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trezor/python-trezor
|
2813522b05cef4e0e545a101f8b3559a3183b45b
|
trezorlib/protobuf.py
|
python
|
dict_to_proto
|
(message_type, d)
|
return message_type(**params)
|
[] |
def dict_to_proto(message_type, d):
params = {}
for fname, ftype, fflags in message_type.get_fields().values():
repeated = fflags & FLAG_REPEATED
value = d.get(fname)
if value is None:
continue
if not repeated:
value = [value]
if issubclass(ftype, MessageType):
function = dict_to_proto
else:
function = value_to_proto
newvalue = [function(ftype, v) for v in value]
if not repeated:
newvalue = newvalue[0]
params[fname] = newvalue
return message_type(**params)
|
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] |
https://github.com/trezor/python-trezor/blob/2813522b05cef4e0e545a101f8b3559a3183b45b/trezorlib/protobuf.py#L389-L411
|
|||
huggingface/transformers
|
623b4f7c63f60cce917677ee704d6c93ee960b4b
|
src/transformers/models/t5/modeling_tf_t5.py
|
python
|
TFT5LayerSelfAttention.call
|
(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
)
|
return outputs
|
[] |
def call(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = hidden_states + self.dropout(attention_output[0], training=training)
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
|
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https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/src/transformers/models/t5/modeling_tf_t5.py#L431-L455
|
|||
rowliny/DiffHelper
|
ab3a96f58f9579d0023aed9ebd785f4edf26f8af
|
Tool/SitePackages/PIL/ImageStat.py
|
python
|
Stat.__getattr__
|
(self, id)
|
return v
|
Calculate missing attribute
|
Calculate missing attribute
|
[
"Calculate",
"missing",
"attribute"
] |
def __getattr__(self, id):
"""Calculate missing attribute"""
if id[:4] == "_get":
raise AttributeError(id)
# calculate missing attribute
v = getattr(self, "_get" + id)()
setattr(self, id, v)
return v
|
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https://github.com/rowliny/DiffHelper/blob/ab3a96f58f9579d0023aed9ebd785f4edf26f8af/Tool/SitePackages/PIL/ImageStat.py#L42-L49
|
|
facebookarchive/sparts
|
c03df928677444ad638d10fa96f4144ca4d644e1
|
sparts/fb303/FacebookService.py
|
python
|
Iface.reinitialize
|
(self)
|
Tell the server to reload its configuration, reopen log files, etc
|
Tell the server to reload its configuration, reopen log files, etc
|
[
"Tell",
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] |
def reinitialize(self):
"""
Tell the server to reload its configuration, reopen log files, etc
"""
pass
|
[
"def",
"reinitialize",
"(",
"self",
")",
":",
"pass"
] |
https://github.com/facebookarchive/sparts/blob/c03df928677444ad638d10fa96f4144ca4d644e1/sparts/fb303/FacebookService.py#L105-L109
|
||
benfred/implicit
|
db83d3f2783441e7dfe3a4ea4743051a8a000fa8
|
implicit/nearest_neighbours.py
|
python
|
BM25Recommender.fit
|
(self, counts, show_progress=True)
|
[] |
def fit(self, counts, show_progress=True):
weighted = bm25_weight(counts.T, self.K1, self.B).T
ItemItemRecommender.fit(self, weighted, show_progress)
|
[
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https://github.com/benfred/implicit/blob/db83d3f2783441e7dfe3a4ea4743051a8a000fa8/implicit/nearest_neighbours.py#L203-L205
|
||||
dickreuter/neuron_poker
|
9f841e5aeead681fa1fb2955524c53081fba2078
|
tools/helper.py
|
python
|
Singleton.__call__
|
(cls, *args, **kwargs)
|
return cls._instances[cls]
|
Is called at instantiation of a class that refers to this metaclass.
|
Is called at instantiation of a class that refers to this metaclass.
|
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] |
def __call__(cls, *args, **kwargs): # called at instantiation of an object that uses this metaclass
"""Is called at instantiation of a class that refers to this metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
|
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https://github.com/dickreuter/neuron_poker/blob/9f841e5aeead681fa1fb2955524c53081fba2078/tools/helper.py#L34-L38
|
|
holzschu/Carnets
|
44effb10ddfc6aa5c8b0687582a724ba82c6b547
|
Library/lib/python3.7/site-packages/docutils/parsers/rst/states.py
|
python
|
Text.literal_block
|
(self)
|
return nodelist
|
Return a list of nodes.
|
Return a list of nodes.
|
[
"Return",
"a",
"list",
"of",
"nodes",
"."
] |
def literal_block(self):
"""Return a list of nodes."""
indented, indent, offset, blank_finish = \
self.state_machine.get_indented()
while indented and not indented[-1].strip():
indented.trim_end()
if not indented:
return self.quoted_literal_block()
data = '\n'.join(indented)
literal_block = nodes.literal_block(data, data)
literal_block.line = offset + 1
nodelist = [literal_block]
if not blank_finish:
nodelist.append(self.unindent_warning('Literal block'))
return nodelist
|
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https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/docutils/parsers/rst/states.py#L2778-L2792
|
|
smallcorgi/Faster-RCNN_TF
|
d9adb24c8ffdbae3b56eb55fc629d719fee3d741
|
lib/roi_data_layer/minibatch2.py
|
python
|
_get_bbox_regression_labels
|
(bbox_target_data, num_classes)
|
return bbox_targets, bbox_loss_weights
|
Bounding-box regression targets are stored in a compact form in the
roidb.
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets). The loss weights
are similarly expanded.
Returns:
bbox_target_data (ndarray): N x 4K blob of regression targets
bbox_loss_weights (ndarray): N x 4K blob of loss weights
|
Bounding-box regression targets are stored in a compact form in the
roidb.
|
[
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] |
def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets are stored in a compact form in the
roidb.
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets). The loss weights
are similarly expanded.
Returns:
bbox_target_data (ndarray): N x 4K blob of regression targets
bbox_loss_weights (ndarray): N x 4K blob of loss weights
"""
clss = bbox_target_data[:, 0]
bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)
bbox_loss_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
inds = np.where(clss > 0)[0]
for ind in inds:
cls = clss[ind]
start = 4 * cls
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_loss_weights[ind, start:end] = [1., 1., 1., 1.]
return bbox_targets, bbox_loss_weights
|
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https://github.com/smallcorgi/Faster-RCNN_TF/blob/d9adb24c8ffdbae3b56eb55fc629d719fee3d741/lib/roi_data_layer/minibatch2.py#L258-L280
|
|
apple/ccs-calendarserver
|
13c706b985fb728b9aab42dc0fef85aae21921c3
|
twistedcaldav/vcard.py
|
python
|
Component.fromStream
|
(clazz, stream, format=None)
|
return clazz._fromData(stream, True, format)
|
Construct a L{Component} from a stream.
@param stream: a C{read()}able stream containing vCard data.
@return: a L{Component} representing the first component described by
C{stream}.
|
Construct a L{Component} from a stream.
|
[
"Construct",
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"}",
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"."
] |
def fromStream(clazz, stream, format=None):
"""
Construct a L{Component} from a stream.
@param stream: a C{read()}able stream containing vCard data.
@return: a L{Component} representing the first component described by
C{stream}.
"""
return clazz._fromData(stream, True, format)
|
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https://github.com/apple/ccs-calendarserver/blob/13c706b985fb728b9aab42dc0fef85aae21921c3/twistedcaldav/vcard.py#L254-L261
|
|
oilshell/oil
|
94388e7d44a9ad879b12615f6203b38596b5a2d3
|
Python-2.7.13/Lib/inspect.py
|
python
|
isdatadescriptor
|
(object)
|
return (hasattr(object, "__set__") and hasattr(object, "__get__"))
|
Return true if the object is a data descriptor.
Data descriptors have both a __get__ and a __set__ attribute. Examples are
properties (defined in Python) and getsets and members (defined in C).
Typically, data descriptors will also have __name__ and __doc__ attributes
(properties, getsets, and members have both of these attributes), but this
is not guaranteed.
|
Return true if the object is a data descriptor.
|
[
"Return",
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"if",
"the",
"object",
"is",
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"data",
"descriptor",
"."
] |
def isdatadescriptor(object):
"""Return true if the object is a data descriptor.
Data descriptors have both a __get__ and a __set__ attribute. Examples are
properties (defined in Python) and getsets and members (defined in C).
Typically, data descriptors will also have __name__ and __doc__ attributes
(properties, getsets, and members have both of these attributes), but this
is not guaranteed."""
return (hasattr(object, "__set__") and hasattr(object, "__get__"))
|
[
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https://github.com/oilshell/oil/blob/94388e7d44a9ad879b12615f6203b38596b5a2d3/Python-2.7.13/Lib/inspect.py#L98-L106
|
|
angr/angr
|
4b04d56ace135018083d36d9083805be8146688b
|
angr/engines/vex/claripy/ccall.py
|
python
|
pc_actions_SBB
|
(state, nbits, cc_dep1, cc_dep2, cc_ndep, platform=None)
|
return pc_make_rdata(data[platform]['size'], cf, pf, af, zf, sf, of, platform=platform)
|
[] |
def pc_actions_SBB(state, nbits, cc_dep1, cc_dep2, cc_ndep, platform=None):
old_c = cc_ndep[data[platform]['CondBitOffsets']['G_CC_SHIFT_C']].zero_extend(nbits-1)
arg_l = cc_dep1
arg_r = cc_dep2 ^ old_c
res = (arg_l - arg_r) - old_c
cf_c = claripy.If(claripy.ULE(arg_l, arg_r), claripy.BVV(1, 1), claripy.BVV(0, 1))
cf_noc = claripy.If(claripy.ULT(arg_l, arg_r), claripy.BVV(1, 1), claripy.BVV(0, 1))
cf = claripy.If(old_c == 1, cf_c, cf_noc)
pf = calc_paritybit(res)
af = (res ^ arg_l ^ arg_r)[data[platform]['CondBitOffsets']['G_CC_SHIFT_A']]
zf = calc_zerobit(res)
sf = res[nbits-1]
of = ((arg_l ^ arg_r) & (arg_l ^ res))[nbits-1]
return pc_make_rdata(data[platform]['size'], cf, pf, af, zf, sf, of, platform=platform)
|
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https://github.com/angr/angr/blob/4b04d56ace135018083d36d9083805be8146688b/angr/engines/vex/claripy/ccall.py#L426-L440
|
|||
shuup/shuup
|
25f78cfe370109b9885b903e503faac295c7b7f2
|
shuup/core/models/_base.py
|
python
|
ChangeProtected._are_changes_protected
|
(self)
|
return True
|
Check if changes of this object should be protected.
This can be overridden in the subclasses to make it possible to
avoid change protection e.g. if object is not in use yet.
The base class implementation just returns True.
|
Check if changes of this object should be protected.
|
[
"Check",
"if",
"changes",
"of",
"this",
"object",
"should",
"be",
"protected",
"."
] |
def _are_changes_protected(self):
"""
Check if changes of this object should be protected.
This can be overridden in the subclasses to make it possible to
avoid change protection e.g. if object is not in use yet.
The base class implementation just returns True.
"""
return True
|
[
"def",
"_are_changes_protected",
"(",
"self",
")",
":",
"return",
"True"
] |
https://github.com/shuup/shuup/blob/25f78cfe370109b9885b903e503faac295c7b7f2/shuup/core/models/_base.py#L123-L132
|
|
riptideio/pymodbus
|
c5772b35ae3f29d1947f3ab453d8d00df846459f
|
pymodbus/framer/tls_framer.py
|
python
|
ModbusTlsFramer.addToFrame
|
(self, message)
|
Adds new packet data to the current frame buffer
:param message: The most recent packet
|
Adds new packet data to the current frame buffer
|
[
"Adds",
"new",
"packet",
"data",
"to",
"the",
"current",
"frame",
"buffer"
] |
def addToFrame(self, message):
""" Adds new packet data to the current frame buffer
:param message: The most recent packet
"""
self._buffer += message
|
[
"def",
"addToFrame",
"(",
"self",
",",
"message",
")",
":",
"self",
".",
"_buffer",
"+=",
"message"
] |
https://github.com/riptideio/pymodbus/blob/c5772b35ae3f29d1947f3ab453d8d00df846459f/pymodbus/framer/tls_framer.py#L72-L77
|
||
urduhack/urduhack
|
44500cd6a78e1a7765bb4f7d6fb92bbb612b7b11
|
urduhack/pipeline/parsers/normalize.py
|
python
|
NormalizeParser.parse
|
(self, document: str)
|
return preprocess(normalize(normalize_whitespace(document)))
|
Normalize|Preprocess text
Args:
document (str): Urdu text
Returns:
str: Return complete urdu document
|
Normalize|Preprocess text
|
[
"Normalize|Preprocess",
"text"
] |
def parse(self, document: str) -> str:
"""
Normalize|Preprocess text
Args:
document (str): Urdu text
Returns:
str: Return complete urdu document
"""
return preprocess(normalize(normalize_whitespace(document)))
|
[
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"(",
"normalize",
"(",
"normalize_whitespace",
"(",
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")",
")"
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https://github.com/urduhack/urduhack/blob/44500cd6a78e1a7765bb4f7d6fb92bbb612b7b11/urduhack/pipeline/parsers/normalize.py#L15-L25
|
|
wistbean/learn_python3_spider
|
73c873f4845f4385f097e5057407d03dd37a117b
|
stackoverflow/venv/lib/python3.6/site-packages/twisted/application/internet.py
|
python
|
_ClientMachine._restarting
|
(self)
|
The service is disconnecting and has been asked to restart.
|
The service is disconnecting and has been asked to restart.
|
[
"The",
"service",
"is",
"disconnecting",
"and",
"has",
"been",
"asked",
"to",
"restart",
"."
] |
def _restarting(self):
"""
The service is disconnecting and has been asked to restart.
"""
|
[
"def",
"_restarting",
"(",
"self",
")",
":"
] |
https://github.com/wistbean/learn_python3_spider/blob/73c873f4845f4385f097e5057407d03dd37a117b/stackoverflow/venv/lib/python3.6/site-packages/twisted/application/internet.py#L617-L620
|
||
kabkabm/defensegan
|
7e3feaebf7b9bbf08b1364e400119ef596cd78fd
|
datasets/utils.py
|
python
|
get_generators
|
(dataset_name, batch_size, randomize=True, attribute='gender')
|
return gens
|
Creates batch generators for datasets.
Args:
dataset_name: A `string`. Name of the dataset.
batch_size: An `integer`. The size of each batch.
randomize: A `boolean`.
attribute: A `string`. If the dataset name is `celeba`, this will
indicate the attribute name that labels should be returned for.
Returns:
Training, validation, and test dataset generators which are the
return values of `create_generator`.
|
Creates batch generators for datasets.
|
[
"Creates",
"batch",
"generators",
"for",
"datasets",
"."
] |
def get_generators(dataset_name, batch_size, randomize=True, attribute='gender'):
"""Creates batch generators for datasets.
Args:
dataset_name: A `string`. Name of the dataset.
batch_size: An `integer`. The size of each batch.
randomize: A `boolean`.
attribute: A `string`. If the dataset name is `celeba`, this will
indicate the attribute name that labels should be returned for.
Returns:
Training, validation, and test dataset generators which are the
return values of `create_generator`.
"""
splits = ['train', 'val', 'test']
gens = []
for i in range(3):
if i > 0:
randomize = False
gens.append(
create_generator(dataset_name, splits[i], batch_size, randomize,
attribute=attribute))
return gens
|
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https://github.com/kabkabm/defensegan/blob/7e3feaebf7b9bbf08b1364e400119ef596cd78fd/datasets/utils.py#L64-L87
|
|
imageworks/OpenColorIO-Configs
|
0bb079c08be410030669cbf5f19ff869b88af953
|
aces_1.0.3/python/aces_ocio/utilities.py
|
python
|
ColorSpace.__init__
|
(self,
name,
aliases=None,
description=None,
bit_depth=ocio.Constants.BIT_DEPTH_F32,
equality_group='',
family=None,
is_data=False,
to_reference_transforms=None,
from_reference_transforms=None,
allocation_type=ocio.Constants.ALLOCATION_UNIFORM,
allocation_vars=None,
aces_transform_id=None)
|
Constructor for ColorSpace container class.
Parameters
----------
name : str or unicode
Name of the colorspace.
All other arguments are optional
|
Constructor for ColorSpace container class.
|
[
"Constructor",
"for",
"ColorSpace",
"container",
"class",
"."
] |
def __init__(self,
name,
aliases=None,
description=None,
bit_depth=ocio.Constants.BIT_DEPTH_F32,
equality_group='',
family=None,
is_data=False,
to_reference_transforms=None,
from_reference_transforms=None,
allocation_type=ocio.Constants.ALLOCATION_UNIFORM,
allocation_vars=None,
aces_transform_id=None):
"""
Constructor for ColorSpace container class.
Parameters
----------
name : str or unicode
Name of the colorspace.
All other arguments are optional
"""
if aliases is None:
aliases = []
if to_reference_transforms is None:
to_reference_transforms = []
if from_reference_transforms is None:
from_reference_transforms = []
if allocation_vars is None:
allocation_vars = [0, 1]
self.name = name
self.aliases = aliases
self.bit_depth = bit_depth
self.description = description
self.equality_group = equality_group
self.family = family
self.is_data = is_data
self.to_reference_transforms = to_reference_transforms
self.from_reference_transforms = from_reference_transforms
self.allocation_type = allocation_type
self.allocation_vars = allocation_vars
self.aces_transform_id = aces_transform_id
|
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https://github.com/imageworks/OpenColorIO-Configs/blob/0bb079c08be410030669cbf5f19ff869b88af953/aces_1.0.3/python/aces_ocio/utilities.py#L40-L86
|
||
arjunvekariyagithub/camelyon16-grand-challenge
|
660000a79775fbc5cfa8c5b44a591e62ce714089
|
camelyon16/postprocess/build_tf_records_heatmap_multi_thread.py
|
python
|
_process_image
|
(patch_path, coder)
|
return image_data, height, width
|
Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide tf image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
|
Process a single image file.
|
[
"Process",
"a",
"single",
"image",
"file",
"."
] |
def _process_image(patch_path, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide tf image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
with tf.gfile.FastGFile(patch_path, 'r') as f:
image_data = f.read()
# Decode the RGB PNG.
image = coder.decode_png(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
|
[
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":",
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https://github.com/arjunvekariyagithub/camelyon16-grand-challenge/blob/660000a79775fbc5cfa8c5b44a591e62ce714089/camelyon16/postprocess/build_tf_records_heatmap_multi_thread.py#L172-L196
|
|
FederatedAI/FATE
|
32540492623568ecd1afcb367360133616e02fa3
|
python/federatedml/ensemble/basic_algorithms/decision_tree/tree_core/feature_histogram.py
|
python
|
FeatureHistogram._trim_node_map
|
(node_map, leaf_sample_counts)
|
return new_node_map, sibling_node_map
|
Only keep the nodes with fewer sample and remove their siblings, for accelerating hist computation
|
Only keep the nodes with fewer sample and remove their siblings, for accelerating hist computation
|
[
"Only",
"keep",
"the",
"nodes",
"with",
"fewer",
"sample",
"and",
"remove",
"their",
"siblings",
"for",
"accelerating",
"hist",
"computation"
] |
def _trim_node_map(node_map, leaf_sample_counts):
"""
Only keep the nodes with fewer sample and remove their siblings, for accelerating hist computation
"""
inverse_node_map = {v: k for k, v in node_map.items()}
sibling_node_map = {}
# if is root node, return directly
if 0 in node_map:
return node_map, None
kept_node_id = []
idx = 0
for left_count, right_count in zip(leaf_sample_counts[0::2], leaf_sample_counts[1::2]):
if left_count < right_count:
kept_node_id.append(inverse_node_map[idx])
sibling_node_map[inverse_node_map[idx]] = inverse_node_map[idx + 1]
else:
kept_node_id.append(inverse_node_map[idx + 1])
sibling_node_map[inverse_node_map[idx + 1]] = inverse_node_map[idx]
idx += 2
new_node_map = {node_id: idx for idx, node_id in enumerate(kept_node_id)}
return new_node_map, sibling_node_map
|
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https://github.com/FederatedAI/FATE/blob/32540492623568ecd1afcb367360133616e02fa3/python/federatedml/ensemble/basic_algorithms/decision_tree/tree_core/feature_histogram.py#L931-L957
|
|
pantsbuild/pants
|
2e126e78ffc40cb108408316b90e8beebee1df9e
|
src/python/pants/option/config.py
|
python
|
Config.get_value
|
(self, section: str, option: str)
|
Returns the value of the option in this config as a string, or None if no value
specified.
|
Returns the value of the option in this config as a string, or None if no value
specified.
|
[
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"or",
"None",
"if",
"no",
"value",
"specified",
"."
] |
def get_value(self, section: str, option: str) -> str | None:
"""Returns the value of the option in this config as a string, or None if no value
specified."""
|
[
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https://github.com/pantsbuild/pants/blob/2e126e78ffc40cb108408316b90e8beebee1df9e/src/python/pants/option/config.py#L175-L177
|
||
explosion/spaCy
|
a784b12eff48df9281b184cb7005e66bbd2e3aca
|
spacy/lang/de/syntax_iterators.py
|
python
|
noun_chunks
|
(doclike: Union[Doc, Span])
|
Detect base noun phrases from a dependency parse. Works on Doc and Span.
|
Detect base noun phrases from a dependency parse. Works on Doc and Span.
|
[
"Detect",
"base",
"noun",
"phrases",
"from",
"a",
"dependency",
"parse",
".",
"Works",
"on",
"Doc",
"and",
"Span",
"."
] |
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""Detect base noun phrases from a dependency parse. Works on Doc and Span."""
# this iterator extracts spans headed by NOUNs starting from the left-most
# syntactic dependent until the NOUN itself for close apposition and
# measurement construction, the span is sometimes extended to the right of
# the NOUN. Example: "eine Tasse Tee" (a cup (of) tea) returns "eine Tasse Tee"
# and not just "eine Tasse", same for "das Thema Familie".
# fmt: off
labels = ["sb", "oa", "da", "nk", "mo", "ag", "ROOT", "root", "cj", "pd", "og", "app"]
# fmt: on
doc = doclike.doc # Ensure works on both Doc and Span.
if not doc.has_annotation("DEP"):
raise ValueError(Errors.E029)
np_label = doc.vocab.strings.add("NP")
np_deps = set(doc.vocab.strings.add(label) for label in labels)
close_app = doc.vocab.strings.add("nk")
rbracket = 0
prev_end = -1
for i, word in enumerate(doclike):
if i < rbracket:
continue
# Prevent nested chunks from being produced
if word.left_edge.i <= prev_end:
continue
if word.pos in (NOUN, PROPN, PRON) and word.dep in np_deps:
rbracket = word.i + 1
# try to extend the span to the right
# to capture close apposition/measurement constructions
for rdep in doc[word.i].rights:
if rdep.pos in (NOUN, PROPN) and rdep.dep == close_app:
rbracket = rdep.i + 1
prev_end = rbracket - 1
yield word.left_edge.i, rbracket, np_label
|
[
"def",
"noun_chunks",
"(",
"doclike",
":",
"Union",
"[",
"Doc",
",",
"Span",
"]",
")",
"->",
"Iterator",
"[",
"Tuple",
"[",
"int",
",",
"int",
",",
"int",
"]",
"]",
":",
"# this iterator extracts spans headed by NOUNs starting from the left-most",
"# syntactic dependent until the NOUN itself for close apposition and",
"# measurement construction, the span is sometimes extended to the right of",
"# the NOUN. Example: \"eine Tasse Tee\" (a cup (of) tea) returns \"eine Tasse Tee\"",
"# and not just \"eine Tasse\", same for \"das Thema Familie\".",
"# fmt: off",
"labels",
"=",
"[",
"\"sb\"",
",",
"\"oa\"",
",",
"\"da\"",
",",
"\"nk\"",
",",
"\"mo\"",
",",
"\"ag\"",
",",
"\"ROOT\"",
",",
"\"root\"",
",",
"\"cj\"",
",",
"\"pd\"",
",",
"\"og\"",
",",
"\"app\"",
"]",
"# fmt: on",
"doc",
"=",
"doclike",
".",
"doc",
"# Ensure works on both Doc and Span.",
"if",
"not",
"doc",
".",
"has_annotation",
"(",
"\"DEP\"",
")",
":",
"raise",
"ValueError",
"(",
"Errors",
".",
"E029",
")",
"np_label",
"=",
"doc",
".",
"vocab",
".",
"strings",
".",
"add",
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"\"NP\"",
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"set",
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"vocab",
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"label",
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"\"nk\"",
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"continue",
"# Prevent nested chunks from being produced",
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"i",
"<=",
"prev_end",
":",
"continue",
"if",
"word",
".",
"pos",
"in",
"(",
"NOUN",
",",
"PROPN",
",",
"PRON",
")",
"and",
"word",
".",
"dep",
"in",
"np_deps",
":",
"rbracket",
"=",
"word",
".",
"i",
"+",
"1",
"# try to extend the span to the right",
"# to capture close apposition/measurement constructions",
"for",
"rdep",
"in",
"doc",
"[",
"word",
".",
"i",
"]",
".",
"rights",
":",
"if",
"rdep",
".",
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"NOUN",
",",
"PROPN",
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"dep",
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"+",
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"-",
"1",
"yield",
"word",
".",
"left_edge",
".",
"i",
",",
"rbracket",
",",
"np_label"
] |
https://github.com/explosion/spaCy/blob/a784b12eff48df9281b184cb7005e66bbd2e3aca/spacy/lang/de/syntax_iterators.py#L8-L40
|
||
home-assistant/core
|
265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1
|
homeassistant/components/homematicip_cloud/cover.py
|
python
|
HomematicipMultiCoverSlats.async_open_cover_tilt
|
(self, **kwargs)
|
Open the slats.
|
Open the slats.
|
[
"Open",
"the",
"slats",
"."
] |
async def async_open_cover_tilt(self, **kwargs) -> None:
"""Open the slats."""
await self._device.set_slats_level(HMIP_SLATS_OPEN, self._channel)
|
[
"async",
"def",
"async_open_cover_tilt",
"(",
"self",
",",
"*",
"*",
"kwargs",
")",
"->",
"None",
":",
"await",
"self",
".",
"_device",
".",
"set_slats_level",
"(",
"HMIP_SLATS_OPEN",
",",
"self",
".",
"_channel",
")"
] |
https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/homematicip_cloud/cover.py#L246-L248
|
||
dib-lab/khmer
|
fb65d21eaedf0d397d49ae3debc578897f9d6eb4
|
versioneer.py
|
python
|
plus_or_dot
|
(pieces)
|
return "+"
|
Return a + if we don't already have one, else return a .
|
Return a + if we don't already have one, else return a .
|
[
"Return",
"a",
"+",
"if",
"we",
"don",
"t",
"already",
"have",
"one",
"else",
"return",
"a",
"."
] |
def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
|
[
"def",
"plus_or_dot",
"(",
"pieces",
")",
":",
"if",
"\"+\"",
"in",
"pieces",
".",
"get",
"(",
"\"closest-tag\"",
",",
"\"\"",
")",
":",
"return",
"\".\"",
"return",
"\"+\""
] |
https://github.com/dib-lab/khmer/blob/fb65d21eaedf0d397d49ae3debc578897f9d6eb4/versioneer.py#L1229-L1233
|
|
spack/spack
|
675210bd8bd1c5d32ad1cc83d898fb43b569ed74
|
lib/spack/spack/util/executable.py
|
python
|
Executable.path
|
(self)
|
return self.exe[0]
|
The path to the executable.
Returns:
str: The path to the executable
|
The path to the executable.
|
[
"The",
"path",
"to",
"the",
"executable",
"."
] |
def path(self):
"""The path to the executable.
Returns:
str: The path to the executable
"""
return self.exe[0]
|
[
"def",
"path",
"(",
"self",
")",
":",
"return",
"self",
".",
"exe",
"[",
"0",
"]"
] |
https://github.com/spack/spack/blob/675210bd8bd1c5d32ad1cc83d898fb43b569ed74/lib/spack/spack/util/executable.py#L70-L76
|
|
Nordeus/pushkin
|
39f7057d3eb82c811c5c6b795d8bc7df9352a217
|
pushkin/database/database.py
|
python
|
update_unregistered_devices
|
(unregistered)
|
Update data for unregistered Android devices.
Unregistered device will not receive notifications and will be deleted when number of devices exceeds maximum.
|
Update data for unregistered Android devices.
|
[
"Update",
"data",
"for",
"unregistered",
"Android",
"devices",
"."
] |
def update_unregistered_devices(unregistered):
'''
Update data for unregistered Android devices.
Unregistered device will not receive notifications and will be deleted when number of devices exceeds maximum.
'''
global ENGINE
binding = [{"p_{}".format(k): v for k, v in u.items()} for u in unregistered]
device_table = model.metadata.tables['device']
stmt = update(device_table).\
values(unregistered_ts=func.now()).\
where(and_(device_table.c.login_id == bindparam('p_login_id'),
func.coalesce(device_table.c.device_token_new, device_table.c.device_token) == bindparam('p_device_token')))
ENGINE.execute(stmt, binding)
|
[
"def",
"update_unregistered_devices",
"(",
"unregistered",
")",
":",
"global",
"ENGINE",
"binding",
"=",
"[",
"{",
"\"p_{}\"",
".",
"format",
"(",
"k",
")",
":",
"v",
"for",
"k",
",",
"v",
"in",
"u",
".",
"items",
"(",
")",
"}",
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"u",
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"]",
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".",
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"[",
"'device'",
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"update",
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"device_table",
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".",
"values",
"(",
"unregistered_ts",
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"func",
".",
"now",
"(",
")",
")",
".",
"where",
"(",
"and_",
"(",
"device_table",
".",
"c",
".",
"login_id",
"==",
"bindparam",
"(",
"'p_login_id'",
")",
",",
"func",
".",
"coalesce",
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"device_table",
".",
"c",
".",
"device_token_new",
",",
"device_table",
".",
"c",
".",
"device_token",
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"==",
"bindparam",
"(",
"'p_device_token'",
")",
")",
")",
"ENGINE",
".",
"execute",
"(",
"stmt",
",",
"binding",
")"
] |
https://github.com/Nordeus/pushkin/blob/39f7057d3eb82c811c5c6b795d8bc7df9352a217/pushkin/database/database.py#L201-L214
|
||
facebookresearch/Large-Scale-VRD
|
7ababfe1023941c3653d7aebe9f835a47f5e8277
|
lib/utils/vis.py
|
python
|
vis_bbox
|
(img, bbox, thick=1)
|
return img
|
Visualizes a bounding box.
|
Visualizes a bounding box.
|
[
"Visualizes",
"a",
"bounding",
"box",
"."
] |
def vis_bbox(img, bbox, thick=1):
"""Visualizes a bounding box."""
(x0, y0, w, h) = bbox
x1, y1 = int(x0 + w), int(y0 + h)
x0, y0 = int(x0), int(y0)
cv2.rectangle(img, (x0, y0), (x1, y1), _GREEN, thickness=thick)
return img
|
[
"def",
"vis_bbox",
"(",
"img",
",",
"bbox",
",",
"thick",
"=",
"1",
")",
":",
"(",
"x0",
",",
"y0",
",",
"w",
",",
"h",
")",
"=",
"bbox",
"x1",
",",
"y1",
"=",
"int",
"(",
"x0",
"+",
"w",
")",
",",
"int",
"(",
"y0",
"+",
"h",
")",
"x0",
",",
"y0",
"=",
"int",
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"x0",
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",",
"int",
"(",
"y0",
")",
"cv2",
".",
"rectangle",
"(",
"img",
",",
"(",
"x0",
",",
"y0",
")",
",",
"(",
"x1",
",",
"y1",
")",
",",
"_GREEN",
",",
"thickness",
"=",
"thick",
")",
"return",
"img"
] |
https://github.com/facebookresearch/Large-Scale-VRD/blob/7ababfe1023941c3653d7aebe9f835a47f5e8277/lib/utils/vis.py#L122-L128
|
|
SforAiDl/Neural-Voice-Cloning-With-Few-Samples
|
33fb609427657c9492f46507184ecba4dcc272b0
|
train_encoder.py
|
python
|
get_cloned_voices
|
(model,no_speakers = 108,no_cloned_texts = 23)
|
return cloned_voices
|
[] |
def get_cloned_voices(model,no_speakers = 108,no_cloned_texts = 23):
try:
with open("./Cloning_Audio/speakers_cloned_voices_mel.p" , "rb") as fp:
cloned_voices = pickle.load(fp)
except:
cloned_voices = generate_cloned_samples(model)
if(np.array(cloned_voices).shape != (no_speakers , no_cloned_texts)):
cloned_voices = generate_cloned_samples(model,"./Cloning_Audio/cloning_text.txt" ,no_speakers,True,0)
print("Cloned_voices Loaded!")
return cloned_voices
|
[
"def",
"get_cloned_voices",
"(",
"model",
",",
"no_speakers",
"=",
"108",
",",
"no_cloned_texts",
"=",
"23",
")",
":",
"try",
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"with",
"open",
"(",
"\"./Cloning_Audio/speakers_cloned_voices_mel.p\"",
",",
"\"rb\"",
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"as",
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"cloned_voices",
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"fp",
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"model",
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"if",
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"array",
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"shape",
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"no_speakers",
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"no_cloned_texts",
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",",
"\"./Cloning_Audio/cloning_text.txt\"",
",",
"no_speakers",
",",
"True",
",",
"0",
")",
"print",
"(",
"\"Cloned_voices Loaded!\"",
")",
"return",
"cloned_voices"
] |
https://github.com/SforAiDl/Neural-Voice-Cloning-With-Few-Samples/blob/33fb609427657c9492f46507184ecba4dcc272b0/train_encoder.py#L42-L51
|
|||
WZMIAOMIAO/deep-learning-for-image-processing
|
a4502c284958d4bf78fb77b089a90e7688ddc196
|
pytorch_classification/vision_transformer/vit_model.py
|
python
|
vit_large_patch32_224_in21k
|
(num_classes: int = 21843, has_logits: bool = True)
|
return model
|
ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
weights ported from official Google JAX impl:
https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth
|
ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights
|
[
"ViT",
"-",
"Large",
"model",
"(",
"ViT",
"-",
"L",
"/",
"32",
")",
"from",
"original",
"paper",
"(",
"https",
":",
"//",
"arxiv",
".",
"org",
"/",
"abs",
"/",
"2010",
".",
"11929",
")",
".",
"ImageNet",
"-",
"21k",
"weights"
] |
def vit_large_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):
"""
ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
weights ported from official Google JAX impl:
https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth
"""
model = VisionTransformer(img_size=224,
patch_size=32,
embed_dim=1024,
depth=24,
num_heads=16,
representation_size=1024 if has_logits else None,
num_classes=num_classes)
return model
|
[
"def",
"vit_large_patch32_224_in21k",
"(",
"num_classes",
":",
"int",
"=",
"21843",
",",
"has_logits",
":",
"bool",
"=",
"True",
")",
":",
"model",
"=",
"VisionTransformer",
"(",
"img_size",
"=",
"224",
",",
"patch_size",
"=",
"32",
",",
"embed_dim",
"=",
"1024",
",",
"depth",
"=",
"24",
",",
"num_heads",
"=",
"16",
",",
"representation_size",
"=",
"1024",
"if",
"has_logits",
"else",
"None",
",",
"num_classes",
"=",
"num_classes",
")",
"return",
"model"
] |
https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/blob/a4502c284958d4bf78fb77b089a90e7688ddc196/pytorch_classification/vision_transformer/vit_model.py#L392-L406
|
|
gammapy/gammapy
|
735b25cd5bbed35e2004d633621896dcd5295e8b
|
gammapy/modeling/models/core.py
|
python
|
DatasetModels.wcs_geom
|
(self)
|
Minimum WCS geom in which all the models are contained
|
Minimum WCS geom in which all the models are contained
|
[
"Minimum",
"WCS",
"geom",
"in",
"which",
"all",
"the",
"models",
"are",
"contained"
] |
def wcs_geom(self):
"""Minimum WCS geom in which all the models are contained"""
regions = self.to_regions()
try:
return RegionGeom.from_regions(regions).to_wcs_geom()
except IndexError:
log.error("No spatial component in any model. Geom not defined")
|
[
"def",
"wcs_geom",
"(",
"self",
")",
":",
"regions",
"=",
"self",
".",
"to_regions",
"(",
")",
"try",
":",
"return",
"RegionGeom",
".",
"from_regions",
"(",
"regions",
")",
".",
"to_wcs_geom",
"(",
")",
"except",
"IndexError",
":",
"log",
".",
"error",
"(",
"\"No spatial component in any model. Geom not defined\"",
")"
] |
https://github.com/gammapy/gammapy/blob/735b25cd5bbed35e2004d633621896dcd5295e8b/gammapy/modeling/models/core.py#L911-L917
|
||
ialbert/biostar-central
|
2dc7bd30691a50b2da9c2833ba354056bc686afa
|
biostar/recipes/forms.py
|
python
|
ProjectForm.custom_save
|
(self, owner)
|
return project
|
Used to save on creation using custom function.
|
Used to save on creation using custom function.
|
[
"Used",
"to",
"save",
"on",
"creation",
"using",
"custom",
"function",
"."
] |
def custom_save(self, owner):
"""Used to save on creation using custom function."""
name = self.cleaned_data["name"]
text = self.cleaned_data["text"]
stream = self.cleaned_data["image"]
project = auth.create_project(user=owner, name=name, text=text, stream=stream)
project.save()
return project
|
[
"def",
"custom_save",
"(",
"self",
",",
"owner",
")",
":",
"name",
"=",
"self",
".",
"cleaned_data",
"[",
"\"name\"",
"]",
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"=",
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"cleaned_data",
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"\"text\"",
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"cleaned_data",
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"auth",
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"create_project",
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"text",
",",
"stream",
"=",
"stream",
")",
"project",
".",
"save",
"(",
")",
"return",
"project"
] |
https://github.com/ialbert/biostar-central/blob/2dc7bd30691a50b2da9c2833ba354056bc686afa/biostar/recipes/forms.py#L182-L191
|
|
jython/frozen-mirror
|
b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99
|
lib-python/2.7/imaplib.py
|
python
|
IMAP4.getannotation
|
(self, mailbox, entry, attribute)
|
return self._untagged_response(typ, dat, 'ANNOTATION')
|
(typ, [data]) = <instance>.getannotation(mailbox, entry, attribute)
Retrieve ANNOTATIONs.
|
(typ, [data]) = <instance>.getannotation(mailbox, entry, attribute)
Retrieve ANNOTATIONs.
|
[
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"getannotation",
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"mailbox",
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"Retrieve",
"ANNOTATIONs",
"."
] |
def getannotation(self, mailbox, entry, attribute):
"""(typ, [data]) = <instance>.getannotation(mailbox, entry, attribute)
Retrieve ANNOTATIONs."""
typ, dat = self._simple_command('GETANNOTATION', mailbox, entry, attribute)
return self._untagged_response(typ, dat, 'ANNOTATION')
|
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"'ANNOTATION'",
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] |
https://github.com/jython/frozen-mirror/blob/b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99/lib-python/2.7/imaplib.py#L468-L473
|
|
cogitas3d/OrtogOnBlender
|
881e93f5beb2263e44c270974dd0e81deca44762
|
__init__.py
|
python
|
ORTOG_PT_ArmatureDynamic.draw
|
(self, context)
|
[] |
def draw(self, context):
layout = self.layout
context = bpy.context
obj = context.object
scn = context.scene
row = layout.row()
row.prop(scn, "my_enum_dynamic")
my_enum_dynamic = scn.my_enum_dynamic
if my_enum_dynamic == ENUM_VALUES_DYNAMIC.DEFAULT:
row = layout.row()
row.label(text="Configure Armature (Classic):")
row = layout.row()
row.operator("object.conf_osteo_auto", text="Setup Osteotomy Auto", icon="FILE_TICK")
row = layout.row()
row.label(text="Soft Tissue:")
row = layout.row()
circle=row.operator("object.configura_dinamica_mole", text="Setup Soft Tissue Dynamics", icon="STYLUS_PRESSURE")
row = layout.row()
circle=row.operator("view3d.clip_border", text="Clipping Border", icon="UV_FACESEL")
if my_enum_dynamic == ENUM_VALUES_DYNAMIC.NOSE:
row = layout.row()
row.operator("object.conf_osteo_auto", text="Setup Osteotomy Auto", icon="FILE_TICK")
row = layout.row()
row.label(text="Mode:")
row = layout.row()
linha=row.operator("wm.tool_set_by_id", text="Cursor", icon="PIVOT_CURSOR").name="builtin.cursor"
linha=row.operator("wm.tool_set_by_id", text="Select", icon="RESTRICT_SELECT_OFF").name="builtin.select_box"
row = layout.row()
row = layout.row()
row.label(text="Anatomical Points:")
row = layout.row()
linha=row.operator("object.trichion_pt", text="Trichion")
row = layout.row()
linha=row.operator("object.radix_pt", text="Radix")
row = layout.row()
linha=row.operator("object.tip_nose_pt", text="Tip of Nose")
row = layout.row()
linha=row.operator("object.alar_groove_right_pt", text="Alar Groove right")
row = layout.row()
linha=row.operator("object.alar_groove_left_pt", text="Alar Groove left")
row = layout.row()
linha=row.operator("object.submental_pt", text="Submental")
row = layout.row()
row = layout.row()
row.label(text="Soft Tissue:")
row = layout.row()
circle=row.operator("object.configura_dinamica_mole", text="Setup Soft Tissue Dynamics", icon="STYLUS_PRESSURE")
row = layout.row()
circle=row.operator("view3d.clip_border", text="Clipping Border", icon="UV_FACESEL")
if my_enum_dynamic == ENUM_VALUES_DYNAMIC.EXPERIMENTAL:
row = layout.row()
row.label(text=" Auto Osteo+Soft Setup (Experimental):")
row = layout.row()
row.operator("object.nome_face_malha", text="Set Face and Hide", icon="USER")
row = layout.row()
row.operator("object.conf_osteo_mole_auto", text="Setup Auto!", icon="BONE_DATA")
row = layout.row()
row = layout.row()
row.label(text="Parent Points:")
row = layout.row()
circle=row.operator("object.parenteia_emp", text="Parent Points", icon="LINKED")
row = layout.row()
row = layout.row()
box = layout.box()
col = box.column(align=True)
row = col.row()
row.scale_y=1.5
row.alignment = 'CENTER'
row.operator("object.gera_dir_nome_paciente_dynamic", text="SAVE!", icon="FILE_TICK")
|
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] |
https://github.com/cogitas3d/OrtogOnBlender/blob/881e93f5beb2263e44c270974dd0e81deca44762/__init__.py#L2404-L2502
|
||||
polakowo/vectorbt
|
6638735c131655760474d72b9f045d1dbdbd8fe9
|
vectorbt/signals/nb.py
|
python
|
generate_enex_nb
|
(shape: tp.Shape,
entry_wait: int,
exit_wait: int,
entry_pick_first: bool,
exit_pick_first: bool,
entry_choice_func_nb: tp.ChoiceFunc,
entry_args: tp.Args,
exit_choice_func_nb: tp.ChoiceFunc,
exit_args: tp.Args)
|
return entries, exits
|
Pick entry signals using `entry_choice_func_nb` and exit signals using
`exit_choice_func_nb` one after another.
Args:
shape (array): Target shape.
entry_wait (int): Number of ticks to wait before placing entries.
!!! note
Setting `entry_wait` to 0 or False assumes that both entry and exit can be processed
within the same bar, and exit can be processed before entry.
exit_wait (int): Number of ticks to wait before placing exits.
!!! note
Setting `exit_wait` to 0 or False assumes that both entry and exit can be processed
within the same bar, and entry can be processed before exit.
entry_pick_first (bool): Whether to pick the first entry out of all returned by `entry_choice_func_nb`.
exit_pick_first (bool): Whether to pick the first exit out of all returned by `exit_choice_func_nb`.
Setting it to False acts similarly to setting `skip_until_exit` to True in `generate_ex_nb`.
entry_choice_func_nb (callable): Entry choice function.
See `choice_func_nb` in `generate_nb`.
entry_args (tuple): Arguments unpacked and passed to `entry_choice_func_nb`.
exit_choice_func_nb (callable): Exit choice function.
See `choice_func_nb` in `generate_nb`.
exit_args (tuple): Arguments unpacked and passed to `exit_choice_func_nb`.
|
Pick entry signals using `entry_choice_func_nb` and exit signals using
`exit_choice_func_nb` one after another.
|
[
"Pick",
"entry",
"signals",
"using",
"entry_choice_func_nb",
"and",
"exit",
"signals",
"using",
"exit_choice_func_nb",
"one",
"after",
"another",
"."
] |
def generate_enex_nb(shape: tp.Shape,
entry_wait: int,
exit_wait: int,
entry_pick_first: bool,
exit_pick_first: bool,
entry_choice_func_nb: tp.ChoiceFunc,
entry_args: tp.Args,
exit_choice_func_nb: tp.ChoiceFunc,
exit_args: tp.Args) -> tp.Tuple[tp.Array2d, tp.Array2d]:
"""Pick entry signals using `entry_choice_func_nb` and exit signals using
`exit_choice_func_nb` one after another.
Args:
shape (array): Target shape.
entry_wait (int): Number of ticks to wait before placing entries.
!!! note
Setting `entry_wait` to 0 or False assumes that both entry and exit can be processed
within the same bar, and exit can be processed before entry.
exit_wait (int): Number of ticks to wait before placing exits.
!!! note
Setting `exit_wait` to 0 or False assumes that both entry and exit can be processed
within the same bar, and entry can be processed before exit.
entry_pick_first (bool): Whether to pick the first entry out of all returned by `entry_choice_func_nb`.
exit_pick_first (bool): Whether to pick the first exit out of all returned by `exit_choice_func_nb`.
Setting it to False acts similarly to setting `skip_until_exit` to True in `generate_ex_nb`.
entry_choice_func_nb (callable): Entry choice function.
See `choice_func_nb` in `generate_nb`.
entry_args (tuple): Arguments unpacked and passed to `entry_choice_func_nb`.
exit_choice_func_nb (callable): Exit choice function.
See `choice_func_nb` in `generate_nb`.
exit_args (tuple): Arguments unpacked and passed to `exit_choice_func_nb`.
"""
entries = np.full(shape, False)
exits = np.full(shape, False)
if entry_wait == 0 and exit_wait == 0:
raise ValueError("entry_wait and exit_wait cannot be both 0")
for col in range(shape[1]):
prev_prev_i = -2
prev_i = -1
i = 0
while True:
to_i = shape[0]
# Cannot assign two functions to a var in numba
if i % 2 == 0:
if i == 0:
from_i = 0
else:
from_i = prev_i + entry_wait
if from_i >= to_i:
break
idxs = entry_choice_func_nb(from_i, to_i, col, *entry_args)
a = entries
pick_first = entry_pick_first
else:
from_i = prev_i + exit_wait
if from_i >= to_i:
break
idxs = exit_choice_func_nb(from_i, to_i, col, *exit_args)
a = exits
pick_first = exit_pick_first
if len(idxs) == 0:
break
first_i = idxs[0]
if first_i == prev_i == prev_prev_i:
raise ValueError("Infinite loop detected")
if first_i < from_i:
raise ValueError("First index is out of bounds")
if pick_first:
# Consider only the first signal
if first_i >= to_i:
raise ValueError("First index is out of bounds")
a[first_i, col] = True
prev_prev_i = prev_i
prev_i = first_i
i += 1
else:
# Consider all signals
last_i = idxs[-1]
if last_i >= to_i:
raise ValueError("Last index is out of bounds")
a[idxs, col] = True
prev_prev_i = prev_i
prev_i = last_i
i += 1
return entries, exits
|
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":",
"# Consider only the first signal",
"if",
"first_i",
">=",
"to_i",
":",
"raise",
"ValueError",
"(",
"\"First index is out of bounds\"",
")",
"a",
"[",
"first_i",
",",
"col",
"]",
"=",
"True",
"prev_prev_i",
"=",
"prev_i",
"prev_i",
"=",
"first_i",
"i",
"+=",
"1",
"else",
":",
"# Consider all signals",
"last_i",
"=",
"idxs",
"[",
"-",
"1",
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"if",
"last_i",
">=",
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"raise",
"ValueError",
"(",
"\"Last index is out of bounds\"",
")",
"a",
"[",
"idxs",
",",
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"]",
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"prev_i",
"prev_i",
"=",
"last_i",
"i",
"+=",
"1",
"return",
"entries",
",",
"exits"
] |
https://github.com/polakowo/vectorbt/blob/6638735c131655760474d72b9f045d1dbdbd8fe9/vectorbt/signals/nb.py#L158-L249
|
|
mesalock-linux/mesapy
|
ed546d59a21b36feb93e2309d5c6b75aa0ad95c9
|
rpython/tool/setuptools_msvc.py
|
python
|
SystemInfo.VCInstallDir
|
(self)
|
return path
|
Microsoft Visual C++ directory.
|
Microsoft Visual C++ directory.
|
[
"Microsoft",
"Visual",
"C",
"++",
"directory",
"."
] |
def VCInstallDir(self):
"""
Microsoft Visual C++ directory.
"""
self.VSInstallDir
guess_vc = self._guess_vc() or self._guess_vc_legacy()
# Try to get "VC++ for Python" path from registry as default path
reg_path = os.path.join(self.ri.vc_for_python, '%0.1f' % self.vc_ver)
python_vc = self.ri.lookup(reg_path, 'installdir')
default_vc = os.path.join(python_vc, 'VC') if python_vc else guess_vc
# Try to get path from registry, if fail use default path
path = self.ri.lookup(self.ri.vc, '%0.1f' % self.vc_ver) or default_vc
if not os.path.isdir(path):
msg = 'Microsoft Visual C++ directory not found'
raise distutils.errors.DistutilsPlatformError(msg)
return path
|
[
"def",
"VCInstallDir",
"(",
"self",
")",
":",
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".",
"VSInstallDir",
"guess_vc",
"=",
"self",
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"_guess_vc",
"(",
")",
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"_guess_vc_legacy",
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"# Try to get \"VC++ for Python\" path from registry as default path",
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"os",
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"'Microsoft Visual C++ directory not found'",
"raise",
"distutils",
".",
"errors",
".",
"DistutilsPlatformError",
"(",
"msg",
")",
"return",
"path"
] |
https://github.com/mesalock-linux/mesapy/blob/ed546d59a21b36feb93e2309d5c6b75aa0ad95c9/rpython/tool/setuptools_msvc.py#L544-L564
|
|
ipython/ipyparallel
|
d35d4fb9501da5b3280b11e83ed633a95f17be1d
|
setupbase.py
|
python
|
_get_package_data
|
(root, file_patterns=None)
|
return _get_files(file_patterns, _glob_pjoin(os.path.abspath(os.getcwd()), root))
|
Expand file patterns to a list of `package_data` paths.
Parameters
-----------
root: str
The relative path to the package root from the current dir.
file_patterns: list or str, optional
A list of glob patterns for the data file locations.
The globs can be recursive if they include a `**`.
They should be relative paths from the root or
absolute paths. If not given, all files will be used.
Note:
Files in `node_modules` are ignored.
|
Expand file patterns to a list of `package_data` paths.
|
[
"Expand",
"file",
"patterns",
"to",
"a",
"list",
"of",
"package_data",
"paths",
"."
] |
def _get_package_data(root, file_patterns=None):
"""Expand file patterns to a list of `package_data` paths.
Parameters
-----------
root: str
The relative path to the package root from the current dir.
file_patterns: list or str, optional
A list of glob patterns for the data file locations.
The globs can be recursive if they include a `**`.
They should be relative paths from the root or
absolute paths. If not given, all files will be used.
Note:
Files in `node_modules` are ignored.
"""
if file_patterns is None:
file_patterns = ['*']
return _get_files(file_patterns, _glob_pjoin(os.path.abspath(os.getcwd()), root))
|
[
"def",
"_get_package_data",
"(",
"root",
",",
"file_patterns",
"=",
"None",
")",
":",
"if",
"file_patterns",
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"None",
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"_glob_pjoin",
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"abspath",
"(",
"os",
".",
"getcwd",
"(",
")",
")",
",",
"root",
")",
")"
] |
https://github.com/ipython/ipyparallel/blob/d35d4fb9501da5b3280b11e83ed633a95f17be1d/setupbase.py#L803-L821
|
|
paulproteus/python-scraping-code-samples
|
4e5396d4e311ca66c784a2b5f859308285e511da
|
new/seleniumrc/selenium-remote-control-1.0-beta-2/selenium-python-client-driver-1.0-beta-2/selenium.py
|
python
|
selenium.ignore_attributes_without_value
|
(self,ignore)
|
Specifies whether Selenium will ignore xpath attributes that have no
value, i.e. are the empty string, when using the non-native xpath
evaluation engine. You'd want to do this for performance reasons in IE.
However, this could break certain xpaths, for example an xpath that looks
for an attribute whose value is NOT the empty string.
The hope is that such xpaths are relatively rare, but the user should
have the option of using them. Note that this only influences xpath
evaluation when using the ajaxslt engine (i.e. not "javascript-xpath").
'ignore' is boolean, true means we'll ignore attributes without value at the expense of xpath "correctness"; false means we'll sacrifice speed for correctness.
|
Specifies whether Selenium will ignore xpath attributes that have no
value, i.e. are the empty string, when using the non-native xpath
evaluation engine. You'd want to do this for performance reasons in IE.
However, this could break certain xpaths, for example an xpath that looks
for an attribute whose value is NOT the empty string.
The hope is that such xpaths are relatively rare, but the user should
have the option of using them. Note that this only influences xpath
evaluation when using the ajaxslt engine (i.e. not "javascript-xpath").
'ignore' is boolean, true means we'll ignore attributes without value at the expense of xpath "correctness"; false means we'll sacrifice speed for correctness.
|
[
"Specifies",
"whether",
"Selenium",
"will",
"ignore",
"xpath",
"attributes",
"that",
"have",
"no",
"value",
"i",
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"are",
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"native",
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"ajaxslt",
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"expense",
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"xpath",
"correctness",
";",
"false",
"means",
"we",
"ll",
"sacrifice",
"speed",
"for",
"correctness",
"."
] |
def ignore_attributes_without_value(self,ignore):
"""
Specifies whether Selenium will ignore xpath attributes that have no
value, i.e. are the empty string, when using the non-native xpath
evaluation engine. You'd want to do this for performance reasons in IE.
However, this could break certain xpaths, for example an xpath that looks
for an attribute whose value is NOT the empty string.
The hope is that such xpaths are relatively rare, but the user should
have the option of using them. Note that this only influences xpath
evaluation when using the ajaxslt engine (i.e. not "javascript-xpath").
'ignore' is boolean, true means we'll ignore attributes without value at the expense of xpath "correctness"; false means we'll sacrifice speed for correctness.
"""
self.do_command("ignoreAttributesWithoutValue", [ignore,])
|
[
"def",
"ignore_attributes_without_value",
"(",
"self",
",",
"ignore",
")",
":",
"self",
".",
"do_command",
"(",
"\"ignoreAttributesWithoutValue\"",
",",
"[",
"ignore",
",",
"]",
")"
] |
https://github.com/paulproteus/python-scraping-code-samples/blob/4e5396d4e311ca66c784a2b5f859308285e511da/new/seleniumrc/selenium-remote-control-1.0-beta-2/selenium-python-client-driver-1.0-beta-2/selenium.py#L1630-L1644
|
||
FederatedAI/FATE
|
32540492623568ecd1afcb367360133616e02fa3
|
python/federatedml/statistic/statics.py
|
python
|
MissingStatistic.__init__
|
(self, missing_val=None)
|
[] |
def __init__(self, missing_val=None):
super(MissingStatistic, self).__init__()
self.missing_val = None
self.feature_summary = {}
self.missing_feature = []
self.all_feature_list = []
self.tag_id_mapping, self.id_tag_mapping = {}, {}
self.dense_missing_val = missing_val
|
[
"def",
"__init__",
"(",
"self",
",",
"missing_val",
"=",
"None",
")",
":",
"super",
"(",
"MissingStatistic",
",",
"self",
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".",
"__init__",
"(",
")",
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".",
"missing_val",
"=",
"None",
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".",
"feature_summary",
"=",
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"missing_feature",
"=",
"[",
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"[",
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",",
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"=",
"{",
"}",
",",
"{",
"}",
"self",
".",
"dense_missing_val",
"=",
"missing_val"
] |
https://github.com/FederatedAI/FATE/blob/32540492623568ecd1afcb367360133616e02fa3/python/federatedml/statistic/statics.py#L237-L245
|
||||
ConsenSys/ethjsonrpc
|
fe525bdcd889924687ba1646fc46cef329410e22
|
ethjsonrpc/client.py
|
python
|
EthJsonRpc.net_version
|
(self)
|
return self._call('net_version')
|
https://github.com/ethereum/wiki/wiki/JSON-RPC#net_version
TESTED
|
https://github.com/ethereum/wiki/wiki/JSON-RPC#net_version
|
[
"https",
":",
"//",
"github",
".",
"com",
"/",
"ethereum",
"/",
"wiki",
"/",
"wiki",
"/",
"JSON",
"-",
"RPC#net_version"
] |
def net_version(self):
'''
https://github.com/ethereum/wiki/wiki/JSON-RPC#net_version
TESTED
'''
return self._call('net_version')
|
[
"def",
"net_version",
"(",
"self",
")",
":",
"return",
"self",
".",
"_call",
"(",
"'net_version'",
")"
] |
https://github.com/ConsenSys/ethjsonrpc/blob/fe525bdcd889924687ba1646fc46cef329410e22/ethjsonrpc/client.py#L153-L159
|
|
paulproteus/python-scraping-code-samples
|
4e5396d4e311ca66c784a2b5f859308285e511da
|
new/seleniumrc/selenium-remote-control-1.0-beta-2/selenium-python-client-driver-1.0-beta-2/selenium.py
|
python
|
selenium.meta_key_down
|
(self)
|
Press the meta key and hold it down until doMetaUp() is called or a new page is loaded.
|
Press the meta key and hold it down until doMetaUp() is called or a new page is loaded.
|
[
"Press",
"the",
"meta",
"key",
"and",
"hold",
"it",
"down",
"until",
"doMetaUp",
"()",
"is",
"called",
"or",
"a",
"new",
"page",
"is",
"loaded",
"."
] |
def meta_key_down(self):
"""
Press the meta key and hold it down until doMetaUp() is called or a new page is loaded.
"""
self.do_command("metaKeyDown", [])
|
[
"def",
"meta_key_down",
"(",
"self",
")",
":",
"self",
".",
"do_command",
"(",
"\"metaKeyDown\"",
",",
"[",
"]",
")"
] |
https://github.com/paulproteus/python-scraping-code-samples/blob/4e5396d4e311ca66c784a2b5f859308285e511da/new/seleniumrc/selenium-remote-control-1.0-beta-2/selenium-python-client-driver-1.0-beta-2/selenium.py#L387-L392
|
||
pantsbuild/pex
|
473c6ac732ed4bc338b4b20a9ec930d1d722c9b4
|
pex/vendor/_vendored/setuptools/pkg_resources/_vendor/packaging/version.py
|
python
|
_parse_version_parts
|
(s)
|
[] |
def _parse_version_parts(s):
for part in _legacy_version_component_re.split(s):
part = _legacy_version_replacement_map.get(part, part)
if not part or part == ".":
continue
if part[:1] in "0123456789":
# pad for numeric comparison
yield part.zfill(8)
else:
yield "*" + part
# ensure that alpha/beta/candidate are before final
yield "*final"
|
[
"def",
"_parse_version_parts",
"(",
"s",
")",
":",
"for",
"part",
"in",
"_legacy_version_component_re",
".",
"split",
"(",
"s",
")",
":",
"part",
"=",
"_legacy_version_replacement_map",
".",
"get",
"(",
"part",
",",
"part",
")",
"if",
"not",
"part",
"or",
"part",
"==",
"\".\"",
":",
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"\"0123456789\"",
":",
"# pad for numeric comparison",
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"else",
":",
"yield",
"\"*\"",
"+",
"part",
"# ensure that alpha/beta/candidate are before final",
"yield",
"\"*final\""
] |
https://github.com/pantsbuild/pex/blob/473c6ac732ed4bc338b4b20a9ec930d1d722c9b4/pex/vendor/_vendored/setuptools/pkg_resources/_vendor/packaging/version.py#L114-L128
|
||||
triaquae/triaquae
|
bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9
|
TriAquae/models/django/utils/timezone.py
|
python
|
_get_timezone_name
|
(timezone)
|
Returns the name of ``timezone``.
|
Returns the name of ``timezone``.
|
[
"Returns",
"the",
"name",
"of",
"timezone",
"."
] |
def _get_timezone_name(timezone):
"""
Returns the name of ``timezone``.
"""
try:
# for pytz timezones
return timezone.zone
except AttributeError:
# for regular tzinfo objects
local_now = datetime.now(timezone)
return timezone.tzname(local_now)
|
[
"def",
"_get_timezone_name",
"(",
"timezone",
")",
":",
"try",
":",
"# for pytz timezones",
"return",
"timezone",
".",
"zone",
"except",
"AttributeError",
":",
"# for regular tzinfo objects",
"local_now",
"=",
"datetime",
".",
"now",
"(",
"timezone",
")",
"return",
"timezone",
".",
"tzname",
"(",
"local_now",
")"
] |
https://github.com/triaquae/triaquae/blob/bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9/TriAquae/models/django/utils/timezone.py#L139-L149
|
||
openfisca/openfisca-france
|
207a58191be6830716693f94d37846f1e5037b51
|
openfisca_france/model/prelevements_obligatoires/impot_revenu/reductions_impot.py
|
python
|
intagr.formula_2005_01_01
|
(foyer_fiscal, period, parameters)
|
return P.taux * min_(f7um, max1)
|
Intérêts pour paiement différé accordé aux agriculteurs
2005-
|
Intérêts pour paiement différé accordé aux agriculteurs
2005-
|
[
"Intérêts",
"pour",
"paiement",
"différé",
"accordé",
"aux",
"agriculteurs",
"2005",
"-"
] |
def formula_2005_01_01(foyer_fiscal, period, parameters):
'''
Intérêts pour paiement différé accordé aux agriculteurs
2005-
'''
f7um = foyer_fiscal('f7um', period)
maries_ou_pacses = foyer_fiscal('maries_ou_pacses', period)
P = parameters(period).impot_revenu.reductions_impots.intagr
max1 = P.max * (1 + maries_ou_pacses)
return P.taux * min_(f7um, max1)
|
[
"def",
"formula_2005_01_01",
"(",
"foyer_fiscal",
",",
"period",
",",
"parameters",
")",
":",
"f7um",
"=",
"foyer_fiscal",
"(",
"'f7um'",
",",
"period",
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"maries_ou_pacses",
"=",
"foyer_fiscal",
"(",
"'maries_ou_pacses'",
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"period",
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"P",
".",
"taux",
"*",
"min_",
"(",
"f7um",
",",
"max1",
")"
] |
https://github.com/openfisca/openfisca-france/blob/207a58191be6830716693f94d37846f1e5037b51/openfisca_france/model/prelevements_obligatoires/impot_revenu/reductions_impot.py#L2723-L2733
|
|
apache/libcloud
|
90971e17bfd7b6bb97b2489986472c531cc8e140
|
libcloud/storage/drivers/dummy.py
|
python
|
DummyStorageDriver.delete_container
|
(self, container)
|
return True
|
>>> driver = DummyStorageDriver('key', 'secret')
>>> container = Container(name = 'test container',
... extra={'object_count': 0}, driver=driver)
>>> driver.delete_container(container=container)
... #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
ContainerDoesNotExistError:
>>> container = driver.create_container(
... container_name='test container 1')
... #doctest: +IGNORE_EXCEPTION_DETAIL
>>> len(driver._containers)
1
>>> driver.delete_container(container=container)
True
>>> len(driver._containers)
0
>>> container = driver.create_container(
... container_name='test container 1')
... #doctest: +IGNORE_EXCEPTION_DETAIL
>>> obj = container.upload_object_via_stream(
... object_name='test object', iterator=DummyFileObject(5, 10),
... extra={})
>>> driver.delete_container(container=container)
... #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
ContainerIsNotEmptyError:
@inherits: :class:`StorageDriver.delete_container`
|
>>> driver = DummyStorageDriver('key', 'secret')
>>> container = Container(name = 'test container',
... extra={'object_count': 0}, driver=driver)
>>> driver.delete_container(container=container)
... #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
ContainerDoesNotExistError:
>>> container = driver.create_container(
... container_name='test container 1')
... #doctest: +IGNORE_EXCEPTION_DETAIL
>>> len(driver._containers)
1
>>> driver.delete_container(container=container)
True
>>> len(driver._containers)
0
>>> container = driver.create_container(
... container_name='test container 1')
... #doctest: +IGNORE_EXCEPTION_DETAIL
>>> obj = container.upload_object_via_stream(
... object_name='test object', iterator=DummyFileObject(5, 10),
... extra={})
>>> driver.delete_container(container=container)
... #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
ContainerIsNotEmptyError:
|
[
">>>",
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":"
] |
def delete_container(self, container):
"""
>>> driver = DummyStorageDriver('key', 'secret')
>>> container = Container(name = 'test container',
... extra={'object_count': 0}, driver=driver)
>>> driver.delete_container(container=container)
... #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
ContainerDoesNotExistError:
>>> container = driver.create_container(
... container_name='test container 1')
... #doctest: +IGNORE_EXCEPTION_DETAIL
>>> len(driver._containers)
1
>>> driver.delete_container(container=container)
True
>>> len(driver._containers)
0
>>> container = driver.create_container(
... container_name='test container 1')
... #doctest: +IGNORE_EXCEPTION_DETAIL
>>> obj = container.upload_object_via_stream(
... object_name='test object', iterator=DummyFileObject(5, 10),
... extra={})
>>> driver.delete_container(container=container)
... #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
ContainerIsNotEmptyError:
@inherits: :class:`StorageDriver.delete_container`
"""
container_name = container.name
if container_name not in self._containers:
raise ContainerDoesNotExistError(
container_name=container_name, value=None, driver=self
)
container = self._containers[container_name]
if len(container["objects"]) > 0:
raise ContainerIsNotEmptyError(
container_name=container_name, value=None, driver=self
)
del self._containers[container_name]
return True
|
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https://github.com/apache/libcloud/blob/90971e17bfd7b6bb97b2489986472c531cc8e140/libcloud/storage/drivers/dummy.py#L335-L380
|
|
JiYou/openstack
|
8607dd488bde0905044b303eb6e52bdea6806923
|
packages/source/cinder/cinder/volume/drivers/netapp/iscsi.py
|
python
|
NetAppCmodeISCSIDriver.create_volume
|
(self, volume)
|
Driver entry point for creating a new volume.
|
Driver entry point for creating a new volume.
|
[
"Driver",
"entry",
"point",
"for",
"creating",
"a",
"new",
"volume",
"."
] |
def create_volume(self, volume):
"""Driver entry point for creating a new volume."""
default_size = '104857600' # 100 MB
gigabytes = 1073741824L # 2^30
name = volume['name']
if int(volume['size']) == 0:
size = default_size
else:
size = str(int(volume['size']) * gigabytes)
extra_args = {}
extra_args['OsType'] = 'linux'
extra_args['QosType'] = self._get_qos_type(volume)
extra_args['Container'] = volume['project_id']
extra_args['Display'] = volume['display_name']
extra_args['Description'] = volume['display_description']
extra_args['SpaceReserved'] = True
server = self.client.service
metadata = self._create_metadata_list(extra_args)
lun = server.ProvisionLun(Name=name, Size=size,
Metadata=metadata)
LOG.debug(_("Created LUN with name %s") % name)
self._add_lun_to_table(
NetAppLun(lun.Handle,
lun.Name,
lun.Size,
self._create_dict_from_meta(lun.Metadata)))
|
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"lun",
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")"
] |
https://github.com/JiYou/openstack/blob/8607dd488bde0905044b303eb6e52bdea6806923/packages/source/cinder/cinder/volume/drivers/netapp/iscsi.py#L1218-L1243
|
||
PaddlePaddle/models
|
511e2e282960ed4c7440c3f1d1e62017acb90e11
|
tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional_tensor.py
|
python
|
_max_value
|
(dtype: torch.dtype)
|
return max_value.item()
|
[] |
def _max_value(dtype: torch.dtype) -> float:
# TODO: replace this method with torch.iinfo when it gets torchscript support.
# https://github.com/pytorch/pytorch/issues/41492
a = torch.tensor(2, dtype=dtype)
signed = 1 if torch.tensor(0, dtype=dtype).is_signed() else 0
bits = 1
max_value = torch.tensor(-signed, dtype=torch.long)
while True:
next_value = a.pow(bits - signed).sub(1)
if next_value > max_value:
max_value = next_value
bits *= 2
else:
break
return max_value.item()
|
[
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https://github.com/PaddlePaddle/models/blob/511e2e282960ed4c7440c3f1d1e62017acb90e11/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional_tensor.py#L34-L49
|
|||
LinkedInAttic/indextank-service
|
880c6295ce8e7a3a55bf9b3777cc35c7680e0d7e
|
api/boto/ec2/autoscale/trigger.py
|
python
|
Trigger.delete
|
(self)
|
return req
|
Delete this trigger.
|
Delete this trigger.
|
[
"Delete",
"this",
"trigger",
"."
] |
def delete(self):
""" Delete this trigger. """
params = {
'TriggerName' : self.name,
'AutoScalingGroupName' : self.autoscale_group_name,
}
req =self.connection.get_object('DeleteTrigger', params,
Request)
self.connection.last_request = req
return req
|
[
"def",
"delete",
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"self",
")",
":",
"params",
"=",
"{",
"'TriggerName'",
":",
"self",
".",
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",",
"'AutoScalingGroupName'",
":",
"self",
".",
"autoscale_group_name",
",",
"}",
"req",
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".",
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".",
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"'DeleteTrigger'",
",",
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",",
"Request",
")",
"self",
".",
"connection",
".",
"last_request",
"=",
"req",
"return",
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] |
https://github.com/LinkedInAttic/indextank-service/blob/880c6295ce8e7a3a55bf9b3777cc35c7680e0d7e/api/boto/ec2/autoscale/trigger.py#L127-L136
|
|
joelgrus/data-science-from-scratch
|
d5d0f117f41b3ccab3b07f1ee1fa21cfcf69afa1
|
first-edition/code/network_analysis.py
|
python
|
vector_as_matrix
|
(v)
|
return [[v_i] for v_i in v]
|
returns the vector v (represented as a list) as a n x 1 matrix
|
returns the vector v (represented as a list) as a n x 1 matrix
|
[
"returns",
"the",
"vector",
"v",
"(",
"represented",
"as",
"a",
"list",
")",
"as",
"a",
"n",
"x",
"1",
"matrix"
] |
def vector_as_matrix(v):
"""returns the vector v (represented as a list) as a n x 1 matrix"""
return [[v_i] for v_i in v]
|
[
"def",
"vector_as_matrix",
"(",
"v",
")",
":",
"return",
"[",
"[",
"v_i",
"]",
"for",
"v_i",
"in",
"v",
"]"
] |
https://github.com/joelgrus/data-science-from-scratch/blob/d5d0f117f41b3ccab3b07f1ee1fa21cfcf69afa1/first-edition/code/network_analysis.py#L127-L129
|
|
Ecogenomics/GTDBTk
|
1e10c56530b4a15eadce519619a62584a490632d
|
gtdbtk/external/pypfam/HMM/HMMResultsIO.py
|
python
|
HMMResultsIO._readUnitData
|
(self, seqId, fh, hmmRes)
|
return
|
# == domain 1 score: 244.0 bits; conditional E-value: 9.5e-76
# SEED 1 medrlallkaisasakdlvalaasrGaksipspvkttavkfdplptPdldalrtrlkeaklPakaiksalsayekaCarWrsdleeafdktaksvsPanlhllealrirlyteqvekWlvqvlevaerWkaemekqrahiaatmgp 146
# m+++la+l++isa+akd++ala+srGa+++ +p++tt+++fd+l++P+ld++rtrl+ea+lP+kaik++lsaye+aCarW++dleeafd+ta+s+sP+n+++l++lr+rly+eqv+kWl++vl+v+erWkaemekqrahi+atmgp
# P37935.1 1 MAELLACLQSISAHAKDMMALARSRGATGS-RPTPTTLPHFDELLPPNLDFVRTRLQEARLPPKAIKGTLSAYESACARWKHDLEEAFDRTAHSISPHNFQRLAQLRTRLYVEQVQKWLYEVLQVPERWKAEMEKQRAHINATMGP 145
# 899***************************.******************************************************************************************************************8 PP
#
# OR....
#
# == domain 1 score: 27.6 bits; conditional E-value: 7.4e-10
# PF00018 17 LsfkkGdvitvleksee.eWwkaelkdg.keGlvPsnYvep 55
# L++++Gd+++++++++e++Ww++++++++++G++P+n+v+p
# P15498.4 617 LRLNPGDIVELTKAEAEqNWWEGRNTSTnEIGWFPCNRVKP 657
# 7899**********9999*******************9987 PP
|
# == domain 1 score: 244.0 bits; conditional E-value: 9.5e-76
# SEED 1 medrlallkaisasakdlvalaasrGaksipspvkttavkfdplptPdldalrtrlkeaklPakaiksalsayekaCarWrsdleeafdktaksvsPanlhllealrirlyteqvekWlvqvlevaerWkaemekqrahiaatmgp 146
# m+++la+l++isa+akd++ala+srGa+++ +p++tt+++fd+l++P+ld++rtrl+ea+lP+kaik++lsaye+aCarW++dleeafd+ta+s+sP+n+++l++lr+rly+eqv+kWl++vl+v+erWkaemekqrahi+atmgp
# P37935.1 1 MAELLACLQSISAHAKDMMALARSRGATGS-RPTPTTLPHFDELLPPNLDFVRTRLQEARLPPKAIKGTLSAYESACARWKHDLEEAFDRTAHSISPHNFQRLAQLRTRLYVEQVQKWLYEVLQVPERWKAEMEKQRAHINATMGP 145
# 899***************************.******************************************************************************************************************8 PP
#
# OR....
#
# == domain 1 score: 27.6 bits; conditional E-value: 7.4e-10
# PF00018 17 LsfkkGdvitvleksee.eWwkaelkdg.keGlvPsnYvep 55
# L++++Gd+++++++++e++Ww++++++++++G++P+n+v+p
# P15498.4 617 LRLNPGDIVELTKAEAEqNWWEGRNTSTnEIGWFPCNRVKP 657
# 7899**********9999*******************9987 PP
|
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def _readUnitData(self, seqId, fh, hmmRes):
if hmmRes.eof:
return
hmmName = hmmRes.seedName
seqName = hmmRes.seqName
units = list()
align = True
recurse = False
eof = False
nextSeqId = None
# Parse the domain hits section
while len(fh) > 0:
hs = fh.popleft()
# Run the regex searches which generate output
unitd_2 = self.re_unitd_2.search(hs)
if self.re_unitd_1.search(hs):
align = False
recurse = False
eof = True
break
elif unitd_2:
nextSeqId = unitd_2.group(1)
align = False
recurse = True
break
elif self.re_unitd_3.search(hs):
align = True
recurse = False
break
elif self.re_unitd_4.search(hs):
# Two human readable lines
continue
elif self.re_unitd_5.search(hs):
# blank line
continue
elif self.re_unitd_6.search(hs):
dMatch = self.re_unitd_7.split(hs)
if len(dMatch) != 17:
sys.exit('Expected 16 elements of data.')
hmmUnit = HMMUnit()
hmmUnit.name = seqId
hmmUnit.domain = dMatch[1]
hmmUnit.bits = float(dMatch[3])
hmmUnit.bias = float(dMatch[4])
hmmUnit.domEvalue = float(dMatch[5])
hmmUnit.evalue = float(dMatch[6])
hmmUnit.hmmFrom = int(dMatch[7])
hmmUnit.hmmTo = int(dMatch[8])
hmmUnit.seqFrom = int(dMatch[10])
hmmUnit.seqTo = int(dMatch[11])
hmmUnit.envFrom = int(dMatch[13])
hmmUnit.envTo = int(dMatch[14])
hmmUnit.aliAcc = float(dMatch[16])
units.append(hmmUnit)
continue
elif self.re_unitd_8.search(hs):
align = False
continue
else:
sys.exit('Did not parse line %s' % hs)
'''
# == domain 1 score: 244.0 bits; conditional E-value: 9.5e-76
# SEED 1 medrlallkaisasakdlvalaasrGaksipspvkttavkfdplptPdldalrtrlkeaklPakaiksalsayekaCarWrsdleeafdktaksvsPanlhllealrirlyteqvekWlvqvlevaerWkaemekqrahiaatmgp 146
# m+++la+l++isa+akd++ala+srGa+++ +p++tt+++fd+l++P+ld++rtrl+ea+lP+kaik++lsaye+aCarW++dleeafd+ta+s+sP+n+++l++lr+rly+eqv+kWl++vl+v+erWkaemekqrahi+atmgp
# P37935.1 1 MAELLACLQSISAHAKDMMALARSRGATGS-RPTPTTLPHFDELLPPNLDFVRTRLQEARLPPKAIKGTLSAYESACARWKHDLEEAFDRTAHSISPHNFQRLAQLRTRLYVEQVQKWLYEVLQVPERWKAEMEKQRAHINATMGP 145
# 899***************************.******************************************************************************************************************8 PP
#
# OR....
#
# == domain 1 score: 27.6 bits; conditional E-value: 7.4e-10
# PF00018 17 LsfkkGdvitvleksee.eWwkaelkdg.keGlvPsnYvep 55
# L++++Gd+++++++++e++Ww++++++++++G++P+n+v+p
# P15498.4 617 LRLNPGDIVELTKAEAEqNWWEGRNTSTnEIGWFPCNRVKP 657
# 7899**********9999*******************9987 PP
'''
if align:
# Specifically for python
pattern1 = None
pattern2 = None
if hmmName and hmmRes.program == 'hmmsearch':
pattern1 = re.compile(r'^\s+%s\s+\d+\s+(\S+)\s+\d+' % hmmName)
seqId = re.sub('(\W)', r'\\\1', seqId)
# $id =~ s/\|/\\|/g; #Escape '|', '[' and ']' characters
# $id =~ s/\[/\\[/g;
# $id =~ s/\]/\\]/g;
pattern2 = re.compile(r'^\s+%s\s+\d+\s+(\S+)\s+\d+' % seqId)
elif seqName and hmmRes.program == 'hmmscan':
tmpSeqName = seqName
tmpSeqName = re.sub('(\W)', r'\\\1', tmpSeqName)
pattern1 = re.compile(r'^\s+%s\s+\d+\s+(\S+)\s+\d+' % seqId)
pattern2 = re.compile(r'^\s+%s\s+\d+\s+(\S+)\s+\d+' % tmpSeqName)
elif seqName and (hmmRes.program == 'phmmer' or hmmRes.program == 'jackhmmer'):
sys.exit("seqName and (hmmRes.program == 'phmmer' or hmmRes.program == 'jackhmmer' is not implemented.")
recurse = False
matchNo = None
hmmlen = 0
while len(fh) > 0:
hs = fh.popleft()
# Run a search for each of the patterns.
pattern1_res = pattern1.search(hs)
pattern2_res = pattern2.search(hs)
re_unitd_9_res = self.re_unitd_9.search(hs)
re_unitd_10_res = self.re_unitd_10.search(hs)
re_unitd_11_res = self.re_unitd_11.search(hs)
re_unitd_12_res = self.re_unitd_12.search(hs)
re_unitd_13_res = self.re_unitd_13.search(hs)
re_unitd_14_res = self.re_unitd_14.search(hs)
re_unitd_15_res = self.re_unitd_15.search(hs)
re_unitd_16_res = self.re_unitd_16.search(hs)
if pattern1_res:
dict_hmmalign = units[matchNo - 1].hmmalign
if 'hmm' in dict_hmmalign:
dict_hmmalign['hmm'] += pattern1_res.group(1)
else:
dict_hmmalign['hmm'] = pattern1_res.group(1)
hmmlen = len(pattern1_res.group(1))
elif pattern2_res:
dict_hmmalign = units[matchNo - 1].hmmalign
if 'seq' in dict_hmmalign:
dict_hmmalign['seq'] += pattern2_res.group(1)
else:
dict_hmmalign['seq'] = pattern2_res.group(1)
# ^\s+([x\.]+)\s+RF$
elif re_unitd_9_res:
rf = re_unitd_9_res.group(1)
dict_hmmalign = units[matchNo - 1].hmmalign
if 'rf' in dict_hmmalign:
dict_hmmalign['rf'] += rf
else:
dict_hmmalign['rf'] = rf
# ^\s+([0-9\*\.]+)\s+PP$
elif re_unitd_10_res:
pp = re_unitd_10_res.group(1)
dict_hmmalign = units[matchNo - 1].hmmalign
if 'pp' in dict_hmmalign:
dict_hmmalign['pp'] += pp
else:
dict_hmmalign['pp'] = pp
# ^\s+(\S+)\s+CS$
elif re_unitd_11_res:
cs = re_unitd_11_res.group(1)
dict_hmmalign = units[matchNo - 1].hmmalign
if 'cs' in dict_hmmalign:
dict_hmmalign['cs'] += cs
else:
dict_hmmalign['cs'] = cs
# ^\s+==\s+domain\s+(\d+)
elif re_unitd_12_res:
matchNo = int(re_unitd_12_res.group(1))
# ^\s+(.*)\s+$
elif re_unitd_13_res:
hs = hs.rstrip()
m1 = hs[-hmmlen:]
# ^$
elif re_unitd_14_res:
continue
# ^[(\/\/|Internal)]
elif re_unitd_15_res:
align = False
recurse = False
eof = True
break
# ^\>\>\s+(\S+)
elif re_unitd_16_res:
nextSeqId = re_unitd_16_res.group(1)
recurse = True
break
else:
sys.exit('Did not parse %s in units' % hs)
# foreach my u (@units)
for u in units:
hmmRes.addHMMUnit(u)
hmmRes.eof = eof
if recurse and nextSeqId:
self._readUnitData(nextSeqId, fh, hmmRes)
return
|
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] |
https://github.com/Ecogenomics/GTDBTk/blob/1e10c56530b4a15eadce519619a62584a490632d/gtdbtk/external/pypfam/HMM/HMMResultsIO.py#L233-L451
|
|
ospalh/anki-addons
|
4ece13423bd541e29d9b40ebe26ca0999a6962b1
|
nachschlagen.py
|
python
|
on_lookup_forvo_selection
|
()
|
u"""Wrapper to look up the selection at Forvo and catch value errors.
|
u"""Wrapper to look up the selection at Forvo and catch value errors.
|
[
"u",
"Wrapper",
"to",
"look",
"up",
"the",
"selection",
"at",
"Forvo",
"and",
"catch",
"value",
"errors",
"."
] |
def on_lookup_forvo_selection():
u"""Wrapper to look up the selection at Forvo and catch value errors."""
try:
lookup_forvo([])
except ValueError as ve:
tooltip(str(ve))
|
[
"def",
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] |
https://github.com/ospalh/anki-addons/blob/4ece13423bd541e29d9b40ebe26ca0999a6962b1/nachschlagen.py#L306-L311
|
||
bids-standard/pybids
|
9449fdc319c4bdff4ed9aa1b299964352f394d56
|
bids/variables/variables.py
|
python
|
DenseRunVariable.split
|
(self, grouper)
|
return [DenseRunVariable(name='%s.%s' % (self.name, name),
values=df[name].values,
run_info=self.run_info,
source=self.source,
sampling_rate=self.sampling_rate)
for i, name in enumerate(df.columns)]
|
Split the current DenseRunVariable into multiple columns.
Parameters
----------
grouper : :obj:`pandas.DataFrame`
Binary DF specifying the design matrix to use for splitting. Number
of rows must match current ``DenseRunVariable``; a new ``DenseRunVariable``
will be generated for each column in the grouper.
Returns
-------
A list of DenseRunVariables, one per unique value in the grouper.
|
Split the current DenseRunVariable into multiple columns.
|
[
"Split",
"the",
"current",
"DenseRunVariable",
"into",
"multiple",
"columns",
"."
] |
def split(self, grouper):
"""Split the current DenseRunVariable into multiple columns.
Parameters
----------
grouper : :obj:`pandas.DataFrame`
Binary DF specifying the design matrix to use for splitting. Number
of rows must match current ``DenseRunVariable``; a new ``DenseRunVariable``
will be generated for each column in the grouper.
Returns
-------
A list of DenseRunVariables, one per unique value in the grouper.
"""
values = grouper.values * self.values.values
df = pd.DataFrame(values, columns=grouper.columns)
return [DenseRunVariable(name='%s.%s' % (self.name, name),
values=df[name].values,
run_info=self.run_info,
source=self.source,
sampling_rate=self.sampling_rate)
for i, name in enumerate(df.columns)]
|
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"values",
"=",
"grouper",
".",
"values",
"*",
"self",
".",
"values",
".",
"values",
"df",
"=",
"pd",
".",
"DataFrame",
"(",
"values",
",",
"columns",
"=",
"grouper",
".",
"columns",
")",
"return",
"[",
"DenseRunVariable",
"(",
"name",
"=",
"'%s.%s'",
"%",
"(",
"self",
".",
"name",
",",
"name",
")",
",",
"values",
"=",
"df",
"[",
"name",
"]",
".",
"values",
",",
"run_info",
"=",
"self",
".",
"run_info",
",",
"source",
"=",
"self",
".",
"source",
",",
"sampling_rate",
"=",
"self",
".",
"sampling_rate",
")",
"for",
"i",
",",
"name",
"in",
"enumerate",
"(",
"df",
".",
"columns",
")",
"]"
] |
https://github.com/bids-standard/pybids/blob/9449fdc319c4bdff4ed9aa1b299964352f394d56/bids/variables/variables.py#L476-L497
|
|
gentoo/portage
|
e5be73709b1a42b40380fd336f9381452b01a723
|
lib/portage/util/backoff.py
|
python
|
ExponentialBackoff.__call__
|
(self, tries)
|
Given a number of previous tries, calculate the amount of time
to delay the next try.
@param tries: number of previous tries
@type tries: int
@return: amount of time to delay the next try
@rtype: int
|
Given a number of previous tries, calculate the amount of time
to delay the next try.
|
[
"Given",
"a",
"number",
"of",
"previous",
"tries",
"calculate",
"the",
"amount",
"of",
"time",
"to",
"delay",
"the",
"next",
"try",
"."
] |
def __call__(self, tries):
"""
Given a number of previous tries, calculate the amount of time
to delay the next try.
@param tries: number of previous tries
@type tries: int
@return: amount of time to delay the next try
@rtype: int
"""
try:
return min(self._limit, self._multiplier * (self._base ** tries))
except OverflowError:
return self._limit
|
[
"def",
"__call__",
"(",
"self",
",",
"tries",
")",
":",
"try",
":",
"return",
"min",
"(",
"self",
".",
"_limit",
",",
"self",
".",
"_multiplier",
"*",
"(",
"self",
".",
"_base",
"**",
"tries",
")",
")",
"except",
"OverflowError",
":",
"return",
"self",
".",
"_limit"
] |
https://github.com/gentoo/portage/blob/e5be73709b1a42b40380fd336f9381452b01a723/lib/portage/util/backoff.py#L32-L45
|
||
datascopeanalytics/traces
|
beed806b548c8c048c62e4224384cd77cf655be0
|
traces/timeseries.py
|
python
|
TimeSeries.items
|
(self)
|
return self._d.items()
|
ts.items() -> list of the (key, value) pairs in ts, as 2-tuples
|
ts.items() -> list of the (key, value) pairs in ts, as 2-tuples
|
[
"ts",
".",
"items",
"()",
"-",
">",
"list",
"of",
"the",
"(",
"key",
"value",
")",
"pairs",
"in",
"ts",
"as",
"2",
"-",
"tuples"
] |
def items(self):
"""ts.items() -> list of the (key, value) pairs in ts, as 2-tuples"""
return self._d.items()
|
[
"def",
"items",
"(",
"self",
")",
":",
"return",
"self",
".",
"_d",
".",
"items",
"(",
")"
] |
https://github.com/datascopeanalytics/traces/blob/beed806b548c8c048c62e4224384cd77cf655be0/traces/timeseries.py#L210-L212
|
|
AppScale/gts
|
46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9
|
AppServer/google/appengine/api/search/search.py
|
python
|
_CheckFieldName
|
(name)
|
return name
|
Checks field name is not too long and matches field name pattern.
Field name pattern: "[A-Za-z][A-Za-z0-9_]*".
|
Checks field name is not too long and matches field name pattern.
|
[
"Checks",
"field",
"name",
"is",
"not",
"too",
"long",
"and",
"matches",
"field",
"name",
"pattern",
"."
] |
def _CheckFieldName(name):
"""Checks field name is not too long and matches field name pattern.
Field name pattern: "[A-Za-z][A-Za-z0-9_]*".
"""
_ValidateString(name, 'name', MAXIMUM_FIELD_NAME_LENGTH)
if not re.match(_FIELD_NAME_PATTERN, name):
raise ValueError('field name "%s" should match pattern: %s' %
(name, _FIELD_NAME_PATTERN))
return name
|
[
"def",
"_CheckFieldName",
"(",
"name",
")",
":",
"_ValidateString",
"(",
"name",
",",
"'name'",
",",
"MAXIMUM_FIELD_NAME_LENGTH",
")",
"if",
"not",
"re",
".",
"match",
"(",
"_FIELD_NAME_PATTERN",
",",
"name",
")",
":",
"raise",
"ValueError",
"(",
"'field name \"%s\" should match pattern: %s'",
"%",
"(",
"name",
",",
"_FIELD_NAME_PATTERN",
")",
")",
"return",
"name"
] |
https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/google/appengine/api/search/search.py#L546-L555
|
|
GNS3/gns3-server
|
aff06572d4173df945ad29ea8feb274f7885d9e4
|
gns3server/controller/compute.py
|
python
|
Compute.forward
|
(self, method, type, path, data=None)
|
return res.json
|
Forward a call to the emulator on compute
|
Forward a call to the emulator on compute
|
[
"Forward",
"a",
"call",
"to",
"the",
"emulator",
"on",
"compute"
] |
async def forward(self, method, type, path, data=None):
"""
Forward a call to the emulator on compute
"""
try:
action = "/{}/{}".format(type, path)
res = await self.http_query(method, action, data=data, timeout=None)
except aiohttp.ServerDisconnectedError:
log.error("Connection lost to %s during %s %s", self._id, method, action)
raise aiohttp.web.HTTPGatewayTimeout()
return res.json
|
[
"async",
"def",
"forward",
"(",
"self",
",",
"method",
",",
"type",
",",
"path",
",",
"data",
"=",
"None",
")",
":",
"try",
":",
"action",
"=",
"\"/{}/{}\"",
".",
"format",
"(",
"type",
",",
"path",
")",
"res",
"=",
"await",
"self",
".",
"http_query",
"(",
"method",
",",
"action",
",",
"data",
"=",
"data",
",",
"timeout",
"=",
"None",
")",
"except",
"aiohttp",
".",
"ServerDisconnectedError",
":",
"log",
".",
"error",
"(",
"\"Connection lost to %s during %s %s\"",
",",
"self",
".",
"_id",
",",
"method",
",",
"action",
")",
"raise",
"aiohttp",
".",
"web",
".",
"HTTPGatewayTimeout",
"(",
")",
"return",
"res",
".",
"json"
] |
https://github.com/GNS3/gns3-server/blob/aff06572d4173df945ad29ea8feb274f7885d9e4/gns3server/controller/compute.py#L583-L593
|
|
ljean/modbus-tk
|
1159c71794071ae67f73f86fa14dd71c989b4859
|
hmi/master_webhmi.py
|
python
|
Master.__init__
|
(self, protocol, address, id, db)
|
[] |
def __init__(self, protocol, address, id, db):
if protocol == "tcp":
try:
(host, port) = address.split(":")
self.modbus = modbus_tcp.TcpMaster(str(host), int(port))
except:
self.modbus = modbus_tcp.TcpMaster(address)
self.modbus.set_timeout(5.0)
elif protocol == "rtu":
if SERIAL:
args = unicode(address).split(',')
kwargs = {}
for a in args:
key, val = a.split(':')
if key=='port':
try:
serial_port = int(val)
except:
serial_port = val
else:
kwargs[key] = val
try:
try:
s = SERIAL_PORTS[serial_port]
except IndexError:
SERIAL_PORTS[serial_port] = s = serial.Serial(port=serial_port, **kwargs)
self.modbus = modbus_rtu.RtuMaster(s)
except Exception, msg:
raise Exception("Protocol {0} error! {1}".format(protocol, msg))
else:
raise Exception("Protocol {0} is disabled!".format(protocol))
else:
raise Exception("Protocol {0} is not supported!".format(protocol))
self.id = id
self._db = db
self.address = address
self.protocol = protocol
self.requests = self._db.get_requests(self.id)
|
[
"def",
"__init__",
"(",
"self",
",",
"protocol",
",",
"address",
",",
"id",
",",
"db",
")",
":",
"if",
"protocol",
"==",
"\"tcp\"",
":",
"try",
":",
"(",
"host",
",",
"port",
")",
"=",
"address",
".",
"split",
"(",
"\":\"",
")",
"self",
".",
"modbus",
"=",
"modbus_tcp",
".",
"TcpMaster",
"(",
"str",
"(",
"host",
")",
",",
"int",
"(",
"port",
")",
")",
"except",
":",
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".",
"modbus",
"=",
"modbus_tcp",
".",
"TcpMaster",
"(",
"address",
")",
"self",
".",
"modbus",
".",
"set_timeout",
"(",
"5.0",
")",
"elif",
"protocol",
"==",
"\"rtu\"",
":",
"if",
"SERIAL",
":",
"args",
"=",
"unicode",
"(",
"address",
")",
".",
"split",
"(",
"','",
")",
"kwargs",
"=",
"{",
"}",
"for",
"a",
"in",
"args",
":",
"key",
",",
"val",
"=",
"a",
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"split",
"(",
"':'",
")",
"if",
"key",
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"'port'",
":",
"try",
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"serial_port",
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"int",
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"kwargs",
"[",
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"]",
"=",
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":",
"try",
":",
"s",
"=",
"SERIAL_PORTS",
"[",
"serial_port",
"]",
"except",
"IndexError",
":",
"SERIAL_PORTS",
"[",
"serial_port",
"]",
"=",
"s",
"=",
"serial",
".",
"Serial",
"(",
"port",
"=",
"serial_port",
",",
"*",
"*",
"kwargs",
")",
"self",
".",
"modbus",
"=",
"modbus_rtu",
".",
"RtuMaster",
"(",
"s",
")",
"except",
"Exception",
",",
"msg",
":",
"raise",
"Exception",
"(",
"\"Protocol {0} error! {1}\"",
".",
"format",
"(",
"protocol",
",",
"msg",
")",
")",
"else",
":",
"raise",
"Exception",
"(",
"\"Protocol {0} is disabled!\"",
".",
"format",
"(",
"protocol",
")",
")",
"else",
":",
"raise",
"Exception",
"(",
"\"Protocol {0} is not supported!\"",
".",
"format",
"(",
"protocol",
")",
")",
"self",
".",
"id",
"=",
"id",
"self",
".",
"_db",
"=",
"db",
"self",
".",
"address",
"=",
"address",
"self",
".",
"protocol",
"=",
"protocol",
"self",
".",
"requests",
"=",
"self",
".",
"_db",
".",
"get_requests",
"(",
"self",
".",
"id",
")"
] |
https://github.com/ljean/modbus-tk/blob/1159c71794071ae67f73f86fa14dd71c989b4859/hmi/master_webhmi.py#L31-L70
|
||||
google/jax
|
bebe9845a873b3203f8050395255f173ba3bbb71
|
jax/_src/util.py
|
python
|
distributed_debug_log
|
(*pairs)
|
Format and log `pairs` if config.jax_distributed_debug is enabled.
Args:
pairs: A sequence of label/value pairs to log. The first pair is treated as
a heading for subsequent pairs.
|
Format and log `pairs` if config.jax_distributed_debug is enabled.
|
[
"Format",
"and",
"log",
"pairs",
"if",
"config",
".",
"jax_distributed_debug",
"is",
"enabled",
"."
] |
def distributed_debug_log(*pairs):
"""Format and log `pairs` if config.jax_distributed_debug is enabled.
Args:
pairs: A sequence of label/value pairs to log. The first pair is treated as
a heading for subsequent pairs.
"""
if config.jax_distributed_debug:
lines = ["\nDISTRIBUTED_DEBUG_BEGIN"]
try:
lines.append(f"{pairs[0][0]}: {pairs[0][1]}")
for label, value in pairs[1:]:
lines.append(f" {label}: {value}")
except Exception as e:
lines.append("DISTRIBUTED_DEBUG logging failed!")
lines.append(f"{e}")
lines.append("DISTRIBUTED_DEBUG_END")
logging.warning("\n".join(lines))
|
[
"def",
"distributed_debug_log",
"(",
"*",
"pairs",
")",
":",
"if",
"config",
".",
"jax_distributed_debug",
":",
"lines",
"=",
"[",
"\"\\nDISTRIBUTED_DEBUG_BEGIN\"",
"]",
"try",
":",
"lines",
".",
"append",
"(",
"f\"{pairs[0][0]}: {pairs[0][1]}\"",
")",
"for",
"label",
",",
"value",
"in",
"pairs",
"[",
"1",
":",
"]",
":",
"lines",
".",
"append",
"(",
"f\" {label}: {value}\"",
")",
"except",
"Exception",
"as",
"e",
":",
"lines",
".",
"append",
"(",
"\"DISTRIBUTED_DEBUG logging failed!\"",
")",
"lines",
".",
"append",
"(",
"f\"{e}\"",
")",
"lines",
".",
"append",
"(",
"\"DISTRIBUTED_DEBUG_END\"",
")",
"logging",
".",
"warning",
"(",
"\"\\n\"",
".",
"join",
"(",
"lines",
")",
")"
] |
https://github.com/google/jax/blob/bebe9845a873b3203f8050395255f173ba3bbb71/jax/_src/util.py#L393-L410
|
||
ANSSI-FR/polichombr
|
e2dc3874ae3d78c3b496e9656c9a6d1b88ae91e1
|
polichombr/controllers/family.py
|
python
|
FamilyController.add_user
|
(user, family)
|
return True
|
Add a user to the family.
|
Add a user to the family.
|
[
"Add",
"a",
"user",
"to",
"the",
"family",
"."
] |
def add_user(user, family):
"""
Add a user to the family.
"""
if user in family.users:
return True
family.users.append(user)
db.session.commit()
return True
|
[
"def",
"add_user",
"(",
"user",
",",
"family",
")",
":",
"if",
"user",
"in",
"family",
".",
"users",
":",
"return",
"True",
"family",
".",
"users",
".",
"append",
"(",
"user",
")",
"db",
".",
"session",
".",
"commit",
"(",
")",
"return",
"True"
] |
https://github.com/ANSSI-FR/polichombr/blob/e2dc3874ae3d78c3b496e9656c9a6d1b88ae91e1/polichombr/controllers/family.py#L145-L153
|
|
cobbler/cobbler
|
eed8cdca3e970c8aa1d199e80b8c8f19b3f940cc
|
cobbler/remote.py
|
python
|
CobblerXMLRPCInterface.get_files
|
(self, page=None, results_per_page=None, token=None, **rest)
|
return self.get_items("file")
|
This returns all files.
:param page: This parameter is not used currently.
:param results_per_page: This parameter is not used currently.
:param token: The API-token obtained via the login() method.
:param rest: This parameter is not used currently.
:return: The list of all files.
|
This returns all files.
|
[
"This",
"returns",
"all",
"files",
"."
] |
def get_files(self, page=None, results_per_page=None, token=None, **rest):
"""
This returns all files.
:param page: This parameter is not used currently.
:param results_per_page: This parameter is not used currently.
:param token: The API-token obtained via the login() method.
:param rest: This parameter is not used currently.
:return: The list of all files.
"""
return self.get_items("file")
|
[
"def",
"get_files",
"(",
"self",
",",
"page",
"=",
"None",
",",
"results_per_page",
"=",
"None",
",",
"token",
"=",
"None",
",",
"*",
"*",
"rest",
")",
":",
"return",
"self",
".",
"get_items",
"(",
"\"file\"",
")"
] |
https://github.com/cobbler/cobbler/blob/eed8cdca3e970c8aa1d199e80b8c8f19b3f940cc/cobbler/remote.py#L938-L948
|
|
aewallin/openvoronoi
|
c4366904dc7ac40c189e95ebb014db7e4b137b86
|
python_examples/chain_3_rpg_loop.py
|
python
|
rpg_vd
|
(Npts, seed, debug)
|
return [is_valid, vd, times]
|
print "polygon is: "
for idx in id_list:
print idx," ",
print "."
|
print "polygon is: "
for idx in id_list:
print idx," ",
print "."
|
[
"print",
"polygon",
"is",
":",
"for",
"idx",
"in",
"id_list",
":",
"print",
"idx",
"print",
"."
] |
def rpg_vd(Npts, seed, debug):
far = 1
vd = ovd.VoronoiDiagram(far, 120)
vd.reset_vertex_count()
poly = rpg.rpg(Npts, seed)
pts = []
for p in poly:
ocl_pt = ovd.Point(p[0], p[1])
pts.append(ocl_pt)
print ocl_pt
times = []
id_list = []
m = 0
t_before = time.time()
for p in pts:
# print " adding vertex ",m
id_list.append(vd.addVertexSite(p))
m = m + 1
"""
print "polygon is: "
for idx in id_list:
print idx," ",
print "."
"""
t_after = time.time()
times.append(t_after - t_before)
# print " pts inserted in ", times[0], " s"
# print " vd-check: ",vd.check()
if (debug):
vd.debug_on()
t_before = time.time()
for n in range(len(id_list)):
n_nxt = n + 1
if n == (len(id_list) - 1):
n_nxt = 0 # point 0 is the endpoint of the last segment
# print " adding line-site ", id_list[n]," - ", id_list[n_nxt]
vd.addLineSite(id_list[n], id_list[n_nxt])
t_after = time.time()
times.append(t_after - t_before)
print " segs inserted in ", times[1], " s"
is_valid = vd.check()
print " vd-check: ", is_valid
return [is_valid, vd, times]
|
[
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",",
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",",
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".",
"VoronoiDiagram",
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",",
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"id_list",
")",
")",
":",
"n_nxt",
"=",
"n",
"+",
"1",
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"n",
"==",
"(",
"len",
"(",
"id_list",
")",
"-",
"1",
")",
":",
"n_nxt",
"=",
"0",
"# point 0 is the endpoint of the last segment",
"# print \" adding line-site \", id_list[n],\" - \", id_list[n_nxt]",
"vd",
".",
"addLineSite",
"(",
"id_list",
"[",
"n",
"]",
",",
"id_list",
"[",
"n_nxt",
"]",
")",
"t_after",
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"(",
")",
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"(",
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",",
"vd",
",",
"times",
"]"
] |
https://github.com/aewallin/openvoronoi/blob/c4366904dc7ac40c189e95ebb014db7e4b137b86/python_examples/chain_3_rpg_loop.py#L70-L119
|
|
mlrun/mlrun
|
4c120719d64327a34b7ee1ab08fb5e01b258b00a
|
mlrun/frameworks/sklearn/__init__.py
|
python
|
apply_mlrun
|
(
model,
context: mlrun.MLClientCtx = None,
X_test=None,
y_test=None,
model_name=None,
tag: str = "",
generate_test_set=True,
**kwargs
)
|
return mh
|
Wrap the given model with MLRun model, saving the model's attributes and methods while giving it mlrun's additional
features.
examples:
model = LogisticRegression()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = apply_mlrun(model, context, X_test=X_test, y_test=y_test)
model.fit(X_train, y_train)
:param model: The model which will have the fit() function wrapped
:param context: MLRun context to work with. If no context is given it will be retrieved via
'mlrun.get_or_create_ctx(None)'
:param X_test: X_test dataset
:param y_test: y_test dataset
:param model_name: The model artifact name (Optional)
:param tag: Tag of a version to give to the logged model.
:param generate_test_set: Generates a test_set dataset artifact
:return: The model in a MLRun model handler.
|
Wrap the given model with MLRun model, saving the model's attributes and methods while giving it mlrun's additional
features.
|
[
"Wrap",
"the",
"given",
"model",
"with",
"MLRun",
"model",
"saving",
"the",
"model",
"s",
"attributes",
"and",
"methods",
"while",
"giving",
"it",
"mlrun",
"s",
"additional",
"features",
"."
] |
def apply_mlrun(
model,
context: mlrun.MLClientCtx = None,
X_test=None,
y_test=None,
model_name=None,
tag: str = "",
generate_test_set=True,
**kwargs
) -> SKLearnModelHandler:
"""
Wrap the given model with MLRun model, saving the model's attributes and methods while giving it mlrun's additional
features.
examples:
model = LogisticRegression()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = apply_mlrun(model, context, X_test=X_test, y_test=y_test)
model.fit(X_train, y_train)
:param model: The model which will have the fit() function wrapped
:param context: MLRun context to work with. If no context is given it will be retrieved via
'mlrun.get_or_create_ctx(None)'
:param X_test: X_test dataset
:param y_test: y_test dataset
:param model_name: The model artifact name (Optional)
:param tag: Tag of a version to give to the logged model.
:param generate_test_set: Generates a test_set dataset artifact
:return: The model in a MLRun model handler.
"""
if context is None:
context = mlrun.get_or_create_ctx("mlrun_sklearn")
kwargs["X_test"] = X_test
kwargs["y_test"] = y_test
kwargs["generate_test_set"] = generate_test_set
mh = SKLearnModelHandler(
model_name=model_name or "model", model=model, context=context
)
# Add MLRun's interface to the model:
MLMLRunInterface.add_interface(mh, context, tag, kwargs)
return mh
|
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] |
https://github.com/mlrun/mlrun/blob/4c120719d64327a34b7ee1ab08fb5e01b258b00a/mlrun/frameworks/sklearn/__init__.py#L16-L60
|
|
nutonomy/nuscenes-devkit
|
05d05b3c994fb3c17b6643016d9f622a001c7275
|
python-sdk/nuscenes/nuscenes.py
|
python
|
NuScenes.__load_table__
|
(self, table_name)
|
return table
|
Loads a table.
|
Loads a table.
|
[
"Loads",
"a",
"table",
"."
] |
def __load_table__(self, table_name) -> dict:
""" Loads a table. """
with open(osp.join(self.table_root, '{}.json'.format(table_name))) as f:
table = json.load(f)
return table
|
[
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"__load_table__",
"(",
"self",
",",
"table_name",
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"->",
"dict",
":",
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"open",
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"as",
"f",
":",
"table",
"=",
"json",
".",
"load",
"(",
"f",
")",
"return",
"table"
] |
https://github.com/nutonomy/nuscenes-devkit/blob/05d05b3c994fb3c17b6643016d9f622a001c7275/python-sdk/nuscenes/nuscenes.py#L134-L138
|
|
PaddlePaddle/Research
|
2da0bd6c72d60e9df403aff23a7802779561c4a1
|
ST_DM/GenRegion/src/region/geometry.py
|
python
|
Segment.__grids
|
(self, x, y, grid_size, grid_set)
|
Desc: 计算指定点的grid, 可能为多个
Args:
self : self
x : x
y : y
grid_size : grid_size
grid_set : 全局grid set
Return:
list of (grid_x, grid_y)
如果点在grid内, 返回一个grid;
如果点在grid边上, 返回两个grid;
如果点在grid顶点上, 返回四个grid;
Raise:
None
|
Desc: 计算指定点的grid, 可能为多个
Args:
self : self
x : x
y : y
grid_size : grid_size
grid_set : 全局grid set
Return:
list of (grid_x, grid_y)
如果点在grid内, 返回一个grid;
如果点在grid边上, 返回两个grid;
如果点在grid顶点上, 返回四个grid;
Raise:
None
|
[
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"计算指定点的grid",
"可能为多个",
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"self",
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";",
"如果点在grid顶点上",
"返回四个grid",
";",
"Raise",
":",
"None"
] |
def __grids(self, x, y, grid_size, grid_set):
"""
Desc: 计算指定点的grid, 可能为多个
Args:
self : self
x : x
y : y
grid_size : grid_size
grid_set : 全局grid set
Return:
list of (grid_x, grid_y)
如果点在grid内, 返回一个grid;
如果点在grid边上, 返回两个grid;
如果点在grid顶点上, 返回四个grid;
Raise:
None
"""
grid_x = int(x) / grid_size
grid_y = int(y) / grid_size
gs = [(grid_x, grid_y)]
if grid_x * grid_size == x:
gs.append((grid_x - 1, grid_y))
if grid_y * grid_size == y:
for i in range(len(gs)):
gs.append((gs[i][0], grid_y - 1))
for gd in gs:
grid_set.add(gd)
|
[
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"__grids",
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":",
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"add",
"(",
"gd",
")"
] |
https://github.com/PaddlePaddle/Research/blob/2da0bd6c72d60e9df403aff23a7802779561c4a1/ST_DM/GenRegion/src/region/geometry.py#L313-L339
|
||
getsentry/sentry
|
83b1f25aac3e08075e0e2495bc29efaf35aca18a
|
src/sentry/utils/locking/lock.py
|
python
|
Lock.blocking_acquire
|
(self, initial_delay: float, timeout: float, exp_base=1.6)
|
Try to acquire the lock in a polling loop.
:param initial_delay: A random retry delay will be picked between 0
and this value (in seconds). The range from which we pick doubles
in every iteration.
:param timeout: Time in seconds after which ``UnableToAcquireLock``
will be raised.
|
Try to acquire the lock in a polling loop.
|
[
"Try",
"to",
"acquire",
"the",
"lock",
"in",
"a",
"polling",
"loop",
"."
] |
def blocking_acquire(self, initial_delay: float, timeout: float, exp_base=1.6):
"""
Try to acquire the lock in a polling loop.
:param initial_delay: A random retry delay will be picked between 0
and this value (in seconds). The range from which we pick doubles
in every iteration.
:param timeout: Time in seconds after which ``UnableToAcquireLock``
will be raised.
"""
stop = time.monotonic() + timeout
attempt = 0
while time.monotonic() < stop:
try:
return self.acquire()
except UnableToAcquireLock:
delay = (exp_base ** attempt) * random.random() * initial_delay
# Redundant check to prevent futile sleep in last iteration:
if time.monotonic() + delay > stop:
break
time.sleep(delay)
attempt += 1
raise UnableToAcquireLock(f"Unable to acquire {self!r} because of timeout")
|
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"1",
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"UnableToAcquireLock",
"(",
"f\"Unable to acquire {self!r} because of timeout\"",
")"
] |
https://github.com/getsentry/sentry/blob/83b1f25aac3e08075e0e2495bc29efaf35aca18a/src/sentry/utils/locking/lock.py#L47-L72
|
||
bbfamily/abu
|
2de85ae57923a720dac99a545b4f856f6b87304b
|
abupy/UtilBu/ABuKLUtil.py
|
python
|
bcut_change_vc
|
(df, bins=None)
|
return _df_dispatch_concat(df, _bcut_change_vc)
|
eg:
tsla = ABuSymbolPd.make_kl_df('usTSLA')
ABuKLUtil.bcut_change_vc(tsla)
out:
p_change rate
(0, 3] 209 0.4147
(-3, 0] 193 0.3829
(3, 7] 47 0.0933
(-7, -3] 44 0.0873
(-10, -7] 6 0.0119
(7, 10] 3 0.0060
(10, inf] 1 0.0020
(-inf, -10] 1 0.0020
:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
:param bins: 默认eg：[-np.inf, -10, -7, -3, 0, 3, 7, 10, np.inf]
:return: pd.DataFrame
|
eg:
tsla = ABuSymbolPd.make_kl_df('usTSLA')
ABuKLUtil.bcut_change_vc(tsla)
|
[
"eg",
":",
"tsla",
"=",
"ABuSymbolPd",
".",
"make_kl_df",
"(",
"usTSLA",
")",
"ABuKLUtil",
".",
"bcut_change_vc",
"(",
"tsla",
")"
] |
def bcut_change_vc(df, bins=None):
"""
eg:
tsla = ABuSymbolPd.make_kl_df('usTSLA')
ABuKLUtil.bcut_change_vc(tsla)
out:
p_change rate
(0, 3] 209 0.4147
(-3, 0] 193 0.3829
(3, 7] 47 0.0933
(-7, -3] 44 0.0873
(-10, -7] 6 0.0119
(7, 10] 3 0.0060
(10, inf] 1 0.0020
(-inf, -10] 1 0.0020
:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
:param bins: 默认eg：[-np.inf, -10, -7, -3, 0, 3, 7, 10, np.inf]
:return: pd.DataFrame
"""
def _bcut_change_vc(p_df, df_name=''):
dww = pd.DataFrame(pd.cut(p_df.p_change, bins=bins).value_counts())
# 计算各个bin所占的百分比
dww['{}rate'.format(df_name)] = dww.p_change.values / dww.p_change.values.sum()
if len(df_name) > 0:
dww.rename(columns={'p_change': '{}'.format(df_name)}, inplace=True)
return dww
if bins is None:
bins = [-np.inf, -10, -7, -3, 0, 3, 7, 10, np.inf]
return _df_dispatch_concat(df, _bcut_change_vc)
|
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https://github.com/bbfamily/abu/blob/2de85ae57923a720dac99a545b4f856f6b87304b/abupy/UtilBu/ABuKLUtil.py#L161-L193
|
|
FederatedAI/FATE
|
32540492623568ecd1afcb367360133616e02fa3
|
python/federatedml/util/data_transform.py
|
python
|
save_missing_imputer_model
|
(missing_fill=False,
missing_replace_method=None,
missing_impute=None,
missing_fill_value=None,
missing_replace_rate=None,
header=None,
model_name="Imputer")
|
return model_meta, model_param
|
[] |
def save_missing_imputer_model(missing_fill=False,
missing_replace_method=None,
missing_impute=None,
missing_fill_value=None,
missing_replace_rate=None,
header=None,
model_name="Imputer"):
model_meta = DataTransformImputerMeta()
model_param = DataTransformImputerParam()
model_meta.is_imputer = missing_fill
if missing_fill:
if missing_replace_method:
model_meta.strategy = str(missing_replace_method)
if missing_impute is not None:
model_meta.missing_value.extend(map(str, missing_impute))
if missing_fill_value is not None:
feature_value_dict = dict(zip(header, map(str, missing_fill_value)))
model_param.missing_replace_value.update(feature_value_dict)
if missing_replace_rate is not None:
missing_replace_rate_dict = dict(zip(header, missing_replace_rate))
model_param.missing_value_ratio.update(missing_replace_rate_dict)
return model_meta, model_param
|
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"missing_replace_rate",
")",
")",
"model_param",
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"missing_value_ratio",
".",
"update",
"(",
"missing_replace_rate_dict",
")",
"return",
"model_meta",
",",
"model_param"
] |
https://github.com/FederatedAI/FATE/blob/32540492623568ecd1afcb367360133616e02fa3/python/federatedml/util/data_transform.py#L1083-L1109
|
|||
jihunchoi/recurrent-batch-normalization-pytorch
|
61736ecd2547bdb43e193ac6aa28545e3918ff9b
|
bnlstm.py
|
python
|
SeparatedBatchNorm1d.__init__
|
(self, num_features, max_length, eps=1e-5, momentum=0.1,
affine=True)
|
Most parts are copied from
torch.nn.modules.batchnorm._BatchNorm.
|
Most parts are copied from
torch.nn.modules.batchnorm._BatchNorm.
|
[
"Most",
"parts",
"are",
"copied",
"from",
"torch",
".",
"nn",
".",
"modules",
".",
"batchnorm",
".",
"_BatchNorm",
"."
] |
def __init__(self, num_features, max_length, eps=1e-5, momentum=0.1,
affine=True):
"""
Most parts are copied from
torch.nn.modules.batchnorm._BatchNorm.
"""
super(SeparatedBatchNorm1d, self).__init__()
self.num_features = num_features
self.max_length = max_length
self.affine = affine
self.eps = eps
self.momentum = momentum
if self.affine:
self.weight = nn.Parameter(torch.FloatTensor(num_features))
self.bias = nn.Parameter(torch.FloatTensor(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
for i in range(max_length):
self.register_buffer(
'running_mean_{}'.format(i), torch.zeros(num_features))
self.register_buffer(
'running_var_{}'.format(i), torch.ones(num_features))
self.reset_parameters()
|
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https://github.com/jihunchoi/recurrent-batch-normalization-pytorch/blob/61736ecd2547bdb43e193ac6aa28545e3918ff9b/bnlstm.py#L15-L39
|
||
nicoboss/nsz
|
ff0c9fd102e4ddb6c2e4d7bada8840943423f419
|
nsz/gui/KivyOnTop.py
|
python
|
set_not_always_on_top
|
(title: str)
|
Sets the HWND_NOTOPMOST flag for the current Kivy Window.
|
Sets the HWND_NOTOPMOST flag for the current Kivy Window.
|
[
"Sets",
"the",
"HWND_NOTOPMOST",
"flag",
"for",
"the",
"current",
"Kivy",
"Window",
"."
] |
def set_not_always_on_top(title: str):
'''
Sets the HWND_NOTOPMOST flag for the current Kivy Window.
'''
global hwnd
if not 'hwnd' in globals():
find_hwnd(title)
rect = win32gui.GetWindowRect(hwnd)
x = rect[0]
y = rect[1]
w = rect[2] - x
h = rect[3] - y
win32gui.SetWindowPos(hwnd, win32con.HWND_NOTOPMOST, x, y, w, h, 0)
|
[
"def",
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https://github.com/nicoboss/nsz/blob/ff0c9fd102e4ddb6c2e4d7bada8840943423f419/nsz/gui/KivyOnTop.py#L41-L57
|
||
fonttools/fonttools
|
892322aaff6a89bea5927379ec06bc0da3dfb7df
|
Lib/fontTools/otlLib/builder.py
|
python
|
LookupBuilder.setBacktrackCoverage_
|
(self, prefix, subtable)
|
[] |
def setBacktrackCoverage_(self, prefix, subtable):
subtable.BacktrackGlyphCount = len(prefix)
subtable.BacktrackCoverage = []
for p in reversed(prefix):
coverage = buildCoverage(p, self.glyphMap)
subtable.BacktrackCoverage.append(coverage)
|
[
"def",
"setBacktrackCoverage_",
"(",
"self",
",",
"prefix",
",",
"subtable",
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":",
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"BacktrackGlyphCount",
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https://github.com/fonttools/fonttools/blob/892322aaff6a89bea5927379ec06bc0da3dfb7df/Lib/fontTools/otlLib/builder.py#L169-L174
|
||||
JoelBender/bacpypes
|
41104c2b565b2ae9a637c941dfb0fe04195c5e96
|
py25/bacpypes/local/file.py
|
python
|
LocalRecordAccessFileObject.__init__
|
(self, **kwargs)
|
Initialize a record accessed file object.
|
Initialize a record accessed file object.
|
[
"Initialize",
"a",
"record",
"accessed",
"file",
"object",
"."
] |
def __init__(self, **kwargs):
""" Initialize a record accessed file object. """
if _debug:
LocalRecordAccessFileObject._debug("__init__ %r",
kwargs,
)
# verify the file access method or provide it
if 'fileAccessMethod' in kwargs:
if kwargs['fileAccessMethod'] != 'recordAccess':
raise ValueError("inconsistent file access method")
else:
kwargs['fileAccessMethod'] = 'recordAccess'
# continue with initialization
FileObject.__init__(self, **kwargs)
|
[
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https://github.com/JoelBender/bacpypes/blob/41104c2b565b2ae9a637c941dfb0fe04195c5e96/py25/bacpypes/local/file.py#L17-L32
|
||
dimagi/commcare-hq
|
d67ff1d3b4c51fa050c19e60c3253a79d3452a39
|
corehq/apps/userreports/indicators/specs.py
|
python
|
LedgerBalancesIndicatorSpec.readable_output
|
(self, context)
|
return "Ledgers from {}".format(str(self.get_case_id_expression(context)))
|
[] |
def readable_output(self, context):
return "Ledgers from {}".format(str(self.get_case_id_expression(context)))
|
[
"def",
"readable_output",
"(",
"self",
",",
"context",
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"\"Ledgers from {}\"",
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"str",
"(",
"self",
".",
"get_case_id_expression",
"(",
"context",
")",
")",
")"
] |
https://github.com/dimagi/commcare-hq/blob/d67ff1d3b4c51fa050c19e60c3253a79d3452a39/corehq/apps/userreports/indicators/specs.py#L144-L145
|
|||
openstack/nova
|
b49b7663e1c3073917d5844b81d38db8e86d05c4
|
nova/api/openstack/compute/views/flavors.py
|
python
|
ViewBuilder.index
|
(self, request, flavors)
|
return self._list_view(self.basic, request, flavors, coll_name,
include_description=include_description)
|
Return the 'index' view of flavors.
|
Return the 'index' view of flavors.
|
[
"Return",
"the",
"index",
"view",
"of",
"flavors",
"."
] |
def index(self, request, flavors):
"""Return the 'index' view of flavors."""
coll_name = self._collection_name
include_description = api_version_request.is_supported(
request, FLAVOR_DESCRIPTION_MICROVERSION)
return self._list_view(self.basic, request, flavors, coll_name,
include_description=include_description)
|
[
"def",
"index",
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"self",
",",
"request",
",",
"flavors",
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"coll_name",
"=",
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",",
"flavors",
",",
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",",
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")"
] |
https://github.com/openstack/nova/blob/b49b7663e1c3073917d5844b81d38db8e86d05c4/nova/api/openstack/compute/views/flavors.py#L78-L84
|
|
materialsproject/pymatgen
|
8128f3062a334a2edd240e4062b5b9bdd1ae6f58
|
pymatgen/analysis/surface_analysis.py
|
python
|
WorkFunctionAnalyzer.get_locpot_along_slab_plot
|
(self, label_energies=True, plt=None, label_fontsize=10)
|
return plt
|
Returns a plot of the local potential (eV) vs the
position along the c axis of the slab model (Ang)
Args:
label_energies (bool): Whether to label relevant energy
quantities such as the work function, Fermi energy,
vacuum locpot, bulk-like locpot
plt (plt): Matplotlib pylab object
label_fontsize (float): Fontsize of labels
Returns plt of the locpot vs c axis
|
Returns a plot of the local potential (eV) vs the
position along the c axis of the slab model (Ang)
|
[
"Returns",
"a",
"plot",
"of",
"the",
"local",
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"eV",
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"position",
"along",
"the",
"c",
"axis",
"of",
"the",
"slab",
"model",
"(",
"Ang",
")"
] |
def get_locpot_along_slab_plot(self, label_energies=True, plt=None, label_fontsize=10):
"""
Returns a plot of the local potential (eV) vs the
position along the c axis of the slab model (Ang)
Args:
label_energies (bool): Whether to label relevant energy
quantities such as the work function, Fermi energy,
vacuum locpot, bulk-like locpot
plt (plt): Matplotlib pylab object
label_fontsize (float): Fontsize of labels
Returns plt of the locpot vs c axis
"""
plt = pretty_plot(width=6, height=4) if not plt else plt
# plot the raw locpot signal along c
plt.plot(self.along_c, self.locpot_along_c, "b--")
# Get the local averaged signal of the locpot along c
xg, yg = [], []
for i, p in enumerate(self.locpot_along_c):
# average signal is just the bulk-like potential when in the slab region
in_slab = False
for r in self.slab_regions:
if r[0] <= self.along_c[i] <= r[1]:
in_slab = True
if len(self.slab_regions) > 1:
if self.along_c[i] >= self.slab_regions[1][1]:
in_slab = True
if self.along_c[i] <= self.slab_regions[0][0]:
in_slab = True
if in_slab:
yg.append(self.ave_bulk_p)
xg.append(self.along_c[i])
elif p < self.ave_bulk_p:
yg.append(self.ave_bulk_p)
xg.append(self.along_c[i])
else:
yg.append(p)
xg.append(self.along_c[i])
xg, yg = zip(*sorted(zip(xg, yg)))
plt.plot(xg, yg, "r", linewidth=2.5, zorder=-1)
# make it look nice
if label_energies:
plt = self.get_labels(plt, label_fontsize=label_fontsize)
plt.xlim([0, 1])
plt.ylim([min(self.locpot_along_c), self.vacuum_locpot + self.ave_locpot * 0.2])
plt.xlabel(r"Fractional coordinates ($\hat{c}$)", fontsize=25)
plt.xticks(fontsize=15, rotation=45)
plt.ylabel(r"Potential (eV)", fontsize=25)
plt.yticks(fontsize=15)
return plt
|
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] |
https://github.com/materialsproject/pymatgen/blob/8128f3062a334a2edd240e4062b5b9bdd1ae6f58/pymatgen/analysis/surface_analysis.py#L1533-L1589
|
|
openshift/openshift-tools
|
1188778e728a6e4781acf728123e5b356380fe6f
|
openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/src/lib/service.py
|
python
|
Service.add_portal_ip
|
(self, pip)
|
add cluster ip
|
add cluster ip
|
[
"add",
"cluster",
"ip"
] |
def add_portal_ip(self, pip):
'''add cluster ip'''
self.put(Service.portal_ip, pip)
|
[
"def",
"add_portal_ip",
"(",
"self",
",",
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":",
"self",
".",
"put",
"(",
"Service",
".",
"portal_ip",
",",
"pip",
")"
] |
https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/src/lib/service.py#L142-L144
|
||
fkie/multimaster_fkie
|
3d23df29d25d71a75c66bbd3cc6e9cbb255724d8
|
fkie_node_manager/src/fkie_node_manager/launch_list_model.py
|
python
|
PathItem.is_launch_file
|
(self)
|
return self.path is not None and self.id in [self.LAUNCH_FILE, self.RECENT_FILE] and self.path.endswith('.launch')
|
:return: True if it is a launch file
:rtype: bool
|
:return: True if it is a launch file
:rtype: bool
|
[
":",
"return",
":",
"True",
"if",
"it",
"is",
"a",
"launch",
"file",
":",
"rtype",
":",
"bool"
] |
def is_launch_file(self):
'''
:return: True if it is a launch file
:rtype: bool
'''
return self.path is not None and self.id in [self.LAUNCH_FILE, self.RECENT_FILE] and self.path.endswith('.launch')
|
[
"def",
"is_launch_file",
"(",
"self",
")",
":",
"return",
"self",
".",
"path",
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"]",
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] |
https://github.com/fkie/multimaster_fkie/blob/3d23df29d25d71a75c66bbd3cc6e9cbb255724d8/fkie_node_manager/src/fkie_node_manager/launch_list_model.py#L295-L300
|
|
googleads/google-ads-python
|
2a1d6062221f6aad1992a6bcca0e7e4a93d2db86
|
google/ads/googleads/v7/services/services/combined_audience_service/client.py
|
python
|
CombinedAudienceServiceClient.__init__
|
(
self,
*,
credentials: Optional[credentials.Credentials] = None,
transport: Union[str, CombinedAudienceServiceTransport, None] = None,
client_options: Optional[client_options_lib.ClientOptions] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
)
|
Instantiate the combined audience service client.
Args:
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
transport (Union[str, ~.CombinedAudienceServiceTransport]): The
transport to use. If set to None, a transport is chosen
automatically.
client_options (google.api_core.client_options.ClientOptions): Custom options for the
client. It won't take effect if a ``transport`` instance is provided.
(1) The ``api_endpoint`` property can be used to override the
default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT
environment variable can also be used to override the endpoint:
"always" (always use the default mTLS endpoint), "never" (always
use the default regular endpoint) and "auto" (auto switch to the
default mTLS endpoint if client certificate is present, this is
the default value). However, the ``api_endpoint`` property takes
precedence if provided.
(2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
is "true", then the ``client_cert_source`` property can be used
to provide client certificate for mutual TLS transport. If
not provided, the default SSL client certificate will be used if
present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
set, no client certificate will be used.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
Raises:
google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
creation failed for any reason.
|
Instantiate the combined audience service client.
|
[
"Instantiate",
"the",
"combined",
"audience",
"service",
"client",
"."
] |
def __init__(
self,
*,
credentials: Optional[credentials.Credentials] = None,
transport: Union[str, CombinedAudienceServiceTransport, None] = None,
client_options: Optional[client_options_lib.ClientOptions] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiate the combined audience service client.
Args:
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
transport (Union[str, ~.CombinedAudienceServiceTransport]): The
transport to use. If set to None, a transport is chosen
automatically.
client_options (google.api_core.client_options.ClientOptions): Custom options for the
client. It won't take effect if a ``transport`` instance is provided.
(1) The ``api_endpoint`` property can be used to override the
default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT
environment variable can also be used to override the endpoint:
"always" (always use the default mTLS endpoint), "never" (always
use the default regular endpoint) and "auto" (auto switch to the
default mTLS endpoint if client certificate is present, this is
the default value). However, the ``api_endpoint`` property takes
precedence if provided.
(2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
is "true", then the ``client_cert_source`` property can be used
to provide client certificate for mutual TLS transport. If
not provided, the default SSL client certificate will be used if
present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
set, no client certificate will be used.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
Raises:
google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
creation failed for any reason.
"""
if isinstance(client_options, dict):
client_options = client_options_lib.from_dict(client_options)
if client_options is None:
client_options = client_options_lib.ClientOptions()
# Create SSL credentials for mutual TLS if needed.
use_client_cert = bool(
util.strtobool(
os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")
)
)
ssl_credentials = None
is_mtls = False
if use_client_cert:
if client_options.client_cert_source:
import grpc # type: ignore
cert, key = client_options.client_cert_source()
ssl_credentials = grpc.ssl_channel_credentials(
certificate_chain=cert, private_key=key
)
is_mtls = True
else:
creds = SslCredentials()
is_mtls = creds.is_mtls
ssl_credentials = creds.ssl_credentials if is_mtls else None
# Figure out which api endpoint to use.
if client_options.api_endpoint is not None:
api_endpoint = client_options.api_endpoint
else:
use_mtls_env = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
if use_mtls_env == "never":
api_endpoint = self.DEFAULT_ENDPOINT
elif use_mtls_env == "always":
api_endpoint = self.DEFAULT_MTLS_ENDPOINT
elif use_mtls_env == "auto":
api_endpoint = (
self.DEFAULT_MTLS_ENDPOINT
if is_mtls
else self.DEFAULT_ENDPOINT
)
else:
raise MutualTLSChannelError(
"Unsupported GOOGLE_API_USE_MTLS_ENDPOINT value. Accepted values: never, auto, always"
)
# Save or instantiate the transport.
# Ordinarily, we provide the transport, but allowing a custom transport
# instance provides an extensibility point for unusual situations.
if isinstance(transport, CombinedAudienceServiceTransport):
# transport is a CombinedAudienceServiceTransport instance.
if credentials:
raise ValueError(
"When providing a transport instance, "
"provide its credentials directly."
)
self._transport = transport
elif isinstance(transport, str):
Transport = type(self).get_transport_class(transport)
self._transport = Transport(
credentials=credentials, host=self.DEFAULT_ENDPOINT
)
else:
self._transport = CombinedAudienceServiceGrpcTransport(
credentials=credentials,
host=api_endpoint,
ssl_channel_credentials=ssl_credentials,
client_info=client_info,
)
|
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] |
https://github.com/googleads/google-ads-python/blob/2a1d6062221f6aad1992a6bcca0e7e4a93d2db86/google/ads/googleads/v7/services/services/combined_audience_service/client.py#L246-L361
|
||
bastula/dicompyler
|
2643e0ee145cb7c699b3d36e3e4f07ac9dc7b1f2
|
dicompyler/baseplugins/2dview.py
|
python
|
plugin2DView.OnZoomOut
|
(self, evt)
|
Zoom the view out.
|
Zoom the view out.
|
[
"Zoom",
"the",
"view",
"out",
"."
] |
def OnZoomOut(self, evt):
"""Zoom the view out."""
if (self.zoom > 1):
self.zoom = self.zoom / 1.1
self.Refresh()
|
[
"def",
"OnZoomOut",
"(",
"self",
",",
"evt",
")",
":",
"if",
"(",
"self",
".",
"zoom",
">",
"1",
")",
":",
"self",
".",
"zoom",
"=",
"self",
".",
"zoom",
"/",
"1.1",
"self",
".",
"Refresh",
"(",
")"
] |
https://github.com/bastula/dicompyler/blob/2643e0ee145cb7c699b3d36e3e4f07ac9dc7b1f2/dicompyler/baseplugins/2dview.py#L582-L587
|
||
aneisch/home-assistant-config
|
86e381fde9609cb8871c439c433c12989e4e225d
|
custom_components/hacs/repositories/base.py
|
python
|
HacsRepository.common_update_data
|
(self, ignore_issues: bool = False, force: bool = False)
|
Common update data.
|
Common update data.
|
[
"Common",
"update",
"data",
"."
] |
async def common_update_data(self, ignore_issues: bool = False, force: bool = False) -> None:
"""Common update data."""
releases = []
try:
repository_object, etag = await self.async_get_legacy_repository_object(
etag=None if force or self.data.installed else self.data.etag_repository,
)
self.repository_object = repository_object
if self.data.full_name.lower() != repository_object.full_name.lower():
self.hacs.common.renamed_repositories[
self.data.full_name
] = repository_object.full_name
raise HacsRepositoryExistException
self.data.update_data(repository_object.attributes)
self.data.etag_repository = etag
except HacsNotModifiedException:
return
except HacsRepositoryExistException:
raise HacsRepositoryExistException from None
except (AIOGitHubAPIException, HacsException) as exception:
if not self.hacs.status.startup:
self.logger.error("%s %s", self, exception)
if not ignore_issues:
self.validate.errors.append("Repository does not exist.")
raise HacsException(exception) from exception
# Make sure the repository is not archived.
if self.data.archived and not ignore_issues:
self.validate.errors.append("Repository is archived.")
if self.data.full_name not in self.hacs.common.archived_repositories:
self.hacs.common.archived_repositories.append(self.data.full_name)
raise HacsRepositoryArchivedException("Repository is archived.")
# Make sure the repository is not in the blacklist.
if self.hacs.repositories.is_removed(self.data.full_name) and not ignore_issues:
self.validate.errors.append("Repository is in the blacklist.")
raise HacsException("Repository is in the blacklist.")
# Get releases.
try:
releases = await self.get_releases(
prerelease=self.data.show_beta,
returnlimit=self.hacs.configuration.release_limit,
)
if releases:
self.data.releases = True
self.releases.objects = [x for x in releases if not x.draft]
self.data.published_tags = [x.tag_name for x in self.releases.objects]
self.data.last_version = next(iter(self.data.published_tags))
except (AIOGitHubAPIException, HacsException):
self.data.releases = False
if not self.force_branch:
self.ref = version_to_download(self)
if self.data.releases:
for release in self.releases.objects or []:
if release.tag_name == self.ref:
assets = release.assets
if assets:
downloads = next(iter(assets)).attributes.get("download_count")
self.data.downloads = downloads
self.hacs.log.debug("%s Running checks against %s", self, self.ref.replace("tags/", ""))
try:
self.tree = await self.get_tree(self.ref)
if not self.tree:
raise HacsException("No files in tree")
self.treefiles = []
for treefile in self.tree:
self.treefiles.append(treefile.full_path)
except (AIOGitHubAPIException, HacsException) as exception:
if not self.hacs.status.startup:
self.logger.error("%s %s", self, exception)
if not ignore_issues:
raise HacsException(exception) from None
|
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"(",
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")",
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"None"
] |
https://github.com/aneisch/home-assistant-config/blob/86e381fde9609cb8871c439c433c12989e4e225d/custom_components/hacs/repositories/base.py#L911-L987
|
||
ClusterHQ/flocker
|
eaa586248986d7cd681c99c948546c2b507e44de
|
flocker/control/_persistence.py
|
python
|
ConfigurationMigration.upgrade_from_v2
|
(cls, config)
|
return dumps(decoded_config)
|
Migrate a v2 JSON configuration to v3.
:param bytes config: The v2 JSON data.
:return bytes: The v3 JSON data.
|
Migrate a v2 JSON configuration to v3.
|
[
"Migrate",
"a",
"v2",
"JSON",
"configuration",
"to",
"v3",
"."
] |
def upgrade_from_v2(cls, config):
"""
Migrate a v2 JSON configuration to v3.
:param bytes config: The v2 JSON data.
:return bytes: The v3 JSON data.
"""
decoded_config = loads(config)
decoded_config[u"version"] = 3
decoded_config[u"deployment"][u"leases"] = {
u"values": [], _CLASS_MARKER: u"PMap",
}
return dumps(decoded_config)
|
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"def",
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https://github.com/ClusterHQ/flocker/blob/eaa586248986d7cd681c99c948546c2b507e44de/flocker/control/_persistence.py#L138-L150
|
|
osmr/imgclsmob
|
f2993d3ce73a2f7ddba05da3891defb08547d504
|
pytorch/pytorchcv/models/efficientnet.py
|
python
|
calc_tf_padding
|
(x,
kernel_size,
stride=1,
dilation=1)
|
return pad_h // 2, pad_h - pad_h // 2, pad_w // 2, pad_w - pad_w // 2
|
Calculate TF-same like padding size.
Parameters:
----------
x : tensor
Input tensor.
kernel_size : int
Convolution window size.
stride : int, default 1
Strides of the convolution.
dilation : int, default 1
Dilation value for convolution layer.
Returns:
-------
tuple of 4 int
The size of the padding.
|
Calculate TF-same like padding size.
|
[
"Calculate",
"TF",
"-",
"same",
"like",
"padding",
"size",
"."
] |
def calc_tf_padding(x,
kernel_size,
stride=1,
dilation=1):
"""
Calculate TF-same like padding size.
Parameters:
----------
x : tensor
Input tensor.
kernel_size : int
Convolution window size.
stride : int, default 1
Strides of the convolution.
dilation : int, default 1
Dilation value for convolution layer.
Returns:
-------
tuple of 4 int
The size of the padding.
"""
height, width = x.size()[2:]
oh = math.ceil(float(height) / stride)
ow = math.ceil(float(width) / stride)
pad_h = max((oh - 1) * stride + (kernel_size - 1) * dilation + 1 - height, 0)
pad_w = max((ow - 1) * stride + (kernel_size - 1) * dilation + 1 - width, 0)
return pad_h // 2, pad_h - pad_h // 2, pad_w // 2, pad_w - pad_w // 2
|
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"//",
"2",
",",
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"-",
"pad_w",
"//",
"2"
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https://github.com/osmr/imgclsmob/blob/f2993d3ce73a2f7ddba05da3891defb08547d504/pytorch/pytorchcv/models/efficientnet.py#L23-L51
|
|
pandas-dev/pandas
|
5ba7d714014ae8feaccc0dd4a98890828cf2832d
|
pandas/core/ops/__init__.py
|
python
|
frame_arith_method_with_reindex
|
(left: DataFrame, right: DataFrame, op)
|
return result
|
For DataFrame-with-DataFrame operations that require reindexing,
operate only on shared columns, then reindex.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
DataFrame
|
For DataFrame-with-DataFrame operations that require reindexing,
operate only on shared columns, then reindex.
|
[
"For",
"DataFrame",
"-",
"with",
"-",
"DataFrame",
"operations",
"that",
"require",
"reindexing",
"operate",
"only",
"on",
"shared",
"columns",
"then",
"reindex",
"."
] |
def frame_arith_method_with_reindex(left: DataFrame, right: DataFrame, op) -> DataFrame:
"""
For DataFrame-with-DataFrame operations that require reindexing,
operate only on shared columns, then reindex.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
DataFrame
"""
# GH#31623, only operate on shared columns
cols, lcols, rcols = left.columns.join(
right.columns, how="inner", level=None, return_indexers=True
)
new_left = left.iloc[:, lcols]
new_right = right.iloc[:, rcols]
result = op(new_left, new_right)
# Do the join on the columns instead of using align_method_FRAME
# to avoid constructing two potentially large/sparse DataFrames
join_columns, _, _ = left.columns.join(
right.columns, how="outer", level=None, return_indexers=True
)
if result.columns.has_duplicates:
# Avoid reindexing with a duplicate axis.
# https://github.com/pandas-dev/pandas/issues/35194
indexer, _ = result.columns.get_indexer_non_unique(join_columns)
indexer = algorithms.unique1d(indexer)
result = result._reindex_with_indexers(
{1: [join_columns, indexer]}, allow_dups=True
)
else:
result = result.reindex(join_columns, axis=1)
return result
|
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"reindex",
"(",
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",",
"axis",
"=",
"1",
")",
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] |
https://github.com/pandas-dev/pandas/blob/5ba7d714014ae8feaccc0dd4a98890828cf2832d/pandas/core/ops/__init__.py#L344-L385
|
|
keras-team/keras
|
5caa668b6a415675064a730f5eb46ecc08e40f65
|
keras/keras_parameterized.py
|
python
|
_test_or_class_decorator
|
(test_or_class, single_method_decorator)
|
return _decorate_test_or_class
|
Decorate a test or class with a decorator intended for one method.
If the test_or_class is a class:
This will apply the decorator to all test methods in the class.
If the test_or_class is an iterable of already-parameterized test cases:
This will apply the decorator to all the cases, and then flatten the
resulting cross-product of test cases. This allows stacking the Keras
parameterized decorators w/ each other, and to apply them to test methods
that have already been marked with an absl parameterized decorator.
Otherwise, treat the obj as a single method and apply the decorator directly.
Args:
test_or_class: A test method (that may have already been decorated with a
parameterized decorator, or a test class that extends
keras_parameterized.TestCase
single_method_decorator:
A parameterized decorator intended for a single test method.
Returns:
The decorated result.
|
Decorate a test or class with a decorator intended for one method.
|
[
"Decorate",
"a",
"test",
"or",
"class",
"with",
"a",
"decorator",
"intended",
"for",
"one",
"method",
"."
] |
def _test_or_class_decorator(test_or_class, single_method_decorator):
"""Decorate a test or class with a decorator intended for one method.
If the test_or_class is a class:
This will apply the decorator to all test methods in the class.
If the test_or_class is an iterable of already-parameterized test cases:
This will apply the decorator to all the cases, and then flatten the
resulting cross-product of test cases. This allows stacking the Keras
parameterized decorators w/ each other, and to apply them to test methods
that have already been marked with an absl parameterized decorator.
Otherwise, treat the obj as a single method and apply the decorator directly.
Args:
test_or_class: A test method (that may have already been decorated with a
parameterized decorator, or a test class that extends
keras_parameterized.TestCase
single_method_decorator:
A parameterized decorator intended for a single test method.
Returns:
The decorated result.
"""
def _decorate_test_or_class(obj):
if isinstance(obj, collections.abc.Iterable):
return itertools.chain.from_iterable(
single_method_decorator(method) for method in obj)
if isinstance(obj, type):
cls = obj
for name, value in cls.__dict__.copy().items():
if callable(value) and name.startswith(
unittest.TestLoader.testMethodPrefix):
setattr(cls, name, single_method_decorator(value))
cls = type(cls).__new__(type(cls), cls.__name__, cls.__bases__,
cls.__dict__.copy())
return cls
return single_method_decorator(obj)
if test_or_class is not None:
return _decorate_test_or_class(test_or_class)
return _decorate_test_or_class
|
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] |
https://github.com/keras-team/keras/blob/5caa668b6a415675064a730f5eb46ecc08e40f65/keras/keras_parameterized.py#L432-L475
|
|
insarlab/MintPy
|
4357b8c726dec8a3f936770e3f3dda92882685b7
|
mintpy/tropo_pyaps.py
|
python
|
cmd_line_parse
|
(iargs=None)
|
return inps
|
Command line parser.
|
Command line parser.
|
[
"Command",
"line",
"parser",
"."
] |
def cmd_line_parse(iargs=None):
"""Command line parser."""
parser = create_parser()
inps = parser.parse_args(args=iargs)
# check the input requirements
key_list = ['date_list', 'hour']
# with timeseries file
if inps.timeseries_file:
for key in key_list+['ref_yx']:
if vars(inps)[key]:
print(('input "{:<10}" is ignored because it will be extracted from '
'timeseries file {}').format(key, inps.timeseries_file))
# without timeseries file
elif any(not vars(inps)[key] for key in key_list):
msg = 'No input timeseries file, all the following options are required: \n{}'.format(key_list)
msg += '\n\n'+EXAMPLE
raise ValueError(msg)
## default values
# Get Grib Source
inps.trop_model = standardize_trop_model(inps.trop_model, standardWeatherModelNames)
print('weather model: '+inps.trop_model)
# weather_dir
inps.weather_dir = os.path.expanduser(inps.weather_dir)
inps.weather_dir = os.path.expandvars(inps.weather_dir)
# Fallback value if WEATHER_DIR is not defined as environment variable
if inps.weather_dir == '${WEATHER_DIR}':
inps.weather_dir = './'
print('weather data directory: '+inps.weather_dir)
return inps
|
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] |
https://github.com/insarlab/MintPy/blob/4357b8c726dec8a3f936770e3f3dda92882685b7/mintpy/tropo_pyaps.py#L105-L139
|
|
Komodo/KomodoEdit
|
61edab75dce2bdb03943b387b0608ea36f548e8e
|
src/apsw/tools/shell.py
|
python
|
Shell.command_tables
|
(self, cmd)
|
tables ?PATTERN?: Lists names of tables matching LIKE pattern
This also returns views.
|
tables ?PATTERN?: Lists names of tables matching LIKE pattern
|
[
"tables",
"?PATTERN?",
":",
"Lists",
"names",
"of",
"tables",
"matching",
"LIKE",
"pattern"
] |
def command_tables(self, cmd):
"""tables ?PATTERN?: Lists names of tables matching LIKE pattern
This also returns views.
"""
self.push_output()
self.output=self.output_list
self.header=False
try:
if len(cmd)==0:
cmd=['%']
# The SQLite shell code filters out sqlite_ prefixes if
# you specified an argument else leaves them in. It also
# has a hand coded output mode that does space separation
# plus wrapping at 80 columns.
for n in cmd:
self.process_sql("SELECT name FROM sqlite_master "
"WHERE type IN ('table', 'view') AND name NOT LIKE 'sqlite_%' "
"AND name like ?1 "
"UNION ALL "
"SELECT name FROM sqlite_temp_master "
"WHERE type IN ('table', 'view') AND name NOT LIKE 'sqlite_%' "
"ORDER BY 1", (n,), internal=True)
finally:
self.pop_output()
|
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"\"SELECT name FROM sqlite_master \"",
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"\"AND name like ?1 \"",
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"\"WHERE type IN ('table', 'view') AND name NOT LIKE 'sqlite_%' \"",
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https://github.com/Komodo/KomodoEdit/blob/61edab75dce2bdb03943b387b0608ea36f548e8e/src/apsw/tools/shell.py#L2240-L2265
|
||
ewels/MultiQC
|
9b953261d3d684c24eef1827a5ce6718c847a5af
|
multiqc/modules/base_module.py
|
python
|
BaseMultiqcModule.write_data_file
|
(self, data, fn, sort_cols=False, data_format=None)
|
Saves raw data to a dictionary for downstream use, then redirects
to report.write_data_file() to create the file in the report directory
|
Saves raw data to a dictionary for downstream use, then redirects
to report.write_data_file() to create the file in the report directory
|
[
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".",
"write_data_file",
"()",
"to",
"create",
"the",
"file",
"in",
"the",
"report",
"directory"
] |
def write_data_file(self, data, fn, sort_cols=False, data_format=None):
"""Saves raw data to a dictionary for downstream use, then redirects
to report.write_data_file() to create the file in the report directory"""
# Append custom module anchor if set
mod_cust_config = getattr(self, "mod_cust_config", {})
if "anchor" in mod_cust_config:
fn = "{}_{}".format(fn, mod_cust_config["anchor"])
# Generate a unique filename if the file already exists (running module multiple times)
i = 1
base_fn = fn
while fn in report.saved_raw_data:
fn = "{}_{}".format(base_fn, i)
i += 1
# Save the file
report.saved_raw_data[fn] = data
util_functions.write_data_file(data, fn, sort_cols, data_format)
|
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https://github.com/ewels/MultiQC/blob/9b953261d3d684c24eef1827a5ce6718c847a5af/multiqc/modules/base_module.py#L469-L487
|
||
holzschu/Carnets
|
44effb10ddfc6aa5c8b0687582a724ba82c6b547
|
Library/lib/python3.7/distutils/archive_util.py
|
python
|
_get_uid
|
(name)
|
return None
|
Returns an uid, given a user name.
|
Returns an uid, given a user name.
|
[
"Returns",
"an",
"uid",
"given",
"a",
"user",
"name",
"."
] |
def _get_uid(name):
"""Returns an uid, given a user name."""
if getpwnam is None or name is None:
return None
try:
result = getpwnam(name)
except KeyError:
result = None
if result is not None:
return result[2]
return None
|
[
"def",
"_get_uid",
"(",
"name",
")",
":",
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"getpwnam",
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"None",
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"[",
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https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/distutils/archive_util.py#L43-L53
|
|
ProjectQ-Framework/ProjectQ
|
0d32c1610ba4e9aefd7f19eb52dadb4fbe5f9005
|
projectq/backends/_sim/_pysim.py
|
python
|
Simulator.allocate_qubit
|
(self, qubit_id)
|
Allocate a qubit.
Args:
qubit_id (int): ID of the qubit which is being allocated.
|
Allocate a qubit.
|
[
"Allocate",
"a",
"qubit",
"."
] |
def allocate_qubit(self, qubit_id):
"""
Allocate a qubit.
Args:
qubit_id (int): ID of the qubit which is being allocated.
"""
self._map[qubit_id] = self._num_qubits
self._num_qubits += 1
self._state.resize(1 << self._num_qubits, refcheck=_USE_REFCHECK)
|
[
"def",
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"qubit_id",
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".",
"_map",
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"(",
"1",
"<<",
"self",
".",
"_num_qubits",
",",
"refcheck",
"=",
"_USE_REFCHECK",
")"
] |
https://github.com/ProjectQ-Framework/ProjectQ/blob/0d32c1610ba4e9aefd7f19eb52dadb4fbe5f9005/projectq/backends/_sim/_pysim.py#L106-L115
|
||
dpressel/mead-baseline
|
9987e6b37fa6525a4ddc187c305e292a718f59a9
|
baseline/tf/deps/train.py
|
python
|
to_tensors
|
(ts, lengths_key)
|
return features, (heads, labels)
|
Convert a data feed into a tuple of `features` (`dict`) and `y` values
This method is required to produce `tf.dataset`s from the input data feed
:param ts: The data feed to convert
:return: A `tuple` of `features` and `y` (labels)
|
Convert a data feed into a tuple of `features` (`dict`) and `y` values
|
[
"Convert",
"a",
"data",
"feed",
"into",
"a",
"tuple",
"of",
"features",
"(",
"dict",
")",
"and",
"y",
"values"
] |
def to_tensors(ts, lengths_key):
"""Convert a data feed into a tuple of `features` (`dict`) and `y` values
This method is required to produce `tf.dataset`s from the input data feed
:param ts: The data feed to convert
:return: A `tuple` of `features` and `y` (labels)
"""
keys = ts[0].keys()
features = dict((k, []) for k in keys)
for sample in ts:
for k in features.keys():
# add each sample
for s in sample[k]:
features[k].append(s)
features = dict((k, np.stack(v)) for k, v in features.items())
features['lengths'] = features[lengths_key]
del features[lengths_key]
heads = features.pop('heads')
labels = features.pop('labels')
return features, (heads, labels)
|
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"(",
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")",
"return",
"features",
",",
"(",
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https://github.com/dpressel/mead-baseline/blob/9987e6b37fa6525a4ddc187c305e292a718f59a9/baseline/tf/deps/train.py#L30-L51
|
|
jgagneastro/coffeegrindsize
|
22661ebd21831dba4cf32bfc6ba59fe3d49f879c
|
App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/matplotlib/transforms.py
|
python
|
interval_contains
|
(interval, val)
|
return a <= val <= b or a >= val >= b
|
Check, inclusively, whether an interval includes a given value.
Parameters
----------
interval : sequence of scalar
A 2-length sequence, endpoints that define the interval.
val : scalar
Value to check is within interval.
Returns
-------
bool
Returns true if given val is within the interval.
|
Check, inclusively, whether an interval includes a given value.
|
[
"Check",
"inclusively",
"whether",
"an",
"interval",
"includes",
"a",
"given",
"value",
"."
] |
def interval_contains(interval, val):
"""
Check, inclusively, whether an interval includes a given value.
Parameters
----------
interval : sequence of scalar
A 2-length sequence, endpoints that define the interval.
val : scalar
Value to check is within interval.
Returns
-------
bool
Returns true if given val is within the interval.
"""
a, b = interval
return a <= val <= b or a >= val >= b
|
[
"def",
"interval_contains",
"(",
"interval",
",",
"val",
")",
":",
"a",
",",
"b",
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"a",
"<=",
"val",
"<=",
"b",
"or",
"a",
">=",
"val",
">=",
"b"
] |
https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/matplotlib/transforms.py#L2915-L2932
|
|
pystruct/pystruct
|
957193a40f3933ae5709336d46289c8ad4a60b7a
|
pystruct/learners/latent_structured_svm.py
|
python
|
LatentSSVM.score
|
(self, X, Y)
|
return 1. - np.sum(losses) / float(np.sum(max_losses))
|
Compute score as 1 - loss over whole data set.
Returns the average accuracy (in terms of model.loss)
over X and Y.
Parameters
----------
X : iterable
Evaluation data.
Y : iterable
True labels.
Returns
-------
score : float
Average of 1 - loss over training examples.
|
Compute score as 1 - loss over whole data set.
|
[
"Compute",
"score",
"as",
"1",
"-",
"loss",
"over",
"whole",
"data",
"set",
"."
] |
def score(self, X, Y):
"""Compute score as 1 - loss over whole data set.
Returns the average accuracy (in terms of model.loss)
over X and Y.
Parameters
----------
X : iterable
Evaluation data.
Y : iterable
True labels.
Returns
-------
score : float
Average of 1 - loss over training examples.
"""
losses = [self.model.base_loss(y, y_pred)
for y, y_pred in zip(Y, self.predict(X))]
max_losses = [self.model.max_loss(y) for y in Y]
return 1. - np.sum(losses) / float(np.sum(max_losses))
|
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"def",
"score",
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",",
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",",
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")",
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"[",
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"/",
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https://github.com/pystruct/pystruct/blob/957193a40f3933ae5709336d46289c8ad4a60b7a/pystruct/learners/latent_structured_svm.py#L136-L158
|
|
scikit-learn/scikit-learn
|
1d1aadd0711b87d2a11c80aad15df6f8cf156712
|
sklearn/feature_extraction/_dict_vectorizer.py
|
python
|
DictVectorizer.inverse_transform
|
(self, X, dict_type=dict)
|
return dicts
|
Transform array or sparse matrix X back to feature mappings.
X must have been produced by this DictVectorizer's transform or
fit_transform method; it may only have passed through transformers
that preserve the number of features and their order.
In the case of one-hot/one-of-K coding, the constructed feature
names and values are returned rather than the original ones.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Sample matrix.
dict_type : type, default=dict
Constructor for feature mappings. Must conform to the
collections.Mapping API.
Returns
-------
D : list of dict_type objects of shape (n_samples,)
Feature mappings for the samples in X.
|
Transform array or sparse matrix X back to feature mappings.
|
[
"Transform",
"array",
"or",
"sparse",
"matrix",
"X",
"back",
"to",
"feature",
"mappings",
"."
] |
def inverse_transform(self, X, dict_type=dict):
"""Transform array or sparse matrix X back to feature mappings.
X must have been produced by this DictVectorizer's transform or
fit_transform method; it may only have passed through transformers
that preserve the number of features and their order.
In the case of one-hot/one-of-K coding, the constructed feature
names and values are returned rather than the original ones.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Sample matrix.
dict_type : type, default=dict
Constructor for feature mappings. Must conform to the
collections.Mapping API.
Returns
-------
D : list of dict_type objects of shape (n_samples,)
Feature mappings for the samples in X.
"""
# COO matrix is not subscriptable
X = check_array(X, accept_sparse=["csr", "csc"])
n_samples = X.shape[0]
names = self.feature_names_
dicts = [dict_type() for _ in range(n_samples)]
if sp.issparse(X):
for i, j in zip(*X.nonzero()):
dicts[i][names[j]] = X[i, j]
else:
for i, d in enumerate(dicts):
for j, v in enumerate(X[i, :]):
if v != 0:
d[names[j]] = X[i, j]
return dicts
|
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https://github.com/scikit-learn/scikit-learn/blob/1d1aadd0711b87d2a11c80aad15df6f8cf156712/sklearn/feature_extraction/_dict_vectorizer.py#L315-L354
|
|
sagemath/sage
|
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
|
src/sage/modules/vector_symbolic_dense.py
|
python
|
apply_map
|
(phi)
|
return apply
|
Returns a function that applies phi to its argument.
EXAMPLES::
sage: from sage.modules.vector_symbolic_dense import apply_map
sage: v = vector([1,2,3])
sage: f = apply_map(lambda x: x+1)
sage: f(v)
(2, 3, 4)
|
Returns a function that applies phi to its argument.
|
[
"Returns",
"a",
"function",
"that",
"applies",
"phi",
"to",
"its",
"argument",
"."
] |
def apply_map(phi):
"""
Returns a function that applies phi to its argument.
EXAMPLES::
sage: from sage.modules.vector_symbolic_dense import apply_map
sage: v = vector([1,2,3])
sage: f = apply_map(lambda x: x+1)
sage: f(v)
(2, 3, 4)
"""
def apply(self, *args, **kwds):
"""
Generic function used to implement common symbolic operations
elementwise as methods of a vector.
EXAMPLES::
sage: var('x,y')
(x, y)
sage: v = vector([sin(x)^2 + cos(x)^2, log(x*y), sin(x/(x^2 + x)), factorial(x+1)/factorial(x)])
sage: v.simplify_trig()
(1, log(x*y), sin(1/(x + 1)), factorial(x + 1)/factorial(x))
sage: v.canonicalize_radical()
(cos(x)^2 + sin(x)^2, log(x) + log(y), sin(1/(x + 1)), factorial(x + 1)/factorial(x))
sage: v.simplify_rational()
(cos(x)^2 + sin(x)^2, log(x*y), sin(1/(x + 1)), factorial(x + 1)/factorial(x))
sage: v.simplify_factorial()
(cos(x)^2 + sin(x)^2, log(x*y), sin(x/(x^2 + x)), x + 1)
sage: v.simplify_full()
(1, log(x*y), sin(1/(x + 1)), x + 1)
sage: v = vector([sin(2*x), sin(3*x)])
sage: v.simplify_trig()
(2*cos(x)*sin(x), (4*cos(x)^2 - 1)*sin(x))
sage: v.simplify_trig(False)
(sin(2*x), sin(3*x))
sage: v.simplify_trig(expand=False)
(sin(2*x), sin(3*x))
"""
return self.apply_map(lambda x: phi(x, *args, **kwds))
apply.__doc__ += "\nSee Expression." + phi.__name__ + "() for optional arguments."
return apply
|
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")",
":",
"\"\"\"\n Generic function used to implement common symbolic operations\n elementwise as methods of a vector.\n\n EXAMPLES::\n\n sage: var('x,y')\n (x, y)\n sage: v = vector([sin(x)^2 + cos(x)^2, log(x*y), sin(x/(x^2 + x)), factorial(x+1)/factorial(x)])\n sage: v.simplify_trig()\n (1, log(x*y), sin(1/(x + 1)), factorial(x + 1)/factorial(x))\n sage: v.canonicalize_radical()\n (cos(x)^2 + sin(x)^2, log(x) + log(y), sin(1/(x + 1)), factorial(x + 1)/factorial(x))\n sage: v.simplify_rational()\n (cos(x)^2 + sin(x)^2, log(x*y), sin(1/(x + 1)), factorial(x + 1)/factorial(x))\n sage: v.simplify_factorial()\n (cos(x)^2 + sin(x)^2, log(x*y), sin(x/(x^2 + x)), x + 1)\n sage: v.simplify_full()\n (1, log(x*y), sin(1/(x + 1)), x + 1)\n\n sage: v = vector([sin(2*x), sin(3*x)])\n sage: v.simplify_trig()\n (2*cos(x)*sin(x), (4*cos(x)^2 - 1)*sin(x))\n sage: v.simplify_trig(False)\n (sin(2*x), sin(3*x))\n sage: v.simplify_trig(expand=False)\n (sin(2*x), sin(3*x))\n \"\"\"",
"return",
"self",
".",
"apply_map",
"(",
"lambda",
"x",
":",
"phi",
"(",
"x",
",",
"*",
"args",
",",
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"+",
"phi",
".",
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"+",
"\"() for optional arguments.\"",
"return",
"apply"
] |
https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/modules/vector_symbolic_dense.py#L61-L105
|
|
jtpereyda/boofuzz
|
64badab7257117bcadab35e903d723223dde9203
|
boofuzz/sessions.py
|
python
|
Session._main_fuzz_loop
|
(self, fuzz_case_iterator)
|
Execute main fuzz logic; takes an iterator of test cases.
Preconditions: `self.total_mutant_index` and `self.total_num_mutations` are set properly.
Args:
fuzz_case_iterator (Iterable): An iterator that walks through fuzz cases and yields MutationContext objects.
See _iterate_single_node() for details.
Returns:
None
|
Execute main fuzz logic; takes an iterator of test cases.
|
[
"Execute",
"main",
"fuzz",
"logic",
";",
"takes",
"an",
"iterator",
"of",
"test",
"cases",
"."
] |
def _main_fuzz_loop(self, fuzz_case_iterator):
"""Execute main fuzz logic; takes an iterator of test cases.
Preconditions: `self.total_mutant_index` and `self.total_num_mutations` are set properly.
Args:
fuzz_case_iterator (Iterable): An iterator that walks through fuzz cases and yields MutationContext objects.
See _iterate_single_node() for details.
Returns:
None
"""
self.server_init()
try:
self._start_target(self.targets[0])
if self._reuse_target_connection:
self.targets[0].open()
self.num_cases_actually_fuzzed = 0
self.start_time = time.time()
for mutation_context in fuzz_case_iterator:
if self.total_mutant_index < self._index_start:
continue
# Check restart interval
if (
self.num_cases_actually_fuzzed
and self.restart_interval
and self.num_cases_actually_fuzzed % self.restart_interval == 0
):
self._fuzz_data_logger.open_test_step("restart interval of %d reached" % self.restart_interval)
self._restart_target(self.targets[0])
self._fuzz_current_case(mutation_context)
self.num_cases_actually_fuzzed += 1
if self._index_end is not None and self.total_mutant_index >= self._index_end:
break
if self._reuse_target_connection:
self.targets[0].close()
if self._keep_web_open and self.web_port is not None:
self.end_time = time.time()
print(
"\nFuzzing session completed. Keeping webinterface up on localhost:{}".format(self.web_port),
"\nPress ENTER to close webinterface",
)
input()
except KeyboardInterrupt:
# TODO: should wait for the end of the ongoing test case, and stop gracefully netmon and procmon
self.export_file()
self._fuzz_data_logger.log_error("SIGINT received ... exiting")
raise
except exception.BoofuzzRestartFailedError:
self._fuzz_data_logger.log_error("Restarting the target failed, exiting.")
self.export_file()
raise
except exception.BoofuzzTargetConnectionFailedError:
# exception should have already been handled but rethrown in order to escape test run
pass
except Exception:
self._fuzz_data_logger.log_error("Unexpected exception! {0}".format(traceback.format_exc()))
self.export_file()
raise
finally:
self._fuzz_data_logger.close_test()
|
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"(",
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] |
https://github.com/jtpereyda/boofuzz/blob/64badab7257117bcadab35e903d723223dde9203/boofuzz/sessions.py#L1362-L1430
|
||
richardaecn/class-balanced-loss
|
1d7857208a2abc03d84e35a9d5383af8225d4b4d
|
tpu/models/official/densenet/densenet_model.py
|
python
|
densenet_imagenet_121
|
(inputs, is_training=True, num_classes=1001)
|
return densenet_imagenet_model(inputs, growth_rate, depths, num_classes,
is_training)
|
DenseNet 121.
|
DenseNet 121.
|
[
"DenseNet",
"121",
"."
] |
def densenet_imagenet_121(inputs, is_training=True, num_classes=1001):
"""DenseNet 121."""
depths = [6, 12, 24, 16]
growth_rate = 32
return densenet_imagenet_model(inputs, growth_rate, depths, num_classes,
is_training)
|
[
"def",
"densenet_imagenet_121",
"(",
"inputs",
",",
"is_training",
"=",
"True",
",",
"num_classes",
"=",
"1001",
")",
":",
"depths",
"=",
"[",
"6",
",",
"12",
",",
"24",
",",
"16",
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"(",
"inputs",
",",
"growth_rate",
",",
"depths",
",",
"num_classes",
",",
"is_training",
")"
] |
https://github.com/richardaecn/class-balanced-loss/blob/1d7857208a2abc03d84e35a9d5383af8225d4b4d/tpu/models/official/densenet/densenet_model.py#L176-L181
|
|
tensorflow/lingvo
|
ce10019243d954c3c3ebe739f7589b5eebfdf907
|
lingvo/jax/base_layer.py
|
python
|
BaseLayer.forward_update_var
|
(self, name: str, new_val: JTensor)
|
Update var 'name' in the forward pass.
|
Update var 'name' in the forward pass.
|
[
"Update",
"var",
"name",
"in",
"the",
"forward",
"pass",
"."
] |
def forward_update_var(self, name: str, new_val: JTensor) -> None:
"""Update var 'name' in the forward pass."""
assert name in self._private_vars
# TODO(yonghui): Maybe lift the constraint below.
# A param can only be updated once.
assert self._forward_updated_vars.dict[name] is None
# Only non-trainable variables can be updated in the forward pass.
assert var_not_trainable(self.vars[name])
self._forward_updated_vars.dict[name] = new_val
|
[
"def",
"forward_update_var",
"(",
"self",
",",
"name",
":",
"str",
",",
"new_val",
":",
"JTensor",
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":",
"assert",
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"# A param can only be updated once.",
"assert",
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".",
"_forward_updated_vars",
".",
"dict",
"[",
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"[",
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"]",
")",
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"_forward_updated_vars",
".",
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"[",
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"=",
"new_val"
] |
https://github.com/tensorflow/lingvo/blob/ce10019243d954c3c3ebe739f7589b5eebfdf907/lingvo/jax/base_layer.py#L957-L965
|
||
yuxiaokui/Intranet-Penetration
|
f57678a204840c83cbf3308e3470ae56c5ff514b
|
proxy/XX-Net/code/default/python27/1.0/lib/noarch/pycparser/c_parser.py
|
python
|
CParser.p_struct_or_union
|
(self, p)
|
struct_or_union : STRUCT
| UNION
|
struct_or_union : STRUCT
| UNION
|
[
"struct_or_union",
":",
"STRUCT",
"|",
"UNION"
] |
def p_struct_or_union(self, p):
""" struct_or_union : STRUCT
| UNION
"""
p[0] = p[1]
|
[
"def",
"p_struct_or_union",
"(",
"self",
",",
"p",
")",
":",
"p",
"[",
"0",
"]",
"=",
"p",
"[",
"1",
"]"
] |
https://github.com/yuxiaokui/Intranet-Penetration/blob/f57678a204840c83cbf3308e3470ae56c5ff514b/proxy/XX-Net/code/default/python27/1.0/lib/noarch/pycparser/c_parser.py#L811-L815
|
||
apache/tvm
|
6eb4ed813ebcdcd9558f0906a1870db8302ff1e0
|
python/tvm/_ffi/_ctypes/ndarray.py
|
python
|
NDArrayBase._copyto
|
(self, target_nd)
|
return target_nd
|
Internal function that implements copy to target ndarray.
|
Internal function that implements copy to target ndarray.
|
[
"Internal",
"function",
"that",
"implements",
"copy",
"to",
"target",
"ndarray",
"."
] |
def _copyto(self, target_nd):
"""Internal function that implements copy to target ndarray."""
check_call(_LIB.TVMArrayCopyFromTo(self.handle, target_nd.handle, None))
return target_nd
|
[
"def",
"_copyto",
"(",
"self",
",",
"target_nd",
")",
":",
"check_call",
"(",
"_LIB",
".",
"TVMArrayCopyFromTo",
"(",
"self",
".",
"handle",
",",
"target_nd",
".",
"handle",
",",
"None",
")",
")",
"return",
"target_nd"
] |
https://github.com/apache/tvm/blob/6eb4ed813ebcdcd9558f0906a1870db8302ff1e0/python/tvm/_ffi/_ctypes/ndarray.py#L88-L91
|
|
oracle/oci-python-sdk
|
3c1604e4e212008fb6718e2f68cdb5ef71fd5793
|
src/oci/_vendor/idna/core.py
|
python
|
valid_contexto
|
(label, pos, exception=False)
|
[] |
def valid_contexto(label, pos, exception=False):
cp_value = ord(label[pos])
if cp_value == 0x00b7:
if 0 < pos < len(label)-1:
if ord(label[pos - 1]) == 0x006c and ord(label[pos + 1]) == 0x006c:
return True
return False
elif cp_value == 0x0375:
if pos < len(label)-1 and len(label) > 1:
return _is_script(label[pos + 1], 'Greek')
return False
elif cp_value == 0x05f3 or cp_value == 0x05f4:
if pos > 0:
return _is_script(label[pos - 1], 'Hebrew')
return False
elif cp_value == 0x30fb:
for cp in label:
if cp == u'\u30fb':
continue
if _is_script(cp, 'Hiragana') or _is_script(cp, 'Katakana') or _is_script(cp, 'Han'):
return True
return False
elif 0x660 <= cp_value <= 0x669:
for cp in label:
if 0x6f0 <= ord(cp) <= 0x06f9:
return False
return True
elif 0x6f0 <= cp_value <= 0x6f9:
for cp in label:
if 0x660 <= ord(cp) <= 0x0669:
return False
return True
|
[
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":",
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] |
https://github.com/oracle/oci-python-sdk/blob/3c1604e4e212008fb6718e2f68cdb5ef71fd5793/src/oci/_vendor/idna/core.py#L198-L236
|
||||
i-pan/kaggle-rsna18
|
2db498fe99615d935aa676f04847d0c562fd8e46
|
models/DeformableConvNets/lib/dataset/imdb.py
|
python
|
IMDB.load_rpn_roidb
|
(self, gt_roidb)
|
return self.create_roidb_from_box_list(box_list, gt_roidb)
|
turn rpn detection boxes into roidb
:param gt_roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
:return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
|
turn rpn detection boxes into roidb
:param gt_roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
:return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
|
[
"turn",
"rpn",
"detection",
"boxes",
"into",
"roidb",
":",
"param",
"gt_roidb",
":",
"[",
"image_index",
"]",
"[",
"boxes",
"gt_classes",
"gt_overlaps",
"flipped",
"]",
":",
"return",
":",
"roidb",
":",
"[",
"image_index",
"]",
"[",
"boxes",
"gt_classes",
"gt_overlaps",
"flipped",
"]"
] |
def load_rpn_roidb(self, gt_roidb):
"""
turn rpn detection boxes into roidb
:param gt_roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
:return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
"""
box_list = self.load_rpn_data()
return self.create_roidb_from_box_list(box_list, gt_roidb)
|
[
"def",
"load_rpn_roidb",
"(",
"self",
",",
"gt_roidb",
")",
":",
"box_list",
"=",
"self",
".",
"load_rpn_data",
"(",
")",
"return",
"self",
".",
"create_roidb_from_box_list",
"(",
"box_list",
",",
"gt_roidb",
")"
] |
https://github.com/i-pan/kaggle-rsna18/blob/2db498fe99615d935aa676f04847d0c562fd8e46/models/DeformableConvNets/lib/dataset/imdb.py#L93-L100
|
|
alexa/alexa-skills-kit-sdk-for-python
|
079de73bc8b827be51ea700a3e4e19c29983a173
|
ask-sdk-local-debug/ask_sdk_local_debug/client/autobahn_client_protocol.py
|
python
|
AutobahnClientProtocol.onConnect
|
(self, response)
|
Callback fired directly after web-socket opening handshake when new
web-socket server connection was established.
:param response: web-socket connection response information.
:type response: instance of
:py:class:`autobahn_client.websocket.protocol.ConnectionResponse`
|
Callback fired directly after web-socket opening handshake when new
web-socket server connection was established.
|
[
"Callback",
"fired",
"directly",
"after",
"web",
"-",
"socket",
"opening",
"handshake",
"when",
"new",
"web",
"-",
"socket",
"server",
"connection",
"was",
"established",
"."
] |
def onConnect(self, response):
# type: (ConnectionResponse) -> None
"""Callback fired directly after web-socket opening handshake when new
web-socket server connection was established.
:param response: web-socket connection response information.
:type response: instance of
:py:class:`autobahn_client.websocket.protocol.ConnectionResponse`
"""
logger.info("*****Starting Skill Debug Session*****")
logger.info('*****Session will last for 1 hour*****')
|
[
"def",
"onConnect",
"(",
"self",
",",
"response",
")",
":",
"# type: (ConnectionResponse) -> None",
"logger",
".",
"info",
"(",
"\"*****Starting Skill Debug Session*****\"",
")",
"logger",
".",
"info",
"(",
"'*****Session will last for 1 hour*****'",
")"
] |
https://github.com/alexa/alexa-skills-kit-sdk-for-python/blob/079de73bc8b827be51ea700a3e4e19c29983a173/ask-sdk-local-debug/ask_sdk_local_debug/client/autobahn_client_protocol.py#L36-L46
|
||
PySimpleGUI/PySimpleGUI
|
6c0d1fb54f493d45e90180b322fbbe70f7a5af3c
|
DemoPrograms/Demo_Matplotlib_Animated.py
|
python
|
draw_figure
|
(canvas, figure, loc=(0, 0))
|
return figure_canvas_agg
|
[] |
def draw_figure(canvas, figure, loc=(0, 0)):
figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
figure_canvas_agg.draw()
figure_canvas_agg.get_tk_widget().pack(side='top', fill='both', expand=1)
return figure_canvas_agg
|
[
"def",
"draw_figure",
"(",
"canvas",
",",
"figure",
",",
"loc",
"=",
"(",
"0",
",",
"0",
")",
")",
":",
"figure_canvas_agg",
"=",
"FigureCanvasTkAgg",
"(",
"figure",
",",
"canvas",
")",
"figure_canvas_agg",
".",
"draw",
"(",
")",
"figure_canvas_agg",
".",
"get_tk_widget",
"(",
")",
".",
"pack",
"(",
"side",
"=",
"'top'",
",",
"fill",
"=",
"'both'",
",",
"expand",
"=",
"1",
")",
"return",
"figure_canvas_agg"
] |
https://github.com/PySimpleGUI/PySimpleGUI/blob/6c0d1fb54f493d45e90180b322fbbe70f7a5af3c/DemoPrograms/Demo_Matplotlib_Animated.py#L9-L13
|
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