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researchmm/tasn | 5dba8ccc096cedc63913730eeea14a9647911129 | tasn-mxnet/3rdparty/tvm/topi/python/topi/nn/depthwise_conv2d.py | python | depthwise_conv2d_nchw | (Input, Filter, stride, padding, out_dtype=None) | return Output | Depthwise convolution nchw forward operator.
Parameters
----------
Input : tvm.Tensor
4-D with shape [batch, in_channel, in_height, in_width]
Filter : tvm.Tensor
4-D with shape [in_channel, channel_multiplier, filter_height, filter_width]
stride : tuple of two ints
The spatial stride along height and width
padding : int or str
Padding size, or ['VALID', 'SAME']
out_dtype: str, optional
Output data type
Returns
-------
Output : tvm.Tensor
4-D with shape [batch, out_channel, out_height, out_width] | Depthwise convolution nchw forward operator. | [
"Depthwise",
"convolution",
"nchw",
"forward",
"operator",
"."
] | def depthwise_conv2d_nchw(Input, Filter, stride, padding, out_dtype=None):
"""Depthwise convolution nchw forward operator.
Parameters
----------
Input : tvm.Tensor
4-D with shape [batch, in_channel, in_height, in_width]
Filter : tvm.Tensor
4-D with shape [in_channel, channel_multiplier, filter_height, filter_width]
stride : tuple of two ints
The spatial stride along height and width
padding : int or str
Padding size, or ['VALID', 'SAME']
out_dtype: str, optional
Output data type
Returns
-------
Output : tvm.Tensor
4-D with shape [batch, out_channel, out_height, out_width]
"""
out_dtype = Input.dtype if out_dtype is None else out_dtype
batch, in_channel, in_height, in_width = Input.shape
filter_channel, channel_multiplier, filter_height, filter_width = Filter.shape
if isinstance(stride, int):
stride_h = stride_w = stride
else:
stride_h, stride_w = stride
pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
padding, (filter_height, filter_width))
out_channel = simplify(in_channel * channel_multiplier)
out_height = simplify((in_height - filter_height + pad_top + pad_down) // stride_h + 1)
out_width = simplify((in_width - filter_width + pad_left + pad_right) // stride_w + 1)
# padding stage
pad_before = [0, 0, pad_top, pad_left]
pad_after = [0, 0, pad_down, pad_right]
PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput")
# depthconv stage
di = tvm.reduce_axis((0, filter_height), name='di')
dj = tvm.reduce_axis((0, filter_width), name='dj')
Output = tvm.compute(
(batch, out_channel, out_height, out_width),
lambda b, c, i, j: tvm.sum(
(PaddedInput[b, c/channel_multiplier, i*stride_h+di, j*stride_w+dj].astype(out_dtype) *
Filter[c/channel_multiplier, c%channel_multiplier, di, dj].astype(out_dtype)),
axis=[di, dj]),
name='DepthwiseConv2d', tag="depthwise_conv2d_nchw")
return Output | [
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tensorflow/tensor2tensor | 2a33b152d7835af66a6d20afe7961751047e28dd | tensor2tensor/data_generators/dialog_abstract.py | python | DialogAbstract.download_data | (self, train_mode) | Download data from official sources.
Args:
train_mode: string, whether we are in train, dev or test mode | Download data from official sources. | [
"Download",
"data",
"from",
"official",
"sources",
"."
] | def download_data(self, train_mode):
"""Download data from official sources.
Args:
train_mode: string, whether we are in train, dev or test mode
"""
# Open the url and download the data with progress bars.
data_stream = requests.get(self._url, stream=True)
with open(self._zipped_data, 'wb') as f:
for chunk in data_stream.iter_content(1024):
if chunk:
f.write(chunk)
f.flush()
# Next step is extracting the data.
print('problem_log: Extracting data to ' + self._zipped_data + '.')
self.extract_data(train_mode) | [
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HoloClean/holoclean | d4f5929a8e4d92d4f41eb058c04c96cdcb0af767 | repair/featurize/featurized_dataset.py | python | FeaturizedDataset.get_infer_data | (self) | return X_infer, mask_infer, infer_idx | Retrieves the samples to be inferred i.e. DK cells. | Retrieves the samples to be inferred i.e. DK cells. | [
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] | def get_infer_data(self):
"""
Retrieves the samples to be inferred i.e. DK cells.
"""
# only infer on those that are DK cells
infer_idx = (self.is_clean == 0).nonzero()[:, 0]
X_infer = self.tensor.index_select(0, infer_idx)
mask_infer = self.var_class_mask.index_select(0, infer_idx)
return X_infer, mask_infer, infer_idx | [
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Parsl/parsl | af2535341152b2640fdd1a3b73b891992bf1b3ea | parsl/executors/base.py | python | ParslExecutor.set_bad_state_and_fail_all | (self, exception: Exception) | Allows external error handlers to mark this executor as irrecoverably bad and cause
all tasks submitted to it now and in the future to fail. The executor is responsible
for checking :method:bad_state_is_set() in the :method:submit() method and raising the
appropriate exception, which is available through :method:executor_exception(). | Allows external error handlers to mark this executor as irrecoverably bad and cause
all tasks submitted to it now and in the future to fail. The executor is responsible
for checking :method:bad_state_is_set() in the :method:submit() method and raising the
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"""Allows external error handlers to mark this executor as irrecoverably bad and cause
all tasks submitted to it now and in the future to fail. The executor is responsible
for checking :method:bad_state_is_set() in the :method:submit() method and raising the
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pass | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | golismero/main/orchestrator.py | python | Orchestrator.__control_c_handler | (self, signum, frame) | Signal handler to catch Control-C interrupts. | Signal handler to catch Control-C interrupts. | [
"Signal",
"handler",
"to",
"catch",
"Control",
"-",
"C",
"interrupts",
"."
] | def __control_c_handler(self, signum, frame):
"""
Signal handler to catch Control-C interrupts.
"""
try:
# Tell the user the message has been sent.
Console.display("User cancel requested, stopping all audits...")
# Send a stop message to the Orchestrator.
message = Message(message_type = MessageType.MSG_TYPE_CONTROL,
message_code = MessageCode.MSG_CONTROL_STOP,
message_info = False,
priority = MessagePriority.MSG_PRIORITY_HIGH)
try:
self.messageManager.put(message)
except:
print_exc()
exit(1)
finally:
# Only do this once, the next time just PANIC.
signal(SIGINT, self.__panic_control_c_handler) | [
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/federatedml/secureprotol/spdz/tensor/fixedpoint_numpy.py | python | PaillierFixedPointTensor.__radd__ | (self, other) | return self.__add__(other) | [] | def __radd__(self, other):
return self.__add__(other) | [
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/pip/_vendor/pkg_resources/__init__.py | python | WorkingSet.subscribe | (self, callback, existing=True) | Invoke `callback` for all distributions
If `existing=True` (default),
call on all existing ones, as well. | Invoke `callback` for all distributions | [
"Invoke",
"callback",
"for",
"all",
"distributions"
] | def subscribe(self, callback, existing=True):
"""Invoke `callback` for all distributions
If `existing=True` (default),
call on all existing ones, as well.
"""
if callback in self.callbacks:
return
self.callbacks.append(callback)
if not existing:
return
for dist in self:
callback(dist) | [
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googlefonts/gftools | 8ad55dd4d7e38729524329c79f236476f1576e67 | Lib/gftools/packager.py | python | _shallow_clone_git | (target_dir, git_url, branch_or_tag='main') | return subprocess.run(['git', 'clone', '--depth', '1', '--bare'
, '-b', branch_or_tag, git_url
, target_dir], check=True
, stdout=subprocess.PIPE) | getting this as a shallow copy, because for some files we want to
search in the filesystem.
branch_or_tag: as used in `git clone -b`
NOTE: libgit2 and hence pygit2 doesn't support shallow clones yet,
but that's the most lightweight way to get the whole directory
structure. | getting this as a shallow copy, because for some files we want to
search in the filesystem. | [
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"because",
"for",
"some",
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"we",
"want",
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"search",
"in",
"the",
"filesystem",
"."
] | def _shallow_clone_git(target_dir, git_url, branch_or_tag='main'):
"""
getting this as a shallow copy, because for some files we want to
search in the filesystem.
branch_or_tag: as used in `git clone -b`
NOTE: libgit2 and hence pygit2 doesn't support shallow clones yet,
but that's the most lightweight way to get the whole directory
structure.
"""
# I don't understand why git clone doesn't take this more explicit form.
# But, I recommended it in the docs, so here's a little fix.
if branch_or_tag.startswith('tags/'):
branch_or_tag = branch_or_tag[len('tags/'):]
return subprocess.run(['git', 'clone', '--depth', '1', '--bare'
, '-b', branch_or_tag, git_url
, target_dir], check=True
, stdout=subprocess.PIPE) | [
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lightforever/mlcomp | c78fdb77ec9c4ec8ff11beea50b90cab20903ad9 | mlcomp/contrib/dataset/classify.py | python | ImageDataset.preprocess_row | (self, row: dict) | [] | def preprocess_row(self, row: dict):
row['image'] = join(self.img_folder, row['image']) | [
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sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | controllers/project.py | python | task | () | return project_task_controller() | RESTful CRUD controller | RESTful CRUD controller | [
"RESTful",
"CRUD",
"controller"
] | def task():
""" RESTful CRUD controller """
from s3db.project import project_task_controller
return project_task_controller() | [
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/mailbox.py | python | Babyl._generate_toc | (self) | Generate key-to-(start, stop) table of contents. | Generate key-to-(start, stop) table of contents. | [
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"-",
"to",
"-",
"(",
"start",
"stop",
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"."
] | def _generate_toc(self):
"""Generate key-to-(start, stop) table of contents."""
starts, stops = [], []
self._file.seek(0)
next_pos = 0
label_lists = []
while True:
line_pos = next_pos
line = self._file.readline()
next_pos = self._file.tell()
if line == '\037\014' + os.linesep:
if len(stops) < len(starts):
stops.append(line_pos - len(os.linesep))
starts.append(next_pos)
labels = [label.strip() for label
in self._file.readline()[1:].split(',')
if label.strip() != '']
label_lists.append(labels)
elif line == '\037' or line == '\037' + os.linesep:
if len(stops) < len(starts):
stops.append(line_pos - len(os.linesep))
elif line == '':
stops.append(line_pos - len(os.linesep))
break
self._toc = dict(enumerate(zip(starts, stops)))
self._labels = dict(enumerate(label_lists))
self._next_key = len(self._toc)
self._file.seek(0, 2)
self._file_length = self._file.tell() | [
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HaoZhang95/Python24 | b897224b8a0e6a5734f408df8c24846a98c553bf | 00Python/venv/Lib/site-packages/pip-10.0.1-py3.7.egg/pip/_vendor/urllib3/contrib/_securetransport/low_level.py | python | _cf_data_from_bytes | (bytestring) | return CoreFoundation.CFDataCreate(
CoreFoundation.kCFAllocatorDefault, bytestring, len(bytestring)
) | Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller. | Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller. | [
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] | def _cf_data_from_bytes(bytestring):
"""
Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller.
"""
return CoreFoundation.CFDataCreate(
CoreFoundation.kCFAllocatorDefault, bytestring, len(bytestring)
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Cadene/tensorflow-model-zoo.torch | 990b10ffc22d4c8eacb2a502f20415b4f70c74c2 | models/research/slim/nets/mobilenet_v1.py | python | mobilenet_v1 | (inputs,
num_classes=1000,
dropout_keep_prob=0.999,
is_training=True,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=tf.contrib.layers.softmax,
spatial_squeeze=True,
reuse=None,
scope='MobilenetV1',
global_pool=False) | return logits, end_points | Mobilenet v1 model for classification.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes. If 0 or None, the logits layer
is omitted and the input features to the logits layer (before dropout)
are returned instead.
dropout_keep_prob: the percentage of activation values that are retained.
is_training: whether is training or not.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
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.
global_pool: Optional boolean flag to control the avgpooling before the
logits layer. If false or unset, pooling is done with a fixed window
that reduces default-sized inputs to 1x1, while larger inputs lead to
larger outputs. If true, any input size is pooled down to 1x1.
Returns:
net: a 2D Tensor with the logits (pre-softmax activations) if num_classes
is a non-zero integer, or the non-dropped-out input to the logits layer
if num_classes is 0 or None.
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: Input rank is invalid. | Mobilenet v1 model for classification. | [
"Mobilenet",
"v1",
"model",
"for",
"classification",
"."
] | def mobilenet_v1(inputs,
num_classes=1000,
dropout_keep_prob=0.999,
is_training=True,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=tf.contrib.layers.softmax,
spatial_squeeze=True,
reuse=None,
scope='MobilenetV1',
global_pool=False):
"""Mobilenet v1 model for classification.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes. If 0 or None, the logits layer
is omitted and the input features to the logits layer (before dropout)
are returned instead.
dropout_keep_prob: the percentage of activation values that are retained.
is_training: whether is training or not.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
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.
global_pool: Optional boolean flag to control the avgpooling before the
logits layer. If false or unset, pooling is done with a fixed window
that reduces default-sized inputs to 1x1, while larger inputs lead to
larger outputs. If true, any input size is pooled down to 1x1.
Returns:
net: a 2D Tensor with the logits (pre-softmax activations) if num_classes
is a non-zero integer, or the non-dropped-out input to the logits layer
if num_classes is 0 or None.
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: Input rank is invalid.
"""
input_shape = inputs.get_shape().as_list()
if len(input_shape) != 4:
raise ValueError('Invalid input tensor rank, expected 4, was: %d' %
len(input_shape))
with tf.variable_scope(scope, 'MobilenetV1', [inputs], reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points = mobilenet_v1_base(inputs, scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier,
conv_defs=conv_defs)
with tf.variable_scope('Logits'):
if global_pool:
# Global average pooling.
net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
end_points['global_pool'] = net
else:
# Pooling with a fixed kernel size.
kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
net = slim.avg_pool2d(net, kernel_size, padding='VALID',
scope='AvgPool_1a')
end_points['AvgPool_1a'] = net
if not num_classes:
return net, end_points
# 1 x 1 x 1024
net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
if prediction_fn:
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points | [
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python-discord/bot | 26c5587ac13e5414361bb6e7ada42983b81014d2 | bot/utils/message_cache.py | python | MessageCache.append | (self, message: Message) | Add the received message to the cache, depending on the order of messages defined by `newest_first`. | Add the received message to the cache, depending on the order of messages defined by `newest_first`. | [
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"""Add the received message to the cache, depending on the order of messages defined by `newest_first`."""
if self.newest_first:
self._appendleft(message)
else:
self._appendright(message) | [
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cyverse/atmosphere | 4a3e522f1f7b58abd9fa944c10b7455dc5cddac1 | service/argo/rest_api.py | python | ArgoAPIClient.run_workflow | (self, wf_json) | return json_resp | Endpoint for running a workflow
Args:
wf_json (dict): workflow definition as JSON object
Returns:
dict: response text as JSON object | Endpoint for running a workflow | [
"Endpoint",
"for",
"running",
"a",
"workflow"
] | def run_workflow(self, wf_json):
"""
Endpoint for running a workflow
Args:
wf_json (dict): workflow definition as JSON object
Returns:
dict: response text as JSON object
"""
api_url = "/api/v1/workflows/" + self._namespace
json_data = {}
json_data["namespace"] = self._namespace
json_data["serverDryRun"] = False
json_data["workflow"] = wf_json
json_resp = self._req("post", api_url, json_data=json_data)
return json_resp | [
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meduza-corp/interstellar | 40a801ccd7856491726f5a126621d9318cabe2e1 | gsutil/third_party/boto/boto/sqs/connection.py | python | SQSConnection.delete_message | (self, queue, message) | return self.get_status('DeleteMessage', params, queue.id) | Delete a message from a queue.
:type queue: A :class:`boto.sqs.queue.Queue` object
:param queue: The Queue from which messages are read.
:type message: A :class:`boto.sqs.message.Message` object
:param message: The Message to be deleted
:rtype: bool
:return: True if successful, False otherwise. | Delete a message from a queue. | [
"Delete",
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"a",
"queue",
"."
] | def delete_message(self, queue, message):
"""
Delete a message from a queue.
:type queue: A :class:`boto.sqs.queue.Queue` object
:param queue: The Queue from which messages are read.
:type message: A :class:`boto.sqs.message.Message` object
:param message: The Message to be deleted
:rtype: bool
:return: True if successful, False otherwise.
"""
params = {'ReceiptHandle' : message.receipt_handle}
return self.get_status('DeleteMessage', params, queue.id) | [
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liuyubobobo/Play-with-Linear-Algebra | e86175adb908b03756618fbeeeadb448a3551321 | 13-Eigenvalues-and-Eigenvectors/06-Eigenvalues-and-Eigenvectors-in-numpy/playLA/Matrix.py | python | Matrix.dot | (self, another) | 返回矩阵乘法的结果 | 返回矩阵乘法的结果 | [
"返回矩阵乘法的结果"
] | def dot(self, another):
"""返回矩阵乘法的结果"""
if isinstance(another, Vector):
# 矩阵和向量的乘法
assert self.col_num() == len(another), \
"Error in Matrix-Vector Multiplication."
return Vector([self.row_vector(i).dot(another) for i in range(self.row_num())])
if isinstance(another, Matrix):
# 矩阵和矩阵的乘法
assert self.col_num() == another.row_num(), \
"Error in Matrix-Matrix Multiplication."
return Matrix([[self.row_vector(i).dot(another.col_vector(j)) for j in range(another.col_num())]
for i in range(self.row_num())]) | [
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deluge-torrent/deluge | 2316088f5c0dd6cb044d9d4832fa7d56dcc79cdc | deluge/ui/console/cmdline/command.py | python | Commander.parse_command | (self, cmd_line) | return options | Parse a console command and process with argparse.
Args:
cmd_line (str): Console command.
Returns:
argparse.Namespace: The parsed command. | Parse a console command and process with argparse. | [
"Parse",
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"console",
"command",
"and",
"process",
"with",
"argparse",
"."
] | def parse_command(self, cmd_line):
"""Parse a console command and process with argparse.
Args:
cmd_line (str): Console command.
Returns:
argparse.Namespace: The parsed command.
"""
if not cmd_line:
return
cmd, _, line = cmd_line.partition(' ')
try:
parser = self._commands[cmd].create_parser()
except KeyError:
self.write('{!error!}Unknown command: %s' % cmd)
return
try:
args = [cmd] + self._commands[cmd].split(line)
except ValueError as ex:
self.write('{!error!}Error parsing command: %s' % ex)
return
# Do a little hack here to print 'command --help' properly
parser._print_help = parser.print_help
def print_help(f=None):
if self.interactive:
self.write(parser.format_help())
else:
parser._print_help(f)
parser.print_help = print_help
# Only these commands can be run when not connected to a daemon
not_connected_cmds = ['help', 'connect', 'quit']
aliases = []
for c in not_connected_cmds:
aliases.extend(self._commands[c].aliases)
not_connected_cmds.extend(aliases)
if not client.connected() and cmd not in not_connected_cmds:
self.write(
'{!error!}Not connected to a daemon, please use the connect command first.'
)
return
try:
options = parser.parse_args(args=args)
options.command = cmd
except TypeError as ex:
self.write('{!error!}Error parsing options: %s' % ex)
import traceback
self.write('%s' % traceback.format_exc())
return
except OptionParserError as ex:
import traceback
log.warning('Error parsing command "%s": %s', args, ex)
self.write('{!error!} %s' % ex)
parser.print_help()
return
if getattr(parser, '_exit', False):
return
return options | [
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postlund/hass-atv-beta | 0ce01623eabc6a11b84a79deaf25cec0359056ea | custom_components/apple_tv/config_flow.py | python | AppleTVConfigFlow.async_step_zeroconf | (
self, discovery_info: zeroconf.ZeroconfServiceInfo
) | return await self.async_find_device_wrapper(self.async_found_zeroconf_device) | Handle device found via zeroconf. | Handle device found via zeroconf. | [
"Handle",
"device",
"found",
"via",
"zeroconf",
"."
] | async def async_step_zeroconf(
self, discovery_info: zeroconf.ZeroconfServiceInfo
) -> data_entry_flow.FlowResult:
"""Handle device found via zeroconf."""
host = discovery_info.host
self._async_abort_entries_match({CONF_ADDRESS: host})
service_type = discovery_info.type[:-1] # Remove leading .
name = discovery_info.name.replace(f".{service_type}.", "")
properties = discovery_info.properties
# Extract unique identifier from service
unique_id = get_unique_id(service_type, name, properties)
if unique_id is None:
return self.async_abort(reason="unknown")
if existing_unique_id := self._entry_unique_id_from_identifers({unique_id}):
await self.async_set_unique_id(existing_unique_id)
self._abort_if_unique_id_configured(updates={CONF_ADDRESS: host})
self._async_abort_entries_match({CONF_ADDRESS: host})
await self._async_aggregate_discoveries(host, unique_id)
# Scan for the device in order to extract _all_ unique identifiers assigned to
# it. Not doing it like this will yield multiple config flows for the same
# device, one per protocol, which is undesired.
self.scan_filter = host
return await self.async_find_device_wrapper(self.async_found_zeroconf_device) | [
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giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter11/immlib/pefile.py | python | COFF.get_qword_at_rva | (self, rva) | Return the quad-word value at the given RVA.
Returns None if the value can't be read, i.e. the RVA can't be mapped
to a file offset. | Return the quad-word value at the given RVA.
Returns None if the value can't be read, i.e. the RVA can't be mapped
to a file offset. | [
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"offset",
"."
] | def get_qword_at_rva(self, rva):
"""Return the quad-word value at the given RVA.
Returns None if the value can't be read, i.e. the RVA can't be mapped
to a file offset.
"""
try:
return self.get_qword_from_data(self.get_data(rva)[:8], 0)
except PEFormatError:
return None | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | tensorflow_dl_models/research/capsules/models/layers/variables.py | python | bias_variable | (shape, verbose=False) | return biases | Creates a CPU variable with constant initialization. Adds summaries.
Args:
shape: list, the shape of the variable.
verbose: if set add histograms.
Returns:
Bias variable tensor with shape=shape. | Creates a CPU variable with constant initialization. Adds summaries. | [
"Creates",
"a",
"CPU",
"variable",
"with",
"constant",
"initialization",
".",
"Adds",
"summaries",
"."
] | def bias_variable(shape, verbose=False):
"""Creates a CPU variable with constant initialization. Adds summaries.
Args:
shape: list, the shape of the variable.
verbose: if set add histograms.
Returns:
Bias variable tensor with shape=shape.
"""
with tf.device('/cpu:0'):
with tf.name_scope('biases'):
biases = tf.get_variable(
'biases',
shape,
initializer=tf.constant_initializer(0.1),
dtype=tf.float32)
variable_summaries(biases, verbose)
return biases | [
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pytorch/fairseq | 1575f30dd0a9f7b3c499db0b4767aa4e9f79056c | fairseq/logging/progress_bar.py | python | TensorboardProgressBarWrapper.log | (self, stats, tag=None, step=None) | Log intermediate stats to tensorboard. | Log intermediate stats to tensorboard. | [
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"intermediate",
"stats",
"to",
"tensorboard",
"."
] | def log(self, stats, tag=None, step=None):
"""Log intermediate stats to tensorboard."""
self._log_to_tensorboard(stats, tag, step)
self.wrapped_bar.log(stats, tag=tag, step=step) | [
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vivisect/vivisect | 37b0b655d8dedfcf322e86b0f144b096e48d547e | envi/config.py | python | EnviConfig.getOptionDoc | (self, optname) | return self.cfgdocs.get(optname) | Retrieve docs about the given option if present.
Example:
doc = config.getOptionDoc('woot')
if doc is not None:
print('woot: %s' % doc) | Retrieve docs about the given option if present. | [
"Retrieve",
"docs",
"about",
"the",
"given",
"option",
"if",
"present",
"."
] | def getOptionDoc(self, optname):
'''
Retrieve docs about the given option if present.
Example:
doc = config.getOptionDoc('woot')
if doc is not None:
print('woot: %s' % doc)
'''
return self.cfgdocs.get(optname) | [
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reingart/pyafipws | 3141894e82d538e297a85b8bc960016a3a871fbe | wsfexv1.py | python | WSFEXv1.GetParamTipoExpo | (self, sep="|") | Recuperador de valores referenciales de códigos de Tipo de exportación | Recuperador de valores referenciales de códigos de Tipo de exportación | [
"Recuperador",
"de",
"valores",
"referenciales",
"de",
"códigos",
"de",
"Tipo",
"de",
"exportación"
] | def GetParamTipoExpo(self, sep="|"):
"Recuperador de valores referenciales de códigos de Tipo de exportación"
ret = self.client.FEXGetPARAM_Tipo_Expo(
Auth={
"Token": self.Token,
"Sign": self.Sign,
"Cuit": self.Cuit,
}
)
result = ret["FEXGetPARAM_Tipo_ExpoResult"]
self.__analizar_errores(result)
ret = []
for u in result["FEXResultGet"]:
u = u["ClsFEXResponse_Tex"]
try:
r = {
"codigo": u.get("Tex_Id"),
"ds": u.get("Tex_Ds"),
"vig_desde": u.get("Tex_vig_desde"),
"vig_hasta": u.get("Tex_vig_hasta"),
}
except Exception as e:
print(e)
ret.append(r)
if sep:
return [
("\t%(codigo)s\t%(ds)s\t%(vig_desde)s\t%(vig_hasta)s\t" % it).replace(
"\t", sep
)
for it in ret
]
else:
return ret | [
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enthought/mayavi | 2103a273568b8f0bd62328801aafbd6252543ae8 | mayavi/plugins/mayavi_workbench_application.py | python | MayaviWorkbenchApplication._about_dialog_default | (self) | return about_dialog | Trait initializer. | Trait initializer. | [
"Trait",
"initializer",
"."
] | def _about_dialog_default(self):
""" Trait initializer. """
from mayavi import api
from vtk import vtkVersion
vtk_version = vtkVersion().GetVTKVersion()
about_dialog = AboutDialog(
parent = self.workbench.active_window.control,
image = ImageResource('m2_about.jpg',
search_path=[IMG_DIR]),
additions = ['Authors: Prabhu Ramachandran',
'and Gael Varoquaux',
'',
'Mayavi version %s \t - \t VTK version %s' %
(api.__version__, vtk_version)],
)
return about_dialog | [
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houtianze/bypy | 10fd0f18378174a775a05a366cc20ba6609f96c6 | .vscode/.ropeproject/config.py | python | project_opened | (project) | This function is called after opening the project | This function is called after opening the project | [
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tenpy/tenpy | bbdd3dbbdb511948eb0e6ba7ff619ac6ca657fff | tenpy/networks/mpo.py | python | MPOEnvironment.full_contraction | (self, i0) | return res | Calculate the energy by a full contraction of the network.
The full contraction of the environments gives the value
``<bra|H|ket> / (norm(|bra>)*norm(|ket>))``,
i.e. if `bra` is `ket` and normalized, the total energy.
For this purpose, this function contracts
``get_LP(i0+1, store=False)`` and ``get_RP(i0, store=False)``.
Parameters
----------
i0 : int
Site index. | Calculate the energy by a full contraction of the network. | [
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"""Calculate the energy by a full contraction of the network.
The full contraction of the environments gives the value
``<bra|H|ket> / (norm(|bra>)*norm(|ket>))``,
i.e. if `bra` is `ket` and normalized, the total energy.
For this purpose, this function contracts
``get_LP(i0+1, store=False)`` and ``get_RP(i0, store=False)``.
Parameters
----------
i0 : int
Site index.
"""
# same as MPSEnvironment.full_contraction, but also contract 'wL' with 'wR'
if self.ket.finite and i0 + 1 == self.L:
# special case to handle `_to_valid_index` correctly:
# get_LP(L) is not valid for finite b.c, so we use need to calculate it explicitly.
LP = self.get_LP(i0, store=False)
LP = self._contract_LP(i0, LP)
else:
LP = self.get_LP(i0 + 1, store=False)
# multiply with `S` on bra and ket side
S_bra = self.bra.get_SR(i0).conj()
if isinstance(S_bra, npc.Array):
LP = npc.tensordot(S_bra, LP, axes=['vL*', 'vR*'])
else:
LP = LP.scale_axis(S_bra, 'vR*')
S_ket = self.ket.get_SR(i0)
if isinstance(S_ket, npc.Array):
LP = npc.tensordot(LP, S_ket, axes=['vR', 'vL'])
else:
LP = LP.scale_axis(S_ket, 'vR')
RP = self.get_RP(i0, store=False)
res = npc.inner(LP, RP, axes=[['vR*', 'wR', 'vR'], ['vL*', 'wL', 'vL']], do_conj=False)
if self.H.explicit_plus_hc:
res = res + np.conj(res)
return res | [
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Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/devops_sdk/v5_0/task_agent/task_agent_client.py | python | TaskAgentClient.add_deployment_group | (self, deployment_group, project) | return self._deserialize('DeploymentGroup', response) | AddDeploymentGroup.
[Preview API] Create a deployment group.
:param :class:`<DeploymentGroupCreateParameter> <azure.devops.v5_0.task_agent.models.DeploymentGroupCreateParameter>` deployment_group: Deployment group to create.
:param str project: Project ID or project name
:rtype: :class:`<DeploymentGroup> <azure.devops.v5_0.task_agent.models.DeploymentGroup>` | AddDeploymentGroup.
[Preview API] Create a deployment group.
:param :class:`<DeploymentGroupCreateParameter> <azure.devops.v5_0.task_agent.models.DeploymentGroupCreateParameter>` deployment_group: Deployment group to create.
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"""AddDeploymentGroup.
[Preview API] Create a deployment group.
:param :class:`<DeploymentGroupCreateParameter> <azure.devops.v5_0.task_agent.models.DeploymentGroupCreateParameter>` deployment_group: Deployment group to create.
:param str project: Project ID or project name
:rtype: :class:`<DeploymentGroup> <azure.devops.v5_0.task_agent.models.DeploymentGroup>`
"""
route_values = {}
if project is not None:
route_values['project'] = self._serialize.url('project', project, 'str')
content = self._serialize.body(deployment_group, 'DeploymentGroupCreateParameter')
response = self._send(http_method='POST',
location_id='083c4d89-ab35-45af-aa11-7cf66895c53e',
version='5.0-preview.1',
route_values=route_values,
content=content)
return self._deserialize('DeploymentGroup', response) | [
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Alfanous-team/alfanous | 594514729473c24efa3908e3107b45a38255de4b | src/alfanous/Support/whoosh/index.py | python | Index.doc_count_all | (self) | Returns the total number of documents, DELETED OR UNDELETED,
in this index. | Returns the total number of documents, DELETED OR UNDELETED,
in this index. | [
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reviewboard/reviewboard | 7395902e4c181bcd1d633f61105012ffb1d18e1b | reviewboard/admin/templatetags/rbadmintags.py | python | split_error_title_text | (error) | return six.text_type(error).split('\n', 1) | Split an exception's text into a title and body text.
Args:
error (Exception):
The error containing text to split.
Returns:
tuple:
A tuple containing:
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"""Split an exception's text into a title and body text.
Args:
error (Exception):
The error containing text to split.
Returns:
tuple:
A tuple containing:
1. The title text.
2. The rest of the error message (or ``None``).
"""
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/third_party/__init__.py | python | _ZipIterator._filter_names | (self, relpath, pattern, group) | [] | def _filter_names(self, relpath, pattern, group):
# We use '/' here because the zip file format spec specifies that paths must use
# forward slashes. See section 4.4.17 of
# https://pkware.cachefly.net/webdocs/casestudies/APPNOTE.TXT.
relpath_pat = "" if not relpath else "{}/".format(relpath.replace(os.sep, "/"))
pat = re.compile(r"^{}{}{}$".format(self.prefix, relpath_pat, pattern))
with contextlib.closing(zipfile.ZipFile(self.zipfile_path)) as zf:
for name in zf.namelist():
match = pat.match(name)
if match:
yield match.group(group) | [
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Qiskit/qiskit-terra | b66030e3b9192efdd3eb95cf25c6545fe0a13da4 | qiskit/result/distributions/probability.py | python | ProbDistribution.hex_probabilities | (self) | return {hex(key): value for key, value in self.items()} | Build a probabilities dictionary with hexadecimal string keys
Returns:
dict: A dictionary where the keys are hexadecimal strings in the
format ``"0x1a"`` | Build a probabilities dictionary with hexadecimal string keys | [
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"""Build a probabilities dictionary with hexadecimal string keys
Returns:
dict: A dictionary where the keys are hexadecimal strings in the
format ``"0x1a"``
"""
return {hex(key): value for key, value in self.items()} | [
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rowliny/DiffHelper | ab3a96f58f9579d0023aed9ebd785f4edf26f8af | Tool/SitePackages/nltk/corpus/reader/framenet.py | python | FramenetCorpusReader.frame_relations | (self, frame=None, frame2=None, type=None) | return PrettyList(
sorted(
rels,
key=lambda frel: (frel.type.ID, frel.superFrameName, frel.subFrameName),
)
) | :param frame: (optional) frame object, name, or ID; only relations involving
this frame will be returned
:param frame2: (optional; 'frame' must be a different frame) only show relations
between the two specified frames, in either direction
:param type: (optional) frame relation type (name or object); show only relations
of this type
:type frame: int or str or AttrDict
:return: A list of all of the frame relations in framenet
:rtype: list(dict)
>>> from nltk.corpus import framenet as fn
>>> frels = fn.frame_relations()
>>> isinstance(frels, list)
True
>>> len(frels) in (1676, 2070) # FN 1.5 and 1.7, resp.
True
>>> PrettyList(fn.frame_relations('Cooking_creation'), maxReprSize=0, breakLines=True)
[<Parent=Intentionally_create -- Inheritance -> Child=Cooking_creation>,
<Parent=Apply_heat -- Using -> Child=Cooking_creation>,
<MainEntry=Apply_heat -- See_also -> ReferringEntry=Cooking_creation>]
>>> PrettyList(fn.frame_relations(274), breakLines=True)
[<Parent=Avoiding -- Inheritance -> Child=Dodging>,
<Parent=Avoiding -- Inheritance -> Child=Evading>, ...]
>>> PrettyList(fn.frame_relations(fn.frame('Cooking_creation')), breakLines=True)
[<Parent=Intentionally_create -- Inheritance -> Child=Cooking_creation>,
<Parent=Apply_heat -- Using -> Child=Cooking_creation>, ...]
>>> PrettyList(fn.frame_relations('Cooking_creation', type='Inheritance'))
[<Parent=Intentionally_create -- Inheritance -> Child=Cooking_creation>]
>>> PrettyList(fn.frame_relations('Cooking_creation', 'Apply_heat'), breakLines=True)
[<Parent=Apply_heat -- Using -> Child=Cooking_creation>,
<MainEntry=Apply_heat -- See_also -> ReferringEntry=Cooking_creation>] | :param frame: (optional) frame object, name, or ID; only relations involving
this frame will be returned
:param frame2: (optional; 'frame' must be a different frame) only show relations
between the two specified frames, in either direction
:param type: (optional) frame relation type (name or object); show only relations
of this type
:type frame: int or str or AttrDict
:return: A list of all of the frame relations in framenet
:rtype: list(dict) | [
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"""
:param frame: (optional) frame object, name, or ID; only relations involving
this frame will be returned
:param frame2: (optional; 'frame' must be a different frame) only show relations
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:type frame: int or str or AttrDict
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>>> from nltk.corpus import framenet as fn
>>> frels = fn.frame_relations()
>>> isinstance(frels, list)
True
>>> len(frels) in (1676, 2070) # FN 1.5 and 1.7, resp.
True
>>> PrettyList(fn.frame_relations('Cooking_creation'), maxReprSize=0, breakLines=True)
[<Parent=Intentionally_create -- Inheritance -> Child=Cooking_creation>,
<Parent=Apply_heat -- Using -> Child=Cooking_creation>,
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>>> PrettyList(fn.frame_relations(274), breakLines=True)
[<Parent=Avoiding -- Inheritance -> Child=Dodging>,
<Parent=Avoiding -- Inheritance -> Child=Evading>, ...]
>>> PrettyList(fn.frame_relations(fn.frame('Cooking_creation')), breakLines=True)
[<Parent=Intentionally_create -- Inheritance -> Child=Cooking_creation>,
<Parent=Apply_heat -- Using -> Child=Cooking_creation>, ...]
>>> PrettyList(fn.frame_relations('Cooking_creation', type='Inheritance'))
[<Parent=Intentionally_create -- Inheritance -> Child=Cooking_creation>]
>>> PrettyList(fn.frame_relations('Cooking_creation', 'Apply_heat'), breakLines=True)
[<Parent=Apply_heat -- Using -> Child=Cooking_creation>,
<MainEntry=Apply_heat -- See_also -> ReferringEntry=Cooking_creation>]
"""
relation_type = type
if not self._frel_idx:
self._buildrelationindex()
rels = None
if relation_type is not None:
if not isinstance(relation_type, dict):
type = [rt for rt in self.frame_relation_types() if rt.name == type][0]
assert isinstance(type, dict)
# lookup by 'frame'
if frame is not None:
if isinstance(frame, dict) and "frameRelations" in frame:
rels = PrettyList(frame.frameRelations)
else:
if not isinstance(frame, int):
if isinstance(frame, dict):
frame = frame.ID
else:
frame = self.frame_by_name(frame).ID
rels = [self._frel_idx[frelID] for frelID in self._frel_f_idx[frame]]
# filter by 'type'
if type is not None:
rels = [rel for rel in rels if rel.type is type]
elif type is not None:
# lookup by 'type'
rels = type.frameRelations
else:
rels = self._frel_idx.values()
# filter by 'frame2'
if frame2 is not None:
if frame is None:
raise FramenetError(
"frame_relations(frame=None, frame2=<value>) is not allowed"
)
if not isinstance(frame2, int):
if isinstance(frame2, dict):
frame2 = frame2.ID
else:
frame2 = self.frame_by_name(frame2).ID
if frame == frame2:
raise FramenetError(
"The two frame arguments to frame_relations() must be different frames"
)
rels = [
rel
for rel in rels
if rel.superFrame.ID == frame2 or rel.subFrame.ID == frame2
]
return PrettyList(
sorted(
rels,
key=lambda frel: (frel.type.ID, frel.superFrameName, frel.subFrameName),
)
) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/decimal.py | python | Context.log10 | (self, a) | return a.log10(context=self) | Returns the base 10 logarithm of the operand.
>>> c = ExtendedContext.copy()
>>> c.Emin = -999
>>> c.Emax = 999
>>> c.log10(Decimal('0'))
Decimal('-Infinity')
>>> c.log10(Decimal('0.001'))
Decimal('-3')
>>> c.log10(Decimal('1.000'))
Decimal('0')
>>> c.log10(Decimal('2'))
Decimal('0.301029996')
>>> c.log10(Decimal('10'))
Decimal('1')
>>> c.log10(Decimal('70'))
Decimal('1.84509804')
>>> c.log10(Decimal('+Infinity'))
Decimal('Infinity')
>>> c.log10(0)
Decimal('-Infinity')
>>> c.log10(1)
Decimal('0') | Returns the base 10 logarithm of the operand. | [
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] | def log10(self, a):
"""Returns the base 10 logarithm of the operand.
>>> c = ExtendedContext.copy()
>>> c.Emin = -999
>>> c.Emax = 999
>>> c.log10(Decimal('0'))
Decimal('-Infinity')
>>> c.log10(Decimal('0.001'))
Decimal('-3')
>>> c.log10(Decimal('1.000'))
Decimal('0')
>>> c.log10(Decimal('2'))
Decimal('0.301029996')
>>> c.log10(Decimal('10'))
Decimal('1')
>>> c.log10(Decimal('70'))
Decimal('1.84509804')
>>> c.log10(Decimal('+Infinity'))
Decimal('Infinity')
>>> c.log10(0)
Decimal('-Infinity')
>>> c.log10(1)
Decimal('0')
"""
a = _convert_other(a, raiseit=True)
return a.log10(context=self) | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/api/rbac_authorization_v1alpha1_api.py | python | RbacAuthorizationV1alpha1Api.delete_namespaced_role_binding | (self, name, namespace, **kwargs) | return self.delete_namespaced_role_binding_with_http_info(name, namespace, **kwargs) | delete_namespaced_role_binding # noqa: E501
delete a RoleBinding # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_namespaced_role_binding(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the RoleBinding (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param V1DeleteOptions body:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1Status
If the method is called asynchronously,
returns the request thread. | delete_namespaced_role_binding # noqa: E501 | [
"delete_namespaced_role_binding",
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":",
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] | def delete_namespaced_role_binding(self, name, namespace, **kwargs): # noqa: E501
"""delete_namespaced_role_binding # noqa: E501
delete a RoleBinding # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_namespaced_role_binding(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the RoleBinding (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param V1DeleteOptions body:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1Status
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.delete_namespaced_role_binding_with_http_info(name, namespace, **kwargs) | [
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bungnoid/glTools | 8ff0899de43784a18bd4543285655e68e28fb5e5 | utils/namespace.py | python | moveNS | (srcNS,dstNS) | return newNS | Move all items from the source namespace to the destination namespace.
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@type srcNS: str
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'''
Move all items from the source namespace to the destination namespace.
@param srcNS: The source namespace
@type srcNS: str
@param dstNS: The destination namespace
@type dstNS: str
'''
# Check NS
if not mc.namespace(exists=srcNS):
raise Exception('Source namespace "'+srcNS+'" does not exist!')
# Check Destination NS
if not mc.namespace(exists=dstNS):
# Create newNS
dstNS = mc.namespace(add=dstNS,f=True)
# Move namespace
mc.namespace(mv=(srcNS,dstNS),f=True)
# Return newNS
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/tempfile.py | python | _TemporaryFileWrapper.close | (self) | Close the temporary file, possibly deleting it. | Close the temporary file, possibly deleting it. | [
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Close the temporary file, possibly deleting it.
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/tkinter/ttk.py | python | Treeview.__init__ | (self, master=None, **kw) | Construct a Ttk Treeview with parent master.
STANDARD OPTIONS
class, cursor, style, takefocus, xscrollcommand,
yscrollcommand
WIDGET-SPECIFIC OPTIONS
columns, displaycolumns, height, padding, selectmode, show
ITEM OPTIONS
text, image, values, open, tags
TAG OPTIONS
foreground, background, font, image | Construct a Ttk Treeview with parent master. | [
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] | def __init__(self, master=None, **kw):
"""Construct a Ttk Treeview with parent master.
STANDARD OPTIONS
class, cursor, style, takefocus, xscrollcommand,
yscrollcommand
WIDGET-SPECIFIC OPTIONS
columns, displaycolumns, height, padding, selectmode, show
ITEM OPTIONS
text, image, values, open, tags
TAG OPTIONS
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"""
Widget.__init__(self, master, "ttk::treeview", kw) | [
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pythoncn/june | 0cb9051e77757871479abe8b5da2a7df7ab191cb | june/handlers/account.py | python | signin | () | return render_template('account/signin.html', form=form) | Sign in page. | Sign in page. | [
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"""Sign in page."""
next_url = request.args.get('next', '/')
if g.user:
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form = SigninForm()
if form.validate_on_submit():
login_user(form.user, form.permanent.data)
return redirect(next_url)
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rapid7/le | 81d98bde8588f5ed74259b42b02ab03b51a0d339 | src/le.py | python | date_patterns | () | Generates date patterns of the form [day<->month year?]. | Generates date patterns of the form [day<->month year?]. | [
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""" Generates date patterns of the form [day<->month year?].
"""
for year in [' %Y', ' %y']:
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yield ['%%d %s%s' % (mon, year), DAY, []]
yield ['%s %%d%s' % (mon, year), DAY, []]
for mon in ['%b', '%B']: # Year empty
yield ['%%d %s' % (mon), DAY, [YEAR]]
yield ['%s %%d' % (mon), DAY, [YEAR]]
yield ['%%Y %%d %s' % (mon), DAY, []]
yield ['%%Y %s %%d' % (mon), DAY, []]
yield ['%Y %m %d', DAY, []] | [
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quantopian/zipline | 014f1fc339dc8b7671d29be2d85ce57d3daec343 | zipline/utils/pool.py | python | ApplyAsyncResult.wait | (self) | Wait until the function is finished executing.
Notes
-----
In the :class:`~zipline.utils.pool.SequentialPool` case, this is a nop
because the function is computed eagerly in the same thread as the
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"""Wait until the function is finished executing.
Notes
-----
In the :class:`~zipline.utils.pool.SequentialPool` case, this is a nop
because the function is computed eagerly in the same thread as the
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exodrifter/unity-python | bef6e4e9ddfbbf1eaf7acbbb973e9aa3dd64a20d | Lib/codecs.py | python | StreamReader.reset | (self) | Resets the codec buffers used for keeping state.
Note that no stream repositioning should take place.
This method is primarily intended to be able to recover
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""" Resets the codec buffers used for keeping state.
Note that no stream repositioning should take place.
This method is primarily intended to be able to recover
from decoding errors.
"""
self.bytebuffer = ""
self.charbuffer = u""
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wechatpy/wechatpy | 5f693a7e90156786c2540ad3c941d12cdf6d88ef | wechatpy/client/api/merchant/__init__.py | python | WeChatMerchant.add_stock | (self, product_id, sku_info, quantity) | return self._post(
"merchant/stock/add",
data={"product_id": product_id, "sku_info": sku_info, "quantity": quantity},
) | 增加库存
:param product_id: 商品ID
:param sku_info: sku信息,格式"id1:vid1;id2:vid2",如商品为统一规格，则此处赋值为空字符串即可
:param quantity: 增加的库存数量
:return: 返回的 JSON 数据包 | 增加库存 | [
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] | def add_stock(self, product_id, sku_info, quantity):
"""
增加库存
:param product_id: 商品ID
:param sku_info: sku信息,格式"id1:vid1;id2:vid2",如商品为统一规格，则此处赋值为空字符串即可
:param quantity: 增加的库存数量
:return: 返回的 JSON 数据包
"""
return self._post(
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data={"product_id": product_id, "sku_info": sku_info, "quantity": quantity},
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tensorwerk/hangar-py | a6deb22854a6c9e9709011b91c1c0eeda7f47bb0 | src/hangar/backends/chunk.py | python | _limit_es | (expected_mb) | return expected_mb | Protection against creating too small or too large chunks. | Protection against creating too small or too large chunks. | [
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if expected_mb < 1: # < 1 MB
expected_mb = 1
elif expected_mb > 10 ** 7: # > 10 TB
expected_mb = 10 ** 7
return expected_mb | [
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m-labs/artiq | eaa1505c947c7987cdbd31c24056823c740e84e0 | artiq/coredevice/novogorny.py | python | adc_channel | (data) | return (data >> 3) & 0x7 | Return the channel index from a result packet | Return the channel index from a result packet | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/op2/tables/geom/mpt.py | python | MPT._read_matt8 | (self, data: bytes, n: int) | return n | common method to read MSC/NX MATT8s | common method to read MSC/NX MATT8s | [
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op2 = self.op2
n = op2.reader_geom2._read_dual_card(
data, n, self._read_matt8_18, self._read_matt8_19,
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almarklein/visvis | 766ed97767b44a55a6ff72c742d7385e074d3d55 | utils/guisupport.py | python | is_event_loop_running_qt4 | (app=None) | Is the qt4 event loop running. | Is the qt4 event loop running. | [
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return app._in_event_loop
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MrH0wl/Cloudmare | 65e5bc9888f9d362ab2abfb103ea6c1e869d67aa | thirdparty/click/types.py | python | convert_type | (ty, default=None) | return FuncParamType(ty) | Converts a callable or python type into the most appropriate
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guessed_type = False
if ty is None and default is not None:
if isinstance(default, tuple):
ty = tuple(map(type, default))
else:
ty = type(default)
guessed_type = True
if isinstance(ty, tuple):
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if isinstance(ty, ParamType):
return ty
if ty is text_type or ty is str or ty is None:
return STRING
if ty is int:
return INT
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if ty is bool and not guessed_type:
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return STRING
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if __debug__:
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raise AssertionError(
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mrJean1/PyGeodesy | 7da5ca71aa3edb7bc49e219e0b8190686e1a7965 | pygeodesy/utm.py | python | _toXtm8 | (Xtm, z, lat, x, y, B, d, c, k, f, # PYCHOK 13+ args
name, latlon, eps, Error=UTMError) | return r | (INTERNAL) Helper for methods L{toEtm8} and L{toUtm8}. | (INTERNAL) Helper for methods L{toEtm8} and L{toUtm8}. | [
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'''
h = _hemi(lat)
if f:
x, y = _false2(x, y, h)
if Xtm is None: # DEPRECATED
r = UtmUps8Tuple(z, h, x, y, B, d, c, k, Error=Error, name=name)
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r = _xnamed(Xtm(z, h, x, y, band=B, datum=d, falsed=f,
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latlon._convergence = c
latlon._scale = k
elif not r._band:
r._band = _toBand(lat)
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owtf/owtf | 22d6d35fb2a232fcc56bf5ed504ec52fd65f15b6 | owtf/net/scanner.py | python | Scanner.target_service | (self, nmap_file, service) | return response | Services for a target
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] | def target_service(self, nmap_file, service):
"""Services for a target
:param nmap_file: Path to nmap file
:type nmap_file: `str`
:param service: Service to get
:type service: `str`
:return: Response
:rtype: `str`
"""
ports_for_service = self.get_ports_for_service(service, "")
f = FileOperations.open(nmap_file.strip())
response = ""
for host_ports in re.findall("Host: (.*?)\tPorts: (.*?)[\t\n]", f.read()):
host = host_ports[0].split(" ")[0] # Remove junk at the end
ports = host_ports[1].split(",")
for port_info in ports:
if len(port_info) < 1:
continue
chunk = port_info.split("/")
port = chunk[0].strip()
port_state = chunk[1].strip()
# No point in wasting time probing closed/filtered ports!!
# (nmap sometimes adds these to the gnmap file for some reason ..)
if port_state in ["closed", "filtered"]:
continue
try:
prot = chunk[2].strip()
except BaseException:
continue
if port in ports_for_service:
response += "{!s}:{!s}:{!s}##".format(host, port, prot)
f.close()
return response | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/threading.py | python | _Condition._is_owned | (self) | [] | def _is_owned(self):
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/integrals/rubi/utility_function.py | python | SumSimplerQ | (u, v) | If u + v is simpler than u, SumSimplerQ(u, v) returns True, else it returns False.
If for every term w of v there is a term of u equal to n*w where n<-1/2, u + v will be simpler than u.
Examples
========
>>> from sympy.integrals.rubi.utility_function import SumSimplerQ
>>> from sympy.abc import x
>>> from sympy import S
>>> SumSimplerQ(S(4 + x),S(3 + x**3))
False | If u + v is simpler than u, SumSimplerQ(u, v) returns True, else it returns False.
If for every term w of v there is a term of u equal to n*w where n<-1/2, u + v will be simpler than u. | [
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If for every term w of v there is a term of u equal to n*w where n<-1/2, u + v will be simpler than u.
Examples
========
>>> from sympy.integrals.rubi.utility_function import SumSimplerQ
>>> from sympy.abc import x
>>> from sympy import S
>>> SumSimplerQ(S(4 + x),S(3 + x**3))
False
"""
if RationalQ(u, v):
if v == S(0):
return False
elif v > S(0):
return u < -S(1)
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return u >= -v
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return SumSimplerAuxQ(Expand(u), Expand(v)) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/setuptools/dist.py | python | Distribution.get_egg_cache_dir | (self) | return egg_cache_dir | [] | def get_egg_cache_dir(self):
egg_cache_dir = os.path.join(os.curdir, '.eggs')
if not os.path.exists(egg_cache_dir):
os.mkdir(egg_cache_dir)
windows_support.hide_file(egg_cache_dir)
readme_txt_filename = os.path.join(egg_cache_dir, 'README.txt')
with open(readme_txt_filename, 'w') as f:
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f.write('This directory caches those eggs to prevent '
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brython-dev/brython | 9cba5fb7f43a9b52fff13e89b403e02a1dfaa5f3 | www/src/Lib/difflib.py | python | unified_diff | (a, b, fromfile='', tofile='', fromfiledate='',
tofiledate='', n=3, lineterm='\n') | r"""
Compare two sequences of lines; generate the delta as a unified diff.
Unified diffs are a compact way of showing line changes and a few
lines of context. The number of context lines is set by 'n' which
defaults to three.
By default, the diff control lines (those with ---, +++, or @@) are
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For inputs that do not have trailing newlines, set the lineterm
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'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
The modification times are normally expressed in the ISO 8601 format.
Example:
>>> for line in unified_diff('one two three four'.split(),
... 'zero one tree four'.split(), 'Original', 'Current',
... '2005-01-26 23:30:50', '2010-04-02 10:20:52',
... lineterm=''):
... print(line) # doctest: +NORMALIZE_WHITESPACE
--- Original 2005-01-26 23:30:50
+++ Current 2010-04-02 10:20:52
@@ -1,4 +1,4 @@
+zero
one
-two
-three
+tree
four | r"""
Compare two sequences of lines; generate the delta as a unified diff. | [
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r"""
Compare two sequences of lines; generate the delta as a unified diff.
Unified diffs are a compact way of showing line changes and a few
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... 'zero one tree four'.split(), 'Original', 'Current',
... '2005-01-26 23:30:50', '2010-04-02 10:20:52',
... lineterm=''):
... print(line) # doctest: +NORMALIZE_WHITESPACE
--- Original 2005-01-26 23:30:50
+++ Current 2010-04-02 10:20:52
@@ -1,4 +1,4 @@
+zero
one
-two
-three
+tree
four
"""
_check_types(a, b, fromfile, tofile, fromfiledate, tofiledate, lineterm)
started = False
for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n):
if not started:
started = True
fromdate = '\t{}'.format(fromfiledate) if fromfiledate else ''
todate = '\t{}'.format(tofiledate) if tofiledate else ''
yield '--- {}{}{}'.format(fromfile, fromdate, lineterm)
yield '+++ {}{}{}'.format(tofile, todate, lineterm)
first, last = group[0], group[-1]
file1_range = _format_range_unified(first[1], last[2])
file2_range = _format_range_unified(first[3], last[4])
yield '@@ -{} +{} @@{}'.format(file1_range, file2_range, lineterm)
for tag, i1, i2, j1, j2 in group:
if tag == 'equal':
for line in a[i1:i2]:
yield ' ' + line
continue
if tag in {'replace', 'delete'}:
for line in a[i1:i2]:
yield '-' + line
if tag in {'replace', 'insert'}:
for line in b[j1:j2]:
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kensho-technologies/graphql-compiler | 4318443b7b2512a059f3616112bfc40bbf8eec06 | graphql_compiler/schema/__init__.py | python | is_vertex_field_name | (field_name: str) | return field_name.startswith(OUTBOUND_EDGE_FIELD_PREFIX) or field_name.startswith(
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arsaboo/homeassistant-config | 53c998986fbe84d793a0b174757154ab30e676e4 | custom_components/futures_cnn/sensor.py | python | CNNFuturesSensor.icon | (self) | return icon | Return the icon to use in the frontend, if any. | Return the icon to use in the frontend, if any. | [
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pythonarcade/arcade | 1ee3eb1900683213e8e8df93943327c2ea784564 | arcade/examples/sprite_move_animation.py | python | load_texture_pair | (filename) | return [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/dcdb/v20180411/models.py | python | DCDBShardInfo.__init__ | (self) | r"""
:param InstanceId: 所属实例Id
:type InstanceId: str
:param ShardSerialId: 分片SQL透传Id，用于将sql透传到指定分片执行
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注意：此字段可能返回 null，表示取不到有效值。
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注意：此字段可能返回 null，表示取不到有效值。
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:param ShardSlaveZones: 分片的从可用区列表
注意：此字段可能返回 null，表示取不到有效值。
:type ShardSlaveZones: list of str
:param Cpu: CPU核数
:type Cpu: int
:param Range: 分片ShardKey的范围（总共64个哈希值），例如： 0-31，32-63
:type Range: str
"""
self.InstanceId = None
self.ShardSerialId = None
self.ShardInstanceId = None
self.Status = None
self.StatusDesc = None
self.CreateTime = None
self.VpcId = None
self.SubnetId = None
self.ProjectId = None
self.Region = None
self.Zone = None
self.Memory = None
self.Storage = None
self.PeriodEndTime = None
self.NodeCount = None
self.StorageUsage = None
self.MemoryUsage = None
self.ShardId = None
self.Pid = None
self.ProxyVersion = None
self.Paymode = None
self.ShardMasterZone = None
self.ShardSlaveZones = None
self.Cpu = None
self.Range = None | [
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PowerScript/KatanaFramework | 0f6ad90a88de865d58ec26941cb4460501e75496 | lib/libusb1/build/lib.linux-i686-2.7/usb1.py | python | USBTransfer.getUserData | (self) | return self.__user_data | Retrieve user data provided on setup. | Retrieve user data provided on setup. | [
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Retrieve user data provided on setup.
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Conchylicultor/MusicGenerator | adea76dccaba923b7d3807082ec6f5b512d16bb9 | deepmusic/modules/decoder.py | python | Rnn.build | (self) | Initialize the weights of the model | Initialize the weights of the model | [
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""" Initialize the weights of the model
"""
self.rnn_cell = tfutils.get_rnn_cell(self.args, "deco_cell")
self.project_key = tfutils.single_layer_perceptron([self.args.hidden_size, 1],
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steeve/xbmctorrent | e6bcb1037668959e1e3cb5ba8cf3e379c6638da9 | resources/site-packages/html5lib/treewalkers/dom.py | python | TreeWalker.getParentNode | (self, node) | return node.parentNode | [] | def getParentNode(self, node):
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GNS3/gns3-gui | da8adbaa18ab60e053af2a619efd468f4c8950f3 | gns3/dialogs/doctor_dialog.py | python | DoctorDialog.checkExperimentalFeaturesEnabled | (self) | return (0, None) | Checking if experimental features are not enabled | Checking if experimental features are not enabled | [
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] | def checkExperimentalFeaturesEnabled(self):
"""Checking if experimental features are not enabled"""
if LocalConfig.instance().experimental():
return (1, "Experimental features are enabled. Turn them off by going to Preferences -> General -> Miscellaneous.")
return (0, None) | [
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BillBillBillBill/Tickeys-linux | 2df31b8665004c58a5d4ab05277f245267d96364 | tickeys/kivy_32/kivy/storage/__init__.py | python | AbstractStore.keys | (self) | return self.store_keys() | Return a list of all the keys in the storage. | Return a list of all the keys in the storage. | [
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JinpengLI/deep_ocr | 450148c0c51b3565a96ac2f3c94ee33022e55307 | deep_ocr/ocrolib/common.py | python | RegionExtractor.bbox | (self,i) | return (r[0].start,r[1].start,r[0].stop,r[1].stop) | Return the bounding box in raster coordinates
(row0,col0,row1,col1). | Return the bounding box in raster coordinates
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"""Return the bounding box in raster coordinates
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r = self.objects[i]
# print("@@@bbox", i, r)
return (r[0].start,r[1].start,r[0].stop,r[1].stop) | [
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qibinlou/SinaWeibo-Emotion-Classification | f336fc104abd68b0ec4180fe2ed80fafe49cb790 | nltk/tag/senna.py | python | NERTagger.batch_tag | (self, sentences) | return tagged_sents | Applies the tag method over a list of sentences. This method will return
for each sentence a list of tuples of (word, tag). | Applies the tag method over a list of sentences. This method will return
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Applies the tag method over a list of sentences. This method will return
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"""
tagged_sents = super(NERTagger, self).batch_tag(sentences)
for i in range(len(tagged_sents)):
for j in range(len(tagged_sents[i])):
annotations = tagged_sents[i][j]
tagged_sents[i][j] = (annotations['word'], annotations['ner'])
return tagged_sents | [
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Tribler/tribler | f1de8bd54f293b01b2646a1dead1c1dc9dfdb356 | src/tribler-core/tribler_core/components/libtorrent/download_manager/download_config.py | python | DownloadConfig.get_selected_files | (self) | return self.config['download_defaults']['selected_file_indexes'] | Returns the list of files selected for download.
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return self.config['download_defaults']['selected_file_indexes'] | [
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iocast/featureserver | 2828532294fe232f1ddf358cfbd2cc81af102e56 | vectorformats/lib/shapefile.py | python | Writer.saveDbf | (self, target) | Save a dbf file. | Save a dbf file. | [
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self.dbf = self.__getFileObj(target)
self.__dbfHeader()
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RomelTorres/alpha_vantage | c637657579950d72605320c68ded42a447566cdf | alpha_vantage/async_support/sectorperformance.py | python | SectorPerformances.__init__ | (self, *args, **kwargs) | Inherit AlphaVantage base class with its default arguments | Inherit AlphaVantage base class with its default arguments | [
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super(SectorPerformances, self).__init__(*args, **kwargs)
self._append_type = False
if self.output_format.lower() == 'csv':
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GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/linux_packages/ycsb.py | python | YCSBExecutor._RunThreaded | (self, vms, **kwargs) | return results | Run a single workload using `vms`. | Run a single workload using `vms`. | [
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target_per_client = target // len(vms)
targets = [
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else:
targets = [target for _ in vms]
results = []
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record_count = int(self.workload_meta.get('recordcount', '1000'))
n_per_client = int(record_count) // len(vms)
loader_counts = [
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def _Run(loader_index):
"""Run YCSB on an individual VM."""
vm = vms[loader_index]
params = copy.deepcopy(kwargs)
params['target'] = targets[loader_index]
if self.perclientparam is not None:
params.update(self.perclientparam[loader_index])
if self.shardkeyspace:
start = sum(loader_counts[:loader_index])
end = start + loader_counts[loader_index]
params.update(insertstart=start,
recordcount=end)
results.append(self._Run(vm, **params))
logging.info('VM %d (%s) finished', loader_index, vm)
vm_util.RunThreaded(_Run, list(range(len(vms))))
if len(results) != len(vms):
raise IOError('Missing results: only {0}/{1} reported\n{2}'.format(
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tensorboy/pytorch_Realtime_Multi-Person_Pose_Estimation | b3e8abf12b172da1ab850e0ff8411c75151154c0 | lib/network/post.py | python | group_limbs_of_same_person | (connected_limbs, joint_list) | return np.array(person_to_joint_assoc) | Associate limbs belonging to the same person together.
:param connected_limbs: See 'return' doc of find_connected_joints()
:param joint_list: unravel'd version of joint_list_per_joint [See 'return' doc of NMS()]
:return: 2d np.array of size num_people x (NUM_JOINTS+2). For each person found:
# First NUM_JOINTS columns contain the index (in joint_list) of the joints associated
with that person (or -1 if their i-th joint wasn't found)
# 2nd-to-last column: Overall score of the joints+limbs that belong to this person
# Last column: Total count of joints found for this person | Associate limbs belonging to the same person together.
:param connected_limbs: See 'return' doc of find_connected_joints()
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:return: 2d np.array of size num_people x (NUM_JOINTS+2). For each person found:
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# 2nd-to-last column: Overall score of the joints+limbs that belong to this person
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person_to_joint_assoc = []
for limb_type in range(NUM_LIMBS):
joint_src_type, joint_dst_type = joint_to_limb_heatmap_relationship[limb_type]
for limb_info in connected_limbs[limb_type]:
person_assoc_idx = []
for person, person_limbs in enumerate(person_to_joint_assoc):
if person_limbs[joint_src_type] == limb_info[0] or person_limbs[joint_dst_type] == limb_info[1]:
person_assoc_idx.append(person)
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# associated to anyone yet:
if len(person_assoc_idx) == 1:
person_limbs = person_to_joint_assoc[person_assoc_idx[0]]
# If the other joint is not associated to anyone yet,
if person_limbs[joint_dst_type] != limb_info[1]:
# Associate it with the current person
person_limbs[joint_dst_type] = limb_info[1]
# Increase the number of limbs associated to this person
person_limbs[-1] += 1
# And update the total score (+= heatmap score of joint_dst
# + score of connecting joint_src with joint_dst)
person_limbs[-2] += joint_list[limb_info[1]
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elif len(person_assoc_idx) == 2: # if found 2 and disjoint, merge them
person1_limbs = person_to_joint_assoc[person_assoc_idx[0]]
person2_limbs = person_to_joint_assoc[person_assoc_idx[1]]
membership = ((person1_limbs >= 0) & (person2_limbs >= 0))[:-2]
if not membership.any(): # If both people have no same joints connected, merge them into a single person
# Update which joints are connected
person1_limbs[:-2] += (person2_limbs[:-2] + 1)
# Update the overall score and total count of joints
# connected by summing their counters
person1_limbs[-2:] += person2_limbs[-2:]
# Add the score of the current joint connection to the
# overall score
person1_limbs[-2] += limb_info[2]
person_to_joint_assoc.pop(person_assoc_idx[1])
else: # Same case as len(person_assoc_idx)==1 above
person1_limbs[joint_dst_type] = limb_info[1]
person1_limbs[-1] += 1
person1_limbs[-2] += joint_list[limb_info[1]
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else: # No person has claimed any of these joints, create a new person
# Initialize person info to all -1 (no joint associations)
row = -1 * np.ones(20)
# Store the joint info of the new connection
row[joint_src_type] = limb_info[0]
row[joint_dst_type] = limb_info[1]
# Total count of connected joints for this person: 2
row[-1] = 2
# Compute overall score: score joint_src + score joint_dst + score connection
# {joint_src,joint_dst}
row[-2] = sum(joint_list[limb_info[:2].astype(int), 2]
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person_to_joint_assoc.append(row)
# Delete people who have very few parts connected
people_to_delete = []
for person_id, person_info in enumerate(person_to_joint_assoc):
if person_info[-1] < 3 or person_info[-2] / person_info[-1] < 0.2:
people_to_delete.append(person_id)
# Traverse the list in reverse order so we delete indices starting from the
# last one (otherwise, removing item for example 0 would modify the indices of
# the remaining people to be deleted!)
for index in people_to_delete[::-1]:
person_to_joint_assoc.pop(index)
# Appending items to a np.array can be very costly (allocating new memory, copying over the array, then adding new row)
# Instead, we treat the set of people as a list (fast to append items) and
# only convert to np.array at the end
return np.array(person_to_joint_assoc) | [
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ssato/python-anyconfig | 09af1950f3226759932f5168d52f5e06ab88815c | src/anyconfig/processors/utils.py | python | load_plugins | (pgroup: str) | A generator function to yield a class object of
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facebookresearch/DetectAndTrack | 9d64bfb16d6ed85c828ca03d195ac618ca04a67b | lib/utils/file_sys.py | python | mkdir_exists | (path) | Makes a directory if it does not exist already. | Makes a directory if it does not exist already. | [
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] | def mkdir_exists(path):
"""Makes a directory if it does not exist already."""
try:
os.mkdir(path)
except OSError as exc:
if exc.errno != errno.EEXIST:
raise | [
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numba/numba | bf480b9e0da858a65508c2b17759a72ee6a44c51 | numba/core/interpreter.py | python | Interpreter.store | (self, value, name, redefine=False) | return target | Store *value* (a Expr or Var instance) into the variable named *name*
(a str object). Returns the target variable. | Store *value* (a Expr or Var instance) into the variable named *name*
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"""
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"""
if redefine or self.current_block_offset in self.cfa.backbone:
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target = self.current_scope.redefine(name, loc=self.loc,
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kedro-org/kedro | e78990c6b606a27830f0d502afa0f639c0830950 | kedro/extras/datasets/spark/spark_hive_dataset.py | python | SparkHiveDataSet.__init__ | (
self, database: str, table: str, write_mode: str, table_pk: List[str] = None
) | Creates a new instance of ``SparkHiveDataSet``.
Args:
database: The name of the hive database.
table: The name of the table within the database.
write_mode: ``insert``, ``upsert`` or ``overwrite`` are supported.
table_pk: If performing an upsert, this identifies the primary key columns used to
resolve preexisting data. Is required for ``write_mode="upsert"``.
Raises:
DataSetError: Invalid configuration supplied | Creates a new instance of ``SparkHiveDataSet``. | [
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] | def __init__(
self, database: str, table: str, write_mode: str, table_pk: List[str] = None
) -> None:
"""Creates a new instance of ``SparkHiveDataSet``.
Args:
database: The name of the hive database.
table: The name of the table within the database.
write_mode: ``insert``, ``upsert`` or ``overwrite`` are supported.
table_pk: If performing an upsert, this identifies the primary key columns used to
resolve preexisting data. Is required for ``write_mode="upsert"``.
Raises:
DataSetError: Invalid configuration supplied
"""
valid_write_modes = ["insert", "upsert", "overwrite"]
if write_mode not in valid_write_modes:
valid_modes = ", ".join(valid_write_modes)
raise DataSetError(
f"Invalid `write_mode` provided: {write_mode}. "
f"`write_mode` must be one of: {valid_modes}"
)
if write_mode == "upsert" and not table_pk:
raise DataSetError("`table_pk` must be set to utilise `upsert` read mode")
self._write_mode = write_mode
self._table_pk = table_pk or []
self._database = database
self._table = table
self._stage_table = "_temp_" + table
# self._table_columns is set up in _save() to speed up initialization
self._table_columns = [] | [
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Yubico/yubikey-manager | 32914673d1d0004aba820e614ac9a9a640b4d196 | yubikit/yubiotp.py | python | HotpSlotConfiguration.__init__ | (self, key: bytes) | [] | def __init__(self, key: bytes):
super(HotpSlotConfiguration, self).__init__()
key = _shorten_hmac_key(key)
# Key is packed into key and uid
self._key = key[:KEY_SIZE].ljust(KEY_SIZE, b"\0")
self._uid = key[KEY_SIZE:].ljust(UID_SIZE, b"\0")
self._update_flags(TKTFLAG.OATH_HOTP, True)
self._update_flags(CFGFLAG.OATH_FIXED_MODHEX2, True) | [
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leapcode/bitmask_client | d2fe20df24fc6eaf146fa5ce1e847de6ab515688 | src/leap/bitmask/services/eip/eipconfig.py | python | EIPConfig.get_gateway_ip | (self, index=0) | Returns the ip of the gateway.
:rtype: An IPv4Address or IPv6Address object. | Returns the ip of the gateway. | [
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"""
Returns the ip of the gateway.
:rtype: An IPv4Address or IPv6Address object.
"""
gateways = self.get_gateways()
leap_assert(len(gateways) > 0, "We don't have any gateway!")
if index > len(gateways):
index = 0
logger.warning("Provided an unknown gateway index %s, " +
"defaulting to 0")
ip_addr_str = gateways[index]["ip_address"]
try:
ipaddr.IPAddress(ip_addr_str)
return ip_addr_str
except ValueError:
logger.error("Invalid ip address in config: %s" % (ip_addr_str,))
return None | [
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nephila/djangocms-blog | d18382808766548c0ec1b9f0dabe443d5430aebf | djangocms_blog/media/base.py | python | MediaAttachmentPluginMixin.get_thumb_image | (self) | return self._media_autoconfiguration["thumb_url"] % self.media_params | URL of the media cover at intermediate resolution
:rtype: str | URL of the media cover at intermediate resolution | [
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"""
URL of the media cover at intermediate resolution
:rtype: str
"""
return self._media_autoconfiguration["thumb_url"] % self.media_params | [
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/django/contrib/gis/db/models/query.py | python | GeoQuerySet._geocol_select | (self, geo_field, field_name) | Helper routine for constructing the SQL to select the geographic
column. Takes into account if the geographic field is in a
ForeignKey relation to the current model. | Helper routine for constructing the SQL to select the geographic
column. Takes into account if the geographic field is in a
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"""
Helper routine for constructing the SQL to select the geographic
column. Takes into account if the geographic field is in a
ForeignKey relation to the current model.
"""
compiler = self.query.get_compiler(self.db)
opts = self.model._meta
if geo_field not in opts.fields:
# Is this operation going to be on a related geographic field?
# If so, it'll have to be added to the select related information
# (e.g., if 'location__point' was given as the field name, then
# chop the non-relational field and add select_related('location')).
# Note: the operation really is defined as "must add select related!"
self.query.add_select_related([field_name.rsplit(LOOKUP_SEP, 1)[0]])
# Call pre_sql_setup() so that compiler.select gets populated.
compiler.pre_sql_setup()
for col, _, _ in compiler.select:
if col.output_field == geo_field:
return col.as_sql(compiler, compiler.connection)[0]
raise ValueError("%r not in compiler's related_select_cols" % geo_field)
elif geo_field not in opts.local_fields:
# This geographic field is inherited from another model, so we have to
# use the db table for the _parent_ model instead.
parent_model = geo_field.model._meta.concrete_model
return self._field_column(compiler, geo_field, parent_model._meta.db_table)
else:
return self._field_column(compiler, geo_field) | [
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openstack/taskflow | 38b9011094dbcfdd00e6446393816201e8256d38 | taskflow/jobs/backends/impl_zookeeper.py | python | ZookeeperJobBoard._process_child | (self, path, request, quiet=True) | Receives the result of a child data fetch request. | Receives the result of a child data fetch request. | [
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"""Receives the result of a child data fetch request."""
job = None
try:
raw_data, node_stat = request.get()
job_data = misc.decode_json(raw_data)
job_created_on = misc.millis_to_datetime(node_stat.ctime)
try:
job_priority = job_data['priority']
job_priority = base.JobPriority.convert(job_priority)
except KeyError:
job_priority = base.JobPriority.NORMAL
job_uuid = job_data['uuid']
job_name = job_data['name']
except (ValueError, TypeError, KeyError):
with excutils.save_and_reraise_exception(reraise=not quiet):
LOG.warning("Incorrectly formatted job data found at path: %s",
path, exc_info=True)
except self._client.handler.timeout_exception:
with excutils.save_and_reraise_exception(reraise=not quiet):
LOG.warning("Operation timed out fetching job data from"
" from path: %s",
path, exc_info=True)
except k_exceptions.SessionExpiredError:
with excutils.save_and_reraise_exception(reraise=not quiet):
LOG.warning("Session expired fetching job data from path: %s",
path, exc_info=True)
except k_exceptions.NoNodeError:
LOG.debug("No job node found at path: %s, it must have"
" disappeared or was removed", path)
except k_exceptions.KazooException:
with excutils.save_and_reraise_exception(reraise=not quiet):
LOG.warning("Internal error fetching job data from path: %s",
path, exc_info=True)
else:
with self._job_cond:
# Now we can officially check if someone already placed this
# jobs information into the known job set (if it's already
# existing then just leave it alone).
if path not in self._known_jobs:
job = ZookeeperJob(self, job_name,
self._client, path,
backend=self._persistence,
uuid=job_uuid,
book_data=job_data.get("book"),
details=job_data.get("details", {}),
created_on=job_created_on,
priority=job_priority)
self._known_jobs[path] = job
self._job_cond.notify_all()
if job is not None:
self._try_emit(base.POSTED, details={'job': job}) | [
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python-modules/twisted/twisted/python/dist.py | python | getVersion | (proj, base="twisted") | return ns['version'].base() | Extract the version number for a given project.
@param proj: the name of the project. Examples are "core",
"conch", "words", "mail".
@rtype: str
@returns: The version number of the project, as a string like
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] | def getVersion(proj, base="twisted"):
"""
Extract the version number for a given project.
@param proj: the name of the project. Examples are "core",
"conch", "words", "mail".
@rtype: str
@returns: The version number of the project, as a string like
"2.0.0".
"""
if proj == 'core':
vfile = os.path.join(base, '_version.py')
else:
vfile = os.path.join(base, proj, '_version.py')
ns = {'__name__': 'Nothing to see here'}
execfile(vfile, ns)
return ns['version'].base() | [
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hhyo/Archery | c9b057d37e47894ca8531e5cd10afdb064cd0122 | sql/engines/models.py | python | ReviewResult.__init__ | (self, inception_result=None, **kwargs) | inception的结果列 = ['ID', 'stage', 'errlevel', 'stagestatus', 'errormessage', 'SQL', 'Affected_rows',
'sequence','backup_dbname', 'execute_time', 'sqlsha1']
go_inception的结果列 = ['order_id', 'stage', 'error_level', 'stage_status', 'error_message', 'sql',
'affected_rows', 'sequence', 'backup_dbname', 'execute_time', 'sqlsha1', 'backup_time'] | inception的结果列 = ['ID', 'stage', 'errlevel', 'stagestatus', 'errormessage', 'SQL', 'Affected_rows',
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go_inception的结果列 = ['order_id', 'stage', 'error_level', 'stage_status', 'error_message', 'sql',
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"""
if inception_result:
self.id = inception_result[0] or 0
self.stage = inception_result[1] or ''
self.errlevel = inception_result[2] or 0
self.stagestatus = inception_result[3] or ''
self.errormessage = inception_result[4] or ''
self.sql = inception_result[5] or ''
self.affected_rows = inception_result[6] or 0
self.sequence = inception_result[7] or ''
self.backup_dbname = inception_result[8] or ''
self.execute_time = inception_result[9] or ''
self.sqlsha1 = inception_result[10] or ''
self.backup_time = inception_result[11] if len(inception_result) >= 12 else ''
self.actual_affected_rows = ''
else:
self.id = kwargs.get('id', 0)
self.stage = kwargs.get('stage', '')
self.errlevel = kwargs.get('errlevel', 0)
self.stagestatus = kwargs.get('stagestatus', '')
self.errormessage = kwargs.get('errormessage', '')
self.sql = kwargs.get('sql', '')
self.affected_rows = kwargs.get('affected_rows', 0)
self.sequence = kwargs.get('sequence', '')
self.backup_dbname = kwargs.get('backup_dbname', '')
self.execute_time = kwargs.get('execute_time', '')
self.sqlsha1 = kwargs.get('sqlsha1', '')
self.backup_time = kwargs.get('backup_time', '')
self.actual_affected_rows = kwargs.get('actual_affected_rows', '')
# 自定义属性
for key, value in kwargs.items():
if not hasattr(self, key):
setattr(self, key, value) | [
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andrewekhalel/edafa | 122da335fa3aada1e4df6b9bc88411f544a23c22 | examples/tensorflow/slim/nets/inception_v3.py | python | inception_v3_base | (inputs,
final_endpoint='Mixed_7c',
min_depth=16,
depth_multiplier=1.0,
scope=None) | Inception model from http://arxiv.org/abs/1512.00567.
Constructs an Inception v3 network from inputs to the given final endpoint.
This method can construct the network up to the final inception block
Mixed_7c.
Note that the names of the layers in the paper do not correspond to the names
of the endpoints registered by this function although they build the same
network.
Here is a mapping from the old_names to the new names:
Old name | New name
=======================================
conv0 | Conv2d_1a_3x3
conv1 | Conv2d_2a_3x3
conv2 | Conv2d_2b_3x3
pool1 | MaxPool_3a_3x3
conv3 | Conv2d_3b_1x1
conv4 | Conv2d_4a_3x3
pool2 | MaxPool_5a_3x3
mixed_35x35x256a | Mixed_5b
mixed_35x35x288a | Mixed_5c
mixed_35x35x288b | Mixed_5d
mixed_17x17x768a | Mixed_6a
mixed_17x17x768b | Mixed_6b
mixed_17x17x768c | Mixed_6c
mixed_17x17x768d | Mixed_6d
mixed_17x17x768e | Mixed_6e
mixed_8x8x1280a | Mixed_7a
mixed_8x8x2048a | Mixed_7b
mixed_8x8x2048b | Mixed_7c
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0 | Inception model from http://arxiv.org/abs/1512.00567. | [
"Inception",
"model",
"from",
"http",
":",
"//",
"arxiv",
".",
"org",
"/",
"abs",
"/",
"1512",
".",
"00567",
"."
] | def inception_v3_base(inputs,
final_endpoint='Mixed_7c',
min_depth=16,
depth_multiplier=1.0,
scope=None):
"""Inception model from http://arxiv.org/abs/1512.00567.
Constructs an Inception v3 network from inputs to the given final endpoint.
This method can construct the network up to the final inception block
Mixed_7c.
Note that the names of the layers in the paper do not correspond to the names
of the endpoints registered by this function although they build the same
network.
Here is a mapping from the old_names to the new names:
Old name | New name
=======================================
conv0 | Conv2d_1a_3x3
conv1 | Conv2d_2a_3x3
conv2 | Conv2d_2b_3x3
pool1 | MaxPool_3a_3x3
conv3 | Conv2d_3b_1x1
conv4 | Conv2d_4a_3x3
pool2 | MaxPool_5a_3x3
mixed_35x35x256a | Mixed_5b
mixed_35x35x288a | Mixed_5c
mixed_35x35x288b | Mixed_5d
mixed_17x17x768a | Mixed_6a
mixed_17x17x768b | Mixed_6b
mixed_17x17x768c | Mixed_6c
mixed_17x17x768d | Mixed_6d
mixed_17x17x768e | Mixed_6e
mixed_8x8x1280a | Mixed_7a
mixed_8x8x2048a | Mixed_7b
mixed_8x8x2048b | Mixed_7c
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0
"""
# end_points will collect relevant activations for external use, for example
# summaries or losses.
end_points = {}
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with tf.variable_scope(scope, 'InceptionV3', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='VALID'):
# 299 x 299 x 3
end_point = 'Conv2d_1a_3x3'
net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 149 x 149 x 32
end_point = 'Conv2d_2a_3x3'
net = slim.conv2d(net, depth(32), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 147 x 147 x 32
end_point = 'Conv2d_2b_3x3'
net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 147 x 147 x 64
end_point = 'MaxPool_3a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 73 x 73 x 64
end_point = 'Conv2d_3b_1x1'
net = slim.conv2d(net, depth(80), [1, 1], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 73 x 73 x 80.
end_point = 'Conv2d_4a_3x3'
net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 71 x 71 x 192.
end_point = 'MaxPool_5a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 35 x 35 x 192.
# Inception blocks
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# mixed: 35 x 35 x 256.
end_point = 'Mixed_5b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(32), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_1: 35 x 35 x 288.
end_point = 'Mixed_5c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1')
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
scope='Conv_1_0c_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_2: 35 x 35 x 288.
end_point = 'Mixed_5d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_3: 17 x 17 x 768.
end_point = 'Mixed_6a'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_1x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed4: 17 x 17 x 768.
end_point = 'Mixed_6b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(128), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(128), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_5: 17 x 17 x 768.
end_point = 'Mixed_6c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_6: 17 x 17 x 768.
end_point = 'Mixed_6d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_7: 17 x 17 x 768.
end_point = 'Mixed_6e'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_8: 8 x 8 x 1280.
end_point = 'Mixed_7a'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_9: 8 x 8 x 2048.
end_point = 'Mixed_7b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat(axis=3, values=[
slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat(axis=3, values=[
slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_10: 8 x 8 x 2048.
end_point = 'Mixed_7c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat(axis=3, values=[
slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat(axis=3, values=[
slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint) | [
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dropbox/nsot | 941b11f84f5c0d210f638654a6ed34a5610af22a | nsot/api/filters.py | python | ProtocolFilter.filter_device | (self, queryset, name, value) | Overload to use natural key. | Overload to use natural key. | [
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"use",
"natural",
"key",
"."
] | def filter_device(self, queryset, name, value):
"""Overload to use natural key."""
if isinstance(value, int):
value = str(value)
if value.isdigit():
return queryset.filter(device=value)
else:
return queryset.filter(device__hostname=value) | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | torch_geometric/nn/to_hetero_with_bases_transformer.py | python | ToHeteroWithBasesTransformer.__init__ | (
self,
module: Module,
metadata: Metadata,
num_bases: int,
in_channels: Optional[Dict[str, int]] = None,
input_map: Optional[Dict[str, str]] = None,
debug: bool = False,
) | [] | def __init__(
self,
module: Module,
metadata: Metadata,
num_bases: int,
in_channels: Optional[Dict[str, int]] = None,
input_map: Optional[Dict[str, str]] = None,
debug: bool = False,
):
super().__init__(module, input_map, debug)
unused_node_types = get_unused_node_types(*metadata)
if len(unused_node_types) > 0:
warnings.warn(
f"There exist node types ({unused_node_types}) whose "
f"representations do not get updated during message passing "
f"as they do not occur as destination type in any edge type. "
f"This may lead to unexpected behaviour.")
self.metadata = metadata
self.num_bases = num_bases
self.in_channels = in_channels or {}
assert len(metadata) == 2
assert len(metadata[0]) > 0 and len(metadata[1]) > 0
# Compute IDs for each node and edge type:
self.node_type2id = {k: i for i, k in enumerate(metadata[0])}
self.edge_type2id = {k: i for i, k in enumerate(metadata[1])} | [
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/dependencies/hdf5.py | python | HDF5ConfigToolDependency.__init__ | (self, name: str, environment: 'Environment', kwargs: T.Dict[str, T.Any], language: T.Optional[str] = None) | [] | def __init__(self, name: str, environment: 'Environment', kwargs: T.Dict[str, T.Any], language: T.Optional[str] = None) -> None:
language = language or 'c'
if language not in {'c', 'cpp', 'fortran'}:
raise DependencyException(f'Language {language} is not supported with HDF5.')
if language == 'c':
cenv = 'CC'
tools = ['h5cc', 'h5pcc']
elif language == 'cpp':
cenv = 'CXX'
tools = ['h5c++', 'h5pc++']
elif language == 'fortran':
cenv = 'FC'
tools = ['h5fc', 'h5pfc']
else:
raise DependencyException('How did you get here?')
# We need this before we call super()
for_machine = self.get_for_machine_from_kwargs(kwargs)
nkwargs = kwargs.copy()
nkwargs['tools'] = tools
# Override the compiler that the config tools are going to use by
# setting the environment variables that they use for the compiler and
# linkers.
compiler = environment.coredata.compilers[for_machine][language]
try:
os.environ[f'HDF5_{cenv}'] = join_args(compiler.get_exelist())
os.environ[f'HDF5_{cenv}LINKER'] = join_args(compiler.get_linker_exelist())
super().__init__(name, environment, nkwargs, language)
finally:
del os.environ[f'HDF5_{cenv}']
del os.environ[f'HDF5_{cenv}LINKER']
if not self.is_found:
return
# We first need to call the tool with -c to get the compile arguments
# and then without -c to get the link arguments.
args = self.get_config_value(['-show', '-c'], 'args')[1:]
args += self.get_config_value(['-show', '-noshlib' if kwargs.get('static', False) else '-shlib'], 'args')[1:]
for arg in args:
if arg.startswith(('-I', '-f', '-D')) or arg == '-pthread':
self.compile_args.append(arg)
elif arg.startswith(('-L', '-l', '-Wl')):
self.link_args.append(arg)
elif Path(arg).is_file():
self.link_args.append(arg)
# If the language is not C we need to add C as a subdependency
if language != 'c':
nkwargs = kwargs.copy()
nkwargs['language'] = 'c'
# I'm being too clever for mypy and pylint
self.is_found = self._add_sub_dependency(hdf5_factory(environment, for_machine, nkwargs)) | [
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returntocorp/bento | 05b365da71b65170d41fe92a702480ab76c1d17c | bento/extra/flake8.py | python | Flake8Tool.select_clause | (self) | return f"--select={RULE_PREFIXES}" | Returns a --select argument to identify which checks flake8 should run | Returns a --select argument to identify which checks flake8 should run | [
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"""Returns a --select argument to identify which checks flake8 should run"""
return f"--select={RULE_PREFIXES}" | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | option/ctp/ApiStruct.py | python | InvestorPositionCombineDetail.__init__ | (self, TradingDay='', OpenDate='', ExchangeID='', SettlementID=0, BrokerID='', InvestorID='', ComTradeID='', TradeID='', InstrumentID='', HedgeFlag=HF_Speculation, Direction=D_Buy, TotalAmt=0, Margin=0.0, ExchMargin=0.0, MarginRateByMoney=0.0, MarginRateByVolume=0.0, LegID=0, LegMultiple=0, CombInstrumentID='', TradeGroupID=0) | [] | def __init__(self, TradingDay='', OpenDate='', ExchangeID='', SettlementID=0, BrokerID='', InvestorID='', ComTradeID='', TradeID='', InstrumentID='', HedgeFlag=HF_Speculation, Direction=D_Buy, TotalAmt=0, Margin=0.0, ExchMargin=0.0, MarginRateByMoney=0.0, MarginRateByVolume=0.0, LegID=0, LegMultiple=0, CombInstrumentID='', TradeGroupID=0):
self.TradingDay = 'Date' #交易日, char[9]
self.OpenDate = 'Date' #开仓日期, char[9]
self.ExchangeID = '' #交易所代码, char[9]
self.SettlementID = '' #结算编号, int
self.BrokerID = '' #经纪公司代码, char[11]
self.InvestorID = '' #投资者代码, char[13]
self.ComTradeID = 'TradeID' #组合编号, char[21]
self.TradeID = '' #撮合编号, char[21]
self.InstrumentID = '' #合约代码, char[31]
self.HedgeFlag = '' #投机套保标志, char
self.Direction = '' #买卖, char
self.TotalAmt = 'Volume' #持仓量, int
self.Margin = 'Money' #投资者保证金, double
self.ExchMargin = 'Money' #交易所保证金, double
self.MarginRateByMoney = 'Ratio' #保证金率, double
self.MarginRateByVolume = 'Ratio' #保证金率(按手数), double
self.LegID = '' #单腿编号, int
self.LegMultiple = '' #单腿乘数, int
self.CombInstrumentID = 'InstrumentID' #组合持仓合约编码, char[31]
self.TradeGroupID = '' | [
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pytorch/fairseq | 1575f30dd0a9f7b3c499db0b4767aa4e9f79056c | fairseq/trainer.py | python | Trainer.checkpoint_suffix | (self) | Suffix to add to the checkpoint file name. | Suffix to add to the checkpoint file name. | [
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return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format(
self.data_parallel_rank
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return self.cfg.checkpoint.checkpoint_suffix or "" | [
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celery/celery | 95015a1d5a60d94d8e1e02da4b9cf16416c747e2 | celery/_state.py | python | set_default_app | (app) | Set default app. | Set default app. | [
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"""Set default app."""
global default_app
default_app = app | [
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jachinlin/geektime_dl | 36df91d4d072758da142378d62492c187689b324 | geektime_dl/data_client/__init__.py | python | DataClient.get_video_collection_content | (self, collection_id: int,
force: bool = False,
pbar=True, pbar_desc='') | return data | 获取每日一课合辑ID 为 collection_id 的所有视频内容 | 获取每日一课合辑ID 为 collection_id 的所有视频内容 | [
"获取每日一课合辑ID",
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] | def get_video_collection_content(self, collection_id: int,
force: bool = False,
pbar=True, pbar_desc='') -> list:
"""
获取每日一课合辑ID 为 collection_id 的所有视频内容
"""
data = []
v_ids = self._gk.get_video_list_of(collection_id)
if pbar:
v_ids = tqdm(v_ids)
v_ids.set_description(pbar_desc)
for v_id in v_ids:
v = self.get_daily_content(v_id['article_id'], force=force)
data.append(v)
return data | [
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rytilahti/python-miio | b6e53dd16fac77915426e7592e2528b78ef65190 | miio/gateway/radio.py | python | Radio.get_mute | (self) | return self._gateway.send("get_mute") | mute of what? | mute of what? | [
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] | def get_mute(self):
"""mute of what?"""
return self._gateway.send("get_mute") | [
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csababarta/ntdsxtract | 7fa1c8c28cbbf97a42bef40f20009dba85e4c25f | ntds/dsobjects.py | python | dsSupplCredentials.Print | (self, indent="") | [] | def Print(self, indent=""):
if self.KerberosNewerKeys != None:
print "{0}Kerberos newer keys".format(indent)
self.KerberosNewerKeys.Print(indent + " ")
if self.KerberosKeys != None:
print "{0}Kerberos keys".format(indent)
self.KerberosKeys.Print(indent + " ")
if self.WDigestHashes != None:
print "{0}WDigest hashes".format(indent)
for h in self.WDigestHashes:
print "{0} {1}".format(indent, hexlify(h))
if self.Packages != None:
print "{0}Packages".format(indent)
for p in self.Packages:
print "{0} {1}".format(indent, p)
if self.Password != None:
print "{0}Password: {1}".format(indent, self.Password)
print "Debug: "
print dump(self.Text,16,16) | [
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BlackLight/platypush | a6b552504e2ac327c94f3a28b607061b6b60cf36 | platypush/plugins/ping/__init__.py | python | PingPlugin.__init__ | (self, executable: str = 'ping', count: int = 1, timeout: float = 5.0, **kwargs) | :param executable: Path to the ``ping`` executable. Default: the first ``ping`` executable found in PATH.
:param count: Default number of packets that should be sent (default: 1).
:param timeout: Default timeout before failing a ping request (default: 5 seconds). | :param executable: Path to the ``ping`` executable. Default: the first ``ping`` executable found in PATH.
:param count: Default number of packets that should be sent (default: 1).
:param timeout: Default timeout before failing a ping request (default: 5 seconds). | [
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:param count: Default number of packets that should be sent (default: 1).
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super().__init__(**kwargs)
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/io/feff/inputs.py | python | Atoms.cluster | (self) | return self._cluster | Returns the atomic cluster as a Molecule object. | Returns the atomic cluster as a Molecule object. | [
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Returns the atomic cluster as a Molecule object.
"""
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/models/basics/polynomial.py | python | Polynomial.num_parameters | (self) | return self._num_parameters | Return the total number of parameters | Return the total number of parameters | [
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"""Return the total number of parameters"""
return self._num_parameters | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Tools/bgen/bgen/scantools.py | python | Scanner.destination | (self, type, name, arglist) | return "FunctionGenerator", "functions" | [] | def destination(self, type, name, arglist):
return "FunctionGenerator", "functions" | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/distlib/resources.py | python | ZipResourceFinder._is_directory | (self, path) | return result | [] | def _is_directory(self, path):
path = path[self.prefix_len:]
if path and path[-1] != os.sep:
path += os.sep
i = bisect.bisect(self.index, path)
try:
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result = False
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Azure/azure-cli | 6c1b085a0910c6c2139006fcbd8ade44006eb6dd | src/azure-cli/azure/cli/command_modules/acs/decorator.py | python | AKSContext.get_load_balancer_outbound_ips | (self) | return load_balancer_outbound_ips | Obtain the value of load_balancer_outbound_ips.
Note: SDK performs the following validation {'maximum': 16, 'minimum': 1}.
:return: string, list of ResourceReference, or None | Obtain the value of load_balancer_outbound_ips. | [
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] | def get_load_balancer_outbound_ips(self) -> Union[str, List[ResourceReference], None]:
"""Obtain the value of load_balancer_outbound_ips.
Note: SDK performs the following validation {'maximum': 16, 'minimum': 1}.
:return: string, list of ResourceReference, or None
"""
# read the original value passed by the command
load_balancer_outbound_ips = self.raw_param.get(
"load_balancer_outbound_ips"
)
# In create mode, try to read the property value corresponding to the parameter from the `mc` object.
if self.decorator_mode == DecoratorMode.CREATE:
if (
self.mc and
self.mc.network_profile and
self.mc.network_profile.load_balancer_profile and
self.mc.network_profile.load_balancer_profile.outbound_i_ps and
self.mc.network_profile.load_balancer_profile.outbound_i_ps.public_i_ps is not None
):
load_balancer_outbound_ips = (
self.mc.network_profile.load_balancer_profile.outbound_i_ps.public_i_ps
)
# this parameter does not need dynamic completion
# this parameter does not need validation
return load_balancer_outbound_ips | [
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"."... | https://github.com/Azure/azure-cli/blob/6c1b085a0910c6c2139006fcbd8ade44006eb6dd/src/azure-cli/azure/cli/command_modules/acs/decorator.py#L2176-L2202 | |
imagr/imagr | e54bcf3f0f951babcd2fa153de2dd8556aa3506d | Imagr/gmacpyutil/macdisk.py | python | Disk.SetStartupDisk | (self) | Sets this disk to be the startup disk. | Sets this disk to be the startup disk. | [
"Sets",
"this",
"disk",
"to",
"be",
"the",
"startup",
"disk",
"."
] | def SetStartupDisk(self):
"""Sets this disk to be the startup disk."""
self.Refresh()
# pylint: disable=no-member
if not self.Mounted():
self.EnsureMountedWithRefresh()
command = ["/usr/sbin/bless", "--mount", self.mountpoint, "--setBoot"]
rc = gmacpyutil.RunProcess(command)[2]
if rc == 0:
return True | [
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... | https://github.com/imagr/imagr/blob/e54bcf3f0f951babcd2fa153de2dd8556aa3506d/Imagr/gmacpyutil/macdisk.py#L190-L199 | ||
mypaint/mypaint | 90b36dbc7b8bd2f323383f7edf608a5e0a3a1a33 | lib/observable.py | python | observable.__get__ | (self, instance, owner) | return wrapper | Creation of the wrapper callable.
The descriptor protocol is used for distinguishing between being
accessed by class and being accessed by instance. For the purposes of
the decorator interface, we return a callable object, which is cached
privately within `instance` so that the callable is associated
permanently with the method. | Creation of the wrapper callable. | [
"Creation",
"of",
"the",
"wrapper",
"callable",
"."
] | def __get__(self, instance, owner):
"""Creation of the wrapper callable.
The descriptor protocol is used for distinguishing between being
accessed by class and being accessed by instance. For the purposes of
the decorator interface, we return a callable object, which is cached
privately within `instance` so that the callable is associated
permanently with the method.
"""
# Return the decorator callable when accessed via the class: normal
# descriptor protocol behaviour for instance things.
if instance is None:
return self
# For second and subsequent calls, use a cache stored in the observable
# object using this class's name mangling.
try:
wrappers_dict = instance.__wrappers
except AttributeError:
wrappers_dict = dict()
instance.__wrappers = wrappers_dict
wrapper = wrappers_dict.get(self.func)
if wrapper is None:
wrapper = _MethodWithObservers(instance, self.func)
wrappers_dict[self.func] = wrapper
elif wrapper.instance_weakref() is not instance:
# Okay, change of identity. Happens with the standard copy().
self._update_observers(instance)
wrappers_dict = instance.__wrappers
old_wrapper = wrapper
wrapper = wrappers_dict.get(self.func)
assert wrapper is not old_wrapper
assert wrapper.instance_weakref() == instance
assert callable(wrapper)
return wrapper | [
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