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ganeti/ganeti | d340a9ddd12f501bef57da421b5f9b969a4ba905 | lib/client/gnt_network.py | python | RenameNetwork | (opts, args) | Rename a network.
@param opts: the command line options selected by the user
@type args: list
@param args: a list of length 2, [old_name, new_name]
@rtype: int
@return: the desired exit code | Rename a network. | [
"Rename",
"a",
"network",
"."
] | def RenameNetwork(opts, args):
"""Rename a network.
@param opts: the command line options selected by the user
@type args: list
@param args: a list of length 2, [old_name, new_name]
@rtype: int
@return: the desired exit code
"""
network_name, new_name = args
op = opcodes.OpNetworkRename(network_name=network_name, new_name=new_name)
SubmitOrSend(op, opts) | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | examples/container_engine.py | python | update_node_pool | (ce_client, node_pool_id) | return | update_node_pool
Currently there are a number of features that can be updated in the node pool.
This example will only update the name. Please see the documentation for
the other features which can be updated | update_node_pool | [
"update_node_pool"
] | def update_node_pool(ce_client, node_pool_id):
"""
update_node_pool
Currently there are a number of features that can be updated in the node pool.
This example will only update the name. Please see the documentation for
the other features which can be updated
"""
update_node_pool_details = oci.container_engine.models.UpdateNodePoolDetails(name="PythonSDK_noodpool_1")
ce_composite_ops = oci.container_engine.ContainerEngineClientCompositeOperations(ce_client)
response = ce_composite_ops.update_node_pool_and_wait_for_state(node_pool_id,
update_node_pool_details,
wait_for_states=[oci.container_engine.models.WorkRequest.STATUS_SUCCEEDED,
oci.container_engine.models.WorkRequest.STATUS_FAILED])
if response.data.status == oci.container_engine.models.WorkRequest.STATUS_FAILED:
get_work_request_errors(ce_client, compartment_id, response.data.id)
else:
print("Update node pool succeeded")
return | [
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hzy46/Deep-Learning-21-Examples | 15c2d9edccad090cd67b033f24a43c544e5cba3e | chapter_6/src/models/inception_resnet_v2.py | python | inception_resnet_v2 | (inputs, is_training=True,
dropout_keep_prob=0.8,
bottleneck_layer_size=128,
reuse=None,
scope='InceptionResnetV2') | return net, end_points | Creates the Inception Resnet V2 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the logits outputs of the model.
end_points: the set of end_points from the inception model. | Creates the Inception Resnet V2 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the logits outputs of the model.
end_points: the set of end_points from the inception model. | [
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dropout_keep_prob=0.8,
bottleneck_layer_size=128,
reuse=None,
scope='InceptionResnetV2'):
"""Creates the Inception Resnet V2 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the logits outputs of the model.
end_points: the set of end_points from the inception model.
"""
end_points = {}
with tf.variable_scope(scope, 'InceptionResnetV2', [inputs], reuse=reuse):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# 149 x 149 x 32
net = slim.conv2d(inputs, 32, 3, stride=2, padding='VALID',
scope='Conv2d_1a_3x3')
end_points['Conv2d_1a_3x3'] = net
# 147 x 147 x 32
net = slim.conv2d(net, 32, 3, padding='VALID',
scope='Conv2d_2a_3x3')
end_points['Conv2d_2a_3x3'] = net
# 147 x 147 x 64
net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
end_points['Conv2d_2b_3x3'] = net
# 73 x 73 x 64
net = slim.max_pool2d(net, 3, stride=2, padding='VALID',
scope='MaxPool_3a_3x3')
end_points['MaxPool_3a_3x3'] = net
# 73 x 73 x 80
net = slim.conv2d(net, 80, 1, padding='VALID',
scope='Conv2d_3b_1x1')
end_points['Conv2d_3b_1x1'] = net
# 71 x 71 x 192
net = slim.conv2d(net, 192, 3, padding='VALID',
scope='Conv2d_4a_3x3')
end_points['Conv2d_4a_3x3'] = net
# 35 x 35 x 192
net = slim.max_pool2d(net, 3, stride=2, padding='VALID',
scope='MaxPool_5a_3x3')
end_points['MaxPool_5a_3x3'] = net
# 35 x 35 x 320
with tf.variable_scope('Mixed_5b'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
scope='Conv2d_0b_3x3')
tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
scope='AvgPool_0a_3x3')
tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
scope='Conv2d_0b_1x1')
net = tf.concat([tower_conv, tower_conv1_1,
tower_conv2_2, tower_pool_1], 3)
end_points['Mixed_5b'] = net
net = slim.repeat(net, 10, block35, scale=0.17)
# 17 x 17 x 1024
with tf.variable_scope('Mixed_6a'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 384, 3, stride=2, padding='VALID',
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
scope='Conv2d_0b_3x3')
tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
stride=2, padding='VALID',
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
end_points['Mixed_6a'] = net
net = slim.repeat(net, 20, block17, scale=0.10)
with tf.variable_scope('Mixed_7a'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
scope='Conv2d_0b_3x3')
tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_3'):
tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat([tower_conv_1, tower_conv1_1,
tower_conv2_2, tower_pool], 3)
end_points['Mixed_7a'] = net
net = slim.repeat(net, 9, block8, scale=0.20)
net = block8(net, activation_fn=None)
net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
end_points['Conv2d_7b_1x1'] = net
with tf.variable_scope('Logits'):
end_points['PrePool'] = net
#pylint: disable=no-member
net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
scope='AvgPool_1a_8x8')
net = slim.flatten(net)
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='Dropout')
end_points['PreLogitsFlatten'] = net
net = slim.fully_connected(net, bottleneck_layer_size, activation_fn=None,
scope='Bottleneck', reuse=False)
return net, end_points | [
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/registry/api_client.py | python | ApiClient.__deserialize_model | (self, data, klass) | return instance | Deserializes list or dict to model.
:param data: dict, list.
:param klass: class literal.
:return: model object. | Deserializes list or dict to model. | [
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] | def __deserialize_model(self, data, klass):
"""
Deserializes list or dict to model.
:param data: dict, list.
:param klass: class literal.
:return: model object.
"""
if not klass.swagger_types:
return data
kwargs = {}
for attr, attr_type in iteritems(klass.swagger_types):
if data is not None \
and klass.attribute_map[attr] in data \
and isinstance(data, (list, dict)):
value = data[klass.attribute_map[attr]]
if attr_type.startswith('list['):
# if this is a list, we may get back a single item
# or a list
# create the list object if it doesn't exist
# append the return
if not kwargs.get(attr):
kwargs[attr] = []
deserialized_value = self.__deserialize(value, attr_type)
if deserialized_value:
if isinstance(deserialized_value, list):
kwargs[attr].extend(deserialized_value)
else:
kwargs[attr].append(deserialized_value)
else:
kwargs[attr] = self.__deserialize(value, attr_type)
instance = klass(**kwargs)
return instance | [
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NVIDIA/NeMo | 5b0c0b4dec12d87d3cd960846de4105309ce938e | nemo/collections/tts/modules/squeezewave.py | python | SqueezeWaveModule.input_example | (self) | return tuple([mel]) | Generates input examples for tracing etc.
Returns:
A tuple of input examples. | Generates input examples for tracing etc.
Returns:
A tuple of input examples. | [
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"""
Generates input examples for tracing etc.
Returns:
A tuple of input examples.
"""
par = next(self.parameters())
mel = torch.randn((1, self.n_mel_channels, 96), device=par.device, dtype=par.dtype)
return tuple([mel]) | [
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tenable/pyTenable | 1ccab9fc6f6e4c9f1cfe5128f694388ea112719d | tenable/sc/current.py | python | CurrentSessionAPI.org | (self, fields=None) | return self._api.get('currentOrganization', params=params).json()['response'] | Returns the organization of the current session.
:sc-api:`current-organization <Current-Organization.html>`
Args:
fields (list, optional):
The list of fields that are desired to be returned. For details
on what fields are available, please refer to the details on the
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Returns:
:obj:`dict`:
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>>> org = sc.current.org() | Returns the organization of the current session. | [
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Returns the organization of the current session.
:sc-api:`current-organization <Current-Organization.html>`
Args:
fields (list, optional):
The list of fields that are desired to be returned. For details
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Returns:
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Examples:
>>> org = sc.current.org()
'''
params = dict()
if fields:
params['fields'] = ','.join([self._check('field', f, str)
for f in fields])
return self._api.get('currentOrganization', params=params).json()['response'] | [
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/datetime.py | python | _days_before_month | (year, month) | return _DAYS_BEFORE_MONTH[month] + (month > 2 and _is_leap(year)) | year, month -> number of days in year preceding first day of month. | year, month -> number of days in year preceding first day of month. | [
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"year, month -> number of days in year preceding first day of month."
assert 1 <= month <= 12, 'month must be in 1..12'
return _DAYS_BEFORE_MONTH[month] + (month > 2 and _is_leap(year)) | [
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git-cola/git-cola | b48b8028e0c3baf47faf7b074b9773737358163d | cola/widgets/status.py | python | show_help | (context) | return text.text_dialog(context, help_text, title) | [] | def show_help(context):
help_text = N_(
r"""
Format String Variables
-----------------------
%(path)s = relative file path
%(abspath)s = absolute file path
%(dirname)s = relative directory path
%(absdirname)s = absolute directory path
%(filename)s = file basename
%(basename)s = file basename without extension
%(ext)s = file extension
"""
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title = N_('Help - Custom Copy Actions')
return text.text_dialog(context, help_text, title) | [
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shiweibsw/Translation-Tools | 2fbbf902364e557fa7017f9a74a8797b7440c077 | venv/Lib/site-packages/pip-9.0.3-py3.6.egg/pip/_vendor/pyparsing.py | python | Or.parseImpl | ( self, instring, loc, doActions=True ) | [] | def parseImpl( self, instring, loc, doActions=True ):
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for _,e in matches:
try:
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err.__traceback__ = None
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maxExcLoc = err.loc
if maxException is not None:
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marcoeilers/nagini | a2a19df7d833e67841e03c9885869c3dddef3327 | src/nagini_contracts/contracts.py | python | LowExit | () | Predicate that states that whether the current loop has been left via a break or
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"""
Predicate that states that whether the current loop has been left via a break or
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"""
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neubig/nn4nlp-code | 970d91a51664b3d91a9822b61cd76abea20218cb | 14-semparsing/ucca/scripts/distances/align.py | python | align | (sen1, sen2, string=True) | return mapping, refactored_indexes | finds the best mapping of words from one sentence to the other
string = a boolean represents if sentences are given as strings or as list of ucca terminal nodes
returns list of word tuples and the corresponding list of indexes tuples | finds the best mapping of words from one sentence to the other
string = a boolean represents if sentences are given as strings or as list of ucca terminal nodes
returns list of word tuples and the corresponding list of indexes tuples | [
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returns list of word tuples and the corresponding list of indexes tuples"""
if string:
sen1 = list(map(preprocess_word, sen1.split()))
sen2 = list(map(preprocess_word, sen2.split()))
else:
sen1 = [preprocess_word(terminal.text) for terminal in sen1]
sen2 = [preprocess_word(terminal.text) for terminal in sen2]
# find lengths
length_dif = len(sen1) - len(sen2)
if length_dif > 0:
shorter = sen2
longer = sen1
switched = False
else:
shorter = sen1
longer = sen2
switched = True
length_dif = abs(length_dif)
shorter += ["emptyWord"] * length_dif
# create matrix
matrix = np.zeros((len(longer), len(longer)))
for i in range(len(longer)):
for j in range(len(longer) - length_dif):
matrix[i, j] = distance.levenshtein(longer[i], shorter[j]) + float(abs(i - j)) / len(longer)
# compare with munkres
m = Munkres()
indexes = m.compute(matrix)
# remove indexing for emptywords and create string mapping
refactored_indexes = []
mapping = []
start = 0 if string else 1
for i, j in indexes:
if j >= len(longer) - length_dif:
j = -1 - start
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mapping.append((shorter[j], longer[i]))
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chdsbd/kodiak | 4c705cea8edaa2792f2a59700a2f7c3d75b6e918 | web_api/web_api/views.py | python | plan_id_from_period | (period: Literal["month", "year"]) | return None | [] | def plan_id_from_period(period: Literal["month", "year"]) -> str:
if period == "month":
return cast(str, settings.STRIPE_PLAN_ID)
if period == "year":
return cast(str, settings.STRIPE_ANNUAL_PLAN_ID)
return None | [
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tensorflow/tfx | b4a6b83269815ed12ba9df9e9154c7376fef2ea0 | tfx/orchestration/portable/mlmd/execution_lib.py | python | get_executions_associated_with_all_contexts | (
metadata_handler: metadata.Metadata,
contexts: Iterable[metadata_store_pb2.Context]
) | return list(executions_dict.values()) if executions_dict else [] | Returns executions that are associated with all given contexts.
Args:
metadata_handler: A handler to access MLMD.
contexts: MLMD contexts for which to fetch associated executions.
Returns:
A list of executions associated with all given contexts. | Returns executions that are associated with all given contexts. | [
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metadata_handler: metadata.Metadata,
contexts: Iterable[metadata_store_pb2.Context]
) -> List[metadata_store_pb2.Execution]:
"""Returns executions that are associated with all given contexts.
Args:
metadata_handler: A handler to access MLMD.
contexts: MLMD contexts for which to fetch associated executions.
Returns:
A list of executions associated with all given contexts.
"""
executions_dict = None
for context in contexts:
executions = metadata_handler.store.get_executions_by_context(context.id)
if executions_dict is None:
executions_dict = {e.id: e for e in executions}
else:
executions_dict = {e.id: e for e in executions if e.id in executions_dict}
return list(executions_dict.values()) if executions_dict else [] | [
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triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/views.py | python | getSuccessLists | (request) | return HttpResponse(json.dumps(ip_list)) | [] | def getSuccessLists(request):
track_id = request.GET['TrackMark']
ret_list = OpsLogTemp.objects.filter(track_mark = track_id,result = "Success")
ip_list = []
for ip in ret_list:
ip_list.append(ip.ip)
return HttpResponse(json.dumps(ip_list)) | [
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pytorch/fairseq | 1575f30dd0a9f7b3c499db0b4767aa4e9f79056c | fairseq/modules/quantization/pq/utils.py | python | SizeTracker.update | (self, W, block_size, n_centroids) | Updates the running statistics when quantizing a new layer. | Updates the running statistics when quantizing a new layer. | [
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"""
Updates the running statistics when quantizing a new layer.
"""
# bits per weights
bits_per_weight = np.log2(n_centroids) / block_size
self.n_quantized_layers += 1
# size of indexing the subvectors of size block_size (in MB)
size_index_layer = bits_per_weight * W.numel() / 8 / 1024 / 1024
self.size_index += size_index_layer
# size of the centroids stored in float16 (in MB)
size_centroids_layer = n_centroids * block_size * 2 / 1024 / 1024
self.size_centroids += size_centroids_layer
# size of non-compressed layers, e.g. LayerNorms or biases (in MB)
size_uncompressed_layer = W.numel() * 4 / 1024 / 1024
self.size_non_quantized -= size_uncompressed_layer | [
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mozman/ezdxf | 59d0fc2ea63f5cf82293428f5931da7e9f9718e9 | src/ezdxf/math/_matrix44.py | python | Matrix44.__imul__ | (self, other: "Matrix44") | return self | Inplace multiplication with another matrix. | Inplace multiplication with another matrix. | [
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"""Inplace multiplication with another matrix."""
m1 = self._matrix
m2 = other._matrix
# fmt: off
self._matrix = [
m1[0] * m2[0] + m1[1] * m2[4] + m1[2] * m2[8] + m1[3] * m2[12],
m1[0] * m2[1] + m1[1] * m2[5] + m1[2] * m2[9] + m1[3] * m2[13],
m1[0] * m2[2] + m1[1] * m2[6] + m1[2] * m2[10] + m1[3] * m2[14],
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m1[8] * m2[3] + m1[9] * m2[7] + m1[10] * m2[11] + m1[11] * m2[15],
m1[12] * m2[0] + m1[13] * m2[4] + m1[14] * m2[8] + m1[15] * m2[12],
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m1[12] * m2[3] + m1[13] * m2[7] + m1[14] * m2[11] + m1[15] * m2[15]
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# fmt: on
return self | [
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cloudera/impyla | 0c736af4cad2bade9b8e313badc08ec50e81c948 | impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py | python | add_partitions_args.read | (self, iprot) | [] | def read(self, iprot):
if iprot._fast_decode is not None and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None:
iprot._fast_decode(self, iprot, [self.__class__, self.thrift_spec])
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.LIST:
self.new_parts = []
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for _i712 in range(_size708):
_elem713 = Partition()
_elem713.read(iprot)
self.new_parts.append(_elem713)
iprot.readListEnd()
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd() | [
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paulproteus/python-scraping-code-samples | 4e5396d4e311ca66c784a2b5f859308285e511da | new/seleniumrc/selenium.py | python | selenium.type | (self,locator,value) | Sets the value of an input field, as though you typed it in.
Can also be used to set the value of combo boxes, check boxes, etc. In these cases,
value should be the value of the option selected, not the visible text.
'locator' is an element locator
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Can also be used to set the value of combo boxes, check boxes, etc. In these cases,
value should be the value of the option selected, not the visible text.
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tensorflow/graphics | 86997957324bfbdd85848daae989b4c02588faa0 | tensorflow_graphics/geometry/transformation/dual_quaternion.py | python | to_rotation_translation | (
dual_quaternion: type_alias.TensorLike,
name: str = "dual_quaternion_to_rot_trans") | Converts a dual quaternion into a quaternion for rotation and translation.
Args:
dual_quaternion: A `[A1, ..., An, 8]`-tensor, where the last dimension
represents a qual quaternion.
name: A name for this op that defaults to "dual_quaternion_to_rot_trans".
Returns:
A tuple with a `[A1, ..., An, 4]`-tensor for rotation in quaternion form,
and a `[A1, ..., An, 3]`-tensor for translation, in that order. | Converts a dual quaternion into a quaternion for rotation and translation. | [
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dual_quaternion: type_alias.TensorLike,
name: str = "dual_quaternion_to_rot_trans") -> Tuple[tf.Tensor, tf.Tensor]:
"""Converts a dual quaternion into a quaternion for rotation and translation.
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"""
with tf.name_scope(name):
dual_quaternion = tf.convert_to_tensor(value=dual_quaternion)
shape.check_static(
tensor=dual_quaternion,
tensor_name="dual_quaternion",
has_dim_equals=(-1, 8))
rotation = dual_quaternion[..., 0:4]
translation = 2 * quaternion.multiply(
dual_quaternion[..., 4:8], quaternion.inverse(rotation))
translation = translation[..., 0:3]
return rotation, translation | [
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vmware/pyvmomi | 5a738bef64efe8daea037ba9e8e0d566a0cc584c | pyVim/sso.py | python | SsoAuthenticator.get_bearer_saml_assertion | (self,
username,
password,
public_key=None,
private_key=None,
request_duration=60,
token_duration=600,
delegatable=False,
renewable=False,
ssl_context=None) | return etree.tostring(
_extract_element(etree.fromstring(bearer_token),
'Assertion',
{'saml2': "urn:oasis:names:tc:SAML:2.0:assertion"}),
pretty_print=False).decode(UTF_8) | Extracts the assertion from the Bearer Token received from the Security
Token Service.
@type username: C{str}
@param username: Username for the user for which bearer token
needs to be requested.
@type password: C{str}
@param password: Password for the user for which bearer token
needs to be requested.
@type public_key: C{str}
@param public_key: File containing the public key for the service
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@type private_key: C{str}
@param private_key: File containing the private key for the service
user registered with SSO, in PEM format.
@type request_duration: C{long}
@param request_duration: The duration for which the request is valid. If
the STS receives this request after this
duration, it is assumed to have expired. The
duration is in seconds and the default is 60s.
@type token_duration: C{long}
@param token_duration: The duration for which the SAML token is issued
for. The duration is specified in seconds and
the default is 600s.
@type delegatable: C{boolean}
@param delegatable: Whether the generated token is delegatable or not
The default value is False
@type ssl_context: C{ssl.SSLContext}
@param ssl_context: SSL context describing the various SSL options.
It is only supported in Python 2.7.9 or higher.
@rtype: C{str}
@return: The SAML assertion in Unicode. | Extracts the assertion from the Bearer Token received from the Security
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password,
public_key=None,
private_key=None,
request_duration=60,
token_duration=600,
delegatable=False,
renewable=False,
ssl_context=None):
'''
Extracts the assertion from the Bearer Token received from the Security
Token Service.
@type username: C{str}
@param username: Username for the user for which bearer token
needs to be requested.
@type password: C{str}
@param password: Password for the user for which bearer token
needs to be requested.
@type public_key: C{str}
@param public_key: File containing the public key for the service
user registered with SSO, in PEM format.
@type private_key: C{str}
@param private_key: File containing the private key for the service
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@type request_duration: C{long}
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the STS receives this request after this
duration, it is assumed to have expired. The
duration is in seconds and the default is 60s.
@type token_duration: C{long}
@param token_duration: The duration for which the SAML token is issued
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the default is 600s.
@type delegatable: C{boolean}
@param delegatable: Whether the generated token is delegatable or not
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@type ssl_context: C{ssl.SSLContext}
@param ssl_context: SSL context describing the various SSL options.
It is only supported in Python 2.7.9 or higher.
@rtype: C{str}
@return: The SAML assertion in Unicode.
'''
request = SecurityTokenRequest(username=username,
password=password,
public_key=public_key,
private_key=private_key,
request_duration=request_duration,
token_duration=token_duration)
soap_message = request.construct_bearer_token_request(
delegatable=delegatable, renewable=renewable)
bearer_token = self.perform_request(soap_message,
public_key,
private_key,
ssl_context)
return etree.tostring(
_extract_element(etree.fromstring(bearer_token),
'Assertion',
{'saml2': "urn:oasis:names:tc:SAML:2.0:assertion"}),
pretty_print=False).decode(UTF_8) | [
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natashamjaques/neural_chat | ddb977bb4602a67c460d02231e7bbf7b2cb49a97 | ParlAI/projects/controllable_dialogue/controllable_seq2seq/modules.py | python | AttentionLayer.forward | (self, xes, hidden, attn_params) | return output, attn_weights | Compute attention over attn_params given input and hidden states.
:param xes: input state. will be combined with applied
attention.
:param hidden: hidden state from model. will be used to select
states to attend to in from the attn_params.
:param attn_params: tuple of encoder output states and a mask showing
which input indices are nonzero.
:returns: output, attn_weights
output is a new state of same size as input state `xes`.
attn_weights are the weights given to each state in the
encoder outputs. | Compute attention over attn_params given input and hidden states. | [
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] | def forward(self, xes, hidden, attn_params):
"""Compute attention over attn_params given input and hidden states.
:param xes: input state. will be combined with applied
attention.
:param hidden: hidden state from model. will be used to select
states to attend to in from the attn_params.
:param attn_params: tuple of encoder output states and a mask showing
which input indices are nonzero.
:returns: output, attn_weights
output is a new state of same size as input state `xes`.
attn_weights are the weights given to each state in the
encoder outputs.
"""
if self.attention == 'none':
# do nothing, no attention
return xes, None
if type(hidden) == tuple:
# for lstms use the "hidden" state not the cell state
hidden = hidden[0]
last_hidden = hidden[-1] # select hidden state from last RNN layer
enc_out, attn_mask = attn_params
bsz, seqlen, hszXnumdir = enc_out.size()
numlayersXnumdir = last_hidden.size(1)
if self.attention == 'local':
# local attention weights aren't based on encoder states
h_merged = torch.cat((xes.squeeze(1), last_hidden), 1)
attn_weights = F.softmax(self.attn(h_merged), dim=1)
# adjust state sizes to the fixed window size
if seqlen > self.max_length:
offset = seqlen - self.max_length
enc_out = enc_out.narrow(1, offset, self.max_length)
seqlen = self.max_length
if attn_weights.size(1) > seqlen:
attn_weights = attn_weights.narrow(1, 0, seqlen)
else:
hid = last_hidden.unsqueeze(1)
if self.attention == 'concat':
# concat hidden state and encoder outputs
hid = hid.expand(bsz, seqlen, numlayersXnumdir)
h_merged = torch.cat((enc_out, hid), 2)
# then do linear combination of them with activation
active = F.tanh(self.attn(h_merged))
attn_w_premask = self.attn_v(active).squeeze(2)
elif self.attention == 'dot':
# dot product between hidden and encoder outputs
if numlayersXnumdir != hszXnumdir:
# enc_out has two directions, so double hid
hid = torch.cat([hid, hid], 2)
enc_t = enc_out.transpose(1, 2)
attn_w_premask = torch.bmm(hid, enc_t).squeeze(1)
elif self.attention == 'general':
# before doing dot product, transform hidden state with linear
# same as dot if linear is identity
hid = self.attn(hid)
enc_t = enc_out.transpose(1, 2)
attn_w_premask = torch.bmm(hid, enc_t).squeeze(1)
# calculate activation scores, apply mask if needed
if attn_mask is not None:
# remove activation from NULL symbols
attn_w_premask.masked_fill_(~attn_mask, -NEAR_INF)
attn_weights = F.softmax(attn_w_premask, dim=1)
# apply the attention weights to the encoder states
attn_applied = torch.bmm(attn_weights.unsqueeze(1), enc_out)
# concatenate the input and encoder states
merged = torch.cat((xes.squeeze(1), attn_applied.squeeze(1)), 1)
# combine them with a linear layer and tanh activation
output = torch.tanh(self.attn_combine(merged).unsqueeze(1))
return output, attn_weights | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/docutils/utils/math/math2html.py | python | Container.locateprocess | (self, locate, process) | Search for all embedded containers and process them | Search for all embedded containers and process them | [
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Ha0Tang/SelectionGAN | 80aa7ad9f79f643c28633c40c621f208f3fb0121 | semantic_synthesis/data/pix2pix_dataset.py | python | Pix2pixDataset.modify_commandline_options | (parser, is_train) | return parser | [] | def modify_commandline_options(parser, is_train):
parser.add_argument('--no_pairing_check', action='store_true',
help='If specified, skip sanity check of correct label-image file pairing')
return parser | [
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tjweir/liftbook | e977a7face13ade1a4558e1909a6951d2f8928dd | elyxer.py | python | ImageFile.__init__ | (self, path) | Create the file based on its path | Create the file based on its path | [
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chainer/chainer | e9da1423255c58c37be9733f51b158aa9b39dc93 | chainer/link.py | python | Link.children | (self) | Returns a generator of all child links.
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dr-costas/mad-twinnet | 446e49a423a4375e5ceedab5eb51bead1057d06b | helpers/data_feeder.py | python | data_feeder_testing | (window_size, fft_size, hop_size, seq_length, context_length,
batch_size, debug, sources_list=None) | return testing_it | Provides an iterator over the testing examples.
:param window_size: The window size to be used for the time-frequency transformation.
:type window_size: int
:param fft_size: The size of the FFT in samples.
:type fft_size: int
:param hop_size: The hop size in samples.
:type hop_size: int
:param seq_length: The sequence length in frames.
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:param context_length: The context length in frames.
:type context_length: int
:param batch_size: The batch size.
:type batch_size: int
:param debug: A flag to indicate debug
:type debug: bool
:param sources_list: The file list provided for using the MaD-TwinNet.
:type sources_list: list[str|pathlib.Path]
:return: An iterator that will provide the input and target values.\
The iterator yields (mix, mix magnitude, mix phase, voice true, bg true) values.
:rtype: callable | Provides an iterator over the testing examples. | [
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] | def data_feeder_testing(window_size, fft_size, hop_size, seq_length, context_length,
batch_size, debug, sources_list=None):
"""Provides an iterator over the testing examples.
:param window_size: The window size to be used for the time-frequency transformation.
:type window_size: int
:param fft_size: The size of the FFT in samples.
:type fft_size: int
:param hop_size: The hop size in samples.
:type hop_size: int
:param seq_length: The sequence length in frames.
:type seq_length: int
:param context_length: The context length in frames.
:type context_length: int
:param batch_size: The batch size.
:type batch_size: int
:param debug: A flag to indicate debug
:type debug: bool
:param sources_list: The file list provided for using the MaD-TwinNet.
:type sources_list: list[str|pathlib.Path]
:return: An iterator that will provide the input and target values.\
The iterator yields (mix, mix magnitude, mix phase, voice true, bg true) values.
:rtype: callable
"""
if sources_list is None:
usage_case = False
sources_list = _get_files_lists('testing')[-1]
else:
usage_case = True
hamming_window = signal.hamming(window_size, True)
def testing_it():
for index in range(len(sources_list)):
yield _get_data_testing(
sources_parent_path=sources_list[index],
window_values=hamming_window, fft_size=fft_size, hop=hop_size,
seq_length=seq_length, context_length=context_length,
batch_size=batch_size, usage_case=usage_case
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if debug:
break
return testing_it | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/io/lammps/data.py | python | LammpsData.write_file | (self, filename, distance=6, velocity=8, charge=4) | Writes LammpsData to file.
Args:
filename (str): Filename.
distance (int): No. of significant figures to output for
box settings (bounds and tilt) and atomic coordinates.
Default to 6.
velocity (int): No. of significant figures to output for
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charge (int): No. of significant figures to output for
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"""
Writes LammpsData to file.
Args:
filename (str): Filename.
distance (int): No. of significant figures to output for
box settings (bounds and tilt) and atomic coordinates.
Default to 6.
velocity (int): No. of significant figures to output for
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charge (int): No. of significant figures to output for
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"""
with open(filename, "w") as f:
f.write(self.get_string(distance=distance, velocity=velocity, charge=charge)) | [
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shubhomoydas/ad_examples | 0a7b86c4f2f7306ff543a15b387fe938f9c06130 | ad_examples/aad/forest_description.py | python | InstancesDescriber.__init__ | (self, x, y, model, opts, sample_negative=False) | :param x: np.ndarray
The instance matrix with ALL instances
:param y: np.array
:param model: Aad
:param opts: AadOpts
:param sample_negative: bool | [] | def __init__(self, x, y, model, opts, sample_negative=False):
"""
:param x: np.ndarray
The instance matrix with ALL instances
:param y: np.array
:param model: Aad
:param opts: AadOpts
:param sample_negative: bool
"""
self.x = x
self.y = y
self.model = model
self.opts = opts
self.sample_negative = sample_negative
self.meta = None | [
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doraemonext/wechat-python-sdk | bf6f6f3d4a5440feb73a51937059d7feddc335a0 | wechat_sdk/ext.py | python | WechatExt.get_message_image | (self, msgid, mode='large') | return r.raw.data | 根据消息 ID 获取图片消息内容
:param msgid: 消息 ID
:param mode: 图片尺寸 ('large'或'small')
:return: 二进制 JPG 图片字符串, 可直接作为 File Object 中 write 的参数
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"""
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headers = {
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token=self.__token,
),
'cookie': self.__cookies,
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r = requests.get(url, headers=headers, stream=True)
# 检测会话是否超时
if r.headers.get('content-type', None) == 'text/html; charset=UTF-8':
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# 检测图片是否存在
if not r.raw.data:
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bbfamily/abu | 2de85ae57923a720dac99a545b4f856f6b87304b | abupy/PickStockBu/ABuPickStockPriceMinMax.py | python | AbuPickStockPriceMinMax.fit_pick | (self, kl_pd, target_symbol) | return False | 开始根据自定义价格边际参数进行选股 | 开始根据自定义价格边际参数进行选股 | [
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if kl_pd.close.max() < self.threshold_price_max and kl_pd.close.min() > self.threshold_price_min:
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return True
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barseghyanartur/django-fobi | a998feae007d7fe3637429a80e42952ec7cda79f | src/fobi/decorators.py | python | permissions_required | (perms, satisfy=DEFAULT_SATISFY, login_url=None,
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:param string login_url:
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>>> @permissions_required(satisfy='any', perms=[
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>>> 'fobi.change_formentry',
>>> 'fobi.delete_formentry',
>>> 'fobi.add_formelemententry',
>>> 'fobi.change_formelemententry',
>>> 'fobi.delete_formelemententry',
>>> ])
>>> def edit_dashboard(request):
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>>> # your code
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def check_perms(user):
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if user.has_perms(perms):
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# In case the 403 handler should be called raise the exception
if raise_exception:
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# As the last resort, show the login form
return False
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for perm in perms:
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# In case the 403 handler should be called raise the exception
if raise_exception:
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return False
return user_passes_test(check_perms, login_url=login_url) | [
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P1sec/pycrate | d12bbccf1df8c9c7891a26967a9d2635610ec5b8 | pycrate_mobile/ISUP.py | python | ISUPNum.get_num | (self) | return the BCD-encoded number, properly decoded according to OE | return the BCD-encoded number, properly decoded according to OE | [
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if self['OE'].get_val():
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return self['Num'].decode() | [
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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/lib-tk/turtle.py | python | ScrolledCanvas.adjustScrolls | (self) | Adjust scrollbars according to window- and canvas-size. | Adjust scrollbars according to window- and canvas-size. | [
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scrtlabs/catalyst | 2e8029780f2381da7a0729f7b52505e5db5f535b | catalyst/pipeline/term.py | python | Term.__init__ | (self, *args, **kwargs) | Noop constructor to play nicely with our caching __new__. Subclasses
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sfepy/sfepy | 02ec7bb2ab39ee1dfe1eb4cd509f0ffb7dcc8b25 | sfepy/discrete/fem/fields_positive.py | python | H1BernsteinVolumeField.create_basis_context | (self) | return ctx | Create the context required for evaluating the field basis. | Create the context required for evaluating the field basis. | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/_erfa/core.py | python | ppp | (a, b) | return apb | Wrapper for ERFA function ``eraPpp``.
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apb = ufunc.ppp(a, b)
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/events/forms.py | python | RegistrationPreForm.clean_pricing | (self) | return pricing | [] | def clean_pricing(self):
if not self.table_only:
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib_pypy/_cffi_ssl/cryptography/x509/name.py | python | Name.rfc4514_string | (self) | return ','.join(attr.rfc4514_string() for attr in self._attributes) | Format as RFC4514 Distinguished Name string.
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awslabs/aws-config-rules | 8dfeacf9d9e5e5f0fbb1b8545ff702dea700ea7a | python/EC2_SECURITY_GROUP_BADINGRESS/EC2_SECURITY_GROUP_BADINGRESS.py | python | get_client | (service, event, region=None) | return boto3.client(service, aws_access_key_id=credentials['AccessKeyId'],
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event -- the event variable given in the lambda handler
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nathanborror/django-basic-apps | 3a90090857549ea4198a72c44f45f6edb238e2a8 | basic/tools/templatetags/listutils.py | python | pop_from_GET | (obj, attr) | return '&%s' % obj.urlencode() | Returns GET parameters sans specified attribute. | Returns GET parameters sans specified attribute. | [
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lebedov/scikit-cuda | 5d3c74f926fe7ce67ecfc85e9623aab7bc0b344f | skcuda/magma.py | python | magma_cheevd_m | (ngpu, jobz, uplo, n, A, lda, w, work, lwork,
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info = c_int_type()
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interpretml/DiCE | e6a1dc3893799a364da993d4a368b7ccfabe4447 | dice_ml/explainer_interfaces/dice_tensorflow2.py | python | DiceTensorFlow2.compute_diversity_loss | (self) | Computes the third part (diversity) of the loss function. | Computes the third part (diversity) of the loss function. | [
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if self.total_CFs == 1:
return tf.constant(0.0)
if "dpp" in self.diversity_loss_type:
submethod = self.diversity_loss_type.split(':')[1]
return tf.reduce_sum(self.dpp_style(submethod))
elif self.diversity_loss_type == "avg_dist":
diversity_loss = 0.0
count = 0.0
# computing pairwise distance and transforming it to normalized similarity
for i in range(self.total_CFs):
for j in range(i+1, self.total_CFs):
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diversity_loss += 1.0/(1.0 + self.compute_dist(self.cfs[i], self.cfs[j]))
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/sqlalchemy/orm/base.py | python | InspectionAttrInfo.info | (self) | return {} | Info dictionary associated with the object, allowing user-defined
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abr/abr_control | a248ec56166f01791857a766ac58ee0920c0861c | abr_control/interfaces/coppeliasim_files/sim.py | python | simxGetDialogInput | (clientID, dialogHandle, operationMode) | return ret, a | Please have a look at the function description/documentation in the CoppeliaSim user manual | Please have a look at the function description/documentation in the CoppeliaSim user manual | [
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inputText = ct.POINTER(ct.c_char)()
ret = c_GetDialogInput(clientID, dialogHandle, ct.byref(inputText), operationMode)
a = bytearray()
if ret == 0:
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/channels/media_player.py | python | ChannelsPlayer.media_previous_track | (self) | Seek back. | Seek back. | [
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conjure-up/conjure-up | d2bf8ab8e71ff01321d0e691a8d3e3833a047678 | conjureup/ui/views/base.py | python | HelpView.build_widget | (self) | return lines | [] | def build_widget(self):
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JimmXinu/FanFicFare | bc149a2deb2636320fe50a3e374af6eef8f61889 | included_dependencies/soupsieve/css_match.py | python | Document.get_children | (self, el, start=None, reverse=False, tags=True, no_iframe=False) | Get children. | Get children. | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/nissan_leaf/switch.py | python | LeafClimateSwitch.async_turn_off | (self, **kwargs: Any) | Turn off climate control. | Turn off climate control. | [
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MaurizioFD/RecSys2019_DeepLearning_Evaluation | 0fb6b7f5c396f8525316ed66cf9c9fdb03a5fa9b | CNN_on_embeddings/IJCAI/CFM_our_interface/LoadData.py | python | LoadData.bind_user | (self) | return len(self.binded_users) | Map the item fields in all files, kept in self.item_fields dictionary
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implus/PytorchInsight | 2864528f8b83f52c3df76f7c3804aa468b91e5cf | detection/mmdet/models/utils/conv_module.py | python | ConvModule.forward | (self, x, activate=True, norm=True) | return x | [] | def forward(self, x, activate=True, norm=True):
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trakt/Plex-Trakt-Scrobbler | aeb0bfbe62fad4b06c164f1b95581da7f35dce0b | Trakttv.bundle/Contents/Libraries/MacOSX/i386/ucs2/cryptography/x509/base.py | python | CertificateBuilder.not_valid_before | (self, time) | return CertificateBuilder(
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phimpme/phimpme-generator | ba6d11190b9016238f27672e1ad55e6a875b74a0 | Phimpme/site-packages/coverage/data.py | python | CoverageData.write | (self, suffix=None) | Write the collected coverage data to a file.
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/dns/rdata.py | python | GenericRdata.from_text | (cls, rdclass, rdtype, tok, origin=None, relativize=True,
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ym2011/POC-EXP | 206b22d3a6b2a172359678df33bbc5b2ad04b6c3 | windows/SYNfulKnock/scapy-2.3.1/scapy/utils6.py | python | in6_addrtovendor | (addr) | return res | Extract the MAC address from a modified EUI-64 constructed IPv6
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taowen/es-monitor | c4deceb4964857f495d13bfaf2d92f36734c9e1c | es_sql/sqlparse/sql.py | python | TokenList.get_token_at_offset | (self, offset) | Returns the token that is on position offset. | Returns the token that is on position offset. | [
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SBCV/Blender-Addon-Photogrammetry-Importer | d964ef04cefde73320749cc346113e16be5bd73b | photogrammetry_importer/operators/open3d_import_op.py | python | ImportOpen3DOperator.invoke | (self, context, event) | return {"RUNNING_MODAL"} | Set the default import options before running the operator. | Set the default import options before running the operator. | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/pydoc.py | python | ispath | (x) | return isinstance(x, str) and find(x, os.sep) >= 0 | [] | def ispath(x):
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | cswitch_t.__eq__ | (self, *args) | return _idaapi.cswitch_t___eq__(self, *args) | __eq__(self, r) -> bool | __eq__(self, r) -> bool | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage_docbuild/ext/inventory_builder.py | python | InventoryBuilder.finish | (self) | Only write the inventory files. | Only write the inventory files. | [
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nixgates/plugin.video.seren | b6091842844337a0de51646be3f5a411d5185997 | resources/lib/database/premiumizeTransfers/__init__.py | python | PremiumizeTransfers.get_premiumize_transfers | (self) | return self.fetchall("SELECT * FROM transfers") | Fetch all transfer created by Seren not removed
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google/closure-linter | c09c885b4e4fec386ff81cebeb8c66c2b0643d49 | closure_linter/tokenutil.py | python | InsertSpaceTokenAfter | (token) | Inserts a space token after the given token.
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cloudtools/troposphere | 62a90a5e88c6e2df8c3f0a5d56284df212438dc1 | troposphere/__init__.py | python | GenericHelperFn.__init__ | (self, data) | [] | def __init__(self, data):
self.data = self.getdata(data) | [
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cupy/cupy | a47ad3105f0fe817a4957de87d98ddccb8c7491f | cupyx/scipy/ndimage/measurements.py | python | sum | (input, labels=None, index=None) | return sum_labels(input, labels, index) | Calculates the sum of the values of an n-D image array, optionally
at specified sub-regions.
Args:
input (cupy.ndarray): Nd-image data to process.
labels (cupy.ndarray or None): Labels defining sub-regions in `input`.
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index (cupy.ndarray or None): `labels` to include in output. If None
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`labels` and `index` are specified.
Notes:
This is an alias for `cupyx.scipy.ndimage.sum_labels` kept for
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.. seealso:: :func:`scipy.ndimage.sum` | Calculates the sum of the values of an n-D image array, optionally
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input (cupy.ndarray): Nd-image data to process.
labels (cupy.ndarray or None): Labels defining sub-regions in `input`.
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Notes:
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/wagtail_bak/wagtailadmin/templatetags/wagtailadmin_tags.py | python | main_nav_js | () | return admin_menu.media['js'] | [] | def main_nav_js():
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pandas-dev/pandas | 5ba7d714014ae8feaccc0dd4a98890828cf2832d | pandas/core/apply.py | python | Apply.apply_str | (self) | return self._try_aggregate_string_function(obj, f, *self.args, **self.kwargs) | Compute apply in case of a string.
Returns
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result: Series or DataFrame | Compute apply in case of a string. | [
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Compute apply in case of a string.
Returns
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result: Series or DataFrame
"""
# Caller is responsible for checking isinstance(self.f, str)
f = cast(str, self.f)
obj = self.obj
# Support for `frame.transform('method')`
# Some methods (shift, etc.) require the axis argument, others
# don't, so inspect and insert if necessary.
func = getattr(obj, f, None)
if callable(func):
sig = inspect.getfullargspec(func)
if "axis" in sig.args:
self.kwargs["axis"] = self.axis
elif self.axis != 0:
raise ValueError(f"Operation {f} does not support axis=1")
return self._try_aggregate_string_function(obj, f, *self.args, **self.kwargs) | [
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googlearchive/simian | fb9c43946ff7ba29be417068d6447cfc0adfe9ef | src/simian/mac/admin/package.py | python | Package.UpdatePackageInfoFromPlist | (self, create_new=False) | Updates or creates a new PackageInfo entity from plist XML. | Updates or creates a new PackageInfo entity from plist XML. | [
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try:
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/venstar/__init__.py | python | VenstarEntity.device_info | (self) | return {
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tztztztztz/eql.detectron2 | 29224acf4ea549c53264e6229da69868bd5470f3 | detectron2/structures/masks.py | python | polygons_to_bitmask | (polygons: List[np.ndarray], height: int, width: int) | return mask_utils.decode(rle).astype(np.bool) | Args:
polygons (list[ndarray]): each array has shape (Nx2,)
height, width (int)
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assert len(polygons) > 0, "COCOAPI does not support empty polygons"
rles = mask_utils.frPyObjects(polygons, height, width)
rle = mask_utils.merge(rles)
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | example/pyctp2/md/minute.py | python | Minutes_Previous.day_finalize | (self) | [] | def day_finalize(self):
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RaRe-Technologies/bounter | 21aeda1b88402bacb44ce92d05c08b632a1edb21 | bounter/bounter.py | python | bounter | (size_mb=None, need_iteration=True, need_counts=True, log_counting=None) | Factory method for bounter implementation.
Args:
size_mb (int): Desired memory footprint of the counter.
need_iteration (Bool): With `True`, create a `HashTable` implementation which can
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With `False`, create a `CountMinSketch` implementation which performs better in limited-memory scenarios,
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Args:
size_mb (int): Desired memory footprint of the counter.
need_iteration (Bool): With `True`, create a `HashTable` implementation which can
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"""
if not need_counts:
return CardinalityEstimator()
if size_mb is None:
raise ValueError("Max size in MB must be provided.")
if need_iteration:
if log_counting:
raise ValueError("Log counting is only supported with CMS implementation (need_iteration=False).")
return HashTable(size_mb=size_mb)
else:
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007gzs/dingtalk-sdk | 7979da2e259fdbc571728cae2425a04dbc65850a | dingtalk/client/api/taobao.py | python | TbDingDing.dingtalk_oapi_role_addrolesforemps | (
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"userIds": user_ids
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) | 批量为员工增加角色信息
企业在做企业员工管理的时候，需要对部分员工进行角色分类，该接口可以批量为员工增加角色信息
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=36985
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:param user_ids: 员工id list | 批量为员工增加角色信息
企业在做企业员工管理的时候，需要对部分员工进行角色分类，该接口可以批量为员工增加角色信息
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批量为员工增加角色信息
企业在做企业员工管理的时候，需要对部分员工进行角色分类，该接口可以批量为员工增加角色信息
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=36985
:param role_ids: 角色id list
:param user_ids: 员工id list
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bbangert/beaker | d4ab7d37387feddd0d544792d9e32533e39ff868 | beaker/middleware.py | python | session_filter_factory | (global_conf, **kwargs) | return filter | [] | def session_filter_factory(global_conf, **kwargs):
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gwpy/gwpy | 82becd78d166a32985cb657a54d0d39f6a207739 | gwpy/frequencyseries/hist.py | python | SpectralVariance.__getitem__ | (self, item) | [] | def __getitem__(self, item):
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kbandla/ImmunityDebugger | 2abc03fb15c8f3ed0914e1175c4d8933977c73e3 | 1.74/Libs/immlib.py | python | Debugger.searchLong | (self, long, flag=None) | return self.search( immutils.int2str32_swapped(long),flag) | Search a short integer on the remote process memory
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trailofbits/manticore | b050fdf0939f6c63f503cdf87ec0ab159dd41159 | manticore/native/cpu/abstractcpu.py | python | Abi.get_argument_values | (self, model: Callable, prefix_args: Tuple) | Extract arguments for model from the environment and return as a tuple that
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:param model: Python model of the function
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:return: Arguments to be passed to the model
"""
if type(model) is partial:
# mypy issue with partial types https://github.com/python/mypy/issues/1484
model = model.args[0] # type: ignore
sig = inspect.signature(model)
if _sig_is_varargs(sig):
model_name = getattr(model, "__qualname__", "<no name>")
logger.warning("ABI: %s: a vararg model must be a unary function.", model_name)
nargs = len(sig.parameters) - len(prefix_args)
def resolve_argument(arg):
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descriptors = self.get_arguments()
argument_iter = map(resolve_argument, descriptors)
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... | https://github.com/trailofbits/manticore/blob/b050fdf0939f6c63f503cdf87ec0ab159dd41159/manticore/native/cpu/abstractcpu.py#L339-L373 | ||
mayank93/Twitter-Sentiment-Analysis | f095c6ca6bf69787582b5dabb140fefaf278eb37 | front-end/web2py/gluon/contrib/markdown/markdown2.py | python | _memoized.__repr__ | (self) | return self.func.__doc__ | Return the function's docstring. | Return the function's docstring. | [
"Return",
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"function",
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"docstring",
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] | def __repr__(self):
"""Return the function's docstring."""
return self.func.__doc__ | [
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sth2018/FastWordQuery | 901ebe8fd5989d8861d20ee3fec3acd15b7f46a5 | addons21/fastwq/libs/mdict/pureSalsa20.py | python | rot32 | ( w, nLeft ) | return RRR | ( sLLLLLL << nLeft ) | Rotate 32-bit word left by nLeft or right by -nLeft
without creating a Python long.
Timing depends on nLeft but not on w. | Rotate 32-bit word left by nLeft or right by -nLeft
without creating a Python long.
Timing depends on nLeft but not on w. | [
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] | def rot32( w, nLeft ):
""" Rotate 32-bit word left by nLeft or right by -nLeft
without creating a Python long.
Timing depends on nLeft but not on w.
"""
nLeft &= 31 # which makes nLeft >= 0
if nLeft == 0:
return w
# Note: now 1 <= nLeft <= 31.
# RRRsLLLLLL There are nLeft RRR's, (31-nLeft) LLLLLL's,
# => sLLLLLLRRR and one s which becomes the sign bit.
RRR = ( ( ( w >> 1 ) & 0x7fffFFFF ) >> ( 31 - nLeft ) )
sLLLLLL = -( (1<<(31-nLeft)) & w ) | (0x7fffFFFF>>nLeft) & w
return RRR | ( sLLLLLL << nLeft ) | [
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facebookresearch/ReAgent | 52f666670a7fa03206812ef48949f6b934d400f7 | reagent/models/cem_planner.py | python | CEMPlannerNetwork.acc_rewards_of_all_solutions | (
self, state: rlt.FeatureData, solutions: torch.Tensor
) | return acc_reward_vec | Calculate accumulated rewards of solutions.
:param state: the input which contains the starting state
:param solutions: its shape is (cem_pop_size, plan_horizon_length, action_dim)
:returns: a vector of size cem_pop_size, which is the reward of each solution | Calculate accumulated rewards of solutions. | [
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"accumulated",
"rewards",
"of",
"solutions",
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] | def acc_rewards_of_all_solutions(
self, state: rlt.FeatureData, solutions: torch.Tensor
) -> float:
"""
Calculate accumulated rewards of solutions.
:param state: the input which contains the starting state
:param solutions: its shape is (cem_pop_size, plan_horizon_length, action_dim)
:returns: a vector of size cem_pop_size, which is the reward of each solution
"""
acc_reward_vec = np.zeros(self.cem_pop_size)
init_state = state.float_features
for i in range(self.cem_pop_size):
if i % (self.cem_pop_size // 10) == 0:
logger.debug(f"Simulating the {i}-th solution...")
acc_reward_vec[i] = self.acc_rewards_of_one_solution(
init_state, solutions[i], i
)
return acc_reward_vec | [
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Epistimio/orion | 732e739d99561020dbe620760acf062ade746006 | src/orion/algo/gridsearch.py | python | GridSearch.build_grid | (space, n_values, max_trials=10000) | return list(itertools.product(*coordinates)) | Build a grid of trials
Parameters
----------
n_values: int or dict
Number of trials for each dimensions, or dictionary specifying number of trials for each
dimension independently (name, n_values). For categorical dimensions, n_values will not be
used, and all categories will be used to build the grid.
max_trials: int
Maximum number of trials for the grid. If n_values lead to more trials than max_trials,
the n_values will be adjusted down. Will raise ValueError if it is impossible to build
a grid smaller than max_trials (for instance if choices are too large). | Build a grid of trials | [
"Build",
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] | def build_grid(space, n_values, max_trials=10000):
"""Build a grid of trials
Parameters
----------
n_values: int or dict
Number of trials for each dimensions, or dictionary specifying number of trials for each
dimension independently (name, n_values). For categorical dimensions, n_values will not be
used, and all categories will be used to build the grid.
max_trials: int
Maximum number of trials for the grid. If n_values lead to more trials than max_trials,
the n_values will be adjusted down. Will raise ValueError if it is impossible to build
a grid smaller than max_trials (for instance if choices are too large).
"""
adjust = 0
n_trials = float("inf")
while n_trials > max_trials:
coordinates = []
capped_values = []
for name, dim in space.items():
capped_value = max(n_values[name] - adjust, 1)
capped_values.append(capped_value)
coordinates.append(list(grid(dim, capped_value)))
if all(value <= 1 for value in capped_values):
raise ValueError(
f"Cannot build a grid smaller than {max_trials}. "
"Try reducing the number of choices in categorical dimensions."
)
n_trials = numpy.prod([len(dim_values) for dim_values in coordinates])
# TODO: Use binary search instead of incrementing by one.
adjust += 1
if adjust > 1:
log.warning(
f"`n_values` reduced by {adjust-1} to limit number of trials below {max_trials}."
)
return list(itertools.product(*coordinates)) | [
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | library/hachoir_core/field/generic_field_set.py | python | GenericFieldSet.__iter__ | (self) | Create a generator to iterate on each field, may create new
fields when needed | Create a generator to iterate on each field, may create new
fields when needed | [
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"""
Create a generator to iterate on each field, may create new
fields when needed
"""
try:
done = 0
while True:
if done == len(self._fields):
if self._field_generator is None:
break
self._addField( self._field_generator.next() )
for field in self._fields.values[done:]:
yield field
done += 1
except HACHOIR_ERRORS, err:
field = self._fixFeedError(err)
if isinstance(field, Field):
yield field
elif hasattr(field, '__iter__'):
for f in field:
yield f
elif field is False:
raise
except StopIteration:
field = self._stopFeeding()
if isinstance(field, Field):
yield field
elif hasattr(field, '__iter__'):
for f in field:
yield f | [
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tableau/rest-api-samples | 0be12fa52c6e66104633ab26e67fdf73ae411b33 | python/publish_workbook.py | python | _encode_for_display | (text) | return text.encode('ascii', errors="backslashreplace").decode('utf-8') | Encodes strings so they can display as ASCII in a Windows terminal window.
This function also encodes strings for processing by xml.etree.ElementTree functions.
Returns an ASCII-encoded version of the text.
Unicode characters are converted to ASCII placeholders (for example, "?"). | Encodes strings so they can display as ASCII in a Windows terminal window.
This function also encodes strings for processing by xml.etree.ElementTree functions.
Returns an ASCII-encoded version of the text.
Unicode characters are converted to ASCII placeholders (for example, "?"). | [
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"""
Encodes strings so they can display as ASCII in a Windows terminal window.
This function also encodes strings for processing by xml.etree.ElementTree functions.
Returns an ASCII-encoded version of the text.
Unicode characters are converted to ASCII placeholders (for example, "?").
"""
return text.encode('ascii', errors="backslashreplace").decode('utf-8') | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/urlparse.py | python | parse_qsl | (qs, keep_blank_values=0, strict_parsing=0) | return r | Parse a query given as a string argument.
Arguments:
qs: percent-encoded query string to be parsed
keep_blank_values: flag indicating whether blank values in
percent-encoded queries should be treated as blank strings. A
true value indicates that blanks should be retained as blank
strings. The default false value indicates that blank values
are to be ignored and treated as if they were not included.
strict_parsing: flag indicating what to do with parsing errors. If
false (the default), errors are silently ignored. If true,
errors raise a ValueError exception.
Returns a list, as G-d intended. | Parse a query given as a string argument. | [
"Parse",
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"given",
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"."
] | def parse_qsl(qs, keep_blank_values=0, strict_parsing=0):
"""Parse a query given as a string argument.
Arguments:
qs: percent-encoded query string to be parsed
keep_blank_values: flag indicating whether blank values in
percent-encoded queries should be treated as blank strings. A
true value indicates that blanks should be retained as blank
strings. The default false value indicates that blank values
are to be ignored and treated as if they were not included.
strict_parsing: flag indicating what to do with parsing errors. If
false (the default), errors are silently ignored. If true,
errors raise a ValueError exception.
Returns a list, as G-d intended.
"""
pairs = [s2 for s1 in qs.split('&') for s2 in s1.split(';')]
r = []
for name_value in pairs:
if not name_value and not strict_parsing:
continue
nv = name_value.split('=', 1)
if len(nv) != 2:
if strict_parsing:
raise ValueError, "bad query field: %r" % (name_value,)
# Handle case of a control-name with no equal sign
if keep_blank_values:
nv.append('')
else:
continue
if len(nv[1]) or keep_blank_values:
name = unquote(nv[0].replace('+', ' '))
value = unquote(nv[1].replace('+', ' '))
r.append((name, value))
return r | [
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certbot/certbot | 30b066f08260b73fc26256b5484a180468b9d0a6 | certbot/certbot/reverter.py | python | Reverter.register_undo_command | (self, temporary: bool, command: Iterable[str]) | Register a command to be run to undo actions taken.
.. warning:: This function does not enforce order of operations in terms
of file modification vs. command registration. All undo commands
are run first before all normal files are reverted to their previous
state. If you need to maintain strict order, you may create
checkpoints before and after the the command registration. This
function may be improved in the future based on demand.
:param bool temporary: Whether the command should be saved in the
IN_PROGRESS or TEMPORARY checkpoints.
:param command: Command to be run.
:type command: list of str | Register a command to be run to undo actions taken. | [
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"run",
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] | def register_undo_command(self, temporary: bool, command: Iterable[str]) -> None:
"""Register a command to be run to undo actions taken.
.. warning:: This function does not enforce order of operations in terms
of file modification vs. command registration. All undo commands
are run first before all normal files are reverted to their previous
state. If you need to maintain strict order, you may create
checkpoints before and after the the command registration. This
function may be improved in the future based on demand.
:param bool temporary: Whether the command should be saved in the
IN_PROGRESS or TEMPORARY checkpoints.
:param command: Command to be run.
:type command: list of str
"""
commands_fp = os.path.join(self._get_cp_dir(temporary), "COMMANDS")
# It is strongly advised to set newline = '' on Python 3 with CSV,
# and it fixes problems on Windows.
kwargs = {'newline': ''}
try:
mode = "a" if os.path.isfile(commands_fp) else "w"
with open(commands_fp, mode, **kwargs) as f: # type: ignore
csvwriter = csv.writer(f)
csvwriter.writerow(command)
except (IOError, OSError):
logger.error("Unable to register undo command")
raise errors.ReverterError(
"Unable to register undo command.") | [
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/invoices/models.py | python | Invoice.save | (self, user=None) | Set guid, creator and owner if any of
these fields are missing. | Set guid, creator and owner if any of
these fields are missing. | [
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] | def save(self, user=None):
"""
Set guid, creator and owner if any of
these fields are missing.
"""
self.guid = self.guid or uuid.uuid4().hex
if hasattr(user, 'pk') and not user.is_anonymous:
self.set_creator(user)
self.set_owner(user)
# assign entity
if not self.entity_id and self.object_type:
self.entity = self.get_entity()
self.verifydata()
super(Invoice, self).save() | [
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atlassian-api/atlassian-python-api | 6d8545a790c3aae10b75bdc225fb5c3a0aee44db | atlassian/bitbucket/cloud/repositories/issues.py | python | Issue.state | (self, state) | return self.update(state=state) | Setter for the issue state | Setter for the issue state | [
"Setter",
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"issue",
"state"
] | def state(self, state):
"""Setter for the issue state"""
return self.update(state=state) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/dynamics/arithmetic_dynamics/projective_ds.py | python | DynamicalSystem_projective.is_PGL_minimal | (self, prime_list=None) | return affine_minimal(self, return_transformation=False, D=prime_list, quick=True) | r"""
Check if this dynamical system is a minimal model in
its conjugacy class.
See [BM2012]_ and [Mol2015]_ for a description of the algorithm.
For polynomial maps it uses [HS2018]_.
INPUT:
- ``prime_list`` -- (optional) list of primes to check minimality
OUTPUT: boolean
EXAMPLES::
sage: PS.<X,Y> = ProjectiveSpace(QQ,1)
sage: f = DynamicalSystem_projective([X^2+3*Y^2, X*Y])
sage: f.is_PGL_minimal()
True
::
sage: PS.<x,y> = ProjectiveSpace(QQ,1)
sage: f = DynamicalSystem_projective([6*x^2+12*x*y+7*y^2, 12*x*y])
sage: f.is_PGL_minimal()
False
::
sage: PS.<x,y> = ProjectiveSpace(QQ,1)
sage: f = DynamicalSystem_projective([6*x^2+12*x*y+7*y^2, y^2])
sage: f.is_PGL_minimal()
False | r"""
Check if this dynamical system is a minimal model in
its conjugacy class. | [
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"this",
"dynamical",
"system",
"is",
"a",
"minimal",
"model",
"in",
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"class",
"."
] | def is_PGL_minimal(self, prime_list=None):
r"""
Check if this dynamical system is a minimal model in
its conjugacy class.
See [BM2012]_ and [Mol2015]_ for a description of the algorithm.
For polynomial maps it uses [HS2018]_.
INPUT:
- ``prime_list`` -- (optional) list of primes to check minimality
OUTPUT: boolean
EXAMPLES::
sage: PS.<X,Y> = ProjectiveSpace(QQ,1)
sage: f = DynamicalSystem_projective([X^2+3*Y^2, X*Y])
sage: f.is_PGL_minimal()
True
::
sage: PS.<x,y> = ProjectiveSpace(QQ,1)
sage: f = DynamicalSystem_projective([6*x^2+12*x*y+7*y^2, 12*x*y])
sage: f.is_PGL_minimal()
False
::
sage: PS.<x,y> = ProjectiveSpace(QQ,1)
sage: f = DynamicalSystem_projective([6*x^2+12*x*y+7*y^2, y^2])
sage: f.is_PGL_minimal()
False
"""
if self.base_ring() != QQ and self.base_ring() != ZZ:
raise NotImplementedError("minimal models only implemented over ZZ or QQ")
if not self.is_morphism():
raise TypeError("the function is not a morphism")
if self.degree() == 1:
raise NotImplementedError("minimality is only for degree 2 or higher")
f = copy(self)
f.normalize_coordinates()
R = f.domain().coordinate_ring()
F = R(f[0].numerator())
G = R(f[0].denominator())
if G.degree() == 0 or F.degree() == 0:
#can't use BM for polynomial
from .endPN_minimal_model import HS_minimal
g, m = HS_minimal(self, return_transformation=True, D=prime_list)
return m == m.parent().one()
from .endPN_minimal_model import affine_minimal
return affine_minimal(self, return_transformation=False, D=prime_list, quick=True) | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/scipy/io/arff/arffread.py | python | get_nominal | (attribute) | return attribute.split(',') | If attribute is nominal, returns a list of the values | If attribute is nominal, returns a list of the values | [
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"values"
] | def get_nominal(attribute):
"""If attribute is nominal, returns a list of the values"""
return attribute.split(',') | [
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mpenning/ciscoconfparse | a6a176e6ceac7c5f3e974272fa70273476ba84a3 | ciscoconfparse/models_junos.py | python | JunosIntfGlobal.is_object_for | (cls, line="", re=re) | return False | [] | def is_object_for(cls, line="", re=re):
if re.search(
r"^(no\s+cdp\s+run)|(logging\s+event\s+link-status\s+global)|(spanning-tree\sportfast\sdefault)|(spanning-tree\sportfast\sbpduguard\sdefault)",
line,
):
return True
return False | [
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shiweibsw/Translation-Tools | 2fbbf902364e557fa7017f9a74a8797b7440c077 | venv/Lib/site-packages/pip-9.0.3-py3.6.egg/pip/_vendor/requests/cookies.py | python | MockRequest.__init__ | (self, request) | [] | def __init__(self, request):
self._r = request
self._new_headers = {}
self.type = urlparse(self._r.url).scheme | [
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nucleic/enaml | 65c2a2a2d765e88f2e1103046680571894bb41ed | enaml/qt/qt_color_dialog.py | python | QtColorDialog.on_color_selected | (self, qcolor) | Handle the 'colorSelected' signal from the widget. | Handle the 'colorSelected' signal from the widget. | [
"Handle",
"the",
"colorSelected",
"signal",
"from",
"the",
"widget",
"."
] | def on_color_selected(self, qcolor):
""" Handle the 'colorSelected' signal from the widget.
"""
d = self.declaration
if d is not None:
d.selected_color = color_from_qcolor(qcolor) | [
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trezor/trezor-firmware | 5c4703c9bbfb962fbbe409c2e40030f30161e4f0 | python/tools/firmware-fingerprint.py | python | firmware_fingerprint | (filename: BinaryIO, output: TextIO) | Display fingerprint of a firmware file. | Display fingerprint of a firmware file. | [
"Display",
"fingerprint",
"of",
"a",
"firmware",
"file",
"."
] | def firmware_fingerprint(filename: BinaryIO, output: TextIO) -> None:
"""Display fingerprint of a firmware file."""
data = filename.read()
try:
version, fw = firmware.parse(data)
# Unsigned production builds for Trezor T do not have valid code hashes.
# Use the internal module which recomputes them first.
if version == firmware.FirmwareFormat.TREZOR_T:
fingerprint = firmware_headers.FirmwareImage(fw).digest()
else:
fingerprint = firmware.digest(version, fw)
except Exception as e:
click.echo(e, err=True)
sys.exit(2)
click.echo(fingerprint.hex(), file=output) | [
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tlsfuzzer/tlslite-ng | 8720db53067ba4f7bb7b5a32d682033d8b5446f9 | tlslite/extensions.py | python | TACKExtension.create | (self, tacks, activation_flags) | return self | Initialize the instance of TACKExtension
:rtype: TACKExtension | Initialize the instance of TACKExtension | [
"Initialize",
"the",
"instance",
"of",
"TACKExtension"
] | def create(self, tacks, activation_flags):
"""
Initialize the instance of TACKExtension
:rtype: TACKExtension
"""
self.tacks = tacks
self.activation_flags = activation_flags
return self | [
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fofix/fofix | 7730d1503c66562b901f62b33a5bd46c3d5e5c34 | fofix/game/guitarscene/Rockmeter.py | python | Layer.render | (self, visibility, playerNum) | Handle the final step of rendering the image. | Handle the final step of rendering the image. | [
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] | def render(self, visibility, playerNum):
""" Handle the final step of rendering the image. """
pass | [
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nicemayi/play-with-data-structures | fa82cfeb9a1ae3faec859f207b1eca6952cee525 | chapter_06_BST/bst.py | python | BST.level_order | (self) | 非常好的BFS例子 | 非常好的BFS例子 | [
"非常好的BFS例子"
] | def level_order(self):
"""非常好的BFS例子"""
queue = deque()
queue.append(self._root)
while queue:
curr = queue.popleft()
print(curr.e)
if curr.left:
queue.append(curr.left)
if curr.right:
queue.append(curr.right) | [
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lululxvi/deepxde | 730c97282636e86c845ce2ba3253482f2178469e | deepxde/model.py | python | LossHistory.append | (self, step, loss_train, loss_test, metrics_test) | [] | def append(self, step, loss_train, loss_test, metrics_test):
self.steps.append(step)
self.loss_train.append(loss_train)
if loss_test is None:
loss_test = self.loss_test[-1]
if metrics_test is None:
metrics_test = self.metrics_test[-1]
self.loss_test.append(loss_test)
self.metrics_test.append(metrics_test) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/urllib3/exceptions.py | python | ProxySchemeUnknown.__init__ | (self, scheme) | [] | def __init__(self, scheme):
message = "Not supported proxy scheme %s" % scheme
super(ProxySchemeUnknown, self).__init__(message) | [
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/outtakes/Peptide.py | python | Peptide._update | (self) | Private method.
Updates all chirality parameters whenever the following attrs are
changed via their set methods:
- n, m,
- type
- bond_length | Private method. | [
"Private",
"method",
"."
] | def _update(self):
"""
Private method.
Updates all chirality parameters whenever the following attrs are
changed via their set methods:
- n, m,
- type
- bond_length
"""
n, m = self.getChirality()
type = self.getType()
bond_length = self.getBondLength()
self.maxlen = maxlen = 1.2 * bond_length
self.maxlensq = maxlen**2
x = (n + 0.5 * m) * sqrt3
y = 1.5 * m
angle = atan2(y, x)
twoPiRoverA = (x**2 + y**2) ** .5
AoverR = (2 * pi) / twoPiRoverA
self.__cos = cos(angle)
self.__sin = sin(angle)
# time to get the constants
s, t = self.x1y1(0,0)
u, v = self.x1y1(1./3, 1./3)
w, x = self.x1y1(0,1)
F = (t - v)**2
G = 2 * (1 - cos(AoverR * (s - u)))
H = (v - x)**2
J = 2 * (1 - cos(AoverR * (u - w)))
denom = F * J - G * H
self.R = (bond_length**2 * (F - H) / denom) ** .5
self.B = (bond_length**2 * (J - G) / denom) ** .5
self.A = self.R * AoverR
if 0:
print "--------------"
print "angle =", angle
print "A =", self.A
print "B =", self.B
print "R =", self.R | [
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Qiskit/qiskit-terra | b66030e3b9192efdd3eb95cf25c6545fe0a13da4 | qiskit/algorithms/phase_estimators/phase_estimation.py | python | PhaseEstimation.estimate_from_pe_circuit | (
self, pe_circuit: QuantumCircuit, num_unitary_qubits: int
) | return PhaseEstimationResult(
self._num_evaluation_qubits, circuit_result=circuit_result, phases=phases
) | Run the the phase estimation algorithm on a phase estimation circuit
Args:
pe_circuit: The phase estimation circuit.
num_unitary_qubits: Must agree with the number of qubits in the unitary in `pe_circuit`.
Returns:
An instance of qiskit.algorithms.phase_estimator_result.PhaseEstimationResult. | Run the the phase estimation algorithm on a phase estimation circuit | [
"Run",
"the",
"the",
"phase",
"estimation",
"algorithm",
"on",
"a",
"phase",
"estimation",
"circuit"
] | def estimate_from_pe_circuit(
self, pe_circuit: QuantumCircuit, num_unitary_qubits: int
) -> PhaseEstimationResult:
"""Run the the phase estimation algorithm on a phase estimation circuit
Args:
pe_circuit: The phase estimation circuit.
num_unitary_qubits: Must agree with the number of qubits in the unitary in `pe_circuit`.
Returns:
An instance of qiskit.algorithms.phase_estimator_result.PhaseEstimationResult.
"""
self._add_measurement_if_required(pe_circuit)
circuit_result = self._quantum_instance.execute(pe_circuit)
phases = self._compute_phases(num_unitary_qubits, circuit_result)
return PhaseEstimationResult(
self._num_evaluation_qubits, circuit_result=circuit_result, phases=phases
) | [
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joblib/joblib | 7742f5882273889f7aaf1d483a8a1c72a97d57e3 | joblib/numpy_pickle.py | python | dump | (value, filename, compress=0, protocol=None, cache_size=None) | return [filename] | Persist an arbitrary Python object into one file.
Read more in the :ref:`User Guide <persistence>`.
Parameters
-----------
value: any Python object
The object to store to disk.
filename: str, pathlib.Path, or file object.
The file object or path of the file in which it is to be stored.
The compression method corresponding to one of the supported filename
extensions ('.z', '.gz', '.bz2', '.xz' or '.lzma') will be used
automatically.
compress: int from 0 to 9 or bool or 2-tuple, optional
Optional compression level for the data. 0 or False is no compression.
Higher value means more compression, but also slower read and
write times. Using a value of 3 is often a good compromise.
See the notes for more details.
If compress is True, the compression level used is 3.
If compress is a 2-tuple, the first element must correspond to a string
between supported compressors (e.g 'zlib', 'gzip', 'bz2', 'lzma'
'xz'), the second element must be an integer from 0 to 9, corresponding
to the compression level.
protocol: int, optional
Pickle protocol, see pickle.dump documentation for more details.
cache_size: positive int, optional
This option is deprecated in 0.10 and has no effect.
Returns
-------
filenames: list of strings
The list of file names in which the data is stored. If
compress is false, each array is stored in a different file.
See Also
--------
joblib.load : corresponding loader
Notes
-----
Memmapping on load cannot be used for compressed files. Thus
using compression can significantly slow down loading. In
addition, compressed files take extra extra memory during
dump and load. | Persist an arbitrary Python object into one file. | [
"Persist",
"an",
"arbitrary",
"Python",
"object",
"into",
"one",
"file",
"."
] | def dump(value, filename, compress=0, protocol=None, cache_size=None):
"""Persist an arbitrary Python object into one file.
Read more in the :ref:`User Guide <persistence>`.
Parameters
-----------
value: any Python object
The object to store to disk.
filename: str, pathlib.Path, or file object.
The file object or path of the file in which it is to be stored.
The compression method corresponding to one of the supported filename
extensions ('.z', '.gz', '.bz2', '.xz' or '.lzma') will be used
automatically.
compress: int from 0 to 9 or bool or 2-tuple, optional
Optional compression level for the data. 0 or False is no compression.
Higher value means more compression, but also slower read and
write times. Using a value of 3 is often a good compromise.
See the notes for more details.
If compress is True, the compression level used is 3.
If compress is a 2-tuple, the first element must correspond to a string
between supported compressors (e.g 'zlib', 'gzip', 'bz2', 'lzma'
'xz'), the second element must be an integer from 0 to 9, corresponding
to the compression level.
protocol: int, optional
Pickle protocol, see pickle.dump documentation for more details.
cache_size: positive int, optional
This option is deprecated in 0.10 and has no effect.
Returns
-------
filenames: list of strings
The list of file names in which the data is stored. If
compress is false, each array is stored in a different file.
See Also
--------
joblib.load : corresponding loader
Notes
-----
Memmapping on load cannot be used for compressed files. Thus
using compression can significantly slow down loading. In
addition, compressed files take extra extra memory during
dump and load.
"""
if Path is not None and isinstance(filename, Path):
filename = str(filename)
is_filename = isinstance(filename, str)
is_fileobj = hasattr(filename, "write")
compress_method = 'zlib' # zlib is the default compression method.
if compress is True:
# By default, if compress is enabled, we want the default compress
# level of the compressor.
compress_level = None
elif isinstance(compress, tuple):
# a 2-tuple was set in compress
if len(compress) != 2:
raise ValueError(
'Compress argument tuple should contain exactly 2 elements: '
'(compress method, compress level), you passed {}'
.format(compress))
compress_method, compress_level = compress
elif isinstance(compress, str):
compress_method = compress
compress_level = None # Use default compress level
compress = (compress_method, compress_level)
else:
compress_level = compress
if compress_method == 'lz4' and lz4 is None:
raise ValueError(LZ4_NOT_INSTALLED_ERROR)
if (compress_level is not None and
compress_level is not False and
compress_level not in range(10)):
# Raising an error if a non valid compress level is given.
raise ValueError(
'Non valid compress level given: "{}". Possible values are '
'{}.'.format(compress_level, list(range(10))))
if compress_method not in _COMPRESSORS:
# Raising an error if an unsupported compression method is given.
raise ValueError(
'Non valid compression method given: "{}". Possible values are '
'{}.'.format(compress_method, _COMPRESSORS))
if not is_filename and not is_fileobj:
# People keep inverting arguments, and the resulting error is
# incomprehensible
raise ValueError(
'Second argument should be a filename or a file-like object, '
'%s (type %s) was given.'
% (filename, type(filename))
)
if is_filename and not isinstance(compress, tuple):
# In case no explicit compression was requested using both compression
# method and level in a tuple and the filename has an explicit
# extension, we select the corresponding compressor.
# unset the variable to be sure no compression level is set afterwards.
compress_method = None
for name, compressor in _COMPRESSORS.items():
if filename.endswith(compressor.extension):
compress_method = name
if compress_method in _COMPRESSORS and compress_level == 0:
# we choose the default compress_level in case it was not given
# as an argument (using compress).
compress_level = None
if cache_size is not None:
# Cache size is deprecated starting from version 0.10
warnings.warn("Please do not set 'cache_size' in joblib.dump, "
"this parameter has no effect and will be removed. "
"You used 'cache_size={}'".format(cache_size),
DeprecationWarning, stacklevel=2)
if compress_level != 0:
with _write_fileobject(filename, compress=(compress_method,
compress_level)) as f:
NumpyPickler(f, protocol=protocol).dump(value)
elif is_filename:
with open(filename, 'wb') as f:
NumpyPickler(f, protocol=protocol).dump(value)
else:
NumpyPickler(filename, protocol=protocol).dump(value)
# If the target container is a file object, nothing is returned.
if is_fileobj:
return
# For compatibility, the list of created filenames (e.g with one element
# after 0.10.0) is returned by default.
return [filename] | [
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google/prettytensor | 75daa0b11252590f548da5647addc0ea610c4c45 | prettytensor/layers.py | python | apply_activation | (
books,
x,
activation,
activation_args=(),
activation_kwargs=None) | return y | Returns activation(x, *activation_args, **activation_kwargs).
This applies the given activation and adds useful summaries specific to the
activation.
Args:
books: The bookkeeper.
x: The tensor to apply activation to.
activation: An activation function.
activation_args: Optional additional arguments for the activation.
activation_kwargs: Optional keyword args for activation.
Returns:
A tensor with activation applied to x. | Returns activation(x, *activation_args, **activation_kwargs). | [
"Returns",
"activation",
"(",
"x",
"*",
"activation_args",
"**",
"activation_kwargs",
")",
"."
] | def apply_activation(
books,
x,
activation,
activation_args=(),
activation_kwargs=None):
"""Returns activation(x, *activation_args, **activation_kwargs).
This applies the given activation and adds useful summaries specific to the
activation.
Args:
books: The bookkeeper.
x: The tensor to apply activation to.
activation: An activation function.
activation_args: Optional additional arguments for the activation.
activation_kwargs: Optional keyword args for activation.
Returns:
A tensor with activation applied to x.
"""
if activation is None:
return x
if activation_kwargs is None:
activation_kwargs = {}
y = activation(x, *activation_args, **activation_kwargs)
if activation in (tf.nn.relu, functions.leaky_relu, functions.softplus):
books.add_scalar_summary(
tf.reduce_mean(tf.cast(tf.less(x, 0.0), tf.float32)),
'%s/zeros' % y.op.name)
elif activation is tf.nn.relu6:
books.add_scalar_summary(
tf.reduce_mean(tf.cast(tf.less(x, 0.0), tf.float32)),
'%s/zeros' % y.op.name)
books.add_scalar_summary(
tf.reduce_mean(tf.cast(tf.greater(x, 6.0), tf.float32)),
'%s/sixes' % y.op.name)
elif activation in (functions.l2_normalize, tf.nn.l2_normalize,
functions.l1_normalize):
books.add_scalar_summary(
tf.reduce_mean(tf.sqrt(tf.reduce_sum(
tf.square(x), 1))), '%s/length' % y.op.name)
return y | [
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":"... | https://github.com/google/prettytensor/blob/75daa0b11252590f548da5647addc0ea610c4c45/prettytensor/layers.py#L31-L72 |
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