xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

2f9dca279923d69029cb436fe4a1ffad45fd7ce9

12,992

py

Python

utils/scripts/OOOlevelGen/src/levels/level_3_2.py

fullscreennl/bullettime

8967449cdf926aaed6bb7ec217d92e0689fb0c3c

[ "MIT" ]

null

utils/scripts/OOOlevelGen/src/levels/level_3_2.py

fullscreennl/bullettime

8967449cdf926aaed6bb7ec217d92e0689fb0c3c

[ "MIT" ]

null

utils/scripts/OOOlevelGen/src/levels/level_3_2.py

fullscreennl/bullettime

8967449cdf926aaed6bb7ec217d92e0689fb0c3c

[ "MIT" ]

null

import LevelBuilder from sprites import * def render(name,bg): lb = LevelBuilder.LevelBuilder(name+".plist",background="NO") lb.addObject(Hero.HeroSprite(x=49, y=58,width=42,height=74)) lb.addObject(Bullet.BulletSprite(x=0, y=0,width=10,height=10,angle='0',restitution=0.5,static='false',friction=0.5,density=3,spawnEvent='onShoot')) #classname='Destructable',firstframe='brittle_brick.png' lb.addObject(Beam.BeamSprite(x=600+262, y=38,width=425,height=26,angle='0' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable')) lb.addObject(Beam.BeamSprite(x=600+240, y=10,width=500,height=30,angle='0',restitution=0.2,static='true',friction=0.5,density=20,classname='SimpleScrollStrategySprite').setName('Beam')) lb.addObject(Beam.BeamSprite(x=600+65, y=71,width=38,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+65, y=118,width=38,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+65, y=165,width=38,height=16,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+65, y=199,width=17,height=16,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+65, y=95,width=8,height=35,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+65, y=142,width=8,height=31,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+65, y=187,width=5,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+65, y=210,width=5,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Enemy.EnemySprite(x=600+65, y=225,width=22,height=22,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Beam.BeamSprite(x=600+455, y=71,width=38,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+455, y=118,width=38,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+455, y=165,width=38,height=16,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+455, y=199,width=17,height=16,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+455, y=95,width=8,height=35,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+455, y=142,width=8,height=31,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+455, y=187,width=5,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+455, y=210,width=5,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Enemy.EnemySprite(x=600+455, y=225,width=22,height=22,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Beam.BeamSprite(x=600+408, y=65,width=26,height=15,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+408, y=97,width=26,height=15,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+408, y=130,width=26,height=11,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+408, y=153,width=12,height=11,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+408, y=81,width=5,height=24,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+408, y=114,width=5,height=22,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+408, y=145,width=3,height=15,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+408, y=161,width=3,height=15,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Enemy.EnemySprite(x=600+408, y=171,width=22,height=22,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Beam.BeamSprite(x=600+114, y=66,width=27,height=15,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+114, y=99,width=27,height=15,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+114, y=133,width=27,height=11,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+114, y=157,width=12,height=11,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+114, y=83,width=6,height=25,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+114, y=116,width=6,height=23,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+114, y=149,width=4,height=16,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+114, y=165,width=4,height=16,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Enemy.EnemySprite(x=600+114, y=176,width=22,height=22,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Beam.BeamSprite(x=600+299, y=93,width=82,height=23,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+223, y=93,width=82,height=21,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+261, y=144,width=85,height=19,angle='-180' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Enemy.EnemySprite(x=600+260, y=201,width=92,height=92,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Enemy.EnemySprite(x=600+260, y=257,width=20,height=20,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Enemy.EnemySprite(x=600+260, y=274,width=22,height=22,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Beam.BeamSprite(x=600+180, y=93,width=82,height=14,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+157, y=93,width=82,height=14,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+340, y=93,width=82,height=14,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+366, y=93,width=82,height=14,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+184, y=139,width=68,height=9,angle='-180' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+339, y=139,width=68,height=9,angle='-180' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+155, y=154,width=20,height=5,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+164, y=151,width=13,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+185, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+194, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+205, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+214, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+199, y=159,width=5,height=38,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Enemy.EnemySprite(x=600+200, y=175,width=26,height=26,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Beam.BeamSprite(x=600+369, y=154,width=20,height=5,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+359, y=151,width=13,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+310, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+318, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+330, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+339, y=150,width=12,height=3,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Beam.BeamSprite(x=600+324, y=159,width=5,height=38,angle='90' ,restitution=0.2,static='false',friction=0.5,density=20, classname='Destructable' )) lb.addObject(Enemy.EnemySprite(x=600+325, y=175,width=26,height=26,angle='0',restitution=0.2,static='false',friction=0.5,density=20)) lb.addObject(Crate.CrateSprite(x=2798-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2759-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2717-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2674-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2638-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2599-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2798-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2759-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2717-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2674-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2638-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2599-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2638-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Crate.CrateSprite(x=2599-50,y=223,width=32, height=32, static='false',angle=0)) lb.addObject(Pickup.PickupSprite(x=2570-50,y=260,width=32, height=32, static='false',angle=0)) lb.addObject(Pickup.PickupSprite(x=2609-50,y=260,width=32, height=32, static='false',angle=0)) lb.addObject(Pickup.PickupSprite(x=2647-50,y=260,width=32, height=32, static='false',angle=0)) lb.addObject(Pickup.PickupSprite(x=2684-50,y=260,width=32, height=32, static='false',angle=0)) lb.addObject(Pickup.PickupSprite(x=2720-50,y=260,width=32, height=32, static='false',angle=0)) lb.addObject(Teleporter.TeleporterSprite(level_id='leveldata/level_3_3')) lb.render()

132.571429

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null

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1

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null

0

7

c824e00f639ba0d802f6b5a65244e7c796b0991a

4,658

py

Python

apps/book/apiview.py

PyCN/BlogBackendProject

144ea98d54c624cf93a69816779e4f3483ab80a2

[ "Apache-2.0" ]

335

2018-02-06T11:40:44.000Z

2022-02-01T07:55:24.000Z

apps/book/apiview.py

PyCN/BlogBackendProject

144ea98d54c624cf93a69816779e4f3483ab80a2

[ "Apache-2.0" ]

18

2018-03-11T05:02:27.000Z

2022-03-11T23:18:34.000Z

apps/book/apiview.py

PyCN/BlogBackendProject

144ea98d54c624cf93a69816779e4f3483ab80a2

[ "Apache-2.0" ]

83

2018-03-02T03:24:06.000Z

2021-12-15T05:04:09.000Z

# _*_ coding: utf-8 _*_ __author__ = 'LennonChin' __date__ = '2017/12/2 12:52' from django_filters.rest_framework import DjangoFilterBackend from rest_framework import status, viewsets, filters, mixins from rest_framework.response import Response from .models import BookInfo, BookNoteInfo from .serializers import BookBaseInfoSerializer, BookDetailInfoSerializer, BookNoteBaseInfoSerializer, BookNoteDetialInfoSerializer from .filters import BookFilter, BookNoteFilter from base.utils import CustomeLimitOffsetPagination class BookBaseInfoListViewset(viewsets.ReadOnlyModelViewSet): """ List: 图书基本信息文章列表页 """ queryset = BookInfo.objects.filter(is_active=True) serializer_class = BookBaseInfoSerializer # 过滤，搜索，排序 filter_backends = (DjangoFilterBackend, filters.SearchFilter, filters.OrderingFilter) filter_class = BookFilter search_fields = ('title', 'subtitle', 'abstract', 'desc') ordering_fields = ('click_num', 'like_num', 'comment_num', 'index', 'add_time') ordering = ('-index', '-add_time') # 分页设置 pagination_class = CustomeLimitOffsetPagination def retrieve(self, request, *args, **kwargs): instance = self.get_object() instance.click_num += 1 instance.save() serializer = self.get_serializer(instance) return Response(serializer.data) class BookDetailInfoListViewset(mixins.RetrieveModelMixin, viewsets.GenericViewSet): """ List: 图书详细信息列表页 """ queryset = BookInfo.objects.filter(is_active=True) serializer_class = BookDetailInfoSerializer # 过滤，搜索，排序 filter_backends = (DjangoFilterBackend, filters.SearchFilter, filters.OrderingFilter) filter_class = BookFilter search_fields = ('title', 'subtitle', 'abstract', 'desc') ordering_fields = ('click_num', 'like_num', 'comment_num') # 分页设置 pagination_class = CustomeLimitOffsetPagination def retrieve(self, request, *args, **kwargs): instance = self.get_object() if instance.browse_password_encrypt: browse_auth = "" if 'browse_auth' in request.query_params: browse_auth = request.query_params['browse_auth'] if browse_auth != instance.browse_password_encrypt: context = { "error": "文章密码错误" } return Response(context, status=status.HTTP_401_UNAUTHORIZED) instance.click_num += 1 instance.save() serializer = self.get_serializer(instance) return Response(serializer.data) # 图书笔记 class BookNoteBaseInfoListViewset(viewsets.ReadOnlyModelViewSet): """ List: 图书笔记信息列表页 """ queryset = BookNoteInfo.objects.filter(is_active=True) serializer_class = BookNoteBaseInfoSerializer # 过滤，搜索，排序 filter_backends = (DjangoFilterBackend, filters.SearchFilter, filters.OrderingFilter) filter_class = BookNoteFilter search_fields = ('title', 'subtitle', 'abstract', 'desc') ordering_fields = ('click_num', 'like_num', 'comment_num', 'index', 'add_time') ordering = ('-index', '-add_time') # 分页设置 pagination_class = CustomeLimitOffsetPagination def retrieve(self, request, *args, **kwargs): instance = self.get_object() instance.click_num += 1 instance.save() serializer = self.get_serializer(instance) return Response(serializer.data) class BookNoteDetailInfoListViewset(mixins.RetrieveModelMixin, viewsets.GenericViewSet): """ List: 图书笔记信息列表页 """ queryset = BookNoteInfo.objects.filter(is_active=True) serializer_class = BookNoteDetialInfoSerializer # 过滤，搜索，排序 # filter_backends = (DjangoFilterBackend, filters.SearchFilter, filters.OrderingFilter) # filter_class = BookFilter search_fields = ('title', 'subtitle', 'abstract', 'desc') ordering_fields = ('click_num', 'like_num', 'comment_num') # 分页设置 pagination_class = CustomeLimitOffsetPagination def retrieve(self, request, *args, **kwargs): instance = self.get_object() if instance.browse_password_encrypt: browse_auth = "" if 'browse_auth' in request.query_params: browse_auth = request.query_params['browse_auth'] if browse_auth != instance.browse_password_encrypt: context = { "error": "文章密码错误" } return Response(context, status=status.HTTP_401_UNAUTHORIZED) instance.click_num += 1 instance.save() serializer = self.get_serializer(instance) return Response(serializer.data)

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7

c82d829f0cb9d94f4748c0ac354c599232344c3a

1,341

py

Python

skqulacs/qsvm/qsvmbase.py

kenjikun/scikit-qulacs

afc502f63927ab61da964698da54ec4b410c30c4

[ "MIT" ]

null

skqulacs/qsvm/qsvmbase.py

kenjikun/scikit-qulacs

afc502f63927ab61da964698da54ec4b410c30c4

[ "MIT" ]

null

skqulacs/qsvm/qsvmbase.py

kenjikun/scikit-qulacs

afc502f63927ab61da964698da54ec4b410c30c4

[ "MIT" ]

null

from qulacs import QuantumState from qulacs.gate import H, RZ, CNOT import numpy as np def get_qvec(x, n_qubit, tlotstep): # xはデータ # n_qubit,tlotstepはそのままの意味 data_state = QuantumState(n_qubit) data_state.set_zero_state() for a in range(n_qubit): H(a).update_quantum_state(data_state) for tlotkai in range(tlotstep): for a in range(n_qubit): RZ(a, x[a] / tlotstep).update_quantum_state(data_state) # aとa+1のゲートの交互作用 b = (a + 1) % n_qubit CNOT(a, b).update_quantum_state(data_state) RZ(b, (np.pi - x[a]) * (np.pi - x[b]) / tlotstep).update_quantum_state( data_state ) CNOT(a, b).update_quantum_state(data_state) for a in range(n_qubit): H(a).update_quantum_state(data_state) for tlotkai in range(tlotstep): for a in range(n_qubit): RZ(a, x[a] / tlotstep).update_quantum_state(data_state) # aとa+1のゲートの交互作用 b = (a + 1) % n_qubit CNOT(a, b).update_quantum_state(data_state) RZ(b, (np.pi - x[a]) * (np.pi - x[b]) / tlotstep).update_quantum_state( data_state ) CNOT(a, b).update_quantum_state(data_state) # 000の行のベクトルを取る return data_state

36.243243

84

0.577927

186

1,341

3.924731

0.204301

0.160274

0.246575

0.30137

0.735616

0

0.007634

0.316182

1,341

36

85

37.25

0.788441

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0

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0

null

0

1

0

1

0

null

0

7

c835a19b1d4422543a1945d892e1b721d91a1295

490

py

Python

eval_mosmed_timm-regnetx_002_Flip.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

eval_mosmed_timm-regnetx_002_Flip.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

eval_mosmed_timm-regnetx_002_Flip.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

import os ls=["python main.py --configs configs/eval_mosmed_unetplusplus_timm-regnetx_002_0_Flip.yml", "python main.py --configs configs/eval_mosmed_unetplusplus_timm-regnetx_002_1_Flip.yml", "python main.py --configs configs/eval_mosmed_unetplusplus_timm-regnetx_002_2_Flip.yml", "python main.py --configs configs/eval_mosmed_unetplusplus_timm-regnetx_002_3_Flip.yml", "python main.py --configs configs/eval_mosmed_unetplusplus_timm-regnetx_002_4_Flip.yml", ] for l in ls: os.system(l)

44.545455

92

0.830612

80

490

4.7125

0.3

0.132626

0.159151

0.251989

0.896552

0

0.043573

0.063265

490

11

93

44.545455

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0

0.86558

0.610998

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false

0

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0.111111

0

null

0

1

0

1

null

0

9

c8687780824b68621b125ca25aa502e1c7c1c16a

161

py

Python

metrics/__init__.py

ryosukehata/severstal

cb54703b820cb27d7b93fb80a42b41f84ec8cf08

[ "Apache-2.0" ]

null

metrics/__init__.py

ryosukehata/severstal

cb54703b820cb27d7b93fb80a42b41f84ec8cf08

[ "Apache-2.0" ]

null

metrics/__init__.py

ryosukehata/severstal

cb54703b820cb27d7b93fb80a42b41f84ec8cf08

[ "Apache-2.0" ]

null

from .metric import dice_channel_torch, dice_channel_torch_with_each_channel __all__ = [ "dice_channel_torch", "dice_channel_torch_with_each_channel" ]

23

76

0.813665

22

161

5.136364

0.409091

0.389381

0.566372

0.353982

0.831858

0

0.124224

161

6

77

26.833333

0.801418

0

0.335404

0.223602

0

1

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false

0

0.2

0

0.2

0

1

0

null

1

0

1

0

null

0

10

2391aaef0023f332758660c17e6870fec503cab1

17,935

py

Python

MagicTelecomAPILib/Controllers/DidsProductsController.py

MagicTelecom/mt_python_api

b014760809cb1d0cab48a3376cbaface0c4bef66

[ "MIT" ]

null

MagicTelecomAPILib/Controllers/DidsProductsController.py

MagicTelecom/mt_python_api

b014760809cb1d0cab48a3376cbaface0c4bef66

[ "MIT" ]

null

MagicTelecomAPILib/Controllers/DidsProductsController.py

MagicTelecom/mt_python_api

b014760809cb1d0cab48a3376cbaface0c4bef66

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ MagicTelecomAPILib.Controllers.DidsProductsController This file was automatically generated by APIMATIC BETA v2.0 on 06/22/2016 """ from MagicTelecomAPILib.APIHelper import APIHelper from MagicTelecomAPILib.APIException import APIException from MagicTelecomAPILib.Configuration import Configuration from MagicTelecomAPILib.Http.HttpRequest import HttpRequest from MagicTelecomAPILib.Http.HttpResponse import HttpResponse from MagicTelecomAPILib.Http.RequestsClient import RequestsClient from MagicTelecomAPILib.Controllers.BaseController import BaseController class DidsProductsController(BaseController): """A Controller to access Endpoints in the MagicTelecomAPILib API.""" def __init__(self, http_client = None): """Constructor which allows a different HTTP client for this controller.""" BaseController.__init__(self, http_client) def get_dids(self, page=None, limit=None, filter=None): """Does a GET request to /dids/products/dids. Allow clients to get the list of available phone_numbers Args: page (int, optional): Zero based offset index for the results. e.g. 0 would start at the first result and 10 would start at the eleventh result limit (int, optional): Maximum number of results to return in the response filter (string, optional): Allowed fields: country_iso2, region_handle, location_handle, location_name, phone_number, phone_number_type, zip_code Returns: mixed: Response from the API. Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # The base uri for api requests query_builder = Configuration.BASE_URI # Prepare query string for API call query_builder += "/dids/products/dids" # Process optional query parameters query_parameters = { "page": page, "limit": limit, "filter": filter } # Validate and preprocess url query_url = APIHelper.clean_url(query_builder) # Prepare headers headers = { "user-agent": "APIMATIC 2.0", "accept": "application/json", "X-Auth-Token": Configuration.x_auth_token, "X-Auth-Token": Configuration.x_auth_token } # Prepare the API call. http_request = self.http_client.get(query_url, headers=headers, query_parameters=query_parameters) # Invoke the API call to fetch the response. response = self.http_client.execute_as_string(http_request); # Endpoint error handling using HTTP status codes. if response.status_code == 401: raise APIException("You are not authenticated", 401, response.raw_body) elif response.status_code == 403: raise APIException("This action needs a valid WSSE header", 403, response.raw_body) elif response.status_code == 404: raise APIException("Resource not found", 404, response.raw_body) # Global error handling using HTTP status codes. self.validate_response(response) return response.raw_body def get_dids_by_phone_number(self, phone_number): """Does a GET request to /dids/products/dids/{phone_number}. Allow clients to get a specific phone_number Args: phone_number (string): Phone Number Returns: mixed: Response from the API. Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # The base uri for api requests query_builder = Configuration.BASE_URI # Prepare query string for API call query_builder += "/dids/products/dids/{phone_number}" # Process optional template parameters query_builder = APIHelper.append_url_with_template_parameters(query_builder, { "phone_number": phone_number }) # Validate and preprocess url query_url = APIHelper.clean_url(query_builder) # Prepare headers headers = { "user-agent": "APIMATIC 2.0", "accept": "application/json", "X-Auth-Token": Configuration.x_auth_token, "X-Auth-Token": Configuration.x_auth_token } # Prepare the API call. http_request = self.http_client.get(query_url, headers=headers) # Invoke the API call to fetch the response. response = self.http_client.execute_as_string(http_request); # Endpoint error handling using HTTP status codes. if response.status_code == 401: raise APIException("You are not authenticated", 401, response.raw_body) elif response.status_code == 403: raise APIException("This action needs a valid WSSE header", 403, response.raw_body) elif response.status_code == 404: raise APIException("Resource not found", 404, response.raw_body) # Global error handling using HTTP status codes. self.validate_response(response) return response.raw_body def get_locations(self, page=None, limit=None, filter=None): """Does a GET request to /dids/products/locations. Allow clients to get the list of available locations. Args: page (int, optional): Zero based offset index for the results. e.g. 0 would start at the first result and 10 would start at the eleventh result limit (int, optional): Maximum number of results to return in the response filter (string, optional): Allowed fields: country_iso2, region_handle, location_handle, location_name, prefix, phone_number_type, zip_code, npa, nxx, fax Returns: mixed: Response from the API. Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # The base uri for api requests query_builder = Configuration.BASE_URI # Prepare query string for API call query_builder += "/dids/products/locations" # Process optional query parameters query_parameters = { "page": page, "limit": limit, "filter": filter } # Validate and preprocess url query_url = APIHelper.clean_url(query_builder) # Prepare headers headers = { "user-agent": "APIMATIC 2.0", "accept": "application/json", "X-Auth-Token": Configuration.x_auth_token, "X-Auth-Token": Configuration.x_auth_token } # Prepare the API call. http_request = self.http_client.get(query_url, headers=headers, query_parameters=query_parameters) # Invoke the API call to fetch the response. response = self.http_client.execute_as_string(http_request); # Endpoint error handling using HTTP status codes. if response.status_code == 401: raise APIException("You are not authenticated", 401, response.raw_body) elif response.status_code == 403: raise APIException("This action needs a valid WSSE header", 403, response.raw_body) elif response.status_code == 404: raise APIException("Resource not found", 404, response.raw_body) # Global error handling using HTTP status codes. self.validate_response(response) return response.raw_body def get_location_by_handle(self, location_handle): """Does a GET request to /dids/products/locations/{location_handle}. Allow clients to get a specific location. Args: location_handle (string): Location Handle Returns: mixed: Response from the API. Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # The base uri for api requests query_builder = Configuration.BASE_URI # Prepare query string for API call query_builder += "/dids/products/locations/{location_handle}" # Process optional template parameters query_builder = APIHelper.append_url_with_template_parameters(query_builder, { "location_handle": location_handle }) # Validate and preprocess url query_url = APIHelper.clean_url(query_builder) # Prepare headers headers = { "user-agent": "APIMATIC 2.0", "accept": "application/json", "X-Auth-Token": Configuration.x_auth_token, "X-Auth-Token": Configuration.x_auth_token } # Prepare the API call. http_request = self.http_client.get(query_url, headers=headers) # Invoke the API call to fetch the response. response = self.http_client.execute_as_string(http_request); # Endpoint error handling using HTTP status codes. if response.status_code == 401: raise APIException("You are not authenticated", 401, response.raw_body) elif response.status_code == 403: raise APIException("This action needs a valid WSSE header", 403, response.raw_body) elif response.status_code == 404: raise APIException("Resource not found", 404, response.raw_body) # Global error handling using HTTP status codes. self.validate_response(response) return response.raw_body def get_trunks(self, page=None, limit=None): """Does a GET request to /dids/products/trunks. Allow clients to get the list of available trunks Args: page (int, optional): Zero based offset index for the results. e.g. 0 would start at the first result and 10 would start at the eleventh result limit (int, optional): Maximum number of results to return in the response Returns: mixed: Response from the API. Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # The base uri for api requests query_builder = Configuration.BASE_URI # Prepare query string for API call query_builder += "/dids/products/trunks" # Process optional query parameters query_parameters = { "page": page, "limit": limit } # Validate and preprocess url query_url = APIHelper.clean_url(query_builder) # Prepare headers headers = { "user-agent": "APIMATIC 2.0", "accept": "application/json", "X-Auth-Token": Configuration.x_auth_token, "X-Auth-Token": Configuration.x_auth_token } # Prepare the API call. http_request = self.http_client.get(query_url, headers=headers, query_parameters=query_parameters) # Invoke the API call to fetch the response. response = self.http_client.execute_as_string(http_request); # Endpoint error handling using HTTP status codes. if response.status_code == 401: raise APIException("You are not authenticated", 401, response.raw_body) elif response.status_code == 403: raise APIException("This action needs a valid WSSE header", 403, response.raw_body) elif response.status_code == 404: raise APIException("Resource not found", 404, response.raw_body) # Global error handling using HTTP status codes. self.validate_response(response) return response.raw_body def get_trunk_by_handle(self, trunk_handle): """Does a GET request to /dids/products/trunks/{trunk_handle}. Allow clients to get a specific trunk Args: trunk_handle (string): Trunk Handle Returns: mixed: Response from the API. Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # The base uri for api requests query_builder = Configuration.BASE_URI # Prepare query string for API call query_builder += "/dids/products/trunks/{trunk_handle}" # Process optional template parameters query_builder = APIHelper.append_url_with_template_parameters(query_builder, { "trunk_handle": trunk_handle }) # Validate and preprocess url query_url = APIHelper.clean_url(query_builder) # Prepare headers headers = { "user-agent": "APIMATIC 2.0", "accept": "application/json", "X-Auth-Token": Configuration.x_auth_token, "X-Auth-Token": Configuration.x_auth_token } # Prepare the API call. http_request = self.http_client.get(query_url, headers=headers) # Invoke the API call to fetch the response. response = self.http_client.execute_as_string(http_request); # Endpoint error handling using HTTP status codes. if response.status_code == 401: raise APIException("You are not authenticated", 401, response.raw_body) elif response.status_code == 403: raise APIException("This action needs a valid WSSE header", 403, response.raw_body) elif response.status_code == 404: raise APIException("Resource not found", 404, response.raw_body) # Global error handling using HTTP status codes. self.validate_response(response) return response.raw_body def get_countries_by_trunk(self, page, limit): """Does a GET request to /dids/products/trunks/countries. Allow clients to get trunk zones. Args: page (int): Zero based offset index for the results. e.g. 0 would start at the first result and 10 would start at the eleventh result. limit (int): Maximum number of results to return in the response. Returns: mixed: Response from the API. Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # The base uri for api requests query_builder = Configuration.BASE_URI # Prepare query string for API call query_builder += "/dids/products/trunks/countries" # Process optional query parameters query_parameters = { "page": page, "limit": limit } # Validate and preprocess url query_url = APIHelper.clean_url(query_builder) # Prepare headers headers = { "user-agent": "APIMATIC 2.0", "accept": "application/json", "X-Auth-Token": Configuration.x_auth_token, "X-Auth-Token": Configuration.x_auth_token } # Prepare the API call. http_request = self.http_client.get(query_url, headers=headers, query_parameters=query_parameters) # Invoke the API call to fetch the response. response = self.http_client.execute_as_string(http_request); # Endpoint error handling using HTTP status codes. if response.status_code == 401: raise APIException("You are not authenticated", 401, response.raw_body) elif response.status_code == 403: raise APIException("This action needs a valid WSSE header", 403, response.raw_body) elif response.status_code == 404: raise APIException("Resource not found", 404, response.raw_body) # Global error handling using HTTP status codes. self.validate_response(response) return response.raw_body

36.453252

107

0.601617

1,997

17,935

5.269905

0.089634

0.013303

0.026606

0.030597

0.891676

0.881319

0.878848

0.872767

0.843311

0

0.01379

0.332869

17,935

491

108

36.527495

0.865775

0.366769

0

0.768421

1

0

0.133749

0.018906

0

1

0.042105

false

0

0.036842

0

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0

null

0

1

0

1

0

null

0

8

23b457644dac6dc44487a932c06e0e8fcfb2037e

29,771

py

Python

eZmaxApi/api/module_sspr_api.py

eZmaxinc/eZmax-SDK-python

5b4d54b69db68aab8ee814a1e26460a0af03784e

[ "MIT" ]

null

eZmaxApi/api/module_sspr_api.py

eZmaxinc/eZmax-SDK-python

5b4d54b69db68aab8ee814a1e26460a0af03784e

[ "MIT" ]

null

eZmaxApi/api/module_sspr_api.py

eZmaxinc/eZmax-SDK-python

5b4d54b69db68aab8ee814a1e26460a0af03784e

[ "MIT" ]

null

""" eZmax API Definition This API expose all the functionnalities for the eZmax and eZsign applications. # noqa: E501 The version of the OpenAPI document: 1.1.3 Contact: support-api@ezmax.ca Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from eZmaxApi.api_client import ApiClient, Endpoint as _Endpoint from eZmaxApi.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types ) from eZmaxApi.model.common_response_error import CommonResponseError from eZmaxApi.model.sspr_reset_password_request_v1_request import SsprResetPasswordRequestV1Request from eZmaxApi.model.sspr_reset_password_v1_request import SsprResetPasswordV1Request from eZmaxApi.model.sspr_send_usernames_v1_request import SsprSendUsernamesV1Request from eZmaxApi.model.sspr_unlock_account_request_v1_request import SsprUnlockAccountRequestV1Request from eZmaxApi.model.sspr_unlock_account_v1_request import SsprUnlockAccountV1Request from eZmaxApi.model.sspr_validate_token_v1_request import SsprValidateTokenV1Request class ModuleSsprApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client self.sspr_reset_password_request_v1_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'Authorization' ], 'endpoint_path': '/1/module/sspr/resetPasswordRequest', 'operation_id': 'sspr_reset_password_request_v1', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'sspr_reset_password_request_v1_request', ], 'required': [ 'sspr_reset_password_request_v1_request', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'sspr_reset_password_request_v1_request': (SsprResetPasswordRequestV1Request,), }, 'attribute_map': { }, 'location_map': { 'sspr_reset_password_request_v1_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.sspr_reset_password_v1_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'Authorization' ], 'endpoint_path': '/1/module/sspr/resetPassword', 'operation_id': 'sspr_reset_password_v1', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'sspr_reset_password_v1_request', ], 'required': [ 'sspr_reset_password_v1_request', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'sspr_reset_password_v1_request': (SsprResetPasswordV1Request,), }, 'attribute_map': { }, 'location_map': { 'sspr_reset_password_v1_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.sspr_send_usernames_v1_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'Authorization' ], 'endpoint_path': '/1/module/sspr/sendUsernames', 'operation_id': 'sspr_send_usernames_v1', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'sspr_send_usernames_v1_request', ], 'required': [ 'sspr_send_usernames_v1_request', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'sspr_send_usernames_v1_request': (SsprSendUsernamesV1Request,), }, 'attribute_map': { }, 'location_map': { 'sspr_send_usernames_v1_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.sspr_unlock_account_request_v1_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'Authorization' ], 'endpoint_path': '/1/module/sspr/unlockAccountRequest', 'operation_id': 'sspr_unlock_account_request_v1', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'sspr_unlock_account_request_v1_request', ], 'required': [ 'sspr_unlock_account_request_v1_request', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'sspr_unlock_account_request_v1_request': (SsprUnlockAccountRequestV1Request,), }, 'attribute_map': { }, 'location_map': { 'sspr_unlock_account_request_v1_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.sspr_unlock_account_v1_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'Authorization' ], 'endpoint_path': '/1/module/sspr/unlockAccount', 'operation_id': 'sspr_unlock_account_v1', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'sspr_unlock_account_v1_request', ], 'required': [ 'sspr_unlock_account_v1_request', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'sspr_unlock_account_v1_request': (SsprUnlockAccountV1Request,), }, 'attribute_map': { }, 'location_map': { 'sspr_unlock_account_v1_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.sspr_validate_token_v1_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'Authorization' ], 'endpoint_path': '/1/module/sspr/validateToken', 'operation_id': 'sspr_validate_token_v1', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'sspr_validate_token_v1_request', ], 'required': [ 'sspr_validate_token_v1_request', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'sspr_validate_token_v1_request': (SsprValidateTokenV1Request,), }, 'attribute_map': { }, 'location_map': { 'sspr_validate_token_v1_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) def sspr_reset_password_request_v1( self, sspr_reset_password_request_v1_request, **kwargs ): """Reset Password Request # noqa: E501 This endpoint sends an email with a link to reset the user's password. sEmailAddress must be set if eUserTypeSSPR = EzsignUser sUserLoginname must be set if eUserTypeSSPR = Native # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sspr_reset_password_request_v1(sspr_reset_password_request_v1_request, async_req=True) >>> result = thread.get() Args: sspr_reset_password_request_v1_request (SsprResetPasswordRequestV1Request): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['sspr_reset_password_request_v1_request'] = \ sspr_reset_password_request_v1_request return self.sspr_reset_password_request_v1_endpoint.call_with_http_info(**kwargs) def sspr_reset_password_v1( self, sspr_reset_password_v1_request, **kwargs ): """Reset Password # noqa: E501 This endpoint resets the user's password. sEmailAddress must be set if eUserTypeSSPR = EzsignUser sUserLoginname must be set if eUserTypeSSPR = Native # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sspr_reset_password_v1(sspr_reset_password_v1_request, async_req=True) >>> result = thread.get() Args: sspr_reset_password_v1_request (SsprResetPasswordV1Request): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['sspr_reset_password_v1_request'] = \ sspr_reset_password_v1_request return self.sspr_reset_password_v1_endpoint.call_with_http_info(**kwargs) def sspr_send_usernames_v1( self, sspr_send_usernames_v1_request, **kwargs ): """Send username(s) # noqa: E501 This endpoint returns an email with the username(s) matching the email address provided in case of forgotten username # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sspr_send_usernames_v1(sspr_send_usernames_v1_request, async_req=True) >>> result = thread.get() Args: sspr_send_usernames_v1_request (SsprSendUsernamesV1Request): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['sspr_send_usernames_v1_request'] = \ sspr_send_usernames_v1_request return self.sspr_send_usernames_v1_endpoint.call_with_http_info(**kwargs) def sspr_unlock_account_request_v1( self, sspr_unlock_account_request_v1_request, **kwargs ): """Unlock Account Request # noqa: E501 This endpoint sends an email with a link to unlock the user account. sEmailAddress must be set if eUserTypeSSPR = EzsignUser sUserLoginname must be set if eUserTypeSSPR = Native # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sspr_unlock_account_request_v1(sspr_unlock_account_request_v1_request, async_req=True) >>> result = thread.get() Args: sspr_unlock_account_request_v1_request (SsprUnlockAccountRequestV1Request): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['sspr_unlock_account_request_v1_request'] = \ sspr_unlock_account_request_v1_request return self.sspr_unlock_account_request_v1_endpoint.call_with_http_info(**kwargs) def sspr_unlock_account_v1( self, sspr_unlock_account_v1_request, **kwargs ): """Unlock Account # noqa: E501 This endpoint unlocks the user account. sEmailAddress must be set if eUserTypeSSPR = EzsignUser sUserLoginname must be set if eUserTypeSSPR = Native # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sspr_unlock_account_v1(sspr_unlock_account_v1_request, async_req=True) >>> result = thread.get() Args: sspr_unlock_account_v1_request (SsprUnlockAccountV1Request): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['sspr_unlock_account_v1_request'] = \ sspr_unlock_account_v1_request return self.sspr_unlock_account_v1_endpoint.call_with_http_info(**kwargs) def sspr_validate_token_v1( self, sspr_validate_token_v1_request, **kwargs ): """Validate Token # noqa: E501 This endpoint validates if a Token is valid and not expired. sEmailAddress must be set if eUserTypeSSPR = EzsignUser sUserLoginname must be set if eUserTypeSSPR = Native # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sspr_validate_token_v1(sspr_validate_token_v1_request, async_req=True) >>> result = thread.get() Args: sspr_validate_token_v1_request (SsprValidateTokenV1Request): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['sspr_validate_token_v1_request'] = \ sspr_validate_token_v1_request return self.sspr_validate_token_v1_endpoint.call_with_http_info(**kwargs)

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py

Python

tests/integration/test_smoke.py

shawn-rusaw/aws-lambda-fsm-workflows

9fcf5af14bf0f4500d4a7e7b3e0eda00423c1d42

[ "Apache-2.0" ]

21

2017-01-26T21:23:57.000Z

2021-08-07T02:56:49.000Z

tests/integration/test_smoke.py

shawn-rusaw/aws-lambda-fsm-workflows

9fcf5af14bf0f4500d4a7e7b3e0eda00423c1d42

[ "Apache-2.0" ]

151

2016-11-29T05:09:33.000Z

2021-05-19T22:47:58.000Z

tests/integration/test_smoke.py

shawn-rusaw/aws-lambda-fsm-workflows

9fcf5af14bf0f4500d4a7e7b3e0eda00423c1d42

[ "Apache-2.0" ]

17

2016-11-29T05:07:58.000Z

2021-05-04T21:22:29.000Z

# Copyright 2016-2020 Workiva 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. # system imports import mock import threading import time # library imports # application imports from aws_lambda_fsm.fsm import FSM from aws_lambda_fsm.config import get_current_configuration from aws_lambda_fsm.constants import AWS as AWS_CONSTANTS from tests.integration.utils import AWSStub from tests.integration.utils import BaseFunctionalTest from .actions import get_counter from .actions import set_counter AWS = AWSStub() @mock.patch("aws_lambda_fsm.client.send_next_event_for_dispatch", wraps=AWS.send_next_event_for_dispatch) @mock.patch("aws_lambda_fsm.fsm.send_next_event_for_dispatch", wraps=AWS.send_next_event_for_dispatch) @mock.patch("aws_lambda_fsm.fsm.set_message_dispatched", wraps=AWS.set_message_dispatched) @mock.patch("aws_lambda_fsm.fsm.get_message_dispatched", wraps=AWS.get_message_dispatched) @mock.patch("aws_lambda_fsm.fsm.acquire_lease", wraps=AWS.acquire_lease) @mock.patch("aws_lambda_fsm.fsm.release_lease", wraps=AWS.release_lease) @mock.patch("aws_lambda_fsm.fsm.start_retries", wraps=AWS.start_retries) @mock.patch("aws_lambda_fsm.fsm.increment_error_counters", wraps=AWS.increment_error_counters) @mock.patch("aws_lambda_fsm.fsm.store_checkpoint", wraps=AWS.store_checkpoint) class Test(BaseFunctionalTest): def setUp(self): AWS.reset() FSM(get_current_configuration('tests/integration/fsm.yaml')) ################################################################################ # START: machine_name="simple" ################################################################################ def test_simple(self, *args): self._execute(AWS, "simple", {}) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), ()), (1, ('start', 'ok', 1, 0), ()) ] self.assertEqual(expected, AWS.all_sources.trace()) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, } self.assertEqual(expected, AWS.all_caches) # check errors expected = [] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_simple_with_primary_failure(self, *args): self._execute(AWS, "simple", {}, primary_stream_chaos=1.0) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), ()), (1, ('start', 'ok', 1, 0), ()) ] self.assertEqual(expected, AWS.all_sources.trace()) expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), ()), ] self.assertEqual(expected, AWS.primary_stream_source.trace()) expected = [ (0, ('start', 'ok', 1, 0), ()) ] self.assertEqual(expected, AWS.secondary_stream_source.trace()) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [{'error': 1}, {'current_state': 'pseudo_init', 'current_event': 'pseudo_init', 'machine_name': 'simple'}] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_simple_with_failure(self, *args): self._execute(AWS, "simple", {'fail_at': [(0, 0)]}) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), ()), (1, ('pseudo_init', 'pseudo_init', 0, 1), ()), # retry (2, ('start', 'ok', 1, 0), ()) ] self.assertEqual(expected, AWS.all_sources.trace()) expected = { 'correlation_id-0': True, 'correlation_id-1': True } self.assertEqual(expected, AWS.primary_cache) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [ {'retry': 1}, {'current_state': 'pseudo_init', 'current_event': 'pseudo_init', 'machine_name': 'simple'} ] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_simple_with_failure_with_primary_retry_failure(self, *args): self._execute(AWS, "simple", {'fail_at': [(0, 0)]}, primary_retry_chaos=1.0) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), ()), (1, ('pseudo_init', 'pseudo_init', 0, 1), ()), # retry (2, ('start', 'ok', 1, 0), ()) ] self.assertEqual(expected, AWS.all_sources.trace()) expected = [] self.assertEqual(expected, AWS.primary_retry_source.trace()) expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 1), ()) ] self.assertEqual(expected, AWS.secondary_retry_source.trace()) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [ {'retry': 1, 'error': 1}, {'current_state': 'pseudo_init', 'current_event': 'pseudo_init', 'machine_name': 'simple'} ] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) ################################################################################ # START: machine_name="looper" ################################################################################ def test_looper(self, *args): set_counter(0) self.assertEqual(0, get_counter()) self._execute(AWS, "looper", {"loops": 3}) self.assertEqual(3, get_counter()) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('start', 'ok', 1, 0), (1,)), (2, ('start', 'ok', 2, 0), (2,)), (3, ('start', 'done', 3, 0), (3,)) ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, 'lease-correlation_id-2': True, 'lease-correlation_id-3': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_looper_with_primary_failure(self, *args): self._execute(AWS, "looper", {"loops": 3}, primary_stream_chaos=1.0) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('start', 'ok', 1, 0), (1,)), (2, ('start', 'ok', 2, 0), (2,)), (3, ('start', 'done', 3, 0), (3,)) ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), ] self.assertEqual(expected, AWS.primary_stream_source.trace(('counter',))) expected = [ (0, ('start', 'ok', 1, 0), (1,)), (1, ('start', 'ok', 2, 0), (2,)), (2, ('start', 'done', 3, 0), (3,)) ] self.assertEqual(expected, AWS.secondary_stream_source.trace(('counter',))) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, 'lease-correlation_id-2': True, 'lease-correlation_id-3': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [ {'error': 1}, {'current_state': 'pseudo_init', 'current_event': 'pseudo_init', 'machine_name': 'looper'} ], [ {'error': 1}, {'current_state': 'start', 'current_event': 'ok', 'machine_name': 'looper'} ], [ {'error': 1}, {'current_state': 'start', 'current_event': 'ok', 'machine_name': 'looper'} ] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_looper_with_failure(self, *args): self._execute(AWS, "looper", {"loops": 3, 'fail_at': [(1, 0)]}) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('start', 'ok', 1, 0), (1,)), (2, ('start', 'ok', 1, 1), (1,)), # retry (3, ('start', 'ok', 2, 0), (2,)), (4, ('start', 'done', 3, 0), (3,)) ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, 'lease-correlation_id-2': True, 'lease-correlation_id-3': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [ {'retry': 1}, {'current_state': 'start', 'current_event': 'ok', 'machine_name': 'looper'} ] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_looper_with_failure_with_primary_retry_failure(self, *args): self._execute(AWS, "looper", {"loops": 3, 'fail_at': [(1, 0)]}, primary_retry_chaos=1.0) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('start', 'ok', 1, 0), (1,)), (2, ('start', 'ok', 1, 1), (1,)), # retry (3, ('start', 'ok', 2, 0), (2,)), (4, ('start', 'done', 3, 0), (3,)) ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) expected = [] self.assertEqual(expected, AWS.primary_retry_source.trace(('counter',))) expected = [ (0, ('start', 'ok', 1, 1), (1,)) ] self.assertEqual(expected, AWS.secondary_retry_source.trace(('counter',))) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, 'lease-correlation_id-2': True, 'lease-correlation_id-3': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [ {'retry': 1, 'error': 1}, {'current_state': 'start', 'current_event': 'ok', 'machine_name': 'looper'} ] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) ################################################################################ # START: machine_name="looper-local" ################################################################################ def test_looper_local(self, *args): set_counter(0) self.assertEqual(0, get_counter()) AWS.add_callback('send_next_event_for_dispatch', mock.Mock(side_effect=([None] + [Exception()] * 100))) self._execute(AWS, "looper-local", {"loops": 3}) self.assertEqual(3, get_counter()) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) # check cache expected = { 'correlation_id-0': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'lease-correlation_id-0': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_looper_local_with_failure(self, *args): set_counter(0) self.assertEqual(0, get_counter()) AWS.add_callback('send_next_event_for_dispatch', mock.Mock(side_effect=([None] + [Exception()] * 100))) self._execute(AWS, "looper-local", {"loops": 3, 'fail_at': [(i, 0) for i in range(100)]}) self.assertEqual(3, get_counter()) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('pseudo_init', 'pseudo_init', 0, 1), (None,)) # retry ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) # check cache expected = { 'correlation_id-0': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'lease-correlation_id-0': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [ {'retry': 1}, {'current_state': 'pseudo_init', 'current_event': 'pseudo_init', 'machine_name': 'looper-local'} ] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) ################################################################################ # START: machine_name="looper-mixed" ################################################################################ def test_looper_mixed(self, *args): set_counter(0) self.assertEqual(0, get_counter()) self._execute(AWS, "looper-mixed", {"loops": 3}) self.assertEqual(6, get_counter()) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('start', 'done', 1, 0), (3,)), (2, ('reset', 'done', 2, 0), (None,)), (3, ('loop', 'done', 3, 0), (3,)) ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, 'lease-correlation_id-2': True, 'lease-correlation_id-3': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_looper_mixed_with_failure(self, *args): set_counter(0) self.assertEqual(0, get_counter()) self._execute(AWS, "looper-mixed", {"loops": 3, 'fail_at': [(i, 0) for i in range(100)]}) self.assertEqual(6, get_counter()) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('pseudo_init', 'pseudo_init', 0, 1), (None,)), # retry (2, ('start', 'done', 1, 0), (3,)), (3, ('reset', 'done', 2, 0), (None,)), (4, ('reset', 'done', 2, 1), (None,)), # retry (5, ('loop', 'done', 3, 0), (3,)) ] self.assertEqual(expected, AWS.all_sources.trace(('counter',))) # check cache expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True } self.assertEqual(expected, AWS.primary_cache) self.assertEqual(expected, AWS.secondary_cache) expected = { 'correlation_id-0': True, 'correlation_id-1': True, 'correlation_id-2': True, 'correlation_id-3': True, 'lease-correlation_id-0': True, 'lease-correlation_id-1': True, 'lease-correlation_id-2': True, 'lease-correlation_id-3': True } self.assertEqual(expected, AWS.all_caches) # check errors expected = [ [ {'retry': 1}, {'current_event': 'pseudo_init', 'current_state': 'pseudo_init', 'machine_name': 'looper-mixed'} ], [ {'retry': 1}, {'current_event': 'done', 'current_state': 'reset', 'machine_name': 'looper-mixed'} ] ] self.assertEqual(expected, AWS.errors.trace(raw=True)) def test_looper_mixed_uses_queue(self, *args): AWS.add_callback('send_next_event_for_dispatch', mock.Mock(side_effect=Exception())) self.assertRaises(Exception, self._execute, AWS, "looper-mixed", {"loops": 3}) ################################################################################ # START: machine_name="serialization" ################################################################################ def test_serialization(self, *args): self._execute(AWS, "serialization", {}) # check messages expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), (None,)), (1, ('start', 'ok', 1, 0), ('<not_serializable>',)), (2, ('middle', 'ok', 2, 0), ('<not_serializable>',)) ] self.assertEqual(expected, AWS.all_sources.trace(uvars={"error"})) ################################################################################ # START: machine_name="longpause" ################################################################################ def test_two_at_same_time(self, *args): thread1 = TestThread(self, "longpause", {'key': 'val1'}) thread2 = TestThread(self, "longpause", {'key': 'val2'}) thread1.start() time.sleep(2) thread2.start() thread1.join() thread2.join() expected = [ (0, ('pseudo_init', 'pseudo_init', 0, 0), ('val1',)), (1, ('pseudo_init', 'pseudo_init', 0, 0), ('val2',)), # both start (2, ('pseudo_init', 'pseudo_init', 0, 1), ('val2',)), # second unable to acquire lease (3, ('start', 'ok', 1, 0), ('val1',)), (4, ('middle', 'ok', 2, 0), ('val1',)), # first finished (5, ('start', 'ok', 1, 0), ('val2',)) # second gets lease, but that has already run ] self.assertEqual(expected, AWS.all_sources.trace(uvars={"key"})) class TestThread(threading.Thread): def __init__(self, test, name, context): threading.Thread.__init__(self) self.test = test self.name = name self.context = context def run(self): self.test._execute(AWS, self.name, self.context) class TestSqs(Test): MESSAGE_TYPE = AWS_CONSTANTS.SQS class TestSns(Test): MESSAGE_TYPE = AWS_CONSTANTS.SNS

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false

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0.071882

0

null

0

1

0

null

0

7

23b5ade293d46ff8fc5136c015ccbac45f12a15f

3,614

py

Python

assignments/assignment5/train.py

nikitakogut/dlcourse_ai

bd5826c04331864e0c37c84cf33674438d3f9b01

[ "MIT" ]

1

2019-03-27T09:18:47.000Z

assignments/assignment5/train.py

nikitakogut/dlcourse_ai

bd5826c04331864e0c37c84cf33674438d3f9b01

[ "MIT" ]

null

assignments/assignment5/train.py

nikitakogut/dlcourse_ai

bd5826c04331864e0c37c84cf33674438d3f9b01

[ "MIT" ]

null

import numpy as np import torch from tqdm.auto import tqdm def tqdm_enumerate(iter): i = 0 for y in tqdm(iter): yield i, y i += 1 def train_model(model, device, dataset, loss, optimizer, scheduler, num_epochs): ''' Trains plain word2vec using cross-entropy loss and regenerating dataset every epoch Returns: loss_history, train_history ''' loss_history = [] train_history = [] for epoch in range(num_epochs): model.train() loss_accum = 0 correct_samples = 0 total_samples = 0 del dataset.samples dataset.samples = [] dataset.generate_dataset() train_loader = torch.utils.data.DataLoader(dataset, batch_size=20) for i_step, (x, y) in tqdm_enumerate(train_loader): x_gpu = x.to(device) y_gpu = y.to(device) prediction = model(x_gpu) loss_value = loss(prediction, y_gpu) optimizer.zero_grad() loss_value.backward() optimizer.step() _, indices = torch.max(prediction, 1) correct_samples += torch.sum(indices == y_gpu) total_samples += y.shape[0] loss_accum += loss_value.detach() ave_loss = loss_accum / i_step train_accuracy = float(correct_samples) / total_samples loss_history.append(float(ave_loss)) train_history.append(train_accuracy) print("Epoch %i, Average loss: %f, Train accuracy: %f" % (epoch+1, ave_loss, train_accuracy)) if scheduler is not None: if type(scheduler) == torch.optim.lr_scheduler.ReduceLROnPlateau: scheduler.step(ave_loss) else: scheduler.step() return loss_history, train_history def train_neg_sample(model, device, dataset, loss, optimizer, scheduler, num_epochs): ''' Trains word2vec with negative samples on and regenerating dataset every epoch Returns: loss_history, train_history ''' loss_history = [] train_history = [] for epoch in range(num_epochs): model.train() loss_accum = 0 correct_samples = 0 total_samples = 0 del dataset.samples dataset.samples = [] dataset.generate_dataset() train_loader = torch.utils.data.DataLoader(dataset, batch_size=20) for i_step, (x, y, z) in tqdm_enumerate(train_loader): x_gpu = x.to(device) y_gpu = y.to(device) z_gpu = z.to(device) prediction = model(x_gpu, y_gpu) loss_value = loss(prediction, z_gpu) optimizer.zero_grad() loss_value.backward() optimizer.step() loss_accum += loss_value.detach() correct_samples += sum(1 / (1+np.exp(-prediction[:, 0].detach().cpu().numpy())) > .5) total_samples += y.shape[0] ave_loss = loss_accum / i_step train_accuracy = float(correct_samples) / total_samples loss_history.append(float(ave_loss)) train_history.append(train_accuracy) print("Epoch %i, Average loss: %f, Train accuracy: %f" % (epoch+1, ave_loss, train_accuracy)) if scheduler is not None: if type(scheduler) == torch.optim.lr_scheduler.ReduceLROnPlateau: scheduler.step(ave_loss) else: scheduler.step() return loss_history, train_history

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1

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7

f1b82185af4b277720b92d0f1200c1bacbf8b5ea

15,772

py

Python

dxm/lib/masking_api/api/application_api.py

experiortec/dxm-toolkit

b2ab6189e163c62fa8d7251cd533d2a36430d44a

[ "Apache-2.0" ]

5

2018-08-23T15:47:05.000Z

2022-01-19T23:38:18.000Z

dxm/lib/masking_api/api/application_api.py

experiortec/dxm-toolkit

b2ab6189e163c62fa8d7251cd533d2a36430d44a

[ "Apache-2.0" ]

59

2018-10-15T10:37:00.000Z

2022-03-22T20:49:25.000Z

dxm/lib/masking_api/api/application_api.py

experiortec/dxm-toolkit

b2ab6189e163c62fa8d7251cd533d2a36430d44a

[ "Apache-2.0" ]

12

2019-03-08T19:59:13.000Z

2021-12-16T03:28:04.000Z

# coding: utf-8 """ Masking API Schema for the Masking Engine API # noqa: E501 OpenAPI spec version: 5.1.8 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from dxm.lib.masking_api.api_client import ApiClient class ApplicationApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_application(self, body, **kwargs): # noqa: E501 """Create application # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_application(body, async_req=True) >>> result = thread.get() :param async_req bool :param Application body: The application to create (required) :return: Application If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_application_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.create_application_with_http_info(body, **kwargs) # noqa: E501 return data def create_application_with_http_info(self, body, **kwargs): # noqa: E501 """Create application # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_application_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param Application body: The application to create (required) :return: Application If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_application" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if self.api_client.client_side_validation and ('body' not in params or params['body'] is None): # noqa: E501 raise ValueError("Missing the required parameter `body` when calling `create_application`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/applications', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Application', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_application(self, application_id, **kwargs): # noqa: E501 """Delete application by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_application(application_id, async_req=True) >>> result = thread.get() :param async_req bool :param int application_id: The ID of the application to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_application_with_http_info(application_id, **kwargs) # noqa: E501 else: (data) = self.delete_application_with_http_info(application_id, **kwargs) # noqa: E501 return data def delete_application_with_http_info(self, application_id, **kwargs): # noqa: E501 """Delete application by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_application_with_http_info(application_id, async_req=True) >>> result = thread.get() :param async_req bool :param int application_id: The ID of the application to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['application_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_application" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'application_id' is set if self.api_client.client_side_validation and ('application_id' not in params or params['application_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `application_id` when calling `delete_application`") # noqa: E501 collection_formats = {} path_params = {} if 'application_id' in params: path_params['applicationId'] = params['application_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/applications/{applicationId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_all_applications(self, **kwargs): # noqa: E501 """Get all applications # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_applications(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: The page number for which to get applications. This will default to the first page if excluded :param int page_size: The maximum number of objects to return. This will default to the DEFAULT_API_PAGE_SIZE property if not provided :return: ApplicationList If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_all_applications_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_all_applications_with_http_info(**kwargs) # noqa: E501 return data def get_all_applications_with_http_info(self, **kwargs): # noqa: E501 """Get all applications # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_applications_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: The page number for which to get applications. This will default to the first page if excluded :param int page_size: The maximum number of objects to return. This will default to the DEFAULT_API_PAGE_SIZE property if not provided :return: ApplicationList If the method is called asynchronously, returns the request thread. """ all_params = ['page_number', 'page_size'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_all_applications" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'page_number' in params: query_params.append(('page_number', params['page_number'])) # noqa: E501 if 'page_size' in params: query_params.append(('page_size', params['page_size'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/applications', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ApplicationList', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_application_by_id(self, application_id, **kwargs): # noqa: E501 """Get application by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_application_by_id(application_id, async_req=True) >>> result = thread.get() :param async_req bool :param int application_id: The ID of the application to get (required) :return: Application If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_application_by_id_with_http_info(application_id, **kwargs) # noqa: E501 else: (data) = self.get_application_by_id_with_http_info(application_id, **kwargs) # noqa: E501 return data def get_application_by_id_with_http_info(self, application_id, **kwargs): # noqa: E501 """Get application by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_application_by_id_with_http_info(application_id, async_req=True) >>> result = thread.get() :param async_req bool :param int application_id: The ID of the application to get (required) :return: Application If the method is called asynchronously, returns the request thread. """ all_params = ['application_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_application_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'application_id' is set if self.api_client.client_side_validation and ('application_id' not in params or params['application_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `application_id` when calling `get_application_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'application_id' in params: path_params['applicationId'] = params['application_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/applications/{applicationId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Application', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

38.751843

142

0.614063

1,809

15,772

5.092869

0.090105

0.044285

0.024313

0.03126

0.922175

0.913492

0.893303

0.871052

0.868447

0

0.01469

0.300786

15,772

406

143

38.847291

0.820729

0.327796

0

0.759259

1

0

0.174339

0.037358

0

1

0.041667

false

0

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0.12037

0

null

0

1

0

1

0

null

0

8

7b2fd2aded75d23271f53e444445b04deea428a0

10,204

py

Python

test/feature/extract/test_nmf.py

ohtaman/pynm

b003962201e4270d0dab681ede37f2d8edd560f2

[ "MIT" ]

1

2018-08-16T20:48:52.000Z

test/feature/extract/test_nmf.py

ohtaman/pynm

b003962201e4270d0dab681ede37f2d8edd560f2

[ "MIT" ]

5

2015-01-12T20:40:46.000Z

2017-11-17T01:27:41.000Z

test/feature/extract/test_nmf.py

ohtaman/pynm

b003962201e4270d0dab681ede37f2d8edd560f2

[ "MIT" ]

null

# -*- coding:utf-8 -*- import logging import math from nose.tools import * import numpy import numpy.linalg from pynm.feature.extract import nmf logger = logging.getLogger(__name__) @istest def can_treat_matrix_without_errors(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix) ok_(True, 'Failed to decomposit a matrix') @istest def result_is_positive_matrix(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix) ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix) ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive') @istest def can_reduce_dimension(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, 2) eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct') @istest def can_approx_original_matrix(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix) diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff/100.0 < 0.12, 'NMF cannot apporximate a matrix (%s > 0.12)' % diff) @istest def can_approx_zero_matrix(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, max_iter=1000) diff += numpy.amax(w.dot(h)) ok_(diff/100.0 < 0.12, 'NMF cannot apporximate zero matrix (%s > 0.12)' % diff) @istest def can_treat_matrix_without_errors_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, distance="kl") ok_(True, 'Failed to decomposit a matrix') @istest def result_is_positive_matrix_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, distance="kl") ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, distance="kl") ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive') @istest def can_reduce_dimension_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, 2, distance="kl") eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct') @istest def can_approx_original_matrix_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, distance="kl") diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff/100.0 < 1, 'NMF cannot apporximate a matrix (%s > 1.)' % diff) @istest def can_approx_zero_matrix_with_kl_divergent(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, max_iter=1000, distance="kl") diff += numpy.amax(w.dot(h)) ok_(diff/100.0 < 0.2, 'NMF cannot apporoximate zero matrix (%s > 0.2)' % diff) @istest def result_is_positive_matrix_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init) ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, init=nmf.random_init) ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive') @istest def can_reduce_dimension_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, 2, init=nmf.random_init) eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct') @istest def can_approx_original_matrix_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init) diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff/100.0 < 0.12, 'NMF cannot apporximate a matrix (%s > 0.12)' % diff) @istest def can_approx_zero_matrix_with_random_init(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init, max_iter=1000) diff += numpy.amax(w.dot(h)) ok_(diff/100.0 < 0.2, 'NMF cannot apporoximate zero matrix (%s > 0.2)' % diff) @istest def can_treat_matrix_without_errors_with_kl_divergent_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="kl") ok_(True, 'Failed to decomposit a matrix') @istest def result_is_positive_matrix_with_kl_divergent_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="kl") ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="kl") ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive') @istest def can_reduce_dimension_with_kl_divergent_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, dim=2, init=nmf.random_init, distance="kl") eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct') @istest def can_approx_original_matrix_with_kl_divergent_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init, distance="kl") diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff/100.0 < 1, 'NMF cannot apporximate a matrix (%s > 1.0)' % diff) @istest def can_approx_zero_matrix_with_kl_divergent_with_random_init(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init, max_iter=1000, distance="kl") diff += numpy.amax(w.dot(h)) ok_(diff/100.0 < 0.2, 'NMF cannot apporoximate zero matrix (%s > 0.2)' % diff) @istest def can_treat_matrix_without_errors_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") ok_(True, 'Failed to decomposit a matrix') @istest def result_is_positive_matrix_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") w_min = numpy.amin(w) h_min = numpy.amin(h) ok_(w_min >= 0, 'W of zero matrix is not positive (%s < 0)' % w_min) ok_(h_min >= 0, 'H of zero matrix is not positive (%s < 0)' % h_min) matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") w_min = numpy.amin(w) h_min = numpy.amin(h) ok_(w_min >= 0, 'W of zero matrix is not positive (%s < 0)' % w_min) ok_(h_min >= 0, 'H of zero matrix is not positive (%s < 0)' % h_min) @istest def can_reduce_dimension_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, dim=2, init=nmf.random_init, distance="beta") eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct') @istest def can_approx_original_matrix_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff/100.0 < 1, 'NMF cannot apporximate a matrix (%s > 1.0)' % diff) @istest def can_approx_zero_matrix_with_beta_divergence_with_random_init(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init, max_iter=1000, distance="beta") diff += numpy.amax(w.dot(h)) ok_(diff/100.0 < 0.2, 'NMF cannot apporoximate zero matrix (%s > 0.2)' % diff)

33.346405

84

0.520776

1,548

10,204

3.271318

0.052972

0.016193

0.028633

0.045814

0.972156

0.968009

0.961888

0.951619

0.949645

0.94688

0

0.065085

0.322423

10,204

305

85

33.455738

0.667342

0.00196

0

0.826613

0

0.144106

0

1

0.096774

false

0

0.024194

0

0.120968

0

null

0

1

0

1

0

null

0

7

9e6ece50ee01b0c747bf6669f828b9ca56a6989b

53,036

py

Python

src/dynamixel_sdk/dynamixel_item.py

lapo5/Dynamixel-Python

81e52621358078e240692da2919362ec71685aa8

[ "Apache-2.0" ]

1

2018-09-16T06:16:01.000Z

src/dynamixel_sdk/dynamixel_item.py

lapo5/Dynamixel-Python

81e52621358078e240692da2919362ec71685aa8

[ "Apache-2.0" ]

null

src/dynamixel_sdk/dynamixel_item.py

lapo5/Dynamixel-Python

81e52621358078e240692da2919362ec71685aa8

[ "Apache-2.0" ]

null

#!/usr/bin/env python # coding: UTF-8 # This Python porting of the original Dynamixel Workbench Toolbox from ROBOTIS # was written by Patrick Roncagliolo and Marco Lapolla as part of a project # developed at the DIBRIS BIOLab of the University of Genoa, Italy. import control_table_item as ct_item # Type of Servo-Motors AX_12A = 12 AX_12W = 300 AX_18A = 18 RX_10 = 10 RX_24F = 24 RX_28 = 28 RX_64 = 64 EX_106 = 107 MX_12W = 360 MX_28 = 29 MX_28_2 = 30 MX_64 = 310 MX_64_2 = 311 MX_106 = 320 MX_106_2 = 321 XL_320 = 350 XL430_W250 = 1060 XM430_W210 = 1030 XM430_W350 = 1020 XM540_W150 = 1130 XM540_W270 = 1120 XH430_V210 = 1050 XH430_V350 = 1040 XH430_W210 = 1010 XH430_W350 = 1000 PRO_L42_10_S300_R = 35072 PRO_L54_30_S400_R = 37928 PRO_L54_30_S500_R = 37896 PRO_L54_50_S290_R = 38176 PRO_L54_50_S500_R = 38152 PRO_M42_10_S260_R = 43288 PRO_M54_40_S250_R = 46096 PRO_M54_60_S250_R = 46352 PRO_H42_20_S300_R = 51200 PRO_H54_100_S500_R = 53768 PRO_H54_200_S500_R = 54024 BYTE = 1 WORD = 2 DWORD = 4 class ModelInfo: def __init__(self): self.velocity_to_value_ratio = None self.torque_to_current_value_ratio = None self.value_of_min_radian_position = None self.value_of_0_radian_position = None self.value_of_max_radian_position = None self.min_radian = None self.max_radian = None def setAXInfo(self): self.velocity_to_value_ratio = 86.03 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 512 self.value_of_max_radian_position = 1023 self.min_radian = -2.61799 self.max_radian = 2.61799 def setRXInfo(self): self.velocity_to_value_ratio = 86.03 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 512 self.value_of_max_radian_position = 1023 self.min_radian = -2.61799 self.max_radian = 2.61799 def setEXInfo(self): self.velocity_to_value_ratio = 86.03 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -2.18969008 self.max_radian = 2.18969008 def setMXInfo(self): self.velocity_to_value_ratio = 86.81 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setMX2Info(self): self.velocity_to_value_ratio = 41.70 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setExtMXInfo(self): self.velocity_to_value_ratio = 86.81 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setExtMX2Info(self): self.velocity_to_value_ratio = 41.70 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setXL320Info(self): self.velocity_to_value_ratio = 86.03 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 512 self.value_of_max_radian_position = 1023 self.min_radian = -2.61799 self.max_radian = 2.61799 def setXLInfo(self): self.velocity_to_value_ratio = 41.70 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setXMInfo(self): self.velocity_to_value_ratio = 41.70 self.torque_to_current_value_ratio = 149.795386991 self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setExtXMInfo(self): self.velocity_to_value_ratio = 41.70 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setXHInfo(self): self.velocity_to_value_ratio = 41.71 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = 0 self.value_of_0_radian_position = 2048 self.value_of_max_radian_position = 4095 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def setPROInfo(self): self.velocity_to_value_ratio = 4792.8 self.torque_to_current_value_ratio = None self.value_of_min_radian_position = -250961 self.value_of_0_radian_position = 0 self.value_of_max_radian_position = 250961 self.min_radian = -3.14159265 self.max_radian = 3.14159265 def getModelInfo(self, num): if num in [AX_12A, AX_12W, AX_18A]: self.setAXInfo() elif num in [RX_10, RX_24F, RX_28, RX_64]: self.setRXInfo() elif num == EX_106: self.setEXInfo() elif num in [MX_12W, MX_28]: self.setMXInfo() elif num in [MX_64, MX_106]: self.setExtMXInfo() elif num == MX_28_2: self.setMX2Info() elif num in [MX_64_2, MX_106_2]: self.setExtMX2Info() elif num == XL_320: self.setXL320Info() elif num == XL430_W250: self.setXLInfo() elif num in [XM430_W210, XM430_W350]: self.setXMInfo() elif num in [XM540_W150, XM540_W270]: self.setExtXMInfo() elif num in [XH430_V210, XH430_V350, XH430_W210, XH430_W350]: self.setXHInfo() elif num in [PRO_L42_10_S300_R, PRO_L54_30_S400_R, PRO_L54_30_S500_R, PRO_L54_50_S290_R, PRO_L54_50_S500_R, PRO_M42_10_S260_R, PRO_M54_40_S250_R, PRO_M54_60_S250_R, PRO_H42_20_S300_R, PRO_H54_100_S500_R,PRO_H54_200_S500_R]: self.setPROInfo() else: self.setXMInfo() return self class ControlTable: def __init__(self): self._item = [] def setAXItem(self): self._item = [] self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(2, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(3, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(4, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(5, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(6, "CW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(8, "CCW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(11, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Min_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Max_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(14, "Max_Torque", WORD)) self._item.append(ct_item.ControlTableItem(16, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(17, "Alarm_LED", BYTE)) self._item.append(ct_item.ControlTableItem(18, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(24, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(25, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(26, "CW_Compliance_Margin", BYTE)) self._item.append(ct_item.ControlTableItem(27, "CCW_Compliance_Margin", BYTE)) self._item.append(ct_item.ControlTableItem(28, "CW_Compliance_Slope", BYTE)) self._item.append(ct_item.ControlTableItem(29, "CCW_Compliance_Slope", BYTE)) self._item.append(ct_item.ControlTableItem(30, "Goal_Position", WORD)) self._item.append(ct_item.ControlTableItem(32, "Moving_Speed", WORD)) self._item.append(ct_item.ControlTableItem(34, "Torque_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "Present_Position", WORD)) self._item.append(ct_item.ControlTableItem(38, "Present_Speed", WORD)) self._item.append(ct_item.ControlTableItem(40, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(42, "Present_Voltage", BYTE)) self._item.append(ct_item.ControlTableItem(43, "Present_Temperature", BYTE)) self._item.append(ct_item.ControlTableItem(44, "Registered", BYTE)) self._item.append(ct_item.ControlTableItem(46, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(47, "Lock", BYTE)) self._item.append(ct_item.ControlTableItem(48, "Punch", WORD)) def setRXItem(self): self._item = [] self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(2, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(3, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(4, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(5, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(6, "CW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(8, "CCW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(11, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Min_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Max_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(14, "Max_Torque", WORD)) self._item.append(ct_item.ControlTableItem(16, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(17, "Alarm_LED", BYTE)) self._item.append(ct_item.ControlTableItem(18, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(24, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(25, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(26, "CW_Compliance_Margin", BYTE)) self._item.append(ct_item.ControlTableItem(27, "CCW_Compliance_Margin", BYTE)) self._item.append(ct_item.ControlTableItem(28, "CW_Compliance_Slope", BYTE)) self._item.append(ct_item.ControlTableItem(29, "CCW_Compliance_Slope", BYTE)) self._item.append(ct_item.ControlTableItem(30, "Goal_Position", WORD)) self._item.append(ct_item.ControlTableItem(32, "Moving_Speed", WORD)) self._item.append(ct_item.ControlTableItem(34, "Torque_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "Present_Position", WORD)) self._item.append(ct_item.ControlTableItem(38, "Present_Speed", WORD)) self._item.append(ct_item.ControlTableItem(40, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(42, "Present_Voltage", BYTE)) self._item.append(ct_item.ControlTableItem(43, "Present_Temperature", BYTE)) self._item.append(ct_item.ControlTableItem(44, "Registered", BYTE)) self._item.append(ct_item.ControlTableItem(46, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(47, "Lock", BYTE)) self._item.append(ct_item.ControlTableItem(48, "Punch", WORD)) def setEXItem(self): self._item = [] self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(2, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(3, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(4, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(5, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(6, "CW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(8, "CCW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(10, "Drive_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Min_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Max_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(14, "Max_Torque", WORD)) self._item.append(ct_item.ControlTableItem(16, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(17, "Alarm_LED", BYTE)) self._item.append(ct_item.ControlTableItem(18, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(24, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(25, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(26, "CW_Compliance_Margin", BYTE)) self._item.append(ct_item.ControlTableItem(27, "CCW_Compliance_Margin", BYTE)) self._item.append(ct_item.ControlTableItem(28, "CW_Compliance_Slope", BYTE)) self._item.append(ct_item.ControlTableItem(29, "CCW_Compliance_Slope", BYTE)) self._item.append(ct_item.ControlTableItem(30, "Goal_Position", WORD)) self._item.append(ct_item.ControlTableItem(32, "Moving_Speed", WORD)) self._item.append(ct_item.ControlTableItem(34, "Torque_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "Present_Position", WORD)) self._item.append(ct_item.ControlTableItem(38, "Present_Speed", WORD)) self._item.append(ct_item.ControlTableItem(40, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(42, "Present_Voltage", BYTE)) self._item.append(ct_item.ControlTableItem(43, "Present_Temperature", BYTE)) self._item.append(ct_item.ControlTableItem(44, "Registered", BYTE)) self._item.append(ct_item.ControlTableItem(46, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(47, "Lock", BYTE)) self._item.append(ct_item.ControlTableItem(48, "Punch", WORD)) self._item.append(ct_item.ControlTableItem(56, "Sensored_Current", WORD)) def setMXItem(self): self._item = [] self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(2, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(3, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(4, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(5, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(6, "CW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(8, "CCW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(11, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Min_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Max_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(14, "Max_Torque", WORD)) self._item.append(ct_item.ControlTableItem(16, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(17, "Alarm_LED", BYTE)) self._item.append(ct_item.ControlTableItem(18, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Multi_Turn_Offset", WORD)) self._item.append(ct_item.ControlTableItem(22, "Resolution_Divider", BYTE)) self._item.append(ct_item.ControlTableItem(24, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(25, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(26, "D_gain", BYTE)) self._item.append(ct_item.ControlTableItem(27, "I_gain", BYTE)) self._item.append(ct_item.ControlTableItem(28, "P_gain", BYTE)) self._item.append(ct_item.ControlTableItem(30, "Goal_Position", WORD)) self._item.append(ct_item.ControlTableItem(32, "Moving_Speed", WORD)) self._item.append(ct_item.ControlTableItem(34, "Torque_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "Present_Position", WORD)) self._item.append(ct_item.ControlTableItem(38, "Present_Speed", WORD)) self._item.append(ct_item.ControlTableItem(40, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(42, "Present_Voltage", BYTE)) self._item.append(ct_item.ControlTableItem(43, "Present_Temperature", BYTE)) self._item.append(ct_item.ControlTableItem(44, "Registered", BYTE)) self._item.append(ct_item.ControlTableItem(46, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(47, "Lock", BYTE)) self._item.append(ct_item.ControlTableItem(48, "Punch", WORD)) self._item.append(ct_item.ControlTableItem(73, "Goal_Acceleration", BYTE)) def setMX2Item(self): self._item = [] self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(6, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(7, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(8, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(9, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(10, "Drive_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Operating_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Secondary_ID", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Protocol_Version", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Homing_Offset", DWORD)) self._item.append(ct_item.ControlTableItem(24, "Moving_Threshold", DWORD)) self._item.append(ct_item.ControlTableItem(31, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(32, "Max_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(34, "Min_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "PWM_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Acceleration_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(44, "Velocity_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(48, "Max_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(52, "Min_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(63, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(64, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(65, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(68, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(69, "Registered_Instruction", BYTE)) self._item.append(ct_item.ControlTableItem(70, "Hardware_Error_Status", BYTE)) self._item.append(ct_item.ControlTableItem(76, "Velocity_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(78, "Velocity_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(80, "Position_D_Gain", WORD)) self._item.append(ct_item.ControlTableItem(82, "Position_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(84, "Position_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(88, "Feedforward_2nd_Gain", WORD)) self._item.append(ct_item.ControlTableItem(90, "Feedforward_1st_Gain", WORD)) self._item.append(ct_item.ControlTableItem(98, "Bus_Watchdog", BYTE)) self._item.append(ct_item.ControlTableItem(100, "Goal_PWM", WORD)) self._item.append(ct_item.ControlTableItem(104, "Goal_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(108, "Profile_Acceleration", DWORD)) self._item.append(ct_item.ControlTableItem(112, "Profile_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(116, "Goal_Position", DWORD)) self._item.append(ct_item.ControlTableItem(120, "Realtime_Tick", WORD)) self._item.append(ct_item.ControlTableItem(122, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(123, "Moving_Status", BYTE)) self._item.append(ct_item.ControlTableItem(124, "Present_PWM", WORD)) self._item.append(ct_item.ControlTableItem(126, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(128, "Present_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(132, "Present_Position", DWORD)) self._item.append(ct_item.ControlTableItem(136, "Velocity_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(140, "Position_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(144, "Present_Input_Voltage", WORD)) self._item.append(ct_item.ControlTableItem(146, "Present_Temperature", BYTE)) def setExtMXItem(self): self._item = [] self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(2, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(3, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(4, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(5, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(6, "CW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(8, "CCW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(11, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Min_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Max_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(14, "Max_Torque", WORD)) self._item.append(ct_item.ControlTableItem(16, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(17, "Alarm_LED", BYTE)) self._item.append(ct_item.ControlTableItem(18, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Multi_Turn_Offset", WORD)) self._item.append(ct_item.ControlTableItem(22, "Resolution_Divider", BYTE)) self._item.append(ct_item.ControlTableItem(24, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(25, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(26, "D_gain", BYTE)) self._item.append(ct_item.ControlTableItem(27, "I_gain", BYTE)) self._item.append(ct_item.ControlTableItem(28, "P_gain", BYTE)) self._item.append(ct_item.ControlTableItem(30, "Goal_Position", WORD)) self._item.append(ct_item.ControlTableItem(32, "Moving_Speed", WORD)) self._item.append(ct_item.ControlTableItem(34, "Torque_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "Present_Position", WORD)) self._item.append(ct_item.ControlTableItem(38, "Present_Speed", WORD)) self._item.append(ct_item.ControlTableItem(40, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(42, "Present_Voltage", BYTE)) self._item.append(ct_item.ControlTableItem(43, "Present_Temperature", BYTE)) self._item.append(ct_item.ControlTableItem(44, "Registered", BYTE)) self._item.append(ct_item.ControlTableItem(46, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(47, "Lock", BYTE)) self._item.append(ct_item.ControlTableItem(48, "Punch", WORD)) self._item.append(ct_item.ControlTableItem(68, "Current", WORD)) self._item.append(ct_item.ControlTableItem(70, "Torque_Control_Mode_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(71, "Goal_Torque", WORD)) self._item.append(ct_item.ControlTableItem(73, "Goal_Acceleration", BYTE)) def setExtMX2Item(self): self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(6, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(7, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(8, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(9, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(10, "Drive_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Operating_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Secondary_ID", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Protocol_Version", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Homing_Offset", DWORD)) self._item.append(ct_item.ControlTableItem(24, "Moving_Threshold", DWORD)) self._item.append(ct_item.ControlTableItem(31, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(32, "Max_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(34, "Min_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "PWM_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Current_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Acceleration_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(44, "Velocity_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(48, "Max_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(52, "Min_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(63, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(64, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(65, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(68, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(69, "Registered_Instruction", BYTE)) self._item.append(ct_item.ControlTableItem(70, "Hardware_Error_Status", BYTE)) self._item.append(ct_item.ControlTableItem(76, "Velocity_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(78, "Velocity_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(80, "Position_D_Gain", WORD)) self._item.append(ct_item.ControlTableItem(82, "Position_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(84, "Position_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(88, "Feedforward_2nd_Gain", WORD)) self._item.append(ct_item.ControlTableItem(90, "Feedforward_1st_Gain", WORD)) self._item.append(ct_item.ControlTableItem(98, "Bus_Watchdog", BYTE)) self._item.append(ct_item.ControlTableItem(100, "Goal_PWM", WORD)) self._item.append(ct_item.ControlTableItem(102, "Goal_Current", WORD)) self._item.append(ct_item.ControlTableItem(104, "Goal_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(108, "Profile_Acceleration", DWORD)) self._item.append(ct_item.ControlTableItem(112, "Profile_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(116, "Goal_Position", DWORD)) self._item.append(ct_item.ControlTableItem(120, "Realtime_Tick", WORD)) self._item.append(ct_item.ControlTableItem(122, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(123, "Moving_Status", BYTE)) self._item.append(ct_item.ControlTableItem(124, "Present_PWM", WORD)) self._item.append(ct_item.ControlTableItem(126, "Present_Current", WORD)) self._item.append(ct_item.ControlTableItem(128, "Present_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(132, "Present_Position", DWORD)) self._item.append(ct_item.ControlTableItem(136, "Velocity_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(140, "Position_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(144, "Present_Input Voltage", WORD)) self._item.append(ct_item.ControlTableItem(146, "Present_Temperature", BYTE)) def setXL320Item(self): self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(2, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(3, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(4, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(5, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(6, "CW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(8, "CCW_Angle_Limit", WORD)) self._item.append(ct_item.ControlTableItem(11, "Control_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Min_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(14, "Max_Voltage_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(15, "Max_Torque", WORD)) self._item.append(ct_item.ControlTableItem(17, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(18, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(24, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(25, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(27, "D_gain", BYTE)) self._item.append(ct_item.ControlTableItem(28, "I_gain", BYTE)) self._item.append(ct_item.ControlTableItem(29, "P_gain", BYTE)) self._item.append(ct_item.ControlTableItem(30, "Goal_Position", WORD)) self._item.append(ct_item.ControlTableItem(32, "Moving_Speed", WORD)) self._item.append(ct_item.ControlTableItem(34, "Torque_Limit", WORD)) self._item.append(ct_item.ControlTableItem(37, "Present_Position", WORD)) self._item.append(ct_item.ControlTableItem(39, "Present_Speed", WORD)) self._item.append(ct_item.ControlTableItem(41, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(45, "Present_Voltage", BYTE)) self._item.append(ct_item.ControlTableItem(46, "Present_Temperature", BYTE)) self._item.append(ct_item.ControlTableItem(47, "Registered", BYTE)) self._item.append(ct_item.ControlTableItem(49, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(50, "Hardware_Error_Status", BYTE)) self._item.append(ct_item.ControlTableItem(51, "Punch", WORD)) def setXLItem(self): self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(6, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(7, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(8, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(9, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(10, "Drive_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Operating_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Secondary_ID", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Protocol_Version", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Homing_Offset", DWORD)) self._item.append(ct_item.ControlTableItem(24, "Moving_Threshold", DWORD)) self._item.append(ct_item.ControlTableItem(31, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(32, "Max_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(34, "Min_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "PWM_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Acceleration_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(44, "Velocity_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(48, "Max_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(52, "Min_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(63, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(64, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(65, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(68, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(69, "Registered_Instruction", BYTE)) self._item.append(ct_item.ControlTableItem(70, "Hardware_Error_Status", BYTE)) self._item.append(ct_item.ControlTableItem(76, "Velocity_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(78, "Velocity_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(80, "Position_D_Gain", WORD)) self._item.append(ct_item.ControlTableItem(82, "Position_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(84, "Position_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(88, "Feedforward_2nd_Gain", WORD)) self._item.append(ct_item.ControlTableItem(90, "Feedforward_1st_Gain", WORD)) self._item.append(ct_item.ControlTableItem(98, "Bus_Watchdog", BYTE)) self._item.append(ct_item.ControlTableItem(100, "Goal_PWM", WORD)) self._item.append(ct_item.ControlTableItem(104, "Goal_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(108, "Profile_Acceleration", DWORD)) self._item.append(ct_item.ControlTableItem(112, "Profile_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(116, "Goal_Position", DWORD)) self._item.append(ct_item.ControlTableItem(120, "Realtime_Tick", WORD)) self._item.append(ct_item.ControlTableItem(122, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(123, "Moving_Status", BYTE)) self._item.append(ct_item.ControlTableItem(124, "Present_PWM", WORD)) self._item.append(ct_item.ControlTableItem(126, "Present_Load", WORD)) self._item.append(ct_item.ControlTableItem(128, "Present_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(132, "Present_Position", DWORD)) self._item.append(ct_item.ControlTableItem(136, "Velocity_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(140, "Position_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(144, "Present_Input_Voltage", WORD)) self._item.append(ct_item.ControlTableItem(146, "Present_Temperature", BYTE)) def setXMItem(self): self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(6, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(7, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(8, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(9, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(10, "Drive_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Operating_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Secondary_ID", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Protocol_Version", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Homing_Offset", DWORD)) self._item.append(ct_item.ControlTableItem(24, "Moving_Threshold", DWORD)) self._item.append(ct_item.ControlTableItem(31, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(32, "Max_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(34, "Min_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "PWM_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Current_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Acceleration_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(44, "Velocity_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(48, "Max_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(52, "Min_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(63, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(64, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(65, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(68, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(69, "Registered_Instruction", BYTE)) self._item.append(ct_item.ControlTableItem(70, "Hardware_Error_Status", BYTE)) self._item.append(ct_item.ControlTableItem(76, "Velocity_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(78, "Velocity_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(80, "Position_D_Gain", WORD)) self._item.append(ct_item.ControlTableItem(82, "Position_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(84, "Position_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(88, "Feedforward_2nd_Gain", WORD)) self._item.append(ct_item.ControlTableItem(90, "Feedforward_1st_Gain", WORD)) self._item.append(ct_item.ControlTableItem(98, "Bus_Watchdog", BYTE)) self._item.append(ct_item.ControlTableItem(100, "Goal_PWM", WORD)) self._item.append(ct_item.ControlTableItem(102, "Goal_Current", WORD)) self._item.append(ct_item.ControlTableItem(104, "Goal_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(108, "Profile_Acceleration", DWORD)) self._item.append(ct_item.ControlTableItem(112, "Profile_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(116, "Goal_Position", DWORD)) self._item.append(ct_item.ControlTableItem(120, "Realtime_Tick", WORD)) self._item.append(ct_item.ControlTableItem(122, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(123, "Moving_Status", BYTE)) self._item.append(ct_item.ControlTableItem(124, "Present_PWM", WORD)) self._item.append(ct_item.ControlTableItem(126, "Present_Current", WORD)) self._item.append(ct_item.ControlTableItem(128, "Present_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(132, "Present_Position", DWORD)) self._item.append(ct_item.ControlTableItem(136, "Velocity_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(140, "Position_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(144, "Present_Input_Voltage", WORD)) self._item.append(ct_item.ControlTableItem(146, "Present_Temperature", BYTE)) def setExtXMItem(self): self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(6, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(7, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(8, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(9, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(10, "Drive_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Operating_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Secondary_ID", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Protocol_Version", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Homing_Offset", DWORD)) self._item.append(ct_item.ControlTableItem(24, "Moving_Threshold", DWORD)) self._item.append(ct_item.ControlTableItem(31, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(32, "Max Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(34, "Min Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "PWM_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Current_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Acceleration_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(44, "Velocity_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(48, "Max_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(52, "Min_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(56, "External_Port_Mode_1", BYTE)) self._item.append(ct_item.ControlTableItem(57, "External_Port_Mode_2", BYTE)) self._item.append(ct_item.ControlTableItem(58, "External_Port_Mode_3", BYTE)) self._item.append(ct_item.ControlTableItem(63, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(64, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(65, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(68, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(69, "Registered_Instruction", BYTE)) self._item.append(ct_item.ControlTableItem(70, "Hardware_Error_Status", BYTE)) self._item.append(ct_item.ControlTableItem(76, "Velocity_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(78, "Velocity_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(80, "Position_D_Gain", WORD)) self._item.append(ct_item.ControlTableItem(82, "Position_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(84, "Position_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(88, "Feedforward_2nd_Gain", WORD)) self._item.append(ct_item.ControlTableItem(90, "Feedforward_1st_Gain", WORD)) self._item.append(ct_item.ControlTableItem(98, "Bus_Watchdog", BYTE)) self._item.append(ct_item.ControlTableItem(100, "Goal_PWM", WORD)) self._item.append(ct_item.ControlTableItem(102, "Goal_Current", WORD)) self._item.append(ct_item.ControlTableItem(104, "Goal_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(108, "Profile_Acceleration", DWORD)) self._item.append(ct_item.ControlTableItem(112, "Profile_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(116, "Goal_Position", DWORD)) self._item.append(ct_item.ControlTableItem(120, "Realtime_Tick", WORD)) self._item.append(ct_item.ControlTableItem(122, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(123, "Moving_Status", BYTE)) self._item.append(ct_item.ControlTableItem(124, "Present_PWM", WORD)) self._item.append(ct_item.ControlTableItem(126, "Present_Current", WORD)) self._item.append(ct_item.ControlTableItem(128, "Present_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(132, "Present_Position", DWORD)) self._item.append(ct_item.ControlTableItem(136, "Velocity_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(140, "Position_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(144, "Present_Input_Voltage", WORD)) self._item.append(ct_item.ControlTableItem(146, "Present_Temperature", BYTE)) def setXHItem(self): self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(6, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(7, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(8, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(9, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(10, "Drive_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Operating_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(12, "Secondary_ID", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Protocol_Version", BYTE)) self._item.append(ct_item.ControlTableItem(20, "Homing_Offset", DWORD)) self._item.append(ct_item.ControlTableItem(24, "Moving_Threshold", DWORD)) self._item.append(ct_item.ControlTableItem(31, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(32, "Max_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(34, "Min_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(36, "PWM_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Current_Limit", WORD)) self._item.append(ct_item.ControlTableItem(40, "Acceleration_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(44, "Velocity_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(48, "Max_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(52, "Min_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(63, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(64, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(65, "LED", BYTE)) self._item.append(ct_item.ControlTableItem(68, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(69, "Registered_Instruction", BYTE)) self._item.append(ct_item.ControlTableItem(70, "Hardware_Error_Status", BYTE)) self._item.append(ct_item.ControlTableItem(76, "Velocity_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(78, "Velocity_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(80, "Position_D_Gain", WORD)) self._item.append(ct_item.ControlTableItem(82, "Position_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(84, "Position_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(88, "Feedforward_2nd_Gain", WORD)) self._item.append(ct_item.ControlTableItem(90, "Feedforward_1st_Gain", WORD)) self._item.append(ct_item.ControlTableItem(98, "Bus_Watchdog", BYTE)) self._item.append(ct_item.ControlTableItem(100, "Goal_PWM", WORD)) self._item.append(ct_item.ControlTableItem(102, "Goal_Current", WORD)) self._item.append(ct_item.ControlTableItem(104, "Goal_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(108, "Profile_Acceleration", DWORD)) self._item.append(ct_item.ControlTableItem(112, "Profile_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(116, "Goal_Position", DWORD)) self._item.append(ct_item.ControlTableItem(120, "Realtime_Tick", WORD)) self._item.append(ct_item.ControlTableItem(122, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(123, "Moving_Status", BYTE)) self._item.append(ct_item.ControlTableItem(124, "Present_PWM", WORD)) self._item.append(ct_item.ControlTableItem(126, "Present_Current", WORD)) self._item.append(ct_item.ControlTableItem(128, "Present_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(132, "Present_Position", DWORD)) self._item.append(ct_item.ControlTableItem(136, "Velocity_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(140, "Position_Trajectory", DWORD)) self._item.append(ct_item.ControlTableItem(144, "Present_Input_Voltage", WORD)) self._item.append(ct_item.ControlTableItem(146, "Present_Temperature", BYTE)) def setPROItem(self): self._item.append(ct_item.ControlTableItem(0, "Model_Number", WORD)) self._item.append(ct_item.ControlTableItem(6, "Firmware_Version", BYTE)) self._item.append(ct_item.ControlTableItem(7, "ID", BYTE)) self._item.append(ct_item.ControlTableItem(8, "Baud_Rate", BYTE)) self._item.append(ct_item.ControlTableItem(9, "Return_Delay_Time", BYTE)) self._item.append(ct_item.ControlTableItem(11, "Operating_Mode", BYTE)) self._item.append(ct_item.ControlTableItem(13, "Homing_Offset", DWORD)) self._item.append(ct_item.ControlTableItem(17, "Moving_Threshold", DWORD)) self._item.append(ct_item.ControlTableItem(21, "Temperature_Limit", BYTE)) self._item.append(ct_item.ControlTableItem(22, "Max_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(24, "Min_Voltage_Limit", WORD)) self._item.append(ct_item.ControlTableItem(26, "Acceleration_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(30, "Torque_Limit", WORD)) self._item.append(ct_item.ControlTableItem(32, "Velocity_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(36, "Max_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(40, "Min_Position_Limit", DWORD)) self._item.append(ct_item.ControlTableItem(44, "External_Port_Mode_1", BYTE)) self._item.append(ct_item.ControlTableItem(45, "External_Port_Mode_2", BYTE)) self._item.append(ct_item.ControlTableItem(46, "External_Port_Mode_3", BYTE)) self._item.append(ct_item.ControlTableItem(47, "External_Port_Mode_4", BYTE)) self._item.append(ct_item.ControlTableItem(48, "Shutdown", BYTE)) self._item.append(ct_item.ControlTableItem(562, "Torque_Enable", BYTE)) self._item.append(ct_item.ControlTableItem(563, "LED_RED", BYTE)) self._item.append(ct_item.ControlTableItem(564, "LED_GREEN", BYTE)) self._item.append(ct_item.ControlTableItem(565, "LED_BLUE", BYTE)) self._item.append(ct_item.ControlTableItem(586, "Velocity_I_Gain", WORD)) self._item.append(ct_item.ControlTableItem(588, "Velocity_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(594, "Position_P_Gain", WORD)) self._item.append(ct_item.ControlTableItem(596, "Goal_Position", DWORD)) self._item.append(ct_item.ControlTableItem(600, "Goal_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(604, "Goal_Torque", WORD)) self._item.append(ct_item.ControlTableItem(606, "Goal_Acceleration", DWORD)) self._item.append(ct_item.ControlTableItem(610, "Moving", BYTE)) self._item.append(ct_item.ControlTableItem(611, "Present_Position", DWORD)) self._item.append(ct_item.ControlTableItem(615, "Present_Velocity", DWORD)) self._item.append(ct_item.ControlTableItem(621, "Present_Current", WORD)) self._item.append(ct_item.ControlTableItem(623, "Present_Input_Voltage", WORD)) self._item.append(ct_item.ControlTableItem(625, "Present_Temperature", BYTE)) self._item.append(ct_item.ControlTableItem(626, "External_Port_Mode_1", WORD)) self._item.append(ct_item.ControlTableItem(628, "External_Port_Mode_2", WORD)) self._item.append(ct_item.ControlTableItem(630, "External_Port_Mode_3", WORD)) self._item.append(ct_item.ControlTableItem(632, "External_Port_Mode_4", WORD)) self._item.append(ct_item.ControlTableItem(890, "Registered_Instruction", BYTE)) self._item.append(ct_item.ControlTableItem(891, "Status_Return_Level", BYTE)) self._item.append(ct_item.ControlTableItem(892, "Hardware_Error_Status", BYTE)) def getControlTableItem(self, num): if num in [AX_12A, AX_12W, AX_18A]: self.setAXItem() elif num in [RX_10, RX_24F, RX_28, RX_64]: self.setRXItem() elif num == EX_106: self.setEXItem() elif num in [MX_12W, MX_28]: self.setMXItem() elif num in [MX_64, MX_106]: self.setExtMXItem() elif num == MX_28_2: self.setMX2Item() elif num in [MX_64_2, MX_106_2]: self.setExtMX2Item() elif num == XL_320: self.setXL320Item() elif num == XL430_W250: self.setXLItem() elif num in [XM430_W210, XM430_W350]: self.setXMItem() elif num in [XM540_W150, XM540_W270]: self.setExtXMItem() elif num in [XH430_V210, XH430_V350, XH430_W210, XH430_W350]: self.setXHItem() elif num in [PRO_L42_10_S300_R, PRO_L54_30_S400_R, PRO_L54_30_S500_R, PRO_L54_50_S290_R, PRO_L54_50_S500_R, PRO_M42_10_S260_R, PRO_M54_40_S250_R, PRO_M54_60_S250_R, PRO_H42_20_S300_R, PRO_H54_100_S500_R,PRO_H54_200_S500_R]: self.setPROItem() else: self.setXMItem() return self._item def getTheNumberOfControlItem(self): return len(self._item)

62.690307

91

0.713383

6,904

53,036

5.149334

0.051854

0.125791

0.21659

0.247532

0.952744

0.94574

0.939974

0.930466

0.91342

0.875503

0

0.05062

0.160419

53,036

845

92

62.764497

0.747777

0.005129

0

0.774275

0

0.144755

0.010179

0

1

0.039092

false

0

0.001261

0.046658

0

null

0

1

0

1

0

null

0

9

7b41895e0a78d41b2d25a506d935af11849ed42c

4,355

py

Python

app/modules/fhir.py

NTUT-108-SE/CMS-Backend

1daa37960ba61f935da5174516f0a2e411b68706

[ "MIT" ]

null

app/modules/fhir.py

NTUT-108-SE/CMS-Backend

1daa37960ba61f935da5174516f0a2e411b68706

[ "MIT" ]

null

app/modules/fhir.py

NTUT-108-SE/CMS-Backend

1daa37960ba61f935da5174516f0a2e411b68706

[ "MIT" ]

1

2019-10-12T02:48:16.000Z

import requests class FHIR: def get_all(self): # Todo GET # Get all data from FHIR pass def get_id(self, id): # Todo GET # Get this id data from FHIR pass def query_patient(self, patient_id): # Todo GET # Query data by patient_id pass def create(self, data): # Todo POST pass def delete(self, id): # Todo DELETE # Delete this id resource from FHIR pass def update(self, id, data): # Todo PUT # Update the data of this ID resource pass class ConditionFHIR(FHIR): def __init__(self, uri="http://localhost:8080/hapi-fhir-jpaserver/fhir/"): self.uri = uri + "Condition" def get_all(self, offset=0, count=20): res = requests.get(self.uri + "?_getpagesoffset={}&_count={}".format(offset, count)) return res.json(), res.status_code def get_id(self, id): res = requests.get(self.uri + "/{}".format(id)) return res.json(), res.status_code def query_patient(self, patient_id, offset=0, count=20): res = requests.get( self.uri + "?patient={}&_getpagesoffset={}&_count={}".format(patient_id, offset, count) ) return res.json(), res.status_code def create(self, data): res = requests.post(self.uri, json=data) return res.json(), res.status_code def delete(self, id): res = requests.delete(self.uri + "/{}".format(id)) return res.json(), res.status_code def update(self, id, data): res = requests.put(self.uri + "/{}".format(id), json=data) return res.json(), res.status_code class PatientFHIR(FHIR): def __init__(self, uri="http://localhost:8080/hapi-fhir-jpaserver/fhir/"): self.uri = uri + "Patient" def get_all(self, offset=0, count=20): res = requests.get(self.uri + "?_getpagesoffset={}&_count={}".format(offset, count)) return res.json(), res.status_code def get_id(self, id): res = requests.get(self.uri + "/{}".format(id)) return res.json(), res.status_code def query_patient(self, identifier): res = requests.get(self.uri + "?identifier={}".format(identifier)) return res.json(), res.status_code def create(self, data): res = requests.post(self.uri, json=data) return res.json(), res.status_code def delete(self, id): res = requests.delete(self.uri + "/{}".format(id)) return res.json(), res.status_code def update(self, id, data): res = requests.put(self.uri + "/{}".format(id), json=data) return res.json(), res.status_code class InvoiceFHIR(FHIR): def __init__(self, uri="http://localhost:8080/hapi-fhir-jpaserver/fhir/"): self.uri = uri + "Invoice" def get_all(self, offset=0, count=20): res = requests.get(self.uri + "?_getpagesoffset={}&_count={}".format(offset, count)) return res.json(), res.status_code def get_id(self, id): res = requests.get(self.uri + "/{}".format(id)) return res.json(), res.status_code def query_patient(self, patient_id, offset=0, count=20): res = requests.get( self.uri + "?patient={}&_getpagesoffset={}&_count={}".format(patient_id, offset, count) ) return res.json(), res.status_code def create(self, data): res = requests.post(self.uri, json=data) return res.json(), res.status_code class MedicationFHIR(FHIR): def __init__(self, uri="http://localhost:8080/hapi-fhir-jpaserver/fhir/"): self.uri = uri + "MedicationKnowledge" def get_all(self, offset=0, count=20): res = requests.get(self.uri + "?_getpagesoffset={}&_count={}".format(offset, count)) return res.json(), res.status_code def get_id(self, id): res = requests.get(self.uri + "/{}".format(id)) return res.json(), res.status_code def create(self, data): res = requests.post(self.uri, json=data) return res.json(), res.status_code def delete(self, id): res = requests.delete(self.uri + "/{}".format(id)) return res.json(), res.status_code def update(self, id, data): res = requests.put(self.uri + "/{}".format(id), json=data) return res.json(), res.status_code

30.886525

99

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575

4,355

4.429565

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0.107185

0.13192

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0.816254

0.814291

0

0.010369

0.247072

4,355

140

100

31.107143

0.766392

0.046383

0

0.827957

0

0.113499

0.047332

0

0.007143

0

1

0.333333

false

0.064516

0.010753

0

0.623656

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

10

7b4be9a3fa1bda8766f1371a20a6a6d001f5968d

13,953

py

Python

src/networks/wav2vec2_components/multi_branch_encoder.py

nikvaessen/w2v2-speaker-few-samples

98139c2234a63532ebc8dffeacb284f3d7403ca2

[ "MIT" ]

null

src/networks/wav2vec2_components/multi_branch_encoder.py

nikvaessen/w2v2-speaker-few-samples

98139c2234a63532ebc8dffeacb284f3d7403ca2

[ "MIT" ]

null

src/networks/wav2vec2_components/multi_branch_encoder.py

nikvaessen/w2v2-speaker-few-samples

98139c2234a63532ebc8dffeacb284f3d7403ca2

[ "MIT" ]

null

######################################################################################## # # Implement a multi-branch encoder for multi-task learning. # # Author(s): Nik Vaessen ######################################################################################## import copy from typing import Optional import torch as t import torch.nn as nn import numpy as np from transformers.deepspeed import is_deepspeed_zero3_enabled from transformers.modeling_outputs import BaseModelOutput from transformers.models.wav2vec2 import configuration_wav2vec2 from src.networks.wav2vec2_components.base_components import ( Wav2vec2Encoder, Wav2Vec2EncoderStableLayerNorm, ) ######################################################################################## # implementation of multi-branch encoder class Wav2vec2MultiBranchEncoder(nn.Module): def __init__( self, cfg: configuration_wav2vec2.Wav2Vec2Config, branch_idx: int, enable_gradient_checkpointing: bool, pretrained_weights: Optional[str] = None, ): super().__init__() base_encoder = Wav2vec2Encoder( cfg, enable_gradient_checkpointing=enable_gradient_checkpointing, pretrained_weights=pretrained_weights, ) self.config = base_encoder.encoder.config self.pos_conv_embed = base_encoder.encoder.pos_conv_embed self.layer_norm = base_encoder.encoder.layer_norm self.dropout = base_encoder.encoder.dropout self.gradient_checkpointing = base_encoder.encoder.gradient_checkpointing if branch_idx == len(base_encoder.encoder.layers): raise ValueError( f"{branch_idx=} should be < {len(base_encoder.encoder.layers)}" ) self.shared_layers = base_encoder.encoder.layers[0:branch_idx] self.branch1 = copy.deepcopy(base_encoder.encoder.layers)[branch_idx:] self.branch2 = copy.deepcopy(self.branch1) del base_encoder def forward( self, hidden_states, attention_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): if not return_dict: raise ValueError("only return_dict=True is supported") shared_output = self._inner_forward( hidden_states, self.shared_layers, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, add_pos_embedding=True, ) branch1_input = shared_output.last_hidden_state branch2_input = t.clone(branch1_input) branch1_output = self._inner_forward( branch1_input, self.branch1, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, add_pos_embedding=False, ) branch2_output = self._inner_forward( branch2_input, self.branch2, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, add_pos_embedding=False, ) if output_attentions: branch1_output.attentions = ( shared_output.attentions + branch1_output.attentions ) branch2_output.attentions = ( shared_output.attentions + branch2_output.attentions ) if output_hidden_states: branch1_output.hidden_states = ( shared_output.hidden_states + branch1_output.hidden_states ) branch2_output.hidden_states = ( shared_output.hidden_states + branch2_output.hidden_states ) return branch1_output, branch2_output def _inner_forward( self, hidden_states, layers: nn.ModuleList, attention_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, add_pos_embedding: bool = True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None if attention_mask is not None: # make sure padded tokens output 0 hidden_states[~attention_mask] = 0.0 # extend attention_mask attention_mask = ( 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) ) * -10000.0 attention_mask = attention_mask.expand( attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1], ) if add_pos_embedding: position_embeddings = self.pos_conv_embed(hidden_states) hidden_states = hidden_states + position_embeddings hidden_states = self.layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() for layer in layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = np.random.uniform(0, 1) skip_the_layer = ( True if self.training and (dropout_probability < self.config.layerdrop) else False ) if not skip_the_layer or deepspeed_zero3_is_enabled: # under deepspeed zero3 all gpus must run in sync if self.gradient_checkpointing and self.training: # create gradient checkpointing function def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = t.utils.checkpoint.checkpoint( create_custom_forward(layer), hidden_states, attention_mask, ) else: layer_outputs = layer( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if skip_the_layer: layer_outputs = (None, None) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class Wav2vec2MultiBranchEncoderStableLayerNorm(nn.Module): def __init__( self, cfg: configuration_wav2vec2.Wav2Vec2Config, branch_idx: int, enable_gradient_checkpointing: bool, pretrained_weights: Optional[str] = None, ): super().__init__() base_encoder = Wav2Vec2EncoderStableLayerNorm( cfg, enable_gradient_checkpointing=enable_gradient_checkpointing, pretrained_weights=pretrained_weights, ) self.config = base_encoder.encoder.config self.pos_conv_embed = base_encoder.encoder.pos_conv_embed self.layer_norm = base_encoder.encoder.layer_norm self.dropout = base_encoder.encoder.dropout self.gradient_checkpointing = base_encoder.encoder.gradient_checkpointing if branch_idx == len(base_encoder.encoder.layers): raise ValueError( f"{branch_idx=} should be < {len(base_encoder.encoder.layers)}" ) self.shared_layers = base_encoder.encoder.layers[0:branch_idx] self.branch1 = copy.deepcopy(base_encoder.encoder.layers)[branch_idx:] self.branch2 = copy.deepcopy(self.branch1) del base_encoder def forward( self, hidden_states, attention_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): if not return_dict: raise ValueError("only return_dict=True is supported") shared_output = self._inner_forward( hidden_states, self.shared_layers, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, add_pos_embedding=True, ) branch1_output = self._inner_forward( shared_output.hidden_states, self.branch1, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, add_pos_embedding=False, ) branch2_output = self._inner_forward( shared_output.hidden_states, self.branch2, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, add_pos_embedding=False, ) if output_attentions: branch1_output.attentions = ( shared_output.attentions + branch1_output.attentions ) branch2_output.attentions = ( shared_output.attentions + branch2_output.attentions ) if output_hidden_states: branch1_output.hidden_states = ( shared_output.hidden_states + branch1_output.hidden_states ) branch2_output.hidden_states = ( shared_output.hidden_states + branch2_output.hidden_states ) return branch1_output, branch2_output def _inner_forward( self, hidden_states, layers: nn.ModuleList, attention_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, add_pos_embedding: bool = True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None if attention_mask is not None: # make sure padded tokens are not attended to hidden_states[~attention_mask] = 0 # extend attention_mask attention_mask = ( 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) ) * -10000.0 attention_mask = attention_mask.expand( attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1], ) if add_pos_embedding: position_embeddings = self.pos_conv_embed(hidden_states) hidden_states = hidden_states + position_embeddings hidden_states = self.dropout(hidden_states) deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() for layer in layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = np.random.uniform(0, 1) skip_the_layer = ( True if self.training and (dropout_probability < self.config.layerdrop) else False ) if not skip_the_layer or deepspeed_zero3_is_enabled: # under deepspeed zero3 all gpus must run in sync # XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth the code complication if self.gradient_checkpointing and self.training: # create gradient checkpointing function def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = t.utils.checkpoint.checkpoint( create_custom_forward(layer), hidden_states, attention_mask, ) else: layer_outputs = layer( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if skip_the_layer: layer_outputs = (None, None) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, )

34.708955

126

0.593134

1,372

13,953

5.680029

0.120262

0.141666

0.087771

0.040036

0.902605

0.892339

0.884897

0.875914

0

0.013898

0.329607

13,953

401

127

34.795511

0.819222

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0

0.828025

0

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false

0

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null

0

1

0

1

0

null

0

7

7b573bfac3397fa32ef6ebc7740446e7ee8b70d6

256,879

py

Python

tests/test_example.py

cariad/ansiscape

07118b68729fcdc2198fc69c35bb1e9ef1bb5b80

[ "MIT" ]

null

tests/test_example.py

cariad/ansiscape

07118b68729fcdc2198fc69c35bb1e9ef1bb5b80

[ "MIT" ]

30

2021-09-05T13:56:21.000Z

2021-10-05T11:14:11.000Z

tests/test_example.py

cariad/ansiscape

07118b68729fcdc2198fc69c35bb1e9ef1bb5b80

[ "MIT" ]

null

from ansiscape.enums import ( Blink, Calligraphy, Font, Frame, Ideogram, MetaInterpretation, NamedColor, Underline, Weight, ) from ansiscape.example import make_example def test_example() -> None: example = make_example() assert list(example.resolved) == [ { "underline": Underline.DOUBLE, "weight": Weight.HEAVY, }, "ansiscape", { "underline": MetaInterpretation.REVERT, "weight": MetaInterpretation.REVERT, }, "\n\nWelcome to the ", {"weight": Weight.HEAVY}, "ansiscape", {"weight": MetaInterpretation.REVERT}, " example!\n\nThese are ", {"weight": Weight.HEAVY}, "heavy", {"weight": MetaInterpretation.REVERT}, " and ", {"weight": Weight.LIGHT}, "light", {"weight": MetaInterpretation.REVERT}, ".\n" "\n" "These are ", {"calligraphy": Calligraphy.ITALIC}, "italic", {"calligraphy": MetaInterpretation.REVERT}, " and ", {"calligraphy": Calligraphy.BLACKLETTER}, "blackletter", {"calligraphy": MetaInterpretation.REVERT}, ".\n" "\n" "These are ", {"underline": Underline.SINGLE}, "single underlined", {"underline": MetaInterpretation.REVERT}, ", ", {"underline": Underline.DOUBLE}, "double underlined", {"underline": MetaInterpretation.REVERT}, " and ", {"overline": True}, "overlined", {"overline": MetaInterpretation.REVERT}, ".\n" "\n" "These are ", {"blink": Blink.SLOW}, "blinking slowly", {"blink": MetaInterpretation.REVERT}, " and ", {"blink": Blink.FAST}, "blinking fast", {"blink": MetaInterpretation.REVERT}, ".\n" "\n" "These are ", {"invert": True}, "inverted", {"invert": MetaInterpretation.REVERT}, ", ", {"conceal": True}, "concealed", {"conceal": MetaInterpretation.REVERT}, " (that's ", {"calligraphy": Calligraphy.ITALIC}, "concealed", {"calligraphy": MetaInterpretation.REVERT}, ") and ", {"strike": True}, "struck", {"strike": MetaInterpretation.REVERT}, ".\n" "\n" "These are the ", {"font": Font.ALT_0}, "first alternative font", {"font": MetaInterpretation.REVERT}, ", the ", {"font": Font.ALT_1}, "second alternative font", {"font": MetaInterpretation.REVERT}, ", the ", {"font": Font.ALT_2}, "third alternative font", {"font": MetaInterpretation.REVERT}, ", the ", {"font": Font.ALT_3}, "fourth alternative font", {"font": MetaInterpretation.REVERT}, ", the ", {"font": Font.ALT_4}, "fifth alternative font", {"font": MetaInterpretation.REVERT}, ", the ", {"font": Font.ALT_5}, "sixth alternative font", {"font": MetaInterpretation.REVERT}, ", the ", {"font": Font.ALT_6}, "seventh alternative font", {"font": MetaInterpretation.REVERT}, ", the ", {"font": Font.ALT_7}, "eighth alternative font", {"font": MetaInterpretation.REVERT}, " and the ", {"font": Font.ALT_8}, "ninth alternative font", {"font": MetaInterpretation.REVERT}, ".\n" "\n", {"proportional_spacing": True}, "This entire line uses proportional spacing.", {"proportional_spacing": MetaInterpretation.REVERT}, "\n" "\n" "These are ", {"foreground": NamedColor.BLACK}, "black", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.RED}, "red", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.GREEN}, "green", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.YELLOW}, "yellow", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BLUE}, "blue", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.MAGENTA}, "magenta", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.CYAN}, "cyan", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.WHITE}, "white", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BRIGHT_BLACK}, "bright black", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BRIGHT_RED}, "bright red", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BRIGHT_GREEN}, "bright green", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BRIGHT_YELLOW}, "bright yellow", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BRIGHT_BLUE}, "bright blue", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BRIGHT_MAGENTA}, "bright magenta", {"foreground": MetaInterpretation.REVERT}, ", ", {"foreground": NamedColor.BRIGHT_CYAN}, "bright cyan", {"foreground": MetaInterpretation.REVERT}, " and ", {"foreground": NamedColor.BRIGHT_WHITE}, "bright white", {"foreground": MetaInterpretation.REVERT}, " foreground.\n" "\n" "These are ", {"background": NamedColor.BLACK}, "black", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.RED}, "red", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.GREEN}, "green", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.YELLOW}, "yellow", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BLUE}, "blue", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.MAGENTA}, "magenta", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.CYAN}, "cyan", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.WHITE}, "white", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BRIGHT_BLACK}, "bright black", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BRIGHT_RED}, "bright red", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BRIGHT_GREEN}, "bright green", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BRIGHT_YELLOW}, "bright yellow", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BRIGHT_BLUE}, "bright blue", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BRIGHT_MAGENTA}, "bright magenta", {"background": MetaInterpretation.REVERT}, ", ", {"background": NamedColor.BRIGHT_CYAN}, "bright cyan", {"background": MetaInterpretation.REVERT}, " and ", {"background": 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c8847354b6aac2703cb31ae548e2b02013841f1a

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Python

easyrequest_hay_app/migrations/0003_auto_20180118_1216.py

birkin/easyrequest_hay_project

0718b3e22485354b45eb27615aba05c56b2b833b

[ "MIT" ]

null

easyrequest_hay_app/migrations/0003_auto_20180118_1216.py

birkin/easyrequest_hay_project

0718b3e22485354b45eb27615aba05c56b2b833b

[ "MIT" ]

4

2018-07-06T17:32:23.000Z

2021-02-05T16:06:03.000Z

easyrequest_hay_app/migrations/0003_auto_20180118_1216.py

Brown-University-Library/easyrequest_hay_project

b993496ec29352409e581f84606684ebcfabe1c9

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.9 on 2018-01-18 17:16 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('easyrequest_hay_app', '0002_itemrequest'), ] operations = [ migrations.AlterField( model_name='itemrequest', name='item_author', field=models.CharField(blank=True, help_text='used by Aeon', max_length=200, null=True), ), migrations.AlterField( model_name='itemrequest', name='item_barcode', field=models.CharField(blank=True, help_text='used by Millennium', max_length=50, null=True), ), migrations.AlterField( model_name='itemrequest', name='item_bib', field=models.CharField(blank=True, help_text='used by Millennium & Aeon', max_length=50, null=True), ), migrations.AlterField( model_name='itemrequest', name='item_callnumber', field=models.CharField(blank=True, help_text='used by Millennium & Aeon', max_length=200, null=True), ), migrations.AlterField( model_name='itemrequest', name='item_digital_version_url', field=models.CharField(blank=True, help_text='used by Aeon', max_length=200, null=True), ), migrations.AlterField( model_name='itemrequest', name='item_id', field=models.CharField(blank=True, help_text='used by Millennium', max_length=50, null=True), ), migrations.AlterField( model_name='itemrequest', name='item_publish_info', field=models.CharField(blank=True, help_text='used by Aeon', max_length=200, null=True), ), migrations.AlterField( model_name='itemrequest', name='item_title', field=models.CharField(blank=True, help_text='used by Millennium & Aeon', max_length=200, null=True), ), migrations.AlterField( model_name='itemrequest', name='patron_barcode', field=models.CharField(blank=True, help_text='used by Millennium', max_length=50, null=True), ), migrations.AlterField( model_name='itemrequest', name='patron_email', field=models.CharField(blank=True, help_text='used by Millennium', max_length=100, null=True), ), migrations.AlterField( model_name='itemrequest', name='patron_name', field=models.CharField(blank=True, help_text='used by Millennium', max_length=100, null=True), ), ]

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c8c322fa4d8b44b73e49e25687b94b69a31f9440

7,204

py

Python

src/hypoxia/option.py

JoshKarpel/hypoxia

31ad7b46b09ca1970e14f9c81275651917a81072

[ "WTFPL" ]

1

2018-03-07T19:44:21.000Z

src/hypoxia/option.py

JoshKarpel/hypoxia

31ad7b46b09ca1970e14f9c81275651917a81072

[ "WTFPL" ]

null

src/hypoxia/option.py

JoshKarpel/hypoxia

31ad7b46b09ca1970e14f9c81275651917a81072

[ "WTFPL" ]

null

import abc from typing import Callable, TypeVar, Generic from .exceptions import Panic from . import result T = TypeVar('T') U = TypeVar('U') class Option(abc.ABC, Generic[T]): def __init__(self, value: T): self._val = value def __hash__(self): return hash(self._val) def __repr__(self): return f'{self.__class__.__name__}({repr(self._val)})' def __eq__(self, other): return self.__class__ == other.__class__ and self._val == other._val @abc.abstractmethod def is_some(self) -> bool: """Returns ``True`` if the ``Option`` is a ``Some``, and ``False`` if it is a ``Nun``.""" raise NotImplementedError @abc.abstractmethod def is_nun(self) -> bool: """Returns ``True`` if the ``Option`` is a ``Nun``, and ``False`` if it is a ``Some``.""" raise NotImplementedError @abc.abstractmethod def unwrap(self) -> T: """If the ``Option`` is a ``Some``, return its value. If it is a ``Nun``, this raises a :class:`Panic`.""" raise NotImplementedError @abc.abstractmethod def unwrap_or(self, default: T) -> T: """If the ``Option`` is a ``Some``, return its value. If it is a ``Nun``, return ``default`` instead.""" raise NotImplementedError @abc.abstractmethod def unwrap_or_else(self, func: Callable[[], T]) -> T: """If the ``Option`` is a ``Some``, return its value. If it is a ``Nun``, return ``func()``.""" raise NotImplementedError @abc.abstractmethod def map(self, func: Callable[[T], U]) -> 'Option[U]': """If the ``Option`` is a ``Some``, return ``Some(func(value))``. If it is a ``Nun``, return ``Nun()``.""" raise NotImplementedError @abc.abstractmethod def map_or(self, func: Callable[[T], U], default: U) -> U: """If the ``Option`` is a ``Some``, return ``func(value)``. If it is a ``Nun``, return ``default``.""" raise NotImplementedError @abc.abstractmethod def map_or_else(self, func: Callable[[T], U], default_func: Callable[[], U]) -> U: """If the ``Option`` is a ``Some``, return ``func(value)``. If it is a ``Nun``, return ``default_func()``.""" raise NotImplementedError @abc.abstractmethod def ok_or(self, err: Exception) -> 'result.Result[T]': """If the ``Option`` is a ``Some``, return ``Ok(value)``. If it is a ``Nun``, return ``Err(err)``.""" raise NotImplementedError @abc.abstractmethod def ok_or_else(self, err_func: Callable[[], Exception]) -> 'result.Result[T]': """If the ``Option`` is a ``Some``, return ``Ok(value)``. If it is a ``Nun``, return ``Err(err())``.""" raise NotImplementedError @abc.abstractmethod def and_(self, other: 'Option[U]') -> 'Option[U]': """If either ``Option`` is ``Nun``, return ``Nun``. If both are ``Some``, return ``other``.""" raise NotImplementedError @abc.abstractmethod def and_then(self, func: Callable[[], U]) -> 'Option[U]': """If the ``Option`` is a ``Some``, return ``func()``. If it is a ``Nun``, return ``Nun()``.""" raise NotImplementedError @abc.abstractmethod def or_(self, other: 'Option[T]') -> 'Option[T]': """If the ``Option`` is a ``Some``, return it. If it is a ``Nun``, return ``other``.""" raise NotImplementedError @abc.abstractmethod def or_else(self, func: Callable[[], T]) -> 'Option[T]': """If the ``Option`` is a ``Some``, return it. If it is a ``Nun``, return ``func()``.""" raise NotImplementedError @abc.abstractmethod def get_or_insert(self, value: T) -> T: """If the ``Option`` is a ``Some``, return its value. If it is a ``Nun``, convert this ``Option`` into ``Some(value)`` and return the value.""" raise NotImplementedError @abc.abstractmethod def get_or_insert_with(self, func: Callable[[], T]) -> T: """If the ``Option`` is a ``Some``, return its value. If it is a ``Nun``, convert this ``Option`` into ``Some(func())`` and return the value.""" raise NotImplementedError class Some(Option): def __iter__(self): yield self._val def is_some(self) -> bool: return True def is_nun(self) -> bool: return False def unwrap(self) -> T: return self._val def unwrap_or(self, default: T) -> T: return self._val def unwrap_or_else(self, func: Callable[[], T]) -> T: return self._val def map(self, func: Callable[[T], U]) -> Option[U]: return Some(func(self._val)) def map_or(self, func: Callable[[T], U], default: U) -> U: return func(self._val) def map_or_else(self, func: Callable[[T], U], default_func: Callable[[], U]) -> U: return func(self._val) def ok_or(self, err: Exception) -> 'result.Result[T]': return result.Ok(self._val) def ok_or_else(self, err_func: Callable[[], Exception]) -> 'result.Result[T]': return result.Ok(self._val) def and_(self, other: 'Option[U]') -> 'Option[U]': if other.is_some(): return other return Nun() def and_then(self, func: Callable[[T], U]) -> 'Option[U]': return Some(func(self._val)) def or_(self, other: 'Option[T]') -> 'Option[T]': return self def or_else(self, func: Callable[[], T]) -> 'Option[T]': return self def get_or_insert(self, value: T) -> T: return self._val def get_or_insert_with(self, func: Callable[[], T]) -> T: return self._val class Nun(Option): def __init__(self): super().__init__(None) def __repr__(self): return 'Nun' def __iter__(self): """This produces an empty iterator.""" for _ in []: yield def is_some(self) -> bool: return False def is_nun(self) -> bool: return True def unwrap(self) -> T: raise Panic('unwrapped Nun') def unwrap_or(self, default: T) -> T: return default def unwrap_or_else(self, func: Callable[[], T]) -> T: return func() def map(self, func: Callable[[T], U]) -> Option[U]: return self def map_or(self, func: Callable[[T], U], default: U) -> U: return default def map_or_else(self, func: Callable[[T], U], default_func: Callable[[], U]) -> U: return default_func() def ok_or(self, err: Exception) -> 'result.Result[T]': return result.Err(err) def ok_or_else(self, err_func: Callable[[], Exception]) -> 'result.Result[T]': return result.Err(err_func()) def and_(self, other: 'Option[U]') -> 'Option[U]': return Nun() def and_then(self, func: Callable[[], U]) -> 'Option[U]': return Nun() def or_(self, other: 'Option[T]') -> 'Option[T]': return other def or_else(self, func: Callable[[], T]) -> 'Option[T]': return Some(func()) def get_or_insert(self, value: T) -> T: self.__class__ = Some self._val = value return self._val def get_or_insert_with(self, func: Callable[[], T]) -> T: self.__class__ = Some self._val = func() return self._val

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cdd24b7b2686df80cda3bb1e206dc848008fe024

62,680

py

Python

tests/test_arguments.py

synesissoftware/CLASP.Python

601f15baa8f79fc95f3e92175dfbe63fe85dff18

[ "BSD-3-Clause" ]

1

2019-08-21T23:30:41.000Z

tests/test_arguments.py

synesissoftware/CLASP.Python

601f15baa8f79fc95f3e92175dfbe63fe85dff18

[ "BSD-3-Clause" ]

null

tests/test_arguments.py

synesissoftware/CLASP.Python

601f15baa8f79fc95f3e92175dfbe63fe85dff18

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python from pyclasp import Arguments from pyclasp import specification, option from pyclasp import Flag from pyclasp import Option import pyclasp as clasp import unittest class Arguments_tester_1(unittest.TestCase): def test_empty_args_via_clasp_parse(self): argv = () with self.assertRaises(IndexError): clasp.parse(argv) def test_no_args_via_clasp_parse(self): argv = ( 'myprog', ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) self.assertFalse(args.values) def test_no_args_via_Arguments_constructor(self): argv = ( 'myprog', ) args = Arguments(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) self.assertFalse(args.values) def test_one_value(self): argv = ( 'myprog', 'value1', ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) def test_two_values(self): argv = ( 'myprog', 'value1', 'value2' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) self.assertTrue(args.values) def test_ten_values(self): argv = [ 'myprog', ] + [ "value%d" % i for i in range(0, 10) ] args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) self.assertTrue(args.values) self.assertEqual(10, len(args.values)) def test_one_flag(self): argv = ( 'myprog', '-f1', ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) def test_two_flags(self): argv = ( 'myprog', '-f1', '--flag2' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertTrue(args.flags) self.assertEqual(2, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') flag = args.flags[1] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 2) self.assertEqual(flag.given_name , '--flag2') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 2) self.assertEqual(flag.given_label , 'flag2') self.assertEqual(flag.name , '--flag2') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--flag2') self.assertEqual(flag , '--flag2') self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) self.assertFalse(args.values) def test_three_flags(self): argv = ( 'myprog', '-f1', '--flag2', '---x' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertTrue(args.flags) self.assertEqual(3, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') flag = args.flags[1] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 2) self.assertEqual(flag.given_name , '--flag2') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 2) self.assertEqual(flag.given_label , 'flag2') self.assertEqual(flag.name , '--flag2') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--flag2') self.assertEqual(flag , '--flag2') flag = args.flags[2] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 3) self.assertEqual(flag.given_name , '---x') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 3) self.assertEqual(flag.given_label , 'x') self.assertEqual(flag.name , '---x') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '---x') self.assertEqual(flag , '---x') self.assertIsInstance(args.options, ( tuple )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple )) self.assertFalse(args.values) def test_one_option(self): argv = ( 'myprog', '-o1=v1', ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o1') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o1') self.assertEqual(option.name , '-o1') self.assertEqual(option.value , 'v1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '-o1=v1') self.assertEqual(option , '-o1=v1') self.assertIsInstance(args.values, ( tuple )) self.assertFalse(args.values) def test_two_options(self): argv = ( 'myprog', '-o1=v1', '--option2=value2' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertTrue(args.options) self.assertEqual(2, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o1') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o1') self.assertEqual(option.name , '-o1') self.assertEqual(option.value , 'v1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '-o1=v1') self.assertEqual(option , '-o1=v1') option = args.options[1] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 2) self.assertEqual(option.given_name , '--option2') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 2) self.assertEqual(option.given_label , 'option2') self.assertEqual(option.name , '--option2') self.assertEqual(option.value , 'value2') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option2=value2') self.assertEqual(option , '--option2=value2') self.assertIsInstance(args.values, ( tuple )) self.assertFalse(args.values) def test_three_options(self): argv = ( 'myprog', '-o1=v1', '--option2=value2', '---the-third-option=the third value' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple )) self.assertFalse(args.flags) self.assertIsInstance(args.options, ( tuple )) self.assertTrue(args.options) self.assertEqual(3, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o1') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o1') self.assertEqual(option.name , '-o1') self.assertEqual(option.value , 'v1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '-o1=v1') self.assertEqual(option , '-o1=v1') option = args.options[1] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 2) self.assertEqual(option.given_name , '--option2') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 2) self.assertEqual(option.given_label , 'option2') self.assertEqual(option.name , '--option2') self.assertEqual(option.value , 'value2') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option2=value2') self.assertEqual(option , '--option2=value2') option = args.options[2] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 3) self.assertEqual(option.given_name , '---the-third-option') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 3) self.assertEqual(option.given_label , 'the-third-option') self.assertEqual(option.name , '---the-third-option') self.assertEqual(option.value , 'the third value') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '---the-third-option=the third value') self.assertEqual(option , '---the-third-option=the third value') self.assertIsInstance(args.values, ( tuple )) self.assertFalse(args.values) def test_one_flag_and_one_option_and_one_value(self): argv = ( 'myprog', '-f1', 'value1', '--first-option=val1' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 3) self.assertEqual(option.given_name , '--first-option') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 2) self.assertEqual(option.given_label , 'first-option') self.assertEqual(option.name , '--first-option') self.assertEqual(option.value , 'val1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--first-option=val1') self.assertEqual(option , '--first-option=val1') self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value1', args.values[0]) def test_double_hyphen_1(self): argv = ( 'myprog', '-f1', 'value1', '--', '-f2' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(2, len(args.values)) self.assertEqual('value1', args.values[0]) self.assertEqual('-f2', args.values[1]) def test_double_hyphen_2(self): argv = ( 'myprog', '-f1', 'value1', '--', '-f2', '--', '--option1=v1' ) args = clasp.parse(argv) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(4, len(args.values)) self.assertEqual('value1', args.values[0]) self.assertEqual('-f2', args.values[1]) self.assertEqual('--', args.values[2]) self.assertEqual('--option1=v1', args.values[3]) def test_one_flag_and_one_option_and_one_value_with_empty_specifications(self): specifications_list = ( tuple(), list(), None ) for specifications in specifications_list: argv = ( 'myprog', '-f1', 'value1', '--first-option=val1' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 3) self.assertEqual(option.given_name , '--first-option') self.assertIsNone(option.argument_specification) self.assertEqual(option.given_hyphens , 2) self.assertEqual(option.given_label , 'first-option') self.assertEqual(option.name , '--first-option') self.assertEqual(option.value , 'val1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--first-option=val1') self.assertEqual(option , '--first-option=val1') self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value1', args.values[0]) def test_alias_of_flag_with_one_specification(self): flag_verbose = clasp.flag('--verbose', alias = '-v', extras = { 'x-name': 'v-val' }) specifications = ( flag_verbose, ) argv = ( 'myprog', '--verbose', '--succinct', 'value', '-v' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(2, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 4) self.assertEqual(flag.given_name , '-v') self.assertEqual(flag.argument_specification, flag_verbose) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'v') self.assertEqual(flag.name , '--verbose') self.assertEqual(flag.extras , { 'x-name': 'v-val' }) self.assertEqual(str(flag) , '--verbose') self.assertEqual(flag , '--verbose') flag = args.flags[1] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 2) self.assertEqual(flag.given_name , '--succinct') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 2) self.assertEqual(flag.given_label , 'succinct') self.assertEqual(flag.name , '--succinct') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--succinct') self.assertEqual(flag , '--succinct') self.assertIsInstance(args.options, ( tuple, )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value', args.values[0]) def alias_of_flag_with_two_specifications(self): flag_expand = clasp.flag('--expand', aliases = ( '-x', '--x', ), extras = { 'some-value': ( 'e', 'x', 't', 'r', 'a', 's', ) }) specifications = ( flag_expand, ) argv = ( 'myprog', '-f1', 'value1', '-x', '--delete', '--x', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(4, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') flag = args.flags[1] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 3) self.assertEqual(flag.given_name , '-x') self.assertEqual(flag.argument_specification, flag_expand) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'x') self.assertEqual(flag.name , '--expand') self.assertTrue(flag.extras) self.assertEqual(str(flag) , '--expand') self.assertEqual(flag , '--expand') flag = args.flags[2] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 4) self.assertEqual(flag.given_name , '--delete') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 2) self.assertEqual(flag.given_label , 'delete') self.assertEqual(flag.name , '--delete') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--delete') self.assertEqual(flag , '--delete') flag = args.flags[3] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 5) self.assertEqual(flag.given_name , '--x') self.assertEqual(flag.argument_specification, specifications[x]) self.assertEqual(flag.given_hyphens , 2) self.assertEqual(flag.given_label , 'x') self.assertEqual(flag.name , '--expand') self.assertTrue(flag.extras) self.assertIsInstance(flag.extras, dict) self.assertEqual(1, len(flag.extras)) self.assertEqual(( 'e', 'x', 't', 'r', 'a', 's', ), flag.extras['some-value']) self.assertEqual(str(flag) , '--expand') self.assertEqual(flag , '--expand') self.assertIsInstance(args.options, ( tuple, )) self.assertFalse(args.options) self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value1', args.values[0]) def test_alias_of_option_with_one_specification(self): option_option = clasp.option('--option', alias = '-o') specifications = ( option_option, ) argv = ( 'myprog', '-f1', 'value1', '-o=value2', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 3) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification, option_option) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'value2') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=value2') self.assertEqual(option , '--option=value2') self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value1', args.values[0]) def test_alias_of_option_with_separate_value(self): option_option = clasp.option('--option', alias = '-o') specifications = ( option_option, ) argv = ( 'myprog', '-o', 'value-1', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertFalse(args.flags) self.assertEqual(0, len(args.flags)) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification, option_option) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'value-1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=value-1') self.assertEqual(option , '--option=value-1') self.assertIsInstance(args.values, ( tuple, )) self.assertFalse(args.values) self.assertEqual(0, len(args.values)) def test_alias_of_option_that_has_default_with_separate_value(self): option_option = clasp.option('--option', alias = '-o', default_value = 'def-val-1') specifications = ( option_option, ) argv = ( 'myprog', '-o', 'value-1', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertFalse(args.flags) self.assertEqual(0, len(args.flags)) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification, option_option) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'value-1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=value-1') self.assertEqual(option , '--option=value-1') self.assertIsInstance(args.values, ( tuple, )) self.assertFalse(args.values) self.assertEqual(0, len(args.values)) def test_alias_of_option_that_has_default_with_separate_value_that_resembles_flag(self): option_option = clasp.option('--option', alias = '-o', default_value = 'def-val-1') specifications = ( option_option, ) argv = ( 'myprog', '-o', '-o', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertFalse(args.flags) self.assertEqual(0, len(args.flags)) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification, option_option) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , '-o') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=-o') self.assertEqual(option , '--option=-o') self.assertIsInstance(args.values, ( tuple, )) self.assertFalse(args.values) self.assertEqual(0, len(args.values)) def test_alias_of_option_that_has_default_with_missing_separate_value(self): option_option = clasp.option('--option', alias = '-o', default_value = 'def-val-1') specifications = ( option_option, ) argv = ( 'myprog', '-o', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertFalse(args.flags) self.assertEqual(0, len(args.flags)) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification, option_option) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'def-val-1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=def-val-1') self.assertEqual(option , '--option=def-val-1') self.assertIsInstance(args.values, ( tuple, )) self.assertFalse(args.values) self.assertEqual(0, len(args.values)) def test_alias_of_option_with_attached_empty(self): specifications = ( clasp.option('--option', alias = '-o', default_value = 'def-val-1'), ) argv = ( 'myprog', '-o=', 'value-2', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertFalse(args.flags) self.assertEqual(0, len(args.flags)) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'def-val-1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=def-val-1') self.assertEqual(option , '--option=def-val-1') self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value-2', args.values[0]) def test_flag_alias_of_option_with_value(self): option_verbosity = clasp.option('--verbosity') specifications = ( option_verbosity, clasp.flag('--verbosity=high', alias = '-v'), ) argv = ( 'myprog', '-v', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertFalse(args.flags) self.assertEqual(0, len(args.flags)) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-v') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'v') self.assertEqual(option.name , '--verbosity') self.assertEqual(option.value , 'high') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--verbosity=high') self.assertEqual(option , '--verbosity=high') self.assertIsInstance(args.values, ( tuple, )) self.assertFalse(args.values) self.assertEqual(0, len(args.values)) def test_alias_of_option_with_value_allowing_multiple(self): option_option = clasp.option('--option', alias = '-o', default_value = 'default-value', on_multiple='allow') specifications = ( option_option, ) argv = ( 'myprog', '-f1', 'value-1', '-o=', '-o=given-value-1', '--option=given-value-2', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(3, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 3) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'default-value') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=default-value') self.assertEqual(option , '--option=default-value') option = args.options[1] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 4) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'given-value-1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=given-value-1') self.assertEqual(option , '--option=given-value-1') option = args.options[2] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 5) self.assertEqual(option.given_name , '--option') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 2) self.assertEqual(option.given_label , 'option') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'given-value-2') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=given-value-2') self.assertEqual(option , '--option=given-value-2') self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value-1', args.values[0]) def test_alias_of_option_with_value_ignoring_multiple(self): option_option = clasp.option('--option', alias = '-o', default_value = 'default-value', on_multiple='ignore') specifications = ( option_option, ) argv = ( 'myprog', '-f1', 'value-1', '-o=', '-o=given-value-1', '--option=given-value-2', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(3, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 3) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'default-value') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=default-value') self.assertEqual(option , '--option=default-value') option = args.options[1] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 4) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'given-value-1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=given-value-1') self.assertEqual(option , '--option=given-value-1') option = args.options[2] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 5) self.assertEqual(option.given_name , '--option') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 2) self.assertEqual(option.given_label , 'option') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'given-value-2') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=given-value-2') self.assertEqual(option , '--option=given-value-2') self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value-1', args.values[0]) def test_alias_of_option_with_value_replacing_multiple(self): option_option = clasp.option('--option', alias = '-o', default_value = 'default-value', on_multiple='replace') specifications = ( option_option, ) argv = ( 'myprog', '-f1', 'value-1', '-o=', '-o=given-value-1', '--option=given-value-2', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(1, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-f1') self.assertIsNone(flag.argument_specification) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'f1') self.assertEqual(flag.name , '-f1') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '-f1') self.assertEqual(flag , '-f1') self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(3, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 3) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'default-value') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=default-value') self.assertEqual(option , '--option=default-value') option = args.options[1] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 4) self.assertEqual(option.given_name , '-o') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'o') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'given-value-1') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=given-value-1') self.assertEqual(option , '--option=given-value-1') option = args.options[2] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 5) self.assertEqual(option.given_name , '--option') self.assertEqual(option.argument_specification , specifications[0]) self.assertEqual(option.given_hyphens , 2) self.assertEqual(option.given_label , 'option') self.assertEqual(option.name , '--option') self.assertEqual(option.value , 'given-value-2') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--option=given-value-2') self.assertEqual(option , '--option=given-value-2') self.assertIsInstance(args.values, ( tuple, )) self.assertTrue(args.values) self.assertEqual(1, len(args.values)) self.assertEqual('value-1', args.values[0]) def test_flags_combined(self): flag_compile = clasp.flag('--compile', alias = '-c') flag_debug = clasp.flag('--debug', alias = '-d') flag_execute = clasp.flag('--execute', alias = '-e') specifications = ( flag_compile, flag_debug, flag_execute, ) self.assertEqual(flag_compile, specifications[0]) self.assertEqual(flag_debug, specifications[1]) self.assertEqual(flag_execute, specifications[2]) argv = ( 'myprog', '-ced', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(3, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-ced') self.assertEqual(flag.argument_specification, flag_compile) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'ced') self.assertEqual(flag.name , '--compile') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--compile') self.assertEqual(flag , '--compile') self.assertEqual(flag, flag_compile) self.assertEqual(flag_compile, flag) flag = args.flags[1] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-ced') self.assertEqual(flag.argument_specification, flag_execute) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'ced') self.assertEqual(flag.name , '--execute') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--execute') self.assertEqual(flag , '--execute') self.assertEqual(flag, flag_execute) self.assertEqual(flag_execute, flag) flag = args.flags[2] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-ced') self.assertEqual(flag.argument_specification, flag_debug) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'ced') self.assertEqual(flag.name , '--debug') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--debug') self.assertEqual(flag , '--debug') self.assertEqual(flag_debug, flag) self.assertEqual(flag, flag_debug) self.assertIsInstance(args.options, ( tuple, )) self.assertFalse(args.options) self.assertEqual(0, len(args.options)) self.assertIsInstance(args.values, ( tuple, )) self.assertFalse(args.values) self.assertEqual(0, len(args.values)) def test_flags_of_flags_and_options_combined(self): flag_compile = clasp.flag('--compile', alias = '-c') flag_execute = clasp.flag('--execute', alias = '-e') option_mode = clasp.option('--mode', alias = '-m') specifications = ( flag_compile, clasp.flag('--mode=debug', alias = '-d'), flag_execute, option_mode, ) argv = ( 'myprog', '-ced', ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(2, len(args.flags)) flag = args.flags[0] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-ced') self.assertEqual(flag.argument_specification, flag_compile) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'ced') self.assertEqual(flag.name , '--compile') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--compile') self.assertEqual(flag , '--compile') self.assertEqual(flag, flag_compile) self.assertEqual(flag_compile, flag) flag = args.flags[1] self.assertIsInstance(flag, ( Flag, )) self.assertEqual(flag.given_index , 1) self.assertEqual(flag.given_name , '-ced') self.assertEqual(flag.argument_specification, flag_execute) self.assertEqual(flag.given_hyphens , 1) self.assertEqual(flag.given_label , 'ced') self.assertEqual(flag.name , '--execute') self.assertEqual(flag.extras , {}) self.assertEqual(str(flag) , '--execute') self.assertEqual(flag , '--execute') self.assertEqual(flag, flag_execute) self.assertEqual(flag_execute, flag) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(1, len(args.options)) option = args.options[0] self.assertIsInstance(option, ( Option, )) self.assertEqual(option.given_index , 1) self.assertEqual(option.given_name , '-ced') self.assertEqual(option.argument_specification, option_mode) self.assertEqual(option.given_hyphens , 1) self.assertEqual(option.given_label , 'ced') self.assertEqual(option.name , '--mode') self.assertEqual(option.extras , {}) self.assertEqual(str(option) , '--mode=debug') self.assertEqual(option , '--mode=debug') self.assertEqual(option_mode, option) self.assertEqual(option, option_mode) self.assertIsInstance(args.values, ( tuple, )) self.assertFalse(args.values) self.assertEqual(0, len(args.values)) def test_first_unused_Flag_via_get_first_unused_flag(self): flag_compile = clasp.flag('--compile', alias = '-c') flag_debug = clasp.flag('--debug', alias = '-d') specifications = ( flag_compile, flag_debug, ) argv = ( 'dir1/myprog', '-cd' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(2, len(args.flags)) # now check the 'unused', iteratively using and testing self.assertIsNone(args.get_first_unused_option()) # before any use()d fu = args.get_first_unused_flag() self.assertIsNotNone(fu) self.assertEqual(flag_compile, fu) # after use() (1st time) fu.use() fu = args.get_first_unused_flag() self.assertIsNotNone(fu) self.assertEqual(flag_debug, fu) # after use() (2nd time) fu.use() fu = args.get_first_unused_flag() self.assertIsNone(fu) def test_first_unused_Flag_via_get_first_unused_flag_or_option(self): flag_compile = clasp.flag('--compile', alias = '-c') flag_debug = clasp.flag('--debug', alias = '-d') specifications = ( flag_compile, flag_debug, ) argv = ( 'dir1/myprog', '-cd' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(2, len(args.flags)) # now check the 'unused', iteratively using and testing self.assertIsNone(args.get_first_unused_option()) # before any use()d fu = args.get_first_unused_flag_or_option() self.assertIsNotNone(fu) self.assertEqual(flag_compile, fu) # after use() (1st time) fu.use() fu = args.get_first_unused_flag_or_option() self.assertIsNotNone(fu) self.assertEqual(flag_debug, fu) # after use() (2nd time) fu.use() fu = args.get_first_unused_flag_or_option() self.assertIsNone(fu) def test_first_unused_Flag_via_get_first_unused(self): flag_compile = clasp.flag('--compile', alias = '-c') flag_debug = clasp.flag('--debug', alias = '-d') specifications = ( flag_compile, flag_debug, ) argv = ( 'dir1/myprog', '-cd' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.flags, ( tuple, )) self.assertTrue(args.flags) self.assertEqual(2, len(args.flags)) # now check the 'unused', iteratively using and testing self.assertIsNone(args.get_first_unused_option()) # before any use()d fu = args.get_first_unused() self.assertIsNotNone(fu) self.assertEqual(flag_compile, fu) # after use() (1st time) fu.use() fu = args.get_first_unused() self.assertIsNotNone(fu) self.assertEqual(flag_debug, fu) # after use() (2nd time) fu.use() fu = args.get_first_unused() self.assertIsNone(fu) def test_first_unused_Option_via_get_first_unused_option(self): option_mode = clasp.option('--mode', alias = '-m') option_option = clasp.option('--option', alias = '-o', default_value = 'default-value', on_multiple='replace') specifications = ( option_mode, option_option, ) argv = ( 'dir1/myprog', '--mode=verbose', '--option=ignore' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(2, len(args.options)) # now check the 'unused', iteratively using and testing self.assertIsNone(args.get_first_unused_flag()) # before any use()d fu = args.get_first_unused_option() self.assertIsNotNone(fu) self.assertEqual(option_mode, fu) # after use() (1st time) fu.use() fu = args.get_first_unused_option() self.assertIsNotNone(fu) self.assertEqual(option_option, fu) # after use() (2nd time) fu.use() fu = args.get_first_unused_option() self.assertIsNone(fu) def test_first_unused_Option_via_get_first_unused_flag_or_option(self): option_mode = clasp.option('--mode', alias = '-m') option_option = clasp.option('--option', alias = '-o', default_value = 'default-value', on_multiple='replace') specifications = ( option_mode, option_option, ) argv = ( 'dir1/myprog', '--mode=verbose', '--option=ignore' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(2, len(args.options)) # now check the 'unused', iteratively using and testing self.assertIsNone(args.get_first_unused_flag()) # before any use()d fu = args.get_first_unused_flag_or_option() self.assertIsNotNone(fu) self.assertEqual(option_mode, fu) # after use() (1st time) fu.use() fu = args.get_first_unused_flag_or_option() self.assertIsNotNone(fu) self.assertEqual(option_option, fu) # after use() (2nd time) fu.use() fu = args.get_first_unused_flag_or_option() self.assertIsNone(fu) def test_first_unused_Option_via_get_first_unused(self): option_mode = clasp.option('--mode', alias = '-m') option_option = clasp.option('--option', alias = '-o', default_value = 'default-value', on_multiple='replace') specifications = ( option_mode, option_option, ) argv = ( 'dir1/myprog', '--mode=verbose', '--option=ignore' ) args = clasp.parse(argv, specifications) self.assertEqual('myprog', args.program_name) self.assertIsInstance(args.options, ( tuple, )) self.assertTrue(args.options) self.assertEqual(2, len(args.options)) # now check the 'unused', iteratively using and testing self.assertIsNone(args.get_first_unused_flag()) # before any use()d fu = args.get_first_unused() self.assertIsNotNone(fu) self.assertEqual(option_mode, fu) # after use() (1st time) fu.use() fu = args.get_first_unused() self.assertIsNotNone(fu) self.assertEqual(option_option, fu) # after use() (2nd time) fu.use() fu = args.get_first_unused() self.assertIsNone(fu) if '__main__' == __name__: unittest.main()

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null

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1

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9

a814113243dd7e14350d08a8430cd81ce14afbc9

213

py

Python

src/apps/episodes/admin.py

ckcollab/foolsnetwork

8043e5486d7bb95eea18d673e04787a43ac728ea

[ "MIT" ]

null

src/apps/episodes/admin.py

ckcollab/foolsnetwork

8043e5486d7bb95eea18d673e04787a43ac728ea

[ "MIT" ]

null

src/apps/episodes/admin.py

ckcollab/foolsnetwork

8043e5486d7bb95eea18d673e04787a43ac728ea

[ "MIT" ]

null

from django.contrib import admin from . import models admin.site.register(models.Character) admin.site.register(models.CharacterAppearance) admin.site.register(models.Episode) admin.site.register(models.Notes)

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a82b93b7d0002f45668d38793a153bab0d51f352

11,444

py

Python

leetcode/1488. Avoid Flood in The City/main.py

huangshiyu13/AlgProblems

70e687e448ab3761d1f8d338ba10bed81b03d6e3

[ "MIT" ]

null

leetcode/1488. Avoid Flood in The City/main.py

huangshiyu13/AlgProblems

70e687e448ab3761d1f8d338ba10bed81b03d6e3

[ "MIT" ]

null

leetcode/1488. Avoid Flood in The City/main.py

huangshiyu13/AlgProblems

70e687e448ab3761d1f8d338ba10bed81b03d6e3

[ "MIT" ]

1

2021-05-28T03:13:01.000Z

# from collections import defaultdict,OrderedDict,Callable class DrySuite(): def __init__(self, linked_lakes=None, dry_days=None, next_suite=None): self.linked_lakes = linked_lakes self.next_suit = next_suite self.dry_days = dry_days class RainDay(): def __init__(self, rain_lake=None, pre_day=None, next_day=None, drySuit=None): self.rain_lake = rain_lake self.pre_day = pre_day self.next_day = next_day self.drySuit = drySuit class ChainManager(): def __init__(self): pass def new_dry_day(self, dry_day): pass def new_rain_day(self, rain_lake): pass class Solution: def avoidFlood(self, rains): chainManager = ChainManager() ans = [-1 if r > 0 else 1 for r in rains] for i in range(len(rains)): lake = rains[i] if lake == 0: chainManager.new_dry_day(i) else: chainManager.new_rain_day(lake) return ans def check_ans(rains, ans): a = [] for i in range(len(ans)): if rains[i] > 0: assert ans[i] == -1, 'i={},rain={},ans={}'.format(i, rains[i], ans[i]) if rains[i] in a: print('wrong for i={},rain={}'.format(i, rains[i])) for ii, r in enumerate(rains): if r == rains[i]: print(ii, r) for ii, l in enumerate(ans): if l == rains[i]: print(ii) a.append(rains[i]) if rains[i] == 0: if ans[i] in a: a.remove(ans[i]) if __name__ == '__main__': import time # rains = [1,2,0,0,2,1] rains = [1, 2, 0, 0, 2, 1] rains = [1, 2, 0, 1, 2] # 300 # 49546 # 519 # 49546 # 229 rains = [98284, 57875, 0, 0, 94301, 94503, 16548, 0, 0, 37144, 0, 0, 0, 63939, 0, 0, 0, 0, 57020, 47710, 3285, 71226, 0, 24745, 0, 0, 70243, 0, 51703, 80321, 95971, 22206, 0, 43959, 84602, 77192, 0, 0, 0, 0, 0, 6407, 6477, 99867, 0, 24520, 0, 0, 0, 0, 9799, 43282, 52055, 96659, 51254, 40585, 79473, 0, 0, 0, 0, 9481, 0, 0, 0, 35881, 54126, 8792, 0, 0, 0, 22570, 0, 0, 879, 2319, 0, 4889, 46458, 0, 0, 0, 36638, 69875, 57212, 57875, 0, 0, 96659, 75448, 51766, 6379, 57212, 99867, 86167, 0, 93231, 52568, 16312, 0, 0, 19402, 0, 0, 8602, 0, 0, 0, 3285, 39361, 36638, 0, 0, 22206, 0, 38549, 94503, 14659, 0, 16548, 0, 0, 54126, 11157, 70915, 0, 0, 81337, 19893, 54920, 51766, 51244, 17717, 69787, 46075, 0, 42139, 0, 0, 0, 4428, 0, 0, 0, 0, 0, 9292, 80984, 17717, 54920, 0, 0, 18568, 0, 19946, 0, 69683, 0, 0, 0, 13735, 79530, 42193, 0, 1149, 78534, 0, 0, 0, 55452, 14864, 24745, 26551, 0, 0, 24233, 0, 79712, 0, 62236, 0, 0, 47800, 0, 6695, 40585, 0, 52402, 879, 68267, 0, 96631, 64057, 84363, 0, 0, 0, 63335, 96878, 0, 47800, 0, 0, 61952, 0, 0, 24297, 0, 0, 0, 14584, 0, 42139, 65252, 64136, 11157, 6695, 0, 14877, 0, 0, 0, 44942, 73999, 0, 0, 45572, 0, 86167, 0, 0, 0, 0, 42193, 0, 84363, 0, 63939, 0, 76503, 0, 51198, 0, 9481, 0, 6407, 0, 64277, 0, 0, 0, 1149, 3072, 0, 24233, 783, 0, 0, 9036, 39361, 22947, 0, 51703, 0, 0, 52055, 97859, 25989, 0, 0, 0, 0, 77192, 0, 0, 12234, 57020, 79530, 0, 0, 19946, 0, 0, 0, 0, 61952, 36135, 0, 0, 34886, 0, 97617, 66393, 0, 0, 80321, 0, 0, 75482, 97859, 49546, 22947, 0, 73999, 0, 0, 68267, 0, 18529, 65252, 2319, 18568, 0, 0, 0, 69939, 508, 0, 0, 0, 65617, 24520, 82199, 93231, 64015, 0, 39813, 0, 0, 0, 16814, 74810, 0, 0, 55452, 0, 43282, 39813, 0, 0, 80942, 0, 70915, 4428, 0, 43683, 0, 82199, 90187, 0, 13584, 36135, 0, 74810, 961, 0, 44942, 0, 0, 32578, 90187, 0, 69683, 0, 38549, 0, 0, 13735, 33424, 0, 59757, 64277, 6477, 0, 0, 94301, 52568, 0, 0, 0, 0, 0, 0, 0, 63505, 0, 0, 0, 51254, 0, 0, 46458, 64015, 0, 0, 66393, 58429, 0, 64136, 69875, 62236, 0, 0, 0, 0, 4770, 0, 89776, 0, 961, 34886, 62059, 0, 65617, 0, 0, 81337, 63335, 0, 0, 0, 43683, 0, 45572, 0, 0, 97617, 0, 9799, 32578, 0, 0, 0, 22570, 95971, 0, 64057, 4770, 0, 46075, 0, 0, 0, 0, 0, 96631, 0, 0, 26551, 0, 8602, 18529, 0, 0, 0, 0, 0, 60713, 0, 70243, 0, 59757, 75482, 0, 0, 78534, 0, 0, 51198, 16814, 0, 0, 58429, 0, 0, 9036, 96878, 0, 0, 71226, 51244, 76503, 0, 0, 64319, 0, 24297, 14864, 0, 25989, 12234, 62059, 0, 84602, 0, 0, 19893, 0, 783, 75448, 69939, 67299, 0, 0, 0, 75087, 0, 0, 37144, 33424, 0, 0, 67299, 0, 0, 20507, 0, 0, 69787, 49546, 14877, 9292, 0, 0, 14659, 89776, 0, 0, 0, 0, 75087, 35881, 3072, 19402, 14584, 0, 0, 13584, 508, 0, 80942, 0, 6379, 0, 0, 60713, 0, 0, 4889, 4528, 0, 0, 0, 0, 0, 0, 8792, 0, 79712, 0, 0, 0, 16312, 0, 47710, 64319, 98284, 0, 4528, 80984, 43959, 79473, 63505, 52402, 20507, 0] rains = [1, 2, 0, 2, 3, 0, 1] rains = [0, 11475, 23148, 0, 91836, 0, 0, 0, 0, 18987, 0, 3057, 0, 0, 0, 69217, 0, 0, 65289, 0, 0, 0, 35467, 33617, 0, 0, 0, 0, 55602, 67935, 0, 0, 2530, 84750, 0, 0, 4411, 0, 0, 81775, 0, 46174, 33617, 0, 60322, 60801, 56836, 72787, 4022, 91465, 21256, 0, 0, 0, 0, 0, 2530, 0, 14817, 57045, 0, 0, 0, 2583, 62414, 4452, 28481, 54082, 36928, 25662, 14817, 95392, 22974, 1040, 0, 93616, 0, 0, 59731, 0, 61094, 0, 65368, 82028, 22053, 54082, 0, 0, 4452, 81775, 98696, 0, 0, 5717, 91465, 0, 0, 20971, 0, 0, 0, 0, 0, 0, 0, 8644, 82028, 55602, 0, 77965, 0, 59578, 0, 0, 0, 42529, 0, 0, 0, 0, 0, 36928, 0, 20971, 25671, 0, 0, 0, 59289, 0, 0, 0, 0, 0, 0, 59289, 72266, 0, 0, 0, 92138, 77364, 59578, 46174, 0, 2583, 60322, 0, 0, 0, 0, 0, 0, 72787, 4022, 0, 95082, 0, 0, 0, 0, 22974, 22053, 60801, 0, 67634, 27785, 0, 91836, 95392, 0, 77364, 28481, 4411, 0, 91988, 0, 0, 0, 27785, 69763, 0, 77965, 7509, 67935, 0, 62414, 18987, 84750, 0, 0, 9118, 0, 9118, 64611, 0, 0, 59731, 0, 0, 69217, 0, 65368, 0, 0, 90771, 0, 0, 56836, 8644, 0, 25662, 1040, 7509, 90771, 0, 0, 5717, 0, 0, 0, 93616, 0, 0, 92138, 91988, 0, 0, 61094, 57045, 0, 0, 0, 95082, 0, 23148, 0, 98696, 25671, 11475, 0, 35467, 21256, 65289, 68210, 69763, 0, 0, 72266, 3057, 67634, 64611, 42529, 68210] rains = [98284, 57875, 0, 0, 94301, 94503, 16548, 0, 0, 37144, 0, 0, 0, 63939, 0, 0, 0, 0, 57020, 47710, 3285, 71226, 0, 24745, 0, 0, 70243, 0, 51703, 80321, 95971, 22206, 0, 43959, 84602, 77192, 0, 0, 0, 0, 0, 6407, 6477, 99867, 0, 24520, 0, 0, 0, 0, 9799, 43282, 52055, 96659, 51254, 40585, 79473, 0, 0, 0, 0, 9481, 0, 0, 0, 35881, 54126, 8792, 0, 0, 0, 22570, 0, 0, 879, 2319, 0, 4889, 46458, 0, 0, 0, 36638, 69875, 57212, 57875, 0, 0, 96659, 75448, 51766, 6379, 57212, 99867, 86167, 0, 93231, 52568, 16312, 0, 0, 19402, 0, 0, 8602, 0, 0, 0, 3285, 39361, 36638, 0, 0, 22206, 0, 38549, 94503, 14659, 0, 16548, 0, 0, 54126, 11157, 70915, 0, 0, 81337, 19893, 54920, 51766, 51244, 17717, 69787, 46075, 0, 42139, 0, 0, 0, 4428, 0, 0, 0, 0, 0, 9292, 80984, 17717, 54920, 0, 0, 18568, 0, 19946, 0, 69683, 0, 0, 0, 13735, 79530, 42193, 0, 1149, 78534, 0, 0, 0, 55452, 14864, 24745, 26551, 0, 0, 24233, 0, 79712, 0, 62236, 0, 0, 47800, 0, 6695, 40585, 0, 52402, 879, 68267, 0, 96631, 64057, 84363, 0, 0, 0, 63335, 96878, 0, 47800, 0, 0, 61952, 0, 0, 24297, 0, 0, 0, 14584, 0, 42139, 65252, 64136, 11157, 6695, 0, 14877, 0, 0, 0, 44942, 73999, 0, 0, 45572, 0, 86167, 0, 0, 0, 0, 42193, 0, 84363, 0, 63939, 0, 76503, 0, 51198, 0, 9481, 0, 6407, 0, 64277, 0, 0, 0, 1149, 3072, 0, 24233, 783, 0, 0, 9036, 39361, 22947, 0, 51703, 0, 0, 52055, 97859, 25989, 0, 0, 0, 0, 77192, 0, 0, 12234, 57020, 79530, 0, 0, 19946, 0, 0, 0, 0, 61952, 36135, 0, 0, 34886, 0, 97617, 66393, 0, 0, 80321, 0, 0, 75482, 97859, 49546, 22947, 0, 73999, 0, 0, 68267, 0, 18529, 65252, 2319, 18568, 0, 0, 0, 69939, 508, 0, 0, 0, 65617, 24520, 82199, 93231, 64015, 0, 39813, 0, 0, 0, 16814, 74810, 0, 0, 55452, 0, 43282, 39813, 0, 0, 80942, 0, 70915, 4428, 0, 43683, 0, 82199, 90187, 0, 13584, 36135, 0, 74810, 961, 0, 44942, 0, 0, 32578, 90187, 0, 69683, 0, 38549, 0, 0, 13735, 33424, 0, 59757, 64277, 6477, 0, 0, 94301, 52568, 0, 0, 0, 0, 0, 0, 0, 63505, 0, 0, 0, 51254, 0, 0, 46458, 64015, 0, 0, 66393, 58429, 0, 64136, 69875, 62236, 0, 0, 0, 0, 4770, 0, 89776, 0, 961, 34886, 62059, 0, 65617, 0, 0, 81337, 63335, 0, 0, 0, 43683, 0, 45572, 0, 0, 97617, 0, 9799, 32578, 0, 0, 0, 22570, 95971, 0, 64057, 4770, 0, 46075, 0, 0, 0, 0, 0, 96631, 0, 0, 26551, 0, 8602, 18529, 0, 0, 0, 0, 0, 60713, 0, 70243, 0, 59757, 75482, 0, 0, 78534, 0, 0, 51198, 16814, 0, 0, 58429, 0, 0, 9036, 96878, 0, 0, 71226, 51244, 76503, 0, 0, 64319, 0, 24297, 14864, 0, 25989, 12234, 62059, 0, 84602, 0, 0, 19893, 0, 783, 75448, 69939, 67299, 0, 0, 0, 75087, 0, 0, 37144, 33424, 0, 0, 67299, 0, 0, 20507, 0, 0, 69787, 49546, 14877, 9292, 0, 0, 14659, 89776, 0, 0, 0, 0, 75087, 35881, 3072, 19402, 14584, 0, 0, 13584, 508, 0, 80942, 0, 6379, 0, 0, 60713, 0, 0, 4889, 4528, 0, 0, 0, 0, 0, 0, 8792, 0, 79712, 0, 0, 0, 16312, 0, 47710, 64319, 98284, 0, 4528, 80984, 43959, 79473, 63505, 52402, 20507, 0] rains = list(range(1, 25001)) + [0] * 50000 + list(range(1, 25001)) rains = [0, 11475, 23148, 0, 91836, 0, 0, 0, 0, 18987, 0, 3057, 0, 0, 0, 69217, 0, 0, 65289, 0, 0, 0, 35467, 33617, 0, 0, 0, 0, 55602, 67935, 0, 0, 2530, 84750, 0, 0, 4411, 0, 0, 81775, 0, 46174, 33617, 0, 60322, 60801, 56836, 72787, 4022, 91465, 21256, 0, 0, 0, 0, 0, 2530, 0, 14817, 57045, 0, 0, 0, 2583, 62414, 4452, 28481, 54082, 36928, 25662, 14817, 95392, 22974, 1040, 0, 93616, 0, 0, 59731, 0, 61094, 0, 65368, 82028, 22053, 54082, 0, 0, 4452, 81775, 98696, 0, 0, 5717, 91465, 0, 0, 20971, 0, 0, 0, 0, 0, 0, 0, 8644, 82028, 55602, 0, 77965, 0, 59578, 0, 0, 0, 42529, 0, 0, 0, 0, 0, 36928, 0, 20971, 25671, 0, 0, 0, 59289, 0, 0, 0, 0, 0, 0, 59289, 72266, 0, 0, 0, 92138, 77364, 59578, 46174, 0, 2583, 60322, 0, 0, 0, 0, 0, 0, 72787, 4022, 0, 95082, 0, 0, 0, 0, 22974, 22053, 60801, 0, 67634, 27785, 0, 91836, 95392, 0, 77364, 28481, 4411, 0, 91988, 0, 0, 0, 27785, 69763, 0, 77965, 7509, 67935, 0, 62414, 18987, 84750, 0, 0, 9118, 0, 9118, 64611, 0, 0, 59731, 0, 0, 69217, 0, 65368, 0, 0, 90771, 0, 0, 56836, 8644, 0, 25662, 1040, 7509, 90771, 0, 0, 5717, 0, 0, 0, 93616, 0, 0, 92138, 91988, 0, 0, 61094, 57045, 0, 0, 0, 95082, 0, 23148, 0, 98696, 25671, 11475, 0, 35467, 21256, 65289, 68210, 69763, 0, 0, 72266, 3057, 67634, 64611, 42529, 68210] solution = Solution() t = time.time() ans = solution.avoidFlood(rains) print(time.time() - t) # res = "\n".join("{} {}".format(x, y) for x, y in zip(rains, ans)) print(ans) check_ans(rains, ans)

66.923977

120

0.521409

1,965

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0.141288

0.092276

0.054081

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false

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0

1

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1

0

1

0

null

0

8

b5219bd1895f4dd93fd8f88b4fcbf7fa4741e60c

174

py

Python

readbin/apps/accounts/admin/__init__.py

asnelzin/readbin

1b546f71955cf5753d63aaf7d7fda0d466fc1332

[ "MIT" ]

null

readbin/apps/accounts/admin/__init__.py

asnelzin/readbin

1b546f71955cf5753d63aaf7d7fda0d466fc1332

[ "MIT" ]

null

readbin/apps/accounts/admin/__init__.py

asnelzin/readbin

1b546f71955cf5753d63aaf7d7fda0d466fc1332

[ "MIT" ]

null

from django.contrib import admin from readbin.apps.accounts.admin.models import UserAdmin from readbin.apps.accounts.models import User admin.site.register(User, UserAdmin)

29

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25

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0

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null

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1

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7

b52a1e100ba3b366d1701bc96940f5b957ec3083

124

py

Python

bin/amp4e_events_input_lib/__init__.py

kbalante/amp4e_splunk_events_input

ff5d15504aff79f0c6b8f886edc946ac4ac28d8f

[ "BSD-2-Clause" ]

9

2017-07-31T16:13:51.000Z

2021-01-06T15:02:36.000Z

bin/amp4e_events_input_lib/__init__.py

kbalante/amp4e_splunk_events_input

ff5d15504aff79f0c6b8f886edc946ac4ac28d8f

[ "BSD-2-Clause" ]

51

2017-10-24T17:25:44.000Z

2022-03-31T16:47:58.000Z

bin/amp4e_events_input_lib/__init__.py

kbalante/amp4e_splunk_events_input

ff5d15504aff79f0c6b8f886edc946ac4ac28d8f

[ "BSD-2-Clause" ]

12

2017-08-01T08:59:39.000Z

2021-02-24T21:10:46.000Z

import sys import os sys.path.insert(0, os.path.realpath(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')))

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0

7

b56069ba379ea3ae385e5a2d93953b8a0a347447

198

py

Python

halotools/empirical_models/phase_space_models/analytic_models/satellites/nfw/__init__.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

83

2015-01-15T14:54:16.000Z

2021-12-09T11:28:02.000Z

halotools/empirical_models/phase_space_models/analytic_models/satellites/nfw/__init__.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

579

2015-01-14T15:57:37.000Z

2022-01-13T18:58:44.000Z

halotools/empirical_models/phase_space_models/analytic_models/satellites/nfw/__init__.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

70

2015-01-14T15:15:58.000Z

2021-12-22T18:18:31.000Z

from .nfw_profile import NFWProfile from .nfw_phase_space import NFWPhaseSpace from .biased_nfw_phase_space import BiasedNFWPhaseSpace from .sfr_biased_nfw_phase_space import SFRBiasedNFWPhaseSpace

39.6

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7

a916e22e3f9256f2465eb008228ffa6d819f256e

34,677

py

Python

post_optimization_studies/mad_analyses/vbf_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_2.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

post_optimization_studies/mad_analyses/vbf_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_2.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

post_optimization_studies/mad_analyses/vbf_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_2.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

def selection_2(): # Library import import numpy import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec # Library version matplotlib_version = matplotlib.__version__ numpy_version = numpy.__version__ # Histo binning xBinning = numpy.linspace(-8.0,8.0,161,endpoint=True) # Creating data sequence: middle of each bin xData = numpy.array([-7.95,-7.85,-7.75,-7.65,-7.55,-7.45,-7.35,-7.25,-7.15,-7.05,-6.95,-6.85,-6.75,-6.65,-6.55,-6.45,-6.35,-6.25,-6.15,-6.05,-5.95,-5.85,-5.75,-5.65,-5.55,-5.45,-5.35,-5.25,-5.15,-5.05,-4.95,-4.85,-4.75,-4.65,-4.55,-4.45,-4.35,-4.25,-4.15,-4.05,-3.95,-3.85,-3.75,-3.65,-3.55,-3.45,-3.35,-3.25,-3.15,-3.05,-2.95,-2.85,-2.75,-2.65,-2.55,-2.45,-2.35,-2.25,-2.15,-2.05,-1.95,-1.85,-1.75,-1.65,-1.55,-1.45,-1.35,-1.25,-1.15,-1.05,-0.95,-0.85,-0.75,-0.65,-0.55,-0.45,-0.35,-0.25,-0.15,-0.05,0.05,0.15,0.25,0.35,0.45,0.55,0.65,0.75,0.85,0.95,1.05,1.15,1.25,1.35,1.45,1.55,1.65,1.75,1.85,1.95,2.05,2.15,2.25,2.35,2.45,2.55,2.65,2.75,2.85,2.95,3.05,3.15,3.25,3.35,3.45,3.55,3.65,3.75,3.85,3.95,4.05,4.15,4.25,4.35,4.45,4.55,4.65,4.75,4.85,4.95,5.05,5.15,5.25,5.35,5.45,5.55,5.65,5.75,5.85,5.95,6.05,6.15,6.25,6.35,6.45,6.55,6.65,6.75,6.85,6.95,7.05,7.15,7.25,7.35,7.45,7.55,7.65,7.75,7.85,7.95]) # Creating weights for histo: y3_PHI_0 y3_PHI_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.83712214407,16.9904494479,17.0764253742,17.539056978,17.4121410867,17.0682373812,17.2360932375,16.9863534514,17.1255533321,17.1419293181,16.4459339142,17.2197172515,16.7202376793,16.892189532,17.1501173111,16.8880975355,17.1951532725,17.0477653988,16.9945414443,16.8103096021,16.7243336758,17.4080450902,17.1705892936,17.4817370271,16.892189532,17.7028208377,17.4612690446,16.7857456232,16.5400978336,16.3845259668,16.9740734619,17.3015971814,17.1050813497,17.0191054233,17.4162330832,17.1255533321,16.9740734619,17.4612690446,17.1705892936,17.1787772866,17.1009893532,17.1173653392,17.2770332024,16.9495094829,17.3097851743,16.908565518,16.6915817038,16.7611816442,17.0395774058,16.7202376793,17.3015971814,17.2770332024,17.1091773462,17.1419293181,17.3752931182,16.7611816442,16.7939336162,17.2197172515,16.7775576302,16.8389655776,17.1746812901,16.6260737599,17.2279052445,7.31203373732,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_1 y3_PHI_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.21129243499,5.92969547579,5.27387254205,5.56527641345,5.97794902812,6.24453518471,5.92925487724,5.12795831796,5.7591477872,5.65006760206,5.70990489086,6.00256246541,5.2249100266,5.83066894851,5.91766713532,5.69816894761,5.68570000859,6.35377959296,5.84436755804,6.16014854569,5.69836921968,5.97753246221,5.699406629,5.77149255754,5.96493134363,6.0626601079,5.78437806247,5.62484934312,5.80754954086,5.8693454905,5.674124283,5.64956692189,5.8446439335,5.63693776521,6.10001084878,5.90517416365,5.8315100912,5.85631178424,5.89364249792,6.36841547577,6.087525888,5.17589143498,6.07518111766,5.74757206161,5.79623016352,5.57748499878,5.86903707151,5.52827414597,5.45573960799,6.28124906042,5.68692967909,5.71011717925,6.12276576127,6.06234367803,5.73429802887,5.54084322102,6.12309020202,5.27303139936,5.7233151086,5.73508309538,6.31891623117,5.96569237749,5.49287405504,1.96824505576,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_2 y3_PHI_2_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.47788304,35.5219335644,36.3544210331,37.740860239,35.7933373135,36.7975377963,35.5304539282,36.5980695527,35.8544095618,36.6263660856,37.3275275601,34.789153359,37.3676542585,35.8145679773,35.5216691109,34.959391218,35.4731129546,34.6774051937,35.7949901484,35.1195137277,36.4563720197,35.1514299689,35.743537399,35.6331073696,35.9951443185,36.384188589,35.1204847682,36.835540602,36.4649337043,36.1331932185,35.8620869797,35.5925591982,36.2248139872,36.0751126013,35.4723939714,36.0541215985,35.9240641551,36.3345250334,36.3748748646,35.3728726521,34.869650549,36.5868592002,36.1323296123,34.9893695104,35.9034780968,36.0032969264,36.7159290749,35.3711206471,35.5914972518,35.2423193582,34.858700518,35.9848926102,35.3999915402,36.244668666,36.9781141375,34.6576868738,35.5218633189,35.8521534422,35.3203455601,35.6007531271,36.033378521,35.9913345341,34.9809731093,15.561589012,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_3 y3_PHI_3_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.8005830699,33.0183245071,32.0848778871,32.7266907399,32.3864242612,31.9233348966,32.0943761145,32.8475961051,31.9517970783,31.9725566769,32.2595428941,32.0234603504,32.0446058907,33.0945500159,32.4417723575,32.502418011,32.5292673627,32.1665715799,33.151405316,32.1771138809,32.2000916285,32.6056269355,32.1167851059,31.9070562837,32.4421461115,32.9414652415,32.2929816919,31.5884269131,32.3923515125,32.7103633765,33.1111780076,32.0511831491,32.8925278392,31.7957344593,33.4798091669,32.5187819373,32.7771028456,32.8697979068,32.3026180457,32.2936723243,31.9228798917,31.6229504101,32.7058945783,33.2926843382,32.8094285063,32.6280074891,32.7882667158,32.6274021701,32.0345023444,32.5726715804,33.2609558713,31.7827668195,32.2709830173,31.9837083596,32.7378464851,32.1657712587,32.8214942613,32.6933535057,32.1281033529,32.4242993568,32.4466555351,32.3586161491,32.5452290972,13.8178366933,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_4 y3_PHI_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.80595139246,4.3845655694,4.59567483129,4.40025005603,4.50294275918,4.50700717765,4.56536805812,4.46258717232,4.55530721953,4.55230099285,4.46355317316,4.47830773367,4.39837016229,4.49100202684,4.45760485265,4.47236342147,4.38064945812,4.43992423151,4.38165955029,4.35099603822,4.27309468437,4.42611963863,4.52661579216,4.38064945812,4.41723323258,4.38560371968,4.48816414886,4.38361961008,4.50298685051,4.47724152528,4.49212835977,4.50399694267,4.42401127166,4.52266360616,4.47028712091,4.45369675798,4.36985510022,4.50484269444,4.43783189775,4.48736649672,4.49817287953,4.4546427173,4.56616571027,4.4823080193,4.35200613038,4.34694364467,4.40721648531,4.38072962417,4.46839520226,4.36289267924,4.4357716304,4.39447810082,4.33727962799,4.49119843365,4.51679545169,4.42878515961,4.45170062347,4.53658844811,4.52578206529,4.40316409175,4.43187155233,4.34319187378,4.42126558463,1.78709192963,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_5 y3_PHI_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.309295417963,0.746123601694,0.736291192735,0.761778637208,0.76256002865,0.753448196234,0.760770390185,0.758503434777,0.749152343742,0.738087632867,0.77618816757,0.746652131185,0.773859196968,0.767297989459,0.767317994361,0.770591196301,0.769835011034,0.777929394174,0.788726839568,0.7660540847,0.751207647295,0.739360344683,0.761026853019,0.789998751189,0.749679672938,0.792282510714,0.783959671603,0.748163301425,0.755459489004,0.762053904649,0.759014760052,0.751448506306,0.75623207829,0.780920126909,0.767578058077,0.759008758582,0.752718017339,0.794066147709,0.753948718863,0.76182264799,0.772590886229,0.743145672096,0.77842031445,0.760274668732,0.756470136615,0.752688009987,0.766797866928,0.735083696897,0.768297434325,0.779412957649,0.776137355121,0.769590951239,0.75645893387,0.734571571425,0.772125572226,0.759774146103,0.78267815763,0.764544514853,0.75875389614,0.744612831553,0.787734196369,0.752961677036,0.750717127117,0.335506799801,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_6 y3_PHI_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0741764853965,0.197273668932,0.196089158804,0.20959051494,0.190692934929,0.177253458426,0.194665427414,0.188387293812,0.195844539319,0.199267412707,0.180112217488,0.185508041495,0.190959947044,0.197020852145,0.19465902952,0.190372740318,0.199254416984,0.184980615093,0.183840790258,0.194399614904,0.19065224832,0.190393433507,0.199555317945,0.191560649327,0.186323073234,0.196976866622,0.191562148834,0.194698416556,0.199307799414,0.193827003375,0.188668201354,0.194998917648,0.205520854436,0.183539689363,0.184656321832,0.199823529666,0.201019536009,0.187800287021,0.174619925233,0.186959463771,0.200433228988,0.188930315081,0.19008653452,0.202400581449,0.196407354073,0.199812133417,0.193805410482,0.18691507838,0.183276276063,0.198091699684,0.196431446143,0.194732905204,0.192985980191,0.198415992945,0.1866871534,0.188391592397,0.180359736019,0.183834292397,0.192993377756,0.197775103889,0.19696557034,0.17925170072,0.185256924149,0.0818637252023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_7 y3_PHI_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00425328456669,0.0106696735777,0.0111867527415,0.0103038911133,0.0104093151833,0.010194146316,0.010666543041,0.0101881199282,0.0112068644489,0.0102580268653,0.0114864335262,0.00999756789145,0.00982175075015,0.0109945159983,0.00978161534238,0.0105829614839,0.0106876312076,0.00987114366192,0.0108348963459,0.0111003130505,0.0106893913487,0.010535265851,0.0110144600732,0.0108368031654,0.0107973885773,0.0105792190886,0.0106663502636,0.0104933158216,0.0112952235319,0.011229972587,0.0106479861249,0.0109865576461,0.0110091545051,0.0105328267984,0.011293593306,0.0105612656495,0.0103673693446,0.010819813613,0.011400777517,0.0109655281508,0.0112494137645,0.0113174264546,0.0109243324676,0.0111240162839,0.0106429571503,0.0103644022497,0.0111835677243,0.00997326537191,0.0107111165188,0.0111810029473,0.0108192059453,0.0109929025356,0.0111669385818,0.0112523682871,0.0107338642471,0.0105368290239,0.011056313714,0.0113148365327,0.0112719184256,0.0113780381704,0.0115964171997,0.0103245182906,0.0104705387577,0.00425447056652,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_8 y3_PHI_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000454216896718,0.000710402331465,0.000793041304089,0.000510495246345,0.000823126733144,0.000707465682248,0.00107861358108,0.00071022824179,0.000794126690828,0.000766573286181,0.00101987985403,0.000622470111758,0.000847971795597,0.000595482201475,0.000823305130499,0.000791025607079,0.000792149911476,0.000767674418232,0.000766641763434,0.000736250043499,0.000850930577375,0.00113302418021,0.000851791073526,0.000791833222749,0.000762406571604,0.00110646391854,0.000738337337116,0.000710370692301,0.00079417971985,0.000738151067105,0.000934747329039,0.000650998983042,0.000594642352478,0.000652568760934,0.000681543105713,0.000681314055986,0.00107653549698,0.0010486058388,0.000937558609923,0.000907795217059,0.000596488267492,0.000650318221112,0.00093479129708,0.000739507837663,0.000764305812721,0.000651886810678,0.000737939693719,0.000737470899337,0.000852118754263,0.000652811476403,0.000880068613981,0.000565727126889,0.00079433078572,0.000679157988035,0.00076235101739,0.000566737649126,0.000965099685694,0.000650389223556,0.000708373414336,0.000652243011226,0.000653148515203,0.000878072524342,0.000766320469946,0.00027945032146,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_9 y3_PHI_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0375632468,20.8492540491,26.0397256058,31.2795107607,28.6898585245,20.8462200169,39.1146355202,23.4744725259,28.6622522923,20.8521111922,26.0622712722,31.2722890719,26.0628865386,28.6973109382,18.2535337485,36.4782191958,31.2908508887,23.4562683327,33.8790534036,15.6400476319,33.891612528,26.066858852,28.6700700204,2.61222059059,33.8848945885,44.2876796575,31.2954923043,20.8576139806,28.6690317585,36.4897054495,18.234483564,20.8459508378,39.0762582814,31.2699818231,23.4645205926,36.4978846466,33.8871095474,33.8750041819,39.1031761844,20.8466814666,46.903715284,39.1056372498,33.8992725941,31.2703394467,23.4437168991,31.2905855551,33.9025142787,26.0528461608,26.0668896153,26.0773914429,36.4982230431,28.6876589473,28.6677435445,18.275421849,26.0754610447,26.0716694658,26.0522655032,23.4467086317,31.2816142026,26.0467780964,23.4670893297,23.4439553148,10.4344289866,13.0373555944,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_10 y3_PHI_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,55.824957344,129.544118577,131.663109299,124.267438071,107.435172771,111.624905265,112.716124117,111.632485044,117.936975895,131.63155895,125.360773105,113.753553471,129.546927328,115.843494786,148.526501713,121.130063633,132.684609574,124.27909631,125.329261232,139.031193209,136.924053107,119.035081958,141.137871599,127.446328152,155.847067718,129.539462977,132.684801955,109.553855684,113.753438043,122.171802303,139.024767711,131.662878443,126.374039509,133.766132466,133.768594932,110.581704505,113.74774359,120.083977171,137.98576084,116.90431757,125.32102736,124.299450133,107.41993626,141.136909698,141.133446855,124.27890393,120.106177844,131.629558196,135.870040581,109.526960935,124.293640252,124.256703257,143.250821583,134.802869251,147.434320959,144.283864669,140.086860204,122.193041075,131.651104776,134.834535029,148.523500582,125.336225395,145.352459612,49.4994201007,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_11 y3_PHI_11_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,45.6054480031,114.928738313,116.795016364,116.774728644,102.270700037,115.408112527,116.55940217,105.726374879,111.25312615,108.954312389,116.998393064,112.172797665,103.192830669,104.80520484,116.09001812,112.868420523,117.241423034,112.395386221,105.25326373,115.631584828,113.786516666,112.156621283,110.787315506,104.111618439,121.155108508,116.326631331,111.480440823,106.418078519,109.872792769,107.337634762,106.641819786,109.413245189,101.80704112,113.32835234,109.405137786,102.489484645,105.728565031,108.719466669,105.951076739,110.330227044,108.951738,110.795691875,105.503325239,113.566041414,116.995127049,107.795261156,107.326568734,108.720004601,116.089095951,108.489423913,114.946528491,113.777026009,110.793962808,108.025957115,110.103796117,109.171636896,109.868681431,123.001713619,101.806080528,110.790427827,110.791695809,105.265905131,115.859053195,49.2922414652,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_12 y3_PHI_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.37396255512,10.772356349,10.3562864185,10.5219911253,10.8840893031,10.3567403829,9.96874315036,9.69200182462,9.96852386247,10.5502561811,9.63670665023,9.94164763006,10.2455768078,9.47122123128,10.2455845022,9.60809535043,9.88530218206,9.47073648961,9.38721857818,9.80251138313,10.7154107446,10.4124202729,10.0238690499,10.1061520242,10.1909279557,10.1899430837,10.6606580192,10.2452959655,10.108464165,10.6048473259,9.63635655902,10.9108962867,10.8543969524,10.0782716841,10.3858210357,10.9087880452,10.3276328,10.7708674995,9.22106760123,9.96940486121,10.2168308576,9.96932022377,9.77473106905,10.5788367036,10.1353250088,10.5781749928,10.7726371913,10.4398620372,10.2460807853,9.69275971437,9.83078028604,9.49720492347,10.7999673882,10.024365333,10.3557708996,9.35977296668,10.9938409719,9.66390604374,10.5503023469,9.27525864183,9.4427830535,10.1076370265,10.1363752824,4.31948682498,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_13 y3_PHI_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.544093096752,1.28067808623,1.36069528394,1.46230950955,1.4115315237,1.35107064683,1.47193293304,1.64296335956,1.51203589108,1.33077522959,1.65374275894,1.30052992427,1.39142603845,1.58202977653,1.50228928486,1.3812394939,1.47223815922,1.61319986257,1.43163882938,1.31083176301,1.37085330836,1.48218015875,1.6334048649,1.66348026299,1.36127418211,1.41151453298,1.47196448724,1.4519172559,1.47189045624,1.50230081428,1.36075960596,1.42178724476,1.44168034599,1.40172423632,1.41137617996,1.44193095913,1.45200281632,1.32082291427,1.34077184214,1.52241722213,1.62310788065,1.37087333314,1.36120803965,1.4823622022,1.50208782345,1.37080961793,1.30077325568,1.34107100021,1.13912656211,1.52217692478,1.26029528813,1.69404839203,1.36100597143,1.42183457606,1.20990626845,1.21976877566,1.61311248171,1.36112733372,1.4114999695,1.40121815554,1.45197186893,1.46186714397,1.56233631629,0.534265117107,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_14 y3_PHI_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.147146226343,0.319770130385,0.345159511045,0.339473797329,0.342287337262,0.339515733613,0.407409461985,0.347945773156,0.356413593829,0.305532069376,0.319627200756,0.36780879798,0.353638719919,0.345173092245,0.314065641525,0.367860468099,0.345219337579,0.396047806861,0.35649508103,0.311253525118,0.362163520075,0.365019958134,0.319660518941,0.356491695349,0.356480807304,0.31688256714,0.387650162136,0.413006994083,0.299898949146,0.331025482799,0.313984731428,0.370607371661,0.367848425845,0.333869917077,0.373433146216,0.367751587654,0.319732118414,0.325397325672,0.322507415533,0.359306158649,0.356473574257,0.316859252105,0.350801403268,0.331018172805,0.319768629912,0.33945875413,0.3734049835,0.424399812868,0.288521404403,0.291459060348,0.356497466397,0.356459415952,0.333771963151,0.370597907142,0.350831374245,0.319673292195,0.325341346503,0.350848456548,0.364952436869,0.384782720612,0.34798170755,0.345079024158,0.393243077395,0.172559768459,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_15 y3_PHI_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0075932686663,0.0258565337607,0.0244568608516,0.0244085379947,0.0182325128962,0.0198093223428,0.0304366873514,0.0259473060433,0.0137504586048,0.0320268154235,0.0121477682444,0.0228123828606,0.0152172918943,0.0259103636979,0.0197830987137,0.0198048670439,0.0152639484458,0.0243029225944,0.0182854210474,0.0182573302108,0.0228626556493,0.0258739413342,0.0197956491842,0.0152406792589,0.0228914200987,0.0243383877188,0.0289325571834,0.0228466780258,0.0137383335739,0.0212813790861,0.0197575132441,0.0319239653811,0.0182884936673,0.0213438832657,0.0121743345888,0.0273807068647,0.019804453422,0.0243749637134,0.0182361645868,0.0304870665001,0.0198245790824,0.00915037907394,0.0273849258082,0.0228432981439,0.0213510448336,0.0198245909002,0.0213585727524,0.033496461342,0.0243301743694,0.0213476176806,0.0213440841677,0.0213439069012,0.0167507419469,0.0197864431423,0.02592078697,0.0227973152052,0.0137294702473,0.0152949819068,0.0167867979597,0.00761402539554,0.0197572768887,0.016818942291,0.0121713565111,0.0061254899553,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y3_PHI_16 y3_PHI_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000180406121736,0.00198429969742,0.00144412419203,0.00216653000546,0.000722458574535,0.00126368968713,0.000902756362704,0.00108315312608,0.00144423202495,0.00144469339577,0.000903043275279,0.0018064693574,0.00126314127974,0.00180620170071,0.00108213410504,0.00144476464252,0.0016251899046,0.00126432821203,0.00108383016274,0.00198679641451,0.00108400154005,0.00126441601883,0.00126358262445,0.000722492079762,0.00144416848055,0.000541889663611,0.00198506877717,0.000903866656604,0.000902182537552,0.000902539156405,0.00198695161688,0.00162621624287,0.000541534585228,0.00234719635113,0.0019866338949,0.00144415615679,0.00270892032189,0.00198758513525,0.00126335155392,0.00198571423419,0.000722191302955,0.00108328599164,0.00180613430514,0.000722770904869,0.00144381879381,0.000901796264648,0.00108311345898,0.00144377643088,0.00162588042037,0.00162413776345,0.00306913424673,0.00126448033347,0.00126420998094,0.000541503005589,0.00180468587802,0.00144459788662,0.00126438906061,0.0012652555751,0.00144427862417,0.00144338322586,0.000902232602834,0.00216762720537,0.00126452616245,0.000360782781979,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating a new Canvas fig = plt.figure(figsize=(12,6),dpi=80) frame = gridspec.GridSpec(1,1,right=0.7) pad = fig.add_subplot(frame[0]) # Creating a new Stack pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights+y3_PHI_12_weights+y3_PHI_13_weights+y3_PHI_14_weights+y3_PHI_15_weights+y3_PHI_16_weights,\ label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#e5e5e5", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights+y3_PHI_12_weights+y3_PHI_13_weights+y3_PHI_14_weights+y3_PHI_15_weights,\ label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#f2f2f2", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights+y3_PHI_12_weights+y3_PHI_13_weights+y3_PHI_14_weights,\ label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights+y3_PHI_12_weights+y3_PHI_13_weights,\ label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights+y3_PHI_12_weights,\ label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights,\ label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights,\ label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights,\ label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights,\ label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights,\ label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights,\ label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights,\ label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights,\ label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights,\ label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights,\ label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights+y3_PHI_1_weights,\ label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y3_PHI_0_weights,\ label="$signal$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") # Axis plt.rc('text',usetex=False) plt.xlabel(r"\eta [ j_{1} ] ",\ fontsize=16,color="black") plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\ fontsize=16,color="black") # Boundary of y-axis ymax=(y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights+y3_PHI_12_weights+y3_PHI_13_weights+y3_PHI_14_weights+y3_PHI_15_weights+y3_PHI_16_weights).max()*1.1 ymin=0 # linear scale #ymin=min([x for x in (y3_PHI_0_weights+y3_PHI_1_weights+y3_PHI_2_weights+y3_PHI_3_weights+y3_PHI_4_weights+y3_PHI_5_weights+y3_PHI_6_weights+y3_PHI_7_weights+y3_PHI_8_weights+y3_PHI_9_weights+y3_PHI_10_weights+y3_PHI_11_weights+y3_PHI_12_weights+y3_PHI_13_weights+y3_PHI_14_weights+y3_PHI_15_weights+y3_PHI_16_weights) if x])/100. # log scale plt.gca().set_ylim(ymin,ymax) # Log/Linear scale for X-axis plt.gca().set_xscale("linear") #plt.gca().set_xscale("log",nonposx="clip") # Log/Linear scale for Y-axis plt.gca().set_yscale("linear") #plt.gca().set_yscale("log",nonposy="clip") # Legend plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.) # Saving the image plt.savefig('../../HTML/MadAnalysis5job_0/selection_2.png') plt.savefig('../../PDF/MadAnalysis5job_0/selection_2.png') plt.savefig('../../DVI/MadAnalysis5job_0/selection_2.eps') # Running! if __name__ == '__main__': selection_2()

178.747423

1,560

0.741731

7,547

34,677

3.311382

0.173711

0.259133

0.384618

0.507383

0.377056

0.375375

0.374255

0.371134

0.369613

0

0.555243

0.047582

34,677

193

1,561

179.673575

0.201441

0.037979

0

0.185841

0

0.00885

0.030849

0.006002

0

1

0.00885

false

0

0.035398

0

0.044248

0

null

1

0

1

0

1

0

null

0

7

a93d11dae5f6b12ab8926a0f93c21a6c8b84c04b

81

py

Python

spid_cie_oidc/authority/utils.py

peppelinux/spid-cie-oidc-authority

816636fece10f410f5d6fce85fd79bb409d0c8b8

[ "Apache-2.0" ]

4

2022-03-08T09:05:13.000Z

2022-03-16T17:59:43.000Z

spid_cie_oidc/authority/utils.py

peppelinux/spid-cie-oidc-authority

816636fece10f410f5d6fce85fd79bb409d0c8b8

[ "Apache-2.0" ]

64

2022-03-08T01:11:40.000Z

2022-03-31T17:23:49.000Z

spid_cie_oidc/authority/utils.py

peppelinux/spid-cie-oidc-authority

816636fece10f410f5d6fce85fd79bb409d0c8b8

[ "Apache-2.0" ]

8

2022-03-09T12:00:08.000Z

2022-03-31T13:52:14.000Z

from secrets import token_hex def random_token(n=254): return token_hex(n)

13.5

29

0.753086

14

81

4.142857

0.714286

0.275862

0

0.044776

0.17284

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null

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a947c523c534bc99056abcf27d1848e30d4ca2e3

419

py

Python

lib/__init__.py

tijko/PyChat

5c80612d9ee0eea6fdec740a2eba200683ca4a4e

[ "MIT" ]

null

lib/__init__.py

tijko/PyChat

5c80612d9ee0eea6fdec740a2eba200683ca4a4e

[ "MIT" ]

null

lib/__init__.py

tijko/PyChat

5c80612d9ee0eea6fdec740a2eba200683ca4a4e

[ "MIT" ]

null

try: from tkinter import Tk, Entry, Label, Button, Scrollbar,\ Checkbutton, TclError, IntVar, Text,\ NORMAL, DISABLED, WORD, CURRENT, END, N, S, E, W except ImportError: from Tkinter import Tk, Entry, Label, Button, Scrollbar,\ Checkbutton, TclError, IntVar, Text,\ NORMAL, DISABLED, WORD, CURRENT, END, N, S, E, W

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8d13a4d5a6aae3b0f90bcf618a2397c0d1555931

6,284

py

Python

tests/components/tasmota/test_init.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

1

2021-07-08T20:09:55.000Z

tests/components/tasmota/test_init.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

47

2021-02-21T23:43:07.000Z

2022-03-31T06:07:10.000Z

tests/components/tasmota/test_init.py

OpenPeerPower/core

f673dfac9f2d0c48fa30af37b0a99df9dd6640ee

[ "Apache-2.0" ]

null

"""The tests for the Tasmota binary sensor platform.""" import copy import json from unittest.mock import call from openpeerpower.components import websocket_api from openpeerpower.components.tasmota.const import DEFAULT_PREFIX from openpeerpower.helpers import device_registry as dr from .test_common import DEFAULT_CONFIG from tests.common import MockConfigEntry, async_fire_mqtt_message async def test_device_remove( opp, mqtt_mock, caplog, device_reg, entity_reg, setup_tasmota ): """Test removing a discovered device through device registry.""" config = copy.deepcopy(DEFAULT_CONFIG) mac = config["mac"] async_fire_mqtt_message(opp, f"{DEFAULT_PREFIX}/{mac}/config", json.dumps(config)) await opp.async_block_till_done() # Verify device entry is created device_entry = device_reg.async_get_device( set(), {(dr.CONNECTION_NETWORK_MAC, mac)} ) assert device_entry is not None device_reg.async_remove_device(device_entry.id) await opp.async_block_till_done() # Verify device entry is removed device_entry = device_reg.async_get_device( set(), {(dr.CONNECTION_NETWORK_MAC, mac)} ) assert device_entry is None # Verify retained discovery topic has been cleared mqtt_mock.async_publish.assert_has_calls( [ call(f"tasmota/discovery/{mac}/config", "", 0, True), call(f"tasmota/discovery/{mac}/sensors", "", 0, True), ], any_order=True, ) async def test_device_remove_non_tasmota_device( opp, device_reg, opp_ws_client, mqtt_mock, setup_tasmota ): """Test removing a non Tasmota device through device registry.""" config_entry = MockConfigEntry(domain="test") config_entry.add_to_opp(opp) mac = "12:34:56:AB:CD:EF" device_entry = device_reg.async_get_or_create( config_entry_id=config_entry.entry_id, connections={(dr.CONNECTION_NETWORK_MAC, mac)}, ) assert device_entry is not None device_reg.async_remove_device(device_entry.id) await opp.async_block_till_done() # Verify device entry is removed device_entry = device_reg.async_get_device( set(), {(dr.CONNECTION_NETWORK_MAC, mac)} ) assert device_entry is None # Verify no Tasmota discovery message was sent mqtt_mock.async_publish.assert_not_called() async def test_device_remove_stale_tasmota_device( opp, device_reg, opp_ws_client, mqtt_mock, setup_tasmota ): """Test removing a stale (undiscovered) Tasmota device through device registry.""" config_entry = opp.config_entries.async_entries("tasmota")[0] mac = "12:34:56:AB:CD:EF" device_entry = device_reg.async_get_or_create( config_entry_id=config_entry.entry_id, connections={(dr.CONNECTION_NETWORK_MAC, mac)}, ) assert device_entry is not None device_reg.async_remove_device(device_entry.id) await opp.async_block_till_done() # Verify device entry is removed device_entry = device_reg.async_get_device( set(), {(dr.CONNECTION_NETWORK_MAC, mac)} ) assert device_entry is None # Verify retained discovery topic has been cleared mac = mac.replace(":", "") mqtt_mock.async_publish.assert_has_calls( [ call(f"tasmota/discovery/{mac}/config", "", 0, True), call(f"tasmota/discovery/{mac}/sensors", "", 0, True), ], any_order=True, ) async def test_tasmota_ws_remove_discovered_device( opp, device_reg, entity_reg, opp_ws_client, mqtt_mock, setup_tasmota ): """Test Tasmota websocket device removal.""" config = copy.deepcopy(DEFAULT_CONFIG) mac = config["mac"] async_fire_mqtt_message(opp, f"{DEFAULT_PREFIX}/{mac}/config", json.dumps(config)) await opp.async_block_till_done() # Verify device entry is created device_entry = device_reg.async_get_device( set(), {(dr.CONNECTION_NETWORK_MAC, mac)} ) assert device_entry is not None client = await opp_ws_client(opp) await client.send_json( {"id": 5, "type": "tasmota/device/remove", "device_id": device_entry.id} ) response = await client.receive_json() assert response["success"] # Verify device entry is cleared device_entry = device_reg.async_get_device( set(), {(dr.CONNECTION_NETWORK_MAC, mac)} ) assert device_entry is None async def test_tasmota_ws_remove_discovered_device_twice( opp, device_reg, opp_ws_client, mqtt_mock, setup_tasmota ): """Test Tasmota websocket device removal.""" config = copy.deepcopy(DEFAULT_CONFIG) mac = config["mac"] async_fire_mqtt_message(opp, f"{DEFAULT_PREFIX}/{mac}/config", json.dumps(config)) await opp.async_block_till_done() # Verify device entry is created device_entry = device_reg.async_get_device( set(), {(dr.CONNECTION_NETWORK_MAC, mac)} ) assert device_entry is not None client = await opp_ws_client(opp) await client.send_json( {"id": 5, "type": "tasmota/device/remove", "device_id": device_entry.id} ) response = await client.receive_json() assert response["success"] await client.send_json( {"id": 6, "type": "tasmota/device/remove", "device_id": device_entry.id} ) response = await client.receive_json() assert not response["success"] assert response["error"]["code"] == websocket_api.const.ERR_NOT_FOUND assert response["error"]["message"] == "Device not found" async def test_tasmota_ws_remove_non_tasmota_device( opp, device_reg, opp_ws_client, mqtt_mock, setup_tasmota ): """Test Tasmota websocket device removal of device belonging to other domain.""" config_entry = MockConfigEntry(domain="test") config_entry.add_to_opp(opp) device_entry = device_reg.async_get_or_create( config_entry_id=config_entry.entry_id, connections={(dr.CONNECTION_NETWORK_MAC, "12:34:56:AB:CD:EF")}, ) assert device_entry is not None client = await opp_ws_client(opp) await client.send_json( {"id": 5, "type": "tasmota/device/remove", "device_id": device_entry.id} ) response = await client.receive_json() assert not response["success"] assert response["error"]["code"] == websocket_api.const.ERR_NOT_FOUND

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a5edd9f5b3d0d282c5be46e78b39f7ea53358fae

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py

Python

tests/test_record.py

Semtexcz/invenio-records-draft

8df87f08bae350b8b50f0bee4edf12c3fbaf3944

[ "MIT" ]

null

tests/test_record.py

Semtexcz/invenio-records-draft

8df87f08bae350b8b50f0bee4edf12c3fbaf3944

[ "MIT" ]

null

tests/test_record.py

Semtexcz/invenio-records-draft

8df87f08bae350b8b50f0bee4edf12c3fbaf3944

[ "MIT" ]

null

import uuid import pytest from invenio_pidstore.errors import PIDDoesNotExistError from invenio_pidstore.models import PersistentIdentifier, PIDStatus from invenio_records import Record from invenio_records_draft.api import RecordContext from invenio_records_draft.proxies import current_drafts from invenio_records_draft.record import ( DraftEnabledRecordMixin, InvalidRecordException, MarshmallowValidator, ) from tests.helpers import disable_test_authenticated class TestDraftRecord(DraftEnabledRecordMixin, Record): schema = None def validate(self, **kwargs): self['$schema'] = self.schema return super().validate(**kwargs) draft_validator = MarshmallowValidator( 'sample.records.marshmallow:MetadataSchemaV1', 'records/record-v1.0.0.json' ) class TestPublishedRecord(DraftEnabledRecordMixin, Record): schema = None def validate(self, **kwargs): self['$schema'] = self.schema return super().validate(**kwargs) def test_publish_record(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): draft_uuid = uuid.uuid4() rec = TestDraftRecord.create({ 'id': '1' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) with pytest.raises(InvalidRecordException): # title is required but not in rec, so should fail with disable_test_authenticated(): current_drafts.publish(RecordContext(record=rec, record_pid=draft_pid)) with pytest.raises(PIDDoesNotExistError): # no record should be created PersistentIdentifier.get(pid_type='recid', pid_value='1') # make the record valid rec['title'] = 'blah' rec.commit() # and publish it again with disable_test_authenticated(): current_drafts.publish(RecordContext(record=rec, record_pid=draft_pid)) # draft should be gone draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.DELETED rec = TestDraftRecord.get_record(draft_uuid, with_deleted=True) assert rec.model.json is None published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.REGISTERED rec = TestPublishedRecord.get_record(published_pid.object_uuid) assert rec.model.json is not None def test_publish_record_marshmallow(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): draft_uuid = uuid.uuid4() rec = TestDraftRecord.create({ 'id': '1' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) with pytest.raises(InvalidRecordException): # title is required but not in rec, so should fail with disable_test_authenticated(): current_drafts.publish(RecordContext(record=rec, record_pid=draft_pid)) with pytest.raises(PIDDoesNotExistError): # no record should be created PersistentIdentifier.get(pid_type='recid', pid_value='1') # make the record valid rec['title'] = 'blah' rec.commit() assert rec['invenio_draft_validation']['valid'] # and publish it again with disable_test_authenticated(): current_drafts.publish(RecordContext(record=rec, record_pid=draft_pid)) # draft should be gone draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.DELETED rec = TestDraftRecord.get_record(draft_uuid, with_deleted=True) assert rec.model.json is None published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.REGISTERED rec = TestPublishedRecord.get_record(published_pid.object_uuid) assert rec.model.json is not None def test_publish_record_with_previous_version(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) assert published_record.revision_id == 0 draft_uuid = uuid.uuid4() draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) draft_record = TestDraftRecord.create({ 'id': '1', 'title': '22' }, id_=draft_uuid) assert draft_record.revision_id == 0 print(draft_record['invenio_draft_validation']) # and publish it again with disable_test_authenticated(): current_drafts.publish(RecordContext(record=draft_record, record_pid=draft_pid)) # draft should be gone draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.DELETED rec = TestDraftRecord.get_record(draft_uuid, with_deleted=True) assert rec.model.json is None published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.REGISTERED rec = TestPublishedRecord.get_record(published_pid.object_uuid) assert rec.model.json is not None assert rec['title'] == '22' assert rec.revision_id == 1 def test_publish_deleted_published(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11', '$schema': 'records/record-v1.0.0.json' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 draft_uuid = uuid.uuid4() rec = TestDraftRecord.create({ 'id': '1', 'title': '22' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) with db.session.begin_nested(): published_record.delete() published_pid.status = PIDStatus.DELETED db.session.add(published_pid) with db.session.begin_nested(): rec = TestDraftRecord.get_record(draft_uuid) draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') with disable_test_authenticated(): current_drafts.publish(RecordContext(record=rec, record_pid=draft_pid)) with db.session.begin_nested(): # draft should be gone draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.DELETED rec = TestDraftRecord.get_record(draft_uuid, with_deleted=True) assert rec.model.json is None published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.REGISTERED rec = TestPublishedRecord.get_record(published_pid.object_uuid) assert rec['title'] == '22' # revision 0 original, 1 deleted, 2 temporarily reverted to orig, 3 published assert rec.revision_id == 3 def test_publish_redirected_published(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 draft_uuid = uuid.uuid4() rec = TestDraftRecord.create({ 'id': '1', 'title': '22' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) with db.session.begin_nested(): published_record.delete() published_pid.status = PIDStatus.REDIRECTED db.session.add(published_pid) with db.session.begin_nested(): rec = TestDraftRecord.get_record(draft_uuid) draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') with pytest.raises(NotImplementedError): with disable_test_authenticated(): current_drafts.publish(RecordContext(record=rec, record_pid=draft_pid)) def test_unpublish_record(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 with disable_test_authenticated(): current_drafts.unpublish(RecordContext(record=published_record, record_pid=published_pid)) # published version should be gone published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.DELETED rec = TestDraftRecord.get_record(published_uuid, with_deleted=True) assert rec.model.json is None draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(draft_pid.object_uuid) assert rec.model.json is not None assert rec['title'] == '11' assert rec.revision_id == 1 def test_unpublish_record_existing_draft(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 draft_uuid = uuid.uuid4() draft_record = TestDraftRecord.create({ 'id': '1', 'title': '22' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) assert draft_record.revision_id == 0 with disable_test_authenticated(): current_drafts.unpublish(RecordContext(record=published_record, record_pid=published_pid)) # published version should be gone published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.DELETED rec = TestDraftRecord.get_record(published_uuid, with_deleted=True) assert rec.model.json is None draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(draft_pid.object_uuid) assert rec.model.json is not None assert rec['title'] == '22' # should not be changed on a newer record assert rec.revision_id == 1 def test_unpublish_record_redirected_draft(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 draft_uuid = uuid.uuid4() draft_record = TestDraftRecord.create({ 'id': '1', 'title': '22' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) assert draft_record.revision_id == 0 with db.session.begin_nested(): draft_record.delete() draft_pid.status = PIDStatus.REDIRECTED db.session.add(draft_pid) with db.session.begin_nested(): with pytest.raises(NotImplementedError): with disable_test_authenticated(): current_drafts.unpublish( RecordContext(record=published_record, record_pid=published_pid)) def test_draft_record(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 with disable_test_authenticated(): current_drafts.edit(RecordContext(record=published_record, record_pid=published_pid)) # published version should be there unchanged published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(published_uuid, with_deleted=True) assert rec['title'] == '11' assert rec.revision_id == 0 # draft version should appear draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(draft_pid.object_uuid) assert rec.model.json is not None assert rec['title'] == '11' assert rec.revision_id == 1 def test_draft_record_existing_draft(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 draft_uuid = uuid.uuid4() draft_record = TestDraftRecord.create({ 'id': '1', 'title': '22' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) assert draft_record.revision_id == 0 with disable_test_authenticated(): current_drafts.edit(RecordContext(record=published_record, record_pid=published_pid)) # published version should be there unchanged published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(published_uuid, with_deleted=True) assert rec['title'] == '11' assert rec.revision_id == 0 # draft version should be there unchanged draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(draft_pid.object_uuid) assert rec.model.json is not None assert rec['title'] == '22' # should not be changed on a newer record assert rec.revision_id == 1 def test_draft_record_deleted_draft(app, db, schemas): TestDraftRecord.schema = schemas['draft'] TestPublishedRecord.schema = schemas['published'] with db.session.begin_nested(): published_uuid = uuid.uuid4() published_record = TestPublishedRecord.create({ 'id': '1', 'title': '11' }, id_=published_uuid) published_pid = PersistentIdentifier.create( pid_type='recid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=published_uuid ) assert published_record.revision_id == 0 draft_uuid = uuid.uuid4() draft_record = TestDraftRecord.create({ 'id': '1', 'title': '22' }, id_=draft_uuid) draft_pid = PersistentIdentifier.create( pid_type='drecid', pid_value='1', status=PIDStatus.REGISTERED, object_type='rec', object_uuid=draft_uuid ) assert draft_record.revision_id == 0 with db.session.begin_nested(): draft_record.delete() draft_pid.status = PIDStatus.DELETED db.session.add(draft_pid) with db.session.begin_nested(): with disable_test_authenticated(): current_drafts.edit(RecordContext(record=published_record, record_pid=published_pid)) # published version should be there unchanged published_pid = PersistentIdentifier.get(pid_type='recid', pid_value='1') assert published_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(published_uuid, with_deleted=True) assert rec['title'] == '11' assert rec.revision_id == 0 # draft version should be there unchanged draft_pid = PersistentIdentifier.get(pid_type='drecid', pid_value='1') assert draft_pid.status == PIDStatus.REGISTERED rec = TestDraftRecord.get_record(draft_pid.object_uuid) assert rec.model.json is not None assert rec['title'] == '11' assert rec.revision_id == 4

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570343d8371a37859e0ddcf3d197742446157ad5

9,095

py

Python

visualise_ba.py

ttk21/lab_05

916bf12185af9668e7c4d71fa282e1bf95a685cc

[ "BSD-3-Clause" ]

null

visualise_ba.py

ttk21/lab_05

916bf12185af9668e7c4d71fa282e1bf95a685cc

[ "BSD-3-Clause" ]

null

visualise_ba.py

ttk21/lab_05

916bf12185af9668e7c4d71fa282e1bf95a685cc

[ "BSD-3-Clause" ]

1

2020-12-19T20:13:20.000Z

import matplotlib import numpy as np import visgeom as vg from matplotlib import pyplot as plt def visualise_moba(true_pose_w_c, true_box_w, measurement, x, cost): # Visualize (press a key to jump to the next iteration). # Use Qt 5 backend in visualisation. matplotlib.use('qt5agg') # Create figure and axis. fig = plt.figure() ax = plt.axes(projection='3d') ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Plot box and true state vg.plot_pose(ax, true_pose_w_c.to_tuple(), scale=1, alpha=0.4) vg.utils.plot_as_box(ax, true_box_w) # Normalised in 3d. xn_3d = np.vstack((measurement.xn, np.ones((1, measurement.num)))) # Plot initial state (to run axis equal first time). ax.set_title('Cost: ' + str(cost[0])) artists = vg.plot_pose(ax, x[0].to_tuple(), scale=1) artists.extend(vg.plot_camera_image_plane(ax, measurement.camera.K(), x[0].to_tuple())) artists.extend(vg.utils.plot_as_box(ax, x[0] * xn_3d, alpha=0.4)) artists.extend( vg.utils.plot_as_box(ax, x[0] * measurement.camera.project_to_normalised_3d(x[0].inverse() * measurement.x_w))) vg.plot.axis_equal(ax) plt.draw() while True: if plt.waitforbuttonpress(): break # Plot iterations for i in range(1, len(x)): for artist in artists: artist.remove() ax.set_title('Cost: ' + str(cost[i])) artists = vg.plot_pose(ax, x[i].to_tuple(), scale=1) artists.extend(vg.plot_camera_image_plane(ax, measurement.camera.K(), x[i].to_tuple())) artists.extend(vg.utils.plot_as_box(ax, x[i] * xn_3d, alpha=0.4)) artists.extend(vg.utils.plot_as_box(ax, x[i] * measurement.camera.project_to_normalised_3d( x[i].inverse() * measurement.x_w))) plt.draw() while True: if plt.waitforbuttonpress(): break plt.close() def visualise_multicam_moba(true_pose_w_c, true_box_w, measurement, x, cost): # Visualize (press a key to jump to the next iteration). # Use Qt 5 backend in visualisation. matplotlib.use('qt5agg') # Create figure and axis. fig = plt.figure() ax = plt.axes(projection='3d') ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Plot box and true state for true_pose in true_pose_w_c: vg.plot_pose(ax, true_pose.to_tuple(), scale=1, alpha=0.4) vg.utils.plot_as_box(ax, true_box_w) # Plot initial state (to run axis equal first time). ax.set_title('Cost: ' + str(cost[0])) artists = [] for pose, meas in zip(x[0], measurement): # Normalised in 3d. xn_3d = np.vstack((meas.xn, np.ones((1, meas.num)))) artists.extend(vg.plot_pose(ax, pose.to_tuple(), scale=1)) artists.extend(vg.plot_camera_image_plane(ax, meas.camera.K(), pose.to_tuple())) artists.extend(vg.utils.plot_as_box(ax, pose * xn_3d, alpha=0.4)) artists.extend( vg.utils.plot_as_box(ax, pose * meas.camera.project_to_normalised_3d(pose.inverse() * meas.x_w))) vg.plot.axis_equal(ax) plt.draw() while True: if plt.waitforbuttonpress(): break # Plot iterations for i in range(1, len(x)): for artist in artists: artist.remove() ax.set_title('Cost: ' + str(cost[i])) artists = [] for pose, meas in zip(x[i], measurement): # Normalised in 3d. xn_3d = np.vstack((meas.xn, np.ones((1, meas.num)))) artists.extend(vg.plot_pose(ax, pose.to_tuple(), scale=1)) artists.extend(vg.plot_camera_image_plane(ax, meas.camera.K(), pose.to_tuple())) artists.extend(vg.utils.plot_as_box(ax, pose * xn_3d, alpha=0.4)) artists.extend(vg.utils.plot_as_box(ax, pose * meas.camera.project_to_normalised_3d( pose.inverse() * meas.x_w))) plt.draw() while True: if plt.waitforbuttonpress(): break plt.close() def visualise_soba(true_pose_w_c, true_box_w, measurement, x, cost): # Visualize (press a key to jump to the next iteration). # Use Qt 5 backend in visualisation. matplotlib.use('qt5agg') # Create figure and axis. fig = plt.figure() ax = plt.axes(projection='3d') ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Plot true box vg.utils.plot_as_box(ax, true_box_w, alpha=0.4) # Plot camera poses for true_pose in true_pose_w_c: vg.plot_pose(ax, true_pose.to_tuple(), scale=1) # Plot initial state (to run axis equal first time). ax.set_title('Cost: ' + str(cost[0])) artists = [] # Extract points as matrix. x_w = np.zeros((3, len(x[0]))) for j, state in enumerate(x[0]): x_w[:, [j]] = state artists.extend(vg.utils.plot_as_box(ax, x_w)) for meas in measurement: # Normalised in 3d. xn_3d = np.vstack((meas.xn, np.ones((1, meas.num)))) artists.extend(vg.plot_camera_image_plane(ax, meas.camera.K(), meas.pose_w_c.to_tuple())) artists.extend(vg.utils.plot_as_box(ax, meas.pose_w_c * xn_3d, alpha=0.4)) artists.extend( vg.utils.plot_as_box(ax, meas.pose_w_c * meas.camera.project_to_normalised_3d(meas.pose_c_w * x_w))) vg.plot.axis_equal(ax) plt.draw() while True: if plt.waitforbuttonpress(): break # Plot iterations for i in range(1, len(x)): for artist in artists: artist.remove() ax.set_title('Cost: ' + str(cost[i])) artists = [] # Extract points as matrix. x_w = np.zeros((3, len(x[i]))) for j, state in enumerate(x[i]): x_w[:, [j]] = state artists.extend(vg.utils.plot_as_box(ax, x_w)) for meas in measurement: # Normalised in 3d. xn_3d = np.vstack((meas.xn, np.ones((1, meas.num)))) artists.extend(vg.plot_camera_image_plane(ax, meas.camera.K(), meas.pose_w_c.to_tuple())) artists.extend(vg.utils.plot_as_box(ax, meas.pose_w_c * xn_3d, alpha=0.4)) artists.extend( vg.utils.plot_as_box(ax, meas.pose_w_c * meas.camera.project_to_normalised_3d(meas.pose_c_w * x_w))) plt.draw() while True: if plt.waitforbuttonpress(): break plt.close() def visualise_full(true_pose_w_c, true_box_w, measurement, x, cost): # Visualize (press a key to jump to the next iteration). # Use Qt 5 backend in visualisation. matplotlib.use('qt5agg') # Create figure and axis. fig = plt.figure() ax = plt.axes(projection='3d') ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Plot true state for true_pose in true_pose_w_c: vg.plot_pose(ax, true_pose.to_tuple(), scale=1, alpha=0.4) vg.utils.plot_as_box(ax, true_box_w, alpha=0.4) num_cameras = x[0].num_poses num_points = x[0].num_points # Plot initial state (to run axis equal first time). ax.set_title('Cost: ' + str(cost[0])) artists = [] # Extract points as matrix. x_w = np.zeros((3, num_points)) for j in range(num_points): x_w[:, [j]] = x[0].get_point(j) artists.extend(vg.utils.plot_as_box(ax, x_w)) for i in range(num_cameras): pose = x[0].get_pose(i) # Normalised in 3d. xn_3d = np.vstack((measurement[i].xn, np.ones((1, measurement[i].num)))) artists.extend(vg.plot_pose(ax, pose.to_tuple(), scale=1)) artists.extend(vg.plot_camera_image_plane(ax, measurement[i].camera.K(), pose.to_tuple())) artists.extend(vg.utils.plot_as_box(ax, pose * xn_3d, alpha=0.4)) artists.extend( vg.utils.plot_as_box(ax, pose * measurement[i].camera.project_to_normalised_3d(pose.inverse() * x_w))) vg.plot.axis_equal(ax) plt.draw() while True: if plt.waitforbuttonpress(): break # Plot iterations for it in range(1, len(x)): for artist in artists: artist.remove() ax.set_title('Cost: ' + str(cost[it])) artists = [] # Extract points as matrix. x_w = np.zeros((3, num_points)) for j in range(num_points): x_w[:, [j]] = x[it].get_point(j) artists.extend(vg.utils.plot_as_box(ax, x_w)) for i in range(num_cameras): pose = x[it].get_pose(i) # Normalised in 3d. xn_3d = np.vstack((measurement[i].xn, np.ones((1, measurement[i].num)))) artists.extend(vg.plot_pose(ax, pose.to_tuple(), scale=1)) artists.extend(vg.plot_camera_image_plane(ax, measurement[i].camera.K(), pose.to_tuple())) artists.extend(vg.utils.plot_as_box(ax, pose * xn_3d, alpha=0.4)) artists.extend( vg.utils.plot_as_box(ax, pose * measurement[i].camera.project_to_normalised_3d(pose.inverse() * x_w))) plt.draw() while True: if plt.waitforbuttonpress(): break plt.close()

32.715827

119

0.610445

1,405

9,095

3.763701

0.079004

0.078669

0.090772

0.059002

0.957262

0.953101

0.93646

0.91528

0.908283

0

0.015964

0.249258

9,095

277

120

32.833935

0.758494

0.115338

0

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0

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0

1

0.022222

false

0

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null

0

1

0

1

0

null

0

7

57079c9d0f7b8a1104c06e1bea819ee6a36ea3cf

2,912

py

Python

email_campaigns/migrations/0003_auto_20200814_1417.py

diffractive/newstream

cf1a1f230e18d01c63b50ab9d360aa44ac5a486f

[ "MIT" ]

1

2020-05-03T12:33:42.000Z

email_campaigns/migrations/0003_auto_20200814_1417.py

diffractive/newstream

cf1a1f230e18d01c63b50ab9d360aa44ac5a486f

[ "MIT" ]

14

2020-07-06T20:05:57.000Z

2022-03-12T00:39:11.000Z

email_campaigns/migrations/0003_auto_20200814_1417.py

diffractive/newstream

cf1a1f230e18d01c63b50ab9d360aa44ac5a486f

[ "MIT" ]

null

# Generated by Django 3.0.8 on 2020-08-14 14:17 from django.db import migrations, models import wagtail.core.fields class Migration(migrations.Migration): dependencies = [ ('email_campaigns', '0002_auto_20200814_1417'), ] operations = [ migrations.AddField( model_name='emailtemplate', name='html_body_en', field=wagtail.core.fields.RichTextField(blank=True, null=True), ), migrations.AddField( model_name='emailtemplate', name='html_body_id_id', field=wagtail.core.fields.RichTextField(blank=True, null=True), ), migrations.AddField( model_name='emailtemplate', name='html_body_ms', field=wagtail.core.fields.RichTextField(blank=True, null=True), ), migrations.AddField( model_name='emailtemplate', name='html_body_tl', field=wagtail.core.fields.RichTextField(blank=True, null=True), ), migrations.AddField( model_name='emailtemplate', name='html_body_zh_hant', field=wagtail.core.fields.RichTextField(blank=True, null=True), ), migrations.AddField( model_name='emailtemplate', name='plain_text_en', field=models.TextField(null=True), ), migrations.AddField( model_name='emailtemplate', name='plain_text_id_id', field=models.TextField(null=True), ), migrations.AddField( model_name='emailtemplate', name='plain_text_ms', field=models.TextField(null=True), ), migrations.AddField( model_name='emailtemplate', name='plain_text_tl', field=models.TextField(null=True), ), migrations.AddField( model_name='emailtemplate', name='plain_text_zh_hant', field=models.TextField(null=True), ), migrations.AddField( model_name='emailtemplate', name='subject_en', field=models.CharField(max_length=255, null=True), ), migrations.AddField( model_name='emailtemplate', name='subject_id_id', field=models.CharField(max_length=255, null=True), ), migrations.AddField( model_name='emailtemplate', name='subject_ms', field=models.CharField(max_length=255, null=True), ), migrations.AddField( model_name='emailtemplate', name='subject_tl', field=models.CharField(max_length=255, null=True), ), migrations.AddField( model_name='emailtemplate', name='subject_zh_hant', field=models.CharField(max_length=255, null=True), ), ]

32.355556

75

0.571085

280

2,912

5.742857

0.192857

0.16791

0.214552

0.251866

0.858209

0.800995

0.796642

0

0.023104

0.316277

2,912

89

76

32.719101

0.78453

0.015453

0

0.722892

1

0

0.150785

0.008028

0

1

0

false

0

0.024096

0

0.060241

0

null

0

1

0

1

0

null

0

10

570b6359116ba2cabc7c9c5c9556313b0daa91f4

25,092

py

Python

scripts/multi_agent_sarsa.py

fb1n15/maddpg

ce0fd8028c09dc4f13c5c4ab015c9ad980469443

[ "MIT" ]

null

scripts/multi_agent_sarsa.py

fb1n15/maddpg

ce0fd8028c09dc4f13c5c4ab015c9ad980469443

[ "MIT" ]

null

scripts/multi_agent_sarsa.py

fb1n15/maddpg

ce0fd8028c09dc4f13c5c4ab015c9ad980469443

[ "MIT" ]

null

import json import matplotlib.pyplot as plt import numpy as np import seaborn as sns # from tqdm import tqdm from tqdm import tqdm_notebook as tqdm # for use in Jupyter Lab from classes_in_reverse_auction_v1 import FogNodeAgent from classes_in_reverse_auction_v1 import ReverseAuctionMDP from classes_in_reverse_auction_v1 import pd from generate_simulation_data import generate_synthetic_data_edge_cloud # (dataframe) set the maximum number of rows and columns to display to unlimited pd.set_option("display.max_rows", None, "display.max_columns", None) def train_multi_agent_sarsa(avg_resource_capacity, avg_unit_cost, seed=0, total_number_of_steps=500, num_fog_nodes=6, resource_coefficient_original=3, alpha=0.001, beta=0.1, epsilon_tuple=(0.2, 0.1, 0.05), epsilon_steps_tuple=(500, 500, 100), plot_bool=False, num_actions=4, time_length=100, high_value_proportion=0.2, high_value_slackness=0, low_value_slackness=6, valuation_coefficient_ratio=10, resource_ratio=1.2, trained_agents=None, verbose=False, auction_type="second-price"): """run multi-agent sarsa Args: :param auction_type: the type of the reverse reverse_auction :param verbose: whether print the details of the execution :param number_of_runs: number of trials :param total_number_of_steps: steps of RL (allocate how many tasks) :param num_fog_nodes: number of fog nodes :param resource_coefficient_original: a coefficient for computing the resource coefficient :param alpha: step size of weights :param beta: step size of estimated average rewards :param epsilon_tuple: probability of exploration :param epsilon_steps_tuple: number of steps to run for each epsilon :param plot_bool: whether plot the results :param num_actions: number of actions :param time_length: tasks arrive within this time length :param low_value_slackness: deadline slackness of low-value tasks :param resource_ratio: resource demand ratio between high-value and low-value tasks :param valuation_coefficient_ratio: valuation coefficient ratio between high-value and low-value tasks :param high_value_slackness: deadline slackness of high-value tasks :param high_value_proportion: the proportion of high-value tasks """ # record the allocation scheme allocation_scheme = pd.DataFrame( columns=['node_id', 'start_time', 'end_time']) # run the trials result_sarsa_list = [] # a list of the lists of average rewards for each step of sarsa social_welfare_list = [] # a list of total social welfare of each trial if verbose: print(f"seed={seed}") sw_list = [] # a list of social welfare after a new task arrives # initialise some parameters n_steps = total_number_of_steps + 1 np.random.seed(seed) # generate the two types of tasks number_of_tasks = total_number_of_steps V = {} # value function: state -> value pi = {} # policy: (state+price) -> probability actions = list( range(num_actions)) # bid [reject, 1/3, 2/3, 1] the value of the task # generate a seqence of analytics tasks # compute the resource coefficient resource_coefficient = ( resource_coefficient_original * number_of_tasks / time_length) # generate the synthetic data for simulations df_tasks, df_nodes, n_time, n_tasks, num_fog_nodes = \ generate_synthetic_data_edge_cloud(avg_resource_capacity, avg_unit_cost, n_tasks=total_number_of_steps, n_time=time_length, seed=seed, n_nodes=num_fog_nodes, p_high_value_tasks=high_value_proportion, high_value_slackness_lower_limit=high_value_slackness, high_value_slackness_upper_limit=high_value_slackness + 2, low_value_slackness_lower_limit=low_value_slackness, low_value_slackness_upper_limit=low_value_slackness + 2, resource_demand_high=resource_ratio, vc_ratio=valuation_coefficient_ratio, k_resource=resource_coefficient) if verbose: print("resource coefficient: ", resource_coefficient) print(f"low value slackness = {low_value_slackness}") print(f"high value slackness = {high_value_slackness}") print('df_tasks:') print(df_tasks.head(10)) print('df_nodes:') print(df_nodes.head()) average_reward_sarsa_list = [] agents_list = [] # with tqdm(total=100) as pbar: mdp = ReverseAuctionMDP(df_tasks, df_nodes, num_nodes=num_fog_nodes, num_actions=num_actions) # several fog nodes # generate several agents representing several fog nodes if trained_agents is not None: for i in range(mdp.num_fog_nodes): agent = FogNodeAgent(n_steps=n_steps - 1, alpha=alpha, beta=beta, fog_index=i, df_tasks=df_tasks, df_nodes=df_nodes, num_actions=num_actions, epsilon=epsilon_tuple[0], mdp=mdp, trained_agent=trained_agents[i]) agents_list.append(agent) else: for i in range(mdp.num_fog_nodes): agent = FogNodeAgent(n_steps=n_steps - 1, alpha=alpha, beta=beta, fog_index=i, df_tasks=df_tasks, df_nodes=df_nodes, num_actions=num_actions, epsilon=epsilon_tuple[0], mdp=mdp) agents_list.append(agent) # actions taken by each node actions = {i: [] for i in range(mdp.num_fog_nodes)} # the reverse reverse_auction for k in tqdm(range(total_number_of_steps)): # fog nodes decide their bidding price, and allocation scheme for the current task if verbose: print() print(f"step: {k}") # epsilon decreases as the number of steps increases if k < epsilon_steps_tuple[0]: epsilon = epsilon_tuple[0] elif k < epsilon_steps_tuple[0] + epsilon_steps_tuple[1]: epsilon = epsilon_tuple[1] elif k < (epsilon_steps_tuple[0] + epsilon_steps_tuple[1] + epsilon_steps_tuple[2]): epsilon = epsilon_tuple[2] else: epsilon = 0 if verbose: print(f'epsilon = {epsilon}') # change the epsilon of all agents for i in range(mdp.num_fog_nodes): agents_list[i].epsilon = epsilon bids_list = [] # bidding price for one time step max_usage_time_list = [] # maximum usage time a fog node can offer start_time_list = [] # start time according to the planned allocation relative_start_time_list = [] # relative start time according to the current task for i in range(mdp.num_fog_nodes): (bidding_price, max_usage_time, relative_start_time, action) = \ agents_list[i].differential_sarsa_decide_action(verbose=verbose) # tranfer relative start_time to absolute start_time start_time = int( df_tasks.loc[k, 'arrive_time'] + relative_start_time + 1) bids_list.append(bidding_price) max_usage_time_list.append(max_usage_time) start_time_list.append(start_time) relative_start_time_list.append(relative_start_time) actions[i].append(action) # find the winner (winner_index, winner_num_time, winner_utility, max_utility) = \ mdp.step(bids_list, max_usage_time_list, start_time_list, verbose=verbose, auction_type=auction_type) sw_list.append( mdp.social_welfare) # a list of social welfare after a new task arrives # modify the allocation scheme if winner_num_time is not None and winner_num_time > 0: allocation_scheme.loc[k] = [winner_index, start_time_list[winner_index], start_time_list[winner_index] + max_usage_time_list[ winner_index] - 1] else: # the task is rejected allocation_scheme.loc[k] = [None, None, None] if verbose: print() print(f"nodes' bids = {bids_list}") print(f"nodes' usage times = {max_usage_time_list}") print(f"nodes' start times = {start_time_list}") print(f"winner's index = {winner_index}") print(f"number of usage time = {winner_num_time}") print(f"winner's utility = {winner_utility}") print(f"user's utility = {max_utility}") # print(f"social_welfare_list={sw_list}") if k < total_number_of_steps - 1: # update sarsa weights for i in range(mdp.num_fog_nodes): if verbose: print(f"updating weights of node{i}:") if i == winner_index: # if fog node i wins this task agents_list[i].differential_sarsa_update_weights(1, max_usage_time_list[i], relative_start_time_list[i], winner_revenue=winner_utility, verbose=verbose) else: # if fog node i lose the reverse_auction agents_list[i].differential_sarsa_update_weights(0, max_usage_time_list[i], relative_start_time_list[i], winner_revenue=winner_utility, verbose=verbose) else: if verbose: print("This is the last task.") # no need to update weights if verbose: print(f"social welfare = {sw_list[-1]}") social_welfare_list.append(sw_list[-1]) # generate a list of average rewards of recent 100 tasks average_reward_sarsa_list = [] for i in range(total_number_of_steps): if i < 100: average_reward = sw_list[i] / (i + 1) average_reward_sarsa_list.append(average_reward) else: average_reward = (sw_list[i] - sw_list[i - 100]) / 100 average_reward_sarsa_list.append(average_reward) result_sarsa_list.append(average_reward_sarsa_list.copy()) # print(result_sarsa_list) # print the total value of tasks total_value = 0 for i in range(total_number_of_steps): total_value += (df_tasks.loc[i, "valuation_coefficient"] * df_tasks.loc[i, "usage_time"]) if verbose: print(f"total value of tasks = {total_value}") print("df_tasks:") print(df_tasks.head()) print("df_nodes:") print(df_nodes.head()) if plot_bool: # plot the result fig, axes = plt.subplots(1 + 2 * num_fog_nodes, 1, figsize=(12, 30)) fig.suptitle('Figures') # plot the social welfare result_df = None for item in result_sarsa_list: result_sarsa_y = item.copy() x_list = range(len(result_sarsa_y)) auction_df = pd.DataFrame({ 'algorithm': 'reverse reverse_auction', 'steps': x_list, 'average_social_welfare (recent 100 tasks)': result_sarsa_y }) result_df = pd.concat([result_df, auction_df]) # print(result_df) sns.lineplot(ax=axes[0], data=result_df, x="steps", y="average_social_welfare (recent 100 tasks)") # plt.show() # plot the learned average rewards of each node for i in range(num_fog_nodes): avg_reward = agents_list[i].list_avg_reward x_list = range(len(avg_reward)) avg_reward_df = pd.DataFrame({ 'steps': x_list, 'average rewards of node 1': avg_reward }) sns.lineplot(ax=axes[i + 1], data=avg_reward_df, x="steps", y="average rewards of node 1") # plot the actions taken by each node for i in range(num_fog_nodes): actions_of_i = actions[i] x_list = range(len(actions_of_i)) actions_of_i_df = pd.DataFrame({ 'steps': x_list, 'action options': actions_of_i }) sns.lineplot(ax=axes[i + num_fog_nodes + 1], data=actions_of_i_df, x='steps', y='action options') # TODO: plot the actions taken by each node plt.show() else: pass return sw_list, total_value, df_tasks, df_nodes, agents_list, allocation_scheme # execute sarsa (do not update weights) def execute_multi_agent_sarsa(avg_resource_capacity, avg_unit_cost, number_of_runs=50, total_number_of_steps=500, num_fog_nodes=6, resource_coefficient_original=3, plot_bool=False, num_actions=4, time_length=100, high_value_proportion=0.2, high_value_slackness=0, low_value_slackness=6, valuation_coefficient_ratio=10, resource_ratio=1.2, agents_list=None, bool_decay=True, training_seed=0, verbose=False, auction_type="second-price"): """execute multi-agent sarsa Args: agents_list: a list of trained agents :param number_of_runs: number of trials :param total_number_of_steps: steps of RL (allocate how many tasks) :param num_fog_nodes: number of fog nodes :param resource_coefficient_original: a coefficient for computing the resource coefficient :param alpha: step size of weights :param beta: step size of estimated average rewards :param epsilon_tuple: probability of exploration :param epsilon_steps_tuple: number of steps to run for each epsilon except for the last one :param plot_bool: whether plot the results :param num_actions: number of actions :param time_length: tasks arrive within this time length :param low_value_slackness: deadline slackness of low-value tasks :param resource_ratio: resource demand ratio between high-value and low-value tasks :param valuation_coefficient_ratio: valuation coefficient ratio between high-value and low-value tasks :param high_value_slackness: deadline slackness of high-value tasks :param high_value_proportion: the proportion of high-value tasks """ # run the trials result_sarsa_list = [] # a list of the lists of average rewards for each step of sarsa social_welfare_list = [] # a list of total social welfare of each trial # record the allocation scheme allocation_scheme = pd.DataFrame( columns=['node_id', 'start_time', 'end_time']) for j in tqdm(range(number_of_runs)): # run all the trials # for j in tqdm(range(number_of_runs - 1, number_of_runs)): # just run one trial if verbose: print(f"run ID = {j}") sw_list = [] # a list of social welfare after a new task arrives # initialise some parameters n_steps = total_number_of_steps + 1 np.random.seed(j) # generate the two types of tasks number_of_tasks = total_number_of_steps V = {} # value function: state -> value pi = {} # policy: (state+price) -> probability actions = list(range( num_actions)) # bid [reject, 1/3, 2/3, 1] the value of the task # generate a seqence of analytics tasks # compute the resource coefficient resource_coefficient = ( resource_coefficient_original * number_of_tasks / time_length) # generate the synthetic data for simulations df_tasks, df_nodes, n_time, n_tasks, num_fog_nodes = \ generate_synthetic_data_edge_cloud(avg_resource_capacity, avg_unit_cost, n_tasks=total_number_of_steps, n_time=time_length, seed=j, n_nodes=num_fog_nodes, p_high_value_tasks=high_value_proportion, high_value_slackness_lower_limit=high_value_slackness, high_value_slackness_upper_limit=high_value_slackness + 2, low_value_slackness_lower_limit=low_value_slackness, low_value_slackness_upper_limit=low_value_slackness + 2, resource_demand_high=resource_ratio, vc_ratio=valuation_coefficient_ratio, k_resource=resource_coefficient) if verbose: print("resource coefficient: ", resource_coefficient) print(f"low value slackness = {low_value_slackness}") print(f"high value slackness = {high_value_slackness}") print("df_tasks:") print(df_tasks.head()) print("df_nodes:") print(df_nodes.head()) # print the total value of tasks total_value = 0 for i in range(total_number_of_steps): total_value += (df_tasks.loc[i, "valuation_coefficient"] * df_tasks.loc[i, "usage_time"]) if verbose: print(f"total_number_of_steps={total_number_of_steps}") print(f"total value of tasks = {total_value}") # with tqdm(total=100) as pbar: mdp = ReverseAuctionMDP(df_tasks, df_nodes, num_nodes=num_fog_nodes, num_actions=num_actions) # several fog nodes # reset the states of the fog node agents for i in range(mdp.num_fog_nodes): agents_list[i].reset_state(df_tasks) # actions taken by each node actions = {i: [] for i in range(mdp.num_fog_nodes)} # the reverse reverse_auction for k in tqdm(range(total_number_of_steps)): # fog nodes decide their bidding price, and allocation scheme for the current task if verbose: print() print(f"step: {k}") bids_list = [] # bidding price for one time step max_usage_time_list = [] # maximum usage time a fog node can offer start_time_list = [] # start time according to the planned allocation relative_start_time_list = [] # relative start time according to the current task for i in range(mdp.num_fog_nodes): (bidding_price, max_usage_time, relative_start_time, action) = \ agents_list[i].differential_sarsa_decide_action( verbose=verbose) # tranfer relative start_time to absolute start_time start_time = int( df_tasks.loc[k, 'arrive_time'] + relative_start_time + 1) bids_list.append(bidding_price) max_usage_time_list.append(max_usage_time) start_time_list.append(start_time) relative_start_time_list.append(relative_start_time) actions[i].append(action) # find the winner (winner_index, winner_num_time, winner_utility, max_utility) = \ mdp.step(bids_list, max_usage_time_list, start_time_list, verbose=verbose, auction_type=auction_type) if verbose: print() print(f"nodes' bids = {bids_list}") print(f"nodes' usage times = {max_usage_time_list}") print(f"nodes' start times = {start_time_list}") print(f"winner's index = {winner_index}") print(f"number of usage time = {winner_num_time}") print(f"winner's utility = {winner_utility}") print(f"user's utility = {max_utility}") # a list of social welfare after a new task arrives sw_list.append(mdp.social_welfare) # modify the overall allocation scheme if winner_num_time is not None and winner_num_time > 0: allocation_scheme.loc[k] = [winner_index, start_time_list[winner_index], start_time_list[winner_index] + winner_num_time - 1] else: # the task is rejected allocation_scheme.loc[k] = [None, None, None] # Do not update weights during execution if k < total_number_of_steps - 1: # update sarsa weights for i in range(mdp.num_fog_nodes): if i == winner_index: # if fog node i wins this task agents_list[i].differential_sarsa_update_weights(1, max_usage_time_list[i], relative_start_time_list[i], winner_revenue=winner_utility, bool_update_weights=False, verbose=verbose) else: # if fog node i lose the reverse_auction agents_list[i].differential_sarsa_update_weights(0, max_usage_time_list[i], relative_start_time_list[i], winner_revenue=winner_utility, bool_update_weights=False, verbose=verbose) else: if verbose: print("This is the last task.") # no need to update weights # enable_print() if verbose: print(f"social welfare = {sw_list[-1]}") social_welfare_list.append(sw_list[-1]) # generate a list of average rewards average_reward_sarsa_list = [] for i in range(total_number_of_steps): average_reward = sw_list[i] / (i + 1) average_reward_sarsa_list.append(average_reward) result_sarsa_list.append(average_reward_sarsa_list.copy()) # print(result_sarsa_list) if plot_bool: # plot the result fig, axes = plt.subplots(1 + num_fog_nodes, 1, figsize=(12, 30)) fig.suptitle('Figures') # plot the social welfare result_df = None for item in result_sarsa_list: result_sarsa_y = item.copy() x_list = range(len(result_sarsa_y)) auction_df = pd.DataFrame({ 'algorithm': 'reverse reverse_auction', 'steps': x_list, 'average_social_welfare': result_sarsa_y }) result_df = pd.concat([result_df, auction_df]) # print(result_df) sns.lineplot(ax=axes[0], data=result_df, x="steps", y="average_social_welfare") # plot the actions taken by each node for i in range(num_fog_nodes): actions_of_i = actions[i] x_list = range(len(actions_of_i)) actions_of_i_df = pd.DataFrame({ 'steps': x_list, 'action options': actions_of_i }) sns.lineplot(ax=axes[i + 1], data=actions_of_i_df, x='steps', y='action options') plt.show() else: # save the result for jupyter notebook if bool_decay: with open( f'../simulation_results/auction_v1_{j + 1}trials' f'_rc={resource_coefficient_original}_seed={training_seed}_decay.txt', 'w') as f: f.write(json.dumps(social_welfare_list)) else: with open( f'../simulation_results/auction_v1_{j + 1}trials' f'_rc={resource_coefficient_original}_seed={training_seed}.txt', 'w') as f: f.write(json.dumps(social_welfare_list)) return sw_list, total_value, df_tasks, df_nodes, agents_list, allocation_scheme if __name__ == "__main__": # code for running simulations number_of_steps = 10000 time_length = number_of_steps / 4 num_trials = 50 num_actions = 4 epsilon_steps_tuple = (3000, 1000) # for resource_coefficient in [2]: # for alpha in [0.02]: # for beta in [0.01]: # for epsilons_tuple in [(0.2, 0.1, 0.05)]: # # run multiple times and save the results # train_multi_agent_sarsa(alpha=alpha, beta=beta, epsilon_tuple=epsilons_tuple, # num_actions=num_actions, n_time=n_time, # epsilon_steps_tuple=epsilon_steps_tuple, # total_number_of_steps=number_of_steps, num_fog_nodes=6, # resource_coefficient_original=resource_coefficient, # number_of_runs=num_trials, plot_bool=False) # run once and plot the average rewards epsilons_tuple = (0.2, 0.1, 0.05) epsilon_steps_tuple = (3000, 1000) resource_coefficient_original = 3 train_multi_agent_sarsa(alpha=0.02, beta=0.01, epsilon_tuple=epsilons_tuple, time_length=time_length, epsilon_steps_tuple=epsilon_steps_tuple, num_actions=num_actions, total_number_of_steps=number_of_steps, num_fog_nodes=6, resource_coefficient_original=resource_coefficient_original, number_of_runs=1, plot_bool=True)

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0.866457

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0.825409

0.812752

0.805295

0.797086

0

0.012586

0.303364

25,092

562

100

44.647687

0.823627

0.256177

0

0.73028

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0.027149

0

0.001779

0

1

0.005089

false

0.002545

0.022901

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1

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0

7

571296272203dc0c0b7c954a4d9cab1957485d71

35,044

py

Python

convcap.py

Zjut-MultimediaPlus/BCIC

22b93b37d4b272db3c09dbd767bb1b89e394569f

[ "Apache-2.0" ]

null

convcap.py

Zjut-MultimediaPlus/BCIC

22b93b37d4b272db3c09dbd767bb1b89e394569f

[ "Apache-2.0" ]

null

convcap.py

Zjut-MultimediaPlus/BCIC

22b93b37d4b272db3c09dbd767bb1b89e394569f

[ "Apache-2.0" ]

null

import sys import math import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import torch.optim as optim from torch.autograd import Variable #import misc.utils as utils import os from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence def Conv1d(in_channels, out_channels, kernel_size, padding, dropout=0): m = nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding) std = math.sqrt((4 * (1.0 - dropout)) / (kernel_size * in_channels)) m.weight.data.normal_(mean=0, std=std) m.bias.data.zero_() return nn.utils.weight_norm(m) def Embedding(num_embeddings, embedding_dim, padding_idx): m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) m.weight.data.normal_(0, 0.1) return m def Linear(in_features, out_features, dropout=0.): m = nn.Linear(in_features, out_features) m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features)) m.bias.data.zero_() return nn.utils.weight_norm(m) class AttentionLayer(nn.Module): def __init__(self, conv_channels, embed_dim): super(AttentionLayer, self).__init__() self.in_projection = Linear(conv_channels, embed_dim) self.out_projection = Linear(embed_dim, conv_channels) self.bmm = torch.bmm def forward(self, x, wordemb, imgsfeats): residual = x x = (self.in_projection(x) + wordemb) * math.sqrt(0.5) b, c, f_h, f_w = imgsfeats.size() y = imgsfeats.view(b, c, f_h*f_w) x = self.bmm(x, y) sz = x.size() x = F.softmax(x.view(sz[0] * sz[1], sz[2]), dim=1) x = x.view(sz) attn_scores = x y = y.permute(0, 2, 1) x = self.bmm(x, y) s = y.size(1) x = x * (s * math.sqrt(1.0 / s)) x = (self.out_projection(x) + residual) * math.sqrt(0.5) return x, attn_scores def sort_pack_padded_sequence(input, lengths): sorted_lengths, indices = torch.sort(lengths, descending=True) tmp = pack_padded_sequence(input[indices], sorted_lengths, batch_first=True) inv_ix = indices.clone() inv_ix[indices] = torch.arange(0,len(indices)).type_as(inv_ix) return tmp, inv_ix def pad_unsort_packed_sequence(input, inv_ix): tmp, _ = pad_packed_sequence(input, batch_first=True) tmp = tmp[inv_ix] return tmp def pack_wrapper(module, att_feats, att_masks): if att_masks is not None: packed, inv_ix = sort_pack_padded_sequence(att_feats, att_masks.data.long().sum(1)) return pad_unsort_packed_sequence(PackedSequence(module(packed[0]), packed[1]), inv_ix) else: return module(att_feats) class convcap_G(nn.Module): #def __init__(self, num_wordclass, num_layers=1, is_attention=True, nfeats=512, dropout=0.1): def __init__(self, num_wordclass, num_layers=1, is_attention=True, nfeats=512, dropout=0.1): super(convcap_G, self).__init__() self.nimgfeats = 2048 self.is_attention = is_attention self.nfeats = nfeats self.dropout = dropout self.emb_0 = Embedding(num_wordclass, nfeats, padding_idx=0) # Linear(9221, 512) self.emb_1 = Linear(nfeats, nfeats, dropout=dropout) self.imgproj = Linear(self.nimgfeats, self.nfeats, dropout=dropout) self.resproj = Linear(nfeats*2, self.nfeats, dropout=dropout) n_in = 2 * self.nfeats n_out = self.nfeats self.n_layers = num_layers self.convs = nn.ModuleList() self.attention = nn.ModuleList() self.kernel_size = 5 self.pad = self.kernel_size - 1 for i in range(self.n_layers): self.convs.append(Conv1d(n_in, 2*n_out, self.kernel_size, self.pad, dropout)) if(self.is_attention): self.attention.append(AttentionLayer(n_out, nfeats)) n_in = n_out self.classifier_0 = Linear(self.nfeats, (nfeats // 2)) self.classifier_1 = Linear((nfeats // 2), num_wordclass, dropout=dropout) ''' self.input_encoding_size = 512 self.rnn_size = 512 self.drop_prob_lm = 0.5 self.fc_feat_size = 2048 self.att_feat_size = 2048 self.att_hid_size = 512 self.seq_per_img = 5 self.index_eval = 0 self.use_rela = False self.vocab_size = 14964 self.use_bn = False self.fc_embed = nn.Sequential(nn.Linear(self.fc_feat_size, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.att_embed = nn.Sequential(*( ((nn.BatchNorm1d(self.att_feat_size),) if self.use_bn else ())+ (nn.Linear(self.att_feat_size, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm))+ ((nn.BatchNorm1d(self.rnn_size),) if self.use_bn==2 else ()))) self.embed = nn.Sequential(nn.Embedding(self.vocab_size + 1, self.input_encoding_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.embed2vis = nn.Sequential(nn.Linear(self.input_encoding_size, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_sbj_rela_fc = nn.Sequential(nn.Linear(self.rnn_size*3, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_obj_rela_fc = nn.Sequential(nn.Linear(self.rnn_size*3, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_rela_fc = nn.Sequential(nn.Linear(self.rnn_size*3, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_attr_fc = nn.Sequential(nn.Linear(self.rnn_size*2, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_ctx2att = nn.Linear(self.rnn_size, self.att_hid_size) ## nn.Linear(512, 512) ''' def clip_att(self, att_feats, att_masks): # Clip the length of att_masks and att_feats to the maximum length if att_masks is not None: max_len = att_masks.data.long().sum(1).max() att_feats = att_feats[:, :max_len].contiguous() att_masks = att_masks[:, :max_len].contiguous() return att_feats, att_masks def _prepare_feature(self, fc_feats, att_feats, att_masks): # embed fc and att feats fc_feats = self.fc_embed(fc_feats) att_feats = pack_wrapper(self.att_embed, att_feats, att_masks) return fc_feats, att_feats def prepare_rela_feats(self, rela_data): """ Change relationship index (one-hot) to relationship features, or change relationship probability to relationship features. :param rela_matrix: :param rela_masks: :return: rela_features, [N_img*5, N_rela_max, rnn_size] """ rela_matrix = rela_data['rela_matrix'] rela_masks = rela_data['rela_masks'] rela_feats_size = rela_matrix.size() N_att = rela_feats_size[0] if self.index_eval == 1: seq_per_img = 1 else: seq_per_img = self.seq_per_img N_img = N_att/seq_per_img rela_feats = torch.zeros([rela_feats_size[0], rela_feats_size[1], self.rnn_size]).cuda() for img_id in range(int(N_img)): N_rela = torch.sum(rela_masks[img_id * seq_per_img, :]) N_rela = int(N_rela) if N_rela>0: rela_index = rela_matrix[img_id*seq_per_img,:N_rela,2].cuda().long() rela_feats_temp = self.embed(rela_index) rela_feats_temp = self.embed2vis(rela_feats_temp) rela_feats[img_id*seq_per_img:(img_id+1)*seq_per_img,:N_rela,:] = rela_feats_temp rela_data['rela_feats'] = rela_feats return rela_data def rela_graph_gfc(self, rela_data): """ :param att_feats: roi features of each bounding box, [N_img*5, N_att_max, rnn_size] :param rela_feats: the embeddings of relationship, [N_img*5, N_rela_max, rnn_size] :param rela_matrix: relationship matrix, [N_img*5, N_rela_max, 3], N_img is the batch size, N_rela_max is the maximum number of relationship in rela_matrix. :param rela_masks: relationship masks, [N_img*5, N_rela_max]. For each row, the sum of that row is the total number of realtionship. :param att_masks: attention masks, [N_img*5, N_att_max]. For each row, the sum of that row is the total number of roi poolings. :param attr_matrix: attribute matrix,[N_img*5, N_attr_max, N_attr_each_max] N_img is the batch size, N_attr_max is the maximum number of attributes of one mini-batch, N_attr_each_max is the maximum number of attributes of each objects in that mini-batch :param attr_masks: attribute masks, [N_img*5, N_attr_max, N_attr_each_max] the sum of attr_masks[img_id*5,:,0] is the number of objects which own attributes, the sum of attr_masks[img_id*5, obj_id, :] is the number of attribute that object has :return: att_feats_new: new roi features rela_feats_new: new relationship embeddings attr_feats_new: new attribute features """ att_feats = rela_data['att_feats'] att_masks = rela_data['att_masks'] rela_matrix = rela_data['rela_matrix'] rela_feats = rela_data['rela_feats'] rela_masks = rela_data['rela_masks'] attr_matrix = rela_data['attr_matrix'] attr_masks = rela_data['attr_masks'] att_feats_size = att_feats.size() attr_masks_size = attr_masks.size() N_att = att_feats_size[0] if self.index_eval == 1: seq_per_img = 1 else: seq_per_img = self.seq_per_img N_img = N_att / seq_per_img att_feats_new = att_feats.clone() rela_feats_new = rela_feats.clone() attr_feats_new = torch.zeros([attr_masks_size[0], attr_masks_size[1], self.rnn_size]).cuda() for img_id in range(int(N_img)): N_rela = torch.sum(rela_masks[img_id * seq_per_img, :]) #N_box = torch.sum(att_masks[img_id * seq_per_img, :]) N_rela = int(N_rela) #N_box = int(N_box) #box_num = np.ones([N_box,]) rela_num = np.ones([N_rela,]) for i in range(N_rela): sub_id = rela_matrix[img_id * seq_per_img, i, 0] sub_id = int(sub_id) #box_num[sub_id] += 1.0 obj_id = rela_matrix[img_id * seq_per_img, i, 1] obj_id = int(obj_id) #box_num[obj_id] += 1.0 rela_id = i rela_num[rela_id] += 1.0 sub_feat_use = att_feats[img_id * seq_per_img, sub_id, :] obj_feat_use = att_feats[img_id * seq_per_img, obj_id, :] rela_feat_use = rela_feats[img_id * seq_per_img, rela_id, :] att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, sub_id, :] += \ self.rela_sbj_rela_fc(torch.cat((sub_feat_use, obj_feat_use, rela_feat_use))) att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, obj_id, :] += \ self.rela_obj_rela_fc(torch.cat((sub_feat_use, obj_feat_use, rela_feat_use))) rela_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, rela_id, :] += \ self.rela_rela_fc(torch.cat((sub_feat_use, obj_feat_use, rela_feat_use))) N_obj_attr = torch.sum(attr_masks[img_id * seq_per_img, :, 0]) N_obj_attr = int(N_obj_attr) for i in range(N_obj_attr): attr_obj_id = int(attr_matrix[img_id * seq_per_img, i, 0]) obj_feat_use = att_feats[img_id * seq_per_img, int(attr_obj_id), :] N_attr_each = torch.sum(attr_masks[img_id * seq_per_img, i, :]) for j in range(N_attr_each-1): attr_index = attr_matrix[img_id * seq_per_img, i, j+1].cuda().long() attr_feat_use = self.embed(attr_index) attr_feat_use = self.embed2vis(attr_feat_use) attr_feats_new[img_id * seq_per_img:(img_id+1) * seq_per_img, i, :] += \ self.rela_attr_fc( torch.cat((attr_feat_use, obj_feat_use)) ) attr_feats_new[img_id * seq_per_img:(img_id+1) * seq_per_img, i, :] = \ attr_feats_new[img_id * seq_per_img:(img_id+1) * seq_per_img, i, :]/(float(N_attr_each)-1) # for i in range(N_box): # att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i] = \ # att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i]/box_num[i] for i in range(N_rela): rela_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i, :] = \ rela_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i, :]/rela_num[i] rela_data['att_feats'] = att_feats_new rela_data['rela_feats'] = rela_feats_new rela_data['attr_feats'] = attr_feats_new return rela_data def merge_rela_att(self, rela_data): """ merge attention features (roi features) and relationship features together :param att_feats: [N_att, N_att_max, rnn_size] :param att_masks: [N_att, N_att_max] :param rela_feats: [N_att, N_rela_max, rnn_size] :param rela_masks: [N_att, N_rela_max] :return: att_feats_new: [N_att, N_att_new_max, rnn_size] att_masks_new: [N_att, N_att_new_max] """ att_feats = rela_data['att_feats'] att_masks = rela_data['att_masks'] rela_feats = rela_data['rela_feats'] rela_masks = rela_data['rela_masks'] attr_feats = rela_data['attr_feats'] attr_masks = rela_data['attr_masks'] att_feats_size = att_feats.size() N_att = att_feats_size[0] if self.index_eval == 1: seq_per_img = 1 else: seq_per_img = self.seq_per_img N_img = N_att/seq_per_img N_att_new_max = -1 for img_id in range(int(N_img)): N_att_new_max = \ max(N_att_new_max,torch.sum(rela_masks[img_id * seq_per_img, :]) + torch.sum(att_masks[img_id * seq_per_img, :]) + torch.sum(attr_masks[img_id * seq_per_img,:,0])) att_masks_new = torch.zeros([N_att, int(N_att_new_max)]).cuda() att_feats_new = torch.zeros([N_att, int(N_att_new_max), self.rnn_size]).cuda() for img_id in range(int(N_img)): N_rela = int(torch.sum(rela_masks[img_id * seq_per_img, :])) N_box = int(torch.sum(att_masks[img_id * seq_per_img, :])) N_attr = int(torch.sum(attr_masks[img_id * seq_per_img,:,0])) att_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_box, :] = \ att_feats[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_box, :] if N_rela > 0: att_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box:N_box + N_rela, :] = \ rela_feats[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_rela, :] if N_attr > 0: att_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box + N_rela: N_box + N_rela + N_attr, :] = \ attr_feats[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_attr, :] att_masks_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_box] = 1 if N_rela > 0: att_masks_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box:N_box + N_rela] = 1 if N_attr > 0: att_masks_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box + N_rela:N_box + N_rela + N_attr] = 1 rela_data['att_feats_new'] = att_feats_new rela_data['att_masks_new'] = att_masks_new return rela_data #def forward(self, fc_feats, att_feats, att_masks, rela_data, use_rela, imgsfeats, imgsfc7, wordclass): def forward(self, imgsfeats, imgsfc7, wordclass, rela_data): # caption word -> (100, 512, 15) attn_buffer = None wordemb = self.emb_0(wordclass) ## Embedding(9221, 512) -> (100, 15, 512) wordemb = self.emb_1(wordemb) ## Linear(512, 512) -> (100, 15, 512) x = wordemb.transpose(2, 1) ## (100, 15, 512) -> (100, 512, 15) batchsize, wordembdim, maxtokens = x.size() y = F.relu(self.imgproj(imgsfc7)) y = y.unsqueeze(2).expand(batchsize, self.nfeats, maxtokens) x = torch.cat([x, y], 1) for i, conv in enumerate(self.convs): if(i == 0): x = x.transpose(2, 1) residual = self.resproj(x) residual = residual.transpose(2, 1) x = x.transpose(2, 1) else: residual = x x = F.dropout(x, p=self.dropout, training=self.training) x = conv(x) x = x[:,:,:-self.pad] x = F.glu(x, dim=1) if (self.is_attention): attn = self.attention[i] x = x.transpose(2, 1) x, attn_buffer = attn(x, wordemb, imgsfeats) x = x.transpose(2, 1) x = (x+residual)*math.sqrt(.5) x = x.transpose(2, 1) x = self.classifier_0(x) x = F.dropout(x, p=self.dropout, training=self.training) x = self.classifier_1(x) x = x.transpose(2, 1) return x, attn_buffer class convcap_D(nn.Module): # def __init__(self, num_wordclass, num_layers=1, is_attention=True, nfeats=512, dropout=0.1): def __init__(self, num_wordclass, num_layers=1, is_attention=True, nfeats=512, dropout=0.1): super(convcap_D, self).__init__() self.nimgfeats = 2048 self.is_attention = is_attention self.nfeats = nfeats self.dropout = dropout self.emb_0 = Embedding(num_wordclass, nfeats, padding_idx=0) # Linear(9221, 512) self.emb_1 = Linear(nfeats, nfeats, dropout=dropout) self.imgproj = Linear(self.nimgfeats, self.nfeats, dropout=dropout) self.resproj = Linear(nfeats * 2, self.nfeats, dropout=dropout) n_in = 2 * self.nfeats n_out = self.nfeats self.n_layers = num_layers self.convs = nn.ModuleList() self.attention = nn.ModuleList() self.kernel_size = 5 self.pad = self.kernel_size - 1 for i in range(self.n_layers): self.convs.append(Conv1d(n_in, 2 * n_out, self.kernel_size, self.pad, dropout)) if (self.is_attention): self.attention.append(AttentionLayer(n_out, nfeats)) n_in = n_out self.classifier_0 = Linear(self.nfeats, (nfeats // 2)) self.classifier_1 = Linear((nfeats // 2), num_wordclass, dropout=dropout) ''' self.input_encoding_size = 512 self.rnn_size = 512 self.drop_prob_lm = 0.5 self.fc_feat_size = 2048 self.att_feat_size = 2048 self.att_hid_size = 512 self.seq_per_img = 5 self.index_eval = 0 self.use_rela = False self.vocab_size = 14964 self.use_bn = False self.fc_embed = nn.Sequential(nn.Linear(self.fc_feat_size, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.att_embed = nn.Sequential(*( ((nn.BatchNorm1d(self.att_feat_size),) if self.use_bn else ()) + (nn.Linear(self.att_feat_size, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) + ((nn.BatchNorm1d(self.rnn_size),) if self.use_bn == 2 else ()))) self.embed = nn.Sequential(nn.Embedding(self.vocab_size + 1, self.input_encoding_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.embed2vis = nn.Sequential(nn.Linear(self.input_encoding_size, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_sbj_rela_fc = nn.Sequential(nn.Linear(self.rnn_size * 3, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_obj_rela_fc = nn.Sequential(nn.Linear(self.rnn_size * 3, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_rela_fc = nn.Sequential(nn.Linear(self.rnn_size * 3, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_attr_fc = nn.Sequential(nn.Linear(self.rnn_size * 2, self.rnn_size), nn.ReLU(inplace=True), nn.Dropout(self.drop_prob_lm)) self.rela_ctx2att = nn.Linear(self.rnn_size, self.att_hid_size) ## nn.Linear(512, 512) ''' def clip_att(self, att_feats, att_masks): # Clip the length of att_masks and att_feats to the maximum length if att_masks is not None: max_len = att_masks.data.long().sum(1).max() att_feats = att_feats[:, :max_len].contiguous() att_masks = att_masks[:, :max_len].contiguous() return att_feats, att_masks def _prepare_feature(self, fc_feats, att_feats, att_masks): # embed fc and att feats fc_feats = self.fc_embed(fc_feats) att_feats = pack_wrapper(self.att_embed, att_feats, att_masks) return fc_feats, att_feats def prepare_rela_feats(self, rela_data): """ Change relationship index (one-hot) to relationship features, or change relationship probability to relationship features. :param rela_matrix: :param rela_masks: :return: rela_features, [N_img*5, N_rela_max, rnn_size] """ rela_matrix = rela_data['rela_matrix'] rela_masks = rela_data['rela_masks'] rela_feats_size = rela_matrix.size() N_att = rela_feats_size[0] if self.index_eval == 1: seq_per_img = 1 else: seq_per_img = self.seq_per_img N_img = N_att / seq_per_img rela_feats = torch.zeros([rela_feats_size[0], rela_feats_size[1], self.rnn_size]).cuda() for img_id in range(int(N_img)): N_rela = torch.sum(rela_masks[img_id * seq_per_img, :]) N_rela = int(N_rela) if N_rela > 0: rela_index = rela_matrix[img_id * seq_per_img, :N_rela, 2].cuda().long() rela_feats_temp = self.embed(rela_index) rela_feats_temp = self.embed2vis(rela_feats_temp) rela_feats[img_id * seq_per_img:(img_id + 1) * seq_per_img, :N_rela, :] = rela_feats_temp rela_data['rela_feats'] = rela_feats return rela_data def rela_graph_gfc(self, rela_data): """ :param att_feats: roi features of each bounding box, [N_img*5, N_att_max, rnn_size] :param rela_feats: the embeddings of relationship, [N_img*5, N_rela_max, rnn_size] :param rela_matrix: relationship matrix, [N_img*5, N_rela_max, 3], N_img is the batch size, N_rela_max is the maximum number of relationship in rela_matrix. :param rela_masks: relationship masks, [N_img*5, N_rela_max]. For each row, the sum of that row is the total number of realtionship. :param att_masks: attention masks, [N_img*5, N_att_max]. For each row, the sum of that row is the total number of roi poolings. :param attr_matrix: attribute matrix,[N_img*5, N_attr_max, N_attr_each_max] N_img is the batch size, N_attr_max is the maximum number of attributes of one mini-batch, N_attr_each_max is the maximum number of attributes of each objects in that mini-batch :param attr_masks: attribute masks, [N_img*5, N_attr_max, N_attr_each_max] the sum of attr_masks[img_id*5,:,0] is the number of objects which own attributes, the sum of attr_masks[img_id*5, obj_id, :] is the number of attribute that object has :return: att_feats_new: new roi features rela_feats_new: new relationship embeddings attr_feats_new: new attribute features """ att_feats = rela_data['att_feats'] att_masks = rela_data['att_masks'] rela_matrix = rela_data['rela_matrix'] rela_feats = rela_data['rela_feats'] rela_masks = rela_data['rela_masks'] attr_matrix = rela_data['attr_matrix'] attr_masks = rela_data['attr_masks'] att_feats_size = att_feats.size() attr_masks_size = attr_masks.size() N_att = att_feats_size[0] if self.index_eval == 1: seq_per_img = 1 else: seq_per_img = self.seq_per_img N_img = N_att / seq_per_img att_feats_new = att_feats.clone() rela_feats_new = rela_feats.clone() attr_feats_new = torch.zeros([attr_masks_size[0], attr_masks_size[1], self.rnn_size]).cuda() for img_id in range(int(N_img)): N_rela = torch.sum(rela_masks[img_id * seq_per_img, :]) # N_box = torch.sum(att_masks[img_id * seq_per_img, :]) N_rela = int(N_rela) # N_box = int(N_box) # box_num = np.ones([N_box,]) rela_num = np.ones([N_rela, ]) for i in range(N_rela): sub_id = rela_matrix[img_id * seq_per_img, i, 0] sub_id = int(sub_id) # box_num[sub_id] += 1.0 obj_id = rela_matrix[img_id * seq_per_img, i, 1] obj_id = int(obj_id) # box_num[obj_id] += 1.0 rela_id = i rela_num[rela_id] += 1.0 sub_feat_use = att_feats[img_id * seq_per_img, sub_id, :] obj_feat_use = att_feats[img_id * seq_per_img, obj_id, :] rela_feat_use = rela_feats[img_id * seq_per_img, rela_id, :] att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, sub_id, :] += \ self.rela_sbj_rela_fc(torch.cat((sub_feat_use, obj_feat_use, rela_feat_use))) att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, obj_id, :] += \ self.rela_obj_rela_fc(torch.cat((sub_feat_use, obj_feat_use, rela_feat_use))) rela_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, rela_id, :] += \ self.rela_rela_fc(torch.cat((sub_feat_use, obj_feat_use, rela_feat_use))) N_obj_attr = torch.sum(attr_masks[img_id * seq_per_img, :, 0]) N_obj_attr = int(N_obj_attr) for i in range(N_obj_attr): attr_obj_id = int(attr_matrix[img_id * seq_per_img, i, 0]) obj_feat_use = att_feats[img_id * seq_per_img, int(attr_obj_id), :] N_attr_each = torch.sum(attr_masks[img_id * seq_per_img, i, :]) for j in range(N_attr_each - 1): attr_index = attr_matrix[img_id * seq_per_img, i, j + 1].cuda().long() attr_feat_use = self.embed(attr_index) attr_feat_use = self.embed2vis(attr_feat_use) attr_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, i, :] += \ self.rela_attr_fc(torch.cat((attr_feat_use, obj_feat_use))) attr_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, i, :] = \ attr_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, i, :] / (float(N_attr_each) - 1) # for i in range(N_box): # att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i] = \ # att_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i]/box_num[i] for i in range(N_rela): rela_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i, :] = \ rela_feats_new[img_id * seq_per_img: (img_id + 1) * seq_per_img, i, :] / rela_num[i] rela_data['att_feats'] = att_feats_new rela_data['rela_feats'] = rela_feats_new rela_data['attr_feats'] = attr_feats_new return rela_data def merge_rela_att(self, rela_data): """ merge attention features (roi features) and relationship features together :param att_feats: [N_att, N_att_max, rnn_size] :param att_masks: [N_att, N_att_max] :param rela_feats: [N_att, N_rela_max, rnn_size] :param rela_masks: [N_att, N_rela_max] :return: att_feats_new: [N_att, N_att_new_max, rnn_size] att_masks_new: [N_att, N_att_new_max] """ att_feats = rela_data['att_feats'] att_masks = rela_data['att_masks'] rela_feats = rela_data['rela_feats'] rela_masks = rela_data['rela_masks'] attr_feats = rela_data['attr_feats'] attr_masks = rela_data['attr_masks'] att_feats_size = att_feats.size() N_att = att_feats_size[0] if self.index_eval == 1: seq_per_img = 1 else: seq_per_img = self.seq_per_img N_img = N_att / seq_per_img N_att_new_max = -1 for img_id in range(int(N_img)): N_att_new_max = \ max(N_att_new_max, torch.sum(rela_masks[img_id * seq_per_img, :]) + torch.sum(att_masks[img_id * seq_per_img, :]) + torch.sum(attr_masks[img_id * seq_per_img, :, 0])) att_masks_new = torch.zeros([N_att, int(N_att_new_max)]).cuda() att_feats_new = torch.zeros([N_att, int(N_att_new_max), self.rnn_size]).cuda() for img_id in range(int(N_img)): N_rela = int(torch.sum(rela_masks[img_id * seq_per_img, :])) N_box = int(torch.sum(att_masks[img_id * seq_per_img, :])) N_attr = int(torch.sum(attr_masks[img_id * seq_per_img, :, 0])) att_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_box, :] = \ att_feats[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_box, :] if N_rela > 0: att_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box:N_box + N_rela, :] = \ rela_feats[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_rela, :] if N_attr > 0: att_feats_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box + N_rela: N_box + N_rela + N_attr, :] = \ attr_feats[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_attr, :] att_masks_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, 0:N_box] = 1 if N_rela > 0: att_masks_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box:N_box + N_rela] = 1 if N_attr > 0: att_masks_new[img_id * seq_per_img:(img_id + 1) * seq_per_img, N_box + N_rela:N_box + N_rela + N_attr] = 1 rela_data['att_feats_new'] = att_feats_new rela_data['att_masks_new'] = att_masks_new return rela_data # def forward(self, fc_feats, att_feats, att_masks, rela_data, use_rela, imgsfeats, imgsfc7, wordclass): def forward(self, imgsfeats, imgsfc7, wordclass, rela_data): # caption word -> (100, 512, 15) attn_buffer = None wordemb = self.emb_0(wordclass) ## Embedding(9221, 512) -> (100, 15, 512) wordemb = self.emb_1(wordemb) ## Linear(512, 512) -> (100, 15, 512) x = wordemb.transpose(2, 1) ## (100, 15, 512) -> (100, 512, 15) batchsize, wordembdim, maxtokens = x.size() y = F.relu(self.imgproj(imgsfc7)) y = y.unsqueeze(2).expand(batchsize, self.nfeats, maxtokens) x = torch.cat([x, y], 1) #### ''' att_masks = None att_feats, att_masks = self.clip_att(att_feats, att_masks) fc_feats, att_feats = self._prepare_feature(fc_feats, att_feats, att_masks) if use_rela == 1: rela_data['att_feats'] = att_feats rela_data['att_masks'] = att_masks rela_data = self.prepare_rela_feats(rela_data) rela_data = self.rela_graph_gfc(rela_data) rela_data = self.merge_rela_att(rela_data) else: rela_data['att_feats_new'] = fc_feats rela_data['att_masks_new'] = att_masks att_feats_rela = rela_data['att_feats_new'] p_att_feats_rela = att_feats_rela.unsqueeze(2).expand((batchsize, self.nfeats, maxtokens)) #x = torch.cat([x, p_att_feats_rela], 1) #### ''' for i, conv in enumerate(self.convs): if (i == 0): x = x.transpose(2, 1) residual = self.resproj(x) residual = residual.transpose(2, 1) x = x.transpose(2, 1) else: residual = x x = F.dropout(x, p=self.dropout, training=self.training) x = conv(x) x = x[:, :, :-self.pad] x = F.glu(x, dim=1) if (self.is_attention): attn = self.attention[i] x = x.transpose(2, 1) x, attn_buffer = attn(x, wordemb, imgsfeats) x = x.transpose(2, 1) x = (x + residual) * math.sqrt(.5) x = x.transpose(2, 1) x = self.classifier_0(x) x = F.dropout(x, p=self.dropout, training=self.training) x = self.classifier_1(x) x = x.transpose(2, 1) return x, attn_buffer

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5724a3c1a663a7ac9cce0159f09922e0cfe0d20a

52

py

Python

instance/config.py

Tellvinch/updater

3f72ac671c1d69ee5b88cad0d0c5ba6d99cb4e84

[ "MIT" ]

null

instance/config.py

Tellvinch/updater

3f72ac671c1d69ee5b88cad0d0c5ba6d99cb4e84

[ "MIT" ]

null

instance/config.py

Tellvinch/updater

3f72ac671c1d69ee5b88cad0d0c5ba6d99cb4e84

[ "MIT" ]

null

NEWS_API_KEY = '<0ef96b496eb7419e8c763bdf082bea06>'

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py

Python

tests/app/main/views/test_service.py

alphagov/notify-api

16dbafbad69e5bb179ba4b2202a7afa299c88d61

[ "MIT" ]

12

2015-10-06T08:58:28.000Z

2016-08-08T17:51:29.000Z

tests/app/main/views/test_service.py

gds-attic/notify-api

16dbafbad69e5bb179ba4b2202a7afa299c88d61

[ "MIT" ]

1

2015-10-27T12:01:26.000Z

tests/app/main/views/test_service.py

gds-attic/notify-api

16dbafbad69e5bb179ba4b2202a7afa299c88d61

[ "MIT" ]

3

2016-05-31T17:40:15.000Z

2021-04-10T20:03:33.000Z

from flask import json from . import uuid_regex from app.models import User, Token from datetime import datetime from app import db def test_should_be_able_to_deactivate_service(notify_api, notify_db, notify_db_session): response_1 = notify_api.test_client().get( '/user/1234/service/1234', headers={ 'Authorization': 'Bearer 1234' } ) data = json.loads(response_1.get_data()) assert data['service']['active'] response_2 = notify_api.test_client().post( '/service/1234/deactivate', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response_2.get_data()) assert not data['service']['active'] def test_should_be_able_to_activate_service(notify_api, notify_db, notify_db_session): response_1 = notify_api.test_client().post( '/service/1234/deactivate', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response_1.get_data()) assert not data['service']['active'] response_2 = notify_api.test_client().post( '/service/1234/activate', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response_2.get_data()) assert data['service']['active'] def test_should_be_able_to_restrict_service(notify_api, notify_db, notify_db_session): response_1 = notify_api.test_client().get( '/user/1234/service/1234', headers={ 'Authorization': 'Bearer 1234' } ) data = json.loads(response_1.get_data()) assert not data['service']['restricted'] response_2 = notify_api.test_client().post( '/service/1234/restrict', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response_2.get_data()) assert data['service']['restricted'] def test_should_be_able_to_unrestrict_service(notify_api, notify_db, notify_db_session): response_1 = notify_api.test_client().post( '/service/1234/restrict', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response_1.get_data()) assert data['service']['restricted'] response_2 = notify_api.test_client().post( '/service/1234/unrestrict', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response_2.get_data()) assert not data['service']['restricted'] def test_should_be_able_to_get_service_by_id_and_user_id(notify_api, notify_db, notify_db_session): response = notify_api.test_client().get( '/user/1234/service/1234', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response.get_data()) assert response.status_code == 200 assert data['service']['id'] == 1234 assert data['service']['name'] == 'service test' assert data['service']['token']['token'] == '1234' def test_should_be_able_to_get_service_as_platform_admin(notify_api, notify_db, notify_db_session): # Setup a dummy user for tests user = User( id=9999, email_address="test-user@example-2.org", mobile_number="+449999123123", password='password', active=True, created_at=datetime.utcnow(), updated_at=datetime.utcnow(), password_changed_at=datetime.utcnow(), role='platform-admin' ) db.session.add(user) db.session.commit() response = notify_api.test_client().get( '/user/9999/service/1234', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response.get_data()) assert response.status_code == 200 assert data['service']['id'] == 1234 assert data['service']['name'] == 'service test' assert data['service']['token']['token'] == '1234' def test_should_be_able_to_get_all_services_for_a_user(notify_api, notify_db, notify_db_session): response = notify_api.test_client().get( '/user/1234/services', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response.get_data()) assert response.status_code == 200 assert len(data['services']) == 1 assert data['services'][0]['id'] == 1234 assert data['services'][0]['name'] == 'service test' def test_should_be_able_to_get_all_services_as_platform_admin(notify_api, notify_db, notify_db_session): # Setup a dummy user for tests user = User( id=9999, email_address="test-user@example-2.org", mobile_number="+449999123123", password='password', active=True, created_at=datetime.utcnow(), updated_at=datetime.utcnow(), password_changed_at=datetime.utcnow(), role='platform-admin' ) db.session.add(user) db.session.commit() response = notify_api.test_client().get( '/user/9999/services', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response.get_data()) assert response.status_code == 200 assert len(data['services']) == 1 assert data['services'][0]['id'] == 1234 assert data['services'][0]['name'] == 'service test' def test_should_return_empty_list_if_no_services_for_user(notify_api, notify_db, notify_db_session): response = notify_api.test_client().get( '/user/12345/services', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response.get_data()) assert response.status_code == 200 assert len(data['services']) == 0 def test_should_be_a_404_of_non_int_org_id(notify_api, notify_db, notify_db_session): response = notify_api.test_client().get( '/user/not-valid/services', headers={ 'Authorization': 'Bearer 1234' }) assert response.status_code == 404 def test_should_be_able_to_create_a_service(notify_api, notify_db, notify_db_session): response = notify_api.test_client().post( '/service', data=json.dumps( { 'service': { 'userId': 1234, 'name': 'my service' } } ), headers={ 'Authorization': 'Bearer 1234' }, content_type='application/json') data = json.loads(response.get_data()) assert response.status_code == 201 assert 'service' in data assert data['service']['name'] == 'my service' assert data['service']['active'] assert data['service']['restricted'] assert data['service']['limit'] == 100 assert uuid_regex.match(data['service']['token']['token']) def test_should_not_be_able_to_create_service_on_client_token(notify_api, notify_db, notify_db_session): token = Token(token='client', type='client') db.session.add(token) db.session.commit() response = notify_api.test_client().post( '/service', data=json.dumps( { 'service': { 'userId': 1234, 'name': 'my service' } } ), headers={ 'Authorization': 'Bearer client' }, content_type='application/json') assert response.status_code == 403 def test_should_reject_a_service_with_invalid_user(notify_api, notify_db, notify_db_session): response = notify_api.test_client().post( '/service', data=json.dumps( { 'service': { 'userId': 9999, 'name': 'this is ok' } } ), headers={ 'Authorization': 'Bearer 1234' }, content_type='application/json') data = json.loads(response.get_data()) assert response.status_code == 400 assert data['error'] == 'failed to create service - invalid user' def test_should_reject_an_invalid_service(notify_api, notify_db, notify_db_session): response = notify_api.test_client().post( '/service', data=json.dumps( { 'service': { 'name': '1', 'userId': 'not-valid' } } ), headers={ 'Authorization': 'Bearer 1234' }, content_type='application/json') data = json.loads(response.get_data()) assert response.status_code == 400 assert data['error'] == 'Invalid JSON' assert len(data['error_details']) == 2 assert {'key': 'userId', 'message': "'not-valid' is not of type 'integer'"} in data['error_details'] assert {'key': 'name', 'message': "'1' is too short"} in data['error_details'] def test_should_reject_if_no_job_root_element(notify_api, notify_db, notify_db_session): response = notify_api.test_client().post( '/service', data=json.dumps({}), content_type='application/json', headers={ 'Authorization': 'Bearer 1234' } ) data = json.loads(response.get_data()) assert data['error'] == "Invalid JSON; must have service as root element" assert response.status_code == 400 def test_should_be_able_to_get_multiple_services_by_user_id(notify_api, notify_db, notify_db_session): response = notify_api.test_client().post( '/service', data=json.dumps( { 'service': { 'userId': 1234, 'name': 'my service' } } ), headers={ 'Authorization': 'Bearer 1234' }, content_type='application/json') assert response.status_code == 201 response = notify_api.test_client().get( '/user/1234/services', headers={ 'Authorization': 'Bearer 1234' }) data = json.loads(response.get_data()) assert response.status_code == 200 assert len(data['services']) == 2 assert data['services'][0]['name'] == 'my service' assert data['services'][1]['name'] == 'service test' def test_should_be_a_404_if_service_does_not_exist(notify_api, notify_db, notify_db_session): response = notify_api.test_client().get( '/user/1234/service/12345', headers={ 'Authorization': 'Bearer 1234' }) assert response.status_code == 404 def test_should_be_a_404_if_service_id_is_not_an_int(notify_api, notify_db, notify_db_session): response = notify_api.test_client().get( '/user/1234/service/invalid-id', headers={ 'Authorization': 'Bearer 1234' }) assert response.status_code == 404

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7

9396896eea1767e47c0c5c8844e6960a55c9c26f

21,745

py

Python

pusion/evaluation/evaluation_metrics.py

IPVS-AS/pusion

58ef24b602f611192430f6005ecf5305f878f412

[ "MIT" ]

5

2021-07-24T16:05:12.000Z

2022-01-21T15:06:03.000Z

pusion/evaluation/evaluation_metrics.py

IPVS-AS/pusion

58ef24b602f611192430f6005ecf5305f878f412

[ "MIT" ]

null

pusion/evaluation/evaluation_metrics.py

IPVS-AS/pusion

58ef24b602f611192430f6005ecf5305f878f412

[ "MIT" ]

2

2021-07-24T16:05:14.000Z

2022-03-25T21:24:40.000Z

import numpy as np from pusion.util.constants import Problem from sklearn.metrics import * from pusion.auto.detector import determine_problem from pusion.util.transformer import multiclass_assignments_to_labels, multilabel_to_multiclass_assignments def micro_precision(y_true, y_pred): """ Calculate the micro precision, i.e. TP / (TP + FP). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The micro precision. """ return precision_score(y_true, y_pred, average='micro') def micro_recall(y_true, y_pred): """ Calculate the micro recall, i.e. TP / (TP + FN). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The micro recall. """ return recall_score(y_true, y_pred, average='micro') def micro_f1(y_true, y_pred): """ Calculate the micro F1-score, i.e. 2 * (Precision * Recall) / (Precision + Recall). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The micro F1-score. """ return f1_score(y_true, y_pred, average='micro') def micro_f2(y_true, y_pred): """ Calculate the micro F2-score (beta=2). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The micro F2-score. """ return fbeta_score(y_true, y_pred, average='micro', beta=2) def micro_jaccard(y_true, y_pred): """ Calculate the micro Jaccard-score, i.e. TP / (TP + FP + FN). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The micro Jaccard-score. """ return jaccard_score(y_true, y_pred, average='micro') def macro_precision(y_true, y_pred): """ Calculate the macro precision, i.e. TP / (TP + FP). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The macro precision. """ return precision_score(y_true, y_pred, average='macro') def macro_recall(y_true, y_pred): """ Calculate the macro recall, i.e. TP / (TP + FN). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The macro recall. """ return recall_score(y_true, y_pred, average='macro') def macro_f1(y_true, y_pred): """ Calculate the macro F1-score, i.e. 2 * (Precision * Recall) / (Precision + Recall). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The macro F1-score. """ return f1_score(y_true, y_pred, average='macro') def macro_f2(y_true, y_pred): """ Calculate the macro F2-score (beta=2). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The macro F2-score. """ return fbeta_score(y_true, y_pred, average='macro', beta=2) def macro_jaccard(y_true, y_pred): """ Calculate the macro Jaccard-score, i.e. TP / (TP + FP + FN). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: The macro Jaccard-score. """ return jaccard_score(y_true, y_pred, average='macro') def accuracy(y_true, y_pred): """ Calculate the accuracy, i.e. (TP + TN) / (TP + FP + FN + TN). :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: Accuracy. """ return accuracy_score(y_true, y_pred) def balanced_multiclass_accuracy(y_true, y_pred): """ Calculate the balanced accuracy, i.e. (Precision + Recall) / 2. :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: Accuracy. """ if y_true.ndim > 1 or y_pred.ndim > 1: y_true = multiclass_assignments_to_labels(y_true) y_pred = multiclass_assignments_to_labels(y_pred) return balanced_accuracy_score(y_true, y_pred) def mean_multilabel_confusion_matrix(y_true, y_pred): """ Calculate the normalized mean confusion matrix across all classes. :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: `numpy.array` of shape `(n_classes, n_classes)`. Normalized mean confusion matrix. """ cm_sum = np.sum(multilabel_confusion_matrix(y_true, y_pred, ), axis=0) return cm_sum / (len(y_pred) * np.max(cm_sum)) def mean_confidence(y_true, y_pred): """ Calculate the mean confidence for continuous multiclass and multilabel classification outputs. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. True class assignments. :param y_pred: `numpy.array` of shape `(n_samples, n_classes)`. Predicted class assignments. :return: Mean confidence. """ return 1 - np.sum(np.abs(y_true - y_pred)) / (y_true.shape[0] * y_true.shape[1]) def hamming(y_true, y_pred): """ Calculate the average Hamming Loss. :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: Average Hamming Loss. """ return hamming_loss(y_true, y_pred) def log(y_true, y_pred): """ Calculate the Logistic Loss. :param y_true: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. True labels or class assignments. :param y_pred: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Predicted labels or class assignments. :return: Logistic Loss. """ return log_loss(y_true, y_pred) def cohens_kappa(y1, y2, labels): """ Calculate the Cohen's Kappa annotator agreement score according to :footcite:`cohen1960coefficient`. .. footbibliography:: :param y1: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Labels or class assignments. :param y2: `numpy.array` of shape `(n_samples,)` or `(n_samples, n_classes)`. Labels or class assignments. :param labels: `list` of all possible labels. :return: Cohen's Kappa score. """ cm = confusion_matrix(y1, y2, labels=labels) a = np.sum(np.diagonal(cm)) / np.sum(cm) e = 0 for i in range(len(cm)): e += np.sum(cm[i, :]) * np.sum(cm[:, i]) / np.sum(cm) ** 2 if e == 1: return 1.0 # case when y1 and y2 are equivalent in their annotation return (a - e) / (1 - e) def pairwise_cohens_kappa(decision_tensor): """ Calculate the average of pairwise Cohen's Kappa scores over all multiclass decision outputs. E.g., for 3 classifiers `(0,1,2)`, the agreement score is calculated for classifier tuples `(0,1)`, `(0,2)` and `(1,2)`. These scores are then averaged over all 3 classifiers. :param decision_tensor: `numpy.array` of shape `(n_classifiers, n_samples, n_classes)`. Tensor of crisp multiclass decision outputs by different classifiers per sample. :return: Pairwise (averages) Cohen's Kappa score. """ decision_tensor = np.array(decision_tensor) if determine_problem(decision_tensor) == Problem.MULTI_LABEL: decision_tensor = multilabel_to_multiclass_assignments(decision_tensor) n_classifiers = decision_tensor.shape[0] n_classes = decision_tensor.shape[2] indices = np.array(np.triu_indices(n_classifiers, k=1)) sum_kappa = 0.0 for i, j in zip(indices[0], indices[1]): decision_labels = multiclass_assignments_to_labels([decision_tensor[i], decision_tensor[j]]) sum_kappa += cohens_kappa(decision_labels[0], decision_labels[1], labels=np.arange(n_classes)) return sum_kappa / len(indices[0]) def __relations(y1, y2, y_true): """ A helper function for calculating the correctness relations between two classifier outputs. `A` accumulates samples which are correctly classified by both classifiers, `B` accumulates those which are correctly classified by `c_1` but not by `c_2` and so on. """ n_samples = len(y_true) a, b, c, d = 0, 0, 0, 0 for i in range(n_samples): if np.all(y1[i] == y_true[i]) and np.all(y_true[i] == y2[i]): a += 1 # both classifiers are correct. elif np.all(y1[i] == y_true[i]) and np.any(y_true[i] != y2[i]): b += 1 # c1 is correct, c2 is wrong. elif np.any(y1[i] != y_true[i]) and np.all(y_true[i] == y2[i]): c += 1 # c1 is wrong, c2 is correct. elif np.any(y1[i] != y_true[i]) and np.any(y_true[i] != y2[i]): d += 1 # both classifiers are wrong. return a/n_samples, b/n_samples, c/n_samples, d/n_samples def __pairwise_avg_score(decision_tensor, true_assignments, score_func): """ A helper function for calculating pairwise average score statistics. """ decision_tensor = np.array(decision_tensor) indices = np.array(np.triu_indices(decision_tensor.shape[0], k=1)) scores = [] for i, j in zip(indices[0], indices[1]): scores.append(score_func(decision_tensor[i], decision_tensor[j], true_assignments)) return np.mean(scores) def correlation(y1, y2, y_true): """ Calculate the correlation score for decision outputs of two classifiers according to Kuncheva :footcite:`kuncheva2014combining`. .. footbibliography:: :param y1: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the first classifier. :param y2: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the second classifier. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Correlation score. """ a, b, c, d = __relations(y1, y2, y_true) return (a * d - b * c) / np.sqrt((a + b) * (c + d) * (a + c) * (b + d)) def q_statistic(y1, y2, y_true): """ Calculate the Q statistic score for decision outputs of two classifiers according to Yule :footcite:`udny1900association`. .. footbibliography:: :param y1: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the first classifier. :param y2: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the second classifier. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Correlation score. """ a, b, c, d = __relations(y1, y2, y_true) return (a * d - b * c) / (a * d + b * c) def kappa_statistic(y1, y2, y_true): """ Calculate the kappa score for decision outputs of two classifiers according to Kuncheva :footcite:`kuncheva2014combining`. .. footbibliography:: :param y1: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the first classifier. :param y2: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the second classifier. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Kappa score. """ a, b, c, d = __relations(y1, y2, y_true) return (2 * (a * d - b * c))/((a + b)*(b + d) + (a + c)*(c + d)) def disagreement(y1, y2, y_true): """ Calculate the disagreement for decision outputs of two classifiers, i.e. the percentage of samples which are correctly classified by exactly one of the classifiers. :param y1: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the first classifier. :param y2: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the second classifier. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Disagreement score. """ a, b, c, d = __relations(y1, y2, y_true) return b + c def double_fault(y1, y2, y_true): """ Calculate the double fault for decision outputs of two classifiers, i.e. the percentage of samples which are misclassified by both classifiers. :param y1: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the first classifier. :param y2: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the second classifier. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Double fault score. """ a, b, c, d = __relations(y1, y2, y_true) return d def abs_correlation(y1, y2, y_true): """ Calculate the absolute correlation score for decision outputs of two classifiers. :param y1: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the first classifier. :param y2: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the second classifier. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Correlation score. """ a, b, c, d = __relations(y1, y2, y_true) return np.abs((a * d - b * c) / np.sqrt((a + b) * (c + d) * (a + c) * (b + d))) def abs_q_statistic(y1, y2, y_true): """ Calculate the absolute Q statistic score for decision outputs of two classifiers. :param y1: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the first classifier. :param y2: `numpy.array` of shape `(n_samples, n_classes)`. Crisp multiclass decision outputs by the second classifier. :param y_true: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Correlation score. """ a, b, c, d = __relations(y1, y2, y_true) return np.abs((a * d - b * c) / (a * d + b * c)) def pairwise_correlation(decision_tensor, true_assignments): """ Calculate the average of the pairwise absolute correlation scores over all decision outputs. :param decision_tensor: `numpy.array` of shape `(n_classifiers, n_samples, n_classes)`. Tensor of crisp multiclass decision outputs by different classifiers per sample. :param true_assignments: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Pairwise correlation score. """ if determine_problem(decision_tensor) == Problem.MULTI_LABEL: decision_tensor = multilabel_to_multiclass_assignments(decision_tensor) true_assignments = multilabel_to_multiclass_assignments(true_assignments) return __pairwise_avg_score(decision_tensor, true_assignments, abs_correlation) def pairwise_q_statistic(decision_tensor, true_assignments): """ Calculate the average of the pairwise absolute Q-statistic scores over all decision outputs. :param decision_tensor: `numpy.array` of shape `(n_classifiers, n_samples, n_classes)`. Tensor of crisp multiclass decision outputs by different classifiers per sample. :param true_assignments: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Pairwise correlation score. """ if determine_problem(decision_tensor) == Problem.MULTI_LABEL: decision_tensor = multilabel_to_multiclass_assignments(decision_tensor) true_assignments = multilabel_to_multiclass_assignments(true_assignments) return __pairwise_avg_score(decision_tensor, true_assignments, abs_q_statistic) def pairwise_kappa_statistic(decision_tensor, true_assignments): """ Calculate the average of pairwise Kappa scores over all decision outputs. Multilabel class assignments are transformed to equivalent multiclass class assignments. :param decision_tensor: `numpy.array` of shape `(n_classifiers, n_samples, n_classes)`. Tensor of crisp multiclass decision outputs by different classifiers per sample. :param true_assignments: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Pairwise kappa score. """ if determine_problem(decision_tensor) == Problem.MULTI_LABEL: decision_tensor = multilabel_to_multiclass_assignments(decision_tensor) true_assignments = multilabel_to_multiclass_assignments(true_assignments) return __pairwise_avg_score(decision_tensor, true_assignments, kappa_statistic) def pairwise_disagreement(decision_tensor, true_assignments): """ Calculate the average of pairwise disagreement scores over all decision outputs. Multilabel class assignments are transformed to equivalent multiclass class assignments. :param decision_tensor: `numpy.array` of shape `(n_classifiers, n_samples, n_classes)`. Tensor of crisp multiclass decision outputs by different classifiers per sample. :param true_assignments: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Pairwise disagreement score. """ if determine_problem(decision_tensor) == Problem.MULTI_LABEL: decision_tensor = multilabel_to_multiclass_assignments(decision_tensor) true_assignments = multilabel_to_multiclass_assignments(true_assignments) return __pairwise_avg_score(decision_tensor, true_assignments, disagreement) def pairwise_double_fault(decision_tensor, true_assignments): """ Calculate the average of pairwise double fault scores over all decision outputs. Multilabel class assignments are transformed to equivalent multiclass class assignments. :param decision_tensor: `numpy.array` of shape `(n_classifiers, n_samples, n_classes)`. Tensor of crisp multiclass decision outputs by different classifiers per sample. :param true_assignments: `numpy.array` of shape `(n_samples, n_classes)`. Matrix of crisp class assignments which are considered as true. :return: Pairwise double fault score. """ if determine_problem(decision_tensor) == Problem.MULTI_LABEL: decision_tensor = multilabel_to_multiclass_assignments(decision_tensor) true_assignments = multilabel_to_multiclass_assignments(true_assignments) return __pairwise_avg_score(decision_tensor, true_assignments, double_fault) def pairwise_euclidean_distance(decision_tensor): """ Calculate the average of pairwise euclidean distance between decision matrices for the given classifiers. :param decision_tensor: `numpy.array` of shape `(n_classifiers, n_samples, n_classes)`. Tensor of crisp multiclass decision outputs by different classifiers per sample. :return: Pairwise euclidean distance. """ decision_tensor = np.array(decision_tensor) indices = np.array(np.triu_indices(decision_tensor.shape[0], k=1)) scores = [] for i, j in zip(indices[0], indices[1]): scores.append(np.mean(np.linalg.norm(decision_tensor[i] - decision_tensor[j], axis=1))) return np.mean(scores)

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8

9e07f5626df5a117ee98bd8f1e78b098812a60a3

99

py

Python

bitcoin-ctf/mapModel/__init__.py

garcilazor/Software_Supply_Chain_CTF

a0cd91d5e72ded132178c3f5868bf78b677316d5

[ "MIT" ]

null

bitcoin-ctf/mapModel/__init__.py

garcilazor/Software_Supply_Chain_CTF

a0cd91d5e72ded132178c3f5868bf78b677316d5

[ "MIT" ]

21

2021-08-06T01:42:28.000Z

2021-08-08T18:57:40.000Z

bitcoin-ctf/mapModel/__init__.py

garcilazor/Software_Supply_Chain_CTF

a0cd91d5e72ded132178c3f5868bf78b677316d5

[ "MIT" ]

1

2021-09-03T22:24:37.000Z

from .Model import model # instatiate client container class def get_client(): return model()

16.5

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5,454

py

Python

plotter.py

ai-se/TimeLIME

eaf8cd44715bb1f6dcac29f4c0bfb2c93809ac98

[ "MIT" ]

null

plotter.py

ai-se/TimeLIME

eaf8cd44715bb1f6dcac29f4c0bfb2c93809ac98

[ "MIT" ]

null

plotter.py

ai-se/TimeLIME

eaf8cd44715bb1f6dcac29f4c0bfb2c93809ac98

[ "MIT" ]

1

2021-04-28T17:21:30.000Z

import matplotlib.pyplot as plt import numpy as np def plot_rq2(scores,bcs,fnames,planner): N = 4 plt.rcParams.update({'font.size':15}) fig, ax = plt.subplots(figsize=(8,8)) ind = np.arange(N) # the x locations for the groups width = 0.08 # the width of the bars result =[] for m in range(0,int(len(scores))): p25,p50,p75,p100 = 0,0,0,0 r25,r50,r75,r100 = 0,0,0,0 a25,a50,a75,a100 = 0,0,0,0 score = scores[m] bugchange = bcs[m] for i in range(0,int(len(score))): if 0<=score[i]<0.25: p25+=1 if bugchange[i]<0: r25-=bugchange[i] if bugchange[i]>0: a25+=bugchange[i] if 0.25<=score[i]<0.5: p50+=1 if bugchange[i]<0: r50-=bugchange[i] if bugchange[i]>0: a50+=bugchange[i] if 0.5<=score[i]<0.75: p75+=1 if bugchange[i]<0: r75-=bugchange[i] if bugchange[i]>0: a75+=bugchange[i] if 0.75<=score[i]<=1: p100+=1 if bugchange[i]<0: r100-=bugchange[i] if bugchange[i]>0: a100+=bugchange[i] s = p25+p50+p75+p100 result.append([p25/s,p50/s,p75/s,p100/s]) if s!=0 else result.append([p25,p50,p75,p100]) ax.set_ylabel("Ratio of plans over all plans") # ax.set_ylabel("Total amount of bugs reduced") # ax.set_ylabel("Number of bugs added") # ax.set_ylabel("Number of bugs reduced") ax.set_xlabel("Overlap percentage") p0 = ax.bar(ind-width*4, result[0], width, bottom=0,label=fnames[0][0].split('-')[0]) p1 = ax.bar(ind-width*3, result[1], width, bottom=0,label=fnames[1][0].split('-')[0]) p2 = ax.bar(ind-width*2, result[2], width, bottom=0,label=fnames[2][0].split('-')[0]) p3 = ax.bar(ind-width*1, result[3], width, bottom=0,label=fnames[3][0].split('-')[0]) p4 = ax.bar(ind+width*0, result[4], width, bottom=0,label=fnames[4][0].split('-')[0]) p5 = ax.bar(ind+width*1, result[5], width, bottom=0,label=fnames[5][0].split('-')[0]) p6 = ax.bar(ind+width*2, result[6], width, bottom=0,label=fnames[6][0].split('-')[0]) p7 = ax.bar(ind+width*3, result[7], width, bottom=0,label=fnames[7][0].split('-')[0]) ax.set_title(planner) ax.set_xticks(ind) ax.set_xticklabels(('0-25', '25-50', '50-75', '75-100')) ax.autoscale_view() plt.grid(axis='y') plt.savefig("rq2"+planner,dpi=100,bbox_inches = 'tight') return result def plot_rq3(scores,bcs,fnames,planner): N = 4 plt.rcParams.update({'font.size':15}) fig, ax = plt.subplots(figsize=(8,8)) ind = np.arange(N) # the x locations for the groups width = 0.08 # the width of the bars result =[] for m in range(0,int(len(scores))): p25,p50,p75,p100 = 0,0,0,0 r25,r50,r75,r100 = 0,0,0,0 a25,a50,a75,a100 = 0,0,0,0 score = scores[m] bugchange = bcs[m] for i in range(0,int(len(score))): if 0<=score[i]<0.25: p25+=1 if bugchange[i]<0: r25-=bugchange[i] if bugchange[i]>0: a25+=bugchange[i] if 0.25<=score[i]<0.5: p50+=1 if bugchange[i]<0: r50-=bugchange[i] if bugchange[i]>0: a50+=bugchange[i] if 0.5<=score[i]<0.75: p75+=1 if bugchange[i]<0: r75-=bugchange[i] if bugchange[i]>0: a75+=bugchange[i] if 0.75<=score[i]<=1: p100+=1 if bugchange[i]<0: r100-=bugchange[i] if bugchange[i]>0: a100+=bugchange[i] s = p25+p50+p75+p100 rate = [a25,a50,a75,a100] rate = [r25 - a25, r50 - a50, r75 - a75, r100 - a100] result.append(rate) # result.append([p25,p50,p75,p100]) # result.append([p25/s,p50/s,p75/s,p100/s]) if s!=0 else result.append([p25,p50,p75,p100]) # result.append([p25/s,p50/s,p75/s,p100/s]) if s!=0 else result.append([p25,p50,p75,p100]) ax.set_ylabel("Total amount of bugs reduced") ax.set_xlabel("Overlap percentage") p0 = ax.bar(ind-width*4, result[0], width, bottom=0,label=fnames[0][0].split('-')[0]) p1 = ax.bar(ind-width*3, result[1], width, bottom=0,label=fnames[1][0].split('-')[0]) p2 = ax.bar(ind-width*2, result[2], width, bottom=0,label=fnames[2][0].split('-')[0]) p3 = ax.bar(ind-width*1, result[3], width, bottom=0,label=fnames[3][0].split('-')[0]) p4 = ax.bar(ind+width*0, result[4], width, bottom=0,label=fnames[4][0].split('-')[0]) p5 = ax.bar(ind+width*1, result[5], width, bottom=0,label=fnames[5][0].split('-')[0]) p6 = ax.bar(ind+width*2, result[6], width, bottom=0,label=fnames[6][0].split('-')[0]) p7 = ax.bar(ind+width*3, result[7], width, bottom=0,label=fnames[7][0].split('-')[0]) ax.set_title(planner) ax.set_xticks(ind) ax.set_xticklabels(('0-25', '25-50', '50-75', '75-100')) ax.autoscale_view() plt.grid(axis='y') plt.savefig("rq3"+planner,dpi=100,bbox_inches = 'tight') return result

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9e12d3af2038cc29768637941c01302825190558

35,764

py

Python

obp/ope/estimators_tuning.py

han20192019/newRL

53598edab284b4364d127ec5662137de3f9c1206

[ "Apache-2.0" ]

387

2020-07-19T14:56:36.000Z

2022-03-29T15:25:21.000Z

obp/ope/estimators_tuning.py

han20192019/newRL

53598edab284b4364d127ec5662137de3f9c1206

[ "Apache-2.0" ]

89

2020-10-04T17:04:42.000Z

2022-03-27T10:43:15.000Z

obp/ope/estimators_tuning.py

han20192019/newRL

53598edab284b4364d127ec5662137de3f9c1206

[ "Apache-2.0" ]

53

2020-08-18T09:52:22.000Z

2022-03-30T23:16:13.000Z

# Copyright (c) Yuta Saito, Yusuke Narita, and ZOZO Technologies, Inc. All rights reserved. # Licensed under the Apache 2.0 License. """Off-Policy Estimators with built-in hyperparameter tuning.""" from dataclasses import dataclass from dataclasses import field from typing import Dict from typing import List from typing import Optional import numpy as np from sklearn.utils import check_scalar from ..utils import check_array from ..utils import check_ope_inputs from .estimators import BaseOffPolicyEstimator from .estimators import DoublyRobust from .estimators import DoublyRobustWithShrinkage from .estimators import InverseProbabilityWeighting from .estimators import SwitchDoublyRobust @dataclass class BaseOffPolicyEstimatorTuning: """Base Class for Off-Policy Estimator with built-in hyperparameter tuning base_ope_estimator: BaseOffPolicyEstimator An OPE estimator with a hyperparameter (such as IPW/DR with clipping, Switch-DR, and DR with Shrinkage). lambdas: List[float] A list of candidate hyperparameter values. use_bias_upper_bound: bool, default=True Whether to use bias upper bound in hyperparameter tuning. If False, direct bias estimator is used to estimate the MSE. delta: float, default=0.05 A confidence delta to construct a high probability upper bound based on the Bernstein’s inequality. References ---------- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. "Doubly Robust Policy Evaluation and Optimization.", 2014. Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, and Miroslav Dudik. "Doubly Robust Off-Policy Evaluation with Shrinkage.", 2020. """ base_ope_estimator: BaseOffPolicyEstimator = field(init=False) lambdas: List[float] = None use_bias_upper_bound: bool = True delta: float = 0.05 def __new__(cls, *args, **kwargs): dataclass(cls) return super().__new__(cls) def _check_lambdas(self) -> None: """Check type and value of lambdas.""" if isinstance(self.lambdas, list): if len(self.lambdas) == 0: raise ValueError("lambdas must not be empty") for hyperparam_ in self.lambdas: check_scalar( hyperparam_, name="an element of lambdas", target_type=(int, float), min_val=0.0, ) if hyperparam_ != hyperparam_: raise ValueError("an element of lambdas must not be nan") else: raise TypeError("lambdas must be a list") def _check_init_inputs(self) -> None: """Initialize Class.""" if not isinstance(self.use_bias_upper_bound, bool): raise TypeError( "`use_bias_upper_bound` must be a bool" ", but {type(self.use_bias_upper_bound)} is given" ) check_scalar(self.delta, "delta", (float), min_val=0.0, max_val=1.0) def _tune_hyperparam( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: Optional[np.ndarray] = None, position: Optional[np.ndarray] = None, ) -> None: """Find the best hyperparameter value from the given candidate set.""" self.estimated_mse_score_dict = dict() for hyperparam_ in self.lambdas: estimated_mse_score = self.base_ope_estimator( hyperparam_ )._estimate_mse_score( reward=reward, action=action, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, position=position, use_bias_upper_bound=self.use_bias_upper_bound, delta=self.delta, ) self.estimated_mse_score_dict[hyperparam_] = estimated_mse_score self.best_hyperparam = min( self.estimated_mse_score_dict.items(), key=lambda x: x[1] )[0] def estimate_policy_value_with_tuning( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: Optional[np.ndarray] = None, position: Optional[np.ndarray] = None, ) -> float: """Estimate the policy value of evaluation policy with a tuned hyperparameter. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list), default=None Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. Returns ---------- V_hat: float Policy value estimated by the DR estimator. """ # tune hyperparameter if necessary if not hasattr(self, "best_hyperparam"): self._tune_hyperparam( reward=reward, action=action, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, position=position, ) return self.base_ope_estimator(self.best_hyperparam).estimate_policy_value( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) def estimate_interval_with_tuning( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: Optional[np.ndarray] = None, position: Optional[np.ndarray] = None, alpha: float = 0.05, n_bootstrap_samples: int = 10000, random_state: Optional[int] = None, **kwargs, ) -> Dict[str, float]: """Estimate confidence interval of policy value by nonparametric bootstrap procedure. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list), default=None Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. alpha: float, default=0.05 Significance level. n_bootstrap_samples: int, default=10000 Number of resampling performed in the bootstrap procedure. random_state: int, default=None Controls the random seed in bootstrap sampling. Returns ---------- estimated_confidence_interval: Dict[str, float] Dictionary storing the estimated mean and upper-lower confidence bounds. """ # tune hyperparameter if necessary if not hasattr(self, "best_hyperparam"): self._tune_hyperparam( reward=reward, action=action, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, position=position, ) return self.base_ope_estimator(self.best_hyperparam).estimate_interval( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, alpha=alpha, n_bootstrap_samples=n_bootstrap_samples, random_state=random_state, ) class InverseProbabilityWeightingTuning(BaseOffPolicyEstimatorTuning): """Inverse Probability Weighting (IPW) with built-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate clipping hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value from the data. use_bias_upper_bound: bool, default=True Whether to use bias upper bound in hyperparameter tuning. If False, direct bias estimator is used to estimate the MSE. delta: float, default=0.05 A confidence delta to construct a high probability upper bound based on the Bernstein’s inequality. estimator_name: str, default='ipw'. Name of the estimator. References ---------- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. "Doubly Robust Policy Evaluation and Optimization.", 2014. Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, and Miroslav Dudik. "Doubly Robust Off-Policy Evaluation with Shrinkage.", 2020. """ estimator_name: str = "ipw" def __post_init__(self) -> None: """Initialize Class.""" self.base_ope_estimator = InverseProbabilityWeighting super()._check_lambdas() super()._check_init_inputs() def estimate_policy_value( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, position: Optional[np.ndarray] = None, **kwargs, ) -> np.ndarray: """Estimate the policy value of evaluation policy. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. Returns ---------- V_hat: float Estimated policy value (performance) of a given evaluation policy. """ check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_policy_value_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, ) def estimate_interval( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, position: Optional[np.ndarray] = None, alpha: float = 0.05, n_bootstrap_samples: int = 10000, random_state: Optional[int] = None, **kwargs, ) -> Dict[str, float]: """Estimate confidence interval of policy value by nonparametric bootstrap procedure. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities by the evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. alpha: float, default=0.05 Significance level. n_bootstrap_samples: int, default=10000 Number of resampling performed in the bootstrap procedure. random_state: int, default=None Controls the random seed in bootstrap sampling. Returns ---------- estimated_confidence_interval: Dict[str, float] Dictionary storing the estimated mean and upper-lower confidence bounds. """ check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_interval_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, alpha=alpha, n_bootstrap_samples=n_bootstrap_samples, random_state=random_state, ) @dataclass class DoublyRobustTuning(BaseOffPolicyEstimatorTuning): """Doubly Robust (DR) with built-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate clipping hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value from the data. estimator_name: str, default='dr'. Name of the estimator. References ---------- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. "Doubly Robust Policy Evaluation and Optimization.", 2014. Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, and Miroslav Dudik. "Doubly Robust Off-Policy Evaluation with Shrinkage.", 2020. """ lambdas: List[float] = None estimator_name: str = "dr" def __post_init__(self) -> None: """Initialize Class.""" self.base_ope_estimator = DoublyRobust super()._check_lambdas() super()._check_init_inputs() def estimate_policy_value( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: np.ndarray, position: Optional[np.ndarray] = None, ) -> float: """Estimate the policy value of evaluation policy with a tuned hyperparameter. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list) Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. Returns ---------- V_hat: float Policy value estimated by the DR estimator. """ check_array( array=estimated_rewards_by_reg_model, name="estimated_rewards_by_reg_model", expected_dim=3, ) check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_policy_value_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) def estimate_interval( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: np.ndarray, position: Optional[np.ndarray] = None, alpha: float = 0.05, n_bootstrap_samples: int = 10000, random_state: Optional[int] = None, **kwargs, ) -> Dict[str, float]: """Estimate confidence interval of policy value by nonparametric bootstrap procedure. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list) Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. alpha: float, default=0.05 Significance level. n_bootstrap_samples: int, default=10000 Number of resampling performed in the bootstrap procedure. random_state: int, default=None Controls the random seed in bootstrap sampling. Returns ---------- estimated_confidence_interval: Dict[str, float] Dictionary storing the estimated mean and upper-lower confidence bounds. """ check_array( array=estimated_rewards_by_reg_model, name="estimated_rewards_by_reg_model", expected_dim=3, ) check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_interval_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, alpha=alpha, n_bootstrap_samples=n_bootstrap_samples, random_state=random_state, ) @dataclass class SwitchDoublyRobustTuning(BaseOffPolicyEstimatorTuning): """Switch Doubly Robust (Switch-DR) with build-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate switching hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value from the data. estimator_name: str, default='switch-dr'. Name of the estimator. References ---------- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. "Doubly Robust Policy Evaluation and Optimization.", 2014. Yu-Xiang Wang, Alekh Agarwal, and Miroslav Dudík. "Optimal and Adaptive Off-policy Evaluation in Contextual Bandits", 2016. """ estimator_name: str = "switch-dr" def __post_init__(self) -> None: """Initialize Class.""" self.base_ope_estimator = SwitchDoublyRobust super()._check_lambdas() super()._check_init_inputs() def estimate_policy_value( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: np.ndarray, position: Optional[np.ndarray] = None, ) -> float: """Estimate the policy value of evaluation policy with a tuned hyperparameter. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list) Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. Returns ---------- V_hat: float Policy value estimated by the DR estimator. """ check_array( array=estimated_rewards_by_reg_model, name="estimated_rewards_by_reg_model", expected_dim=3, ) check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_policy_value_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) def estimate_interval( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: np.ndarray, position: Optional[np.ndarray] = None, alpha: float = 0.05, n_bootstrap_samples: int = 10000, random_state: Optional[int] = None, **kwargs, ) -> Dict[str, float]: """Estimate confidence interval of policy value by nonparametric bootstrap procedure. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list) Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. alpha: float, default=0.05 Significance level. n_bootstrap_samples: int, default=10000 Number of resampling performed in the bootstrap procedure. random_state: int, default=None Controls the random seed in bootstrap sampling. Returns ---------- estimated_confidence_interval: Dict[str, float] Dictionary storing the estimated mean and upper-lower confidence bounds. """ check_array( array=estimated_rewards_by_reg_model, name="estimated_rewards_by_reg_model", expected_dim=3, ) check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_interval_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, alpha=alpha, n_bootstrap_samples=n_bootstrap_samples, random_state=random_state, ) @dataclass class DoublyRobustWithShrinkageTuning(BaseOffPolicyEstimatorTuning): """Doubly Robust with optimistic shrinkage (DRos) with built-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate shrinkage hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value from the data. estimator_name: str, default='dr-os'. Name of the estimator. References ---------- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. "Doubly Robust Policy Evaluation and Optimization.", 2014. Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, and Miroslav Dudik. "Doubly Robust Off-Policy Evaluation with Shrinkage.", 2020. """ estimator_name: str = "dr-os" def __post_init__(self) -> None: """Initialize Class.""" self.base_ope_estimator = DoublyRobustWithShrinkage super()._check_lambdas() super()._check_init_inputs() def estimate_policy_value( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: np.ndarray, position: Optional[np.ndarray] = None, ) -> float: """Estimate the policy value of evaluation policy with a tuned hyperparameter. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list) Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. Returns ---------- V_hat: float Policy value estimated by the DR estimator. """ check_array( array=estimated_rewards_by_reg_model, name="estimated_rewards_by_reg_model", expected_dim=3, ) check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_policy_value_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) def estimate_interval( self, reward: np.ndarray, action: np.ndarray, pscore: np.ndarray, action_dist: np.ndarray, estimated_rewards_by_reg_model: np.ndarray, position: Optional[np.ndarray] = None, alpha: float = 0.05, n_bootstrap_samples: int = 10000, random_state: Optional[int] = None, **kwargs, ) -> Dict[str, float]: """Estimate confidence interval of policy value by nonparametric bootstrap procedure. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of the logged bandit feedback, i.e., :math:`r_t`. action: array-like, shape (n_rounds,) Action sampled by behavior policy in each round of the logged bandit feedback, i.e., :math:`a_t`. pscore: array-like, shape (n_rounds,) Action choice probabilities of behavior policy (propensity scores), i.e., :math:`\\pi_b(a_t|x_t)`. action_dist: array-like, shape (n_rounds, n_actions, len_list) Action choice probabilities of evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`. estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list) Expected rewards given context, action, and position estimated by regression model, i.e., :math:`\\hat{q}(x_t,a_t)`. position: array-like, shape (n_rounds,), default=None Position of recommendation interface where action was presented in each round of the given logged bandit data. alpha: float, default=0.05 Significance level. n_bootstrap_samples: int, default=10000 Number of resampling performed in the bootstrap procedure. random_state: int, default=None Controls the random seed in bootstrap sampling. Returns ---------- estimated_confidence_interval: Dict[str, float] Dictionary storing the estimated mean and upper-lower confidence bounds. """ check_array( array=estimated_rewards_by_reg_model, name="estimated_rewards_by_reg_model", expected_dim=3, ) check_array(array=reward, name="reward", expected_dim=1) check_array(array=action, name="action", expected_dim=1) check_array(array=pscore, name="pscore", expected_dim=1) check_ope_inputs( action_dist=action_dist, position=position, action=action, reward=reward, pscore=pscore, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, ) if position is None: position = np.zeros(action_dist.shape[0], dtype=int) return super().estimate_interval_with_tuning( reward=reward, action=action, position=position, pscore=pscore, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, alpha=alpha, n_bootstrap_samples=n_bootstrap_samples, random_state=random_state, )

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128

0.633822

4,287

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0.063914

0.032558

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false

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0

null

0

1

0

1

0

null

0

7

f547668045352af962baf40f72a717cbaff007d5

4,271

py

Python

RFEM/BasicObjects/memberSet.py

Dlubal-Software/RFEM_Python_Client

9e29c598dadf380d49677c463931f0be659ccc40

[ "MIT" ]

16

2021-10-13T21:00:11.000Z

2022-03-21T11:12:09.000Z

RFEM/BasicObjects/memberSet.py

Dlubal-Software/RFEM_Python_Client

9e29c598dadf380d49677c463931f0be659ccc40

[ "MIT" ]

49

2021-10-19T13:18:51.000Z

2022-03-30T08:20:17.000Z

RFEM/BasicObjects/memberSet.py

Dlubal-Software/RFEM_Python_Client

9e29c598dadf380d49677c463931f0be659ccc40

[ "MIT" ]

7

2021-10-13T06:06:24.000Z

2022-03-29T17:48:39.000Z

from RFEM.initModel import Model, clearAtributes, ConvertToDlString from RFEM.enums import SetType class MemberSet(): def __init__(self, no: int = 1, members_no: str = '1 4 5 8 9 12 13 16 17 20 21 24', member_set_type = SetType.SET_TYPE_GROUP, comment: str = '', params: dict = None, model = Model): ''' Args: no (int): Member Set Tag members_no (str): Tags of Members Contained Within Member Set member_set_type (enum): Member Set Type Enumeration comment (str, optional): Comments params (dict, optional): Any WS Parameter relevant to the object and its value in form of a dictionary ''' # Client model | Member Set clientObject = model.clientModel.factory.create('ns0:member_set') # Clears object atributes | Sets all atributes to None clearAtributes(clientObject) # Member Set No. clientObject.no = no # Members number clientObject.members = ConvertToDlString(members_no) # Member Set Type clientObject.set_type = member_set_type.name # Comment clientObject.comment = comment # Adding optional parameters via dictionary if params: for key in params: clientObject[key] = params[key] # Add Member Set to client model model.clientModel.service.set_member_set(clientObject) @staticmethod def ContinuousMembers( no: int = 1, members_no: str = '1 4 5 8 9 12 13 16 17 20 21 24', comment: str = '', params: dict = None, model = Model): ''' Args: no (int): Member Set Tag members_no (str): Tags of Members Contained Within Continuous Member Set comment (str, optional): Comments params (dict, optional): Any WS Parameter relevant to the object and its value in form of a dictionary ''' # Client model | Member Set clientObject = model.clientModel.factory.create('ns0:member_set') # Clears object atributes | Sets all atributes to None clearAtributes(clientObject) # Member Set No. clientObject.no = no # Members number clientObject.members = ConvertToDlString(members_no) # Member Set Type clientObject.set_type = SetType.SET_TYPE_CONTINUOUS.name # Comment clientObject.comment = comment # Adding optional parameters via dictionary if params: for key in params: clientObject[key] = params[key] # Add Member Set to client model model.clientModel.service.set_member_set(clientObject) @staticmethod def GroupOfmembers( no: int = 1, members_no: str = '1 4 5 8 9 12 13 16 17 20 21 24', comment: str = '', params: dict = None, model = Model): ''' Args: no (int): Member Set Tag members_no (str): Tags of Members Contained Within Group of Members Member Set comment (str, optional): Comments params (dict, optional): Any WS Parameter relevant to the object and its value in form of a dictionary ''' # Client model | Member Set clientObject = model.clientModel.factory.create('ns0:member_set') # Clears object atributes | Sets all atributes to None clearAtributes(clientObject) # Member Set No. clientObject.no = no # Members number clientObject.members = ConvertToDlString(members_no) # Member Set Type clientObject.set_type = SetType.SET_TYPE_GROUP.name # Comment clientObject.comment = comment # Adding optional parameters via dictionary if params: for key in params: clientObject[key] = params[key] # Add Member Set to client model model.clientModel.service.set_member_set(clientObject)

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114

0.575509

464

4,271

5.217672

0.185345

0.104089

0.037588

0.016109

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0

0.023043

0.359869

4,271

130

115

32.853846

0.862473

0.337158

0

0.814815

0

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0

1

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false

0

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0

null

0

1

0

1

0

null

0

7

f572f6faf65636cfedf0030aeff5669803ce0835

29,921

py

Python

sdk/python/pulumi_azuread/service_principal_password.py

ragnarstolsmark/pulumi-azuread

b9398511c142f0aad349e492ded419f870edc925

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

null

sdk/python/pulumi_azuread/service_principal_password.py

ragnarstolsmark/pulumi-azuread

b9398511c142f0aad349e492ded419f870edc925

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

null

sdk/python/pulumi_azuread/service_principal_password.py

ragnarstolsmark/pulumi-azuread

b9398511c142f0aad349e492ded419f870edc925

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

null

# 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 __all__ = ['ServicePrincipalPasswordArgs', 'ServicePrincipalPassword'] @pulumi.input_type class ServicePrincipalPasswordArgs: def __init__(__self__, *, service_principal_id: pulumi.Input[str], description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_date: Optional[pulumi.Input[str]] = None, end_date_relative: Optional[pulumi.Input[str]] = None, key_id: Optional[pulumi.Input[str]] = None, start_date: Optional[pulumi.Input[str]] = None, value: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a ServicePrincipalPassword resource. :param pulumi.Input[str] service_principal_id: The ID of the Service Principal for which this password should be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] description: A description for the Password. Deprecated in favour of `display_name`. :param pulumi.Input[str] display_name: The display name for the password. :param pulumi.Input[str] end_date: The End Date which the Password is valid until, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). Changing this field forces a new resource to be created. :param pulumi.Input[str] end_date_relative: A relative duration for which the Password is valid until, for example `240h` (10 days) or `2400h30m`. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". Changing this field forces a new resource to be created. :param pulumi.Input[str] key_id: A GUID used to uniquely identify this Key. If not specified a GUID will be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] start_date: The Start Date which the Password is valid from, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). If this isn't specified, the current date is used. Changing this field forces a new resource to be created. :param pulumi.Input[str] value: The Password for this Service Principal. """ pulumi.set(__self__, "service_principal_id", service_principal_id) if description is not None: warnings.warn("""This property has been renamed to `display_name` and will be removed in version 2.0 of the AzureAD provider""", DeprecationWarning) pulumi.log.warn("""description is deprecated: This property has been renamed to `display_name` and will be removed in version 2.0 of the AzureAD provider""") if description is not None: pulumi.set(__self__, "description", description) if display_name is not None: pulumi.set(__self__, "display_name", display_name) if end_date is not None: pulumi.set(__self__, "end_date", end_date) if end_date_relative is not None: pulumi.set(__self__, "end_date_relative", end_date_relative) if key_id is not None: pulumi.set(__self__, "key_id", key_id) if start_date is not None: pulumi.set(__self__, "start_date", start_date) if value is not None: warnings.warn("""In version 2.0 of the AzureAD provider, this attribute will become read-only as it will no longer be possible to specify a password value. It will be generated by Azure Active Directory and persisted to state for reuse in your Terraform configuration.""", DeprecationWarning) pulumi.log.warn("""value is deprecated: In version 2.0 of the AzureAD provider, this attribute will become read-only as it will no longer be possible to specify a password value. It will be generated by Azure Active Directory and persisted to state for reuse in your Terraform configuration.""") if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter(name="servicePrincipalId") def service_principal_id(self) -> pulumi.Input[str]: """ The ID of the Service Principal for which this password should be created. Changing this field forces a new resource to be created. """ return pulumi.get(self, "service_principal_id") @service_principal_id.setter def service_principal_id(self, value: pulumi.Input[str]): pulumi.set(self, "service_principal_id", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ A description for the Password. Deprecated in favour of `display_name`. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="displayName") def display_name(self) -> Optional[pulumi.Input[str]]: """ The display name for the password. """ return pulumi.get(self, "display_name") @display_name.setter def display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "display_name", value) @property @pulumi.getter(name="endDate") def end_date(self) -> Optional[pulumi.Input[str]]: """ The End Date which the Password is valid until, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). Changing this field forces a new resource to be created. """ return pulumi.get(self, "end_date") @end_date.setter def end_date(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "end_date", value) @property @pulumi.getter(name="endDateRelative") def end_date_relative(self) -> Optional[pulumi.Input[str]]: """ A relative duration for which the Password is valid until, for example `240h` (10 days) or `2400h30m`. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". Changing this field forces a new resource to be created. """ return pulumi.get(self, "end_date_relative") @end_date_relative.setter def end_date_relative(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "end_date_relative", value) @property @pulumi.getter(name="keyId") def key_id(self) -> Optional[pulumi.Input[str]]: """ A GUID used to uniquely identify this Key. If not specified a GUID will be created. Changing this field forces a new resource to be created. """ return pulumi.get(self, "key_id") @key_id.setter def key_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "key_id", value) @property @pulumi.getter(name="startDate") def start_date(self) -> Optional[pulumi.Input[str]]: """ The Start Date which the Password is valid from, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). If this isn't specified, the current date is used. Changing this field forces a new resource to be created. """ return pulumi.get(self, "start_date") @start_date.setter def start_date(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "start_date", value) @property @pulumi.getter def value(self) -> Optional[pulumi.Input[str]]: """ The Password for this Service Principal. """ return pulumi.get(self, "value") @value.setter def value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "value", value) @pulumi.input_type class _ServicePrincipalPasswordState: def __init__(__self__, *, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_date: Optional[pulumi.Input[str]] = None, end_date_relative: Optional[pulumi.Input[str]] = None, key_id: Optional[pulumi.Input[str]] = None, service_principal_id: Optional[pulumi.Input[str]] = None, start_date: Optional[pulumi.Input[str]] = None, value: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering ServicePrincipalPassword resources. :param pulumi.Input[str] description: A description for the Password. Deprecated in favour of `display_name`. :param pulumi.Input[str] display_name: The display name for the password. :param pulumi.Input[str] end_date: The End Date which the Password is valid until, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). Changing this field forces a new resource to be created. :param pulumi.Input[str] end_date_relative: A relative duration for which the Password is valid until, for example `240h` (10 days) or `2400h30m`. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". Changing this field forces a new resource to be created. :param pulumi.Input[str] key_id: A GUID used to uniquely identify this Key. If not specified a GUID will be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] service_principal_id: The ID of the Service Principal for which this password should be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] start_date: The Start Date which the Password is valid from, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). If this isn't specified, the current date is used. Changing this field forces a new resource to be created. :param pulumi.Input[str] value: The Password for this Service Principal. """ if description is not None: warnings.warn("""This property has been renamed to `display_name` and will be removed in version 2.0 of the AzureAD provider""", DeprecationWarning) pulumi.log.warn("""description is deprecated: This property has been renamed to `display_name` and will be removed in version 2.0 of the AzureAD provider""") if description is not None: pulumi.set(__self__, "description", description) if display_name is not None: pulumi.set(__self__, "display_name", display_name) if end_date is not None: pulumi.set(__self__, "end_date", end_date) if end_date_relative is not None: pulumi.set(__self__, "end_date_relative", end_date_relative) if key_id is not None: pulumi.set(__self__, "key_id", key_id) if service_principal_id is not None: pulumi.set(__self__, "service_principal_id", service_principal_id) if start_date is not None: pulumi.set(__self__, "start_date", start_date) if value is not None: warnings.warn("""In version 2.0 of the AzureAD provider, this attribute will become read-only as it will no longer be possible to specify a password value. It will be generated by Azure Active Directory and persisted to state for reuse in your Terraform configuration.""", DeprecationWarning) pulumi.log.warn("""value is deprecated: In version 2.0 of the AzureAD provider, this attribute will become read-only as it will no longer be possible to specify a password value. It will be generated by Azure Active Directory and persisted to state for reuse in your Terraform configuration.""") if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ A description for the Password. Deprecated in favour of `display_name`. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="displayName") def display_name(self) -> Optional[pulumi.Input[str]]: """ The display name for the password. """ return pulumi.get(self, "display_name") @display_name.setter def display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "display_name", value) @property @pulumi.getter(name="endDate") def end_date(self) -> Optional[pulumi.Input[str]]: """ The End Date which the Password is valid until, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). Changing this field forces a new resource to be created. """ return pulumi.get(self, "end_date") @end_date.setter def end_date(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "end_date", value) @property @pulumi.getter(name="endDateRelative") def end_date_relative(self) -> Optional[pulumi.Input[str]]: """ A relative duration for which the Password is valid until, for example `240h` (10 days) or `2400h30m`. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". Changing this field forces a new resource to be created. """ return pulumi.get(self, "end_date_relative") @end_date_relative.setter def end_date_relative(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "end_date_relative", value) @property @pulumi.getter(name="keyId") def key_id(self) -> Optional[pulumi.Input[str]]: """ A GUID used to uniquely identify this Key. If not specified a GUID will be created. Changing this field forces a new resource to be created. """ return pulumi.get(self, "key_id") @key_id.setter def key_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "key_id", value) @property @pulumi.getter(name="servicePrincipalId") def service_principal_id(self) -> Optional[pulumi.Input[str]]: """ The ID of the Service Principal for which this password should be created. Changing this field forces a new resource to be created. """ return pulumi.get(self, "service_principal_id") @service_principal_id.setter def service_principal_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "service_principal_id", value) @property @pulumi.getter(name="startDate") def start_date(self) -> Optional[pulumi.Input[str]]: """ The Start Date which the Password is valid from, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). If this isn't specified, the current date is used. Changing this field forces a new resource to be created. """ return pulumi.get(self, "start_date") @start_date.setter def start_date(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "start_date", value) @property @pulumi.getter def value(self) -> Optional[pulumi.Input[str]]: """ The Password for this Service Principal. """ return pulumi.get(self, "value") @value.setter def value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "value", value) class ServicePrincipalPassword(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_date: Optional[pulumi.Input[str]] = None, end_date_relative: Optional[pulumi.Input[str]] = None, key_id: Optional[pulumi.Input[str]] = None, service_principal_id: Optional[pulumi.Input[str]] = None, start_date: Optional[pulumi.Input[str]] = None, value: Optional[pulumi.Input[str]] = None, __props__=None): """ Manages a password credential associated with a service principal within Azure Active Directory. See also the ApplicationPassword resource. > **NOTE:** If you're authenticating using a Service Principal then it must have permissions to both `Read and write all applications` and `Sign in and read user profile` within the `Windows Azure Active Directory` API. ## Example Usage ```python import pulumi import pulumi_azuread as azuread example_application = azuread.Application("exampleApplication") example_service_principal = azuread.ServicePrincipal("exampleServicePrincipal", application_id=example_application.application_id) example_service_principal_password = azuread.ServicePrincipalPassword("exampleServicePrincipalPassword", service_principal_id=example_service_principal.object_id) ``` ## Import Passwords can be imported using the `object id` of a Service Principal and the `key id` of the password, e.g. ```sh $ pulumi import azuread:index/servicePrincipalPassword:ServicePrincipalPassword test 00000000-0000-0000-0000-000000000000/password/11111111-1111-1111-1111-111111111111 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] description: A description for the Password. Deprecated in favour of `display_name`. :param pulumi.Input[str] display_name: The display name for the password. :param pulumi.Input[str] end_date: The End Date which the Password is valid until, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). Changing this field forces a new resource to be created. :param pulumi.Input[str] end_date_relative: A relative duration for which the Password is valid until, for example `240h` (10 days) or `2400h30m`. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". Changing this field forces a new resource to be created. :param pulumi.Input[str] key_id: A GUID used to uniquely identify this Key. If not specified a GUID will be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] service_principal_id: The ID of the Service Principal for which this password should be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] start_date: The Start Date which the Password is valid from, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). If this isn't specified, the current date is used. Changing this field forces a new resource to be created. :param pulumi.Input[str] value: The Password for this Service Principal. """ ... @overload def __init__(__self__, resource_name: str, args: ServicePrincipalPasswordArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Manages a password credential associated with a service principal within Azure Active Directory. See also the ApplicationPassword resource. > **NOTE:** If you're authenticating using a Service Principal then it must have permissions to both `Read and write all applications` and `Sign in and read user profile` within the `Windows Azure Active Directory` API. ## Example Usage ```python import pulumi import pulumi_azuread as azuread example_application = azuread.Application("exampleApplication") example_service_principal = azuread.ServicePrincipal("exampleServicePrincipal", application_id=example_application.application_id) example_service_principal_password = azuread.ServicePrincipalPassword("exampleServicePrincipalPassword", service_principal_id=example_service_principal.object_id) ``` ## Import Passwords can be imported using the `object id` of a Service Principal and the `key id` of the password, e.g. ```sh $ pulumi import azuread:index/servicePrincipalPassword:ServicePrincipalPassword test 00000000-0000-0000-0000-000000000000/password/11111111-1111-1111-1111-111111111111 ``` :param str resource_name: The name of the resource. :param ServicePrincipalPasswordArgs 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(ServicePrincipalPasswordArgs, 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, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_date: Optional[pulumi.Input[str]] = None, end_date_relative: Optional[pulumi.Input[str]] = None, key_id: Optional[pulumi.Input[str]] = None, service_principal_id: Optional[pulumi.Input[str]] = None, start_date: Optional[pulumi.Input[str]] = None, value: Optional[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__ = ServicePrincipalPasswordArgs.__new__(ServicePrincipalPasswordArgs) if description is not None and not opts.urn: warnings.warn("""This property has been renamed to `display_name` and will be removed in version 2.0 of the AzureAD provider""", DeprecationWarning) pulumi.log.warn("""description is deprecated: This property has been renamed to `display_name` and will be removed in version 2.0 of the AzureAD provider""") __props__.__dict__["description"] = description __props__.__dict__["display_name"] = display_name __props__.__dict__["end_date"] = end_date __props__.__dict__["end_date_relative"] = end_date_relative __props__.__dict__["key_id"] = key_id if service_principal_id is None and not opts.urn: raise TypeError("Missing required property 'service_principal_id'") __props__.__dict__["service_principal_id"] = service_principal_id __props__.__dict__["start_date"] = start_date if value is not None and not opts.urn: warnings.warn("""In version 2.0 of the AzureAD provider, this attribute will become read-only as it will no longer be possible to specify a password value. It will be generated by Azure Active Directory and persisted to state for reuse in your Terraform configuration.""", DeprecationWarning) pulumi.log.warn("""value is deprecated: In version 2.0 of the AzureAD provider, this attribute will become read-only as it will no longer be possible to specify a password value. It will be generated by Azure Active Directory and persisted to state for reuse in your Terraform configuration.""") __props__.__dict__["value"] = value super(ServicePrincipalPassword, __self__).__init__( 'azuread:index/servicePrincipalPassword:ServicePrincipalPassword', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_date: Optional[pulumi.Input[str]] = None, end_date_relative: Optional[pulumi.Input[str]] = None, key_id: Optional[pulumi.Input[str]] = None, service_principal_id: Optional[pulumi.Input[str]] = None, start_date: Optional[pulumi.Input[str]] = None, value: Optional[pulumi.Input[str]] = None) -> 'ServicePrincipalPassword': """ Get an existing ServicePrincipalPassword 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[str] description: A description for the Password. Deprecated in favour of `display_name`. :param pulumi.Input[str] display_name: The display name for the password. :param pulumi.Input[str] end_date: The End Date which the Password is valid until, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). Changing this field forces a new resource to be created. :param pulumi.Input[str] end_date_relative: A relative duration for which the Password is valid until, for example `240h` (10 days) or `2400h30m`. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". Changing this field forces a new resource to be created. :param pulumi.Input[str] key_id: A GUID used to uniquely identify this Key. If not specified a GUID will be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] service_principal_id: The ID of the Service Principal for which this password should be created. Changing this field forces a new resource to be created. :param pulumi.Input[str] start_date: The Start Date which the Password is valid from, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). If this isn't specified, the current date is used. Changing this field forces a new resource to be created. :param pulumi.Input[str] value: The Password for this Service Principal. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _ServicePrincipalPasswordState.__new__(_ServicePrincipalPasswordState) __props__.__dict__["description"] = description __props__.__dict__["display_name"] = display_name __props__.__dict__["end_date"] = end_date __props__.__dict__["end_date_relative"] = end_date_relative __props__.__dict__["key_id"] = key_id __props__.__dict__["service_principal_id"] = service_principal_id __props__.__dict__["start_date"] = start_date __props__.__dict__["value"] = value return ServicePrincipalPassword(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def description(self) -> pulumi.Output[str]: """ A description for the Password. Deprecated in favour of `display_name`. """ return pulumi.get(self, "description") @property @pulumi.getter(name="displayName") def display_name(self) -> pulumi.Output[str]: """ The display name for the password. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="endDate") def end_date(self) -> pulumi.Output[str]: """ The End Date which the Password is valid until, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). Changing this field forces a new resource to be created. """ return pulumi.get(self, "end_date") @property @pulumi.getter(name="endDateRelative") def end_date_relative(self) -> pulumi.Output[Optional[str]]: """ A relative duration for which the Password is valid until, for example `240h` (10 days) or `2400h30m`. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". Changing this field forces a new resource to be created. """ return pulumi.get(self, "end_date_relative") @property @pulumi.getter(name="keyId") def key_id(self) -> pulumi.Output[str]: """ A GUID used to uniquely identify this Key. If not specified a GUID will be created. Changing this field forces a new resource to be created. """ return pulumi.get(self, "key_id") @property @pulumi.getter(name="servicePrincipalId") def service_principal_id(self) -> pulumi.Output[str]: """ The ID of the Service Principal for which this password should be created. Changing this field forces a new resource to be created. """ return pulumi.get(self, "service_principal_id") @property @pulumi.getter(name="startDate") def start_date(self) -> pulumi.Output[str]: """ The Start Date which the Password is valid from, formatted as a RFC3339 date string (e.g. `2018-01-01T01:02:03Z`). If this isn't specified, the current date is used. Changing this field forces a new resource to be created. """ return pulumi.get(self, "start_date") @property @pulumi.getter def value(self) -> pulumi.Output[str]: """ The Password for this Service Principal. """ return pulumi.get(self, "value")

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f5743ebfb8c29d01fea27ea81f62611880cb4751

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Python

XCS229ii-PS1-Sandbox/src/grader.py

bearbearyu1223/Stanford-XCS-229-II

7e5743fb326352a168400bb96694c54ed476773f

[ "MIT" ]

2

2021-04-16T20:15:20.000Z

2021-04-23T08:37:27.000Z

XCS229ii-PS1-Sandbox/src/grader.py

bearbearyu1223/Stanford-XCS-229-II

7e5743fb326352a168400bb96694c54ed476773f

[ "MIT" ]

null

XCS229ii-PS1-Sandbox/src/grader.py

bearbearyu1223/Stanford-XCS-229-II

7e5743fb326352a168400bb96694c54ed476773f

[ "MIT" ]

null

#!/usr/bin/env python3 import unittest, random, sys, copy, argparse, inspect from graderUtil import graded, CourseTestRunner, GradedTestCase # Import student submission import submission ############################################# # HELPER FUNCTIONS FOR CREATING TEST INPUTS # ############################################# ######### # TESTS # ######### class Test_1a(GradedTestCase): @graded() def test_0(self): """1a-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1a()]) self.assertTrue(response.issubset(set(['a','b','c'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1a-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1a', lambda f: set([choice.lower() for choice in f()])) class Test_1b(GradedTestCase): @graded() def test_0(self): """1b-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1b()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1b-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1b', lambda f: set([choice.lower() for choice in f()])) class Test_1c(GradedTestCase): @graded() def test_0(self): """1c-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1c()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1c-1-hidden: Multiple choice response.""" self.assertTrue(True) # self.compare_with_solution_or_wait(submission, 'multiple_choice_1c', lambda f: set([choice.lower() for choice in f()])) class Test_1d(GradedTestCase): @graded() def test_0(self): """1d-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1d()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1d-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1d', lambda f: set([choice.lower() for choice in f()])) class Test_1e(GradedTestCase): @graded() def test_0(self): """1e-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1e()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1e-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1e', lambda f: set([choice.lower() for choice in f()])) class Test_1f(GradedTestCase): @graded() def test_0(self): """1f-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1f()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1f-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1f', lambda f: set([choice.lower() for choice in f()])) class Test_1g(GradedTestCase): @graded() def test_0(self): """1g-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1g()]) self.assertTrue(response.issubset(set(['a','b','c','d','e'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),2, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1g-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1g', lambda f: set([choice.lower() for choice in f()])) class Test_1h(GradedTestCase): @graded() def test_0(self): """1h-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1h()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),2, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1h-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1h', lambda f: set([choice.lower() for choice in f()])) class Test_1i(GradedTestCase): @graded() def test_0(self): """1i-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1i()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1i-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1i', lambda f: set([choice.lower() for choice in f()])) class Test_1j(GradedTestCase): @graded() def test_0(self): """1j-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_1j()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """1j-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_1j', lambda f: set([choice.lower() for choice in f()])) class Test_2a(GradedTestCase): @graded() def test_0(self): """2a-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_2a()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """2a-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2a', lambda f: set([choice.lower() for choice in f()])) class Test_2b(GradedTestCase): @graded() def test_0(self): """2b-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_2b()]) self.assertTrue(response.issubset(set(['a','b'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """2b-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2b', lambda f: set([choice.lower() for choice in f()])) class Test_2c(GradedTestCase): @graded() def test_0(self): """2c-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_2c()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """2c-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2c', lambda f: set([choice.lower() for choice in f()])) class Test_2d(GradedTestCase): @graded() def test_0(self): """2d-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_2d()]) self.assertTrue(response.issubset(set(['a','b','c'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """2d-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2d', lambda f: set([choice.lower() for choice in f()])) class Test_2e(GradedTestCase): @graded() def test_0(self): """2e-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_2e_i()]) self.assertTrue(response.issubset(set(['a','b'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') response = set([choice.lower() for choice in submission.multiple_choice_2e_ii()]) self.assertTrue(response.issubset(set(['a','b'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') response = set([choice.lower() for choice in submission.multiple_choice_2e_iii()]) self.assertTrue(response.issubset(set(['a','b'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') response = set([choice.lower() for choice in submission.multiple_choice_2e_iv()]) self.assertTrue(response.issubset(set(['a','b'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_i(self): """2e-i-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2e_i', lambda f: set([choice.lower() for choice in f()])) @graded(is_hidden=True, after_published=False, hide_errors=True) def test_ii(self): """2e-ii-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2e_ii', lambda f: set([choice.lower() for choice in f()])) @graded(is_hidden=True, after_published=False, hide_errors=True) def test_iii(self): """2e-iii-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2e_iii', lambda f: set([choice.lower() for choice in f()])) @graded(is_hidden=True, after_published=False, hide_errors=True) def test_iv(self): """2e-iv-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_2e_iv', lambda f: set([choice.lower() for choice in f()])) class Test_3a(GradedTestCase): @graded() def test_0(self): """3a-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_3a()]) self.assertTrue(response.issubset(set(['a','b','c'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """3a-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_3a', lambda f: set([choice.lower() for choice in f()])) class Test_3b(GradedTestCase): @graded() def test_0(self): """3b-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_3b()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """3b-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_3b', lambda f: set([choice.lower() for choice in f()])) class Test_3c(GradedTestCase): @graded() def test_0(self): """3c-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_3c()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """3c-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_3c', lambda f: set([choice.lower() for choice in f()])) class Test_4a(GradedTestCase): @graded() def test_0(self): """4a-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_4a()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """4a-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_4a', lambda f: set([choice.lower() for choice in f()])) class Test_4b(GradedTestCase): @graded() def test_0(self): """4b-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_4b()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """4b-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_4b', lambda f: set([choice.lower() for choice in f()])) class Test_4c(GradedTestCase): @graded() def test_0(self): """4c-0-helper: Sanity check.""" response = set([choice.lower() for choice in submission.multiple_choice_4c()]) self.assertTrue(response.issubset(set(['a','b','c','d'])), msg='Checks that the response contains only the options available.') self.assertGreaterEqual(len(response),1, msg='Checks that the response is within the cardinality of possible options.') @graded(is_hidden=True, after_published=False, hide_errors=True) def test_1(self): """4c-1-hidden: Multiple choice response.""" self.compare_with_solution_or_wait(submission, 'multiple_choice_4c', lambda f: set([choice.lower() for choice in f()])) def getTestCaseForTestID(test_id): question, part, _ = test_id.split('-') g = globals().copy() for name, obj in g.items(): if inspect.isclass(obj) and name == ('Test_'+question): return obj('test_'+part) if __name__ == '__main__': # Parse for a specific test_core parser = argparse.ArgumentParser() parser.add_argument('test_case', nargs='?', default='all') test_id = parser.parse_args().test_case assignment = unittest.TestSuite() if test_id != 'all': assignment.addTest(getTestCaseForTestID(test_id)) else: assignment.addTests(unittest.defaultTestLoader.discover('.', pattern='grader.py')) CourseTestRunner().run(assignment)

54.818182

135

0.71882

2,467

17,487

4.949737

0.056344

0.082549

0.055032

0.066825

0.925395

0.924085

0.86635

0.856277

0

0.014512

0.129124

17,487

318

136

54.990566

0.787314

0.102934

0

0.588496

0

0.242604

0.00417

0

0.216814

1

0.20354

false

0

0.013274

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0.314159

0

null

0

1

0

null

0

1

0

7

19136b81aa430fea83fa8c561abde96c4199dfbc

93

py

Python

agent/continuous/seperate/__init__.py

SunandBean/tensorflow_RL

a248cbfb99b2041f6f7cc008fcad53fb83ac486e

[ "MIT" ]

60

2019-01-29T14:13:00.000Z

2020-11-24T09:08:05.000Z

agent/continuous/seperate/__init__.py

SunandBean/tensorflow_RL

a248cbfb99b2041f6f7cc008fcad53fb83ac486e

[ "MIT" ]

2

2019-08-14T06:44:32.000Z

2020-11-12T12:57:55.000Z

agent/continuous/seperate/__init__.py

SunandBean/tensorflow_RL

a248cbfb99b2041f6f7cc008fcad53fb83ac486e

[ "MIT" ]

37

2019-01-22T05:19:34.000Z

2021-04-12T02:27:50.000Z

from agent.continuous.seperate.ddpg import DDPG from agent.continuous.seperate.td3 import TD3

46.5

47

0.860215

14

93

5.714286

0.5

0.225

0.475

0.675

0

0.023256

0.075269

93

2

48

46.5

0.906977

0

1

0

true

0

1

0

1

0

1

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null

1

0

1

0

null

0

1

0

1

0

7

1927f7b0302955a77045e3d4e6299a01d8d06ff6

78

py

Python

python/testData/inspections/PyPep8NamingInspection/ignored/ignoreN806.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/PyPep8NamingInspection/ignored/ignoreN806.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/PyPep8NamingInspection/ignored/ignoreN806.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def do_stuff(): return 1 def func(): Test = do_stuff() return Test

11.142857

24

0.602564

12

78

3.75

0.583333

0.311111

0.577778

0

0.017857

0.282051

78

6

25

13

0.785714

0

1

0.5

false

0

0.25

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

19334e8926876a858fc7ded1b9c7dfe427149d57

81

py

Python

TOPSIS_RATISH_101803004/__init__.py

zeearo/TOPSIS_THAPAR

c40c58fe0e706eca5487e53fd424d229f7bc5642

[ "MIT" ]

null

TOPSIS_RATISH_101803004/__init__.py

zeearo/TOPSIS_THAPAR

c40c58fe0e706eca5487e53fd424d229f7bc5642

[ "MIT" ]

null

TOPSIS_RATISH_101803004/__init__.py

zeearo/TOPSIS_THAPAR

c40c58fe0e706eca5487e53fd424d229f7bc5642

[ "MIT" ]

null

name="TOPSIS_RATISH_101803004/TOPSIS_RATISH_101803004" __version__ = "1.0.0"

20.25

55

0.790123

11

81

5.090909

0.636364

0.428571

0.75

0

0.287671

0.098765

81

3

56

27

0.479452

0

0.675325

0.61039

0

1

0

false

0

1

0

null

1

0

1

0

1

0

1

0

1

null

0

8

19930b62e22186d94f517c643d9ae714f32db65b

347

py

Python

bitmovin_api_sdk/analytics/outputs/s3_role_based/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/analytics/outputs/s3_role_based/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/analytics/outputs/s3_role_based/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

from bitmovin_api_sdk.analytics.outputs.s3_role_based.s3_role_based_api import S3RoleBasedApi from bitmovin_api_sdk.analytics.outputs.s3_role_based.customdata.customdata_api import CustomdataApi from bitmovin_api_sdk.analytics.outputs.s3_role_based.analytics_s3_role_based_output_list_query_params import AnalyticsS3RoleBasedOutputListQueryParams

86.75

151

0.927954

48

347

6.229167

0.375

0.100334

0.183946

0.180602

0.451505

0

0.020896

0.034582

347

3

152

115.666667

0.871642

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

1

0

7

199895a547c9ad690b831a03c54cce0845e370b1

5,539

py

Python

test/test_bin.py

kmanalo/qhost

8d681da52451d3687053532fa25c041cd31ad8bf

[ "Apache-2.0" ]

4

2015-01-07T21:36:25.000Z

2017-09-11T02:25:57.000Z

test/test_bin.py

kmanalo/qhost

8d681da52451d3687053532fa25c041cd31ad8bf

[ "Apache-2.0" ]

10

2015-01-08T20:40:33.000Z

2015-09-17T15:09:28.000Z

test/test_bin.py

kmanalo/qhost

8d681da52451d3687053532fa25c041cd31ad8bf

[ "Apache-2.0" ]

1

2019-01-02T15:18:53.000Z

import unittest import sys import os class TestBin(unittest.TestCase): def test_run(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_00.xml') ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_00.txt') ).read() self.assertEquals(actual, expected) def test_longer_run(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_04.xml') ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_04_4.txt') ).read() self.assertEquals(actual, expected) def test_filter_by_state(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_04.xml'), '-s EO -x', ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_04_1.txt') ).read() self.assertEquals(actual, expected) def test_filter_by_state_ODE(self): ''' State filter on 'ODE' ''' top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_05.xml'), '-s DEO', ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_05.txt') ).read() self.assertEquals(actual, expected) def test_filter_by_state_and_node(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_04.xml'), '-s EO -x', 'n0[35]' ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_04_2.txt') ).read() self.assertEquals(actual, expected) def test_filter_by_node(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_04.xml'), 'n0[15]' ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_04_3.txt') ).read() self.assertEquals(actual, expected) def test_filter_by_jobid(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_04.xml'), '-J 1158770' ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_04_5.txt') ).read() self.assertEquals(actual, expected) def test_job_note_notification(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_06.xml') ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_06_1.txt') ).read() self.assertEquals(actual, expected) def test_job_note_display(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_06.xml'), '-N' ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_06_2.txt') ).read() self.assertEquals(actual, expected) def test_any_state_display(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_07.xml'), '-s E' ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_07_1.txt') ).read() self.assertEquals(actual, expected) def test_exclusive_state_display(self): top = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../') cmd = [ os.path.join(top, 'bin', 'qhost'), '-X', os.path.join(top, 'test', 'output', 'output_07.xml'), '-s E -x' ] actual = os.popen(' '.join(cmd)).read() expected = open( os.path.join(top, 'test', 'output', 'output_07_2.txt') ).read() self.assertEquals(actual, expected)

34.61875

78

0.501715

659

5,539

4.053111

0.098634

0.148259

0.164732

0.160614

0.942344

0.928117

0.908648

0.87383

0

0.017259

0.309623

5,539

159

79

34.836478

0.681224

0.003791

0

0.662069

0

0.133782

0

0.075862

1

0.075862

false

0

0.02069

0

0.103448

0

null

0

1

0

null

0

7

274c4bdd71fa36740b0323aed6b95f484a1965fe

113,261

py

Python

Montefx.py

fireballpoint1/fortranTOpy

55843a62c6f0a2f8e2a777ef70193940d3d2d141

[ "Apache-2.0" ]

1

2018-08-26T05:10:56.000Z

Montefx.py

fireballpoint1/fortranTOpy

55843a62c6f0a2f8e2a777ef70193940d3d2d141

[ "Apache-2.0" ]

null

Montefx.py

fireballpoint1/fortranTOpy

55843a62c6f0a2f8e2a777ef70193940d3d2d141

[ "Apache-2.0" ]

1

2018-06-26T18:06:44.000Z

# global NGAS,NSTEP,NANISO,EFINAL,ESTEP,AKT,ARY,TEMPC,TORR,IPEN # #COMMON/INPT1/ # global NDVEC # #COMMON/CNSTS1/ # global CONST1,CONST2,CONST3,CONST4,CONST5 # #COMMON/SETP/ # global TMAX,SMALL,API,ESTART,THETA,PHI,TCFMAX#(10) # global TCFMAX1,RSTART,EFIELD,ETHRM,ECUT,NDELTA,IMIP,IWRITE # #COMMON/LARGE/ # global CF # CF=[[0 for x in range(20000)] for y in range(512)] # global EIN#(512) # EIN=[0 for x in range(512)] # global TCF#(20000) # TCF=[0 for x in range(20000)] # global IARRY#(512) # IARRY=[0 for x in range(512)] # global RGAS#(512) # RGAS=[0 for x in range(512)] # global IPN#(512) # IPN=[0 for x in range(512)] # global WPL#(512) # WPL=[0 for x in range(512)] # global IZBR#(512) # IZBR=[0 for x in range(512)] # global IPLAST # IPLAST=1 # global PENFRA#(3,512) # IARRY=[[0 for y in range(3)] for x in range(512)] # #COMMON/LARGEN/ # global CFN#(20000,60) # CFN=[[0 for x in range(20000)] for y in range(60)] # global TCFN#(20000) # TCFN=[0 for x in range(20000)] # global SCLENUL#(60) # SCLENUL=[0 for x in range(60)] # global NPLAST # #COMMON/OUTPT/ # global ICOLL#(30) # ICOLL==[0 for x in range(30)] # global NETOT,NPRIME,TMAX1,TIME#(300) # TIME=[0 for x in range(300)] # global NNULL,NITOT,ICOLN#(512) # ICOLN=[0 for x in range(512)] # global ICOLNN#(60) # ICOLNN=[0 for x in range(60)] # global NREAL,NEXCTOT # #COMMON/RLTVY/ # global BET#(2000) # BET=[0 for x in range(2000)] # global GAM#(20000) # GAM=[0 for x in range(20000)] # global VC,EMS # #COMMON/STTS/ # global XST#(150000) # XST=[0 for x in range(150000)] # global YST#(150000) # YST=[0 for x in range(150000)] # global ZST#(150000) # ZST=[0 for x in range(150000)] # global TST#(150000) # TST=[0 for x in range(150000)] # global TTIME#(150000) # TTIME=[0 for x in range(150000)] # global NFGF#(150000) # NFGF=[0 for x in range(150000)] # global NFGPP#(150000) # NFGPP=[0 for x in range(150000)] # global NFGBR#(150000) # NFGBR=[0 for x in range(150000)] # global NELEC,NEGION,EST1,EST2 # #COMMON/STEXC/ # global XSTEXC#(150000) # XSTEXC=[0 for x in range(150000)] # global YSTEXC#(150000) # YSTEXC=[0 for x in range(150000)] # global ZSTEXC#(150000) # ZSTEXC=[0 for x in range(150000)] # global TSTEXC#(150000) # TSTEXC=[0 for x in range(150000)] # global NSTEXC # #COMMON/STEXCNUL/ # global XSTN#(150000) # XSTN=[0 for x in range(150000)] # global YSTN#(150000) # YSTN=[0 for x in range(150000)] # global ZSTN#(150000) # ZSTN=[0 for x in range(150000)] # global TSTN#(150000) # TSTN=[0 for x in range(150000)] # global IDNUL#(150000) # IDNUL=[0 for x in range(150000)] # global NEXCNUL # #COMMON/IONC/ # global DOUBLE#(6,20000) # DOUBLE=[[0 for x in range(6)] for y in range(20000)] # global CMINIXSC#(6) # CMINIXSC=[0 for x in range(6)] # global CMINEXSC#(6) # CMINEXSC=[0 for x in range(6)] # global ECLOSS#(6) # ECLOSS=[0 for x in range(6)] # global WPLN#(6) # WPLN=[0 for x in range(6)] # global ICOUNT,AVPFRAC#(3,6) # AVOFRAC=[[0 for x in range(3)] for y in range(6)] # #COMMON/IONFL/ # global NC0#(512) # NC0=[0 for x in range(512)] # global EC0#(512) # EC0=[0 for x in range(512)] # global NG1#(512) # NG1=[0 for x in range(512)] # global EG1#(512) # EG1=[0 for x in range(512)] # global NG2#(512) # NG2=[0 for x in range(512)] # global EG2#(512) # EG2=[0 for x in range(512)] # global WKLM#(512) # WKLM=[0 for x in range(512)] # global DSTFL#(512) # DSTFL=[0 for x in range(512)] # #COMMON/IONMOD/ # global ESPLIT#(512,20) # ESPLIT=[[0 for x in range(512)] for y in range(20)] # global IONMODEL#(512) # IONMODEL=[0 for x in range(512)] # #COMMON/ANIS/ # global PSCT#(20000,512) # PSCT=[[0 for y in range(20000)] for x in range(512)] # global ANGCT#(20000,512) # ANGCT=[[0 for y in range(20000)] for x in range(512)] # global INDEX#(512) # INDEX=[0 for x in range(512)] # global NISO # #COMMON/CASRS/ # global ECAS#(400) # ECAS=[0 for x in range(400)] # global XCAS#(400) # XCAS=[0 for x in range(400)] # global YCAS#(400) # YCAS=[0 for x in range(400)] # global ZCAS#(400) # ZCAS=[0 for x in range(400)] # global DRXS#(400) # DRXS=[0 for x in range(400)] # global DRYS#(400) # DRYS=[0 for x in range(400)] # global DRZS#(400) # DRZS=[0 for x in range(400)] # global TT1#(400) # global NFLGF#(400) # NFLGF=[0 for x in range(400)] # global NFLGPP#(400) # NFLGPP=[0 for x in range(400)] # global IEVNTL # #COMMON/COMP/ # global LCMP,LCFLG,LRAY,LRFLG,LPAP,LPFLG,LBRM,LBFLG,LPEFLG # #COMMON/BREMG/ # global EBRGAM#(10) # EBRGAM=[0 for x in range(10)] # global BRDCOSX#(10) # BRDCOSX=[0 for x in range(10)] # global BRDCOSY#(10) # BRDCOSY=[0 for x in range(10)] # global BRDCOSZ#(10) # BRDCOSZ=[0 for x in range(10)] # global BRX#(10) # BRX=[0 for x in range(10)] # global BRY#(10) # BRY==[0 for x in range(10)] # global BRZ#(10) # BRZ=[0 for x in range(10)] # global BRT#(10) # BRT=[0 for x in range(10)] # global EBRTOT#(6) # EBRTOT=[0 for x in range(6)] # global NBREM#(6) # NBREM=[0 for x in range(6)] # #COMMON/CASRSB/ # global ECASB#(400) # ECASB=[0 for x in range(400)] # global XCASB#(400) # XCASB=[0 for x in range(400)] # global YCASB#(400) # YCASB=[0 for x in range(400)] # global ZCASB#(400) # ZCASB=[0 for x in range(400)] # global DRXB#(400) # DRXB=[0 for x in range(400)] # global DRYB#(400) # DRYB=[0 for x in range(400)] # global DRZB#(400) # DRZB=[0 for x in range(400)] # global TTB1#(400) # TTB1=[0 for x in range(400)] # global NFLGFB#(400) # NFLGFB=[0 for x in range(400)] # global NFLGPPB#(400) # NFLGPPB=[0 for x in range(400)] # global IEVNTLB # #COMMON/CASRSE/ # global ECASE#(400) # ECASE=[0 for x in range(400)] # global XCASE#(400) # XCASE=[0 for x in range(400)] # global YCASE#(400) # YCASE=[0 for x in range(400)] # global ZCASE#(400) # ZCASE=[0 for x in range(400)] # global DRXCE#(400) # DRXCE=[0 for x in range(400)] # global DRYCE#(400) # DRYCE=[0 for x in range(400)] # global DRZCE#(400) # DRZCE=[0 for x in range(400)] # global TCASE#(400) # TCASE=[0 for x in range(400)] # global NFLGFE#(400) # NFLGFE=[0 for x in range(400)] # global NFLGPPE#(400) # NFLGPPE=[0 for x in range(400)] # global IEVENTE # #COMMON/ECASC/ # global NEGAS#(512) # NEGAS=[0 for x in range(512)] # global LEGAS#(512) # LEGAS=[0 for x in range(512)] # global IESHELL#(512) # IESHELL=[0 for x in range(512)] # global IECASC # #COMMON/IDEXC/ # global NGEXC1,NGEXC2,NGEXC3,NGEXC4,NGEXC5,NGEXC6,IDG1,IDG2,IDG3,IDG4,IDG5,IDG6 def MONTEFE(): # IMPLICIT #real*8 (A-H,O-Z) # IMPLICIT #integer*8 (I-N) # COMMON/INPT/NGAS,NSTEP,NANISO,EFINAL,ESTEP,AKT,ARY,TEMPC,TORR,IPEN # COMMON/INPT1/NDVEC # COMMON/CNSTS1/CONST1,CONST2,CONST3,CONST4,CONST5 # COMMON/SETP/TMAX,SMALL,API,ESTART,THETA,PHI,TCFMAX(10),TCFMAX1,RSTART,EFIELD,ETHRM,ECUT,NDELTA,IMIP,IWRITE # COMMON/LARGE/CF(20000,512),EIN(512),TCF(20000),IARRY(512),RGAS(512),IPN(512),WPL(512),IZBR(512),IPLAST,PENFRA[3,512] # COMMON/LARGEN/CFN(20000,60),TCFN(20000),SCLENUL(60),NPLAST # COMMON/OUTPT/ICOLL(30),NETOT,NPRIME,TMAX1,TIME(300),NNULL,NITOT,ICOLN(512),ICOLNN(60),NREAL,NEXCTOT # COMMON/RLTVY/BET[2000],GAM(20000),VC,EMS # COMMON/STTS/XST(150000),YST(150000),ZST(150000),TST(150000),TTIME(150000),NFGF(150000),NFGPP(150000),NFGBR(150000),NELEC,NEGION,EST1,EST2 # COMMON/STEXC/XSTEXC(150000),YSTEXC(150000),ZSTEXC(150000),TSTEXC(150000),NSTEXC # COMMON/STEXCNUL/XSTN(150000),YSTN(150000),ZSTN(150000),TSTN(150000),IDNUL(150000),NEXCNUL # COMMON/IONC/DOUBLE(6,20000),CMINIXSC[6],CMINEXSC[6],ECLOSS[6],WPLN[6],ICOUNT,AVPFRAC(3,6) # COMMON/IONFL/NC0(512),EC0(512),NG1(512),EG1(512),NG2(512),EG2(512),WKLM(512),DSTFL(512) # COMMON/IONMOD/ESPLIT(512,20),IONMODEL(512) # COMMON/ANIS/PSCT(20000,512),ANGCT(20000,512),INDEX(512),NISO # COMMON/CASRS/ECAS(400),XCAS(400),YCAS(400),ZCAS(400),DRXS(400),DRYS(400),DRZS(400),TT1(400),NFLGF(400),NFLGPP(400),IEVNTL # COMMON/COMP/LCMP,LCFLG,LRAY,LRFLG,LPAP,LPFLG,LBRM,LBFLG,LPEFLG # COMMON/BREMG/EBRGAM(10),BRnumpy.cosX(10),BRnumpy.cosY(10),BRnumpy.cosZ[10],BRX(10),BRY(10),BRZ[10],BRT(10),EBRTOT[6],NBREM[6] # COMMON/CASRSB/ECASB[400],XCASB[400],YCASB[400],ZCASB[400],DRXB[400],DRYB[400],DRZB[400],TTB1(400),NFLGFB[400],NFLGPPB[400],IEVNTLB # COMMON/CASRSE/ECASE(400),XCASE(400),YCASE(400),ZCASE(400),DRXCE(400),DRYCE(400),DRZCE(400),TCASE(400),NFLGFE(400),NFLGPPE(400),IEVENTE # COMMON/ECASC/NEGAS(512),LEGAS(512),IESHELL(512),IECASC # COMMON/IDEXC/NGEXC1,NGEXC2,NGEXC3,NGEXC4,NGEXC5,NGEXC6,IDG1,IDG2,IDG3,IDG4,IDG5,IDG6 #COMMON/INPT/ global NGAS,NSTEP,NANISO,EFINAL,ESTEP,AKT,ARY,TEMPC,TORR,IPEN #COMMON/INPT1/ global NDVEC #COMMON/CNSTS1/ global CONST1,CONST2,CONST3,CONST4,CONST5 #COMMON/SETP/ global TMAX,SMALL,API,ESTART,THETA,PHI,TCFMAX#(10) global TCFMAX1,RSTART,EFIELD,ETHRM,ECUT,NDELTA,IMIP,IWRITE #COMMON/LARGE/ global CF#(20000,512) global EIN#(512) global TCF#(20000) global IARRY#(512) global RGAS#(512) global IPN#(512) global WPL#(512) global IZBR#(512) global IPLAST global PENFRA#(3,512) #COMMON/LARGEN/ global CFN#(20000,60) global TCFN#(20000) global SCLENUL#(60) global NPLAST #COMMON/OUTPT/ global ICOLL#(30) global NETOT,NPRIME,TMAX1,TIME#(300) global NNULL,NITOT,ICOLN#(512) global ICOLNN#(60) global NREAL,NEXCTOT #COMMON/RLTVY/ global BET#(2000) global GAM#(20000) global VC,EMS #COMMON/STTS/ global XST#(150000) global YST#(150000) global ZST#(150000) global TST#(150000) global TTIME#(150000) global NFGF#(150000) global NFGPP#(150000) global NFGBR#(150000) global NELEC,NEGION,EST1,EST2 #COMMON/STEXC/ global XSTEXC#(150000) global YSTEXC#(150000) global ZSTEXC#(150000) global TSTEXC#(150000) global NSTEXC #COMMON/STEXCNUL/ global XSTN#(150000) global YSTN#(150000) global ZSTN#(150000) global TSTN#(150000) global IDNUL#(150000) global NEXCNUL #COMMON/IONC/ global DOUBLE#(6,20000) global CMINIXSC#(6) global CMINEXSC#(6) global ECLOSS#(6) global WPLN#(6) global ICOUNT,AVPFRAC#(3,6) #COMMON/IONFL/ global NC0#(512) global EC0#(512) global NG1#(512) global EG1#(512) global NG2#(512) global EG2#(512) global WKLM#(512) global DSTFL#(512) #COMMON/IONMOD/ global ESPLIT#(512,20) global IONMODEL#(512) #COMMON/ANIS/ global PSCT#(20000,512) global ANGCT#(20000,512) global INDEX#(512) global NISO #COMMON/CASRS/ global ECAS#(400) global XCAS#(400) global YCAS#(400) global ZCAS#(400) global DRXS#(400) global DRYS#(400) global DRZS#(400) global TT1#(400) global NFLGF#(400) global NFLGPP#(400) global IEVNTL #COMMON/COMP/ global LCMP,LCFLG,LRAY,LRFLG,LPAP,LPFLG,LBRM,LBFLG,LPEFLG #COMMON/BREMG/ global EBRGAM#(10) global BRDCOSX#(10) global BRDCOSY#(10) global BRDCOSZ#(10) global BRX#(10) global BRY#(10) global BRZ#(10) global BRT#(10) global EBRTOT#(6) global NBREM#(6) #COMMON/CASRSB/ global ECASB#(400) global XCASB#(400) global YCASB#(400) global ZCASB#(400) global DRXB#(400) global DRYB#(400) global DRZB#(400) global TTB1#(400) global NFLGFB#(400) global NFLGPPB#(400) global IEVNTLB #COMMON/CASRSE/ global ECASE#(400) global XCASE#(400) global YCASE#(400) global ZCASE#(400) global DRXCE#(400) global DRYCE#(400) global DRZCE#(400) global TCASE#(400) global NFLGFE#(400) global NFLGPPE#(400) global IEVENTE #COMMON/ECASC/ global NEGAS#(512) global LEGAS#(512) global IESHELL#(512) global IECASC #COMMON/IDEXC/ global NGEXC1,NGEXC2,NGEXC3,NGEXC4,NGEXC5,NGEXC6,IDG1,IDG2,IDG3,IDG4,IDG5,IDG6 #DIMENSION XS(150000),YS(150000),ZS(150000),TS(150000),ES(150000),DCX(150000),DCY(150000),DCZ(150000),NFLGFC(150000),NFLGPPC(150000),NFLGBRMC(150000) #DIMENSION TEMP(20000) # DIMENSION ETEMP(1000) # ---------------------------------------------------------------------- # RELATIVISTIC VERSION SEPTEMBER 2013 # ELECTRIC FIELD ALONG Z AXIS. NO MAGNETIC FIELD. # TRACKS DELTA ELECTRONS AND UPDATES ARRAYS CONTAINING POSITION AND # TIME OF THERMALISED ELECTRONS. # CALCULATES NUMBER OF PRODUCED ELECTRONS PER PRIMARY AND OTHER # HIGHER FANO FACTORS. # RANGE IS ACCURATE ONLY FOR ANISOTROPIC X-SECTIONS # ---------------------------------------------------------------------- # VARYING ENERGY STEPS if(EFINAL <= 140000.): ESTEP1=(EFINAL-16000.0)/float(4000) else: ESTEP1=20.0 ESTEP2=(EFINAL-92000.0)/float(4000) # endif NPRINT=0 J20000=20000 J300=300 API=numpy.arccos(-1.00) SMALL=1.0E-20 TMAX1=0.00 EMAX=0.00 RDUM=RSTART CONST9=CONST3*0.010 for I in range(1,300): TIME[I]=0.00 for I in range(1,30): ICOLL[I]=0 for I in range(1,512): ICOLN[I]=0 NREAL=0 NNULL=0 NETOT=0 NEXCTOT=0 NITOT=0 NMXADD=0 NTMPFLG=0 BP=EFIELD*EFIELD*CONST1 F1=EFIELD*CONST2 F2=EFIELD*CONST3 F4=2.00*API THETA1=THETA PHI1=PHI # CALCULATE MAXIMUM COLLISION FREQUENCY TLIM=0.0 for J in range(1,20000): TEMP[J]=TCFN[J]+TCF[J] if(TLIM < TEMP[J]): TLIM=TEMP[J] NEOVFL=0 J1=0 # START OF PRIMARY EVENT LOOP for J11 in range(1,NDELTA): J1=J1+1 NPRIME=J1 NGEXC1=0 NGEXC2=0 NGEXC3=0 NGEXC4=0 NGEXC5=0 NGEXC6=0 # INITIAL DIRECTION COSINES FOR ELECTRON BEAM DCZ1=numpy.cos(THETA1) DCX1=numpy.sin(THETA1)*numpy.cos(PHI1) DCY1=numpy.sin(THETA1)*numpy.sin(PHI1) NFLGFF=0 NFLGPPP=0 NFLGBRMM=0 NFLGHIGH=0 EST1=ESTART E1=ESTART X=0.00 Y=0.00 Z=0.00 K1=0 KEXC=0 NSTEXC=0 NEXCNUL=0 NCLUS=0 NELEC=0 NEGION=0 TLAST=0.00 ST=0.00 TDASH=0.00 if(IMIP == 2): pass else: if(IMIP > 2): # READIN FIRST ELECTRON FROM BETA DECAY OR XRAY UNTHERMALISED CLUSTERS CASRES(J11,IBADTOT,IBAD1) # SKIP IF BAD EVENT if(IBAD1 == 1): J1=J1-1 continue # endif elif(IMIP == 1) : # READ IN FIRST ELECTRON FROM MIP INTERACTION CASREM(J11) EST1=ECAS[1] EST2=EST1 # endif X=XCAS[1] Y=YCAS[1] Z=ZCAS[1] ST=TT1[1] TS[1]=TT1[1] E1=ECAS[1] DCZ1=DRZS[1] DCY1=DRYS[1] DCX1=DRXS[1] NFLGFF=NFLGF[1] NFLGPPP=NFLGPP[1] NFLGBRMM=0 NFLGHIGH=NFLGFF # PUT REMAINDER OF ELECTRONS INTO CLUSTER STORE ISDUM=0 for IST in range(2,IEVNTL): ISDUM=ISDUM+1 XS[ISDUM]=XCAS[IST] YS[ISDUM]=YCAS[IST] ZS[ISDUM]=ZCAS[IST] TS[ISDUM]=TT1[IST] ES[ISDUM]=ECAS[IST] DCX[ISDUM]=DRXS[IST] DCY[ISDUM]=DRYS[IST] DCZ[ISDUM]=DRZS[IST] NFLGFC[ISDUM]=NFLGF[IST] NFLGPPC[ISDUM]=NFLGPP[IST] NFLGBRMC[ISDUM]=0 NCLUS=ISDUM if(NFLGF[IST]> NFLGHIGH): NFLGHIGH=NFLGF[IST] # START OF LOOP FOR NEWLY CREATED ELECTRONS flag190=0 def GOTO1(): R1=DRAND48(RDUM) T=-math.log(R1)/TLIM+TDASH TDASH=T # AP=DCZ1*F2*math.sqrt(E1) GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) AP=DCZ1*EFIELD*BET1*VC*1.0E-10 BP1=BP/GAM1 E=E1+(AP+BP1*T)*T if(E < 0.00): E=0.0010 # endif # INSERT NEW ALGORITHM TO FIND IE FOR VARYING ENERGY STEP if(IMIP == 1): IE=int(E/ESTEP)+1 else: if(EFINAL <= 20000.): IE=int(E/ESTEP)+1 elif(EFINAL <= 140000.) : if(E <= 16000.): IE=int(E)+1 else: IE=16000+int((E-16000.)/ESTEP1) # endif else: if(E <= 12000.): IE=int(E)+1 elif(E <= 92000.) : IE=12000+int((E-12000.)/ESTEP1) else: IE=16000+int((E-92000.)/ESTEP2) # endif # endif # endif IE=DMIN0[IE][J20000] # # TEST FOR #real OR NULL COLLISION # R5=DRAND48(RDUM) TEST1=TCF[IE]/TLIM if(R5 <= TEST1): pass else: NNULL=NNULL+1 TEST2=TEMP[IE]/TLIM if(R5 < TEST2): # TEST FOR NULL LEVELS if(NPLAST == 0): GOTO1() R2=DRAND48(RDUM) I=0 flag888=1 while(flag888): flag888=0 I=I+1 if(CFN[IE][I]< R2): flag888=1 # INCREMENT NULL LEVEL SUM NEXCNUL=NEXCNUL+1 ICOLNN[I]=ICOLNN[I]+1 # STORE X Y Z T ID FOR MOLECULAR LIGHT EMISSION FROM NULL EXCITATION # NOTE: SMALL APPROX USED POSITION OF PREVIOUS #real COLLISION XSTN[NEXCNUL]=X YSTN[NEXCNUL]=Y ZSTN[NEXCNUL]=Z TSTN[NEXCNUL]=ST IDNUL[NEXCNUL]=I GOTO1() else: # NULL GOTO1() # endif # # CALCULATE DIRECTION COSINES AND POSITIONS AT INSTANT BEFORE COLLISION # 137 T2=T*T if(E > EMAX): EMAX=E if(T > TMAX1): TMAX1=T TDASH=0.00 NREAL=NREAL+1 # CONST6=math.sqrt(E1/E) GAM2=(EMS+E)/EMS GAM12=(GAM1+GAM2)/2.00 BET2=math.sqrt(1.00-1.00/(GAM2*GAM2)) CONST6=BET1/BET2 DCX2=DCX1*CONST6 DCY2=DCY1*CONST6 # DCZ2=DCZ1*CONST6+EFIELD*T*CONST5/math.sqrt(E) DCZ2=DCZ1*CONST6+EFIELD*T*2.0*10**(10*CONST1/(VC*BET2)) # CONST7=CONST9*math.sqrt(E1) CONST7=VC*BET1*1.0E-12 A=T*CONST7 X=X+DCX1*A Y=Y+DCY1*A Z=Z+DCZ1*A+T2*F1/GAM12 # Z=Z+DCZ1*A+T2*F1 ST=ST+T IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 # --------------------------------------------------------------------- # DETERMINATION OF #real COLLISION TYPE # --------------------------------------------------------------------- R2=DRAND48(RDUM) I=0 flag140=1 while(flag140): flag140=0 I=I+1 if(CF[IE][I]< R2): flag140=1 #************************************************************ # CHECK IF BREMSSTRAHLUNG if(IZBR[I]!= 0 and LBRM == 1): NFLGBRMM=1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 for KNGS in range(1,NGAS): if(IPT == (KNGS*5)-1): break IATOMNO=IZBR[I] BREMS(IATOMNO,E,DCX2,DCY2,DCZ2,EOUT,EDCX,EDCY,EDCZ,EGAMMA,GDCX,GDCY,GDCZ) NBREM[KNGS]=NBREM[KNGS]+1 EBRTOT[KNGS]=EBRTOT[KNGS]+EGAMMA # GET NEW DRCOS DRCOSY DRCOSX AND ENERGY OF ELECTRON E1=EOUT DCX1=EDCX DCY1=EDCY DCZ1=EDCZ # RUN BREMSSTRAHLUNG GAMMA THROUGH CASCADE : STORE CONVERTED # ELECTRONS IN COMMON/CASRSB/ # BREMSCASC(J11,EGAMMA,X,Y,Z,ST,GDCX,GDCY,GDCZ,ILOW) # BREMSSTRAHLUNG ENERGY TOO LOW TO IONISE if(ILOW == 1): GO TO 190 # # STORE BREMSSTRAHLUNG DATA IN CLUSTER STORE # ETSUM=0.0 for KBR in range(1,IEVNTLB): NCLUS=NCLUS+1 if(NCLUS > 150000): print(' def STOPPED: . NCLUS=',NCLUS,' NREAL=',NREAL) sys.exit() # endif ES[NCLUS]=ECASB[KBR] ETSUM=ETSUM+ES[NCLUS] XS[NCLUS]=XCASB[KBR] YS[NCLUS]=YCASB[KBR] ZS[NCLUS]=ZCASB[KBR] TS[NCLUS]=TTB1[KBR] DCX[NCLUS]=DRXB[KBR] DCY[NCLUS]=DRYB[KBR] DCZ[NCLUS]=DRZB[KBR] NFLGFC[NCLUS]=NFLGFB[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPB[KBR] NFLGBRMC[NCLUS]=2 if(NFLGFC[NCLUS]> NFLGHIGH): NFLGHIGH=NFLGFC[NCLUS] GO TO 190 # endif 891 CONTINUE #***************************************************************** # S1=RGAS[I] S1=1.00+GAM2*(RGAS[I]-1.00) EI=EIN[I] # WRITE(6,8890) EIN[I],I #8890 print(' EIN=','%.4f' % ,' I=',I3) if(E < EI): EI=E-0.00010 # endif if(IPN[I]== 0): GO TO 666 # ATTACHMENT flag335=0 if(IPN[I]== -1): NETOT=NETOT+1 NITOT=NITOT+1 NELEC=NELEC+1 NEGION=NEGION+1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 flag335=1 # endif else: EISTR=EI if(IONMODEL[I]> 0): # CALCULATE SECONDARY ENERGY,ESEC,IN IONISATION COLLISION USING # FIVE DIFFERENT MODELS IONSPLIT(I,E,EI,ESEC) pass # endif else: R9=DRAND48(RDUM) # USE OPAL PETERSON AND BEATY SPLITTING FACTOR. ESEC=WPL[I]*numpy.tan(R9*numpy.arctan((E-EI)/(2.00*WPL[I]))) ESEC=WPL[I]*(ESEC/WPL[I])**0.9524 # 544 CONTINUE EI=ESEC+EI # STORE POSITION ,ENERGY, DIRECTION COSINES AND TIME OF GENERATION # OF SECONDARY IONISATION ELECTRONS NCLUS=NCLUS+1 NMXADD=MAX[NCLUS][NMXADD] if(NCLUS > 150000): #546 print(' ROUTINE STOPPED: . NCLUS=',NCLUS,' NREAL=',NREAL) sys.exit() # endif XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST ES[NCLUS]=ESEC NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM NTMPFLG=1 NCLTMP=NCLUS # ES[NCLUS]=ESEC # RANDOMISE SECONDARY ELECTRON DIRECTION # R3=drand48(RDUM) # F3=1.0-2.00*R3 # THETA0=DACOS(F3) # F6=DCOS(THETA0) # F5=DSIN(THETA0) # R4=drand48(RDUM) # PHI0=F4*R4 # F8=DSIN(PHI0) # F9=DCOS(PHI0) # DCX[NCLUS]=F9*F5 # DCY[NCLUS]=F8*F5 # DCZ[NCLUS]=F6 #********************************************************* flag666=1 if(IECASC == 0): pass elif(LEGAS[I]== 0): # changed if to elif cause same destination pass else: # USE COMPLETE CASCADE FOR ELECTRON IONISATION KG1=NEGAS[I] LG1=LEGAS[I] IGSHEL=IESHELL[I] CASCADEE(J11,KG1,LG1,X,Y,Z,ST,ESEC,IGSHEL) # # STORE CASCADE IN CLUSTER STORE # ETSUM=0.0 for KBR in range(1,IEVENTE): NCLUS=NCLUS+1 if(NCLUS > 150000): print(' SUBROUTINE STOPPED: . NCLUS=',NCLUS,' NREAL=',NREAL) sys.exit() # endif ES[NCLUS]=ECASE[KBR] ETSUM=ETSUM+ES[NCLUS] XS[NCLUS]=XCASE[KBR] YS[NCLUS]=YCASE[KBR] ZS[NCLUS]=ZCASE[KBR] TS[NCLUS]=TCASE[KBR] DCX[NCLUS]=DRXCE[KBR] DCY[NCLUS]=DRYCE[KBR] DCZ[NCLUS]=DRZCE[KBR] NFLGFC[NCLUS]=NFLGFE[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPE[KBR] NFLGBRMC[NCLUS]=NFLGBRMM if(NFLGFC[NCLUS]> NFLGHIGH): NFLGHIGH=NFLGFC[NCLUS] flag666=0 #********************************************************* # STORE POSSIBLE SHELL EMISSIONS AUGER OR FLUORESCENCE # 333 if(flag666): if(EISTR > 30.0) : # WRITE(6,8891) EISTR #8891 print(' EISTR=','%.4f' % ) # TEST IF FLUORESCENCE EMISSION IFLTST=0: if(WKLM[I]> 0.0): R9=DRAND48(RDUM) if(R9 < WKLM[I]): IFLTST=1 # endif if(IFLTST == 0): # AUGER EMISSION WITHOUT FLUORESCENCE NAUG=NC0[I] EAVAUG=EC0[I]/float(NAUG) for JFL in range(1,NC0[I]): NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 else: # AUGER EMISSION AND FLUORESENCE if(NG2[I]== 0): pass else: NAUG=NG2[I] EAVAUG=EG2[I]/float(NAUG) for JFL in range(1,NG2[I]): NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 if(NG1[I] == 0): pass else: NAUG=NG1[I] EAVAUG=EG1[I]/float(NAUG) R9=DRAND48(RDUM) DFL=-math.log(R9)*DSTFL[I] for JFL in range(1,NG1[I]): NCLUS=NCLUS+1 R3=DRAND48(RDUM) THEFL=numpy.arccos(1.0-2.00*R3) R4=DRAND48(RDUM) PHIFL=F4*R4 XS[NCLUS]=X+DFL*numpy.sin(THEFL)*numpy.cos(PHIFL) YS[NCLUS]=Y+DFL*numpy.sin(THEFL)*numpy.sin(PHIFL) ZS[NCLUS]=Z+DFL*numpy.cos(THEFL) NFLGFC[NCLUS]=NFLGHIGH+1 NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 NFLGHIGH=NFLGFC[NCLUS] # endif # endif # # GENERATE SCATTERING ANGLES AND UPDATE LABORATORY COSINES AFTER # COLLISION ALSO UPDATE ENERGY OF ELECTRON. # #666 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 # IF EXCITATION : ADD PROBABILITY ,PENFRA(1,I),OF TRANSFER TO GIVE # IONISATION OF THE OTHER GASES IN MIXTURE flag6=1 if(IPEN == 0 or NGAS == 1): pass else: if(PENFRA[1][I] != 0.0): RAN=DRAND48(RDUM) if(RAN > PENFRA[1][I]): pass else: NCLUS=NCLUS+1 # ENTER HERE POSSIBLE DELOCALISATION LENGTH FOR PENNING TRANSFER if(PENFRA[2][I] == 0.0): XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM pass # endif else: ASIGN=1.0 RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN XS[NCLUS]=X-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN YS[NCLUS]=Y-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN ZS[NCLUS]=Z-math.log(RAN)*PENFRA[2][I]*ASIGN #667 RAN=DRAND48(RDUM) TS[NCLUS]=ST-math.log(RAN)*PENFRA[3][I] # ASSIGN EXCESS ENERGY OF 1EV TO PENNING CREATED ELECTRON ES[NCLUS]=1.0 DCX[NCLUS]=DCX1 DCY[NCLUS]=DCY1 DCZ[NCLUS]=DCZ1 flag6=0 # endif # GO TO 6 # CALCULATE SUM OF EXCITATION PER CLUSTER AND STORE EXCITATION X Y Z T # 5 if(flag6): if(IPN[I] == 0) : if((RGAS[I]*EIN[I]) > 4.0): KEXC=KEXC+1 if(KEXC > 150000): print(2X,' def STOPPED: . KEXC=',KEXC) sys.exit() # endif # FIND GAS IN WHICH EXCITATION OCCURED AND INCREMENT COUNTER if(I <= IDG1): NGEXC1=NGEXC1+1 elif(I <= IDG2) : NGEXC2=NGEXC2+1 elif(I <= IDG3) : NGEXC3=NGEXC3+1 elif(I <= IDG4) : NGEXC4=NGEXC4+1 elif(I <= IDG5) : NGEXC5=NGEXC5+1 elif(I <= IDG6) : NGEXC6=NGEXC6+1 else: print(' def STOPPED: BAD GAS ID IN MONTE') sys.exit() # endif NEXCTOT=NEXCTOT+1 NSTEXC=NSTEXC+1 XSTEXC[KEXC]=X YSTEXC[KEXC]=Y ZSTEXC[KEXC]=Z TSTEXC[KEXC]=ST # endif # endif # 6 S2=(S1*S1)/(S1-1.00) # ANISOTROPIC SCATTERING R3=DRAND48(RDUM) if(INDEX[I]== 1): R31=DRAND48(RDUM) F3=1.00-R3*ANGCT[IE][I] if(R31 > PSCT[IE][I]): F3=-F3 elif(INDEX[I] == 2) : EPSI=PSCT[IE][I] F3=1.00-(2.00*R3*(1.00-EPSI)/(1.00+EPSI*(1.00-2.00*R3))) else: # ISOTROPIC SCATTERING F3=1.00-2.00*R3 # endif THETA0=numpy.arccos(F3) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) if(E < EI): EI=0.00 ARG1=1.00-S1*EI/E ARG1=DMAX1[ARG1][SMALL] D=1.00-F3*math.sqrt(ARG1) E1=E*(1.00-EI/(S1*E)-2.00*D/S2) E1=DMAX1[E1][SMALL] Q=math.sqrt((E/E1)*ARG1)/S1 Q=DMIN1[Q][1.00] THETA=numpy.arcsin(Q*numpy.sin(THETA0)) F6=numpy.cos(THETA) U=(S1-1.00)*(S1-1.00)/ARG1 CSQD=F3*F3 if(F3 < 0.00 and CSQD > U): F6=-1.00*F6 F5=numpy.sin(THETA) DCZ2=DMIN1[DCZ2][1.00] ARGZ=math.sqrt(DCX2*DCX2+DCY2*DCY2) if(ARGZ == 0.00): DCZ1=F6 DCX1=F9*F5 DCY1=F8*F5 if(NTMPFLG == 1): # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S > 1.0): F5S=1.0 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=F6S DCX[NCLTMP]=F9S*F5S DCY[NCLTMP]=F8S*F5S NTMPFLG=0 # endif pass # endif else: DCZ1=DCZ2*F6+ARGZ*F5*F8 DCY1=DCY2*F6+(F5/ARGZ)*(DCX2*F9-DCY2*DCZ2*F8) DCX1=DCX2*F6-(F5/ARGZ)*(DCY2*F9+DCX2*DCZ2*F8) if(NTMPFLG == 1): # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S > 1.0): F5S=1.0 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=DCZ2*F6S+ARGZ*F5S*F8S DCY[NCLTMP]=DCY2*F6S+(F5S/ARGZ)*(DCX2*F9S-DCY2*DCZ2*F8S) DCX[NCLTMP]=DCX2*F6S-(F5S/ARGZ)*(DCY2*F9S+DCX2*DCZ2*F8S) NTMPFLG=0 # endif # 190 CONTINUE # TEST IF ELECTRON IS THERMALISED if(E1 > ETHRM): GOTO1() # STORE POSITION AND TIME OF ELECTRON AND COLLISION HISTORY #191 flag191=1 while (flag191): flag191=0 if(flag335==0): K1=K1+1 XST[K1]=X YST[K1]=Y ZST[K1]=Z TST[K1]=ST NFGF[K1]=NFLGFF NFGPP[K1]=NFLGPPP NFGBR[K1]=NFLGBRMM TTIME[K1]=ST-TLAST NELEC=NELEC+1 NETOT=NETOT+1 #335 if(K1 == 150000): GOTO889() # CATCH SINGLE ELECTRON CLUSTER THAT WAS ATTACHED. # if(NELEC == 1 and NCLUS == 0) GO TO 210 # if(NELEC == (NCLUS+1)): # WRITE(6,884) NELEC,NCLUS,NEGION,J11 # 884 print(' NELEC=',I6,' NCLUS=',I6,' NEGION=',I3,' J11=',I6) # LAST ELECTRON IN CLUSTER DO STATISTICS OVER PRIMARY CLUSTER STATS(J11,J1) pass # endif else: if(NELEC < (NCLUS+1)) : # GET NEW IONISATION ELECTRON FROM STORE X=XS[NELEC] Y=YS[NELEC] Z=ZS[NELEC] ST=TS[NELEC] NFLGFF=NFLGFC[NELEC] NFLGPPP=NFLGPPC[NELEC] NFLGBRMM=NFLGBRMC[NELEC] TLAST=TS[NELEC] E1=ES[NELEC] DCX1=DCX[NELEC] DCY1=DCY[NELEC] DCZ1=DCZ[NELEC] if(E1 < ETHRM): flag191=1 else: GOTO1() # endif # MAIN LOOP # end GOTO1() # RESET NUMBER OF EVENTS FOR BAD EVENTS if(IMIP > 2): NDELTA=NDELTA-IBADTOT # print(' EMAX=','%.7f' % EMAX,' NEOVFL=',NEOVFL) if(EMAX > EFINAL): print('INCREASE ENERGY LIMIT FROM','%.6f' % EFINAL,' EV TO AT LEAST','%.6f' % EMAX,' EV.') sys.exit() # endif return def GOTO889(): NLEFT=NCLUS-NELEC print('\n\n\n WARNING STOPPED: AFTER NPRIME=',NPRIME,' LAST PRIMARY HASAT LEAST ',NLEFT,' SECONDARIES LEFT TO TRACK OUT OF ',NCLUS,' ELECTRONS ALREADY IN CLUSTER') sys.exit() GOTO889() return # end def MONTEF(): # IMPLICIT #real*8 (A-H,O-Z) # IMPLICIT #integer*8 (I-N) COMMON/INPT/NGAS,NSTEP,NANISO,EFINAL,ESTEP,AKT,ARY,TEMPC,TORR,IPEN COMMON/INPT1/NDVEC COMMON/CNSTS1/CONST1,CONST2,CONST3,CONST4,CONST5 COMMON/SETP/TMAX,SMALL,API,ESTART,THETA,PHI,TCFMAX(10),TCFMAX1,RSTART,EFIELD,ETHRM,ECUT,NDELTA,IMIP,IWRITE COMMON/BFLD/EOVB,WB,BTHETA,BMAG COMMON/LARGE/CF(20000,512),EIN(512),TCF(20000),IARRY(512), RGAS(512),IPN(512),WPL(512),IZBR(512),IPLAST,PENFRA[3,512] COMMON/LARGEN/CFN(20000,60),TCFN(20000),SCLENUL(60),NPLAST COMMON/OUTPT/ICOLL(30),NETOT,NPRIME,TMAX1,TIME(300),NNULL,NITOT,ICOLN(512),ICOLNN(60),NREAL,NEXCTOT COMMON/RLTVY/BET[2000],GAM(20000),VC,EMS COMMON/STTS/XST(150000),YST(150000),ZST(150000),TST(150000),TTIME(150000),NFGF(150000),NFGPP(150000),NFGBR(150000),NELEC,NEGION,EST1,EST2 COMMON/STEXC/XSTEXC(150000),YSTEXC(150000),ZSTEXC(150000),TSTEXC(150000),NSTEXC COMMON/STEXCNUL/XSTN(150000),YSTN(150000),ZSTN(150000),TSTN(150000),IDNUL(150000),NEXCNUL COMMON/IONC/DOUBLE(6,20000),CMINIXSC[6],CMINEXSC[6],ECLOSS[6],WPLN[6],ICOUNT,AVPFRAC(3,6) COMMON/IONFL/NC0(512),EC0(512),NG1(512),EG1(512),NG2(512),EG2(512),WKLM(512),DSTFL(512) COMMON/IONMOD/ESPLIT(512,20),IONMODEL(512) COMMON/ANIS/PSCT(20000,512),ANGCT(20000,512),INDEX(512),NISO COMMON/CASRS/ECAS(400),XCAS(400),YCAS(400),ZCAS(400),DRXS(400),DRYS(400),DRZS(400),TT1(400),NFLGF(400),NFLGPP(400),IEVNTL COMMON/COMP/LCMP,LCFLG,LRAY,LRFLG,LPAP,LPFLG,LBRM,LBFLG,LPEFLG COMMON/BREMG/EBRGAM(10),BRDCOSX(10),BRDCOSY(10),BRDCOSZ[10],BRX(10),BRY(10),BRZ[10],BRT(10),EBRTOT[6],NBREM[6] COMMON/CASRSB/ECASB[400],XCASB[400],YCASB[400],ZCASB[400],DRXB[400],DRYB[400],DRZB[400],TTB1(400),NFLGFB[400],NFLGPPB[400],IEVNTLB COMMON/CASRSE/ECASE(400),XCASE(400),YCASE(400),ZCASE(400),DRXCE(400),DRYCE(400),DRZCE(400),TCASE(400),NFLGFE(400),NFLGPPE(400),IEVENTE COMMON/ECASC/NEGAS(512),LEGAS(512),IESHELL(512),IECASC COMMON/IDEXC/NGEXC1,NGEXC2,NGEXC3,NGEXC4,NGEXC5,NGEXC6,IDG1,IDG2,IDG3,IDG4,IDG5,IDG6 DIMENSION XS(150000),YS(150000),ZS(150000),TS(150000),ES(150000),DCX(150000),DCY(150000),DCZ[150000],NFLGFC(150000),NFLGPPC(150000),NFLGBRMC(150000) DIMENSION TEMP(20000) # ---------------------------------------------------------------------- # RELATIVISTIC KINEMATICS # ELECTRIC AND MAGNETIC FIELDS PARALLEL TO Z-AXIS # TRACKS DELTA ELECTRONS AND UPDATES ARRAYS CONTAINING POSITION AND # TIME OF THERMALISED ELECTRONS. # CALCULATES NUMBER OF PRODUCED ELECTRONS PER PRIMARY DELTA AND OTHER # HIGHER FANO FACTORS # RANGE CALCULATION IS ACCURATE ONLY FOR ANISOTROPIC X-SECTIONS. # ---------------------------------------------------------------------- # VARYING ENERGY STEPS if(EFINAL <= 140000.): : ESTEP1=(EFINAL-16000.0)/float(4000) else: ESTEP1=20.0 ESTEP2=(EFINAL-92000.0)/float(4000) # endif NPRINT=0 J300=300 J20000=20000 API=numpy.arccos(-1.00) SMALL=1.0D-20 EMAX=0.00 TMAX1=0.00 RDUM=RSTART CONST9=CONST3*0.010 DO 25 I=1,300 25 TIME[I]=0.00 DO 26 I=1,30 26 ICOLL[I]=0 DO 27 I=1,512 27 ICOLN[I]=0 NREAL=0 NNULL=0 NETOT=0 NEXCTOT=0 NITOT=0 NMXADD=0 NTMPFLG=0 BP=EFIELD*EFIELD*CONST1 F1=EFIELD*CONST2 F2=EFIELD*CONST3 F4=2.00*API THETA1=THETA PHI1=PHI NEOVFL=0 # CALCULATE MAXIMUM COLLISION FREQUENCY TLIM=0.0 DO 111 J=1,20000 TEMP[J]=TCFN[J]+TCF[J] if(TLIM < TEMP[J]: ) TLIM=TEMP[J] 111 CONTINUE # START OF PRIMARY DELTA LOOP J1=0 DO 210 J11=1,NDELTA J1=J1+1 NPRIME=J1 NGEXC1=0 NGEXC2=0 NGEXC3=0 NGEXC4=0 NGEXC5=0 NGEXC6=0 # INITIAL DIRECTION COSINES FOR ELECTRON BEAM DCZ1=numpy.cos(THETA1) DCX1=numpy.sin(THETA1)*numpy.cos(PHI1) DCY1=numpy.sin(THETA1)*numpy.sin(PHI1) NFLGFF=0 NFLGPPP=0 NFLGBRMM=0 NFLGHIGH=0 EST1=ESTART # INITIAL VELOCITY E1=ESTART # VTOT=CONST9*math.sqrt(E1) GAM1=(EMS+E1)/EMS GAM12=GAM1 BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.0D-12 CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT X=0.00 Y=0.00 Z=0.00 K1=0 KEXC=0 NSTEXC=0 NEXCNUL=0 NCLUS=0 NELEC=0 NEGION=0 TLAST=0.00 ST=0.00 TDASH=0.00 if(IMIP == 2): GO TO 1 if(IMIP > 2): : # READIN FIRST ELECTRON FROM BETA DECAY OR XRAY UNTHERMALISED CLUSTERS CALL CASRES(J11,IBADTOT,IBAD1) # SKIP IF BAD EVENT if(IBAD1 == 1): : J1=J1-1 GO TO 210 # endif else if(IMIP == 1) : # READ IN FIRST ELECTRON FROM MIP INTERACTION CALL CASREM(J11) EST1=ECAS[1] EST2=EST1 # endif X=XCAS[1] Y=YCAS[1] Z=ZCAS[1] ST=TT1[1] TS[1]=TT1[1] E1=ECAS[1] DCZ1=DRZS[1] DCY1=DRYS[1] DCX1=DRXS[1] NFLGFF=NFLGF[1] NFLGPPP=NFLGPP[1] NFLGBRMM=0 NFLGHIGH=NFLGFF GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) # VTOT=CONST9*math.sqrt(E1) VTOT=BET1*VC*1.0D-12 # CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT # PUT REMAINDER OF ELECTRONS INTO CLUSTER STORE ISDUM=0 DO 35 IST=2,IEVNTL ISDUM=ISDUM+1 XS[ISDUM]=XCAS[IST] YS[ISDUM]=YCAS[IST] ZS[ISDUM]=ZCAS[IST] TS[ISDUM]=TT1[IST] ES[ISDUM]=ECAS[IST] DCX[ISDUM]=DRXS[IST] DCY[ISDUM]=DRYS[IST] DCZ[ISDUM]=DRZS[IST] NFLGFC[ISDUM]=NFLGF[IST] NFLGPPC[ISDUM]=NFLGPP[IST] NFLGBRMC[ISDUM]=0 NCLUS=ISDUM if(NFLGF[IST]: > NFLGHIGH) NFLGHIGH=NFLGF[IST] 35 CONTINUE GAM12=GAM1 # START OF LOOP FOR NEWLY CREATED ELECTRONS 1 CONTINUE R1=DRAND48(RDUM) T=-math.log(R1)/TLIM+TDASH TDASH=T # AP=DCZ1*F2*math.sqrt(E1) GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) AP=DCZ1*EFIELD*BET1*VC*1.0D-10 BP1=BP/GAM1 913 print(3X,' AFTER STORE NREAL=',I10,' E1=',E12.3,' T=',E12.3,' AP=',E12.3,' BP=',E12.3,' DCZ1=',E12.3) # E=E1+(AP+BP*T)*T E=E1+(AP+BP1*T)*T if(E < 0.00): : if(NPRINT == 0): WRITE(6,913)NREAL,E1,T,AP,BP,DCZ1 NPRINT=1 E=0.0010 # endif # INSERT NEW ALGORITHM TO FIND IE FOR VARYING ENERGY STEP if(IMIP == 1): : IE=int(E/ESTEP)+1 else: if(EFINAL <= 20000.): : IE=int(E/ESTEP)+1 else if(EFINAL <= 140000.) : if(E <= 16000.): : IE=int(E)+1 else: IE=16000+int((E-16000.)/ESTEP1) # endif else: if(E <= 12000.): : IE=int(E)+1 else if(E <= 92000.) : IE=12000+int((E-12000.)/ESTEP1) else: IE=16000+int((E-92000.)/ESTEP2) # endif # endif # endif IE=DMIN0(IE,J20000) # # TEST FOR #real OR NULL COLLISION # R5=DRAND48(RDUM) TEST1=TCF[IE]/TLIM if(R5 <= TEST1): GO TO 137 NNULL=NNULL+1 TEST2=TEMP[IE]/TLIM if(R5 < TEST2): : # TEST FOR NULL LEVELS if(NPLAST == 0): GO TO 1 R2=DRAND48(RDUM) I=0 888 I=I+1 if(CFN[IE][I]: < R2) GO TO 888 # INCREMENT NULL LEVEL SUM NEXCNUL=NEXCNUL+1 ICOLNN[I]=ICOLNN[I]+1 # STORE X Y Z T ID FOR MOLECULAR LIGHT EMISSION AND DISSOCIATION FROM # NULL EXCITATION # NOTE: SMALL APPROX USED POSITION OF PREVIOUS COLLISION XSTN[NEXCNUL]=X YSTN[NEXCNUL]=Y ZSTN[NEXCNUL]=Z TSTN[NEXCNUL]=ST IDNUL[NEXCNUL]=I GO TO 1 else: # NULL GO TO 1 # endif # # CALCULATE DIRECTION COSINES AND POSITIONS AT INSTANT BEFORE COLLISION 137 T2=T*T GAM2=(EMS+E)/EMS BET2=math.sqrt(1.00-1.00/(GAM2*GAM2)) GAM12=(GAM1+GAM2)/2.00 if(E > EMAX): EMAX=E if(T > TMAX1): TMAX1=T TDASH=0.00 NREAL=NREAL+1 WBT=WB*T/GAM12 # WBT=WB*T WBR=WB/GAM12 COSWT=numpy.cos(WBT) SINWT=numpy.sin(WBT) # CONST6=math.sqrt(E1/E) CONST6=BET1/BET2 CX2=CX1*COSWT-CY1*SINWT CY2=CY1*COSWT+CX1*SINWT # VTOT=CONST9*math.sqrt(E) VTOT=VC*BET2*1.0D-12 DCX2=CX2/VTOT DCY2=CY2/VTOT # DCZ2=DCZ1*CONST6+EFIELD*T*CONST5/math.sqrt(E) DCZ2=DCZ1*CONST6+EFIELD*T*2.0D10*CONST1/(VC*BET2) # CONST7=CONST9*math.sqrt(E1) CONST7=VC*BET1*1.0D-12 A=T*CONST7 # DX=(CX1*SINWT-CY1*(1.00-COSWT))/WB DX=(CX1*SINWT-CY1*(1.00-COSWT))/WBR X=X+DX # DY=(CY1*SINWT+CX1*(1.00-COSWT))/WB DY=(CY1*SINWT+CX1*(1.00-COSWT))/WBR Y=Y+DY # Z=Z+DCZ1*A+T2*F1 Z=Z+DCZ1*A+T2*F1/GAM12 ST=ST+T IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 # --------------------------------------------------------------------- # DETERMINATION OF #real COLLISION TYPE # --------------------------------------------------------------------- R2=DRAND48(RDUM) I=0 140 I=I+1 if(I <= 0 or I > 512): : WRITE(6,945) I 945 print(' BAD SELECTION I=',I8) sys.exit() # endif if(CF[IE][I]: < R2) GO TO 140 #************************************************************ # CHECK IF BREMSSTRAHLUNG if(IZBR[I]: != 0 and LBRM == 1) : NFLGBRMM=1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 DO 141 KNGS=1,NGAS if(IPT == (KNGS*5): -1) GO TO 142 141 CONTINUE 142 IATOMNO=IZBR[I] CALL BREMS(IATOMNO,E,DCX2,DCY2,DCZ2,EOUT,EDCX,EDCY,EDCZ,EGAMMA,GDCX,GDCY,GDCZ) NBREM[KNGS]=NBREM[KNGS]+1 EBRTOT[KNGS]=EBRTOT[KNGS]+EGAMMA # WRITE(6,668) EGAMMA,J11 # 668 print(' BREM EGAMMA=','%.4f' % ,' EVENT NO=',I5) # GET NEW DRCOS DRCOSY DRCOSX AND ENERGY OF ELECTRON E1=EOUT DCX1=EDCX DCY1=EDCY DCZ1=EDCZ # RUN BREMSSTRAHLUNG GAMMA THROUGH CASCADE : STORE CONVERTED # ELECTRONS IN COMMON/CASRSB/ # CALL BREMSCASC(J11,EGAMMA,X,Y,Z,ST,GDCX,GDCY,GDCZ,ILOW) # BREMSSTRAHLUNG ENERGY TOO LOW TO IONISE if(ILOW == 1): GO TO 190 # # STORE BREMSSTRAHLUNG DATA IN CLUSTER STORE # DO 890 KBR=1,IEVNTLB NCLUS=NCLUS+1 if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL sys.exit() # endif ES[NCLUS]=ECASB[KBR] XS[NCLUS]=XCASB[KBR] YS[NCLUS]=YCASB[KBR] ZS[NCLUS]=ZCASB[KBR] TS[NCLUS]=TTB1[KBR] DCX[NCLUS]=DRXB[KBR] DCY[NCLUS]=DRYB[KBR] DCZ[NCLUS]=DRZB[KBR] NFLGFC[NCLUS]=NFLGFB[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPB[KBR] NFLGBRMC[NCLUS]=2 890 CONTINUE if(NFLGFC[NCLUS]: > NFLGHIGH) NFLGHIGH=NFLGFC[NCLUS] GO TO 190 # endif 891 CONTINUE #**************************************************************** # S1=RGAS[I] S1=1.00+GAM2*(RGAS[I]-1.00) EI=EIN[I] if(E < EI): : EI=E-0.00010 # endif if(IPN[I]: == 0) GO TO 666 # ATTACHMENT if(IPN[I]: == -1) : NETOT=NETOT+1 NITOT=NITOT+1 NELEC=NELEC+1 NEGION=NEGION+1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 GO TO 335 # endif EISTR=EI if(IONMODEL[I]: > 0) : # CALCULATE SECONDARY ENERGY,ESEC,IN IONISATION COLLISION USING # FIVE DIFFERENT MODELS CALL IONSPLIT(I,E,EI,ESEC) GO TO 544 # endif R9=DRAND48(RDUM) # USE OPAL PETERSON AND BEATY SPLITTING FACTOR. ESEC=WPL[I]*TAN(R9*ATAN((E-EI)/(2.00*WPL[I]))) ESEC=WPL[I]*(ESEC/WPL[I])**0.9524 544 CONTINUE EI=ESEC+EI # STORE POSITION ,ENERGY, DIRECTION COSINES AND TIME OF GENERATION # OF SECONDARY IONISATION ELECTRON NCLUS=NCLUS+1 NMXADD=MAX[NCLUS][NMXADD] if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL 546 print(2X,' def STOPPED: . NCLUS=',I7,' NREAL =',I10) sys.exit() # endif XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST ES[NCLUS]=ESEC NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM NTMPFLG=1 NCLTMP=NCLUS # RANDOMISE SECONDARY ELECTRON DIRECTION # R3=drand48(RDUM) # F3=1.0-2.00*R3 # THETA0=DACOS(F3) # F6=DCOS(THETA0) # F5=DSIN(THETA0) # R4=drand48(RDUM) # PHI0=F4*R4 # F8=DSIN(PHI0) # F9=DCOS(PHI0) # DCX[NCLUS]=F9*F5 # DCY[NCLUS]=F8*F5 # DCZ[NCLUS]=F6 #********************************************************* if(IECASC == 0): GO TO 333 if(LEGAS[I]: == 0) GO TO 333 # USE COMPLETE CASCADE FOR ELECTRON IONISATION KG1=NEGAS[I] LG1=LEGAS[I] IGSHEL=IESHELL[I] CALL CASCADEE(J11,KG1,LG1,X,Y,Z,ST,ESEC,IGSHEL) # # STORE CASCADE IN CLUSTER STORE # ETSUM=0.0 DO 844 KBR=1,IEVENTE NCLUS=NCLUS+1 if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL sys.exit() # endif ES[NCLUS]=ECASE[KBR] ETSUM=ETSUM+ES[NCLUS] XS[NCLUS]=XCASE[KBR] YS[NCLUS]=YCASE[KBR] ZS[NCLUS]=ZCASE[KBR] TS[NCLUS]=TCASE[KBR] DCX[NCLUS]=DRXCE[KBR] DCY[NCLUS]=DRYCE[KBR] DCZ[NCLUS]=DRZCE[KBR] NFLGFC[NCLUS]=NFLGFE[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPE[KBR] NFLGBRMC[NCLUS]=NFLGBRMM 844 CONTINUE if(NFLGFC[NCLUS]: > NFLGHIGH) NFLGHIGH=NFLGFC[NCLUS] GO TO 666 #********************************************************* # STORE POSSIBLE SHELL EMISSSIONS BY AUGER OR FLUORESCENCE 333 if (EISTR > 30.0) : # TEST IF FLUORESCENCE EMISSION IFLTST=0: if(WKLM[I]: > 0.0) : R9=DRAND48(RDUM) if(R9 < WKLM[I]: ) IFLTST=1 # endif if(IFLTST == 0): : # AUGER EMISSION WITHOUT FLUORESCENCE NAUG=NC0[I] EAVAUG=EC0[I]/float(NAUG) DO 700 JFL=1,NC0[I] NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 700 CONTINUE else: # AUGER EMISSION AND FLUORESCENCE if(NG2[I]: == 0) GO TO 702 NAUG=NG2[I] EAVAUG=EG2[I]/float(NAUG) DO 701 JFL=1,NG2[I] NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 701 CONTINUE 702 if(NG1[I] == 0) GO TO 704 NAUG=NG1[I] EAVAUG=EG1[I]/float(NAUG) R9=DRAND48(RDUM) DFL=-math.log(R9)*DSTFL[I] DO 703 JFL=1,NG1[I] NCLUS=NCLUS+1 R3=DRAND48(RDUM) THEFL=numpy.arccos(1.0-2.00*R3) R4=DRAND48(RDUM) PHIFL=F4*R4 XS[NCLUS]=X+DFL*numpy.sin(THEFL)*numpy.cos(PHIFL) YS[NCLUS]=Y+DFL*numpy.sin(THEFL)*numpy.sin(PHIFL) ZS[NCLUS]=Z+DFL*numpy.cos(THEFL) NFLGFC[NCLUS]=NFLGHIGH+1 NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 NFLGHIGH=NFLGFC[NCLUS] 703 CONTINUE 704 CONTINUE # endif # endif # # GENERATE SCATTERING ANGLES AND UPDATE LABORATORY COSINES AFTER # COLLISION ALSO UPDATE ENERGY OF ELECTRON. # 666 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 # IF EXCITATION : ADD PROBABILITY,PENFRA(1,I), OF TRANSFER TO GIVE # IONISATION OF THE OTHER GASES IN MIXTURE if(IPEN == 0 or NGAS == 1): GO TO 5 if(PENFRA[1][I] != 0.0): : RAN=DRAND48(RDUM) if(RAN > PENFRA[1][I]): GO TO 5 NCLUS=NCLUS+1 # ENTER HERE POSSIBLE DELOCALISATION LENGTH FOR PENNING TRANSFER if(PENFRA[2][I] == 0.0): : XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM GO TO 667 # endif ASIGN=1.0 RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN XS[NCLUS]=X-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN YS[NCLUS]=Y-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN ZS[NCLUS]=Z-math.log(RAN)*PENFRA[2][I]*ASIGN 667 RAN=DRAND48(RDUM) TS[NCLUS]=ST-math.log(RAN)*PENFRA[3][I] # ASSIGN EXCESS ENERGY OF 1EV TO PENNING CREATED ELECTRON ES[NCLUS]=1.0 DCX[NCLUS]=DCX1 DCY[NCLUS]=DCY1 DCZ[NCLUS]=DCZ1 GO TO 6 # endif # GO TO 6 # CALCULATE SUM OF EXCITATION PER CLUSTER AND STORE EXCITATION X Y Z T 5 if(IPN[I] == 0) : if((RGAS[I]: *EIN[I]) > 4.0) : KEXC=KEXC+1 if(KEXC > 150000): : WRITE(6,548) KEXC 548 print(2X,' def STOPPED: . KEXC=',I7) sys.exit() # endif # FIND GAS IN WHICH EXCITATION OCCURED AND INCREMENT COUNTER if(I <= IDG1): : NGEXC1=NGEXC1+1 else if(I <= IDG2) : NGEXC2=NGEXC2+1 else if(I <= IDG3) : NGEXC3=NGEXC3+1 else if(I <= IDG4) : NGEXC4=NGEXC4+1 else if(I <= IDG5) : NGEXC5=NGEXC5+1 else if(I <= IDG6) : NGEXC6=NGEXC6+1 else: WRITE(6,9911) 9911 print(' def STOPPED: BAD GAS ID IN MONTE') sys.exit() # endif NEXCTOT=NEXCTOT+1 NSTEXC=NSTEXC+1 XSTEXC[KEXC]=X YSTEXC[KEXC]=Y ZSTEXC[KEXC]=Z TSTEXC[KEXC]=ST # endif # endif 6 S2=(S1*S1)/(S1-1.00) # ANISOTROPIC SCATTERING R3=DRAND48(RDUM) if(INDEX[I]: == 1) : R31=DRAND48(RDUM) F3=1.00-R3*ANGCT[IE][I] if(R31 > PSCT[IE][I]: ) F3=-F3 else if (INDEX[I] == 2) : EPSI=PSCT[IE][I] F3=1.00-(2.00*R3*(1.00-EPSI)/(1.00+EPSI*(1.00-2.00*R3))) else: # ISOTROPIC SCATTERING F3=1.00-2.00*R3 # endif THETA0=numpy.arccos(F3) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) if(E < EI): EI=0.00 ARG1=1.00-S1*EI/E ARG1=DMAX1[ARG1][SMALL] D=1.00-F3*math.sqrt(ARG1) E1=E*(1.00-EI/(S1*E)-2.00*D/S2) E1=DMAX1[E1][SMALL] Q=math.sqrt((E/E1)*ARG1)/S1 Q=DMIN1[Q][1.00] THETA=numpy.arcsin(Q*numpy.sin(THETA0)) F6=numpy.cos(THETA) U=(S1-1.00)*(S1-1.00)/ARG1 CSQD=F3*F3 if(F3 < 0.00 and CSQD > U): F6=-1.00*F6 F5=numpy.sin(THETA) DCZ2=DMIN1[DCZ2][1.00] ARGZ=math.sqrt(DCX2*DCX2+DCY2*DCY2) if(ARGZ == 0.00): : DCZ1=F6 DCX1=F9*F5 DCY1=F8*F5 if(NTMPFLG == 1): : # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S >= 1.0): F5S=0.999 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=F6S DCX[NCLTMP]=F9S*F5S DCY[NCLTMP]=F8S*F5S NTMPFLG=0 # endif GO TO 190 # endif DCZ1=DCZ2*F6+ARGZ*F5*F8 DCY1=DCY2*F6+(F5/ARGZ)*(DCX2*F9-DCY2*DCZ2*F8) DCX1=DCX2*F6-(F5/ARGZ)*(DCY2*F9+DCX2*DCZ2*F8) if(NTMPFLG == 1): : # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S >= 1.0): F5S=0.999 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=DCZ2*F6S+ARGZ*F5S*F8S DCY[NCLTMP]=DCY2*F6S+(F5S/ARGZ)*(DCX2*F9S-DCY2*DCZ2*F8S) DCX[NCLTMP]=DCX2*F6S-(F5S/ARGZ)*(DCY2*F9S+DCX2*DCZ2*F8S) NTMPFLG=0 # endif 190 CONTINUE # VTOT=CONST9*math.sqrt(E1) GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.0D-12 CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT # TEST IF ELECTRON IS THERMALISED if(E1 > ETHRM): GO TO 1 # STORE POSITION AND TIME OF THERMALISED ELECTRON 191 CONTINUE K1=K1+1 XST[K1]=X YST[K1]=Y ZST[K1]=Z TST[K1]=ST NFGF[K1]=NFLGFF NFGPP[K1]=NFLGPPP NFGBR[K1]=NFLGBRMM TTIME[K1]=ST-TLAST NELEC=NELEC+1 NETOT=NETOT+1 # WRITE(6,777) XST[K1],YST[K1],ZST[K1],TST[K1],NFGF[K1],NFGPP[K1], # /NFGBR[K1],NELEC,NETOT,K1 # 777 print(' XST=','%.4f' % ,' YST=','%.4f' % ,' ZST=','%.4f' % ,' TST=','%.4f' % ,/, # /' NFGF=',I4,' NFGPP=',I4,' NFGBR=',I4,' NELEC=',I4,' NETOT=',I4, # /' K1=',I4) 335 if(K1 == 150000) GO TO 889 if(NELEC == (NCLUS+1): ) : # LAST ELECTRON IN CLUSTER, DO STATISTICS ON PRIMARY CALL STATS(J11,J1) GO TO 210 # endif # GET NEW IONISATION ELECTRON FROM STORE X=XS[NELEC] Y=YS[NELEC] Z=ZS[NELEC] ST=TS[NELEC] NFLGFF=NFLGFC[NELEC] NFLGPPP=NFLGPPC[NELEC] NFLGBRMM=NFLGBRMC[NELEC] TLAST=TS[NELEC] E1=ES[NELEC] DCX1=DCX[NELEC] DCY1=DCY[NELEC] DCZ1=DCZ[NELEC] # IF(NELEC > 94) WRITE(6,766) X,Y,Z,ST,E1,DCX1,DCY1,DCZ1,NELEC # 766 print(' X=','%.4f' % ,' Y=','%.4f' % ,' Z=','%.4f' % ,' T=','%.4f' % ,/,' E=', # /'%.4f' % ,' DCX=','%.4f' % ,' DCY=','%.4f' % ,' DCZ=','%.4f' % ,' NELEC=',I6,/) # STORE ALREADY THERMALISED ELECTRONS if(E1 < ETHRM): GO TO 191 GO TO 1 # MAIN LOOP # end 210 CONTINUE # RESET NUMBER OF EVENTS FOR BAD EVENTS if(IMIP > 2): NDELTA=NDELTA-IBADTOT # WRITE(6,887) EMAX,NEOVFL 887 print(' EMAX=','%.7f' % ,' NEOVFL =',I5) if(EMAX > EFINAL): : WRITE(6,989) EFINAL,EMAX 989 print('INCREASE ENERGY LIMIT FROM','%.6f' % ,' EV TO AT LEAST','%.6f' % ,' EV.') sys.exit() # endif return 889 NLEFT=NCLUS-NELEC WRITE(6,992) NPRIME,NLEFT,NCLUS 992 print(3(/),' WARNING STOPPED: AFTER NPRIME=',I6,' LAST PRIMARY HAS AT LEAST ',I6,' SECONDARIES LEFT TO TRACK, OUT OF ',I6,' ELECTRONS ALREADY IN CLUSTER') sys.exit() return # end def MONTEFB(): # IMPLICIT #real*8 (A-H,O-Z) # IMPLICIT #integer*8 (I-N) COMMON/INPT/NGAS,NSTEP,NANISO,EFINAL,ESTEP,AKT,ARY,TEMPC,TORR,IPEN COMMON/INPT1/NDVEC COMMON/CNSTS1/CONST1,CONST2,CONST3,CONST4,CONST5 COMMON/SETP/TMAX,SMALL,API,ESTART,THETA,PHI,TCFMAX(10),TCFMAX1,RSTART,EFIELD,ETHRM,ECUT,NDELTA,IMIP,IWRITE COMMON/BFLD/EOVB,WB,BTHETA,BMAG COMMON/LARGE/CF(20000,512),EIN(512),TCF(20000),IARRY(512), RGAS(512),IPN(512),WPL(512),IZBR(512),IPLAST,PENFRA[3,512] COMMON/LARGEN/CFN(20000,60),TCFN(20000),SCLENUL(60),NPLAST COMMON/OUTPT/ICOLL(30),NETOT,NPRIME,TMAX1,TIME(300),NNULL, NITOT,ICOLN(512),ICOLNN(60),NREAL,NEXCTOT COMMON/RLTVY/BET[2000],GAM(20000),VC,EMS COMMON/STTS/XST(150000),YST(150000),ZST(150000),TST(150000),TTIME(150000),NFGF(150000),NFGPP(150000),NFGBR(150000),NELEC,NEGION,EST1,EST2 COMMON/STEXC/XSTEXC(150000),YSTEXC(150000),ZSTEXC(150000),TSTEXC(150000),NSTEXC COMMON/STEXCNUL/XSTN(150000),YSTN(150000),ZSTN(150000),TSTN(150000),IDNUL(150000),NEXCNUL COMMON/IONC/DOUBLE(6,20000),CMINIXSC[6],CMINEXSC[6],ECLOSS[6],WPLN[6],ICOUNT,AVPFRAC(3,6) COMMON/IONFL/NC0(512),EC0(512),NG1(512),EG1(512),NG2(512),EG2(512),WKLM(512),DSTFL(512) COMMON/IONMOD/ESPLIT(512,20),IONMODEL(512) COMMON/ANIS/PSCT(20000,512),ANGCT(20000,512),INDEX(512),NISO COMMON/CASRS/ECAS(400),XCAS(400),YCAS(400),ZCAS(400),DRXS(400),DRYS(400),DRZS(400),TT1(400),NFLGF(400),NFLGPP(400),IEVNTL COMMON/COMP/LCMP,LCFLG,LRAY,LRFLG,LPAP,LPFLG,LBRM,LBFLG,LPEFLG COMMON/BREMG/EBRGAM(10),BRDCOSX(10),BRDCOSY(10),BRDCOSZ[10],BRX(10),BRY(10),BRZ[10],BRT(10),EBRTOT[6],NBREM[6] COMMON/CASRSB/ECASB[400],XCASB[400],YCASB[400],ZCASB[400],DRXB[400],DRYB[400],DRZB[400],TTB1(400),NFLGFB[400],NFLGPPB[400],IEVNTLB COMMON/CASRSE/ECASE(400),XCASE(400),YCASE(400),ZCASE(400),DRXCE(400),DRYCE(400),DRZCE(400),TCASE(400),NFLGFE(400),NFLGPPE(400),IEVENTE COMMON/ECASC/NEGAS(512),LEGAS(512),IESHELL(512),IECASC COMMON/IDEXC/NGEXC1,NGEXC2,NGEXC3,NGEXC4,NGEXC5,NGEXC6,IDG1,IDG2,IDG3,IDG4,IDG5,IDG6 DIMENSION XS(150000),YS(150000),ZS(150000),TS(150000),ES(150000),DCX(150000),DCY(150000),DCZ[150000],NFLGFC(150000),NFLGPPC(150000),NFLGBRMC(150000) DIMENSION TEMP(20000) # ------------------------------------------------------------------- # RELATIVISTIC VERSION # ELECTRIC FIELD ALONG Z-AXIS MAGNETIC FIELD ALONG X-AXIS. # TRACKS DELTA ELECTRONS AND UPDATES ARRAYS CONTAINING POSITION AND # TIME OF THERMALISED ELECTRONS. # CALCULATES NUMBER OF PRODUCED ELECTRONS PER PRIMARY DELTA AND OTHER # HIGHER FANO FACTORS . # ------------------------------------------------------------------- # VARYING ENERGY STEPS if(EFINAL <= 140000.): : ESTEP1=(EFINAL-16000.0)/float(4000) else: ESTEP1=20.0 ESTEP2=(EFINAL-92000.0)/float(4000) # endif NPRINT=0 J20000=20000 J300=300 API=numpy.arccos(-1.00) SMALL=1.0D-20 EMAX=0.00 TMAX1=0.00 RDUM=RSTART CONST9=CONST3*0.010 DO 25 I=1,300 25 TIME[I]=0.00 DO 26 I=1,30 26 ICOLL[I]=0 DO 27 I=1,512 27 ICOLN[I]=0 NREAL=0 NNULL=0 NETOT=0 NEXCTOT=0 NITOT=0 NMXADD=0 NTMPFLG=0 THETA1=THETA PHI1=PHI F4=2.00*API NEOVFL=0 # CALCULATE MAXIMUM COLLISION FREQUENCY TLIM=0.0 DO 111 J=1,20000 TEMP[J]=TCFN[J]+TCF[J] if(TLIM < TEMP[J]: ) TLIM=TEMP[J] 111 CONTINUE J1=0 # START OF PRIMARY EVENT LOOP DO 210 J11=1,NDELTA J1=J1+1 NPRIME=J1 NGEXC1=0 NGEXC2=0 NGEXC3=0 NGEXC4=0 NGEXC5=0 NGEXC6=0 # INITIAL DIRECTION COSINES DCZ1=numpy.cos(THETA1) DCX1=numpy.sin(THETA1)*numpy.cos(PHI1) DCY1=numpy.sin(THETA1)*numpy.sin(PHI1) NFLGFF=0 NFLGPPP=0 NFLGBRMM=0 NFLGHIGH=0 EST1=ESTART # INITIAL VELOCITY,TIME AND POSITION E1=ESTART GAM1=(EMS+E1)/EMS GAM12=GAM1 BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.0D-12 # VTOT=CONST9*math.sqrt(E1) CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT X=0.00 Y=0.00 Z=0.00 K1=0 KEXC=0 NSTEXC=0 NEXCNUL=0 NCLUS=0 NELEC=0 NEGION=0 TLAST=0.00 ST=0.00 TDASH=0.00 if(IMIP == 2): GO TO 1 if(IMIP > 2): : # READ IN FIRST ELECTRON FROM BETA DECAY OR XRAY UNTHERMALISED CLUSTERS CALL CASRES(J11,IBADTOT,IBAD1) # SKIP IF BAD EVENT if(IBAD1 == 1): : J1=J1-1 GO TO 210 # endif else if(IMIP == 1) : # READ IN FIRST ELECTRON FROM MIP INTERACTION CALL CASREM(J11) EST1=ECAS[1] EST2=EST1 # endif X=XCAS[1] Y=YCAS[1] Z=ZCAS[1] ST=TT1[1] TS[1]=TT1[1] E1=ECAS[1] DCZ1=DRZS[1] DCY1=DRYS[1] DCX1=DRXS[1] NFLGFF=NFLGF[1] NFLGPPP=NFLGPP[1] NFLGBRMM=0 NFLGHIGH=NFLGFF GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=VC*BET1*1.0D-12 # VTOT=CONST9*math.sqrt(E1) CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT # PUT REMAINDER OF ELECTRONS INTO CLUSTER STORE ISDUM=0 DO 35 IST=2,IEVNTL ISDUM=ISDUM+1 XS[ISDUM]=XCAS[IST] YS[ISDUM]=YCAS[IST] ZS[ISDUM]=ZCAS[IST] TS[ISDUM]=TT1[IST] ES[ISDUM]=ECAS[IST] DCX[ISDUM]=DRXS[IST] DCY[ISDUM]=DRYS[IST] DCZ[ISDUM]=DRZS[IST] NFLGFC[ISDUM]=NFLGF[IST] NFLGPPC[ISDUM]=NFLGPP[IST] NFLGBRMC[ISDUM]=0 NCLUS=ISDUM if(NFLGFC[IST]: > NFLGHIGH) NFLGHIGH=NFLGFC[IST] 35 CONTINUE GAM12=GAM1 # START OF LOOP FOR NEWLY CREATED ELECTRONS 1 CONTINUE R1=DRAND48(RDUM) T=-math.log(R1)/TLIM+TDASH TDASH=T WBT=WB*T/GAM12 # WBT=WB*T COSWT=numpy.cos(WBT) SINWT=numpy.sin(WBT) DZ=GAM12*(CZ1*SINWT+(EOVB-CY1)*(1.00-COSWT))/WB # DZ=(CZ1*SINWT+(EOVB-CY1)*(1.00-COSWT))/WB E=E1+DZ*EFIELD*100.00 GAM2=(EMS+E)/EMS BET2=math.sqrt(1.00-1.00/(GAM2*GAM2)) #913 print(3X,' AFTER STORE NREAL=',I10,' DZ=','%.3f' %,'E1=','%.3f' %,' COS # /WT=','%.3f' %,' SINWT=','%.3f' %,' WBT=','%.3f' %,' CY1=','%.3f' %) if(E < 0.00): : # IF(NPRINT == 0) WRITE(6,913)NREAL,DZ,E1,COSWT,SINWT,WBT,CY1 # NPRINT=1 E=0.0010 # endif # INSERT NEW ALGORITHM TO FIND IE FOR VARYING ENERGY STEP if(IMIP == 1): : IE=int(E/ESTEP)+1 else: if(EFINAL <= 20000.): : IE=int(E/ESTEP)+1 else if(EFINAL <= 140000.) : if(E <= 16000.): : IE=int(E)+1 else: IE=16000+int((E-16000.)/ESTEP1) # endif else: if(E <= 12000.): : IE=int(E)+1 else if(E <= 92000.) : IE=12000+int((E-12000.)/ESTEP1) else: IE=16000+int((E-92000.)/ESTEP2) # endif # endif # endif IE=DMIN0(IE,J20000) # # TEST FOR #real OR NULL COLLISION # R5=DRAND48(RDUM) TEST1=TCF[IE]/TLIM if(R5 <= TEST1): GO TO 137 NNULL=NNULL+1 TEST2=TEMP[IE]/TLIM if(R5 < TEST2): : # TEST FOR NULL LEVELS if(NPLAST == 0): GO TO 1 R2=DRAND48(RDUM) I=0 888 I=I+1 if(CFN[IE][I]: < R2) GO TO 888 # INCREMENT NULL LEVEL SUM NEXCNUL=NEXCNUL+1 ICOLNN[I]=ICOLNN[I]+1 # STORE X Y Z T ID FOR MOLECULAR LIGHT EMISSION AND DISSOCIATION FROM # NULL EXCITATION # NOTE: SMALL APPROX USED POSITION OF PREVIOUS #real COLLISION XSTN[NEXCNUL]=X YSTN[NEXCNUL]=Y ZSTN[NEXCNUL]=Z TSTN[NEXCNUL]=ST IDNUL[NEXCNUL]=I GO TO 1 else: # NULL GO TO 1 # endif # # CALCULATE DIRECTION COSINES AND POSITIONS AT INSTANT BEFORE COLLISION 137 T2=T*T if(E > EMAX): EMAX=E if(T > TMAX1): TMAX1=T TDASH=0.00 NREAL=NREAL+1 # CALC VELOCITY CX2=CX1 CY2=(CY1-EOVB)*COSWT+CZ1*SINWT+EOVB CZ2=CZ1*COSWT-(CY1-EOVB)*SINWT # CALC DIRECTION COSINES VTOT=math.sqrt(CX2*CX2+CY2*CY2+CZ2*CZ2) DCX2=CX2/VTOT DCY2=CY2/VTOT DCZ2=CZ2/VTOT # CALC NEW POSITION X=X+CX1*T Y=Y+EOVB*T+GAM12*((CY1-EOVB)*SINWT+CZ1*(1.00-COSWT))/WB Z=Z+DZ GAM12=(GAM1+GAM2)/2.00 ST=ST+T IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 # --------------------------------------------------------------------- # DETERMINATION OF #real COLLISION TYPE # --------------------------------------------------------------------- R2=DRAND48(RDUM) I=0 140 I=I+1 if(CF[IE][I]: < R2) GO TO 140 #************************************************************ # CHECK IF BREMSSTRAHLUNG if(IZBR[I]: != 0 and LBRM == 1) : NFLGBRMM=1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 DO 141 KNGS=1,NGAS if(IPT == (KNGS*5): -1) GO TO 142 141 CONTINUE 142 IATOMNO=IZBR[I] CALL BREMS(IATOMNO,E,DCX2,DCY2,DCZ2,EOUT,EDCX,EDCY,EDCZ,EGAMMA,GDCX,GDCY,GDCZ) NBREM[KNGS]=NBREM[KNGS]+1 EBRTOT[KNGS]=EBRTOT[KNGS]+EGAMMA # WRITE(6,668) EGAMMA,J11 # 668 print(' BREM EGAMMA=','%.4f' % ,' EVENT NO=',I5) # GET NEW DRCOS DRCOSY DRCOSX AND ENERGY OF ELECTRON E1=EOUT DCX1=EDCX DCY1=EDCY DCZ1=EDCZ # RUN BREMSSTRAHLUNG GAMMA THROUGH CASCADE : STORE CONVERTED # ELECTRONS IN COMMON/CASRSB/ # CALL BREMSCASC(J11,EGAMMA,X,Y,Z,ST,GDCX,GDCY,GDCZ,ILOW) # BREMSSTRAHLUNG ENERGY TOO LOW TO IONISE if(ILOW == 1): GO TO 190 # # STORE BREMSSTARHLUNG DATA IN CLUSTER STORE # DO 890 KBR=1,IEVNTLB NCLUS=NCLUS+1 if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL sys.exit() # endif ES[NCLUS]=ECASB[KBR] XS[NCLUS]=XCASB[KBR] YS[NCLUS]=YCASB[KBR] ZS[NCLUS]=ZCASB[KBR] TS[NCLUS]=TTB1[KBR] DCX[NCLUS]=DRXB[KBR] DCY[NCLUS]=DRYB[KBR] DCZ[NCLUS]=DRZB[KBR] NFLGFC[NCLUS]=NFLGFB[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPB[KBR] NFLGBRMC[NCLUS]=2 890 CONTINUE if(NFLGFC[NCLUS]: > NFLGHIGH) NFLGHIGH=NFLGFC[NCLUS] GO TO 190 # endif 891 CONTINUE #**************************************************************** # S1=RGAS[I] S1=1.00+GAM2*(RGAS[I]-1.00) EI=EIN[I] if(E < EI): : EI=E-0.00010 # endif if(IPN[I]: == 0) GO TO 666 # ATTACHMENT if(IPN[I]: == -1) : NETOT=NETOT+1 NITOT=NITOT+1 NELEC=NELEC+1 NEGION=NEGION+1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 GO TO 335 # endif EISTR=EI if(IONMODEL[I]: > 0) : # CALCULATE SECONDARY ENERGY,ESEC,IN IONISATION COLLISION USING # FIVE DIFFERENT MODELS CALL IONSPLIT(I,E,EI,ESEC) GO TO 544 # endif R9=DRAND48(RDUM) # USE OPAL PETERSON AND BEATY SPLITTING FACTOR. ESEC=WPL[I]*TAN(R9*ATAN((E-EI)/(2.00*WPL[I]))) ESEC=WPL[I]*(ESEC/WPL[I])**0.9524 544 CONTINUE EI=ESEC+EI # STORE POSITION ,ENERGY, DIRECTION COSINES AND TIME OF GENERATION # OF SECONDARY IONISATION ELECTRON NCLUS=NCLUS+1 NMXADD=MAX[NCLUS][NMXADD] if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL 546 print(2X,' def STOPPED: . NCLUS=',I7,' NREAL=',I10) sys.exit() # endif XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST ES[NCLUS]=ESEC NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM NTMPFLG=1 NCLTMP=NCLUS # RANDOMISE SECONDARY ELECTRON DIRECTION # R3=drand48(RDUM) # F3=1.0-2.00*R3 # THETA0=DACOS(F3) # F6=DCOS(THETA0) # F5=DSIN(THETA0) # R4=drand48(RDUM) # PHI0=F4*R4 # F8=DSIN(PHI0) # F9=DCOS(PHI0) # DCX[NCLUS]=F9*F5 # DCY[NCLUS]=F8*F5 # DCZ[NCLUS]=F6 #********************************************************* if(IECASC == 0): GO TO 333 if(LEGAS[I]: == 0) GO TO 333 # USE COMPLETE CASCADE FOR ELECTRON IONISATION KG1=NEGAS[I] LG1=LEGAS[I] IGSHEL=IESHELL[I] CALL CASCADEE(J11,KG1,LG1,X,Y,Z,ST,ESEC,IGSHEL) # # STORE CASCADE IN CLUSTER STORE # ETSUM=0.0 DO 844 KBR=1,IEVENTE NCLUS=NCLUS+1 if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL sys.exit() # endif ES[NCLUS]=ECASE[KBR] ETSUM=ETSUM+ES[NCLUS] XS[NCLUS]=XCASE[KBR] YS[NCLUS]=YCASE[KBR] ZS[NCLUS]=ZCASE[KBR] TS[NCLUS]=TCASE[KBR] DCX[NCLUS]=DRXCE[KBR] DCY[NCLUS]=DRYCE[KBR] DCZ[NCLUS]=DRZCE[KBR] NFLGFC[NCLUS]=NFLGFE[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPE[KBR] NFLGBRMC[NCLUS]=NFLGBRMM 844 CONTINUE if(NFLGFC[NCLUS]: > NFLGHIGH) NFLGHIGH=NFLGFC[NCLUS] GO TO 666 #********************************************************* # STORE POSSIBLE SHELL EMISSIONS AUGER OR FLUORESCENCE 333 if(EISTR > 30.0) : # TEST IF FLUORESCENCE EMISSION IFLTST=0: if(WKLM[I]: > 0.0) : R9=DRAND48(RDUM) if(R9 < WKLM[I]: ) IFLTST=1 # endif if(IFLTST == 0): : # AUGER EMISSION WITHOUT FLUORESCENCE NAUG=NC0[I] EAVAUG=EC0[I]/float(NAUG) DO 700 JFL=1,NC0[I] NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 700 CONTINUE else: # AUGER EMISSION AND FLUORESENCE if(NG2[I]: == 0) GO TO 702 NAUG=NG2[I] EAVAUG=EG2[I]/float(NAUG) DO 701 JFL=1,NG2[I] NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 701 CONTINUE 702 if(NG1[I] == 0) GO TO 704 NAUG=NG1[I] EAVAUG=EG1[I]/float(NAUG) R9=DRAND48(RDUM) DFL=-math.log(R9)*DSTFL[I] DO 703 JFL=1,NG1[I] NCLUS=NCLUS+1 R3=DRAND48(RDUM) THEFL=numpy.arccos(1.0-2.00*R3) R4=DRAND48(RDUM) PHIFL=F4*R4 XS[NCLUS]=X+DFL*numpy.sin(THEFL)*numpy.cos(PHIFL) YS[NCLUS]=Y+DFL*numpy.sin(THEFL)*numpy.sin(PHIFL) ZS[NCLUS]=Z+DFL*numpy.cos(THEFL) NFLGFC[NCLUS]=NFLGHIGH+1 NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 NFLGHIGH=NFLGFC[NCLUS] 703 CONTINUE 704 CONTINUE # endif # endif # # GENERATE SCATTERING ANGLES AND UPDATE LABORATORY COSINES AFTER # COLLISION ALSO UPDATE ENERGY OF ELECTRON. # 666 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 # IF EXCITATION : ADD PROBABILITY ,PENFRA(1,I), OF TRANSFER TO GIVE # IONISATION OF THE OTHER GASES IN MIXTURE if(IPEN == 0 or NGAS == 1): GO TO 5 if(PENFRA[1][I] != 0.0): : RAN=DRAND48(RDUM) if(RAN > PENFRA[1][I]): GO TO 5 NCLUS=NCLUS+1 # ENTER HERE POSSIBLE DELOCALISATION LENGTH FOR PENNING TRANSFER if(PENFRA[2][I] == 0.0): : XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM GO TO 667 # endif ASIGN=1.0 RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN XS[NCLUS]=X-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN YS[NCLUS]=Y-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN ZS[NCLUS]=Z-math.log(RAN)*PENFRA[2][I]*ASIGN 667 RAN=DRAND48(RDUM) TS[NCLUS]=ST-math.log(RAN)*PENFRA[3][I] # ASSIGN EXCESS ENERGY OF 1EV TO PENNING CREATED ELECTRON ES[NCLUS]=1.0 DCX[NCLUS]=DCX1 DCY[NCLUS]=DCY1 DCZ[NCLUS]=DCZ1 GO TO 6 # endif # GO TO 6 # CALCULATE SUM OF EXCITATION PER CLUSTER AND STORE EXCITATION X Y Z T 5 if(IPN[I] == 0) : if((RGAS[I]: *EIN[I]) > 4.0) : KEXC=KEXC+1 if(KEXC > 150000): : WRITE(6,548) KEXC 548 print(2X,' def STOPPED: . KEXC=',I7) sys.exit() # endif # FIND GAS IN WHICH EXCITATION OCCURED AND INCREMENT COUNTER if(I <= IDG1): : NGEXC1=NGEXC1+1 else if(I <= IDG2) : NGEXC2=NGEXC2+1 else if(I <= IDG3) : NGEXC3=NGEXC3+1 else if(I <= IDG4) : NGEXC4=NGEXC4+1 else if(I <= IDG5) : NGEXC5=NGEXC5+1 else if(I <= IDG6) : NGEXC6=NGEXC6+1 else: WRITE(6,9911) 9911 print(' def STOPPED: BAD GAS ID IN MONTE') sys.exit() # endif NEXCTOT=NEXCTOT+1 NSTEXC=NSTEXC+1 XSTEXC[KEXC]=X YSTEXC[KEXC]=Y ZSTEXC[KEXC]=Z TSTEXC[KEXC]=ST # endif # endif 6 S2=(S1*S1)/(S1-1.00) # ANISOTROPIC SCATTERING R3=DRAND48(RDUM) if(INDEX[I]: == 1) : R31=DRAND48(RDUM) F3=1.00-R3*ANGCT[IE][I] if(R31 > PSCT[IE][I]: ) F3=-F3 else if(INDEX[I] == 2) : EPSI=PSCT[IE][I] F3=1.00-(2.00*R3*(1.00-EPSI)/(1.00+EPSI*(1.00-2.00*R3))) else: # ISOTROPIC SCATTERING F3=1.00-2.00*R3 # endif THETA0=numpy.arccos(F3) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) if(E < EI): EI=0.00 ARG1=1.00-S1*EI/E ARG1=DMAX1[ARG1][SMALL] D=1.00-F3*math.sqrt(ARG1) E1=E*(1.00-EI/(S1*E)-2.00*D/S2) E1=DMAX1[E1][SMALL] Q=math.sqrt((E/E1)*ARG1)/S1 Q=DMIN1[Q][1.00] THETA=numpy.arcsin(Q*numpy.sin(THETA0)) F6=numpy.cos(THETA) U=(S1-1.00)*(S1-1.00)/ARG1 CSQD=F3*F3 if(F3 < 0.00 and CSQD > U): F6=-1.00*F6 F5=numpy.sin(THETA) DCZ2=DMIN1[DCZ2][1.00] ARGZ=math.sqrt(DCX2*DCX2+DCY2*DCY2) if(ARGZ == 0.00): : DCZ1=F6 DCX1=F9*F5 DCY1=F8*F5 if(NTMPFLG == 1): : # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S > 1.0): F5S=1.0 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=F6S DCX[NCLTMP]=F9S*F5S DCY[NCLTMP]=F8S*F5S NTMPFLG=0 # endif GO TO 190 # endif DCZ1=DCZ2*F6+ARGZ*F5*F8 DCY1=DCY2*F6+(F5/ARGZ)*(DCX2*F9-DCY2*DCZ2*F8) DCX1=DCX2*F6-(F5/ARGZ)*(DCY2*F9+DCX2*DCZ2*F8) if(NTMPFLG == 1): : # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S > 1.0): F5S=1.0 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=DCZ2*F6S+ARGZ*F5S*F8S DCY[NCLTMP]=DCY2*F6S+(F5S/ARGZ)*(DCX2*F9S-DCY2*DCZ2*F8S) DCX[NCLTMP]=DCX2*F6S-(F5S/ARGZ)*(DCY2*F9S+DCX2*DCZ2*F8S) NTMPFLG=0 # endif 190 CONTINUE GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.D-12 # VTOT=CONST9*math.sqrt(E1) CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT # TEST IF ELECTRON IS THERMALISED if(E1 > ETHRM): GO TO 1 # STORE POSITION AND TIME OF THERMALISED ELECTRON 191 CONTINUE K1=K1+1 XST[K1]=X YST[K1]=Y ZST[K1]=Z TST[K1]=ST NFGF[K1]=NFLGFF NFGPP[K1]=NFLGPPP NFGBR[K1]=NFLGBRMM TTIME[K1]=ST-TLAST NELEC=NELEC+1 NETOT=NETOT+1 335 if(K1 == 150000) GO TO 889 if(NELEC == (NCLUS+1): ) : # LAST ELECTRON IN CLUSTER , DO STATISTICS ON CLUSTER CALL STATS(J11,J1) GO TO 210 # endif # GET NEW IONISATION ELECTRON FROM STORE X=XS[NELEC] Y=YS[NELEC] Z=ZS[NELEC] ST=TS[NELEC] NFLGFF=NFLGFC[NELEC] NFLGPPP=NFLGPPC[NELEC] NFLGBRMM=NFLGBRMC[NELEC] TLAST=TS[NELEC] E1=ES[NELEC] DCX1=DCX[NELEC] DCY1=DCY[NELEC] DCZ1=DCZ[NELEC] if(E1 < ETHRM): GO TO 191 GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.D-12 CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT GO TO 1 # MAIN LOOP # end 210 CONTINUE # RESET NUMBER OF EVENTS FOR BAD EVENTS if(IMIP > 2): NDELTA=NDELTA-IBADTOT # WRITE(6,887) EMAX,NEOVFL 887 print(' EMAX=','%.7f' % ,' NEOVFL =',I5) if(EMAX > EFINAL): : WRITE(6,989) EFINAL,EMAX 989 print('INCREASE ENERGY LIMIT FROM','%.6f' % ,' EV TO AT LEAST','%.6f' % ,' EV.') sys.exit() # endif return 889 NLEFT=NCLUS-NELEC WRITE(6,992) NPRIME,NLEFT,NCLUS 992 print(3(/),' WARNING STOPPED: AFTER NPRIME=',I6,' LAST PRIMARY HAS AT LEAST ',I6,' SECONDARIES LEFT TO TRACK,OUT OF ',I6,' ELECTRONS ALREADY IN CLUSTER') sys.exit() return # end def MONTEFC(): # IMPLICIT #real*8 (A-H,O-Z) # IMPLICIT #integer*8 (I-N) COMMON/INPT/NGAS,NSTEP,NANISO,EFINAL,ESTEP,AKT,ARY,TEMPC,TORR,IPEN COMMON/INPT1/NDVEC COMMON/CNSTS1/CONST1,CONST2,CONST3,CONST4,CONST5 COMMON/SETP/TMAX,SMALL,API,ESTART,THETA,PHI,TCFMAX(10),TCFMAX1,RSTART,EFIELD,ETHRM,ECUT,NDELTA,IMIP,IWRITE COMMON/BFLD/EOVB,WB,BTHETA,BMAG COMMON/LARGE/CF(20000,512),EIN(512),TCF(20000),IARRY(512), RGAS(512),IPN(512),WPL(512),IZBR(512),IPLAST,PENFRA[3,512] COMMON/LARGEN/CFN(20000,60),TCFN(20000),SCLENUL(60),NPLAST COMMON/OUTPT/ICOLL(30),NETOT,NPRIME,TMAX1,TIME(300),NNULL, NITOT,ICOLN(512),ICOLNN(60),NREAL,NEXCTOT COMMON/RLTVY/BET[2000],GAM(20000),VC,EMS COMMON/STTS/XST(150000),YST(150000),ZST(150000),TST(150000),TTIME(150000),NFGF(150000),NFGPP(150000),NFGBR(150000),NELEC,NEGION,EST1,EST2 COMMON/STEXC/XSTEXC(150000),YSTEXC(150000),ZSTEXC(150000),TSTEXC(150000),NSTEXC COMMON/STEXCNUL/XSTN(150000),YSTN(150000),ZSTN(150000),TSTN(150000),IDNUL(150000),NEXCNUL COMMON/IONC/DOUBLE(6,20000),CMINIXSC[6],CMINEXSC[6],ECLOSS[6],WPLN[6],ICOUNT,AVPFRAC(3,6) COMMON/IONFL/NC0(512),EC0(512),NG1(512),EG1(512),NG2(512),EG2(512),WKLM(512),DSTFL(512) COMMON/IONMOD/ESPLIT(512,20),IONMODEL(512) COMMON/ANIS/PSCT(20000,512),ANGCT(20000,512),INDEX(512),NISO COMMON/CASRS/ECAS(400),XCAS(400),YCAS(400),ZCAS(400),DRXS(400),DRYS(400),DRZS(400),TT1(400),NFLGF(400),NFLGPP(400),IEVNTL COMMON/COMP/LCMP,LCFLG,LRAY,LRFLG,LPAP,LPFLG,LBRM,LBFLG,LPEFLG COMMON/BREMG/EBRGAM(10),BRDCOSX(10),BRDCOSY(10),BRDCOSZ[10],BRX(10),BRY(10),BRZ[10],BRT(10),EBRTOT[6],NBREM[6] COMMON/CASRSB/ECASB[400],XCASB[400],YCASB[400],ZCASB[400],DRXB[400],DRYB[400],DRZB[400],TTB1(400),NFLGFB[400],NFLGPPB[400],IEVNTLB COMMON/CASRSE/ECASE(400),XCASE(400),YCASE(400),ZCASE(400),DRXCE(400),DRYCE(400),DRZCE(400),TCASE(400),NFLGFE(400),NFLGPPE(400),IEVENTE COMMON/ECASC/NEGAS(512),LEGAS(512),IESHELL(512),IECASC COMMON/IDEXC/NGEXC1,NGEXC2,NGEXC3,NGEXC4,NGEXC5,NGEXC6,IDG1,IDG2,IDG3,IDG4,IDG5,IDG6 DIMENSION XS(150000),YS(150000),ZS(150000),TS(150000),ES(150000),DCX(150000),DCY(150000),DCZ[150000],NFLGFC(150000),NFLGPPC(150000),NFLGBRMC(150000) DIMENSION TEMP(20000) # ------------------------------------------------------------------- # RELATIVISTIC VERSION # CALCULATES COLLISION EVENTS AND UPDATES DIFFUSION AND VELOCITY. # THIS ROUTINE HANDLES TERMINATIONS AT FIXED DRIFT TIMES. # SOLVES MOTION IN COORDINATE SYSTEM WITH BFIELD ALIGNED TO X-AXIS # ELECTRIC FIELD AT AN ANGLE BTHETA IN THE X-Z PLANE. # THE RESULTS FOR THE VELOCITY VECTORS ARE : # ROTATED INTO THE STANDARD COORDINATE FRAME WITH THE ELECTRIC FIELD # ALONG THE Z-AXIS AND THE BFIELD AT AN ANGLE BTHETA TO THE ELECTRIC # FIELD IN THE X-Z PLANE # ------------------------------------------------------------------- # VARYING ENERGY STEPS if(EFINAL <= 140000.): : ESTEP1=(EFINAL-16000.0)/float(4000) else: ESTEP1=20.0 ESTEP2=(EFINAL-92000.0)/float(4000) # endif NPRINT=0 J20000=20000 J300=300 API=numpy.arccos(-1.00) SMALL=1.0D-20 EMAX=0.00 TMAX1=0.00 RDUM=RSTART CONST9=CONST3*0.010 DO 25 I=1,300 25 TIME[I]=0.00 DO 26 I=1,30 26 ICOLL[I]=0 DO 27 I=1,512 27 ICOLN[I]=0 NREAL=0 NNULL=0 NETOT=0 NEXCTOT=0 NITOT=0 NMXADD=0 NTMPFLG=0 # CALC ROTATION MATRIX ANGLES RCS=numpy.cos((BTHETA-90.00)*API/180.00) RSN=numpy.sin((BTHETA-90.00)*API/180.00) # RTHETA=BTHETA*API/180.00 EFZ100=EFIELD*100.00*numpy.sin(RTHETA) EFX100=EFIELD*100.00*numpy.cos(RTHETA) F1=EFIELD*CONST2*numpy.cos(RTHETA) F4=2.00*API EOVBR=EOVB*numpy.sin(RTHETA) THETA1=THETA PHI1=PHI # CALCULATE MAXIMUM COLLISION FREQUENCY TLIM=0.0 DO 111 J=1,20000 TEMP[J]=TCFN[J]+TCF[J] if(TLIM < TEMP[J]: ) TLIM=TEMP[J] 111 CONTINUE NEOVFL=0 J1=0 # START OF PRIMARY EVENT LOOP DO 210 J11=1,NDELTA J1=J1+1 NPRIME=J1 NGEXC1=0 NGEXC2=0 NGEXC3=0 NGEXC4=0 NGEXC5=0 NGEXC6=0 # INITIAL DIRECTION COSINES if(THETA1 == (API/2.0): or NDVEC != 1) : # ONLY ALLOW CASE WHERE DELTA IS ALONG E-FIELD DIRECTION WRITE(6,22) 22 print(2(/),3X,'def STOPPED: ONLY ALLOWED TO HAVE DELTA ELECTRON PRALLEL TO E-FIELD IN CASE WITH ARBITRARY ANGLE FOR B-FIELD') sys.exit() # endif # FIX DELTA TO E - FIELD DIRECTION PHI1=0.00 THETA1=(API/2.0)-RTHETA DCZ1=numpy.cos(THETA1) DCX1=numpy.sin(THETA1)*numpy.cos(PHI1) DCY1=numpy.sin(THETA1)*numpy.sin(PHI1) NFLGFF=0 NFLGPPP=0 NFLGBRMM=0 NFLGHIGH=0 EST1=ESTART # INITIAL VELOCITY E1=ESTART GAM1=(EMS+E1)/EMS GAM12=GAM1 BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.0D-12 # VTOT=CONST9*math.sqrt(E1) CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT X=0.00 Y=0.00 Z=0.00 K1=0 KEXC=0 NSTEXC=0 NEXCNUL=0 NCLUS=0 NELEC=0 NEGION=0 TLAST=0.00 ST=0.00 TDASH=0.00 if(IMIP == 2): GO TO 1 if(IMIP > 2): : # READIN FIRST ELECTRON FROM BETA DECAY OR X-RAY UNTHERMALISED CLUSTERS CALL CASRES(J11,IBADTOT,IBAD1) # SKIP BAD EVENT if(IBAD1 == 1): : J1=J1-1 GO TO 210 # endif else if(IMIP == 1) : # READ IN FIRST ELECTRON FROM MIP INTERACTION CALL CASREM(J11) EST1=ECAS[1] EST2=EST1 # endif X=XCAS[1] Y=YCAS[1] Z=ZCAS[1] ST=TT1[1] TS[1]=TT1[1] E1=ECAS[1] DCZ1=DRZS[1] DCY1=DRYS[1] DCX1=DRXS[1] NFLGFF=NFLGF[1] NFLGPPP=NFLGPP[1] NFLGBRMM=0 NFLGHIGH=NFLGFF GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.0D-12 # VTOT=CONST9*math.sqrt(E1) CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT # PUT REMAINDER OF ELECTRONS INTO CLUSTER STORE ISDUM=0 DO 35 IST=2,IEVNTL ISDUM=ISDUM+1 XS[ISDUM]=XCAS[IST] YS[ISDUM]=YCAS[IST] ZS[ISDUM]=ZCAS[IST] TS[ISDUM]=TT1[IST] ES[ISDUM]=ECAS[IST] DCX[ISDUM]=DRXS[IST] DCY[ISDUM]=DRYS[IST] DCZ[ISDUM]=DRZS[IST] NFLGFC[ISDUM]=NFLGF[IST] NFLGPPC[ISDUM]=NFLGPP[IST] NFLGBRMC[ISDUM]=0 NCLUS=ISDUM if(NFLGFC[IST]: > NFLGHIGH) NFLGHIGH=NFLGFC[IST] 35 CONTINUE GAM12=GAM1 # START OF LOOP FOR NEW ELECTRONS 1 CONTINUE R1=DRAND48(RDUM) T=-math.log(R1)/TLIM+TDASH TDASH=T WBT=WB*T/GAM12 # WBT=WB*T COSWT=numpy.cos(WBT) SINWT=numpy.sin(WBT) DZ=GAM12*(CZ1*SINWT+(EOVBR-CY1)*(1.00-COSWT))/WB # DZ=(CZ1*SINWT+(EOVBR-CY1)*(1.00-COSWT))/WB DX=CX1*T+F1*T*T/GAM12 # DX=CX1*T+F1*T*T E=E1+DZ*EFZ100+DX*EFX100 GAM2=(EMS+E)/EMS BET2=math.sqrt(1.00-1.00/(GAM2+GAM2)) if(E < 0.00): : E=0.0010 # endif # INSERT NEW ALGORITHM TO FIND IE FOR VARYING ENERGY STEP if(IMIP == 1): : IE=int(E/ESTEP)+1 else: if(EFINAL <= 20000.): : IE=int(E/ESTEP)+1 else if(EFINAL <= 140000.) : if(E <= 16000.): : IE=int(E)+1 else: IE=16000+int((E-16000.)/ESTEP1) # endif else: if(E <= 12000.): : IE=int(E)+1 else if(E <= 92000.) : IE=12000+int((E-12000.)/ESTEP1) else: IE=16000+int((E-92000.)/ESTEP2) # endif # endif # endif IE=DMIN0(IE,J20000) # # TEST FOR #real OR NULL COLLISION # R5=DRAND48(RDUM) TEST1=TCF[IE]/TLIM if(R5 <= TEST1): GO TO 137 NNULL=NNULL+1 TEST2=TEMP[IE]/TLIM if(R5 < TEST2): : # TEST FOR NULL LEVELS if(NPLAST == 0): GO TO 1 R2=DRAND48(RDUM) I=0 888 I=I+1 if(CFN[IE][I]: < R2) GOTO 888 # INCREMENT NULL LEVEL SUM NEXCNUL=NEXCNUL+1 ICOLNN[I]=ICOLNN[I]+1 # STORE X Y Z T ID FOR MOLECULAR LIGHT EMISSION AND DISSOCIATION FROM # NULL EXCITATION # NOTE: SMALL APPROX USED POSITION OF PREVIOUS #real COLLISION XSTN[NEXCNUL]=X YSTN[NEXCNUL]=Y ZSTN[NEXCNUL]=Z TSTN[NEXCNUL]=ST IDNUL[NEXCNUL]=I GO TO 1 else: # NULL GO TO 1 # endif # # CALCULATE DIRECTION COSINES AND POSITIONS AT INSTANT BEFORE COLLISION 137 T2=T*T if(E > EMAX): EMAX=E if(T > TMAX1): TMAX1=T TDASH=0.00 NREAL=NREAL+1 # CALC VELOCITY # CX2=CX1+2.0*F1*T CX2=CX1+2.0*F1*T/GAM12 CY2=(CY1-EOVBR)*COSWT+CZ1*SINWT+EOVBR CZ2=CZ1*COSWT-(CY1-EOVBR)*SINWT # CALC DIRECTION COSINES VTOT=math.sqrt(CX2*CX2+CY2*CY2+CZ2*CZ2) DCX2=CX2/VTOT DCY2=CY2/VTOT DCZ2=CZ2/VTOT # CALC NEW POSITION X=X+DX Y=Y+EOVBR*T+GAM12*((CY1-EOVBR)*SINWT+CZ1*(1.00-COSWT))/WB # Y=Y+EOVBR*T+((CY1-EOVBR)*SINWT+CZ1*(1.00-COSWT))/WB Z=Z+DZ GAM12=(GAM1+GAM2)/2.00 ST=ST+T IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 # --------------------------------------------------------------------- # DETERMINATION OF #real COLLISION TYPE # --------------------------------------------------------------------- R2=DRAND48(RDUM) I=0 140 I=I+1 if(CF[IE][I]: < R2) GO TO 140 #************************************************************ # CHECK IF BREMSSTRAHLUNG if(IZBR[I]: != 0 and LBRM == 1) : NFLGBRMM=1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 DO 141 KNGS=1,NGAS if(IPT == (KNGS*5): -1) GO TO 142 141 CONTINUE 142 IATOMNO=IZBR[I] CALL BREMS(IATOMNO,E,DCX2,DCY2,DCZ2,EOUT,EDCX,EDCY,EDCZ,EGAMMA,GDCX,GDCY,GDCZ) NBREM[KNGS]=NBREM[KNGS]+1 EBRTOT[KNGS]=EBRTOT[KNGS]+EGAMMA # WRITE(6,668) EGAMMA,J11 # 668 print(' BREM EGAMMA=','%.4f' % ,' EVENT NO=',I5) # GET NEW DRCOS DRCOSY DRCOSX AND ENERGY OF ELECTRON E1=EOUT DCX1=EDCX DCY1=EDCY DCZ1=EDCZ # RUN BREMSSTRAHLUNG GAMMA THROUGH CASCADE : STORE CONVERTED # ELECTRONS IN COMMON/CASRSB/ # CALL BREMSCASC(J11,EGAMMA,X,Y,Z,ST,GDCX,GDCY,GDCZ,ILOW) # BREMSSTRAHLUNG ENERGY TOO LOW TO IONISE if(ILOW == 1): GO TO 190 # # STORE BREMSSTRAHLUNG DATA IN CLUSTER STORE DO 890 KBR=1,IEVNTLB NCLUS=NCLUS+1 if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL sys.exit() # endif ES[NCLUS]=ECASB[KBR] XS[NCLUS]=XCASB[KBR] YS[NCLUS]=YCASB[KBR] ZS[NCLUS]=ZCASB[KBR] TS[NCLUS]=TTB1[KBR] DCX[NCLUS]=DRXB[KBR] DCY[NCLUS]=DRYB[KBR] DCZ[NCLUS]=DRZB[KBR] NFLGFC[NCLUS]=NFLGFB[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPB[KBR] NFLGBRMC[NCLUS]=2 890 CONTINUE if(NFLGFC[NCLUS]: > NFLGHIGH) NFLGHIGH=NFLGFC[NCLUS] GO TO 190 # endif 891 CONTINUE #**************************************************************** # S1=RGAS[I] S1=1.00+GAM2*(RGAS[I]-1.00) EI=EIN[I] if(E < EI): : EI=E-0.00010 # endif if(IPN[I]: == 0) GO TO 666 # ATTACHMENT if(IPN[I]== -1) : NETOT=NETOT+1 NITOT=NITOT+1 NELEC=NELEC+1 NEGION=NEGION+1 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 IT=int(T+1.00) IT=DMIN0[IT][J300] TIME[IT]=TIME[IT]+1.00 GO TO 335 # endif EISTR=EI if(IONMODEL[I]> 0): # CALCULATE SECONDARY ENERGY,ESEC,IN IONISATION COLLISION USING # FIVE DIFFERENT MODELS CALL IONSPLIT(I,E,EI,ESEC) GO TO 544 # endif R9=DRAND48(RDUM) # USE OPAL PETERSON AND BEATY SPLITTING FACTOR. ESEC=WPL[I]*TAN(R9*ATAN((E-EI)/(2.00*WPL[I]))) ESEC=WPL[I]*(ESEC/WPL[I])**0.9524 544 CONTINUE EI=ESEC+EI # STORE POSITION ,ENERGY, DIRECTION COSINES AND TIME OF GENERATION # OF SECONDARY IONISATION ELECTRON NCLUS=NCLUS+1 NMXADD=MAX[NCLUS][NMXADD] if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL 546 print(2X,' def STOPPED: . NCLUS=',I7,' NREAL=',I10) sys.exit() # endif XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST ES[NCLUS]=ESEC NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM NTMPFLG=1 NCLTMP=NCLUS # RANDOMISE SECONDARY ELECTRON DIRECTION # R3=drand48(RDUM) # F3=1.0-2.00*R3 # THETA0=DACOS(F3) # F6=DCOS(THETA0) # F5=DSIN(THETA0) # R4=drand48(RDUM) # PHI0=F4*R4 # F8=DSIN(PHI0) # F9=DCOS(PHI0) # DCX[NCLUS]=F9*F5 # DCY[NCLUS]=F8*F5 # DCZ[NCLUS]=F6 #********************************************************* if(IECASC == 0): GO TO 333 if(LEGAS[I]: == 0) GO TO 333 # USE COMPLETE CASCADE FOR ELECTRON IONISATION KG1=NEGAS[I] LG1=LEGAS[I] IGSHEL=IESHELL[I] CALL CASCADEE(J11,KG1,LG1,X,Y,Z,ST,ESEC,IGSHEL) # # STORE CASCADE IN CLUSTER STORE # ETSUM=0.0 DO 844 KBR=1,IEVENTE NCLUS=NCLUS+1 if(NCLUS > 150000): : WRITE(6,546) NCLUS,NREAL sys.exit() # endif ES[NCLUS]=ECASE[KBR] ETSUM=ETSUM+ES[NCLUS] XS[NCLUS]=XCASE[KBR] YS[NCLUS]=YCASE[KBR] ZS[NCLUS]=ZCASE[KBR] TS[NCLUS]=TCASE[KBR] DCX[NCLUS]=DRXCE[KBR] DCY[NCLUS]=DRYCE[KBR] DCZ[NCLUS]=DRZCE[KBR] NFLGFC[NCLUS]=NFLGFE[KBR]+NFLGHIGH NFLGPPC[NCLUS]=NFLGPPE[KBR] NFLGBRMC[NCLUS]=NFLGBRMM 844 CONTINUE if(NFLGFC[NCLUS]: > NFLGHIGH) NFLGHIGH=NFLGFC[NCLUS] GO TO 666 #********************************************************* # STORE POSSIBLE SHELL EMISSIONS AUGER OR FLUORESCENCE 333 if(EISTR > 30.0) : # TEST IF FLUORESCENCE EMISSION IFLTST=0: if(WKLM[I]: > 0.0) : R9=DRAND48(RDUM) if(R9 < WKLM[I]: ) IFLTST=1 # endif if(IFLTST == 0): : # AUGER EMISSION WITHOUT FLUORESCENCE NAUG=NC0[I] EAVAUG=EC0[I]/float(NAUG) DO 700 JFL=1,NC0[I] NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z TS[NCLUS]=ST NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 700 CONTINUE else: # AUGER EMISSION AND FLUORESENCE if(NG2[I]: == 0) GO TO 702 NAUG=NG2[I] EAVAUG=EG2[I]/float(NAUG) DO 701 JFL=1,NG2[I] NCLUS=NCLUS+1 XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 701 CONTINUE 702 if(NG1[I] == 0) GO TO 704 NAUG=NG1[I] EAVAUG=EG1[I]/float(NAUG) R9=DRAND48(RDUM) DFL=-math.log(R9)*DSTFL[I] DO 703 JFL=1,NG1[I] NCLUS=NCLUS+1 R3=DRAND48(RDUM) THEFL=numpy.arccos(1.0-2.00*R3) R4=DRAND48(RDUM) PHIFL=F4*R4 XS[NCLUS]=X+DFL*numpy.sin(THEFL)*numpy.cos(PHIFL) YS[NCLUS]=Y+DFL*numpy.sin(THEFL)*numpy.sin(PHIFL) ZS[NCLUS]=Z+DFL*numpy.cos(THEFL) NFLGFC[NCLUS]=NFLGHIGH+1 NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM TS[NCLUS]=ST ES[NCLUS]=EAVAUG R3=DRAND48(RDUM) F3=1.0-2.00*R3 THETA0=numpy.arccos(F3) F6=numpy.cos(THETA0) F5=numpy.sin(THETA0) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) DCX[NCLUS]=F9*F5 DCY[NCLUS]=F8*F5 DCZ[NCLUS]=F6 NFLGHIGH=NFLGFC[NCLUS] 703 CONTINUE 704 CONTINUE # endif # endif # # GENERATE SCATTERING ANGLES AND UPDATE LABORATORY COSINES AFTER # COLLISION ALSO UPDATE ENERGY OF ELECTRON. # 666 IPT=IARRY[I] ICOLL[IPT]=ICOLL[IPT]+1 ICOLN[I]=ICOLN[I]+1 # IF EXCITATION : ADD PROBABILITY ,PENFRA(1,I), OF TRANSFER TO GIVE # IONISATION OF THE OTHER GASES IN MIXTURE if(IPEN == 0 or NGAS == 1): GO TO 5 if(PENFRA[1][I] != 0.0): : RAN=DRAND48(RDUM) if(RAN > PENFRA[1][I]): GO TO 5 NCLUS=NCLUS+1 # ENTER HERE POSSIBLE DELOCALISATION LENGTH FOR PENNING TRANSFER if(PENFRA[2][I] == 0.0): : XS[NCLUS]=X YS[NCLUS]=Y ZS[NCLUS]=Z NFLGFC[NCLUS]=NFLGFF NFLGPPC[NCLUS]=NFLGPPP NFLGBRMC[NCLUS]=NFLGBRMM GO TO 667 # endif ASIGN=1.0 RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN XS[NCLUS]=X-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN YS[NCLUS]=Y-math.log(RAN)*PENFRA[2][I]*ASIGN RAN=DRAND48(RDUM) RAN1=DRAND48(RDUM) if(RAN1 < 0.5): ASIGN=-ASIGN ZS[NCLUS]=Z-math.log(RAN)*PENFRA[2][I]*ASIGN 667 RAN=DRAND48(RDUM) TS[NCLUS]=ST-math.log(RAN)*PENFRA[3][I] # ASSIGN EXCESS ENERGY OF 1EV TO PENNING CREATED ELECTRON ES[NCLUS]=1.0 DCX[NCLUS]=DCX1 DCY[NCLUS]=DCY1 DCZ[NCLUS]=DCZ1 GO TO 6 # endif # GO TO 6 # CALCULATE SUM OF EXCITATION PER CLUSTER AND STORE EXCITATION X Y Z T 5 if(IPN[I] == 0) : if((RGAS[I]: *EIN[I]) > 4.0) : KEXC=KEXC+1 if(KEXC > 150000): : WRITE(6,548) KEXC 548 print(2X,' def STOPPED: . KEXC=',I7) sys.exit() # endif # FIND GAS IN WHICH EXCITATION OCCURED AND INCREMENT COUNTER if(I <= IDG1): : NGEXC1=NGEXC1+1 else if(I <= IDG2) : NGEXC2=NGEXC2+1 else if(I <= IDG3) : NGEXC3=NGEXC3+1 else if(I <= IDG4) : NGEXC4=NGEXC4+1 else if(I <= IDG5) : NGEXC5=NGEXC5+1 else if(I <= IDG6) : NGEXC6=NGEXC6+1 else: WRITE(6,9911) 9911 print(' def STOPPED: BAD GAS ID IN MONTE') sys.exit() # endif NEXCTOT=NEXCTOT+1 NSTEXC=NSTEXC+1 XSTEXC[KEXC]=X YSTEXC[KEXC]=Y ZSTEXC[KEXC]=Z TSTEXC[KEXC]=ST # endif # endif 6 S2=(S1*S1)/(S1-1.00) # ANISOTROPIC SCATTERING R3=DRAND48(RDUM) if(INDEX[I]: == 1) : R31=DRAND48(RDUM) F3=1.00-R3*ANGCT[IE][I] if(R31 > PSCT[IE][I]: ) F3=-F3 else if(INDEX[I] == 2) : EPSI=PSCT[IE][I] F3=1.00-(2.00*R3*(1.00-EPSI)/(1.00+EPSI*(1.00-2.00*R3))) else: # ISOTROPIC SCATTERING F3=1.00-2.00*R3 # endif THETA0=numpy.arccos(F3) R4=DRAND48(RDUM) PHI0=F4*R4 F8=numpy.sin(PHI0) F9=numpy.cos(PHI0) if(E < EI): EI=0.00 ARG1=1.00-S1*EI/E ARG1=DMAX1[ARG1][SMALL] D=1.00-F3*math.sqrt(ARG1) E1=E*(1.00-EI/(S1*E)-2.00*D/S2) E1=DMAX1[E1][SMALL] Q=math.sqrt((E/E1)*ARG1)/S1 Q=DMIN1[Q][1.00] THETA=numpy.arcsin(Q*numpy.sin(THETA0)) F6=numpy.cos(THETA) U=(S1-1.00)*(S1-1.00)/ARG1 CSQD=F3*F3 if(F3 < 0.00 and CSQD > U): F6=-1.00*F6 F5=numpy.sin(THETA) DCZ2=DMIN1[DCZ2][1.00] ARGZ=math.sqrt(DCX2*DCX2+DCY2*DCY2) if(ARGZ == 0.00): DCZ1=F6 DCX1=F9*F5 DCY1=F8*F5 if(NTMPFLG == 1): # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S > 1.0): F5S=1.0 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=F6S DCX[NCLTMP]=F9S*F5S DCY[NCLTMP]=F8S*F5S NTMPFLG=0 # endif pass # endif else: DCZ1=DCZ2*F6+ARGZ*F5*F8 DCY1=DCY2*F6+(F5/ARGZ)*(DCX2*F9-DCY2*DCZ2*F8) DCX1=DCX2*F6-(F5/ARGZ)*(DCY2*F9+DCX2*DCZ2*F8) if(NTMPFLG == 1): # USE FREE KINEMATICS FOR IONISATION SECONDARY ANGLES F5S=F5*math.sqrt(E1/ES[NCLTMP]) if(F5S > 1.0): F5S=1.0 THSEC=numpy.arcsin(F5S) F5S=numpy.sin(THSEC) F6S=numpy.cos(THSEC) if(F6 < 0.0): F6S=-F6S PHIS=PHI0+API if(PHIS > F4): PHIS=PHI0-F4 F8S=numpy.sin(PHIS) F9S=numpy.cos(PHIS) DCZ[NCLTMP]=DCZ2*F6S+ARGZ*F5S*F8S DCY[NCLTMP]=DCY2*F6S+(F5S/ARGZ)*(DCX2*F9S-DCY2*DCZ2*F8S) DCX[NCLTMP]=DCX2*F6S-(F5S/ARGZ)*(DCY2*F9S+DCX2*DCZ2*F8S) NTMPFLG=0 # endif #190 CONTINUE GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.E-12 # VTOT=CONST9*math.sqrt(E1) CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT # TEST IF ELECTRON IS THERMALISED if(E1 > ETHRM): GO TO 1 191 CONTINUE # STORE POSITION AND TIME OF THERMALISED ELECTRONS K1=K1+1 # ROTATE INTO COORDINATE SYSTEM WITH EFIELD ALONG Z ZR=Z*RCS-X*RSN YR=Y XR=Z*RSN+X*RCS XST[K1]=XR YST[K1]=YR ZST[K1]=ZR TST[K1]=ST NFGF[K1]=NFLGFF NFGPP[K1]=NFLGPPP NFGBR[K1]=NFLGBRMM TTIME[K1]=ST-TLAST NELEC=NELEC+1 NETOT=NETOT+1 335 if(K1 == 150000): GO TO 889 if(NELEC == (NCLUS+1)): # LAST ELECTRON IN CLUSTER. DO STATISTICS ON CLUSTER STATS(J11,J1) GO TO 210 # endif # GET NEW IONISATION ELECTRON FROM STORE X=XS[NELEC] Y=YS[NELEC] Z=ZS[NELEC] ST=TS[NELEC] NFLGFF=NFLGFC[NELEC] NFLGPPP=NFLGPPC[NELEC] NFLGBRMM=NFLGBRMC[NELEC] TLAST=TS[NELEC] E1=ES[NELEC] DCX1=DCX[NELEC] DCY1=DCY[NELEC] DCZ1=DCZ[NELEC] if(E1 < ETHRM): GO TO 191 GAM1=(EMS+E1)/EMS BET1=math.sqrt(1.00-1.00/(GAM1*GAM1)) VTOT=BET1*VC*1.E-12 CX1=DCX1*VTOT CY1=DCY1*VTOT CZ1=DCZ1*VTOT GO TO 1 # MAIN LOOP # end 210 CONTINUE # RESET NUMBER OF EVENTS FOR BAD EVENTS if(IMIP > 2): NDELTA=NDELTA-IBADTOT print(' EMAX=','%.7f' % EMAX,' NEOVFL =',NEOVFL) if(EMAX > EFINAL): print('INCREASE ENERGY LIMIT FROM','%.6f' % EFINAL,' EV TO AT LEAST','%.6f' % EMAX,' EV.') sys.exit() # endif return 889 NLEFT=NCLUS-NELEC print('\n\n\n WARNING STOPPED: AFTER NPRIME=',NPRIME,' LAST PRIMARY HAS AT LEAST ',NLEFT,' SECONDARIES LEFT TO TRACK. OUT OF ',NCLUS,' ELECTRONS ALREADY IN CLUSTER') sys.exit() return # end

30.64421

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0.502865

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0.018541

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0

1

0

null

0

1

0

8

27de1f1415a967826ef5c397e0c4153d0dfcb6e7

1,258

py

Python

008.py

wittycoder/project_euler

27878bcccf6c1d4cd6e51b220d8575ad398c7762

[ "MIT" ]

null

008.py

wittycoder/project_euler

27878bcccf6c1d4cd6e51b220d8575ad398c7762

[ "MIT" ]

null

008.py

wittycoder/project_euler

27878bcccf6c1d4cd6e51b220d8575ad398c7762

[ "MIT" ]

null

from functools import reduce num = '7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450' digits = 13 max_prod = 0 for i in range(0, len(num)+1-digits): #print(num[i:i+4]) prod = reduce((lambda x, y: int(x) * int(y)), num[i:i+digits]) if prod > max_prod: max_prod = prod print(max_prod)

89.857143

1,008

0.919714

48

1,258

24.020833

0.5

0.024284

0.008673

0

0.839033

0.0469

1,258

14

1,009

89.857143

0.122602

0.013514

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0.111111

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0

1

0

1

null

1

0

7

fd77793cec6b95df2b9cf05805f92c4d78e59c96

5,752

py

Python

tests/test_unit/test_detect_pattern.py

loftwah/CleverCSV

c7d0cab7b19d969dbbda2bcafb414ca2973facf5

[ "MIT" ]

989

2019-02-22T12:14:17.000Z

2022-03-28T01:33:20.000Z

tests/test_unit/test_detect_pattern.py

loftwah/CleverCSV

c7d0cab7b19d969dbbda2bcafb414ca2973facf5

[ "MIT" ]

27

2019-12-22T00:09:28.000Z

2022-03-30T22:45:50.000Z

tests/test_unit/test_detect_pattern.py

loftwah/CleverCSV

c7d0cab7b19d969dbbda2bcafb414ca2973facf5

[ "MIT" ]

55

2019-10-22T13:09:53.000Z

2022-01-03T04:28:26.000Z

# -*- coding: utf-8 -*- """ Unit tests for the pattern score. Author: Gertjan van den Burg """ import unittest from clevercsv import detect_pattern from clevercsv.dialect import SimpleDialect class PatternTestCase(unittest.TestCase): """ Abstraction tests """ def test_abstraction_1(self): out = detect_pattern.make_abstraction( "A,B,C", SimpleDialect(delimiter=",", quotechar="", escapechar="") ) exp = "CDCDC" self.assertEqual(exp, out) def test_abstraction_2(self): out = detect_pattern.make_abstraction( "A,\rA,A,A\r", SimpleDialect(delimiter=",", quotechar="", escapechar=""), ) exp = "CDCRCDCDC" self.assertEqual(exp, out) def test_abstraction_3(self): out = detect_pattern.make_abstraction( "a,a,\n,a,a\ra,a,a\r\n", SimpleDialect(delimiter=",", quotechar="", escapechar=""), ) exp = "CDCDCRCDCDCRCDCDC" self.assertEqual(exp, out) def test_abstraction_4(self): out = detect_pattern.make_abstraction( 'a,"bc""d""e""f""a",\r\n', SimpleDialect(delimiter=",", quotechar='"', escapechar=""), ) exp = "CDCDC" self.assertEqual(exp, out) def test_abstraction_5(self): out = detect_pattern.make_abstraction( 'a,"bc""d"",|"f|""', SimpleDialect(delimiter=",", quotechar='"', escapechar="|"), ) exp = "CDC" self.assertEqual(exp, out) def test_abstraction_6(self): out = detect_pattern.make_abstraction( ",,,", SimpleDialect(delimiter=",", quotechar="", escapechar="") ) exp = "CDCDCDC" self.assertEqual(exp, out) def test_abstraction_7(self): out = detect_pattern.make_abstraction( ',"",,', SimpleDialect(delimiter=",", quotechar='"', escapechar="") ) exp = "CDCDCDC" self.assertEqual(exp, out) def test_abstraction_8(self): out = detect_pattern.make_abstraction( ',"",,\r\n', SimpleDialect(delimiter=",", quotechar='"', escapechar=""), ) exp = "CDCDCDC" self.assertEqual(exp, out) """ Escape char tests """ def test_abstraction_9(self): out = detect_pattern.make_abstraction( "A,B|,C", SimpleDialect(delimiter=",", quotechar="", escapechar="|"), ) exp = "CDC" self.assertEqual(exp, out) def test_abstraction_10(self): out = detect_pattern.make_abstraction( 'A,"B,C|"D"', SimpleDialect(delimiter=",", quotechar='"', escapechar="|"), ) exp = "CDC" self.assertEqual(exp, out) def test_abstraction_11(self): out = detect_pattern.make_abstraction( "a,|b,c", SimpleDialect(delimiter=",", quotechar="", escapechar="|"), ) exp = "CDCDC" self.assertEqual(exp, out) def test_abstraction_12(self): out = detect_pattern.make_abstraction( "a,b|,c", SimpleDialect(delimiter=",", quotechar="", escapechar="|"), ) exp = "CDC" self.assertEqual(exp, out) def test_abstraction_13(self): out = detect_pattern.make_abstraction( 'a,"b,c|""', SimpleDialect(delimiter=",", quotechar='"', escapechar="|"), ) exp = "CDC" self.assertEqual(exp, out) def test_abstraction_14(self): out = detect_pattern.make_abstraction( "a,b||c", SimpleDialect(delimiter=",", quotechar="", escapechar="|"), ) exp = "CDC" self.assertEqual(exp, out) def test_abstraction_15(self): out = detect_pattern.make_abstraction( 'a,"b|"c||d|"e"', SimpleDialect(delimiter=",", quotechar='"', escapechar="|"), ) exp = "CDC" self.assertEqual(exp, out) def test_abstraction_16(self): out = detect_pattern.make_abstraction( 'a,"b|"c||d","e"', SimpleDialect(delimiter=",", quotechar='"', escapechar="|"), ) exp = "CDCDC" self.assertEqual(exp, out) """ Fill empties """ def test_fill_empties_1(self): out = detect_pattern.fill_empties("DDD") exp = "CDCDCDC" self.assertEqual(exp, out) """ Pattern Score tests """ def test_pattern_score_1(self): # theta_1 from paper data = ( "7,5; Mon, Jan 12;6,40\n100; Fri, Mar 21;8,23\n8,2; Thu, Sep 17;" '2,71\n538,0;;7,26\n"NA"; Wed, Oct 4;6,93' ) d = SimpleDialect(delimiter=",", quotechar="", escapechar="") out = detect_pattern.pattern_score(data, d) exp = 7 / 4 self.assertAlmostEqual(exp, out) def test_pattern_score_2(self): # theta_2 from paper data = ( "7,5; Mon, Jan 12;6,40\n100; Fri, Mar 21;8,23\n8,2; Thu, Sep 17;" '2,71\n538,0;;7,26\n"NA"; Wed, Oct 4;6,93' ) d = SimpleDialect(delimiter=";", quotechar="", escapechar="") out = detect_pattern.pattern_score(data, d) exp = 10 / 3 self.assertAlmostEqual(exp, out) def test_pattern_score_3(self): # theta_3 from paper data = ( "7,5; Mon, Jan 12;6,40\n100; Fri, Mar 21;8,23\n8,2; Thu, Sep 17;" '2,71\n538,0;;7,26\n"NA"; Wed, Oct 4;6,93' ) d = SimpleDialect(delimiter=";", quotechar='"', escapechar="") out = detect_pattern.pattern_score(data, d) exp = 10 / 3 self.assertAlmostEqual(exp, out) if __name__ == "__main__": unittest.main()

28.76

79

0.547114

627

5,752

4.866029

0.154705

0.089479

0.104884

0.255326

0.856441

0.831858

0.810226

0.760406

0.722714

0.697148

0

0.034158

0.297636

5,752

199

80

28.904523

0.72104

0.02799

0

0.534247

0

0.041096

0.113021

0.021214

0

0.136986

1

0.136986

false

0

0.020548

0

0.164384

0

null

0

1

0

null

0

7

fd7bbce9b5f593c37883d20ef97f1c6d6ce75522

6,784

py

Python

tests/test_algorithms/test_transformTextToIndex.py

elangovana/kegg-pathway-extractor

08e9a28199bb4454e2e1a09c5d833f243f6f5768

[ "MIT" ]

10

2019-12-17T01:17:06.000Z

2022-02-25T22:08:09.000Z

tests/test_algorithms/test_transformTextToIndex.py

elangovana/kegg-pathway-extractor

08e9a28199bb4454e2e1a09c5d833f243f6f5768

[ "MIT" ]

2

2021-03-31T18:40:32.000Z

2021-12-13T20:15:20.000Z

tests/test_algorithms/test_transformTextToIndex.py

elangovana/kegg-pathway-extractor

08e9a28199bb4454e2e1a09c5d833f243f6f5768

[ "MIT" ]

2

2020-08-25T19:31:33.000Z

2021-11-11T15:15:02.000Z

from unittest import TestCase from unittest.mock import MagicMock from torch.utils.data import DataLoader from algorithms.transform_text_index import TransformTextToIndex class TestTransformTextToIndex(TestCase): def test_transform_no_vocab(self): mock_dataset = MagicMock() initial_vocab_dict = None # ["random", "initial"] mock_dataset.data = [[["This is sample text", "entity1", "entity2", "phosphorylation"], ["yes"]], [["Completey random text2", "entity11", "entity12", "phosphorylation1"], ["no"]]] max_feature_lens = [10, 1, 1, 1] # Unique words + pad character ( ignore labels) expected_unique_item_no = 13 + 1 mock_dataset.__len__.return_value = len(mock_dataset.data) mock_dataset.__getitem__.side_effect = lambda i: (mock_dataset.data[i][0], mock_dataset.data[i][1]) sut = TransformTextToIndex(max_feature_lens, vocab_dict=initial_vocab_dict, min_vocab_doc_frequency=1) data_loader = DataLoader(mock_dataset, batch_size=1) # Act actual = list(sut.fit_transform(data_loader)) # Assert the max feature length matchs unique_items = set() for b, y in actual: for ci, c_tensor in enumerate(b): c = c_tensor.tolist() for r in c: unique_items = unique_items.union(r) feature_len = max_feature_lens[ci] self.assertEqual(feature_len, len(r), "The feature length for column {} should match the max_feature_length".format( feature_len)) # Assert self.assertEqual(expected_unique_item_no, len(unique_items), "The number of unique words doesnt match to unique indexes including padding{}".format( unique_items)) def test_transform_min_frequnct(self): mock_dataset = MagicMock() initial_vocab_dict = None # ["random", "initial"] mock_dataset.data = [[["This is sample sample text", "entity1", "entity2", "phosphorylation"], ["yes"]], [["Completey random random sample text2", "entity11", "entity12", "phosphorylation1"], ["no"]]] max_feature_lens = [10, 1, 1, 1] # Unique words + pad character ( ignore labels) expected_unique_item_no = 1 + 1 + 1 # unknown words mock_dataset.__len__.return_value = len(mock_dataset.data) mock_dataset.__getitem__.side_effect = lambda i: (mock_dataset.data[i][0], mock_dataset.data[i][1]) sut = TransformTextToIndex(max_feature_lens, vocab_dict=initial_vocab_dict, min_vocab_doc_frequency=2) data_loader = DataLoader(mock_dataset, batch_size=1) # Act actual = list(sut.fit_transform(data_loader)) # Assert the max feature length matchs unique_items = set() for b, y in actual: for ci, c_tensor in enumerate(b): c = c_tensor.tolist() for r in c: unique_items = unique_items.union(r) feature_len = max_feature_lens[ci] self.assertEqual(feature_len, len(r), "The feature length for column {} should match the max_feature_length".format( feature_len)) # Assert self.assertEqual(expected_unique_item_no, len(unique_items), "The number of unique words doesnt match to unique indexes including padding{}".format( unique_items)) def test_transform_with_vocab(self): mock_dataset = MagicMock() initial_vocab_dict = {"random": 0, "initial": 1} mock_dataset.data = [[["This is sample text", "entity1", "entity2", "phosphorylation"], ["yes"]], [["Minoritt word random unknown", "entity1", "entity2", "phosphorylation"], ["no"]]] max_feature_lens = [10, 1, 1, 1] # Unique words + pad character ( ignore labels) expected_unique_item_no = 12 mock_dataset.__len__.return_value = len(mock_dataset.data) mock_dataset.__getitem__.side_effect = lambda i: (mock_dataset.data[i][0], mock_dataset.data[i][1]) sut = TransformTextToIndex(max_feature_lens, vocab_dict=initial_vocab_dict, use_dataset_vocab=True, min_vocab_doc_frequency=1) data_loader = DataLoader(mock_dataset, batch_size=2) # Act vocab_dict = sut.construct_vocab_dict(data_loader) sut.vocab_dict = vocab_dict actual = list(sut.fit_transform(data_loader)) # Assert the max feature length matchs unique_items = set() for b, y in actual: for ci, c_tensor in enumerate(b): c = c_tensor.tolist() for r in c: unique_items = unique_items.union(r) feature_len = max_feature_lens[ci] self.assertEqual(feature_len, len(r), "The feature length for column {} should match the max_feature_length".format( feature_len)) # Assert self.assertEqual(expected_unique_item_no, len(unique_items), "The number of unique words doesnt match to unique indexes including padding{}".format( unique_items)) def test_transform_pad(self): """ Test case to make ensure that pad index is zero """ mock_dataset = MagicMock() initial_vocab_dict = {"random": 0, "initial": 1} mock_dataset.data = [[["This is sample text", "entity1", "entity2", "phosphorylation"], ["yes"]], [["This is sample text2", "entity1", "entity2", "phosphorylation"], ["no"]]] max_feature_lens = [10, 1, 1, 1] # Unique words + pad character ( ignore labels) expected_unique_item_no = 9 mock_dataset.__len__.return_value = len(mock_dataset.data) mock_dataset.__getitem__.side_effect = lambda i: (mock_dataset.data[i][0], mock_dataset.data[i][1]) sut = TransformTextToIndex(max_feature_lens, vocab_dict=initial_vocab_dict, use_dataset_vocab=True, min_vocab_doc_frequency=1) data_loader = DataLoader(mock_dataset, batch_size=2) # Act vocab_dict = sut.construct_vocab_dict(data_loader) # Assert the max feature length matchs self.assertEqual(vocab_dict[sut.pad_token()], 0, "Index of pas token {} must be zero".format(sut.pad_token()))

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7

fdd70e73afa9c1b5c8e686cc00e57881e761fc19

96

py

Python

rbig_jax/density/test_histogram.py

jejjohnson/rbig_jax

112e064d5b62631aa03b7563c9eb9f115ab23eb0

[ "MIT" ]

null

rbig_jax/density/test_histogram.py

jejjohnson/rbig_jax

112e064d5b62631aa03b7563c9eb9f115ab23eb0

[ "MIT" ]

null

rbig_jax/density/test_histogram.py

jejjohnson/rbig_jax

112e064d5b62631aa03b7563c9eb9f115ab23eb0

[ "MIT" ]

null

import pytest from src.density.histogram import get_bin_edges def test_bin_edges(): pass

12

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8

e329156c3c46920af5c8cf6aea71ef24c0a1102e

25

py

Python

tests/bytecode/mp-tests/slice2.py

LabAixBidouille/micropython

11aa6ba456287d6c80598a7ebbebd2887ce8f5a2

[ "MIT" ]

303

2015-07-11T17:12:55.000Z

2018-01-08T03:02:37.000Z

tests/bytecode/mp-tests/slice2.py

LabAixBidouille/micropython

11aa6ba456287d6c80598a7ebbebd2887ce8f5a2

[ "MIT" ]

13

2016-05-12T16:51:22.000Z

2018-01-10T22:33:25.000Z

tests/bytecode/mp-tests/slice2.py

LabAixBidouille/micropython

11aa6ba456287d6c80598a7ebbebd2887ce8f5a2

[ "MIT" ]

26

2018-01-18T09:15:33.000Z

2022-02-07T13:09:14.000Z

x = x[a, b] x[a, b] = x

6.25

11

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8

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25

3

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false

0

1

null

1

0

1

0

null

0

7

e33044dac4ac02b711b219fcdbe7973fa99da2dc

1,805

py

Python

Day 4/Rock_Paper_Scissors.py

hamzaoda/100-Days-of-Code---The-Complete-Python-Pro-Bootcamp-for-2021

5340007d8405df2e29643b47d3ff9fa4f7af9e10

[ "Unlicense" ]

null

Day 4/Rock_Paper_Scissors.py

hamzaoda/100-Days-of-Code---The-Complete-Python-Pro-Bootcamp-for-2021

5340007d8405df2e29643b47d3ff9fa4f7af9e10

[ "Unlicense" ]

null

Day 4/Rock_Paper_Scissors.py

hamzaoda/100-Days-of-Code---The-Complete-Python-Pro-Bootcamp-for-2021

5340007d8405df2e29643b47d3ff9fa4f7af9e10

[ "Unlicense" ]

null

import random rock = ''' _______ ---' ____) (_____) (_____) (____) ---.__(___) ''' paper = ''' _______ ---' ____)____ ______) _______) _______) ---.__________) ''' scissors = ''' _______ ---' ____)____ ______) __________) (____) ---.__(___) ''' game_images = [rock, paper, scissors] user_choice=int(input("what is your choice? 0 for Rock, 1 for Paper, 2 for scissors :")) computer_choice=random.randint(0,2) if user_choice == 0: print(f"You Choosed\n {game_images[user_choice]}") if computer_choice == 0: print(f"Computer Choose{game_images[computer_choice]}") print("it's Draw ") elif computer_choice == 1: print(f"Computer Choose{game_images[computer_choice]}") print("You Have Lost :(") else : print(f"Computer Choose{game_images[computer_choice]}") print("You Have Win! :)") elif user_choice == 1: print(f"You Choosed\n {game_images[user_choice]}") if computer_choice == 0: print(f"Computer Choose{game_images[computer_choice]}") print("You Have Win! :)") elif computer_choice == 1: print(f"Computer Choose{game_images[computer_choice]}") print("it's Draw ") else : print(f"Computer Choose{game_images[computer_choice]}") print("You Have Lost :(") else: print(f"You Choosed\n {game_images[user_choice]}") if computer_choice == 0: print(f"Computer Choose{game_images[computer_choice]}") print("You Have Lost :(") elif computer_choice == 1: print(f"Computer Choose{game_images[computer_choice]}") print("You Have Win! :)") else : print(f"Computer Choose{game_images[computer_choice]}") print("it's Draw ")

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1

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null

0

9

8b73433d6edcd43401b52bb9cbcda7f746eef8f1

14,185

py

Python

test/comp/comp_aggregate.py

bomtuckle/pyrolite

c0af0ade14ff26b4e9fdd5a033b27e73df085c55

[ "BSD-3-Clause" ]

69

2019-02-25T00:17:53.000Z

2022-03-31T17:26:48.000Z

test/comp/comp_aggregate.py

bomtuckle/pyrolite

c0af0ade14ff26b4e9fdd5a033b27e73df085c55

[ "BSD-3-Clause" ]

68

2018-07-20T09:01:01.000Z

2022-03-31T16:28:36.000Z

test/comp/comp_aggregate.py

bomtuckle/pyrolite

c0af0ade14ff26b4e9fdd5a033b27e73df085c55

[ "BSD-3-Clause" ]

24

2018-10-02T04:32:10.000Z

2021-11-10T08:24:17.000Z

import unittest import numpy as np from pyrolite.comp.aggregate import * from pyrolite.util.synthetic import normal_frame import logging class TestCompositionalMean(unittest.TestCase): """Tests pandas compositional mean operator.""" def setUp(self): self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.df = normal_frame(columns=self.cols) def test_1D(self): """Checks results on single records.""" df = pd.DataFrame(self.df.iloc[:, 0].head(1)) out = compositional_mean(df) # Check closure self.assertTrue(np.allclose(np.sum(out.values, axis=-1), 1.0)) def test_single(self): """Checks results on single records.""" df = self.df.head(1).copy() out = compositional_mean(df) # Check closure self.assertTrue(np.allclose(np.sum(out.values, axis=-1), 1.0)) def test_multiple(self): """Checks results on multiple records.""" df = self.df.copy() out = compositional_mean(df) # Check closure self.assertTrue(np.allclose(np.sum(out.values, axis=-1), 1.0)) @unittest.expectedFailure def test_contrasting(self): """Checks results on multiple contrasting records.""" df = self.df.copy() # Create some nans to imitate contrasting analysis sets df.iloc[ np.random.randint(1, 10, size=2), np.random.randint(1, len(self.cols), size=2), ] = np.nan out = compositional_mean(df) # Check closure self.assertTrue(np.allclose(np.sum(out.values, axis=-1), 1.0)) def test_mean(self): """Checks whether the mean is accurate.""" pass class TestWeightsFromArray(unittest.TestCase): """Tests the numpy array-weight generator for weighted averages.""" def setUp(self): self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.df = normal_frame(columns=self.cols) def test_single(self): """Checks results on single records.""" df = self.df.head(1).copy() out = weights_from_array(df.values) self.assertTrue(out.size == 1) def test_multiple(self): """Checks results on multiple records.""" df = self.df.copy() out = weights_from_array(df.values) self.assertTrue(out.size == df.index.size) class TestGetFullColumn(unittest.TestCase): """Tests the nan-column checking function for numpy arrays.""" def setUp(self): self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.df = normal_frame(columns=self.cols) nans = 10 self.df.iloc[ np.random.randint(1, 10, size=nans), np.random.randint(1, len(self.cols), size=nans), ] = np.nan def test_single(self): """Checks results on single records.""" df = self.df.head(1).copy() out = get_full_column(df.values) self.assertTrue(out == 0) def test_multiple(self): """Checks results on multiple records.""" df = self.df.copy() out = get_full_column(df.values) self.assertTrue(out == 0) class TestNANWeightedMean(unittest.TestCase): """Tests numpy weighted NaN-mean operator.""" def setUp(self): self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.df = normal_frame(columns=self.cols) def test_single(self): """Checks results on single records.""" df = self.df.head(1).copy() out = nan_weighted_mean(df.values) self.assertTrue(np.allclose(out, df.values)) def test_multiple(self): """Checks results on multiple records.""" df = self.df.copy() out = nan_weighted_mean(df.values) self.assertTrue(np.allclose(out, np.mean(df.values, axis=0))) def test_multiple_equal_weights(self): """Checks results on multiple records with equal weights.""" df = self.df.copy() weights = np.array([1.0 / len(df.index)] * len(df.index)) out = nan_weighted_mean(df.values, weights=weights) self.assertTrue( np.allclose(out, np.average(df.values, weights=weights, axis=0)) ) def test_multiple_unequal_weights(self): """Checks results on multiple records with unequal weights.""" df = self.df.copy() weights = np.random.rand(1, df.index.size).squeeze() out = nan_weighted_mean(df.values, weights=weights) check = np.average(df.values.T, weights=weights, axis=1) self.assertTrue( np.allclose(out, np.average(df.values, weights=weights, axis=0)) ) def test_multiple_unequal_weights_withnan(self): """ Checks results on multiple records with unequal weights, where the data includes some null data. """ df = self.df.copy() df.iloc[0, :] = np.nan # make one record nan # Some non-negative weights weights = np.random.rand(1, df.index.size).squeeze() weights = np.array(weights) / np.nansum(weights) out = nan_weighted_mean(df.values, weights=weights) check = np.average(df.iloc[1:, :].values, weights=weights[1:], axis=0) self.assertTrue(np.allclose(out, check)) class TestNANWeightedCompositionalMean(unittest.TestCase): """Tests numpy weighted compositonal NaN-mean operator.""" def setUp(self): self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.df = normal_frame(columns=self.cols) self.df = self.df.apply(lambda x: x / np.sum(x), axis="columns") def test_single(self): """Checks results on single records.""" # Should not change result, once closure is considered df = self.df.head(1).copy() for renorm in [True, False]: with self.subTest(renorm=renorm): out = nan_weighted_compositional_mean(df.values, renorm=renorm) if renorm: self.assertTrue(np.allclose(np.sum(out, axis=-1), 1.0)) self.assertTrue(np.allclose(out, df.values.reshape(out.shape))) def test_multiple(self): """Checks results on multiple records.""" df = self.df.copy() for renorm in [True, False]: with self.subTest(renorm=renorm): out = nan_weighted_compositional_mean(df.values, renorm=renorm) if renorm: self.assertTrue(np.allclose(np.sum(out, axis=-1), 1.0)) def test_contrasting(self): """Checks results on multiple contrasting records.""" # This should succeed for this function df = self.df.copy() # Create some nans to imitate contrasting analysis sets df.iloc[ np.random.randint(1, 10, size=2), np.random.randint(1, len(self.cols), size=2), ] = np.nan out = nan_weighted_compositional_mean(df.values) # Check closure self.assertTrue(np.allclose(np.sum(out, axis=-1), 1.0)) def test_mean(self): """Checks whether the mean is accurate.""" pass class TestCrossRatios(unittest.TestCase): """Tests pandas cross ratios utility.""" def setUp(self): self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.d = len(self.cols) self.n = 10 self.df = normal_frame(columns=self.cols, size=self.n) def test_single(self): """Checks results on single record.""" df = self.df.head(1).copy() n = df.index.size out = cross_ratios(df) self.assertTrue(np.isfinite(out).any()) self.assertTrue((out[np.isfinite(out)] > 0).all()) self.assertTrue(out.shape == (n, self.d, self.d)) def test_multiple(self): """Checks results on multiple records.""" df = self.df.copy() n = df.index.size out = cross_ratios(df) self.assertTrue(np.isfinite(out).any()) self.assertTrue((out[np.isfinite(out)] > 0).all()) self.assertTrue(out.shape == (n, self.d, self.d)) def test_contrasting(self): """Checks results on multiple contrasting records.""" df = self.df.copy() n = df.index.size # Create some nans to imitate contrasting analysis sets df.iloc[ np.random.randint(1, self.n, size=2), np.random.randint(1, self.d, size=2) ] = np.nan out = cross_ratios(df) self.assertTrue(np.isfinite(out).any()) self.assertTrue((out[np.isfinite(out)] > 0).all()) self.assertTrue(out.shape == (n, self.d, self.d)) class TestNPCrossRatios(unittest.TestCase): """Tests numpy cross ratios utility.""" def setUp(self): self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.d = len(self.cols) self.n = 10 self.df = normal_frame(columns=self.cols, size=self.n) def test_single(self): """Checks results on single record.""" df = self.df.head(1).copy() n = df.index.size arr = df.values out = np_cross_ratios(arr) self.assertTrue(np.isfinite(out).any()) self.assertTrue((out[np.isfinite(out)] > 0).all()) self.assertTrue(out.shape == (n, self.d, self.d)) def test_multiple(self): """Checks results on multiple records.""" df = self.df.copy() n = df.index.size arr = df.values out = np_cross_ratios(arr) self.assertTrue(np.isfinite(out).any()) self.assertTrue((out[np.isfinite(out)] > 0).all()) self.assertTrue(out.shape == (n, self.d, self.d)) def test_contrasting(self): """Checks results on multiple contrasting records.""" df = self.df.copy() n = df.index.size # Create some nans to imitate contrasting analysis sets df.iloc[ np.random.randint(1, self.n, size=2), np.random.randint(1, self.d, size=2) ] = np.nan arr = df.values out = np_cross_ratios(arr) self.assertTrue(np.isfinite(out).any()) self.assertTrue((out[np.isfinite(out)] > 0).all()) self.assertTrue(out.shape == (n, self.d, self.d)) class TestStandardiseAggregate(unittest.TestCase): """Tests pandas internal standardisation aggregation method.""" def setUp(self): self.mcols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"] self.mdf = pd.DataFrame( {k: v for k, v in zip(self.mcols, np.random.rand(len(self.mcols), 10))} ) self.mdf = self.mdf.apply(lambda x: x / np.sum(x), axis="columns") self.tcols = ["SiO2", "Ni", "Cr", "Sn"] self.tdf = pd.DataFrame( {k: v for k, v in zip(self.tcols, np.random.rand(len(self.tcols), 10))} ) self.df = self.mdf.append(self.tdf, ignore_index=True, sort=False) def test_single(self): """Checks results on single records.""" df = self.df.head(1).copy() for renorm in [True, False]: with self.subTest(renorm=renorm): out = standardise_aggregate(df, renorm=renorm) outvals = out.values[~np.isnan(out.values)] dfvals = df.values[~np.isnan(df.values)] self.assertTrue(np.allclose(outvals, dfvals)) def test_multiple_with_IS(self): """ Checks results on multiple records with internal standard specifed. """ df = self.mdf.copy() fixed_record_idx = 0 int_std = "SiO2" for renorm in [True, False]: with self.subTest(renorm=renorm): out = standardise_aggregate( df, int_std=int_std, renorm=renorm, fixed_record_idx=fixed_record_idx, ) if not renorm: self.assertTrue( np.allclose( out[int_std], df.iloc[fixed_record_idx, df.columns.get_loc(int_std)], ) ) def test_multiple_without_IS(self): """ Checks results on multiple records without internal standard specifed. """ df = self.mdf fixed_record_idx = 0 for renorm in [True, False]: with self.subTest(renorm=renorm): out = standardise_aggregate( df, renorm=renorm, fixed_record_idx=fixed_record_idx ) if not renorm: self.assertTrue( np.isclose( out.values, df.iloc[fixed_record_idx, :].values ).any() ) def test_contrasting_with_IS(self): """Checks results on multiple contrasting records.""" # This should succeed for records which differ by all-but-one element df = self.df fixed_record_idx = 0 int_std = "SiO2" for renorm in [True, False]: with self.subTest(renorm=renorm): out = standardise_aggregate( df, int_std=int_std, renorm=renorm, fixed_record_idx=fixed_record_idx, ) if not renorm: self.assertTrue( np.allclose( out[int_std], df.iloc[fixed_record_idx, df.columns.get_loc(int_std)], ) ) def test_contrasting_without_IS(self): """ Checks results on multiple contrasting records without internal standard specifed. """ df = self.df fixed_record_idx = 0 for renorm in [True, False]: with self.subTest(renorm=renorm): out = standardise_aggregate( df, renorm=renorm, fixed_record_idx=fixed_record_idx ) if not renorm: self.assertTrue( np.isclose( out.values, df.iloc[fixed_record_idx, :].values ).any() ) if __name__ == "__main__": unittest.main()

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7

8bcfde0fabdf44fa913fc966d320025ac02b2f42

183

py

Python

data/micro-benchmark/mro/super_call/main.py

vitsalis/pycg-evaluation

ce37eb5668465b0c17371914e863d699826447ee

[ "Apache-2.0" ]

121

2020-12-16T20:31:37.000Z

2022-03-21T20:32:43.000Z

data/micro-benchmark/mro/super_call/main.py

vitsalis/pycg-evaluation

ce37eb5668465b0c17371914e863d699826447ee

[ "Apache-2.0" ]

24

2021-03-13T00:04:00.000Z

2022-03-21T17:28:11.000Z

data/micro-benchmark/mro/super_call/main.py

vitsalis/pycg-evaluation

ce37eb5668465b0c17371914e863d699826447ee

[ "Apache-2.0" ]

19

2021-03-23T10:58:47.000Z

2022-03-24T19:46:50.000Z

class A: def __init__(self): pass class B(A): def __init__(self): super().__init__() class C(B): def __init__(self): super().__init__() c = C()

13.071429

26

0.530055

24

183

3.208333

0.375

0.272727

0.428571

0.311688

0.519481

0

0.311475

183

13

27

14.076923

0.611111

0

0.5

0

1

0.3

false

0.1

0

0.6

0

1

0

null

1

0

null

0

1

0

1

0

1

0

7

4750ec1a8164c84d661f4b6fcbda93f93661ece2

4,507

py

Python

route_sidewalk/src/planning.py

nutorbit/route_sidewalk

df32df5a0c637c129efc82d15eef62ac7d0daf13

[ "MIT" ]

null

route_sidewalk/src/planning.py

nutorbit/route_sidewalk

df32df5a0c637c129efc82d15eef62ac7d0daf13

[ "MIT" ]

null

route_sidewalk/src/planning.py

nutorbit/route_sidewalk

df32df5a0c637c129efc82d15eef62ac7d0daf13

[ "MIT" ]

null

from heapq import heappush, heappop def cal_weight(from_x, from_y, to_x, to_y): """ calculate distance Args: from_x: x coordinate from_y: y coordinate to_x: x coordinate to_y: y coordinate Returns: distance """ # return abs(from_x - to_x) + abs(from_y - to_y) # manhattan return ((from_x - to_x) ** 2 + (from_y - to_y) ** 2) ** 0.5 # euclidean dist def find_closest_road(data, from_): """ Find closest road with Breadth First Search (BFS). Args: data: array for search from_: [x, y] starting point for search """ queue = [(from_, [tuple(from_)])] visited = set() visited.add(tuple(from_)) while len(queue): # pop position & paths current, paths = queue.pop(0) if data[current[0], current[1]] != data[from_[0], from_[1]]: # if found road then return paths return paths for (dix, diy) in [(-1, -1), (-1, 0), (0, -1), (1, 1), (1, 0), (0, 1), (1, -1), (-1, 1)]: to_ = ( current[0] + dix, current[1] + diy ) if to_ not in visited and 0 <= to_[0] < data.shape[0] and 0 <= to_[1] < data.shape[1]: # add to queue queue.append((to_, paths + [to_])) visited.add(to_) def move_point_inside_road(data, from_): """ Find closest road with Breadth First Search (BFS). [Modify] Args: data: array for search from_: [x, y] starting point for search """ queue = [(from_, [tuple(from_)])] visited = set() visited.add(tuple(from_)) while len(queue): # pop position & paths current, paths = queue.pop(0) if data[current[0], current[1]] == 255: # if found road then return paths return paths for (dix, diy) in [(-1, -1), (-1, 0), (0, -1), (1, 1), (1, 0), (0, 1), (1, -1), (-1, 1)]: to_ = ( current[0] + dix, current[1] + diy ) if to_ not in visited and 0 <= to_[0] < data.shape[0] and 0 <= to_[1] < data.shape[1]: # add to queue queue.append((to_, paths + [to_])) visited.add(to_) def route_condition(data, from_, to_, v): """ Route with condition Args: data: array of map from_: (x, y) coordinate to_: (x, y) coordinate v: value Returns: list of path to target """ heap = [] heappush(heap, (0, from_[0], from_[1], [(from_[0], from_[1])])) # (weight, x, y, path) visited = set() visited.add((from_[0], from_[1])) while heap: weight, current_x, current_y, path = heappop(heap) if current_x == to_[0] and current_y == to_[1]: # if reach target return path for dix in range(-1, 2): for diy in range(-1, 2): to_x = current_x + dix to_y = current_y + diy if (to_x, to_y) not in visited and \ 0 <= to_x < data.shape[0] and \ 0 <= to_y < data.shape[1] and \ data[to_x, to_y] == v: weight = cal_weight(to_x, to_y, to_[0], to_[1]) + len(path) * 1000 heappush(heap, (weight, to_x, to_y, path + [(to_x, to_y)])) visited.add((to_x, to_y)) def route(data, from_, to_): """ Route without condition Args: data: array of map from_: (x, y) coordinate to_: (x, y) coordinate Returns: list of path to target """ heap = [] heappush(heap, (0, from_[0], from_[1], [(from_[0], from_[1])])) # (weight, x, y, path) visited = set() visited.add((from_[0], from_[1])) while heap: weight, current_x, current_y, path = heappop(heap) if current_x == to_[0] and current_y == to_[1]: # if reach target return path for dix in range(-1, 2): for diy in range(-1, 2): to_x = current_x + dix to_y = current_y + diy if (to_x, to_y) not in visited and \ 0 <= to_x < data.shape[0] and \ 0 <= to_y < data.shape[1]: weight = cal_weight(to_x, to_y, to_[0], to_[1]) + len(path) * 1000 heappush(heap, (weight, to_x, to_y, path + [(to_x, to_y)])) visited.add((to_x, to_y))

30.248322

103

0.490126

623

4,507

3.351525

0.123596

0.030172

0.028736

0.034483

0.824713

0

0.038259

0.367872

4,507

148

104

30.452703

0.69463

0.22099

0

0.810811

0

1

0.067568

false

0

0.013514

0

0.148649

0

null

0

1

0

1

0

null

0

7

4758f94b26862bbe4d311fbeca5395e4093d6f7b

1,282

py

Python

tests/empire/test_empire_scrape.py

magnublo/msc-darkweb-scraping

7cfb20d9013534d2ad71c388ee59f70b8450773c

[ "MIT" ]

null

tests/empire/test_empire_scrape.py

magnublo/msc-darkweb-scraping

7cfb20d9013534d2ad71c388ee59f70b8450773c

[ "MIT" ]

null

tests/empire/test_empire_scrape.py

magnublo/msc-darkweb-scraping

7cfb20d9013534d2ad71c388ee59f70b8450773c

[ "MIT" ]

null

from unittest import TestCase from src.empire.empire_scrape import _get_final_quantity_in_stock class TestGetFinalQuantityInStock(TestCase): def test_should_return_0(self): first_quantity_in_stock = 0 second_quantity_in_stock = None final_quantity_in_stock = _get_final_quantity_in_stock(first_quantity_in_stock, second_quantity_in_stock) self.assertEqual(0, final_quantity_in_stock) def test_should_return_12(self): first_quantity_in_stock = None second_quantity_in_stock = 12 final_quantity_in_stock = _get_final_quantity_in_stock(first_quantity_in_stock, second_quantity_in_stock) self.assertEqual(12, final_quantity_in_stock) def test_should_return_6(self): first_quantity_in_stock = 6 second_quantity_in_stock = 12 final_quantity_in_stock = _get_final_quantity_in_stock(first_quantity_in_stock, second_quantity_in_stock) self.assertEqual(6, final_quantity_in_stock) def test_should_return_none(self): first_quantity_in_stock = None second_quantity_in_stock = None final_quantity_in_stock = _get_final_quantity_in_stock(first_quantity_in_stock, second_quantity_in_stock) self.assertEqual(None, final_quantity_in_stock)

32.871795

113

0.780031

178

1,282

5

0.146067

0.325843

0.488764

0.292135

0.85618

0.747191

0.61573

0

0.013245

0.175507

1,282

38

114

33.736842

0.828761

0

0.434783

0

0.173913

1

0.173913

false

0

0.086957

0

0.304348

0

null

1

0

1

0

null

0

8

477402d59483741bc2ba9815642155df7c49a132

20,246

py

Python

src/models/srl.py

diegma/span-gcn

b3abf2950055886d148004ddbc8b7edf71c99420

[ "MIT" ]

9

2020-09-28T12:51:22.000Z

2021-12-06T03:09:31.000Z

src/models/srl.py

diegma/span-gcn

b3abf2950055886d148004ddbc8b7edf71c99420

[ "MIT" ]

1

2021-03-30T08:19:03.000Z

src/models/srl.py

diegma/span-gcn

b3abf2950055886d148004ddbc8b7edf71c99420

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence from torch.autograd import Variable import numpy as np from models.gcn import GCNLayer from models.bilinear_scorer import BilinearScorer from models.custom_allennlp.stacked_alternating_lstm import StackedAlternatingLstm from models.custom_allennlp.elmo import Elmo class SRL(nn.Module): def __init__( self, hidden_dim, tagset_size, num_layers, w_c_vocab_size, c_c_vocab_size, use_syntax, eln, num_layers_top, use_elmo, use_bert, params, gpu_id=-1, ): super(SRL, self).__init__() if gpu_id > -1: self.use_gpu = True else: self.use_gpu = False self.hidden_dim = hidden_dim self.num_layers = num_layers self.num_layers_top = num_layers_top self.eln = eln self.use_elmo = use_elmo self.use_bert = use_bert self.params = params self.dropout = nn.Dropout(p=params.gcn_dropout) self.embedding_dropout = nn.Dropout(p=params.emb_dropout) if self.use_elmo: weight_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_weights.hdf5" options_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_options.json" fixed_dim = 1024 self.elmo = Elmo( options_file, weight_file, 1, dropout=0, do_layer_norm=False ) fixed_dim += 100 elif self.use_bert: fixed_dim = 768 else: fixed_dim = 100 embedding_dim = self.params.emb_dim self.indicator_embeddings = nn.Embedding(2, embedding_dim) self.tagset_size = tagset_size self.use_syntax = use_syntax self.num_layers_top = num_layers_top gcn_type = GCNLayer if self.params.non_linearity == "relu": self.non_linearity = nn.ReLU() elif self.params.non_linearity == "tanh": self.non_linearity = nn.Tanh() elif self.params.non_linearity == "leakyrelu": self.non_linearity = nn.LeakyReLU() elif self.params.non_linearity == "celu": self.non_linearity = nn.CELU() elif self.params.non_linearity == "selu": self.non_linearity = nn.SELU() else: raise NotImplementedError self.lstm = StackedAlternatingLstm( fixed_dim + embedding_dim, hidden_dim, num_layers=num_layers, recurrent_dropout_probability=0.1, ) if self.use_syntax: if num_layers_top > 0: self.lstm_top = StackedAlternatingLstm( hidden_dim, hidden_dim, num_layers=num_layers_top, recurrent_dropout_probability=0.1, ) self.hidden2predicate = nn.Linear(hidden_dim, hidden_dim) self.hidden2argument = nn.Linear(hidden_dim, hidden_dim) self.bilinear_scorer = BilinearScorer( hidden_dim, tagset_size, params.bilinear_dropout ) else: self.hidden2predicate = nn.Linear(hidden_dim, hidden_dim) self.hidden2argument = nn.Linear(hidden_dim, hidden_dim) self.bilinear_scorer = BilinearScorer( hidden_dim, tagset_size, params.bilinear_dropout ) self.gcn_w_c = gcn_type( hidden_dim, hidden_dim, w_c_vocab_size, in_arcs=True, out_arcs=True, use_gates=True, batch_first=True, residual=True, no_loop=True, dropout=self.params.gcn_dropout, non_linearity=self.non_linearity, edge_dropout=self.params.edge_dropout, ) self.gcn_c_w = gcn_type( hidden_dim, hidden_dim, w_c_vocab_size, in_arcs=True, out_arcs=True, use_gates=True, batch_first=True, residual=True, no_loop=True, dropout=self.params.gcn_dropout, non_linearity=self.non_linearity, edge_dropout=self.params.edge_dropout, ) self.gcn_c_c = gcn_type( hidden_dim, hidden_dim, c_c_vocab_size, in_arcs=True, out_arcs=True, use_gates=True, batch_first=True, residual=True, no_loop=False, dropout=self.params.gcn_dropout, non_linearity=self.non_linearity, edge_dropout=self.params.edge_dropout, ) else: self.hidden2predicate = nn.Linear(hidden_dim, hidden_dim) self.hidden2argument = nn.Linear(hidden_dim, hidden_dim) self.bilinear_scorer = BilinearScorer( hidden_dim, tagset_size, params.bilinear_dropout ) if self.eln: self.layernorm = nn.LayerNorm(fixed_dim) def forward( self, sentence, predicate_flags, sent_mask, lengths, fixed_embs, constituents, GCN_w_c, GCN_c_w, GCN_c_c, mask_const_batch, predicate_index, elmo_character_ids, bert_embs, ): if self.use_elmo: embeds = self.elmo(elmo_character_ids)["elmo_representations"][0] if not self.params.elmo_proj: embeds = torch.cat([embeds, fixed_embs], dim=2) elif self.use_bert: embeds = bert_embs else: embeds = fixed_embs if self.eln: embeds = self.layernorm(embeds * sent_mask.unsqueeze(2)) embeds = self.embedding_dropout(embeds) embeds = torch.cat( (embeds, self.indicator_embeddings(predicate_flags.long())), 2 ) b, t, e = embeds.data.shape sent_len = torch.sort(lengths, descending=True)[0] idx_sort = torch.argsort(-lengths) if self.use_gpu: embeds = embeds.index_select( 0, Variable(torch.cuda.LongTensor(idx_sort.cuda())) ) else: embeds = embeds.index_select(0, Variable(torch.LongTensor(idx_sort))) packed = pack_padded_sequence(embeds, sent_len, batch_first=True) lstm_out, _ = self.lstm(packed) lstm_out, _ = pad_packed_sequence(lstm_out, batch_first=True) # [b, t, h] # Un-sort by length idx_unsort = torch.argsort(idx_sort) if self.use_gpu: lstm_out = lstm_out.index_select( 0, Variable(torch.cuda.LongTensor(idx_unsort.cuda())) ) else: lstm_out = lstm_out.index_select(0, Variable(torch.LongTensor(idx_unsort))) if self.use_syntax: # Here I must concatenate the constituents with the lstm_out gcn_in = torch.cat([lstm_out, constituents], dim=1) mask_all = torch.cat([sent_mask, mask_const_batch], dim=1) # Apply graph conv adj_arc_in_w_c, adj_arc_out_w_c, adj_lab_in_w_c, adj_lab_out_w_c, mask_in_w_c, mask_out_w_c, mask_loop_w_c = ( GCN_w_c ) adj_arc_in_c_w, adj_arc_out_c_w, adj_lab_in_c_w, adj_lab_out_c_w, mask_in_c_w, mask_out_c_w, mask_loop_c_w = ( GCN_c_w ) adj_arc_in_c_c, adj_arc_out_c_c, adj_lab_in_c_c, adj_lab_out_c_c, mask_in_c_c, mask_out_c_c, mask_loop_c_c = ( GCN_c_c ) gcn_out = self.gcn_w_c( gcn_in, adj_arc_in_w_c, adj_arc_out_w_c, adj_lab_in_w_c, adj_lab_out_w_c, mask_in_w_c, mask_out_w_c, mask_loop_w_c, mask_all, ) gcn_out = self.gcn_c_c( gcn_out, adj_arc_in_c_c, adj_arc_out_c_c, adj_lab_in_c_c, adj_lab_out_c_c, mask_in_c_c, mask_out_c_c, mask_loop_c_c, mask_all, ) gcn_out = self.gcn_c_w( gcn_out, adj_arc_in_c_w, adj_arc_out_c_w, adj_lab_in_c_w, adj_lab_out_c_w, mask_in_c_w, mask_out_c_w, mask_loop_c_w, mask_all, ) # Take back the lstm out only lstm_out = gcn_out.narrow(1, 0, t) if self.num_layers_top > 0: if self.use_gpu: lstm_out = lstm_out.index_select( 0, Variable(torch.cuda.LongTensor(idx_sort.cuda())) ) else: lstm_out = lstm_out.index_select( 0, Variable(torch.LongTensor(idx_sort)) ) packed = pack_padded_sequence(lstm_out, sent_len, batch_first=True) lstm_out_, _ = self.lstm_top(packed) lstm_out_, _ = pad_packed_sequence( lstm_out_, batch_first=True ) # [b, t, h] # Un-sort by length if self.use_gpu: lstm_out_ = lstm_out_.index_select( 0, Variable(torch.cuda.LongTensor(idx_unsort.cuda())) ) else: lstm_out_ = lstm_out_.index_select( 0, Variable(torch.LongTensor(idx_unsort)) ) lstm_out = lstm_out_ lstm_out_view = lstm_out.contiguous().view(b * t, -1) predicate_index = predicate_index.view(b * t) predicates_repr = lstm_out_view.index_select(0, predicate_index).view(b, t, -1) pred_repr = self.non_linearity( self.hidden2predicate(self.dropout(predicates_repr)) ) arg_repr = self.non_linearity(self.hidden2argument(self.dropout(lstm_out))) tag_scores = self.bilinear_scorer(pred_repr, arg_repr) # [b*t, label_size] return tag_scores.view(b, t, self.tagset_size) class SRL_Framenet(nn.Module): def __init__( self, hidden_dim, tagset_size, num_layers, w_c_vocab_size, c_c_vocab_size, use_syntax, eln, num_layers_top, params, gpu_id=-1, ): super(SRL_Framenet, self).__init__() if gpu_id > -1: self.use_gpu = True else: self.use_gpu = False self.hidden_dim = hidden_dim self.num_layers = num_layers self.num_layers_top = num_layers_top self.eln = eln self.params = params self.dropout = nn.Dropout(p=params.gcn_dropout) fixed_dim = 100 embedding_dim = self.params.emb_dim self.indicator_embeddings = nn.Embedding(2, embedding_dim) self.tagset_size = tagset_size self.use_syntax = use_syntax self.num_layers_top = num_layers_top gcn_type = GCNLayer self.lstm = StackedAlternatingLstm( fixed_dim + embedding_dim, hidden_dim, num_layers=num_layers, recurrent_dropout_probability=0.1, ) if self.use_syntax: if num_layers_top > 0: if params.alter_top: self.lstm_top = StackedAlternatingLstm( hidden_dim, hidden_dim, num_layers=num_layers_top, recurrent_dropout_probability=0.1, ) self.hidden2predicate = nn.Linear(hidden_dim, hidden_dim) self.hidden2argument = nn.Linear(hidden_dim, hidden_dim) self.bilinear_scorer = BilinearScorer( hidden_dim, tagset_size, params.bilinear_dropout ) else: self.lstm_top = nn.LSTM( hidden_dim, hidden_dim, num_layers=num_layers_top, batch_first=True, bidirectional=True, dropout=self.params.gcn_dropout, ) self.hidden2predicate = nn.Linear(2 * hidden_dim, hidden_dim) self.hidden2argument = nn.Linear(2 * hidden_dim, hidden_dim) self.bilinear_scorer = BilinearScorer( hidden_dim, tagset_size, params.bilinear_dropout ) else: self.hidden2predicate = nn.Linear(2 * hidden_dim, hidden_dim) self.hidden2argument = nn.Linear(2 * hidden_dim, hidden_dim) self.bilinear_scorer = BilinearScorer( hidden_dim, tagset_size, params.bilinear_dropout ) self.gcn_w_c = gcn_type( hidden_dim, hidden_dim, w_c_vocab_size, in_arcs=True, out_arcs=True, use_gates=True, batch_first=True, residual=True, no_loop=True, dropout=self.params.gcn_dropout, ) self.gcn_c_w = gcn_type( hidden_dim, hidden_dim, w_c_vocab_size, in_arcs=True, out_arcs=True, use_gates=True, batch_first=True, residual=True, no_loop=True, dropout=self.params.gcn_dropout, ) self.gcn_c_c = gcn_type( hidden_dim, hidden_dim, c_c_vocab_size, in_arcs=True, out_arcs=True, use_gates=True, batch_first=True, residual=True, no_loop=False, dropout=self.params.gcn_dropout, ) else: self.hidden2predicate = nn.Linear(hidden_dim, hidden_dim) self.hidden2argument = nn.Linear(hidden_dim, hidden_dim) self.bilinear_scorer = BilinearScorer( hidden_dim, tagset_size, params.bilinear_dropout ) if self.eln: self.layernorm = nn.LayerNorm(fixed_dim) def forward( self, sentence, predicate_flags, sent_mask, lengths, fixed_embs, constituents, GCN_w_c, GCN_c_w, GCN_c_c, mask_const_batch, predicate_index, softmax_constraints, frame_emb_batch, ): embeds = fixed_embs if self.eln: embeds = self.layernorm(embeds * sent_mask.unsqueeze(2)) if self.params.emb_dropout: embeds = self.dropout(embeds) embeds = torch.cat( (embeds, self.indicator_embeddings(predicate_flags.long())), 2 ) b, t, e = embeds.data.shape # Sort by length (keep idx) sent_len = torch.sort(lengths, descending=True)[0] idx_sort = torch.argsort(-lengths) if self.use_gpu: embeds = embeds.index_select( 0, Variable(torch.cuda.LongTensor(idx_sort.cuda())) ) else: embeds = embeds.index_select(0, Variable(torch.LongTensor(idx_sort))) packed = pack_padded_sequence(embeds, sent_len, batch_first=True) lstm_out, _ = self.lstm(packed) lstm_out, _ = pad_packed_sequence(lstm_out, batch_first=True) # [b, t, h] # Un-sort by length idx_unsort = np.argsort(idx_sort) if self.use_gpu: lstm_out = lstm_out.index_select( 0, Variable(torch.cuda.LongTensor(idx_unsort.cuda())) ) else: lstm_out = lstm_out.index_select(0, Variable(torch.LongTensor(idx_unsort))) if self.use_syntax: # Here I must concatenate the constituents with the lstm_out gcn_in = torch.cat([lstm_out, constituents], dim=1) mask_all = torch.cat([sent_mask, mask_const_batch], dim=1) # Apply graph conv adj_arc_in_w_c, adj_arc_out_w_c, adj_lab_in_w_c, adj_lab_out_w_c, mask_in_w_c, mask_out_w_c, mask_loop_w_c = ( GCN_w_c ) adj_arc_in_c_w, adj_arc_out_c_w, adj_lab_in_c_w, adj_lab_out_c_w, mask_in_c_w, mask_out_c_w, mask_loop_c_w = ( GCN_c_w ) adj_arc_in_c_c, adj_arc_out_c_c, adj_lab_in_c_c, adj_lab_out_c_c, mask_in_c_c, mask_out_c_c, mask_loop_c_c = ( GCN_c_c ) gcn_out = self.gcn_w_c( gcn_in, adj_arc_in_w_c, adj_arc_out_w_c, adj_lab_in_w_c, adj_lab_out_w_c, mask_in_w_c, mask_out_w_c, mask_loop_w_c, mask_all, ) gcn_out = self.gcn_c_c( gcn_out, adj_arc_in_c_c, adj_arc_out_c_c, adj_lab_in_c_c, adj_lab_out_c_c, mask_in_c_c, mask_out_c_c, mask_loop_c_c, mask_all, ) gcn_out = self.gcn_c_w( gcn_out, adj_arc_in_c_w, adj_arc_out_c_w, adj_lab_in_c_w, adj_lab_out_c_w, mask_in_c_w, mask_out_c_w, mask_loop_c_w, mask_all, ) # Take back the lstm out only lstm_out = gcn_out.narrow(1, 0, t) if self.num_layers_top > 0: if self.use_gpu: lstm_out = lstm_out.index_select( 0, Variable(torch.cuda.LongTensor(idx_sort.cuda())) ) else: lstm_out = lstm_out.index_select( 0, Variable(torch.LongTensor(idx_sort)) ) packed = pack_padded_sequence(lstm_out, sent_len, batch_first=True) lstm_out, _ = self.lstm_top(packed) lstm_out, _ = pad_packed_sequence( lstm_out, batch_first=True ) # [b, t, h] # Un-sort by length if self.use_gpu: lstm_out = lstm_out.index_select( 0, Variable(torch.cuda.LongTensor(idx_unsort.cuda())) ) else: lstm_out = lstm_out.index_select( 0, Variable(torch.LongTensor(idx_unsort)) ) lstm_out_view = lstm_out.contiguous().view(b * t, -1) predicate_index = predicate_index.view(b * t) predicates_repr = lstm_out_view.index_select(0, predicate_index).view(b, t, -1) pred_repr = F.relu(self.hidden2predicate(self.dropout(predicates_repr))) arg_repr = F.relu(self.hidden2argument(self.dropout(lstm_out))) tag_scores = self.bilinear_scorer(pred_repr, arg_repr) # [b*t, label_size] tag_scores = tag_scores.view(b, t, -1) tag_scores = tag_scores.masked_fill( (1 - softmax_constraints.view(b, 1, -1)).byte(), float("-1e13") ) # 1e-13) tag_scores = tag_scores.view(b * t, -1) return tag_scores.view(b, t, self.tagset_size)

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4777e199f387ab79fe32bc29c1da1101931c482a

104,508

py

Python

msgraph-cli-extensions/v1_0/usersactions_v1_0/azext_usersactions_v1_0/generated/commands.py

thewahome/msgraph-cli

33127d9efa23a0e5f5303c93242fbdbb73348671

[ "MIT" ]

null

msgraph-cli-extensions/v1_0/usersactions_v1_0/azext_usersactions_v1_0/generated/commands.py

thewahome/msgraph-cli

33127d9efa23a0e5f5303c93242fbdbb73348671

[ "MIT" ]

null

msgraph-cli-extensions/v1_0/usersactions_v1_0/azext_usersactions_v1_0/generated/commands.py

thewahome/msgraph-cli

33127d9efa23a0e5f5303c93242fbdbb73348671

[ "MIT" ]

null

# -------------------------------------------------------------------------- # 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. # -------------------------------------------------------------------------- # pylint: disable=too-many-statements # pylint: disable=too-many-locals # pylint: disable=bad-continuation # pylint: disable=line-too-long from msgraph.cli.core.commands import CliCommandType from azext_usersactions_v1_0.generated._client_factory import ( cf_user_calendar_calendar_view_attachment, cf_user_calendar_calendar_view_calendar, cf_user_calendar_calendar_view_instance, cf_user_calendar_calendar_view, cf_user_calendar_event_attachment, cf_user_calendar_event_calendar, cf_user_calendar_event_instance, cf_user_calendar_event, cf_user_calendar, cf_user_calendar_group_calendar_calendar_view_attachment, cf_user_calendar_group_calendar_calendar_view_calendar, cf_user_calendar_group_calendar_calendar_view_instance, cf_user_calendar_group_calendar_calendar_view, cf_user_calendar_group_calendar_event_attachment, cf_user_calendar_group_calendar_event_calendar, cf_user_calendar_group_calendar_event_instance, cf_user_calendar_group_calendar_event, cf_user_calendar_group_calendar, cf_user_calendar_calendar_view_attachment, cf_user_calendar_calendar_view_calendar, cf_user_calendar_calendar_view_instance, cf_user_calendar_calendar_view, cf_user_calendar_event_attachment, cf_user_calendar_event_calendar, cf_user_calendar_event_instance, cf_user_calendar_event, cf_user_calendar, cf_user_calendar_view_attachment, cf_user_calendar_view_calendar_calendar_view, cf_user_calendar_view_calendar_event, cf_user_calendar_view_calendar, cf_user_calendar_view_instance, cf_user_calendar_view, cf_user_event_attachment, cf_user_event_calendar_calendar_view, cf_user_event_calendar_event, cf_user_event_calendar, cf_user_event_instance, cf_user_event, cf_user_mail_folder_child_folder, cf_user_mail_folder_message_attachment, cf_user_mail_folder_message, cf_user_mail_folder, cf_user_managed_device, cf_user_message_attachment, cf_user_message, cf_user, cf_user_onenote_notebook, cf_user_onenote_notebook_section_group_parent_notebook, cf_user_onenote_notebook_section_group_section, cf_user_onenote_notebook_section_group_section_page, cf_user_onenote_notebook_section_group_section_page_parent_notebook, cf_user_onenote_notebook_section_group_section_page_parent_section, cf_user_onenote_notebook_section_group_section_parent_notebook, cf_user_onenote_notebook_section, cf_user_onenote_notebook_section_page, cf_user_onenote_notebook_section_page_parent_notebook, cf_user_onenote_notebook_section_page_parent_section, cf_user_onenote_notebook_section_parent_notebook, cf_user_onenote_notebook_section_parent_section_group_parent_notebook, cf_user_onenote_notebook_section_parent_section_group_section, cf_user_onenote_page, cf_user_onenote_page_parent_notebook, cf_user_onenote_page_parent_notebook_section_group_parent_notebook, cf_user_onenote_page_parent_notebook_section_group_section, cf_user_onenote_page_parent_notebook_section_group_section_page, cf_user_onenote_page_parent_notebook_section_group_section_parent_notebook, cf_user_onenote_page_parent_notebook_section, cf_user_onenote_page_parent_notebook_section_page, cf_user_onenote_page_parent_notebook_section_parent_notebook, cf_user_onenote_page_parent_notebook_section_parent_section_group_parent_notebook, cf_user_onenote_page_parent_notebook_section_parent_section_group_section, cf_user_onenote_page_parent_section, cf_user_onenote_page_parent_section_page, cf_user_onenote_page_parent_section_parent_notebook, cf_user_onenote_page_parent_section_parent_notebook_section_group_parent_notebook, cf_user_onenote_page_parent_section_parent_notebook_section_group_section, cf_user_onenote_page_parent_section_parent_notebook_section, cf_user_onenote_page_parent_section_parent_section_group_parent_notebook, cf_user_onenote_page_parent_section_parent_section_group_parent_notebook_section, cf_user_onenote_page_parent_section_parent_section_group_section, cf_user_onenote_section_group_parent_notebook, cf_user_onenote_section_group_parent_notebook_section, cf_user_onenote_section_group_parent_notebook_section_page, cf_user_onenote_section_group_parent_notebook_section_page_parent_notebook, cf_user_onenote_section_group_parent_notebook_section_page_parent_section, cf_user_onenote_section_group_parent_notebook_section_parent_notebook, cf_user_onenote_section_group_section, cf_user_onenote_section_group_section_page, cf_user_onenote_section_group_section_page_parent_notebook, cf_user_onenote_section_group_section_page_parent_notebook_section, cf_user_onenote_section_group_section_page_parent_section, cf_user_onenote_section_group_section_parent_notebook, cf_user_onenote_section_group_section_parent_notebook_section, cf_user_onenote_section, cf_user_onenote_section_page, cf_user_onenote_section_page_parent_notebook, cf_user_onenote_section_page_parent_notebook_section_group_parent_notebook, cf_user_onenote_section_page_parent_notebook_section_group_section, cf_user_onenote_section_page_parent_notebook_section, cf_user_onenote_section_page_parent_section, cf_user_onenote_section_parent_notebook, cf_user_onenote_section_parent_notebook_section_group_parent_notebook, cf_user_onenote_section_parent_notebook_section_group_section, cf_user_onenote_section_parent_notebook_section, cf_user_onenote_section_parent_section_group_parent_notebook, cf_user_onenote_section_parent_section_group_parent_notebook_section, cf_user_onenote_section_parent_section_group_section, cf_user_online_meeting, ) usersactions_v1_0_user_calendar_calendar_view_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_calendar_view_attachments_operations#UsersCalendarCalendarViewAttachmentsOperations.{}', client_factory=cf_user_calendar_calendar_view_attachment, ) usersactions_v1_0_user_calendar_calendar_view_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_calendar_view_calendar_operations#UsersCalendarCalendarViewCalendarOperations.{}', client_factory=cf_user_calendar_calendar_view_calendar, ) usersactions_v1_0_user_calendar_calendar_view_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_calendar_view_instances_operations#UsersCalendarCalendarViewInstancesOperations.{}', client_factory=cf_user_calendar_calendar_view_instance, ) usersactions_v1_0_user_calendar_calendar_view = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_calendar_view_operations#UsersCalendarCalendarViewOperations.{}', client_factory=cf_user_calendar_calendar_view, ) usersactions_v1_0_user_calendar_event_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_events_attachments_operations#UsersCalendarEventsAttachmentsOperations.{}', client_factory=cf_user_calendar_event_attachment, ) usersactions_v1_0_user_calendar_event_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_events_calendar_operations#UsersCalendarEventsCalendarOperations.{}', client_factory=cf_user_calendar_event_calendar, ) usersactions_v1_0_user_calendar_event_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_events_instances_operations#UsersCalendarEventsInstancesOperations.{}', client_factory=cf_user_calendar_event_instance, ) usersactions_v1_0_user_calendar_event = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_events_operations#UsersCalendarEventsOperations.{}', client_factory=cf_user_calendar_event, ) usersactions_v1_0_user_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_operations#UsersCalendarOperations.{}', client_factory=cf_user_calendar, ) usersactions_v1_0_user_calendar_group_calendar_calendar_view_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_calendar_view_attachments_operations#UsersCalendarGroupsCalendarsCalendarViewAttachmentsOperations.{}', client_factory=cf_user_calendar_group_calendar_calendar_view_attachment, ) usersactions_v1_0_user_calendar_group_calendar_calendar_view_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_calendar_view_calendar_operations#UsersCalendarGroupsCalendarsCalendarViewCalendarOperations.{}', client_factory=cf_user_calendar_group_calendar_calendar_view_calendar, ) usersactions_v1_0_user_calendar_group_calendar_calendar_view_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_calendar_view_instances_operations#UsersCalendarGroupsCalendarsCalendarViewInstancesOperations.{}', client_factory=cf_user_calendar_group_calendar_calendar_view_instance, ) usersactions_v1_0_user_calendar_group_calendar_calendar_view = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_calendar_view_operations#UsersCalendarGroupsCalendarsCalendarViewOperations.{}', client_factory=cf_user_calendar_group_calendar_calendar_view, ) usersactions_v1_0_user_calendar_group_calendar_event_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_events_attachments_operations#UsersCalendarGroupsCalendarsEventsAttachmentsOperations.{}', client_factory=cf_user_calendar_group_calendar_event_attachment, ) usersactions_v1_0_user_calendar_group_calendar_event_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_events_calendar_operations#UsersCalendarGroupsCalendarsEventsCalendarOperations.{}', client_factory=cf_user_calendar_group_calendar_event_calendar, ) usersactions_v1_0_user_calendar_group_calendar_event_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_events_instances_operations#UsersCalendarGroupsCalendarsEventsInstancesOperations.{}', client_factory=cf_user_calendar_group_calendar_event_instance, ) usersactions_v1_0_user_calendar_group_calendar_event = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_events_operations#UsersCalendarGroupsCalendarsEventsOperations.{}', client_factory=cf_user_calendar_group_calendar_event, ) usersactions_v1_0_user_calendar_group_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_groups_calendars_operations#UsersCalendarGroupsCalendarsOperations.{}', client_factory=cf_user_calendar_group_calendar, ) usersactions_v1_0_user_calendar_calendar_view_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_calendar_view_attachments_operations#UsersCalendarsCalendarViewAttachmentsOperations.{}', client_factory=cf_user_calendar_calendar_view_attachment, ) usersactions_v1_0_user_calendar_calendar_view_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_calendar_view_calendar_operations#UsersCalendarsCalendarViewCalendarOperations.{}', client_factory=cf_user_calendar_calendar_view_calendar, ) usersactions_v1_0_user_calendar_calendar_view_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_calendar_view_instances_operations#UsersCalendarsCalendarViewInstancesOperations.{}', client_factory=cf_user_calendar_calendar_view_instance, ) usersactions_v1_0_user_calendar_calendar_view = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_calendar_view_operations#UsersCalendarsCalendarViewOperations.{}', client_factory=cf_user_calendar_calendar_view, ) usersactions_v1_0_user_calendar_event_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_events_attachments_operations#UsersCalendarsEventsAttachmentsOperations.{}', client_factory=cf_user_calendar_event_attachment, ) usersactions_v1_0_user_calendar_event_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_events_calendar_operations#UsersCalendarsEventsCalendarOperations.{}', client_factory=cf_user_calendar_event_calendar, ) usersactions_v1_0_user_calendar_event_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_events_instances_operations#UsersCalendarsEventsInstancesOperations.{}', client_factory=cf_user_calendar_event_instance, ) usersactions_v1_0_user_calendar_event = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_events_operations#UsersCalendarsEventsOperations.{}', client_factory=cf_user_calendar_event, ) usersactions_v1_0_user_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendars_operations#UsersCalendarsOperations.{}', client_factory=cf_user_calendar, ) usersactions_v1_0_user_calendar_view_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_view_attachments_operations#UsersCalendarViewAttachmentsOperations.{}', client_factory=cf_user_calendar_view_attachment, ) usersactions_v1_0_user_calendar_view_calendar_calendar_view = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_view_calendar_calendar_view_operations#UsersCalendarViewCalendarCalendarViewOperations.{}', client_factory=cf_user_calendar_view_calendar_calendar_view, ) usersactions_v1_0_user_calendar_view_calendar_event = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_view_calendar_events_operations#UsersCalendarViewCalendarEventsOperations.{}', client_factory=cf_user_calendar_view_calendar_event, ) usersactions_v1_0_user_calendar_view_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_view_calendar_operations#UsersCalendarViewCalendarOperations.{}', client_factory=cf_user_calendar_view_calendar, ) usersactions_v1_0_user_calendar_view_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_view_instances_operations#UsersCalendarViewInstancesOperations.{}', client_factory=cf_user_calendar_view_instance, ) usersactions_v1_0_user_calendar_view = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_calendar_view_operations#UsersCalendarViewOperations.{}', client_factory=cf_user_calendar_view, ) usersactions_v1_0_user_event_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_events_attachments_operations#UsersEventsAttachmentsOperations.{}', client_factory=cf_user_event_attachment, ) usersactions_v1_0_user_event_calendar_calendar_view = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_events_calendar_calendar_view_operations#UsersEventsCalendarCalendarViewOperations.{}', client_factory=cf_user_event_calendar_calendar_view, ) usersactions_v1_0_user_event_calendar_event = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_events_calendar_events_operations#UsersEventsCalendarEventsOperations.{}', client_factory=cf_user_event_calendar_event, ) usersactions_v1_0_user_event_calendar = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_events_calendar_operations#UsersEventsCalendarOperations.{}', client_factory=cf_user_event_calendar, ) usersactions_v1_0_user_event_instance = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_events_instances_operations#UsersEventsInstancesOperations.{}', client_factory=cf_user_event_instance, ) usersactions_v1_0_user_event = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_events_operations#UsersEventsOperations.{}', client_factory=cf_user_event, ) usersactions_v1_0_user_mail_folder_child_folder = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_mail_folders_child_folders_operations#UsersMailFoldersChildFoldersOperations.{}', client_factory=cf_user_mail_folder_child_folder, ) usersactions_v1_0_user_mail_folder_message_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_mail_folders_messages_attachments_operations#UsersMailFoldersMessagesAttachmentsOperations.{}', client_factory=cf_user_mail_folder_message_attachment, ) usersactions_v1_0_user_mail_folder_message = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_mail_folders_messages_operations#UsersMailFoldersMessagesOperations.{}', client_factory=cf_user_mail_folder_message, ) usersactions_v1_0_user_mail_folder = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_mail_folders_operations#UsersMailFoldersOperations.{}', client_factory=cf_user_mail_folder, ) usersactions_v1_0_user_managed_device = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_managed_devices_operations#UsersManagedDevicesOperations.{}', client_factory=cf_user_managed_device, ) usersactions_v1_0_user_message_attachment = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_messages_attachments_operations#UsersMessagesAttachmentsOperations.{}', client_factory=cf_user_message_attachment, ) usersactions_v1_0_user_message = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_messages_operations#UsersMessagesOperations.{}', client_factory=cf_user_message, ) usersactions_v1_0_user = CliCommandType( operations_tmpl=( 'azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_operations#UsersOperations.{}' ), client_factory=cf_user, ) usersactions_v1_0_user_onenote_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_operations#UsersOnenoteNotebooksOperations.{}', client_factory=cf_user_onenote_notebook, ) usersactions_v1_0_user_onenote_notebook_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_section_groups_parent_notebook_operations#UsersOnenoteNotebooksSectionGroupsParentNotebookOperations.{}', client_factory=cf_user_onenote_notebook_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_notebook_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_section_groups_sections_operations#UsersOnenoteNotebooksSectionGroupsSectionsOperations.{}', client_factory=cf_user_onenote_notebook_section_group_section, ) usersactions_v1_0_user_onenote_notebook_section_group_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_section_groups_sections_pages_operations#UsersOnenoteNotebooksSectionGroupsSectionsPagesOperations.{}', client_factory=cf_user_onenote_notebook_section_group_section_page, ) usersactions_v1_0_user_onenote_notebook_section_group_section_page_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_section_groups_sections_pages_parent_notebook_operations#UsersOnenoteNotebooksSectionGroupsSectionsPagesParentNotebookOperations.{}', client_factory=cf_user_onenote_notebook_section_group_section_page_parent_notebook, ) usersactions_v1_0_user_onenote_notebook_section_group_section_page_parent_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_section_groups_sections_pages_parent_section_operations#UsersOnenoteNotebooksSectionGroupsSectionsPagesParentSectionOperations.{}', client_factory=cf_user_onenote_notebook_section_group_section_page_parent_section, ) usersactions_v1_0_user_onenote_notebook_section_group_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_section_groups_sections_parent_notebook_operations#UsersOnenoteNotebooksSectionGroupsSectionsParentNotebookOperations.{}', client_factory=cf_user_onenote_notebook_section_group_section_parent_notebook, ) usersactions_v1_0_user_onenote_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_sections_operations#UsersOnenoteNotebooksSectionsOperations.{}', client_factory=cf_user_onenote_notebook_section, ) usersactions_v1_0_user_onenote_notebook_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_sections_pages_operations#UsersOnenoteNotebooksSectionsPagesOperations.{}', client_factory=cf_user_onenote_notebook_section_page, ) usersactions_v1_0_user_onenote_notebook_section_page_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_sections_pages_parent_notebook_operations#UsersOnenoteNotebooksSectionsPagesParentNotebookOperations.{}', client_factory=cf_user_onenote_notebook_section_page_parent_notebook, ) usersactions_v1_0_user_onenote_notebook_section_page_parent_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_sections_pages_parent_section_operations#UsersOnenoteNotebooksSectionsPagesParentSectionOperations.{}', client_factory=cf_user_onenote_notebook_section_page_parent_section, ) usersactions_v1_0_user_onenote_notebook_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_sections_parent_notebook_operations#UsersOnenoteNotebooksSectionsParentNotebookOperations.{}', client_factory=cf_user_onenote_notebook_section_parent_notebook, ) usersactions_v1_0_user_onenote_notebook_section_parent_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_sections_parent_section_group_parent_notebook_operations#UsersOnenoteNotebooksSectionsParentSectionGroupParentNotebookOperations.{}', client_factory=cf_user_onenote_notebook_section_parent_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_notebook_section_parent_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_notebooks_sections_parent_section_group_sections_operations#UsersOnenoteNotebooksSectionsParentSectionGroupSectionsOperations.{}', client_factory=cf_user_onenote_notebook_section_parent_section_group_section, ) usersactions_v1_0_user_onenote_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_operations#UsersOnenotePagesOperations.{}', client_factory=cf_user_onenote_page, ) usersactions_v1_0_user_onenote_page_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_operations#UsersOnenotePagesParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_section_groups_parent_notebook_operations#UsersOnenotePagesParentNotebookSectionGroupsParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_section_groups_sections_operations#UsersOnenotePagesParentNotebookSectionGroupsSectionsOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_group_section, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_group_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_section_groups_sections_pages_operations#UsersOnenotePagesParentNotebookSectionGroupsSectionsPagesOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_group_section_page, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_group_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_section_groups_sections_parent_notebook_operations#UsersOnenotePagesParentNotebookSectionGroupsSectionsParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_group_section_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_sections_operations#UsersOnenotePagesParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_sections_pages_operations#UsersOnenotePagesParentNotebookSectionsPagesOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_page, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_sections_parent_notebook_operations#UsersOnenotePagesParentNotebookSectionsParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_parent_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_sections_parent_section_group_parent_notebook_operations#UsersOnenotePagesParentNotebookSectionsParentSectionGroupParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_parent_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_notebook_section_parent_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_notebook_sections_parent_section_group_sections_operations#UsersOnenotePagesParentNotebookSectionsParentSectionGroupSectionsOperations.{}', client_factory=cf_user_onenote_page_parent_notebook_section_parent_section_group_section, ) usersactions_v1_0_user_onenote_page_parent_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_operations#UsersOnenotePagesParentSectionOperations.{}', client_factory=cf_user_onenote_page_parent_section, ) usersactions_v1_0_user_onenote_page_parent_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_pages_operations#UsersOnenotePagesParentSectionPagesOperations.{}', client_factory=cf_user_onenote_page_parent_section_page, ) usersactions_v1_0_user_onenote_page_parent_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_parent_notebook_operations#UsersOnenotePagesParentSectionParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_section_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_section_parent_notebook_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_parent_notebook_section_groups_parent_notebook_operations#UsersOnenotePagesParentSectionParentNotebookSectionGroupsParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_section_parent_notebook_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_section_parent_notebook_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_parent_notebook_section_groups_sections_operations#UsersOnenotePagesParentSectionParentNotebookSectionGroupsSectionsOperations.{}', client_factory=cf_user_onenote_page_parent_section_parent_notebook_section_group_section, ) usersactions_v1_0_user_onenote_page_parent_section_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_parent_notebook_sections_operations#UsersOnenotePagesParentSectionParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_page_parent_section_parent_notebook_section, ) usersactions_v1_0_user_onenote_page_parent_section_parent_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_parent_section_group_parent_notebook_operations#UsersOnenotePagesParentSectionParentSectionGroupParentNotebookOperations.{}', client_factory=cf_user_onenote_page_parent_section_parent_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_page_parent_section_parent_section_group_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_parent_section_group_parent_notebook_sections_operations#UsersOnenotePagesParentSectionParentSectionGroupParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_page_parent_section_parent_section_group_parent_notebook_section, ) usersactions_v1_0_user_onenote_page_parent_section_parent_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_pages_parent_section_parent_section_group_sections_operations#UsersOnenotePagesParentSectionParentSectionGroupSectionsOperations.{}', client_factory=cf_user_onenote_page_parent_section_parent_section_group_section, ) usersactions_v1_0_user_onenote_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_parent_notebook_operations#UsersOnenoteSectionGroupsParentNotebookOperations.{}', client_factory=cf_user_onenote_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_section_group_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_parent_notebook_sections_operations#UsersOnenoteSectionGroupsParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_section_group_parent_notebook_section, ) usersactions_v1_0_user_onenote_section_group_parent_notebook_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_parent_notebook_sections_pages_operations#UsersOnenoteSectionGroupsParentNotebookSectionsPagesOperations.{}', client_factory=cf_user_onenote_section_group_parent_notebook_section_page, ) usersactions_v1_0_user_onenote_section_group_parent_notebook_section_page_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_parent_notebook_sections_pages_parent_notebook_operations#UsersOnenoteSectionGroupsParentNotebookSectionsPagesParentNotebookOperations.{}', client_factory=cf_user_onenote_section_group_parent_notebook_section_page_parent_notebook, ) usersactions_v1_0_user_onenote_section_group_parent_notebook_section_page_parent_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_parent_notebook_sections_pages_parent_section_operations#UsersOnenoteSectionGroupsParentNotebookSectionsPagesParentSectionOperations.{}', client_factory=cf_user_onenote_section_group_parent_notebook_section_page_parent_section, ) usersactions_v1_0_user_onenote_section_group_parent_notebook_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_parent_notebook_sections_parent_notebook_operations#UsersOnenoteSectionGroupsParentNotebookSectionsParentNotebookOperations.{}', client_factory=cf_user_onenote_section_group_parent_notebook_section_parent_notebook, ) usersactions_v1_0_user_onenote_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_sections_operations#UsersOnenoteSectionGroupsSectionsOperations.{}', client_factory=cf_user_onenote_section_group_section, ) usersactions_v1_0_user_onenote_section_group_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_sections_pages_operations#UsersOnenoteSectionGroupsSectionsPagesOperations.{}', client_factory=cf_user_onenote_section_group_section_page, ) usersactions_v1_0_user_onenote_section_group_section_page_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_sections_pages_parent_notebook_operations#UsersOnenoteSectionGroupsSectionsPagesParentNotebookOperations.{}', client_factory=cf_user_onenote_section_group_section_page_parent_notebook, ) usersactions_v1_0_user_onenote_section_group_section_page_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_sections_pages_parent_notebook_sections_operations#UsersOnenoteSectionGroupsSectionsPagesParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_section_group_section_page_parent_notebook_section, ) usersactions_v1_0_user_onenote_section_group_section_page_parent_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_sections_pages_parent_section_operations#UsersOnenoteSectionGroupsSectionsPagesParentSectionOperations.{}', client_factory=cf_user_onenote_section_group_section_page_parent_section, ) usersactions_v1_0_user_onenote_section_group_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_sections_parent_notebook_operations#UsersOnenoteSectionGroupsSectionsParentNotebookOperations.{}', client_factory=cf_user_onenote_section_group_section_parent_notebook, ) usersactions_v1_0_user_onenote_section_group_section_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_section_groups_sections_parent_notebook_sections_operations#UsersOnenoteSectionGroupsSectionsParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_section_group_section_parent_notebook_section, ) usersactions_v1_0_user_onenote_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_operations#UsersOnenoteSectionsOperations.{}', client_factory=cf_user_onenote_section, ) usersactions_v1_0_user_onenote_section_page = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_pages_operations#UsersOnenoteSectionsPagesOperations.{}', client_factory=cf_user_onenote_section_page, ) usersactions_v1_0_user_onenote_section_page_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_pages_parent_notebook_operations#UsersOnenoteSectionsPagesParentNotebookOperations.{}', client_factory=cf_user_onenote_section_page_parent_notebook, ) usersactions_v1_0_user_onenote_section_page_parent_notebook_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_pages_parent_notebook_section_groups_parent_notebook_operations#UsersOnenoteSectionsPagesParentNotebookSectionGroupsParentNotebookOperations.{}', client_factory=cf_user_onenote_section_page_parent_notebook_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_section_page_parent_notebook_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_pages_parent_notebook_section_groups_sections_operations#UsersOnenoteSectionsPagesParentNotebookSectionGroupsSectionsOperations.{}', client_factory=cf_user_onenote_section_page_parent_notebook_section_group_section, ) usersactions_v1_0_user_onenote_section_page_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_pages_parent_notebook_sections_operations#UsersOnenoteSectionsPagesParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_section_page_parent_notebook_section, ) usersactions_v1_0_user_onenote_section_page_parent_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_pages_parent_section_operations#UsersOnenoteSectionsPagesParentSectionOperations.{}', client_factory=cf_user_onenote_section_page_parent_section, ) usersactions_v1_0_user_onenote_section_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_parent_notebook_operations#UsersOnenoteSectionsParentNotebookOperations.{}', client_factory=cf_user_onenote_section_parent_notebook, ) usersactions_v1_0_user_onenote_section_parent_notebook_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_parent_notebook_section_groups_parent_notebook_operations#UsersOnenoteSectionsParentNotebookSectionGroupsParentNotebookOperations.{}', client_factory=cf_user_onenote_section_parent_notebook_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_section_parent_notebook_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_parent_notebook_section_groups_sections_operations#UsersOnenoteSectionsParentNotebookSectionGroupsSectionsOperations.{}', client_factory=cf_user_onenote_section_parent_notebook_section_group_section, ) usersactions_v1_0_user_onenote_section_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_parent_notebook_sections_operations#UsersOnenoteSectionsParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_section_parent_notebook_section, ) usersactions_v1_0_user_onenote_section_parent_section_group_parent_notebook = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_parent_section_group_parent_notebook_operations#UsersOnenoteSectionsParentSectionGroupParentNotebookOperations.{}', client_factory=cf_user_onenote_section_parent_section_group_parent_notebook, ) usersactions_v1_0_user_onenote_section_parent_section_group_parent_notebook_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_parent_section_group_parent_notebook_sections_operations#UsersOnenoteSectionsParentSectionGroupParentNotebookSectionsOperations.{}', client_factory=cf_user_onenote_section_parent_section_group_parent_notebook_section, ) usersactions_v1_0_user_onenote_section_parent_section_group_section = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_onenote_sections_parent_section_group_sections_operations#UsersOnenoteSectionsParentSectionGroupSectionsOperations.{}', client_factory=cf_user_onenote_section_parent_section_group_section, ) usersactions_v1_0_user_online_meeting = CliCommandType( operations_tmpl='azext_usersactions_v1_0.vendored_sdks.usersactions.operations._users_online_meetings_operations#UsersOnlineMeetingsOperations.{}', client_factory=cf_user_online_meeting, ) def load_command_table(self, _): with self.command_group( 'usersactions user-calendar-calendar-view-attachment', usersactions_v1_0_user_calendar_calendar_view_attachment, client_factory=cf_user_calendar_calendar_view_attachment, ) as g: g.custom_command( 'create-upload-session', 'usersactions_user_calendar_calendar_view_attachment_create_upload_session' ) with self.command_group( 'usersactions user-calendar-calendar-view-calendar', usersactions_v1_0_user_calendar_calendar_view_calendar, client_factory=cf_user_calendar_calendar_view_calendar, ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_calendar_view_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-calendar-view-instance', usersactions_v1_0_user_calendar_calendar_view_instance, client_factory=cf_user_calendar_calendar_view_instance, ) as g: g.custom_command('accept', 'usersactions_user_calendar_calendar_view_instance_accept') g.custom_command('decline', 'usersactions_user_calendar_calendar_view_instance_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_calendar_view_instance_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_calendar_view_instance_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_calendar_view_instance_tentatively_accept') with self.command_group( 'usersactions user-calendar-calendar-view', usersactions_v1_0_user_calendar_calendar_view, client_factory=cf_user_calendar_calendar_view, ) as g: g.custom_command('accept', 'usersactions_user_calendar_calendar_view_accept') g.custom_command('decline', 'usersactions_user_calendar_calendar_view_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_calendar_view_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_calendar_view_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_calendar_view_tentatively_accept') with self.command_group( 'usersactions user-calendar-event-attachment', usersactions_v1_0_user_calendar_event_attachment, client_factory=cf_user_calendar_event_attachment, ) as g: g.custom_command('create-upload-session', 'usersactions_user_calendar_event_attachment_create_upload_session') with self.command_group( 'usersactions user-calendar-event-calendar', usersactions_v1_0_user_calendar_event_calendar, client_factory=cf_user_calendar_event_calendar, ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_event_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-event-instance', usersactions_v1_0_user_calendar_event_instance, client_factory=cf_user_calendar_event_instance, ) as g: g.custom_command('accept', 'usersactions_user_calendar_event_instance_accept') g.custom_command('decline', 'usersactions_user_calendar_event_instance_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_event_instance_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_event_instance_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_event_instance_tentatively_accept') with self.command_group( 'usersactions user-calendar-event', usersactions_v1_0_user_calendar_event, client_factory=cf_user_calendar_event ) as g: g.custom_command('accept', 'usersactions_user_calendar_event_accept') g.custom_command('decline', 'usersactions_user_calendar_event_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_event_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_event_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_event_tentatively_accept') with self.command_group( 'usersactions user-calendar', usersactions_v1_0_user_calendar, client_factory=cf_user_calendar ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-group-calendar-calendar-view-attachment', usersactions_v1_0_user_calendar_group_calendar_calendar_view_attachment, client_factory=cf_user_calendar_group_calendar_calendar_view_attachment, ) as g: g.custom_command( 'create-upload-session', 'usersactions_user_calendar_group_calendar_calendar_view_attachment_create_upload_session', ) with self.command_group( 'usersactions user-calendar-group-calendar-calendar-view-calendar', usersactions_v1_0_user_calendar_group_calendar_calendar_view_calendar, client_factory=cf_user_calendar_group_calendar_calendar_view_calendar, ) as g: g.custom_command( 'get-schedule', 'usersactions_user_calendar_group_calendar_calendar_view_calendar_get_schedule' ) with self.command_group( 'usersactions user-calendar-group-calendar-calendar-view-instance', usersactions_v1_0_user_calendar_group_calendar_calendar_view_instance, client_factory=cf_user_calendar_group_calendar_calendar_view_instance, ) as g: g.custom_command('accept', 'usersactions_user_calendar_group_calendar_calendar_view_instance_accept') g.custom_command('decline', 'usersactions_user_calendar_group_calendar_calendar_view_instance_decline') g.custom_command( 'dismiss-reminder', 'usersactions_user_calendar_group_calendar_calendar_view_instance_dismiss_reminder' ) g.custom_command( 'snooze-reminder', 'usersactions_user_calendar_group_calendar_calendar_view_instance_snooze_reminder' ) g.custom_command( 'tentatively-accept', 'usersactions_user_calendar_group_calendar_calendar_view_instance_tentatively_accept' ) with self.command_group( 'usersactions user-calendar-group-calendar-calendar-view', usersactions_v1_0_user_calendar_group_calendar_calendar_view, client_factory=cf_user_calendar_group_calendar_calendar_view, ) as g: g.custom_command('accept', 'usersactions_user_calendar_group_calendar_calendar_view_accept') g.custom_command('decline', 'usersactions_user_calendar_group_calendar_calendar_view_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_group_calendar_calendar_view_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_group_calendar_calendar_view_snooze_reminder') g.custom_command( 'tentatively-accept', 'usersactions_user_calendar_group_calendar_calendar_view_tentatively_accept' ) with self.command_group( 'usersactions user-calendar-group-calendar-event-attachment', usersactions_v1_0_user_calendar_group_calendar_event_attachment, client_factory=cf_user_calendar_group_calendar_event_attachment, ) as g: g.custom_command( 'create-upload-session', 'usersactions_user_calendar_group_calendar_event_attachment_create_upload_session' ) with self.command_group( 'usersactions user-calendar-group-calendar-event-calendar', usersactions_v1_0_user_calendar_group_calendar_event_calendar, client_factory=cf_user_calendar_group_calendar_event_calendar, ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_group_calendar_event_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-group-calendar-event-instance', usersactions_v1_0_user_calendar_group_calendar_event_instance, client_factory=cf_user_calendar_group_calendar_event_instance, ) as g: g.custom_command('accept', 'usersactions_user_calendar_group_calendar_event_instance_accept') g.custom_command('decline', 'usersactions_user_calendar_group_calendar_event_instance_decline') g.custom_command( 'dismiss-reminder', 'usersactions_user_calendar_group_calendar_event_instance_dismiss_reminder' ) g.custom_command('snooze-reminder', 'usersactions_user_calendar_group_calendar_event_instance_snooze_reminder') g.custom_command( 'tentatively-accept', 'usersactions_user_calendar_group_calendar_event_instance_tentatively_accept' ) with self.command_group( 'usersactions user-calendar-group-calendar-event', usersactions_v1_0_user_calendar_group_calendar_event, client_factory=cf_user_calendar_group_calendar_event, ) as g: g.custom_command('accept', 'usersactions_user_calendar_group_calendar_event_accept') g.custom_command('decline', 'usersactions_user_calendar_group_calendar_event_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_group_calendar_event_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_group_calendar_event_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_group_calendar_event_tentatively_accept') with self.command_group( 'usersactions user-calendar-group-calendar', usersactions_v1_0_user_calendar_group_calendar, client_factory=cf_user_calendar_group_calendar, ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_group_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-calendar-view-attachment', usersactions_v1_0_user_calendar_calendar_view_attachment, client_factory=cf_user_calendar_calendar_view_attachment, ) as g: g.custom_command( 'create-upload-session', 'usersactions_user_calendar_calendar_view_attachment_create_upload_session' ) with self.command_group( 'usersactions user-calendar-calendar-view-calendar', usersactions_v1_0_user_calendar_calendar_view_calendar, client_factory=cf_user_calendar_calendar_view_calendar, ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_calendar_view_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-calendar-view-instance', usersactions_v1_0_user_calendar_calendar_view_instance, client_factory=cf_user_calendar_calendar_view_instance, ) as g: g.custom_command('accept', 'usersactions_user_calendar_calendar_view_instance_accept') g.custom_command('decline', 'usersactions_user_calendar_calendar_view_instance_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_calendar_view_instance_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_calendar_view_instance_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_calendar_view_instance_tentatively_accept') with self.command_group( 'usersactions user-calendar-calendar-view', usersactions_v1_0_user_calendar_calendar_view, client_factory=cf_user_calendar_calendar_view, ) as g: g.custom_command('accept', 'usersactions_user_calendar_calendar_view_accept') g.custom_command('decline', 'usersactions_user_calendar_calendar_view_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_calendar_view_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_calendar_view_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_calendar_view_tentatively_accept') with self.command_group( 'usersactions user-calendar-event-attachment', usersactions_v1_0_user_calendar_event_attachment, client_factory=cf_user_calendar_event_attachment, ) as g: g.custom_command('create-upload-session', 'usersactions_user_calendar_event_attachment_create_upload_session') with self.command_group( 'usersactions user-calendar-event-calendar', usersactions_v1_0_user_calendar_event_calendar, client_factory=cf_user_calendar_event_calendar, ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_event_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-event-instance', usersactions_v1_0_user_calendar_event_instance, client_factory=cf_user_calendar_event_instance, ) as g: g.custom_command('accept', 'usersactions_user_calendar_event_instance_accept') g.custom_command('decline', 'usersactions_user_calendar_event_instance_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_event_instance_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_event_instance_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_event_instance_tentatively_accept') with self.command_group( 'usersactions user-calendar-event', usersactions_v1_0_user_calendar_event, client_factory=cf_user_calendar_event ) as g: g.custom_command('accept', 'usersactions_user_calendar_event_accept') g.custom_command('decline', 'usersactions_user_calendar_event_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_event_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_event_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_event_tentatively_accept') with self.command_group( 'usersactions user-calendar', usersactions_v1_0_user_calendar, client_factory=cf_user_calendar ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-view-attachment', usersactions_v1_0_user_calendar_view_attachment, client_factory=cf_user_calendar_view_attachment, ) as g: g.custom_command('create-upload-session', 'usersactions_user_calendar_view_attachment_create_upload_session') with self.command_group( 'usersactions user-calendar-view-calendar-calendar-view', usersactions_v1_0_user_calendar_view_calendar_calendar_view, client_factory=cf_user_calendar_view_calendar_calendar_view, ) as g: g.custom_command('accept', 'usersactions_user_calendar_view_calendar_calendar_view_accept') g.custom_command('decline', 'usersactions_user_calendar_view_calendar_calendar_view_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_view_calendar_calendar_view_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_view_calendar_calendar_view_snooze_reminder') g.custom_command( 'tentatively-accept', 'usersactions_user_calendar_view_calendar_calendar_view_tentatively_accept' ) with self.command_group( 'usersactions user-calendar-view-calendar-event', usersactions_v1_0_user_calendar_view_calendar_event, client_factory=cf_user_calendar_view_calendar_event, ) as g: g.custom_command('accept', 'usersactions_user_calendar_view_calendar_event_accept') g.custom_command('decline', 'usersactions_user_calendar_view_calendar_event_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_view_calendar_event_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_view_calendar_event_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_view_calendar_event_tentatively_accept') with self.command_group( 'usersactions user-calendar-view-calendar', usersactions_v1_0_user_calendar_view_calendar, client_factory=cf_user_calendar_view_calendar, ) as g: g.custom_command('get-schedule', 'usersactions_user_calendar_view_calendar_get_schedule') with self.command_group( 'usersactions user-calendar-view-instance', usersactions_v1_0_user_calendar_view_instance, client_factory=cf_user_calendar_view_instance, ) as g: g.custom_command('accept', 'usersactions_user_calendar_view_instance_accept') g.custom_command('decline', 'usersactions_user_calendar_view_instance_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_view_instance_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_view_instance_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_view_instance_tentatively_accept') with self.command_group( 'usersactions user-calendar-view', usersactions_v1_0_user_calendar_view, client_factory=cf_user_calendar_view ) as g: g.custom_command('accept', 'usersactions_user_calendar_view_accept') g.custom_command('decline', 'usersactions_user_calendar_view_decline') g.custom_command('dismiss-reminder', 'usersactions_user_calendar_view_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_calendar_view_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_calendar_view_tentatively_accept') with self.command_group( 'usersactions user-event-attachment', usersactions_v1_0_user_event_attachment, client_factory=cf_user_event_attachment, ) as g: g.custom_command('create-upload-session', 'usersactions_user_event_attachment_create_upload_session') with self.command_group( 'usersactions user-event-calendar-calendar-view', usersactions_v1_0_user_event_calendar_calendar_view, client_factory=cf_user_event_calendar_calendar_view, ) as g: g.custom_command('accept', 'usersactions_user_event_calendar_calendar_view_accept') g.custom_command('decline', 'usersactions_user_event_calendar_calendar_view_decline') g.custom_command('dismiss-reminder', 'usersactions_user_event_calendar_calendar_view_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_event_calendar_calendar_view_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_event_calendar_calendar_view_tentatively_accept') with self.command_group( 'usersactions user-event-calendar-event', usersactions_v1_0_user_event_calendar_event, client_factory=cf_user_event_calendar_event, ) as g: g.custom_command('accept', 'usersactions_user_event_calendar_event_accept') g.custom_command('decline', 'usersactions_user_event_calendar_event_decline') g.custom_command('dismiss-reminder', 'usersactions_user_event_calendar_event_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_event_calendar_event_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_event_calendar_event_tentatively_accept') with self.command_group( 'usersactions user-event-calendar', usersactions_v1_0_user_event_calendar, client_factory=cf_user_event_calendar ) as g: g.custom_command('get-schedule', 'usersactions_user_event_calendar_get_schedule') with self.command_group( 'usersactions user-event-instance', usersactions_v1_0_user_event_instance, client_factory=cf_user_event_instance ) as g: g.custom_command('accept', 'usersactions_user_event_instance_accept') g.custom_command('decline', 'usersactions_user_event_instance_decline') g.custom_command('dismiss-reminder', 'usersactions_user_event_instance_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_event_instance_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_event_instance_tentatively_accept') with self.command_group('usersactions user-event', usersactions_v1_0_user_event, client_factory=cf_user_event) as g: g.custom_command('accept', 'usersactions_user_event_accept') g.custom_command('decline', 'usersactions_user_event_decline') g.custom_command('dismiss-reminder', 'usersactions_user_event_dismiss_reminder') g.custom_command('snooze-reminder', 'usersactions_user_event_snooze_reminder') g.custom_command('tentatively-accept', 'usersactions_user_event_tentatively_accept') with self.command_group( 'usersactions user-mail-folder-child-folder', usersactions_v1_0_user_mail_folder_child_folder, client_factory=cf_user_mail_folder_child_folder, ) as g: g.custom_command('copy', 'usersactions_user_mail_folder_child_folder_copy') g.custom_command('move', 'usersactions_user_mail_folder_child_folder_move') with self.command_group( 'usersactions user-mail-folder-message-attachment', usersactions_v1_0_user_mail_folder_message_attachment, client_factory=cf_user_mail_folder_message_attachment, ) as g: g.custom_command( 'create-upload-session', 'usersactions_user_mail_folder_message_attachment_create_upload_session' ) with self.command_group( 'usersactions user-mail-folder-message', usersactions_v1_0_user_mail_folder_message, client_factory=cf_user_mail_folder_message, ) as g: g.custom_command('copy', 'usersactions_user_mail_folder_message_copy') g.custom_command('create-forward', 'usersactions_user_mail_folder_message_create_forward') g.custom_command('create-reply', 'usersactions_user_mail_folder_message_create_reply') g.custom_command('create-reply-all', 'usersactions_user_mail_folder_message_create_reply_all') g.custom_command('forward', 'usersactions_user_mail_folder_message_forward') g.custom_command('move', 'usersactions_user_mail_folder_message_move') g.custom_command('reply', 'usersactions_user_mail_folder_message_reply') g.custom_command('reply-all', 'usersactions_user_mail_folder_message_reply_all') g.custom_command('send', 'usersactions_user_mail_folder_message_send') with self.command_group( 'usersactions user-mail-folder', usersactions_v1_0_user_mail_folder, client_factory=cf_user_mail_folder ) as g: g.custom_command('copy', 'usersactions_user_mail_folder_copy') g.custom_command('move', 'usersactions_user_mail_folder_move') with self.command_group( 'usersactions user-managed-device', usersactions_v1_0_user_managed_device, client_factory=cf_user_managed_device ) as g: g.custom_command('bypass-activation-lock', 'usersactions_user_managed_device_bypass_activation_lock') g.custom_command('clean-window-device', 'usersactions_user_managed_device_clean_window_device') g.custom_command( 'delete-user-from-shared-apple-device', 'usersactions_user_managed_device_delete_user_from_shared_apple_device', ) g.custom_command('disable-lost-mode', 'usersactions_user_managed_device_disable_lost_mode') g.custom_command('locate-device', 'usersactions_user_managed_device_locate_device') g.custom_command( 'logout-shared-apple-device-active-user', 'usersactions_user_managed_device_logout_shared_apple_device_active_user', ) g.custom_command('reboot-now', 'usersactions_user_managed_device_reboot_now') g.custom_command('recover-passcode', 'usersactions_user_managed_device_recover_passcode') g.custom_command('remote-lock', 'usersactions_user_managed_device_remote_lock') g.custom_command('request-remote-assistance', 'usersactions_user_managed_device_request_remote_assistance') g.custom_command('reset-passcode', 'usersactions_user_managed_device_reset_passcode') g.custom_command('retire', 'usersactions_user_managed_device_retire') g.custom_command('shut-down', 'usersactions_user_managed_device_shut_down') g.custom_command('sync-device', 'usersactions_user_managed_device_sync_device') g.custom_command( 'update-window-device-account', 'usersactions_user_managed_device_update_window_device_account' ) g.custom_command('window-defender-scan', 'usersactions_user_managed_device_window_defender_scan') g.custom_command( 'window-defender-update-signature', 'usersactions_user_managed_device_window_defender_update_signature' ) g.custom_command('wipe', 'usersactions_user_managed_device_wipe') with self.command_group( 'usersactions user-message-attachment', usersactions_v1_0_user_message_attachment, client_factory=cf_user_message_attachment, ) as g: g.custom_command('create-upload-session', 'usersactions_user_message_attachment_create_upload_session') with self.command_group( 'usersactions user-message', usersactions_v1_0_user_message, client_factory=cf_user_message ) as g: g.custom_command('copy', 'usersactions_user_message_copy') g.custom_command('create-forward', 'usersactions_user_message_create_forward') g.custom_command('create-reply', 'usersactions_user_message_create_reply') g.custom_command('create-reply-all', 'usersactions_user_message_create_reply_all') g.custom_command('forward', 'usersactions_user_message_forward') g.custom_command('move', 'usersactions_user_message_move') g.custom_command('reply', 'usersactions_user_message_reply') g.custom_command('reply-all', 'usersactions_user_message_reply_all') g.custom_command('send', 'usersactions_user_message_send') with self.command_group('usersactions user', usersactions_v1_0_user, client_factory=cf_user) as g: g.custom_command('assign-license', 'usersactions_user_assign_license') g.custom_command('change-password', 'usersactions_user_change_password') g.custom_command('check-member-group', 'usersactions_user_check_member_group') g.custom_command('check-member-object', 'usersactions_user_check_member_object') g.custom_command('export-personal-data', 'usersactions_user_export_personal_data') g.custom_command('find-meeting-time', 'usersactions_user_find_meeting_time') g.custom_command('get-available-extension-property', 'usersactions_user_get_available_extension_property') g.custom_command('get-by-id', 'usersactions_user_get_by_id') g.custom_command('get-mail-tip', 'usersactions_user_get_mail_tip') g.custom_command('get-member-group', 'usersactions_user_get_member_group') g.custom_command('get-member-object', 'usersactions_user_get_member_object') g.custom_command('remove-all-device-from-management', 'usersactions_user_remove_all_device_from_management') g.custom_command('reprocess-license-assignment', 'usersactions_user_reprocess_license_assignment') g.custom_command('restore', 'usersactions_user_restore') g.custom_command('revoke-sign-in-session', 'usersactions_user_revoke_sign_in_session') g.custom_command('send-mail', 'usersactions_user_send_mail') g.custom_command('translate-exchange-id', 'usersactions_user_translate_exchange_id') g.custom_command('validate-property', 'usersactions_user_validate_property') g.custom_command( 'wipe-managed-app-registration-by-device-tag', 'usersactions_user_wipe_managed_app_registration_by_device_tag', ) with self.command_group( 'usersactions user-onenote-notebook', usersactions_v1_0_user_onenote_notebook, client_factory=cf_user_onenote_notebook, ) as g: g.custom_command('copy-notebook', 'usersactions_user_onenote_notebook_copy_notebook') g.custom_command('get-notebook-from-web-url', 'usersactions_user_onenote_notebook_get_notebook_from_web_url') with self.command_group( 'usersactions user-onenote-notebook-section-group-parent-notebook', usersactions_v1_0_user_onenote_notebook_section_group_parent_notebook, client_factory=cf_user_onenote_notebook_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_notebook_section_group_parent_notebook_copy_notebook' ) with self.command_group( 'usersactions user-onenote-notebook-section-group-section', usersactions_v1_0_user_onenote_notebook_section_group_section, client_factory=cf_user_onenote_notebook_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_notebook_section_group_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_notebook_section_group_section_copy_to_section_group' ) with self.command_group( 'usersactions user-onenote-notebook-section-group-section-page', usersactions_v1_0_user_onenote_notebook_section_group_section_page, client_factory=cf_user_onenote_notebook_section_group_section_page, ) as g: g.custom_command( 'copy-to-section', 'usersactions_user_onenote_notebook_section_group_section_page_copy_to_section' ) g.custom_command( 'onenote-patch-content', 'usersactions_user_onenote_notebook_section_group_section_page_onenote_patch_content', ) with self.command_group( 'usersactions user-onenote-notebook-section-group-section-page-parent-notebook', usersactions_v1_0_user_onenote_notebook_section_group_section_page_parent_notebook, client_factory=cf_user_onenote_notebook_section_group_section_page_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_notebook_section_group_section_page_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-notebook-section-group-section-page-parent-section', usersactions_v1_0_user_onenote_notebook_section_group_section_page_parent_section, client_factory=cf_user_onenote_notebook_section_group_section_page_parent_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_notebook_section_group_section_page_parent_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_notebook_section_group_section_page_parent_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-notebook-section-group-section-parent-notebook', usersactions_v1_0_user_onenote_notebook_section_group_section_parent_notebook, client_factory=cf_user_onenote_notebook_section_group_section_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_notebook_section_group_section_parent_notebook_copy_notebook' ) with self.command_group( 'usersactions user-onenote-notebook-section', usersactions_v1_0_user_onenote_notebook_section, client_factory=cf_user_onenote_notebook_section, ) as g: g.custom_command('copy-to-notebook', 'usersactions_user_onenote_notebook_section_copy_to_notebook') g.custom_command('copy-to-section-group', 'usersactions_user_onenote_notebook_section_copy_to_section_group') with self.command_group( 'usersactions user-onenote-notebook-section-page', usersactions_v1_0_user_onenote_notebook_section_page, client_factory=cf_user_onenote_notebook_section_page, ) as g: g.custom_command('copy-to-section', 'usersactions_user_onenote_notebook_section_page_copy_to_section') g.custom_command( 'onenote-patch-content', 'usersactions_user_onenote_notebook_section_page_onenote_patch_content' ) with self.command_group( 'usersactions user-onenote-notebook-section-page-parent-notebook', usersactions_v1_0_user_onenote_notebook_section_page_parent_notebook, client_factory=cf_user_onenote_notebook_section_page_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_notebook_section_page_parent_notebook_copy_notebook' ) with self.command_group( 'usersactions user-onenote-notebook-section-page-parent-section', usersactions_v1_0_user_onenote_notebook_section_page_parent_section, client_factory=cf_user_onenote_notebook_section_page_parent_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_notebook_section_page_parent_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_notebook_section_page_parent_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-notebook-section-parent-notebook', usersactions_v1_0_user_onenote_notebook_section_parent_notebook, client_factory=cf_user_onenote_notebook_section_parent_notebook, ) as g: g.custom_command('copy-notebook', 'usersactions_user_onenote_notebook_section_parent_notebook_copy_notebook') with self.command_group( 'usersactions user-onenote-notebook-section-parent-section-group-parent-notebook', usersactions_v1_0_user_onenote_notebook_section_parent_section_group_parent_notebook, client_factory=cf_user_onenote_notebook_section_parent_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_notebook_section_parent_section_group_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-notebook-section-parent-section-group-section', usersactions_v1_0_user_onenote_notebook_section_parent_section_group_section, client_factory=cf_user_onenote_notebook_section_parent_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_notebook_section_parent_section_group_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_notebook_section_parent_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-page', usersactions_v1_0_user_onenote_page, client_factory=cf_user_onenote_page ) as g: g.custom_command('copy-to-section', 'usersactions_user_onenote_page_copy_to_section') g.custom_command('onenote-patch-content', 'usersactions_user_onenote_page_onenote_patch_content') with self.command_group( 'usersactions user-onenote-page-parent-notebook', usersactions_v1_0_user_onenote_page_parent_notebook, client_factory=cf_user_onenote_page_parent_notebook, ) as g: g.custom_command('copy-notebook', 'usersactions_user_onenote_page_parent_notebook_copy_notebook') with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-group-parent-notebook', usersactions_v1_0_user_onenote_page_parent_notebook_section_group_parent_notebook, client_factory=cf_user_onenote_page_parent_notebook_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_page_parent_notebook_section_group_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-group-section', usersactions_v1_0_user_onenote_page_parent_notebook_section_group_section, client_factory=cf_user_onenote_page_parent_notebook_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_page_parent_notebook_section_group_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_page_parent_notebook_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-group-section-page', usersactions_v1_0_user_onenote_page_parent_notebook_section_group_section_page, client_factory=cf_user_onenote_page_parent_notebook_section_group_section_page, ) as g: g.custom_command( 'copy-to-section', 'usersactions_user_onenote_page_parent_notebook_section_group_section_page_copy_to_section', ) g.custom_command( 'onenote-patch-content', 'usersactions_user_onenote_page_parent_notebook_section_group_section_page_onenote_patch_content', ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-group-section-parent-notebook', usersactions_v1_0_user_onenote_page_parent_notebook_section_group_section_parent_notebook, client_factory=cf_user_onenote_page_parent_notebook_section_group_section_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_page_parent_notebook_section_group_section_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section', usersactions_v1_0_user_onenote_page_parent_notebook_section, client_factory=cf_user_onenote_page_parent_notebook_section, ) as g: g.custom_command('copy-to-notebook', 'usersactions_user_onenote_page_parent_notebook_section_copy_to_notebook') g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_page_parent_notebook_section_copy_to_section_group' ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-page', usersactions_v1_0_user_onenote_page_parent_notebook_section_page, client_factory=cf_user_onenote_page_parent_notebook_section_page, ) as g: g.custom_command( 'copy-to-section', 'usersactions_user_onenote_page_parent_notebook_section_page_copy_to_section' ) g.custom_command( 'onenote-patch-content', 'usersactions_user_onenote_page_parent_notebook_section_page_onenote_patch_content' ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-parent-notebook', usersactions_v1_0_user_onenote_page_parent_notebook_section_parent_notebook, client_factory=cf_user_onenote_page_parent_notebook_section_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_page_parent_notebook_section_parent_notebook_copy_notebook' ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-parent-section-group-parent-notebook', usersactions_v1_0_user_onenote_page_parent_notebook_section_parent_section_group_parent_notebook, client_factory=cf_user_onenote_page_parent_notebook_section_parent_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_page_parent_notebook_section_parent_section_group_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-page-parent-notebook-section-parent-section-group-section', usersactions_v1_0_user_onenote_page_parent_notebook_section_parent_section_group_section, client_factory=cf_user_onenote_page_parent_notebook_section_parent_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_page_parent_notebook_section_parent_section_group_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_page_parent_notebook_section_parent_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-page-parent-section', usersactions_v1_0_user_onenote_page_parent_section, client_factory=cf_user_onenote_page_parent_section, ) as g: g.custom_command('copy-to-notebook', 'usersactions_user_onenote_page_parent_section_copy_to_notebook') g.custom_command('copy-to-section-group', 'usersactions_user_onenote_page_parent_section_copy_to_section_group') with self.command_group( 'usersactions user-onenote-page-parent-section-page', usersactions_v1_0_user_onenote_page_parent_section_page, client_factory=cf_user_onenote_page_parent_section_page, ) as g: g.custom_command('copy-to-section', 'usersactions_user_onenote_page_parent_section_page_copy_to_section') g.custom_command( 'onenote-patch-content', 'usersactions_user_onenote_page_parent_section_page_onenote_patch_content' ) with self.command_group( 'usersactions user-onenote-page-parent-section-parent-notebook', usersactions_v1_0_user_onenote_page_parent_section_parent_notebook, client_factory=cf_user_onenote_page_parent_section_parent_notebook, ) as g: g.custom_command('copy-notebook', 'usersactions_user_onenote_page_parent_section_parent_notebook_copy_notebook') with self.command_group( 'usersactions user-onenote-page-parent-section-parent-notebook-section-group-parent-notebook', usersactions_v1_0_user_onenote_page_parent_section_parent_notebook_section_group_parent_notebook, client_factory=cf_user_onenote_page_parent_section_parent_notebook_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_page_parent_section_parent_notebook_section_group_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-page-parent-section-parent-notebook-section-group-section', usersactions_v1_0_user_onenote_page_parent_section_parent_notebook_section_group_section, client_factory=cf_user_onenote_page_parent_section_parent_notebook_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_page_parent_section_parent_notebook_section_group_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_page_parent_section_parent_notebook_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-page-parent-section-parent-notebook-section', usersactions_v1_0_user_onenote_page_parent_section_parent_notebook_section, client_factory=cf_user_onenote_page_parent_section_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_page_parent_section_parent_notebook_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_page_parent_section_parent_notebook_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-page-parent-section-parent-section-group-parent-notebook', usersactions_v1_0_user_onenote_page_parent_section_parent_section_group_parent_notebook, client_factory=cf_user_onenote_page_parent_section_parent_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_page_parent_section_parent_section_group_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-page-parent-section-parent-section-group-parent-notebook-section', usersactions_v1_0_user_onenote_page_parent_section_parent_section_group_parent_notebook_section, client_factory=cf_user_onenote_page_parent_section_parent_section_group_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_page_parent_section_parent_section_group_parent_notebook_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_page_parent_section_parent_section_group_parent_notebook_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-page-parent-section-parent-section-group-section', usersactions_v1_0_user_onenote_page_parent_section_parent_section_group_section, client_factory=cf_user_onenote_page_parent_section_parent_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_page_parent_section_parent_section_group_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_page_parent_section_parent_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-group-parent-notebook', usersactions_v1_0_user_onenote_section_group_parent_notebook, client_factory=cf_user_onenote_section_group_parent_notebook, ) as g: g.custom_command('copy-notebook', 'usersactions_user_onenote_section_group_parent_notebook_copy_notebook') with self.command_group( 'usersactions user-onenote-section-group-parent-notebook-section', usersactions_v1_0_user_onenote_section_group_parent_notebook_section, client_factory=cf_user_onenote_section_group_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_group_parent_notebook_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_group_parent_notebook_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-group-parent-notebook-section-page', usersactions_v1_0_user_onenote_section_group_parent_notebook_section_page, client_factory=cf_user_onenote_section_group_parent_notebook_section_page, ) as g: g.custom_command( 'copy-to-section', 'usersactions_user_onenote_section_group_parent_notebook_section_page_copy_to_section' ) g.custom_command( 'onenote-patch-content', 'usersactions_user_onenote_section_group_parent_notebook_section_page_onenote_patch_content', ) with self.command_group( 'usersactions user-onenote-section-group-parent-notebook-section-page-parent-notebook', usersactions_v1_0_user_onenote_section_group_parent_notebook_section_page_parent_notebook, client_factory=cf_user_onenote_section_group_parent_notebook_section_page_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_section_group_parent_notebook_section_page_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-section-group-parent-notebook-section-page-parent-section', usersactions_v1_0_user_onenote_section_group_parent_notebook_section_page_parent_section, client_factory=cf_user_onenote_section_group_parent_notebook_section_page_parent_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_group_parent_notebook_section_page_parent_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_group_parent_notebook_section_page_parent_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-group-parent-notebook-section-parent-notebook', usersactions_v1_0_user_onenote_section_group_parent_notebook_section_parent_notebook, client_factory=cf_user_onenote_section_group_parent_notebook_section_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_section_group_parent_notebook_section_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-section-group-section', usersactions_v1_0_user_onenote_section_group_section, client_factory=cf_user_onenote_section_group_section, ) as g: g.custom_command('copy-to-notebook', 'usersactions_user_onenote_section_group_section_copy_to_notebook') g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_group_section_copy_to_section_group' ) with self.command_group( 'usersactions user-onenote-section-group-section-page', usersactions_v1_0_user_onenote_section_group_section_page, client_factory=cf_user_onenote_section_group_section_page, ) as g: g.custom_command('copy-to-section', 'usersactions_user_onenote_section_group_section_page_copy_to_section') g.custom_command( 'onenote-patch-content', 'usersactions_user_onenote_section_group_section_page_onenote_patch_content' ) with self.command_group( 'usersactions user-onenote-section-group-section-page-parent-notebook', usersactions_v1_0_user_onenote_section_group_section_page_parent_notebook, client_factory=cf_user_onenote_section_group_section_page_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_section_group_section_page_parent_notebook_copy_notebook' ) with self.command_group( 'usersactions user-onenote-section-group-section-page-parent-notebook-section', usersactions_v1_0_user_onenote_section_group_section_page_parent_notebook_section, client_factory=cf_user_onenote_section_group_section_page_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_group_section_page_parent_notebook_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_group_section_page_parent_notebook_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-group-section-page-parent-section', usersactions_v1_0_user_onenote_section_group_section_page_parent_section, client_factory=cf_user_onenote_section_group_section_page_parent_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_group_section_page_parent_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_group_section_page_parent_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-group-section-parent-notebook', usersactions_v1_0_user_onenote_section_group_section_parent_notebook, client_factory=cf_user_onenote_section_group_section_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_section_group_section_parent_notebook_copy_notebook' ) with self.command_group( 'usersactions user-onenote-section-group-section-parent-notebook-section', usersactions_v1_0_user_onenote_section_group_section_parent_notebook_section, client_factory=cf_user_onenote_section_group_section_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_group_section_parent_notebook_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_group_section_parent_notebook_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section', usersactions_v1_0_user_onenote_section, client_factory=cf_user_onenote_section, ) as g: g.custom_command('copy-to-notebook', 'usersactions_user_onenote_section_copy_to_notebook') g.custom_command('copy-to-section-group', 'usersactions_user_onenote_section_copy_to_section_group') with self.command_group( 'usersactions user-onenote-section-page', usersactions_v1_0_user_onenote_section_page, client_factory=cf_user_onenote_section_page, ) as g: g.custom_command('copy-to-section', 'usersactions_user_onenote_section_page_copy_to_section') g.custom_command('onenote-patch-content', 'usersactions_user_onenote_section_page_onenote_patch_content') with self.command_group( 'usersactions user-onenote-section-page-parent-notebook', usersactions_v1_0_user_onenote_section_page_parent_notebook, client_factory=cf_user_onenote_section_page_parent_notebook, ) as g: g.custom_command('copy-notebook', 'usersactions_user_onenote_section_page_parent_notebook_copy_notebook') with self.command_group( 'usersactions user-onenote-section-page-parent-notebook-section-group-parent-notebook', usersactions_v1_0_user_onenote_section_page_parent_notebook_section_group_parent_notebook, client_factory=cf_user_onenote_section_page_parent_notebook_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_section_page_parent_notebook_section_group_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-section-page-parent-notebook-section-group-section', usersactions_v1_0_user_onenote_section_page_parent_notebook_section_group_section, client_factory=cf_user_onenote_section_page_parent_notebook_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_page_parent_notebook_section_group_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_page_parent_notebook_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-page-parent-notebook-section', usersactions_v1_0_user_onenote_section_page_parent_notebook_section, client_factory=cf_user_onenote_section_page_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_page_parent_notebook_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_page_parent_notebook_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-page-parent-section', usersactions_v1_0_user_onenote_section_page_parent_section, client_factory=cf_user_onenote_section_page_parent_section, ) as g: g.custom_command('copy-to-notebook', 'usersactions_user_onenote_section_page_parent_section_copy_to_notebook') g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_page_parent_section_copy_to_section_group' ) with self.command_group( 'usersactions user-onenote-section-parent-notebook', usersactions_v1_0_user_onenote_section_parent_notebook, client_factory=cf_user_onenote_section_parent_notebook, ) as g: g.custom_command('copy-notebook', 'usersactions_user_onenote_section_parent_notebook_copy_notebook') with self.command_group( 'usersactions user-onenote-section-parent-notebook-section-group-parent-notebook', usersactions_v1_0_user_onenote_section_parent_notebook_section_group_parent_notebook, client_factory=cf_user_onenote_section_parent_notebook_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_section_parent_notebook_section_group_parent_notebook_copy_notebook', ) with self.command_group( 'usersactions user-onenote-section-parent-notebook-section-group-section', usersactions_v1_0_user_onenote_section_parent_notebook_section_group_section, client_factory=cf_user_onenote_section_parent_notebook_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_parent_notebook_section_group_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_parent_notebook_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-parent-notebook-section', usersactions_v1_0_user_onenote_section_parent_notebook_section, client_factory=cf_user_onenote_section_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_parent_notebook_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_parent_notebook_section_copy_to_section_group' ) with self.command_group( 'usersactions user-onenote-section-parent-section-group-parent-notebook', usersactions_v1_0_user_onenote_section_parent_section_group_parent_notebook, client_factory=cf_user_onenote_section_parent_section_group_parent_notebook, ) as g: g.custom_command( 'copy-notebook', 'usersactions_user_onenote_section_parent_section_group_parent_notebook_copy_notebook' ) with self.command_group( 'usersactions user-onenote-section-parent-section-group-parent-notebook-section', usersactions_v1_0_user_onenote_section_parent_section_group_parent_notebook_section, client_factory=cf_user_onenote_section_parent_section_group_parent_notebook_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_parent_section_group_parent_notebook_section_copy_to_notebook', ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_parent_section_group_parent_notebook_section_copy_to_section_group', ) with self.command_group( 'usersactions user-onenote-section-parent-section-group-section', usersactions_v1_0_user_onenote_section_parent_section_group_section, client_factory=cf_user_onenote_section_parent_section_group_section, ) as g: g.custom_command( 'copy-to-notebook', 'usersactions_user_onenote_section_parent_section_group_section_copy_to_notebook' ) g.custom_command( 'copy-to-section-group', 'usersactions_user_onenote_section_parent_section_group_section_copy_to_section_group', ) with self.command_group( 'usersactions user-online-meeting', usersactions_v1_0_user_online_meeting, client_factory=cf_user_online_meeting ) as g: g.custom_command('create-or-get', 'usersactions_user_online_meeting_create_or_get') with self.command_group('usersactions_v1_0', is_experimental=True): pass

54.318087

263

0.82209

12,112

104,508

6.457645

0.024769

0.065256

0.063095

0.052957

0.887707

0.872045

0.8549

0.840082

0.817541

0.790616

0

0.007124

0.116173

104,508

1,923

264

54.346334

0.839661

0.005454

0

0.419886

0

0.0019

0.447625

0.404767

0

1

0.000633

false

0.003167

0.001267

0

0.0019

0

null

0

1

0

1

null

0

7

47ad9c165ce4d425211db91d27aa1bdf0ad1b23b

1,356

py

Python

tests/test_quotes.py

anterokangas/ManuscriptManagerOld

194bc6c7b899bb4ab61966af3ba1e619fc74c20c

[ "MIT" ]

null

tests/test_quotes.py

anterokangas/ManuscriptManagerOld

194bc6c7b899bb4ab61966af3ba1e619fc74c20c

[ "MIT" ]

null

tests/test_quotes.py

anterokangas/ManuscriptManagerOld

194bc6c7b899bb4ab61966af3ba1e619fc74c20c

[ "MIT" ]

null

import pytest from manuscript.tools.quotes import add_quotes from manuscript.tools.quotes import remove_quotes def test_add_quotes(): assert add_quotes("abc") == "abc" assert add_quotes("abc def") == '"abc def"' assert add_quotes("abc \"def\"") == "'abc \"def\"'" assert add_quotes("abc\"def\"") == "abc\"def\"" assert add_quotes("abc\" def\"") == "'abc\" def\"'" assert add_quotes("abc'cde\"fgg") == "abc'cde\"fgg" assert add_quotes("abc 'cde\"fgg") == '"abc \'cde\"fgg"' def test_remove_quotes(): assert remove_quotes("") == "" assert remove_quotes("a") == "a" assert remove_quotes("ab") == "ab" assert remove_quotes("abc") == "abc" assert remove_quotes("'abc'") == "abc" assert remove_quotes('"abc"') == "abc" assert remove_quotes("abc def") == "abc def" assert remove_quotes("'") == "'" assert remove_quotes("''") == "" assert remove_quotes("'''") == "'" assert remove_quotes("'\"'") == "\"" assert remove_quotes('"') == '"' assert remove_quotes('""') == '' assert remove_quotes('"""') == '"' assert remove_quotes('"\'"') == "\'" assert remove_quotes("'abc \"def\"'") == "abc \"def\"" assert remove_quotes("abc\"def\"") == "abc\"def\"" assert remove_quotes("'abc\" def\"'") == "abc\" def\"" assert remove_quotes("abc'cde\"fgg") == "abc'cde\"fgg"

39.882353

60

0.581858

164

1,356

4.615854

0.097561

0.332893

0.451783

0.317041

0.866579

0.784676

0.764861

0.701453

0.638045

0

0.175516

1,356

34

61

39.882353

0.677102

0

0.129032

0

0.15549

0

0.83871

1

0.064516

true

0

0.096774

0

0.16129

0

null

1

0

null

0

1

0

1

0

11

9a212507394544c0c2e01f0f0d625bbb7d17f307

94,208

py

Python

sdk/python/pulumi_oci/marketplace/outputs.py

EladGabay/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

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

5

2021-08-17T11:14:46.000Z

2021-12-31T02:07:03.000Z

sdk/python/pulumi_oci/marketplace/outputs.py

pulumi-oci/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

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

1

2021-09-06T11:21:29.000Z

sdk/python/pulumi_oci/marketplace/outputs.py

pulumi-oci/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

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

2

2021-08-24T23:31:30.000Z

2022-01-02T19:26:54.000Z

# 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 __all__ = [ 'PublicationIcon', 'PublicationPackageDetails', 'PublicationPackageDetailsEula', 'PublicationPackageDetailsOperatingSystem', 'PublicationSupportContact', 'PublicationSupportedOperatingSystem', 'GetAcceptedAgreementsAcceptedAgreementResult', 'GetAcceptedAgreementsFilterResult', 'GetCategoriesCategoryResult', 'GetCategoriesFilterResult', 'GetListingBannerResult', 'GetListingDocumentationLinkResult', 'GetListingIconResult', 'GetListingLanguageResult', 'GetListingLinkResult', 'GetListingPackageAgreementsAgreementResult', 'GetListingPackageAgreementsFilterResult', 'GetListingPackageOperatingSystemResult', 'GetListingPackagePricingResult', 'GetListingPackageRegionResult', 'GetListingPackageRegionCountryResult', 'GetListingPackageVariableResult', 'GetListingPackagesFilterResult', 'GetListingPackagesListingPackageResult', 'GetListingPackagesListingPackageOperatingSystemResult', 'GetListingPackagesListingPackageRegionResult', 'GetListingPackagesListingPackageRegionCountryResult', 'GetListingPublisherResult', 'GetListingPublisherLinkResult', 'GetListingPublisherLogoResult', 'GetListingRegionResult', 'GetListingRegionCountryResult', 'GetListingScreenshotResult', 'GetListingSupportContactResult', 'GetListingSupportLinkResult', 'GetListingSupportedOperatingSystemResult', 'GetListingTaxesFilterResult', 'GetListingTaxesTaxResult', 'GetListingVideoResult', 'GetListingsFilterResult', 'GetListingsListingResult', 'GetListingsListingIconResult', 'GetListingsListingPublisherResult', 'GetListingsListingRegionResult', 'GetListingsListingRegionCountryResult', 'GetListingsListingSupportedOperatingSystemResult', 'GetPublicationIconResult', 'GetPublicationPackageDetailsResult', 'GetPublicationPackageDetailsEulaResult', 'GetPublicationPackageDetailsOperatingSystemResult', 'GetPublicationPackageOperatingSystemResult', 'GetPublicationPackageVariableResult', 'GetPublicationPackagesFilterResult', 'GetPublicationPackagesPublicationPackageResult', 'GetPublicationSupportContactResult', 'GetPublicationSupportedOperatingSystemResult', 'GetPublicationsFilterResult', 'GetPublicationsPublicationResult', 'GetPublicationsPublicationIconResult', 'GetPublicationsPublicationPackageDetailsResult', 'GetPublicationsPublicationPackageDetailsEulaResult', 'GetPublicationsPublicationPackageDetailsOperatingSystemResult', 'GetPublicationsPublicationSupportContactResult', 'GetPublicationsPublicationSupportedOperatingSystemResult', 'GetPublishersFilterResult', 'GetPublishersPublisherResult', ] @pulumi.output_type class PublicationIcon(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "contentUrl": suggest = "content_url" elif key == "fileExtension": suggest = "file_extension" elif key == "mimeType": suggest = "mime_type" if suggest: pulumi.log.warn(f"Key '{key}' not found in PublicationIcon. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: PublicationIcon.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: PublicationIcon.__key_warning(key) return super().get(key, default) def __init__(__self__, *, content_url: Optional[str] = None, file_extension: Optional[str] = None, mime_type: Optional[str] = None, name: Optional[str] = None): """ :param str content_url: The content URL of the upload data. :param str file_extension: The file extension of the upload data. :param str mime_type: The MIME type of the upload data. :param str name: (Updatable) The name of the contact. """ if content_url is not None: pulumi.set(__self__, "content_url", content_url) if file_extension is not None: pulumi.set(__self__, "file_extension", file_extension) if mime_type is not None: pulumi.set(__self__, "mime_type", mime_type) if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> Optional[str]: """ The content URL of the upload data. """ return pulumi.get(self, "content_url") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> Optional[str]: """ The file extension of the upload data. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> Optional[str]: """ The MIME type of the upload data. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> Optional[str]: """ (Updatable) The name of the contact. """ return pulumi.get(self, "name") @pulumi.output_type class PublicationPackageDetails(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "operatingSystem": suggest = "operating_system" elif key == "packageType": suggest = "package_type" elif key == "packageVersion": suggest = "package_version" elif key == "imageId": suggest = "image_id" if suggest: pulumi.log.warn(f"Key '{key}' not found in PublicationPackageDetails. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: PublicationPackageDetails.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: PublicationPackageDetails.__key_warning(key) return super().get(key, default) def __init__(__self__, *, eulas: Sequence['outputs.PublicationPackageDetailsEula'], operating_system: 'outputs.PublicationPackageDetailsOperatingSystem', package_type: str, package_version: str, image_id: Optional[str] = None): """ :param Sequence['PublicationPackageDetailsEulaArgs'] eulas: End User License Agreeement that a consumer of this listing has to accept :param 'PublicationPackageDetailsOperatingSystemArgs' operating_system: OS used by the listing. :param str package_type: Type of the artifact of the listing :param str package_version: The version of the package :param str image_id: base image id of the listing """ pulumi.set(__self__, "eulas", eulas) pulumi.set(__self__, "operating_system", operating_system) pulumi.set(__self__, "package_type", package_type) pulumi.set(__self__, "package_version", package_version) if image_id is not None: pulumi.set(__self__, "image_id", image_id) @property @pulumi.getter def eulas(self) -> Sequence['outputs.PublicationPackageDetailsEula']: """ End User License Agreeement that a consumer of this listing has to accept """ return pulumi.get(self, "eulas") @property @pulumi.getter(name="operatingSystem") def operating_system(self) -> 'outputs.PublicationPackageDetailsOperatingSystem': """ OS used by the listing. """ return pulumi.get(self, "operating_system") @property @pulumi.getter(name="packageType") def package_type(self) -> str: """ Type of the artifact of the listing """ return pulumi.get(self, "package_type") @property @pulumi.getter(name="packageVersion") def package_version(self) -> str: """ The version of the package """ return pulumi.get(self, "package_version") @property @pulumi.getter(name="imageId") def image_id(self) -> Optional[str]: """ base image id of the listing """ return pulumi.get(self, "image_id") @pulumi.output_type class PublicationPackageDetailsEula(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "eulaType": suggest = "eula_type" elif key == "licenseText": suggest = "license_text" if suggest: pulumi.log.warn(f"Key '{key}' not found in PublicationPackageDetailsEula. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: PublicationPackageDetailsEula.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: PublicationPackageDetailsEula.__key_warning(key) return super().get(key, default) def __init__(__self__, *, eula_type: str, license_text: Optional[str] = None): """ :param str eula_type: the specified eula's type :param str license_text: text of the eula """ pulumi.set(__self__, "eula_type", eula_type) if license_text is not None: pulumi.set(__self__, "license_text", license_text) @property @pulumi.getter(name="eulaType") def eula_type(self) -> str: """ the specified eula's type """ return pulumi.get(self, "eula_type") @property @pulumi.getter(name="licenseText") def license_text(self) -> Optional[str]: """ text of the eula """ return pulumi.get(self, "license_text") @pulumi.output_type class PublicationPackageDetailsOperatingSystem(dict): def __init__(__self__, *, name: Optional[str] = None): """ :param str name: (Updatable) The name of the contact. """ if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> Optional[str]: """ (Updatable) The name of the contact. """ return pulumi.get(self, "name") @pulumi.output_type class PublicationSupportContact(dict): def __init__(__self__, *, email: Optional[str] = None, name: Optional[str] = None, phone: Optional[str] = None, subject: Optional[str] = None): """ :param str email: (Updatable) The email of the contact. :param str name: (Updatable) The name of the contact. :param str phone: (Updatable) The phone number of the contact. :param str subject: (Updatable) The email subject line to use when contacting support. """ if email is not None: pulumi.set(__self__, "email", email) if name is not None: pulumi.set(__self__, "name", name) if phone is not None: pulumi.set(__self__, "phone", phone) if subject is not None: pulumi.set(__self__, "subject", subject) @property @pulumi.getter def email(self) -> Optional[str]: """ (Updatable) The email of the contact. """ return pulumi.get(self, "email") @property @pulumi.getter def name(self) -> Optional[str]: """ (Updatable) The name of the contact. """ return pulumi.get(self, "name") @property @pulumi.getter def phone(self) -> Optional[str]: """ (Updatable) The phone number of the contact. """ return pulumi.get(self, "phone") @property @pulumi.getter def subject(self) -> Optional[str]: """ (Updatable) The email subject line to use when contacting support. """ return pulumi.get(self, "subject") @pulumi.output_type class PublicationSupportedOperatingSystem(dict): def __init__(__self__, *, name: Optional[str] = None): """ :param str name: (Updatable) The name of the contact. """ if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> Optional[str]: """ (Updatable) The name of the contact. """ return pulumi.get(self, "name") @pulumi.output_type class GetAcceptedAgreementsAcceptedAgreementResult(dict): def __init__(__self__, *, agreement_id: str, compartment_id: str, defined_tags: Mapping[str, Any], display_name: str, freeform_tags: Mapping[str, Any], id: str, listing_id: str, package_version: str, signature: str, time_accepted: str): """ :param str agreement_id: The unique identifier for the terms of use agreement itself. :param str compartment_id: The unique identifier for the compartment. :param Mapping[str, Any] defined_tags: The defined tags associated with this resource, if any. Each key is predefined and scoped to namespaces. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Operations.CostCenter": "42"}` :param str display_name: The display name of the resource. :param Mapping[str, Any] freeform_tags: The freeform tags associated with this resource, if any. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}` :param str id: The unique identifier for the acceptance of the agreement within a specific compartment. :param str listing_id: The unique identifier for the listing. :param str package_version: The version of the package. Package versions are unique within a listing. :param str time_accepted: The time the agreement was accepted. """ pulumi.set(__self__, "agreement_id", agreement_id) pulumi.set(__self__, "compartment_id", compartment_id) pulumi.set(__self__, "defined_tags", defined_tags) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "freeform_tags", freeform_tags) pulumi.set(__self__, "id", id) pulumi.set(__self__, "listing_id", listing_id) pulumi.set(__self__, "package_version", package_version) pulumi.set(__self__, "signature", signature) pulumi.set(__self__, "time_accepted", time_accepted) @property @pulumi.getter(name="agreementId") def agreement_id(self) -> str: """ The unique identifier for the terms of use agreement itself. """ return pulumi.get(self, "agreement_id") @property @pulumi.getter(name="compartmentId") def compartment_id(self) -> str: """ The unique identifier for the compartment. """ return pulumi.get(self, "compartment_id") @property @pulumi.getter(name="definedTags") def defined_tags(self) -> Mapping[str, Any]: """ The defined tags associated with this resource, if any. Each key is predefined and scoped to namespaces. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Operations.CostCenter": "42"}` """ return pulumi.get(self, "defined_tags") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ The display name of the resource. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="freeformTags") def freeform_tags(self) -> Mapping[str, Any]: """ The freeform tags associated with this resource, if any. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}` """ return pulumi.get(self, "freeform_tags") @property @pulumi.getter def id(self) -> str: """ The unique identifier for the acceptance of the agreement within a specific compartment. """ return pulumi.get(self, "id") @property @pulumi.getter(name="listingId") def listing_id(self) -> str: """ The unique identifier for the listing. """ return pulumi.get(self, "listing_id") @property @pulumi.getter(name="packageVersion") def package_version(self) -> str: """ The version of the package. Package versions are unique within a listing. """ return pulumi.get(self, "package_version") @property @pulumi.getter def signature(self) -> str: return pulumi.get(self, "signature") @property @pulumi.getter(name="timeAccepted") def time_accepted(self) -> str: """ The time the agreement was accepted. """ return pulumi.get(self, "time_accepted") @pulumi.output_type class GetAcceptedAgreementsFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetCategoriesCategoryResult(dict): def __init__(__self__, *, name: str): """ :param str name: Name of the product category. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ Name of the product category. """ return pulumi.get(self, "name") @pulumi.output_type class GetCategoriesFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): """ :param str name: Name of the product category. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: """ Name of the product category. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetListingBannerResult(dict): def __init__(__self__, *, content_url: str, file_extension: str, mime_type: str, name: str): """ :param str content_url: The content URL of the screenshot. :param str file_extension: The file extension of the screenshot. :param str mime_type: The MIME type of the screenshot. :param str name: Text that describes the resource. """ pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "file_extension", file_extension) pulumi.set(__self__, "mime_type", mime_type) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the screenshot. """ return pulumi.get(self, "content_url") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> str: """ The file extension of the screenshot. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> str: """ The MIME type of the screenshot. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingDocumentationLinkResult(dict): def __init__(__self__, *, document_category: str, name: str, url: str): """ :param str document_category: The category that the document belongs to. :param str name: Text that describes the resource. :param str url: The URL of the resource. """ pulumi.set(__self__, "document_category", document_category) pulumi.set(__self__, "name", name) pulumi.set(__self__, "url", url) @property @pulumi.getter(name="documentCategory") def document_category(self) -> str: """ The category that the document belongs to. """ return pulumi.get(self, "document_category") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @property @pulumi.getter def url(self) -> str: """ The URL of the resource. """ return pulumi.get(self, "url") @pulumi.output_type class GetListingIconResult(dict): def __init__(__self__, *, content_url: str, file_extension: str, mime_type: str, name: str): """ :param str content_url: The content URL of the screenshot. :param str file_extension: The file extension of the screenshot. :param str mime_type: The MIME type of the screenshot. :param str name: Text that describes the resource. """ pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "file_extension", file_extension) pulumi.set(__self__, "mime_type", mime_type) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the screenshot. """ return pulumi.get(self, "content_url") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> str: """ The file extension of the screenshot. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> str: """ The MIME type of the screenshot. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingLanguageResult(dict): def __init__(__self__, *, code: str, name: str): """ :param str code: A code assigned to the item. :param str name: Text that describes the resource. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingLinkResult(dict): def __init__(__self__, *, href: str, rel: str): """ :param str href: The anchor tag. :param str rel: Reference links to the previous page, next page, and other pages. """ pulumi.set(__self__, "href", href) pulumi.set(__self__, "rel", rel) @property @pulumi.getter def href(self) -> str: """ The anchor tag. """ return pulumi.get(self, "href") @property @pulumi.getter def rel(self) -> str: """ Reference links to the previous page, next page, and other pages. """ return pulumi.get(self, "rel") @pulumi.output_type class GetListingPackageAgreementsAgreementResult(dict): def __init__(__self__, *, author: str, content_url: str, id: str, prompt: str): """ :param str author: Who authored the agreement. :param str content_url: The content URL of the agreement. :param str id: The unique identifier for the agreement. :param str prompt: Textual prompt to read and accept the agreement. """ pulumi.set(__self__, "author", author) pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "id", id) pulumi.set(__self__, "prompt", prompt) @property @pulumi.getter def author(self) -> str: """ Who authored the agreement. """ return pulumi.get(self, "author") @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the agreement. """ return pulumi.get(self, "content_url") @property @pulumi.getter def id(self) -> str: """ The unique identifier for the agreement. """ return pulumi.get(self, "id") @property @pulumi.getter def prompt(self) -> str: """ Textual prompt to read and accept the agreement. """ return pulumi.get(self, "prompt") @pulumi.output_type class GetListingPackageAgreementsFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetListingPackageOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: The name of the variable. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingPackagePricingResult(dict): def __init__(__self__, *, currency: str, pay_go_strategy: str, rate: float, type: str): """ :param str currency: The currency of the pricing model. :param str pay_go_strategy: The type of pricing for a PAYGO model, eg PER_OCPU_LINEAR, PER_OCPU_MIN_BILLING, PER_INSTANCE. Null if type is not PAYGO. :param float rate: The pricing rate. :param str type: The type of the pricing model. """ pulumi.set(__self__, "currency", currency) pulumi.set(__self__, "pay_go_strategy", pay_go_strategy) pulumi.set(__self__, "rate", rate) pulumi.set(__self__, "type", type) @property @pulumi.getter def currency(self) -> str: """ The currency of the pricing model. """ return pulumi.get(self, "currency") @property @pulumi.getter(name="payGoStrategy") def pay_go_strategy(self) -> str: """ The type of pricing for a PAYGO model, eg PER_OCPU_LINEAR, PER_OCPU_MIN_BILLING, PER_INSTANCE. Null if type is not PAYGO. """ return pulumi.get(self, "pay_go_strategy") @property @pulumi.getter def rate(self) -> float: """ The pricing rate. """ return pulumi.get(self, "rate") @property @pulumi.getter def type(self) -> str: """ The type of the pricing model. """ return pulumi.get(self, "type") @pulumi.output_type class GetListingPackageRegionResult(dict): def __init__(__self__, *, code: str, countries: Sequence['outputs.GetListingPackageRegionCountryResult'], name: str): """ :param str code: A code assigned to the item. :param Sequence['GetListingPackageRegionCountryArgs'] countries: Countries in the region. :param str name: The name of the variable. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "countries", countries) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def countries(self) -> Sequence['outputs.GetListingPackageRegionCountryResult']: """ Countries in the region. """ return pulumi.get(self, "countries") @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingPackageRegionCountryResult(dict): def __init__(__self__, *, code: str, name: str): """ :param str code: A code assigned to the item. :param str name: The name of the variable. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingPackageVariableResult(dict): def __init__(__self__, *, data_type: str, default_value: str, description: str, hint_message: str, is_mandatory: bool, name: str): """ :param str data_type: The data type of the variable. :param str default_value: The variable's default value. :param str description: A description of the variable. :param str hint_message: A brief textual description that helps to explain the variable. :param bool is_mandatory: Whether the variable is mandatory. :param str name: The name of the variable. """ pulumi.set(__self__, "data_type", data_type) pulumi.set(__self__, "default_value", default_value) pulumi.set(__self__, "description", description) pulumi.set(__self__, "hint_message", hint_message) pulumi.set(__self__, "is_mandatory", is_mandatory) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="dataType") def data_type(self) -> str: """ The data type of the variable. """ return pulumi.get(self, "data_type") @property @pulumi.getter(name="defaultValue") def default_value(self) -> str: """ The variable's default value. """ return pulumi.get(self, "default_value") @property @pulumi.getter def description(self) -> str: """ A description of the variable. """ return pulumi.get(self, "description") @property @pulumi.getter(name="hintMessage") def hint_message(self) -> str: """ A brief textual description that helps to explain the variable. """ return pulumi.get(self, "hint_message") @property @pulumi.getter(name="isMandatory") def is_mandatory(self) -> bool: """ Whether the variable is mandatory. """ return pulumi.get(self, "is_mandatory") @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingPackagesFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): """ :param str name: The name of the variable. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetListingPackagesListingPackageResult(dict): def __init__(__self__, *, listing_id: str, operating_system: 'outputs.GetListingPackagesListingPackageOperatingSystemResult', package_type: str, package_version: str, regions: Sequence['outputs.GetListingPackagesListingPackageRegionResult'], resource_id: str, time_created: str): """ :param str listing_id: The unique identifier for the listing. :param 'GetListingPackagesListingPackageOperatingSystemArgs' operating_system: OS used by the listing. :param str package_type: A filter to return only packages that match the given package type exactly. :param str package_version: The version of the package. Package versions are unique within a listing. :param Sequence['GetListingPackagesListingPackageRegionArgs'] regions: The regions where the listing is available. :param str resource_id: The unique identifier for the package resource. :param str time_created: The date and time this listing package was created, expressed in [RFC 3339](https://tools.ietf.org/html/rfc3339) timestamp format. Example: `2016-08-25T21:10:29.600Z` """ pulumi.set(__self__, "listing_id", listing_id) pulumi.set(__self__, "operating_system", operating_system) pulumi.set(__self__, "package_type", package_type) pulumi.set(__self__, "package_version", package_version) pulumi.set(__self__, "regions", regions) pulumi.set(__self__, "resource_id", resource_id) pulumi.set(__self__, "time_created", time_created) @property @pulumi.getter(name="listingId") def listing_id(self) -> str: """ The unique identifier for the listing. """ return pulumi.get(self, "listing_id") @property @pulumi.getter(name="operatingSystem") def operating_system(self) -> 'outputs.GetListingPackagesListingPackageOperatingSystemResult': """ OS used by the listing. """ return pulumi.get(self, "operating_system") @property @pulumi.getter(name="packageType") def package_type(self) -> str: """ A filter to return only packages that match the given package type exactly. """ return pulumi.get(self, "package_type") @property @pulumi.getter(name="packageVersion") def package_version(self) -> str: """ The version of the package. Package versions are unique within a listing. """ return pulumi.get(self, "package_version") @property @pulumi.getter def regions(self) -> Sequence['outputs.GetListingPackagesListingPackageRegionResult']: """ The regions where the listing is available. """ return pulumi.get(self, "regions") @property @pulumi.getter(name="resourceId") def resource_id(self) -> str: """ The unique identifier for the package resource. """ return pulumi.get(self, "resource_id") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The date and time this listing package was created, expressed in [RFC 3339](https://tools.ietf.org/html/rfc3339) timestamp format. Example: `2016-08-25T21:10:29.600Z` """ return pulumi.get(self, "time_created") @pulumi.output_type class GetListingPackagesListingPackageOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: The name of the variable. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingPackagesListingPackageRegionResult(dict): def __init__(__self__, *, code: str, countries: Sequence['outputs.GetListingPackagesListingPackageRegionCountryResult'], name: str): """ :param str code: A code assigned to the item. :param Sequence['GetListingPackagesListingPackageRegionCountryArgs'] countries: Countries in the region. :param str name: The name of the variable. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "countries", countries) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def countries(self) -> Sequence['outputs.GetListingPackagesListingPackageRegionCountryResult']: """ Countries in the region. """ return pulumi.get(self, "countries") @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingPackagesListingPackageRegionCountryResult(dict): def __init__(__self__, *, code: str, name: str): """ :param str code: A code assigned to the item. :param str name: The name of the variable. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingPublisherResult(dict): def __init__(__self__, *, contact_email: str, contact_phone: str, description: str, hq_address: str, id: str, links: Sequence['outputs.GetListingPublisherLinkResult'], logo: 'outputs.GetListingPublisherLogoResult', name: str, website_url: str, year_founded: str): """ :param str contact_email: The email address of the publisher. :param str contact_phone: The phone number of the publisher. :param str description: A description of the screenshot. :param str hq_address: The address of the publisher's headquarters. :param str id: Unique identifier for the publisher. :param Sequence['GetListingPublisherLinkArgs'] links: Reference links. :param 'GetListingPublisherLogoArgs' logo: The model for upload data for images and icons. :param str name: Text that describes the resource. :param str website_url: The publisher's website. :param str year_founded: The year the publisher's company or organization was founded. """ pulumi.set(__self__, "contact_email", contact_email) pulumi.set(__self__, "contact_phone", contact_phone) pulumi.set(__self__, "description", description) pulumi.set(__self__, "hq_address", hq_address) pulumi.set(__self__, "id", id) pulumi.set(__self__, "links", links) pulumi.set(__self__, "logo", logo) pulumi.set(__self__, "name", name) pulumi.set(__self__, "website_url", website_url) pulumi.set(__self__, "year_founded", year_founded) @property @pulumi.getter(name="contactEmail") def contact_email(self) -> str: """ The email address of the publisher. """ return pulumi.get(self, "contact_email") @property @pulumi.getter(name="contactPhone") def contact_phone(self) -> str: """ The phone number of the publisher. """ return pulumi.get(self, "contact_phone") @property @pulumi.getter def description(self) -> str: """ A description of the screenshot. """ return pulumi.get(self, "description") @property @pulumi.getter(name="hqAddress") def hq_address(self) -> str: """ The address of the publisher's headquarters. """ return pulumi.get(self, "hq_address") @property @pulumi.getter def id(self) -> str: """ Unique identifier for the publisher. """ return pulumi.get(self, "id") @property @pulumi.getter def links(self) -> Sequence['outputs.GetListingPublisherLinkResult']: """ Reference links. """ return pulumi.get(self, "links") @property @pulumi.getter def logo(self) -> 'outputs.GetListingPublisherLogoResult': """ The model for upload data for images and icons. """ return pulumi.get(self, "logo") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="websiteUrl") def website_url(self) -> str: """ The publisher's website. """ return pulumi.get(self, "website_url") @property @pulumi.getter(name="yearFounded") def year_founded(self) -> str: """ The year the publisher's company or organization was founded. """ return pulumi.get(self, "year_founded") @pulumi.output_type class GetListingPublisherLinkResult(dict): def __init__(__self__, *, href: str, rel: str): """ :param str href: The anchor tag. :param str rel: Reference links to the previous page, next page, and other pages. """ pulumi.set(__self__, "href", href) pulumi.set(__self__, "rel", rel) @property @pulumi.getter def href(self) -> str: """ The anchor tag. """ return pulumi.get(self, "href") @property @pulumi.getter def rel(self) -> str: """ Reference links to the previous page, next page, and other pages. """ return pulumi.get(self, "rel") @pulumi.output_type class GetListingPublisherLogoResult(dict): def __init__(__self__, *, content_url: str, file_extension: str, mime_type: str, name: str): """ :param str content_url: The content URL of the screenshot. :param str file_extension: The file extension of the screenshot. :param str mime_type: The MIME type of the screenshot. :param str name: Text that describes the resource. """ pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "file_extension", file_extension) pulumi.set(__self__, "mime_type", mime_type) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the screenshot. """ return pulumi.get(self, "content_url") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> str: """ The file extension of the screenshot. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> str: """ The MIME type of the screenshot. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingRegionResult(dict): def __init__(__self__, *, code: str, countries: Sequence['outputs.GetListingRegionCountryResult'], name: str): """ :param str code: A code assigned to the item. :param Sequence['GetListingRegionCountryArgs'] countries: Countries in the region. :param str name: Text that describes the resource. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "countries", countries) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def countries(self) -> Sequence['outputs.GetListingRegionCountryResult']: """ Countries in the region. """ return pulumi.get(self, "countries") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingRegionCountryResult(dict): def __init__(__self__, *, code: str, name: str): """ :param str code: A code assigned to the item. :param str name: Text that describes the resource. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingScreenshotResult(dict): def __init__(__self__, *, content_url: str, description: str, file_extension: str, mime_type: str, name: str): """ :param str content_url: The content URL of the screenshot. :param str description: A description of the screenshot. :param str file_extension: The file extension of the screenshot. :param str mime_type: The MIME type of the screenshot. :param str name: Text that describes the resource. """ pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "description", description) pulumi.set(__self__, "file_extension", file_extension) pulumi.set(__self__, "mime_type", mime_type) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the screenshot. """ return pulumi.get(self, "content_url") @property @pulumi.getter def description(self) -> str: """ A description of the screenshot. """ return pulumi.get(self, "description") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> str: """ The file extension of the screenshot. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> str: """ The MIME type of the screenshot. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingSupportContactResult(dict): def __init__(__self__, *, email: str, name: str, phone: str, subject: str): """ :param str email: The email of the contact. :param str name: Text that describes the resource. :param str phone: The phone number of the contact. :param str subject: The email subject line to use when contacting support. """ pulumi.set(__self__, "email", email) pulumi.set(__self__, "name", name) pulumi.set(__self__, "phone", phone) pulumi.set(__self__, "subject", subject) @property @pulumi.getter def email(self) -> str: """ The email of the contact. """ return pulumi.get(self, "email") @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @property @pulumi.getter def phone(self) -> str: """ The phone number of the contact. """ return pulumi.get(self, "phone") @property @pulumi.getter def subject(self) -> str: """ The email subject line to use when contacting support. """ return pulumi.get(self, "subject") @pulumi.output_type class GetListingSupportLinkResult(dict): def __init__(__self__, *, name: str, url: str): """ :param str name: Text that describes the resource. :param str url: The URL of the resource. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "url", url) @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @property @pulumi.getter def url(self) -> str: """ The URL of the resource. """ return pulumi.get(self, "url") @pulumi.output_type class GetListingSupportedOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: Text that describes the resource. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingTaxesFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): """ :param str name: Name of the tax code. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: """ Name of the tax code. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetListingTaxesTaxResult(dict): def __init__(__self__, *, code: str, country: str, name: str, url: str): """ :param str code: Unique code for the tax. :param str country: Country, which imposes the tax. :param str name: Name of the tax code. :param str url: The URL with more details about this tax. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "country", country) pulumi.set(__self__, "name", name) pulumi.set(__self__, "url", url) @property @pulumi.getter def code(self) -> str: """ Unique code for the tax. """ return pulumi.get(self, "code") @property @pulumi.getter def country(self) -> str: """ Country, which imposes the tax. """ return pulumi.get(self, "country") @property @pulumi.getter def name(self) -> str: """ Name of the tax code. """ return pulumi.get(self, "name") @property @pulumi.getter def url(self) -> str: """ The URL with more details about this tax. """ return pulumi.get(self, "url") @pulumi.output_type class GetListingVideoResult(dict): def __init__(__self__, *, name: str, url: str): """ :param str name: Text that describes the resource. :param str url: The URL of the resource. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "url", url) @property @pulumi.getter def name(self) -> str: """ Text that describes the resource. """ return pulumi.get(self, "name") @property @pulumi.getter def url(self) -> str: """ The URL of the resource. """ return pulumi.get(self, "url") @pulumi.output_type class GetListingsFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): """ :param str name: The name of the listing. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetListingsListingResult(dict): def __init__(__self__, *, categories: Sequence[str], icon: 'outputs.GetListingsListingIconResult', id: str, is_featured: bool, listing_type: str, name: str, package_type: str, pricing_types: Sequence[str], publishers: Sequence['outputs.GetListingsListingPublisherResult'], regions: Sequence['outputs.GetListingsListingRegionResult'], short_description: str, supported_operating_systems: Sequence['outputs.GetListingsListingSupportedOperatingSystemResult']): """ :param Sequence[str] categories: Product categories that the listing belongs to. :param 'GetListingsListingIconArgs' icon: The model for upload data for images and icons. :param str id: Unique identifier for the publisher. :param bool is_featured: Indicates whether to show only featured listings. If this is set to `false` or is omitted, then all listings will be returned. :param str listing_type: In which catalog the listing should exist. :param str name: The name of the listing. :param str package_type: A filter to return only packages that match the given package type exactly. :param Sequence['GetListingsListingPublisherArgs'] publishers: Summary details about the publisher of the listing. :param Sequence['GetListingsListingRegionArgs'] regions: The regions where the listing is eligible to be deployed. :param str short_description: A short description of the listing. :param Sequence['GetListingsListingSupportedOperatingSystemArgs'] supported_operating_systems: List of operating systems supported. """ pulumi.set(__self__, "categories", categories) pulumi.set(__self__, "icon", icon) pulumi.set(__self__, "id", id) pulumi.set(__self__, "is_featured", is_featured) pulumi.set(__self__, "listing_type", listing_type) pulumi.set(__self__, "name", name) pulumi.set(__self__, "package_type", package_type) pulumi.set(__self__, "pricing_types", pricing_types) pulumi.set(__self__, "publishers", publishers) pulumi.set(__self__, "regions", regions) pulumi.set(__self__, "short_description", short_description) pulumi.set(__self__, "supported_operating_systems", supported_operating_systems) @property @pulumi.getter def categories(self) -> Sequence[str]: """ Product categories that the listing belongs to. """ return pulumi.get(self, "categories") @property @pulumi.getter def icon(self) -> 'outputs.GetListingsListingIconResult': """ The model for upload data for images and icons. """ return pulumi.get(self, "icon") @property @pulumi.getter def id(self) -> str: """ Unique identifier for the publisher. """ return pulumi.get(self, "id") @property @pulumi.getter(name="isFeatured") def is_featured(self) -> bool: """ Indicates whether to show only featured listings. If this is set to `false` or is omitted, then all listings will be returned. """ return pulumi.get(self, "is_featured") @property @pulumi.getter(name="listingType") def listing_type(self) -> str: """ In which catalog the listing should exist. """ return pulumi.get(self, "listing_type") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @property @pulumi.getter(name="packageType") def package_type(self) -> str: """ A filter to return only packages that match the given package type exactly. """ return pulumi.get(self, "package_type") @property @pulumi.getter(name="pricingTypes") def pricing_types(self) -> Sequence[str]: return pulumi.get(self, "pricing_types") @property @pulumi.getter def publishers(self) -> Sequence['outputs.GetListingsListingPublisherResult']: """ Summary details about the publisher of the listing. """ return pulumi.get(self, "publishers") @property @pulumi.getter def regions(self) -> Sequence['outputs.GetListingsListingRegionResult']: """ The regions where the listing is eligible to be deployed. """ return pulumi.get(self, "regions") @property @pulumi.getter(name="shortDescription") def short_description(self) -> str: """ A short description of the listing. """ return pulumi.get(self, "short_description") @property @pulumi.getter(name="supportedOperatingSystems") def supported_operating_systems(self) -> Sequence['outputs.GetListingsListingSupportedOperatingSystemResult']: """ List of operating systems supported. """ return pulumi.get(self, "supported_operating_systems") @pulumi.output_type class GetListingsListingIconResult(dict): def __init__(__self__, *, content_url: str, file_extension: str, mime_type: str, name: str): """ :param str content_url: The content URL of the screenshot. :param str file_extension: The file extension of the screenshot. :param str mime_type: The MIME type of the screenshot. :param str name: The name of the listing. """ pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "file_extension", file_extension) pulumi.set(__self__, "mime_type", mime_type) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the screenshot. """ return pulumi.get(self, "content_url") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> str: """ The file extension of the screenshot. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> str: """ The MIME type of the screenshot. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingsListingPublisherResult(dict): def __init__(__self__, *, description: str, id: str, name: str): """ :param str description: A description of the screenshot. :param str id: Unique identifier for the publisher. :param str name: The name of the listing. """ pulumi.set(__self__, "description", description) pulumi.set(__self__, "id", id) pulumi.set(__self__, "name", name) @property @pulumi.getter def description(self) -> str: """ A description of the screenshot. """ return pulumi.get(self, "description") @property @pulumi.getter def id(self) -> str: """ Unique identifier for the publisher. """ return pulumi.get(self, "id") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingsListingRegionResult(dict): def __init__(__self__, *, code: str, countries: Sequence['outputs.GetListingsListingRegionCountryResult'], name: str): """ :param str code: A code assigned to the item. :param Sequence['GetListingsListingRegionCountryArgs'] countries: Countries in the region. :param str name: The name of the listing. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "countries", countries) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def countries(self) -> Sequence['outputs.GetListingsListingRegionCountryResult']: """ Countries in the region. """ return pulumi.get(self, "countries") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingsListingRegionCountryResult(dict): def __init__(__self__, *, code: str, name: str): """ :param str code: A code assigned to the item. :param str name: The name of the listing. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "name", name) @property @pulumi.getter def code(self) -> str: """ A code assigned to the item. """ return pulumi.get(self, "code") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetListingsListingSupportedOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: The name of the listing. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationIconResult(dict): def __init__(__self__, *, content_url: str, file_extension: str, mime_type: str, name: str): """ :param str content_url: The content URL of the upload data. :param str file_extension: The file extension of the upload data. :param str mime_type: The MIME type of the upload data. :param str name: name of the operating system """ pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "file_extension", file_extension) pulumi.set(__self__, "mime_type", mime_type) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the upload data. """ return pulumi.get(self, "content_url") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> str: """ The file extension of the upload data. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> str: """ The MIME type of the upload data. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> str: """ name of the operating system """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationPackageDetailsResult(dict): def __init__(__self__, *, eulas: Sequence['outputs.GetPublicationPackageDetailsEulaResult'], image_id: str, operating_system: 'outputs.GetPublicationPackageDetailsOperatingSystemResult', package_type: str, package_version: str): """ :param str package_type: The listing's package type. """ pulumi.set(__self__, "eulas", eulas) pulumi.set(__self__, "image_id", image_id) pulumi.set(__self__, "operating_system", operating_system) pulumi.set(__self__, "package_type", package_type) pulumi.set(__self__, "package_version", package_version) @property @pulumi.getter def eulas(self) -> Sequence['outputs.GetPublicationPackageDetailsEulaResult']: return pulumi.get(self, "eulas") @property @pulumi.getter(name="imageId") def image_id(self) -> str: return pulumi.get(self, "image_id") @property @pulumi.getter(name="operatingSystem") def operating_system(self) -> 'outputs.GetPublicationPackageDetailsOperatingSystemResult': return pulumi.get(self, "operating_system") @property @pulumi.getter(name="packageType") def package_type(self) -> str: """ The listing's package type. """ return pulumi.get(self, "package_type") @property @pulumi.getter(name="packageVersion") def package_version(self) -> str: return pulumi.get(self, "package_version") @pulumi.output_type class GetPublicationPackageDetailsEulaResult(dict): def __init__(__self__, *, eula_type: str, license_text: str): pulumi.set(__self__, "eula_type", eula_type) pulumi.set(__self__, "license_text", license_text) @property @pulumi.getter(name="eulaType") def eula_type(self) -> str: return pulumi.get(self, "eula_type") @property @pulumi.getter(name="licenseText") def license_text(self) -> str: return pulumi.get(self, "license_text") @pulumi.output_type class GetPublicationPackageDetailsOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: name of the operating system """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ name of the operating system """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationPackageOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: The name of the variable. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationPackageVariableResult(dict): def __init__(__self__, *, data_type: str, default_value: str, description: str, hint_message: str, is_mandatory: bool, name: str): """ :param str data_type: The data type of the variable. :param str default_value: The variable's default value. :param str description: A description of the variable. :param str hint_message: A brief textual description that helps to explain the variable. :param bool is_mandatory: Whether the variable is mandatory. :param str name: The name of the variable. """ pulumi.set(__self__, "data_type", data_type) pulumi.set(__self__, "default_value", default_value) pulumi.set(__self__, "description", description) pulumi.set(__self__, "hint_message", hint_message) pulumi.set(__self__, "is_mandatory", is_mandatory) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="dataType") def data_type(self) -> str: """ The data type of the variable. """ return pulumi.get(self, "data_type") @property @pulumi.getter(name="defaultValue") def default_value(self) -> str: """ The variable's default value. """ return pulumi.get(self, "default_value") @property @pulumi.getter def description(self) -> str: """ A description of the variable. """ return pulumi.get(self, "description") @property @pulumi.getter(name="hintMessage") def hint_message(self) -> str: """ A brief textual description that helps to explain the variable. """ return pulumi.get(self, "hint_message") @property @pulumi.getter(name="isMandatory") def is_mandatory(self) -> bool: """ Whether the variable is mandatory. """ return pulumi.get(self, "is_mandatory") @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationPackagesFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): """ :param str name: The name of the variable. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: """ The name of the variable. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetPublicationPackagesPublicationPackageResult(dict): def __init__(__self__, *, listing_id: str, package_type: str, package_version: str, resource_id: str, time_created: str): """ :param str listing_id: The ID of the listing that the specified package belongs to. :param str package_type: A filter to return only packages that match the given package type exactly. :param str package_version: The version of the package. Package versions are unique within a listing. :param str resource_id: The unique identifier for the package resource. :param str time_created: The date and time this listing package was created, expressed in [RFC 3339](https://tools.ietf.org/html/rfc3339) timestamp format. Example: `2016-08-25T21:10:29.600Z` """ pulumi.set(__self__, "listing_id", listing_id) pulumi.set(__self__, "package_type", package_type) pulumi.set(__self__, "package_version", package_version) pulumi.set(__self__, "resource_id", resource_id) pulumi.set(__self__, "time_created", time_created) @property @pulumi.getter(name="listingId") def listing_id(self) -> str: """ The ID of the listing that the specified package belongs to. """ return pulumi.get(self, "listing_id") @property @pulumi.getter(name="packageType") def package_type(self) -> str: """ A filter to return only packages that match the given package type exactly. """ return pulumi.get(self, "package_type") @property @pulumi.getter(name="packageVersion") def package_version(self) -> str: """ The version of the package. Package versions are unique within a listing. """ return pulumi.get(self, "package_version") @property @pulumi.getter(name="resourceId") def resource_id(self) -> str: """ The unique identifier for the package resource. """ return pulumi.get(self, "resource_id") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The date and time this listing package was created, expressed in [RFC 3339](https://tools.ietf.org/html/rfc3339) timestamp format. Example: `2016-08-25T21:10:29.600Z` """ return pulumi.get(self, "time_created") @pulumi.output_type class GetPublicationSupportContactResult(dict): def __init__(__self__, *, email: str, name: str, phone: str, subject: str): """ :param str email: The email of the contact. :param str name: name of the operating system :param str phone: The phone number of the contact. :param str subject: The email subject line to use when contacting support. """ pulumi.set(__self__, "email", email) pulumi.set(__self__, "name", name) pulumi.set(__self__, "phone", phone) pulumi.set(__self__, "subject", subject) @property @pulumi.getter def email(self) -> str: """ The email of the contact. """ return pulumi.get(self, "email") @property @pulumi.getter def name(self) -> str: """ name of the operating system """ return pulumi.get(self, "name") @property @pulumi.getter def phone(self) -> str: """ The phone number of the contact. """ return pulumi.get(self, "phone") @property @pulumi.getter def subject(self) -> str: """ The email subject line to use when contacting support. """ return pulumi.get(self, "subject") @pulumi.output_type class GetPublicationSupportedOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: name of the operating system """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ name of the operating system """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationsFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): """ :param str name: The name of the listing. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetPublicationsPublicationResult(dict): def __init__(__self__, *, compartment_id: str, defined_tags: Mapping[str, Any], freeform_tags: Mapping[str, Any], icon: 'outputs.GetPublicationsPublicationIconResult', id: str, is_agreement_acknowledged: bool, listing_type: str, long_description: str, name: str, package_details: 'outputs.GetPublicationsPublicationPackageDetailsResult', package_type: str, short_description: str, state: str, support_contacts: Sequence['outputs.GetPublicationsPublicationSupportContactResult'], supported_operating_systems: Sequence['outputs.GetPublicationsPublicationSupportedOperatingSystemResult'], time_created: str): """ :param str compartment_id: The unique identifier for the compartment. :param Mapping[str, Any] defined_tags: The defined tags associated with this resource, if any. Each key is predefined and scoped to namespaces. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Operations.CostCenter": "42"}` :param Mapping[str, Any] freeform_tags: The freeform tags associated with this resource, if any. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}` :param 'GetPublicationsPublicationIconArgs' icon: The model for upload data for images and icons. :param str id: The unique identifier for the listing in Marketplace. :param str listing_type: The type of the listing :param str long_description: A long description of the listing. :param str name: The name of the listing. :param str package_type: The listing's package type. :param str short_description: A short description of the listing. :param str state: The state of the listing in its lifecycle :param Sequence['GetPublicationsPublicationSupportContactArgs'] support_contacts: Contact information to use to get support from the publisher for the listing. :param Sequence['GetPublicationsPublicationSupportedOperatingSystemArgs'] supported_operating_systems: List of operating systems supprted. :param str time_created: The date and time this publication was created, expressed in [RFC 3339](https://tools.ietf.org/html/rfc3339) timestamp format. Example: `2016-08-25T21:10:29.600Z` """ pulumi.set(__self__, "compartment_id", compartment_id) pulumi.set(__self__, "defined_tags", defined_tags) pulumi.set(__self__, "freeform_tags", freeform_tags) pulumi.set(__self__, "icon", icon) pulumi.set(__self__, "id", id) pulumi.set(__self__, "is_agreement_acknowledged", is_agreement_acknowledged) pulumi.set(__self__, "listing_type", listing_type) pulumi.set(__self__, "long_description", long_description) pulumi.set(__self__, "name", name) pulumi.set(__self__, "package_details", package_details) pulumi.set(__self__, "package_type", package_type) pulumi.set(__self__, "short_description", short_description) pulumi.set(__self__, "state", state) pulumi.set(__self__, "support_contacts", support_contacts) pulumi.set(__self__, "supported_operating_systems", supported_operating_systems) pulumi.set(__self__, "time_created", time_created) @property @pulumi.getter(name="compartmentId") def compartment_id(self) -> str: """ The unique identifier for the compartment. """ return pulumi.get(self, "compartment_id") @property @pulumi.getter(name="definedTags") def defined_tags(self) -> Mapping[str, Any]: """ The defined tags associated with this resource, if any. Each key is predefined and scoped to namespaces. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Operations.CostCenter": "42"}` """ return pulumi.get(self, "defined_tags") @property @pulumi.getter(name="freeformTags") def freeform_tags(self) -> Mapping[str, Any]: """ The freeform tags associated with this resource, if any. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}` """ return pulumi.get(self, "freeform_tags") @property @pulumi.getter def icon(self) -> 'outputs.GetPublicationsPublicationIconResult': """ The model for upload data for images and icons. """ return pulumi.get(self, "icon") @property @pulumi.getter def id(self) -> str: """ The unique identifier for the listing in Marketplace. """ return pulumi.get(self, "id") @property @pulumi.getter(name="isAgreementAcknowledged") def is_agreement_acknowledged(self) -> bool: return pulumi.get(self, "is_agreement_acknowledged") @property @pulumi.getter(name="listingType") def listing_type(self) -> str: """ The type of the listing """ return pulumi.get(self, "listing_type") @property @pulumi.getter(name="longDescription") def long_description(self) -> str: """ A long description of the listing. """ return pulumi.get(self, "long_description") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @property @pulumi.getter(name="packageDetails") def package_details(self) -> 'outputs.GetPublicationsPublicationPackageDetailsResult': return pulumi.get(self, "package_details") @property @pulumi.getter(name="packageType") def package_type(self) -> str: """ The listing's package type. """ return pulumi.get(self, "package_type") @property @pulumi.getter(name="shortDescription") def short_description(self) -> str: """ A short description of the listing. """ return pulumi.get(self, "short_description") @property @pulumi.getter def state(self) -> str: """ The state of the listing in its lifecycle """ return pulumi.get(self, "state") @property @pulumi.getter(name="supportContacts") def support_contacts(self) -> Sequence['outputs.GetPublicationsPublicationSupportContactResult']: """ Contact information to use to get support from the publisher for the listing. """ return pulumi.get(self, "support_contacts") @property @pulumi.getter(name="supportedOperatingSystems") def supported_operating_systems(self) -> Sequence['outputs.GetPublicationsPublicationSupportedOperatingSystemResult']: """ List of operating systems supprted. """ return pulumi.get(self, "supported_operating_systems") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The date and time this publication was created, expressed in [RFC 3339](https://tools.ietf.org/html/rfc3339) timestamp format. Example: `2016-08-25T21:10:29.600Z` """ return pulumi.get(self, "time_created") @pulumi.output_type class GetPublicationsPublicationIconResult(dict): def __init__(__self__, *, content_url: str, file_extension: str, mime_type: str, name: str): """ :param str content_url: The content URL of the upload data. :param str file_extension: The file extension of the upload data. :param str mime_type: The MIME type of the upload data. :param str name: The name of the listing. """ pulumi.set(__self__, "content_url", content_url) pulumi.set(__self__, "file_extension", file_extension) pulumi.set(__self__, "mime_type", mime_type) pulumi.set(__self__, "name", name) @property @pulumi.getter(name="contentUrl") def content_url(self) -> str: """ The content URL of the upload data. """ return pulumi.get(self, "content_url") @property @pulumi.getter(name="fileExtension") def file_extension(self) -> str: """ The file extension of the upload data. """ return pulumi.get(self, "file_extension") @property @pulumi.getter(name="mimeType") def mime_type(self) -> str: """ The MIME type of the upload data. """ return pulumi.get(self, "mime_type") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationsPublicationPackageDetailsResult(dict): def __init__(__self__, *, eulas: Sequence['outputs.GetPublicationsPublicationPackageDetailsEulaResult'], image_id: str, operating_system: 'outputs.GetPublicationsPublicationPackageDetailsOperatingSystemResult', package_type: str, package_version: str): """ :param str package_type: The listing's package type. """ pulumi.set(__self__, "eulas", eulas) pulumi.set(__self__, "image_id", image_id) pulumi.set(__self__, "operating_system", operating_system) pulumi.set(__self__, "package_type", package_type) pulumi.set(__self__, "package_version", package_version) @property @pulumi.getter def eulas(self) -> Sequence['outputs.GetPublicationsPublicationPackageDetailsEulaResult']: return pulumi.get(self, "eulas") @property @pulumi.getter(name="imageId") def image_id(self) -> str: return pulumi.get(self, "image_id") @property @pulumi.getter(name="operatingSystem") def operating_system(self) -> 'outputs.GetPublicationsPublicationPackageDetailsOperatingSystemResult': return pulumi.get(self, "operating_system") @property @pulumi.getter(name="packageType") def package_type(self) -> str: """ The listing's package type. """ return pulumi.get(self, "package_type") @property @pulumi.getter(name="packageVersion") def package_version(self) -> str: return pulumi.get(self, "package_version") @pulumi.output_type class GetPublicationsPublicationPackageDetailsEulaResult(dict): def __init__(__self__, *, eula_type: str, license_text: str): pulumi.set(__self__, "eula_type", eula_type) pulumi.set(__self__, "license_text", license_text) @property @pulumi.getter(name="eulaType") def eula_type(self) -> str: return pulumi.get(self, "eula_type") @property @pulumi.getter(name="licenseText") def license_text(self) -> str: return pulumi.get(self, "license_text") @pulumi.output_type class GetPublicationsPublicationPackageDetailsOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: The name of the listing. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetPublicationsPublicationSupportContactResult(dict): def __init__(__self__, *, email: str, name: str, phone: str, subject: str): """ :param str email: The email of the contact. :param str name: The name of the listing. :param str phone: The phone number of the contact. :param str subject: The email subject line to use when contacting support. """ pulumi.set(__self__, "email", email) pulumi.set(__self__, "name", name) pulumi.set(__self__, "phone", phone) pulumi.set(__self__, "subject", subject) @property @pulumi.getter def email(self) -> str: """ The email of the contact. """ return pulumi.get(self, "email") @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @property @pulumi.getter def phone(self) -> str: """ The phone number of the contact. """ return pulumi.get(self, "phone") @property @pulumi.getter def subject(self) -> str: """ The email subject line to use when contacting support. """ return pulumi.get(self, "subject") @pulumi.output_type class GetPublicationsPublicationSupportedOperatingSystemResult(dict): def __init__(__self__, *, name: str): """ :param str name: The name of the listing. """ pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> str: """ The name of the listing. """ return pulumi.get(self, "name") @pulumi.output_type class GetPublishersFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): """ :param str name: The name of the publisher. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: """ The name of the publisher. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex") @pulumi.output_type class GetPublishersPublisherResult(dict): def __init__(__self__, *, description: str, id: str, name: str): """ :param str description: A description of the publisher. :param str id: Unique identifier for the publisher. :param str name: The name of the publisher. """ pulumi.set(__self__, "description", description) pulumi.set(__self__, "id", id) pulumi.set(__self__, "name", name) @property @pulumi.getter def description(self) -> str: """ A description of the publisher. """ return pulumi.get(self, "description") @property @pulumi.getter def id(self) -> str: """ Unique identifier for the publisher. """ return pulumi.get(self, "id") @property @pulumi.getter def name(self) -> str: """ The name of the publisher. """ return pulumi.get(self, "name")

31.122564

343

0.603112

9,925

94,208

5.504685

0.042821

0.030109

0.055204

0.080682

0.79068

0.776732

0.761028

0.734744

0.71267

0.693927

0

0.002374

0.289041

94,208

3,026

344

31.132849

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0.238164

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null

0

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1

0

null

0

7

9a6e49cee57ef1529a3df692eac1133d101cf3e1

5,657

py

Python

overseed_tests/test_create_plant.py

Raihanbook/overseed

73d7bfdd967d4c43b88d8aadd0cd0abbbd5f2842

[ "MIT" ]

null

overseed_tests/test_create_plant.py

Raihanbook/overseed

73d7bfdd967d4c43b88d8aadd0cd0abbbd5f2842

[ "MIT" ]

null

overseed_tests/test_create_plant.py

Raihanbook/overseed

73d7bfdd967d4c43b88d8aadd0cd0abbbd5f2842

[ "MIT" ]

null

from overseed_tests.overseed_test_case import OverseedTestCase # Create Plant test # --------------- # This test case covers all the Create Plant pages (both Admin and Supervisor) class TestCreatePlant(OverseedTestCase): def test_create_plant_admin(self): self.client.post("/login", data=dict(email='admin@admin.com', password='admin', remember=False), follow_redirects=True) # now navigate to the create plant page and create a new plant (with new valid data) result = self.client.post("create/plant", data=dict(type='Aloe Vera', icon='bush_healthy.png', company='Company X'), follow_redirects=True) self.assert_template_used('plants_list.html') self.assert_message_flashed('The new plant has been created.', 'success') self.assertIn(b'bush_healthy.png', result.data) def test_create_plant_supervisor(self): self.client.post("/login", data=dict(email='supervisor@supervisor.com', password='supervisor', remember=False), follow_redirects=True) # now navigate to the create plant page and create a new plant (with new valid data) result = self.client.post("create/plant", data=dict(type='Aloe Vera', icon='bush_healthy.png', company='Company X'), follow_redirects=True) self.assert_template_used('plants_list.html') self.assert_message_flashed('The new plant has been created.', 'success') self.assertIn(b'bush_healthy.png', result.data) def test_create_plant_admin_invalid_type(self): self.client.post("/login", data=dict(email='admin@admin.com', password='admin', remember=False), follow_redirects=True) # now navigate to the create plant page and create a new plant (with invalid type data) result = self.client.post("create/plant", data=dict(type='Fictional Plant', icon='pilea_healthy.png', company='Company X'), follow_redirects=True) self.assert_template_used('create_plant.html') result = self.client.get("/plants") self.assertNotIn(b'Fictional Plant', result.data) def test_create_plant_admin_invalid_icon(self): self.client.post("/login", data=dict(email='admin@admin.com', password='admin', remember=False), follow_redirects=True) # now navigate to the create plant page and create a new plant (with invalid icon data) result = self.client.post("create/plant", data=dict(type='Aloe Vera', icon='fictional.png', company='Company X'), follow_redirects=True) self.assert_template_used('create_plant.html') result = self.client.get("/plants") self.assertNotIn(b'fictional.png', result.data) def test_create_plant_admin_invalid_company(self): self.client.post("/login", data=dict(email='admin@admin.com', password='admin', remember=False), follow_redirects=True) # now navigate to the create plant page and create a new plant (with invalid company data) result = self.client.post("create/plant", data=dict(type='Aloe Vera', icon='tree_healthy.png', company='Fictional Company'), follow_redirects=True) self.assert_template_used('create_plant.html') result = self.client.get("/plants") self.assertNotIn(b'Fictional Company', result.data) def test_create_plant_user(self): self.client.post("/login", data=dict(email='user@user.com', password='user', remember=False), follow_redirects=True) # now navigate to the create plant page and create a new plant (with new valid data) result = self.client.post("create/plant", data=dict(type='Aloe Vera', icon='bush_healthy.png', company='Company X'), follow_redirects=True) self.assert403(result) def test_create_plant_logged_out(self): # DON'T log in. # now navigate to the create plant page and create a new plant (with new valid data) result = self.client.post("create/plant", data=dict(type='Aloe Vera', icon='bush_healthy.png', company='Company X'), follow_redirects=True) self.assert403(result)

46.368852

109

0.501326

555

5,657

4.981982

0.145946

0.103436

0.065823

0.04557

0.85642

0.848101

0.837975

0.797468

0.777577

0

0.001793

0.408344

5,657

121

110

46.752066

0.82432

0.126746

0

0.759494

0

0.154018

0.005073

0

0.177215

1

0.088608

false

0.075949

0.012658

0

0.113924

0

null

0

1

0

1

0

null

0

1

0

8

9a6e97bb736bd52130b81168ba7495160de773c2

30,540

py

Python

bin/ML/model.py

ArtaSeify/BOSS-SC2

a86db4bcb1cd0a9cfd71c4583ccb9bd87c0cb415

[ "MIT" ]

null

bin/ML/model.py

ArtaSeify/BOSS-SC2

a86db4bcb1cd0a9cfd71c4583ccb9bd87c0cb415

[ "MIT" ]

null

bin/ML/model.py

ArtaSeify/BOSS-SC2

a86db4bcb1cd0a9cfd71c4583ccb9bd87c0cb415

[ "MIT" ]

null

import tensorflow as tf from tensorflow.keras import layers import os import math import numpy as np from scipy.special import softmax from datetime import datetime # from data_loader import DataLoader class CustomTensorBoard(tf.keras.callbacks.TensorBoard): def __init__(self, model, *args, **kwargs): super(CustomTensorBoard, self).__init__(*args, **kwargs) self.model = model def get_lr(self): return tf.keras.backend.eval(self.model.optimizer.lr) def on_epoch_end(self, epoch, logs=None): # test_dataset = DataLoader(self.model.feature_shape, self.model.prediction_shape, True, self.model.batch_size, 8) # test_iterator = test_dataset.make_iterator(sess, [args.testset_file]) # testset_samples = sum(1 for line in open(args.testset_file)) # evaluations.append(network.evaluate(test_iterator, floor(testset_samples/self.model.batch_size), verbose)) logs.update({"learning rate": np.float_(self.get_lr()) }) super(CustomTensorBoard, self).on_epoch_end(epoch, logs) class Model(): def __init__(self): return class IntegralValueNN(Model): def __init__(self, input_shape, output_shape, model_name, batch_size, learning_rate, model_path, create_network=True): self.model_name = model_name self.model_path = model_path self.feature_shape = input_shape self.prediction_shape = output_shape self.batch_size = batch_size self.epochs = 0 self.checkpoint_best = tf.keras.callbacks.ModelCheckpoint(self.model_path.split(".")[0] + "_best.h5", monitor='loss', save_best_only=True, mode='min') self.checkpoint = tf.keras.callbacks.ModelCheckpoint(self.model_path) if create_network: self.create(input_shape, output_shape, model_name, batch_size, learning_rate) def percent_error(self, y_true, y_pred): return tf.math.multiply(tf.math.divide(tf.math.abs(tf.math.subtract(y_true, y_pred)), tf.math.maximum(y_true, 1)), 100) def exponential_decay(self, epoch, lr): decay_rate = 0.70 reduce_every_epochs = 1.0 return lr * pow(decay_rate, math.floor((epoch+1) / reduce_every_epochs)) def create(self, input_shape, output_shape, model_name, batch_size, learning_rate): inputs = tf.keras.Input(shape=(input_shape, ), name="state") layer = layers.Dense(2048, activation='elu')(inputs) layer = layers.Dense(1024, activation='elu')(layer) layer = layers.Dense(1024, activation='elu')(layer) #layer = layers.Dropout(0.30)(layer) #layer = layers.Dropout(0.30)(layer) layer = layers.Dense(512, activation='elu')(layer) layer = layers.Dense(512, activation='elu')(layer) prediction = layers.Dense(output_shape, name="value")(layer) self.model = tf.keras.Model(inputs=inputs, outputs=prediction) #self.lrs = tf.keras.callbacks.LearningRateScheduler(self.exponential_decay) self.model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate), loss='mse', metrics=['mae', self.percent_error]) def train(self, iterator, epochs, steps_per_epoch, verbose, class_weight=None): return self.model.fit(iterator, epochs=epochs, steps_per_epoch=steps_per_epoch, verbose=verbose, callbacks=[CustomTensorBoard(self.model, log_dir=os.path.join(os.getcwd(), os.path.join("logs", self.model_name)), write_graph=False, batch_size=self.batch_size), self.checkpoint, self.checkpoint_best], class_weight = class_weight) def evaluate(self, iterator, steps, verbose): return self.model.evaluate(iterator, steps=steps, verbose=verbose) def predict(self, nn_input, batch_size=None, steps=1, verbose=0): return self.model.predict(nn_input, batch_size=batch_size, steps=steps, verbose=verbose) def predict_on_batch(self, nn_input): output = self.model.predict_on_batch(nn_input) output = np.ndarray.tolist(np.squeeze(output)) if isinstance(output, float): output = [output] return output def save(self, path): tf.keras.models.save_model(self.model, path) def load(self, path): self.model = tf.keras.models.load_model(path, custom_objects={"percent_error": self.percent_error}) class PolicyNetwork(Model): def __init__(self, input_shape, output_shape, model_name, batch_size, learning_rate, model_path, create_network=True): self.model_name = model_name self.model_path = model_path self.feature_shape = input_shape self.prediction_shape = output_shape self.batch_size = batch_size self.epochs = 0 self.checkpoint_best = tf.keras.callbacks.ModelCheckpoint(self.model_path.split(".")[0] + "_best.h5", monitor='categorical_accuracy', save_best_only=True, mode='max') self.checkpoint = tf.keras.callbacks.ModelCheckpoint(self.model_path) if create_network: self.create(input_shape, output_shape, model_name, batch_size, learning_rate) def exponential_decay(self, epoch, lr): decay_rate = 0.70 reduce_every_epochs = 1.0 return lr * pow(decay_rate, math.floor((epoch+1) / reduce_every_epochs)) def top_2_accuracy(self, y_true, y_pred): return tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=2) def CCELogits(self, y_true, y_pred): return tf.keras.backend.categorical_crossentropy(y_true, y_pred, from_logits=True) def accuracy(self, y_true, y_pred): indices = tf.concat([tf.convert_to_tensor([[i] for i in range(self.batch_size)], dtype=tf.int64), tf.expand_dims(tf.keras.backend.argmax(y_pred, axis=-1), 1)], 1) nonzeros = tf.math.divide(tf.math.count_nonzero(tf.gather_nd(y_true, indices)),self.batch_size) return nonzeros # BEST MODEL SO FAR: # inputs = tf.keras.Input(shape=(input_shape, )) # layer = layers.Dense(512, activation='elu')(inputs) # layer = layers.Dense(256, activation='elu')(layer) # layer = layers.Dense(256, activation='elu')(layer) # layer = layers.Dense(128, activation='elu')(layer) # layer = layers.Dense(128, activation='elu')(layer) # prediction = layers.Dense(output_shape, activation='linear')(layer) def create(self, input_shape, output_shape, model_name, batch_size, learning_rate): inputs = tf.keras.Input(shape=(input_shape, ), name="state") layer = layers.Dense(512, activation='elu')(inputs) layer = layers.Dense(256, activation='elu')(layer) layer = layers.Dense(256, activation='elu')(layer) layer = layers.Dense(128, activation='elu')(layer) layer = layers.Dense(128, activation='elu')(layer) prediction = layers.Dense(output_shape, activation='linear', name="policy")(layer) #prediction = layers.Dense(output_shape, activation='softmax')(layer) self.model = tf.keras.Model(inputs=inputs, outputs=prediction) #self.lrs = tf.keras.callbacks.LearningRateScheduler(self.exponential_decay) self.model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate), #loss='categorical_crossentropy', #loss='kld', loss = self.CCELogits, metrics=['categorical_accuracy', self.top_2_accuracy, self.accuracy]) def train(self, iterator, epochs, steps_per_epoch, verbose, class_weight=None): return self.model.fit(iterator, epochs=epochs, steps_per_epoch=steps_per_epoch, verbose=verbose, callbacks=[CustomTensorBoard(self.model, log_dir=os.path.join(os.getcwd(), os.path.join("logs", self.model_name)), write_graph=False, batch_size=self.batch_size), self.checkpoint, self.checkpoint_best], class_weight = class_weight) def evaluate(self, iterator, steps, verbose): return self.model.evaluate(iterator, steps=steps, verbose=verbose) def predict(self, nn_input, batch_size=None, steps=1, verbose=0): return softmax(self.model.predict(nn_input, batch_size=batch_size, steps=steps, verbose=verbose)) def predict_on_batch(self, nn_input): return np.ndarray.tolist(np.squeeze(softmax(self.model.predict_on_batch(nn_input)))) def save(self, path): tf.keras.models.save_model(self.model, path) def load(self, path): self.model = tf.keras.models.load_model(path, custom_objects={"top_2_accuracy": self.top_2_accuracy, "CCELogits": self.CCELogits, "accuracy": self.accuracy}) class PolicyAndValueNetwork(Model): def __init__(self, input_shape, policy_shape, value_shape, model_name, batch_size, learning_rate, model_path, create_network=True): self.model_name = model_name self.model_path = model_path self.feature_shape = input_shape self.policy_shape = policy_shape self.value_shape = value_shape self.batch_size = batch_size self.learning_rate = learning_rate self.epochs = 0 self.checkpoint_best = tf.keras.callbacks.ModelCheckpoint(self.model_path.split(".")[0] + "_best.h5", monitor='categorical_accuracy', save_best_only=True, mode='max') self.checkpoint = tf.keras.callbacks.ModelCheckpoint(self.model_path) if create_network: self.create() def exponential_decay(self, epoch, lr): decay_rate = 0.70 reduce_every_epochs = 1.0 return lr * pow(decay_rate, math.floor((epoch+1) / reduce_every_epochs)) def top_2_accuracy(self, y_true, y_pred): return tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=2) def CCELogits(self, y_true, y_pred): return tf.keras.backend.categorical_crossentropy(y_true, y_pred, from_logits=True) def accuracy(self, y_true, y_pred): indices = tf.concat([tf.convert_to_tensor([[i] for i in range(self.batch_size)], dtype=tf.int64), tf.expand_dims(tf.keras.backend.argmax(y_pred, axis=-1), 1)], 1) nonzeros = tf.math.divide(tf.math.count_nonzero(tf.gather_nd(y_true, indices)),self.batch_size) return nonzeros def percent_error(self, y_true, y_pred): return tf.math.multiply(tf.math.divide(tf.math.abs(tf.math.subtract(y_true, y_pred)), tf.math.maximum(y_true, 1)), 100) def create(self): inputs = tf.keras.Input(shape=(self.feature_shape, ), name="state") layer = layers.Dense(512, activation='elu')(inputs) layer = layers.Dense(512, activation='elu')(layer) layer = layers.Dense(256, activation='elu')(layer) layer = layers.Dense(128, activation='elu')(layer) layer = layers.Dense(128, activation='elu')(layer) policy = layers.Dense(self.policy_shape, activation='linear', name="policy")(layer) value = layers.Dense(self.value_shape, name="value")(layer) self.model = tf.keras.Model(inputs=inputs, outputs=[policy, value]) #self.lrs = tf.keras.callbacks.LearningRateScheduler(self.exponential_decay) self.model.compile(optimizer=tf.keras.optimizers.Adam(self.learning_rate), #loss='categorical_crossentropy', #loss='kld', loss = {"policy": self.CCELogits, "value" : 'mse'}, metrics={"policy": ['categorical_accuracy', self.top_2_accuracy, self.accuracy], "value" : ['mae', self.percent_error]}) def train(self, iterator, epochs, steps_per_epoch, verbose, class_weight=None): return self.model.fit(iterator, epochs=epochs, steps_per_epoch=steps_per_epoch, verbose=verbose, callbacks=[CustomTensorBoard(self.model, log_dir=os.path.join(os.getcwd(), os.path.join("logs", self.model_name)), write_graph=False, batch_size=self.batch_size), self.checkpoint, self.checkpoint_best], class_weight = class_weight) def evaluate(self, iterator, steps, verbose): return self.model.evaluate(iterator, steps=steps, verbose=verbose) def predict(self, nn_input, batch_size=None, steps=1, verbose=0): return softmax(self.model.predict(nn_input, batch_size=batch_size, steps=steps, verbose=verbose)) def predict_on_batch(self, nn_input): prediction = self.model.predict_on_batch(nn_input) value = np.ndarray.tolist(np.squeeze(prediction[1])) if isinstance(value, float): value = [value] policy = softmax(prediction[0]) policy = np.ndarray.tolist(policy) return [policy, value] def save(self, path): tf.keras.models.save_model(self.model, path) def load(self, path): self.model = tf.keras.models.load_model(path, custom_objects={"top_2_accuracy": self.top_2_accuracy, "CCELogits": self.CCELogits, "accuracy": self.accuracy , "percent_error": self.percent_error}) class RelationsPolicyNetwork(Model): def __init__(self, units_features_size, extra_features_size, policy_shape, model_name, batch_size, learning_rate, model_path, create_network=True): self.model_name = model_name self.model_path = model_path self.units_features_size = units_features_size self.extra_features_size = extra_features_size self.policy_shape = policy_shape self.batch_size = batch_size self.learning_rate = learning_rate self.epochs = 0 #self.checkpoint_best = tf.keras.callbacks.ModelCheckpoint(self.model_path.split(".")[0] + "_bestCA.h5", monitor='categorical_accuracy', save_best_only=True, mode='max') self.checkpoint = tf.keras.callbacks.ModelCheckpoint(self.model_path) self.early_stop = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=7, mode="min", restore_best_weights=True) if create_network: self.create() #def exponential_decay(self, epoch, lr): # decay_rate = 0.70 # reduce_every_epochs = 1.0 # return lr * pow(decay_rate, math.floor((epoch+1) / reduce_every_epochs)) def top_3_accuracy(self, y_true, y_pred): return tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=3) def CCELogits(self, y_true, y_pred): return tf.keras.backend.categorical_crossentropy(y_true, y_pred, from_logits=True) def accuracy(self, y_true, y_pred): indices = tf.concat([tf.convert_to_tensor([[i] for i in range(self.batch_size)], dtype=tf.int64), tf.expand_dims(tf.keras.backend.argmax(y_pred, axis=-1), 1)], 1) #indices = tf.keras.backend.argmax(y_pred, axis=-1) nonzeros = tf.math.divide(tf.math.count_nonzero(tf.gather_nd(y_true, indices)),self.batch_size) return nonzeros def create(self): units_output_size = 512 units_input = tf.keras.Input(shape=(None, self.units_features_size), name="units_input") layer_units = layers.Dense(1024, activation='elu', name="units_layer1")(units_input) layer_units = layers.Dense(512, activation='elu', name="units_layer2")(layer_units) layer_units = layers.Dense(512, activation='elu', name="units_layer3")(layer_units) units_output = layers.Dense(units_output_size, activation='elu', name="units_output")(layer_units) units_output = layers.Lambda(lambda x: tf.keras.backend.mean(x, axis=1), name="average_units_output")(units_output) extra_features_input = tf.keras.Input(shape=(self.extra_features_size, ), name="extra_features_input") concatenate_layer = layers.Concatenate()([units_output, extra_features_input]) layer = layers.Dense(2048, activation='elu', name="state_layer1")(concatenate_layer) layer = layers.Dense(1024, activation='elu', name="state_layer2")(layer) layer = layers.Dense(512, activation='elu', name="state_layer3")(layer) layer = layers.Dense(512, activation='elu', name="state_layer4")(layer) layer = layers.Dense(256, activation='elu', name="state_layer5")(layer) layer = layers.Dense(256, activation='elu', name="state_layer6")(layer) layer = layers.Dense(256, activation='elu', name="state_layer7")(layer) layer = layers.Dense(128, activation='elu', name="state_layer8")(layer) layer = layers.Dense(128, activation='elu', name="state_layer9")(layer) layer = layers.Dense(128, activation='elu', name="state_layer10")(layer) policy = layers.Dense(self.policy_shape, activation='linear', name="policy")(layer) self.model = tf.keras.Model(inputs=[units_input, extra_features_input], outputs=policy) #self.lrs = tf.keras.callbacks.LearningRateScheduler(self.exponential_decay) self.model.compile(optimizer=tf.keras.optimizers.Nadam(self.learning_rate), loss = {"policy" : self.CCELogits}, metrics= {"policy" : ['categorical_accuracy', self.top_3_accuracy, self.accuracy]}) def train(self, iterator, epochs, steps_per_epoch, verbose, validation_iterator, validation_steps): return self.model.fit(iterator, epochs=epochs, steps_per_epoch=steps_per_epoch, verbose=verbose, validation_data=validation_iterator, validation_steps=validation_steps, callbacks=[CustomTensorBoard(self.model, log_dir=os.path.join(os.getcwd(), os.path.join("logs", self.model_name)), write_graph=False, batch_size=self.batch_size), self.checkpoint, self.early_stop]) #self.checkpoint_best]) def evaluate(self, iterator, steps, verbose): return self.model.evaluate(iterator, steps=steps, verbose=verbose) def predict(self, nn_input, batch_size=None, steps=1, verbose=0): predictions = softmax(self.model.predict(nn_input, batch_size=batch_size, steps=steps, verbose=verbose), axis=1) output = [] for i in range(len(predictions)): output.append(np.ndarray.tolist(predictions[i])) return output def predict_on_batch(self, nn_input): return np.ndarray.tolist(np.squeeze(softmax(self.model.predict_on_batch(nn_input)))) def save(self, path): tf.keras.models.save_model(self.model, path,) def load(self, path): self.model = tf.keras.models.load_model(path, compile=False, custom_objects={"top_3_accuracy": self.top_3_accuracy, "CCELogits": self.CCELogits, "accuracy": self.accuracy}) def compile(self): self.model.compile(optimizer=tf.keras.optimizers.Adam(self.learning_rate), loss = {"policy" : self.CCELogits}, metrics= {"policy" : ['categorical_accuracy', self.top_3_accuracy, self.accuracy]}) class RelationsValueNetwork(Model): def __init__(self, units_features_size, extra_features_size, value_normalize_factor, model_name, batch_size, learning_rate, model_path, create_network=True): self.model_name = model_name self.model_path = model_path self.units_features_size = units_features_size self.extra_features_size = extra_features_size self.batch_size = batch_size self.learning_rate = learning_rate self.epochs = 0 #self.checkpoint_best = tf.keras.callbacks.ModelCheckpoint(self.model_path.split(".")[0] + "_bestCA.h5", monitor='categorical_accuracy', save_best_only=True, mode='max') self.checkpoint = tf.keras.callbacks.ModelCheckpoint(self.model_path) self.early_stop = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=7, mode="min", restore_best_weights=True) if create_network: self.create() def create(self): units_output_size = 512 units_input = tf.keras.Input(shape=(None, self.units_features_size), name="units_input") layer_units = layers.Dense(1024, activation='elu', name="units_layer1")(units_input) layer_units = layers.Dense(512, activation='elu', name="units_layer2")(layer_units) layer_units = layers.Dense(512, activation='elu', name="units_layer3")(layer_units) units_output = layers.Dense(units_output_size, activation='elu', name="units_output")(layer_units) units_output = layers.Lambda(lambda x: tf.keras.backend.mean(x, axis=1), name="average_units_output")(units_output) extra_features_input = tf.keras.Input(shape=(self.extra_features_size, ), name="extra_features_input") concatenate_layer = layers.Concatenate()([units_output, extra_features_input]) layer = layers.Dense(2048, activation='elu', name="state_layer1")(concatenate_layer) layer = layers.Dense(1024, activation='elu', name="state_layer2")(layer) layer = layers.Dense(512, activation='elu', name="state_layer3")(layer) layer = layers.Dense(512, activation='elu', name="state_layer4")(layer) layer = layers.Dense(256, activation='elu', name="state_layer5")(layer) layer = layers.Dense(256, activation='elu', name="state_layer6")(layer) layer = layers.Dense(256, activation='elu', name="state_layer7")(layer) layer = layers.Dense(128, activation='elu', name="state_layer8")(layer) layer = layers.Dense(128, activation='elu', name="state_layer9")(layer) layer = layers.Dense(128, activation='elu', name="state_layer10")(layer) value = layers.Dense(1, activation='relu', name="value")(layer) self.model = tf.keras.Model(inputs=[units_input, extra_features_input], outputs=value) #self.lrs = tf.keras.callbacks.LearningRateScheduler(self.exponential_decay) self.model.compile(optimizer=tf.keras.optimizers.Nadam(self.learning_rate), loss = 'mae', metrics = ['mae']) def train(self, iterator, epochs, steps_per_epoch, verbose, validation_iterator, validation_steps): return self.model.fit(iterator, epochs=epochs, steps_per_epoch=steps_per_epoch, verbose=verbose, validation_data=validation_iterator, validation_steps=validation_steps, callbacks=[CustomTensorBoard(self.model, log_dir=os.path.join(os.getcwd(), os.path.join("logs", self.model_name)), write_graph=False, batch_size=self.batch_size), self.checkpoint, self.early_stop]) #self.checkpoint_best]) def evaluate(self, iterator, steps, verbose): return self.model.evaluate(iterator, steps=steps, verbose=verbose) def predict(self, nn_input, batch_size=None, steps=1, verbose=0): predictions = softmax(self.model.predict(nn_input, batch_size=batch_size, steps=steps, verbose=verbose), axis=1) output = [] for i in range(len(predictions)): output.append(np.ndarray.tolist(predictions[i])) return output def predict_on_batch(self, nn_input): return np.ndarray.tolist(np.squeeze(softmax(self.model.predict_on_batch(nn_input)))) def save(self, path): tf.keras.models.save_model(self.model, path,) def load(self, path): self.model = tf.keras.models.load_model(path, compile=False) def compile(self): self.model.compile(optimizer=tf.keras.optimizers.Adam(self.learning_rate), loss = 'mae', metrics = 'mae') class RelationsPolicyAndValueNetwork(Model): def __init__(self, units_features_size, extra_features_size, policy_shape, value_normalize_factor, model_name, batch_size, learning_rate, model_path, create_network=True): self.model_name = model_name self.model_path = model_path self.units_features_size = units_features_size self.extra_features_size = extra_features_size self.policy_shape = policy_shape self.batch_size = batch_size self.learning_rate = learning_rate self.epochs = 0 #self.checkpoint_best = tf.keras.callbacks.ModelCheckpoint(self.model_path.split(".")[0] + "_bestCA.h5", monitor='categorical_accuracy', save_best_only=True, mode='max') self.checkpoint = tf.keras.callbacks.ModelCheckpoint(self.model_path) self.early_stop = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=7, mode="min", restore_best_weights=True) self.value_loss_scale = tf.keras.backend.variable(value_normalize_factor, dtype=tf.float32) if create_network: self.create() def top_3_accuracy(self, y_true, y_pred): return tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=3) def CCELogits(self, y_true, y_pred): return tf.keras.backend.categorical_crossentropy(y_true, y_pred, from_logits=True) def accuracy(self, y_true, y_pred): indices = tf.concat([tf.convert_to_tensor([[i] for i in range(self.batch_size)], dtype=tf.int64), tf.expand_dims(tf.keras.backend.argmax(y_pred, axis=-1), 1)], 1) #indices = tf.keras.backend.argmax(y_pred, axis=-1) nonzeros = tf.math.divide(tf.math.count_nonzero(tf.gather_nd(y_true, indices)),self.batch_size) return nonzeros def MAEWithScalar(self, y_true, y_pred): return tf.math.divide(tf.keras.losses.MAE(y_true, y_pred), self.value_loss_scale) def MSEWithScaling(self, y_true, y_pred): return tf.keras.losses.MSE(tf.math.divide(y_true, 30000), tf.math.divide(y_pred, 30000)) def create(self): units_output_size = 512 units_input = tf.keras.Input(shape=(None, self.units_features_size), name="units_input") layer_units = layers.Dense(1024, activation='elu', name="units_layer1")(units_input) layer_units = layers.Dense(512, activation='elu', name="units_layer2")(layer_units) layer_units = layers.Dense(512, activation='elu', name="units_layer3")(layer_units) units_output = layers.Dense(units_output_size, activation='elu', name="units_output")(layer_units) units_output = layers.Lambda(lambda x: tf.keras.backend.mean(x, axis=1), name="average_units_output")(units_output) extra_features_input = tf.keras.Input(shape=(self.extra_features_size, ), name="extra_features_input") concatenate_layer = layers.Concatenate()([units_output, extra_features_input]) layer = layers.Dense(2048, activation='elu', name="state_layer1")(concatenate_layer) layer = layers.Dense(1024, activation='elu', name="state_layer2")(layer) layer = layers.Dense(512, activation='elu', name="state_layer3")(layer) layer = layers.Dense(512, activation='elu', name="state_layer4")(layer) layer = layers.Dense(256, activation='elu', name="state_layer5")(layer) layer = layers.Dense(256, activation='elu', name="state_layer6")(layer) layer = layers.Dense(256, activation='elu', name="state_layer7")(layer) layer = layers.Dense(128, activation='elu', name="state_layer8")(layer) layer = layers.Dense(128, activation='elu', name="state_layer9")(layer) layer = layers.Dense(128, activation='elu', name="state_layer10")(layer) policy_layer = layers.Dense(128, activation='elu', name="policy_layer1")(layer) policy_layer = layers.Dense(128, activation='elu', name="policy_layer2")(policy_layer) policy = layers.Dense(self.policy_shape, activation='linear', name="policy")(policy_layer) value_layer = layers.Dense(64, activation='elu', name="value_layer1")(layer) value_layer = layers.Dense(64, activation='elu', name="value_layer2")(value_layer) value = layers.Dense(1, activation='relu', name="value")(value_layer) self.model = tf.keras.Model(inputs=[units_input, extra_features_input], outputs=[policy, value]) #self.lrs = tf.keras.callbacks.LearningRateScheduler(self.exponential_decay) self.model.compile(optimizer=tf.keras.optimizers.Nadam(self.learning_rate), loss = {"policy" : self.CCELogits, "value" : self.MAEWithScalar}, metrics= {"policy" : ['categorical_accuracy', self.top_3_accuracy, self.accuracy], "value" : ['mae', 'msle']}) def train(self, iterator, epochs, steps_per_epoch, verbose, validation_iterator, validation_steps): return self.model.fit(iterator, epochs=epochs, steps_per_epoch=steps_per_epoch, verbose=verbose, validation_data=validation_iterator, validation_steps=validation_steps, callbacks=[CustomTensorBoard(self.model, log_dir=os.path.join(os.getcwd(), os.path.join("logs", self.model_name)), write_graph=False, batch_size=self.batch_size), self.checkpoint, self.early_stop]) #self.checkpoint_best]) def evaluate(self, iterator, steps, verbose): return self.model.evaluate(iterator, steps=steps, verbose=verbose) def predict(self, nn_input, batch_size=None, steps=1, verbose=0): prediction = self.model.predict(nn_input, batch_size=batch_size, steps=steps, verbose=verbose) softmaxed_values = softmax(prediction[0], axis=1) output = [] for i in range(len(prediction[0])): output.append([np.ndarray.tolist(softmaxed_values[i]), np.ndarray.tolist(prediction[1][i])]) return output def predict_on_batch(self, nn_input): return np.ndarray.tolist(np.squeeze(softmax(self.model.predict_on_batch(nn_input)))) def save(self, path): tf.keras.models.save_model(self.model, path,) def load(self, path): self.model = tf.keras.models.load_model(path, compile=False, custom_objects={"top_3_accuracy": self.top_3_accuracy, "CCELogits": self.CCELogits, "accuracy": self.accuracy , "MAEWithScalar": self.MAEWithScalar}) def compile(self): self.model.compile(optimizer=tf.keras.optimizers.Nadam(self.learning_rate), loss = {"policy" : self.CCELogits, "value" : self.MAEWithScalar}, metrics= {"policy" : ['categorical_accuracy', self.top_3_accuracy, self.accuracy], "value" : ['mae', 'msle']})

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null

0

7

7be05eb610b6b1b99369bc0bb423c4e716a229c7

104

py

Python

tests/test_polyshape.py

abey79/lines

09fbd84f9eaaba40d24b07227e8c95c0493a75c2

[ "MIT" ]

39

2019-10-23T09:19:34.000Z

2022-02-16T21:44:12.000Z

tests/test_polyshape.py

abey79/lines

09fbd84f9eaaba40d24b07227e8c95c0493a75c2

[ "MIT" ]

2

2020-11-13T14:06:02.000Z

2021-09-29T08:18:44.000Z

tests/test_polyshape.py

abey79/lines

09fbd84f9eaaba40d24b07227e8c95c0493a75c2

[ "MIT" ]

2

2020-11-06T22:21:00.000Z

2021-06-09T18:40:02.000Z

def test_transform_identity(): # TODO pass def test_transform_rotation(): # TODO pass

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0.5

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1

0

1

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8

d035f09f377a472f6d08cceaf7e1debfa748e7ee

1,551

py

Python

netbox_aws/choices.py

lampwins/interop2020-netbox-plugins

e983b1875c58230228448deca6b129be9fc40c1e

[ "Apache-2.0" ]

4

2020-10-06T19:00:01.000Z

2021-04-26T19:37:00.000Z

netbox_aws/choices.py

lampwins/interop2020-netbox-plugins

e983b1875c58230228448deca6b129be9fc40c1e

[ "Apache-2.0" ]

null

netbox_aws/choices.py

lampwins/interop2020-netbox-plugins

e983b1875c58230228448deca6b129be9fc40c1e

[ "Apache-2.0" ]

1

2021-04-16T15:24:24.000Z

from utilities.choices import ChoiceSet class VPCRegionChoices(ChoiceSet): REGION_US_EAST_2 = "US-East(Ohio)" REGION_US_EAST_1 = "US-East(N-Virginia)" REGION_US_WEST_1 = "US-West(N-California)" REGION_US_WEST_2 = "US-West-(Oregon)" REGION_AP_NORTHEAST_1 = "Asia-Pacific-(Tokyo)" REGION_AP_NORTHEAST_2 = "Asia-Pacific-(Seoul)" REGION_AP_SOUTH_1 = "Asia-Pacific-(Mumbai)" REGION_AP_SOUTHEAST_1 = "Asia-Pacific-(Singapore)" REGION_AP_SOUTHEAST_2 = "Asia-Pacific-(Sydney)" REGION_CA_CENTRAL_1 = "Canada-(Central)" REGION_EU_CENTRAL_1 = "EU-(Frankfurt)" REGION_EU_WEST_1 = "EU-(Ireland)" REGION_EU_WEST_2 = "EU-(London)" REGION_EU_WEST_3 = "EU-(Paris)" REGION_SA_EAST_1 = "South-America-(São-Paulo)" CHOICES = ( (REGION_US_EAST_2, "US-East(Ohio)"), (REGION_US_EAST_1, "US-East(N-Virginia)"), (REGION_US_WEST_1, "US-West(N-California)"), (REGION_US_WEST_2, "US-West-(Oregon)"), (REGION_AP_NORTHEAST_1, "Asia-Pacific-(Tokyo)"), (REGION_AP_NORTHEAST_2, "Asia-Pacific-(Seoul)"), (REGION_AP_SOUTH_1, "Asia-Pacific-(Mumbai)"), (REGION_AP_SOUTHEAST_1, "Asia-Pacific-(Singapore)"), (REGION_AP_SOUTHEAST_2, "Asia-Pacific-(Sydney)"), (REGION_CA_CENTRAL_1, "Canada-(Central)"), (REGION_EU_CENTRAL_1, "EU-(Frankfurt)"), (REGION_EU_WEST_1, "EU-(Ireland)"), (REGION_EU_WEST_2, "EU-(London)"), (REGION_EU_WEST_3, "EU-(Paris)"), (REGION_SA_EAST_1, "South-America-(São-Paulo)"), )

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1,551

4.334884

0.195349

0.085837

0.077253

0.027897

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0.178594

1,551

38

61

40.815789

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0

0.339136

0.144423

0

1

0

false

0

0.029412

0

0.529412

0

null

0

1

0

null

0

1

0

7

d0a7be0a378d5903371dc911d082c2ade426a942

3,637

py

Python

Arase/Tools/FitKappaDist.py

mattkjames7/Arase

996167be35a13bbb1fdddfbe75e3a06d124b1d25

[ "MIT" ]

null

Arase/Tools/FitKappaDist.py

mattkjames7/Arase

996167be35a13bbb1fdddfbe75e3a06d124b1d25

[ "MIT" ]

1

2021-06-10T22:51:09.000Z

Arase/Tools/FitKappaDist.py

mattkjames7/Arase

996167be35a13bbb1fdddfbe75e3a06d124b1d25

[ "MIT" ]

null

import numpy as np from .KappaDist import KappaDist,KappaDistCts from .KappaDist import KappaDistE,KappaDistCtsE from scipy.optimize import minimize def _GetMisfitFunc(v,f,mass): def Func(X): n,T,K = X fk = KappaDist(n,v,T,mass,K) #if np.isnan(fk[0]): # print(n,T,K,fk) lf = np.log10(f) lk = np.log10(fk) diff = np.sqrt(np.sum(((lf-lk)**2))/f.size) return diff return Func def FitKappaDist(v,f,n0,T0,mass,Verbose=False,MaxIter=None): #select only good data to fit to good = np.where(np.isfinite(f) & (f > 0))[0] if (good.size < 3.0): return -1, -1, -1, False Func = _GetMisfitFunc(v[good],f[good],mass) if MaxIter is None: opt = {} else: opt = { 'maxiter' : MaxIter } res = minimize(Func,[n0,T0,5.0],method='nelder-mead',options=opt) n,t,k = res.x if not res.success and Verbose: print('Warning - potentially bad Kappa fit') print(res.message) #return n,T and Kappa fitted return n,t,k,res.success def _GetMisfitFuncCts(v,C,mass,dOmega=1.0,Eff=1.0,nSpec=1.0,Tau=1.0,g=1.0): def Func(X): n,T,K = X Cm = KappaDistCts(n,v,T,mass,K,Eff,dOmega,nSpec,Tau,g) diff = np.sqrt(np.sum(((C-Cm)**2))/C.size) return diff return Func def FitKappaDistCts(v,Counts,n0,T0,mass,dOmega=1.0,Eff=1.0,nSpec=1.0,Tau=1.0,g=1.0,Verbose=False,MaxIter=None): bad = np.where(np.isfinite(Counts) == False)[0] Cs = np.copy(Counts) Cs[bad] = 0.0 #select only good data to fit to good = np.where((Cs >= 0.0))[0] if (good.size < 3.0): return -1, -1, -1, False Func = _GetMisfitFuncCts(v[good],Cs[good],mass,dOmega,Eff,nSpec,Tau,g) if MaxIter is None: opt = {} else: opt = { 'maxiter' : MaxIter } res = minimize(Func,[n0,T0,5.0],method='nelder-mead',options=opt) n,t,k = res.x if not res.success and Verbose: print('Warning - potentially bad Kappa fit') print(res.message) #return n,T fitted return n,t,k,res.success def _GetMisfitFuncE(E,f,mass): def Func(X): n,T,K = X fk = KappaDistE(n,E,T,mass,K) #if np.isnan(fk[0]): # print(n,T,K,fk) lf = np.log10(f) lk = np.log10(fk) diff = np.sqrt(np.sum(((lf-lk)**2))/f.size) return diff return Func def FitKappaDistE(E,f,n0,T0,mass,Verbose=False,MaxIter=None): #select only good data to fit to good = np.where(np.isfinite(f) & (f > 0))[0] if (good.size < 3.0): return -1, -1, -1, False Func = _GetMisfitFuncE(E[good],f[good],mass) if MaxIter is None: opt = {} else: opt = { 'maxiter' : MaxIter } res = minimize(Func,[n0,T0,5.0],method='nelder-mead',options=opt) n,t,k = res.x if not res.success and Verbose: print('Warning - potentially bad Kappa fit') print(res.message) #return n,T and Kappa fitted return n,t,k,res.success def _GetMisfitFuncCtsE(E,C,mass,dOmega=1.0,Eff=1.0,nSpec=1.0,Tau=1.0,g=1.0): def Func(X): n,T,K = X Cm = KappaDistCtsE(n,E,T,mass,K,Eff,dOmega,nSpec,Tau,g) diff = np.sqrt(np.sum(((C-Cm)**2))/C.size) return diff return Func def FitKappaDistCtsE(E,Counts,n0,T0,mass,dOmega=1.0,Eff=1.0,nSpec=1.0,Tau=1.0,g=1.0,Verbose=False,MaxIter=None): bad = np.where(np.isfinite(Counts) == False)[0] Cs = np.copy(Counts) Cs[bad] = 0.0 #select only good data to fit to good = np.where((Cs >= 0.0))[0] if (good.size < 3.0): return -1, -1, -1, False Func = _GetMisfitFuncCtsE(E[good],Cs[good],mass,dOmega,Eff,nSpec,Tau,g) if MaxIter is None: opt = {} else: opt = { 'maxiter' : MaxIter } res = minimize(Func,[n0,T0,5.0],method='nelder-mead',options=opt) n,t,k = res.x if not res.success and Verbose: print('Warning - potentially bad Kappa fit') print(res.message) #return n,T fitted return n,t,k,res.success

24.086093

112

0.649986

679

3,637

3.469809

0.129602

0.016978

0.017827

0.020374

0.845925

0.84253

0.841256

0

0.037587

0.166071

3,637

150

113

24.246667

0.739202

0.078086

0

0.803922

0

0.063454

0

1

0.117647

false

0

0.039216

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0.078431

0

null

0

1

0

1

0

null

0

7

190517b24d3ead7752c44ae48efcf168ac4cbb3d

86

py

Python

hyperspherical_vae_pytorch/__init__.py

pimdh/svae-temp

49d3974e66abc761312432f28ae57fe714d17451

[ "MIT" ]

3

2018-06-10T00:15:50.000Z

2021-12-08T11:07:59.000Z

hyperspherical_vae_pytorch/__init__.py

pimdh/svae-temp

49d3974e66abc761312432f28ae57fe714d17451

[ "MIT" ]

null

hyperspherical_vae_pytorch/__init__.py

pimdh/svae-temp

49d3974e66abc761312432f28ae57fe714d17451

[ "MIT" ]

null

import hyperspherical_vae_pytorch.ops import hyperspherical_vae_pytorch.distributions

28.666667

47

0.930233

10

86

7.6

0.6

0.526316

0.605263

0.789474

0

0.046512

86

2

48

43

0.926829

0

1

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true

0

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null

0

1

0

1

0

1

0

8

efb37752dd7214c7300db5ebcde51f141fa275eb

79,605

py

Python

GR/bssnUtils.py

paralab/SymPyGR

3aa4164a64773b9015b83744cd104550ae465e8a

[ "MIT" ]

7

2019-08-29T20:41:39.000Z

2022-03-26T17:47:16.000Z

GR/bssnUtils.py

paralab/SymPyGR

3aa4164a64773b9015b83744cd104550ae465e8a

[ "MIT" ]

2

2019-02-01T22:20:48.000Z

2019-05-24T20:39:33.000Z

GR/bssnUtils.py

paralab/SymPyGR

3aa4164a64773b9015b83744cd104550ae465e8a

[ "MIT" ]

1

2018-12-18T19:36:13.000Z

########################################################################## # author: Milinda Fernando # email: milinda@cs.utah.edu, # date: 08/13/2018 # # python module to generate bssn derivative calls and support function # calls to call the generated code by bssn.py # (python code for perl script written by David) # ########################################################################## # Note: gbx, gby, gbz are not needed for the RHS, but the derivatives # are needed for the boundary conditions. The allocation of derivatives # and calls to derivative routines for the boundaries uses the functions # required for the rhs, so I include them here. from collections import namedtuple from datetime import datetime from time import strftime import dendro as dendro import math as math #import bssn_stages as bssn_stages import bssn as bssn import sympy as sympy import re as re import os as os import cudaSharedMemManager as SharedMemManager ## ==== BSSN GPU code generation paramerters D = ["alpha", "beta0", "beta1", "beta2", "B0", "B1", "B2", "chi", "Gt0", "Gt1", "Gt2", "K", "gt0", "gt1", "gt2", "gt3", "gt4", "gt5", "At0", "At1", "At2", "At3", "At4", "At5" ] # variable names, to access the 2D array. VAR_ENUM=["cuda::VAR::U_ALPHA", "cuda::VAR::U_BETA0", "cuda::VAR::U_BETA1", "cuda::VAR::U_BETA2", "cuda::VAR::U_B0", "cuda::VAR::U_B1", "cuda::VAR::U_B2", "cuda::VAR::U_CHI", "cuda::VAR::U_GT0", "cuda::VAR::U_GT1", "cuda::VAR::U_GT2", "cuda::VAR::U_K", "cuda::VAR::U_SYMGT0", "cuda::VAR::U_SYMGT1", "cuda::VAR::U_SYMGT2", "cuda::VAR::U_SYMGT3", "cuda::VAR::U_SYMGT4", "cuda::VAR::U_SYMGT5", "cuda::VAR::U_SYMAT0", "cuda::VAR::U_SYMAT1", "cuda::VAR::U_SYMAT2", "cuda::VAR::U_SYMAT3", "cuda::VAR::U_SYMAT4", "cuda::VAR::U_SYMAT5"] # enum to symbolic input vars dictionary VAR_ENUM_TO_INPUT_SYM = { "alpha" : "cuda::VAR::U_ALPHA", "beta0" : "cuda::VAR::U_BETA0", "beta1" : "cuda::VAR::U_BETA1", "beta2" : "cuda::VAR::U_BETA2", "B0" : "cuda::VAR::U_B0", "B1" : "cuda::VAR::U_B1", "B2" : "cuda::VAR::U_B2", "chi" : "cuda::VAR::U_CHI", "Gt0" : "cuda::VAR::U_GT0", "Gt1" : "cuda::VAR::U_GT1", "Gt2" : "cuda::VAR::U_GT2", "K" : "cuda::VAR::U_K", "gt0" : "cuda::VAR::U_SYMGT0", "gt1" : "cuda::VAR::U_SYMGT1", "gt2" : "cuda::VAR::U_SYMGT2", "gt3" : "cuda::VAR::U_SYMGT3", "gt4" : "cuda::VAR::U_SYMGT4", "gt5" : "cuda::VAR::U_SYMGT5", "At0" :"cuda::VAR::U_SYMAT0", "At1" :"cuda::VAR::U_SYMAT1", "At2" :"cuda::VAR::U_SYMAT2", "At3" :"cuda::VAR::U_SYMAT3", "At4" :"cuda::VAR::U_SYMAT4", "At5" :"cuda::VAR::U_SYMAT5" } # enum to symbolic output vars dictionary VAR_ENUM_TO_OUTPUT_SYM={ "a_rhs" : "cuda::VAR::U_ALPHA", "b_rhs0" : "cuda::VAR::U_BETA0", "b_rhs1" : "cuda::VAR::U_BETA1", "b_rhs2" : "cuda::VAR::U_BETA2", "B_rhs0" : "cuda::VAR::U_B0", "B_rhs1" : "cuda::VAR::U_B1", "B_rhs2" : "cuda::VAR::U_B2", "chi_rhs" : "cuda::VAR::U_CHI", "Gt_rhs0" : "cuda::VAR::U_GT0", "Gt_rhs1" : "cuda::VAR::U_GT1", "Gt_rhs2" : "cuda::VAR::U_GT2", "K_rhs" : "cuda::VAR::U_K", "gt_rhs00" : "cuda::VAR::U_SYMGT0", "gt_rhs01" : "cuda::VAR::U_SYMGT1", "gt_rhs02" : "cuda::VAR::U_SYMGT2", "gt_rhs11" : "cuda::VAR::U_SYMGT3", "gt_rhs12" : "cuda::VAR::U_SYMGT4", "gt_rhs22" : "cuda::VAR::U_SYMGT5", "At_rhs00" :"cuda::VAR::U_SYMAT0", "At_rhs01" :"cuda::VAR::U_SYMAT1", "At_rhs02" :"cuda::VAR::U_SYMAT2", "At_rhs11" :"cuda::VAR::U_SYMAT3", "At_rhs12" :"cuda::VAR::U_SYMAT4", "At_rhs22" :"cuda::VAR::U_SYMAT5" } # custom functions for code generation in cse. custom_functions = {'grad': 'grad', 'grad2': 'grad2', 'agrad': 'agrad', 'kograd': 'kograd'} # second derivs required for RHS DD = ["gt0", "gt1", "gt2", "gt3", "gt4", "gt5", "chi", "alpha", "beta0", "beta1", "beta2" ] # advective derivatives AD = ["gt0", "gt1", "gt2", "gt3", "gt4", "gt5", "At0", "At1", "At2", "At3", "At4", "At5", "alpha", "beta0", "beta1", "beta2", "chi", "Gt0", "Gt1", "Gt2", "K", "B0", "B1", "B2"] KO=AD # first derivs required for constraints--no gauge variables CONSTRAINT_D = [ "chi", "Gt0", "Gt1", "Gt2", "K", "gt0", "gt1", "gt2", "gt3", "gt4", "gt5", "At0", "At1", "At2", "At3", "At4", "At5" ] # second derivs required for constraints--no gauge variables CONSTRAINT_DD = ["gt0", "gt1", "gt2", "gt3", "gt4", "gt5", "chi"] PREFIX_D = ["grad_0_", "grad_1_", "grad_2_"] PREFIX_AD = ["agrad_0_", "agrad_1_", "agrad_2_"] PREFIX_KOD = ["kograd_0_", "kograd_1_", "kograd_2_"] PREFIX_DD = ["grad2_0_0_", "grad2_0_1_", "grad2_0_2_", "grad2_1_1_", "grad2_1_2_", "grad2_2_2_"] # first derivative in i direction FUNC_D_I=[] for f in D: for p in PREFIX_D: FUNC_D_I.append(p+f) # second derivative in ij direction FUNC_D_IJ=[] for f in DD: for p in PREFIX_DD: FUNC_D_IJ.append(p+f) #advective derivative in i direction FUNC_AD_I=[] for f in AD: for p in PREFIX_AD: FUNC_AD_I.append(p+f) #Kriess-Oliger derivative in i direction FUNC_KOD_I=[] for f in D: for p in PREFIX_KOD: FUNC_KOD_I.append(p+f) # cuda utility functions ## Note all the device vars which is global starts with __ FUNC_LOAD_VAR="cuda::__loadGlobalToShared3D<double>" FUNC_STORE_VAR="cuda::__storeSharedToGlobal3D<double>" FUNC_SIGN_EXT="cuda::__extractSign3D<double>" VAR_UNZIP_IN="__unzipInVar" VAR_UNZIP_OUT="__unzipOutVar" ## shift vector block shared variables to compute advective derivs VAR_BETA0="beta0" VAR_BETA1="beta1" VAR_BETA2="beta2" VAR_BETA0_BOOL="beta0_bool" VAR_BETA1_BOOL="beta1_bool" VAR_BETA2_BOOL="beta2_bool" # shared input variable name for derivative kernels VAR_IN_SHARED="unzipVarInShared" # shared output variable name for derivative kernels VAR_OUT_SHARED_0="unzipVarOutShared0" VAR_OUT_SHARED_1="unzipVarOutShared1" # block ids VAR_BLK_ID_X="blockIdx.x" VAR_BLK_ID_Y="blockIdx.y" VAR_BLK_ID_Z="blockIdx.z" # thread ids VAR_TRD_ID_X="threadIdx.x" VAR_TRD_ID_Y="threadIdx.y" VAR_TRD_ID_Z="threadIdx.z" # block dim VAR_BLK_DIM_X="blockDim.x" VAR_BLK_DIM_Y="blockDim.y" VAR_BLK_DIM_Z="blockDim.z" # x,y,z bounds of the time i_lm[0] is the min and i_lm[1] is the max. VAR_TILE_SZ="tile_sz" VAR_DENDRO_BLK_ALIGNED_SZ="alignedSz" VAR_TILE_LIMITS="ijk_lm" VAR_TILE_LIMITS_STORE="tile_lm" TYPE_DERIV_STRUCT="MemoryDerivs" TYPE_BLK_CU="cuda::_Block" TYPE_BSSN_COMP_PARS="BSSNComputeParams" ## # generate the code to allocate derivative memory variables (allocated size unzip_dof) ## def cudaDerivAllocDeallocHeader(fname,headers=[]): func_i=FUNC_D_I func_ij=FUNC_D_IJ afunc_i=FUNC_AD_I kofunc_i=FUNC_KOD_I with open(fname, 'w') as ofile: ofile.write("// generated by Dendro-GR SymPyGR code gernation framework\n") ofile.write("//date: "+str(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))+"\n") fileName,fileExt=os.path.splitext(os.path.basename(fname)) ofile.write("#ifndef "+fileName.upper()+"_"+fileExt[1:].upper()+" \n") ofile.write("#define "+fileName.upper()+"_"+fileExt[1:].upper()+" \n") ofile.write("\n") ofile.write("#include <iostream>\n") ofile.write("#include \"cuda_runtime.h\"\n") for header in headers: ofile.write("#include \""+header+"\"\n") ofile.write("\n") ofile.write("namespace cuda {\n") ofile.write("\tstruct "+TYPE_DERIV_STRUCT+"{\n\n") ofile.write("/**@brief upper bound of the block size processed by the GPU*/\n") ofile.write("\t unsigned int __maxBlkSz;\n") ofile.write("/**@brief number of streams the kernel get executed*/\n") ofile.write("\t unsigned int __numStream;\n") ofile.write("/**@brief size per stream*/\n") ofile.write("\t unsigned int __szPerStream;\n") for deriv in func_i: ofile.write("\t double* __"+deriv+";\n") for deriv in func_ij: ofile.write("\t double* __"+deriv+";\n") for deriv in afunc_i: ofile.write("\t double* __"+deriv+";\n") for deriv in kofunc_i: ofile.write("\t double* __"+deriv+";\n") ofile.write("\n") ofile.write("\n") ofile.write("\t/**@brief memory allocation for deriv variables */\n") ofile.write("\tvoid allocateDerivMemory(unsigned int maxBlkSz, unsigned int numSM,unsigned int numStreams=1); \n") ofile.write("\n") ofile.write("\n") ofile.write("\t/**@brief memory deallocation for deriv variables */\n") ofile.write("\tvoid deallocateDerivMemory(); \n") ofile.write("\n") ofile.write("\n") ofile.write("\t};\n\n") ofile.write("}// end of namespace cuda\n") ofile.write("\n") ofile.write("#endif\n") ofile.close() ## # generate the code to allocate derivative memory variables (allocated size unzip_dof) ## def cudaDerivAllocDeallocSource(fname,headers=[]): func_i=FUNC_D_I func_ij=FUNC_D_IJ afunc_i=FUNC_AD_I kofunc_i=FUNC_KOD_I with open(fname, 'w') as ofile: ofile.write("// generated by Dendro-GR SymPyGR code gernation framework\n") ofile.write("//date: "+str(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))+"\n") for header in headers: ofile.write("#include \""+header+"\"\n") ofile.write("\n") ofile.write("namespace cuda {\n") ofile.write("\n") ofile.write("\n") ofile.write("\t/**@brief memory allocation for deriv variables */\n") ofile.write("\tvoid cuda::"+TYPE_DERIV_STRUCT+"::allocateDerivMemory(unsigned int maxBlkSz, unsigned int numSM,unsigned int numStreams){ \n") ofile.write("\t\t __maxBlkSz=maxBlkSz;\n") ofile.write("\t\t __numStream=numStreams;\n") ofile.write("\t\t __szPerStream=numSM*maxBlkSz;\n") ofile.write("\t\t const size_t bytes=sizeof(double)*numSM*maxBlkSz*numStreams;\n") for deriv in func_i: ofile.write("\t\t cudaMalloc((void **)&__"+deriv+",bytes);\n") #ofile.write("\t\tCUDA_CHECK_ERROR();\n") for deriv in func_ij: ofile.write("\t\t cudaMalloc((void **)&__"+deriv+",bytes);\n") #ofile.write("\t\tCUDA_CHECK_ERROR();\n") for deriv in afunc_i: ofile.write("\t\t cudaMalloc((void **)&__"+deriv+",bytes);\n") #ofile.write("\t\tCUDA_CHECK_ERROR();\n") for deriv in kofunc_i: ofile.write("\t\t cudaMalloc((void **)&__"+deriv+",bytes);\n") #ofile.write("\t\tCUDA_CHECK_ERROR()\n") ofile.write("\n") ofile.write("\n") ofile.write("} \n") ofile.write("\n") ofile.write("\n") ofile.write("\t/**@brief memory deallocation for deriv variables */\n") ofile.write("\tvoid cuda::"+TYPE_DERIV_STRUCT+"::deallocateDerivMemory(){ \n") for deriv in func_i: ofile.write("\t\t cudaFree((void *)__"+deriv+");\n") #ofile.write("\t\tCUDA_CHECK_ERROR()\n") for deriv in func_ij: ofile.write("\t\t cudaFree((void *)__"+deriv+");\n") #ofile.write("\t\tCUDA_CHECK_ERROR()\n") for deriv in afunc_i: ofile.write("\t\t cudaFree((void *)__"+deriv+");\n") #ofile.write("\t\tCUDA_CHECK_ERROR()\n") for deriv in kofunc_i: ofile.write("\t\t cudaFree((void *)__"+deriv+");\n") #ofile.write("\t\tCUDA_CHECK_ERROR()\n") ofile.write("\n") ofile.write("\n") ofile.write("} \n") ofile.write("\n") ofile.write("\n") ofile.write("}// end of namespace cuda\n") ofile.close() def computeTileStore(dir,out,padWidth=3): padWidth=str(padWidth) out.write("\n") if(dir=="x"): out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[0]="+padWidth+";\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[1]=("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0]);\n") out.write("\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[2]=(iter_y)? "+padWidth+": 0;\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[3]=(iter_y==(BLK_ITERATIONS_Y-1)) ? ("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]) : "+"("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]-"+padWidth+")" +";\n") out.write("\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[4]=(iter_z)? "+padWidth+": 0;\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[5]=(iter_z==(BLK_ITERATIONS_Z-1)) ? ("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]) : "+"("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]-"+padWidth+")" +";\n") out.write("\n") elif(dir=="y"): out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[0]=(iter_x)? "+padWidth+": 0;\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[1]=(iter_x==(BLK_ITERATIONS_X-1)) ? ("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0]) : "+"("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0]-"+padWidth+")" +";\n") out.write("\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[2]="+padWidth+";\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[3]=("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]);\n") out.write("\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[4]=(iter_z)? "+padWidth+": 0;\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[5]=(iter_z==(BLK_ITERATIONS_Z-1)) ? ("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]) : "+"("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]-"+padWidth+")" +";\n") out.write("\n") elif(dir=="z"): out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[0]=(iter_x)? "+padWidth+": 0;\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[1]=(iter_x==(BLK_ITERATIONS_X-1)) ? ("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0]) : "+"("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0]-"+padWidth+")" +";\n") out.write("\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[2]=(iter_y)? "+padWidth+": 0;\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[3]=(iter_y==(BLK_ITERATIONS_Y-1)) ? ("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]) : "+"("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]-"+padWidth+")" +";\n") out.write("\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[4]="+padWidth+";\n") out.write("\t\t"+VAR_TILE_LIMITS_STORE+"[5]=("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]);\n") out.write("\n") out.write("\n") def cudaCompute(fname_cuh,fname_cu,derivs,outs,varnames,kernelName,headers=[]): # cuda device properties VAR_CUDA_DEVICE="__deviceProperties" # dendro block list parameters VAR_DENDRO_BLK_LIST="__dendroBlkList" VAR_NUM_BLOCKS="cuda::__DENDRO_NUM_BLOCKS" VAR_DERIV_WORKSPACE="__derivWorkspace" VAR_GPU_BLOCK_MAP="__gpuBlockMap" VAR_MAX_DENDRO_BLK_SZ=VAR_DERIV_WORKSPACE+"->__maxBlkSz" VAR_DW_SZ_PER_STREAM=VAR_DERIV_WORKSPACE+"->__szPerStream" VAR_BSSN_PARAMS="__bssnParams" TYPE_BSSN_PARAMS="cuda::BSSNComputeParams" FUNC_DERIV_COMP="__compute_derivatives" FUNC_COMP_RHS_PRE="__compute" FUNC_KO_DISS="__ko_dissipation" VAR_SHARED_MEM="__sm_base" TYPE_SHARED_MEM="double" VAR_ADV_COMPRESS_0=VAR_BETA0_BOOL VAR_ADV_COMPRESS_1=VAR_BETA1_BOOL VAR_ADV_COMPRESS_2=VAR_BETA2_BOOL TYPE_ADV_COMPRESS="bool" VAR_DBLOCK="dblock" VAR_STREAM_ID="stream_id" VAR_DERIV_WORKSPACE_OFFSET=VAR_STREAM_ID+"*("+VAR_DW_SZ_PER_STREAM+") + SM_ID*("+VAR_MAX_DENDRO_BLK_SZ+")" ###################################################### # Writing the header ###################################################### with open(fname_cuh, 'w') as ofile: ofile.write("// generated by Dendro-GR SymPyGR code gernation framework\n") ofile.write("//date: "+str(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))+"\n") fileName,fileExt=os.path.splitext(os.path.basename(fname_cuh)) ofile.write("#ifndef "+fileName.upper()+"_"+fileExt[1:].upper()+" \n") ofile.write("#define "+fileName.upper()+"_"+fileExt[1:].upper()+" \n") ofile.write("#include<iostream>\n") ofile.write("#include\"cuda_runtime.h\"\n") ofile.write("#include<device_launch_parameters.h>\n") for header in headers: ofile.write("#include \""+header+"\"\n") ofile.write("namespace cuda {\n") ofile.write("\n") ofile.write("/**@brief compute derivs \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") #ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") ofile.write("__device__ void "+FUNC_DERIV_COMP+"(const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* dblock, const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+",const cudaDeviceProp* "+VAR_CUDA_DEVICE+", "+TYPE_SHARED_MEM+"* "+VAR_SHARED_MEM+", "+TYPE_ADV_COMPRESS+"* "+VAR_ADV_COMPRESS_0+", "+TYPE_ADV_COMPRESS+"* "+VAR_ADV_COMPRESS_1+", "+TYPE_ADV_COMPRESS+"* "+VAR_ADV_COMPRESS_2+",unsigned int "+VAR_STREAM_ID+");\n") ofile.write("\n") for var in varnames: ofile.write("/**@brief compute "+var+" \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") ofile.write("__device__ void "+FUNC_COMP_RHS_PRE+"_"+var+"(double **"+VAR_UNZIP_OUT+", const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* dblock, const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+",const cudaDeviceProp* "+VAR_CUDA_DEVICE+", "+TYPE_SHARED_MEM+"* "+VAR_SHARED_MEM+", unsigned int "+VAR_STREAM_ID+");\n") ofile.write("\n") ofile.write("/**@brief apply KO dissipation \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") ofile.write("__device__ void "+FUNC_KO_DISS+"(double **"+VAR_UNZIP_OUT+", const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* dblock, const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+",const cudaDeviceProp* "+VAR_CUDA_DEVICE+", "+TYPE_SHARED_MEM+"* "+VAR_SHARED_MEM+", unsigned int "+VAR_STREAM_ID+");\n") ofile.write("\n") ofile.write("/**@brief compute RHS \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") ofile.write("__global__ void "+kernelName+"(double **"+VAR_UNZIP_OUT+", const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* "+VAR_DENDRO_BLK_LIST+", const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+",const cudaDeviceProp* "+VAR_CUDA_DEVICE+", unsigned int "+VAR_STREAM_ID+");\n") ofile.write("\n") ofile.write("}// end of namespace cuda\n") ofile.write("\n") ofile.write("\n") ofile.write("#endif\n") ofile.close() ###################################################### # Writing the source ###################################################### with open(fname_cu, 'w') as ofile: ofile.write("// generated by Dendro-GR SymPyGR code gernation framework\n") ofile.write("//date: "+str(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))+"\n") ofile.write("#include \""+os.path.basename(fname_cuh)+"\"\n") # namespace begin ofile.write("namespace cuda {\n\n") ofile.write("/**@brief compute RHS \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") # function begin ofile.write("__global__ void "+kernelName+"(double **"+VAR_UNZIP_OUT+", const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* "+VAR_DENDRO_BLK_LIST+", const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+" ,const cudaDeviceProp* "+VAR_CUDA_DEVICE+", unsigned int "+VAR_STREAM_ID+"){\n\n") ofile.write("// shared memory allocation for deriv and rhs computation\n") memManager=SharedMemManager.MemoryManager(maxMemSz=48*1024,memUsable=41*1024,cout=ofile,baseName=VAR_SHARED_MEM,varType=TYPE_SHARED_MEM) deriv_tile_sz_1d=0 deriv_req_pad=0 deriv_max_pad=3 for deriv in derivs: if deriv_tile_sz_1d <deriv.DerivTile1D : deriv_tile_sz_1d=deriv.DerivTile1D if deriv_req_pad < deriv.padWidth : deriv_req_pad =deriv.padWidth deriv_tile_sz=deriv_tile_sz_1d**3 ofile.write("\t__shared__ bool "+VAR_BETA0_BOOL+ "["+str(deriv_tile_sz)+"];\n") ofile.write("\t__shared__ bool "+VAR_BETA1_BOOL+ "["+str(deriv_tile_sz)+"];\n") ofile.write("\t__shared__ bool "+VAR_BETA2_BOOL+ "["+str(deriv_tile_sz)+"];\n\n") ofile.write("\tfor(unsigned int blk="+VAR_GPU_BLOCK_MAP+"[2*"+VAR_BLK_ID_X+"];blk<"+VAR_GPU_BLOCK_MAP+"[2*"+VAR_BLK_ID_X+"+1];++blk){\n\n\n") ofile.write("\t// blocks assigned to each gpu block \n") ofile.write("\tconst _Block * "+VAR_DBLOCK+"=&"+VAR_DENDRO_BLK_LIST+"[blk];\n") ofile.write("\t// compute the derivatives\n") ofile.write("\t"+FUNC_DERIV_COMP+"("+VAR_UNZIP_IN+","+VAR_DERIV_WORKSPACE+","+VAR_DBLOCK+","+VAR_GPU_BLOCK_MAP+","+VAR_BSSN_PARAMS+","+VAR_CUDA_DEVICE+","+VAR_SHARED_MEM+","+VAR_ADV_COMPRESS_0+","+VAR_ADV_COMPRESS_1+","+VAR_ADV_COMPRESS_2+","+VAR_STREAM_ID+");\n") ofile.write("\t__syncthreads();\n") ofile.write("\t// compute the RHS\n") for var in varnames: ofile.write("\t"+FUNC_COMP_RHS_PRE+"_"+var+"("+VAR_UNZIP_OUT+","+VAR_UNZIP_IN+","+VAR_DERIV_WORKSPACE+","+VAR_DBLOCK+","+VAR_GPU_BLOCK_MAP+","+VAR_BSSN_PARAMS+","+VAR_CUDA_DEVICE+","+VAR_SHARED_MEM+","+VAR_STREAM_ID+");\n") ofile.write("\t__syncthreads();\n") ofile.write("\t"+FUNC_KO_DISS+"("+VAR_UNZIP_OUT+","+VAR_UNZIP_IN+","+VAR_DERIV_WORKSPACE+","+VAR_DBLOCK+","+VAR_GPU_BLOCK_MAP+","+VAR_BSSN_PARAMS+","+VAR_CUDA_DEVICE+","+VAR_SHARED_MEM+","+VAR_STREAM_ID+");\n") ofile.write("\t__syncthreads();\n") ofile.write("\t}// end of the block loop\n") ofile.write("} // end of kernel \n\n") ofile.write("\n") ofile.write("/**@brief compute derivs \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") #ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") ofile.write("__device__ void "+FUNC_DERIV_COMP+"(const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* dblock, const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+",const cudaDeviceProp* "+VAR_CUDA_DEVICE+", "+TYPE_SHARED_MEM+"* "+VAR_SHARED_MEM+", "+TYPE_ADV_COMPRESS+"* "+VAR_ADV_COMPRESS_0+", "+TYPE_ADV_COMPRESS+"* "+VAR_ADV_COMPRESS_1+", "+TYPE_ADV_COMPRESS+"* "+VAR_ADV_COMPRESS_2+", unsigned int "+VAR_STREAM_ID+"){\n") ofile.write("\n") ofile.write("\tconst unsigned int NUM_SM_UNITS="+VAR_CUDA_DEVICE+"->multiProcessorCount;\n") ofile.write("\tconst unsigned int SM_ID=get_smid();//"+VAR_BLK_ID_X+"%NUM_SM_UNITS;\n") ofile.write("\tconst unsigned int offset=dblock->getOffset();\n") ofile.write("\tconst unsigned int *sz=dblock->getSz();\n") ofile.write("\tconst unsigned int *"+VAR_DENDRO_BLK_ALIGNED_SZ+"=dblock->getAlignedSz();\n") ofile.write("\tconst double* hx=dblock->getDx();\n") ofile.write("\tconst double dx=hx[0];\n") ofile.write("\tconst double dy=hx[1];\n") ofile.write("\tconst double dz=hx[2];\n") ofile.write("\tconst double* ptmin=dblock->getPtMin();\n") ofile.write("\tconst double* ptmax=dblock->getPtMax();\n") ofile.write("\tconst unsigned int bflag=dblock->getBFlag();\n") ofile.write("\n") if(deriv_req_pad>deriv_max_pad): print("code generation error : maxPadwith for derivatives is larger than the dendro block pad width\n") os.sys.exit(0) ofile.write("\tconst unsigned int "+VAR_TILE_SZ+"[3]={"+str(deriv_tile_sz_1d)+","+str(deriv_tile_sz_1d)+","+str(deriv_tile_sz_1d)+"};\n") memManager.malloc(VAR_IN_SHARED,deriv_tile_sz,ofile,prefix="\t") memManager.malloc(VAR_OUT_SHARED_0,deriv_tile_sz,ofile,prefix="\t") memManager.malloc(VAR_OUT_SHARED_1,deriv_tile_sz,ofile,prefix="\t") ofile.write("\tconst unsigned int Lb = "+str(deriv_max_pad-deriv_req_pad)+";// load begin bound\n") ofile.write("\tconst unsigned int Le = sz[0]-"+str(deriv_max_pad-deriv_req_pad)+";// load end bound\n") # !! Note that we assume tile size are cubic. ofile.write("//!! Note that we assume tile size are cubic.\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_X = ((Le-Lb)<"+VAR_TILE_SZ+"[0])? 1: ((int)ceil((double)(Le-Lb-"+VAR_TILE_SZ+"[0])/("+VAR_TILE_SZ+"[0]-2*" +str(deriv_req_pad)+")))+1;\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_Y = BLK_ITERATIONS_X;\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_Z = BLK_ITERATIONS_X;\n") ofile.write("\n") ofile.write("\tunsigned int "+VAR_TILE_LIMITS+"[3*2];\n") ofile.write("\tunsigned int "+VAR_TILE_LIMITS_STORE+"[3*2];\n") ofile.write("\tfor(unsigned int iter_z=0;iter_z<BLK_ITERATIONS_Z;iter_z++){\n\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*2+0]=max("+str(deriv_max_pad-deriv_req_pad)+",(int)("+str(deriv_max_pad-deriv_req_pad)+" + "+VAR_TILE_SZ+"[2]*iter_z -2*iter_z*"+str(deriv_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*2+1]=min("+VAR_TILE_LIMITS+"[2*2+0]+"+VAR_TILE_SZ+"[2]-1,sz[2]-"+str(deriv_max_pad-deriv_req_pad)+ "-1);\n") ofile.write("\n") ofile.write("\n") ofile.write("\t\t if(("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]+1)<="+str(2*deriv_req_pad+3)+") \n\t\t "+VAR_TILE_LIMITS+"[4]="+VAR_TILE_LIMITS+"[4]-("+str(2*deriv_req_pad+3)+"-("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]+1)) ; \n ") ofile.write("\n") ofile.write("\t for(unsigned int iter_y=0;iter_y<BLK_ITERATIONS_Y;iter_y++){\n\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*1+0]=max("+str(deriv_max_pad-deriv_req_pad)+",(int)("+str(deriv_max_pad-deriv_req_pad)+" + "+VAR_TILE_SZ+"[1]*iter_y -2*iter_y*"+str(deriv_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*1+1]=min("+VAR_TILE_LIMITS+"[2*1+0]+"+VAR_TILE_SZ+"[1]-1,sz[1]-"+str(deriv_max_pad-deriv_req_pad)+ "-1);\n") ofile.write("\n") ofile.write("\t\t if(("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]+1)<="+str(2*deriv_req_pad+3)+") \n\t\t "+VAR_TILE_LIMITS+"[2]="+VAR_TILE_LIMITS+"[2]-("+str(2*deriv_req_pad+3)+"-("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]+1)) ; \n ") ofile.write("\n") ofile.write("\t for(unsigned int iter_x=0;iter_x<BLK_ITERATIONS_X;iter_x++){\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*0+0]=max("+str(deriv_max_pad-deriv_req_pad)+",(int)("+str(deriv_max_pad-deriv_req_pad)+" + "+VAR_TILE_SZ+"[0]*iter_x -2*iter_x*"+str(deriv_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*0+1]=min("+VAR_TILE_LIMITS+"[2*0+0]+"+VAR_TILE_SZ+"[0]-1,sz[0]-"+str(deriv_max_pad-deriv_req_pad)+ "-1);\n") ofile.write("\n") ofile.write("\t\t if(("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0]+1)<="+str(2*deriv_req_pad+3)+") \n\t\t "+VAR_TILE_LIMITS+"[0]="+VAR_TILE_LIMITS+"[0]-("+str(2*deriv_req_pad+3)+"-("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0]+1)) ; \n ") ofile.write("\n") ofile.write("\n") ofile.write("\t\t //if(threadIdx.x ==0 && threadIdx.y==0 && threadIdx.z==0)\n") ofile.write("\t\t //printf(\" iter %d %d %d : threadid (%d,%d,%d) tile begin: (%d,%d,%d) tile end: (%d,%d,%d) \\n\",iter_x,iter_y,iter_z, threadIdx.x,threadIdx.y,threadIdx.z,ijk_lm[0],ijk_lm[2],ijk_lm[4],ijk_lm[1],ijk_lm[3],ijk_lm[5]);\n\n") ofile.write("\n") ofile.write("\t\t"+FUNC_LOAD_VAR+"(&"+VAR_UNZIP_IN+"[cuda::VAR::U_BETA0][offset],(double *) "+VAR_IN_SHARED+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t"+FUNC_LOAD_VAR+"(&"+VAR_UNZIP_IN+"[cuda::VAR::U_BETA1][offset],(double *) "+VAR_OUT_SHARED_0+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t"+FUNC_LOAD_VAR+"(&"+VAR_UNZIP_IN+"[cuda::VAR::U_BETA2][offset],(double *) "+VAR_OUT_SHARED_1+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") ofile.write("\t\t"+FUNC_SIGN_EXT+"((double *)"+VAR_IN_SHARED+",(bool *) "+VAR_BETA0_BOOL+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t"+FUNC_SIGN_EXT+"((double *)"+VAR_OUT_SHARED_0+",(bool *) "+VAR_BETA1_BOOL+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t"+FUNC_SIGN_EXT+"((double *)"+VAR_OUT_SHARED_1+",(bool *) "+VAR_BETA2_BOOL+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") for e in D: enumStr=VAR_ENUM[D.index(e)] ofile.write("\n") ofile.write("\t\t//load input data from global to shared memory\n") ofile.write("\t\t"+FUNC_LOAD_VAR+"(&"+VAR_UNZIP_IN+"["+enumStr+"][offset],(double *) "+VAR_IN_SHARED+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") ofile.write("\t\t//sync to make sure all the data is loaded\n") for deriv in derivs: if((deriv.DerivType=="d") and (deriv.DerivDir=="x")): ofile.write("\t\t// computing deriv "+deriv.DerivDir+" for variable "+e+" \n") ofile.write("\t\t"+deriv.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. for deriv1 in derivs: if((e in DD) and (deriv1.DerivType=="dd") and ((deriv1.DerivDir=="xy") or (deriv1.DerivDir=="xz"))): ofile.write("\t\t// computing deriv "+deriv1.DerivDir+" for variable "+e+" \n") ofile.write("\t\t"+deriv1.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. if(deriv1.DerivDir=="xy"): computeTileStore("y",ofile,deriv_req_pad) elif(deriv1.DerivDir=="xz"): computeTileStore("z",ofile,deriv_req_pad) #!!!! NOTE that for mixed derivs you need to store the padding region as well. ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_1+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv1.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") if((deriv1.DerivDir=="xy")): ofile.write("\t\t__syncthreads();\n") ofile.write("\n") computeTileStore("x",ofile,deriv_req_pad) ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_0+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") ofile.write("\n") if((deriv.DerivType=="d") and (deriv.DerivDir=="y")): ofile.write("\t\t// computing deriv "+deriv.DerivDir+" for variable "+e+" \n") ofile.write("\t\t"+deriv.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. for deriv1 in derivs: if((e in DD) and (deriv1.DerivType=="dd") and (deriv1.DerivDir=="yz")): ofile.write("\t\t// computing deriv "+deriv1.DerivDir+" for variable "+e+" \n") ofile.write("\t\t"+deriv1.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. computeTileStore("z",ofile,deriv_req_pad) ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_1+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv1.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") #ofile.write("\t\t__syncthreads();\n") ofile.write("\n") #write the x, y,z derivs. computeTileStore("y",ofile,deriv_req_pad) ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_0+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") ofile.write("\n") if((deriv.DerivType=="d") and (deriv.DerivDir=="z")): ofile.write("\t\t// computing deriv "+deriv.DerivDir+" for variable "+e+" \n") ofile.write("\t\t"+deriv.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. computeTileStore("z",ofile,deriv_req_pad) ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_0+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") ofile.write("\n") derivCount=0 for deriv in derivs: if((e in DD) and (deriv.DerivType=="dd") and ((deriv.DerivDir=="xx") or (deriv.DerivDir=="yy") or (deriv.DerivDir=="zz"))): ofile.write("\t\t"+deriv.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. if(deriv.DerivDir=="xx"): computeTileStore("x",ofile,deriv_req_pad) elif(deriv.DerivDir=="yy"): computeTileStore("y",ofile,deriv_req_pad) elif(deriv.DerivDir=="zz"): computeTileStore("z",ofile,deriv_req_pad) ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_0+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") ofile.write("\n") for deriv in derivs: if( (e in KO) and (deriv.DerivType=="ko")): ofile.write("\t\t"+deriv.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. if(deriv.DerivDir=="x"): computeTileStore("x",ofile,deriv_req_pad) elif(deriv.DerivDir=="y"): computeTileStore("y",ofile,deriv_req_pad) elif(deriv.DerivDir=="z"): computeTileStore("z",ofile,deriv_req_pad) ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_0+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") for deriv in derivs: if( (e in AD) and (deriv.DerivType=="ad")): ofile.write("\t\t"+deriv.DerivFuncCall+"\n") ofile.write("\t\t__syncthreads();\n") # not essential if each thread writes only the points it has computed in the block. if(deriv.DerivDir=="x"): computeTileStore("x",ofile,deriv_req_pad) elif(deriv.DerivDir=="y"): computeTileStore("y",ofile,deriv_req_pad) elif(deriv.DerivDir=="z"): computeTileStore("z",ofile,deriv_req_pad) ofile.write("\t\t"+FUNC_STORE_VAR+"((double *) "+VAR_OUT_SHARED_0+",&("+VAR_DERIV_WORKSPACE+"->__"+deriv.DerivOutput+"_"+ e +"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n") ofile.write("\t\t } // end of block tile loop x\n") ofile.write("\t\t } // end of block tile loop y\n") ofile.write("\t\t} // end of block tile loop z\n\n") ofile.write("} // end of function "+FUNC_DERIV_COMP+"\n\n") ############################################################################## ## RHS code generation ############################################################################## for var_id in range(0,len(varnames)): memManager.deallocAll() memManager.clearScopeVariables() ofile.write("/**@brief compute "+varnames[var_id]+" \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") ofile.write("__device__ void "+FUNC_COMP_RHS_PRE+"_"+varnames[var_id]+"(double **"+VAR_UNZIP_OUT+", const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* dblock, const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+",const cudaDeviceProp* "+VAR_CUDA_DEVICE+", "+TYPE_SHARED_MEM+"* "+VAR_SHARED_MEM+", unsigned int "+VAR_STREAM_ID+"){\n") ofile.write("\n\n") mi = [0, 1, 2, 4, 5, 8] midx = ['00', '01', '02', '11', '12', '22'] ofile.write("\n") idx="[pp]" ofile.write("\t///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n") ofile.write("\t// generated code for "+varnames[var_id]+" begin \n") ofile.write("\t///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n") varOut=varnames[var_id] exp=outs[var_id] ofile.write("\t// bssn compute parameters \n") ofile.write("\tconst double lambda[4]={"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[0],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[1],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[2],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[3]};\n") ofile.write("\tconst double lambda_f[2]={"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA_F[0],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA_F[1]};\n") ofile.write("\tconst double kosigma="+VAR_BSSN_PARAMS+"->KO_DISS_SIGMA;\n") ofile.write("\tconst double ETA_R0="+VAR_BSSN_PARAMS+"->ETA_R0;\n") ofile.write("\tconst double R0="+VAR_BSSN_PARAMS+"->ETA_R0;\n") ofile.write("\tconst double ETA_DAMPING="+VAR_BSSN_PARAMS+"->ETA_DAMPING;\n") ofile.write("\tconst double ETA_DAMPING_EXP="+VAR_BSSN_PARAMS+"->ETA_DAMPING_EXP;\n") ofile.write("\tconst double ETA_CONST="+VAR_BSSN_PARAMS+"->ETA_CONST;\n") ofile.write("\tconst double eta_power[2]={"+VAR_BSSN_PARAMS+"->BSSN_ETA_POWER[0],"+VAR_BSSN_PARAMS+"->BSSN_ETA_POWER[1]};\n") print("code generation for : "+varOut) ofile.write("\tconst unsigned int NUM_SM_UNITS="+VAR_CUDA_DEVICE+"->multiProcessorCount;\n") ofile.write("\tconst unsigned int SM_ID=get_smid();//"+VAR_BLK_ID_X+"%NUM_SM_UNITS;\n") ofile.write("\tconst unsigned int offset=dblock->getOffset();\n") ofile.write("\tconst unsigned int *sz=dblock->getSz();\n") ofile.write("\tconst unsigned int *"+VAR_DENDRO_BLK_ALIGNED_SZ+"=dblock->getAlignedSz();\n") ofile.write("\tconst double* hx=dblock->getDx();\n") ofile.write("\tconst double dx=hx[0];\n") ofile.write("\tconst double dy=hx[1];\n") ofile.write("\tconst double dz=hx[2];\n") ofile.write("\tconst double* ptmin=dblock->getPtMin();\n") ofile.write("\tconst double* ptmax=dblock->getPtMax();\n") ofile.write("\tconst unsigned int bflag=dblock->getBFlag();\n") ofile.write("\n") num_e = 0 lexp = [] lname = [] if type(exp) == list: num_e = num_e + len(exp) for j, ev in enumerate(exp): lexp.append(ev) lname.append(varOut+repr(j)+idx) elif type(exp) == sympy.Matrix: num_e = num_e + len(exp) for j, k in enumerate(mi): lexp.append(exp[k]) lname.append(varOut+midx[j]+idx) else: num_e = num_e + 1 lexp.append(exp) lname.append(varOut+idx) print("cse tree build begin") ee_name = 'DENDRO_' #''.join(random.choice(string.ascii_uppercase) for _ in range(5)) ee_syms = sympy.utilities.numbered_symbols(prefix=ee_name) _v = sympy.cse(lexp, symbols=ee_syms, optimizations='basic') print("cse tree build completed") # bssn variables needed for rhs computation. bssnInputVars=[] # bssn variables output bssnOutputVars=[] # derivative variables needed for rhs computation derivVars=[] # staged bssn variables. bssnStagedVars=[] if type(exp) == list: #print("list \n") for j, ev in enumerate(exp): regm=re.findall(re.compile(r"([A-Z,a-z,0-9,_]*\[pp\])"),dendro.change_deriv_names(str(ev))) for varDep in regm: if varDep[0:-4] in VAR_ENUM_TO_INPUT_SYM.keys(): bssnInputVars.append(varDep[0:-4]) elif varDep[0:-4] in VAR_ENUM_TO_OUTPUT_SYM.keys(): bssnOutputVars.append(varDep[0:-4]) else: for key,value in custom_functions.items(): if value in varDep[0:-4]: derivVars.append(varDep[0:-4]) break elif type(exp)==sympy.Matrix: #print(dendro.change_deriv_names(str(exp))) #print(exp.free_symbols) regm=re.findall(re.compile(r"([A-Z,a-z,0-9,_]*\[pp\])"),dendro.change_deriv_names(str(exp))) for varDep in regm: if varDep[0:-4] in VAR_ENUM_TO_INPUT_SYM.keys(): bssnInputVars.append(varDep[0:-4]) elif varDep[0:-4] in VAR_ENUM_TO_OUTPUT_SYM.keys(): bssnOutputVars.append(varDep[0:-4]) else: for key,value in custom_functions.items(): if value in varDep[0:-4]: derivVars.append(varDep[0:-4]) break else: #print(dendro.change_deriv_names(str(exp))) regm=re.findall(re.compile(r"([A-Z,a-z,0-9,_]*\[pp\])"),dendro.change_deriv_names(str(exp))) for varDep in regm: #print (varDep[0:-4]) if varDep[0:-4] in VAR_ENUM_TO_INPUT_SYM.keys(): bssnInputVars.append(varDep[0:-4]) elif varDep[0:-4] in VAR_ENUM_TO_OUTPUT_SYM.keys(): bssnOutputVars.append(varDep[0:-4]) else: for key,value in custom_functions.items(): if value in varDep[0:-4]: derivVars.append(varDep[0:-4]) break for lvar in lname: if lvar[0:-4] in VAR_ENUM_TO_OUTPUT_SYM.keys(): bssnOutputVars.append(lvar[0:-4]) else: bssnStagedVars.append(lvar[0:-4]) bssnInputVars=list(set(bssnInputVars)) bssnOutputVars=list(set(bssnOutputVars)) bssnStagedVars=list(set(bssnStagedVars)) derivVars=list(set(derivVars)) total_dep=len(bssnInputVars)+len(bssnStagedVars)+len(derivVars)+len(bssnOutputVars) rhs_tile_size_1d=math.floor(((memManager.getMemUsable())/(total_dep*8))**(1.0/3.0)) ofile.write("\tconst unsigned int "+VAR_TILE_SZ+"[3]={"+str(rhs_tile_size_1d)+","+str(rhs_tile_size_1d)+","+str(rhs_tile_size_1d)+"};\n") rhs_tile_size=rhs_tile_size_1d**3 ofile.write("\t\n") # no padding region required for rhs computation rhs_req_pad=0 ofile.write("\t //input vars begin\n") for var in bssnInputVars: memManager.malloc(var,rhs_tile_size,ofile,prefix="\t") ofile.write("\t //input vars end\n") ofile.write("\t // staged vars begin\n") for var in bssnStagedVars: memManager.malloc(var,rhs_tile_size,ofile,prefix="\t") ofile.write("\t // staged vars end\n") ofile.write("\t // deriv vars begin\n") for var in derivVars: memManager.malloc(var,rhs_tile_size,ofile,prefix="\t") ofile.write("\t // deriv vars end\n") ofile.write("\t // output vars begin\n") for var in bssnOutputVars: memManager.malloc(var,rhs_tile_size,ofile,prefix="\t") ofile.write("\t // output vars end\n") ofile.write("\tconst unsigned int Lb = "+str(deriv_max_pad-rhs_req_pad)+";// load begin bound\n") ofile.write("\tconst unsigned int Le = sz[0]-"+str(deriv_max_pad-rhs_req_pad)+";// load end bound\n") # !! Note that we assume tile size are cubic. ofile.write("//!! Note that we assume tile size are cubic.\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_X = ((Le-Lb)<"+VAR_TILE_SZ+"[0])? 1: ((int)ceil((double)(Le-Lb-"+VAR_TILE_SZ+"[0])/("+VAR_TILE_SZ+"[0]-2*" +str(rhs_req_pad)+")))+1;\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_Y = BLK_ITERATIONS_X;\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_Z = BLK_ITERATIONS_X;\n") ofile.write("\n") ofile.write("\tunsigned int "+VAR_TILE_LIMITS+"[3*2];\n") ofile.write("\tunsigned int "+VAR_TILE_LIMITS_STORE+"[3*2];\n") ofile.write("\tfor(unsigned int iter_z=0;iter_z<BLK_ITERATIONS_Z;iter_z++){\n\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*2+0]=max("+str(deriv_max_pad-rhs_req_pad)+",(int)("+str(deriv_max_pad-rhs_req_pad)+" + "+VAR_TILE_SZ+"[2]*iter_z -2*iter_z*"+str(rhs_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*2+1]=min("+VAR_TILE_LIMITS+"[2*2+0]+"+VAR_TILE_SZ+"[2]-1,sz[2]-"+str(deriv_max_pad-rhs_req_pad)+"-1);\n") ofile.write("\n") ofile.write("\t for(unsigned int iter_y=0;iter_y<BLK_ITERATIONS_Y;iter_y++){\n\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*1+0]=max("+str(deriv_max_pad-rhs_req_pad)+",(int)("+str(deriv_max_pad-rhs_req_pad)+" + "+VAR_TILE_SZ+"[1]*iter_y -2*iter_y*"+str(rhs_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*1+1]=min("+VAR_TILE_LIMITS+"[2*1+0]+"+VAR_TILE_SZ+"[1]-1,sz[1]-"+str(deriv_max_pad-rhs_req_pad)+"-1);\n") ofile.write("\n") ofile.write("\t for(unsigned int iter_x=0;iter_x<BLK_ITERATIONS_X;iter_x++){\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*0+0]=max("+str(deriv_max_pad-rhs_req_pad)+",(int)("+str(deriv_max_pad-rhs_req_pad)+" + "+VAR_TILE_SZ+"[0]*iter_x -2*iter_x*"+str(rhs_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*0+1]=min("+VAR_TILE_LIMITS+"[2*0+0]+"+VAR_TILE_SZ+"[0]-1,sz[0]-"+str(deriv_max_pad-rhs_req_pad)+"-1);\n") ofile.write("\n") ofile.write("\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[0]=0;\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[1]="+VAR_TILE_LIMITS+"[1] - "+VAR_TILE_LIMITS+"[0];\n") ofile.write("\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[2]=0;\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[3]="+VAR_TILE_LIMITS+"[3] - "+VAR_TILE_LIMITS+"[2];\n") ofile.write("\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[4]=0;\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[5]="+VAR_TILE_LIMITS+"[5] - "+VAR_TILE_LIMITS+"[4];\n") ofile.write("\t\t //if(threadIdx.x ==0 && threadIdx.y==0 && threadIdx.z==0)\n") ofile.write("\t\t //printf(\" iter %d %d %d : threadid (%d,%d,%d) tile begin: (%d,%d,%d) tile end: (%d,%d,%d) \\n\",iter_x,iter_y,iter_z, threadIdx.x,threadIdx.y,threadIdx.z,ijk_lm[0],ijk_lm[2],ijk_lm[4],ijk_lm[1],ijk_lm[3],ijk_lm[5]);\n\n") ofile.write("\n\n") ofile.write("\t\t //load data from global to shared memory\n") for var in bssnInputVars: ofile.write("\t\t "+FUNC_LOAD_VAR+"(&"+VAR_UNZIP_IN+"["+VAR_ENUM_TO_INPUT_SYM[var]+"][offset],(double *) "+var+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") for var in derivVars: ofile.write("\t\t "+FUNC_LOAD_VAR+"(&("+VAR_DERIV_WORKSPACE+"->__"+var+"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(double *) "+var+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t __syncthreads();\n\n") ofile.write("\n\n") #/*|| ("+VAR_TRD_ID_Z+">=("+VAR_TILE_LIMITS+"[5]-"+VAR_TILE_LIMITS+"[4]))*/ ofile.write("\tif(!(("+VAR_TRD_ID_X+">("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0])) || ("+VAR_TRD_ID_Y+">("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]))) ){ \n\n") ofile.write("\t\t double x,y,z,r_coord,eta;\n") ofile.write("\t\t unsigned int pp=0*"+VAR_TILE_SZ+"[0]*"+VAR_TILE_SZ+"[1]+"+VAR_TRD_ID_Y+"*"+VAR_TILE_SZ+"[1]+"+VAR_TRD_ID_X+";\n") ofile.write("\t\t for(unsigned int k=0;k<=(ijk_lm[5]-ijk_lm[4]);++k,pp+="+VAR_TILE_SZ+"[0]*"+VAR_TILE_SZ+"[1]){\n") ofile.write("\t\t\t z = ptmin[2] + (k+"+VAR_TILE_LIMITS+"[4])*dz;\n") ofile.write("\t\t\t y = ptmin[1] + ("+VAR_TRD_ID_Y+"+"+VAR_TILE_LIMITS+"[2])*dy;\n") ofile.write("\t\t\t x = ptmin[0] + ("+VAR_TRD_ID_X+"+"+VAR_TILE_LIMITS+"[0])*dx;\n") ofile.write("\t\t\t r_coord = sqrt(x*x + y*y + z*z);\n") ofile.write("\t\t\t eta=ETA_CONST;\n") ofile.write("\t\t\t if (r_coord >= ETA_R0) {\n") ofile.write("\t\t\t eta *= pow( (ETA_R0/r_coord), ETA_DAMPING_EXP);\n") ofile.write("\t\t\t }\n\n") ofile.write("\t\t\t // Dendro: {{{ \n") ofile.write("\t\t\t // Dendro: original ops: "+str(sympy.count_ops(lexp))+"\n") rops=0 ofile.write("\t\t\t // Dendro: printing temp variables\n") for (v1, v2) in _v[0]: ofile.write("\t\t const double ") ofile.write(dendro.change_deriv_names(sympy.ccode(v2, assign_to=v1, user_functions=custom_functions))+"\n") rops = rops + sympy.count_ops(v2) ofile.write("\t\t\t // Dendro: printing variables\n\n") for i, e in enumerate(_v[1]): ofile.write("\t\t "+dendro.change_deriv_names(sympy.ccode(e, assign_to=lname[i], user_functions=custom_functions))+"\n") rops = rops + sympy.count_ops(e) ofile.write("\t\t\t // Dendro: reduced ops: "+str(rops)+"\n") ofile.write("\t\t\t // Dendro: }}} \n") ofile.write("\t\t\t } //loop z end \n") ofile.write("\t}// end of the if for the thread idx \n") ofile.write("\t\t\t__syncthreads();\n\n") ofile.write("\t\t\t// sotre computed variables\n\n") for var in bssnOutputVars: ofile.write("\t\t"+FUNC_STORE_VAR+"("+var+", &"+VAR_UNZIP_OUT+"["+VAR_ENUM_TO_OUTPUT_SYM[var]+"][offset],(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t __syncthreads();\n") ofile.write("\t } // end of block assigned to gpu block loop x \n\n") ofile.write("\t } // end of block assigned to gpu block loop y \n\n") ofile.write("\t} // end of block assigned to gpu block loop z \n\n") ofile.write("} // end of function" +FUNC_COMP_RHS_PRE+"_"+varnames[var_id]+" \n\n") memManager.deallocAll() memManager.clearScopeVariables() ofile.write("/**@brief apply KO dissipation \n") ofile.write(" @param[in] "+VAR_UNZIP_IN+": unzipped input array (global memory) \n") ofile.write(" @param[in] "+TYPE_DERIV_STRUCT+": allocated workspace for derivative computations \n") ofile.write(" @param[in] "+VAR_DENDRO_BLK_LIST+": dendro block list \n") ofile.write(" @param[in] "+VAR_GPU_BLOCK_MAP+": gpu block map \n") ofile.write(" @param[in] "+VAR_CUDA_DEVICE+": cuda device properties \n") ofile.write(" @param[out] "+VAR_UNZIP_OUT+": unzipped output computed rhs \n") ofile.write("*/ \n") ofile.write("__device__ void "+FUNC_KO_DISS+"(double **"+VAR_UNZIP_OUT+", const double**"+VAR_UNZIP_IN+","+TYPE_DERIV_STRUCT+"* __derivWorkspace, const "+ TYPE_BLK_CU+ "* dblock, const unsigned int * "+VAR_GPU_BLOCK_MAP+",const "+TYPE_BSSN_PARAMS+" * "+VAR_BSSN_PARAMS+",const cudaDeviceProp* "+VAR_CUDA_DEVICE+", "+TYPE_SHARED_MEM+"* "+VAR_SHARED_MEM+", unsigned int "+VAR_STREAM_ID+"){\n") ofile.write("\n") ofile.write("\t// bssn compute parameters \n") ofile.write("\tconst double lambda[4]={"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[0],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[1],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[2],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA[3]};\n") ofile.write("\tconst double lambda_f[2]={"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA_F[0],"+VAR_BSSN_PARAMS+"->BSSN_LAMBDA_F[1]};\n") ofile.write("\tconst double kosigma="+VAR_BSSN_PARAMS+"->KO_DISS_SIGMA;\n") ofile.write("\tconst double ETA_R0="+VAR_BSSN_PARAMS+"->ETA_R0;\n") ofile.write("\tconst double R0="+VAR_BSSN_PARAMS+"->ETA_R0;\n") ofile.write("\tconst double ETA_DAMPING="+VAR_BSSN_PARAMS+"->ETA_DAMPING;\n") ofile.write("\tconst double ETA_DAMPING_EXP="+VAR_BSSN_PARAMS+"->ETA_DAMPING_EXP;\n") ofile.write("\tconst double ETA_CONST="+VAR_BSSN_PARAMS+"->ETA_CONST;\n") ofile.write("\tconst double eta_power[2]={"+VAR_BSSN_PARAMS+"->BSSN_ETA_POWER[0],"+VAR_BSSN_PARAMS+"->BSSN_ETA_POWER[1]};\n") ofile.write("\tconst unsigned int NUM_SM_UNITS="+VAR_CUDA_DEVICE+"->multiProcessorCount;\n") ofile.write("\tconst unsigned int SM_ID=get_smid();//"+VAR_BLK_ID_X+"%NUM_SM_UNITS;\n") ofile.write("\tconst unsigned int offset=dblock->getOffset();\n") ofile.write("\tconst unsigned int *sz=dblock->getSz();\n") ofile.write("\tconst unsigned int *"+VAR_DENDRO_BLK_ALIGNED_SZ+"=dblock->getAlignedSz();\n") ofile.write("\tconst double* hx=dblock->getDx();\n") ofile.write("\tconst double dx=hx[0];\n") ofile.write("\tconst double dy=hx[1];\n") ofile.write("\tconst double dz=hx[2];\n") ofile.write("\tconst double* ptmin=dblock->getPtMin();\n") ofile.write("\tconst double* ptmax=dblock->getPtMax();\n") ofile.write("\tconst unsigned int bflag=dblock->getBFlag();\n") total_dep=4 rhs_req_pad=0 rhs_tile_size_1d=math.floor(((memManager.getMemUsable())/(total_dep*8))**(1.0/3.0)) ofile.write("\tconst unsigned int "+VAR_TILE_SZ+"[3]={"+str(rhs_tile_size_1d)+","+str(rhs_tile_size_1d)+","+str(rhs_tile_size_1d)+"};\n") rhs_tile_size=rhs_tile_size_1d**3 VAR_KO_TEMP=["kograd_0","kograd_1","kograd_2"] VAR_KO_TEMP_RHS=["unZipSharedOut"] for var in VAR_KO_TEMP: memManager.malloc(var,rhs_tile_size,ofile,prefix="\t") for var in VAR_KO_TEMP_RHS: memManager.malloc(var,rhs_tile_size,ofile,prefix="\t") ofile.write("\tconst unsigned int Lb = "+str(deriv_max_pad-rhs_req_pad)+";// load begin bound\n") ofile.write("\tconst unsigned int Le = sz[0]-"+str(deriv_max_pad-rhs_req_pad)+";// load end bound\n") # !! Note that we assume tile size are cubic. ofile.write("//!! Note that we assume tile size are cubic.\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_X = ((Le-Lb)<"+VAR_TILE_SZ+"[0])? 1: ((int)ceil((double)(Le-Lb-"+VAR_TILE_SZ+"[0])/("+VAR_TILE_SZ+"[0]-2*" +str(rhs_req_pad)+")))+1;\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_Y = BLK_ITERATIONS_X;\n") ofile.write("\tconst unsigned int BLK_ITERATIONS_Z = BLK_ITERATIONS_X;\n") ofile.write("\n") ofile.write("\tunsigned int "+VAR_TILE_LIMITS+"[3*2];\n") ofile.write("\tunsigned int "+VAR_TILE_LIMITS_STORE+"[3*2];\n") ofile.write("\tfor(unsigned int iter_z=0;iter_z<BLK_ITERATIONS_Z;iter_z++){\n\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*2+0]=max("+str(deriv_max_pad-rhs_req_pad)+",(int)("+str(deriv_max_pad-rhs_req_pad)+" + "+VAR_TILE_SZ+"[2]*iter_z -2*iter_z*"+str(rhs_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*2+1]=min("+VAR_TILE_LIMITS+"[2*2+0]+"+VAR_TILE_SZ+"[2]-1,sz[2]-"+str(deriv_max_pad-rhs_req_pad)+"-1);\n") ofile.write("\n") ofile.write("\t for(unsigned int iter_y=0;iter_y<BLK_ITERATIONS_Y;iter_y++){\n\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*1+0]=max("+str(deriv_max_pad-rhs_req_pad)+",(int)("+str(deriv_max_pad-rhs_req_pad)+" + "+VAR_TILE_SZ+"[1]*iter_y -2*iter_y*"+str(rhs_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*1+1]=min("+VAR_TILE_LIMITS+"[2*1+0]+"+VAR_TILE_SZ+"[1]-1,sz[1]-"+str(deriv_max_pad-rhs_req_pad)+"-1);\n") ofile.write("\n") ofile.write("\t for(unsigned int iter_x=0;iter_x<BLK_ITERATIONS_X;iter_x++){\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*0+0]=max("+str(deriv_max_pad-rhs_req_pad)+",(int)("+str(deriv_max_pad-rhs_req_pad)+" + "+VAR_TILE_SZ+"[0]*iter_x -2*iter_x*"+str(rhs_req_pad)+"));\n") ofile.write("\t\t "+VAR_TILE_LIMITS+"[2*0+1]=min("+VAR_TILE_LIMITS+"[2*0+0]+"+VAR_TILE_SZ+"[0]-1,sz[0]-"+str(deriv_max_pad-rhs_req_pad)+"-1);\n") ofile.write("\n") ofile.write("\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[0]=0;\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[1]="+VAR_TILE_LIMITS+"[1] - "+VAR_TILE_LIMITS+"[0];\n") ofile.write("\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[2]=0;\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[3]="+VAR_TILE_LIMITS+"[3] - "+VAR_TILE_LIMITS+"[2];\n") ofile.write("\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[4]=0;\n") ofile.write("\t\t"+VAR_TILE_LIMITS_STORE+"[5]="+VAR_TILE_LIMITS+"[5] - "+VAR_TILE_LIMITS+"[4];\n") ofile.write("\t\t //if(threadIdx.x ==0 && threadIdx.y==0 && threadIdx.z==0)\n") ofile.write("\t\t //printf(\" iter %d %d %d : threadid (%d,%d,%d) tile begin: (%d,%d,%d) tile end: (%d,%d,%d) \\n\",iter_x,iter_y,iter_z, threadIdx.x,threadIdx.y,threadIdx.z,ijk_lm[0],ijk_lm[2],ijk_lm[4],ijk_lm[1],ijk_lm[3],ijk_lm[5]);\n\n") ofile.write("\n\n") ofile.write("\t\t unsigned int pp;\n") for var_id in range(0,len(varnames)): mi = [0, 1, 2, 4, 5, 8] midx = ['00', '01', '02', '11', '12', '22'] varOut=varnames[var_id] exp=outs[var_id] num_e = 0 lexp = [] lname = [] idx="" if type(exp) == list: num_e = num_e + len(exp) for j, ev in enumerate(exp): lexp.append(ev) lname.append(varOut+repr(j)+idx) elif type(exp) == sympy.Matrix: num_e = num_e + len(exp) for j, k in enumerate(mi): lexp.append(exp[k]) lname.append(varOut+midx[j]+idx) else: num_e = num_e + 1 lexp.append(exp) lname.append(varOut+idx) for rhs in lname: var_d=list(VAR_ENUM_TO_INPUT_SYM.keys())[list(VAR_ENUM_TO_INPUT_SYM.values()).index(VAR_ENUM_TO_OUTPUT_SYM[rhs])] ofile.write("\t\t //ko dissipation for variable "+var_d+"\n\n") for var in VAR_KO_TEMP: ofile.write("\t\t "+FUNC_LOAD_VAR+"(&("+VAR_DERIV_WORKSPACE+"->__"+var+"_"+var_d+"[("+VAR_DERIV_WORKSPACE_OFFSET+")]),(double *) "+var+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") #ofile.write("\t\t "+FUNC_LOAD_VAR+"(&"+VAR_UNZIP_IN+"["+VAR_ENUM_TO_INPUT_SYM[var_d]+"][offset],(double *) "+VAR_KO_TEMP_RHS[0]+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t "+FUNC_LOAD_VAR+"(&"+VAR_UNZIP_OUT+"["+VAR_ENUM_TO_OUTPUT_SYM[rhs]+"][offset],(double *) "+VAR_KO_TEMP_RHS[0]+",(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t __syncthreads();\n\n") ofile.write("\t\tif(!(("+VAR_TRD_ID_X+">("+VAR_TILE_LIMITS+"[1]-"+VAR_TILE_LIMITS+"[0])) || ("+VAR_TRD_ID_Y+">("+VAR_TILE_LIMITS+"[3]-"+VAR_TILE_LIMITS+"[2]))) ){ \n\n") ofile.write("\t\t pp=0*"+VAR_TILE_SZ+"[0]*"+VAR_TILE_SZ+"[1]+"+VAR_TRD_ID_Y+"*"+VAR_TILE_SZ+"[1]+"+VAR_TRD_ID_X+";\n") ofile.write("\t\t for(unsigned int k=0;k<=(ijk_lm[5]-ijk_lm[4]);++k,pp+="+VAR_TILE_SZ+"[0]*"+VAR_TILE_SZ+"[1]){\n") ofile.write("\t\t "+VAR_KO_TEMP_RHS[0]+"[pp] += kosigma * ("+VAR_KO_TEMP[0]+"[pp] +"+VAR_KO_TEMP[1]+"[pp] + "+VAR_KO_TEMP[2]+"[pp]);\n") ofile.write("\t\t } //loop z end \n") ofile.write("\t\t}// end of the if for the thread idx \n") ofile.write("\t\t__syncthreads();\n\n") ofile.write("\t\t// sotre computed variables\n\n") ofile.write("\t\t"+FUNC_STORE_VAR+"("+VAR_KO_TEMP_RHS[0]+", &"+VAR_UNZIP_OUT+"["+VAR_ENUM_TO_OUTPUT_SYM[rhs]+"][offset],(const unsigned int *) "+VAR_TILE_LIMITS+",(const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+", (const unsigned int *) "+VAR_TILE_LIMITS_STORE+",(const unsigned int *) "+VAR_TILE_SZ+");\n") ofile.write("\t\t__syncthreads();\n\n") ofile.write("\t } // end of block assigned to gpu block loop x \n\n") ofile.write("\t } // end of block assigned to gpu block loop y \n\n") ofile.write("\t} // end of block assigned to gpu block loop z \n\n") ofile.write("}// end of function "+FUNC_KO_DISS+"\n") ofile.write("}// end of namespace cuda\n") ofile.close() def main(): # shared derivs ''' dxn = "grad_0" dxxn = "grad2_0_0" dyn = "grad_1" dyyn = "grad2_1_1" dzn = "grad_2" dzzn = "grad2_2_2" dxyn = "grad2_0_1" dxzn = "grad2_0_2" dyzn = "grad2_1_2" adxn = "agrad_0" adyn = "agrad_1" adzn = "agrad_2" kodxn = "kograd_0" kodyn = "kograd_1" kodzn = "kograd_2" ''' dxn = VAR_OUT_SHARED_0 dxxn = VAR_OUT_SHARED_0 dyn = VAR_OUT_SHARED_0 dyyn = VAR_OUT_SHARED_0 dzn = VAR_OUT_SHARED_0 dzzn = VAR_OUT_SHARED_0 dxyn = VAR_OUT_SHARED_1 dxzn = VAR_OUT_SHARED_1 dyzn = VAR_OUT_SHARED_1 adxn = VAR_OUT_SHARED_0 adyn = VAR_OUT_SHARED_0 adzn = VAR_OUT_SHARED_0 kodxn = VAR_OUT_SHARED_0 kodyn = VAR_OUT_SHARED_0 kodzn = VAR_OUT_SHARED_0 func_dx="deriv42_x((double *) "+dxn+",(const double *) "+VAR_IN_SHARED+",dx, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dy="deriv42_y((double *) "+dyn+",(const double *) "+VAR_IN_SHARED+",dy, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dz="deriv42_z((double *) "+dzn+",(const double *) "+VAR_IN_SHARED+",dz, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dxx="deriv42_xx((double *) "+dxxn+",(const double *) "+VAR_IN_SHARED+",dx, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dxy="deriv42_y((double *) "+dxyn+",(const double *) "+dxn+",dy, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dxz="deriv42_z((double *) "+dxzn+",(const double *) "+dxn+",dz, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dyy="deriv42_yy((double *) "+dyyn+",(const double *) "+VAR_IN_SHARED+",dy, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dyz="deriv42_z((double *) " +dyzn+",(const double *) "+dyn+",dz, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_dzz="deriv42_zz((double *) "+dzzn+",(const double *) "+VAR_IN_SHARED+",dz, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_adx="deriv42adv_x((double *) "+adxn+",(const double *) "+VAR_IN_SHARED+",dx, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", (const bool*) "+ VAR_BETA0_BOOL+" , 3, bflag);" func_ady="deriv42adv_y((double *) "+adyn+",(const double *) "+VAR_IN_SHARED+",dy, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", (const bool*) "+ VAR_BETA1_BOOL+" , 3, bflag);" func_adz="deriv42adv_z((double *) "+adzn+",(const double *) "+VAR_IN_SHARED+",dz, (const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", (const bool*) "+ VAR_BETA2_BOOL+" , 3, bflag);" func_kodx="ko_deriv42_x((double *) "+kodxn+",(const double *) "+VAR_IN_SHARED+",dx,(const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_kody="ko_deriv42_y((double *) "+kodyn+",(const double *) "+VAR_IN_SHARED+",dy,(const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) "+VAR_TILE_SZ+", 3, bflag);" func_kodz="ko_deriv42_z((double *) "+kodzn+",(const double *) "+VAR_IN_SHARED+",dz,(const unsigned int *) "+VAR_TILE_LIMITS+" , (const unsigned int *) "+VAR_DENDRO_BLK_ALIGNED_SZ+" , (const unsigned int *) " +VAR_TILE_SZ+", 3, bflag);" ## number of passes for cuda derivatives. Derivative = namedtuple("Derivative", "DerivType DerivDir DerivName DerivTile1D DerivInput DerivOutput IB IE JB JE KB KE padWidth DerivFuncCall") #### ## Since the block shared memory is not enough to compute the all the derivs (15) for a given variable, ## we use multiple passes of deriv computations. ## ## We assume that the deriv TILE is cubic, for simplicity !!! ## ### !!!!!!! NOTE: WHEN SPECIFYING THE TILE SZ MAKE SURE YOU HAVE 5 POINTS FOR ONE SIDED DERIVS, WHEN THE TILE LOAD THE BLOCK IN THE ITERATIONS TileSz1D=12 bssn_derivs=[ Derivative(DerivType="d",DerivDir="x",DerivName="deriv_x",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="grad_0",IB=3,IE=-3,JB=1,JE=-1,KB=1,KE=-1,padWidth=3,DerivFuncCall="_RSWS_"+func_dx), Derivative(DerivType="d",DerivDir="y",DerivName="deriv_y",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="grad_1",IB=3,IE=-3,JB=3,JE=-3,KB=1,KE=-1,padWidth=3,DerivFuncCall="_RSWS_"+func_dy), Derivative(DerivType="d",DerivDir="z",DerivName="deriv_z",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="grad_2",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_dz), Derivative(DerivType="dd",DerivDir="xx",DerivName="deriv_xx",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="grad2_0_0",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_dxx), Derivative(DerivType="dd",DerivDir="yy",DerivName="deriv_yy",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="grad2_1_1",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_dyy), Derivative(DerivType="dd",DerivDir="zz",DerivName="deriv_zz",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="grad2_2_2",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_dzz), Derivative(DerivType="ko",DerivDir="x",DerivName="ko_deriv_x",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="kograd_0",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_kodx), Derivative(DerivType="ko",DerivDir="y",DerivName="ko_deriv_y",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="kograd_1",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_kody), Derivative(DerivType="ko",DerivDir="z",DerivName="ko_deriv_z",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="kograd_2",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_kodz), Derivative(DerivType="ad",DerivDir="x",DerivName="adv_deriv_x",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="agrad_0",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_adx), Derivative(DerivType="ad",DerivDir="y",DerivName="adv_deriv_y",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="agrad_1",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_ady), Derivative(DerivType="ad",DerivDir="z",DerivName="adv_deriv_z",DerivTile1D=TileSz1D,DerivInput=VAR_IN_SHARED,DerivOutput="agrad_2",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_adz), Derivative(DerivType="dd",DerivDir="xy",DerivName="deriv_xy",DerivTile1D=TileSz1D,DerivInput=dxn,DerivOutput="grad2_0_1",IB=3,IE=-3,JB=3,JE=-3,KB=1,KE=-1,padWidth=3,DerivFuncCall="_RSWS_"+func_dxy), Derivative(DerivType="dd",DerivDir="xz",DerivName="deriv_xz",DerivTile1D=TileSz1D,DerivInput=dxn,DerivOutput="grad2_0_2",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_dxz), Derivative(DerivType="dd",DerivDir="yz",DerivName="deriv_yz",DerivTile1D=TileSz1D,DerivInput=dyn,DerivOutput="grad2_1_2",IB=3,IE=-3,JB=3,JE=-3,KB=3,KE=-3,padWidth=3,DerivFuncCall="_RSWS_"+func_dyz) ] #cudaDerivAllocDeallocHeader("../bssn/cuda_gr/include/bssn_rhs_deriv_mem_cuda.h") #cudaDerivAllocDeallocSource("../bssn/cuda_gr/src/bssn_rhs_deriv_mem_cuda.cpp",["bssn_rhs_deriv_mem_cuda.h"]) subset_exp=bssn.outs#[0:4] subset_var=bssn.vnames#[0:4] cudaCompute("../bssn/cuda_gr/include/rhs_bssn.cuh","../bssn/cuda_gr/src/rhs_bssn.cu",bssn_derivs,subset_exp,subset_var,"__computeBSSNRHS",["block_cu.h","params_cu.h","bssn_rhs_deriv_mem_cuda.h","cudaUtils.cuh","derivs.cuh","cudaUtils.h"]) if __name__ == "__main__": main()

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null

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null

0

7

eff4b6fabb2796ca12858b6ff5d04ad67c5cc7b5

200

py

Python

src/UQpy/dimension_reduction/__init__.py

SURGroup/UncertaintyQuantification

a94c8db47d07134ea2b3b0a3ca53ca818532c3e6

[ "MIT" ]

null

src/UQpy/dimension_reduction/__init__.py

SURGroup/UncertaintyQuantification

a94c8db47d07134ea2b3b0a3ca53ca818532c3e6

[ "MIT" ]

null

src/UQpy/dimension_reduction/__init__.py

SURGroup/UncertaintyQuantification

a94c8db47d07134ea2b3b0a3ca53ca818532c3e6

[ "MIT" ]

null

from UQpy.dimension_reduction.grassmann_manifold import * from UQpy.dimension_reduction.pod import * from UQpy.dimension_reduction.hosvd import * from UQpy.dimension_reduction.diffusion_maps import *

40

57

0.86

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0.409639

0.626506

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7

4bfcd4d8d46c263e7d31a85670b6c126d12503ac

7,290

py

Python

babilim/model/layers/convolution.py

penguinmenac3/babilim

d3b1dd7c38a9de8f1e553cc5c0b2dfa62fe25c27

[ "MIT" ]

1

2020-05-04T15:20:55.000Z

babilim/model/layers/convolution.py

penguinmenac3/babilim

d3b1dd7c38a9de8f1e553cc5c0b2dfa62fe25c27

[ "MIT" ]

1

2019-11-28T09:03:20.000Z

babilim/model/layers/convolution.py

penguinmenac3/babilim

d3b1dd7c38a9de8f1e553cc5c0b2dfa62fe25c27

[ "MIT" ]

1

2019-11-28T08:30:13.000Z

# AUTOGENERATED FROM: babilim/model/layers/convolution.ipynb # Cell: 0 """doc # babilim.model.layers.convolution > Convolution for 1d and 2d. """ # Cell: 1 from typing import Optional, Any, Tuple from babilim.core.annotations import RunOnlyOnce from babilim.core.module_native import ModuleNative from babilim.model.layers.activation import Activation # Cell: 2 class Conv1D(ModuleNative): def __init__(self, filters: int, kernel_size: int, padding: Optional[str] = None, stride: int = 1, dilation_rate: int = 1, kernel_initializer: Optional[Any] = None, activation=None): """ A 1d convolution layer. :param filters: The number of filters in the convolution. Defines the number of output channels. :param kernel_size: The kernel size of the convolution. Defines the area over which is convolved. Typically 1, 3 or 5 are recommended. :param padding: What type of padding should be applied. The string "none" means no padding is applied, None or "same" means the input is padded in a way that the output stays the same size if no stride is applied. :param stride: The offset between two convolutions that are applied. Typically 1. Stride affects also the resolution of the output feature map. A stride 2 halves the resolution, since convolutions are only applied every odd pixel. :param dilation_rate: The dilation rate for a convolution. :param kernel_initializer: A kernel initializer function. By default orthonormal weight initialization is used. :param activation: The activation function that should be added after the dense layer. """ super().__init__() self.filters = filters self.kernel_size = kernel_size self.padding = padding self.dilation = dilation_rate self.stride = stride self.kernel_initializer = kernel_initializer self.activation = Activation(activation, axis=1) @RunOnlyOnce def _build_pytorch(self, features): import torch from torch.nn import Conv1d as _Conv1d if self.kernel_initializer is None: from torch.nn.init import orthogonal_ self.kernel_initializer = orthogonal_ if self.padding == "same" or self.padding is None: self.padding = int((self.kernel_size - 1) / 2) elif self.padding == "none": self.padding = 0 else: raise NotImplementedError("Padding {} is not implemented.".format(padding)) in_channels = features.shape[1] self.conv = _Conv1d(in_channels, self.filters, self.kernel_size, self.stride, self.padding, self.dilation) self.conv.weight.data = self.kernel_initializer(self.conv.weight.data) if torch.cuda.is_available(): self.conv = self.conv.to(torch.device("cuda")) # TODO move to correct device from babilim.core.tensor_pt import Tensor as _Tensor self.weight = _Tensor(data=None, trainable=True, native=self.conv.weight) self.bias = _Tensor(data=None, trainable=True, native=self.conv.bias) def _call_pytorch(self, features): return self.activation(self.conv(features)) @RunOnlyOnce def _build_tf(self, features): #TODO Implement raise NotImplementedError() def _call_tf(self, features): #TODO Implement raise NotImplementedError() # Cell: 3 class Conv2D(ModuleNative): def __init__(self, filters: int, kernel_size: Tuple[int, int], padding: Optional[str] = None, strides: Tuple[int, int] = (1, 1), dilation_rate: Tuple[int, int] = (1, 1), kernel_initializer: Optional[Any] = None, activation=None): """ A 2d convolution layer. :param filters: The number of filters in the convolution. Defines the number of output channels. :param kernel_size: The kernel size of the convolution. Defines the area over which is convolved. Typically (1,1) (3,3) or (5,5) are recommended. :param padding: What type of padding should be applied. The string "none" means no padding is applied, None or "same" means the input is padded in a way that the output stays the same size if no stride is applied. :param stride: The offset between two convolutions that are applied. Typically (1, 1). Stride affects also the resolution of the output feature map. A stride 2 halves the resolution, since convolutions are only applied every odd pixel. :param dilation_rate: The dilation rate for a convolution. :param kernel_initializer: A kernel initializer function. By default orthonormal weight initialization is used. :param activation: The activation function that should be added after the dense layer. """ super().__init__() self.filters = filters self.kernel_size = kernel_size self.padding = padding self.dilation = dilation_rate self.stride = strides self.kernel_initializer = kernel_initializer self.activation = Activation(activation) @RunOnlyOnce def _build_pytorch(self, features): import torch from torch.nn import Conv2d as _Conv2d if self.kernel_initializer is None: from torch.nn.init import orthogonal_ self.kernel_initializer = orthogonal_ if self.padding == "same" or self.padding is None: px = int((self.kernel_size[0] - 1) / 2) py = int((self.kernel_size[1] - 1) / 2) self.padding = (px, py) elif self.padding == "none": self.padding = (0, 0) else: raise NotImplementedError("Padding {} is not implemented.".format(padding)) in_channels = features.shape[1] self.conv = _Conv2d(in_channels, self.filters, self.kernel_size, self.stride, self.padding, self.dilation) self.conv.weight.data = self.kernel_initializer(self.conv.weight.data) if torch.cuda.is_available(): self.conv = self.conv.to(torch.device("cuda")) # TODO move to correct device from babilim.core.tensor_pt import Tensor as _Tensor self.weight = _Tensor(data=None, trainable=True, native=self.conv.weight) self.bias = _Tensor(data=None, trainable=True, native=self.conv.bias) def _call_pytorch(self, features): return self.activation(self.conv(features)) @RunOnlyOnce def _build_tf(self, features): from tensorflow.keras.layers import Conv2D as _Conv2D if self.kernel_initializer is None: from tensorflow.keras.initializers import Orthogonal self.kernel_initializer = Orthogonal() if self.padding is None: self.padding = "same" self.conv = _Conv2D(filters=self.filters, kernel_size=self.kernel_size, strides=self.stride, dilation_rate=self.dilation_rate, padding=self.padding, activation=None, kernel_initializer=self.kernel_initializer) self.conv.build(features.shape) from babilim.core.tensor_tf import Tensor as _Tensor self.weight = _Tensor(data=None, trainable=True, native=self.conv.kernel) self.bias = _Tensor(data=None, trainable=True, native=self.conv.bias) def _call_tf(self, features): raise self.activation(self.conv(features))

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8

ef2f05ba7a7c491439a6a7b86b89eb5f2087bf95

795

py

Python

tests/test_provider_bltavares_zerotier.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

tests/test_provider_bltavares_zerotier.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

tests/test_provider_bltavares_zerotier.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# tests/test_provider_bltavares_zerotier.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:31:25 UTC) def test_provider_import(): import terrascript.provider.bltavares.zerotier def test_resource_import(): from terrascript.resource.bltavares.zerotier import zerotier_member from terrascript.resource.bltavares.zerotier import zerotier_network # TODO: Shortcut imports without namespace for official and supported providers. # TODO: This has to be moved into a required_providers block. # def test_version_source(): # # import terrascript.provider.bltavares.zerotier # # t = terrascript.provider.bltavares.zerotier.zerotier() # s = str(t) # # assert 'https://github.com/bltavares/terraform-provider-zerotier' in s # assert '0.3.0' in s

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ef4e9fc07b282bbcf421d54c15af9310733a3b4b

4,578

py

Python

tests/terraform/checks/resource/azure/test_AzureFrontDoorEnablesWAF.py

kylelaker/checkov

6eada26030a87f397a6bf1831827b3dc6c5dad2d

[ "Apache-2.0" ]

4,013

2019-12-09T13:16:54.000Z

2022-03-31T14:31:01.000Z

tests/terraform/checks/resource/azure/test_AzureFrontDoorEnablesWAF.py

kylelaker/checkov

6eada26030a87f397a6bf1831827b3dc6c5dad2d

[ "Apache-2.0" ]

1,258

2019-12-17T09:55:51.000Z

2022-03-31T19:17:17.000Z

tests/terraform/checks/resource/azure/test_AzureFrontDoorEnablesWAF.py

kylelaker/checkov

6eada26030a87f397a6bf1831827b3dc6c5dad2d

[ "Apache-2.0" ]

638

2019-12-19T08:57:38.000Z

2022-03-30T21:38:37.000Z

import unittest import hcl2 from checkov.terraform.checks.resource.azure.AzureFrontDoorEnablesWAF import check from checkov.common.models.enums import CheckResult class TestAzureFrontDoorEnablesWAF(unittest.TestCase): def test_failure(self): hcl_res = hcl2.loads(""" resource "azurerm_frontdoor" "example" { name = "example-FrontDoor" location = "EastUS2" resource_group_name = azurerm_resource_group.example.name enforce_backend_pools_certificate_name_check = false routing_rule { name = "exampleRoutingRule1" accepted_protocols = ["Http", "Https"] patterns_to_match = ["/*"] frontend_endpoints = ["exampleFrontendEndpoint1"] forwarding_configuration { forwarding_protocol = "MatchRequest" backend_pool_name = "exampleBackendBing" } } backend_pool_load_balancing { name = "exampleLoadBalancingSettings1" } backend_pool_health_probe { name = "exampleHealthProbeSetting1" } backend_pool { name = "exampleBackendBing" backend { host_header = "www.bing.com" address = "www.bing.com" http_port = 80 https_port = 443 } load_balancing_name = "exampleLoadBalancingSettings1" health_probe_name = "exampleHealthProbeSetting1" } frontend_endpoint { name = "exampleFrontendEndpoint1" host_name = "example-FrontDoor.azurefd.net" custom_https_provisioning_enabled = false } } """) resource_conf = hcl_res['resource'][0]['azurerm_frontdoor']['example'] scan_result = check.scan_resource_conf(conf=resource_conf) self.assertEqual(CheckResult.FAILED, scan_result) def test_success(self): hcl_res = hcl2.loads(""" resource "azurerm_frontdoor" "example" { name = "example-FrontDoor" location = "EastUS2" resource_group_name = azurerm_resource_group.example.name enforce_backend_pools_certificate_name_check = false web_application_firewall_policy_link_id = "this_is_id" routing_rule { name = "exampleRoutingRule1" accepted_protocols = ["Http", "Https"] patterns_to_match = ["/*"] frontend_endpoints = ["exampleFrontendEndpoint1"] forwarding_configuration { forwarding_protocol = "MatchRequest" backend_pool_name = "exampleBackendBing" } } backend_pool_load_balancing { name = "exampleLoadBalancingSettings1" } backend_pool_health_probe { name = "exampleHealthProbeSetting1" } backend_pool { name = "exampleBackendBing" backend { host_header = "www.bing.com" address = "www.bing.com" http_port = 80 https_port = 443 } load_balancing_name = "exampleLoadBalancingSettings1" health_probe_name = "exampleHealthProbeSetting1" } frontend_endpoint { name = "exampleFrontendEndpoint1" host_name = "example-FrontDoor.azurefd.net" custom_https_provisioning_enabled = false } } """) resource_conf = hcl_res['resource'][0]['azurerm_frontdoor']['example'] scan_result = check.scan_resource_conf(conf=resource_conf) self.assertEqual(CheckResult.PASSED, scan_result) if __name__ == '__main__': unittest.main()

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4,578

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1

0

1

0

1

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null

0

8

322d79aad7f5d87cfa794f7179d95c58d29e0df4

3,296

py

Python

plot_creation_scripts/set_making_weight_non_negative_before_cen_cal/set_making_weights_non_negative_before_cen_cal_dis_histograms.py

andrewjh9/CenBench

afd960b77ade05be2d2368bed3b47d54f7e229b6

[ "MIT" ]

null

plot_creation_scripts/set_making_weight_non_negative_before_cen_cal/set_making_weights_non_negative_before_cen_cal_dis_histograms.py

andrewjh9/CenBench

afd960b77ade05be2d2368bed3b47d54f7e229b6

[ "MIT" ]

null

plot_creation_scripts/set_making_weight_non_negative_before_cen_cal/set_making_weights_non_negative_before_cen_cal_dis_histograms.py

andrewjh9/CenBench

afd960b77ade05be2d2368bed3b47d54f7e229b6

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np import tikzplotlib read_dataset_0_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_0__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') read_dataset_25_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_25__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') read_dataset_50_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_50__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') read_dataset_75_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_75__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') read_dataset_100_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_100__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') read_dataset_150_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_150__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') read_dataset_125_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_125__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') read_dataset_175_fminst = np.genfromtxt('results/set_making_weight_non_negative_before_cen_cal/SET__fashion_mnist_for_200_epochs_20210604-081818_num_sd_None_cen_dis_lap_epoch_175__testing_set_make_weights_non_negative_before_lap_cen_cal.csv',delimiter='') # plt.hist(read_dataset_175_fminst , bins= np.arange(int(min(read_dataset_175_fminst)), max(read_dataset_175_fminst) + 0.5, 0.5), label="175") # plt.hist(read_dataset_150_fminst , bins= np.arange(int(min(read_dataset_175_fminst)), max(read_dataset_175_fminst) + 0.5, 0.5), label="150") # plt.hist(read_dataset_125_fminst , bins= np.arange(int(min(read_dataset_175_fminst)), int(max(read_dataset_125_fminst)) + 0.5, 0.5), label="125") # plt.hist(read_dataset_100_fminst , bins= np.arange(int(min(read_dataset_175_fminst)), max(read_dataset_175_fminst) + 0.5, 0.5), label="100") # plt.hist(read_dataset_75_fminst , bins= np.arange(int(min(read_dataset_175_fminst)), max(read_dataset_175_fminst) + 0.5, 0.5), label="75") # plt.hist(read_dataset_50_fminst , bins= np.arange(int(min(read_dataset_175_fminst)), max(read_dataset_175_fminst) + 0.5, 0.5), label="50") plt.hist(read_dataset_0_fminst , bins=10, label="0") plt.legend( title="At Epoch[#]") plt.xlabel("Laplacian centrality") plt.ylabel("Frequency") plt.title("Frequency Distribution of Centrality of Nodes with Making weight non negative") plt.show() # tikzplotlib.save("plots/tex/histogram_lap/cifar_250_epochs.tex")

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1

null

0

8

329fedc21fa62b0a53eeb32beb376d82f2851b0d

122

py

Python

axial_positional_embedding/__init__.py

lucidrains/axial-positional-embedding

fa6bee65ae45ce373004e33eea40a3625a126787

[ "MIT" ]

43

2020-06-08T09:38:19.000Z

2022-03-17T02:58:26.000Z

axial_positional_embedding/__init__.py

lucidrains/axial-positional-embedding

fa6bee65ae45ce373004e33eea40a3625a126787

[ "MIT" ]

2

2020-08-12T00:18:29.000Z

2021-05-02T02:42:35.000Z

axial_positional_embedding/__init__.py

lucidrains/axial-positional-embedding

fa6bee65ae45ce373004e33eea40a3625a126787

[ "MIT" ]

5

2021-07-10T05:02:50.000Z

2021-12-14T15:00:03.000Z

from axial_positional_embedding.axial_positional_embedding import AxialPositionalEmbedding, AxialPositionalEmbeddingImage

61

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0.942623

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11.1

0.7

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1

0

9

088e0dea06148c771774b461fd3d1aa5ea5bc774

22,830

py

Python

yyds/jd_scripts_check_dependence.py

demo2099/js

f0b3850fcc386e55241e2b9dc79c91032cbebadd

[ "MIT" ]

15

2022-02-08T06:56:31.000Z

2022-03-23T05:21:27.000Z

yyds/jd_scripts_check_dependence.py

demo2099/js

f0b3850fcc386e55241e2b9dc79c91032cbebadd

[ "MIT" ]

null

yyds/jd_scripts_check_dependence.py

demo2099/js

f0b3850fcc386e55241e2b9dc79c91032cbebadd

[ "MIT" ]

33

2022-02-07T12:31:03.000Z

2022-03-21T06:42:33.000Z

# -*- coding:utf-8 -*- # 作者仓库:https://jihulab.com/spiritlhl/qinglong_auto_tools.git # 觉得不错麻烦点个star谢谢 # 频道：https://t.me/qinglong_auto_tools ''' cron: 1 new Env('单容器二叉树修复脚本依赖文件'); ''' import os, requests import os.path import time # from os import popen # 版本号 2.10.9 ，其他环境自测 # 只修复依赖文件（jdCookie.js那种）！！不修复环境依赖（pip install aiohttp）！！ # 默认不做任何操作只查询依赖脚本存在与否，有需求请在配置文件中配置对应变量进行操作，更新不会增加缺失文件 # 如果你有发现更多的脚本依赖文件没有新增，欢迎提交issues到https://jihulab.com/spiritlhl/dependence_scripts # 增加缺失依赖文件(推荐) # export ec_fix_dep="true" # 更新老旧依赖文件(慎填，默认的依赖我使用的魔改版本，非必要别选) # export ec_ref_dep="true" # 2021.11.27 支持新版本仓库拉取的脚本目录结构，针对各个仓库进行依赖检索 txtx = "青龙配置文件中的config中填写下列变量启用对应功能\n\n增加缺失依赖文件(推荐)\n填写export ec_fix_dep=\"true\"\n更新老旧依赖文件(日常使用别填，默认的依赖我使用的魔改版本，非必要别选)\n如果选择使用请使用对应code文件等相关文件：https://jihulab.com/spiritlhl/dependence_config \n填写export ec_ref_dep=\"true\"\n" print(txtx) try: if os.environ["ec_fix_dep"] == "true": print("已配置依赖文件缺失修复\n") fix = 1 else: fix = 0 except: fix = 0 print("#默认不修复缺失依赖文件，有需求") print("#请在配置文件中配置\nexport ec_fix_dep=\"true\" \n#开启脚本依赖文件缺失修复\n") try: if os.environ["ec_ref_dep"] == "true": print("已配置依赖文件老旧更新\n") ref = 1 else: ref = 0 except: ref = 0 print("#默认不更新老旧依赖文件，有需求") print("#请在配置文件中配置\nexport ec_re_dep=\"true\" #开启脚本依赖文件更新\n") def traversalDir_FirstDir(path): list = [] if (os.path.exists(path)): files = os.listdir(path) for file in files: m = os.path.join(path, file) if (os.path.isdir(m)): h = os.path.split(m) list.append(h[1]) print("文件夹名字有：") print(list) return list def check_dependence(file_path): try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/contents.json").json() except: print("网络波动，稍后尝试") time.sleep(5) try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/contents.json").json() except: print("网络问题无法获取仓库文件列表，终止检索") return dependence_scripts_name = [] for i in res: dependence_scripts_name.append(i["name"]) if "db" in os.listdir("../"): dir_list = os.listdir(file_path) else: dir_list = os.listdir("." + file_path) # 查询 for i in dependence_scripts_name: if i not in dir_list and i != "utils" and i != "function": print("缺失文件 {}{}".format(file_path, i)) # 修补 try: if fix == 1: print("增加文件 {}{}".format(file_path, i)) r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/" + i).text if "db" in os.listdir("../"): with open(file_path + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("." + file_path + i, "w", encoding="utf-8") as fe: fe.write(r) except: temp = 1 try: if temp == 1: print("未配置ec_fix_dep，默认不修复增加缺失的依赖文件") except: pass # 更新 try: if ref == 1: for i in dependence_scripts_name: if i != "utils" and i != "function": if "db" in os.listdir("../"): with open(i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/" + i).text d = f.read() if r == d: print("无需修改 {}".format(i)) else: print("更新文件 {}".format(i)) with open(file_path + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open(i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/" + i).text d = f.read() if r == d: print("无需修改 {}".format(i)) else: print("更新文件 {}".format(i)) with open("." + file_path + i, "w", encoding="utf-8") as fe: fe.write(r) except: print("未配置ec_ref_dep，默认不更新依赖文件") ######################################################################################################### # utils try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils.json").json() except: print("网络波动，稍后尝试") time.sleep(5) try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils.json").json() except: print("网络问题无法获取仓库文件列表，终止检索") return dependence_scripts_utils = [] for i in res: dependence_scripts_utils.append(i["name"]) try: if "db" in os.listdir("../"): utils_list = os.listdir(file_path + "utils") else: utils_list = os.listdir("." + file_path + "utils") except: if "db" in os.listdir("../"): os.makedirs(file_path + "utils") utils_list = os.listdir(file_path + "utils") else: os.makedirs("." + file_path + "utils") utils_list = os.listdir("." + file_path + "utils") # 查询 for i in dependence_scripts_utils: if i not in utils_list and i != "utils" and i != "function": print("缺失文件 {}utils/{}".format(file_path, i)) # 修补 try: if fix == 1: print("增加文件 {}utils/{}".format(file_path, i)) r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils/" + i).text if "db" in os.listdir("../"): with open(file_path + "utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("." + file_path + "utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: temp = 1 try: if temp == 1: print("未配置ec_fix_dep，默认不修复增加缺失的依赖文件") except: pass # 更新 try: if ref == 1: for i in dependence_scripts_utils: if i != "utils" and i != "function": if "db" in os.listdir("../"): with open(file_path + "utils/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils/" + i).text d = f.read() if r == d: print("已存在文件 {}utils/{}".format(file_path, i)) else: print("更新文件 {}utils/{}".format(file_path, i)) with open(file_path + "utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("." + file_path + "utils/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils/" + i).text d = f.read() if r == d: print("已存在文件 {}utils/{}".format(file_path, i)) else: print("更新文件 {}utils/{}".format(file_path, i)) with open("." + file_path + "utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: print("未配置ec_ref_dep，默认不更新依赖文件") #################################################################################################### # function try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function.json").json() except: print("网络波动，稍后尝试") time.sleep(5) try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function.json").json() except: print("网络问题无法获取仓库文件列表，终止检索") return dependence_scripts_function = [] for i in res: dependence_scripts_function.append(i["name"]) try: if "db" in os.listdir("../"): function_list = os.listdir(file_path + "function") else: function_list = os.listdir("." + file_path + "function") except: if "db" in os.listdir("../"): os.makedirs(file_path + "function") function_list = os.listdir(file_path + "function") else: os.makedirs("." + file_path + "function") function_list = os.listdir("." + file_path + "function") # 查询 for i in dependence_scripts_function: if i not in function_list and i != "utils" and i != "function": print("缺失文件 {}function/{}".format(file_path, i)) # 修补 try: if fix == 1: print("增加文件 {}function/{}".format(file_path, i)) r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function/" + i).text if "db" in os.listdir("../"): with open(file_path + "function/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("." + file_path + "function/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: temp = 1 try: if temp == 1: print("未配置ec_fix_dep，默认不修复增加缺失的依赖文件") except: pass # 更新 try: if ref == 1: for i in dependence_scripts_function: if i != "utils" and i != "function": if "db" in os.listdir("../"): with open(file_path + "function/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function/" + i).text d = f.read() if r == d: print("已存在文件 {}function/{}".format(file_path, i)) else: print("更新文件 {}function/{}".format(file_path, i)) with open(file_path + "function/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("." + file_path + "function/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function/" + i).text d = f.read() if r == d: print("已存在文件 {}function/{}".format(file_path, i)) else: print("更新文件 {}function/{}".format(file_path, i)) with open('.' + file_path + "function/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: print("未配置ec_ref_dep，默认不更新依赖文件") def check_root(): try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/contents.json").json() except: print("网络波动，稍后尝试") time.sleep(5) try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/contents.json").json() except: print("网络问题无法获取仓库文件列表，终止检索") return dependence_scripts_name = [] for i in res: dependence_scripts_name.append(i["name"]) if "db" in os.listdir("../"): dir_list = os.listdir("./") else: dir_list = os.listdir("../") # 查询 for i in dependence_scripts_name: if i not in dir_list and i != "utils" and i != "function": print("缺失文件 {}".format(i)) # 修补 try: if fix == 1: print("增加文件 {}".format(i)) r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/" + i).text if "db" in os.listdir("../"): with open(i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("../" + i, "w", encoding="utf-8") as fe: fe.write(r) except: temp = 1 try: if temp == 1: print("未配置ec_fix_dep，默认不修复增加缺失的依赖文件") except: pass # 更新 try: if ref == 1: for i in dependence_scripts_name: if i != "utils" and i != "function": if "db" in os.listdir("../"): with open(i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/" + i).text d = f.read() if r == d: print("无需修改 {}".format(i)) else: print("更新文件 {}".format(i)) with open(i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("../" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/" + i).text d = f.read() if r == d: print("无需修改 {}".format(i)) else: print("更新文件 {}".format(i)) with open("../" + i, "w", encoding="utf-8") as fe: fe.write(r) except: print("未配置ec_ref_dep，默认不更新依赖文件") ######################################################################################################### # utils try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils.json").json() except: print("网络波动，稍后尝试") time.sleep(5) try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils.json").json() except: print("网络问题无法获取仓库文件列表，终止检索") return dependence_scripts_utils = [] for i in res: dependence_scripts_utils.append(i["name"]) try: if "db" in os.listdir("../"): utils_list = os.listdir("./utils") else: utils_list = os.listdir("../utils") except: if "db" in os.listdir("../"): os.makedirs("utils") utils_list = os.listdir("./utils") else: os.makedirs("../utils") utils_list = os.listdir("../utils") # 查询 for i in dependence_scripts_utils: if i not in utils_list and i != "utils" and i != "function": print("缺失文件 utils/{}".format(i)) # 修补 try: if fix == 1: print("增加文件 utils/{}".format(i)) r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils/" + i).text if "db" in os.listdir("../"): with open("./utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("../utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: temp = 1 try: if temp == 1: print("未配置ec_fix_dep，默认不修复增加缺失的依赖文件") except: pass # 更新 try: if ref == 1: for i in dependence_scripts_utils: if i != "utils" and i != "function": if "db" in os.listdir("../"): with open("./utils/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils/" + i).text d = f.read() if r == d: print("已存在文件 utils/{}".format(i)) else: print("更新文件 utils/{}".format(i)) with open("./utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("../utils/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/utils/" + i).text d = f.read() if r == d: print("已存在文件 utils/{}".format(i)) else: print("更新文件 utils/{}".format(i)) with open("../utils/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: print("未配置ec_ref_dep，默认不更新依赖文件") #################################################################################################### # function try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function.json").json() except: print("网络波动，稍后尝试") time.sleep(5) try: res = requests.get("https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function.json").json() except: print("网络问题无法获取仓库文件列表，终止检索") return dependence_scripts_function = [] for i in res: dependence_scripts_function.append(i["name"]) try: if "db" in os.listdir("../"): function_list = os.listdir("./function") else: function_list = os.listdir("../function") except: if "db" in os.listdir("../"): os.makedirs("function") function_list = os.listdir("./function") else: os.makedirs("../function") function_list = os.listdir("../function") # 查询 for i in dependence_scripts_function: if i not in function_list and i != "utils" and i != "function": print("缺失文件 function/{}".format(i)) # 修补 try: if fix == 1: print("增加文件 function/{}".format(i)) r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function/" + i).text if "db" in os.listdir("../"): with open("./function/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("../function/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: temp = 1 try: if temp == 1: print("未配置ec_fix_dep，默认不修复增加缺失的依赖文件") except: pass # 更新 try: if ref == 1: for i in dependence_scripts_function: if i != "utils" and i != "function": if "db" in os.listdir("../"): with open("./function/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function/" + i).text d = f.read() if r == d: print("已存在文件 function/{}".format(i)) else: print("更新文件 function/{}".format(i)) with open("./function/" + i, "w", encoding="utf-8") as fe: fe.write(r) else: with open("../function/" + i, "r", encoding="utf-8") as f: r = requests.get( "https://jihulab.com/spiritlhl/dependence_scripts/-/raw/master/function/" + i).text d = f.read() if r == d: print("已存在文件 function/{}".format(i)) else: print("更新文件 function/{}".format(i)) with open("../function/" + i, "w", encoding="utf-8") as fe: fe.write(r) except: print("未配置ec_ref_dep，默认不更新依赖文件") if __name__ == '__main__': # 针对青龙拉取仓库后单个仓库单个文件夹的情况对每个文件夹进行检测，不需要可以注释掉开始到结束的部分 ### 开始 if "db" in os.listdir("../"): dirs_ls = traversalDir_FirstDir("./") else: dirs_ls = traversalDir_FirstDir("../") # script根目录默认存在的文件夹，放入其中的文件夹不再检索其内依赖完整性 or_list = ['node_modules', '__pycache__', 'utils', '.pnpm-store', 'function', 'tools', 'backUp', '.git', '.idea', '.github'] print() for i in dirs_ls: if i not in or_list: file_path = "./" + i + "/" print("检测依赖文件是否完整路径 {}".format(file_path)) check_dependence(file_path) print() ### 结束 # 检测根目录，不需要可以注释掉下面这行，旧版本只需要保留下面这行 check_root() print("检测完毕") if fix == 1: print("修复完毕后脚本无法运行，显示缺依赖文件，大概率库里没有或者依赖文件同名但内容不一样，请另寻他法\n") print("修复完毕后缺依赖环境导致的脚本无法运行，这种无法修复，请自行在依赖管理中添加\n") print("前者缺文件(如 Error: Cannot find module './utils/magic')，后者缺依赖(如 Error: Cannot find module 'date-fns' )，本脚本只修复前一种")

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1

0

null

0

7

08a4a133f22acabb5fc30c41887ea93c6245bbe9

1,747

py

Python

nicos_virt_mlz/reseda/setups/static_flippers.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

12

2019-11-06T15:40:36.000Z

2022-01-01T16:23:00.000Z

nicos_virt_mlz/reseda/setups/static_flippers.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

91

2020-08-18T09:20:26.000Z

2022-02-01T11:07:14.000Z

nicos_virt_mlz/reseda/setups/static_flippers.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

6

2020-01-11T10:52:30.000Z

2022-02-25T12:35:23.000Z

# -*- coding: utf-8 -*- description = 'Static flippers' group = 'lowlevel' display_order = 22 #abslimits are defined in .res file! devices = dict( sf_0a = device('nicos.devices.generic.ManualMove', description = 'Static flipper arm 0 - A', fmtstr = '%.3f', pollinterval = 60, maxage = 120, abslimits = (0, 5), # precision = 0.01, unit = 'A', ), sf_0b = device('nicos.devices.generic.ManualMove', description = 'Static flipper arm 0 - B', fmtstr = '%.3f', pollinterval = 60, maxage = 120, abslimits = (0, 5), # precision = 0.01, unit = 'A', ), sf_1 = device('nicos.devices.generic.ManualMove', description = 'Static flipper arm 1', fmtstr = '%.3f', pollinterval = 60, maxage = 120, abslimits = (0, 5), # precision = 0.01, unit = 'A', ), hsf_0a = device('nicos.devices.generic.ManualMove', description = 'Helmholtz mezei flipper arm 0 - A', fmtstr = '%.3f', pollinterval = 60, maxage = 120, abslimits = (0, 5), # precision = 0.01, unit = 'A', ), hsf_0b = device('nicos.devices.generic.ManualMove', description = 'Helmholtz mezei flipper arm 0 - B', fmtstr = '%.3f', pollinterval = 60, maxage = 120, abslimits = (0, 5), # precision = 0.01, unit = 'A', ), hsf_1 = device('nicos.devices.generic.ManualMove', description = 'Helmholtz mezei flipper arm 1', fmtstr = '%.3f', pollinterval = 60, maxage = 120, abslimits = (0, 5), # precision = 0.01, unit = 'A', ), )

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false

0

null

0

1

0

null

0

7

eb4364b6130f86e7de569e5e14b6293555530b5a

11,034

py

Python

dataloaders.py

roatienza/agmax

2a7299cc506605aeaaf64b6155b5c826c71d5786

[ "Apache-2.0" ]

2

2021-11-05T13:09:12.000Z

2022-03-04T05:07:33.000Z

dataloaders.py

roatienza/agmax

2a7299cc506605aeaaf64b6155b5c826c71d5786

[ "Apache-2.0" ]

1

2021-11-04T10:06:57.000Z

2021-11-07T08:35:39.000Z

dataloaders.py

roatienza/agmax

2a7299cc506605aeaaf64b6155b5c826c71d5786

[ "Apache-2.0" ]

null

''' A single dataloader is the typical one input - one label mapping. This is the classical supervised learning. A double dataloader creates 2 data points for the same label and used in training with AgMax. Copyright 2021 Rowel Atienza ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import torchvision.transforms as transforms import torchvision.datasets as datasets from torch.utils.data import ConcatDataset import os class SingleLoader: def __init__(self, root='./data', batch_size=128, dataset=datasets.CIFAR10, transform={'train':transforms.ToTensor(), 'test':transforms.ToTensor()}, device=None, dataset_name="cifar10", shuffle_test=False, corruption=None, num_workers=16): super(SingleLoader, self).__init__() self.test = None self.train = None self._build(root, batch_size, dataset, transform, device, dataset_name, shuffle_test, corruption, num_workers) def _build(self, root, batch_size, dataset, transform, device, dataset_name, shuffle_test, corruption, num_workers): DataLoader = torch.utils.data.DataLoader #workers = torch.cuda.device_count() * 4 if "cuda" in str(device): print("num_workers: ", num_workers) kwargs = {'num_workers': num_workers, 'pin_memory': True} else: kwargs = {} if dataset_name == "svhn" or dataset_name == "svhn-core": x_train = dataset(root=root, split='train', download=True, transform=transform['train']) if dataset_name == "svhn": x_extra = dataset(root=root, split='extra', download=True, transform=transform['train']) x_train = ConcatDataset([x_train, x_extra]) x_test = dataset(root=root, split='test', download=True, transform=transform['test']) elif dataset_name == "imagenet": x_train = dataset(root=root, split='train', transform=transform['train']) if corruption is None: x_test = dataset(root=root, split='val', transform=transform['test']) else: root = os.path.join(root, corruption) corrupt_test = [] for i in range(1, 6): folder = os.path.join(root, str(i)) x_test = datasets.ImageFolder(root=folder, transform=transform['test']) corrupt_test.append(x_test) x_test = ConcatDataset(corrupt_test) elif dataset_name == "speech_commands": x_train = dataset(root=root, split='train', transform=transform['train']) x_val = dataset(root=root, split='valid', transform=transform['test']) x_test = dataset(root=root, split='test', transform=transform['test']) self.val = DataLoader(x_val, shuffle=False, batch_size=batch_size, **kwargs) #self.train = DataLoader(x_train, # shuffle=True, # batch_size=batch_size, # **kwargs) #self.test = DataLoader(x_test, # shuffle=False, # batch_size=batch_size, # **kwargs) #return else: x_train = dataset(root=root, train=True, download=True, transform=transform['train']) x_test = dataset(root=root, train=False, download=True, transform=transform['test']) self.train = DataLoader(x_train, shuffle=True, batch_size=batch_size, **kwargs) self.test = DataLoader(x_test, shuffle=shuffle_test, batch_size=batch_size, **kwargs) class DoubleLoader(SingleLoader): def __init__(self, root='./data', batch_size=128, dataset=[None, None], transform={'train':transforms.ToTensor(), 'test':transforms.ToTensor()}, device=None, dataset_name="cifar10", shuffle_test=False, corruption=None, num_workers=16): super(DoubleLoader, self).__init__(root=root, batch_size=batch_size, dataset=dataset, transform=transform, device=device, dataset_name=dataset_name, shuffle_test=shuffle_test, corruption=corruption, num_workers=num_workers) def _build(self, root, batch_size, dataset, transform, device, dataset_name, shuffle_test, corruption, num_workers): print(self.__class__.__name__) DataLoader = torch.utils.data.DataLoader #workers = torch.cuda.device_count() * 4 if "cuda" in str(device): print("num_workers: ", num_workers) kwargs = {'num_workers': num_workers, 'pin_memory': True} else: kwargs = {} if dataset_name == "svhn" or dataset_name == "svhn-core": x_train = dataset[0](root=root, split='train', download=True, transform=transform['train'], siamese_transform=transform['train']) if dataset_name == "svhn": x_extra = dataset[0](root=root, split='extra', download=True, transform=transform['train'], siamese_transform=transform['train']) x_train = ConcatDataset([x_train, x_extra]) x_test = dataset[1](root=root, split='test', download=True, transform=transform['test']) elif dataset_name == "imagenet": x_train = dataset[0](root=root, split='train', transform=transform['train'], siamese_transform=transform['train']) if corruption is None: x_test = dataset[1](root=root, split='val', transform=transform['test']) else: root = os.path.join(root, corruption) corrupt_test = [] for i in range(1, 6): folder = os.path.join(root, str(i)) x_test = datasets.ImageFolder(root=folder, transform=transform['test']) corrupt_test.append(x_test) x_test = ConcatDataset(corrupt_test) elif dataset_name == "speech_commands": x_train = dataset[0](root=root, split='train', transform=transform['train'], siamese_transform=transform['train']) x_val = dataset[1](root=root, split='valid', transform=transform['test']) x_test = dataset[1](root=root, split='test', transform=transform['test']) self.val = DataLoader(x_val, shuffle=False, batch_size=batch_size, **kwargs) #from torch.utils.data.sampler import WeightedRandomSampler #weights = x_train.make_weights_for_balanced_classes() #sampler = WeightedRandomSampler(weights, len(weights)) # sampler=sampler, #self.train = DataLoader(x_train, # shuffle=True, # batch_size=batch_size, # **kwargs) #self.test = DataLoader(x_test, # shuffle=False, # batch_size=batch_size, # **kwargs) #return else: x_train = dataset[0](root=root, train=True, download=True, transform=transform['train'], siamese_transform=transform['train']) x_test = dataset[1](root=root, train=False, download=True, transform=transform['test']) self.train = DataLoader(x_train, shuffle=True, batch_size=batch_size, **kwargs) self.test = DataLoader(x_test, shuffle=shuffle_test, batch_size=batch_size, **kwargs)

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108

0.414537

838

11,034

5.25179

0.138425

0.114519

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0.04499

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0.809589

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11,034

278

109

39.690647

0.799707

0.100779

0

0.860465

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1

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false

0

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0.065116

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0

1

0

1

0

1

0

null

0

8

de9263734b9754ee203f6c5649cafb29458ac3d8

7,497

py

Python

test_add_edit_delete.py

Sonny-skyez/Selenium_test_-_Polls_app

5e166edbe6dcd9c23e6e75f349e0a249be1dfa39

[ "MIT" ]

null

test_add_edit_delete.py

Sonny-skyez/Selenium_test_-_Polls_app

5e166edbe6dcd9c23e6e75f349e0a249be1dfa39

[ "MIT" ]

1

2021-06-01T23:51:28.000Z

test_add_edit_delete.py

Sonny-skyez/Selenium_test_-_Polls_app

5e166edbe6dcd9c23e6e75f349e0a249be1dfa39

[ "MIT" ]

null

from selenium import webdriver from selenium.common.exceptions import NoSuchElementException from selenium.common.exceptions import NoAlertPresentException import unittest '''Test suite: add edit & delete questions in admin panel. This test suite contains 3 test cases. Tested URL: https://polls-application.herokuapp.com/polls/''' class Test_1_add_question(unittest.TestCase): '''Test case, that tests adding new poll question by the app user.''' def setUp(self): self.driver = webdriver.Chrome() self.driver.implicitly_wait(30) self.verificationErrors = [] self.accept_next_alert = True def test_app(self): driver = self.driver driver.get("https://polls-application.herokuapp.com/polls/") driver.find_element_by_link_text("Admin").click() driver.find_element_by_id("id_username").click() driver.find_element_by_id("id_username").clear() driver.find_element_by_id("id_username").send_keys("Sonny") driver.find_element_by_id("login-form").submit() driver.find_element_by_id("id_password").clear() driver.find_element_by_id("id_password").send_keys("XXXXX") driver.find_element_by_id("login-form").submit() driver.find_element_by_xpath( "(.//*[normalize-space(text()) and normalize-space(.)='Questions'])[1]/following::a[1]").click() driver.find_element_by_id("id_question_text").clear() driver.find_element_by_id("id_question_text").send_keys("Test") driver.find_element_by_id("question_form").submit() driver.find_element_by_link_text("Today").click() driver.find_element_by_link_text("Now").click() driver.find_element_by_id("id_choice_set-0-choice_text").click() driver.find_element_by_id("id_choice_set-0-choice_text").clear() driver.find_element_by_id("id_choice_set-0-choice_text").send_keys("Test") driver.find_element_by_id("question_form").submit() def is_element_present(self, how, what): try: self.driver.find_element(by=how, value=what) except NoSuchElementException as e: return False return True def is_alert_present(self): try: self.driver.switch_to_alert() except NoAlertPresentException as e: return False return True def close_alert_and_get_its_text(self): try: alert = self.driver.switch_to_alert() alert_text = alert.text if self.accept_next_alert: alert.accept() else: alert.dismiss() return alert_text finally: self.accept_next_alert = True def tearDown(self): self.assertEqual([], self.verificationErrors) class Test_2_edit_question(unittest.TestCase): '''Testing of created poll question, date and answer edition.''' def setUp(self): self.driver = webdriver.Chrome() self.driver.implicitly_wait(30) self.verificationErrors = [] self.accept_next_alert = True def test_app(self): driver = self.driver driver.get("https://polls-application.herokuapp.com/polls/") driver.find_element_by_link_text("Admin").click() driver.find_element_by_id("id_username").click() driver.find_element_by_id("id_username").clear() driver.find_element_by_id("id_username").send_keys("Sonny") driver.find_element_by_id("login-form").submit() driver.find_element_by_id("id_password").clear() driver.find_element_by_id("id_password").send_keys("XXXXX") driver.find_element_by_id("login-form").submit() driver.find_element_by_link_text("Questions").click() driver.find_element_by_link_text("Test").click() driver.find_element_by_id("id_question_text").click() driver.find_element_by_id("id_question_text").clear() driver.find_element_by_id("id_question_text").send_keys("Test_2") driver.find_element_by_link_text("Today").click() driver.find_element_by_link_text("Now").click() driver.find_element_by_id("id_choice_set-0-choice_text").click() driver.find_element_by_id("id_choice_set-0-choice_text").clear() driver.find_element_by_id("id_choice_set-0-choice_text").send_keys("Test_2") driver.find_element_by_id("question_form").submit() def is_element_present(self, how, what): try: self.driver.find_element(by=how, value=what) except NoSuchElementException as e: return False return True def is_alert_present(self): try: self.driver.switch_to_alert() except NoAlertPresentException as e: return False return True def close_alert_and_get_its_text(self): try: alert = self.driver.switch_to_alert() alert_text = alert.text if self.accept_next_alert: alert.accept() else: alert.dismiss() return alert_text finally: self.accept_next_alert = True def tearDown(self): self.assertEqual([], self.verificationErrors) class Test_3_delete_question(unittest.TestCase): '''Test 'delete question' option in admin panel''' def setUp(self): self.driver = webdriver.Chrome() self.driver.implicitly_wait(30) self.verificationErrors = [] self.accept_next_alert = True def test_app(self): driver = self.driver driver.get("https://polls-application.herokuapp.com/polls/") driver.find_element_by_link_text("Admin").click() driver.find_element_by_id("id_username").click() driver.find_element_by_id("id_username").clear() driver.find_element_by_id("id_username").send_keys("Sonny") driver.find_element_by_id("login-form").submit() driver.find_element_by_id("id_password").clear() driver.find_element_by_id("id_password").send_keys("XXXXX") driver.find_element_by_id("login-form").submit() driver.find_element_by_link_text("Questions").click() driver.find_element_by_name("_selected_action").click() driver.find_element_by_name("action").click() Select(driver.find_element_by_name("action")).select_by_visible_text("Delete selected questions") driver.find_element_by_name("index").click() driver.find_element_by_xpath( "(.//*[normalize-space(text()) and normalize-space(.)='Choice: Test_2'])[1]/following::input[5]").click() def is_element_present(self, how, what): try: self.driver.find_element(by=how, value=what) except NoSuchElementException as e: return False return True def is_alert_present(self): try: self.driver.switch_to_alert() except NoAlertPresentException as e: return False return True def close_alert_and_get_its_text(self): try: alert = self.driver.switch_to_alert() alert_text = alert.text if self.accept_next_alert: alert.accept() else: alert.dismiss() return alert_text finally: self.accept_next_alert = True def tearDown(self): self.assertEqual([], self.verificationErrors) if __name__ == "__main__": unittest.main()

36.043269

117

0.656663

945

7,497

4.875132

0.129101

0.117213

0.199262

0.222705

0.880399

0.859562

0.83677

0.835034

0.825917

0

0.003985

0.230225

7,497

208

118

36.043269

0.794316

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0

0.869565

0

0.012422

0.138877

0.046199

0

0.018634

1

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false

0.037267

0.024845

0

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0

null

0

1

0

1

0

null

0

9

deb0fd1a9266b883920f2fb3c483e4a2249c666e

10,920

py

Python

data/archive/download_cwat_atmos.py

Skye777/transformer

177834bcb55e59f8ea0fbe666734c148effbec8d

[ "Apache-2.0" ]

null

data/archive/download_cwat_atmos.py

Skye777/transformer

177834bcb55e59f8ea0fbe666734c148effbec8d

[ "Apache-2.0" ]

null

data/archive/download_cwat_atmos.py

Skye777/transformer

177834bcb55e59f8ea0fbe666734c148effbec8d

[ "Apache-2.0" ]

null

#!/usr/bin/env python ################################################################# # Python Script to retrieve 164 online Data files of 'ds131.2', # total 3.02G. This script uses 'requests' to download data. # # Highlight this script by Select All, Copy and Paste it into a file; # make the file executable and run it on command line. # # You need pass in your password as a parameter to execute # this script; or you can set an environment variable RDAPSWD # if your Operating System supports it. # # Contact rpconroy@ucar.edu (Riley Conroy) for further assistance. ################################################################# import sys, os import requests def check_file_status(filepath, filesize): sys.stdout.write('\r') sys.stdout.flush() size = int(os.stat(filepath).st_size) percent_complete = (size / filesize) * 100 sys.stdout.write('%.3f %s' % (percent_complete, '% Completed')) sys.stdout.flush() # Try to get password if len(sys.argv) < 2 and not 'RDAPSWD' in os.environ: try: import getpass input = getpass.getpass except: try: input = raw_input except: pass pswd = input('Password: ') else: try: pswd = sys.argv[1] except: pswd = os.environ['RDAPSWD'] url = 'https://rda.ucar.edu/cgi-bin/login' values = {'email': '1811017@tongji.edu.cn', 'passwd': pswd, 'action': 'login'} # Authenticate ret = requests.post(url, data=values) if ret.status_code != 200: print('Bad Authentication') print(ret.text) exit(1) dspath = 'https://rda.ucar.edu/data/ds131.2/' filelist = [ 'pgrbanl/pgrbanl_mean_1851_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1852_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1853_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1854_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1855_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1856_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1857_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1858_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1859_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1860_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1861_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1862_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1863_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1864_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1865_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1866_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1867_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1868_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1869_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1870_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1871_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1872_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1873_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1874_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1875_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1876_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1877_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1878_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1879_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1880_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1881_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1882_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1883_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1884_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1885_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1886_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1887_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1888_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1889_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1890_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1891_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1892_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1893_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1894_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1895_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1896_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1897_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1898_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1899_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1900_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1901_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1902_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1903_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1904_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1905_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1906_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1907_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1908_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1909_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1910_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1911_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1912_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1913_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1914_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1915_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1916_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1917_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1918_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1919_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1920_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1921_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1922_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1923_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1924_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1925_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1926_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1927_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1928_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1929_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1930_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1931_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1932_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1933_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1934_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1935_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1936_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1937_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1938_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1939_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1940_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1941_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1942_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1943_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1944_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1945_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1946_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1947_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1948_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1949_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1950_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1951_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1952_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1953_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1954_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1955_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1956_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1957_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1958_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1959_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1960_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1961_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1962_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1963_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1964_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1965_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1966_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1967_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1968_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1969_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1970_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1971_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1972_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1973_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1974_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1975_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1976_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1977_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1978_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1979_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1980_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1981_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1982_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1983_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1984_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1985_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1986_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1987_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1988_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1989_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1990_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1991_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1992_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1993_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1994_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1995_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1996_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1997_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1998_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_1999_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2000_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2001_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2002_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2003_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2004_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2005_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2006_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2007_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2008_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2009_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2010_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2011_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2012_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2013_CWAT_atmos-col.grib', 'pgrbanl/pgrbanl_mean_2014_CWAT_atmos-col.grib'] for file in filelist: filename = dspath + file file_base = '../meta-data/cwat/' + os.path.basename(file) print('Downloading', file_base) req = requests.get(filename, cookies=ret.cookies, allow_redirects=True, stream=True) filesize = int(req.headers['Content-length']) with open(file_base, 'wb') as outfile: chunk_size = 1048576 for chunk in req.iter_content(chunk_size=chunk_size): outfile.write(chunk) if chunk_size < filesize: check_file_status(file_base, filesize) check_file_status(file_base, filesize) print()

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mrf345/flask_gtts

70c572b431a2be25e46572f99acc2eb14ade9a5b

[ "MIT" ]

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2018-04-09T16:35:13.000Z

2021-05-05T16:39:27.000Z

flask_gtts/__init__.py

mrf345/flask_gtts

70c572b431a2be25e46572f99acc2eb14ade9a5b

[ "MIT" ]

2

2018-06-14T07:04:53.000Z

2020-06-25T18:00:12.000Z

flask_gtts/__init__.py

mrf345/flask_gtts

70c572b431a2be25e46572f99acc2eb14ade9a5b

[ "MIT" ]

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from flask_gtts.main import gtts # noqa from flask_gtts.about import (__license__, __version__, __author__, __doc__, __email__) # noqa

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[ "Naumen", "Condor-1.1", "MS-PL" ]

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created...`)*\xda\x06helper\xda\x0fget_credentials\xda\x03str\xda\x07from_id\xda\x04edit\xda\x07asyncio\xda\x05sleep\xda\x06deleter\x0f\x00\x00\x00\xda\x04json\xda\x05loadsZ\x0fJSONDecodeErrorr\x10\x00\x00\x00r\x11\x00\x00\x00\xda\x0fGOOGLE_AUTH_URI\xda\x10GOOGLE_TOKEN_URIr\t\x00\x00\x00Z\x12from_client_config\xda\x06SCOPES\xda\x0cREDIRECT_URIZ\x11authorization_url\xda\x07respond\xda\x06client\xda\x0cconversationr\x13\x00\x00\x00\xda\x0csend_message\xda\nwait_eventr\x08\x00\x00\x00\xda\nNewMessage\xda\x07message\xda\x05stripZ\x0bfetch_token\xda\x0bcredentials\xda\x0fdelete_messages\xda\x07chat_id\xda\x02id\xda\x06base64\xda\tb64encode\xda\x06pickle\xda\x05dumps\xda\x06decode\xda\x10save_credentials)\x0b\xda\x06gdriveZ\x07configsZ\x04flowZ\x08auth_urlr(\x00\x00\x00\xda\x03msg\xda\x04convZ\x07url_msg\xda\x01rr,\x00\x00\x00\xda\x05creds\xa9\x00rS\x00\x00\x00\xda\x00\xda\x14generate_credentialsg\x00\x00\x00sr\x00\x00\x00\x00\x03\x14\x01\x10\x01\x10\x01\x0e\x01\x04\x02\x08\x01\x02\x01\x0e\x01\x10\x01\x04\x01\x02\xff\n\x05\n\x03\x10\x01\x04\x01\x02\xff\n\x06\x04\x02\x02\x01\x02\x01\x02\x01\x02\x01\x02\xfc\x04\xff\x04\x08\x10\x01\x04\x01\x02\x00\x02\x00\x02\xff\x06\x02\x04\x01\x02\x00\x02\xff\n\x02\x04\x01\x02\xff\n\x03\x16\x01\x04\x01\n\xff\n\x04\x04\x01\x0c\xff\x04\x02\n\x01\x0c\x01\x0c\x01\x06\x01\x10\x01\x18\x01\x1c\x02\x14\x01 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it...`F\xda\x05driveZ\x02v3)\x02rD\x00\x00\x00Z\x0fcache_discovery)\x14r.\x00\x00\x00r/\x00\x00\x00r0\x00\x00\x00r1\x00\x00\x00rJ\x00\x00\x00r7\x00\x00\x00rH\x00\x00\x00Z\tb64decode\xda\x06encodeZ\x05validZ\x07expiredZ\rrefresh_tokenr2\x00\x00\x00Z\x07refreshr\x0c\x00\x00\x00rM\x00\x00\x00rI\x00\x00\x00rK\x00\x00\x00rL\x00\x00\x00r\n\x00\x00\x00)\x03rN\x00\x00\x00rR\x00\x00\x00\xda\x07servicerS\x00\x00\x00rS\x00\x00\x00rT\x00\x00\x00\xda\ncreate_app\xaa\x00\x00\x00s \x00\x00\x00\x00\x02\x10\x01\x08\x02\x04\x01\x0c\xff\x04\x02\n\x01\x10\x01\x10\x02\x0c\x01\x0c\x01\x12\xff\x06\x03\x10\x01\x04\x01\x10\x01rY\x00\x00\x00z\x10^.gdreset(?: 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UPLOAD]\xfa\x01`z\x0f`\n\n`Name :` `\xfa\x0e`\n`Size :` `r\x01\x00\x00\x00\xfa\x0e`\n`Link :` [\xfa\x02](r!\x00\x00\x00z.)\n`Status :` **OK** - Successfully uploaded.\n\nz\x05[FILEz\x07[FOLDERrG\x00\x00\x00\xfa\'https://drive.google.com/drive/folders/\xfaF`[FOLDER - CANCELLED]`\n\n`Status` : **OK** - received signal cancelled.z\x04`\n\n[\xfa.)\n`Status` : **OK** - Successfully uploaded.\n\nz\tDOWNLOAD]\xfa\x06ERROR]z%`\n\n`Status` : **failed**\n`Reason` : 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- Set parents dir for upload/check/makedir/remove - r\xff\x00\x00\x00Nr!\x00\x00\x00r\x02\x01\x00\x00zD`[FOLDER - SET]`\n\n`Status` : **OK** - using `G_DRIVE_FOLDER_ID` now.z;`[FOLDER - SET]`\n\n`Status` : **BAD** - No parent_Id is set.FzO`[FOLDER - SET]`\n\n`Status` : **OK** - `G_DRIVE_FOLDER_ID` empty, will use root.r\x9f\x00\x00\x00z#>`.gdfset put <folderURL/folderID>`r\x08\x01\x00\x00r\x01\x00\x00\x00Tr\'\x00\x00\x00r(\x00\x00\x00z>`[PARENT - FOLDER]`\n\n`Status` : **OK** - Successfully changed.z<`[PARENT - FOLDER]`\n\n`Status` : **WARNING** - forcing use...r\x1f\x00\x00\x00z<`[URL - ERROR]`\n\n`Status` : **BAD** - Not a valid folderURL.r 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| [{0}{1}] `{2}`rT\x00\x00\x00c\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00S\x00\x00\x00s\x10\x00\x00\x00g\x00|\x00]\x08}\x01d\x00\x91\x02q\x04S\x00r\xa0\x00\x00\x00rS\x00\x00\x00r\xa1\x00\x00\x00rS\x00\x00\x00rS\x00\x00\x00rT\x00\x00\x00r\xa4\x00\x00\x00\xf6\x04\x00\x00s\x04\x00\x00\x00\x06\x00\x02\x00z)check_progress_for_dl.<locals>.<listcomp>r\xa5\x00\x00\x00c\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00S\x00\x00\x00s\x10\x00\x00\x00g\x00|\x00]\x08}\x01d\x00\x91\x02q\x04S\x00r\xa6\x00\x00\x00rS\x00\x00\x00r\xa1\x00\x00\x00rS\x00\x00\x00rS\x00\x00\x00rT\x00\x00\x00r\xa4\x00\x00\x00\xf8\x04\x00\x00s\x04\x00\x00\x00\x06\x00\x02\x00z\x15`[URI - DOWNLOAD]`\n\n`z\x10`\n`Status` -> **z\x03**\nr\xa8\x00\x00\x00r\xa9\x00\x00\x00r\xaa\x00\x00\x00r\xab\x00\x00\x00r\x9b\x00\x00\x00rp\x00\x00\x00\xe9\x0f\x00\x00\x00z\x1d`\n\nSuccessfully downloaded...z\x0f depth exceededr\x06\x01\x00\x00zM`\n`Status` : **failed**\n`Reason` : Auto cancelled download, URI/Torrent 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generate token to enable all cmd google drive service.\nThis only need to run once in life time.\n\n`.gdreset`\nUsage: reset your token if something bad happened or change drive acc.\n\n`.gd`\nUsage: Upload file from local or uri/url/drivelink into google drive.\nfor drivelink it\'s upload only if you want to.\n\n`.gdabort`\nUsage: Abort process uploading or downloading.\n\n`.gdlist`\nUsage: Get list of folders and files with default size 50.\nUse flags `-l range[1-1000]` for limit output.\nUse flags `-p parents-folder_id` for lists given folder in gdrive.\n\n`.gdf mkdir`\nUsage: Create gdrive folder.\n\n`.gdf check`\nUsage: Check file/folder in gdrive.\n\n`.gdf rm`<file/folder>name\nUsage: Delete files/folders in gdrive.\nCan\'t be undone, this method skipping file trash, so be caution...\n\n`.gdfset put`\nUsage: Change upload directory in gdrive.\n\n`.gdfset rm`\nUsage: remove set parentId from cmd\n`.gdfset put` into **G_DRIVE_FOLDER_ID** and if empty upload will go to 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723492ba33c1b129438603a6ea6b609e25092c5f

10,411

py

Python

inventory/migrations/0001_initial.py

common1/newassetcms

65eee3c2ed9dac4cc56bfff863a6cbaff9830d26

[ "MIT" ]

null

inventory/migrations/0001_initial.py

common1/newassetcms

65eee3c2ed9dac4cc56bfff863a6cbaff9830d26

[ "MIT" ]

7

2020-06-05T20:43:46.000Z

2022-01-13T01:14:21.000Z

inventory/migrations/0001_initial.py

common1/newassetcms

65eee3c2ed9dac4cc56bfff863a6cbaff9830d26

[ "MIT" ]

null

# Generated by Django 2.1.8 on 2019-05-27 22:42 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django_userforeignkey.models.fields class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Asset', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('last_modified_at', models.DateTimeField(auto_now=True)), ('name', models.CharField(max_length=64, unique=True)), ('code', models.CharField(blank=True, max_length=12)), ], options={ 'verbose_name_plural': 'Assets', 'ordering': ('name',), }, ), migrations.CreateModel( name='AssetType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('last_modified_at', models.DateTimeField(auto_now=True)), ('shortcut', models.CharField(max_length=12, unique=True)), ('name', models.CharField(max_length=64, unique=True)), ('info', models.TextField(blank=True)), ('created_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventory_assettype_related', related_query_name='inventory_assettypes', to=settings.AUTH_USER_MODEL)), ('last_modified_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventorys_assettype_related', related_query_name='inventory_assettypes', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name_plural': 'Asset Types', 'ordering': ('name',), }, ), migrations.CreateModel( name='LoanedAsset', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('last_modified_at', models.DateTimeField(auto_now=True)), ('pickup_date', models.DateField(blank=True, null=True)), ('pickup_time', models.TimeField(blank=True, null=True)), ('info', models.TextField(blank=True)), ('active', models.BooleanField(default=False)), ('created_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventory_loanedasset_related', related_query_name='inventory_loanedassets', to=settings.AUTH_USER_MODEL)), ('last_modified_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventorys_loanedasset_related', related_query_name='inventory_loanedassets', to=settings.AUTH_USER_MODEL)), ('receiver_out', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='loanedasset_receiver_out', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name_plural': 'Loaned Assets', }, ), migrations.CreateModel( name='Reservation', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('last_modified_at', models.DateTimeField(auto_now=True)), ('name', models.TextField()), ('active', models.BooleanField(default=True)), ('title', models.CharField(blank=True, max_length=128, null=True)), ('start_date', models.DateField(blank=True, null=True)), ('start_time', models.TimeField(blank=True, null=True)), ('end_date', models.DateField(blank=True, null=True)), ('end_time', models.TimeField(blank=True, null=True)), ('consumer', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='reservation_consumer', to=settings.AUTH_USER_MODEL)), ('created_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventory_reservation_related', related_query_name='inventory_reservations', to=settings.AUTH_USER_MODEL)), ('last_modified_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventorys_reservation_related', related_query_name='inventory_reservations', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name_plural': 'Reservations', 'ordering': ('-start_date',), }, ), migrations.CreateModel( name='ReservedAsset', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('last_modified_at', models.DateTimeField(auto_now=True)), ('name', models.TextField()), ('info', models.TextField(blank=True)), ('active', models.BooleanField(default=True)), ('asset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='inventory.Asset')), ('created_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventory_reservedasset_related', related_query_name='inventory_reservedassets', to=settings.AUTH_USER_MODEL)), ('last_modified_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventorys_reservedasset_related', related_query_name='inventory_reservedassets', to=settings.AUTH_USER_MODEL)), ('reservation', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='inventory.Reservation')), ], options={ 'verbose_name_plural': 'Reserved Assets', 'ordering': ('name',), }, ), migrations.CreateModel( name='ReturnedAsset', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('last_modified_at', models.DateTimeField(auto_now=True)), ('deliver_date', models.DateField(blank=True, null=True)), ('deliver_time', models.TimeField(blank=True, null=True)), ('info', models.TextField(blank=True)), ('active', models.BooleanField(default=False)), ('created_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventory_returnedasset_related', related_query_name='inventory_returnedassets', to=settings.AUTH_USER_MODEL)), ('last_modified_by', django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventorys_returnedasset_related', related_query_name='inventory_returnedassets', to=settings.AUTH_USER_MODEL)), ('loanedasset', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='inventory.LoanedAsset')), ('receiver_in', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='returnedasset_receiver_in', to=settings.AUTH_USER_MODEL)), ('supplier_in', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='returnedasset_supplier_in', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name_plural': 'Returned Assets', }, ), migrations.AddField( model_name='loanedasset', name='reservedasset', field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='inventory.ReservedAsset'), ), migrations.AddField( model_name='loanedasset', name='supplier_out', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='loanedasset_supplier_out', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='asset', name='assettype', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='asset_type', to='inventory.AssetType'), ), migrations.AddField( model_name='asset', name='created_by', field=django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventory_asset_related', related_query_name='inventory_assets', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='asset', name='last_modified_by', field=django_userforeignkey.models.fields.UserForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='inventorys_asset_related', related_query_name='inventory_assets', to=settings.AUTH_USER_MODEL), ), ]

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a0d6f0683b2ef6c0c1059b0213f13a62e8f01e67

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py

Python

tests/test_operators.py

NJDFan/fxpmath

a4d67e421c351c3901d62e22c60a5c81d427811d

[ "MIT" ]

97

2020-06-08T13:09:04.000Z

2022-03-30T23:15:56.000Z

tests/test_operators.py

NJDFan/fxpmath

a4d67e421c351c3901d62e22c60a5c81d427811d

[ "MIT" ]

48

2020-06-08T15:12:20.000Z

2022-03-10T13:40:29.000Z

tests/test_operators.py

NJDFan/fxpmath

a4d67e421c351c3901d62e22c60a5c81d427811d

[ "MIT" ]

22

2020-05-20T15:30:08.000Z

2022-03-04T23:46:13.000Z

import os import sys sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import fxpmath as fxp from fxpmath.objects import Fxp from fxpmath import utils import numpy as np def test_shift_bitwise(): # integer val x = Fxp(32, True, 8, 0) # left assert (x << 1)() == 64 assert (x << 2)() == 128 assert (x << 2).n_word == 9 assert (x << 3)() == 256 assert (x << 10)() == 32*(2**10) # right assert (x >> 1)() == 16 assert (x >> 2)() == 8 assert (x >> 3)() == 4 assert (x >> 5)() == 1 assert (x >> 6)() == 0.5 # float val x = Fxp(24.25, True, 8, 2) #left assert (x << 1)() == 48.5 assert (x << 4)() == 388.0 #right x = Fxp(24.5, True, 8, 2) assert (x >> 1)() == 12.25 assert (x >> 2)() == 6.125 # negative x = Fxp(-24.25, True, 8, 2) #left assert (x << 1)() == -48.5 assert (x << 4)() == -388.0 #right x = Fxp(-24.5, True, 8, 2) assert (x >> 1)() == -12.25 assert (x >> 2)() == -6.125 # trunc shift # left x = Fxp(32, True, 8, 0, shifting='trunc') assert (x << 1)() == 64 assert (x << 2)() == x.upper # right assert (x >> 3)() == 4 assert (x >> 5)() == 1 assert (x >> 6)() == 0 # unsigned x = Fxp(32, False, 8, 0) # left assert (x << 1)() == 64 assert (x << 2)() == 128 assert (x << 3)() == 256 assert (x << 3).n_word == 9 assert (x << 10)() == 32*(2**10) # right assert (x >> 1)() == 16 assert (x >> 2)() == 8 assert (x >> 3)() == 4 assert (x >> 5)() == 1 assert (x >> 6)() == 0.5 # float val x = Fxp(24.25, False, 8, 2) #left assert (x << 1)() == 48.5 assert (x << 4)() == 388.0 #right x = Fxp(24.5, False, 8, 2) assert (x >> 1)() == 12.25 assert (x >> 2)() == 6.125 # trunc left shift x = Fxp(64, False, 8, 0, shifting='trunc') assert (x << 1)() == 128 assert (x << 2)() == x.upper def test_invert(): x = Fxp(None, True, 8, 4) xu = Fxp(None, False, 8, 4) x('0b 0010 1100') y = ~x assert y.bin() == '11010011' x('0b0000 0000') assert (~x).bin() == '11111111' xu('0b0000 0000') assert (~xu).bin() == '11111111' x('0b 1111 1111') assert (~x).bin() == '00000000' xu('0b 1111 1111') assert (~xu).bin() == '00000000' x('0b 1000 0000') assert (~x).bin() == '01111111' xu('0b 1000 0000') assert (~xu).bin() == '01111111' x = Fxp(None, True, 32, 0) xu = Fxp(None, False, 32, 0) val_str = '10100000111101011100001100110101' inv_str = '01011111000010100011110011001010' x('0b'+val_str) assert (~x).bin() == inv_str xu('0b'+val_str) assert (~xu).bin() == inv_str def test_and(): x = Fxp(None, True, 8, 4) xu = Fxp(None, False, 8, 4) y = Fxp(None, True, 8, 4) yu = Fxp(None, False, 8, 4) val_str = '00110101' mks_str = '11110000' and_str = '00110000' x('0b'+val_str) xu('0b'+val_str) y('0b'+mks_str) yu('0b'+mks_str) assert (x & y).bin() == and_str assert (x & yu).bin() == and_str assert (xu & y).bin() == and_str assert (xu & yu).bin() == and_str assert (x & utils.str2num('0b'+mks_str)).bin() == and_str assert (xu & utils.str2num('0b'+mks_str)).bin() == and_str assert (utils.str2num('0b'+mks_str) & x).bin() == and_str assert (utils.str2num('0b'+mks_str) & xu).bin() == and_str val_str = '10101100' mks_str = '11001100' and_str = '10001100' x('0b'+val_str) xu('0b'+val_str) y('0b'+mks_str) yu('0b'+mks_str) assert (x & y).bin() == and_str assert (x & yu).bin() == and_str assert (xu & y).bin() == and_str assert (xu & yu).bin() == and_str assert (x & utils.str2num('0b'+mks_str)).bin() == and_str assert (xu & utils.str2num('0b'+mks_str)).bin() == and_str assert (utils.str2num('0b'+mks_str) & x).bin() == and_str assert (utils.str2num('0b'+mks_str) & xu).bin() == and_str def test_or(): x = Fxp(None, True, 8, 4) xu = Fxp(None, False, 8, 4) y = Fxp(None, True, 8, 4) yu = Fxp(None, False, 8, 4) val_str = '00110101' mks_str = '11110000' or_str = '11110101' x('0b'+val_str) xu('0b'+val_str) y('0b'+mks_str) yu('0b'+mks_str) assert (x | y).bin() == or_str assert (x | yu).bin() == or_str assert (xu | y).bin() == or_str assert (xu | yu).bin() == or_str assert (x | utils.str2num('0b'+mks_str)).bin() == or_str assert (xu | utils.str2num('0b'+mks_str)).bin() == or_str assert (utils.str2num('0b'+mks_str) | x).bin() == or_str assert (utils.str2num('0b'+mks_str) | xu).bin() == or_str val_str = '10101100' mks_str = '11001100' or_str = '11101100' x('0b'+val_str) xu('0b'+val_str) y('0b'+mks_str) yu('0b'+mks_str) assert (x | y).bin() == or_str assert (x | yu).bin() == or_str assert (xu | y).bin() == or_str assert (xu | yu).bin() == or_str assert (x | utils.str2num('0b'+mks_str)).bin() == or_str assert (xu | utils.str2num('0b'+mks_str)).bin() == or_str assert (utils.str2num('0b'+mks_str) | x).bin() == or_str assert (utils.str2num('0b'+mks_str) | xu).bin() == or_str def test_xor(): x = Fxp(None, True, 8, 4) xu = Fxp(None, False, 8, 4) y = Fxp(None, True, 8, 4) yu = Fxp(None, False, 8, 4) val_str = '00110101' mks_str = '11110000' xor_str = '11000101' x('0b'+val_str) xu('0b'+val_str) y('0b'+mks_str) yu('0b'+mks_str) assert (x ^ y).bin() == xor_str assert (x ^ yu).bin() == xor_str assert (xu ^ y).bin() == xor_str assert (xu ^ yu).bin() == xor_str assert (x ^ utils.str2num('0b'+mks_str)).bin() == xor_str assert (xu ^ utils.str2num('0b'+mks_str)).bin() == xor_str assert (utils.str2num('0b'+mks_str) ^ x).bin() == xor_str assert (utils.str2num('0b'+mks_str) ^ xu).bin() == xor_str val_str = '10101100' mks_str = '11001100' xor_str = '01100000' x('0b'+val_str) xu('0b'+val_str) y('0b'+mks_str) yu('0b'+mks_str) assert (x ^ y).bin() == xor_str assert (x ^ yu).bin() == xor_str assert (xu ^ y).bin() == xor_str assert (xu ^ yu).bin() == xor_str assert (x ^ utils.str2num('0b'+mks_str)).bin() == xor_str assert (xu ^ utils.str2num('0b'+mks_str)).bin() == xor_str assert (utils.str2num('0b'+mks_str) ^ x).bin() == xor_str assert (utils.str2num('0b'+mks_str) ^ xu).bin() == xor_str def test_arrays(): x = Fxp(None, True, 8, 4) y = Fxp(None, True, 8, 4) x(['0b00110101', '0b10101100']) y('0b11110000') z = x & y assert z.bin()[0] == '00110000' assert z.bin()[1] == '10100000' def test_operations_with_combinations(): v = [-256, -64, -16, -4.75, -3.75, -3.25, -1, -0.75, -0.125, 0.0, 0.125, 0.75, 1, 1.5, 3.75, 4.0, 8.0, 32, 128] for i in range(len(v)): for j in range(len(v)): vx, vy = v[i], v[j] x = Fxp(vx) y = Fxp(vy) assert (vx + vy) == (x + y)() assert (vy + vx) == (y + x)() assert (vx - vy) == (x - y)() assert -(vy - vx) == -(y - x)() assert (vx * vy) == (x * y)() assert (vy * vx) == (y * x)() v = [-256, -64, -16, -4.75, -4.25, -1, -0.75, -0.125, 0.125, 0.75, 1, 1.5, 2.75, 4.0, 8.0, 32, 128] d = [-256, -64, -16, -1, -0.5, -0.125, 0.125, 0.5, 1, 2, 4.0, 8.0, 32, 128] for i in range(len(v)): for j in range(len(d)): vx, vy = v[i], d[j] x = Fxp(vx) y = Fxp(vy) assert (vx / vy) == (x / y)() assert (vx // vy) == (x // y)() assert (vx % vy) == (x % y)() def test_operations_with_constants_with_combinations(): v = [-256, -64, -16, -4.75, -3.75, -3.25, -1, -0.75, -0.125, 0.0, 0.125, 0.75, 1, 1.5, 3.75, 4.0, 8.0, 32, 128] for i in range(len(v)): for j in range(len(v)): vx, vy = v[i], v[j] x = Fxp(vx, True, 16, 3) y = Fxp(vy, True, 16, 3) assert (x + vy)() == (vx + vy) == (vx + y)() == (x + y)() assert (vy + x)() == (vy + vx) == (y + vx)() == (y + x)() assert (x - vy)() == (vx - vy) == (vx - y)() == (x - y)() assert -(vy - x)() == -(vy - vx) == -(y - vx)() == -(y - x)() for i in range(len(v)): for j in range(len(v)): vx, vy = v[i], v[j] x = Fxp(vx, True, 24, 6) y = Fxp(vy, True, 24, 6) assert (x * vy)() == (vx * vy) == (vx * y)() == (x * y)() assert (vy * x)() == (vy * vx) == (y * vx)() == (y * x)() v = [-256, -64, -16, -4.75, -4.25, -1, -0.75, -0.125, 0.125, 0.75, 1, 1.5, 2.75, 4.0, 8.0, 32, 128] d = [-256, -64, -16, -1, -0.5, -0.125, 0.125, 0.5, 1, 2, 4.0, 8.0, 32, 128] for i in range(len(v)): for j in range(len(d)): vx, vy = v[i], d[j] x = Fxp(vx, True, 32, 12) y = Fxp(vy, True, 32, 12) assert (x / vy)() == (vx / vy) == (vx / y)() == (x / y)() # assert (vy / x)() == (vy / vx) == (y / vx)() == (y / x)() assert (x // vy)() == (vx // vy) == (vx // y)() == (x // y)() # assert (vy // x)() == (vy // vx) == (y // vx)() == (y // x)() assert (x % vy)() == (vx % vy) == (vx % y)() == (x % y)() # assert (vy % x)() == (vy % vx) == (y % vx)() == (y % x)() def test_pow(): x = Fxp(16, True, n_int=14, n_frac=8) n = Fxp(-1, True, n_int=14, n_frac=8) assert(x**n)() == 1/16 v = 15 n_vals = [0, 1, 2, 3] x = Fxp(v, signed=True, n_int=12, n_frac=0) xu = Fxp(v, signed=False, n_int=12, n_frac=0) for n in n_vals: assert (x**n)() == v**n assert (xu**n)() == v**n v = -16 x = Fxp(v, signed=True, n_int=12, n_frac=0) for n in n_vals: assert (x**n)() == v**n v = 16.0 n_vals = [-2, -1, 0, 1, 2, 3] x = Fxp(v, signed=True, n_int=14, n_frac=8) # xu = Fxp(v, signed=False, n_int=12, n_frac=0) for n in n_vals: assert (x**n)() == v**n # assert (xu**n)() == v**n v = -16.0 x = Fxp(v, signed=True, n_int=14, n_frac=8) for n in n_vals: assert (x**n)() == (v)**n v = 81 n_vals = [0, 0.25, 0.5] x = Fxp(v, signed=True, n_int=14, n_frac=8) xu = Fxp(v, signed=False, n_int=14, n_frac=8) for n in n_vals: assert (x**n)() == v**n assert (xu**n)() == v**n v = 16. n = 2 v_vals = [-4, -2, -1, 0, 1, 2, 4] n_vals = [-2, -1, 0, 1, 2] x = Fxp(v, signed=True, n_int=12, n_frac=0) xu = Fxp(v, signed=False, n_int=12, n_frac=0) p = Fxp(n, signed=True, n_int=8, n_frac=0) assert ((x**p)() == np.power(v, n)).all() assert ((xu**p)() == np.power(v, n)).all() x = Fxp(v, signed=True, n_int=12, n_frac=8) p_vals = Fxp(n_vals, signed=True, n_int=8, n_frac=0) x.config.op_sizing = 'same' assert ((x**p_vals)() == np.power(v, n_vals)).all() p_vals = Fxp(n_vals, signed=True, n_int=8, n_frac=0) assert ((x**p_vals)() == np.power(v, n_vals)).all() x_vals = Fxp(v_vals, signed=True, n_int=12, n_frac=8) p = Fxp(n, signed=True, n_int=8, n_frac=0) x_vals.config.op_sizing = 'same' assert ((x_vals**p)() == np.power(v_vals, n)).all() p = Fxp(n, signed=True, n_int=8, n_frac=2) assert ((x_vals**p)() == np.power(v_vals, n)).all() v_vals = [-1, 1, 2, 3, 4] n_vals = [-2, -1, 0, 1, 2] x_vals = Fxp(v_vals, signed=True, n_int=12, n_frac=8) p_vals = Fxp(n_vals, signed=True, n_int=8, n_frac=0) x_vals.config.op_sizing = 'same' assert ((x_vals**p_vals)() == np.array([vi**ni for vi, ni in zip(v_vals, n_vals)])).all() p_vals = Fxp(n_vals, signed=True, n_int=8, n_frac=2) assert ((x_vals**p_vals)() == np.array([vi**ni for vi, ni in zip(v_vals, n_vals)])).all() v_vals = [[1, 2],[3, 4]] n_vals = [[1, 2],[3, 4]] x_vals = Fxp(v_vals, signed=True, n_int=12, n_frac=8) p_vals = Fxp(n_vals, signed=True, n_int=8, n_frac=0) x_vals.config.op_sizing = 'same' assert ((x_vals**p_vals)() == np.power(v_vals, n_vals)).all() def test_scaled(): x = Fxp(10.5, True, 16, 8, scale=2, bias=1) assert x() == 10.5 assert x + 2 == 12.5 assert x - 2.5 == 8.0 assert x * 3 == 31.5 assert x / 2 == 5.25

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19e7540196ad90163feb7018771ccb4ab2bcb873

10,175

py

Python

cpm_kernels/kernels/arith.py

Achazwl/cpm_kernels

926d06461ad460dc8e80a66239328739eed16618

[ "Apache-2.0" ]

1

2022-03-04T11:04:09.000Z

cpm_kernels/kernels/arith.py

Achazwl/cpm_kernels

926d06461ad460dc8e80a66239328739eed16618

[ "Apache-2.0" ]

1

2022-03-07T03:45:00.000Z

2022-03-19T06:16:37.000Z

cpm_kernels/kernels/arith.py

Achazwl/cpm_kernels

926d06461ad460dc8e80a66239328739eed16618

[ "Apache-2.0" ]

1

2022-03-04T16:52:08.000Z

from .base import Kernel, DevicePointer, CUDAStream, round_up import ctypes arith_kernel = Kernel( "arith", [ "cu_arith_global_scale", "cu_arith_element_add", "cu_arith_element_mul", "cu_arith_batch_add_forward", "cu_arith_batch_add_backward", "cu_arith_ln_mul_add", "cu_arith_ln_add", "cu_arith_ln_mul", "cu_arith_ln_div", "cu_arith_ln_sub_div", "cu_arith_ln_mul_backward", "cu_arith_ln_add_backward", "cu_arith_batch_mul_add", "cu_arith_batch_mul" ] ) def arith_global_scale( n : int, inp : DevicePointer, # (n,) fp16 scale : float, out : DevicePointer, # (n,) fp16 stream : CUDAStream ): threads = min(round_up(n, 32), 1024) gridDim = (round_up(n, threads) // threads, 1, 1) blockDim = (threads, 1, 1) arith_kernel.cu_arith_global_scale( gridDim, blockDim, 0, stream, [ ctypes.c_int64(n), ctypes.c_void_p(inp), ctypes.c_float(scale), ctypes.c_void_p(out) ] ) def arith_element_add( batch : int, n : int, x : DevicePointer, # (batch, n) fp16 y : DevicePointer, # (batch, n) fp16 out : DevicePointer, # (batch, n) fp16 stream : CUDAStream ): """ out = x + y """ assert n % 2 == 0 n = n // 2 threads = min(round_up(n, 32), 1024) gridDim = (batch, round_up(n, threads) // threads, 1) blockDim = (threads, 1, 1) arith_kernel.cu_arith_element_add( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_void_p(x), ctypes.c_void_p(y), ctypes.c_void_p(out) ] ) def arith_element_mul( batch : int, n : int, x : DevicePointer, # (batch, n) fp16 y : DevicePointer, # (batch, n) fp16 out : DevicePointer, # (batch, n) fp16 stream : CUDAStream ): """ out = x * y """ assert n % 2 == 0 n = n // 2 threads = min(round_up(n, 32), 1024) gridDim = (batch, round_up(n, threads) // threads, 1) blockDim = (threads, 1, 1) arith_kernel.cu_arith_element_mul( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_void_p(x), ctypes.c_void_p(y), ctypes.c_void_p(out) ] ) def arith_batch_add_forward( batch : int, n : int, x : DevicePointer, # (batch, n) fp16 y : DevicePointer, # (n) fp16 out : DevicePointer, # (batch, n) fp16 stream : CUDAStream ): """ out = x + y[None, :] """ assert n % 2 == 0 n = n // 2 threads = min(round_up(n, 32), 1024) gridDim = (batch, round_up(n, threads) // threads, 1) blockDim = (threads, 1, 1) arith_kernel.cu_arith_batch_add_forward( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_void_p(x), ctypes.c_void_p(y), ctypes.c_void_p(out) ] ) def arith_batch_add_backward( batch : int, n : int, grad_out : DevicePointer, # (batch, n) fp16 grad : DevicePointer, # (n) fp16 stream : CUDAStream ): gridDim = ( round_up(n, 32) // 32, 1, 1 ) blockDim = (32, 32, 1) arith_kernel.cu_arith_batch_add_backward( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_void_p(grad_out), ctypes.c_void_p(grad) ] ) def arith_ln_mul_add( batch : int, n : int, m : int, inp : DevicePointer, # (batch, n, m) fp16 alpha : DevicePointer, # (n) fp16 beta : DevicePointer, # (n) fp16 out : DevicePointer, # (batch, n, m) fp16 stream : CUDAStream ): """ out = x * alpha[None, :, None] + beta[None, :, None] """ assert m % 2 == 0 m = m // 2 threads = min(round_up(m, 32), 1024) gridDim = (batch, n, round_up(m, threads) // threads) blockDim = (threads, 1, 1) arith_kernel.cu_arith_ln_mul_add( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_int64(m), ctypes.c_void_p(inp), ctypes.c_void_p(alpha), ctypes.c_void_p(beta), ctypes.c_void_p(out) ] ) def arith_ln_add( batch : int, n : int, m : int, inp : DevicePointer, # (batch, n, m) fp16 beta : DevicePointer, # (n) fp16 out : DevicePointer, # (batch, n, m) fp16 stream : CUDAStream ): """ out = x + beta[None, :, None] """ assert m % 2 == 0 m = m // 2 threads = min(round_up(m, 32), 1024) gridDim = (batch, n, round_up(m, threads) // threads) blockDim = (threads, 1, 1) arith_kernel.cu_arith_ln_add( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_int64(m), ctypes.c_void_p(inp), ctypes.c_void_p(beta), ctypes.c_void_p(out) ] ) def arith_ln_mul( batch : int, n : int, m : int, inp : DevicePointer, # (batch, n, m) fp16 alpha : DevicePointer, # (n) fp16 out : DevicePointer, # (batch, n, m) fp16 stream : CUDAStream ): """ out = x * alpha[None, :, None] """ assert m % 2 == 0 m = m // 2 threads = min(round_up(m, 32), 1024) gridDim = (batch, n, round_up(m, threads) // threads) blockDim = (threads, 1, 1) arith_kernel.cu_arith_ln_mul( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_int64(m), ctypes.c_void_p(inp), ctypes.c_void_p(alpha), ctypes.c_void_p(out) ] ) def arith_ln_div( batch : int, n : int, m : int, inp : DevicePointer, # (batch, n, m) fp16 alpha : DevicePointer, # (n) fp16 out : DevicePointer, # (batch, n, m) fp16 stream : CUDAStream ): """ out = x / alpha[None, :, None] """ assert m % 2 == 0 m = m // 2 threads = min(round_up(m, 32), 1024) gridDim = (batch, n, round_up(m, threads) // threads) blockDim = (threads, 1, 1) arith_kernel.cu_arith_ln_div( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_int64(m), ctypes.c_void_p(inp), ctypes.c_void_p(alpha), ctypes.c_void_p(out) ] ) def arith_ln_sub_div( batch : int, n : int, m : int, inp : DevicePointer, # (batch, n, m) fp16 alpha : DevicePointer, # (n) fp16 beta : DevicePointer, # (n) fp16 out : DevicePointer, # (batch, n, m) fp16 stream : CUDAStream ): """ out = (x - beta[None, :, None]) / alpha[None, :, None] """ assert m % 2 == 0 m = m // 2 threads = min(round_up(m, 32), 1024) gridDim = (batch, n, round_up(m, threads) // threads) blockDim = (threads, 1, 1) arith_kernel.cu_arith_ln_sub_div( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_int64(m), ctypes.c_void_p(inp), ctypes.c_void_p(alpha), ctypes.c_void_p(beta), ctypes.c_void_p(out) ] ) def arith_ln_mul_backward( batch : int, n : int, m : int, inp : DevicePointer, # (batch, n, m) fp16 grad_out : DevicePointer, # (batch, n, m) fp16 grad : DevicePointer, # (n) fp16 stream : CUDAStream ): gridDim = (n, 1, 1) blockDim = (32, 32, 1) arith_kernel.cu_arith_ln_mul_backward( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_int64(m), ctypes.c_void_p(inp), ctypes.c_void_p(grad_out), ctypes.c_void_p(grad) ] ) def arith_ln_add_backward( batch : int, n : int, m : int, grad_out : DevicePointer, # (batch, n, m) fp16 grad : DevicePointer, # (n) fp16 stream : CUDAStream ): gridDim = (n, 1, 1) blockDim = (32, 32, 1) arith_kernel.cu_arith_ln_add_backward( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_int64(m), ctypes.c_void_p(grad_out), ctypes.c_void_p(grad) ] ) def arith_batch_mul_add( batch : int, n : int, x : DevicePointer, # (batch, n) alpha : DevicePointer, # (n) beta : DevicePointer, # (n) out : DevicePointer, # (batch, n) stream : CUDAStream ): assert n % 2 == 0 n = n // 2 threads = min(round_up(n, 32), 1024) gridDim = (batch, round_up(n, threads) // threads, 1) blockDim = (threads, 1, 1) arith_kernel.cu_arith_batch_mul_add( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_void_p(x), ctypes.c_void_p(alpha), ctypes.c_void_p(beta), ctypes.c_void_p(out) ] ) def arith_batch_mul( batch : int, n : int, x : DevicePointer, # (batch, n) alpha : DevicePointer, # (n) out : DevicePointer, # (batch, n) stream : CUDAStream ): assert n % 2 == 0 n = n // 2 threads = min(round_up(n, 32), 1024) gridDim = (batch, round_up(n, threads) // threads, 1) blockDim = (threads, 1, 1) arith_kernel.cu_arith_batch_mul( gridDim, blockDim, 0, stream, [ ctypes.c_int64(batch), ctypes.c_int64(n), ctypes.c_void_p(x), ctypes.c_void_p(alpha), ctypes.c_void_p(out) ] )

28.661972

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null

0

7

df9d237726d5d359f7bdb462a59f3b344116b9ca

212

py

Python

sisvac_appointments/services/__init__.py

opticrd/sisvac-odoo-modules

8ca71ec8f116a04416d62a780acc1ff5784acd3a

[ "MIT" ]

3

2021-03-16T17:14:25.000Z

2021-08-15T17:40:04.000Z

sisvac_appointments/services/__init__.py

opticrd/sisvac-odoo-modules

8ca71ec8f116a04416d62a780acc1ff5784acd3a

[ "MIT" ]

3

2021-03-19T01:37:40.000Z

2021-04-14T12:27:20.000Z

sisvac_appointments/services/__init__.py

opticrd/sisvac-odoo-modules

8ca71ec8f116a04416d62a780acc1ff5784acd3a

[ "MIT" ]

null

from . import common from . import appointments_service from . import application_services from . import symptom_services from . import consent_services from . import location_services from . import lot_services

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1

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7

261166e77197b46a8c6f5cd00a6a1e19768d8823

4,015

py

Python

flaskie/api/v1/tests/test_logout.py

asheuh/flaskie

290fd2a6602abdf3b11434e2a72a3428acc0c0f4

[ "MIT" ]

7

2018-06-20T19:06:05.000Z

2019-11-03T02:23:20.000Z

flaskie/api/v1/tests/test_logout.py

asheux/flaskie

290fd2a6602abdf3b11434e2a72a3428acc0c0f4

[ "MIT" ]

23

2018-07-09T13:00:22.000Z

2018-08-04T10:48:42.000Z

flaskie/api/v1/tests/test_logout.py

asheux/flaskie

290fd2a6602abdf3b11434e2a72a3428acc0c0f4

[ "MIT" ]

1

2018-09-22T15:39:20.000Z

import json from .base_test import BaseTestCase class TestLogout(BaseTestCase): def test_logout(self): with self.client: response_login = self.client.post( '/api/v1/auth/login', data=json.dumps(dict( username='paulla', password='mermaid' )), content_type='application/json' ) response_data = json.loads(response_login.data.decode()) self.assertTrue(response_data['status'] == 'success') self.assertTrue(response_data['message'] == 'Successfully logged in as Paulla Mboya') self.assertTrue(response_data['Authorization']['access_token']) self.assertEqual(response_login.status_code, 201) # valid logout response = self.client.post( '/api/v1/auth/logout_access', headers=dict( Authorization='Bearer ' + json.loads( response_login.data.decode() )['Authorization']['access_token'] ) ) data = json.loads(response.data.decode()) self.assertTrue(data['status'] == 'success') self.assertTrue(data['message'] == 'Access token has been revoked, you are now logged out') self.assertEqual(response.status_code, 200) def test_logout_refresh(self): with self.client: response_login = self.client.post( '/api/v1/auth/login', data=json.dumps(dict( username='paulla', password='mermaid' )), content_type='application/json' ) response_data = json.loads(response_login.data.decode()) self.assertTrue(response_data['status'] == 'success') self.assertTrue(response_data['message'] == 'Successfully logged in as Paulla Mboya') self.assertTrue(response_data['Authorization']['access_token']) self.assertEqual(response_login.status_code, 201) # valid logout response = self.client.post( '/api/v1/auth/logout_refresh', headers=dict( Authorization='Bearer ' + json.loads( response_login.data.decode() )['Authorization']['refresh_token'] ) ) data = json.loads(response.data.decode()) self.assertTrue(data['status'] == 'success') self.assertTrue(data['message'] == 'Refresh token has been revoked') self.assertEqual(response.status_code, 200) def test_token_refresh(self): with self.client: response_login = self.client.post( '/api/v1/auth/login', data=json.dumps(dict( username='paulla', password='mermaid' )), content_type='application/json' ) response_data = json.loads(response_login.data.decode()) self.assertTrue(response_data['status'] == 'success') self.assertTrue(response_data['message'] == 'Successfully logged in as Paulla Mboya') self.assertTrue(response_data['Authorization']['access_token']) self.assertEqual(response_login.status_code, 201) # valid logout response = self.client.post( '/api/v1/auth/refresh_token', headers=dict( Authorization='Bearer ' + json.loads( response_login.data.decode() )['Authorization']['refresh_token'] ) ) data = json.loads(response.data.decode()) self.assertTrue(data['status'] == 'success') self.assertTrue(data['message'] == 'token refreshed successfully') self.assertEqual(response.status_code, 201)

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2622545d6da9d4e0b483916a9e3466b892e652a8

2,299

py

Python

tests/unit/butterfree/transform/transformations/user_defined_functions/conftest.py

fossabot/butterfree

8a7da8c540b51c6560b2825cb926c40a351f202b

[ "Apache-2.0" ]

208

2020-07-17T18:46:10.000Z

2022-03-21T12:44:12.000Z

tests/unit/butterfree/transform/transformations/user_defined_functions/conftest.py

fossabot/butterfree

8a7da8c540b51c6560b2825cb926c40a351f202b

[ "Apache-2.0" ]

124

2020-07-17T19:42:47.000Z

2021-07-21T00:38:05.000Z

tests/unit/butterfree/transform/transformations/user_defined_functions/conftest.py

fossabot/butterfree

8a7da8c540b51c6560b2825cb926c40a351f202b

[ "Apache-2.0" ]

30

2020-07-17T20:24:09.000Z

2022-03-17T00:50:37.000Z

from pytest import fixture @fixture def feature_set_dataframe(spark_context, spark_session): data = [ {"id": 1, "feature1": 100}, {"id": 1, "feature1": 100}, {"id": 1, "feature1": 200}, {"id": 1, "feature1": 200}, {"id": 1, "feature1": 200}, {"id": 1, "feature1": 300}, {"id": 1, "feature1": 300}, {"id": 1, "feature1": 300}, {"id": 1, "feature1": 300}, {"id": 1, "feature1": 300}, {"id": 2, "feature1": 100}, {"id": 2, "feature1": 100}, {"id": 2, "feature1": 200}, {"id": 2, "feature1": 200}, {"id": 2, "feature1": 200}, {"id": 2, "feature1": 300}, {"id": 2, "feature1": 300}, {"id": 2, "feature1": 300}, {"id": 2, "feature1": 300}, {"id": 2, "feature1": 300}, ] return spark_session.read.json(spark_context.parallelize(data, 1)) @fixture def feature_set_custom_dataframe(spark_context, spark_session): data = [ {"id": 1, "feature1": "abc"}, {"id": 1, "feature1": "abc"}, {"id": 1, "feature1": "abc"}, {"id": 1, "feature1": "def"}, {"id": 1, "feature1": "def"}, {"id": 2, "feature1": "def"}, {"id": 2, "feature1": "def"}, {"id": 2, "feature1": "def"}, {"id": 2, "feature1": "abc"}, {"id": 2, "feature1": "abc"}, ] return spark_session.read.json(spark_context.parallelize(data, 1)) @fixture def mode_target_df(spark_context, spark_session): data = [ {"id": 1, "mode(feature1)": "300"}, {"id": 2, "mode(feature1)": "300"}, ] return spark_session.read.json(spark_context.parallelize(data, 1)) @fixture def most_frequent_set_target_df(spark_context, spark_session): data = [ {"id": 1, "most_frequent_set(feature1)": ["300", "200", "100"]}, {"id": 2, "most_frequent_set(feature1)": ["300", "200", "100"]}, ] return spark_session.read.json(spark_context.parallelize(data, 1)) @fixture def most_frequent_set_str_target_df(spark_context, spark_session): data = [ {"id": 1, "most_frequent_set(feature1)": ["abc", "def"]}, {"id": 2, "most_frequent_set(feature1)": ["def", "abc"]}, ] return spark_session.read.json(spark_context.parallelize(data, 1))

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null

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26b40eaaa82b9f2ed4027bee64eeace23b2d04e5

174

py

Python

tests/test_punters_client.py

predictive-punter/punters_client

87fa0ba29f716937ae2a602b6946662f79b3452d

[ "MIT" ]

4

2019-11-03T06:07:53.000Z

2021-04-20T17:33:24.000Z

tests/test_punters_client.py

justjasongreen/punters_client

87fa0ba29f716937ae2a602b6946662f79b3452d

[ "MIT" ]

50

2016-07-20T05:14:40.000Z

2016-07-27T07:12:12.000Z

tests/test_punters_client.py

predictive-punter/punters_client

87fa0ba29f716937ae2a602b6946662f79b3452d

[ "MIT" ]

5

2016-12-15T06:04:46.000Z

2020-09-15T07:02:58.000Z

import punters_client def test_version(): """punters_client.__version__ should return the correct version string""" assert punters_client.__version__ == '1.0.0b8'

21.75

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null

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1

0

1

0

8

f803ecd26c75201d8b091079b451b29c2f847fca

5,372

py

Python

src/go/outcome.py

sadakatsu/strawman

b374f2bb6268ebe9aa25da8578fb0f0f25af5b87

[ "MIT" ]

null

src/go/outcome.py

sadakatsu/strawman

b374f2bb6268ebe9aa25da8578fb0f0f25af5b87

[ "MIT" ]

null

src/go/outcome.py

sadakatsu/strawman

b374f2bb6268ebe9aa25da8578fb0f0f25af5b87

[ "MIT" ]

null

#!/usr/bin/env python3 from enum import auto, Enum from math import isclose from .color import Color class Outcome: @property def over(self) -> bool: raise NotImplementedError() @property def black_points_on_board(self) -> int: raise NotImplementedError() @property def black_score(self) -> float: raise NotImplementedError() @property def white_points_on_board(self) -> int: raise NotImplementedError() @property def white_score(self) -> float: raise NotImplementedError() @property def margin(self) -> float: raise NotImplementedError() @property def winner(self) -> Color: raise NotImplementedError() class CompleteButNotScored(Outcome, Enum): INSTANCE = auto() @property def over(self) -> bool: return True @property def black_points_on_board(self) -> int: return None @property def black_score(self) -> float: return None @property def white_points_on_board(self) -> int: return None @property def white_score(self) -> float: return None @property def margin(self) -> float: return None @property def winner(self) -> Color: return None def __str__(self): return 'Complete but not Scored' class Draw(Outcome): def __init__( self, black_points_on_board: int, black_score: float, white_points_on_board: int, white_score: float ): self._black_points_on_board = black_points_on_board self._black_score = black_score self._white_points_on_board = white_points_on_board self._white_score = white_score @property def over(self) -> bool: return True @property def black_points_on_board(self) -> int: return self._black_points_on_board @property def black_score(self) -> float: return self._black_score @property def white_points_on_board(self) -> int: return self._white_points_on_board @property def white_score(self) -> float: return self._white_score @property def margin(self) -> float: return 0. @property def winner(self) -> Color: return None def __str__(self): return 'Draw' class InProgress(Outcome, Enum): INSTANCE = auto() @property def over(self) -> bool: return False @property def black_points_on_board(self) -> int: return None @property def black_score(self) -> float: return None @property def white_points_on_board(self) -> int: return None @property def white_score(self) -> float: return None @property def margin(self) -> float: return None @property def winner(self) -> Color: return None def __str__(self): return 'In Progress' class Invalidated(Outcome, Enum): INSTANCE = auto() @property def over(self) -> bool: return True @property def black_points_on_board(self) -> int: return None @property def black_score(self) -> float: return None @property def white_points_on_board(self) -> int: return None @property def white_score(self) -> float: return None @property def margin(self) -> float: return None @property def winner(self) -> Color: return None def __str__(self): return 'Invalidated' class Win(Outcome): def __init__( self, black_points_on_board: int, black_score: float, white_points_on_board: int, white_score: float ): self._black_points_on_board = black_points_on_board self._black_score = black_score self._white_points_on_board = white_points_on_board self._white_score = white_score self._margin = abs(black_score - white_score) self._winner = Color.BLACK if black_score > white_score else Color.WHITE @property def over(self) -> bool: return True @property def black_points_on_board(self) -> int: return self._black_points_on_board @property def black_score(self) -> float: return self._black_score @property def white_points_on_board(self) -> int: return self._white_points_on_board @property def white_score(self) -> float: return self._white_score @property def margin(self) -> float: return self._margin @property def winner(self) -> Color: return self._winner def __str__(self): winner = 'Black' if self._winner is Color.BLACK else 'White' return f'{winner} won by {self._margin} points' def calculate_outcome( black_points_on_board: int, black_point_adjustment: float, white_points_on_board: int, white_point_adjustment: float, ): final_black = black_points_on_board + black_point_adjustment final_white = white_points_on_board + white_point_adjustment if isclose(final_black, final_white): outcome = Draw(black_points_on_board, final_black, white_points_on_board, final_white) else: outcome = Win(black_points_on_board, final_black, white_points_on_board, final_white) return outcome

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0.638496

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0.093023

0.143345

0.145206

0.100527

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0.776295

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0.281646

5,372

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1

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null

0

1

0

1

0

9

f80e74da52382a01276f3649b5a1d96628728874

1,782

py

Python

common/src/main/python/gov/nasa/jpl/edrn/labcas/preprocess/utils.py

EDRN/labcas-backend

87a0a4bfbb98782b3ed58270aebcc8ba6a392106

[ "Apache-2.0" ]

null

common/src/main/python/gov/nasa/jpl/edrn/labcas/preprocess/utils.py

EDRN/labcas-backend

87a0a4bfbb98782b3ed58270aebcc8ba6a392106

[ "Apache-2.0" ]

3

2020-01-30T01:02:14.000Z

2021-01-01T00:55:59.000Z

common/src/main/python/gov/nasa/jpl/edrn/labcas/preprocess/utils.py

EDRN/labcas-backend

87a0a4bfbb98782b3ed58270aebcc8ba6a392106

[ "Apache-2.0" ]

null

# Collection of Python utilities for LabCAS operations def write_description(metadata_filepath, description): '''Writes the file description to the ancillary metadata file.''' print "Writing metadata file: %s" % metadata_filepath with open(metadata_filepath,'w') as file: file.write('<cas:metadata xmlns:cas="http://oodt.jpl.nasa.gov/1.0/cas">\n') file.write('\t<keyval type="vector">\n') file.write('\t\t<key>_File_Description</key>\n') file.write('\t\t<val>%s</val>\n' % description) file.write('\t</keyval>\n') file.write('</cas:metadata>\n') def write_file_metadata(metadata_filepath, metadata): '''Writes file metadata to the ancillary file.''' print "Writing metadata file: %s" % metadata_filepath with open(metadata_filepath,'w') as file: file.write('<cas:metadata xmlns:cas="http://oodt.jpl.nasa.gov/1.0/cas">\n') for key, value in metadata.items(): file.write('\t<keyval type="vector">\n') file.write('\t\t<key>_File_%s</key>\n' % key) file.write('\t\t<val>%s</val>\n' % value) file.write('\t</keyval>\n') file.write('</cas:metadata>\n') def write_dataset_metadata(filepath, metadata): '''Writes file metadata to the ancillary file.''' print "Writing metadata file: %s" % filepath with open(filepath,'w') as file: file.write('<cas:metadata xmlns:cas="http://oodt.jpl.nasa.gov/1.0/cas">\n') for key, value in metadata.items(): file.write('\t<keyval type="vector">\n') file.write('\t\t<key>%s</key>\n' % key) file.write('\t\t<val>%s</val>\n' % value) file.write('\t</keyval>\n') file.write('</cas:metadata>\n')

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83

0.602694

252

1,782

4.202381

0.174603

0.152975

0.113314

0.813031

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0

0.004283

0.213805

1,782

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0.751606

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0

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0.037676

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null

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null

0.103448

0

null

0

1

0

null

0

1

0

7

f81b7fadf51f8e70075bb54b3346cb40132c7496

1,578

py

Python

Blinker/BlinkerDebug.py

zhcong/blinker-mpy

b52538497a5e734c63d00a72c3f8ed28ff749a86

[ "MIT" ]

17

2019-07-30T08:43:32.000Z

2021-12-08T21:47:10.000Z

Blinker/BlinkerDebug.py

zhcong/blinker-mpy

b52538497a5e734c63d00a72c3f8ed28ff749a86

[ "MIT" ]

3

2020-05-05T10:51:57.000Z

2021-04-21T03:06:20.000Z

Blinker/BlinkerDebug.py

zhcong/blinker-mpy

b52538497a5e734c63d00a72c3f8ed28ff749a86

[ "MIT" ]

10

2019-08-10T16:01:02.000Z

2021-12-13T08:43:22.000Z

from BlinkerUtility.BlinkerUtility import * class BlinkerDebug(): def __init__(self): self.isDebug = False self.isDebugAll = False def debug(self): self.isDebug = True self.isDebugAll = False def debugAll(self): self.isDebug = True self.isDebugAll = True BLINKER_DEBUG = BlinkerDebug() def BLINKER_LOG(arg1, *vartuple): # timeInfo = time.strftime("%H:%M:%S %Y", time.localtime()) if BLINKER_DEBUG.isDebug == False : return data = str(arg1) for var in vartuple: data = data + str(var) data = '[' + str(millis()) + '] ' + data print(data) def BLINKER_ERR_LOG(arg1, *vartuple): # timeInfo = time.strftime("%H:%M:%S %Y", time.localtime()) if BLINKER_DEBUG.isDebug == False : return data = str(arg1) for var in vartuple: data = data + str(var) data = '[' + str(millis()) + '] Error: ' + data print(data) def BLINKER_LOG_ALL(arg1, *vartuple): # timeInfo = time.strftime("%H:%M:%S %Y", time.localtime()) if BLINKER_DEBUG.isDebugAll == False : return data = str(arg1) for var in vartuple: data = data + str(var) data = '[' + str(millis()) + '] ' + data print(data) def BLINKER_ERR_LOG_ALL(arg1, *vartuple): # timeInfo = time.strftime("%H:%M:%S %Y", time.localtime()) if BLINKER_DEBUG.isDebugAll == False : return data = str(arg1) for var in vartuple: data = data + str(var) data = '[' + str(millis()) + '] Error: ' + data print(data)

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63

0.575412

190

1,578

4.689474

0.2

0.094276

0.089787

0.107744

0.817059

0.805836

0.731762

0

0.007024

0.2782

1,578

60

64

26.3

0.775241

0.146388

0

0.727273

0

0.019374

0

1

0.159091

false

0

0.022727

0

0.295455

0.090909

0

null

0

1

0

1

0

null

0

7

f8613fdae4ef7c8224f173f11a7de0c6dc0d49f3

1,925

py

Python

backend/wod_board/tests/models/test_user.py

GuillaumeOj/P13-WOD-Board

36df7979e63c354507edb56eabdfc548b1964d08

[ "MIT" ]

null

backend/wod_board/tests/models/test_user.py

GuillaumeOj/P13-WOD-Board

36df7979e63c354507edb56eabdfc548b1964d08

[ "MIT" ]

82

2021-01-17T18:12:23.000Z

2021-06-12T21:46:49.000Z

backend/wod_board/tests/models/test_user.py

GuillaumeOj/WodBoard

1ac12404f6094909c9bf116bcaf6ccd60e85bc00

[ "MIT" ]

null

import pytest import sqlalchemy.exc from wod_board.models import user def test_user(db): new_user = user.User( email="foo@bar.com", password="foo-password", username="foo-username", first_name="foo", last_name="bar", is_admin=False, ) db.add(new_user) db.commit() db.refresh(new_user) assert str(new_user) == f"<User {new_user.email}>" new_user = user.User( password="foo-password", username="foo-username", is_admin=False, ) db.add(new_user) with pytest.raises(sqlalchemy.exc.IntegrityError) as error: db.commit() db.rollback() assert ( 'null value in column "email" of relation "user" ' "violates not-null constraint" in str(error) ) new_user = user.User( email="foo@bar.com", username="foo-username", is_admin=False, ) db.add(new_user) with pytest.raises(sqlalchemy.exc.IntegrityError) as error: db.commit() db.rollback() assert ( 'null value in column "password" of relation "user" ' "violates not-null constraint" in str(error) ) new_user = user.User( email="foo@bar.com", password="foo-password", is_admin=False, ) db.add(new_user) with pytest.raises(sqlalchemy.exc.IntegrityError) as error: db.commit() db.rollback() assert ( 'null value in column "username" of relation "user" ' "violates not-null constraint" in str(error) ) new_user = user.User( email="foo@bar.com", password="foo-password", username="foo-username", ) db.add(new_user) with pytest.raises(sqlalchemy.exc.IntegrityError) as error: db.commit() db.rollback() assert ( 'null value in column "is_admin" of relation "user" ' "violates not-null constraint" in str(error) )

25.328947

63

0.598961

241

1,925

4.692946

0.190871

0.08046

0.04863

0.066313

0.827586

0.810787

0.789567

0

0.277922

1,925

75

64

25.666667

0.813669

0

0.691176

0

0.25039

0

0.073529

1

0.014706

false

0.073529

0.044118

0

0.058824

0

null

0

1

0

null

0

1

0

7

3e29f0eb4ce004dc76ef6dd0876aa930cd9d76f6

40,996

py

Python

o/soft_robot/prev/kinematics.py

YoshimitsuMatsutaIe/ctrlab2021_soudan

7841c981e6804cc92d34715a00e7c3efce41d1d0

[ "MIT" ]

null

o/soft_robot/prev/kinematics.py

YoshimitsuMatsutaIe/ctrlab2021_soudan

7841c981e6804cc92d34715a00e7c3efce41d1d0

[ "MIT" ]

null

o/soft_robot/prev/kinematics.py

YoshimitsuMatsutaIe/ctrlab2021_soudan

7841c981e6804cc92d34715a00e7c3efce41d1d0

[ "MIT" ]

null

import numpy as np from math import sin, cos, sqrt from math_utils import * class Base: """ベース""" # モーダル同時変換行列のパラメータ c1 = 837019575 c2 = 4133430 c3 = 32805 c4 = 486 c5 = 18 c6 = 55801305 c7 = 688905 c8 = 3645 c9 = 81 c10 = 279006525 c11 = 1377810 c12 = 10935 c13 = 162 c14 = 243 c15 = 2066715 r = 0.0125 L0 = 0.15 sq3 = sqrt(3) def calc_P(self, q, xi): """線形化されたアクチュエータ空間からタスク空間への写像順運動学 """ l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] A1 = l1**2 + l2**2 + l3**2 - l1*l2 - l1*l3 - l2*l3 A2 = 2*l1 - l2 - l3 A3 = l2 - l3 A4 = 3*self.L0 + l1 + l2 + l3 x = -(A2 * A1**4 * A4 * xi**10) / ((self.c1 * self.r**9)) + \ (A2 * A1**3 * A4 * xi**8) / (self.c2 * self.r**7) - \ (A2 * A1**2 * A4 * xi**6) / (self.c3 * self.r**5) + \ (A2 * A1 * A4 * xi**4) / (self.c4 * self.r**3) - \ (A2 * A4 * xi**2) / (self.c5 * self.r) y = -(self.sq3 * A4 * A3 * A1**4 * xi**10) / (self.c1 * self.r**9) + \ (self.sq3 * A4 * A3 * A1**3 * xi**8) / (self.c2 * self.r**7) - \ (self.sq3 * A4 * A3 * A1**2 * xi**6) / (self.c3 * self.r**5) + \ (self.sq3 * A4 * A1 * A2 * xi**4) / (self.c4 * self.r**3) - \ (self.sq3 * A4 * A3 * xi**2) / (self.c5 * self.r) z = (2 * A1**4 * A4 * xi**9) / (self.c6 * self.r**8) - \ (4 * A1**3 * A4 * xi**7) / (self.c7 * self.r**6) + \ (2 * A1**2 * A4 * xi**5) / (self.c8 * self.r**4) - \ (2 * A1 *A4 * xi**3) / (self.c9 * self.r**2) + \ (A4 * xi) / 3 return np.array([[x, y, z]]).T def calc_R(self, q, xi): """線形化された回転行列""" l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] A1 = l1**2 + l2**2 + l3**2 - l1*l2 - l1*l3 - l2*l3 A2 = 2*l1 - l2 - l3 A3 = l2 - l3 A4 = 3*self.L0 + l1 + l2 + l3 R11 = 1 - (A2**2 * A1**4 * xi**10) / (self.c1 * self.r**10) + \ (A2**2 * A1**3 * xi**8) / (self.c1 * self.r**8) - \ (A2**2 * A1**2 * xi**6) / (self.c3 * self.r**6) + \ (A1 * A2**2 * xi**4) / (self.c4 * self.r**4) - \ (A2**2 * xi**2) / (self.c5 * self.r**2) R12 = (self.sq3 * A2 * A3 * A1**4 * xi**10) / (self.c1 * self.r**10) + \ (self.sq3 * A2 * A3 * A1**3 * xi**8) / (self.c2 * self.r**8) - \ (self.sq3 * A2 * A3 * A1**2 * xi**6) / (self.c3 * self.r**6) + \ (self.sq3 * A2 * A3 * A1 * xi**4) / (self.c4 * self.r**4) - \ (self.sq3 * A2 * A3 * xi**2) / (self.c5 * self.r**2) R13 = -(2 * A2 * A1**4 * xi**9) / (self.c6 * self.r**9) + \ (4 * A2 * A1**3 * xi**7) / (self.c7 * self.r**7) - \ (2 * A2 * A1**2 * xi**5) / (self.c8 * self.r**5) + \ (2 * A2 * A1 * xi**3) / (self.c9 * self.r**3) - \ (A2 * xi) / (3 * self.r) R22 = 1 - (A3**2 * A1**4 * xi**10) / (self.c10 * self.r**10) + \ (A3**2 * A1**3 * xi**8) / (self.c11 * self.r**8) - \ (A3**2 * A1**2 * xi**6) / (self.c12 * self.r**6) + \ (A3**2 * A1 * xi**4) / (self.c13 * self.r**4) - \ (A3**2 * xi**2) / (6 * self.r**2) R23 = -(2*self.sq3 * A3 * A1**4 * xi**9) / (self.c6 * self.r**9) + \ (4*self.sq3 * A3 * A1**3 * xi**7) / (self.c7 * self.r**7) - \ (2*self.sq3 * A3 * A1**2 * xi**5) / (self.c8 * self.r**5) + \ (2*self.sq3 * A3 * A1 * xi**3) / (self.c9 * self.r**3) - \ (self.sq3 * A3 * xi) / (3 * self.r) R33 = 1 - (2 * xi**2 * A1) / (9 * self.r**2) + \ (2 * xi**4 * A1**2) / (self.c14 * self.r**4) - \ (4 * xi**6 * A1**3) / (self.c3 * self.r**6) + \ (2 * xi**8 * A1**4) / (self.c15 * self.r**8) - \ (4 * xi**10 * A1**5) / (self.c1 * self.r**10) R21 = R12 R31 = -R13 R32 = -R23 return np.array([ [R11, R12, R13], [R21, R22, R23], [R31, R32, R33], ]) def calc_MHTM(self, q, xi): """モーダル同時変換行列線形化されたHomogeneous Transformation Matrix """ return np.block([ [self.calc_R(q, xi), self.calc_P(q, xi)], [np.zeros((1, 3)), np.eye(1)], ]) def calc_dPdl1(self, q, xi): l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] return np.array([[ -xi**2*(2*l1 - l2 - l3)/(self.c5*self.r) - 2*xi**2*(3*self.L0 + l1 + l2 + l3)/(self.c5*self.r) + xi**4*(2*l1 - l2 - l3)**2*(3*self.L0 + l1 + l2 + l3)/(self.c4*self.r**3) + xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) + 2*xi**4*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - xi**6*(2*l1 - l2 - l3)*(4*l1 - 2*l2 - 2*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c3*self.r**5) - xi**6*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) - 2*xi**6*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + xi**8*(2*l1 - l2 - l3)*(6*l1 - 3*l2 - 3*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c2*self.r**7) + xi**8*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) + 2*xi**8*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - xi**10*(2*l1 - l2 - l3)*(8*l1 - 4*l2 - 4*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c1*self.r**9) - xi**10*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9) - 2*xi**10*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9), -self.sq3*xi**2*(l2 - l3)/(self.c5*self.r) + self.sq3*xi**4*(2*l1 - l2 - l3)**2*(3*self.L0 + l1 + l2 + l3)/(self.c4*self.r**3) + self.sq3*xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) + 2*self.sq3*xi**4*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - self.sq3*xi**6*(l2 - l3)*(4*l1 - 2*l2 - 2*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c3*self.r**5) - self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + self.sq3*xi**8*(l2 - l3)*(6*l1 - 3*l2 - 3*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c2*self.r**7) + self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - self.sq3*xi**10*(l2 - l3)*(8*l1 - 4*l2 - 4*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c1*self.r**9) - self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9), xi/3 - xi**3*(4*l1 - 2*l2 - 2*l3)*(3*self.L0 + l1 + l2 + l3)/(self.c9*self.r**2) - xi**3*(2*l1**2 - 2*l1*l2 - 2*l1*l3 + 4*l2**2 - 2*l2*l3)/(self.c9*self.r**2) + 2*xi**5*(4*l1 - 2*l2 - 2*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c8*self.r**4) + 2*xi**5*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c8*self.r**4) - 4*xi**7*(6*l1 - 3*l2 - 3*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c7*self.r**6) - 4*xi**7*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c7*self.r**6) + 2*xi**9*(8*l1 - 4*l2 - 4*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c6*self.r**8) + 2*xi**9*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c6*self.r**8), ]]).T def calc_dPdl2(self, q, xi): l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] return np.array([[ -xi**2*(2*l1 - l2 - l3)/(self.c5*self.r) + xi**2*(3*self.L0 + l1 + l2 + l3)/(self.c5*self.r) + xi**4*(-l1 + 4*l2 - l3)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)/(self.c4*self.r**3) + xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - xi**4*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - xi**6*(-2*l1 + 8*l2 - 2*l3)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c3*self.r**5) - xi**6*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + xi**6*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + xi**8*(-3*l1 + 12*l2 - 3*l3)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c2*self.r**7) + xi**8*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - xi**8*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - xi**10*(-4*l1 + 16*l2 - 4*l3)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c1*self.r**9) - xi**10*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9) + xi**10*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9), -self.sq3*xi**2*(l2 - l3)/(self.c5*self.r) - self.sq3*xi**2*(3*self.L0 + l1 + l2 + l3)/(self.c5*self.r) + self.sq3*xi**4*(-l1 + 4*l2 - l3)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)/(self.c4*self.r**3) + self.sq3*xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - self.sq3*xi**4*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - self.sq3*xi**6*(l2 - l3)*(-2*l1 + 8*l2 - 2*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c3*self.r**5) - self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) - self.sq3*xi**6*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + self.sq3*xi**8*(l2 - l3)*(-3*l1 + 12*l2 - 3*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c2*self.r**7) + self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) + self.sq3*xi**8*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - self.sq3*xi**10*(l2 - l3)*(-4*l1 + 16*l2 - 4*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c1*self.r**9) - self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9) - self.sq3*xi**10*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9), xi/3 - xi**3*(-2*l1 + 8*l2 - 2*l3)*(3*self.L0 + l1 + l2 + l3)/(self.c9*self.r**2) - xi**3*(2*l1**2 - 2*l1*l2 - 2*l1*l3 + 4*l2**2 - 2*l2*l3)/(self.c9*self.r**2) + 2*xi**5*(-2*l1 + 8*l2 - 2*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c8*self.r**4) + 2*xi**5*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c8*self.r**4) - 4*xi**7*(-3*l1 + 12*l2 - 3*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c7*self.r**6) - 4*xi**7*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c7*self.r**6) + 2*xi**9*(-4*l1 + 16*l2 - 4*l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c6*self.r**8) + 2*xi**9*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c6*self.r**8), ]]).T def calc_dPdl3(self, q, xi): l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] return np.array([[ -xi**2*(2*l1 - l2 - l3)/(self.c5*self.r) + xi**2*(3*self.L0 + l1 + l2 + l3)/(self.c5*self.r) + xi**4*(-l1 - l2)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)/(self.c4*self.r**3) + xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - xi**4*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - xi**6*(-2*l1 - 2*l2)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c3*self.r**5) - xi**6*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + xi**6*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + xi**8*(-3*l1 - 3*l2)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c2*self.r**7) + xi**8*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - xi**8*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - xi**10*(-4*l1 - 4*l2)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c1*self.r**9) - xi**10*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9) + xi**10*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9), -self.sq3*xi**2*(l2 - l3)/(self.c5*self.r) + self.sq3*xi**2*(3*self.L0 + l1 + l2 + l3)/(self.c5*self.r) + self.sq3*xi**4*(-l1 - l2)*(2*l1 - l2 - l3)*(3*self.L0 + l1 + l2 + l3)/(self.c4*self.r**3) + self.sq3*xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - self.sq3*xi**4*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c4*self.r**3) - self.sq3*xi**6*(-2*l1 - 2*l2)*(l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c3*self.r**5) - self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + self.sq3*xi**6*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c3*self.r**5) + self.sq3*xi**8*(-3*l1 - 3*l2)*(l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c2*self.r**7) + self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - self.sq3*xi**8*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c2*self.r**7) - self.sq3*xi**10*(-4*l1 - 4*l2)*(l2 - l3)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c1*self.r**9) - self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9) + self.sq3*xi**10*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c1*self.r**9), xi/3 - xi**3*(-2*l1 - 2*l2)*(3*self.L0 + l1 + l2 + l3)/(self.c9*self.r**2) - xi**3*(2*l1**2 - 2*l1*l2 - 2*l1*l3 + 4*l2**2 - 2*l2*l3)/(self.c9*self.r**2) + 2*xi**5*(-2*l1 - 2*l2)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)/(self.c8*self.r**4) + 2*xi**5*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c8*self.r**4) - 4*xi**7*(-3*l1 - 3*l2)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**2/(self.c7*self.r**6) - 4*xi**7*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c7*self.r**6) + 2*xi**9*(-4*l1 - 4*l2)*(3*self.L0 + l1 + l2 + l3)*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**3/(self.c6*self.r**8) + 2*xi**9*(l1**2 - l1*l2 - l1*l3 + 2*l2**2 - l2*l3)**4/(self.c6*self.r**8) ]]).T def calc_dRdl1(self, q, xi): l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] return np.array([ [ -xi**2*(8*l1 - 4*l2 - 4*l3)/(self.c5*self.r**2) + xi**4*(2*l1 - l2 - l3)**3/(self.c4*self.r**4) + xi**4*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - xi**6*(2*l1 - l2 - l3)**2*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) - xi**6*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + xi**8*(2*l1 - l2 - l3)**2*(6*l1 - 3*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c1*self.r**8) + xi**8*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**8) - xi**10*(2*l1 - l2 - l3)**2*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) - xi**10*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), -2*self.sq3*xi**2*(l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**4*(l2 - l3)*(2*l1 - l2 - l3)**2/(self.c4*self.r**4) + 2*self.sq3*xi**4*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**6*(l2 - l3)*(2*l1 - l2 - l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) - 2*self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**8*(l2 - l3)*(2*l1 - l2 - l3)*(6*l1 - 3*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c2*self.r**8) + 2*self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**10*(l2 - l3)*(2*l1 - l2 - l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) + 2*self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), -2*xi/(3*self.r) + xi**3*(2*l1 - l2 - l3)*(4*l1 - 2*l2 - 2*l3)/(self.c9*self.r**3) + 4*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) - xi**5*(4*l1 - 2*l2 - 2*l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) - 4*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) + xi**7*(6*l1 - 3*l2 - 3*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) + 8*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) - xi**9*(4*l1 - 2*l2 - 2*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) - 4*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9) ], [ -2*self.sq3*xi**2*(l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**4*(l2 - l3)*(2*l1 - l2 - l3)**2/(self.c4*self.r**4) + 2*self.sq3*xi**4*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**6*(l2 - l3)*(2*l1 - l2 - l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) - 2*self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**8*(l2 - l3)*(2*l1 - l2 - l3)*(6*l1 - 3*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c2*self.r**8) + 2*self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**10*(l2 - l3)*(2*l1 - l2 - l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) + 2*self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), xi**4*(l2 - l3)**2*(2*l1 - l2 - l3)/(self.c13*self.r**4) - xi**6*(l2 - l3)**2*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c12*self.r**6) + xi**8*(l2 - l3)**2*(6*l1 - 3*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c11*self.r**8) - xi**10*(l2 - l3)**2*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c10*self.r**10), 2*self.sq3*xi**3*(l2 - l3)*(2*l1 - l2 - l3)/(self.c9*self.r**3) - 2*self.sq3*xi**5*(l2 - l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) + 4*self.sq3*xi**7*(l2 - l3)*(6*l1 - 3*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) - 2*self.sq3*xi**9*(l2 - l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) ], [ 2*xi/(3*self.r) - xi**3*(2*l1 - l2 - l3)*(4*l1 - 2*l2 - 2*l3)/(self.c9*self.r**3) - 4*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) + xi**5*(4*l1 - 2*l2 - 2*l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) + 4*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) - xi**7*(6*l1 - 3*l2 - 3*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) - 8*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) + xi**9*(4*l1 - 2*l2 - 2*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) + 4*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9), -2*self.sq3*xi**3*(l2 - l3)*(2*l1 - l2 - l3)/(self.c9*self.r**3) + 2*self.sq3*xi**5*(l2 - l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) - 4*self.sq3*xi**7*(l2 - l3)*(6*l1 - 3*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) + 2*self.sq3*xi**9*(l2 - l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9), -2*xi**2*(2*l1 - l2 - l3)/(9*self.r**2) - 4*xi**6*(6*l1 - 3*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + 2*xi**8*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c15*self.r**8) + 2*xi**4*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c14*self.r**4) - 4*xi**10*(10*l1 - 5*l2 - 5*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) ] ]) def calc_dRdl2(self, q, xi): l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] return np.array([ [ -xi**2*(-4*l1 + 2*l2 + 2*l3)/(self.c5*self.r**2) + xi**4*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) + xi**4*(-l1 + 2*l2 - l3)*(2*l1 - l2 - l3)**2/(self.c4*self.r**4) - xi**6*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) - xi**6*(-2*l1 + 4*l2 - 2*l3)*(2*l1 - l2 - l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) + xi**8*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**8) + xi**8*(-3*l1 + 6*l2 - 3*l3)*(2*l1 - l2 - l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c1*self.r**8) - xi**10*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) - xi**10*(-4*l1 + 8*l2 - 4*l3)*(2*l1 - l2 - l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10), self.sq3*xi**2*(l2 - l3)/(self.c5*self.r**2) - self.sq3*xi**2*(2*l1 - l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**4*(l2 - l3)*(-l1 + 2*l2 - l3)*(2*l1 - l2 - l3)/(self.c4*self.r**4) - self.sq3*xi**4*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) + self.sq3*xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**6*(l2 - l3)*(-2*l1 + 4*l2 - 2*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) + self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) - self.sq3*xi**6*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**8*(l2 - l3)*(-3*l1 + 6*l2 - 3*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c2*self.r**8) - self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**8*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**10*(l2 - l3)*(-4*l1 + 8*l2 - 4*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) - self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) + self.sq3*xi**10*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), xi/(3*self.r) + xi**3*(-l1 + 2*l2 - l3)*(4*l1 - 2*l2 - 2*l3)/(self.c9*self.r**3) - 2*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) - xi**5*(-2*l1 + 4*l2 - 2*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) + 2*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) + xi**7*(-3*l1 + 6*l2 - 3*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) - 4*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) - xi**9*(-4*l1 + 8*l2 - 4*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) + 2*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9), ], [ self.sq3*xi**2*(l2 - l3)/(self.c5*self.r**2) - self.sq3*xi**2*(2*l1 - l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**4*(l2 - l3)*(-l1 + 2*l2 - l3)*(2*l1 - l2 - l3)/(self.c4*self.r**4) - self.sq3*xi**4*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) + self.sq3*xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**6*(l2 - l3)*(-2*l1 + 4*l2 - 2*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) + self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) - self.sq3*xi**6*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**8*(l2 - l3)*(-3*l1 + 6*l2 - 3*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c2*self.r**8) - self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**8*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**10*(l2 - l3)*(-4*l1 + 8*l2 - 4*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) - self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) + self.sq3*xi**10*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), -xi**2*(2*l2 - 2*l3)/(6*self.r**2) + xi**4*(l2 - l3)**2*(-l1 + 2*l2 - l3)/(self.c13*self.r**4) + xi**4*(2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c13*self.r**4) - xi**6*(l2 - l3)**2*(-2*l1 + 4*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c12*self.r**6) - xi**6*(2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c12*self.r**6) + xi**8*(l2 - l3)**2*(-3*l1 + 6*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c11*self.r**8) + xi**8*(2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c11*self.r**8) - xi**10*(l2 - l3)**2*(-4*l1 + 8*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c10*self.r**10) - xi**10*(2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c10*self.r**10), -self.sq3*xi/(3*self.r) + 2*self.sq3*xi**3*(l2 - l3)*(-l1 + 2*l2 - l3)/(self.c9*self.r**3) + 2*self.sq3*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) - 2*self.sq3*xi**5*(l2 - l3)*(-2*l1 + 4*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) - 2*self.sq3*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) + 4*self.sq3*xi**7*(l2 - l3)*(-3*l1 + 6*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) + 4*self.sq3*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) - 2*self.sq3*xi**9*(l2 - l3)*(-4*l1 + 8*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) - 2*self.sq3*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9), ], [ -xi/(3*self.r) - xi**3*(-l1 + 2*l2 - l3)*(4*l1 - 2*l2 - 2*l3)/(self.c9*self.r**3) + 2*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) + xi**5*(-2*l1 + 4*l2 - 2*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) - 2*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) - xi**7*(-3*l1 + 6*l2 - 3*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) + 4*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) + xi**9*(-4*l1 + 8*l2 - 4*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) - 2*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9), self.sq3*xi/(3*self.r) - 2*self.sq3*xi**3*(l2 - l3)*(-l1 + 2*l2 - l3)/(self.c9*self.r**3) - 2*self.sq3*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) + 2*self.sq3*xi**5*(l2 - l3)*(-2*l1 + 4*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) + 2*self.sq3*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) - 4*self.sq3*xi**7*(l2 - l3)*(-3*l1 + 6*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) - 4*self.sq3*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) + 2*self.sq3*xi**9*(l2 - l3)*(-4*l1 + 8*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) + 2*self.sq3*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9), -2*xi**2*(-l1 + 2*l2 - l3)/(9*self.r**2) - 4*xi**6*(-3*l1 + 6*l2 - 3*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + 2*xi**8*(-4*l1 + 8*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c15*self.r**8) + 2*xi**4*(-2*l1 + 4*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c14*self.r**4) - 4*xi**10*(-5*l1 + 10*l2 - 5*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) ] ]) def calc_dRdl3(self, q, xi): l1 = q[0,0] l2 = q[1,0] l3 = q[2,0] return np.array([ [ -xi**2*(-4*l1 + 2*l2 + 2*l3)/(self.c5*self.r**2) + xi**4*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) + xi**4*(-l1 - l2 + 2*l3)*(2*l1 - l2 - l3)**2/(self.c4*self.r**4) - xi**6*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) - xi**6*(-2*l1 - 2*l2 + 4*l3)*(2*l1 - l2 - l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) + xi**8*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**8) + xi**8*(-3*l1 - 3*l2 + 6*l3)*(2*l1 - l2 - l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c1*self.r**8) - xi**10*(-4*l1 - 4*l2 + 8*l3)*(2*l1 - l2 - l3)**2*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) - xi**10*(-4*l1 + 2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), self.sq3*xi**2*(l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**2*(2*l1 - l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**4*(l2 - l3)*(-l1 - l2 + 2*l3)*(2*l1 - l2 - l3)/(self.c4*self.r**4) - self.sq3*xi**4*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**6*(l2 - l3)*(-2*l1 - 2*l2 + 4*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) + self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**6*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**8*(l2 - l3)*(-3*l1 - 3*l2 + 6*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c2*self.r**8) - self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) - self.sq3*xi**8*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**10*(l2 - l3)*(-4*l1 - 4*l2 + 8*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) - self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) - self.sq3*xi**10*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), xi/(3*self.r) + xi**3*(-l1 - l2 + 2*l3)*(4*l1 - 2*l2 - 2*l3)/(self.c9*self.r**3) - 2*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) - xi**5*(-2*l1 - 2*l2 + 4*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) + 2*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) + xi**7*(-3*l1 - 3*l2 + 6*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) - 4*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) - xi**9*(-4*l1 - 4*l2 + 8*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) + 2*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9) ], [ self.sq3*xi**2*(l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**2*(2*l1 - l2 - l3)/(self.c5*self.r**2) + self.sq3*xi**4*(l2 - l3)*(-l1 - l2 + 2*l3)*(2*l1 - l2 - l3)/(self.c4*self.r**4) - self.sq3*xi**4*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**4*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c4*self.r**4) - self.sq3*xi**6*(l2 - l3)*(-2*l1 - 2*l2 + 4*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c3*self.r**6) + self.sq3*xi**6*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**6*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + self.sq3*xi**8*(l2 - l3)*(-3*l1 - 3*l2 + 6*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c2*self.r**8) - self.sq3*xi**8*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) - self.sq3*xi**8*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c2*self.r**8) + self.sq3*xi**10*(l2 - l3)*(-4*l1 - 4*l2 + 8*l3)*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c1*self.r**10) - self.sq3*xi**10*(l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) - self.sq3*xi**10*(2*l1 - l2 - l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10), -xi**2*(-2*l2 + 2*l3)/(6*self.r**2) + xi**4*(-2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c13*self.r**4) + xi**4*(l2 - l3)**2*(-l1 - l2 + 2*l3)/(self.c13*self.r**4) - xi**6*(-2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c12*self.r**6) - xi**6*(l2 - l3)**2*(-2*l1 - 2*l2 + 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c12*self.r**6) + xi**8*(-2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c11*self.r**8) + xi**8*(l2 - l3)**2*(-3*l1 - 3*l2 + 6*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c11*self.r**8) - xi**10*(-2*l2 + 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c10*self.r**10) - xi**10*(l2 - l3)**2*(-4*l1 - 4*l2 + 8*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c10*self.r**10), self.sq3*xi/(3*self.r) + 2*self.sq3*xi**3*(l2 - l3)*(-l1 - l2 + 2*l3)/(self.c9*self.r**3) - 2*self.sq3*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) - 2*self.sq3*xi**5*(l2 - l3)*(-2*l1 - 2*l2 + 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) + 2*self.sq3*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) + 4*self.sq3*xi**7*(l2 - l3)*(-3*l1 - 3*l2 + 6*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) - 4*self.sq3*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) - 2*self.sq3*xi**9*(l2 - l3)*(-4*l1 - 4*l2 + 8*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) + 2*self.sq3*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9)], [ -xi/(3*self.r) - xi**3*(-l1 - l2 + 2*l3)*(4*l1 - 2*l2 - 2*l3)/(self.c9*self.r**3) + 2*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) + xi**5*(-2*l1 - 2*l2 + 4*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) - 2*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) - xi**7*(-3*l1 - 3*l2 + 6*l3)*(8*l1 - 4*l2 - 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) + 4*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) + xi**9*(-4*l1 - 4*l2 + 8*l3)*(4*l1 - 2*l2 - 2*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) - 2*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9), -self.sq3*xi/(3*self.r) - 2*self.sq3*xi**3*(l2 - l3)*(-l1 - l2 + 2*l3)/(self.c9*self.r**3) + 2*self.sq3*xi**3*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c9*self.r**3) + 2*self.sq3*xi**5*(l2 - l3)*(-2*l1 - 2*l2 + 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c8*self.r**5) - 2*self.sq3*xi**5*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c8*self.r**5) - 4*self.sq3*xi**7*(l2 - l3)*(-3*l1 - 3*l2 + 6*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c7*self.r**7) + 4*self.sq3*xi**7*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c7*self.r**7) + 2*self.sq3*xi**9*(l2 - l3)*(-4*l1 - 4*l2 + 8*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c6*self.r**9) - 2*self.sq3*xi**9*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c6*self.r**9), -2*xi**2*(-l1 - l2 + 2*l3)/(9*self.r**2) - 4*xi**6*(-3*l1 - 3*l2 + 6*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**2/(self.c3*self.r**6) + 2*xi**8*(-4*l1 - 4*l2 + 8*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**3/(self.c15*self.r**8) + 2*xi**4*(-2*l1 - 2*l2 + 4*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)/(self.c14*self.r**4) - 4*xi**10*(-5*l1 - 5*l2 + 10*l3)*(l1**2 - l1*l2 - l1*l3 + l2**2 - l2*l3 + l3**2)**4/(self.c1*self.r**10) ] ]) class OneSection(Base): """1つセクションのローカル変数""" def __init__(self, n, n_step): self.n = n # セクション番号 self.J_OMEGAs = None self.J_omegas = None self.J_vs = None def P(): pass class AllSection: """アーム全体の運動学""" def __init__(self, N): self.N = N # セクションの数 self.n_step = 10 self.set_section() def set_section(self,): """ローカルセクションを追加""" self.sections = [ OneSection(i, self.n_step) for i in range(self.N) ] def update_all(self, q_all, q_dot_all): """全部更新""" self.q_all = q_all # 縦ベクトル self.q_dot_all = q_dot_all def update_local(self,): """ローカル位置，回転行列を更新""" for i in range(self.N): self.sections[i].update_local_state(self.q_all[i:i+3, :]) def update_J_OMEGA_ij(self,): """J_OMEGAを更新""" for k in range(self.N): print("k = ", k) J_OMEGA_ijs_all = [] for l in range(self.n_step): # xiの一個一個の分を順番に計算 J_OMEGA_ijs = [] for i in range(k+1): print("i = ", i) Ri = self.sections[i].Rs[l] J_OMEGA_ij = [] for j in range(3): if i == k: print("ketu") dRidlj = self.sections[i].dRdls[l][j] J_OMEGA_ij.append(Ri.T @ dRidlj) else: print("mae") print(self.sections[i].J_OMEGAs) J_OMEGGA_prev = self.sections[i-1].J_OMEGAs[-1][i][j] J_OMEGA_ij.append(Ri.T @ J_OMEGGA_prev @ Ri) J_OMEGA_ijs.append(J_OMEGA_ij) J_OMEGA_ijs_all.append(J_OMEGA_ijs) self.sections[k].J_OMEGAs = J_OMEGA_ijs_all return def update_J_v_ij(self,): pass if __name__ == "__main__": N = 3 q_all = np.zeros((3*N, 1)) q_dot_all = np.zeros((3*N, 1)) hoge = AllSection(N) hoge.update_all(q_all, q_dot_all) hoge.update_local() hoge.update_J_OMEGA_ij()

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null

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11

e4267ee5d7be19604157f354de614036a3513084

4,467

py

Python

api/migrations/0005_disasterneighborhoodgrid.py

hackoregon/disaster-resilience-backend

7776ca37bc50ef79e8bbf0830b6ca4b798f0df9f

[ "MIT" ]

2

2018-04-27T09:10:08.000Z

2018-05-01T08:38:29.000Z

api/migrations/0005_disasterneighborhoodgrid.py

hackoregon/disaster-resilience-backend

7776ca37bc50ef79e8bbf0830b6ca4b798f0df9f

[ "MIT" ]

21

2018-05-27T23:51:40.000Z

2021-06-10T20:15:17.000Z

api/migrations/0005_disasterneighborhoodgrid.py

hackoregon/disaster-resilience-backend

7776ca37bc50ef79e8bbf0830b6ca4b798f0df9f

[ "MIT" ]

3

2018-04-27T09:11:06.000Z

2019-03-10T19:32:26.000Z

# Generated by Django 2.0.1 on 2018-06-21 01:22 import django.contrib.gis.db.models.fields from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('api', '0004_disasterneighborhoods_disasterneighborhoodview'), ] operations = [ migrations.CreateModel( name='DisasterNeighborhoodGrid', fields=[ ('id', models.IntegerField(primary_key=True, serialize=False)), ('centroidx', models.CharField(blank=True, max_length=255, null=True)), ('centroidy', models.CharField(blank=True, max_length=255, null=True)), ('x_simple', models.CharField(blank=True, max_length=255, null=True)), ('y_simple', models.CharField(blank=True, max_length=255, null=True)), ('wkb_geometry', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('pgv_site_count', models.CharField(blank=True, max_length=255, null=True)), ('pgv_site_max', models.CharField(blank=True, max_length=255, null=True)), ('pgv_site_mean', models.CharField(blank=True, max_length=255, null=True)), ('pgv_site_min', models.CharField(blank=True, max_length=255, null=True)), ('pgv_site_std', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_dry_count', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_dry_max', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_dry_mean', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_dry_min', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_dry_std', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_count', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_max', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_mean', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_min', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_std', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_count', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_max', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_mean', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_min', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_std', models.CharField(blank=True, max_length=255, null=True)), ('pgd_total_wet_mean', models.CharField(blank=True, max_length=255, null=True)), ('pgv_site_min_mmi', models.IntegerField(blank=True, null=True)), ('pgv_site_max_mmi', models.IntegerField(blank=True, null=True)), ('pgv_site_mean_mmi', models.IntegerField(blank=True, null=True)), ('pgd_landslide_dry_min_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_dry_max_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_dry_mean_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_min_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_max_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_landslide_wet_mean_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_min_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_max_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_liquefaction_wet_mean_di', models.CharField(blank=True, max_length=255, null=True)), ('pgd_total_wet_mean_di', models.CharField(blank=True, max_length=255, null=True)), ], options={ 'db_table': 'disaster_neighborhood_grid', 'managed': False, }, ), ]

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9

e4d55357f38e2ca42648fb7ea064b6a959bc2b42

55,657

py

Python

external/TriBITS/test/python_utils/gitdist_UnitTests.py

murraypurves/BootsOnTheGround

15acc4ed064e368f6af5114408f1be8a62749f32

[ "MIT" ]

4

2017-02-01T00:39:29.000Z

2018-08-09T11:53:18.000Z

external/TriBITS/test/python_utils/gitdist_UnitTests.py

murraypurves/BootsOnTheGround

15acc4ed064e368f6af5114408f1be8a62749f32

[ "MIT" ]

14

2017-01-19T17:56:04.000Z

2017-08-27T21:52:35.000Z

external/TriBITS/test/python_utils/gitdist_UnitTests.py

murraypurves/BootsOnTheGround

15acc4ed064e368f6af5114408f1be8a62749f32

[ "MIT" ]

1

2019-10-03T12:13:36.000Z

# @HEADER # ************************************************************************ # # TriBITS: Tribal Build, Integrate, and Test System # Copyright 2013 Sandia Corporation # # Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, # the U.S. Government retains certain rights in this software. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. 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. # # 3. Neither the name of the Corporation nor the names of the # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "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 SANDIA CORPORATION OR THE # 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. # # ************************************************************************ # @HEADER ################################# # Unit testing code for gitdist # ################################# import sys import imp import shutil from unittest_helpers import * pythonDir = os.path.abspath(GeneralScriptSupport.getScriptBaseDir()) utilsDir = pythonDir+"/utils" tribitsDir = os.path.abspath(pythonDir+"/..") sys.path = [pythonUtilsDir] + sys.path from gitdist import * # # Utility functions for testing # gitdistPath = pythonUtilsDir+"/gitdist" gitdistPathNoColor = gitdistPath+" --dist-no-color" gitdistPathMock = gitdistPathNoColor+" --dist-use-git=mockgit --dist-no-opt" mockGitPath = pythonUtilsDir+"/mockprogram.py" unitTestDataDir = testPythonUtilsDir tempMockProjectDir = "MockProjectDir" testBaseDir = os.getcwd() def getCmndOutputInMockProjectDir(cmnd): os.chdir(mockProjectDir) cmndOut = getCmndOutput(cmnd) os.chdir(testBaseDir) return cmndOut def createAndMoveIntoTestDir(testDir): if os.path.exists(testDir): shutil.rmtree(testDir) os.mkdir(testDir) os.chdir(testDir) if not os.path.exists(tempMockProjectDir): os.mkdir(tempMockProjectDir) os.chdir(tempMockProjectDir) return os.path.join(testBaseDir, testDir, tempMockProjectDir) class GitDistOptions: def __init__(self, useGit): self.useGit = useGit # # Unit tests for createAsciiTable # class test_createAsciiTable(unittest.TestCase): def test_full_table(self): tableData = [ { "label" : "ID", "align" : "R", "fields" : ["0", "1", "2"] }, { "label" : "Repo Dir", "align" : "L", "fields" : ["Base: BaseRepo", "ExtraRepo1", "Path/To/ExtraRepo2" ] }, { "label":"Branch", "align":"L", "fields" : ["dummy", "master", "HEAD" ] }, { "label" : "Tracking Branch", "align":"L", "fields" : ["", "origin/master", "" ] }, { "label" : "C", "align":"R", "fields" : ["", "1", "" ] }, { "label" : "M", "align":"R", "fields" : ["0", "2", "25" ] }, { "label" : "?", "align":"R", "fields" : ["0", "0", "4" ] }, ] asciiTable = createAsciiTable(tableData) #print(asciiTable) asciiTable_expected = \ "-------------------------------------------------------------------\n" \ "| ID | Repo Dir | Branch | Tracking Branch | C | M | ? |\n" \ "|----|--------------------|--------|-----------------|---|----|---|\n" \ "| 0 | Base: BaseRepo | dummy | | | 0 | 0 |\n" \ "| 1 | ExtraRepo1 | master | origin/master | 1 | 2 | 0 |\n" \ "| 2 | Path/To/ExtraRepo2 | HEAD | | | 25 | 4 |\n" \ "-------------------------------------------------------------------\n" self.assertEqual(asciiTable, asciiTable_expected) def test_no_rows(self): tableData = [ { "label" : "ID", "align" : "R", "fields" : [] }, { "label" : "Repo Dir", "align" : "L", "fields" : [] }, { "label":"Branch", "align":"L", "fields" : [] }, { "label" : "Tracking Branch", "align":"L", "fields" : [] }, { "label" : "C", "align":"R", "fields" : [] }, { "label" : "M", "align":"R", "fields" : [] }, { "label" : "?", "align":"R", "fields" : [] }, ] asciiTable = createAsciiTable(tableData) #print(asciiTable) asciiTable_expected = \ "--------------------------------------------------------\n" \ "| ID | Repo Dir | Branch | Tracking Branch | C | M | ? |\n" \ "|----|----------|--------|-----------------|---|---|---|\n" \ "--------------------------------------------------------\n" self.assertEqual(asciiTable, asciiTable_expected) def test_one_row(self): tableData = [ { "label" : "ID", "align" : "R", "fields" : ["0"] }, { "label" : "Repo Dir", "align" : "L", "fields" : ["Base: BaseRepo"] }, { "label":"Branch", "align":"L", "fields" : ["dummy"] }, { "label" : "Tracking Branch", "align":"L", "fields" : ["origin/master"] }, { "label" : "C", "align":"R", "fields" : ["24"] }, { "label" : "M", "align":"R", "fields" : ["25"] }, { "label" : "?", "align":"R", "fields" : ["4"] }, ] asciiTable = createAsciiTable(tableData) #print(asciiTable) asciiTable_expected = \ "----------------------------------------------------------------\n" \ "| ID | Repo Dir | Branch | Tracking Branch | C | M | ? |\n" \ "|----|----------------|--------|-----------------|----|----|---|\n" \ "| 0 | Base: BaseRepo | dummy | origin/master | 24 | 25 | 4 |\n" \ "----------------------------------------------------------------\n" self.assertEqual(asciiTable, asciiTable_expected) def test_row_mismatch(self): tableData = [ { "label" : "ID", "align" : "R", "fields" : ["0", "1"] }, { "label" : "Repo Dir", "align" : "L", "fields" : ["Base: BaseRepo"] }, ] #createAsciiTable(tableData) self.assertRaises(Exception, createAsciiTable, tableData) # # Unit tests for functions in gitdist # class test_gitdist_getRepoStats(unittest.TestCase): def test_no_change(self): try: testDir = createAndMoveIntoTestDir("gitdist_getRepoStats_no_change") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo/remote_branch\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo/remote_branch\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) options = GitDistOptions(mockGitPath) repoStats = getRepoStats(options) repoStats_expected = "{branch='local_branch'," \ " trackingBranch='origin_repo/remote_branch', numCommits='0'," \ " numModified='0', numUntracked='0'}" self.assertEqual(str(repoStats), repoStats_expected) finally: os.chdir(testBaseDir) def test_all_changed_no_tracking_branch(self): try: testDir = createAndMoveIntoTestDir( "gitdist_getRepoStats_all_changed_no_tracking_branch") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 55\n" \ "MOCK_PROGRAM_OUTPUT: error: blah blahh blah\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ " T file2\n" \ " D file3\n" \ "?? file4\n" \ "?? file5\n" \ ) options = GitDistOptions(mockGitPath) repoStats = getRepoStats(options) repoStats_expected = "{branch='local_branch'," \ " trackingBranch='', numCommits=''," \ " numModified='3', numUntracked='2'}" self.assertEqual(str(repoStats), repoStats_expected) finally: os.chdir(testBaseDir) def test_modified_and_staged_no_tracking_branch(self): try: testDir = createAndMoveIntoTestDir( "gitdist_getRepoStats_all_changed_no_tracking_branch") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 55\n" \ "MOCK_PROGRAM_OUTPUT: error: blah blahh blah\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ "MM file1b\n" \ " T file2\n" \ "MT file2b\n" \ " D file3\n" \ "MD file3\n" \ "?? file4\n" \ "?? file5\n" \ "?? file5b\n" \ " A file6\n" \ "A file6b\n" \ " U file7\n" \ "U file7b\n" \ "R file8\n" \ ) options = GitDistOptions(mockGitPath) repoStats = getRepoStats(options) repoStats_expected = "{branch='local_branch'," \ " trackingBranch='', numCommits=''," \ " numModified='11', numUntracked='3'}" self.assertEqual(str(repoStats), repoStats_expected) finally: os.chdir(testBaseDir) def test_all_changed_detached_head(self): try: testDir = createAndMoveIntoTestDir("gitdist_getRepoStats_all_changed_detached_head") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: HEAD\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 128\n" \ "MOCK_PROGRAM_OUTPUT: fatal: blah blahh blah\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ " M file2\n" \ "?? file3\n" \ "?? file4\n" \ "?? file5\n" \ ) options = GitDistOptions(mockGitPath) repoStats = getRepoStats(options) repoStats_expected = "{branch='HEAD'," \ " trackingBranch='', numCommits=''," \ " numModified='2', numUntracked='3'}" self.assertEqual(str(repoStats), repoStats_expected) finally: os.chdir(testBaseDir) def test_all_ambiguous_head(self): try: testDir = createAndMoveIntoTestDir("gitdist_getRepoStats_all_changed_detached_head") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: warning: refname 'HEAD' is ambiguous.\n" \ "error: refname 'HEAD' is ambiguous\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: remoterepo/trackingbranch\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^remoterepo/trackingbranch\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 7\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ " M file2\n" \ "?? file3\n" \ "?? file4\n" \ "?? file5\n" \ ) options = GitDistOptions(mockGitPath) repoStats = getRepoStats(options) repoStats_expected = "{branch='<AMBIGUOUS-HEAD>'," \ " trackingBranch='remoterepo/trackingbranch', numCommits='7'," \ " numModified='2', numUntracked='3'}" self.assertEqual(str(repoStats), repoStats_expected) finally: os.chdir(testBaseDir) # NOTE: Above is a very strange test case. It is what happens when # someone creates a tag called 'HEAD' using the command 'git tag HEAD' # (which was an accident obviously). But amazingly, 'git rev-parse # --abbrev HEAD' still returns 0 but returns no name! See TriBITS #100 # for details. def test_all_changed_1_author(self): try: testDir = createAndMoveIntoTestDir("gitdist_getRepoStats_all_changed_1_author") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo/remote_branch\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo/remote_branch\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 1\tsome author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ " M file2\n" \ "?? file3\n" \ "?? file4\n" \ "?? file5\n" \ ) options = GitDistOptions(mockGitPath) repoStats = getRepoStats(options) repoStats_expected = "{branch='local_branch'," \ " trackingBranch='origin_repo/remote_branch', numCommits='1'," \ " numModified='2', numUntracked='3'}" self.assertEqual(str(repoStats), repoStats_expected) finally: os.chdir(testBaseDir) def test_all_changed_3_authors(self): try: testDir = createAndMoveIntoTestDir("gitdist_getRepoStats_all_changed_3_authors") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo/remote_branch\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo/remote_branch\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 1 some author1\n" \ "2 some author2\n" \ "3 some author2\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ " M file2\n" \ "?? file3\n" \ "?? file4\n" \ "?? file5\n" \ ) options = GitDistOptions(mockGitPath) repoStats = getRepoStats(options) repoStats_expected = "{branch='local_branch'," \ " trackingBranch='origin_repo/remote_branch', numCommits='6'," \ " numModified='2', numUntracked='3'}" self.assertEqual(str(repoStats), repoStats_expected) finally: os.chdir(testBaseDir) repoVersionFile_withSummary_1 = """*** Base Git Repo: MockTrilinos sha1_1 [Mon Sep 23 11:34:59 2013 -0400] <author_1@ornl.gov> First summary message *** Git Repo: extraTrilinosRepo sha1_2 [Fri Aug 30 09:55:07 2013 -0400] <author_2@ornl.gov> Second summary message *** Git Repo: extraRepoOnePackage sha1_3 [Thu Dec 1 23:34:06 2011 -0500] <author_3@ornl.gov> Third summary message """ repoVersionFile_withoutSummary_1 = """*** Base Git Repo: MockTrilinos sha1_1 [Mon Sep 23 11:34:59 2013 -0400] <author_1@ornl.gov> *** Git Repo: extraRepoTwoPackages sha1_2 [Fri Aug 30 09:55:07 2013 -0400] <author_2@ornl.gov> *** Git Repo: extraRepoOnePackageThreeSubpackages sha1_3 [Thu Dec 1 23:34:06 2011 -0500] <author_3@ornl.gov> """ def writeGitMockProgram_base_3_2_1_repo1_22_0_2_repo2_0_0_0(): open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch0\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 3 some author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ " M file2\n" \ "?? file2\n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch0\n" \ "Your branch is ahead of 'origin_repo0/remote_branch0' by 3 commits.\n" \ ) open("ExtraRepo1/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch1\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 22 some author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: ?? file1\n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch1\n" \ "Your branch is ahead of 'origin_repo1/remote_branch1' by 22 commits.\n" \ ) open("ExtraRepo2/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch2\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) def writeGitMockProgram_base_3_2_1_repo1_0_0_0_repo2_4_0_2(): open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch0\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 3 some author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ " M file2\n" \ "?? file3\n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch0\n" \ "Your branch is ahead of 'origin_repo0/remote_branch0' by 3 commits.\n" \ ) open("ExtraRepo1/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch1\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) open("ExtraRepo2/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch2\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 3 some author\n" \ "1 some other author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: ?? file1\n" \ "?? file3\n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch2\n" \ "Your branch is ahead of 'origin_repo2/remote_branch2' by 4 commits.\n" \ ) class test_gitdist_getRepoVersionDictFromRepoVersionFileString(unittest.TestCase): def setUp(self): None def test_repoVersionFile_withSummary_1(self): repoVersionDict = \ getRepoVersionDictFromRepoVersionFileString(repoVersionFile_withSummary_1) expectedDict = { 'MockTrilinos': 'sha1_1', 'extraTrilinosRepo': 'sha1_2', 'extraRepoOnePackage': 'sha1_3' } #print("repoVersionDict =\n" + str(repoVersionDict)) self.assertEqual(repoVersionDict, expectedDict) def test_repoVersionFile_withoutSummary_1(self): repoVersionDict = \ getRepoVersionDictFromRepoVersionFileString(repoVersionFile_withoutSummary_1) expectedDict = { 'MockTrilinos': 'sha1_1', 'extraRepoTwoPackages': 'sha1_2', 'extraRepoOnePackageThreeSubpackages': 'sha1_3' } #print("repoVersionDict =\n" + str(repoVersionDict)) self.assertEqual(repoVersionDict, expectedDict) # ToDo: Add unit tests for requoteCmndLineArgsIntoArray! # # Test entire script gitdist # def assertContainsGitdistHelpHeader(testObj, cmndOut): cmndOutList = cmndOut.splitlines() cmndOutFirstLine = cmndOutList[0] cmndOutFirstLineAfterComma = cmndOutFirstLine.split(s(":"))[1].strip() cmndOutFirstLineAfterComma_expected = s("gitdist [gitdist arguments] <raw-git-command> [git arguments]") testObj.assertEqual(cmndOutFirstLineAfterComma, cmndOutFirstLineAfterComma_expected) def assertContainsAllGitdistHelpSections(testObj, cmndOut): testObj.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^OVERVIEW:$"), "OVERVIEW:\n") testObj.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^REPO SELECTION AND SETUP:$"), "REPO SELECTION AND SETUP:\n") testObj.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^SUMMARY OF REPO STATUS:$"), "SUMMARY OF REPO STATUS:\n") testObj.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^REPO VERSION FILES:$"), "REPO VERSION FILES:\n") testObj.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^USEFUL ALIASES:$"), "USEFUL ALIASES:\n") testObj.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^USAGE TIPS:$"), "USAGE TIPS:\n") testObj.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^SCRIPT DEPENDENCIES:$"), "SCRIPT DEPENDENCIES:\n") class test_gitdist(unittest.TestCase): def setUp(self): None def test_default(self): (cmndOut, errOut) = getCmndOutput(gitdistPathNoColor, rtnCode=True) cmndOut_expected = "Must specify git command. See 'git --help' for options.\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) self.assertEqual(errOut, 1) # Make sure the default --help shows the section "OVERVIEW" def test_help(self): cmndOut = getCmndOutput(gitdistPath+" --help") assertContainsGitdistHelpHeader(self, cmndOut) self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^OVERVIEW:$"), "") self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^REPO SELECTION AND SETUP:$"), "") # Make sure --dist-help= does not print OVERVIEW section def test_dist_help_none_help(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help= --help") assertContainsGitdistHelpHeader(self, cmndOut) self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^OVERVIEW:$"), "") self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^Options:$"), "Options:\n") # --dist-help=aliases --help def test_dist_help_aliases_help(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help=aliases --help") assertContainsGitdistHelpHeader(self, cmndOut) self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^USEFUL ALIASES:$"), "USEFUL ALIASES:\n") self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^REPO SELECTION AND SETUP:$"), "") # Make sure --dist-help=all prints all the topic headers def test_dist_help_all_help(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help=all --help") assertContainsGitdistHelpHeader(self, cmndOut) assertContainsAllGitdistHelpSections(self, cmndOut) # Tet that --dist-help --help prints nice error message def test_dist_help_help(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help --help") cmndOut_expected = "gitdist: error: option --dist-help: invalid choice: '--help' (choose from '', 'overview', 'repo-selection-and-setup', 'dist-repo-status', 'repo-versions', 'aliases', 'usage-tips', 'script-dependencies', 'all')\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) # Test --dist-helps=invalid-pick picked up as invalid value. def test_dist_help_invalid_pick_help(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help=invalid-pick --help") assertContainsGitdistHelpHeader(self, cmndOut) errorToFind = "gitdist: error: option --dist-help: invalid choice: 'invalid-pick' (choose from '', 'overview', 'repo-selection-and-setup', 'dist-repo-status', 'repo-versions', 'aliases', 'usage-tips', 'script-dependencies', 'all')" self.assertEqual( GeneralScriptSupport.extractLinesMatchingSubstr(cmndOut,errorToFind), errorToFind+"\n") # Test --dist-help (show error string) def test_dist_help(self): (cmndOut, errOut) = getCmndOutput(gitdistPath+" --dist-help", rtnCode=True) if sys.version_info < (3,): anOrOne = "an" else: anOrOne = "1" self.assertEqual( s(cmndOut), s("gitdist: error: --dist-help option requires "+anOrOne+" argument\n")) self.assertEqual(errOut, 2) # Test --dist-help= (show no-op string) def test_dist_help_none(self): (cmndOut, errOut) = getCmndOutput(gitdistPathNoColor+" --dist-help=", rtnCode=True) self.assertEqual( s(cmndOut), s("Must specify git command. See 'git --help' for options.\n")) self.assertEqual(errOut, 1) # Test --dist-help=overview def test_dist_help_overview(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help=overview") self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^OVERVIEW:$"), "OVERVIEW:\n") self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^Options:$"), "") # Test --dist-help=usage-tips def test_dist_help_usage_tips(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help=usage-tips") self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^USAGE TIPS:$"), "USAGE TIPS:\n") self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^Options:$"), "") # Test --dist-help=all def test_dist_help_all(self): cmndOut = getCmndOutput(gitdistPath+" --dist-help=all") assertContainsAllGitdistHelpSections(self, cmndOut) self.assertEqual( GeneralScriptSupport.extractLinesMatchingRegex(cmndOut,"^Options:$"), "") def test_noEgGit(self): (cmndOut, errOut) = getCmndOutput(gitdistPathNoColor+" --dist-use-git= log", rtnCode=True) cmndOut_expected = "Can't find git, please set --dist-use-git\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) self.assertEqual(errOut, 1) def test_log_args(self): cmndOut = getCmndOutputInMockProjectDir(gitdistPathMock+" log HEAD -1") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_dot_gitdist(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("gitdist_dot_gitdist") os.mkdir("ExtraRepo1") os.makedirs("Path/To/ExtraRepo2") os.mkdir("ExtraRepo3") # Make sure .gitdist.default is found and read correctly open(".gitdist.default", "w").write( ".\n" \ "ExtraRepo1\n" \ "Path/To/ExtraRepo2\n" \ "MissingExtraRep\n" \ "ExtraRepo3\n" ) cmndOut = GeneralScriptSupport.getCmndOutput(gitdistPathMock+" status", workingDir=testDir) cmndOut_expected = \ "\n*** Base Git Repo: MockProjectDir\n" \ "['mockgit', 'status']\n\n" \ "*** Git Repo: ExtraRepo1\n" \ "['mockgit', 'status']\n\n" \ "*** Git Repo: Path/To/ExtraRepo2\n" \ "['mockgit', 'status']\n\n" \ "*** Git Repo: ExtraRepo3\n" \ "['mockgit', 'status']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) # NOTE: Above ensures that all of the paths are read correctly and that # missing paths (MissingExtraRepo) are ignored. # Make sure that .gitdist overrides .gitdist.default open(".gitdist", "w").write( ".\n" \ "ExtraRepo1\n" \ "ExtraRepo3\n" ) cmndOut = GeneralScriptSupport.getCmndOutput(gitdistPathMock+" status", workingDir=testDir) cmndOut_expected = \ "\n*** Base Git Repo: MockProjectDir\n" \ "['mockgit', 'status']\n\n" \ "*** Git Repo: ExtraRepo1\n" \ "['mockgit', 'status']\n\n" \ "*** Git Repo: ExtraRepo3\n" \ "['mockgit', 'status']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) # Make sure that --dist-repos overrides all files cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPathMock+" --dist-repos=.,ExtraRepo1,Path/To/ExtraRepo2 status", workingDir=testDir) cmndOut_expected = \ "\n*** Base Git Repo: MockProjectDir\n" \ "['mockgit', 'status']\n\n" \ "*** Git Repo: ExtraRepo1\n" \ "['mockgit', 'status']\n\n" \ "*** Git Repo: Path/To/ExtraRepo2\n" \ "['mockgit', 'status']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_log_args_extra_repo_1(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+" --dist-repos=.,extraTrilinosRepo log HEAD -1") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" \ "*** Git Repo: extraTrilinosRepo\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_args_extra_repo_2_not_first(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+\ " --dist-repos=.,extraTrilinosRepo,extraRepoOnePackage "+\ " --dist-not-repos=extraTrilinosRepo "+\ " log HEAD -1" ) cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" \ "*** Git Repo: extraRepoOnePackage\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_args_extra_repo_2_not_second(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+\ " --dist-repos=.,extraTrilinosRepo,extraRepoOnePackage "+\ " --dist-not-repos=extraTrilinosRepo "+\ " log HEAD -1" ) cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" \ "*** Git Repo: extraRepoOnePackage\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_args_extra_repo_1_not_base(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+\ " --dist-repos=.,extraTrilinosRepo "+\ " --dist-not-repos=. "+\ " log HEAD -1" ) cmndOut_expected = \ "\n*** Git Repo: extraTrilinosRepo\n" \ "['mockgit', 'log', 'HEAD', '-1']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_dist_mod_only_1_change_base(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("gitdist_dist_mod_only_1_change_base") os.mkdir("ExtraRepo1") os.mkdir("ExtraRepo2") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch0\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 3 some author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch0\n" \ "Your branch is ahead of 'origin_repo0/remote_branch0' by 3 commits.\n" \ ) open("ExtraRepo1/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch1\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) open("ExtraRepo2/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch2\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-mod-only --dist-repos=.,ExtraRepo1,ExtraRepo2 status", workingDir=testDir) cmndOut_expected = \ "\n*** Base Git Repo: MockProjectDir\n" \ "On branch local_branch0\n" \ "Your branch is ahead of 'origin_repo0/remote_branch0' by 3 commits.\n\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_dist_mod_only_1_change_extrarepo1(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("gitdist_dist_mod_only_1_change_extrarepo1") os.mkdir("ExtraRepo1") os.mkdir("ExtraRepo2") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch0\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) open("ExtraRepo1/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch1\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo1/remote_branch1\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 1 some author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch1\n" \ "Your branch is ahead of 'origin_repo1/remote_branch1' by 1 commits.\n" \ ) open("ExtraRepo2/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch2\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo2/remote_branch2\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-mod-only --dist-repos=.,ExtraRepo1,ExtraRepo2 status", workingDir=testDir) cmndOut_expected = \ "\n*** Git Repo: ExtraRepo1\nOn branch local_branch1\n" \ "Your branch is ahead of 'origin_repo1/remote_branch1' by 1 commits.\n\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_dist_mod_only_1_extrarepo1_not_tracking_branch(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("dist_mod_only_1_extrarepo1_not_tracking_branch") os.mkdir("ExtraRepo1") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch0\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: 3 some author\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch0\n" \ "Your branch is ahead of 'origin_repo0/remote_branch0' by 3 commits.\n" \ ) open("ExtraRepo1/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch1\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 128\n" \ "MOCK_PROGRAM_OUTPUT: error: No upstream branch found for ''\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-mod-only --dist-repos=.,ExtraRepo1,ExtraRepo2 status", workingDir=testDir) cmndOut_expected = \ "\n*** Base Git Repo: MockProjectDir\n" \ "On branch local_branch0\n" \ "Your branch is ahead of 'origin_repo0/remote_branch0' by 3 commits.\n\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_dist_mod_only_1_extrarepo1_not_tracking_branch_with_mods(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("dist_mod_only_1_extrarepo1_not_tracking_branch_with_mods") os.mkdir("ExtraRepo1") open(".mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch0\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_INPUT: shortlog -s HEAD ^origin_repo0/remote_branch0\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: \n" \ ) open("ExtraRepo1/.mockprogram_inout.txt", "w").write( "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref HEAD\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: local_branch1\n" \ "MOCK_PROGRAM_INPUT: rev-parse --abbrev-ref --symbolic-full-name @{u}\n" \ "MOCK_PROGRAM_RETURN: 128\n" \ "MOCK_PROGRAM_OUTPUT: error: No upstream branch found for ''\n" \ "MOCK_PROGRAM_INPUT: status --porcelain\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: M file1\n" \ "MOCK_PROGRAM_INPUT: status\n" \ "MOCK_PROGRAM_RETURN: 0\n" \ "MOCK_PROGRAM_OUTPUT: On branch local_branch1\n" \ "Your branch is ahead of 'origin_repo1/remote_branch1' by 1 commits.\n" \ ) # Make sure that --dist-repos overrides all files cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-mod-only --dist-repos=.,ExtraRepo1,ExtraRepo2 status", workingDir=testDir) cmndOut_expected = \ "\n*** Git Repo: ExtraRepo1\n" \ "On branch local_branch1\n" \ "Your branch is ahead of 'origin_repo1/remote_branch1' by 1 commits.\n\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_log_version_file(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+\ " log _VERSION_ --some -other args") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'sha1_1', '--some', '-other', 'args']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_version_file_extra_repo_1(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+ \ " --dist-repos=.,extraTrilinosRepo"+ \ " log _VERSION_") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'sha1_1']\n" \ "\n*** Git Repo: extraTrilinosRepo\n['mockgit', 'log', 'sha1_2']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_version_file_extra_repo_2(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+ \ " --dist-repos=.,extraRepoOnePackage,extraTrilinosRepo"+ \ " log _VERSION_") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'sha1_1']\n" \ "\n*** Git Repo: extraRepoOnePackage\n['mockgit', 'log', 'sha1_3']\n" \ "\n*** Git Repo: extraTrilinosRepo\n['mockgit', 'log', 'sha1_2']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_HEAD_version_file_extra_repo_1(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+ \ " --dist-repos=.,extraTrilinosRepo"+ \ " log HEAD ^_VERSION_") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'HEAD', '^sha1_1']\n" \ "\n*** Git Repo: extraTrilinosRepo\n['mockgit', 'log', 'HEAD', '^sha1_2']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_version_file_invalid_extra_repo(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+ \ " --dist-repos=.,extraRepoTwoPackages"+ \ " log _VERSION_") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n['mockgit', 'log', 'sha1_1']\n" \ "\n*** Git Repo: extraRepoTwoPackages\nRepo 'extraRepoTwoPackages' is not in the list of repos ['.', 'extraRepoOnePackage', 'extraTrilinosRepo'] read in from the version file.\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_not_version_file_2(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+ \ " --dist-version-file2="+unitTestDataDir+"/versionFile_withSummary_1_2.txt"+ \ " log _VERSION_ ^_VERSION2_") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'sha1_1', '^sha1_1_2']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_not_version_file_2_extra_repo_1(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+ \ " --dist-version-file2="+unitTestDataDir+"/versionFile_withSummary_1_2.txt"+ \ " --dist-repos=.,extraTrilinosRepo"+ \ " log _VERSION_ ^_VERSION2_") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'sha1_1', '^sha1_1_2']\n" \ "\n*** Git Repo: extraTrilinosRepo\n['mockgit', 'log', 'sha1_2', '^sha1_2_2']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) def test_log_since_until_version_file_2_extra_repo_1(self): cmndOut = getCmndOutputInMockProjectDir( gitdistPathMock+ \ " --dist-version-file="+unitTestDataDir+"/versionFile_withSummary_1.txt"+ \ " --dist-version-file2="+unitTestDataDir+"/versionFile_withSummary_1_2.txt"+ \ " --dist-repos=.,extraTrilinosRepo"+ \ " log _VERSION2_.._VERSION_") cmndOut_expected = \ "\n*** Base Git Repo: MockTrilinos\n" \ "['mockgit', 'log', 'sha1_1_2..sha1_1']\n" \ "\n*** Git Repo: extraTrilinosRepo\n['mockgit', 'log', 'sha1_2_2..sha1_2']\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) # The above test ensures that it repalces the SHA1s for in the same cmndline args def test_dist_repo_status_all(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("gitdist_dist_repo_status_all") os.mkdir("ExtraRepo1") os.mkdir("ExtraRepo2") writeGitMockProgram_base_3_2_1_repo1_22_0_2_repo2_0_0_0() cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-repos=.,ExtraRepo1,ExtraRepo2 dist-repo-status", workingDir=testDir) #print(cmndOut) cmndOut_expected = \ "-----------------------------------------------------------------------------------------\n" \ "| ID | Repo Dir | Branch | Tracking Branch | C | M | ? |\n" \ "|----|-----------------------|---------------|-----------------------------|----|---|---|\n" \ "| 0 | MockProjectDir (Base) | local_branch0 | origin_repo0/remote_branch0 | 3 | 2 | 1 |\n" \ "| 1 | ExtraRepo1 | local_branch1 | origin_repo1/remote_branch1 | 22 | | 1 |\n" \ "| 2 | ExtraRepo2 | local_branch2 | origin_repo2/remote_branch2 | | | |\n" \ "-----------------------------------------------------------------------------------------\n" \ "\n" \ "(tip: to see a legend, pass in --dist-legend.)\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_dist_repo_status_mod_only_first(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("gitdist_dist_repo_status_mod_only_first") os.mkdir("ExtraRepo1") os.mkdir("ExtraRepo2") writeGitMockProgram_base_3_2_1_repo1_22_0_2_repo2_0_0_0() cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-repos=.,ExtraRepo1,ExtraRepo2 --dist-mod-only dist-repo-status", workingDir=testDir) #print(cmndOut) cmndOut_expected = \ "-----------------------------------------------------------------------------------------\n" \ "| ID | Repo Dir | Branch | Tracking Branch | C | M | ? |\n" \ "|----|-----------------------|---------------|-----------------------------|----|---|---|\n" \ "| 0 | MockProjectDir (Base) | local_branch0 | origin_repo0/remote_branch0 | 3 | 2 | 1 |\n" \ "| 1 | ExtraRepo1 | local_branch1 | origin_repo1/remote_branch1 | 22 | | 1 |\n" \ "-----------------------------------------------------------------------------------------\n" \ "\n" \ "(tip: to see a legend, pass in --dist-legend.)\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_dist_repo_status_mod_only_first_legend(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("gitdist_dist_repo_status_mod_only_first_legend") os.mkdir("ExtraRepo1") os.mkdir("ExtraRepo2") writeGitMockProgram_base_3_2_1_repo1_22_0_2_repo2_0_0_0() cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-repos=.,ExtraRepo1,ExtraRepo2 --dist-mod-only" \ +" --dist-legend dist-repo-status", workingDir=testDir) #print("+++++++++\n" + cmndOut + "+++++++\n") cmndOut_expected = \ "-----------------------------------------------------------------------------------------\n" \ "| ID | Repo Dir | Branch | Tracking Branch | C | M | ? |\n" \ "|----|-----------------------|---------------|-----------------------------|----|---|---|\n" \ "| 0 | MockProjectDir (Base) | local_branch0 | origin_repo0/remote_branch0 | 3 | 2 | 1 |\n" \ "| 1 | ExtraRepo1 | local_branch1 | origin_repo1/remote_branch1 | 22 | | 1 |\n" \ "-----------------------------------------------------------------------------------------\n" \ "\n" \ "Legend:\n" \ "* ID: Repository ID, zero based (order git commands are run)\n" \ "* Repo Dir: Relative to base repo (base repo shown first with '(Base)')\n" \ "* Branch: Current branch (or detached HEAD)\n" \ "* Tracking Branch: Tracking branch (or empty if no tracking branch exists)\n" \ "* C: Number local commits w.r.t. tracking branch (empty if zero or no TB)\n" \ "* M: Number of tracked modified (uncommitted) files (empty if zero)\n" \ "* ?: Number of untracked, non-ignored files (empty if zero)\n\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_dist_repo_status_mod_only_first_last(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("gitdist_dist_repo_status_mod_only_first_last") os.mkdir("ExtraRepo1") os.mkdir("ExtraRepo2") writeGitMockProgram_base_3_2_1_repo1_0_0_0_repo2_4_0_2() cmndOut = GeneralScriptSupport.getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-repos=.,ExtraRepo1,ExtraRepo2 --dist-mod-only dist-repo-status", workingDir=testDir) #print(cmndOut) cmndOut_expected = \ "----------------------------------------------------------------------------------------\n" \ "| ID | Repo Dir | Branch | Tracking Branch | C | M | ? |\n" \ "|----|-----------------------|---------------|-----------------------------|---|---|---|\n" \ "| 0 | MockProjectDir (Base) | local_branch0 | origin_repo0/remote_branch0 | 3 | 2 | 1 |\n" \ "| 2 | ExtraRepo2 | local_branch2 | origin_repo2/remote_branch2 | 4 | | 2 |\n" \ "----------------------------------------------------------------------------------------\n" \ "\n" \ "(tip: to see a legend, pass in --dist-legend.)\n" self.assertEqual(s(cmndOut), s(cmndOut_expected)) finally: os.chdir(testBaseDir) def test_dist_repo_status_extra_args_fail(self): os.chdir(testBaseDir) try: # Create a mock git meta-project testDir = createAndMoveIntoTestDir("dist_repo_status_extra_args_fail") (cmndOut, errOut) = getCmndOutput( gitdistPath + " --dist-no-color --dist-use-git="+mockGitPath \ +" --dist-repos=.,ExtraRepo1,ExtraRepo2 --dist-mod-only" \ +" --dist-legend dist-repo-status --name-status", rtnCode=True) #print(cmndOut) cmndOut_expected = \ "Error, passing in extra git commands/args ='--name-status' with special comamnd 'dist-repo-status is not allowed!\n" self.assertEqual(cmndOut, s(cmndOut_expected)) self.assertEqual(errOut, 1) finally: os.chdir(testBaseDir) if __name__ == '__main__': unittest.main()

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e4e2f7c240033460fd897db29fd259c8392ba838

205

py

Python

config.py

vortex-17/file-split-Cryptography

621673ad0aa327f97f0e38d9d94216cfd12f8e0e

[ "MIT" ]

null

config.py

vortex-17/file-split-Cryptography

621673ad0aa327f97f0e38d9d94216cfd12f8e0e

[ "MIT" ]

null

config.py

vortex-17/file-split-Cryptography

621673ad0aa327f97f0e38d9d94216cfd12f8e0e

[ "MIT" ]

null

{ "data_storage" : ["/Users/vivek/Desktop/file-split/storage1/", "/Users/vivek/Desktop/file-split/storage2/", "/Users/vivek/Desktop/file-split/storage3/", "/Users/vivek/Desktop/file-split/storage4/"] }

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py

Python

NiaPy/tests/test_pso.py

lucijabrezocnik/NiaPy

1582d1af835c022c77224ea0234178a399efc106

[ "MIT" ]

null

NiaPy/tests/test_pso.py

lucijabrezocnik/NiaPy

1582d1af835c022c77224ea0234178a399efc106

[ "MIT" ]

null

NiaPy/tests/test_pso.py

lucijabrezocnik/NiaPy

1582d1af835c022c77224ea0234178a399efc106

[ "MIT" ]

1

2018-06-13T08:10:23.000Z

# encoding=utf8 from NiaPy.algorithms.basic import ParticleSwarmOptimization, ParticleSwarmAlgorithm, OppositionVelocityClampingParticleSwarmOptimization, CenterParticleSwarmOptimization, MutatedParticleSwarmOptimization, MutatedCenterParticleSwarmOptimization, ComprehensiveLearningParticleSwarmOptimizer, MutatedCenterUnifiedParticleSwarmOptimization from NiaPy.tests.test_algorithm import AlgorithmTestCase, MyBenchmark class PSOTestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = ParticleSwarmOptimization def test_algorithm_info(self): al = self.algo.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = self.algo.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) def test_custom_works_fine(self): pso_custom = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) pso_customc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, pso_custom, pso_customc, MyBenchmark()) def test_griewank_works_fine(self): pso_griewank = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) pso_griewankc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, pso_griewank, pso_griewankc) class PSATestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = ParticleSwarmAlgorithm def test_algorithm_info(self): al = ParticleSwarmAlgorithm.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = ParticleSwarmAlgorithm.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['vMax'](10)) self.assertTrue(d['vMin'](10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) self.assertFalse(d['vMin'](None)) self.assertFalse(d['vMax'](None)) self.assertFalse(d['w'](None)) self.assertFalse(d['w'](-.1)) self.assertFalse(d['w'](-10)) self.assertTrue(d['w'](.01)) self.assertTrue(d['w'](10.01)) def test_custom_works_fine(self): wvcpso_custom = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) wvcpso_customc = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, wvcpso_custom, wvcpso_customc, MyBenchmark()) def test_griewank_works_fine(self): wvcpso_griewank = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) wvcpso_griewankc = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, wvcpso_griewank, wvcpso_griewankc) class OVCPSOTestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = OppositionVelocityClampingParticleSwarmOptimization def test_algorithm_info(self): al = self.algo.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = self.algo.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['vMax'](10)) self.assertTrue(d['vMin'](10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) self.assertFalse(d['vMin'](None)) self.assertFalse(d['vMax'](None)) self.assertFalse(d['w'](None)) self.assertFalse(d['w'](-.1)) self.assertFalse(d['w'](-10)) self.assertTrue(d['w'](.01)) self.assertTrue(d['w'](10.01)) def test_custom_works_fine(self): wvcpso_custom = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) wvcpso_customc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, wvcpso_custom, wvcpso_customc, MyBenchmark()) def test_griewank_works_fine(self): wvcpso_griewank = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) wvcpso_griewankc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, wvcpso_griewank, wvcpso_griewankc) class CPSOTestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = CenterParticleSwarmOptimization def test_algorithm_info(self): al = self.algo.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = self.algo.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['vMax'](10)) self.assertTrue(d['vMin'](10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) self.assertFalse(d['vMin'](None)) self.assertFalse(d['vMax'](None)) self.assertFalse(d['w'](None)) self.assertFalse(d['w'](-.1)) self.assertFalse(d['w'](-10)) self.assertTrue(d['w'](.01)) self.assertTrue(d['w'](10.01)) def test_custom_works_fine(self): cpso_custom = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) cpso_customc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, cpso_custom, cpso_customc, MyBenchmark()) def test_griewank_works_fine(self): cpso_griewank = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) cpso_griewankc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, cpso_griewank, cpso_griewankc) class MPSOTestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = MutatedParticleSwarmOptimization def test_algorithm_info(self): al = MutatedParticleSwarmOptimization.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = MutatedParticleSwarmOptimization.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['vMax'](10)) self.assertTrue(d['vMin'](10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) self.assertFalse(d['vMin'](None)) self.assertFalse(d['vMax'](None)) self.assertFalse(d['w'](None)) self.assertFalse(d['w'](-.1)) self.assertFalse(d['w'](-10)) self.assertTrue(d['w'](.01)) self.assertTrue(d['w'](10.01)) def test_custom_works_fine(self): mpso_custom = MutatedParticleSwarmOptimization(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) mpso_customc = MutatedParticleSwarmOptimization(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, mpso_custom, mpso_customc, MyBenchmark()) def test_griewank_works_fine(self): mpso_griewank = MutatedParticleSwarmOptimization(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) mpso_griewankc = MutatedParticleSwarmOptimization(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, mpso_griewank, mpso_griewankc) class MCPSOTestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = MutatedCenterParticleSwarmOptimization def test_algorithm_info(self): al = self.algo.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = self.algo.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['vMax'](10)) self.assertTrue(d['vMin'](10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) self.assertFalse(d['vMin'](None)) self.assertFalse(d['vMax'](None)) self.assertFalse(d['w'](None)) self.assertFalse(d['w'](-.1)) self.assertFalse(d['w'](-10)) self.assertTrue(d['w'](.01)) self.assertTrue(d['w'](10.01)) def test_custom_works_fine(self): mcpso_custom = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) mcpso_customc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, mcpso_custom, mcpso_customc, MyBenchmark()) def test_griewank_works_fine(self): mcpso_griewank = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) mcpso_griewankc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, mcpso_griewank, mcpso_griewankc) class MCUPSOTestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = MutatedCenterUnifiedParticleSwarmOptimization def test_algorithm_info(self): al = self.algo.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = self.algo.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['vMax'](10)) self.assertTrue(d['vMin'](10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) self.assertFalse(d['vMin'](None)) self.assertFalse(d['vMax'](None)) self.assertFalse(d['w'](None)) self.assertFalse(d['w'](-.1)) self.assertFalse(d['w'](-10)) self.assertTrue(d['w'](.01)) self.assertTrue(d['w'](10.01)) def test_custom_works_fine(self): mcupso_custom = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) mcupso_customc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, mcupso_custom, mcupso_customc, MyBenchmark()) def test_griewank_works_fine(self): mcupso_griewank = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) mcupso_griewankc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, mcupso_griewank, mcupso_griewankc) class CLPSOTestCase(AlgorithmTestCase): def setUp(self): AlgorithmTestCase.setUp(self) self.algo = ComprehensiveLearningParticleSwarmOptimizer def test_algorithm_info(self): al = self.algo.algorithmInfo() self.assertIsNotNone(al) def test_parameter_type(self): d = self.algo.typeParameters() self.assertTrue(d['C1'](10)) self.assertTrue(d['C2'](10)) self.assertTrue(d['C1'](0)) self.assertTrue(d['C2'](0)) self.assertFalse(d['C1'](-10)) self.assertFalse(d['C2'](-10)) self.assertTrue(d['vMax'](10)) self.assertTrue(d['vMin'](10)) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](-10)) self.assertFalse(d['NP'](0)) def test_custom_works_fine(self): clpso_custom = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) clpso_customc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, clpso_custom, clpso_customc, MyBenchmark()) def test_griewank_works_fine(self): clpso_griewank = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) clpso_griewankc = self.algo(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, seed=self.seed) AlgorithmTestCase.test_algorithm_run(self, clpso_griewank, clpso_griewankc) # vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3

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0.753575

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8

39c56c5758a7476eba61e1c186c655d329a1c6a8

17,781

py

Python

fonts/romfonts/vga2_8x16.py

slabua/st7789py_mpy

31e6f94592563e2b5ad716c48486e605ca3911bb

[ "MIT" ]

153

2020-02-02T11:03:14.000Z

2022-03-30T05:47:07.000Z

fonts/bitmap/vga2_8x16.py

skylin008/st7789_mpy

f304991fc5558be653df5f0de928494b85cbc60d

[ "MIT" ]

58

2020-04-11T23:23:02.000Z

2022-03-26T20:45:23.000Z

fonts/bitmap/vga2_8x16.py

skylin008/st7789_mpy

f304991fc5558be653df5f0de928494b85cbc60d

[ "MIT" ]

50

2020-02-02T11:05:23.000Z

2022-03-22T15:24:42.000Z

"""converted from vga_8x16.bin """ WIDTH = 8 HEIGHT = 16 FIRST = 0x00 LAST = 0xff _FONT =\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x7e\x81\xa5\x81\x81\xbd\x99\x81\x81\x7e\x00\x00\x00\x00'\ b'\x00\x00\x7e\xff\xdb\xff\xff\xc3\xe7\xff\xff\x7e\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x6c\xfe\xfe\xfe\xfe\x7c\x38\x10\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x10\x38\x7c\xfe\x7c\x38\x10\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x18\x3c\x3c\xe7\xe7\xe7\x18\x18\x3c\x00\x00\x00\x00'\ b'\x00\x00\x00\x18\x3c\x7e\xff\xff\x7e\x18\x18\x3c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x18\x3c\x3c\x18\x00\x00\x00\x00\x00\x00'\ b'\xff\xff\xff\xff\xff\xff\xe7\xc3\xc3\xe7\xff\xff\xff\xff\xff\xff'\ b'\x00\x00\x00\x00\x00\x3c\x66\x42\x42\x66\x3c\x00\x00\x00\x00\x00'\ b'\xff\xff\xff\xff\xff\xc3\x99\xbd\xbd\x99\xc3\xff\xff\xff\xff\xff'\ b'\x00\x00\x1e\x0e\x1a\x32\x78\xcc\xcc\xcc\xcc\x78\x00\x00\x00\x00'\ b'\x00\x00\x3c\x66\x66\x66\x66\x3c\x18\x7e\x18\x18\x00\x00\x00\x00'\ b'\x00\x00\x3f\x33\x3f\x30\x30\x30\x30\x70\xf0\xe0\x00\x00\x00\x00'\ b'\x00\x00\x7f\x63\x7f\x63\x63\x63\x63\x67\xe7\xe6\xc0\x00\x00\x00'\ b'\x00\x00\x00\x18\x18\xdb\x3c\xe7\x3c\xdb\x18\x18\x00\x00\x00\x00'\ b'\x00\x80\xc0\xe0\xf0\xf8\xfe\xf8\xf0\xe0\xc0\x80\x00\x00\x00\x00'\ b'\x00\x02\x06\x0e\x1e\x3e\xfe\x3e\x1e\x0e\x06\x02\x00\x00\x00\x00'\ b'\x00\x00\x18\x3c\x7e\x18\x18\x18\x7e\x3c\x18\x00\x00\x00\x00\x00'\ b'\x00\x00\x66\x66\x66\x66\x66\x66\x66\x00\x66\x66\x00\x00\x00\x00'\ b'\x00\x00\x7f\xdb\xdb\xdb\x7b\x1b\x1b\x1b\x1b\x1b\x00\x00\x00\x00'\ b'\x00\x7c\xc6\x60\x38\x6c\xc6\xc6\x6c\x38\x0c\xc6\x7c\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\xfe\xfe\xfe\xfe\x00\x00\x00\x00'\ b'\x00\x00\x18\x3c\x7e\x18\x18\x18\x7e\x3c\x18\x7e\x00\x00\x00\x00'\ b'\x00\x00\x18\x3c\x7e\x18\x18\x18\x18\x18\x18\x18\x00\x00\x00\x00'\ b'\x00\x00\x18\x18\x18\x18\x18\x18\x18\x7e\x3c\x18\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x18\x0c\xfe\x0c\x18\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x30\x60\xfe\x60\x30\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\xc0\xc0\xc0\xfe\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x28\x6c\xfe\x6c\x28\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x10\x38\x38\x7c\x7c\xfe\xfe\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\xfe\xfe\x7c\x7c\x38\x38\x10\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x18\x3c\x3c\x3c\x18\x18\x18\x00\x18\x18\x00\x00\x00\x00'\ b'\x00\x66\x66\x66\x24\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x6c\x6c\xfe\x6c\x6c\x6c\xfe\x6c\x6c\x00\x00\x00\x00'\ b'\x18\x18\x7c\xc6\xc2\xc0\x7c\x06\x06\x86\xc6\x7c\x18\x18\x00\x00'\ b'\x00\x00\x00\x00\xc2\xc6\x0c\x18\x30\x60\xc6\x86\x00\x00\x00\x00'\ b'\x00\x00\x38\x6c\x6c\x38\x76\xdc\xcc\xcc\xcc\x76\x00\x00\x00\x00'\ b'\x00\x30\x30\x30\x60\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x0c\x18\x30\x30\x30\x30\x30\x30\x18\x0c\x00\x00\x00\x00'\ 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b'\x18\x18\x18\x18\x18\x18\x18\x1f\x18\x18\x18\x18\x18\x18\x18\x18'\ b'\x00\x00\x00\x00\x00\x00\x00\xff\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x18\x18\x18\x18\x18\x18\x18\xff\x18\x18\x18\x18\x18\x18\x18\x18'\ b'\x18\x18\x18\x18\x18\x1f\x18\x1f\x18\x18\x18\x18\x18\x18\x18\x18'\ b'\x36\x36\x36\x36\x36\x36\x36\x37\x36\x36\x36\x36\x36\x36\x36\x36'\ b'\x36\x36\x36\x36\x36\x37\x30\x3f\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x3f\x30\x37\x36\x36\x36\x36\x36\x36\x36\x36'\ b'\x36\x36\x36\x36\x36\xf7\x00\xff\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\xff\x00\xf7\x36\x36\x36\x36\x36\x36\x36\x36'\ b'\x36\x36\x36\x36\x36\x37\x30\x37\x36\x36\x36\x36\x36\x36\x36\x36'\ b'\x00\x00\x00\x00\x00\xff\x00\xff\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x36\x36\x36\x36\x36\xf7\x00\xf7\x36\x36\x36\x36\x36\x36\x36\x36'\ b'\x18\x18\x18\x18\x18\xff\x00\xff\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x36\x36\x36\x36\x36\x36\x36\xff\x00\x00\x00\x00\x00\x00\x00\x00'\ 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b'\xff\xff\xff\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x76\xdc\xd8\xd8\xd8\xdc\x76\x00\x00\x00\x00'\ b'\x00\x00\x78\xcc\xcc\xcc\xd8\xcc\xc6\xc6\xc6\xcc\x00\x00\x00\x00'\ b'\x00\x00\xfe\xc6\xc6\xc0\xc0\xc0\xc0\xc0\xc0\xc0\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\xfe\x6c\x6c\x6c\x6c\x6c\x6c\x00\x00\x00\x00'\ b'\x00\x00\xfe\xc6\x60\x30\x18\x18\x30\x60\xc6\xfe\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x7e\xd8\xd8\xd8\xd8\xd8\x70\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x66\x66\x66\x66\x66\x66\x7c\x60\x60\xc0\x00'\ b'\x00\x00\x00\x00\x76\xdc\x18\x18\x18\x18\x18\x18\x00\x00\x00\x00'\ b'\x00\x00\x7e\x18\x3c\x66\x66\x66\x66\x3c\x18\x7e\x00\x00\x00\x00'\ b'\x00\x00\x38\x6c\xc6\xc6\xfe\xc6\xc6\xc6\x6c\x38\x00\x00\x00\x00'\ b'\x00\x00\x38\x6c\xc6\xc6\xc6\x6c\x6c\x6c\x6c\xee\x00\x00\x00\x00'\ b'\x00\x00\x1e\x30\x18\x0c\x3e\x66\x66\x66\x66\x3c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x7e\xdb\xdb\xdb\x7e\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x03\x06\x7e\xdb\xdb\xf3\x7e\x60\xc0\x00\x00\x00\x00'\ b'\x00\x00\x1c\x30\x60\x60\x7c\x60\x60\x60\x30\x1c\x00\x00\x00\x00'\ b'\x00\x00\x00\x7c\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\xfe\x00\x00\xfe\x00\x00\xfe\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x18\x18\x7e\x18\x18\x00\x00\x7e\x00\x00\x00\x00'\ b'\x00\x00\x00\x30\x18\x0c\x06\x0c\x18\x30\x00\x7e\x00\x00\x00\x00'\ b'\x00\x00\x00\x0c\x18\x30\x60\x30\x18\x0c\x00\x7e\x00\x00\x00\x00'\ b'\x00\x00\x0e\x1b\x1b\x18\x18\x18\x18\x18\x18\x18\x18\x18\x18\x18'\ b'\x18\x18\x18\x18\x18\x18\x18\x18\x18\xd8\xd8\xd8\x70\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x18\x00\x7e\x00\x18\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x76\xdc\x00\x76\xdc\x00\x00\x00\x00\x00\x00'\ b'\x00\x38\x6c\x6c\x38\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x18\x18\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x18\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x0f\x0c\x0c\x0c\x0c\x0c\xec\x6c\x6c\x3c\x1c\x00\x00\x00\x00'\ b'\x00\x6c\x36\x36\x36\x36\x36\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x3c\x66\x0c\x18\x32\x7e\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x7e\x7e\x7e\x7e\x7e\x7e\x7e\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ FONT = memoryview(_FONT)

67.098113

68

0.709915

4,369

17,781

2.888533

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1

0

1

0

1

0

1

null

1

0

15

39db27b1d77d155111d280f5ec244c06ca9b69cf

5,108

py

Python

tests/test_api.py

ProjetPP/PPP-Logger

dc529fea565e795a93a160c9415724b9f4cda24c

[ "MIT" ]

1

2015-02-26T21:07:24.000Z

tests/test_api.py

ProjetPP/PPP-Logger

dc529fea565e795a93a160c9415724b9f4cda24c

[ "MIT" ]

4

2015-03-01T09:05:08.000Z

2015-03-01T09:58:19.000Z

tests/test_api.py

ProjetPP/PPP-Logger

dc529fea565e795a93a160c9415724b9f4cda24c

[ "MIT" ]

null

import sqlite3 import json import tempfile from ppp_logger import app from ppp_libmodule.tests import PPPTestCase class HttpTest(PPPTestCase(app)): config_var = 'PPP_LOGGER_CONFIG' def setUp(self): self.fd = tempfile.NamedTemporaryFile('w+') self.config = '{"database_url": "sqlite:///%s"}' % self.fd.name super(HttpTest, self).setUp() def tearDown(self): super(HttpTest, self).tearDown() self.fd.close() def testEmpty(self): r = self.app.get('/') self.assertEqual(r.content_type, 'application/json') r = json.loads(r.body.decode()) self.assertEqual(r, []) def testUnknownOrder(self): self.assertEqual(self.app.get('/', {'order': 'foobar'}, status='*').status_int, 405) def testLast(self): q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) r = self.app.get('/') self.assertEqual(r.content_type, 'application/json') r = json.loads(r.body.decode()) self.assertEqual(len(r), 2, r) self.assertEqual(r[0][0], 'Baz qux?') self.assertEqual(r[1][0], 'Foo bar?') def testLimitLast(self): q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) r = self.app.get('/', params={'limit': 1}) self.assertEqual(r.content_type, 'application/json') r = json.loads(r.body.decode()) self.assertEqual(len(r), 1, r) self.assertEqual(r[0][0], 'Baz qux?') def testFirst(self): q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) r = self.app.get('/', {'order': 'first', 'limit': '1'}) self.assertEqual(r.content_type, 'application/json') r = json.loads(r.body.decode()) self.assertEqual(len(r), 1, r) self.assertEqual(r[0][0], 'Foo bar?') r = self.app.get('/', {'order': 'first', 'limit': '1', 'offset': '1'}) self.assertEqual(r.content_type, 'application/json') r = json.loads(r.body.decode()) self.assertEqual(len(r), 1, r) self.assertEqual(r[0][0], 'Baz qux?') def testTop(self): q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'quux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) r = self.app.get('/', {'order': 'top'}) self.assertEqual(r.content_type, 'application/json') r = json.loads(r.body.decode()) self.assertEqual(len(r), 3, r) self.assertEqual(r[0][0], 'Baz qux?', r) self.assertEqual(r[1][0], 'Foo bar?', r) self.assertEqual(r[2][0], 'quux?', r) """ def testTopAmong(self): q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Baz qux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'quux?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) q = {'id': 'foo', 'question': 'Foo bar?', 'responses': []} self.assertStatusInt(q, 200) r = self.app.get('/', {'order': 'top', 'among': 6}) self.assertEqual(r.content_type, 'application/json') r = json.loads(r.body.decode()) self.assertEqual(len(r), 3, r) self.assertEqual(r[0][0], 'Foo bar?', r) self.assertEqual(r[1][0], 'Baz qux?', r) self.assertEqual(r[2][0], 'quux?', r) """

42.214876

92

0.536022

609

5,108

4.472906

0.119869

0.143172

0.052863

0.123348

0.825624

0.825257

0.800294

0.76138

0

0.028297

0.245889

5,108

120

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42.566667

0.678868

0

0.580247

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0.188976

0

0.432099

1

0.098765

false

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0

0.185185

0

null

0

1

0

null

0

1

0

7

f2e6ef9b0f9999ef0cf5f83497ce17ad9dca6b12

8,769

py

Python

tests/test_registration_page.py

PopkovS/Testing_assistant_lk

8e935a5709570deb9e7c459cb3b8cbcd81f587e0

[ "Apache-2.0" ]

null

tests/test_registration_page.py

PopkovS/Testing_assistant_lk

8e935a5709570deb9e7c459cb3b8cbcd81f587e0

[ "Apache-2.0" ]

1

2021-06-02T00:59:36.000Z

tests/test_registration_page.py

PopkovS/Testing_assistant_lk

8e935a5709570deb9e7c459cb3b8cbcd81f587e0

[ "Apache-2.0" ]

null

from time import sleep import pytest from pages.locators import Links, TestData, BaseLocators from pages.registration_page import RegistrationPage from pages.mailforforspam_page import MailForSpamPage @pytest.fixture(scope="module", autouse=True) def setup_for_module(browser): global page page = RegistrationPage(browser, Links.LOGIN_LINK) page.check_new_user_exist() page.change_sys_paran(auth_ad="False") page.open() page.go_to_reg_page() yield page class TestsRegNegative(): @pytest.fixture(scope="function", autouse=True) def setup_for_login_neg_function(self, browser): browser.refresh() yield page page.should_be_no_more_necessary_alert() page.should_not_be_user_in_bd() def test_reg_with_empty_fields(self, browser): page.registration("", "", "", "") page.should_be_err_reg_fields(email="email_empty", name="name_empty", pas="pass_empty", conf_pass="conf_pass_empty_what") page.should_be_alert("not_valid_pass_or_log") def test_reg_with_empty_email(self, browser): page.registration("", name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_err_reg_fields(email="email_empty") page.should_be_alert("not_valid_pass_or_log") def test_reg_with_empty_name(self, browser): page.registration(email=TestData.NEW_USER_EMAIL, name="", password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_err_reg_fields(name="name_empty") page.should_be_alert("not_valid_pass_or_log") def test_reg_with_empty_pass_and_conf_pass(self, browser): page.registration(email=TestData.NEW_USER_EMAIL, name=TestData.NEW_USER_NAME, password="", conf_password="") page.should_be_err_reg_fields(pas="pass_empty", conf_pass="conf_pass_empty_what") page.should_be_alert("not_valid_pass_or_log") def test_reg_with_empty_pass(self, browser): page.registration(email=TestData.NEW_USER_EMAIL, name=TestData.NEW_USER_NAME, password="", conf_password=TestData.PASSWORD_USER_NORMAL) page.submit_click() page.should_be_err_reg_fields(pas="pass_empty") page.should_be_alert("err_to_admin") def test_reg_with_empty_conf_pass(self, browser): page.registration(email=TestData.NEW_USER_EMAIL, name=TestData.NEW_USER_NAME, password="", conf_password=TestData.PASSWORD_USER_NORMAL) page.submit_click() page.should_be_err_reg_fields(pas="pass_empty", conf_pass="pass_and_cof_pass_diff") page.should_be_alert("err_to_admin") def test_reg_with_diff_passwords(self, browser): page.registration(email=TestData.NEW_USER_EMAIL, name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_AD) page.submit_click() page.should_be_err_reg_fields(conf_pass="pass_and_cof_pass_diff") page.should_be_alert("err_to_admin") def test_reg_with_not_format_email(self, browser): page.registration(email=TestData.NEW_USER_EMAIL.replace("@", ""), name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_err_reg_fields(email="not_valid_email") page.should_be_alert("not_valid_pass_or_log") def test_reg_with_taken_email(self, browser): page.registration(email=TestData.USER_NORMAL_EMAIL, name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_alert("email_is_taken", text=TestData.USER_NORMAL_EMAIL) def test_reg_with_dangerous_content_in_email(self, browser): page.registration(email=f"<{TestData.NEW_USER_EMAIL}>", name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_err_reg_fields(email="not_valid_email") page.should_be_alert("dang_cont") def test_reg_with_dangerous_content_in_name(self, browser): page.registration(email=TestData.NEW_USER_EMAIL, name=f"<{TestData.NEW_USER_NAME}>", password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_alert("dang_cont") class TestsNegativeAfterReg(): @pytest.fixture(scope="function", autouse=True) def setup_for_login_neg_function(self, browser): browser.refresh() page.go_to_reg_page() yield page page.check_new_user_exist() def test_reg_link_from_letters(self): page.registration(email=TestData.NEW_USER_EMAIL, name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_success_reg_page() page.go_to_login_page_from_confirm_reg() page.login(email=TestData.NEW_USER_EMAIL, password=TestData.PASSWORD_USER_NORMAL) page.should_be_alert("acc_not_conf") page.should_be_user_in_bd() class TestsRegPositive(): @pytest.fixture(scope="function", autouse=True) def setup_for_login_neg_function(self, browser): browser.refresh() global mail_num mail_num = page.old_letters_count() page.go_to_reg_page() yield page page.should_be_user_in_bd() page.check_new_user_exist() def test_reg_link_from_letters(self): page.registration(email=TestData.NEW_USER_EMAIL, name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_success_reg_page() page.go_to_account_activation(old_lett=mail_num) page.should_be_reg_confirm_page() page.go_to_login_page_from_confirm_reg() page.login_new_user() page.close_tab() def test_reg_link_from_reg_page(self): page.registration(email=TestData.NEW_USER_EMAIL, name=TestData.NEW_USER_NAME, password=TestData.PASSWORD_USER_NORMAL, conf_password=TestData.PASSWORD_USER_NORMAL) page.should_be_success_reg_page() page.go_to_account_activation(old_lett=mail_num) page.should_be_reg_confirm_page() page.close_tab() page.go_to_login_page_from_confirm_reg() page.login_new_user() # class TestsDeleteMe(): # def test_registr2(self): # page.change_sys_paran(auth_ad="True", dir_control="False") # mail_num = page.old_letters_count(link=Links.MAIL_FOR_SPAM_NEW_US + "2") # page.registration(email=TestData.NEW_USER.replace("ser@", "ser2@"), # name=TestData.NEW_USER_NAME + "2", # password=TestData.PASSWORD_USER_NORMAL, # conf_password=TestData.PASSWORD_USER_NORMAL) # page.go_to_account_activation(old_lett=mail_num, link=Links.MAIL_FOR_SPAM_NEW_US + "2") # page.change_sys_paran(auth_ad="True", dir_control="True") # # def test_registr1(self): # page.change_sys_paran(auth_ad="True", dir_control="False") # mail_num = page.old_letters_count(link=Links.MAIL_FOR_SPAM_NEW_US) # page.registration(email=TestData.NEW_USER, # name=TestData.NEW_USER_NAME, # password=TestData.PASSWORD_USER_NORMAL, # conf_password=TestData.PASSWORD_USER_NORMAL) # page.go_to_account_activation(old_lett=mail_num, link=Links.MAIL_FOR_SPAM_NEW_US) # page.change_sys_paran(auth_ad="True", dir_control="True") # sleep(6)

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