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lucadelu/pyModis
|
de86ccf28fffcb759d18b4b5b5a601304ec4fd14
|
scripts/modis_multiparse.py
|
python
|
main
|
()
|
Main function
|
Main function
|
[
"Main",
"function"
] |
def main():
"""Main function"""
#usage
usage = "usage: %prog [options] hdf_files_list"
if 1 == len(sys.argv) and wxpython:
option_parser_class = optparse_gui.OptionParser
else:
option_parser_class = optparse_required.OptionParser
parser = option_parser_class(usage=usage, description='modis_multiparse')
#spatial extent
parser.add_option("-b", action="store_true", dest="bound", default=False,
help="print the values related to the spatial max extent")
#write into file
parser.add_option("-w", "--write", dest="output", metavar="OUTPUT_FILE",
help="write the MODIS XML metadata file for MODIS mosaic")
(options, args) = parser.parse_args()
#create modis object
if len(args) == 0 and not wxpython:
parser.print_help()
sys.exit(1)
if len(args) < 2:
parser.error("You have to define the name of multiple HDF files")
for arg in args:
if not os.path.isfile(arg):
parser.error(arg + " does not exist or is not a file")
modisOgg = parsemodis.parseModisMulti(args)
if options.bound:
modisOgg.valBound()
print(readDict(modisOgg.boundary))
elif options.output:
modisOgg.writexml(options.output)
print("%s write correctly" % options.output)
else:
parser.error("You have to choose at least one option")
|
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https://github.com/lucadelu/pyModis/blob/de86ccf28fffcb759d18b4b5b5a601304ec4fd14/scripts/modis_multiparse.py#L46-L81
|
||
compas-dev/compas
|
0b33f8786481f710115fb1ae5fe79abc2a9a5175
|
src/compas/geometry/intersections/intersections.py
|
python
|
intersection_segment_polyline_xy
|
(segment, polyline, tol=1e-6)
|
Calculate the intersection point of a segment and a polyline on the XY-plane.
Parameters
----------
segment : [point, point] or :class:`compas.geometry.Line`
A line segment defined by two points, with at least XY coordinates.
polyline : sequence[point] or :class:`compas.geometry.Polyline`
A polyline defined by a sequence of points, with at least XY coordinates.
tol : float, optional
The tolerance for intersection verification.
Returns
-------
[float, float, 0.0] or None
XYZ coordinates of the first intersection point if one exists.
None otherwise
Examples
--------
>>> from compas.geometry import is_point_on_polyline_xy
>>> from compas.geometry import is_point_on_segment_xy
>>> from compas.geometry import distance_point_point
>>> p = [(0.0, 0.0, 0.0), (1.0, 0.0, 0.0), (2.0, 0.0, 0.0)]
>>> s = [(0.5, -0.5, 0.0), (0.5, 0.5, 0.0)]
>>> x = intersection_segment_polyline_xy(s, p)
>>> is_point_on_polyline_xy(x, p)
True
>>> is_point_on_segment_xy(x, s)
True
>>> distance_point_point((0.5, 0.0, 0.0), x) < 1e-6
True
|
Calculate the intersection point of a segment and a polyline on the XY-plane.
|
[
"Calculate",
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"of",
"a",
"segment",
"and",
"a",
"polyline",
"on",
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"-",
"plane",
"."
] |
def intersection_segment_polyline_xy(segment, polyline, tol=1e-6):
"""
Calculate the intersection point of a segment and a polyline on the XY-plane.
Parameters
----------
segment : [point, point] or :class:`compas.geometry.Line`
A line segment defined by two points, with at least XY coordinates.
polyline : sequence[point] or :class:`compas.geometry.Polyline`
A polyline defined by a sequence of points, with at least XY coordinates.
tol : float, optional
The tolerance for intersection verification.
Returns
-------
[float, float, 0.0] or None
XYZ coordinates of the first intersection point if one exists.
None otherwise
Examples
--------
>>> from compas.geometry import is_point_on_polyline_xy
>>> from compas.geometry import is_point_on_segment_xy
>>> from compas.geometry import distance_point_point
>>> p = [(0.0, 0.0, 0.0), (1.0, 0.0, 0.0), (2.0, 0.0, 0.0)]
>>> s = [(0.5, -0.5, 0.0), (0.5, 0.5, 0.0)]
>>> x = intersection_segment_polyline_xy(s, p)
>>> is_point_on_polyline_xy(x, p)
True
>>> is_point_on_segment_xy(x, s)
True
>>> distance_point_point((0.5, 0.0, 0.0), x) < 1e-6
True
"""
for cd in pairwise(polyline):
pt = intersection_segment_segment_xy(segment, cd, tol)
if pt:
return pt
|
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https://github.com/compas-dev/compas/blob/0b33f8786481f710115fb1ae5fe79abc2a9a5175/src/compas/geometry/intersections/intersections.py#L908-L946
|
||
vikasverma1077/manifold_mixup
|
870ef77caaa5092144d82c56f26b07b29eefabec
|
gan/interactive.py
|
python
|
pp_interp
|
(net, alpha)
|
Only works with model_resnet_preproc.py as your
architecture!!!
|
Only works with model_resnet_preproc.py as your
architecture!!!
|
[
"Only",
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"with",
"model_resnet_preproc",
".",
"py",
"as",
"your",
"architecture!!!"
] |
def pp_interp(net, alpha):
"""
Only works with model_resnet_preproc.py as your
architecture!!!
"""
conv2d = net.d.preproc
deconv2d = nn.ConvTranspose2d(16, 3, 3, stride=1, padding=1)
deconv2d = deconv2d.cuda()
deconv2d.weight = conv2d.weight
gz1 = net.sample(bs=128)
gz2 = net.sample(bs=128)
#alpha = net.sample_lambda(gz1.size(0))
gz_mix = alpha*gz1 + (1.-alpha)*gz2
save_image(gz1*0.5 + 0.5, filename="gz1.png")
save_image(gz2*0.5 + 0.5, filename="gz2.png")
save_image(gz_mix*0.5 + 0.5, filename="gz_mix.png")
# Ok, do the mixup in hidden space.
gz1_h = conv2d(gz1)
gz2_h = conv2d(gz2)
#alpha = 0.05
gz_mix_h = alpha*gz1_h + (1.-alpha)*gz2_h
gz_mix_h_dec = deconv2d(gz_mix_h)
save_image(gz_mix_h_dec*0.5 + 0.5, filename="gz_mix_h_dec.png")
print(conv2d.weight == deconv2d.weight)
import pdb
pdb.set_trace()
|
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https://github.com/vikasverma1077/manifold_mixup/blob/870ef77caaa5092144d82c56f26b07b29eefabec/gan/interactive.py#L5-L38
|
||
sshaoshuai/PointRCNN
|
1d0dee91262b970f460135252049112d80259ca0
|
lib/utils/bbox_transform.py
|
python
|
rotate_pc_along_y_torch
|
(pc, rot_angle)
|
return pc
|
:param pc: (N, 3 + C)
:param rot_angle: (N)
:return:
|
:param pc: (N, 3 + C)
:param rot_angle: (N)
:return:
|
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def rotate_pc_along_y_torch(pc, rot_angle):
"""
:param pc: (N, 3 + C)
:param rot_angle: (N)
:return:
"""
cosa = torch.cos(rot_angle).view(-1, 1)
sina = torch.sin(rot_angle).view(-1, 1)
raw_1 = torch.cat([cosa, -sina], dim=1)
raw_2 = torch.cat([sina, cosa], dim=1)
R = torch.cat((raw_1.unsqueeze(dim=1), raw_2.unsqueeze(dim=1)), dim=1) # (N, 2, 2)
pc_temp = pc[:, [0, 2]].unsqueeze(dim=1) # (N, 1, 2)
pc[:, [0, 2]] = torch.matmul(pc_temp, R.permute(0, 2, 1)).squeeze(dim=1)
return pc
|
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https://github.com/sshaoshuai/PointRCNN/blob/1d0dee91262b970f460135252049112d80259ca0/lib/utils/bbox_transform.py#L5-L21
|
|
mne-tools/mne-python
|
f90b303ce66a8415e64edd4605b09ac0179c1ebf
|
mne/io/curry/curry.py
|
python
|
_read_curry_info
|
(curry_paths)
|
return info, curry_params.n_samples, curry_params.is_ascii
|
Extract info from curry parameter files.
|
Extract info from curry parameter files.
|
[
"Extract",
"info",
"from",
"curry",
"parameter",
"files",
"."
] |
def _read_curry_info(curry_paths):
"""Extract info from curry parameter files."""
curry_params = _read_curry_parameters(curry_paths['info'])
R = np.eye(4)
R[[0, 1], [0, 1]] = -1 # rotate 180 deg
# shift down and back
# (chosen by eyeballing to make the CTF helmet look roughly correct)
R[:3, 3] = [0., -0.015, -0.12]
curry_dev_dev_t = Transform('ctf_meg', 'meg', R)
# read labels from label files
label_fname = curry_paths['labels']
types = ["meg", "eeg", "misc"]
labels = _read_curry_lines(label_fname,
["LABELS" + CHANTYPES[key] for key in types])
sensors = _read_curry_lines(label_fname,
["SENSORS" + CHANTYPES[key] for key in types])
normals = _read_curry_lines(label_fname,
['NORMALS' + CHANTYPES[key] for key in types])
assert len(labels) == len(sensors) == len(normals)
all_chans = list()
for key in ["meg", "eeg", "misc"]:
chanidx_is_explicit = (len(curry_params.chanidx_in_file["CHAN_IN_FILE"
+ CHANTYPES[key]]) > 0) # channel index
# position in the datafile may or may not be explicitly declared,
# based on the CHAN_IN_FILE section in info file
for ind, chan in enumerate(labels["LABELS" + CHANTYPES[key]]):
chanidx = len(all_chans) + 1 # by default, just assume the
# channel index in the datafile is in order of the channel
# names as we found them in the labels file
if chanidx_is_explicit: # but, if explicitly declared, use
# that index number
chanidx = int(curry_params.chanidx_in_file["CHAN_IN_FILE"
+ CHANTYPES[key]][ind])
if chanidx <= 0: # if chanidx was explicitly declared to be ' 0',
# it means the channel is not actually saved in the data file
# (e.g. the "Ref" channel), so don't add it to our list.
# Git issue #8391
continue
ch = {"ch_name": chan,
"unit": curry_params.unit_dict[key],
"kind": FIFFV_CHANTYPES[key],
"coil_type": FIFFV_COILTYPES[key],
"ch_idx": chanidx
}
if key == "eeg":
loc = np.array(sensors["SENSORS" + CHANTYPES[key]][ind], float)
# XXX just the sensor, where is ref (next 3)?
assert loc.shape == (3,)
loc /= 1000. # to meters
loc = np.concatenate([loc, np.zeros(9)])
ch['loc'] = loc
# XXX need to check/ensure this
ch['coord_frame'] = FIFF.FIFFV_COORD_HEAD
elif key == 'meg':
pos = np.array(sensors["SENSORS" + CHANTYPES[key]][ind], float)
pos /= 1000. # to meters
pos = pos[:3] # just the inner coil
pos = apply_trans(curry_dev_dev_t, pos)
nn = np.array(normals["NORMALS" + CHANTYPES[key]][ind], float)
assert np.isclose(np.linalg.norm(nn), 1., atol=1e-4)
nn /= np.linalg.norm(nn)
nn = apply_trans(curry_dev_dev_t, nn, move=False)
trans = np.eye(4)
trans[:3, 3] = pos
trans[:3, :3] = _normal_orth(nn).T
ch['loc'] = _coil_trans_to_loc(trans)
ch['coord_frame'] = FIFF.FIFFV_COORD_DEVICE
all_chans.append(ch)
ch_count = len(all_chans)
assert (ch_count == curry_params.n_chans) # ensure that we have assembled
# the same number of channels as declared in the info (.DAP) file in the
# DATA_PARAMETERS section. Git issue #8391
# sort the channels to assure they are in the order that matches how
# recorded in the datafile. In general they most likely are already in
# the correct order, but if the channel index in the data file was
# explicitly declared we might as well use it.
all_chans = sorted(all_chans, key=lambda ch: ch['ch_idx'])
ch_names = [chan["ch_name"] for chan in all_chans]
info = create_info(ch_names, curry_params.sfreq)
with info._unlock():
info['meas_date'] = curry_params.dt_start # for Git issue #8398
_make_trans_dig(curry_paths, info, curry_dev_dev_t)
for ind, ch_dict in enumerate(info["chs"]):
all_chans[ind].pop('ch_idx')
ch_dict.update(all_chans[ind])
assert ch_dict['loc'].shape == (12,)
ch_dict['unit'] = SI_UNITS[all_chans[ind]['unit'][1]]
ch_dict['cal'] = SI_UNIT_SCALE[all_chans[ind]['unit'][0]]
return info, curry_params.n_samples, curry_params.is_ascii
|
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"# the same number of channels as declared in the info (.DAP) file in the",
"# DATA_PARAMETERS section. Git issue #8391",
"# sort the channels to assure they are in the order that matches how",
"# recorded in the datafile. In general they most likely are already in",
"# the correct order, but if the channel index in the data file was",
"# explicitly declared we might as well use it.",
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"info",
",",
"curry_params",
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",",
"curry_params",
".",
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] |
https://github.com/mne-tools/mne-python/blob/f90b303ce66a8415e64edd4605b09ac0179c1ebf/mne/io/curry/curry.py#L208-L303
|
|
osmr/imgclsmob
|
f2993d3ce73a2f7ddba05da3891defb08547d504
|
pytorch/pytorchcv/models/unet.py
|
python
|
get_unet
|
(model_name=None,
pretrained=False,
root=os.path.join("~", ".torch", "models"),
**kwargs)
|
return net
|
Create U-Net model with specific parameters.
Parameters:
----------
model_name : str or None, default None
Model name for loading pretrained model.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.
|
Create U-Net model with specific parameters.
|
[
"Create",
"U",
"-",
"Net",
"model",
"with",
"specific",
"parameters",
"."
] |
def get_unet(model_name=None,
pretrained=False,
root=os.path.join("~", ".torch", "models"),
**kwargs):
"""
Create U-Net model with specific parameters.
Parameters:
----------
model_name : str or None, default None
Model name for loading pretrained model.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.
"""
channels = [[128, 256, 512, 512], [512, 256, 128, 64]]
init_block_channels = 64
net = UNet(
channels=channels,
init_block_channels=init_block_channels,
**kwargs)
if pretrained:
if (model_name is None) or (not model_name):
raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.")
from .model_store import download_model
download_model(
net=net,
model_name=model_name,
local_model_store_dir_path=root)
return net
|
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https://github.com/osmr/imgclsmob/blob/f2993d3ce73a2f7ddba05da3891defb08547d504/pytorch/pytorchcv/models/unet.py#L240-L273
|
|
ales-tsurko/cells
|
4cf7e395cd433762bea70cdc863a346f3a6fe1d0
|
packaging/macos/python/lib/python3.7/timeit.py
|
python
|
repeat
|
(stmt="pass", setup="pass", timer=default_timer,
repeat=default_repeat, number=default_number, globals=None)
|
return Timer(stmt, setup, timer, globals).repeat(repeat, number)
|
Convenience function to create Timer object and call repeat method.
|
Convenience function to create Timer object and call repeat method.
|
[
"Convenience",
"function",
"to",
"create",
"Timer",
"object",
"and",
"call",
"repeat",
"method",
"."
] |
def repeat(stmt="pass", setup="pass", timer=default_timer,
repeat=default_repeat, number=default_number, globals=None):
"""Convenience function to create Timer object and call repeat method."""
return Timer(stmt, setup, timer, globals).repeat(repeat, number)
|
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"repeat",
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"\"pass\"",
",",
"timer",
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",",
"number",
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",",
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"setup",
",",
"timer",
",",
"globals",
")",
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"(",
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",",
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")"
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https://github.com/ales-tsurko/cells/blob/4cf7e395cd433762bea70cdc863a346f3a6fe1d0/packaging/macos/python/lib/python3.7/timeit.py#L234-L237
|
|
CedricGuillemet/Imogen
|
ee417b42747ed5b46cb11b02ef0c3630000085b3
|
bin/Lib/email/message.py
|
python
|
Message.__getitem__
|
(self, name)
|
return self.get(name)
|
Get a header value.
Return None if the header is missing instead of raising an exception.
Note that if the header appeared multiple times, exactly which
occurrence gets returned is undefined. Use get_all() to get all
the values matching a header field name.
|
Get a header value.
|
[
"Get",
"a",
"header",
"value",
"."
] |
def __getitem__(self, name):
"""Get a header value.
Return None if the header is missing instead of raising an exception.
Note that if the header appeared multiple times, exactly which
occurrence gets returned is undefined. Use get_all() to get all
the values matching a header field name.
"""
return self.get(name)
|
[
"def",
"__getitem__",
"(",
"self",
",",
"name",
")",
":",
"return",
"self",
".",
"get",
"(",
"name",
")"
] |
https://github.com/CedricGuillemet/Imogen/blob/ee417b42747ed5b46cb11b02ef0c3630000085b3/bin/Lib/email/message.py#L382-L391
|
|
convexengineering/gpkit
|
3d4dd34ba4e95f1fe58fe9ea45401a6ff2fde1fa
|
gpkit/globals.py
|
python
|
load_settings
|
(path=None, trybuild=True)
|
return settings_
|
Load the settings file at SETTINGS_PATH; return settings dict
|
Load the settings file at SETTINGS_PATH; return settings dict
|
[
"Load",
"the",
"settings",
"file",
"at",
"SETTINGS_PATH",
";",
"return",
"settings",
"dict"
] |
def load_settings(path=None, trybuild=True):
"Load the settings file at SETTINGS_PATH; return settings dict"
if path is None:
path = os.sep.join([os.path.dirname(__file__), "env", "settings"])
try: # if the settings file already exists, read it
with open(path) as settingsfile:
lines = [line[:-1].split(" : ") for line in settingsfile
if len(line.split(" : ")) == 2]
settings_ = {name: value.split(", ") for name, value in lines}
for name, value in settings_.items():
# flatten 1-element lists unless they're the solver list
if len(value) == 1 and name != "installed_solvers":
settings_[name], = value
except IOError: # pragma: no cover
settings_ = {"installed_solvers": [""]}
if settings_["installed_solvers"] == [""] and trybuild: # pragma: no cover
print("Found no installed solvers, beginning a build.")
build()
settings_ = load_settings(path, trybuild=False)
if settings_["installed_solvers"] != [""]:
settings_["just built!"] = True
else:
print("""
=============
Build failed! :(
=============
You may need to install a solver and then `import gpkit` again;
see https://gpkit.readthedocs.io/en/latest/installation.html
for troubleshooting details.
But before you go, please post the output above
(starting from "Found no installed solvers, beginning a build.")
to [email protected] or https://github.com/convexengineering/gpkit/issues/new
so we can prevent others from having to see this message.
Thanks! :)
""")
settings_["default_solver"] = settings_["installed_solvers"][0]
return settings_
|
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"[",
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] |
https://github.com/convexengineering/gpkit/blob/3d4dd34ba4e95f1fe58fe9ea45401a6ff2fde1fa/gpkit/globals.py#L7-L45
|
|
dmlc/dgl
|
8d14a739bc9e446d6c92ef83eafe5782398118de
|
examples/pytorch/ogb/deepwalk/model.py
|
python
|
SkipGramModel.forward
|
(self, pos_u, pos_v, neg_v)
|
return torch.sum(score), torch.sum(neg_score)
|
Do forward and backward. It is designed for future use.
|
Do forward and backward. It is designed for future use.
|
[
"Do",
"forward",
"and",
"backward",
".",
"It",
"is",
"designed",
"for",
"future",
"use",
"."
] |
def forward(self, pos_u, pos_v, neg_v):
''' Do forward and backward. It is designed for future use. '''
emb_u = self.u_embeddings(pos_u)
emb_v = self.v_embeddings(pos_v)
emb_neg_v = self.v_embeddings(neg_v)
score = torch.sum(torch.mul(emb_u, emb_v), dim=1)
score = torch.clamp(score, max=6, min=-6)
score = -F.logsigmoid(score)
neg_score = torch.bmm(emb_neg_v, emb_u.unsqueeze(2)).squeeze()
neg_score = torch.clamp(neg_score, max=6, min=-6)
neg_score = -torch.sum(F.logsigmoid(-neg_score), dim=1)
#return torch.mean(score + neg_score)
return torch.sum(score), torch.sum(neg_score)
|
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",",
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"neg_score",
")"
] |
https://github.com/dmlc/dgl/blob/8d14a739bc9e446d6c92ef83eafe5782398118de/examples/pytorch/ogb/deepwalk/model.py#L458-L473
|
|
securityclippy/elasticintel
|
aa08d3e9f5ab1c000128e95161139ce97ff0e334
|
ingest_feed_lambda/pandas/core/dtypes/concat.py
|
python
|
_concat_compat
|
(to_concat, axis=0)
|
return np.concatenate(to_concat, axis=axis)
|
provide concatenation of an array of arrays each of which is a single
'normalized' dtypes (in that for example, if it's object, then it is a
non-datetimelike and provide a combined dtype for the resulting array that
preserves the overall dtype if possible)
Parameters
----------
to_concat : array of arrays
axis : axis to provide concatenation
Returns
-------
a single array, preserving the combined dtypes
|
provide concatenation of an array of arrays each of which is a single
'normalized' dtypes (in that for example, if it's object, then it is a
non-datetimelike and provide a combined dtype for the resulting array that
preserves the overall dtype if possible)
|
[
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"-",
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"resulting",
"array",
"that",
"preserves",
"the",
"overall",
"dtype",
"if",
"possible",
")"
] |
def _concat_compat(to_concat, axis=0):
"""
provide concatenation of an array of arrays each of which is a single
'normalized' dtypes (in that for example, if it's object, then it is a
non-datetimelike and provide a combined dtype for the resulting array that
preserves the overall dtype if possible)
Parameters
----------
to_concat : array of arrays
axis : axis to provide concatenation
Returns
-------
a single array, preserving the combined dtypes
"""
# filter empty arrays
# 1-d dtypes always are included here
def is_nonempty(x):
try:
return x.shape[axis] > 0
except Exception:
return True
nonempty = [x for x in to_concat if is_nonempty(x)]
# If all arrays are empty, there's nothing to convert, just short-cut to
# the concatenation, #3121.
#
# Creating an empty array directly is tempting, but the winnings would be
# marginal given that it would still require shape & dtype calculation and
# np.concatenate which has them both implemented is compiled.
typs = get_dtype_kinds(to_concat)
_contains_datetime = any(typ.startswith('datetime') for typ in typs)
_contains_period = any(typ.startswith('period') for typ in typs)
if 'category' in typs:
# this must be priort to _concat_datetime,
# to support Categorical + datetime-like
return _concat_categorical(to_concat, axis=axis)
elif _contains_datetime or 'timedelta' in typs or _contains_period:
return _concat_datetime(to_concat, axis=axis, typs=typs)
# these are mandated to handle empties as well
elif 'sparse' in typs:
return _concat_sparse(to_concat, axis=axis, typs=typs)
if not nonempty:
# we have all empties, but may need to coerce the result dtype to
# object if we have non-numeric type operands (numpy would otherwise
# cast this to float)
typs = get_dtype_kinds(to_concat)
if len(typs) != 1:
if (not len(typs - set(['i', 'u', 'f'])) or
not len(typs - set(['bool', 'i', 'u']))):
# let numpy coerce
pass
else:
# coerce to object
to_concat = [x.astype('object') for x in to_concat]
return np.concatenate(to_concat, axis=axis)
|
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"(",
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",",
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"=",
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")"
] |
https://github.com/securityclippy/elasticintel/blob/aa08d3e9f5ab1c000128e95161139ce97ff0e334/ingest_feed_lambda/pandas/core/dtypes/concat.py#L102-L168
|
|
Runbook/runbook
|
7b68622f75ef09f654046f0394540025f3ee7445
|
src/bridge/bridge.py
|
python
|
decimateRedis
|
(itemkey, item)
|
return True
|
This will parse out a dictionary and kill the redis data
|
This will parse out a dictionary and kill the redis data
|
[
"This",
"will",
"parse",
"out",
"a",
"dictionary",
"and",
"kill",
"the",
"redis",
"data"
] |
def decimateRedis(itemkey, item):
''' This will parse out a dictionary and kill the redis data '''
if "timer" in item['data']:
try:
r_server.srem(item['data']['timer'], item['cid'])
except:
pass
try:
r_server.delete(itemkey)
except:
pass
return True
|
[
"def",
"decimateRedis",
"(",
"itemkey",
",",
"item",
")",
":",
"if",
"\"timer\"",
"in",
"item",
"[",
"'data'",
"]",
":",
"try",
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"r_server",
".",
"srem",
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"]",
",",
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":",
"r_server",
".",
"delete",
"(",
"itemkey",
")",
"except",
":",
"pass",
"return",
"True"
] |
https://github.com/Runbook/runbook/blob/7b68622f75ef09f654046f0394540025f3ee7445/src/bridge/bridge.py#L134-L145
|
|
ni/nidaqmx-python
|
62fc6b48cbbb330fe1bcc9aedadc86610a1269b6
|
nidaqmx/_task_modules/channels/ci_channel.py
|
python
|
CIChannel.ci_freq_div
|
(self)
|
[] |
def ci_freq_div(self):
cfunc = lib_importer.windll.DAQmxResetCIFreqDiv
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes_byte_str]
error_code = cfunc(
self._handle, self._name)
check_for_error(error_code)
|
[
"def",
"ci_freq_div",
"(",
"self",
")",
":",
"cfunc",
"=",
"lib_importer",
".",
"windll",
".",
"DAQmxResetCIFreqDiv",
"if",
"cfunc",
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"_handle",
",",
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".",
"_name",
")",
"check_for_error",
"(",
"error_code",
")"
] |
https://github.com/ni/nidaqmx-python/blob/62fc6b48cbbb330fe1bcc9aedadc86610a1269b6/nidaqmx/_task_modules/channels/ci_channel.py#L5149-L5159
|
||||
CouchPotato/CouchPotatoServer
|
7260c12f72447ddb6f062367c6dfbda03ecd4e9c
|
libs/tornado/autoreload.py
|
python
|
add_reload_hook
|
(fn)
|
Add a function to be called before reloading the process.
Note that for open file and socket handles it is generally
preferable to set the ``FD_CLOEXEC`` flag (using `fcntl` or
``tornado.platform.auto.set_close_exec``) instead
of using a reload hook to close them.
|
Add a function to be called before reloading the process.
|
[
"Add",
"a",
"function",
"to",
"be",
"called",
"before",
"reloading",
"the",
"process",
"."
] |
def add_reload_hook(fn):
"""Add a function to be called before reloading the process.
Note that for open file and socket handles it is generally
preferable to set the ``FD_CLOEXEC`` flag (using `fcntl` or
``tornado.platform.auto.set_close_exec``) instead
of using a reload hook to close them.
"""
_reload_hooks.append(fn)
|
[
"def",
"add_reload_hook",
"(",
"fn",
")",
":",
"_reload_hooks",
".",
"append",
"(",
"fn",
")"
] |
https://github.com/CouchPotato/CouchPotatoServer/blob/7260c12f72447ddb6f062367c6dfbda03ecd4e9c/libs/tornado/autoreload.py#L149-L157
|
||
aleju/imgaug
|
0101108d4fed06bc5056c4a03e2bcb0216dac326
|
imgaug/augmenters/debug.py
|
python
|
_DebugGridImageCell._resize_overlay
|
(cls, arr, size)
|
return arr_rs
|
[] |
def _resize_overlay(cls, arr, size):
arr_rs = ia.imresize_single_image(arr, size, interpolation="nearest")
return arr_rs
|
[
"def",
"_resize_overlay",
"(",
"cls",
",",
"arr",
",",
"size",
")",
":",
"arr_rs",
"=",
"ia",
".",
"imresize_single_image",
"(",
"arr",
",",
"size",
",",
"interpolation",
"=",
"\"nearest\"",
")",
"return",
"arr_rs"
] |
https://github.com/aleju/imgaug/blob/0101108d4fed06bc5056c4a03e2bcb0216dac326/imgaug/augmenters/debug.py#L254-L256
|
|||
pyvista/pyvista
|
012dbb95a9aae406c3cd4cd94fc8c477f871e426
|
pyvista/themes.py
|
python
|
DefaultTheme.hidden_line_removal
|
(self)
|
return self._hidden_line_removal
|
Return or set hidden line removal.
Wireframe geometry will be drawn using hidden line removal if
the rendering engine supports it.
See Also
--------
pyvista.BasePlotter.enable_hidden_line_removal
Examples
--------
Enable hidden line removal.
>>> import pyvista
>>> pyvista.global_theme.hidden_line_removal = True # doctest:+SKIP
>>> pyvista.global_theme.hidden_line_removal # doctest:+SKIP
True
|
Return or set hidden line removal.
|
[
"Return",
"or",
"set",
"hidden",
"line",
"removal",
"."
] |
def hidden_line_removal(self) -> bool:
"""Return or set hidden line removal.
Wireframe geometry will be drawn using hidden line removal if
the rendering engine supports it.
See Also
--------
pyvista.BasePlotter.enable_hidden_line_removal
Examples
--------
Enable hidden line removal.
>>> import pyvista
>>> pyvista.global_theme.hidden_line_removal = True # doctest:+SKIP
>>> pyvista.global_theme.hidden_line_removal # doctest:+SKIP
True
"""
return self._hidden_line_removal
|
[
"def",
"hidden_line_removal",
"(",
"self",
")",
"->",
"bool",
":",
"return",
"self",
".",
"_hidden_line_removal"
] |
https://github.com/pyvista/pyvista/blob/012dbb95a9aae406c3cd4cd94fc8c477f871e426/pyvista/themes.py#L1221-L1241
|
|
beeware/ouroboros
|
a29123c6fab6a807caffbb7587cf548e0c370296
|
ouroboros/_osx_support.py
|
python
|
_save_modified_value
|
(_config_vars, cv, newvalue)
|
Save modified and original unmodified value of configuration var
|
Save modified and original unmodified value of configuration var
|
[
"Save",
"modified",
"and",
"original",
"unmodified",
"value",
"of",
"configuration",
"var"
] |
def _save_modified_value(_config_vars, cv, newvalue):
"""Save modified and original unmodified value of configuration var"""
oldvalue = _config_vars.get(cv, '')
if (oldvalue != newvalue) and (_INITPRE + cv not in _config_vars):
_config_vars[_INITPRE + cv] = oldvalue
_config_vars[cv] = newvalue
|
[
"def",
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"(",
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",",
"cv",
",",
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"=",
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"]",
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"oldvalue",
"_config_vars",
"[",
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"]",
"=",
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] |
https://github.com/beeware/ouroboros/blob/a29123c6fab6a807caffbb7587cf548e0c370296/ouroboros/_osx_support.py#L120-L126
|
||
rrmina/fast-neural-style-pytorch
|
f32f6cac3ac7906df4655fa70ab831ca6336f9b9
|
utils.py
|
python
|
transfer_color
|
(src, dest)
|
return cv2.cvtColor(src_yiq, cv2.COLOR_YCrCb2BGR).clip(0,255)
|
Transfer Color using YIQ colorspace. Useful in preserving colors in style transfer.
This method assumes inputs of shape [Height, Width, Channel] in BGR Color Space
|
Transfer Color using YIQ colorspace. Useful in preserving colors in style transfer.
This method assumes inputs of shape [Height, Width, Channel] in BGR Color Space
|
[
"Transfer",
"Color",
"using",
"YIQ",
"colorspace",
".",
"Useful",
"in",
"preserving",
"colors",
"in",
"style",
"transfer",
".",
"This",
"method",
"assumes",
"inputs",
"of",
"shape",
"[",
"Height",
"Width",
"Channel",
"]",
"in",
"BGR",
"Color",
"Space"
] |
def transfer_color(src, dest):
"""
Transfer Color using YIQ colorspace. Useful in preserving colors in style transfer.
This method assumes inputs of shape [Height, Width, Channel] in BGR Color Space
"""
src, dest = src.clip(0,255), dest.clip(0,255)
# Resize src to dest's size
H,W,_ = src.shape
dest = cv2.resize(dest, dsize=(W, H), interpolation=cv2.INTER_CUBIC)
dest_gray = cv2.cvtColor(dest, cv2.COLOR_BGR2GRAY) #1 Extract the Destination's luminance
src_yiq = cv2.cvtColor(src, cv2.COLOR_BGR2YCrCb) #2 Convert the Source from BGR to YIQ/YCbCr
src_yiq[...,0] = dest_gray #3 Combine Destination's luminance and Source's IQ/CbCr
return cv2.cvtColor(src_yiq, cv2.COLOR_YCrCb2BGR).clip(0,255)
|
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",",
"cv2",
".",
"COLOR_YCrCb2BGR",
")",
".",
"clip",
"(",
"0",
",",
"255",
")"
] |
https://github.com/rrmina/fast-neural-style-pytorch/blob/f32f6cac3ac7906df4655fa70ab831ca6336f9b9/utils.py#L77-L92
|
|
ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework
|
cb692f527e4e819b6c228187c5702d990a180043
|
bin/x86/Debug/scripting_engine/Lib/decimal.py
|
python
|
Decimal.__abs__
|
(self, round=True, context=None)
|
return ans
|
Returns the absolute value of self.
If the keyword argument 'round' is false, do not round. The
expression self.__abs__(round=False) is equivalent to
self.copy_abs().
|
Returns the absolute value of self.
|
[
"Returns",
"the",
"absolute",
"value",
"of",
"self",
"."
] |
def __abs__(self, round=True, context=None):
"""Returns the absolute value of self.
If the keyword argument 'round' is false, do not round. The
expression self.__abs__(round=False) is equivalent to
self.copy_abs().
"""
if not round:
return self.copy_abs()
if self._is_special:
ans = self._check_nans(context=context)
if ans:
return ans
if self._sign:
ans = self.__neg__(context=context)
else:
ans = self.__pos__(context=context)
return ans
|
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"def",
"__abs__",
"(",
"self",
",",
"round",
"=",
"True",
",",
"context",
"=",
"None",
")",
":",
"if",
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"round",
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"(",
")",
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"_is_special",
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"=",
"self",
".",
"__pos__",
"(",
"context",
"=",
"context",
")",
"return",
"ans"
] |
https://github.com/ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework/blob/cb692f527e4e819b6c228187c5702d990a180043/bin/x86/Debug/scripting_engine/Lib/decimal.py#L1110-L1130
|
|
gunthercox/ChatterBot
|
4ff8af28567ed446ae796d37c246bb6a14032fe7
|
chatterbot/storage/mongodb.py
|
python
|
MongoDatabaseAdapter.get_statement_model
|
(self)
|
return statement
|
Return the class for the statement model.
|
Return the class for the statement model.
|
[
"Return",
"the",
"class",
"for",
"the",
"statement",
"model",
"."
] |
def get_statement_model(self):
"""
Return the class for the statement model.
"""
from chatterbot.conversation import Statement
# Create a storage-aware statement
statement = Statement
statement.storage = self
return statement
|
[
"def",
"get_statement_model",
"(",
"self",
")",
":",
"from",
"chatterbot",
".",
"conversation",
"import",
"Statement",
"# Create a storage-aware statement",
"statement",
"=",
"Statement",
"statement",
".",
"storage",
"=",
"self",
"return",
"statement"
] |
https://github.com/gunthercox/ChatterBot/blob/4ff8af28567ed446ae796d37c246bb6a14032fe7/chatterbot/storage/mongodb.py#L44-L54
|
|
chromium/web-page-replay
|
472351e1122bb1beb936952c7e75ae58bf8a69f1
|
replay.py
|
python
|
OptionsWrapper.__getattr__
|
(self, name)
|
return getattr(self._options, name)
|
Make the original option values available.
|
Make the original option values available.
|
[
"Make",
"the",
"original",
"option",
"values",
"available",
"."
] |
def __getattr__(self, name):
"""Make the original option values available."""
return getattr(self._options, name)
|
[
"def",
"__getattr__",
"(",
"self",
",",
"name",
")",
":",
"return",
"getattr",
"(",
"self",
".",
"_options",
",",
"name",
")"
] |
https://github.com/chromium/web-page-replay/blob/472351e1122bb1beb936952c7e75ae58bf8a69f1/replay.py#L277-L279
|
|
ipython/ipython
|
c0abea7a6dfe52c1f74c9d0387d4accadba7cc14
|
IPython/utils/frame.py
|
python
|
extract_vars
|
(*names,**kw)
|
return dict((k,callerNS[k]) for k in names)
|
Extract a set of variables by name from another frame.
Parameters
----------
*names : str
One or more variable names which will be extracted from the caller's
frame.
**kw : integer, optional
How many frames in the stack to walk when looking for your variables.
The default is 0, which will use the frame where the call was made.
Examples
--------
::
In [2]: def func(x):
...: y = 1
...: print(sorted(extract_vars('x','y').items()))
...:
In [3]: func('hello')
[('x', 'hello'), ('y', 1)]
|
Extract a set of variables by name from another frame.
|
[
"Extract",
"a",
"set",
"of",
"variables",
"by",
"name",
"from",
"another",
"frame",
"."
] |
def extract_vars(*names,**kw):
"""Extract a set of variables by name from another frame.
Parameters
----------
*names : str
One or more variable names which will be extracted from the caller's
frame.
**kw : integer, optional
How many frames in the stack to walk when looking for your variables.
The default is 0, which will use the frame where the call was made.
Examples
--------
::
In [2]: def func(x):
...: y = 1
...: print(sorted(extract_vars('x','y').items()))
...:
In [3]: func('hello')
[('x', 'hello'), ('y', 1)]
"""
depth = kw.get('depth',0)
callerNS = sys._getframe(depth+1).f_locals
return dict((k,callerNS[k]) for k in names)
|
[
"def",
"extract_vars",
"(",
"*",
"names",
",",
"*",
"*",
"kw",
")",
":",
"depth",
"=",
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".",
"get",
"(",
"'depth'",
",",
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"(",
"k",
",",
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"[",
"k",
"]",
")",
"for",
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")"
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https://github.com/ipython/ipython/blob/c0abea7a6dfe52c1f74c9d0387d4accadba7cc14/IPython/utils/frame.py#L23-L51
|
|
OpenMDAO/OpenMDAO-Framework
|
f2e37b7de3edeaaeb2d251b375917adec059db9b
|
openmdao.main/src/openmdao/main/interfaces.py
|
python
|
IHasConstraints.get_constraints
|
()
|
Returns an ordered dict of constraint objects.
|
Returns an ordered dict of constraint objects.
|
[
"Returns",
"an",
"ordered",
"dict",
"of",
"constraint",
"objects",
"."
] |
def get_constraints():
"""Returns an ordered dict of constraint objects."""
|
[
"def",
"get_constraints",
"(",
")",
":"
] |
https://github.com/OpenMDAO/OpenMDAO-Framework/blob/f2e37b7de3edeaaeb2d251b375917adec059db9b/openmdao.main/src/openmdao/main/interfaces.py#L724-L725
|
||
sagemath/sage
|
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
|
src/sage/schemes/elliptic_curves/heegner.py
|
python
|
HeegnerPointOnEllipticCurve._trace_numerical_conductor_1
|
(self, prec=53)
|
return s
|
Return numerical approximation using ``prec`` terms of working
precision to the trace down to the quadratic imaginary field
`K` of this Heegner point.
INPUT:
- `prec` -- bits precision (default: 53)
EXAMPLES::
sage: E = EllipticCurve('57a1')
sage: P = E.heegner_point(-8); P
Heegner point of discriminant -8 on elliptic curve of conductor 57
sage: P._trace_numerical_conductor_1() # approx. (1 : 0 : 1)
(1.00000000000000 + ...e-16*I : ...e-16 - ...e-16*I : 1.00000000000000)
sage: P = E(2,1) # a generator
sage: E([1,0]).height()
0.150298370947295
sage: P.height()
0.0375745927368238
sage: E.heegner_index(-8)
2.0000?
sage: E.torsion_order()
1
sage: 2*P
(1 : 0 : 1)
|
Return numerical approximation using ``prec`` terms of working
precision to the trace down to the quadratic imaginary field
`K` of this Heegner point.
|
[
"Return",
"numerical",
"approximation",
"using",
"prec",
"terms",
"of",
"working",
"precision",
"to",
"the",
"trace",
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"to",
"the",
"quadratic",
"imaginary",
"field",
"K",
"of",
"this",
"Heegner",
"point",
"."
] |
def _trace_numerical_conductor_1(self, prec=53):
"""
Return numerical approximation using ``prec`` terms of working
precision to the trace down to the quadratic imaginary field
`K` of this Heegner point.
INPUT:
- `prec` -- bits precision (default: 53)
EXAMPLES::
sage: E = EllipticCurve('57a1')
sage: P = E.heegner_point(-8); P
Heegner point of discriminant -8 on elliptic curve of conductor 57
sage: P._trace_numerical_conductor_1() # approx. (1 : 0 : 1)
(1.00000000000000 + ...e-16*I : ...e-16 - ...e-16*I : 1.00000000000000)
sage: P = E(2,1) # a generator
sage: E([1,0]).height()
0.150298370947295
sage: P.height()
0.0375745927368238
sage: E.heegner_index(-8)
2.0000?
sage: E.torsion_order()
1
sage: 2*P
(1 : 0 : 1)
"""
if self.conductor() != 1:
raise ValueError("conductor must be 1")
R, U = self._good_tau_representatives()
E = self.__E
phi = E.modular_parametrization()
C = rings.ComplexField(prec)
F = E.change_ring(C)
s = 0
for u, weight in U:
P = phi(C(self._qf_to_tau(u)))
z = F.point(list(P),check=False)
if abs(weight) == 2:
t = F.point(z,check=False) + F.point(tuple([x.conjugate() for x in z]), check=False)
if weight < 0:
s -= t
else:
s += t
else:
if weight < 0:
s -= z
else:
s += z
return s
|
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"-=",
"z",
"else",
":",
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] |
https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/schemes/elliptic_curves/heegner.py#L3706-L3757
|
|
ifwe/digsby
|
f5fe00244744aa131e07f09348d10563f3d8fa99
|
digsby/src/mail/emailobj.py
|
python
|
Email.fromEmailMessage
|
(cls, id, email, sendtime_if_error = None)
|
return email
|
Creates an Email from a Python email.message.Message object.
|
Creates an Email from a Python email.message.Message object.
|
[
"Creates",
"an",
"Email",
"from",
"a",
"Python",
"email",
".",
"message",
".",
"Message",
"object",
"."
] |
def fromEmailMessage(cls, id, email, sendtime_if_error = None):
'Creates an Email from a Python email.message.Message object.'
encoding = email.get_content_charset()
# parse name, address
realname, email_address = parseaddr(email['From'])
realname = unicode_hdr(realname, encoding)
# parse date
_email = email
try:
datetuple = parsedate(email['Date'])
sendtime = datetime(*datetuple[:7])
except Exception:
traceback.print_exc()
print >> sys.stderr, 'using %s for "sendtime" instead' % sendtime_if_error
sendtime = sendtime_if_error
try:
attachments = find_attachments(email)
except:
attachments = {}
part = find_part(email, ('text/plain', 'text/html'))
if part is None:
content = u''
else:
content = parse_content(part)
content = replace_newlines(content)
prev_length = pref('email.preview_length', 200)
if len(content) > prev_length:
content = content[:prev_length] + '...'
else:
content
email = cls(id, realname, email_address, sendtime, email['Subject'],
content = content, attachments=attachments)
return email
|
[
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",",
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"=",
"attachments",
")",
"return",
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] |
https://github.com/ifwe/digsby/blob/f5fe00244744aa131e07f09348d10563f3d8fa99/digsby/src/mail/emailobj.py#L110-L152
|
|
huggingface/transformers
|
623b4f7c63f60cce917677ee704d6c93ee960b4b
|
examples/research_projects/rag/utils_rag.py
|
python
|
set_extra_model_params
|
(extra_params, hparams, config)
|
return hparams, config
|
[] |
def set_extra_model_params(extra_params, hparams, config):
equivalent_param = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
equivalent_param["dropout"] = "dropout_rate"
for p in extra_params:
if getattr(hparams, p, None):
if not hasattr(config, p) and not hasattr(config, equivalent_param[p]):
logger.info("config doesn't have a `{}` attribute".format(p))
delattr(hparams, p)
continue
set_p = p if hasattr(config, p) else equivalent_param[p]
setattr(config, set_p, getattr(hparams, p))
delattr(hparams, p)
return hparams, config
|
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https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/examples/research_projects/rag/utils_rag.py#L231-L244
|
|||
beeware/ouroboros
|
a29123c6fab6a807caffbb7587cf548e0c370296
|
ouroboros/mimetypes.py
|
python
|
guess_type
|
(url, strict=True)
|
return _db.guess_type(url, strict)
|
Guess the type of a file based on its URL.
Return value is a tuple (type, encoding) where type is None if the
type can't be guessed (no or unknown suffix) or a string of the
form type/subtype, usable for a MIME Content-type header; and
encoding is None for no encoding or the name of the program used
to encode (e.g. compress or gzip). The mappings are table
driven. Encoding suffixes are case sensitive; type suffixes are
first tried case sensitive, then case insensitive.
The suffixes .tgz, .taz and .tz (case sensitive!) are all mapped
to ".tar.gz". (This is table-driven too, using the dictionary
suffix_map).
Optional `strict' argument when false adds a bunch of commonly found, but
non-standard types.
|
Guess the type of a file based on its URL.
|
[
"Guess",
"the",
"type",
"of",
"a",
"file",
"based",
"on",
"its",
"URL",
"."
] |
def guess_type(url, strict=True):
"""Guess the type of a file based on its URL.
Return value is a tuple (type, encoding) where type is None if the
type can't be guessed (no or unknown suffix) or a string of the
form type/subtype, usable for a MIME Content-type header; and
encoding is None for no encoding or the name of the program used
to encode (e.g. compress or gzip). The mappings are table
driven. Encoding suffixes are case sensitive; type suffixes are
first tried case sensitive, then case insensitive.
The suffixes .tgz, .taz and .tz (case sensitive!) are all mapped
to ".tar.gz". (This is table-driven too, using the dictionary
suffix_map).
Optional `strict' argument when false adds a bunch of commonly found, but
non-standard types.
"""
if _db is None:
init()
return _db.guess_type(url, strict)
|
[
"def",
"guess_type",
"(",
"url",
",",
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"=",
"True",
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"init",
"(",
")",
"return",
"_db",
".",
"guess_type",
"(",
"url",
",",
"strict",
")"
] |
https://github.com/beeware/ouroboros/blob/a29123c6fab6a807caffbb7587cf548e0c370296/ouroboros/mimetypes.py#L269-L289
|
|
QCoDeS/Qcodes
|
3cda2cef44812e2aa4672781f2423bf5f816f9f9
|
qcodes/utils/command.py
|
python
|
Command.call_cmd_parsed_out
|
(self, *args)
|
return self.output_parser(self._cmd(*args))
|
Execute a function with output parsing.
|
Execute a function with output parsing.
|
[
"Execute",
"a",
"function",
"with",
"output",
"parsing",
"."
] |
def call_cmd_parsed_out(self, *args):
"""Execute a function with output parsing."""
return self.output_parser(self._cmd(*args))
|
[
"def",
"call_cmd_parsed_out",
"(",
"self",
",",
"*",
"args",
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"return",
"self",
".",
"output_parser",
"(",
"self",
".",
"_cmd",
"(",
"*",
"args",
")",
")"
] |
https://github.com/QCoDeS/Qcodes/blob/3cda2cef44812e2aa4672781f2423bf5f816f9f9/qcodes/utils/command.py#L155-L157
|
|
smart-mobile-software/gitstack
|
d9fee8f414f202143eb6e620529e8e5539a2af56
|
python/Lib/site-packages/django/contrib/gis/gdal/field.py
|
python
|
Field.type
|
(self)
|
return capi.get_field_type(self.ptr)
|
Returns the OGR type of this Field.
|
Returns the OGR type of this Field.
|
[
"Returns",
"the",
"OGR",
"type",
"of",
"this",
"Field",
"."
] |
def type(self):
"Returns the OGR type of this Field."
return capi.get_field_type(self.ptr)
|
[
"def",
"type",
"(",
"self",
")",
":",
"return",
"capi",
".",
"get_field_type",
"(",
"self",
".",
"ptr",
")"
] |
https://github.com/smart-mobile-software/gitstack/blob/d9fee8f414f202143eb6e620529e8e5539a2af56/python/Lib/site-packages/django/contrib/gis/gdal/field.py#L77-L79
|
|
aws-samples/aws-kube-codesuite
|
ab4e5ce45416b83bffb947ab8d234df5437f4fca
|
src/networkx/readwrite/adjlist.py
|
python
|
parse_adjlist
|
(lines, comments='#', delimiter=None,
create_using=None, nodetype=None)
|
return G
|
Parse lines of a graph adjacency list representation.
Parameters
----------
lines : list or iterator of strings
Input data in adjlist format
create_using: NetworkX graph container
Use given NetworkX graph for holding nodes or edges.
nodetype : Python type, optional
Convert nodes to this type.
comments : string, optional
Marker for comment lines
delimiter : string, optional
Separator for node labels. The default is whitespace.
Returns
-------
G: NetworkX graph
The graph corresponding to the lines in adjacency list format.
Examples
--------
>>> lines = ['1 2 5',
... '2 3 4',
... '3 5',
... '4',
... '5']
>>> G = nx.parse_adjlist(lines, nodetype=int)
>>> nodes = [1, 2, 3, 4, 5]
>>> all(node in G for node in nodes)
True
>>> edges = [(1, 2), (1, 5), (2, 3), (2, 4), (3, 5)]
>>> all((u, v) in G.edges() or (v, u) in G.edges() for (u, v) in edges)
True
See Also
--------
read_adjlist
|
Parse lines of a graph adjacency list representation.
|
[
"Parse",
"lines",
"of",
"a",
"graph",
"adjacency",
"list",
"representation",
"."
] |
def parse_adjlist(lines, comments='#', delimiter=None,
create_using=None, nodetype=None):
"""Parse lines of a graph adjacency list representation.
Parameters
----------
lines : list or iterator of strings
Input data in adjlist format
create_using: NetworkX graph container
Use given NetworkX graph for holding nodes or edges.
nodetype : Python type, optional
Convert nodes to this type.
comments : string, optional
Marker for comment lines
delimiter : string, optional
Separator for node labels. The default is whitespace.
Returns
-------
G: NetworkX graph
The graph corresponding to the lines in adjacency list format.
Examples
--------
>>> lines = ['1 2 5',
... '2 3 4',
... '3 5',
... '4',
... '5']
>>> G = nx.parse_adjlist(lines, nodetype=int)
>>> nodes = [1, 2, 3, 4, 5]
>>> all(node in G for node in nodes)
True
>>> edges = [(1, 2), (1, 5), (2, 3), (2, 4), (3, 5)]
>>> all((u, v) in G.edges() or (v, u) in G.edges() for (u, v) in edges)
True
See Also
--------
read_adjlist
"""
if create_using is None:
G = nx.Graph()
else:
try:
G = create_using
G.clear()
except:
raise TypeError("Input graph is not a NetworkX graph type")
for line in lines:
p = line.find(comments)
if p >= 0:
line = line[:p]
if not len(line):
continue
vlist = line.strip().split(delimiter)
u = vlist.pop(0)
# convert types
if nodetype is not None:
try:
u = nodetype(u)
except:
raise TypeError("Failed to convert node ({}) to type {}"
.format(u, nodetype))
G.add_node(u)
if nodetype is not None:
try:
vlist = map(nodetype, vlist)
except:
raise TypeError("Failed to convert nodes ({}) to type {}"
.format(','.join(vlist), nodetype))
G.add_edges_from([(u, v) for v in vlist])
return G
|
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https://github.com/aws-samples/aws-kube-codesuite/blob/ab4e5ce45416b83bffb947ab8d234df5437f4fca/src/networkx/readwrite/adjlist.py#L148-L226
|
|
tadejmagajna/HereIsWally
|
eba5274f65c1b9b636aba23942364933a632efc1
|
object_detection/core/model.py
|
python
|
DetectionModel.__init__
|
(self, num_classes)
|
Constructor.
Args:
num_classes: number of classes. Note that num_classes *does not* include
background categories that might be implicitly be predicted in various
implementations.
|
Constructor.
|
[
"Constructor",
"."
] |
def __init__(self, num_classes):
"""Constructor.
Args:
num_classes: number of classes. Note that num_classes *does not* include
background categories that might be implicitly be predicted in various
implementations.
"""
self._num_classes = num_classes
self._groundtruth_lists = {}
|
[
"def",
"__init__",
"(",
"self",
",",
"num_classes",
")",
":",
"self",
".",
"_num_classes",
"=",
"num_classes",
"self",
".",
"_groundtruth_lists",
"=",
"{",
"}"
] |
https://github.com/tadejmagajna/HereIsWally/blob/eba5274f65c1b9b636aba23942364933a632efc1/object_detection/core/model.py#L57-L66
|
||
aiidateam/aiida-core
|
c743a335480f8bb3a5e4ebd2463a31f9f3b9f9b2
|
aiida/schedulers/plugins/direct.py
|
python
|
DirectScheduler._parse_joblist_output
|
(self, retval, stdout, stderr)
|
return job_list
|
Parse the queue output string, as returned by executing the
command returned by _get_joblist_command command (qstat -f).
Return a list of JobInfo objects, one of each job,
each relevant parameters implemented.
.. note:: depending on the scheduler configuration, finished jobs
may either appear here, or not.
This function will only return one element for each job find
in the qstat output; missing jobs (for whatever reason) simply
will not appear here.
|
Parse the queue output string, as returned by executing the
command returned by _get_joblist_command command (qstat -f).
|
[
"Parse",
"the",
"queue",
"output",
"string",
"as",
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"the",
"command",
"returned",
"by",
"_get_joblist_command",
"command",
"(",
"qstat",
"-",
"f",
")",
"."
] |
def _parse_joblist_output(self, retval, stdout, stderr):
"""
Parse the queue output string, as returned by executing the
command returned by _get_joblist_command command (qstat -f).
Return a list of JobInfo objects, one of each job,
each relevant parameters implemented.
.. note:: depending on the scheduler configuration, finished jobs
may either appear here, or not.
This function will only return one element for each job find
in the qstat output; missing jobs (for whatever reason) simply
will not appear here.
"""
import re
filtered_stderr = '\n'.join(l for l in stderr.split('\n'))
if filtered_stderr.strip():
self.logger.warning(f"Warning in _parse_joblist_output, non-empty (filtered) stderr='{filtered_stderr}'")
if retval != 0:
raise SchedulerError('Error during direct execution parsing (_parse_joblist_output function)')
# Create dictionary and parse specific fields
job_list = []
for line in stdout.split('\n'):
if re.search(r'^\s*PID', line) or line == '':
# Skip the header if present
continue
line = re.sub(r'^\s+', '', line)
job = re.split(r'\s+', line)
this_job = JobInfo()
this_job.job_id = job[0]
if len(job) < 3:
raise SchedulerError(f"Unexpected output from the scheduler, not enough fields in line '{line}'")
try:
job_state_string = job[1][0] # I just check the first character
except IndexError:
self.logger.debug(f"No 'job_state' field for job id {this_job.job_id}")
this_job.job_state = JobState.UNDETERMINED
else:
try:
this_job.job_state = \
_MAP_STATUS_PS[job_state_string]
except KeyError:
self.logger.warning(f"Unrecognized job_state '{job_state_string}' for job id {this_job.job_id}")
this_job.job_state = JobState.UNDETERMINED
try:
# I strip the part after the @: is this always ok?
this_job.job_owner = job[2]
except KeyError:
self.logger.debug(f"No 'job_owner' field for job id {this_job.job_id}")
try:
this_job.wallclock_time_seconds = self._convert_time(job[3])
except KeyError:
# May not have started yet
pass
except ValueError:
self.logger.warning(f"Error parsing 'resources_used.walltime' for job id {this_job.job_id}")
# I append to the list of jobs to return
job_list.append(this_job)
return job_list
|
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"f\"No 'job_owner' field for job id {this_job.job_id}\"",
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"_convert_time",
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":",
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":",
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"logger",
".",
"warning",
"(",
"f\"Error parsing 'resources_used.walltime' for job id {this_job.job_id}\"",
")",
"# I append to the list of jobs to return",
"job_list",
".",
"append",
"(",
"this_job",
")",
"return",
"job_list"
] |
https://github.com/aiidateam/aiida-core/blob/c743a335480f8bb3a5e4ebd2463a31f9f3b9f9b2/aiida/schedulers/plugins/direct.py#L199-L265
|
|
Jajcus/pyxmpp2
|
59e5fd7c8837991ac265dc6aad23a6bd256768a7
|
pyxmpp2/ext/muc/muccore.py
|
python
|
MucPresence.copy
|
(self)
|
return MucPresence(self)
|
Return a copy of `self`.
|
Return a copy of `self`.
|
[
"Return",
"a",
"copy",
"of",
"self",
"."
] |
def copy(self):
"""
Return a copy of `self`.
"""
return MucPresence(self)
|
[
"def",
"copy",
"(",
"self",
")",
":",
"return",
"MucPresence",
"(",
"self",
")"
] |
https://github.com/Jajcus/pyxmpp2/blob/59e5fd7c8837991ac265dc6aad23a6bd256768a7/pyxmpp2/ext/muc/muccore.py#L703-L707
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/sqlalchemy/engine/base.py
|
python
|
Connection.recover_twophase
|
(self)
|
return self.engine.dialect.do_recover_twophase(self)
|
[] |
def recover_twophase(self):
return self.engine.dialect.do_recover_twophase(self)
|
[
"def",
"recover_twophase",
"(",
"self",
")",
":",
"return",
"self",
".",
"engine",
".",
"dialect",
".",
"do_recover_twophase",
"(",
"self",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/sqlalchemy/engine/base.py#L665-L666
|
|||
neuropsychology/NeuroKit
|
d01111b9b82364d28da01c002e6cbfc45d9493d9
|
neurokit2/complexity/optim_complexity_k.py
|
python
|
complexity_k
|
(signal, k_max="max", show=False)
|
return kmax_optimal, {
"Values": kmax_range,
"Scores": slopes,
"Intercepts": intercepts,
"Average_Values": average_values,
}
|
Automated selection of the optimal k_max parameter for Higuchi Fractal Dimension (HFD).
The optimal kmax is computed based on the point at which HFD values plateau for a range of kmax values (see Vega, 2015).
Parameters
----------
signal : Union[list, np.array, pd.Series]
The signal (i.e., a time series) in the form of a vector of values.
k_max : Union[int, str, list], optional
Maximum number of interval times (should be greater than or equal to 3) to be tested. If 'max',
it selects the maximum possible value corresponding to half the length of the signal.
show : bool
Visualise the slope of the curve for the selected kmax value.
Returns
--------
k : float
The optimal kmax of the time series.
info : dict
A dictionary containing additional information regarding the parameters used
to compute optimal kmax.
See Also
--------
fractal_higuchi
Examples
----------
>>> import neurokit2 as nk
>>>
>>> signal = nk.signal_simulate(duration=2, sampling_rate=100, frequency=[5, 6], noise=0.5)
>>> k_max, info = nk.complexity_k(signal, k_max='default', show=True)
>>> k_max #doctest: +SKIP
Reference
----------
- Higuchi, T. (1988). Approach to an irregular time series on the basis of the fractal theory.
Physica D: Nonlinear Phenomena, 31(2), 277-283.
- Vega, C. F., & Noel, J. (2015, June). Parameters analyzed of Higuchi's fractal dimension for EEG brain signals.
In 2015 Signal Processing Symposium (SPSympo) (pp. 1-5). IEEE. https://ieeexplore.ieee.org/document/7168285
|
Automated selection of the optimal k_max parameter for Higuchi Fractal Dimension (HFD).
|
[
"Automated",
"selection",
"of",
"the",
"optimal",
"k_max",
"parameter",
"for",
"Higuchi",
"Fractal",
"Dimension",
"(",
"HFD",
")",
"."
] |
def complexity_k(signal, k_max="max", show=False):
"""Automated selection of the optimal k_max parameter for Higuchi Fractal Dimension (HFD).
The optimal kmax is computed based on the point at which HFD values plateau for a range of kmax values (see Vega, 2015).
Parameters
----------
signal : Union[list, np.array, pd.Series]
The signal (i.e., a time series) in the form of a vector of values.
k_max : Union[int, str, list], optional
Maximum number of interval times (should be greater than or equal to 3) to be tested. If 'max',
it selects the maximum possible value corresponding to half the length of the signal.
show : bool
Visualise the slope of the curve for the selected kmax value.
Returns
--------
k : float
The optimal kmax of the time series.
info : dict
A dictionary containing additional information regarding the parameters used
to compute optimal kmax.
See Also
--------
fractal_higuchi
Examples
----------
>>> import neurokit2 as nk
>>>
>>> signal = nk.signal_simulate(duration=2, sampling_rate=100, frequency=[5, 6], noise=0.5)
>>> k_max, info = nk.complexity_k(signal, k_max='default', show=True)
>>> k_max #doctest: +SKIP
Reference
----------
- Higuchi, T. (1988). Approach to an irregular time series on the basis of the fractal theory.
Physica D: Nonlinear Phenomena, 31(2), 277-283.
- Vega, C. F., & Noel, J. (2015, June). Parameters analyzed of Higuchi's fractal dimension for EEG brain signals.
In 2015 Signal Processing Symposium (SPSympo) (pp. 1-5). IEEE. https://ieeexplore.ieee.org/document/7168285
"""
# Get the range of k-max values to be tested
# ------------------------------------------
if isinstance(k_max, str): # e.g., "default"
# upper limit for k value (max possible value)
k_max = int(np.floor(len(signal) / 2)) # so that normalizing factor is positive
if isinstance(k_max, int):
kmax_range = np.arange(2, k_max + 1)
elif isinstance(k_max, (list, np.ndarray, pd.Series)):
kmax_range = np.array(k_max)
else:
warn(
"k_max should be an int or a list of values of kmax to be tested.",
category=NeuroKitWarning,
)
# Compute the slope for each kmax value
# --------------------------------------
vectorized_k_slope = np.vectorize(_complexity_k_slope, excluded=[1])
slopes, intercepts, info = vectorized_k_slope(kmax_range, signal)
# k_values = [d["k_values"] for d in info]
average_values = [d["average_values"] for d in info]
# Find plateau (the saturation point of slope)
# --------------------------------------------
optimal_point = find_plateau(slopes, show=False)
if optimal_point is not None:
kmax_optimal = kmax_range[optimal_point]
else:
kmax_optimal = np.max(kmax_range)
warn(
"The optimal kmax value detected is 2 or less. There may be no plateau in this case. "
+ f"You can inspect the plot by set `show=True`. We will return optimal k_max = {kmax_optimal} (the max).",
category=NeuroKitWarning,
)
# Plot
if show:
_complexity_k_plot(kmax_range, slopes, kmax_optimal, ax=None)
# Return optimal tau and info dict
return kmax_optimal, {
"Values": kmax_range,
"Scores": slopes,
"Intercepts": intercepts,
"Average_Values": average_values,
}
|
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",",
"\"Intercepts\"",
":",
"intercepts",
",",
"\"Average_Values\"",
":",
"average_values",
",",
"}"
] |
https://github.com/neuropsychology/NeuroKit/blob/d01111b9b82364d28da01c002e6cbfc45d9493d9/neurokit2/complexity/optim_complexity_k.py#L10-L99
|
|
JiYou/openstack
|
8607dd488bde0905044b303eb6e52bdea6806923
|
packages/source/ceilometer/ceilometer/openstack/common/timeutils.py
|
python
|
normalize_time
|
(timestamp)
|
return timestamp.replace(tzinfo=None) - offset
|
Normalize time in arbitrary timezone to UTC naive object.
|
Normalize time in arbitrary timezone to UTC naive object.
|
[
"Normalize",
"time",
"in",
"arbitrary",
"timezone",
"to",
"UTC",
"naive",
"object",
"."
] |
def normalize_time(timestamp):
"""Normalize time in arbitrary timezone to UTC naive object."""
offset = timestamp.utcoffset()
if offset is None:
return timestamp
return timestamp.replace(tzinfo=None) - offset
|
[
"def",
"normalize_time",
"(",
"timestamp",
")",
":",
"offset",
"=",
"timestamp",
".",
"utcoffset",
"(",
")",
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"offset",
"is",
"None",
":",
"return",
"timestamp",
"return",
"timestamp",
".",
"replace",
"(",
"tzinfo",
"=",
"None",
")",
"-",
"offset"
] |
https://github.com/JiYou/openstack/blob/8607dd488bde0905044b303eb6e52bdea6806923/packages/source/ceilometer/ceilometer/openstack/common/timeutils.py#L68-L73
|
|
tomplus/kubernetes_asyncio
|
f028cc793e3a2c519be6a52a49fb77ff0b014c9b
|
kubernetes_asyncio/client/models/v1_service_spec.py
|
python
|
V1ServiceSpec.selector
|
(self)
|
return self._selector
|
Gets the selector of this V1ServiceSpec. # noqa: E501
Route service traffic to pods with label keys and values matching this selector. If empty or not present, the service is assumed to have an external process managing its endpoints, which Kubernetes will not modify. Only applies to types ClusterIP, NodePort, and LoadBalancer. Ignored if type is ExternalName. More info: https://kubernetes.io/docs/concepts/services-networking/service/ # noqa: E501
:return: The selector of this V1ServiceSpec. # noqa: E501
:rtype: dict(str, str)
|
Gets the selector of this V1ServiceSpec. # noqa: E501
|
[
"Gets",
"the",
"selector",
"of",
"this",
"V1ServiceSpec",
".",
"#",
"noqa",
":",
"E501"
] |
def selector(self):
"""Gets the selector of this V1ServiceSpec. # noqa: E501
Route service traffic to pods with label keys and values matching this selector. If empty or not present, the service is assumed to have an external process managing its endpoints, which Kubernetes will not modify. Only applies to types ClusterIP, NodePort, and LoadBalancer. Ignored if type is ExternalName. More info: https://kubernetes.io/docs/concepts/services-networking/service/ # noqa: E501
:return: The selector of this V1ServiceSpec. # noqa: E501
:rtype: dict(str, str)
"""
return self._selector
|
[
"def",
"selector",
"(",
"self",
")",
":",
"return",
"self",
".",
"_selector"
] |
https://github.com/tomplus/kubernetes_asyncio/blob/f028cc793e3a2c519be6a52a49fb77ff0b014c9b/kubernetes_asyncio/client/models/v1_service_spec.py#L356-L364
|
|
Lonero-Team/Decentralized-Internet
|
3cb157834fcc19ff8c2316e66bf07b103c137068
|
packages/p2lara/src/storages/bigchaindb/lib.py
|
python
|
BigchainDB.migrate_abci_chain
|
(self)
|
Generate and record a new ABCI chain ID. New blocks are not
accepted until we receive an InitChain ABCI request with
the matching chain ID and validator set.
Chain ID is generated based on the current chain and height.
`chain-X` => `chain-X-migrated-at-height-5`.
`chain-X-migrated-at-height-5` => `chain-X-migrated-at-height-21`.
If there is no known chain (we are at genesis), the function returns.
|
Generate and record a new ABCI chain ID. New blocks are not
accepted until we receive an InitChain ABCI request with
the matching chain ID and validator set.
|
[
"Generate",
"and",
"record",
"a",
"new",
"ABCI",
"chain",
"ID",
".",
"New",
"blocks",
"are",
"not",
"accepted",
"until",
"we",
"receive",
"an",
"InitChain",
"ABCI",
"request",
"with",
"the",
"matching",
"chain",
"ID",
"and",
"validator",
"set",
"."
] |
def migrate_abci_chain(self):
"""Generate and record a new ABCI chain ID. New blocks are not
accepted until we receive an InitChain ABCI request with
the matching chain ID and validator set.
Chain ID is generated based on the current chain and height.
`chain-X` => `chain-X-migrated-at-height-5`.
`chain-X-migrated-at-height-5` => `chain-X-migrated-at-height-21`.
If there is no known chain (we are at genesis), the function returns.
"""
latest_chain = self.get_latest_abci_chain()
if latest_chain is None:
return
block = self.get_latest_block()
suffix = '-migrated-at-height-'
chain_id = latest_chain['chain_id']
block_height_str = str(block['height'])
new_chain_id = chain_id.split(suffix)[0] + suffix + block_height_str
self.store_abci_chain(block['height'] + 1, new_chain_id, False)
|
[
"def",
"migrate_abci_chain",
"(",
"self",
")",
":",
"latest_chain",
"=",
"self",
".",
"get_latest_abci_chain",
"(",
")",
"if",
"latest_chain",
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"'-migrated-at-height-'",
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"[",
"'height'",
"]",
"+",
"1",
",",
"new_chain_id",
",",
"False",
")"
] |
https://github.com/Lonero-Team/Decentralized-Internet/blob/3cb157834fcc19ff8c2316e66bf07b103c137068/packages/p2lara/src/storages/bigchaindb/lib.py#L478-L500
|
||
log2timeline/plaso
|
fe2e316b8c76a0141760c0f2f181d84acb83abc2
|
plaso/multi_process/extraction_engine.py
|
python
|
ExtractionMultiProcessEngine._StopExtractionProcesses
|
(self, abort=False)
|
Stops the extraction processes.
Args:
abort (bool): True to indicated the stop is issued on abort.
|
Stops the extraction processes.
|
[
"Stops",
"the",
"extraction",
"processes",
"."
] |
def _StopExtractionProcesses(self, abort=False):
"""Stops the extraction processes.
Args:
abort (bool): True to indicated the stop is issued on abort.
"""
logger.debug('Stopping extraction processes.')
self._StopMonitoringProcesses()
if abort:
# Signal all the processes to abort.
self._AbortTerminate()
logger.debug('Emptying task queue.')
self._task_queue.Empty()
# Wake the processes to make sure that they are not blocking
# waiting for the queue new items.
for _ in self._processes_per_pid:
try:
self._task_queue.PushItem(plaso_queue.QueueAbort(), block=False)
except errors.QueueFull:
logger.warning('Task queue full, unable to push abort message.')
# Try waiting for the processes to exit normally.
self._AbortJoin(timeout=self._PROCESS_JOIN_TIMEOUT)
self._task_queue.Close(abort=abort)
if not abort:
# Check if the processes are still alive and terminate them if necessary.
self._AbortTerminate()
self._AbortJoin(timeout=self._PROCESS_JOIN_TIMEOUT)
self._task_queue.Close(abort=True)
# Kill any lingering processes.
self._AbortKill()
|
[
"def",
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"(",
"self",
",",
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"abort",
"=",
"True",
")",
"# Kill any lingering processes.",
"self",
".",
"_AbortKill",
"(",
")"
] |
https://github.com/log2timeline/plaso/blob/fe2e316b8c76a0141760c0f2f181d84acb83abc2/plaso/multi_process/extraction_engine.py#L790-L825
|
||
DataDog/integrations-core
|
934674b29d94b70ccc008f76ea172d0cdae05e1e
|
openmetrics/datadog_checks/openmetrics/config_models/shared.py
|
python
|
SharedConfig._final_validation
|
(cls, values)
|
return validation.core.finalize_config(getattr(validators, 'finalize_shared', identity)(values))
|
[] |
def _final_validation(cls, values):
return validation.core.finalize_config(getattr(validators, 'finalize_shared', identity)(values))
|
[
"def",
"_final_validation",
"(",
"cls",
",",
"values",
")",
":",
"return",
"validation",
".",
"core",
".",
"finalize_config",
"(",
"getattr",
"(",
"validators",
",",
"'finalize_shared'",
",",
"identity",
")",
"(",
"values",
")",
")"
] |
https://github.com/DataDog/integrations-core/blob/934674b29d94b70ccc008f76ea172d0cdae05e1e/openmetrics/datadog_checks/openmetrics/config_models/shared.py#L59-L60
|
|||
robotlearn/pyrobolearn
|
9cd7c060723fda7d2779fa255ac998c2c82b8436
|
pyrobolearn/exploration/actions/boltzmann.py
|
python
|
BoltzmannActionExploration.__init__
|
(self, policy, action)
|
Initialize the Boltzmann action exploration strategy.
Args:
policy (Policy): policy to wrap.
action (Action): discrete actions.
|
Initialize the Boltzmann action exploration strategy.
|
[
"Initialize",
"the",
"Boltzmann",
"action",
"exploration",
"strategy",
"."
] |
def __init__(self, policy, action):
"""
Initialize the Boltzmann action exploration strategy.
Args:
policy (Policy): policy to wrap.
action (Action): discrete actions.
"""
super(BoltzmannActionExploration, self).__init__(policy, action=action)
# create Categorical module
logits = IdentityModule()
self._module = CategoricalModule(logits=logits)
|
[
"def",
"__init__",
"(",
"self",
",",
"policy",
",",
"action",
")",
":",
"super",
"(",
"BoltzmannActionExploration",
",",
"self",
")",
".",
"__init__",
"(",
"policy",
",",
"action",
"=",
"action",
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"# create Categorical module",
"logits",
"=",
"IdentityModule",
"(",
")",
"self",
".",
"_module",
"=",
"CategoricalModule",
"(",
"logits",
"=",
"logits",
")"
] |
https://github.com/robotlearn/pyrobolearn/blob/9cd7c060723fda7d2779fa255ac998c2c82b8436/pyrobolearn/exploration/actions/boltzmann.py#L39-L51
|
||
angr/angr
|
4b04d56ace135018083d36d9083805be8146688b
|
angr/analyses/typehoon/translator.py
|
python
|
TypeTranslator._translate_Pointer64
|
(self, tc)
|
return sim_type.SimTypePointer(internal).with_arch(self.arch)
|
[] |
def _translate_Pointer64(self, tc):
if isinstance(tc.basetype, typeconsts.BottomType):
# void *
internal = sim_type.SimTypeBottom(label="void").with_arch(self.arch)
else:
internal = self._tc2simtype(tc.basetype)
return sim_type.SimTypePointer(internal).with_arch(self.arch)
|
[
"def",
"_translate_Pointer64",
"(",
"self",
",",
"tc",
")",
":",
"if",
"isinstance",
"(",
"tc",
".",
"basetype",
",",
"typeconsts",
".",
"BottomType",
")",
":",
"# void *",
"internal",
"=",
"sim_type",
".",
"SimTypeBottom",
"(",
"label",
"=",
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".",
"with_arch",
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".",
"arch",
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"else",
":",
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"=",
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"_tc2simtype",
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"basetype",
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"return",
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".",
"SimTypePointer",
"(",
"internal",
")",
".",
"with_arch",
"(",
"self",
".",
"arch",
")"
] |
https://github.com/angr/angr/blob/4b04d56ace135018083d36d9083805be8146688b/angr/analyses/typehoon/translator.py#L81-L88
|
|||
kubernetes-client/python
|
47b9da9de2d02b2b7a34fbe05afb44afd130d73a
|
kubernetes/client/models/v1beta1_priority_level_configuration_reference.py
|
python
|
V1beta1PriorityLevelConfigurationReference.__init__
|
(self, name=None, local_vars_configuration=None)
|
V1beta1PriorityLevelConfigurationReference - a model defined in OpenAPI
|
V1beta1PriorityLevelConfigurationReference - a model defined in OpenAPI
|
[
"V1beta1PriorityLevelConfigurationReference",
"-",
"a",
"model",
"defined",
"in",
"OpenAPI"
] |
def __init__(self, name=None, local_vars_configuration=None): # noqa: E501
"""V1beta1PriorityLevelConfigurationReference - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._name = None
self.discriminator = None
self.name = name
|
[
"def",
"__init__",
"(",
"self",
",",
"name",
"=",
"None",
",",
"local_vars_configuration",
"=",
"None",
")",
":",
"# noqa: E501",
"# noqa: E501",
"if",
"local_vars_configuration",
"is",
"None",
":",
"local_vars_configuration",
"=",
"Configuration",
"(",
")",
"self",
".",
"local_vars_configuration",
"=",
"local_vars_configuration",
"self",
".",
"_name",
"=",
"None",
"self",
".",
"discriminator",
"=",
"None",
"self",
".",
"name",
"=",
"name"
] |
https://github.com/kubernetes-client/python/blob/47b9da9de2d02b2b7a34fbe05afb44afd130d73a/kubernetes/client/models/v1beta1_priority_level_configuration_reference.py#L43-L52
|
||
tensorflow/lingvo
|
ce10019243d954c3c3ebe739f7589b5eebfdf907
|
lingvo/tools/gke_launch.py
|
python
|
tensorboard_template
|
(job_name, logdir, port)
|
return """
apiVersion: apps/v1
kind: Deployment
metadata:
name: {job_name}
spec:
replicas: 1
selector:
matchLabels:
name: {job_name}
template:
metadata:
labels:
name: {job_name}
spec:
restartPolicy: Always
containers:
- name: {container_name}
image: gcr.io/tensorflow/tpu-util:r1.11
command:
- tensorboard
- --logdir=$(MODEL_BUCKET)
env:
- name: MODEL_BUCKET
value: {logdir}
ports:
- containerPort: {port}
---
apiVersion: v1
kind: Service
metadata:
name: {container_name}-service
spec:
type: LoadBalancer
selector:
name: {job_name}
ports:
- port: {port}
targetPort: {port}
""".format(
job_name=job_name, container_name=container_name, logdir=logdir, port=port)
|
Constructs the tensorboard YAML template.
|
Constructs the tensorboard YAML template.
|
[
"Constructs",
"the",
"tensorboard",
"YAML",
"template",
"."
] |
def tensorboard_template(job_name, logdir, port):
"""Constructs the tensorboard YAML template."""
job_name = six.ensure_str(job_name) + ".tensorboard"
container_name = job_name.replace(".", "-")
print("To poll for tensorboard address, run: $ kubectl get service %s -w" %
(container_name + "-service"))
return """
apiVersion: apps/v1
kind: Deployment
metadata:
name: {job_name}
spec:
replicas: 1
selector:
matchLabels:
name: {job_name}
template:
metadata:
labels:
name: {job_name}
spec:
restartPolicy: Always
containers:
- name: {container_name}
image: gcr.io/tensorflow/tpu-util:r1.11
command:
- tensorboard
- --logdir=$(MODEL_BUCKET)
env:
- name: MODEL_BUCKET
value: {logdir}
ports:
- containerPort: {port}
---
apiVersion: v1
kind: Service
metadata:
name: {container_name}-service
spec:
type: LoadBalancer
selector:
name: {job_name}
ports:
- port: {port}
targetPort: {port}
""".format(
job_name=job_name, container_name=container_name, logdir=logdir, port=port)
|
[
"def",
"tensorboard_template",
"(",
"job_name",
",",
"logdir",
",",
"port",
")",
":",
"job_name",
"=",
"six",
".",
"ensure_str",
"(",
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"\".tensorboard\"",
"container_name",
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"job_name",
".",
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"(",
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",",
"\"-\"",
")",
"print",
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"\"To poll for tensorboard address, run: $ kubectl get service %s -w\"",
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"(",
"container_name",
"+",
"\"-service\"",
")",
")",
"return",
"\"\"\"\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: {job_name}\nspec:\n replicas: 1\n selector:\n matchLabels:\n name: {job_name}\n template:\n metadata:\n labels:\n name: {job_name}\n spec:\n restartPolicy: Always\n containers:\n - name: {container_name}\n image: gcr.io/tensorflow/tpu-util:r1.11\n command:\n - tensorboard\n - --logdir=$(MODEL_BUCKET)\n env:\n - name: MODEL_BUCKET\n value: {logdir}\n ports:\n - containerPort: {port}\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: {container_name}-service\nspec:\n type: LoadBalancer\n selector:\n name: {job_name}\n ports:\n - port: {port}\n targetPort: {port}\n\"\"\"",
".",
"format",
"(",
"job_name",
"=",
"job_name",
",",
"container_name",
"=",
"container_name",
",",
"logdir",
"=",
"logdir",
",",
"port",
"=",
"port",
")"
] |
https://github.com/tensorflow/lingvo/blob/ce10019243d954c3c3ebe739f7589b5eebfdf907/lingvo/tools/gke_launch.py#L229-L276
|
|
plotly/plotly.py
|
cfad7862594b35965c0e000813bd7805e8494a5b
|
packages/python/plotly/plotly/graph_objs/indicator/gauge/_axis.py
|
python
|
Axis.tickwidth
|
(self)
|
return self["tickwidth"]
|
Sets the tick width (in px).
The 'tickwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
|
Sets the tick width (in px).
The 'tickwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
|
[
"Sets",
"the",
"tick",
"width",
"(",
"in",
"px",
")",
".",
"The",
"tickwidth",
"property",
"is",
"a",
"number",
"and",
"may",
"be",
"specified",
"as",
":",
"-",
"An",
"int",
"or",
"float",
"in",
"the",
"interval",
"[",
"0",
"inf",
"]"
] |
def tickwidth(self):
"""
Sets the tick width (in px).
The 'tickwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["tickwidth"]
|
[
"def",
"tickwidth",
"(",
"self",
")",
":",
"return",
"self",
"[",
"\"tickwidth\"",
"]"
] |
https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/graph_objs/indicator/gauge/_axis.py#L753-L764
|
|
EricSteinberger/Deep-CFR
|
2e664ebbe5bf3c8f8ab56057205cbe4c2c7baeb5
|
DeepCFR/IterationStrategy.py
|
python
|
IterationStrategy.get_a_probs_for_each_hand
|
(self, pub_obs, legal_actions_list)
|
return self._get_a_probs_of_hands(pub_obs=pub_obs, legal_actions_list=legal_actions_list,
range_idxs_tensor=self._all_range_idxs)
|
Args:
pub_obs (np.array(shape=(seq_len, n_features,)))
legal_actions_list (list): list of ints representing legal actions
|
Args:
pub_obs (np.array(shape=(seq_len, n_features,)))
legal_actions_list (list): list of ints representing legal actions
|
[
"Args",
":",
"pub_obs",
"(",
"np",
".",
"array",
"(",
"shape",
"=",
"(",
"seq_len",
"n_features",
")))",
"legal_actions_list",
"(",
"list",
")",
":",
"list",
"of",
"ints",
"representing",
"legal",
"actions"
] |
def get_a_probs_for_each_hand(self, pub_obs, legal_actions_list):
"""
Args:
pub_obs (np.array(shape=(seq_len, n_features,)))
legal_actions_list (list): list of ints representing legal actions
"""
if self._t_prof.DEBUGGING:
assert isinstance(pub_obs, np.ndarray)
assert len(pub_obs.shape) == 2, "all hands have the same public obs"
assert isinstance(legal_actions_list[0],
int), "all hands do the same actions. no need to batch, just parse int"
return self._get_a_probs_of_hands(pub_obs=pub_obs, legal_actions_list=legal_actions_list,
range_idxs_tensor=self._all_range_idxs)
|
[
"def",
"get_a_probs_for_each_hand",
"(",
"self",
",",
"pub_obs",
",",
"legal_actions_list",
")",
":",
"if",
"self",
".",
"_t_prof",
".",
"DEBUGGING",
":",
"assert",
"isinstance",
"(",
"pub_obs",
",",
"np",
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"ndarray",
")",
"assert",
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"pub_obs",
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"shape",
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"==",
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",",
"\"all hands have the same public obs\"",
"assert",
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"(",
"legal_actions_list",
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"0",
"]",
",",
"int",
")",
",",
"\"all hands do the same actions. no need to batch, just parse int\"",
"return",
"self",
".",
"_get_a_probs_of_hands",
"(",
"pub_obs",
"=",
"pub_obs",
",",
"legal_actions_list",
"=",
"legal_actions_list",
",",
"range_idxs_tensor",
"=",
"self",
".",
"_all_range_idxs",
")"
] |
https://github.com/EricSteinberger/Deep-CFR/blob/2e664ebbe5bf3c8f8ab56057205cbe4c2c7baeb5/DeepCFR/IterationStrategy.py#L116-L130
|
|
kivymd/KivyMD
|
1cb82f7d2437770f71be7c5a4f7de4b8da61f352
|
kivymd/uix/bottomnavigation/bottomnavigation.py
|
python
|
MDBottomNavigation.refresh_tabs
|
(self, *args)
|
Refresh all tabs.
|
Refresh all tabs.
|
[
"Refresh",
"all",
"tabs",
"."
] |
def refresh_tabs(self, *args) -> NoReturn:
"""Refresh all tabs."""
if self.ids:
tab_bar = self.ids.tab_bar
tab_bar.clear_widgets()
tab_manager = self.ids.tab_manager
self._active_color = self.theme_cls.primary_color
if self.text_color_active != [1, 1, 1, 1]:
self._active_color = self.text_color_active
for tab in tab_manager.screens:
self.tab_header = MDBottomNavigationHeader(tab=tab, panel=self)
tab.header = self.tab_header
tab_bar.add_widget(self.tab_header)
if tab is self.first_widget:
self.tab_header._text_color_normal = self._active_color
self.tab_header._label_font_size = sp(14)
self.tab_header.active = True
else:
self.tab_header.ids._label.font_size = sp(12)
self.tab_header._label_font_size = sp(12)
|
[
"def",
"refresh_tabs",
"(",
"self",
",",
"*",
"args",
")",
"->",
"NoReturn",
":",
"if",
"self",
".",
"ids",
":",
"tab_bar",
"=",
"self",
".",
"ids",
".",
"tab_bar",
"tab_bar",
".",
"clear_widgets",
"(",
")",
"tab_manager",
"=",
"self",
".",
"ids",
".",
"tab_manager",
"self",
".",
"_active_color",
"=",
"self",
".",
"theme_cls",
".",
"primary_color",
"if",
"self",
".",
"text_color_active",
"!=",
"[",
"1",
",",
"1",
",",
"1",
",",
"1",
"]",
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"self",
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"_active_color",
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"text_color_active",
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".",
"screens",
":",
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"=",
"MDBottomNavigationHeader",
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"tab",
",",
"panel",
"=",
"self",
")",
"tab",
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"tab_bar",
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"(",
"self",
".",
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".",
"first_widget",
":",
"self",
".",
"tab_header",
".",
"_text_color_normal",
"=",
"self",
".",
"_active_color",
"self",
".",
"tab_header",
".",
"_label_font_size",
"=",
"sp",
"(",
"14",
")",
"self",
".",
"tab_header",
".",
"active",
"=",
"True",
"else",
":",
"self",
".",
"tab_header",
".",
"ids",
".",
"_label",
".",
"font_size",
"=",
"sp",
"(",
"12",
")",
"self",
".",
"tab_header",
".",
"_label_font_size",
"=",
"sp",
"(",
"12",
")"
] |
https://github.com/kivymd/KivyMD/blob/1cb82f7d2437770f71be7c5a4f7de4b8da61f352/kivymd/uix/bottomnavigation/bottomnavigation.py#L586-L606
|
||
bungnoid/glTools
|
8ff0899de43784a18bd4543285655e68e28fb5e5
|
utils/nDynamics.py
|
python
|
connectToNucleus
|
(object,nucleus)
|
return nucleus
|
Connect the specified nDynamics node to an existing nucleus node
@param object: nDynamics node to connect to the nucleus solver
@type object: str
@param nucleus: nucleus solver to connect to
@type nucleus: str
|
Connect the specified nDynamics node to an existing nucleus node
|
[
"Connect",
"the",
"specified",
"nDynamics",
"node",
"to",
"an",
"existing",
"nucleus",
"node"
] |
def connectToNucleus(object,nucleus):
'''
Connect the specified nDynamics node to an existing nucleus node
@param object: nDynamics node to connect to the nucleus solver
@type object: str
@param nucleus: nucleus solver to connect to
@type nucleus: str
'''
# Check nucleus
if not isNucleus(nucleus):
preNucleusList = mc.ls(type='nucleus')
# Check nDynamics node
if isNDynamicsNode(object):
nNode = object
else:
nNode = getConnectedNNode(nNode)
if not nNode: raise Exception('Object "'+object+'" is not a valid nDynamics node, or connected to a valid nDynamics node!')
nNode = nNode[0]
# Check nRigid
if isNRigid(nNode): connectNRigidToNucleus(nNode,nucleus,True)
# Assign nNode to nucleus solver
mc.select(nNode)
mm.eval('assignNSolver '+nucleus)
# Rename new nucleus node
if not mc.objExists(nucleus):
postNucleusList = mc.ls(type='nucleus')
newNucleus = list(set(postNucleusList) - set(preNucleusList))
if not newNucleus: raise Exception('Unable to determine new nucleus node attached to "'+object+'"!')
nucleus = mc.rename(newNucleus[0],nucleus)
# Return result
mc.select(nucleus)
return nucleus
|
[
"def",
"connectToNucleus",
"(",
"object",
",",
"nucleus",
")",
":",
"# Check nucleus",
"if",
"not",
"isNucleus",
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"nucleus",
")",
":",
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"'Object \"'",
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"nNode",
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"nucleus",
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":",
"postNucleusList",
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")",
"newNucleus",
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"(",
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"(",
"postNucleusList",
")",
"-",
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"(",
"preNucleusList",
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"(",
"'Unable to determine new nucleus node attached to \"'",
"+",
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"+",
"'\"!'",
")",
"nucleus",
"=",
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"newNucleus",
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"0",
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",",
"nucleus",
")",
"# Return result",
"mc",
".",
"select",
"(",
"nucleus",
")",
"return",
"nucleus"
] |
https://github.com/bungnoid/glTools/blob/8ff0899de43784a18bd4543285655e68e28fb5e5/utils/nDynamics.py#L442-L478
|
|
dimagi/commcare-hq
|
d67ff1d3b4c51fa050c19e60c3253a79d3452a39
|
custom/inddex/filters.py
|
python
|
GapTypeFilter.options
|
(self)
|
return [
(ConvFactorGaps.slug, ConvFactorGaps.name),
(FctGaps.slug, FctGaps.name),
]
|
[] |
def options(self):
return [
(ConvFactorGaps.slug, ConvFactorGaps.name),
(FctGaps.slug, FctGaps.name),
]
|
[
"def",
"options",
"(",
"self",
")",
":",
"return",
"[",
"(",
"ConvFactorGaps",
".",
"slug",
",",
"ConvFactorGaps",
".",
"name",
")",
",",
"(",
"FctGaps",
".",
"slug",
",",
"FctGaps",
".",
"name",
")",
",",
"]"
] |
https://github.com/dimagi/commcare-hq/blob/d67ff1d3b4c51fa050c19e60c3253a79d3452a39/custom/inddex/filters.py#L143-L147
|
|||
openstack/barbican
|
a9d2b133c8dc3307974f119f9a2b23a4ba82e8ce
|
barbican/common/exception.py
|
python
|
MultipleSecretStoreLookupFailed.__init__
|
(self)
|
[] |
def __init__(self):
msg = u._("Plugin lookup property 'stores_lookup_suffix' is not "
"defined in service configuration")
super(MultipleSecretStoreLookupFailed, self).__init__(msg)
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"msg",
"=",
"u",
".",
"_",
"(",
"\"Plugin lookup property 'stores_lookup_suffix' is not \"",
"\"defined in service configuration\"",
")",
"super",
"(",
"MultipleSecretStoreLookupFailed",
",",
"self",
")",
".",
"__init__",
"(",
"msg",
")"
] |
https://github.com/openstack/barbican/blob/a9d2b133c8dc3307974f119f9a2b23a4ba82e8ce/barbican/common/exception.py#L372-L375
|
||||
rwth-i6/returnn
|
f2d718a197a280b0d5f0fd91a7fcb8658560dddb
|
returnn/tf/layers/rec.py
|
python
|
BaseChoiceLayer.__init__
|
(self, beam_size, search=NotSpecified, **kwargs)
|
:param int|None beam_size: the outgoing beam size. i.e. our output will be (batch * beam_size, ...)
:param NotSpecified|bool search: whether to perform search, or use the ground truth (`target` option).
If not specified, it will depend on `network.search_flag`.
|
:param int|None beam_size: the outgoing beam size. i.e. our output will be (batch * beam_size, ...)
:param NotSpecified|bool search: whether to perform search, or use the ground truth (`target` option).
If not specified, it will depend on `network.search_flag`.
|
[
":",
"param",
"int|None",
"beam_size",
":",
"the",
"outgoing",
"beam",
"size",
".",
"i",
".",
"e",
".",
"our",
"output",
"will",
"be",
"(",
"batch",
"*",
"beam_size",
"...",
")",
":",
"param",
"NotSpecified|bool",
"search",
":",
"whether",
"to",
"perform",
"search",
"or",
"use",
"the",
"ground",
"truth",
"(",
"target",
"option",
")",
".",
"If",
"not",
"specified",
"it",
"will",
"depend",
"on",
"network",
".",
"search_flag",
"."
] |
def __init__(self, beam_size, search=NotSpecified, **kwargs):
"""
:param int|None beam_size: the outgoing beam size. i.e. our output will be (batch * beam_size, ...)
:param NotSpecified|bool search: whether to perform search, or use the ground truth (`target` option).
If not specified, it will depend on `network.search_flag`.
"""
super(BaseChoiceLayer, self).__init__(**kwargs)
|
[
"def",
"__init__",
"(",
"self",
",",
"beam_size",
",",
"search",
"=",
"NotSpecified",
",",
"*",
"*",
"kwargs",
")",
":",
"super",
"(",
"BaseChoiceLayer",
",",
"self",
")",
".",
"__init__",
"(",
"*",
"*",
"kwargs",
")"
] |
https://github.com/rwth-i6/returnn/blob/f2d718a197a280b0d5f0fd91a7fcb8658560dddb/returnn/tf/layers/rec.py#L4969-L4975
|
||
williballenthin/python-registry
|
11e857623469dd28ed14519a08d2db7c8228ca0c
|
Registry/RegistryParse.py
|
python
|
REGFBlock.is_old_transaction_log_file
|
(self)
|
return (self.file_type() == FileType.FILE_TYPE_LOG_OLD_1) or (self.file_type() == FileType.FILE_TYPE_LOG_OLD_2)
|
Check if this REGF block belongs to an old transaction log file (used before Windows 8.1).
|
Check if this REGF block belongs to an old transaction log file (used before Windows 8.1).
|
[
"Check",
"if",
"this",
"REGF",
"block",
"belongs",
"to",
"an",
"old",
"transaction",
"log",
"file",
"(",
"used",
"before",
"Windows",
"8",
".",
"1",
")",
"."
] |
def is_old_transaction_log_file(self):
"""
Check if this REGF block belongs to an old transaction log file (used before Windows 8.1).
"""
return (self.file_type() == FileType.FILE_TYPE_LOG_OLD_1) or (self.file_type() == FileType.FILE_TYPE_LOG_OLD_2)
|
[
"def",
"is_old_transaction_log_file",
"(",
"self",
")",
":",
"return",
"(",
"self",
".",
"file_type",
"(",
")",
"==",
"FileType",
".",
"FILE_TYPE_LOG_OLD_1",
")",
"or",
"(",
"self",
".",
"file_type",
"(",
")",
"==",
"FileType",
".",
"FILE_TYPE_LOG_OLD_2",
")"
] |
https://github.com/williballenthin/python-registry/blob/11e857623469dd28ed14519a08d2db7c8228ca0c/Registry/RegistryParse.py#L396-L400
|
|
jwasham/code-catalog-python
|
c8645a1058b970206e688bfcb1782c18c64bcc00
|
catalog/suggested/lists/circular_queue.py
|
python
|
CircularQueue.dequeue
|
(self)
|
return oldhead._element
|
Remove and return the first element of the queue (i.e., FIFO).
Raise Empty exception if the queue is empty.
|
Remove and return the first element of the queue (i.e., FIFO).
|
[
"Remove",
"and",
"return",
"the",
"first",
"element",
"of",
"the",
"queue",
"(",
"i",
".",
"e",
".",
"FIFO",
")",
"."
] |
def dequeue(self):
"""Remove and return the first element of the queue (i.e., FIFO).
Raise Empty exception if the queue is empty.
"""
if self.is_empty():
raise Empty('Queue is empty')
oldhead = self._tail._next
if self._size == 1: # removing only element
self._tail = None # queue becomes empty
else:
self._tail._next = oldhead._next # bypass the old head
self._size -= 1
return oldhead._element
|
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https://github.com/jwasham/code-catalog-python/blob/c8645a1058b970206e688bfcb1782c18c64bcc00/catalog/suggested/lists/circular_queue.py#L45-L58
|
|
linxid/Machine_Learning_Study_Path
|
558e82d13237114bbb8152483977806fc0c222af
|
Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/requests/packages/urllib3/contrib/pyopenssl.py
|
python
|
ssl_wrap_socket
|
(sock, keyfile=None, certfile=None, cert_reqs=None,
ca_certs=None, server_hostname=None,
ssl_version=None, ca_cert_dir=None)
|
return WrappedSocket(cnx, sock)
|
[] |
def ssl_wrap_socket(sock, keyfile=None, certfile=None, cert_reqs=None,
ca_certs=None, server_hostname=None,
ssl_version=None, ca_cert_dir=None):
ctx = OpenSSL.SSL.Context(_openssl_versions[ssl_version])
if certfile:
keyfile = keyfile or certfile # Match behaviour of the normal python ssl library
ctx.use_certificate_file(certfile)
if keyfile:
ctx.use_privatekey_file(keyfile)
if cert_reqs != ssl.CERT_NONE:
ctx.set_verify(_openssl_verify[cert_reqs], _verify_callback)
if ca_certs or ca_cert_dir:
try:
ctx.load_verify_locations(ca_certs, ca_cert_dir)
except OpenSSL.SSL.Error as e:
raise ssl.SSLError('bad ca_certs: %r' % ca_certs, e)
else:
ctx.set_default_verify_paths()
# Disable TLS compression to mitigate CRIME attack (issue #309)
OP_NO_COMPRESSION = 0x20000
ctx.set_options(OP_NO_COMPRESSION)
# Set list of supported ciphersuites.
ctx.set_cipher_list(DEFAULT_SSL_CIPHER_LIST)
cnx = OpenSSL.SSL.Connection(ctx, sock)
if isinstance(server_hostname, six.text_type): # Platform-specific: Python 3
server_hostname = server_hostname.encode('utf-8')
cnx.set_tlsext_host_name(server_hostname)
cnx.set_connect_state()
while True:
try:
cnx.do_handshake()
except OpenSSL.SSL.WantReadError:
rd, _, _ = select.select([sock], [], [], sock.gettimeout())
if not rd:
raise timeout('select timed out')
continue
except OpenSSL.SSL.Error as e:
raise ssl.SSLError('bad handshake: %r' % e)
break
return WrappedSocket(cnx, sock)
|
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"return",
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"(",
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",",
"sock",
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] |
https://github.com/linxid/Machine_Learning_Study_Path/blob/558e82d13237114bbb8152483977806fc0c222af/Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/requests/packages/urllib3/contrib/pyopenssl.py#L315-L358
|
|||
CuriousAI/mean-teacher
|
546348ff863c998c26be4339021425df973b4a36
|
pytorch/mean_teacher/data.py
|
python
|
TwoStreamBatchSampler.__init__
|
(self, primary_indices, secondary_indices, batch_size, secondary_batch_size)
|
[] |
def __init__(self, primary_indices, secondary_indices, batch_size, secondary_batch_size):
self.primary_indices = primary_indices
self.secondary_indices = secondary_indices
self.secondary_batch_size = secondary_batch_size
self.primary_batch_size = batch_size - secondary_batch_size
assert len(self.primary_indices) >= self.primary_batch_size > 0
assert len(self.secondary_indices) >= self.secondary_batch_size > 0
|
[
"def",
"__init__",
"(",
"self",
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"primary_indices",
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"secondary_indices",
",",
"batch_size",
",",
"secondary_batch_size",
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">",
"0"
] |
https://github.com/CuriousAI/mean-teacher/blob/546348ff863c998c26be4339021425df973b4a36/pytorch/mean_teacher/data.py#L112-L119
|
||||
vericast/spylon-kernel
|
2d0ddf2aca1b91738f938b72a500c20293e3156c
|
spylon_kernel/_version.py
|
python
|
git_get_keywords
|
(versionfile_abs)
|
return keywords
|
Extract version information from the given file.
|
Extract version information from the given file.
|
[
"Extract",
"version",
"information",
"from",
"the",
"given",
"file",
"."
] |
def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
f = open(versionfile_abs, "r")
for line in f.readlines():
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
f.close()
except EnvironmentError:
pass
return keywords
|
[
"def",
"git_get_keywords",
"(",
"versionfile_abs",
")",
":",
"# the code embedded in _version.py can just fetch the value of these",
"# keywords. When used from setup.py, we don't want to import _version.py,",
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"(",
")",
"except",
"EnvironmentError",
":",
"pass",
"return",
"keywords"
] |
https://github.com/vericast/spylon-kernel/blob/2d0ddf2aca1b91738f938b72a500c20293e3156c/spylon_kernel/_version.py#L133-L158
|
|
MontrealCorpusTools/Montreal-Forced-Aligner
|
63473f9a4fabd31eec14e1e5022882f85cfdaf31
|
montreal_forced_aligner/transcription/multiprocessing.py
|
python
|
compose_g
|
(arpa_path: str, words_path: str, g_path: str, log_file: TextIO)
|
Create G.fst from an ARPA formatted language model
See Also
--------
:kaldi_src:`arpa2fst`
Relevant Kaldi binary
Parameters
----------
arpa_path: str
Path to ARPA file
words_path: str
Path to words symbols file
g_path: str
Path to output G.fst file
log_file: TextIO
Log file handler to output logging info to
|
Create G.fst from an ARPA formatted language model
|
[
"Create",
"G",
".",
"fst",
"from",
"an",
"ARPA",
"formatted",
"language",
"model"
] |
def compose_g(arpa_path: str, words_path: str, g_path: str, log_file: TextIO) -> None:
"""
Create G.fst from an ARPA formatted language model
See Also
--------
:kaldi_src:`arpa2fst`
Relevant Kaldi binary
Parameters
----------
arpa_path: str
Path to ARPA file
words_path: str
Path to words symbols file
g_path: str
Path to output G.fst file
log_file: TextIO
Log file handler to output logging info to
"""
arpafst_proc = subprocess.Popen(
[
thirdparty_binary("arpa2fst"),
"--disambig-symbol=#0",
f"--read-symbol-table={words_path}",
arpa_path,
g_path,
],
stderr=log_file,
stdout=log_file,
)
arpafst_proc.communicate()
|
[
"def",
"compose_g",
"(",
"arpa_path",
":",
"str",
",",
"words_path",
":",
"str",
",",
"g_path",
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",",
"log_file",
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",",
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",",
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",",
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",",
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"(",
")"
] |
https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/blob/63473f9a4fabd31eec14e1e5022882f85cfdaf31/montreal_forced_aligner/transcription/multiprocessing.py#L392-L423
|
||
jwkvam/bowtie
|
220cd41367a70f2e206db846278cb7b6fd3649eb
|
bowtie/control.py
|
python
|
DatePicker.get
|
(self, data)
|
return data
|
Get the currently selected date.
Returns
-------
str
Date in the format "YYYY-MM-DD"
|
Get the currently selected date.
|
[
"Get",
"the",
"currently",
"selected",
"date",
"."
] |
def get(self, data):
"""
Get the currently selected date.
Returns
-------
str
Date in the format "YYYY-MM-DD"
"""
return data
|
[
"def",
"get",
"(",
"self",
",",
"data",
")",
":",
"return",
"data"
] |
https://github.com/jwkvam/bowtie/blob/220cd41367a70f2e206db846278cb7b6fd3649eb/bowtie/control.py#L273-L283
|
|
wxWidgets/Phoenix
|
b2199e299a6ca6d866aa6f3d0888499136ead9d6
|
wx/py/frame.py
|
python
|
Frame.OnHelp
|
(self, event)
|
Display a Help window.
|
Display a Help window.
|
[
"Display",
"a",
"Help",
"window",
"."
] |
def OnHelp(self, event):
"""Display a Help window."""
title = 'Help'
text = "Type 'shell.help()' in the shell window."
dialog = wx.MessageDialog(self, text, title,
wx.OK | wx.ICON_INFORMATION)
dialog.ShowModal()
dialog.Destroy()
|
[
"def",
"OnHelp",
"(",
"self",
",",
"event",
")",
":",
"title",
"=",
"'Help'",
"text",
"=",
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"=",
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".",
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",",
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",",
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"ICON_INFORMATION",
")",
"dialog",
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"ShowModal",
"(",
")",
"dialog",
".",
"Destroy",
"(",
")"
] |
https://github.com/wxWidgets/Phoenix/blob/b2199e299a6ca6d866aa6f3d0888499136ead9d6/wx/py/frame.py#L447-L454
|
||
quic/aimet
|
dae9bae9a77ca719aa7553fefde4768270fc3518
|
TrainingExtensions/torch/src/python/aimet_torch/qc_quantize_recurrent.py
|
python
|
QcQuantizeRecurrent.grouped_quantizers
|
(self)
|
return self._grouped_quantizers
|
Return dictionary of grouped quantizer
|
Return dictionary of grouped quantizer
|
[
"Return",
"dictionary",
"of",
"grouped",
"quantizer"
] |
def grouped_quantizers(self):
""" Return dictionary of grouped quantizer """
return self._grouped_quantizers
|
[
"def",
"grouped_quantizers",
"(",
"self",
")",
":",
"return",
"self",
".",
"_grouped_quantizers"
] |
https://github.com/quic/aimet/blob/dae9bae9a77ca719aa7553fefde4768270fc3518/TrainingExtensions/torch/src/python/aimet_torch/qc_quantize_recurrent.py#L198-L200
|
|
aws/aws-parallelcluster
|
f1fe5679a01c524e7ea904c329bd6d17318c6cd9
|
cli/src/pcluster/templates/cw_dashboard_builder.py
|
python
|
CWDashboardConstruct._generate_graph_widget
|
(self, title, metric_list)
|
return widget
|
Generate a graph widget and update the coordinates.
|
Generate a graph widget and update the coordinates.
|
[
"Generate",
"a",
"graph",
"widget",
"and",
"update",
"the",
"coordinates",
"."
] |
def _generate_graph_widget(self, title, metric_list):
"""Generate a graph widget and update the coordinates."""
widget = cloudwatch.GraphWidget(
title=title,
left=metric_list,
region=self._stack_region,
width=self.graph_width,
height=self.graph_height,
)
widget.position(x=self.coord.x_value, y=self.coord.y_value)
self._update_coord(self.graph_width, self.graph_height)
return widget
|
[
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"_generate_graph_widget",
"(",
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",",
"title",
",",
"metric_list",
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":",
"widget",
"=",
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",",
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"(",
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"graph_width",
",",
"self",
".",
"graph_height",
")",
"return",
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] |
https://github.com/aws/aws-parallelcluster/blob/f1fe5679a01c524e7ea904c329bd6d17318c6cd9/cli/src/pcluster/templates/cw_dashboard_builder.py#L170-L181
|
|
svinota/pyroute2
|
d320acd67067206b4217bb862afdae23bcb55266
|
pyroute2.core/pr2modules/netlink/generic/l2tp.py
|
python
|
L2tp.delete_tunnel
|
(self, tunnel_id)
|
return self._do_request(msg)
|
Delete a tunnel
:param tunnel_id: tunnel id of the tunnel to be deleted
:return: netlink response
|
Delete a tunnel
:param tunnel_id: tunnel id of the tunnel to be deleted
:return: netlink response
|
[
"Delete",
"a",
"tunnel",
":",
"param",
"tunnel_id",
":",
"tunnel",
"id",
"of",
"the",
"tunnel",
"to",
"be",
"deleted",
":",
"return",
":",
"netlink",
"response"
] |
def delete_tunnel(self, tunnel_id):
"""
Delete a tunnel
:param tunnel_id: tunnel id of the tunnel to be deleted
:return: netlink response
"""
msg = l2tpmsg()
msg["cmd"] = L2TP_CMD_TUNNEL_DELETE
msg["version"] = L2TP_GENL_VERSION
msg["attrs"].append(["L2TP_ATTR_CONN_ID", tunnel_id])
return self._do_request(msg)
|
[
"def",
"delete_tunnel",
"(",
"self",
",",
"tunnel_id",
")",
":",
"msg",
"=",
"l2tpmsg",
"(",
")",
"msg",
"[",
"\"cmd\"",
"]",
"=",
"L2TP_CMD_TUNNEL_DELETE",
"msg",
"[",
"\"version\"",
"]",
"=",
"L2TP_GENL_VERSION",
"msg",
"[",
"\"attrs\"",
"]",
".",
"append",
"(",
"[",
"\"L2TP_ATTR_CONN_ID\"",
",",
"tunnel_id",
"]",
")",
"return",
"self",
".",
"_do_request",
"(",
"msg",
")"
] |
https://github.com/svinota/pyroute2/blob/d320acd67067206b4217bb862afdae23bcb55266/pyroute2.core/pr2modules/netlink/generic/l2tp.py#L336-L347
|
|
yianjiajia/django_web_ansible
|
1103343082a65abf9d37310f5048514d74930753
|
devops/apps/ansible/elfinder/volumes/storage.py
|
python
|
ElfinderVolumeStorage._rmdir
|
(self, path)
|
Remove a directory. This implementation calls the
:ref:`setting-rmDir` callable driver option, if it is available.
If not, it raises an ``os.error``.
|
Remove a directory. This implementation calls the
:ref:`setting-rmDir` callable driver option, if it is available.
If not, it raises an ``os.error``.
|
[
"Remove",
"a",
"directory",
".",
"This",
"implementation",
"calls",
"the",
":",
"ref",
":",
"setting",
"-",
"rmDir",
"callable",
"driver",
"option",
"if",
"it",
"is",
"available",
".",
"If",
"not",
"it",
"raises",
"an",
"os",
".",
"error",
"."
] |
def _rmdir(self, path):
"""
Remove a directory. This implementation calls the
:ref:`setting-rmDir` callable driver option, if it is available.
If not, it raises an ``os.error``.
"""
if 'rmDir' in self._options and callable(self._options['rmDir']):
return self._options['rmDir'](path, self._options['storage'])
raise os.error
|
[
"def",
"_rmdir",
"(",
"self",
",",
"path",
")",
":",
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"[",
"'storage'",
"]",
")",
"raise",
"os",
".",
"error"
] |
https://github.com/yianjiajia/django_web_ansible/blob/1103343082a65abf9d37310f5048514d74930753/devops/apps/ansible/elfinder/volumes/storage.py#L398-L406
|
||
google/diff-match-patch
|
62f2e689f498f9c92dbc588c58750addec9b1654
|
python3/diff_match_patch.py
|
python
|
diff_match_patch.diff_xIndex
|
(self, diffs, loc)
|
return last_chars2 + (loc - last_chars1)
|
loc is a location in text1, compute and return the equivalent location
in text2. e.g. "The cat" vs "The big cat", 1->1, 5->8
Args:
diffs: Array of diff tuples.
loc: Location within text1.
Returns:
Location within text2.
|
loc is a location in text1, compute and return the equivalent location
in text2. e.g. "The cat" vs "The big cat", 1->1, 5->8
|
[
"loc",
"is",
"a",
"location",
"in",
"text1",
"compute",
"and",
"return",
"the",
"equivalent",
"location",
"in",
"text2",
".",
"e",
".",
"g",
".",
"The",
"cat",
"vs",
"The",
"big",
"cat",
"1",
"-",
">",
"1",
"5",
"-",
">",
"8"
] |
def diff_xIndex(self, diffs, loc):
"""loc is a location in text1, compute and return the equivalent location
in text2. e.g. "The cat" vs "The big cat", 1->1, 5->8
Args:
diffs: Array of diff tuples.
loc: Location within text1.
Returns:
Location within text2.
"""
chars1 = 0
chars2 = 0
last_chars1 = 0
last_chars2 = 0
for x in range(len(diffs)):
(op, text) = diffs[x]
if op != self.DIFF_INSERT: # Equality or deletion.
chars1 += len(text)
if op != self.DIFF_DELETE: # Equality or insertion.
chars2 += len(text)
if chars1 > loc: # Overshot the location.
break
last_chars1 = chars1
last_chars2 = chars2
if len(diffs) != x and diffs[x][0] == self.DIFF_DELETE:
# The location was deleted.
return last_chars2
# Add the remaining len(character).
return last_chars2 + (loc - last_chars1)
|
[
"def",
"diff_xIndex",
"(",
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"loc",
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"return",
"last_chars2",
"+",
"(",
"loc",
"-",
"last_chars1",
")"
] |
https://github.com/google/diff-match-patch/blob/62f2e689f498f9c92dbc588c58750addec9b1654/python3/diff_match_patch.py#L1027-L1057
|
|
as-ideas/TransformerTTS
|
363805548abdd93b33508da2c027ae514bfc1a07
|
utils/alignments.py
|
python
|
get_durations_from_alignment
|
(batch_alignments, mels, phonemes, weighted=False)
|
return durations, final_alignment, jumpiness, peakiness, diag_measure
|
:param batch_alignments: attention weights from autoregressive model.
:param mels: mel spectrograms.
:param phonemes: phoneme sequence.
:param weighted: if True use weighted average of durations of heads, best head if False.
:param binary: if True take maximum attention peak, sum if False.
:param fill_gaps: if True fills zeros durations with ones.
:param fix_jumps: if True, tries to scan alingments for attention jumps and interpolate.
:param fill_mode: used only if fill_gaps is True. Is either 'max' or 'next'. Defines where to take the duration
needed to fill the gap. Next takes it from the next non-zeros duration value, max from the sequence maximum.
:return:
|
[] |
def get_durations_from_alignment(batch_alignments, mels, phonemes, weighted=False):
"""
:param batch_alignments: attention weights from autoregressive model.
:param mels: mel spectrograms.
:param phonemes: phoneme sequence.
:param weighted: if True use weighted average of durations of heads, best head if False.
:param binary: if True take maximum attention peak, sum if False.
:param fill_gaps: if True fills zeros durations with ones.
:param fix_jumps: if True, tries to scan alingments for attention jumps and interpolate.
:param fill_mode: used only if fill_gaps is True. Is either 'max' or 'next'. Defines where to take the duration
needed to fill the gap. Next takes it from the next non-zeros duration value, max from the sequence maximum.
:return:
"""
# mel_len - 1 because we remove last timestep, which is end_vector. start vector is not predicted (or removed from GTA)
mel_len = mel_lengths(mels, padding_value=0.) - 1 # [N]
# phonemes contain start and end tokens (start will be removed later)
phon_len = phoneme_lengths(phonemes) - 1
jumpiness, peakiness, diag_measure = attention_score(att=batch_alignments, mel_len=mel_len, phon_len=phon_len, r=1)
attn_scores = diag_measure + jumpiness + peakiness
durations = []
final_alignment = []
for batch_num, al in enumerate(batch_alignments):
unpad_mel_len = mel_len[batch_num]
unpad_phon_len = phon_len[batch_num]
unpad_alignments = al[:, 1:unpad_mel_len, 1:unpad_phon_len] # first dim is heads
scored_attention = unpad_alignments * attn_scores[batch_num][:, None, None]
if weighted:
ref_attention_weights = np.sum(scored_attention, axis=0)
else:
best_head = np.argmax(attn_scores[batch_num])
ref_attention_weights = unpad_alignments[best_head]
integer_durations = extract_durations_with_dijkstra(ref_attention_weights)
assert np.sum(integer_durations) == mel_len[batch_num]-1, f'{np.sum(integer_durations)} vs {mel_len[batch_num]-1}'
new_alignment = duration_to_alignment_matrix(integer_durations.astype(int))
best_head = np.argmax(attn_scores[batch_num])
best_attention = unpad_alignments[best_head]
final_alignment.append(best_attention.T + new_alignment)
durations.append(integer_durations)
return durations, final_alignment, jumpiness, peakiness, diag_measure
|
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",",
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",",
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",",
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",",
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] |
https://github.com/as-ideas/TransformerTTS/blob/363805548abdd93b33508da2c027ae514bfc1a07/utils/alignments.py#L102-L143
|
||
eirannejad/pyRevit
|
49c0b7eb54eb343458ce1365425e6552d0c47d44
|
site-packages/sqlalchemy/dialects/oracle/cx_oracle.py
|
python
|
OracleDialect_cx_oracle._detect_decimal_char
|
(self, connection)
|
detect if the decimal separator character is not '.', as
is the case with European locale settings for NLS_LANG.
cx_oracle itself uses similar logic when it formats Python
Decimal objects to strings on the bind side (as of 5.0.3),
as Oracle sends/receives string numerics only in the
current locale.
|
detect if the decimal separator character is not '.', as
is the case with European locale settings for NLS_LANG.
|
[
"detect",
"if",
"the",
"decimal",
"separator",
"character",
"is",
"not",
".",
"as",
"is",
"the",
"case",
"with",
"European",
"locale",
"settings",
"for",
"NLS_LANG",
"."
] |
def _detect_decimal_char(self, connection):
"""detect if the decimal separator character is not '.', as
is the case with European locale settings for NLS_LANG.
cx_oracle itself uses similar logic when it formats Python
Decimal objects to strings on the bind side (as of 5.0.3),
as Oracle sends/receives string numerics only in the
current locale.
"""
if self.cx_oracle_ver < (5,):
# no output type handlers before version 5
return
cx_Oracle = self.dbapi
conn = connection.connection
# override the output_type_handler that's
# on the cx_oracle connection with a plain
# one on the cursor
def output_type_handler(cursor, name, defaultType,
size, precision, scale):
return cursor.var(
cx_Oracle.STRING,
255, arraysize=cursor.arraysize)
cursor = conn.cursor()
cursor.outputtypehandler = output_type_handler
cursor.execute("SELECT 0.1 FROM DUAL")
val = cursor.fetchone()[0]
cursor.close()
char = re.match(r"([\.,])", val).group(1)
if char != '.':
_detect_decimal = self._detect_decimal
self._detect_decimal = \
lambda value: _detect_decimal(value.replace(char, '.'))
self._to_decimal = \
lambda value: decimal.Decimal(value.replace(char, '.'))
|
[
"def",
"_detect_decimal_char",
"(",
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",",
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":",
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"(",
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",",
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")",
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"Decimal",
"(",
"value",
".",
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"(",
"char",
",",
"'.'",
")",
")"
] |
https://github.com/eirannejad/pyRevit/blob/49c0b7eb54eb343458ce1365425e6552d0c47d44/site-packages/sqlalchemy/dialects/oracle/cx_oracle.py#L802-L840
|
||
obspy/obspy
|
0ee5a0d2db293c8d5d4c3b1f148a6c5a85fea55f
|
obspy/clients/fdsn/mass_downloader/download_helpers.py
|
python
|
Station.remove_files
|
(self, logger, reason)
|
Delete all files under it. Only delete stuff that actually has been
downloaded!
|
Delete all files under it. Only delete stuff that actually has been
downloaded!
|
[
"Delete",
"all",
"files",
"under",
"it",
".",
"Only",
"delete",
"stuff",
"that",
"actually",
"has",
"been",
"downloaded!"
] |
def remove_files(self, logger, reason):
"""
Delete all files under it. Only delete stuff that actually has been
downloaded!
"""
for chan in self.channels:
for ti in chan.intervals:
if ti.status != STATUS.DOWNLOADED or not ti.filename:
continue
if os.path.exists(ti.filename):
logger.info("Deleting MiniSEED file '%s'. Reason: %s" % (
ti.filename, reason))
utils.safe_delete(ti.filename)
if self.stationxml_status == STATUS.DOWNLOADED and \
self.stationxml_filename and \
os.path.exists(self.stationxml_filename):
logger.info("Deleting StationXMl file '%s'. Reason: %s" %
(self.stationxml_filename, reason))
utils.safe_delete(self.stationxml_filename)
|
[
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")",
")",
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"(",
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"stationxml_filename",
")"
] |
https://github.com/obspy/obspy/blob/0ee5a0d2db293c8d5d4c3b1f148a6c5a85fea55f/obspy/clients/fdsn/mass_downloader/download_helpers.py#L126-L145
|
||
MeanEYE/Sunflower
|
1024bbdde3b8e202ddad3553b321a7b6230bffc9
|
sunflower/gui/input_dialog.py
|
python
|
CreateDialog._entry_activate
|
(self, widget, data=None)
|
Handle octal mode change
|
Handle octal mode change
|
[
"Handle",
"octal",
"mode",
"change"
] |
def _entry_activate(self, widget, data=None):
"""Handle octal mode change"""
self._mode = int(widget.get_text(), 8)
self.update_mode()
|
[
"def",
"_entry_activate",
"(",
"self",
",",
"widget",
",",
"data",
"=",
"None",
")",
":",
"self",
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"(",
"widget",
".",
"get_text",
"(",
")",
",",
"8",
")",
"self",
".",
"update_mode",
"(",
")"
] |
https://github.com/MeanEYE/Sunflower/blob/1024bbdde3b8e202ddad3553b321a7b6230bffc9/sunflower/gui/input_dialog.py#L303-L306
|
||
svip-lab/impersonator
|
b041dd415157c1e7f5b46e579a1ad4dffabb2e66
|
thirdparty/his_evaluators/his_evaluators/evaluators/appearance_transfer.py
|
python
|
IPERAppearanceTransferEvaluator.__init__
|
(self, data_dir, dataset="iPER_Appearance_Transfer")
|
Args:
data_dir (str): the data directory
dataset (str): the dataset name, it can be
--iPER_Appearance_Transfer: the iPER dataset;
|
[] |
def __init__(self, data_dir, dataset="iPER_Appearance_Transfer"):
"""
Args:
data_dir (str): the data directory
dataset (str): the dataset name, it can be
--iPER_Appearance_Transfer: the iPER dataset;
"""
super().__init__(dataset=dataset, data_dir=data_dir)
|
[
"def",
"__init__",
"(",
"self",
",",
"data_dir",
",",
"dataset",
"=",
"\"iPER_Appearance_Transfer\"",
")",
":",
"super",
"(",
")",
".",
"__init__",
"(",
"dataset",
"=",
"dataset",
",",
"data_dir",
"=",
"data_dir",
")"
] |
https://github.com/svip-lab/impersonator/blob/b041dd415157c1e7f5b46e579a1ad4dffabb2e66/thirdparty/his_evaluators/his_evaluators/evaluators/appearance_transfer.py#L205-L213
|
|||
ym2011/POC-EXP
|
206b22d3a6b2a172359678df33bbc5b2ad04b6c3
|
K8/Web-Exp/sqlmap/thirdparty/oset/_abc.py
|
python
|
ABCMeta._dump_registry
|
(cls, file=None)
|
Debug helper to print the ABC registry.
|
Debug helper to print the ABC registry.
|
[
"Debug",
"helper",
"to",
"print",
"the",
"ABC",
"registry",
"."
] |
def _dump_registry(cls, file=None):
"""Debug helper to print the ABC registry."""
print >> file, "Class: %s.%s" % (cls.__module__, cls.__name__)
print >> file, "Inv.counter: %s" % ABCMeta._abc_invalidation_counter
for name in sorted(cls.__dict__.keys()):
if name.startswith("_abc_"):
value = getattr(cls, name)
print >> file, "%s: %r" % (name, value)
|
[
"def",
"_dump_registry",
"(",
"cls",
",",
"file",
"=",
"None",
")",
":",
"print",
">>",
"file",
",",
"\"Class: %s.%s\"",
"%",
"(",
"cls",
".",
"__module__",
",",
"cls",
".",
"__name__",
")",
"print",
">>",
"file",
",",
"\"Inv.counter: %s\"",
"%",
"ABCMeta",
".",
"_abc_invalidation_counter",
"for",
"name",
"in",
"sorted",
"(",
"cls",
".",
"__dict__",
".",
"keys",
"(",
")",
")",
":",
"if",
"name",
".",
"startswith",
"(",
"\"_abc_\"",
")",
":",
"value",
"=",
"getattr",
"(",
"cls",
",",
"name",
")",
"print",
">>",
"file",
",",
"\"%s: %r\"",
"%",
"(",
"name",
",",
"value",
")"
] |
https://github.com/ym2011/POC-EXP/blob/206b22d3a6b2a172359678df33bbc5b2ad04b6c3/K8/Web-Exp/sqlmap/thirdparty/oset/_abc.py#L97-L104
|
||
mars-project/mars
|
6afd7ed86db77f29cc9470485698ef192ecc6d33
|
mars/tensor/statistics/ptp.py
|
python
|
ptp
|
(a, axis=None, out=None, keepdims=None)
|
return t
|
Range of values (maximum - minimum) along an axis.
The name of the function comes from the acronym for 'peak to peak'.
Parameters
----------
a : array_like
Input values.
axis : int, optional
Axis along which to find the peaks. By default, flatten the
array.
out : array_like
Alternative output tensor in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type of the output values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `ptp` method of sub-classes of
`Tensor`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
ptp : Tensor
A new tensor holding the result, unless `out` was
specified, in which case a reference to `out` is returned.
Examples
--------
>>> import mars.tensor as mt
>>> x = mt.arange(4).reshape((2,2))
>>> x.execute()
array([[0, 1],
[2, 3]])
>>> mt.ptp(x, axis=0).execute()
array([2, 2])
>>> mt.ptp(x, axis=1).execute()
array([1, 1])
|
Range of values (maximum - minimum) along an axis.
|
[
"Range",
"of",
"values",
"(",
"maximum",
"-",
"minimum",
")",
"along",
"an",
"axis",
"."
] |
def ptp(a, axis=None, out=None, keepdims=None):
"""
Range of values (maximum - minimum) along an axis.
The name of the function comes from the acronym for 'peak to peak'.
Parameters
----------
a : array_like
Input values.
axis : int, optional
Axis along which to find the peaks. By default, flatten the
array.
out : array_like
Alternative output tensor in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type of the output values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `ptp` method of sub-classes of
`Tensor`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
ptp : Tensor
A new tensor holding the result, unless `out` was
specified, in which case a reference to `out` is returned.
Examples
--------
>>> import mars.tensor as mt
>>> x = mt.arange(4).reshape((2,2))
>>> x.execute()
array([[0, 1],
[2, 3]])
>>> mt.ptp(x, axis=0).execute()
array([2, 2])
>>> mt.ptp(x, axis=1).execute()
array([1, 1])
"""
a = astensor(a)
if axis is None:
a = ravel(a)
else:
validate_axis(a.ndim, axis)
t = a.max(axis=axis, keepdims=keepdims) - a.min(axis=axis, keepdims=keepdims)
if out is not None:
if not isinstance(out, Tensor):
raise TypeError(f"out should be Tensor object, got {type(out)} instead")
check_out_param(out, t, "same_kind")
out.data = t.data
return out
return t
|
[
"def",
"ptp",
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"a",
",",
"axis",
"=",
"None",
",",
"out",
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"None",
",",
"keepdims",
"=",
"None",
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":",
"a",
"=",
"astensor",
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"if",
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"ravel",
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"else",
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"validate_axis",
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",",
"axis",
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"axis",
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",",
"keepdims",
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"-",
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"axis",
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",",
"keepdims",
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"out",
",",
"Tensor",
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"f\"out should be Tensor object, got {type(out)} instead\"",
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"check_out_param",
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",",
"\"same_kind\"",
")",
"out",
".",
"data",
"=",
"t",
".",
"data",
"return",
"out",
"return",
"t"
] |
https://github.com/mars-project/mars/blob/6afd7ed86db77f29cc9470485698ef192ecc6d33/mars/tensor/statistics/ptp.py#L23-L90
|
|
rowliny/DiffHelper
|
ab3a96f58f9579d0023aed9ebd785f4edf26f8af
|
Tool/SitePackages/lxml/html/diff.py
|
python
|
copy_annotations
|
(src, dest)
|
Copy annotations from the tokens listed in src to the tokens in dest
|
Copy annotations from the tokens listed in src to the tokens in dest
|
[
"Copy",
"annotations",
"from",
"the",
"tokens",
"listed",
"in",
"src",
"to",
"the",
"tokens",
"in",
"dest"
] |
def copy_annotations(src, dest):
"""
Copy annotations from the tokens listed in src to the tokens in dest
"""
assert len(src) == len(dest)
for src_tok, dest_tok in zip(src, dest):
dest_tok.annotation = src_tok.annotation
|
[
"def",
"copy_annotations",
"(",
"src",
",",
"dest",
")",
":",
"assert",
"len",
"(",
"src",
")",
"==",
"len",
"(",
"dest",
")",
"for",
"src_tok",
",",
"dest_tok",
"in",
"zip",
"(",
"src",
",",
"dest",
")",
":",
"dest_tok",
".",
"annotation",
"=",
"src_tok",
".",
"annotation"
] |
https://github.com/rowliny/DiffHelper/blob/ab3a96f58f9579d0023aed9ebd785f4edf26f8af/Tool/SitePackages/lxml/html/diff.py#L96-L102
|
||
MDudek-ICS/TRISIS-TRITON-HATMAN
|
15a00af7fd1040f0430729d024427601f84886a1
|
decompiled_code/library/os2emxpath.py
|
python
|
dirname
|
(p)
|
return split(p)[0]
|
Returns the directory component of a pathname
|
Returns the directory component of a pathname
|
[
"Returns",
"the",
"directory",
"component",
"of",
"a",
"pathname"
] |
def dirname(p):
"""Returns the directory component of a pathname"""
return split(p)[0]
|
[
"def",
"dirname",
"(",
"p",
")",
":",
"return",
"split",
"(",
"p",
")",
"[",
"0",
"]"
] |
https://github.com/MDudek-ICS/TRISIS-TRITON-HATMAN/blob/15a00af7fd1040f0430729d024427601f84886a1/decompiled_code/library/os2emxpath.py#L82-L84
|
|
aimagelab/meshed-memory-transformer
|
e0fe3fae68091970407e82e5b907cbc423f25df2
|
data/dataset.py
|
python
|
COCO.get_samples
|
(cls, roots, ids_dataset=None)
|
return train_samples, val_samples, test_samples
|
[] |
def get_samples(cls, roots, ids_dataset=None):
train_samples = []
val_samples = []
test_samples = []
for split in ['train', 'val', 'test']:
if isinstance(roots[split]['cap'], tuple):
coco_dataset = (pyCOCO(roots[split]['cap'][0]), pyCOCO(roots[split]['cap'][1]))
root = roots[split]['img']
else:
coco_dataset = (pyCOCO(roots[split]['cap']),)
root = (roots[split]['img'],)
if ids_dataset is None:
ids = list(coco_dataset.anns.keys())
else:
ids = ids_dataset[split]
if isinstance(ids, tuple):
bp = len(ids[0])
ids = list(ids[0]) + list(ids[1])
else:
bp = len(ids)
for index in range(len(ids)):
if index < bp:
coco = coco_dataset[0]
img_root = root[0]
else:
coco = coco_dataset[1]
img_root = root[1]
ann_id = ids[index]
caption = coco.anns[ann_id]['caption']
img_id = coco.anns[ann_id]['image_id']
filename = coco.loadImgs(img_id)[0]['file_name']
example = Example.fromdict({'image': os.path.join(img_root, filename), 'text': caption})
if split == 'train':
train_samples.append(example)
elif split == 'val':
val_samples.append(example)
elif split == 'test':
test_samples.append(example)
return train_samples, val_samples, test_samples
|
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"def",
"get_samples",
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"cls",
",",
"roots",
",",
"ids_dataset",
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"val_samples",
"=",
"[",
"]",
"test_samples",
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"pyCOCO",
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"ann_id",
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"[",
"'image_id'",
"]",
"filename",
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"coco",
".",
"loadImgs",
"(",
"img_id",
")",
"[",
"0",
"]",
"[",
"'file_name'",
"]",
"example",
"=",
"Example",
".",
"fromdict",
"(",
"{",
"'image'",
":",
"os",
".",
"path",
".",
"join",
"(",
"img_root",
",",
"filename",
")",
",",
"'text'",
":",
"caption",
"}",
")",
"if",
"split",
"==",
"'train'",
":",
"train_samples",
".",
"append",
"(",
"example",
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"==",
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":",
"val_samples",
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":",
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".",
"append",
"(",
"example",
")",
"return",
"train_samples",
",",
"val_samples",
",",
"test_samples"
] |
https://github.com/aimagelab/meshed-memory-transformer/blob/e0fe3fae68091970407e82e5b907cbc423f25df2/data/dataset.py#L232-L278
|
|||
twisted/twisted
|
dee676b040dd38b847ea6fb112a712cb5e119490
|
src/twisted/names/dns.py
|
python
|
_nameToLabels
|
(name)
|
return labels
|
Split a domain name into its constituent labels.
@type name: L{bytes}
@param name: A fully qualified domain name (with or without a
trailing dot).
@return: A L{list} of labels ending with an empty label
representing the DNS root zone.
@rtype: L{list} of L{bytes}
|
Split a domain name into its constituent labels.
|
[
"Split",
"a",
"domain",
"name",
"into",
"its",
"constituent",
"labels",
"."
] |
def _nameToLabels(name):
"""
Split a domain name into its constituent labels.
@type name: L{bytes}
@param name: A fully qualified domain name (with or without a
trailing dot).
@return: A L{list} of labels ending with an empty label
representing the DNS root zone.
@rtype: L{list} of L{bytes}
"""
if name in (b"", b"."):
return [b""]
labels = name.split(b".")
if labels[-1] != b"":
labels.append(b"")
return labels
|
[
"def",
"_nameToLabels",
"(",
"name",
")",
":",
"if",
"name",
"in",
"(",
"b\"\"",
",",
"b\".\"",
")",
":",
"return",
"[",
"b\"\"",
"]",
"labels",
"=",
"name",
".",
"split",
"(",
"b\".\"",
")",
"if",
"labels",
"[",
"-",
"1",
"]",
"!=",
"b\"\"",
":",
"labels",
".",
"append",
"(",
"b\"\"",
")",
"return",
"labels"
] |
https://github.com/twisted/twisted/blob/dee676b040dd38b847ea6fb112a712cb5e119490/src/twisted/names/dns.py#L298-L315
|
|
TRI-ML/packnet-sfm
|
f59b1d615777a9987285a10e45b5d87b0369fa7d
|
packnet_sfm/loggers/wandb_logger.py
|
python
|
WandbLogger.log_images
|
(self, func, mode, batch, output,
args, dataset, world_size, config)
|
Adds images to metrics for later logging.
Parameters
----------
func : Function
Function used to process the image before logging
mode : str {"train", "val"}
Training stage where the images come from (serve as prefix for logging)
batch : dict
Data batch
output : dict
Model output
args : tuple
Step arguments
dataset : CfgNode
Dataset configuration
world_size : int
Number of GPUs, used to get logging samples at consistent intervals
config : CfgNode
Model configuration
|
Adds images to metrics for later logging.
|
[
"Adds",
"images",
"to",
"metrics",
"for",
"later",
"logging",
"."
] |
def log_images(self, func, mode, batch, output,
args, dataset, world_size, config):
"""
Adds images to metrics for later logging.
Parameters
----------
func : Function
Function used to process the image before logging
mode : str {"train", "val"}
Training stage where the images come from (serve as prefix for logging)
batch : dict
Data batch
output : dict
Model output
args : tuple
Step arguments
dataset : CfgNode
Dataset configuration
world_size : int
Number of GPUs, used to get logging samples at consistent intervals
config : CfgNode
Model configuration
"""
dataset_idx = 0 if len(args) == 1 else args[1]
prefix = prepare_dataset_prefix(config, dataset_idx)
interval = len(dataset[dataset_idx]) // world_size // config.num_logs
if args[0] % interval == 0:
prefix_idx = '{}-{}-{}'.format(mode, prefix, batch['idx'][0].item())
func(prefix_idx, batch, output)
|
[
"def",
"log_images",
"(",
"self",
",",
"func",
",",
"mode",
",",
"batch",
",",
"output",
",",
"args",
",",
"dataset",
",",
"world_size",
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"config",
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"dataset_idx",
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"==",
"0",
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"prefix_idx",
"=",
"'{}-{}-{}'",
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"format",
"(",
"mode",
",",
"prefix",
",",
"batch",
"[",
"'idx'",
"]",
"[",
"0",
"]",
".",
"item",
"(",
")",
")",
"func",
"(",
"prefix_idx",
",",
"batch",
",",
"output",
")"
] |
https://github.com/TRI-ML/packnet-sfm/blob/f59b1d615777a9987285a10e45b5d87b0369fa7d/packnet_sfm/loggers/wandb_logger.py#L133-L162
|
||
saltstack/salt
|
fae5bc757ad0f1716483ce7ae180b451545c2058
|
salt/runners/drac.py
|
python
|
__connect
|
(hostname, timeout=20, username=None, password=None)
|
return client
|
Connect to the DRAC
|
Connect to the DRAC
|
[
"Connect",
"to",
"the",
"DRAC"
] |
def __connect(hostname, timeout=20, username=None, password=None):
"""
Connect to the DRAC
"""
drac_cred = __opts__.get("drac")
err_msg = (
"No drac login credentials found. Please add the 'username' and 'password' "
"fields beneath a 'drac' key in the master configuration file. Or you can "
"pass in a username and password as kwargs at the CLI."
)
if not username:
if drac_cred is None:
log.error(err_msg)
return False
username = drac_cred.get("username", None)
if not password:
if drac_cred is None:
log.error(err_msg)
return False
password = drac_cred.get("password", None)
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
try:
client.connect(hostname, username=username, password=password, timeout=timeout)
except Exception as e: # pylint: disable=broad-except
log.error("Unable to connect to %s: %s", hostname, e)
return False
return client
|
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] |
https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/runners/drac.py#L38-L69
|
|
sympy/sympy
|
d822fcba181155b85ff2b29fe525adbafb22b448
|
sympy/physics/quantum/qasm.py
|
python
|
stripquotes
|
(s)
|
return s
|
Replace explicit quotes in a string.
>>> from sympy.physics.quantum.qasm import stripquotes
>>> stripquotes("'S'") == 'S'
True
>>> stripquotes('"S"') == 'S'
True
>>> stripquotes('S') == 'S'
True
|
Replace explicit quotes in a string.
|
[
"Replace",
"explicit",
"quotes",
"in",
"a",
"string",
"."
] |
def stripquotes(s):
"""Replace explicit quotes in a string.
>>> from sympy.physics.quantum.qasm import stripquotes
>>> stripquotes("'S'") == 'S'
True
>>> stripquotes('"S"') == 'S'
True
>>> stripquotes('S') == 'S'
True
"""
s = s.replace('"', '') # Remove second set of quotes?
s = s.replace("'", '')
return s
|
[
"def",
"stripquotes",
"(",
"s",
")",
":",
"s",
"=",
"s",
".",
"replace",
"(",
"'\"'",
",",
"''",
")",
"# Remove second set of quotes?",
"s",
"=",
"s",
".",
"replace",
"(",
"\"'\"",
",",
"''",
")",
"return",
"s"
] |
https://github.com/sympy/sympy/blob/d822fcba181155b85ff2b29fe525adbafb22b448/sympy/physics/quantum/qasm.py#L104-L117
|
|
AppScale/gts
|
46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9
|
AppServer/google/appengine/ext/ndb/polymodel.py
|
python
|
_ClassKeyProperty.__init__
|
(self, name=_CLASS_KEY_PROPERTY, indexed=True)
|
Constructor.
If you really want to you can give this a different datastore name
or make it unindexed. For example:
class Foo(PolyModel):
class_ = _ClassKeyProperty(indexed=False)
|
Constructor.
|
[
"Constructor",
"."
] |
def __init__(self, name=_CLASS_KEY_PROPERTY, indexed=True):
"""Constructor.
If you really want to you can give this a different datastore name
or make it unindexed. For example:
class Foo(PolyModel):
class_ = _ClassKeyProperty(indexed=False)
"""
super(_ClassKeyProperty, self).__init__(name=name, indexed=indexed,
repeated=True)
|
[
"def",
"__init__",
"(",
"self",
",",
"name",
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"_CLASS_KEY_PROPERTY",
",",
"indexed",
"=",
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"name",
"=",
"name",
",",
"indexed",
"=",
"indexed",
",",
"repeated",
"=",
"True",
")"
] |
https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/google/appengine/ext/ndb/polymodel.py#L41-L51
|
||
ucsb-seclab/karonte
|
427ac313e596f723e40768b95d13bd7a9fc92fd8
|
eval/multi_bin/all_bins/binary_dependency_graph/binary_dependency_graph.py
|
python
|
BdgNode.__setstate__
|
(self, info)
|
[] |
def __setstate__(self, info):
self._p = info[0]
self._bin = info[1]
self._role_strings_info = info[2]
self._root = info[3]
self._generator_strings = info[4]
self._plugins_used = []
|
[
"def",
"__setstate__",
"(",
"self",
",",
"info",
")",
":",
"self",
".",
"_p",
"=",
"info",
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"0",
"]",
"self",
".",
"_bin",
"=",
"info",
"[",
"1",
"]",
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"_role_strings_info",
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"[",
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"]",
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".",
"_root",
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"[",
"3",
"]",
"self",
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"_generator_strings",
"=",
"info",
"[",
"4",
"]",
"self",
".",
"_plugins_used",
"=",
"[",
"]"
] |
https://github.com/ucsb-seclab/karonte/blob/427ac313e596f723e40768b95d13bd7a9fc92fd8/eval/multi_bin/all_bins/binary_dependency_graph/binary_dependency_graph.py#L96-L102
|
||||
wxWidgets/Phoenix
|
b2199e299a6ca6d866aa6f3d0888499136ead9d6
|
wx/lib/agw/ribbon/buttonbar.py
|
python
|
RibbonButtonBar.DoGetNextSmallerSize
|
(self, direction, _result)
|
return result
|
Implementation of :meth:`RibbonControl.GetNextSmallerSize() <lib.agw.ribbon.control.RibbonControl.GetNextSmallerSize>`.
Controls which have non-continuous sizing must override this virtual function
rather than :meth:`RibbonControl.GetNextSmallerSize() <lib.agw.ribbon.control.RibbonControl.GetNextSmallerSize>`.
:return: An instance of :class:`wx.Size`.
|
Implementation of :meth:`RibbonControl.GetNextSmallerSize() <lib.agw.ribbon.control.RibbonControl.GetNextSmallerSize>`.
|
[
"Implementation",
"of",
":",
"meth",
":",
"RibbonControl",
".",
"GetNextSmallerSize",
"()",
"<lib",
".",
"agw",
".",
"ribbon",
".",
"control",
".",
"RibbonControl",
".",
"GetNextSmallerSize",
">",
"."
] |
def DoGetNextSmallerSize(self, direction, _result):
"""
Implementation of :meth:`RibbonControl.GetNextSmallerSize() <lib.agw.ribbon.control.RibbonControl.GetNextSmallerSize>`.
Controls which have non-continuous sizing must override this virtual function
rather than :meth:`RibbonControl.GetNextSmallerSize() <lib.agw.ribbon.control.RibbonControl.GetNextSmallerSize>`.
:return: An instance of :class:`wx.Size`.
"""
result = wx.Size(*_result)
for i, layout in enumerate(self._layouts):
size = wx.Size(*layout.overall_size)
if direction == wx.HORIZONTAL:
if size.x < result.x and size.y <= result.y:
result.x = size.x
break
elif direction == wx.VERTICAL:
if size.x <= result.x and size.y < result.y:
result.y = size.y
break
elif direction == wx.BOTH:
if size.x < result.x and size.y < result.y:
result = size
break
return result
|
[
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"DoGetNextSmallerSize",
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",",
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",",
"_result",
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"y",
"<",
"result",
".",
"y",
":",
"result",
"=",
"size",
"break",
"return",
"result"
] |
https://github.com/wxWidgets/Phoenix/blob/b2199e299a6ca6d866aa6f3d0888499136ead9d6/wx/lib/agw/ribbon/buttonbar.py#L746-L776
|
|
snowkylin/ntm
|
7db406826d6109f44c61a857ef2d1aadbbec7f54
|
utils.py
|
python
|
baseN
|
(num,b)
|
return ((num == 0) and "0" ) or ( baseN(num // b, b).lstrip("0") + "0123456789abcdefghijklmnopqrstuvwxyz"[num % b])
|
[] |
def baseN(num,b):
return ((num == 0) and "0" ) or ( baseN(num // b, b).lstrip("0") + "0123456789abcdefghijklmnopqrstuvwxyz"[num % b])
|
[
"def",
"baseN",
"(",
"num",
",",
"b",
")",
":",
"return",
"(",
"(",
"num",
"==",
"0",
")",
"and",
"\"0\"",
")",
"or",
"(",
"baseN",
"(",
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"//",
"b",
",",
"b",
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"\"0\"",
")",
"+",
"\"0123456789abcdefghijklmnopqrstuvwxyz\"",
"[",
"num",
"%",
"b",
"]",
")"
] |
https://github.com/snowkylin/ntm/blob/7db406826d6109f44c61a857ef2d1aadbbec7f54/utils.py#L34-L35
|
|||
google/grr
|
8ad8a4d2c5a93c92729206b7771af19d92d4f915
|
grr/server/grr_response_server/access_control.py
|
python
|
AccessControlManager.CheckClientAccess
|
(self, context, client_urn)
|
Checks access to the given client.
Args:
context: User credentials context.
client_urn: URN of a client to check.
Returns:
True if access is allowed, raises otherwise.
|
Checks access to the given client.
|
[
"Checks",
"access",
"to",
"the",
"given",
"client",
"."
] |
def CheckClientAccess(self, context, client_urn):
"""Checks access to the given client.
Args:
context: User credentials context.
client_urn: URN of a client to check.
Returns:
True if access is allowed, raises otherwise.
"""
logging.debug("Checking %s for client %s access.", context, client_urn)
raise NotImplementedError()
|
[
"def",
"CheckClientAccess",
"(",
"self",
",",
"context",
",",
"client_urn",
")",
":",
"logging",
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"debug",
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"\"Checking %s for client %s access.\"",
",",
"context",
",",
"client_urn",
")",
"raise",
"NotImplementedError",
"(",
")"
] |
https://github.com/google/grr/blob/8ad8a4d2c5a93c92729206b7771af19d92d4f915/grr/server/grr_response_server/access_control.py#L62-L73
|
||
jliljebl/flowblade
|
995313a509b80e99eb1ad550d945bdda5995093b
|
flowblade-trunk/Flowblade/tools/toolsencoding.py
|
python
|
RenderFilePanel.enable_file_selections
|
(self, enabled)
|
[] |
def enable_file_selections(self, enabled):
self.movie_name.set_sensitive(enabled)
self.extension_label.set_sensitive(enabled)
self.out_folder.set_sensitive(enabled)
self.out_folder_label.set_sensitive(enabled)
self.name_label.set_sensitive(enabled)
self.frame_name_label.set_sensitive(enabled)
self.frame_name.set_sensitive(enabled)
|
[
"def",
"enable_file_selections",
"(",
"self",
",",
"enabled",
")",
":",
"self",
".",
"movie_name",
".",
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"(",
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")",
"self",
".",
"frame_name",
".",
"set_sensitive",
"(",
"enabled",
")"
] |
https://github.com/jliljebl/flowblade/blob/995313a509b80e99eb1ad550d945bdda5995093b/flowblade-trunk/Flowblade/tools/toolsencoding.py#L329-L336
|
||||
phantomcyber/playbooks
|
9e850ecc44cb98c5dde53784744213a1ed5799bd
|
user_prompt_and_block_domain.py
|
python
|
on_finish
|
(container, summary)
|
return
|
[] |
def on_finish(container, summary):
phantom.debug('on_finish() called')
# This function is called after all actions are completed.
# summary of all the action and/or all details of actions
# can be collected here.
# summary_json = phantom.get_summary()
# if 'result' in summary_json:
# for action_result in summary_json['result']:
# if 'action_run_id' in action_result:
# action_results = phantom.get_action_results(action_run_id=action_result['action_run_id'], result_data=False, flatten=False)
# phantom.debug(action_results)
return
|
[
"def",
"on_finish",
"(",
"container",
",",
"summary",
")",
":",
"phantom",
".",
"debug",
"(",
"'on_finish() called'",
")",
"# This function is called after all actions are completed.",
"# summary of all the action and/or all details of actions",
"# can be collected here.",
"# summary_json = phantom.get_summary()",
"# if 'result' in summary_json:",
"# for action_result in summary_json['result']:",
"# if 'action_run_id' in action_result:",
"# action_results = phantom.get_action_results(action_run_id=action_result['action_run_id'], result_data=False, flatten=False)",
"# phantom.debug(action_results)",
"return"
] |
https://github.com/phantomcyber/playbooks/blob/9e850ecc44cb98c5dde53784744213a1ed5799bd/user_prompt_and_block_domain.py#L158-L171
|
|||
CoinAlpha/hummingbot
|
36f6149c1644c07cd36795b915f38b8f49b798e7
|
hummingbot/strategy/twap/twap.py
|
python
|
TwapTradeStrategy.process_market
|
(self, market_info)
|
Checks if enough time has elapsed from previous order to place order and if so, calls place_orders_for_market() and
cancels orders if they are older than self._cancel_order_wait_time.
:param market_info: a market trading pair
|
Checks if enough time has elapsed from previous order to place order and if so, calls place_orders_for_market() and
cancels orders if they are older than self._cancel_order_wait_time.
|
[
"Checks",
"if",
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"time",
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"elapsed",
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"previous",
"order",
"to",
"place",
"order",
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"if",
"they",
"are",
"older",
"than",
"self",
".",
"_cancel_order_wait_time",
"."
] |
def process_market(self, market_info):
"""
Checks if enough time has elapsed from previous order to place order and if so, calls place_orders_for_market() and
cancels orders if they are older than self._cancel_order_wait_time.
:param market_info: a market trading pair
"""
if self._quantity_remaining > 0:
# If current timestamp is greater than the start timestamp and its the first order
if (self.current_timestamp > self._previous_timestamp) and self._first_order:
self.logger().info("Trying to place orders now. ")
self._previous_timestamp = self.current_timestamp
self.place_orders_for_market(market_info)
self._first_order = False
# If current timestamp is greater than the start timestamp + time delay place orders
elif (self.current_timestamp > self._previous_timestamp + self._order_delay_time) and (self._first_order is False):
self.logger().info("Current time: "
f"{datetime.fromtimestamp(self.current_timestamp).strftime('%Y-%m-%d %H:%M:%S')} "
"is now greater than "
"Previous time: "
f"{datetime.fromtimestamp(self._previous_timestamp).strftime('%Y-%m-%d %H:%M:%S')} "
f" with time delay: {self._order_delay_time}. Trying to place orders now. ")
self._previous_timestamp = self.current_timestamp
self.place_orders_for_market(market_info)
active_orders = self.market_info_to_active_orders.get(market_info, [])
orders_to_cancel = (active_order
for active_order
in active_orders
if self.current_timestamp >= self._time_to_cancel[active_order.client_order_id])
for order in orders_to_cancel:
self.cancel_order(market_info, order.client_order_id)
|
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"\"Previous time: \"",
"f\"{datetime.fromtimestamp(self._previous_timestamp).strftime('%Y-%m-%d %H:%M:%S')} \"",
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"orders_to_cancel",
":",
"self",
".",
"cancel_order",
"(",
"market_info",
",",
"order",
".",
"client_order_id",
")"
] |
https://github.com/CoinAlpha/hummingbot/blob/36f6149c1644c07cd36795b915f38b8f49b798e7/hummingbot/strategy/twap/twap.py#L257-L293
|
||
guildai/guildai
|
1665985a3d4d788efc1a3180ca51cc417f71ca78
|
guild/external/pkg_resources/__init__.py
|
python
|
ResourceManager._warn_unsafe_extraction_path
|
(path)
|
If the default extraction path is overridden and set to an insecure
location, such as /tmp, it opens up an opportunity for an attacker to
replace an extracted file with an unauthorized payload. Warn the user
if a known insecure location is used.
See Distribute #375 for more details.
|
If the default extraction path is overridden and set to an insecure
location, such as /tmp, it opens up an opportunity for an attacker to
replace an extracted file with an unauthorized payload. Warn the user
if a known insecure location is used.
|
[
"If",
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"overridden",
"and",
"set",
"to",
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"insecure",
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"such",
"as",
"/",
"tmp",
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".",
"Warn",
"the",
"user",
"if",
"a",
"known",
"insecure",
"location",
"is",
"used",
"."
] |
def _warn_unsafe_extraction_path(path):
"""
If the default extraction path is overridden and set to an insecure
location, such as /tmp, it opens up an opportunity for an attacker to
replace an extracted file with an unauthorized payload. Warn the user
if a known insecure location is used.
See Distribute #375 for more details.
"""
if os.name == 'nt' and not path.startswith(os.environ['windir']):
# On Windows, permissions are generally restrictive by default
# and temp directories are not writable by other users, so
# bypass the warning.
return
mode = os.stat(path).st_mode
if mode & stat.S_IWOTH or mode & stat.S_IWGRP:
msg = (
"%s is writable by group/others and vulnerable to attack "
"when "
"used with get_resource_filename. Consider a more secure "
"location (set with .set_extraction_path or the "
"PYTHON_EGG_CACHE environment variable)." % path
)
warnings.warn(msg, UserWarning)
|
[
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".",
"name",
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"%",
"path",
")",
"warnings",
".",
"warn",
"(",
"msg",
",",
"UserWarning",
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] |
https://github.com/guildai/guildai/blob/1665985a3d4d788efc1a3180ca51cc417f71ca78/guild/external/pkg_resources/__init__.py#L1221-L1244
|
||
fffonion/xeHentai
|
26063154a238d4df280f8d17f14d090e679084ec
|
xeHentai/rpc.py
|
python
|
RPCServer.run
|
(self)
|
[] |
def run(self):
try:
self.server = ThreadedHTTPServer(self.bind_addr, lambda *x: Handler(self.xeH, self.secret, *x))
except Exception as ex:
self.logger.error(i18n.RPC_CANNOT_BIND % traceback.format_exc())
else:
self.logger.info(i18n.RPC_STARTED % (self.bind_addr[0], self.bind_addr[1]))
url = "http://%s:%s/ui/#host=%s,port=%s,https=no" % (
self.bind_addr[0], self.bind_addr[1],
self.bind_addr[0], self.bind_addr[1]
)
if self.secret:
url = url + ",token=" + self.secret
if self.open_browser:
import webbrowser
webbrowser.open(url)
else:
self.logger.info(i18n.RPC_WEBUI_PATH % url)
while not self._exit("rpc"):
self.server.handle_request()
|
[
"def",
"run",
"(",
"self",
")",
":",
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"bind_addr",
",",
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"RPC_CANNOT_BIND",
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"(",
")",
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":",
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")",
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",",
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".",
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":",
"url",
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"+",
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"+",
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"_exit",
"(",
"\"rpc\"",
")",
":",
"self",
".",
"server",
".",
"handle_request",
"(",
")"
] |
https://github.com/fffonion/xeHentai/blob/26063154a238d4df280f8d17f14d090e679084ec/xeHentai/rpc.py#L50-L69
|
||||
Runbook/runbook
|
7b68622f75ef09f654046f0394540025f3ee7445
|
src/actions/actions/stathat/stathat.py
|
python
|
_StatHatBase._send
|
(self, path, data, async)
|
return True
|
[] |
def _send(self, path, data, async):
endpoint = STATHAT_ENDPOINT + path
payload = self._auth.copy()
payload.update(data)
if HAS_GEVENT and async is not False:
# Async request should be completely silent and ignore any
# errors that may be thrown.
async_group.spawn(self._send_inner, endpoint, payload, silent=True)
else:
# If the request isn't async, we should make an effort
# to parse the response and return it, or raise a proper exception
try:
raw = self._send_inner(endpoint, payload)
except urllib2.URLError, e:
# Network issue or something else affecting the general request
raise StatHatError(e)
try:
resp = json.loads(raw)
except Exception:
# JSON decoding false meaning StatHat returned something bad
raise StatHatError('Something bad happened: %s' % raw)
if 'msg' in resp and 'status' in resp:
if resp['status'] != 200:
# Normal error from StatHat
raise StatHatError(resp['msg'])
else:
# 'msg' and 'status' keys weren't returned, something bad happened
raise StatHatError('Something bad happened: %s' % raw)
return True
|
[
"def",
"_send",
"(",
"self",
",",
"path",
",",
"data",
",",
"async",
")",
":",
"endpoint",
"=",
"STATHAT_ENDPOINT",
"+",
"path",
"payload",
"=",
"self",
".",
"_auth",
".",
"copy",
"(",
")",
"payload",
".",
"update",
"(",
"data",
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"if",
"HAS_GEVENT",
"and",
"async",
"is",
"not",
"False",
":",
"# Async request should be completely silent and ignore any",
"# errors that may be thrown.",
"async_group",
".",
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"self",
".",
"_send_inner",
",",
"endpoint",
",",
"payload",
",",
"silent",
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"raise",
"StatHatError",
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"e",
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"try",
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"resp",
"=",
"json",
".",
"loads",
"(",
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"except",
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"# JSON decoding false meaning StatHat returned something bad",
"raise",
"StatHatError",
"(",
"'Something bad happened: %s'",
"%",
"raw",
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"if",
"'msg'",
"in",
"resp",
"and",
"'status'",
"in",
"resp",
":",
"if",
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"[",
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"]",
"!=",
"200",
":",
"# Normal error from StatHat",
"raise",
"StatHatError",
"(",
"resp",
"[",
"'msg'",
"]",
")",
"else",
":",
"# 'msg' and 'status' keys weren't returned, something bad happened",
"raise",
"StatHatError",
"(",
"'Something bad happened: %s'",
"%",
"raw",
")",
"return",
"True"
] |
https://github.com/Runbook/runbook/blob/7b68622f75ef09f654046f0394540025f3ee7445/src/actions/actions/stathat/stathat.py#L88-L117
|
|||
ales-tsurko/cells
|
4cf7e395cd433762bea70cdc863a346f3a6fe1d0
|
packaging/macos/python/lib/python3.7/asyncio/transports.py
|
python
|
ReadTransport.pause_reading
|
(self)
|
Pause the receiving end.
No data will be passed to the protocol's data_received()
method until resume_reading() is called.
|
Pause the receiving end.
|
[
"Pause",
"the",
"receiving",
"end",
"."
] |
def pause_reading(self):
"""Pause the receiving end.
No data will be passed to the protocol's data_received()
method until resume_reading() is called.
"""
raise NotImplementedError
|
[
"def",
"pause_reading",
"(",
"self",
")",
":",
"raise",
"NotImplementedError"
] |
https://github.com/ales-tsurko/cells/blob/4cf7e395cd433762bea70cdc863a346f3a6fe1d0/packaging/macos/python/lib/python3.7/asyncio/transports.py#L51-L57
|
||
runawayhorse001/LearningApacheSpark
|
67f3879dce17553195f094f5728b94a01badcf24
|
pyspark/sql/session.py
|
python
|
SparkSession._convert_from_pandas
|
(self, pdf, schema, timezone)
|
return [r.tolist() for r in np_records]
|
Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
:return list of records
|
Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
:return list of records
|
[
"Convert",
"a",
"pandas",
".",
"DataFrame",
"to",
"list",
"of",
"records",
"that",
"can",
"be",
"used",
"to",
"make",
"a",
"DataFrame",
":",
"return",
"list",
"of",
"records"
] |
def _convert_from_pandas(self, pdf, schema, timezone):
"""
Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
:return list of records
"""
if timezone is not None:
from pyspark.sql.types import _check_series_convert_timestamps_tz_local
copied = False
if isinstance(schema, StructType):
for field in schema:
# TODO: handle nested timestamps, such as ArrayType(TimestampType())?
if isinstance(field.dataType, TimestampType):
s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
if s is not pdf[field.name]:
if not copied:
# Copy once if the series is modified to prevent the original
# Pandas DataFrame from being updated
pdf = pdf.copy()
copied = True
pdf[field.name] = s
else:
for column, series in pdf.iteritems():
s = _check_series_convert_timestamps_tz_local(series, timezone)
if s is not series:
if not copied:
# Copy once if the series is modified to prevent the original
# Pandas DataFrame from being updated
pdf = pdf.copy()
copied = True
pdf[column] = s
# Convert pandas.DataFrame to list of numpy records
np_records = pdf.to_records(index=False)
# Check if any columns need to be fixed for Spark to infer properly
if len(np_records) > 0:
record_dtype = self._get_numpy_record_dtype(np_records[0])
if record_dtype is not None:
return [r.astype(record_dtype).tolist() for r in np_records]
# Convert list of numpy records to python lists
return [r.tolist() for r in np_records]
|
[
"def",
"_convert_from_pandas",
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",",
"schema",
",",
"timezone",
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",",
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":",
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"=",
"True",
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"[",
"column",
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"np_records",
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".",
"to_records",
"(",
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"False",
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"# Check if any columns need to be fixed for Spark to infer properly",
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"[",
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"]",
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"[",
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")",
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"tolist",
"(",
")",
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"return",
"[",
"r",
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"tolist",
"(",
")",
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"in",
"np_records",
"]"
] |
https://github.com/runawayhorse001/LearningApacheSpark/blob/67f3879dce17553195f094f5728b94a01badcf24/pyspark/sql/session.py#L455-L496
|
|
sagemath/sage
|
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
|
src/sage/combinat/sloane_functions.py
|
python
|
A000108._eval
|
(self, n)
|
return combinat.catalan_number(n)
|
EXAMPLES::
sage: [sloane.A000108._eval(n) for n in range(10)]
[1, 1, 2, 5, 14, 42, 132, 429, 1430, 4862]
|
EXAMPLES::
|
[
"EXAMPLES",
"::"
] |
def _eval(self, n):
"""
EXAMPLES::
sage: [sloane.A000108._eval(n) for n in range(10)]
[1, 1, 2, 5, 14, 42, 132, 429, 1430, 4862]
"""
return combinat.catalan_number(n)
|
[
"def",
"_eval",
"(",
"self",
",",
"n",
")",
":",
"return",
"combinat",
".",
"catalan_number",
"(",
"n",
")"
] |
https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/combinat/sloane_functions.py#L4031-L4038
|
|
jkwill87/mnamer
|
c8bbc63a8847e9b15b0f512f7ae01de0b98cf739
|
mnamer/endpoints.py
|
python
|
tvdb_search_series
|
(
token: str,
series: Optional[str] = None,
id_imdb: Optional[str] = None,
id_zap2it: Optional[str] = None,
language: Optional[Language] = None,
cache: bool = True,
)
|
return content
|
Allows the user to search for a series based on the following parameters.
Online docs: https://api.thetvdb.com/swagger#!/Search/get_search_series
Note: results a maximum of 100 entries per page, no option for pagination.
|
Allows the user to search for a series based on the following parameters.
|
[
"Allows",
"the",
"user",
"to",
"search",
"for",
"a",
"series",
"based",
"on",
"the",
"following",
"parameters",
"."
] |
def tvdb_search_series(
token: str,
series: Optional[str] = None,
id_imdb: Optional[str] = None,
id_zap2it: Optional[str] = None,
language: Optional[Language] = None,
cache: bool = True,
) -> dict:
"""
Allows the user to search for a series based on the following parameters.
Online docs: https://api.thetvdb.com/swagger#!/Search/get_search_series
Note: results a maximum of 100 entries per page, no option for pagination.
"""
Language.ensure_valid_for_tvdb(language)
url = "https://api.thetvdb.com/search/series"
parameters = {"name": series, "imdbId": id_imdb, "zap2itId": id_zap2it}
headers = {"Authorization": f"Bearer {token}"}
if language:
headers["Accept-Language"] = language.a2
status, content = request_json(
url,
parameters,
headers=headers,
cache=cache is True and language is None,
)
if status == 401:
raise MnamerException("invalid token")
elif status == 405:
raise MnamerException(
"series, id_imdb, id_zap2it parameters are mutually exclusive"
)
elif status == 404:
raise MnamerNotFoundException
elif status != 200 or not content.get("data"): # pragma: no cover
raise MnamerNetworkException("TVDb down or unavailable?")
return content
|
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"language",
"is",
"None",
",",
")",
"if",
"status",
"==",
"401",
":",
"raise",
"MnamerException",
"(",
"\"invalid token\"",
")",
"elif",
"status",
"==",
"405",
":",
"raise",
"MnamerException",
"(",
"\"series, id_imdb, id_zap2it parameters are mutually exclusive\"",
")",
"elif",
"status",
"==",
"404",
":",
"raise",
"MnamerNotFoundException",
"elif",
"status",
"!=",
"200",
"or",
"not",
"content",
".",
"get",
"(",
"\"data\"",
")",
":",
"# pragma: no cover",
"raise",
"MnamerNetworkException",
"(",
"\"TVDb down or unavailable?\"",
")",
"return",
"content"
] |
https://github.com/jkwill87/mnamer/blob/c8bbc63a8847e9b15b0f512f7ae01de0b98cf739/mnamer/endpoints.py#L388-L424
|
|
bruderstein/PythonScript
|
df9f7071ddf3a079e3a301b9b53a6dc78cf1208f
|
PythonLib/min/uuid.py
|
python
|
UUID.bytes_le
|
(self)
|
return (bytes[4-1::-1] + bytes[6-1:4-1:-1] + bytes[8-1:6-1:-1] +
bytes[8:])
|
[] |
def bytes_le(self):
bytes = self.bytes
return (bytes[4-1::-1] + bytes[6-1:4-1:-1] + bytes[8-1:6-1:-1] +
bytes[8:])
|
[
"def",
"bytes_le",
"(",
"self",
")",
":",
"bytes",
"=",
"self",
".",
"bytes",
"return",
"(",
"bytes",
"[",
"4",
"-",
"1",
":",
":",
"-",
"1",
"]",
"+",
"bytes",
"[",
"6",
"-",
"1",
":",
"4",
"-",
"1",
":",
"-",
"1",
"]",
"+",
"bytes",
"[",
"8",
"-",
"1",
":",
"6",
"-",
"1",
":",
"-",
"1",
"]",
"+",
"bytes",
"[",
"8",
":",
"]",
")"
] |
https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/min/uuid.py#L289-L292
|
|||
JimmXinu/FanFicFare
|
bc149a2deb2636320fe50a3e374af6eef8f61889
|
fanficfare/adapters/adapter_storiesonlinenet.py
|
python
|
StoriesOnlineNetAdapter.getSiteAbbrev
|
(cls)
|
return 'strol'
|
[] |
def getSiteAbbrev(cls):
return 'strol'
|
[
"def",
"getSiteAbbrev",
"(",
"cls",
")",
":",
"return",
"'strol'"
] |
https://github.com/JimmXinu/FanFicFare/blob/bc149a2deb2636320fe50a3e374af6eef8f61889/fanficfare/adapters/adapter_storiesonlinenet.py#L69-L70
|
|||
ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework
|
cb692f527e4e819b6c228187c5702d990a180043
|
external/Scripting Engine/packages/IronPython.StdLib.2.7.4/content/Lib/filecmp.py
|
python
|
cmpfiles
|
(a, b, common, shallow=1)
|
return res
|
Compare common files in two directories.
a, b -- directory names
common -- list of file names found in both directories
shallow -- if true, do comparison based solely on stat() information
Returns a tuple of three lists:
files that compare equal
files that are different
filenames that aren't regular files.
|
Compare common files in two directories.
|
[
"Compare",
"common",
"files",
"in",
"two",
"directories",
"."
] |
def cmpfiles(a, b, common, shallow=1):
"""Compare common files in two directories.
a, b -- directory names
common -- list of file names found in both directories
shallow -- if true, do comparison based solely on stat() information
Returns a tuple of three lists:
files that compare equal
files that are different
filenames that aren't regular files.
"""
res = ([], [], [])
for x in common:
ax = os.path.join(a, x)
bx = os.path.join(b, x)
res[_cmp(ax, bx, shallow)].append(x)
return res
|
[
"def",
"cmpfiles",
"(",
"a",
",",
"b",
",",
"common",
",",
"shallow",
"=",
"1",
")",
":",
"res",
"=",
"(",
"[",
"]",
",",
"[",
"]",
",",
"[",
"]",
")",
"for",
"x",
"in",
"common",
":",
"ax",
"=",
"os",
".",
"path",
".",
"join",
"(",
"a",
",",
"x",
")",
"bx",
"=",
"os",
".",
"path",
".",
"join",
"(",
"b",
",",
"x",
")",
"res",
"[",
"_cmp",
"(",
"ax",
",",
"bx",
",",
"shallow",
")",
"]",
".",
"append",
"(",
"x",
")",
"return",
"res"
] |
https://github.com/ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework/blob/cb692f527e4e819b6c228187c5702d990a180043/external/Scripting Engine/packages/IronPython.StdLib.2.7.4/content/Lib/filecmp.py#L240-L258
|
|
Calysto/calysto_scheme
|
15bf81987870bcae1264e5a0a06feb9a8ee12b8b
|
calysto_scheme/scheme.py
|
python
|
list_head
|
(lyst, pos)
|
return retval
|
[] |
def list_head(lyst, pos):
stack = symbol_emptylist
current = lyst
for i in range(pos):
stack = cons(current.car, stack)
current = current.cdr
retval = symbol_emptylist
for i in range(pos):
retval = cons(stack.car, retval)
stack = stack.cdr
return retval
|
[
"def",
"list_head",
"(",
"lyst",
",",
"pos",
")",
":",
"stack",
"=",
"symbol_emptylist",
"current",
"=",
"lyst",
"for",
"i",
"in",
"range",
"(",
"pos",
")",
":",
"stack",
"=",
"cons",
"(",
"current",
".",
"car",
",",
"stack",
")",
"current",
"=",
"current",
".",
"cdr",
"retval",
"=",
"symbol_emptylist",
"for",
"i",
"in",
"range",
"(",
"pos",
")",
":",
"retval",
"=",
"cons",
"(",
"stack",
".",
"car",
",",
"retval",
")",
"stack",
"=",
"stack",
".",
"cdr",
"return",
"retval"
] |
https://github.com/Calysto/calysto_scheme/blob/15bf81987870bcae1264e5a0a06feb9a8ee12b8b/calysto_scheme/scheme.py#L482-L492
|
|||
basho/riak-python-client
|
91de13a16607cdf553d1a194e762734e3bec4231
|
riak/riak_object.py
|
python
|
RiakObject.link
|
(self, *args)
|
return mr.link(*args)
|
Start assembling a Map/Reduce operation.
A shortcut for :meth:`~riak.mapreduce.RiakMapReduce.link`.
:rtype: :class:`~riak.mapreduce.RiakMapReduce`
|
Start assembling a Map/Reduce operation.
A shortcut for :meth:`~riak.mapreduce.RiakMapReduce.link`.
|
[
"Start",
"assembling",
"a",
"Map",
"/",
"Reduce",
"operation",
".",
"A",
"shortcut",
"for",
":",
"meth",
":",
"~riak",
".",
"mapreduce",
".",
"RiakMapReduce",
".",
"link",
"."
] |
def link(self, *args):
"""
Start assembling a Map/Reduce operation.
A shortcut for :meth:`~riak.mapreduce.RiakMapReduce.link`.
:rtype: :class:`~riak.mapreduce.RiakMapReduce`
"""
mr = RiakMapReduce(self.client)
mr.add(self.bucket.name, self.key)
return mr.link(*args)
|
[
"def",
"link",
"(",
"self",
",",
"*",
"args",
")",
":",
"mr",
"=",
"RiakMapReduce",
"(",
"self",
".",
"client",
")",
"mr",
".",
"add",
"(",
"self",
".",
"bucket",
".",
"name",
",",
"self",
".",
"key",
")",
"return",
"mr",
".",
"link",
"(",
"*",
"args",
")"
] |
https://github.com/basho/riak-python-client/blob/91de13a16607cdf553d1a194e762734e3bec4231/riak/riak_object.py#L379-L388
|
|
beeware/ouroboros
|
a29123c6fab6a807caffbb7587cf548e0c370296
|
ouroboros/base64.py
|
python
|
b16decode
|
(s, casefold=False)
|
return binascii.unhexlify(s)
|
Decode a Base16 encoded byte string.
s is the byte string to decode. Optional casefold is a flag
specifying whether a lowercase alphabet is acceptable as input.
For security purposes, the default is False.
The decoded byte string is returned. binascii.Error is raised if
s were incorrectly padded or if there are non-alphabet characters
present in the string.
|
Decode a Base16 encoded byte string.
|
[
"Decode",
"a",
"Base16",
"encoded",
"byte",
"string",
"."
] |
def b16decode(s, casefold=False):
"""Decode a Base16 encoded byte string.
s is the byte string to decode. Optional casefold is a flag
specifying whether a lowercase alphabet is acceptable as input.
For security purposes, the default is False.
The decoded byte string is returned. binascii.Error is raised if
s were incorrectly padded or if there are non-alphabet characters
present in the string.
"""
s = _bytes_from_decode_data(s)
if casefold:
s = s.upper()
if re.search(b'[^0-9A-F]', s):
raise binascii.Error('Non-base16 digit found')
return binascii.unhexlify(s)
|
[
"def",
"b16decode",
"(",
"s",
",",
"casefold",
"=",
"False",
")",
":",
"s",
"=",
"_bytes_from_decode_data",
"(",
"s",
")",
"if",
"casefold",
":",
"s",
"=",
"s",
".",
"upper",
"(",
")",
"if",
"re",
".",
"search",
"(",
"b'[^0-9A-F]'",
",",
"s",
")",
":",
"raise",
"binascii",
".",
"Error",
"(",
"'Non-base16 digit found'",
")",
"return",
"binascii",
".",
"unhexlify",
"(",
"s",
")"
] |
https://github.com/beeware/ouroboros/blob/a29123c6fab6a807caffbb7587cf548e0c370296/ouroboros/base64.py#L267-L283
|
|
kuri65536/python-for-android
|
26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891
|
python-modules/pybluez/bluetooth/bluez.py
|
python
|
DeviceDiscoverer.cancel_inquiry
|
(self)
|
Call this method to cancel an inquiry in process. inquiry_complete
will still be called.
|
Call this method to cancel an inquiry in process. inquiry_complete
will still be called.
|
[
"Call",
"this",
"method",
"to",
"cancel",
"an",
"inquiry",
"in",
"process",
".",
"inquiry_complete",
"will",
"still",
"be",
"called",
"."
] |
def cancel_inquiry (self):
"""
Call this method to cancel an inquiry in process. inquiry_complete
will still be called.
"""
self.names_to_find = {}
if self.is_inquiring:
try:
_bt.hci_send_cmd (self.sock, _bt.OGF_LINK_CTL, \
_bt.OCF_INQUIRY_CANCEL)
self.sock.close ()
self.sock = None
except:
raise BluetoothError ("error canceling inquiry")
self.is_inquiring = False
|
[
"def",
"cancel_inquiry",
"(",
"self",
")",
":",
"self",
".",
"names_to_find",
"=",
"{",
"}",
"if",
"self",
".",
"is_inquiring",
":",
"try",
":",
"_bt",
".",
"hci_send_cmd",
"(",
"self",
".",
"sock",
",",
"_bt",
".",
"OGF_LINK_CTL",
",",
"_bt",
".",
"OCF_INQUIRY_CANCEL",
")",
"self",
".",
"sock",
".",
"close",
"(",
")",
"self",
".",
"sock",
"=",
"None",
"except",
":",
"raise",
"BluetoothError",
"(",
"\"error canceling inquiry\"",
")",
"self",
".",
"is_inquiring",
"=",
"False"
] |
https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-modules/pybluez/bluetooth/bluez.py#L405-L420
|
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