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linxid/Machine_Learning_Study_Path
|
558e82d13237114bbb8152483977806fc0c222af
|
Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/requests/packages/chardet/sbcsgroupprober.py
|
python
|
SBCSGroupProber.__init__
|
(self)
|
[] |
def __init__(self):
CharSetGroupProber.__init__(self)
self._mProbers = [
SingleByteCharSetProber(Win1251CyrillicModel),
SingleByteCharSetProber(Koi8rModel),
SingleByteCharSetProber(Latin5CyrillicModel),
SingleByteCharSetProber(MacCyrillicModel),
SingleByteCharSetProber(Ibm866Model),
SingleByteCharSetProber(Ibm855Model),
SingleByteCharSetProber(Latin7GreekModel),
SingleByteCharSetProber(Win1253GreekModel),
SingleByteCharSetProber(Latin5BulgarianModel),
SingleByteCharSetProber(Win1251BulgarianModel),
SingleByteCharSetProber(Latin2HungarianModel),
SingleByteCharSetProber(Win1250HungarianModel),
SingleByteCharSetProber(TIS620ThaiModel),
]
hebrewProber = HebrewProber()
logicalHebrewProber = SingleByteCharSetProber(Win1255HebrewModel,
False, hebrewProber)
visualHebrewProber = SingleByteCharSetProber(Win1255HebrewModel, True,
hebrewProber)
hebrewProber.set_model_probers(logicalHebrewProber, visualHebrewProber)
self._mProbers.extend([hebrewProber, logicalHebrewProber,
visualHebrewProber])
self.reset()
|
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https://github.com/linxid/Machine_Learning_Study_Path/blob/558e82d13237114bbb8152483977806fc0c222af/Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/requests/packages/chardet/sbcsgroupprober.py#L43-L69
|
||||
AppScale/gts
|
46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9
|
AppServer/google/appengine/tools/bulkloader.py
|
python
|
ParseKey
|
(key_string)
|
return datastore.Key(encoded=key_string)
|
Turn a key stored in the database into a Key or None.
Args:
key_string: The string representation of a Key.
Returns:
A datastore.Key instance or None
|
Turn a key stored in the database into a Key or None.
|
[
"Turn",
"a",
"key",
"stored",
"in",
"the",
"database",
"into",
"a",
"Key",
"or",
"None",
"."
] |
def ParseKey(key_string):
"""Turn a key stored in the database into a Key or None.
Args:
key_string: The string representation of a Key.
Returns:
A datastore.Key instance or None
"""
if not key_string:
return None
if key_string == 'None':
return None
return datastore.Key(encoded=key_string)
|
[
"def",
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"key_string",
")",
":",
"if",
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":",
"return",
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"None",
"return",
"datastore",
".",
"Key",
"(",
"encoded",
"=",
"key_string",
")"
] |
https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/google/appengine/tools/bulkloader.py#L2599-L2612
|
|
riptano/ccm
|
ce612ea71587bf263ed513cb8f8d5dfcf7c8dadb
|
ccmlib/remote.py
|
python
|
SSHClient.execute_python_script
|
(self, script)
|
return output
|
Execute a python script of the remote server
:param script: Inline script to convert to a file and execute remotely
:return: The output of the script execution
|
Execute a python script of the remote server
|
[
"Execute",
"a",
"python",
"script",
"of",
"the",
"remote",
"server"
] |
def execute_python_script(self, script):
"""
Execute a python script of the remote server
:param script: Inline script to convert to a file and execute remotely
:return: The output of the script execution
"""
# Create the local file to copy to remote
file_handle, filename = tempfile.mkstemp()
temp_file = os.fdopen(file_handle, "wt")
temp_file.write(script)
temp_file.close()
# Put the file into the remote user directory
self.put(filename, "python_execute.py")
command = ["python", "python_execute.py"]
# Execute the python script on the remote system, clean up, and return the output
output = self.execute(command, False)
self.remove("python_execute.py")
os.unlink(filename)
return output
|
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] |
https://github.com/riptano/ccm/blob/ce612ea71587bf263ed513cb8f8d5dfcf7c8dadb/ccmlib/remote.py#L257-L278
|
|
idanr1986/cuckoo-droid
|
1350274639473d3d2b0ac740cae133ca53ab7444
|
analyzer/android/lib/api/androguard/dvm.py
|
python
|
FieldAnnotation.get_field_idx
|
(self)
|
return self.get_field_idx
|
Return the index into the field_ids list for the identity of the field being annotated
:rtype: int
|
Return the index into the field_ids list for the identity of the field being annotated
|
[
"Return",
"the",
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"the",
"field_ids",
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"for",
"the",
"identity",
"of",
"the",
"field",
"being",
"annotated"
] |
def get_field_idx(self) :
"""
Return the index into the field_ids list for the identity of the field being annotated
:rtype: int
"""
return self.get_field_idx
|
[
"def",
"get_field_idx",
"(",
"self",
")",
":",
"return",
"self",
".",
"get_field_idx"
] |
https://github.com/idanr1986/cuckoo-droid/blob/1350274639473d3d2b0ac740cae133ca53ab7444/analyzer/android/lib/api/androguard/dvm.py#L724-L730
|
|
asappresearch/flambe
|
98f10f859fe9223fd2d1d76d430f77cdbddc0956
|
flambe/tokenizer/label.py
|
python
|
LabelTokenizer.__init__
|
(self, multilabel_sep: Optional[str] = None)
|
Initialize the tokenizer.
Parameters
----------
multilabel_sep : Optional[str], optional
Used to split multi label inputs, if given
|
Initialize the tokenizer.
|
[
"Initialize",
"the",
"tokenizer",
"."
] |
def __init__(self, multilabel_sep: Optional[str] = None) -> None:
"""Initialize the tokenizer.
Parameters
----------
multilabel_sep : Optional[str], optional
Used to split multi label inputs, if given
"""
self.multilabel_sep = multilabel_sep
|
[
"def",
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"Optional",
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"str",
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"=",
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")",
"->",
"None",
":",
"self",
".",
"multilabel_sep",
"=",
"multilabel_sep"
] |
https://github.com/asappresearch/flambe/blob/98f10f859fe9223fd2d1d76d430f77cdbddc0956/flambe/tokenizer/label.py#L14-L23
|
||
jellyfin/jellyfin-kodi
|
e21e059e000f06890b33e2794a7e57959fdf19a3
|
jellyfin_kodi/objects/utils.py
|
python
|
get_grouped_set
|
()
|
return result.get('result', {}).get('value', False)
|
Get if boxsets should be grouped
|
Get if boxsets should be grouped
|
[
"Get",
"if",
"boxsets",
"should",
"be",
"grouped"
] |
def get_grouped_set():
''' Get if boxsets should be grouped
'''
result = JSONRPC('Settings.GetSettingValue').execute({'setting': "videolibrary.groupmoviesets"})
return result.get('result', {}).get('value', False)
|
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"def",
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"JSONRPC",
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",",
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"}",
")",
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",",
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] |
https://github.com/jellyfin/jellyfin-kodi/blob/e21e059e000f06890b33e2794a7e57959fdf19a3/jellyfin_kodi/objects/utils.py#L16-L21
|
|
brython-dev/brython
|
9cba5fb7f43a9b52fff13e89b403e02a1dfaa5f3
|
www/src/Lib/html/parser.py
|
python
|
HTMLParser.get_starttag_text
|
(self)
|
return self.__starttag_text
|
Return full source of start tag: '<...>'.
|
Return full source of start tag: '<...>'.
|
[
"Return",
"full",
"source",
"of",
"start",
"tag",
":",
"<",
"...",
">",
"."
] |
def get_starttag_text(self):
"""Return full source of start tag: '<...>'."""
return self.__starttag_text
|
[
"def",
"get_starttag_text",
"(",
"self",
")",
":",
"return",
"self",
".",
"__starttag_text"
] |
https://github.com/brython-dev/brython/blob/9cba5fb7f43a9b52fff13e89b403e02a1dfaa5f3/www/src/Lib/html/parser.py#L118-L120
|
|
hzlzh/AlfredWorkflow.com
|
7055f14f6922c80ea5943839eb0caff11ae57255
|
Sources/Workflows/Toggl-Time-Tracking/alp/request/requests/packages/oauthlib/oauth1/rfc5849/__init__.py
|
python
|
Server.validate_timestamp_and_nonce
|
(self, client_key, timestamp, nonce,
request_token=None, access_token=None)
|
Validates that the nonce has not been used before.
Per `Section 3.3`_ of the spec.
"A nonce is a random string, uniquely generated by the client to allow
the server to verify that a request has never been made before and
helps prevent replay attacks when requests are made over a non-secure
channel. The nonce value MUST be unique across all requests with the
same timestamp, client credentials, and token combinations."
.. _`Section 3.3`: http://tools.ietf.org/html/rfc5849#section-3.3
|
Validates that the nonce has not been used before.
|
[
"Validates",
"that",
"the",
"nonce",
"has",
"not",
"been",
"used",
"before",
"."
] |
def validate_timestamp_and_nonce(self, client_key, timestamp, nonce,
request_token=None, access_token=None):
"""Validates that the nonce has not been used before.
Per `Section 3.3`_ of the spec.
"A nonce is a random string, uniquely generated by the client to allow
the server to verify that a request has never been made before and
helps prevent replay attacks when requests are made over a non-secure
channel. The nonce value MUST be unique across all requests with the
same timestamp, client credentials, and token combinations."
.. _`Section 3.3`: http://tools.ietf.org/html/rfc5849#section-3.3
"""
raise NotImplementedError("Subclasses must implement this function.")
|
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https://github.com/hzlzh/AlfredWorkflow.com/blob/7055f14f6922c80ea5943839eb0caff11ae57255/Sources/Workflows/Toggl-Time-Tracking/alp/request/requests/packages/oauthlib/oauth1/rfc5849/__init__.py#L548-L563
|
||
darksigma/Fundamentals-of-Deep-Learning-Book
|
fa75f5186834b59de1950838b18803fdcef93101
|
archive/seq2seq/tmp_seq2seq_model.py
|
python
|
Seq2SeqModel.__init__
|
(self, source_vocab_size, target_vocab_size, buckets, size,
num_layers, max_gradient_norm, batch_size, learning_rate,
learning_rate_decay_factor, use_lstm=False,
num_samples=512, forward_only=False, dtype=tf.float32)
|
Create the model.
Args:
source_vocab_size: size of the source vocabulary.
target_vocab_size: size of the target vocabulary.
buckets: a list of pairs (I, O), where I specifies maximum input length
that will be processed in that bucket, and O specifies maximum output
length. Training instances that have inputs longer than I or outputs
longer than O will be pushed to the next bucket and padded accordingly.
We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
size: number of units in each layer of the model.
num_layers: number of layers in the model.
max_gradient_norm: gradients will be clipped to maximally this norm.
batch_size: the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.
learning_rate: learning rate to start with.
learning_rate_decay_factor: decay learning rate by this much when needed.
use_lstm: if true, we use LSTM cells instead of GRU cells.
num_samples: number of samples for sampled softmax.
forward_only: if set, we do not construct the backward pass in the model.
|
Create the model.
|
[
"Create",
"the",
"model",
"."
] |
def __init__(self, source_vocab_size, target_vocab_size, buckets, size,
num_layers, max_gradient_norm, batch_size, learning_rate,
learning_rate_decay_factor, use_lstm=False,
num_samples=512, forward_only=False, dtype=tf.float32):
"""Create the model.
Args:
source_vocab_size: size of the source vocabulary.
target_vocab_size: size of the target vocabulary.
buckets: a list of pairs (I, O), where I specifies maximum input length
that will be processed in that bucket, and O specifies maximum output
length. Training instances that have inputs longer than I or outputs
longer than O will be pushed to the next bucket and padded accordingly.
We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
size: number of units in each layer of the model.
num_layers: number of layers in the model.
max_gradient_norm: gradients will be clipped to maximally this norm.
batch_size: the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.
learning_rate: learning rate to start with.
learning_rate_decay_factor: decay learning rate by this much when needed.
use_lstm: if true, we use LSTM cells instead of GRU cells.
num_samples: number of samples for sampled softmax.
forward_only: if set, we do not construct the backward pass in the model.
"""
self.source_vocab_size = source_vocab_size
self.target_vocab_size = target_vocab_size
self.buckets = buckets
self.batch_size = batch_size
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
# If we use sampled softmax, we need an output projection.
output_projection = None
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
if num_samples > 0 and num_samples < self.target_vocab_size:
with tf.device("/cpu:0"):
w = tf.get_variable("proj_w", [size, self.target_vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.target_vocab_size])
output_projection = (w, b)
def sampled_loss(inputs, labels):
with tf.device("/cpu:0"):
labels = tf.reshape(labels, [-1, 1])
return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
self.target_vocab_size)
softmax_loss_function = sampled_loss
# Create the internal multi-layer cell for our RNN.
single_cell = tf.nn.rnn_cell.GRUCell(size)
if use_lstm:
single_cell = tf.nn.rnn_cell.BasicLSTMCell(size)
cell = single_cell
if num_layers > 1:
cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs, decoder_inputs, cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=size,
output_projection=output_projection,
feed_previous=do_decode)
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="encoder{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}".format(i)))
# Our targets are decoder inputs shifted by one.
targets = [self.decoder_inputs[i + 1]
for i in xrange(len(self.decoder_inputs) - 1)]
# Training outputs and losses.
if forward_only:
self.outputs, self.losses = seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=softmax_loss_function)
# If we use output projection, we need to project outputs for decoding.
if output_projection is not None:
for b in xrange(len(buckets)):
self.outputs[b] = [
tf.matmul(output, output_projection[0]) + output_projection[1]
for output in self.outputs[b]
]
else:
self.outputs, self.losses = seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets,
lambda x, y: seq2seq_f(x, y, False),
softmax_loss_function=softmax_loss_function)
# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.updates = []
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[b], params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients,
max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step))
self.saver = tf.train.Saver(tf.all_variables())
|
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https://github.com/darksigma/Fundamentals-of-Deep-Learning-Book/blob/fa75f5186834b59de1950838b18803fdcef93101/archive/seq2seq/tmp_seq2seq_model.py#L47-L169
|
||
gramps-project/gramps
|
04d4651a43eb210192f40a9f8c2bad8ee8fa3753
|
gramps/gui/widgets/undoablebuffer.py
|
python
|
UndoableBuffer.redo
|
(self)
|
redo inserts or deletions
redone actions are moved to undo stack
|
redo inserts or deletions
|
[
"redo",
"inserts",
"or",
"deletions"
] |
def redo(self):
"""redo inserts or deletions
redone actions are moved to undo stack"""
if not self.redo_stack:
return
self.begin_not_undoable_action()
self.undo_in_progress = True
redo_action = self.redo_stack.pop()
self.undo_stack.append(redo_action)
if isinstance(redo_action, self.insertclass):
self._redo_insert(redo_action)
elif isinstance(redo_action, self.deleteclass):
self._redo_delete(redo_action)
else:
self._handle_redo(redo_action)
self.end_not_undoable_action()
self.undo_in_progress = False
|
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] |
https://github.com/gramps-project/gramps/blob/04d4651a43eb210192f40a9f8c2bad8ee8fa3753/gramps/gui/widgets/undoablebuffer.py#L272-L289
|
||
DSE-MSU/DeepRobust
|
2bcde200a5969dae32cddece66206a52c87c43e8
|
deeprobust/image/defense/AWP.py
|
python
|
pgd_AWP.__init__
|
(self, model, proxy, proxy_optim, gamma)
|
[] |
def __init__(self, model, proxy, proxy_optim, gamma):
super(pgd_AWP, self).__init__()
self.model = model
self.proxy = proxy
self.proxy_optim = proxy_optim
self.gamma = gamma
|
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https://github.com/DSE-MSU/DeepRobust/blob/2bcde200a5969dae32cddece66206a52c87c43e8/deeprobust/image/defense/AWP.py#L45-L50
|
||||
microsoft/nni
|
31f11f51249660930824e888af0d4e022823285c
|
nni/algorithms/compression/v2/pytorch/pruning/movement_pruner.py
|
python
|
MovementPruner._wrap_modules
|
(self, layer: LayerInfo, config: Dict)
|
return wrapper
|
Create a wrapper module to replace the original one.
Different from the parent function, use `PrunerScoredModuleWrapper` instead of `PrunerModuleWrapper`.
Parameters
----------
layer
The layer to instrument the mask.
config
The configuration for generating the mask.
|
Create a wrapper module to replace the original one.
Different from the parent function, use `PrunerScoredModuleWrapper` instead of `PrunerModuleWrapper`.
|
[
"Create",
"a",
"wrapper",
"module",
"to",
"replace",
"the",
"original",
"one",
".",
"Different",
"from",
"the",
"parent",
"function",
"use",
"PrunerScoredModuleWrapper",
"instead",
"of",
"PrunerModuleWrapper",
"."
] |
def _wrap_modules(self, layer: LayerInfo, config: Dict):
"""
Create a wrapper module to replace the original one.
Different from the parent function, use `PrunerScoredModuleWrapper` instead of `PrunerModuleWrapper`.
Parameters
----------
layer
The layer to instrument the mask.
config
The configuration for generating the mask.
"""
_logger.debug("Module detected to compress : %s.", layer.name)
wrapper = PrunerScoredModuleWrapper(layer.module, layer.name, config, self)
assert hasattr(layer.module, 'weight'), "module %s does not have 'weight' attribute" % layer.name
# move newly registered buffers to the same device of weight
wrapper.to(layer.module.weight.device)
return wrapper
|
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https://github.com/microsoft/nni/blob/31f11f51249660930824e888af0d4e022823285c/nni/algorithms/compression/v2/pytorch/pruning/movement_pruner.py#L254-L271
|
|
plaid/plaid-python
|
8c60fca608e426f3ff30da8857775946d29e122c
|
plaid/model/account_base.py
|
python
|
AccountBase.__init__
|
(self, account_id, balances, mask, name, official_name, type, subtype, *args, **kwargs)
|
AccountBase - a model defined in OpenAPI
Args:
account_id (str): Plaid’s unique identifier for the account. This value will not change unless Plaid can't reconcile the account with the data returned by the financial institution. This may occur, for example, when the name of the account changes. If this happens a new `account_id` will be assigned to the account. The `account_id` can also change if the `access_token` is deleted and the same credentials that were used to generate that `access_token` are used to generate a new `access_token` on a later date. In that case, the new `account_id` will be different from the old `account_id`. If an account with a specific `account_id` disappears instead of changing, the account is likely closed. Closed accounts are not returned by the Plaid API. Like all Plaid identifiers, the `account_id` is case sensitive.
balances (AccountBalance):
mask (str, none_type): The last 2-4 alphanumeric characters of an account's official account number. Note that the mask may be non-unique between an Item's accounts, and it may also not match the mask that the bank displays to the user.
name (str): The name of the account, either assigned by the user or by the financial institution itself
official_name (str, none_type): The official name of the account as given by the financial institution
type (AccountType):
subtype (AccountSubtype):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
verification_status (str): The current verification status of an Auth Item initiated through Automated or Manual micro-deposits. Returned for Auth Items only. `pending_automatic_verification`: The Item is pending automatic verification `pending_manual_verification`: The Item is pending manual micro-deposit verification. Items remain in this state until the user successfully verifies the two amounts. `automatically_verified`: The Item has successfully been automatically verified `manually_verified`: The Item has successfully been manually verified `verification_expired`: Plaid was unable to automatically verify the deposit within 7 calendar days and will no longer attempt to validate the Item. Users may retry by submitting their information again through Link. `verification_failed`: The Item failed manual micro-deposit verification because the user exhausted all 3 verification attempts. Users may retry by submitting their information again through Link. . [optional] # noqa: E501
|
AccountBase - a model defined in OpenAPI
|
[
"AccountBase",
"-",
"a",
"model",
"defined",
"in",
"OpenAPI"
] |
def __init__(self, account_id, balances, mask, name, official_name, type, subtype, *args, **kwargs): # noqa: E501
"""AccountBase - a model defined in OpenAPI
Args:
account_id (str): Plaid’s unique identifier for the account. This value will not change unless Plaid can't reconcile the account with the data returned by the financial institution. This may occur, for example, when the name of the account changes. If this happens a new `account_id` will be assigned to the account. The `account_id` can also change if the `access_token` is deleted and the same credentials that were used to generate that `access_token` are used to generate a new `access_token` on a later date. In that case, the new `account_id` will be different from the old `account_id`. If an account with a specific `account_id` disappears instead of changing, the account is likely closed. Closed accounts are not returned by the Plaid API. Like all Plaid identifiers, the `account_id` is case sensitive.
balances (AccountBalance):
mask (str, none_type): The last 2-4 alphanumeric characters of an account's official account number. Note that the mask may be non-unique between an Item's accounts, and it may also not match the mask that the bank displays to the user.
name (str): The name of the account, either assigned by the user or by the financial institution itself
official_name (str, none_type): The official name of the account as given by the financial institution
type (AccountType):
subtype (AccountSubtype):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
verification_status (str): The current verification status of an Auth Item initiated through Automated or Manual micro-deposits. Returned for Auth Items only. `pending_automatic_verification`: The Item is pending automatic verification `pending_manual_verification`: The Item is pending manual micro-deposit verification. Items remain in this state until the user successfully verifies the two amounts. `automatically_verified`: The Item has successfully been automatically verified `manually_verified`: The Item has successfully been manually verified `verification_expired`: Plaid was unable to automatically verify the deposit within 7 calendar days and will no longer attempt to validate the Item. Users may retry by submitting their information again through Link. `verification_failed`: The Item failed manual micro-deposit verification because the user exhausted all 3 verification attempts. Users may retry by submitting their information again through Link. . [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
self.account_id = account_id
self.balances = balances
self.mask = mask
self.name = name
self.official_name = official_name
self.type = type
self.subtype = subtype
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
|
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] |
https://github.com/plaid/plaid-python/blob/8c60fca608e426f3ff30da8857775946d29e122c/plaid/model/account_base.py#L135-L218
|
||
krintoxi/NoobSec-Toolkit
|
38738541cbc03cedb9a3b3ed13b629f781ad64f6
|
NoobSecToolkit - MAC OSX/tools/sqli/thirdparty/odict/odict.py
|
python
|
Keys.__setitem__
|
(self, index, name)
|
You cannot assign to keys, but you can do slice assignment to re-order
them.
You can only do slice assignment if the new set of keys is a reordering
of the original set.
|
You cannot assign to keys, but you can do slice assignment to re-order
them.
|
[
"You",
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"assign",
"to",
"keys",
"but",
"you",
"can",
"do",
"slice",
"assignment",
"to",
"re",
"-",
"order",
"them",
"."
] |
def __setitem__(self, index, name):
"""
You cannot assign to keys, but you can do slice assignment to re-order
them.
You can only do slice assignment if the new set of keys is a reordering
of the original set.
"""
if isinstance(index, types.SliceType):
# FIXME: efficiency?
# check length is the same
indexes = range(len(self._main._sequence))[index]
if len(indexes) != len(name):
raise ValueError('attempt to assign sequence of size %s '
'to slice of size %s' % (len(name), len(indexes)))
# check they are the same keys
# FIXME: Use set
old_keys = self._main._sequence[index]
new_keys = list(name)
old_keys.sort()
new_keys.sort()
if old_keys != new_keys:
raise KeyError('Keylist is not the same as current keylist.')
orig_vals = [self._main[k] for k in name]
del self._main[index]
vals = zip(indexes, name, orig_vals)
vals.sort()
for i, k, v in vals:
if self._main.strict and k in self._main:
raise ValueError('slice assignment must be from '
'unique keys')
self._main.insert(i, k, v)
else:
raise ValueError('Cannot assign to keys')
|
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",",
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https://github.com/krintoxi/NoobSec-Toolkit/blob/38738541cbc03cedb9a3b3ed13b629f781ad64f6/NoobSecToolkit - MAC OSX/tools/sqli/thirdparty/odict/odict.py#L898-L931
|
||
buke/GreenOdoo
|
3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df
|
runtime/python/lib/python2.7/site-packages/gdata/apps/multidomain/data.py
|
python
|
AliasEntry.SetUserEmail
|
(self, value)
|
Set the user email address of this Alias object.
Args:
value: string The new user email address to give this object.
|
Set the user email address of this Alias object.
|
[
"Set",
"the",
"user",
"email",
"address",
"of",
"this",
"Alias",
"object",
"."
] |
def SetUserEmail(self, value):
"""Set the user email address of this Alias object.
Args:
value: string The new user email address to give this object.
"""
self._SetProperty(USER_EMAIL, value)
|
[
"def",
"SetUserEmail",
"(",
"self",
",",
"value",
")",
":",
"self",
".",
"_SetProperty",
"(",
"USER_EMAIL",
",",
"value",
")"
] |
https://github.com/buke/GreenOdoo/blob/3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df/runtime/python/lib/python2.7/site-packages/gdata/apps/multidomain/data.py#L366-L372
|
||
a1600012888/YOPO-You-Only-Propagate-Once
|
b8ae668be829a0ca50647ae21676a932a9634365
|
lib/base_model/network.py
|
python
|
PreActBlock.forward
|
(self, x)
|
return out
|
[] |
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out += shortcut
return out
|
[
"def",
"forward",
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",",
"x",
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",",
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"(",
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")",
")",
")",
"out",
"+=",
"shortcut",
"return",
"out"
] |
https://github.com/a1600012888/YOPO-You-Only-Propagate-Once/blob/b8ae668be829a0ca50647ae21676a932a9634365/lib/base_model/network.py#L28-L34
|
|||
vt-vl-lab/DF-Net
|
53f4e016b881d55624042f755235eb8d7d248209
|
core/UnFlow/src/e2eflow/util.py
|
python
|
config_dict
|
(config_path=CONFIG_PATH)
|
return d
|
Returns the config as dictionary,
where the elements have intuitively correct types.
|
Returns the config as dictionary,
where the elements have intuitively correct types.
|
[
"Returns",
"the",
"config",
"as",
"dictionary",
"where",
"the",
"elements",
"have",
"intuitively",
"correct",
"types",
"."
] |
def config_dict(config_path=CONFIG_PATH):
"""Returns the config as dictionary,
where the elements have intuitively correct types.
"""
config = configparser.ConfigParser()
config.read(config_path)
d = dict()
for section_key in config.sections():
sd = dict()
section = config[section_key]
for key in section:
val = section[key]
try:
sd[key] = int(val)
except ValueError:
try:
sd[key] = float(val)
except ValueError:
try:
sd[key] = section.getboolean(key)
except ValueError:
sd[key] = val
d[section_key] = sd
return d
|
[
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https://github.com/vt-vl-lab/DF-Net/blob/53f4e016b881d55624042f755235eb8d7d248209/core/UnFlow/src/e2eflow/util.py#L37-L62
|
|
beeware/ouroboros
|
a29123c6fab6a807caffbb7587cf548e0c370296
|
ouroboros/multiprocessing/pool.py
|
python
|
Pool._join_exited_workers
|
(self)
|
return cleaned
|
Cleanup after any worker processes which have exited due to reaching
their specified lifetime. Returns True if any workers were cleaned up.
|
Cleanup after any worker processes which have exited due to reaching
their specified lifetime. Returns True if any workers were cleaned up.
|
[
"Cleanup",
"after",
"any",
"worker",
"processes",
"which",
"have",
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"due",
"to",
"reaching",
"their",
"specified",
"lifetime",
".",
"Returns",
"True",
"if",
"any",
"workers",
"were",
"cleaned",
"up",
"."
] |
def _join_exited_workers(self):
"""Cleanup after any worker processes which have exited due to reaching
their specified lifetime. Returns True if any workers were cleaned up.
"""
cleaned = False
for i in reversed(range(len(self._pool))):
worker = self._pool[i]
if worker.exitcode is not None:
# worker exited
util.debug('cleaning up worker %d' % i)
worker.join()
cleaned = True
del self._pool[i]
return cleaned
|
[
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"True",
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"[",
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"]",
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https://github.com/beeware/ouroboros/blob/a29123c6fab6a807caffbb7587cf548e0c370296/ouroboros/multiprocessing/pool.py#L204-L217
|
|
isnowfy/pydown
|
71ecc891868cd2a34b7e5fe662c99474f2d0fd7f
|
markdown/extensions/abbr.py
|
python
|
AbbrExtension.extendMarkdown
|
(self, md, md_globals)
|
Insert AbbrPreprocessor before ReferencePreprocessor.
|
Insert AbbrPreprocessor before ReferencePreprocessor.
|
[
"Insert",
"AbbrPreprocessor",
"before",
"ReferencePreprocessor",
"."
] |
def extendMarkdown(self, md, md_globals):
""" Insert AbbrPreprocessor before ReferencePreprocessor. """
md.preprocessors.add('abbr', AbbrPreprocessor(md), '<reference')
|
[
"def",
"extendMarkdown",
"(",
"self",
",",
"md",
",",
"md_globals",
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":",
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"preprocessors",
".",
"add",
"(",
"'abbr'",
",",
"AbbrPreprocessor",
"(",
"md",
")",
",",
"'<reference'",
")"
] |
https://github.com/isnowfy/pydown/blob/71ecc891868cd2a34b7e5fe662c99474f2d0fd7f/markdown/extensions/abbr.py#L36-L38
|
||
michael-lazar/rtv
|
b3d5bf16a70dba685e05db35308cc8a6d2b7f7aa
|
rtv/packages/praw/__init__.py
|
python
|
AuthenticatedReddit.is_logged_in
|
(self)
|
return self._authentication is True
|
Return True when the session is authenticated via username/password.
Username and passwords are provided via
:meth:`~praw.__init__.AuthenticatedReddit.login`.
|
Return True when the session is authenticated via username/password.
|
[
"Return",
"True",
"when",
"the",
"session",
"is",
"authenticated",
"via",
"username",
"/",
"password",
"."
] |
def is_logged_in(self):
"""Return True when the session is authenticated via username/password.
Username and passwords are provided via
:meth:`~praw.__init__.AuthenticatedReddit.login`.
"""
return self._authentication is True
|
[
"def",
"is_logged_in",
"(",
"self",
")",
":",
"return",
"self",
".",
"_authentication",
"is",
"True"
] |
https://github.com/michael-lazar/rtv/blob/b3d5bf16a70dba685e05db35308cc8a6d2b7f7aa/rtv/packages/praw/__init__.py#L1455-L1462
|
|
kaaedit/kaa
|
e6a8819a5ecba04b7db8303bd5736b5a7c9b822d
|
kaa/filetype/python/pythonmode.py
|
python
|
PythonMode.init_themes
|
(self)
|
[] |
def init_themes(self):
super().init_themes()
self.themes.append(PythonThemes)
|
[
"def",
"init_themes",
"(",
"self",
")",
":",
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"(",
")",
".",
"init_themes",
"(",
")",
"self",
".",
"themes",
".",
"append",
"(",
"PythonThemes",
")"
] |
https://github.com/kaaedit/kaa/blob/e6a8819a5ecba04b7db8303bd5736b5a7c9b822d/kaa/filetype/python/pythonmode.py#L96-L98
|
||||
JiYou/openstack
|
8607dd488bde0905044b303eb6e52bdea6806923
|
packages/source/nova/nova/db/sqlalchemy/api.py
|
python
|
instance_info_cache_delete
|
(context, instance_uuid)
|
Deletes an existing instance_info_cache record
:param instance_uuid: = uuid of the instance tied to the cache record
:param session: = optional session object
|
Deletes an existing instance_info_cache record
|
[
"Deletes",
"an",
"existing",
"instance_info_cache",
"record"
] |
def instance_info_cache_delete(context, instance_uuid):
"""Deletes an existing instance_info_cache record
:param instance_uuid: = uuid of the instance tied to the cache record
:param session: = optional session object
"""
model_query(context, models.InstanceInfoCache).\
filter_by(instance_uuid=instance_uuid).\
soft_delete()
|
[
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"instance_info_cache_delete",
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"context",
",",
"instance_uuid",
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":",
"model_query",
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"filter_by",
"(",
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"=",
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"soft_delete",
"(",
")"
] |
https://github.com/JiYou/openstack/blob/8607dd488bde0905044b303eb6e52bdea6806923/packages/source/nova/nova/db/sqlalchemy/api.py#L2027-L2035
|
||
ifwe/digsby
|
f5fe00244744aa131e07f09348d10563f3d8fa99
|
digsby/lib/pyxmpp/clientstream.py
|
python
|
ClientStream.do_bind
|
(self,stanza)
|
Do the resource binding requested by a client connected.
[server only]
:Parameters:
- `stanza`: resource binding request stanza.
:Types:
- `stanza`: `pyxmpp.Iq`
|
Do the resource binding requested by a client connected.
|
[
"Do",
"the",
"resource",
"binding",
"requested",
"by",
"a",
"client",
"connected",
"."
] |
def do_bind(self,stanza):
"""Do the resource binding requested by a client connected.
[server only]
:Parameters:
- `stanza`: resource binding request stanza.
:Types:
- `stanza`: `pyxmpp.Iq`"""
fr=stanza.get_from()
if fr and fr!=self.peer:
r=stanza.make_error_response("forbidden")
self.send(r)
r.free()
return
resource_n=stanza.xpath_eval("bind:bind/bind:resource",{"bind":BIND_NS})
if resource_n:
resource=resource_n[0].getContent()
else:
resource="auto"
if not resource:
r=stanza.make_error_response("bad-request")
else:
self.unset_iq_set_handler("bind",BIND_NS)
r=stanza.make_result_response()
self.peer.set_resource(resource)
q=r.new_query(BIND_NS,"bind")
q.newTextChild(None,"jid",to_utf8(self.peer.as_unicode()))
self.state_change("authorized",self.peer)
r.set_to(None)
self.send(r)
r.free()
|
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https://github.com/ifwe/digsby/blob/f5fe00244744aa131e07f09348d10563f3d8fa99/digsby/lib/pyxmpp/clientstream.py#L219-L250
|
||
hhyo/Archery
|
c9b057d37e47894ca8531e5cd10afdb064cd0122
|
sql/utils/sql_utils.py
|
python
|
get_base_sqlitem_list
|
(full_sql)
|
return list
|
把参数 full_sql 转变为 SqlItem列表
:param full_sql: 完整sql字符串, 每个SQL以分号;间隔, 不包含plsql执行块和plsql对象定义块
:return: SqlItem对象列表
|
把参数 full_sql 转变为 SqlItem列表
:param full_sql: 完整sql字符串, 每个SQL以分号;间隔, 不包含plsql执行块和plsql对象定义块
:return: SqlItem对象列表
|
[
"把参数",
"full_sql",
"转变为",
"SqlItem列表",
":",
"param",
"full_sql",
":",
"完整sql字符串",
"每个SQL以分号",
";",
"间隔",
"不包含plsql执行块和plsql对象定义块",
":",
"return",
":",
"SqlItem对象列表"
] |
def get_base_sqlitem_list(full_sql):
''' 把参数 full_sql 转变为 SqlItem列表
:param full_sql: 完整sql字符串, 每个SQL以分号;间隔, 不包含plsql执行块和plsql对象定义块
:return: SqlItem对象列表
'''
list = []
for statement in sqlparse.split(full_sql):
statement = sqlparse.format(statement, strip_comments=True, reindent=True, keyword_case='lower')
if len(statement) <= 0:
continue
item = SqlItem(statement=statement)
list.append(item)
return list
|
[
"def",
"get_base_sqlitem_list",
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"full_sql",
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"list",
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"[",
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".",
"append",
"(",
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")",
"return",
"list"
] |
https://github.com/hhyo/Archery/blob/c9b057d37e47894ca8531e5cd10afdb064cd0122/sql/utils/sql_utils.py#L133-L145
|
|
sfepy/sfepy
|
02ec7bb2ab39ee1dfe1eb4cd509f0ffb7dcc8b25
|
sfepy/discrete/materials.py
|
python
|
Material.reset
|
(self)
|
Clear all data created by a call to ``time_update()``, set
``self.mode`` to ``None``.
|
Clear all data created by a call to ``time_update()``, set
``self.mode`` to ``None``.
|
[
"Clear",
"all",
"data",
"created",
"by",
"a",
"call",
"to",
"time_update",
"()",
"set",
"self",
".",
"mode",
"to",
"None",
"."
] |
def reset(self):
"""
Clear all data created by a call to ``time_update()``, set
``self.mode`` to ``None``.
"""
self.mode = None
self.datas = {}
self.special_names = set()
self.constant_names = set()
self.extra_args = {}
|
[
"def",
"reset",
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"self",
")",
":",
"self",
".",
"mode",
"=",
"None",
"self",
".",
"datas",
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".",
"constant_names",
"=",
"set",
"(",
")",
"self",
".",
"extra_args",
"=",
"{",
"}"
] |
https://github.com/sfepy/sfepy/blob/02ec7bb2ab39ee1dfe1eb4cd509f0ffb7dcc8b25/sfepy/discrete/materials.py#L344-L353
|
||
analysiscenter/batchflow
|
294747da0bca309785f925be891441fdd824e9fa
|
batchflow/pipeline_executor.py
|
python
|
PipelineExecutor.run
|
(self, *args, **kwargs)
|
return self.pipeline
|
Execute all lazy actions for each batch in the dataset
See also
--------
:meth:`~.PipelineExecutor.gen_batch`
|
Execute all lazy actions for each batch in the dataset
|
[
"Execute",
"all",
"lazy",
"actions",
"for",
"each",
"batch",
"in",
"the",
"dataset"
] |
def run(self, *args, **kwargs):
""" Execute all lazy actions for each batch in the dataset
See also
--------
:meth:`~.PipelineExecutor.gen_batch`
"""
for _ in self.pipeline.gen_batch(*args, **kwargs):
pass
return self.pipeline
|
[
"def",
"run",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
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"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"pass",
"return",
"self",
".",
"pipeline"
] |
https://github.com/analysiscenter/batchflow/blob/294747da0bca309785f925be891441fdd824e9fa/batchflow/pipeline_executor.py#L254-L264
|
|
edfungus/Crouton
|
ada98b3930192938a48909072b45cb84b945f875
|
clients/python_clients/cf_demo_client/cf_env/lib/python2.7/site-packages/pip/_vendor/ipaddress.py
|
python
|
_BaseV4._string_from_ip_int
|
(cls, ip_int)
|
return '.'.join(_compat_str(struct.unpack(b'!B', b)[0]
if isinstance(b, bytes)
else b)
for b in _compat_to_bytes(ip_int, 4, 'big'))
|
Turns a 32-bit integer into dotted decimal notation.
Args:
ip_int: An integer, the IP address.
Returns:
The IP address as a string in dotted decimal notation.
|
Turns a 32-bit integer into dotted decimal notation.
|
[
"Turns",
"a",
"32",
"-",
"bit",
"integer",
"into",
"dotted",
"decimal",
"notation",
"."
] |
def _string_from_ip_int(cls, ip_int):
"""Turns a 32-bit integer into dotted decimal notation.
Args:
ip_int: An integer, the IP address.
Returns:
The IP address as a string in dotted decimal notation.
"""
return '.'.join(_compat_str(struct.unpack(b'!B', b)[0]
if isinstance(b, bytes)
else b)
for b in _compat_to_bytes(ip_int, 4, 'big'))
|
[
"def",
"_string_from_ip_int",
"(",
"cls",
",",
"ip_int",
")",
":",
"return",
"'.'",
".",
"join",
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"_compat_to_bytes",
"(",
"ip_int",
",",
"4",
",",
"'big'",
")",
")"
] |
https://github.com/edfungus/Crouton/blob/ada98b3930192938a48909072b45cb84b945f875/clients/python_clients/cf_demo_client/cf_env/lib/python2.7/site-packages/pip/_vendor/ipaddress.py#L1309-L1322
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/jinja2/filters.py
|
python
|
do_wordcount
|
(s)
|
return len(_word_re.findall(s))
|
Count the words in that string.
|
Count the words in that string.
|
[
"Count",
"the",
"words",
"in",
"that",
"string",
"."
] |
def do_wordcount(s):
"""Count the words in that string."""
return len(_word_re.findall(s))
|
[
"def",
"do_wordcount",
"(",
"s",
")",
":",
"return",
"len",
"(",
"_word_re",
".",
"findall",
"(",
"s",
")",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/jinja2/filters.py#L636-L638
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/Python-2.7.9/Parser/asdl.py
|
python
|
ASDLParser.typestring
|
(self, tok)
|
return tok.type
|
[] |
def typestring(self, tok):
return tok.type
|
[
"def",
"typestring",
"(",
"self",
",",
"tok",
")",
":",
"return",
"tok",
".",
"type"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/Python-2.7.9/Parser/asdl.py#L105-L106
|
|||
roam-qgis/Roam
|
6bfa836a2735f611b7f26de18ae4a4581f7e83ef
|
scripts/fabricate.py
|
python
|
AtimesRunner._age_atimes
|
(self, filetimes)
|
return adjusted
|
Age files' atimes and mtimes to be at least FAT_xx_resolution old.
Only adjust if the given filetimes dict says it isn't that old,
and return a new dict of filetimes with the ages adjusted.
|
Age files' atimes and mtimes to be at least FAT_xx_resolution old.
Only adjust if the given filetimes dict says it isn't that old,
and return a new dict of filetimes with the ages adjusted.
|
[
"Age",
"files",
"atimes",
"and",
"mtimes",
"to",
"be",
"at",
"least",
"FAT_xx_resolution",
"old",
".",
"Only",
"adjust",
"if",
"the",
"given",
"filetimes",
"dict",
"says",
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"that",
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"and",
"return",
"a",
"new",
"dict",
"of",
"filetimes",
"with",
"the",
"ages",
"adjusted",
"."
] |
def _age_atimes(self, filetimes):
""" Age files' atimes and mtimes to be at least FAT_xx_resolution old.
Only adjust if the given filetimes dict says it isn't that old,
and return a new dict of filetimes with the ages adjusted. """
adjusted = {}
now = time.time()
for filename, entry in filetimes.items():
if now-entry[0] < FAT_atime_resolution or now-entry[1] < FAT_mtime_resolution:
entry = entry[0] - FAT_atime_resolution, entry[1] - FAT_mtime_resolution
self._utime(filename, entry[0], entry[1])
adjusted[filename] = entry
return adjusted
|
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https://github.com/roam-qgis/Roam/blob/6bfa836a2735f611b7f26de18ae4a4581f7e83ef/scripts/fabricate.py#L408-L419
|
|
houtianze/bypy
|
10fd0f18378174a775a05a366cc20ba6609f96c6
|
bypy/bypy.py
|
python
|
ByPy.__proceed_remote_gather
|
(self, walkresult, remotepath, dirjs, filejs, args = None)
|
return walkresult
|
[] |
def __proceed_remote_gather(self, walkresult, remotepath, dirjs, filejs, args = None):
# NOTE: the '+ 1' is due to the trailing slash '/'
# be careful about the trailing '/', it bit me once, bitterly
rootrdir = args
rootlen = len(rootrdir)
dlen = len(remotepath) + 1
for d in dirjs:
self.__remote_dir_contents.get(remotepath[rootlen:]).add(
d['path'][dlen:], PathDictTree('D', size = d['size'], md5 = d['md5'] if 'md5' in d else ''))
# Baidu made another fuck up here:
# f['md5'] doesn't have the correct MD5 value, but f['block_list'][0] has
# This makes no sense, and I'm not going to change the correct code to adapt its wrong behaviors
# --- Code below for reference ---
# fmd5 = f['md5']
# bl = 'block_list'
# if bl in f and f[bl]:
# fmd5 = f[bl][0]
# f['path'][dlen:], PathDictTree('F', size = f['size'], md5 = fmd5))
for f in filejs:
self.__remote_dir_contents.get(remotepath[rootlen:]).add(
f['path'][dlen:], PathDictTree('F', size = f['size'], md5 = f['md5']))
return walkresult
|
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https://github.com/houtianze/bypy/blob/10fd0f18378174a775a05a366cc20ba6609f96c6/bypy/bypy.py#L2670-L2693
|
|||
hzlzh/AlfredWorkflow.com
|
7055f14f6922c80ea5943839eb0caff11ae57255
|
Sources/Workflows/Toggl-Time-Tracking/alp/request/requests/packages/urllib3/packages/ordered_dict.py
|
python
|
OrderedDict.clear
|
(self)
|
od.clear() -> None. Remove all items from od.
|
od.clear() -> None. Remove all items from od.
|
[
"od",
".",
"clear",
"()",
"-",
">",
"None",
".",
"Remove",
"all",
"items",
"from",
"od",
"."
] |
def clear(self):
'od.clear() -> None. Remove all items from od.'
try:
for node in self.__map.itervalues():
del node[:]
root = self.__root
root[:] = [root, root, None]
self.__map.clear()
except AttributeError:
pass
dict.clear(self)
|
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"AttributeError",
":",
"pass",
"dict",
".",
"clear",
"(",
"self",
")"
] |
https://github.com/hzlzh/AlfredWorkflow.com/blob/7055f14f6922c80ea5943839eb0caff11ae57255/Sources/Workflows/Toggl-Time-Tracking/alp/request/requests/packages/urllib3/packages/ordered_dict.py#L80-L90
|
||
mikedh/trimesh
|
6b1e05616b44e6dd708d9bc748b211656ebb27ec
|
trimesh/util.py
|
python
|
grid_arange
|
(bounds, step)
|
return grid
|
Return a grid from an (2,dimension) bounds with samples step distance apart.
Parameters
------------
bounds: (2,dimension) list of [[min x, min y, etc], [max x, max y, etc]]
step: float, or (dimension) floats, separation between points
Returns
---------
grid: (n, dimension), points inside the specified bounds
|
Return a grid from an (2,dimension) bounds with samples step distance apart.
|
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"(",
"2",
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def grid_arange(bounds, step):
"""
Return a grid from an (2,dimension) bounds with samples step distance apart.
Parameters
------------
bounds: (2,dimension) list of [[min x, min y, etc], [max x, max y, etc]]
step: float, or (dimension) floats, separation between points
Returns
---------
grid: (n, dimension), points inside the specified bounds
"""
bounds = np.asanyarray(bounds, dtype=np.float64)
if len(bounds) != 2:
raise ValueError('bounds must be (2, dimension!')
# allow single float or per-dimension spacing
step = np.asanyarray(step, dtype=np.float64)
if step.shape == ():
step = np.tile(step, bounds.shape[1])
grid_elements = [np.arange(*b, step=s) for b, s in zip(bounds.T, step)]
grid = np.vstack(np.meshgrid(*grid_elements, indexing='ij')
).reshape(bounds.shape[1], -1).T
return grid
|
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https://github.com/mikedh/trimesh/blob/6b1e05616b44e6dd708d9bc748b211656ebb27ec/trimesh/util.py#L708-L733
|
|
researchmm/tasn
|
5dba8ccc096cedc63913730eeea14a9647911129
|
tasn-mxnet/python/mxnet/symbol/symbol.py
|
python
|
Symbol.sqrt
|
(self, *args, **kwargs)
|
return op.sqrt(self, *args, **kwargs)
|
Convenience fluent method for :py:func:`sqrt`.
The arguments are the same as for :py:func:`sqrt`, with
this array as data.
|
Convenience fluent method for :py:func:`sqrt`.
|
[
"Convenience",
"fluent",
"method",
"for",
":",
"py",
":",
"func",
":",
"sqrt",
"."
] |
def sqrt(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sqrt`.
The arguments are the same as for :py:func:`sqrt`, with
this array as data.
"""
return op.sqrt(self, *args, **kwargs)
|
[
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"sqrt",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/researchmm/tasn/blob/5dba8ccc096cedc63913730eeea14a9647911129/tasn-mxnet/python/mxnet/symbol/symbol.py#L2346-L2352
|
|
carpedm20/NAF-tensorflow
|
5754bd40fe135f79272b333ba2b911b02ca293f7
|
src/exploration.py
|
python
|
OUExploration.__init__
|
(self, env, sigma=0.3, mu=0, theta=0.15)
|
[] |
def __init__(self, env, sigma=0.3, mu=0, theta=0.15):
super(OUExploration, self).__init__(env)
self.mu = mu
self.theta = theta
self.sigma = sigma
self.state = np.ones(self.action_size) * self.mu
self.reset()
|
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https://github.com/carpedm20/NAF-tensorflow/blob/5754bd40fe135f79272b333ba2b911b02ca293f7/src/exploration.py#L17-L25
|
||||
mrkipling/maraschino
|
c6be9286937783ae01df2d6d8cebfc8b2734a7d7
|
lib/sqlalchemy/orm/query.py
|
python
|
Query.except_
|
(self, *q)
|
return self._from_selectable(
expression.except_(*([self]+ list(q)))
)
|
Produce an EXCEPT of this Query against one or more queries.
Works the same way as :meth:`~sqlalchemy.orm.query.Query.union`. See
that method for usage examples.
|
Produce an EXCEPT of this Query against one or more queries.
|
[
"Produce",
"an",
"EXCEPT",
"of",
"this",
"Query",
"against",
"one",
"or",
"more",
"queries",
"."
] |
def except_(self, *q):
"""Produce an EXCEPT of this Query against one or more queries.
Works the same way as :meth:`~sqlalchemy.orm.query.Query.union`. See
that method for usage examples.
"""
return self._from_selectable(
expression.except_(*([self]+ list(q)))
)
|
[
"def",
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"self",
",",
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"(",
"[",
"self",
"]",
"+",
"list",
"(",
"q",
")",
")",
")",
")"
] |
https://github.com/mrkipling/maraschino/blob/c6be9286937783ae01df2d6d8cebfc8b2734a7d7/lib/sqlalchemy/orm/query.py#L1243-L1252
|
|
demisto/content
|
5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07
|
Packs/FreshDesk/Integrations/FreshDesk/FreshDesk.py
|
python
|
ticket_reply_command
|
()
|
Reply to a specified ticket.
demisto parameter: (number) ticket_id
ID of the ticket you wish to respond to
demisto parameter: (string) body
Content of the reply in HTML format
demisto parameter: (string) from_email
The email address from which the reply is sent. By default the global support email will be used.
demisto parameter: (number) user_id
ID of the agent who is adding the note
demisto parameter: (list) cc_emails
Array of email address strings added in the 'cc' field of the outgoing ticket email.
demisto parameter: (list) bcc_emails
Array of email address strings added in the 'bcc' field of the outgoing ticket email.
demisto parameter: (list) attachments
Entry IDs of files to attach to the reply. The total size of these attachments cannot exceed 15MB.
returns:
Ticket Reply Object
|
Reply to a specified ticket.
|
[
"Reply",
"to",
"a",
"specified",
"ticket",
"."
] |
def ticket_reply_command():
"""
Reply to a specified ticket.
demisto parameter: (number) ticket_id
ID of the ticket you wish to respond to
demisto parameter: (string) body
Content of the reply in HTML format
demisto parameter: (string) from_email
The email address from which the reply is sent. By default the global support email will be used.
demisto parameter: (number) user_id
ID of the agent who is adding the note
demisto parameter: (list) cc_emails
Array of email address strings added in the 'cc' field of the outgoing ticket email.
demisto parameter: (list) bcc_emails
Array of email address strings added in the 'bcc' field of the outgoing ticket email.
demisto parameter: (list) attachments
Entry IDs of files to attach to the reply. The total size of these attachments cannot exceed 15MB.
returns:
Ticket Reply Object
"""
args = demisto.args()
# Make request and get raw response
reply = ticket_reply(args)
# Parse response into context
context = {string_to_context_key(key): val for key, val in reply.iteritems() if val}
context = reformat_conversation_context(context)
# Parse attachments into context
context, context_readable = attachments_into_context(reply, context)
context = reformat_ticket_context(context)
context_readable = reformat_ticket_context(context_readable)
complete_context = {
'ID': int(reply.get('ticket_id')),
'Conversation': context
}
title = 'Reply to Ticket #{}'.format(reply.get('ticket_id'))
human_readable = tableToMarkdown(title, context_readable, removeNull=True)
demisto.results({
'Type': entryTypes['note'],
'ContentsFormat': formats['json'],
'Contents': reply,
'ReadableContentsFormat': formats['markdown'],
'HumanReadable': human_readable,
'EntryContext': {
'Freshdesk.Ticket(val.ID && val.ID === obj.ID)': complete_context
}
})
|
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] |
https://github.com/demisto/content/blob/5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07/Packs/FreshDesk/Integrations/FreshDesk/FreshDesk.py#L1313-L1366
|
||
CrowdStrike/automactc
|
0df8d738be5f3e5053c4a6facda4014ea8ba4b36
|
modules/common/dep/cffi38/api.py
|
python
|
FFI.from_buffer
|
(self, cdecl, python_buffer=_unspecified,
require_writable=False)
|
return self._backend.from_buffer(cdecl, python_buffer,
require_writable)
|
Return a cdata of the given type pointing to the data of the
given Python object, which must support the buffer interface.
Note that this is not meant to be used on the built-in types
str or unicode (you can build 'char[]' arrays explicitly)
but only on objects containing large quantities of raw data
in some other format, like 'array.array' or numpy arrays.
The first argument is optional and default to 'char[]'.
|
Return a cdata of the given type pointing to the data of the
given Python object, which must support the buffer interface.
Note that this is not meant to be used on the built-in types
str or unicode (you can build 'char[]' arrays explicitly)
but only on objects containing large quantities of raw data
in some other format, like 'array.array' or numpy arrays.
|
[
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"the",
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"numpy",
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"."
] |
def from_buffer(self, cdecl, python_buffer=_unspecified,
require_writable=False):
"""Return a cdata of the given type pointing to the data of the
given Python object, which must support the buffer interface.
Note that this is not meant to be used on the built-in types
str or unicode (you can build 'char[]' arrays explicitly)
but only on objects containing large quantities of raw data
in some other format, like 'array.array' or numpy arrays.
The first argument is optional and default to 'char[]'.
"""
if python_buffer is _unspecified:
cdecl, python_buffer = self.BCharA, cdecl
elif isinstance(cdecl, basestring):
cdecl = self._typeof(cdecl)
return self._backend.from_buffer(cdecl, python_buffer,
require_writable)
|
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https://github.com/CrowdStrike/automactc/blob/0df8d738be5f3e5053c4a6facda4014ea8ba4b36/modules/common/dep/cffi38/api.py#L350-L366
|
|
jtriley/StarCluster
|
bc7c950e73f193eac9aab986b6764939cfdad978
|
starcluster/node.py
|
python
|
Node.getpwnam
|
(self, username)
|
return umap.get(username)
|
Remote version of the getpwnam method in the standard pwd module
returns a pwd.struct_passwd
|
Remote version of the getpwnam method in the standard pwd module
|
[
"Remote",
"version",
"of",
"the",
"getpwnam",
"method",
"in",
"the",
"standard",
"pwd",
"module"
] |
def getpwnam(self, username):
"""
Remote version of the getpwnam method in the standard pwd module
returns a pwd.struct_passwd
"""
umap = self.get_user_map()
return umap.get(username)
|
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https://github.com/jtriley/StarCluster/blob/bc7c950e73f193eac9aab986b6764939cfdad978/starcluster/node.py#L454-L461
|
|
F8LEFT/DecLLVM
|
d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c
|
python/idaapi.py
|
python
|
can_define_item
|
(*args)
|
return _idaapi.can_define_item(*args)
|
can_define_item(ea, length, flags) -> bool
|
can_define_item(ea, length, flags) -> bool
|
[
"can_define_item",
"(",
"ea",
"length",
"flags",
")",
"-",
">",
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] |
def can_define_item(*args):
"""
can_define_item(ea, length, flags) -> bool
"""
return _idaapi.can_define_item(*args)
|
[
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"_idaapi",
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https://github.com/F8LEFT/DecLLVM/blob/d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c/python/idaapi.py#L23080-L23084
|
|
aws-samples/aws-kube-codesuite
|
ab4e5ce45416b83bffb947ab8d234df5437f4fca
|
src/kubernetes/client/models/v1_api_group.py
|
python
|
V1APIGroup.to_dict
|
(self)
|
return result
|
Returns the model properties as a dict
|
Returns the model properties as a dict
|
[
"Returns",
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] |
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
|
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"result"
] |
https://github.com/aws-samples/aws-kube-codesuite/blob/ab4e5ce45416b83bffb947ab8d234df5437f4fca/src/kubernetes/client/models/v1_api_group.py#L202-L226
|
|
openstack/cinder
|
23494a6d6c51451688191e1847a458f1d3cdcaa5
|
cinder/volume/drivers/nec/volume_helper.py
|
python
|
MStorageDriver.manage_existing_snapshot
|
(self, snapshot, existing_ref)
|
Brings an existing backend storage object under Cinder management.
Rename the backend storage object so that it matches the
snapshot['name'] which is how drivers traditionally map between a
cinder snapshot and the associated backend storage object.
|
Brings an existing backend storage object under Cinder management.
|
[
"Brings",
"an",
"existing",
"backend",
"storage",
"object",
"under",
"Cinder",
"management",
"."
] |
def manage_existing_snapshot(self, snapshot, existing_ref):
"""Brings an existing backend storage object under Cinder management.
Rename the backend storage object so that it matches the
snapshot['name'] which is how drivers traditionally map between a
cinder snapshot and the associated backend storage object.
"""
LOG.debug('manage_existing_snapshots Start.')
xml = self._cli.view_all(self._properties['ismview_path'])
pools, lds, ldsets, used_ldns, hostports, max_ld_count = (
self.configs(xml))
newname, oldname = self._reference_to_ldname('snapshot',
snapshot,
existing_ref)
param_source = self.get_ldname(snapshot.volume_id,
self._properties['ld_name_format'])
ref_source = self._cli.get_bvname(oldname)
if param_source[3:] != ref_source:
reason = _('Snapshot source is unmatched.')
raise exception.ManageExistingInvalidReference(
existing_ref=existing_ref, reason=reason)
if (lds[oldname]['pool_num']
not in self._properties['pool_backup_pools']):
reason = _('Volume type is unmatched.')
raise exception.ManageExistingVolumeTypeMismatch(
existing_ref=existing_ref, reason=reason)
try:
self._cli.changeldname(None, newname, oldname)
except exception.CinderException as e:
LOG.warning('Unable to manage existing snapshot '
'(reference = %(ref)s), (%(exception)s)',
{'ref': existing_ref['source-name'], 'exception': e})
|
[
"def",
"manage_existing_snapshot",
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"self",
",",
"snapshot",
",",
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")",
":",
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".",
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"'manage_existing_snapshots Start.'",
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"'snapshot'",
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"'Volume type is unmatched.'",
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",",
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"'ref'",
":",
"existing_ref",
"[",
"'source-name'",
"]",
",",
"'exception'",
":",
"e",
"}",
")"
] |
https://github.com/openstack/cinder/blob/23494a6d6c51451688191e1847a458f1d3cdcaa5/cinder/volume/drivers/nec/volume_helper.py#L1725-L1759
|
||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/Python-2.7.9/Tools/bgen/bgen/bgenBuffer.py
|
python
|
FixedInputOutputBufferType.getargsCheck
|
(self, name)
|
[] |
def getargsCheck(self, name):
Output("if (%s__in_len__ != %s)", name, self.size)
OutLbrace()
Output('PyErr_SetString(PyExc_TypeError, "buffer length should be %s");',
self.size)
Output("goto %s__error__;", name)
self.label_needed = 1
OutRbrace()
self.transferSize(name)
|
[
"def",
"getargsCheck",
"(",
"self",
",",
"name",
")",
":",
"Output",
"(",
"\"if (%s__in_len__ != %s)\"",
",",
"name",
",",
"self",
".",
"size",
")",
"OutLbrace",
"(",
")",
"Output",
"(",
"'PyErr_SetString(PyExc_TypeError, \"buffer length should be %s\");'",
",",
"self",
".",
"size",
")",
"Output",
"(",
"\"goto %s__error__;\"",
",",
"name",
")",
"self",
".",
"label_needed",
"=",
"1",
"OutRbrace",
"(",
")",
"self",
".",
"transferSize",
"(",
"name",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/Python-2.7.9/Tools/bgen/bgen/bgenBuffer.py#L83-L91
|
||||
thouska/spotpy
|
92c3aad416ccd6becbeb345c58cae36b3a63d892
|
spotpy/algorithms/dds.py
|
python
|
DDSGenerator.neigh_value_continuous
|
(self, s, x_min, x_max, r)
|
return x_new
|
select a RANDOM neighbouring real value of a SINGLE decision variable
CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT
variables:
x_range is the range of the real variable (s_max-s_min)
:param s: is a current SINGLE decision variable VALUE
:param x_min: is the min of variable s
:param x_max: is the max of variable s
:param r: is the neighbourhood parameter (replaces V parameter~see not
It is defined as the ratio of the std deviation of the desired
normal random number/x_range. Eg:
std dev desired = r * x_range
for comparison: variance (V) = (r * x_range)^2
:return: x_new, a new sample of values in beetween a given range
|
select a RANDOM neighbouring real value of a SINGLE decision variable
CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT
variables:
x_range is the range of the real variable (s_max-s_min)
|
[
"select",
"a",
"RANDOM",
"neighbouring",
"real",
"value",
"of",
"a",
"SINGLE",
"decision",
"variable",
"CEE",
"509",
"HW",
"5",
"by",
"Bryan",
"Tolson",
"Mar",
"5",
"2003",
"AND",
"ALSO",
"CEE",
"PROJECT",
"variables",
":",
"x_range",
"is",
"the",
"range",
"of",
"the",
"real",
"variable",
"(",
"s_max",
"-",
"s_min",
")"
] |
def neigh_value_continuous(self, s, x_min, x_max, r):
"""
select a RANDOM neighbouring real value of a SINGLE decision variable
CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT
variables:
x_range is the range of the real variable (s_max-s_min)
:param s: is a current SINGLE decision variable VALUE
:param x_min: is the min of variable s
:param x_max: is the max of variable s
:param r: is the neighbourhood parameter (replaces V parameter~see not
It is defined as the ratio of the std deviation of the desired
normal random number/x_range. Eg:
std dev desired = r * x_range
for comparison: variance (V) = (r * x_range)^2
:return: x_new, a new sample of values in beetween a given range
"""
x_range = x_max - x_min
x_new = s + self.np_random.normal(0, 1) * r * x_range
# NEED to deal with variable upper and lower bounds:
# Originally bounds in DDS were 100# reflective
# But some times DVs are right on the boundary and with 100# reflective
# boundaries it is hard to detect them. Therefore, we decided to make the
# boundaries reflective with 50# chance and absorptive with 50# chance.
# M. Asadzadeh and B. Tolson Dec 2008
p_abs_or_ref = self.np_random.rand()
if x_new < x_min: # works for any pos or neg x_min
if p_abs_or_ref <= 0.5: # with 50%chance reflect
x_new = x_min + (x_min - x_new)
else: # with 50% chance absorb
x_new = x_min
# if reflection goes past x_max then value should be x_min since without reflection
# the approach goes way past lower bound. This keeps X close to lower bound when X current
# is close to lower bound:
if x_new > x_max:
x_new = x_min
elif x_new > x_max: # works for any pos or neg x_max
if p_abs_or_ref <= 0.5: # with 50% chance reflect
x_new = x_max - (x_new - x_max)
else: # with 50% chance absorb
x_new = x_max
# if reflection goes past x_min then value should be x_max for same reasons as above
if x_new < x_min:
x_new = x_max
return x_new
|
[
"def",
"neigh_value_continuous",
"(",
"self",
",",
"s",
",",
"x_min",
",",
"x_max",
",",
"r",
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":",
"x_range",
"=",
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"-",
"x_min",
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"+",
"self",
".",
"np_random",
".",
"normal",
"(",
"0",
",",
"1",
")",
"*",
"r",
"*",
"x_range",
"# NEED to deal with variable upper and lower bounds:",
"# Originally bounds in DDS were 100# reflective",
"# But some times DVs are right on the boundary and with 100# reflective",
"# boundaries it is hard to detect them. Therefore, we decided to make the",
"# boundaries reflective with 50# chance and absorptive with 50# chance.",
"# M. Asadzadeh and B. Tolson Dec 2008",
"p_abs_or_ref",
"=",
"self",
".",
"np_random",
".",
"rand",
"(",
")",
"if",
"x_new",
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":",
"# works for any pos or neg x_min",
"if",
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":",
"# with 50%chance reflect",
"x_new",
"=",
"x_min",
"+",
"(",
"x_min",
"-",
"x_new",
")",
"else",
":",
"# with 50% chance absorb",
"x_new",
"=",
"x_min",
"# if reflection goes past x_max then value should be x_min since without reflection",
"# the approach goes way past lower bound. This keeps X close to lower bound when X current",
"# is close to lower bound:",
"if",
"x_new",
">",
"x_max",
":",
"x_new",
"=",
"x_min",
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"x_new",
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"x_max",
":",
"# works for any pos or neg x_max",
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"p_abs_or_ref",
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":",
"# with 50% chance reflect",
"x_new",
"=",
"x_max",
"-",
"(",
"x_new",
"-",
"x_max",
")",
"else",
":",
"# with 50% chance absorb",
"x_new",
"=",
"x_max",
"# if reflection goes past x_min then value should be x_max for same reasons as above",
"if",
"x_new",
"<",
"x_min",
":",
"x_new",
"=",
"x_max",
"return",
"x_new"
] |
https://github.com/thouska/spotpy/blob/92c3aad416ccd6becbeb345c58cae36b3a63d892/spotpy/algorithms/dds.py#L15-L68
|
|
google/nogotofail
|
7037dcb23f1fc370de784c36dbb24ae93cd5a58d
|
nogotofail/mitm/util/extras.py
|
python
|
get_extras_path
|
(file_path)
|
return os.path.join(extras_dir, file_path)
|
Return a full path to file_path in the extras directory.
|
Return a full path to file_path in the extras directory.
|
[
"Return",
"a",
"full",
"path",
"to",
"file_path",
"in",
"the",
"extras",
"directory",
"."
] |
def get_extras_path(file_path):
""" Return a full path to file_path in the extras directory."""
if extras_dir is None:
return file_path
return os.path.join(extras_dir, file_path)
|
[
"def",
"get_extras_path",
"(",
"file_path",
")",
":",
"if",
"extras_dir",
"is",
"None",
":",
"return",
"file_path",
"return",
"os",
".",
"path",
".",
"join",
"(",
"extras_dir",
",",
"file_path",
")"
] |
https://github.com/google/nogotofail/blob/7037dcb23f1fc370de784c36dbb24ae93cd5a58d/nogotofail/mitm/util/extras.py#L20-L24
|
|
jython/jython3
|
def4f8ec47cb7a9c799ea4c745f12badf92c5769
|
lib-python/3.5.1/idlelib/HyperParser.py
|
python
|
HyperParser.set_index
|
(self, index)
|
Set the index to which the functions relate.
The index must be in the same statement.
|
Set the index to which the functions relate.
|
[
"Set",
"the",
"index",
"to",
"which",
"the",
"functions",
"relate",
"."
] |
def set_index(self, index):
"""Set the index to which the functions relate.
The index must be in the same statement.
"""
indexinrawtext = (len(self.rawtext) -
len(self.text.get(index, self.stopatindex)))
if indexinrawtext < 0:
raise ValueError("Index %s precedes the analyzed statement"
% index)
self.indexinrawtext = indexinrawtext
# find the rightmost bracket to which index belongs
self.indexbracket = 0
while (self.indexbracket < len(self.bracketing)-1 and
self.bracketing[self.indexbracket+1][0] < self.indexinrawtext):
self.indexbracket += 1
if (self.indexbracket < len(self.bracketing)-1 and
self.bracketing[self.indexbracket+1][0] == self.indexinrawtext and
not self.isopener[self.indexbracket+1]):
self.indexbracket += 1
|
[
"def",
"set_index",
"(",
"self",
",",
"index",
")",
":",
"indexinrawtext",
"=",
"(",
"len",
"(",
"self",
".",
"rawtext",
")",
"-",
"len",
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"index",
",",
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")",
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"\"Index %s precedes the analyzed statement\"",
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"(",
"self",
".",
"indexbracket",
"<",
"len",
"(",
"self",
".",
"bracketing",
")",
"-",
"1",
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"[",
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"+",
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"]",
"[",
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"indexbracket",
"+",
"1",
"]",
")",
":",
"self",
".",
"indexbracket",
"+=",
"1"
] |
https://github.com/jython/jython3/blob/def4f8ec47cb7a9c799ea4c745f12badf92c5769/lib-python/3.5.1/idlelib/HyperParser.py#L82-L101
|
||
robotlearn/pyrobolearn
|
9cd7c060723fda7d2779fa255ac998c2c82b8436
|
pyrobolearn/utils/data_structures/orderedset.py
|
python
|
OrderedSet2.__reversed__
|
(self)
|
Iterate over the ordered set in the reverse order the items have been added.
Time complexity: O(N)
|
Iterate over the ordered set in the reverse order the items have been added.
Time complexity: O(N)
|
[
"Iterate",
"over",
"the",
"ordered",
"set",
"in",
"the",
"reverse",
"order",
"the",
"items",
"have",
"been",
"added",
".",
"Time",
"complexity",
":",
"O",
"(",
"N",
")"
] |
def __reversed__(self):
"""
Iterate over the ordered set in the reverse order the items have been added.
Time complexity: O(N)
"""
curr = self._end
while curr != self.NonePtr:
yield curr
curr = self._map[curr][0]
|
[
"def",
"__reversed__",
"(",
"self",
")",
":",
"curr",
"=",
"self",
".",
"_end",
"while",
"curr",
"!=",
"self",
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"NonePtr",
":",
"yield",
"curr",
"curr",
"=",
"self",
".",
"_map",
"[",
"curr",
"]",
"[",
"0",
"]"
] |
https://github.com/robotlearn/pyrobolearn/blob/9cd7c060723fda7d2779fa255ac998c2c82b8436/pyrobolearn/utils/data_structures/orderedset.py#L771-L779
|
||
pyg-team/pytorch_geometric
|
b920e9a3a64e22c8356be55301c88444ff051cae
|
torch_geometric/datasets/webkb.py
|
python
|
WebKB.download
|
(self)
|
[] |
def download(self):
for f in self.raw_file_names[:2]:
download_url(f'{self.url}/new_data/{self.name}/{f}', self.raw_dir)
for f in self.raw_file_names[2:]:
download_url(f'{self.url}/splits/{f}', self.raw_dir)
|
[
"def",
"download",
"(",
"self",
")",
":",
"for",
"f",
"in",
"self",
".",
"raw_file_names",
"[",
":",
"2",
"]",
":",
"download_url",
"(",
"f'{self.url}/new_data/{self.name}/{f}'",
",",
"self",
".",
"raw_dir",
")",
"for",
"f",
"in",
"self",
".",
"raw_file_names",
"[",
"2",
":",
"]",
":",
"download_url",
"(",
"f'{self.url}/splits/{f}'",
",",
"self",
".",
"raw_dir",
")"
] |
https://github.com/pyg-team/pytorch_geometric/blob/b920e9a3a64e22c8356be55301c88444ff051cae/torch_geometric/datasets/webkb.py#L59-L63
|
||||
DataBrewery/cubes
|
140133e8c2e3f2ff60631cc3ebc9966d16c1655e
|
cubes/common.py
|
python
|
sorted_dependencies
|
(graph)
|
return L
|
Return keys from `deps` ordered by dependency (topological sort).
`deps` is a dictionary where keys are strings and values are list of
strings where keys is assumed to be dependant on values.
Example::
A ---> B -+--> C
|
+--> D --> E
Will be: ``{"A": ["B"], "B": ["C", "D"], "D": ["E"],"E": []}``
|
Return keys from `deps` ordered by dependency (topological sort).
`deps` is a dictionary where keys are strings and values are list of
strings where keys is assumed to be dependant on values.
|
[
"Return",
"keys",
"from",
"deps",
"ordered",
"by",
"dependency",
"(",
"topological",
"sort",
")",
".",
"deps",
"is",
"a",
"dictionary",
"where",
"keys",
"are",
"strings",
"and",
"values",
"are",
"list",
"of",
"strings",
"where",
"keys",
"is",
"assumed",
"to",
"be",
"dependant",
"on",
"values",
"."
] |
def sorted_dependencies(graph):
"""Return keys from `deps` ordered by dependency (topological sort).
`deps` is a dictionary where keys are strings and values are list of
strings where keys is assumed to be dependant on values.
Example::
A ---> B -+--> C
|
+--> D --> E
Will be: ``{"A": ["B"], "B": ["C", "D"], "D": ["E"],"E": []}``
"""
graph = dict((key, set(value)) for key, value in graph.items())
# L ← Empty list that will contain the sorted elements
L = []
# S ← Set of all nodes with no dependencies (incoming edges)
S = set(parent for parent, req in graph.items() if not req)
while S:
# remove a node n from S
n = S.pop()
# insert n into L
L.append(n)
# for each node m with an edge e from n to m do
# (n that depends on m)
parents = [parent for parent, req in graph.items() if n in req]
for parent in parents:
graph[parent].remove(n)
# remove edge e from the graph
# if m has no other incoming edges then insert m into S
if not graph[parent]:
S.add(parent)
# if graph has edges then -> error
nonempty = [k for k, v in graph.items() if v]
if nonempty:
raise ArgumentError("Cyclic dependency of: %s"
% ", ".join(nonempty))
return L
|
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https://github.com/DataBrewery/cubes/blob/140133e8c2e3f2ff60631cc3ebc9966d16c1655e/cubes/common.py#L272-L317
|
|
openstack/manila
|
142990edc027e14839d5deaf4954dd6fc88de15e
|
manila/db/sqlalchemy/api.py
|
python
|
message_get_all
|
(context, filters=None, limit=None, offset=None,
sort_key='created_at', sort_dir='desc')
|
Retrieves all messages.
If no sort parameters are specified then the returned messages are
sorted by the 'created_at' key in descending order.
:param context: context to query under
:param limit: maximum number of items to return
:param offset: the number of items to skip from the marker or from the
first element.
:param sort_key: attributes by which results should be sorted.
:param sort_dir: directions in which results should be sorted.
:param filters: dictionary of filters; values that are in lists, tuples,
or sets cause an 'IN' operation, while exact matching
is used for other values, see exact_filter function for
more information
:returns: list of matching messages
|
Retrieves all messages.
|
[
"Retrieves",
"all",
"messages",
"."
] |
def message_get_all(context, filters=None, limit=None, offset=None,
sort_key='created_at', sort_dir='desc'):
"""Retrieves all messages.
If no sort parameters are specified then the returned messages are
sorted by the 'created_at' key in descending order.
:param context: context to query under
:param limit: maximum number of items to return
:param offset: the number of items to skip from the marker or from the
first element.
:param sort_key: attributes by which results should be sorted.
:param sort_dir: directions in which results should be sorted.
:param filters: dictionary of filters; values that are in lists, tuples,
or sets cause an 'IN' operation, while exact matching
is used for other values, see exact_filter function for
more information
:returns: list of matching messages
"""
messages = models.Message
session = get_session()
with session.begin():
query = model_query(context,
messages,
read_deleted="no",
project_only="yes")
legal_filter_keys = ('request_id', 'resource_type', 'resource_id',
'action_id', 'detail_id', 'message_level',
'created_since', 'created_before')
if not filters:
filters = {}
query = exact_filter(query, messages, filters, legal_filter_keys)
query = utils.paginate_query(query, messages, limit,
sort_key=sort_key,
sort_dir=sort_dir,
offset=offset)
return query.all()
|
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https://github.com/openstack/manila/blob/142990edc027e14839d5deaf4954dd6fc88de15e/manila/db/sqlalchemy/api.py#L5879-L5921
|
||
burke-software/schooldriver
|
a07262ba864aee0182548ecceb661e49c925725f
|
appy/shared/sap.py
|
python
|
Sap.getFunctionInfo
|
(self, functionName)
|
Returns information about the RFC function named p_functionName.
|
Returns information about the RFC function named p_functionName.
|
[
"Returns",
"information",
"about",
"the",
"RFC",
"function",
"named",
"p_functionName",
"."
] |
def getFunctionInfo(self, functionName):
'''Returns information about the RFC function named p_functionName.'''
try:
res = ''
usedTypes = set() # Names of type definitions used in parameters.
fDesc = self.sap.get_interface_desc(functionName)
functionDescr = str(fDesc).strip()
if functionDescr: res += functionDescr
# Import parameters
if fDesc.imports:
res += '\nIMPORTS\n'
for iDesc in fDesc.imports:
res += ' %s\n' % str(iDesc)
usedTypes.add(iDesc.field_def)
# Export parameters
if fDesc.exports:
res += '\nEXPORTS\n'
for eDesc in fDesc.exports:
res += ' %s\n' % str(eDesc)
usedTypes.add(eDesc.field_def)
if fDesc.tables:
res += '\nTABLES\n'
for tDesc in fDesc.tables:
res += ' %s\n' % str(tDesc)
usedTypes.add(tDesc.field_def)
if fDesc.exceptions:
res += '\nEXCEPTIONS\n'
for eDesc in fDesc.exceptions:
res += ' %s\n' % str(eDesc)
# Add information about used types
if usedTypes:
res += '\nTypes used by the parameters:\n'
for typeName in usedTypes:
# Dump info only if it is a structure, not a simple type
try:
self.sap.get_structure(typeName)
res += '%s\n%s\n\n' % \
(typeName, self.getTypeInfo(typeName))
except pysap.BaseSapRfcError, ee:
pass
return res
except pysap.BaseSapRfcError, se:
if se.value == 'FU_NOT_FOUND':
raise SapError(SAP_FUNCTION_NOT_FOUND % (functionName))
else:
raise SapError(SAP_FUNCTION_INFO_ERROR % (functionName,str(se)))
|
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https://github.com/burke-software/schooldriver/blob/a07262ba864aee0182548ecceb661e49c925725f/appy/shared/sap.py#L163-L208
|
||
hakril/PythonForWindows
|
61e027a678d5b87aa64fcf8a37a6661a86236589
|
ctypes_generation/definitions/structures/template.py
|
python
|
EnumValue.__init__
|
(self, enum_name, name, value)
|
[] |
def __init__(self, enum_name, name, value):
self.enum_name = enum_name
self.name = name
|
[
"def",
"__init__",
"(",
"self",
",",
"enum_name",
",",
"name",
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"self",
".",
"enum_name",
"=",
"enum_name",
"self",
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https://github.com/hakril/PythonForWindows/blob/61e027a678d5b87aa64fcf8a37a6661a86236589/ctypes_generation/definitions/structures/template.py#L11-L13
|
||||
loli/medpy
|
39131b94f0ab5328ab14a874229320efc2f74d98
|
medpy/iterators/patchwise.py
|
python
|
CentredPatchIterator.applyslicer
|
(array, slicer, pmask, cval = 0)
|
return patch
|
r"""
Apply a slicer returned by the iterator to a new array of the same
dimensionality as the one used to initialize the iterator.
Notes
-----
If ``array`` has more dimensions than ``slicer`` and ``pmask``, the first ones
are sliced.
Parameters
----------
array : array_like
A n-dimensional array.
slicer : list
List if `slice()` instances as returned by `next()`.
pmask : narray
The array mask as returned by `next()`.
cval : number
Value to fill undefined positions.
Experiments
-----------
>>> import numpy
>>> from medpy.iterators import CentredPatchIterator
>>> arr = numpy.arange(0, 25).reshape((5,5))
>>> for patch, pmask, _, slicer in CentredPatchIterator(arr, 3):
>>> new_patch = CentredPatchIterator.applyslicer(arr, slicer, pmask)
>>> print numpy.all(new_patch == patch)
True
...
|
r"""
Apply a slicer returned by the iterator to a new array of the same
dimensionality as the one used to initialize the iterator.
Notes
-----
If ``array`` has more dimensions than ``slicer`` and ``pmask``, the first ones
are sliced.
Parameters
----------
array : array_like
A n-dimensional array.
slicer : list
List if `slice()` instances as returned by `next()`.
pmask : narray
The array mask as returned by `next()`.
cval : number
Value to fill undefined positions.
Experiments
-----------
>>> import numpy
>>> from medpy.iterators import CentredPatchIterator
>>> arr = numpy.arange(0, 25).reshape((5,5))
>>> for patch, pmask, _, slicer in CentredPatchIterator(arr, 3):
>>> new_patch = CentredPatchIterator.applyslicer(arr, slicer, pmask)
>>> print numpy.all(new_patch == patch)
True
...
|
[
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"slicer",
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] |
def applyslicer(array, slicer, pmask, cval = 0):
r"""
Apply a slicer returned by the iterator to a new array of the same
dimensionality as the one used to initialize the iterator.
Notes
-----
If ``array`` has more dimensions than ``slicer`` and ``pmask``, the first ones
are sliced.
Parameters
----------
array : array_like
A n-dimensional array.
slicer : list
List if `slice()` instances as returned by `next()`.
pmask : narray
The array mask as returned by `next()`.
cval : number
Value to fill undefined positions.
Experiments
-----------
>>> import numpy
>>> from medpy.iterators import CentredPatchIterator
>>> arr = numpy.arange(0, 25).reshape((5,5))
>>> for patch, pmask, _, slicer in CentredPatchIterator(arr, 3):
>>> new_patch = CentredPatchIterator.applyslicer(arr, slicer, pmask)
>>> print numpy.all(new_patch == patch)
True
...
"""
l = len(slicer)
patch = numpy.zeros(list(pmask.shape[:l]) + list(array.shape[l:]), array.dtype)
if not 0 == cval: patch.fill(cval)
sliced = array[slicer]
patch[pmask] = sliced.reshape([numpy.prod(sliced.shape[:l])] + list(sliced.shape[l:]))
return patch
|
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https://github.com/loli/medpy/blob/39131b94f0ab5328ab14a874229320efc2f74d98/medpy/iterators/patchwise.py#L320-L358
|
|
PaddlePaddle/PaddleX
|
2bab73f81ab54e328204e7871e6ae4a82e719f5d
|
paddlex/paddleseg/models/gcnet.py
|
python
|
GCNetHead.__init__
|
(self,
num_classes,
backbone_indices,
backbone_channels,
gc_channels,
ratio,
enable_auxiliary_loss=True)
|
[] |
def __init__(self,
num_classes,
backbone_indices,
backbone_channels,
gc_channels,
ratio,
enable_auxiliary_loss=True):
super().__init__()
in_channels = backbone_channels[1]
self.conv_bn_relu1 = layers.ConvBNReLU(
in_channels=in_channels,
out_channels=gc_channels,
kernel_size=3,
padding=1)
self.gc_block = GlobalContextBlock(
gc_channels=gc_channels, in_channels=gc_channels, ratio=ratio)
self.conv_bn_relu2 = layers.ConvBNReLU(
in_channels=gc_channels,
out_channels=gc_channels,
kernel_size=3,
padding=1)
self.conv_bn_relu3 = layers.ConvBNReLU(
in_channels=in_channels + gc_channels,
out_channels=gc_channels,
kernel_size=3,
padding=1)
self.dropout = nn.Dropout(p=0.1)
self.conv = nn.Conv2D(
in_channels=gc_channels, out_channels=num_classes, kernel_size=1)
if enable_auxiliary_loss:
self.auxlayer = layers.AuxLayer(
in_channels=backbone_channels[0],
inter_channels=backbone_channels[0] // 4,
out_channels=num_classes)
self.backbone_indices = backbone_indices
self.enable_auxiliary_loss = enable_auxiliary_loss
|
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] |
https://github.com/PaddlePaddle/PaddleX/blob/2bab73f81ab54e328204e7871e6ae4a82e719f5d/paddlex/paddleseg/models/gcnet.py#L98-L142
|
||||
huggingface/transformers
|
623b4f7c63f60cce917677ee704d6c93ee960b4b
|
src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
|
python
|
BlenderbotSmallForCausalLM.get_decoder
|
(self)
|
return self.model.decoder
|
[] |
def get_decoder(self):
return self.model.decoder
|
[
"def",
"get_decoder",
"(",
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")",
":",
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"self",
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] |
https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py#L1389-L1390
|
|||
AndreasMadsen/stable-nalu
|
b3296ace137ffa4854edeef3759f1578b7650210
|
stable_nalu/layer/regualized_linear_mnac.py
|
python
|
RegualizedLinearMNACCell.__init__
|
(self, input_size, hidden_size, **kwargs)
|
[] |
def __init__(self, input_size, hidden_size, **kwargs):
super().__init__(ReRegualizedLinearMNACLayer, input_size, hidden_size, **kwargs)
|
[
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https://github.com/AndreasMadsen/stable-nalu/blob/b3296ace137ffa4854edeef3759f1578b7650210/stable_nalu/layer/regualized_linear_mnac.py#L72-L73
|
||||
kerlomz/captcha_platform
|
f7d719bd1239a987996e266bd7fe35c96003b378
|
middleware/impl/gif_frames.py
|
python
|
concat_arr
|
(img_arr)
|
return all_slice
|
[] |
def concat_arr(img_arr):
if len(img_arr) < 2:
return img_arr[0]
all_slice = img_arr[0]
for im_slice in img_arr[1:]:
all_slice = np.concatenate((all_slice, im_slice), axis=1)
return all_slice
|
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https://github.com/kerlomz/captcha_platform/blob/f7d719bd1239a987996e266bd7fe35c96003b378/middleware/impl/gif_frames.py#L19-L25
|
|||
kerlomz/captcha_platform
|
f7d719bd1239a987996e266bd7fe35c96003b378
|
middleware/impl/corp_to_multi.py
|
python
|
coord_calc
|
(param, is_range=True, is_integer=True)
|
return result_group
|
[] |
def coord_calc(param, is_range=True, is_integer=True):
result_group = []
start_h = param['start_pos'][1]
end_h = start_h + param['corp_size'][1]
for row in range(param['corp_num'][1]):
start_w = param['start_pos'][0]
end_w = start_w + param['corp_size'][0]
for col in range(param['corp_num'][0]):
pos_range = [[start_w, end_w], [start_h, end_h]]
t = lambda x: int(x) if is_integer else x
pos_center = [t((start_w + end_w)/2), t((start_h + end_h)/2)]
result_group.append(pos_range if is_range else pos_center)
start_w = end_w + param['interval_size'][0]
end_w = start_w + param['corp_size'][0]
start_h = end_h + param['interval_size'][1]
end_h = start_h + param['corp_size'][1]
return result_group
|
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https://github.com/kerlomz/captcha_platform/blob/f7d719bd1239a987996e266bd7fe35c96003b378/middleware/impl/corp_to_multi.py#L10-L27
|
|||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/Python-2.7.9/Lib/plat-irix6/IN.py
|
python
|
IS_IPV4SOCKADDR
|
(a)
|
return
|
[] |
def IS_IPV4SOCKADDR(a): return
|
[
"def",
"IS_IPV4SOCKADDR",
"(",
"a",
")",
":",
"return"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/Python-2.7.9/Lib/plat-irix6/IN.py#L296-L296
|
|||
globocom/m3u8
|
cf7ae5fda4681efcea796cd7c51c02f152c36009
|
m3u8/model.py
|
python
|
M3U8._initialize_attributes
|
(self)
|
[] |
def _initialize_attributes(self):
self.keys = [ Key(base_uri=self.base_uri, **params) if params else None
for params in self.data.get('keys', []) ]
self.segments = SegmentList([ Segment(base_uri=self.base_uri, keyobject=find_key(segment.get('key', {}), self.keys), **segment)
for segment in self.data.get('segments', []) ])
#self.keys = get_uniques([ segment.key for segment in self.segments ])
for attr, param in self.simple_attributes:
setattr(self, attr, self.data.get(param))
self.files = []
for key in self.keys:
# Avoid None key, it could be the first one, don't repeat them
if key and key.uri not in self.files:
self.files.append(key.uri)
self.files.extend(self.segments.uri)
self.media = MediaList([ Media(base_uri=self.base_uri, **media)
for media in self.data.get('media', []) ])
self.playlists = PlaylistList([ Playlist(base_uri=self.base_uri, media=self.media, **playlist)
for playlist in self.data.get('playlists', []) ])
self.iframe_playlists = PlaylistList()
for ifr_pl in self.data.get('iframe_playlists', []):
self.iframe_playlists.append(IFramePlaylist(base_uri=self.base_uri,
uri=ifr_pl['uri'],
iframe_stream_info=ifr_pl['iframe_stream_info'])
)
self.segment_map = self.data.get('segment_map')
start = self.data.get('start', None)
self.start = start and Start(**start)
server_control = self.data.get('server_control', None)
self.server_control = server_control and ServerControl(**server_control)
part_inf = self.data.get('part_inf', None)
self.part_inf = part_inf and PartInformation(**part_inf)
skip = self.data.get('skip', None)
self.skip = skip and Skip(**skip)
self.rendition_reports = RenditionReportList([ RenditionReport(base_uri=self.base_uri, **rendition_report)
for rendition_report in self.data.get('rendition_reports', []) ])
self.session_data = SessionDataList([ SessionData(**session_data)
for session_data in self.data.get('session_data', [])
if 'data_id' in session_data ])
self.session_keys = [ SessionKey(base_uri=self.base_uri, **params) if params else None
for params in self.data.get('session_keys', []) ]
preload_hint = self.data.get('preload_hint', None)
self.preload_hint = preload_hint and PreloadHint(base_uri=self.base_uri, **preload_hint)
|
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",",
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] |
https://github.com/globocom/m3u8/blob/cf7ae5fda4681efcea796cd7c51c02f152c36009/m3u8/model.py#L159-L212
|
||||
swisscom/ai-research-keyphrase-extraction
|
78c0b13633f0e443cf43892b098b4c8dabf3dad9
|
swisscom_ai/research_keyphrase/preprocessing/postagging.py
|
python
|
PosTagging.pos_tag_file
|
(self, input_path, output_path=None)
|
POS Tag a file.
Either we have a list of list (for each sentence a list of tuple (word,tag))
Or a file with the POS tagged text
Note : The jumpline is only for readibility purpose , when reading a tagged file we'll use again
sent_tokenize to find the sentences boundaries.
:param input_path: path of the source file
:param output_path: If set write POS tagged text with separator (self.pos_tag_raw_text with as_tuple_list False)
If not set, return list of list of tuple (self.post_tag_raw_text with as_tuple_list = True)
:return: resulting POS tagged text as a list of list of tuple or nothing if output path is set.
|
POS Tag a file.
Either we have a list of list (for each sentence a list of tuple (word,tag))
Or a file with the POS tagged text
|
[
"POS",
"Tag",
"a",
"file",
".",
"Either",
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"have",
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"of",
"list",
"(",
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"sentence",
"a",
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"(",
"word",
"tag",
"))",
"Or",
"a",
"file",
"with",
"the",
"POS",
"tagged",
"text"
] |
def pos_tag_file(self, input_path, output_path=None):
"""
POS Tag a file.
Either we have a list of list (for each sentence a list of tuple (word,tag))
Or a file with the POS tagged text
Note : The jumpline is only for readibility purpose , when reading a tagged file we'll use again
sent_tokenize to find the sentences boundaries.
:param input_path: path of the source file
:param output_path: If set write POS tagged text with separator (self.pos_tag_raw_text with as_tuple_list False)
If not set, return list of list of tuple (self.post_tag_raw_text with as_tuple_list = True)
:return: resulting POS tagged text as a list of list of tuple or nothing if output path is set.
"""
original_text = read_file(input_path)
if output_path is not None:
tagged_text = self.pos_tag_raw_text(original_text, as_tuple_list=False)
# Write to the output the POS-Tagged text.
write_string(tagged_text, output_path)
else:
return self.pos_tag_raw_text(original_text, as_tuple_list=True)
|
[
"def",
"pos_tag_file",
"(",
"self",
",",
"input_path",
",",
"output_path",
"=",
"None",
")",
":",
"original_text",
"=",
"read_file",
"(",
"input_path",
")",
"if",
"output_path",
"is",
"not",
"None",
":",
"tagged_text",
"=",
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"pos_tag_raw_text",
"(",
"original_text",
",",
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"False",
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"# Write to the output the POS-Tagged text.",
"write_string",
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"self",
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"pos_tag_raw_text",
"(",
"original_text",
",",
"as_tuple_list",
"=",
"True",
")"
] |
https://github.com/swisscom/ai-research-keyphrase-extraction/blob/78c0b13633f0e443cf43892b098b4c8dabf3dad9/swisscom_ai/research_keyphrase/preprocessing/postagging.py#L65-L89
|
||
argoai/argoverse-api
|
575c222e7d7fa0c56bdc9dc20f71464c4bea2e6d
|
argoverse/utils/sim2.py
|
python
|
Sim2.scale
|
(self)
|
return self.s_
|
Return the scale.
|
Return the scale.
|
[
"Return",
"the",
"scale",
"."
] |
def scale(self) -> float:
"""Return the scale."""
return self.s_
|
[
"def",
"scale",
"(",
"self",
")",
"->",
"float",
":",
"return",
"self",
".",
"s_"
] |
https://github.com/argoai/argoverse-api/blob/575c222e7d7fa0c56bdc9dc20f71464c4bea2e6d/argoverse/utils/sim2.py#L88-L90
|
|
pypa/pipenv
|
b21baade71a86ab3ee1429f71fbc14d4f95fb75d
|
pipenv/patched/notpip/_internal/utils/parallel.py
|
python
|
_map_multiprocess
|
(func, iterable, chunksize=1)
|
Chop iterable into chunks and submit them to a process pool.
For very long iterables using a large value for chunksize can make
the job complete much faster than using the default value of 1.
Return an unordered iterator of the results.
|
Chop iterable into chunks and submit them to a process pool.
|
[
"Chop",
"iterable",
"into",
"chunks",
"and",
"submit",
"them",
"to",
"a",
"process",
"pool",
"."
] |
def _map_multiprocess(func, iterable, chunksize=1):
# type: (Callable[[S], T], Iterable[S], int) -> Iterator[T]
"""Chop iterable into chunks and submit them to a process pool.
For very long iterables using a large value for chunksize can make
the job complete much faster than using the default value of 1.
Return an unordered iterator of the results.
"""
with closing(ProcessPool()) as pool:
return pool.imap_unordered(func, iterable, chunksize)
|
[
"def",
"_map_multiprocess",
"(",
"func",
",",
"iterable",
",",
"chunksize",
"=",
"1",
")",
":",
"# type: (Callable[[S], T], Iterable[S], int) -> Iterator[T]",
"with",
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"(",
"ProcessPool",
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")",
")",
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":",
"return",
"pool",
".",
"imap_unordered",
"(",
"func",
",",
"iterable",
",",
"chunksize",
")"
] |
https://github.com/pypa/pipenv/blob/b21baade71a86ab3ee1429f71fbc14d4f95fb75d/pipenv/patched/notpip/_internal/utils/parallel.py#L71-L81
|
||
danielgtaylor/arista
|
edcf2565eea92014b55c1084acd12a832aab1ca2
|
arista/inputs/haldisco.py
|
python
|
InputFinder.device_removed
|
(self, udi)
|
Called when a device has been removed from the signal. If the
device is a volume with a video DVD the "video-lost" signal is
emitted.
|
Called when a device has been removed from the signal. If the
device is a volume with a video DVD the "video-lost" signal is
emitted.
|
[
"Called",
"when",
"a",
"device",
"has",
"been",
"removed",
"from",
"the",
"signal",
".",
"If",
"the",
"device",
"is",
"a",
"volume",
"with",
"a",
"video",
"DVD",
"the",
"video",
"-",
"lost",
"signal",
"is",
"emitted",
"."
] |
def device_removed(self, udi):
"""
Called when a device has been removed from the signal. If the
device is a volume with a video DVD the "video-lost" signal is
emitted.
"""
for block, drive in self.drives.items():
if drive.video_udi == udi:
drive.video = False
drive.udi = ""
label = drive.label
drive.label = ""
self.emit("disc-lost", drive, label)
break
for device, capture in self.capture_devices.items():
if capture.udi == udi:
self.emit("v4l-capture-lost", self.capture_devices[device])
del self.capture_devices[device]
break
|
[
"def",
"device_removed",
"(",
"self",
",",
"udi",
")",
":",
"for",
"block",
",",
"drive",
"in",
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"]",
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"self",
".",
"capture_devices",
"[",
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"]",
"break"
] |
https://github.com/danielgtaylor/arista/blob/edcf2565eea92014b55c1084acd12a832aab1ca2/arista/inputs/haldisco.py#L246-L265
|
||
IJDykeman/wangTiles
|
7c1ee2095ebdf7f72bce07d94c6484915d5cae8b
|
experimental_code/tiles_3d/venv_mac_py3/lib/python2.7/site-packages/setuptools/command/install_lib.py
|
python
|
install_lib._exclude_pkg_path
|
(self, pkg, exclusion_path)
|
return os.path.join(self.install_dir, *parts)
|
Given a package name and exclusion path within that package,
compute the full exclusion path.
|
Given a package name and exclusion path within that package,
compute the full exclusion path.
|
[
"Given",
"a",
"package",
"name",
"and",
"exclusion",
"path",
"within",
"that",
"package",
"compute",
"the",
"full",
"exclusion",
"path",
"."
] |
def _exclude_pkg_path(self, pkg, exclusion_path):
"""
Given a package name and exclusion path within that package,
compute the full exclusion path.
"""
parts = pkg.split('.') + [exclusion_path]
return os.path.join(self.install_dir, *parts)
|
[
"def",
"_exclude_pkg_path",
"(",
"self",
",",
"pkg",
",",
"exclusion_path",
")",
":",
"parts",
"=",
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"'.'",
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"+",
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".",
"path",
".",
"join",
"(",
"self",
".",
"install_dir",
",",
"*",
"parts",
")"
] |
https://github.com/IJDykeman/wangTiles/blob/7c1ee2095ebdf7f72bce07d94c6484915d5cae8b/experimental_code/tiles_3d/venv_mac_py3/lib/python2.7/site-packages/setuptools/command/install_lib.py#L31-L37
|
|
git-cola/git-cola
|
b48b8028e0c3baf47faf7b074b9773737358163d
|
cola/widgets/browse.py
|
python
|
BrowseBranch.setModel
|
(self, model)
|
[] |
def setModel(self, model):
self.tree.setModel(model)
|
[
"def",
"setModel",
"(",
"self",
",",
"model",
")",
":",
"self",
".",
"tree",
".",
"setModel",
"(",
"model",
")"
] |
https://github.com/git-cola/git-cola/blob/b48b8028e0c3baf47faf7b074b9773737358163d/cola/widgets/browse.py#L655-L656
|
||||
kennethreitz-archive/requests3
|
69eb662703b40db58fdc6c095d0fe130c56649bb
|
requests3/core/_http/contrib/securetransport.py
|
python
|
WrappedSocket._reuse
|
(self)
|
[] |
def _reuse(self):
self._makefile_refs += 1
|
[
"def",
"_reuse",
"(",
"self",
")",
":",
"self",
".",
"_makefile_refs",
"+=",
"1"
] |
https://github.com/kennethreitz-archive/requests3/blob/69eb662703b40db58fdc6c095d0fe130c56649bb/requests3/core/_http/contrib/securetransport.py#L642-L643
|
||||
larryhastings/gilectomy
|
4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a
|
Lib/asyncio/events.py
|
python
|
AbstractEventLoop.is_running
|
(self)
|
Return whether the event loop is currently running.
|
Return whether the event loop is currently running.
|
[
"Return",
"whether",
"the",
"event",
"loop",
"is",
"currently",
"running",
"."
] |
def is_running(self):
"""Return whether the event loop is currently running."""
raise NotImplementedError
|
[
"def",
"is_running",
"(",
"self",
")",
":",
"raise",
"NotImplementedError"
] |
https://github.com/larryhastings/gilectomy/blob/4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a/Lib/asyncio/events.py#L232-L234
|
||
pypa/setuptools
|
9f37366aab9cd8f6baa23e6a77cfdb8daf97757e
|
pkg_resources/_vendor/packaging/tags.py
|
python
|
Tag.__repr__
|
(self)
|
return "<{self} @ {self_id}>".format(self=self, self_id=id(self))
|
[] |
def __repr__(self) -> str:
return "<{self} @ {self_id}>".format(self=self, self_id=id(self))
|
[
"def",
"__repr__",
"(",
"self",
")",
"->",
"str",
":",
"return",
"\"<{self} @ {self_id}>\"",
".",
"format",
"(",
"self",
"=",
"self",
",",
"self_id",
"=",
"id",
"(",
"self",
")",
")"
] |
https://github.com/pypa/setuptools/blob/9f37366aab9cd8f6baa23e6a77cfdb8daf97757e/pkg_resources/_vendor/packaging/tags.py#L92-L93
|
|||
openshift/openshift-tools
|
1188778e728a6e4781acf728123e5b356380fe6f
|
openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_vendored_deps/library/oc_serviceaccount.py
|
python
|
main
|
()
|
ansible oc module for service accounts
|
ansible oc module for service accounts
|
[
"ansible",
"oc",
"module",
"for",
"service",
"accounts"
] |
def main():
'''
ansible oc module for service accounts
'''
module = AnsibleModule(
argument_spec=dict(
kubeconfig=dict(default='/etc/origin/master/admin.kubeconfig', type='str'),
state=dict(default='present', type='str',
choices=['present', 'absent', 'list']),
debug=dict(default=False, type='bool'),
name=dict(default=None, required=True, type='str'),
namespace=dict(default=None, required=True, type='str'),
secrets=dict(default=None, type='list'),
image_pull_secrets=dict(default=None, type='list'),
),
supports_check_mode=True,
)
rval = OCServiceAccount.run_ansible(module.params, module.check_mode)
if 'failed' in rval:
module.fail_json(**rval)
module.exit_json(**rval)
|
[
"def",
"main",
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")",
":",
"module",
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"AnsibleModule",
"(",
"argument_spec",
"=",
"dict",
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"kubeconfig",
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"dict",
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"default",
"=",
"'/etc/origin/master/admin.kubeconfig'",
",",
"type",
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"'str'",
")",
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"state",
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"*",
"*",
"rval",
")",
"module",
".",
"exit_json",
"(",
"*",
"*",
"rval",
")"
] |
https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_vendored_deps/library/oc_serviceaccount.py#L1761-L1784
|
||
iiau-tracker/SPLT
|
a196e603798e9be969d9d985c087c11cad1cda43
|
lib/object_detection/core/losses.py
|
python
|
WeightedL2LocalizationLoss._compute_loss
|
(self, prediction_tensor, target_tensor, weights)
|
return tf.reduce_sum(square_diff)
|
Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the (encoded) predicted locations of objects.
target_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the regression targets
weights: a float tensor of shape [batch_size, num_anchors]
Returns:
loss: a (scalar) tensor representing the value of the loss function
or a float tensor of shape [batch_size, num_anchors]
|
Compute loss function.
|
[
"Compute",
"loss",
"function",
"."
] |
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the (encoded) predicted locations of objects.
target_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the regression targets
weights: a float tensor of shape [batch_size, num_anchors]
Returns:
loss: a (scalar) tensor representing the value of the loss function
or a float tensor of shape [batch_size, num_anchors]
"""
weighted_diff = (prediction_tensor - target_tensor) * tf.expand_dims(
weights, 2)
square_diff = 0.5 * tf.square(weighted_diff)
if self._anchorwise_output:
return tf.reduce_sum(square_diff, 2)
return tf.reduce_sum(square_diff)
|
[
"def",
"_compute_loss",
"(",
"self",
",",
"prediction_tensor",
",",
"target_tensor",
",",
"weights",
")",
":",
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",",
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",",
"2",
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"reduce_sum",
"(",
"square_diff",
")"
] |
https://github.com/iiau-tracker/SPLT/blob/a196e603798e9be969d9d985c087c11cad1cda43/lib/object_detection/core/losses.py#L103-L122
|
|
holzschu/Carnets
|
44effb10ddfc6aa5c8b0687582a724ba82c6b547
|
Library/lib/python3.7/site-packages/qtconsole/kill_ring.py
|
python
|
QtKillRing.__init__
|
(self, text_edit)
|
Create a kill ring attached to the specified Qt text edit.
|
Create a kill ring attached to the specified Qt text edit.
|
[
"Create",
"a",
"kill",
"ring",
"attached",
"to",
"the",
"specified",
"Qt",
"text",
"edit",
"."
] |
def __init__(self, text_edit):
""" Create a kill ring attached to the specified Qt text edit.
"""
assert isinstance(text_edit, (QtGui.QTextEdit, QtGui.QPlainTextEdit))
super(QtKillRing, self).__init__()
self._ring = KillRing()
self._prev_yank = None
self._skip_cursor = False
self._text_edit = text_edit
text_edit.cursorPositionChanged.connect(self._cursor_position_changed)
|
[
"def",
"__init__",
"(",
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",",
"text_edit",
")",
":",
"assert",
"isinstance",
"(",
"text_edit",
",",
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",",
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"QPlainTextEdit",
")",
")",
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",",
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"__init__",
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")",
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"_prev_yank",
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"_skip_cursor",
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"text_edit",
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".",
"cursorPositionChanged",
".",
"connect",
"(",
"self",
".",
"_cursor_position_changed",
")"
] |
https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/qtconsole/kill_ring.py#L62-L73
|
||
m-rtijn/mpu6050
|
0626053a5e1182f4951b78b8326691a9223a5f7d
|
mpu6050/mpu6050.py
|
python
|
mpu6050.set_accel_range
|
(self, accel_range)
|
Sets the range of the accelerometer to range.
accel_range -- the range to set the accelerometer to. Using a
pre-defined range is advised.
|
Sets the range of the accelerometer to range.
|
[
"Sets",
"the",
"range",
"of",
"the",
"accelerometer",
"to",
"range",
"."
] |
def set_accel_range(self, accel_range):
"""Sets the range of the accelerometer to range.
accel_range -- the range to set the accelerometer to. Using a
pre-defined range is advised.
"""
# First change it to 0x00 to make sure we write the correct value later
self.bus.write_byte_data(self.address, self.ACCEL_CONFIG, 0x00)
# Write the new range to the ACCEL_CONFIG register
self.bus.write_byte_data(self.address, self.ACCEL_CONFIG, accel_range)
|
[
"def",
"set_accel_range",
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"self",
",",
"accel_range",
")",
":",
"# First change it to 0x00 to make sure we write the correct value later",
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",",
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"ACCEL_CONFIG",
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"(",
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".",
"address",
",",
"self",
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"ACCEL_CONFIG",
",",
"accel_range",
")"
] |
https://github.com/m-rtijn/mpu6050/blob/0626053a5e1182f4951b78b8326691a9223a5f7d/mpu6050/mpu6050.py#L108-L118
|
||
alanhamlett/pip-update-requirements
|
ce875601ef278c8ce00ad586434a978731525561
|
pur/packages/pip/_vendor/urllib3/contrib/_securetransport/low_level.py
|
python
|
_cf_data_from_bytes
|
(bytestring)
|
return CoreFoundation.CFDataCreate(
CoreFoundation.kCFAllocatorDefault, bytestring, len(bytestring)
)
|
Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller.
|
Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller.
|
[
"Given",
"a",
"bytestring",
"create",
"a",
"CFData",
"object",
"from",
"it",
".",
"This",
"CFData",
"object",
"must",
"be",
"CFReleased",
"by",
"the",
"caller",
"."
] |
def _cf_data_from_bytes(bytestring):
"""
Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller.
"""
return CoreFoundation.CFDataCreate(
CoreFoundation.kCFAllocatorDefault, bytestring, len(bytestring)
)
|
[
"def",
"_cf_data_from_bytes",
"(",
"bytestring",
")",
":",
"return",
"CoreFoundation",
".",
"CFDataCreate",
"(",
"CoreFoundation",
".",
"kCFAllocatorDefault",
",",
"bytestring",
",",
"len",
"(",
"bytestring",
")",
")"
] |
https://github.com/alanhamlett/pip-update-requirements/blob/ce875601ef278c8ce00ad586434a978731525561/pur/packages/pip/_vendor/urllib3/contrib/_securetransport/low_level.py#L27-L34
|
|
bruderstein/PythonScript
|
df9f7071ddf3a079e3a301b9b53a6dc78cf1208f
|
PythonLib/min/pydoc.py
|
python
|
TextDoc.indent
|
(self, text, prefix=' ')
|
return '\n'.join(lines)
|
Indent text by prepending a given prefix to each line.
|
Indent text by prepending a given prefix to each line.
|
[
"Indent",
"text",
"by",
"prepending",
"a",
"given",
"prefix",
"to",
"each",
"line",
"."
] |
def indent(self, text, prefix=' '):
"""Indent text by prepending a given prefix to each line."""
if not text: return ''
lines = [prefix + line for line in text.split('\n')]
if lines: lines[-1] = lines[-1].rstrip()
return '\n'.join(lines)
|
[
"def",
"indent",
"(",
"self",
",",
"text",
",",
"prefix",
"=",
"' '",
")",
":",
"if",
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"return",
"''",
"lines",
"=",
"[",
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"+",
"line",
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"text",
".",
"split",
"(",
"'\\n'",
")",
"]",
"if",
"lines",
":",
"lines",
"[",
"-",
"1",
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"=",
"lines",
"[",
"-",
"1",
"]",
".",
"rstrip",
"(",
")",
"return",
"'\\n'",
".",
"join",
"(",
"lines",
")"
] |
https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/min/pydoc.py#L1169-L1174
|
|
yuxiaokui/Intranet-Penetration
|
f57678a204840c83cbf3308e3470ae56c5ff514b
|
proxy/XX-Net/code/default/gae_proxy/server/lib/google/appengine/api/images/__init__.py
|
python
|
create_rpc
|
(deadline=None, callback=None)
|
return apiproxy_stub_map.UserRPC("images", deadline, callback)
|
Creates an RPC object for use with the images API.
Args:
deadline: Optional deadline in seconds for the operation; the default
is a system-specific deadline (typically 5 seconds).
callback: Optional callable to invoke on completion.
Returns:
An apiproxy_stub_map.UserRPC object specialized for this service.
|
Creates an RPC object for use with the images API.
|
[
"Creates",
"an",
"RPC",
"object",
"for",
"use",
"with",
"the",
"images",
"API",
"."
] |
def create_rpc(deadline=None, callback=None):
"""Creates an RPC object for use with the images API.
Args:
deadline: Optional deadline in seconds for the operation; the default
is a system-specific deadline (typically 5 seconds).
callback: Optional callable to invoke on completion.
Returns:
An apiproxy_stub_map.UserRPC object specialized for this service.
"""
return apiproxy_stub_map.UserRPC("images", deadline, callback)
|
[
"def",
"create_rpc",
"(",
"deadline",
"=",
"None",
",",
"callback",
"=",
"None",
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"return",
"apiproxy_stub_map",
".",
"UserRPC",
"(",
"\"images\"",
",",
"deadline",
",",
"callback",
")"
] |
https://github.com/yuxiaokui/Intranet-Penetration/blob/f57678a204840c83cbf3308e3470ae56c5ff514b/proxy/XX-Net/code/default/gae_proxy/server/lib/google/appengine/api/images/__init__.py#L1018-L1029
|
|
davidastephens/pandas-finance
|
3b7a80df8159fa253f08191b7aec5587976c26e7
|
pandas_finance/api.py
|
python
|
OptionChain.near_calls
|
(self)
|
return self._pdr._chop_data(self.calls, 5, self.underlying.price)
|
[] |
def near_calls(self):
return self._pdr._chop_data(self.calls, 5, self.underlying.price)
|
[
"def",
"near_calls",
"(",
"self",
")",
":",
"return",
"self",
".",
"_pdr",
".",
"_chop_data",
"(",
"self",
".",
"calls",
",",
"5",
",",
"self",
".",
"underlying",
".",
"price",
")"
] |
https://github.com/davidastephens/pandas-finance/blob/3b7a80df8159fa253f08191b7aec5587976c26e7/pandas_finance/api.py#L257-L258
|
|||
QUANTAXIS/QUANTAXIS
|
d6eccb97c8385854aa596d6ba8d70ec0655519ff
|
QUANTAXIS/QAData/base_datastruct.py
|
python
|
_quotation_base.stdev
|
(self)
|
return res
|
返回DataStruct.price的样本标准差 Sample standard deviation
|
返回DataStruct.price的样本标准差 Sample standard deviation
|
[
"返回DataStruct",
".",
"price的样本标准差",
"Sample",
"standard",
"deviation"
] |
def stdev(self):
'返回DataStruct.price的样本标准差 Sample standard deviation'
res = self.price.groupby(level=1).apply(lambda x: statistics.stdev(x))
res.name = 'stdev'
return res
|
[
"def",
"stdev",
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"self",
")",
":",
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"=",
"self",
".",
"price",
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"groupby",
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"level",
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"apply",
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"statistics",
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"stdev",
"(",
"x",
")",
")",
"res",
".",
"name",
"=",
"'stdev'",
"return",
"res"
] |
https://github.com/QUANTAXIS/QUANTAXIS/blob/d6eccb97c8385854aa596d6ba8d70ec0655519ff/QUANTAXIS/QAData/base_datastruct.py#L516-L520
|
|
Fantomas42/django-blog-zinnia
|
881101a9d1d455b2fc581d6f4ae0947cdd8126c6
|
zinnia/templatetags/zinnia.py
|
python
|
get_random_entries
|
(number=5, template='zinnia/tags/entries_random.html')
|
return {'template': template,
'entries': Entry.published.order_by('?')[:number]}
|
Return random entries.
|
Return random entries.
|
[
"Return",
"random",
"entries",
"."
] |
def get_random_entries(number=5, template='zinnia/tags/entries_random.html'):
"""
Return random entries.
"""
return {'template': template,
'entries': Entry.published.order_by('?')[:number]}
|
[
"def",
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"(",
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"=",
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",",
"template",
"=",
"'zinnia/tags/entries_random.html'",
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"return",
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"template",
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"'entries'",
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"Entry",
".",
"published",
".",
"order_by",
"(",
"'?'",
")",
"[",
":",
"number",
"]",
"}"
] |
https://github.com/Fantomas42/django-blog-zinnia/blob/881101a9d1d455b2fc581d6f4ae0947cdd8126c6/zinnia/templatetags/zinnia.py#L116-L121
|
|
i-pan/kaggle-rsna18
|
2db498fe99615d935aa676f04847d0c562fd8e46
|
models/RetinaNet/keras_retinanet/models/retinanet.py
|
python
|
retinanet_bbox
|
(
model = None,
anchor_parameters = AnchorParameters.default,
nms = True,
class_specific_filter = True,
name = 'retinanet-bbox',
**kwargs
)
|
return keras.models.Model(inputs=model.inputs, outputs=outputs, name=name)
|
Construct a RetinaNet model on top of a backbone and adds convenience functions to output boxes directly.
This model uses the minimum retinanet model and appends a few layers to compute boxes within the graph.
These layers include applying the regression values to the anchors and performing NMS.
Args
model : RetinaNet model to append bbox layers to. If None, it will create a RetinaNet model using **kwargs.
anchor_parameters : Struct containing configuration for anchor generation (sizes, strides, ratios, scales).
nms : Whether to use non-maximum suppression for the filtering step.
class_specific_filter : Whether to use class specific filtering or filter for the best scoring class only.
name : Name of the model.
*kwargs : Additional kwargs to pass to the minimal retinanet model.
Returns
A keras.models.Model which takes an image as input and outputs the detections on the image.
The order is defined as follows:
```
[
boxes, scores, labels, other[0], other[1], ...
]
```
|
Construct a RetinaNet model on top of a backbone and adds convenience functions to output boxes directly.
|
[
"Construct",
"a",
"RetinaNet",
"model",
"on",
"top",
"of",
"a",
"backbone",
"and",
"adds",
"convenience",
"functions",
"to",
"output",
"boxes",
"directly",
"."
] |
def retinanet_bbox(
model = None,
anchor_parameters = AnchorParameters.default,
nms = True,
class_specific_filter = True,
name = 'retinanet-bbox',
**kwargs
):
""" Construct a RetinaNet model on top of a backbone and adds convenience functions to output boxes directly.
This model uses the minimum retinanet model and appends a few layers to compute boxes within the graph.
These layers include applying the regression values to the anchors and performing NMS.
Args
model : RetinaNet model to append bbox layers to. If None, it will create a RetinaNet model using **kwargs.
anchor_parameters : Struct containing configuration for anchor generation (sizes, strides, ratios, scales).
nms : Whether to use non-maximum suppression for the filtering step.
class_specific_filter : Whether to use class specific filtering or filter for the best scoring class only.
name : Name of the model.
*kwargs : Additional kwargs to pass to the minimal retinanet model.
Returns
A keras.models.Model which takes an image as input and outputs the detections on the image.
The order is defined as follows:
```
[
boxes, scores, labels, other[0], other[1], ...
]
```
"""
if model is None:
model = retinanet(num_anchors=anchor_parameters.num_anchors(), **kwargs)
# compute the anchors
features = [model.get_layer(p_name).output for p_name in ['P3', 'P4', 'P5', 'P6', 'P7']]
anchors = __build_anchors(anchor_parameters, features)
# we expect the anchors, regression and classification values as first output
regression = model.outputs[0]
classification = model.outputs[1]
# "other" can be any additional output from custom submodels, by default this will be []
other = model.outputs[2:]
# apply predicted regression to anchors
boxes = layers.RegressBoxes(name='boxes')([anchors, regression])
boxes = layers.ClipBoxes(name='clipped_boxes')([model.inputs[0], boxes])
# filter detections (apply NMS / score threshold / select top-k)
detections = layers.FilterDetections(
nms = nms,
class_specific_filter = class_specific_filter,
name = 'filtered_detections'
)([boxes, classification] + other)
outputs = detections
# construct the model
return keras.models.Model(inputs=model.inputs, outputs=outputs, name=name)
|
[
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"inputs",
",",
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",",
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"name",
")"
] |
https://github.com/i-pan/kaggle-rsna18/blob/2db498fe99615d935aa676f04847d0c562fd8e46/models/RetinaNet/keras_retinanet/models/retinanet.py#L312-L371
|
|
scikit-image/scikit-image
|
ed642e2bc822f362504d24379dee94978d6fa9de
|
skimage/morphology/extrema.py
|
python
|
_add_constant_clip
|
(image, const_value)
|
return(result)
|
Add constant to the image while handling overflow issues gracefully.
|
Add constant to the image while handling overflow issues gracefully.
|
[
"Add",
"constant",
"to",
"the",
"image",
"while",
"handling",
"overflow",
"issues",
"gracefully",
"."
] |
def _add_constant_clip(image, const_value):
"""Add constant to the image while handling overflow issues gracefully.
"""
min_dtype, max_dtype = dtype_limits(image, clip_negative=False)
if const_value > (max_dtype - min_dtype):
raise ValueError("The added constant is not compatible"
"with the image data type.")
result = image + const_value
result[image > max_dtype-const_value] = max_dtype
return(result)
|
[
"def",
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",",
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")",
":",
"min_dtype",
",",
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] |
https://github.com/scikit-image/scikit-image/blob/ed642e2bc822f362504d24379dee94978d6fa9de/skimage/morphology/extrema.py#L20-L31
|
|
XX-net/XX-Net
|
a9898cfcf0084195fb7e69b6bc834e59aecdf14f
|
python3.8.2/Lib/asynchat.py
|
python
|
async_chat.close_when_done
|
(self)
|
automatically close this channel once the outgoing queue is empty
|
automatically close this channel once the outgoing queue is empty
|
[
"automatically",
"close",
"this",
"channel",
"once",
"the",
"outgoing",
"queue",
"is",
"empty"
] |
def close_when_done(self):
"automatically close this channel once the outgoing queue is empty"
self.producer_fifo.append(None)
|
[
"def",
"close_when_done",
"(",
"self",
")",
":",
"self",
".",
"producer_fifo",
".",
"append",
"(",
"None",
")"
] |
https://github.com/XX-net/XX-Net/blob/a9898cfcf0084195fb7e69b6bc834e59aecdf14f/python3.8.2/Lib/asynchat.py#L220-L222
|
||
general03/flask-autoindex
|
424246242c9f40aeb9ac2c8c63f4d2234024256e
|
.eggs/Werkzeug-1.0.1-py3.7.egg/werkzeug/http.py
|
python
|
parse_csp_header
|
(value, on_update=None, cls=None)
|
return cls(items, on_update)
|
Parse a Content Security Policy header.
.. versionadded:: 1.0.0
Support for Content Security Policy headers was added.
:param value: a csp header to be parsed.
:param on_update: an optional callable that is called every time a value
on the object is changed.
:param cls: the class for the returned object. By default
:class:`~werkzeug.datastructures.ContentSecurityPolicy` is used.
:return: a `cls` object.
|
Parse a Content Security Policy header.
|
[
"Parse",
"a",
"Content",
"Security",
"Policy",
"header",
"."
] |
def parse_csp_header(value, on_update=None, cls=None):
"""Parse a Content Security Policy header.
.. versionadded:: 1.0.0
Support for Content Security Policy headers was added.
:param value: a csp header to be parsed.
:param on_update: an optional callable that is called every time a value
on the object is changed.
:param cls: the class for the returned object. By default
:class:`~werkzeug.datastructures.ContentSecurityPolicy` is used.
:return: a `cls` object.
"""
if cls is None:
cls = ContentSecurityPolicy
if value is None:
return cls(None, on_update)
items = []
for policy in value.split(";"):
policy = policy.strip()
# Ignore badly formatted policies (no space)
if " " in policy:
directive, value = policy.strip().split(" ", 1)
items.append((directive.strip(), value.strip()))
return cls(items, on_update)
|
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https://github.com/general03/flask-autoindex/blob/424246242c9f40aeb9ac2c8c63f4d2234024256e/.eggs/Werkzeug-1.0.1-py3.7.egg/werkzeug/http.py#L527-L552
|
|
ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework
|
cb692f527e4e819b6c228187c5702d990a180043
|
external/Scripting Engine/Xenotix Python Scripting Engine/Lib/mhlib.py
|
python
|
Message.__init__
|
(self, f, n, fp = None)
|
Constructor.
|
Constructor.
|
[
"Constructor",
"."
] |
def __init__(self, f, n, fp = None):
"""Constructor."""
self.folder = f
self.number = n
if fp is None:
path = f.getmessagefilename(n)
fp = open(path, 'r')
mimetools.Message.__init__(self, fp)
|
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",",
"'r'",
")",
"mimetools",
".",
"Message",
".",
"__init__",
"(",
"self",
",",
"fp",
")"
] |
https://github.com/ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework/blob/cb692f527e4e819b6c228187c5702d990a180043/external/Scripting Engine/Xenotix Python Scripting Engine/Lib/mhlib.py#L665-L672
|
||
scikit-hep/awkward-0.x
|
dd885bef15814f588b58944d2505296df4aaae0e
|
awkward0/array/base.py
|
python
|
AwkwardArray.unzip
|
(self)
|
return tuple(self[column_name] for column_name in self._util_columns(set()))
|
[] |
def unzip(self):
return tuple(self[column_name] for column_name in self._util_columns(set()))
|
[
"def",
"unzip",
"(",
"self",
")",
":",
"return",
"tuple",
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"self",
"[",
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"]",
"for",
"column_name",
"in",
"self",
".",
"_util_columns",
"(",
"set",
"(",
")",
")",
")"
] |
https://github.com/scikit-hep/awkward-0.x/blob/dd885bef15814f588b58944d2505296df4aaae0e/awkward0/array/base.py#L689-L690
|
|||
dmlc/dgl
|
8d14a739bc9e446d6c92ef83eafe5782398118de
|
python/dgl/core.py
|
python
|
invoke_udf_reduce
|
(graph, func, msgdata, *, orig_nid=None)
|
return retf
|
Invoke user-defined reduce function on all the nodes in the graph.
It analyzes the graph, groups nodes by their degrees and applies the UDF on each
group -- a strategy called *degree-bucketing*.
Parameters
----------
graph : DGLGraph
The input graph.
func : callable
The user-defined function.
msgdata : dict[str, Tensor]
Message data.
orig_nid : Tensor, optional
Original node IDs. Useful if the input graph is an extracted subgraph.
Returns
-------
dict[str, Tensor]
Results from running the UDF.
|
Invoke user-defined reduce function on all the nodes in the graph.
|
[
"Invoke",
"user",
"-",
"defined",
"reduce",
"function",
"on",
"all",
"the",
"nodes",
"in",
"the",
"graph",
"."
] |
def invoke_udf_reduce(graph, func, msgdata, *, orig_nid=None):
"""Invoke user-defined reduce function on all the nodes in the graph.
It analyzes the graph, groups nodes by their degrees and applies the UDF on each
group -- a strategy called *degree-bucketing*.
Parameters
----------
graph : DGLGraph
The input graph.
func : callable
The user-defined function.
msgdata : dict[str, Tensor]
Message data.
orig_nid : Tensor, optional
Original node IDs. Useful if the input graph is an extracted subgraph.
Returns
-------
dict[str, Tensor]
Results from running the UDF.
"""
degs = graph.in_degrees()
nodes = graph.dstnodes()
if orig_nid is None:
orig_nid = nodes
ntype = graph.dsttypes[0]
ntid = graph.get_ntype_id_from_dst(ntype)
dstdata = graph._node_frames[ntid]
msgdata = Frame(msgdata)
# degree bucketing
unique_degs, bucketor = _bucketing(degs)
bkt_rsts = []
bkt_nodes = []
for deg, node_bkt, orig_nid_bkt in zip(unique_degs, bucketor(nodes), bucketor(orig_nid)):
if deg == 0:
# skip reduce function for zero-degree nodes
continue
bkt_nodes.append(node_bkt)
ndata_bkt = dstdata.subframe(node_bkt)
# order the incoming edges per node by edge ID
eid_bkt = F.zerocopy_to_numpy(graph.in_edges(node_bkt, form='eid'))
assert len(eid_bkt) == deg * len(node_bkt)
eid_bkt = np.sort(eid_bkt.reshape((len(node_bkt), deg)), 1)
eid_bkt = F.zerocopy_from_numpy(eid_bkt.flatten())
msgdata_bkt = msgdata.subframe(eid_bkt)
# reshape all msg tensors to (num_nodes_bkt, degree, feat_size)
maildata = {}
for k, msg in msgdata_bkt.items():
newshape = (len(node_bkt), deg) + F.shape(msg)[1:]
maildata[k] = F.reshape(msg, newshape)
# invoke udf
nbatch = NodeBatch(graph, orig_nid_bkt, ntype, ndata_bkt, msgs=maildata)
bkt_rsts.append(func(nbatch))
# prepare a result frame
retf = Frame(num_rows=len(nodes))
retf._initializers = dstdata._initializers
retf._default_initializer = dstdata._default_initializer
# merge bucket results and write to the result frame
if len(bkt_rsts) != 0: # if all the nodes have zero degree, no need to merge results.
merged_rst = {}
for k in bkt_rsts[0].keys():
merged_rst[k] = F.cat([rst[k] for rst in bkt_rsts], dim=0)
merged_nodes = F.cat(bkt_nodes, dim=0)
retf.update_row(merged_nodes, merged_rst)
return retf
|
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] |
https://github.com/dmlc/dgl/blob/8d14a739bc9e446d6c92ef83eafe5782398118de/python/dgl/core.py#L87-L158
|
|
XX-net/XX-Net
|
a9898cfcf0084195fb7e69b6bc834e59aecdf14f
|
python3.8.2/Lib/site-packages/setuptools/unicode_utils.py
|
python
|
try_encode
|
(string, enc)
|
turn unicode encoding into a functional routine
|
turn unicode encoding into a functional routine
|
[
"turn",
"unicode",
"encoding",
"into",
"a",
"functional",
"routine"
] |
def try_encode(string, enc):
"turn unicode encoding into a functional routine"
try:
return string.encode(enc)
except UnicodeEncodeError:
return None
|
[
"def",
"try_encode",
"(",
"string",
",",
"enc",
")",
":",
"try",
":",
"return",
"string",
".",
"encode",
"(",
"enc",
")",
"except",
"UnicodeEncodeError",
":",
"return",
"None"
] |
https://github.com/XX-net/XX-Net/blob/a9898cfcf0084195fb7e69b6bc834e59aecdf14f/python3.8.2/Lib/site-packages/setuptools/unicode_utils.py#L39-L44
|
||
BrikerMan/Kashgari
|
ffe730d33f894e99a6fd7aa17ca67d161bf70359
|
kashgari/tasks/classification/abc_model.py
|
python
|
ABCClassificationModel.fit
|
(self,
x_train: TextSamplesVar,
y_train: Union[ClassificationLabelVar, MultiLabelClassificationLabelVar],
x_validate: TextSamplesVar = None,
y_validate: Union[ClassificationLabelVar, MultiLabelClassificationLabelVar] = None,
*,
batch_size: int = 64,
epochs: int = 5,
callbacks: List['keras.callbacks.Callback'] = None,
fit_kwargs: Dict = None)
|
return self.fit_generator(train_sample_gen=train_gen,
valid_sample_gen=valid_gen,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
fit_kwargs=fit_kwargs)
|
Trains the model for a given number of epochs with given data set list.
Args:
x_train: Array of train feature data (if the model has a single input),
or tuple of train feature data array (if the model has multiple inputs)
y_train: Array of train label data
x_validate: Array of validation feature data (if the model has a single input),
or tuple of validation feature data array (if the model has multiple inputs)
y_validate: Array of validation label data
batch_size: Number of samples per gradient update, default to 64.
epochs: Number of epochs to train the model.
An epoch is an iteration over the entire `x` and `y` data provided.
callbacks: List of `tf.keras.callbacks.Callback` instances.
List of callbacks to apply during training.
See :class:`tf.keras.callbacks`.
fit_kwargs: fit_kwargs: additional arguments passed to :meth:`tf.keras.Model.fit`
Returns:
A :class:`tf.keras.callback.History` object. Its `History.history` attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
|
Trains the model for a given number of epochs with given data set list.
|
[
"Trains",
"the",
"model",
"for",
"a",
"given",
"number",
"of",
"epochs",
"with",
"given",
"data",
"set",
"list",
"."
] |
def fit(self,
x_train: TextSamplesVar,
y_train: Union[ClassificationLabelVar, MultiLabelClassificationLabelVar],
x_validate: TextSamplesVar = None,
y_validate: Union[ClassificationLabelVar, MultiLabelClassificationLabelVar] = None,
*,
batch_size: int = 64,
epochs: int = 5,
callbacks: List['keras.callbacks.Callback'] = None,
fit_kwargs: Dict = None) -> 'keras.callbacks.History':
"""
Trains the model for a given number of epochs with given data set list.
Args:
x_train: Array of train feature data (if the model has a single input),
or tuple of train feature data array (if the model has multiple inputs)
y_train: Array of train label data
x_validate: Array of validation feature data (if the model has a single input),
or tuple of validation feature data array (if the model has multiple inputs)
y_validate: Array of validation label data
batch_size: Number of samples per gradient update, default to 64.
epochs: Number of epochs to train the model.
An epoch is an iteration over the entire `x` and `y` data provided.
callbacks: List of `tf.keras.callbacks.Callback` instances.
List of callbacks to apply during training.
See :class:`tf.keras.callbacks`.
fit_kwargs: fit_kwargs: additional arguments passed to :meth:`tf.keras.Model.fit`
Returns:
A :class:`tf.keras.callback.History` object. Its `History.history` attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
"""
train_gen = CorpusGenerator(x_train, y_train)
if x_validate is not None:
valid_gen = CorpusGenerator(x_validate, y_validate)
else:
valid_gen = None
return self.fit_generator(train_sample_gen=train_gen,
valid_sample_gen=valid_gen,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
fit_kwargs=fit_kwargs)
|
[
"def",
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",",
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"TextSamplesVar",
",",
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",",
"MultiLabelClassificationLabelVar",
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",",
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",",
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")"
] |
https://github.com/BrikerMan/Kashgari/blob/ffe730d33f894e99a6fd7aa17ca67d161bf70359/kashgari/tasks/classification/abc_model.py#L164-L208
|
|
makerbot/ReplicatorG
|
d6f2b07785a5a5f1e172fb87cb4303b17c575d5d
|
skein_engines/skeinforge-47/skeinforge_application/skeinforge_plugins/craft_plugins/comb.py
|
python
|
getCraftedTextFromText
|
(gcodeText, repository=None)
|
return CombSkein().getCraftedGcode(gcodeText, repository)
|
Comb a gcode linear move text.
|
Comb a gcode linear move text.
|
[
"Comb",
"a",
"gcode",
"linear",
"move",
"text",
"."
] |
def getCraftedTextFromText(gcodeText, repository=None):
"Comb a gcode linear move text."
if gcodec.isProcedureDoneOrFileIsEmpty(gcodeText, 'comb'):
return gcodeText
if repository == None:
repository = settings.getReadRepository(CombRepository())
if not repository.activateComb.value:
return gcodeText
return CombSkein().getCraftedGcode(gcodeText, repository)
|
[
"def",
"getCraftedTextFromText",
"(",
"gcodeText",
",",
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")",
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"if",
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"isProcedureDoneOrFileIsEmpty",
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",",
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"(",
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",",
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")"
] |
https://github.com/makerbot/ReplicatorG/blob/d6f2b07785a5a5f1e172fb87cb4303b17c575d5d/skein_engines/skeinforge-47/skeinforge_application/skeinforge_plugins/craft_plugins/comb.py#L63-L71
|
|
JimmXinu/FanFicFare
|
bc149a2deb2636320fe50a3e374af6eef8f61889
|
included_dependencies/urllib3/packages/six.py
|
python
|
assertRegex
|
(self, *args, **kwargs)
|
return getattr(self, _assertRegex)(*args, **kwargs)
|
[] |
def assertRegex(self, *args, **kwargs):
return getattr(self, _assertRegex)(*args, **kwargs)
|
[
"def",
"assertRegex",
"(",
"self",
",",
"*",
"args",
",",
"*",
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"kwargs",
")",
":",
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"(",
"self",
",",
"_assertRegex",
")",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/JimmXinu/FanFicFare/blob/bc149a2deb2636320fe50a3e374af6eef8f61889/included_dependencies/urllib3/packages/six.py#L753-L754
|
|||
makerbot/ReplicatorG
|
d6f2b07785a5a5f1e172fb87cb4303b17c575d5d
|
skein_engines/skeinforge-50/skeinforge_application/skeinforge_plugins/analyze_plugins/skeiniso.py
|
python
|
SkeinWindow.update
|
(self)
|
Update the screen.
|
Update the screen.
|
[
"Update",
"the",
"screen",
"."
] |
def update(self):
"Update the screen."
if len( self.skeinPanes ) < 1:
return
self.limitIndexSetArrowMouseDeleteCanvas()
self.repository.viewpointLatitude.value = view_rotate.getBoundedLatitude( self.repository.viewpointLatitude.value )
self.repository.viewpointLongitude.value = round( self.repository.viewpointLongitude.value, 1 )
projectiveSpace = euclidean.ProjectiveSpace().getByLatitudeLongitude( self.repository.viewpointLatitude.value, self.repository.viewpointLongitude.value )
skeinPanesCopy = self.getUpdateSkeinPanes()[:]
skeinPanesCopy.sort( compareLayerSequence )
if projectiveSpace.basisZ.z > 0.0:
self.drawXYAxisLines( projectiveSpace )
else:
skeinPanesCopy.reverse()
self.drawZAxisLine( projectiveSpace )
for skeinPane in skeinPanesCopy:
self.drawSkeinPane( projectiveSpace, skeinPane )
if projectiveSpace.basisZ.z > 0.0:
self.drawZAxisLine( projectiveSpace )
else:
self.drawXYAxisLines( projectiveSpace )
if self.repository.widthOfAxisNegativeSide.value > 0:
self.drawRulings( self.negativeAxisLineX, projectiveSpace, self.negativeRulings )
self.drawRulings( self.negativeAxisLineY, projectiveSpace, self.negativeRulings )
self.drawRulings( self.negativeAxisLineZ, projectiveSpace, self.negativeRulings )
if self.repository.widthOfAxisPositiveSide.value > 0:
self.drawRulings( self.positiveAxisLineX, projectiveSpace, self.positiveRulings )
self.drawRulings( self.positiveAxisLineY, projectiveSpace, self.positiveRulings )
self.drawRulings( self.positiveAxisLineZ, projectiveSpace, self.positiveRulings )
self.setDisplayLayerIndex()
|
[
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",",
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")",
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"(",
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"positiveAxisLineZ",
",",
"projectiveSpace",
",",
"self",
".",
"positiveRulings",
")",
"self",
".",
"setDisplayLayerIndex",
"(",
")"
] |
https://github.com/makerbot/ReplicatorG/blob/d6f2b07785a5a5f1e172fb87cb4303b17c575d5d/skein_engines/skeinforge-50/skeinforge_application/skeinforge_plugins/analyze_plugins/skeiniso.py#L848-L877
|
||
mozillazg/pypy
|
2ff5cd960c075c991389f842c6d59e71cf0cb7d0
|
lib-python/2.7/email/header.py
|
python
|
Header.__unicode__
|
(self)
|
return UEMPTYSTRING.join(uchunks)
|
Helper for the built-in unicode function.
|
Helper for the built-in unicode function.
|
[
"Helper",
"for",
"the",
"built",
"-",
"in",
"unicode",
"function",
"."
] |
def __unicode__(self):
"""Helper for the built-in unicode function."""
uchunks = []
lastcs = None
for s, charset in self._chunks:
# We must preserve spaces between encoded and non-encoded word
# boundaries, which means for us we need to add a space when we go
# from a charset to None/us-ascii, or from None/us-ascii to a
# charset. Only do this for the second and subsequent chunks.
nextcs = charset
if uchunks:
if lastcs not in (None, 'us-ascii'):
if nextcs in (None, 'us-ascii'):
uchunks.append(USPACE)
nextcs = None
elif nextcs not in (None, 'us-ascii'):
uchunks.append(USPACE)
lastcs = nextcs
uchunks.append(unicode(s, str(charset)))
return UEMPTYSTRING.join(uchunks)
|
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"def",
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"=",
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"return",
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"join",
"(",
"uchunks",
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] |
https://github.com/mozillazg/pypy/blob/2ff5cd960c075c991389f842c6d59e71cf0cb7d0/lib-python/2.7/email/header.py#L202-L221
|
|
ronf/asyncssh
|
ee1714c598d8c2ea6f5484e465443f38b68714aa
|
asyncssh/connection.py
|
python
|
connect_reverse
|
(
host: str, port: DefTuple[int] = (), *,
tunnel: DefTuple[_TunnelConnector] = (),
family: DefTuple[int] = (), flags: int = 0,
local_addr: DefTuple[HostPort] = (),
config: DefTuple[ConfigPaths] = (),
options: Optional[SSHServerConnectionOptions] = None,
**kwargs: object)
|
return await asyncio.wait_for(
_connect(new_options, loop, flags, conn_factory,
'Opening reverse SSH connection to'),
timeout=new_options.connect_timeout)
|
Create a reverse direction SSH connection
This function is a coroutine which behaves similar to :func:`connect`,
making an outbound TCP connection to a remote server. However, instead
of starting up an SSH client which runs on that outbound connection,
this function starts up an SSH server, expecting the remote system to
start up a reverse-direction SSH client.
Arguments to this function are the same as :func:`connect`, except
that the `options` are of type :class:`SSHServerConnectionOptions`
instead of :class:`SSHClientConnectionOptions`.
:param host:
The hostname or address to connect to.
:param port: (optional)
The port number to connect to. If not specified, the default
SSH port is used.
:param tunnel: (optional)
An existing SSH client connection that this new connection should
be tunneled over. If set, a direct TCP/IP tunnel will be opened
over this connection to the requested host and port rather than
connecting directly via TCP. A string of the form
[user@]host[:port] may also be specified, in which case a
connection will first be made to that host and it will then be
used as a tunnel.
:param family: (optional)
The address family to use when creating the socket. By default,
the address family is automatically selected based on the host.
:param flags: (optional)
The flags to pass to getaddrinfo() when looking up the host address
:param local_addr: (optional)
The host and port to bind the socket to before connecting
:param config: (optional)
Paths to OpenSSH server configuration files to load. This
configuration will be used as a fallback to override the
defaults for settings which are not explcitly specified using
AsyncSSH's configuration options. By default, no OpenSSH
configuration files will be loaded. See
:ref:`SupportedServerConfigOptions` for details on what
configuration options are currently supported.
:param options: (optional)
Options to use when starting the reverse-direction SSH server.
These options can be specified either through this parameter
or as direct keyword arguments to this function.
:type host: `str`
:type port: `int`
:type tunnel: :class:`SSHClientConnection` or `str`
:type family: `socket.AF_UNSPEC`, `socket.AF_INET`, or `socket.AF_INET6`
:type flags: flags to pass to :meth:`getaddrinfo() <socket.getaddrinfo>`
:type local_addr: tuple of `str` and `int`
:type config: `list` of `str`
:type options: :class:`SSHServerConnectionOptions`
:returns: :class:`SSHServerConnection`
|
Create a reverse direction SSH connection
|
[
"Create",
"a",
"reverse",
"direction",
"SSH",
"connection"
] |
async def connect_reverse(
host: str, port: DefTuple[int] = (), *,
tunnel: DefTuple[_TunnelConnector] = (),
family: DefTuple[int] = (), flags: int = 0,
local_addr: DefTuple[HostPort] = (),
config: DefTuple[ConfigPaths] = (),
options: Optional[SSHServerConnectionOptions] = None,
**kwargs: object) -> SSHServerConnection:
"""Create a reverse direction SSH connection
This function is a coroutine which behaves similar to :func:`connect`,
making an outbound TCP connection to a remote server. However, instead
of starting up an SSH client which runs on that outbound connection,
this function starts up an SSH server, expecting the remote system to
start up a reverse-direction SSH client.
Arguments to this function are the same as :func:`connect`, except
that the `options` are of type :class:`SSHServerConnectionOptions`
instead of :class:`SSHClientConnectionOptions`.
:param host:
The hostname or address to connect to.
:param port: (optional)
The port number to connect to. If not specified, the default
SSH port is used.
:param tunnel: (optional)
An existing SSH client connection that this new connection should
be tunneled over. If set, a direct TCP/IP tunnel will be opened
over this connection to the requested host and port rather than
connecting directly via TCP. A string of the form
[user@]host[:port] may also be specified, in which case a
connection will first be made to that host and it will then be
used as a tunnel.
:param family: (optional)
The address family to use when creating the socket. By default,
the address family is automatically selected based on the host.
:param flags: (optional)
The flags to pass to getaddrinfo() when looking up the host address
:param local_addr: (optional)
The host and port to bind the socket to before connecting
:param config: (optional)
Paths to OpenSSH server configuration files to load. This
configuration will be used as a fallback to override the
defaults for settings which are not explcitly specified using
AsyncSSH's configuration options. By default, no OpenSSH
configuration files will be loaded. See
:ref:`SupportedServerConfigOptions` for details on what
configuration options are currently supported.
:param options: (optional)
Options to use when starting the reverse-direction SSH server.
These options can be specified either through this parameter
or as direct keyword arguments to this function.
:type host: `str`
:type port: `int`
:type tunnel: :class:`SSHClientConnection` or `str`
:type family: `socket.AF_UNSPEC`, `socket.AF_INET`, or `socket.AF_INET6`
:type flags: flags to pass to :meth:`getaddrinfo() <socket.getaddrinfo>`
:type local_addr: tuple of `str` and `int`
:type config: `list` of `str`
:type options: :class:`SSHServerConnectionOptions`
:returns: :class:`SSHServerConnection`
"""
def conn_factory() -> SSHServerConnection:
"""Return an SSH client connection factory"""
return SSHServerConnection(loop, new_options, wait='auth')
loop = asyncio.get_event_loop()
new_options = SSHServerConnectionOptions(options, config=config, host=host,
port=port, tunnel=tunnel,
family=family,
local_addr=local_addr, **kwargs)
return await asyncio.wait_for(
_connect(new_options, loop, flags, conn_factory,
'Opening reverse SSH connection to'),
timeout=new_options.connect_timeout)
|
[
"async",
"def",
"connect_reverse",
"(",
"host",
":",
"str",
",",
"port",
":",
"DefTuple",
"[",
"int",
"]",
"=",
"(",
")",
",",
"*",
",",
"tunnel",
":",
"DefTuple",
"[",
"_TunnelConnector",
"]",
"=",
"(",
")",
",",
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"DefTuple",
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"=",
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")",
",",
"flags",
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"int",
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"kwargs",
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"object",
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":",
"\"\"\"Return an SSH client connection factory\"\"\"",
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"loop",
",",
"new_options",
",",
"wait",
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"'auth'",
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"options",
",",
"config",
"=",
"config",
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"host",
"=",
"host",
",",
"port",
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"port",
",",
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"=",
"tunnel",
",",
"family",
"=",
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",",
"local_addr",
"=",
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",",
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"(",
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",",
"loop",
",",
"flags",
",",
"conn_factory",
",",
"'Opening reverse SSH connection to'",
")",
",",
"timeout",
"=",
"new_options",
".",
"connect_timeout",
")"
] |
https://github.com/ronf/asyncssh/blob/ee1714c598d8c2ea6f5484e465443f38b68714aa/asyncssh/connection.py#L7644-L7724
|
|
devstructure/blueprint
|
574a9fc0dd3031c66970387f1105d8c89e61218f
|
blueprint/io/server/backend.py
|
python
|
put
|
(key, data)
|
Store an object in S3. This function makes one billable request.
|
Store an object in S3. This function makes one billable request.
|
[
"Store",
"an",
"object",
"in",
"S3",
".",
"This",
"function",
"makes",
"one",
"billable",
"request",
"."
] |
def put(key, data):
"""
Store an object in S3. This function makes one billable request.
"""
librato.count('blueprint-io-server.requests.put')
statsd.increment('blueprint-io-server.requests.put')
# TODO librato.something('blueprint-io-server.storage', len(data))
statsd.update('blueprint-io-server.storage', len(data))
c = boto.connect_s3(access_key, secret_key)
b = c.get_bucket(bucket, validate=False)
k = b.new_key(key)
try:
k.set_contents_from_string(data,
policy='public-read',
reduced_redundancy=True)
return True
except (boto.exception.BotoClientError,
boto.exception.BotoServerError,
httplib.HTTPException,
socket.error,
socket.gaierror):
return False
|
[
"def",
"put",
"(",
"key",
",",
"data",
")",
":",
"librato",
".",
"count",
"(",
"'blueprint-io-server.requests.put'",
")",
"statsd",
".",
"increment",
"(",
"'blueprint-io-server.requests.put'",
")",
"# TODO librato.something('blueprint-io-server.storage', len(data))",
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"'blueprint-io-server.storage'",
",",
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")",
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"BotoClientError",
",",
"boto",
".",
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".",
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",",
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".",
"HTTPException",
",",
"socket",
".",
"error",
",",
"socket",
".",
"gaierror",
")",
":",
"return",
"False"
] |
https://github.com/devstructure/blueprint/blob/574a9fc0dd3031c66970387f1105d8c89e61218f/blueprint/io/server/backend.py#L144-L165
|
||
arthurdejong/python-stdnum
|
02dec52602ae0709b940b781fc1fcebfde7340b7
|
stdnum/pl/regon.py
|
python
|
is_valid
|
(number)
|
Check if the number is a valid REGON number.
|
Check if the number is a valid REGON number.
|
[
"Check",
"if",
"the",
"number",
"is",
"a",
"valid",
"REGON",
"number",
"."
] |
def is_valid(number):
"""Check if the number is a valid REGON number."""
try:
return bool(validate(number))
except ValidationError:
return False
|
[
"def",
"is_valid",
"(",
"number",
")",
":",
"try",
":",
"return",
"bool",
"(",
"validate",
"(",
"number",
")",
")",
"except",
"ValidationError",
":",
"return",
"False"
] |
https://github.com/arthurdejong/python-stdnum/blob/02dec52602ae0709b940b781fc1fcebfde7340b7/stdnum/pl/regon.py#L89-L94
|
||
ganeti/ganeti
|
d340a9ddd12f501bef57da421b5f9b969a4ba905
|
lib/hypervisor/hv_kvm/__init__.py
|
python
|
KVMHypervisor._SocatUnixConsoleParams
|
()
|
Returns the correct parameters for socat
If we have a new-enough socat we can use raw mode with an escape character.
|
Returns the correct parameters for socat
|
[
"Returns",
"the",
"correct",
"parameters",
"for",
"socat"
] |
def _SocatUnixConsoleParams():
"""Returns the correct parameters for socat
If we have a new-enough socat we can use raw mode with an escape character.
"""
if constants.SOCAT_USE_ESCAPE:
return "raw,echo=0,escape=%s" % constants.SOCAT_ESCAPE_CODE
else:
return "echo=0,icanon=0"
|
[
"def",
"_SocatUnixConsoleParams",
"(",
")",
":",
"if",
"constants",
".",
"SOCAT_USE_ESCAPE",
":",
"return",
"\"raw,echo=0,escape=%s\"",
"%",
"constants",
".",
"SOCAT_ESCAPE_CODE",
"else",
":",
"return",
"\"echo=0,icanon=0\""
] |
https://github.com/ganeti/ganeti/blob/d340a9ddd12f501bef57da421b5f9b969a4ba905/lib/hypervisor/hv_kvm/__init__.py#L823-L832
|
||
xonsh/xonsh
|
b76d6f994f22a4078f602f8b386f4ec280c8461f
|
xonsh/ansi_colors.py
|
python
|
ansi_style_by_name
|
(name)
|
return astyle
|
Gets or makes an ANSI color style by name. If the styles does not
exist, it will look for a style using the pygments name.
|
Gets or makes an ANSI color style by name. If the styles does not
exist, it will look for a style using the pygments name.
|
[
"Gets",
"or",
"makes",
"an",
"ANSI",
"color",
"style",
"by",
"name",
".",
"If",
"the",
"styles",
"does",
"not",
"exist",
"it",
"will",
"look",
"for",
"a",
"style",
"using",
"the",
"pygments",
"name",
"."
] |
def ansi_style_by_name(name):
"""Gets or makes an ANSI color style by name. If the styles does not
exist, it will look for a style using the pygments name.
"""
if name in ANSI_STYLES:
return ANSI_STYLES[name]
elif not HAS_PYGMENTS:
raise KeyError(f"could not find style {name!r}")
from xonsh.pygments_cache import get_style_by_name
from pygments.util import ClassNotFound
try:
pstyle = get_style_by_name(name)
except (ModuleNotFoundError, ClassNotFound):
pstyle = get_style_by_name("default")
palette = make_palette(pstyle.styles.values())
astyle = make_ansi_style(palette)
ANSI_STYLES[name] = astyle
return astyle
|
[
"def",
"ansi_style_by_name",
"(",
"name",
")",
":",
"if",
"name",
"in",
"ANSI_STYLES",
":",
"return",
"ANSI_STYLES",
"[",
"name",
"]",
"elif",
"not",
"HAS_PYGMENTS",
":",
"raise",
"KeyError",
"(",
"f\"could not find style {name!r}\"",
")",
"from",
"xonsh",
".",
"pygments_cache",
"import",
"get_style_by_name",
"from",
"pygments",
".",
"util",
"import",
"ClassNotFound",
"try",
":",
"pstyle",
"=",
"get_style_by_name",
"(",
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")",
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"ModuleNotFoundError",
",",
"ClassNotFound",
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":",
"pstyle",
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"\"default\"",
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"palette",
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"(",
"pstyle",
".",
"styles",
".",
"values",
"(",
")",
")",
"astyle",
"=",
"make_ansi_style",
"(",
"palette",
")",
"ANSI_STYLES",
"[",
"name",
"]",
"=",
"astyle",
"return",
"astyle"
] |
https://github.com/xonsh/xonsh/blob/b76d6f994f22a4078f602f8b386f4ec280c8461f/xonsh/ansi_colors.py#L1146-L1164
|
|
securesystemslab/zippy
|
ff0e84ac99442c2c55fe1d285332cfd4e185e089
|
zippy/benchmarks/src/benchmarks/sympy/sympy/utilities/iterables.py
|
python
|
multiset
|
(seq)
|
return dict(rv)
|
Return the hashable sequence in multiset form with values being the
multiplicity of the item in the sequence.
Examples
========
>>> from sympy.utilities.iterables import multiset
>>> multiset('mississippi')
{'i': 4, 'm': 1, 'p': 2, 's': 4}
See Also
========
group
|
Return the hashable sequence in multiset form with values being the
multiplicity of the item in the sequence.
|
[
"Return",
"the",
"hashable",
"sequence",
"in",
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"with",
"values",
"being",
"the",
"multiplicity",
"of",
"the",
"item",
"in",
"the",
"sequence",
"."
] |
def multiset(seq):
"""Return the hashable sequence in multiset form with values being the
multiplicity of the item in the sequence.
Examples
========
>>> from sympy.utilities.iterables import multiset
>>> multiset('mississippi')
{'i': 4, 'm': 1, 'p': 2, 's': 4}
See Also
========
group
"""
rv = defaultdict(int)
for s in seq:
rv[s] += 1
return dict(rv)
|
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] |
https://github.com/securesystemslab/zippy/blob/ff0e84ac99442c2c55fe1d285332cfd4e185e089/zippy/benchmarks/src/benchmarks/sympy/sympy/utilities/iterables.py#L196-L214
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/babel/numbers.py
|
python
|
format_scientific
|
(number, format=None, locale=LC_NUMERIC)
|
return pattern.apply(number, locale)
|
Return value formatted in scientific notation for a specific locale.
>>> format_scientific(10000, locale='en_US')
u'1E4'
The format pattern can also be specified explicitly:
>>> format_scientific(1234567, u'##0E00', locale='en_US')
u'1.23E06'
:param number: the number to format
:param format:
:param locale: the `Locale` object or locale identifier
|
Return value formatted in scientific notation for a specific locale.
|
[
"Return",
"value",
"formatted",
"in",
"scientific",
"notation",
"for",
"a",
"specific",
"locale",
"."
] |
def format_scientific(number, format=None, locale=LC_NUMERIC):
"""Return value formatted in scientific notation for a specific locale.
>>> format_scientific(10000, locale='en_US')
u'1E4'
The format pattern can also be specified explicitly:
>>> format_scientific(1234567, u'##0E00', locale='en_US')
u'1.23E06'
:param number: the number to format
:param format:
:param locale: the `Locale` object or locale identifier
"""
locale = Locale.parse(locale)
if not format:
format = locale.scientific_formats.get(format)
pattern = parse_pattern(format)
return pattern.apply(number, locale)
|
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"format",
")",
"return",
"pattern",
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"apply",
"(",
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",",
"locale",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/babel/numbers.py#L451-L470
|
|
nasa/CrisisMappingToolkit
|
0296487974d74cec6aa8be42eafbb5cd24dc6a51
|
app_engine/libs/bs4/__init__.py
|
python
|
BeautifulSoup.reset
|
(self)
|
[] |
def reset(self):
Tag.__init__(self, self, self.builder, self.ROOT_TAG_NAME)
self.hidden = 1
self.builder.reset()
self.current_data = []
self.currentTag = None
self.tagStack = []
self.preserve_whitespace_tag_stack = []
self.pushTag(self)
|
[
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",",
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] |
https://github.com/nasa/CrisisMappingToolkit/blob/0296487974d74cec6aa8be42eafbb5cd24dc6a51/app_engine/libs/bs4/__init__.py#L245-L253
|
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