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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import decorator
import logging
import numbers
import paddle
from ...common import get_logger
from .utils.utils import get_paddle_version
pd_ver = get_paddle_version()
if pd_ver == 185:
import paddle.fluid.dygraph.nn as nn
from paddle.fluid.dygraph.nn import Conv2D, Conv2DTranspose, Linear, LayerNorm, Embedding
from paddle.fluid import ParamAttr
from .layers_old import *
from . import layers_old as layers
Layer = paddle.fluid.dygraph.Layer
else:
import paddle.nn as nn
from paddle.nn import Conv2D, Conv2DTranspose, Linear, LayerNorm, Embedding, SyncBatchNorm
from paddle import ParamAttr
from .layers import *
from . import layers
Layer = paddle.nn.Layer
from .layers_base import Block
from . import layers_old
_logger = get_logger(__name__, level=logging.INFO)
__all__ = ['supernet', 'Convert']
WEIGHT_LAYER = ['conv', 'linear', 'embedding']
class Convert:
"""
Convert network to the supernet according to the search space.
Parameters:
context(paddleslim.nas.ofa.supernet): search space defined by the user.
Examples:
.. code-block:: python
from paddleslim.nas.ofa import supernet, Convert
sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4])
convert = Convert(sp_net_config)
"""
def convert(self, network):
"""
The function to convert the network to a supernet.
Parameters:
network(paddle.nn.Layer|list(paddle.nn.Layer)): instance of the model or list of instance of layers.
Examples:
.. code-block:: python
from paddle.vision.models import mobilenet_v1
from paddleslim.nas.ofa import supernet, Convert
sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4])
convert = Convert(sp_net_config).convert(mobilenet_v1())
"""
# search the first and last weight layer, don't change out channel of the last weight layer
# don't change in channel of the first weight layer
model = []
if isinstance(network, Layer):
for name, sublayer in network.named_sublayers():
model.append(sublayer)
else:
model = network
first_weight_layer_idx = -1
last_weight_layer_idx = -1
weight_layer_count = 0
# NOTE: pre_channel store for shortcut module
pre_channel = None
cur_channel = None
for idx, layer in enumerate(model):
cls_name = layer.__class__.__name__.lower()
### basic api in paddle
if len(layer.sublayers()) == 0:
if 'conv' in cls_name or 'linear' in cls_name or 'embedding' in cls_name:
weight_layer_count += 1
last_weight_layer_idx = idx
if first_weight_layer_idx == -1:
first_weight_layer_idx = idx
if getattr(self.context, 'channel', None) != None:
assert len(
self.context.channel
) == weight_layer_count, "length of channel must same as weight layer."
for idx, layer in enumerate(model):
if isinstance(layer, Conv2D):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
new_attr_name = [
'stride', 'padding', 'dilation', 'groups', 'bias_attr'
]
if pd_ver == 185:
new_attr_name += ['param_attr', 'use_cudnn', 'act', 'dtype']
else:
new_attr_name += [
'weight_attr', 'data_format', 'padding_mode'
]
self._change_name(layer, pd_ver, conv=True)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
if pd_ver == 185:
new_attr_dict['num_channels'] = None
new_attr_dict['num_filters'] = None
new_attr_dict['filter_size'] = None
else:
new_attr_dict['in_channels'] = None
new_attr_dict['out_channels'] = None
new_attr_dict['kernel_size'] = None
self.kernel_size = getattr(self.context, 'kernel_size', None)
# if the kernel_size of conv is 1, don't change it.
fks = '_filter_size' if '_filter_size' in attr_dict.keys(
) else '_kernel_size'
ks = [attr_dict[fks]] if isinstance(
attr_dict[fks], numbers.Integral) else attr_dict[fks]
if self.kernel_size and int(ks[0]) != 1:
new_attr_dict['transform_kernel'] = True
new_attr_dict[fks[1:]] = max(self.kernel_size)
new_attr_dict['candidate_config'].update({
'kernel_size': self.kernel_size
})
else:
new_attr_dict[fks[1:]] = attr_dict[fks]
in_key = '_num_channels' if '_num_channels' in attr_dict.keys(
) else '_in_channels'
out_key = '_num_filters' if '_num_filters' in attr_dict.keys(
) else '_out_channels'
if self.context.expand:
### first super convolution
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = int(self.context.expand *
attr_dict[in_key])
### last super convolution
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = int(self.context.expand *
attr_dict[out_key])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
if attr_dict['_groups'] != None and (
int(attr_dict['_groups']) == int(attr_dict[in_key])
):
### depthwise conv, if conv is depthwise, use pre channel as cur_channel
_logger.warn(
"If convolution is a depthwise conv, output channel change" \
" to the same channel with input, output channel in search is not used."
)
cur_channel = pre_channel
else:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = max(pre_channel)
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
new_attr_dict[out_key[1:]] = attr_dict[out_key]
for attr in new_attr_name:
if attr == 'weight_attr':
new_attr_dict[attr] = attr_dict['_param_attr']
else:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer
if attr_dict['_groups'] == None or int(attr_dict[
'_groups']) == 1:
### standard conv
layer = Block(SuperConv2D(**new_attr_dict), key=key)
elif int(attr_dict['_groups']) == int(attr_dict[in_key]):
# if conv is depthwise conv, groups = in_channel, out_channel = in_channel,
# channel in candidate_config = in_channel_list
if 'channel' in new_attr_dict['candidate_config']:
new_attr_dict[in_key[1:]] = max(cur_channel)
new_attr_dict[out_key[1:]] = new_attr_dict[in_key[1:]]
new_attr_dict['candidate_config'][
'channel'] = cur_channel
new_attr_dict['groups'] = new_attr_dict[in_key[1:]]
layer = Block(
SuperDepthwiseConv2D(**new_attr_dict), key=key)
else:
### group conv
layer = Block(SuperGroupConv2D(**new_attr_dict), key=key)
model[idx] = layer
elif (isinstance(layer, nn.BatchNorm2D) or
isinstance(layer, nn.BatchNorm)) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
# num_features in BatchNorm don't change after last weight operators
if idx > last_weight_layer_idx:
continue
use_bn_old = False
if isinstance(layer, nn.BatchNorm):
use_bn_old = True
attr_dict = layer.__dict__
new_attr_name = ['momentum', 'epsilon', 'bias_attr']
if pd_ver == 185 or use_bn_old:
new_attr_name += [
'param_attr', 'act', 'dtype', 'in_place', 'data_layout',
'is_test', 'use_global_stats', 'trainable_statistics'
]
else:
new_attr_name += ['weight_attr', 'data_format', 'name']
self._change_name(layer, pd_ver, use_bn_old=use_bn_old)
new_attr_dict = dict.fromkeys(new_attr_name, None)
if pd_ver == 185 or use_bn_old:
new_attr_dict['num_channels'] = None
else:
new_attr_dict['num_features'] = None
new_key = 'num_channels' if 'num_channels' in new_attr_dict.keys(
) else 'num_features'
if self.context.expand:
new_attr_dict[new_key] = int(
self.context.expand *
layer._parameters['weight'].shape[0])
elif self.context.channel:
new_attr_dict[new_key] = max(cur_channel)
else:
new_attr_dict[new_key] = attr_dict[
'_num_channels'] if '_num_channels' in attr_dict.keys(
) else attr_dict['_num_features']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = layers_old.SuperBatchNorm(
**new_attr_dict
) if pd_ver == 185 or use_bn_old else layers.SuperBatchNorm2D(
**new_attr_dict)
model[idx] = layer
elif isinstance(layer, SyncBatchNorm) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
# num_features in SyncBatchNorm don't change after last weight operators
if idx > last_weight_layer_idx:
continue
attr_dict = layer.__dict__
new_attr_name = ['momentum', 'epsilon', 'bias_attr']
new_attr_name += ['weight_attr', 'data_format', 'name']
self._change_name(layer, pd_ver)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['num_features'] = None
new_key = 'num_channels' if 'num_channels' in new_attr_dict.keys(
) else 'num_features'
if self.context.expand:
new_attr_dict[new_key] = int(
self.context.expand *
layer._parameters['weight'].shape[0])
elif self.context.channel:
new_attr_dict[new_key] = max(cur_channel)
else:
new_attr_dict[new_key] = attr_dict[
'_num_channels'] if '_num_channels' in attr_dict.keys(
) else attr_dict['_num_features']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = layers.SuperSyncBatchNorm(**new_attr_dict)
model[idx] = layer
### assume output_size = None, filter_size != None
### NOTE: output_size != None may raise error, solve when it happend.
elif isinstance(layer, Conv2DTranspose):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
new_attr_name = [
'stride', 'padding', 'dilation', 'groups', 'bias_attr'
]
assert getattr(
attr_dict, '_filter_size', '_kernel_size'
) != None, "Conv2DTranspose only support kernel size != None now"
if pd_ver == 185:
new_attr_name += [
'output_size', 'param_attr', 'use_cudnn', 'act', 'dtype'
]
else:
new_attr_name += [
'output_padding', 'weight_attr', 'data_format'
]
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
if pd_ver == 185:
new_attr_dict['num_channels'] = None
new_attr_dict['num_filters'] = None
new_attr_dict['filter_size'] = None
else:
new_attr_dict['in_channels'] = None
new_attr_dict['out_channels'] = None
new_attr_dict['kernel_size'] = None
self._change_name(layer, pd_ver, conv=True)
self.kernel_size = getattr(self.context, 'kernel_size', None)
# if the kernel_size of conv transpose is 1, don't change it.
fks = '_filter_size' if '_filter_size' in attr_dict.keys(
) else '_kernel_size'
ks = [attr_dict[fks]] if isinstance(
attr_dict[fks], numbers.Integral) else attr_dict[fks]
if self.kernel_size and int(ks[0]) != 1:
new_attr_dict['transform_kernel'] = True
new_attr_dict[fks[1:]] = max(self.kernel_size)
new_attr_dict['candidate_config'].update({
'kernel_size': self.kernel_size
})
else:
new_attr_dict[fks[1:]] = attr_dict[fks]
in_key = '_num_channels' if '_num_channels' in attr_dict.keys(
) else '_in_channels'
out_key = '_num_filters' if '_num_filters' in attr_dict.keys(
) else '_out_channels'
if self.context.expand:
### first super convolution transpose
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = int(self.context.expand *
attr_dict[in_key])
### last super convolution transpose
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = int(self.context.expand *
attr_dict[out_key])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
if attr_dict['_groups'] != None and (
int(attr_dict['_groups']) == int(attr_dict[in_key])
):
### depthwise conv_transpose
_logger.warn(
"If convolution is a depthwise conv_transpose, output channel " \
"change to the same channel with input, output channel in search is not used."
)
cur_channel = pre_channel
else:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = max(pre_channel)
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
new_attr_dict[out_key[1:]] = attr_dict[out_key]
for attr in new_attr_name:
if attr == 'weight_attr':
new_attr_dict[attr] = attr_dict['_param_attr']
elif attr == 'output_padding':
new_attr_dict[attr] = attr_dict[attr]
else:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer
if getattr(new_attr_dict, 'output_size', None) == []:
new_attr_dict['output_size'] = None
if attr_dict['_groups'] == None or int(attr_dict[
'_groups']) == 1:
### standard conv_transpose
layer = Block(
SuperConv2DTranspose(**new_attr_dict), key=key)
elif int(attr_dict['_groups']) == int(attr_dict[in_key]):
# if conv is depthwise conv, groups = in_channel, out_channel = in_channel,
# channel in candidate_config = in_channel_list
if 'channel' in new_attr_dict['candidate_config']:
new_attr_dict[in_key[1:]] = max(cur_channel)
new_attr_dict[out_key[1:]] = new_attr_dict[in_key[1:]]
new_attr_dict['candidate_config'][
'channel'] = cur_channel
new_attr_dict['groups'] = new_attr_dict[in_key[1:]]
layer = Block(
SuperDepthwiseConv2DTranspose(**new_attr_dict), key=key)
else:
### group conv_transpose
layer = Block(
SuperGroupConv2DTranspose(**new_attr_dict), key=key)
model[idx] = layer
elif isinstance(layer, Linear) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
if pd_ver == 185:
new_attr_name = ['act', 'dtype']
else:
new_attr_name = ['weight_attr', 'bias_attr']
self._change_name(layer, pd_ver) if pd_ver != 185 else None
in_nc, out_nc = layer._parameters['weight'].shape
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
if pd_ver == 185:
new_attr_dict['input_dim'] = None
new_attr_dict['output_dim'] = None
else:
new_attr_dict['in_features'] = None
new_attr_dict['out_features'] = None
in_key = '_input_dim' if pd_ver == 185 else '_in_features'
out_key = '_output_dim' if pd_ver == 185 else '_out_features'
attr_dict[in_key] = in_nc
attr_dict[out_key] = out_nc
if self.context.expand:
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = int(attr_dict[in_key])
else:
new_attr_dict[in_key[1:]] = int(self.context.expand *
attr_dict[in_key])
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = int(attr_dict[out_key])
else:
new_attr_dict[out_key[1:]] = int(self.context.expand *
attr_dict[out_key])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = int(attr_dict[in_key])
else:
new_attr_dict[in_key[1:]] = max(pre_channel)
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = int(attr_dict[out_key])
else:
new_attr_dict[out_key[1:]] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
new_attr_dict[in_key[1:]] = int(attr_dict[in_key])
new_attr_dict[out_key[1:]] = int(attr_dict[out_key])
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = Block(SuperLinear(**new_attr_dict), key=key)
model[idx] = layer
elif isinstance(
layer,
getattr(nn, 'InstanceNorm2D',
paddle.fluid.dygraph.nn.InstanceNorm)) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
# num_features in InstanceNorm don't change after last weight operators
if idx > last_weight_layer_idx:
continue
attr_dict = layer.__dict__
if pd_ver == 185:
new_attr_name = [
'bias_attr', 'epsilon', 'param_attr', 'dtype'
]
else:
new_attr_name = ['bias_attr', 'epsilon', 'weight_attr']
self._change_name(layer, pd_ver)
new_attr_dict = dict.fromkeys(new_attr_name, None)
if pd_ver == 185:
new_attr_dict['num_channels'] = None
else:
new_attr_dict['num_features'] = None
new_key = '_num_channels' if '_num_channels' in new_attr_dict.keys(
) else '_num_features'
### 10 is a default channel in the case of weight_attr=False, in this condition, num of channels if useless, so give it arbitrarily.
attr_dict[new_key] = layer._parameters['scale'].shape[0] if len(
layer._parameters) != 0 else 10
if self.context.expand:
new_attr_dict[new_key[1:]] = int(self.context.expand *
attr_dict[new_key])
elif self.context.channel:
new_attr_dict[new_key[1:]] = max(cur_channel)
else:
new_attr_dict[new_key[1:]] = attr_dict[new_key]
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = layers.SuperInstanceNorm(
**new_attr_dict
) if pd_ver == 185 else layers.SuperInstanceNorm2D(
**new_attr_dict)
model[idx] = layer
elif isinstance(layer, LayerNorm) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
### TODO(ceci3): fix when normalized_shape != last_dim_of_input
if idx > last_weight_layer_idx:
continue
attr_dict = layer.__dict__
new_attr_name = ['epsilon', 'bias_attr']
if pd_ver == 185:
new_attr_name += [
'scale', 'shift', 'param_attr', 'act', 'dtype'
]
else:
new_attr_name += ['weight_attr']
self._change_name(layer, pd_ver)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['normalized_shape'] = None
if self.context.expand:
new_attr_dict['normalized_shape'] = int(
self.context.expand * attr_dict['_normalized_shape'][0])
elif self.context.channel:
new_attr_dict['normalized_shape'] = max(cur_channel)
else:
new_attr_dict['normalized_shape'] = attr_dict[
'_normalized_shape']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = SuperLayerNorm(**new_attr_dict)
model[idx] = layer
elif isinstance(layer, Embedding) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
new_attr_name = []
if pd_ver == 185:
new_attr_name += [
'is_sparse', 'is_distributed', 'param_attr', 'dtype'
]
else:
new_attr_name += ['sparse', 'weight_attr', 'name']
self._change_name(layer, pd_ver, has_bias=False)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
bef_size = attr_dict['_size']
if self.context.expand:
if pd_ver == 185:
new_attr_dict['size'] = [
bef_size[0], int(self.context.expand * bef_size[1])
]
else:
new_attr_dict['num_embeddings'] = attr_dict[
'_num_embeddings']
new_attr_dict['embedding_dim'] = int(
self.context.expand * attr_dict['_embedding_dim'])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if pd_ver == 185:
new_attr_dict['size'] = [bef_size[0], max(cur_channel)]
else:
new_attr_dict['num_embeddings'] = attr_dict[
'_num_embeddings']
new_attr_dict['embedding_dim'] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
if pf_ver == 185:
new_attr_dict['size'] = bef_size
else:
new_attr_dict['num_embeddings'] = attr_dict[
'_num_embeddings']
new_attr_dict['embedding_dim'] = attr_dict[
'_embedding_dim']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
new_attr_dict['padding_idx'] = None if attr_dict[
'_padding_idx'] == -1 else attr_dict['_padding_idx']
del layer, attr_dict
layer = Block(SuperEmbedding(**new_attr_dict), key=key)
model[idx] = layer
if isinstance(network, Layer):
curr_id = 0
self.name_list = []
get_split_names(network, [])
for idx, nl in enumerate(self.name_list):
if len(nl) > 1:
net = split_prefix(network, nl[:-1])
else:
net = network
setattr(net, nl[-1], model[idx])
return network
class supernet:
"""
Search space of the network.
Parameters:
kernel_size(list|tuple, optional): search space for the kernel size of the Conv2D.
expand_ratio(list|tuple, optional): the search space for the expand ratio of the number of channels of Conv2D, the expand ratio of the output dimensions of the Embedding or Linear, which means this parameter get the number of channels of each OP in the converted super network based on the the channels of each OP in the original model, so this parameter The length is 1. Just set one between this parameter and ``channel``.
channel(list|tuple, optional): the search space for the number of channels of Conv2D, the output dimensions of the Embedding or Linear, this parameter directly sets the number of channels of each OP in the super network, so the length of this parameter needs to be the same as the total number that of Conv2D, Embedding, and Linear included in the network. Just set one between this parameter and ``expand_ratio``.
"""
#def ofa_supernet(kernel_size, expand_ratio):
# def _ofa_supernet(func):
# @functools.wraps(func)
# def convert(*args, **kwargs):
# supernet_convert(*args, **kwargs)
# return convert
# return _ofa_supernet | [
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2073,
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62,
6,
1343,
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81,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1619,
7679,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
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7,
3605,
62,
35226,
62,
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11,
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22915,
62,
7857,
3256,
6045,
8,
6624,
685,
5974,
198,
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220,
220,
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24432,
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7,
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62,
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58,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
62,
24432,
6,
12962,
6624,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44386,
3210,
3063,
62,
7645,
3455,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7679,
796,
9726,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
3115,
3103,
85,
17,
35,
8291,
3455,
7,
1174,
3605,
62,
35226,
62,
11600,
828,
1994,
28,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
493,
7,
35226,
62,
11600,
17816,
62,
24432,
6,
12962,
6624,
493,
7,
35226,
62,
11600,
58,
259,
62,
2539,
60,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
3063,
318,
6795,
3083,
3063,
11,
2628,
796,
287,
62,
17620,
11,
503,
62,
17620,
796,
287,
62,
17620,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6518,
287,
4540,
62,
11250,
796,
287,
62,
17620,
62,
4868,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
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17620,
6,
287,
649,
62,
35226,
62,
11600,
17816,
46188,
20540,
62,
11250,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
649,
62,
35226,
62,
11600,
58,
259,
62,
2539,
58,
16,
25,
11907,
796,
3509,
7,
22019,
62,
17620,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
62,
35226,
62,
11600,
58,
448,
62,
2539,
58,
16,
25,
11907,
796,
649,
62,
35226,
62,
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58,
259,
62,
2539,
58,
16,
25,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
62,
35226,
62,
11600,
17816,
46188,
20540,
62,
11250,
6,
7131,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
17620,
20520,
796,
1090,
62,
17620,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
62,
35226,
62,
11600,
17816,
24432,
20520,
796,
649,
62,
35226,
62,
11600,
58,
259,
62,
2539,
58,
16,
25,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7679,
796,
9726,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3115,
48791,
3083,
3103,
85,
17,
35,
8291,
3455,
7,
1174,
3605,
62,
35226,
62,
11600,
828,
1994,
28,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44386,
1448,
3063,
62,
7645,
3455,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7679,
796,
9726,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3115,
13247,
3103,
85,
17,
35,
8291,
3455,
7,
1174,
3605,
62,
35226,
62,
11600,
828,
1994,
28,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2746,
58,
312,
87,
60,
796,
7679,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
318,
39098,
7,
29289,
11,
44800,
8,
290,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
651,
35226,
7,
944,
13,
22866,
11,
705,
11201,
392,
3256,
6045,
8,
14512,
6045,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
651,
35226,
7,
944,
13,
22866,
11,
705,
17620,
3256,
6045,
8,
14512,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
708,
81,
62,
11600,
796,
7679,
13,
834,
11600,
834,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
708,
81,
62,
11600,
17816,
62,
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1441,
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198,
2,
220,
220,
220,
1441,
4808,
1659,
64,
62,
16668,
3262
] | 1.76956 | 18,239 |
from gensim import models
w = models.Word2Vec.load_word2vec_format('/home/jalaj/Downloads/GoogleNews-vectors-negative300.bin', binary=True)
if 'the' in w.wv.vocab:
print("Vector for word 'the' \n")
print(w.wv['the'])
else:
print("Vocabulary doesn't include word 'the'\n")
if 'a' in w.wv.vocab:
print("Vector for word 'a' \n")
print(w.wv['a'])
else:
print("Vocabulary doesn't include word 'a'\n") | [
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420,
22528,
1595,
470,
2291,
1573,
705,
64,
6,
59,
77,
4943
] | 2.340782 | 179 |
import itertools
def peek(sequence):
"""
Returns the value of the top of the sequence without removing the value
from the data.
"""
iterable = iter(sequence)
try:
first = next(iterable)
return first, itertools.chain([first], iterable)
except StopIteration:
return None, []
| [
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] | 2.680328 | 122 |
import numpy as np
from scipy import stats
class H0:
'''
Applies binomial hypothesis test to data processed with RadClass.
Capable of applying a hypothesis test to gross or channel-wise count-rate.
This test relies on the assumption that count-rates measured consecutively
in time will not be statistical different from each other. That is, they
should come from the same baseline background environment. If they are
different (and therefore the null hypothesis is rejected), presumably a
radioactive event occurred (e.g. SNM transfer).
For information on testing regime, see doi: 10.2307/2332612.
For information on scipy's binomial test, see:
docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binom_test.html
Attributes:
significance: Significance level for hypothesis test. If the resulting
binomial test's p-value is smaller than significance, null hypothesis
is rejected.
gross: Boolean; determines whether only the gross count-rate is analyzed
(True) or each energy channel individually (False).
x1: The first timestamp's count-rate to be tested.
x2: The second timestamp's count-rate to be compared with x1.
triggers: Three things are saved in this array. x1, x2, and the p-value for
rejected null hypotheses per the inputted significance level.
trigger_times: The timestamp (in Unix epoch timestamp) for rejected null
hypothesis via triggers.
'''
def run(self, data, timestamp):
'''
Method required by RadClass. Called at each integration step.
Completes hypothesis testing for passed data and stores results.
Wrapper method that chooses run_gross or run_channel based on user
input variable: gross.
'''
run_method = { True: self.run_gross, False: self.run_channels }
run_method[self.gross](data, timestamp)
def write(self, filename):
'''
Writes results of hypothesis test to file using numpy.savetxt.
'''
with open(filename, 'a') as f:
header = ''
# build/include header if file is new
if f.tell() == 0:
if self.gross:
header = ['timestamps', 'pval', 'x1', 'x2']
elif not self.gross:
header = np.append(['timestamp'],
np.arange(self.triggers.shape[0]-1).astype(str))
header = ', '.join(col for col in header)
np.savetxt(fname=f,
X=self.triggers,
delimiter=',',
header=header)
| [
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#!/usr/bin/env python
#
# faq.py
#
# Routines to assemble a FAQ list for the Wireshark web site.
# Questions and answer content can be found below. Section and
# question numbers will be automatically generated.
#
# Wireshark - Network traffic analyzer
# By Gerald Combs <[email protected]>
# Copyright 1998 Gerald Combs
#
# SPDX-License-Identifier: GPL-2.0-or-later
import sys
import string
sec_num = 0
subsec_num = 0
subsubsec_num = 0
sections = []
current_section = None
parent_section = None
grandparent_section = None
current_question = None
current_tag = None
# Make a URL of itself
# Add a section
# Add a subsection
# Add a subsubsection
# Add a question
# Add an answer
# Create the index
# Print result
#################################################################
section("General Questions")
#################################################################
question("What is Wireshark?")
answer("""
Wireshark® is a network protocol analyzer. It lets you capture and
interactively browse the traffic running on a computer network. It has
a rich and powerful feature set and is world's most popular tool of its
kind. It runs on most computing platforms including Windows, macOS,
Linux, and UNIX. Network professionals, security experts, developers,
and educators around the world use it regularly. It is freely available
as open source, and is released under the GNU General Public License
version 2.
<br>
It is developed and maintained by a global team of protocol experts, and
it is an example of a
<a href="https://en.wikipedia.org/wiki/Disruptive_technology">disruptive
technology</a>.
<br>
Wireshark used to be known as Ethereal®. See the next question
for details about the name change. If you're still using Ethereal, it
is strongly recommended that you upgrade to Wireshark as Ethereal is
unsupported and has known security vulnerabilities.
<br>
For more information, please see the
<a href="https://www.wireshark.org/about.html">About Wireshark</a>
page.
""")
question("What's up with the name change? Is Wireshark a fork?")
answer("""
In May of 2006, Gerald Combs (the original author of Ethereal)
went to work for CACE Technologies (best known for WinPcap).
Unfortunately, he had to leave the Ethereal trademarks behind.
<br>
This left the project in an awkward position. The only reasonable way
to ensure the continued success of the project was to change the name.
This is how Wireshark was born.
<br>
Wireshark is almost (but not quite) a fork. Normally a "fork" of an open source
project results in two names, web sites, development teams, support
infrastructures, etc. This is the case with Wireshark except for one notable
exception -- every member of the core development team is now working on
Wireshark. There has been no active development on Ethereal since the name
change. Several parts of the Ethereal web site (such as the mailing lists,
source code repository, and build farm) have gone offline.
<br>
More information on the name change can be found here:
</p>
<ul class="item_list">
<li><a href="http://www.prweb.com/releases/2006/6/prweb396098.htm">Original press release</a>
<li><a href="http://archive09.linux.com/articles/54968">NewsForge article</a>
<li>Many other articles in <a href="https://www.wireshark.org/bibliography.html">our bibliography</a>
</ul>
<p>
""")
question("Where can I get help?")
answer("""
Community support is available on the
<a href="https://ask.wireshark.org/">Q&A site</a> and on the
wireshark-users mailing list. Subscription information and archives for
all of Wireshark's mailing lists can be found at %s. An IRC channel
dedicated to Wireshark can be found at %s.
<br>
Self-paced and instructor-led training is available at <a
href="http://www.wiresharktraining.com">Wireshark University</a>.
Wireshark University also offers certification via the Wireshark
Certified Network Analyst program.
""" % (selflink("https://www.wireshark.org/mailman/listinfo"),
selflink("irc://irc.freenode.net/wireshark")
))
question("What kind of shark is Wireshark?")
answer("""
<i>carcharodon photoshopia</i>.
""")
question("How is Wireshark pronounced, spelled and capitalized?")
answer("""
Wireshark is pronounced as the word <i>wire</i> followed immediately by
the word <i>shark</i>. Exact pronunciation and emphasis may vary
depending on your locale (e.g. Arkansas).
<br>
It's spelled with a capital <i>W</i>, followed by a lower-case
<i>ireshark</i>. It is not a CamelCase word, i.e., <i>WireShark</i>
is incorrect.
""")
question("How much does Wireshark cost?", "but_thats_not_all")
answer("""
Wireshark is "free software"; you can download it without paying any
license fee. The version of Wireshark you download isn't a "demo"
version, with limitations not present in a "full" version; it
<em>is</em> the full version.
<br>
The license under which Wireshark is issued is <a
href="https://www.gnu.org/licenses/gpl-2.0.html">the GNU General Public
License version 2</a>. See <a
href="https://www.gnu.org/licenses/old-licenses/gpl-2.0-faq.html">the GNU
GPL FAQ</a> for some more information.
""")
question("But I just paid someone on eBay for a copy of Wireshark! Did I get ripped off?")
answer("""
That depends. Did they provide any sort of value-added product or service, such
as installation support, installation media, training, trace file analysis, or
funky-colored shark-themed socks? Probably not.
<br>
Wireshark is <a href="https://www.wireshark.org/download.html">available for
anyone to download, absolutely free, at any time</a>. Paying for a copy implies
that you should get something for your money.
""")
question("Can I use Wireshark commercially?")
answer("""
Yes, if, for example, you mean "I work for a commercial organization;
can I use Wireshark to capture and analyze network traffic in our
company's networks or in our customer's networks?"
<br>
If you mean "Can I use Wireshark as part of my commercial product?", see
<a href="#derived_work_gpl">the next entry in the FAQ</a>.
""")
question("Can I use Wireshark as part of my commercial product?",
"derived_work_gpl")
answer("""
As noted, Wireshark is licensed under <a
href="https://www.gnu.org/licenses/gpl-2.0.html">the GNU General Public
License, version 2</a>. The GPL imposes conditions on your use of GPL'ed
code in your own products; you cannot, for example, make a "derived
work" from Wireshark, by making modifications to it, and then sell the
resulting derived work and not allow recipients to give away the
resulting work. You must also make the changes you've made to the
Wireshark source available to all recipients of your modified version;
those changes must also be licensed under the terms of the GPL. See the
<a href="https://www.gnu.org/licenses/old-licenses/gpl-2.0-faq.html">GPL
FAQ</a> for more details; in particular, note the answer to <a
href="https://www.gnu.org/licenses/old-licenses/gpl-2.0-faq.html#GPLCommercially">the
question about modifying a GPLed program and selling it
commercially</a>, and <a
href="https://www.gnu.org/licenses/old-licenses/gpl-2.0-faq.html#LinkingWithGPL">the
question about linking GPLed code with other code to make a proprietary
program</a>.
<br>
You can combine a GPLed program such as Wireshark and a commercial
program as long as they communicate "at arm's length", as per <a
href="https://www.gnu.org/licenses/old-licenses/gpl-2.0-faq.html#GPLInProprietarySystem">this
item in the GPL FAQ</a>.
<br>
We recommend keeping Wireshark and your product completely separate,
communicating over sockets or pipes. If you're loading any part of
Wireshark as a DLL, you're probably doing it wrong.
""")
question("What protocols are currently supported?")
answer("""
There are currently hundreds of supported
protocols and media. Details can be found in the
<a
href="https://www.wireshark.org/docs/man-pages/wireshark.html">wireshark(1)</a>
man page.
""")
question("Are there any plans to support {your favorite protocol}?")
answer("""
Support for particular protocols is added to Wireshark as a result of
people contributing that support; no formal plans for adding support for
particular protocols in particular future releases exist.
""")
question("""Can Wireshark read capture files from {your favorite network
analyzer}?""")
answer("""
Support for particular capture file formats is added to Wireshark as a result
of people contributing that support; no formal plans for adding support for
particular capture file formats in particular future releases exist.
<br>
If a network analyzer writes out files in a format already supported by
Wireshark (e.g., in libpcap format), Wireshark may already be able to read
them, unless the analyzer has added its own proprietary extensions to
that format.
<br>
If a network analyzer writes out files in its own format, or has added
proprietary extensions to another format, in order to make Wireshark read
captures from that network analyzer, we would either have to have a
specification for the file format, or the extensions, sufficient to give
us enough information to read the parts of the file relevant to
Wireshark, or would need at least one capture file in that format
<strong>AND</strong> a detailed textual analysis of the packets in that
capture file (showing packet time stamps, packet lengths, and the
top-level packet header) in order to reverse-engineer the file
format.
<br>
Note that there is no guarantee that we will be able to reverse-engineer
a capture file format.
""")
question("What devices can Wireshark use to capture packets?")
answer("""
Wireshark can read live data from Ethernet, Token-Ring, FDDI, serial (PPP
and SLIP) (if the OS on which it's running allows Wireshark to do so),
802.11 wireless LAN (if the OS on which it's running allows Wireshark to
do so), ATM connections (if the OS on which it's running allows Wireshark
to do so), and the "any" device supported on Linux by recent versions of
libpcap.
<br>
See <a href="https://wiki.wireshark.org/CaptureSetup/NetworkMedia">the list of
supported capture media on various OSes</a> for details (several items
in there say "Unknown", which doesn't mean "Wireshark can't capture on
them", it means "we don't know whether it can capture on them"; we
expect that it will be able to capture on many of them, but we haven't
tried it ourselves - if you try one of those types and it works, please
update the wiki page accordingly.
<br>
It can also read a variety of capture file formats, including:
</p>
<ul>
<li> AG Group/WildPackets/Savvius EtherPeek/TokenPeek/AiroPeek/EtherHelp/Packet Grabber captures
<li> AIX's iptrace captures
<li> Accellent's 5Views LAN agent output
<li> Cinco Networks NetXRay captures
<li> Cisco Secure Intrusion Detection System IPLog output
<li> CoSine L2 debug output
<li> DBS Etherwatch VMS text output
<li> Endace Measurement Systems' ERF format captures
<li> EyeSDN USB S0 traces
<li> HP-UX nettl captures
<li> ISDN4BSD project i4btrace captures
<li> Linux Bluez Bluetooth stack hcidump -w traces
<li> Lucent/Ascend router debug output
<li> Microsoft Network Monitor captures
<li> Network Associates Windows-based Sniffer captures
<li> Network General/Network Associates DOS-based Sniffer (compressed or uncompressed) captures
<li> Network Instruments Observer version 9 captures
<li> Novell LANalyzer captures
<li> RADCOM's WAN/LAN analyzer captures
<li> Shomiti/Finisar Surveyor captures
<li> Toshiba's ISDN routers dump output
<li> VMS TCPIPtrace/TCPtrace/UCX$TRACE output
<li> Visual Networks' Visual UpTime traffic capture
<li> libpcap, tcpdump and various other tools using tcpdump's capture format
<li> snoop and atmsnoop output
</ul>
<p>
so that it can read traces from various network types, as captured by
other applications or equipment, even if it cannot itself capture on
those network types.
""")
question("""
Does Wireshark work on Windows Vista or Windows Server 2008?
""")
answer("""
Yes, but if you want to capture packets as a normal user, you must make sure
npf.sys is loaded. Wireshark's installer enables this by default. This is not a
concern if you run Wireshark as Administrator, but this is discouraged. See the
<a
href="https://wiki.wireshark.org/CaptureSetup/CapturePrivileges#windows">CapturePrivileges</a>
page on the wiki for more details.
""")
#################################################################
section("Installing Wireshark")
#################################################################
question("""I installed the Wireshark RPM (or other package); why did
it install TShark but not Wireshark?""")
answer("""
Many distributions have separate Wireshark packages, one for non-GUI
components such as TShark, editcap, dumpcap, etc. and one for the GUI.
If this is the case on your system, there's probably a separate package
named <code>wireshark-qt</code>. Find it and install it.
""")
#################################################################
section("Building Wireshark")
#################################################################
question("""I have libpcap installed; why did the configure script not
find pcap.h or bpf.h?""")
answer("""
Are you sure pcap.h and bpf.h are installed? The official distribution
of libpcap only installs the libpcap.a library file when "make install"
is run. To install pcap.h and bpf.h, you must run "make install-incl".
If you're running Debian or Redhat, make sure you have the "libpcap-dev"
or "libpcap-devel" packages installed.
<br>
It's also possible that pcap.h and bpf.h have been installed in a strange
location. If this is the case, you may have to tweak aclocal.m4.
""")
#################################################################
section("Starting Wireshark")
#################################################################
question("""When I try to run Wireshark, why does it complain about
<code>sprint_realloc_objid</code> being undefined?""")
answer("""
Wireshark can only be linked with version 4.2.2 or later of UCD SNMP.
Your version of Wireshark was dynamically linked with such a version of
UCD SNMP; however, you have an older version of UCD SNMP installed,
which means that when Wireshark is run, it tries to link to the older
version, and fails. You will have to replace that version of UCD SNMP
with version 4.2.2 or a later version.
""")
question("""
I've installed Wireshark from Fink on macOS; why is it very slow to
start up?
""")
answer("""
When an application is installed on macOS, prior to 10.4, it is usually
"prebound" to speed up launching the application. (That's what the
"Optimizing" phase of installation is.)
<br>
Fink normally performs prebinding automatically when you install a
package. However, in some rare cases, for whatever reason the prebinding
caches get corrupt, and then not only does prebinding fail, but startup
actually becomes much slower, because the system tries in vain to
perform prebinding "on the fly" as you launch the application. This
fails, causing sometimes huge delays.
<br>
To fix the prebinding caches, run the command
</p>
<pre>
sudo /sw/var/lib/fink/prebound/update-package-prebinding.pl -f
</pre>
<p>
""")
#################################################################
section("Crashes and other fatal errors")
#################################################################
question("""
I have an XXX network card on my machine; if I try to capture on it, why
does my machine crash or reset itself?
""")
answer("""
This is almost certainly a problem with one or more of:
</p>
<ul>
<li>the operating system you're using;
<li>the device driver for the interface you're using;
<li>the libpcap/Npcap library and, if this is Windows, the Npcap
device driver;
</ul>
<p>
so:
</p>
<ul>
<li>if you are using Windows, see <a
href="https://nmap.org/npcap/">the Npcap support
page</a> - check the "Patches, Bug Reports, Questions, Suggestions, etc" section;
<li>if you are using some Linux distribution, some version of BSD, or
some other UNIX-flavored OS, you should report the problem to the
company or organization that produces the OS (in the case of a Linux
distribution, report the problem to whoever produces the distribution).
</ul>
<p>
""")
question("""
Why does my machine crash or reset itself when I select "Start" from the
"Capture" menu or select "Preferences" from the "Edit" menu?
""")
answer("""
Both of those operations cause Wireshark to try to build a list of the
interfaces that it can open; it does so by getting a list of interfaces
and trying to open them. There is probably an OS, driver, or, for
Windows, Npcap bug that causes the system to crash when this happens;
see the previous question.
""")
#################################################################
section("Capturing packets")
#################################################################
question("""When I use Wireshark to capture packets, why do I see only
packets to and from my machine, or not see all the traffic I'm expecting
to see from or to the machine I'm trying to monitor?""", "promiscsniff")
answer("""
This might be because the interface on which you're capturing is plugged
into an Ethernet or Token Ring switch; on a switched network, unicast
traffic between two ports will not necessarily appear on other ports -
only broadcast and multicast traffic will be sent to all ports.
<br>
Note that even if your machine is plugged into a hub, the "hub" may be
a switched hub, in which case you're still on a switched network.
<br>
Note also that on the Linksys Web site, they say that their
auto-sensing hubs "broadcast the 10Mb packets to the port that operate
at 10Mb only and broadcast the 100Mb packets to the ports that operate
at 100Mb only", which would indicate that if you sniff on a 10Mb port,
you will not see traffic coming sent to a 100Mb port, and <i>vice
versa</i>. This problem has also been reported for Netgear dual-speed
hubs, and may exist for other "auto-sensing" or "dual-speed" hubs.
<br>
Some switches have the ability to replicate all traffic on all ports to
a single port so that you can plug your analyzer into that single port to
sniff all traffic. You would have to check the documentation for the
switch to see if this is possible and, if so, to see how to do this.
See <a href="https://wiki.wireshark.org/SwitchReference">the switch
reference page</a> on <a href="https://wiki.wireshark.org/">the Wireshark
Wiki</a> for information on some switches. (Note that it's a Wiki, so
you can update or fix that information, or add additional information on
those switches or information on new switches, yourself.)
<br>
Note also that many firewall/NAT boxes have a switch built into them;
this includes many of the "cable/DSL router" boxes. If you have a box
of that sort, that has a switch with some number of Ethernet ports into
which you plug machines on your network, and another Ethernet port used
to connect to a cable or DSL modem, you can, at least, sniff traffic
between the machines on your network and the Internet by plugging
the Ethernet port on the router going to the modem, the Ethernet port on
the modem, and the machine on which you're running Wireshark into a hub
(make sure it's not a switching hub, and that, if it's a dual-speed hub,
all three of those ports are running at the same speed.
<br>
If your machine is <em>not</em> plugged into a switched network or a
dual-speed hub, or it is plugged into a switched network but the port is
set up to have all traffic replicated to it, the problem might be that
the network interface on which you're capturing doesn't support
"promiscuous" mode, or because your OS can't put the interface into
promiscuous mode. Normally, network interfaces supply to the host only:
</p>
<ul>
<li>packets sent to one of that host's link-layer addresses;
<li>broadcast packets;
<li>multicast packets sent to a multicast address that the host has
configured the interface to accept.
</ul>
<p>
Most network interfaces can also be put in "promiscuous" mode, in which
they supply to the host all network packets they see. Wireshark will try
to put the interface on which it's capturing into promiscuous mode
unless the "Capture packets in promiscuous mode" option is turned off in
the "Capture Options" dialog box, and TShark will try to put the
interface on which it's capturing into promiscuous mode unless the
<code>-p</code> option was specified. However, some network interfaces
don't support promiscuous mode, and some OSes might not allow interfaces
to be put into promiscuous mode.
<br>
If the interface is not running in promiscuous mode, it won't see any
traffic that isn't intended to be seen by your machine. It
<strong>will</strong> see broadcast packets, and multicast packets sent
to a multicast MAC address the interface is set up to receive.
<br>
You should ask the vendor of your network interface whether it supports
promiscuous mode. If it does, you should ask whoever supplied the
driver for the interface (the vendor, or the supplier of the OS you're
running on your machine) whether it supports promiscuous mode with that
network interface.
<br>
In the case of token ring interfaces, the drivers for some of them, on
Windows, may require you to enable promiscuous mode in order to capture
in promiscuous mode. See <a
href="https://wiki.wireshark.org/CaptureSetup/TokenRing">the Wireshark
Wiki item on Token Ring capturing</a> for details.
<br>
In the case of wireless LAN interfaces, it appears that, when those
interfaces are promiscuously sniffing, they're running in a
significantly different mode from the mode that they run in when they're
just acting as network interfaces (to the extent that it would be a
significant effort for those drivers to support for promiscuously
sniffing <em>and</em> acting as regular network interfaces at the same
time), so it may be that Windows drivers for those interfaces don't
support promiscuous mode.
""")
question("""When I capture with Wireshark, why can't I see any TCP
packets other than packets to and from my machine, even though another
analyzer on the network sees those packets?""")
answer("""
You're probably not seeing <em>any</em> packets other than unicast
packets to or from your machine, and broadcast and multicast packets; a
switch will normally send to a port only unicast traffic sent to the MAC
address for the interface on that port, and broadcast and multicast
traffic - it won't send to that port unicast traffic sent to a MAC
address for some other interface - and a network interface not in
promiscuous mode will receive only unicast traffic sent to the MAC
address for that interface, broadcast traffic, and multicast traffic
sent to a multicast MAC address the interface is set up to receive.
<br>
TCP doesn't use broadcast or multicast, so you will only see your own
TCP traffic, but UDP services may use broadcast or multicast so you'll
see some UDP traffic - however, this is not a problem with TCP traffic,
it's a problem with unicast traffic, as you also won't see all UDP
traffic between other machines.
<br>
I.e., this is probably <a href="#promiscsniff">the same question
as this earlier one</a>; see the response to that question.
""")
question("""Why am I only seeing ARP packets when I try to capture
traffic?""")
answer("""
You're probably on a switched network, and running Wireshark on a machine
that's not sending traffic to the switch and not being sent any traffic
from other machines on the switch. ARP packets are often broadcast
packets, which are sent to all switch ports.
<br>
I.e., this is probably <a href="#promiscsniff">the same question
as this earlier one</a>; see the response to that question.
""")
question("""
Why am I not seeing any traffic when I try to capture traffic?""")
answer("""
Is the machine running Wireshark sending out any traffic on the network
interface on which you're capturing, or receiving any traffic on that
network, or is there any broadcast traffic on the network or multicast
traffic to a multicast group to which the machine running Wireshark
belongs?
<br>
If not, this may just be a problem with promiscuous sniffing, either due
to running on a switched network or a dual-speed hub, or due to problems
with the interface not supporting promiscuous mode; see the response to
<a href="#promiscsniff">this earlier question</a>.
<br>
Otherwise, on Windows, see the response to <a href="#capprobwin">this
question</a> and, on a UNIX-flavored OS, see the response to <a
href="#capprobunix">this question</a>.
""")
question("""
Can Wireshark capture on (my T1/E1 line, SS7 links, etc.)?
""")
answer("""
Wireshark can only capture on devices supported by libpcap/Npcap. On
most OSes, only devices that can act as network interfaces of the type
that support IP are supported as capture devices for libpcap/Npcap,
although the device doesn't necessarily have to be running as an IP
interface in order to support traffic capture.
<br>
On Linux and FreeBSD, libpcap 0.8 and later support the API for <a
href="http://www.endace.com/products.htm">Endace Measurement Systems'
DAG cards</a>, so that a system with one of those cards, and its driver
and libraries, installed can capture traffic with those cards with
libpcap-based applications. You would either have to have a version of
Wireshark built with that version of libpcap, or a dynamically-linked
version of Wireshark and a shared libpcap library with DAG support, in
order to do so with Wireshark. You should ask Endace whether that could
be used to capture traffic on, for example, your T1/E1 link.
<br>
See <a href="https://wiki.wireshark.org/CaptureSetup/SS7">the SS7 capture
setup page</a> on <a href="https://wiki.wireshark.org/">the Wireshark
Wiki</a> for current information on capturing SS7 traffic on TDM
links.
""")
question("""How do I put an interface into promiscuous mode?""")
answer("""
By not disabling promiscuous mode when running Wireshark or TShark.
<br>
Note, however, that:
</p>
<ul>
<li>the form of promiscuous mode that libpcap (the library that
programs such as tcpdump, Wireshark, etc. use to do packet capture)
turns on will <strong>not</strong> necessarily be shown if you run
<code>ifconfig</code> on the interface on a UNIX system;
<li>some network interfaces might not support promiscuous mode, and some
drivers might not allow promiscuous mode to be turned on - see <a
href="#promiscsniff">this earlier question</a> for more information on
that;
<li>the fact that you're not seeing any traffic, or are only seeing
broadcast traffic, or aren't seeing any non-broadcast traffic other than
traffic to or from the machine running Wireshark, does not mean that
promiscuous mode isn't on - see <a href="#promiscsniff">this earlier
question</a> for more information on that.
</ul>
<p>
I.e., this is probably <a href="#promiscsniff">the same question
as this earlier one</a>; see the response to that question.
""")
question("""
I can set a display filter just fine; why don't capture filters work?
""")
answer("""
Capture filters currently use a different syntax than display filters. Here's
the corresponding section from the
<a
href="https://www.wireshark.org/docs/man-pages/wireshark.html">wireshark(1)</a>
man page:
<br>
"Display filters in Wireshark are very powerful; more fields are filterable
in Wireshark than in other protocol analyzers, and the syntax you can
use to create your filters is richer. As Wireshark progresses, expect
more and more protocol fields to be allowed in display filters.
<br>
Packet capturing is performed with the pcap library. The capture filter
syntax follows the rules of the pcap library. This syntax is different
from the display filter syntax."
<br>
The capture filter syntax used by libpcap can be found in the
<a href="http://www.tcpdump.org/tcpdump_man.html">tcpdump(8)</a>
man page.
""")
question("""
How can I capture packets with CRC errors?
""")
answer("""
Wireshark can capture only the packets that the packet capture library -
libpcap on UNIX-flavored OSes, and the Npcap port to Windows of libpcap
on Windows - can capture, and libpcap/Npcap can capture only the
packets that the OS's raw packet capture mechanism (or the Npcap
driver, and the underlying OS networking code and network interface
drivers, on Windows) will allow it to capture.
<br>
Unless the OS always supplies packets with errors such as invalid CRCs
to the raw packet capture mechanism, or can be configured to do so,
invalid CRCs to the raw packet capture mechanism, Wireshark - and other
programs that capture raw packets, such as tcpdump - cannot capture
those packets. You will have to determine whether your OS needs to be
so configured and, if so, can be so configured, configure it if
necessary and possible, and make whatever changes to libpcap and the
packet capture program you're using are necessary, if any, to support
capturing those packets.
<br>
Most OSes probably do <strong>not</strong> support capturing packets
with invalid CRCs on Ethernet, and probably do not support it on most
other link-layer types. Some drivers on some OSes do support it, such
as some Ethernet drivers on FreeBSD; in those OSes, you might always get
those packets, or you might only get them if you capture in promiscuous
mode (you'd have to determine which is the case).
<br>
Note that libpcap does not currently supply to programs that use it an
indication of whether the packet's CRC was invalid (because the drivers
themselves do not supply that information to the raw packet capture
mechanism); therefore, Wireshark will not indicate which packets had CRC
errors unless the FCS was captured (see the next question) and you're
using Wireshark 0.9.15 and later, in which case Wireshark will check the
CRC and indicate whether it's correct or not.
""")
question("""
How can I capture entire frames, including the FCS?
""")
answer("""
Wireshark can only capture data that the packet capture library -
libpcap on UNIX-flavored OSes, and the Npcap port to Windows of
libpcap on Windows - can capture, and libpcap/Npcap can capture only
the data that the OS's raw packet capture mechanism (or the Npcap
driver, and the underlying OS networking code and network interface
drivers, on Windows) will allow it to capture.
<br>
For any particular link-layer network type, unless the OS supplies the
FCS of a frame as part of the frame, or can be configured to do so,
Wireshark - and other programs that capture raw packets, such as tcpdump
- cannot capture the FCS of a frame. You will have to determine whether
your OS needs to be so configured and, if so, can be so configured,
configure it if necessary and possible, and make whatever changes to
libpcap and the packet capture program you're using are necessary, if
any, to support capturing the FCS of a frame.
<br>
Most OSes do <strong>not</strong> support capturing the FCS of a frame
on Ethernet, and probably do not support it on most other link-layer
types. Some drivres on some OSes do support it, such as some (all?)
Ethernet drivers on NetBSD and possibly the driver for Apple's gigabit
Ethernet interface in macOS; in those OSes, you might always get the
FCS, or you might only get the FCS if you capture in promiscuous mode
(you'd have to determine which is the case).
<br>
Versions of Wireshark prior to 0.9.15 will not treat an Ethernet FCS in a
captured packet as an FCS. 0.9.15 and later will attempt to determine
whether there's an FCS at the end of the frame and, if it thinks there
is, will display it as such, and will check whether it's the correct
CRC-32 value or not.
""")
question("""
I'm capturing packets on a machine on a VLAN; why don't the packets I'm
capturing have VLAN tags?
""")
answer("""
You might be capturing on what might be called a "VLAN interface" - the
way a particular OS makes VLANs plug into the networking stack might,
for example, be to have a network device object for the physical
interface, which takes VLAN packets, strips off the VLAN header and
constructs an Ethernet header, and passes that packet to an internal
network device object for the VLAN, which then passes the packets onto
various higher-level protocol implementations.
<br>
In order to see the raw Ethernet packets, rather than "de-VLANized"
packets, you would have to capture not on the virtual interface for the
VLAN, but on the interface corresponding to the physical network device,
if possible. See <a
href="https://wiki.wireshark.org/CaptureSetup/VLAN">the Wireshark Wiki
item on VLAN capturing</a> for details.
""")
question("""
Why does Wireshark hang after I stop a capture?
""")
answer("""
The most likely reason for this is that Wireshark is trying to look up an
IP address in the capture to convert it to a name (so that, for example,
it can display the name in the source address or destination address
columns), and that lookup process is taking a very long time.
<br>
Wireshark calls a routine in the OS of the machine on which it's running
to convert of IP addresses to the corresponding names. That routine
probably does one or more of:
</p>
<ul><li>a search of a system file listing IP addresses and names;
<li>a lookup using DNS;
<li>on UNIX systems, a lookup using NIS;
<li>on Windows systems, a NetBIOS-over-TCP query.
</ul>
<p>
If a DNS server that's used in an address lookup is not responding, the
lookup will fail, but will only fail after a timeout while the system
routine waits for a reply.
<br>
In addition, on Windows systems, if the DNS lookup of the address fails,
either because the server isn't responding or because there are no
records in the DNS that could be used to map the address to a name, a
NetBIOS-over-TCP query will be made. That query involves sending a
message to the NetBIOS-over-TCP name service on that machine, asking for
the name and other information about the machine. If the machine isn't
running software that responds to those queries - for example, many
non-Windows machines wouldn't be running that software - the lookup will
only fail after a timeout. Those timeouts can cause the lookup to take
a long time.
<br>
If you disable network address-to-name translation - for example, by
turning off the "Enable network name resolution" option in the "Capture
Options" dialog box for starting a network capture - the lookups of the
address won't be done, which may speed up the process of reading the
capture file after the capture is stopped. You can make that setting
the default by selecting "Preferences" from the "Edit" menu, turning off
the "Enable network name resolution" option in the "Name resolution"
options in the preferences disalog box, and using the "Save" button in
that dialog box; note that this will save <em>all</em> your current
preference settings.
<br>
If Wireshark hangs when reading a capture even with network name
resolution turned off, there might, for example, be a bug in one of
Wireshark's dissectors for a protocol causing it to loop infinitely. If
you're not running the most recent release of Wireshark, you should first
upgrade to that release, as, if there's a bug of that sort, it might've
been fixed in a release after the one you're running. If the hang
occurs in the most recent release of Wireshark, the bug should be
reported to <a href="mailto:[email protected]">the Wireshark
developers' mailing list</a> at <code>[email protected]</code>.
<br>
On UNIX-flavored OSes, please try to force Wireshark to dump core, by
sending it a <code>SIGABRT</code> signal (usually signal 6) with the
<code>kill</code> command, and then get a stack trace if you have a debugger
installed. A stack trace can be obtained by using your debugger
(<code>gdb</code> in this example), the Wireshark binary, and the resulting
core file. Here's an example of how to use the gdb command
<code>backtrace</code> to do so.
</p>
<pre>
$ gdb wireshark core
(gdb) backtrace
..... prints the stack trace
(gdb) quit
$
</pre>
<p>
The core dump file may be named "wireshark.core" rather than "core" on
some platforms (e.g., BSD systems).
<br>
Also, if at all possible, please send a copy of the capture file that caused
the problem. When capturing packets, Wireshark normally writes captured
packets to a temporary file, which will probably be in <code>/tmp</code> or
<code>/var/tmp</code> on UNIX-flavored OSes, <code>\\TEMP</code> on the main system disk
(normally <code>\\Documents and Settings\\</code><var>your login name</var>
<code>\\Local Settings\\Temp</code> on the main system disk on Windows
Windows XP and Server 2003, and
<code>\\Users\\<var>your login name</var>\\AppData\\Local\\Temp</code> on the main
system disk on Windows Vista and later, so the capture file will probably be there. If you
are capturing on a single interface, it will have a name of the form,
<code>wireshark_<iface>_YYYYmmddHHMMSS_XXXXXX.<fmt></code>, where
<fmt> is the capture file format (pcap or pcapng), and <iface> is
the actual name of the interface you are capturing on; otherwise, if you are
capturing on multiple interfaces, it will have a name of the form,
<code>wireshark_<N>_interfaces_YYYYmmddHHMMSS_XXXXXX.<fmt></code>, where <N>
is the number of simultaneous interfaces you are capturing on. Please don't
send a trace file greater than 1 MB when compressed; instead, make it available
via FTP or HTTP, or say it's available but leave it up to a developer to ask
for it. If the trace file contains sensitive information (e.g., passwords),
then please do not send it.
""")
#################################################################
section("Capturing packets on Windows")
#################################################################
question("""
I'm running Wireshark on Windows; why does some network interface on my
machine not show up in the list of interfaces in the "Interface:" field
in the dialog box popped up by "Capture->Start", and/or why does
Wireshark give me an error if I try to capture on that interface?
""", "capprobwin")
answer("""
Wireshark relies on the Npcap library, on the Npcap device driver, and
and on the facilities that come with the OS on which it's running in
order to do captures.
<br>
Therefore, if the OS, the Npcap library, or the Npcap driver don't
support capturing on a particular network interface device, Wireshark
won't be able to capture on that device.
<br>
If an interface doesn't show up in the list of interfaces in the
"Interface:" field, and you know the name of the interface, try entering
that name in the "Interface:" field and capturing on that device.
<br>
If the attempt to capture on it succeeds, the interface is somehow not
being reported by the mechanism Wireshark uses to get a list of
interfaces. Try listing the interfaces with WinDump; see <a
href="https://www.windump.org/">the WinDump Web site</a>
for information on using WinDump.
<br>
You would run WinDump with the <code>-D</code> flag; if it lists the
interface, please report this to <a
href="mailto:[email protected]">[email protected]</a>
giving full details of the problem, including
</p>
<ul>
<li>the operating system you're using, and the version of that operating
system;
<li>the type of network device you're using;
<li>the output of WinDump.
</ul>
<p>
If WinDump does <em>not</em> list the interface,
this is almost certainly a problem with one or more of:
</p>
<ul>
<li>the operating system you're using;
<li>the device driver for the interface you're using;
<li>the WinPcap library and/or the WinPcap device driver;
</ul>
<p>
so first check <a href="https://nmap.org/npcap/guide/">the
Npcap User's Guide</a> to see if your problem is mentioned there. If not, then see <a
href="https://nmap.org/npcap/">the main Npcap page</a>
- check the "Patches, Bug Reports, Questions, Suggestions, etc" section.
<br>
If you are having trouble capturing on a particular network interface,
first try capturing on that device with WinDump; see <a
href="https://www.windump.org/">the WinDump Web site</a>
for information on using WinDump.
<br>
If you can capture on the interface with WinDump, send mail to <a
href="mailto:[email protected]">[email protected]</a>
giving full details of the problem, including
</p>
<ul>
<li>the operating system you're using, and the version of that operating
system;
<li>the type of network device you're using;
<li>the error message you get from Wireshark.
</ul>
<p>
If you <em>cannot</em> capture on the interface with WinDump,
this is almost certainly a problem with one or more of:
</p>
<ul>
<li>the operating system you're using;
<li>the device driver for the interface you're using;
<li>the Npcap library and/or the Npcap device driver;
</ul>
<p>
so first check <a href="https://nmap.org/npcap/guide/">the
Npcap User's Guide</a> to see if your problem is mentioned there. If not, then see <a
href="https://nmap.org/npcap/">the main Npcap page</a>
- check the "Patches, Bug Reports, Questions, Suggestions, etc" section.
<br>
You may also want to ask the <a
href="mailto:[email protected]">[email protected]</a>
and the <a
href="mailto:[email protected]">[email protected]</a>
mailing lists to see if anybody happens to know about the problem and
know a workaround or fix for the problem. (Note that you will have to
subscribe to that list in order to be allowed to mail to it; see <a
href="https://nmap.org/npcap/">the Npcap support
page</a> for information on the mailing list.) In your mail,
please give full details of the problem, as described above, and also
indicate that the problem occurs with WinDump, not just with Wireshark.
""")
question("""
I'm running Wireshark on Windows; why do no network interfaces show up in
the list of interfaces in the "Interface:" field in the dialog box
popped up by "Capture->Start"?
""")
answer("""
This is really <a href="#capprobwin">the same question as a previous
one</a>; see the response to that question.
""")
question("""
I'm running Wireshark on Windows; why am I not seeing any traffic being
sent by the machine running Wireshark?""")
answer("""
If you are running some form of VPN client software, it might be causing
this problem; people have seen this problem when they have Check Point's
VPN software installed on their machine. If that's the cause of the
problem, you will have to remove the VPN software in order to have
Wireshark (or any other application using Npcap) see outgoing packets;
unfortunately, neither we nor the Npcap developers know any way to
make Npcap and the VPN software work well together.
<br>
Also, some drivers for Windows (especially some wireless network
interface drivers) apparently do not, when running in promiscuous mode,
arrange that outgoing packets are delivered to the software that
requested that the interface run promiscuously; try turning promiscuous
mode off.
""")
question("""
When I capture on Windows in promiscuous mode, I can see packets other
than those sent to or from my machine; however, those packets show up
with a "Short Frame" indication, unlike packets to or from my machine.
What should I do to arrange that I see those packets in their entirety?
""")
answer("""
In at least some cases, this appears to be the result of PGPnet running
on the network interface on which you're capturing; turn it off on that
interface.
""")
question("""
I'm trying to capture 802.11 traffic on Windows; why am I not seeing any
packets?
""", "win802_11promisc")
answer("""
At least some 802.11 card drivers on Windows appear not to see any
packets if they're running in promiscuous mode. Try turning promiscuous
mode off; you'll only be able to see packets sent by and received by
your machine, not third-party traffic, and it'll look like Ethernet
traffic and won't include any management or control frames, but that's a
limitation of the card drivers.
<br>
See the archived <a
href="https://web.archive.org/web/20090226193157/http://www.micro-logix.com/winpcap/Supported.asp">MicroLogix's
list of cards supported with WinPcap</a> for information on
support of various adapters and drivers with WinPcap.
""")
question("""
I'm trying to capture 802.11 traffic on Windows; why am I seeing packets
received by the machine on which I'm capturing traffic, but not packets
sent by that machine?
""")
answer("""
This appears to be another problem with promiscuous mode; try turning it
off.
""")
question("""
I'm trying to capture Ethernet VLAN traffic on Windows, and I'm
capturing on a "raw" Ethernet device rather than a "VLAN interface", so
that I can see the VLAN headers; why am I seeing packets received by the
machine on which I'm capturing traffic, but not packets sent by that
machine?
""")
answer("""
The way the Windows networking code works probably means that packets
are sent on a "VLAN interface" rather than the "raw" device, so packets
sent by the machine will only be seen when you capture on the "VLAN
interface". If so, you will be unable to see outgoing packets when
capturing on the "raw" device, so you are stuck with a choice between
seeing VLAN headers and seeing outgoing packets.
""")
#################################################################
section("Capturing packets on UN*Xes")
#################################################################
question("""
I'm running Wireshark on a UNIX-flavored OS; why does some network
interface on my machine not show up in the list of interfaces in the
"Interface:" field in the dialog box popped up by "Capture->Start",
and/or why does Wireshark give me an error if I try to capture on that
interface? """, "capprobunix")
answer("""
You may need to run Wireshark from an account with sufficient privileges
to capture packets, such as the super-user account, or may need to give
your account sufficient privileges to capture packets. Only those
interfaces that Wireshark can open for capturing show up in that list; if
you don't have sufficient privileges to capture on any interfaces, no
interfaces will show up in the list. See
<a href="https://wiki.wireshark.org/CaptureSetup/CapturePrivileges">the
Wireshark Wiki item on capture privileges</a> for details on how to give
a particular account or account group capture privileges on platforms
where that can be done.
<br>
If you are running Wireshark from an account with sufficient privileges,
then note that Wireshark relies on the libpcap library, and on the
facilities that come with the OS on which it's running in order to do
captures. On some OSes, those facilities aren't present by default; see
<a href="https://wiki.wireshark.org/CaptureSetup/CaptureSupport">the
Wireshark Wiki item on adding capture support</a> for details.
<br>
And, even if you're running with an account that has sufficient
privileges to capture, and capture support is present in your OS, if the
OS or the libpcap library don't support capturing on a particular
network interface device or particular types of devices, Wireshark won't
be able to capture on that device.
<br>
On Solaris, note that libpcap 0.6.2 and earlier didn't support Token
Ring interfaces; the current version, 0.7.2, does support Token Ring,
and the current version of Wireshark works with libpcap 0.7.2 and later.
<br>
If an interface doesn't show up in the list of interfaces in the
"Interface:" field, and you know the name of the interface, try entering
that name in the "Interface:" field and capturing on that device.
<br>
If the attempt to capture on it succeeds, the interface is somehow not
being reported by the mechanism Wireshark uses to get a list of
interfaces; please report this to <a
href="mailto:[email protected]">[email protected]</a>
giving full details of the problem, including
</p>
<ul>
<li>the operating system you're using, and the version of that operating
system (for Linux, give both the version number of the kernel and the
name and version number of the distribution you're using);
<li>the type of network device you're using.
</ul>
<p>
If you are having trouble capturing on a particular network interface,
and you've made sure that (on platforms that require it) you've arranged
that packet capture support is present, as per the above, first try
capturing on that device with <code>tcpdump</code>.
<br>
If you can capture on the interface with <code>tcpdump</code>, send mail to
<a
href="mailto:[email protected]">[email protected]</a>
giving full details of the problem, including
</p>
<ul>
<li>the operating system you're using, and the version of that operating
system (for Linux, give both the version number of the kernel and the
name and version number of the distribution you're using);
<li>the type of network device you're using;
<li>the error message you get from Wireshark.
</ul>
<p>
If you <em>cannot</em> capture on the interface with <code>tcpdump</code>,
this is almost certainly a problem with one or more of:
</p>
<ul>
<li>the operating system you're using;
<li>the device driver for the interface you're using;
<li>the libpcap library;
</ul>
<p>
so you should report the problem to the company or organization that
produces the OS (in the case of a Linux distribution, report the problem
to whoever produces the distribution).
<br>
You may also want to ask the <a
href="mailto:[email protected]">[email protected]</a>
and the <a
href="mailto:[email protected]">[email protected]</a>
mailing lists to see if anybody happens to know about the problem and
know a workaround or fix for the problem. In your mail, please give
full details of the problem, as described above, and also indicate that
the problem occurs with <code>tcpdump</code> not just with Wireshark.
""")
question("""
I'm running Wireshark on a UNIX-flavored OS; why do no network interfaces
show up in the list of interfaces in the "Interface:" field in the
dialog box popped up by "Capture->Start"?
""")
answer("""
This is really <a href="#capprobunix">the same question as the previous
one</a>; see the response to that question.
""")
question("""I'm capturing packets on Linux; why do the time stamps have
only 100ms resolution, rather than 1us resolution?""")
answer("""
Wireshark gets time stamps from libpcap/Npcap, and
libpcap/Npcap get them from the OS kernel, so Wireshark - and any other
program using libpcap, such as tcpdump - is at the mercy of the time
stamping code in the OS for time stamps.
<br>
At least on x86-based machines, Linux can get high-resolution time
stamps on newer processors with the Time Stamp Counter (TSC) register;
for example, Intel x86 processors, starting with the Pentium Pro, and
including all x86 processors since then, have had a TSC, and other
vendors probably added the TSC at some point to their families of x86
processors.
The Linux kernel must be configured with the CONFIG_X86_TSC option
enabled in order to use the TSC. Make sure this option is enabled in
your kernel.
<br>
In addition, some Linux distributions may have bugs in their versions of
the kernel that cause packets not to be given high-resolution time
stamps even if the TSC is enabled. See, for example, bug 61111 for Red
Hat Linux 7.2. If your distribution has a bug such as this, you may
have to run a standard kernel from kernel.org in order to get
high-resolution time stamps.
""")
#################################################################
section("Capturing packets on wireless LANs")
#################################################################
question("""
How can I capture raw 802.11 frames, including non-data (management,
beacon) frames?
""", "raw_80211_sniff")
answer("""
That depends on the operating system on which you're running, and on the
802.11 interface on which you're capturing.
<br>
This would probably require that you capture in promiscuous mode or in
the mode called "monitor mode" or "RFMON mode". On some platforms, or
with some cards, this might require that you capture in monitor mode -
promiscuous mode might not be sufficient. If you want to capture
traffic on networks other than the one with which you're associated, you
will have to capture in monitor mode.
<br>
Not all operating systems support capturing non-data packets and, even
on operating systems that do support it, not all drivers, and thus not
all interfaces, support it. Even on those that do, monitor mode might
not be supported by the operating system or by the drivers for all
interfaces.
<br>
<strong>NOTE:</strong> an interface running in monitor mode will, on
most if not all platforms, not be able to act as a regular network
interface; putting it into monitor mode will, in effect, take your
machine off of whatever network it's on as long as the interface is in
monitor mode, allowing it only to passively capture packets.
<br>
This means that you should disable name resolution when capturing in
monitor mode; otherwise, when Wireshark (or TShark, or tcpdump) tries
to display IP addresses as host names, it will probably block for a long
time trying to resolve the name because it will not be able to
communicate with any DNS or NIS servers.
<br>
See <a
href="https://wiki.wireshark.org/CaptureSetup/WLAN">the Wireshark
Wiki item on 802.11 capturing</a> for details.
""")
question("""
How do I capture on an 802.11 device in monitor mode?""",
"monitor")
answer("""
Whether you will be able to capture in monitor mode depends on the
operating system, adapter, and driver you're using.
See <a href="#raw_80211_sniff">the previous question</a> for information
on monitor mode, including a link to the Wireshark Wiki page that gives
details on 802.11 capturing.
""")
#################################################################
section("Viewing traffic")
#################################################################
question("Why am I seeing lots of packets with incorrect TCP checksums?")
answer("""
If the packets that have incorrect TCP checksums are all being sent by
the machine on which Wireshark is running, this is probably because the
network interface on which you're capturing does TCP checksum
offloading. That means that the TCP checksum is added to the packet by
the network interface, not by the OS's TCP/IP stack; when capturing on
an interface, packets being sent by the host on which you're capturing
are directly handed to the capture interface by the OS, which means that
they are handed to the capture interface without a TCP checksum being
added to them.
<br>
The only way to prevent this from happening would be to disable TCP
checksum offloading, but
</p>
<ol>
<li>that might not even be possible on some OSes;
<li>that could reduce networking performance significantly.
</ol>
<p>
However, you can disable the check that Wireshark does of the TCP
checksum, so that it won't report any packets as having TCP checksum
errors, and so that it won't refuse to do TCP reassembly due to a packet
having an incorrect TCP checksum. That can be set as an Wireshark
preference by selecting "Preferences" from the "Edit" menu, opening up
the "Protocols" list in the left-hand pane of the "Preferences" dialog
box, selecting "TCP", from that list, turning off the "Check the
validity of the TCP checksum when possible" option, clicking "Save" if
you want to save that setting in your preference file, and clicking
"OK".
<br>
It can also be set on the Wireshark or TShark command line with a
<code>-o tcp.check_checksum:false</code> command-line flag, or manually set
in your preferences file by adding a <code>tcp.check_checksum:false</code>
line.
""")
question("""
I've just installed Wireshark, and the traffic on my local LAN
is boring. Where can I find more interesting captures?
""")
answer("""
We have a collection of strange and exotic sample capture
files at %s""" % (selflink("https://wiki.wireshark.org/SampleCaptures")))
question("""
Why doesn't Wireshark correctly identify RTP packets? It shows them
only as UDP.""")
answer("""
Wireshark can identify a UDP datagram as containing a packet of a
particular protocol running atop UDP only if
</p>
<ol>
<li> The protocol in question has a particular standard port
number, and the UDP source or destination port number is that port
<li> Packets of that protocol can be identified by looking for a
"signature" of some type in the packet - i.e., some data
that, if Wireshark finds it in some particular part of a
packet, means that the packet is almost certainly a packet of
that type.
<li> Some <em>other</em> traffic earlier in the capture indicated that,
for example, UDP traffic between two particular addresses and
ports will be RTP traffic.
</ol>
<p>
RTP doesn't have a standard port number, so 1) doesn't work; it doesn't,
as far as I know, have any "signature", so 2) doesn't work.
<br>
That leaves 3). If there's RTSP traffic that sets up an RTP session,
then, at least in some cases, the RTSP dissector will set things up so
that subsequent RTP traffic will be identified. Currently, that's the
only place we do that; there may be other places.
<br>
However, there will always be places where Wireshark is simply
<b>incapable</b> of deducing that a given UDP flow is RTP; a mechanism
would be needed to allow the user to specify that a given conversation
should be treated as RTP. As of Wireshark 0.8.16, such a mechanism
exists; if you select a UDP or TCP packet, the right mouse button menu
will have a "Decode As..." menu item, which will pop up a dialog box
letting you specify that the source port, the destination port, or both
the source and destination ports of the packet should be dissected as
some particular protocol.
""")
question("""
Why doesn't Wireshark show Yahoo Messenger packets in captures that
contain Yahoo Messenger traffic?""")
answer("""
Wireshark only recognizes as Yahoo Messenger traffic packets to or from TCP
port 3050 that begin with "YPNS", "YHOO", or "YMSG". TCP segments that
start with the middle of a Yahoo Messenger packet that takes more than one
TCP segment will not be recognized as Yahoo Messenger packets (even if the
TCP segment also contains the beginning of another Yahoo Messenger
packet).
""")
#################################################################
section("Filtering traffic")
#################################################################
question("""I saved a filter and tried to use its name to filter the
display; why do I get an "Unexpected end of filter string" error?""")
answer("""
You cannot use the name of a saved display filter as a filter. To
filter the display, you can enter a display filter expression -
<strong>not</strong> the name of a saved display filter - in the
"Filter:" box at the bottom of the display, and type the <Enter> key or
press the "Apply" button (that does not require you to have a saved
filter), or, if you want to use a saved filter, you can press the
"Filter:" button, select the filter in the dialog box that pops up, and
press the "OK" button.""")
question("""
How can I search for, or filter, packets that have a particular string
anywhere in them?
""")
answer("""
If you want to do this when capturing, you can't. That's a feature that
would be hard to implement in capture filters without changes to the
capture filter code, which, on many platforms, is in the OS kernel and,
on other platforms, is in the libpcap library.
<br>
After capture, you can search for text by selecting <i>Edit→Find
Packet...</i> and making sure <i>String</i> is selected. Alternately, you can
use the "contains" display filter operator or "matches" operator if it's
supported on your system.
""")
question("""
How do I filter a capture to see traffic for virus XXX?
""")
answer("""
For some viruses/worms there might be a capture filter to recognize the
virus traffic. Check the <a
href="https://wiki.wireshark.org/CaptureFilters">CaptureFilters</a> page
on the <a href="https://wiki.wireshark.org/">Wireshark Wiki</a> to see if
anybody's added such a filter.
<br>
Note that Wireshark was not designed to be an intrusion detection system;
you might be able to use it as an IDS, but in most cases software
designed to be an IDS, such as <a href="https://www.snort.org/">Snort</a>
or <a href="https://www.prelude-siem.org/">Prelude</a>, will probably work
better.
""")
#################################################################
if __name__ == '__main__':
sys.exit(main())
#################################################################
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] | 3.61219 | 16,784 |
filter_cfg = dict(
type='SavizkyGolayFilter',
window_size=11,
polyorder=2,
)
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] | 2.119048 | 42 |
import torch.nn as nn
import torch.nn.functional as F
# define the CNN architecture
### TODO: choose an architecture, and complete the class | [
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] | 3.717949 | 39 |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import pytest
import pickle
import os
from .. import Quat, normalize
ra = 10.
dec = 20.
roll = 30.
q0 = Quat([ra, dec, roll])
equatorial_23 = np.array([[[10, 20, 30],
[10, 20, -30],
[10, -60, 30]],
[[10, 20, 0],
[10, 50, 30],
[10, -50, -30]]], dtype=float)
q_23 = np.zeros(equatorial_23[..., 0].shape + (4,))
for _i, _j in indices(equatorial_23.shape[:-1]):
q_23[_i, _j] = Quat(equatorial_23[_i, _j]).q
transform_23 = np.zeros(equatorial_23[..., 0].shape + (3, 3))
for _i, _j in indices(transform_23.shape[:-2]):
transform_23[_i, _j] = Quat(equatorial_23[_i, _j]).transform
def test_from_transform():
"""Initialize from inverse of q0 via transform matrix"""
q = Quat(q0.transform.transpose())
assert np.allclose(q.q[0], -0.26853582)
assert np.allclose(q.q[1], 0.14487813)
assert np.allclose(q.q[2], -0.12767944)
assert np.allclose(q.q[3], 0.94371436)
q = Quat(q0.transform)
assert np.allclose(q.roll0, 30)
assert np.allclose(q.ra0, 10)
q1 = Quat(transform=q0.transform)
assert np.all(q1.q == q.q)
def test_vector_to_scalar_correspondence():
"""
Simple test that all possible transform pathways give the same
answer when done in vectorized form as they do for the scalar version.
"""
atol = 1e-12
# Input equatorial has roll not in 0:360, so fix that for comparisons.
eq_23 = equatorial_23.copy()
normalize_angles(eq_23[..., -1], 0, 360)
# Compare vectorized computations for all possible input/output combos
# with the same for the scalar calculation.
q = Quat(equatorial=equatorial_23)
assert np.all(q.q == q_23)
assert np.all(q.equatorial == equatorial_23)
assert np.all(q.transform == transform_23)
q = Quat(q=q_23)
assert np.all(q.q == q_23)
assert np.allclose(q.equatorial, eq_23, rtol=0, atol=atol)
assert np.allclose(q.transform, transform_23, rtol=0, atol=atol)
q = Quat(transform=transform_23)
assert np.allclose(q.q, q_23, rtol=0, atol=atol)
assert np.allclose(q.equatorial, eq_23, rtol=0, atol=atol)
assert np.all(q.transform == transform_23)
def test_numeric_underflow():
"""
Test new code (below) for numeric issue https://github.com/sot/Quaternion/issues/1.
If this code is not included then the test fails with a MathDomainError::
one_minus_xn2 = 1 - xn**2
if one_minus_xn2 < 0:
if one_minus_xn2 < -1e-12:
raise ValueError('Unexpected negative norm: {}'.format(one_minus_xn2))
one_minus_xn2 = 0
"""
quat = Quat((0, 0, 0))
angle = 0
while angle < 360:
q = Quat((0, angle, 0))
quat = q * quat
_ = quat.equatorial
angle += 0.1
def test_mult_and_dq_broadcasted():
"""Test mult and delta quat of Quats with different but broadcastable shapes.
"""
q2 = Quat(equatorial=np.arange(18).reshape(3, 2, 3))
q1 = Quat(equatorial=[[10, 20, 30], [40, 50, 60]])
q0 = Quat(equatorial=[10, 20, 30])
# (3,2) * () = (3,2)
q20 = q2 * q0
dq20 = q2.dq(q0)
assert q20.shape == (3, 2)
assert dq20.shape == (3, 2)
for ii in range(3):
for jj in range(2):
qq = q2[ii, jj] * q0
dq = q2[ii, jj].dq(q0)
assert np.allclose(qq.q, q20.q[ii, jj])
assert np.allclose(dq.q, dq20.q[ii, jj])
# (3,2) * (2,) = (3,2)
q21 = q2 * q1
dq21 = q2.dq(q1)
assert q21.shape == (3, 2)
assert dq21.shape == (3, 2)
for ii in range(3):
for jj in range(2):
qq = q2[ii, jj] * q1[jj]
dq = q2[ii, jj].dq(q1[jj])
assert np.allclose(qq.q, q21.q[ii, jj])
assert np.allclose(dq.q, dq21.q[ii, jj])
def test_pickle():
"""
Pickle file generated using Quaternion v3.4.1:
from Quaternion import Quat
import pickle
q = Quat([10., 20., 30.])
quats = [Quat(q.q), Quat(q.transform), Quat(q.equatorial)]
quats.append(q)
with open('quaternion-v3.4.1.pkl', 'wb') as f:
pickle.dump(quats, f)
"""
# testing we can unpickle older versions
filename = os.path.join(os.path.dirname(__file__), 'data', 'quaternion-v3.4.1.pkl')
with open(filename, 'rb') as f:
quaternions = pickle.load(f)
for q in quaternions:
assert np.all(np.isclose(q.q, [0.26853582, -0.14487813, 0.12767944, 0.94371436]))
assert np.all(np.isclose(q.equatorial, [10., 20., 30.]))
assert np.all(np.isclose(q.transform, [[0.92541658, -0.31879578, -0.20487413],
[0.16317591, 0.82317294, -0.54383814],
[0.34202014, 0.46984631, 0.81379768]]))
def test_rotate_x_to_vec_regress():
"""Note that truth values are just results from original code in Ska.quatutil.
They have not been independently validated"""
vec = [1, 2, 3]
q = Quat.rotate_x_to_vec(vec) # method='radec', default
assert np.allclose(q.q, [0.2358142, -0.38155539, 0.4698775, 0.76027777])
q = Quat.rotate_x_to_vec(vec, method='shortest')
assert np.allclose(q.q, [0., -0.50362718, 0.33575146, 0.79600918])
q = Quat.rotate_x_to_vec(vec, method='keep_z')
assert np.allclose(q.q, [-0.16269544, -0.56161937, 0.22572786, 0.77920525])
@pytest.mark.parametrize('method', ('keep_z', 'shortest', 'radec'))
@pytest.mark.parametrize('attr', ['q', 'equatorial', 'transform'])
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] | 2.027441 | 2,806 |
#!/usr/bin/env python
#===============================================================================
# Copyright (c) 2014 Geoscience Australia
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither Geoscience Australia nor the names of its contributors may be
# used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#===============================================================================
'''
Created on 12/03/2015
@author: Alex Ip
Tests for the gdf._ConfigFile.py module.
'''
import os
import unittest
from gdf._config_file import ConfigFile
#
# Test cases
#
# pylint: disable=too-many-public-methods
#
# Disabled to avoid complaints about the unittest.TestCase class.
#
class TestConfigFile(unittest.TestCase):
"""Unit tests for utility functions."""
MODULE = 'gdf._config_file'
SUITE = 'TestConfigFile'
def test_ConfigFile(self):
"Test ConfigFile constructor"
# Default config file should be ../gdf/gdf_default.conf
default_config_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'gdf', 'gdf_default.conf')
config_file_object = ConfigFile(default_config_file)
assert config_file_object.path == os.path.abspath(default_config_file), 'path property is not set correctly'
#
# Define test suites
#
def test_suite():
"""Returns a test suite of all the tests in this module."""
test_classes = [TestConfigFile
]
suite_list = map(unittest.defaultTestLoader.loadTestsFromTestCase,
test_classes)
suite = unittest.TestSuite(suite_list)
return suite
# Define main function
#
# Run unit tests if in __main__
#
if __name__ == '__main__':
main()
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2,
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201,
198,
220,
220,
220,
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201,
198
] | 2.876476 | 1,101 |
if __name__ == "__main__":
print("The upper bound of P(A∪B) = P(A) + P(B)\n")
print("The lower bound of P(A∪B) = max(P(A), P(B))\n")
print("The upper bound of P(A∩B) = max(P(A), P(B))\n")
print("The lower bound of P(A∩B) = 0\n")
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] | 1.937984 | 129 |
import pytest
from pype import *
__author__ = "Mynti207"
__copyright__ = "Mynti207"
__license__ = "mit"
| [
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# -*- coding: utf-8 -*-
#
# Copyright (C) 2012 Tommy Winther
# http://tommy.winther.nu
#
# Modified for FTV Guide (09/2014 onwards)
# by Thomas Geppert [bluezed] - [email protected]
#
# This Program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2, or (at your option)
# any later version.
#
# This Program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this Program; see the file LICENSE.txt. If not, write to
# the Free Software Foundation, 675 Mass Ave, Cambridge, MA 02139, USA.
# http://www.gnu.org/copyleft/gpl.html
#
import xbmcaddon
ADDON = xbmcaddon.Addon(id = 'script.ftvguide')
NO_DESCRIPTION = 30000
CALCULATING_REMAINING_TIME = 30002
TIME_LEFT = 30003
BACKGROUND_UPDATE_IN_PROGRESS = 30004
NO_PROGRAM_AVAILABLE = 30009
NO_STREAM_AVAILABLE_TITLE = 30100
NO_STREAM_AVAILABLE_LINE1 = 30101
NO_STREAM_AVAILABLE_LINE2 = 30102
CLEAR_CACHE = 30104
CLEAR_NOTIFICATIONS = 30108
DONE = 30105
LOAD_ERROR_TITLE = 30150
LOAD_ERROR_LINE1 = 30151
LOAD_ERROR_LINE2 = 30152
CONFIGURATION_ERROR_LINE2 = 30153
SKIN_ERROR_LINE1 = 30154
SKIN_ERROR_LINE2 = 30155
SKIN_ERROR_LINE3 = 30156
NOTIFICATION_5_MINS = 30200
NOTIFICATION_NOW = 30201
WATCH_CHANNEL = 30300
REMIND_PROGRAM = 30301
DONT_REMIND_PROGRAM = 30302
CHOOSE_STRM_FILE = 30304
REMOVE_STRM_FILE = 30306
PREVIEW_STREAM = 30604
STOP_PREVIEW = 30607
WEEBTV_WEBTV_MISSING_1 = 30802
WEEBTV_WEBTV_MISSING_2 = 30803
WEEBTV_WEBTV_MISSING_3 = 30804
DATABASE_SCHEMA_ERROR_1 = 30157
DATABASE_SCHEMA_ERROR_2 = 30158
DATABASE_SCHEMA_ERROR_3 = 30159
FETCH_ERROR_TITLE = 31000
FETCH_ERROR_LINE1 = 31001
FETCH_ERROR_LINE2 = 31002
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62,
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62,
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17,
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513,
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] | 2.431579 | 855 |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : imports.py
@Time : 2020/05/07 23:59:19
@Author : Benjin Zhu
@Contact : [email protected]
@Last Modified by : Benjin Zhu
@Last Modified time : 2020/05/07 23:59:19
'''
import imp
def dynamic_import(config_name, config_path):
"""
Dynamic import a project.
Args:
config_name (str): module name
config_path (str): the dir that contains the .py with this module.
Examples::
>>> root = "/data/repos/cvpods_playground/zhubenjin/retinanet/"
>>> project = root + "retinanet.res50.fpn.coco.800size.1x.mrcnn_sigmoid"
>>> cfg = dynamic_import("config", project).config
>>> net = dynamic_import("net", project)
"""
fp, pth, desc = imp.find_module(config_name, [config_path])
return imp.load_module(config_name, fp, pth, desc)
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] | 2.213942 | 416 |
# Copyright (C) strawberryhacker
import os
import sys
import serial
import time
from citrus import citrus_packet
from citrus import citrus_file
from loading import loading_simple
from loading import loading_bar
# Used for loading a custom application to the CitrusOS and execute it
main()
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# -*- coding: utf-8 -*-
"""Unit test package for document_clipper."""
| [
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from intensity_normalization.normalize import fcm, gmm, kde, lsq, nyul, whitestripe, zscore
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import sys
import json
import argparse
from blocksec2go import open_pyscard, CardError
from blocksec2go import select_app, unlock_pin, set_pin
from blocksec2go.util import bytes_from_hex
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pkgname = "libdaemon"
pkgver = "0.14"
pkgrel = 0
build_style = "gnu_configure"
configure_args = ["--disable-lynx"]
hostmakedepends = ["pkgconf"]
pkgdesc = "Lightweight C library that eases the writing of UNIX daemons"
maintainer = "q66 <[email protected]>"
license = "LGPL-2.1-or-later"
url = "http://0pointer.de/lennart/projects/libdaemon"
source = f"{url}/{pkgname}-{pkgver}.tar.gz"
sha256 = "fd23eb5f6f986dcc7e708307355ba3289abe03cc381fc47a80bca4a50aa6b834"
@subpackage("libdaemon-devel")
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] | 2.34434 | 212 |
#===============================================================================
# pyQuASAR.py
#===============================================================================
# Imports ======================================================================
import funcgenom
import gzip
import itertools
import os
import os.path
import pipes
import subprocess
import tempfile
from Bio.bgzf import BgzfWriter
from pyQuASAR.env import DIR, SNPS_BED_PATH
# Constants ====================================================================
GENOTYPE_DICT = {0: ('0/0', '0/1'), 1: ('0/1', '0/0'), 2: ('1/1', '0/0')}
# Functions ====================================================================
def write_compressed_pileup(pileup_bytes: bytes, pileup_file_path: str):
"""Write a compressed QuASAR intermediate pileup to disk
Parameters
----------
pileup_bytes : bytes
Pileup file as a bytes object
pileup_file_path : str
File path to write data
"""
with gzip.open(pileup_file_path, 'wb') as f:
f.write(pileup_bytes)
def pileup_to_bed(
pileup_bytes,
snps_bed_path: str = SNPS_BED_PATH,
temp_file_dir=None
) -> str:
"""Convert a pileup to a BED file in memory
Parameters
----------
pileup_bytes : bytes
Pileup file as a bytes object
snps_bed_path : str
Path to BED file containing SNPs
temp_file_dir
directory for temporary files
Returns
-------
str
BED file
"""
with tempfile.NamedTemporaryFile(dir=temp_file_dir) as temp_file:
temp_file_name = temp_file.name
t = pipes.Template()
for cmd, kind in (
('less', '--'),
(
(
r"awk -v OFS='\t' "
"'{ "
"if ("
"$4>0 "
"&& $5 !~ /[^\^][<>]/ "
"&& $5 !~ /\+[0-9]+[ACGTNacgtn]+/ "
"&& $5 !~ /-[0-9]+[ACGTNacgtn]+/ "
"&& $5 !~ /[^\^]\*/"
") "
"print $1,$2-1,$2,$3,$4,$5,$6"
"}'"
),
'--'
),
('sortBed -i stdin', '--'),
(f'intersectBed -a stdin -b {snps_bed_path} -wo', '--'),
('cut -f 1-7,11-14', '--'),
):
t.append(cmd, kind)
f = t.open(temp_file_name, 'w')
f.write(pileup_bytes.decode())
f.close()
return open(temp_file_name).read()
def write_compressed_pileup_bed(
pileup_bytes,
pileup_bed_path,
snps_bed_path=None,
temp_file_dir=None
):
"""Write a compressed QuASAR intermediate pileup.bed file to disk
Parameters
----------
pileup_bytes : bytes
Pileup file as a bytes object
pileup_bed_path : str
File path to write data
snps_bed_path : str
Path to BED file containing SNPs
temp_file_dir
directory for temporary files
"""
with gzip.open(pileup_bed_path, 'wt') as f:
f.write(
pileup_to_bed(
pileup_bytes,
snps_bed_path=snps_bed_path,
temp_file_dir=temp_file_dir
)
)
def bed_to_quasar(bed_path):
"""Convert the pileup.bed file to quasar input format
Write the new file to disk next to the input file.
Parameters
----------
bed_path
Path to a pileup.bed.gz file
"""
working_dir = os.getcwd()
os.chdir(os.path.dirname(bed_path))
subprocess.call(
(
'Rscript', '--vanilla',
f'{DIR}/QuASAR/scripts/convertPileupToQuasar.R',
os.path.basename(bed_path)
),
stdout=subprocess.DEVNULL
)
os.chdir(working_dir)
def genotype(*input_file_paths):
"""Run quasar on formatted input files to obtain genotypes
Parameters
----------
input_file_paths
paths to input files
Returns
-------
bytes
Standard output from QuASAR
"""
with subprocess.Popen(
(
(
'Rscript',
os.path.join(os.path.dirname(__file__), 'QuASAR_genotype.R')
)
+ tuple(input_file_paths)
),
stdout=subprocess.PIPE
) as quasar_genotype:
return quasar_genotype.communicate()[0]
def generate_allele_dict_items(snps_bed_path: str):
"""Generate tuples defining an allele dict
Parameters
----------
snps_bed_path : str
Path to BED file containing SNPs
Yields
------
tuple
Variant ID and a pair of alleles
"""
with open(snps_bed_path, 'r') as f:
for line in f:
chr, _, pos, id, ref, alt, maf = line.split()
yield id, (ref, alt)
def genotype_to_vcf(
genotype_bytes: bytes,
sample_name: str = 'SAMPLE',
snps_bed_path: str = SNPS_BED_PATH,
threshold: float = 0.99,
het_only: bool = False,
temp_file_dir=None
):
"""Convert genotype probabilities from QuASAR to VCF format
Parameters
----------
genotype_bytes : bytes
Standard output from QuASAR as a bytes object
sample_name : str
Sample name for the VCF file
snps_bed_path : str
Path to BED file containing SNPs
threshold : float
Genotype probability threshold for variants to include
het_only : bool
Include only heterozygous variants if true
temp_file_dir
directory for temporary files
Yields
------
str
A line of a VCF file
"""
yield from (
'##fileformat=VCFv4.0',
'##reference=GRCh37',
'##INFO=<ID=BUILD,Number=1,Type=Integer,Description="Genome build">',
(
'##INFO='
'<ID=GP,Number=3,Type=Float,Description="Genotype probabilities">'
),
'##FORMAT=<ID=GT,Number=1,Type=String,Description=Genotype>',
'\t'.join(
(
'#CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO',
'FORMAT', sample_name, '{}_COMP'.format(sample_name)
)
)
)
allele_dict = dict(generate_allele_dict_items(snps_bed_path))
with tempfile.NamedTemporaryFile(dir=temp_file_dir) as temp_variants:
temp_variants.write(genotype_bytes)
with funcgenom.Genome() as genome:
genome.load_variants(
temp_variants.name,
add_header=('chr', '_', 'pos', 'id', 'g0', 'g1', 'g2')
)
genome.sort_variants()
for variant in genome.variants():
g0, g1, g2 = (float(g) for g in variant.tuple[-3:])
if (
any(g > threshold for g in (g0, g1, g2))
and
((not het_only) or (g1 > threshold))
):
ref, alt = allele_dict[variant.id]
genotype, complementary_genotype = GENOTYPE_DICT[
(g0, g1, g2).index(max(g0, g1, g2))
]
yield '\t'.join(
(
variant.chromosome,
str(variant.position),
variant.id,
ref,
alt,
'.',
'PASS',
'BUILD=37;GP={},{},{}'.format(g0, g1, g2),
'GT',
genotype,
complementary_genotype
)
)
def write_split_vcf(vcf, prefix: str):
"""Write VCF data split by chromosome
Parameters
----------
vcf
Iterator giving lines of VCF data
prefix : str
Output prefix for split VCF files
"""
header = []
for key, group in itertools.groupby(vcf, key=lambda l: l[:2]):
if key in {'##', '#C'}:
header.extend(list(group))
else:
with BgzfWriter(f'{prefix}.chr{key.rstrip()}.vcf.gz', 'wb') as f:
f.write(
'\n'
.join(itertools.chain(header, tuple(group), ('',)))
.encode()
)
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] | 1.93712 | 4,278 |
import re
import idna
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.views.decorators.http import require_http_methods
from helper.models import UserData, NotesData
EMAIL_REGEX = '^[a-z0-9-]+(\.[_a-z0-9-]+)*@[a-z0-9-]+(\.[a-z0-9-]+)*(\.[a-z]{2,4})$'
@csrf_exempt
@require_http_methods(['POST'])
@csrf_exempt
@require_http_methods(['POST'])
@require_http_methods(['GET'])
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] | 2.305263 | 190 |
import json
import datetime
import decimal
from math import sqrt
access_control = {'Access-Control-Allow-Origin': '*','Access-Control-Allow-Credentials': 'true'}
| [
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] | 3.326531 | 49 |
from os.path import abspath, join, dirname, realpath
from flask import Flask
from flask_cors import CORS
from cache import initialize_cache
from compress import initialize_compress
from controllers import initialize_routes
from flask_restful import Api
from mailer import initialize_mailer
from middleware.error_handling import errors
from server.logging import initialize_logs
from swagger import initialize_swagger
from config import Config
app: Flask = Flask(
__name__,
static_folder=abspath(join(dirname(dirname(realpath(__file__))), "static")),
static_url_path="/static",
)
app.config.from_object(Config)
if __name__ != "__main__":
initialize_logs(app)
cors: CORS = CORS(
app,
resources={r"/*": {"origins": ["*"]}},
)
api: Api = Api(app, errors=errors)
initialize_mailer(app)
initialize_cache(app)
initialize_compress(app)
initialize_swagger(app)
initialize_routes(api)
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] | 2.986799 | 303 |
# Generated by Django 3.0.5 on 2020-04-18 10:36
import datetime
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
| [
2,
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] | 3.089286 | 56 |
"""
Filename: table_manager.py
Purpose: A program to define and create all tables needed in the database
Authors: Jordan Smith
Group: Wholesome as Heck Programmers (WaHP)
Last modified: 11/13/21
"""
from db_manager import db_mgr
DROP_ALL = True
tables = {}
###
# TABLES TO BE ADDED
###
tables['fitnessGoal'] = {
'fitness_id': 'int NOT NULL AUTO_INCREMENT',
'name': 'varchar(16) NOT NULL',
'constraints': {
'PRIMARY KEY': 'fitness_id'
}
}
tables['users'] = {
'user_id': 'int NOT NULL AUTO_INCREMENT',
'email': 'varchar(64) NOT NULL',
'username': 'varchar(64) NOT NULL',
'password': 'varchar(255) NOT NULL',
'created_at': 'timestamp DEFAULT CURRENT_TIMESTAMP',
'last_logged_in': 'timestamp DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP',
'login_streak': 'int DEFAULT 1',
'height': 'float',
'weight': 'float',
'fitness_goal_id': 'int',
'has_finished_quiz': 'boolean DEFAULT false',
'wants_emails': 'boolean',
'show_tips': 'boolean DEFAULT true',
'experience': 'varchar(16)',
'daysPerWeek': 'int',
'availableEquipment': 'varchar(255)',
'constraints': {
'UNIQUE': 'email',
'UNIQUE': 'username',
'PRIMARY KEY': 'user_id',
'FOREIGN KEY': ['fitness_goal_id', 'fitnessGoal(fitness_id)']
}
}
tables['monsters'] = {
'monster_id': 'int NOT NULL AUTO_INCREMENT',
'user_id': 'int NOT NULL',
'name': 'varchar(64)',
'species': 'varchar(64) NOT NULL',
'exp': 'int NOT NULL DEFAULT 0',
'level': 'int NOT NULL DEFAULT 1',
'image_name': 'varchar(32)',
'constraints': {
'PRIMARY KEY': 'monster_id',
'FOREIGN KEY': ['user_id', 'users(user_id)']
}
}
tables['workouts'] = {
'workout_id': 'int NOT NULL AUTO_INCREMENT',
'type': 'varchar(16) NOT NULL',
'name': 'varchar(32) NOT NULL',
'equipment': 'varchar(64) NOT NULL',
'difficulty': 'varchar(16) NOT NULL',
'is_priority': 'bool NOT NULL',
'constraints': {
'PRIMARY KEY': 'workout_id'
}
}
tables['workoutConnection'] = {
'workout_id': 'int NOT NULL',
'fitness_goal_id': 'int NOT NULL',
'constraints': {
'FOREIGN KEY': ['workout_id', 'workouts(workout_id)'],
'FOREIGN KEY': ['fitness_goal_id', 'fitnessGoal(fitness_id)']
}
}
tables['workoutTips'] = {
'tip_id': 'int NOT NULL AUTO_INCREMENT',
'tip_str': 'varchar(255) NOT NULL',
'workout_id': 'int NOT NULL',
'constraints': {
'PRIMARY KEY': 'tip_id',
'FOREIGN KEY': ['workout_id', 'workouts(workout_id)']
}
}
tables['workoutLogs'] = {
'log_id': 'int NOT NULL AUTO_INCREMENT',
'user_id': 'int NOT NULL',
'workout_ids': 'varchar(255) NOT NULL',
'time_created': 'timestamp DEFAULT CURRENT_TIMESTAMP',
'user_has_completed': 'bool NOT NULL DEFAULT false',
'reps': 'int',
'sets': 'int',
'weight': 'float',
'time_duration': 'float',
'distance_duration': 'float',
'details': 'varchar(255) NOT NULL',
'user_enjoyment': 'int NOT NULL',
'constraints': {
'PRIMARY KEY': 'log_id',
'FOREIGN KEY': ['user_id', 'users(user_id)']
# 'FOREIGN KEY': ['workout_type_id', 'workouts(workout_id)']
}
}
tables['userRelationship'] = {
'user_first_id': 'int NOT NULL',
'user_second_id': 'int NOT NULL',
'relationship_type': 'varchar(64) NOT NULL',
'constraints': {
'FOREIGN KEY': ['user_first_id', 'users(user_id)'],
'FOREIGN KEY': ['user_second_id', 'users(user_id)'],
}
}
tables['userPRs'] = {
'user_id': 'int NOT NULL',
'workout_id': 'int NOT NULL',
'weight': 'float',
'time': 'float',
'distance': 'float',
'constraints': {
'FOREIGN KEY': ['user_id', 'users(user_id)'],
'FOREIGN KEY': ['workout_id', 'workouts(workout_id)']
}
}
# Drop all of the tables for debugging and configuration
if DROP_ALL:
db_mgr.drop_tables(list(tables.keys())[::-1])
# Add the tables into the database
for table_name, table_description in tables.items():
db_mgr.create_table(table_name, table_description)
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] | 2.272225 | 1,811 |
from backend.app import app | [
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1330,
598
] | 4.5 | 6 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.10 on 2018-10-09 14:26
from __future__ import unicode_literals
from django.db import migrations
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#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
import os
import zipfile
import pandas as pd
from PIL import Image
import tensorflow as tf
from tensorflow.python.keras import layers
from tensorflow.python.keras import losses
from tensorflow.python.keras import models
from tensorflow.python.keras import backend as K
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
from bigdl.orca import init_orca_context, stop_orca_context
from bigdl.orca.data import XShards
from bigdl.orca.learn.tf.estimator import Estimator
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cluster_mode', type=str, default="local",
help='The mode for the Spark cluster. local, yarn or spark-submit.')
parser.add_argument('--file_path', type=str, default="/tmp/carvana/",
help="The path to carvana train.zip, train_mask.zip and train_mask.csv.zip")
parser.add_argument('--epochs', type=int, default=8,
help="The number of epochs to train the model")
parser.add_argument('--batch_size', type=int, default=8,
help="Batch size for training and prediction")
parser.add_argument('--platform', type=str, default="linux",
help="The platform you used. Only linux and mac are supported.")
parser.add_argument('--non_interactive', default=False, action="store_true",
help="Flag to not visualize the result.")
args = parser.parse_args()
main(args.cluster_mode, args.epochs, args.file_path, args.batch_size, args.platform,
args.non_interactive)
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"""
Algorithms for ode propagation with adaptive step selection
using explicit embedded runge-kutta method (rk_step):
- select_initial_step: select size of first step
- rk_variable_step: make an adaptive rk step according to tolerance
- rk_prop: integrate ode from s0, t0 to t (Cauchy's problem)
- event_detector: root separation for given event function
- prop_event: calculate event function from s0, t0 to t
- calc_root_brentq: root calculation procedure for events time location
- accurate_events: calculate state and time of given events
- rk_prop_ev: integrate ode from s0, t0 up to terminal events or time t (boundary problem)
Parts of code was used (and rewrited) from scipy.integrate.solve_ivp:
https://github.com/scipy/scipy/tree/v1.7.0/scipy/integrate/_ivp
and scipy.optimize.brentq:
https://github.com/scipy/scipy/blob/v1.7.0/scipy/optimize/zeros.py
"""
import numpy as np
# minimum feasible tolerance
EPS = np.finfo(float).eps
# rk_variable_step constants
SAFETY = 0.9 # Multiply steps computed from asymptotic behaviour of errors by this
MIN_FACTOR = 0.2 # Minimum allowed decrease in a step size
MAX_FACTOR = 10 # Maximum allowed increase in a step size
# rk_prop constants
N_STEPS = 2 ** 13 # 8192, standard array size for rk steps
N_STEPS_MUL = 2 # Array size multiplier when array is filled up
N_STEPS_MAX = 2 ** 24 # 16777216, maximum allowed steps count
# rk_prop_ev constants
N_EVENTS = 2 ** 10 # 1024, standard events count
N_EVENTS_MUL = 2 # Array size multiplier when array is filled up
N_EVENTS_MAX = 2 ** 24 # 16777216, maximum allowed events count
def rk_variable_step(fode, t, s, h_abs, direction, t_bound, max_step, atol, rtol, out, *fargs):
"""
Make one RK step of h_abs size in given direction and calculate size of the next step.
:param fode: right part of ODE system, should be @cfunc
:param s: current state
:param t: current time
:param h_abs: absolute value of current step
:param direction: step direction (+1/-1)
:param t_bound: boundary time
:param max_step: maximum allowed step size
:param atol: absolute tolerance
:param rtol: relative tolerance
:param out: (writable) array for time and state after successful step
:param fargs: fode additional arguments
:return: assumption for size of next step
"""
min_step = 10 * np.abs(np.nextafter(t, direction * np.inf) - t)
error_exponent = -1 / (RK_ORDER[1] + 1)
if h_abs > max_step:
h_abs = max_step
elif h_abs < min_step:
h_abs = min_step
step_accepted = False
step_rejected = False
while not step_accepted:
if h_abs < min_step:
raise ValueError('Step size becomes too small')
h = h_abs * direction
t_new = t + h
if direction * (t_new - t_bound) > 0: # in case of last step
t_new = t_bound
h = t_new - t
h_abs = abs(h)
sarr = rk_step(fode, t, s, h, *fargs)
s_new = sarr[:, 0]
s_err = sarr[:, 1]
scale = atol + np.maximum(np.abs(s), np.abs(s_new)) * rtol
error_norm = rms_norm(s_err / scale) # _estimate_error_norm(self.K, h, scale)
if error_norm < 1:
if error_norm == 0:
factor = MAX_FACTOR
else:
factor = min(MAX_FACTOR, SAFETY * error_norm ** error_exponent)
if step_rejected:
factor = min(1, factor)
h_abs *= factor
step_accepted = True
else:
h_abs *= max(MIN_FACTOR, SAFETY * error_norm ** error_exponent)
step_rejected = True
out[0] = t_new
out[1:] = s_new
return h_abs
def select_initial_step(fode, t0, s0, direction, rtol, atol, *fargs):
"""
Select good initial step size.
See scipy.integrate.solve_ivp for details.
:param fode: right part of ODE system, should be @cfunc
:param t0: initial time
:param s0: initial state
:param direction: step direction (+1/-1)
:param atol: absolute tolerance
:param rtol: relative tolerance
:param fargs: fode additional arguments
:return:
"""
if s0.size == 0:
return np.inf
f0 = fode(t0, s0, *fargs)
scale = atol + np.abs(s0) * rtol
d0 = rms_norm(s0 / scale)
d1 = rms_norm(f0 / scale)
if d0 < 1e-5 or d1 < 1e-5:
h0 = 1e-6
else:
h0 = 0.01 * d0 / d1
s1 = s0 + h0 * direction * f0
f1 = fode(t0 + h0 * direction, s1, *fargs)
d2 = rms_norm((f1 - f0) / scale) / h0
if d1 <= 1e-15 and d2 <= 1e-15:
h1 = max(1e-6, h0 * 1e-3)
else:
h1 = (0.01 / max(d1, d2)) ** (1 / (RK_ORDER[1] + 1))
return min(100 * h0, h1)
def rk_prop(fode, s0, t0, t, max_step, rtol, atol, *fargs):
"""
Integrate ODE from t0, s0 to t.
See scipy.integrate.solve_ivp for details.
:param fode: right part of ODE system, should be @cfunc
:param s0: initial state
:param t0: initial time
:param t: boundary time
:param max_step: maximum allowed step size
:param rtol: relative tolerance
:param atol: absolute tolerance
:param fargs: fode additional arguments
:return: array [[time, state]] for each integrator step
"""
direction = 1. if t >= t0 else -1.
h_abs = select_initial_step(fode, t0, s0, direction, rtol, atol, *fargs)
# h_abs = abs(first_step)
n_steps = N_STEPS
trajectory = np.empty((n_steps, s0.size + 1), dtype=s0.dtype)
trajectory[0, 0] = t0
trajectory[0, 1:] = s0
i = 0
while abs(trajectory[i, 0] - t) > EPS:
h_abs = rk_variable_step(fode, trajectory[i, 0], trajectory[i, 1:],
h_abs, direction, t, max_step, rtol, atol,
trajectory[i + 1], *fargs)
i += 1
if i >= n_steps - 1:
# resize array
n_steps *= N_STEPS_MUL
if n_steps > N_STEPS_MAX:
raise RuntimeError('Maximum allowed steps count exceeded')
tmp = trajectory
trajectory = _resize_2darray_axis0(trajectory, n_steps)
return trajectory[:i + 1]
def event_detector(call_event, t, s, evvals, it, counters, values, terminals, directions, counts, evout, n_evout):
"""
Separate roots of event functions, i.e. time moments when events triggered.
:param call_event: call_event function
:param t: current time
:param s: current state
:param evvals: array with event function values at previous times
:param it: number of current iteration (index of state in trajectory)
:param counters: current event counters
:param values: array of values for event functions
:param terminals: array of bools (whether event is terminal)
:param directions: array of direction for events
:param counts: array of
:param evout: (writable) array [[event_index, event_counter, trajectory_index]]
:param n_evout: (writable) array of one int (size of evout)
:return:
"""
terminal = False
n_events = evvals.shape[1]
for i in range(n_events):
evvals[it, i] = call_event(t, s, values, i)
if it == 0:
counters[...] = counts
return 0
for i in range(n_events):
cur_val = evvals[it][i]
prev_val = evvals[it - 1][i]
f1 = (prev_val < 0) and (cur_val > 0) and ((directions[i] == 1) or (directions[i] == 0))
f2 = (prev_val > 0) and (cur_val < 0) and ((directions[i] == -1) or (directions[i] == 0))
if (f1 or f2) and ((counters[i] == -1) or (counters[i] > 0)):
if counters[i] > 0:
counters[i] -= 1
cnt = -1 if counters[i] == -1 else counts[i] - counters[i]
# event index, event trigger counter, index of state before event
evout[n_evout[0]] = np.array([i, cnt, it - 1])
n_evout[0] += 1
if terminals[i] and ((counters[i] == -1) or (counters[i] == 0)):
terminal = True
if terminal:
return -1
return 0
def prop_event(call_event, values, idx,
_fode, _s0, _t0, _t, _max_step, _rtol, _atol, *fargs):
"""
Propagate ODE state up to t and calculate value of [idx] event function
:param call_event: call_event function
:param values: events values
:param idx: event index
:param _rest: rk_prop arguments
:param fargs: _fode additional arguments
:return:
"""
trj = rk_prop(_fode, _s0, _t0, _t, _max_step, _rtol, _atol, *fargs)
return call_event(trj[-1, 0], trj[-1, 1:], values, idx)
def calc_root_brentq(xa, xb, xtol, rtol, maxiter,
_fode, _s0, _max_step, _rtol, _atol,
__call_event, __values, __idx, *fargs):
"""
Brent's root finding algorithm for prop_event(t) function.
See scipy.optimize.brentq for details.
:param xa: left side of segment
:param xb: right side of segment
:param xtol: absolute tolerance by function argument
:param rtol: relative tolerance
:param maxiter: maximum number of iterations
:param rest: prop_event arguments
:return: <x>, where f(<x>) = 0 with specified tolerance
"""
xpre, xcur = xa, xb
xblk, fblk = 0., 0.
spre, scur = 0., 0.
fpre = prop_event(__call_event, __values, __idx, _fode, _s0, xa, xpre, _max_step, _rtol, _atol, *fargs)
fcur = prop_event(__call_event, __values, __idx, _fode, _s0, xa, xcur, _max_step, _rtol, _atol, *fargs)
if fpre * fcur > 0:
raise ValueError('The event function has the same signs at both ends of the segment')
if fpre == 0:
return xpre
if fcur == 0:
return xcur
for i in range(maxiter):
if fpre * fcur < 0:
xblk = xpre
fblk = fpre
spre = scur = xcur - xpre
if abs(fblk) < abs(fcur):
xpre = xcur
xcur = xblk
xblk = xpre
fpre = fcur
fcur = fblk
fblk = fpre
delta = (xtol + rtol * abs(xcur)) / 2
sbis = (xblk - xcur) / 2
if fcur == 0 or abs(sbis) < delta:
return xcur
if abs(spre) > delta and abs(fcur) < abs(fpre):
if xpre == xblk:
stry = -fcur * (xcur - xpre) / (fcur - fpre)
else:
dpre = (fpre - fcur) / (xpre - xcur)
dblk = (fblk - fcur) / (xblk - xcur)
stry = -fcur * (fblk * dblk - fpre * dpre) / (dblk * dpre * (fblk - fpre))
if 2 * abs(stry) < min(abs(spre), 3 * abs(sbis) - delta):
spre = scur
scur = stry
else:
spre = sbis
scur = sbis
else:
spre = sbis
scur = sbis
xpre = xcur
fpre = fcur
if abs(scur) > delta:
xcur += scur
else:
xcur += delta if sbis > 0 else -delta
fcur = prop_event(__call_event, __values, __idx, _fode, _s0, xa, xcur, _max_step, _rtol, _atol, *fargs)
raise ValueError('Convergence error')
def accurate_events(trajectory, evout, accurates,
_call_event, _values, _xtol, _rtol, _maxiter,
__fode, __max_step, __rtol, __atol, *fargs):
"""
Calculate event time and state where it needs to with given tolerance.
:param trajectory: calculated trajectory (array of time-states)
:param evout: array of [event_index, event_counter, trajectory_index]
:param accurates: array of bools (True if event should be calculated accurate)
:param _rest: calc_root_brentq, rk_prop arguments
:param fargs: _fode additional arguments
:return: event-states-array [[event_index, event_counter, time, state]]
"""
# evout [[event_index, event_counter, trajectory_index]]
n = evout.shape[0]
res = np.empty((n, trajectory.shape[1] + 2), dtype=trajectory.dtype)
for i in range(n):
ei = evout[i, 0]
ti = evout[i, 2]
t0 = trajectory[ti, 0]
t1 = trajectory[ti + 1, 0]
s0 = trajectory[ti, 1:]
res[i, :2] = evout[i, :2]
if accurates[ei]:
t = calc_root_brentq(t0, t1, _xtol, _rtol, _maxiter,
__fode, s0, __max_step, __rtol, __atol,
_call_event, _values, ei, *fargs)
res[i, 2:] = rk_prop(__fode, s0, t0, t, __max_step, __rtol, __atol, *fargs)[-1]
else:
res[i, 2:] = trajectory[ti + 1]
return res
def rk_prop_ev(fode, s0, t0, t, max_step, rtol, atol,
_values, _terminals, _directions, _counts, _accurates,
__call_event, __xtol, __rtol, __maxiter, *fargs):
"""
Integrate ODE from t0, s0 up to any terminal event or time t.
:param fode: right part of ODE system, should be @cfunc
:param s0: initial state
:param t0: initial time
:param t: end time
:param max_step: maximum step size
:param rtol: relative tolerance
:param atol: absolute tolerance
:param _rest: event_detector, rk_variable_step arguments
:param fargs: fode additional arguments
:return: trajectory-array, event-states-array
"""
direction = 1. if t >= t0 else -1.
h_abs = select_initial_step(fode, t0, s0, direction, rtol, atol, *fargs)
n_steps = N_STEPS
trajectory = np.empty((n_steps, s0.size + 1), dtype=s0.dtype)
trajectory[0, 0] = t0
trajectory[0, 1:] = s0
n_events = N_EVENTS
ev_n = _terminals.size
evvals = np.empty((n_steps, ev_n), dtype=s0.dtype)
counters = np.empty(ev_n, dtype=np.int32)
evout = np.empty((n_events, 3), dtype=np.int32)
n_evout = np.zeros(1, dtype=np.int32)
# first call
event_detector(__call_event, t, s0, evvals, 0, counters,
_values, _terminals, _directions, _counts,
evout, n_evout)
i = 0
while abs(trajectory[i, 0] - t) > EPS:
h_abs = rk_variable_step(fode, trajectory[i, 0], trajectory[i, 1:],
h_abs, direction, t, max_step, rtol, atol,
trajectory[i + 1], *fargs)
i += 1
if i >= n_steps - 1:
n_steps *= N_STEPS_MUL
if n_steps > N_STEPS_MAX:
raise RuntimeError('Maximum allowed steps count exceeded')
tmp = trajectory
trajectory = _resize_2darray_axis0(trajectory, n_steps)
evvals = _resize_2darray_axis0(evvals, n_steps)
trm = event_detector(__call_event, trajectory[i, 0], trajectory[i, 1:], evvals, i,
counters, _values, _terminals, _directions, _counts,
evout, n_evout)
if n_evout[0] >= n_events - 1:
n_events *= N_EVENTS_MUL
if n_steps > N_EVENTS_MAX:
raise RuntimeError('Maximum allowed count of event records exceeded')
tmp = evout
evout = _resize_2darray_axis0(evout, n_events)
if trm != 0:
break
event_out = accurate_events(trajectory, evout[:n_evout[0]], _accurates,
__call_event, _values, __xtol, __rtol, __maxiter,
fode, max_step, rtol, atol, *fargs)
return trajectory[:i + 1], event_out
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] | 2.139975 | 7,137 |
# Copyright 2022 The Pigweed Authors
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
"""WORK IN PROGRESS!
This is intended to be a replacement for the proto codegen in proto.bzl, which
relies on the transitive proto compilation support removed from newer versions
of rules_proto_grpc. However, the version checked in here does not yet support,
1. Proto libraries with dependencies in external repositories.
2. Proto libraries with strip_import_prefix or import_prefix attributes.
In addition, nanopb proto files are not yet generated.
TODO(pwbug/621): Close these gaps and start using this implementation.
# Overview of implementation
(If you just want to use pw_proto_library, see its docstring; this section is
intended to orient future maintainers.)
Proto code generation is carried out by the _pw_proto_library,
_pw_raw_rpc_proto_library and _pw_nanopb_rpc_proto_library rules using aspects
(https://docs.bazel.build/versions/main/skylark/aspects.html). A
_pw_proto_library has a single proto_library as a dependency, but that
proto_library may depend on other proto_library targets; as a result, the
generated .pwpb.h file #include's .pwpb.h files generated from the dependency
proto_libraries. The aspect propagates along the proto_library dependency
graph, running the proto compiler on each proto_library in the original
target's transitive dependencies, ensuring that we're not missing any .pwpb.h
files at C++ compile time.
Although we have a separate rule for each protocol compiler plugin
(_pw_proto_library, _pw_raw_rpc_proto_library, _pw_nanopb_rpc_proto_library),
they actually share an implementation (_pw _impl_pw_proto_library) and use
similar aspects, all generated by _proto_compiler_aspect. The only difference
between the rules are captured in the PIGWEED_PLUGIN dictonary and the aspect
instantiations (_pw_proto_compiler_aspect, etc).
"""
load("//pw_build:pigweed.bzl", "pw_cc_library")
load("@rules_proto//proto:defs.bzl", "ProtoInfo")
load("//pw_protobuf_compiler:pw_nanopb_cc_library", "pw_nanopb_cc_library")
def pw_proto_library(
name = "",
deps = [],
nanopb_options = None,
enabled_targets = None):
"""Generate Pigweed proto C++ code.
This is the only public symbol in this file: everything else is
implementation details.
Args:
name: The name of the target.
deps: proto_library targets from which to generate Pigweed C++.
nanopb_options: path to file containing nanopb options, if any
(https://jpa.kapsi.fi/nanopb/docs/reference.html#proto-file-options).
enabled_targets: Specifies which libraries should be generated. Libraries
will only be generated as needed, but unnecessary outputs may conflict
with other build rules and thus cause build failures. This filter allows
manual selection of which libraries should be supported by this build
target in order to prevent such conflicts. The argument, if provided,
should be a subset of ["pwpb", "nanopb", "raw_rpc", "nanopb_rpc"]. All
are enabled by default. Note that "nanopb_rpc" relies on "nanopb".
Example usage:
proto_library(
name = "benchmark_proto",
srcs = [
"benchmark.proto",
],
)
pw_proto_library(
name = "benchmark_pw_proto",
deps = [":benchmark_proto"],
)
pw_cc_binary(
name = "proto_user",
srcs = ["proto_user.cc"],
deps = [":benchmark_pw_proto.pwpb"],
)
The pw_proto_library generates the following targets in this example:
"benchmark_pw_proto.pwpb": C++ library exposing the "benchmark.pwpb.h" header.
"benchmark_pw_proto.pwpb_rpc": C++ library exposing the
"benchmark.rpc.pwpb.h" header.
"benchmark_pw_proto.raw_rpc": C++ library exposing the "benchmark.raw_rpc.h"
header.
"benchmark_pw_proto.nanopb": C++ library exposing the "benchmark.pb.h"
header.
"benchmark_pw_proto.nanopb_rpc": C++ library exposing the
"benchmark.rpc.pb.h" header.
"""
if is_plugin_enabled("nanobp"):
# Use nanopb to generate the pb.h and pb.c files, and the target
# exposing them.
pw_nanopb_cc_library(name + ".nanopb", deps, options = nanopb_options)
# Use Pigweed proto plugins to generate the remaining files and targets.
for plugin_name, info in PIGWEED_PLUGIN.items():
if not is_plugin_enabled(plugin_name):
continue
name_pb = name + "_pb." + plugin_name
plugin_rule = info["compiler"]
plugin_rule(
name = name_pb,
deps = deps,
)
# The rpc.pb.h header depends on the generated nanopb or pwpb code.
if info["include_nanopb_dep"]:
lib_deps = info["deps"] + [":" + name + ".nanopb"]
elif info["include_pwpb_dep"]:
lib_deps = info["deps"] + [":" + name + ".pwpb"]
else:
lib_deps = info["deps"]
pw_cc_library(
name = name + "." + plugin_name,
hdrs = [name_pb],
deps = lib_deps,
linkstatic = 1,
)
PwProtoInfo = provider(
"Returned by PW proto compilation aspect",
fields = {
"genfiles": "generated C++ header files",
},
)
def _proto_compiler_aspect(extension, protoc_plugin):
"""Returns an aspect that runs the proto compiler.
The aspect propagates through the deps of proto_library targets, running
the proto compiler with the specified plugin for each of their source
files. The proto compiler is assumed to produce one output file per input
.proto file. That file is placed under bazel-bin at the same path as the
input file, but with the specified extension (i.e., with _extension =
.pwpb.h, the aspect converts pw_log/log.proto into
bazel-bin/pw_log/log.pwpb.h).
The aspect returns a provider exposing all the File objects generated from
the dependency graph.
"""
return aspect(
attr_aspects = ["deps"],
attrs = {
"_extension": attr.string(default = extension),
"_protoc": attr.label(
default = Label("@com_google_protobuf//:protoc"),
executable = True,
cfg = "exec",
),
"_protoc_plugin": attr.label(
default = Label(protoc_plugin),
executable = True,
cfg = "exec",
),
},
implementation = _proto_compiler_aspect_impl,
provides = [PwProtoInfo],
)
def _impl_pw_proto_library(ctx):
"""Implementation of the proto codegen rule.
The work of actually generating the code is done by the aspect, so here we
just gather up all the generated files and return them.
"""
# Note that we don't distinguish between the files generated from the
# target, and the files generated from its dependencies. We return all of
# them together, and in pw_proto_library expose all of them as hdrs.
# Pigweed's plugins happen to only generate .h files, so this works, but
# strictly speaking we should expose only the files generated from the
# target itself in hdrs, and place the headers generated from dependencies
# in srcs. We don't perform layering_check in Pigweed, so this is not a big
# deal.
#
# TODO(pwbug/621): Tidy this up.
all_genfiles = []
for dep in ctx.attr.deps:
for f in dep[PwProtoInfo].genfiles:
all_genfiles.append(f)
return [DefaultInfo(files = depset(all_genfiles))]
# Instantiate the aspects and rules for generating code using specific plugins.
_pw_proto_compiler_aspect = _proto_compiler_aspect("pwpb.h", "//pw_protobuf/py:plugin")
_pw_proto_library = rule(
implementation = _impl_pw_proto_library,
attrs = {
"deps": attr.label_list(
providers = [ProtoInfo],
aspects = [_pw_proto_compiler_aspect],
),
},
)
_pw_pwpb_rpc_proto_compiler_aspect = _proto_compiler_aspect("rpc.pwpb.h", "//pw_rpc/py:plugin_pwpb")
_pw_pwpb_rpc_proto_library = rule(
implementation = _impl_pw_proto_library,
attrs = {
"deps": attr.label_list(
providers = [ProtoInfo],
aspects = [_pw_pwpb_rpc_proto_compiler_aspect],
),
},
)
_pw_raw_rpc_proto_compiler_aspect = _proto_compiler_aspect("raw_rpc.pb.h", "//pw_rpc/py:plugin_raw")
_pw_raw_rpc_proto_library = rule(
implementation = _impl_pw_proto_library,
attrs = {
"deps": attr.label_list(
providers = [ProtoInfo],
aspects = [_pw_raw_rpc_proto_compiler_aspect],
),
},
)
_pw_nanopb_rpc_proto_compiler_aspect = _proto_compiler_aspect("rpc.pb.h", "//pw_rpc/py:plugin_nanopb")
_pw_nanopb_rpc_proto_library = rule(
implementation = _impl_pw_proto_library,
attrs = {
"deps": attr.label_list(
providers = [ProtoInfo],
aspects = [_pw_nanopb_rpc_proto_compiler_aspect],
),
},
)
PIGWEED_PLUGIN = {
"pwpb": {
"compiler": _pw_proto_library,
"deps": [
"//pw_assert:facade",
"//pw_containers:vector",
"//pw_preprocessor",
"//pw_protobuf",
"//pw_result",
"//pw_span",
"//pw_status",
],
"include_nanopb_dep": False,
"include_pwpb_dep": False,
},
"pwpb_rpc": {
"compiler": _pw_pwpb_rpc_proto_library,
"deps": [
"//pw_protobuf:pw_protobuf",
"//pw_rpc",
"//pw_rpc/pwpb:client_api",
"//pw_rpc/pwpb:server_api",
],
"include_nanopb_dep": False,
"include_pwpb_dep": True,
},
"raw_rpc": {
"compiler": _pw_raw_rpc_proto_library,
"deps": [
"//pw_rpc",
"//pw_rpc/raw:client_api",
"//pw_rpc/raw:server_api",
],
"include_nanopb_dep": False,
"include_pwpb_dep": False,
},
"nanopb_rpc": {
"compiler": _pw_nanopb_rpc_proto_library,
"deps": [
"//pw_rpc",
"//pw_rpc/nanopb:client_api",
"//pw_rpc/nanopb:server_api",
],
"include_nanopb_dep": True,
"include_pwpb_dep": False,
},
}
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] | 2.341363 | 4,608 |
import hashlib
import json
import logging
import os
from aquarium.provenance import (FileEntity, FileTypes,
JobActivity, OperationActivity,
ProvenanceTrace)
from typing import List, Union
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import math
import numpy as np
from metadrive.utils.math_utils import norm
class InterpolatingLine:
"""
This class provides point set with interpolating function
"""
@staticmethod
@staticmethod
@staticmethod
@staticmethod
def get_point(self, longitudinal, lateral=None):
"""
Get point on this line by interpolating
"""
accumulate_len = 0
for seg in self.segment_property:
accumulate_len += seg["length"]
if accumulate_len + 0.1 >= longitudinal:
break
if lateral is not None:
return (seg["start_point"] + (longitudinal - accumulate_len + seg["length"]) *
seg["direction"]) + lateral * seg["lateral_direction"]
else:
return seg["start_point"] + (longitudinal - accumulate_len + seg["length"]) * seg["direction"]
def get_heading_theta(self, longitudinal: float) -> float:
"""
In rad
"""
accumulate_len = 0
for seg in self.segment_property:
accumulate_len += seg["length"]
if accumulate_len > longitudinal:
return seg["heading"]
return seg["heading"]
def segment(self, longitudinal: float):
"""
Return the segment piece on this lane of current position
"""
accumulate_len = 0
for index, seg in enumerate(self.segment_property):
accumulate_len += seg["length"]
if accumulate_len + 0.1 >= longitudinal:
return self.segment_property[index]
return self.segment_property[index]
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] | 2.348637 | 697 |
import pytest
# pylint: disable=unused-import
from .context import Storage, Asset, Order
from .fixtures import (
ASSET_ID,
ORDER_ID,
WORKSPACE_ID,
auth_mock,
auth_live,
storage_mock,
storage_live,
JSON_ASSET,
JSON_ORDER,
)
def test_paginate_with_limit_smaller_page_size(storage_mock, requests_mock):
"""
Test pagination with limit <= pagination size.
"""
url = "http://some_url/assets"
limit = 5
size = limit
total_pages = 2
total_elements = 12
expected = 5
requests_mock.get(
url + f"&size={limit}",
json=_mock_one_page_reponse(0, size, total_pages, total_elements),
)
requests_mock.get(
url + f"&size={limit}&page=1",
json=_mock_one_page_reponse(0, size, total_pages, total_elements),
)
res = storage_mock._query_paginated(url=url, limit=limit, size=size)
assert len(res) == expected
@pytest.mark.live
def test_get_assets_live(storage_live):
"""
SDK test account holds too few results to query multiple pages via pagination,
needs to be mocked.
"""
assets = storage_live.get_assets()
assert len(assets) >= 2
dates = [asset.info["createdAt"] for asset in assets]
# default descending, newest to oldest.
descending_dates = sorted(dates)[::-1]
assert descending_dates == dates
def test_get_assets_pagination(auth_mock, requests_mock):
"""
Mock result holds 2 pages, each with 50 results.
"""
json_assets_paginated = {
"data": {
"content": [JSON_ASSET["data"]] * 50,
"pageable": {
"sort": {"sorted": True, "unsorted": False, "empty": False},
"pageNumber": 0,
"pageSize": 50,
"offset": 0,
"paged": True,
"unpaged": False,
},
"totalPages": 2,
"totalElements": 100,
"last": True,
"sort": {"sorted": True, "unsorted": False, "empty": False},
"numberOfElements": 100,
"first": True,
"size": 50,
"number": 0,
"empty": False,
},
"error": None,
}
# assets pages
url_storage_assets_paginated = (
f"{auth_mock._endpoint()}/workspaces/{auth_mock.workspace_id}/"
f"assets?format=paginated&sort=createdAt,asc&size=50"
)
requests_mock.get(url=url_storage_assets_paginated, json=json_assets_paginated)
storage = Storage(auth=auth_mock)
assets = storage.get_assets(limit=74, sortby="createdAt", descending=False)
assert len(assets) == 74
assert isinstance(assets[0], Asset)
assert assets[0].asset_id == ASSET_ID
@pytest.mark.live
def test_get_orders_live(storage_live):
"""
SDK test account holds too few results to query multiple pages via pagination,
needs to be mocked.
"""
orders = storage_live.get_orders()
assert len(orders) >= 1
dates = [order.info["createdAt"] for order in orders]
# default descending, newest to oldest.
descending_dates = sorted(dates)[::-1]
assert descending_dates == dates
def test_get_orders_pagination(auth_mock, requests_mock):
"""
Mock result holds 2 pages, each with 50 results.
"""
json_orders_paginated = {
"data": {
"content": [JSON_ORDER["data"]] * 50,
"pageable": {
"sort": {"sorted": True, "unsorted": False, "empty": False},
"pageNumber": 0,
"pageSize": 50,
"offset": 0,
"paged": True,
"unpaged": False,
},
"totalPages": 2,
"totalElements": 100,
"last": True,
"sort": {"sorted": True, "unsorted": False, "empty": False},
"numberOfElements": 100,
"first": True,
"size": 50,
"number": 0,
"empty": False,
},
"error": None,
}
# assets pages
url_storage_orders_paginated = (
f"{auth_mock._endpoint()}/workspaces/{auth_mock.workspace_id}/"
f"orders?format=paginated&sort=createdAt,asc&size=50"
)
requests_mock.get(url=url_storage_orders_paginated, json=json_orders_paginated)
storage = Storage(auth=auth_mock)
orders = storage.get_orders(limit=74, sortby="createdAt", descending=False)
assert len(orders) == 74
assert isinstance(orders[0], Order)
assert orders[0].order_id == ORDER_ID
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] | 2.181026 | 2,066 |
# -*- coding: utf-8 -*-
# Copyright: (c) 2016, Matt Davis, <[email protected]>
# Copyright: (c) 2016, Chris Houseknecht, <[email protected]>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
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] | 2.484211 | 95 |
import numpy.testing
import cupy
# NumPy-like assertion functions that accept both NumPy and CuPy arrays
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] | 3.5625 | 32 |
from collections import Counter
import random
class LPA:
"""
Louvain community detection
"""
def train(self,networks):
"""
No training actually takes place in this class.
:param networks: list of egonet objects
:return: Predictions on training networks
"""
# Normally some training would take place here...
return self.predict(networks)
def predict(self,networks):
"""
Randomly constract valid circles given initialized hyper parameters
:param networks:
:return:
"""
predictions = []
for egonet in networks:
n = egonet.G
# Initially each node is labeled by themselves
label = dict(zip(n.nodes(),n.nodes()))
isConverged = False
while not isConverged:
isConverged = True
for node in n.nodes():
c = Counter(label[_] for _ in n.edge[node])
most = max(c.values())
# When the label of the node is not among the most of its neighbors' labels
if c[label[node]] != max(c.values()):
# Update the label to be one of the most choosed label of its beighborhood
label[node] = random.choice([_ for _ in c if c[_] == most])
isConverged = False
predicted_circles = {}
for node in n.nodes():
if label[node] not in predicted_circles:
predicted_circles[label[node]] = [node]
else:
predicted_circles[label[node]].append(node)
predictions.append(predicted_circles)
print("circles")
print(predicted_circles)
return predictions
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
#plot maps of ensemble mean annual mean dslc and wind stress change
@author: thermans
"""
import xarray as xr
import matplotlib.pyplot as plt
import os
import numpy as np
import fnmatch
import cmocean
import cartopy.crs as ccrs
import matplotlib
import matplotlib.gridspec as gridspec
from seasonal_deviations_from_monthly_means import seasonal_deviations_from_monthly_means
plt.close('all')
ssp = 'ssp585'
in_dir = '/Volumes/Naamloos/PhD_Data/CMIP6/ensemble_netcdfs/' #input directory ensemble data
out_dir = '/Users/thermans/Documents/PhD/Phase4_seasonal/Figures' #where to store the figure
model_list = list(np.genfromtxt('/Users/thermans/Documents/PhD/Phase4_seasonal/Analysis/compiling_ensembles/ens_model_list_'+ssp+'.txt',dtype='str'))
baseyears = np.arange(1995,2015) #reference periods
futyears = np.arange(2081,2101)
#load variant averaged ensemble for NWES (1x1)
nwes_ds = xr.open_dataset(os.path.join(in_dir,fnmatch.filter(os.listdir(in_dir), "zos_CMIP6_ssp585_n38_nwes_variant_averaged.nc")[0]))
nwes_ds = nwes_ds.sel(model=model_list)
nwes_ds = nwes_ds.isel(time=np.arange(11,3011)) #start in December end in November
bathy = xr.open_dataset('/Users/thermans/Documents/Modeling/ROMS/preprocessing/bathymetry/etopo1_bedrock_bigNorthSea1.nc')
#get seasonal anomalies
ens_djf_mean_anom, ens_jja_mean_anom, ens_mam_mean_anom, ens_son_mean_anom, ens_dec2nov_mean = seasonal_deviations_from_monthly_means(nwes_ds.zos)
ens_dec2nov_mean_d = ens_dec2nov_mean - ens_dec2nov_mean.sel(year=baseyears).mean(dim='year')#change relative to base period
#prepare data for plotting
numfin = np.sum(np.isfinite(ens_dec2nov_mean_d.isel(year=0)),axis=0) #get number of models with ocean value for each grid point
min_num = 5
a = ens_dec2nov_mean_d #winter
a = a.where(np.isfinite(ens_dec2nov_mean_d.sel(year=futyears).mean(dim='year').mean(dim='model',skipna=True) )) #mask where slc masked
a = a.where(numfin>min_num-1) #mask where minimum number of models with ocean value not exceeded
#load wind stress
taux_ds = xr.open_dataset(os.path.join(in_dir,fnmatch.filter(os.listdir(in_dir), "tauu_CMIP6_ssp585_n33_nwes_variant_averaged.nc")[0]))
taux_ds = taux_ds.isel(time=np.arange(11+124*12,3011))
taux_ds = taux_ds.sel(model=model_list)
taux_ds = taux_ds.where(numfin>min_num-1) #apply mask from zos
taux_ds.coords['lon'] = ((taux_ds.coords['lon'] + 180) % 360) - 180 #wrap around 0
taux_ds = taux_ds.reindex({ 'lon' : np.sort(taux_ds['lon'])})
modi = np.where(np.isin(taux_ds.model.values,['CIESM', 'CMCC-CM2-SR5', 'CMCC-ESM2', 'CAS-ESM2-0', 'FIO-ESM-2-0']))[0] #defined positive westward
taux_ds['tauu'][modi,...] = -1*taux_ds['tauu'][modi,...] #change sign
taux_djf_mean_anom, taux_jja_mean_anom, taux_mam_mean_anom, taux_son_mean_anom, taux_dec2nov_mean = seasonal_deviations_from_monthly_means(taux_ds.tauu)
#open ensemble dataset wind stress y
tauy_ds = xr.open_dataset(os.path.join(in_dir,fnmatch.filter(os.listdir(in_dir), "tauv_CMIP6_ssp585_n33_nwes_variant_averaged.nc")[0]))
tauy_ds = tauy_ds.isel(time=np.arange(11+124*12,3011))
tauy_ds = tauy_ds.sel(model=model_list)
tauy_ds = tauy_ds.where(numfin>min_num-1) #apply mask from zos
tauy_ds.coords['lon'] = ((tauy_ds.coords['lon'] + 180) % 360) - 180 #wrap around 0
tauy_ds = tauy_ds.reindex({ 'lon' : np.sort(tauy_ds['lon'])})
modi = np.where(np.isin(tauy_ds.model.values,['CIESM', 'CMCC-CM2-SR5', 'CMCC-ESM2', 'CAS-ESM2-0', 'FIO-ESM-2-0']))[0]
tauy_ds['tauv'][modi,...] = -1*tauy_ds['tauv'][modi,...]
tauy_djf_mean_anom, tauy_jja_mean_anom, tauy_mam_mean_anom, tauy_son_mean_anom, tauy_dec2nov_mean = seasonal_deviations_from_monthly_means(tauy_ds.tauv)
#calculate change future relative to baseline
taux_dec2nov_mean_d = taux_dec2nov_mean.sel(year=futyears).mean(dim='year') - taux_dec2nov_mean.sel(year=baseyears).mean(dim='year')
tauy_dec2nov_mean_d = tauy_dec2nov_mean.sel(year=futyears).mean(dim='year') - tauy_dec2nov_mean.sel(year=baseyears).mean(dim='year')
####plotting
my_cmap = cmocean.cm.balance #continuous colormap
my_cmap.set_bad('grey')
fig=plt.figure(figsize=(8,5))
gs = fig.add_gridspec(1,2)
gs.update(hspace = 0.75,wspace=0.3,right=.84,top=.95,bottom=.05)
#annual mean slc
ax3 = plt.subplot(gs[0,0], projection=ccrs.Orthographic(0, 50)) #maps
p=(100*a).sel(year=futyears).mean(dim='year').mean(dim='model').plot(transform=ccrs.PlateCarree(),ax=ax3,vmin=-25,vmax=25,cmap=my_cmap,add_colorbar=False)
p.set_edgecolor('face') #avoid white lines in pdf rendering
bathy.Band1.plot.contour(transform=ccrs.PlateCarree(),ax=ax3,levels=[-200],colors=['white'],add_colorbar=False) #shelf break
ax3.coastlines(resolution='50m',color='black',linewidth=1)
gl = ax3.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,linewidth=1, color='lightgrey', alpha=0, linestyle='-')
gl.top_labels = gl.right_labels = False #don't label top and right axes
gl.xlabel_style = {'color': 'black','rotation':0}
gl.ylabel_style = {'color': 'black','rotation':0}
ax3.set_extent([-9.5,9,45,60]) #set longitude & latitude extent
ax3.set_title('(a)') #subplot title
cax = fig.add_axes([0.125, .15, .312, .02])
fig.colorbar(p, cax=cax,orientation='horizontal',label='SLC [cm]')
#annual mean wind-stress change
my_cmap = cmocean.cm.tempo
my_cmap.set_bad('grey')
abs_d = np.sqrt(taux_dec2nov_mean_d.mean(dim='model')**2+tauy_dec2nov_mean_d.mean(dim='model')**2) #absolute change
ax1 = plt.subplot(gs[0,1], projection=ccrs.Orthographic(0, 50)) #maps
im = abs_d.plot(transform=ccrs.PlateCarree(),ax=ax1,vmin=0,vmax=.03,cmap=my_cmap,add_colorbar=False)
bathy.Band1.plot.contour(transform=ccrs.PlateCarree(),ax=ax1,levels=[-200],colors=['white'],add_colorbar=False)
im.set_edgecolor('face') #avoid whitespace between grid cells when rendering
x=taux_djf_mean_anom.lon.values #quivers
y=taux_djf_mean_anom.lat.values
u = taux_dec2nov_mean_d.mean(dim='model').values
v = tauy_dec2nov_mean_d.mean(dim='model').values
q=ax1.quiver(x,y,u,v,transform=ccrs.PlateCarree(),scale=.2,color='black',width=.007,edgecolors='k',zorder=5)
qk = ax1.quiverkey(q, 0.73, 0.239, 0.02, label='0.02 N/m'r'$^{2}$', labelpos='E',
coordinates='figure') #quiver key on scale
ax1.coastlines(resolution='50m',color='black')
ax1.set_extent([-9.5,9,45,60])
ax1.set_title('(b)')
gl = ax1.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,linewidth=1, color='lightgrey', alpha=0, linestyle='-')
gl.top_labels = gl.right_labels = gl.left_labels = False #don't label top and right axes
gl.xlabel_style = {'color': 'black','rotation':0}
gl.ylabel_style = {'color': 'black','rotation':0}
cax = fig.add_axes([0.529, .15, .312, .02])
fig.colorbar(im, cax=cax,orientation='horizontal',label='Wind-stress change [N/m'r'$^{2}$'']')
#fig.savefig(os.path.join(out_dir,'suppFigure1_annualmean_change_ssp585.pdf'),dpi=300)
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] | 2.290463 | 2,978 |
import pytest
from src.conanbuilder.signature import Signature
@pytest.fixture
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] | 3.222222 | 27 |
import torch
import torch.nn
from models.blocks import *
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] | 2.5 | 28 |
#!/usr/bin/env python
import os
from typing import Any
import pytest
import tcod as libtcod
@pytest.mark.filterwarnings("ignore")
if __name__ == "__main__":
test_parser()
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# Copyright (c) 2019-present, HuggingFace Inc.
# All rights reserved. This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
import os
import tarfile
import tempfile
import torch
from pytorch_pretrained_bert import cached_path
from collections import defaultdict
PERSONACHAT_URL = "https://s3.amazonaws.com/datasets.huggingface.co/personachat/personachat_self_original.json"
HF_FINETUNED_MODEL = "https://s3.amazonaws.com/models.huggingface.co/transfer-learning-chatbot/finetuned_chatbot_gpt.tar.gz"
logger = logging.getLogger(__file__)
SPECIAL_TOKENS = ["<bos>", "<eos>", "<speaker1>", "<speaker2>", "<pad>"]
QN_WORDS = ['who', 'what', 'where', 'why', 'when', 'how', 'which', 'whom', 'whose', '?']
lm_special_tokens = ['_start_', '_delimiter_', '_classify_']
def download_pretrained_model():
""" Download and extract finetuned model from S3 """
resolved_archive_file = cached_path(HF_FINETUNED_MODEL,cache_dir='./cache/')
tempdir = tempfile.mkdtemp()
logger.info("extracting archive file {} to temp dir {}".format(resolved_archive_file, tempdir))
with tarfile.open(resolved_archive_file, 'r:gz') as archive:
archive.extractall(tempdir)
return tempdir
def get_dataset(tokenizer, dataset_path, dataset_cache=None):
""" Get PERSONACHAT from S3 """
dataset_path = dataset_path or PERSONACHAT_URL
dataset_cache = dataset_cache + '_' + type(tokenizer).__name__ # Do avoid using GPT cache for GPT-2 and vice-versa
if dataset_cache and os.path.isfile(dataset_cache):
logger.info("Load tokenized dataset from cache at %s", dataset_cache)
dataset = torch.load(dataset_cache)
else:
logger.info("Download dataset from %s", dataset_path)
personachat_file = cached_path(dataset_path,cache_dir='./cache/')
# personachat_file = cached_path(dataset_path, cache_dir='../../.pytorch_pretrained_bert')
with open(personachat_file, "r", encoding="utf-8") as f:
dataset = json.loads(f.read())
logger.info("Tokenize and encode the dataset")
dataset = tokenize(dataset)
if dataset_cache:
torch.save(dataset, dataset_cache)
# FIXME: only for testing, delete later:
'''
dataset['train'] = dataset['train'][:1]
while len(dataset['train'][0]['utterances']) != 1:
dataset['train'][0]['utterances'].pop()
# dataset['valid'] = dataset['valid'][:1]
dataset['valid'] = dataset['train']
'''
dataset['train'] = dataset['train'][:int(len(dataset['train'])*0.9)]
# dataset['train'] = dataset['train'][:int(len(dataset['train']) * 0.1)]
# train_len = int(len(dataset['train']) * 0.9)
# dataset['train'] = dataset['train'][int(len(dataset['train']) * 0.9):]
# dataset['train'] = dataset['train'][: 1]
dataset['valid'] = dataset['valid'][: 1] # 这里不要乱改啊！！！
# dataset['train'] = dataset['train'][:int(len(dataset['train'])*0.9)]
# dataset['dev'] = dataset['train'][int(len(dataset['train']) * 0.9):]
personachat_file = cached_path(dataset_path,cache_dir='./cache/')
# personachat_file = cached_path(dataset_path, cache_dir='../../.pytorch_pretrained_bert')
with open(personachat_file, "r", encoding="utf-8") as f:
org_dataset = json.loads(f.read())
# org_dataset_tmp = org_dataset['train'][train_len:]
# personas = defaultdict(list)
for dataset_name in org_dataset:
for i, dialogue in enumerate(org_dataset[dataset_name]):
if i >= len(dataset[dataset_name]):
break
dataset[dataset_name][i]['persona_org'] = dialogue['personality'].copy()
'''
for _ in range(len(dialogue['utterances'])):
personas[dataset_name].append(dialogue['personality'])
'''
return dataset
def get_dataset_personalities(tokenizer, dataset_path, dataset_cache=None):
""" Get personalities from PERSONACHAT """
dataset_path = dataset_path or PERSONACHAT_URL
dataset_cache = dataset_cache + '_' + type(tokenizer).__name__ # Do avoid using GPT cache for GPT-2 and vice-versa
if os.path.isfile(dataset_cache):
logger.info("Load tokenized dataset from cache at %s", dataset_cache)
personachat = torch.load(dataset_cache)
else:
logger.info("Download PERSONACHAT dataset from %s", dataset_path)
personachat_file = cached_path(dataset_path,cache_dir='./cache/')
with open(personachat_file, "r", encoding="utf-8") as f:
personachat = json.loads(f.read())
print(personachat)
logger.info("Tokenize and encode the dataset")
personachat = tokenize(personachat)
torch.save(personachat, dataset_cache)
logger.info("Filter personalities")
personalities = []
for dataset in personachat.values():
for dialog in dataset:
personalities.append(dialog["personality"])
logger.info("Gathered {} personalities".format(len(personalities)))
return personalities
def build_input_from_segments(persona, history, reply, tokenizer, lm_labels=False, with_eos=True):
""" Build a sequence of input from 3 segments: persona, history and last reply """
bos, eos, speaker1, speaker2 = tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS[:-1])
# YW: speaker1 is user
instance = {}
sequence = [[bos] + list(chain(*persona))] + history + [reply + ([eos] if with_eos else [])]
sequence = [sequence[0]] + [[speaker2 if (len(sequence)-i) % 2 else speaker1] + s for i, s in enumerate(sequence[1:])] # YW: add speaker infomation
instance["input_ids"] = list(chain(*sequence)) # YW: concat all the context
instance["token_type_ids"] = [speaker2 if i % 2 else speaker1 for i, s in enumerate(sequence) for _ in s] # YW: TODO: persona is speaker1?
instance["mc_token_ids"] = len(instance["input_ids"]) - 1
instance["lm_labels"] = [-1] * len(instance["input_ids"]) # YW: all -1 (mask?) -> because it's not the right candidate(label)!
if lm_labels: # YW: if it's label
instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:]
return instance, sequence | [
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] | 2.521564 | 2,481 |
import numpy as np
import cv2
from core.config import cfg
import utils.blob as blob_utils
import roi_data.rpn
def get_minibatch_blob_names(is_training=True):
"""Return blob names in the order in which they are read by the data loader.
"""
# data blob: holds a batch of N images, each with 3 channels
blob_names = ['data']
if cfg.RPN.RPN_ON:
# RPN-only or end-to-end Faster R-CNN
blob_names += roi_data.rpn.get_rpn_blob_names(is_training=is_training)
elif cfg.RETINANET.RETINANET_ON:
raise NotImplementedError
else:
# Fast R-CNN like models trained on precomputed proposals
blob_names += roi_data.fast_rcnn.get_fast_rcnn_blob_names(
is_training=is_training
)
return blob_names
def get_minibatch(roidb, coco):
"""Given a roidb, construct a minibatch sampled from it."""
# We collect blobs from each image onto a list and then concat them into a
# single tensor, hence we initialize each blob to an empty list
blobs = {k: [] for k in get_minibatch_blob_names()}
# Get the input image blob
im_blob, im_scales, roidb = _get_image_blob(roidb, coco)
blobs['data'] = im_blob
if cfg.RPN.RPN_ON:
# RPN-only or end-to-end Faster/Mask R-CNN
valid = roi_data.rpn.add_rpn_blobs(blobs, im_scales, roidb)
elif cfg.RETINANET.RETINANET_ON:
raise NotImplementedError
else:
# Fast R-CNN like models trained on precomputed proposals
valid = roi_data.fast_rcnn.add_fast_rcnn_blobs(blobs, im_scales, roidb)
return blobs, valid
def _get_image_blob(roidb, coco):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
# Sample random scales to use for each image in this batch
scale_inds = np.random.randint(
0, high=len(cfg.TRAIN.SCALES), size=num_images)
processed_ims = []
im_scales = []
for i in range(num_images):
im = cv2.imread(roidb[i]['image'])
assert im is not None, \
'Failed to read image \'{}\''.format(roidb[i]['image'])
# AUG BEGIN--------------------------------
backupim = im
backuproidb = roidb[i]
try:
from AugSeg.get_instance_group import extract
from AugSeg.affine_transform import transform_image, transform_annotation
img_id = roidb[i]['id']
ann_ids = coco.getAnnIds(imgIds=img_id)
anns = coco.loadAnns(ann_ids)
background, instances_list, transforms_list, groupbnds_list, groupidx_list = extract(anns, im)
new_img = transform_image(background, instances_list, transforms_list)
new_ann = transform_annotation(anns, transforms_list, groupbnds_list, groupidx_list,
background.shape[1], background.shape[0])
im = new_img
from datasetsAug.roidb import combined_roidb_for_training
new_roidb, ratio_list, ratio_index = combined_roidb_for_training( \
('coco_2017_train',), cfg.TRAIN.PROPOSAL_FILES, \
img_id, new_ann, coco
)
if roidb[i]['flipped']:
roidb[i] = new_roidb[1]
else:
roidb[i] = new_roidb[0]
except:
roidb[i] = backuproidb
im = backupim
# AUG END----------------------------------
# If NOT using opencv to read in images, uncomment following lines
# if len(im.shape) == 2:
# im = im[:, :, np.newaxis]
# im = np.concatenate((im, im, im), axis=2)
# # flip the channel, since the original one using cv2
# # rgb -> bgr
# im = im[:, :, ::-1]
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale = blob_utils.prep_im_for_blob(
im, cfg.PIXEL_MEANS, [target_size], cfg.TRAIN.MAX_SIZE)
im_scales.append(im_scale[0])
processed_ims.append(im[0])
# Create a blob to hold the input images [n, c, h, w]
blob = blob_utils.im_list_to_blob(processed_ims)
return blob, im_scales, roidb
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1441,
44812,
11,
545,
62,
1416,
2040,
11,
686,
312,
65,
198
] | 2.112711 | 2,014 |
import sys, os
class Extractor:
'''
Returns the variable name if a variable with
the value <value> is found.
'''
'''
Scans a list of <lines> containing CSS and
returns a list of strings containing the
rendered LESS version.
'''
'''
Returns the output for the variables.less
file as a list of strings
'''
if __name__ == '__main__':
if len(sys.argv) > 1:
for path in sys.argv[1:]:
name = '.'.join(path.split('.')[:-1])
extractor = Extractor(name)
read = open(path)
write = open(name + '.less', 'w')
variables = open(name + '_variables.less', 'w')
try:
for line in extractor.scan(read.readlines()):
write.write(line)
for line in extractor.get_variables():
variables.write(line + os.linesep)
finally:
variables.close()
write.close()
read.close()
else:
print('usage: python extract.py [file]')
| [
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3601,
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25,
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9078,
685,
7753,
60,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
628
] | 1.965096 | 573 |
import unittest
import numpy as np
from auv_models import linear_surge, diagonal_slow_without_g
from helper import degrees_to_quat_rotation
if __name__ == "__main__":
unittest.main()
| [
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] | 2.838235 | 68 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import argparse
import requests
import json
from dateutil.parser import parse
from lxml import html
if __name__ == '__main__':
arg_parser = argparse.ArgumentParser(
description='Download videos from ZDF media library.')
arg_parser.add_argument(
'-d', '--directory', dest='tgt_dir', metavar='target_directory',
required=True, help='Target directory for video downloads.')
arg_parser.add_argument(
'-q', '--quality', dest='quality', metavar='video quality',
required=False, choices=['veryhigh', 'high', 'low'], default='high',
help='Quality of downloaded videos')
arg_parser.add_argument(
'urls', metavar='video_urls',
help='Comma-separated list of video urls.')
args = arg_parser.parse_args()
dl = ZdfDownloader(args.tgt_dir, args.urls, quality=args.quality)
dl.download_all()
| [
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8,
198,
220,
220,
220,
288,
75,
13,
15002,
62,
439,
3419,
198
] | 2.619444 | 360 |
import os
import yaml
import tempfile
import json
import sys
import copy
from collections import namedtuple
import pytest
from mock import patch
from aeon_ztp.bin import aztp_os_selector
dev_data = {
'os_name': 'cumulus-vx',
'vendor': 'cumulus',
'hw_part_number': '1234',
'hostname': 'cumulus',
'fqdn': 'cumulus.localhost',
'virtual': True,
'service_tag': '1234',
'os_version': '3.1.1',
'hw_version': '1234',
'mac_address': '0123456789012',
'serial_number': '09786554',
'hw_model': 'cvx1000'
}
cfg_data = {
'default': {
'regex_match': '3\.1\.[12]',
'image': 'CumulusLinux-3.1.2-amd64.bin'
},
'group_a': {
'regex_match': '3\.1\.[12]',
'image': 'CumulusLinux-3.1.2-amd64.bin',
'matches': {
'hw_model': ['cvx1000'],
'mac_address': ['0123456789012', '2109876543210']
}
}
}
@pytest.fixture()
def os_sel_file(contents=None):
"""
Used to create a temporary os-selector file
:param contents: python dictionary that will be converted to yaml
:return: returns a temporary file string
"""
if not contents:
contents = cfg_data
os_sel = tempfile.NamedTemporaryFile(mode='w+t')
os_sel.file.write(yaml.dump(contents, default_flow_style=False))
os_sel.file.flush()
return os_sel
@patch('aeon_ztp.bin.aztp_os_selector.exit_results', side_effect=SystemExit)
@patch('aeon_ztp.bin.aztp_os_selector.json.loads')
@patch('aeon_ztp.bin.aztp_os_selector.load_cfg', return_value=cfg_data)
@patch('aeon_ztp.bin.aztp_os_selector.cli_parse')
@patch('aeon_ztp.bin.aztp_os_selector.exit_results', side_effect=SystemExit)
@patch('aeon_ztp.bin.aztp_os_selector.cli_parse')
@patch('aeon_ztp.bin.aztp_os_selector.match_hw_model', side_effect=aztp_os_selector.HwNoMatchError)
@patch('aeon_ztp.bin.aztp_os_selector.exit_results', side_effect=SystemExit)
@patch('aeon_ztp.bin.aztp_os_selector.json.loads')
@patch('aeon_ztp.bin.aztp_os_selector.load_cfg', return_value=cfg_data)
@patch('aeon_ztp.bin.aztp_os_selector.cli_parse')
@patch('aeon_ztp.bin.aztp_os_selector.match_hw_model', side_effect=aztp_os_selector.HwMultiMatchError)
@patch('aeon_ztp.bin.aztp_os_selector.exit_results', side_effect=SystemExit)
@patch('aeon_ztp.bin.aztp_os_selector.json.loads')
@patch('aeon_ztp.bin.aztp_os_selector.load_cfg', return_value=cfg_data)
@patch('aeon_ztp.bin.aztp_os_selector.cli_parse')
@patch('aeon_ztp.bin.aztp_os_selector.match_os_version')
@patch('aeon_ztp.bin.aztp_os_selector.match_hw_model')
@patch('aeon_ztp.bin.aztp_os_selector.exit_results', side_effect=SystemExit)
@patch('aeon_ztp.bin.aztp_os_selector.json.loads')
@patch('aeon_ztp.bin.aztp_os_selector.load_cfg', return_value=cfg_data)
@patch('aeon_ztp.bin.aztp_os_selector.cli_parse')
@patch('aeon_ztp.bin.aztp_os_selector.match_os_version')
@patch('aeon_ztp.bin.aztp_os_selector.match_hw_model')
@patch('aeon_ztp.bin.aztp_os_selector.exit_results', side_effect=SystemExit)
@patch('aeon_ztp.bin.aztp_os_selector.json.loads')
@patch('aeon_ztp.bin.aztp_os_selector.load_cfg', return_value=cfg_data)
@patch('aeon_ztp.bin.aztp_os_selector.cli_parse')
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] | 2.226415 | 1,431 |
import unittest
from lymph.core.monitoring.metrics import RawMetric
from lymph.core.monitoring.aggregator import Aggregator
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2017 eelium <eelium@eez008>
#
# Distributed under terms of the MIT license.
import random
"""
parameters
"""
# The window refresh rate
REFRESH_INTERVAL = 50
# the ground size
margin_left = 100
margin_right = 100
margin_top = 100
margin_bottom = 100
L = 50 # unit value in pixel; as scalar
# the window size
world_height = 16 * L + margin_top + margin_bottom
world_width = 22 * L + margin_left + margin_right
# robot numbers
NUM_AGENTS = 8
# agent speed
agent_speed = 40
# TODO: parameterize this magic number
TIME_RATE = 2
FRAME_PER_SECOND = 20.0
# # collision allows the angle
# th_close_angle = 45
all_finish_mark = 0
# good position letter (forward,backward)
ROW1_LEFT = [
('B1', 'A1') , ('A2', 'B2') , ('B3', 'A3') , ('A4', 'B4') , ('B5', 'A5') ,
('A6', 'B6') , ('B7', 'A7') ]
ROW1_RIGHT = [
('A2', 'B2') , ('B3', 'A3') , ('A4', 'B4') , ('B5', 'A5') ,
('A6', 'B6') , ('B7', 'A7') , ('A8', 'B8')]
ROW2_LEFT = [
('C1', 'B1') , ('B2', 'C2') , ('C3', 'B3') , ('B4', 'C4') , ('C5', 'B5') ,
('B6', 'C6') , ('C7', 'B7') ]
ROW2_RIGHT = [
('B2', 'C2') , ('C3', 'B3') , ('B4', 'C4') , ('C5', 'B5') ,
('B6', 'C6') , ('C7', 'B7') , ('B8', 'C8') ]
ROW3_LEFT = [
('D1', 'C1') , ('C2', 'D2') , ('D3', 'C3') , ('C4', 'D4') , ('D5', 'C5') ,
('C6', 'D6') , ('D7', 'C7') ]
ROW3_RIGHT = [
('C2', 'D2') , ('D3', 'C3') , ('C4', 'D4') , ('D5', 'C5') ,
('C6', 'D6') , ('D7', 'C7') , ('C8', 'D8') ]
# #Class goods
# CLASS_GOODS_DIC = {
# 0: [0, 1, 2, 3, 4, 5, 6, 7],
# 1: [8, 9, 10, 11, 12, 13, 14, 15],
# 2: [16, 17, 18, 19, 20, 21, 22, 23],
# 3: [24, 25, 26, 27, 28, 29, 30, 31],
# 4: [32, 33, 34, 35, 36, 37, 38, 39],
# 5: [40, 41, 42, 43, 44, 45, 46, 47],
# 6: [48, 49, 50, 51, 52, 53, 54, 55],
# 7: [56, 57, 58, 59, 60, 61, 62, 63],
# 8: [64, 65, 66, 67, 68, 69, 70, 71],
# 9: [72, 73, 74, 75, 76, 77, 78, 79],
# 10: [80, 81, 82, 83, 84, 85, 86, 87],
# 11: [88, 89, 90, 91, 92, 93, 94, 95],
# 12: [96, 97, 98, 99, 100, 101, 102, 103],
# 13: [104, 105, 106, 107, 108, 109, 110, 111],
# 14: [112, 113, 114, 115, 116, 117, 118, 119],
# 15: [120, 121, 122, 123, 124, 125, 126, 127],
# 16: [128, 129, 130, 131, 132, 133, 134, 135],
# 17: [136, 137, 138, 139, 140, 141, 142, 143],
# 18: [144, 145, 146, 147, 148, 149, 150, 151],
# 19: [152, 153, 154, 155, 156, 157, 158, 159],
# 20: [160, 161, 162, 163, 164, 165, 166, 167]}
# charging park
CHARGING_PARK = ['Y1', 'Y2', 'Y3', 'Y4', 'Y5', 'Y6', 'Y7', 'Y8']
# order points
UNLOADING_POSITION = ['Z1', 'Z5']
#goods colour
SHELVES_COLOR = {0:'BLUE', 1:'SLATE BLUE', 2:'GREEN', 3:'SPRING GREEN', 4:'CYAN', 5:'NAVY',
6:'STEEL BLUE', 7:'FOREST GREEN', 8:'SEA GREEN', 9:'SEA GREEN', 10:'MIDNIGHT BLUE',
11:'DARK GREEN', 12:'DARK SLATE GREY', 13:'MEDIUM BLUE', 14:'SKY BLUE', 15:'LIME GREEN',
16:'MEDIUM AQUAMARINE', 17:'CORNFLOWER BLUE', 18:'MEDIUM SEA GREEN', 19:'INDIAN RED', 20:'VIOLET',
21:'DARK OLIVE GREEN'}
# goods = 0
# #total goods
# for i in xrange(ORDER_NUM):
# goods += len(GOODS_ORDER[i])
# TOTAL_GOODS = goods
# goods order
# GOODS_ORDER = {0:[790, 160, 1210, 1010, 130, 220, 590, 980, 1400, 1020, 1040, 370],
# 1:[650, 1170, 950, 1100, 540, 1650, 60, 470, 1500, 210, 890, 830, 990]} #1
# GOODS_ORDER = {0:[1150, 1340, 350, 110, 600, 670, 120, 1160, 1490, 790, 730, 1540],
# 1:[1210, 520, 1050, 400, 1140, 190, 210, 90, 870, 1560, 1130, 1640, 690]} #2
# GOODS_ORDER = {0:[40, 860, 150, 400, 690, 1570, 1630, 760, 1370, 1420, 320, 230],
# 1:[480, 1670, 1320, 1430, 1300, 1610, 280, 570, 920, 530, 910, 200, 260]} #3
# GOODS_ORDER = {0:[140, 450, 1250, 1230, 150, 1350, 970, 350, 120, 1430, 1550, 300],
# 1:[1100, 570, 1640, 160, 1070, 790, 770, 130, 1010, 830, 90, 550, 890]} #4
GOODS_ORDER = {0:[950, 1130, 1510, 550, 1200, 890, 1370, 1240, 990, 450, 1170, 440],
1:[1180, 1640, 1290, 60, 480, 230, 370, 140, 740, 1310, 1480, 150, 870]} #5
# # # goods order
# GOODS_ORDER = {0:[790, 160, 1210, 1010, 130],
# 1:[650, 1170, 950]}
#order number
ORDER_NUM = len(GOODS_ORDER) | [
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] | 1.956541 | 2,209 |
from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.contrib.auth.models import User
from django.core.exceptions import PermissionDenied, SuspiciousOperation
from django.db import transaction
from django.db.utils import IntegrityError
from django.http.response import HttpResponseBadRequest, HttpResponseForbidden, HttpResponseNotFound, HttpResponse, \
Http404
from django.shortcuts import render, redirect, get_object_or_404
from django.utils.decorators import method_decorator
from django.views import generic, View
from django.utils import timezone
from django.db.models import Q
import django_excel as excel
from django.views.decorators.http import require_http_methods, require_GET
from django.views.generic import TemplateView, FormView
from permission_utils import *
from dashboard.views import bad_request
import re
from Grades.exceptions import GradeException
from Grades.models import ModuleEdition, Grade, Test, Person, ModulePart, Studying, Module, Study
from importer.forms import GradeUploadForm, TestGradeUploadForm, ImportStudentForm, ImportStudentModule, \
ImportModuleEditionStructureForm, COLUMN_TITLE_ROW, ImportModuleForm
# Create your views here.
class ImporterIndexView(LoginRequiredMixin, View):
"""Index view of the importer module. Serves both Module Coordinators and Teachers.
Module coordinators are presented with an overview of the module editions they are a coordinator of, for which they
can perform administrative actions. They can add students to a module edition, and download and upload a form that
contains grades for the tests which are in the module. This can be done for the whole module, or per test individually.
Teachers receive a list of module parts that are a teacher of, with their tests. They can, like module coordinators,
download and upload an excel sheet that contains grades. This can be done per test individually.
"""
model = ModuleEdition
@login_required
@require_http_methods(["GET", "POST"])
@transaction.atomic
def import_module(request, pk):
"""Module import. Use an .xlsx file to submit grades to a module edition
On GET the user is presented with a file upload form.
On POST, the submitted .xlsx file is processed by the system, registering Grade object for each grade in the excel
file. It dynamically detects the tests that are submitted (by exact name match or database ID), and omits extra
columns silently. Also, lines that do not have a filled in student number are ignored. Students that are not
declared as part of the module (def:import_student_to_module) raise an import error.
:param request: Django request
:param pk: Module that grades should be submitted to
:return: A redirect to the Grades module's module view on success. Otherwise a 404 (module does not exist), 403
(no permissions) or 400 (bad excel file or other import error)
"""
person = Person.objects.filter(user=request.user).first()
module_edition = get_object_or_404(ModuleEdition, pk=pk)
if not is_coordinator_or_assistant_of_module(person, module_edition):
raise PermissionDenied('You are not the module coordinator for this course')
if request.method == "POST":
form = GradeUploadForm(request.POST, request.FILES)
if form.is_valid():
title_row = form.cleaned_data.get('title_row') - 1
# Check if /any/ tests and/or grades are imported.
any_tests = False
# List of all tests that are imported.
all_tests = []
sheet = request.FILES['file'].get_book_dict()
for table in sheet:
# Check if the sheet has enough rows
if title_row >= len(sheet[table]) :
return bad_request(request, {'message': 'The file that was uploaded was not recognised as a grade '
'excel file. Are you sure the file is an .xlsx file, and '
'that all fields are present? Otherwise, download a new '
'gradesheet and try using that instead.'})
test_rows = dict()
university_number_field = None
# Detect university_number and test columns
for title_index in range(0, len(sheet[table][title_row])):
# This is the university number column
if ('number' in str(sheet[table][title_row][title_index]).lower()) or \
('nummer' in str(sheet[table][title_row][title_index]).lower()):
university_number_field = title_index
else:
# Attempt to find a Test
# search by ID
try:
test = Test.objects.filter(
pk=sheet[table][title_row][title_index])
if test and test.filter(module_part__module_edition=module_edition):
test_rows[title_index] = sheet[table][title_row][title_index] # pk of Test
any_tests = True
except (ValueError, TypeError):
# search by name
if Test.objects.filter(
name=sheet[table][title_row][title_index]
).filter(module_part__module_edition=pk):
test_rows[title_index] = Test.objects.filter(
name=sheet[table][title_row][title_index]
).filter(module_part__module_edition=pk)[0].pk # pk of Test
any_tests = True
# Else not a test, continue...
if university_number_field is None:
continue # Ignore this sheet
if len(test_rows.keys()) == 0:
continue # Ignore this sheet
# The current user's Person is the corrector of the grades.
teacher = Person.objects.filter(user=request.user).first()
grades = []
# Retrieve Test object beforehand to validate permissions on tests and speed up Grade creation
tests = dict()
test_prefetch = Test.objects.prefetch_related('module_part__module_edition__module')
for test_column in test_rows.keys():
tests[test_column] = test_prefetch.get(pk=test_rows[test_column])
[all_tests.append(test) for test in tests.values() if test]
# Check excel file for invalid students
invalid_students = []
for row in sheet[table][(title_row + 1):]:
if not Studying.objects.filter(person__university_number__contains=row[university_number_field]).filter(
module_edition=pk):
invalid_students.append(row[university_number_field])
# Check for invalid student numbers in the university_number column, but ignore empty fields.
if [student for student in invalid_students if student is not '']:
return bad_request(request, {'message': 'Students {} are not enrolled in this module. '
'Enroll these students first before retrying.'
.format(invalid_students)})
# Make Grades
for row in sheet[table][(title_row + 1):]: # Walk horizontally over table
student = Person.objects.filter(university_number__contains=row[university_number_field]).first()
# check if this is not an empty line, else continue.
if student:
for test_column in test_rows.keys():
try:
grades.append(make_grade(
student=student,
corrector=teacher,
test=tests[test_column],
grade=row[test_column]
))
except GradeException as e: # Called for either: bad grade, grade out of bounds
return HttpResponseBadRequest(e)
save_grades(grades) # Bulk-save grades. Also prevents a partial import of the sheet.
# Check if anything was imported.
if not any_tests:
return bad_request(request, {'message': 'There were no tests recognized to import.'})
return render(request=request,
template_name='importer/successfully_imported.html',
context={'tests': all_tests})
else:
return bad_request(request, {'message': 'The file that was uploaded was not recognised as a grade excel '
'file. Are you sure the file is an .xlsx file? Otherwise, download '
'a new gradesheet and try using that instead.'})
else: # GET request
form = GradeUploadForm()
return render(request, 'importer/importmodule.html', {'form': form, 'pk': pk})
@login_required
@require_http_methods(["GET", "POST"])
@transaction.atomic
def import_module_part(request, pk):
"""Module part import. Use an .xlsx file to submit grades to a module part
On GET the user is presented with a file upload form.
On POST, the submitted .xlsx file is processed by the system, registering Grade object for each grade in the excel
file. It dynamically detects the tests that are submitted (by exact name match or database ID), and omits extra
columns silently. Also, lines that do not have a filled in student number are ignored. Students that are not
declared as part of the module (def:import_student_to_module) raise an import error.
:param request: Django request
:param pk: Module part that grades should be submitted to
:return: A redirect to the Grades course view on success. Otherwise a 404 (module does not exist), 403
(no permissions) or 400 (bad excel file or other import error)
"""
module_part = get_object_or_404(ModulePart, pk=pk)
module_edition = get_object_or_404(ModuleEdition, modulepart=module_part)
person = Person.objects.filter(user=request.user).filter(
Q(coordinator__module_edition__modulepart=module_part) | Q(teacher__module_part=module_part)
).first()
if not ModuleEdition.objects.filter(modulepart=module_part):
raise Http404('Module does not exist.')
if not (is_coordinator_or_assistant_of_module(person, module_edition) or is_coordinator_or_teacher_of_module_part(person, module_part)):
raise PermissionDenied('You are not allowed to do this.')
if request.method == "POST":
form = GradeUploadForm(request.POST, request.FILES)
if form.is_valid():
title_row = form.cleaned_data.get('title_row') - 1
# Check if /any/ tests and/or grades are imported.
any_tests = False
# List of all tests that are imported.
all_tests = []
sheet = request.FILES['file'].get_book_dict()
for table in sheet:
# Check if the sheet has enough rows
if title_row >= len(sheet[table]):
return bad_request(request, {'message': 'The file that was uploaded was not recognised as a grade'
' excel file. Are you sure the file is an .xlsx file, and'
' that all fields are present? Otherwise, download a new'
' gradesheet and try using that instead.'})
test_rows = dict()
university_number_field = None
# Detect university_number and test columns
for title_index in range(0, len(sheet[table][title_row])):
# This is the university number column
if ('number' in str(sheet[table][title_row][title_index]).lower()) or \
('nummer' in str(sheet[table][title_row][title_index]).lower()):
university_number_field = title_index
else:
# Attempt to find a Test
# search by ID
try:
test = Test.objects.filter(
pk=sheet[table][title_row][title_index])
if test and test.filter(module_part=module_part):
test_rows[title_index] = sheet[table][title_row][title_index] # pk of Test
any_tests = True
except (ValueError, TypeError):
pass # Not an int.
# search by name
if Test.objects.filter(module_part=module_part).filter(
name=sheet[table][title_row][title_index]):
test_rows[title_index] = Test.objects.filter(
name=sheet[table][title_row][title_index]
).filter(module_part=module_part)[0].pk # pk of Test
any_tests = True
# Attempt to ignore test altogether.
else:
pass
if university_number_field is None:
continue # Ignore this sheet
if len(test_rows.keys()) == 0:
continue # Ignore this sheet
# The current user's Person is the corrector of the grades.
teacher = Person.objects.filter(user=request.user).first()
grades = []
# Retrieve Test object beforehand to validate permissions on tests and speed up Grade creation
tests = dict()
for test_column in test_rows.keys():
tests[test_column] = Test.objects.get(pk=test_rows[test_column])
[all_tests.append(test) for test in tests.values() if test]
# Check excel file for invalid students
invalid_students = []
for row in sheet[table][(title_row + 1):]:
if not Studying.objects.filter(person__university_number__contains=row[university_number_field]).filter(
module_edition=module_edition):
invalid_students.append(row[university_number_field])
# Check for invalid student numbers in the university_number column, but ignore empty fields.
if [student for student in invalid_students if student is not '']:
return bad_request(request, {'message': 'Students {} are not enrolled in this module.\n '
'Enroll these students first before retrying'
.format(invalid_students)})
# Make Grades
for row in sheet[table][(title_row + 1):]: # Walk horizontally over table
student = Person.objects.filter(university_number__contains=row[university_number_field]).first()
# check if this is not an empty line, else continue.
if student:
for test_column in test_rows.keys():
try:
grades.append(make_grade(
student=student,
corrector=teacher,
test=tests[test_column],
grade=row[test_column]
))
except GradeException as e: # Called for either: bad grade, grade out of bounds
return bad_request(request, {'message': e})
save_grades(grades) # Bulk-save grades. Also prevents a partial import of the sheet.
# Check if anything was imported.
if not any_tests:
return bad_request(request, {'message': 'There were no tests recognized to import.'})
return render(request=request,
template_name='importer/successfully_imported.html',
context={'tests': all_tests})
else:
return bad_request(request, {'message': 'The file uploaded was not recognised as a grade excel file.'
' Are you sure the file is an .xlsx file? Otherwise, download a new'
' gradesheet and try using that instead'})
else: # GET request
form = GradeUploadForm()
return render(request, 'importer/importmodulepart.html', {'form': form, 'pk': pk, 'module_part': module_part})
@login_required
@require_http_methods(["GET", "POST"])
@transaction.atomic
def import_test(request, pk):
""" Test import. Use an .xlsx file to submit grades to a test
On GET the user is presented with a file upload form.
On POST, the submitted .xlsx file is processed by the system, registering Grade object for each grade in the excel
file. Lines that do not have a filled in student number are ignored. Students that are not declared as part of the
module (def:import_student_to_module) the test is in raise an import error.
:param request: Django request
:param pk: Test that grades should be submitted to
:return: A redirect to the Grades module's Test view on success. Otherwise a 404 (module does not exist), 403
(no permissions) or 400 (bad excel file or other import error)
"""
# Check if user is either the module coordinator or teacher of this test.
test = get_object_or_404(Test, pk=pk)
person = Person.objects.filter(user=request.user).first()
if not is_coordinator_or_teacher_of_test(person, test):
raise PermissionDenied('You are not a module coordinator or teacher of this test. Please refer to the'
'module coordinator of this test if you think this is in error.')
if request.method == "POST":
form = TestGradeUploadForm(request.POST, request.FILES)
if form.is_valid():
title_row = form.cleaned_data.get('title_row') - 1
sheet = request.FILES['file'].get_book_dict()
for table in sheet:
# Check dimensions
if not len(sheet[table]) > title_row and len(sheet[table][0]) == 3:
return bad_request(request, {'message': 'The file that was uploaded was not recognised as a grade '
'excel file. Are you sure all columns are present? '
'Otherwise, download a new gradesheet and try using that '
'instead.'})
# Identify columns
try:
student_id_field = sheet[table][title_row].index('university_number')
grade_field = sheet[table][title_row].index('grade')
description_field = sheet[table][title_row].index('description')
except ValueError:
return bad_request(request, {'message': 'One of the required field [student_id, grade, description]'
' could not be found.'})
# The current user's Person is the corrector of the grades.
teacher = Person.objects.filter(user=request.user).first()
# Check excel file for invalid students
invalid_students = []
for row in sheet[table][(title_row + 1):]:
if not Studying.objects.filter(person__university_number=row[0]).filter(
module_edition=test.module_part.module_edition_id):
invalid_students.append(row[0])
# Check for invalid student numbers in the university_number column, but ignore empty fields.
if [student for student in invalid_students if student is not '']:
return bad_request(request, {'message': 'Students {} are not enrolled in this module. '
'Enroll these students first before retrying.'
.format(invalid_students)})
elif invalid_students:
return bad_request(request, {'message': 'There are grades or description fields in this excel sheet'
' that do not have a student number filled in. Please'
' check the contents of your excel file for stale values'
' in rows.'})
grades = []
for row in sheet[table][(title_row + 1):]:
try:
student = Person.objects.get(university_number=row[student_id_field])
# check if this is not an empty line, else continue.
if student:
grades.append(make_grade(
student=student,
corrector=teacher,
test=test,
grade=row[grade_field],
description=row[description_field]
))
except GradeException as e: # Called for either: bad grade, grade out of bounds
return bad_request(request, {'message': e})
save_grades(grades) # Bulk-save grades. Also prevents a partial import of the sheet.
return redirect('grades:test', pk)
else:
return bad_request(request, {'message': 'Bad POST'})
else:
if Test.objects.filter(pk=pk):
form = TestGradeUploadForm()
return render(request, 'importer/importtest.html',
{'test': Test.objects.get(pk=pk), 'form': form, 'pk': pk})
else:
return bad_request(request, {'message', 'Test does not exists'})
@login_required()
@require_http_methods(["GET"])
def export_module(request, pk):
""" Creates an excel sheet that contains all tests against all students in the module. This sheet is compatible with
def:import_module.
:param request: Django request
:param pk: Module ID
:return: A file response containing an .xlsx file.
"""
module_edition = get_object_or_404(ModuleEdition, pk=pk)
person = Person.objects.filter(user=request.user).first()
# Check if user is a module coordinator.
if not is_coordinator_or_assistant_of_module(person, module_edition):
raise PermissionDenied('You are not the module coordinator for this course.')
students = Person.objects.filter(studying__module_edition=module_edition).values('university_number', 'name')
tests = Test.objects.filter(module_part__module_edition=module_edition)
# Pre-fill first few columns.
table = [
['Module:', str(module_edition)]+['' for _ in range(len(tests))],
] + [['' for _ in range(len(tests) + 2)] for _ in range(COLUMN_TITLE_ROW - 4)]
# Add the module part name and test name for each test if there is enough header room.
if COLUMN_TITLE_ROW > 2:
table.append(['', 'Module part >'] + [test.module_part.name for test in tests])
if COLUMN_TITLE_ROW > 1:
table.append(['', 'Test name >'] + [test.name for test in tests])
if COLUMN_TITLE_ROW > 0:
table.append(['', 'Grade between >'] + ['{} - {}'.format(test.minimum_grade, test.maximum_grade) for test in tests])
# Add machine-readable header row.
table.append(['university_number', 'name'] + [test.name for test in tests])
# pre-fill student numbers
for student in students:
table.append([student['university_number'], student['name']] + [None for _ in range(len(tests))])
return excel.make_response_from_array(table,
file_name='Module Grades {} {}-{}.xlsx'.format(module_edition.module.name,
module_edition.year,
module_edition.block),
file_type='xlsx')
@login_required()
@require_http_methods(["GET"])
def export_module_part(request, pk):
""" Creates an excel sheet that contains all tests that are in a course. Used mainly to download sign off excel sheets.
:param request: Django request
:param pk: Module part ID
:return: A file response containing an .xlsx file.
"""
module_part = get_object_or_404(ModulePart, pk=pk)
module_edition = ModuleEdition.objects.get(modulepart=module_part)
person = Person.objects.filter(user=request.user).first()
# Check if user is a module coordinator.
if not is_coordinator_or_teacher_of_module_part(person, module_part):
raise PermissionDenied('You are not the module coordinator for this course.')
students = Person.objects.filter(studying__module_edition=module_edition).values('university_number', 'name')
tests = Test.objects.filter(module_part=module_part).values('pk', 'name', 'minimum_grade', 'maximum_grade')
# Pre-fill first few columns.
table = [['' for _ in range(len(tests) + 2)] for _ in range(COLUMN_TITLE_ROW - 2)]
# Add the module part name and test name for each test if there is enough header room.
if COLUMN_TITLE_ROW > 1:
table.append(['', 'Test name >'] + [test['name'] for test in tests])
if COLUMN_TITLE_ROW > 0:
table.append(['', 'Grade between >'] + ['{} - {}'.format(test['minimum_grade'], test['maximum_grade']) for test in tests])
# Add machine-readable header row.
table.append(['university_number', 'name'] + [test['name'] for test in tests])
# pre-fill student numbers
for student in students:
table.append([student['university_number'], student['name']] + [None for _ in range(len(tests))])
return excel.make_response_from_array(table,
file_name='Sign-off sheet {} {}-{}.xlsx'.format(module_edition.module.name,
module_edition.year,
module_edition.block),
file_type='xlsx')
@login_required()
@require_http_methods(["GET"])
def export_module_part_signoff(request, pk):
""" Creates an excel sheet that contains all tests that are in a course. Used mainly to download sign off excel sheets.
:param request: Django request
:param pk: Module part ID
:return: A file response containing an .xlsx file.
"""
module_part = get_object_or_404(ModulePart, pk=pk)
module_edition = ModuleEdition.objects.get(modulepart=module_part)
person = Person.objects.filter(user=request.user).first()
# Check if user is a module coordinator.
if not is_coordinator_or_teacher_of_module_part(person, module_part):
raise PermissionDenied('You are not the module coordinator for this course.')
students = Person.objects.filter(studying__module_edition=module_edition).values('university_number', 'name')
tests = Test.objects.filter(module_part=module_part, type='A').values('pk', 'name', 'minimum_grade', 'maximum_grade')
# Pre-fill first few columns.
table = [['' for _ in range(len(tests) + 2)] for _ in range(COLUMN_TITLE_ROW - 2)]
# Add the module part name and test name for each test if there is enough header room.
if COLUMN_TITLE_ROW > 1:
table.append(['', 'Test name >'] + [test['name'] for test in tests])
if COLUMN_TITLE_ROW > 0:
table.append(['', 'Grade between >'] + ['{} - {}'.format(test['minimum_grade'], test['maximum_grade']) for test in tests])
# Add machine-readable header row.
table.append(['university_number', 'name'] + [test['name'] for test in tests])
# pre-fill student numbers
for student in students:
table.append([student['university_number'], student['name']] + [None for _ in range(len(tests))])
return excel.make_response_from_array(table,
file_name='Sign-off sheet {} {}-{}.xlsx'.format(module_edition.module.name,
module_edition.year,
module_edition.block),
file_type='xlsx')
@login_required()
@require_http_methods(["GET"])
def export_test(request, pk):
""" Creates an excel sheet that contains all students that may take the test. This sheet is compatible with
def:import_test. It contains a description row, which can be used to submit feedback through.
:param request: Django request
:param pk: Test ID
:return: A file response containing an .xlsx file.
"""
test = get_object_or_404(Test, pk=pk)
person = Person.objects.filter(user=request.user).first()
# Check if user is either the module coordinator or teacher of this test.
if not is_coordinator_or_teacher_of_test(person, test):
raise PermissionDenied('Not allowed to export test.')
students = Person.objects.filter(studying__module_edition__modulepart=test.module_part).values('university_number',
'name')
# Insert padding
table = [
[test.name, "", "grade between:", '{} - {}'.format(test.minimum_grade, test.maximum_grade)]
] + [['', '', '', ''] for _ in range(COLUMN_TITLE_ROW - 1)]
# Insert title row
table.append(['university_number', 'name', 'grade', 'description'])
# Insert student numbers
for student in students:
table.append([student['university_number'], student['name'], '', ''])
return excel.make_response_from_array(table, file_name='Test Grades {} {}-{}.xlsx'
.format(test.name,
test.module_part.module_edition.year,
test.module_part.module_edition.block),
file_type='xlsx')
def make_grade(student: Person, corrector: Person, test: Test, grade, description: str = ''):
""" Helper function that makes Grade objects so they can be inserted in bulk with def:save_grades.
:param student: Person object of the student.
:param corrector: Person object of the corrector.
:param test: Test object.
:param grade: A float that is the grade.
:param description: An optional description.
:return: A grade object, or None (:param grade is empty).
"""
interpreted_grade = 0
if grade == '':
return # Field is empty, assume it does not need to be imported.
try:
interpreted_grade = float(grade)
except (ValueError, TypeError):
if test.type == 'A':
interpreted_grade = 1
else:
raise GradeException('\'{}\' is not a valid input for a grade (found at {}\'s grade for {}.)'
.format(grade, student.name, test)) # Probably a typo, give an error.
if test.minimum_grade > interpreted_grade or interpreted_grade > test.maximum_grade:
raise GradeException(
'Cannot register {}\'s ({}) grade for test {} because it\'s grade ({}) is outside the defined bounds '
'({}-{}).'.format(student.name, student.university_number, test.name, grade, test.minimum_grade,
test.maximum_grade))
# try:
grade_obj = Grade(
student=student,
teacher=corrector,
test=test,
grade=interpreted_grade,
time=timezone.now(),
description=description
)
# except Exception as e:
# raise GradeException(e)
return grade_obj
def save_grades(grades):
""" Helper function that saves all grades to the database.
:param grades: List of Grade objects, which may contain None values. These are ignored.
:return: Nothing.
:raises: GradeException if a grade object is malformed. No grades are saved when this happens.
"""
try:
Grade.objects.bulk_create([grade for grade in grades if grade is not None])
except Exception as e:
raise GradeException('Error when saving grades to te database.' + str(e))
@login_required
def workbook_student_to_module(request, pk):
""" Creates an excel sheet that may be filled in to register students to a module. This sheet is compatible with
def:import_student_to_module.
:param request: Django request
:param pk: Test ID
:return: A file response containing an .xlsx file.
"""
# Check if user is a module coordinator.
module_edition = get_object_or_404(ModuleEdition, pk=pk)
person = Person.objects.filter(user=request.user).first()
if not is_coordinator_or_assistant_of_module(person, module_edition):
raise PermissionDenied('You are not the module coordinator for this course.')
# Insert column titles
table = [['university_number', 'name', 'email', 'role']]
return excel.make_response_from_array(table, file_name='Module import Sheet.xlsx', file_type='xlsx')
@login_required
@require_http_methods(["GET", "POST"])
@transaction.atomic
def import_student_to_module(request, pk):
"""
Take .xlsx file, as produced by def:export_student_import_format and retrieve all students and metainformation from it.
Case 1 - Known User - Add the user to the active module edition with the right study and role by making a new Studying object
Case 2 - Unknown User - Add the student to the database by making a new Person object and proceed like Case 1
Case 3 - Already Added User - Ignore row
The function returns a view in which all newly added users, all users that are now added to the module edition and all users that were already in the module are shown.
:param request: Django request
:param pk: The module edition id
:return: /students-module-imported.html redirect in which all fails, successes and addeds are given
:raises: Permission denied if the user is not the Module Coordinator
:raises: SuspiciousOperation in case of faulty file input
"""
# Check if user is a module coordinator.
module_edition = get_object_or_404(ModuleEdition, pk=pk)
person = Person.objects.filter(user=request.user).first()
if not is_coordinator_or_assistant_of_module(person, module_edition):
raise PermissionDenied('Not allowed to upload students to module.')
if request.method == "POST":
student_form = ImportStudentForm(request.POST, request.FILES)
if student_form.is_valid():
file = request.FILES['file']
dict = file.get_book_dict()
# Select first page
students_to_module = dict[list(dict.keys())[0]]
key_rows = {}
emailpattern = re.compile('e[-]?mail*')
for i in range(0,len(students_to_module[0])):
if 'number' in students_to_module[0][i].lower():
key_rows['number'] = i
elif 'name' in students_to_module[0][i].lower():
key_rows['name'] = i
elif emailpattern.match(students_to_module[0][i].lower()):
key_rows['email'] = i
elif 'role' in students_to_module[0][i].lower():
key_rows['role'] = i
# Check dimensions
if not (len(students_to_module) > 1 and len(key_rows) == 4):
return bad_request(request, {'message': 'Not all required columns [university_number, name, email, '
'role] are in the excel sheet, or no rows to import.'})
context = {'created': [], 'studying': [], 'failed': []}
for i in range(1, len(students_to_module)):
# Sanitize number input
try:
if str(students_to_module[i][key_rows['number']])[0] == 's' and int(students_to_module[i][key_rows['number']][1:]) > 0:
username = str(students_to_module[i][key_rows['number']])
elif str(students_to_module[i][key_rows['number']])[0] == 'm' and int(students_to_module[i][key_rows['number']][1:]) > 0:
return HttpResponseBadRequest('Trying to add an employee as a student to a module.')
elif int(students_to_module[i][key_rows['number']]) > 0:
username = 's{}'.format(str(students_to_module[i][key_rows['number']]))
else:
raise ValueError
except ValueError:
return bad_request(request, {'message': '{} is not a student number.'
.format(students_to_module[i][key_rows['number']])})
user, created = User.objects.get_or_create(
username=username,
defaults={
'email': students_to_module[i][key_rows['email']]
}
)
student, created = Person.objects.get_or_create(
university_number=username,
defaults={
'user': user,
'name': students_to_module[i][key_rows['name']],
'email': students_to_module[i][key_rows['email']],
}
)
# Update name and email
student.name = students_to_module[i][key_rows['name']]
student.email = students_to_module[i][key_rows['email']]
student.save()
if created:
context['created'].append([student.name, student.full_id])
studying, created = Studying.objects.get_or_create(
person=student,
module_edition=ModuleEdition.objects.get(pk=pk),
defaults={
'role': students_to_module[i][key_rows['role']],
}
)
if created:
module_ed = ModuleEdition.objects.get(id=studying.module_edition.pk)
module = Module.objects.get(moduleedition=module_ed)
context['studying'].append(
[student.name, student.full_id, module.name, module_ed.code]) # studying.study])
else:
module_ed = ModuleEdition.objects.get(id=studying.module_edition.pk)
module = Module.objects.get(moduleedition=module_ed)
context['failed'].append(
[student.name, student.full_id, module.name, module_ed.code]) # studying.study])
context['studying'].append(
[student.name, student.full_id, module.name, module_ed.code]) # studying.study])
return render(request, 'importer/students-module-imported.html', context={'context': context})
else:
raise SuspiciousOperation('Bad POST')
else: # if Module_ed.objects.filter(pk=pk):
student_form = ImportStudentModule()
return render(request, 'importer/import-module-student.html',
{'form': student_form, 'pk': pk, 'module_edition': ModuleEdition.objects.get(pk=pk)})
class SuccessfullyImportedView(LoginRequiredMixin, TemplateView):
"""Show a message that summarizes the imported data and warns of extra steps to be taken
"""
template_name = 'importer/successfully_imported.html'
tests = []
class ModuleStructureImporter(LoginRequiredMixin, View):
"""Import to bulk-create Module parts and Tests for a module.
"""
@transaction.atomic
def export_module_structure(request, pk):
""" Returns an excel sheet which can be used to upload the module structure.
:param request: Django request
:param pk: Module edition primary key
:return: Excel worksheet as response.
"""
# Check if user is a module coordinator.
module_edition = get_object_or_404(ModuleEdition, pk=pk)
person = Person.objects.filter(user=request.user).first()
if not is_coordinator_or_assistant_of_module(person, module_edition):
raise PermissionDenied('You are not the module coordinator for this course.')
# Insert column titles
table = [
['Module part:', '<<example>>', '', '(Duplicate this page for each module part)'],
['Test:', '<<example test>>', '<<example signoff>>', ''],
['Min. grade', '1', '0', ''],
['Max. grade', '10', '1', '']
]
return excel.make_response_from_array(table, file_name='Module Structure {}.xlsx'.format(module_edition), file_type='xlsx')
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329,
23884,
2014,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
764,
18982,
7,
9526,
11,
3710,
13,
3672,
11,
1332,
4008,
220,
1303,
18578,
257,
46517,
11,
1577,
281,
4049,
13,
198,
220,
220,
220,
611,
1332,
13,
39504,
62,
9526,
1875,
16173,
62,
9526,
393,
16173,
62,
9526,
1875,
1332,
13,
47033,
62,
9526,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
22653,
16922,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
34,
34574,
7881,
23884,
43054,
82,
37913,
30072,
9559,
329,
1332,
23884,
780,
340,
43054,
82,
9559,
37913,
30072,
318,
2354,
262,
5447,
22303,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
15090,
92,
12,
90,
92,
737,
4458,
18982,
7,
50139,
13,
3672,
11,
3710,
13,
403,
1608,
62,
17618,
11,
1332,
13,
3672,
11,
9559,
11,
1332,
13,
39504,
62,
9526,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1332,
13,
47033,
62,
9526,
4008,
628,
220,
220,
220,
1303,
1949,
25,
198,
220,
220,
220,
9559,
62,
26801,
796,
22653,
7,
198,
220,
220,
220,
220,
220,
220,
220,
3710,
28,
50139,
11,
198,
220,
220,
220,
220,
220,
220,
220,
4701,
28,
30283,
273,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1332,
28,
9288,
11,
198,
220,
220,
220,
220,
220,
220,
220,
9559,
28,
27381,
276,
62,
9526,
11,
198,
220,
220,
220,
220,
220,
220,
220,
640,
28,
2435,
11340,
13,
2197,
22784,
198,
220,
220,
220,
220,
220,
220,
220,
6764,
28,
11213,
198,
220,
220,
220,
1267,
198,
220,
220,
220,
1303,
2845,
35528,
355,
304,
25,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
5298,
22653,
16922,
7,
68,
8,
198,
220,
220,
220,
1441,
9559,
62,
26801,
628,
198,
4299,
3613,
62,
31177,
7,
31177,
2599,
198,
220,
220,
220,
37227,
5053,
525,
2163,
326,
16031,
477,
19051,
284,
262,
6831,
13,
628,
220,
220,
220,
1058,
17143,
19051,
25,
7343,
286,
22653,
5563,
11,
543,
743,
3994,
6045,
3815,
13,
2312,
389,
9514,
13,
198,
220,
220,
220,
1058,
7783,
25,
10528,
13,
198,
220,
220,
220,
1058,
430,
2696,
25,
22653,
16922,
611,
257,
9559,
2134,
318,
6428,
12214,
13,
1400,
19051,
389,
7448,
618,
428,
4325,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
22653,
13,
48205,
13,
65,
12171,
62,
17953,
26933,
9526,
329,
9559,
287,
19051,
611,
9559,
318,
407,
6045,
12962,
198,
220,
220,
220,
2845,
35528,
355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
22653,
16922,
10786,
12331,
618,
8914,
19051,
284,
573,
6831,
2637,
1343,
965,
7,
68,
4008,
628,
198,
31,
38235,
62,
35827,
198,
4299,
670,
2070,
62,
50139,
62,
1462,
62,
21412,
7,
25927,
11,
279,
74,
2599,
198,
220,
220,
220,
37227,
7921,
274,
281,
27336,
9629,
326,
743,
307,
5901,
287,
284,
7881,
2444,
284,
257,
8265,
13,
770,
9629,
318,
11670,
351,
198,
220,
220,
220,
220,
220,
220,
220,
825,
25,
11748,
62,
50139,
62,
1462,
62,
21412,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
2581,
25,
37770,
2581,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
279,
74,
25,
6208,
4522,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
317,
2393,
2882,
7268,
281,
764,
87,
7278,
87,
2393,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
1303,
6822,
611,
2836,
318,
257,
8265,
16052,
13,
198,
220,
220,
220,
8265,
62,
28736,
796,
651,
62,
15252,
62,
273,
62,
26429,
7,
26796,
7407,
653,
11,
279,
74,
28,
79,
74,
8,
198,
220,
220,
220,
1048,
796,
7755,
13,
48205,
13,
24455,
7,
7220,
28,
25927,
13,
7220,
737,
11085,
3419,
198,
220,
220,
220,
611,
407,
318,
62,
37652,
20900,
62,
273,
62,
562,
10167,
62,
1659,
62,
21412,
7,
6259,
11,
8265,
62,
28736,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
2448,
3411,
21306,
798,
10786,
1639,
389,
407,
262,
8265,
16052,
329,
428,
1781,
2637,
8,
628,
220,
220,
220,
1303,
35835,
5721,
8714,
198,
220,
220,
220,
3084,
796,
16410,
6,
403,
1608,
62,
17618,
3256,
705,
3672,
3256,
705,
12888,
3256,
705,
18090,
6,
11907,
628,
220,
220,
220,
1441,
27336,
13,
15883,
62,
26209,
62,
6738,
62,
18747,
7,
11487,
11,
2393,
62,
3672,
11639,
26796,
1330,
21616,
13,
87,
7278,
87,
3256,
2393,
62,
4906,
11639,
87,
7278,
87,
11537,
628,
198,
31,
38235,
62,
35827,
198,
31,
46115,
62,
4023,
62,
24396,
82,
7,
14692,
18851,
1600,
366,
32782,
8973,
8,
198,
31,
7645,
2673,
13,
47116,
198,
4299,
1330,
62,
50139,
62,
1462,
62,
21412,
7,
25927,
11,
279,
74,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
7214,
764,
87,
7278,
87,
2393,
11,
355,
4635,
416,
825,
25,
39344,
62,
50139,
62,
11748,
62,
18982,
290,
19818,
477,
2444,
290,
1138,
391,
1161,
422,
340,
13,
198,
220,
220,
220,
8913,
352,
532,
29454,
11787,
532,
3060,
262,
2836,
284,
262,
4075,
8265,
8313,
351,
262,
826,
2050,
290,
2597,
416,
1642,
257,
649,
3604,
1112,
2134,
198,
220,
220,
220,
8913,
362,
532,
16185,
11787,
532,
3060,
262,
3710,
284,
262,
6831,
416,
1642,
257,
649,
7755,
2134,
290,
5120,
588,
8913,
352,
198,
220,
220,
220,
8913,
513,
532,
27511,
10687,
11787,
532,
41032,
5752,
198,
220,
220,
220,
383,
2163,
5860,
257,
1570,
287,
543,
477,
8308,
2087,
2985,
11,
477,
2985,
326,
389,
783,
2087,
284,
262,
8265,
8313,
290,
477,
2985,
326,
547,
1541,
287,
262,
8265,
389,
3402,
13,
628,
220,
220,
220,
1058,
17143,
2581,
25,
37770,
2581,
198,
220,
220,
220,
1058,
17143,
279,
74,
25,
383,
8265,
8313,
4686,
198,
220,
220,
220,
1058,
7783,
25,
1220,
19149,
658,
12,
21412,
12,
320,
9213,
13,
6494,
18941,
287,
543,
477,
10143,
11,
23682,
290,
2087,
82,
389,
1813,
198,
220,
220,
220,
1058,
430,
2696,
25,
2448,
3411,
6699,
611,
262,
2836,
318,
407,
262,
19937,
36831,
198,
220,
220,
220,
1058,
430,
2696,
25,
31922,
6243,
32180,
287,
1339,
286,
30954,
2393,
5128,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1303,
6822,
611,
2836,
318,
257,
8265,
16052,
13,
198,
220,
220,
220,
8265,
62,
28736,
796,
651,
62,
15252,
62,
273,
62,
26429,
7,
26796,
7407,
653,
11,
279,
74,
28,
79,
74,
8,
198,
220,
220,
220,
1048,
796,
7755,
13,
48205,
13,
24455,
7,
7220,
28,
25927,
13,
7220,
737,
11085,
3419,
198,
220,
220,
220,
611,
407,
318,
62,
37652,
20900,
62,
273,
62,
562,
10167,
62,
1659,
62,
21412,
7,
6259,
11,
8265,
62,
28736,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
2448,
3411,
21306,
798,
10786,
3673,
3142,
284,
9516,
2444,
284,
8265,
2637,
8,
628,
220,
220,
220,
611,
2581,
13,
24396,
6624,
366,
32782,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
3710,
62,
687,
796,
17267,
38778,
8479,
7,
25927,
13,
32782,
11,
2581,
13,
46700,
1546,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3710,
62,
687,
13,
271,
62,
12102,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2393,
796,
2581,
13,
46700,
1546,
17816,
7753,
20520,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8633,
796,
2393,
13,
1136,
62,
2070,
62,
11600,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9683,
717,
2443,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2444,
62,
1462,
62,
21412,
796,
8633,
58,
4868,
7,
11600,
13,
13083,
28955,
58,
15,
11907,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
8516,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3053,
33279,
796,
302,
13,
5589,
576,
10786,
68,
58,
12,
60,
30,
4529,
9,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
15,
11,
11925,
7,
19149,
658,
62,
1462,
62,
21412,
58,
15,
12962,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
705,
17618,
6,
287,
2444,
62,
1462,
62,
21412,
58,
15,
7131,
72,
4083,
21037,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
8516,
17816,
17618,
20520,
796,
1312,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
705,
3672,
6,
287,
2444,
62,
1462,
62,
21412,
58,
15,
7131,
72,
4083,
21037,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
8516,
17816,
3672,
20520,
796,
1312,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
3053,
33279,
13,
15699,
7,
19149,
658,
62,
1462,
62,
21412,
58,
15,
7131,
72,
4083,
21037,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
8516,
17816,
12888,
20520,
796,
1312,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
705,
18090,
6,
287,
2444,
62,
1462,
62,
21412,
58,
15,
7131,
72,
4083,
21037,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
8516,
17816,
18090,
20520,
796,
1312,
628,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
15225,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
357,
11925,
7,
19149,
658,
62,
1462,
62,
21412,
8,
1875,
352,
290,
18896,
7,
2539,
62,
8516,
8,
6624,
604,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2089,
62,
25927,
7,
25927,
11,
1391,
6,
20500,
10354,
705,
3673,
477,
2672,
15180,
685,
403,
1608,
62,
17618,
11,
1438,
11,
3053,
11,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
18090,
60,
389,
287,
262,
27336,
9629,
11,
393,
645,
15274,
284,
1330,
2637,
30072,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4732,
796,
1391,
6,
25598,
10354,
685,
4357,
705,
19149,
1112,
10354,
685,
4357,
705,
47904,
10354,
17635,
92,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
16,
11,
18896,
7,
19149,
658,
62,
1462,
62,
21412,
8,
2599,
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] | 2.22077 | 19,047 |
#coding=utf-8
import os
import math
#from tqdm import tqdm
def lsp(x,y,th,mv_red_th,mv_green_th):#x与y为五人机绝对坐标系，后二者为mv返回大致角度值（为绝对坐标系中y轴方向顺时针转动角度）
'''
'''
times=30#雷达扫描次数
num_data_line=600#雷达数据行数
data_ls = [[round(0.6 * x,2),0] for x in range(num_data_line)]#创建存储雷达数据的数组
error_range = 6#mv角度数据误差（度）
mv_red_th //= 0.6
mv_green_th //= 0.6
mv_red_th = int(mv_red_th) #取整
mv_green_th = int(mv_green_th) #取整
#for i in tqdm(range(times)):#多次扫描
for i in range(times):#多次扫描
#执行雷达扫视代码
os.system("./simple_grabber /dev/ttyUSB0")
with open("data.txt", "r") as f:
for line in f.readlines():
angle_dis = line.strip('\n').split(" ") #去掉列表中每一个元素的换行符,以空格作为分割符号，分割角度和距离
#雷达数据下标
j = int(float(angle_dis[0]) // 0.6)
#print(j)
dis = dis2int(angle_dis[1])
if dis != 0:
if ((x + dis * math.sin(math.radians(float(angle_dis[0])))) < 4300) & ((x + dis * math.sin(math.radians(float(angle_dis[0])))) > 0):
if ((y + dis * math.cos(math.radians(float(angle_dis[0])))) < 3250) & ((y + dis * math.cos(math.radians(float(angle_dis[0])))) > 0):
data_ls[j][1]=dis #更新数据
#angle_dis = deal_ls_data(angle_dis,data_ls[j]) #处理角度和距离数据
j += 1
for i in range(num_data_line):
if data_ls[i][1] != 0:
print(i," ",data_ls[i])
#print("len",len(data_ls))
mpr = []
mpg = []
f = 0.
a = 0.
b = 0.
print("mv",mv_red_th)
for i in range(1,11):
if data_ls[mv_red_th - 6 + i][1] != 0:
mpr.append(data_ls[mv_red_th - 6 + i])
print("mpr",mpr)
f += 1
for i in range(1,int(f)+1):
a += mpr[i - 1][0]
b += mpr[i - 1][1]
print("f",f)
if f!=0:decision_red = [a / f,b / f]
else : decision_red = [0,0]
print("de",decision_red)
f = 0.
a = 0.
b = 0.
print("mv",mv_green_th)
for i in range(1,11):
if data_ls[mv_green_th - 6 + i][1] != 0:
mpg.append(data_ls[mv_green_th - 6 + i])
print("mpg",mpg)
f += 1
for i in range(1,int(f)+1):
a += mpg[i - 1][0]
b += mpg[i - 1][1]
print("f",f)
if f!=0:decision_green = [a / f,b / f]
else: decision_green = [0,0]
return (x + decision_red[1] * math.sin(math.radians(decision_red[0]))),(y + decision_red[1] * math.cos(math.radians(decision_red[0]))),(x + decision_green[1] * math.sin(math.radians(decision_green[0]))),(y + decision_green[1] * math.cos(math.radians(decision_green[0])))
print("result:",lsp(0,0,45,90))
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198
] | 1.504891 | 1,840 |
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from BasicSR (https://github.com/xinntao/BasicSR)
# Copyright 2018-2020 BasicSR Authors
# ------------------------------------------------------------------------
import sys
sys.path.append("/content/drive/MyDrive/DERAIN/HINet")
import importlib
import logging
from unittest import TestLoader
import torch
from os import path as osp
from basicsr.data import create_dataloader, create_dataset
from basicsr.models import create_model
from basicsr.train import parse_options
from basicsr.utils import (get_env_info, get_root_logger, get_time_str,
make_exp_dirs)
from basicsr.utils.options import dict2str
from basicsr.utils import get_root_logger, imwrite, tensor2img
from tqdm import tqdm
from copy import deepcopy
import time, cv2
from skimage.measure import compare_psnr, compare_ssim
import numpy as np
# ----------------- from TransWeather ------------------
# ------------------------------------------------------
if __name__ == '__main__':
main()
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] | 3.728395 | 324 |
a = [1,2,3,1,2,3]
l = 0
m = 0
h = len(a)-1
while(m<=h):
if(a[m]==1):
a[l],a[m] = a[m],a[l]
l+=1
m+=1
elif(a[m]==2):
m+=1
else:
a[h],a[m] = a[m],a[h]
h-=1
print a | [
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] | 1.276316 | 152 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 The Plaso Project Authors.
# Please see the AUTHORS file for details on individual authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Analysis plugin related functions and classes for testing."""
import os
import unittest
from dfvfs.lib import definitions
from dfvfs.path import factory as path_spec_factory
from dfvfs.resolver import resolver as path_spec_resolver
from plaso.analysis import context
from plaso.artifacts import knowledge_base
from plaso.lib import event
from plaso.lib import queue
from plaso.parsers import context as parsers_context
class TestAnalysisReportQueueConsumer(queue.AnalysisReportQueueConsumer):
"""Class that implements a test analysis report queue consumer."""
def __init__(self, queue_object):
"""Initializes the queue consumer.
Args:
queue_object: the queue object (instance of Queue).
"""
super(TestAnalysisReportQueueConsumer, self).__init__(queue_object)
self.analysis_reports = []
def _ConsumeAnalysisReport(self, analysis_report):
"""Consumes an analysis report callback for ConsumeAnalysisReports."""
self.analysis_reports.append(analysis_report)
@property
def number_of_analysis_reports(self):
"""The number of analysis reports."""
return len(self.analysis_reports)
class AnalysisPluginTestCase(unittest.TestCase):
"""The unit test case for an analysis plugin."""
_TEST_DATA_PATH = os.path.join(os.getcwd(), 'test_data')
# Show full diff results, part of TestCase so does not follow our naming
# conventions.
maxDiff = None
def _GetAnalysisReportsFromQueue(self, analysis_report_queue_consumer):
"""Retrieves the analysis reports from the queue consumer.
Args:
analysis_report_queue_consumer: the analysis report queue consumer
object (instance of
TestAnalysisReportQueueConsumer).
Returns:
A list of analysis reports (instances of AnalysisReport).
"""
analysis_report_queue_consumer.ConsumeAnalysisReports()
analysis_reports = []
for analysis_report in analysis_report_queue_consumer.analysis_reports:
self.assertIsInstance(analysis_report, event.AnalysisReport)
analysis_reports.append(analysis_report)
return analysis_reports
def _GetTestFilePath(self, path_segments):
"""Retrieves the path of a test file relative to the test data directory.
Args:
path_segments: the path segments inside the test data directory.
Returns:
A path of the test file.
"""
# Note that we need to pass the individual path segments to os.path.join
# and not a list.
return os.path.join(self._TEST_DATA_PATH, *path_segments)
def _ParseFile(self, parser_object, path, knowledge_base_object):
"""Parses a file using the parser object.
Args:
parser_object: the parser object.
path: the path of the file to parse.
knowledge_base_object: the knowledge base object (instance of
KnowledgeBase).
Returns:
An event object queue object (instance of Queue).
"""
event_queue = queue.SingleThreadedQueue()
event_queue_producer = queue.EventObjectQueueProducer(event_queue)
parse_error_queue = queue.SingleThreadedQueue()
parser_context = parsers_context.ParserContext(
event_queue_producer, parse_error_queue, knowledge_base_object)
path_spec = path_spec_factory.Factory.NewPathSpec(
definitions.TYPE_INDICATOR_OS, location=path)
file_entry = path_spec_resolver.Resolver.OpenFileEntry(path_spec)
parser_object.Parse(parser_context, file_entry)
event_queue.SignalEndOfInput()
return event_queue
def _RunAnalysisPlugin(self, analysis_plugin, knowledge_base_object):
"""Analyzes an event object queue using the plugin object.
Args:
analysis_plugin: the analysis plugin object (instance of AnalysisPlugin).
knowledge_base_object: the knowledge base object (instance of
KnowledgeBase).
Returns:
An event object queue object (instance of Queue).
"""
analysis_report_queue = queue.SingleThreadedQueue()
analysis_report_queue_consumer = TestAnalysisReportQueueConsumer(
analysis_report_queue)
analysis_report_queue_producer = queue.AnalysisReportQueueProducer(
analysis_report_queue)
analysis_context = context.AnalysisContext(
analysis_report_queue_producer, knowledge_base_object)
analysis_plugin.RunPlugin(analysis_context)
analysis_report_queue.SignalEndOfInput()
return analysis_report_queue_consumer
def _SetUpKnowledgeBase(self, knowledge_base_values=None):
"""Sets up a knowledge base.
Args:
knowledge_base_values: optional dict containing the knowledge base
values. The default is None.
Returns:
An knowledge base object (instance of KnowledgeBase).
"""
knowledge_base_object = knowledge_base.KnowledgeBase()
if knowledge_base_values:
for identifier, value in knowledge_base_values.iteritems():
knowledge_base_object.SetValue(identifier, value)
return knowledge_base_object
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] | 3.052548 | 1,884 |
"""Provide pytest fixtures for the entire test suite.
These fixtures create data and data modules that can be reused by other tests. Their
construction relies heavily on the utility functions provided in `utils/scripts.py`.
"""
import copy
import imgaug.augmenters as iaa
from omegaconf import ListConfig, OmegaConf
import os
import pytest
import pytorch_lightning as pl
import shutil
import torch
from typing import Callable, List, Optional
import yaml
from lightning_pose.data.datamodules import BaseDataModule, UnlabeledDataModule
from lightning_pose.data.datasets import BaseTrackingDataset, HeatmapDataset
from lightning_pose.utils.predictions import get_videos_in_dir
from lightning_pose.utils.scripts import (
get_data_module,
get_dataset,
get_imgaug_transform,
)
_TORCH_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
TOY_DATA_ROOT_DIR = "toy_datasets/toymouseRunningData"
@pytest.fixture
def cfg() -> dict:
"""Load all toy data config files without hydra."""
base_dir = os.path.dirname(os.path.dirname(os.path.join(__file__)))
config_dir = os.path.join(base_dir, "scripts", "configs")
keys = ["data", "losses", "model", "training"]
cfg = {}
for key in keys:
cfg_tmp = os.path.join(config_dir, key, "%s_params.yaml" % key)
with open(cfg_tmp) as f:
dict_tmp = yaml.load(f, Loader=yaml.FullLoader)
cfg[key] = dict_tmp
return OmegaConf.create(cfg)
@pytest.fixture
def imgaug_transform(cfg) -> iaa.Sequential:
"""Create basic resizing transform."""
return get_imgaug_transform(cfg)
@pytest.fixture
def base_dataset(cfg, imgaug_transform) -> BaseTrackingDataset:
"""Create a dataset for regression models from toy data."""
# setup
cfg_tmp = copy.deepcopy(cfg)
cfg_tmp.model.model_type = "regression"
base_dataset = get_dataset(
cfg_tmp, data_dir=TOY_DATA_ROOT_DIR, imgaug_transform=imgaug_transform
)
# return to tests
yield base_dataset
# cleanup after all tests have run (no more calls to yield)
del base_dataset
torch.cuda.empty_cache()
@pytest.fixture
def heatmap_dataset(cfg, imgaug_transform) -> HeatmapDataset:
"""Create a dataset for heatmap models from toy data."""
# setup
cfg_tmp = copy.deepcopy(cfg)
cfg_tmp.model.model_type = "heatmap"
heatmap_dataset = get_dataset(
cfg_tmp, data_dir=TOY_DATA_ROOT_DIR, imgaug_transform=imgaug_transform
)
# return to tests
yield heatmap_dataset
# cleanup after all tests have run (no more calls to yield)
del heatmap_dataset
torch.cuda.empty_cache()
@pytest.fixture
def base_data_module(cfg, base_dataset) -> BaseDataModule:
"""Create a labeled data module for regression models."""
# setup
cfg_tmp = copy.deepcopy(cfg)
cfg_tmp.model.losses_to_use = []
# bump up training data so we can test pca_singleview loss
cfg_tmp.training.train_prob = 0.95
cfg_tmp.training.val_prob = 0.025
data_module = get_data_module(cfg_tmp, dataset=base_dataset, video_dir=None)
data_module.setup()
# return to tests
yield data_module
# cleanup after all tests have run (no more calls to yield)
del data_module
torch.cuda.empty_cache()
@pytest.fixture
def heatmap_data_module(cfg, heatmap_dataset) -> BaseDataModule:
"""Create a labeled data module for heatmap models."""
# setup
cfg_tmp = copy.deepcopy(cfg)
cfg_tmp.model.losses_to_use = []
# bump up training data so we can test pca_singleview loss
cfg_tmp.training.train_prob = 0.95
cfg_tmp.training.val_prob = 0.025
data_module = get_data_module(cfg_tmp, dataset=heatmap_dataset, video_dir=None)
data_module.setup()
# return to tests
yield data_module
# cleanup after all tests have run (no more calls to yield)
del data_module
torch.cuda.empty_cache()
@pytest.fixture
def base_data_module_combined(cfg, base_dataset) -> UnlabeledDataModule:
"""Create a combined data module for regression models."""
# setup
cfg_tmp = copy.deepcopy(cfg)
cfg_tmp.model.losses_to_use = ["temporal"]
data_module = get_data_module(
cfg_tmp,
dataset=base_dataset,
video_dir=os.path.join(TOY_DATA_ROOT_DIR, "unlabeled_videos")
)
# data_module.setup() # already done in UnlabeledDataModule constructor
# return to tests
yield data_module
# cleanup after all tests have run (no more calls to yield)
del data_module
torch.cuda.empty_cache()
@pytest.fixture
def heatmap_data_module_combined(cfg, heatmap_dataset) -> UnlabeledDataModule:
"""Create a combined data module for heatmap models."""
# setup
cfg_tmp = copy.deepcopy(cfg)
cfg_tmp.model.losses_to_use = ["temporal"]
data_module = get_data_module(
cfg_tmp,
dataset=heatmap_dataset,
video_dir=os.path.join(TOY_DATA_ROOT_DIR, "unlabeled_videos")
)
# data_module.setup() # already done in UnlabeledDataModule constructor
# return to tests
yield data_module
# cleanup after all tests have run (no more calls to yield)
del data_module
torch.cuda.empty_cache()
@pytest.fixture
def trainer(cfg) -> pl.Trainer:
"""Create a basic pytorch lightning trainer for testing models."""
ckpt_callback = pl.callbacks.model_checkpoint.ModelCheckpoint(
monitor="val_supervised_loss"
)
transfer_unfreeze_callback = pl.callbacks.BackboneFinetuning(
unfreeze_backbone_at_epoch=10,
lambda_func=lambda epoch: 1.5,
backbone_initial_ratio_lr=0.1,
should_align=True,
train_bn=True,
)
# determine gpu setup
if _TORCH_DEVICE == "cpu":
gpus = 0
elif isinstance(cfg.training.gpu_id, list):
gpus = cfg.training.gpu_id
elif isinstance(cfg.training.gpu_id, ListConfig):
gpus = list(cfg.training.gpu_id)
elif isinstance(cfg.training.gpu_id, int):
gpus = [cfg.training.gpu_id]
else:
raise NotImplementedError(
"training.gpu_id must be list or int, not {}".format(
type(cfg.training.gpu_id)
)
)
trainer = pl.Trainer(
gpus=gpus,
max_epochs=2,
min_epochs=2,
check_val_every_n_epoch=1,
log_every_n_steps=1,
callbacks=[ckpt_callback, transfer_unfreeze_callback],
limit_train_batches=2,
)
return trainer
@pytest.fixture
@pytest.fixture
@pytest.fixture
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] | 2.491923 | 2,600 |
from .utils import load_query
| [
6738,
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62,
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198
] | 3.75 | 8 |
import datetime
import os
import platform
import pytest
import subprocess
import sys
import uuid
sys.path.insert(0, 'src')
from cosalib import cmdlib
PY_MAJOR, PY_MINOR, PY_PATCH = platform.python_version_tuple()
def test_run_verbose():
"""
Verify run_verbose returns expected information
"""
result = cmdlib.run_verbose(['echo', 'hi'])
assert result.stdout is None
with pytest.raises(FileNotFoundError):
cmdlib.run_verbose(['idonotexist'])
# If we are not at least on Python 3.7 we must skip the following test
if PY_MAJOR == 3 and PY_MINOR >= 7:
result = cmdlib.run_verbose(['echo', 'hi'], capture_output=True)
assert result.stdout == b'hi\n'
def test_write_and_load_json(tmpdir):
"""
Ensure write_json writes loadable json
"""
data = {
'test': ['data'],
}
path = os.path.join(tmpdir, 'data.json')
cmdlib.write_json(path, data)
# Ensure the file exists
assert os.path.isfile(path)
# Ensure the data matches
assert cmdlib.load_json(path) == data
def test_sha256sum_file(tmpdir):
"""
Verify we get the proper sha256 sum
"""
test_file = os.path.join(tmpdir, 'testfile')
with open(test_file, 'w') as f:
f.write('test')
# $ sha256sum testfile
# 9f86d081884c7d659a2feaa0c55ad015a3bf4f1b2b0b822cd15d6c15b0f00a08
e = '9f86d081884c7d659a2feaa0c55ad015a3bf4f1b2b0b822cd15d6c15b0f00a08'
shasum = cmdlib.sha256sum_file(test_file)
assert shasum == e
def test_fatal(capsys):
"""
Ensure that fatal does indeed attempt to exit
"""
test_string = str(uuid.uuid4())
err = None
with pytest.raises(SystemExit) as err:
cmdlib.fatal(test_string)
# Check that our test string is in stderr
assert test_string in str(err)
def test_info(capsys):
"""
Verify test_info writes properly to stderr without exit
"""
test_string = str(uuid.uuid4())
cmdlib.info(test_string)
captured = capsys.readouterr()
assert test_string in captured.err
def test_rfc3339_time():
"""
Verify the format returned from rfc3339_time
"""
t = cmdlib.rfc3339_time()
assert datetime.datetime.strptime(t, '%Y-%m-%dT%H:%M:%SZ')
# now and utcnow don't set TZ's. We should get a raise
with pytest.raises(AssertionError):
cmdlib.rfc3339_time(datetime.datetime.now())
def test_rm_allow_noent(tmpdir):
"""
Ensure rm_allow_noent works both with existing and non existing files
"""
test_path = os.path.join(tmpdir, 'testfile')
with open(test_path, 'w') as f:
f.write('test')
# Exists
cmdlib.rm_allow_noent(test_path)
# Doesn't exist
cmdlib.rm_allow_noent(test_path)
def test_import_ostree_commit(monkeypatch, tmpdir):
"""
Verify the correct ostree/tar commands are executed when
import_ostree_commit is called.
"""
repo_tmp = os.path.join(tmpdir, 'tmp')
os.mkdir(repo_tmp)
class monkeyspcheck_call:
"""
Verifies each subprocess.check_call matches what is required.
"""
check_call_count = 0
def monkeyspcall(*args, **kwargs):
"""
Verifies suprocess.call matches what we need.
"""
assert args[0] == ['ostree', 'show', '--repo', tmpdir, 'commit']
# Monkey patch the subprocess function
monkeypatch.setattr(subprocess, 'check_call', monkeyspcheck_call())
monkeypatch.setattr(subprocess, 'call', monkeyspcall)
build = {
'ostree-commit': 'commit',
'images': {
'ostree': {
'path': 'tarfile.tar'
}
}
}
cmdlib.import_ostree_commit(tmpdir, './', build)
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7,
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15908,
11,
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19571,
3256,
1382,
8,
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] | 2.319095 | 1,592 |
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# ##### END GPL LICENSE BLOCK #####
# <pep8 compliant>
import bpy
from bpy.types import (
Panel,
)
from rna_prop_ui import PropertyPanel
classes = (
WORKSPACE_PT_main,
WORKSPACE_PT_addons,
WORKSPACE_PT_custom_props,
)
if __name__ == "__main__": # only for live edit.
from bpy.utils import register_class
for cls in classes:
register_class(cls)
| [
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] | 3.195531 | 358 |
import setuptools, sys, os
with open("README.rst", "r") as fh:
long_description = fh.read()
# The wce triage is designed to work with Ubuntu 18.04LTS and after
# that comes with Python 3.6.
python_version = sys.version_info
need_python_version = (3, 6)
if python_version < need_python_version:
raise RuntimeError("wce_triage requires Python version %d.%d or higher"
% need_python_version)
sys.path.append(os.getcwd())
from wce_triage.version import *
setuptools.setup(
name="wce_triage",
version=TRIAGE_VERSION,
author="Naoyuki Tai",
author_email="[email protected]",
description="WCE Triage",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/ntai/wce-triage-v2",
packages=['wce_triage',
'wce_triage.bin',
'wce_triage.components',
'wce_triage.lib',
'wce_triage.http',
'wce_triage.ops',
'wce_triage.setup',
'test'],
include_package_data=True,
install_requires=[
'python-socketio',
'aiohttp',
'aiohttp_cors'
],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: POSIX :: Linux",
],
)
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] | 2.348066 | 543 |
"""
The `dicts` module provides a set of utility functions to simplify dict operations
"""
# Copyright 2022 the original author or authors (i.e.: St4rG00se for Dev4py).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Optional, Any
from dev4py.utils.JOptional import JOptional
from dev4py.utils.objects import non_none, require_non_none, to_none
from dev4py.utils.types import K, V, Supplier
def is_dict(value: Any) -> bool:
"""
If the given value is a dict, returns true, otherwise false
Returns:
bool: true if the given value is a dict, otherwise false
"""
return isinstance(value, dict)
def _get_value(dictionary: dict[K, V], key: K) -> Optional[V]:
"""
private function to replace get_joptional_value lambda
Note: lambda are not used in order to be compatible with multiprocessing (lambda are not serializable)
"""
# lambda d: d.get(key)
return dictionary.get(key)
def get_joptional_value(dictionary: Optional[dict[K, V]], key: K) -> JOptional[V]:
"""
Tries to get a value from a dict with the given key and returns a JOptional describing the result
Args:
dictionary: The dict
key: The key
Returns:
JOptional[V]: An empty JOptional if dictionary is None or the searched key result is None, otherwise a JOptional
with a present value
"""
if non_none(dictionary) and not is_dict(dictionary):
raise TypeError("Optional[dict[K, V]] parameter is required")
return JOptional \
.of_noneable(dictionary) \
.map(partial(_get_value, key=key))
def get_value(
dictionary: Optional[dict[K, V]], key: K, default_supplier: Supplier[Optional[V]] = to_none
) -> Optional[V]:
"""
Returns the value from a dict with the given key if presents, otherwise returns the result produced by the supplying
function
Args:
dictionary: The dict
key: The searched key
default_supplier: The supplying function that produces a value to be returned
Returns:
Optional[V]: The value, if present, otherwise the result produced by the supplying function (even if dictionary
is None)
"""
return get_joptional_value(dictionary, key).or_else_get(default_supplier)
def get_value_from_path(
dictionary: Optional[dict[K, Any]], path: list[Any], default_supplier: Supplier[Optional[V]] = to_none
) -> Optional[V]:
"""
Returns the value from a deep dict (dict of dicts) with the given key path if present, otherwise returns the result
produced by the supplying function
Args:
dictionary: The dict
path: The searched key path
default_supplier: The supplying function that produces a value to be returned
Returns:
Optional[V]: The value, if present, otherwise the result produced by the supplying function (even if dictionary
is None)
Raises:
TypeError: if dictionary is not None and not a dict
"""
if non_none(dictionary) and not is_dict(dictionary):
raise TypeError("Optional[dict[K, V]] dictionary parameter must be a dict or None value")
if not path:
return default_supplier()
if len(path) == 1:
return get_value(dictionary, path[0], default_supplier)
current_path_value: Any = get_value(dictionary, path[0])
return get_value_from_path(
current_path_value,
path[1:],
default_supplier
) if is_dict(current_path_value) else default_supplier()
def put_value(dictionary: dict[K, V], key: K, value: V) -> Optional[V]:
"""
Associates the specified value with the specified key in the given map. If the map previously contained a mapping
for the key, the old value is returned and replaced by the specified value
Args:
dictionary: The dict
key: The key with which the specified value is to be associated
value: The value to be associated with the specified key
Returns:
Optional[V]: The previous value associated with key, or None if there was no mapping for key.
Raises:
TypeError: if dictionary is not a dict
"""
if not is_dict(dictionary):
raise TypeError("dictionary must be a dict value")
result: Optional[V] = dictionary.get(key)
dictionary[key] = value
return result
def empty_dict() -> dict[Any, Any]:
"""
Returns an empty dict
Returns:
dict[Any, Any]: an empty dict
"""
return {}
def update(dict_1: dict[K, V], dict_2: dict[K, V]) -> dict[K, V]:
"""
Adds all elements of the second dict to the first one and returns it
Args:
dict_1: The dict where add elements
dict_2: The dict with elements to add
Returns:
dict_1 (dict[K, V]): The first dict with added elements from dict_2
Raises:
TypeError: if dict_1 or dict_2 is None
"""
require_non_none(dict_1).update(require_non_none(dict_2))
return dict_1
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] | 2.933333 | 1,875 |
from rest_framework import serializers
from django.contrib.auth.models import User
from snippets.models import Contact
from snippets.models import Snippet
from snippets.serializers import ContactSerializer
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# -*- coding: utf-8 -*-
"""
hskl
Classification Module
Data I/O conventions
X: (DimX, DimY, NumSpectralChannels) <np.array>
Y: (DimX, DimY) <np.array,dtype=uint8>
0 - ignored
1 - first label
2 - second label
...
"""
# Author: Qian Cao
# License: BSD 3-Clause
import numpy as np
from sklearn.utils import all_estimators
from sklearn.base import BaseEstimator, ClassifierMixin
from .base import flatten_with_label, HyperspectralMixin
from sklearn.metrics import accuracy_score
# TODO: Search for list of models will be moved to models/sklearn
_sklearn_methods = dict(all_estimators(type_filter='classifier'))
_methods_list = ['AdaBoostClassifier', 'BaggingClassifier', 'BernoulliNB',
'DecisionTreeClassifier','ExtraTreesClassifier',
'GaussianNB','GradientBoostingClassifier',
'KNeighborsClassifier','LinearDiscriminantAnalysis',
'LinearSVC','LogisticRegression','MLPClassifier',
'QuadraticDiscriminantAnalysis','RandomForestClassifier',
'RidgeClassifier','SGDClassifier','SVC']
_parallel_methods = []
_methods = {k:_sklearn_methods[k] for k in _methods_list
if k in _sklearn_methods}
def list_sklearn_methods():
"""List Available Classifier Methods in Scikit-Learn
Returns
-------
list of classifier names
"""
return list(_sklearn_methods.keys())
def list_methods():
"""List Available Classifier Methods Tested for Hyperspectral-sklearn
Returns
-------
list of classifier names
"""
return list(_methods.keys())
class HyperspectralClassifier(BaseEstimator, HyperspectralMixin, ClassifierMixin):
"""Hyperspectral Classifier
Wrapper of classifiers in sklearn
Parameters
----------
method_name : str, default='RandomForest'
Invokes correponding classification algorithm in scikit-learn.
method_param : dict, default={}
A parameter used for demonstation of how to pass and store paramters.
"""
_estimator_type = "classifier"
def fit(self, X, Y, sample_fraction=None):
""" Fit model to inputs and labels.
"""
self.est = self._fit(self.est, X, Y, sample_fraction)
return self
def predict(self, X, Y = None):
""" Use model to predict input labels.
"""
Y = self._predict(self.est, X, Y)
return Y
def score(self, X, Y):
""" Evaluate Classification accuracy
Adapted from Classifier Mixin
"""
sample_weight = None # TODO: Implement sample weighting
X, Y = flatten_with_label(X, Y)
return super().score(X, Y, sample_weight)
if __name__ == "__main__":
pass | [
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6624,
366,
834,
12417,
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1298,
198,
220,
220,
220,
1208
] | 2.459964 | 1,124 |
from datetime import date, timedelta
from random import uniform
from numpy import ndarray
def polygen(*coefficients):
'''Polynomial generating function'''
if not coefficients:
return lambda i: 0
else:
c0 = coefficients[0]
coefficients = coefficients[1:]
return _
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] | 2.455882 | 136 |
#!/usr/bin/env python3
import re
import sys
import argparse
import asyncio
import json
from os import environ
from typing import List
from pprint import pprint
import logging
import subprocess
from urllib.parse import urlparse
from aiohttp import ClientSession, ClientError
import requests
from bs4 import BeautifulSoup
from tabulate import tabulate
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler(sys.stdout))
forbidden_usernames = ["apps", "site", "topics"]
def main():
"""Scrape and parse a github page. For all linked github projects, determine the number
of stars asynchronously. Order the results by decreasing number of stars.
Authentication can be provided by the GITHUB_API_TOKEN environmental variable, or
preferentially via the --token argument. If not given, am unauthenticated query will
be attempted.
"""
parser = argparse.ArgumentParser()
parser.add_argument("url", type=str)
parser.add_argument("--token", type=str)
parser.add_argument("--limit", type=int)
parser.add_argument("--debug", dest="debug", action="store_true")
parser.add_argument("--open", dest="open", action="store_true")
args = parser.parse_args()
if args.debug:
logger.setLevel(logging.DEBUG)
token = environ.get("GITHUB_API_TOKEN", None) or args.token
logger.debug(f"Using authentication token: {token}")
projects = get_linked_projects(args.url)
repo_endpoints = get_repo_api_endpoints(projects)
ranking = asyncio.run(get_stargazer_counts(repo_endpoints, token=token))
if args.limit:
ranking = ranking[: args.limit]
if args.open:
open_urls(ranking)
else:
print_ranking(ranking)
def open_urls(ranking):
"""Open the urls in the ranking in firefox. Add option to choose the web browser?"""
urls = [item["url"] for item in ranking]
firefox_cmd = "firefox".split() + urls
logger.debug(f"Executing: {' '.join (firefox_cmd)}")
subprocess.run(firefox_cmd)
def get_linked_projects(url):
"""Get github projects linked a given webpage"""
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
links = soup.find_all("a")
links = [a["href"] for a in links if "href" in a.attrs]
links = list (set(links))
pattern = r"^https://github.com/(?P<user>[\w-]+)/(?P<repo>[\w-]+)$"
prog = re.compile(pattern).search
matches = map(prog, links)
repos = list(filter(None, matches))
return repos
def get_repo_api_endpoints(projects: List[re.Match]):
""" Transform a project URL into an API repo endpoint"""
groups = [project.groupdict() for project in projects]
return [
f"https://api.github.com/repos/{group['user']}/{group['repo']}"
for group in groups
if group["user"] not in forbidden_usernames
]
async def get_ranking_data(session: ClientSession, repo_url):
"""Get individual repos data"""
logger.debug(f"beginning request to {repo_url}")
try:
async with session.get(repo_url) as response:
logger.debug(f"get response to {repo_url}")
data = await response.text()
data = json.loads(data)
return {
"name": data["name"],
"owner": data["owner"]["login"],
"stargazers": data["stargazers_count"],
"url": data["html_url"],
}
except KeyError as e:
logger.error(f"Response malformed at {repo_url} - {data}")
except ClientError:
logger.error(f"Request failed at {repo_url}")
if __name__ == "__main__":
main()
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] | 2.625538 | 1,394 |
"""
.. module:: BEST
:platform: Unix, linux, Windows
.. moduleauthor:: Sunkyu Kim <[email protected]>
================================
Biomedical Entity Query API v2
================================
API Description
================
This API is for use of BEST(Biomedical Entity Search Tool) in various purposes.
All users can access BEST at : http://best.korea.ac.kr/
For bugs and inquiries, please contact:
* Jaewoo Kang([email protected])
* Sunkyu Kim([email protected])
Reference : https://doi.org/10.1371/journal.pone.0164680
Usage Examples
===============
To see ‘gene’s related ‘breast cancer’, use this sample code.
>>> bestQuery = best.BESTQuery("breast cancer",
filterObjectName="gene",
noAbsTxt=False)
>>> searchResult = best.getRelevantBioEntities(bestQuery)
>>> print(searchResult)
[{ 'entityname' : 'ERBB2',
'score' : 8098.43,
'PMIDs' : ['28427196', '28341751', '28199325'],
'abstracts' : [
'Molecular-based cancer tests...',
'The molecular subtype of breast...'
'Breast cancer is the second leading cause of...'],
'numArticles':14537
'rank' : 1},
{ 'entityname' : 'ESR1',
'score' : 7340.54,
'PMIDs' : ['27923387', '28274211', '26276891'],
'abstracts' : [
'Several studies have shown that mammographic..',
'A shift towards less burdening and more...'
'The complete molecular basis of the organ-...'],
'numArticles':18084
'rank' : 2},
...
]
Changing noAbsTxt=True can make the process faster.
>>> bestQuery = best.BESTQuery("breast cancer",
filterObjectName="gene",
noAbsTxt=True)
>>> searchResult = best.getRelevantBioEntities(bestQuery)
>>> print(searchResult)
[{ 'entityname' : 'ERBB2',
'score' : 8098.43,
'PMIDs' : [],
'abstracts' : [],
'numArticles':14537
'rank' : 1},
{ 'entityname' : 'ESR1',
'score' : 7340.54,
'PMIDs' : [],
'abstracts' : [],
'numArticles':18084
'rank' : 2},
...
]
If you want to see other entity types, change filterObjectName.
.. note:: Total 10 filterObjects(entity types) are available.
* gene
* drug
* chemical compound
* target
* disease
* toxin
* transcription factor
* mirna
* pathway
* mutation
>>> bestQuery = best.BESTQuery("breast cancer",
filterObjectName="drug",
noAbsTxt=True)
>>> searchResult = best.getRelevantBioEntities(bestQuery)
>>> print(searchResult)
[{ 'entityname' : 'tamoxifen',
'score' : 3208.687,
'abstracts' : [],
'numArticles':10583
'rank' : 1},
{ 'entityname' : 'doxorubicin',
'score' : 1639.867,
'abstracts' : [],
'numArticles':6074
'rank' : 2},
...
]
Class/Function Description
===========================
"""
import http
#from http.client import HTTPException
import socket
class BESTQuery():
"""
BESTQuery class is basic query object for BEST API.
"""
__besturl = "http://best.korea.ac.kr/s?"
def __init__(self, querystr, filterObjectName="All Entity Type", topN=20, noAbsTxt=True):
"""BESTQuery
:param querystr, filterObjectName : result type, topN, noAbsTxt : if True, the result doesn't include the abstract texts.
.
>>> query = BESTQuery("lung cancer", filterObjectName="gene", topN=10, noAbsTxt=False)
>>> # 10 genes related with lung cancer is searched including the abstract texts.
"""
self.querystr = querystr
self.filterObjectName = filterObjectName
self.topN = topN
self.noAbsTxt = noAbsTxt
def setQuerystr (self, querystr):
"""Setting the query
:param querystr: a string object
>>> query.setQuery(["cancer"])
"""
if type(querystr) is not str:
print ("Initialize error : invalid query. It should be a string object.")
print (querystr)
return
if len(querystr) == 0:
return
self.querystr = querystr
def getQuerystr (self):
"""Getting the query String
:return: A string
>>> querystr = query.getQuerystr()
>>> print (querystr)
["cancer"]
"""
return self.querystr
def setTopN (self, n):
""" Setting the number of results retrieved by query
:param n: the number of results to be retrieved
>>> query.setTopN(100)
"""
self.topN = n
def getTopN (self):
""" Getting the number of results retrieved by query
:return: the number of results to be retrieved
>>> print (query.getTopN())
100
"""
return self.topN
def setFilterObjectName (self, oname):
""" Setting the filtering object.
Total 10 types are available.
* gene
* drug
* chemical compound
* target
* disease
* toxin
* transcription factor
* mirna
* pathway
* mutation
>>> qeury.setFilterObjectName("Gene")
"""
self.filterObjectName = oname
def getFilterObjectName (self):
""" Getting the filtering entity type.
>>> print(query.getFilterObjectName())
"breast cancer"
"""
return self.filterObjectName
def getRelevantBioEntities(bestQuery):
""" Function for retrieval from BEST
:param bestQuery: BESTQuery
:return: parsed objects (dict-BIOENTITY).
* BIOENTITY (dict): {"entityName":str, "rank":int, "score":float, "numArticles":int, "abstracts":[str]}
>>> bestQuery = BESTQuery( "lung cancer",
filterObjectName="gene",
topN=10,
noAbsTxt=True )
>>> relevantEntities = getRelevantBioEntities(bestQuery)
"""
if not (type(bestQuery) is BESTQuery):
print ("query is invalid! please check your query object.")
return None
if not bestQuery._isValid() :
print ("Query object is invalid. Please check the query")
print ("Query : ")
print (" query: " + str(bestQuery.query))
print (" topN: " + str(bestQuery.topN))
return None
urlquery = bestQuery.makeQueryString()
import urllib
resultStr = ""
again = 0
while (again < 5):
try:
request = urllib.request.Request(urlquery)
request.add_header('User-Agent', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)')
geneUrl = urllib.request.urlopen(request, timeout=5)
resultStr = geneUrl.read().decode('utf-8')
again = 10
except http.client.BadStatusLine:
again += 1
except http.client.HTTPException:
again += 1
except socket.timeout:
again += 1
except socket.error:
again += 1
except urllib.error.URLError:
again += 1
except Exception:
again += 1
if again == 5:
print("Network status is not good")
return None
result = __makeDataFromBestQueryResult(resultStr)
return result | [
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13290,
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628,
220,
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] | 2.308866 | 3,147 |
import io
import unittest
import unittest.mock
from haste.benchmarking import log
| [
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555,
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198,
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198,
198,
6738,
36797,
13,
26968,
4102,
278,
1330,
2604,
628
] | 3.230769 | 26 |
from RandomBot.MainCog import *
| [
6738,
14534,
20630,
13,
13383,
34,
519,
1330,
1635,
198
] | 3.2 | 10 |
# coding: utf-8
# In[1]:
import sys
sys.path.append("../../examples")
from full_adder import FullAdder
from magma import *
# In[2]:
T = Bits(4)
# In[3]:
from magma.simulator import PythonSimulator
from magma.bit_vector import BitVector
simulator = PythonSimulator(Adder4)
simulator.set_value(Adder4.a, BitVector(2, num_bits=4))
simulator.set_value(Adder4.b, BitVector(3, num_bits=4))
simulator.set_value(Adder4.cin, True)
simulator.evaluate()
assert simulator.get_value(Adder4.out) == BitVector(6, num_bits=4)
assert simulator.get_value(Adder4.cout) == False
print("Success!")
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] | 2.615044 | 226 |
# Space: O(1)
# Time: O(n)
# Recursive approach
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
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] | 2.186047 | 86 |
import errno
import logging
import os
import shutil
import stat
import sys
from typing import TYPE_CHECKING
from dvc.exceptions import DvcException
if TYPE_CHECKING:
from dvc.types import StrPath
logger = logging.getLogger(__name__)
def path_isin(child: "StrPath", parent: "StrPath") -> bool:
"""Check if given `child` path is inside `parent`."""
parent = os.path.join(normalize_path(parent), "")
child = normalize_path(child)
return child != parent and child.startswith(parent)
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] | 3.005882 | 170 |
''' Figure utilities. '''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import types
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
import six
from io import BytesIO
from tensorflow import Summary
from . import mpl_figure
def subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True,
subplot_kw=None, gridspec_kw=None, **fig_kw):
"""
Create a figure and a set of subplots, as in `pyplot.subplots()`.
It works almost similar to `pyplot.subplots()`, but differ from it in that
it does not involve any side effect as pyplot does (e.g. modifying thread
states such as current figure or current subplot).
(docstrings inherited from `matplotlib.pyplot.subplots`)
"""
FigureClass = fig_kw.pop('FigureClass', Figure)
fig = FigureClass(**fig_kw)
# attach a new Agg canvas
if fig.canvas is None:
FigureCanvasAgg(fig)
# create subplots, e.g. fig.subplots() in matplotlib 2.1+
if not hasattr(fig, 'subplots'):
if six.PY2:
fig.subplots = types.MethodType(mpl_figure.subplots, fig, FigureClass)
else:
fig.subplots = types.MethodType(mpl_figure.subplots, fig)
axs = fig.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey,
squeeze=squeeze, subplot_kw=subplot_kw,
gridspec_kw=gridspec_kw)
return fig, axs
# inherit and append a part of the docstring from pyplot.subplots()
subplots.__doc__ += plt.subplots.__doc__[plt.subplots.__doc__.find('Parameters'):]\
.replace('plt.subplots', 'tfplot.subplots')
def to_array(fig):
"""
Convert a matplotlib figure ``fig`` into a 3D numpy array.
Example:
>>> fig, ax = tfplot.subplots(figsize=(4, 4))
>>> # draw whatever, e.g. ax.text(0.5, 0.5, "text")
>>> im = to_array(fig) # ndarray [288, 288, 4]
Args:
fig: A ``matplotlib.figure.Figure`` object.
Returns:
A numpy ``ndarray`` of shape ``(?, ?, 4)``, containing an RGB-A image of
the figure.
"""
# attach a new canvas if not exists
if fig.canvas is None:
FigureCanvasAgg(fig)
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
img = np.frombuffer(fig.canvas.tostring_argb(), dtype=np.uint8)
img = img.reshape((h, w, 4))
img = img[:, :, (1, 2, 3, 0)] # argb -> rgba
return img
def to_summary(fig, tag):
"""
Convert a matplotlib figure ``fig`` into a TensorFlow Summary object
that can be directly fed into ``Summary.FileWriter``.
Example:
>>> fig, ax = ... # (as above)
>>> summary = to_summary(fig, tag='MyFigure/image')
>>> type(summary)
tensorflow.core.framework.summary_pb2.Summary
>>> summary_writer.add_summary(summary, global_step=global_step)
Args:
fig: A ``matplotlib.figure.Figure`` object.
tag (string): The tag name of the created summary.
Returns:
A TensorFlow ``Summary`` protobuf object containing the plot image
as a image summary.
"""
if not isinstance(tag, six.string_types):
raise TypeError("tag must be a string type")
# attach a new canvas if not exists
if fig.canvas is None:
FigureCanvasAgg(fig)
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
# get PNG data from the figure
png_buffer = BytesIO()
fig.canvas.print_png(png_buffer)
png_encoded = png_buffer.getvalue()
png_buffer.close()
summary_image = Summary.Image(height=h, width=w, colorspace=4, # RGB-A
encoded_image_string=png_encoded)
summary = Summary(value=[Summary.Value(tag=tag, image=summary_image)])
return summary
__all__ = (
'to_array',
'to_summary',
'subplots',
)
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220,
13163,
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11,
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796,
2644,
220,
220,
220,
1303,
357,
292,
2029,
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198,
220,
220,
220,
220,
220,
13163,
10638,
796,
284,
62,
49736,
7,
5647,
11,
7621,
11639,
3666,
11337,
14,
9060,
11537,
628,
220,
220,
220,
220,
220,
13163,
2099,
7,
49736,
8,
198,
220,
220,
220,
220,
220,
11192,
273,
11125,
13,
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13,
30604,
13,
49736,
62,
40842,
17,
13,
22093,
198,
220,
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220,
220,
220,
13163,
10638,
62,
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11,
3298,
62,
9662,
28,
20541,
62,
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8,
628,
220,
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220,
943,
14542,
25,
198,
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220,
220,
220,
220,
2336,
25,
317,
7559,
6759,
29487,
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13,
26875,
13,
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15506,
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62,
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3256,
198,
220,
220,
220,
705,
7266,
489,
1747,
3256,
198,
8,
198
] | 2.433519 | 1,617 |
"""
Java Backend Format Strings
The following section lists format strings for the Java backend, and can/
should be updated as the text API to the Stanford Portable Library changes.
The strings are marked using keyword format syntax, which can be expanded by
keyword in Python
"""
# Platform::filelib_fileExists
# Platform::filelib_isFile
# Platform::filelib_isSymbolicLink
# Platform::filelib_isDirectory
# Platform::filelib_setCurrentDirectory
# Platform::filelib_getCurrentDirectory
# Platform::filelib_getTempDirectory
# Platform::filelib_createDirectory
# Platform::filelib_deleteFile
# Platform::filelib_getDirectoryPathSeparator
# Platform::filelib_getSearchPathSeparator
# Platform::filelib_expandPathname
# Platform::filelib_listDirectory
# Platform::filelib_fileExists
# Platform::filelib_isFile
# Platform::filelib_isSymbolicLink
# Platform::filelib_isDirectory
# Platform::filelib_setCurrentDirectory
# Platform::filelib_getCurrentDirectory
# Platform::filelib_getTempDirectory
# Platform::filelib_createDirectory
# Platform::filelib_deleteFile
# Platform::filelib_getDirectoryPathSeparator
# Platform::filelib_getSearchPathSeparator
# Platform::filelib_expandPathname
# Platform::filelib_listDirectory
# Platform::setStackSize
# Platform::os_getLastError
# Platform::regex_match
# Platform::regex_matchCount
# Platform::regex_matchCountWithLines
# Platform::regex_replace
# Transfer to functions: DONE:
# File_
# TODO: special
File_openFileDialog = 'File.openFileDialog("{title!q}", "{mode}", "{path!q}")' # TODO check if needs to be quoted
GWindow_constructor = 'GWindow.create("{id}", {width}, {height}, "{top_compound}", {visible!b})'
GWindow_delete = 'GWindow.delete("{id}")'
GWindow_close = 'GWindow.close("{id}")'
GWindow_requestFocus = 'GWindow.requestFocus("{id}")'
GWindow_setExitOnClose = 'GWindow.setExitOnClose("{id}", {value!b})'
GWindow_clear = 'GWindow.clear("{id}")'
GWindow_clearCanvas = 'GWindow.clearCanvas("{id}")'
GWindow_repaint = 'GWindow.repaint("{id}")'
GWindow_saveCanvasPixels = 'GWindow.saveCanvasPixels("{id}", {filename!q}'
GWindow_setSize = 'GWindow.setSize("{id}", {width}, {height})'
GWindow_setCanvasSize = 'GWindow.setCanvasSize("{id}", {width}, {height})'
GWindow_setCloseOperation = 'GWindow.setCloseOperation("{id}", {op}'
GWindow_minimize = 'GWindow.minimize("{id}")'
GWindow_pack = 'GWindow.pack("{id}")'
GWindow_setTitle = 'GWindow.setTitle("{id}", {title!q})'
GWindow_setLocation = 'GWindow.setLocation("{id}", {x}, {y})'
GWindow_setLocationSaved = 'GWindow.setLocationSaved("{id}", {value!b})'
GWindow_setPixel = 'GWindow.setPixel("{id}", {x}, {y}, {rgb}, {repaint!b})'
GWindow_setPixels = 'GWindow.setPixels("{id}", {base64!q})' # TODO: Special
GWindow_toBack = 'GWindow.toBack("{id}")'
GWindow_toFront = 'GWindow.toFront("{id}")'
GWindow_getScreenHeight = 'GWindow.getScreenHeight()'
GWindow_getScreenSize = 'GWindow.getScreenSize()'
GWindow_getScreenWidth = 'GWindow.getScreenWidth()'
GWindow_draw = 'GWindow.draw("{id}", "{gobj_id}")'
GWindow_drawInBackground = 'GWindow.drawInBackground("{id}", "{gobj_id}")'
GWindow_exitGraphics = 'GWindow.exitGraphics()' # TODO: special
GWindow_setRegionAlignment = 'GWindow.setRegionAlignment("{id}", "{region}", "{align}")'
GWindow_setResizable = 'GWindow.setResizable("{id}", {value!b})'
GWindow_addToRegion = 'GWindow.addToRegion("{id}", "{gobj_id}", "{region}")'
GWindow_getLocation = 'GWindow.getLocation("{id}")' # TODO special
GWindow_getPixel = 'GWindow.getPixel("{id}", {x}, {y})' # TODO special
GWindow_getPixels = 'GWindow.getPixels("{id}")' # TODO special
GWindow_getRegionSize = 'GWindow.getRegionSize("{id}", "{region}")' # TODO: special
GWindow_removeFromRegion = 'GWindow.removeFromRegion("{id}", "{gobj_id}", "{region}")'
GWindow_getSize = 'GWindow.getSize("{id}")' # TODO special
GWindow_getCanvasSize = 'GWindow.getCanvasSize("{id}")' # TODO: special
GWindow_getContentPaneSize = 'GWindow.getContentPaneSize("{id}")' # TODO: special
GWindow_setVisible = 'GWindow.setVisible("{id}", {flag!b})'
GTimer_constructor = 'GTimer.create("{id}", {millis})'
GTimer_delete = 'GTimer.deleteTimer("{id}")'
GTimer_start = 'GTimer.startTimer("{id}")'
GTimer_pause = 'GTimer.pause({millis})'
GTimer_stop = 'GTimer.stopTimer("{id}")'
HttpServer_sendResponse = 'HttpServer.sendResponse({request_id}, {http_error_code}, {contentType!q}, {responseText!q})'
HttpServer_sendResponseFile = 'HttpServer.sendResponseFile({request_id}, {content_type!q}, {response_file_path!q})'
HttpServer_start = 'HttpServer.start({port})'
HttpServer_stop = 'HttpServer.stop()'
Sound_constructor = 'Sound.create("{id}", {filename!q})'
Sound_delete = 'Sound.delete("{id}")'
Sound_play = 'Sound.play("{id}")'
# Platform::url_download
GCompound_constructor = 'GCompound.create("{id}")'
GCompound_add = 'GCompound.add("{compound_id}", "{gobj_id}")'
GObject_delete = 'GObject.delete("{id}")'
GObject_remove = 'GObject.remove("{id}")'
GObject_sendForward = 'GObject.sendForward("{id}")'
GObject_sendToFront = 'GObject.sendToFront("{id}")'
GObject_sendBackward = 'GObject.sendBackward("{id}")'
GObject_sendToBack = 'GObject.sendToBack("{id}")'
GObject_setVisible = 'GObject.setVisible("{id}", {flag!b})'
GObject_setColor = 'GObject.setColor("{id}", "{color}")'
GObject_scale = 'GObject.scale("{id}", {sx}, {sy})'
GObject_rotate = 'GObject.rotate("{id}", {theta})'
GObject_contains = 'GObject.contains("{id}", {x}, {y})' # TODO: special move into gobjects.py
GObject_getBounds = 'GObject.getBounds("{id}")' # TODO: special
GObject_setLineWidth = 'GObject.setLineWidth("{id}", {line_width})'
GObject_setLocation = 'GObject.setLocation("{id}", {x}, {y})'
GObject_setSize = 'GObject.setSize("{id}", {width}, {height})'
GObject_setAntialiasing = 'GObject.setAntialiasing({value!b})'
GObject_setFilled = 'GObject.setFilled("{id}", {flag!b})'
GObject_setFillColor = 'GObject.setFillColor("{id}", "{color}")'
GInteractor_isEnabled = 'GInteractor.isEnabled("{id}")' # TODO: special
GInteractor_setEnabled = 'GInteractor.setEnabled("{id}", {value!b})'
GInteractor_setFont = 'GInteractor.setFont("{id}", "{font!u}")'
GInteractor_setIcon = 'GInteractor.setIcon("{id}", {filename!q})'
GInteractor_setMnemonic = 'GInteractor.setMnemonic("{id}", {mnemonic})' # TODO: special
GInteractor_setText = 'GInteractor.setText("{id}", {text!u})'
GInteractor_setTextPosition = 'GInteractor.setTextPosition("{id}", {horizontal}, {vertical})'
GInteractor_setTooltip = 'GInteractor.setTooltip("{id}", {tooltip_text!q})'
GRect_constructor = 'GRect.create("{id}", {width}, {height})'
GRoundRect_constructor = 'GRoundRect.create("{id}", {width}, {height}, {corner})'
G3DRect_constructor = 'G3DRect.create("{id}", {width}, {height}, {raised!b})'
G3DRect_setRaised = 'G3DRect.setRaised("{id}", {raised!b})'
GOval_constructor = 'GOval.create("{id}", {width}, {height})'
GArc_constructor = 'GArc.create("{id}", {width}, {height}, {start}, {sweep})'
GArc_setStartAngle = 'GArc.setStartAngle("{id}", {angle})'
GArc_setSweepAngle = 'GArc.setSweepAngle("{id}", {angle})'
GArc_setFrameRectangle = 'GArc.setFrameRectangle("{id}", {x}, {y}, {width}, {height})' # TODO: special
GLine_constructor = 'GLine.create("{id}", {x1}, {y1}, {x2}, {y2})'
GLine_setStartPoint = 'GLine.setStartPoint("{id}", {x}, {y})' # TODO special
GLine_setEndPoint = 'GLine.setEndPoint("{id}", {x}, {y})'
GImage_constructor = 'GImage.create("{id}", "{filename}")' # TODO: special
GLabel_constructor = 'GLabel.create("{id}", {label!q})'
GLabel_setFont = 'GLabel.setFont("{id}", "{font}")'
GLabel_setLabel = 'GLabel.setLabel("{id}", {label!q})'
GLabel_getFontAscent = 'GLabel.getFontAscent("{id}")' # TODO: special
GLabel_getFontDescent = 'GLabel.getFontDescent("{id}")' # TODO: special
GLabel_getSize = 'GLabel.getGLabelSize("{id}")' # TODO: special
GPolygon_constructor = 'GPolygon.create("{id}")'
GPolygon_addVertex = 'GPolygon.addVertex("{id}", {x}, {y})' # TODO: special
DiffImage_compareImages = 'DiffImage.compareImages({file1!q}, {file2!q}, {outfile!q})'
DiffImage_compareWindowToImage = 'DiffImage.compareWindowToImage("{id}", {file2!q}, {ignore_winodw_size!b})'
DiffImage_show = 'DiffImage.show({file1!q}, {file2!q})'
GBufferedImage_constructor = 'GBufferedImage.create("{id}", {x}, {y}, {width}, {height}, {rgb})' # TODO special and round
GBufferedImage_fill = 'GBufferedImage.fill("{id}", {rgb})'
GBufferedImage_fillRegion = 'GBufferedImage.fillRegion("{id}", {x}, {y}, {width}, {height}, {rgb})' # TODO rounding
GBufferedImage_load = 'GBufferedImage.load("{id}", {filename!q})'
GBufferedImage_resize = 'GBufferedImage.resize("{id}", {width}, {height}, {retain!b})' # TODO: round
GBufferedImage_save = 'GBufferedImage.save("{id}", {filename!q})'
GBufferedImage_setRGB = 'GBufferedImage.setRGB("{id}", {x}, {y}, {rgb})' # TODO round
GBufferedImage_updateAllPixels = 'GBufferedImage.updateAllPixels("{id}", "{base64}")'
# SECTION: GInteractor
GInteractor_setAccelerator = 'GInteractor.setAccelerator("{id}", {accelerator!u})'
GInteractor_setActionCommand = 'GInteractor.setActionCommand("{id}", {cmd!q})'
GInteractor_setBackground = 'GInteractor.setBackground("{id}", "{color}")'
GInteractor_addActionListener = 'GInteractor.addActionListener("{id}")'
GInteractor_removeActionListener = 'GInteractor.removeActionListener("{id}")'
GInteractor_requestFocus = 'GInteractor.requestFocus("{id}")'
GInteractor_getFont = 'GInteractor.getFont("{id}")' # TODO: special
GInteractor_getMnemonic = 'GInteractor.getMnemonic("{id}")' # TODO: special
GInteractor_getSize = 'GInteractor.getSize("{id}")' # TODO: special
GButton_constructor = 'GButton.create("{id}", {label!q})' # TODO: special
GCheckBox_constructor = 'GCheckBox.create("{id}", {label!q})'
GCheckBox_isSelected = 'GCheckBox.isSelected("{id}")' # TODO: special
GCheckBox_setSelected = 'GCheckBox.setSelected("{id}", {state!b})'
GRadioButton_constructor = 'GRadioButton.create("{id}", {label!q}, {group!q})'
GRadioButton_isSelected = 'GRadioButton.isSelected("{id}")' # TODO: special
GRadioButton_setSelected = 'GRadioButton.setSelected("{id}", {state!b})'
GSlider_constructor = 'GSlider.create("{id}", {min}, {max}, {value})'
GSlider_getMajorTickSpacing = 'GSlider.getMajorTickSpacing("{id}")' # TODO: special
GSlider_getMinorTickSpacing = 'GSlider.getMinorTickSpacing("{id}")' # TODO: special
GSlider_getPaintLabels = 'GSlider.getPaintLabels("{id}")' # TODO: special
GSlider_getPaintTicks = 'GSlider.getPaintTicks("{id}")' # TODO: special
GSlider_getSnapToTicks = 'GSlider.getSnapToTicks("{id}")' # TODO: special
GSlider_getValue = 'GSlider.getValue("{id}")' # TODO: special
GSlider_setMajorTickSpacing = 'GSlider.setMajorTickSpacing("{id}", {value})'
GSlider_setMinorTickSpacing = 'GSlider.setMinorTickSpacing("{id}", {value})'
GSlider_setPaintLabels = 'GSlider.setPaintLabels("{id}", {value!b})'
GSlider_setPaintTicks = 'GSlider.setPaintTicks("{id}", {value!b})'
GSlider_setSnapToTicks = 'GSlider.setSnapToTicks("{id}", {value!b})'
GSlider_setValue = 'GSlider.setValue("{id}", {value})'
GTable_constructor = 'GTable.create("{id}", {num_rows}, {num_cols}, {x}, {y}, {width}, {height})' # TODO: special
GTable_autofitColumnWidths = 'GTable.autofitColumnWidths("{id}")'
GTable_clear = 'GTable.clear("{id}")'
GTable_clearFormatting = 'GTable.clearFormatting("{id}")'
GTable_get = 'GTable.get("{id}", {row}, {column})' # TODO: special
GTable_getColumnWidth = 'GTable.getColumnWidth("{id}", {column})' # TODO: special
GTable_getSelection = 'GTable.getSelection("{id}")' # TODO: special
GTable_resize = 'GTable.resize("{id}", {num_rows}, {num_cols})' # TODO: special
GTable_select = 'GTable.select("{id}", {row}, {column})'
GTable_set = 'GTable.set("{id}", {row}, {column}, {value!q})'
GTable_setCellAlignment = 'GTable.setCellAlignment("{id}", {row}, {column}, {align})'
GTable_setCellBackground = 'GTable.setCellBackground("{id}", {row}, {column}, {color!q})'
GTable_setCellFont = 'GTable.setCellFont("{id}", {row}, {column}, {font!q})'
GTable_setCellForeground = 'GTable.setCellForeground("{id}", {row}, {column}, {color!q}))'
GTable_setColumnAlignment = 'GTable.setColumnAlignment("{id}", {column}, {align}))'
GTable_setColumnBackground = 'GTable.setColumnBackground("{id}", {column}, {color!q})'
GTable_setColumnFont = 'GTable.setColumnFont("{id}", {column}, {font!q})'
GTable_setColumnForeground = 'GTable.setColumnForeground("{id}", {column}, {color!q})'
GTable_setColumnHeaderStyle = 'GTable.setColumnHeaderStyle("{id}", {style})'
GTable_setColumnWidth = 'GTable.setColumnWidth("{id}", {column}, {width})'
GTable_setEditable = 'GTable.setEditable("{id}", {editable!b})'
GTable_setEditorValue = 'GTable.setEditorValue("{id}", {row}, {column}, {value!q})'
GTable_setEventEnabled = 'GTable.setEventEnabled("{id}", {type}, {enabled!b})'
GTable_setFont = 'GTable.setFont("{id}", {font!q})'
GTable_setHorizontalAlignment = 'GTable.setHorizontalAlignment("{id}", {alignment!q})'
GTable_setRowAlignment = 'GTable.setRowAlignment("{id}", {row}, {align})'
GTable_setRowBackground = 'GTable.setRowBackground("{id}", {row}, {color!q})'
GTable_setRowColumnHeadersVisible = 'GTable.setRowColumnHeadersVisible("{id}", {visible!b})'
GTable_setRowFont = 'GTable.setRowFont("{id}", {row}, {font!q})'
GTable_setRowForeground = 'GTable.setRowForeground("{id}", {row}, {color!q})'
GTextArea_constructor = 'GTextArea.create("{id}", {width}, {height})'
GTextArea_getText = 'GTextArea.getText("{id}")' # TODO: special
GTextArea_setEditable = 'GTextArea.setEditable("{id}", {editable!b})'
GTextArea_setFont = 'GTextArea.setFont("{id}", {font!q})'
GTextArea_setText = 'GTextArea.setText("{id}", {text!q})'
GTextField_constructor = 'GTextField.create("{id}", {num_chars})' # TODO: special
GTextField_getText = 'GTextField.getText("{id}")' # TODO: special
GTextField_isEditable = 'GTextField.isEditable("{id}")' # TODO: special
GTextField_setEditable = 'GTextField.setEditable("{id}", {editable!b})'
GTextField_setPlaceholder = 'GTextField.setPlaceholder("{id}", {text!q})'
GTextField_setText = 'GTextField.setText("{id}", {text!q})'
GChooser_constructor = 'GChooser.create("{id}")' #TODO: special
GChooser_addItem = 'GChooser.addItem("{id}", {item!q})'
GChooser_getSelectedItem = 'GChooser.getSelectedItem("{id}")' # TODO: special
GChooser_setSelectedItem = 'GChooser.setSelectedItem("{id}", {item!q})'
# END SECTION: GInteractor
GEvent_getNextEvent = 'GEvent.getNextEvent({mask})' # TODO: special
GEvent_waitForEvent = 'GEvent.waitForEvent({mask})' # TODO: special
GFileChooser_showOpenDialog = 'GFileChooser.showOpenDialog({current_dir!q}, {file_filter!q})' # TODO: special
GFileChooser_showSaveDialog = 'GFileChooser.showSaveDialog({current_dir!q}, {file_filter!q})' # TODO: special
GOptionPane_showConfirmDialog = 'GOptionPane.showConfirmDialog({message!u}, {title!u}, {type})' # TODO: special
GOptionPane_showInputDialog = 'GOptionPane.showInputDialog({message!u}, {title!u})' # TODO: special
GOptionPane_showMessageDialog = 'GOptionPane.showMessageDialog({message!u}, {title!u}, {type})' # TODO: special
GOptionPane_showOptionDialog = 'GOptionPane.showOptionDialog({message!u}, {title!u}, {options}, {initially_selected!q})' #TODO: very special
GOptionPane_showTextFileDialog = 'GOptionPane.showTextFileDialog({message!u}, {title!u}, {rows}, {cols})' # TODO: special
Clipboard_get = 'Clipboard.get()' # TODO: special
Clipboard_set = 'Clipboard.set({text!u})'
# Platform::cpplib_setCppLibraryVersion
# Platform::cpplib_getCppLibraryVersion
SPL_getJavaBackEndVersion = 'StanfordCppLib.getJbeVersion()' # TODO: special
# JBEConsole_isBlocked = 'JBEConsole.isBlocked()'
JBEConsole_print = 'JBEConsole.print({line!q}, {stderr!b})' # TODO: special
JBEConsole_getLine = 'JBEConsole.getLine()' # TODO: special
JBEConsole_clear = 'JBEConsole.clear()'
JBEConsole_minimize = 'JBEConsole.minimize()'
JBEConsole_setFont = 'JBEConsole.setFont({font})'
JBEConsole_setSize = 'JBEConsole.setSize({width}, {height})'
JBEConsole_setTitle = 'JBEConsole.setTitle({title!q})'
JBEConsole_setLocation = 'JBEConsole.setLocation({x}, {y})'
JBEConsole_setCloseOperation = 'JBEConsole.setCloseOperation({value})'
JBEConsole_setErrorColor = 'JBEConsole.setErrorColor({color!q})'
JBEConsole_setExitProgramOnClose = 'JBEConsole.setExitProgramOnClose({value!b})'
JBEConsole_setLocationSaved = 'JBEConsole.setLocationSaved({value!b})'
JBEConsole_setOutputColor = 'JBEConsole.setOutputColor({color!q})'
JBEConsole_setVisible = 'JBEConsole.setVisible({value!b})'
JBEConsole_toFront = 'JBEConsole.toFront()'
Note_play = 'Note.play({note!u})' # TODO: special
AutograderInput_addButton = 'AutograderInput.addButton({text!u}, {input!u})' # TODO: special
AutograderInput_removeButton = 'AutograderInput.removeButton({text!u})'
AutograderInput_addCategory = 'AutograderInput.addCategory({name!u})'
AutograderInput_removeCategory = 'AutograderInput.removeCategory({name!u})'
AutograderInput_setVisible = 'AutograderInput.setVisible({visible!b})'
AutograderUnitTest_addTest = 'AutograderUnitTest.addTest({test_name!u}, {category!u}, {style_check!b})'
AutograderUnitTest_catchExceptions = 'AutograderUnitTest.catchExceptions()' # TODO: special
AutograderUnitTest_clearTests = 'AutograderUnitTest.clearTests({style_check!b})'
AutograderUnitTest_clearTestResults = 'AutograderUnitTest.clearTestResults({style_check!b})'
AutograderUnitTest_isChecked = 'AutograderUnitTest.isChecked({full_test_name!u})' # TODO special
AutograderUnitTest_setChecked = 'AutograderUnitTest.setChecked({full_test_name!u}, {checked!b})'
AutograderUnitTest_setTestCounts = 'AutograderUnitTest.setTestCounts({pass_count}, {test_count}, {style_check!b})'
AutograderUnitTest_setTestDetails = 'AutograderUnitTest.setTestDetails({test_full_name!u}, {deets!u}, {style_check!b})'
AutograderUnitTest_setTestingCompleted = 'AutograderUnitTest.setTestingCompleted({completed!b}, {style_check!b})'
AutograderUnitTest_setTestResult = 'AutograderUnitTest.setTestResult({test_full_name!u}, {result!u}, {style_check!b})'
AutograderUnitTest_setTestRuntime = 'AutograderUnitTest.setTestRuntime({test_full_name}, {runtime_ms})'
AutograderUnitTest_setVisible = 'AutograderUnitTest.setVisible({visible!b}, {style_check!b})'
AutograderUnitTest_setWindowDescriptionText = 'AutograderUnitTest.setWindowDescriptionText({text!u}, {style_check!b})'
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] | 2.612779 | 6,996 |
# coding=utf-8
import urllib
import re
from proxy import Proxy
from .basespider import BaseSpider
from bs4 import BeautifulSoup
| [
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] | 3.195122 | 41 |
from tracardi.service.storage.elastic_client import ElasticClient
from tracardi.service.storage.factory import storage_manager
from tracardi.domain.storage_result import StorageResult
from tracardi.service.storage.index import resources
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491,
330,
22490,
13,
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13,
35350,
13,
9630,
1330,
4133,
628,
628
] | 3.934426 | 61 |
#!/usr/bin/env python3
# Copyright (c) Yugabyte, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License
# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
# or implied. See the License for the specific language governing permissions and limitations
# under the License.
#
import build_definitions
from build_definitions import * # noqa
from yugabyte_db_thirdparty.builder import Builder
from yugabyte_db_thirdparty.custom_logging import (
log_separator,
heading,
configure_logging,
)
from yugabyte_db_thirdparty.multi_build import MultiBuilder
from yugabyte_db_thirdparty.remote_build import build_remotely
from yugabyte_db_thirdparty.shared_library_checking import get_lib_tester
from yugabyte_db_thirdparty.download_manager import DownloadManager
import json
import_submodules(build_definitions)
if __name__ == "__main__":
main()
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834,
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220,
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220,
1388,
3419,
198
] | 3.421512 | 344 |
"""
GoogleViews:
/s(earch) <term>
/i(mage) <term>
youtube urls
"""
from ..utils import media_sender
import requests, urllib
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] | 2.366667 | 60 |
import subprocess
from glob import glob
from os.path import join
import numpy as np
import seisflows.seistools.specfem3d as solvertools
from seisflows.seistools.shared import getpar, setpar
from seisflows.tools import unix
from seisflows.tools.code import exists
from seisflows.tools.config import SeisflowsParameters, SeisflowsPaths, \
ParameterError, loadclass
PAR = SeisflowsParameters()
PATH = SeisflowsPaths()
import system
class specfem3d(loadclass('solver', 'base')):
""" Python interface for SPECFEM3D
See base class for method descriptions
"""
def check(self):
""" Checks parameters and paths
"""
super(specfem3d, self).check()
# check time stepping parameters
if 'NT' not in PAR:
raise Exception
if 'DT' not in PAR:
raise Exception
if 'F0' not in PAR:
raise Exception
def generate_data(self, **model_kwargs):
""" Generates data
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.getpath)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
self.mpirun('bin/xspecfem3D')
unix.mv(self.data_wildcard, 'traces/obs')
self.export_traces(PATH.OUTPUT, 'traces/obs')
def generate_mesh(self, model_path=None, model_name=None, model_type='gll'):
""" Performs meshing and database generation
"""
assert(model_name)
assert(model_type)
self.initialize_solver_directories()
unix.cd(self.getpath)
if model_type in ['gll']:
par = getpar('MODEL').strip()
if par != 'gll':
if self.getnode == 0:
print 'WARNING: Unexpected Par_file setting:'
print 'MODEL =', par
assert(exists(model_path))
self.check_mesh_properties(model_path)
src = glob(model_path +'/'+ '*')
dst = self.model_databases
unix.cp(src, dst)
self.mpirun('bin/xmeshfem3D')
self.mpirun('bin/xgenerate_databases')
self.export_model(PATH.OUTPUT +'/'+ model_name)
else:
raise NotImplementedError
### low-level solver interface
def forward(self):
""" Calls SPECFEM3D forward solver
"""
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
self.mpirun('bin/xgenerate_databases')
self.mpirun('bin/xspecfem3D')
def adjoint(self):
""" Calls SPECFEM3D adjoint solver
"""
setpar('SIMULATION_TYPE', '3')
setpar('SAVE_FORWARD', '.false.')
unix.rm('SEM')
unix.ln('traces/adj', 'SEM')
self.mpirun('bin/xspecfem3D')
### input file writers
def check_solver_parameter_files(self):
""" Checks solver parameters
"""
nt = getpar('NSTEP', cast=int)
dt = getpar('DT', cast=float)
if nt != PAR.NT:
if self.getnode == 0: print "WARNING: nt != PAR.NT"
setpar('NSTEP', PAR.NT)
if dt != PAR.DT:
if self.getnode == 0: print "WARNING: dt != PAR.DT"
setpar('DT', PAR.DT)
if self.mesh.nproc != PAR.NPROC:
if self.getnode == 0:
print 'Warning: mesh.nproc != PAR.NPROC'
if 'MULTIPLES' in PAR:
raise NotImplementedError
### miscellaneous
@property
@property
@property
@property
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] | 2.090261 | 1,684 |
""" TensorMONK :: loss :: utils """
__all__ = ["compute_n_embedding", "compute_top15", "one_hot", "one_hot_idx",
"hard_negative_mask"]
import torch
import numpy as np
@torch.no_grad()
@torch.no_grad()
@torch.no_grad()
@torch.no_grad()
def hard_negative_mask(prediction: torch.Tensor,
targets: torch.Tensor,
pos_to_neg_ratio: float = 0.25):
r""" Hard negative mask generator for object detection (includes both
positives and hard negatives).
Args:
prediction (torch.Tensor): label predictions of the network must be
2D/3D tensor.
2D: Two class problem with scores ranging from 0 to 1, where 0 is
background.
3D: N-class predictions before softmax where
F.softmax(prediction, -1)[:, :, 0] are the probabilities of
background.
targets (torch.Tensor): A 2D tensor of labels/targets. 0 is background.
pos_to_neg_ratio (float): Ratio of positives to negatives.
default = 0.25
"""
assert prediction.ndim == 2 or prediction.ndim == 3, \
"hard_negative_mask: prediction must be 2D/3D tensor."
assert targets.ndim == 2, \
"hard_negative_mask: targets must be 2D tensor."
prediction = prediction.clone()
ns = prediction.size(0)
foreground_mask = targets > 0
ignore_mask = targets < 0
if prediction.shape == foreground_mask.shape:
# assumes, two class problem and sigmoid is applied to output
assert 1. >= prediction.max() and prediction.min() >= 0., \
"hard_negative_mask: Use torch.sigmoid(prediction)."
# Mark the background_mask with hard negatives
background_mask = torch.zeros_like(targets).bool()
for i in range(ns):
retain = max(1, int(foreground_mask[i].sum() / pos_to_neg_ratio))
probs = prediction[i]
# foreground prob to minimum
probs[foreground_mask[i]] = 0.
# remove invalid targets
probs[ignore_mask[i]] = 0.
background_mask[i, torch.argsort(probs)[-retain:]] = True
else:
# assumes, N-class problem and softmax is not applied to output
assert ~ prediction.sum(-1).eq(1).all(), \
"hard_negative_mask: Requires predictions before softmax."
background_probs = torch.nn.functional.softmax(prediction, -1)[:, :, 0]
# Mark the background_mask with hard negatives
background_mask = torch.zeros_like(targets).bool()
for i in range(ns):
retain = max(1, int(foreground_mask[i].sum() / pos_to_neg_ratio))
probs = background_probs[i]
# foreground prob to maximum
probs[foreground_mask[i]] = 1.
# remove invalid targets
probs[ignore_mask[i]] = 1.
background_mask[i, torch.argsort(probs)[:retain]] = True
mask = foreground_mask.bool() | background_mask.bool()
return mask
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] | 2.327399 | 1,292 |
'''
The module linear.py provides the linear
preparation analogous of parallelize.py.
'''
from qiskit import *
from qiskit.circuit import Parameter
def get_measurement_circ(n, qregname, cregname, full_measurement=True):
'''
Creates a measurement circuit that can toggle between
measuring the first control qubit or measuring all qubits.
The default is measurement of all qubits.
Args:
n: number of qubits
full_measurement:
Whether to append full measurement, or only on the first qubit.
Returns:
The measurement suffix for a circuit
'''
q = QuantumRegister(n, qregname)
if full_measurement:
cla = ClassicalRegister(n, cregname)
meas = QuantumCircuit(q, cla)
meas.barrier()
meas.measure(q, cla)
return meas
cla = ClassicalRegister(1, cregname)
meas = QuantumCircuit(q, cla)
meas.barrier()
meas.measure(q[0], cla)
return meas
def get_ghz_simple(n, measure=True, full_measurement=True):
'''
Creates a linear GHZ state
with the option of measurement
Args:
n: number of qubits
measure (Boolean): Whether to add measurement gates
full_measurement:
Whether to append full measurement, or only on the first qubit.
Relevant only for measure=True
Returns:
A linear GHZ Circuit
'''
q = QuantumRegister(n, 'q')
circ = QuantumCircuit(q)
circ.h(q[0])
for i in range(1, n):
circ.cx(q[i - 1], q[i])
if measure:
meas = get_measurement_circ(n, 'q', 'c', full_measurement)
circ = circ + meas
return circ
def get_ghz_mqc(n, delta, full_measurement):
'''
This function creates an MQC circuit with n qubits,
where the middle phase rotation around the z axis is by delta
'''
q = QuantumRegister(n, 'q')
circ = get_ghz_simple(n, measure=False)
circinv = circ.inverse()
circ.barrier()
circ.u1(delta, q)
circ.x(q)
circ.barrier()
circ += circinv
meas = get_measurement_circ(n, 'q', 'c', full_measurement)
circ = circ + meas
return circ
def get_ghz_mqc_para(n, full_measurement=True):
'''
This function creates an MQC circuit with n qubits,
where the middle phase rotation around the z axis is by delta
Args:
n: number of qubits
full_measurement:
Whether to append full measurement, or only on the first qubit.
Returns:
An mqc circuit and its Delta parameter
'''
q = QuantumRegister(n, 'q')
circ = get_ghz_simple(n, measure=False)
delta = Parameter('t')
circinv = circ.inverse()
circ.barrier()
circ.u1(delta, q)
circ.x(q)
circ.barrier()
circ += circinv
meas = get_measurement_circ(n, 'q', 'c', full_measurement)
circ = circ + meas
return circ, delta
def get_ghz_po(n, delta):
'''
This function creates an Parity Oscillation circuit
with n qubits, where the middle superposition rotation around
the x and y axes is by delta
'''
q = QuantumRegister(n, 'q')
circ = get_ghz_simple(n, measure=False)
circ.barrier()
circ.u2(delta, -delta, q)
circ.barrier()
meas = get_measurement_circ(n, 'q', 'c', True)
circ = circ + meas
return circ
def get_ghz_po_para(n):
'''
This function creates a Parity Oscillation circuit with n qubits,
where the middle superposition rotation around
the x and y axes is by delta
Args:
n: number of qubits
Returns:
A parity oscillation circuit and its Delta/minus-delta parameters
'''
q = QuantumRegister(n, 'q')
delta = Parameter('t')
deltaneg = Parameter('-t')
circ = get_ghz_simple(n, measure=False)
circ.barrier()
circ.u2(delta, deltaneg, q)
meas = get_measurement_circ(n, 'q', 'c', True)
circ = circ + meas
return circ, [delta, deltaneg]
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11,
685,
67,
12514,
11,
1619,
83,
1531,
70,
60,
198
] | 2.443396 | 1,590 |
import time
import unittest
import sqlalchemy as _sqla
import sqlalchemy.orm as _sqla_orm
from .. import models
| [
11748,
640,
198,
11748,
555,
715,
395,
198,
198,
11748,
44161,
282,
26599,
355,
4808,
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5031,
198,
11748,
44161,
282,
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355,
4808,
31166,
5031,
62,
579,
198,
198,
6738,
11485,
1330,
4981,
628,
628,
198
] | 3.025641 | 39 |
#!/usr/bin/env python
# coding=utf-8
# Created by JTProgru
# Date: 2019-08-13
# https://jtprog.ru/
__author__ = 'jtprogru'
__version__ = '0.0.1'
__author_email__ = '[email protected]'
import twitter
import dotenv as d
from pathlib import Path
from speedtest import Speedtest
import json
env = d.get_variables(str(Path(__file__).parent / '.env'))
servers = []
# If you want to test against a specific server
# servers = [1234]
threads = None
# If you want to use a single threaded test
# threads = 1
s = Speedtest()
# s.get_servers(servers)
s.get_best_server()
s.download(threads=threads)
s.upload(threads=threads)
# s.results.json()
resp = json.loads(s.results.json(pretty=True))
print(resp)
print()
print('%.2f' % (resp['bytes_received'] / 1024 / 1024))
# my_auth = twitter.OAuth(env['TOKEN'], env['TOKEN_KEY'], env['CON_SEC'], env['CON_SEC_KEY'])
# twit = twitter.Twitter(auth=my_auth)
# try to tweet if speedtest couldnt even connet.
# probably wont work if the internet is down
# if "Cannot" in resp:
# try:
# tweet_status = "WTF, что с моим интернетом? #JTProgru #"
# twit.statuses.update(status=tweet_status)
# except Exception as e:
# print(str(e))
# pass
# tweet if down speed is less than whatever I set
# elif eval(resp['download']) < eval(env['ISP_PAYED_SPEED']):
# print(eval(dowloadspeed))
# print(eval(uploadspeed))
# print(eval(env['ISP_PAYED_SPEED']))
# print("trying to tweet")
# try:
# i know there must be a better way than to do (str(int(eval())))
# tweet = str(float(eval(dowloadspeed))) + " down\\" + str(float(eval(uploadspeed))) + " up #speedtest"
# twit.statuses.update(status='My speedtest now is: ' + tweet)
# except Exception as e:
# print(str(e))
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] | 2.413279 | 738 |
"""Caching utilities for the merge-counts command line tool."""
import glob
import os
import json
import tempfile
from pathlib import Path
from typing import Dict, Optional
from logzero import logger
from . import errors
CACHE_POINTER_LOCATION = Path.home() / ".mergecounts-cache"
class DNAnexusFileCache:
"""A utility class for keeping up with cached DNAnexus objects."""
def __init__(self):
"""Creates a DNAnexusFileCache object to hold the list of known properties
and describe calls for each DNAnexus file id.
"""
self.properties = {}
self.describes = {}
self.counts = {}
def load_from_filesystem(self):
"""Loads any cached information from the filesystem."""
self.properties = load_cached_properties_from_filesystem()
self.describes = load_cached_describes_from_filesystem()
##############################
# Cache folder manipulations #
##############################
def get_cache_folder() -> Optional[str]:
"""Gets the top level cache folder if it exists in the file at CACHE_POINTER_LOCATION.
If that file does not exist, merge-counts has not instantiated a cache folder, so None
is returned.
Returns:
Optional[str]: the cache folder directory or None if a cache has not been created by merge-counts.
"""
if not os.path.exists(CACHE_POINTER_LOCATION):
return None
# will always be the first line of the file
cache_loc = [l.strip() for l in open(CACHE_POINTER_LOCATION, "r").readlines()][0]
if not os.path.exists(cache_loc):
errors.raise_error(
f"Cache pointed to in {CACHE_POINTER_LOCATION} does not exist! {cache_loc}."
)
return Path(cache_loc)
def create_new_cache_folder() -> None:
"""Creates a new cache folder as a temporary dir (assumes that an existing cache instantiated
by merge-counts does not exist and errors if it does).
"""
cache_folder_loc = get_cache_folder()
if cache_folder_loc:
errors.raise_error(
f"Refusing to overwrite existing cache: {cache_folder_loc}",
suggest_report=False,
)
new_cache_loc = tempfile.mkdtemp()
with open(CACHE_POINTER_LOCATION, "w") as cache_pointer:
cache_pointer.writelines(new_cache_loc)
logger.info(
"Created new cache folder pointer at %s to %s.",
CACHE_POINTER_LOCATION,
new_cache_loc,
)
def clean_cache() -> None:
"""Assuming a cache instantied by merge-counts exists, it will clean and remove the cache or
silently succeed otherwise (e.g. if the cache folder does not exist).
"""
cache_folder_loc = get_cache_folder()
if not cache_folder_loc or not os.path.exists(cache_folder_loc):
logger.debug("No cache folder to delete.")
else:
logger.debug("Removing cache folder: %s.", cache_folder_loc)
os.removedirs(cache_folder_loc)
if os.path.exists(CACHE_POINTER_LOCATION):
logger.debug("Removing cache folder pointer.")
os.remove(CACHE_POINTER_LOCATION)
def get_cached_properties_folder(silently_create: bool = True) -> Path:
"""Returns the subfolder within the cache that contains all DNAnexus properties for
each dxid. In this folder, the filename is the dxid and the contents of each property
are the DNAnexus properties as JSON objects.
Arguments:
silently_create (bool): if the subfolder does not exist, create before returning.
Defaults to True. If False, this will error if the folder
doesn't exist.
Returns:
Path: path to the subfolder containing the cached DNAnexus property files.
"""
properties_folder = get_cache_folder() / "properties"
if not os.path.exists(properties_folder):
if silently_create:
os.makedirs(properties_folder)
else:
errors.raise_error(
f"Properties subfolder in cache does not exist: {properties_folder}!"
)
return properties_folder
def get_cached_describes_folder(silently_create: bool = True) -> Path:
"""Returns the subfolder within the cache that contains all DNAnexus describe calls for
each dxid. In this folder, the filename is the dxid and the contents of each property
are the DNAnexus describe calls as JSON objects.
Arguments:
silently_create (bool): if the subfolder does not exist, create before returning.
Defaults to True. If False, this will error if the folder
doesn't exist.
Returns:
Path: path to the subfolder containing the cached DNAnexus property files.
"""
describes_folder = get_cache_folder() / "describes"
if not os.path.exists(describes_folder):
if silently_create:
os.makedirs(describes_folder)
else:
errors.raise_error(
f"Properties subfolder in cache does not exist: {describes_folder}!"
)
return describes_folder
def cache_properties_on_filesystem(dxid: str, properties: Dict) -> None:
"""Caches DNAnexus properties in the property subfolder of the cache.
Args:
dxid (str): DNAnexus id of the file in question.
properties (Dict): DNAnexus properties as a dict.
"""
cache_filepath = get_cached_properties_folder() / dxid
with open(cache_filepath, "w") as cache:
json.dump(properties, cache)
def cache_describes_on_filesystem(dxid: str, describe: Dict) -> None:
"""Caches DNAnexus describe calls in the describes subfolder of the cache.
Args:
dxid (str): DNAnexus id of the file in question.
describe (Dict): DNAnexus describe call as a dict.
"""
cache_filepath = get_cached_describes_folder() / dxid
with open(cache_filepath, "w") as cache:
json.dump(describe, cache)
def load_cached_properties_from_filesystem() -> Dict:
"""Loads the cached DNAnexus properties from the appropriate subfolder in the
merge-counts cache.
Returns:
Dict: all cached properties where the key is the DNAnexus file id and the value
is the DNAnexus properties as a dict.
"""
result = dict()
path = str(get_cached_properties_folder() / "*")
for filename in glob.glob(path):
basename = os.path.basename(filename)
result[basename] = json.load(open(filename, "r"))
logger.info("Loaded %d entries from the properties cache.", len(result.items()))
return result
def load_cached_describes_from_filesystem() -> Dict:
"""Loads the cached DNAnexus describe calls from the appropriate subfolder in the
merge-counts cache.
Returns:
Dict: all cached describes where the key is the DNAnexus file id and the value
is the DNAnexus describe call as a dict.
"""
result = dict()
for filename in glob.glob(str(get_cached_describes_folder() / "*")):
basename = os.path.basename(filename)
result[basename] = json.load(open(filename, "r"))
logger.info("Loaded %d entries from the describes cache.", len(result.items()))
return result
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] | 2.71504 | 2,653 |
# Connor Sanders
# 10/16/2017
# Yahoo Finance Web Scraper
# Tested on python 2.7 and 3.5
# Returns a JSON File containing financial statement data for a given company or list of companies
# How to use this script:
# Make sure you have the PhantomJS Driver set up. If you need the manually add the path, go to line 150
# python yahoo_surface_scraper.py StockTicker StatementType JSONFilename Frequency
# If running multiple stock tickers at once, separate them each by commas with no spaces in between
# Examples:
# python yahoo_surface_scraper.py AAPL Income test.json Annual
# or
# python3 yahoo_surface_scraper.py WFC Balance testfile Quarterly
# or
# python3 yahoo_surface_scraper.py WFC,AAPL Balance extracts.json Quarterly
# Feel free to email [email protected] with any questions, concerns, or improvement ideas!
import os
import sys
import re
import pip
from json import load, dump
import signal
import time
import datetime
# Package installer function to handle missing packages
# Ensure beautifulsoup4 is installed
try:
from bs4 import BeautifulSoup
except:
install('beautifulsoup4')
from bs4 import BeautifulSoup
# Ensure selenium is installed
try:
from selenium import webdriver
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
except:
install('selenium')
from selenium import webdriver
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
# Declare datetime time stamp, and current working directory and file variables
dt = datetime.datetime.today()
c_dir = os.getcwd()
# OS Specific Code for Cross Platform Purposes
os_system = os.name
if os_system == 'nt':
json_local_dir = c_dir + '\\json_extracts\\'
fields_dir = c_dir + '\\data_fields\\'
else:
json_local_dir = c_dir + '/json_extracts/'
fields_dir = c_dir + '/data_fields/'
# Function to check user inputs
# Function to create JSON Extract File Name
# Function to write JSON Data to Timestamped JSON Output File
# Main Function
# Main Process Handler for Script
if __name__ == '__main__':
check_inputs(sys.argv)
if len(sys.argv) == 5:
main(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
elif len(sys.argv) == 4:
main(sys.argv[1], sys.argv[2], sys.argv[3]) | [
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#!/usr/bin/env python
import argparse
import numpy as np
import rospy
import message_filters
from message_filters import TimeSynchronizer
from ros_x_habitat.msg import PointGoalWithGPSCompass, DepthImage
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
from PIL import Image as PILImage
from nav_msgs.msg import Odometry
from std_msgs.msg import Header, Int16
from geometry_msgs.msg import PoseStamped, Pose, Point
import quaternion
from habitat.tasks.utils import cartesian_to_polar
from tf.transformations import euler_from_quaternion, rotation_matrix
from visualization_msgs.msg import Marker, MarkerArray
from threading import Lock
from ros_x_habitat.srv import GetAgentPose
from src.constants.constants import PACKAGE_NAME, ServiceNames
from src.utils import utils_logging
class GazeboToHabitatAgent:
r"""
A class to represent a ROS node which subscribes from Gazebo sensor
topics and publishes to Habitat agent's sensor topics.
"""
def __init__(
self,
node_name: str,
gazebo_rgb_topic_name: str,
gazebo_depth_topic_name: str,
gazebo_odom_topic_name: str,
move_base_goal_topic_name: str,
fetch_goal_from_move_base: bool=False,
final_pointgoal_pos: np.ndarray = np.array([0.0, 0.0, 0.0]),
):
r"""
Instantiates the Gazebo->Habitat agent bridge.
:param node_name: name of the bridge node
:param gazebo_rgb_topic_name: name of the topic on which Gazebo
publishes RGB observations
:param gazebo_depth_topic_name: name of the topic on which Gazebo
publishes depth observations
:param gazebo_odom_topic_name: name of the topic on which Gazebo
publishes odometry data
:param move_base_goal_topic_name: name of the topic on which the
move_base package publishes navigation goals
:param fetch_goal_from_move_base: True if we fetch the goal position
from topic <move_base_goal_topic_name>
:param final_pointgoal_pos: goal location of navigation, measured
in the world frame. If `fetch_goal_from_move_base` is True, this
position is ignored
"""
# initialize the node
self.node_name = node_name
rospy.init_node(self.node_name)
# bridge's publish and subscribe queue size
# TODO: make them configurable by constructor argument
self.sub_queue_size = 1
self.pub_queue_size = 1
# set up logger
self.logger = utils_logging.setup_logger(self.node_name)
# set max number of steps for navigation
# TODO: make them configurable by constructor argument
self.max_steps = 500
# last_action_done controls when to publish the next set
# of observations
self.last_action_lock = Lock()
with self.last_action_lock:
self.count_steps = 0
self.last_action_done = True
# current pose of the agent
self.curr_pose_lock = Lock()
with self.curr_pose_lock:
self.prev_pos = None # NOTE: self.prev_pos is for visualization only
self.curr_pos = None
self.curr_rotation = None
# initialize pointogal_reached flag
self.pointgoal_reached_lock = Lock()
with self.pointgoal_reached_lock:
self.pointgoal_reached = False
# initialize final pointgoal position
self.final_pointgoal_lock = Lock()
with self.final_pointgoal_lock:
self.final_pointgoal_pos = None
self.pointgoal_set = False
if fetch_goal_from_move_base:
# subscribe from goal position topic and get
# the point-goal from there
self.sub_goal = rospy.Subscriber(
move_base_goal_topic_name,
PoseStamped,
self.callback_register_goal,
queue_size=self.sub_queue_size
)
else:
# get goal directly from constructor argument
with self.final_pointgoal_lock:
self.final_pointgoal_pos = final_pointgoal_pos
self.logger.info(f"Final pointgoal position set to: {self.final_pointgoal_pos}")
self.pointgoal_set = True
# subscribe from Gazebo-facing sensor topics
self.sub_rgb = message_filters.Subscriber(gazebo_rgb_topic_name, Image)
self.sub_depth = message_filters.Subscriber(gazebo_depth_topic_name, Image)
self.sub_odom = message_filters.Subscriber(gazebo_odom_topic_name, Odometry)
self.ts = TimeSynchronizer(
[self.sub_rgb, self.sub_depth, self.sub_odom],
queue_size=self.sub_queue_size,
)
self.ts.registerCallback(self.callback_obs_from_gazebo)
# subscribe from `last_action_done/`
self.sub_last_action_done = rospy.Subscriber(
"last_action_done",
Int16,
self.callback_signal_last_action,
queue_size=self.sub_queue_size
)
# publish to Habitat-agent-facing sensor topics
self.pub_rgb = rospy.Publisher(
"rgb",
Image,
queue_size=self.pub_queue_size
)
self.pub_depth = rospy.Publisher(
"depth",
DepthImage,
queue_size=self.pub_queue_size
)
self.pub_pointgoal_with_gps_compass = rospy.Publisher(
"pointgoal_with_gps_compass",
PointGoalWithGPSCompass,
queue_size=self.pub_queue_size
)
# establish get_agent_pose service server
self.get_agent_pose_service = rospy.Service(
f"{PACKAGE_NAME}/{node_name}/{ServiceNames.GET_AGENT_POSE}",
GetAgentPose,
self.get_agent_pose,
)
# publish initial/goal position for visualization
self.marker_array_lock = Lock()
with self.marker_array_lock:
self.marker_array = MarkerArray()
self.pub_init_and_goal_pos = rospy.Publisher(
"visualization_marker_array",
MarkerArray,
queue_size=self.pub_queue_size
)
self.logger.info("gazebo -> habitat agent bridge initialized")
def pos_to_point(self, pos):
r"""
Produce a ROS point from a position.
:param pos: position as a (3, ) numpy array.
:return a ROS point.
"""
point = Point()
point.x = pos[0]
point.y = pos[1]
point.z = pos[2]
return point
def add_pos_to_marker_array(self, pos_type, pos_0, pos_1=None, rot=None):
r"""
Add position(s) to the marker array for visualization.
Require:
1) self.marker_array_lock not being held by the calling
thread
2) the lock guarding `pos_0` and `pos_1` being held by the
calling thread
3) self.last_action_lock is being held by the calling thread
:param pos_type: can only be one of the following:
1) "curr" - path from previous to current position of the agent
2) "init" - inital position of the agent
3) "goal" - final goal position
:param pos_0: position to add marker to
:param pos_1: second position for drawing line segment; Only used if
`pos_type` is "curr"
:param rot: orientation of the marker
"""
# precondition checks
assert pos_type in ["curr", "init", "goal"]
if pos_type == "curr":
pos_1 is not None
# code adapted from
# https://answers.ros.org/question/11135/plotting-a-markerarray-of-spheres-with-rviz/
pos_marker = Marker()
pos_marker.id = self.count_steps
pos_marker.header.frame_id = "odom"
pos_marker.action = pos_marker.ADD
if pos_type == "curr":
pos_marker.type = pos_marker.LINE_STRIP
point_0 = self.pos_to_point(pos_0)
point_1 = self.pos_to_point(pos_1)
pos_marker.points.append(point_0)
pos_marker.points.append(point_1)
pos_marker.scale.x = 0.05
pos_marker.color.a = 1.0
pos_marker.color.g = 1.0
elif pos_type == "init":
pos_marker.type = pos_marker.SPHERE
pos_marker.pose.position.x = pos_0[0]
pos_marker.pose.position.y = pos_0[1]
pos_marker.pose.position.z = pos_0[2]
pos_marker.scale.x = 0.4
pos_marker.scale.y = 0.4
pos_marker.scale.z = 0.4
pos_marker.color.a = 1.0
pos_marker.color.b = 1.0
elif pos_type == "goal":
pos_marker.type = pos_marker.SPHERE
pos_marker.pose.position.x = pos_0[0]
pos_marker.pose.position.y = pos_0[1]
pos_marker.pose.position.z = pos_0[2]
pos_marker.scale.x = 0.4
pos_marker.scale.y = 0.4
pos_marker.scale.z = 0.4
pos_marker.color.a = 1.0
pos_marker.color.r = 1.0
with self.marker_array_lock:
self.marker_array.markers.append(pos_marker)
def publish_marker_array(self):
r"""
Publish the marker array.
"""
with self.marker_array_lock:
self.pub_init_and_goal_pos.publish(self.marker_array)
def compute_pointgoal(self):
r"""
Compute distance-to-goal and angle-to-goal. Modified upon
habitat.task.nav.nav.PointGoalSensor._compute_pointgoal().
Require 1) `self.curr_pose_lock` and `self.final_pointgoal_lock`
already acquired in the calling thread
:return tuple 1) distance-to-goal, 2) angle-to-goal
"""
# get the yaw angle
_, _, yaw = euler_from_quaternion(self.curr_rotation)
# build local->world matrix
local_to_world = rotation_matrix(yaw, (0, 0, 1))
# build world->local matrix
world_to_local = np.linalg.inv(local_to_world)
# compute direction vector in world frame
direction_vector_world = np.zeros((4, ))
direction_vector_world[0:3] = self.final_pointgoal_pos - self.curr_pos
direction_vector_world[-1] = 0
# compute direction vector in local frame
direction_vector_local = np.matmul(world_to_local, direction_vector_world)[0:3]
rho, phi = cartesian_to_polar(
direction_vector_local[0], direction_vector_local[1]
)
return rho, phi
def rgb_msg_to_img(self, rgb_msg, dim):
r"""
Extract RGB image from RGB message. Further compress the RGB
image to size `dim` x `dim`.
:param rgb_msg: RGB sensor reading from Gazebo
:param dim: dimension of the RGB observation
:return: RGB observation as a numpy array
"""
rgb_img = CvBridge().imgmsg_to_cv2(rgb_msg, desired_encoding="rgb8")
rgb_img = cv2.resize(rgb_img, (dim, dim), interpolation = cv2.INTER_AREA)
return rgb_img
def depth_msg_to_img(self, depth_msg, dim):
r"""
Extract depth image from depth message. Further compress the depth
image to size `dim` x `dim`.
:param depth_msg: Depth sensor reading from Gazebo
:param dim: dimension of the depth observation
:return: Depth observation as a numpy array
"""
depth_img_original = np.copy(CvBridge().imgmsg_to_cv2(
depth_msg,
desired_encoding="passthrough"))
# remove nan values by replacing with 0's
# idea: https://github.com/stereolabs/zed-ros-wrapper/issues/67
for row in range(depth_img_original.shape[0]):
for col in range(depth_img_original.shape[1]):
if np.isnan(depth_img_original[row][col]):
depth_img_original[row][col] = 0.0
#depth_img_normalized = cv2.normalize(
# depth_img_original,
# None,
# alpha=0,
# beta=255.0,
# norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F
#)
#depth_img_resized = cv2.resize(depth_img_normalized,
# (dim, dim),
# interpolation = cv2.INTER_AREA
#)
depth_img_resized = cv2.resize(depth_img_original,
(dim, dim),
interpolation = cv2.INTER_AREA
)
depth_img = np.array(depth_img_resized, dtype=np.float64)
#depth_img_pil = PILImage.fromarray(depth_img)
#depth_img_pil.save("gazebo_obs/depth.png", mode="L")
return depth_img
def update_pose(self, odom_msg):
r"""
Update current position, current rotation, previous position.
Require `self.curr_pose_lock` already acquired by the calling
thread
:param odom_msg: Odometry data from Gazebo
"""
self.prev_pos = self.curr_pos
self.curr_pos = np.array(
[odom_msg.pose.pose.position.x,
odom_msg.pose.pose.position.y,
odom_msg.pose.pose.position.z
]
)
self.curr_rotation = [
odom_msg.pose.pose.orientation.x,
odom_msg.pose.pose.orientation.y,
odom_msg.pose.pose.orientation.z,
odom_msg.pose.pose.orientation.w
]
def callback_obs_from_gazebo(self, rgb_msg, depth_msg, odom_msg):
r"""
Upon receiving a set of RGBD observations and odometry data from
Gazebo, publishes
1) the observations to `rgb/` and `depth/` topic for a Habitat
agent to read;
2) current GPS+Compass data.
Only publishes iff all following conditions are satisfied:
1) on reset or after the last action has been completed,
2) pointogoal is set,
3) pointgoal not reached yet.
:param rgb_msg: RGB sensor reading from Gazebo
:param depth_msg: Depth sensor reading from Gazebo
:param odom_msg: Odometry data from Gazebo
"""
with self.last_action_lock:
with self.pointgoal_reached_lock:
with self.final_pointgoal_lock:
if (
self.last_action_done
and self.pointgoal_set
and not self.pointgoal_reached
and not (self.count_steps >= self.max_steps)
):
# publish a new set of observations if last action's done,
# pointgoal's set and navigation not completed yet
# get a header object and assign time
h = Header()
h.stamp = rospy.Time.now()
# create RGB message for Habitat
rgb_img = self.rgb_msg_to_img(rgb_msg, 720)
rgb_msg_for_hab = CvBridge().cv2_to_imgmsg(rgb_img, encoding="rgb8")
rgb_msg_for_hab.header = h
# create depth message for Habitat
depth_img = self.depth_msg_to_img(depth_msg, 720)
depth_msg_for_hab = DepthImage()
depth_msg_for_hab.height, depth_msg_for_hab.width = depth_img.shape
depth_msg_for_hab.step = depth_msg_for_hab.width
depth_msg_for_hab.data = np.ravel(depth_img)
depth_msg_for_hab.header = h
with self.curr_pose_lock:
# update pose and compute current GPS+Compass info
self.update_pose(odom_msg)
distance_to_goal, angle_to_goal = self.compute_pointgoal()
ptgoal_gps_msg = PointGoalWithGPSCompass()
ptgoal_gps_msg.distance_to_goal = distance_to_goal
ptgoal_gps_msg.angle_to_goal = angle_to_goal
ptgoal_gps_msg.header = h
# publish observations
self.pub_rgb.publish(rgb_msg_for_hab)
self.pub_depth.publish(depth_msg_for_hab)
self.pub_pointgoal_with_gps_compass.publish(ptgoal_gps_msg)
# block further sensor callbacks
self.last_action_done = False
# add the current position to the marker array for
# visualization
with self.curr_pose_lock:
if self.count_steps == 0:
self.logger.info(f"initial agent position: {self.curr_pos}")
self.add_pos_to_marker_array(
"init",
self.curr_pos
)
else:
self.add_pos_to_marker_array(
"curr",
self.prev_pos,
self.curr_pos,
self.curr_rotation
)
elif(
self.last_action_done
and self.pointgoal_set
and (
self.pointgoal_reached
or self.count_steps >= self.max_steps
)
):
# if the navigation is completed, publish data
# for visualization
self.logger.info(f"navigation completed after {self.count_steps} steps")
with self.curr_pose_lock:
self.logger.info(f"final agent position: {self.curr_pos}")
self.add_pos_to_marker_array(
"goal",
self.final_pointgoal_pos
)
self.publish_marker_array()
self.last_action_done = False
else:
# otherwise, drop the observations
return
def callback_register_goal(self, goal_msg):
r"""
Register point-goal position w.r.t. the world frame.
:param goal_msg: point-goal position from move_base
"""
with self.final_pointgoal_lock:
self.final_pointgoal_pos = np.array([
goal_msg.pose.position.x,
goal_msg.pose.position.y,
goal_msg.pose.position.z
])
self.pointgoal_set = True
self.logger.info(f"Final pointgoal position set to: {self.final_pointgoal_pos}")
def callback_signal_last_action(self, done_msg):
r"""
Signal the last action being done; toggle `pointgoal_reached`
flag if the last action is `STOP`.
"""
if done_msg.data == 1:
# last action is `STOP`
with self.pointgoal_reached_lock:
self.pointgoal_reached = True
with self.last_action_lock:
# increment step counter and signal last action being done
self.count_steps += 1
self.last_action_done = True
def get_agent_pose(self, request):
r"""
ROS service handler which returns the current pose of the agent.
:param request: not used
:returns: current pose of the agent
"""
pose = Pose()
with self.curr_pose_lock:
pose.position.x = self.curr_pos[0]
pose.position.y = self.curr_pos[1]
pose.position.z = self.curr_pos[2]
pose.orientation.x = self.curr_rotation[0]
pose.orientation.y = self.curr_rotation[1]
pose.orientation.z = self.curr_rotation[2]
pose.orientation.w = self.curr_rotation[3]
return pose
def spin_until_shutdown(self):
r"""
Let the node spin until shutdown.
"""
rospy.spin()
if __name__ == "__main__":
main() | [
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] | 1.958217 | 10,363 |
# ================================================== #
# DASHBOARD #
# ================================================== #
# Author: Brady Hammond #
# Created: 05/04/2018 #
# Last Edited: #
# Last Edited By: #
# ================================================== #
# IMPORTS #
# ================================================== #
from __future__ import generator_stop
import falcon
import mimetypes
import os
# ================================================== #
# CLASS DEFINITIONS #
# ================================================== #
class StaticSink(object):
"""
Class for Falcon sink endpoint serving static files.
"""
# ================================================== #
# EOF #
# ================================================== # | [
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220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
198,
2,
46111,
4770,
28,
1303,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
30023,
33002,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
198,
2,
46111,
4770,
28,
1303,
198,
198,
6738,
11593,
37443,
834,
1330,
17301,
62,
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198,
11748,
24215,
1102,
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17007,
2963,
12272,
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198,
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2,
46111,
4770,
28,
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2,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
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42715,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
198,
2,
46111,
4770,
28,
1303,
198,
198,
4871,
36125,
50,
676,
7,
15252,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
5016,
329,
17621,
14595,
36123,
7351,
9037,
3696,
13,
198,
220,
220,
220,
37227,
198,
198,
2,
46111,
4770,
28,
1303,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
412,
19238,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
198,
2,
46111,
4770,
28,
1303
] | 2.331868 | 455 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import locale
from PySide2.QtCore import Qt, QTranslator
from PySide2.QtGui import QGuiApplication, QIcon, QFont
from scihubeva.scihubeva_dialog import SciHubEVADialog
from scihubeva.utils import *
import scihubeva.resources
if hasattr(Qt, 'AA_EnableHighDpiScaling'):
QGuiApplication.setAttribute(Qt.AA_EnableHighDpiScaling, True)
if hasattr(Qt, 'AA_UseHighDpiPixmaps'):
QGuiApplication.setAttribute(Qt.AA_UseHighDpiPixmaps, True)
if __name__ == '__main__':
app_path = os.path.abspath(os.path.dirname(sys.argv[0]))
os.environ['QT_QUICK_CONTROLS_CONF'] = (CONF_DIR / 'qtquickcontrols2.conf').resolve().as_posix()
app = QGuiApplication(sys.argv)
lang = locale.getdefaultlocale()[0]
lang_file_path = (TRANSLATION_DIR / 'SciHubEVA_{lang}.qm'.format(lang=lang)).resolve().as_posix()
translator = QTranslator()
translator.load(lang_file_path)
app.installTranslator(translator)
icon_file_path = (IMAGES_DIR / 'SciHubEVA-icon.png').resolve().as_posix()
app.setWindowIcon(QIcon(icon_file_path))
if is_windows():
app.setFont(QFont('Microsoft YaHei'))
eva = SciHubEVADialog()
sys.exit(app.exec_())
| [
2,
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50,
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80,
76,
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7,
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28,
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292,
62,
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844,
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198,
220,
220,
220,
33417,
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220,
220,
220,
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198,
220,
220,
220,
598,
13,
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7,
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8,
628,
220,
220,
220,
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62,
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62,
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796,
357,
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62,
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50,
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27881,
12,
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13,
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27691,
411,
6442,
22446,
292,
62,
1930,
844,
3419,
198,
220,
220,
220,
598,
13,
2617,
27703,
19578,
7,
48,
19578,
7,
4749,
62,
7753,
62,
6978,
4008,
628,
220,
220,
220,
611,
318,
62,
28457,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
598,
13,
2617,
23252,
7,
48,
23252,
10786,
15905,
26421,
1544,
72,
6,
4008,
628,
220,
220,
220,
819,
64,
796,
10286,
16066,
20114,
2885,
498,
519,
3419,
198,
220,
220,
220,
25064,
13,
37023,
7,
1324,
13,
18558,
62,
28955,
198
] | 2.391473 | 516 |
# A basic script to remove any part of the configuration that is described in "hassci"
#Open the file
with open(r'configuration.yaml', 'r') as file:
data = file.read()
# Open ignore file
with open('.hass_ci') as ignore_file:
# Open ignore file and replace matches line by line
for search_text in ignore_file:
data = data.replace(search_text, "#REDACTED")
# Write to file
with open(r'configuration.yaml', 'w') as file:
file.write(data)
print("configuration.yaml has been redacted.")
| [
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25,
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220,
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4798,
7203,
11250,
3924,
13,
88,
43695,
468,
587,
44740,
19570,
198
] | 2.824468 | 188 |
import platform
import doctest
import unittest
from nose.tools import assert_raises
from urlblocks import urlblocks as urlblocks_module
from urlblocks import URL
from urlblocks.six import text_type, u, print_
def dictsort(d):
"""``repr()`` a dictionary with sorted key/value pairs, for doctests."""
items = sorted(d.items())
print_('{' + ', '.join('%r: %r' % (k, v) for k, v in items) + '}')
| [
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] | 2.957143 | 140 |
import json
import re
from collections import OrderedDict, defaultdict
import cchardet
import requests
from colorama import Fore
from selenium import webdriver
from toapi.cache import CacheSetting
from toapi.log import logger
from toapi.server import Server
from toapi.settings import Settings
from toapi.storage import Storage
class Api:
"""Api handle the routes dispatch"""
def register(self, item):
"""Register items"""
if item in self.item_classes:
logger.error('Register', 'Repeat register item <%s>' % (item.__name__))
exit()
self.item_classes.append(item)
item.__base_url__ = item.__base_url__ or self.base_url
for define_alias, define_route in OrderedDict(item.Meta.route).items():
alias = '^' + define_alias.replace('?', '\?') + '$'
_alias_re = re.compile(re.sub(':(?P<params>[a-z_]+)',
lambda m: '(?P<{}>[A-Za-z0-9_?&/=\s\-\u4e00-\u9fa5]+)'.format(
m.group('params')),
alias))
self.alias_re.append((define_alias, _alias_re))
self.items[define_alias].append({
'item': item,
'alias_re': _alias_re,
'alias': define_alias,
'route': item.__base_url__ + define_route
})
logger.info(Fore.GREEN, 'Register', '<%s>' % (item.__name__))
item_with_ajax = getattr(item.Meta, 'web', {}).get('with_ajax', False)
if self.browser is None and item_with_ajax:
self.browser = self.get_browser(settings=self.settings, item_with_ajax=item_with_ajax)
def parse(self, path, params=None, **kwargs):
"""Parse items from a url"""
items = self.prepare_parsing_items(path)
if items is None:
return None
results = OrderedDict()
cached_html = {}
for index, item in enumerate(items):
converted_path = item['converted_path']
html = cached_html.get(converted_path) or self.get_storage(converted_path) or self.fetch_page_source(
converted_path,
item=item['item'],
params=params,
**kwargs)
if html is not None:
cached_html[converted_path] = html
parsed_item = self.parse_item(html, item['item'])
results[item['item'].__name__] = parsed_item
return json.dumps(results) if results else None
def fetch_page_source(self, url, item, params=None, **kwargs):
"""Fetch the html of given url"""
self.update_status('_status_sent')
if getattr(item.Meta, 'web', {}).get('with_ajax', False) and self.browser is not None:
self.browser.get(url)
text = self.browser.page_source
if text != '':
logger.info(Fore.GREEN, 'Sent', '%s %s 200' % (url, len(text)))
else:
logger.error('Sent', '%s %s' % (url, len(text)))
result = text
else:
request_config = getattr(item.Meta, 'web', {}).get('request_config', {}) or self.web.get(
'request_config', {})
response = requests.get(url, params=params, timeout=15, **request_config)
content = response.content
charset = cchardet.detect(content)
text = content.decode(charset['encoding'] or 'utf-8')
if response.status_code != 200:
logger.error('Sent', '%s %s %s' % (url, len(text), response.status_code))
else:
logger.info(Fore.GREEN, 'Sent', '%s %s %s' % (url, len(text), response.status_code))
result = text
self.set_storage(url, result)
return result
def get_browser(self, settings, item_with_ajax=False):
"""Get browser"""
if not getattr(self.settings, 'web', {}).get('with_ajax', False) and not item_with_ajax:
return None
if getattr(settings, 'headers', None) is not None:
for key, value in settings.headers.items():
capability_key = 'phantomjs.page.customHeaders.{}'.format(key)
webdriver.DesiredCapabilities.PHANTOMJS[capability_key] = value
phantom_options = []
phantom_options.append('--load-images=false')
return webdriver.PhantomJS(service_args=phantom_options)
def update_status(self, key):
"""Increment Status"""
self.cache.incr(key)
def get_status(self, key):
"""Get Status"""
return int(self.cache.get(key, 0))
def set_cache(self, key, value):
"""Set cache"""
if self.cache.get(key) is None and self.cache.set(key, value):
logger.info(Fore.YELLOW, 'Cache', 'Set<%s>' % key)
self.update_status('_status_cache_set')
return True
return False
def get_cache(self, key, default=None):
"""Set cache"""
result = self.cache.get(key)
if result is not None:
logger.info(Fore.YELLOW, 'Cache', 'Get<%s>' % key)
self.update_status('_status_cache_get')
return result
return default
def set_storage(self, key, value):
"""Set storage"""
try:
if self.storage.get(key) is None and self.storage.save(key, value):
logger.info(Fore.BLUE, 'Storage', 'Set<%s>' % key)
self.update_status('_status_storage_set')
return True
return False
except Exception as e:
logger.error('Storage', 'Set<{}>'.format(str(e)))
return False
def get_storage(self, key, default=None):
"""Set storage"""
result = self.storage.get(key)
if result is not None:
logger.info(Fore.BLUE, 'Storage', 'Get<%s>' % key)
self.update_status('_status_storage_get')
return result
return default
def parse_item(self, html, item):
"""Parse item from html"""
result = item.parse(html)
if len(result) == 0:
logger.error('Parsed', 'Item<%s[%s]>' % (item.__name__.title(), len(result)))
else:
logger.info(Fore.CYAN, 'Parsed', 'Item<%s[%s]>' % (item.__name__.title(), len(result)))
return result
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"""
Exercise 2
Write a program to look for lines of the form
New Revision: 39772
And extract the number from each of the lines using a regular expression and
the findall() method. Compute the average of the numbers and print out the
average.
Enter file:mbox.txt
38549.7949721
Enter file:mbox-short.txt
39756.9259259
"""
import re
file = open("mbox-short.txt")
lines = [line.strip("\n") for line in file]
total = 0
count = 0
for line in lines:
if re.findall("New Revision: 397*", line):
count += 1
total += float(line[14:19])
print(total / count)
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] | 2.874372 | 199 |
###########################################################################
# Imports
###########################################################################
# Standard library imports
import argparse
import time as time
import numpy as np
import math as math
import matplotlib.pyplot as plt
from matplotlib import path
# Local imports
from helper_funcs import *
###########################################################################
# Code
###########################################################################
# KNOWNS
args = cli_parser()
Vinf = args.Vinf
AoA = args.AoA
numB = args.numB
# Convert AoA to radians [rad]
AoAR = AoA * (np.pi / 180)
# Plotting flags
flagPlot = [1, # Shape polygon with panel normal vectors
1, # Geometry boundary pts, control pts, first panel
1, # Analytical and SPM pressure coefficient plot
1, # Streamline plot
1] # Pressure coefficient contour plot
# Grid parameters
# X & Y grid for streamlines and contours
nGridX = nGridY = 150
# X-grid extents [min, max]
xVals = [-2, 2]
# Y-grid extents [min, max]
yVals = [-2, 2]
# %% FUNCTIONS
XB, YB, numPan = create_elliptical_panels(
numB=numB, a=args.ellipse_a, b=args.ellipse_b
)
XB, YB = correct_panels_orientation(numPan, XB, YB)
XC, YC, S, beta, delta, phi = compute_panel_geometries(numPan, XB, YB, AoA)
K, L = compute_kl_vpm(XC, YC, XB, YB, phi, S)
A, b = populate_matrices_vpm(numPan, K, beta, Vinf)
A, b = satisfy_kutta_condition_vpm(numPan, A, b, pct=args.replacement_pct)
gamma = np.linalg.solve(A, b)
print("Sum of gamma: ", sum(gamma * S))
Vt, Cp = compute_panel_velocities(numPan, gamma, beta, L, Vinf)
# Analytical angles and pressure coefficients
# Analytical theta angles [rad]
analyticTheta = np.linspace(0, 2 * np.pi, 200)
# Analytical pressure coefficient []
analyticCP = 1 - 4 * np.sin(analyticTheta)**2
CN, CA, CL, CD, CM = compute_force_coefficients(XC, phi, beta, AoAR, Cp, S)
# Print the results to the Console
print("\n======= RESULTS =======")
print("Lift Coefficient (CL)")
# From Kutta-Joukowski lift equation
print(f" K-J : {2*sum(gamma*S)}")
# From this VPM code
print(f" VPM : {CL}")
print("\nMoment Coefficient (CM)")
print(f" VPM : {CM}")
# %% COMPUTE STREAMLINES - REF [4]
if (flagPlot[3] == 1 or flagPlot[4] == 1):
# Streamline parameters
# Percentage of streamlines of the grid
slPct = 25
# Create array of Y streamline starting points
Ysl = np.linspace(yVals[0], yVals[1], int((slPct / 100) * nGridY))
# Create array of X streamline starting points
Xsl = xVals[0] * np.ones(len(Ysl))
# Concatenate X and Y streamline starting points
XYsl = np.vstack((Xsl.T, Ysl.T)).T
# Generate the grid points
# X-values in evenly spaced grid
Xgrid = np.linspace(xVals[0], xVals[1], nGridX)
# Y-values in evenly spaced grid
Ygrid = np.linspace(yVals[0], yVals[1], nGridY)
# Create meshgrid from X and Y grid arrays
XX, YY = np.meshgrid(Xgrid, Ygrid)
# Initialize velocities
# Initialize X velocity matrix
Vx = np.zeros([nGridX, nGridY])
# Initialize Y velocity matrix
Vy = np.zeros([nGridX, nGridY])
# Path to figure out if grid point is inside polygon or not
# Concatenate XB and YB geometry points
AF = np.vstack((XB.T, YB.T)).T
# Create a path for the geometry
afPath = path.Path(AF)
# Solve for grid point X and Y velocities
tic = time.perf_counter()
# Loop over X-grid points
for m in range(nGridX):
# Loop over Y-grid points
for n in range(nGridY):
# Current iteration's X grid point
XP = XX[m, n]
# Current iteration's Y grid point
YP = YY[m, n]
# Compute Nx and Ny geometric integrals
Nx, Ny = streamline_vpn(XP, YP, XB, YB, phi, S)
# Check if grid points are in object
# - If they are, assign a velocity of zero
# If (XP,YP) is in the body
if afPath.contains_points([(XP, YP)]):
# Set X-velocity equal to zero
Vx[m, n] = 0
# Set Y-velocity equal to zero
Vy[m, n] = 0
else:
# Compute X-velocity
Vx[m, n] = Vinf * np.cos(AoAR) + sum(-gamma * Nx / (2 * np.pi))
# Compute Y-velocity
Vy[m, n] = Vinf * np.sin(AoAR) + sum(-gamma * Ny / (2 * np.pi))
toc = time.perf_counter()
print("\n\nSTREAMLINE_VPM: %.2f seconds" % (toc - tic))
# Compute grid point velocity magnitude and pressure coefficient
# Compute magnitude of velocity vector []
Vxy = np.sqrt(Vx**2 + Vy**2)
# Pressure coefficient []
CpXY = 1 - (Vxy / Vinf)**2
# %% PLOTTING
# FIGURE: Shape polygon with panel normal vectors
if (flagPlot[0] == 1):
# Angles for "perfect" circle
angCirc = np.linspace(0, 2 * np.pi, 1000)
# "Perfect" circle X values
xCirc = np.cos(angCirc)
# "Perfect" circle Y values
yCirc = np.sin(angCirc)
# Create figure
fig = plt.figure(1)
# Clear the axes
plt.cla()
# Plot the circle that polygon is approximating
plt.plot(xCirc, yCirc, 'k--')
# Plot the paneled circle
plt.fill(XB, YB, 'k')
# Initialize 'X'
X = np.zeros(2)
# Initialize 'Y'
Y = np.zeros(2)
# Loop over all panels
for i in range(numPan):
# Set X start of panel orientation vector
X[0] = XC[i]
# Set X end of panel orientation vector
X[1] = XC[i] + S[i] * np.cos(delta[i])
# Set Y start of panel orientation vector
Y[0] = YC[i]
# Set Y end of panel orientation vector
Y[1] = YC[i] + S[i] * np.sin(delta[i])
# If it's the first panel index
if (i == 0):
# Plot the first panel
plt.plot(X, Y, 'b-', label='First Panel')
# If it's the second panel index
elif (i == 1):
# Plot the second panel
plt.plot(X, Y, 'g-', label='Second Panel')
# If it's neither the first nor second panel index
else:
# Plot the rest of the panels
plt.plot(X, Y, 'r-')
# Set X-label
plt.xlabel('X-Axis')
# Set Y-label
plt.ylabel('Y-Axis')
# Set title
plt.title('Panel Geometry')
# Set axes equal
plt.axis('equal')
# Show legend
plt.legend()
fname = os.path.join('figs', 'ellipses', 'panel_geometry.png')
plt.savefig(fname, dpi=args.dpi, bbox_inches='tight')
# FIGURE: Geometry with the following indicated:
# - Boundary points, control points, first panel, second panel
if (flagPlot[1] == 1):
# Create figure
fig = plt.figure(2)
# Get ready for plotting
plt.cla()
# Plot polygon
plt.plot(XB, YB, 'k-', label='Panels')
plt.plot([XB[0], XB[1]], [YB[0], YB[1]], 'b-',
label='First Panel') # Plot first panel
plt.plot([XB[1], XB[2]], [YB[1], YB[2]], 'g-',
label='Second Panel') # Plot second panel
# Plot boundary points
plt.plot(
XB, YB, 'ko', markerfacecolor='k', label='Boundary Points'
)
# Plot control points
plt.plot(
XC, YC, 'ko', markerfacecolor='r', label='Control Points'
)
# Set X-label
plt.xlabel('X-Axis')
# Set Y-label
plt.ylabel('Y-Axis')
# Set title
plt.title('Panel Geometry 2')
# Set axes equal
plt.axis('equal')
# Show legend
plt.legend()
fname = os.path.join('figs', 'ellipses', 'panel_geometry2.png')
plt.savefig(fname, dpi=args.dpi, bbox_inches='tight')
# FIGURE: Analytical and SPM pressure coefficient
if (flagPlot[2] == 1):
# Create figure
fig = plt.figure(3)
# Get ready for plotting
plt.cla()
# Plot analytical pressure coefficient
plt.plot(
analyticTheta * (180 / np.pi), analyticCP, 'b-', label='Analytical'
)
# Plot panel method pressure coefficient
plt.plot(
beta * (180 / np.pi), Cp, 'ks', markerfacecolor='r', label='VPM'
)
# Set X-label
plt.xlabel('Angle [deg]')
# Set Y-label
plt.ylabel('Pressure Coefficient')
# Set title
plt.title('Pressure Coefficient Comparison')
# Set X-limits
plt.xlim(0, 360)
# Set Y-limits
plt.ylim(-3.5, 1.5)
# Show legend
plt.legend()
fname = os.path.join('figs', 'ellipses', 'pressure_coefficient_comparison.png')
plt.savefig(fname, dpi=args.dpi, bbox_inches='tight')
# FIGURE: Streamlines
if (flagPlot[3] == 1):
# Create figure
fig = plt.figure(5)
# Get ready for plotting
plt.cla()
# Ignore underflow error message
np.seterr(under="ignore")
# Plot streamlines
plt.streamplot(
XX, YY, Vx, Vy, linewidth=0.5, density=40, color='r', arrowstyle='-',
start_points=XYsl
)
plt.clim(vmin=0, vmax=2)
# Plot airfoil as black polygon
plt.fill(XB, YB, 'k')
# Set X-label
plt.xlabel('X Units')
# Set Y-label
plt.ylabel('Y Units')
# Set axes equal
plt.gca().set_aspect('equal')
# Set X-limits
plt.xlim(xVals)
# Set Y-limits
plt.ylim(yVals)
fname = os.path.join('figs', 'ellipses', 'streamlines.png')
plt.savefig(fname, dpi=args.dpi, bbox_inches='tight')
# FIGURE: Pressure coefficient contours
if (flagPlot[4] == 1):
# Create figure
fig = plt.figure(6)
# Get ready for plotting
plt.cla()
# Plot contour
plt.contourf(XX, YY, CpXY, 500, cmap='jet')
# Plot airfoil as black polygon
plt.fill(XB, YB, 'k')
# Set X-label
plt.xlabel('X Units')
# Set Y-label
plt.ylabel('Y Units')
# Set axes equal
plt.gca().set_aspect('equal')
# Set X-limits
plt.xlim(xVals)
# Set Y-limits
plt.ylim(yVals)
plt.colorbar()
fname = os.path.join('figs', 'ellipses', 'pressure_coefficient_contours.png')
plt.savefig(fname, dpi=args.dpi, bbox_inches='tight')
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] | 2.252322 | 4,415 |
from .devices import data_parallel, empty_cache, find, move_to_device, CPU, GPU | [
6738,
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] | 3.291667 | 24 |
##
##
## Interface to allow remote kerberos authentication via Multiplexor
##
##
##
##
##
## TODO: RPC auth type is not implemented or tested!!!!
from msldap.authentication.spnego.asn1_structs import KRB5Token
from msldap.authentication.kerberos.gssapi import get_gssapi, GSSWrapToken, KRB5_MECH_INDEP_TOKEN
from minikerberos.protocol.asn1_structs import AP_REQ, AP_REP, TGS_REP
from minikerberos.protocol.encryption import Enctype, Key, _enctype_table
from multiplexor.operator.external.sspi import KerberosSSPIClient
from multiplexor.operator import MultiplexorOperator
import enum
# mutual auth not supported
# encryption is always on
# we dont get the output flags back (lack of time to do the multiplexor protocol... TODO
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] | 3.024691 | 243 |
#!/usr/bin/python
# Copyright: (c) 2020, Tatsuya Naganawa <[email protected]>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
ANSIBLE_METADATA = {
'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'
}
DOCUMENTATION = '''
---
module: firewall_rule
short_description: create tungstenfabirc firewall-rule
version_added: "2.9"
description:
- "create / delete tungstenfabric firewall-rule"
options:
name:
description:
- firewall-rule name
required: true
controller_ip:
description:
- tungstenfabric controller ip
required: true
project:
description:
- project name (if it is defined, firewall-rule will be project scoped rule)
required: false
firewall_rule:
description:
- rule of this firewall-rule (see EXAMPLES)
required: false
author:
- Tatsuya Naganawa (@tnaganawa)
'''
EXAMPLES = '''
- name: create firewall_rule
tungstenfabric.networking.firewall_rule:
name: firewall_rule1
controller_ip: x.x.x.x
state: present
endpoint_1: {virtual_network: default-domain:admin:vn1}
endpoint_2: {virtual_network: default-domain:admin:vn2}
service: {protocol: any}
action_list: {simple_action: pass}
- name: create project-scope firewall_rule
tungstenfabric.networking.firewall_rule:
name: firewall_rule1
controller_ip: x.x.x.x
state: present
project: admin
endpoint_1: {virtual_network: default-domain:admin:vn1}
endpoint_2: {virtual_network: default-domain:admin:vn2}
service: {protocol: any}
action_list: {simple_action: pass}
- name: delete firewall_rule
tungstenfabric.networking.firewall_rule:
name: firewall_rule1
controller_ip: x.x.x.x
state: absent
'''
RETURN = '''
message:
description: The output message that this module generates
type: str
returned: always
'''
import sys
import json
import requests
from ansible.module_utils.basic import AnsibleModule
from ansible_collections.tungstenfabric.networking.plugins.module_utils.common import login_and_check_id, crud
if __name__ == '__main__':
main()
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] | 2.552632 | 874 |
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import annotations # isort:skip
import pytest ; pytest
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Bokeh imports
from bokeh.models import (
FixedTicker,
MathText,
PlainText,
TeX,
)
# Module under test
import bokeh.models.axes as bma # isort:skip
#-----------------------------------------------------------------------------
# Setup
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------
| [
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] | 7.364017 | 239 |
import unittest
from ..sdk import Client, Results
from . import config
if __name__ == '__main__':
unittest.main()
| [
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] | 2.880952 | 42 |
from django.test import TestCase
from django.test import override_settings
@override_settings(ES_ENABLED=False)
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] | 3.257143 | 35 |
import hashlib
import os
import shutil
from concurrent.futures import ThreadPoolExecutor, as_completed
from contextlib import suppress
from functools import wraps
from typing import List, Any, Union
from funcy import contextmanager
from toolz import keyfilter
logger = None
def ensure_directory(directory, force_recreate=True):
""" Ensure directory will nuke provided path, and create a fresh directory.
Args:
directory (str): A nested path we want to ensure.
force_recreate (bool): If True, it will always nuke the path and re-create it.
Otherwise, it checks and returns if path already exists first.
"""
if not force_recreate:
with suppress(Exception):
if os.path.isdir(directory):
return
with suppress(FileNotFoundError):
shutil.rmtree(directory)
os.makedirs(directory)
@contextmanager
def changewd(path, is_child=False):
"""
This is a workaround function to make ipfs api work.
If relative path is /foo/bar/baz, and global path is /x/foo/bar/baz,
we change the current directory to /x/foo/bar, then we add baz to ipfs.
After that, we change the directory back to the original (/x/).
"""
current_dir = os.getcwd()
parent_dir = "/".join(path.split('/')[:-1]).lstrip('/')
if is_child:
change_to_dir = os.path.join(current_dir, parent_dir)
else:
change_to_dir = os.path.join('/', parent_dir)
os.chdir(change_to_dir)
yield
os.chdir(current_dir)
def cleanup_after(fn):
""" A decorator to be used on methods that finalize actions on temporary directory.
The temporary directory is destroyed when wrapped function returns.
"""
@wraps(fn)
return wrapper
# logging
# -------
def log_exception():
""" Log to sentry.io. Alternatively,
fallback to stdout stacktrace dump."""
global logger
dsn = os.getenv('SENTRY_DSN')
if dsn:
import raven
logger = raven.Client(dsn)
if logger:
logger.captureException()
else:
import traceback
print(traceback.format_exc())
@contextmanager
# toolz
# -----
# ---------------
# Multi-Threading
# ---------------
def dependency_injection(fn_args, dep_args):
"""
>>> dependency_injection([1, None, None], [2,3])
[1, 2, 3]
"""
fn_args = ensure_list(fn_args)
dep_args = ensure_list(dep_args)[::-1]
args = []
for fn_arg in fn_args:
next_arg = fn_arg if fn_arg is not None else dep_args.pop()
args.append(next_arg)
return args
def thread_multi(
fn,
fn_args: List[Any],
dep_args: List[Union[Any, List[Any]]],
fn_kwargs=None,
max_workers=100,
re_raise_errors=True):
""" Run a function /w variable inputs concurrently.
Args:
fn: A pointer to the function that will be executed in parallel.
fn_args: A list of arguments the function takes. None arguments will be
displaced trough `dep_args`.
dep_args: A list of lists of arguments to displace in `fn_args`.
fn_kwargs: Keyword arguments that `fn` takes.
max_workers: A cap of threads to run in parallel.
re_raise_errors: Throw exceptions that happen in the worker pool.
"""
if not fn_kwargs:
fn_kwargs = dict()
fn_args = ensure_list(fn_args)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = (executor.submit(fn, *dependency_injection(fn_args, args), **fn_kwargs)
for args in dep_args)
for future in as_completed(futures):
try:
yield future.result()
except Exception as e:
log_exception()
if re_raise_errors:
raise e
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5298,
304,
198
] | 2.494426 | 1,525 |
from refactor.default_interface import DefaultHandler
from refactor.tilde_essentials.example import Example
from refactor.tilde_essentials.leaf_strategy import LeafBuilder
from refactor.tilde_essentials.splitter import Splitter
from refactor.tilde_essentials.stop_criterion import StopCriterion
from refactor.tilde_essentials.tree_builder import TreeBuilder
from refactor.query_testing_back_end.subtle.clause_handling import build_clause
from refactor.query_testing_back_end.subtle.evaluation import SubtleQueryEvaluator
from refactor.query_testing_back_end.subtle.test_generation import SubtleTestGeneratorBuilder
from refactor.representation.example_collection import ExampleCollection
from tilde_config import subtle_path, split_criterion
| [
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] | 3.715 | 200 |
from identixone.base.choices import Conf
| [
6738,
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505,
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13,
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1063,
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7326,
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] | 3.5 | 12 |
#!/usr/bin/python
import argparse
import collections
import json
import math
import os
import struct
import sys
ANGLE_FACTOR = 2 * math.pi / 40000.0
SPEED_FACTOR = 1 / 1000.0
if __name__ == '__main__':
main()
| [
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198
] | 2.708861 | 79 |
#!/usr/env/bin python3
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Trampoline handles launching into a docker container for running tests."""
import errno
import os
import shutil
import subprocess
import sys
import tempfile
from subprocess import Popen, PIPE
ENV_BLACKLIST = ["ALIASES", "ALLUSERSPROFILE", "ANDROID_HOME", "ANSICON",
"ANSICON_DEF", "ANT_HOME", "APPDATA", "ARCHITECTURE", "ARCHITECTURE_BITS", "CCALL",
"CEXEC", "CHOCOLATEYINSTALL", "CHOCOLATEYLASTPATHUPDATE", "CHOCOLATEYTOOLSLOCATION",
"CLASSPATH", "CLIENTNAME", "CLOUDSDK_CONFIG", "CLOUD_SDK_VERSION", "CMDER_ALIASES",
"CMDER_CLINK", "CMDER_CONFIGURED", "CMDER_INIT_END", "CMDER_INIT_START", "CMDER_ROOT",
"CMDER_SHELL", "CMDER_USER_FLAGS", "COMPUTERNAME", "CONEMUANSI", "CONEMUARGS", "CONEMUBACKHWND",
"CONEMUBASEDIR", "CONEMUBASEDIRSHORT", "CONEMUBUILD", "CONEMUCFGDIR", "CONEMUDIR", "CONEMUDRAWHWND",
"CONEMUDRIVE", "CONEMUHOOKS", "CONEMUHWND", "CONEMUPALETTE", "CONEMUPID", "CONEMUSERVERPID",
"CONEMUTASK", "CONEMUWORKDIR", "CONEMUWORKDRIVE", "CUDA_PATH", "CUDA_PATH_V9_0", "CUDA_PATH_V9_1",
"ComSpec", "CommonProgramFiles", "CommonProgramFiles(x86)", "CommonProgramW6432", "DEBUG_OUTPUT",
"DERBY_HOME", "FAST_INIT", "FEFLAGNAME", "FSHARPINSTALLDIR", "GIT_INSTALL_ROOT", "GOOGETROOT",
"GOPATH", "GOROOT", "GRADLE_HOME", "GRADLE_USER_HOME", "HOME", "HOMEDRIVE", "HOMEPATH", "J2REDIR",
"J2SDKDIR", "JAVA_HOME", "LANG", "LIB_BASE", "LIB_CONSOLE",
"LIB_GIT", "LIB_PATH", "LIB_PROFILE", "LOCALAPPDATA", "LOGNAME", "LOGONSERVER", "M2", "M2_HOME",
"M2_REPO", "MAIL", "MAVEN_OPTS", "MAX_DEPTH", "MSMPI_BIN", "NIX_TOOLS", "NUMBER_OF_PROCESSORS",
"NVCUDASAMPLES9_0_ROOT", "NVCUDASAMPLES9_1_ROOT", "NVCUDASAMPLES_ROOT", "OS", "PATH",
"PATHEXT", "PLINK_PROTOCOL", "PROCESSOR_ARCHITECTURE", "PROCESSOR_IDENTIFIER", "PROCESSOR_LEVEL",
"PROCESSOR_REVISION", "PROMPT", "PSModulePath", "PUBLIC", "PWD", "PYENV_DIR", "PYENV_HOOK_PATH",
"PYENV_ROOT", "PYENV_SHELL", "PYENV_VERSION", "PYENV_VIRTUALENV_INIT", "Path", "ProgramData",
"ProgramFiles", "ProgramFiles(x86)", "ProgramW6432", "QT_QPA_PLATFORMTHEME",
"SESSIONNAME", "SHELL", "SHLVL", "SSH_CLIENT", "SSH_CONNECTION", "SVN_SSH", "SystemDrive",
"SystemRoot", "TEMP", "TERM", "TIME_INIT", "TMP", "TMPDIR", "TRAMPOLINE_BUILD_FILE",
"TRAMPOLINE_IMAGE", "USER", "USERDOMAIN", "USERDOMAIN_ROAMINGPROFILE", "USERNAME", "USERPROFILE",
"USER_ALIASES", "VERBOSE_OUTPUT", "VS110COMNTOOLS", "VS120COMNTOOLS", "VS140COMNTOOLS",
"VSSDK140INSTALL", "XDG_RUNTIME_DIR", "XDG_SESSION_COOKIE", "XDG_SESSION_ID", "_system_arch"
"_system_name", "_system_type", "_system_version", "rvm_bin_path", "rvm_path", "rvm_prefix",
"rvm_version", "windir"]
if __name__ == '__main__':
main()
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198
] | 2.069452 | 1,771 |
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