xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

77f919c514d25d45bf6b2a0656c719e2e387903e

1,065

py

Python

molecule/python/molecule_api/api/__init__.py

sumit4-ttn/SDK

b3ae385e5415e47ac70abd0b3fdeeaeee9aa7cff

[ "Apache-2.0" ]

null

molecule/python/molecule_api/api/__init__.py

sumit4-ttn/SDK

b3ae385e5415e47ac70abd0b3fdeeaeee9aa7cff

[ "Apache-2.0" ]

null

molecule/python/molecule_api/api/__init__.py

sumit4-ttn/SDK

b3ae385e5415e47ac70abd0b3fdeeaeee9aa7cff

[ "Apache-2.0" ]

null

from __future__ import absolute_import # flake8: noqa # import apis into api package from molecule_api.api.currency_api import CurrencyApi from molecule_api.api.currency_balance_api import CurrencyBalanceApi from molecule_api.api.currency_transfer_api import CurrencyTransferApi from molecule_api.api.document_api import DocumentApi from molecule_api.api.escrow_api import EscrowApi from molecule_api.api.escrow_transaction_api import EscrowTransactionApi from molecule_api.api.token_api import TokenApi from molecule_api.api.token_balance_api import TokenBalanceApi from molecule_api.api.token_crowdsale_api import TokenCrowdsaleApi from molecule_api.api.token_supply_api import TokenSupplyApi from molecule_api.api.token_transfer_api import TokenTransferApi from molecule_api.api.transaction_status_api import TransactionStatusApi from molecule_api.api.wallet_api import WalletApi from molecule_api.api.wallet_key_api import WalletKeyApi from molecule_api.api.wallet_permission_api import WalletPermissionApi from molecule_api.api.webhook_api import WebhookApi

48.409091

72

0.892958

150

1,065

6.026667

0.273333

0.212389

0.265487

0.318584

0.346239

0

0.001014

0.074178

1,065

21

73

50.714286

0.915822

0.038498

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

2479210d36899d3c3493c10f0323675ec7462f38

31

py

Python

Xana/XpcsAna/__init__.py

ClLov/Xana

83d880432a457cff0f1fab2801e2530ddecb4019

[ "MIT" ]

1

2021-01-25T08:57:57.000Z

Xana/XpcsAna/__init__.py

ClLov/Xana

83d880432a457cff0f1fab2801e2530ddecb4019

[ "MIT" ]

21

2020-03-23T12:50:32.000Z

2021-05-07T07:54:38.000Z

Xana/XpcsAna/__init__.py

ClLov/Xana

83d880432a457cff0f1fab2801e2530ddecb4019

[ "MIT" ]

2

2020-03-22T10:31:09.000Z

2020-07-01T14:00:28.000Z

from .CorrFunc import CorrFunc

15.5

30

0.83871

4

31

6.5

0.75

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

24b789436e689bdf8e0b033d12fbb2bfd15e4b48

37

py

Python

asaplib/kde/__init__.py

FelixFaber/ASAP

951d9667143095e42f1566816b4ab90d901b56a8

[ "MIT" ]

1

2020-02-25T19:05:25.000Z

asaplib/kde/__init__.py

FelixFaber/ASAP

951d9667143095e42f1566816b4ab90d901b56a8

[ "MIT" ]

null

asaplib/kde/__init__.py

FelixFaber/ASAP

951d9667143095e42f1566816b4ab90d901b56a8

[ "MIT" ]

null

from .ml_density_estimation import *

18.5

36

0.837838

5

37

5.8

1

0

0.108108

37

1

37

0.878788

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

24ccf5e3daf961e1aa9e805413523fc06711d58c

187

py

Python

src/rxn_network/reactions/__init__.py

bigboyabhisthi/reaction-network

b84f16b7261ecd62d7aa8e2681907f6ea0c35565

[ "BSD-3-Clause-LBNL" ]

1

2022-02-22T23:09:47.000Z

src/rxn_network/reactions/__init__.py

bigboyabhisthi/reaction-network

b84f16b7261ecd62d7aa8e2681907f6ea0c35565

[ "BSD-3-Clause-LBNL" ]

null

src/rxn_network/reactions/__init__.py

bigboyabhisthi/reaction-network

b84f16b7261ecd62d7aa8e2681907f6ea0c35565

[ "BSD-3-Clause-LBNL" ]

null

" Implementations of various reaction classes for the reaction-network " from rxn_network.reactions.basic import BasicReaction from rxn_network.reactions.computed import ComputedReaction

46.75

72

0.86631

23

187

6.956522

0.695652

0.0875

0.175

0.2875

0

0.096257

187

3

73

62.333333

0.946746

0.363636

0

0.374332

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

24d4efb365fba66c1f11e60fd1b3d5f10d86a9ef

213

py

Python

Codewars/5kyu/where-my-anagrams-at/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/5kyu/where-my-anagrams-at/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/5kyu/where-my-anagrams-at/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 Test.assert_equals(anagrams('abba', ['aabb', 'abcd', 'bbaa', 'dada']), ['aabb', 'bbaa']) Test.assert_equals(anagrams('racer', ['crazer', 'carer', 'racar', 'caers', 'racer']), ['carer', 'racer'])

42.6

105

0.600939

27

213

4.666667

0.666667

0.15873

0.253968

0.380952

0

0.015544

0.093897

213

4

106

53.25

0.637306

0.065728

0

0.350254

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

70239c6156014ca8d337bee056f5e3acfc721a45

31

py

Python

src/amuse/community/galaxia/__init__.py

sibonyves/amuse

5557bf88d14df1aa02133a199b6d60c0c57dcab7

[ "Apache-2.0" ]

null

src/amuse/community/galaxia/__init__.py

sibonyves/amuse

5557bf88d14df1aa02133a199b6d60c0c57dcab7

[ "Apache-2.0" ]

12

2021-11-15T09:13:03.000Z

2022-02-02T14:53:04.000Z

src/amuse/community/galaxia/__init__.py

sibonyves/amuse

5557bf88d14df1aa02133a199b6d60c0c57dcab7

[ "Apache-2.0" ]

null

from .interface import Galaxia

15.5

30

0.83871

4

31

6.5

1

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

561391cefa78fddffa49079f2c75a37e39f54b4a

5,572

py

Python

PyChunks.py

PolsCommits/isometric-chunks

06c4049dfbfb99571d3b872b8989a19a545936d8

[ "MIT" ]

null

PyChunks.py

PolsCommits/isometric-chunks

06c4049dfbfb99571d3b872b8989a19a545936d8

[ "MIT" ]

null

PyChunks.py

PolsCommits/isometric-chunks

06c4049dfbfb99571d3b872b8989a19a545936d8

[ "MIT" ]

null

from Block import Blocks import pygame from Helper import DISPLAY_SURFACE from Helper import RESOLUTION from ImageHelper import PLAYER_TEXTURES import random pygame.display.init() display = DISPLAY_SURFACE player = PLAYER_TEXTURES['placeholder'] smoothness = 2 # chunk = Blocks(16, 16, 32, smoothness, water_level=16 + random.randint(-6, 0), water=True) chunk_index = 0 FPS_CLOCK = pygame.time.Clock() FPS = 60 running = True offset_x = int(RESOLUTION[0] / 2) offset_y = int(RESOLUTION[1] / 4) move_distance = 48 chunks = None map_width = 4 map_height = 4 while running: for event in pygame.event.get(): if event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: running = False if event.key == pygame.K_w: offset_y += move_distance if event.key == pygame.K_a: offset_x += move_distance if event.key == pygame.K_s: offset_y -= move_distance if event.key == pygame.K_d: offset_x -= move_distance if event.key == pygame.K_e: pygame.image.save(display, "chunk" + str(chunk_index) + ".png") chunk_index += 1 display.fill((25, 100, 200)) if chunks: for chunks_y in range(0, map_height): chunks_offset_y = chunks_y * 8 * 24 chunks_offset_x = (chunks_y % 2) * 8 * 48 for chunks_x in range(0, map_width): chunks_offset_x -= chunks_x * 16 * 48 #print(chunks_offset_x, chunks_offset_y) for x in range(0, chunks[chunks_x][chunks_y].size[0]): for y in range(0, chunks[chunks_x][chunks_y].size[1]): for z in range(0, chunks[chunks_x][chunks_y].size[2]): if chunks[chunks_x][chunks_y].blocks[x][y][z].type != 'empty': if x < len(chunks[chunks_x][chunks_y].blocks) - 1 and y < len(chunks[chunks_x][chunks_y].blocks) - 1 and z < len( chunks[chunks_x][chunks_y].blocks[1]) - 1: if chunks[chunks_x][chunks_y].blocks[x + 1][y + 1][z + 1].type != 'normal': display.blit(chunks[chunks_x][chunks_y].blocks[x][y][z].texture, (chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[0] + chunks_offset_x + offset_x, chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[1] + chunks_offset_y + offset_y)) elif chunks[chunks_x][chunks_y].blocks[x][y][z].type != 'empty': display.blit(chunks[chunks_x][chunks_y].blocks[x][y][z].texture, (chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[0] + chunks_offset_x + offset_x, chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[1] + chunks_offset_y + offset_y)) if event.key == pygame.K_p: chunks = [[Blocks(16, 16, 16, 1.75, water_level=6, water=True) for x in range(0, map_width)] for y in range(0, map_height)] for chunks_y in range(0, map_height): chunks_offset_y = chunks_y * 8 * 24 chunks_offset_x = (chunks_y % 2) * 8 * 48 for chunks_x in range(0, map_width): chunks_offset_x += chunks_x * 16 * 48 #print(chunks_offset_x, chunks_offset_y) for x in range(0, chunks[chunks_x][chunks_y].size[0]): for y in range(0, chunks[chunks_x][chunks_y].size[1]): for z in range(0, chunks[chunks_x][chunks_y].size[2]): if chunks[chunks_x][chunks_y].blocks[x][y][z].type != 'empty': if x < len(chunks[chunks_x][chunks_y].blocks) - 1 and y < len(chunks[chunks_x][chunks_y].blocks) - 1 and z < len( chunks[chunks_x][chunks_y].blocks[1]) - 1: if chunks[chunks_x][chunks_y].blocks[x + 1][y + 1][z + 1].type != 'normal': display.blit(chunks[chunks_x][chunks_y].blocks[x][y][z].texture, (chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[0] + chunks_offset_x + offset_x, chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[1] + chunks_offset_y + offset_y)) elif chunks[chunks_x][chunks_y].blocks[x][y][z].type != 'empty': display.blit(chunks[chunks_x][chunks_y].blocks[x][y][z].texture, (chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[0] + chunks_offset_x + offset_x, chunks[chunks_x][chunks_y].blocks[x][y][z].screen_position[1] + chunks_offset_y + offset_y)) pygame.display.flip() FPS_CLOCK.tick(FPS)

43.874016

154

0.491206

695

5,572

3.709353

0.129496

0.108611

0.099302

0.221102

0.746315

0.724205

0.710628

0.654771

0

0.030437

0.392678

5,572

126

155

44.222222

0.731383

0.030151

0

0.45

0

0.00963

0

1

0

false

0

0.075

0

0.075

0

null

0

1

0

1

0

null

0

6

563f0b9f054fb1ac13e4b2070aaa6bde92c56cef

6,457

py

Python

tests/utils/test_mock_backend.py

coxwave/manta

fc55357e139dbf61854595475f401656440d53aa

[ "MIT" ]

17

2021-11-19T08:26:33.000Z

2022-01-27T10:22:09.000Z

tests/utils/test_mock_backend.py

coxwave/manta

fc55357e139dbf61854595475f401656440d53aa

[ "MIT" ]

95

2021-11-19T05:29:22.000Z

2022-03-03T09:39:46.000Z

tests/utils/test_mock_backend.py

coxwave/manta

fc55357e139dbf61854595475f401656440d53aa

[ "MIT" ]

2

2021-12-10T07:31:54.000Z

2021-12-10T07:43:37.000Z

import json from werkzeug.sansio.response import Response from tests.conftest import DefaultData from tests.utils.mock_backend import backend, ctx, Project def test_greetings(): response: Response = backend.get("/hello") assert response.status_code == 200 def test_get_user(default_data: DefaultData): response: Response = backend.get("/api/user", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}) assert response.status_code == 200 assert response.json == default_data.user.__dict__ def test_get_user_detail(default_data: DefaultData): response: Response = backend.get( "/api/user/profile", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"} ) assert response.status_code == 200 assert response.json == default_data.user.__dict__ def test_create_team(default_data: DefaultData): response: Response = backend.post( "/api/team", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, json={"uid": "foo"}, ) assert response.status_code == 201 assert response.json["Id"] is not None team_id = response.json["Id"] response: Response = backend.get( f"/api/team/{team_id}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"} ) assert response.status_code == 200 def test_get_team(default_data: DefaultData): team_id = default_data.team.Id response: Response = backend.get( f"/api/team/{team_id}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"} ) assert response.status_code == 200 assert response.json == default_data.team.__dict__ def test_get_my_teams(default_data: DefaultData): response: Response = backend.get( "/api/team/my", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"} ) assert response.status_code == 200 assert response.json == {"teams": [default_data.team.__dict__]} def test_get_personal_team(default_data: DefaultData): response: Response = backend.get( "/api/team/my/personal", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"} ) assert response.status_code == 200 assert response.json == default_data.team.__dict__ def test_project(): project = Project(**{"teamId": "zoo", "name": "foo", "description": "bar"}) assert project.teamId == "zoo" assert project.name == "foo" assert project.description == "bar" def test_create_project(default_data: DefaultData): team_id = default_data.team.Id response: Response = backend.post( f"/api/team/{team_id}/project", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, json={"name": "foo", "description": "bar"}, ) assert response.status_code == 201 assert response.json is not None assert response.json["Id"] is not None response: Response = backend.get( f"/api/project/{response.json['Id']}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 200 def test_get_project_detail(default_data: DefaultData): project_id = default_data.project.Id response: Response = backend.get( f"/api/project/{project_id}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 200 def test_get_my_projects(default_data: DefaultData): team_id = default_data.team.Id response: Response = backend.get( f"/api/team/{team_id}/project/my", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 200 assert response.json == {"projects": [default_data.project.__dict__]} def test_delete_project(default_data: DefaultData): project_id = default_data.project.Id response: Response = backend.delete( f"/api/project/{project_id}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 204 def test_create_run(default_data: DefaultData): project_id = default_data.project.Id response: Response = backend.post( f"/api/project/{project_id}/run", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, json={"name": "foo"}, ) assert response.status_code == 201 def test_get_runs_by_project_id(default_data: DefaultData): project_id = default_data.project.Id response: Response = backend.get( f"/api/project/{project_id}/run", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 200 assert response.json == {"runs": [default_data.run.__dict__]} def test_get_run(default_data: DefaultData): run_id = default_data.run.Id response: Response = backend.get( f"/api/run/{run_id}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 200 assert response.json == default_data.run.__dict__ def test_delete_run(default_data: DefaultData): run_id = default_data.run.Id response: Response = backend.delete( f"/api/run/{run_id}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 204 def test_add_run_record(default_data: DefaultData): run_id = default_data.run.Id response: Response = backend.post( f"/api/run/{run_id}/record", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, json={"histories": [{"foo": "foo"}], "systems": [{"bar": "bar"}], "logs": [{"zoo": "zoo"}]}, ) assert response.status_code == 204 response: Response = backend.get( f"/api/run/{run_id}", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, ) assert response.status_code == 200 assert response.json["histories"] == [{"foo": "foo"}] assert response.json["systems"] == [{"bar": "bar"}] assert response.json["logs"] == [{"zoo": "zoo"}] def test_add_run_record_empty(default_data: DefaultData): run_id = default_data.run.Id response: Response = backend.post( f"/api/run/{run_id}/record", headers={"Authorization": f"manta-apikey {default_data.api_key.key}"}, json={}, ) assert response.status_code == 204

31.34466

120

0.676475

824

6,457

5.063107

0.080097

0.142378

0.110259

0.115053

0.845638

0.815436

0.793145

0.748562

0.718121

0.691755

0

0.011325

0.179495

6,457

205

121

31.497561

0.776142

0

0.541096

0

0.243147

0.118011

0

0.253425

1

0.123288

false

0

0.027397

0

0.150685

0

null

0

1

0

null

0

6

5643fcfc82d05927c2b3146ec3240e9744a847bb

35

py

Python

main.py

ransh93/fake_news

899e01e3c63dbf60d8a16455792eaf35365f8410

[ "MIT" ]

1

2020-05-18T12:03:11.000Z

main.py

ransh93/fake_news

899e01e3c63dbf60d8a16455792eaf35365f8410

[ "MIT" ]

1

2020-02-11T13:31:14.000Z

main.py

ransh93/fake_news

899e01e3c63dbf60d8a16455792eaf35365f8410

[ "MIT" ]

null

print("GIT!!!!!!!!!!") print("new")

17.5

22

0.457143

4

35

4

0.75

0

0.028571

35

2

23

17.5

0.470588

0

0.444444

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

56970207628072edc614468de4b8fc38243d3f99

35

py

Python

microservices/api/config.py

clodonil/pipeline_aws_custom

8ca517d0bad48fe528461260093f0035f606f9be

[ "Apache-2.0" ]

null

microservices/api/config.py

clodonil/pipeline_aws_custom

8ca517d0bad48fe528461260093f0035f606f9be

[ "Apache-2.0" ]

null

microservices/api/config.py

clodonil/pipeline_aws_custom

8ca517d0bad48fe528461260093f0035f606f9be

[ "Apache-2.0" ]

null

HOST='0.0.0.0' PORT=8080 DEBUG=True

11.666667

14

0.714286

9

35

2.777778

0.666667

0.24

0

0.242424

0.057143

35

3

15

11.666667

0.515152

0

0.194444

0

1

0

false

0

1

0

null

1

0

1

0

1

0

null

0

6

3b49f20c87905ba368fa5c004441f41f324e0d3f

191

py

Python

graph4nlp/__init__.py

stjordanis/graph4nlp

c6ebde32bc77d3a7b78f86a93f19b1c057963ffa

[ "Apache-2.0" ]

1

2021-06-06T15:23:11.000Z

graph4nlp/__init__.py

stjordanis/graph4nlp

c6ebde32bc77d3a7b78f86a93f19b1c057963ffa

[ "Apache-2.0" ]

null

graph4nlp/__init__.py

stjordanis/graph4nlp

c6ebde32bc77d3a7b78f86a93f19b1c057963ffa

[ "Apache-2.0" ]

1

2021-11-01T08:41:26.000Z

from .pytorch import data, datasets, models from .pytorch.data import log_level from .pytorch.modules import evaluation, graph_construction, graph_embedding, loss, prediction, config, utils

38.2

109

0.82199

25

191

6.16

0.68

0.214286

0

0.109948

191

4

110

47.75

0.905882

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

3b7eedcc35cf4144a0970df0ab0b2fc6f2c2b215

166

py

Python

django_geo/urls.py

roverdotcom/django-geo

f832a7f434fa4e2baa54a859596468a283ce7f43

[ "MIT" ]

null

django_geo/urls.py

roverdotcom/django-geo

f832a7f434fa4e2baa54a859596468a283ce7f43

[ "MIT" ]

1

2018-05-14T20:04:03.000Z

2018-05-17T22:11:09.000Z

django_geo/urls.py

roverdotcom/django-geo

f832a7f434fa4e2baa54a859596468a283ce7f43

[ "MIT" ]

null

from __future__ import print_function from __future__ import division from __future__ import absolute_import from __future__ import unicode_literals urlpatterns = []

27.666667

39

0.86747

20

166

6.25

0.5

0.32

0.512

0

0.114458

166

5

40

33.2

0.85034

0

1

0

false

0

0.8

0

0.8

0.2

1

0

null

1

0

1

0

null

0

1

0

1

0

6

8e5386dcb675035e10d15e63e270d268d28e6f21

140

py

Python

application/resource/__init__.py

thec0sm0s/Quick-Notes

09940a1dc7780b16fadb1e43d7734b101dd989de

[ "MIT" ]

1

2020-10-18T02:34:26.000Z

application/resource/__init__.py

thec0sm0s/Quick-Notes

09940a1dc7780b16fadb1e43d7734b101dd989de

[ "MIT" ]

8

2020-09-28T10:01:31.000Z

2020-10-12T04:51:25.000Z

application/resource/__init__.py

thec0sm0s/cosnote

09940a1dc7780b16fadb1e43d7734b101dd989de

[ "MIT" ]

4

2020-09-28T11:47:27.000Z

2020-10-12T06:54:06.000Z

from mongoengine import connect def initialize_mongo_connection(configs): connect(configs.MONGODB_DATABASE, host=configs.MONGODB_URI)

23.333333

63

0.835714

17

140

6.647059

0.764706

0.247788

0

0.1

140

5

64

28

0.896825

0

1

0.333333

false

0

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d922ebb8c9e9eef59ccf9035fa86ca895f048873

15,617

py

Python

Tests/OSGroups.py

davidavg/Pytest_UI_Automation

ee1859506ea66446a496a6590d24093782277a07

[ "MIT" ]

null

Tests/OSGroups.py

davidavg/Pytest_UI_Automation

ee1859506ea66446a496a6590d24093782277a07

[ "MIT" ]

null

Tests/OSGroups.py

davidavg/Pytest_UI_Automation

ee1859506ea66446a496a6590d24093782277a07

[ "MIT" ]

null

''' Created on Nov 1, 2018 @author: dguerra ''' import pytest from FrameworkLib.GeneralLib import Browser, AUTCommon, General, nDelay from selenium.webdriver.common.by import By from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.action_chains import ActionChains import time general = General() groupName = general.getRandName() @pytest.fixture(scope="module") def control(): browser = Browser() control = browser.initBrowser() yield control browser.endBroswer(control) def test_OSGroupsExist(control): common = AUTCommon() assert common.login(control) assert common.navigateOSGroups(control) def test_WidgetElementsVisibility(control): general = General() #validate pagination is present general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table > div.grid-record-count > div", "Validate pagination is present on OS Groups Widget", control ) #filter is present general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > div > input", "Validate filter is present on OS Groups Widget", control ) #reload button exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked .top-icons.ui-icon-container > span:nth-child(1) > a", "Validate reload is present on OS Groups widget", control ) #Maximize button exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked .top-icons.ui-icon-container > span:nth-child(4) > a:nth-child(2)", "Validate maximize button exists on OS Groups widget", control ) #Help button exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked .top-icons.ui-icon-container > span:nth-child(3) > a", "Validate maximize button exists on OS Groups widget", control ) #Add button exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked .widgetcontent > div > vne-grid > div.grid-actions.ui-button-container > span:nth-child(1) > button", "Validate add button exists on OS Groups widget", control ) #Edit button exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked .widgetcontent > div > vne-grid > div.grid-actions.ui-button-container > span:nth-child(2) > button", "Validate add button exists on OS Groups widget", control ) #Delete button exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked .widgetcontent > div > vne-grid > div.grid-actions.ui-button-container > span:nth-child(3) > button", "Validate add button exists on OS Groups widget", control ) #validate items per page dropdown exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div.grid-pagination.ui-button-container.ng-scope > div > select", "Validate items per page dropdown exists on OS Groups widget", control ) #Validate items per page label exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div.grid-pagination.ui-button-container.ng-scope > div > span", "Validate items per page dropdown exists on OS Groups widget", control ) #validate selected page exists general.toVerifyElement( By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div.grid-pagination.ui-button-container.ng-scope > form > input", "Validate page selection input exists on OS Groups widget", control ) #Validate selectAll checkbox exists general.toVerifyElement( By.XPATH, "//div[@class='tile os_groups locked']/div[3]/div/vne-grid/vne-grid-table/div[2]/div/div/div/div/div/div/div/div/div/div/div/input", "Validate 'Select all' checkbox exists on OS Groups widget", control ) assert general.verify() def test_itemCount(control): general = General() try: itemCount = control.find_element_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table > div.grid-record-count > div").get_attribute("innerHTML") OSGroupsList = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div") assert general.getItemCount(itemCount) == len(OSGroupsList) except: assert False def test_reload(control): try: control.find_element_by_css_selector(".tile.os_groups.locked .top-icons.ui-icon-container > span:nth-child(1) > a").click() #Wait for item count to display WebDriverWait(control, nDelay).until( EC.visibility_of_element_located((By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table > div.grid-record-count > div")) ) except: assert False def test_itemsPerPage(control): perPage = (20, 50 , 100, 200) wSelect = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div.grid-pagination.ui-button-container.ng-scope > div > select > option") for index in range(len(wSelect)): assert int(wSelect[index].get_attribute("label")) == perPage[index] def test_validateDefaultOS(control): defaultOS = ("Tripwire: Cisco", "Tripwire: Linux", "Tripwire: Network Infrastructure", "Tripwire: Sun Microsystems","Tripwire: Unix Variant","Tripwire: Windows") wOSGroups = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div > div > div:nth-child(2) > div") OSgroupList = [] for element in wOSGroups: OSgroupList.append(element.get_attribute("innerHTML")) for index in range(len(defaultOS)): assert defaultOS[index] in OSgroupList def test_addOSGroup(control): actions = ActionChains(control) try: control.find_element_by_css_selector(".tile.os_groups.locked .widgetcontent > div > vne-grid > div.grid-actions.ui-button-container > span:nth-child(1) > button").click() WebDriverWait(control, nDelay).until(EC.visibility_of_element_located((By.CSS_SELECTOR, "#node-tree-item-1882"))) control.find_element_by_css_selector( "body > div.modal.modal-wizard.modal-wizard-os_groups.fade.ng-scope.ng-isolate-scope.in > div > div > div > div.wizard-content > div > div > div > form > div > div:nth-child(1) > div > label > input" ).send_keys(groupName) actions.double_click(control.find_element_by_xpath("//form[@name='osgroups_form']/div/div[3]/div/div/ul/li[1]/div")).perform() actions.double_click(control.find_element_by_xpath("//form[@name='osgroups_form']/div/div[3]/div/div/ul/li[5]/div")).perform() control.find_element_by_css_selector("body > div.modal.modal-wizard.modal-wizard-os_groups.fade.ng-scope.ng-isolate-scope.in > div > div > div > div.wizard-content > div > div > div > div > button.action").click() WebDriverWait(control, nDelay).until( EC.visibility_of_element_located((By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table > div.grid-record-count > div")) ) WebDriverWait(control, nDelay).until( EC.invisibility_of_element_located((By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div:nth-child(2) > div.grid-loading-overlay.ng-scope.ng-hide > div > span")) ) WebDriverWait(control, nDelay).until( EC.visibility_of_element_located((By.XPATH, "//*[contains(text(), '"+groupName+"')]")) ) wOSGroups = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div > div > div:nth-child(2) > div") OSgroupList = [] for element in wOSGroups: OSgroupList.append(element.get_attribute("innerHTML")) assert groupName in OSgroupList except: assert False def test_editOSGroup(control): try: wOSGroups = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div > div > div:nth-child(2) > div") checkBoxList = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div > div > div > div > input") for index in range(len(wOSGroups)): if groupName == str(wOSGroups[index].get_attribute("innerHTML")): checkBoxList[index].click() checkBox = checkBoxList[index] OSGroup = wOSGroups[index] break control.find_element_by_css_selector(".tile.os_groups.locked .widgetcontent > div > vne-grid > div.grid-actions.ui-button-container > span:nth-child(2) > button").click() WebDriverWait(control, nDelay).until(EC.visibility_of_element_located((By.CSS_SELECTOR, "#node-tree-item-1882"))) control.find_element_by_css_selector( "body > div.modal.modal-wizard.modal-wizard-os_groups.fade.ng-scope.ng-isolate-scope.in > div > div > div > div.wizard-content > div > div > div > form > div > div:nth-child(1) > div > label > input" ).send_keys("_EDITED") control.find_element_by_css_selector("body > div.modal.modal-wizard.modal-wizard-os_groups.fade.ng-scope.ng-isolate-scope.in > div > div > div > div.wizard-content > div > div > div > div > button.action").click() WebDriverWait(control, nDelay).until( EC.visibility_of_element_located((By.XPATH, "//*[contains(text(), '"+groupName + "_EDITED"+"')]")) ) assert groupName + "_EDITED" == str(OSGroup.get_attribute("innerHTML")) checkBox.click() except: assert False def test_deleteOSGroup(control): general = General() try: wOSGroups = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div > div > div:nth-child(2) > div") checkBoxList = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div > div > div > div > input") for index in range(len(wOSGroups)): if groupName + "_EDITED" == str(wOSGroups[index].get_attribute("innerHTML")): checkBoxList[index].click() break control.find_element_by_css_selector(".tile.os_groups.locked .widgetcontent > div > vne-grid > div.grid-actions.ui-button-container > span:nth-child(3) > button").click() time.sleep(2) control.find_element_by_css_selector(".actions.ui-button-container > button:nth-child(2)").click() WebDriverWait(control, nDelay).until( EC.visibility_of_element_located((By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table > div.grid-record-count > div")) ) WebDriverWait(control, nDelay).until( EC.invisibility_of_element_located((By.CSS_SELECTOR, ".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div:nth-child(2) > div.grid-loading-overlay.ng-scope.ng-hide > div > span")) ) time.sleep(2) assert len(control.find_elements_by_xpath("//*[contains(text(), '"+groupName + "_EDITED"+"')]")) == 0 except Exception as e: print(e) assert False def test_selectedPage(control): wInputPage = control.find_element_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div.grid-pagination.ui-button-container.ng-scope > form > input") nMinPage = int(wInputPage.get_attribute("min")) nMaxPage = int(wInputPage.get_attribute("max")) if nMinPage != nMaxPage: wInputPage.send_keys(nMaxPage) wOSGroupList = control.find_element_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div") assert len(wOSGroupList) > 0 def test_checkAll(control): wCheckAll = control.find_element_by_xpath("//div[@class='tile os_groups locked focused']/div[3]/div/vne-grid/vne-grid-table/div[2]/div/div/div/div/div/div/div/div/div/div/div/input") wCheckAll.click() checkBoxList = control.find_elements_by_css_selector(".tile.os_groups.locked > div.widgetcontent > div > vne-grid > vne-grid-table :nth-child(6) > div > div .ui-grid-viewport.ng-isolate-scope > div > div > div > div > div > input") assert bool(wCheckAll.get_attribute("checked")) for checkboxAttribute in checkBoxList: assert bool(checkboxAttribute.get_attribute("checked")) def test_maximize(control): try: control.find_element_by_css_selector(".tile.os_groups.locked .top-icons.ui-icon-container > span:nth-child(4) > a:nth-child(2)").click() WebDriverWait(control, nDelay).until(EC.visibility_of_element_located((By.ID, "dash-sidebar"))) WebDriverWait(control, nDelay).until(EC.visibility_of_element_located((By.XPATH, "//div[@class='tile os_groups locked focused expanded']/div[2]/span[4]/a[1]"))) minimizeButton = control.find_element_by_xpath("//div[@class='tile os_groups locked focused expanded']/div[2]/span[4]/a[1]") maximizeButton = control.find_element_by_xpath("//div[@class='tile os_groups locked focused expanded']/div[2]/span[4]/a[2]") assert str(maximizeButton.get_attribute("class")) == "ng-hide" minimizeButton.click() control.find_element_by_xpath("//div[@class='tile os_groups locked focused']/div[2]/span[4]/a[2]") assert str(control.find_element_by_xpath("//div[@class='tile os_groups locked focused']/div[2]/span[4]/a[1]").get_attribute("class")) == "ng-hide" assert control.find_elements_by_xpath("//div[@class='tile os_groups locked focused']/div[2]/span[4]/a[2]") #assert control.find_element_by_id("dash-sidebar") except: assert False

47.75841

241

0.658193

1,997

15,617

5.020531

0.10666

0.050868

0.043986

0.073609

0.782466

0.760523

0.749152

0.735887

0.731299

0.709156

0

0.007394

0.211949

15,617

327

242

47.75841

0.807264

0.027022

0

0.462222

0

0.204444

0.461483

0.175222

0

0.093333

1

0.057778

false

0

0.031111

0

0.088889

0.004444

0

null

0

1

0

1

0

null

0

6

d94cf7787342249604ed6698cf43d321e3fef022

9,753

py

Python

tests/src/Diksha_Reports/usage_by_textbook/column_chart.py

JalajaTR/cQube

6bf58ab25f0c36709630987ab730bbd5d9192c03

[ "MIT" ]

null

tests/src/Diksha_Reports/usage_by_textbook/column_chart.py

JalajaTR/cQube

6bf58ab25f0c36709630987ab730bbd5d9192c03

[ "MIT" ]

2

2022-02-01T00:55:12.000Z

2022-03-29T22:29:09.000Z

tests/src/Diksha_Reports/usage_by_textbook/column_chart.py

JalajaTR/cQube

6bf58ab25f0c36709630987ab730bbd5d9192c03

[ "MIT" ]

null

import unittest from Data.parameters import Data from Diksha_Reports.Diksha_column_chart.check_course_type_content_play_counts import test_course_types_data from Diksha_Reports.Diksha_column_chart.check_overall_type_content_play_counts import test_overall_types_data from Diksha_Reports.Diksha_column_chart.check_textbook_type_content_plays_count import test_textbook_types_data from Diksha_Reports.Diksha_column_chart.check_with_all_collection_records import All_records from Diksha_Reports.Diksha_column_chart.check_with_course_collection_records import course_records from Diksha_Reports.Diksha_column_chart.check_with_others_collection_records import others_records from Diksha_Reports.Diksha_column_chart.check_with_textbook_collection_records import textbook_records from Diksha_Reports.Diksha_column_chart.click_on_homeicon import Diksha_column_homeicon from Diksha_Reports.Diksha_column_chart.click_on_hyperlink import Diksha_column_hyperlink from Diksha_Reports.Diksha_column_chart.click_on_logout import Diksha_column_logout from Diksha_Reports.Diksha_column_chart.download_all_collection_records import All_records_download from Diksha_Reports.Diksha_column_chart.download_course_collection_records import course_records_download from Diksha_Reports.Diksha_column_chart.download_other_collection_records import others_records_download from Diksha_Reports.Diksha_column_chart.download_textbook_collection_records import textbook_records_download from reuse_func import GetData class cQube_diskha_column_report(unittest.TestCase): @classmethod def setUpClass(self): self.data = GetData() self.driver = self.data.get_driver() self.data.open_cqube_appln(self.driver) self.data.login_cqube(self.driver) self.data.navigate_to_diksha_column_chart() self.data.page_loading(self.driver) def test_Diksha_homeicon(self): b = Diksha_column_homeicon(self.driver) res = b.test_homeicon() #self.assertEqual(res, 0, msg="Homeicon is not working ") self.data.page_loading(self.driver) def test_homebtn(self): count = 0 self.driver.find_element_by_xpath(Data.hyper_link).click() self.data.page_loading(self.driver) self.driver.find_element_by_id('homeBtn').click() if "home" in self.driver.current_url: print("Navigated to landing page") else: print('Home button is not working') count = count + 1 self.assertEqual(0,count,msg="Home button is not working") self.driver.find_element_by_xpath("//img[@alt='dikshaColumn']").click() self.data.page_loading(self.driver) def test_Diksha_logout(self): b = Diksha_column_logout(self.driver) res = b.test_logout() self.assertEqual(res, 'Log in to cQube', msg="Logout is not working") self.data.page_loading(self.driver) def test_hyperlink(self): b = Diksha_column_hyperlink(self.driver) result = b.test_hyperlink() self.data.page_loading(self.driver) self.data.page_loading(self.driver) def test_all_lastmonth(self): b = All_records(self.driver) res = b.allrecords_of_last30days() self.assertNotEqual(0,res,msg="Collection names list is empty") self.data.page_loading(self.driver) def test_all_lastweek(self): b = All_records(self.driver) res = b.allrecords_of_last7days() self.assertNotEqual(0,res,msg="Collection names list is empty") self.data.page_loading(self.driver) def test_all_lastday(self): b = All_records(self.driver) res = b.allrecords_of_lastday() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_course_lastmonth(self): b = course_records(self.driver) res = b.courserecords_of_last30days() self.assertNotEqual(0,res,msg="Collection names list is empty") self.data.page_loading(self.driver) def test_course_lastweek(self): b = course_records(self.driver) res = b.courserecords_of_last7days() self.assertNotEqual(0,res,msg="Collection names list is empty") self.data.page_loading(self.driver) def test_course_lastday(self): b = course_records(self.driver) res = b.courserecords_of_lastday() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_textbook_lastmonth(self): b = textbook_records(self.driver) res = b.textbookrecords_of_last30days() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_textbook_lastweek(self): b = textbook_records(self.driver) res = b.textbookrecords_of_last7days() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_textbook_lastday(self): b = textbook_records(self.driver) res = b.textbookrecords_of_lastday() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_others_lastmonth(self): b = others_records(self.driver) res = b.othersrecords_of_last30days() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_others_lastweek(self): b = others_records(self.driver) res = b.othersrecords_of_last7days() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_others_lastday(self): b = others_records(self.driver) res = b.othersrecords_of_lastday() self.assertNotEqual(0, res, msg="Collection names list is empty") self.data.page_loading(self.driver) def test_download_alltype(self): b = All_records_download(self.driver) res = b.test_download_csv() self.assertTrue(res , msg='Alltype records file is not downloaded') self.data.page_loading(self.driver) def test_download_coursetype(self): b =course_records_download(self.driver) res = b.test_download_csv() self.assertTrue(res , msg='coursetype records file is not downloaded') self.data.page_loading(self.driver) def test_download_textbooktype(self): b =textbook_records_download(self.driver) res = b.test_download_csv() self.assertTrue(res , msg='textbooktype records file is not downloaded') self.data.page_loading(self.driver) def test_download_otherstype(self): b =others_records_download(self.driver) res = b.test_download_csv() # self.assertTrue(res , msg='otherstype records file is not downloaded') self.data.page_loading(self.driver) def test_check_course_last30_contentplays(self): b = test_course_types_data(self.driver) res = b.test_last30_days() self.assertEqual(0,res,msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_course_lastday_contentplays(self): b = test_course_types_data(self.driver) res = b.test_last_day() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_course_lastweek_contentplays(self): b = test_course_types_data(self.driver) res = b.test_last7_days() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_overall_last30days_plays(self): b = test_overall_types_data(self.driver) res = b.test_last30_days() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_overall_last7days_plays(self): b = test_overall_types_data(self.driver) res = b.test_last7_days() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_overall_lastday_plays(self): b = test_overall_types_data(self.driver) res = b.test_last_day() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_textbook_last30days_content(self): b = test_textbook_types_data(self.driver) res = b.test_last30_days() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_textbook_last7days_content(self): b = test_textbook_types_data(self.driver) res = b.test_last7_days() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) def test_check_textbook_lastday_content(self): b = test_textbook_types_data(self.driver) res = b.test_last_day() self.assertEqual(0, res, msg="Mismatch found at csv file sum of content play count and ui content play count") self.data.page_loading(self.driver) @classmethod def tearDownClass(cls): cls.driver.close()

44.131222

119

0.720804

1,339

9,753

4.986557

0.09186

0.100344

0.057511

0.091059

0.824772

0.801258

0.751685

0.733563

0.708102

0.618841

0

0.006883

0.195632

9,753

221

120

44.131222

0.844232

0.013022

0

0.494444

0

0.139532

0.002701

0

0.144444

1

0.172222

false

0

0.094444

0

0.272222

0.011111

0

null

0

1

0

null

0

6

79410e2c6369500a0ffe3b8c2e0cbc351890d3de

142

py

Python

scripts/save_next_appointment.py

rugo/sac_client

64b4a2bb1c2fadbc28facac3149e8d5cc097ba63

[ "Apache-2.0" ]

null

scripts/save_next_appointment.py

rugo/sac_client

64b4a2bb1c2fadbc28facac3149e8d5cc097ba63

[ "Apache-2.0" ]

null

scripts/save_next_appointment.py

rugo/sac_client

64b4a2bb1c2fadbc28facac3149e8d5cc097ba63

[ "Apache-2.0" ]

null

#!/opt/sac/bin/python from sac import util from sac import config util.save_appointment_to_file(config.APP_CACHE_PATH, config.APP_ERROR_PATH)

28.4

75

0.838028

25

142

4.48

0.64

0.125

0.232143

0

0.077465

142

5

75

28.4

0.854962

0.140845

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

8dc55477b8ce71e54041769e6f2cc7a0b7733fe4

43

py

Python

bind_manager_v3/__init__.py

the-elven-archer/bind_manager

8c56bb8602bd823a44cf2531960f2fad0e2bb7db

[ "Apache-2.0" ]

null

bind_manager_v3/__init__.py

the-elven-archer/bind_manager

8c56bb8602bd823a44cf2531960f2fad0e2bb7db

[ "Apache-2.0" ]

null

bind_manager_v3/__init__.py

the-elven-archer/bind_manager

8c56bb8602bd823a44cf2531960f2fad0e2bb7db

[ "Apache-2.0" ]

null

#!/usr/bin/env python2 from .web import *

10.75

22

0.674419

7

43

4.142857

1

0

0.027778

0.162791

43

3

23

14.333333

0.777778

0.488372

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8def1dc491fc20dbb138c3c824fc2bb878ed5f11

43

py

Python

mosa/__init__.py

siyuan0/Multi-objective-simulated-annealing

562e17d8274fed6bd6d870973de837aefde63cea

[ "MIT" ]

null

mosa/__init__.py

siyuan0/Multi-objective-simulated-annealing

562e17d8274fed6bd6d870973de837aefde63cea

[ "MIT" ]

null

mosa/__init__.py

siyuan0/Multi-objective-simulated-annealing

562e17d8274fed6bd6d870973de837aefde63cea

[ "MIT" ]

null

from .solution import Solution as Solution

21.5

42

0.837209

6

43

6

0.666667

0

0.139535

43

1

43

0.972973

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8def354fd005c450023a964c5dfcbc0bc5ffbeb1

191

py

Python

flash/image/segmentation/__init__.py

Isaac-Flath/lightning-flash

320f87707587d92a13c8831778864b33af4fe421

[ "Apache-2.0" ]

2

2021-04-23T11:02:21.000Z

2021-04-23T11:22:19.000Z

flash/image/segmentation/__init__.py

Isaac-Flath/lightning-flash

320f87707587d92a13c8831778864b33af4fe421

[ "Apache-2.0" ]

1

2021-06-16T14:46:06.000Z

flash/image/segmentation/__init__.py

Isaac-Flath/lightning-flash

320f87707587d92a13c8831778864b33af4fe421

[ "Apache-2.0" ]

3

2021-06-03T10:03:04.000Z

2021-08-08T21:49:16.000Z

from flash.image.segmentation.data import SemanticSegmentationData, SemanticSegmentationPreprocess # noqa: F401 from flash.image.segmentation.model import SemanticSegmentation # noqa: F401

63.666667

112

0.853403

19

191

8.578947

0.631579

0.110429

0.171779

0.319018

0

0.034483

0.089005

191

2

113

95.5

0.902299

0.109948

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

5c0ec76f1f23de3b79b029255b4c5795624af739

128

py

Python

telerembash/artifacts/__init__.py

kpe/telerembash

139fa21584e6cdaa4e84cec4193779d7d3d39a96

[ "MIT" ]

2

2021-04-25T16:47:13.000Z

2021-12-09T15:38:34.000Z

telerembash/artifacts/__init__.py

thomasbiege/telerembash

4c8bc061d6cffff4d0e43389602ced4749dac092

[ "MIT" ]

1

2021-09-10T07:35:13.000Z

telerembash/artifacts/__init__.py

thomasbiege/telerembash

4c8bc061d6cffff4d0e43389602ced4749dac092

[ "MIT" ]

1

2021-04-25T16:47:17.000Z

# coding=utf-8 # # created by kpe on 10.01.2021 at 12:55 PM # from __future__ import division, absolute_import, print_function

18.285714

64

0.757813

22

128

4.136364

0.954545

0

0.12037

0.15625

128

6

65

21.333333

0.722222

0.414063

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5c1cb3400a164c3e136775cff73810aae11a3b83

1,683

py

Python

Virtual Assistant/API/weatherCom.py

Codingmace/JARVIS

826d8eeaac9472511ffe5cae6f6924aa041cc723

[ "MIT" ]

1

2021-02-06T13:08:37.000Z

Virtual Assistant/API/weatherCom.py

Codingmace/JARVIS

826d8eeaac9472511ffe5cae6f6924aa041cc723

[ "MIT" ]

null

Virtual Assistant/API/weatherCom.py

Codingmace/JARVIS

826d8eeaac9472511ffe5cae6f6924aa041cc723

[ "MIT" ]

null

import requests from variables import rapidApiKey def covid19(language): url = "https://weather-com.p.rapidapi.com/v3/wx/disease/tracker/countryList/current" querystring = {"language": language} headers = { 'x-rapidapi-key': rapidApiKey, 'x-rapidapi-host': "weather-com.p.rapidapi.com" } response = requests.request("GET", url, headers=headers, params=querystring) return response def forecastDaily(geocode, units, lang): url = "https://weather-com.p.rapidapi.com/v3/wx/forecast/daily/3day" querystring = {"geocode":geocode,"units":units,"language":lang} headers = { 'x-rapidapi-key': rapidApiKey, 'x-rapidapi-host': "weather-com.p.rapidapi.com" } response = requests.request("GET", url, headers=headers, params=querystring) return response def forecastHourly(geocode, units, lang): url = "https://weather-com.p.rapidapi.com/v3/wx/forecast/hourly/1day" querystring = {"geocode":geocode,"units":units,"language":lang} headers = { 'x-rapidapi-key': rapidApiKey, 'x-rapidapi-host': "weather-com.p.rapidapi.com" } response = requests.request("GET", url, headers=headers, params=querystring) return response def historical30d(geocode, units, lang): url = "https://weather-com.p.rapidapi.com/v3/wx/conditions/historical/dailysummary/30day" querystring = {"geocode":geocode,"units":units,"language":lang} headers = { 'x-rapidapi-key': rapidApiKey, 'x-rapidapi-host': "weather-com.p.rapidapi.com" } response = requests.request("GET", url, headers=headers, params=querystring) return response

29.526316

93

0.667261

194

1,683

5.78866

0.231959

0.071238

0.078362

0.135352

0.806768

0.776492

0

0.008727

0.183007

1,683

56

94

30.053571

0.808

0

0.605263

0

0.105263

0.343843

0.061868

0

1

0.105263

false

0

0.052632

0

0.263158

0

null

0

1

0

null

0

6

5c2bdc5a41eda9baa59bbd8fd90686dd365764e0

176

py

Python

tests/webserver/test_data_model.py

cybercamp18isecurity/iSecurity

016e1bb7d73864654323e2aac00024483741f8ed

[ "MIT" ]

4

2018-11-30T22:49:52.000Z

2019-06-20T22:36:23.000Z

tests/webserver/test_data_model.py

cybercamp18isecurity/iSecurity

016e1bb7d73864654323e2aac00024483741f8ed

[ "MIT" ]

3

2018-11-30T12:06:21.000Z

2018-12-11T21:09:07.000Z

tests/webserver/test_data_model.py

cybercamp18isecurity/iSecurity

016e1bb7d73864654323e2aac00024483741f8ed

[ "MIT" ]

4

2018-12-01T01:19:36.000Z

2019-10-22T05:54:48.000Z

import pytest from isecurity_webserver.data_model import get_data_model def test_load_data_model(): data_model = get_data_model() assert len(data_model._elk._HOST) > 0

29.333333

57

0.801136

28

176

4.571429

0.571429

0.421875

0.1875

0

0.006536

0.130682

176

6

58

29.333333

0.830065

0

0.2

1

0.2

false

0

0.4

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

5c3cc3657dbc609c066ba36d12a43f4607850af1

12,790

py

Python

tests/test_yt_selection.py

ytree-project/ytree

4ba1d3e6efd1e647c594b2c0de51b0217de7a45c

[ "BSD-3-Clause-Clear" ]

3

2020-02-28T10:25:39.000Z

2021-04-13T07:18:35.000Z

tests/test_yt_selection.py

ytree-project/ytree

4ba1d3e6efd1e647c594b2c0de51b0217de7a45c

[ "BSD-3-Clause-Clear" ]

36

2019-12-03T17:33:29.000Z

2022-02-18T10:39:25.000Z

tests/test_yt_selection.py

ytree-project/ytree

4ba1d3e6efd1e647c594b2c0de51b0217de7a45c

[ "BSD-3-Clause-Clear" ]

6

2019-08-22T16:01:34.000Z

2020-08-25T09:57:17.000Z

""" tests for yt frontend and selection functions """ #----------------------------------------------------------------------------- # Copyright (c) ytree development team. All rights reserved. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file COPYING.txt, distributed with this software. #----------------------------------------------------------------------------- import numpy as np from numpy.testing import assert_raises from unyt import uconcatenate import ytree from ytree.utilities.testing import \ TempDirTest, \ assert_array_rel_equal, \ requires_file from ytree.yt_frontend import YTreeDataset CTG = "tiny_ctrees/locations.dat" class YTSelectionTest(TempDirTest): _arbor = None @property def arbor(self): if self._arbor is not None: return self._arbor a = ytree.load(CTG) fn = a.save_arbor() self._arbor = ytree.load(fn) return self._arbor @requires_file(CTG) def test_yt_all_data(self): a = self.arbor ds = a.ytds assert isinstance(ds, YTreeDataset) ad = ds.all_data() for field, units in zip(["mass", "redshift"], ["Msun", ""]): yt_data = ad["halos", field].to(units) yt_data.sort() ytree_data = uconcatenate([t["forest", field] for t in a]) ytree_data.sort() assert_array_rel_equal(yt_data, ytree_data, decimals=5) @requires_file(CTG) def test_yt_sphere(self): a = self.arbor ds = a.ytds sp = ds.sphere(0.5*ds.domain_center, (20, "Mpc/h")) ytree_pos = uconcatenate([t["forest", "position"] for t in a]) ytree_mass = uconcatenate([t["forest", "mass"] for t in a]) r = a.quan(sp.radius.to("unitary")) c = a.arr(sp.center.to("unitary")) ytree_r = np.sqrt(((ytree_pos - c)**2).sum(axis=1)) in_sphere = ytree_r <= r ytree_sp_r = ytree_r[in_sphere].to("unitary") ytree_sp_r.sort() sp_r = sp["halos", "particle_radius"].to("unitary") sp_r.sort() assert_array_rel_equal(ytree_sp_r, sp_r, decimals=5) sp_mass = sp["halos", "mass"].to("Msun") sp_mass.sort() ytree_sp_mass = ytree_mass[in_sphere].to("Msun") ytree_sp_mass.sort() assert_array_rel_equal(ytree_sp_mass, sp_mass, decimals=5) @requires_file(CTG) def test_above(self): a = self.arbor ds = a.ytds mt = 1e10 sel = a.get_yt_selection(above=[("mass", mt, "Msun")]) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass >= mt).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") yt_mass = ad_mass[ad_mass >= mt] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_above_no_units(self): a = self.arbor ds = a.ytds mt = 1e10 sel = a.get_yt_selection(above=[("mass", mt)]) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass >= mt).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") yt_mass = ad_mass[ad_mass >= mt] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_above_sphere(self): a = self.arbor ds = a.ytds sp = ds.sphere(0.5*ds.domain_center, (20, "Mpc/h")) mt = 1e10 sel = a.get_yt_selection(above=[("mass", mt, "Msun")], data_source=sp) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass >= mt).all() sel_mass.sort() sp_mass = sp["halos", "mass"].to("Msun") yt_mass = sp_mass[sp_mass >= mt] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_below(self): a = self.arbor ds = a.ytds mt = 1e10 sel = a.get_yt_selection(below=[("mass", mt, "Msun")]) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass <= mt).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") yt_mass = ad_mass[ad_mass <= mt] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_below_no_units(self): a = self.arbor ds = a.ytds mt = 1e10 sel = a.get_yt_selection(below=[("mass", mt)]) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass <= mt).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") yt_mass = ad_mass[ad_mass <= mt] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_below_sphere(self): a = self.arbor ds = a.ytds sp = ds.sphere(0.5*ds.domain_center, (20, "Mpc/h")) mt = 1e10 sel = a.get_yt_selection(below=[("mass", mt, "Msun")], data_source=sp) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass <= mt).all() sel_mass.sort() sp_mass = sp["halos", "mass"].to("Msun") yt_mass = sp_mass[sp_mass <= mt] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_about(self): a = self.arbor ds = a.ytds mt = 1e10 within = 0.5 sel = a.get_yt_selection(about=[("mass", mt, "Msun", within)]) sel_mass = sel["halos", "mass"].to("Msun") assert ((sel_mass >= (1-within)*mt) & (sel_mass <= (1+within)*mt)).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") yt_mass = ad_mass[(ad_mass >= (1-within)*mt) & (ad_mass <= (1+within)*mt)] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_about_no_units(self): a = self.arbor ds = a.ytds mt = 1e10 within = 0.5 sel = a.get_yt_selection(about=[("mass", mt, within)]) sel_mass = sel["halos", "mass"].to("Msun") assert ((sel_mass >= (1-within)*mt) & (sel_mass <= (1+within)*mt)).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") yt_mass = ad_mass[(ad_mass >= (1-within)*mt) & (ad_mass <= (1+within)*mt)] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_about_sphere(self): a = self.arbor ds = a.ytds sp = ds.sphere(0.5*ds.domain_center, (20, "Mpc/h")) mt = 1e10 within = 0.5 sel = a.get_yt_selection(about=[("mass", mt, within)], data_source=sp) sel_mass = sel["halos", "mass"].to("Msun") assert ((sel_mass >= (1-within)*mt) & (sel_mass <= (1+within)*mt)).all() sel_mass.sort() sp_mass = sp["halos", "mass"].to("Msun") yt_mass = sp_mass[(sp_mass >= (1-within)*mt) & (sp_mass <= (1+within)*mt)] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_equal(self): a = self.arbor ds = a.ytds sel = a.get_yt_selection(equal=[("mmp?", 1, "")]) sel_mmp = sel["halos", "mmp?"].to("") assert (sel_mmp == 1).all() sel_mmp.sort() ad = ds.all_data() ad_mmp = ad["halos", "mmp?"].to("") yt_mmp = ad_mmp[ad_mmp == 1] yt_mmp.sort() assert_array_rel_equal(sel_mmp, yt_mmp, decimals=5) @requires_file(CTG) def test_equal_no_units(self): a = self.arbor ds = a.ytds sel = a.get_yt_selection(equal=[("mmp?", 1)]) sel_mmp = sel["halos", "mmp?"].to("") assert (sel_mmp == 1).all() sel_mmp.sort() ad = ds.all_data() ad_mmp = ad["halos", "mmp?"].to("") yt_mmp = ad_mmp[ad_mmp == 1] yt_mmp.sort() assert_array_rel_equal(sel_mmp, yt_mmp, decimals=5) @requires_file(CTG) def test_equal_sphere(self): a = self.arbor ds = a.ytds sp = ds.sphere(0.5*ds.domain_center, (20, "Mpc/h")) sel = a.get_yt_selection(equal=[("mmp?", 1, "")], data_source=sp) sel_mmp = sel["halos", "mmp?"].to("") assert (sel_mmp == 1).all() sel_mmp.sort() sp_mmp = sp["halos", "mmp?"].to("") yt_mmp = sp_mmp[sp_mmp == 1] yt_mmp.sort() assert_array_rel_equal(sel_mmp, yt_mmp, decimals=5) @requires_file(CTG) def test_conditionals(self): a = self.arbor ds = a.ytds sel = a.get_yt_selection(conditionals=['obj["halos", "mass"] > 1e10']) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass >= 1e10).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") yt_mass = ad_mass[ad_mass >= 1e10] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_conditionals_sphere(self): a = self.arbor ds = a.ytds sp = ds.sphere(0.5*ds.domain_center, (20, "Mpc/h")) sel = a.get_yt_selection(conditionals=['obj["halos", "mass"] > 1e10'], data_source=sp) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass >= 1e10).all() sel_mass.sort() sp_mass = sp["halos", "mass"].to("Msun") yt_mass = sp_mass[sp_mass >= 1e10] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_yt_selection_bad_input(self): a = self.arbor with assert_raises(ValueError): a.get_yt_selection( conditionals=['obj["halos", "mass"] > 1e10'], above=["mass", 1e10]) @requires_file(CTG) def test_multiple_criteria_1(self): a = self.arbor ds = a.ytds sel = a.get_yt_selection(above=[("mass", 1e10, "Msun"), ("redshift", 0.5)]) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass >= 1e10).all() sel_redshift = sel["halos", "redshift"] assert (sel_redshift >= 0.5).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") ad_redshift = ad["halos", "redshift"] yt_mass = ad_mass[(ad_mass >= 1e10) & (ad_redshift >= 0.5)] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_multiple_criteria_2(self): a = self.arbor ds = a.ytds sel = a.get_yt_selection(above=[("mass", 1e10, "Msun")], below=[("redshift", 0.5)]) sel_mass = sel["halos", "mass"].to("Msun") assert (sel_mass >= 1e10).all() sel_redshift = sel["halos", "redshift"] assert (sel_redshift <= 0.5).all() sel_mass.sort() ad = ds.all_data() ad_mass = ad["halos", "mass"].to("Msun") ad_redshift = ad["halos", "redshift"] yt_mass = ad_mass[(ad_mass >= 1e10) & (ad_redshift <= 0.5)] yt_mass.sort() assert_array_rel_equal(sel_mass, yt_mass, decimals=5) @requires_file(CTG) def test_nodes_from_sphere(self): a = self.arbor ds = a.ytds sp = ds.sphere(0.5*ds.domain_center, (20, "Mpc/h")) nodes = list(a.get_nodes_from_selection(sp)) sp_mass = sp["halos", "mass"].to("Msun") sp_mass.sort() node_mass = a.arr([node["mass"] for node in nodes]) node_mass.sort() assert_array_rel_equal(node_mass, sp_mass, decimals=5) node_pos = a.arr([node["position"] for node in nodes]) r = a.quan(sp.radius.to("unitary")) c = a.arr(sp.center.to("unitary")) node_r = np.sqrt(((node_pos - c)**2).sum(axis=1)) assert (node_r <= r).all() @requires_file(CTG) def test_nodes_from_selection(self): a = self.arbor sel = a.get_yt_selection(above=[("mass", 1e10, "Msun")]) sel_mass = sel["halos", "mass"].to("Msun") sel_mass.sort() nodes = list(a.get_nodes_from_selection(sel)) node_mass = a.arr([node["mass"] for node in nodes]).to("Msun") node_mass.sort() assert_array_rel_equal(node_mass, sel_mass, decimals=5)

31.580247

82

0.547615

1,781

12,790

3.687254

0.072431

0.061824

0.048576

0.06624

0.815898

0.801584

0.793361

0.757271

0.742348

0.711436

0

0.018148

0.284832

12,790

404

83

31.658416

0.699792

0.030727

0

0.683386

0

0.070517

0.002019

0

0.137931

1

0.068966

false

0

0.018809

0

0.100313

0

null

0

1

0

null

0

6

5c48edd451c129e0c61fc8bd7cc147f6c3637857

7,079

py

Python

xscale/spectral/tests/test_fft.py

serazing/xscale

a804866aa6f6a5a0f293a7f6765ea17403159134

[ "Apache-2.0" ]

24

2017-02-28T15:01:29.000Z

2022-02-22T08:26:23.000Z

xscale/spectral/tests/test_fft.py

serazing/xscale

a804866aa6f6a5a0f293a7f6765ea17403159134

[ "Apache-2.0" ]

19

2017-02-24T12:30:26.000Z

2022-02-25T04:57:32.000Z

xscale/spectral/tests/test_fft.py

serazing/xscale

a804866aa6f6a5a0f293a7f6765ea17403159134

[ "Apache-2.0" ]

10

2017-03-04T02:59:42.000Z

2021-11-14T12:40:54.000Z

# Python 2/3 compatibility from __future__ import absolute_import, division, print_function import xscale.spectral.fft as xfft import xarray as xr import numpy as np import pytest def test_fft_real_1d(): """ Compare the result from the spectrum._fft function to numpy.fft.rfft """ a = [0, 1, 0, 0] dummy_array = xr.DataArray(a, dims=['x']) chunked_array = dummy_array.chunk(chunks={'x': 2}) spectrum_array, spectrum_coords, spectrum_dims = \ xfft._fft(chunked_array, nfft={'x': 4}, dim=['x'], dx={'x': 0.01}, sym=False) assert np.array_equal(spectrum_array.compute(), np.fft.rfft(a)) assert np.array_equal(spectrum_coords['f_x'], np.fft.rfftfreq(4, d=0.01)) assert 'f_x' in spectrum_dims def test_fft_complex_1d(): """ Compare the result from the spectrum.fft function to numpy.fft.fft """ a = np.exp(2j * np.pi * np.arange(8) / 8) dummy_array = xr.DataArray(a, dims=['x']) chunked_array = dummy_array.chunk(chunks={'x': 2}) spectrum_array, spectrum_coords, spectrum_dims = \ xfft._fft(chunked_array, nfft={'x': 16}, dim=['x'], dx={'x': 0.5}) assert np.array_equal(spectrum_array.compute(), np.fft.fft(a, n=16)) assert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(16, d=0.5)) assert 'f_x' in spectrum_dims def test_fft_real_2d(): """ Compare the result from the spectrum.fft function to numpy.fft.rfftn """ a = np.mgrid[:5, :5, :5][0] dummy_array = xr.DataArray(a, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2}) spectrum_array, spectrum_coords, spectrum_dims = \ xfft._fft(chunked_array, nfft={'y': 14, 'z': 18}, dim=['y', 'z'], dx={'y': 0.01, 'z': 0.02}, sym=False) assert np.allclose(spectrum_array.compute(), np.fft.rfftn(a, s=(18, 14), axes=(2, 1))) assert np.array_equal(spectrum_coords['f_y'], np.fft.rfftfreq(14, d=0.01)) assert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02)) assert ('x', 'f_y', 'f_z') == spectrum_dims def test_fft_complex_2d(): """ Compare the result from the spectrum.fft function to numpy.fft.fftn """ a, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1]) dummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2}) spectrum_array, spectrum_coords, spectrum_dims = \ xfft._fft(chunked_array, nfft={'y': 6, 'z': 8}, dim=['y', 'z'], dx={'y': 0.01, 'z': 0.02}) assert np.allclose(spectrum_array.compute(), np.fft.fftn(a * b * c, s=(8, 6), axes=(2, 1))) assert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01)) assert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02)) assert ('x', 'f_y', 'f_z') == spectrum_dims #@pytest.mark.skip(reason="Core dumped") def test_fft_real_3d(): """ Compare the result from the spectrum.fft function to numpy.fft.rfftn """ a = np.mgrid[:7, :5, :5][0] dummy_array = xr.DataArray(a, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 7, 'y': 5, 'z': 5}) spectrum_array, spectrum_coords, spectrum_dims = \ xfft._fft(chunked_array, nfft={'x': 11, 'y': 14, 'z': 18}, dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02}, sym=False) assert np.allclose(spectrum_array.compute(), np.fft.rfftn(a.T, s=(18, 14, 11)).T) assert np.array_equal(spectrum_coords['f_x'], np.fft.rfftfreq(11, d=12)) assert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(14, d=0.01)) assert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02)) assert ('f_x', 'f_y', 'f_z') == spectrum_dims def test_fft_complex_3d(): """ Compare the result from the spectrum.fft function to numpy.fft.fftn """ a, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1]) dummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2}) spectrum_array, spectrum_coords, spectrum_dims = \ xfft._fft(chunked_array, nfft={'x': 8, 'y': 6, 'z': 8}, dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02}) assert np.allclose(spectrum_array.compute(), np.fft.fftn(a * b * c, s=(8, 6, 8))) assert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(8, d=12)) assert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01)) assert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02)) assert ('f_x', 'f_y', 'f_z') == spectrum_dims def test_fft_warning(): """Test if a warning is raise if a wrong dimension is used """ a = np.mgrid[:5, :5, :5][0] dummy_array = xr.DataArray(a, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2}) with pytest.warns(UserWarning): xfft.fft(chunked_array, dim=['x', 'y', 'time']) @pytest.mark.parametrize("tapering", [True, False]) def test_spectrum_1d(tapering): a = np.mgrid[:5, :5, :5][0] dummy_array = xr.DataArray(a, dims=['time', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'time': 2, 'y': 2, 'z': 2}) xfft.fft(chunked_array, dim='time', dx=1., tapering=tapering).load() @pytest.mark.parametrize("tapering", [True, False]) def test_spectrum_2d(tapering): a = np.mgrid[:5, :5, :5][0] dummy_array = xr.DataArray(a, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2}) xfft.fft(chunked_array, dim=['y', 'z'], tapering=tapering).load() def test_parserval_real_1d(): """Test if the Parseval theorem is verified""" a = [0, 1, 0, 1, 1, 0, 1] dummy_array = xr.DataArray(a, dims=['x']) chunked_array = dummy_array.chunk(chunks={'x': 2}) spec = xfft.fft(chunked_array, dim=['x'], detrend='mean') assert np.isclose(np.var(a), xfft.ps(spec).sum()) def test_parserval_complex_1d(): """Test if the Parseval theorem is verified""" a = np.exp(2j * np.pi * np.arange(8) / 8) dummy_array = xr.DataArray(a, dims=['x']) chunked_array = dummy_array.chunk(chunks={'x': 2}) spec = xfft.fft(chunked_array, dim=['x'], detrend='mean') assert np.var(a) == xfft.ps(spec).sum() def test_parseval_real_2d(): """Test if the Parseval theorem is verified""" a = np.mgrid[:5, :5, :5][0] dummy_array = xr.DataArray(a, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2}) chunked_array_zeromean = chunked_array - chunked_array.mean(dim=['y', 'z']) spec = xfft.fft(chunked_array_zeromean, dim=['y', 'z']) assert np.allclose(np.var(a, axis=(1, 2)), xfft.ps(spec).sum(dim=['f_y','f_z'])) def test_parserval_complex_2d(): """ Compare the result from the spectrum.fft function to numpy.fft.fftn """ a, b, c = np.meshgrid([1j, 1, 1, 1j], [0, 1j, 1j], [0, 1, 1, 1]) dummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z']) chunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2}) chunked_array_zeromean = chunked_array - chunked_array.mean(dim=['y', 'z']) spec = xfft.fft(chunked_array_zeromean, dim=['y', 'z'], sym=True) assert np.allclose(np.var(a * b * c, axis=(1, 2)), xfft.ps(spec).sum(dim=['f_y','f_z']))

41.397661

76

0.633564

1,222

7,079

3.510638

0.099836

0.08951

0.042424

0.058741

0.877855

0.869697

0.852214

0.843823

0.747552

0

0.041167

0.152423

7,079

171

77

41.397661

0.673833

0.109479

0

0.528926

0

0.037598

0

0.231405

1

0.107438

false

0

0.041322

0

0.14876

0.008264

0

null

0

1

0

null

0

6

30b324b74ce787286bb0b194cade3809939e5dd4

19,345

py

Python

sdk/digitaltwins/azure-digitaltwins-core/tests/test_models.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/digitaltwins/azure-digitaltwins-core/tests/test_models.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/digitaltwins/azure-digitaltwins-core/tests/test_models.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See LICENSE.txt in the project root for # license information. # ------------------------------------------------------------------------- import pytest import random from devtools_testutils import AzureTestCase from _preparer import DigitalTwinsRGPreparer, DigitalTwinsPreparer from azure.digitaltwins.core import DigitalTwinsClient, DigitalTwinsModelData from azure.core.exceptions import ResourceNotFoundError, HttpResponseError, ResourceExistsError class DigitalTwinsModelsTests(AzureTestCase): def _get_client(self, endpoint, **kwargs): credential = self.get_credential(DigitalTwinsClient) return self.create_client_from_credential( DigitalTwinsClient, credential, endpoint=endpoint, **kwargs) def _clean_up_models(self, client, *models): models = [m.id for m in client.list_models()] while models: for model in models: try: client.delete_model(model) except: pass models = [m.id for m in client.list_models()] def _get_unique_component_id(self, client): id = "dtmi:com:samples:{};1".format(self.create_random_name("ComponentModel")) try: client.get_model(id) except ResourceNotFoundError: return id self._clean_up_models(client) return id def _get_unique_model_id(self, client): id = "dtmi:com:samples:{};1".format(self.create_random_name("TempModel")) try: client.get_model(id) except ResourceNotFoundError: return id self._clean_up_models(client) return id @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_create_models_empty(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) with pytest.raises(HttpResponseError): client.create_models([]) with pytest.raises(HttpResponseError): client.create_models([{}]) with pytest.raises(HttpResponseError): client.create_models(None) @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_create_models(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) model_id = self._get_unique_model_id(client) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } model = { "@id": model_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "TempModel", "contents": [ { "@type": "Property", "name": "Prop1", "schema": "string" }, { "@type": "Component", "name": "Component1", "schema": component_id }, { "@type": "Telemetry", "name": "Telemetry1", "schema": "integer" } ] } models = client.create_models([component, model]) assert isinstance(models, list) assert len(models) == 2 assert isinstance(models[0], DigitalTwinsModelData) assert models[0].id == component_id assert isinstance(models[1], DigitalTwinsModelData) assert models[1].id == model_id @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_create_model_existing(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) model_id = self._get_unique_model_id(client) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } model = { "@id": model_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "TempModel", "contents": [ { "@type": "Property", "name": "Prop1", "schema": "string" }, { "@type": "Telemetry", "name": "Telemetry1", "schema": "integer" } ] } models = client.create_models([model]) assert len(models) == 1 with pytest.raises(ResourceExistsError): client.create_models([component, model]) with pytest.raises(ResourceNotFoundError): client.get_model(component_id) @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_create_model_invalid_model(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) model = { "@context": "dtmi:dtdl:context;2", "displayName": "TempModel", "contents": [ { "@type": "Property", "name": "Prop1", "schema": "string" }, { "@type": "Component", "name": "Component1", "schema": component_id }, { "@type": "Telemetry", "name": "Telemetry1", "schema": "integer" } ] } with pytest.raises(HttpResponseError): client.create_models([model]) @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_create_model_invalid_reference(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) model_id = self._get_unique_model_id(client) model = { "@id": model_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "TempModel", "contents": [ { "@type": "Property", "name": "Prop1", "schema": "string" }, { "@type": "Component", "name": "Component1", "schema": component_id }, { "@type": "Telemetry", "name": "Telemetry1", "schema": "integer" } ] } with pytest.raises(HttpResponseError): client.create_models([model]) @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_get_model(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } models = client.create_models([component]) model = client.get_model(component_id) assert models[0].id == model.id assert model.id == component_id assert model.model is None @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_get_model_with_definition(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": ["dtmi:dtdl:context;2"], "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } models = client.create_models([component]) model = client.get_model(component_id, include_model_definition=True) assert models[0].id == model.id assert model.id == component_id assert model.model == component @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_get_model_not_existing(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) with pytest.raises(ResourceNotFoundError): client.get_model("dtmi:com:samples:NonExistingModel;1") @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_list_models(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": ["dtmi:dtdl:context;2"], "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } client.create_models([component]) listed_models = [m.id for m in client.list_models()] assert len(listed_models) >= 1 assert component_id in listed_models @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_list_models_with_definition(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": ["dtmi:dtdl:context;2"], "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } client.create_models([component]) listed_models = [m.model for m in client.list_models(include_model_definition=True)] assert len(listed_models) >= 1 assert component in listed_models assert all(listed_models) @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_decommission_model(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } client.create_models([component]) model = client.get_model(component_id) assert not model.decommissioned decommissioned = client.decommission_model(component_id) assert decommissioned is None model = client.get_model(component_id) assert model.decommissioned @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_decommission_model_not_existing(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) with pytest.raises(ResourceNotFoundError): client.decommission_model("dtmi:com:samples:NonExistingModel;1") @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_decommission_model_already_decommissioned(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } client.create_models([component]) model = client.get_model(component_id) assert not model.decommissioned client.decommission_model(component_id) client.decommission_model(component_id) model = client.get_model(component_id) assert model.decommissioned @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_delete_model(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } client.create_models([component]) deleted = client.delete_model(component_id) assert deleted is None with pytest.raises(ResourceNotFoundError): client.get_model(component_id) @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_delete_model_not_existing(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) with pytest.raises(ResourceNotFoundError): client.delete_model("dtmi:com:samples:NonExistingModel;1") @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_delete_model_already_deleted(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } client.create_models([component]) client.delete_model(component_id) with pytest.raises(ResourceNotFoundError): client.delete_model(component_id) @DigitalTwinsRGPreparer(name_prefix="dttest") @DigitalTwinsPreparer(name_prefix="dttest") def test_delete_models_with_dependencies(self, resource_group, location, digitaltwin): client = self._get_client(digitaltwin.host_name) component_id = self._get_unique_component_id(client) model_id = self._get_unique_model_id(client) component = { "@id": component_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "Component1", "contents": [ { "@type": "Property", "name": "ComponentProp1", "schema": "string" }, { "@type": "Telemetry", "name": "ComponentTelemetry1", "schema": "integer" } ] } model = { "@id": model_id, "@type": "Interface", "@context": "dtmi:dtdl:context;2", "displayName": "TempModel", "contents": [ { "@type": "Property", "name": "Prop1", "schema": "string" }, { "@type": "Component", "name": "Component1", "schema": component_id }, { "@type": "Telemetry", "name": "Telemetry1", "schema": "integer" } ] } client.create_models([component, model]) with pytest.raises(ResourceExistsError): client.delete_model(component_id) client.get_model(component_id) client.delete_model(model_id) client.delete_model(component_id) with pytest.raises(ResourceNotFoundError): client.get_model(component_id)

35.04529

100

0.537607

1,583

19,345

6.334807

0.075174

0.08227

0.054248

0.06442

0.884424

0.862385

0.852014

0.843139

0.833765

0.823295

0

0.006113

0.34045

19,345

551

101

35.108893

0.779842

0.015405

0

0.660118

0

0.160391

0.00772

0

0.047151

1

0.041257

false

0.001965

0.011788

0

0.064833

0

null

0

1

0

null

0

6

30b39bd8871bd22dd1f5dffd1e8533878fbd1be9

33

py

Python

malcolm/modules/pandablocks/__init__.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

malcolm/modules/pandablocks/__init__.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

malcolm/modules/pandablocks/__init__.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

from . import controllers, parts

16.5

32

0.787879

4

33

6.5

1

0

0.151515

33

1

33

0.928571

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

30b3e2184f9c83334de38f38f5c7e3a8c0320a0b

239

py

Python

tests/test_fibonnaci.py

robchrob/python-boilerplate

aa51b37344fc339a62702f427e64fd78ec852970

[ "MIT" ]

1

2021-07-11T22:53:59.000Z

tests/test_fibonnaci.py

robchrob/python-boilerplate

aa51b37344fc339a62702f427e64fd78ec852970

[ "MIT" ]

null

tests/test_fibonnaci.py

robchrob/python-boilerplate

aa51b37344fc339a62702f427e64fd78ec852970

[ "MIT" ]

null

from app.math_func import fibonacci def test_fibonacci_basic_init(): assert fibonacci(0) == 0 assert fibonacci(1) == 1 def test_fibonacci_2(): assert fibonacci(2) == 1 def test_fibonacci_3(): assert fibonacci(3) == 2

15.933333

35

0.690377

35

239

4.485714

0.428571

0.382166

0.305732

0.216561

0

0.052356

0.200837

239

14

36

17.071429

0.769634

0

0.5

1

0.375

true

0

0.125

0

0.5

0

null

1

0

null

0

1

0

1

0

6

30dd1cfcb3c401c0ff078e8b1e79d65d1f3b0668

326

py

Python

terrascript/librato/r.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/librato/r.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/librato/r.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

# terrascript/librato/r.py import terrascript class librato_space(terrascript.Resource): pass class librato_space_chart(terrascript.Resource): pass class librato_metric(terrascript.Resource): pass class librato_alert(terrascript.Resource): pass class librato_service(terrascript.Resource): pass

13.583333

48

0.773006

37

326

6.648649

0.351351

0.243902

0.46748

0.455285

0.569106

0

0.153374

326

23

49

14.173913

0.891304

0.07362

0

0.454545

0

1

0

true

0.454545

0.090909

0

0.545455

0

null

1

0

null

0

1

0

1

0

6

eb73d2ce1b5470e210d1552d59f859baa865830e

196

py

Python

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/modify/dot1x/modify.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/modify/dot1x/modify.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/modify/dot1x/modify.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

'''Implementation for Dot1x modify triggers''' # import genie.libs from genie.libs.sdk.triggers.modify.modify import TriggerModify class TriggerModifyDot1xUserCredential(TriggerModify): pass

28

63

0.816327

21

196

7.619048

0.666667

0.1125

0

0.011364

0.102041

196

7

64

28

0.897727

0.30102

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

eb8f5f84ea03fd69fb7073c3e4f5bf6d4b9138b4

1,891

py

Python

atom/electron/python/electron_api/__init__.py

AbhiGupta03/SDK

f3a61aae7a847f07f0c22a154ca88dc378e9d25e

[ "Apache-2.0" ]

null

atom/electron/python/electron_api/__init__.py

AbhiGupta03/SDK

f3a61aae7a847f07f0c22a154ca88dc378e9d25e

[ "Apache-2.0" ]

null

atom/electron/python/electron_api/__init__.py

AbhiGupta03/SDK

f3a61aae7a847f07f0c22a154ca88dc378e9d25e

[ "Apache-2.0" ]

null

# coding: utf-8 # flake8: noqa """ Hydrogen Electron API The Hydrogen Electron API # noqa: E501 OpenAPI spec version: 1.3.1 Contact: info@hydrogenplatform.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import # import apis into sdk package from electron_api.api.ach_api import ACHApi from electron_api.api.card_api import CardApi # import ApiClient from electron_api.api_client import ApiClient from electron_api.configuration import Configuration from electron_api.auth_api import AuthApi from electron_api.environment import Environment # import models into sdk package from electron_api.models.ach_card_request_co import AchCardRequestCO from electron_api.models.ach_card_response_vo import AchCardResponseVO from electron_api.models.base_response_vo import BaseResponseVO from electron_api.models.card_base_request_co import CardBaseRequestCO from electron_api.models.card_client_request_co import CardClientRequestCO from electron_api.models.card_load_request_co import CardLoadRequestCO from electron_api.models.card_load_unload_response_vo import CardLoadUnloadResponseVO from electron_api.models.card_replace_response_vo import CardReplaceResponseVO from electron_api.models.card_reserve_account_response_vo import CardReserveAccountResponseVO from electron_api.models.card_spending_control_request_co import CardSpendingControlRequestCO from electron_api.models.card_spending_control_response_vo import CardSpendingControlResponseVO from electron_api.models.card_unload_request_co import CardUnloadRequestCO from electron_api.models.create_card_client_response_vo import CreateCardClientResponseVO from electron_api.models.spending_control_vendor_request_data_vo import SpendingControlVendorRequestDataVO from electron_api.models.update_card_client_response_vo import UpdateCardClientResponseVO

43.976744

106

0.8789

250

1,891

6.32

0.304

0.160127

0.199367

0.3

0.15

0.050633

0

0.004619

0.084083

1,891

42

107

45.02381

0.907621

0.156531

0

1

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

eba5f222c3541de195057b10d27801c818985d80

167

py

Python

tests/test_util.py

phanirithvij/anime-downloader

421b487e18ea83b31b38ceee812adf1c5a764380

[ "Unlicense" ]

null

tests/test_util.py

phanirithvij/anime-downloader

421b487e18ea83b31b38ceee812adf1c5a764380

[ "Unlicense" ]

null

tests/test_util.py

phanirithvij/anime-downloader

421b487e18ea83b31b38ceee812adf1c5a764380

[ "Unlicense" ]

null

import pytest from anime_downloader import util def test_split_anime(): anime_list = list(range(20)) assert len(util.split_anime(anime_list, '1:10')) == 9

16.7

57

0.718563

26

167

4.384615

0.653846

0.175439

0.263158

0.333333

0

0.043165

0.167665

167

9

58

18.555556

0.776978

0

0.023952

0

0.2

1

0.2

false

0

0.4

0

0.6

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

ebb301337d4fda8930c51cbc18cfe03a3395831f

49

py

Python

glosysnet/nn/optimize/__init__.py

NareshAtnPLUS/glosysnet

e85df44727b8784766be7e728267e5699997e226

[ "MIT" ]

null

glosysnet/nn/optimize/__init__.py

NareshAtnPLUS/glosysnet

e85df44727b8784766be7e728267e5699997e226

[ "MIT" ]

null

glosysnet/nn/optimize/__init__.py

NareshAtnPLUS/glosysnet

e85df44727b8784766be7e728267e5699997e226

[ "MIT" ]

null

from glosysnet.nn.optimizations.optimize import *

49

0.857143

6

49

7

1

0

0.061224

49

1

49

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ebbdfda518d8f437c1afe08f1064ebe908ba3137

30

py

Python

map-parser/zlib_raw/__init__.py

ZetaTwo/AoE2ScenarioParser

1e130facf7871e08c9aaa456180940878d975219

[ "Apache-2.0" ]

1

2022-01-17T00:05:43.000Z

map-parser/zlib_raw/__init__.py

ZetaTwo/AoE2ScenarioParser

1e130facf7871e08c9aaa456180940878d975219

[ "Apache-2.0" ]

null

map-parser/zlib_raw/__init__.py

ZetaTwo/AoE2ScenarioParser

1e130facf7871e08c9aaa456180940878d975219

[ "Apache-2.0" ]

1

2022-01-17T00:05:48.000Z

from .zlib_raw import ZlibRaw

15

29

0.833333

5

30

4.8

1

0

0.133333

30

1

30

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ebd9a56fa7a697f8f32aac4e5414f4b9da78270a

38

py

Python

myscript.py

jakevdp/try-out-git

c8bdfc917612f6b49519e91f3374fdd289f40dcb

[ "MIT" ]

4

2018-06-21T02:28:13.000Z

2021-08-29T05:03:13.000Z

myscript.py

jakevdp/try-out-git

c8bdfc917612f6b49519e91f3374fdd289f40dcb

[ "MIT" ]

2

2018-06-15T22:49:32.000Z

2018-06-16T14:39:38.000Z

myscript.py

jakevdp/try-out-git

c8bdfc917612f6b49519e91f3374fdd289f40dcb

[ "MIT" ]

1

2021-08-29T05:03:14.000Z

import math print(math.sin(math.pi))

9.5

24

0.736842

7

38

4

0.714286

0

0.105263

38

3

25

12.666667

0.823529

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cce8134f5bf76931d70e650051ce9fa77b4f2cac

3,678

py

Python

core/api/sprinklers.py

One-Green/admin-ui

194d5878d0f737bd121ed8356434bf9fae3914e1

[ "CC0-1.0" ]

null

core/api/sprinklers.py

One-Green/admin-ui

194d5878d0f737bd121ed8356434bf9fae3914e1

[ "CC0-1.0" ]

null

core/api/sprinklers.py

One-Green/admin-ui

194d5878d0f737bd121ed8356434bf9fae3914e1

[ "CC0-1.0" ]

null

import os import requests from json.decoder import JSONDecodeError def create_tag(api: str, tag=str, _basic_auth: tuple = None) -> bool: """ Create new sprinkler tag by api :param api: :param tag: :param _basic_auth: :return: """ if _basic_auth: if requests.post(api, data={"tag": tag}, auth=_basic_auth).json()[ "acknowledge" ]: return True else: return False else: if requests.post(api, data={"tag": tag}).json()["acknowledge"]: return True else: return False def delete_tag(api: str, tag=str, _basic_auth: tuple = None) -> bool: """ Delete sprinklers tags by api :param api: :param tag: :param _basic_auth: :return: """ if _basic_auth: if requests.delete(api, data={"tag": tag}, auth=_basic_auth).ok: return True else: return False else: if requests.delete(api, data={"tag": tag}).ok: return True else: return False def get_tags(api: str, _basic_auth: tuple = None) -> list: """ Get sprinklers tags by api :param api: :param _basic_auth: :return: """ if _basic_auth: return requests.get(api, auth=_basic_auth).json() else: return requests.get(api).json() def get_configuration(api: str, sprinkler_tag: str, _basic_auth: tuple = None) -> dict: """ Configure a sprinkler with is tag :param api: :param sprinkler_tag: :param config: :param _basic_auth: :return: """ _api = os.path.join(api, sprinkler_tag) try: if _basic_auth: return requests.get(_api, auth=_basic_auth).json() else: return requests.get(_api).json() except JSONDecodeError: return { "soil_moisture_min_level": "not set", "soil_moisture_max_level": "not set", } def post_configuration( api: str, sprinkler_tag: str, config: dict, _basic_auth: tuple = None ) -> bool: """ Configure a sprinkler with is tag :param api: :param sprinkler_tag: :param config: :param _basic_auth: :return: """ _api = os.path.join(api, sprinkler_tag) if _basic_auth: if requests.post(_api, json=config, auth=_basic_auth).ok: return True else: return False else: if requests.post(_api, json=config).ok: return True else: return False def get_controller_force( api: str, sprinkler_tag: str, _basic_auth: tuple = None ) -> dict: """ Configure a sprinkler with is tag :param api: :param sprinkler_tag: :param config: :param _basic_auth: :return: """ _api = os.path.join(api, sprinkler_tag) try: if _basic_auth: return requests.get(_api, auth=_basic_auth).json() else: return requests.get(_api).json() except JSONDecodeError: return {"force_water_valve_signal": False, "water_valve_signal": False} def post_controller_force( api: str, sprinkler_tag: str, config: dict, _basic_auth: tuple = None ) -> bool: """ Configure a sprinkler with is tag :param api: :param sprinkler_tag: :param config: :param _basic_auth: :return: """ _api = os.path.join(api, sprinkler_tag) if _basic_auth: if requests.post(_api, json=config, auth=_basic_auth).ok: return True else: return False else: if requests.post(_api, json=config).ok: return True else: return False

24.039216

87

0.582926

445

3,678

4.593258

0.130337

0.123288

0.073386

0.078278

0.886008

0.875734

0.861546

0.751957

0.732877

0

0.309679

3,678

152

88

24.197368

0.805041

0.185699

0

0.698795

0

0.049186

0.025316

0

1

0.084337

false

0

0.036145

0

0.409639

0

null

0

1

0

null

0

6

cceb1726bd58f0efe0918b0121a4fe7a6311ed8c

12,184

py

Python

django_eve_data/esi_api/alliances.py

SvenMatzke/EveData

a890c5bf4197f63092938de5d35c63054e0bb18c

[ "MIT" ]

1

2017-02-26T20:34:11.000Z

django_eve_data/esi_api/alliances.py

SvenMatzke/eve_data

a890c5bf4197f63092938de5d35c63054e0bb18c

[ "MIT" ]

null

django_eve_data/esi_api/alliances.py

SvenMatzke/eve_data

a890c5bf4197f63092938de5d35c63054e0bb18c

[ "MIT" ]

null

# coding utf-8 """ Autogenerated Template File """ from .base import EsiRequestObject class AlliancesDetailIcons(object): base_url = "https://esi.tech.ccp.is/latest/alliances/{alliance_id}/icons/" get_responses = {'500': {'schema': {'type': 'object', 'properties': {'error': {'type': 'string', 'description': 'Internal server error message', 'title': 'get_alliances_alliance_id_icons_500_internal_server_error'}}, 'description': 'Internal server error', 'title': 'get_alliances_alliance_id_icons_internal_server_error'}, 'examples': {'application/json': {'error': "uncaught exception: IOError('out of memory')"}}, 'description': 'Internal server error'}, '200': {'schema': {'type': 'object', 'properties': {'px64x64': {'type': 'string', 'description': 'px64x64 string', 'title': 'get_alliances_alliance_id_icons_px64x64'}, 'px128x128': {'type': 'string', 'description': 'px128x128 string', 'title': 'get_alliances_alliance_id_icons_px128x128'}}, 'description': '200 ok object', 'title': 'get_alliances_alliance_id_icons_ok'}, 'examples': {'application/json': {'px64x64': 'https://imageserver.eveonline.com/Alliance/503818424_64.png', 'px128x128': 'https://imageserver.eveonline.com/Alliance/503818424_128.png'}}, 'headers': {'Expires': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}, 'Cache-Control': {'type': 'string', 'description': 'The caching mechanism used'}, 'Last-Modified': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}}, 'description': 'Urls for icons for the given alliance id and server'}, '404': {'schema': {'type': 'object', 'properties': {'error': {'type': 'string', 'description': 'error message', 'title': 'get_alliances_alliance_id_icons_error'}}, 'description': 'No image server for this datasource', 'title': 'get_alliances_alliance_id_icons_not_found'}, 'examples': {'application/json': {'error': 'No image server for this datasource'}}, 'description': 'No image server for this datasource'}} def get(self, alliance_id, datasource="tranquility",**kwargs): """ Get the icon urls for a alliance --- Alternate route: `/v1/alliances/{alliance_id}/icons/` Alternate route: `/legacy/alliances/{alliance_id}/icons/` Alternate route: `/dev/alliances/{alliance_id}/icons/` --- This route is cached for up to 3600 seconds :type alliance_id: int :param alliance_id: An EVE alliance ID :type datasource: str :param datasource: The server name you would like data from :param kwargs: user_agent, X-User-Agent """ kwargs_dict ={ "alliance_id" : alliance_id, "datasource" : datasource, } kwargs_dict.update(kwargs) return EsiRequestObject(self.base_url, self.get_responses) \ .get(**kwargs_dict) class AlliancesDetailCorporations(object): base_url = "https://esi.tech.ccp.is/latest/alliances/{alliance_id}/corporations/" get_responses = {'500': {'schema': {'type': 'object', 'properties': {'error': {'type': 'string', 'description': 'Internal server error message', 'title': 'get_alliances_alliance_id_corporations_500_internal_server_error'}}, 'description': 'Internal server error', 'title': 'get_alliances_alliance_id_corporations_internal_server_error'}, 'examples': {'application/json': {'error': "uncaught exception: IOError('out of memory')"}}, 'description': 'Internal server error'}, '200': {'schema': {'items': {'format': 'int32', 'minimum': 0, 'type': 'integer', 'uniqueItems': True, 'description': '200 ok integer', 'title': 'get_alliances_alliance_id_corporations_200_ok'}, 'type': 'array', 'description': '200 ok array', 'title': 'get_alliances_alliance_id_corporations_ok'}, 'examples': {'application/json': [98000001]}, 'headers': {'Expires': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}, 'Cache-Control': {'type': 'string', 'description': 'The caching mechanism used'}, 'Last-Modified': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}}, 'description': 'List of corporation IDs'}} def get(self, alliance_id, datasource="tranquility",**kwargs): """ List all current member corporations of an alliance --- Alternate route: `/v1/alliances/{alliance_id}/corporations/` Alternate route: `/legacy/alliances/{alliance_id}/corporations/` Alternate route: `/dev/alliances/{alliance_id}/corporations/` --- This route is cached for up to 3600 seconds :type alliance_id: int :param alliance_id: An EVE alliance ID :type datasource: str :param datasource: The server name you would like data from :param kwargs: user_agent, X-User-Agent """ kwargs_dict ={ "alliance_id" : alliance_id, "datasource" : datasource, } kwargs_dict.update(kwargs) return EsiRequestObject(self.base_url, self.get_responses) \ .get(**kwargs_dict) class AlliancesDetail(object): base_url = "https://esi.tech.ccp.is/latest/alliances/{alliance_id}/" get_responses = {'500': {'schema': {'type': 'object', 'properties': {'error': {'type': 'string', 'description': 'Internal server error message', 'title': 'get_alliances_alliance_id_500_internal_server_error'}}, 'description': 'Internal server error', 'title': 'get_alliances_alliance_id_internal_server_error'}, 'examples': {'application/json': {'error': "uncaught exception: IOError('out of memory')"}}, 'description': 'Internal server error'}, '200': {'schema': {'title': 'get_alliances_alliance_id_ok', 'type': 'object', 'properties': {'executor_corp': {'format': 'int32', 'type': 'integer', 'description': 'the executor corporation ID, if this alliance is not closed', 'title': 'get_alliances_alliance_id_executor_corp'}, 'date_founded': {'format': 'date-time', 'type': 'string', 'description': 'date_founded string', 'title': 'get_alliances_alliance_id_date_founded'}, 'alliance_name': {'type': 'string', 'description': 'the full name of the alliance', 'title': 'get_alliances_alliance_id_alliance_name'}, 'ticker': {'type': 'string', 'description': 'the short name of the alliance', 'title': 'get_alliances_alliance_id_ticker'}}, 'description': '200 ok object', 'required': ['alliance_name', 'ticker', 'date_founded']}, 'examples': {'application/json': {'executor_corp': 98356193, 'date_founded': '2016-06-26T21:00:00Z', 'alliance_name': 'C C P Alliance', 'ticker': '<C C P>'}}, 'headers': {'Expires': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}, 'Cache-Control': {'type': 'string', 'description': 'The caching mechanism used'}, 'Last-Modified': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}}, 'description': 'Public data about an alliance'}, '404': {'schema': {'type': 'object', 'properties': {'error': {'type': 'string', 'description': 'error message', 'title': 'get_alliances_alliance_id_error'}}, 'description': 'Alliance not found', 'title': 'get_alliances_alliance_id_not_found'}, 'examples': {'application/json': {'error': 'Alliance not found'}}, 'description': 'Alliance not found'}} def get(self, alliance_id, datasource="tranquility",**kwargs): """ Public information about an alliance --- Alternate route: `/v2/alliances/{alliance_id}/` --- This route is cached for up to 3600 seconds :type alliance_id: int :param alliance_id: An Eve alliance ID :type datasource: str :param datasource: The server name you would like data from :param kwargs: user_agent, X-User-Agent """ kwargs_dict ={ "alliance_id" : alliance_id, "datasource" : datasource, } kwargs_dict.update(kwargs) return EsiRequestObject(self.base_url, self.get_responses) \ .get(**kwargs_dict) class AlliancesNames(object): base_url = "https://esi.tech.ccp.is/latest/alliances/names/" get_responses = {'500': {'schema': {'type': 'object', 'properties': {'error': {'type': 'string', 'description': 'Internal server error message', 'title': 'get_alliances_names_500_internal_server_error'}}, 'description': 'Internal server error', 'title': 'get_alliances_names_internal_server_error'}, 'examples': {'application/json': {'error': "uncaught exception: IOError('out of memory')"}}, 'description': 'Internal server error'}, '200': {'schema': {'items': {'title': 'get_alliances_names_200_ok', 'type': 'object', 'properties': {'alliance_name': {'type': 'string', 'description': 'alliance_name string', 'title': 'get_alliances_names_alliance_name'}, 'alliance_id': {'format': 'int32', 'type': 'integer', 'description': 'alliance_id integer', 'title': 'get_alliances_names_alliance_id'}}, 'description': '200 ok object', 'required': ['alliance_id', 'alliance_name']}, 'type': 'array', 'description': '200 ok array', 'title': 'get_alliances_names_ok'}, 'examples': {'application/json': [{'alliance_name': 'Republic University', 'alliance_id': 1000171}]}, 'headers': {'Expires': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}, 'Cache-Control': {'type': 'string', 'description': 'The caching mechanism used'}, 'Last-Modified': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}}, 'description': 'List of id/name associations'}} def get(self, alliance_ids, datasource="tranquility",**kwargs): """ Resolve a set of alliance IDs to alliance names --- Alternate route: `/v1/alliances/names/` Alternate route: `/legacy/alliances/names/` Alternate route: `/dev/alliances/names/` --- This route is cached for up to 3600 seconds :type alliance_ids: list :param alliance_ids: A comma separated list of alliance IDs :type datasource: str :param datasource: The server name you would like data from :param kwargs: user_agent, X-User-Agent """ kwargs_dict ={ "alliance_ids" : alliance_ids, "datasource" : datasource, } kwargs_dict.update(kwargs) return EsiRequestObject(self.base_url, self.get_responses) \ .get(**kwargs_dict) class Alliances(object): base_url = "https://esi.tech.ccp.is/latest/alliances/" get_responses = {'500': {'schema': {'type': 'object', 'properties': {'error': {'type': 'string', 'description': 'Internal server error message', 'title': 'get_alliances_500_internal_server_error'}}, 'description': 'Internal server error', 'title': 'get_alliances_internal_server_error'}, 'examples': {'application/json': {'error': "uncaught exception: IOError('out of memory')"}}, 'description': 'Internal server error'}, '200': {'schema': {'items': {'format': 'int32', 'minimum': 0, 'type': 'integer', 'uniqueItems': True, 'description': '200 ok integer', 'title': 'get_alliances_200_ok'}, 'type': 'array', 'description': '200 ok array', 'title': 'get_alliances_ok'}, 'examples': {'application/json': [99000001, 99000002]}, 'headers': {'Expires': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}, 'Cache-Control': {'type': 'string', 'description': 'The caching mechanism used'}, 'Last-Modified': {'type': 'string', 'description': 'RFC7231 formatted datetime string'}}, 'description': 'List of Alliance IDs'}} def get(self, datasource="tranquility",**kwargs): """ List all active player alliances --- Alternate route: `/v1/alliances/` Alternate route: `/legacy/alliances/` Alternate route: `/dev/alliances/` --- This route is cached for up to 3600 seconds :type datasource: str :param datasource: The server name you would like data from :param kwargs: user_agent, X-User-Agent """ kwargs_dict ={ "datasource" : datasource, } kwargs_dict.update(kwargs) return EsiRequestObject(self.base_url, self.get_responses) \ .get(**kwargs_dict)

67.688889

2,053

0.66612

1,364

12,184

5.77346

0.1239

0.068571

0.072381

0.063492

0.788825

0.748952

0.669968

0.638984

0.61981

0.602667

0

0.02724

0.168418

12,184

180

2,054

67.688889

0.75

0.222177

0

0.509804

1

0

0.613752

0.11927

0

1

0.098039

false

0

0.019608

0

0.509804

0

null

0

1

0

1

0

1

0

1

0

1

0

null

0

1

0

6

693128e8d0570b1f5ed32820fbfafb4505d3d015

48

py

Python

project/Packaging/sta663/__init__.py

taotangtt/sta-663-2018

67dac909477f81d83ebe61e0753de2328af1be9c

[ "BSD-3-Clause" ]

72

2018-01-20T20:50:22.000Z

2022-02-27T23:24:21.000Z

project/Packaging/sta663/__init__.py

taotangtt/sta-663-2018

67dac909477f81d83ebe61e0753de2328af1be9c

[ "BSD-3-Clause" ]

1

2020-02-03T13:43:46.000Z

project/Packaging/sta663/__init__.py

taotangtt/sta-663-2018

67dac909477f81d83ebe61e0753de2328af1be9c

[ "BSD-3-Clause" ]

64

2018-01-12T17:13:14.000Z

2022-03-14T20:22:46.000Z

import pkg from pkg.sub1.sub1_stuff import g1

12

34

0.791667

9

48

4.111111

0.666667

0

0.075

0.166667

48

3

35

16

0.85

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

15e70d68afc700fc3846dc9cdfad68aa353d844d

159

py

Python

tests/test_integration.py

ENVIRO-Module/enbios

10e93df9a168627833eca6d04e4e2b864de8e8d9

[ "BSD-3-Clause" ]

2

2022-01-28T09:38:28.000Z

2022-01-28T09:38:32.000Z

tests/test_integration.py

ENVIRO-Module/enbios

10e93df9a168627833eca6d04e4e2b864de8e8d9

[ "BSD-3-Clause" ]

1

2022-01-27T21:42:42.000Z

tests/test_integration.py

ENVIRO-Module/enbios

10e93df9a168627833eca6d04e4e2b864de8e8d9

[ "BSD-3-Clause" ]

null

import os import sys import pytest from ..enbios.processing.main import enviro_musiasem def test_example_1(): enviro_musiasem("../example_config.yaml")

15.9

52

0.779874

22

159

5.409091

0.727273

0.235294

0

0.007194

0.125786

159

9

53

17.666667

0.848921

0

0.138365

0

1

0.166667

true

0

0.666667

0

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

c618b4be487a9b92b4878363ad44c86c833125b9

26

py

Python

cloud_broker/__init__.py

mridhul/minion-manager

7301ac6360a82dfdd27e682d070c34e90f080149

[ "Apache-2.0" ]

54

2018-07-06T18:06:54.000Z

2019-06-03T15:21:01.000Z

cloud_broker/__init__.py

mridhul/minion-manager

7301ac6360a82dfdd27e682d070c34e90f080149

[ "Apache-2.0" ]

28

2018-07-05T23:32:22.000Z

2019-07-19T17:19:26.000Z

cloud_broker/__init__.py

mridhul/minion-manager

7301ac6360a82dfdd27e682d070c34e90f080149

[ "Apache-2.0" ]

15

2018-07-28T04:51:01.000Z

2019-07-30T14:50:25.000Z

from broker import Broker

13

25

0.846154

4

26

5.5

0.75

0

0.153846

26

1

26

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c619529557146fe2c49428bfed9251ade8ce67b7

188

py

Python

scripts/andc1_memory.py

cferge-auroranetworx/Aurora-Networx-Cloud-Mgmt

9ac116c5e8509325498265103b1dd00fb3714b95

[ "Apache-2.0" ]

null

scripts/andc1_memory.py

cferge-auroranetworx/Aurora-Networx-Cloud-Mgmt

9ac116c5e8509325498265103b1dd00fb3714b95

[ "Apache-2.0" ]

null

scripts/andc1_memory.py

cferge-auroranetworx/Aurora-Networx-Cloud-Mgmt

9ac116c5e8509325498265103b1dd00fb3714b95

[ "Apache-2.0" ]

null

import os import commands memory = commands.getoutput("""free -t -m | grep "Total" | awk '{ print "Total (Inc Swap) : "$2" MB >> " "Used : "$3" MB >> Free : "$4" MB";}'""") print memory

26.857143

146

0.574468

27

188

4

0.703704

0

0.019868

0.196809

188

6

147

31.333333

0.695364

0

0.25

0.590426

0

null

0

0.5

null

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

c643f97e68c0652f0872753ba57afc59bd066300

3,532

py

Python

test_dht.py

AndreaPi/DHT

103cd286b804f52222f50055b36788c83c0fe231

[ "MIT" ]

1

2021-08-06T06:23:39.000Z

test_dht.py

AndreaPi/DHT

103cd286b804f52222f50055b36788c83c0fe231

[ "MIT" ]

null

test_dht.py

AndreaPi/DHT

103cd286b804f52222f50055b36788c83c0fe231

[ "MIT" ]

null

from dht import dht, idht, flip_periodic, dht_conv, conv import numpy as np import inspect def test_dht(): # DHT transform of a constant sequence, based on DFT transform of the same N = 20 x = np.ones((N, )) X = dht(x) X1 = np.zeros((N, )) X1[0] = N msg = f"{inspect.stack()[0][3]}: constant sequence test failed" np.testing.assert_allclose(X, X1, atol=1e-08, err_msg=msg) # DHT transform of a cosine, based on DFT transform of the same k = 3 c = 2.*np.pi*k/N i = np.arange(N) x = np.cos(c * i) X = dht(x) X1 = np.zeros((N, )) X1[k] = N/2. X1[-k] = X1[-k] + N/2. msg = f"{inspect.stack()[0][3]}: cosine sequence test failed" np.testing.assert_allclose(X, X1, atol=1e-08, err_msg=msg) # DHT transform of a cosine: corner cases k = 0 c = 2.*np.pi*k/N i = np.arange(N) x = np.cos(c * i) X = dht(x) X1 = np.zeros((N, )) X1[k] = N/2. X1[-k] = X1[-k] + N/2. msg = f"{inspect.stack()[0][3]}: cosine corner case #1 test failed" np.testing.assert_allclose(X, X1, atol=1e-08, err_msg=msg) N = 20 k = 19 c = 2.*np.pi*k/N i = np.arange(N) x = np.cos(c * i) X = dht(x) X1 = np.zeros((N, )) X1[k] = N/2. X1[-k] = X1[-k] + N/2. msg = f"{inspect.stack()[0][3]}: cosine corner case #2 test failed" np.testing.assert_allclose(X, X1, atol=1e-08, err_msg=msg) def test_idht(): # IDHT transform of an impulse at 0, based on IDFT transform of the same N = 20 X = np.zeros((N, )) X[0] = N x = idht(X) x1 = np.ones((N, )) msg = f"{inspect.stack()[0][3]}: impulse test failed" np.testing.assert_allclose(x, x1, atol=1e-08, err_msg=msg) # IDHT transform of sum of impulses at k and N-k, based on IDFT transform of the same k = 3 X = np.zeros((N, )) X[k] = N/2. X[-k] = X[-k] + N/2. x = idht(X) c = 2.*np.pi*k/N i = np.arange(N) x1 = np.cos(c * i) msg = f"{inspect.stack()[0][3]}: sum of impulses test failed" np.testing.assert_allclose(x, x1, atol=1e-08, err_msg=msg) # IDHT transform of sum of impulses at k and N-k, corner cases k = 0 X = np.zeros((N, )) X[k] = N/2. X[-k] = X[-k] + N/2. x = idht(X) c = 2.*np.pi*k/N i = np.arange(N) x1 = np.cos(c * i) msg = f"{inspect.stack()[0][3]}: sum of impulses corner case #1 test failed" np.testing.assert_allclose(x, x1, atol=1e-08, err_msg=msg) # IDHT transform of sum of impulses at k and N-k, corner cases k = 19 X = np.zeros((N, )) X[k] = N/2. X[-k] = X[-k] + N/2. x = idht(X) c = 2.*np.pi*k/N i = np.arange(N) x1 = np.cos(c * i) msg = f"{inspect.stack()[0][3]}: sum of impulses corner case #2 test failed" np.testing.assert_allclose(x, x1, atol=1e-08, err_msg=msg) def test_dht_conv(): N = 20 x = np.ones((N, )) y = np.copy(x) Z = dht_conv(x, y) Z1 = np.real(np.fft.fft(x)*np.fft.fft(y)) msg = f"{inspect.stack()[0][3]} test failed" np.testing.assert_allclose(Z, Z1, err_msg=msg) def test_conv(): N = 20 x = np.ones((N, )) y = np.copy(x) z = conv(x, y) z1 = np.real(np.fft.ifft(np.fft.fft(x)*np.fft.fft(y))) msg = f"{inspect.stack()[0][3]} test failed" np.testing.assert_allclose(z, z1, err_msg=msg) if (__name__=='__main__'): test_dht() print("test_dht() passed") test_idht() print("test_idht() passed") test_dht_conv() print("test_dht_conv() passed") test_conv() print("test_conv() passed")

29.680672

90

0.55974

662

3,532

2.916918

0.111782

0.018643

0.082859

0.829622

0.822372

0.817711

0.757639

0.706888

0

0.044554

0.256512

3,532

119

91

29.680672

0.690784

0.128539

0

0.685714

0

0.197004

0.077499

0

0.095238

1

0.038095

false

0.038095

0.028571

0

0.066667

0.038095

0

null

0

1

0

null

0

6

d65f87af2506f0299a9465f9755d43378d0ae3e1

15,746

py

Python

barbican/api/controllers/acls.py

mail2nsrajesh/barbican

d16d932b77486e9b2f8c6d30e628a6e66517b1a6

[ "Apache-2.0" ]

177

2015-01-02T09:35:53.000Z

2022-02-26T01:43:55.000Z

barbican/api/controllers/acls.py

kkutysllb/barbican

7b14d983e0dce6dcffe9781b05c52335b8203fc7

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

barbican/api/controllers/acls.py

kkutysllb/barbican

7b14d983e0dce6dcffe9781b05c52335b8203fc7

[ "Apache-2.0" ]

87

2015-01-13T17:33:40.000Z

2021-11-09T05:30:36.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import pecan import six from barbican import api from barbican.api import controllers from barbican.common import hrefs from barbican.common import utils from barbican.common import validators from barbican import i18n as u from barbican.model import models from barbican.model import repositories as repo LOG = utils.getLogger(__name__) def _convert_acl_to_response_format(acl, acls_dict): fields = acl.to_dict_fields() operation = fields['operation'] acl_data = {} # dict for each acl operation data acl_data['project-access'] = fields['project_access'] acl_data['users'] = fields.get('users', []) acl_data['created'] = fields['created'] acl_data['updated'] = fields['updated'] acls_dict[operation] = acl_data DEFAULT_ACL = {'read': {'project-access': True}} class SecretACLsController(controllers.ACLMixin): """Handles SecretACL requests by a given secret id.""" def __init__(self, secret): self.secret = secret self.secret_project_id = self.secret.project.external_id self.acl_repo = repo.get_secret_acl_repository() self.validator = validators.ACLValidator() def get_acl_tuple(self, req, **kwargs): d = {'project_id': self.secret_project_id, 'creator_id': self.secret.creator_id} return 'secret', d @pecan.expose(generic=True) def index(self, **kwargs): pecan.abort(405) # HTTP 405 Method Not Allowed as default @index.when(method='GET', template='json') @controllers.handle_exceptions(u._('SecretACL(s) retrieval')) @controllers.enforce_rbac('secret_acls:get') def on_get(self, external_project_id, **kw): LOG.debug('Start secret ACL on_get ' 'for secret-ID %s:', self.secret.id) return self._return_acl_list_response(self.secret.id) @index.when(method='PATCH', template='json') @controllers.handle_exceptions(u._('SecretACL(s) Update')) @controllers.enforce_rbac('secret_acls:put_patch') @controllers.enforce_content_types(['application/json']) def on_patch(self, external_project_id, **kwargs): """Handles update of existing secret acl requests. At least one secret ACL needs to exist for update to proceed. In update, multiple operation ACL payload can be specified as mentioned in sample below. A specific ACL can be updated by its own id via SecretACLController patch request. { "read":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a", "20b63d71f90848cf827ee48074f213b7", "c7753f8da8dc4fbea75730ab0b6e0ef4" ] }, "write":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a" ], "project-access":true } } """ data = api.load_body(pecan.request, validator=self.validator) LOG.debug('Start on_patch...%s', data) existing_acls_map = {acl.operation: acl for acl in self.secret.secret_acls} for operation in six.moves.filter(lambda x: data.get(x), validators.ACL_OPERATIONS): project_access = data[operation].get('project-access') user_ids = data[operation].get('users') s_acl = None if operation in existing_acls_map: # update if matching acl exists s_acl = existing_acls_map[operation] if project_access is not None: s_acl.project_access = project_access else: s_acl = models.SecretACL(self.secret.id, operation=operation, project_access=project_access) self.acl_repo.create_or_replace_from(self.secret, secret_acl=s_acl, user_ids=user_ids) acl_ref = '{0}/acl'.format( hrefs.convert_secret_to_href(self.secret.id)) return {'acl_ref': acl_ref} @index.when(method='PUT', template='json') @controllers.handle_exceptions(u._('SecretACL(s) Update')) @controllers.enforce_rbac('secret_acls:put_patch') @controllers.enforce_content_types(['application/json']) def on_put(self, external_project_id, **kwargs): """Handles update of existing secret acl requests. Replaces existing secret ACL(s) with input ACL(s) data. Existing ACL operation not specified in input are removed as part of update. For missing project-access in ACL, true is used as default. In update, multiple operation ACL payload can be specified as mentioned in sample below. A specific ACL can be updated by its own id via SecretACLController patch request. { "read":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a", "20b63d71f90848cf827ee48074f213b7", "c7753f8da8dc4fbea75730ab0b6e0ef4" ] }, "write":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a" ], "project-access":false } } Every secret, by default, has an implicit ACL in case client has not defined an explicit ACL. That default ACL definition, DEFAULT_ACL, signifies that a secret by default has project based access i.e. client with necessary roles on secret project can access the secret. That's why when ACL is added to a secret, it always returns 200 (and not 201) indicating existence of implicit ACL on a secret. """ data = api.load_body(pecan.request, validator=self.validator) LOG.debug('Start on_put...%s', data) existing_acls_map = {acl.operation: acl for acl in self.secret.secret_acls} for operation in six.moves.filter(lambda x: data.get(x), validators.ACL_OPERATIONS): project_access = data[operation].get('project-access', True) user_ids = data[operation].get('users', []) s_acl = None if operation in existing_acls_map: # update if matching acl exists s_acl = existing_acls_map.pop(operation) s_acl.project_access = project_access else: s_acl = models.SecretACL(self.secret.id, operation=operation, project_access=project_access) self.acl_repo.create_or_replace_from(self.secret, secret_acl=s_acl, user_ids=user_ids) # delete remaining existing acls as they are not present in input. for acl in existing_acls_map.values(): self.acl_repo.delete_entity_by_id(entity_id=acl.id, external_project_id=None) acl_ref = '{0}/acl'.format( hrefs.convert_secret_to_href(self.secret.id)) return {'acl_ref': acl_ref} @index.when(method='DELETE', template='json') @controllers.handle_exceptions(u._('SecretACL(s) deletion')) @controllers.enforce_rbac('secret_acls:delete') def on_delete(self, external_project_id, **kwargs): count = self.acl_repo.get_count(self.secret.id) if count > 0: self.acl_repo.delete_acls_for_secret(self.secret) def _return_acl_list_response(self, secret_id): result = self.acl_repo.get_by_secret_id(secret_id) acls_data = {} if result: for acl in result: _convert_acl_to_response_format(acl, acls_data) if not acls_data: acls_data = DEFAULT_ACL.copy() return acls_data class ContainerACLsController(controllers.ACLMixin): """Handles ContainerACL requests by a given container id.""" def __init__(self, container): self.container = container self.container_id = container.id self.acl_repo = repo.get_container_acl_repository() self.container_repo = repo.get_container_repository() self.validator = validators.ACLValidator() self.container_project_id = container.project.external_id def get_acl_tuple(self, req, **kwargs): d = {'project_id': self.container_project_id, 'creator_id': self.container.creator_id} return 'container', d @pecan.expose(generic=True) def index(self, **kwargs): pecan.abort(405) # HTTP 405 Method Not Allowed as default @index.when(method='GET', template='json') @controllers.handle_exceptions(u._('ContainerACL(s) retrieval')) @controllers.enforce_rbac('container_acls:get') def on_get(self, external_project_id, **kw): LOG.debug('Start container ACL on_get ' 'for container-ID %s:', self.container_id) return self._return_acl_list_response(self.container.id) @index.when(method='PATCH', template='json') @controllers.handle_exceptions(u._('ContainerACL(s) Update')) @controllers.enforce_rbac('container_acls:put_patch') @controllers.enforce_content_types(['application/json']) def on_patch(self, external_project_id, **kwargs): """Handles update of existing container acl requests. At least one container ACL needs to exist for update to proceed. In update, multiple operation ACL payload can be specified as mentioned in sample below. A specific ACL can be updated by its own id via ContainerACLController patch request. { "read":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a", "20b63d71f90848cf827ee48074f213b7", "c7753f8da8dc4fbea75730ab0b6e0ef4" ] }, "write":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a" ], "project-access":false } } """ data = api.load_body(pecan.request, validator=self.validator) LOG.debug('Start ContainerACLsController on_patch...%s', data) existing_acls_map = {acl.operation: acl for acl in self.container.container_acls} for operation in six.moves.filter(lambda x: data.get(x), validators.ACL_OPERATIONS): project_access = data[operation].get('project-access') user_ids = data[operation].get('users') if operation in existing_acls_map: # update if matching acl exists c_acl = existing_acls_map[operation] if project_access is not None: c_acl.project_access = project_access else: c_acl = models.ContainerACL(self.container.id, operation=operation, project_access=project_access) self.acl_repo.create_or_replace_from(self.container, container_acl=c_acl, user_ids=user_ids) acl_ref = '{0}/acl'.format( hrefs.convert_container_to_href(self.container.id)) return {'acl_ref': acl_ref} @index.when(method='PUT', template='json') @controllers.handle_exceptions(u._('ContainerACL(s) Update')) @controllers.enforce_rbac('container_acls:put_patch') @controllers.enforce_content_types(['application/json']) def on_put(self, external_project_id, **kwargs): """Handles update of existing container acl requests. Replaces existing container ACL(s) with input ACL(s) data. Existing ACL operation not specified in input are removed as part of update. For missing project-access in ACL, true is used as default. In update, multiple operation ACL payload can be specified as mentioned in sample below. A specific ACL can be updated by its own id via ContainerACLController patch request. { "read":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a", "20b63d71f90848cf827ee48074f213b7", "c7753f8da8dc4fbea75730ab0b6e0ef4" ] }, "write":{ "users":[ "5ecb18f341894e94baca9e8c7b6a824a" ], "project-access":false } } Every container, by default, has an implicit ACL in case client has not defined an explicit ACL. That default ACL definition, DEFAULT_ACL, signifies that a container by default has project based access i.e. client with necessary roles on container project can access the container. That's why when ACL is added to a container, it always returns 200 (and not 201) indicating existence of implicit ACL on a container. """ data = api.load_body(pecan.request, validator=self.validator) LOG.debug('Start ContainerACLsController on_put...%s', data) existing_acls_map = {acl.operation: acl for acl in self.container.container_acls} for operation in six.moves.filter(lambda x: data.get(x), validators.ACL_OPERATIONS): project_access = data[operation].get('project-access', True) user_ids = data[operation].get('users', []) if operation in existing_acls_map: # update if matching acl exists c_acl = existing_acls_map.pop(operation) c_acl.project_access = project_access else: c_acl = models.ContainerACL(self.container.id, operation=operation, project_access=project_access) self.acl_repo.create_or_replace_from(self.container, container_acl=c_acl, user_ids=user_ids) # delete remaining existing acls as they are not present in input. for acl in existing_acls_map.values(): self.acl_repo.delete_entity_by_id(entity_id=acl.id, external_project_id=None) acl_ref = '{0}/acl'.format( hrefs.convert_container_to_href(self.container.id)) return {'acl_ref': acl_ref} @index.when(method='DELETE', template='json') @controllers.handle_exceptions(u._('ContainerACL(s) deletion')) @controllers.enforce_rbac('container_acls:delete') def on_delete(self, external_project_id, **kwargs): count = self.acl_repo.get_count(self.container_id) if count > 0: self.acl_repo.delete_acls_for_container(self.container) def _return_acl_list_response(self, container_id): result = self.acl_repo.get_by_container_id(container_id) acls_data = {} if result: for acl in result: _convert_acl_to_response_format(acl, acls_data) if not acls_data: acls_data = DEFAULT_ACL.copy() return acls_data

41.989333

79

0.618252

1,812

15,746

5.178808

0.129691

0.048487

0.016411

0.024723

0.808824

0.772805

0.765345

0.748188

0.735401

0.730072

0

0.030254

0.294678

15,746

374

80

42.101604

0.814695

0.273593

0

0.663415

0

0.089932

0.0148

0

1

0.082927

false

0

0.04878

0

0.190244

0

null

0

1

0

null

0

6

d661f83e953a22fa7c71d719f617ef546a36e45d

78

py

Python

open_close_mixin/__init__.py

CarlosAdp/open-close-mixin

bc9a62ee0b97f1023b8d82048513137dde0e96e6

[ "MIT" ]

null

open_close_mixin/__init__.py

CarlosAdp/open-close-mixin

bc9a62ee0b97f1023b8d82048513137dde0e96e6

[ "MIT" ]

null

open_close_mixin/__init__.py

CarlosAdp/open-close-mixin

bc9a62ee0b97f1023b8d82048513137dde0e96e6

[ "MIT" ]

null

from .open_close_mixin import * # noqa from .decorators import * # noqa

26

42

0.692308

10

78

5.2

0.7

0.384615

0

0.230769

78

2

43

39

0.866667

0.115385

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d66e4444097bd3e27b1fbe37441ceeed2a87a257

47

py

Python

__init__.py

alissonit/azure_migrate

bc6d5bf6ef290d1bfb0d22854c3429eaf1c074ba

[ "MIT" ]

1

2021-01-14T09:19:34.000Z

__init__.py

mohammedamirk/azure_migrate

bc6d5bf6ef290d1bfb0d22854c3429eaf1c074ba

[ "MIT" ]

null

__init__.py

mohammedamirk/azure_migrate

bc6d5bf6ef290d1bfb0d22854c3429eaf1c074ba

[ "MIT" ]

1

2021-01-14T09:18:41.000Z

from . import credentials from . import migrate

23.5

25

0.808511

6

47

6.333333

0.666667

0.526316

0

0.148936

47

2

26

23.5

0.95

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d68680d3449b966a74617b8ca7322c57e329feb8

630

py

Python

src/cho_util/math/rotation/quaternion.py

yycho0108/cho-util

331efc7aac8cddfb4258620b80cb7fc5f0688d1f

[ "MIT" ]

null

src/cho_util/math/rotation/quaternion.py

yycho0108/cho-util

331efc7aac8cddfb4258620b80cb7fc5f0688d1f

[ "MIT" ]

null

src/cho_util/math/rotation/quaternion.py

yycho0108/cho-util

331efc7aac8cddfb4258620b80cb7fc5f0688d1f

[ "MIT" ]

null

import numpy as np from cho_util.math.rotation import _matrix from cho_util.math.rotation import _quaternion from cho_util.math.rotation import _euler from cho_util.math.rotation import _axis_angle from cho_util.math.rotation._quaternion import * def to_matrix(x, *args, **kwargs): return _matrix.from_quaternion(x, *args, **kwargs) def to_quaternion(x, *args, **kwargs): return _quaternion.from_quaternion(x, *args, **kwargs) def to_euler(x, *args, **kwargs): return _euler.from_quaternion(x, *args, **kwargs) def to_axis_angle(x, *args, **kwargs): return _axis_angle.from_quaternion(x, *args, **kwargs)

26.25

58

0.750794

94

630

4.765957

0.212766

0.089286

0.196429

0.167411

0.566964

0.459821

0.200893

0

0.128571

630

23

59

27.391304

0.816029

0

1

0.285714

false

0

0.428571

0.285714

1

0

null

0

1

0

null

0

1

0

1

0

6

d6a20001c44684d78ac527c61fc8639ae7afe33a

38

py

Python

python/lutmatmul/__init__.py

tbiasi/tom-convwconv

554ec31fe3f3a9e633828e91e42d93f49eb3679e

[ "MIT" ]

null

python/lutmatmul/__init__.py

tbiasi/tom-convwconv

554ec31fe3f3a9e633828e91e42d93f49eb3679e

[ "MIT" ]

null

python/lutmatmul/__init__.py

tbiasi/tom-convwconv

554ec31fe3f3a9e633828e91e42d93f49eb3679e

[ "MIT" ]

null

from .lut_wrapper import MithralMatmul

38

0.894737

5

38

6.6

1

0

0.078947

38

1

38

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d6e2cfaeadef78b0529f4bde23b6a51ba6fbc791

2,777

py

Python

tests/test_curation.py

alex4200/morphology-workflows

aaf79369202939d0e2a86f37818d760893040a34

[ "Apache-2.0" ]

null

tests/test_curation.py

alex4200/morphology-workflows

aaf79369202939d0e2a86f37818d760893040a34

[ "Apache-2.0" ]

null

tests/test_curation.py

alex4200/morphology-workflows

aaf79369202939d0e2a86f37818d760893040a34

[ "Apache-2.0" ]

null

"""Test curation functions.""" from morphology_workflows import curation from . import create_morphology class Test_fix_soma_radius: """Test the function curation.fix_soma_radius().""" def test_nothing_to_fix(self): """No point in soma.""" morph = create_morphology( """ 1 1 0 0 0 1. -1 2 2 1 0 0 1. 1 3 2 2 0 0 1. 2 4 2 3 0 0 1. 3 """, "swc", ) former_radius, new_radius = curation.fix_soma_radius(morph) assert former_radius == 1 assert new_radius is None def test_first_point_in_soma(self): """First point in soma.""" morph = create_morphology( """ 1 1 0 0 0 1.5 -1 2 2 1 0 0 1. 1 3 2 2 0 0 1. 2 4 2 3 0 0 1. 3 """, "swc", ) former_radius, new_radius = curation.fix_soma_radius(morph) assert former_radius == 1.5 assert new_radius is None def test_last_point_in_soma(self): """All points in soma.""" morph = create_morphology( """ 1 1 0 0 0 10 -1 2 2 1 0 0 1. 1 3 2 2 0 0 1. 2 4 2 3 0 0 1. 3 """, "swc", ) former_radius, new_radius = curation.fix_soma_radius(morph) assert former_radius == 10 assert new_radius == 2.5 def test_last_point_closer_than_before_last(self): """All points in soma and the last points is closer than the before last point.""" morph = create_morphology( """ 1 1 0 0 0 10 -1 2 2 1 0 0 1. 1 3 2 3 0 0 1. 2 4 2 2 0 0 1. 3 """, "swc", ) former_radius, new_radius = curation.fix_soma_radius(morph) assert former_radius == 10 assert new_radius == 2.5 def test_two_points(self): """Only 2 points and both in soma.""" morph = create_morphology( """ 1 1 0 0 0 10 -1 2 2 2 0 0 1. 1 3 2 3 0 0 1. 2 """, "swc", ) former_radius, new_radius = curation.fix_soma_radius(morph) assert former_radius == 10 assert new_radius == 2.5 def test_two_points_with_last_closer(self): """Only 2 points and the last points is closer than the before last point.""" morph = create_morphology( """ 1 1 0 0 0 10 -1 2 2 3 0 0 1. 1 3 2 2 0 0 1. 2 """, "swc", ) former_radius, new_radius = curation.fix_soma_radius(morph) assert former_radius == 10 assert new_radius == 2.5

26.961165

90

0.49874

402

2,777

3.263682

0.116915

0.042683

0.041159

0.112043

0.804878

0.753049

0.751524

0.708841

0

0.111043

0.416277

2,777

102

91

27.22549

0.698334

0.111631

0

0.54902

0

0.01052

0

0.235294

1

0.117647

false

0

0.039216

0

0.176471

0

null

0

1

0

null

0

6

ba383b2f4619255e54fdbf61160bbb0870536257

27

py

Python

universaldatatool/nb/edit_local.py

bribroder/python-universaldatatool

343b3c0f137bf94a99941c299f25dac16680afcc

[ "MIT" ]

4

2020-04-30T22:15:43.000Z

2021-01-16T15:48:35.000Z

universaldatatool/nb/edit_local.py

bribroder/python-universaldatatool

343b3c0f137bf94a99941c299f25dac16680afcc

[ "MIT" ]

6

2020-06-22T13:26:32.000Z

2020-08-08T23:59:01.000Z

universaldatatool/nb/edit_local.py

bribroder/python-universaldatatool

343b3c0f137bf94a99941c299f25dac16680afcc

[ "MIT" ]

5

2020-05-25T04:39:41.000Z

2021-08-11T23:59:26.000Z

def edit_local(): pass

9

17

0.62963

4

27

4

1

0

0.259259

27

2

18

13.5

0.8

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

bab00170f9fb517a7b1e363c02feb0758118f7d8

104

py

Python

tests/__init__.py

Arsh0023/netengine

dd0264cfdc03d630774c9e3bfb5c19310444837b

[ "X11" ]

1

2020-08-06T06:38:11.000Z

tests/__init__.py

shildenbrand/NetEngine

86da540aa9f0aeb7448ac74829f4443af119b2b6

[ "X11" ]

null

tests/__init__.py

shildenbrand/NetEngine

86da540aa9f0aeb7448ac74829f4443af119b2b6

[ "X11" ]

null

from .base import * from .dummy import * from .ssh import * from .snmp import * from .bin import *

17.333333

21

0.663462

15

104

4.6

0.466667

0.57971

0

0.240385

104

5

22

20.8

0.873418

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

242e85efbc6206f633e719b5265cf2554d026b8a

9,050

py

Python

tests/test_polygons.py

lycantropos/hypothesis_geometry

23e1638144ffba089eee21eb623b0499713e0b1c

[ "MIT" ]

9

2020-01-16T13:52:16.000Z

2022-03-16T00:01:26.000Z

tests/test_polygons.py

lycantropos/hypothesis_geometry

23e1638144ffba089eee21eb623b0499713e0b1c

[ "MIT" ]

38

2020-01-16T12:08:51.000Z

2021-01-11T11:06:32.000Z

tests/test_polygons.py

lycantropos/hypothesis_geometry

23e1638144ffba089eee21eb623b0499713e0b1c

[ "MIT" ]

1

2020-03-12T10:29:44.000Z

from typing import Tuple import pytest from ground.hints import Scalar from hypothesis import given from hypothesis.errors import HypothesisWarning from hypothesis.strategies import DataObject from hypothesis_geometry.hints import Strategy from hypothesis_geometry.planar import polygons from tests import strategies from tests.utils import (ScalarsLimitsType, SizesPair, contours_do_not_cross_or_overlap, is_contour_counterclockwise, is_contour_non_self_intersecting, is_polygon, is_polygon_strict, polygon_has_coordinates_in_range, polygon_has_coordinates_types, polygon_has_valid_sizes) @given(strategies.scalars_strategies, strategies.concave_contours_sizes_pairs, strategies.polygon_holes_sizes_pairs, strategies.convex_contours_sizes_pairs) def test_basic(coordinates: Strategy[Scalar], sizes_pair: SizesPair, holes_sizes_pair: SizesPair, hole_sizes_pair: SizesPair) -> None: min_size, max_size = sizes_pair min_holes_size, max_holes_size = holes_sizes_pair min_hole_size, max_hole_size = hole_sizes_pair result = polygons(coordinates, min_size=min_size, max_size=max_size, min_holes_size=min_holes_size, max_holes_size=max_holes_size, min_hole_size=min_hole_size, max_hole_size=max_hole_size) assert isinstance(result, Strategy) @given(strategies.data, strategies.scalars_strategy_with_limit_and_type_pairs, strategies.concave_contours_sizes_pairs, strategies.polygon_holes_sizes_pairs, strategies.convex_contours_sizes_pairs) def test_properties(data: DataObject, coordinates_limits_type_pair: Tuple[ScalarsLimitsType, ScalarsLimitsType], sizes_pair: SizesPair, holes_sizes_pair: SizesPair, hole_sizes_pair: SizesPair) -> None: (x_coordinates_limits_type, y_coordinates_limits_type) = coordinates_limits_type_pair ((x_coordinates, (min_x_value, max_x_value)), x_type) = x_coordinates_limits_type ((y_coordinates, (min_y_value, max_y_value)), y_type) = y_coordinates_limits_type min_size, max_size = sizes_pair min_holes_size, max_holes_size = holes_sizes_pair min_hole_size, max_hole_size = hole_sizes_pair strategy = polygons(x_coordinates, y_coordinates, min_size=min_size, max_size=max_size, min_holes_size=min_holes_size, max_holes_size=max_holes_size, min_hole_size=min_hole_size, max_hole_size=max_hole_size) result = data.draw(strategy) assert is_polygon(result) assert polygon_has_valid_sizes(result, min_size=min_size, max_size=max_size, min_holes_size=min_holes_size, max_holes_size=max_holes_size, min_hole_size=min_hole_size, max_hole_size=max_hole_size) assert polygon_has_coordinates_types(result, x_type=x_type, y_type=y_type) assert polygon_has_coordinates_in_range(result, min_x_value=min_x_value, max_x_value=max_x_value, min_y_value=min_y_value, max_y_value=max_y_value) assert is_polygon_strict(result) assert is_contour_non_self_intersecting(result.border) assert all(is_contour_non_self_intersecting(hole) for hole in result.holes) assert contours_do_not_cross_or_overlap(result.holes) assert is_contour_counterclockwise(result.border) assert all(not is_contour_counterclockwise(hole) for hole in result.holes) @given(strategies.data, strategies.scalars_strategies_with_limits_and_types, strategies.concave_contours_sizes_pairs, strategies.polygon_holes_sizes_pairs, strategies.convex_contours_sizes_pairs) def test_same_coordinates(data: DataObject, coordinates_limits_type: ScalarsLimitsType, sizes_pair: SizesPair, holes_sizes_pair: SizesPair, hole_sizes_pair: SizesPair) -> None: (coordinates, (min_value, max_value)), type_ = coordinates_limits_type min_size, max_size = sizes_pair min_holes_size, max_holes_size = holes_sizes_pair min_hole_size, max_hole_size = hole_sizes_pair strategy = polygons(coordinates, min_size=min_size, max_size=max_size, min_holes_size=min_holes_size, max_holes_size=max_holes_size, min_hole_size=min_hole_size, max_hole_size=max_hole_size) result = data.draw(strategy) assert is_polygon(result) assert polygon_has_valid_sizes(result, min_size=min_size, max_size=max_size, min_holes_size=min_holes_size, max_holes_size=max_holes_size, min_hole_size=min_hole_size, max_hole_size=max_hole_size) assert polygon_has_coordinates_types(result, x_type=type_, y_type=type_) assert polygon_has_coordinates_in_range(result, min_x_value=min_value, max_x_value=max_value, min_y_value=min_value, max_y_value=max_value) assert is_polygon_strict(result) assert is_contour_non_self_intersecting(result.border) assert all(is_contour_non_self_intersecting(hole) for hole in result.holes) assert contours_do_not_cross_or_overlap(result.holes) assert is_contour_counterclockwise(result.border) assert all(not is_contour_counterclockwise(hole) for hole in result.holes) @given(strategies.scalars_strategies, strategies.invalid_convex_contours_sizes_pairs) def test_invalid_border_sizes(coordinates: Strategy[Scalar], invalid_sizes_pair: SizesPair) -> None: min_size, max_size = invalid_sizes_pair with pytest.raises(ValueError): polygons(coordinates, min_size=min_size, max_size=max_size) @given(strategies.scalars_strategies, strategies.invalid_polygon_holes_sizes_pairs) def test_invalid_holes_list_sizes(coordinates: Strategy[Scalar], invalid_sizes_pair: SizesPair ) -> None: min_holes_size, max_holes_size = invalid_sizes_pair with pytest.raises(ValueError): polygons(coordinates, min_holes_size=min_holes_size, max_holes_size=max_holes_size) @given(strategies.scalars_strategies, strategies.invalid_convex_contours_sizes_pairs) def test_invalid_holes_sizes(coordinates: Strategy[Scalar], invalid_sizes_pair: SizesPair ) -> None: min_hole_size, max_hole_size = invalid_sizes_pair with pytest.raises(ValueError): polygons(coordinates, min_hole_size=min_hole_size, max_hole_size=max_hole_size) @given(strategies.scalars_strategies, strategies.non_valid_convex_contours_sizes_pairs) def test_non_valid_border_sizes(coordinates: Strategy[Scalar], non_valid_sizes_pair: SizesPair) -> None: min_size, max_size = non_valid_sizes_pair with pytest.warns(HypothesisWarning) as warnings: polygons(coordinates, min_size=min_size, max_size=max_size) assert len(warnings) == 1 @given(strategies.scalars_strategies, strategies.non_valid_convex_contours_sizes_pairs) def test_non_valid_holes_sizes(coordinates: Strategy[Scalar], non_valid_sizes_pair: SizesPair) -> None: min_size, max_size = non_valid_sizes_pair with pytest.warns(HypothesisWarning) as warnings: polygons(coordinates, min_hole_size=min_size, max_hole_size=max_size) assert len(warnings) == 1

41.898148

79

0.613591

983

9,050

5.163784

0.085453

0.074468

0.045508

0.050236

0.86052

0.795311

0.747833

0.72892

0.727541

0

0.000333

0.335912

9,050

215

80

42.093023

0.84426

0

0.651934

0

0.127072

1

0.044199

false

0

0.055249

0

0.099448

0

null

0

1

0

null

0

6

243ab2416f11ff2da6d02262f3c90d40193c5193

4,973

py

Python

pygsti/extras/rpe/rpeconfig_GxPi2_GyPi2_UpDn.py

drewrisinger/pyGSTi

dd4ad669931c7f75e026456470cf33ac5b682d0d

[ "Apache-2.0" ]

1

2021-12-19T15:11:09.000Z

pygsti/extras/rpe/rpeconfig_GxPi2_GyPi2_UpDn.py

drewrisinger/pyGSTi

dd4ad669931c7f75e026456470cf33ac5b682d0d

[ "Apache-2.0" ]

null

pygsti/extras/rpe/rpeconfig_GxPi2_GyPi2_UpDn.py

drewrisinger/pyGSTi

dd4ad669931c7f75e026456470cf33ac5b682d0d

[ "Apache-2.0" ]

null

""" RPE configuration for X(pi/2), Y(pi/2) single qubit model """ #*************************************************************************************************** # Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS). # Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights # in this software. # 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 or in the LICENSE file in the root pyGSTi directory. #*************************************************************************************************** import numpy as _np from . import rpeconfig as _rpeconfig rpeconfig_GxPi2_GyPi2_UpDn_dict = {} rpeconfig_GxPi2_GyPi2_UpDn_dict['fixed_axis_gate_label'] = 'Gx' rpeconfig_GxPi2_GyPi2_UpDn_dict['fixed_axis_label'] = 'X' rpeconfig_GxPi2_GyPi2_UpDn_dict['fixed_axis_target'] = [0, 1, 0, 0] rpeconfig_GxPi2_GyPi2_UpDn_dict['loose_axis_gate_label'] = 'Gy' rpeconfig_GxPi2_GyPi2_UpDn_dict['loose_axis_label'] = 'Y' rpeconfig_GxPi2_GyPi2_UpDn_dict['loose_axis_target'] = [0, 0, 1, 0] rpeconfig_GxPi2_GyPi2_UpDn_dict['auxiliary_axis_gate_label'] = 'Gz' rpeconfig_GxPi2_GyPi2_UpDn_dict['auxiliary_axis_label'] = 'Z' rpeconfig_GxPi2_GyPi2_UpDn_dict['rhoExpressions'] = ["0"] rpeconfig_GxPi2_GyPi2_UpDn_dict['ELabels'] = ["0", "1"] rpeconfig_GxPi2_GyPi2_UpDn_dict['EExpressions'] = ["0", "1"] #rpeconfig_GxPi2_GyPi2_UpDn_dict['spamLabelDict'] = {'plus': (0,0), 'minus': (0,-1) } rpeconfig_GxPi2_GyPi2_UpDn_dict['spamLabelDict'] = {'plus': ('rho0', 'E0'), 'minus': ('rho0', 'remainder')} rpeconfig_GxPi2_GyPi2_UpDn_dict['dn_labels'] = ['1'] rpeconfig_GxPi2_GyPi2_UpDn_dict['up_labels'] = ['0'] rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha'] = _np.pi / 2 rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon'] = _np.pi / 2 rpeconfig_GxPi2_GyPi2_UpDn_dict['theta'] = 0 # This should always be 0. rpeconfig_GxPi2_GyPi2_UpDn_dict['new_epsilon_func'] = lambda epsilon: (epsilon / (_np.pi / 2)) - 1 rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_hat_func'] = lambda xhat, yhat, Nx, Ny: _np.arctan2( (xhat - Nx / 2.) / Nx, -(yhat - Ny / 2.) / Ny) rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_hat_func'] = lambda xhat, yhat, Nx, Ny: _np.arctan2( (xhat - Nx / 2.) / Nx, -(yhat - Ny / 2.) / Ny) rpeconfig_GxPi2_GyPi2_UpDn_dict['Phi_hat_func'] = lambda xhat, yhat, Nx, Ny: _np.arctan2( (xhat - Nx / 2.) / Nx, -(yhat - Ny / 2.) / Ny) rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_cos_prep_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_cos_prep_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_cos_germ_tuple'] = ('Gx',) rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_cos_germ_str'] = 'Gx' rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_cos_meas_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_cos_meas_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_sin_prep_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_sin_prep_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_sin_germ_tuple'] = ('Gx',) rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_sin_germ_str'] = 'Gx' rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_sin_meas_tuple'] = ('Gx',) rpeconfig_GxPi2_GyPi2_UpDn_dict['alpha_sin_meas_str'] = 'Gx' rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_cos_prep_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_cos_prep_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_cos_germ_tuple'] = ('Gy',) rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_cos_germ_str'] = 'Gy' rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_cos_meas_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_cos_meas_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_sin_prep_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_sin_prep_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_sin_germ_tuple'] = ('Gy',) rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_sin_germ_str'] = 'Gy' rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_sin_meas_tuple'] = ('Gy',) rpeconfig_GxPi2_GyPi2_UpDn_dict['epsilon_sin_meas_str'] = 'Gy' rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_cos_prep_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_cos_prep_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_cos_germ_tuple'] = ('Gx', 'Gy', 'Gy', 'Gx', 'Gx', 'Gy', 'Gy', 'Gx') rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_cos_germ_str'] = 'GxGyGyGxGxGyGyGx' rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_cos_meas_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_cos_meas_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_sin_prep_tuple'] = () rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_sin_prep_str'] = '' rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_sin_germ_tuple'] = ('Gx', 'Gy', 'Gy', 'Gx', 'Gx', 'Gy', 'Gy', 'Gx') rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_sin_germ_str'] = 'GxGyGyGxGxGyGyGx' rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_sin_meas_tuple'] = ('Gy',) rpeconfig_GxPi2_GyPi2_UpDn_dict['theta_sin_meas_str'] = 'Gy' rpeconfig_GxPi2_GyPi2_UpDn = _rpeconfig(rpeconfig_GxPi2_GyPi2_UpDn_dict)

63.75641

107

0.748442

729

4,973

4.572016

0.163237

0.256226

0.347735

0.420942

0.80378

0.787579

0.713471

0.614461

0.315632

0.105311

0

0.03937

0.080635

4,973

77

108

64.584416

0.689633

0.167907

0

0.046875

0

0.275594

0.048278

0

1

0

false

0

0.03125

0

0.03125

0

null

1

0

null

0

6

2453bee06881fae176465c24b872184d27215860

32

py

Python

vis_tools/__init__.py

JessikaSmith/AutomatedTrainTestSplit

02d5e65d4a1f21b359884c1307f351fa5e3a2590

[ "MIT" ]

null

vis_tools/__init__.py

JessikaSmith/AutomatedTrainTestSplit

02d5e65d4a1f21b359884c1307f351fa5e3a2590

[ "MIT" ]

null

vis_tools/__init__.py

JessikaSmith/AutomatedTrainTestSplit

02d5e65d4a1f21b359884c1307f351fa5e3a2590

[ "MIT" ]

null

from vis_tools.ga_tools import *

32

0.84375

6

32

4.166667

0.833333

0

0.09375

32

1

32

0.862069

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

79fbb8e2b3e6151112ab4957bf3c24a2edd515ad

17,484

py

Python

tests/api/test_prpoint.py

RUrlus/ModelMetricUncertainty

f401a25dd196d6e4edf4901fcfee4b56ebd7c10b

[ "Apache-2.0" ]

null

tests/api/test_prpoint.py

RUrlus/ModelMetricUncertainty

f401a25dd196d6e4edf4901fcfee4b56ebd7c10b

[ "Apache-2.0" ]

11

2021-12-08T10:34:17.000Z

2022-01-20T13:40:05.000Z

tests/api/test_prpoint.py

RUrlus/ModelMetricUncertainty

f401a25dd196d6e4edf4901fcfee4b56ebd7c10b

[ "Apache-2.0" ]

null

import os import itertools import numpy as np import pytest import scipy.stats as sts import sklearn.metrics as skm from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split import mmu from mmu.commons._testing import generate_test_labels from mmu.commons._testing import greater_equal_tol Y_DTYPES = [ bool, np.bool_, int, np.int32, np.int64, float, np.float32, np.float64, ] YHAT_DTYPES = [ bool, np.bool_, int, np.int32, np.int64, float, np.float32, np.float64, ] PROBA_DTYPES = [ float, np.float32, np.float64, ] def test_PRMU_from_scores(): """Test PRMU.from_scores""" np.random.seed(412) thresholds = np.random.uniform(1e-6, 1-1e-6, 10) for y_dtype, proba_dtype, threshold in itertools.product( Y_DTYPES, PROBA_DTYPES, thresholds ): proba, _, y = generate_test_labels( N=1000, y_dtype=y_dtype, proba_dtype=proba_dtype ) yhat = greater_equal_tol(proba, threshold) sk_conf_mat = skm.confusion_matrix(y, yhat) pr_err = mmu.PRU.from_scores(y=y, scores=proba, threshold=threshold) assert pr_err.conf_mat is not None assert pr_err.conf_mat.dtype == np.dtype(np.int64) prec, rec, _, _ = skm.precision_recall_fscore_support( y, yhat, zero_division=0.0 # type: ignore ) assert pr_err.chi2_scores.shape == (pr_err.n_bins, pr_err.n_bins) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat), ( f"test failed for dtypes: {y_dtype}, {proba_dtype}" f" and threshold: {threshold}" ) def test_PRMU_from_predictions(): """Test PRMU.from_predictions""" for y_dtype, yhat_dtype in itertools.product(Y_DTYPES, YHAT_DTYPES): _, yhat, y = generate_test_labels( N=1000, y_dtype=y_dtype, yhat_dtype=yhat_dtype ) sk_conf_mat = skm.confusion_matrix(y, yhat) pr_err = mmu.PRU.from_predictions(y=y, yhat=yhat) assert pr_err.conf_mat is not None assert pr_err.conf_mat.dtype == np.dtype(np.int64) prec, rec, _, _ = skm.precision_recall_fscore_support( y, yhat, zero_division=0.0 # type: ignore ) assert pr_err.chi2_scores.shape == (pr_err.n_bins, pr_err.n_bins) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat), ( f"test failed for dtypes: {y_dtype}, {yhat_dtype}" ) def test_PRMU_from_confusion_matrix(): """Test PRMU.from_confusion_matrix""" for y_dtype, yhat_dtype in itertools.product(Y_DTYPES, YHAT_DTYPES): _, yhat, y = generate_test_labels( N=1000, y_dtype=y_dtype, yhat_dtype=yhat_dtype ) sk_conf_mat = skm.confusion_matrix(y, yhat) pr_err = mmu.PRU.from_confusion_matrix(sk_conf_mat) prec, rec, _, _ = skm.precision_recall_fscore_support( y, yhat, zero_division=0.0 # type: ignore ) assert pr_err.chi2_scores.shape == (pr_err.n_bins, pr_err.n_bins) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat.flatten()), ( f"test failed for dtypes: {y_dtype}, {yhat_dtype}" ) def test_PRMU_from_classifier(): """Test PRMU.from_classifier""" # generate seeds to be used by sklearn # do not use this in real scenarios, # it's a convenience only used in the tutorial notebooks seeds = mmu.commons.utils.SeedGenerator(234) # generate 2 class dataset X, y = make_classification( n_samples=1000, n_classes=2, random_state=seeds() ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=seeds() ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_scores = model.predict_proba(X_test)[:, 1] np.random.seed(2345) thresholds = np.random.uniform(1e-6, 1-1e-6, 10) for threshold in thresholds: yhat = greater_equal_tol(y_scores, threshold) sk_conf_mat = skm.confusion_matrix(y_test, yhat) pr_err = mmu.PRU.from_classifier( model, X_test, y_test, threshold=threshold ) prec, rec, _, _ = skm.precision_recall_fscore_support( y_test, yhat, zero_division=0.0 # type: ignore ) assert pr_err.chi2_scores.shape == (pr_err.n_bins, pr_err.n_bins) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat), ( f"test failed for threshold: {threshold}" ) def test_PRMU_exceptions(): """Test PRMU exceptions""" proba, _, y = generate_test_labels(N=1000,) yhat = greater_equal_tol(proba, 0.5) pr_err = mmu.PRU.from_scores( y=y, scores=proba, threshold=0.5, n_bins=40 ) assert pr_err.n_bins == 40 assert pr_err.chi2_scores.shape == (40, 40) # n_bins >= 1 with pytest.raises(ValueError): pr_err = mmu.PRU.from_scores( y=y, scores=proba, threshold=0.5, n_bins=-20 ) # n_bins >= 1 with pytest.raises(ValueError): pr_err = mmu.PRU.from_scores( y=y, scores=proba, threshold=0.5, n_bins=0 ) # n_bins must be an int with pytest.raises(TypeError): pr_err = mmu.PRU.from_scores( y=y, scores=proba, threshold=0.5, n_bins=20. ) pr_err = mmu.PRU.from_scores( y=y, scores=proba, threshold=0.5, n_sigmas=1. ) with pytest.raises(TypeError): pr_err = mmu.PRU.from_scores( y=y, scores=proba, threshold=0.5, n_sigmas=[1., ] ) def test_PRMU_ref_chi2(): X, y = make_classification( n_samples=1000, n_classes=2, random_state=1949933174 ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=3437779408 ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_score = model.predict_proba(X_test)[:, 1] pr_err = mmu.PRU.from_scores( y_test, scores=y_score, threshold=0.5, ) ref_path = os.path.join( os.path.abspath(os.path.dirname(__file__)), 'multn_chi2_scores.npy' ) ref_chi2_scores = np.load(ref_path) assert np.allclose(pr_err.chi2_scores, ref_chi2_scores) def test_PRMU_compute_score_for(): X, y = make_classification( n_samples=1000, n_classes=2, random_state=1949933174 ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=3437779408 ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_score = model.predict_proba(X_test)[:, 1] pr_err = mmu.PRU.from_scores( y_test, scores=y_score, threshold=0.5, ) # check the the profile loglikelihood with itself is zero assert ( pr_err.compute_score_for(pr_err.precision, pr_err.recall) < 1e-12 ) ref_path = os.path.join( os.path.abspath(os.path.dirname(__file__)), 'multn_chi2_scores.npy' ) ref_chi2_scores = np.load(ref_path) ref_prec = 0.7852763945527024 ref_rec = 0.8305165343173831 ref_score = pr_err.compute_score_for(ref_prec, ref_rec) assert np.isclose(ref_score, ref_chi2_scores.min()) prec = np.linspace(0, 1, 100) rec = prec[::-1].copy() scores = pr_err.compute_score_for(prec, rec) assert scores.size == prec.size assert np.isnan(scores).sum() == 0 def test_PRMU_compute_pvalue_for(): X, y = make_classification( n_samples=1000, n_classes=2, random_state=1949933174 ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=3437779408 ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_score = model.predict_proba(X_test)[:, 1] pr_err = mmu.PRU.from_scores( y_test, scores=y_score, threshold=0.5, ) # check the the profile loglikelihood with itself is zero assert ( abs(pr_err.compute_pvalue_for(pr_err.precision, pr_err.recall) - 1) < 1e-12 ) ref_path = os.path.join( os.path.abspath(os.path.dirname(__file__)), 'multn_chi2_scores.npy' ) ref_chi2_scores = np.load(ref_path) ref_prec = 0.7852763945527024 ref_rec = 0.8305165343173831 ref_score = pr_err.compute_pvalue_for(ref_prec, ref_rec) assert np.isclose(ref_score, sts.chi2.sf(ref_chi2_scores.min(), 2)) prec = np.linspace(0, 1, 100) rec = prec[::-1].copy() scores = pr_err.compute_score_for(prec, rec) assert scores.size == prec.size assert np.isnan(scores).sum() == 0 def test_PREU_ref_cov(): """Test PREU.from_scores""" # generate 2 class dataset X, y = make_classification( n_samples=1000, n_classes=2, random_state=1949933174 ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=3437779408 ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_score = model.predict_proba(X_test)[:, 1] pr_err = mmu.PRU.from_scores( y_test, scores=y_score, threshold=0.5, method='bvn' ) ref_cov_mat = np.asarray([0.00064625, 0.00011629, 0.00011629, 0.00057463]) assert np.isclose(ref_cov_mat, pr_err.cov_mat.flatten(), rtol=7e-3).all() def test_PREU_from_scores(): """Test PREU.from_scores""" np.random.seed(43) thresholds = np.random.uniform(1e-6, 1-1e-6, 10) for y_dtype, proba_dtype, threshold in itertools.product( Y_DTYPES, PROBA_DTYPES, thresholds ): proba, _, y = generate_test_labels( N=1000, y_dtype=y_dtype, proba_dtype=proba_dtype ) yhat = greater_equal_tol(proba, threshold) sk_conf_mat = skm.confusion_matrix(y, yhat) pr_err = mmu.PRU.from_scores( y=y, scores=proba, threshold=threshold, method='bvn' ) assert pr_err.conf_mat is not None assert pr_err.conf_mat.dtype == np.dtype(np.int64) assert pr_err.cov_mat.shape == (2, 2) prec, rec, _, _ = skm.precision_recall_fscore_support( y, yhat, zero_division=0.0 # type: ignore ) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat), ( f"test failed for dtypes: {y_dtype}, {proba_dtype}" f" and threshold: {threshold}" ) def test_PREU_from_predictions(): """Test PREU.from_predictions""" for y_dtype, yhat_dtype in itertools.product(Y_DTYPES, YHAT_DTYPES): _, yhat, y = generate_test_labels( N=1000, y_dtype=y_dtype, yhat_dtype=yhat_dtype ) sk_conf_mat = skm.confusion_matrix(y, yhat) pr_err = mmu.PRU.from_predictions(y=y, yhat=yhat, method='bvn') assert pr_err.conf_mat is not None assert pr_err.conf_mat.dtype == np.dtype(np.int64) prec, rec, _, _ = skm.precision_recall_fscore_support( y, yhat, zero_division=0.0 # type: ignore ) assert pr_err.cov_mat.shape == (2, 2) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat), ( f"test failed for dtypes: {y_dtype}, {yhat_dtype}" ) def test_PREU_from_confusion_matrix(): """Test PREU.from_confusion_matrix""" for y_dtype, yhat_dtype in itertools.product(Y_DTYPES, YHAT_DTYPES): _, yhat, y = generate_test_labels( N=1000, y_dtype=y_dtype, yhat_dtype=yhat_dtype ) sk_conf_mat = skm.confusion_matrix(y, yhat) pr_err = mmu.PRU.from_confusion_matrix(sk_conf_mat, method='bvn') prec, rec, _, _ = skm.precision_recall_fscore_support( y, yhat, zero_division=0.0 # type: ignore ) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat.flatten()), ( f"test failed for dtypes: {y_dtype}, {yhat_dtype}" ) def test_PREU_from_classifier(): """Test PREU.from_classifier""" # generate seeds to be used by sklearn # do not use this in real scenarios, # it's a convenience only used in the tutorial notebooks seeds = mmu.commons.utils.SeedGenerator(234) # generate 2 class dataset X, y = make_classification( n_samples=1000, n_classes=2, random_state=seeds() ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=seeds() ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_scores = model.predict_proba(X_test)[:, 1] thresholds = np.random.uniform(0, 1, 10) for threshold in thresholds: yhat = greater_equal_tol(y_scores, threshold) sk_conf_mat = skm.confusion_matrix(y_test, yhat) pr_err = mmu.PRU.from_classifier( model, X_test, y_test, threshold=threshold, method='bvn' ) prec, rec, _, _ = skm.precision_recall_fscore_support( y_test, yhat, zero_division=0.0 # type: ignore ) assert np.isclose(pr_err.precision, prec[1]) assert np.isclose(pr_err.recall, rec[1]) assert np.array_equal(pr_err.conf_mat, sk_conf_mat), ( f"test failed for threshold: {threshold}" ) def test_PREU_compute_score_for(): X, y = make_classification( n_samples=1000, n_classes=2, random_state=1949933174 ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=3437779408 ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_score = model.predict_proba(X_test)[:, 1] pr_err = mmu.PRU.from_scores( y_test, scores=y_score, threshold=0.5, method='bvn' ) # check the the profile loglikelihood with itself is zero assert ( pr_err.compute_score_for(pr_err.precision, pr_err.recall) < 1e-12 ) prec = np.linspace(0, 1, 100) rec = prec[::-1].copy() scores = pr_err.compute_score_for(prec, rec) assert scores.size == prec.size assert np.isnan(scores).sum() == 0 def test_PREU_compute_pvalue_for(): X, y = make_classification( n_samples=1000, n_classes=2, random_state=1949933174 ) # split into train/test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=3437779408 ) # fit a model model = LogisticRegression(solver='lbfgs') model.fit(X_train, y_train) # predict probabilities, for the positive outcome only y_score = model.predict_proba(X_test)[:, 1] pr_err = mmu.PRU.from_scores( y_test, scores=y_score, threshold=0.5, method='bvn' ) # check the the profile loglikelihood with itself is zero assert ( abs(pr_err.compute_pvalue_for(pr_err.precision, pr_err.recall) - 1) < 1e-12 ) prec = np.linspace(0, 1, 100) rec = prec[::-1].copy() scores = pr_err.compute_score_for(prec, rec) assert scores.size == prec.size assert np.isnan(scores).sum() == 0 def test_PRU_from_scores_with_train(): """Test PREU.from_classifier""" ref_path = os.path.join( os.path.abspath(os.path.dirname(__file__)), 'train_reference_sets.npz' ) ll = np.load(ref_path) y_test = ll.get('y_test') y_score = ll.get('y_score') scores_bs = ll.get('scores_bs') pr_err = mmu.PRU.from_scores_with_train( y_test, scores=y_score, scores_bs=scores_bs, threshold=0.5, obs_axis=0 ) ref_set_test = [0.0006159 , 0.00010284, 0.00010284, 0.00057342] ref_set_train = [2.30241132e-04, -8.41839128e-05, -8.41839128e-05, 3.88862912e-04] assert np.allclose(pr_err.cov_mat.flatten(), ref_set_test, rtol=5e3) assert np.allclose(pr_err.train_cov_mat.flatten(), ref_set_train)

30.566434

87

0.641615

2,532

17,484

4.156793

0.081754

0.04323

0.015962

0.021948

0.904228

0.867648

0.861283

0.858527

0.855012

0

0.046664

0.249886

17,484

571

88

30.619965

0.755852

0.09849

0

0.675355

0

0.037295

0.005556

0

0.137441

1

0.037915

false

0

0.028436

0

0.066351

0

null

0

1

0

null

0

6

0306b211b832bee3f7e8bb9e7eedde1551b0cf74

183

py

Python

template/app/accounts/admin.py

iYasha/django-template-generator

954d3ffda328390118993ef5016a6811d6fe306d

[ "MIT" ]

null

template/app/accounts/admin.py

iYasha/django-template-generator

954d3ffda328390118993ef5016a6811d6fe306d

[ "MIT" ]

null

template/app/accounts/admin.py

iYasha/django-template-generator

954d3ffda328390118993ef5016a6811d6fe306d

[ "MIT" ]

null

from django.contrib import admin from django.contrib.auth.admin import UserAdmin as BaseUserAdmin from accounts.models import * from django.utils.translation import gettext_lazy as _

36.6

64

0.846995

26

183

5.884615

0.576923

0.196078

0.222222

0

0.10929

183

4

65

45.75

0.93865

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

032c0589ffdd67df686d1efae229b5e36681299d

188

py

Python

cesium/features/__init__.py

acrellin/cesium

9d33edc0f9b3a79c68070826c0f390896abe294d

[ "BSD-3-Clause" ]

603

2016-04-15T00:11:07.000Z

2022-03-18T09:10:39.000Z

cesium/features/__init__.py

acrellin/cesium

9d33edc0f9b3a79c68070826c0f390896abe294d

[ "BSD-3-Clause" ]

146

2016-03-17T19:58:24.000Z

2022-02-05T20:36:03.000Z

cesium/features/__init__.py

acrellin/cesium

9d33edc0f9b3a79c68070826c0f390896abe294d

[ "BSD-3-Clause" ]

84

2016-04-13T23:30:58.000Z

2022-03-18T07:34:09.000Z

from .graphs import (CADENCE_FEATS, GENERAL_FEATS, LOMB_SCARGLE_FEATS, generate_dask_graph, feature_categories, dask_feature_graph, feature_tags)

47

70

0.675532

20

188

5.85

0.7

0.205128

0

0.276596

188

3

71

62.666667

0.860294

0

1

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

0367f989c2a90b84925377ba987c842c0e442960

180

py

Python

tour2_constant_bond/po_symb/__init__.py

bmcs-group/bmcs_tutorial

4e008e72839fad8820a6b663a20d3f188610525d

[ "MIT" ]

null

tour2_constant_bond/po_symb/__init__.py

bmcs-group/bmcs_tutorial

4e008e72839fad8820a6b663a20d3f188610525d

[ "MIT" ]

null

tour2_constant_bond/po_symb/__init__.py

bmcs-group/bmcs_tutorial

4e008e72839fad8820a6b663a20d3f188610525d

[ "MIT" ]

null

from .CB_ELF_ELM_Symb import CB_ELF_ELM_Symb from .PO_ELF_RLM_Symb import PO_ELF_RLM_Symb from .PO_ELF_ELM_Symb import PO_ELF_ELM_Symb from .PO_ESF_RLM_Symb import PO_ESF_RLM_Symb

36

44

0.888889

40

180

3.4

0.225

0.176471

0.294118

0.176471

0.235294

0

0.088889

180

5

45

36

0.829268

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

03785d3b7e3e77f5e4243076771845566a206f5b

47

py

Python

qg-model/src/run/__init__.py

alpgokcek/turkish-qg-model

e90050d869958325aeaf639a2b1ff5eb2856e318

[ "MIT" ]

null

qg-model/src/run/__init__.py

alpgokcek/turkish-qg-model

e90050d869958325aeaf639a2b1ff5eb2856e318

[ "MIT" ]

null

qg-model/src/run/__init__.py

alpgokcek/turkish-qg-model

e90050d869958325aeaf639a2b1ff5eb2856e318

[ "MIT" ]

null

from .train import train from .eval import eval

23.5

24

0.808511

8

47

4.75

0.5

0

0.148936

47

2

25

23.5

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3013aae949b5a8f73487bd834b104bc72f062e8d

25

py

Python

main.py

IlyasDiker/TodoApp

7c772d3d707738073ecb0a2420f0465a8211916a

[ "MIT" ]

3

2020-11-21T10:02:24.000Z

2021-11-28T23:54:05.000Z

main.py

IlyasDiker/TodoApp

7c772d3d707738073ecb0a2420f0465a8211916a

[ "MIT" ]

null

main.py

IlyasDiker/TodoApp

7c772d3d707738073ecb0a2420f0465a8211916a

[ "MIT" ]

null

print("Django Server...")

25

0.68

3

25

5.666667

1

0

0.04

25

1

25

0.708333

0

0.615385

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

3016f4f017eb040b7314e92349771afc6bbe6084

176

py

Python

src/brouwers/migration/forms.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

6

2015-03-03T13:23:07.000Z

2021-12-19T18:12:41.000Z

src/brouwers/migration/forms.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

95

2015-02-07T00:55:39.000Z

2022-02-08T20:22:05.000Z

src/brouwers/migration/forms.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

2

2016-03-22T16:53:26.000Z

2019-02-09T22:46:04.000Z

from django import forms class PhotoMigrationForm(forms.Form): start = forms.IntegerField(min_value=0, initial=0) end = forms.IntegerField(min_value=0, initial=2000)

25.142857

55

0.761364

24

176

5.5

0.625

0.257576

0.30303

0.378788

0.5

0

0.046053

0.136364

176

6

56

29.333333

0.822368

0

1

0

false

0

0.25

0

1

0

1

0

null

1

0

1

0

null

0

1

0

6

303a6cd1603cdc0d61a6d5b2953b580075b0bff8

228

py

Python

office365/planner/tasks/task_details.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/planner/tasks/task_details.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/planner/tasks/task_details.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

from office365.entity import Entity class PlannerTaskDetails(Entity): """ The plannerTaskDetails resource represents the additional information about a task. Each task object has a details object. """ pass

22.8

87

0.736842

26

228

6.461538

0.730769

0

0.016667

0.210526

228

9

88

25.333333

0.916667

0.535088

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

304e7da42aa5ce4586c53fa66ceaf334a33e36a5

63

py

Python

src/vulnpy/aiohttp/__init__.py

davidaustinarcher/vulnpy

692703dae701197fd42ae7fc5a9d52f05a501550

[ "MIT" ]

null

src/vulnpy/aiohttp/__init__.py

davidaustinarcher/vulnpy

692703dae701197fd42ae7fc5a9d52f05a501550

[ "MIT" ]

1

2022-02-07T07:43:35.000Z

src/vulnpy/aiohttp/__init__.py

davidaustinarcher/vulnpy

692703dae701197fd42ae7fc5a9d52f05a501550

[ "MIT" ]

1

2022-01-12T02:50:14.000Z

from .vulnerable_routes import vulnerable_routes # noqa: F401

31.5

62

0.825397

8

63

6.25

0.75

0.64

0

0.054545

0.126984

63

1

63

0.854545

0.15873

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

306d8ef77e3bf60dffba737cc6884a271353d303

82

py

Python

test_plus.py

denisrosset/sphinx_example

aa75c4beb4febd616e638e83f8727a11aab7219f

[ "MIT" ]

null

test_plus.py

denisrosset/sphinx_example

aa75c4beb4febd616e638e83f8727a11aab7219f

[ "MIT" ]

null

test_plus.py

denisrosset/sphinx_example

aa75c4beb4febd616e638e83f8727a11aab7219f

[ "MIT" ]

null

from sphinx_example import * def test_plus() -> None: assert plus(2,3) == 5

13.666667

28

0.646341

13

82

3.923077

0.923077

0

0.046875

0.219512

82

5

29

16.4

0.75

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

062a2cad8cd74e9169038b4b1c22235a19967b60

175

py

Python

railguns/rest_framework_jwt/utils.py

larryhq/railguns-1

d76367a2d12f2330f8a370fba5e3c07013a763f6

[ "MIT" ]

null

railguns/rest_framework_jwt/utils.py

larryhq/railguns-1

d76367a2d12f2330f8a370fba5e3c07013a763f6

[ "MIT" ]

1

2019-11-18T02:03:04.000Z

railguns/rest_framework_jwt/utils.py

larryhq/railguns-1

d76367a2d12f2330f8a370fba5e3c07013a763f6

[ "MIT" ]

1

2020-07-23T16:58:09.000Z

from ..rest_framework.serializers import UserCreatedSerializer def jwt_response_payload_handler(token, user=None, request=None): return UserCreatedSerializer(user).data

29.166667

65

0.834286

20

175

7.1

0.85

0

0.091429

175

5

66

35

0.893082

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

23364bfbd6142155ffc349de84299db69c84b34e

119

py

Python

src/onceml/utils/time.py

lzmchina/OnceML

f30d9037d2e492d8d45b858f2be3b27fc5258356

[ "MIT" ]

1

2022-01-01T07:15:03.000Z

src/onceml/utils/time.py

lzmchina/OnceML

f30d9037d2e492d8d45b858f2be3b27fc5258356

[ "MIT" ]

null

src/onceml/utils/time.py

lzmchina/OnceML

f30d9037d2e492d8d45b858f2be3b27fc5258356

[ "MIT" ]

null

import calendar import time def get_timestamp(): '''获得一个秒的时间戳 ''' return calendar.timegm(time.gmtime())

11.9

41

0.663866

13

119

6

0.769231

0

0.210084

119

9

42

13.222222

0.829787

0.07563

0

1

0.25

true

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

236837ea43f9e4d8bb33367a3d21625d89c42964

21

py

Python

msgtools/__init__.py

JohannesLiu/Apollo-Cyber-Parser

ef7e99ea29b2586a72c4cf0b74c18370102be65c

[ "MIT" ]

2

2022-03-11T07:45:59.000Z

2022-03-31T17:38:04.000Z

msgtools/__init__.py

JohannesLiu/Apollo-Cyber-Parser

ef7e99ea29b2586a72c4cf0b74c18370102be65c

[ "MIT" ]

1

2022-03-20T12:22:49.000Z

2022-03-31T17:47:06.000Z

msgtools/__init__.py

JohannesLiu/Apollo-Cyber-Parser

ef7e99ea29b2586a72c4cf0b74c18370102be65c

[ "MIT" ]

null

from .Parser import *

21

0.761905

3

21

5.333333

1

0

0.142857

21

1

21

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

88ccf5c775521f3a73f27163ca84292e3e32930d

137

py

Python

src/main.py

BWAI-SWmaestro/BWAI_Crawler

b7402b9753dfcbe5189cca12f9446b09820c61d6

[ "MIT" ]

null

src/main.py

BWAI-SWmaestro/BWAI_Crawler

b7402b9753dfcbe5189cca12f9446b09820c61d6

[ "MIT" ]

1

2021-06-02T03:57:49.000Z

src/main.py

BWAI-SWmaestro/BWAI_Crawler

b7402b9753dfcbe5189cca12f9446b09820c61d6

[ "MIT" ]

null

from crawler.crawler_ilbe import run_ilbe from crawler.crawler_dc import run_dc if __name__ == "__main__": run_ilbe() #run_dc()

19.571429

41

0.744526

21

137

4.190476

0.428571

0.25

0.409091

0

0.167883

137

6

42

22.833333

0.77193

0.058394

0

0.0625

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

002a31835404c3d513dad33a19fba974b9927e22

14,541

py

Python

tests/api/v3_1_1/test_node_deployment.py

CiscoISE/ciscoisesdk

860b0fc7cc15d0c2a39c64608195a7ab3d5f4885

[ "MIT" ]

36

2021-05-18T16:24:19.000Z

2022-03-05T13:44:41.000Z

tests/api/v3_1_1/test_node_deployment.py

CiscoISE/ciscoisesdk

860b0fc7cc15d0c2a39c64608195a7ab3d5f4885

[ "MIT" ]

15

2021-06-08T19:03:37.000Z

2022-02-25T14:47:33.000Z

tests/api/v3_1_1/test_node_deployment.py

CiscoISE/ciscoisesdk

860b0fc7cc15d0c2a39c64608195a7ab3d5f4885

[ "MIT" ]

6

2021-06-10T09:32:01.000Z

2022-01-12T08:34:39.000Z

# -*- coding: utf-8 -*- """IdentityServicesEngineAPI node_deployment API fixtures and tests. Copyright (c) 2021 Cisco and/or its affiliates. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import pytest from fastjsonschema.exceptions import JsonSchemaException from ciscoisesdk.exceptions import MalformedRequest from ciscoisesdk.exceptions import ciscoisesdkException from tests.environment import IDENTITY_SERVICES_ENGINE_VERSION pytestmark = pytest.mark.skipif(IDENTITY_SERVICES_ENGINE_VERSION != '3.1.1', reason='version does not match') def is_valid_get_nodes(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_51b95cb82a8954c5a785140a9a8f3156_v3_1_1').validate(obj.response) return True def get_nodes(api): endpoint_result = api.node_deployment.get_nodes( filter='value1,value2', filter_type='string' ) return endpoint_result @pytest.mark.node_deployment def test_get_nodes(api, validator): try: assert is_valid_get_nodes( validator, get_nodes(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def get_nodes_default(api): endpoint_result = api.node_deployment.get_nodes( filter=None, filter_type=None ) return endpoint_result @pytest.mark.node_deployment def test_get_nodes_default(api, validator): try: assert is_valid_get_nodes( validator, get_nodes_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_register_node(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_58d0ee193cc65780af11ed96b1758755_v3_1_1').validate(obj.response) return True def register_node(api): endpoint_result = api.node_deployment.register_node( active_validation=False, allow_cert_import=True, fqdn='string', password='string', payload=None, roles=['string'], services=['string'], user_name='string' ) return endpoint_result @pytest.mark.node_deployment def test_register_node(api, validator): try: assert is_valid_register_node( validator, register_node(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def register_node_default(api): endpoint_result = api.node_deployment.register_node( active_validation=False, allow_cert_import=None, fqdn=None, password=None, payload=None, roles=None, services=None, user_name=None ) return endpoint_result @pytest.mark.node_deployment def test_register_node_default(api, validator): try: assert is_valid_register_node( validator, register_node_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_get_node_details(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_ae8d7c8f33bb52ceb04880845f2f45ba_v3_1_1').validate(obj.response) return True def get_node_details(api): endpoint_result = api.node_deployment.get_node_details( hostname='string' ) return endpoint_result @pytest.mark.node_deployment def test_get_node_details(api, validator): try: assert is_valid_get_node_details( validator, get_node_details(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def get_node_details_default(api): endpoint_result = api.node_deployment.get_node_details( hostname='string' ) return endpoint_result @pytest.mark.node_deployment def test_get_node_details_default(api, validator): try: assert is_valid_get_node_details( validator, get_node_details_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_update_node(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_682c1fa3bf115c77be99b602aca1493b_v3_1_1').validate(obj.response) return True def update_node(api): endpoint_result = api.node_deployment.update_node( active_validation=False, hostname='string', payload=None, roles=['string'], services=['string'] ) return endpoint_result @pytest.mark.node_deployment def test_update_node(api, validator): try: assert is_valid_update_node( validator, update_node(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def update_node_default(api): endpoint_result = api.node_deployment.update_node( active_validation=False, hostname='string', payload=None, roles=None, services=None ) return endpoint_result @pytest.mark.node_deployment def test_update_node_default(api, validator): try: assert is_valid_update_node( validator, update_node_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_delete_node(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_161d26670a205a78800cb50673027a6e_v3_1_1').validate(obj.response) return True def delete_node(api): endpoint_result = api.node_deployment.delete_node( hostname='string' ) return endpoint_result @pytest.mark.node_deployment def test_delete_node(api, validator): try: assert is_valid_delete_node( validator, delete_node(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def delete_node_default(api): endpoint_result = api.node_deployment.delete_node( hostname='string' ) return endpoint_result @pytest.mark.node_deployment def test_delete_node_default(api, validator): try: assert is_valid_delete_node( validator, delete_node_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_make_primary(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_5d2e0f05045c5459824d9f24f2827608_v3_1_1').validate(obj.response) return True def make_primary(api): endpoint_result = api.node_deployment.make_primary( active_validation=False, payload=None ) return endpoint_result @pytest.mark.node_deployment def test_make_primary(api, validator): try: assert is_valid_make_primary( validator, make_primary(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def make_primary_default(api): endpoint_result = api.node_deployment.make_primary( active_validation=False, payload=None ) return endpoint_result @pytest.mark.node_deployment def test_make_primary_default(api, validator): try: assert is_valid_make_primary( validator, make_primary_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_promote_node(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_31351f27497451e4aac524c2d7fc4bf0_v3_1_1').validate(obj.response) return True def promote_node(api): endpoint_result = api.node_deployment.promote_node( active_validation=False, payload=None ) return endpoint_result @pytest.mark.node_deployment def test_promote_node(api, validator): try: assert is_valid_promote_node( validator, promote_node(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def promote_node_default(api): endpoint_result = api.node_deployment.promote_node( active_validation=False, payload=None ) return endpoint_result @pytest.mark.node_deployment def test_promote_node_default(api, validator): try: assert is_valid_promote_node( validator, promote_node_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_make_standalone(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_c63819cf4d3b5854bcbbadbc383236a0_v3_1_1').validate(obj.response) return True def make_standalone(api): endpoint_result = api.node_deployment.make_standalone( active_validation=False, payload=None ) return endpoint_result @pytest.mark.node_deployment def test_make_standalone(api, validator): try: assert is_valid_make_standalone( validator, make_standalone(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def make_standalone_default(api): endpoint_result = api.node_deployment.make_standalone( active_validation=False, payload=None ) return endpoint_result @pytest.mark.node_deployment def test_make_standalone_default(api, validator): try: assert is_valid_make_standalone( validator, make_standalone_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_sync_node(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') json_schema_validate('jsd_4eb4709af3a7528e947ad10d2f2141a8_v3_1_1').validate(obj.response) return True def sync_node(api): endpoint_result = api.node_deployment.sync_node( active_validation=False, hostname='string', payload=None ) return endpoint_result @pytest.mark.node_deployment def test_sync_node(api, validator): try: assert is_valid_sync_node( validator, sync_node(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def sync_node_default(api): endpoint_result = api.node_deployment.sync_node( active_validation=False, hostname='string', payload=None ) return endpoint_result @pytest.mark.node_deployment def test_sync_node_default(api, validator): try: assert is_valid_sync_node( validator, sync_node_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e

28.234951

109

0.691149

1,690

14,541

5.695266

0.116568

0.042078

0.059844

0.037403

0.812883

0.810286

0.805714

0.797818

0.782649

0.753974

0

0.020487

0.231277

14,541

514

110

28.289883

0.84058

0.079843

0

0.675

0

0.066029

0.028939

0

0.135

1

0.1125

false

0.005

0.0175

0

0.22

0.0225

0

null

0

1

0

null

0

6

cc3990bd408f416aec7691870035e9a126eeaa8d

197

py

Python

server/frequency.py

MadJukesInc/WhatTheHellIs.com

5e270037a3d9196df62daebf679c034cbd75b79a

[ "MIT" ]

1

2016-04-24T18:13:58.000Z

server/frequency.py

MadJukesInc/WhatTheHellIs.com

5e270037a3d9196df62daebf679c034cbd75b79a

[ "MIT" ]

3

2015-07-25T03:43:42.000Z

2016-04-24T18:14:35.000Z

server/frequency.py

MadJukesInc/WhatTheHellIs.com

5e270037a3d9196df62daebf679c034cbd75b79a

[ "MIT" ]

null

from textblob import TextBlob def get_noun_frequencies(text): blob = TextBlob(text) return blob.np_counts def get_frequencies(text): blob = TextBlob(text) return blob.word_counts

19.7

31

0.746193

27

197

5.259259

0.481481

0.084507

0.267606

0.380282

0.577465

0

0.182741

197

9

32

21.888889

0.881988

0

0.285714

0

1

0.285714

false

0

0.142857

0

0.714286

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

cc6d9472d734abee46d29837547c3ad5f87bc96b

2,114

py

Python

openpnm/models/physics/electrical_conductance.py

lixuekai2001/OpenPNM

9026f0fed427d37f4caf1a79e4a7684490d52cf6

[ "MIT" ]

2

2019-08-24T09:17:40.000Z

2020-07-05T07:21:21.000Z

openpnm/models/physics/electrical_conductance/_funcs.py

xu-kai-xu/OpenPNM

61d5fc4729a0a29291cf6c53c07c4246e7a13714

[ "MIT" ]

null

openpnm/models/physics/electrical_conductance/_funcs.py

xu-kai-xu/OpenPNM

61d5fc4729a0a29291cf6c53c07c4246e7a13714

[ "MIT" ]

null

from openpnm.models.physics._utils import _poisson_conductance from openpnm.models import _doctxt __all__ = ["generic_electrical", "series_resistors"] @_doctxt def generic_electrical( target, pore_conductivity='pore.electrical_conductivity', throat_conductivity='throat.electrical_conductivity', size_factors='throat.diffusive_size_factors' ): r""" Calculate the electrical conductance of conduits in network, where a conduit is ( 1/2 pore - full throat - 1/2 pore ). See the notes section. Parameters ---------- %(target_blurb)s pore_conductivity : str %(dict_blurb)s pore electrical conductivity throat_conductivity : str %(dict_blurb)s throat electrical conductivity size_factors: str %(dict_blurb)s conduit diffusive size factors Returns ------- %(return_arr)s electrical conductance """ return _poisson_conductance(target=target, pore_conductivity=pore_conductivity, throat_conductivity=throat_conductivity, size_factors=size_factors) def series_resistors( target, pore_conductivity='pore.electrical_conductivity', throat_conductivity='throat.electrical_conductivity', size_factors='throat.diffusive_size_factors' ): r""" Calculate the electrical conductance of conduits in network, where a conduit is ( 1/2 pore - full throat - 1/2 pore ). See the notes section. Parameters ---------- %(target_blurb)s pore_conductivity : str %(dict_blurb)s pore electrical conductivity throat_conductivity : str %(dict_blurb)s throat electrical conductivity size_factors: str %(dict_blurb)s conduit diffusive size factors Returns ------- %(return_arr)s electrical conductance """ return _poisson_conductance(target=target, pore_conductivity=pore_conductivity, throat_conductivity=throat_conductivity, size_factors=size_factors)

30.637681

76

0.662252

219

2,114

6.123288

0.214612

0.098434

0.178971

0.058166

0.87994

0

0.005105

0.258751

2,114

68

77

31.088235

0.85067

0.428098

0

0.692308

0

0.19403

0.162313

0

1

0.076923

false

0

0.076923

0

0.230769

0

null

0

1

0

null

0

6

cc9b536993bc6a82dc40edac2914779505012562

33

py

Python

new.py

ChenOuyang/hangman

e88c7d18fa3c5e92f3d23a3f3be94ed670e4f479

[ "MIT" ]

null

new.py

ChenOuyang/hangman

e88c7d18fa3c5e92f3d23a3f3be94ed670e4f479

[ "MIT" ]

null

new.py

ChenOuyang/hangman

e88c7d18fa3c5e92f3d23a3f3be94ed670e4f479

[ "MIT" ]

null

import os print('I love fishc')

8.25

21

0.69697

6

33

3.833333

1

0

0.181818

33

3

22

11

0.851852

0

0.363636

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cca10ffb5352042314fc72a85cbab6334bbf2085

596

py

Python

calendartools/views/generic/__init__.py

chrischambers/django-calendartools

7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe

[ "BSD-3-Clause" ]

1

2015-12-15T19:12:14.000Z

calendartools/views/generic/__init__.py

chrischambers/django-calendartools

7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe

[ "BSD-3-Clause" ]

null

calendartools/views/generic/__init__.py

chrischambers/django-calendartools

7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe

[ "BSD-3-Clause" ]

null

from calendartools.views.generic.base import View, TemplateView, RedirectView from calendartools.views.generic.dates import (ArchiveIndexView, YearArchiveView, MonthArchiveView, WeekArchiveView, DayArchiveView, TodayArchiveView, DateDetailView) from calendartools.views.generic.detail import DetailView from calendartools.views.generic.edit import CreateView, UpdateView, DeleteView from calendartools.views.generic.list import ListView class GenericViewError(Exception): """A problem in a generic view.""" pass

45.846154

99

0.731544

55

596

7.927273

0.581818

0.194954

0.252294

0.332569

0

0.206376

596

12

100

49.666667

0.921776

0.04698

0

1

0

true

0.111111

0.555556

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

6

aeb0fc60223a23c58c5218ccec0a7895399955d6

234

py

Python

backend/src/core/services/streamer/__init__.py

uesleicarvalhoo/ProjectStore

9b7518eab6b0c21bf7b908cdd9a1b063485c5943

[ "MIT" ]

1

2021-10-10T13:26:44.000Z

backend/src/core/services/streamer/__init__.py

uesleicarvalhoo/Store

9b7518eab6b0c21bf7b908cdd9a1b063485c5943

[ "MIT" ]

null

backend/src/core/services/streamer/__init__.py

uesleicarvalhoo/Store

9b7518eab6b0c21bf7b908cdd9a1b063485c5943

[ "MIT" ]

null

from abc import ABC, abstractclassmethod from src.core.events import EventDescription class Streamer(ABC): @abstractclassmethod def send_event(cls, description: EventDescription, context: str, **data) -> None: pass

23.4

85

0.74359

26

234

6.653846

0.769231

0.254335

0

0.175214

234

9

86

26

0.896373

0

1

0.166667

false

0.166667

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

aeb7cfb5466773ecfb56bdc22c018364cdca93b8

17,177

py

Python

src/pcns_v2/helpers/layer_helpers.py

rogerwaleffe/Principal-Component-Networks

018a288a120cbdb9db5792432c147d9e0a74ecdc

[ "Apache-2.0" ]

null

src/pcns_v2/helpers/layer_helpers.py

rogerwaleffe/Principal-Component-Networks

018a288a120cbdb9db5792432c147d9e0a74ecdc

[ "Apache-2.0" ]

null

src/pcns_v2/helpers/layer_helpers.py

rogerwaleffe/Principal-Component-Networks

018a288a120cbdb9db5792432c147d9e0a74ecdc

[ "Apache-2.0" ]

null

import tensorflow as tf class DenseTransformLayer(tf.keras.layers.Layer): def __init__(self, units, mu, Vt, Vb, Wt, Wb, bp, include_offset=False, train_top_basis='NO', add_bias_if_nec=True, name=None, **kwargs): super(DenseTransformLayer, self).__init__(name=name) assert train_top_basis in ['NO', 'YES', 'AS_KERNEL'], 'train_top_basis must be in [NO, YES, AS_KERNEL]' should_train = False if train_top_basis == 'NO' else True regularizer = kwargs.get('kernel_regularizer', None) if train_top_basis == 'AS_KERNEL' else None constraint = kwargs.get('kernel_constraint', None) if train_top_basis == 'AS_KERNEL' else None regularizer = tf.keras.regularizers.get(regularizer) constraint = tf.keras.constraints.get(constraint) if mu is not None: shape = [1, mu.shape[0]] if tf.is_tensor(mu) else mu initializer = cift(tf.expand_dims(mu, 0)) if tf.is_tensor(mu) else tf.keras.initializers.get('zeros') self.mu = self.add_weight('mu', shape=shape, initializer=initializer, regularizer=regularizer, constraint=constraint, dtype=self.dtype, trainable=should_train) else: self.mu = None shape = Vt.shape if tf.is_tensor(Vt) else Vt initializer = cift(Vt) if tf.is_tensor(Vt) else tf.keras.initializers.get('zeros') self.Vt = self.add_weight('Vt', shape=shape, initializer=initializer, regularizer=regularizer, constraint=constraint, dtype=self.dtype, trainable=should_train) if Vb is not None: shape = Vb.shape if tf.is_tensor(Vb) else Vb initializer = cift(Vb) if tf.is_tensor(Vb) else tf.keras.initializers.get('zeros') self.Vb = self.add_weight('Vb', shape=shape, initializer=initializer, regularizer=None, constraint=None, dtype=self.dtype, trainable=False) shape = Wb.shape if tf.is_tensor(Wb) else Wb initializer = cift(Wb) if tf.is_tensor(Wb) else tf.keras.initializers.get('zeros') self.Wb = self.add_weight('Wb', shape=shape, initializer=initializer, regularizer=None, constraint=None, dtype=self.dtype, trainable=False) else: self.Vb, self.Wb = None, None self.activation = tf.keras.activations.get(kwargs.pop('activation', None)) use_bias = kwargs.get('use_bias', True) self.previous_layer_used_bias = kwargs.pop('previous_layer_used_bias', use_bias) if self.previous_layer_used_bias is False and mu is not None: if add_bias_if_nec is True: use_bias = True kwargs.update({'use_bias': use_bias}) else: self.mu = None kwargs.pop('kernel_initializer', '') kwargs.pop('bias_initializer', '') k_init = cift(Wt) if Wt is not None else tf.keras.initializers.get('zeros') b_init = cift(tf.squeeze(bp)) if bp is not None else tf.keras.initializers.get('zeros') self.dense_layer = tf.keras.layers.Dense(units, activation=None, kernel_initializer=k_init, bias_initializer=b_init, **kwargs) self.units = units self.include_offset = include_offset self.train_top_basis = train_top_basis self.add_bias_if_nec = add_bias_if_nec def build(self, input_shape): super(DenseTransformLayer, self).build(input_shape) def call(self, inputs, **kwargs): h_top = tf.matmul(inputs, self.Vt) if self.mu is None else tf.matmul(tf.subtract(inputs, self.mu), self.Vt) output = self.dense_layer(h_top) if self.include_offset: h_bot = tf.matmul(inputs, self.Vb) if self.mu is None else tf.matmul(tf.subtract(inputs, self.mu), self.Vb) output += tf.matmul(h_bot, self.Wb) return self.activation(output) def get_config(self): config = super(DenseTransformLayer, self).get_config() config.update({'units': self.units}) config.update({'mu': self.mu.shape if self.mu is not None else None}) config.update({'Vt': self.Vt.shape}) config.update({'Vb': self.Vb.shape if self.Vb is not None else None}) config.update({'Wt': None}) config.update({'Wb': self.Wb.shape if self.Wb is not None else None}) config.update({'bp': None}) config.update({'include_offset': self.include_offset}) config.update({'train_top_basis': self.train_top_basis}) config.update({'add_bias_if_nec': self.add_bias_if_nec}) config.update({'previous_layer_used_bias': self.previous_layer_used_bias}) config.update({ 'activation': tf.keras.activations.serialize(self.activation), 'use_bias': self.dense_layer.use_bias, 'kernel_initializer': None, 'bias_initializer': None, 'kernel_regularizer': tf.keras.regularizers.serialize(self.dense_layer.kernel_regularizer), 'bias_regularizer': tf.keras.regularizers.serialize(self.dense_layer.bias_regularizer), 'activity_regularizer': tf.keras.regularizers.serialize(self.dense_layer.activity_regularizer), 'kernel_constraint': tf.keras.constraints.serialize(self.dense_layer.kernel_constraint), 'bias_constraint': tf.keras.constraints.serialize(self.dense_layer.bias_constraint) }) class Conv2DTransformLayer(tf.keras.layers.Layer): def __init__(self, filters, kernel_size, mu, Vt, Vb, Wt, Wb, bp, include_offset=False, train_top_basis='NO', add_bias_if_nec=True, previous_layer_type=None, name=None, **kwargs): super(Conv2DTransformLayer, self).__init__(name=name) assert train_top_basis in ['NO', 'YES', 'AS_KERNEL'], 'train_top_basis must be in [NO, YES, AS_KERNEL]' should_train = False if train_top_basis == 'NO' else True regularizer = kwargs.get('kernel_regularizer', None) if train_top_basis == 'AS_KERNEL' else None constraint = kwargs.get('kernel_constraint', None) if train_top_basis == 'AS_KERNEL' else None regularizer = tf.keras.regularizers.get(regularizer) constraint = tf.keras.constraints.get(constraint) if mu is not None: shape = [1, mu.shape[0]] if tf.is_tensor(mu) else mu initializer = cift(tf.expand_dims(mu, 0)) if tf.is_tensor(mu) else tf.keras.initializers.get('zeros') self.mu = self.add_weight('mu', shape=shape, initializer=initializer, regularizer=regularizer, constraint=constraint, dtype=self.dtype, trainable=should_train) else: self.mu = None shape = Vt.shape if tf.is_tensor(Vt) else Vt initializer = cift(Vt) if tf.is_tensor(Vt) else tf.keras.initializers.get('zeros') self.Vt = self.add_weight('Vt', shape=shape, initializer=initializer, regularizer=regularizer, constraint=constraint, dtype=self.dtype, trainable=should_train) if Vb is not None: shape = Vb.shape if tf.is_tensor(Vb) else Vb initializer = cift(Vb) if tf.is_tensor(Vb) else tf.keras.initializers.get('zeros') self.Vb = self.add_weight('Vb', shape=shape, initializer=initializer, regularizer=None, constraint=None, dtype=self.dtype, trainable=False) shape = Wb.shape if tf.is_tensor(Wb) else Wb initializer = cift(Wb) if tf.is_tensor(Wb) else tf.keras.initializers.get('zeros') self.Wb = self.add_weight('Wb', shape=shape, initializer=initializer, regularizer=None, constraint=None, dtype=self.dtype, trainable=False) else: self.Vb, self.Wb = None, None # TODO: support tuple kernel sizes if isinstance(kernel_size, (list, tuple)): kernel_size = kernel_size[0] pad_total = kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg self.zero_pad = ((pad_beg, pad_end), (pad_beg, pad_end)) self.zero_pad_layer = tf.keras.layers.ZeroPadding2D(padding=self.zero_pad, data_format=kwargs.get('data_format', None)) self.activation = tf.keras.activations.get(kwargs.pop('activation', None)) use_bias = kwargs.get('use_bias', True) self.previous_layer_used_bias = kwargs.pop('previous_layer_used_bias', use_bias) if self.previous_layer_used_bias is False and mu is not None: if add_bias_if_nec is True: use_bias = True kwargs.update({'use_bias': use_bias}) else: self.mu = None self.previous_layer_padding = kwargs.pop('previous_layer_padding', kwargs.get('padding', 'valid')) kwargs.pop('padding', '') kwargs.pop('kernel_initializer', '') kwargs.pop('bias_initializer', '') k_init = cift(Wt) if Wt is not None else tf.keras.initializers.get('zeros') b_init = cift(bp) if bp is not None else tf.keras.initializers.get('zeros') self.conv_layer = tf.keras.layers.Conv2D(filters, kernel_size, padding='valid', activation=None, kernel_initializer=k_init, bias_initializer=b_init, **kwargs) self.filters = filters self.kernel_size = kernel_size self.include_offset = include_offset self.train_top_basis = train_top_basis self.add_bias_if_nec = add_bias_if_nec self.previous_layer_type = previous_layer_type self.height, self.width, self.pre_transform_depth = None, None, None self.post_transform_top_depth, self.post_transform_bot_depth = None, None def build(self, input_shape): super(Conv2DTransformLayer, self).build(input_shape) self.height = input_shape[-3] + self.zero_pad[0][0] + self.zero_pad[0][1] self.width = input_shape[-2] + self.zero_pad[1][0] + self.zero_pad[1][1] self.pre_transform_depth = input_shape[-1] self.post_transform_top_depth = self.Vt.shape[1] self.post_transform_bot_depth = self.Vb.shape[1] if self.Vb is not None else None def call(self, inputs, **kwargs): inputs = self.zero_pad_layer(inputs) inputs = tf.reshape(inputs, (-1, self.pre_transform_depth)) h_top = tf.matmul(inputs, self.Vt) if self.mu is None else tf.matmul(tf.subtract(inputs, self.mu), self.Vt) h_top = tf.reshape(h_top, (-1, self.height, self.width, self.post_transform_top_depth)) output = self.conv_layer(h_top) if self.include_offset: h_bot = tf.matmul(inputs, self.Vb) if self.mu is None else tf.matmul(tf.subtract(inputs, self.mu), self.Vb) h_bot = tf.reshape(h_bot, (-1, self.height, self.width, self.post_transform_bot_depth)) output += tf.nn.conv2d(h_bot, self.Wb, strides=self.conv_layer.strides, padding='VALID', data_format='NHWC', dilations=None) return self.activation(output) def get_config(self): config = super(Conv2DTransformLayer, self).get_config() config.update({'filters': self.filters}) config.update({'kernel_size': self.kernel_size}) config.update({'mu': self.mu.shape if self.mu is not None else None}) config.update({'Vt': self.Vt.shape}) config.update({'Vb': self.Vb.shape if self.Vb is not None else None}) config.update({'Wt': None}) config.update({'Wb': self.Wb.shape if self.Wb is not None else None}) config.update({'bp': None}) config.update({'include_offset': self.include_offset}) config.update({'train_top_basis': self.train_top_basis}) config.update({'add_bias_if_nec': self.add_bias_if_nec}) config.update({'previous_layer_type': self.previous_layer_type}) config.update({'previous_layer_used_bias': self.previous_layer_used_bias}) config.update({'previous_layer_padding': self.previous_layer_padding}) config.update({ 'strides': self.conv_layer.strides, 'padding': self.conv_layer.padding, 'data_format': self.conv_layer.data_format, 'dilation_rate': self.conv_layer.dilation_rate, 'groups': self.conv_layer.groups, 'activation': tf.keras.activations.serialize(self.activation), 'use_bias': self.conv_layer.use_bias, 'kernel_initializer': None, 'bias_initializer': None, 'kernel_regularizer': tf.keras.regularizers.serialize(self.conv_layer.kernel_regularizer), 'bias_regularizer': tf.keras.regularizers.serialize(self.conv_layer.bias_regularizer), 'activity_regularizer': tf.keras.regularizers.serialize(self.conv_layer.activity_regularizer), 'kernel_constraint': tf.keras.constraints.serialize(self.conv_layer.kernel_constraint), 'bias_constraint': tf.keras.constraints.serialize(self.conv_layer.bias_constraint) }) return config class Conv2DExplicitPadding(tf.keras.layers.Layer): def __init__(self, filters, kernel_size, strides=1, data_format='channels_last', name=None, **kwargs): super(Conv2DExplicitPadding, self).__init__(name=name) # TODO: support tuple strides and kernel sizes if isinstance(strides, (list, tuple)): strides = strides[0] if isinstance(kernel_size, (list, tuple)): kernel_size = kernel_size[0] if strides > 1: pad_total = kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg zero_pad = ((pad_beg, pad_end), (pad_beg, pad_end)) self.zero_pad_layer = tf.keras.layers.ZeroPadding2D(padding=zero_pad, data_format=data_format) self.explicit_pad = True else: self.explicit_pad = False kwargs.pop('padding', '') self.conv_layer = tf.keras.layers.Conv2D(filters, kernel_size, strides=strides, padding=('SAME' if strides == 1 else 'VALID'), data_format=data_format, **kwargs) self.filters = filters self.kernel_size = kernel_size self.strides = strides self.data_format = data_format def build(self, input_shape): super(Conv2DExplicitPadding, self).build(input_shape) def call(self, inputs, **kwargs): if self.explicit_pad: inputs = self.zero_pad_layer(inputs) return self.conv_layer(inputs) def get_config(self): config = super(Conv2DExplicitPadding, self).get_config() config.update({'filters': self.filters}) config.update({'kernel_size': self.kernel_size}) config.update({'strides': self.strides}) config.update({'data_format': self.data_format}) config.update({ 'padding': self.conv_layer.padding, 'dilation_rate': self.conv_layer.dilation_rate, 'groups': self.conv_layer.groups, 'activation': tf.keras.activations.serialize(self.conv_layer.activation), 'use_bias': self.conv_layer.use_bias, 'kernel_initializer': tf.keras.initializers.serialize(tf.keras.initializers.get('zeros')), 'bias_initializer': tf.keras.initializers.serialize(tf.keras.initializers.get('zeros')), 'kernel_regularizer': tf.keras.regularizers.serialize(self.conv_layer.kernel_regularizer), 'bias_regularizer': tf.keras.regularizers.serialize(self.conv_layer.bias_regularizer), 'activity_regularizer': tf.keras.regularizers.serialize(self.conv_layer.activity_regularizer), 'kernel_constraint': tf.keras.constraints.serialize(self.conv_layer.kernel_constraint), 'bias_constraint': tf.keras.constraints.serialize(self.conv_layer.bias_constraint) }) return config class IdentityLayer(tf.keras.layers.Layer): def __init__(self, **kwargs): super(IdentityLayer, self).__init__(**kwargs) def build(self, input_shape): super(IdentityLayer, self).build(input_shape) def call(self, inputs, **kwargs): return inputs class cift(tf.keras.initializers.Initializer): def __init__(self, value=None): # TODO: check value is a Tensor if not none self.value = value def __call__(self, shape, dtype=None): """ Returns a tensor object initialized as specified by the initializer. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. If not provided the dtype of the tensor created will be the type of the initial value. """ if self.value is None: return tf.zeros(shape, dtype=dtype) # TODO: check shape matches shape of value, possibly convert dtype (if dtype not None and diff than value dtype) return self.value

49.788406

120

0.643302

2,206

17,177

4.802811

0.069356

0.033034

0.031902

0.018122

0.804814

0.781784

0.751864

0.74639

0.734214

0.722322

0

0.003466

0.244047

17,177

345

121

49.788406

0.812476

0.027479

0

0.674157

0

0.079834

0.00841

0

0.005797

0.007491

1

0.06367

false

0

0.003745

0.116105

0

null

0

1

0

null

0

6

aebbc24d5889b54b894725a2c5b6cbb3dc851e74

1,069

py

Python

calculation/gmhazard_calc/gmhazard_calc/plots/__init__.py

ucgmsim/gmhazard

d3d90b4c94b3d9605597a3efeccc8523a1e50c0e

[ "MIT" ]

null

calculation/gmhazard_calc/gmhazard_calc/plots/__init__.py

ucgmsim/gmhazard

d3d90b4c94b3d9605597a3efeccc8523a1e50c0e

[ "MIT" ]

8

2021-10-13T02:33:23.000Z

2022-03-29T21:01:08.000Z

calculation/gmhazard_calc/gmhazard_calc/plots/__init__.py

ucgmsim/gmhazard

d3d90b4c94b3d9605597a3efeccc8523a1e50c0e

[ "MIT" ]

null

from .plotly_plotting import plot_uhs as plotly_uhs from .plotly_plotting import plot_hazard as plotly_hazard from .plt_plotting import plot_hazard as plt_hazard from .plt_plotting import plot_uhs as plt_uhs from .plt_plotting import plot_uhs_branches as plt_uhs_branches from .plt_plotting import plot_disagg_src_type as plt_disagg_src_type from .plt_plotting import plot_disagg_epsilon as plt_disagg_epsilon from .plt_plotting import plot_hazard_totals as plt_hazard_totals from .plt_plotting import plot_gms_im_distribution as plt_gms_im_distribution from .plt_plotting import plot_gms_mw_rrup as plt_gms_mw_rrup from .plt_plotting import plot_gms_causal_param as plt_gms_causal_param from .plt_plotting import plot_gms_spectra as plt_gms_spectra from .plt_plotting import plot_gms_disagg_distribution as plt_gms_disagg_distribution from .plt_plotting import plot_gms_available_gm as plt_gms_available_gm from .gmt_plotting import plot_disagg as gmt_disagg from .gmt_plotting import plot_context as gmt_context from .gmt_plotting import plot_site_vs30 as gmt_vs30

53.45

85

0.88681

186

1,069

4.645161

0.155914

0.275463

0.354167

0.291667

0.615741

0.444444

0.092593

0

0.004145

0.097287

1,069

19

86

56.263158

0.891192

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

aec206356fe67fc7126a2428215e4354ceb3ea43

44

py

Python

tests/_utilities/factories/__init__.py

BinSquare/busy-beaver

b8063a7e434eb47e638697719896880781f9783f

[ "MIT" ]

55

2019-05-05T01:20:58.000Z

2022-01-10T18:03:05.000Z

tests/_utilities/factories/__init__.py

BinSquare/busy-beaver

b8063a7e434eb47e638697719896880781f9783f

[ "MIT" ]

222

2019-05-03T16:31:26.000Z

2021-08-28T23:49:03.000Z

tests/_utilities/factories/__init__.py

BinSquare/busy-beaver

b8063a7e434eb47e638697719896880781f9783f

[ "MIT" ]

19

2019-04-27T19:49:32.000Z

2020-06-30T19:52:09.000Z

from .manager import FactoryManager # noqa

22

43

0.795455

5

44

7

1

0

0.159091

44

1

44

0.945946

0.090909

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9dc152a605fa2294806ea6eec72e8f167a12daf7

40

py

Python

kerastuner/engine/logger.py

haifeng-jin/kt-legacy

15686b5e2d25b7094134d68956b2edce5dffa7a0

[ "Apache-2.0" ]

1

2022-03-29T21:49:22.000Z

kerastuner/engine/logger.py

haifeng-jin/kt-legacy

15686b5e2d25b7094134d68956b2edce5dffa7a0

[ "Apache-2.0" ]

null

kerastuner/engine/logger.py

haifeng-jin/kt-legacy

15686b5e2d25b7094134d68956b2edce5dffa7a0

[ "Apache-2.0" ]

1

2022-02-14T18:57:19.000Z

from keras_tuner.engine.logger import *

20

39

0.825

6

40

5.333333

1

0

0.1

40

1

40

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9deb819c8d78b491d6f0191d57db7ba6e497426d

34

py

Python

drawing/drawer/plane/utils/__init__.py

akyruu/blender-cartography-addon

4f34b029d9b6a72619227ab3ceaed9393506934e

[ "Apache-2.0" ]

null

drawing/drawer/plane/utils/__init__.py

akyruu/blender-cartography-addon

4f34b029d9b6a72619227ab3ceaed9393506934e

[ "Apache-2.0" ]

null

drawing/drawer/plane/utils/__init__.py

akyruu/blender-cartography-addon

4f34b029d9b6a72619227ab3ceaed9393506934e

[ "Apache-2.0" ]

null

from . import edge, face, vertice

17

33

0.735294

5

34

5

1

0

0.176471

34

1

34

0.892857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d17e795ae10f8c97965cf89cd463a0d905a32000

53

py

Python

tests/test_runnable.py

jasonqiao36/pingtop

be6696504d856feef9b7d44d7436543c5bfac3ba

[ "MIT" ]

420

2019-04-09T15:34:49.000Z

2022-03-27T01:07:31.000Z

tests/test_runnable.py

jasonqiao36/pingtop

be6696504d856feef9b7d44d7436543c5bfac3ba

[ "MIT" ]

29

2019-04-05T04:38:08.000Z

2021-12-05T04:17:11.000Z

tests/test_runnable.py

jasonqiao36/pingtop

be6696504d856feef9b7d44d7436543c5bfac3ba

[ "MIT" ]

37

2019-04-09T15:36:25.000Z

2021-12-20T03:54:09.000Z

def test_hello(): import pingtop return True

13.25

18

0.679245

7

53

5

1

0

0.264151

53

3

19

17.666667

0.897436

0

1

0.333333

true

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d18af759df0cae63ff5a271f27a4b4824e7aba85

103

py

Python

modulo-08-django-no-python-basico/aula-01/sobre/views.py

leandropinheiroalves/python-bas-to-adv

c1ad02f4f53e12cc1a1d2805fa0b1a3aa13c609f

[ "MIT" ]

null

modulo-08-django-no-python-basico/aula-01/sobre/views.py

leandropinheiroalves/python-bas-to-adv

c1ad02f4f53e12cc1a1d2805fa0b1a3aa13c609f

[ "MIT" ]

null

modulo-08-django-no-python-basico/aula-01/sobre/views.py

leandropinheiroalves/python-bas-to-adv

c1ad02f4f53e12cc1a1d2805fa0b1a3aa13c609f

[ "MIT" ]

null

from django.shortcuts import render def teste(request): return render(request, 'sobre/bla.html')

17.166667

44

0.747573

14

103

5.5

0.857143

0

0.145631

103

5

45

20.6

0.875

0

0.135922

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

d1f3ba57d08b2303f9fcc942f67f403af30f951c

13,812

py

Python

autoarray/simulator/simulator.py

Sketos/PyAutoArray

72dc7e8d1c38786915f82a7e7284239e5ce87624

[ "MIT" ]

null

autoarray/simulator/simulator.py

Sketos/PyAutoArray

72dc7e8d1c38786915f82a7e7284239e5ce87624

[ "MIT" ]

null

autoarray/simulator/simulator.py

Sketos/PyAutoArray

72dc7e8d1c38786915f82a7e7284239e5ce87624

[ "MIT" ]

null

import os from autoarray.util import array_util from autoarray.structures import grids, kernel from autoarray.dataset import imaging, interferometer from autoarray.operators import transformer class ImagingSimulator: def __init__( self, shape_2d, pixel_scales, sub_size, psf, exposure_time, background_level, add_noise=True, noise_if_add_noise_false=0.1, noise_seed=-1, origin=(0.0, 0.0), ): """A class representing a Imaging observation, using the shape of the image, the pixel scale, psf, exposure time, etc. Parameters ---------- shape_2d : (int, int) The shape of the observation. Note that we do not simulator a full Imaging frame (e.g. 2000 x 2000 pixels for \ Hubble imaging), but instead just a cut-out around the strong lens. pixel_scales : float The size of each pixel in arc seconds. psf : PSF An arrays describing the PSF kernel of the image. exposure_time : float The exposure time of an observation using this data_type. background_level : float The level of the background sky of an observationg using this data_type. """ if type(pixel_scales) is float: pixel_scales = (pixel_scales, pixel_scales) self.shape_2d = shape_2d self.pixel_scales = pixel_scales self.sub_size = sub_size self.origin = origin self.psf = psf self.exposure_time = exposure_time self.background_level = background_level self.add_noise = add_noise self.noise_if_add_noise_false = noise_if_add_noise_false self.noise_seed = noise_seed @classmethod def lsst( cls, shape=(101, 101), pixel_scales=0.2, sub_size=8, psf_shape_2d=(31, 31), psf_sigma=0.5, exposure_time=100.0, background_level=1.0, add_noise=True, noise_if_add_noise_false=0.1, noise_seed=-1, ): """Default settings for an observation with the Large Synotpic Survey Telescope. This can be customized by over-riding the default input values.""" psf = kernel.Kernel.from_gaussian( shape_2d=psf_shape_2d, sigma=psf_sigma, pixel_scales=pixel_scales ) return cls( shape_2d=shape, pixel_scales=pixel_scales, sub_size=sub_size, psf=psf, exposure_time=exposure_time, background_level=background_level, add_noise=add_noise, noise_if_add_noise_false=noise_if_add_noise_false, noise_seed=noise_seed, ) @classmethod def euclid( cls, shape=(151, 151), pixel_scales=0.1, sub_size=8, psf_shape_2d=(31, 31), psf_sigma=0.1, exposure_time=565.0, background_level=1.0, add_noise=True, noise_if_add_noise_false=0.1, noise_seed=-1, ): """Default settings for an observation with the Euclid space satellite. This can be customized by over-riding the default input values.""" psf = kernel.Kernel.from_gaussian( shape_2d=psf_shape_2d, sigma=psf_sigma, pixel_scales=pixel_scales ) return cls( shape_2d=shape, pixel_scales=pixel_scales, sub_size=sub_size, psf=psf, exposure_time=exposure_time, background_level=background_level, add_noise=add_noise, noise_if_add_noise_false=noise_if_add_noise_false, noise_seed=noise_seed, ) @classmethod def hst( cls, shape=(251, 251), pixel_scales=0.05, sub_size=8, psf_shape_2d=(31, 31), psf_sigma=0.05, exposure_time=2000.0, background_level=1.0, add_noise=True, noise_if_add_noise_false=0.1, noise_seed=-1, ): """Default settings for an observation with the Hubble Space Telescope. This can be customized by over-riding the default input values.""" psf = kernel.Kernel.from_gaussian( shape_2d=psf_shape_2d, sigma=psf_sigma, pixel_scales=pixel_scales ) return cls( shape_2d=shape, pixel_scales=pixel_scales, sub_size=sub_size, psf=psf, exposure_time=exposure_time, background_level=background_level, add_noise=add_noise, noise_if_add_noise_false=noise_if_add_noise_false, noise_seed=noise_seed, ) @classmethod def hst_up_sampled( cls, shape=(401, 401), pixel_scales=0.03, sub_size=8, psf_shape_2d=(31, 31), psf_sigma=0.05, exposure_time=2000.0, background_level=1.0, add_noise=True, noise_if_add_noise_false=0.1, noise_seed=-1, ): """Default settings for an observation with the Hubble Space Telescope which has been upscaled to a higher \ pixel-scale to better sample the PSF. This can be customized by over-riding the default input values.""" psf = kernel.Kernel.from_gaussian( shape_2d=psf_shape_2d, sigma=psf_sigma, pixel_scales=pixel_scales ) return cls( shape_2d=shape, pixel_scales=pixel_scales, sub_size=sub_size, psf=psf, exposure_time=exposure_time, background_level=background_level, add_noise=add_noise, noise_if_add_noise_false=noise_if_add_noise_false, noise_seed=noise_seed, ) @classmethod def keck_adaptive_optics( cls, shape=(751, 751), pixel_scales=0.01, sub_size=8, psf_shape_2d=(31, 31), psf_sigma=0.025, exposure_time=1000.0, background_level=1.0, add_noise=True, noise_if_add_noise_false=0.1, noise_seed=-1, ): """Default settings for an observation using Keck Adaptive Optics imaging. This can be customized by over-riding the default input values.""" psf = kernel.Kernel.from_gaussian( shape_2d=psf_shape_2d, sigma=psf_sigma, pixel_scales=pixel_scales ) return cls( shape_2d=shape, pixel_scales=pixel_scales, sub_size=sub_size, psf=psf, exposure_time=exposure_time, background_level=background_level, add_noise=add_noise, noise_if_add_noise_false=noise_if_add_noise_false, noise_seed=noise_seed, ) @property def grid(self): return grids.Grid.uniform( shape_2d=self.shape_2d, pixel_scales=self.pixel_scales, sub_size=self.sub_size, origin=self.origin, ) def from_image(self, image, name=None): """ Create a realistic simulated image by applying effects to a plain simulated image. Parameters ---------- name image : ndarray The image before simulating (e.g. the lens and source galaxies before optics blurring and Imaging read-out). pixel_scales: float The scale of each pixel in arc seconds exposure_time_map : ndarray An arrays representing the effective exposure time of each pixel. psf: PSF An arrays describing the PSF the simulated image is blurred with. background_sky_map : ndarray The value of background sky in every image pixel (electrons per second). add_noise: Bool If True poisson noise_maps is simulated and added to the image, based on the total counts in each image pixel noise_seed: int A seed for random noise_maps generation """ return imaging.SimulatedImaging.simulate( image=image, exposure_time=self.exposure_time, psf=self.psf, background_level=self.background_level, add_noise=self.add_noise, noise_if_add_noise_false=self.noise_if_add_noise_false, noise_seed=self.noise_seed, name=name, ) class InterferometerSimulator: def __init__( self, real_space_shape_2d, real_space_pixel_scales, uv_wavelengths, sub_size, exposure_time, background_level, primary_beam=None, noise_sigma=0.1, noise_if_add_noise_false=0.1, noise_seed=-1, origin=(0.0, 0.0), ): """A class representing a Imaging observation, using the shape of the image, the pixel scale, psf, exposure time, etc. Parameters ---------- real_space_shape_2d : (int, int) The shape of the observation. Note that we do not simulator a full Imaging frame (e.g. 2000 x 2000 pixels for \ Hubble imaging), but instead just a cut-out around the strong lens. real_space_pixel_scales : float The size of each pixel in arc seconds. psf : PSF An arrays describing the PSF kernel of the image. exposure_time : float The exposure time of an observation using this data_type. background_level : float The level of the background sky of an observationg using this data_type. """ if type(real_space_pixel_scales) is float: real_space_pixel_scales = (real_space_pixel_scales, real_space_pixel_scales) self.real_space_shape_2d = real_space_shape_2d self.real_space_pixel_scales = real_space_pixel_scales self.uv_wavelengths = uv_wavelengths self.sub_size = sub_size self.origin = origin self.transformer = transformer.Transformer( uv_wavelengths=self.uv_wavelengths, grid_radians=self.grid.in_1d_binned.in_radians, ) self.exposure_time = exposure_time self.background_level = background_level self.primary_beam = primary_beam self.noise_sigma = noise_sigma self.noise_if_add_noise_false = noise_if_add_noise_false self.noise_seed = noise_seed @property def grid(self): return grids.Grid.uniform( shape_2d=self.real_space_shape_2d, pixel_scales=self.real_space_pixel_scales, sub_size=self.sub_size, origin=self.origin, ) @classmethod def sma( cls, real_space_shape_2d=(151, 151), real_space_pixel_scales=(0.05, 0.05), sub_size=8, primary_beam_shape_2d=None, primary_beam_sigma=None, exposure_time=100.0, background_level=1.0, noise_sigma=0.1, noise_if_add_noise_false=0.1, noise_seed=-1, ): """Default settings for an observation with the Large Synotpic Survey Telescope. This can be customized by over-riding the default input values.""" uv_wavelengths_path = "{}/dataset/sma_uv_wavelengths.fits".format( os.path.dirname(os.path.realpath(__file__)) ) uv_wavelengths = array_util.numpy_array_1d_from_fits( file_path=uv_wavelengths_path, hdu=0 ) if primary_beam_shape_2d is not None and primary_beam_sigma is not None: primary_beam = kernel.Kernel.from_gaussian( shape_2d=primary_beam_shape_2d, sigma=primary_beam_sigma, pixel_scales=real_space_pixel_scales, ) else: primary_beam = None return cls( real_space_shape_2d=real_space_shape_2d, real_space_pixel_scales=real_space_pixel_scales, uv_wavelengths=uv_wavelengths, sub_size=sub_size, primary_beam=primary_beam, exposure_time=exposure_time, background_level=background_level, noise_sigma=noise_sigma, noise_if_add_noise_false=noise_if_add_noise_false, noise_seed=noise_seed, ) def from_real_space_image(self, real_space_image): """ Create a realistic simulated image by applying effects to a plain simulated image. Parameters ---------- name real_space_image : ndarray The image before simulating (e.g. the lens and source galaxies before optics blurring and Imaging read-out). pixel_scales: float The scale of each pixel in arc seconds exposure_time_map : ndarray An arrays representing the effective exposure time of each pixel. psf: PSF An arrays describing the PSF the simulated image is blurred with. background_sky_map : ndarray The value of background sky in every image pixel (electrons per second). add_noise: Bool If True poisson noise_maps is simulated and added to the image, based on the total counts in each image pixel noise_seed: int A seed for random noise_maps generation """ return interferometer.SimulatedInterferometer.simulate( real_space_image=real_space_image, real_space_pixel_scales=self.real_space_pixel_scales, exposure_time=self.exposure_time, transformer=self.transformer, primary_beam=self.primary_beam, background_level=self.background_level, noise_sigma=self.noise_sigma, noise_if_add_noise_false=self.noise_if_add_noise_false, noise_seed=self.noise_seed, )

34.272953

123

0.620909

1,750

13,812

4.606286

0.114286

0.073688

0.034735

0.052103

0.800397

0.763181

0.750527

0.738122

0.70562

0.685275

0

0.024099

0.315016

13,812

402

124

34.358209

0.827925

0.27773

0

0.620567

0

0.003648

0

1

0.042553

false

0

0.017731

0.007092

0.102837

0

null

0

1

0

null

0

6

883efe5aff47233a66a4a748cd4e6773c105e664

17,247

py

Python

tests/python/renderer/test__wrap_renderer.py

dotmodules/dm

ec2ebf6c8b9ac707440a81d0f25003af6f0603c2

[ "MIT" ]

null

tests/python/renderer/test__wrap_renderer.py

dotmodules/dm

ec2ebf6c8b9ac707440a81d0f25003af6f0603c2

[ "MIT" ]

null

tests/python/renderer/test__wrap_renderer.py

dotmodules/dm

ec2ebf6c8b9ac707440a81d0f25003af6f0603c2

[ "MIT" ]

null

import pytest from pytest_mock.plugin import MockerFixture from dotmodules.renderer import Colors, WrapRenderer from dotmodules.settings import Settings @pytest.fixture def settings() -> Settings: return Settings() @pytest.fixture def colors() -> Colors: return Colors() @pytest.fixture def wrap_renderer(settings: Settings, colors: Colors) -> WrapRenderer: return WrapRenderer(settings=settings, colors=colors) class TestWrapRenderingWithoutColoringCases: def test__empty_string_remains_empty(self, wrap_renderer: WrapRenderer) -> None: dummy_string = "" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "", ] def test__wrapping_wont_happen_for_shorter_text( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = "short text" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "short text", ] def test__wrapping_will_happen_on_longer_text( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = "longer text" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "longer", "text", ] def test__wrapping_will_happen_on_longer_text_with_multiple_words( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = "a b c d e f g h" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "a b c d e", "f g h", ] def test__longer_word_than_wrap_limit_will_be_an_overhagning_word( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = "extralongword" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "extralongword", ] def test__longer_text_than_wrap_limit_in_context_will_overhang( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = "extralongword it should be left as is" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "extralongword", "it should", "be left as", "is", ] def test__indentation_will_be_kept(self, wrap_renderer: WrapRenderer) -> None: # |----10----| dummy_string = " short text" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ " short", "text", ] def test__indentation_longer_than_the_limit__full_indentation_will_be_kept( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = " short text" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ " short", "text", ] def test__indentation_will_be_kept_before_the_too_long_word( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = " extralongword" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ " extralongword", ] def test__indentation_will_be_kept_before_the_too_long_word_even_if_it_is_long( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = " extralongword" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ " extralongword", ] def test__longer_text_than_wrap_limit_in_context_with_indentation( self, wrap_renderer: WrapRenderer ) -> None: # |----10----| dummy_string = " extralongword it should be left as is" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ " extralongword", "it should", "be left as", "is", ] def test__multiple_input_lines__empty_lines( self, wrap_renderer: WrapRenderer ) -> None: dummy_string = "\n\n\n" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "", "", "", ] def test__multiple_input_lines_can_be_handled( self, wrap_renderer: WrapRenderer ) -> None: dummy_string = "\n".join( [ # |----------20--------| "This is a bit longer text that will be tested.", "", "It also has some empty lines.", "- it contains", "- multiple levels", " - of indentations", "", "Longer lines should be wrapped as it is expected.", ] ) result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=20, print_lines=False ) assert result == [ "This is a bit longer", "text that will be", "tested.", "", "It also has some", "empty lines.", "- it contains", "- multiple levels", " - of indentations", "", "Longer lines should", "be wrapped as it is", "expected.", ] class TestWrapRenderingPrinOutputCases: def test__multiple_input_lines_can_be_handled( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_print = mocker.patch("dotmodules.renderer.print") dummy_string = "\n".join( [ # |----------20--------| "This is a bit longer text that will be tested.", "", "It also has some empty lines.", "- it contains", "- multiple levels", " - of indentations", "", "Longer lines should be wrapped as it is expected.", ] ) wrap_renderer.render(string=dummy_string, indent=False, wrap_limit=20) mock_print.assert_has_calls( [ mocker.call("This is a bit longer"), mocker.call("text that will be"), mocker.call("tested."), mocker.call(""), mocker.call("It also has some"), mocker.call("empty lines."), mocker.call("- it contains"), mocker.call("- multiple levels"), mocker.call(" - of indentations"), mocker.call(""), mocker.call("Longer lines should"), mocker.call("be wrapped as it is"), mocker.call("expected."), ] ) assert mock_print.call_count == 13 class TestWrapRenderingWithColoringTagsCases: def test__coloring_tags_will_be_ignored( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) # |----10----| dummy_string = "<<BOLD>>short text<<RESET>>" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "boldshort textreset", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RESET"), ] ) def test__arbitraty_number_of_coloring_tags_can_be_handled( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) dummy_string = "<<BOLD>><<RED>><<DIM>><<UNDERLINE>>short text<<RESET>>" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "boldreddimunderlineshort textreset", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RED"), mocker.call(tag="DIM"), mocker.call(tag="UNDERLINE"), mocker.call(tag="RESET"), ] ) def test__coloring_tag_will_be_ignored_on_multiline_input_too( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) dummy_string = "<<BOLD>><<RED>>longer<<RESET>> <<BOLD>><<BLUE>>text<<RESET>>" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "boldredlongerreset", "boldbluetextreset", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RED"), mocker.call(tag="RESET"), mocker.call(tag="BLUE"), ] ) def test__coloring_wont_interfere_with_extra_long_words( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) # |----10----| dummy_string = "<<BOLD>>extralongword<<RESET>>" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "boldextralongwordreset", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RESET"), ] ) def test__coloring_wont_interfere_with_extra_long_words_in_context( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) # |----10----| dummy_string = ( "<<BOLD>>extralongword<<RESET>> it should be <<BOLD>>left<<RESET>> as is" ) result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ "boldextralongwordreset", "it should", "be boldleftreset as", "is", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RESET"), ] ) def test__coloring_wont_interfere_with_indented_extra_long_words( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) # |----10----| dummy_string = " <<BOLD>>extralongword<<RESET>>" result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ " boldextralongwordreset", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RESET"), ] ) def test__coloring_wont_interfere_with_indented_extra_long_words_in_context( self, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) # |----10----| dummy_string = ( " <<BOLD>>extralongword<<RESET>> it should be <<BOLD>>left<<RESET>> as is" ) result = wrap_renderer.render( string=dummy_string, indent=False, wrap_limit=10, print_lines=False ) assert result == [ " boldextralongwordreset", "it should", "be boldleftreset as", "is", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RESET"), ] ) class TestWrapRenderingGlobalIndentationCases: def test__global_indentation_will_be_respected_with_global_width( self, settings: Settings, wrap_renderer: WrapRenderer ) -> None: settings.text_wrap_limit = 10 settings.indent = " " # The wrap limit will be shortened by the indentation size. Without # indentation this input won't be wrapped. # |----8---| dummy_string = "short text" result = wrap_renderer.render(string=dummy_string, print_lines=False) assert result == [ " short", " text", ] def test__global_indentation_will_be_respected_with_defined_width( self, settings: Settings, wrap_renderer: WrapRenderer ) -> None: settings.indent = " " # The wrap limit will be shortened by the indentation size. Without # indentation this input won't be wrapped. # |----8---| dummy_string = "short text" result = wrap_renderer.render( string=dummy_string, wrap_limit=10, print_lines=False ) assert result == [ " short", " text", ] def test__global_indentation_will_be_respected_while_colored_with_global_width( self, settings: Settings, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) settings.text_wrap_limit = 10 settings.indent = " " # |----8---| dummy_string = ( "<<BOLD>>extralongword<<RESET>> it should be <<BOLD>>left<<RESET>> as is" ) result = wrap_renderer.render(string=dummy_string, print_lines=False) assert result == [ " boldextralongwordreset", " it", " should", " be boldleftreset", " as is", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RESET"), ] ) def test__global_indentation_will_be_respected_while_colored_with_defined_width( self, settings: Settings, wrap_renderer: WrapRenderer, mocker: MockerFixture ) -> None: mock_load_color_for_tag = mocker.patch( "dotmodules.renderer.ColorAdapter._load_color_for_tag", wraps=lambda tag: tag.lower(), ) settings.indent = " " # |----8---| dummy_string = ( "<<BOLD>>extralongword<<RESET>> it should be <<BOLD>>left<<RESET>> as is" ) result = wrap_renderer.render( string=dummy_string, wrap_limit=10, print_lines=False ) assert result == [ " boldextralongwordreset", " it", " should", " be boldleftreset", " as is", ] mock_load_color_for_tag.assert_has_calls( [ mocker.call(tag="BOLD"), mocker.call(tag="RESET"), ] )

29.890815

86

0.542413

1,678

17,247

5.27056

0.087604

0.069199

0.036635

0.045794

0.859

0.847128

0.835142

0.818408

0.784487

0

0.008055

0.352177

17,247

576

87

29.942708

0.783496

0.043602

0

0.608989

0

0.156964

0.061497

0

0.078652

1

0.062921

false

0

0.008989

0.006742

0.08764

0.060674

0

null

0

1

0

null

0

6

884d4abaa2aff12b7707df3727ea42d2c4a4cc56

2,775

py

Python

test.py

Guaguago/PPLM

c03b184803c3d57851016c788b41f54153547cc4

[ "Apache-2.0" ]

null

test.py

Guaguago/PPLM

c03b184803c3d57851016c788b41f54153547cc4

[ "Apache-2.0" ]

null

test.py

Guaguago/PPLM

c03b184803c3d57851016c788b41f54153547cc4

[ "Apache-2.0" ]

null

import unittest from run_pplm import run_pplm_example class TestMethods(unittest.TestCase): def test_BC_untached(self): with open('test_cases/BC/output', 'w') as file: run_pplm_example( cond_text='Once upon a time', num_samples=1, discrim='sentiment', class_label=3, length=5, # influence random seed=0, stepsize=0.05, sample=True, num_iterations=3, gamma=1, gm_scale=0.9, kl_scale=0.02, verbosity='quiet', file=file, sample_method='BC' ) with open('test_cases/BC/output', 'r') as file: output = file.read() with open('test_cases/BC/known_output', 'r') as file: known_output = file.read() self.assertEqual(output, known_output) def test_BC_VAD_untached(self): with open('test_cases/BC_VAD/output', 'w') as file: run_pplm_example( cond_text='The book', num_samples=1, discrim='sentiment', class_label=3, # very_negative length=5, # influence random seed=0, stepsize=0.05, sample=True, num_iterations=3, gamma=1, gm_scale=0.9, kl_scale=0.02, verbosity='quiet', file=file, sample_method='BC_VAD' ) with open('test_cases/BC_VAD/output', 'r') as file: output = file.read() with open('test_cases/BC_VAD/known_output', 'r') as file: known_output = file.read() self.assertEqual(output, known_output) def test_BC_VAD_ABS_untached(self): with open('test_cases/BC_VAD_ABS/output', 'w') as file: run_pplm_example( cond_text='The book', num_samples=1, discrim='sentiment', class_label=3, # very_negative length=5, # influence random seed=0, stepsize=0.05, sample=True, num_iterations=3, gamma=1, gm_scale=0.9, kl_scale=0.02, verbosity='quiet', file=file, sample_method='BC_VAD_ABS' ) with open('test_cases/BC_VAD_ABS/output', 'r') as file: output = file.read() with open('test_cases/BC_VAD_ABS/known_output', 'r') as file: known_output = file.read() self.assertEqual(output, known_output) unittest.main()

33.433735

69

0.492252

309

2,775

4.190939

0.210356

0.03861

0.083398

0.118147

0.909653

0.894981

0.851737

0.748263

0.721236

0

0.025625

0.409369

2,775

82

70

33.841463

0.764491

0.028108

0

0.697368

0

0.124489

0.072092

0

0.039474

1

0.039474

false

0

0.026316

0

0.078947

0

null

0

1

0

null

0

6

8896bb3e1b11df0f1b5f1b4c6922b52bfa2bec78

175

py

Python

realesrgan/losses/__init__.py

theleokul/Real-ESRGAN

0afbc090d012d729e6cb3ff47a80018d53bce3f6

[ "BSD-3-Clause" ]

null

realesrgan/losses/__init__.py

theleokul/Real-ESRGAN

0afbc090d012d729e6cb3ff47a80018d53bce3f6

[ "BSD-3-Clause" ]

null

realesrgan/losses/__init__.py

theleokul/Real-ESRGAN

0afbc090d012d729e6cb3ff47a80018d53bce3f6

[ "BSD-3-Clause" ]

null

from .losses import GANFeatureMatchingLoss, BCEWithLogitsLoss, PerceptualContextualLoss __all__ = ['GANFeatureMatchingLoss', 'BCEWithLogitsLoss', 'PerceptualContextualLoss']

43.75

87

0.851429

10

175

14.5

0.7

0.537931

0.868966

0

0.068571

175

3

88

58.333333

0.889571

0

0.36

0.262857

0

1

0

false

0

0.5

0

0.5

0

1

0

1

null

1

0

1

0

null

0

1

0

6

31f297a68e016f8b02fa0c0dba04f6d7b20d1da2

176

py

Python

PythonInheritence.py

rvalusa2108/PythonOOP

4329c84c0016cb3d300fd6ca759cf701512efe87

[ "Apache-2.0" ]

null

PythonInheritence.py

rvalusa2108/PythonOOP

4329c84c0016cb3d300fd6ca759cf701512efe87

[ "Apache-2.0" ]

null

PythonInheritence.py

rvalusa2108/PythonOOP

4329c84c0016cb3d300fd6ca759cf701512efe87

[ "Apache-2.0" ]

null

# from PythonOOP import PythonInheritence as PyInh from PythonInheritence import pythoninheritence as PyInh if __name__ == '__main__': pyInheritenceObj = PyInh.myBird()

22

56

0.789773

18

176

7.277778

0.611111

0.351145

0.381679

0.458015

0

0.153409

176

7

57

25.142857

0.879195

0.272727

0

0.064516

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

ee6d786381fbb5c9e3df9fa83f1fbe59553f37ee

223

py

Python

lib/models/__init__.py

vikkio88/pyDsManager

018e08f7db0852f4653c4da6db851551783584a1

[ "MIT" ]

null

lib/models/__init__.py

vikkio88/pyDsManager

018e08f7db0852f4653c4da6db851551783584a1

[ "MIT" ]

null

lib/models/__init__.py

vikkio88/pyDsManager

018e08f7db0852f4653c4da6db851551783584a1

[ "MIT" ]

null

from lib.models.abstract.person import Person from lib.models.player import Player from lib.models.coach import Coach from lib.models.team import Team from lib.models.module import Module from lib.models.match import Match

31.857143

45

0.834081

37

223

5.027027

0.297297

0.225806

0.419355

0

0.107623

223

6

46

37.166667

0.934673

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ee800d75103f49d2f04d95f05721e5d9686a96cd

35

py

Python

garageberry/__init__.py

arcadecoffee/garageberry

66dc89d7eb37e2a5d7e69707b2d36f085a62befc

[ "MIT" ]

null

garageberry/__init__.py

arcadecoffee/garageberry

66dc89d7eb37e2a5d7e69707b2d36f085a62befc

[ "MIT" ]

null

garageberry/__init__.py

arcadecoffee/garageberry

66dc89d7eb37e2a5d7e69707b2d36f085a62befc

[ "MIT" ]

null

from .garageberry import GarageDoor

35

0.885714

4

35

7.75

1

0

0.085714

35

1

35

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c9912431afe0c77c5348756d61b3149c75e6823e

289

py

Python

etna/clustering/hierarchical/__init__.py

Pacman1984/etna

9b3ccb980e576d56858f14aca2e06ce2957b0fa9

[ "Apache-2.0" ]

326

2021-11-18T15:30:50.000Z

2022-03-31T09:44:15.000Z

etna/clustering/hierarchical/__init__.py

Pacman1984/etna

9b3ccb980e576d56858f14aca2e06ce2957b0fa9

[ "Apache-2.0" ]

305

2021-11-17T10:28:31.000Z

2022-03-31T18:05:03.000Z

etna/clustering/hierarchical/__init__.py

Pacman1984/etna

9b3ccb980e576d56858f14aca2e06ce2957b0fa9

[ "Apache-2.0" ]

29

2021-11-21T12:10:48.000Z

2022-03-31T22:55:06.000Z

from etna.clustering.hierarchical.base import ClusteringLinkageMode from etna.clustering.hierarchical.base import HierarchicalClustering from etna.clustering.hierarchical.dtw_clustering import DTWClustering from etna.clustering.hierarchical.euclidean_clustering import EuclideanClustering

57.8

81

0.903114

30

289

8.633333

0.4

0.123552

0.277992

0.46332

0.30888

0

0.055363

289

4

82

72.25

0.948718

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

c9bf7e42227eb3b496da643f78123d631461fee4

169

py

Python

ezotv/views/__init__.py

marcsello/ezotv-frontend

405c440a567e8a0f1577f10d45385f3171398afe

[ "CC0-1.0" ]

null

ezotv/views/__init__.py

marcsello/ezotv-frontend

405c440a567e8a0f1577f10d45385f3171398afe

[ "CC0-1.0" ]

7

2020-01-23T00:50:39.000Z

2020-04-18T20:34:40.000Z

ezotv/views/__init__.py

marcsello/ezotv-frontend

405c440a567e8a0f1577f10d45385f3171398afe

[ "CC0-1.0" ]

null

#!/usr/bin/env python3 from .dashboard_view import DashboardView from .home_view import HomeView from .backups_view import BackupsView from .admin_view import AdminView

28.166667

41

0.840237

24

169

5.75

0.625

0.289855

0

0.006623

0.106509

169

5

42

33.8

0.907285

0.12426

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

c9d4b5b0b4d29f047cc0cb42a233ea9a341ea2ac

22

py

Python

app/LOGS/__init__.py

innovationb1ue/XMU_HealthReport

6ee0c7830a0e30fc9730401585a303873f382bac

[ "MIT" ]

2

2021-09-03T18:13:46.000Z

2022-01-13T08:48:36.000Z

app/LOGS/__init__.py

buuuuuuug/XMU_HealthReport

cb545959eceddf676b34237c38b1ba6f797764f5

[ "MIT" ]

null

app/LOGS/__init__.py

buuuuuuug/XMU_HealthReport

cb545959eceddf676b34237c38b1ba6f797764f5

[ "MIT" ]

1

2021-07-14T09:48:19.000Z

from .logger_ import *

22

0.772727

3

22

5.333333

1

0

0.136364

22

1

22

0.842105

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a0068e492e563a8f36cf9a408da1fc774dbb3579

14,949

py

Python

tests/extensions/test_version.py

emmanuelmathot/pystac

6552327f30658d25972c97024ed71865ed131e52

[ "Apache-2.0" ]

null

tests/extensions/test_version.py

emmanuelmathot/pystac

6552327f30658d25972c97024ed71865ed131e52

[ "Apache-2.0" ]

null

tests/extensions/test_version.py

emmanuelmathot/pystac

6552327f30658d25972c97024ed71865ed131e52

[ "Apache-2.0" ]

null

"""Tests for pystac.extensions.version.""" import datetime import unittest import pystac from pystac.extensions import version from tests.utils import TestCases URL_TEMPLATE: str = 'http://example.com/catalog/%s.json' def make_item(year: int) -> pystac.Item: """Create basic test items that are only slightly different.""" asset_id = f'USGS/GAP/CONUS/{year}' start = datetime.datetime(year, 1, 2) item = pystac.Item(id=asset_id, geometry=None, bbox=None, datetime=start, properties={}) item.set_self_href(URL_TEMPLATE % year) item.ext.enable(pystac.Extensions.VERSION) return item class VersionItemExtTest(unittest.TestCase): version: str = '1.2.3' def setUp(self): super().setUp() self.item = make_item(2011) self.item.ext.enable(pystac.Extensions.VERSION) def test_stac_extensions(self): self.assertEqual([pystac.Extensions.VERSION], self.item.stac_extensions) def test_add_version(self): self.item.ext.version.apply(self.version) self.assertEqual(self.version, self.item.ext.version.version) self.assertNotIn(version.DEPRECATED, self.item.properties) self.assertFalse(self.item.ext.version.deprecated) self.item.validate() def test_version_in_properties(self): self.item.ext.version.apply(self.version, deprecated=True) self.assertIn(version.VERSION, self.item.properties) self.assertIn(version.DEPRECATED, self.item.properties) self.item.validate() def test_add_not_deprecated_version(self): self.item.ext.version.apply(self.version, deprecated=False) self.assertIn(version.DEPRECATED, self.item.properties) self.assertFalse(self.item.ext.version.deprecated) self.item.validate() def test_add_deprecated_version(self): self.item.ext.version.apply(self.version, deprecated=True) self.assertIn(version.DEPRECATED, self.item.properties) self.assertTrue(self.item.ext.version.deprecated) self.item.validate() def test_latest(self): year = 2013 latest = make_item(year) self.item.ext.version.apply(self.version, latest=latest) latest_result = self.item.ext.version.latest self.assertIs(latest, latest_result) expected_href = URL_TEMPLATE % year link = self.item.get_links(version.LATEST)[0] self.assertEqual(expected_href, link.get_href()) self.item.validate() def test_predecessor(self): year = 2010 predecessor = make_item(year) self.item.ext.version.apply(self.version, predecessor=predecessor) predecessor_result = self.item.ext.version.predecessor self.assertIs(predecessor, predecessor_result) expected_href = URL_TEMPLATE % year link = self.item.get_links(version.PREDECESSOR)[0] self.assertEqual(expected_href, link.get_href()) self.item.validate() def test_successor(self): year = 2012 successor = make_item(year) self.item.ext.version.apply(self.version, successor=successor) successor_result = self.item.ext.version.successor self.assertIs(successor, successor_result) expected_href = URL_TEMPLATE % year link = self.item.get_links(version.SUCCESSOR)[0] self.assertEqual(expected_href, link.get_href()) self.item.validate() def test_fail_validate(self): with self.assertRaises(pystac.validation.STACValidationError): self.item.validate() def test_all_links(self): deprecated = True latest = make_item(2013) predecessor = make_item(2010) successor = make_item(2012) self.item.ext.version.apply(self.version, deprecated, latest, predecessor, successor) self.item.validate() def test_full_copy(self): cat = TestCases.test_case_1() # Fetch two items from the catalog item1 = cat.get_item('area-1-1-imagery', recursive=True) item2 = cat.get_item('area-2-2-imagery', recursive=True) # Enable the version extension on each, and link them # as if they are different versions of the same Item item1.ext.enable(pystac.Extensions.VERSION) item2.ext.enable(pystac.Extensions.VERSION) item1.ext.version.apply(version='2.0', predecessor=item2) item2.ext.version.apply(version='1.0', successor=item1, latest=item1) # Make a full copy of the catalog cat_copy = cat.full_copy() # Retrieve the copied version of the items item1_copy = cat_copy.get_item('area-1-1-imagery', recursive=True) item2_copy = cat_copy.get_item('area-2-2-imagery', recursive=True) # Check to see if the version links point to the instances of the # item objects as they should. predecessor = item1_copy.get_single_link(version.PREDECESSOR).target successor = item2_copy.get_single_link(version.SUCCESSOR).target latest = item2_copy.get_single_link(version.LATEST).target self.assertIs(predecessor, item2_copy) self.assertIs(successor, item1_copy) self.assertIs(latest, item1_copy) def test_setting_none_clears_link(self): deprecated = False latest = make_item(2013) predecessor = make_item(2010) successor = make_item(2012) self.item.ext.version.apply(self.version, deprecated, latest, predecessor, successor) self.item.ext.version.latest = None links = self.item.get_links(version.LATEST) self.assertEqual(0, len(links)) self.assertIsNone(self.item.ext.version.latest) self.item.ext.version.predecessor = None links = self.item.get_links(version.PREDECESSOR) self.assertEqual(0, len(links)) self.assertIsNone(self.item.ext.version.predecessor) self.item.ext.version.successor = None links = self.item.get_links(version.SUCCESSOR) self.assertEqual(0, len(links)) self.assertIsNone(self.item.ext.version.successor) def test_multiple_link_setting(self): deprecated = False latest1 = make_item(2013) predecessor1 = make_item(2010) successor1 = make_item(2012) self.item.ext.version.apply(self.version, deprecated, latest1, predecessor1, successor1) year = 2015 latest2 = make_item(year) expected_href = URL_TEMPLATE % year self.item.ext.version.latest = latest2 links = self.item.get_links(version.LATEST) self.assertEqual(1, len(links)) self.assertEqual(expected_href, links[0].get_href()) year = 2009 predecessor2 = make_item(year) expected_href = URL_TEMPLATE % year self.item.ext.version.predecessor = predecessor2 links = self.item.get_links(version.PREDECESSOR) self.assertEqual(1, len(links)) self.assertEqual(expected_href, links[0].get_href()) year = 2014 successor2 = make_item(year) expected_href = URL_TEMPLATE % year self.item.ext.version.successor = successor2 links = self.item.get_links(version.SUCCESSOR) self.assertEqual(1, len(links)) self.assertEqual(expected_href, links[0].get_href()) def make_collection(year: int) -> pystac.Collection: asset_id = f'my/collection/of/things/{year}' start = datetime.datetime(2014, 8, 10) end = datetime.datetime(year, 1, 3, 4, 5) bboxes = [[-180, -90, 180, 90]] spatial_extent = pystac.SpatialExtent(bboxes) temporal_extent = pystac.TemporalExtent([[start, end]]) extent = pystac.Extent(spatial_extent, temporal_extent) collection = pystac.Collection(asset_id, 'desc', extent) collection.set_self_href(URL_TEMPLATE % year) collection.ext.enable(pystac.Extensions.VERSION) return collection class VersionCollectionExtTest(unittest.TestCase): version: str = '1.2.3' def setUp(self): super().setUp() self.collection = make_collection(2011) def test_stac_extensions(self): self.assertEqual([pystac.Extensions.VERSION], self.collection.stac_extensions) def test_add_version(self): self.collection.ext.version.apply(self.version) self.assertEqual(self.version, self.collection.ext.version.version) self.assertNotIn(version.DEPRECATED, self.collection.extra_fields) self.assertFalse(self.collection.ext.version.deprecated) self.collection.validate() def test_version_deprecated(self): self.collection.ext.version.apply(self.version, deprecated=True) self.assertIn(version.VERSION, self.collection.extra_fields) self.assertIn(version.DEPRECATED, self.collection.extra_fields) self.collection.validate() def test_add_not_deprecated_version(self): self.collection.ext.version.apply(self.version, deprecated=False) self.assertIn(version.DEPRECATED, self.collection.extra_fields) self.assertFalse(self.collection.ext.version.deprecated) self.collection.validate() def test_add_deprecated_version(self): self.collection.ext.version.apply(self.version, deprecated=True) self.assertIn(version.DEPRECATED, self.collection.extra_fields) self.assertTrue(self.collection.ext.version.deprecated) self.collection.validate() def test_latest(self): year = 2013 latest = make_collection(year) self.collection.ext.version.apply(self.version, latest=latest) latest_result = self.collection.ext.version.latest self.assertIs(latest, latest_result) expected_href = URL_TEMPLATE % year link = self.collection.get_links(version.LATEST)[0] self.assertEqual(expected_href, link.get_href()) self.collection.validate() def test_predecessor(self): year = 2010 predecessor = make_collection(year) self.collection.ext.version.apply(self.version, predecessor=predecessor) predecessor_result = self.collection.ext.version.predecessor self.assertIs(predecessor, predecessor_result) expected_href = URL_TEMPLATE % year link = self.collection.get_links(version.PREDECESSOR)[0] self.assertEqual(expected_href, link.get_href()) self.collection.validate() def test_successor(self): year = 2012 successor = make_collection(year) self.collection.ext.version.apply(self.version, successor=successor) successor_result = self.collection.ext.version.successor self.assertIs(successor, successor_result) expected_href = URL_TEMPLATE % year link = self.collection.get_links(version.SUCCESSOR)[0] self.assertEqual(expected_href, link.get_href()) self.collection.validate() def test_fail_validate(self): with self.assertRaises(pystac.validation.STACValidationError): self.collection.validate() def test_validate_all(self): deprecated = True latest = make_collection(2013) predecessor = make_collection(2010) successor = make_collection(2012) self.collection.ext.version.apply(self.version, deprecated, latest, predecessor, successor) self.collection.validate() def test_full_copy(self): cat = TestCases.test_case_1() # Fetch two collections from the catalog col1 = cat.get_child('area-1-1', recursive=True) col2 = cat.get_child('area-2-2', recursive=True) # Enable the version extension on each, and link them # as if they are different versions of the same Collection col1.ext.enable(pystac.Extensions.VERSION) col2.ext.enable(pystac.Extensions.VERSION) col1.ext.version.apply(version='2.0', predecessor=col2) col2.ext.version.apply(version='1.0', successor=col1, latest=col1) # Make a full copy of the catalog cat_copy = cat.full_copy() # Retrieve the copied version of the items col1_copy = cat_copy.get_child('area-1-1', recursive=True) col2_copy = cat_copy.get_child('area-2-2', recursive=True) # Check to see if the version links point to the instances of the # col objects as they should. predecessor = col1_copy.get_single_link(version.PREDECESSOR).target successor = col2_copy.get_single_link(version.SUCCESSOR).target latest = col2_copy.get_single_link(version.LATEST).target self.assertIs(predecessor, col2_copy) self.assertIs(successor, col1_copy) self.assertIs(latest, col1_copy) def test_setting_none_clears_link(self): deprecated = False latest = make_collection(2013) predecessor = make_collection(2010) successor = make_collection(2012) self.collection.ext.version.apply(self.version, deprecated, latest, predecessor, successor) self.collection.ext.version.latest = None links = self.collection.get_links(version.LATEST) self.assertEqual(0, len(links)) self.assertIsNone(self.collection.ext.version.latest) self.collection.ext.version.predecessor = None links = self.collection.get_links(version.PREDECESSOR) self.assertEqual(0, len(links)) self.assertIsNone(self.collection.ext.version.predecessor) self.collection.ext.version.successor = None links = self.collection.get_links(version.SUCCESSOR) self.assertEqual(0, len(links)) self.assertIsNone(self.collection.ext.version.successor) def test_multiple_link_setting(self): deprecated = False latest1 = make_collection(2013) predecessor1 = make_collection(2010) successor1 = make_collection(2012) self.collection.ext.version.apply(self.version, deprecated, latest1, predecessor1, successor1) year = 2015 latest2 = make_collection(year) expected_href = URL_TEMPLATE % year self.collection.ext.version.latest = latest2 links = self.collection.get_links(version.LATEST) self.assertEqual(1, len(links)) self.assertEqual(expected_href, links[0].get_href()) year = 2009 predecessor2 = make_collection(year) expected_href = URL_TEMPLATE % year self.collection.ext.version.predecessor = predecessor2 links = self.collection.get_links(version.PREDECESSOR) self.assertEqual(1, len(links)) self.assertEqual(expected_href, links[0].get_href()) year = 2014 successor2 = make_collection(year) expected_href = URL_TEMPLATE % year self.collection.ext.version.successor = successor2 links = self.collection.get_links(version.SUCCESSOR) self.assertEqual(1, len(links)) self.assertEqual(expected_href, links[0].get_href()) if __name__ == '__main__': unittest.main()

38.727979

99

0.685665

1,805

14,949

5.54072

0.093629

0.055994

0.029697

0.046795

0.865014

0.831917

0.777522

0.758724

0.710829

0.649535

0

0.022482

0.211519

14,949

385

100

38.828571

0.825995

0.047495

0

0.532872

0

0.015125

0.003588

0

0.221453

1

0.103806

false

0

0.017301

0

0.141869

0

null

0

1

0

null

0

6

a00d76b2f200eddda1c1831571b6067de469d5aa

152

py

Python

Leetcode/832. Flipping an Image/solution1.py

asanoviskhak/Outtalent

c500e8ad498f76d57eb87a9776a04af7bdda913d

[ "MIT" ]

51

2020-07-12T21:27:47.000Z

2022-02-11T19:25:36.000Z

Leetcode/832. Flipping an Image/solution1.py

CrazySquirrel/Outtalent

8a10b23335d8e9f080e5c39715b38bcc2916ff00

[ "MIT" ]

null

Leetcode/832. Flipping an Image/solution1.py

CrazySquirrel/Outtalent

8a10b23335d8e9f080e5c39715b38bcc2916ff00

[ "MIT" ]

32

2020-07-27T13:54:24.000Z

2021-12-25T18:12:50.000Z

class Solution: def flipAndInvertImage(self, A: List[List[int]]) -> List[List[int]]: return [[(i + 1) % 2 for i in row[::-1]] for row in A]

38

72

0.585526

25

152

3.56

0.6

0.179775

0.247191

0

0.025424

0.223684

152

3

73

50.666667

0.728814

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

a016f26fa011016d54bf18e5ea0270729fa16d4b

20,194

py

Python

tests/views/test_event.py

DanielGrams/gsevp

e94034f7b64de76f38754b56455e83092378261f

[ "MIT" ]

1

2021-06-01T14:49:18.000Z

tests/views/test_event.py

DanielGrams/gsevp

e94034f7b64de76f38754b56455e83092378261f

[ "MIT" ]

286

2020-12-04T14:13:00.000Z

2022-03-09T19:05:16.000Z

tests/views/test_event.py

DanielGrams/gsevpt

a92f71694388e227e65ed1b24446246ee688d00e

[ "MIT" ]

null

import pytest from psycopg2.errors import UniqueViolation @pytest.mark.parametrize( "external_link", [None, "https://example.com", "www.example.com"] ) def test_read(client, seeder, utils, external_link): user_id, admin_unit_id = seeder.setup_base(log_in=False) event_id = seeder.create_event(admin_unit_id, external_link=external_link) url = utils.get_url("event", event_id=event_id) utils.get_ok(url) event_id = seeder.create_event(admin_unit_id, draft=True) url = utils.get_url("event", event_id=event_id) response = utils.get(url) utils.assert_response_unauthorized(response) utils.login() utils.get_ok(url) _, _, event_id = seeder.create_event_unverified() url = utils.get_url("event", event_id=event_id) response = utils.get(url) utils.assert_response_unauthorized(response) def test_read_containsActionLink(seeder, utils): user_id, admin_unit_id = seeder.setup_base() other_user_id = seeder.create_user("other@test.de") other_admin_unit_id = seeder.create_admin_unit( other_user_id, "Other Crew", verified=True ) event_id = seeder.create_event(other_admin_unit_id) url = utils.get_url("event", event_id=event_id) response = utils.get_ok(url) action_url = utils.get_url("event_actions", event_id=event_id) assert action_url in str(response.data) def test_read_co_organizers(seeder, utils): user_id, admin_unit_id = seeder.setup_base() event_id, organizer_a_id, organizer_b_id = seeder.create_event_with_co_organizers( admin_unit_id ) url = utils.get_url("event", event_id=event_id) response = utils.get_ok(url) utils.assert_response_contains(response, "Organizer A") utils.assert_response_contains(response, "Organizer B") @pytest.mark.parametrize("variant", ["normal", "db_error", "two_date_definitions"]) def test_create(client, app, utils, seeder, mocker, variant): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) organizer_id = seeder.upsert_default_event_organizer(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) if variant == "db_error": utils.mock_db_commit(mocker, UniqueViolation("MockException", "MockException")) data = { "name": "Name", "description": "Beschreibung", "date_definitions-0-start": ["2030-12-31", "23:59"], "event_place_id": place_id, "organizer_id": organizer_id, "photo-image_base64": seeder.get_default_image_upload_base64(), } if variant == "two_date_definitions": data["date_definitions-1-start"] = ["2030-12-31", "14:00"] response = utils.post_form( url, response, data, ) if variant == "db_error": utils.assert_response_db_error(response) return utils.assert_response_redirect(response, "event_actions", event_id=1) with app.app_context(): from project.models import Event event = ( Event.query.filter(Event.admin_unit_id == admin_unit_id) .filter(Event.name == "Name") .first() ) assert event is not None if variant == "two_date_definitions": assert len(event.date_definitions) == 2 else: assert len(event.date_definitions) == 1 def test_create_allday(client, app, utils, seeder): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) organizer_id = seeder.upsert_default_event_organizer(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "description": "Beschreibung", "date_definitions-0-start": ["2030-12-31", "00:00"], "date_definitions-0-end": ["2030-12-31", "23:59"], "date_definitions-0-allday": "y", "event_place_id": place_id, "organizer_id": organizer_id, "photo-image_base64": seeder.get_default_image_upload_base64(), }, ) utils.assert_response_redirect(response, "event_actions", event_id=1) with app.app_context(): from project.models import Event event = ( Event.query.filter(Event.admin_unit_id == admin_unit_id) .filter(Event.name == "Name") .first() ) assert event is not None assert event.date_definitions[0].allday def test_create_newPlaceAndOrganizer(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "description": "Beschreibung", "date_definitions-0-start": ["2030-12-31", "23:59"], "organizer_choice": 2, "new_organizer-name": "Neuer Veranstalter", "event_place_choice": 2, "new_event_place-name": "Neuer Ort", }, ) utils.assert_response_redirect(response, "event_actions", event_id=1) with app.app_context(): from project.models import Event event = ( Event.query.filter(Event.admin_unit_id == admin_unit_id) .filter(Event.name == "Name") .first() ) assert event is not None def test_create_missingName(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, {}, ) utils.assert_response_error_message(response) def test_create_missingPlace(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "description": "Beschreibung", "date_definitions-0-start": ["2030-12-31", "23:59"], }, ) utils.assert_response_error_message(response) def test_create_missingOrganizer(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "description": "Beschreibung", "date_definitions-0-start": ["2030-12-31", "23:59"], "event_place_id": place_id, }, ) utils.assert_response_error_message(response) def test_create_invalidOrganizer(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) organizer_id = seeder.upsert_default_event_organizer(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "description": "Beschreibung", "date_definitions-0-start": ["2030-12-31", "23:59"], "event_place_id": place_id, "organizer_id": organizer_id, "co_organizer_ids": [organizer_id], }, ) utils.assert_response_error_message(response) utils.assert_response_contains(response, "Ungültiger Mitveranstalter") def test_create_invalidDateFormat(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "description": "Beschreibung", "date_definitions-0-start": ["2030-12-31", "23:59"], "event_place_id": place_id, }, ) utils.assert_response_error_message(response) def test_create_startInvalid(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "date_definitions-0-start": ["31.12.2030", "23:59"], "date_definitions-0-end": ["2030-12-31", "23:58"], "event_place_id": place_id, }, ) utils.assert_response_error_message(response) def test_create_startAfterEnd(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) organizer_id = seeder.upsert_default_event_organizer(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "date_definitions-0-start": ["2030-12-31", "23:59"], "date_definitions-0-end": ["2030-12-31", "23:58"], "event_place_id": place_id, "organizer_id": organizer_id, }, ) utils.assert_response_error_message( response, "Der Start muss vor dem Ende sein", ) def test_create_durationMoreThanMaxAllowedDuration(client, app, utils, seeder, mocker): user_id, admin_unit_id = seeder.setup_base() place_id = seeder.upsert_default_event_place(admin_unit_id) url = utils.get_url("event_create_for_admin_unit_id", id=admin_unit_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Name", "date_definitions-0-start": ["2030-12-30", "12:00"], "date_definitions-0-end": ["2031-01-13", "12:01"], "event_place_id": place_id, }, ) utils.assert_response_error_message( response, "Eine Veranstaltung darf maximal 14 Tage dauern", ) @pytest.mark.parametrize("allday", [True, False]) def test_duplicate(client, app, utils, seeder, mocker, allday): user_id, admin_unit_id = seeder.setup_base() template_event_id = seeder.create_event(admin_unit_id, allday=allday) url = utils.get_url( "event_create_for_admin_unit_id", id=admin_unit_id, template_id=template_event_id, ) response = utils.get_ok(url) response = utils.post_form(url, response, {}) utils.assert_response_redirect(response, "event_actions", event_id=2) with app.app_context(): from project.models import Event events = ( Event.query.filter(Event.admin_unit_id == admin_unit_id) .filter(Event.name == "Name") .all() ) assert len(events) == 2 assert events[1].category.name == events[0].category.name assert ( events[1].date_definitions[0].allday == events[0].date_definitions[0].allday ) @pytest.mark.parametrize("free_text", [True, False]) @pytest.mark.parametrize("allday", [True, False]) def test_create_fromSuggestion(client, app, utils, seeder, mocker, free_text, allday): user_id, admin_unit_id = seeder.setup_base() suggestion_id = seeder.create_event_suggestion(admin_unit_id, free_text, allday) url = utils.get_url( "event_create_for_admin_unit_id", id=admin_unit_id, event_suggestion_id=suggestion_id, ) response = utils.get_ok(url) response = utils.post_form(url, response, {}) utils.assert_response_redirect(response, "event_actions", event_id=1) with app.app_context(): from project.models import Event, EventSuggestion event = ( Event.query.filter(Event.admin_unit_id == admin_unit_id) .filter(Event.name == "Vorschlag") .first() ) assert event is not None assert event.date_definitions[0].allday == allday suggestion = EventSuggestion.query.get(suggestion_id) assert suggestion is not None assert suggestion.verified assert suggestion.event_id == event.id def test_create_verifiedSuggestionRedirectsToReviewStatus( client, app, utils, seeder, mocker ): user_id, admin_unit_id = seeder.setup_base() suggestion_id = seeder.create_event_suggestion(admin_unit_id) url = utils.get_url( "event_create_for_admin_unit_id", id=admin_unit_id, event_suggestion_id=suggestion_id, ) response = utils.get_ok(url) response = utils.post_form(url, response, {}) utils.assert_response_redirect(response, "event_actions", event_id=1) response = client.get(url) utils.assert_response_redirect( response, "event_suggestion_review_status", event_suggestion_id=suggestion_id ) def test_actions(seeder, utils): user_id, admin_unit_id = seeder.setup_base(log_in=False) event_id = seeder.create_event(admin_unit_id) url = utils.get_url("event_actions", event_id=event_id) response = utils.get_ok(url) # Nutzer ist alleine auf der Welt. Deshalb darf es keine Referenz-Links geben assert b"Empfehlung anfragen" not in response.data assert b"Veranstaltung empfehlen" not in response.data event_id = seeder.create_event(admin_unit_id, draft=True) url = utils.get_url("event_actions", event_id=event_id) response = utils.get(url) utils.assert_response_unauthorized(response) utils.login() utils.get_ok(url) _, _, event_id = seeder.create_event_unverified() url = utils.get_url("event_actions", event_id=event_id) response = utils.get(url) utils.assert_response_unauthorized(response) def test_actions_withReferenceRequestLink(seeder, utils): user_id, admin_unit_id = seeder.setup_base() event_id = seeder.create_event(admin_unit_id) other_user_id = seeder.create_user("other@test.de") seeder.create_admin_unit(other_user_id, "Other Crew") url = utils.get_url("event_actions", event_id=event_id) response = utils.get_ok(url) # 'Empfehlung anfragen' erlaubt: Referenz-Anfrage an andere AdminUnit assert b"Empfehlung anfragen" in response.data # Es gibt keine andere AdminUnit, bei der der aktuelle Nutzer Mitglied ist. Deshalb kann er die Veranstaltung nicht empfehlen. assert b"Veranstaltung empfehlen" not in response.data def test_actions_unverifiedWithoutReferenceRequestLink(seeder, utils): user_id, admin_unit_id = seeder.setup_base(admin_unit_verified=False) event_id = seeder.create_event(admin_unit_id) other_user_id = seeder.create_user("other@test.de") seeder.create_admin_unit(other_user_id, "Other Crew") url = utils.get_url("event_actions", event_id=event_id) response = utils.get_ok(url) # 'Empfehlung anfragen' nicht erlaubt assert b"Empfehlung anfragen" not in response.data def test_actions_withReferenceLink(seeder, utils): user_id, admin_unit_id = seeder.setup_base() other_user_id = seeder.create_user("other@test.de") other_admin_unit_id = seeder.create_admin_unit( other_user_id, "Other Crew", verified=True ) event_id = seeder.create_event(other_admin_unit_id) url = utils.get_url("event_actions", event_id=event_id) response = utils.get_ok(url) # 'Empfehlung anfragen' nicht erlaubt: Der aktuelle Nutzer ist nicht Mitglied der anderen AdminUnit. assert b"Empfehlung anfragen" not in response.data # Referenz auf Veranstaltung der anderen AdminUnit erlaubt assert b"Veranstaltung empfehlen" in response.data @pytest.mark.parametrize( "variant", ["normal", "db_error", "add_date_definition", "remove_date_definition"] ) def test_update(client, seeder, utils, app, mocker, variant): user_id, admin_unit_id = seeder.setup_base() event_id = seeder.create_event(admin_unit_id) if variant == "remove_date_definition": seeder.add_event_date_definition(event_id) url = utils.get_url("event_update", event_id=event_id) response = utils.get_ok(url) if variant == "db_error": utils.mock_db_commit(mocker) data = { "name": "Neuer Name", } if variant == "add_date_definition": data["date_definitions-1-start"] = ["2030-12-31", "14:00"] if variant == "remove_date_definition": data["date_definitions-1-csrf_token"] = None data["date_definitions-1-start"] = None data["date_definitions-1-end"] = None data["date_definitions-1-allday"] = None data["date_definitions-1-recurrence_rule"] = None response = utils.post_form( url, response, data, ) if variant == "db_error": utils.assert_response_db_error(response) return utils.assert_response_redirect( response, "manage_admin_unit_events", id=admin_unit_id ) with app.app_context(): from project.models import Event event = ( Event.query.filter(Event.admin_unit_id == admin_unit_id) .filter(Event.name == "Neuer Name") .first() ) assert event is not None if variant == "add_date_definition": assert len(event.date_definitions) == 2 else: assert len(event.date_definitions) == 1 def test_update_co_organizers(client, seeder, utils, app): user_id, admin_unit_id = seeder.setup_base() event_id, organizer_a_id, organizer_b_id = seeder.create_event_with_co_organizers( admin_unit_id ) url = utils.get_url("event_update", event_id=event_id) response = utils.get_ok(url) response = utils.post_form( url, response, { "name": "Neuer Name", }, ) utils.assert_response_redirect( response, "manage_admin_unit_events", id=admin_unit_id ) @pytest.mark.parametrize("db_error", [True, False]) def test_delete(client, seeder, utils, app, mocker, db_error): user_id, admin_unit_id = seeder.setup_base() event_id = seeder.create_event(admin_unit_id) url = utils.get_url("event_delete", event_id=event_id) response = utils.get_ok(url) if db_error: utils.mock_db_commit(mocker) response = utils.post_form( url, response, {}, ) if db_error: utils.assert_response_db_error(response) return utils.assert_response_redirect( response, "manage_admin_unit_events", id=admin_unit_id ) with app.app_context(): from project.models import Event event = ( Event.query.filter(Event.admin_unit_id == admin_unit_id) .filter(Event.name == "Name") .first() ) assert event is None def test_rrule(client, seeder, utils, app): url = utils.get_url("event_rrule") response = utils.post_json( url, { "year": 2020, "month": 11, "day": 25, "rrule": "RRULE:FREQ=DAILY;COUNT=7", "start": 0, }, ) json = response.json assert json["batch"]["batch_size"] == 10 occurence = json["occurrences"][0] assert occurence["date"] == "20201125T000000" assert occurence["formattedDate"] == '"25.11.2020"' def test_report(seeder, utils): user_id, admin_unit_id = seeder.setup_base() event_id = seeder.create_event(admin_unit_id) url = utils.get_url("event_report", event_id=event_id) utils.get_ok(url)

30.972393

130

0.66287

2,596

20,194

4.831279

0.084746

0.0775

0.087705

0.050789

0.817892

0.778664

0.762079

0.750917

0.710891

0.687291

0

0.017986

0.226354

20,194

651

131

31.019969

0.784804

0.022779

0

0.668699

0

0.152481

0.058144

0

0.115854

1

0.052846

false

0

0.018293

0

0.077236

0

null

0

1

0

null

0

6

4e4afe24eae76781f0bd16e5edb6e113cad1b27b

252

py

Python

lib/collect/backend.py

csmsx/mark0

5e6a2ead0f2ecd3f64fce505faa60de987b56cce

[ "MIT" ]

4

2018-10-04T09:34:44.000Z

2021-12-20T22:02:11.000Z

lib/collect/backend.py

csmsx/mark0

5e6a2ead0f2ecd3f64fce505faa60de987b56cce

[ "MIT" ]

5

2017-08-08T07:35:48.000Z

2017-08-08T07:55:03.000Z

lib/collect/backend.py

csmsx/mark0

5e6a2ead0f2ecd3f64fce505faa60de987b56cce

[ "MIT" ]

3

2017-07-06T04:55:58.000Z

2021-03-07T11:45:57.000Z

import lib.collect.config as config try: if config.BACKEND == 'aws': import lib.collect.backends.aws as api else: import lib.collect.backends.localfs as api except AttributeError: import lib.collect.backends.localfs as api

25.2

50

0.710317

35

252

5.114286

0.428571

0.201117

0.357542

0.402235

0

0.210317

252

9

51

28

0.899497

0

0.25

0

0.011905

0

1

0

true

0

0.5

0

0.5

0

null

1

0

1

0

null

0

1

0

1

0

6

4e59c80771c8629ced1f66eeacd94e28e7e7a677

151

py

Python

arc/arc020/arc020a.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

arc/arc020/arc020a.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

arc/arc020/arc020a.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

A, B = map(int, input().split()) if abs(A) == abs(B): print('Draw') elif abs(A) < abs(B): print('Ant') elif abs(A) > abs(B): print('Bug')

16.777778

32

0.509934

27

151

2.851852

0.481481

0.155844

0.272727

0.311688

0.61039

0.441558

0

0.218543

151

8

33

18.875

0.652542

0

0.066225

0

1

0

true

0

0.428571

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

14c3a3e7c858dcee774c3312bc9a21d5f8dab470

33,142

py

Python

web/apis/comments/tests.py

KhomDrake/Remember-Backend

233f72ef3a54300e25946bdc3a1baae5b56d7a25

[ "MIT" ]

null

web/apis/comments/tests.py

KhomDrake/Remember-Backend

233f72ef3a54300e25946bdc3a1baae5b56d7a25

[ "MIT" ]

null

web/apis/comments/tests.py

KhomDrake/Remember-Backend

233f72ef3a54300e25946bdc3a1baae5b56d7a25

[ "MIT" ]

null

from django.urls import reverse from rest_framework.test import APITestCase import json from apis.accounts.models import RememberAccount from .models import Comment class CommentsTest(APITestCase): def createType(self, name): dataType = { 'name': name } return self.client.post( "/api/v1/type/", dataType, format='json', HTTP_AUTHORIZATION=self.bearerToken ) def requestTypes(self, page=1): return self.client.get( "/api/v1/type/?page=" + str(page), format='json', HTTP_AUTHORIZATION=self.bearerToken ) def createMemoryLine(self, typeId, name, description): self.client.post( "/api/v1/memory-lines/", data = { "title": name, "type": typeId, "description": description }, format='json', HTTP_AUTHORIZATION=self.bearerToken ) def memoryLineByType(self, typeId, page=1): return self.client.get( "/api/v1/memory-lines/?type=" + typeId + "&page=" + str(page), format='json', HTTP_AUTHORIZATION=self.bearerToken ) def createMoment(self, memoryLineId, description): self.client.post( "/api/v1/memory-lines/" + memoryLineId + "/moments/create/", data = { "file": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDABsSFBcUERsXFhceHBsgKEIrKCUlKFE6PTBCYFVlZF9V XVtqeJmBanGQc1tdhbWGkJ6jq62rZ4C8ybqmx5moq6T/2wBDARweHigjKE4rK06kbl1upKSkpKSk pKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKT/wAARCAMwAmQDASIA AhEBAxEB/8QAGwABAQEBAQEBAQAAAAAAAAAAAAECAwQFBgf/xAA6EAACAgECBAQFAgQGAgIDAAAA AQIRAwQhEjFBURNxgdEFIjJhsRTBM0KCkSM0UnKh4SRiBhU2Y3P/xAAZAQEBAQEBAQAAAAAAAAAA AAAAAQIDBAX/xAAiEQEBAQEAAwEBAAIDAQAAAAAAARECEiExA0FRYQQTIjL/2gAMAwEAAhEDEQA/ APzQAA9GH6Doc8H0nQ1AAAQKddPp5521jVtb0cmnFuMlTWzQUBlvmdcMPEg31RLcXnm9XI5g1CDn xVvwkaae41MQAFQDAAAg6gUEsAXqOhAgKUyUCoEAFBABQQAVggAoIQKpSCyIFICiggCqCACggApA AKQEAoJZQAJYIKCACglgCggAoIAKQAAAAARAFbwfWenI7o4YI/NZ2lzOfU969P5desE9ztJ1ibOa irMaib4KRzza7W5HllLdswVkR3eO1qJszFGishClhFzkkubCSa3jwznG0tgfZwYIwxRjXIHkv/I9 vTPyj8sAD1PK74OTOpxwdTsWIFUW+RDvgcbpsnVyN8c7Wvh+qek1Km1a5NGviU8eTVSyY+Ukmy5t NxO4nlyY3jnwvqjPPUt1eubGWuT72ez4VCM8uTFJ05RqPmY0EIZtTHHk2i0/wTU43pNU4xlbi00y 336Zlz2xCXh6h9raZ7MmCOVOl818z53E3O3ze59XBvCMu6v/AIOf6erLFnt8ycXB8MlTMn09bgU8 fHF7xStdz5h0568oydSFIzaABAABmTA0mEzKbQi2BsA76XTy1OZQh5vyIOKB9Cfw2o8UJ3BJW+xn TfD3kxeJkfDF/T9xsXHhB7P0GT9W8D502q67DNoXilig5Ljyf2Q0eM34M3heVL5E6s+g/hieKThO 5RVvsXhS+At9fF9iXpcfLIGDTIAAAACgAAAAgAhQAAAAAAAAAIAKCACggApAABYxbdI6QiuFtmYU pE1UnHhZk1N3JmSooIAAAAFStkAV6MWx1pPzPNCdM3KdRtFObZddW6dHPLXUypUjE5WzjOfb1ddz GGrYSKuQOrzBQAgd9Gl4vE+nI850wz4Zmepsa49V9uE/l5g8cc6oHhv5169fBAB73hdsHU7HDBzZ 3LBYQc20udDgmlTVM1gfDNSPTqJLwbVWc++rLjtxzPHUjklDDFt87OGplKU48XRHoxVnwrG9nF2j z6uLx5nB9EvwZ5/+j9PjnGTTuPNFlklklxTdskN5KlyRucUsi22dHXfbn4+nP79jrHUyhFRMZIrj ahypMxJU9h66TLHp/VtJ9b2o4GVupfbcRd3ZJMStEYDNogYIAMt7GjLW4FvYRDWxUQU+n8Bf/nVz 4otHzD6nwTJHHkm5fVWzF+LHsz8WHQ5oxi221v8AazqnKOh03hx4lwb7GPEhhwZeKXHcOp87T/EX iwrHJP5bSoxI1r6WKbn8ai5Kv8J7dtjwYJ/rPimOM6SUqOMde1rXqEnyr/ijhps7wamOXs7LjL9F htLNFx4Y8Do+c/8A8ff/APV/scZ/E7TUU94tbnnlqW9EtPvtNyJl1deZkANoAAoAAgWAAAAAAAAA QCgAACFAAgAoIAKCAAbxxcpUYN45KKfcUejhXDSOOSPDujXiHPJKzElarAIU2yoIUCFIAKCFAobI AqgAKAACgEYQZAALxtdQQDDXlABEdcHNnc8+D6juWCqVG3xtWnt2OZqE3B2uROprfPWeq74Mrg91 zOWefjZ5S77GZSbZORmc5dXruX0kW4S+5ricp7vmzLbbV9yUaxnXfFKMNXBypxTVnCysz1EmJbo3 Tsq5s7YtOpY8kpSrhTa++xzk7k33oS7SzEDBGaZCMACApAKAKAGoycJJxdNGSgdJZsk1UpNo52AR QWBQQFgBQhSAAAAAAAdQAAAAAAAAAAAAAAAAQCkKQAAAAAAAAAAUAAAAIUAAAqgACgAKAFAEKQIg AAgAA8wAIjpg+s7nnw/WegsAm9soKhYAApl8vMo4bjxMlUfIQXFNJjd7H0Ph+mwZdLky5XTg0luS 3Fk/rwy4opxtrmZRZScm2ydSxKnQMBlRAQAUAgFBLBFUpChAAgFAAABgKgAAAAAAAAAAABgAQoAE KAAIBQQAUEAAAAAAABCgAAAKQAAAFAABSoiPVpNJPUN0vlXNktxZNcsWKWWahFbs34Duj6UMK08J ZKpr5UcIxtnPz16vy/GX64rTKrZiWCro96haLLGlj+7J5O3X5c4+VwPsyVsfb0mkU4u1Z4ddo3hk 2l8v4OnPWvF1zlx4SGmjJpzQFo9Ohw+LmTa2W4tHmcJJ7poHr1qk9TK9vsCaY+SAAjeL60dzz4vr R6CwAAVFBAANwcVJ8XIwCWasuNNqOT5OQU5KLim1Fu2jPQDDS9gQFRSMBgQAACMEsir0G4sAUpCx VsCrk/Ify+olv5C040QF9LIty8otd2WlwgKuSX2M31Lydilf2ASVSokeYbt2IvcAuZer9SLnZLts AgXlFksor+leY6DnFIco0QSivdhbJsyBS1uSP1LzF/MBa3oncr52RfSwHQDoSwKB0IUAAAAAAAAA AABCgUEAFAAUKRFQHTDjeTJGEVbbo/SQhHSaZQjV1uz53wHTeJneVraC28z6nxCKx4d+bZx7trrz MfN1OTiaj0RiKI/qNwVmY+j+czl3w43JHWWBvfsd9NBRxpsznmoJ1zZccr3bcj1aPGvC5cznrNOp 42q5nq0irEjWWNxLy8Xd3qvxmaHBkcexyPd8Uhw6qTqk+R4jswh9j4NjuHFXXmfIP0HwpKGj426S 3Md/Bzno1lySnLm2D0PMlyBzV+OAB2Yax/Wj0Hmx/Wj0FgoILKgGzLZANcQsyANWUwVOgNAllAEY DAgBCCkAChQABSAC2OhABRZABbFkAAAALAIBQQAUWQAWwQAW9wQAWwQAWyAAUgAAAgFBABQQAUEA FBCgUEAVQABUaiYNx5kWP1fwHEseiTreTs5fFsvFnWNcorc6/Cs0VpFJ8oxo8Ck8+rcnybOV+O3E 3pnLiqMaTbfMuKG6PoRxp7tHKONeI2uSYkevnv1jWXIseJR6s88eLJNJlztyytvyR20+Omu5cPXP OvqYFWNI1JXFkxtcJZyUYOTeyVkj59+vy/xtr9VS6I+aejXZfG1M53ds8zOyC3aR9LVarwdHj0+N 71cj5l77GoRllnXNmbB6f10usQeRrcF9DyAAMrD6keg88PqXmd+hYKZkzRhlQBABQFzOupgoTSiq VX/yzNvvE33jkAQqqmaMGk9gKR8gGUQAhFUEKAAAAAACkAFBABQQAUgAAAgFBABQQAUEAFBABQQA AAAAAAAgFBABQQAUEAFBABSkAFBAFU0mYNJgfX0mpb00cEXu3ufVw4IQgpdWfnNFmjjzJy5H2Y66 EpNJ8jjZ7eji78e6TpOiRjSMY5Xh4m+ZJZeCNmnaT/DlNcWoXZHrx7bnhwz4srZ7FLYNdz+PR4lR Pn/Ftfwabw4v5p/g663NHTae7+Z8j83nzSyzcpMcz+vJ1jDdkZLIzbmp79Fi4MUskubR89cz6mOS /T09rRnv41y+fJOcm0ge7Dghwc6vkCeR6fEABtzWP1I7nnjzR6CwGYNPkZKgajCU74VdGTUMkoXw vmqJf9F/02sGTiqup21WOU5RlFWuH92clqJ8XrdHXVZJY3BR6wX5OV8tjlfLY4Swzim2tkczo885 JpvnzOZ0m/10m/0LEgRWmiPkUjKiAAihUiGobyQBr5nXcNbIi3ZZ/UQZNRSb3MlVt7FHTgUotrml ZIw+TjfLka/hY2v5pIT/AMvjru/2M6MyiuHiXK6CUaV8zUP8vPzX7kxwu5S5IoSxtTUV1LwxcuE3 jlx521/pdf2OEb413sgjVOmWEeKVI6ZknnnfRlxJKU6/0v8ABd9DPCpPhXoZhG+K+isuC/Gj5hSU XkXR7f8AJBmVVsjJ04U8Tl2MuKUeZRrgXA3fIxFNukbh/Cn6FguHHxJ7vkQZyQUUqMHbKn4cGzm4 pK+pRkU2dppQhHu0VwUYQ+6saOAOyjGWalyoQqXH9kxo4GopPdhRTW7EU5OkBZRqNowdcrSjGC6c zkIAAKABAKCACggAoIAKAAKQAClRkqCtWdceVx5s4gljXPVnx9xar5IRi+S3Mz1LlzeyPkRnJcmb WWVGcejn9pH1cGZR3Z1euhGS4nsj43iy7mXJvmxh3+2z09Ot1ktTNu/lXJHlsjZDTzatkYIBbPTL PHw4xT8zy2QZpr3vPH/UgeAE8U1xABUFzO5wXM7lgGXzNEZUQgBBVzO+rnGcoOL5QSPOCWe9Sz3q kAKoaRlGgoGCMIgAChuMtn9kYAG4y+ZEk7bMggHTDJQmpPocwUdpTjK7SskMlR4XujmCYOk5px4Y qldm3ljwKNLZHBAYN8ajkuOxtZIp8VbnEDBW7bfcsJ8MrMADssii+JVZiLW9mAB0lkXBwrq7ZiyA DcZ1jlHuTje32MgDpkycUYrsjFtkAHTLPi4ftGjXicUUnvWxxJYwdo5KySf2Jjnwqb7qjkBgts7Y pKMH3ZwFgbm16mBYAAAoAAACACgAAAAAAAAAClMo0FCkBBopkqCtIpAwiMEYAMhSAAAUAAEcQAQF zOxxR2XIsAAFRGiGiEEBaFAQFoAAAFAwRhEKQBVAIBQQoAAAAAgKAwAIUhAAAAEAFBABSAAAAAAA AAAAQAAAAAAAAAAAAAAAAACkKAAAFAQCqAVIAjSLHG2dI4trLlVzDOnh3yMuDGI5g04tOmtyMgyA AAFGlCTVpMDANOLXNAqOAAIB1RyOq5FgoBCooILIKDNgDRCFAAACBgMKgAAAAgFIAKAABUiGlyAg AAEKQAAAIAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAKAAKUiKFWKcnSPXiwLZPmYwR4Vxv0N LI033JzZq2OlLjfCvsiz+RcHXqfQ0OgvFDJPvZn9E8nxJRS+W7Y/7p7XxZhoa0fiP6nujnj0/wA0 e5+hy44KHDWyR8WHy6+UeaOfP6Wy6We3PXaC4ccFvR8iUWnTP2jwcWJP7H5r4tpvBz2ls/8Ag1zd Zr5wKDY6afDLPmjjit2z9Q9NDT4cWOMem58j4DG9U32R+mdKVz5JGLfY+RqdHheS5wVtdQTV6XPq dRLJxUnsvIAx+UABtA6rkcjpHkUUAASwARAG4Yp5PpVnWWkypXVk8pGb1I84LKLi6apkK0WACqEK QACxi5OlzLwSpunS5kGQbxQeTJGC5t0enLHBjlLHVtdQPGD1aLBHJKUp/Sthj06etWGS2v8A4Gjy lPZ+kS1PDzg02v7HPSYY5Mr4vojuxo85T0T06jq1jr5W9jrj00HPNtahKkv7jR4gdMyisnyql2PT LDix48Tkrc43zGjxA90dJF6lR/llHiRx1EIQUUotMmjzA9KwqWkeRfUnv5B6dLDjdfPke3kXR5Qe " + "2eHFDJ4dW1s3fU5vDHHnlCatJ7E0eYHq1WCGLFB8pS3r7HlKAAAAEAAAAAAAAAAAAAAAAAAAAAAA AAAFAAFAG4R4pJdzJ10/8REvxqfXrlDkuiRz02PxdTGHdnWctmccE/DzqTOXO5XS/X63FGMoKuVU jeLDHHJyrdnHQ5oyxp9KO7lxO0cFpkpxbfLmfAwTU/iF/wCp/wDB9X4pqY6bSNX88tkfA0Mr1UW+ 9HXif+axvt+uhXhK+VH5/wD+QSx1GKfzXZ9TVauODSn5bW5nmyuTfM6cs152QEOiPr/ApKGTJNuk kfRjr/1VtPZM/NQyzhGUYypS5lx58mNNQlV8zOD9Vh1SeNNPYH5nHrM2OPDGWwJ41deAAG2Q6R5H M3HkBogKUQ6YMby5FFGD2fDUnObfNLb+5ju5NY7uc2vdjxxxrhitk0JzjCNyaWzYz5PCxyl1t0eC cPFjiam3KXNdjy8877rxc8+XuvRmxR1OJyit9qfc+Wz7sY8MVFclR8fUpLUZEuXEzt+XX8d/x63Y 5EKDs9KAAosW4u0dMmbjgo1Xc5Ag6aefh5oT7Oz1Z8EJ5XkjkXDLc8JrifdkHuhkx4dLCLpufzP7 HTxMctXgyppJrc+ZbKpNVu9uQwfR0meMlKE3vFSpvyMYcmPFpm5O3kluvseFNrkxbaqxhr6M5wyS wZU0nfCzMd9RmlGaX+I6++54U33NKTXJjB6Nc4vJHhpvh3O2XGsuPB86VQ3PA23zNccuVjB9HHlg 9ZBJ7QglZ49VF1GTkn0OKk07T3JKcpKm7Eg9Ohkm5YpOlNFz5ktZjp/LjSSPIpOLTTpkbbdjB7p4 VPVuamqcm2bisefW5Mra4ItV99jweLOqskMko3TGK76tSk+OUk75JHlNynKSSb2RliIgAKAAAgKQ AAAAAAAAAAAAAAAACAoAAAAAAKUiKBUbxupGEai6ZKsexv5DjHHObuMW/JHTTQlqMsMa6s/VafQ4 sWNRUVscr14em779vkfDZ5qWPgl5s+xKSwx3e51UMeFcVI8NvUZ5N8kYzfbcuvF8TwZc8fFb2XQ+ fihkxTjJRezP0Gq+aMMMf5md46SEcaVLY35TmYxnt8DX6lzSheyPl5HbPsfGcccU01sfGfM3yyyC g0yhrw5VfC6PX8N0yz5+Ka/w47v7nvz43xfLFOPYlqvn49Jj4IvJJqTV0D6EsSVWrdA1IuPzgADI bjyOZ0jyApSBAU7aXL4WZN8upxKSzZjNmzH18+J5+FKXyW35lx4IY4/Kt6Pn4NXPFtzR3l8QThtH eqPPeOvjydfn38nx7MuRY4uUnsn+x8ScnKTb5s659RPM7fK+RxOn58eMd/y/PwntCFIzq7AAAAAA UAACgAAWgBRRaAAooIyRmmiMKywVkAgKQAAAICgCAAAAAICgCAoAgAAAAAAAAAAAAAAAAKABSIoF KiFIr3/Cs0cWfik9+h+o0+pUoq2fioume/S/EJ4tpO0ce+NuxuX+Pva7PS4ovlsl9zpgxeHp4p/V LeTPiR1kcuoi5P5VuezN8RisLaluOeVt9ZHfTzeXXNdIbI9+o1OPBjcpNbH5zTfEFhjKT3nJnm1W tyah/NKl2Hhb1qdU+Iap6nO5X8vQ8bK2Q7RgBAEfV+GTTx8HJJ7/AHPfOePG1FfVJf2R8TFq3igo qP8A2R6vI8ksje7VeRM9q+zHNGV0rp0D5GHWvFDh4b3uwZs60184AHRENx5GDceQGiAFGkymDSZE UAEEAZGwDZkpCqFIVACgADriwznNKnuc1zPt44rhi0ui/BLR4NTp4Y44kurps45cDguJO49z261b 4O3E/wBjDS/x4fyJJ+oHgPX8P0c9bqY4obXzfZHlR93/AOLJfqcr/m4Nv7lHq/8AqPh8H4cpNy/3 HxdfpoafWZMWJuUU9j6Hw/Fj1GfN+sclkT2+anZ7IfD8K+NcNOUVDjqTu2RcfnZYckFcoSiu7VBY puqjJ3y25n6/VYseXTZI5OFpRbX2PPCen0nw/TZckVfDSdd0NTH5dYpy3jCT8ker4Vo46nXRxZVt TbXoff8AhkWtFCTio8XzN+rMrFHH/wDII8KSvE2/Pcqx+X1eNY9TlhHlGbS/ucaPVrv85m/3v8nm YRkGiAQhogEBQBAUAZBaAEBQBAAAAAAAACFAAhQAAAAAAAAARoiKAKAiKqNIiNAVCwgAIykYGQAB AUhUAAABQB5wABDceRg3HkBQAAAAApAECFAVAAAKiFQFKQoA9OHVzhJcTuJ5wQfQ1mSElhkntbOO bPHw3jx7J8/ueZttU2AB7fhesei1Kyc4vaS+x4jSKP1T1Xw7JkWZuHF3s8z+KY//ALfxl/DcVCz4 CZtDDX6TUazSxw5JRmnKUWkrvfyPn/ENVjy6LSY4tNwjv/ZHzAMNfpcet00tLi4ppcMVtdcjj+uw z+NrJxLgWKrs/PNmWwa3qZrJnyTXKUm/+TiVgDIKAMgoAhClSAiQooAjRDRAIQ0yAQFAEBQBACgQ AACFAAAoEBQBAUAEUIpAKgWgCNERQqkKABlmiMDIotCgMg1RKKiAtCgICgDzAEAG48jBuPICgAAA AAAAAAAQpABVzAXMDRQVAQqBQIXoCgRFQSNJBBG0hCEpOkrZ7Mehm/qdblHkB656KcVa32s884OD qSoI5MyzbRloKyQoIqENUQCUCgDJRRaAgKAiENECoQoAgKAIQ0QCAooCAoAgLQoAQtFAgooAlAtC gCRaLFFoglFotFSthSMXJ0kdcuNYoLi+p9Ox9DHplptH4s188v8Ag+bmk8s3JmJdrdmRybFjhfYU dGFsq3Mm8MeKRKqUKPTn07xpPoeegjNCjVG8MIyyxjJ0mwOmPQ5cmnlmS+VGMGmy6jJwY42z9Ngh BYVjivlo454YvhWjnwb5sr2fYmj83kxvHNwlVrZ0D7Ol+EeNhWTK2pS3A0x+ZIUhpA3HkYNRA0AA AAAAAAAAAAABcwVc0BsItFAhUKLQEKkCgEjaRlHo0sVLU44vk5ID6Wh0nDFKrnJM+l+hn3XNGvh0 Lm5dl+57nOKnw3uB856GfC7rl+54NdpL4k18yf7H6BzipcN7nk+Iw3hKupYlj8lKNGGj06uCjmlF dKOKjxSS7uhSOdCj2S0aWOUoytxSbItH/hKcpU5JtEV46FHpwafxU25cKuiPST8bwlzq/QDz0+ZK Poy06xaHLvxNzSOM9HKGNylJWknQHkotHpx6SU4KXElabS7no0WHgx5ckq4ovhX2A+dRKPVDSyyR " + "47ST5N9T0aXCoaXNkdOX0oD5rRKPVHS5MkePZJ8r6nPHp55E2tkurA40Q9EtNkjNRa3fImXTzxRU nVPsBwoFoAQhqgBkGqFAQUWhQEBaFASjUISm6irYo+p8KxxhjyZZ8+SJR8qUJRlwtUyUfVzxhj48 00uOW0Y9j5j3dlGQaoUQWKLQijVBUo9WgwqeZOXJHnSPXpMihJLuzPfxrie30fiavTJLkj4uPHKW 6i6Pu6qPi4VFG8GiisSRx56yOt532+P4Djj3jvI5zwW0knS5s/SLTR6qzGXTJQ+WO7Ok7Z8X5ecV xbLY9Ggx8WdI9+r0cYRcnGifCdO7eRryLetiTnHXWYE8TX2PitUz9LnjxQZ+eyr55eZefjF+uQWz KwaR+i+DZvGiuL+XmcfiE46j4rCFqSW1Hg0etWm0+SMfrlyOWmz+HqY5Ju992TB+pjKMYpdgfKfx PBe7bBFflCFIbZDceRg3ADQAAAAAAAAAAgKAIVcwVc0B0oFKBC0KLQRAWioAjrgn4eaE/wDS0zkj a5gfq/huSKg1fPc9UpY1kTdcR+b0WrcFwyfR0z6Hiccr4r3X4C6+opY5ZFJVxUctfKPhxV78R4Fk 4FfFXy/ueXW6y7jF22936FS18/Uy48spLkzOnUXqcaly4tzMjPJ7CkfTnJLDm+ZU4KkTD82OKlJc PC1/wfOc5NbybDyTquJ0TF178EYrBcGk3Lr2NylF6rPwtW8VJ+p81ZJxVKTSIpyUuJPcYPbKDx6J xnJOTyxO2VeJhm5tVwp/fmfNlknJ7y62V5pyjwt7DB9TDBQeKKpqtzzcdabVNP8An2PL+oyKt+Rj jfA43s3YwfS01zwY4Umq6nKbUNFnjF7LJSPLHUTjFJbUc3kk4cHS7A+ngTyYcceFcPDzvkTT4lLS 8Ke0ZvfueGOpnGNLbaj04tRHwIQ6R3fmB0UXl10m41GEdkZ1yX6FPhpuf7HLPrP8X5F8qVHnzZ5Z YqL5J3QHChRaFBEolGhQGaFFotAZBaKBkGhQFxR4siVWfXxxeKKi95vlHsfM081inxvdrkd9PqXG WTLPeVbEqtayXBiqT4sk3v8AZHgN5JynJyk92ZKiApQqxWxaEeRqgCRYtxmpLoEjpBKrM341z9fV 0mXxIo+rjpQPh/DYSlKktkfcjFxgefx9u1uxUbUVW5ytpHROoWzTNeLWY/FmoLk+ZqEYxioxVJGZ 5E8jNLkFtY1MuHDJn56e7bPrfFM3DjWNPd8z5LO3M9OVYaJRposYuTSS5lRigerNpJY3BLdyNfoM jzLFHeVXL7AeMHvfw6nXiX6AD4BCkKgbgYNwA0UAAAABCgCAoAgKAIaXNEKuaA6loooCUWiivyBK /BUvyK29DVb+oREvwaS39SJbehtLf1AsVt6HaOWceUnzMY8bntFXsetaJ7cUqdjYzepPrzvLOS3f Q5vn6nseifDal0R58mOWOVSVbl0nUvxwZDTRKDTNCjVCgMkNUAM1uDVEoCENVuAMko0xQGaBaLQG QWhQGaBqhQGRRqiUBKFFooGaFGiUBKBaLRBmgaFAZoUaoUFZotFAGorYqEVsaoCUbh2MmkFff+GK GPEltbPoNLhPzek1TxOnyPr4dZGUeZJEtu69KJO2jl+pg3zE9VBLmc7z7dd/ry5YOMrLkzLBjuXP ojGfVwW/NnzM2WWWVyZqcM3rWc2SWWbnLmzkzTIzbLB6tCo+Nv6HmOmGXBkjLsyD70IQ4Hnml/h8 vMxCoYZtfxMm7Z8/Va3jjHHjfyLd/c7Q1UHFNyS2JiuGSOplNuEJJAxm1uSWR8DqPQFHwCFIVkN4 +Zg3j5sDoAUggAKAAAhQAAKAIVc0CrmB26ii1uWgJQ9zVcxW4RK29C1v6jp6FXP1ALl6G4puSS5t mUtvQ76ZJ6jGv/2IUr6mi0vyxhH6pRtvtud54/C1cndRi0/Puej4etsr6qkv7WfP1+oUnmg5fOpb P+5JGeZ63+vrSwY82NWluj5ut0vzOEt7bcX6Hp+DSnPTNzle+x2+IJeFGXVTS/vsF75/sflpwcW0 +gUXKSSVtvY9OtVama+y/A0MU9ZhXTjRo5uyOU9Lmxxbnjkkq3aOfhy4ePhfDbV9D9ZKHFPIpRTh XI+XDB4nwzDDkpZHvRNafFolH2M/wmOPBPLHI2o1W3Mr+DLglJZXsm3sUx8aiUfV0vwr9RhjkeTh 4nsqsxD4VlnkywT3x16gx82txwurrY+pD4ROWonjU1wwq5UdfiunWn+HabGqbT3fclpj4rRKN0Si ozQo1QoDNEo3QoDFA1QoDNCjVCgMijVCgM0KNUKAzQo1QoDNCjVADNA1QogyKNUKCtRWxaKlsWgI kVFoqQBI0pNPZ0Ei0FXxJ8+Jh5JvqSiBUe/MlGqFFRhkaNtGWiDFA1RKAgKKAgLQKj5JACAbx8zB 0xfUB0BQQQFAEBQUQFFAQtFAEKuYKgO/X1Ffgtb+pa/AEoJfuWvyh/2BK29Ea6+rFfsXr6sIi5ei O2GShmhJ8lKzlW3oja5+rA+5i1T0+NySuMop+vL9jitNjy5XqZ5Fw+I3R5NPqEocGTeNL8nqlFTx PHGWzuv7GWJ14+q9+ky4+KUcVKNX+w1uVT4Ma/1W/tR5MUlhwuMWlaW/2OGo1KimoO5Nu36Fh13v rl5NVJTzza5bGtC0tZh/3o4P9gm07Xdmmp6fp3OMMmTLLL8lcjyYuHNosMOLhcsjf5Z8V5JtU5Ov MLLONVNquVMmNa+58QhLJhUMeSMcca27nTVY5R0jw4siTp8TfNnwXqMrVPJJrbqV6rM008knf3GG v0OkjHHpsMYtbJWcZ5vCWtyRatNV50fFWrzRgoqboxLPklGScnUnb+7LDX2dI55vhr8OaWWUrbZx +Npx0mnjKVyV3/Y+dg1WXAmoSpMznz5M7XiSuiYa4Nbko0SiogLQoCUSjRAJQotCgJRKNUKAlEo1 QAzRaLQoDNFotCgM0WjVCgM0KLRaAzQo1QoitJbFoqWxqijNFSLRUiAkWjVCiqzRKN0KAxQo1QoD m0ZaOjRloDAo1QogzQo1RKKiUC0APigAgHTD9RzOmH6wOyLQFEEoGqAGSgFEBS0BAUAEFzKi0B3r f+46ehf+xW3oArf1Qrb0Zqt/UiW3owFb/wBh1/uXr6oL3CCW39jS5+rFfsVLf+5Q6eiOkZyi9n1Z lRb2XZHVYZWu9saZrLnJxpvoYfP1ZqSlHmuxH+7CMeyLW/qxW3oi1v6sKz/0OvqXp6IVv6lE6B8y 9PQVuBkGgBklG6JQGRRqiEEohoUBmhRqhRRmhRqhRBmhRaFAShRoUBmhRaLQGaFFotAZoUaoUBKF GqFAZoqRaKkFaSLRUipASjSRUjvptPLPkUIrz+xFc4YpTdRVnsj8OcYpze76I+lg02PBBKK82ST4 pWTW+ZHihoIVurJP4fF/TsfQijaiqGrbHwsuknBulaR53Gj9KsceqPn/ABDRJLxILzLK518dojR0 kjNFGKFG6PofDdOn/itJvoiD5jiSj3ZdNkyyy5dkosabQzypTe0LGjwUD62oSx5FGOONJAo/GgpC IHTD9ZzOmH+IgPSCigJQotACAooCAo6gAVIASi1uUUB6KFfhFXL0Za/YCJb+orZf7TSXzLzJXy/0 lCt/VDp6M1W/qTp6MIV+xpL9xW/qjSX7gS+FLbdpCOSTap72fQ1Gj8XSQyY186S9ThodG5TmskWq 5Gbcmt8TXqy6Jfo4yi7kkmz5m/F6s+7p4S8Hw57LY+RrorDq5RXK2yc30dTK41t6IVv6sq3j6IvX 1ZthHCSipNbNbErf1PbkxvJh00I85R/dklpsbU1jdyg367AeLp6Ct/U9sNEvDjxXxSje3Q8uSHBk lF84yaAxQrctfgtb+pRnoQ10FAZFFotAZoUWhQEolGqFAZoUaoUBKJRqhQGaFGqFAZoUaoUQZoUa FAZotFotAZoGqFASipbloqW4VpI0kEipAIq2fd+H4FhwJtfNLdnytJj8TPCPSz7y2Rm/V/jnkk0m cY8ztl3WxxjzJzHTn47Y0dKJBUhKaWy5mnO+6qVElFSi0+pY8tyhH5/WYvDzSR52j6XxWNZE+6Pn soxR9r4ZjS099WfHS3Ps4ckdNooub3a5GarpPGvA4EubOscajCMa2R41rsaabfoddNqPGc8jdQXI guXDGc22DlLWY3J1JUC+1fhCAFZDph/iI5nTB/FiB66FGqFAZoUaoUBkUaoUBmgkaoUESgarYUFQ ooqA9Fbegr9ir6fQtb+qAi5rzYr5f6TSX7krb0RUK39QuXozVb/1Er5f6QoufqjS5f3Fb+qKuXow j7mklGWmhXZFUf8AEeypWfL0uplgbXNOtjq9ZNZeNbq3aJefKNc3K9by1OXFySR8rWNazXPw+Vcz 6GTUQnp5S/maSPm44+G3XN2iczF7us8PDGulGoRi8lT2Vuw+Xog+fqzTD0yzwjlwqH0440ac8WLx ZxdynaSvueLp6F6+oV71mjOMHxVUaPLLBLJOU4tU5OrOPT0NrJJbJhGcmN43T6qzPublJy3fYzW5 RK2JRqhQGRRqhQGaFGqFAZoUWi0BmhRaAEFFoAShRaFAZotFoUBmhRoUBkpaFAQFKQQseYLFbgbR pERpBXr+G/5heR9eTpHxNJPgzRfQ+yn4i25Gf600lcTCx8LOiVKiliay9omYxNT5FSpA/igGZSUY uTeyCPmfFZJ5UuyPns76nJ4uWUu7OLKMp07NZMs8lcTuiUbhhnkVxVgcjq9RPwvDW0fsaemyJpNc z1fpIRxbxuuvcivm2DU1872r7AqPzIAIB0w/xY+Zg3h/ix8wPcgVD2AgotACUKLRaAzRQlyLQEFF otARfuEiovsB3S+X0Rqt/UL6fRFrf1KiVt/cVt6ItbejLX7BRLf+on8v9Jqt/VhL5f6QFb+qCW3o zVfN/Uglt6MBX7Gkv3PRpdJLO21tFNbnujo8Ua2vnzJqyPk9P7Cvyz7E9HilF1FLlyPFqtHLD8y3" + "jb9C6WPE+XoK39Waa29CNb+pWWa29BW/qWtvQtb+oGa29B7l6egAlbDqUAZFGqFAZoUWigZoUWi0 BkUaoUBkUWi0BmhRqiUBKBolAShRaFAShRaAGSloUBKFFAELFbgseZBsoQA1F0z6mj1MfDSfM+Wj UJOLtEsalfejJS5Gj52l1KXNnr8eLXML4/4bm1sjR5uNOa3OktRCC3YLzjsfM1+p4m8cHt1Lqddx Lhx7LqzwNlZRmSsARLc+rBRw6ZUt2fLj9SPTk1HFKMU6iiVXqlG4xVW2zq4rk220eWGqTb3rsWet UYVF22+ZB0hp8KT8RXJuwcfGT34kBg/FEAKimsX8SPmZNY/rXmB9EtFoUBKFF6+oAgLXIV+AJX4B a/BaAgr8lr8igiUWi+xQO8fpXoa6+pI/SvQ0ufqyjK5ehX19C1t/SVrn5oKlbrzYr5f6S9vUVt6A VL5v6v2NY48TSXN7D+b+o6af+Lj81+SEfax4/BxRhHpVmY7v+52k7aRiMOF/3I3K1VIsoKcHGStN Ee7fY2VmvgajH4WSUOxiMOLIorrKj0/EP81k9Dlp1eoxrvNGmXGUeFuPZFhBzmorm2kemcF4WeVb p0v7o1ixrj0//s7ZB43GnXYuPG8s1GPM7yxx8DJLqpNHbBjjDLirrC2NHgolHscMeTDNwVcBvHp4 Sik481zA8FA01uKKM0DVEoCUKKKAgLQoCUKLQoCUC0AJQotCgIKLQAlCigCUbnilBJyVWdNLj4s0 XJfKmezPw5GpS2gnsv8AUyaPmOLXMlHXNLjyN1XZGKKIWPMUWK3A1QKCAigoURpTkuTMgK34k+5l yb5sgBozJojCMgpAAAAgKQAAAPzAAIBYfWvMhYfUgPqotFXQJEERKNCvwUZ9h7GqFAShRaLX5Az7 lotCtvQIlbehaLQ6lHoitl6FXT1EPpiVcvQKlbehevqg1s/IvX+oIiX7lrZ+SC9y9H5IAvq/qNQd U10RP5v6irl6BX2dLmjkxpt/MqR6D4WOcoSuL6o90Ncmqku5Ma+vbHmxmyrFDilyPF+ujGLpbnmz 6ieaXzck+RcSuOWbySlN82rN6VL9RFt0ou2cunoXe/UMuyzJPImrjJs6Ycvi54N1FY1Z5P8Asbq/ QK7wzRUZxkri5Nl/UrxYyrZRo85AjvLLBY3DHGuJ7nX9TBS4utUeQgB8yUUdCiCigCUCgCUC0AIC 0AJQotACENCgIC0KAyKNUOTA9WKKx41KfpHubytQfHkdyr5Y9jy+I3NSl0GXI8k+Jkwc3bdslGiU UQ1EhqKAoooIBQAAKAqAoAyQ0RgZBQAo9ENJcU5Spvoc8EXLKkj6sUltGnS3ZLVfMWlnLI4rkubL LS1Fy4tkfRguJyS+nqzE6clFR+SO/mTR4I6PJKKk6V9wevLKcp2lt9gNH4QAFQLH6kQseYH148l5 GvYkPoXkjTXMDNCjVEAjXMV+TVCvyBmvyVFBRPYexa/Be4RO5evqWt/UIDvBfLEq/YQ+heRa/AUa 5+SL1/qD6+gXP1YREvwyvr5Itbeg/wCgL19QuXoFz9Srl6BV6+qNKLfJPqdsGFP5p8rVHqSUY2ls rEHz3GSW6fJE6+p9N414SlJbNHHUaVOHiY+9so8HT0D5+qLW3oH7BE/7J/0ar9yf9ACdPQ119SdP QAToa9ydAICgCAoAgKAIKKAIKKAIKKAICgCAoAgLQAgLQAgLQIIaiiGogAUAAUFEKAFAABCM0Qgy CkA3im8crOr1MmuFbWecoH0ceRLGoLl3OebUwh8kN+7PFxPuyExden9Wltw2Dy0Bia/KgAAVcyFX MD7GPfHHyRvuZw/wYeSN0RalCvyX3CRUShRR7FEHT0K1+CgShXMo9whX5CXIvX1C6Ad4fQvI1X4J D6F5Gu4VOvqVc/Vjr6hBE6ehrv6CtvQvfzQES3Xmairpd1+5FzXmahs15L8hY9edcMVXRpHtxwjP Cl0aPFmucairbo7aJ5VHhlFqO+7Mz4r0Z6WCV8kjOnaeJ9rZz1nHOCjBfL1MY5uONwaadpF/iPFn io5ZRXJExQU8sYvk2iTfFJvub0/+Yh5oqOssON8cY7ONnlcXw3XNHulJSjljBJSt39zk4OWHDSvv /cK5Qx/XxbOJJY6hCrbkj1TS49T5fsYmnWBRVtxA8zhJLdNGT1amVRWNK6dt/c8xUQFAEBQBAUEE BQUQFAEBQBBRQBAUAQUWhQFx43kmox5msmGUJqPNs9GlXhSurbWyLklTk1TnW77EV4mqdPmgUFRD USGokAFBQBQBAUBUBQBkjNEZEQhQFQFAEIUAQFAH5MAEAqIVAfZ02+CHkdF0Oek/y8H9jt7EVmhR r2HsVE9iF7lKMvqUtDr6hEr8lS5eYSCXIAugrb0LX4LX4A7Q+hGjMPoN+4VOvqEVIVt6BCtvQdfU o9wC6FXL0C6eoS29Ar26HLFSkp83VHtk+SXU+OufqdY58kaqXKyYuvpcpKH2OGuyRjFRVcVnklqM knxcW9Uc223bd7jEtZ6egTcXa57AdP7FRpSkm2nu2yrLOMeFPajHuOnoBrjk+Lf6uZVlknF/6VSM dQBueWU40zmUUUQUUAQFAEBQBKBQQQFBRAUAQFAEBQBCxTckkByA9cXFTWOD3f1SOWaaSeOC2vd9 zlGTi7RCCAoKIajyIajyIFFBQqCi0dsOCWXlsu4HGhwvsfQjp8cFVWzpHHFdBq4+XwvsSj6zxRfQ 8+TSKT+XYaPAQ65McscqaOdBGQaIBAdMeOWSVRW5mUXFtPoBgGkm3SLPHKD+ZUBzBaAH5IAEAqAA +zo/8tDyO559DvpYnp7gQdy+49wI1zJ7l9y9vMonuF08wuhV0CIuha29B7D2AexR3L3A6w+n1N+5 nH9K8zS6BQexfYBBj3KPcAug6egRfYCxi5Spbts7R08mt2lzNqtPi4mvmbPPkWXLvGe25E9346S0 8qdO9jnKLi6arc5KWfDLna5Hti1qIbqpplT3Pryr3BaLGMpbRTbroGmfcexpJuVJb2RprZregICl pgZBa2FAQFBRAUAQFAEBQBAUASgUAQFIABSACFAEKAAAAA0uRDSAFBUiK66fD4s//Vcz6CSiqSpI xp4cGJLq92bkZqst2zUTmuZ0iVa0AAy5Z8Sywe258yUeFtM+wfO1kOHLa6lg8wooA9mjheN7UjEt MlicnvJvY76XbAdPlkkr5GVccWmUIp1cu5jJp+PJJyeyPWpRbpO6Mt3a7kHgjpJTV8gfQjwwSTYL tPT+cAAqKAAPsfD99LHzPVR5fhv+WX+5nrXJEE7eYXQoKJ2IuhfYexQ7eQ9h7FoCew7lL7hE7l6+ o9ygdMf0rzNozj+g37BT2AooEKAggv2Klv8A2BeoHulBZJyi+Vo6rBBKqOMJcXDNc+TPVe3oYXm+ nnzaWEovbkjlixPFGLfOU0vQ9mSVR3OMpKUk/wCWLuynXx4syrLJF02Tw8qb5PZmZvim5d2Z6ehp I+h4cMU5Z3y5pfc8soOeGWdve+RrLkUtPihdtczpgUMmleOUq+awrn+lbnCKfNW/sdngWPTyjfE3 LmdPEh41KX8lGHGOLDwOdtyRBw1qrKlVfKjznp1zUsyp3UaPMWCAoCAAKAKQAQoAAAAQoAgKAICg CAoAgKAIAUCG0ZNICmsaucV9zJqDqSZKsfUXJB7ki7imVGRzUfmOnJFIyrbqbsoKEDxa/wCqPke0 +fq58WV/bYsHnIUAdp6h+GoQ2Ry8Sf8AqZkAejHqFixtR3k+py8afcwQYLLJKTttgyAPx4AIKAig fW+F/wCXfmez2PF8L/gy8z3dwJX4HsXuH1AgK+o7lE7lrmK5+Y9wh7l9x7hAF+5V0CC/YDri+k37 GMf0s33ChR19R7gACgQoKEbxZHje3Kz0wzxrZ0zxgYY9k8sJL5pWjhlzcXyxVRs5D3GJgAAp1AAA tu/UhQIAAAKQoAoAgKQACgCAoAgKAIAAAAAgKAICgCAoAhtGTQAqAA92lyXGm9z0Hy4ScXaPXhzX s2Zxr69JGLXcxKTsGNlJZyy54wWzthDUZvDjS5s+e3bs1ObnJt8zBQIULmgPRjwxhBTyc3yRZ4Yy SpUaTjNJSexubSqiK86wqWVr+VGo6ZNylLl0R1UseNOTe76DxOJUgOMtOr5UCZJx4t5tv7AD8KAA iopEUD6nwr+FNfdHv7+Z4PhHKa+6/c+gugDv5j3FcglyKHuK/I7FAnuOxV0HYCLoULp5F9gJX4KP YoR0x8mbMY+T8zp7hUKh2C6AEAUAAUAAAgT3KPcoiL0AIAAADqAUAAABQBAUAQFAEBQBACgQFAEB SAAAAAAAhQBAUAEaMo0AAKACbQAV0WWa6l8aRyANrcss5dTDYAEIUgAhSAWMmmn2LKcpO2zIIDbH E+4IyiAAg/HAAgqKRFA+n8I5z8j6S6Hy/hH15P8AafU9gHYexfYexRPYexfYdwJ7D2L7AB7B9fIF CHcAvuB0x9fM37mMX7m10Ci6GowcuSsi6Ho0rSTt9ALDAljk5LfhOSxcULT37Hsn9EvI88Ppgl9V kHCtyxXFJLuzWWvElXcYv4sfMo6PTu2lJNroc1jbjKXSPM9TjGOSU7ba6HPFUsOTi2TYHCEHOVLm TqerDHGslxds8vX1AgKCgAAAACAACgKAICgIgKAIAUCApAAKQACgCAoAgAAEKAICgAjRlGgAAAAA AAUKgAAjAAEBSACFIAIUgEBQB+NKQpkVAIAfQ+E/xZr/ANf3R9bufI+E/wAdr/1Pr8/7gH1Hce5f concdy+49wIX3HuPcB38x7gq6BD3KiIq6BW8R0RzxfsdPYAW2uRCgdoZqg4y7GVl4Y1Fb9zmAKWD 4ZJ9iBAdVlfiuffmg8i4JxS+o5ADphmoT4mc+oAAFBQABAABQAAAFIEAUAQFAEBSAAAAAAAAAAAA AAAAACFABAIoEKAANwxSl02O+HT7cU/RHdIxesax5VppMPTNPY9XFFdUFKL5Mz50eGeJx6mGj3Sx puznlwrgs3OtSx5CFIaAhTphgpzp8iDkD05sXFl4YLkjm9PkTW27GjiDq9PkSvhMywzirkqQ0cwb WHI1aiwNH4spCkFRSIoHt+E/5peTPsR6Hxvhn+aj5P8AB9qPTyALoRdCrp5Dt5FBdAugXQLoAS5F XQewXTyALoOwXTyKv2CCA9i+wGsfXyOnsYx82bCqAABR7hAEUiKAABQA6hAAAAAKBAUAQFAAABAA BQABAAAAAAAAAhQAIUAQpAAAKBAUgBFCKBDrp4ceRXyRzPVpKUWZ6+K3nzRxRtnilq8mWXBDazvr YcaTXQ8eni1mSe25M9aR9DDpklcm22Zy4JJ8UGepcgRdeXHmaVTVG8008La6mdYrUUudmcb4skYd EMz2fXneKUIpy6mT3atf4Z4TcRDvpP4hwNQm4XXUUe9NJyZjBNy4pvdnkeWTjw3sRZJKHCtkTF17 " + "ozfA5PmSScoLa2zxvNKlHojS1Mr35EwezaO0nuDy/rGtkgMo/CFIUqKikRQPV8Of/lR9fwfcXXyP haD/ADUPM+538wL38g/2D6+YfUoexfYPqO4D2HsH1HcC+w7+QHcAXuO4A3j5s6HOHNnT3Ae5fchU AAAAoAAAAAAigAUgAAoAAAAAAKAAAIAAKIUACFACICkAAAAAABCgAAAAAAIoQAHp0ytUeY64J8Mi VY9fCl0sw8cLuqZvi2slN7swrSaWxHkinRl8vuZUUpc9xFJzuWy3GLEoy4nzYpcWx0clGNstRy1k vkUTxHTNPjm2czUQIU6YsTyPsgORDvLCknwu2RYLVt0+w0cSHVYJOTXRdRLA0rvYaOIOqwSrdpAD 8SUhSCopEUD0aF1q8X+5H3l08z4Gj21OP/cvyfej0AvuPcLoF08yh38y9/MLp5hdPMA+vmXv5kX7 hfuEX3HfzHuPcC+49x7l7BWoczp7nOH1HTsARURdCgEAAKAAABSiFAIAAAAAoAFAAAAAAAAIAAKA AAAAAAAgQoAEKAIAUCAAACkAqAAAIADtjytOmeniUo0meA0ptGbF17nHbYkYJSs8y1Ekg9RI52dK 9UnGKt0jy583HsuRylNye7Mm5zn1BkANgenAn4To8p0WVxhwx2JR2SUYKLknJm40vppLq2ePid3e 4c5PmyYr2R+e3fymMicpJR2iupzjmSil07Gcme9oKkMHfl9MG13B5fGn3AwfiikKEVFIigddM6zw f/sj9Aufkj87h2yRf3P0X+pAXlXkF08g+vkO/kUVdAuhPYewF7DsO3kO3kEVdAugXTyHsBV0KuhE PYDcPqR0OcPq9Dp7BQvsT2L7AAABQAAAAFBCgAAAKQoAEKUAAAAQAAoAgAApCkAAAAAAgAAAAAAh QICkAAACgIAAAAAAAhSAAABAAFQ0kZNogKHE6StnT9NKt9jthjwQut2dU9jj1+l301jwTxOPMw0e 6fzJ7Hjkqk0b561LGOEFoG0fiykKQEaMo0BqH1I/RLr92fnI80fooO1EDXfzK+pOi+7HuUV9R38i d/MvcA+vkXv5EfUvcIexfYncvcB7F9iPqXuVWofV6HX2OUeZ07kF9ik7juBQQoApCgAgggBSIFFA BAAAApClAAAAAAAAFIAAHUAIAAKAAAAAgAABCgCFIAAAAoAAAAAAAIAAIAABAABtdDmaUiK92KSc Sttv7HjjnceRr9SzneF16cklCDZ4ZO3ZuWbiObkanOGgJYNI/GFIUgGkZNAWPM/QYn8kP9tn59cz 72F/4UX/AOi/AHVfyhdPMcq8gunkUVdPMdPUi6FT5AX3D6+ZFyXmX3AvfzHci/cvuUXuXuT3HuQa jzOpyj9XqdPcCl7k9x7gX3A9x7gVAIICoIgQFABRQAAAAApABQAAAAAAAAAAAAAABAAAACAUgAAA AAAAAAGgQAUEAAAACAACAACABUALwyfRgQhrhfZk+xBkHSWPhhb5nMCAAD8eUhSAaMmkBVzPuad/ 4EP9sfwj4S5n29M//Gx+SA9D6+RXz9DL6+Ze5RV+EF08iPr5F9gKugXQi/Yq6eRRV0C6eZF0KugF X7j3IuhV0CNR5rzOq/c5Q+peZ0XTzIq+5fcyuhV0AvbzCC6BdAKuhexnsXsBQRAClICiggIKACig gApTJQBSACggAoIUAQAAAAAACAACgACAAAAgAAADQIAKQAAAQCkAAEAAEACtQ3mjvKfzqK2VHmUu F2VZHxX1JR6HK5NckjL4catK23zOKyNW+rNLL3VkxXWVSS4uRnJy+VKjDzcT3JPKnGor1A57dQZb BUfkCkKQDSMlAp9rRu9Pi8j4p9jRP/x8fk/yB6b2X3Zb2fmRfyhdPMor6+ZW+Zn3L38wK+vkX2I+ ofUo1f4C6eRnv5Fv8AVdPIq6eRPYLp5BG484nRdDlHmjounkRWl0C6EXTyC6AaXQLoRdAgNLoF0I ugX7AUpABQQpRbBAQUpAUUEAFAAFBABQQAUEAFBABQQAUEAAAAAAAAIBSABAAAaIAAAIBRZAABBY AEAFICBUlJQVs5fqY9jWXdHNQQTWv1ER48O5jgTYeFVYV08eHceLDucViTI8IHbxI9wefwl9wB+c ABkUqIVAU+toX/40fsn+T5J9TQv/AMb1A9i6eRVziZfXyL7FFT5Dp5si5ryCf0lRfcr6+ZldPMvT 1Ar6l7k7+Y7+YVp9fIvsZ7l7hGlzXkdfY4p7nX2Cqv2LfLyM+xfYgq/YvsZ9i+wF9gT2HsUa9gQo FBABSkBBQSylApBYFBLAFAAFBABQQAUEAFIAECmShQAgRQQBVBAEACAUEAGgQAWwQAAQAUgIAAIB SEsASatHNJs7VaNqNJKKRNXHldojmzpm+o49QjXGRz7l+ShUAM8aBeGAA/MAAiqVEKgKfT0D/wDG X+4+WfT+H/wF/uA9j6+Ze/kZXL1K+pUX9kF08h3D5vyKKugXTzJ19CroBe3mPci6BdPMDXfzL3M+ 5b/IVq+Z0fU5dfU6XzA13L38jL6lfUC+xfYzfMoFsvsZ9i2BfYWQAaFkAGhZLAFLZkAasEAFKZFg aBABQQAUEAFBABQSwBQQBFIAAAIBRZLAFICAUqMlQFKQgFBAAAJYFIQWBSCyAUgJYGuKoh5ZVSMM JbEVh7k4LfMsjFuwOvgbczLwPuROT6jiknSdkXDwJA1eQFTH5UAEFKjJpAD6Xw5/4X9T/Y+afQ+H /wAKXmB7lyj5l5r1JHoF0KD6+ZX/ADE6eo7+ZRXzfkW+v2J3D6+QFXQqfInX0C6eQFXTzL7kXQLo Ea6vzOnfzOS6eZ09wrXcvcz38y9/MC9y3zM9y9wL3LZnuUClM9y2BRZABoEAFKZstgUEsWBSmSgU EFgUEAFBABQQAUEsAUEFhFBABQQAAQAUEAAqMlQGrICAUEsNgLBABSEsWBSWQAAQAGFOjLI9gEnZ grIBU2yq4vYymLa5GXT+OnFLsDn4k+wDL80AAgaRk0gB7/h/8OfmeA93w/6J+aA96/Yq6GV18i9f Qoq6DovMi6BdCi9/Mvcz09Svr5gXv5C/wR9Q+vkBpdPIq6GfYq5ryAsXyOvucV0Oq6AX3HuRdPMv uBS3zM+5fcC3zKZ7lvmBe47kvmO4GrBLFgaBmy2BSmQBoEsWBQQAasGSgWwSwBRZABbBABRZLARQ QAUEIBohABRZABbFkIBSozZUBbFkAFBLFgAQAASwABLAAEslgOpqk1uYb3KpMFZnszFmps52CNpl Uq6GVyKpcJl0vxfF/wDUDxF2AZfmgAEDSMlQFPb8P5T80eI9nw/nJeQH0OjHV+RFy9SvqUXr6BdC dx7FFXTzHT1IugXTzAr6+ZX1M3+S9wL38i9fQzfMt8/ICrp5HVPkcvY6Lp5AVPl5lXTzMroVPkBr 3F/kz28y+4Fvn5juT3HuBodyXzAGgQAUpkoFFkAFspABQQAUpkoFBABRZLAFsGbKEUEJYGrBCWBR ZLAFFksWBQSyWBbFkAFKjIQGrFkFgUhLFgUgsgFsWQgFJYJYFJYsjYAUItByIMSOZuRkKsXsVTro Qiml0I1fjfir/SDPiR/0gI//2Q==", "description": description }, format='json', HTTP_AUTHORIZATION=self.bearerToken ) def getMoments(self, memoryLineId, page=1): return self.client.get( '/api/v1/memory-lines/' + memoryLineId + "/moments/?page=" + str(page), format='json', HTTP_AUTHORIZATION=self.bearerToken ) def createComment(self, momentId, content): return self.client.post( "/api/v1/moments/" + momentId + "/comments/create/", data={ "content": content }, format="json", HTTP_AUTHORIZATION=self.bearerToken ) def getComments(self, momentId, page=1): return self.client.get( "/api/v1/moments/" + momentId + "/comments/?page=" + str(page), format="json", HTTP_AUTHORIZATION=self.bearerToken ) def deleteComment(self, commentId): return self.client.delete( "/api/v1/comments/" + commentId + "/", format="json", HTTP_AUTHORIZATION=self.bearerToken ) def createAccount(self, username, email): user = { "username": username, "password": "testpassword", "email": email, "name": "alkdjaldksaj", "photo": None, "nickname": "teste", "bio": "dksajldajdkl", "birth_date": "1997-09-05", "gender": "gender" } RememberAccount.create_account(**user) def configAuthentication(self, username): data = {'username': username, 'password':'testpassword'} response = self.client.post(self.urlLogin, data, format='json') body = json.loads(response.content) authorization = body["access"] self.bearerToken = "Bearer " + authorization def setUp(self): self.urlLogin = reverse('api.accounts.login') self.createAccount("typeuser", "type@hotmail.com") self.configAuthentication("typeuser") self.createType("Mistborn") body = json.loads(self.requestTypes().content) typeId = body["results"][0]["type"]["id"] self.createMemoryLine(typeId, "Era 1", "Primeira era de mistborn") memoryLines = json.loads(self.memoryLineByType(typeId).content) memoryLineId = memoryLines["results"][0]["id"] self.createMoment(memoryLineId, "Testando 1") self.moments = json.loads(self.getMoments(memoryLineId).content) def test_create_comment(self): momentId = self.moments["results"][0]["id"] self.createComment(momentId, "Memory Line are awesome") comment = Comment.objects.first() self.assertEqual(str(comment.moment.id), momentId) self.assertEqual(comment.content, "Memory Line are awesome") def test_get_comments(self): momentId = self.moments["results"][0]["id"] self.createComment(momentId, "Memory Line are awesome 1") self.createComment(momentId, "Memory Line are awesome 2") self.createComment(momentId, "Memory Line are awesome 3") self.createComment(momentId, "Memory Line are awesome 4") comments = json.loads(self.getComments(momentId).content) self.assertEqual(comments["count"], 4) self.assertEqual(comments["previous"], None) self.assertEqual(comments["next"], None) self.assertEqual(comments["results"][0]["content"], "Memory Line are awesome 4") self.assertEqual(comments["results"][1]["content"], "Memory Line are awesome 3") self.assertEqual(comments["results"][2]["content"], "Memory Line are awesome 2") self.assertEqual(comments["results"][3]["content"], "Memory Line are awesome 1") def test_get_comments_has_second_page(self): momentId = self.moments["results"][0]["id"] self.createComment(momentId, "Memory Line are awesome 1") self.createComment(momentId, "Memory Line are awesome 2") self.createComment(momentId, "Memory Line are awesome 3") self.createComment(momentId, "Memory Line are awesome 4") self.createComment(momentId, "Memory Line are awesome 5") self.createComment(momentId, "Memory Line are awesome 6") self.createComment(momentId, "Memory Line are awesome 7") comments = json.loads(self.getComments(momentId).content) self.assertEqual(comments["count"], 7) self.assertEqual(comments["previous"], None) self.assertTrue(comments["next"] != None) self.assertEqual(comments["results"][0]["content"], "Memory Line are awesome 7") self.assertEqual(comments["results"][1]["content"], "Memory Line are awesome 6") self.assertEqual(comments["results"][2]["content"], "Memory Line are awesome 5") self.assertEqual(comments["results"][3]["content"], "Memory Line are awesome 4") self.assertEqual(comments["results"][4]["content"], "Memory Line are awesome 3") self.assertEqual(comments["results"][5]["content"], "Memory Line are awesome 2") def test_get_comments_second_page(self): momentId = self.moments["results"][0]["id"] self.createComment(momentId, "Memory Line are awesome 1") self.createComment(momentId, "Memory Line are awesome 2") self.createComment(momentId, "Memory Line are awesome 3") self.createComment(momentId, "Memory Line are awesome 4") self.createComment(momentId, "Memory Line are awesome 5") self.createComment(momentId, "Memory Line are awesome 6") self.createComment(momentId, "Memory Line are awesome 7") comments = json.loads(self.getComments(momentId,page=2).content) self.assertEqual(comments["count"], 7) self.assertEqual(comments["next"], None) self.assertTrue(comments["previous"] != None) self.assertEqual(comments["results"][0]["content"], "Memory Line are awesome 1") def test_get_comments_second_page_and_dont_have_more_than_six(self): momentId = self.moments["results"][0]["id"] self.createComment(momentId, "Memory Line are awesome 1") self.createComment(momentId, "Memory Line are awesome 2") self.createComment(momentId, "Memory Line are awesome 3") self.createComment(momentId, "Memory Line are awesome 4") request = self.getComments(momentId,page=2) self.assertEqual(request.status_code, 404) def test_delete_comment(self): self.assertEqual(True, True) momentId = self.moments["results"][0]["id"] self.createComment(momentId, "Não deletado") body = json.loads(self.createComment(momentId, "Testando de outra conta").content) commentId = body["id"] self.deleteComment(commentId) comments = json.loads(self.getComments(momentId).content) self.assertEqual(comments["count"], 1) self.assertEqual(comments["next"], None) self.assertEqual(comments["previous"], None) self.assertEqual(comments["results"][0]["content"], "Não deletado") def test_create_comment_moment_from_another_account_should_not_work(self): momentId = self.moments["results"][0]["id"] self.createAccount("hoid", "hoid@hotmail.com") self.configAuthentication("hoid") request = self.createComment(momentId, "Testando de outra conta") self.assertEqual(request.status_code, 403) def test_get_comments_from_another_account_should_not_work(self): momentId = self.moments["results"][0]["id"] self.createAccount("hoid", "hoid@hotmail.com") self.configAuthentication("hoid") request = self.getComments(momentId) self.assertEqual(request.status_code, 403) def test_deleting_comment_from_another_account_should_not_work(self): momentId = self.moments["results"][0]["id"] body = json.loads(self.createComment(momentId, "Testando de outra conta").content) commentId = body["id"] self.createAccount("hoid", "hoid@hotmail.com") self.configAuthentication("hoid") request = self.deleteComment(commentId) self.assertEqual(request.status_code, 403)

133.637097

5,800

0.859423

1,948

33,142

14.587782

0.533368

0.012317

0.016012

0.024633

0.150121

0.14523

0.13784

0.131647

0.120386

0.108245

0

0.10624

0.082644

33,142

247

5,801

134.178138

0.828438

0

0.421569

0

0.02451

0.763895

0.698389

0

1

0

0.151961

1

0.102941

false

0.009804

0.02451

0.029412

0.166667

0

1

null

0

1

0

1

null

1

0

6

14e8fb06882ffea404b31596c1861d91aa0af527

188,370

py

Python

ion_functions/data/opt_functions_tscor.py

steinermg/ion-functions

cea532ad9af51e86768572c8deb48547d99567c5

[ "Apache-2.0" ]

10

2015-04-03T15:32:21.000Z

2018-11-21T11:57:26.000Z

ion_functions/data/opt_functions_tscor.py

steinermg/ion-functions

cea532ad9af51e86768572c8deb48547d99567c5

[ "Apache-2.0" ]

8

2015-01-07T15:19:22.000Z

2015-12-08T18:14:04.000Z

ion_functions/data/opt_functions_tscor.py

steinermg/ion-functions

cea532ad9af51e86768572c8deb48547d99567c5

[ "Apache-2.0" ]

17

2015-01-14T16:23:00.000Z

2021-07-19T08:26:52.000Z

#!/usr/bin/env python """ @package ion_functions.data.opt_functions_tscor @file ion_functions/data/opt_functions_tscor.py @author Christopher Wingard @brief Module containing OPTAA related temperature and salinity correction coefficients. """ import numpy as np # Returns the Temperature and Salinity (T/S) correction coefficients as a # dictionary for use in the ion_functions/data/opt_functions module. Values are # from published literature and are available in: # # WET Labs, Inc. 2009. AC Meter Protocol Document, Revision P. # # The original file is composed of 4 columns corresponding to the wavelength # (nm) and the wavelength dependent temperature correction coefficients, # salinity correction coefficients for the 'c' channel, and salinity correction # coefficients for the 'a' channel, respectively. The dictionary uses the # wavelength as the key. # # Created April 25, 2013 by Christopher Wingard from vendor provided TS4.cor # file, available in ion_functions/data/matlab_scripts/optaa. # awk '{ # printf("tscor[%4.1f] = [%.5f, %.6f, %.6f]\n", $1, $2, $3, $4) # }' TS4.cor > temp.txt # # Modified April 29, 2014 by Russell Desiderio: padded dictionary at both ends # with nan values so that acs (OPTAA) wavelength values outside of the # wavelength range of the empirically determined temperature and salinity # correction coefficients will not cause a dictionary look-up error. no_value = np.nan # initialize dictionary tscor = {} # populate dictionary tscor[380.0] = [no_value, no_value, no_value] tscor[380.1] = [no_value, no_value, no_value] tscor[380.2] = [no_value, no_value, no_value] tscor[380.3] = [no_value, no_value, no_value] tscor[380.4] = [no_value, no_value, no_value] tscor[380.5] = [no_value, no_value, no_value] tscor[380.6] = [no_value, no_value, no_value] tscor[380.7] = [no_value, no_value, no_value] tscor[380.8] = [no_value, no_value, no_value] tscor[380.9] = [no_value, no_value, no_value] tscor[381.0] = [no_value, no_value, no_value] tscor[381.1] = [no_value, no_value, no_value] tscor[381.2] = [no_value, no_value, no_value] tscor[381.3] = [no_value, no_value, no_value] tscor[381.4] = [no_value, no_value, no_value] tscor[381.5] = [no_value, no_value, no_value] tscor[381.6] = [no_value, no_value, no_value] tscor[381.7] = [no_value, no_value, no_value] tscor[381.8] = [no_value, no_value, no_value] tscor[381.9] = [no_value, no_value, no_value] tscor[382.0] = [no_value, no_value, no_value] tscor[382.1] = [no_value, no_value, no_value] tscor[382.2] = [no_value, no_value, no_value] tscor[382.3] = [no_value, no_value, no_value] tscor[382.4] = [no_value, no_value, no_value] tscor[382.5] = [no_value, no_value, no_value] tscor[382.6] = [no_value, no_value, no_value] tscor[382.7] = [no_value, no_value, no_value] tscor[382.8] = [no_value, no_value, no_value] tscor[382.9] = [no_value, no_value, no_value] tscor[383.0] = [no_value, no_value, no_value] tscor[383.1] = [no_value, no_value, no_value] tscor[383.2] = [no_value, no_value, no_value] tscor[383.3] = [no_value, no_value, no_value] tscor[383.4] = [no_value, no_value, no_value] tscor[383.5] = [no_value, no_value, no_value] tscor[383.6] = [no_value, no_value, no_value] tscor[383.7] = [no_value, no_value, no_value] tscor[383.8] = [no_value, no_value, no_value] tscor[383.9] = [no_value, no_value, no_value] tscor[384.0] = [no_value, no_value, no_value] tscor[384.1] = [no_value, no_value, no_value] tscor[384.2] = [no_value, no_value, no_value] tscor[384.3] = [no_value, no_value, no_value] tscor[384.4] = [no_value, no_value, no_value] tscor[384.5] = [no_value, no_value, no_value] tscor[384.6] = [no_value, no_value, no_value] tscor[384.7] = [no_value, no_value, no_value] tscor[384.8] = [no_value, no_value, no_value] tscor[384.9] = [no_value, no_value, no_value] tscor[385.0] = [no_value, no_value, no_value] tscor[385.1] = [no_value, no_value, no_value] tscor[385.2] = [no_value, no_value, no_value] tscor[385.3] = [no_value, no_value, no_value] tscor[385.4] = [no_value, no_value, no_value] tscor[385.5] = [no_value, no_value, no_value] tscor[385.6] = [no_value, no_value, no_value] tscor[385.7] = [no_value, no_value, no_value] tscor[385.8] = [no_value, no_value, no_value] tscor[385.9] = [no_value, no_value, no_value] tscor[386.0] = [no_value, no_value, no_value] tscor[386.1] = [no_value, no_value, no_value] tscor[386.2] = [no_value, no_value, no_value] tscor[386.3] = [no_value, no_value, no_value] tscor[386.4] = [no_value, no_value, no_value] tscor[386.5] = [no_value, no_value, no_value] tscor[386.6] = [no_value, no_value, no_value] tscor[386.7] = [no_value, no_value, no_value] tscor[386.8] = [no_value, no_value, no_value] tscor[386.9] = [no_value, no_value, no_value] tscor[387.0] = [no_value, no_value, no_value] tscor[387.1] = [no_value, no_value, no_value] tscor[387.2] = [no_value, no_value, no_value] tscor[387.3] = [no_value, no_value, no_value] tscor[387.4] = [no_value, no_value, no_value] tscor[387.5] = [no_value, no_value, no_value] tscor[387.6] = [no_value, no_value, no_value] tscor[387.7] = [no_value, no_value, no_value] tscor[387.8] = [no_value, no_value, no_value] tscor[387.9] = [no_value, no_value, no_value] tscor[388.0] = [no_value, no_value, no_value] tscor[388.1] = [no_value, no_value, no_value] tscor[388.2] = [no_value, no_value, no_value] tscor[388.3] = [no_value, no_value, no_value] tscor[388.4] = [no_value, no_value, no_value] tscor[388.5] = [no_value, no_value, no_value] tscor[388.6] = [no_value, no_value, no_value] tscor[388.7] = [no_value, no_value, no_value] tscor[388.8] = [no_value, no_value, no_value] tscor[388.9] = [no_value, no_value, no_value] tscor[389.0] = [no_value, no_value, no_value] tscor[389.1] = [no_value, no_value, no_value] tscor[389.2] = [no_value, no_value, no_value] tscor[389.3] = [no_value, no_value, no_value] tscor[389.4] = [no_value, no_value, no_value] tscor[389.5] = [no_value, no_value, no_value] tscor[389.6] = [no_value, no_value, no_value] tscor[389.7] = [no_value, no_value, no_value] tscor[389.8] = [no_value, no_value, no_value] tscor[389.9] = [no_value, no_value, no_value] tscor[390.0] = [no_value, no_value, no_value] tscor[390.1] = [no_value, no_value, no_value] tscor[390.2] = [no_value, no_value, no_value] tscor[390.3] = [no_value, no_value, no_value] tscor[390.4] = [no_value, no_value, no_value] tscor[390.5] = [no_value, no_value, no_value] tscor[390.6] = [no_value, no_value, no_value] tscor[390.7] = [no_value, no_value, no_value] tscor[390.8] = [no_value, no_value, no_value] tscor[390.9] = [no_value, no_value, no_value] tscor[391.0] = [no_value, no_value, no_value] tscor[391.1] = [no_value, no_value, no_value] tscor[391.2] = [no_value, no_value, no_value] tscor[391.3] = [no_value, no_value, no_value] tscor[391.4] = [no_value, no_value, no_value] tscor[391.5] = [no_value, no_value, no_value] tscor[391.6] = [no_value, no_value, no_value] tscor[391.7] = [no_value, no_value, no_value] tscor[391.8] = [no_value, no_value, no_value] tscor[391.9] = [no_value, no_value, no_value] tscor[392.0] = [no_value, no_value, no_value] tscor[392.1] = [no_value, no_value, no_value] tscor[392.2] = [no_value, no_value, no_value] tscor[392.3] = [no_value, no_value, no_value] tscor[392.4] = [no_value, no_value, no_value] tscor[392.5] = [no_value, no_value, no_value] tscor[392.6] = [no_value, no_value, no_value] tscor[392.7] = [no_value, no_value, no_value] tscor[392.8] = [no_value, no_value, no_value] tscor[392.9] = [no_value, no_value, no_value] tscor[393.0] = [no_value, no_value, no_value] tscor[393.1] = [no_value, no_value, no_value] tscor[393.2] = [no_value, no_value, no_value] tscor[393.3] = [no_value, no_value, no_value] tscor[393.4] = [no_value, no_value, no_value] tscor[393.5] = [no_value, no_value, no_value] tscor[393.6] = [no_value, no_value, no_value] tscor[393.7] = [no_value, no_value, no_value] tscor[393.8] = [no_value, no_value, no_value] tscor[393.9] = [no_value, no_value, no_value] tscor[394.0] = [no_value, no_value, no_value] tscor[394.1] = [no_value, no_value, no_value] tscor[394.2] = [no_value, no_value, no_value] tscor[394.3] = [no_value, no_value, no_value] tscor[394.4] = [no_value, no_value, no_value] tscor[394.5] = [no_value, no_value, no_value] tscor[394.6] = [no_value, no_value, no_value] tscor[394.7] = [no_value, no_value, no_value] tscor[394.8] = [no_value, no_value, no_value] tscor[394.9] = [no_value, no_value, no_value] tscor[395.0] = [no_value, no_value, no_value] tscor[395.1] = [no_value, no_value, no_value] tscor[395.2] = [no_value, no_value, no_value] tscor[395.3] = [no_value, no_value, no_value] tscor[395.4] = [no_value, no_value, no_value] tscor[395.5] = [no_value, no_value, no_value] tscor[395.6] = [no_value, no_value, no_value] tscor[395.7] = [no_value, no_value, no_value] tscor[395.8] = [no_value, no_value, no_value] tscor[395.9] = [no_value, no_value, no_value] tscor[396.0] = [no_value, no_value, no_value] tscor[396.1] = [no_value, no_value, no_value] tscor[396.2] = [no_value, no_value, no_value] tscor[396.3] = [no_value, no_value, no_value] tscor[396.4] = [no_value, no_value, no_value] tscor[396.5] = [no_value, no_value, no_value] tscor[396.6] = [no_value, no_value, no_value] tscor[396.7] = [no_value, no_value, no_value] tscor[396.8] = [no_value, no_value, no_value] tscor[396.9] = [no_value, no_value, no_value] tscor[397.0] = [no_value, no_value, no_value] tscor[397.1] = [no_value, no_value, no_value] tscor[397.2] = [no_value, no_value, no_value] tscor[397.3] = [no_value, no_value, no_value] tscor[397.4] = [no_value, no_value, no_value] tscor[397.5] = [no_value, no_value, no_value] tscor[397.6] = [no_value, no_value, no_value] tscor[397.7] = [no_value, no_value, no_value] tscor[397.8] = [no_value, no_value, no_value] tscor[397.9] = [no_value, no_value, no_value] tscor[398.0] = [no_value, no_value, no_value] tscor[398.1] = [no_value, no_value, no_value] tscor[398.2] = [no_value, no_value, no_value] tscor[398.3] = [no_value, no_value, no_value] tscor[398.4] = [no_value, no_value, no_value] tscor[398.5] = [no_value, no_value, no_value] tscor[398.6] = [no_value, no_value, no_value] tscor[398.7] = [no_value, no_value, no_value] tscor[398.8] = [no_value, no_value, no_value] tscor[398.9] = [no_value, no_value, no_value] tscor[399.0] = [no_value, no_value, no_value] tscor[399.1] = [no_value, no_value, no_value] tscor[399.2] = [no_value, no_value, no_value] tscor[399.3] = [no_value, no_value, no_value] tscor[399.4] = [no_value, no_value, no_value] tscor[399.5] = [no_value, no_value, no_value] tscor[399.6] = [no_value, no_value, no_value] tscor[399.7] = [no_value, no_value, no_value] tscor[399.8] = [no_value, no_value, no_value] tscor[399.9] = [no_value, no_value, no_value] tscor[400.0] = [0.00010, -0.000012, 0.000033] tscor[400.1] = [0.00010, -0.000012, 0.000033] tscor[400.2] = [0.00010, -0.000012, 0.000033] tscor[400.3] = [0.00010, -0.000012, 0.000033] tscor[400.4] = [0.00010, -0.000013, 0.000033] tscor[400.5] = [0.00010, -0.000013, 0.000033] tscor[400.6] = [0.00010, -0.000013, 0.000033] tscor[400.7] = [0.00010, -0.000013, 0.000033] tscor[400.8] = [0.00010, -0.000013, 0.000033] tscor[400.9] = [0.00010, -0.000013, 0.000033] tscor[401.0] = [0.00010, -0.000014, 0.000034] tscor[401.1] = [0.00010, -0.000014, 0.000034] tscor[401.2] = [0.00010, -0.000014, 0.000034] tscor[401.3] = [0.00010, -0.000014, 0.000034] tscor[401.4] = [0.00010, -0.000014, 0.000034] tscor[401.5] = [0.00010, -0.000014, 0.000034] tscor[401.6] = [0.00010, -0.000014, 0.000034] tscor[401.7] = [0.00010, -0.000015, 0.000034] tscor[401.8] = [0.00010, -0.000015, 0.000034] tscor[401.9] = [0.00010, -0.000015, 0.000034] tscor[402.0] = [0.00010, -0.000015, 0.000034] tscor[402.1] = [0.00010, -0.000015, 0.000034] tscor[402.2] = [0.00010, -0.000015, 0.000034] tscor[402.3] = [0.00010, -0.000016, 0.000034] tscor[402.4] = [0.00010, -0.000016, 0.000034] tscor[402.5] = [0.00010, -0.000016, 0.000034] tscor[402.6] = [0.00010, -0.000016, 0.000034] tscor[402.7] = [0.00010, -0.000016, 0.000034] tscor[402.8] = [0.00010, -0.000017, 0.000034] tscor[402.9] = [0.00010, -0.000017, 0.000034] tscor[403.0] = [0.00010, -0.000017, 0.000035] tscor[403.1] = [0.00009, -0.000017, 0.000035] tscor[403.2] = [0.00009, -0.000017, 0.000035] tscor[403.3] = [0.00009, -0.000018, 0.000035] tscor[403.4] = [0.00009, -0.000018, 0.000035] tscor[403.5] = [0.00009, -0.000018, 0.000035] tscor[403.6] = [0.00009, -0.000018, 0.000035] tscor[403.7] = [0.00009, -0.000018, 0.000035] tscor[403.8] = [0.00009, -0.000019, 0.000035] tscor[403.9] = [0.00009, -0.000019, 0.000035] tscor[404.0] = [0.00009, -0.000019, 0.000035] tscor[404.1] = [0.00009, -0.000019, 0.000035] tscor[404.2] = [0.00009, -0.000019, 0.000035] tscor[404.3] = [0.00009, -0.000019, 0.000035] tscor[404.4] = [0.00009, -0.000019, 0.000035] tscor[404.5] = [0.00009, -0.000019, 0.000036] tscor[404.6] = [0.00008, -0.000019, 0.000036] tscor[404.7] = [0.00008, -0.000019, 0.000036] tscor[404.8] = [0.00008, -0.000019, 0.000036] tscor[404.9] = [0.00008, -0.000019, 0.000036] tscor[405.0] = [0.00008, -0.000020, 0.000036] tscor[405.1] = [0.00008, -0.000020, 0.000036] tscor[405.2] = [0.00008, -0.000020, 0.000036] tscor[405.3] = [0.00008, -0.000020, 0.000036] tscor[405.4] = [0.00008, -0.000020, 0.000036] tscor[405.5] = [0.00008, -0.000020, 0.000037] tscor[405.6] = [0.00007, -0.000020, 0.000037] tscor[405.7] = [0.00007, -0.000020, 0.000037] tscor[405.8] = [0.00007, -0.000020, 0.000037] tscor[405.9] = [0.00007, -0.000020, 0.000037] tscor[406.0] = [0.00007, -0.000020, 0.000037] tscor[406.1] = [0.00007, -0.000020, 0.000037] tscor[406.2] = [0.00007, -0.000020, 0.000037] tscor[406.3] = [0.00007, -0.000020, 0.000037] tscor[406.4] = [0.00006, -0.000020, 0.000037] tscor[406.5] = [0.00006, -0.000020, 0.000038] tscor[406.6] = [0.00006, -0.000020, 0.000038] tscor[406.7] = [0.00006, -0.000020, 0.000038] tscor[406.8] = [0.00006, -0.000020, 0.000038] tscor[406.9] = [0.00006, -0.000020, 0.000038] tscor[407.0] = [0.00006, -0.000020, 0.000038] tscor[407.1] = [0.00005, -0.000020, 0.000038] tscor[407.2] = [0.00005, -0.000020, 0.000038] tscor[407.3] = [0.00005, -0.000020, 0.000038] tscor[407.4] = [0.00005, -0.000020, 0.000038] tscor[407.5] = [0.00005, -0.000020, 0.000039] tscor[407.6] = [0.00005, -0.000020, 0.000039] tscor[407.7] = [0.00004, -0.000020, 0.000039] tscor[407.8] = [0.00004, -0.000020, 0.000039] tscor[407.9] = [0.00004, -0.000020, 0.000039] tscor[408.0] = [0.00004, -0.000020, 0.000039] tscor[408.1] = [0.00004, -0.000020, 0.000039] tscor[408.2] = [0.00004, -0.000020, 0.000039] tscor[408.3] = [0.00004, -0.000020, 0.000039] tscor[408.4] = [0.00004, -0.000020, 0.000039] tscor[408.5] = [0.00004, -0.000020, 0.000039] tscor[408.6] = [0.00003, -0.000020, 0.000039] tscor[408.7] = [0.00003, -0.000020, 0.000039] tscor[408.8] = [0.00003, -0.000020, 0.000039] tscor[408.9] = [0.00003, -0.000020, 0.000039] tscor[409.0] = [0.00003, -0.000020, 0.000040] tscor[409.1] = [0.00003, -0.000020, 0.000040] tscor[409.2] = [0.00003, -0.000020, 0.000040] tscor[409.3] = [0.00003, -0.000020, 0.000040] tscor[409.4] = [0.00003, -0.000020, 0.000040] tscor[409.5] = [0.00003, -0.000020, 0.000040] tscor[409.6] = [0.00002, -0.000020, 0.000040] tscor[409.7] = [0.00002, -0.000020, 0.000040] tscor[409.8] = [0.00002, -0.000020, 0.000040] tscor[409.9] = [0.00002, -0.000020, 0.000040] tscor[410.0] = [0.00002, -0.000020, 0.000040] tscor[410.1] = [0.00002, -0.000020, 0.000040] tscor[410.2] = [0.00002, -0.000020, 0.000040] tscor[410.3] = [0.00002, -0.000020, 0.000040] tscor[410.4] = [0.00002, -0.000020, 0.000040] tscor[410.5] = [0.00002, -0.000020, 0.000040] tscor[410.6] = [0.00002, -0.000020, 0.000040] tscor[410.7] = [0.00002, -0.000020, 0.000040] tscor[410.8] = [0.00002, -0.000020, 0.000040] tscor[410.9] = [0.00002, -0.000020, 0.000040] tscor[411.0] = [0.00003, -0.000021, 0.000040] tscor[411.1] = [0.00003, -0.000021, 0.000040] tscor[411.2] = [0.00003, -0.000021, 0.000040] tscor[411.3] = [0.00003, -0.000021, 0.000040] tscor[411.4] = [0.00003, -0.000021, 0.000040] tscor[411.5] = [0.00003, -0.000021, 0.000040] tscor[411.6] = [0.00003, -0.000021, 0.000040] tscor[411.7] = [0.00003, -0.000021, 0.000040] tscor[411.8] = [0.00003, -0.000021, 0.000040] tscor[411.9] = [0.00003, -0.000021, 0.000040] tscor[412.0] = [0.00003, -0.000021, 0.000040] tscor[412.1] = [0.00003, -0.000021, 0.000040] tscor[412.2] = [0.00003, -0.000021, 0.000040] tscor[412.3] = [0.00003, -0.000021, 0.000040] tscor[412.4] = [0.00003, -0.000021, 0.000040] tscor[412.5] = [0.00004, -0.000021, 0.000040] tscor[412.6] = [0.00004, -0.000021, 0.000040] tscor[412.7] = [0.00004, -0.000021, 0.000040] tscor[412.8] = [0.00004, -0.000021, 0.000040] tscor[412.9] = [0.00004, -0.000021, 0.000040] tscor[413.0] = [0.00004, -0.000022, 0.000040] tscor[413.1] = [0.00004, -0.000022, 0.000040] tscor[413.2] = [0.00004, -0.000022, 0.000040] tscor[413.3] = [0.00004, -0.000022, 0.000040] tscor[413.4] = [0.00004, -0.000022, 0.000040] tscor[413.5] = [0.00005, -0.000022, 0.000040] tscor[413.6] = [0.00005, -0.000022, 0.000040] tscor[413.7] = [0.00005, -0.000022, 0.000040] tscor[413.8] = [0.00005, -0.000022, 0.000040] tscor[413.9] = [0.00005, -0.000022, 0.000040] tscor[414.0] = [0.00005, -0.000022, 0.000040] tscor[414.1] = [0.00005, -0.000022, 0.000040] tscor[414.2] = [0.00005, -0.000022, 0.000040] tscor[414.3] = [0.00005, -0.000022, 0.000040] tscor[414.4] = [0.00005, -0.000023, 0.000040] tscor[414.5] = [0.00005, -0.000023, 0.000040] tscor[414.6] = [0.00005, -0.000023, 0.000040] tscor[414.7] = [0.00005, -0.000023, 0.000040] tscor[414.8] = [0.00005, -0.000023, 0.000040] tscor[414.9] = [0.00005, -0.000023, 0.000040] tscor[415.0] = [0.00005, -0.000024, 0.000040] tscor[415.1] = [0.00004, -0.000024, 0.000040] tscor[415.2] = [0.00004, -0.000024, 0.000040] tscor[415.3] = [0.00004, -0.000024, 0.000040] tscor[415.4] = [0.00004, -0.000024, 0.000040] tscor[415.5] = [0.00004, -0.000024, 0.000040] tscor[415.6] = [0.00004, -0.000024, 0.000040] tscor[415.7] = [0.00004, -0.000025, 0.000040] tscor[415.8] = [0.00004, -0.000025, 0.000040] tscor[415.9] = [0.00004, -0.000025, 0.000040] tscor[416.0] = [0.00004, -0.000025, 0.000040] tscor[416.1] = [0.00004, -0.000025, 0.000040] tscor[416.2] = [0.00004, -0.000025, 0.000040] tscor[416.3] = [0.00004, -0.000025, 0.000040] tscor[416.4] = [0.00004, -0.000025, 0.000040] tscor[416.5] = [0.00004, -0.000026, 0.000040] tscor[416.6] = [0.00003, -0.000026, 0.000040] tscor[416.7] = [0.00003, -0.000026, 0.000040] tscor[416.8] = [0.00003, -0.000026, 0.000040] tscor[416.9] = [0.00003, -0.000026, 0.000040] tscor[417.0] = [0.00003, -0.000026, 0.000040] tscor[417.1] = [0.00003, -0.000026, 0.000040] tscor[417.2] = [0.00003, -0.000026, 0.000040] tscor[417.3] = [0.00003, -0.000026, 0.000040] tscor[417.4] = [0.00003, -0.000026, 0.000040] tscor[417.5] = [0.00003, -0.000027, 0.000040] tscor[417.6] = [0.00002, -0.000027, 0.000040] tscor[417.7] = [0.00002, -0.000027, 0.000040] tscor[417.8] = [0.00002, -0.000027, 0.000040] tscor[417.9] = [0.00002, -0.000027, 0.000040] tscor[418.0] = [0.00002, -0.000027, 0.000040] tscor[418.1] = [0.00002, -0.000027, 0.000040] tscor[418.2] = [0.00002, -0.000027, 0.000040] tscor[418.3] = [0.00002, -0.000027, 0.000040] tscor[418.4] = [0.00002, -0.000027, 0.000039] tscor[418.5] = [0.00002, -0.000027, 0.000039] tscor[418.6] = [0.00002, -0.000027, 0.000039] tscor[418.7] = [0.00002, -0.000027, 0.000039] tscor[418.8] = [0.00002, -0.000027, 0.000039] tscor[418.9] = [0.00002, -0.000027, 0.000039] tscor[419.0] = [0.00002, -0.000028, 0.000039] tscor[419.1] = [0.00001, -0.000028, 0.000038] tscor[419.2] = [0.00001, -0.000028, 0.000038] tscor[419.3] = [0.00001, -0.000028, 0.000038] tscor[419.4] = [0.00001, -0.000028, 0.000038] tscor[419.5] = [0.00001, -0.000028, 0.000038] tscor[419.6] = [0.00001, -0.000028, 0.000038] tscor[419.7] = [0.00001, -0.000028, 0.000037] tscor[419.8] = [0.00001, -0.000028, 0.000037] tscor[419.9] = [0.00001, -0.000028, 0.000037] tscor[420.0] = [0.00001, -0.000028, 0.000037] tscor[420.1] = [0.00001, -0.000028, 0.000037] tscor[420.2] = [0.00001, -0.000028, 0.000037] tscor[420.3] = [0.00001, -0.000028, 0.000037] tscor[420.4] = [0.00001, -0.000028, 0.000037] tscor[420.5] = [0.00001, -0.000029, 0.000037] tscor[420.6] = [0.00001, -0.000029, 0.000036] tscor[420.7] = [0.00001, -0.000029, 0.000036] tscor[420.8] = [0.00001, -0.000029, 0.000036] tscor[420.9] = [0.00001, -0.000029, 0.000036] tscor[421.0] = [0.00001, -0.000029, 0.000036] tscor[421.1] = [0.00000, -0.000029, 0.000036] tscor[421.2] = [0.00000, -0.000029, 0.000036] tscor[421.3] = [0.00000, -0.000029, 0.000036] tscor[421.4] = [0.00000, -0.000029, 0.000036] tscor[421.5] = [0.00000, -0.000030, 0.000036] tscor[421.6] = [0.00000, -0.000030, 0.000035] tscor[421.7] = [0.00000, -0.000030, 0.000035] tscor[421.8] = [0.00000, -0.000030, 0.000035] tscor[421.9] = [0.00000, -0.000030, 0.000035] tscor[422.0] = [0.00000, -0.000030, 0.000035] tscor[422.1] = [0.00000, -0.000030, 0.000035] tscor[422.2] = [0.00000, -0.000030, 0.000035] tscor[422.3] = [0.00000, -0.000030, 0.000035] tscor[422.4] = [0.00000, -0.000030, 0.000035] tscor[422.5] = [0.00000, -0.000030, 0.000035] tscor[422.6] = [0.00000, -0.000030, 0.000034] tscor[422.7] = [0.00000, -0.000030, 0.000034] tscor[422.8] = [0.00000, -0.000030, 0.000034] tscor[422.9] = [0.00000, -0.000030, 0.000034] tscor[423.0] = [-0.00001, -0.000031, 0.000034] tscor[423.1] = [-0.00001, -0.000031, 0.000034] tscor[423.2] = [-0.00001, -0.000031, 0.000034] tscor[423.3] = [-0.00001, -0.000031, 0.000034] tscor[423.4] = [-0.00001, -0.000031, 0.000034] tscor[423.5] = [-0.00001, -0.000031, 0.000034] tscor[423.6] = [-0.00001, -0.000031, 0.000033] tscor[423.7] = [-0.00001, -0.000031, 0.000033] tscor[423.8] = [-0.00001, -0.000031, 0.000033] tscor[423.9] = [-0.00001, -0.000031, 0.000033] tscor[424.0] = [-0.00001, -0.000031, 0.000033] tscor[424.1] = [-0.00001, -0.000031, 0.000033] tscor[424.2] = [-0.00001, -0.000031, 0.000033] tscor[424.3] = [-0.00001, -0.000031, 0.000033] tscor[424.4] = [-0.00001, -0.000031, 0.000033] tscor[424.5] = [-0.00001, -0.000031, 0.000033] tscor[424.6] = [-0.00001, -0.000031, 0.000033] tscor[424.7] = [-0.00001, -0.000031, 0.000033] tscor[424.8] = [-0.00001, -0.000031, 0.000033] tscor[424.9] = [-0.00001, -0.000031, 0.000033] tscor[425.0] = [-0.00001, -0.000031, 0.000033] tscor[425.1] = [-0.00001, -0.000030, 0.000032] tscor[425.2] = [-0.00001, -0.000030, 0.000032] tscor[425.3] = [-0.00001, -0.000030, 0.000032] tscor[425.4] = [-0.00001, -0.000030, 0.000032] tscor[425.5] = [-0.00001, -0.000030, 0.000032] tscor[425.6] = [-0.00001, -0.000030, 0.000032] tscor[425.7] = [-0.00001, -0.000030, 0.000032] tscor[425.8] = [-0.00001, -0.000030, 0.000032] tscor[425.9] = [-0.00001, -0.000030, 0.000032] tscor[426.0] = [-0.00001, -0.000030, 0.000032] tscor[426.1] = [-0.00001, -0.000030, 0.000032] tscor[426.2] = [-0.00001, -0.000030, 0.000032] tscor[426.3] = [-0.00001, -0.000030, 0.000032] tscor[426.4] = [-0.00001, -0.000030, 0.000032] tscor[426.5] = [-0.00001, -0.000030, 0.000032] tscor[426.6] = [-0.00001, -0.000030, 0.000032] tscor[426.7] = [-0.00001, -0.000030, 0.000032] tscor[426.8] = [-0.00001, -0.000030, 0.000032] tscor[426.9] = [-0.00001, -0.000030, 0.000032] tscor[427.0] = [-0.00001, -0.000030, 0.000032] tscor[427.1] = [0.00000, -0.000030, 0.000031] tscor[427.2] = [0.00000, -0.000030, 0.000031] tscor[427.3] = [0.00000, -0.000030, 0.000031] tscor[427.4] = [0.00000, -0.000030, 0.000031] tscor[427.5] = [0.00000, -0.000030, 0.000031] tscor[427.6] = [0.00000, -0.000030, 0.000031] tscor[427.7] = [0.00000, -0.000030, 0.000031] tscor[427.8] = [0.00000, -0.000030, 0.000031] tscor[427.9] = [0.00000, -0.000030, 0.000031] tscor[428.0] = [0.00000, -0.000030, 0.000031] tscor[428.1] = [0.00000, -0.000030, 0.000031] tscor[428.2] = [0.00000, -0.000030, 0.000031] tscor[428.3] = [0.00000, -0.000030, 0.000031] tscor[428.4] = [0.00000, -0.000030, 0.000031] tscor[428.5] = [0.00000, -0.000031, 0.000031] tscor[428.6] = [0.00000, -0.000031, 0.000030] tscor[428.7] = [0.00000, -0.000031, 0.000030] tscor[428.8] = [0.00000, -0.000031, 0.000030] tscor[428.9] = [0.00000, -0.000031, 0.000030] tscor[429.0] = [-0.00001, -0.000031, 0.000030] tscor[429.1] = [-0.00001, -0.000031, 0.000030] tscor[429.2] = [-0.00001, -0.000031, 0.000030] tscor[429.3] = [-0.00001, -0.000031, 0.000030] tscor[429.4] = [-0.00001, -0.000031, 0.000030] tscor[429.5] = [-0.00001, -0.000032, 0.000030] tscor[429.6] = [-0.00001, -0.000032, 0.000029] tscor[429.7] = [-0.00001, -0.000032, 0.000029] tscor[429.8] = [-0.00001, -0.000032, 0.000029] tscor[429.9] = [-0.00001, -0.000032, 0.000029] tscor[430.0] = [-0.00001, -0.000032, 0.000029] tscor[430.1] = [-0.00001, -0.000032, 0.000029] tscor[430.2] = [-0.00001, -0.000032, 0.000029] tscor[430.3] = [-0.00001, -0.000032, 0.000029] tscor[430.4] = [-0.00001, -0.000032, 0.000029] tscor[430.5] = [-0.00001, -0.000033, 0.000029] tscor[430.6] = [-0.00001, -0.000033, 0.000029] tscor[430.7] = [-0.00001, -0.000033, 0.000029] tscor[430.8] = [-0.00001, -0.000033, 0.000029] tscor[430.9] = [-0.00001, -0.000033, 0.000029] tscor[431.0] = [-0.00001, -0.000033, 0.000029] tscor[431.1] = [-0.00001, -0.000033, 0.000028] tscor[431.2] = [-0.00001, -0.000033, 0.000028] tscor[431.3] = [-0.00001, -0.000033, 0.000028] tscor[431.4] = [-0.00001, -0.000033, 0.000028] tscor[431.5] = [-0.00001, -0.000034, 0.000028] tscor[431.6] = [-0.00001, -0.000034, 0.000028] tscor[431.7] = [-0.00001, -0.000034, 0.000028] tscor[431.8] = [-0.00001, -0.000034, 0.000028] tscor[431.9] = [-0.00001, -0.000034, 0.000028] tscor[432.0] = [-0.00001, -0.000034, 0.000028] tscor[432.1] = [-0.00001, -0.000034, 0.000028] tscor[432.2] = [-0.00001, -0.000034, 0.000028] tscor[432.3] = [-0.00001, -0.000034, 0.000028] tscor[432.4] = [-0.00001, -0.000034, 0.000028] tscor[432.5] = [-0.00001, -0.000034, 0.000028] tscor[432.6] = [-0.00001, -0.000034, 0.000028] tscor[432.7] = [-0.00001, -0.000034, 0.000028] tscor[432.8] = [-0.00001, -0.000034, 0.000028] tscor[432.9] = [-0.00001, -0.000034, 0.000028] tscor[433.0] = [-0.00002, -0.000034, 0.000028] tscor[433.1] = [-0.00002, -0.000034, 0.000028] tscor[433.2] = [-0.00002, -0.000034, 0.000028] tscor[433.3] = [-0.00002, -0.000034, 0.000028] tscor[433.4] = [-0.00002, -0.000034, 0.000028] tscor[433.5] = [-0.00002, -0.000034, 0.000028] tscor[433.6] = [-0.00002, -0.000034, 0.000028] tscor[433.7] = [-0.00002, -0.000034, 0.000028] tscor[433.8] = [-0.00002, -0.000034, 0.000028] tscor[433.9] = [-0.00002, -0.000034, 0.000028] tscor[434.0] = [-0.00002, -0.000034, 0.000028] tscor[434.1] = [-0.00002, -0.000034, 0.000028] tscor[434.2] = [-0.00002, -0.000034, 0.000028] tscor[434.3] = [-0.00002, -0.000034, 0.000028] tscor[434.4] = [-0.00002, -0.000034, 0.000028] tscor[434.5] = [-0.00002, -0.000034, 0.000028] tscor[434.6] = [-0.00002, -0.000034, 0.000028] tscor[434.7] = [-0.00002, -0.000034, 0.000028] tscor[434.8] = [-0.00002, -0.000034, 0.000028] tscor[434.9] = [-0.00002, -0.000034, 0.000028] tscor[435.0] = [-0.00003, -0.000035, 0.000028] tscor[435.1] = [-0.00003, -0.000035, 0.000027] tscor[435.2] = [-0.00003, -0.000035, 0.000027] tscor[435.3] = [-0.00003, -0.000035, 0.000027] tscor[435.4] = [-0.00003, -0.000035, 0.000027] tscor[435.5] = [-0.00003, -0.000035, 0.000027] tscor[435.6] = [-0.00003, -0.000035, 0.000027] tscor[435.7] = [-0.00003, -0.000035, 0.000027] tscor[435.8] = [-0.00003, -0.000035, 0.000027] tscor[435.9] = [-0.00003, -0.000035, 0.000027] tscor[436.0] = [-0.00003, -0.000035, 0.000027] tscor[436.1] = [-0.00003, -0.000035, 0.000027] tscor[436.2] = [-0.00003, -0.000035, 0.000027] tscor[436.3] = [-0.00003, -0.000035, 0.000027] tscor[436.4] = [-0.00003, -0.000035, 0.000027] tscor[436.5] = [-0.00003, -0.000035, 0.000027] tscor[436.6] = [-0.00003, -0.000035, 0.000027] tscor[436.7] = [-0.00003, -0.000035, 0.000027] tscor[436.8] = [-0.00003, -0.000035, 0.000027] tscor[436.9] = [-0.00003, -0.000035, 0.000027] tscor[437.0] = [-0.00003, -0.000035, 0.000027] tscor[437.1] = [-0.00003, -0.000035, 0.000026] tscor[437.2] = [-0.00003, -0.000035, 0.000026] tscor[437.3] = [-0.00003, -0.000035, 0.000026] tscor[437.4] = [-0.00003, -0.000035, 0.000026] tscor[437.5] = [-0.00003, -0.000035, 0.000026] tscor[437.6] = [-0.00003, -0.000035, 0.000026] tscor[437.7] = [-0.00003, -0.000035, 0.000026] tscor[437.8] = [-0.00003, -0.000035, 0.000026] tscor[437.9] = [-0.00003, -0.000035, 0.000026] tscor[438.0] = [-0.00003, -0.000035, 0.000026] tscor[438.1] = [-0.00003, -0.000035, 0.000026] tscor[438.2] = [-0.00003, -0.000035, 0.000026] tscor[438.3] = [-0.00003, -0.000035, 0.000026] tscor[438.4] = [-0.00003, -0.000035, 0.000026] tscor[438.5] = [-0.00003, -0.000035, 0.000026] tscor[438.6] = [-0.00003, -0.000035, 0.000026] tscor[438.7] = [-0.00003, -0.000035, 0.000026] tscor[438.8] = [-0.00003, -0.000035, 0.000026] tscor[438.9] = [-0.00003, -0.000035, 0.000026] tscor[439.0] = [-0.00003, -0.000035, 0.000026] tscor[439.1] = [-0.00002, -0.000035, 0.000025] tscor[439.2] = [-0.00002, -0.000035, 0.000025] tscor[439.3] = [-0.00002, -0.000035, 0.000025] tscor[439.4] = [-0.00002, -0.000035, 0.000025] tscor[439.5] = [-0.00002, -0.000035, 0.000025] tscor[439.6] = [-0.00002, -0.000035, 0.000025] tscor[439.7] = [-0.00002, -0.000035, 0.000025] tscor[439.8] = [-0.00002, -0.000035, 0.000025] tscor[439.9] = [-0.00002, -0.000035, 0.000025] tscor[440.0] = [-0.00002, -0.000035, 0.000025] tscor[440.1] = [-0.00002, -0.000035, 0.000025] tscor[440.2] = [-0.00002, -0.000035, 0.000025] tscor[440.3] = [-0.00002, -0.000035, 0.000025] tscor[440.4] = [-0.00002, -0.000035, 0.000025] tscor[440.5] = [-0.00002, -0.000035, 0.000025] tscor[440.6] = [-0.00002, -0.000035, 0.000025] tscor[440.7] = [-0.00002, -0.000035, 0.000025] tscor[440.8] = [-0.00002, -0.000035, 0.000025] tscor[440.9] = [-0.00002, -0.000035, 0.000025] tscor[441.0] = [-0.00002, -0.000035, 0.000025] tscor[441.1] = [-0.00001, -0.000035, 0.000024] tscor[441.2] = [-0.00001, -0.000035, 0.000024] tscor[441.3] = [-0.00001, -0.000035, 0.000024] tscor[441.4] = [-0.00001, -0.000035, 0.000024] tscor[441.5] = [-0.00001, -0.000035, 0.000024] tscor[441.6] = [-0.00001, -0.000035, 0.000024] tscor[441.7] = [-0.00001, -0.000035, 0.000024] tscor[441.8] = [-0.00001, -0.000035, 0.000024] tscor[441.9] = [-0.00001, -0.000035, 0.000024] tscor[442.0] = [-0.00001, -0.000035, 0.000024] tscor[442.1] = [-0.00001, -0.000035, 0.000024] tscor[442.2] = [-0.00001, -0.000035, 0.000024] tscor[442.3] = [-0.00001, -0.000035, 0.000024] tscor[442.4] = [-0.00001, -0.000035, 0.000024] tscor[442.5] = [-0.00001, -0.000035, 0.000024] tscor[442.6] = [-0.00001, -0.000035, 0.000024] tscor[442.7] = [-0.00001, -0.000035, 0.000024] tscor[442.8] = [-0.00001, -0.000035, 0.000024] tscor[442.9] = [-0.00001, -0.000035, 0.000024] tscor[443.0] = [-0.00001, -0.000035, 0.000024] tscor[443.1] = [-0.00001, -0.000035, 0.000023] tscor[443.2] = [-0.00001, -0.000035, 0.000023] tscor[443.3] = [-0.00001, -0.000035, 0.000023] tscor[443.4] = [-0.00001, -0.000035, 0.000023] tscor[443.5] = [-0.00001, -0.000035, 0.000023] tscor[443.6] = [-0.00001, -0.000035, 0.000023] tscor[443.7] = [-0.00001, -0.000035, 0.000023] tscor[443.8] = [-0.00001, -0.000035, 0.000023] tscor[443.9] = [-0.00001, -0.000035, 0.000023] tscor[444.0] = [-0.00001, -0.000035, 0.000023] tscor[444.1] = [-0.00001, -0.000035, 0.000023] tscor[444.2] = [-0.00001, -0.000035, 0.000023] tscor[444.3] = [-0.00001, -0.000035, 0.000023] tscor[444.4] = [-0.00001, -0.000035, 0.000023] tscor[444.5] = [-0.00001, -0.000035, 0.000023] tscor[444.6] = [-0.00001, -0.000035, 0.000023] tscor[444.7] = [-0.00001, -0.000035, 0.000023] tscor[444.8] = [-0.00001, -0.000035, 0.000023] tscor[444.9] = [-0.00001, -0.000035, 0.000023] tscor[445.0] = [-0.00001, -0.000036, 0.000023] tscor[445.1] = [-0.00001, -0.000036, 0.000022] tscor[445.2] = [-0.00001, -0.000036, 0.000022] tscor[445.3] = [-0.00001, -0.000036, 0.000022] tscor[445.4] = [-0.00001, -0.000036, 0.000022] tscor[445.5] = [-0.00001, -0.000036, 0.000022] tscor[445.6] = [-0.00001, -0.000036, 0.000022] tscor[445.7] = [-0.00001, -0.000036, 0.000022] tscor[445.8] = [-0.00001, -0.000036, 0.000022] tscor[445.9] = [-0.00001, -0.000036, 0.000022] tscor[446.0] = [-0.00001, -0.000036, 0.000022] tscor[446.1] = [-0.00001, -0.000036, 0.000022] tscor[446.2] = [-0.00001, -0.000036, 0.000022] tscor[446.3] = [-0.00001, -0.000036, 0.000022] tscor[446.4] = [-0.00001, -0.000036, 0.000022] tscor[446.5] = [-0.00001, -0.000036, 0.000022] tscor[446.6] = [-0.00001, -0.000036, 0.000022] tscor[446.7] = [-0.00001, -0.000036, 0.000022] tscor[446.8] = [-0.00001, -0.000036, 0.000022] tscor[446.9] = [-0.00001, -0.000036, 0.000022] tscor[447.0] = [-0.00002, -0.000037, 0.000022] tscor[447.1] = [-0.00002, -0.000037, 0.000022] tscor[447.2] = [-0.00002, -0.000037, 0.000022] tscor[447.3] = [-0.00002, -0.000037, 0.000022] tscor[447.4] = [-0.00002, -0.000037, 0.000022] tscor[447.5] = [-0.00002, -0.000037, 0.000022] tscor[447.6] = [-0.00002, -0.000037, 0.000022] tscor[447.7] = [-0.00002, -0.000037, 0.000022] tscor[447.8] = [-0.00002, -0.000037, 0.000022] tscor[447.9] = [-0.00002, -0.000037, 0.000022] tscor[448.0] = [-0.00002, -0.000037, 0.000022] tscor[448.1] = [-0.00002, -0.000037, 0.000022] tscor[448.2] = [-0.00002, -0.000037, 0.000022] tscor[448.3] = [-0.00002, -0.000037, 0.000022] tscor[448.4] = [-0.00002, -0.000037, 0.000022] tscor[448.5] = [-0.00002, -0.000037, 0.000022] tscor[448.6] = [-0.00002, -0.000037, 0.000022] tscor[448.7] = [-0.00002, -0.000037, 0.000022] tscor[448.8] = [-0.00002, -0.000037, 0.000022] tscor[448.9] = [-0.00002, -0.000037, 0.000022] tscor[449.0] = [-0.00002, -0.000038, 0.000022] tscor[449.1] = [-0.00002, -0.000038, 0.000021] tscor[449.2] = [-0.00002, -0.000038, 0.000021] tscor[449.3] = [-0.00002, -0.000038, 0.000021] tscor[449.4] = [-0.00002, -0.000038, 0.000021] tscor[449.5] = [-0.00002, -0.000038, 0.000021] tscor[449.6] = [-0.00002, -0.000038, 0.000021] tscor[449.7] = [-0.00002, -0.000038, 0.000021] tscor[449.8] = [-0.00002, -0.000038, 0.000021] tscor[449.9] = [-0.00002, -0.000038, 0.000021] tscor[450.0] = [-0.00002, -0.000038, 0.000021] tscor[450.1] = [-0.00002, -0.000038, 0.000021] tscor[450.2] = [-0.00002, -0.000038, 0.000021] tscor[450.3] = [-0.00002, -0.000038, 0.000021] tscor[450.4] = [-0.00002, -0.000038, 0.000021] tscor[450.5] = [-0.00002, -0.000038, 0.000021] tscor[450.6] = [-0.00002, -0.000038, 0.000021] tscor[450.7] = [-0.00002, -0.000038, 0.000021] tscor[450.8] = [-0.00002, -0.000038, 0.000021] tscor[450.9] = [-0.00002, -0.000038, 0.000021] tscor[451.0] = [-0.00003, -0.000039, 0.000021] tscor[451.1] = [-0.00003, -0.000039, 0.000021] tscor[451.2] = [-0.00003, -0.000039, 0.000021] tscor[451.3] = [-0.00003, -0.000039, 0.000021] tscor[451.4] = [-0.00003, -0.000039, 0.000021] tscor[451.5] = [-0.00003, -0.000039, 0.000021] tscor[451.6] = [-0.00003, -0.000039, 0.000021] tscor[451.7] = [-0.00003, -0.000039, 0.000021] tscor[451.8] = [-0.00003, -0.000039, 0.000021] tscor[451.9] = [-0.00003, -0.000039, 0.000021] tscor[452.0] = [-0.00003, -0.000039, 0.000021] tscor[452.1] = [-0.00003, -0.000039, 0.000021] tscor[452.2] = [-0.00003, -0.000039, 0.000021] tscor[452.3] = [-0.00003, -0.000039, 0.000021] tscor[452.4] = [-0.00003, -0.000039, 0.000021] tscor[452.5] = [-0.00003, -0.000039, 0.000021] tscor[452.6] = [-0.00003, -0.000039, 0.000021] tscor[452.7] = [-0.00003, -0.000039, 0.000021] tscor[452.8] = [-0.00003, -0.000039, 0.000021] tscor[452.9] = [-0.00003, -0.000039, 0.000021] tscor[453.0] = [-0.00003, -0.000039, 0.000021] tscor[453.1] = [-0.00003, -0.000038, 0.000020] tscor[453.2] = [-0.00003, -0.000038, 0.000020] tscor[453.3] = [-0.00003, -0.000038, 0.000020] tscor[453.4] = [-0.00003, -0.000038, 0.000020] tscor[453.5] = [-0.00003, -0.000038, 0.000020] tscor[453.6] = [-0.00003, -0.000038, 0.000020] tscor[453.7] = [-0.00003, -0.000038, 0.000020] tscor[453.8] = [-0.00003, -0.000038, 0.000020] tscor[453.9] = [-0.00003, -0.000038, 0.000020] tscor[454.0] = [-0.00003, -0.000038, 0.000020] tscor[454.1] = [-0.00003, -0.000038, 0.000020] tscor[454.2] = [-0.00003, -0.000038, 0.000020] tscor[454.3] = [-0.00003, -0.000038, 0.000020] tscor[454.4] = [-0.00003, -0.000038, 0.000020] tscor[454.5] = [-0.00003, -0.000038, 0.000020] tscor[454.6] = [-0.00003, -0.000038, 0.000020] tscor[454.7] = [-0.00003, -0.000038, 0.000020] tscor[454.8] = [-0.00003, -0.000038, 0.000020] tscor[454.9] = [-0.00003, -0.000038, 0.000020] tscor[455.0] = [-0.00003, -0.000038, 0.000020] tscor[455.1] = [-0.00002, -0.000037, 0.000020] tscor[455.2] = [-0.00002, -0.000037, 0.000020] tscor[455.3] = [-0.00002, -0.000037, 0.000020] tscor[455.4] = [-0.00002, -0.000037, 0.000020] tscor[455.5] = [-0.00002, -0.000037, 0.000020] tscor[455.6] = [-0.00002, -0.000037, 0.000020] tscor[455.7] = [-0.00002, -0.000037, 0.000020] tscor[455.8] = [-0.00002, -0.000037, 0.000020] tscor[455.9] = [-0.00002, -0.000037, 0.000020] tscor[456.0] = [-0.00002, -0.000037, 0.000020] tscor[456.1] = [-0.00002, -0.000037, 0.000020] tscor[456.2] = [-0.00002, -0.000037, 0.000020] tscor[456.3] = [-0.00002, -0.000037, 0.000020] tscor[456.4] = [-0.00002, -0.000037, 0.000020] tscor[456.5] = [-0.00002, -0.000037, 0.000020] tscor[456.6] = [-0.00002, -0.000037, 0.000020] tscor[456.7] = [-0.00002, -0.000037, 0.000020] tscor[456.8] = [-0.00002, -0.000037, 0.000020] tscor[456.9] = [-0.00002, -0.000037, 0.000020] tscor[457.0] = [-0.00002, -0.000037, 0.000020] tscor[457.1] = [-0.00001, -0.000037, 0.000019] tscor[457.2] = [-0.00001, -0.000037, 0.000019] tscor[457.3] = [-0.00001, -0.000037, 0.000019] tscor[457.4] = [-0.00001, -0.000037, 0.000019] tscor[457.5] = [-0.00001, -0.000037, 0.000019] tscor[457.6] = [-0.00001, -0.000037, 0.000019] tscor[457.7] = [-0.00001, -0.000037, 0.000019] tscor[457.8] = [-0.00001, -0.000037, 0.000019] tscor[457.9] = [-0.00001, -0.000037, 0.000019] tscor[458.0] = [-0.00001, -0.000037, 0.000019] tscor[458.1] = [-0.00001, -0.000037, 0.000019] tscor[458.2] = [-0.00001, -0.000037, 0.000019] tscor[458.3] = [-0.00001, -0.000037, 0.000019] tscor[458.4] = [-0.00001, -0.000037, 0.000019] tscor[458.5] = [-0.00001, -0.000037, 0.000019] tscor[458.6] = [-0.00001, -0.000037, 0.000019] tscor[458.7] = [-0.00001, -0.000037, 0.000019] tscor[458.8] = [-0.00001, -0.000037, 0.000019] tscor[458.9] = [-0.00001, -0.000037, 0.000019] tscor[459.0] = [-0.00001, -0.000037, 0.000019] tscor[459.1] = [-0.00001, -0.000037, 0.000018] tscor[459.2] = [-0.00001, -0.000037, 0.000018] tscor[459.3] = [-0.00001, -0.000037, 0.000018] tscor[459.4] = [-0.00001, -0.000037, 0.000018] tscor[459.5] = [-0.00001, -0.000037, 0.000018] tscor[459.6] = [-0.00001, -0.000037, 0.000018] tscor[459.7] = [-0.00001, -0.000037, 0.000018] tscor[459.8] = [-0.00001, -0.000037, 0.000018] tscor[459.9] = [-0.00001, -0.000037, 0.000018] tscor[460.0] = [-0.00001, -0.000037, 0.000018] tscor[460.1] = [-0.00001, -0.000037, 0.000018] tscor[460.2] = [-0.00001, -0.000037, 0.000018] tscor[460.3] = [-0.00001, -0.000037, 0.000018] tscor[460.4] = [-0.00001, -0.000037, 0.000018] tscor[460.5] = [-0.00001, -0.000037, 0.000018] tscor[460.6] = [-0.00001, -0.000037, 0.000018] tscor[460.7] = [-0.00001, -0.000037, 0.000018] tscor[460.8] = [-0.00001, -0.000037, 0.000018] tscor[460.9] = [-0.00001, -0.000037, 0.000018] tscor[461.0] = [-0.00001, -0.000038, 0.000018] tscor[461.1] = [-0.00001, -0.000038, 0.000018] tscor[461.2] = [-0.00001, -0.000038, 0.000018] tscor[461.3] = [-0.00001, -0.000038, 0.000018] tscor[461.4] = [-0.00001, -0.000038, 0.000018] tscor[461.5] = [-0.00001, -0.000038, 0.000018] tscor[461.6] = [-0.00001, -0.000038, 0.000018] tscor[461.7] = [-0.00001, -0.000038, 0.000018] tscor[461.8] = [-0.00001, -0.000038, 0.000018] tscor[461.9] = [-0.00001, -0.000038, 0.000018] tscor[462.0] = [-0.00001, -0.000038, 0.000018] tscor[462.1] = [-0.00001, -0.000038, 0.000018] tscor[462.2] = [-0.00001, -0.000038, 0.000018] tscor[462.3] = [-0.00001, -0.000038, 0.000018] tscor[462.4] = [-0.00001, -0.000038, 0.000018] tscor[462.5] = [-0.00001, -0.000038, 0.000018] tscor[462.6] = [-0.00001, -0.000038, 0.000018] tscor[462.7] = [-0.00001, -0.000038, 0.000018] tscor[462.8] = [-0.00001, -0.000038, 0.000018] tscor[462.9] = [-0.00001, -0.000038, 0.000018] tscor[463.0] = [-0.00001, -0.000039, 0.000018] tscor[463.1] = [-0.00001, -0.000039, 0.000018] tscor[463.2] = [-0.00001, -0.000039, 0.000018] tscor[463.3] = [-0.00001, -0.000039, 0.000018] tscor[463.4] = [-0.00001, -0.000039, 0.000018] tscor[463.5] = [-0.00001, -0.000039, 0.000018] tscor[463.6] = [-0.00001, -0.000039, 0.000018] tscor[463.7] = [-0.00001, -0.000039, 0.000018] tscor[463.8] = [-0.00001, -0.000039, 0.000018] tscor[463.9] = [-0.00001, -0.000039, 0.000018] tscor[464.0] = [-0.00001, -0.000039, 0.000018] tscor[464.1] = [-0.00001, -0.000039, 0.000018] tscor[464.2] = [-0.00001, -0.000039, 0.000018] tscor[464.3] = [-0.00001, -0.000039, 0.000018] tscor[464.4] = [-0.00001, -0.000039, 0.000018] tscor[464.5] = [-0.00001, -0.000039, 0.000018] tscor[464.6] = [-0.00001, -0.000039, 0.000018] tscor[464.7] = [-0.00001, -0.000039, 0.000018] tscor[464.8] = [-0.00001, -0.000039, 0.000018] tscor[464.9] = [-0.00001, -0.000039, 0.000018] tscor[465.0] = [-0.00001, -0.000040, 0.000018] tscor[465.1] = [-0.00001, -0.000040, 0.000017] tscor[465.2] = [-0.00001, -0.000040, 0.000017] tscor[465.3] = [-0.00001, -0.000040, 0.000017] tscor[465.4] = [-0.00001, -0.000040, 0.000017] tscor[465.5] = [-0.00001, -0.000040, 0.000017] tscor[465.6] = [-0.00001, -0.000040, 0.000017] tscor[465.7] = [-0.00001, -0.000040, 0.000017] tscor[465.8] = [-0.00001, -0.000040, 0.000017] tscor[465.9] = [-0.00001, -0.000040, 0.000017] tscor[466.0] = [-0.00001, -0.000040, 0.000017] tscor[466.1] = [-0.00001, -0.000040, 0.000017] tscor[466.2] = [-0.00001, -0.000040, 0.000017] tscor[466.3] = [-0.00001, -0.000040, 0.000017] tscor[466.4] = [-0.00001, -0.000040, 0.000017] tscor[466.5] = [-0.00001, -0.000040, 0.000017] tscor[466.6] = [-0.00001, -0.000040, 0.000017] tscor[466.7] = [-0.00001, -0.000040, 0.000017] tscor[466.8] = [-0.00001, -0.000040, 0.000017] tscor[466.9] = [-0.00001, -0.000040, 0.000017] tscor[467.0] = [-0.00001, -0.000040, 0.000017] tscor[467.1] = [-0.00001, -0.000040, 0.000016] tscor[467.2] = [-0.00001, -0.000040, 0.000016] tscor[467.3] = [-0.00001, -0.000040, 0.000016] tscor[467.4] = [-0.00001, -0.000040, 0.000016] tscor[467.5] = [-0.00001, -0.000040, 0.000016] tscor[467.6] = [-0.00001, -0.000040, 0.000016] tscor[467.7] = [-0.00001, -0.000040, 0.000016] tscor[467.8] = [-0.00001, -0.000040, 0.000016] tscor[467.9] = [-0.00001, -0.000040, 0.000016] tscor[468.0] = [-0.00001, -0.000040, 0.000016] tscor[468.1] = [-0.00001, -0.000040, 0.000016] tscor[468.2] = [-0.00001, -0.000040, 0.000016] tscor[468.3] = [-0.00001, -0.000040, 0.000016] tscor[468.4] = [-0.00001, -0.000040, 0.000016] tscor[468.5] = [-0.00001, -0.000040, 0.000016] tscor[468.6] = [-0.00001, -0.000040, 0.000016] tscor[468.7] = [-0.00001, -0.000040, 0.000016] tscor[468.8] = [-0.00001, -0.000040, 0.000016] tscor[468.9] = [-0.00001, -0.000040, 0.000016] tscor[469.0] = [-0.00001, -0.000040, 0.000016] tscor[469.1] = [0.00000, -0.000039, 0.000015] tscor[469.2] = [0.00000, -0.000039, 0.000015] tscor[469.3] = [0.00000, -0.000039, 0.000015] tscor[469.4] = [0.00000, -0.000039, 0.000015] tscor[469.5] = [0.00000, -0.000039, 0.000015] tscor[469.6] = [0.00000, -0.000039, 0.000015] tscor[469.7] = [0.00000, -0.000039, 0.000015] tscor[469.8] = [0.00000, -0.000039, 0.000015] tscor[469.9] = [0.00000, -0.000039, 0.000015] tscor[470.0] = [0.00000, -0.000039, 0.000015] tscor[470.1] = [0.00000, -0.000039, 0.000015] tscor[470.2] = [0.00000, -0.000039, 0.000015] tscor[470.3] = [0.00000, -0.000039, 0.000015] tscor[470.4] = [0.00000, -0.000039, 0.000015] tscor[470.5] = [0.00000, -0.000039, 0.000015] tscor[470.6] = [0.00000, -0.000039, 0.000015] tscor[470.7] = [0.00000, -0.000039, 0.000015] tscor[470.8] = [0.00000, -0.000039, 0.000015] tscor[470.9] = [0.00000, -0.000039, 0.000015] tscor[471.0] = [0.00000, -0.000039, 0.000015] tscor[471.1] = [0.00000, -0.000039, 0.000014] tscor[471.2] = [0.00000, -0.000039, 0.000014] tscor[471.3] = [0.00000, -0.000039, 0.000014] tscor[471.4] = [0.00000, -0.000039, 0.000014] tscor[471.5] = [0.00000, -0.000039, 0.000014] tscor[471.6] = [0.00000, -0.000039, 0.000014] tscor[471.7] = [0.00000, -0.000039, 0.000014] tscor[471.8] = [0.00000, -0.000039, 0.000014] tscor[471.9] = [0.00000, -0.000039, 0.000014] tscor[472.0] = [0.00000, -0.000039, 0.000014] tscor[472.1] = [0.00000, -0.000039, 0.000014] tscor[472.2] = [0.00000, -0.000039, 0.000014] tscor[472.3] = [0.00000, -0.000039, 0.000014] tscor[472.4] = [0.00000, -0.000039, 0.000014] tscor[472.5] = [0.00000, -0.000039, 0.000014] tscor[472.6] = [0.00000, -0.000039, 0.000014] tscor[472.7] = [0.00000, -0.000039, 0.000014] tscor[472.8] = [0.00000, -0.000039, 0.000014] tscor[472.9] = [0.00000, -0.000039, 0.000014] tscor[473.0] = [0.00000, -0.000040, 0.000014] tscor[473.1] = [0.00000, -0.000040, 0.000014] tscor[473.2] = [0.00000, -0.000040, 0.000014] tscor[473.3] = [0.00000, -0.000040, 0.000014] tscor[473.4] = [0.00000, -0.000040, 0.000014] tscor[473.5] = [0.00000, -0.000040, 0.000014] tscor[473.6] = [0.00000, -0.000040, 0.000014] tscor[473.7] = [0.00000, -0.000040, 0.000014] tscor[473.8] = [0.00000, -0.000040, 0.000014] tscor[473.9] = [0.00000, -0.000040, 0.000014] tscor[474.0] = [0.00000, -0.000040, 0.000014] tscor[474.1] = [0.00000, -0.000040, 0.000014] tscor[474.2] = [0.00000, -0.000040, 0.000014] tscor[474.3] = [0.00000, -0.000040, 0.000014] tscor[474.4] = [0.00000, -0.000040, 0.000014] tscor[474.5] = [0.00000, -0.000040, 0.000014] tscor[474.6] = [0.00000, -0.000040, 0.000014] tscor[474.7] = [0.00000, -0.000040, 0.000014] tscor[474.8] = [0.00000, -0.000040, 0.000014] tscor[474.9] = [0.00000, -0.000040, 0.000014] tscor[475.0] = [-0.00001, -0.000040, 0.000014] tscor[475.1] = [-0.00001, -0.000040, 0.000013] tscor[475.2] = [-0.00001, -0.000040, 0.000013] tscor[475.3] = [-0.00001, -0.000040, 0.000013] tscor[475.4] = [-0.00001, -0.000040, 0.000013] tscor[475.5] = [-0.00001, -0.000040, 0.000013] tscor[475.6] = [-0.00001, -0.000040, 0.000013] tscor[475.7] = [-0.00001, -0.000040, 0.000013] tscor[475.8] = [-0.00001, -0.000040, 0.000013] tscor[475.9] = [-0.00001, -0.000040, 0.000013] tscor[476.0] = [-0.00001, -0.000040, 0.000013] tscor[476.1] = [-0.00001, -0.000040, 0.000013] tscor[476.2] = [-0.00001, -0.000040, 0.000013] tscor[476.3] = [-0.00001, -0.000040, 0.000013] tscor[476.4] = [-0.00001, -0.000040, 0.000013] tscor[476.5] = [-0.00001, -0.000040, 0.000013] tscor[476.6] = [-0.00001, -0.000040, 0.000013] tscor[476.7] = [-0.00001, -0.000040, 0.000013] tscor[476.8] = [-0.00001, -0.000040, 0.000013] tscor[476.9] = [-0.00001, -0.000040, 0.000013] tscor[477.0] = [-0.00001, -0.000040, 0.000013] tscor[477.1] = [-0.00001, -0.000040, 0.000013] tscor[477.2] = [-0.00001, -0.000040, 0.000013] tscor[477.3] = [-0.00001, -0.000040, 0.000013] tscor[477.4] = [-0.00001, -0.000040, 0.000013] tscor[477.5] = [-0.00001, -0.000040, 0.000013] tscor[477.6] = [-0.00001, -0.000040, 0.000013] tscor[477.7] = [-0.00001, -0.000040, 0.000013] tscor[477.8] = [-0.00001, -0.000040, 0.000013] tscor[477.9] = [-0.00001, -0.000040, 0.000013] tscor[478.0] = [-0.00001, -0.000040, 0.000013] tscor[478.1] = [-0.00001, -0.000040, 0.000013] tscor[478.2] = [-0.00001, -0.000040, 0.000013] tscor[478.3] = [-0.00001, -0.000040, 0.000013] tscor[478.4] = [-0.00001, -0.000040, 0.000013] tscor[478.5] = [-0.00001, -0.000040, 0.000013] tscor[478.6] = [-0.00001, -0.000040, 0.000013] tscor[478.7] = [-0.00001, -0.000040, 0.000013] tscor[478.8] = [-0.00001, -0.000040, 0.000013] tscor[478.9] = [-0.00001, -0.000040, 0.000013] tscor[479.0] = [-0.00001, -0.000040, 0.000013] tscor[479.1] = [0.00000, -0.000040, 0.000012] tscor[479.2] = [0.00000, -0.000040, 0.000012] tscor[479.3] = [0.00000, -0.000040, 0.000012] tscor[479.4] = [0.00000, -0.000040, 0.000012] tscor[479.5] = [0.00000, -0.000040, 0.000012] tscor[479.6] = [0.00000, -0.000040, 0.000012] tscor[479.7] = [0.00000, -0.000040, 0.000012] tscor[479.8] = [0.00000, -0.000040, 0.000012] tscor[479.9] = [0.00000, -0.000040, 0.000012] tscor[480.0] = [0.00000, -0.000040, 0.000012] tscor[480.1] = [0.00000, -0.000040, 0.000012] tscor[480.2] = [0.00000, -0.000040, 0.000012] tscor[480.3] = [0.00000, -0.000040, 0.000012] tscor[480.4] = [0.00000, -0.000040, 0.000012] tscor[480.5] = [0.00000, -0.000040, 0.000012] tscor[480.6] = [0.00000, -0.000040, 0.000012] tscor[480.7] = [0.00000, -0.000040, 0.000012] tscor[480.8] = [0.00000, -0.000040, 0.000012] tscor[480.9] = [0.00000, -0.000040, 0.000012] tscor[481.0] = [0.00000, -0.000041, 0.000012] tscor[481.1] = [0.00000, -0.000041, 0.000011] tscor[481.2] = [0.00000, -0.000041, 0.000011] tscor[481.3] = [0.00000, -0.000041, 0.000011] tscor[481.4] = [0.00000, -0.000041, 0.000011] tscor[481.5] = [0.00000, -0.000041, 0.000011] tscor[481.6] = [0.00000, -0.000041, 0.000011] tscor[481.7] = [0.00000, -0.000041, 0.000011] tscor[481.8] = [0.00000, -0.000041, 0.000011] tscor[481.9] = [0.00000, -0.000041, 0.000011] tscor[482.0] = [0.00000, -0.000041, 0.000011] tscor[482.1] = [0.00000, -0.000041, 0.000011] tscor[482.2] = [0.00000, -0.000041, 0.000011] tscor[482.3] = [0.00000, -0.000041, 0.000011] tscor[482.4] = [0.00000, -0.000041, 0.000011] tscor[482.5] = [0.00000, -0.000041, 0.000011] tscor[482.6] = [0.00000, -0.000041, 0.000011] tscor[482.7] = [0.00000, -0.000041, 0.000011] tscor[482.8] = [0.00000, -0.000041, 0.000011] tscor[482.9] = [0.00000, -0.000041, 0.000011] tscor[483.0] = [0.00001, -0.000042, 0.000011] tscor[483.1] = [0.00001, -0.000042, 0.000011] tscor[483.2] = [0.00001, -0.000042, 0.000011] tscor[483.3] = [0.00001, -0.000042, 0.000011] tscor[483.4] = [0.00001, -0.000042, 0.000011] tscor[483.5] = [0.00001, -0.000042, 0.000011] tscor[483.6] = [0.00001, -0.000042, 0.000011] tscor[483.7] = [0.00001, -0.000042, 0.000011] tscor[483.8] = [0.00001, -0.000042, 0.000011] tscor[483.9] = [0.00001, -0.000042, 0.000011] tscor[484.0] = [0.00001, -0.000042, 0.000011] tscor[484.1] = [0.00001, -0.000042, 0.000011] tscor[484.2] = [0.00001, -0.000042, 0.000011] tscor[484.3] = [0.00001, -0.000042, 0.000011] tscor[484.4] = [0.00001, -0.000042, 0.000011] tscor[484.5] = [0.00001, -0.000042, 0.000011] tscor[484.6] = [0.00001, -0.000042, 0.000011] tscor[484.7] = [0.00001, -0.000042, 0.000011] tscor[484.8] = [0.00001, -0.000042, 0.000011] tscor[484.9] = [0.00001, -0.000042, 0.000011] tscor[485.0] = [0.00001, -0.000042, 0.000011] tscor[485.1] = [0.00001, -0.000042, 0.000010] tscor[485.2] = [0.00001, -0.000042, 0.000010] tscor[485.3] = [0.00001, -0.000042, 0.000010] tscor[485.4] = [0.00001, -0.000042, 0.000010] tscor[485.5] = [0.00001, -0.000042, 0.000010] tscor[485.6] = [0.00001, -0.000042, 0.000010] tscor[485.7] = [0.00001, -0.000042, 0.000010] tscor[485.8] = [0.00001, -0.000042, 0.000010] tscor[485.9] = [0.00001, -0.000042, 0.000010] tscor[486.0] = [0.00001, -0.000042, 0.000010] tscor[486.1] = [0.00001, -0.000042, 0.000010] tscor[486.2] = [0.00001, -0.000042, 0.000010] tscor[486.3] = [0.00001, -0.000042, 0.000010] tscor[486.4] = [0.00001, -0.000042, 0.000010] tscor[486.5] = [0.00001, -0.000042, 0.000010] tscor[486.6] = [0.00001, -0.000042, 0.000010] tscor[486.7] = [0.00001, -0.000042, 0.000010] tscor[486.8] = [0.00001, -0.000042, 0.000010] tscor[486.9] = [0.00001, -0.000042, 0.000010] tscor[487.0] = [0.00001, -0.000042, 0.000010] tscor[487.1] = [0.00001, -0.000041, 0.000009] tscor[487.2] = [0.00001, -0.000041, 0.000009] tscor[487.3] = [0.00001, -0.000041, 0.000009] tscor[487.4] = [0.00001, -0.000041, 0.000009] tscor[487.5] = [0.00001, -0.000041, 0.000009] tscor[487.6] = [0.00001, -0.000041, 0.000009] tscor[487.7] = [0.00001, -0.000041, 0.000009] tscor[487.8] = [0.00001, -0.000041, 0.000009] tscor[487.9] = [0.00001, -0.000041, 0.000009] tscor[488.0] = [0.00001, -0.000041, 0.000009] tscor[488.1] = [0.00001, -0.000041, 0.000009] tscor[488.2] = [0.00001, -0.000041, 0.000009] tscor[488.3] = [0.00001, -0.000041, 0.000009] tscor[488.4] = [0.00001, -0.000041, 0.000009] tscor[488.5] = [0.00001, -0.000041, 0.000009] tscor[488.6] = [0.00001, -0.000041, 0.000009] tscor[488.7] = [0.00001, -0.000041, 0.000009] tscor[488.8] = [0.00001, -0.000041, 0.000009] tscor[488.9] = [0.00001, -0.000041, 0.000009] tscor[489.0] = [0.00001, -0.000041, 0.000009] tscor[489.1] = [0.00000, -0.000041, 0.000009] tscor[489.2] = [0.00000, -0.000041, 0.000009] tscor[489.3] = [0.00000, -0.000041, 0.000009] tscor[489.4] = [0.00000, -0.000041, 0.000009] tscor[489.5] = [0.00000, -0.000041, 0.000009] tscor[489.6] = [0.00000, -0.000041, 0.000009] tscor[489.7] = [0.00000, -0.000041, 0.000009] tscor[489.8] = [0.00000, -0.000041, 0.000009] tscor[489.9] = [0.00000, -0.000041, 0.000009] tscor[490.0] = [0.00000, -0.000041, 0.000009] tscor[490.1] = [0.00000, -0.000041, 0.000009] tscor[490.2] = [0.00000, -0.000041, 0.000009] tscor[490.3] = [0.00000, -0.000041, 0.000009] tscor[490.4] = [0.00000, -0.000041, 0.000009] tscor[490.5] = [0.00000, -0.000041, 0.000009] tscor[490.6] = [0.00000, -0.000041, 0.000009] tscor[490.7] = [0.00000, -0.000041, 0.000009] tscor[490.8] = [0.00000, -0.000041, 0.000009] tscor[490.9] = [0.00000, -0.000041, 0.000009] tscor[491.0] = [0.00001, -0.000042, 0.000009] tscor[491.1] = [0.00001, -0.000042, 0.000009] tscor[491.2] = [0.00001, -0.000042, 0.000009] tscor[491.3] = [0.00001, -0.000042, 0.000009] tscor[491.4] = [0.00001, -0.000042, 0.000009] tscor[491.5] = [0.00001, -0.000042, 0.000009] tscor[491.6] = [0.00001, -0.000042, 0.000009] tscor[491.7] = [0.00001, -0.000042, 0.000009] tscor[491.8] = [0.00001, -0.000042, 0.000009] tscor[491.9] = [0.00001, -0.000042, 0.000009] tscor[492.0] = [0.00001, -0.000042, 0.000009] tscor[492.1] = [0.00001, -0.000042, 0.000009] tscor[492.2] = [0.00001, -0.000042, 0.000009] tscor[492.3] = [0.00001, -0.000042, 0.000009] tscor[492.4] = [0.00001, -0.000042, 0.000009] tscor[492.5] = [0.00001, -0.000042, 0.000009] tscor[492.6] = [0.00001, -0.000042, 0.000009] tscor[492.7] = [0.00001, -0.000042, 0.000009] tscor[492.8] = [0.00001, -0.000042, 0.000009] tscor[492.9] = [0.00001, -0.000042, 0.000009] tscor[493.0] = [0.00001, -0.000042, 0.000009] tscor[493.1] = [0.00001, -0.000042, 0.000009] tscor[493.2] = [0.00001, -0.000042, 0.000009] tscor[493.3] = [0.00001, -0.000042, 0.000009] tscor[493.4] = [0.00001, -0.000042, 0.000009] tscor[493.5] = [0.00001, -0.000042, 0.000009] tscor[493.6] = [0.00001, -0.000042, 0.000009] tscor[493.7] = [0.00001, -0.000042, 0.000009] tscor[493.8] = [0.00001, -0.000042, 0.000009] tscor[493.9] = [0.00001, -0.000042, 0.000009] tscor[494.0] = [0.00001, -0.000042, 0.000009] tscor[494.1] = [0.00001, -0.000042, 0.000009] tscor[494.2] = [0.00001, -0.000042, 0.000009] tscor[494.3] = [0.00001, -0.000042, 0.000009] tscor[494.4] = [0.00001, -0.000042, 0.000009] tscor[494.5] = [0.00001, -0.000042, 0.000009] tscor[494.6] = [0.00001, -0.000042, 0.000009] tscor[494.7] = [0.00001, -0.000042, 0.000009] tscor[494.8] = [0.00001, -0.000042, 0.000009] tscor[494.9] = [0.00001, -0.000042, 0.000009] tscor[495.0] = [0.00002, -0.000042, 0.000009] tscor[495.1] = [0.00002, -0.000042, 0.000008] tscor[495.2] = [0.00002, -0.000042, 0.000008] tscor[495.3] = [0.00002, -0.000042, 0.000008] tscor[495.4] = [0.00002, -0.000042, 0.000008] tscor[495.5] = [0.00002, -0.000042, 0.000008] tscor[495.6] = [0.00002, -0.000042, 0.000008] tscor[495.7] = [0.00002, -0.000042, 0.000008] tscor[495.8] = [0.00002, -0.000042, 0.000008] tscor[495.9] = [0.00002, -0.000042, 0.000008] tscor[496.0] = [0.00002, -0.000042, 0.000008] tscor[496.1] = [0.00002, -0.000042, 0.000008] tscor[496.2] = [0.00002, -0.000042, 0.000008] tscor[496.3] = [0.00002, -0.000042, 0.000008] tscor[496.4] = [0.00002, -0.000042, 0.000008] tscor[496.5] = [0.00002, -0.000042, 0.000008] tscor[496.6] = [0.00002, -0.000042, 0.000008] tscor[496.7] = [0.00002, -0.000042, 0.000008] tscor[496.8] = [0.00002, -0.000042, 0.000008] tscor[496.9] = [0.00002, -0.000042, 0.000008] tscor[497.0] = [0.00003, -0.000043, 0.000008] tscor[497.1] = [0.00003, -0.000043, 0.000008] tscor[497.2] = [0.00003, -0.000043, 0.000008] tscor[497.3] = [0.00003, -0.000043, 0.000008] tscor[497.4] = [0.00003, -0.000043, 0.000008] tscor[497.5] = [0.00003, -0.000043, 0.000008] tscor[497.6] = [0.00003, -0.000043, 0.000008] tscor[497.7] = [0.00003, -0.000043, 0.000008] tscor[497.8] = [0.00003, -0.000043, 0.000008] tscor[497.9] = [0.00003, -0.000043, 0.000008] tscor[498.0] = [0.00003, -0.000043, 0.000008] tscor[498.1] = [0.00003, -0.000043, 0.000008] tscor[498.2] = [0.00003, -0.000043, 0.000008] tscor[498.3] = [0.00003, -0.000043, 0.000008] tscor[498.4] = [0.00003, -0.000043, 0.000008] tscor[498.5] = [0.00003, -0.000043, 0.000008] tscor[498.6] = [0.00003, -0.000043, 0.000008] tscor[498.7] = [0.00003, -0.000043, 0.000008] tscor[498.8] = [0.00003, -0.000043, 0.000008] tscor[498.9] = [0.00003, -0.000043, 0.000008] tscor[499.0] = [0.00003, -0.000043, 0.000008] tscor[499.1] = [0.00003, -0.000043, 0.000008] tscor[499.2] = [0.00003, -0.000043, 0.000008] tscor[499.3] = [0.00003, -0.000043, 0.000008] tscor[499.4] = [0.00003, -0.000043, 0.000008] tscor[499.5] = [0.00003, -0.000043, 0.000008] tscor[499.6] = [0.00003, -0.000043, 0.000008] tscor[499.7] = [0.00003, -0.000043, 0.000008] tscor[499.8] = [0.00003, -0.000043, 0.000008] tscor[499.9] = [0.00003, -0.000043, 0.000008] tscor[500.0] = [0.00003, -0.000043, 0.000008] tscor[500.1] = [0.00003, -0.000043, 0.000008] tscor[500.2] = [0.00003, -0.000043, 0.000008] tscor[500.3] = [0.00003, -0.000043, 0.000008] tscor[500.4] = [0.00003, -0.000043, 0.000008] tscor[500.5] = [0.00003, -0.000043, 0.000008] tscor[500.6] = [0.00003, -0.000043, 0.000008] tscor[500.7] = [0.00003, -0.000043, 0.000008] tscor[500.8] = [0.00003, -0.000043, 0.000008] tscor[500.9] = [0.00003, -0.000043, 0.000008] tscor[501.0] = [0.00003, -0.000043, 0.000008] tscor[501.1] = [0.00003, -0.000043, 0.000007] tscor[501.2] = [0.00003, -0.000043, 0.000007] tscor[501.3] = [0.00003, -0.000043, 0.000007] tscor[501.4] = [0.00003, -0.000043, 0.000007] tscor[501.5] = [0.00003, -0.000043, 0.000007] tscor[501.6] = [0.00003, -0.000043, 0.000007] tscor[501.7] = [0.00003, -0.000043, 0.000007] tscor[501.8] = [0.00003, -0.000043, 0.000007] tscor[501.9] = [0.00003, -0.000043, 0.000007] tscor[502.0] = [0.00003, -0.000043, 0.000007] tscor[502.1] = [0.00003, -0.000043, 0.000007] tscor[502.2] = [0.00003, -0.000043, 0.000007] tscor[502.3] = [0.00003, -0.000043, 0.000007] tscor[502.4] = [0.00003, -0.000043, 0.000007] tscor[502.5] = [0.00003, -0.000043, 0.000007] tscor[502.6] = [0.00003, -0.000043, 0.000007] tscor[502.7] = [0.00003, -0.000043, 0.000007] tscor[502.8] = [0.00003, -0.000043, 0.000007] tscor[502.9] = [0.00003, -0.000043, 0.000007] tscor[503.0] = [0.00003, -0.000043, 0.000007] tscor[503.1] = [0.00003, -0.000043, 0.000007] tscor[503.2] = [0.00003, -0.000043, 0.000007] tscor[503.3] = [0.00003, -0.000043, 0.000007] tscor[503.4] = [0.00003, -0.000043, 0.000007] tscor[503.5] = [0.00003, -0.000043, 0.000007] tscor[503.6] = [0.00003, -0.000043, 0.000007] tscor[503.7] = [0.00003, -0.000043, 0.000007] tscor[503.8] = [0.00003, -0.000043, 0.000007] tscor[503.9] = [0.00003, -0.000043, 0.000007] tscor[504.0] = [0.00003, -0.000043, 0.000007] tscor[504.1] = [0.00003, -0.000043, 0.000007] tscor[504.2] = [0.00003, -0.000043, 0.000007] tscor[504.3] = [0.00003, -0.000043, 0.000007] tscor[504.4] = [0.00003, -0.000043, 0.000007] tscor[504.5] = [0.00003, -0.000043, 0.000007] tscor[504.6] = [0.00003, -0.000043, 0.000007] tscor[504.7] = [0.00003, -0.000043, 0.000007] tscor[504.8] = [0.00003, -0.000043, 0.000007] tscor[504.9] = [0.00003, -0.000043, 0.000007] tscor[505.0] = [0.00004, -0.000043, 0.000007] tscor[505.1] = [0.00004, -0.000043, 0.000007] tscor[505.2] = [0.00004, -0.000043, 0.000007] tscor[505.3] = [0.00004, -0.000043, 0.000007] tscor[505.4] = [0.00004, -0.000043, 0.000007] tscor[505.5] = [0.00004, -0.000043, 0.000007] tscor[505.6] = [0.00004, -0.000043, 0.000007] tscor[505.7] = [0.00004, -0.000043, 0.000007] tscor[505.8] = [0.00004, -0.000043, 0.000007] tscor[505.9] = [0.00004, -0.000043, 0.000007] tscor[506.0] = [0.00004, -0.000043, 0.000007] tscor[506.1] = [0.00004, -0.000043, 0.000007] tscor[506.2] = [0.00004, -0.000043, 0.000007] tscor[506.3] = [0.00004, -0.000043, 0.000007] tscor[506.4] = [0.00004, -0.000043, 0.000007] tscor[506.5] = [0.00004, -0.000043, 0.000007] tscor[506.6] = [0.00004, -0.000043, 0.000007] tscor[506.7] = [0.00004, -0.000043, 0.000007] tscor[506.8] = [0.00004, -0.000043, 0.000007] tscor[506.9] = [0.00004, -0.000043, 0.000007] tscor[507.0] = [0.00005, -0.000043, 0.000007] tscor[507.1] = [0.00005, -0.000043, 0.000007] tscor[507.2] = [0.00005, -0.000043, 0.000007] tscor[507.3] = [0.00005, -0.000043, 0.000007] tscor[507.4] = [0.00005, -0.000043, 0.000007] tscor[507.5] = [0.00005, -0.000043, 0.000007] tscor[507.6] = [0.00005, -0.000043, 0.000007] tscor[507.7] = [0.00005, -0.000043, 0.000007] tscor[507.8] = [0.00005, -0.000043, 0.000007] tscor[507.9] = [0.00005, -0.000043, 0.000007] tscor[508.0] = [0.00005, -0.000043, 0.000007] tscor[508.1] = [0.00005, -0.000043, 0.000007] tscor[508.2] = [0.00005, -0.000043, 0.000007] tscor[508.3] = [0.00005, -0.000043, 0.000007] tscor[508.4] = [0.00005, -0.000043, 0.000007] tscor[508.5] = [0.00005, -0.000043, 0.000007] tscor[508.6] = [0.00005, -0.000043, 0.000007] tscor[508.7] = [0.00005, -0.000043, 0.000007] tscor[508.8] = [0.00005, -0.000043, 0.000007] tscor[508.9] = [0.00005, -0.000043, 0.000007] tscor[509.0] = [0.00005, -0.000043, 0.000007] tscor[509.1] = [0.00005, -0.000042, 0.000007] tscor[509.2] = [0.00005, -0.000042, 0.000007] tscor[509.3] = [0.00005, -0.000042, 0.000007] tscor[509.4] = [0.00005, -0.000042, 0.000007] tscor[509.5] = [0.00005, -0.000042, 0.000007] tscor[509.6] = [0.00005, -0.000042, 0.000007] tscor[509.7] = [0.00005, -0.000042, 0.000007] tscor[509.8] = [0.00005, -0.000042, 0.000007] tscor[509.9] = [0.00005, -0.000042, 0.000007] tscor[510.0] = [0.00005, -0.000042, 0.000007] tscor[510.1] = [0.00005, -0.000042, 0.000007] tscor[510.2] = [0.00005, -0.000042, 0.000007] tscor[510.3] = [0.00005, -0.000042, 0.000007] tscor[510.4] = [0.00005, -0.000042, 0.000007] tscor[510.5] = [0.00005, -0.000042, 0.000007] tscor[510.6] = [0.00005, -0.000042, 0.000007] tscor[510.7] = [0.00005, -0.000042, 0.000007] tscor[510.8] = [0.00005, -0.000042, 0.000007] tscor[510.9] = [0.00005, -0.000042, 0.000007] tscor[511.0] = [0.00006, -0.000042, 0.000007] tscor[511.1] = [0.00006, -0.000042, 0.000007] tscor[511.2] = [0.00006, -0.000042, 0.000007] tscor[511.3] = [0.00006, -0.000042, 0.000007] tscor[511.4] = [0.00006, -0.000042, 0.000007] tscor[511.5] = [0.00006, -0.000042, 0.000007] tscor[511.6] = [0.00006, -0.000042, 0.000007] tscor[511.7] = [0.00006, -0.000042, 0.000007] tscor[511.8] = [0.00006, -0.000042, 0.000007] tscor[511.9] = [0.00006, -0.000042, 0.000007] tscor[512.0] = [0.00006, -0.000042, 0.000007] tscor[512.1] = [0.00006, -0.000042, 0.000007] tscor[512.2] = [0.00006, -0.000042, 0.000007] tscor[512.3] = [0.00006, -0.000042, 0.000007] tscor[512.4] = [0.00006, -0.000042, 0.000007] tscor[512.5] = [0.00006, -0.000042, 0.000007] tscor[512.6] = [0.00006, -0.000042, 0.000007] tscor[512.7] = [0.00006, -0.000042, 0.000007] tscor[512.8] = [0.00006, -0.000042, 0.000007] tscor[512.9] = [0.00006, -0.000042, 0.000007] tscor[513.0] = [0.00006, -0.000042, 0.000008] tscor[513.1] = [0.00006, -0.000041, 0.000008] tscor[513.2] = [0.00006, -0.000041, 0.000008] tscor[513.3] = [0.00006, -0.000041, 0.000008] tscor[513.4] = [0.00006, -0.000041, 0.000008] tscor[513.5] = [0.00006, -0.000041, 0.000008] tscor[513.6] = [0.00006, -0.000041, 0.000008] tscor[513.7] = [0.00006, -0.000041, 0.000008] tscor[513.8] = [0.00006, -0.000041, 0.000008] tscor[513.9] = [0.00006, -0.000041, 0.000008] tscor[514.0] = [0.00006, -0.000041, 0.000008] tscor[514.1] = [0.00006, -0.000041, 0.000008] tscor[514.2] = [0.00006, -0.000041, 0.000008] tscor[514.3] = [0.00006, -0.000041, 0.000008] tscor[514.4] = [0.00006, -0.000041, 0.000008] tscor[514.5] = [0.00006, -0.000041, 0.000008] tscor[514.6] = [0.00006, -0.000041, 0.000008] tscor[514.7] = [0.00006, -0.000041, 0.000008] tscor[514.8] = [0.00006, -0.000041, 0.000008] tscor[514.9] = [0.00006, -0.000041, 0.000008] tscor[515.0] = [0.00006, -0.000041, 0.000009] tscor[515.1] = [0.00006, -0.000040, 0.000009] tscor[515.2] = [0.00006, -0.000040, 0.000009] tscor[515.3] = [0.00006, -0.000040, 0.000009] tscor[515.4] = [0.00006, -0.000040, 0.000009] tscor[515.5] = [0.00006, -0.000040, 0.000009] tscor[515.6] = [0.00006, -0.000040, 0.000009] tscor[515.7] = [0.00006, -0.000040, 0.000009] tscor[515.8] = [0.00006, -0.000040, 0.000009] tscor[515.9] = [0.00006, -0.000040, 0.000009] tscor[516.0] = [0.00006, -0.000040, 0.000009] tscor[516.1] = [0.00006, -0.000040, 0.000009] tscor[516.2] = [0.00006, -0.000040, 0.000009] tscor[516.3] = [0.00006, -0.000040, 0.000009] tscor[516.4] = [0.00006, -0.000040, 0.000009] tscor[516.5] = [0.00006, -0.000040, 0.000009] tscor[516.6] = [0.00006, -0.000040, 0.000009] tscor[516.7] = [0.00006, -0.000040, 0.000009] tscor[516.8] = [0.00006, -0.000040, 0.000009] tscor[516.9] = [0.00006, -0.000040, 0.000009] tscor[517.0] = [0.00006, -0.000040, 0.000009] tscor[517.1] = [0.00006, -0.000039, 0.000009] tscor[517.2] = [0.00006, -0.000039, 0.000009] tscor[517.3] = [0.00006, -0.000039, 0.000009] tscor[517.4] = [0.00006, -0.000039, 0.000009] tscor[517.5] = [0.00006, -0.000039, 0.000009] tscor[517.6] = [0.00006, -0.000039, 0.000009] tscor[517.7] = [0.00006, -0.000039, 0.000009] tscor[517.8] = [0.00006, -0.000039, 0.000009] tscor[517.9] = [0.00006, -0.000039, 0.000009] tscor[518.0] = [0.00006, -0.000039, 0.000009] tscor[518.1] = [0.00006, -0.000039, 0.000009] tscor[518.2] = [0.00006, -0.000039, 0.000009] tscor[518.3] = [0.00006, -0.000039, 0.000009] tscor[518.4] = [0.00006, -0.000039, 0.000009] tscor[518.5] = [0.00006, -0.000039, 0.000009] tscor[518.6] = [0.00006, -0.000039, 0.000009] tscor[518.7] = [0.00006, -0.000039, 0.000009] tscor[518.8] = [0.00006, -0.000039, 0.000009] tscor[518.9] = [0.00006, -0.000039, 0.000009] tscor[519.0] = [0.00006, -0.000039, 0.000010] tscor[519.1] = [0.00006, -0.000038, 0.000010] tscor[519.2] = [0.00006, -0.000038, 0.000010] tscor[519.3] = [0.00006, -0.000038, 0.000010] tscor[519.4] = [0.00006, -0.000038, 0.000010] tscor[519.5] = [0.00006, -0.000038, 0.000010] tscor[519.6] = [0.00006, -0.000038, 0.000010] tscor[519.7] = [0.00006, -0.000038, 0.000010] tscor[519.8] = [0.00006, -0.000038, 0.000010] tscor[519.9] = [0.00006, -0.000038, 0.000010] tscor[520.0] = [0.00006, -0.000038, 0.000010] tscor[520.1] = [0.00006, -0.000038, 0.000010] tscor[520.2] = [0.00006, -0.000038, 0.000010] tscor[520.3] = [0.00006, -0.000038, 0.000010] tscor[520.4] = [0.00006, -0.000038, 0.000010] tscor[520.5] = [0.00006, -0.000038, 0.000010] tscor[520.6] = [0.00006, -0.000038, 0.000010] tscor[520.7] = [0.00006, -0.000038, 0.000010] tscor[520.8] = [0.00006, -0.000038, 0.000010] tscor[520.9] = [0.00006, -0.000038, 0.000010] tscor[521.0] = [0.00006, -0.000038, 0.000010] tscor[521.1] = [0.00006, -0.000037, 0.000010] tscor[521.2] = [0.00006, -0.000037, 0.000010] tscor[521.3] = [0.00006, -0.000037, 0.000010] tscor[521.4] = [0.00006, -0.000037, 0.000010] tscor[521.5] = [0.00006, -0.000037, 0.000010] tscor[521.6] = [0.00006, -0.000037, 0.000010] tscor[521.7] = [0.00006, -0.000037, 0.000010] tscor[521.8] = [0.00006, -0.000037, 0.000010] tscor[521.9] = [0.00006, -0.000037, 0.000010] tscor[522.0] = [0.00006, -0.000037, 0.000010] tscor[522.1] = [0.00006, -0.000037, 0.000010] tscor[522.2] = [0.00006, -0.000037, 0.000010] tscor[522.3] = [0.00006, -0.000037, 0.000010] tscor[522.4] = [0.00006, -0.000037, 0.000010] tscor[522.5] = [0.00006, -0.000037, 0.000010] tscor[522.6] = [0.00006, -0.000037, 0.000010] tscor[522.7] = [0.00006, -0.000037, 0.000010] tscor[522.8] = [0.00006, -0.000037, 0.000010] tscor[522.9] = [0.00006, -0.000037, 0.000010] tscor[523.0] = [0.00006, -0.000037, 0.000011] tscor[523.1] = [0.00006, -0.000036, 0.000011] tscor[523.2] = [0.00006, -0.000036, 0.000011] tscor[523.3] = [0.00006, -0.000036, 0.000011] tscor[523.4] = [0.00006, -0.000036, 0.000011] tscor[523.5] = [0.00006, -0.000036, 0.000011] tscor[523.6] = [0.00006, -0.000036, 0.000011] tscor[523.7] = [0.00006, -0.000036, 0.000011] tscor[523.8] = [0.00006, -0.000036, 0.000011] tscor[523.9] = [0.00006, -0.000036, 0.000011] tscor[524.0] = [0.00006, -0.000036, 0.000011] tscor[524.1] = [0.00006, -0.000036, 0.000011] tscor[524.2] = [0.00006, -0.000036, 0.000011] tscor[524.3] = [0.00006, -0.000036, 0.000011] tscor[524.4] = [0.00006, -0.000036, 0.000011] tscor[524.5] = [0.00006, -0.000036, 0.000011] tscor[524.6] = [0.00006, -0.000036, 0.000011] tscor[524.7] = [0.00006, -0.000036, 0.000011] tscor[524.8] = [0.00006, -0.000036, 0.000011] tscor[524.9] = [0.00006, -0.000036, 0.000011] tscor[525.0] = [0.00006, -0.000037, 0.000011] tscor[525.1] = [0.00005, -0.000037, 0.000011] tscor[525.2] = [0.00005, -0.000037, 0.000011] tscor[525.3] = [0.00005, -0.000037, 0.000011] tscor[525.4] = [0.00005, -0.000037, 0.000011] tscor[525.5] = [0.00005, -0.000037, 0.000011] tscor[525.6] = [0.00005, -0.000037, 0.000011] tscor[525.7] = [0.00005, -0.000037, 0.000011] tscor[525.8] = [0.00005, -0.000037, 0.000011] tscor[525.9] = [0.00005, -0.000037, 0.000011] tscor[526.0] = [0.00005, -0.000037, 0.000011] tscor[526.1] = [0.00005, -0.000037, 0.000011] tscor[526.2] = [0.00005, -0.000037, 0.000011] tscor[526.3] = [0.00005, -0.000037, 0.000011] tscor[526.4] = [0.00005, -0.000037, 0.000011] tscor[526.5] = [0.00005, -0.000037, 0.000011] tscor[526.6] = [0.00005, -0.000037, 0.000011] tscor[526.7] = [0.00005, -0.000037, 0.000011] tscor[526.8] = [0.00005, -0.000037, 0.000011] tscor[526.9] = [0.00005, -0.000037, 0.000011] tscor[527.0] = [0.00005, -0.000037, 0.000011] tscor[527.1] = [0.00004, -0.000037, 0.000011] tscor[527.2] = [0.00004, -0.000037, 0.000011] tscor[527.3] = [0.00004, -0.000037, 0.000011] tscor[527.4] = [0.00004, -0.000037, 0.000011] tscor[527.5] = [0.00004, -0.000037, 0.000011] tscor[527.6] = [0.00004, -0.000037, 0.000011] tscor[527.7] = [0.00004, -0.000037, 0.000011] tscor[527.8] = [0.00004, -0.000037, 0.000011] tscor[527.9] = [0.00004, -0.000037, 0.000011] tscor[528.0] = [0.00004, -0.000037, 0.000011] tscor[528.1] = [0.00004, -0.000037, 0.000011] tscor[528.2] = [0.00004, -0.000037, 0.000011] tscor[528.3] = [0.00004, -0.000037, 0.000011] tscor[528.4] = [0.00004, -0.000037, 0.000011] tscor[528.5] = [0.00004, -0.000037, 0.000011] tscor[528.6] = [0.00004, -0.000037, 0.000011] tscor[528.7] = [0.00004, -0.000037, 0.000011] tscor[528.8] = [0.00004, -0.000037, 0.000011] tscor[528.9] = [0.00004, -0.000037, 0.000011] tscor[529.0] = [0.00004, -0.000037, 0.000011] tscor[529.1] = [0.00003, -0.000037, 0.000010] tscor[529.2] = [0.00003, -0.000037, 0.000010] tscor[529.3] = [0.00003, -0.000037, 0.000010] tscor[529.4] = [0.00003, -0.000037, 0.000010] tscor[529.5] = [0.00003, -0.000037, 0.000010] tscor[529.6] = [0.00003, -0.000037, 0.000010] tscor[529.7] = [0.00003, -0.000037, 0.000010] tscor[529.8] = [0.00003, -0.000037, 0.000010] tscor[529.9] = [0.00003, -0.000037, 0.000010] tscor[530.0] = [0.00003, -0.000037, 0.000010] tscor[530.1] = [0.00003, -0.000037, 0.000010] tscor[530.2] = [0.00003, -0.000037, 0.000010] tscor[530.3] = [0.00003, -0.000037, 0.000010] tscor[530.4] = [0.00003, -0.000037, 0.000010] tscor[530.5] = [0.00003, -0.000037, 0.000010] tscor[530.6] = [0.00003, -0.000037, 0.000010] tscor[530.7] = [0.00003, -0.000037, 0.000010] tscor[530.8] = [0.00003, -0.000037, 0.000010] tscor[530.9] = [0.00003, -0.000037, 0.000010] tscor[531.0] = [0.00003, -0.000037, 0.000010] tscor[531.1] = [0.00003, -0.000037, 0.000010] tscor[531.2] = [0.00003, -0.000037, 0.000010] tscor[531.3] = [0.00003, -0.000037, 0.000010] tscor[531.4] = [0.00003, -0.000037, 0.000010] tscor[531.5] = [0.00003, -0.000037, 0.000010] tscor[531.6] = [0.00003, -0.000037, 0.000010] tscor[531.7] = [0.00003, -0.000037, 0.000010] tscor[531.8] = [0.00003, -0.000037, 0.000010] tscor[531.9] = [0.00003, -0.000037, 0.000010] tscor[532.0] = [0.00003, -0.000037, 0.000010] tscor[532.1] = [0.00003, -0.000037, 0.000010] tscor[532.2] = [0.00003, -0.000037, 0.000010] tscor[532.3] = [0.00003, -0.000037, 0.000010] tscor[532.4] = [0.00003, -0.000037, 0.000010] tscor[532.5] = [0.00003, -0.000037, 0.000010] tscor[532.6] = [0.00003, -0.000037, 0.000010] tscor[532.7] = [0.00003, -0.000037, 0.000010] tscor[532.8] = [0.00003, -0.000037, 0.000010] tscor[532.9] = [0.00003, -0.000037, 0.000010] tscor[533.0] = [0.00003, -0.000037, 0.000010] tscor[533.1] = [0.00003, -0.000037, 0.000009] tscor[533.2] = [0.00003, -0.000037, 0.000009] tscor[533.3] = [0.00003, -0.000037, 0.000009] tscor[533.4] = [0.00003, -0.000037, 0.000009] tscor[533.5] = [0.00003, -0.000037, 0.000009] tscor[533.6] = [0.00003, -0.000037, 0.000009] tscor[533.7] = [0.00003, -0.000037, 0.000009] tscor[533.8] = [0.00003, -0.000037, 0.000009] tscor[533.9] = [0.00003, -0.000037, 0.000009] tscor[534.0] = [0.00003, -0.000037, 0.000009] tscor[534.1] = [0.00003, -0.000037, 0.000009] tscor[534.2] = [0.00003, -0.000037, 0.000009] tscor[534.3] = [0.00003, -0.000037, 0.000009] tscor[534.4] = [0.00003, -0.000037, 0.000009] tscor[534.5] = [0.00003, -0.000037, 0.000009] tscor[534.6] = [0.00003, -0.000037, 0.000009] tscor[534.7] = [0.00003, -0.000037, 0.000009] tscor[534.8] = [0.00003, -0.000037, 0.000009] tscor[534.9] = [0.00003, -0.000037, 0.000009] tscor[535.0] = [0.00003, -0.000038, 0.000009] tscor[535.1] = [0.00003, -0.000038, 0.000008] tscor[535.2] = [0.00003, -0.000038, 0.000008] tscor[535.3] = [0.00003, -0.000038, 0.000008] tscor[535.4] = [0.00003, -0.000038, 0.000008] tscor[535.5] = [0.00003, -0.000038, 0.000008] tscor[535.6] = [0.00003, -0.000038, 0.000008] tscor[535.7] = [0.00003, -0.000038, 0.000008] tscor[535.8] = [0.00003, -0.000038, 0.000008] tscor[535.9] = [0.00003, -0.000038, 0.000008] tscor[536.0] = [0.00003, -0.000038, 0.000008] tscor[536.1] = [0.00003, -0.000038, 0.000008] tscor[536.2] = [0.00003, -0.000038, 0.000008] tscor[536.3] = [0.00003, -0.000038, 0.000008] tscor[536.4] = [0.00003, -0.000038, 0.000008] tscor[536.5] = [0.00003, -0.000038, 0.000008] tscor[536.6] = [0.00003, -0.000038, 0.000008] tscor[536.7] = [0.00003, -0.000038, 0.000008] tscor[536.8] = [0.00003, -0.000038, 0.000008] tscor[536.9] = [0.00003, -0.000038, 0.000008] tscor[537.0] = [0.00003, -0.000038, 0.000008] tscor[537.1] = [0.00002, -0.000038, 0.000007] tscor[537.2] = [0.00002, -0.000038, 0.000007] tscor[537.3] = [0.00002, -0.000038, 0.000007] tscor[537.4] = [0.00002, -0.000038, 0.000007] tscor[537.5] = [0.00002, -0.000038, 0.000007] tscor[537.6] = [0.00002, -0.000038, 0.000007] tscor[537.7] = [0.00002, -0.000038, 0.000007] tscor[537.8] = [0.00002, -0.000038, 0.000007] tscor[537.9] = [0.00002, -0.000038, 0.000007] tscor[538.0] = [0.00002, -0.000038, 0.000007] tscor[538.1] = [0.00002, -0.000038, 0.000007] tscor[538.2] = [0.00002, -0.000038, 0.000007] tscor[538.3] = [0.00002, -0.000038, 0.000007] tscor[538.4] = [0.00002, -0.000038, 0.000007] tscor[538.5] = [0.00002, -0.000038, 0.000007] tscor[538.6] = [0.00002, -0.000038, 0.000007] tscor[538.7] = [0.00002, -0.000038, 0.000007] tscor[538.8] = [0.00002, -0.000038, 0.000007] tscor[538.9] = [0.00002, -0.000038, 0.000007] tscor[539.0] = [0.00002, -0.000039, 0.000007] tscor[539.1] = [0.00002, -0.000039, 0.000006] tscor[539.2] = [0.00002, -0.000039, 0.000006] tscor[539.3] = [0.00002, -0.000039, 0.000006] tscor[539.4] = [0.00002, -0.000039, 0.000006] tscor[539.5] = [0.00002, -0.000039, 0.000006] tscor[539.6] = [0.00002, -0.000039, 0.000006] tscor[539.7] = [0.00002, -0.000039, 0.000006] tscor[539.8] = [0.00002, -0.000039, 0.000006] tscor[539.9] = [0.00002, -0.000039, 0.000006] tscor[540.0] = [0.00002, -0.000039, 0.000006] tscor[540.1] = [0.00002, -0.000039, 0.000006] tscor[540.2] = [0.00002, -0.000039, 0.000006] tscor[540.3] = [0.00002, -0.000039, 0.000006] tscor[540.4] = [0.00002, -0.000039, 0.000006] tscor[540.5] = [0.00002, -0.000039, 0.000006] tscor[540.6] = [0.00002, -0.000039, 0.000006] tscor[540.7] = [0.00002, -0.000039, 0.000006] tscor[540.8] = [0.00002, -0.000039, 0.000006] tscor[540.9] = [0.00002, -0.000039, 0.000006] tscor[541.0] = [0.00002, -0.000039, 0.000006] tscor[541.1] = [0.00002, -0.000039, 0.000006] tscor[541.2] = [0.00002, -0.000039, 0.000006] tscor[541.3] = [0.00002, -0.000039, 0.000006] tscor[541.4] = [0.00002, -0.000039, 0.000006] tscor[541.5] = [0.00002, -0.000039, 0.000006] tscor[541.6] = [0.00002, -0.000039, 0.000006] tscor[541.7] = [0.00002, -0.000039, 0.000006] tscor[541.8] = [0.00002, -0.000039, 0.000006] tscor[541.9] = [0.00002, -0.000039, 0.000006] tscor[542.0] = [0.00002, -0.000039, 0.000006] tscor[542.1] = [0.00002, -0.000039, 0.000006] tscor[542.2] = [0.00002, -0.000039, 0.000006] tscor[542.3] = [0.00002, -0.000039, 0.000006] tscor[542.4] = [0.00002, -0.000039, 0.000006] tscor[542.5] = [0.00002, -0.000039, 0.000006] tscor[542.6] = [0.00002, -0.000039, 0.000006] tscor[542.7] = [0.00002, -0.000039, 0.000006] tscor[542.8] = [0.00002, -0.000039, 0.000006] tscor[542.9] = [0.00002, -0.000039, 0.000006] tscor[543.0] = [0.00002, -0.000039, 0.000006] tscor[543.1] = [0.00002, -0.000039, 0.000005] tscor[543.2] = [0.00002, -0.000039, 0.000005] tscor[543.3] = [0.00002, -0.000039, 0.000005] tscor[543.4] = [0.00002, -0.000039, 0.000005] tscor[543.5] = [0.00002, -0.000039, 0.000005] tscor[543.6] = [0.00002, -0.000039, 0.000005] tscor[543.7] = [0.00002, -0.000039, 0.000005] tscor[543.8] = [0.00002, -0.000039, 0.000005] tscor[543.9] = [0.00002, -0.000039, 0.000005] tscor[544.0] = [0.00002, -0.000039, 0.000005] tscor[544.1] = [0.00002, -0.000039, 0.000005] tscor[544.2] = [0.00002, -0.000039, 0.000005] tscor[544.3] = [0.00002, -0.000039, 0.000005] tscor[544.4] = [0.00002, -0.000039, 0.000005] tscor[544.5] = [0.00002, -0.000039, 0.000005] tscor[544.6] = [0.00002, -0.000039, 0.000005] tscor[544.7] = [0.00002, -0.000039, 0.000005] tscor[544.8] = [0.00002, -0.000039, 0.000005] tscor[544.9] = [0.00002, -0.000039, 0.000005] tscor[545.0] = [0.00002, -0.000040, 0.000005] tscor[545.1] = [0.00002, -0.000040, 0.000005] tscor[545.2] = [0.00002, -0.000040, 0.000005] tscor[545.3] = [0.00002, -0.000040, 0.000005] tscor[545.4] = [0.00002, -0.000040, 0.000005] tscor[545.5] = [0.00002, -0.000040, 0.000005] tscor[545.6] = [0.00002, -0.000040, 0.000005] tscor[545.7] = [0.00002, -0.000040, 0.000005] tscor[545.8] = [0.00002, -0.000040, 0.000005] tscor[545.9] = [0.00002, -0.000040, 0.000005] tscor[546.0] = [0.00002, -0.000040, 0.000005] tscor[546.1] = [0.00002, -0.000040, 0.000005] tscor[546.2] = [0.00002, -0.000040, 0.000005] tscor[546.3] = [0.00002, -0.000040, 0.000005] tscor[546.4] = [0.00002, -0.000040, 0.000005] tscor[546.5] = [0.00002, -0.000040, 0.000005] tscor[546.6] = [0.00002, -0.000040, 0.000005] tscor[546.7] = [0.00002, -0.000040, 0.000005] tscor[546.8] = [0.00002, -0.000040, 0.000005] tscor[546.9] = [0.00002, -0.000040, 0.000005] tscor[547.0] = [0.00002, -0.000040, 0.000005] tscor[547.1] = [0.00002, -0.000040, 0.000005] tscor[547.2] = [0.00002, -0.000040, 0.000005] tscor[547.3] = [0.00002, -0.000040, 0.000005] tscor[547.4] = [0.00002, -0.000040, 0.000005] tscor[547.5] = [0.00002, -0.000040, 0.000005] tscor[547.6] = [0.00002, -0.000040, 0.000005] tscor[547.7] = [0.00002, -0.000040, 0.000005] tscor[547.8] = [0.00002, -0.000040, 0.000005] tscor[547.9] = [0.00002, -0.000040, 0.000005] tscor[548.0] = [0.00002, -0.000040, 0.000005] tscor[548.1] = [0.00002, -0.000040, 0.000005] tscor[548.2] = [0.00002, -0.000040, 0.000005] tscor[548.3] = [0.00002, -0.000040, 0.000005] tscor[548.4] = [0.00002, -0.000040, 0.000005] tscor[548.5] = [0.00002, -0.000040, 0.000005] tscor[548.6] = [0.00002, -0.000040, 0.000005] tscor[548.7] = [0.00002, -0.000040, 0.000005] tscor[548.8] = [0.00002, -0.000040, 0.000005] tscor[548.9] = [0.00002, -0.000040, 0.000005] tscor[549.0] = [0.00002, -0.000040, 0.000005] tscor[549.1] = [0.00002, -0.000040, 0.000005] tscor[549.2] = [0.00002, -0.000040, 0.000005] tscor[549.3] = [0.00002, -0.000040, 0.000005] tscor[549.4] = [0.00002, -0.000040, 0.000005] tscor[549.5] = [0.00002, -0.000040, 0.000005] tscor[549.6] = [0.00002, -0.000040, 0.000005] tscor[549.7] = [0.00002, -0.000040, 0.000005] tscor[549.8] = [0.00002, -0.000040, 0.000005] tscor[549.9] = [0.00002, -0.000040, 0.000005] tscor[550.0] = [0.00002, -0.000040, 0.000005] tscor[550.1] = [0.00002, -0.000040, 0.000005] tscor[550.2] = [0.00002, -0.000040, 0.000005] tscor[550.3] = [0.00002, -0.000040, 0.000005] tscor[550.4] = [0.00002, -0.000040, 0.000005] tscor[550.5] = [0.00002, -0.000040, 0.000005] tscor[550.6] = [0.00002, -0.000040, 0.000005] tscor[550.7] = [0.00002, -0.000040, 0.000005] tscor[550.8] = [0.00002, -0.000040, 0.000005] tscor[550.9] = [0.00002, -0.000040, 0.000005] tscor[551.0] = [0.00002, -0.000040, 0.000005] tscor[551.1] = [0.00001, -0.000040, 0.000005] tscor[551.2] = [0.00001, -0.000040, 0.000005] tscor[551.3] = [0.00001, -0.000040, 0.000005] tscor[551.4] = [0.00001, -0.000040, 0.000005] tscor[551.5] = [0.00001, -0.000040, 0.000005] tscor[551.6] = [0.00001, -0.000040, 0.000005] tscor[551.7] = [0.00001, -0.000040, 0.000005] tscor[551.8] = [0.00001, -0.000040, 0.000005] tscor[551.9] = [0.00001, -0.000040, 0.000005] tscor[552.0] = [0.00001, -0.000040, 0.000005] tscor[552.1] = [0.00001, -0.000040, 0.000005] tscor[552.2] = [0.00001, -0.000040, 0.000005] tscor[552.3] = [0.00001, -0.000040, 0.000005] tscor[552.4] = [0.00001, -0.000040, 0.000005] tscor[552.5] = [0.00001, -0.000040, 0.000005] tscor[552.6] = [0.00001, -0.000040, 0.000005] tscor[552.7] = [0.00001, -0.000040, 0.000005] tscor[552.8] = [0.00001, -0.000040, 0.000005] tscor[552.9] = [0.00001, -0.000040, 0.000005] tscor[553.0] = [0.00001, -0.000040, 0.000005] tscor[553.1] = [0.00001, -0.000039, 0.000004] tscor[553.2] = [0.00001, -0.000039, 0.000004] tscor[553.3] = [0.00001, -0.000039, 0.000004] tscor[553.4] = [0.00001, -0.000039, 0.000004] tscor[553.5] = [0.00001, -0.000039, 0.000004] tscor[553.6] = [0.00001, -0.000039, 0.000004] tscor[553.7] = [0.00001, -0.000039, 0.000004] tscor[553.8] = [0.00001, -0.000039, 0.000004] tscor[553.9] = [0.00001, -0.000039, 0.000004] tscor[554.0] = [0.00001, -0.000039, 0.000004] tscor[554.1] = [0.00001, -0.000039, 0.000004] tscor[554.2] = [0.00001, -0.000039, 0.000004] tscor[554.3] = [0.00001, -0.000039, 0.000004] tscor[554.4] = [0.00001, -0.000039, 0.000004] tscor[554.5] = [0.00001, -0.000039, 0.000004] tscor[554.6] = [0.00001, -0.000039, 0.000004] tscor[554.7] = [0.00001, -0.000039, 0.000004] tscor[554.8] = [0.00001, -0.000039, 0.000004] tscor[554.9] = [0.00001, -0.000039, 0.000004] tscor[555.0] = [0.00001, -0.000039, 0.000004] tscor[555.1] = [0.00001, -0.000039, 0.000004] tscor[555.2] = [0.00001, -0.000039, 0.000004] tscor[555.3] = [0.00001, -0.000039, 0.000004] tscor[555.4] = [0.00001, -0.000039, 0.000004] tscor[555.5] = [0.00001, -0.000039, 0.000004] tscor[555.6] = [0.00001, -0.000039, 0.000004] tscor[555.7] = [0.00001, -0.000039, 0.000004] tscor[555.8] = [0.00001, -0.000039, 0.000004] tscor[555.9] = [0.00001, -0.000039, 0.000004] tscor[556.0] = [0.00001, -0.000039, 0.000004] tscor[556.1] = [0.00001, -0.000039, 0.000004] tscor[556.2] = [0.00001, -0.000039, 0.000004] tscor[556.3] = [0.00001, -0.000039, 0.000004] tscor[556.4] = [0.00001, -0.000039, 0.000004] tscor[556.5] = [0.00001, -0.000039, 0.000004] tscor[556.6] = [0.00001, -0.000039, 0.000004] tscor[556.7] = [0.00001, -0.000039, 0.000004] tscor[556.8] = [0.00001, -0.000039, 0.000004] tscor[556.9] = [0.00001, -0.000039, 0.000004] tscor[557.0] = [0.00001, -0.000039, 0.000004] tscor[557.1] = [0.00001, -0.000039, 0.000003] tscor[557.2] = [0.00001, -0.000039, 0.000003] tscor[557.3] = [0.00001, -0.000039, 0.000003] tscor[557.4] = [0.00001, -0.000039, 0.000003] tscor[557.5] = [0.00001, -0.000039, 0.000003] tscor[557.6] = [0.00001, -0.000039, 0.000003] tscor[557.7] = [0.00001, -0.000039, 0.000003] tscor[557.8] = [0.00001, -0.000039, 0.000003] tscor[557.9] = [0.00001, -0.000039, 0.000003] tscor[558.0] = [0.00001, -0.000039, 0.000003] tscor[558.1] = [0.00001, -0.000039, 0.000003] tscor[558.2] = [0.00001, -0.000039, 0.000003] tscor[558.3] = [0.00001, -0.000039, 0.000003] tscor[558.4] = [0.00001, -0.000039, 0.000003] tscor[558.5] = [0.00001, -0.000039, 0.000003] tscor[558.6] = [0.00001, -0.000039, 0.000003] tscor[558.7] = [0.00001, -0.000039, 0.000003] tscor[558.8] = [0.00001, -0.000039, 0.000003] tscor[558.9] = [0.00001, -0.000039, 0.000003] tscor[559.0] = [0.00001, -0.000040, 0.000003] tscor[559.1] = [0.00000, -0.000040, 0.000002] tscor[559.2] = [0.00000, -0.000040, 0.000002] tscor[559.3] = [0.00000, -0.000040, 0.000002] tscor[559.4] = [0.00000, -0.000040, 0.000002] tscor[559.5] = [0.00000, -0.000040, 0.000002] tscor[559.6] = [0.00000, -0.000040, 0.000002] tscor[559.7] = [0.00000, -0.000040, 0.000002] tscor[559.8] = [0.00000, -0.000040, 0.000002] tscor[559.9] = [0.00000, -0.000040, 0.000002] tscor[560.0] = [0.00000, -0.000040, 0.000002] tscor[560.1] = [0.00000, -0.000040, 0.000002] tscor[560.2] = [0.00000, -0.000040, 0.000002] tscor[560.3] = [0.00000, -0.000040, 0.000002] tscor[560.4] = [0.00000, -0.000040, 0.000002] tscor[560.5] = [0.00000, -0.000040, 0.000002] tscor[560.6] = [0.00000, -0.000040, 0.000001] tscor[560.7] = [0.00000, -0.000040, 0.000001] tscor[560.8] = [0.00000, -0.000040, 0.000001] tscor[560.9] = [0.00000, -0.000040, 0.000001] tscor[561.0] = [0.00000, -0.000041, 0.000001] tscor[561.1] = [0.00000, -0.000041, 0.000001] tscor[561.2] = [0.00000, -0.000041, 0.000001] tscor[561.3] = [0.00000, -0.000041, 0.000001] tscor[561.4] = [0.00000, -0.000041, 0.000001] tscor[561.5] = [0.00000, -0.000041, 0.000001] tscor[561.6] = [0.00000, -0.000041, 0.000000] tscor[561.7] = [0.00000, -0.000041, 0.000000] tscor[561.8] = [0.00000, -0.000041, 0.000000] tscor[561.9] = [0.00000, -0.000041, 0.000000] tscor[562.0] = [0.00000, -0.000041, 0.000000] tscor[562.1] = [0.00000, -0.000041, 0.000000] tscor[562.2] = [0.00000, -0.000041, 0.000000] tscor[562.3] = [0.00000, -0.000041, 0.000000] tscor[562.4] = [0.00000, -0.000041, 0.000000] tscor[562.5] = [0.00000, -0.000041, -0.000001] tscor[562.6] = [0.00000, -0.000041, -0.000001] tscor[562.7] = [0.00000, -0.000041, -0.000001] tscor[562.8] = [0.00000, -0.000041, -0.000001] tscor[562.9] = [0.00000, -0.000041, -0.000001] tscor[563.0] = [0.00000, -0.000042, -0.000001] tscor[563.1] = [0.00000, -0.000042, -0.000001] tscor[563.2] = [0.00000, -0.000042, -0.000001] tscor[563.3] = [0.00000, -0.000042, -0.000001] tscor[563.4] = [0.00000, -0.000042, -0.000001] tscor[563.5] = [0.00000, -0.000042, -0.000002] tscor[563.6] = [0.00000, -0.000042, -0.000002] tscor[563.7] = [0.00000, -0.000042, -0.000002] tscor[563.8] = [0.00000, -0.000042, -0.000002] tscor[563.9] = [0.00000, -0.000042, -0.000002] tscor[564.0] = [0.00000, -0.000042, -0.000002] tscor[564.1] = [0.00000, -0.000042, -0.000002] tscor[564.2] = [0.00000, -0.000042, -0.000002] tscor[564.3] = [0.00000, -0.000042, -0.000002] tscor[564.4] = [0.00000, -0.000042, -0.000002] tscor[564.5] = [0.00000, -0.000042, -0.000002] tscor[564.6] = [0.00000, -0.000042, -0.000002] tscor[564.7] = [0.00000, -0.000042, -0.000002] tscor[564.8] = [0.00000, -0.000042, -0.000002] tscor[564.9] = [0.00000, -0.000042, -0.000002] tscor[565.0] = [0.00000, -0.000042, -0.000003] tscor[565.1] = [0.00000, -0.000042, -0.000003] tscor[565.2] = [0.00000, -0.000042, -0.000003] tscor[565.3] = [0.00000, -0.000042, -0.000003] tscor[565.4] = [0.00000, -0.000042, -0.000003] tscor[565.5] = [0.00000, -0.000042, -0.000003] tscor[565.6] = [0.00000, -0.000042, -0.000003] tscor[565.7] = [0.00000, -0.000042, -0.000003] tscor[565.8] = [0.00000, -0.000042, -0.000003] tscor[565.9] = [0.00000, -0.000042, -0.000003] tscor[566.0] = [0.00000, -0.000042, -0.000003] tscor[566.1] = [0.00000, -0.000042, -0.000003] tscor[566.2] = [0.00000, -0.000042, -0.000003] tscor[566.3] = [0.00000, -0.000042, -0.000003] tscor[566.4] = [0.00000, -0.000042, -0.000003] tscor[566.5] = [0.00000, -0.000042, -0.000004] tscor[566.6] = [0.00000, -0.000042, -0.000004] tscor[566.7] = [0.00000, -0.000042, -0.000004] tscor[566.8] = [0.00000, -0.000042, -0.000004] tscor[566.9] = [0.00000, -0.000042, -0.000004] tscor[567.0] = [0.00000, -0.000043, -0.000004] tscor[567.1] = [0.00000, -0.000043, -0.000004] tscor[567.2] = [0.00000, -0.000043, -0.000004] tscor[567.3] = [0.00000, -0.000043, -0.000004] tscor[567.4] = [0.00000, -0.000043, -0.000004] tscor[567.5] = [0.00000, -0.000043, -0.000005] tscor[567.6] = [0.00000, -0.000043, -0.000005] tscor[567.7] = [0.00000, -0.000043, -0.000005] tscor[567.8] = [0.00000, -0.000043, -0.000005] tscor[567.9] = [0.00000, -0.000043, -0.000005] tscor[568.0] = [0.00000, -0.000043, -0.000005] tscor[568.1] = [0.00000, -0.000043, -0.000005] tscor[568.2] = [0.00000, -0.000043, -0.000005] tscor[568.3] = [0.00000, -0.000043, -0.000005] tscor[568.4] = [0.00000, -0.000043, -0.000005] tscor[568.5] = [0.00001, -0.000043, -0.000005] tscor[568.6] = [0.00001, -0.000043, -0.000005] tscor[568.7] = [0.00001, -0.000043, -0.000005] tscor[568.8] = [0.00001, -0.000043, -0.000005] tscor[568.9] = [0.00001, -0.000043, -0.000005] tscor[569.0] = [0.00001, -0.000044, -0.000006] tscor[569.1] = [0.00001, -0.000044, -0.000006] tscor[569.2] = [0.00001, -0.000044, -0.000006] tscor[569.3] = [0.00001, -0.000044, -0.000006] tscor[569.4] = [0.00001, -0.000044, -0.000006] tscor[569.5] = [0.00002, -0.000044, -0.000006] tscor[569.6] = [0.00002, -0.000044, -0.000006] tscor[569.7] = [0.00002, -0.000044, -0.000006] tscor[569.8] = [0.00002, -0.000044, -0.000006] tscor[569.9] = [0.00002, -0.000044, -0.000006] tscor[570.0] = [0.00002, -0.000044, -0.000006] tscor[570.1] = [0.00002, -0.000044, -0.000006] tscor[570.2] = [0.00002, -0.000044, -0.000006] tscor[570.3] = [0.00002, -0.000044, -0.000006] tscor[570.4] = [0.00002, -0.000044, -0.000006] tscor[570.5] = [0.00003, -0.000045, -0.000006] tscor[570.6] = [0.00003, -0.000045, -0.000006] tscor[570.7] = [0.00003, -0.000045, -0.000006] tscor[570.8] = [0.00003, -0.000045, -0.000006] tscor[570.9] = [0.00003, -0.000045, -0.000006] tscor[571.0] = [0.00003, -0.000045, -0.000007] tscor[571.1] = [0.00003, -0.000045, -0.000007] tscor[571.2] = [0.00003, -0.000045, -0.000007] tscor[571.3] = [0.00003, -0.000045, -0.000007] tscor[571.4] = [0.00003, -0.000045, -0.000007] tscor[571.5] = [0.00004, -0.000046, -0.000007] tscor[571.6] = [0.00004, -0.000046, -0.000007] tscor[571.7] = [0.00004, -0.000046, -0.000007] tscor[571.8] = [0.00004, -0.000046, -0.000007] tscor[571.9] = [0.00004, -0.000046, -0.000007] tscor[572.0] = [0.00004, -0.000046, -0.000007] tscor[572.1] = [0.00004, -0.000046, -0.000007] tscor[572.2] = [0.00004, -0.000046, -0.000007] tscor[572.3] = [0.00004, -0.000046, -0.000007] tscor[572.4] = [0.00004, -0.000046, -0.000007] tscor[572.5] = [0.00005, -0.000046, -0.000007] tscor[572.6] = [0.00005, -0.000046, -0.000007] tscor[572.7] = [0.00005, -0.000046, -0.000007] tscor[572.8] = [0.00005, -0.000046, -0.000007] tscor[572.9] = [0.00005, -0.000046, -0.000007] tscor[573.0] = [0.00005, -0.000047, -0.000007] tscor[573.1] = [0.00005, -0.000047, -0.000007] tscor[573.2] = [0.00005, -0.000047, -0.000007] tscor[573.3] = [0.00005, -0.000047, -0.000007] tscor[573.4] = [0.00005, -0.000047, -0.000007] tscor[573.5] = [0.00006, -0.000047, -0.000007] tscor[573.6] = [0.00006, -0.000047, -0.000007] tscor[573.7] = [0.00006, -0.000047, -0.000007] tscor[573.8] = [0.00006, -0.000047, -0.000007] tscor[573.9] = [0.00006, -0.000047, -0.000007] tscor[574.0] = [0.00006, -0.000047, -0.000007] tscor[574.1] = [0.00006, -0.000047, -0.000007] tscor[574.2] = [0.00006, -0.000047, -0.000007] tscor[574.3] = [0.00007, -0.000047, -0.000007] tscor[574.4] = [0.00007, -0.000047, -0.000007] tscor[574.5] = [0.00007, -0.000047, -0.000007] tscor[574.6] = [0.00007, -0.000047, -0.000007] tscor[574.7] = [0.00007, -0.000047, -0.000007] tscor[574.8] = [0.00008, -0.000047, -0.000007] tscor[574.9] = [0.00008, -0.000047, -0.000007] tscor[575.0] = [0.00008, -0.000048, -0.000008] tscor[575.1] = [0.00008, -0.000048, -0.000008] tscor[575.2] = [0.00008, -0.000048, -0.000008] tscor[575.3] = [0.00009, -0.000048, -0.000008] tscor[575.4] = [0.00009, -0.000048, -0.000008] tscor[575.5] = [0.00009, -0.000048, -0.000008] tscor[575.6] = [0.00009, -0.000048, -0.000008] tscor[575.7] = [0.00009, -0.000048, -0.000008] tscor[575.8] = [0.00010, -0.000048, -0.000008] tscor[575.9] = [0.00010, -0.000048, -0.000008] tscor[576.0] = [0.00010, -0.000048, -0.000008] tscor[576.1] = [0.00010, -0.000048, -0.000008] tscor[576.2] = [0.00010, -0.000048, -0.000008] tscor[576.3] = [0.00011, -0.000048, -0.000008] tscor[576.4] = [0.00011, -0.000048, -0.000008] tscor[576.5] = [0.00011, -0.000048, -0.000008] tscor[576.6] = [0.00011, -0.000048, -0.000008] tscor[576.7] = [0.00011, -0.000048, -0.000008] tscor[576.8] = [0.00012, -0.000048, -0.000008] tscor[576.9] = [0.00012, -0.000048, -0.000008] tscor[577.0] = [0.00012, -0.000049, -0.000009] tscor[577.1] = [0.00012, -0.000049, -0.000009] tscor[577.2] = [0.00012, -0.000049, -0.000009] tscor[577.3] = [0.00013, -0.000049, -0.000009] tscor[577.4] = [0.00013, -0.000049, -0.000009] tscor[577.5] = [0.00013, -0.000049, -0.000009] tscor[577.6] = [0.00013, -0.000049, -0.000009] tscor[577.7] = [0.00013, -0.000049, -0.000009] tscor[577.8] = [0.00014, -0.000049, -0.000009] tscor[577.9] = [0.00014, -0.000049, -0.000009] tscor[578.0] = [0.00014, -0.000049, -0.000009] tscor[578.1] = [0.00014, -0.000049, -0.000009] tscor[578.2] = [0.00015, -0.000049, -0.000009] tscor[578.3] = [0.00015, -0.000049, -0.000009] tscor[578.4] = [0.00015, -0.000049, -0.000009] tscor[578.5] = [0.00016, -0.000050, -0.000009] tscor[578.6] = [0.00016, -0.000050, -0.000009] tscor[578.7] = [0.00016, -0.000050, -0.000009] tscor[578.8] = [0.00016, -0.000050, -0.000009] tscor[578.9] = [0.00017, -0.000050, -0.000009] tscor[579.0] = [0.00017, -0.000050, -0.000010] tscor[579.1] = [0.00017, -0.000050, -0.000010] tscor[579.2] = [0.00018, -0.000050, -0.000010] tscor[579.3] = [0.00018, -0.000050, -0.000010] tscor[579.4] = [0.00018, -0.000050, -0.000010] tscor[579.5] = [0.00019, -0.000051, -0.000010] tscor[579.6] = [0.00019, -0.000051, -0.000010] tscor[579.7] = [0.00019, -0.000051, -0.000010] tscor[579.8] = [0.00019, -0.000051, -0.000010] tscor[579.9] = [0.00020, -0.000051, -0.000010] tscor[580.0] = [0.00020, -0.000051, -0.000010] tscor[580.1] = [0.00020, -0.000051, -0.000010] tscor[580.2] = [0.00021, -0.000051, -0.000010] tscor[580.3] = [0.00021, -0.000051, -0.000010] tscor[580.4] = [0.00021, -0.000051, -0.000010] tscor[580.5] = [0.00022, -0.000051, -0.000010] tscor[580.6] = [0.00022, -0.000051, -0.000010] tscor[580.7] = [0.00022, -0.000051, -0.000010] tscor[580.8] = [0.00022, -0.000051, -0.000010] tscor[580.9] = [0.00023, -0.000051, -0.000010] tscor[581.0] = [0.00023, -0.000052, -0.000011] tscor[581.1] = [0.00023, -0.000052, -0.000011] tscor[581.2] = [0.00024, -0.000052, -0.000011] tscor[581.3] = [0.00024, -0.000052, -0.000011] tscor[581.4] = [0.00024, -0.000052, -0.000011] tscor[581.5] = [0.00025, -0.000052, -0.000011] tscor[581.6] = [0.00025, -0.000052, -0.000011] tscor[581.7] = [0.00025, -0.000052, -0.000011] tscor[581.8] = [0.00025, -0.000052, -0.000011] tscor[581.9] = [0.00026, -0.000052, -0.000011] tscor[582.0] = [0.00026, -0.000052, -0.000011] tscor[582.1] = [0.00026, -0.000052, -0.000011] tscor[582.2] = [0.00027, -0.000052, -0.000011] tscor[582.3] = [0.00027, -0.000052, -0.000011] tscor[582.4] = [0.00027, -0.000052, -0.000011] tscor[582.5] = [0.00028, -0.000052, -0.000011] tscor[582.6] = [0.00028, -0.000052, -0.000011] tscor[582.7] = [0.00028, -0.000052, -0.000011] tscor[582.8] = [0.00028, -0.000052, -0.000011] tscor[582.9] = [0.00029, -0.000052, -0.000011] tscor[583.0] = [0.00029, -0.000052, -0.000012] tscor[583.1] = [0.00029, -0.000052, -0.000012] tscor[583.2] = [0.00030, -0.000052, -0.000012] tscor[583.3] = [0.00030, -0.000052, -0.000012] tscor[583.4] = [0.00030, -0.000052, -0.000012] tscor[583.5] = [0.00031, -0.000052, -0.000012] tscor[583.6] = [0.00031, -0.000052, -0.000012] tscor[583.7] = [0.00031, -0.000052, -0.000012] tscor[583.8] = [0.00031, -0.000052, -0.000012] tscor[583.9] = [0.00032, -0.000052, -0.000012] tscor[584.0] = [0.00032, -0.000052, -0.000012] tscor[584.1] = [0.00032, -0.000052, -0.000012] tscor[584.2] = [0.00033, -0.000052, -0.000012] tscor[584.3] = [0.00033, -0.000052, -0.000012] tscor[584.4] = [0.00033, -0.000052, -0.000012] tscor[584.5] = [0.00034, -0.000052, -0.000012] tscor[584.6] = [0.00034, -0.000052, -0.000012] tscor[584.7] = [0.00034, -0.000052, -0.000012] tscor[584.8] = [0.00035, -0.000052, -0.000012] tscor[584.9] = [0.00035, -0.000052, -0.000012] tscor[585.0] = [0.00036, -0.000052, -0.000012] tscor[585.1] = [0.00036, -0.000052, -0.000012] tscor[585.2] = [0.00036, -0.000052, -0.000012] tscor[585.3] = [0.00037, -0.000052, -0.000012] tscor[585.4] = [0.00037, -0.000052, -0.000012] tscor[585.5] = [0.00037, -0.000052, -0.000012] tscor[585.6] = [0.00038, -0.000052, -0.000012] tscor[585.7] = [0.00038, -0.000052, -0.000012] tscor[585.8] = [0.00038, -0.000052, -0.000012] tscor[585.9] = [0.00039, -0.000052, -0.000012] tscor[586.0] = [0.00039, -0.000052, -0.000012] tscor[586.1] = [0.00039, -0.000052, -0.000012] tscor[586.2] = [0.00040, -0.000052, -0.000012] tscor[586.3] = [0.00040, -0.000052, -0.000012] tscor[586.4] = [0.00041, -0.000052, -0.000012] tscor[586.5] = [0.00041, -0.000052, -0.000012] tscor[586.6] = [0.00041, -0.000052, -0.000012] tscor[586.7] = [0.00042, -0.000052, -0.000012] tscor[586.8] = [0.00042, -0.000052, -0.000012] tscor[586.9] = [0.00043, -0.000052, -0.000012] tscor[587.0] = [0.00043, -0.000052, -0.000013] tscor[587.1] = [0.00043, -0.000052, -0.000013] tscor[587.2] = [0.00044, -0.000052, -0.000013] tscor[587.3] = [0.00044, -0.000052, -0.000013] tscor[587.4] = [0.00045, -0.000052, -0.000013] tscor[587.5] = [0.00045, -0.000052, -0.000013] tscor[587.6] = [0.00045, -0.000052, -0.000013] tscor[587.7] = [0.00046, -0.000052, -0.000013] tscor[587.8] = [0.00046, -0.000052, -0.000013] tscor[587.9] = [0.00047, -0.000052, -0.000013] tscor[588.0] = [0.00047, -0.000052, -0.000013] tscor[588.1] = [0.00047, -0.000052, -0.000013] tscor[588.2] = [0.00048, -0.000052, -0.000013] tscor[588.3] = [0.00048, -0.000052, -0.000013] tscor[588.4] = [0.00049, -0.000052, -0.000013] tscor[588.5] = [0.00049, -0.000052, -0.000013] tscor[588.6] = [0.00049, -0.000052, -0.000013] tscor[588.7] = [0.00050, -0.000052, -0.000013] tscor[588.8] = [0.00050, -0.000052, -0.000013] tscor[588.9] = [0.00051, -0.000052, -0.000013] tscor[589.0] = [0.00051, -0.000052, -0.000013] tscor[589.1] = [0.00051, -0.000052, -0.000013] tscor[589.2] = [0.00052, -0.000052, -0.000013] tscor[589.3] = [0.00052, -0.000052, -0.000013] tscor[589.4] = [0.00053, -0.000052, -0.000013] tscor[589.5] = [0.00053, -0.000052, -0.000013] tscor[589.6] = [0.00053, -0.000052, -0.000013] tscor[589.7] = [0.00054, -0.000052, -0.000013] tscor[589.8] = [0.00054, -0.000052, -0.000013] tscor[589.9] = [0.00055, -0.000052, -0.000013] tscor[590.0] = [0.00055, -0.000052, -0.000013] tscor[590.1] = [0.00055, -0.000052, -0.000013] tscor[590.2] = [0.00056, -0.000052, -0.000013] tscor[590.3] = [0.00056, -0.000052, -0.000013] tscor[590.4] = [0.00057, -0.000052, -0.000013] tscor[590.5] = [0.00057, -0.000052, -0.000013] tscor[590.6] = [0.00058, -0.000052, -0.000013] tscor[590.7] = [0.00058, -0.000052, -0.000013] tscor[590.8] = [0.00059, -0.000052, -0.000013] tscor[590.9] = [0.00059, -0.000052, -0.000013] tscor[591.0] = [0.00060, -0.000052, -0.000013] tscor[591.1] = [0.00060, -0.000051, -0.000012] tscor[591.2] = [0.00060, -0.000051, -0.000012] tscor[591.3] = [0.00061, -0.000051, -0.000012] tscor[591.4] = [0.00061, -0.000051, -0.000012] tscor[591.5] = [0.00062, -0.000051, -0.000012] tscor[591.6] = [0.00062, -0.000051, -0.000012] tscor[591.7] = [0.00063, -0.000051, -0.000012] tscor[591.8] = [0.00063, -0.000051, -0.000012] tscor[591.9] = [0.00064, -0.000051, -0.000012] tscor[592.0] = [0.00064, -0.000051, -0.000012] tscor[592.1] = [0.00065, -0.000051, -0.000012] tscor[592.2] = [0.00065, -0.000051, -0.000012] tscor[592.3] = [0.00066, -0.000051, -0.000012] tscor[592.4] = [0.00066, -0.000051, -0.000012] tscor[592.5] = [0.00067, -0.000051, -0.000012] tscor[592.6] = [0.00067, -0.000050, -0.000012] tscor[592.7] = [0.00068, -0.000050, -0.000012] tscor[592.8] = [0.00068, -0.000050, -0.000012] tscor[592.9] = [0.00069, -0.000050, -0.000012] tscor[593.0] = [0.00069, -0.000050, -0.000012] tscor[593.1] = [0.00070, -0.000050, -0.000011] tscor[593.2] = [0.00070, -0.000050, -0.000011] tscor[593.3] = [0.00071, -0.000050, -0.000011] tscor[593.4] = [0.00071, -0.000050, -0.000011] tscor[593.5] = [0.00072, -0.000050, -0.000011] tscor[593.6] = [0.00072, -0.000049, -0.000011] tscor[593.7] = [0.00073, -0.000049, -0.000011] tscor[593.8] = [0.00073, -0.000049, -0.000011] tscor[593.9] = [0.00074, -0.000049, -0.000011] tscor[594.0] = [0.00074, -0.000049, -0.000011] tscor[594.1] = [0.00074, -0.000049, -0.000011] tscor[594.2] = [0.00075, -0.000049, -0.000011] tscor[594.3] = [0.00075, -0.000049, -0.000011] tscor[594.4] = [0.00076, -0.000048, -0.000010] tscor[594.5] = [0.00076, -0.000048, -0.000010] tscor[594.6] = [0.00077, -0.000048, -0.000010] tscor[594.7] = [0.00077, -0.000048, -0.000010] tscor[594.8] = [0.00078, -0.000048, -0.000010] tscor[594.9] = [0.00078, -0.000048, -0.000010] tscor[595.0] = [0.00079, -0.000048, -0.000010] tscor[595.1] = [0.00079, -0.000047, -0.000009] tscor[595.2] = [0.00079, -0.000047, -0.000009] tscor[595.3] = [0.00080, -0.000047, -0.000009] tscor[595.4] = [0.00080, -0.000047, -0.000009] tscor[595.5] = [0.00081, -0.000047, -0.000009] tscor[595.6] = [0.00081, -0.000047, -0.000009] tscor[595.7] = [0.00082, -0.000046, -0.000008] tscor[595.8] = [0.00082, -0.000046, -0.000008] tscor[595.9] = [0.00083, -0.000046, -0.000008] tscor[596.0] = [0.00083, -0.000046, -0.000008] tscor[596.1] = [0.00083, -0.000046, -0.000008] tscor[596.2] = [0.00084, -0.000045, -0.000008] tscor[596.3] = [0.00084, -0.000045, -0.000008] tscor[596.4] = [0.00085, -0.000045, -0.000008] tscor[596.5] = [0.00085, -0.000045, -0.000008] tscor[596.6] = [0.00085, -0.000044, -0.000007] tscor[596.7] = [0.00086, -0.000044, -0.000007] tscor[596.8] = [0.00086, -0.000044, -0.000007] tscor[596.9] = [0.00087, -0.000043, -0.000007] tscor[597.0] = [0.00087, -0.000043, -0.000007] tscor[597.1] = [0.00087, -0.000043, -0.000007] tscor[597.2] = [0.00088, -0.000042, -0.000007] tscor[597.3] = [0.00088, -0.000042, -0.000007] tscor[597.4] = [0.00089, -0.000042, -0.000007] tscor[597.5] = [0.00089, -0.000042, -0.000007] tscor[597.6] = [0.00089, -0.000041, -0.000006] tscor[597.7] = [0.00090, -0.000041, -0.000006] tscor[597.8] = [0.00090, -0.000041, -0.000006] tscor[597.9] = [0.00091, -0.000040, -0.000006] tscor[598.0] = [0.00091, -0.000040, -0.000006] tscor[598.1] = [0.00091, -0.000040, -0.000006] tscor[598.2] = [0.00092, -0.000039, -0.000005] tscor[598.3] = [0.00092, -0.000039, -0.000005] tscor[598.4] = [0.00092, -0.000039, -0.000005] tscor[598.5] = [0.00093, -0.000039, -0.000004] tscor[598.6] = [0.00093, -0.000038, -0.000004] tscor[598.7] = [0.00093, -0.000038, -0.000004] tscor[598.8] = [0.00094, -0.000038, -0.000003] tscor[598.9] = [0.00094, -0.000037, -0.000003] tscor[599.0] = [0.00095, -0.000037, -0.000003] tscor[599.1] = [0.00095, -0.000037, -0.000002] tscor[599.2] = [0.00095, -0.000036, -0.000002] tscor[599.3] = [0.00096, -0.000036, -0.000001] tscor[599.4] = [0.00096, -0.000036, -0.000001] tscor[599.5] = [0.00096, -0.000036, -0.000001] tscor[599.6] = [0.00097, -0.000035, 0.000000] tscor[599.7] = [0.00097, -0.000035, 0.000000] tscor[599.8] = [0.00097, -0.000035, 0.000000] tscor[599.9] = [0.00098, -0.000034, 0.000001] tscor[600.0] = [0.00098, -0.000034, 0.000001] tscor[600.1] = [0.00098, -0.000034, 0.000001] tscor[600.2] = [0.00098, -0.000033, 0.000002] tscor[600.3] = [0.00099, -0.000033, 0.000002] tscor[600.4] = [0.00099, -0.000032, 0.000002] tscor[600.5] = [0.00099, -0.000032, 0.000003] tscor[600.6] = [0.00099, -0.000032, 0.000003] tscor[600.7] = [0.00099, -0.000031, 0.000003] tscor[600.8] = [0.00100, -0.000031, 0.000004] tscor[600.9] = [0.00100, -0.000030, 0.000004] tscor[601.0] = [0.00100, -0.000030, 0.000005] tscor[601.1] = [0.00100, -0.000030, 0.000005] tscor[601.2] = [0.00100, -0.000029, 0.000005] tscor[601.3] = [0.00101, -0.000029, 0.000006] tscor[601.4] = [0.00101, -0.000028, 0.000006] tscor[601.5] = [0.00101, -0.000028, 0.000006] tscor[601.6] = [0.00101, -0.000028, 0.000007] tscor[601.7] = [0.00101, -0.000027, 0.000007] tscor[601.8] = [0.00102, -0.000027, 0.000007] tscor[601.9] = [0.00102, -0.000026, 0.000008] tscor[602.0] = [0.00102, -0.000026, 0.000008] tscor[602.1] = [0.00102, -0.000026, 0.000008] tscor[602.2] = [0.00102, -0.000025, 0.000009] tscor[602.3] = [0.00102, -0.000025, 0.000009] tscor[602.4] = [0.00102, -0.000024, 0.000010] tscor[602.5] = [0.00102, -0.000024, 0.000010] tscor[602.6] = [0.00102, -0.000023, 0.000011] tscor[602.7] = [0.00102, -0.000023, 0.000011] tscor[602.8] = [0.00102, -0.000022, 0.000012] tscor[602.9] = [0.00102, -0.000022, 0.000012] tscor[603.0] = [0.00103, -0.000022, 0.000013] tscor[603.1] = [0.00103, -0.000021, 0.000013] tscor[603.2] = [0.00103, -0.000021, 0.000013] tscor[603.3] = [0.00103, -0.000020, 0.000014] tscor[603.4] = [0.00103, -0.000020, 0.000014] tscor[603.5] = [0.00103, -0.000019, 0.000015] tscor[603.6] = [0.00103, -0.000019, 0.000015] tscor[603.7] = [0.00103, -0.000018, 0.000016] tscor[603.8] = [0.00103, -0.000018, 0.000016] tscor[603.9] = [0.00103, -0.000017, 0.000017] tscor[604.0] = [0.00103, -0.000017, 0.000017] tscor[604.1] = [0.00103, -0.000017, 0.000017] tscor[604.2] = [0.00103, -0.000016, 0.000018] tscor[604.3] = [0.00103, -0.000016, 0.000018] tscor[604.4] = [0.00103, -0.000015, 0.000019] tscor[604.5] = [0.00103, -0.000015, 0.000019] tscor[604.6] = [0.00102, -0.000014, 0.000020] tscor[604.7] = [0.00102, -0.000014, 0.000020] tscor[604.8] = [0.00102, -0.000013, 0.000021] tscor[604.9] = [0.00102, -0.000013, 0.000021] tscor[605.0] = [0.00102, -0.000013, 0.000022] tscor[605.1] = [0.00102, -0.000012, 0.000022] tscor[605.2] = [0.00102, -0.000012, 0.000022] tscor[605.3] = [0.00102, -0.000011, 0.000023] tscor[605.4] = [0.00102, -0.000011, 0.000023] tscor[605.5] = [0.00102, -0.000010, 0.000024] tscor[605.6] = [0.00101, -0.000010, 0.000024] tscor[605.7] = [0.00101, -0.000009, 0.000025] tscor[605.8] = [0.00101, -0.000009, 0.000025] tscor[605.9] = [0.00101, -0.000008, 0.000026] tscor[606.0] = [0.00101, -0.000008, 0.000026] tscor[606.1] = [0.00101, -0.000008, 0.000026] tscor[606.2] = [0.00101, -0.000007, 0.000027] tscor[606.3] = [0.00100, -0.000007, 0.000027] tscor[606.4] = [0.00100, -0.000006, 0.000028] tscor[606.5] = [0.00100, -0.000006, 0.000028] tscor[606.6] = [0.00100, -0.000005, 0.000028] tscor[606.7] = [0.00100, -0.000005, 0.000029] tscor[606.8] = [0.00099, -0.000004, 0.000029] tscor[606.9] = [0.00099, -0.000004, 0.000030] tscor[607.0] = [0.00099, -0.000004, 0.000030] tscor[607.1] = [0.00099, -0.000003, 0.000030] tscor[607.2] = [0.00099, -0.000003, 0.000031] tscor[607.3] = [0.00098, -0.000002, 0.000031] tscor[607.4] = [0.00098, -0.000002, 0.000032] tscor[607.5] = [0.00098, -0.000001, 0.000032] tscor[607.6] = [0.00098, -0.000001, 0.000032] tscor[607.7] = [0.00098, 0.000000, 0.000033] tscor[607.8] = [0.00097, 0.000000, 0.000033] tscor[607.9] = [0.00097, 0.000001, 0.000034] tscor[608.0] = [0.00097, 0.000001, 0.000034] tscor[608.1] = [0.00097, 0.000001, 0.000034] tscor[608.2] = [0.00097, 0.000002, 0.000035] tscor[608.3] = [0.00096, 0.000002, 0.000035] tscor[608.4] = [0.00096, 0.000003, 0.000035] tscor[608.5] = [0.00096, 0.000003, 0.000036] tscor[608.6] = [0.00096, 0.000003, 0.000036] tscor[608.7] = [0.00095, 0.000004, 0.000036] tscor[608.8] = [0.00095, 0.000004, 0.000037] tscor[608.9] = [0.00095, 0.000005, 0.000037] tscor[609.0] = [0.00095, 0.000005, 0.000038] tscor[609.1] = [0.00094, 0.000005, 0.000038] tscor[609.2] = [0.00094, 0.000006, 0.000038] tscor[609.3] = [0.00094, 0.000006, 0.000039] tscor[609.4] = [0.00094, 0.000007, 0.000039] tscor[609.5] = [0.00093, 0.000007, 0.000039] tscor[609.6] = [0.00093, 0.000007, 0.000040] tscor[609.7] = [0.00093, 0.000008, 0.000040] tscor[609.8] = [0.00093, 0.000008, 0.000040] tscor[609.9] = [0.00092, 0.000009, 0.000041] tscor[610.0] = [0.00092, 0.000009, 0.000041] tscor[610.1] = [0.00092, 0.000009, 0.000041] tscor[610.2] = [0.00091, 0.000010, 0.000042] tscor[610.3] = [0.00091, 0.000010, 0.000042] tscor[610.4] = [0.00091, 0.000010, 0.000042] tscor[610.5] = [0.00091, 0.000010, 0.000043] tscor[610.6] = [0.00090, 0.000011, 0.000043] tscor[610.7] = [0.00090, 0.000011, 0.000043] tscor[610.8] = [0.00090, 0.000011, 0.000044] tscor[610.9] = [0.00089, 0.000011, 0.000044] tscor[611.0] = [0.00089, 0.000012, 0.000045] tscor[611.1] = [0.00089, 0.000012, 0.000045] tscor[611.2] = [0.00088, 0.000012, 0.000045] tscor[611.3] = [0.00088, 0.000012, 0.000046] tscor[611.4] = [0.00088, 0.000013, 0.000046] tscor[611.5] = [0.00088, 0.000013, 0.000046] tscor[611.6] = [0.00087, 0.000013, 0.000047] tscor[611.7] = [0.00087, 0.000013, 0.000047] tscor[611.8] = [0.00087, 0.000014, 0.000047] tscor[611.9] = [0.00086, 0.000014, 0.000048] tscor[612.0] = [0.00086, 0.000014, 0.000048] tscor[612.1] = [0.00086, 0.000014, 0.000048] tscor[612.2] = [0.00085, 0.000015, 0.000048] tscor[612.3] = [0.00085, 0.000015, 0.000049] tscor[612.4] = [0.00084, 0.000015, 0.000049] tscor[612.5] = [0.00084, 0.000015, 0.000049] tscor[612.6] = [0.00084, 0.000016, 0.000049] tscor[612.7] = [0.00083, 0.000016, 0.000049] tscor[612.8] = [0.00083, 0.000016, 0.000050] tscor[612.9] = [0.00082, 0.000016, 0.000050] tscor[613.0] = [0.00082, 0.000017, 0.000050] tscor[613.1] = [0.00082, 0.000017, 0.000050] tscor[613.2] = [0.00081, 0.000017, 0.000050] tscor[613.3] = [0.00081, 0.000017, 0.000051] tscor[613.4] = [0.00080, 0.000018, 0.000051] tscor[613.5] = [0.00080, 0.000018, 0.000051] tscor[613.6] = [0.00080, 0.000018, 0.000051] tscor[613.7] = [0.00079, 0.000018, 0.000051] tscor[613.8] = [0.00079, 0.000019, 0.000052] tscor[613.9] = [0.00078, 0.000019, 0.000052] tscor[614.0] = [0.00078, 0.000019, 0.000052] tscor[614.1] = [0.00078, 0.000019, 0.000052] tscor[614.2] = [0.00077, 0.000019, 0.000052] tscor[614.3] = [0.00077, 0.000019, 0.000053] tscor[614.4] = [0.00077, 0.000019, 0.000053] tscor[614.5] = [0.00076, 0.000020, 0.000053] tscor[614.6] = [0.00076, 0.000020, 0.000053] tscor[614.7] = [0.00076, 0.000020, 0.000053] tscor[614.8] = [0.00075, 0.000020, 0.000054] tscor[614.9] = [0.00075, 0.000020, 0.000054] tscor[615.0] = [0.00075, 0.000020, 0.000054] tscor[615.1] = [0.00074, 0.000020, 0.000054] tscor[615.2] = [0.00074, 0.000020, 0.000054] tscor[615.3] = [0.00073, 0.000020, 0.000055] tscor[615.4] = [0.00073, 0.000020, 0.000055] tscor[615.5] = [0.00073, 0.000021, 0.000055] tscor[615.6] = [0.00072, 0.000021, 0.000055] tscor[615.7] = [0.00072, 0.000021, 0.000055] tscor[615.8] = [0.00072, 0.000021, 0.000056] tscor[615.9] = [0.00071, 0.000021, 0.000056] tscor[616.0] = [0.00071, 0.000021, 0.000056] tscor[616.1] = [0.00071, 0.000021, 0.000056] tscor[616.2] = [0.00070, 0.000021, 0.000056] tscor[616.3] = [0.00070, 0.000021, 0.000056] tscor[616.4] = [0.00070, 0.000021, 0.000056] tscor[616.5] = [0.00069, 0.000022, 0.000057] tscor[616.6] = [0.00069, 0.000022, 0.000057] tscor[616.7] = [0.00069, 0.000022, 0.000057] tscor[616.8] = [0.00068, 0.000022, 0.000057] tscor[616.9] = [0.00068, 0.000022, 0.000057] tscor[617.0] = [0.00068, 0.000022, 0.000057] tscor[617.1] = [0.00067, 0.000022, 0.000057] tscor[617.2] = [0.00067, 0.000022, 0.000057] tscor[617.3] = [0.00066, 0.000022, 0.000057] tscor[617.4] = [0.00066, 0.000022, 0.000057] tscor[617.5] = [0.00066, 0.000023, 0.000058] tscor[617.6] = [0.00065, 0.000023, 0.000058] tscor[617.7] = [0.00065, 0.000023, 0.000058] tscor[617.8] = [0.00065, 0.000023, 0.000058] tscor[617.9] = [0.00064, 0.000023, 0.000058] tscor[618.0] = [0.00064, 0.000023, 0.000058] tscor[618.1] = [0.00064, 0.000023, 0.000058] tscor[618.2] = [0.00063, 0.000023, 0.000058] tscor[618.3] = [0.00063, 0.000023, 0.000058] tscor[618.4] = [0.00062, 0.000023, 0.000058] tscor[618.5] = [0.00062, 0.000023, 0.000058] tscor[618.6] = [0.00062, 0.000023, 0.000058] tscor[618.7] = [0.00061, 0.000023, 0.000058] tscor[618.8] = [0.00061, 0.000023, 0.000058] tscor[618.9] = [0.00060, 0.000023, 0.000058] tscor[619.0] = [0.00060, 0.000024, 0.000059] tscor[619.1] = [0.00060, 0.000024, 0.000059] tscor[619.2] = [0.00059, 0.000024, 0.000059] tscor[619.3] = [0.00059, 0.000024, 0.000059] tscor[619.4] = [0.00058, 0.000024, 0.000059] tscor[619.5] = [0.00058, 0.000024, 0.000059] tscor[619.6] = [0.00058, 0.000024, 0.000059] tscor[619.7] = [0.00057, 0.000024, 0.000059] tscor[619.8] = [0.00057, 0.000024, 0.000059] tscor[619.9] = [0.00056, 0.000024, 0.000059] tscor[620.0] = [0.00056, 0.000024, 0.000059] tscor[620.1] = [0.00056, 0.000024, 0.000059] tscor[620.2] = [0.00055, 0.000024, 0.000059] tscor[620.3] = [0.00055, 0.000024, 0.000059] tscor[620.4] = [0.00054, 0.000024, 0.000059] tscor[620.5] = [0.00054, 0.000024, 0.000059] tscor[620.6] = [0.00054, 0.000024, 0.000059] tscor[620.7] = [0.00053, 0.000024, 0.000059] tscor[620.8] = [0.00053, 0.000024, 0.000059] tscor[620.9] = [0.00052, 0.000024, 0.000059] tscor[621.0] = [0.00052, 0.000024, 0.000059] tscor[621.1] = [0.00052, 0.000023, 0.000059] tscor[621.2] = [0.00051, 0.000023, 0.000059] tscor[621.3] = [0.00051, 0.000023, 0.000059] tscor[621.4] = [0.00050, 0.000023, 0.000059] tscor[621.5] = [0.00050, 0.000023, 0.000059] tscor[621.6] = [0.00050, 0.000023, 0.000059] tscor[621.7] = [0.00049, 0.000023, 0.000059] tscor[621.8] = [0.00049, 0.000023, 0.000059] tscor[621.9] = [0.00048, 0.000023, 0.000059] tscor[622.0] = [0.00048, 0.000023, 0.000059] tscor[622.1] = [0.00048, 0.000023, 0.000059] tscor[622.2] = [0.00047, 0.000023, 0.000059] tscor[622.3] = [0.00047, 0.000023, 0.000059] tscor[622.4] = [0.00046, 0.000023, 0.000059] tscor[622.5] = [0.00046, 0.000023, 0.000059] tscor[622.6] = [0.00046, 0.000023, 0.000059] tscor[622.7] = [0.00045, 0.000023, 0.000059] tscor[622.8] = [0.00045, 0.000023, 0.000059] tscor[622.9] = [0.00044, 0.000023, 0.000059] tscor[623.0] = [0.00044, 0.000023, 0.000059] tscor[623.1] = [0.00044, 0.000023, 0.000058] tscor[623.2] = [0.00043, 0.000023, 0.000058] tscor[623.3] = [0.00043, 0.000023, 0.000058] tscor[623.4] = [0.00042, 0.000023, 0.000058] tscor[623.5] = [0.00042, 0.000023, 0.000058] tscor[623.6] = [0.00042, 0.000023, 0.000058] tscor[623.7] = [0.00041, 0.000023, 0.000058] tscor[623.8] = [0.00041, 0.000023, 0.000058] tscor[623.9] = [0.00040, 0.000023, 0.000058] tscor[624.0] = [0.00040, 0.000023, 0.000058] tscor[624.1] = [0.00040, 0.000023, 0.000058] tscor[624.2] = [0.00039, 0.000023, 0.000058] tscor[624.3] = [0.00039, 0.000023, 0.000058] tscor[624.4] = [0.00039, 0.000023, 0.000058] tscor[624.5] = [0.00038, 0.000023, 0.000058] tscor[624.6] = [0.00038, 0.000023, 0.000058] tscor[624.7] = [0.00038, 0.000023, 0.000058] tscor[624.8] = [0.00037, 0.000023, 0.000058] tscor[624.9] = [0.00037, 0.000023, 0.000058] tscor[625.0] = [0.00037, 0.000023, 0.000058] tscor[625.1] = [0.00036, 0.000022, 0.000057] tscor[625.2] = [0.00036, 0.000022, 0.000057] tscor[625.3] = [0.00035, 0.000022, 0.000057] tscor[625.4] = [0.00035, 0.000022, 0.000057] tscor[625.5] = [0.00035, 0.000022, 0.000057] tscor[625.6] = [0.00034, 0.000022, 0.000057] tscor[625.7] = [0.00034, 0.000022, 0.000057] tscor[625.8] = [0.00034, 0.000022, 0.000057] tscor[625.9] = [0.00033, 0.000022, 0.000057] tscor[626.0] = [0.00033, 0.000022, 0.000057] tscor[626.1] = [0.00033, 0.000022, 0.000057] tscor[626.2] = [0.00032, 0.000022, 0.000057] tscor[626.3] = [0.00032, 0.000022, 0.000057] tscor[626.4] = [0.00032, 0.000022, 0.000057] tscor[626.5] = [0.00031, 0.000022, 0.000057] tscor[626.6] = [0.00031, 0.000021, 0.000056] tscor[626.7] = [0.00031, 0.000021, 0.000056] tscor[626.8] = [0.00030, 0.000021, 0.000056] tscor[626.9] = [0.00030, 0.000021, 0.000056] tscor[627.0] = [0.00030, 0.000021, 0.000056] tscor[627.1] = [0.00029, 0.000021, 0.000056] tscor[627.2] = [0.00029, 0.000021, 0.000056] tscor[627.3] = [0.00028, 0.000021, 0.000056] tscor[627.4] = [0.00028, 0.000021, 0.000056] tscor[627.5] = [0.00028, 0.000021, 0.000056] tscor[627.6] = [0.00027, 0.000020, 0.000055] tscor[627.7] = [0.00027, 0.000020, 0.000055] tscor[627.8] = [0.00027, 0.000020, 0.000055] tscor[627.9] = [0.00026, 0.000020, 0.000055] tscor[628.0] = [0.00026, 0.000020, 0.000055] tscor[628.1] = [0.00026, 0.000020, 0.000055] tscor[628.2] = [0.00025, 0.000020, 0.000055] tscor[628.3] = [0.00025, 0.000020, 0.000055] tscor[628.4] = [0.00025, 0.000020, 0.000055] tscor[628.5] = [0.00024, 0.000020, 0.000055] tscor[628.6] = [0.00024, 0.000019, 0.000054] tscor[628.7] = [0.00024, 0.000019, 0.000054] tscor[628.8] = [0.00023, 0.000019, 0.000054] tscor[628.9] = [0.00023, 0.000019, 0.000054] tscor[629.0] = [0.00023, 0.000019, 0.000054] tscor[629.1] = [0.00022, 0.000019, 0.000054] tscor[629.2] = [0.00022, 0.000019, 0.000054] tscor[629.3] = [0.00021, 0.000019, 0.000054] tscor[629.4] = [0.00021, 0.000019, 0.000054] tscor[629.5] = [0.00021, 0.000019, 0.000054] tscor[629.6] = [0.00020, 0.000018, 0.000053] tscor[629.7] = [0.00020, 0.000018, 0.000053] tscor[629.8] = [0.00020, 0.000018, 0.000053] tscor[629.9] = [0.00019, 0.000018, 0.000053] tscor[630.0] = [0.00019, 0.000018, 0.000053] tscor[630.1] = [0.00019, 0.000018, 0.000053] tscor[630.2] = [0.00019, 0.000018, 0.000053] tscor[630.3] = [0.00018, 0.000018, 0.000053] tscor[630.4] = [0.00018, 0.000018, 0.000053] tscor[630.5] = [0.00018, 0.000018, 0.000053] tscor[630.6] = [0.00018, 0.000017, 0.000052] tscor[630.7] = [0.00017, 0.000017, 0.000052] tscor[630.8] = [0.00017, 0.000017, 0.000052] tscor[630.9] = [0.00017, 0.000017, 0.000052] tscor[631.0] = [0.00017, 0.000017, 0.000052] tscor[631.1] = [0.00016, 0.000017, 0.000052] tscor[631.2] = [0.00016, 0.000017, 0.000052] tscor[631.3] = [0.00016, 0.000017, 0.000052] tscor[631.4] = [0.00016, 0.000017, 0.000052] tscor[631.5] = [0.00015, 0.000017, 0.000052] tscor[631.6] = [0.00015, 0.000016, 0.000051] tscor[631.7] = [0.00015, 0.000016, 0.000051] tscor[631.8] = [0.00015, 0.000016, 0.000051] tscor[631.9] = [0.00014, 0.000016, 0.000051] tscor[632.0] = [0.00014, 0.000016, 0.000051] tscor[632.1] = [0.00014, 0.000016, 0.000051] tscor[632.2] = [0.00014, 0.000016, 0.000051] tscor[632.3] = [0.00013, 0.000016, 0.000051] tscor[632.4] = [0.00013, 0.000016, 0.000051] tscor[632.5] = [0.00013, 0.000016, 0.000051] tscor[632.6] = [0.00013, 0.000015, 0.000050] tscor[632.7] = [0.00012, 0.000015, 0.000050] tscor[632.8] = [0.00012, 0.000015, 0.000050] tscor[632.9] = [0.00012, 0.000015, 0.000050] tscor[633.0] = [0.00012, 0.000015, 0.000050] tscor[633.1] = [0.00011, 0.000015, 0.000050] tscor[633.2] = [0.00011, 0.000015, 0.000050] tscor[633.3] = [0.00011, 0.000015, 0.000050] tscor[633.4] = [0.00011, 0.000015, 0.000050] tscor[633.5] = [0.00010, 0.000015, 0.000050] tscor[633.6] = [0.00010, 0.000014, 0.000049] tscor[633.7] = [0.00010, 0.000014, 0.000049] tscor[633.8] = [0.00010, 0.000014, 0.000049] tscor[633.9] = [0.00009, 0.000014, 0.000049] tscor[634.0] = [0.00009, 0.000014, 0.000049] tscor[634.1] = [0.00009, 0.000014, 0.000049] tscor[634.2] = [0.00009, 0.000014, 0.000049] tscor[634.3] = [0.00008, 0.000014, 0.000049] tscor[634.4] = [0.00008, 0.000014, 0.000049] tscor[634.5] = [0.00008, 0.000014, 0.000049] tscor[634.6] = [0.00008, 0.000013, 0.000048] tscor[634.7] = [0.00007, 0.000013, 0.000048] tscor[634.8] = [0.00007, 0.000013, 0.000048] tscor[634.9] = [0.00007, 0.000013, 0.000048] tscor[635.0] = [0.00007, 0.000013, 0.000048] tscor[635.1] = [0.00006, 0.000013, 0.000048] tscor[635.2] = [0.00006, 0.000013, 0.000048] tscor[635.3] = [0.00006, 0.000013, 0.000048] tscor[635.4] = [0.00006, 0.000013, 0.000048] tscor[635.5] = [0.00005, 0.000013, 0.000048] tscor[635.6] = [0.00005, 0.000012, 0.000047] tscor[635.7] = [0.00005, 0.000012, 0.000047] tscor[635.8] = [0.00005, 0.000012, 0.000047] tscor[635.9] = [0.00004, 0.000012, 0.000047] tscor[636.0] = [0.00004, 0.000012, 0.000047] tscor[636.1] = [0.00004, 0.000012, 0.000047] tscor[636.2] = [0.00004, 0.000012, 0.000047] tscor[636.3] = [0.00004, 0.000012, 0.000047] tscor[636.4] = [0.00003, 0.000012, 0.000046] tscor[636.5] = [0.00003, 0.000012, 0.000046] tscor[636.6] = [0.00003, 0.000011, 0.000046] tscor[636.7] = [0.00003, 0.000011, 0.000046] tscor[636.8] = [0.00003, 0.000011, 0.000046] tscor[636.9] = [0.00003, 0.000011, 0.000046] tscor[637.0] = [0.00003, 0.000011, 0.000046] tscor[637.1] = [0.00002, 0.000011, 0.000045] tscor[637.2] = [0.00002, 0.000011, 0.000045] tscor[637.3] = [0.00002, 0.000011, 0.000045] tscor[637.4] = [0.00002, 0.000011, 0.000045] tscor[637.5] = [0.00002, 0.000011, 0.000045] tscor[637.6] = [0.00002, 0.000010, 0.000045] tscor[637.7] = [0.00001, 0.000010, 0.000044] tscor[637.8] = [0.00001, 0.000010, 0.000044] tscor[637.9] = [0.00001, 0.000010, 0.000044] tscor[638.0] = [0.00001, 0.000010, 0.000044] tscor[638.1] = [0.00001, 0.000010, 0.000044] tscor[638.2] = [0.00001, 0.000010, 0.000044] tscor[638.3] = [0.00001, 0.000010, 0.000044] tscor[638.4] = [0.00000, 0.000009, 0.000044] tscor[638.5] = [0.00000, 0.000009, 0.000044] tscor[638.6] = [0.00000, 0.000009, 0.000043] tscor[638.7] = [0.00000, 0.000009, 0.000043] tscor[638.8] = [0.00000, 0.000009, 0.000043] tscor[638.9] = [0.00000, 0.000009, 0.000043] tscor[639.0] = [-0.00001, 0.000009, 0.000043] tscor[639.1] = [-0.00001, 0.000008, 0.000043] tscor[639.2] = [-0.00001, 0.000008, 0.000043] tscor[639.3] = [-0.00001, 0.000008, 0.000043] tscor[639.4] = [-0.00001, 0.000008, 0.000043] tscor[639.5] = [-0.00001, 0.000008, 0.000043] tscor[639.6] = [-0.00001, 0.000008, 0.000042] tscor[639.7] = [-0.00002, 0.000007, 0.000042] tscor[639.8] = [-0.00002, 0.000007, 0.000042] tscor[639.9] = [-0.00002, 0.000007, 0.000042] tscor[640.0] = [-0.00002, 0.000007, 0.000042] tscor[640.1] = [-0.00002, 0.000007, 0.000042] tscor[640.2] = [-0.00002, 0.000007, 0.000042] tscor[640.3] = [-0.00002, 0.000007, 0.000042] tscor[640.4] = [-0.00002, 0.000007, 0.000041] tscor[640.5] = [-0.00003, 0.000007, 0.000041] tscor[640.6] = [-0.00003, 0.000006, 0.000041] tscor[640.7] = [-0.00003, 0.000006, 0.000041] tscor[640.8] = [-0.00003, 0.000006, 0.000041] tscor[640.9] = [-0.00003, 0.000006, 0.000041] tscor[641.0] = [-0.00003, 0.000006, 0.000041] tscor[641.1] = [-0.00003, 0.000006, 0.000040] tscor[641.2] = [-0.00003, 0.000006, 0.000040] tscor[641.3] = [-0.00003, 0.000006, 0.000040] tscor[641.4] = [-0.00003, 0.000006, 0.000040] tscor[641.5] = [-0.00004, 0.000006, 0.000040] tscor[641.6] = [-0.00004, 0.000005, 0.000040] tscor[641.7] = [-0.00004, 0.000005, 0.000039] tscor[641.8] = [-0.00004, 0.000005, 0.000039] tscor[641.9] = [-0.00004, 0.000005, 0.000039] tscor[642.0] = [-0.00004, 0.000005, 0.000039] tscor[642.1] = [-0.00004, 0.000005, 0.000039] tscor[642.2] = [-0.00004, 0.000005, 0.000039] tscor[642.3] = [-0.00004, 0.000004, 0.000039] tscor[642.4] = [-0.00004, 0.000004, 0.000038] tscor[642.5] = [-0.00004, 0.000004, 0.000038] tscor[642.6] = [-0.00004, 0.000004, 0.000038] tscor[642.7] = [-0.00004, 0.000004, 0.000038] tscor[642.8] = [-0.00004, 0.000003, 0.000038] tscor[642.9] = [-0.00004, 0.000003, 0.000038] tscor[643.0] = [-0.00004, 0.000003, 0.000038] tscor[643.1] = [-0.00004, 0.000003, 0.000037] tscor[643.2] = [-0.00004, 0.000003, 0.000037] tscor[643.3] = [-0.00004, 0.000002, 0.000037] tscor[643.4] = [-0.00004, 0.000002, 0.000037] tscor[643.5] = [-0.00004, 0.000002, 0.000037] tscor[643.6] = [-0.00004, 0.000002, 0.000037] tscor[643.7] = [-0.00004, 0.000002, 0.000036] tscor[643.8] = [-0.00004, 0.000001, 0.000036] tscor[643.9] = [-0.00004, 0.000001, 0.000036] tscor[644.0] = [-0.00004, 0.000001, 0.000036] tscor[644.1] = [-0.00004, 0.000001, 0.000036] tscor[644.2] = [-0.00004, 0.000001, 0.000036] tscor[644.3] = [-0.00004, 0.000001, 0.000036] tscor[644.4] = [-0.00004, 0.000000, 0.000035] tscor[644.5] = [-0.00004, 0.000000, 0.000035] tscor[644.6] = [-0.00004, 0.000000, 0.000035] tscor[644.7] = [-0.00004, 0.000000, 0.000035] tscor[644.8] = [-0.00004, 0.000000, 0.000035] tscor[644.9] = [-0.00004, 0.000000, 0.000035] tscor[645.0] = [-0.00004, -0.000001, 0.000035] tscor[645.1] = [-0.00004, -0.000001, 0.000034] tscor[645.2] = [-0.00004, -0.000001, 0.000034] tscor[645.3] = [-0.00004, -0.000001, 0.000034] tscor[645.4] = [-0.00004, -0.000001, 0.000034] tscor[645.5] = [-0.00004, -0.000001, 0.000034] tscor[645.6] = [-0.00004, -0.000001, 0.000034] tscor[645.7] = [-0.00004, -0.000002, 0.000033] tscor[645.8] = [-0.00004, -0.000002, 0.000033] tscor[645.9] = [-0.00004, -0.000002, 0.000033] tscor[646.0] = [-0.00004, -0.000002, 0.000033] tscor[646.1] = [-0.00004, -0.000002, 0.000033] tscor[646.2] = [-0.00004, -0.000002, 0.000033] tscor[646.3] = [-0.00004, -0.000003, 0.000032] tscor[646.4] = [-0.00004, -0.000003, 0.000032] tscor[646.5] = [-0.00004, -0.000003, 0.000032] tscor[646.6] = [-0.00003, -0.000003, 0.000032] tscor[646.7] = [-0.00003, -0.000003, 0.000032] tscor[646.8] = [-0.00003, -0.000004, 0.000031] tscor[646.9] = [-0.00003, -0.000004, 0.000031] tscor[647.0] = [-0.00003, -0.000004, 0.000031] tscor[647.1] = [-0.00003, -0.000004, 0.000031] tscor[647.2] = [-0.00003, -0.000004, 0.000031] tscor[647.3] = [-0.00003, -0.000005, 0.000030] tscor[647.4] = [-0.00003, -0.000005, 0.000030] tscor[647.5] = [-0.00003, -0.000005, 0.000030] tscor[647.6] = [-0.00002, -0.000005, 0.000030] tscor[647.7] = [-0.00002, -0.000005, 0.000030] tscor[647.8] = [-0.00002, -0.000006, 0.000029] tscor[647.9] = [-0.00002, -0.000006, 0.000029] tscor[648.0] = [-0.00002, -0.000006, 0.000029] tscor[648.1] = [-0.00002, -0.000006, 0.000029] tscor[648.2] = [-0.00002, -0.000006, 0.000029] tscor[648.3] = [-0.00002, -0.000006, 0.000029] tscor[648.4] = [-0.00001, -0.000007, 0.000028] tscor[648.5] = [-0.00001, -0.000007, 0.000028] tscor[648.6] = [-0.00001, -0.000007, 0.000028] tscor[648.7] = [-0.00001, -0.000007, 0.000028] tscor[648.8] = [-0.00001, -0.000007, 0.000028] tscor[648.9] = [-0.00001, -0.000007, 0.000028] tscor[649.0] = [-0.00001, -0.000008, 0.000028] tscor[649.1] = [0.00000, -0.000008, 0.000027] tscor[649.2] = [0.00000, -0.000008, 0.000027] tscor[649.3] = [0.00000, -0.000008, 0.000027] tscor[649.4] = [0.00000, -0.000008, 0.000027] tscor[649.5] = [0.00000, -0.000008, 0.000027] tscor[649.6] = [0.00000, -0.000008, 0.000027] tscor[649.7] = [0.00001, -0.000009, 0.000026] tscor[649.8] = [0.00001, -0.000009, 0.000026] tscor[649.9] = [0.00001, -0.000009, 0.000026] tscor[650.0] = [0.00001, -0.000009, 0.000026] tscor[650.1] = [0.00001, -0.000009, 0.000026] tscor[650.2] = [0.00001, -0.000009, 0.000026] tscor[650.3] = [0.00001, -0.000009, 0.000026] tscor[650.4] = [0.00002, -0.000010, 0.000025] tscor[650.5] = [0.00002, -0.000010, 0.000025] tscor[650.6] = [0.00002, -0.000010, 0.000025] tscor[650.7] = [0.00002, -0.000010, 0.000025] tscor[650.8] = [0.00002, -0.000010, 0.000025] tscor[650.9] = [0.00002, -0.000010, 0.000025] tscor[651.0] = [0.00003, -0.000011, 0.000025] tscor[651.1] = [0.00003, -0.000011, 0.000024] tscor[651.2] = [0.00003, -0.000011, 0.000024] tscor[651.3] = [0.00003, -0.000011, 0.000024] tscor[651.4] = [0.00003, -0.000011, 0.000024] tscor[651.5] = [0.00003, -0.000011, 0.000024] tscor[651.6] = [0.00003, -0.000011, 0.000024] tscor[651.7] = [0.00004, -0.000012, 0.000023] tscor[651.8] = [0.00004, -0.000012, 0.000023] tscor[651.9] = [0.00004, -0.000012, 0.000023] tscor[652.0] = [0.00004, -0.000012, 0.000023] tscor[652.1] = [0.00004, -0.000012, 0.000023] tscor[652.2] = [0.00004, -0.000012, 0.000023] tscor[652.3] = [0.00004, -0.000012, 0.000023] tscor[652.4] = [0.00005, -0.000012, 0.000022] tscor[652.5] = [0.00005, -0.000013, 0.000022] tscor[652.6] = [0.00005, -0.000013, 0.000022] tscor[652.7] = [0.00005, -0.000013, 0.000022] tscor[652.8] = [0.00005, -0.000013, 0.000022] tscor[652.9] = [0.00005, -0.000013, 0.000022] tscor[653.0] = [0.00006, -0.000013, 0.000022] tscor[653.1] = [0.00006, -0.000013, 0.000021] tscor[653.2] = [0.00006, -0.000013, 0.000021] tscor[653.3] = [0.00006, -0.000013, 0.000021] tscor[653.4] = [0.00006, -0.000013, 0.000021] tscor[653.5] = [0.00006, -0.000014, 0.000021] tscor[653.6] = [0.00006, -0.000014, 0.000021] tscor[653.7] = [0.00007, -0.000014, 0.000020] tscor[653.8] = [0.00007, -0.000014, 0.000020] tscor[653.9] = [0.00007, -0.000014, 0.000020] tscor[654.0] = [0.00007, -0.000014, 0.000020] tscor[654.1] = [0.00007, -0.000014, 0.000020] tscor[654.2] = [0.00007, -0.000014, 0.000020] tscor[654.3] = [0.00008, -0.000014, 0.000020] tscor[654.4] = [0.00008, -0.000014, 0.000020] tscor[654.5] = [0.00008, -0.000015, 0.000020] tscor[654.6] = [0.00008, -0.000015, 0.000020] tscor[654.7] = [0.00008, -0.000015, 0.000020] tscor[654.8] = [0.00009, -0.000015, 0.000020] tscor[654.9] = [0.00009, -0.000015, 0.000020] tscor[655.0] = [0.00009, -0.000015, 0.000020] tscor[655.1] = [0.00009, -0.000015, 0.000019] tscor[655.2] = [0.00009, -0.000015, 0.000019] tscor[655.3] = [0.00010, -0.000015, 0.000019] tscor[655.4] = [0.00010, -0.000015, 0.000019] tscor[655.5] = [0.00010, -0.000016, 0.000019] tscor[655.6] = [0.00010, -0.000016, 0.000019] tscor[655.7] = [0.00010, -0.000016, 0.000019] tscor[655.8] = [0.00011, -0.000016, 0.000019] tscor[655.9] = [0.00011, -0.000016, 0.000019] tscor[656.0] = [0.00011, -0.000016, 0.000019] tscor[656.1] = [0.00011, -0.000016, 0.000019] tscor[656.2] = [0.00011, -0.000016, 0.000019] tscor[656.3] = [0.00011, -0.000016, 0.000019] tscor[656.4] = [0.00012, -0.000016, 0.000019] tscor[656.5] = [0.00012, -0.000016, 0.000019] tscor[656.6] = [0.00012, -0.000016, 0.000018] tscor[656.7] = [0.00012, -0.000016, 0.000018] tscor[656.8] = [0.00012, -0.000016, 0.000018] tscor[656.9] = [0.00012, -0.000016, 0.000018] tscor[657.0] = [0.00013, -0.000016, 0.000018] tscor[657.1] = [0.00013, -0.000016, 0.000018] tscor[657.2] = [0.00013, -0.000016, 0.000018] tscor[657.3] = [0.00013, -0.000016, 0.000018] tscor[657.4] = [0.00013, -0.000016, 0.000018] tscor[657.5] = [0.00013, -0.000016, 0.000018] tscor[657.6] = [0.00013, -0.000016, 0.000017] tscor[657.7] = [0.00014, -0.000016, 0.000017] tscor[657.8] = [0.00014, -0.000016, 0.000017] tscor[657.9] = [0.00014, -0.000016, 0.000017] tscor[658.0] = [0.00014, -0.000016, 0.000017] tscor[658.1] = [0.00014, -0.000016, 0.000017] tscor[658.2] = [0.00014, -0.000016, 0.000017] tscor[658.3] = [0.00014, -0.000016, 0.000017] tscor[658.4] = [0.00014, -0.000016, 0.000017] tscor[658.5] = [0.00014, -0.000016, 0.000017] tscor[658.6] = [0.00014, -0.000016, 0.000017] tscor[658.7] = [0.00014, -0.000016, 0.000017] tscor[658.8] = [0.00014, -0.000016, 0.000017] tscor[658.9] = [0.00014, -0.000016, 0.000017] tscor[659.0] = [0.00015, -0.000016, 0.000017] tscor[659.1] = [0.00015, -0.000016, 0.000017] tscor[659.2] = [0.00015, -0.000016, 0.000017] tscor[659.3] = [0.00015, -0.000016, 0.000017] tscor[659.4] = [0.00015, -0.000016, 0.000017] tscor[659.5] = [0.00015, -0.000016, 0.000017] tscor[659.6] = [0.00015, -0.000016, 0.000017] tscor[659.7] = [0.00015, -0.000016, 0.000017] tscor[659.8] = [0.00015, -0.000016, 0.000017] tscor[659.9] = [0.00015, -0.000016, 0.000017] tscor[660.0] = [0.00015, -0.000016, 0.000017] tscor[660.1] = [0.00015, -0.000016, 0.000017] tscor[660.2] = [0.00015, -0.000016, 0.000017] tscor[660.3] = [0.00015, -0.000016, 0.000017] tscor[660.4] = [0.00015, -0.000016, 0.000017] tscor[660.5] = [0.00015, -0.000016, 0.000017] tscor[660.6] = [0.00015, -0.000016, 0.000017] tscor[660.7] = [0.00015, -0.000016, 0.000017] tscor[660.8] = [0.00015, -0.000016, 0.000017] tscor[660.9] = [0.00015, -0.000016, 0.000017] tscor[661.0] = [0.00015, -0.000016, 0.000017] tscor[661.1] = [0.00015, -0.000016, 0.000016] tscor[661.2] = [0.00015, -0.000016, 0.000016] tscor[661.3] = [0.00015, -0.000016, 0.000016] tscor[661.4] = [0.00015, -0.000016, 0.000016] tscor[661.5] = [0.00015, -0.000016, 0.000016] tscor[661.6] = [0.00015, -0.000016, 0.000016] tscor[661.7] = [0.00015, -0.000016, 0.000016] tscor[661.8] = [0.00015, -0.000016, 0.000016] tscor[661.9] = [0.00015, -0.000016, 0.000016] tscor[662.0] = [0.00015, -0.000016, 0.000016] tscor[662.1] = [0.00015, -0.000016, 0.000016] tscor[662.2] = [0.00015, -0.000016, 0.000016] tscor[662.3] = [0.00015, -0.000016, 0.000016] tscor[662.4] = [0.00015, -0.000016, 0.000016] tscor[662.5] = [0.00015, -0.000016, 0.000016] tscor[662.6] = [0.00015, -0.000016, 0.000016] tscor[662.7] = [0.00015, -0.000016, 0.000016] tscor[662.8] = [0.00015, -0.000016, 0.000016] tscor[662.9] = [0.00015, -0.000016, 0.000016] tscor[663.0] = [0.00015, -0.000016, 0.000016] tscor[663.1] = [0.00015, -0.000015, 0.000016] tscor[663.2] = [0.00015, -0.000015, 0.000016] tscor[663.3] = [0.00015, -0.000015, 0.000016] tscor[663.4] = [0.00015, -0.000015, 0.000016] tscor[663.5] = [0.00015, -0.000015, 0.000016] tscor[663.6] = [0.00015, -0.000015, 0.000016] tscor[663.7] = [0.00015, -0.000015, 0.000016] tscor[663.8] = [0.00015, -0.000015, 0.000016] tscor[663.9] = [0.00015, -0.000015, 0.000016] tscor[664.0] = [0.00015, -0.000015, 0.000016] tscor[664.1] = [0.00015, -0.000015, 0.000016] tscor[664.2] = [0.00015, -0.000015, 0.000016] tscor[664.3] = [0.00015, -0.000015, 0.000016] tscor[664.4] = [0.00015, -0.000015, 0.000016] tscor[664.5] = [0.00015, -0.000015, 0.000016] tscor[664.6] = [0.00014, -0.000015, 0.000016] tscor[664.7] = [0.00014, -0.000015, 0.000016] tscor[664.8] = [0.00014, -0.000015, 0.000016] tscor[664.9] = [0.00014, -0.000015, 0.000016] tscor[665.0] = [0.00014, -0.000016, 0.000016] tscor[665.1] = [0.00014, -0.000016, 0.000015] tscor[665.2] = [0.00014, -0.000016, 0.000015] tscor[665.3] = [0.00014, -0.000016, 0.000015] tscor[665.4] = [0.00014, -0.000016, 0.000015] tscor[665.5] = [0.00014, -0.000016, 0.000015] tscor[665.6] = [0.00013, -0.000016, 0.000015] tscor[665.7] = [0.00013, -0.000016, 0.000015] tscor[665.8] = [0.00013, -0.000016, 0.000015] tscor[665.9] = [0.00013, -0.000016, 0.000015] tscor[666.0] = [0.00013, -0.000016, 0.000015] tscor[666.1] = [0.00013, -0.000016, 0.000015] tscor[666.2] = [0.00013, -0.000016, 0.000015] tscor[666.3] = [0.00013, -0.000016, 0.000015] tscor[666.4] = [0.00012, -0.000016, 0.000015] tscor[666.5] = [0.00012, -0.000016, 0.000015] tscor[666.6] = [0.00012, -0.000016, 0.000015] tscor[666.7] = [0.00012, -0.000016, 0.000015] tscor[666.8] = [0.00012, -0.000016, 0.000015] tscor[666.9] = [0.00012, -0.000016, 0.000015] tscor[667.0] = [0.00012, -0.000017, 0.000015] tscor[667.1] = [0.00011, -0.000017, 0.000014] tscor[667.2] = [0.00011, -0.000017, 0.000014] tscor[667.3] = [0.00011, -0.000017, 0.000014] tscor[667.4] = [0.00011, -0.000017, 0.000014] tscor[667.5] = [0.00011, -0.000017, 0.000014] tscor[667.6] = [0.00011, -0.000017, 0.000014] tscor[667.7] = [0.00010, -0.000017, 0.000014] tscor[667.8] = [0.00010, -0.000017, 0.000014] tscor[667.9] = [0.00010, -0.000017, 0.000014] tscor[668.0] = [0.00010, -0.000017, 0.000014] tscor[668.1] = [0.00010, -0.000017, 0.000014] tscor[668.2] = [0.00010, -0.000017, 0.000014] tscor[668.3] = [0.00009, -0.000017, 0.000014] tscor[668.4] = [0.00009, -0.000018, 0.000013] tscor[668.5] = [0.00009, -0.000018, 0.000013] tscor[668.6] = [0.00009, -0.000018, 0.000013] tscor[668.7] = [0.00009, -0.000018, 0.000013] tscor[668.8] = [0.00008, -0.000018, 0.000013] tscor[668.9] = [0.00008, -0.000018, 0.000013] tscor[669.0] = [0.00008, -0.000019, 0.000013] tscor[669.1] = [0.00008, -0.000019, 0.000012] tscor[669.2] = [0.00008, -0.000019, 0.000012] tscor[669.3] = [0.00007, -0.000019, 0.000012] tscor[669.4] = [0.00007, -0.000019, 0.000012] tscor[669.5] = [0.00007, -0.000019, 0.000012] tscor[669.6] = [0.00007, -0.000019, 0.000012] tscor[669.7] = [0.00007, -0.000020, 0.000011] tscor[669.8] = [0.00006, -0.000020, 0.000011] tscor[669.9] = [0.00006, -0.000020, 0.000011] tscor[670.0] = [0.00006, -0.000020, 0.000011] tscor[670.1] = [0.00006, -0.000020, 0.000011] tscor[670.2] = [0.00006, -0.000021, 0.000011] tscor[670.3] = [0.00005, -0.000021, 0.000011] tscor[670.4] = [0.00005, -0.000021, 0.000010] tscor[670.5] = [0.00005, -0.000021, 0.000010] tscor[670.6] = [0.00005, -0.000022, 0.000010] tscor[670.7] = [0.00005, -0.000022, 0.000010] tscor[670.8] = [0.00004, -0.000022, 0.000010] tscor[670.9] = [0.00004, -0.000022, 0.000010] tscor[671.0] = [0.00004, -0.000023, 0.000010] tscor[671.1] = [0.00004, -0.000023, 0.000009] tscor[671.2] = [0.00004, -0.000023, 0.000009] tscor[671.3] = [0.00003, -0.000023, 0.000009] tscor[671.4] = [0.00003, -0.000024, 0.000009] tscor[671.5] = [0.00003, -0.000024, 0.000009] tscor[671.6] = [0.00003, -0.000024, 0.000009] tscor[671.7] = [0.00003, -0.000024, 0.000008] tscor[671.8] = [0.00002, -0.000025, 0.000008] tscor[671.9] = [0.00002, -0.000025, 0.000008] tscor[672.0] = [0.00002, -0.000025, 0.000008] tscor[672.1] = [0.00002, -0.000025, 0.000008] tscor[672.2] = [0.00002, -0.000026, 0.000007] tscor[672.3] = [0.00002, -0.000026, 0.000007] tscor[672.4] = [0.00001, -0.000026, 0.000007] tscor[672.5] = [0.00001, -0.000026, 0.000007] tscor[672.6] = [0.00001, -0.000027, 0.000006] tscor[672.7] = [0.00001, -0.000027, 0.000006] tscor[672.8] = [0.00001, -0.000027, 0.000006] tscor[672.9] = [0.00001, -0.000027, 0.000005] tscor[673.0] = [0.00001, -0.000028, 0.000005] tscor[673.1] = [0.00000, -0.000028, 0.000005] tscor[673.2] = [0.00000, -0.000028, 0.000004] tscor[673.3] = [0.00000, -0.000028, 0.000004] tscor[673.4] = [0.00000, -0.000029, 0.000004] tscor[673.5] = [0.00000, -0.000029, 0.000004] tscor[673.6] = [0.00000, -0.000029, 0.000003] tscor[673.7] = [-0.00001, -0.000029, 0.000003] tscor[673.8] = [-0.00001, -0.000030, 0.000003] tscor[673.9] = [-0.00001, -0.000030, 0.000002] tscor[674.0] = [-0.00001, -0.000030, 0.000002] tscor[674.1] = [-0.00001, -0.000030, 0.000002] tscor[674.2] = [-0.00001, -0.000031, 0.000001] tscor[674.3] = [-0.00002, -0.000031, 0.000001] tscor[674.4] = [-0.00002, -0.000032, 0.000001] tscor[674.5] = [-0.00002, -0.000032, 0.000001] tscor[674.6] = [-0.00002, -0.000032, 0.000000] tscor[674.7] = [-0.00002, -0.000033, 0.000000] tscor[674.8] = [-0.00003, -0.000033, 0.000000] tscor[674.9] = [-0.00003, -0.000034, -0.000001] tscor[675.0] = [-0.00003, -0.000034, -0.000001] tscor[675.1] = [-0.00003, -0.000034, -0.000001] tscor[675.2] = [-0.00003, -0.000035, -0.000002] tscor[675.3] = [-0.00004, -0.000035, -0.000002] tscor[675.4] = [-0.00004, -0.000036, -0.000002] tscor[675.5] = [-0.00004, -0.000036, -0.000003] tscor[675.6] = [-0.00004, -0.000036, -0.000003] tscor[675.7] = [-0.00004, -0.000037, -0.000003] tscor[675.8] = [-0.00005, -0.000037, -0.000003] tscor[675.9] = [-0.00005, -0.000038, -0.000004] tscor[676.0] = [-0.00005, -0.000038, -0.000004] tscor[676.1] = [-0.00005, -0.000038, -0.000004] tscor[676.2] = [-0.00005, -0.000039, -0.000005] tscor[676.3] = [-0.00005, -0.000039, -0.000005] tscor[676.4] = [-0.00005, -0.000040, -0.000006] tscor[676.5] = [-0.00006, -0.000040, -0.000006] tscor[676.6] = [-0.00006, -0.000040, -0.000006] tscor[676.7] = [-0.00006, -0.000041, -0.000007] tscor[676.8] = [-0.00006, -0.000041, -0.000007] tscor[676.9] = [-0.00006, -0.000042, -0.000008] tscor[677.0] = [-0.00006, -0.000042, -0.000008] tscor[677.1] = [-0.00006, -0.000042, -0.000008] tscor[677.2] = [-0.00006, -0.000043, -0.000009] tscor[677.3] = [-0.00006, -0.000043, -0.000009] tscor[677.4] = [-0.00006, -0.000044, -0.000010] tscor[677.5] = [-0.00007, -0.000044, -0.000010] tscor[677.6] = [-0.00007, -0.000044, -0.000010] tscor[677.7] = [-0.00007, -0.000045, -0.000011] tscor[677.8] = [-0.00007, -0.000045, -0.000011] tscor[677.9] = [-0.00007, -0.000046, -0.000012] tscor[678.0] = [-0.00007, -0.000046, -0.000012] tscor[678.1] = [-0.00007, -0.000046, -0.000012] tscor[678.2] = [-0.00007, -0.000047, -0.000013] tscor[678.3] = [-0.00007, -0.000047, -0.000013] tscor[678.4] = [-0.00007, -0.000048, -0.000014] tscor[678.5] = [-0.00007, -0.000048, -0.000014] tscor[678.6] = [-0.00007, -0.000049, -0.000015] tscor[678.7] = [-0.00007, -0.000049, -0.000015] tscor[678.8] = [-0.00007, -0.000050, -0.000016] tscor[678.9] = [-0.00007, -0.000050, -0.000016] tscor[679.0] = [-0.00008, -0.000051, -0.000017] tscor[679.1] = [-0.00008, -0.000051, -0.000017] tscor[679.2] = [-0.00008, -0.000051, -0.000017] tscor[679.3] = [-0.00008, -0.000052, -0.000018] tscor[679.4] = [-0.00008, -0.000052, -0.000018] tscor[679.5] = [-0.00008, -0.000053, -0.000019] tscor[679.6] = [-0.00008, -0.000053, -0.000019] tscor[679.7] = [-0.00008, -0.000054, -0.000020] tscor[679.8] = [-0.00008, -0.000054, -0.000020] tscor[679.9] = [-0.00008, -0.000055, -0.000021] tscor[680.0] = [-0.00008, -0.000055, -0.000021] tscor[680.1] = [-0.00008, -0.000055, -0.000022] tscor[680.2] = [-0.00008, -0.000056, -0.000022] tscor[680.3] = [-0.00008, -0.000056, -0.000023] tscor[680.4] = [-0.00008, -0.000057, -0.000023] tscor[680.5] = [-0.00008, -0.000057, -0.000024] tscor[680.6] = [-0.00008, -0.000058, -0.000024] tscor[680.7] = [-0.00008, -0.000058, -0.000025] tscor[680.8] = [-0.00008, -0.000059, -0.000025] tscor[680.9] = [-0.00008, -0.000059, -0.000026] tscor[681.0] = [-0.00009, -0.000060, -0.000027] tscor[681.1] = [-0.00009, -0.000060, -0.000027] tscor[681.2] = [-0.00009, -0.000060, -0.000028] tscor[681.3] = [-0.00009, -0.000061, -0.000028] tscor[681.4] = [-0.00009, -0.000061, -0.000029] tscor[681.5] = [-0.00009, -0.000062, -0.000029] tscor[681.6] = [-0.00009, -0.000062, -0.000030] tscor[681.7] = [-0.00009, -0.000063, -0.000030] tscor[681.8] = [-0.00009, -0.000063, -0.000031] tscor[681.9] = [-0.00009, -0.000064, -0.000031] tscor[682.0] = [-0.00009, -0.000064, -0.000032] tscor[682.1] = [-0.00009, -0.000065, -0.000033] tscor[682.2] = [-0.00009, -0.000065, -0.000033] tscor[682.3] = [-0.00009, -0.000066, -0.000034] tscor[682.4] = [-0.00009, -0.000066, -0.000034] tscor[682.5] = [-0.00009, -0.000067, -0.000035] tscor[682.6] = [-0.00009, -0.000067, -0.000036] tscor[682.7] = [-0.00009, -0.000068, -0.000036] tscor[682.8] = [-0.00009, -0.000068, -0.000037] tscor[682.9] = [-0.00009, -0.000069, -0.000037] tscor[683.0] = [-0.00009, -0.000070, -0.000038] tscor[683.1] = [-0.00008, -0.000070, -0.000039] tscor[683.2] = [-0.00008, -0.000071, -0.000039] tscor[683.3] = [-0.00008, -0.000071, -0.000040] tscor[683.4] = [-0.00008, -0.000072, -0.000040] tscor[683.5] = [-0.00008, -0.000072, -0.000041] tscor[683.6] = [-0.00008, -0.000073, -0.000042] tscor[683.7] = [-0.00008, -0.000073, -0.000042] tscor[683.8] = [-0.00008, -0.000074, -0.000043] tscor[683.9] = [-0.00008, -0.000074, -0.000043] tscor[684.0] = [-0.00008, -0.000075, -0.000044] tscor[684.1] = [-0.00008, -0.000076, -0.000045] tscor[684.2] = [-0.00008, -0.000076, -0.000045] tscor[684.3] = [-0.00008, -0.000077, -0.000046] tscor[684.4] = [-0.00008, -0.000078, -0.000046] tscor[684.5] = [-0.00008, -0.000078, -0.000047] tscor[684.6] = [-0.00007, -0.000079, -0.000048] tscor[684.7] = [-0.00007, -0.000080, -0.000048] tscor[684.8] = [-0.00007, -0.000080, -0.000049] tscor[684.9] = [-0.00007, -0.000081, -0.000049] tscor[685.0] = [-0.00007, -0.000082, -0.000050] tscor[685.1] = [-0.00007, -0.000082, -0.000051] tscor[685.2] = [-0.00007, -0.000083, -0.000051] tscor[685.3] = [-0.00007, -0.000083, -0.000052] tscor[685.4] = [-0.00007, -0.000084, -0.000052] tscor[685.5] = [-0.00007, -0.000085, -0.000053] tscor[685.6] = [-0.00006, -0.000085, -0.000054] tscor[685.7] = [-0.00006, -0.000086, -0.000054] tscor[685.8] = [-0.00006, -0.000087, -0.000055] tscor[685.9] = [-0.00006, -0.000087, -0.000055] tscor[686.0] = [-0.00006, -0.000088, -0.000056] tscor[686.1] = [-0.00006, -0.000089, -0.000057] tscor[686.2] = [-0.00006, -0.000089, -0.000057] tscor[686.3] = [-0.00005, -0.000090, -0.000058] tscor[686.4] = [-0.00005, -0.000090, -0.000058] tscor[686.5] = [-0.00005, -0.000091, -0.000059] tscor[686.6] = [-0.00005, -0.000092, -0.000060] tscor[686.7] = [-0.00005, -0.000092, -0.000060] tscor[686.8] = [-0.00004, -0.000093, -0.000061] tscor[686.9] = [-0.00004, -0.000093, -0.000061] tscor[687.0] = [-0.00004, -0.000094, -0.000062] tscor[687.1] = [-0.00004, -0.000095, -0.000063] tscor[687.2] = [-0.00004, -0.000095, -0.000063] tscor[687.3] = [-0.00003, -0.000096, -0.000064] tscor[687.4] = [-0.00003, -0.000096, -0.000064] tscor[687.5] = [-0.00003, -0.000097, -0.000065] tscor[687.6] = [-0.00003, -0.000098, -0.000066] tscor[687.7] = [-0.00003, -0.000098, -0.000066] tscor[687.8] = [-0.00002, -0.000099, -0.000067] tscor[687.9] = [-0.00002, -0.000099, -0.000067] tscor[688.0] = [-0.00002, -0.000100, -0.000068] tscor[688.1] = [-0.00002, -0.000101, -0.000069] tscor[688.2] = [-0.00002, -0.000101, -0.000069] tscor[688.3] = [-0.00001, -0.000102, -0.000070] tscor[688.4] = [-0.00001, -0.000103, -0.000071] tscor[688.5] = [-0.00001, -0.000104, -0.000071] tscor[688.6] = [-0.00001, -0.000104, -0.000072] tscor[688.7] = [-0.00001, -0.000105, -0.000073] tscor[688.8] = [0.00000, -0.000106, -0.000073] tscor[688.9] = [0.00000, -0.000106, -0.000074] tscor[689.0] = [0.00000, -0.000107, -0.000075] tscor[689.1] = [0.00000, -0.000108, -0.000075] tscor[689.2] = [0.00000, -0.000108, -0.000076] tscor[689.3] = [0.00001, -0.000109, -0.000076] tscor[689.4] = [0.00001, -0.000110, -0.000077] tscor[689.5] = [0.00001, -0.000111, -0.000078] tscor[689.6] = [0.00001, -0.000111, -0.000078] tscor[689.7] = [0.00001, -0.000112, -0.000079] tscor[689.8] = [0.00002, -0.000113, -0.000080] tscor[689.9] = [0.00002, -0.000113, -0.000080] tscor[690.0] = [0.00002, -0.000114, -0.000081] tscor[690.1] = [0.00002, -0.000115, -0.000082] tscor[690.2] = [0.00003, -0.000115, -0.000082] tscor[690.3] = [0.00003, -0.000116, -0.000083] tscor[690.4] = [0.00004, -0.000117, -0.000084] tscor[690.5] = [0.00004, -0.000117, -0.000084] tscor[690.6] = [0.00005, -0.000118, -0.000085] tscor[690.7] = [0.00005, -0.000119, -0.000086] tscor[690.8] = [0.00006, -0.000119, -0.000086] tscor[690.9] = [0.00006, -0.000120, -0.000087] tscor[691.0] = [0.00007, -0.000121, -0.000088] tscor[691.1] = [0.00007, -0.000121, -0.000088] tscor[691.2] = [0.00007, -0.000122, -0.000089] tscor[691.3] = [0.00008, -0.000122, -0.000089] tscor[691.4] = [0.00008, -0.000123, -0.000090] tscor[691.5] = [0.00009, -0.000124, -0.000091] tscor[691.6] = [0.00009, -0.000124, -0.000091] tscor[691.7] = [0.00010, -0.000125, -0.000092] tscor[691.8] = [0.00010, -0.000126, -0.000093] tscor[691.9] = [0.00011, -0.000126, -0.000093] tscor[692.0] = [0.00011, -0.000127, -0.000094] tscor[692.1] = [0.00011, -0.000128, -0.000095] tscor[692.2] = [0.00012, -0.000128, -0.000095] tscor[692.3] = [0.00012, -0.000129, -0.000096] tscor[692.4] = [0.00013, -0.000130, -0.000097] tscor[692.5] = [0.00013, -0.000131, -0.000098] tscor[692.6] = [0.00014, -0.000131, -0.000098] tscor[692.7] = [0.00014, -0.000132, -0.000099] tscor[692.8] = [0.00015, -0.000133, -0.000100] tscor[692.9] = [0.00015, -0.000133, -0.000100] tscor[693.0] = [0.00016, -0.000134, -0.000101] tscor[693.1] = [0.00016, -0.000135, -0.000102] tscor[693.2] = [0.00016, -0.000135, -0.000102] tscor[693.3] = [0.00017, -0.000136, -0.000103] tscor[693.4] = [0.00017, -0.000137, -0.000104] tscor[693.5] = [0.00018, -0.000138, -0.000105] tscor[693.6] = [0.00018, -0.000138, -0.000105] tscor[693.7] = [0.00019, -0.000139, -0.000106] tscor[693.8] = [0.00019, -0.000140, -0.000107] tscor[693.9] = [0.00020, -0.000140, -0.000107] tscor[694.0] = [0.00020, -0.000141, -0.000108] tscor[694.1] = [0.00021, -0.000142, -0.000109] tscor[694.2] = [0.00021, -0.000142, -0.000110] tscor[694.3] = [0.00022, -0.000143, -0.000110] tscor[694.4] = [0.00023, -0.000144, -0.000111] tscor[694.5] = [0.00023, -0.000145, -0.000112] tscor[694.6] = [0.00024, -0.000145, -0.000113] tscor[694.7] = [0.00025, -0.000146, -0.000113] tscor[694.8] = [0.00025, -0.000147, -0.000114] tscor[694.9] = [0.00026, -0.000147, -0.000115] tscor[695.0] = [0.00027, -0.000148, -0.000116] tscor[695.1] = [0.00027, -0.000149, -0.000116] tscor[695.2] = [0.00028, -0.000149, -0.000117] tscor[695.3] = [0.00028, -0.000150, -0.000118] tscor[695.4] = [0.00029, -0.000151, -0.000119] tscor[695.5] = [0.00030, -0.000152, -0.000119] tscor[695.6] = [0.00030, -0.000152, -0.000120] tscor[695.7] = [0.00031, -0.000153, -0.000121] tscor[695.8] = [0.00032, -0.000154, -0.000122] tscor[695.9] = [0.00032, -0.000154, -0.000122] tscor[696.0] = [0.00033, -0.000155, -0.000123] tscor[696.1] = [0.00034, -0.000156, -0.000124] tscor[696.2] = [0.00035, -0.000156, -0.000124] tscor[696.3] = [0.00035, -0.000157, -0.000125] tscor[696.4] = [0.00036, -0.000158, -0.000126] tscor[696.5] = [0.00037, -0.000158, -0.000127] tscor[696.6] = [0.00038, -0.000159, -0.000127] tscor[696.7] = [0.00039, -0.000160, -0.000128] tscor[696.8] = [0.00039, -0.000160, -0.000129] tscor[696.9] = [0.00040, -0.000161, -0.000129] tscor[697.0] = [0.00041, -0.000162, -0.000130] tscor[697.1] = [0.00042, -0.000162, -0.000131] tscor[697.2] = [0.00043, -0.000163, -0.000131] tscor[697.3] = [0.00043, -0.000163, -0.000132] tscor[697.4] = [0.00044, -0.000164, -0.000133] tscor[697.5] = [0.00045, -0.000165, -0.000134] tscor[697.6] = [0.00046, -0.000165, -0.000134] tscor[697.7] = [0.00047, -0.000166, -0.000135] tscor[697.8] = [0.00047, -0.000167, -0.000136] tscor[697.9] = [0.00048, -0.000167, -0.000136] tscor[698.0] = [0.00049, -0.000168, -0.000137] tscor[698.1] = [0.00050, -0.000169, -0.000138] tscor[698.2] = [0.00051, -0.000169, -0.000138] tscor[698.3] = [0.00052, -0.000170, -0.000139] tscor[698.4] = [0.00053, -0.000171, -0.000140] tscor[698.5] = [0.00054, -0.000171, -0.000141] tscor[698.6] = [0.00055, -0.000172, -0.000141] tscor[698.7] = [0.00056, -0.000173, -0.000142] tscor[698.8] = [0.00057, -0.000173, -0.000143] tscor[698.9] = [0.00058, -0.000174, -0.000143] tscor[699.0] = [0.00060, -0.000175, -0.000144] tscor[699.1] = [0.00061, -0.000175, -0.000145] tscor[699.2] = [0.00062, -0.000176, -0.000145] tscor[699.3] = [0.00063, -0.000176, -0.000146] tscor[699.4] = [0.00064, -0.000177, -0.000147] tscor[699.5] = [0.00065, -0.000178, -0.000148] tscor[699.6] = [0.00066, -0.000178, -0.000148] tscor[699.7] = [0.00067, -0.000179, -0.000149] tscor[699.8] = [0.00068, -0.000180, -0.000150] tscor[699.9] = [0.00069, -0.000180, -0.000150] tscor[700.0] = [0.00070, -0.000181, -0.000151] tscor[700.1] = [0.00071, -0.000182, -0.000152] tscor[700.2] = [0.00072, -0.000182, -0.000152] tscor[700.3] = [0.00074, -0.000183, -0.000153] tscor[700.4] = [0.00075, -0.000183, -0.000153] tscor[700.5] = [0.00076, -0.000184, -0.000154] tscor[700.6] = [0.00077, -0.000184, -0.000155] tscor[700.7] = [0.00078, -0.000185, -0.000155] tscor[700.8] = [0.00080, -0.000185, -0.000156] tscor[700.9] = [0.00081, -0.000186, -0.000156] tscor[701.0] = [0.00082, -0.000187, -0.000157] tscor[701.1] = [0.00083, -0.000187, -0.000158] tscor[701.2] = [0.00084, -0.000188, -0.000158] tscor[701.3] = [0.00086, -0.000188, -0.000159] tscor[701.4] = [0.00087, -0.000189, -0.000159] tscor[701.5] = [0.00088, -0.000189, -0.000160] tscor[701.6] = [0.00089, -0.000190, -0.000161] tscor[701.7] = [0.00090, -0.000190, -0.000161] tscor[701.8] = [0.00092, -0.000191, -0.000162] tscor[701.9] = [0.00093, -0.000191, -0.000162] tscor[702.0] = [0.00094, -0.000192, -0.000163] tscor[702.1] = [0.00096, -0.000193, -0.000164] tscor[702.2] = [0.00097, -0.000193, -0.000164] tscor[702.3] = [0.00099, -0.000194, -0.000165] tscor[702.4] = [0.00101, -0.000194, -0.000165] tscor[702.5] = [0.00102, -0.000195, -0.000166] tscor[702.6] = [0.00104, -0.000195, -0.000166] tscor[702.7] = [0.00106, -0.000196, -0.000167] tscor[702.8] = [0.00107, -0.000196, -0.000167] tscor[702.9] = [0.00109, -0.000197, -0.000168] tscor[703.0] = [0.00111, -0.000198, -0.000169] tscor[703.1] = [0.00112, -0.000198, -0.000169] tscor[703.2] = [0.00114, -0.000199, -0.000170] tscor[703.3] = [0.00115, -0.000199, -0.000170] tscor[703.4] = [0.00117, -0.000200, -0.000171] tscor[703.5] = [0.00119, -0.000200, -0.000171] tscor[703.6] = [0.00120, -0.000201, -0.000172] tscor[703.7] = [0.00122, -0.000201, -0.000172] tscor[703.8] = [0.00124, -0.000202, -0.000173] tscor[703.9] = [0.00125, -0.000202, -0.000173] tscor[704.0] = [0.00127, -0.000203, -0.000174] tscor[704.1] = [0.00129, -0.000203, -0.000174] tscor[704.2] = [0.00131, -0.000204, -0.000175] tscor[704.3] = [0.00133, -0.000204, -0.000175] tscor[704.4] = [0.00135, -0.000205, -0.000176] tscor[704.5] = [0.00137, -0.000205, -0.000176] tscor[704.6] = [0.00138, -0.000206, -0.000177] tscor[704.7] = [0.00140, -0.000206, -0.000177] tscor[704.8] = [0.00142, -0.000207, -0.000178] tscor[704.9] = [0.00144, -0.000207, -0.000178] tscor[705.0] = [0.00146, -0.000208, -0.000179] tscor[705.1] = [0.00148, -0.000208, -0.000179] tscor[705.2] = [0.00150, -0.000208, -0.000179] tscor[705.3] = [0.00152, -0.000209, -0.000180] tscor[705.4] = [0.00154, -0.000209, -0.000180] tscor[705.5] = [0.00156, -0.000210, -0.000181] tscor[705.6] = [0.00157, -0.000210, -0.000181] tscor[705.7] = [0.00159, -0.000211, -0.000182] tscor[705.8] = [0.00161, -0.000211, -0.000182] tscor[705.9] = [0.00163, -0.000212, -0.000183] tscor[706.0] = [0.00165, -0.000212, -0.000183] tscor[706.1] = [0.00167, -0.000212, -0.000183] tscor[706.2] = [0.00170, -0.000213, -0.000184] tscor[706.3] = [0.00172, -0.000213, -0.000184] tscor[706.4] = [0.00174, -0.000213, -0.000185] tscor[706.5] = [0.00177, -0.000214, -0.000185] tscor[706.6] = [0.00179, -0.000214, -0.000185] tscor[706.7] = [0.00181, -0.000214, -0.000186] tscor[706.8] = [0.00184, -0.000215, -0.000186] tscor[706.9] = [0.00186, -0.000215, -0.000187] tscor[707.0] = [0.00189, -0.000216, -0.000187] tscor[707.1] = [0.00191, -0.000216, -0.000187] tscor[707.2] = [0.00193, -0.000216, -0.000188] tscor[707.3] = [0.00196, -0.000217, -0.000188] tscor[707.4] = [0.00198, -0.000217, -0.000189] tscor[707.5] = [0.00200, -0.000217, -0.000189] tscor[707.6] = [0.00203, -0.000218, -0.000189] tscor[707.7] = [0.00205, -0.000218, -0.000190] tscor[707.8] = [0.00207, -0.000218, -0.000190] tscor[707.9] = [0.00210, -0.000219, -0.000191] tscor[708.0] = [0.00212, -0.000219, -0.000191] tscor[708.1] = [0.00215, -0.000219, -0.000191] tscor[708.2] = [0.00217, -0.000220, -0.000192] tscor[708.3] = [0.00220, -0.000220, -0.000192] tscor[708.4] = [0.00222, -0.000220, -0.000192] tscor[708.5] = [0.00225, -0.000221, -0.000193] tscor[708.6] = [0.00227, -0.000221, -0.000193] tscor[708.7] = [0.00230, -0.000221, -0.000193] tscor[708.8] = [0.00232, -0.000221, -0.000193] tscor[708.9] = [0.00235, -0.000222, -0.000194] tscor[709.0] = [0.00237, -0.000222, -0.000194] tscor[709.1] = [0.00240, -0.000222, -0.000194] tscor[709.2] = [0.00242, -0.000223, -0.000195] tscor[709.3] = [0.00245, -0.000223, -0.000195] tscor[709.4] = [0.00247, -0.000223, -0.000195] tscor[709.5] = [0.00250, -0.000224, -0.000196] tscor[709.6] = [0.00252, -0.000224, -0.000196] tscor[709.7] = [0.00255, -0.000224, -0.000196] tscor[709.8] = [0.00257, -0.000224, -0.000196] tscor[709.9] = [0.00260, -0.000225, -0.000197] tscor[710.0] = [0.00262, -0.000225, -0.000197] tscor[710.1] = [0.00265, -0.000225, -0.000197] tscor[710.2] = [0.00268, -0.000225, -0.000198] tscor[710.3] = [0.00270, -0.000226, -0.000198] tscor[710.4] = [0.00273, -0.000226, -0.000198] tscor[710.5] = [0.00276, -0.000226, -0.000198] tscor[710.6] = [0.00279, -0.000226, -0.000199] tscor[710.7] = [0.00281, -0.000226, -0.000199] tscor[710.8] = [0.00284, -0.000227, -0.000199] tscor[710.9] = [0.00287, -0.000227, -0.000199] tscor[711.0] = [0.00290, -0.000227, -0.000200] tscor[711.1] = [0.00292, -0.000227, -0.000200] tscor[711.2] = [0.00295, -0.000227, -0.000200] tscor[711.3] = [0.00298, -0.000228, -0.000200] tscor[711.4] = [0.00301, -0.000228, -0.000201] tscor[711.5] = [0.00303, -0.000228, -0.000201] tscor[711.6] = [0.00306, -0.000228, -0.000201] tscor[711.7] = [0.00309, -0.000228, -0.000201] tscor[711.8] = [0.00312, -0.000229, -0.000202] tscor[711.9] = [0.00314, -0.000229, -0.000202] tscor[712.0] = [0.00317, -0.000229, -0.000202] tscor[712.1] = [0.00320, -0.000229, -0.000202] tscor[712.2] = [0.00323, -0.000229, -0.000202] tscor[712.3] = [0.00326, -0.000229, -0.000202] tscor[712.4] = [0.00330, -0.000229, -0.000202] tscor[712.5] = [0.00333, -0.000230, -0.000203] tscor[712.6] = [0.00336, -0.000230, -0.000203] tscor[712.7] = [0.00339, -0.000230, -0.000203] tscor[712.8] = [0.00342, -0.000230, -0.000203] tscor[712.9] = [0.00345, -0.000230, -0.000203] tscor[713.0] = [0.00349, -0.000230, -0.000203] tscor[713.1] = [0.00352, -0.000230, -0.000203] tscor[713.2] = [0.00355, -0.000230, -0.000203] tscor[713.3] = [0.00358, -0.000230, -0.000203] tscor[713.4] = [0.00361, -0.000230, -0.000203] tscor[713.5] = [0.00364, -0.000231, -0.000204] tscor[713.6] = [0.00367, -0.000231, -0.000204] tscor[713.7] = [0.00371, -0.000231, -0.000204] tscor[713.8] = [0.00374, -0.000231, -0.000204] tscor[713.9] = [0.00377, -0.000231, -0.000204] tscor[714.0] = [0.00380, -0.000231, -0.000204] tscor[714.1] = [0.00384, -0.000231, -0.000204] tscor[714.2] = [0.00387, -0.000231, -0.000204] tscor[714.3] = [0.00391, -0.000231, -0.000204] tscor[714.4] = [0.00394, -0.000231, -0.000205] tscor[714.5] = [0.00398, -0.000232, -0.000205] tscor[714.6] = [0.00401, -0.000232, -0.000205] tscor[714.7] = [0.00405, -0.000232, -0.000205] tscor[714.8] = [0.00408, -0.000232, -0.000205] tscor[714.9] = [0.00412, -0.000232, -0.000205] tscor[715.0] = [0.00416, -0.000232, -0.000206] tscor[715.1] = [0.00419, -0.000232, -0.000206] tscor[715.2] = [0.00423, -0.000232, -0.000206] tscor[715.3] = [0.00426, -0.000232, -0.000206] tscor[715.4] = [0.00430, -0.000232, -0.000206] tscor[715.5] = [0.00433, -0.000233, -0.000206] tscor[715.6] = [0.00437, -0.000233, -0.000206] tscor[715.7] = [0.00440, -0.000233, -0.000207] tscor[715.8] = [0.00444, -0.000233, -0.000207] tscor[715.9] = [0.00447, -0.000233, -0.000207] tscor[716.0] = [0.00451, -0.000233, -0.000207] tscor[716.1] = [0.00455, -0.000233, -0.000207] tscor[716.2] = [0.00459, -0.000233, -0.000207] tscor[716.3] = [0.00464, -0.000233, -0.000207] tscor[716.4] = [0.00468, -0.000233, -0.000208] tscor[716.5] = [0.00472, -0.000234, -0.000208] tscor[716.6] = [0.00476, -0.000234, -0.000208] tscor[716.7] = [0.00480, -0.000234, -0.000208] tscor[716.8] = [0.00485, -0.000234, -0.000208] tscor[716.9] = [0.00489, -0.000234, -0.000208] tscor[717.0] = [0.00493, -0.000234, -0.000209] tscor[717.1] = [0.00497, -0.000234, -0.000209] tscor[717.2] = [0.00501, -0.000234, -0.000209] tscor[717.3] = [0.00506, -0.000234, -0.000209] tscor[717.4] = [0.00510, -0.000234, -0.000209] tscor[717.5] = [0.00514, -0.000235, -0.000209] tscor[717.6] = [0.00518, -0.000235, -0.000209] tscor[717.7] = [0.00522, -0.000235, -0.000210] tscor[717.8] = [0.00527, -0.000235, -0.000210] tscor[717.9] = [0.00531, -0.000235, -0.000210] tscor[718.0] = [0.00535, -0.000235, -0.000210] tscor[718.1] = [0.00540, -0.000235, -0.000210] tscor[718.2] = [0.00544, -0.000235, -0.000210] tscor[718.3] = [0.00549, -0.000235, -0.000210] tscor[718.4] = [0.00554, -0.000236, -0.000210] tscor[718.5] = [0.00558, -0.000236, -0.000210] tscor[718.6] = [0.00563, -0.000236, -0.000210] tscor[718.7] = [0.00568, -0.000236, -0.000210] tscor[718.8] = [0.00572, -0.000236, -0.000210] tscor[718.9] = [0.00577, -0.000236, -0.000210] tscor[719.0] = [0.00582, -0.000237, -0.000211] tscor[719.1] = [0.00586, -0.000237, -0.000211] tscor[719.2] = [0.00591, -0.000237, -0.000211] tscor[719.3] = [0.00595, -0.000237, -0.000211] tscor[719.4] = [0.00600, -0.000237, -0.000211] tscor[719.5] = [0.00605, -0.000237, -0.000211] tscor[719.6] = [0.00609, -0.000237, -0.000211] tscor[719.7] = [0.00614, -0.000238, -0.000211] tscor[719.8] = [0.00619, -0.000238, -0.000211] tscor[719.9] = [0.00623, -0.000238, -0.000211] tscor[720.0] = [0.00628, -0.000238, -0.000211] tscor[720.1] = [0.00633, -0.000238, -0.000211] tscor[720.2] = [0.00638, -0.000238, -0.000211] tscor[720.3] = [0.00643, -0.000238, -0.000211] tscor[720.4] = [0.00648, -0.000238, -0.000211] tscor[720.5] = [0.00653, -0.000238, -0.000211] tscor[720.6] = [0.00658, -0.000238, -0.000211] tscor[720.7] = [0.00663, -0.000238, -0.000211] tscor[720.8] = [0.00668, -0.000238, -0.000211] tscor[720.9] = [0.00673, -0.000238, -0.000211] tscor[721.0] = [0.00678, -0.000239, -0.000212] tscor[721.1] = [0.00683, -0.000239, -0.000212] tscor[721.2] = [0.00688, -0.000239, -0.000212] tscor[721.3] = [0.00693, -0.000239, -0.000212] tscor[721.4] = [0.00698, -0.000239, -0.000212] tscor[721.5] = [0.00703, -0.000239, -0.000212] tscor[721.6] = [0.00708, -0.000239, -0.000212] tscor[721.7] = [0.00713, -0.000239, -0.000212] tscor[721.8] = [0.00718, -0.000239, -0.000212] tscor[721.9] = [0.00723, -0.000239, -0.000212] tscor[722.0] = [0.00728, -0.000239, -0.000212] tscor[722.1] = [0.00733, -0.000239, -0.000212] tscor[722.2] = [0.00739, -0.000239, -0.000211] tscor[722.3] = [0.00744, -0.000239, -0.000211] tscor[722.4] = [0.00749, -0.000238, -0.000211] tscor[722.5] = [0.00754, -0.000238, -0.000211] tscor[722.6] = [0.00760, -0.000238, -0.000210] tscor[722.7] = [0.00765, -0.000238, -0.000210] tscor[722.8] = [0.00770, -0.000238, -0.000210] tscor[722.9] = [0.00775, -0.000238, -0.000209] tscor[723.0] = [0.00781, -0.000238, -0.000209] tscor[723.1] = [0.00786, -0.000237, -0.000209] tscor[723.2] = [0.00791, -0.000237, -0.000208] tscor[723.3] = [0.00796, -0.000237, -0.000208] tscor[723.4] = [0.00802, -0.000237, -0.000208] tscor[723.5] = [0.00807, -0.000237, -0.000208] tscor[723.6] = [0.00812, -0.000237, -0.000207] tscor[723.7] = [0.00817, -0.000236, -0.000207] tscor[723.8] = [0.00823, -0.000236, -0.000207] tscor[723.9] = [0.00828, -0.000236, -0.000206] tscor[724.0] = [0.00833, -0.000236, -0.000206] tscor[724.1] = [0.00838, -0.000235, -0.000205] tscor[724.2] = [0.00844, -0.000235, -0.000205] tscor[724.3] = [0.00849, -0.000234, -0.000204] tscor[724.4] = [0.00854, -0.000234, -0.000204] tscor[724.5] = [0.00859, -0.000233, -0.000203] tscor[724.6] = [0.00865, -0.000233, -0.000203] tscor[724.7] = [0.00870, -0.000232, -0.000202] tscor[724.8] = [0.00875, -0.000232, -0.000202] tscor[724.9] = [0.00880, -0.000231, -0.000201] tscor[725.0] = [0.00886, -0.000231, -0.000201] tscor[725.1] = [0.00891, -0.000230, -0.000200] tscor[725.2] = [0.00896, -0.000229, -0.000199] tscor[725.3] = [0.00901, -0.000229, -0.000199] tscor[725.4] = [0.00907, -0.000228, -0.000198] tscor[725.5] = [0.00912, -0.000228, -0.000198] tscor[725.6] = [0.00917, -0.000227, -0.000197] tscor[725.7] = [0.00922, -0.000227, -0.000197] tscor[725.8] = [0.00928, -0.000226, -0.000196] tscor[725.9] = [0.00933, -0.000226, -0.000196] tscor[726.0] = [0.00938, -0.000225, -0.000195] tscor[726.1] = [0.00943, -0.000224, -0.000194] tscor[726.2] = [0.00948, -0.000223, -0.000193] tscor[726.3] = [0.00953, -0.000222, -0.000192] tscor[726.4] = [0.00958, -0.000222, -0.000190] tscor[726.5] = [0.00963, -0.000221, -0.000189] tscor[726.6] = [0.00967, -0.000220, -0.000188] tscor[726.7] = [0.00972, -0.000219, -0.000187] tscor[726.8] = [0.00977, -0.000218, -0.000186] tscor[726.9] = [0.00982, -0.000217, -0.000185] tscor[727.0] = [0.00987, -0.000217, -0.000184] tscor[727.1] = [0.00992, -0.000216, -0.000182] tscor[727.2] = [0.00997, -0.000215, -0.000181] tscor[727.3] = [0.01002, -0.000214, -0.000180] tscor[727.4] = [0.01007, -0.000213, -0.000179] tscor[727.5] = [0.01012, -0.000212, -0.000178] tscor[727.6] = [0.01016, -0.000211, -0.000177] tscor[727.7] = [0.01021, -0.000211, -0.000175] tscor[727.8] = [0.01026, -0.000210, -0.000174] tscor[727.9] = [0.01031, -0.000209, -0.000173] tscor[728.0] = [0.01036, -0.000208, -0.000172] tscor[728.1] = [0.01041, -0.000206, -0.000170] tscor[728.2] = [0.01045, -0.000204, -0.000168] tscor[728.3] = [0.01050, -0.000202, -0.000166] tscor[728.4] = [0.01055, -0.000200, -0.000165] tscor[728.5] = [0.01059, -0.000198, -0.000163] tscor[728.6] = [0.01064, -0.000196, -0.000161] tscor[728.7] = [0.01069, -0.000194, -0.000159] tscor[728.8] = [0.01073, -0.000192, -0.000157] tscor[728.9] = [0.01078, -0.000190, -0.000155] tscor[729.0] = [0.01083, -0.000189, -0.000154] tscor[729.1] = [0.01087, -0.000187, -0.000152] tscor[729.2] = [0.01092, -0.000185, -0.000150] tscor[729.3] = [0.01096, -0.000183, -0.000148] tscor[729.4] = [0.01101, -0.000181, -0.000146] tscor[729.5] = [0.01106, -0.000179, -0.000144] tscor[729.6] = [0.01110, -0.000177, -0.000142] tscor[729.7] = [0.01115, -0.000175, -0.000141] tscor[729.8] = [0.01120, -0.000173, -0.000139] tscor[729.9] = [0.01124, -0.000171, -0.000137] tscor[730.0] = [0.01129, -0.000169, -0.000135] tscor[730.1] = [0.01133, -0.000167, -0.000132] tscor[730.2] = [0.01137, -0.000164, -0.000130] tscor[730.3] = [0.01141, -0.000162, -0.000127] tscor[730.4] = [0.01145, -0.000160, -0.000125] tscor[730.5] = [0.01150, -0.000157, -0.000122] tscor[730.6] = [0.01154, -0.000155, -0.000120] tscor[730.7] = [0.01158, -0.000153, -0.000117] tscor[730.8] = [0.01162, -0.000150, -0.000115] tscor[730.9] = [0.01166, -0.000148, -0.000112] tscor[731.0] = [0.01170, -0.000146, -0.000110] tscor[731.1] = [0.01174, -0.000143, -0.000107] tscor[731.2] = [0.01178, -0.000141, -0.000104] tscor[731.3] = [0.01182, -0.000138, -0.000102] tscor[731.4] = [0.01186, -0.000136, -0.000099] tscor[731.5] = [0.01191, -0.000134, -0.000097] tscor[731.6] = [0.01195, -0.000131, -0.000094] tscor[731.7] = [0.01199, -0.000129, -0.000092] tscor[731.8] = [0.01203, -0.000127, -0.000089] tscor[731.9] = [0.01207, -0.000124, -0.000087] tscor[732.0] = [0.01211, -0.000122, -0.000084] tscor[732.1] = [0.01215, -0.000119, -0.000081] tscor[732.2] = [0.01218, -0.000116, -0.000077] tscor[732.3] = [0.01222, -0.000112, -0.000074] tscor[732.4] = [0.01225, -0.000109, -0.000070] tscor[732.5] = [0.01229, -0.000106, -0.000067] tscor[732.6] = [0.01232, -0.000103, -0.000063] tscor[732.7] = [0.01236, -0.000100, -0.000060] tscor[732.8] = [0.01239, -0.000096, -0.000056] tscor[732.9] = [0.01243, -0.000093, -0.000053] tscor[733.0] = [0.01247, -0.000090, -0.000049] tscor[733.1] = [0.01250, -0.000087, -0.000046] tscor[733.2] = [0.01254, -0.000084, -0.000042] tscor[733.3] = [0.01257, -0.000080, -0.000039] tscor[733.4] = [0.01261, -0.000077, -0.000035] tscor[733.5] = [0.01264, -0.000074, -0.000032] tscor[733.6] = [0.01268, -0.000071, -0.000028] tscor[733.7] = [0.01271, -0.000068, -0.000025] tscor[733.8] = [0.01275, -0.000064, -0.000021] tscor[733.9] = [0.01278, -0.000061, -0.000018] tscor[734.0] = [0.01282, -0.000058, -0.000014] tscor[734.1] = [0.01285, -0.000054, -0.000010] tscor[734.2] = [0.01287, -0.000050, -0.000006] tscor[734.3] = [0.01290, -0.000046, -0.000002] tscor[734.4] = [0.01292, -0.000041, 0.000002] tscor[734.5] = [0.01295, -0.000037, 0.000006] tscor[734.6] = [0.01297, -0.000033, 0.000010] tscor[734.7] = [0.01300, -0.000029, 0.000014] tscor[734.8] = [0.01302, -0.000025, 0.000018] tscor[734.9] = [0.01305, -0.000021, 0.000022] tscor[735.0] = [0.01308, -0.000017, 0.000026] tscor[735.1] = [0.01310, -0.000012, 0.000030] tscor[735.2] = [0.01313, -0.000008, 0.000034] tscor[735.3] = [0.01315, -0.000004, 0.000038] tscor[735.4] = [0.01318, 0.000000, 0.000042] tscor[735.5] = [0.01320, 0.000004, 0.000046] tscor[735.6] = [0.01323, 0.000008, 0.000050] tscor[735.7] = [0.01325, 0.000013, 0.000054] tscor[735.8] = [0.01328, 0.000017, 0.000058] tscor[735.9] = [0.01330, 0.000021, 0.000062] tscor[736.0] = [0.01333, 0.000025, 0.000066] tscor[736.1] = [0.01335, 0.000030, 0.000071] tscor[736.2] = [0.01336, 0.000034, 0.000075] tscor[736.3] = [0.01338, 0.000039, 0.000080] tscor[736.4] = [0.01339, 0.000044, 0.000084] tscor[736.5] = [0.01341, 0.000048, 0.000089] tscor[736.6] = [0.01342, 0.000053, 0.000093] tscor[736.7] = [0.01344, 0.000058, 0.000098] tscor[736.8] = [0.01345, 0.000062, 0.000102] tscor[736.9] = [0.01347, 0.000067, 0.000107] tscor[737.0] = [0.01349, 0.000072, 0.000111] tscor[737.1] = [0.01350, 0.000076, 0.000116] tscor[737.2] = [0.01352, 0.000081, 0.000120] tscor[737.3] = [0.01353, 0.000085, 0.000125] tscor[737.4] = [0.01355, 0.000090, 0.000129] tscor[737.5] = [0.01356, 0.000095, 0.000134] tscor[737.6] = [0.01358, 0.000099, 0.000138] tscor[737.7] = [0.01359, 0.000104, 0.000143] tscor[737.8] = [0.01361, 0.000109, 0.000147] tscor[737.9] = [0.01362, 0.000113, 0.000152] tscor[738.0] = [0.01364, 0.000118, 0.000156] tscor[738.1] = [0.01364, 0.000123, 0.000161] tscor[738.2] = [0.01364, 0.000128, 0.000167] tscor[738.3] = [0.01363, 0.000133, 0.000172] tscor[738.4] = [0.01363, 0.000138, 0.000177] tscor[738.5] = [0.01363, 0.000143, 0.000183] tscor[738.6] = [0.01363, 0.000147, 0.000188] tscor[738.7] = [0.01362, 0.000152, 0.000193] tscor[738.8] = [0.01362, 0.000157, 0.000198] tscor[738.9] = [0.01362, 0.000162, 0.000204] tscor[739.0] = [0.01362, 0.000167, 0.000209] tscor[739.1] = [0.01361, 0.000172, 0.000214] tscor[739.2] = [0.01361, 0.000177, 0.000220] tscor[739.3] = [0.01361, 0.000182, 0.000225] tscor[739.4] = [0.01361, 0.000187, 0.000230] tscor[739.5] = [0.01360, 0.000192, 0.000236] tscor[739.6] = [0.01360, 0.000196, 0.000241] tscor[739.7] = [0.01360, 0.000201, 0.000246] tscor[739.8] = [0.01360, 0.000206, 0.000251] tscor[739.9] = [0.01359, 0.000211, 0.000257] tscor[740.0] = [0.01359, 0.000216, 0.000262] tscor[740.1] = [0.01358, 0.000221, 0.000267] tscor[740.2] = [0.01357, 0.000226, 0.000272] tscor[740.3] = [0.01355, 0.000231, 0.000277] tscor[740.4] = [0.01354, 0.000236, 0.000283] tscor[740.5] = [0.01353, 0.000241, 0.000288] tscor[740.6] = [0.01352, 0.000246, 0.000293] tscor[740.7] = [0.01350, 0.000251, 0.000298] tscor[740.8] = [0.01349, 0.000256, 0.000303] tscor[740.9] = [0.01348, 0.000261, 0.000308] tscor[741.0] = [0.01347, 0.000266, 0.000314] tscor[741.1] = [0.01345, 0.000270, 0.000319] tscor[741.2] = [0.01344, 0.000275, 0.000324] tscor[741.3] = [0.01343, 0.000280, 0.000329] tscor[741.4] = [0.01342, 0.000285, 0.000334] tscor[741.5] = [0.01340, 0.000290, 0.000339] tscor[741.6] = [0.01339, 0.000295, 0.000344] tscor[741.7] = [0.01338, 0.000300, 0.000350] tscor[741.8] = [0.01337, 0.000305, 0.000355] tscor[741.9] = [0.01335, 0.000310, 0.000360] tscor[742.0] = [0.01334, 0.000315, 0.000365] tscor[742.1] = [0.01332, 0.000320, 0.000370] tscor[742.2] = [0.01330, 0.000325, 0.000375] tscor[742.3] = [0.01328, 0.000330, 0.000380] tscor[742.4] = [0.01325, 0.000334, 0.000384] tscor[742.5] = [0.01323, 0.000339, 0.000389] tscor[742.6] = [0.01321, 0.000344, 0.000394] tscor[742.7] = [0.01319, 0.000349, 0.000399] tscor[742.8] = [0.01317, 0.000354, 0.000404] tscor[742.9] = [0.01315, 0.000359, 0.000409] tscor[743.0] = [0.01313, 0.000364, 0.000414] tscor[743.1] = [0.01310, 0.000368, 0.000418] tscor[743.2] = [0.01308, 0.000373, 0.000423] tscor[743.3] = [0.01306, 0.000378, 0.000428] tscor[743.4] = [0.01304, 0.000383, 0.000433] tscor[743.5] = [0.01302, 0.000388, 0.000438] tscor[743.6] = [0.01300, 0.000393, 0.000443] tscor[743.7] = [0.01297, 0.000397, 0.000447] tscor[743.8] = [0.01295, 0.000402, 0.000452] tscor[743.9] = [0.01293, 0.000407, 0.000457] tscor[744.0] = [0.01291, 0.000412, 0.000462] tscor[744.1] = [0.01288, 0.000416, 0.000466] tscor[744.2] = [0.01286, 0.000420, 0.000470] tscor[744.3] = [0.01283, 0.000424, 0.000474] tscor[744.4] = [0.01280, 0.000428, 0.000478] tscor[744.5] = [0.01278, 0.000433, 0.000483] tscor[744.6] = [0.01275, 0.000437, 0.000487] tscor[744.7] = [0.01272, 0.000441, 0.000491] tscor[744.8] = [0.01270, 0.000445, 0.000495] tscor[744.9] = [0.01267, 0.000449, 0.000499] tscor[745.0] = [0.01265, 0.000453, 0.000503] tscor[745.1] = [0.01262, 0.000457, 0.000507] tscor[745.2] = [0.01259, 0.000461, 0.000511] tscor[745.3] = [0.01257, 0.000465, 0.000515] tscor[745.4] = [0.01254, 0.000469, 0.000519] tscor[745.5] = [0.01251, 0.000474, 0.000524] tscor[745.6] = [0.01249, 0.000478, 0.000528] tscor[745.7] = [0.01246, 0.000482, 0.000532] tscor[745.8] = [0.01243, 0.000486, 0.000536] tscor[745.9] = [0.01241, 0.000490, 0.000540] tscor[746.0] = [0.01238, 0.000494, 0.000544] tscor[746.1] = [0.01234, 0.000498, 0.000547] tscor[746.2] = [0.01230, 0.000501, 0.000551] tscor[746.3] = [0.01226, 0.000505, 0.000554] tscor[746.4] = [0.01223, 0.000508, 0.000558] tscor[746.5] = [0.01219, 0.000512, 0.000561] tscor[746.6] = [0.01215, 0.000515, 0.000564] tscor[746.7] = [0.01211, 0.000519, 0.000568] tscor[746.8] = [0.01207, 0.000522, 0.000571] tscor[746.9] = [0.01203, 0.000526, 0.000575] tscor[747.0] = [0.01200, 0.000529, 0.000578] tscor[747.1] = [0.01196, 0.000533, 0.000581] tscor[747.2] = [0.01192, 0.000536, 0.000585] tscor[747.3] = [0.01188, 0.000540, 0.000588] tscor[747.4] = [0.01184, 0.000543, 0.000592] tscor[747.5] = [0.01180, 0.000547, 0.000595] tscor[747.6] = [0.01176, 0.000550, 0.000598] tscor[747.7] = [0.01173, 0.000554, 0.000602] tscor[747.8] = [0.01169, 0.000557, 0.000605] tscor[747.9] = [0.01165, 0.000561, 0.000609] tscor[748.0] = [0.01161, 0.000564, 0.000612] tscor[748.1] = [0.01157, 0.000567, 0.000615] tscor[748.2] = [0.01152, 0.000570, 0.000618] tscor[748.3] = [0.01148, 0.000573, 0.000621] tscor[748.4] = [0.01143, 0.000575, 0.000624] tscor[748.5] = [0.01139, 0.000578, 0.000627] tscor[748.6] = [0.01135, 0.000581, 0.000630] tscor[748.7] = [0.01130, 0.000584, 0.000633] tscor[748.8] = [0.01126, 0.000587, 0.000636] tscor[748.9] = [0.01121, 0.000590, 0.000639] tscor[749.0] = [0.01117, 0.000593, 0.000642] tscor[749.1] = [0.01113, 0.000595, 0.000644] tscor[749.2] = [0.01108, 0.000598, 0.000647] tscor[749.3] = [0.01104, 0.000601, 0.000650] tscor[749.4] = [0.01099, 0.000604, 0.000653] tscor[749.5] = [0.01095, 0.000607, 0.000656] tscor[749.6] = [0.01091, 0.000610, 0.000659] tscor[749.7] = [0.01086, 0.000612, 0.000662] tscor[749.8] = [0.01082, 0.000615, 0.000665] tscor[749.9] = [0.01077, 0.000618, 0.000668] tscor[750.0] = [0.01073, 0.000621, 0.000671] tscor[750.1] = [0.01069, 0.000625, 0.000671] tscor[750.2] = [0.01064, 0.000628, 0.000674] tscor[750.3] = [0.01060, 0.000631, 0.000677] tscor[750.4] = [0.01055, 0.000634, 0.000680] tscor[750.5] = [0.01051, 0.000637, 0.000683] tscor[750.6] = [0.01047, 0.000640, 0.000686] tscor[750.7] = [0.01042, 0.000643, 0.000689] tscor[750.8] = [0.01038, 0.000646, 0.000692] tscor[750.9] = [0.01033, 0.000649, 0.000695] tscor[751.0] = [0.01029, 0.000652, 0.000698] tscor[751.1] = [0.01025, 0.000655, 0.000701] tscor[751.2] = [0.01020, 0.000658, 0.000704] tscor[751.3] = [0.01016, 0.000661, 0.000707] tscor[751.4] = [0.01011, 0.000665, 0.000710] tscor[751.5] = [0.01007, 0.000668, 0.000713] tscor[751.6] = [0.01003, 0.000671, 0.000716] tscor[751.7] = [0.00998, 0.000674, 0.000719] tscor[751.8] = [0.00994, 0.000677, 0.000722] tscor[751.9] = [0.00989, 0.000680, 0.000725] tscor[752.0] = [0.00985, 0.000683, 0.000728] tscor[752.1] = [0.00981, 0.000686, 0.000731] tscor[752.2] = [0.00976, 0.000689, 0.000734] tscor[752.3] = [0.00972, 0.000692, 0.000737] tscor[752.4] = [0.00967, 0.000695, 0.000740] tscor[752.5] = [0.00963, 0.000698, 0.000743] tscor[752.6] = [0.00959, 0.000701, 0.000746] tscor[752.7] = [0.00954, 0.000704, 0.000749] tscor[752.8] = [0.00950, 0.000707, 0.000752] tscor[752.9] = [0.00945, 0.000710, 0.000755] tscor[753.0] = [0.00941, 0.000713, 0.000758] tscor[753.1] = [0.00937, 0.000716, 0.000761] tscor[753.2] = [0.00932, 0.000719, 0.000764] tscor[753.3] = [0.00928, 0.000722, 0.000767] tscor[753.4] = [0.00923, 0.000725, 0.000770] tscor[753.5] = [0.00919, 0.000728, 0.000773] tscor[753.6] = [0.00915, 0.000731, 0.000776] tscor[753.7] = [0.00910, 0.000734, 0.000779] tscor[753.8] = [0.00906, 0.000737, 0.000782] tscor[753.9] = [0.00901, 0.000740, 0.000785] tscor[754.0] = [0.00897, 0.000743, 0.000788] tscor[754.1] = [0.00893, 0.000746, 0.000791] tscor[754.2] = [0.00888, 0.000749, 0.000794] tscor[754.3] = [0.00884, 0.000752, 0.000797] tscor[754.4] = [0.00879, 0.000755, 0.000800] tscor[754.5] = [0.00875, 0.000758, 0.000803] tscor[754.6] = [0.00871, 0.000761, 0.000806] tscor[754.7] = [0.00866, 0.000764, 0.000809] tscor[754.8] = [0.00862, 0.000767, 0.000812] tscor[754.9] = [0.00857, 0.000770, 0.000815] tscor[755.0] = [0.00853, 0.000773, 0.000818] tscor[755.1] = [no_value, no_value, no_value] tscor[755.2] = [no_value, no_value, no_value] tscor[755.3] = [no_value, no_value, no_value] tscor[755.4] = [no_value, no_value, no_value] tscor[755.5] = [no_value, no_value, no_value] tscor[755.6] = [no_value, no_value, no_value] tscor[755.7] = [no_value, no_value, no_value] tscor[755.8] = [no_value, no_value, no_value] tscor[755.9] = [no_value, no_value, no_value] tscor[756.0] = [no_value, no_value, no_value] tscor[756.1] = [no_value, no_value, no_value] tscor[756.2] = [no_value, no_value, no_value] tscor[756.3] = [no_value, no_value, no_value] tscor[756.4] = [no_value, no_value, no_value] tscor[756.5] = [no_value, no_value, no_value] tscor[756.6] = [no_value, no_value, no_value] tscor[756.7] = [no_value, no_value, no_value] tscor[756.8] = [no_value, no_value, no_value] tscor[756.9] = [no_value, no_value, no_value] tscor[757.0] = [no_value, no_value, no_value] tscor[757.1] = [no_value, no_value, no_value] tscor[757.2] = [no_value, no_value, no_value] tscor[757.3] = [no_value, no_value, no_value] tscor[757.4] = [no_value, no_value, no_value] tscor[757.5] = [no_value, no_value, no_value] tscor[757.6] = [no_value, no_value, no_value] tscor[757.7] = [no_value, no_value, no_value] tscor[757.8] = [no_value, no_value, no_value] tscor[757.9] = [no_value, no_value, no_value] tscor[758.0] = [no_value, no_value, no_value] tscor[758.1] = [no_value, no_value, no_value] tscor[758.2] = [no_value, no_value, no_value] tscor[758.3] = [no_value, no_value, no_value] tscor[758.4] = [no_value, no_value, no_value] tscor[758.5] = [no_value, no_value, no_value] tscor[758.6] = [no_value, no_value, no_value] tscor[758.7] = [no_value, no_value, no_value] tscor[758.8] = [no_value, no_value, no_value] tscor[758.9] = [no_value, no_value, no_value] tscor[759.0] = [no_value, no_value, no_value] tscor[759.1] = [no_value, no_value, no_value] tscor[759.2] = [no_value, no_value, no_value] tscor[759.3] = [no_value, no_value, no_value] tscor[759.4] = [no_value, no_value, no_value] tscor[759.5] = [no_value, no_value, no_value] tscor[759.6] = [no_value, no_value, no_value] tscor[759.7] = [no_value, no_value, no_value] tscor[759.8] = [no_value, no_value, no_value] tscor[759.9] = [no_value, no_value, no_value] tscor[760.0] = [no_value, no_value, no_value] tscor[760.1] = [no_value, no_value, no_value] tscor[760.2] = [no_value, no_value, no_value] tscor[760.3] = [no_value, no_value, no_value] tscor[760.4] = [no_value, no_value, no_value] tscor[760.5] = [no_value, no_value, no_value] tscor[760.6] = [no_value, no_value, no_value] tscor[760.7] = [no_value, no_value, no_value] tscor[760.8] = [no_value, no_value, no_value] tscor[760.9] = [no_value, no_value, no_value] tscor[761.0] = [no_value, no_value, no_value] tscor[761.1] = [no_value, no_value, no_value] tscor[761.2] = [no_value, no_value, no_value] tscor[761.3] = [no_value, no_value, no_value] tscor[761.4] = [no_value, no_value, no_value] tscor[761.5] = [no_value, no_value, no_value] tscor[761.6] = [no_value, no_value, no_value] tscor[761.7] = [no_value, no_value, no_value] tscor[761.8] = [no_value, no_value, no_value] tscor[761.9] = [no_value, no_value, no_value] tscor[762.0] = [no_value, no_value, no_value] tscor[762.1] = [no_value, no_value, no_value] tscor[762.2] = [no_value, no_value, no_value] tscor[762.3] = [no_value, no_value, no_value] tscor[762.4] = [no_value, no_value, no_value] tscor[762.5] = [no_value, no_value, no_value] tscor[762.6] = [no_value, no_value, no_value] tscor[762.7] = [no_value, no_value, no_value] tscor[762.8] = [no_value, no_value, no_value] tscor[762.9] = [no_value, no_value, no_value] tscor[763.0] = [no_value, no_value, no_value] tscor[763.1] = [no_value, no_value, no_value] tscor[763.2] = [no_value, no_value, no_value] tscor[763.3] = [no_value, no_value, no_value] tscor[763.4] = [no_value, no_value, no_value] tscor[763.5] = [no_value, no_value, no_value] tscor[763.6] = [no_value, no_value, no_value] tscor[763.7] = [no_value, no_value, no_value] tscor[763.8] = [no_value, no_value, no_value] tscor[763.9] = [no_value, no_value, no_value] tscor[764.0] = [no_value, no_value, no_value] tscor[764.1] = [no_value, no_value, no_value] tscor[764.2] = [no_value, no_value, no_value] tscor[764.3] = [no_value, no_value, no_value] tscor[764.4] = [no_value, no_value, no_value] tscor[764.5] = [no_value, no_value, no_value] tscor[764.6] = [no_value, no_value, no_value] tscor[764.7] = [no_value, no_value, no_value] tscor[764.8] = [no_value, no_value, no_value] tscor[764.9] = [no_value, no_value, no_value] tscor[765.0] = [no_value, no_value, no_value] tscor[765.1] = [no_value, no_value, no_value] tscor[765.2] = [no_value, no_value, no_value] tscor[765.3] = [no_value, no_value, no_value] tscor[765.4] = [no_value, no_value, no_value] tscor[765.5] = [no_value, no_value, no_value] tscor[765.6] = [no_value, no_value, no_value] tscor[765.7] = [no_value, no_value, no_value] tscor[765.8] = [no_value, no_value, no_value] tscor[765.9] = [no_value, no_value, no_value] tscor[766.0] = [no_value, no_value, no_value] tscor[766.1] = [no_value, no_value, no_value] tscor[766.2] = [no_value, no_value, no_value] tscor[766.3] = [no_value, no_value, no_value] tscor[766.4] = [no_value, no_value, no_value] tscor[766.5] = [no_value, no_value, no_value] tscor[766.6] = [no_value, no_value, no_value] tscor[766.7] = [no_value, no_value, no_value] tscor[766.8] = [no_value, no_value, no_value] tscor[766.9] = [no_value, no_value, no_value] tscor[767.0] = [no_value, no_value, no_value] tscor[767.1] = [no_value, no_value, no_value] tscor[767.2] = [no_value, no_value, no_value] tscor[767.3] = [no_value, no_value, no_value] tscor[767.4] = [no_value, no_value, no_value] tscor[767.5] = [no_value, no_value, no_value] tscor[767.6] = [no_value, no_value, no_value] tscor[767.7] = [no_value, no_value, no_value] tscor[767.8] = [no_value, no_value, no_value] tscor[767.9] = [no_value, no_value, no_value] tscor[768.0] = [no_value, no_value, no_value] tscor[768.1] = [no_value, no_value, no_value] tscor[768.2] = [no_value, no_value, no_value] tscor[768.3] = [no_value, no_value, no_value] tscor[768.4] = [no_value, no_value, no_value] tscor[768.5] = [no_value, no_value, no_value] tscor[768.6] = [no_value, no_value, no_value] tscor[768.7] = [no_value, no_value, no_value] tscor[768.8] = [no_value, no_value, no_value] tscor[768.9] = [no_value, no_value, no_value] tscor[769.0] = [no_value, no_value, no_value] tscor[769.1] = [no_value, no_value, no_value] tscor[769.2] = [no_value, no_value, no_value] tscor[769.3] = [no_value, no_value, no_value] tscor[769.4] = [no_value, no_value, no_value] tscor[769.5] = [no_value, no_value, no_value] tscor[769.6] = [no_value, no_value, no_value] tscor[769.7] = [no_value, no_value, no_value] tscor[769.8] = [no_value, no_value, no_value] tscor[769.9] = [no_value, no_value, no_value] tscor[770.0] = [no_value, no_value, no_value] tscor[770.1] = [no_value, no_value, no_value] tscor[770.2] = [no_value, no_value, no_value] tscor[770.3] = [no_value, no_value, no_value] tscor[770.4] = [no_value, no_value, no_value] tscor[770.5] = [no_value, no_value, no_value] tscor[770.6] = [no_value, no_value, no_value] tscor[770.7] = [no_value, no_value, no_value] tscor[770.8] = [no_value, no_value, no_value] tscor[770.9] = [no_value, no_value, no_value] tscor[771.0] = [no_value, no_value, no_value] tscor[771.1] = [no_value, no_value, no_value] tscor[771.2] = [no_value, no_value, no_value] tscor[771.3] = [no_value, no_value, no_value] tscor[771.4] = [no_value, no_value, no_value] tscor[771.5] = [no_value, no_value, no_value] tscor[771.6] = [no_value, no_value, no_value] tscor[771.7] = [no_value, no_value, no_value] tscor[771.8] = [no_value, no_value, no_value] tscor[771.9] = [no_value, no_value, no_value] tscor[772.0] = [no_value, no_value, no_value] tscor[772.1] = [no_value, no_value, no_value] tscor[772.2] = [no_value, no_value, no_value] tscor[772.3] = [no_value, no_value, no_value] tscor[772.4] = [no_value, no_value, no_value] tscor[772.5] = [no_value, no_value, no_value] tscor[772.6] = [no_value, no_value, no_value] tscor[772.7] = [no_value, no_value, no_value] tscor[772.8] = [no_value, no_value, no_value] tscor[772.9] = [no_value, no_value, no_value] tscor[773.0] = [no_value, no_value, no_value] tscor[773.1] = [no_value, no_value, no_value] tscor[773.2] = [no_value, no_value, no_value] tscor[773.3] = [no_value, no_value, no_value] tscor[773.4] = [no_value, no_value, no_value] tscor[773.5] = [no_value, no_value, no_value] tscor[773.6] = [no_value, no_value, no_value] tscor[773.7] = [no_value, no_value, no_value] tscor[773.8] = [no_value, no_value, no_value] tscor[773.9] = [no_value, no_value, no_value] tscor[774.0] = [no_value, no_value, no_value] tscor[774.1] = [no_value, no_value, no_value] tscor[774.2] = [no_value, no_value, no_value] tscor[774.3] = [no_value, no_value, no_value] tscor[774.4] = [no_value, no_value, no_value] tscor[774.5] = [no_value, no_value, no_value] tscor[774.6] = [no_value, no_value, no_value] tscor[774.7] = [no_value, no_value, no_value] tscor[774.8] = [no_value, no_value, no_value] tscor[774.9] = [no_value, no_value, no_value] tscor[775.0] = [no_value, no_value, no_value]

47.186874

80

0.622801

35,770

188,370

3.245904

0.043668

0.072408

0.062012

0.096464

0.848354

0.82857

0.762553

0.74643

0

0.528358

0.12735

188,370

3,991

81

47.198697

0.177965

0.007384

0

1

0

false

0

0.000253

0

0.000253

0

null

0

1

0

1

0

1

0

null

0

6

0922df708f07ee89dca56d5a7342793146fecafe

207

py

Python

fastsrv/src/tests/test_config.py

lingluadmin/operwrt-fast-cli

8f7ad9f9138bea3fd89749aaff37f325ea0aa5a5

[ "Apache-2.0" ]

1

2018-03-08T08:14:08.000Z

fastsrv/src/tests/test_config.py

lingluadmin/operwrt-fast-cli

8f7ad9f9138bea3fd89749aaff37f325ea0aa5a5

[ "Apache-2.0" ]

null

fastsrv/src/tests/test_config.py

lingluadmin/operwrt-fast-cli

8f7ad9f9138bea3fd89749aaff37f325ea0aa5a5

[ "Apache-2.0" ]

null

import unittest class TestConfig(unittest.TestCase): def test_render(self): self.assertEqual('foo'.upper(), 'FOO') def test_load_yaml(self): self.assertEqual('foo'.upper(), 'FOO')

20.7

46

0.657005

25

207

5.32

0.56

0.105263

0.285714

0.330827

0.451128

0

0.188406

207

9

47

23

0.791667

0

0.333333

0

0.057971

0

0.333333

1

0.333333

false

0

0.166667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

11a8572f89964ed3f4aac5fb111939fade28fb43

79

py

Python

get_started.py

chrissaher/datascience-bowl-2018

83da94ff3885593f4ea55b88a484f9d3e92be233

[ "MIT" ]

null

get_started.py

chrissaher/datascience-bowl-2018

83da94ff3885593f4ea55b88a484f9d3e92be233

[ "MIT" ]

null

get_started.py

chrissaher/datascience-bowl-2018

83da94ff3885593f4ea55b88a484f9d3e92be233

[ "MIT" ]

null

import distutils.dir_util distutils.dir_util.copy_tree("./demo", "./my_test")

19.75

51

0.759494

12

79

4.666667

0.75

0.428571

0.571429

0

0.063291

79

3

52

26.333333

0.756757

0

0.189873

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

11b6f096c3dedabdfaeeb81ef43e8527c93cb38d

6,919

py

Python

tests/test_nested_parameters.py

Algomorph/ext_argparse

fbca26f8a551f84677475a11fb5415ddda78abd9

[ "Apache-2.0" ]

1

2021-09-06T23:22:07.000Z

tests/test_nested_parameters.py

Algomorph/ext_argparse

fbca26f8a551f84677475a11fb5415ddda78abd9

[ "Apache-2.0" ]

11

2021-09-07T14:13:39.000Z

2021-09-29T15:17:46.000Z

tests/test_nested_parameters.py

Algomorph/ext_argparse

fbca26f8a551f84677475a11fb5415ddda78abd9

[ "Apache-2.0" ]

null

import os import pathlib from ext_argparse import process_arguments, Parameter, ParameterEnum from typing import Type class LevelTwoGroupA(ParameterEnum): string_param = Parameter(arg_type=str, default="Istanbul", arg_help="Point of origin") int_param = Parameter(arg_type=int, default=8, arg_help="Number of hairs on chest") float_param = Parameter(arg_type=float, default=0.1, arg_help="Litres of vodka left to drink") class LevelTwoGroupB(ParameterEnum): string_param = Parameter(arg_type=str, default="Kabul", arg_help="Point of origin") int_param = Parameter(arg_type=int, default=8, arg_help="Number of hairs on chest") float_param = Parameter(arg_type=float, default=0.1, arg_help="Litres of vodka left to drink") class LevelTwoGroupC(ParameterEnum): string_param = Parameter(arg_type=str, default="Istanbul", arg_help="Point of origin") int_param = Parameter(arg_type=int, default=8, arg_help="Number of hairs on chest") float_param = Parameter(arg_type=float, default=0.1, arg_help="Litres of vodka left to drink") path_param = Parameter(arg_type=str, default=".", arg_help="Some path", setting_file_location=True) class LevelOneGroupD(ParameterEnum): int_param = Parameter(arg_type=int, default=5, arg_help="Number of hairs on chest") group_a: Type[LevelTwoGroupA] = LevelTwoGroupA group_b: Type[LevelTwoGroupB] = LevelTwoGroupB group_c: Type[LevelTwoGroupC] = LevelTwoGroupC class LevelOneGroupA(ParameterEnum): string_param = Parameter(arg_type=str, default="Istanbul", arg_help="Point of origin") int_param = Parameter(arg_type=int, default=8, arg_help="Number of hairs on chest") float_param = Parameter(arg_type=float, default=0.1, arg_help="Litres of vodka left to drink") class LevelOneGroupB(ParameterEnum): string_param = Parameter(arg_type=str, default="Istanbul", arg_help="Point of origin") int_param = Parameter(arg_type=int, default=8, arg_help="Number of hairs on chest") float_param = Parameter(arg_type=float, default=0.1, arg_help="Litres of vodka left to drink") class LevelOneGroupC(ParameterEnum): string_param = Parameter(arg_type=str, default="Istanbul", arg_help="Point of origin") int_param = Parameter(arg_type=int, default=9, arg_help="Number of hairs on chest") float_param = Parameter(arg_type=float, default=0.1, arg_help="Litres of vodka left to drink") class BaseLevelParams(ParameterEnum): string_param = Parameter(arg_type=str, default="Istanbul", arg_help="Point of origin") int_param = Parameter(arg_type=int, default=8, arg_help="Number of hairs on chest") group_a: Type[LevelOneGroupA] = LevelOneGroupA group_b: Type[LevelOneGroupB] = LevelOneGroupB group_c: Type[LevelOneGroupC] = LevelOneGroupC float_param = Parameter(arg_type=float, default=0.1, arg_help="Litres of coolaid left to drink") group_d = LevelOneGroupD def test_default_nested_parameters(): process_arguments(BaseLevelParams, "Test parameter parser", argv=[]) assert BaseLevelParams.float_param.value == 0.1 assert BaseLevelParams.group_a.float_param.value == 0.1 assert BaseLevelParams.group_c.int_param.value == 9 assert BaseLevelParams.group_d.int_param.value == 5 assert BaseLevelParams.group_d.group_a.parameter.float_param.value == 0.1 assert BaseLevelParams.group_d.group_b.parameter.string_param.value == "Kabul" def test_full_nested_parameters(): process_arguments(BaseLevelParams, "Test parameter parser", argv=[ "--float_param=0.2", "--int_param=1", "--group_a.float_param=0.4", "--group_c.string_param=Constantinople", "--group_d.int_param=9", "--group_d.group_a.float_param=0.32", "--group_d.group_b.string_param=Liverpool" ]) assert BaseLevelParams.float_param.value == 0.2 assert BaseLevelParams.int_param.value == 1 assert BaseLevelParams.group_a.float_param.value == 0.4 assert BaseLevelParams.group_c.string_param.value == "Constantinople" assert BaseLevelParams.group_d.int_param.value == 9 assert BaseLevelParams.group_d.group_a.parameter.float_param.value == 0.32 assert BaseLevelParams.group_d.group_b.parameter.string_param.value == "Liverpool" def test_shorthand_nested_parameters(): process_arguments(BaseLevelParams, "Test parameter parser", argv=[ "-fp=0.2", "-ip=1", "-ga.fp=0.4", "-gc.sp=Constantinople", "-gd.ip=9", "-gd.ga.fp=0.32", "-gd.gb.sp=Liverpool" ]) assert BaseLevelParams.float_param.value == 0.2 assert BaseLevelParams.int_param.value == 1 assert BaseLevelParams.group_a.float_param.value == 0.4 assert BaseLevelParams.group_c.string_param.value == "Constantinople" assert BaseLevelParams.group_d.int_param.value == 9 assert BaseLevelParams.group_d.group_a.float_param.value == 0.32 assert BaseLevelParams.group_d.group_b.string_param.value == "Liverpool" def test_nested_parameter_save_load(): test_data_dir = os.path.join(pathlib.Path(__file__).parent.resolve(), "test_data") output_settings_path = os.path.join(test_data_dir, "nested_settings.yaml") process_arguments(BaseLevelParams, "Test parameter parser", argv=[ f"--settings_file={output_settings_path}", "--save_settings", "--float_param=0.2", "--int_param=1", "--group_a.float_param=0.4", "--group_c.string_param=Constantinople", "--group_d.int_param=9", "--group_d.group_a.float_param=0.32", "--group_d.group_b.string_param=Liverpool", "--group_d.group_c.path_param=!settings_file_location" ]) assert BaseLevelParams.float_param.value == 0.2 assert BaseLevelParams.int_param.value == 1 assert BaseLevelParams.group_a.float_param.value == 0.4 assert BaseLevelParams.group_c.string_param.value == "Constantinople" assert BaseLevelParams.group_d.int_param.value == 9 assert BaseLevelParams.group_d.group_a.float_param.value == 0.32 assert BaseLevelParams.group_d.group_b.string_param.value == "Liverpool" assert BaseLevelParams.group_d.group_c.path_param.value == test_data_dir # load defaults process_arguments(BaseLevelParams, "Test parameter parser", argv=[]) assert BaseLevelParams.group_d.group_c.path_param.value == "." process_arguments(BaseLevelParams, "Test parameter parser", argv=[ f"--settings_file={output_settings_path}", "--int_param=2" ]) assert BaseLevelParams.int_param.value == 2 print() print(BaseLevelParams.group_d.group_c.path_param.value) assert BaseLevelParams.group_d.group_c.path_param.value == test_data_dir # test that settings file was not overwritten process_arguments(BaseLevelParams, "Test parameter parser", argv=[ f"--settings_file={output_settings_path}" ]) assert BaseLevelParams.int_param.value == 1

44.352564

103

0.733198

943

6,919

5.136797

0.104984

0.068126

0.080718

0.099711

0.821429

0.791495

0.781792

0.745458

0.667837

0

0.014816

0.151322

6,919

155

104

44.63871

0.810116

0.008238

0

0.545455

0

0.214025

0.073043

0

0.264463

1

0.033058

false

0

0.033058

0

0.380165

0.016529

0

null

0

1

0

null

0

6

11df6b25f5f3bf935cf4fd1a168a822fbe73def9

143

py

Python

tools/__init__.py

vitae-gravitas/model-tester

c6de6f7e26043047fd30c9ed66f4dfb75a68a29b

[ "MIT" ]

null

tools/__init__.py

vitae-gravitas/model-tester

c6de6f7e26043047fd30c9ed66f4dfb75a68a29b

[ "MIT" ]

null

tools/__init__.py

vitae-gravitas/model-tester

c6de6f7e26043047fd30c9ed66f4dfb75a68a29b

[ "MIT" ]

null

from tools.predict import predict from tools.slice import slice_vids from tools.visualize import visualize from tools.evaluate import evaluate

28.6

37

0.86014

21

143

5.809524

0.380952

0.295082

0

0.111888

143

4

38

35.75

0.96063

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6