hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6bc769665a28b261a9bb5c92755fd84fc80df5a4 | 181 | py | Python | actions/objects/stock.py | kevincheng96/robin_stocks | a17b133b0e814a258667bbb6ca390dbdbb2561fe | [
"MIT"
] | 1 | 2019-11-17T21:31:00.000Z | 2019-11-17T21:31:00.000Z | actions/objects/stock.py | kevincheng96/robin_stocks | a17b133b0e814a258667bbb6ca390dbdbb2561fe | [
"MIT"
] | null | null | null | actions/objects/stock.py | kevincheng96/robin_stocks | a17b133b0e814a258667bbb6ca390dbdbb2561fe | [
"MIT"
] | null | null | null | class Stock:
def __init__(self, name, ticker, news = []):
self.name = name
self.ticker = ticker
self.news = news
def __str__(self):
return self.name + " - " + self.ticker | 22.625 | 45 | 0.646409 | 25 | 181 | 4.36 | 0.4 | 0.220183 | 0.256881 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.209945 | 181 | 8 | 46 | 22.625 | 0.762238 | 0 | 0 | 0 | 0 | 0 | 0.016484 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.285714 | false | 0 | 0 | 0.142857 | 0.571429 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
d46eba2c8f8e5e92c83cb529ec7c86e6f5dc32cd | 253 | py | Python | definitions.py | Inlinesoft/POC.ECS.Python.App | d1aefed9ebeb0a3f4183bf8deffb24c14f7baba2 | [
"Apache-2.0"
] | null | null | null | definitions.py | Inlinesoft/POC.ECS.Python.App | d1aefed9ebeb0a3f4183bf8deffb24c14f7baba2 | [
"Apache-2.0"
] | null | null | null | definitions.py | Inlinesoft/POC.ECS.Python.App | d1aefed9ebeb0a3f4183bf8deffb24c14f7baba2 | [
"Apache-2.0"
] | null | null | null | import os
import urllib.parse
from prettyconf import config
VERSION="0.0.1"
EMAIL_ACCOUNT_USERNAME=config('EMAIL_ACCOUNT_USERNAME')
EMAIL_ACCOUNT_PASSWORD=config('EMAIL_ACCOUNT_PASSWORD')
EMAIL_PRIMARY_SMTP_ADDRESS=config('EMAIL_PRIMARY_SMTP_ADDRESS') | 28.111111 | 63 | 0.865613 | 36 | 253 | 5.694444 | 0.472222 | 0.234146 | 0.195122 | 0.22439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0125 | 0.051383 | 253 | 9 | 63 | 28.111111 | 0.841667 | 0 | 0 | 0 | 0 | 0 | 0.295276 | 0.275591 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.142857 | 0.428571 | 0 | 0.428571 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 6 |
2e041d30122c28eea3062e04a4351a2b225945a6 | 69 | py | Python | marshmallow_extended/validate/__init__.py | blackacornlabs/marshmallow_extended | 0cda702d65c850044ff58f00f4eb29d2969077d0 | [
"MIT"
] | null | null | null | marshmallow_extended/validate/__init__.py | blackacornlabs/marshmallow_extended | 0cda702d65c850044ff58f00f4eb29d2969077d0 | [
"MIT"
] | null | null | null | marshmallow_extended/validate/__init__.py | blackacornlabs/marshmallow_extended | 0cda702d65c850044ff58f00f4eb29d2969077d0 | [
"MIT"
] | null | null | null | from marshmallow.validate import *
from .not_blank import not_blank
| 17.25 | 34 | 0.826087 | 10 | 69 | 5.5 | 0.6 | 0.290909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 69 | 3 | 35 | 23 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2e2d8a93ec433a1d1a935326c207d14d3becd361 | 41 | py | Python | spikesorters/mountainsort4/__init__.py | tjd2002/spikeforest2 | 2e393564b858b2995aa2ccccd9bd73065681b5de | [
"Apache-2.0"
] | null | null | null | spikesorters/mountainsort4/__init__.py | tjd2002/spikeforest2 | 2e393564b858b2995aa2ccccd9bd73065681b5de | [
"Apache-2.0"
] | null | null | null | spikesorters/mountainsort4/__init__.py | tjd2002/spikeforest2 | 2e393564b858b2995aa2ccccd9bd73065681b5de | [
"Apache-2.0"
] | null | null | null | from .mountainsort4 import MountainSort4
| 20.5 | 40 | 0.878049 | 4 | 41 | 9 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.054054 | 0.097561 | 41 | 1 | 41 | 41 | 0.918919 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2e75c31236252c6f267c5d07c8908cebafcf1f31 | 118 | py | Python | models/modules/__init__.py | mauriliosalg/Seis_Shift-Net_pytorch | 26f777d5e3be9d0828972202a61f0e01e2c04a1a | [
"MIT"
] | 350 | 2018-04-12T15:08:27.000Z | 2022-03-15T09:55:16.000Z | models/modules/__init__.py | qianbenb/Shift-Net_pytorch | c765939bed64b9604e9ea7ce2c14b2b2c69046d4 | [
"MIT"
] | 87 | 2018-07-13T05:15:14.000Z | 2022-02-07T06:20:43.000Z | models/modules/__init__.py | qianbenb/Shift-Net_pytorch | c765939bed64b9604e9ea7ce2c14b2b2c69046d4 | [
"MIT"
] | 88 | 2018-04-23T13:41:15.000Z | 2022-03-29T06:39:59.000Z | from .discrimators import *
from .losses import *
from .modules import *
from .shift_unet import *
from .unet import * | 23.6 | 27 | 0.754237 | 16 | 118 | 5.5 | 0.4375 | 0.454545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.161017 | 118 | 5 | 28 | 23.6 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2e7daaa12f6492875e6fca9f665d22fcbf0f5be8 | 207 | py | Python | torchqf/__init__.py | simaki/torchqf | e4dfd154c62ccd858847048f77d8c2f82924ae80 | [
"BSD-3-Clause"
] | 7 | 2021-05-18T17:03:10.000Z | 2021-12-01T07:58:41.000Z | torchqf/__init__.py | vishalbelsare/torchqf | e4dfd154c62ccd858847048f77d8c2f82924ae80 | [
"BSD-3-Clause"
] | 27 | 2021-05-18T03:54:17.000Z | 2022-01-31T15:16:16.000Z | torchqf/__init__.py | vishalbelsare/torchqf | e4dfd154c62ccd858847048f77d8c2f82924ae80 | [
"BSD-3-Clause"
] | 3 | 2021-07-13T12:56:12.000Z | 2021-12-26T23:00:06.000Z | from .functional import compound
from .functional import cumcompound
from .functional import log_return
from .functional import npv
from .functional import pv
from .model import bs
from .tensor import steps
| 25.875 | 35 | 0.830918 | 29 | 207 | 5.896552 | 0.448276 | 0.409357 | 0.584795 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135266 | 207 | 7 | 36 | 29.571429 | 0.955307 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
cf3896fe1e32ed63cdfd3e211a5ac827a24e061e | 7,310 | py | Python | tests/test_op_binarized_conv2d.py | HephaestusProject/pytorch-binaryconnect | 0a07a524522e993366749a865ae4bdf927cea3b5 | [
"MIT"
] | 5 | 2020-07-21T16:19:00.000Z | 2021-08-17T10:32:21.000Z | tests/test_op_binarized_conv2d.py | HephaestusProject/pytorch-binaryconnect | 0a07a524522e993366749a865ae4bdf927cea3b5 | [
"MIT"
] | 22 | 2020-07-18T08:20:59.000Z | 2020-12-22T13:51:30.000Z | tests/test_op_binarized_conv2d.py | HephaestusProject/pytorch-binaryconnect | 0a07a524522e993366749a865ae4bdf927cea3b5 | [
"MIT"
] | null | null | null | import os
import sys
import pytest
import pytorch_lightning
import torch
from src.ops.binarized_conv2d import BinarizedConv2d, binarized_conv2d
@pytest.fixture(scope="module")
def fix_seed():
pytorch_lightning.seed_everything(777)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
mode_test_case = [
# (test_input, test_weight, test_bias, test_mode)
(
torch.tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]),
torch.tensor([[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]),
None,
"test",
)
]
@pytest.mark.parametrize(
"test_input, test_weight, test_bias, test_mode", mode_test_case
)
def test_supported_mode(fix_seed, test_input, test_weight, test_bias, test_mode):
with pytest.raises(RuntimeError):
binarized_conv2d(test_input, test_weight, test_bias, 1, 0, 1, 1, test_mode)
forward_test_case = [
# (test_input, test_weight, test_bias, test_mode, expected)
(
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]]),
None,
"deterministic",
torch.tensor([[[[3.0]]]]),
),
(
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]]),
torch.tensor([1.0]),
"deterministic",
torch.tensor([[[[4.0]]]]),
),
(
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]]),
None,
"stochastic",
torch.tensor([[[[1.0]]]]),
),
(
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]]),
torch.tensor([1.0]),
"stochastic",
torch.tensor([[[[2.0]]]]),
),
]
@pytest.mark.parametrize(
"test_input, test_weight, test_bias, test_mode, expected", forward_test_case
)
def test_forward(fix_seed, test_input, test_weight, test_bias, test_mode, expected):
assert torch.allclose(
input=binarized_conv2d(
test_input, test_weight, test_bias, 1, 0, 1, 1, test_mode
),
other=expected,
rtol=1e-04,
atol=1e-04,
equal_nan=True,
)
indirectly_backward_test_case = [
# (test_input, test_weight, test_bias, test_mode, expected_weight_grad, expected_input_grad)
(
torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], requires_grad=True
),
torch.tensor(
[[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]],
requires_grad=True,
),
None,
"deterministic",
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -1.0, 1.0], [1.0, -1.0, 1.0]]]]),
),
(
torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], requires_grad=True
),
torch.tensor(
[[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]],
requires_grad=True,
),
torch.tensor([1.0]),
"deterministic",
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -1.0, 1.0], [1.0, -1.0, 1.0]]]]),
),
(
torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], requires_grad=True
),
torch.tensor(
[[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]],
requires_grad=True,
),
None,
"stochastic",
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, -1.0, -1.0], [1.0, -1.0, 1.0], [-1.0, -1.0, -1.0]]]]),
),
(
torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], requires_grad=True
),
torch.tensor(
[[[[-1.0, 1.0, 1.0], [1.0, -0.8, 1.0], [1.0, -0.3, 1.0]]]],
requires_grad=True,
),
torch.tensor([1.0]),
"stochastic",
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -1.0, 1.0], [-1.0, 1.0, -1.0]]]]),
),
]
@pytest.mark.parametrize(
"test_input, test_weight, test_bias, test_mode, expected_weight_grad, expected_input_grad",
indirectly_backward_test_case,
)
def test_backward_indirectly(
fix_seed,
test_input,
test_weight,
test_bias,
test_mode,
expected_weight_grad,
expected_input_grad,
):
binarized_conv2d(
test_input, test_weight, test_bias, 1, 0, 1, 1, test_mode
).backward()
assert torch.allclose(
input=test_input.grad,
other=expected_input_grad,
rtol=1e-04,
atol=1e-04,
equal_nan=True,
)
assert torch.allclose(
input=test_weight.grad,
other=expected_weight_grad,
rtol=1e-04,
atol=1e-04,
equal_nan=True,
)
directly_backward_test_case = [
# (saved_tensors, needs_input_grad, grad_output, expected_weight_grad, expected_input_grad, expected_bias_grad)
(
(
torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]],
requires_grad=True,
),
torch.tensor(
[[[[-1.0, 1.0, 1.0], [1.0, -1.0, 1.0], [1.0, -1.0, 1.0]]]],
requires_grad=True,
),
torch.tensor([1]),
),
(True, True, True, False),
torch.tensor([[[[1.0]]]]),
torch.tensor([[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]),
torch.tensor([[[[-1.0, 1.0, 1.0], [1.0, -1.0, 1.0], [1.0, -1.0, 1.0]]]]),
torch.tensor(1.0),
),
]
@pytest.mark.parametrize(
"saved_tensors, needs_input_grad, grad_output, expected_weight_grad, expected_input_grad, expected_bias_grad",
directly_backward_test_case,
)
def test_backward_directly(
fix_seed,
saved_tensors,
needs_input_grad,
grad_output,
expected_weight_grad,
expected_input_grad,
expected_bias_grad,
):
class CTX:
def __init__(self, saved_tensors, needs_input_grad):
self.saved_tensors = saved_tensors
self.needs_input_grad = needs_input_grad
self.stride = 1
self.padding = 0
self.dilation = 1
self.groups = 1
ctx = CTX(saved_tensors, needs_input_grad)
input_grad, weight_grad, bias_grad, _, _, _, _, _ = BinarizedConv2d.backward(
ctx, grad_output
)
assert torch.allclose(
input=input_grad,
other=expected_input_grad,
rtol=1e-04,
atol=1e-04,
equal_nan=True,
)
assert torch.allclose(
input=weight_grad,
other=expected_weight_grad,
rtol=1e-04,
atol=1e-04,
equal_nan=True,
)
assert torch.allclose(
input=bias_grad,
other=expected_bias_grad,
rtol=1e-04,
atol=1e-04,
equal_nan=True,
)
| 28.893281 | 115 | 0.511081 | 1,148 | 7,310 | 3.092334 | 0.066202 | 0.147606 | 0.17493 | 0.229859 | 0.780563 | 0.750986 | 0.730704 | 0.718028 | 0.718028 | 0.710141 | 0 | 0.118015 | 0.277839 | 7,310 | 252 | 116 | 29.007937 | 0.554461 | 0.04186 | 0 | 0.559633 | 0 | 0 | 0.056722 | 0.006001 | 0 | 0 | 0 | 0 | 0.027523 | 1 | 0.027523 | false | 0 | 0.027523 | 0 | 0.059633 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cf3ab4fed3ea39a43c915b28030fef70bde13994 | 8,749 | py | Python | fpn_pretrained.py | fregu856/retinanet | 408cc34aac9a30233ac3a23661654997d0cd5641 | [
"MIT"
] | null | null | null | fpn_pretrained.py | fregu856/retinanet | 408cc34aac9a30233ac3a23661654997d0cd5641 | [
"MIT"
] | 1 | 2019-09-15T11:18:53.000Z | 2019-09-15T11:22:59.000Z | fpn_pretrained.py | fregu856/retinanet | 408cc34aac9a30233ac3a23661654997d0cd5641 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.models as models
class FPN_Bottleneck(nn.Module):
def __init__(self, num_layers):
super(FPN_Bottleneck, self).__init__()
if num_layers == 50:
resnet = models.resnet50()
# load pretrained model:
resnet.load_state_dict(torch.load("/root/retinanet/pretrained_models/resnet/resnet50-19c8e357.pth"))
# remove fully connected layer and avg pool:
self.resnet_layers = nn.ModuleList(list(resnet.children())[:-2])
print ("pretrained resnet, 50")
elif num_layers == 101:
resnet = models.resnet101()
# load pretrained model:
resnet.load_state_dict(torch.load("/root/retinanet/pretrained_models/resnet/resnet101-5d3b4d8f.pth"))
# remove fully connected layer and avg pool:
self.resnet_layers = nn.ModuleList(list(resnet.children())[:-2])
print ("pretrained resnet, 101")
elif num_layers == 152:
resnet = models.resnet152()
# load pretrained model:
resnet.load_state_dict(torch.load("/root/retinanet/pretrained_models/resnet/resnet152-b121ed2d.pth"))
# remove fully connected layer and avg pool:
self.resnet_layers = nn.ModuleList(list(resnet.children())[:-2])
print ("pretrained resnet, 152")
else:
raise Exception("num_layers must be in {50, 101, 152}!")
self.conv6 = nn.Conv2d(4*512, 256, kernel_size=3, stride=2, padding=1)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
self.lateral_conv5 = nn.Conv2d(4*512, 256, kernel_size=1, stride=1, padding=0)
self.lateral_conv4 = nn.Conv2d(4*256, 256, kernel_size=1, stride=1, padding=0)
self.lateral_conv3 = nn.Conv2d(4*128, 256, kernel_size=1, stride=1, padding=0)
self.smoothing_conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smoothing_conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
def _upsample_and_add(self, feature_map, small_feature_map):
# (feature_map has shape (batch_size, channels, h, w))
# (small_feature_map has shape (batch_size, channels, h/2, w/2)) (integer division)
_, _, h, w = feature_map.size()
out = F.upsample(small_feature_map, size=(h, w), mode="bilinear") + feature_map # (shape: (batch_size, channels, h, w)))
return out
def forward(self, x):
# (x has shape (batch_size, 3, h, w))
# x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) # (shape: (batch_size, 3, h/2, w/2))
# pass x through the pretrained ResNet and collect feature maps:
c = []
for layer in self.resnet_layers:
x = layer(x)
if isinstance(layer, nn.Sequential):
c.append(x)
################################################################## NOTE! all spatial dimensons below should actually be divided by 2 (because of the initial max pool)
c2 = c[0] # (shape: (batch_size, 4*64, h/4, w/4))
c3 = c[1] # (shape: (batch_size, 4*128, h/8, w/8))
c4 = c[2] # (shape: (batch_size, 4*256, h/16, w/16))
c5 = c[3] # (shape: (batch_size, 4*512, h/32, w/32))
p6 = self.conv6(c5) # (shape: (batch_size, 256, h/64, w/64))
p7 = self.conv7(F.relu(p6)) # (shape: (batch_size, 256, h/128, w/128))
p5 = self.lateral_conv5(c5) # (shape: (batch_size, 256, h/32, w/32))
p4 = self._upsample_and_add(feature_map=self.lateral_conv4(c4),
small_feature_map=p5) # (shape: (batch_size, 256, h/16, w/16))
p4 = self.smoothing_conv4(p4) # (shape: (batch_size, 256, h/16, w/16))
p3 = self._upsample_and_add(feature_map=self.lateral_conv3(c3),
small_feature_map=p4) # (shape: (batch_size, 256, h/8, w/8))
p3 = self.smoothing_conv3(p3) # (shape: (batch_size, 256, h/8, w/8))
# (p3 has shape: (batch_size, 256, h/8, w/8))
# (p4 has shape: (batch_size, 256, h/16, w/16))
# (p5 has shape: (batch_size, 256, h/32, w/32))
# (p6 has shape: (batch_size, 256, h/64, w/64))
# (p7 has shape: (batch_size, 256, h/128, w/128))
return (p3, p4, p5, p6, p7)
class FPN_BasicBlock(nn.Module):
def __init__(self, num_layers):
super(FPN_BasicBlock, self).__init__()
if num_layers == 18:
resnet = models.resnet18()
# load pretrained model:
resnet.load_state_dict(torch.load("/root/retinanet/pretrained_models/resnet/resnet18-5c106cde.pth"))
# remove fully connected layer and avg pool:
self.resnet_layers = nn.ModuleList(list(resnet.children())[:-2])
print ("pretrained resnet, 18")
elif num_layers == 34:
resnet = models.resnet34()
# load pretrained model:
resnet.load_state_dict(torch.load("/root/retinanet/pretrained_models/resnet/resnet34-333f7ec4.pth"))
# remove fully connected layer and avg pool:
self.resnet_layers = nn.ModuleList(list(resnet.children())[:-2])
print ("pretrained resnet, 34")
else:
raise Exception("num_layers must be in {18, 34}!")
self.conv6 = nn.Conv2d(512, 256, kernel_size=3, stride=2, padding=1)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
self.lateral_conv5 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
self.lateral_conv4 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
self.lateral_conv3 = nn.Conv2d(128, 256, kernel_size=1, stride=1, padding=0)
self.smoothing_conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smoothing_conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
def _upsample_and_add(self, feature_map, small_feature_map):
# (feature_map has shape (batch_size, channels, h, w))
# (small_feature_map has shape (batch_size, channels, h/2, w/2)) (integer division)
_, _, h, w = feature_map.size()
out = F.upsample(small_feature_map, size=(h, w), mode="bilinear") + feature_map # (shape: (batch_size, channels, h, w)))
return out
def forward(self, x):
# (x has shape (batch_size, 3, h, w))
#x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) # (shape: (batch_size, 3, h/2, w/2))
# pass x through the pretrained ResNet and collect feature maps:
c = []
for layer in self.resnet_layers:
x = layer(x)
if isinstance(layer, nn.Sequential):
c.append(x)
################################################################## NOTE! all spatial dimensons below should actually be divided by 2 (because of the initial max pool)
c2 = c[0] # (shape: (batch_size, 64, h/4, w/4))
c3 = c[1] # (shape: (batch_size, 128, h/8, w/8))
c4 = c[2] # (shape: (batch_size, 256, h/16, w/16))
c5 = c[3] # (shape: (batch_size, 512, h/32, w/32))
p6 = self.conv6(c5) # (shape: (batch_size, 256, h/64, w/64))
p7 = self.conv7(F.relu(p6)) # (shape: (batch_size, 256, h/128, w/128))
p5 = self.lateral_conv5(c5) # (shape: (batch_size, 256, h/32, w/32))
p4 = self._upsample_and_add(feature_map=self.lateral_conv4(c4),
small_feature_map=p5) # (shape: (batch_size, 256, h/16, w/16))
p4 = self.smoothing_conv4(p4) # (shape: (batch_size, 256, h/16, w/16))
p3 = self._upsample_and_add(feature_map=self.lateral_conv3(c3),
small_feature_map=p4) # (shape: (batch_size, 256, h/8, w/8))
p3 = self.smoothing_conv3(p3) # (shape: (batch_size, 256, h/8, w/8))
# (p3 has shape: (batch_size, 256, h/8, w/8))
# (p4 has shape: (batch_size, 256, h/16, w/16))
# (p5 has shape: (batch_size, 256, h/32, w/32))
# (p6 has shape: (batch_size, 256, h/64, w/64))
# (p7 has shape: (batch_size, 256, h/128, w/128))
return (p3, p4, p5, p6, p7)
def FPN18():
return FPN_BasicBlock(num_layers=18)
def FPN34():
return FPN_BasicBlock(num_layers=34)
def FPN50():
return FPN_Bottleneck(num_layers=50)
def FPN101():
return FPN_Bottleneck(num_layers=101)
def FPN152():
return FPN_Bottleneck(num_layers=152)
# x = Variable(torch.randn(1, 3, 512, 512))
# network = FPN_BasicBlock(num_layers=34)
# out = network(x)
# x = Variable(torch.randn(1, 3, 512, 512))
# network = FPN_Bottleneck(num_layers=50)
# out = network(x)
| 44.637755 | 174 | 0.597097 | 1,262 | 8,749 | 3.981775 | 0.118067 | 0.083582 | 0.117015 | 0.084577 | 0.895124 | 0.850547 | 0.848955 | 0.846965 | 0.833035 | 0.818706 | 0 | 0.0925 | 0.248714 | 8,749 | 195 | 175 | 44.866667 | 0.671991 | 0.316379 | 0 | 0.5625 | 0 | 0 | 0.086934 | 0.053923 | 0 | 0 | 0 | 0 | 0 | 1 | 0.098214 | false | 0 | 0.044643 | 0.044643 | 0.241071 | 0.044643 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cf60222ac02178e928aa440d8cd51a8d3d650852 | 94 | py | Python | tests/python/myorg/myapi/conftest.py | Aigeruth/pants-webapps | 3a206bb32853a712443a124f0769648ce1139bc9 | [
"MIT"
] | null | null | null | tests/python/myorg/myapi/conftest.py | Aigeruth/pants-webapps | 3a206bb32853a712443a124f0769648ce1139bc9 | [
"MIT"
] | null | null | null | tests/python/myorg/myapi/conftest.py | Aigeruth/pants-webapps | 3a206bb32853a712443a124f0769648ce1139bc9 | [
"MIT"
] | null | null | null | import pytest
from myorg.myapi.app import myapi
@pytest.fixture
def app():
return myapi
| 11.75 | 33 | 0.744681 | 14 | 94 | 5 | 0.642857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.180851 | 94 | 7 | 34 | 13.428571 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0 | 0.4 | 0.2 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
d87e5168dc02d37f5cb769960e572be723a6d4cb | 34 | py | Python | fpgaedu/subcommands/__init__.py | fpgaedu/fpgaedu | da7b0c1871d8172243ee77156df8e6c8bb1006d1 | [
"Apache-2.0"
] | null | null | null | fpgaedu/subcommands/__init__.py | fpgaedu/fpgaedu | da7b0c1871d8172243ee77156df8e6c8bb1006d1 | [
"Apache-2.0"
] | null | null | null | fpgaedu/subcommands/__init__.py | fpgaedu/fpgaedu | da7b0c1871d8172243ee77156df8e6c8bb1006d1 | [
"Apache-2.0"
] | null | null | null | from .shell import ShellSubcommand | 34 | 34 | 0.882353 | 4 | 34 | 7.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 34 | 1 | 34 | 34 | 0.967742 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9972f32f56f1d951bd81b7d51ca5047a1e3d5369 | 162 | py | Python | miscellanies/parser/txt.py | zhangzhengde0225/SwinTrack | 526be17f8ef266cb924c6939bd8dda23e9b73249 | [
"MIT"
] | 143 | 2021-12-03T02:33:36.000Z | 2022-03-29T00:01:48.000Z | miscellanies/parser/txt.py | zhangzhengde0225/SwinTrack | 526be17f8ef266cb924c6939bd8dda23e9b73249 | [
"MIT"
] | 33 | 2021-12-03T10:32:05.000Z | 2022-03-31T02:13:55.000Z | miscellanies/parser/txt.py | zhangzhengde0225/SwinTrack | 526be17f8ef266cb924c6939bd8dda23e9b73249 | [
"MIT"
] | 24 | 2021-12-04T06:46:42.000Z | 2022-03-30T07:57:47.000Z | import numpy as np
def load_numpy_array_from_txt(path: str, dtype=np.float, delimiter: str=None):
return np.loadtxt(path, dtype=dtype, delimiter=delimiter)
| 27 | 78 | 0.771605 | 26 | 162 | 4.653846 | 0.653846 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.123457 | 162 | 5 | 79 | 32.4 | 0.852113 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
99a1dcd4ea82317dd1d180d73b4ebb6d1c745f56 | 3,488 | py | Python | tests/validators/test_capacity_per_plant_unit_validator.py | NOWUM/EnSysMod | 18c8a2198db3510e667c1f0298d00a3dfcb0aab7 | [
"MIT"
] | 1 | 2021-12-10T19:41:01.000Z | 2021-12-10T19:41:01.000Z | tests/validators/test_capacity_per_plant_unit_validator.py | NOWUM/EnSysMod | 18c8a2198db3510e667c1f0298d00a3dfcb0aab7 | [
"MIT"
] | 83 | 2021-10-20T22:54:28.000Z | 2022-03-24T19:07:06.000Z | tests/validators/test_capacity_per_plant_unit_validator.py | NOWUM/EnSysMod | 18c8a2198db3510e667c1f0298d00a3dfcb0aab7 | [
"MIT"
] | null | null | null | from typing import Type, List, Tuple, Dict, Any
import pytest
from pydantic import BaseModel, ValidationError
from ensysmod.model import EnergyComponentType
from ensysmod.schemas import EnergyComponentUpdate, EnergyComponentCreate
schemas_with_capacity_per_plant_unit_required: List[Tuple[Type[BaseModel], Dict[str, Any]]] = []
schemas_with_capacity_per_plant_unit_optional: List[Tuple[Type[BaseModel], Dict[str, Any]]] = [
(EnergyComponentUpdate, {}),
(EnergyComponentCreate,
{"name": "test", "description": "foo", "ref_dataset": 42, "type": EnergyComponentType.SOURCE})
]
schemas_with_capacity_per_plant_unit = schemas_with_capacity_per_plant_unit_required + schemas_with_capacity_per_plant_unit_optional
@pytest.mark.parametrize("schema,data", schemas_with_capacity_per_plant_unit_required)
def test_error_missing_capacity_per_plant_unit(schema: Type[BaseModel], data: Dict[str, Any]):
"""
Test that a capacity per plant unit is required for a schema
"""
with pytest.raises(ValidationError) as exc_info:
schema(**data)
assert len(exc_info.value.errors()) == 1
assert exc_info.value.errors()[0]["loc"] == ("capacity_per_plant_unit",)
assert exc_info.value.errors()[0]["msg"] == "field required"
assert exc_info.value.errors()[0]["type"] == "value_error.missing"
@pytest.mark.parametrize("schema,data", schemas_with_capacity_per_plant_unit_optional)
def test_ok_missing_capacity_per_plant_unit(schema: Type[BaseModel], data: Dict[str, Any]):
"""
Test that a capacity per plant unit is optional for a schema
"""
schema(**data)
@pytest.mark.parametrize("schema,data", schemas_with_capacity_per_plant_unit_optional)
def test_ok_none_capacity_per_plant_unit(schema: Type[BaseModel], data: Dict[str, Any]):
"""
Test that a capacity per plant unit is optional for a schema
"""
schema(capacity_per_plant_unit=None, **data)
@pytest.mark.parametrize("schema,data", schemas_with_capacity_per_plant_unit)
def test_error_on_zero_capacity_per_plant_unit(schema: Type[BaseModel], data: Dict[str, Any]):
"""
Test that a capacity per plant unit is not zero
"""
with pytest.raises(ValidationError) as exc_info:
schema(capacity_per_plant_unit=0, **data)
assert len(exc_info.value.errors()) == 1
assert exc_info.value.errors()[0]["loc"] == ("capacity_per_plant_unit",)
assert exc_info.value.errors()[0]["msg"] == "Capacity per plant per unit must be positive."
assert exc_info.value.errors()[0]["type"] == "value_error"
@pytest.mark.parametrize("schema,data", schemas_with_capacity_per_plant_unit)
def test_error_on_negative_capacity_per_plant_unit(schema: Type[BaseModel], data: Dict[str, Any]):
"""
Test that a capacity per plant unit is not negative
"""
with pytest.raises(ValidationError) as exc_info:
schema(capacity_per_plant_unit=-0.5, **data)
assert len(exc_info.value.errors()) == 1
assert exc_info.value.errors()[0]["loc"] == ("capacity_per_plant_unit",)
assert exc_info.value.errors()[0]["msg"] == "Capacity per plant per unit must be positive."
assert exc_info.value.errors()[0]["type"] == "value_error"
@pytest.mark.parametrize("schema,data", schemas_with_capacity_per_plant_unit)
def test_ok_capacity_per_plant_units(schema: Type[BaseModel], data: Dict[str, Any]):
"""
Test that a capacity per plant unit with everything over 0.001 is valid
"""
schema(capacity_per_plant_unit=0.001, **data)
| 41.52381 | 132 | 0.741112 | 492 | 3,488 | 4.971545 | 0.148374 | 0.143908 | 0.209321 | 0.237122 | 0.817661 | 0.809485 | 0.785773 | 0.692559 | 0.673753 | 0.657809 | 0 | 0.008278 | 0.134174 | 3,488 | 83 | 133 | 42.024096 | 0.801656 | 0.101491 | 0 | 0.444444 | 0 | 0 | 0.114032 | 0.022675 | 0 | 0 | 0 | 0 | 0.266667 | 1 | 0.133333 | false | 0 | 0.111111 | 0 | 0.244444 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
41d9aa5a26358f9468c2875169172ec3fda36df1 | 173 | py | Python | forager_server/forager_server_api/apps.py | jeremyephron/forager | 6db1590686e0e34b2e42ff5deb70f62fcee73d7d | [
"MIT"
] | 1 | 2020-12-01T23:25:58.000Z | 2020-12-01T23:25:58.000Z | forager_server/forager_server_api/apps.py | jeremyephron/forager | 6db1590686e0e34b2e42ff5deb70f62fcee73d7d | [
"MIT"
] | 2 | 2020-10-07T01:03:06.000Z | 2020-10-12T19:08:55.000Z | forager_server/forager_server_api/apps.py | jeremyephron/forager | 6db1590686e0e34b2e42ff5deb70f62fcee73d7d | [
"MIT"
] | null | null | null | from django.apps import AppConfig
class ForagerServerApiConfig(AppConfig):
name = 'forager_server_api'
def ready(self):
import forager_server_api.signals
| 19.222222 | 41 | 0.751445 | 20 | 173 | 6.3 | 0.75 | 0.206349 | 0.253968 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.184971 | 173 | 8 | 42 | 21.625 | 0.893617 | 0 | 0 | 0 | 0 | 0 | 0.104046 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.4 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
41ea86a2dd91eb586e723d9f432a431247a311ac | 6,891 | py | Python | backend/wod_board/tests/routers/test_goal.py | GuillaumeOj/P13-WOD-Board | 36df7979e63c354507edb56eabdfc548b1964d08 | [
"MIT"
] | null | null | null | backend/wod_board/tests/routers/test_goal.py | GuillaumeOj/P13-WOD-Board | 36df7979e63c354507edb56eabdfc548b1964d08 | [
"MIT"
] | 82 | 2021-01-17T18:12:23.000Z | 2021-06-12T21:46:49.000Z | backend/wod_board/tests/routers/test_goal.py | GuillaumeOj/WodBoard | 1ac12404f6094909c9bf116bcaf6ccd60e85bc00 | [
"MIT"
] | null | null | null | import pytest
from wod_board.models import goal
from wod_board.schemas import goal_schemas
@pytest.mark.asyncio
async def test_create_goal(db, client, db_round, db_movement, token, token_admin):
assert db.query(goal.Goal).count() == 0
goal_json = {
"movementId": db_movement.id,
"roundId": db_round.id,
"repetition": 10,
"durationSeconds": 60 * 5,
}
response = await client.post(
"/api/goal",
json=goal_json,
headers={"Authorization": f"Bearer {token.access_token}"},
)
expected_response = goal_json | {
"id": 1,
"equipments": [],
"movement": {
"equipments": db_movement.equipments.all(),
"id": db_movement.id,
"name": db_movement.name,
"unit": {
"id": db_movement.unit.id,
"name": db_movement.unit.name,
"symbol": db_movement.unit.symbol,
},
"unitId": db_movement.unit.id,
},
}
assert response.status_code == 200
assert response.json() == expected_response
assert db.query(goal.Goal).count() == 1
response = await client.post("/api/goal", json=goal_json)
assert response.status_code == 401
assert response.json() == {"detail": "Not authenticated"}
assert db.query(goal.Goal).count() == 1
response = await client.post(
"/api/goal",
json=goal_json,
headers={"Authorization": f"Bearer {token_admin.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "Author don't match with authenticated user"}
assert db.query(goal.Goal).count() == 1
goal_json = {
"movementId": db_movement.id,
"roundId": 2,
"repetition": 10,
}
response = await client.post(
"/api/goal",
json=goal_json,
headers={"Authorization": f"Bearer {token.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "This round doesn't exist"}
assert db.query(goal.Goal).count() == 1
goal_json = {"repetition": 10, "round_id": db_round.id, "movement_id": 2}
response = await client.post(
"/api/goal",
json=goal_json,
headers={"Authorization": f"Bearer {token.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "This movement doesn't exist"}
assert db.query(goal.Goal).count() == 1
@pytest.mark.asyncio
async def test_update_goal(db, client, db_goal, token, token_admin):
assert db.query(goal.Goal).count() == 1
goal_json = {
"movementId": db_goal.movement_id,
"roundId": db_goal.round_id,
"repetition": 10,
"durationSeconds": 60 * 5,
}
response = await client.put(
f"/api/goal/{db_goal.id}",
json=goal_json,
headers={"Authorization": f"Bearer {token.access_token}"},
)
expected_response = goal_json | {
"id": 1,
"equipments": [],
"movement": {
"equipments": db_goal.movement.equipments.all(),
"id": db_goal.movement.id,
"name": db_goal.movement.name,
"unit": {
"id": db_goal.movement.unit.id,
"name": db_goal.movement.unit.name,
"symbol": db_goal.movement.unit.symbol,
},
"unitId": db_goal.movement.unit.id,
},
}
assert response.status_code == 200
assert response.json() == expected_response
assert db.query(goal.Goal).count() == 1
response = await client.put(f"/api/goal/{db_goal.id}", json=goal_json)
assert response.status_code == 401
assert response.json() == {"detail": "Not authenticated"}
response = await client.put(
f"/api/goal/{db_goal.id}",
json=goal_json,
headers={"Authorization": f"Bearer {token_admin.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "Author don't match with authenticated user"}
response = await client.put(
"/api/goal/2",
json=goal_json,
headers={"Authorization": f"Bearer {token.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "This goal doesn't exist"}
goal_json = {
"movementId": 2,
"roundId": db_goal.round_id,
"repetition": 10,
"durationSeconds": 60 * 5,
}
response = await client.put(
f"/api/goal/{db_goal.id}",
json=goal_json,
headers={"Authorization": f"Bearer {token.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "This movement doesn't exist"}
goal_json = {
"movementId": db_goal.movement_id,
"roundId": 2,
"repetition": 10,
"durationSeconds": 60 * 5,
}
response = await client.put(
f"/api/goal/{db_goal.id}",
json=goal_json,
headers={"Authorization": f"Bearer {token.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "This round doesn't exist"}
@pytest.mark.asyncio
async def test_delete_goal_by_id(db, client, db_goal, token, token_admin):
assert db.query(goal.Goal).count() == 1
response = await client.delete(
"/api/goal/2", headers={"Authorization": f"Bearer {token.access_token}"}
)
assert response.status_code == 422
assert response.json() == {"detail": "This goal doesn't exist"}
assert db.query(goal.Goal).count() == 1
response = await client.delete(f"/api/goal/{db_goal.id}")
assert response.status_code == 401
assert response.json() == {"detail": "Not authenticated"}
assert db.query(goal.Goal).count() == 1
response = await client.delete(
f"/api/goal/{db_goal.id}",
headers={"Authorization": f"Bearer {token_admin.access_token}"},
)
assert response.status_code == 422
assert response.json() == {"detail": "Author don't match with authenticated user"}
assert db.query(goal.Goal).count() == 1
response = await client.delete(
f"/api/goal/{db_goal.id}",
headers={"Authorization": f"Bearer {token.access_token}"},
)
assert response.status_code == 200
assert response.json() == {"detail": "Goal successfully deleted"}
assert db.query(goal.Goal).count() == 0
@pytest.mark.asyncio
async def test_get_goals_by_round_id(db, client, db_goal):
assert db.query(goal.Goal).count() == 1
response = await client.get(f"/api/goal/goals/{db_goal.round_id}")
assert response.status_code == 200
assert response.json() == [goal_schemas.Goal.from_orm(db_goal).dict(by_alias=True)]
response = await client.get(f"/api/goal/goals/{db_goal.round_id + 1}")
assert response.status_code == 200
assert response.json() == []
| 33.289855 | 87 | 0.608475 | 843 | 6,891 | 4.827995 | 0.094899 | 0.116953 | 0.079361 | 0.100246 | 0.917199 | 0.850369 | 0.814005 | 0.793857 | 0.764128 | 0.741032 | 0 | 0.018752 | 0.24162 | 6,891 | 206 | 88 | 33.451456 | 0.760046 | 0 | 0 | 0.683333 | 0 | 0 | 0.22943 | 0.046583 | 0 | 0 | 0 | 0 | 0.266667 | 1 | 0 | false | 0 | 0.016667 | 0 | 0.016667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
41f422a48cb461ef4a68004dbae3e0a8bdd124b5 | 2,553 | py | Python | epytope/Data/pssms/smm/mat/B_07_02_10.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 7 | 2021-02-01T18:11:28.000Z | 2022-01-31T19:14:07.000Z | epytope/Data/pssms/smm/mat/B_07_02_10.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 22 | 2021-01-02T15:25:23.000Z | 2022-03-14T11:32:53.000Z | epytope/Data/pssms/smm/mat/B_07_02_10.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 4 | 2021-05-28T08:50:38.000Z | 2022-03-14T11:45:32.000Z | B_07_02_10 = {0: {'A': -0.177, 'C': -0.499, 'E': 0.513, 'D': 1.283, 'G': -0.247, 'F': -0.204, 'I': 0.109, 'H': -0.128, 'K': -0.402, 'M': -0.023, 'L': -0.047, 'N': 0.065, 'Q': 0.113, 'P': 0.605, 'S': -0.323, 'R': -0.745, 'T': -0.156, 'W': 0.381, 'V': 0.125, 'Y': -0.243}, 1: {'A': -0.02, 'C': 0.0, 'E': 0.505, 'D': 0.0, 'G': -0.168, 'F': -0.032, 'I': -0.029, 'H': 0.06, 'K': -0.431, 'M': 0.176, 'L': 0.313, 'N': 0.454, 'Q': 0.224, 'P': -1.41, 'S': -0.026, 'R': 0.421, 'T': -0.059, 'W': 0.006, 'V': -0.146, 'Y': 0.162}, 2: {'A': -0.155, 'C': -0.001, 'E': 0.161, 'D': 0.147, 'G': -0.02, 'F': 0.222, 'I': -0.158, 'H': -0.045, 'K': -0.153, 'M': -0.496, 'L': 0.323, 'N': 0.158, 'Q': 0.086, 'P': 0.135, 'S': 0.225, 'R': -0.562, 'T': -0.084, 'W': 0.134, 'V': 0.097, 'Y': -0.013}, 3: {'A': 0.019, 'C': 0.019, 'E': 0.063, 'D': -0.011, 'G': 0.042, 'F': -0.101, 'I': 0.131, 'H': -0.068, 'K': 0.055, 'M': -0.076, 'L': 0.058, 'N': -0.07, 'Q': 0.088, 'P': 0.1, 'S': -0.057, 'R': -0.245, 'T': 0.066, 'W': -0.046, 'V': 0.054, 'Y': -0.021}, 4: {'A': -0.194, 'C': 0.116, 'E': 0.215, 'D': 0.168, 'G': -0.051, 'F': -0.101, 'I': -0.116, 'H': 0.011, 'K': -0.011, 'M': 0.111, 'L': -0.139, 'N': 0.127, 'Q': 0.068, 'P': 0.086, 'S': -0.033, 'R': 0.0, 'T': -0.033, 'W': -0.075, 'V': -0.141, 'Y': -0.006}, 5: {'A': 0.009, 'C': 0.085, 'E': 0.035, 'D': 0.087, 'G': -0.08, 'F': 0.041, 'I': 0.071, 'H': 0.029, 'K': 0.097, 'M': 0.003, 'L': -0.047, 'N': 0.012, 'Q': -0.006, 'P': -0.063, 'S': 0.071, 'R': -0.049, 'T': -0.066, 'W': -0.081, 'V': -0.087, 'Y': -0.061}, 6: {'A': 0.004, 'C': -0.123, 'E': 0.086, 'D': 0.427, 'G': -0.277, 'F': 0.027, 'I': 0.174, 'H': -0.067, 'K': -0.15, 'M': -0.099, 'L': -0.098, 'N': 0.022, 'Q': 0.055, 'P': 0.169, 'S': 0.081, 'R': -0.352, 'T': -0.101, 'W': -0.045, 'V': 0.142, 'Y': 0.126}, 7: {'A': -0.111, 'C': 0.02, 'E': 0.004, 'D': 0.069, 'G': 0.067, 'F': -0.013, 'I': 0.086, 'H': -0.03, 'K': 0.054, 'M': -0.037, 'L': 0.008, 'N': 0.01, 'Q': -0.041, 'P': 0.01, 'S': -0.057, 'R': 0.032, 'T': -0.093, 'W': -0.01, 'V': -0.001, 'Y': 0.031}, 8: {'A': -0.262, 'C': 0.27, 'E': -0.268, 'D': 0.153, 'G': 0.084, 'F': 0.216, 'I': -0.226, 'H': 0.018, 'K': 0.081, 'M': -0.036, 'L': 0.13, 'N': 0.334, 'Q': 0.23, 'P': -0.439, 'S': -0.391, 'R': 0.41, 'T': -0.218, 'W': 0.158, 'V': -0.245, 'Y': 0.001}, 9: {'A': -0.082, 'C': 0.0, 'E': 0.804, 'D': -0.102, 'G': -0.255, 'F': -0.506, 'I': -0.353, 'H': 0.0, 'K': 0.055, 'M': -0.825, 'L': -0.659, 'N': 0.447, 'Q': -0.006, 'P': 0.421, 'S': 0.084, 'R': 0.158, 'T': 0.784, 'W': -0.028, 'V': -0.465, 'Y': 0.527}, -1: {'con': 4.55182}} | 2,553 | 2,553 | 0.392479 | 618 | 2,553 | 1.616505 | 0.286408 | 0.02002 | 0.01001 | 0.012012 | 0.094094 | 0 | 0 | 0 | 0 | 0 | 0 | 0.371669 | 0.162162 | 2,553 | 1 | 2,553 | 2,553 | 0.095372 | 0 | 0 | 0 | 0 | 0 | 0.079483 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
41fb148ffd38d0601c72b12643b0970ccbfa153e | 202 | py | Python | python/models/yolo.py | lindsayshuo/yolov5_TRT_C-_python_api | 29f7a9f1a3eda0c99fb843cfe0689b8e1e1f0bac | [
"Info-ZIP"
] | 5 | 2021-10-09T05:57:57.000Z | 2022-03-22T23:11:32.000Z | python/models/yolo.py | lindsayshuo/yolov5_TRT_C-_python_api | 29f7a9f1a3eda0c99fb843cfe0689b8e1e1f0bac | [
"Info-ZIP"
] | null | null | null | python/models/yolo.py | lindsayshuo/yolov5_TRT_C-_python_api | 29f7a9f1a3eda0c99fb843cfe0689b8e1e1f0bac | [
"Info-ZIP"
] | null | null | null | from models.common import *
class Detect(nn.Module):
def __init__(self):
super(Detect, self).__init__()
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
| 25.25 | 38 | 0.663366 | 26 | 202 | 4.538462 | 0.5 | 0.135593 | 0.186441 | 0.254237 | 0.40678 | 0.40678 | 0 | 0 | 0 | 0 | 0 | 0 | 0.19802 | 202 | 7 | 39 | 28.857143 | 0.728395 | 0 | 0 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.285714 | false | 0 | 0.142857 | 0 | 0.714286 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
51161c8833d60abe7a4cdbfb000a77d8caea721f | 220 | py | Python | ports/sysutils/accountsservice/dragonfly/patch-meson_post_install.py | liweitianux/DeltaPorts | b907de0ceb9c0e46ae8961896e97b361aa7c62c0 | [
"BSD-2-Clause-FreeBSD"
] | 31 | 2015-02-06T17:06:37.000Z | 2022-03-08T19:53:28.000Z | ports/sysutils/accountsservice/dragonfly/patch-meson_post_install.py | liweitianux/DeltaPorts | b907de0ceb9c0e46ae8961896e97b361aa7c62c0 | [
"BSD-2-Clause-FreeBSD"
] | 236 | 2015-06-29T19:51:17.000Z | 2021-12-16T22:46:38.000Z | ports/sysutils/accountsservice/dragonfly/patch-meson_post_install.py | liweitianux/DeltaPorts | b907de0ceb9c0e46ae8961896e97b361aa7c62c0 | [
"BSD-2-Clause-FreeBSD"
] | 52 | 2015-02-06T17:05:36.000Z | 2021-10-21T12:13:06.000Z | --- meson_post_install.py.orig 2021-07-29 19:16:51.295622000 +0200
+++ meson_post_install.py 2021-07-29 19:16:59.705396000 +0200
@@ -1,4 +1,4 @@
-#!/usr/bin/env python3
+#!/usr/bin/env python3.8
import os
import sys
| 24.444444 | 66 | 0.690909 | 42 | 220 | 3.52381 | 0.595238 | 0.121622 | 0.216216 | 0.243243 | 0.162162 | 0 | 0 | 0 | 0 | 0 | 0 | 0.314433 | 0.118182 | 220 | 8 | 67 | 27.5 | 0.448454 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.285714 | null | null | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5134bcae81faace8e1ecccde9d17611d2fa953bc | 35 | py | Python | pycroscopy/learn/ml/__init__.py | pycroscopy/pyCroscopy | c187d456a4063566b6ac2597b1ada2791200002b | [
"MIT"
] | 1 | 2016-06-08T21:07:14.000Z | 2016-06-08T21:07:14.000Z | pycroscopy/learn/ml/__init__.py | pycroscopy/pyCroscopy | c187d456a4063566b6ac2597b1ada2791200002b | [
"MIT"
] | null | null | null | pycroscopy/learn/ml/__init__.py | pycroscopy/pyCroscopy | c187d456a4063566b6ac2597b1ada2791200002b | [
"MIT"
] | null | null | null | from .decompose import TensorFactor | 35 | 35 | 0.885714 | 4 | 35 | 7.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085714 | 35 | 1 | 35 | 35 | 0.96875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5139f8ceb6fc91699c5cdf125f5d6d099172789e | 283 | py | Python | backend/app/utils/token.py | luccasPh/gobarber | 3bc84c5098b534352ef794428ffb7b937bd3bbd6 | [
"MIT"
] | 1 | 2021-05-05T15:43:25.000Z | 2021-05-05T15:43:25.000Z | backend/app/utils/token.py | luccasPh/gobarber | 3bc84c5098b534352ef794428ffb7b937bd3bbd6 | [
"MIT"
] | null | null | null | backend/app/utils/token.py | luccasPh/gobarber | 3bc84c5098b534352ef794428ffb7b937bd3bbd6 | [
"MIT"
] | null | null | null | import jwt
from app.core.config import settings
def encode_payload(payload: dict) -> bytes:
return jwt.encode(payload, settings.AUTH_SECRET_KEY, algorithm='HS256')
def decode_token(token: str) -> dict:
return jwt.decode(token, settings.AUTH_SECRET_KEY, algorithm='HS256')
| 28.3 | 75 | 0.763251 | 40 | 283 | 5.25 | 0.525 | 0.12381 | 0.171429 | 0.2 | 0.333333 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0.024194 | 0.123675 | 283 | 9 | 76 | 31.444444 | 0.822581 | 0 | 0 | 0 | 0 | 0 | 0.035336 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
513efa9b101813ce70d46b0fc3f2533ab51511e5 | 37 | py | Python | inference/models/__init__.py | Benjamin-deLaverny/RootNav-2.0 | 14b6d7353687acf640e5efbd224a35d9131e7275 | [
"BSD-3-Clause"
] | 23 | 2019-07-25T10:15:20.000Z | 2022-01-26T03:28:56.000Z | inference/models/__init__.py | rootnav2/RootNav-2.0 | 3e973c0f7fc34b3938a2294e858d1a0de76e9f0f | [
"BSD-3-Clause"
] | 7 | 2019-08-07T15:56:26.000Z | 2022-01-13T01:28:22.000Z | inference/models/__init__.py | rootnav2/RootNav-2.0 | 3e973c0f7fc34b3938a2294e858d1a0de76e9f0f | [
"BSD-3-Clause"
] | 11 | 2019-07-25T10:15:25.000Z | 2022-02-15T09:14:49.000Z | from .model_loader import ModelLoader | 37 | 37 | 0.891892 | 5 | 37 | 6.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.081081 | 37 | 1 | 37 | 37 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5146d3c65138983c6d992831aab4e203852fb373 | 162 | py | Python | plots/__main__.py | evansosenko/aps-spin-lifetime-plots | d73d5665e77ef8871ce3f17289efc9102465f625 | [
"MIT"
] | null | null | null | plots/__main__.py | evansosenko/aps-spin-lifetime-plots | d73d5665e77ef8871ce3f17289efc9102465f625 | [
"MIT"
] | null | null | null | plots/__main__.py | evansosenko/aps-spin-lifetime-plots | d73d5665e77ef8871ce3f17289efc9102465f625 | [
"MIT"
] | null | null | null | import os
import shutil
if __name__ == '__main__':
if os.path.isdir('build'): shutil.rmtree('build')
if not os.path.isdir('build'): os.makedirs('build')
| 23.142857 | 55 | 0.67284 | 24 | 162 | 4.208333 | 0.5 | 0.118812 | 0.217822 | 0.316832 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148148 | 162 | 6 | 56 | 27 | 0.731884 | 0 | 0 | 0 | 0 | 0 | 0.17284 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
5163f9e6f1f8341072251f138096845d82ca0872 | 144 | py | Python | ambra_sdk/service/entrypoints/dicomdata.py | dyens/sdk-python | 24bf05268af2832c70120b84fd53bf44862cffec | [
"Apache-2.0"
] | null | null | null | ambra_sdk/service/entrypoints/dicomdata.py | dyens/sdk-python | 24bf05268af2832c70120b84fd53bf44862cffec | [
"Apache-2.0"
] | null | null | null | ambra_sdk/service/entrypoints/dicomdata.py | dyens/sdk-python | 24bf05268af2832c70120b84fd53bf44862cffec | [
"Apache-2.0"
] | null | null | null | from ambra_sdk.service.entrypoints.generated.dicomdata import \
Dicomdata as GDicomdata
class Dicomdata(GDicomdata):
"""Dicomdata."""
| 20.571429 | 63 | 0.756944 | 15 | 144 | 7.2 | 0.733333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.138889 | 144 | 6 | 64 | 24 | 0.870968 | 0.069444 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5abbf9c199238b2eced4c8df710d056463651ff3 | 1,274 | py | Python | tests/test_load.py | POSTECH-CVLab/Geometric-Primitives | e4b16d8930f4a9d1c906d06255988d02f54a6deb | [
"MIT"
] | 1 | 2022-03-16T13:01:33.000Z | 2022-03-16T13:01:33.000Z | tests/test_load.py | POSTECH-CVLab/Geometric-Primitives | e4b16d8930f4a9d1c906d06255988d02f54a6deb | [
"MIT"
] | null | null | null | tests/test_load.py | POSTECH-CVLab/Geometric-Primitives | e4b16d8930f4a9d1c906d06255988d02f54a6deb | [
"MIT"
] | null | null | null | import pytest
def test_load_geometric_primitives():
import geometric_primitives
def test_load_brick():
import geometric_primitives.brick
from geometric_primitives import brick
def test_load_bricks():
import geometric_primitives.bricks
from geometric_primitives import bricks
def test_load_voxels():
import geometric_primitives.voxels
from geometric_primitives import voxels
def test_load_utils_validation():
import geometric_primitives.utils_validation
from geometric_primitives import utils_validation
def test_load_utils_bricks():
import geometric_primitives.utils_bricks
from geometric_primitives import utils_bricks
def test_load_utils_meshes():
import geometric_primitives.utils_meshes
from geometric_primitives import utils_meshes
def test_load_utils_io():
import geometric_primitives.utils_io
from geometric_primitives import utils_io
def test_load_rules():
import geometric_primitives.rules
from geometric_primitives import rules
def test_load_rules_2_4():
import geometric_primitives.rules.rules_2_4
from geometric_primitives.rules import rules_2_4
def test_load_rules_1_2():
import geometric_primitives.rules.rules_1_2
from geometric_primitives.rules import rules_1_2
| 27.695652 | 53 | 0.820251 | 168 | 1,274 | 5.815476 | 0.113095 | 0.42784 | 0.123849 | 0.237462 | 0.332651 | 0.079836 | 0 | 0 | 0 | 0 | 0 | 0.010989 | 0.142857 | 1,274 | 45 | 54 | 28.311111 | 0.8837 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.666667 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5abc6c631aad38f3acb7287dcaa3c05938cf66c1 | 987 | py | Python | volume_provider/credentials/faas.py | bento-dbaas/volume-provider | eb945c36dd68696f71849adbfa406727e7331688 | [
"BSD-3-Clause"
] | 1 | 2021-02-19T21:59:29.000Z | 2021-02-19T21:59:29.000Z | volume_provider/credentials/faas.py | bento-dbaas/volume-provider | eb945c36dd68696f71849adbfa406727e7331688 | [
"BSD-3-Clause"
] | 16 | 2021-02-19T21:59:33.000Z | 2022-03-29T19:36:11.000Z | volume_provider/credentials/faas.py | bento-dbaas/volume-provider | eb945c36dd68696f71849adbfa406727e7331688 | [
"BSD-3-Clause"
] | null | null | null | from volume_provider.credentials.base import CredentialBase, CredentialAdd
class CredentialFaaS(CredentialBase):
@property
def user(self):
return self.content['user']
@property
def password(self):
return self.content['password']
@property
def endpoint(self):
return self.content['endpoint']
@property
def project(self):
return self.content['project']
@property
def is_secure(self):
return self.content['is_secure']
@property
def category_id(self):
return self.content['category_id']
@property
def access_type(self):
return self.content['access_type']
@property
def tenant_id(self):
return self.content['tenant_id']
class CredentialAddFaaS(CredentialAdd):
@property
def valid_fields(self):
return [
'user', 'password', 'endpoint', 'is_secure', 'project',
'category_id', 'access_type', 'tenant_id'
]
| 21 | 74 | 0.633232 | 104 | 987 | 5.875 | 0.278846 | 0.162029 | 0.183306 | 0.274959 | 0.075286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.255319 | 987 | 46 | 75 | 21.456522 | 0.831293 | 0 | 0 | 0.272727 | 0 | 0 | 0.135765 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.272727 | false | 0.090909 | 0.030303 | 0.272727 | 0.636364 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 |
85096f92f97c73da34cbafc51bb98a5e8bde0f3e | 71 | py | Python | fast3tree/__init__.py | cosmicshear/fast3tree | 052e100216a6ab22b5adfd6f6b2c54fda87c0ec5 | [
"MIT"
] | null | null | null | fast3tree/__init__.py | cosmicshear/fast3tree | 052e100216a6ab22b5adfd6f6b2c54fda87c0ec5 | [
"MIT"
] | null | null | null | fast3tree/__init__.py | cosmicshear/fast3tree | 052e100216a6ab22b5adfd6f6b2c54fda87c0ec5 | [
"MIT"
] | null | null | null | from .core import *
from .fof import *
from .version import __version__ | 23.666667 | 32 | 0.774648 | 10 | 71 | 5.1 | 0.5 | 0.392157 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15493 | 71 | 3 | 32 | 23.666667 | 0.85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
51743c05aaae0b795fc253d2d8355eee64a3ffb9 | 646 | py | Python | src/ToolChainSCDG/procedures/linux/custom_package/sigprocmask.py | AnonymousSEMA/SEMA-ToolChain | 05d6a7e43e10d4b1f6c5dfb70fbabeab3d4daf82 | [
"BSD-2-Clause"
] | null | null | null | src/ToolChainSCDG/procedures/linux/custom_package/sigprocmask.py | AnonymousSEMA/SEMA-ToolChain | 05d6a7e43e10d4b1f6c5dfb70fbabeab3d4daf82 | [
"BSD-2-Clause"
] | null | null | null | src/ToolChainSCDG/procedures/linux/custom_package/sigprocmask.py | AnonymousSEMA/SEMA-ToolChain | 05d6a7e43e10d4b1f6c5dfb70fbabeab3d4daf82 | [
"BSD-2-Clause"
] | null | null | null | import angr
class sigprocmask(angr.SimProcedure):
def run(self, how, set_, oldset):
# self.state.memory.store(oldset, self.state.posix.sigmask(sigsetsize=sigsetsize), condition=oldset != 0)
# self.state.posix.sigprocmask(how, self.state.memory.load(set_, sigsetsize), sigsetsize, valid_ptr=set_!=0)
return 0
# TODO: EFAULT
# return self.state.solver.If(self.state.solver.And(how != self.state.posix.SIG_BLOCK,how != self.state.posix.SIG_UNBLOCK,how != self.state.posix.SIG_SETMASK),self.state.solver.BVV(self.state.posix.EINVAL, self.state.arch.bits),self.state.solver.BVV(0, self.state.arch.bits),)
| 58.727273 | 284 | 0.718266 | 94 | 646 | 4.861702 | 0.382979 | 0.275711 | 0.183807 | 0.111597 | 0.131291 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007105 | 0.128483 | 646 | 10 | 285 | 64.6 | 0.804618 | 0.770898 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0.25 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
51a7118986d389688590683565b00533a8047370 | 205 | py | Python | python/tvm/relay/annotation.py | weberlo/tvm | e4b9f986dab8c48ba109a52106565fc4be6b67c4 | [
"Apache-2.0"
] | 2 | 2020-06-24T03:16:34.000Z | 2020-06-24T03:16:36.000Z | python/tvm/relay/annotation.py | weberlo/tvm | e4b9f986dab8c48ba109a52106565fc4be6b67c4 | [
"Apache-2.0"
] | 4 | 2020-12-04T21:00:38.000Z | 2022-01-22T12:49:30.000Z | python/tvm/relay/annotation.py | weberlo/tvm | e4b9f986dab8c48ba109a52106565fc4be6b67c4 | [
"Apache-2.0"
] | 1 | 2020-02-09T10:42:31.000Z | 2020-02-09T10:42:31.000Z | # pylint: disable=wildcard-import, unused-import, unused-wildcard-import
"""Annotation related operators."""
# Re-export in a specific file name so that autodoc can pick it up
from .op.annotation import *
| 41 | 72 | 0.770732 | 30 | 205 | 5.266667 | 0.8 | 0.177215 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126829 | 205 | 4 | 73 | 51.25 | 0.882682 | 0.809756 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
51c4f21cd806b0dea4214d9ab6b6018c573fecd4 | 165 | py | Python | sim/__init__.py | ayoung11/pySuStaIn | 13e924a98f28f0f2d47983f4f265070064ac791a | [
"MIT"
] | 52 | 2019-03-19T21:50:41.000Z | 2022-03-29T15:34:55.000Z | sim/__init__.py | ElsevierSoftwareX/SOFTX-D-21-00098 | 225e083eff46277016104ad0191b79115b9de478 | [
"MIT"
] | 24 | 2018-11-28T14:10:42.000Z | 2022-03-23T11:13:01.000Z | sim/__init__.py | ElsevierSoftwareX/SOFTX-D-21-00098 | 225e083eff46277016104ad0191b79115b9de478 | [
"MIT"
] | 30 | 2018-11-13T16:19:18.000Z | 2022-03-29T14:38:59.000Z | # Authors: Leon Aksman <l.aksman@ucl.ac.uk>
# License: TBC
from simfuncs import *
from ..pySuStaIn.MixtureSustain import *
from ..pySuStaIn.ZscoreSustain import *
| 20.625 | 43 | 0.751515 | 21 | 165 | 5.904762 | 0.714286 | 0.16129 | 0.306452 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133333 | 165 | 7 | 44 | 23.571429 | 0.867133 | 0.327273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a40a918bb0f444ebf6bc238ba2ebf2e41f61db19 | 92,559 | py | Python | backend/test_game.py | unicorn1337x/stopthevirus | 7a67d8a6a6d0cbc5f58b45b605aeef0c5c407304 | [
"MIT"
] | 9 | 2020-03-30T00:20:28.000Z | 2020-11-29T07:24:02.000Z | backend/test_game.py | unicorn1337x/stopthevirus | 7a67d8a6a6d0cbc5f58b45b605aeef0c5c407304 | [
"MIT"
] | 109 | 2020-03-28T20:51:48.000Z | 2020-12-21T11:01:15.000Z | backend/test_game.py | unicorn1337x/stopthevirus | 7a67d8a6a6d0cbc5f58b45b605aeef0c5c407304 | [
"MIT"
] | 4 | 2020-04-01T03:05:56.000Z | 2020-11-29T07:24:14.000Z | import unittest
import mock
import types
from game import Game
from game import GameOptions
from game_engine.engine import Engine
from game_engine.database import Database, Data
from game_engine.database import Player, Team, Tribe
from game_engine.database import Challenge, Entry, Vote, Ballot
import attr
from typing import Any, Iterable, Dict, Text, Tuple, List, Optional
import uuid
from mock import Mock
import time
import pprint
from multiprocessing import Process
from game_engine import events
from queue import Queue
class MockPlayEngine(Mock):
def CreateEngine(self, mygamedb):
def challenge1_worker(gamedb):
# round 1: [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 y3 k1 k2 k3 x1 x2 x3]
# africa wins
# asia teams vote
# y votes out y3
# k votes out k3
# x votes out x3
# [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 k1 k2 x1 x2]
# asia teams of 2 would deadlock and must merge
# [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id='AFRICA', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='y1', tribe_id='ASIA', challenge_id='challenge/1'),
}
def council1_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='y1', to_id='y3'),
'vote/2': Vote(id='vote/2', from_id='y2', to_id='y3'),
'vote/3': Vote(id='vote/3', from_id='y3', to_id='y1'),
'vote/4': Vote(id='vote/4', from_id='k1', to_id='k3'),
'vote/5': Vote(id='vote/5', from_id='k2', to_id='k3'),
'vote/6': Vote(id='vote/6', from_id='k3', to_id='k1'),
'vote/7': Vote(id='vote/7', from_id='x1', to_id='x3'),
'vote/8': Vote(id='vote/8', from_id='x2', to_id='x3'),
'vote/9': Vote(id='vote/9', from_id='x3', to_id='x1'),
}
def challenge2_worker(gamedb):
# round 2: [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# asia wins
# africa teams vote
# r votes out r3
# g votes out g3
# b votes out b3
# [Africa r1 r2 g1 g2 b1 b2] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# africa teams of 2 would deadlock and must merge
# [Africa r1 r2 g1 g2 b1 b2 (all on team r)] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id='AFRICA', challenge_id='challenge/2'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id='ASIA', challenge_id='challenge/2'),
}
def council2_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='r3'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='r3'),
'vote/3': Vote(id='vote/3', from_id='r3', to_id='r1'),
'vote/4': Vote(id='vote/4', from_id='g1', to_id='g3'),
'vote/5': Vote(id='vote/5', from_id='g2', to_id='g3'),
'vote/6': Vote(id='vote/6', from_id='g3', to_id='g1'),
'vote/7': Vote(id='vote/7', from_id='b1', to_id='b3'),
'vote/8': Vote(id='vote/8', from_id='b2', to_id='b3'),
'vote/9': Vote(id='vote/9', from_id='b3', to_id='b1'),
}
def challenge3_worker(gamedb):
# round 3: [Africa r1 r2 g1 g2 b1 b2 (all on team r)] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# asia wins
# africa single team votes
# b2 is voted out
# [Africa r1 r2 g1 g2 b1 (all on team r)] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# africa tribe size has reached minimum, tribes merge
# [a$apmob (r1 r2 g1 g2 b1) (y1 y2 k1 k2 x1 x2)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id='AFRICA', challenge_id='challenge/3'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id='ASIA', challenge_id='challenge/3'),
}
def council3_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='b2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='b2'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='b2'),
'vote/4': Vote(id='vote/4', from_id='g2', to_id='b2'),
'vote/5': Vote(id='vote/5', from_id='b1', to_id='b2'),
'vote/6': Vote(id='vote/6', from_id='b2', to_id='r1'),
}
def challenge4_worker(gamedb):
# round 4: [a$apmob (team L: r1 r2 g1 g2 b1) (team R: y1 y2 k1 k2 x1 x2)]
# team L wins
# team R votes out x2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/4'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/4'),
}
def council4_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='y1', to_id='x2'),
'vote/2': Vote(id='vote/2', from_id='y2', to_id='x2'),
'vote/3': Vote(id='vote/3', from_id='k1', to_id='x2'),
'vote/4': Vote(id='vote/4', from_id='k2', to_id='x2'),
'vote/5': Vote(id='vote/5', from_id='x1', to_id='x2'),
'vote/6': Vote(id='vote/6', from_id='x2', to_id='x1'),
}
def challenge5_worker(gamedb):
# round 5: [a$apmob (team L: r1 r2 g1 g2 b1) (team R: y1 y2 k1 k2 x1)]
# team L wins
# team R votes out x1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/5'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/5'),
}
def council5_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='y1', to_id='x1'),
'vote/2': Vote(id='vote/2', from_id='y2', to_id='x1'),
'vote/3': Vote(id='vote/3', from_id='k1', to_id='x1'),
'vote/4': Vote(id='vote/4', from_id='k2', to_id='x1'),
'vote/5': Vote(id='vote/5', from_id='x1', to_id='y1'),
}
def challenge6_worker(gamedb):
# round 6: [a$apmob (team L: r1 r2 g1 g2 b1) (team R: y1 y2 k1 k2)]
# team R wins
# team L votes out b1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/6'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/6'),
}
def council6_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='b1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='b1'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='b1'),
'vote/4': Vote(id='vote/4', from_id='g2', to_id='b1'),
'vote/5': Vote(id='vote/5', from_id='b1', to_id='r1'),
}
def challenge7_worker(gamedb):
# round 7: [a$apmob (team L: r1 r2 g1 g2) (team R: y1 y2 k1 k2)]
# team R wins
# team L votes out g2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/7'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/7'),
}
def council7_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='g2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='g2'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='g2'),
'vote/4': Vote(id='vote/4', from_id='g2', to_id='g1'),
}
def challenge8_worker(gamedb):
# round 8: [a$apmob (team L: r1 r2 g1) (team R: y1 y2 k1 k2)]
# team R wins
# team L votes out g1
# team L would deadlock and must merge
# a$apmob: r1 r2 y1 y2 k1 k2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/8'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/8'),
}
def council8_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='g1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='g1'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='r1'),
}
def challenge9_worker(gamedb):
# round 9: a$apmob: r1 r2 y1 y2 k1 k2
# r1 wins immunity
# team votes out k2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=6, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/2': Entry(id='entry/2', likes=5, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/3': Entry(id='entry/3', likes=4, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/4': Entry(id='entry/4', likes=3, views=1, player_id='y2', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/5': Entry(id='entry/5', likes=2, views=1, player_id='k1', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/6': Entry(id='entry/6', likes=1, views=1, player_id='k2', tribe_id=tribe_id, challenge_id='challenge/9'),
}
def council9_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='k2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='k2'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='k2'),
'vote/4': Vote(id='vote/4', from_id='y2', to_id='k2'),
'vote/5': Vote(id='vote/5', from_id='k1', to_id='k2'),
'vote/6': Vote(id='vote/6', from_id='k2', to_id='k1'),
}
def challenge10_worker(gamedb):
# round 10: a$apmob: r1 r2 y1 y2 k1
# r1 wins immunity
# team votes out k1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=5, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/2': Entry(id='entry/2', likes=4, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/3': Entry(id='entry/3', likes=3, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/4': Entry(id='entry/4', likes=2, views=1, player_id='y2', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/5': Entry(id='entry/5', likes=1, views=1, player_id='k1', tribe_id=tribe_id, challenge_id='challenge/10'),
}
def council10_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='k1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='k1'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='k1'),
'vote/4': Vote(id='vote/4', from_id='y2', to_id='k1'),
'vote/5': Vote(id='vote/5', from_id='k1', to_id='r1'),
}
def challenge11_worker(gamedb):
# round 11: a$apmob: r1 r2 y1 y2
# r1 wins immunity
# team votes out y2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=4, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/11'),
'entry/2': Entry(id='entry/2', likes=3, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/11'),
'entry/3': Entry(id='entry/3', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/11'),
'entry/4': Entry(id='entry/4', likes=1, views=1, player_id='y2', tribe_id=tribe_id, challenge_id='challenge/11'),
}
def council11_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='y2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='y2'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='y2'),
'vote/4': Vote(id='vote/4', from_id='y2', to_id='r1'),
}
def challenge12_worker(gamedb):
# round 12: a$apmob: r1 r2 y1
# y1 wins immunity
# team votes out r1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/12'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/12'),
'entry/3': Entry(id='entry/3', likes=3, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/12'),
}
def council12_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='y1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='r1'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='r1'),
}
def challenge13_worker(gamedb):
# round 13: a$apmob: r2 y1
# community votes y1 to win (no tribal challenge)
# y1 wins
pass
def council13_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='y1', is_for_win=True),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='y1', is_for_win=True),
'vote/3': Vote(id='vote/3', from_id='y2', to_id='y1', is_for_win=True),
'vote/4': Vote(id='vote/4', from_id='k1', to_id='y1', is_for_win=True),
'vote/5': Vote(id='vote/5', from_id='k2', to_id='y1', is_for_win=True),
}
challenge_worker_queue = Queue()
for worker in [challenge1_worker, challenge2_worker, challenge3_worker, challenge4_worker, challenge5_worker,
challenge6_worker, challenge7_worker, challenge8_worker, challenge9_worker, challenge10_worker,
challenge11_worker, challenge12_worker, challenge13_worker]:
challenge_worker_queue.put(worker)
council_worker_queue = Queue()
for worker in [council1_worker, council2_worker, council3_worker, council4_worker, council5_worker, council6_worker,
council7_worker, council8_worker, council9_worker, council10_worker, council11_worker, council12_worker,
council13_worker]:
council_worker_queue.put(worker)
def event_fn(event):
if isinstance(event, events.NotifyTribalChallengeEvent) and not challenge_worker_queue.empty():
challenge_worker = challenge_worker_queue.get_nowait()
challenge_worker(mygamedb)
elif isinstance(event, events.NotifyMultiTribeCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(mygamedb)
elif isinstance(event, events.NotifySingleTribeCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(mygamedb)
elif isinstance(event, events.NotifySingleTeamCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(mygamedb)
elif isinstance(event, events.NotifyFinalTribalCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(mygamedb)
eng = mock.MagicMock()
eng.add_event = event_fn
return eng
class MockDatabase(Database):
def __init__(self):
self._games = dict()
self._players = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
'player/03': Player(id='player/03', tribe_id='tribe/1', team_id='team/1'),
'player/04': Player(id='player/04', tribe_id='tribe/1', team_id='team/1'),
'player/05': Player(id='player/05', tribe_id='tribe/1', team_id='team/2'),
'player/06': Player(id='player/06', tribe_id='tribe/1', team_id='team/2'),
'player/07': Player(id='player/07', tribe_id='tribe/1', team_id='team/2'),
'player/08': Player(id='player/08', tribe_id='tribe/1', team_id='team/2'),
'player/09': Player(id='player/09', tribe_id='tribe/2', team_id='team/3'),
'player/10': Player(id='player/10', tribe_id='tribe/2', team_id='team/3'),
'player/11': Player(id='player/11', tribe_id='tribe/2', team_id='team/3'),
'player/12': Player(id='player/12', tribe_id='tribe/2', team_id='team/3'),
'player/13': Player(id='player/13', tribe_id='tribe/2', team_id='team/4'),
'player/14': Player(id='player/14', tribe_id='tribe/2', team_id='team/4'),
'player/15': Player(id='player/15', tribe_id='tribe/2', team_id='team/4'),
'player/16': Player(id='player/16', tribe_id='tribe/2', team_id='team/4'),
'player/17': Player(id='player/17', tribe_id='tribe/2', team_id='team/5'),
'player/18': Player(id='player/18', tribe_id='tribe/2', team_id='team/5'),
'player/19': Player(id='player/19', tribe_id='tribe/2', team_id='team/6'),
'player/20': Player(id='player/20', tribe_id='tribe/2', team_id='team/6'),
'player/21': Player(id='player/21', tribe_id='tribe/2', team_id='team/7'),
'player/22': Player(id='player/22', tribe_id='tribe/2', team_id='team/7'),
'player/23': Player(id='player/23', tribe_id='tribe/2', team_id='team/7')
}
self._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=4, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=4, tribe_id='tribe/1'),
'team/3': Team(id='team/3', name='name/team3', count_players=4, tribe_id='tribe/2'),
'team/4': Team(id='team/4', name='name/team4', count_players=4, tribe_id='tribe/2'),
'team/5': Team(id='team/5', name='name/team5', count_players=2, tribe_id='tribe/2'),
'team/6': Team(id='team/6', name='name/team6', count_players=2, tribe_id='tribe/2'),
'team/7': Team(id='team/7', name='name/team7', count_players=3, tribe_id='tribe/2')
}
self._tribes = {
'tribe/1': Tribe(id='tribe/1', name='name/tribe1', count_players=8),
'tribe/2': Tribe(id='tribe/2', name='name/tribe2', count_players=15)
}
self._challenges = {
'challenge/1': Challenge(id='challenge/1', name='name/challenge1'),
'challenge/2': Challenge(id='challenge/2', name='name/challenge2'),
'challenge/3': Challenge(id='challenge/3', name='name/challenge3'),
'challenge/4': Challenge(id='challenge/4', name='name/challenge4'),
'challenge/5': Challenge(id='challenge/5', name='name/challenge5'),
}
self._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='player/01', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='player/02', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/3': Entry(id='entry/3', likes=1, views=1, player_id='player/03', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/4': Entry(id='entry/4', likes=1, views=1, player_id='player/04', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/5': Entry(id='entry/5', likes=1, views=1, player_id='player/12', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/6': Entry(id='entry/6', likes=1, views=1, player_id='player/13', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/7': Entry(id='entry/7', likes=1, views=1, player_id='player/14', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/8': Entry(id='entry/8', likes=1, views=1, player_id='player/15', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/9': Entry(id='entry/9', likes=1, views=1, player_id='player/16', tribe_id='tribe/2', challenge_id='challenge/1'),
}
self._games = {
"7rPwCJaiSkxYgDocGDw1": {
"count_teams": 6,
"count_players": 8,
"name": "test_game1",
"country_code": "US",
"game_has_started": False,
"id": "7rPwCJaiSkxYgDocGDw1"
},
"FFFFFFFFFFFFFFFFFFFF": {
"count_teams": 6,
"count_players": 5,
"name": "test_game2",
"country_code": "EU",
"game_has_started": True,
"id": "FFFFFFFFFFFFFFFFFFFF"
}
}
self._votes = {}
def get_game_id(self) -> str:
pass
def batch_update_tribe(self, from_tribe: Tribe, to_tribe: Tribe) -> None:
new_active_players_count = 0
new_active_teams_count = 0
for key in self._teams:
if self._teams[key].tribe_id == from_tribe.id:
self._teams[key].tribe_id = to_tribe.id
new_active_teams_count += 1
for key in self._players:
if self._players[key].tribe_id == from_tribe.id:
self._players[key].tribe_id = to_tribe.id
if self._players[key].active:
new_active_players_count = new_active_players_count + 1
self._tribes[to_tribe.id].count_players += new_active_players_count
self._tribes[to_tribe.id].count_teams += new_active_teams_count
self._tribes[from_tribe.id].count_players = 0
self._tribes[from_tribe.id].active = False
def stream_entries(self, from_tribe: Tribe = None, from_team: Team = None, from_challenge: Challenge = None) -> Iterable[Entry]:
if from_tribe:
return [entry for entry in self._entries.values() if
(entry.challenge_id == from_challenge.id and entry.tribe_id == from_tribe.id)]
if from_team:
players_ids = [player.id for player in self._players.values(
) if player.team_id == from_team.id]
return [entry for entry in self._entries.values() if
(entry.challenge_id == from_challenge.id and entry.player_id in players_ids)]
def stream_teams(self, from_tribe: Tribe,
team_size_predicate_value: [int, None] = None,
order_by_size=True,
descending=False
) -> Iterable[Team]:
if team_size_predicate_value:
return sorted([team for team in self._teams.values() if (team.count_players == team_size_predicate_value
and team.active
and team.tribe_id == from_tribe.id)],
key=lambda team: team.count_players, reverse=True)
else:
return sorted([team for team in self._teams.values() if (team.active and team.tribe_id == from_tribe.id)],
key=lambda team: team.count_players, reverse=True)
def stream_players(self, active_player_predicate_value: bool = True) -> Iterable[Player]:
return self._players.values()
def count_players(self, from_tribe: Tribe = None, from_team: Team = None) -> int:
count = 0
for key in self._players:
if from_tribe:
if self._players[key].tribe_id == from_tribe.id:
count = count + 1
elif from_team:
if self._players[key].team_id == from_team.id:
count = count + 1
return count
def count_teams(self, from_tribe: Tribe = None, active_team_predicate_value=True) -> int:
if from_tribe:
return len([team for team in self._teams.values() if team.tribe_id == from_tribe.id and team.active == active_team_predicate_value])
else:
return len([team for team in self._teams.values() if team.active == active_team_predicate_value])
def deactivate_player(self, player: Player) -> None:
player.active = False
self._players[player.id].active = False
self._teams[player.team_id].count_players -= 1
self._tribes[player.tribe_id].count_players -= 1
def deactivate_team(self, team: Team) -> None:
team.active = False
self._teams[team.id].active = False
pprint.pprint(self._teams)
def count_votes(self, from_team: Team = None, is_for_win: bool = False) -> Dict[Text, int]:
player_counts = {}
if from_team:
for vote in self._votes.values():
print(vote)
voter = self.player_from_id(vote.from_id)
team = self._teams[voter.team_id]
if team.id != from_team.id or not voter.active:
continue
if vote.to_id not in player_counts:
player_counts[vote.to_id] = 1
else:
player_counts[vote.to_id] = player_counts[vote.to_id] + 1
else:
for vote in self._votes.values():
if not vote.is_for_win:
continue
if vote.to_id not in player_counts:
player_counts[vote.to_id] = 1
else:
player_counts[vote.to_id] = player_counts[vote.to_id] + 1
return player_counts
def clear_votes(self) -> None:
self._votes = {}
def list_challenges(self, challenge_completed_predicate_value=False) -> Iterable[Challenge]:
return [challenge for challenge in self._challenges.values() if not challenge.complete]
def list_players(self, from_team: Team, active_player_predicate_value=True) -> Iterable[Player]:
return [player for player in self._players.values() if player.team_id == from_team.id and player.active == active_player_predicate_value]
def list_teams(self, active_team_predicate_value=True) -> Iterable[Team]:
return [team for team in self._teams.values() if team.active == active_team_predicate_value]
def player(self, name: Text) -> Player:
pass
def game_from_id(self, id: Text) -> Game:
return Game(game_id='', options=None)
def player_from_id(self, id: Text) -> Player:
return self._players[id]
def tribe(self, name: Text) -> Tribe:
tribe_id = uuid.uuid1()
tribe = Tribe(id=tribe_id, name=name)
self._tribes[tribe_id] = tribe
return tribe
def team_from_id(self, id: Text) -> Team:
return self._teams[id]
def tribe_from_id(self, id: Text) -> Tribe:
return self._tribes[id]
def challenge_from_id(self, id: Text) -> Challenge:
return self._challenges[id]
def save(self, data: Data) -> None:
if isinstance(data, Player):
self._players[data.id] = data
if isinstance(data, Team):
self._teams[data.id] = data
if isinstance(data, Challenge):
self._challenges[data.id] = data
def find_matchmaker_games(self, region="US") -> list:
class TestGame(dict):
class Reference():
class Stream(dict):
def stream(self3):
players_dict = self._players
players_list = []
for key, val in players_dict.items():
players_list.append(val)
return players_list
def collection(self2, inp):
if inp == "players":
return self2.Stream()
reference = Reference()
def to_dict(self):
return self
filtered = filter(lambda elem: elem[1]['country_code'] ==
region and not elem[1]['game_has_started'], self._games.items())
games = list(filtered)
games_list = []
for g_tuple in games:
game = TestGame(g_tuple[1])
games_list.append(game)
return games_list
def ballot(self, player_id: str, challenge_id: str, options: Dict[str, str]) -> None:
pass
def find_ballot(self, player: Player) -> Iterable[Ballot]:
pass
def find_player(self, phone_number: str) -> Optional[Player]:
pass
def find_user(self, phone_number: str) -> Optional[object]:
pass
class GameTest(unittest.TestCase):
def setUp(self):
self._game = Game(game_id=str(uuid.uuid4()), options=GameOptions(
game_wait_sleep_interval_sec=0.1,
tribe_council_time_sec=.2,
single_tribe_top_k_threshold=0.5,
multi_tribe_min_tribe_size=5,
multi_tribe_team_immunity_likelihood=0.0))
def test_play(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
# [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 y3 k1 k2 k3 x1 x2 x3]
gamedb._players = {
'r1': Player(id='r1', tribe_id='AFRICA', team_id='r'),
'r2': Player(id='r2', tribe_id='AFRICA', team_id='r'),
'r3': Player(id='r3', tribe_id='AFRICA', team_id='r'),
'g1': Player(id='g1', tribe_id='AFRICA', team_id='g'),
'g2': Player(id='g2', tribe_id='AFRICA', team_id='g'),
'g3': Player(id='g3', tribe_id='AFRICA', team_id='g'),
'b1': Player(id='b1', tribe_id='AFRICA', team_id='b'),
'b2': Player(id='b2', tribe_id='AFRICA', team_id='b'),
'b3': Player(id='b3', tribe_id='AFRICA', team_id='b'),
'y1': Player(id='y1', tribe_id='ASIA', team_id='y'),
'y2': Player(id='y2', tribe_id='ASIA', team_id='y'),
'y3': Player(id='y3', tribe_id='ASIA', team_id='y'),
'k1': Player(id='k1', tribe_id='ASIA', team_id='k'),
'k2': Player(id='k2', tribe_id='ASIA', team_id='k'),
'k3': Player(id='k3', tribe_id='ASIA', team_id='k'),
'x1': Player(id='x1', tribe_id='ASIA', team_id='x'),
'x2': Player(id='x2', tribe_id='ASIA', team_id='x'),
'x3': Player(id='x3', tribe_id='ASIA', team_id='x'),
}
gamedb._teams = {
'r': Team(id='r', name='name/r', count_players=3, tribe_id='AFRICA'),
'g': Team(id='g', name='name/g', count_players=3, tribe_id='AFRICA'),
'b': Team(id='b', name='name/b', count_players=3, tribe_id='AFRICA'),
'y': Team(id='y', name='name/y', count_players=3, tribe_id='ASIA'),
'k': Team(id='k', name='name/k', count_players=3, tribe_id='ASIA'),
'x': Team(id='x', name='name/x', count_players=3, tribe_id='ASIA'),
}
gamedb._tribes = {
'AFRICA': Tribe(id='AFRICA', name='name/AFRICA', count_players=9),
'ASIA': Tribe(id='ASIA', name='name/ASIA', count_players=9),
}
gamedb._challenges = {
'challenge/1': Challenge(id='challenge/1', name='name/challenge1'),
'challenge/2': Challenge(id='challenge/2', name='name/challenge2'),
'challenge/3': Challenge(id='challenge/3', name='name/challenge3'),
'challenge/4': Challenge(id='challenge/4', name='name/challenge4'),
'challenge/5': Challenge(id='challenge/5', name='name/challenge5'),
'challenge/6': Challenge(id='challenge/6', name='name/challenge6'),
'challenge/7': Challenge(id='challenge/7', name='name/challenge7'),
'challenge/8': Challenge(id='challenge/8', name='name/challenge8'),
'challenge/9': Challenge(id='challenge/9', name='name/challenge9'),
'challenge/10': Challenge(id='challenge/10', name='name/challenge10'),
'challenge/11': Challenge(id='challenge/11', name='name/challenge11'),
'challenge/12': Challenge(id='challenge/12', name='name/challenge12'),
'challenge/13': Challenge(id='challenge/13', name='name/challenge13'),
'challenge/14': Challenge(id='challenge/14', name='name/challenge14'),
'challenge/15': Challenge(id='challenge/15', name='name/challenge15'),
}
gamedb._entries = {}
def challenge1_worker(gamedb):
# round 1: [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 y3 k1 k2 k3 x1 x2 x3]
# africa wins
# asia teams vote
# y votes out y3
# k votes out k3
# x votes out x3
# [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 k1 k2 x1 x2]
# asia teams of 2 would deadlock and must merge
# [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id='AFRICA', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='y1', tribe_id='ASIA', challenge_id='challenge/1'),
}
def council1_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='y1', to_id='y3'),
'vote/2': Vote(id='vote/2', from_id='y2', to_id='y3'),
'vote/3': Vote(id='vote/3', from_id='y3', to_id='y1'),
'vote/4': Vote(id='vote/4', from_id='k1', to_id='k3'),
'vote/5': Vote(id='vote/5', from_id='k2', to_id='k3'),
'vote/6': Vote(id='vote/6', from_id='k3', to_id='k1'),
'vote/7': Vote(id='vote/7', from_id='x1', to_id='x3'),
'vote/8': Vote(id='vote/8', from_id='x2', to_id='x3'),
'vote/9': Vote(id='vote/9', from_id='x3', to_id='x1'),
}
def challenge2_worker(gamedb):
# round 2: [Africa r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# asia wins
# africa teams vote
# r votes out r3
# g votes out g3
# b votes out b3
# [Africa r1 r2 g1 g2 b1 b2] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# africa teams of 2 would deadlock and must merge
# [Africa r1 r2 g1 g2 b1 b2 (all on team r)] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id='AFRICA', challenge_id='challenge/2'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id='ASIA', challenge_id='challenge/2'),
}
def council2_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='r3'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='r3'),
'vote/3': Vote(id='vote/3', from_id='r3', to_id='r1'),
'vote/4': Vote(id='vote/4', from_id='g1', to_id='g3'),
'vote/5': Vote(id='vote/5', from_id='g2', to_id='g3'),
'vote/6': Vote(id='vote/6', from_id='g3', to_id='g1'),
'vote/7': Vote(id='vote/7', from_id='b1', to_id='b3'),
'vote/8': Vote(id='vote/8', from_id='b2', to_id='b3'),
'vote/9': Vote(id='vote/9', from_id='b3', to_id='b1'),
}
def challenge3_worker(gamedb):
# round 3: [Africa r1 r2 g1 g2 b1 b2 (all on team r)] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# asia wins
# africa single team votes
# b2 is voted out
# [Africa r1 r2 g1 g2 b1 (all on team r)] vs [Asia y1 y2 k1 k2 x1 x2 (all on k team)]
# africa tribe size has reached minimum, tribes merge
# [a$apmob (r1 r2 g1 g2 b1) (y1 y2 k1 k2 x1 x2)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id='AFRICA', challenge_id='challenge/3'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id='ASIA', challenge_id='challenge/3'),
}
def council3_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='b2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='b2'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='b2'),
'vote/4': Vote(id='vote/4', from_id='g2', to_id='b2'),
'vote/5': Vote(id='vote/5', from_id='b1', to_id='b2'),
'vote/6': Vote(id='vote/6', from_id='b2', to_id='r1'),
}
def challenge4_worker(gamedb):
# round 4: [a$apmob (team L: r1 r2 g1 g2 b1) (team R: y1 y2 k1 k2 x1 x2)]
# team L wins
# team R votes out x2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/4'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/4'),
}
def council4_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='y1', to_id='x2'),
'vote/2': Vote(id='vote/2', from_id='y2', to_id='x2'),
'vote/3': Vote(id='vote/3', from_id='k1', to_id='x2'),
'vote/4': Vote(id='vote/4', from_id='k2', to_id='x2'),
'vote/5': Vote(id='vote/5', from_id='x1', to_id='x2'),
'vote/6': Vote(id='vote/6', from_id='x2', to_id='x1'),
}
def challenge5_worker(gamedb):
# round 5: [a$apmob (team L: r1 r2 g1 g2 b1) (team R: y1 y2 k1 k2 x1)]
# team L wins
# team R votes out x1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/5'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/5'),
}
def council5_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='y1', to_id='x1'),
'vote/2': Vote(id='vote/2', from_id='y2', to_id='x1'),
'vote/3': Vote(id='vote/3', from_id='k1', to_id='x1'),
'vote/4': Vote(id='vote/4', from_id='k2', to_id='x1'),
'vote/5': Vote(id='vote/5', from_id='x1', to_id='y1'),
}
def challenge6_worker(gamedb):
# round 6: [a$apmob (team L: r1 r2 g1 g2 b1) (team R: y1 y2 k1 k2)]
# team R wins
# team L votes out b1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/6'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/6'),
}
def council6_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='b1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='b1'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='b1'),
'vote/4': Vote(id='vote/4', from_id='g2', to_id='b1'),
'vote/5': Vote(id='vote/5', from_id='b1', to_id='r1'),
}
def challenge7_worker(gamedb):
# round 7: [a$apmob (team L: r1 r2 g1 g2) (team R: y1 y2 k1 k2)]
# team R wins
# team L votes out g2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/7'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/7'),
}
def council7_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='g2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='g2'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='g2'),
'vote/4': Vote(id='vote/4', from_id='g2', to_id='g1'),
}
def challenge8_worker(gamedb):
# round 8: [a$apmob (team L: r1 r2 g1) (team R: y1 y2 k1 k2)]
# team R wins
# team L votes out g1
# team L would deadlock and must merge
# a$apmob: r1 r2 y1 y2 k1 k2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/8'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/8'),
}
def council8_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='g1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='g1'),
'vote/3': Vote(id='vote/3', from_id='g1', to_id='r1'),
}
def challenge9_worker(gamedb):
# round 9: a$apmob: r1 r2 y1 y2 k1 k2
# r1 wins immunity
# team votes out k2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=6, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/2': Entry(id='entry/2', likes=5, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/3': Entry(id='entry/3', likes=4, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/4': Entry(id='entry/4', likes=3, views=1, player_id='y2', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/5': Entry(id='entry/5', likes=2, views=1, player_id='k1', tribe_id=tribe_id, challenge_id='challenge/9'),
'entry/6': Entry(id='entry/6', likes=1, views=1, player_id='k2', tribe_id=tribe_id, challenge_id='challenge/9'),
}
def council9_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='k2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='k2'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='k2'),
'vote/4': Vote(id='vote/4', from_id='y2', to_id='k2'),
'vote/5': Vote(id='vote/5', from_id='k1', to_id='k2'),
'vote/6': Vote(id='vote/6', from_id='k2', to_id='k1'),
}
def challenge10_worker(gamedb):
# round 10: a$apmob: r1 r2 y1 y2 k1
# r1 wins immunity
# team votes out k1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=5, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/2': Entry(id='entry/2', likes=4, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/3': Entry(id='entry/3', likes=3, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/4': Entry(id='entry/4', likes=2, views=1, player_id='y2', tribe_id=tribe_id, challenge_id='challenge/10'),
'entry/5': Entry(id='entry/5', likes=1, views=1, player_id='k1', tribe_id=tribe_id, challenge_id='challenge/10'),
}
def council10_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='k1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='k1'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='k1'),
'vote/4': Vote(id='vote/4', from_id='y2', to_id='k1'),
'vote/5': Vote(id='vote/5', from_id='k1', to_id='r1'),
}
def challenge11_worker(gamedb):
# round 11: a$apmob: r1 r2 y1 y2
# r1 wins immunity
# team votes out y2
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=4, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/11'),
'entry/2': Entry(id='entry/2', likes=3, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/11'),
'entry/3': Entry(id='entry/3', likes=2, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/11'),
'entry/4': Entry(id='entry/4', likes=1, views=1, player_id='y2', tribe_id=tribe_id, challenge_id='challenge/11'),
}
def council11_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='y2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='y2'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='y2'),
'vote/4': Vote(id='vote/4', from_id='y2', to_id='r1'),
}
def challenge12_worker(gamedb):
# round 12: a$apmob: r1 r2 y1
# y1 wins immunity
# team votes out r1
tribe_id = [tribe for tribe in gamedb._tribes.values()
if tribe.name == "a$apmob"][0].id
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id=tribe_id, challenge_id='challenge/12'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='r2', tribe_id=tribe_id, challenge_id='challenge/12'),
'entry/3': Entry(id='entry/3', likes=3, views=1, player_id='y1', tribe_id=tribe_id, challenge_id='challenge/12'),
}
def council12_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='y1'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='r1'),
'vote/3': Vote(id='vote/3', from_id='y1', to_id='r1'),
}
def challenge13_worker(gamedb):
# round 13: a$apmob: r2 y1
# community votes y1 to win (no tribal challenge)
# y1 wins
pass
def council13_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='y1', is_for_win=True),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='y1', is_for_win=True),
'vote/3': Vote(id='vote/3', from_id='y2', to_id='y1', is_for_win=True),
'vote/4': Vote(id='vote/4', from_id='k1', to_id='y1', is_for_win=True),
'vote/5': Vote(id='vote/5', from_id='k2', to_id='y1', is_for_win=True),
}
challenge_worker_queue = Queue()
for worker in [challenge1_worker, challenge2_worker, challenge3_worker, challenge4_worker, challenge5_worker,
challenge6_worker, challenge7_worker, challenge8_worker, challenge9_worker, challenge10_worker,
challenge11_worker, challenge12_worker, challenge13_worker]:
challenge_worker_queue.put(worker)
council_worker_queue = Queue()
for worker in [council1_worker, council2_worker, council3_worker, council4_worker, council5_worker, council6_worker,
council7_worker, council8_worker, council9_worker, council10_worker, council11_worker, council12_worker,
council13_worker]:
council_worker_queue.put(worker)
def event_fn(event):
if isinstance(event, events.NotifyTribalChallengeEvent) and not challenge_worker_queue.empty():
challenge_worker = challenge_worker_queue.get_nowait()
challenge_worker(gamedb)
elif isinstance(event, events.NotifyMultiTribeCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(gamedb)
elif isinstance(event, events.NotifySingleTribeCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(gamedb)
elif isinstance(event, events.NotifySingleTeamCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(gamedb)
elif isinstance(event, events.NotifyFinalTribalCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(gamedb)
engine.add_event = event_fn
eng = MockPlayEngine().CreateEngine(gamedb)
winner = self._game.play(tribe1=gamedb.tribe_from_id('AFRICA'), tribe2=gamedb.tribe_from_id('ASIA'),
gamedb=gamedb, engine=eng)
self.assertEqual(winner, gamedb.player_from_id('y1'))
def test_play_multi_tribe(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
# [Tokyo r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [London y1 y2 y3 k1 k2 k3 x1 x2 x3]
gamedb._players = {
'r1': Player(id='r1', tribe_id='TOKYO', team_id='r'),
'r2': Player(id='r2', tribe_id='TOKYO', team_id='r'),
'r3': Player(id='r3', tribe_id='TOKYO', team_id='r'),
'g1': Player(id='g1', tribe_id='TOKYO', team_id='g'),
'g2': Player(id='g2', tribe_id='TOKYO', team_id='g'),
'g3': Player(id='g3', tribe_id='TOKYO', team_id='g'),
'b1': Player(id='b1', tribe_id='TOKYO', team_id='b'),
'b2': Player(id='b2', tribe_id='TOKYO', team_id='b'),
'b3': Player(id='b3', tribe_id='TOKYO', team_id='b'),
'y1': Player(id='y1', tribe_id='LONDON', team_id='y'),
'y2': Player(id='y2', tribe_id='LONDON', team_id='y'),
'y3': Player(id='y3', tribe_id='LONDON', team_id='y'),
'k1': Player(id='k1', tribe_id='LONDON', team_id='k'),
'k2': Player(id='k2', tribe_id='LONDON', team_id='k'),
'k3': Player(id='k3', tribe_id='LONDON', team_id='k'),
'x1': Player(id='x1', tribe_id='LONDON', team_id='x'),
'x2': Player(id='x2', tribe_id='LONDON', team_id='x'),
'x3': Player(id='x3', tribe_id='LONDON', team_id='x'),
}
gamedb._teams = {
'r': Team(id='r', name='name/r', count_players=3, tribe_id='TOKYO'),
'g': Team(id='g', name='name/g', count_players=3, tribe_id='TOKYO'),
'b': Team(id='b', name='name/b', count_players=3, tribe_id='TOKYO'),
'y': Team(id='y', name='name/y', count_players=3, tribe_id='LONDON'),
'k': Team(id='k', name='name/k', count_players=3, tribe_id='LONDON'),
'x': Team(id='x', name='name/x', count_players=3, tribe_id='LONDON'),
}
gamedb._tribes = {
'TOKYO': Tribe(id='TOKYO', name='name/TOKYO', count_players=9),
'LONDON': Tribe(id='LONDON', name='name/LONDON', count_players=9),
}
gamedb._challenges = {
'challenge/1': Challenge(id='challenge/1', name='name/challenge1'),
'challenge/2': Challenge(id='challenge/2', name='name/challenge2'),
'challenge/3': Challenge(id='challenge/3', name='name/challenge3'),
'challenge/4': Challenge(id='challenge/4', name='name/challenge4'),
}
gamedb._entries = {}
def challenge1_worker(gamedb):
# round 1: [Tokyo r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [London y1 y2 y3 k1 k2 k3 x1 x2 x3]
# tokyo wins
# london teams vote
# y votes out y3
# k votes out k3
# x votes out x3
# [Tokyo r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [London y1 y2 k1 k2 x1 x2]
# london teams of 2 would deadlock and must merge
# [Tokyo r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [London y1 y2 k1 k2 x1 x2 (all on k team)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id='TOKYO', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='y1', tribe_id='LONDON', challenge_id='challenge/1'),
}
def council1_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='y1', to_id='y3'),
'vote/2': Vote(id='vote/2', from_id='y2', to_id='y3'),
'vote/3': Vote(id='vote/3', from_id='y3', to_id='y1'),
'vote/4': Vote(id='vote/4', from_id='k1', to_id='k3'),
'vote/5': Vote(id='vote/5', from_id='k2', to_id='k3'),
'vote/6': Vote(id='vote/6', from_id='k3', to_id='k1'),
'vote/7': Vote(id='vote/7', from_id='x1', to_id='x3'),
'vote/8': Vote(id='vote/8', from_id='x2', to_id='x3'),
'vote/9': Vote(id='vote/9', from_id='x3', to_id='x1'),
}
def challenge2_worker(gamedb):
# round 2: [Tokyo r1 r2 r3 g1 g2 g3 b1 b2 b3] vs [London y1 y2 k1 k2 x1 x2 (all on k team)]
# london wins
# tokyo teams vote
# r votes out r3
# g votes out g3
# b votes out b3
# [Tokyo r1 r2 g1 g2 b1 b2] vs [London y1 y2 k1 k2 x1 x2 (all on k team)]
# tokyo teams of 2 would deadlock and must merge
# [Tokyo r1 r2 g1 g2 b1 b2 (all on team r)] vs [London y1 y2 k1 k2 x1 x2 (all on k team)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id='TOKYO', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id='LONDON', challenge_id='challenge/1'),
}
def council2_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='r3'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='r3'),
'vote/3': Vote(id='vote/3', from_id='r3', to_id='r1'),
'vote/4': Vote(id='vote/4', from_id='g1', to_id='g3'),
'vote/5': Vote(id='vote/5', from_id='g2', to_id='g3'),
'vote/6': Vote(id='vote/6', from_id='g3', to_id='g1'),
'vote/7': Vote(id='vote/7', from_id='b1', to_id='b3'),
'vote/8': Vote(id='vote/8', from_id='b2', to_id='b3'),
'vote/9': Vote(id='vote/9', from_id='b3', to_id='b1'),
}
def challenge3_worker(gamedb):
# round 3: [Tokyo r1 r2 g1 g2 b1 b2 (all on team r)] vs [London y1 y2 k1 k2 x1 x2 (all on k team)]
# london wins
# tokyo single team votes
# b2 is voted out
# [Tokyo r1 r2 g1 g2 b1 (all on team r)] vs [London y1 y2 k1 k2 x1 x2 (all on k team)]
# tokyo tribe size has reached minimum, tribes merge
# [a$apmob (r1 r2 g1 g2 b1) (y1 y2 k1 k2 x1 x2)]
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id='TOKYO', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='y1', tribe_id='LONDON', challenge_id='challenge/1'),
}
def council3_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='b2'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='b2'),
'vote/4': Vote(id='vote/4', from_id='g1', to_id='b2'),
'vote/5': Vote(id='vote/5', from_id='g2', to_id='b2'),
'vote/7': Vote(id='vote/7', from_id='b1', to_id='b2'),
'vote/8': Vote(id='vote/8', from_id='b2', to_id='r1'),
}
challenge_worker_queue = Queue()
for worker in [challenge1_worker, challenge2_worker, challenge3_worker]:
challenge_worker_queue.put(worker)
council_worker_queue = Queue()
for worker in [council1_worker, council2_worker, council3_worker]:
council_worker_queue.put(worker)
def event_fn(event):
if isinstance(event, events.NotifyTribalChallengeEvent) and not challenge_worker_queue.empty():
challenge_worker = challenge_worker_queue.get_nowait()
challenge_worker(gamedb)
elif isinstance(event, events.NotifyMultiTribeCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(gamedb)
engine.add_event = event_fn
tribe = self._game._play_multi_tribe(tribe1=gamedb.tribe_from_id('TOKYO'),
tribe2=gamedb.tribe_from_id('LONDON'), gamedb=gamedb, engine=engine)
# round 4: [a$apmob (r1 r2 g1 g2 b1) (y1 y2 k1 k2 x1 x2)]
# single tribe should be returned
self.assertEqual(tribe.name, self._game._options.merge_tribe_name)
self.assertListEqual([player.id for player in gamedb._players.values() if player.active and player.tribe_id == tribe.id], [
'r1', 'r2', 'g1', 'g2', 'b1', 'y1', 'y2', 'k1', 'k2', 'x1', 'x2'
])
def test_play_single_tribe(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
# start with 8 players in tribe
gamedb._players = {
'r1': Player(id='r1', tribe_id='tribe/1', team_id='red'),
'r2': Player(id='r2', tribe_id='tribe/1', team_id='red'),
'r3': Player(id='r3', tribe_id='tribe/1', team_id='red'),
'r4': Player(id='r4', tribe_id='tribe/1', team_id='red'),
'b1': Player(id='b1', tribe_id='tribe/1', team_id='blue'),
'b2': Player(id='b2', tribe_id='tribe/1', team_id='blue'),
'b3': Player(id='b3', tribe_id='tribe/1', team_id='blue'),
'b4': Player(id='b4', tribe_id='tribe/1', team_id='blue'),
}
gamedb._teams = {
'red': Team(id='red', name='name/red', count_players=4, tribe_id='tribe/1'),
'blue': Team(id='blue', name='name/blue', count_players=4, tribe_id='tribe/1'),
}
gamedb._tribes = {
'tribe/1': Tribe(id='tribe/1', name='name/tribe1', count_players=8),
}
gamedb._challenges = {
'challenge/1': Challenge(id='challenge/1', name='name/challenge1'),
'challenge/2': Challenge(id='challenge/2', name='name/challenge2'),
'challenge/3': Challenge(id='challenge/3', name='name/challenge3'),
'challenge/4': Challenge(id='challenge/4', name='name/challenge4'),
}
gamedb._entries = {}
def challenge1_worker(gamedb):
# round1: red1, red2, red3, red4 vs blue1, blue2, blue3, blue4
# blue wins challenge
# red4 is voted out
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=1, views=1, player_id='r1', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=1, views=1, player_id='r2', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/3': Entry(id='entry/3', likes=1, views=1, player_id='r3', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/4': Entry(id='entry/4', likes=1, views=1, player_id='r4', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/5': Entry(id='entry/5', likes=2, views=1, player_id='b1', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/6': Entry(id='entry/6', likes=2, views=1, player_id='b2', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/7': Entry(id='entry/7', likes=2, views=1, player_id='b3', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/8': Entry(id='entry/8', likes=2, views=1, player_id='b4', tribe_id='tribe/1', challenge_id='challenge/1'),
}
def council1_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='r1', to_id='r4'),
'vote/2': Vote(id='vote/2', from_id='r2', to_id='r4'),
'vote/3': Vote(id='vote/3', from_id='r3', to_id='r4'),
'vote/4': Vote(id='vote/4', from_id='r4', to_id='r1'),
}
def challenge2_worker(gamedb):
# round2: red1, red2, red3 vs blue1, blue2, blue3, blue4
# red wins challenge
# blue4 is voted out
# TODO(brandon): only count votes from active players
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id='tribe/1', challenge_id='challenge/2'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='r2', tribe_id='tribe/1', challenge_id='challenge/2'),
'entry/3': Entry(id='entry/3', likes=2, views=1, player_id='r3', tribe_id='tribe/1', challenge_id='challenge/2'),
'entry/5': Entry(id='entry/5', likes=1, views=1, player_id='b1', tribe_id='tribe/1', challenge_id='challenge/2'),
'entry/6': Entry(id='entry/6', likes=1, views=1, player_id='b2', tribe_id='tribe/1', challenge_id='challenge/2'),
'entry/7': Entry(id='entry/7', likes=1, views=1, player_id='b3', tribe_id='tribe/1', challenge_id='challenge/2'),
'entry/8': Entry(id='entry/8', likes=1, views=1, player_id='b4', tribe_id='tribe/1', challenge_id='challenge/2'),
}
def council2_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='b1', to_id='b4'),
'vote/2': Vote(id='vote/2', from_id='b2', to_id='b4'),
'vote/3': Vote(id='vote/3', from_id='b3', to_id='b4'),
'vote/4': Vote(id='vote/4', from_id='b4', to_id='b1'),
}
def challenge3_worker(gamedb):
# round3: red1, red2, red3 vs blue1, blue2, blue3
# red wins challenge
# blue3 is voted out
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=2, views=1, player_id='r1', tribe_id='tribe/1', challenge_id='challenge/3'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='r2', tribe_id='tribe/1', challenge_id='challenge/3'),
'entry/3': Entry(id='entry/3', likes=2, views=1, player_id='r3', tribe_id='tribe/1', challenge_id='challenge/3'),
'entry/5': Entry(id='entry/5', likes=1, views=1, player_id='b1', tribe_id='tribe/1', challenge_id='challenge/3'),
'entry/6': Entry(id='entry/6', likes=1, views=1, player_id='b2', tribe_id='tribe/1', challenge_id='challenge/3'),
'entry/7': Entry(id='entry/7', likes=1, views=1, player_id='b3', tribe_id='tribe/1', challenge_id='challenge/3'),
}
def council3_worker(gamedb):
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='b1', to_id='b3'),
'vote/2': Vote(id='vote/2', from_id='b2', to_id='b3'),
'vote/3': Vote(id='vote/3', from_id='b3', to_id='b1'),
}
challenge_worker_queue = Queue()
for worker in [challenge1_worker, challenge2_worker, challenge3_worker]:
challenge_worker_queue.put(worker)
council_worker_queue = Queue()
for worker in [council1_worker, council2_worker, council3_worker]:
council_worker_queue.put(worker)
def event_fn(event):
if isinstance(event, events.NotifyTribalChallengeEvent) and not challenge_worker_queue.empty():
challenge_worker = challenge_worker_queue.get_nowait()
challenge_worker(gamedb)
elif isinstance(event, events.NotifySingleTribeCouncilEvent) and not council_worker_queue.empty():
council_worker = council_worker_queue.get_nowait()
council_worker(gamedb)
engine.add_event = event_fn
team = self._game._play_single_tribe(gamedb.tribe_from_id('tribe/1'), gamedb=gamedb,
engine=engine)
# round4: red1, red2, red3 vs blue1, blue2
# blue has two players and would deadlock
# blue merges into red
# single team with 5 members should be returned
self.assertEqual(team.id, 'red')
self.assertListEqual([player.id for player in gamedb.list_players(from_team=team) if player.active], [
'r1', 'r2', 'r3', 'b1', 'b2'
])
def test_get_voted_out_player(self):
gamedb = MockDatabase()
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='player/20', to_id='player/22'),
'vote/2': Vote(id='vote/2', from_id='player/21', to_id='player/22'),
'vote/3': Vote(id='vote/3', from_id='player/22', to_id='player/21'),
'vote/4': Vote(id='vote/4', from_id='player/23', to_id='player/22'),
}
player = self._game._get_voted_out_player(gamedb.team_from_id('team/7'),
gamedb=gamedb)
self.assertEqual(player.id, 'player/22')
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='player/20', to_id='player/23'),
'vote/2': Vote(id='vote/2', from_id='player/21', to_id='player/23'),
'vote/3': Vote(id='vote/3', from_id='player/22', to_id='player/23'),
'vote/4': Vote(id='vote/4', from_id='player/23', to_id='player/20'),
}
player = self._game._get_voted_out_player(gamedb.team_from_id('team/7'),
gamedb=gamedb)
self.assertEqual(player.id, 'player/23')
def test_get_voted_out_player_with_tie(self):
gamedb = MockDatabase()
gamedb._players = {
'player/20': Player(id='player/20', tribe_id='tribe/2', team_id='team/7'),
'player/21': Player(id='player/21', tribe_id='tribe/2', team_id='team/7'),
'player/22': Player(id='player/22', tribe_id='tribe/2', team_id='team/7'),
'player/23': Player(id='player/23', tribe_id='tribe/2', team_id='team/7')
}
gamedb._teams = {
'team/7': Team(id='team/7', name='name/team7', count_players=4, tribe_id='tribe/2')
}
# in a tie situation, the algorithm leaves it to chance to
# decide the winner.
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='player/20', to_id='player/21'),
'vote/2': Vote(id='vote/2', from_id='player/21', to_id='player/20'),
'vote/3': Vote(id='vote/3', from_id='player/22', to_id='player/21'),
'vote/4': Vote(id='vote/4', from_id='player/23', to_id='player/20'),
}
player = self._game._get_voted_out_player(gamedb.team_from_id('team/7'),
gamedb=gamedb)
self.assertIn(player.id, ['player/20', 'player/21'])
def test_run_multi_tribe_council(self):
gamedb = MockDatabase()
gamedb.clear_votes = Mock()
engine = mock.MagicMock()
gamedb._players = {
'player/1': Player(id='player/1', tribe_id='tribe/1', team_id='team/1'),
'player/2': Player(id='player/2', tribe_id='tribe/1', team_id='team/1'),
'player/3': Player(id='player/3', tribe_id='tribe/1', team_id='team/1'),
'player/4': Player(id='player/4', tribe_id='tribe/1', team_id='team/1'),
'player/5': Player(id='player/5', tribe_id='tribe/2', team_id='team/2'),
'player/6': Player(id='player/6', tribe_id='tribe/2', team_id='team/2'),
'player/7': Player(id='player/7', tribe_id='tribe/2', team_id='team/2'),
'player/8': Player(id='player/8', tribe_id='tribe/2', team_id='team/2'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=4, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=4, tribe_id='tribe/2'),
}
gamedb._tribes = {
'tribe/1': Tribe(id='tribe/1', name='name/tribe1', count_players=4),
'tribe/2': Tribe(id='tribe/2', name='name/tribe2', count_players=4)
}
# inject votes
gamedb._votes = {
'vote/5': Vote(id='vote/5', from_id='player/5', to_id='player/8'),
'vote/6': Vote(id='vote/6', from_id='player/6', to_id='player/8'),
'vote/7': Vote(id='vote/7', from_id='player/7', to_id='player/8'),
'vote/8': Vote(id='vote/8', from_id='player/8', to_id='player/5'),
}
self._game._run_multi_tribe_council(winning_tribe=gamedb.tribe_from_id('tribe/1'),
losing_tribe=gamedb.tribe_from_id('tribe/2'), gamedb=gamedb, engine=engine)
engine.add_event.assert_called()
gamedb.clear_votes.assert_called_once()
self.assertFalse(gamedb.player_from_id('player/8').active)
def test_run_single_tribe_council(self):
gamedb = MockDatabase()
gamedb.clear_votes = Mock()
engine = mock.MagicMock()
# inject votes
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='player/01', to_id='player/04'),
'vote/2': Vote(id='vote/2', from_id='player/02', to_id='player/04'),
'vote/3': Vote(id='vote/3', from_id='player/03', to_id='player/04'),
'vote/4': Vote(id='vote/4', from_id='player/04', to_id='player/01'),
}
self._game._run_single_tribe_council(winning_teams=[gamedb._teams['team/2']],
losing_teams=[
gamedb._teams['team/1']],
gamedb=gamedb, engine=engine)
engine.add_event.assert_called()
gamedb.clear_votes.assert_called_once()
self.assertFalse(gamedb.player_from_id('player/04').active)
def test_run_finalist_tribe_council(self):
gamedb = MockDatabase()
gamedb.clear_votes = Mock()
engine = mock.MagicMock()
finalists = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
'player/03': Player(id='player/03', tribe_id='tribe/1', team_id='team/1'),
'player/04': Player(id='player/04', tribe_id='tribe/1', team_id='team/1'),
}
# inject votes
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='player/01', to_id='player/04', is_for_win=True),
'vote/2': Vote(id='vote/2', from_id='player/02', to_id='player/04', is_for_win=True),
'vote/3': Vote(id='vote/3', from_id='player/03', to_id='player/04', is_for_win=True),
'vote/4': Vote(id='vote/4', from_id='player/04', to_id='player/01', is_for_win=True),
}
winner = self._game._run_finalist_tribe_council(
finalists=finalists, gamedb=gamedb, engine=engine)
engine.add_event.assert_called()
self.assertEqual(winner, gamedb._players['player/04'])
def test_run_single_team_council(self):
gamedb = MockDatabase()
gamedb.clear_votes = Mock()
engine = mock.MagicMock()
gamedb._players = {
'player/1': Player(id='player/1', tribe_id='tribe/1', team_id='team/1'),
'player/2': Player(id='player/2', tribe_id='tribe/1', team_id='team/1'),
'player/3': Player(id='player/3', tribe_id='tribe/1', team_id='team/1'),
'player/4': Player(id='player/4', tribe_id='tribe/1', team_id='team/1'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=4, tribe_id='tribe/1'),
}
gamedb._tribes = {
'tribe/1': Tribe(id='tribe/1', name='name/tribe1', count_players=4)
}
# inject votes
gamedb._votes = {
'vote/1': Vote(id='vote/1', from_id='player/1', to_id='player/4'),
'vote/2': Vote(id='vote/2', from_id='player/2', to_id='player/4'),
'vote/3': Vote(id='vote/3', from_id='player/3', to_id='player/4'),
'vote/4': Vote(id='vote/4', from_id='player/4', to_id='player/1'),
}
self._game._run_single_team_council(team=gamedb._teams['team/1'], losing_players=[
gamedb._players['player/2'],
gamedb._players['player/3'],
gamedb._players['player/4'],
], gamedb=gamedb, engine=engine)
# TODO(brandon): assert result
engine.add_event.assert_called()
self.assertFalse(gamedb._players['player/4'].active)
def test_merge_teams_2player(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
gamedb._players = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=1, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=1, tribe_id='tribe/1'),
}
tribe = gamedb.tribe_from_id('tribe/1')
self._game._merge_teams(
target_team_size=5, tribe=tribe, gamedb=gamedb, engine=engine)
engine.add_event.assert_not_called()
expected_player_to_team_dict = {
'player/01': 'team/1',
'player/02': 'team/1',
}
for k, v in gamedb._players.items():
self.assertEqual(v.team_id, expected_player_to_team_dict[k])
def test_merge_teams_3player(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
gamedb._players = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
'player/03': Player(id='player/03', tribe_id='tribe/1', team_id='team/2'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=2, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=1, tribe_id='tribe/1'),
}
tribe = gamedb.tribe_from_id('tribe/1')
self._game._merge_teams(
target_team_size=5, tribe=tribe, gamedb=gamedb, engine=engine)
engine.add_event.assert_called()
expected_player_to_team_dict = {
'player/01': 'team/2',
'player/02': 'team/2',
'player/03': 'team/2',
}
for k, v in gamedb._players.items():
self.assertEqual(v.team_id, expected_player_to_team_dict[k])
def test_merge_teams_5player(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
gamedb._players = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
'player/03': Player(id='player/03', tribe_id='tribe/1', team_id='team/2'),
'player/04': Player(id='player/04', tribe_id='tribe/1', team_id='team/2'),
'player/05': Player(id='player/05', tribe_id='tribe/1', team_id='team/2'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=2, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=3, tribe_id='tribe/1'),
}
tribe = gamedb.tribe_from_id('tribe/1')
self._game._merge_teams(
target_team_size=5, tribe=tribe, gamedb=gamedb, engine=engine)
engine.add_event.assert_called()
expected_player_to_team_dict = {
'player/01': 'team/2',
'player/02': 'team/2',
'player/03': 'team/2',
'player/04': 'team/2',
'player/05': 'team/2',
}
for k, v in gamedb._players.items():
self.assertEqual(v.team_id, expected_player_to_team_dict[k])
def test_merge_teams_6player(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
gamedb._players = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
'player/03': Player(id='player/03', tribe_id='tribe/1', team_id='team/1'),
'player/04': Player(id='player/04', tribe_id='tribe/1', team_id='team/2'),
'player/05': Player(id='player/05', tribe_id='tribe/1', team_id='team/2'),
'player/06': Player(id='player/06', tribe_id='tribe/1', team_id='team/2'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=3, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=3, tribe_id='tribe/1'),
}
tribe = gamedb.tribe_from_id('tribe/1')
self._game._merge_teams(
target_team_size=5, tribe=tribe, gamedb=gamedb, engine=engine)
engine.add_event.assert_not_called()
expected_player_to_team_dict = {
'player/01': 'team/1',
'player/02': 'team/1',
'player/03': 'team/1',
'player/04': 'team/2',
'player/05': 'team/2',
'player/06': 'team/2',
}
for k, v in gamedb._players.items():
self.assertEqual(v.team_id, expected_player_to_team_dict[k])
def test_merge_teams_9player(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
gamedb._players = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
'player/03': Player(id='player/03', tribe_id='tribe/1', team_id='team/2'),
'player/04': Player(id='player/04', tribe_id='tribe/1', team_id='team/2'),
'player/05': Player(id='player/05', tribe_id='tribe/1', team_id='team/3'),
'player/06': Player(id='player/06', tribe_id='tribe/1', team_id='team/3'),
'player/07': Player(id='player/07', tribe_id='tribe/1', team_id='team/4'),
'player/08': Player(id='player/08', tribe_id='tribe/1', team_id='team/4'),
'player/09': Player(id='player/09', tribe_id='tribe/1', team_id='team/4'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=2, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=2, tribe_id='tribe/1'),
'team/3': Team(id='team/3', name='name/team3', count_players=2, tribe_id='tribe/1'),
'team/4': Team(id='team/4', name='name/team4', count_players=3, tribe_id='tribe/1'),
}
tribe = gamedb.tribe_from_id('tribe/1')
self._game._merge_teams(
target_team_size=5, tribe=tribe, gamedb=gamedb, engine=engine)
engine.add_event.assert_called()
expected_player_to_team_dict = {
'player/01': 'team/4',
'player/02': 'team/4',
'player/03': 'team/4',
'player/04': 'team/4',
'player/05': 'team/4',
'player/06': 'team/4',
'player/07': 'team/4',
'player/08': 'team/4',
'player/09': 'team/4',
}
for k, v in gamedb._players.items():
self.assertEqual(v.team_id, expected_player_to_team_dict[k])
def test_merge_teams_10player(self):
gamedb = MockDatabase()
engine = mock.MagicMock()
gamedb._players = {
'player/01': Player(id='player/01', tribe_id='tribe/1', team_id='team/1'),
'player/02': Player(id='player/02', tribe_id='tribe/1', team_id='team/1'),
'player/03': Player(id='player/03', tribe_id='tribe/1', team_id='team/2'),
'player/04': Player(id='player/04', tribe_id='tribe/1', team_id='team/2'),
'player/05': Player(id='player/05', tribe_id='tribe/1', team_id='team/2'),
'player/06': Player(id='player/06', tribe_id='tribe/1', team_id='team/2'),
'player/07': Player(id='player/07', tribe_id='tribe/1', team_id='team/3'),
'player/08': Player(id='player/08', tribe_id='tribe/1', team_id='team/3'),
'player/09': Player(id='player/09', tribe_id='tribe/1', team_id='team/3'),
'player/10': Player(id='player/10', tribe_id='tribe/1', team_id='team/3'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=2, tribe_id='tribe/1'),
'team/2': Team(id='team/2', name='name/team2', count_players=4, tribe_id='tribe/1'),
'team/3': Team(id='team/3', name='name/team3', count_players=4, tribe_id='tribe/1'),
}
tribe = gamedb.tribe_from_id('tribe/1')
self._game._merge_teams(
target_team_size=5, tribe=tribe, gamedb=gamedb, engine=engine)
engine.add_event.assert_called()
expected_player_to_team_dict = {
'player/01': 'team/2',
'player/02': 'team/3',
'player/03': 'team/2',
'player/04': 'team/2',
'player/05': 'team/2',
'player/06': 'team/2',
'player/07': 'team/3',
'player/08': 'team/3',
'player/09': 'team/3',
'player/10': 'team/3',
}
for k, v in gamedb._players.items():
self.assertEqual(v.team_id, expected_player_to_team_dict[k])
def test_get_next_challenge(self):
gamedb = MockDatabase()
challenges = set()
for _ in range(5):
challenge = self._game._get_next_challenge(gamedb=gamedb)
challenges.add(challenge.name)
self.assertSetEqual(challenges, set(
['name/challenge1']))
for _ in range(5):
challenge = self._game._get_next_challenge(gamedb=gamedb)
challenges.add(challenge.name)
gamedb._challenges[challenge.id].complete = True
self.assertSetEqual(challenges, set(
['name/challenge1', 'name/challenge2', 'name/challenge3', 'name/challenge4', 'name/challenge5']))
def test_run_challenge(self):
engine = mock.MagicMock()
gamedb = MockDatabase()
challenge = gamedb.challenge_from_id('challenge/1')
self._game._run_challenge(
challenge=challenge, gamedb=gamedb, engine=engine)
engine.add_event.assert_called_once()
def test_score_entries_tribe_aggregate(self):
engine = mock.MagicMock()
gamedb = MockDatabase()
tribe = gamedb.tribe_from_id('tribe/1')
challenge = gamedb.challenge_from_id('challenge/1')
self.assertEqual(self._game._score_entries_tribe_aggregate(
tribe=tribe, challenge=challenge, gamedb=gamedb, engine=engine), 50)
engine.add_event.assert_called()
def test_score_entries_top_k_teams(self):
engine = mock.MagicMock()
gamedb = MockDatabase()
tribe = gamedb.tribe_from_id('tribe/2')
challenge = gamedb.challenge_from_id('challenge/1')
gamedb._players = {
'player/09': Player(id='player/09', tribe_id='tribe/2', team_id='team/3'),
'player/10': Player(id='player/10', tribe_id='tribe/2', team_id='team/3'),
'player/11': Player(id='player/11', tribe_id='tribe/2', team_id='team/3'),
'player/12': Player(id='player/12', tribe_id='tribe/2', team_id='team/3'),
'player/13': Player(id='player/13', tribe_id='tribe/2', team_id='team/4'),
'player/14': Player(id='player/14', tribe_id='tribe/2', team_id='team/4'),
'player/15': Player(id='player/15', tribe_id='tribe/2', team_id='team/4'),
'player/16': Player(id='player/16', tribe_id='tribe/2', team_id='team/4'),
'player/17': Player(id='player/17', tribe_id='tribe/2', team_id='team/5'),
'player/18': Player(id='player/18', tribe_id='tribe/2', team_id='team/5'),
}
gamedb._entries = {
'entry/01': Entry(id='entry/01', likes=5, views=1, player_id='player/09', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/02': Entry(id='entry/02', likes=5, views=1, player_id='player/10', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/03': Entry(id='entry/03', likes=5, views=1, player_id='player/11', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/04': Entry(id='entry/04', likes=5, views=1, player_id='player/12', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/05': Entry(id='entry/05', likes=2, views=1, player_id='player/13', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/06': Entry(id='entry/06', likes=2, views=1, player_id='player/14', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/07': Entry(id='entry/07', likes=2, views=1, player_id='player/15', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/08': Entry(id='entry/08', likes=2, views=1, player_id='player/16', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/09': Entry(id='entry/09', likes=1, views=1, player_id='player/17', tribe_id='tribe/2', challenge_id='challenge/1'),
'entry/10': Entry(id='entry/10', likes=1, views=1, player_id='player/18', tribe_id='tribe/2', challenge_id='challenge/1'),
}
winning_teams, losing_teams = self._game._score_entries_top_k_teams(k=self._game._options.single_tribe_top_k_threshold,
tribe=tribe, challenge=challenge, gamedb=gamedb, engine=engine)
self.assertListEqual(winning_teams, [
gamedb.team_from_id('team/3'),
])
self.assertListEqual(losing_teams, [
gamedb.team_from_id('team/5'),
gamedb.team_from_id('team/4'),
])
engine.add_event.assert_called()
def test_score_entries_top_k_players(self):
engine = mock.MagicMock()
gamedb = MockDatabase()
tribe = gamedb.tribe_from_id('tribe/1')
challenge = gamedb.challenge_from_id('challenge/1')
gamedb._players = {
'player/1': Player(id='player/1', tribe_id='tribe/1', team_id='team/1'),
'player/2': Player(id='player/2', tribe_id='tribe/1', team_id='team/1'),
'player/3': Player(id='player/3', tribe_id='tribe/1', team_id='team/1'),
}
gamedb._teams = {
'team/1': Team(id='team/1', name='name/team1', count_players=3, tribe_id='tribe/1'),
}
gamedb._entries = {
'entry/1': Entry(id='entry/1', likes=3, views=1, player_id='player/1', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/2': Entry(id='entry/2', likes=2, views=1, player_id='player/2', tribe_id='tribe/1', challenge_id='challenge/1'),
'entry/3': Entry(id='entry/3', likes=1, views=1, player_id='player/3', tribe_id='tribe/1', challenge_id='challenge/1'),
}
losing_players = self._game._score_entries_top_k_players(team=gamedb.team_from_id(
'team/1'), challenge=challenge, gamedb=gamedb, engine=engine)
self.assertListEqual(losing_players, [
gamedb.player_from_id('player/3'),
gamedb.player_from_id('player/2')
])
engine.add_event.assert_called()
def test_merge_tribes(self):
gamedb = MockDatabase()
tribe1 = gamedb.tribe_from_id(id='tribe/1')
tribe2 = gamedb.tribe_from_id(id='tribe/2')
tribe1_count = gamedb.count_players(from_tribe=tribe1)
tribe2_count = gamedb.count_players(from_tribe=tribe2)
tribe3 = self._game._merge_tribes(tribe1=tribe1, tribe2=tribe2,
new_tribe_name='test/tribe3', gamedb=gamedb, engine=mock.MagicMock())
self.assertEqual(gamedb.count_players(
from_tribe=tribe3), tribe1_count + tribe2_count)
self.assertEqual(gamedb.count_players(from_tribe=tribe1), 0)
self.assertEqual(gamedb.count_players(from_tribe=tribe2), 0)
self.assertEqual(gamedb.count_teams(from_tribe=tribe1), 0)
self.assertEqual(gamedb.count_teams(from_tribe=tribe2), 0)
self.assertFalse(gamedb.tribe_from_id(tribe1.id).active)
self.assertFalse(gamedb.tribe_from_id(tribe2.id).active)
if __name__ == '__main__':
unittest.main()
| 51.45025 | 145 | 0.556045 | 12,990 | 92,559 | 3.78291 | 0.027098 | 0.057835 | 0.062027 | 0.031746 | 0.860724 | 0.841351 | 0.817114 | 0.799654 | 0.78024 | 0.74827 | 0 | 0.054852 | 0.276148 | 92,559 | 1,798 | 146 | 51.478865 | 0.678592 | 0.063171 | 0 | 0.5803 | 0 | 0 | 0.165334 | 0 | 0 | 0 | 0 | 0.000556 | 0.033547 | 1 | 0.087081 | false | 0.00571 | 0.012848 | 0.007138 | 0.120628 | 0.002141 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a40c00c17ef76b2826c55c8ef3ffc21c477e3c30 | 298 | py | Python | people/datasets/__init__.py | dluvizon/3d-pose-consensus | 7a829d5713d2c45c6b265c9886add0b69e0050a8 | [
"MIT"
] | 5 | 2020-05-11T14:18:12.000Z | 2022-03-10T12:10:17.000Z | people/datasets/__init__.py | dluvizon/3d-pose-consensus | 7a829d5713d2c45c6b265c9886add0b69e0050a8 | [
"MIT"
] | null | null | null | people/datasets/__init__.py | dluvizon/3d-pose-consensus | 7a829d5713d2c45c6b265c9886add0b69e0050a8 | [
"MIT"
] | null | null | null | from .coco import Coco
from .human36m import Human36M
from .human36m import Human36MTest
from .mpii import MPII
from .mpii3dhp import MpiInf3D
from .generic import project_gt_poses_to_anchors
from .generic import inverse_project_2dposes_from_anchors
from .generic import compute_anchors_reference
| 29.8 | 57 | 0.862416 | 42 | 298 | 5.880952 | 0.428571 | 0.133603 | 0.206478 | 0.194332 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.041509 | 0.110738 | 298 | 9 | 58 | 33.111111 | 0.890566 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
cf9d760706d4273dd70ace7b117a929ef4b2bbbf | 259,537 | py | Python | instances/passenger_demand/pas-20210422-1717-int6000000000000001e-1/41.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int6000000000000001e-1/41.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int6000000000000001e-1/41.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 11504
passenger_arriving = (
(2, 6, 3, 3, 0, 1, 2, 0, 1, 2, 0, 0, 0, 4, 1, 1, 1, 2, 2, 1, 0, 2, 1, 0, 0, 0), # 0
(4, 3, 2, 1, 0, 1, 2, 0, 1, 2, 1, 1, 0, 3, 4, 5, 2, 0, 2, 1, 0, 2, 3, 1, 0, 0), # 1
(5, 4, 4, 3, 2, 2, 2, 2, 0, 2, 0, 2, 0, 7, 2, 4, 2, 2, 3, 0, 0, 2, 1, 0, 0, 0), # 2
(5, 4, 1, 3, 1, 0, 1, 0, 1, 0, 1, 0, 0, 2, 5, 4, 2, 5, 1, 1, 3, 0, 1, 3, 0, 0), # 3
(5, 0, 1, 3, 1, 0, 2, 5, 0, 1, 0, 0, 0, 3, 0, 3, 3, 2, 0, 3, 1, 5, 0, 2, 0, 0), # 4
(5, 2, 2, 5, 4, 1, 1, 1, 1, 0, 1, 0, 0, 2, 4, 3, 2, 4, 2, 0, 0, 2, 3, 0, 1, 0), # 5
(6, 2, 5, 3, 1, 1, 3, 1, 0, 1, 4, 1, 0, 3, 4, 3, 1, 3, 1, 2, 0, 0, 1, 0, 0, 0), # 6
(3, 5, 7, 3, 0, 1, 4, 0, 0, 1, 1, 0, 0, 3, 4, 4, 4, 1, 2, 1, 2, 1, 3, 1, 0, 0), # 7
(5, 8, 1, 5, 4, 1, 1, 0, 4, 1, 2, 1, 0, 3, 4, 3, 2, 1, 0, 3, 0, 0, 0, 2, 2, 0), # 8
(6, 8, 4, 4, 3, 1, 1, 4, 3, 0, 2, 1, 0, 4, 4, 4, 2, 5, 2, 2, 2, 2, 1, 1, 0, 0), # 9
(4, 1, 4, 4, 3, 2, 1, 3, 2, 0, 0, 0, 0, 11, 3, 2, 5, 3, 0, 1, 2, 4, 1, 0, 0, 0), # 10
(5, 5, 3, 4, 2, 2, 2, 3, 1, 0, 1, 2, 0, 5, 3, 4, 8, 4, 1, 2, 0, 0, 1, 1, 0, 0), # 11
(5, 7, 4, 3, 0, 2, 1, 1, 4, 1, 0, 0, 0, 7, 7, 2, 1, 2, 3, 3, 2, 0, 2, 0, 0, 0), # 12
(5, 1, 4, 4, 3, 2, 1, 3, 4, 3, 0, 1, 0, 9, 2, 3, 3, 2, 2, 3, 0, 3, 3, 2, 1, 0), # 13
(5, 10, 4, 7, 2, 3, 1, 3, 1, 0, 4, 0, 0, 6, 4, 2, 5, 6, 1, 5, 1, 1, 3, 0, 1, 0), # 14
(8, 6, 6, 7, 3, 2, 6, 2, 4, 0, 1, 1, 0, 0, 2, 3, 2, 8, 2, 2, 2, 2, 0, 2, 0, 0), # 15
(10, 6, 1, 2, 2, 3, 1, 2, 0, 0, 1, 2, 0, 3, 4, 8, 4, 4, 3, 0, 2, 0, 1, 0, 1, 0), # 16
(8, 9, 5, 5, 4, 2, 2, 3, 4, 1, 1, 1, 0, 4, 6, 3, 6, 4, 3, 4, 1, 3, 1, 1, 0, 0), # 17
(5, 5, 9, 7, 8, 2, 3, 3, 4, 1, 0, 0, 0, 9, 6, 4, 3, 5, 1, 1, 2, 5, 3, 1, 0, 0), # 18
(8, 6, 6, 9, 3, 2, 4, 3, 4, 1, 0, 0, 0, 7, 3, 2, 0, 7, 2, 0, 1, 6, 4, 0, 1, 0), # 19
(10, 9, 5, 6, 3, 2, 3, 1, 4, 1, 0, 0, 0, 7, 3, 6, 2, 3, 2, 8, 1, 2, 1, 0, 1, 0), # 20
(5, 6, 11, 4, 5, 1, 0, 1, 3, 0, 1, 0, 0, 5, 8, 5, 4, 3, 1, 3, 1, 3, 2, 2, 1, 0), # 21
(7, 3, 10, 4, 3, 4, 2, 2, 3, 1, 0, 0, 0, 4, 4, 5, 5, 4, 3, 2, 1, 0, 2, 1, 0, 0), # 22
(4, 7, 9, 1, 9, 1, 4, 1, 2, 2, 1, 0, 0, 3, 9, 2, 2, 7, 1, 3, 1, 2, 2, 1, 0, 0), # 23
(7, 3, 6, 3, 2, 1, 1, 2, 5, 3, 0, 0, 0, 7, 3, 1, 5, 6, 3, 0, 3, 3, 2, 0, 0, 0), # 24
(9, 7, 4, 2, 2, 4, 1, 1, 2, 1, 1, 0, 0, 7, 5, 2, 2, 1, 2, 2, 0, 1, 3, 1, 1, 0), # 25
(5, 12, 7, 10, 5, 1, 4, 6, 4, 2, 0, 2, 0, 6, 4, 4, 1, 1, 2, 3, 1, 1, 1, 0, 0, 0), # 26
(8, 4, 4, 7, 3, 2, 5, 2, 2, 3, 0, 0, 0, 6, 4, 4, 2, 3, 2, 3, 3, 1, 2, 1, 1, 0), # 27
(5, 6, 3, 9, 4, 3, 5, 4, 3, 0, 0, 0, 0, 3, 2, 2, 4, 5, 4, 1, 3, 1, 0, 0, 0, 0), # 28
(5, 4, 6, 8, 2, 1, 1, 0, 4, 1, 2, 0, 0, 2, 2, 2, 3, 2, 1, 1, 1, 2, 3, 4, 0, 0), # 29
(8, 8, 1, 6, 0, 0, 1, 0, 3, 1, 0, 1, 0, 5, 6, 6, 4, 7, 5, 2, 3, 1, 3, 1, 0, 0), # 30
(7, 9, 8, 4, 4, 1, 0, 5, 4, 1, 0, 1, 0, 9, 4, 5, 1, 2, 3, 4, 2, 5, 0, 1, 0, 0), # 31
(3, 8, 4, 8, 8, 1, 5, 2, 1, 0, 1, 0, 0, 4, 7, 3, 5, 1, 4, 1, 1, 4, 2, 2, 1, 0), # 32
(13, 3, 7, 6, 3, 4, 1, 1, 3, 0, 0, 1, 0, 4, 1, 4, 5, 1, 4, 3, 1, 1, 2, 2, 0, 0), # 33
(6, 11, 5, 11, 6, 5, 3, 3, 0, 0, 0, 0, 0, 9, 3, 5, 2, 2, 7, 3, 3, 4, 2, 1, 1, 0), # 34
(2, 5, 6, 7, 9, 3, 1, 3, 1, 0, 1, 0, 0, 6, 7, 3, 3, 2, 6, 1, 2, 3, 1, 2, 0, 0), # 35
(4, 4, 2, 6, 6, 5, 3, 0, 0, 1, 2, 3, 0, 15, 4, 5, 2, 9, 7, 1, 0, 4, 2, 0, 0, 0), # 36
(10, 4, 2, 5, 2, 3, 7, 0, 3, 0, 0, 1, 0, 8, 0, 3, 3, 4, 4, 2, 0, 2, 1, 0, 2, 0), # 37
(10, 6, 2, 10, 4, 0, 2, 3, 3, 1, 1, 0, 0, 3, 5, 4, 1, 5, 6, 2, 2, 2, 2, 1, 0, 0), # 38
(11, 6, 3, 5, 4, 2, 3, 1, 4, 2, 0, 1, 0, 4, 6, 4, 8, 1, 3, 3, 0, 0, 3, 2, 1, 0), # 39
(5, 3, 4, 3, 4, 3, 2, 2, 1, 0, 0, 1, 0, 9, 2, 2, 3, 6, 3, 3, 3, 2, 1, 0, 0, 0), # 40
(3, 8, 10, 2, 4, 3, 4, 1, 0, 2, 1, 0, 0, 7, 7, 4, 2, 4, 2, 2, 1, 2, 2, 0, 0, 0), # 41
(5, 11, 6, 8, 3, 3, 3, 5, 2, 1, 1, 1, 0, 11, 6, 7, 5, 7, 1, 0, 0, 2, 5, 2, 1, 0), # 42
(7, 7, 3, 8, 2, 3, 4, 0, 1, 1, 0, 0, 0, 8, 4, 5, 5, 4, 1, 4, 2, 3, 2, 4, 1, 0), # 43
(7, 7, 4, 6, 2, 1, 4, 2, 4, 1, 1, 0, 0, 6, 1, 5, 2, 3, 4, 1, 2, 2, 1, 0, 2, 0), # 44
(4, 3, 10, 4, 3, 2, 2, 0, 5, 2, 0, 1, 0, 6, 4, 4, 4, 5, 2, 0, 1, 0, 3, 1, 1, 0), # 45
(2, 6, 4, 2, 5, 2, 5, 2, 2, 0, 0, 0, 0, 5, 5, 4, 1, 5, 2, 6, 1, 2, 0, 0, 0, 0), # 46
(7, 7, 2, 3, 3, 3, 1, 2, 3, 2, 0, 0, 0, 5, 6, 3, 2, 1, 2, 1, 1, 1, 0, 0, 1, 0), # 47
(6, 4, 4, 7, 4, 2, 4, 3, 0, 1, 0, 0, 0, 4, 5, 2, 2, 6, 5, 1, 0, 2, 2, 0, 1, 0), # 48
(5, 5, 10, 2, 4, 1, 0, 2, 3, 1, 1, 1, 0, 6, 7, 2, 0, 2, 4, 1, 1, 2, 1, 2, 0, 0), # 49
(6, 5, 6, 6, 4, 3, 1, 2, 2, 2, 3, 0, 0, 8, 5, 6, 0, 4, 4, 2, 2, 1, 1, 0, 2, 0), # 50
(6, 4, 4, 0, 6, 5, 2, 1, 1, 0, 0, 0, 0, 6, 5, 6, 1, 7, 3, 0, 4, 1, 3, 0, 2, 0), # 51
(10, 12, 6, 6, 3, 0, 3, 3, 4, 2, 2, 0, 0, 4, 7, 9, 6, 4, 4, 3, 0, 1, 1, 1, 0, 0), # 52
(4, 5, 3, 6, 4, 2, 1, 1, 2, 4, 1, 1, 0, 4, 2, 4, 3, 8, 6, 2, 3, 3, 4, 0, 0, 0), # 53
(7, 2, 3, 4, 5, 4, 3, 0, 4, 2, 0, 0, 0, 4, 5, 4, 8, 2, 4, 0, 1, 2, 1, 1, 1, 0), # 54
(6, 6, 9, 6, 5, 3, 4, 2, 3, 2, 2, 1, 0, 8, 7, 6, 2, 1, 4, 2, 1, 2, 0, 1, 0, 0), # 55
(9, 5, 7, 1, 7, 1, 1, 2, 5, 0, 0, 0, 0, 4, 5, 2, 4, 5, 2, 3, 1, 6, 3, 1, 0, 0), # 56
(5, 3, 12, 8, 1, 0, 6, 1, 2, 0, 0, 0, 0, 4, 5, 3, 0, 4, 2, 0, 0, 2, 1, 0, 1, 0), # 57
(6, 7, 7, 9, 0, 3, 1, 2, 1, 0, 0, 0, 0, 4, 9, 7, 3, 8, 2, 2, 0, 3, 2, 0, 0, 0), # 58
(11, 2, 4, 8, 4, 0, 5, 2, 0, 2, 1, 2, 0, 7, 4, 4, 1, 1, 2, 2, 3, 1, 1, 1, 0, 0), # 59
(7, 2, 5, 4, 8, 2, 1, 2, 3, 0, 1, 1, 0, 5, 4, 6, 6, 7, 2, 1, 1, 4, 1, 1, 0, 0), # 60
(3, 3, 2, 3, 5, 0, 2, 3, 3, 1, 0, 1, 0, 5, 4, 4, 3, 6, 2, 3, 1, 3, 2, 2, 1, 0), # 61
(4, 10, 6, 6, 4, 1, 4, 2, 2, 2, 0, 1, 0, 4, 5, 5, 3, 5, 3, 4, 1, 2, 3, 1, 2, 0), # 62
(5, 4, 9, 4, 6, 4, 2, 1, 5, 0, 1, 0, 0, 5, 10, 7, 4, 4, 2, 3, 3, 1, 4, 3, 2, 0), # 63
(4, 7, 6, 11, 4, 0, 2, 0, 4, 3, 1, 3, 0, 5, 6, 2, 2, 5, 1, 0, 1, 4, 2, 1, 1, 0), # 64
(11, 3, 6, 6, 4, 2, 2, 1, 4, 1, 2, 2, 0, 8, 6, 4, 2, 6, 2, 2, 1, 5, 2, 1, 0, 0), # 65
(4, 7, 7, 4, 6, 2, 2, 1, 1, 2, 1, 0, 0, 6, 4, 3, 6, 5, 1, 2, 2, 3, 4, 0, 0, 0), # 66
(6, 10, 6, 12, 8, 5, 1, 3, 3, 0, 0, 3, 0, 8, 3, 5, 1, 3, 2, 1, 4, 2, 5, 2, 0, 0), # 67
(10, 4, 2, 5, 3, 3, 1, 2, 3, 1, 1, 0, 0, 7, 5, 7, 7, 7, 2, 0, 3, 3, 2, 2, 0, 0), # 68
(8, 2, 6, 7, 5, 4, 1, 1, 4, 0, 1, 1, 0, 2, 4, 8, 2, 9, 5, 2, 0, 4, 2, 0, 1, 0), # 69
(6, 6, 3, 2, 5, 2, 4, 3, 1, 0, 1, 0, 0, 13, 2, 4, 7, 2, 2, 4, 1, 2, 2, 0, 0, 0), # 70
(8, 7, 5, 5, 8, 2, 0, 2, 0, 0, 2, 0, 0, 9, 6, 2, 2, 8, 3, 1, 0, 3, 2, 1, 0, 0), # 71
(2, 1, 1, 9, 2, 4, 1, 1, 3, 3, 0, 0, 0, 5, 3, 5, 0, 6, 6, 2, 1, 3, 1, 0, 2, 0), # 72
(9, 2, 3, 5, 5, 2, 5, 2, 1, 1, 0, 1, 0, 5, 3, 5, 2, 6, 2, 2, 3, 2, 2, 1, 2, 0), # 73
(8, 4, 3, 8, 7, 5, 2, 1, 3, 1, 1, 0, 0, 5, 8, 4, 3, 4, 3, 4, 1, 3, 4, 0, 0, 0), # 74
(8, 4, 8, 7, 2, 2, 1, 3, 2, 0, 2, 0, 0, 9, 3, 4, 2, 2, 3, 0, 1, 1, 1, 0, 0, 0), # 75
(6, 3, 4, 3, 2, 0, 1, 0, 1, 1, 2, 1, 0, 8, 5, 6, 3, 4, 3, 2, 1, 3, 3, 1, 0, 0), # 76
(4, 10, 3, 2, 6, 3, 1, 1, 1, 2, 0, 0, 0, 6, 7, 2, 3, 0, 2, 1, 0, 3, 3, 2, 5, 0), # 77
(5, 4, 5, 4, 3, 2, 2, 1, 1, 2, 1, 0, 0, 6, 5, 3, 4, 5, 6, 3, 3, 5, 1, 2, 0, 0), # 78
(3, 5, 6, 7, 2, 1, 2, 2, 2, 0, 3, 0, 0, 9, 4, 3, 2, 2, 0, 4, 1, 1, 6, 0, 0, 0), # 79
(4, 9, 5, 4, 5, 1, 1, 2, 1, 2, 2, 0, 0, 12, 3, 6, 3, 4, 0, 1, 3, 2, 4, 1, 0, 0), # 80
(6, 10, 3, 11, 4, 5, 2, 2, 0, 0, 0, 1, 0, 4, 8, 5, 2, 5, 1, 3, 0, 1, 0, 2, 1, 0), # 81
(5, 4, 4, 4, 6, 6, 1, 3, 2, 1, 1, 0, 0, 5, 6, 6, 6, 7, 3, 3, 2, 5, 1, 0, 1, 0), # 82
(8, 7, 4, 3, 1, 1, 3, 0, 0, 0, 1, 1, 0, 1, 4, 5, 3, 9, 1, 4, 4, 1, 2, 0, 2, 0), # 83
(4, 9, 2, 4, 5, 4, 3, 0, 3, 0, 0, 0, 0, 4, 7, 3, 5, 5, 2, 3, 2, 1, 1, 2, 0, 0), # 84
(5, 6, 5, 8, 3, 3, 2, 2, 0, 4, 0, 0, 0, 7, 7, 5, 5, 4, 4, 1, 3, 4, 4, 0, 0, 0), # 85
(7, 4, 3, 3, 1, 3, 1, 2, 2, 0, 1, 0, 0, 5, 4, 3, 6, 6, 4, 3, 3, 2, 4, 2, 0, 0), # 86
(6, 3, 3, 3, 4, 2, 1, 2, 0, 0, 1, 1, 0, 7, 6, 3, 1, 3, 2, 2, 1, 3, 0, 1, 1, 0), # 87
(9, 1, 7, 7, 3, 1, 2, 4, 3, 1, 3, 0, 0, 6, 5, 5, 1, 6, 2, 2, 3, 3, 1, 1, 0, 0), # 88
(8, 6, 8, 8, 3, 3, 3, 0, 2, 1, 0, 0, 0, 4, 7, 4, 2, 9, 5, 2, 3, 2, 0, 1, 2, 0), # 89
(7, 6, 7, 6, 5, 5, 2, 3, 2, 0, 1, 0, 0, 7, 4, 2, 1, 3, 1, 2, 2, 1, 0, 2, 0, 0), # 90
(7, 3, 5, 6, 6, 1, 0, 4, 3, 1, 1, 0, 0, 4, 5, 7, 3, 5, 4, 2, 2, 4, 3, 4, 1, 0), # 91
(5, 5, 4, 12, 5, 6, 0, 0, 2, 1, 1, 0, 0, 7, 4, 3, 4, 4, 1, 3, 1, 3, 0, 0, 1, 0), # 92
(3, 3, 4, 3, 7, 1, 2, 0, 1, 1, 0, 1, 0, 4, 6, 3, 0, 9, 2, 1, 1, 2, 1, 0, 1, 0), # 93
(5, 3, 6, 6, 3, 1, 0, 3, 2, 1, 0, 1, 0, 6, 7, 3, 3, 2, 0, 0, 3, 2, 1, 0, 0, 0), # 94
(3, 2, 7, 6, 7, 0, 1, 1, 0, 3, 0, 0, 0, 6, 5, 0, 1, 2, 3, 0, 3, 3, 1, 2, 0, 0), # 95
(2, 3, 4, 6, 2, 0, 3, 1, 3, 0, 0, 1, 0, 10, 1, 4, 6, 4, 2, 2, 2, 2, 1, 0, 0, 0), # 96
(7, 8, 9, 3, 6, 3, 1, 0, 3, 2, 0, 0, 0, 6, 6, 1, 4, 3, 2, 2, 1, 1, 2, 1, 0, 0), # 97
(8, 6, 7, 4, 5, 1, 4, 0, 0, 0, 1, 0, 0, 2, 5, 3, 4, 8, 0, 0, 1, 0, 3, 0, 0, 0), # 98
(7, 5, 5, 2, 8, 3, 2, 2, 3, 1, 0, 1, 0, 5, 5, 2, 3, 9, 1, 2, 2, 5, 0, 1, 0, 0), # 99
(8, 3, 2, 7, 4, 3, 3, 0, 3, 0, 0, 0, 0, 4, 9, 2, 3, 6, 3, 0, 3, 0, 3, 0, 0, 0), # 100
(8, 3, 4, 6, 2, 4, 4, 1, 3, 0, 1, 2, 0, 6, 1, 4, 4, 6, 0, 2, 2, 2, 1, 2, 0, 0), # 101
(4, 1, 3, 7, 8, 2, 1, 1, 5, 2, 0, 0, 0, 2, 5, 3, 2, 2, 1, 4, 2, 1, 3, 1, 1, 0), # 102
(5, 5, 7, 3, 6, 4, 1, 3, 4, 0, 3, 0, 0, 10, 7, 4, 4, 3, 1, 3, 1, 2, 1, 0, 0, 0), # 103
(9, 6, 8, 2, 0, 2, 1, 3, 2, 2, 0, 1, 0, 8, 8, 4, 5, 1, 1, 2, 2, 3, 4, 2, 1, 0), # 104
(5, 2, 10, 6, 5, 4, 4, 2, 1, 0, 1, 1, 0, 6, 6, 4, 2, 3, 2, 1, 1, 1, 1, 3, 0, 0), # 105
(7, 3, 6, 6, 1, 3, 4, 2, 0, 2, 2, 0, 0, 10, 4, 3, 2, 5, 3, 3, 3, 2, 2, 1, 1, 0), # 106
(5, 8, 4, 9, 5, 4, 0, 2, 3, 3, 0, 1, 0, 5, 6, 2, 5, 1, 3, 3, 1, 2, 0, 0, 0, 0), # 107
(6, 2, 7, 5, 1, 1, 1, 2, 4, 1, 1, 1, 0, 10, 4, 6, 2, 5, 0, 0, 1, 0, 0, 2, 0, 0), # 108
(6, 3, 3, 7, 4, 5, 3, 1, 3, 0, 0, 0, 0, 6, 5, 3, 0, 3, 3, 2, 3, 2, 1, 1, 1, 0), # 109
(6, 0, 3, 3, 4, 3, 0, 3, 2, 1, 3, 1, 0, 9, 4, 5, 1, 8, 2, 5, 2, 1, 4, 2, 1, 0), # 110
(12, 5, 1, 3, 2, 0, 2, 3, 4, 1, 0, 0, 0, 10, 4, 5, 2, 3, 1, 1, 1, 2, 2, 0, 2, 0), # 111
(3, 6, 5, 4, 6, 2, 0, 1, 2, 2, 0, 1, 0, 4, 2, 3, 1, 9, 0, 0, 2, 2, 4, 1, 0, 0), # 112
(6, 1, 0, 5, 4, 5, 2, 2, 0, 0, 1, 0, 0, 6, 9, 2, 2, 2, 2, 0, 0, 1, 1, 1, 0, 0), # 113
(3, 2, 3, 4, 4, 0, 2, 1, 2, 1, 0, 0, 0, 7, 2, 3, 4, 4, 1, 1, 2, 1, 3, 1, 0, 0), # 114
(5, 3, 6, 4, 5, 1, 2, 3, 5, 0, 0, 1, 0, 3, 4, 2, 1, 5, 0, 0, 1, 2, 1, 1, 0, 0), # 115
(4, 4, 5, 1, 4, 2, 1, 1, 3, 0, 0, 0, 0, 6, 5, 1, 6, 0, 2, 0, 1, 1, 1, 1, 0, 0), # 116
(8, 6, 4, 1, 4, 2, 1, 1, 1, 0, 0, 0, 0, 5, 10, 2, 4, 4, 1, 1, 4, 3, 3, 0, 0, 0), # 117
(4, 1, 6, 5, 7, 2, 2, 0, 6, 1, 1, 0, 0, 3, 3, 2, 4, 3, 1, 2, 3, 1, 2, 2, 0, 0), # 118
(6, 3, 7, 4, 5, 3, 3, 3, 1, 0, 2, 0, 0, 5, 3, 2, 4, 5, 1, 3, 4, 1, 2, 0, 0, 0), # 119
(5, 7, 5, 5, 6, 1, 5, 0, 3, 1, 0, 0, 0, 6, 5, 2, 1, 3, 0, 0, 1, 3, 3, 0, 0, 0), # 120
(6, 2, 9, 7, 6, 5, 5, 4, 2, 1, 1, 1, 0, 5, 5, 3, 4, 3, 3, 1, 0, 2, 4, 0, 0, 0), # 121
(4, 5, 6, 1, 3, 2, 3, 2, 1, 2, 1, 0, 0, 8, 5, 0, 4, 4, 7, 4, 1, 1, 1, 1, 1, 0), # 122
(10, 3, 8, 6, 2, 5, 1, 1, 3, 1, 0, 0, 0, 10, 7, 3, 3, 5, 4, 0, 2, 1, 4, 0, 1, 0), # 123
(4, 6, 6, 8, 6, 1, 3, 0, 2, 0, 1, 1, 0, 3, 2, 3, 3, 6, 1, 3, 3, 1, 3, 2, 1, 0), # 124
(2, 5, 3, 1, 3, 1, 2, 0, 4, 1, 1, 2, 0, 5, 2, 3, 1, 2, 0, 1, 2, 2, 1, 1, 0, 0), # 125
(7, 3, 7, 4, 4, 2, 3, 1, 5, 1, 0, 0, 0, 4, 4, 2, 3, 4, 1, 3, 2, 5, 1, 1, 0, 0), # 126
(6, 5, 7, 4, 3, 1, 2, 1, 0, 0, 0, 2, 0, 9, 4, 3, 3, 3, 3, 0, 2, 4, 1, 0, 0, 0), # 127
(8, 1, 3, 7, 6, 2, 2, 1, 4, 2, 2, 0, 0, 11, 3, 14, 2, 2, 1, 0, 1, 1, 2, 1, 0, 0), # 128
(6, 4, 1, 4, 5, 5, 2, 2, 1, 2, 2, 3, 0, 14, 11, 2, 3, 8, 4, 4, 1, 2, 1, 1, 0, 0), # 129
(4, 3, 4, 7, 5, 2, 2, 1, 1, 1, 1, 0, 0, 7, 11, 3, 2, 4, 0, 1, 2, 5, 4, 1, 0, 0), # 130
(4, 2, 4, 3, 5, 3, 1, 0, 1, 1, 0, 1, 0, 4, 3, 2, 1, 4, 0, 3, 2, 1, 2, 1, 0, 0), # 131
(5, 3, 7, 8, 5, 0, 3, 1, 1, 0, 2, 0, 0, 4, 4, 3, 1, 3, 3, 2, 1, 1, 2, 1, 2, 0), # 132
(6, 3, 6, 0, 6, 1, 1, 4, 1, 0, 0, 0, 0, 7, 6, 0, 7, 2, 2, 3, 3, 2, 1, 0, 0, 0), # 133
(5, 6, 6, 5, 3, 1, 3, 1, 4, 0, 1, 0, 0, 9, 4, 5, 2, 5, 1, 0, 1, 3, 0, 2, 0, 0), # 134
(5, 3, 3, 4, 6, 1, 3, 0, 3, 0, 0, 0, 0, 9, 6, 5, 6, 6, 3, 1, 2, 1, 0, 0, 0, 0), # 135
(9, 4, 6, 4, 4, 2, 3, 3, 2, 2, 0, 0, 0, 5, 3, 4, 2, 2, 2, 2, 0, 0, 0, 1, 0, 0), # 136
(6, 4, 3, 4, 1, 2, 0, 1, 1, 1, 0, 1, 0, 7, 3, 1, 2, 1, 4, 2, 0, 0, 0, 2, 0, 0), # 137
(5, 3, 1, 6, 1, 2, 4, 4, 1, 0, 0, 0, 0, 5, 6, 2, 0, 3, 2, 3, 0, 1, 1, 0, 1, 0), # 138
(8, 2, 6, 2, 4, 2, 2, 3, 3, 2, 1, 1, 0, 4, 5, 2, 1, 4, 1, 4, 3, 3, 3, 0, 0, 0), # 139
(2, 4, 5, 3, 3, 2, 4, 4, 1, 0, 1, 1, 0, 7, 8, 5, 1, 3, 4, 1, 0, 2, 1, 2, 0, 0), # 140
(3, 1, 6, 4, 3, 1, 2, 3, 2, 0, 1, 0, 0, 4, 4, 1, 2, 8, 3, 3, 1, 3, 2, 0, 0, 0), # 141
(3, 6, 7, 6, 3, 3, 2, 1, 2, 2, 1, 2, 0, 0, 6, 5, 2, 8, 2, 0, 0, 2, 3, 3, 0, 0), # 142
(3, 4, 2, 5, 6, 3, 1, 2, 2, 0, 2, 1, 0, 8, 4, 3, 0, 6, 3, 2, 1, 4, 1, 0, 0, 0), # 143
(3, 6, 4, 4, 3, 1, 1, 1, 2, 1, 1, 1, 0, 9, 4, 1, 3, 6, 4, 1, 1, 2, 1, 0, 1, 0), # 144
(8, 6, 3, 6, 4, 2, 2, 1, 1, 3, 1, 0, 0, 6, 2, 3, 3, 3, 2, 1, 1, 3, 3, 0, 0, 0), # 145
(3, 2, 2, 3, 1, 2, 1, 1, 4, 0, 0, 0, 0, 6, 8, 4, 0, 4, 2, 3, 1, 0, 2, 1, 1, 0), # 146
(3, 4, 6, 7, 1, 0, 2, 1, 2, 0, 0, 3, 0, 6, 7, 5, 1, 1, 2, 2, 1, 1, 3, 0, 0, 0), # 147
(5, 5, 5, 3, 5, 2, 4, 1, 3, 2, 0, 0, 0, 7, 1, 4, 2, 2, 2, 3, 0, 2, 0, 2, 0, 0), # 148
(3, 2, 3, 5, 3, 1, 0, 0, 2, 1, 0, 1, 0, 1, 7, 4, 3, 2, 1, 0, 0, 2, 3, 1, 0, 0), # 149
(2, 5, 7, 4, 7, 3, 0, 0, 6, 0, 0, 0, 0, 4, 4, 5, 2, 4, 5, 2, 1, 2, 3, 0, 0, 0), # 150
(7, 4, 3, 2, 6, 3, 1, 0, 4, 0, 1, 1, 0, 7, 4, 5, 0, 4, 2, 3, 1, 2, 1, 2, 0, 0), # 151
(7, 4, 3, 5, 1, 3, 2, 1, 0, 1, 0, 1, 0, 2, 5, 2, 2, 5, 3, 1, 0, 2, 2, 1, 0, 0), # 152
(6, 0, 5, 2, 2, 2, 2, 0, 2, 3, 1, 0, 0, 8, 3, 3, 3, 7, 0, 1, 3, 4, 0, 1, 0, 0), # 153
(6, 4, 1, 10, 6, 2, 1, 1, 6, 0, 0, 0, 0, 4, 5, 1, 1, 4, 5, 0, 0, 3, 3, 0, 0, 0), # 154
(6, 4, 4, 7, 3, 3, 3, 2, 0, 0, 1, 0, 0, 11, 3, 6, 3, 1, 2, 0, 2, 2, 1, 1, 0, 0), # 155
(5, 3, 3, 8, 5, 1, 2, 1, 1, 1, 1, 0, 0, 6, 3, 1, 3, 1, 1, 2, 1, 2, 1, 1, 0, 0), # 156
(4, 2, 3, 2, 4, 1, 4, 1, 3, 0, 0, 0, 0, 4, 5, 5, 0, 7, 4, 5, 3, 0, 1, 1, 0, 0), # 157
(4, 5, 8, 1, 4, 2, 2, 1, 5, 0, 1, 0, 0, 1, 1, 1, 4, 5, 2, 0, 2, 1, 0, 0, 1, 0), # 158
(2, 3, 4, 5, 4, 1, 0, 3, 3, 1, 0, 0, 0, 9, 4, 6, 1, 1, 1, 2, 0, 2, 0, 1, 1, 0), # 159
(2, 5, 6, 4, 5, 2, 3, 1, 2, 0, 0, 1, 0, 3, 5, 1, 2, 3, 2, 2, 1, 1, 0, 1, 0, 0), # 160
(7, 4, 2, 6, 2, 1, 1, 1, 0, 0, 1, 0, 0, 4, 3, 2, 3, 4, 1, 1, 0, 2, 3, 2, 0, 0), # 161
(3, 7, 5, 9, 6, 3, 0, 2, 2, 2, 2, 0, 0, 6, 4, 4, 2, 4, 3, 3, 1, 1, 0, 2, 0, 0), # 162
(1, 2, 1, 3, 3, 3, 5, 3, 1, 0, 3, 1, 0, 9, 4, 4, 2, 1, 2, 0, 2, 0, 3, 3, 0, 0), # 163
(5, 3, 2, 8, 6, 1, 1, 2, 2, 1, 1, 0, 0, 3, 1, 1, 1, 4, 3, 2, 1, 3, 0, 2, 0, 0), # 164
(3, 3, 3, 5, 3, 0, 0, 6, 4, 1, 0, 0, 0, 2, 2, 3, 3, 4, 2, 0, 0, 2, 2, 0, 0, 0), # 165
(1, 2, 4, 4, 2, 2, 3, 1, 1, 0, 1, 0, 0, 3, 3, 1, 2, 5, 0, 0, 1, 2, 0, 2, 0, 0), # 166
(4, 3, 2, 6, 1, 0, 2, 2, 2, 0, 1, 0, 0, 3, 1, 1, 1, 2, 0, 1, 2, 3, 0, 0, 0, 0), # 167
(6, 1, 4, 0, 6, 2, 5, 0, 1, 1, 1, 1, 0, 2, 2, 2, 1, 2, 2, 2, 2, 3, 1, 0, 0, 0), # 168
(3, 2, 2, 1, 3, 1, 2, 3, 3, 1, 2, 0, 0, 3, 1, 0, 0, 1, 3, 0, 1, 2, 1, 1, 0, 0), # 169
(4, 4, 3, 5, 1, 1, 0, 0, 2, 1, 0, 1, 0, 2, 2, 0, 2, 4, 3, 1, 0, 1, 0, 0, 0, 0), # 170
(3, 2, 7, 2, 5, 1, 1, 1, 3, 0, 1, 0, 0, 2, 4, 2, 2, 2, 0, 1, 0, 1, 0, 0, 1, 0), # 171
(0, 3, 8, 5, 1, 1, 1, 2, 3, 0, 2, 1, 0, 2, 2, 1, 3, 5, 0, 0, 1, 0, 1, 1, 0, 0), # 172
(4, 2, 2, 2, 0, 1, 3, 0, 0, 1, 0, 0, 0, 4, 3, 2, 0, 2, 0, 0, 1, 1, 0, 0, 0, 0), # 173
(2, 4, 2, 2, 1, 0, 0, 3, 0, 0, 1, 0, 0, 7, 1, 3, 1, 2, 1, 2, 2, 1, 2, 1, 0, 0), # 174
(2, 2, 1, 2, 2, 3, 3, 1, 1, 0, 0, 0, 0, 2, 3, 1, 4, 2, 0, 2, 1, 0, 1, 0, 1, 0), # 175
(3, 0, 2, 1, 5, 0, 2, 1, 4, 1, 0, 0, 0, 1, 3, 3, 1, 3, 0, 1, 0, 2, 1, 1, 0, 0), # 176
(2, 5, 1, 4, 2, 1, 1, 0, 2, 0, 1, 1, 0, 0, 1, 1, 0, 2, 1, 0, 0, 1, 3, 2, 0, 0), # 177
(3, 1, 4, 5, 1, 2, 1, 1, 3, 1, 0, 0, 0, 3, 2, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(3.012519347023061, 3.3151731358159005, 3.1267699377675044, 3.72880066431806, 3.3328113645657673, 1.8831929592224035, 2.487621369224349, 2.7919057037876542, 3.6540594373008046, 2.374809009313422, 2.523159098331362, 2.9387490469837045, 3.050328127547018), # 0
(3.2125962912119848, 3.5340364164868556, 3.333207954798092, 3.9750865519659704, 3.5534942024845426, 2.007590150425534, 2.6516813647582165, 2.975708233743425, 3.895347195799746, 2.53138841536629, 2.689857681573679, 3.1327299964886133, 3.2518749884639133), # 1
(3.412033805387839, 3.752031102534588, 3.5388253713284485, 4.220392622798877, 3.773377784249847, 2.1314910687074673, 2.815091104776423, 3.158775148742652, 4.13567208412653, 2.6873482650123277, 2.855893889265788, 3.3259395912318332, 3.4526188969258147), # 2
(3.6100546758879375, 3.9682923011618687, 3.742806521591913, 4.463745844519245, 3.991597005601142, 2.254404373551068, 2.9772021849887738, 3.3403806944967047, 4.374081096552656, 2.842069619055709, 3.0206090470964297, 3.517611410958277, 3.651763683752536), # 3
(3.8058816890495892, 4.181955119571465, 3.9443357398218226, 4.704173184829543, 4.207286762277896, 2.375838724439201, 3.1373662011050754, 3.51979911671695, 4.609621227349625, 2.9949335383006077, 3.1833444807543456, 3.706979035412859, 3.848513179763891), # 4
(3.9987376312101066, 4.392154664966154, 4.142597360251511, 4.940701611432237, 4.419581950019569, 2.495302780854729, 3.294934748835135, 3.696304661114756, 4.841339470788936, 3.1453210835511984, 3.34344151592828, 3.8932760443404892, 4.042071215779696), # 5
(4.1878452887068, 4.598026044548702, 4.33677571711432, 5.172358092029792, 4.627617464565627, 2.6123052022805187, 3.449259423888758, 3.8691715734014935, 5.068282821142089, 3.2926133156116553, 3.50024147830697, 4.0757360174860855, 4.231641622619764), # 6
(4.372427447876982, 4.798704365521879, 4.526055144643582, 5.398169594324678, 4.830528201655533, 2.726354648199433, 3.5996918219757514, 4.037674099288531, 5.2894982726805875, 3.4361912952861524, 3.6530856935791616, 4.253592534594558, 4.416428231103912), # 7
(4.551706895057961, 4.99332473508846, 4.709619977072638, 5.6171630860193575, 5.027449057028752, 2.836959778094334, 3.745583538805919, 4.201086484487235, 5.504032819675924, 3.575436083378865, 3.801315487433593, 4.426079175410822, 4.595634872051951), # 8
(4.724906416587052, 5.181022260451214, 4.886654548634823, 5.828365534816302, 5.217514926424746, 2.943629251448091, 3.8862861700890723, 4.358682974708978, 5.710933456399605, 3.7097287406939654, 3.9442721855590075, 4.592429519679789, 4.768465376283698), # 9
(4.89124879880156, 5.36093204881291, 5.0563431935634755, 6.030803908417973, 5.399860705582981, 3.045871727743565, 4.0211513115350135, 4.509737815665127, 5.909247177123129, 3.838450328035629, 4.081297113644145, 4.751877147146372, 4.934123574618967), # 10
(5.049956828038804, 5.532189207376325, 5.2178702460919295, 6.2235051745268395, 5.573621290242921, 3.1431958664636213, 4.1495305588535505, 4.653525253067049, 6.098020976117997, 3.960981906208032, 4.21173159737775, 4.903655637555487, 5.091813297877567), # 11
(5.200253290636088, 5.69392884334422, 5.370420040453524, 6.4054963008453685, 5.737931576144027, 3.235110327091122, 4.270775507754488, 4.789319532626113, 6.276301847655708, 4.0767045360153435, 4.3349169624485615, 5.0469985706520415, 5.240738376879321), # 12
(5.341360972930726, 5.845286063919373, 5.513176910881598, 6.575804255076027, 5.891926459025762, 3.321123769108935, 4.384237753947634, 4.91639490005369, 6.44313678600776, 4.184999278261743, 4.4501945345453215, 5.181139526180953, 5.380102642444042), # 13
(5.47250266126003, 5.985395976304554, 5.645325191609489, 6.733456004921276, 6.034740834627595, 3.400744851999921, 4.489268893142797, 5.034025601061148, 6.597572785445654, 4.285247193751401, 4.5569056393567715, 5.305312083887137, 5.509109925391539), # 14
(5.592901141961314, 6.113393687702531, 5.766049216870527, 6.877478518083592, 6.165509598688986, 3.473482235246948, 4.585220521049776, 5.141485881359854, 6.738656840240893, 4.376829343288495, 4.6543916025716525, 5.418749823515504, 5.626964056541629), # 15
(5.701779201371881, 6.22841430531608, 5.874533320898055, 7.006898762265431, 6.283367646949401, 3.538844578332876, 4.671444233378386, 5.238049986661177, 6.865435944664973, 4.4591267876771985, 4.741993749878708, 5.520686324810964, 5.732868866714128), # 16
(5.798359625829046, 6.329592936347969, 5.969961837925412, 7.120743705169268, 6.387449875148303, 3.596340540740572, 4.747291625838427, 5.322992162676491, 6.976957092989391, 4.531520587721681, 4.81905340696668, 5.610355167518434, 5.82602818672885), # 17
(5.881865201670123, 6.416064688000965, 6.051519102185929, 7.218040314497568, 6.476891179025154, 3.645478781952902, 4.812114294139711, 5.395586655117157, 7.072267279485659, 4.593391804226123, 4.8849118995243055, 5.68698993138283, 5.905645847405608), # 18
(5.95151871523242, 6.486964667477848, 6.118389447912948, 7.29781555795279, 6.55082645431942, 3.6857679614527257, 4.865263833992037, 5.4551077096945475, 7.1504134984252685, 4.644121497994697, 4.938910553240327, 5.749824196149059, 5.9709256795642185), # 19
(6.00654295285325, 6.541427981981378, 6.169757209339802, 7.359096403237413, 6.608390596770567, 3.7167167387229116, 4.906091841105218, 5.500829572120031, 7.21044274407972, 4.683090729831576, 4.980390693803491, 5.7980915415620355, 6.021071514024495), # 20
(6.046160700869921, 6.578589738714336, 6.204806720699832, 7.400909818053893, 6.648718502118054, 3.737833773246322, 4.933949911189056, 5.532026488104974, 7.251402010720514, 4.709680560540933, 5.008693646902536, 5.831025547366677, 6.055287181606248), # 21
(6.0695947456197485, 6.597585044879487, 6.222722316226372, 7.422282770104703, 6.670945066101346, 3.7486277245058206, 4.948189639953361, 5.54797270336075, 7.27233829261915, 4.723272050926946, 5.0231607382262045, 5.847859793307895, 6.0727765131292974), # 22
(6.078236018005584, 6.599834156378602, 6.224953909465022, 7.424958487654322, 6.676639233619559, 3.7500000000000004, 4.9498824013556035, 5.549696296296298, 7.274955740740742, 4.724875363511661, 5.024974822297045, 5.8499385459533615, 6.075000000000001), # 23
(6.084607447820493, 6.598522222222224, 6.224588888888889, 7.424629166666668, 6.679864568168912, 3.7500000000000004, 4.948952287581701, 5.547300000000001, 7.274605000000001, 4.723890370370371, 5.024774747474749, 5.849451851851852, 6.075000000000001), # 24
(6.090844338126949, 6.59593621399177, 6.223868312757203, 7.423977623456792, 6.683018967356703, 3.7500000000000004, 4.947119341563787, 5.542592592592594, 7.2739120370370385, 4.7219513031550076, 5.024378039655818, 5.84849108367627, 6.075000000000001), # 25
(6.096946211428821, 6.592115637860084, 6.22280205761317, 7.423011265432099, 6.6861023210526636, 3.7500000000000004, 4.944412030985234, 5.535662962962964, 7.272885740740741, 4.719090425240055, 5.023788290310513, 5.847069410150893, 6.075000000000001), # 26
(6.102912590229983, 6.5871, 6.221400000000001, 7.421737500000001, 6.689114519126529, 3.7500000000000004, 4.940858823529413, 5.526600000000001, 7.271535000000001, 4.71534, 5.023009090909092, 5.845200000000001, 6.075000000000001), # 27
(6.108742997034302, 6.580928806584363, 6.2196720164609065, 7.420163734567902, 6.6920554514480415, 3.7500000000000004, 4.936488186879691, 5.515492592592594, 7.269868703703704, 4.710732290809329, 5.022044032921811, 5.842896021947875, 6.075000000000001), # 28
(6.114436954345651, 6.573641563786009, 6.217627983539096, 7.418297376543211, 6.694925007886933, 3.7500000000000004, 4.93132858871944, 5.5024296296296304, 7.267895740740742, 4.705299561042525, 5.02089670781893, 5.8401706447187935, 6.075000000000001), # 29
(6.119993984667899, 6.565277777777779, 6.215277777777779, 7.416145833333334, 6.697723078312943, 3.7500000000000004, 4.925408496732027, 5.4875, 7.265625000000001, 4.699074074074075, 5.019570707070708, 5.837037037037037, 6.075000000000001), # 30
(6.125413610504916, 6.555876954732512, 6.212631275720166, 7.41371651234568, 6.700449552595806, 3.7500000000000004, 4.918756378600825, 5.470792592592594, 7.263065370370372, 4.692088093278464, 5.018069622147401, 5.833508367626888, 6.075000000000001), # 31
(6.130695354360573, 6.5454786008230466, 6.209698353909465, 7.411016820987656, 6.7031043206052585, 3.7500000000000004, 4.9114007020092, 5.452396296296298, 7.260225740740742, 4.6843738820301795, 5.016397044519267, 5.829597805212622, 6.075000000000001), # 32
(6.135838738738739, 6.534122222222223, 6.20648888888889, 7.408054166666667, 6.705687272211039, 3.7500000000000004, 4.903369934640524, 5.432400000000001, 7.257115000000001, 4.6759637037037045, 5.014556565656566, 5.825318518518519, 6.075000000000001), # 33
(6.140843286143286, 6.521847325102881, 6.2030127572016465, 7.404835956790125, 6.70819829728288, 3.7500000000000004, 4.894692544178167, 5.410892592592593, 7.253742037037039, 4.666889821673526, 5.012551777029555, 5.820683676268862, 6.075000000000001), # 34
(6.145708519078085, 6.508693415637861, 6.199279835390948, 7.401369598765433, 6.710637285690523, 3.7500000000000004, 4.885396998305496, 5.3879629629629635, 7.250115740740743, 4.65718449931413, 5.0103862701084925, 5.81570644718793, 6.075000000000001), # 35
(6.150433960047004, 6.4947, 6.1953000000000005, 7.397662500000001, 6.713004127303702, 3.7500000000000004, 4.875511764705883, 5.3637000000000015, 7.246245, 4.64688, 5.008063636363637, 5.810400000000001, 6.075000000000001), # 36
(6.155019131553915, 6.479906584362141, 6.191083127572017, 7.393722067901235, 6.715298711992154, 3.7500000000000004, 4.865065311062697, 5.338192592592593, 7.242138703703705, 4.636008587105625, 5.005587467265246, 5.8047775034293565, 6.075000000000001), # 37
(6.159463556102686, 6.46435267489712, 6.186639094650206, 7.389555709876545, 6.717520929625615, 3.7500000000000004, 4.8540861050593085, 5.311529629629631, 7.2378057407407415, 4.624602524005488, 5.002961354283578, 5.798852126200276, 6.075000000000001), # 38
(6.163766756197193, 6.448077777777779, 6.181977777777779, 7.385170833333334, 6.719670670073823, 3.7500000000000004, 4.842602614379086, 5.2838, 7.2332550000000015, 4.612694074074075, 5.000188888888889, 5.7926370370370375, 6.075000000000001), # 39
(6.167928254341299, 6.431121399176956, 6.177109053497943, 7.380574845679013, 6.721747823206512, 3.7500000000000004, 4.830643306705399, 5.255092592592594, 7.228495370370371, 4.6003155006858725, 4.997273662551441, 5.786145404663925, 6.075000000000001), # 40
(6.171947573038878, 6.413523045267491, 6.17204279835391, 7.375775154320989, 6.723752278893422, 3.7500000000000004, 4.818236649721618, 5.225496296296297, 7.223535740740742, 4.587499067215364, 4.99421926674149, 5.779390397805213, 6.075000000000001), # 41
(6.175824234793801, 6.395322222222224, 6.166788888888891, 7.370779166666669, 6.725683927004286, 3.7500000000000004, 4.805411111111112, 5.195100000000001, 7.218385000000001, 4.574277037037038, 4.991029292929293, 5.772385185185186, 6.075000000000001), # 42
(6.179557762109936, 6.376558436213993, 6.161357201646092, 7.365594290123458, 6.727542657408843, 3.7500000000000004, 4.79219515855725, 5.163992592592594, 7.213052037037038, 4.560681673525378, 4.987707332585111, 5.7651429355281225, 6.075000000000001), # 43
(6.183147677491157, 6.357271193415639, 6.155757613168726, 7.360227932098767, 6.729328359976828, 3.7500000000000004, 4.778617259743404, 5.132262962962964, 7.207545740740741, 4.546745240054871, 4.9842569771792, 5.7576768175583, 6.075000000000001), # 44
(6.186593503441331, 6.337500000000001, 6.150000000000001, 7.354687500000001, 6.731040924577979, 3.7500000000000004, 4.764705882352942, 5.1000000000000005, 7.201875, 4.532500000000001, 4.980681818181819, 5.75, 6.075000000000001), # 45
(6.18989476246433, 6.317284362139919, 6.144094238683128, 7.348980401234569, 6.732680241082031, 3.7500000000000004, 4.7504894940692335, 5.067292592592594, 7.196048703703704, 4.517978216735254, 4.976985447063225, 5.7421256515775045, 6.075000000000001), # 46
(6.193050977064022, 6.296663786008231, 6.138050205761318, 7.343114043209877, 6.734246199358721, 3.7500000000000004, 4.735996562575648, 5.03422962962963, 7.190075740740742, 4.5032121536351175, 4.973171455293678, 5.73406694101509, 6.075000000000001), # 47
(6.196061669744278, 6.275677777777779, 6.131877777777778, 7.337095833333335, 6.735738689277787, 3.7500000000000004, 4.721255555555556, 5.0009000000000015, 7.1839650000000015, 4.488234074074074, 4.969243434343435, 5.725837037037038, 6.075000000000001), # 48
(6.198926363008972, 6.254365843621399, 6.125586831275721, 7.330933179012347, 6.737157600708965, 3.7500000000000004, 4.7062949406923265, 4.967392592592593, 7.177725370370371, 4.473076241426613, 4.965204975682754, 5.717449108367628, 6.075000000000001), # 49
(6.201644579361971, 6.232767489711936, 6.119187242798356, 7.324633487654322, 6.73850282352199, 3.7500000000000004, 4.691143185669331, 4.933796296296297, 7.171365740740741, 4.457770919067216, 4.961059670781894, 5.70891632373114, 6.075000000000001), # 50
(6.204215841307147, 6.210922222222223, 6.112688888888889, 7.318204166666668, 6.7397742475866, 3.7500000000000004, 4.675828758169935, 4.900200000000001, 7.164895000000001, 4.442350370370371, 4.956811111111112, 5.700251851851854, 6.075000000000001), # 51
(6.206639671348368, 6.188869547325105, 6.1061016460905355, 7.311652623456791, 6.74097176277253, 3.7500000000000004, 4.660380125877512, 4.866692592592592, 7.158322037037038, 4.426846858710563, 4.9524628881406665, 5.691468861454048, 6.075000000000001), # 52
(6.2089155919895065, 6.166648971193417, 6.099435390946504, 7.304986265432099, 6.742095258949519, 3.7500000000000004, 4.644825756475431, 4.833362962962964, 7.151655740740742, 4.411292647462278, 4.948018593340816, 5.682580521262003, 6.075000000000001), # 53
(6.211043125734431, 6.144300000000001, 6.0927, 7.298212500000001, 6.743144625987302, 3.7500000000000004, 4.629194117647059, 4.8003, 7.144905000000001, 4.395720000000001, 4.943481818181818, 5.6736, 6.075000000000001), # 54
(6.213021795087014, 6.121862139917696, 6.08590534979424, 7.291338734567901, 6.744119753755615, 3.7500000000000004, 4.613513677075769, 4.767592592592593, 7.138078703703704, 4.380161179698217, 4.938856154133933, 5.664540466392319, 6.075000000000001), # 55
(6.214851122551123, 6.099374897119342, 6.079061316872429, 7.28437237654321, 6.7450205321241965, 3.7500000000000004, 4.59781290244493, 4.73532962962963, 7.131185740740742, 4.364648449931414, 4.934145192667415, 5.655415089163238, 6.075000000000001), # 56
(6.21653063063063, 6.076877777777779, 6.072177777777779, 7.277320833333334, 6.745846850962781, 3.7500000000000004, 4.582120261437909, 4.703600000000002, 7.124235000000001, 4.349214074074075, 4.929352525252526, 5.646237037037038, 6.075000000000001), # 57
(6.218059841829408, 6.054410288065845, 6.065264609053499, 7.270191512345679, 6.746598600141105, 3.7500000000000004, 4.566464221738078, 4.672492592592593, 7.117235370370372, 4.333890315500687, 4.9244817433595225, 5.637019478737998, 6.075000000000001), # 58
(6.219438278651324, 6.03201193415638, 6.0583316872427995, 7.262991820987655, 6.747275669528908, 3.7500000000000004, 4.550873251028808, 4.642096296296297, 7.110195740740743, 4.318709437585735, 4.91953643845866, 5.627775582990399, 6.075000000000001), # 59
(6.220665463600247, 6.009722222222224, 6.051388888888889, 7.255729166666668, 6.747877948995923, 3.7500000000000004, 4.535375816993464, 4.612500000000001, 7.103125000000002, 4.303703703703705, 4.914520202020203, 5.61851851851852, 6.075000000000001), # 60
(6.22174091918005, 5.987580658436215, 6.044446090534981, 7.248410956790124, 6.748405328411888, 3.7500000000000004, 4.52000038731542, 4.5837925925925935, 7.0960320370370376, 4.2889053772290815, 4.909436625514404, 5.60926145404664, 6.075000000000001), # 61
(6.222664167894603, 5.965626748971194, 6.037513168724281, 7.241044598765434, 6.7488576976465415, 3.7500000000000004, 4.504775429678045, 4.556062962962963, 7.088925740740741, 4.274346721536352, 4.9042893004115236, 5.600017558299041, 6.075000000000001), # 62
(6.223434732247776, 5.9439, 6.030600000000001, 7.233637500000002, 6.749234946569615, 3.7500000000000004, 4.489729411764706, 4.529400000000001, 7.081815000000001, 4.26006, 4.899081818181819, 5.590800000000001, 6.075000000000001), # 63
(6.224052134743439, 5.922439917695474, 6.023716460905351, 7.226197067901236, 6.74953696505085, 3.7500000000000004, 4.474890801258776, 4.503892592592594, 7.074708703703704, 4.246077475994514, 4.893817770295548, 5.581621947873801, 6.075000000000001), # 64
(6.224515897885464, 5.901286008230453, 6.01687242798354, 7.218730709876544, 6.749763642959981, 3.7500000000000004, 4.460288065843622, 4.47962962962963, 7.067615740740742, 4.2324314128943765, 4.888500748222972, 5.572496570644719, 6.075000000000001), # 65
(6.224825544177719, 5.88047777777778, 6.010077777777779, 7.211245833333335, 6.749914870166744, 3.7500000000000004, 4.445949673202615, 4.456700000000001, 7.060545000000001, 4.2191540740740745, 4.883134343434344, 5.563437037037039, 6.075000000000001), # 66
(6.224980596124076, 5.860054732510289, 6.003342386831276, 7.203749845679013, 6.749990536540879, 3.7500000000000004, 4.431904091019124, 4.435192592592594, 7.053505370370371, 4.206277722908094, 4.877722147399926, 5.554456515775034, 6.075000000000001), # 67
(6.224874968688496, 5.839949183592195, 5.996643569958848, 7.196185044283416, 6.749926773178408, 3.7499304069501602, 4.418109116897789, 4.415006310013718, 7.046452709190674, 4.193772264988301, 4.872171593124226, 5.545518013139847, 6.074925090020577), # 68
(6.2238850241545896, 5.819547311827958, 5.989793055555555, 7.188170108695653, 6.749346405228758, 3.749380246913581, 4.4041609088079685, 4.39505925925926, 7.039078703703705, 4.181283834422658, 4.865917703349282, 5.536331384015596, 6.074331597222224), # 69
(6.221931472535338, 5.798755475906014, 5.982761059670782, 7.179652274557167, 6.748199588477366, 3.7482967535436673, 4.389996080736823, 4.375171467764061, 7.031341735253774, 4.168751714677641, 4.858889331915648, 5.526853656775686, 6.073159400720166), # 70
(6.219041796385758, 5.777586101154919, 5.9755500514403295, 7.17064410225443, 6.746500847252483, 3.7466974851394608, 4.375620995723393, 4.355349519890262, 7.023253326474624, 4.156176215605584, 4.851112422470318, 5.51709176232763, 6.07142393261317), # 71
(6.215243478260871, 5.756051612903226, 5.9681625, 7.161158152173914, 6.744264705882354, 3.744600000000001, 4.361042016806723, 4.3356, 7.014825000000002, 4.143557647058824, 4.842612918660288, 5.507052631578948, 6.069140625000001), # 72
(6.210564000715693, 5.734164436479491, 5.960600874485597, 7.151206984702094, 6.741505688695232, 3.7420218564243255, 4.346265507025856, 4.315929492455419, 7.00606827846365, 4.130896318889696, 4.833416764132553, 5.496743195437152, 6.066324909979425), # 73
(6.2050308463052435, 5.711936997212267, 5.952867644032922, 7.140803160225444, 6.7382383200193665, 3.7389806127114777, 4.331297829419834, 4.296344581618656, 6.996994684499315, 4.118192540950537, 4.823549902534114, 5.486170384809761, 6.062992219650208), # 74
(6.198671497584542, 5.689381720430108, 5.944965277777778, 7.129959239130436, 6.734477124183008, 3.7354938271604947, 4.3161453470277005, 4.276851851851853, 6.9876157407407415, 4.105446623093682, 4.813038277511962, 5.475341130604289, 6.059157986111113), # 75
(6.191513437108607, 5.666511031461571, 5.936896244855968, 7.118687781803544, 6.730236625514404, 3.731579058070417, 4.3008144228884975, 4.257457887517147, 6.977942969821674, 4.092658875171468, 4.801907832713096, 5.46426236372825, 6.054837641460906), # 76
(6.183584147432457, 5.643337355635206, 5.928663014403292, 7.10700134863124, 6.725531348341806, 3.727253863740284, 4.2853114200412685, 4.238169272976681, 6.967987894375859, 4.079829607036231, 4.7901845117845125, 5.452941015089165, 6.050046617798356), # 77
(6.174911111111112, 5.619873118279572, 5.920268055555556, 7.094912500000001, 6.7203758169934655, 3.722535802469136, 4.269642701525056, 4.218992592592594, 6.957762037037037, 4.066959128540305, 4.777894258373206, 5.441384015594544, 6.044800347222224), # 78
(6.165521810699589, 5.59613074472322, 5.91171383744856, 7.082433796296297, 6.714784555797629, 3.717442432556013, 4.253814630378901, 4.199934430727024, 6.947276920438958, 4.0540477495360285, 4.765063016126174, 5.429598296151903, 6.039114261831276), # 79
(6.155443728752909, 5.5721226602947045, 5.903002829218107, 7.069577797906604, 6.708772089082548, 3.7119913122999546, 4.237833569641849, 4.181001371742113, 6.936544067215364, 4.041095779875737, 4.751716728690414, 5.417590787668762, 6.033003793724281), # 80
(6.144704347826088, 5.547861290322582, 5.8941375, 7.0563570652173935, 6.702352941176471, 3.7062000000000004, 4.221705882352942, 4.1622, 6.925575, 4.028103529411766, 4.73788133971292, 5.405368421052633, 6.026484375), # 81
(6.133331150474146, 5.523359060135405, 5.885120318930043, 7.042784158615138, 6.6955416364076505, 3.7000860539551907, 4.205437931551223, 4.143536899862826, 6.914381241426613, 4.0150713079964495, 4.723582792840689, 5.392938127211033, 6.019571437757203), # 82
(6.121351619252104, 5.498628395061729, 5.8759537551440335, 7.028871638486313, 6.688352699104334, 3.693667032464564, 4.1890360802757325, 4.12501865569273, 6.902974314128945, 4.001999425482127, 4.708847031720717, 5.380306837051478, 6.0122804140946515), # 83
(6.108793236714976, 5.4736817204301085, 5.866640277777779, 7.014632065217393, 6.680800653594773, 3.686960493827161, 4.172506691565515, 4.106651851851852, 6.891365740740741, 3.988888191721134, 4.693700000000001, 5.367481481481482, 6.004626736111112), # 84
(6.095683485417786, 5.448531461569097, 5.857182355967079, 7.000077999194849, 6.672900024207215, 3.679983996342022, 4.1558561284596145, 4.088443072702333, 6.879567043895747, 3.9757379165658038, 4.678167641325537, 5.354468991408563, 5.996625835905351), # 85
(6.082049847915549, 5.423190043807249, 5.8475824588477385, 6.985222000805155, 6.664665335269911, 3.6727550983081856, 4.139090753997072, 4.0703989026063105, 6.867589746227711, 3.962548909868475, 4.662275899344321, 5.341276297740237, 5.988293145576132), # 86
(6.067919806763285, 5.397669892473119, 5.837843055555556, 6.970076630434783, 6.656111111111113, 3.6652913580246915, 4.122216931216932, 4.052525925925926, 6.855445370370372, 3.9493214814814817, 4.64605071770335, 5.327910331384016, 5.979644097222223), # 87
(6.053320844516015, 5.371983432895261, 5.827966615226338, 6.954654448470211, 6.647251876059067, 3.657610333790581, 4.105241023158235, 4.03483072702332, 6.843145438957477, 3.936055941257161, 4.62951804004962, 5.31437802324742, 5.970694122942389), # 88
(6.0382804437287545, 5.346143090402231, 5.817955606995886, 6.938968015297908, 6.638102154442025, 3.649729583904893, 4.088169392860025, 4.017319890260632, 6.8307014746227726, 3.92275259904785, 4.612703810030127, 5.300686304237962, 5.961458654835392), # 89
(6.022826086956522, 5.320161290322582, 5.807812500000001, 6.923029891304349, 6.628676470588237, 3.6416666666666675, 4.071008403361345, 4.0, 6.818125000000002, 3.909411764705883, 4.595633971291867, 5.286842105263158, 5.951953125000001), # 90
(6.006985256754339, 5.294050457984866, 5.797539763374486, 6.906852636876009, 6.61898934882595, 3.633439140374943, 4.053764417701237, 3.9828776406035673, 6.80542753772291, 3.896033748083596, 4.578334467481836, 5.2728523572305255, 5.942192965534979), # 91
(5.990785435677224, 5.267823018717643, 5.787139866255145, 6.890448812399357, 6.6090553134834185, 3.6250645633287615, 4.036443798918746, 3.9659593964334716, 6.792620610425241, 3.882618859033326, 4.5608312422470325, 5.258723991047579, 5.932193608539095), # 92
(5.974254106280194, 5.2414913978494635, 5.776615277777778, 6.873830978260871, 6.59888888888889, 3.6165604938271607, 4.019052910052911, 3.949251851851853, 6.779715740740742, 3.8691674074074074, 4.543150239234451, 5.244463937621833, 5.921970486111111), # 93
(5.957418751118269, 5.215068020708881, 5.765968467078191, 6.8570116948470226, 6.588504599370613, 3.607944490169182, 4.001598114142777, 3.9327615912208516, 6.766724451303157, 3.8556797030581786, 4.525317402091087, 5.230079127860805, 5.911539030349795), # 94
(5.940306852746467, 5.188565312624453, 5.755201903292182, 6.840003522544285, 6.577916969256839, 3.5992341106538643, 3.984085774227387, 3.916495198902607, 6.753658264746229, 3.842156055837973, 4.507358674463938, 5.2155764926720085, 5.900914673353911), # 95
(5.922945893719809, 5.161995698924733, 5.744318055555557, 6.822819021739131, 6.5671405228758175, 3.590446913580247, 3.966522253345783, 3.9004592592592604, 6.7405287037037045, 3.828596775599129, 4.489300000000001, 5.200962962962964, 5.890112847222223), # 96
(5.90536335659331, 5.135371604938273, 5.733319393004115, 6.805470752818036, 6.556189784555799, 3.581600457247371, 3.9489139145370085, 3.88466035665295, 6.7273472908093295, 3.815002172193981, 4.47116732234627, 5.186245469641182, 5.8791489840535), # 97
(5.8875867239219914, 5.108705455993629, 5.722208384773663, 6.7879712761674735, 6.545079278625032, 3.572712299954276, 3.931267120840106, 3.869105075445817, 6.714125548696845, 3.8013725554748654, 4.452986585149744, 5.17143094361418, 5.868038515946503), # 98
(5.86964347826087, 5.0820096774193555, 5.710987500000001, 6.770333152173914, 6.533823529411766, 3.5638000000000005, 3.9135882352941187, 3.8538000000000006, 6.700875000000001, 3.787708235294118, 4.434783732057417, 5.156526315789474, 5.8567968750000015), # 99
(5.851561102164967, 5.0552966945440065, 5.6996592078189305, 6.752568941223833, 6.5224370612442515, 3.5548811156835853, 3.8958836209380876, 3.8387517146776413, 6.687607167352539, 3.7740095215040754, 4.416584706716287, 5.141538517074581, 5.8454394933127585), # 100
(5.833367078189301, 5.028578932696138, 5.6882259773662565, 6.734691203703705, 6.510934398450739, 3.54597320530407, 3.8781596408110577, 3.823966803840878, 6.674333573388204, 3.760276723957073, 4.398415452773348, 5.126474478377013, 5.83398180298354), # 101
(5.81508888888889, 5.001868817204302, 5.676690277777778, 6.716712500000002, 6.499330065359478, 3.537093827160495, 3.8604226579520704, 3.8094518518518523, 6.661065740740741, 3.7465101525054476, 4.3803019138755985, 5.1113411306042895, 5.822439236111112), # 102
(5.796754016818752, 4.975178773397054, 5.665054578189301, 6.6986453904991965, 6.4876385862987185, 3.5282605395518982, 3.8426790354001685, 3.795213443072703, 6.647815192043897, 3.7327101170015338, 4.362270033670034, 5.0961454046639245, 5.810827224794239), # 103
(5.778389944533907, 4.9485212266029475, 5.653321347736626, 6.680502435587763, 6.475874485596709, 3.519490900777321, 3.824935136194396, 3.78125816186557, 6.634593449931414, 3.7188769272976687, 4.3443457558036505, 5.080894231463433, 5.799161201131688), # 104
(5.760024154589373, 4.921908602150538, 5.641493055555556, 6.662296195652175, 6.4640522875817, 3.510802469135803, 3.8071973233737952, 3.7675925925925933, 6.621412037037038, 3.7050108932461883, 4.326555023923445, 5.0655945419103325, 5.787456597222223), # 105
(5.741684129540169, 4.89535332536838, 5.629572170781894, 6.6440392310789065, 6.452186516581943, 3.5022128029263833, 3.7894719599774076, 3.754223319615913, 6.608282475994513, 3.6911123246994273, 4.308923781676413, 5.050253266912137, 5.775728845164609), # 106
(5.723397351941315, 4.868867821585027, 5.617561162551442, 6.625744102254429, 6.4402916969256845, 3.4937394604481034, 3.7717654090442774, 3.741156927297669, 6.5952162894375865, 3.677181531509724, 4.291477972709552, 5.034877337376363, 5.763993377057614), # 107
(5.705191304347827, 4.842464516129033, 5.605462500000002, 6.607423369565219, 6.428382352941177, 3.4854000000000007, 3.7540840336134456, 3.7284000000000006, 6.582225000000001, 3.6632188235294123, 4.274243540669857, 5.019473684210528, 5.7522656250000015), # 108
(5.687093469314727, 4.816155834328954, 5.5932786522633755, 6.589089593397746, 6.416473008956671, 3.4772119798811163, 3.7364341967239576, 3.715959122085049, 6.569320130315502, 3.649224510610829, 4.257246429204325, 5.0040492383221435, 5.740561021090536), # 109
(5.66913132939703, 4.789954201513343, 5.581012088477368, 6.570755334138488, 6.4045781893004134, 3.4691929583904897, 3.718822261414854, 3.7038408779149523, 6.5565132030178335, 3.6351989026063105, 4.240512581959951, 4.988610930618729, 5.728894997427985), # 110
(5.6513323671497595, 4.763872043010754, 5.56866527777778, 6.552433152173914, 6.392712418300655, 3.4613604938271614, 3.7012545907251795, 3.6920518518518524, 6.543815740740742, 3.6211423093681923, 4.224067942583733, 4.9731656920077985, 5.717282986111112), # 111
(5.633724065127931, 4.737921784149741, 5.556240689300413, 6.5341356078905, 6.380890220285646, 3.45373214449017, 3.6837375476939753, 3.680598628257888, 6.53123926611797, 3.60705504074881, 4.207938454722666, 4.957720453396867, 5.705740419238684), # 112
(5.616302534221828, 4.712159171271572, 5.543770696951242, 6.515900329495225, 6.3691054081042235, 3.4463218615287086, 3.6663155781940624, 3.6695115412683643, 6.518827686755173, 3.592982841967638, 4.192154343691721, 4.942315761902657, 5.6942663405059335), # 113
(5.59888853874004, 4.686838310115322, 5.531427405012972, 6.497873652766403, 6.357236012739796, 3.439112765117941, 3.649210925046348, 3.6589267557030722, 6.506771421427837, 3.57918910074767, 4.176746602267919, 4.927147303267786, 5.682765248496022), # 114
(5.581430941802398, 4.661968319209828, 5.519218159624391, 6.480050703109068, 6.345244606733162, 3.4320861074992592, 3.6324357901149376, 3.648841592567226, 6.495074987201274, 3.5656951967928467, 4.161692706856257, 4.912222549535311, 5.6712039789962265), # 115
(5.5639079239425815, 4.637512968775022, 5.507119313990325, 6.4623996901598435, 6.333113115830268, 3.425225326372259, 3.6159628905167365, 3.6392281877393966, 6.483708803536699, 3.552476015069729, 4.146963558762889, 4.897513918492408, 5.659564355853536), # 116
(5.546297665694264, 4.613436029030833, 5.4951072213155925, 6.444888823555347, 6.320823465777067, 3.4185138594365347, 3.599764943368652, 3.6300586770981513, 6.472643289895323, 3.5395064405448777, 4.132530059293971, 4.882993827926251, 5.647828202914936), # 117
(5.528578347591128, 4.589701270197195, 5.483158234805021, 6.427486312932201, 6.308357582319506, 3.4119351443916837, 3.58381466578759, 3.6213051965220586, 6.461848865738362, 3.526761358184856, 4.118363109755655, 4.86863469562401, 5.635977344027416), # 118
(5.5107281501668455, 4.566272462494038, 5.471248707663435, 6.410160367927024, 6.295697391203533, 3.4054726189373, 3.568084774890454, 3.6129398818896874, 6.451295950527027, 3.5142156529562234, 4.104433611454097, 4.8544089393728616, 5.6239936030379605), # 119
(5.492725253955098, 4.543113376141296, 5.4593549930956575, 6.392879198176438, 6.282824818175099, 3.3991097207729797, 3.5525479877941524, 3.604934869079606, 6.440954963722536, 3.501844209825544, 4.09071246569545, 4.840288976959979, 5.611858803793559), # 120
(5.474547839489562, 4.520187781358898, 5.447453444306514, 6.375611013317061, 6.269721788980152, 3.3928298875983187, 3.5371770216155887, 3.5972622939703847, 6.4307963247861, 3.489621913759378, 4.07717057378587, 4.8262472261725335, 5.599554770141197), # 121
(5.456174087303913, 4.497459448366778, 5.435520414500828, 6.3583240229855145, 6.2563702293646415, 3.386616557112912, 3.521944593471671, 3.5898942924405897, 6.420790453178935, 3.4775236497242865, 4.063778837031511, 4.812256104797701, 5.587063325927863), # 122
(5.437582177931832, 4.474892147384865, 5.423532256883422, 6.340986436818417, 6.242752065074515, 3.3804531670163556, 3.5068234204793036, 3.5828030003687905, 6.410907768362253, 3.4655243026868328, 4.050508156738527, 4.798288030622653, 5.5743662950005435), # 123
(5.418750291906993, 4.452449648633093, 5.411465324659123, 6.323566464452393, 6.228849221855723, 3.374323155008244, 3.491786219755392, 3.575960553633556, 6.40111868979727, 3.453598757613577, 4.037329434213072, 4.784315421434566, 5.561445501206228), # 124
(5.399656609763076, 4.430095722331393, 5.399295971032755, 6.306032315524057, 6.214643625454214, 3.368209958788174, 3.476805708416844, 3.569339088113455, 6.391393636945197, 3.441721899471081, 4.024213570761301, 4.770310695020609, 5.548282768391898), # 125
(5.380279312033758, 4.407794138699697, 5.38700054920914, 6.288352199670033, 6.200117201615937, 3.362097016055741, 3.4618546035805626, 3.562910739687056, 6.381703029267252, 3.429868613225906, 4.011131467689368, 4.756246269167961, 5.534859920404548), # 126
(5.360596579252717, 4.385508667957935, 5.374555412393104, 6.27049432652694, 6.18525187608684, 3.3559677645105395, 3.446905622363457, 3.556647644232928, 6.372017286224645, 3.4180137838446147, 3.9980540263034277, 4.74209456166379, 5.52115878109116), # 127
(5.340586591953628, 4.363203080326041, 5.361936913789471, 6.2524269057314, 6.170029574612875, 3.349805641852167, 3.4319314818824296, 3.5505219376296377, 6.362306827278592, 3.406132296293768, 3.984952147909635, 4.727827990295274, 5.507161174298723), # 128
(5.320227530670169, 4.340841146023945, 5.349121406603064, 6.2341181469200295, 6.154432222939987, 3.343594085780217, 3.4169048992543885, 3.5445057557557567, 6.352542071890306, 3.394199035539928, 3.971796733814143, 4.713418972849582, 5.492848923874224), # 129
(5.29949757593602, 4.3183866352715805, 5.336085244038708, 6.215536259729452, 6.138441746814129, 3.3373165339942856, 3.401798591596238, 3.538571234489851, 6.3426934395210015, 3.3821888865496548, 3.958558685323107, 4.698839927113892, 5.47820385366465), # 130
(5.2783749082848574, 4.295803318288876, 5.322804779301229, 6.196649453796286, 6.122040071981248, 3.330956424193969, 3.386585276024886, 3.5326905097104904, 6.332731349631892, 3.3700767342895115, 3.945208903742681, 4.6840632708753756, 5.463207787516988), # 131
(5.256837708250356, 4.273054965295767, 5.309256365595449, 6.177425938757153, 6.105209124187293, 3.324497194078862, 3.3712376696572357, 3.5268357172962443, 6.322626221684193, 3.3578374637260593, 3.931718290379019, 4.669061421921206, 5.447842549278226), # 132
(5.234864156366198, 4.250105346512182, 5.295416356126191, 6.1578339242486715, 6.087930829178212, 3.3179222813485603, 3.355728489610194, 3.52097899312568, 6.312348475139117, 3.34544595982586, 3.9180577465382767, 4.6538067980385565, 5.432089962795352), # 133
(5.212432433166057, 4.226918232158054, 5.281261104098283, 6.137841619907463, 6.070187112699956, 3.3112151237026595, 3.3400304530006677, 3.5150924730773667, 6.301868529457878, 3.3328771075554746, 3.904198173526608, 4.6382718170146005, 5.41593185191535), # 134
(5.189520719183613, 4.203457392453315, 5.266766962716546, 6.117417235370148, 6.051959900498473, 3.3043591588407555, 3.324116276945561, 3.5091482930298725, 6.291156804101688, 3.320105791881466, 3.890110472650166, 4.622428896636512, 5.399350040485213), # 135
(5.1661071949525414, 4.179686597617896, 5.251910285185806, 6.0965289802733444, 6.033231118319711, 3.2973378244624443, 3.307958678561781, 3.5031185888617666, 6.280183718531765, 3.307106897770394, 3.8757655452151067, 4.606250454691465, 5.382326352351923), # 136
(5.142170041006521, 4.155569617871729, 5.236667424710887, 6.075145064253676, 6.0139826919096215, 3.290134558267319, 3.2915303749662335, 3.496975496451618, 6.26891969220932, 3.2938553101888215, 3.8611342925275838, 4.5897089089666325, 5.364842611362467), # 137
(5.117687437879229, 4.131070223434746, 5.221014734496611, 6.053233696947759, 5.994196547014152, 3.2827327979549787, 3.274804083275823, 3.4906911516779946, 6.25733514459557, 3.2803259141033085, 3.8461876158937516, 4.572776677249188, 5.346880641363835), # 138
(5.092637566104342, 4.106152184526877, 5.204928567747805, 6.030763087992216, 5.9738546093792495, 3.2751159812250163, 3.2577525206074562, 3.484237690419465, 6.245400495151723, 3.2664935944804188, 3.8308964166197645, 4.5554261773263045, 5.328422266203012), # 139
(5.066998606215539, 4.080779271368056, 5.188385277669293, 6.007701447023668, 5.952938804750868, 3.267267545777028, 3.2403484040780386, 3.477587248554598, 6.233086163339, 3.2523332362867117, 3.8152315960117766, 4.537629826985156, 5.30944930972699), # 140
(5.040748738746498, 4.054915254178213, 5.171361217465897, 5.984016983678733, 5.931431058874953, 3.2591709293106095, 3.2225644508044775, 3.4707119619619626, 6.220362568618609, 3.237819724488751, 3.7991640553759436, 4.519360044012916, 5.2899435957827485), # 141
(5.013866144230894, 4.02852390317728, 5.153832740342443, 5.959677907594033, 5.9093132974974525, 3.2508095695253565, 3.2043733779036763, 3.4635839665201273, 6.207200130451766, 3.222927944053097, 3.7826646960184185, 4.500589246196757, 5.26988694821728), # 142
(4.986329003202405, 4.001568988585189, 5.135776199503756, 5.934652428406186, 5.886567446364318, 3.242166904120864, 3.1857479024925417, 3.4561753981076597, 6.193569268299689, 3.207632779946312, 3.765704419245356, 4.481289851323854, 5.249261190877571), # 143
(4.958115496194711, 3.9740142806218723, 5.117167948154657, 5.908908755751814, 5.8631754312214985, 3.2332263707967286, 3.1666607416879797, 3.44845839260313, 6.179440401623586, 3.1919091171349563, 3.748254126362911, 4.46143427718138, 5.228048147610609), # 144
(4.929203803741487, 3.94582354950726, 5.097984339499974, 5.882415099267537, 5.839119177814941, 3.2239714072525447, 3.1470846126068963, 3.4404050858851063, 6.164783949884673, 3.175731840585593, 3.7302847186772374, 4.440994941556509, 5.206229642263381), # 145
(4.899572106376411, 3.916960565461286, 5.07820172674453, 5.855139668589977, 5.814380611890596, 3.214385451187909, 3.1269922323661983, 3.4319876138321566, 6.149570332544164, 3.1590758352647828, 3.71176709749449, 4.419944262236412, 5.183787498682872), # 146
(4.8691985846331605, 3.8873890987038786, 5.057796463093147, 5.827050673355749, 5.788941659194411, 3.204451940302415, 3.106356318082789, 3.4231781123228497, 6.133769969063275, 3.1419159861390877, 3.692672164120822, 4.398254657008267, 5.160703540716072), # 147
(4.838061419045413, 3.857072919454973, 5.036744901750651, 5.798116323201479, 5.7627842454723375, 3.194154312295661, 3.0851495868735763, 3.4139487172357548, 6.117353278903218, 3.124227178175069, 3.67297081986239, 4.375898543659242, 5.136959592209966), # 148
(4.806138790146848, 3.8259757979344986, 5.015023395921867, 5.7683048277637825, 5.735890296470323, 3.18347600486724, 3.0633447558554647, 3.4042715644494406, 6.100290681525204, 3.1059842963392885, 3.652633966025346, 4.352848339976515, 5.112537477011543), # 149
(4.773408878471139, 3.794061504362388, 4.992608298811617, 5.737584396679283, 5.708241737934316, 3.17240045571675, 3.040914542145361, 3.3941187898424743, 6.082552596390452, 3.0871622255983064, 3.631632503915846, 4.329076463747258, 5.087419018967789), # 150
(4.7398498645519656, 3.761293808958573, 4.969475963624726, 5.705923239584599, 5.679820495610267, 3.1609111025437833, 3.0178316628601705, 3.3834625292934266, 6.0641094429601745, 3.067735850918687, 3.609937334840043, 4.304555332758643, 5.0615860419256915), # 151
(4.705439928923006, 3.727636481942984, 4.945602743566021, 5.673289566116352, 5.650608495244122, 3.1489913830479384, 2.994068835116799, 3.372274918680865, 6.044931640695583, 3.04768005726699, 3.5875193601040936, 4.2792573647978465, 5.035020369732239), # 152
(4.670157252117937, 3.6930532935355544, 4.9209649918403215, 5.63965158591116, 5.620587662581834, 3.1366247349288097, 2.969598776032152, 3.3605280938833575, 6.0249896090578945, 3.0269697296097777, 3.5643494810141503, 4.253154977652039, 5.007703826234417), # 153
(4.6339800146704375, 3.657508013956215, 4.895539061652456, 5.604977508605646, 5.589739923369349, 3.1237945958859927, 2.9443942027231373, 3.3481941907794743, 6.00425376750832, 3.005579752913612, 3.5403985988763678, 4.2262205891083955, 4.979618235279215), # 154
(4.596886397114182, 3.6209644134248973, 4.869301306207246, 5.569235543836427, 5.5580472033526185, 3.110484403619083, 2.9184278323066573, 3.335245345247782, 5.982694535508077, 2.983485012145053, 3.5156376149969004, 4.198426616954089, 4.950745420713616), # 155
(4.5588545799828495, 3.5833862621615333, 4.842228078709517, 5.532393901240126, 5.525491428277589, 3.096677595827677, 2.89167238189962, 3.3216536931668514, 5.960282332518377, 2.9606603922706642, 3.490037430681903, 4.169745478976294, 4.9210672063846115), # 156
(4.519862743810118, 3.5447373303860545, 4.814295732364092, 5.494420790453363, 5.492054523890212, 3.0823576102113686, 2.8641005686189316, 3.3073913704152496, 5.936987578000434, 2.937080778257005, 3.4635689472375297, 4.1401495929621825, 4.890565416139187), # 157
(4.478808567843144, 3.5042718724633555, 4.784155172341414, 5.453861748990747, 5.4562086635226, 3.0666146857902663, 2.8350640325567147, 3.291478171409624, 5.910997254959459, 2.9120195497746866, 3.435357451523366, 4.108559738516604, 4.857891515649208), # 158
(4.429372060187042, 3.457839191759687, 4.744042691041793, 5.402386295273073, 5.409114785868978, 3.0442137888042624, 2.80092765803143, 3.26832811965863, 5.8718563567332875, 2.8813685964592475, 3.400450161371397, 4.068817514209865, 4.815256588152117), # 159
(4.370923256942587, 3.405058037124429, 4.693152574773534, 5.339146506245316, 5.349852078808078, 3.0146047776286107, 2.7613462490302707, 3.237359513716408, 5.8184551363371915, 2.8447233911177365, 3.3583557051657245, 4.020301169332709, 4.761852365336149), # 160
(4.303933232751577, 3.3462725208482818, 4.632028183146186, 5.264743502254038, 5.279035874569268, 2.9781463449421888, 2.7166088676041262, 3.1989603198658685, 5.751497860199412, 2.8023949159025627, 3.3094449927598246, 3.963460481344543, 4.698224426891459), # 161
(4.228873062255815, 3.281826755221944, 4.561212875769298, 5.179778403645797, 5.197281505381923, 2.9351971834238735, 2.667004575803886, 3.1535185043899214, 5.671688794748182, 2.7546941529661395, 3.254088934007173, 3.8987452277047767, 4.6249183525082005), # 162
(4.146213820097099, 3.212064852536115, 4.4812500122524215, 5.084852330767161, 5.105204303475413, 2.886115985752544, 2.6128224356804406, 3.1014220335714753, 5.5797322064117445, 2.7019320844608754, 3.1926584387612453, 3.82660518587282, 4.5424797218765285), # 163
(4.056426580917231, 3.1373309250814927, 4.3926829522051065, 4.9805664039646915, 5.003419601079114, 2.831261444607078, 2.55435150928468, 3.043058873693442, 5.476332361618335, 2.644419692539181, 3.125524416875518, 3.7474901333080823, 4.451454114686597), # 164
(3.9599824193580107, 3.0579690851487795, 4.296055055236902, 4.867521743584952, 4.892542730422395, 2.770992252666352, 2.4918808586674936, 2.978816991038728, 5.362193526796188, 2.5824679593534685, 3.0530577782034674, 3.6618498474699726, 4.3523871106285625), # 165
(3.857352410061239, 2.974323445028673, 4.191909680957357, 4.746319469974502, 4.773189023734629, 2.7056671026092456, 2.425699545879772, 2.9090843518902467, 5.238019968373544, 2.5163878670561477, 2.9756294325985677, 3.5701341058179015, 4.245824289392578), # 166
(3.749007627668714, 2.886738117011873, 4.080790188976023, 4.617560703479906, 4.645973813245188, 2.6356446871146355, 2.356096632972405, 2.834248922530906, 5.10451595277864, 2.4464903977996286, 2.8936102899142964, 3.4727926858112754, 4.132311230668798), # 167
(3.6354191468222377, 2.7955572133890776, 3.9632399389024493, 4.4818465644477286, 4.511512431183446, 2.5612836988614, 2.2833611819962827, 2.754698669243616, 4.962385746439714, 2.3730865337363234, 2.8073712600041287, 3.3702753649095074, 4.012393514147377), # 168
(3.5170580421636095, 2.701124846450988, 3.839802290346186, 4.339778173224531, 4.370420209778772, 2.482942830528417, 2.207782255002295, 2.6708215583112875, 4.812333615785003, 2.2964872570186423, 2.717283252721541, 3.2630319205720038, 3.8866167195184715), # 169
(3.3943953883346305, 2.603785128488303, 3.7110206029167814, 4.191956650156873, 4.223312481260541, 2.400980774794564, 2.129648914041332, 2.5830055560168286, 4.655063827242744, 2.217003549798995, 2.62371717792001, 3.1515121302581757, 3.7555264264722337), # 170
(3.2679022599771006, 2.503882171791721, 3.577438236223787, 4.038983115591321, 4.070804577858124, 2.3157562243387195, 2.0492502211642836, 2.49163862864315, 4.491280647241174, 2.1349463942297935, 2.527043945453009, 3.036165771427432, 3.6196682146988195), # 171
(3.1380497317328193, 2.401760088651942, 3.4395985498767523, 3.8814586898744383, 3.913511831800893, 2.2276278718397604, 1.9668752384220396, 2.3971087424731627, 4.321688342208532, 2.050626772463448, 2.4276344651740165, 2.917442621539183, 3.4795876638883825), # 172
(3.0053088782435884, 2.2977629913596656, 3.2980449034852275, 3.719984493352786, 3.7520495753182215, 2.1369544099765654, 1.88281302786549, 2.2998038637897746, 4.146991178573054, 1.9643556666523692, 2.325859646936507, 2.7957924580528353, 3.335830353731078), # 173
(2.8701507741512065, 2.192234992205591, 3.1533206566587615, 3.555161646372925, 3.5870331406394804, 2.0440945314280112, 1.7973526515455256, 2.2001119588758966, 3.967893422762979, 1.8764440589489682, 2.222090400593957, 2.671665058427801, 3.1889418639170604), # 174
(2.7330464940974735, 2.0855202034804172, 3.0059691690069053, 3.3875912692814207, 3.419077859994042, 1.9494069288729774, 1.710783171513035, 2.098420994014438, 3.7850993412065437, 1.7872029315056548, 2.116697635999842, 2.545510200123489, 3.039467774136485), # 175
(2.5944671127241916, 1.977962737474844, 2.8565338001392075, 3.2178744824248353, 3.2487990656112786, 1.8532502949903402, 1.6233936498189092, 1.9951189354883097, 3.5993132003319857, 1.6969432664748405, 2.0100522630076383, 2.4177776605993078, 2.8879536640795047), # 176
(2.45488370467316, 1.8699067064795711, 2.7055579096652185, 3.0466124061497304, 3.076812089720564, 1.7559833224589783, 1.5354731485140378, 1.89059374958042, 3.4112392665675424, 1.605976046008935, 1.9025251914708217, 2.2889172173146677, 2.734945113436275), # 177
(2.3147673445861785, 1.7616962227852973, 2.5535848571944886, 2.87440616080267, 2.9037322645512686, 1.6579647039577698, 1.4473107296493106, 1.7852334025736796, 3.221581806341452, 1.5146122522603502, 1.7944873312428677, 2.1593786477289765, 2.5809877018969516), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(2, 6, 3, 3, 0, 1, 2, 0, 1, 2, 0, 0, 0, 4, 1, 1, 1, 2, 2, 1, 0, 2, 1, 0, 0, 0), # 0
(6, 9, 5, 4, 0, 2, 4, 0, 2, 4, 1, 1, 0, 7, 5, 6, 3, 2, 4, 2, 0, 4, 4, 1, 0, 0), # 1
(11, 13, 9, 7, 2, 4, 6, 2, 2, 6, 1, 3, 0, 14, 7, 10, 5, 4, 7, 2, 0, 6, 5, 1, 0, 0), # 2
(16, 17, 10, 10, 3, 4, 7, 2, 3, 6, 2, 3, 0, 16, 12, 14, 7, 9, 8, 3, 3, 6, 6, 4, 0, 0), # 3
(21, 17, 11, 13, 4, 4, 9, 7, 3, 7, 2, 3, 0, 19, 12, 17, 10, 11, 8, 6, 4, 11, 6, 6, 0, 0), # 4
(26, 19, 13, 18, 8, 5, 10, 8, 4, 7, 3, 3, 0, 21, 16, 20, 12, 15, 10, 6, 4, 13, 9, 6, 1, 0), # 5
(32, 21, 18, 21, 9, 6, 13, 9, 4, 8, 7, 4, 0, 24, 20, 23, 13, 18, 11, 8, 4, 13, 10, 6, 1, 0), # 6
(35, 26, 25, 24, 9, 7, 17, 9, 4, 9, 8, 4, 0, 27, 24, 27, 17, 19, 13, 9, 6, 14, 13, 7, 1, 0), # 7
(40, 34, 26, 29, 13, 8, 18, 9, 8, 10, 10, 5, 0, 30, 28, 30, 19, 20, 13, 12, 6, 14, 13, 9, 3, 0), # 8
(46, 42, 30, 33, 16, 9, 19, 13, 11, 10, 12, 6, 0, 34, 32, 34, 21, 25, 15, 14, 8, 16, 14, 10, 3, 0), # 9
(50, 43, 34, 37, 19, 11, 20, 16, 13, 10, 12, 6, 0, 45, 35, 36, 26, 28, 15, 15, 10, 20, 15, 10, 3, 0), # 10
(55, 48, 37, 41, 21, 13, 22, 19, 14, 10, 13, 8, 0, 50, 38, 40, 34, 32, 16, 17, 10, 20, 16, 11, 3, 0), # 11
(60, 55, 41, 44, 21, 15, 23, 20, 18, 11, 13, 8, 0, 57, 45, 42, 35, 34, 19, 20, 12, 20, 18, 11, 3, 0), # 12
(65, 56, 45, 48, 24, 17, 24, 23, 22, 14, 13, 9, 0, 66, 47, 45, 38, 36, 21, 23, 12, 23, 21, 13, 4, 0), # 13
(70, 66, 49, 55, 26, 20, 25, 26, 23, 14, 17, 9, 0, 72, 51, 47, 43, 42, 22, 28, 13, 24, 24, 13, 5, 0), # 14
(78, 72, 55, 62, 29, 22, 31, 28, 27, 14, 18, 10, 0, 72, 53, 50, 45, 50, 24, 30, 15, 26, 24, 15, 5, 0), # 15
(88, 78, 56, 64, 31, 25, 32, 30, 27, 14, 19, 12, 0, 75, 57, 58, 49, 54, 27, 30, 17, 26, 25, 15, 6, 0), # 16
(96, 87, 61, 69, 35, 27, 34, 33, 31, 15, 20, 13, 0, 79, 63, 61, 55, 58, 30, 34, 18, 29, 26, 16, 6, 0), # 17
(101, 92, 70, 76, 43, 29, 37, 36, 35, 16, 20, 13, 0, 88, 69, 65, 58, 63, 31, 35, 20, 34, 29, 17, 6, 0), # 18
(109, 98, 76, 85, 46, 31, 41, 39, 39, 17, 20, 13, 0, 95, 72, 67, 58, 70, 33, 35, 21, 40, 33, 17, 7, 0), # 19
(119, 107, 81, 91, 49, 33, 44, 40, 43, 18, 20, 13, 0, 102, 75, 73, 60, 73, 35, 43, 22, 42, 34, 17, 8, 0), # 20
(124, 113, 92, 95, 54, 34, 44, 41, 46, 18, 21, 13, 0, 107, 83, 78, 64, 76, 36, 46, 23, 45, 36, 19, 9, 0), # 21
(131, 116, 102, 99, 57, 38, 46, 43, 49, 19, 21, 13, 0, 111, 87, 83, 69, 80, 39, 48, 24, 45, 38, 20, 9, 0), # 22
(135, 123, 111, 100, 66, 39, 50, 44, 51, 21, 22, 13, 0, 114, 96, 85, 71, 87, 40, 51, 25, 47, 40, 21, 9, 0), # 23
(142, 126, 117, 103, 68, 40, 51, 46, 56, 24, 22, 13, 0, 121, 99, 86, 76, 93, 43, 51, 28, 50, 42, 21, 9, 0), # 24
(151, 133, 121, 105, 70, 44, 52, 47, 58, 25, 23, 13, 0, 128, 104, 88, 78, 94, 45, 53, 28, 51, 45, 22, 10, 0), # 25
(156, 145, 128, 115, 75, 45, 56, 53, 62, 27, 23, 15, 0, 134, 108, 92, 79, 95, 47, 56, 29, 52, 46, 22, 10, 0), # 26
(164, 149, 132, 122, 78, 47, 61, 55, 64, 30, 23, 15, 0, 140, 112, 96, 81, 98, 49, 59, 32, 53, 48, 23, 11, 0), # 27
(169, 155, 135, 131, 82, 50, 66, 59, 67, 30, 23, 15, 0, 143, 114, 98, 85, 103, 53, 60, 35, 54, 48, 23, 11, 0), # 28
(174, 159, 141, 139, 84, 51, 67, 59, 71, 31, 25, 15, 0, 145, 116, 100, 88, 105, 54, 61, 36, 56, 51, 27, 11, 0), # 29
(182, 167, 142, 145, 84, 51, 68, 59, 74, 32, 25, 16, 0, 150, 122, 106, 92, 112, 59, 63, 39, 57, 54, 28, 11, 0), # 30
(189, 176, 150, 149, 88, 52, 68, 64, 78, 33, 25, 17, 0, 159, 126, 111, 93, 114, 62, 67, 41, 62, 54, 29, 11, 0), # 31
(192, 184, 154, 157, 96, 53, 73, 66, 79, 33, 26, 17, 0, 163, 133, 114, 98, 115, 66, 68, 42, 66, 56, 31, 12, 0), # 32
(205, 187, 161, 163, 99, 57, 74, 67, 82, 33, 26, 18, 0, 167, 134, 118, 103, 116, 70, 71, 43, 67, 58, 33, 12, 0), # 33
(211, 198, 166, 174, 105, 62, 77, 70, 82, 33, 26, 18, 0, 176, 137, 123, 105, 118, 77, 74, 46, 71, 60, 34, 13, 0), # 34
(213, 203, 172, 181, 114, 65, 78, 73, 83, 33, 27, 18, 0, 182, 144, 126, 108, 120, 83, 75, 48, 74, 61, 36, 13, 0), # 35
(217, 207, 174, 187, 120, 70, 81, 73, 83, 34, 29, 21, 0, 197, 148, 131, 110, 129, 90, 76, 48, 78, 63, 36, 13, 0), # 36
(227, 211, 176, 192, 122, 73, 88, 73, 86, 34, 29, 22, 0, 205, 148, 134, 113, 133, 94, 78, 48, 80, 64, 36, 15, 0), # 37
(237, 217, 178, 202, 126, 73, 90, 76, 89, 35, 30, 22, 0, 208, 153, 138, 114, 138, 100, 80, 50, 82, 66, 37, 15, 0), # 38
(248, 223, 181, 207, 130, 75, 93, 77, 93, 37, 30, 23, 0, 212, 159, 142, 122, 139, 103, 83, 50, 82, 69, 39, 16, 0), # 39
(253, 226, 185, 210, 134, 78, 95, 79, 94, 37, 30, 24, 0, 221, 161, 144, 125, 145, 106, 86, 53, 84, 70, 39, 16, 0), # 40
(256, 234, 195, 212, 138, 81, 99, 80, 94, 39, 31, 24, 0, 228, 168, 148, 127, 149, 108, 88, 54, 86, 72, 39, 16, 0), # 41
(261, 245, 201, 220, 141, 84, 102, 85, 96, 40, 32, 25, 0, 239, 174, 155, 132, 156, 109, 88, 54, 88, 77, 41, 17, 0), # 42
(268, 252, 204, 228, 143, 87, 106, 85, 97, 41, 32, 25, 0, 247, 178, 160, 137, 160, 110, 92, 56, 91, 79, 45, 18, 0), # 43
(275, 259, 208, 234, 145, 88, 110, 87, 101, 42, 33, 25, 0, 253, 179, 165, 139, 163, 114, 93, 58, 93, 80, 45, 20, 0), # 44
(279, 262, 218, 238, 148, 90, 112, 87, 106, 44, 33, 26, 0, 259, 183, 169, 143, 168, 116, 93, 59, 93, 83, 46, 21, 0), # 45
(281, 268, 222, 240, 153, 92, 117, 89, 108, 44, 33, 26, 0, 264, 188, 173, 144, 173, 118, 99, 60, 95, 83, 46, 21, 0), # 46
(288, 275, 224, 243, 156, 95, 118, 91, 111, 46, 33, 26, 0, 269, 194, 176, 146, 174, 120, 100, 61, 96, 83, 46, 22, 0), # 47
(294, 279, 228, 250, 160, 97, 122, 94, 111, 47, 33, 26, 0, 273, 199, 178, 148, 180, 125, 101, 61, 98, 85, 46, 23, 0), # 48
(299, 284, 238, 252, 164, 98, 122, 96, 114, 48, 34, 27, 0, 279, 206, 180, 148, 182, 129, 102, 62, 100, 86, 48, 23, 0), # 49
(305, 289, 244, 258, 168, 101, 123, 98, 116, 50, 37, 27, 0, 287, 211, 186, 148, 186, 133, 104, 64, 101, 87, 48, 25, 0), # 50
(311, 293, 248, 258, 174, 106, 125, 99, 117, 50, 37, 27, 0, 293, 216, 192, 149, 193, 136, 104, 68, 102, 90, 48, 27, 0), # 51
(321, 305, 254, 264, 177, 106, 128, 102, 121, 52, 39, 27, 0, 297, 223, 201, 155, 197, 140, 107, 68, 103, 91, 49, 27, 0), # 52
(325, 310, 257, 270, 181, 108, 129, 103, 123, 56, 40, 28, 0, 301, 225, 205, 158, 205, 146, 109, 71, 106, 95, 49, 27, 0), # 53
(332, 312, 260, 274, 186, 112, 132, 103, 127, 58, 40, 28, 0, 305, 230, 209, 166, 207, 150, 109, 72, 108, 96, 50, 28, 0), # 54
(338, 318, 269, 280, 191, 115, 136, 105, 130, 60, 42, 29, 0, 313, 237, 215, 168, 208, 154, 111, 73, 110, 96, 51, 28, 0), # 55
(347, 323, 276, 281, 198, 116, 137, 107, 135, 60, 42, 29, 0, 317, 242, 217, 172, 213, 156, 114, 74, 116, 99, 52, 28, 0), # 56
(352, 326, 288, 289, 199, 116, 143, 108, 137, 60, 42, 29, 0, 321, 247, 220, 172, 217, 158, 114, 74, 118, 100, 52, 29, 0), # 57
(358, 333, 295, 298, 199, 119, 144, 110, 138, 60, 42, 29, 0, 325, 256, 227, 175, 225, 160, 116, 74, 121, 102, 52, 29, 0), # 58
(369, 335, 299, 306, 203, 119, 149, 112, 138, 62, 43, 31, 0, 332, 260, 231, 176, 226, 162, 118, 77, 122, 103, 53, 29, 0), # 59
(376, 337, 304, 310, 211, 121, 150, 114, 141, 62, 44, 32, 0, 337, 264, 237, 182, 233, 164, 119, 78, 126, 104, 54, 29, 0), # 60
(379, 340, 306, 313, 216, 121, 152, 117, 144, 63, 44, 33, 0, 342, 268, 241, 185, 239, 166, 122, 79, 129, 106, 56, 30, 0), # 61
(383, 350, 312, 319, 220, 122, 156, 119, 146, 65, 44, 34, 0, 346, 273, 246, 188, 244, 169, 126, 80, 131, 109, 57, 32, 0), # 62
(388, 354, 321, 323, 226, 126, 158, 120, 151, 65, 45, 34, 0, 351, 283, 253, 192, 248, 171, 129, 83, 132, 113, 60, 34, 0), # 63
(392, 361, 327, 334, 230, 126, 160, 120, 155, 68, 46, 37, 0, 356, 289, 255, 194, 253, 172, 129, 84, 136, 115, 61, 35, 0), # 64
(403, 364, 333, 340, 234, 128, 162, 121, 159, 69, 48, 39, 0, 364, 295, 259, 196, 259, 174, 131, 85, 141, 117, 62, 35, 0), # 65
(407, 371, 340, 344, 240, 130, 164, 122, 160, 71, 49, 39, 0, 370, 299, 262, 202, 264, 175, 133, 87, 144, 121, 62, 35, 0), # 66
(413, 381, 346, 356, 248, 135, 165, 125, 163, 71, 49, 42, 0, 378, 302, 267, 203, 267, 177, 134, 91, 146, 126, 64, 35, 0), # 67
(423, 385, 348, 361, 251, 138, 166, 127, 166, 72, 50, 42, 0, 385, 307, 274, 210, 274, 179, 134, 94, 149, 128, 66, 35, 0), # 68
(431, 387, 354, 368, 256, 142, 167, 128, 170, 72, 51, 43, 0, 387, 311, 282, 212, 283, 184, 136, 94, 153, 130, 66, 36, 0), # 69
(437, 393, 357, 370, 261, 144, 171, 131, 171, 72, 52, 43, 0, 400, 313, 286, 219, 285, 186, 140, 95, 155, 132, 66, 36, 0), # 70
(445, 400, 362, 375, 269, 146, 171, 133, 171, 72, 54, 43, 0, 409, 319, 288, 221, 293, 189, 141, 95, 158, 134, 67, 36, 0), # 71
(447, 401, 363, 384, 271, 150, 172, 134, 174, 75, 54, 43, 0, 414, 322, 293, 221, 299, 195, 143, 96, 161, 135, 67, 38, 0), # 72
(456, 403, 366, 389, 276, 152, 177, 136, 175, 76, 54, 44, 0, 419, 325, 298, 223, 305, 197, 145, 99, 163, 137, 68, 40, 0), # 73
(464, 407, 369, 397, 283, 157, 179, 137, 178, 77, 55, 44, 0, 424, 333, 302, 226, 309, 200, 149, 100, 166, 141, 68, 40, 0), # 74
(472, 411, 377, 404, 285, 159, 180, 140, 180, 77, 57, 44, 0, 433, 336, 306, 228, 311, 203, 149, 101, 167, 142, 68, 40, 0), # 75
(478, 414, 381, 407, 287, 159, 181, 140, 181, 78, 59, 45, 0, 441, 341, 312, 231, 315, 206, 151, 102, 170, 145, 69, 40, 0), # 76
(482, 424, 384, 409, 293, 162, 182, 141, 182, 80, 59, 45, 0, 447, 348, 314, 234, 315, 208, 152, 102, 173, 148, 71, 45, 0), # 77
(487, 428, 389, 413, 296, 164, 184, 142, 183, 82, 60, 45, 0, 453, 353, 317, 238, 320, 214, 155, 105, 178, 149, 73, 45, 0), # 78
(490, 433, 395, 420, 298, 165, 186, 144, 185, 82, 63, 45, 0, 462, 357, 320, 240, 322, 214, 159, 106, 179, 155, 73, 45, 0), # 79
(494, 442, 400, 424, 303, 166, 187, 146, 186, 84, 65, 45, 0, 474, 360, 326, 243, 326, 214, 160, 109, 181, 159, 74, 45, 0), # 80
(500, 452, 403, 435, 307, 171, 189, 148, 186, 84, 65, 46, 0, 478, 368, 331, 245, 331, 215, 163, 109, 182, 159, 76, 46, 0), # 81
(505, 456, 407, 439, 313, 177, 190, 151, 188, 85, 66, 46, 0, 483, 374, 337, 251, 338, 218, 166, 111, 187, 160, 76, 47, 0), # 82
(513, 463, 411, 442, 314, 178, 193, 151, 188, 85, 67, 47, 0, 484, 378, 342, 254, 347, 219, 170, 115, 188, 162, 76, 49, 0), # 83
(517, 472, 413, 446, 319, 182, 196, 151, 191, 85, 67, 47, 0, 488, 385, 345, 259, 352, 221, 173, 117, 189, 163, 78, 49, 0), # 84
(522, 478, 418, 454, 322, 185, 198, 153, 191, 89, 67, 47, 0, 495, 392, 350, 264, 356, 225, 174, 120, 193, 167, 78, 49, 0), # 85
(529, 482, 421, 457, 323, 188, 199, 155, 193, 89, 68, 47, 0, 500, 396, 353, 270, 362, 229, 177, 123, 195, 171, 80, 49, 0), # 86
(535, 485, 424, 460, 327, 190, 200, 157, 193, 89, 69, 48, 0, 507, 402, 356, 271, 365, 231, 179, 124, 198, 171, 81, 50, 0), # 87
(544, 486, 431, 467, 330, 191, 202, 161, 196, 90, 72, 48, 0, 513, 407, 361, 272, 371, 233, 181, 127, 201, 172, 82, 50, 0), # 88
(552, 492, 439, 475, 333, 194, 205, 161, 198, 91, 72, 48, 0, 517, 414, 365, 274, 380, 238, 183, 130, 203, 172, 83, 52, 0), # 89
(559, 498, 446, 481, 338, 199, 207, 164, 200, 91, 73, 48, 0, 524, 418, 367, 275, 383, 239, 185, 132, 204, 172, 85, 52, 0), # 90
(566, 501, 451, 487, 344, 200, 207, 168, 203, 92, 74, 48, 0, 528, 423, 374, 278, 388, 243, 187, 134, 208, 175, 89, 53, 0), # 91
(571, 506, 455, 499, 349, 206, 207, 168, 205, 93, 75, 48, 0, 535, 427, 377, 282, 392, 244, 190, 135, 211, 175, 89, 54, 0), # 92
(574, 509, 459, 502, 356, 207, 209, 168, 206, 94, 75, 49, 0, 539, 433, 380, 282, 401, 246, 191, 136, 213, 176, 89, 55, 0), # 93
(579, 512, 465, 508, 359, 208, 209, 171, 208, 95, 75, 50, 0, 545, 440, 383, 285, 403, 246, 191, 139, 215, 177, 89, 55, 0), # 94
(582, 514, 472, 514, 366, 208, 210, 172, 208, 98, 75, 50, 0, 551, 445, 383, 286, 405, 249, 191, 142, 218, 178, 91, 55, 0), # 95
(584, 517, 476, 520, 368, 208, 213, 173, 211, 98, 75, 51, 0, 561, 446, 387, 292, 409, 251, 193, 144, 220, 179, 91, 55, 0), # 96
(591, 525, 485, 523, 374, 211, 214, 173, 214, 100, 75, 51, 0, 567, 452, 388, 296, 412, 253, 195, 145, 221, 181, 92, 55, 0), # 97
(599, 531, 492, 527, 379, 212, 218, 173, 214, 100, 76, 51, 0, 569, 457, 391, 300, 420, 253, 195, 146, 221, 184, 92, 55, 0), # 98
(606, 536, 497, 529, 387, 215, 220, 175, 217, 101, 76, 52, 0, 574, 462, 393, 303, 429, 254, 197, 148, 226, 184, 93, 55, 0), # 99
(614, 539, 499, 536, 391, 218, 223, 175, 220, 101, 76, 52, 0, 578, 471, 395, 306, 435, 257, 197, 151, 226, 187, 93, 55, 0), # 100
(622, 542, 503, 542, 393, 222, 227, 176, 223, 101, 77, 54, 0, 584, 472, 399, 310, 441, 257, 199, 153, 228, 188, 95, 55, 0), # 101
(626, 543, 506, 549, 401, 224, 228, 177, 228, 103, 77, 54, 0, 586, 477, 402, 312, 443, 258, 203, 155, 229, 191, 96, 56, 0), # 102
(631, 548, 513, 552, 407, 228, 229, 180, 232, 103, 80, 54, 0, 596, 484, 406, 316, 446, 259, 206, 156, 231, 192, 96, 56, 0), # 103
(640, 554, 521, 554, 407, 230, 230, 183, 234, 105, 80, 55, 0, 604, 492, 410, 321, 447, 260, 208, 158, 234, 196, 98, 57, 0), # 104
(645, 556, 531, 560, 412, 234, 234, 185, 235, 105, 81, 56, 0, 610, 498, 414, 323, 450, 262, 209, 159, 235, 197, 101, 57, 0), # 105
(652, 559, 537, 566, 413, 237, 238, 187, 235, 107, 83, 56, 0, 620, 502, 417, 325, 455, 265, 212, 162, 237, 199, 102, 58, 0), # 106
(657, 567, 541, 575, 418, 241, 238, 189, 238, 110, 83, 57, 0, 625, 508, 419, 330, 456, 268, 215, 163, 239, 199, 102, 58, 0), # 107
(663, 569, 548, 580, 419, 242, 239, 191, 242, 111, 84, 58, 0, 635, 512, 425, 332, 461, 268, 215, 164, 239, 199, 104, 58, 0), # 108
(669, 572, 551, 587, 423, 247, 242, 192, 245, 111, 84, 58, 0, 641, 517, 428, 332, 464, 271, 217, 167, 241, 200, 105, 59, 0), # 109
(675, 572, 554, 590, 427, 250, 242, 195, 247, 112, 87, 59, 0, 650, 521, 433, 333, 472, 273, 222, 169, 242, 204, 107, 60, 0), # 110
(687, 577, 555, 593, 429, 250, 244, 198, 251, 113, 87, 59, 0, 660, 525, 438, 335, 475, 274, 223, 170, 244, 206, 107, 62, 0), # 111
(690, 583, 560, 597, 435, 252, 244, 199, 253, 115, 87, 60, 0, 664, 527, 441, 336, 484, 274, 223, 172, 246, 210, 108, 62, 0), # 112
(696, 584, 560, 602, 439, 257, 246, 201, 253, 115, 88, 60, 0, 670, 536, 443, 338, 486, 276, 223, 172, 247, 211, 109, 62, 0), # 113
(699, 586, 563, 606, 443, 257, 248, 202, 255, 116, 88, 60, 0, 677, 538, 446, 342, 490, 277, 224, 174, 248, 214, 110, 62, 0), # 114
(704, 589, 569, 610, 448, 258, 250, 205, 260, 116, 88, 61, 0, 680, 542, 448, 343, 495, 277, 224, 175, 250, 215, 111, 62, 0), # 115
(708, 593, 574, 611, 452, 260, 251, 206, 263, 116, 88, 61, 0, 686, 547, 449, 349, 495, 279, 224, 176, 251, 216, 112, 62, 0), # 116
(716, 599, 578, 612, 456, 262, 252, 207, 264, 116, 88, 61, 0, 691, 557, 451, 353, 499, 280, 225, 180, 254, 219, 112, 62, 0), # 117
(720, 600, 584, 617, 463, 264, 254, 207, 270, 117, 89, 61, 0, 694, 560, 453, 357, 502, 281, 227, 183, 255, 221, 114, 62, 0), # 118
(726, 603, 591, 621, 468, 267, 257, 210, 271, 117, 91, 61, 0, 699, 563, 455, 361, 507, 282, 230, 187, 256, 223, 114, 62, 0), # 119
(731, 610, 596, 626, 474, 268, 262, 210, 274, 118, 91, 61, 0, 705, 568, 457, 362, 510, 282, 230, 188, 259, 226, 114, 62, 0), # 120
(737, 612, 605, 633, 480, 273, 267, 214, 276, 119, 92, 62, 0, 710, 573, 460, 366, 513, 285, 231, 188, 261, 230, 114, 62, 0), # 121
(741, 617, 611, 634, 483, 275, 270, 216, 277, 121, 93, 62, 0, 718, 578, 460, 370, 517, 292, 235, 189, 262, 231, 115, 63, 0), # 122
(751, 620, 619, 640, 485, 280, 271, 217, 280, 122, 93, 62, 0, 728, 585, 463, 373, 522, 296, 235, 191, 263, 235, 115, 64, 0), # 123
(755, 626, 625, 648, 491, 281, 274, 217, 282, 122, 94, 63, 0, 731, 587, 466, 376, 528, 297, 238, 194, 264, 238, 117, 65, 0), # 124
(757, 631, 628, 649, 494, 282, 276, 217, 286, 123, 95, 65, 0, 736, 589, 469, 377, 530, 297, 239, 196, 266, 239, 118, 65, 0), # 125
(764, 634, 635, 653, 498, 284, 279, 218, 291, 124, 95, 65, 0, 740, 593, 471, 380, 534, 298, 242, 198, 271, 240, 119, 65, 0), # 126
(770, 639, 642, 657, 501, 285, 281, 219, 291, 124, 95, 67, 0, 749, 597, 474, 383, 537, 301, 242, 200, 275, 241, 119, 65, 0), # 127
(778, 640, 645, 664, 507, 287, 283, 220, 295, 126, 97, 67, 0, 760, 600, 488, 385, 539, 302, 242, 201, 276, 243, 120, 65, 0), # 128
(784, 644, 646, 668, 512, 292, 285, 222, 296, 128, 99, 70, 0, 774, 611, 490, 388, 547, 306, 246, 202, 278, 244, 121, 65, 0), # 129
(788, 647, 650, 675, 517, 294, 287, 223, 297, 129, 100, 70, 0, 781, 622, 493, 390, 551, 306, 247, 204, 283, 248, 122, 65, 0), # 130
(792, 649, 654, 678, 522, 297, 288, 223, 298, 130, 100, 71, 0, 785, 625, 495, 391, 555, 306, 250, 206, 284, 250, 123, 65, 0), # 131
(797, 652, 661, 686, 527, 297, 291, 224, 299, 130, 102, 71, 0, 789, 629, 498, 392, 558, 309, 252, 207, 285, 252, 124, 67, 0), # 132
(803, 655, 667, 686, 533, 298, 292, 228, 300, 130, 102, 71, 0, 796, 635, 498, 399, 560, 311, 255, 210, 287, 253, 124, 67, 0), # 133
(808, 661, 673, 691, 536, 299, 295, 229, 304, 130, 103, 71, 0, 805, 639, 503, 401, 565, 312, 255, 211, 290, 253, 126, 67, 0), # 134
(813, 664, 676, 695, 542, 300, 298, 229, 307, 130, 103, 71, 0, 814, 645, 508, 407, 571, 315, 256, 213, 291, 253, 126, 67, 0), # 135
(822, 668, 682, 699, 546, 302, 301, 232, 309, 132, 103, 71, 0, 819, 648, 512, 409, 573, 317, 258, 213, 291, 253, 127, 67, 0), # 136
(828, 672, 685, 703, 547, 304, 301, 233, 310, 133, 103, 72, 0, 826, 651, 513, 411, 574, 321, 260, 213, 291, 253, 129, 67, 0), # 137
(833, 675, 686, 709, 548, 306, 305, 237, 311, 133, 103, 72, 0, 831, 657, 515, 411, 577, 323, 263, 213, 292, 254, 129, 68, 0), # 138
(841, 677, 692, 711, 552, 308, 307, 240, 314, 135, 104, 73, 0, 835, 662, 517, 412, 581, 324, 267, 216, 295, 257, 129, 68, 0), # 139
(843, 681, 697, 714, 555, 310, 311, 244, 315, 135, 105, 74, 0, 842, 670, 522, 413, 584, 328, 268, 216, 297, 258, 131, 68, 0), # 140
(846, 682, 703, 718, 558, 311, 313, 247, 317, 135, 106, 74, 0, 846, 674, 523, 415, 592, 331, 271, 217, 300, 260, 131, 68, 0), # 141
(849, 688, 710, 724, 561, 314, 315, 248, 319, 137, 107, 76, 0, 846, 680, 528, 417, 600, 333, 271, 217, 302, 263, 134, 68, 0), # 142
(852, 692, 712, 729, 567, 317, 316, 250, 321, 137, 109, 77, 0, 854, 684, 531, 417, 606, 336, 273, 218, 306, 264, 134, 68, 0), # 143
(855, 698, 716, 733, 570, 318, 317, 251, 323, 138, 110, 78, 0, 863, 688, 532, 420, 612, 340, 274, 219, 308, 265, 134, 69, 0), # 144
(863, 704, 719, 739, 574, 320, 319, 252, 324, 141, 111, 78, 0, 869, 690, 535, 423, 615, 342, 275, 220, 311, 268, 134, 69, 0), # 145
(866, 706, 721, 742, 575, 322, 320, 253, 328, 141, 111, 78, 0, 875, 698, 539, 423, 619, 344, 278, 221, 311, 270, 135, 70, 0), # 146
(869, 710, 727, 749, 576, 322, 322, 254, 330, 141, 111, 81, 0, 881, 705, 544, 424, 620, 346, 280, 222, 312, 273, 135, 70, 0), # 147
(874, 715, 732, 752, 581, 324, 326, 255, 333, 143, 111, 81, 0, 888, 706, 548, 426, 622, 348, 283, 222, 314, 273, 137, 70, 0), # 148
(877, 717, 735, 757, 584, 325, 326, 255, 335, 144, 111, 82, 0, 889, 713, 552, 429, 624, 349, 283, 222, 316, 276, 138, 70, 0), # 149
(879, 722, 742, 761, 591, 328, 326, 255, 341, 144, 111, 82, 0, 893, 717, 557, 431, 628, 354, 285, 223, 318, 279, 138, 70, 0), # 150
(886, 726, 745, 763, 597, 331, 327, 255, 345, 144, 112, 83, 0, 900, 721, 562, 431, 632, 356, 288, 224, 320, 280, 140, 70, 0), # 151
(893, 730, 748, 768, 598, 334, 329, 256, 345, 145, 112, 84, 0, 902, 726, 564, 433, 637, 359, 289, 224, 322, 282, 141, 70, 0), # 152
(899, 730, 753, 770, 600, 336, 331, 256, 347, 148, 113, 84, 0, 910, 729, 567, 436, 644, 359, 290, 227, 326, 282, 142, 70, 0), # 153
(905, 734, 754, 780, 606, 338, 332, 257, 353, 148, 113, 84, 0, 914, 734, 568, 437, 648, 364, 290, 227, 329, 285, 142, 70, 0), # 154
(911, 738, 758, 787, 609, 341, 335, 259, 353, 148, 114, 84, 0, 925, 737, 574, 440, 649, 366, 290, 229, 331, 286, 143, 70, 0), # 155
(916, 741, 761, 795, 614, 342, 337, 260, 354, 149, 115, 84, 0, 931, 740, 575, 443, 650, 367, 292, 230, 333, 287, 144, 70, 0), # 156
(920, 743, 764, 797, 618, 343, 341, 261, 357, 149, 115, 84, 0, 935, 745, 580, 443, 657, 371, 297, 233, 333, 288, 145, 70, 0), # 157
(924, 748, 772, 798, 622, 345, 343, 262, 362, 149, 116, 84, 0, 936, 746, 581, 447, 662, 373, 297, 235, 334, 288, 145, 71, 0), # 158
(926, 751, 776, 803, 626, 346, 343, 265, 365, 150, 116, 84, 0, 945, 750, 587, 448, 663, 374, 299, 235, 336, 288, 146, 72, 0), # 159
(928, 756, 782, 807, 631, 348, 346, 266, 367, 150, 116, 85, 0, 948, 755, 588, 450, 666, 376, 301, 236, 337, 288, 147, 72, 0), # 160
(935, 760, 784, 813, 633, 349, 347, 267, 367, 150, 117, 85, 0, 952, 758, 590, 453, 670, 377, 302, 236, 339, 291, 149, 72, 0), # 161
(938, 767, 789, 822, 639, 352, 347, 269, 369, 152, 119, 85, 0, 958, 762, 594, 455, 674, 380, 305, 237, 340, 291, 151, 72, 0), # 162
(939, 769, 790, 825, 642, 355, 352, 272, 370, 152, 122, 86, 0, 967, 766, 598, 457, 675, 382, 305, 239, 340, 294, 154, 72, 0), # 163
(944, 772, 792, 833, 648, 356, 353, 274, 372, 153, 123, 86, 0, 970, 767, 599, 458, 679, 385, 307, 240, 343, 294, 156, 72, 0), # 164
(947, 775, 795, 838, 651, 356, 353, 280, 376, 154, 123, 86, 0, 972, 769, 602, 461, 683, 387, 307, 240, 345, 296, 156, 72, 0), # 165
(948, 777, 799, 842, 653, 358, 356, 281, 377, 154, 124, 86, 0, 975, 772, 603, 463, 688, 387, 307, 241, 347, 296, 158, 72, 0), # 166
(952, 780, 801, 848, 654, 358, 358, 283, 379, 154, 125, 86, 0, 978, 773, 604, 464, 690, 387, 308, 243, 350, 296, 158, 72, 0), # 167
(958, 781, 805, 848, 660, 360, 363, 283, 380, 155, 126, 87, 0, 980, 775, 606, 465, 692, 389, 310, 245, 353, 297, 158, 72, 0), # 168
(961, 783, 807, 849, 663, 361, 365, 286, 383, 156, 128, 87, 0, 983, 776, 606, 465, 693, 392, 310, 246, 355, 298, 159, 72, 0), # 169
(965, 787, 810, 854, 664, 362, 365, 286, 385, 157, 128, 88, 0, 985, 778, 606, 467, 697, 395, 311, 246, 356, 298, 159, 72, 0), # 170
(968, 789, 817, 856, 669, 363, 366, 287, 388, 157, 129, 88, 0, 987, 782, 608, 469, 699, 395, 312, 246, 357, 298, 159, 73, 0), # 171
(968, 792, 825, 861, 670, 364, 367, 289, 391, 157, 131, 89, 0, 989, 784, 609, 472, 704, 395, 312, 247, 357, 299, 160, 73, 0), # 172
(972, 794, 827, 863, 670, 365, 370, 289, 391, 158, 131, 89, 0, 993, 787, 611, 472, 706, 395, 312, 248, 358, 299, 160, 73, 0), # 173
(974, 798, 829, 865, 671, 365, 370, 292, 391, 158, 132, 89, 0, 1000, 788, 614, 473, 708, 396, 314, 250, 359, 301, 161, 73, 0), # 174
(976, 800, 830, 867, 673, 368, 373, 293, 392, 158, 132, 89, 0, 1002, 791, 615, 477, 710, 396, 316, 251, 359, 302, 161, 74, 0), # 175
(979, 800, 832, 868, 678, 368, 375, 294, 396, 159, 132, 89, 0, 1003, 794, 618, 478, 713, 396, 317, 251, 361, 303, 162, 74, 0), # 176
(981, 805, 833, 872, 680, 369, 376, 294, 398, 159, 133, 90, 0, 1003, 795, 619, 478, 715, 397, 317, 251, 362, 306, 164, 74, 0), # 177
(984, 806, 837, 877, 681, 371, 377, 295, 401, 160, 133, 90, 0, 1006, 797, 619, 479, 716, 397, 318, 251, 363, 307, 165, 74, 0), # 178
(984, 806, 837, 877, 681, 371, 377, 295, 401, 160, 133, 90, 0, 1006, 797, 619, 479, 716, 397, 318, 251, 363, 307, 165, 74, 0), # 179
)
passenger_arriving_rate = (
(3.012519347023061, 3.038908707831242, 2.605641614806254, 2.796600498238545, 2.2218742430438447, 1.0985292262130688, 1.2438106846121746, 1.163294043244856, 1.2180198124336015, 0.5937022523283556, 0.42052651638856037, 0.24489575391530874, 0.0, 3.050328127547018, 2.6938532930683956, 2.102632581942802, 1.7811067569850665, 2.436039624867203, 1.6286116605427985, 1.2438106846121746, 0.7846637330093348, 1.1109371215219224, 0.9322001660795152, 0.5211283229612508, 0.2762644279846584, 0.0), # 0
(3.2125962912119848, 3.2395333817796175, 2.777673295665077, 2.9813149139744777, 2.368996134989695, 1.1710942544148948, 1.3258406823791082, 1.2398784307264272, 1.2984490652665819, 0.6328471038415726, 0.4483096135956133, 0.2610608330407178, 0.0, 3.2518749884639133, 2.8716691634478955, 2.241548067978066, 1.8985413115247174, 2.5968981305331638, 1.735829803016998, 1.3258406823791082, 0.8364958960106391, 1.1844980674948475, 0.9937716379914928, 0.5555346591330154, 0.294503034707238, 0.0), # 1
(3.412033805387839, 3.439361843990039, 2.949021142773707, 3.165294467099158, 2.5155851894998977, 1.243369790079356, 1.4075455523882114, 1.316156311976105, 1.37855736137551, 0.671837066253082, 0.47598231487763143, 0.2771616326026528, 0.0, 3.4526188969258147, 3.0487779586291803, 2.379911574388157, 2.0155111987592456, 2.75711472275102, 1.842618836766547, 1.4075455523882114, 0.8881212786281114, 1.2577925947499489, 1.0550981556997194, 0.5898042285547415, 0.312669258544549, 0.0), # 2
(3.6100546758879375, 3.637601276065046, 3.1190054346599276, 3.347809383389434, 2.6610646704007612, 1.3150692179047898, 1.4886010924943869, 1.391825289373627, 1.4580270321842186, 0.7105174047639273, 0.5034348411827384, 0.2931342842465231, 0.0, 3.651763683752536, 3.224477126711754, 2.5171742059136917, 2.1315522142917818, 2.916054064368437, 1.9485554051230778, 1.4886010924943869, 0.9393351556462783, 1.3305323352003806, 1.1159364611298115, 0.6238010869319855, 0.3306910250968224, 0.0), # 3
(3.8058816890495892, 3.833458859607176, 3.286946449851519, 3.528129888622157, 2.804857841518597, 1.385905922589534, 1.5686831005525377, 1.4665829652987292, 1.5365404091165416, 0.748733384575152, 0.5305574134590577, 0.30891491961773826, 0.0, 3.848513179763891, 3.3980641157951204, 2.652787067295288, 2.246200153725456, 3.073080818233083, 2.053216151418221, 1.5686831005525377, 0.9899328018496671, 1.4024289207592986, 1.176043296207386, 0.6573892899703039, 0.3484962599642888, 0.0), # 4
(3.9987376312101066, 4.026141776218974, 3.452164466876259, 3.705526208574178, 2.9463879666797124, 1.4555932888319254, 1.6474673744175674, 1.5401269421311483, 1.6137798235963121, 0.7863302708877998, 0.5572402526547134, 0.32443967036170746, 0.0, 4.042071215779696, 3.568836373978782, 2.7862012632735667, 2.3589908126633987, 3.2275596471926242, 2.1561777189836078, 1.6474673744175674, 1.0397094920228038, 1.4731939833398562, 1.2351754028580595, 0.6904328933752518, 0.3660128887471795, 0.0), # 5
(4.1878452887068, 4.214857207502976, 3.613979764261934, 3.8792685690223436, 3.0850783097104175, 1.5238447013303027, 1.724629711944379, 1.6121548222506223, 1.689427607047363, 0.823153328902914, 0.5833735797178284, 0.3396446681238405, 0.0, 4.231641622619764, 3.736091349362245, 2.9168678985891416, 2.4694599867087415, 3.378855214094726, 2.2570167511508714, 1.724629711944379, 1.0884605009502162, 1.5425391548552088, 1.2930895230074482, 0.7227959528523867, 0.3831688370457252, 0.0), # 6
(4.372427447876982, 4.398812335061723, 3.7717126205363183, 4.048627195743508, 3.220352134437022, 1.5903735447830027, 1.7998459109878757, 1.6823642080368881, 1.7631660908935292, 0.8590478238215383, 0.608847615596527, 0.35446604454954656, 0.0, 4.416428231103912, 3.8991264900450116, 3.0442380779826346, 2.5771434714646144, 3.5263321817870583, 2.3553098912516433, 1.7998459109878757, 1.1359811034164304, 1.610176067218511, 1.3495423985811696, 0.7543425241072638, 0.39989203046015664, 0.0), # 7
(4.551706895057961, 4.577214340497755, 3.9246833142271984, 4.212872314514518, 3.3516327046858345, 1.6548932038883617, 1.8727917694029594, 1.7504527018696814, 1.8346776065586412, 0.8938590208447165, 0.6335525812389322, 0.36883993128423526, 0.0, 4.595634872051951, 4.057239244126587, 3.167762906194661, 2.681577062534149, 3.6693552131172824, 2.450633782617554, 1.8727917694029594, 1.1820665742059726, 1.6758163523429173, 1.4042907715048396, 0.7849366628454397, 0.41611039459070503, 0.0), # 8
(4.724906416587052, 4.749270405413613, 4.072212123862353, 4.371274151112226, 3.478343284283164, 1.7171170633447197, 1.9431430850445361, 1.8161179061287411, 1.9036444854665349, 0.9274321851734916, 0.657378697593168, 0.3827024599733158, 0.0, 4.768465376283698, 4.2097270597064735, 3.28689348796584, 2.782296555520474, 3.8072889709330697, 2.5425650685802377, 1.9431430850445361, 1.2265121881033711, 1.739171642141582, 1.4570913837040758, 0.8144424247724705, 0.4317518550376012, 0.0), # 9
(4.89124879880156, 4.914187711411834, 4.213619327969563, 4.52310293131348, 3.5999071370553204, 1.7767585078504131, 2.0105756557675067, 1.8790574231938029, 1.969749059041043, 0.9596125820089074, 0.6802161856073575, 0.3959897622621977, 0.0, 4.934123574618967, 4.355887384884174, 3.4010809280367877, 2.8788377460267216, 3.939498118082086, 2.6306803924713242, 2.0105756557675067, 1.269113219893152, 1.7999535685276602, 1.5077009771044938, 0.8427238655939127, 0.4467443374010759, 0.0), # 10
(5.049956828038804, 5.071173440094964, 4.348225205076608, 4.66762888089513, 3.715747526828614, 1.8335309221037792, 2.0747652794267752, 1.9389688554446038, 2.032673658705999, 0.9902454765520082, 0.7019552662296251, 0.40863796979629063, 0.0, 5.091813297877567, 4.495017667759196, 3.5097763311481254, 2.970736429656024, 4.065347317411998, 2.7145563976224456, 2.0747652794267752, 1.3096649443598423, 1.857873763414307, 1.5558762936317103, 0.8696450410153217, 0.46101576728136046, 0.0), # 11
(5.200253290636088, 5.219434773065535, 4.475350033711271, 4.804122225634027, 3.8252877174293514, 1.8871476908031546, 2.135387753877244, 1.9955498052608804, 2.092100615885236, 1.019176134003836, 0.7224861604080937, 0.42058321422100353, 0.0, 5.240738376879321, 4.626415356431038, 3.612430802040468, 3.0575284020115077, 4.184201231770472, 2.7937697273652327, 2.135387753877244, 1.3479626362879675, 1.9126438587146757, 1.6013740752113426, 0.8950700067422541, 0.47449407027868507, 0.0), # 12
(5.341360972930726, 5.358178891926092, 4.594314092401332, 4.93185319130702, 3.927950972683841, 1.9373221986468787, 2.192118876973817, 2.048497875022371, 2.1477122620025866, 1.046249819565436, 0.741699089090887, 0.43176162718174615, 0.0, 5.380102642444042, 4.749377898999207, 3.7084954454544348, 3.1387494586963074, 4.295424524005173, 2.8678970250313194, 2.192118876973817, 1.3838015704620563, 1.9639754863419205, 1.6439510637690071, 0.9188628184802665, 0.48710717199328113, 0.0), # 13
(5.47250266126003, 5.486612978279174, 4.704437659674574, 5.050092003690957, 4.0231605564183965, 1.9837678303332873, 2.2446344465713985, 2.097510667108812, 2.1991909284818845, 1.0713117984378504, 0.7594842732261287, 0.4421093403239281, 0.0, 5.509109925391539, 4.863202743563209, 3.797421366130643, 3.2139353953135505, 4.398381856963769, 2.9365149339523366, 2.2446344465713985, 1.4169770216666338, 2.0115802782091983, 1.6833640012303195, 0.9408875319349149, 0.49878299802537956, 0.0), # 14
(5.592901141961314, 5.60394421372732, 4.805041014058772, 5.158108888562694, 4.110339732459323, 2.0261979705607196, 2.292610260524888, 2.1422857838999394, 2.246218946746964, 1.094207335822124, 0.7757319337619422, 0.4515624852929587, 0.0, 5.626964056541629, 4.967187338222545, 3.8786596688097106, 3.2826220074663714, 4.492437893493928, 2.999200097459915, 2.292610260524888, 1.4472842646862283, 2.0551698662296616, 1.7193696295208984, 0.9610082028117546, 0.509449473975211, 0.0), # 15
(5.701779201371881, 5.709379779873073, 4.895444434081713, 5.255174071699074, 4.188911764632934, 2.064326004027511, 2.335722116689193, 2.1825208277754906, 2.2884786482216573, 1.1147816969192998, 0.7903322916464515, 0.4600571937342471, 0.0, 5.732868866714128, 5.060629131076717, 3.951661458232257, 3.3443450907578987, 4.5769572964433145, 3.0555291588856868, 2.335722116689193, 1.474518574305365, 2.094455882316467, 1.7517246905663582, 0.9790888868163427, 0.5190345254430068, 0.0), # 16
(5.798359625829046, 5.802126858318971, 4.974968198271177, 5.340557778876951, 4.2582999167655355, 2.0978653154320006, 2.3736458129192135, 2.217913401115205, 2.325652364329797, 1.1328801469304204, 0.8031755678277801, 0.46752959729320287, 0.0, 5.82602818672885, 5.142825570225231, 4.0158778391389, 3.3986404407912607, 4.651304728659594, 3.1050787615612867, 2.3736458129192135, 1.4984752253085718, 2.1291499583827678, 1.7801859262923174, 0.9949936396542355, 0.5274660780289975, 0.0), # 17
(5.881865201670123, 5.881392630667551, 5.042932585154941, 5.413530235873176, 4.317927452683436, 2.1265292894725265, 2.4060571470698555, 2.2481611062988156, 2.3574224264952193, 1.1483479510565309, 0.814151983254051, 0.4739158276152359, 0.0, 5.905645847405608, 5.213074103767594, 4.070759916270255, 3.445043853169592, 4.7148448529904385, 3.1474255488183416, 2.4060571470698555, 1.518949492480376, 2.158963726341718, 1.8045100786243924, 1.0085865170309882, 0.5346720573334138, 0.0), # 18
(5.95151871523242, 5.94638427852136, 5.09865787326079, 5.473361668464593, 4.3672176362129465, 2.1500313108474236, 2.4326319169960184, 2.2729615457060617, 2.383471166141756, 1.1610303744986745, 0.823151758873388, 0.479152016345755, 0.0, 5.9709256795642185, 5.270672179803304, 4.11575879436694, 3.483091123496023, 4.766942332283512, 3.1821461639884863, 2.4326319169960184, 1.5357366506053025, 2.1836088181064732, 1.8244538894881979, 1.0197315746521582, 0.5405803889564874, 0.0), # 19
(6.00654295285325, 5.996308983482929, 5.141464341116502, 5.51932230242806, 4.405593731180378, 2.168084764255032, 2.453045920552609, 2.2920123217166797, 2.40348091469324, 1.1707726824578941, 0.8300651156339153, 0.4831742951301697, 0.0, 6.021071514024495, 5.3149172464318655, 4.150325578169576, 3.5123180473736815, 4.80696182938648, 3.2088172504033516, 2.453045920552609, 1.5486319744678798, 2.202796865590189, 1.8397741008093538, 1.0282928682233006, 0.5451189984984482, 0.0), # 20
(6.046160700869921, 6.030373927154808, 5.17067226724986, 5.550682363540419, 4.432479001412036, 2.180403034393688, 2.466974955594528, 2.305011036710406, 2.4171340035735045, 1.1774201401352336, 0.8347822744837561, 0.4859187956138898, 0.0, 6.055287181606248, 5.345106751752787, 4.173911372418781, 3.5322604204057, 4.834268007147009, 3.2270154513945686, 2.466974955594528, 1.557430738852634, 2.216239500706018, 1.8502274545134736, 1.0341344534499721, 0.548215811559528, 0.0), # 21
(6.0695947456197485, 6.04778629113953, 5.185601930188644, 5.5667120775785275, 4.44729671073423, 2.186699505961729, 2.4740948199766803, 2.311655293066979, 2.424112764206383, 1.1808180127317367, 0.8371934563710342, 0.4873216494423246, 0.0, 6.0727765131292974, 5.36053814386557, 4.185967281855171, 3.5424540381952094, 4.848225528412766, 3.236317410293771, 2.4740948199766803, 1.561928218544092, 2.223648355367115, 1.8555706925261761, 1.037120386037729, 0.5497987537399573, 0.0), # 22
(6.078236018005584, 6.049847976680385, 5.187461591220852, 5.568718865740742, 4.451092822413039, 2.1875000000000004, 2.4749412006778018, 2.312373456790124, 2.4249852469135806, 1.1812188408779154, 0.8374958037161743, 0.48749487882944686, 0.0, 6.075000000000001, 5.362443667123914, 4.187479018580871, 3.5436565226337455, 4.849970493827161, 3.2373228395061737, 2.4749412006778018, 1.5625000000000002, 2.2255464112065195, 1.856239621913581, 1.0374923182441704, 0.549986179698217, 0.0), # 23
(6.084607447820493, 6.048645370370371, 5.187157407407408, 5.568471875000001, 4.453243045445941, 2.1875000000000004, 2.4744761437908505, 2.3113750000000004, 2.4248683333333334, 1.180972592592593, 0.8374624579124582, 0.4874543209876544, 0.0, 6.075000000000001, 5.361997530864198, 4.187312289562291, 3.5429177777777783, 4.849736666666667, 3.235925000000001, 2.4744761437908505, 1.5625000000000002, 2.2266215227229704, 1.8561572916666675, 1.0374314814814818, 0.5498768518518521, 0.0), # 24
(6.090844338126949, 6.046274862825789, 5.186556927297669, 5.567983217592594, 4.455345978237801, 2.1875000000000004, 2.4735596707818934, 2.3094135802469142, 2.4246373456790127, 1.180487825788752, 0.8373963399426364, 0.4873742569730225, 0.0, 6.075000000000001, 5.361116826703247, 4.186981699713182, 3.5414634773662557, 4.849274691358025, 3.23317901234568, 2.4735596707818934, 1.5625000000000002, 2.2276729891189007, 1.8559944058641984, 1.037311385459534, 0.5496613511659809, 0.0), # 25
(6.096946211428821, 6.04277266803841, 5.185668381344309, 5.567258449074075, 4.457401547368442, 2.1875000000000004, 2.472206015492617, 2.306526234567902, 2.42429524691358, 1.179772606310014, 0.8372980483850856, 0.48725578417924115, 0.0, 6.075000000000001, 5.359813625971652, 4.186490241925428, 3.5393178189300416, 4.84859049382716, 3.2291367283950625, 2.472206015492617, 1.5625000000000002, 2.228700773684221, 1.8557528163580252, 1.0371336762688619, 0.5493429698216737, 0.0), # 26
(6.102912590229983, 6.038175, 5.184500000000001, 5.566303125000001, 4.459409679417686, 2.1875000000000004, 2.4704294117647065, 2.3027500000000005, 2.423845, 1.1788350000000003, 0.8371681818181821, 0.48710000000000014, 0.0, 6.075000000000001, 5.358100000000001, 4.18584090909091, 3.536505, 4.84769, 3.223850000000001, 2.4704294117647065, 1.5625000000000002, 2.229704839708843, 1.8554343750000006, 1.0369000000000004, 0.5489250000000001, 0.0), # 27
(6.108742997034302, 6.032518072702333, 5.183060013717422, 5.565122800925927, 4.461370300965361, 2.1875000000000004, 2.4682440934398455, 2.2981219135802475, 2.4232895679012345, 1.1776830727023324, 0.837007338820302, 0.48690800182898963, 0.0, 6.075000000000001, 5.355988020118885, 4.18503669410151, 3.5330492181069966, 4.846579135802469, 3.2173706790123466, 2.4682440934398455, 1.5625000000000002, 2.2306851504826803, 1.855040933641976, 1.0366120027434846, 0.5484107338820303, 0.0), # 28
(6.114436954345651, 6.025838100137175, 5.181356652949247, 5.563723032407408, 4.463283338591289, 2.1875000000000004, 2.46566429435972, 2.2926790123456793, 2.422631913580247, 1.1763248902606314, 0.8368161179698218, 0.48668088705989954, 0.0, 6.075000000000001, 5.353489757658894, 4.184080589849109, 3.5289746707818934, 4.845263827160494, 3.209750617283951, 2.46566429435972, 1.5625000000000002, 2.2316416692956444, 1.8545743441358031, 1.0362713305898494, 0.5478034636488341, 0.0), # 29
(6.119993984667899, 6.018171296296297, 5.179398148148149, 5.562109375, 4.4651487188752945, 2.1875000000000004, 2.4627042483660135, 2.2864583333333335, 2.421875, 1.174768518518519, 0.8365951178451181, 0.4864197530864198, 0.0, 6.075000000000001, 5.350617283950617, 4.18297558922559, 3.5243055555555562, 4.84375, 3.201041666666667, 2.4627042483660135, 1.5625000000000002, 2.2325743594376473, 1.854036458333334, 1.03587962962963, 0.5471064814814817, 0.0), # 30
(6.125413610504916, 6.009553875171469, 5.177192729766805, 5.56028738425926, 4.466966368397204, 2.1875000000000004, 2.4593781893004123, 2.2794969135802474, 2.421021790123457, 1.1730220233196162, 0.8363449370245669, 0.48612569730224076, 0.0, 6.075000000000001, 5.347382670324647, 4.181724685122834, 3.519066069958848, 4.842043580246914, 3.1912956790123466, 2.4593781893004123, 1.5625000000000002, 2.233483184198602, 1.8534291280864204, 1.035438545953361, 0.5463230795610428, 0.0), # 31
(6.130695354360573, 6.000022050754459, 5.174748628257888, 5.558262615740742, 4.468736213736839, 2.1875000000000004, 2.4557003510046, 2.2718317901234575, 2.4200752469135804, 1.171093470507545, 0.8360661740865446, 0.48579981710105186, 0.0, 6.075000000000001, 5.34379798811157, 4.180330870432723, 3.5132804115226346, 4.840150493827161, 3.1805645061728405, 2.4557003510046, 1.5625000000000002, 2.2343681068684194, 1.8527542052469144, 1.0349497256515776, 0.5454565500685873, 0.0), # 32
(6.135838738738739, 5.989612037037038, 5.172074074074075, 5.5560406250000005, 4.470458181474026, 2.1875000000000004, 2.451684967320262, 2.2635000000000005, 2.4190383333333334, 1.1689909259259263, 0.8357594276094278, 0.4854432098765433, 0.0, 6.075000000000001, 5.339875308641975, 4.1787971380471385, 3.5069727777777784, 4.838076666666667, 3.1689000000000007, 2.451684967320262, 1.5625000000000002, 2.235229090737013, 1.8520135416666672, 1.0344148148148151, 0.5445101851851853, 0.0), # 33
(6.140843286143286, 5.978360048010974, 5.169177297668039, 5.553626967592594, 4.472132198188587, 2.1875000000000004, 2.4473462720890833, 2.254538580246914, 2.4179140123456797, 1.1667224554183817, 0.8354252961715926, 0.48505697302240525, 0.0, 6.075000000000001, 5.335626703246457, 4.177126480857963, 3.5001673662551447, 4.835828024691359, 3.1563540123456795, 2.4473462720890833, 1.5625000000000002, 2.2360660990942933, 1.8512089891975316, 1.033835459533608, 0.5434872770919068, 0.0), # 34
(6.145708519078085, 5.966302297668039, 5.166066529492457, 5.551027199074074, 4.473758190460348, 2.1875000000000004, 2.442698499152748, 2.244984567901235, 2.4167052469135806, 1.1642961248285326, 0.8350643783514156, 0.48464220393232754, 0.0, 6.075000000000001, 5.331064243255602, 4.175321891757077, 3.492888374485597, 4.833410493827161, 3.142978395061729, 2.442698499152748, 1.5625000000000002, 2.236879095230174, 1.8503423996913586, 1.0332133058984916, 0.5423911179698219, 0.0), # 35
(6.150433960047004, 5.953475, 5.162750000000001, 5.548246875, 4.475336084869134, 2.1875000000000004, 2.4377558823529415, 2.2348750000000006, 2.415415, 1.1617200000000003, 0.834677272727273, 0.4842000000000002, 0.0, 6.075000000000001, 5.326200000000001, 4.173386363636364, 3.4851600000000005, 4.83083, 3.128825000000001, 2.4377558823529415, 1.5625000000000002, 2.237668042434567, 1.8494156250000007, 1.0325500000000003, 0.5412250000000001, 0.0), # 36
(6.155019131553915, 5.939914368998629, 5.159235939643348, 5.545291550925926, 4.476865807994769, 2.1875000000000004, 2.4325326555313485, 2.2242469135802474, 2.4140462345679015, 1.1590021467764065, 0.8342645778775412, 0.4837314586191131, 0.0, 6.075000000000001, 5.3210460448102435, 4.171322889387706, 3.4770064403292187, 4.828092469135803, 3.113945679012346, 2.4325326555313485, 1.5625000000000002, 2.2384329039973845, 1.8484305169753092, 1.0318471879286697, 0.5399922153635118, 0.0), # 37
(6.159463556102686, 5.925656618655693, 5.155532578875172, 5.542166782407408, 4.478347286417076, 2.1875000000000004, 2.4270430525296542, 2.213137345679013, 2.412601913580247, 1.1561506310013723, 0.8338268923805964, 0.4832376771833564, 0.0, 6.075000000000001, 5.31561444901692, 4.169134461902981, 3.468451893004116, 4.825203827160494, 3.0983922839506186, 2.4270430525296542, 1.5625000000000002, 2.239173643208538, 1.8473889274691366, 1.0311065157750345, 0.5386960562414268, 0.0), # 38
(6.163766756197193, 5.910737962962964, 5.1516481481481495, 5.538878125000001, 4.479780446715882, 2.1875000000000004, 2.421301307189543, 2.2015833333333337, 2.4110850000000004, 1.153173518518519, 0.833364814814815, 0.48271975308641984, 0.0, 6.075000000000001, 5.309917283950617, 4.166824074074075, 3.4595205555555566, 4.822170000000001, 3.082216666666667, 2.421301307189543, 1.5625000000000002, 2.239890223357941, 1.846292708333334, 1.03032962962963, 0.537339814814815, 0.0), # 39
(6.167928254341299, 5.895194615912209, 5.147590877914952, 5.53543113425926, 4.481165215471008, 2.1875000000000004, 2.4153216533526995, 2.1896219135802477, 2.4094984567901236, 1.1500788751714683, 0.8328789437585736, 0.4821787837219938, 0.0, 6.075000000000001, 5.303966620941931, 4.164394718792868, 3.450236625514404, 4.818996913580247, 3.0654706790123467, 2.4153216533526995, 1.5625000000000002, 2.240582607735504, 1.8451437114197538, 1.0295181755829905, 0.5359267832647464, 0.0), # 40
(6.171947573038878, 5.879062791495199, 5.143368998628259, 5.531831365740741, 4.482501519262281, 2.1875000000000004, 2.409118324860809, 2.1772901234567907, 2.4078452469135803, 1.1468747668038413, 0.8323698777902485, 0.4816158664837678, 0.0, 6.075000000000001, 5.297774531321445, 4.161849388951242, 3.440624300411523, 4.8156904938271605, 3.048206172839507, 2.409118324860809, 1.5625000000000002, 2.2412507596311406, 1.8439437885802477, 1.0286737997256519, 0.534460253772291, 0.0), # 41
(6.175824234793801, 5.862378703703705, 5.138990740740742, 5.5280843750000015, 4.483789284669523, 2.1875000000000004, 2.402705555555556, 2.1646250000000005, 2.4061283333333336, 1.1435692592592597, 0.8318382154882157, 0.48103209876543224, 0.0, 6.075000000000001, 5.291353086419754, 4.159191077441078, 3.430707777777778, 4.812256666666667, 3.030475000000001, 2.402705555555556, 1.5625000000000002, 2.2418946423347617, 1.8426947916666676, 1.0277981481481486, 0.5329435185185187, 0.0), # 42
(6.179557762109936, 5.845178566529493, 5.1344643347050765, 5.524195717592594, 4.485028438272561, 2.1875000000000004, 2.396097579278625, 2.151663580246914, 2.404350679012346, 1.1401704183813448, 0.8312845554308519, 0.48042857796067695, 0.0, 6.075000000000001, 5.284714357567445, 4.156422777154259, 3.4205112551440338, 4.808701358024692, 3.01232901234568, 2.396097579278625, 1.5625000000000002, 2.2425142191362806, 1.841398572530865, 1.0268928669410153, 0.5313798696844995, 0.0), # 43
(6.183147677491157, 5.827498593964336, 5.129798010973938, 5.520170949074076, 4.486218906651218, 2.1875000000000004, 2.389308629871702, 2.1384429012345687, 2.4025152469135804, 1.136686310013718, 0.8307094961965334, 0.47980640146319176, 0.0, 6.075000000000001, 5.277870416095109, 4.153547480982667, 3.410058930041153, 4.805030493827161, 2.993820061728396, 2.389308629871702, 1.5625000000000002, 2.243109453325609, 1.8400569830246922, 1.0259596021947879, 0.5297725994513033, 0.0), # 44
(6.186593503441331, 5.809375000000001, 5.125000000000001, 5.5160156250000005, 4.487360616385319, 2.1875000000000004, 2.382352941176471, 2.1250000000000004, 2.400625, 1.1331250000000004, 0.8301136363636366, 0.47916666666666674, 0.0, 6.075000000000001, 5.270833333333333, 4.1505681818181825, 3.3993750000000005, 4.80125, 2.9750000000000005, 2.382352941176471, 1.5625000000000002, 2.2436803081926593, 1.8386718750000006, 1.0250000000000004, 0.5281250000000002, 0.0), # 45
(6.18989476246433, 5.790843998628259, 5.12007853223594, 5.511735300925927, 4.488453494054687, 2.1875000000000004, 2.3752447470346167, 2.1113719135802476, 2.398682901234568, 1.1294945541838137, 0.8294975745105375, 0.4785104709647921, 0.0, 6.075000000000001, 5.263615180612712, 4.147487872552688, 3.3884836625514403, 4.797365802469136, 2.9559206790123467, 2.3752447470346167, 1.5625000000000002, 2.2442267470273434, 1.8372451003086427, 1.0240157064471882, 0.52644036351166, 0.0), # 46
(6.193050977064022, 5.771941803840877, 5.115041838134432, 5.507335532407408, 4.489497466239147, 2.1875000000000004, 2.367998281287824, 2.097595679012346, 2.396691913580247, 1.1258030384087796, 0.8288619092156131, 0.4778389117512576, 0.0, 6.075000000000001, 5.256228029263832, 4.144309546078065, 3.377409115226338, 4.793383827160494, 2.9366339506172845, 2.367998281287824, 1.5625000000000002, 2.2447487331195735, 1.8357785108024698, 1.0230083676268864, 0.5247219821673527, 0.0), # 47
(6.196061669744278, 5.752704629629631, 5.109898148148149, 5.502821875000001, 4.490492459518524, 2.1875000000000004, 2.360627777777778, 2.083708333333334, 2.394655, 1.1220585185185188, 0.8282072390572391, 0.4771530864197532, 0.0, 6.075000000000001, 5.248683950617284, 4.141036195286196, 3.3661755555555555, 4.78931, 2.9171916666666675, 2.360627777777778, 1.5625000000000002, 2.245246229759262, 1.8342739583333343, 1.02197962962963, 0.5229731481481483, 0.0), # 48
(6.198926363008972, 5.733168689986282, 5.104655692729768, 5.49819988425926, 4.491438400472643, 2.1875000000000004, 2.3531474703461632, 2.0697469135802473, 2.3925751234567905, 1.1182690603566534, 0.8275341626137924, 0.4764540923639691, 0.0, 6.075000000000001, 5.240995016003659, 4.137670813068962, 3.3548071810699596, 4.785150246913581, 2.897645679012346, 2.3531474703461632, 1.5625000000000002, 2.2457192002363215, 1.8327332947530872, 1.0209311385459536, 0.5211971536351167, 0.0), # 49
(6.201644579361971, 5.7133701989026076, 5.099322702331963, 5.4934751157407415, 4.492335215681326, 2.1875000000000004, 2.3455715928346654, 2.055748456790124, 2.3904552469135805, 1.1144427297668043, 0.826843278463649, 0.4757430269775951, 0.0, 6.075000000000001, 5.233173296753545, 4.134216392318245, 3.3433281893004123, 4.780910493827161, 2.8780478395061735, 2.3455715928346654, 1.5625000000000002, 2.246167607840663, 1.8311583719135809, 1.0198645404663929, 0.5193972908093281, 0.0), # 50
(6.204215841307147, 5.693345370370371, 5.093907407407408, 5.488653125000001, 4.4931828317244, 2.1875000000000004, 2.3379143790849675, 2.0417500000000004, 2.3882983333333336, 1.110587592592593, 0.8261351851851855, 0.4750209876543212, 0.0, 6.075000000000001, 5.225230864197532, 4.130675925925927, 3.3317627777777785, 4.776596666666667, 2.858450000000001, 2.3379143790849675, 1.5625000000000002, 2.2465914158622, 1.8295510416666674, 1.0187814814814817, 0.517576851851852, 0.0), # 51
(6.206639671348368, 5.673130418381346, 5.08841803840878, 5.483739467592593, 4.493981175181686, 2.1875000000000004, 2.330190062938756, 2.0277885802469138, 2.3861073456790125, 1.106711714677641, 0.8254104813567779, 0.4742890717878374, 0.0, 6.075000000000001, 5.21717978966621, 4.127052406783889, 3.3201351440329225, 4.772214691358025, 2.838904012345679, 2.330190062938756, 1.5625000000000002, 2.246990587590843, 1.8279131558641981, 1.0176836076817561, 0.5157391289437588, 0.0), # 52
(6.2089155919895065, 5.652761556927298, 5.082862825788753, 5.478739699074074, 4.494730172633012, 2.1875000000000004, 2.3224128782377154, 2.0139012345679017, 2.3838852469135805, 1.1028231618655697, 0.8246697655568027, 0.47354837677183365, 0.0, 6.075000000000001, 5.20903214449017, 4.123348827784014, 3.3084694855967083, 4.767770493827161, 2.8194617283950625, 2.3224128782377154, 1.5625000000000002, 2.247365086316506, 1.8262465663580252, 1.0165725651577509, 0.5138874142661182, 0.0), # 53
(6.211043125734431, 5.632275000000001, 5.07725, 5.473659375, 4.495429750658201, 2.1875000000000004, 2.3145970588235296, 2.000125, 2.381635, 1.0989300000000004, 0.8239136363636363, 0.4728000000000001, 0.0, 6.075000000000001, 5.2008, 4.119568181818182, 3.2967900000000006, 4.76327, 2.8001750000000003, 2.3145970588235296, 1.5625000000000002, 2.2477148753291005, 1.8245531250000007, 1.0154500000000002, 0.5120250000000002, 0.0), # 54
(6.213021795087014, 5.611706961591222, 5.0715877914952, 5.468504050925926, 4.496079835837076, 2.1875000000000004, 2.3067568385378845, 1.9864969135802473, 2.3793595679012345, 1.0950402949245546, 0.8231426923556555, 0.47204503886602667, 0.0, 6.075000000000001, 5.192495427526293, 4.115713461778277, 3.2851208847736633, 4.758719135802469, 2.7810956790123464, 2.3067568385378845, 1.5625000000000002, 2.248039917918538, 1.8228346836419758, 1.0143175582990402, 0.5101551783264747, 0.0), # 55
(6.214851122551123, 5.59109365569273, 5.065884430727024, 5.463279282407408, 4.496680354749464, 2.1875000000000004, 2.298906451222465, 1.9730540123456795, 2.3770619135802473, 1.0911621124828537, 0.822357532111236, 0.47128459076360324, 0.0, 6.075000000000001, 5.184130498399635, 4.11178766055618, 3.2734863374485603, 4.754123827160495, 2.7622756172839513, 2.298906451222465, 1.5625000000000002, 2.248340177374732, 1.821093094135803, 1.013176886145405, 0.5082812414266119, 0.0), # 56
(6.21653063063063, 5.570471296296297, 5.06014814814815, 5.457990625000001, 4.497231233975187, 2.1875000000000004, 2.2910601307189546, 1.959833333333334, 2.3747450000000003, 1.087303518518519, 0.8215587542087543, 0.47051975308641986, 0.0, 6.075000000000001, 5.175717283950617, 4.107793771043772, 3.261910555555556, 4.749490000000001, 2.7437666666666676, 2.2910601307189546, 1.5625000000000002, 2.2486156169875935, 1.819330208333334, 1.01202962962963, 0.5064064814814816, 0.0), # 57
(6.218059841829408, 5.549876097393691, 5.054387174211249, 5.4526436342592595, 4.49773240009407, 2.1875000000000004, 2.283232110869039, 1.946871913580247, 2.372411790123457, 1.083472578875172, 0.8207469572265872, 0.46975162322816655, 0.0, 6.075000000000001, 5.167267855509832, 4.103734786132936, 3.2504177366255154, 4.744823580246914, 2.725620679012346, 2.283232110869039, 1.5625000000000002, 2.248866200047035, 1.8175478780864203, 1.01087743484225, 0.5045341906721538, 0.0), # 58
(6.219438278651324, 5.529344272976681, 5.048609739369, 5.447243865740742, 4.498183779685938, 2.1875000000000004, 2.275436625514404, 1.9342067901234572, 2.3700652469135806, 1.079677359396434, 0.8199227397431101, 0.4689812985825333, 0.0, 6.075000000000001, 5.158794284407866, 4.099613698715551, 3.239032078189301, 4.740130493827161, 2.7078895061728403, 2.275436625514404, 1.5625000000000002, 2.249091889842969, 1.8157479552469142, 1.0097219478738, 0.5026676611796984, 0.0), # 59
(6.220665463600247, 5.508912037037038, 5.042824074074074, 5.4417968750000005, 4.498585299330615, 2.1875000000000004, 2.267687908496732, 1.9218750000000004, 2.3677083333333337, 1.0759259259259264, 0.8190867003367005, 0.46820987654321, 0.0, 6.075000000000001, 5.150308641975309, 4.095433501683503, 3.2277777777777787, 4.7354166666666675, 2.6906250000000007, 2.267687908496732, 1.5625000000000002, 2.2492926496653074, 1.8139322916666674, 1.008564814814815, 0.5008101851851854, 0.0), # 60
(6.22174091918005, 5.48861560356653, 5.037038408779151, 5.436308217592593, 4.498936885607925, 2.1875000000000004, 2.26000019365771, 1.909913580246914, 2.365344012345679, 1.0722263443072706, 0.8182394375857341, 0.46743845450388677, 0.0, 6.075000000000001, 5.141822999542754, 4.09119718792867, 3.216679032921811, 4.730688024691358, 2.67387901234568, 2.26000019365771, 1.5625000000000002, 2.2494684428039626, 1.8121027391975315, 1.0074076817558304, 0.4989650548696847, 0.0), # 61
(6.222664167894603, 5.4684911865569275, 5.031260973936901, 5.430783449074076, 4.499238465097694, 2.1875000000000004, 2.2523877148390223, 1.8983595679012348, 2.3629752469135803, 1.0685866803840882, 0.8173815500685874, 0.46666812985825346, 0.0, 6.075000000000001, 5.133349428440788, 4.086907750342936, 3.205760041152264, 4.725950493827161, 2.657703395061729, 2.2523877148390223, 1.5625000000000002, 2.249619232548847, 1.810261149691359, 1.0062521947873804, 0.4971355624142662, 0.0), # 62
(6.223434732247776, 5.448575, 5.025500000000001, 5.425228125000002, 4.499489964379743, 2.1875000000000004, 2.244864705882353, 1.8872500000000003, 2.360605, 1.0650150000000003, 0.8165136363636366, 0.4659000000000001, 0.0, 6.075000000000001, 5.1249, 4.082568181818183, 3.1950450000000004, 4.72121, 2.6421500000000004, 2.244864705882353, 1.5625000000000002, 2.2497449821898714, 1.808409375000001, 1.0051000000000003, 0.49532500000000007, 0.0), # 63
(6.224052134743439, 5.428903257887518, 5.019763717421125, 5.419647800925927, 4.499691310033899, 2.1875000000000004, 2.237445400629388, 1.8766219135802475, 2.3582362345679013, 1.0615193689986286, 0.815636295049258, 0.4651351623228168, 0.0, 6.075000000000001, 5.116486785550984, 4.07818147524629, 3.184558106995885, 4.716472469135803, 2.6272706790123466, 2.237445400629388, 1.5625000000000002, 2.2498456550169497, 1.8065492669753094, 1.0039527434842253, 0.49353665980795625, 0.0), # 64
(6.224515897885464, 5.409512174211248, 5.0140603566529505, 5.414048032407408, 4.499842428639987, 2.1875000000000004, 2.230144032921811, 1.8665123456790127, 2.355871913580247, 1.0581078532235944, 0.8147501247038288, 0.46437471422039334, 0.0, 6.075000000000001, 5.108121856424326, 4.073750623519143, 3.1743235596707824, 4.711743827160494, 2.613117283950618, 2.230144032921811, 1.5625000000000002, 2.2499212143199934, 1.8046826774691365, 1.0028120713305901, 0.49177383401920444, 0.0), # 65
(6.224825544177719, 5.390437962962965, 5.008398148148149, 5.408434375000001, 4.499943246777829, 2.1875000000000004, 2.2229748366013076, 1.856958333333334, 2.3535150000000002, 1.0547885185185188, 0.8138557239057241, 0.46361975308641995, 0.0, 6.075000000000001, 5.099817283950618, 4.06927861952862, 3.164365555555556, 4.7070300000000005, 2.5997416666666675, 2.2229748366013076, 1.5625000000000002, 2.2499716233889147, 1.8028114583333341, 1.00167962962963, 0.49003981481481507, 0.0), # 66
(6.224980596124076, 5.371716838134431, 5.002785322359397, 5.40281238425926, 4.499993691027252, 2.1875000000000004, 2.215952045509562, 1.8479969135802476, 2.3511684567901234, 1.0515694307270238, 0.8129536912333212, 0.46287137631458625, 0.0, 6.075000000000001, 5.091585139460448, 4.064768456166606, 3.1547082921810707, 4.702336913580247, 2.5871956790123467, 2.215952045509562, 1.5625000000000002, 2.249996845513626, 1.8009374614197537, 1.0005570644718795, 0.48833789437585745, 0.0), # 67
(6.224874968688496, 5.353286751626178, 4.997202974965707, 5.3971387832125615, 4.499951182118938, 2.1874594040542603, 2.2090545584488943, 1.8395859625057158, 2.3488175697302243, 1.0484430662470754, 0.8120285988540378, 0.4621265010949873, 0.0, 6.074925090020577, 5.08339151204486, 4.060142994270189, 3.1453291987412255, 4.6976351394604485, 2.575420347508002, 2.2090545584488943, 1.5624710028959001, 2.249975591059469, 1.7990462610708544, 0.9994405949931414, 0.4866624319660163, 0.0), # 68
(6.2238850241545896, 5.334585035842295, 4.991494212962963, 5.39112758152174, 4.499564270152505, 2.187138477366256, 2.2020804544039843, 1.8312746913580251, 2.346359567901235, 1.0453209586056647, 0.8109862838915471, 0.4613609486679664, 0.0, 6.074331597222224, 5.0749704353476295, 4.054931419457736, 3.1359628758169933, 4.69271913580247, 2.5637845679012354, 2.2020804544039843, 1.5622417695473254, 2.2497821350762526, 1.7970425271739137, 0.9982988425925927, 0.4849622759856632, 0.0), # 69
(6.221931472535338, 5.315525852913846, 4.985634216392318, 5.384739205917875, 4.498799725651577, 2.186506439567139, 2.1949980403684113, 1.8229881115683588, 2.3437805784179244, 1.0421879286694105, 0.8098148886526081, 0.46057113806464056, 0.0, 6.073159400720166, 5.066282518711046, 4.049074443263041, 3.126563786008231, 4.687561156835849, 2.5521833561957026, 2.1949980403684113, 1.5617903139765281, 2.2493998628257885, 1.7949130686392922, 0.9971268432784638, 0.4832296229921679, 0.0), # 70
(6.219041796385758, 5.296120592725343, 4.9796250428669415, 5.377983076690823, 4.497667231501655, 2.185573532998019, 2.1878104978616966, 1.8147289666209425, 2.3410844421582078, 1.0390440539013963, 0.8085187370783864, 0.4597576468606359, 0.0, 6.07142393261317, 5.057334115466994, 4.042593685391932, 3.1171321617041885, 4.6821688843164155, 2.54062055326932, 2.1878104978616966, 1.561123952141442, 2.2488336157508275, 1.792661025563608, 0.9959250085733884, 0.4814655084295767, 0.0), # 71
(6.215243478260871, 5.27638064516129, 4.97346875, 5.370868614130435, 4.496176470588235, 2.1843500000000007, 2.1805210084033617, 1.8065000000000002, 2.3382750000000003, 1.0358894117647062, 0.8071021531100481, 0.45892105263157906, 0.0, 6.069140625000001, 5.048131578947369, 4.0355107655502405, 3.107668235294118, 4.676550000000001, 2.5291000000000006, 2.1805210084033617, 1.5602500000000006, 2.2480882352941176, 1.790289538043479, 0.9946937500000002, 0.47967096774193557, 0.0), # 72
(6.210564000715693, 5.2563174001062, 4.9671673954046645, 5.363405238526571, 4.494337125796821, 2.18284608291419, 2.173132753512928, 1.7983039551897582, 2.3353560928212165, 1.0327240797224242, 0.805569460688759, 0.45806193295309605, 0.0, 6.066324909979425, 5.038681262484056, 4.027847303443795, 3.0981722391672717, 4.670712185642433, 2.5176255372656615, 2.173132753512928, 1.5591757735101357, 2.2471685628984104, 1.787801746175524, 0.9934334790809329, 0.47784703637329096, 0.0), # 73
(6.2050308463052435, 5.235942247444578, 4.960723036694102, 5.355602370169082, 4.49215888001291, 2.1810720240816956, 2.165648914709917, 1.79014357567444, 2.3323315614997715, 1.0295481352376346, 0.8039249837556858, 0.4571808654008135, 0.0, 6.062992219650208, 5.028989519408948, 4.019624918778429, 3.0886444057129028, 4.664663122999543, 2.506201005944216, 2.165648914709917, 1.5579085886297825, 2.246079440006455, 1.7852007900563613, 0.9921446073388204, 0.47599474976768896, 0.0), # 74
(6.198671497584542, 5.215266577060932, 4.954137731481482, 5.347469429347827, 4.489651416122005, 2.179038065843622, 2.1580726735138502, 1.782021604938272, 2.32920524691358, 1.0263616557734208, 0.8021730462519937, 0.4562784275503575, 0.0, 6.059157986111113, 5.019062703053931, 4.010865231259968, 3.0790849673202616, 4.65841049382716, 2.494830246913581, 2.1580726735138502, 1.5564557613168728, 2.2448257080610023, 1.7824898097826094, 0.9908275462962965, 0.4741151433691757, 0.0), # 75
(6.191513437108607, 5.194301778839773, 4.9474135373799735, 5.339015836352658, 4.486824417009602, 2.1767544505410767, 2.1504072114442487, 1.7739407864654781, 2.325980989940558, 1.0231647187928672, 0.8003179721188494, 0.4553551969773542, 0.0, 6.054837641460906, 5.0089071667508955, 4.001589860594247, 3.069494156378601, 4.651961979881116, 2.4835171010516697, 2.1504072114442487, 1.5548246075293404, 2.243412208504801, 1.7796719454508865, 0.9894827074759949, 0.47220925262179764, 0.0), # 76
(6.183584147432457, 5.173059242665606, 4.940552512002744, 5.33025101147343, 4.483687565561204, 2.1742314205151656, 2.1426557100206343, 1.765903863740284, 2.3226626314586194, 1.019957401759058, 0.7983640852974189, 0.45441175125743044, 0.0, 6.050046617798356, 4.998529263831734, 3.991820426487094, 3.0598722052771734, 4.645325262917239, 2.472265409236398, 2.1426557100206343, 1.5530224432251183, 2.241843782780602, 1.7767503371578104, 0.9881105024005488, 0.4702781129696006, 0.0), # 77
(6.174911111111112, 5.1515503584229405, 4.933556712962964, 5.321184375000001, 4.48025054466231, 2.171479218106996, 2.134821350762528, 1.7579135802469141, 2.319254012345679, 1.0167397821350765, 0.7963157097288678, 0.453448667966212, 0.0, 6.044800347222224, 4.987935347628332, 3.9815785486443387, 3.050219346405229, 4.638508024691358, 2.46107901234568, 2.134821350762528, 1.5510565843621402, 2.240125272331155, 1.7737281250000008, 0.9867113425925927, 0.4683227598566311, 0.0), # 78
(6.165521810699589, 5.129786515996284, 4.9264281978738005, 5.311825347222223, 4.476523037198419, 2.1685080856576744, 2.1269073151894506, 1.7499726794695933, 2.3157589734796526, 1.0135119373840074, 0.7941771693543623, 0.45246652467932535, 0.0, 6.039114261831276, 4.977131771472578, 3.970885846771812, 3.0405358121520214, 4.631517946959305, 2.4499617512574305, 2.1269073151894506, 1.5489343468983388, 2.2382615185992094, 1.7706084490740748, 0.9852856395747601, 0.46634422872693504, 0.0), # 79
(6.155443728752909, 5.107779105270145, 4.919169024348423, 5.302183348429953, 4.4725147260550315, 2.165328265508307, 2.1189167848209247, 1.7420839048925472, 2.3121813557384545, 1.0102739449689344, 0.7919527881150691, 0.45146589897239686, 0.0, 6.033003793724281, 4.966124888696365, 3.959763940575345, 3.030821834906803, 4.624362711476909, 2.438917466849566, 2.1189167848209247, 1.5466630467916478, 2.2362573630275158, 1.7673944494766514, 0.9838338048696846, 0.46434355502455876, 0.0), # 80
(6.144704347826088, 5.0855395161290335, 4.911781250000001, 5.292267798913045, 4.4682352941176475, 2.1619500000000005, 2.110852941176471, 1.7342500000000003, 2.308525, 1.0070258823529417, 0.7896468899521534, 0.4504473684210528, 0.0, 6.026484375, 4.95492105263158, 3.9482344497607667, 3.0210776470588243, 4.61705, 2.4279500000000005, 2.110852941176471, 1.5442500000000001, 2.2341176470588238, 1.7640892663043488, 0.9823562500000002, 0.4623217741935486, 0.0), # 81
(6.133331150474146, 5.0630791384574545, 4.904266932441702, 5.2820881189613536, 4.463694424271766, 2.1583835314738615, 2.1027189657756113, 1.7264737082761776, 2.304793747142204, 1.0037678269991126, 0.7872637988067815, 0.44941151060091944, 0.0, 6.019571437757203, 4.9435266166101135, 3.936318994033907, 3.011303480997337, 4.609587494284408, 2.417063191586649, 2.1027189657756113, 1.5417025224813294, 2.231847212135883, 1.760696039653785, 0.9808533864883406, 0.4602799216779505, 0.0), # 82
(6.121351619252104, 5.040409362139918, 4.896628129286695, 5.2716537288647345, 4.458901799402889, 2.1546391022709956, 2.0945180401378662, 1.7187577732053043, 2.3009914380429812, 1.000499856370532, 0.7848078386201196, 0.4483589030876232, 0.0, 6.0122804140946515, 4.931947933963855, 3.924039193100598, 3.0014995691115955, 4.6019828760859625, 2.4062608824874263, 2.0945180401378662, 1.5390279301935683, 2.2294508997014444, 1.7572179096215788, 0.9793256258573391, 0.4582190329218108, 0.0), # 83
(6.108793236714976, 5.017541577060933, 4.888866898148149, 5.260974048913045, 4.453867102396515, 2.1507269547325105, 2.0862533457827577, 1.7111049382716053, 2.2971219135802468, 0.9972220479302837, 0.7822833333333336, 0.4472901234567902, 0.0, 6.004626736111112, 4.920191358024692, 3.911416666666667, 2.9916661437908503, 4.5942438271604935, 2.3955469135802474, 2.0862533457827577, 1.5362335390946504, 2.2269335511982575, 1.7536580163043487, 0.97777337962963, 0.45614014336917574, 0.0), # 84
(6.095683485417786, 4.994487173105005, 4.880985296639232, 5.250058499396136, 4.448600016138143, 2.146657331199513, 2.0779280642298072, 1.7035179469593054, 2.2931890146319156, 0.9939344791414512, 0.7796946068875895, 0.446205749284047, 0.0, 5.996625835905351, 4.908263242124516, 3.8984730344379477, 2.981803437424353, 4.586378029263831, 2.3849251257430275, 2.0779280642298072, 1.533326665142509, 2.2243000080690716, 1.7500194997987126, 0.9761970593278466, 0.45404428846409145, 0.0), # 85
(6.082049847915549, 4.971257540156645, 4.872985382373115, 5.2389165006038665, 4.443110223513274, 2.1424404740131084, 2.069545376998536, 1.6959995427526295, 2.2891965820759035, 0.990637227467119, 0.7770459832240536, 0.44510635814501975, 0.0, 5.988293145576132, 4.896169939595216, 3.8852299161202675, 2.9719116824013563, 4.578393164151807, 2.3743993598536814, 2.069545376998536, 1.5303146242950774, 2.221555111756637, 1.7463055002012893, 0.9745970764746232, 0.45193250365060417, 0.0), # 86
(6.067919806763285, 4.947864068100359, 4.864869212962963, 5.227557472826088, 4.437407407407409, 2.1380866255144033, 2.061108465608466, 1.6885524691358027, 2.285148456790124, 0.9873303703703706, 0.7743417862838917, 0.44399252761533475, 0.0, 5.979644097222223, 4.883917803768681, 3.8717089314194584, 2.9619911111111112, 4.570296913580248, 2.363973456790124, 2.061108465608466, 1.5272047325102882, 2.2187037037037043, 1.7425191576086962, 0.9729738425925928, 0.44980582437275995, 0.0), # 87
(6.053320844516015, 4.924318146820656, 4.856638846021949, 5.215990836352658, 4.431501250706044, 2.1336060280445057, 2.0526205115791174, 1.6811794695930502, 2.281048479652492, 0.9840139853142905, 0.77158634000827, 0.4428648352706184, 0.0, 5.970694122942389, 4.871513187976801, 3.85793170004135, 2.952041955942871, 4.562096959304984, 2.35365125743027, 2.0526205115791174, 1.5240043057460755, 2.215750625353022, 1.7386636121175532, 0.9713277692043898, 0.44766528607460515, 0.0), # 88
(6.0382804437287545, 4.900631166202045, 4.848296339163238, 5.2042260114734304, 4.425401436294683, 2.129008923944521, 2.0440846964300126, 1.6738832876085967, 2.276900491540924, 0.9806881497619627, 0.7687839683383546, 0.4417238586864969, 0.0, 5.961458654835392, 4.858962445551465, 3.843919841691773, 2.9420644492858874, 4.553800983081848, 2.3434366026520355, 2.0440846964300126, 1.5207206599603722, 2.2127007181473415, 1.7347420038244774, 0.9696592678326478, 0.445511924200186, 0.0), # 89
(6.022826086956522, 4.876814516129033, 4.839843750000001, 5.192272418478262, 4.419117647058824, 2.1243055555555563, 2.0355042016806726, 1.6666666666666667, 2.272708333333334, 0.977352941176471, 0.7659389952153112, 0.44057017543859656, 0.0, 5.951953125000001, 4.846271929824561, 3.829694976076556, 2.9320588235294123, 4.545416666666668, 2.3333333333333335, 2.0355042016806726, 1.5173611111111116, 2.209558823529412, 1.7307574728260877, 0.9679687500000003, 0.4433467741935485, 0.0), # 90
(6.006985256754339, 4.852879586486127, 4.831283136145405, 5.180139477657006, 4.412659565883967, 2.119506165218717, 2.0268822088506186, 1.6595323502514865, 2.2684758459076364, 0.9740084370208992, 0.7630557445803061, 0.4394043631025438, 0.0, 5.942192965534979, 4.833447994127981, 3.8152787229015304, 2.922025311062697, 4.536951691815273, 2.3233452903520813, 2.0268822088506186, 1.5139329751562263, 2.2063297829419835, 1.7267131592190026, 0.9662566272290811, 0.4411708714987389, 0.0), # 91
(5.990785435677224, 4.8288377671578395, 4.822616555212621, 5.167836609299518, 4.406036875655612, 2.114620995275111, 2.018221899459373, 1.65248308184728, 2.264206870141747, 0.9706547147583318, 0.7601385403745055, 0.43822699925396497, 0.0, 5.932193608539095, 4.820496991793614, 3.8006927018725274, 2.911964144274995, 4.528413740283494, 2.313476314586192, 2.018221899459373, 1.5104435680536508, 2.203018437827806, 1.7226122030998396, 0.9645233110425242, 0.43898525155980367, 0.0), # 92
(5.974254106280194, 4.804700448028675, 4.813846064814816, 5.155373233695654, 4.39925925925926, 2.109660288065844, 2.0095264550264553, 1.6455216049382722, 2.2599052469135805, 0.9672918518518521, 0.7571917065390752, 0.43703866146848613, 0.0, 5.921970486111111, 4.807425276153347, 3.7859585326953757, 2.9018755555555558, 4.519810493827161, 2.303730246913581, 2.0095264550264553, 1.506900205761317, 2.19962962962963, 1.7184577445652183, 0.9627692129629631, 0.4367909498207887, 0.0), # 93
(5.957418751118269, 4.780479018983141, 4.804973722565159, 5.1427587711352665, 4.392336399580408, 2.104634285932023, 2.0007990570713887, 1.6386506630086881, 2.255574817101052, 0.9639199257645449, 0.7542195670151812, 0.4358399273217338, 0.0, 5.911539030349795, 4.794239200539072, 3.771097835075906, 2.891759777293634, 4.511149634202104, 2.2941109282121634, 2.0007990570713887, 1.503310204237159, 2.196168199790204, 1.714252923711756, 0.960994744513032, 0.43458900172574016, 0.0), # 94
(5.940306852746467, 4.7561848699057485, 4.796001586076819, 5.130002641908213, 4.3852779795045596, 2.0995532312147542, 1.9920428871136935, 1.631872999542753, 2.251219421582076, 0.9605390139594935, 0.7512264457439898, 0.4346313743893341, 0.0, 5.900914673353911, 4.780945118282674, 3.7561322287199483, 2.8816170418784797, 4.502438843164152, 2.284622199359854, 1.9920428871136935, 1.49968087943911, 2.1926389897522798, 1.7100008806360716, 0.9592003172153638, 0.43238044271870446, 0.0), # 95
(5.922945893719809, 4.731829390681005, 4.786931712962964, 5.117114266304348, 4.378093681917211, 2.094427366255144, 1.9832611266728915, 1.625191358024692, 2.246842901234568, 0.9571491938997825, 0.7482166666666669, 0.43341358024691373, 0.0, 5.890112847222223, 4.767549382716051, 3.7410833333333344, 2.8714475816993468, 4.493685802469136, 2.2752679012345687, 1.9832611266728915, 1.496019547325103, 2.1890468409586057, 1.7057047554347833, 0.957386342592593, 0.4301663082437278, 0.0), # 96
(5.90536335659331, 4.707423971193417, 4.777766160836763, 5.104103064613527, 4.370793189703866, 2.0892669333943, 1.9744569572685042, 1.6186084819387292, 2.242449096936443, 0.9537505430484955, 0.7451945537243784, 0.4321871224700986, 0.0, 5.8791489840535, 4.7540583471710836, 3.725972768621892, 2.8612516291454857, 4.484898193872886, 2.266051874714221, 1.9744569572685042, 1.4923335238530713, 2.185396594851933, 1.7013676882045095, 0.9555532321673527, 0.4279476337448561, 0.0), # 97
(5.8875867239219914, 4.682980001327493, 4.768506987311386, 5.090978457125605, 4.363386185750021, 2.084082174973328, 1.965633560420053, 1.6121271147690905, 2.238041849565615, 0.9503431388687166, 0.7421644308582908, 0.430952578634515, 0.0, 5.868038515946503, 4.740478364979665, 3.7108221542914532, 2.851029416606149, 4.47608369913123, 2.2569779606767266, 1.965633560420053, 1.4886301249809484, 2.1816930928750105, 1.6969928190418688, 0.9537013974622772, 0.4257254546661358, 0.0), # 98
(5.86964347826087, 4.658508870967743, 4.759156250000001, 5.0777498641304355, 4.355882352941177, 2.0788833333333336, 1.9567941176470594, 1.6057500000000002, 2.233625, 0.9469270588235297, 0.7391306220095696, 0.42971052631578954, 0.0, 5.8567968750000015, 4.726815789473684, 3.6956531100478474, 2.840781176470588, 4.46725, 2.2480500000000005, 1.9567941176470594, 1.4849166666666669, 2.1779411764705885, 1.692583288043479, 0.9518312500000002, 0.42350080645161303, 0.0), # 99
(5.851561102164967, 4.634021969998672, 4.749716006515776, 5.0644267059178745, 4.348291374162834, 2.073680650815425, 1.9479418104690438, 1.5994798811156838, 2.229202389117513, 0.9435023803760191, 0.7360974511193812, 0.4284615430895485, 0.0, 5.8454394933127585, 4.713076973985032, 3.6804872555969057, 2.8305071411280567, 4.458404778235026, 2.2392718335619577, 1.9479418104690438, 1.4812004648681607, 2.174145687081417, 1.6881422353059587, 0.9499432013031552, 0.42127472454533393, 0.0), # 100
(5.833367078189301, 4.609530688304793, 4.74018831447188, 5.051018402777779, 4.340622932300493, 2.0684843697607076, 1.9390798204055288, 1.5933195016003658, 2.224777857796068, 0.9400691809892685, 0.7330692421288914, 0.4272062065314178, 0.0, 5.83398180298354, 4.699268271845595, 3.6653462106444565, 2.820207542967805, 4.449555715592136, 2.230647302240512, 1.9390798204055288, 1.4774888355433624, 2.1703114661502463, 1.6836728009259267, 0.9480376628943762, 0.41904824439134486, 0.0), # 101
(5.81508888888889, 4.58504641577061, 4.730575231481482, 5.037534375000002, 4.332886710239652, 2.063304732510289, 1.9302113289760352, 1.5872716049382718, 2.2203552469135803, 0.9366275381263621, 0.7300503189792665, 0.4259450942170242, 0.0, 5.822439236111112, 4.6853960363872655, 3.650251594896332, 2.809882614379086, 4.440710493827161, 2.2221802469135805, 1.9302113289760352, 1.4737890946502061, 2.166443355119826, 1.6791781250000009, 0.9461150462962965, 0.4168224014336919, 0.0), # 102
(5.796754016818752, 4.560580542280633, 4.720878815157751, 5.023984042874398, 4.325092390865812, 2.058151981405274, 1.9213395177000843, 1.5813389346136262, 2.2159383973479656, 0.9331775292503837, 0.7270450056116725, 0.42467878372199375, 0.0, 5.810827224794239, 4.67146662094193, 3.635225028058362, 2.79953258775115, 4.431876794695931, 2.2138745084590767, 1.9213395177000843, 1.4701085581466242, 2.162546195432906, 1.6746613476247996, 0.9441757630315504, 0.4145982311164212, 0.0), # 103
(5.778389944533907, 4.536144457719368, 4.711101123113855, 5.010376826690822, 4.3172496570644725, 2.0530363587867706, 1.912467568097198, 1.5755242341106543, 2.2115311499771377, 0.9297192318244174, 0.7240576259672752, 0.4234078526219528, 0.0, 5.799161201131688, 4.65748637884148, 3.6202881298363754, 2.7891576954732518, 4.4230622999542755, 2.205733927754916, 1.912467568097198, 1.4664545419905504, 2.1586248285322363, 1.6701256088969412, 0.9422202246227711, 0.412376768883579, 0.0), # 104
(5.760024154589373, 4.5117495519713255, 4.701244212962964, 4.996722146739131, 4.309368191721133, 2.047968106995885, 1.9035986616868976, 1.5698302469135805, 2.2071373456790124, 0.9262527233115473, 0.721092503987241, 0.4221328784925278, 0.0, 5.787456597222223, 4.6434616634178045, 3.6054625199362045, 2.7787581699346413, 4.414274691358025, 2.197762345679013, 1.9035986616868976, 1.462834362139918, 2.1546840958605666, 1.6655740489130442, 0.9402488425925928, 0.4101590501792115, 0.0), # 105
(5.741684129540169, 4.487407214921015, 4.691310142318245, 4.98302942330918, 4.301457677721295, 2.0429574683737237, 1.8947359799887038, 1.5642597165066305, 2.202760825331504, 0.922778081174857, 0.7181539636127356, 0.4208544389093448, 0.0, 5.775728845164609, 4.629398828002793, 3.590769818063678, 2.7683342435245706, 4.405521650663008, 2.1899636031092826, 1.8947359799887038, 1.4592553345526598, 2.1507288388606476, 1.661009807769727, 0.9382620284636491, 0.40794611044736506, 0.0), # 106
(5.723397351941315, 4.463128836452942, 4.681300968792868, 4.9693080766908215, 4.293527797950456, 2.038014685261394, 1.8858827045221387, 1.5588153863740288, 2.198405429812529, 0.9192953828774312, 0.7152463287849253, 0.41957311144803033, 0.0, 5.763993377057614, 4.615304225928333, 3.5762316439246264, 2.7578861486322928, 4.396810859625058, 2.18234154092364, 1.8858827045221387, 1.4557247751867097, 2.146763898975228, 1.6564360255636077, 0.9362601937585737, 0.40573898513208567, 0.0), # 107
(5.705191304347827, 4.438925806451614, 4.671218750000001, 4.955567527173915, 4.285588235294117, 2.0331500000000005, 1.8770420168067228, 1.5535000000000003, 2.194075, 0.9158047058823533, 0.7123739234449762, 0.4182894736842107, 0.0, 5.7522656250000015, 4.601184210526317, 3.5618696172248807, 2.7474141176470592, 4.38815, 2.1749000000000005, 1.8770420168067228, 1.4522500000000003, 2.1427941176470586, 1.6518558423913052, 0.9342437500000004, 0.4035387096774195, 0.0), # 108
(5.687093469314727, 4.414809514801541, 4.661065543552813, 4.941817195048309, 4.27764867263778, 2.0283736549306512, 1.8682170983619788, 1.5483163008687704, 2.189773376771834, 0.9123061276527075, 0.7095410715340542, 0.417004103193512, 0.0, 5.740561021090536, 4.587045135128632, 3.547705357670271, 2.736918382958122, 4.379546753543668, 2.167642821216279, 1.8682170983619788, 1.4488383249504653, 2.13882433631889, 1.6472723983494368, 0.9322131087105627, 0.40134631952741284, 0.0), # 109
(5.66913132939703, 4.3907913513872305, 4.650843407064474, 4.928066500603865, 4.269718792866942, 2.0236958923944526, 1.859411130707427, 1.5432670324645634, 2.1855044010059443, 0.9087997256515778, 0.7067520969933253, 0.4157175775515608, 0.0, 5.728894997427985, 4.572893353067168, 3.533760484966626, 2.726399176954733, 4.371008802011889, 2.160573845450389, 1.859411130707427, 1.4454970659960373, 2.134859396433471, 1.6426888335346224, 0.9301686814128948, 0.3991628501261119, 0.0), # 110
(5.6513323671497595, 4.366882706093191, 4.64055439814815, 4.914324864130435, 4.261808278867103, 2.019126954732511, 1.8506272953625897, 1.5383549382716053, 2.181271913580247, 0.9052855773420483, 0.7040113237639555, 0.4144304743339832, 0.0, 5.717282986111112, 4.558735217673815, 3.5200566188197775, 2.715856732026144, 4.362543827160494, 2.1536969135802475, 1.8506272953625897, 1.4422335390946506, 2.1309041394335515, 1.638108288043479, 0.92811087962963, 0.3969893369175629, 0.0), # 111
(5.633724065127931, 4.343094968803928, 4.630200574417011, 4.900601705917875, 4.253926813523764, 2.0146770842859327, 1.8418687738469877, 1.5335827617741202, 2.1770797553726564, 0.9017637601872027, 0.7013230757871111, 0.4131433711164056, 0.0, 5.705740419238684, 4.544577082280461, 3.5066153789355554, 2.7052912805616076, 4.354159510745313, 2.1470158664837684, 1.8418687738469877, 1.4390550602042376, 2.126963406761882, 1.6335339019726254, 0.9260401148834023, 0.39482681534581177, 0.0), # 112
(5.616302534221828, 4.319479240332274, 4.619808914126035, 4.886925247121419, 4.246070272069482, 2.01035441922508, 1.8331577890970312, 1.528963142195152, 2.172942562251724, 0.8982457104919097, 0.6986923906152869, 0.41185964682522147, 0.0, 5.6942663405059335, 4.530456115077436, 3.493461953076434, 2.6947371314757285, 4.345885124503448, 2.1405483990732126, 1.8331577890970312, 1.4359674423036286, 2.123035136034741, 1.6289750823738067, 0.9239617828252071, 0.3926799309392977, 0.0), # 113
(5.59888853874004, 4.296268450939045, 4.60952283751081, 4.873405239574803, 4.238157341826531, 2.0061491129854656, 1.824605462523174, 1.5245528148762801, 2.1689238071426122, 0.8947972751869178, 0.6961244337113198, 0.41059560860564887, 0.0, 5.682765248496022, 4.5165516946621365, 3.4806221685565992, 2.684391825560753, 4.3378476142852245, 2.134373940826792, 1.824605462523174, 1.4329636521324756, 2.1190786709132654, 1.6244684131916012, 0.9219045675021622, 0.39056985917627685, 0.0), # 114
(5.581430941802398, 4.273470959275675, 4.59934846635366, 4.860038027331801, 4.230163071155441, 2.002050229374568, 1.8162178950574688, 1.5203506635696775, 2.165024995733758, 0.8914237991982119, 0.6936154511427095, 0.40935187912794263, 0.0, 5.6712039789962265, 4.502870670407368, 3.4680772557135473, 2.674271397594635, 4.330049991467516, 2.1284909289975484, 1.8162178950574688, 1.4300358781246914, 2.1150815355777204, 1.6200126757772675, 0.919869693270732, 0.3884973599341524, 0.0), # 115
(5.5639079239425815, 4.251053554710436, 4.589266094991937, 4.846799767619883, 4.222075410553511, 1.9980481070504847, 1.8079814452583682, 1.5163450782247487, 2.1612362678455663, 0.8881190037674325, 0.6911605931271482, 0.4081261598743674, 0.0, 5.659564355853536, 4.489387758618041, 3.455802965635741, 2.6643570113022967, 4.322472535691133, 2.1228831095146483, 1.8079814452583682, 1.4271772193217747, 2.1110377052767557, 1.6155999225399613, 0.9178532189983876, 0.3864594140645852, 0.0), # 116
(5.546297665694264, 4.228983026611597, 4.579256017762994, 4.833666617666511, 4.213882310518045, 1.9941330846713121, 1.799882471684326, 1.5125244487908964, 2.1575477632984406, 0.8848766101362197, 0.6887550098823285, 0.40691615232718764, 0.0, 5.647828202914936, 4.476077675599063, 3.4437750494116424, 2.654629830408658, 4.315095526596881, 2.117534228307255, 1.799882471684326, 1.4243807747652228, 2.1069411552590225, 1.6112222058888372, 0.9158512035525989, 0.38445300241923613, 0.0), # 117
(5.528578347591128, 4.2072261643474285, 4.569298529004184, 4.82061473469915, 4.205571721546337, 1.9902955008951488, 1.791907332893795, 1.5088771652175246, 2.153949621912787, 0.8816903395462142, 0.6863938516259426, 0.40571955796866754, 0.0, 5.635977344027416, 4.462915137655342, 3.4319692581297123, 2.645071018638642, 4.307899243825574, 2.1124280313045345, 1.791907332893795, 1.4216396434965348, 2.1027858607731686, 1.6068715782330507, 0.913859705800837, 0.3824751058497663, 0.0), # 118
(5.5107281501668455, 4.185749757286201, 4.5593739230528625, 4.807620275945268, 4.197131594135689, 1.986525694380092, 1.784042387445227, 1.5053916174540365, 2.1504319835090087, 0.8785539132390561, 0.6840722685756829, 0.40453407828107185, 0.0, 5.6239936030379605, 4.449874861091789, 3.4203613428784148, 2.6356617397171673, 4.300863967018017, 2.107548264435651, 1.784042387445227, 1.4189469245572084, 2.0985657970678444, 1.6025400919817565, 0.9118747846105726, 0.38052270520783654, 0.0), # 119
(5.492725253955098, 4.164520594796188, 4.549462494246382, 4.794659398632328, 4.188549878783399, 1.9828140037842383, 1.7762739938970762, 1.502056195449836, 2.146984987907512, 0.8754610524563862, 0.6817854109492417, 0.4033574147466649, 0.0, 5.611858803793559, 4.436931562213314, 3.4089270547462087, 2.626383157369158, 4.293969975815024, 2.10287867362977, 1.7762739938970762, 1.4162957169887416, 2.0942749393916995, 1.5982197995441099, 0.9098924988492764, 0.37859278134510804, 0.0), # 120
(5.474547839489562, 4.1435054662456565, 4.539544536922095, 4.781708259987796, 4.179814525986767, 1.979150767765686, 1.7685885108077943, 1.498859289154327, 2.1435987749286998, 0.8724054784398447, 0.6795284289643118, 0.40218726884771117, 0.0, 5.599554770141197, 4.424059957324822, 3.3976421448215586, 2.6172164353195333, 4.2871975498573995, 2.098403004816058, 1.7685885108077943, 1.4136791198326328, 2.0899072629933837, 1.5939027533292658, 0.9079089073844191, 0.3766823151132416, 0.0), # 121
(5.456174087303913, 4.122671161002879, 4.529600345417356, 4.768743017239136, 4.170913486243094, 1.9755263249825321, 1.7609722967358354, 1.4957892885169124, 2.140263484392978, 0.8693809124310719, 0.6772964728385852, 0.4010213420664751, 0.0, 5.587063325927863, 4.411234762731225, 3.386482364192926, 2.608142737293215, 4.280526968785956, 2.0941050039236773, 1.7609722967358354, 1.41109023213038, 2.085456743121547, 1.589581005746379, 0.9059200690834714, 0.3747882873638982, 0.0), # 122
(5.437582177931832, 4.101984468436126, 4.519610214069519, 4.755739827613812, 4.161834710049677, 1.9719310140928743, 1.7534117102396518, 1.492834583486996, 2.136969256120751, 0.8663810756717084, 0.6750846927897546, 0.39985733588522115, 0.0, 5.5743662950005435, 4.398430694737432, 3.3754234639487724, 2.5991432270151247, 4.273938512241502, 2.0899684168817947, 1.7534117102396518, 1.4085221529234815, 2.0809173550248383, 1.5852466092046047, 0.9039220428139038, 0.3729076789487388, 0.0), # 123
(5.418750291906993, 4.081412177913668, 4.509554437215936, 4.742674848339295, 4.152566147903816, 1.968355173754809, 1.745893109877696, 1.4899835640139818, 2.1337062299324234, 0.8633996894033945, 0.6728882390355121, 0.39869295178621383, 0.0, 5.561445501206228, 4.385622469648352, 3.3644411951775606, 2.590199068210183, 4.267412459864847, 2.0859769896195743, 1.745893109877696, 1.4059679812534351, 2.076283073951908, 1.5808916161130986, 0.9019108874431874, 0.37103747071942444, 0.0), # 124
(5.399656609763076, 4.060921078803776, 4.4994133091939625, 4.729524236643043, 4.143095750302809, 1.964789142626435, 1.738402854208422, 1.487224620047273, 2.130464545648399, 0.8604304748677705, 0.6707022617935502, 0.3975258912517175, 0.0, 5.548282768391898, 4.372784803768892, 3.353511308967751, 2.581291424603311, 4.260929091296798, 2.082114468066182, 1.738402854208422, 1.4034208161617392, 2.0715478751514045, 1.5765080788810146, 0.8998826618387926, 0.3691746435276161, 0.0), # 125
(5.380279312033758, 4.040477960474722, 4.48916712434095, 4.716264149752525, 4.133411467743958, 1.9612232593658492, 1.7309273017902813, 1.4845461415362735, 2.127234343089084, 0.8574671533064767, 0.6685219112815614, 0.3963538557639968, 0.0, 5.534859920404548, 4.359892413403964, 3.342609556407807, 2.5724014599194294, 4.254468686178168, 2.078364598150783, 1.7309273017902813, 1.4008737566898921, 2.066705733871979, 1.5720880499175087, 0.8978334248681901, 0.3673161782249748, 0.0), # 126
(5.360596579252717, 4.020049612294773, 4.478796176994253, 4.702870744895205, 4.123501250724559, 1.9576478626311482, 1.7234528111817284, 1.4819365184303865, 2.124005762074882, 0.8545034459611539, 0.666342337717238, 0.3951745468053159, 0.0, 5.52115878109116, 4.346920014858474, 3.3317116885861897, 2.563510337883461, 4.248011524149764, 2.0747111258025415, 1.7234528111817284, 1.3983199018793915, 2.0617506253622797, 1.5676235816317354, 0.8957592353988507, 0.3654590556631613, 0.0), # 127
(5.340586591953628, 3.9996028236322045, 4.468280761491226, 4.68932017929855, 4.113353049741916, 1.954053291080431, 1.7159657409412148, 1.4793841406790158, 2.120768942426197, 0.8515330740734423, 0.6641586913182725, 0.39398566585793954, 0.0, 5.507161174298723, 4.3338423244373345, 3.3207934565913626, 2.554599222220326, 4.241537884852394, 2.071137796950622, 1.7159657409412148, 1.3957523507717362, 2.056676524870958, 1.5631067264328504, 0.8936561522982452, 0.36360025669383683, 0.0), # 128
(5.320227530670169, 3.9791043838552826, 4.457601172169221, 4.675588610190022, 4.102954815293325, 1.9504298833717935, 1.7084524496271942, 1.4768773982315653, 2.117514023963435, 0.8485497588849822, 0.6619661223023573, 0.3927849144041319, 0.0, 5.492848923874224, 4.32063405844545, 3.309830611511786, 2.545649276654946, 4.23502804792687, 2.0676283575241916, 1.7084524496271942, 1.3931642024084239, 2.0514774076466624, 1.5585295367300078, 0.8915202344338442, 0.36173676216866213, 0.0), # 129
(5.29949757593602, 3.958521082332282, 4.44673770336559, 4.661652194797089, 4.092294497876086, 1.9467679781633334, 1.700899295798119, 1.4744046810374378, 2.1142311465070005, 0.8455472216374139, 0.6597597808871846, 0.3915699939261577, 0.0, 5.47820385366465, 4.307269933187734, 3.2987989044359227, 2.536641664912241, 4.228462293014001, 2.064166553452413, 1.700899295798119, 1.3905485558309525, 2.046147248938043, 1.5538840649323635, 0.8893475406731182, 0.3598655529392984, 0.0), # 130
(5.2783749082848574, 3.9378197084314697, 4.435670649417691, 4.647487090347214, 4.0813600479874985, 1.9430579141131485, 1.693292638012443, 1.4719543790460377, 2.1109104498772973, 0.8425191835723781, 0.6575348172904469, 0.39033860590628133, 0.0, 5.463207787516988, 4.293724664969094, 3.2876740864522342, 2.5275575507171335, 4.2218208997545945, 2.060736130664453, 1.693292638012443, 1.3878985100808203, 2.0406800239937493, 1.5491623634490719, 0.8871341298835383, 0.3579836098574064, 0.0), # 131
(5.256837708250356, 3.9169670515211195, 4.424380304662874, 4.633069454067865, 4.070139416124862, 1.9392900298793363, 1.6856188348286179, 1.4695148822067685, 2.107542073894731, 0.839459365931515, 0.6552863817298366, 0.38908845182676716, 0.0, 5.447842549278226, 4.2799729700944384, 3.2764319086491827, 2.5183780977945442, 4.215084147789462, 2.057320835089476, 1.6856188348286179, 1.3852071641995258, 2.035069708062431, 1.5443564846892888, 0.8848760609325749, 0.35608791377464727, 0.0), # 132
(5.234864156366198, 3.8959299009695, 4.412846963438493, 4.618375443186504, 4.058620552785475, 1.9354546641199937, 1.677864244805097, 1.4670745804690333, 2.1041161583797052, 0.8363614899564652, 0.6530096244230462, 0.38781723316987976, 0.0, 5.432089962795352, 4.2659895648686765, 3.2650481221152305, 2.509084469869395, 4.2082323167594105, 2.053904412656647, 1.677864244805097, 1.3824676172285668, 2.0293102763927373, 1.5394584810621683, 0.8825693926876986, 0.3541754455426819, 0.0), # 133
(5.212432433166057, 3.874675046144883, 4.401050920081903, 4.6033812149305975, 4.046791408466637, 1.9315421554932182, 1.6700152265003338, 1.4646218637822361, 2.100622843152626, 0.8332192768888689, 0.6506996955877681, 0.3865226514178834, 0.0, 5.41593185191535, 4.251749165596717, 3.25349847793884, 2.4996578306666057, 4.201245686305252, 2.0504706092951306, 1.6700152265003338, 1.3796729682094415, 2.0233957042333186, 1.5344604049768662, 0.8802101840163805, 0.35224318601317123, 0.0), # 134
(5.189520719183613, 3.8531692764155387, 4.388972468930455, 4.588062926527611, 4.034639933665648, 1.9275428426571075, 1.6620581384727806, 1.4621451220957802, 2.097052268033896, 0.8300264479703667, 0.6483517454416944, 0.3852024080530427, 0.0, 5.399350040485213, 4.237226488583469, 3.241758727208472, 2.4900793439110993, 4.194104536067792, 2.0470031709340923, 1.6620581384727806, 1.3768163161836482, 2.017319966832824, 1.5293543088425374, 0.8777944937860911, 0.3502881160377763, 0.0), # 135
(5.1661071949525414, 3.8313793811497376, 4.376591904321505, 4.572396735205008, 4.022154078879807, 1.9234470642697592, 1.6539793392808906, 1.4596327453590694, 2.0933945728439216, 0.8267767244425988, 0.6459609242025178, 0.38385420455762215, 0.0, 5.382326352351923, 4.222396250133843, 3.229804621012589, 2.4803301733277956, 4.186789145687843, 2.0434858435026975, 1.6539793392808906, 1.373890760192685, 2.0110770394399036, 1.5241322450683366, 0.8753183808643011, 0.34830721646815804, 0.0), # 136
(5.142170041006521, 3.8092721497157513, 4.363889520592406, 4.556358798190257, 4.009321794606414, 1.9192451589892696, 1.6457651874831167, 1.4570731235215075, 2.0896398974031065, 0.8234638275472056, 0.6435223820879307, 0.3824757424138861, 0.0, 5.364842611362467, 4.207233166552746, 3.2176119104396532, 2.4703914826416162, 4.179279794806213, 2.0399023729301105, 1.6457651874831167, 1.3708893992780498, 2.004660897303207, 1.5187862660634195, 0.8727779041184812, 0.34629746815597745, 0.0), # 137
(5.117687437879229, 3.78681437148185, 4.3508456120805095, 4.5399252727108195, 3.9961310313427676, 1.9149274654737378, 1.6374020416379116, 1.454454646532498, 2.0857783815318562, 0.8200814785258274, 0.6410312693156254, 0.381064723104099, 0.0, 5.346880641363835, 4.191711954145088, 3.2051563465781263, 2.4602444355774815, 4.1715567630637125, 2.036236505145497, 1.6374020416379116, 1.3678053324812411, 1.9980655156713838, 1.5133084242369401, 0.870169122416102, 0.3442558519528955, 0.0), # 138
(5.092637566104342, 3.763972835816304, 4.337440473123171, 4.5230723159941615, 3.9825697395861663, 1.9104843223812598, 1.6288762603037281, 1.4517657043414438, 2.0818001650505744, 0.8166233986201049, 0.6384827361032942, 0.37961884811052543, 0.0, 5.328422266203012, 4.175807329215779, 3.1924136805164705, 2.449870195860314, 4.163600330101149, 2.032471986078021, 1.6288762603037281, 1.3646316588437568, 1.9912848697930832, 1.5076907719980543, 0.8674880946246343, 0.34217934871057315, 0.0), # 139
(5.066998606215539, 3.7407143320873844, 4.323654398057744, 4.505776085267751, 3.968625869833912, 1.9059060683699331, 1.6201742020390193, 1.4489946868977492, 2.0776953877796664, 0.8130833090716781, 0.6358719326686295, 0.3781358189154297, 0.0, 5.30944930972699, 4.159494008069726, 3.1793596633431473, 2.439249927215034, 4.155390775559333, 2.0285925616568488, 1.6201742020390193, 1.361361477407095, 1.984312934916956, 1.5019253617559174, 0.8647308796115489, 0.34006493928067133, 0.0), # 140
(5.040748738746498, 3.7170056496633617, 4.309467681221581, 4.48801273775905, 3.954287372583302, 1.9011830420978557, 1.6112822254022388, 1.4461299841508177, 2.073454189539536, 0.809454931122188, 0.633194009229324, 0.37661333700107635, 0.0, 5.2899435957827485, 4.142746707011839, 3.1659700461466196, 2.4283647933665633, 4.146908379079072, 2.024581977811145, 1.6112822254022388, 1.357987887212754, 1.977143686291651, 1.4960042459196834, 0.8618935362443163, 0.33790960451485114, 0.0), # 141
(5.013866144230894, 3.692813577912507, 4.294860616952036, 4.469758430695524, 3.939542198331635, 1.8963055822231247, 1.6021866889518381, 1.443159986050053, 2.0690667101505884, 0.8057319860132744, 0.6304441160030698, 0.3750491038497298, 0.0, 5.26988694821728, 4.125540142347027, 3.152220580015349, 2.4171959580398226, 4.138133420301177, 2.0204239804700745, 1.6021866889518381, 1.354503987302232, 1.9697710991658175, 1.4899194768985085, 0.8589721233904073, 0.3357103252647734, 0.0), # 142
(4.986329003202405, 3.6681049062030895, 4.279813499586464, 4.45098932130464, 3.924378297576212, 1.8912640274038375, 1.5928739512462708, 1.4400730825448582, 2.0645230894332296, 0.8019081949865782, 0.6276174032075594, 0.37344082094365455, 0.0, 5.249261190877571, 4.1078490303801996, 3.138087016037797, 2.4057245849597337, 4.129046178866459, 2.0161023155628017, 1.5928739512462708, 1.3509028767170268, 1.962189148788106, 1.4836631071015467, 0.8559626999172927, 0.33346408238209907, 0.0), # 143
(4.958115496194711, 3.6428464239033826, 4.264306623462215, 4.431681566813861, 3.908783620814332, 1.886048716298092, 1.5833303708439899, 1.4368576635846375, 2.059813467207862, 0.7979772792837393, 0.6247090210604852, 0.371786189765115, 0.0, 5.228048147610609, 4.089648087416265, 3.123545105302426, 2.393931837851217, 4.119626934415724, 2.0116007290184927, 1.5833303708439899, 1.347177654498637, 1.954391810407166, 1.4772271889379538, 0.852861324692443, 0.3311678567184894, 0.0), # 144
(4.929203803741487, 3.617004920381655, 4.248320282916645, 4.411811324450653, 3.8927461185432937, 1.8806499875639846, 1.5735423063034482, 1.4335021191187944, 2.054927983294891, 0.7939329601463985, 0.6217141197795396, 0.3700829117963758, 0.0, 5.206229642263381, 4.070912029760133, 3.108570598897698, 2.3817988804391947, 4.109855966589782, 2.006902966766312, 1.5735423063034482, 1.3433214196885603, 1.9463730592716468, 1.4706037748168845, 0.8496640565833291, 0.32881862912560506, 0.0), # 145
(4.899572106376411, 3.5905471850061783, 4.231834772287109, 4.391354751442482, 3.8762537412603972, 1.8750581798596135, 1.5634961161830991, 1.4299948390967319, 2.049856777514721, 0.7897689588161959, 0.618627849582415, 0.3683286885197011, 0.0, 5.183787498682872, 4.051615573716711, 3.093139247912075, 2.369306876448587, 4.099713555029442, 2.0019927747354247, 1.5634961161830991, 1.3393272713282953, 1.9381268706301986, 1.4637849171474944, 0.8463669544574218, 0.3264133804551072, 0.0), # 146
(4.8691985846331605, 3.563440007145222, 4.214830385910956, 4.370288005016811, 3.859294439462941, 1.8692636318430758, 1.5531781590413944, 1.4263242134678542, 2.044589989687758, 0.7854789965347722, 0.6154453606868038, 0.3665212214173556, 0.0, 5.160703540716072, 4.031733435590911, 3.0772268034340184, 2.356436989604316, 4.089179979375516, 1.9968538988549958, 1.5531781590413944, 1.3351883084593397, 1.9296472197314705, 1.4567626683389374, 0.8429660771821912, 0.3239490915586566, 0.0), # 147
(4.838061419045413, 3.5356501761670587, 4.197287418125542, 4.348587242401109, 3.8418561636482247, 1.863256682172469, 1.5425747934367882, 1.4224786321815646, 2.039117759634406, 0.7810567945437674, 0.6121618033103984, 0.3646582119716036, 0.0, 5.136959592209966, 4.011240331687639, 3.0608090165519917, 2.3431703836313016, 4.078235519268812, 1.9914700850541904, 1.5425747934367882, 1.330897630123192, 1.9209280818241123, 1.44952908080037, 0.8394574836251085, 0.3214227432879145, 0.0), # 148
(4.806138790146848, 3.507144481439957, 4.179186163268223, 4.326228620822837, 3.823926864313549, 1.8570276695058903, 1.5316723779277324, 1.418446485187267, 2.0334302271750677, 0.7764960740848224, 0.6087723276708911, 0.36273736166470966, 0.0, 5.112537477011543, 3.9901109783118054, 3.0438616383544552, 2.3294882222544664, 4.066860454350135, 1.9858250792621739, 1.5316723779277324, 1.32644833536135, 1.9119634321567744, 1.4420762069409458, 0.8358372326536446, 0.3188313164945416, 0.0), # 149
(4.773408878471139, 3.477889712332189, 4.160506915676348, 4.303188297509463, 3.805494491956211, 1.8505669325014376, 1.5204572710726805, 1.4142161624343643, 2.0275175321301506, 0.7717905563995768, 0.6052720839859744, 0.36075637197893823, 0.0, 5.087419018967789, 3.96832009176832, 3.0263604199298717, 2.31537166919873, 4.055035064260301, 1.9799026274081102, 1.5204572710726805, 1.3218335232153124, 1.9027472459781054, 1.434396099169821, 0.8321013831352696, 0.316171792030199, 0.0), # 150
(4.7398498645519656, 3.447852658212025, 4.141229969687272, 4.279442429688449, 3.7865469970735113, 1.843864809817207, 1.5089158314300852, 1.4097760538722612, 2.021369814320058, 0.766933962729672, 0.6016562224733406, 0.35871294439655366, 0.0, 5.0615860419256915, 3.9458423883620894, 3.008281112366703, 2.300801888189015, 4.042739628640116, 1.9736864754211656, 1.5089158314300852, 1.3170462927265765, 1.8932734985367556, 1.42648080989615, 0.8282459939374544, 0.31344115074654777, 0.0), # 151
(4.705439928923006, 3.4170001084477355, 4.1213356196383515, 4.2549671745872635, 3.767072330162748, 1.8369116401112975, 1.4970344175583996, 1.4051145494503605, 2.014977213565194, 0.7619200143167477, 0.5979198933506823, 0.3566047803998206, 0.0, 5.035020369732239, 3.9226525843980258, 2.9895994667534116, 2.2857600429502423, 4.029954427130388, 1.9671603692305049, 1.4970344175583996, 1.312079742936641, 1.883536165081374, 1.4183223915290881, 0.8242671239276703, 0.3106363734952487, 0.0), # 152
(4.670157252117937, 3.3852988524075913, 4.100804159866935, 4.22973868943337, 3.7470584417212227, 1.8296977620418058, 1.484799388016076, 1.4002200391180657, 2.008329869685965, 0.7567424324024447, 0.5940582468356918, 0.3544295814710033, 0.0, 5.007703826234417, 3.8987253961810353, 2.970291234178459, 2.270227297207333, 4.01665973937193, 1.960308054765292, 1.484799388016076, 1.306926972887004, 1.8735292208606114, 1.4099128964777903, 0.820160831973387, 0.3077544411279629, 0.0), # 153
(4.6339800146704375, 3.3527156794598634, 4.07961588471038, 4.203733131454235, 3.726493282246232, 1.8222135142668292, 1.4721971013615687, 1.395080912824781, 2.0014179225027733, 0.7513949382284032, 0.5900664331460613, 0.3521850490923663, 0.0, 4.979618235279215, 3.874035540016029, 2.9503321657303068, 2.254184814685209, 4.002835845005547, 1.9531132779546936, 1.4721971013615687, 1.3015810816191637, 1.863246641123116, 1.4012443771514118, 0.8159231769420761, 0.3047923344963513, 0.0), # 154
(4.596886397114182, 3.3192173789728225, 4.057751088506039, 4.1769266578773205, 3.7053648022350787, 1.8144492354444652, 1.4592139161533286, 1.3896855605199092, 1.9942315118360256, 0.7458712530362633, 0.5859396024994835, 0.3498688847461741, 0.0, 4.950745420713616, 3.8485577322079147, 2.929698012497417, 2.2376137591087897, 3.9884630236720513, 1.945559784727873, 1.4592139161533286, 1.296035168174618, 1.8526824011175393, 1.392308885959107, 0.8115502177012078, 0.3017470344520748, 0.0), # 155
(4.5588545799828495, 3.284770740314739, 4.035190065591264, 4.149295425930094, 3.6836609521850594, 1.8063952642328118, 1.44583619094981, 1.3840223721528548, 1.9867607775061256, 0.7401650980676662, 0.5816729051136506, 0.3474787899146912, 0.0, 4.9210672063846115, 3.822266689061603, 2.9083645255682526, 2.220495294202998, 3.973521555012251, 1.9376313210139968, 1.44583619094981, 1.2902823315948655, 1.8418304760925297, 1.3830984753100317, 0.8070380131182529, 0.29861552184679446, 0.0), # 156
(4.519862743810118, 3.249342552853883, 4.01191311030341, 4.120815592840023, 3.6613696825934743, 1.7980419392899651, 1.4320502843094658, 1.3780797376730207, 1.978995859333478, 0.7342701945642514, 0.577261491206255, 0.34501246608018193, 0.0, 4.890565416139187, 3.7951371268820004, 2.886307456031275, 2.2028105836927536, 3.957991718666956, 1.929311632742229, 1.4320502843094658, 1.2843156709214036, 1.8306848412967371, 1.373605197613341, 0.802382622060682, 0.29539477753217125, 0.0), # 157
(4.478808567843144, 3.2122492164247425, 3.9867959769511785, 4.09039631174306, 3.6374724423483995, 1.7888585667109889, 1.4175320162783573, 1.3714492380873433, 1.9703324183198196, 0.7280048874436718, 0.5725595752538944, 0.34237997820971705, 0.0, 4.857891515649208, 3.766179760306887, 2.8627978762694717, 2.184014662331015, 3.9406648366396393, 1.9200289333222809, 1.4175320162783573, 1.2777561190792777, 1.8187362211741998, 1.3634654372476869, 0.7973591953902358, 0.292022656038613, 0.0), # 158
(4.429372060187042, 3.169685925779713, 3.9533689092014943, 4.051789721454805, 3.6060765239126518, 1.7757913768024864, 1.400463829015715, 1.3618033831910958, 1.9572854522444292, 0.720342149114812, 0.5667416935618995, 0.33906812618415544, 0.0, 4.815256588152117, 3.7297493880257093, 2.8337084678094975, 2.161026447344436, 3.9145709044888584, 1.9065247364675344, 1.400463829015715, 1.268422412001776, 1.8030382619563259, 1.3505965738182684, 0.7906737818402989, 0.288153265979974, 0.0), # 159
(4.370923256942587, 3.121303200697393, 3.910960478977945, 4.004359879683987, 3.5665680525387184, 1.7585194536166897, 1.3806731245151354, 1.3488997973818369, 1.9394850454457304, 0.7111808477794342, 0.5597259508609541, 0.3350250974443924, 0.0, 4.761852365336149, 3.685276071888316, 2.7986297543047707, 2.1335425433383026, 3.878970090891461, 1.8884597163345715, 1.3806731245151354, 1.2560853240119212, 1.7832840262693592, 1.3347866265613293, 0.7821920957955891, 0.28375483642703575, 0.0), # 160
(4.303933232751577, 3.067416477444258, 3.860023485955155, 3.948557626690528, 3.5193572497128454, 1.7372520345496103, 1.3583044338020631, 1.3329001332774453, 1.9171659533998038, 0.7005987289756408, 0.5515741654599708, 0.3302883734453786, 0.0, 4.698224426891459, 3.6331721078991643, 2.757870827299854, 2.101796186926922, 3.8343319067996076, 1.8660601865884234, 1.3583044338020631, 1.2408943103925787, 1.7596786248564227, 1.3161858755635096, 0.7720046971910312, 0.27885604340402353, 0.0), # 161
(4.228873062255815, 3.0083411922867818, 3.8010107298077487, 3.8848338027343474, 3.464854336921282, 1.7121983569972596, 1.333502287901943, 1.3139660434958007, 1.8905629315827275, 0.688673538241535, 0.5423481556678622, 0.32489543564206474, 0.0, 4.6249183525082005, 3.573849792062712, 2.7117407783393106, 2.0660206147246045, 3.781125863165455, 1.839552460894121, 1.333502287901943, 1.222998826426614, 1.732427168460641, 1.2949446009114494, 0.7602021459615498, 0.273485562935162, 0.0), # 162
(4.146213820097099, 2.9443927814914383, 3.7343750102103512, 3.8136392480753707, 3.4034695356502755, 1.6835676583556507, 1.3064112178402203, 1.2922591806547814, 1.8599107354705815, 0.675483021115219, 0.5321097397935409, 0.3188837654894017, 0.0, 4.5424797218765285, 3.5077214203834184, 2.6605486989677045, 2.0264490633456567, 3.719821470941163, 1.809162852916694, 1.3064112178402203, 1.2025483273968933, 1.7017347678251378, 1.2712130826917905, 0.7468750020420704, 0.2676720710446763, 0.0), # 163
(4.056426580917231, 2.8758866813247015, 3.660569126837589, 3.735424802973519, 3.3356130673860758, 1.6515691760207956, 1.27717575464234, 1.2679411973722674, 1.8254441205394447, 0.6611049231347954, 0.5209207361459197, 0.31229084444234023, 0.0, 4.451454114686597, 3.435199288865742, 2.6046036807295985, 1.983314769404386, 3.6508882410788894, 1.7751176763211745, 1.27717575464234, 1.1796922685862825, 1.6678065336930379, 1.245141600991173, 0.7321138253675178, 0.2614442437567911, 0.0), # 164
(3.9599824193580107, 2.803138328053048, 3.5800458793640852, 3.650641307688714, 3.26169515361493, 1.6164121473887054, 1.2459404293337468, 1.2411737462661367, 1.787397842265396, 0.6456169898383672, 0.5088429630339113, 0.30515415395583106, 0.0, 4.3523871106285625, 3.3566956935141414, 2.544214815169556, 1.9368509695151013, 3.574795684530792, 1.7376432447725916, 1.2459404293337468, 1.1545801052776468, 1.630847576807465, 1.2168804358962382, 0.7160091758728171, 0.25483075709573166, 0.0), # 165
(3.857352410061239, 2.72646315794295, 3.493258067464464, 3.5597396024808763, 3.1821260158230857, 1.5783058098553933, 1.212849772939886, 1.2121184799542695, 1.7460066561245147, 0.629096966764037, 0.495938238766428, 0.29751117548482514, 0.0, 4.245824289392578, 3.272622930333076, 2.47969119383214, 1.8872909002921108, 3.4920133122490293, 1.6969658719359773, 1.212849772939886, 1.1273612927538523, 1.5910630079115429, 1.1865798674936257, 0.698651613492893, 0.24786028708572277, 0.0), # 166
(3.749007627668714, 2.6461766072608834, 3.4006584908133526, 3.46317052760993, 3.097315875496792, 1.5374594008168707, 1.1780483164862026, 1.1809370510545443, 1.70150531759288, 0.6116225994499073, 0.4822683816523828, 0.289399390484273, 0.0, 4.132311230668798, 3.1833932953270025, 2.411341908261914, 1.8348677983497215, 3.40301063518576, 1.653311871476362, 1.1780483164862026, 1.0981852862977648, 1.548657937748396, 1.1543901758699768, 0.6801316981626705, 0.24056150975098944, 0.0), # 167
(3.6354191468222377, 2.562594112273321, 3.3026999490853743, 3.3613849233357964, 3.0076749541222974, 1.49408215766915, 1.1416805909981413, 1.14779111218484, 1.6541285821465712, 0.593271633434081, 0.4678952100006882, 0.28085628040912564, 0.0, 4.012393514147377, 3.0894190845003817, 2.339476050003441, 1.7798149003022425, 3.3082571642931424, 1.606907557058776, 1.1416805909981413, 1.06720154119225, 1.5038374770611487, 1.1204616411119324, 0.660539989817075, 0.2329631011157565, 0.0), # 168
(3.5170580421636095, 2.476031109246739, 3.1998352419551552, 3.254833629918398, 2.913613473185848, 1.4483833178082435, 1.1038911275011476, 1.1128423159630365, 1.6041112052616677, 0.5741218142546607, 0.4528805421202569, 0.27191932671433366, 0.0, 3.8866167195184715, 2.99111259385767, 2.2644027106012845, 1.7223654427639818, 3.2082224105233355, 1.5579792423482512, 1.1038911275011476, 1.0345595127201739, 1.456806736592924, 1.0849445433061329, 0.639967048391031, 0.22509373720424902, 0.0), # 169
(3.3943953883346305, 2.386803034447611, 3.092517169097318, 3.1439674876176547, 2.8155416541736935, 1.4005721186301625, 1.064824457020666, 1.076252315007012, 1.5516879424142478, 0.5542508874497488, 0.4372861963200017, 0.262626010854848, 0.0, 3.7555264264722337, 2.8888861194033275, 2.1864309816000085, 1.6627526623492461, 3.1033758848284956, 1.5067532410098168, 1.064824457020666, 1.0004086561644019, 1.4077708270868468, 1.0479891625392184, 0.6185034338194636, 0.21698209404069194, 0.0), # 170
(3.2679022599771006, 2.2952253241424105, 2.9811985301864894, 3.029237336693491, 2.7138697185720826, 1.3508577975309197, 1.0246251105821418, 1.038182761934646, 1.4970935490803914, 0.5337365985574485, 0.4211739909088349, 0.25301381428561937, 0.0, 3.6196682146988195, 2.7831519571418126, 2.1058699545441746, 1.6012097956723452, 2.9941870981607828, 1.4534558667085042, 1.0246251105821418, 0.9648984268077998, 1.3569348592860413, 1.0097457788978306, 0.5962397060372979, 0.20865684764931008, 0.0), # 171
(3.1380497317328193, 2.2016134145976136, 2.8663321248972937, 2.9110940174058286, 2.6090078878672616, 1.299449591906527, 0.9834376192110198, 0.9987953093638179, 1.4405627807361772, 0.5126566931158621, 0.40460574419566947, 0.24312021846159862, 0.0, 3.4795876638883825, 2.6743224030775843, 2.0230287209783473, 1.5379700793475861, 2.8811255614723543, 1.398313433109345, 0.9834376192110198, 0.9281782799332335, 1.3045039439336308, 0.9703646724686098, 0.5732664249794588, 0.20014667405432854, 0.0), # 172
(3.0053088782435884, 2.1062827420796935, 2.748370752904356, 2.7899883700145893, 2.5013663835454807, 1.2465567391529966, 0.941406513932745, 0.9582516099124061, 1.3823303928576847, 0.4910889166630924, 0.3876432744894179, 0.2329827048377363, 0.0, 3.335830353731078, 2.562809753215099, 1.938216372447089, 1.4732667499892769, 2.7646607857153693, 1.3415522538773685, 0.941406513932745, 0.8903976708235689, 1.2506831917727403, 0.9299961233381967, 0.5496741505808713, 0.19148024927997215, 0.0), # 173
(2.8701507741512065, 2.009548742855125, 2.627767213882301, 2.6663712347796937, 2.3913554270929867, 1.19238847666634, 0.8986763257727628, 0.9167133161982903, 1.322631140920993, 0.46911101473724215, 0.37034840009899284, 0.22263875486898346, 0.0, 3.1889418639170604, 2.4490263035588176, 1.851742000494964, 1.4073330442117262, 2.645262281841986, 1.2833986426776065, 0.8986763257727628, 0.8517060547616714, 1.1956777135464933, 0.8887904115932315, 0.5255534427764603, 0.18268624935046593, 0.0), # 174
(2.7330464940974735, 1.9117268531903824, 2.5049743075057544, 2.5406934519610656, 2.279385239996028, 1.1371540418425703, 0.8553915857565175, 0.8743420808393492, 1.2616997804021812, 0.4468007328764138, 0.352782939333307, 0.21212585001029077, 0.0, 3.039467774136485, 2.333384350113198, 1.763914696666535, 1.3404021986292411, 2.5233995608043625, 1.2240789131750889, 0.8553915857565175, 0.8122528870304073, 1.139692619998014, 0.8468978173203554, 0.5009948615011509, 0.1737933502900348, 0.0), # 175
(2.5944671127241916, 1.8131325093519404, 2.3804448334493395, 2.4134058618186263, 2.165866043740852, 1.0810626720776986, 0.8116968249094546, 0.8312995564534624, 1.1997710667773285, 0.42423581661871024, 0.33500871050127307, 0.201481471716609, 0.0, 2.8879536640795047, 2.2162961888826986, 1.6750435525063654, 1.2727074498561304, 2.399542133554657, 1.1638193790348474, 0.8116968249094546, 0.7721876229126418, 1.082933021870426, 0.804468620606209, 0.476088966689868, 0.16483022812290368, 0.0), # 176
(2.45488370467316, 1.7140811476062734, 2.254631591387682, 2.284959304612298, 2.0512080598137095, 1.0243236047677373, 0.7677365742570189, 0.7877473956585084, 1.137079755522514, 0.40149401150223385, 0.31708753191180367, 0.190743101442889, 0.0, 2.734945113436275, 2.0981741158717786, 1.585437659559018, 1.204482034506701, 2.274159511045028, 1.1028463539219118, 0.7677365742570189, 0.7316597176912409, 1.0256040299068547, 0.7616531015374328, 0.4509263182775365, 0.1558255588732976, 0.0), # 177
(2.3147673445861785, 1.6148882042198558, 2.1279873809954073, 2.1558046206020025, 1.9358215097008458, 0.9671460773086992, 0.7236553648246553, 0.7438472510723665, 1.0738606021138173, 0.37865306306508767, 0.2990812218738113, 0.1799482206440814, 0.0, 2.5809877018969516, 1.979430427084895, 1.4954061093690565, 1.1359591891952627, 2.1477212042276346, 1.0413861515013132, 0.7236553648246553, 0.6908186266490708, 0.9679107548504229, 0.7186015402006676, 0.4255974761990815, 0.14680801856544146, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 156
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 157
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 158
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 159
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 160
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
40, # 1
)
| 277.579679 | 500 | 0.770653 | 32,987 | 259,537 | 6.063055 | 0.205778 | 0.356396 | 0.341997 | 0.647994 | 0.382841 | 0.371766 | 0.365581 | 0.362516 | 0.361876 | 0.361826 | 0 | 0.850598 | 0.0953 | 259,537 | 934 | 501 | 277.876874 | 0.001188 | 0.015454 | 0 | 0.200873 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.005459 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cff4e4986be3b644fefb038b92d171b8bc465c3c | 15,187 | py | Python | robot/install/lib/python2.7/dist-packages/intera_core_msgs/msg/_IONodeStatus.py | satvu/TeachBot | 5888aea544fea952afa36c097a597c5d575c8d6d | [
"BSD-3-Clause"
] | null | null | null | robot/install/lib/python2.7/dist-packages/intera_core_msgs/msg/_IONodeStatus.py | satvu/TeachBot | 5888aea544fea952afa36c097a597c5d575c8d6d | [
"BSD-3-Clause"
] | null | null | null | robot/install/lib/python2.7/dist-packages/intera_core_msgs/msg/_IONodeStatus.py | satvu/TeachBot | 5888aea544fea952afa36c097a597c5d575c8d6d | [
"BSD-3-Clause"
] | null | null | null | # This Python file uses the following encoding: utf-8
"""autogenerated by genpy from intera_core_msgs/IONodeStatus.msg. Do not edit."""
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
import genpy
import intera_core_msgs.msg
class IONodeStatus(genpy.Message):
_md5sum = "260fce3c02f43bd977c92642b3c09c1d"
_type = "intera_core_msgs/IONodeStatus"
_has_header = False #flag to mark the presence of a Header object
_full_text = """# IO Node Status
time time # time the message was created
IOComponentStatus node # IO Node status
IOComponentStatus[] devices # status of IO Devices in this node
time[] commands # recent command timestamps, for syncing
================================================================================
MSG: intera_core_msgs/IOComponentStatus
## IO Component status data
string name # component name
IOStatus status # component status
#
================================================================================
MSG: intera_core_msgs/IOStatus
## IO status data
#
string tag # one of the values listed below
# down Inoperative, not fully instantiated
# ready OK, fully operational
# busy OK, not ready to output data; input data value may be stale
# unready OK, not operational; data is invalid
# error Error, not operational
string DOWN = down
string READY = ready
string BUSY = busy
string UNREADY = unready
string ERROR = error
#
string id # message id, for internationalization
#
string detail # optional additional status detail
#
"""
__slots__ = ['time','node','devices','commands']
_slot_types = ['time','intera_core_msgs/IOComponentStatus','intera_core_msgs/IOComponentStatus[]','time[]']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
time,node,devices,commands
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(IONodeStatus, self).__init__(*args, **kwds)
#message fields cannot be None, assign default values for those that are
if self.time is None:
self.time = genpy.Time()
if self.node is None:
self.node = intera_core_msgs.msg.IOComponentStatus()
if self.devices is None:
self.devices = []
if self.commands is None:
self.commands = []
else:
self.time = genpy.Time()
self.node = intera_core_msgs.msg.IOComponentStatus()
self.devices = []
self.commands = []
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
_x = self
buff.write(_get_struct_2I().pack(_x.time.secs, _x.time.nsecs))
_x = self.node.name
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.node.status.tag
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.node.status.id
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.node.status.detail
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
length = len(self.devices)
buff.write(_struct_I.pack(length))
for val1 in self.devices:
_x = val1.name
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_v1 = val1.status
_x = _v1.tag
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = _v1.id
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = _v1.detail
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
length = len(self.commands)
buff.write(_struct_I.pack(length))
for val1 in self.commands:
_x = val1
buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
try:
if self.time is None:
self.time = genpy.Time()
if self.node is None:
self.node = intera_core_msgs.msg.IOComponentStatus()
if self.devices is None:
self.devices = None
if self.commands is None:
self.commands = None
end = 0
_x = self
start = end
end += 8
(_x.time.secs, _x.time.nsecs,) = _get_struct_2I().unpack(str[start:end])
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.name = str[start:end].decode('utf-8')
else:
self.node.name = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.status.tag = str[start:end].decode('utf-8')
else:
self.node.status.tag = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.status.id = str[start:end].decode('utf-8')
else:
self.node.status.id = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.status.detail = str[start:end].decode('utf-8')
else:
self.node.status.detail = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.devices = []
for i in range(0, length):
val1 = intera_core_msgs.msg.IOComponentStatus()
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
val1.name = str[start:end].decode('utf-8')
else:
val1.name = str[start:end]
_v2 = val1.status
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
_v2.tag = str[start:end].decode('utf-8')
else:
_v2.tag = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
_v2.id = str[start:end].decode('utf-8')
else:
_v2.id = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
_v2.detail = str[start:end].decode('utf-8')
else:
_v2.detail = str[start:end]
self.devices.append(val1)
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.commands = []
for i in range(0, length):
val1 = genpy.Time()
_x = val1
start = end
end += 8
(_x.secs, _x.nsecs,) = _get_struct_2I().unpack(str[start:end])
self.commands.append(val1)
self.time.canon()
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
_x = self
buff.write(_get_struct_2I().pack(_x.time.secs, _x.time.nsecs))
_x = self.node.name
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.node.status.tag
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.node.status.id
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.node.status.detail
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
length = len(self.devices)
buff.write(_struct_I.pack(length))
for val1 in self.devices:
_x = val1.name
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_v3 = val1.status
_x = _v3.tag
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = _v3.id
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = _v3.detail
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
length = len(self.commands)
buff.write(_struct_I.pack(length))
for val1 in self.commands:
_x = val1
buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
try:
if self.time is None:
self.time = genpy.Time()
if self.node is None:
self.node = intera_core_msgs.msg.IOComponentStatus()
if self.devices is None:
self.devices = None
if self.commands is None:
self.commands = None
end = 0
_x = self
start = end
end += 8
(_x.time.secs, _x.time.nsecs,) = _get_struct_2I().unpack(str[start:end])
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.name = str[start:end].decode('utf-8')
else:
self.node.name = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.status.tag = str[start:end].decode('utf-8')
else:
self.node.status.tag = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.status.id = str[start:end].decode('utf-8')
else:
self.node.status.id = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.node.status.detail = str[start:end].decode('utf-8')
else:
self.node.status.detail = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.devices = []
for i in range(0, length):
val1 = intera_core_msgs.msg.IOComponentStatus()
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
val1.name = str[start:end].decode('utf-8')
else:
val1.name = str[start:end]
_v4 = val1.status
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
_v4.tag = str[start:end].decode('utf-8')
else:
_v4.tag = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
_v4.id = str[start:end].decode('utf-8')
else:
_v4.id = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
_v4.detail = str[start:end].decode('utf-8')
else:
_v4.detail = str[start:end]
self.devices.append(val1)
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.commands = []
for i in range(0, length):
val1 = genpy.Time()
_x = val1
start = end
end += 8
(_x.secs, _x.nsecs,) = _get_struct_2I().unpack(str[start:end])
self.commands.append(val1)
self.time.canon()
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_2I = None
def _get_struct_2I():
global _struct_2I
if _struct_2I is None:
_struct_2I = struct.Struct("<2I")
return _struct_2I
| 32.381663 | 145 | 0.569039 | 2,015 | 15,187 | 4.131514 | 0.101241 | 0.092252 | 0.073994 | 0.057658 | 0.74967 | 0.74967 | 0.735255 | 0.726366 | 0.694895 | 0.682643 | 0 | 0.017586 | 0.292355 | 15,187 | 468 | 146 | 32.450855 | 0.757048 | 0.083032 | 0 | 0.80622 | 1 | 0 | 0.123021 | 0.025418 | 0 | 0 | 0.000726 | 0 | 0 | 1 | 0.019139 | false | 0 | 0.011962 | 0 | 0.059809 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
320f9dce4a70ab49308f49f9be6dd55f7d2683b7 | 21 | py | Python | pointsbot/__init__.py | Watchful1/PointsBot | 56550b82bd12ff41f1e3a92bf6c2da7562654fea | [
"MIT"
] | 4 | 2020-03-10T15:06:23.000Z | 2021-07-27T19:11:33.000Z | pointsbot/__init__.py | Watchful1/PointsBot | 56550b82bd12ff41f1e3a92bf6c2da7562654fea | [
"MIT"
] | 3 | 2020-12-28T23:47:33.000Z | 2021-11-02T18:56:52.000Z | pointsbot/__init__.py | Watchful1/PointsBot | 56550b82bd12ff41f1e3a92bf6c2da7562654fea | [
"MIT"
] | 2 | 2020-12-13T20:37:51.000Z | 2021-07-31T02:57:09.000Z | from .bot import run
| 10.5 | 20 | 0.761905 | 4 | 21 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.190476 | 21 | 1 | 21 | 21 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5c7a161bf08ce8f7e9810d791fd59a12bbec5998 | 2,951 | py | Python | firecares/firestation/migrations/0010_auto_20150812_1225.py | FireCARES/firecares | aa708d441790263206dd3a0a480eb6ca9031439d | [
"MIT"
] | 12 | 2016-01-30T02:28:35.000Z | 2019-05-29T15:49:56.000Z | firecares/firestation/migrations/0010_auto_20150812_1225.py | FireCARES/firecares | aa708d441790263206dd3a0a480eb6ca9031439d | [
"MIT"
] | 455 | 2015-07-27T20:21:56.000Z | 2022-03-11T23:26:20.000Z | firecares/firestation/migrations/0010_auto_20150812_1225.py | FireCARES/firecares | aa708d441790263206dd3a0a480eb6ca9031439d | [
"MIT"
] | 14 | 2015-07-29T09:45:53.000Z | 2020-10-21T20:03:17.000Z | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.db import transaction
class Migration(migrations.Migration):
dependencies = [
('firestation', '0009_auto_20150807_1552'),
]
operations = [
migrations.AddField(
model_name='firedepartment',
name='risk_model_fires',
field=models.FloatField(db_index=True, null=True, verbose_name=b'Predicted number of fires per year.', blank=True),
),
migrations.AddField(
model_name='firedepartment',
name='risk_model_fires_size0_percentage',
field=models.FloatField(null=True, verbose_name=b'Percentage of size 0 fires.', blank=True),
),
migrations.AddField(
model_name='firedepartment',
name='risk_model_fires_size0',
field=models.FloatField(db_index=True, null=True, verbose_name=b'Predicted number of size 0 fires.', blank=True),
),
migrations.AddField(
model_name='firedepartment',
name='risk_model_fires_size1',
field=models.FloatField(db_index=True, null=True, verbose_name=b'Predicted number of size 1 fires.', blank=True),
),
migrations.AddField(
model_name='firedepartment',
name='risk_model_fires_size1_percentage',
field=models.FloatField(null=True, verbose_name=b'Percentage of size 1 fires.', blank=True),
),
migrations.AddField(
model_name='firedepartment',
name='risk_model_fires_size2',
field=models.FloatField(db_index=True, null=True, verbose_name=b'Predicted number of size 2 firese.', blank=True),
),
migrations.AddField(
model_name='firedepartment',
name='risk_model_fires_size2_percentage',
field=models.FloatField(null=True, verbose_name=b'Percentage of size 2 fires.', blank=True),
),
migrations.RunSQL("UPDATE firestation_firedepartment SET risk_model_fires=risk_model_fires_room;"),
migrations.RunSQL("UPDATE firestation_firedepartment SET risk_model_fires_size0_percentage=1-risk_model_fires_floor_percentage-risk_model_fires_structure_percentage;"),
migrations.RunSQL("UPDATE firestation_firedepartment SET risk_model_fires_size0=risk_model_fires*risk_model_fires_size0_percentage;"),
migrations.RunSQL("UPDATE firestation_firedepartment SET risk_model_fires_size1=risk_model_fires_floor;"),
migrations.RunSQL("UPDATE firestation_firedepartment SET risk_model_fires_size1_percentage=risk_model_fires_floor_percentage;"),
migrations.RunSQL("UPDATE firestation_firedepartment SET risk_model_fires_size2=risk_model_fires_structure;"),
migrations.RunSQL("UPDATE firestation_firedepartment SET risk_model_fires_size2_percentage=risk_model_fires_structure_percentage;"),
]
| 45.4 | 176 | 0.704846 | 336 | 2,951 | 5.863095 | 0.166667 | 0.105076 | 0.163452 | 0.095939 | 0.862437 | 0.830457 | 0.770558 | 0.770558 | 0.770558 | 0.708122 | 0 | 0.015731 | 0.202982 | 2,951 | 64 | 177 | 46.109375 | 0.821854 | 0.007116 | 0 | 0.411765 | 0 | 0 | 0.427596 | 0.282445 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.058824 | 0 | 0.117647 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5c84756f6ca7519baff95e7738549095c5b3a1c9 | 7,227 | py | Python | apps/user_accounts/views.py | viallymboma/mlm_osw_network | d46a938f4a1295b59e863105926f9c249de00f9e | [
"MIT"
] | null | null | null | apps/user_accounts/views.py | viallymboma/mlm_osw_network | d46a938f4a1295b59e863105926f9c249de00f9e | [
"MIT"
] | null | null | null | apps/user_accounts/views.py | viallymboma/mlm_osw_network | d46a938f4a1295b59e863105926f9c249de00f9e | [
"MIT"
] | null | null | null | from django.shortcuts import render
from django.http import JsonResponse, HttpResponse, HttpResponseRedirect
from django.contrib.auth.decorators import login_required
from django.contrib.auth import logout
import json
from django.template import loader
# Create your views here.
# @login_required
def admin_dashboard(request):
if request.user.is_authenticated:
if request.method == "POST":
# data = json.loads(request.body)
# print(data)
result = {
"boolean_val": True,
"user": "Admin",
"page": "Admin Dashboard"
}
return JsonResponse(result)
else:
segment = {
"components-notifications": True,
"components": True,
"components-": True,
"components-forms": True,
"components-modals": True,
"components-typography": True,
"tables-bootstrap-": True,
"settings": True,
"transactions": True,
"dashboard": True,
"settings": True,
}
html_template = loader.get_template('accounts/dashboard.html')
return HttpResponse(html_template.render(segment, request))
# return render(request, 'accounts/dashboard.html')
elif not request.user.is_authenticated:
segment = {
"boolean_false": "False",
"user": "Admin",
"msg": "Please Login before you can access Back Office"
}
# return HttpResponseRedirect('../../../management/backend/admin/')
html_template = loader.get_template('auths/login.html')
return HttpResponse(html_template.render(segment, request))
def graphical_tree_view(request):
# We are gonna pull data in two different ways
# first one is by selecting a user or IBA and serializing him
# then pulling the 2 (left and right) users who hava him as
# sponsor. and in turn also pulling data of those 2 left and
# right users. this i gonna give us an object that we can
# easily pass to json response to our javascript on the UI
# the second way we can get the data is by using an incremental
# Number for each users this number will pull data and organize it
# as object and send it via json and javascript will do BFS on it
# and plot a tree with that data.
if request.user.is_authenticated:
if request.method == "POST":
# data = json.loads(request.body)
# print(data)
result = {
"boolean_val": True,
"user": "Admin",
"page": "Admin Dashboard"
}
return JsonResponse(result)
else:
segment = {
"components-notifications": True,
"components": True,
"components-": True,
"components-forms": True,
"components-modals": True,
"components-typography": True,
"tables-bootstrap-": True,
"settings": True,
"transactions": True,
"dashboard": True,
"settings": True,
}
html_template = loader.get_template('accounts/graphical_tree_display.html')
return HttpResponse(html_template.render(segment, request))
# return render(request, 'accounts/dashboard.html')
elif not request.user.is_authenticated:
segment = {
"boolean_false": "False",
"user": "Admin",
"msg": "Please Login before you can access Back Office"
}
# return HttpResponseRedirect('../../../management/backend/admin/')
html_template = loader.get_template('auths/login.html')
return HttpResponse(html_template.render(segment, request))
# @login_required
def admin_profile(request):
if request.user.is_authenticated:
if request.method == "POST":
result = {
"boolean_val": True,
"user": "Admin",
"page": "Admin Profile Page"
}
return JsonResponse(result)
else:
context = {
"boolean_admin": True,
"user": "Admin",
"page": "Admin Profile Page"
}
html_template = loader.get_template('accounts/profile.html')
return HttpResponse(html_template.render(context, request))
# return render(request, 'accounts/dashboard.html')
elif not request.user.is_authenticated:
segment = {
"boolean_false": "False",
"user": "Admin",
"msg": "Please Login before you can access Back Office"
}
# return HttpResponseRedirect('../../../management/backend/admin/')
html_template = loader.get_template('auths/login.html')
return HttpResponse(html_template.render(segment, request))
@login_required
def subadmin_dashboard(request):
if request.method == "POST":
result = {
"boolean_val": True,
"user": "Subadmin",
"page": "Subadmin Dashboard"
}
return JsonResponse(result)
else:
html_template = loader.get_template('accounts/dashboard.html')
return HttpResponse(html_template.render(context, request))
# return render(request, 'accounts/dashboard.html')
@login_required
def subadmin_profile(request):
if request.method == "POST":
result = {
"boolean_val": True,
"user": "Subadmin",
"page": "Subadmin Profile Page"
}
return JsonResponse(result)
else:
html_template = loader.get_template('accounts/profile.html')
return HttpResponse(html_template.render(context, request))
# return render(request, 'accounts/dashboard.html')
@login_required
def iba_dashboard(request):
if request.method == "POST":
result = {
"boolean_val": True,
"user": "IBA",
"page": "IBA Dashboard"
}
return JsonResponse(result)
else:
html_template = loader.get_template('accounts/dasboard.html')
return HttpResponse(html_template.render(context, request))
# return render(request, 'accounts/settings.html')
@login_required
def iba_profile(request):
if request.method == "POST":
result = {
"boolean_val": True,
"user": "IBA",
"page": "IBA Profile Page"
}
return JsonResponse(result)
else:
html_template = loader.get_template('accounts/profile.html')
return HttpResponse(html_template.render(context, request))
# return render(request, 'accounts/dashboard.html')
@login_required
def logout_view(request):
logout(request)
context = {
"logout_message": "You are Logout! See you soon!"
}
return HttpResponseRedirect('../management/backend/admin/')
# html_template = loader.get_template('accounts/login.html')
# return HttpResponse(html_template.render(context, request))
| 32.701357 | 87 | 0.583506 | 718 | 7,227 | 5.775766 | 0.20195 | 0.06366 | 0.047745 | 0.055703 | 0.79455 | 0.7796 | 0.770919 | 0.759103 | 0.742706 | 0.736918 | 0 | 0.000402 | 0.311886 | 7,227 | 220 | 88 | 32.85 | 0.833501 | 0.189567 | 0 | 0.75 | 0 | 0 | 0.202371 | 0.048961 | 0 | 0 | 0 | 0 | 0 | 1 | 0.052632 | false | 0 | 0.039474 | 0 | 0.210526 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5c8d7a1167e3c7437d9cb953c3375480d84105d8 | 173 | py | Python | netmiko/mrv/__init__.py | chrisotter92/netmiko | 013135c9ebe6a36f8d6ab0c61d519275c47e7626 | [
"MIT"
] | null | null | null | netmiko/mrv/__init__.py | chrisotter92/netmiko | 013135c9ebe6a36f8d6ab0c61d519275c47e7626 | [
"MIT"
] | null | null | null | netmiko/mrv/__init__.py | chrisotter92/netmiko | 013135c9ebe6a36f8d6ab0c61d519275c47e7626 | [
"MIT"
] | null | null | null | from __future__ import unicode_literals
from netmiko.mrv.mrv_lx import MrvLxSSH
from netmiko.mrv.mrv_ssh import MrvOptiswitchSSH
__all__ = ['MrvOptiswitchSSH', 'MrvLxSSH']
| 28.833333 | 48 | 0.83237 | 22 | 173 | 6.045455 | 0.545455 | 0.165414 | 0.210526 | 0.255639 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.098266 | 173 | 5 | 49 | 34.6 | 0.852564 | 0 | 0 | 0 | 0 | 0 | 0.138728 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.75 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5cadd84cfeba3a99d6e9e488af886d64ba397636 | 8,032 | py | Python | discovery-provider/integration_tests/tasks/test_index_aggregate_monthly_plays.py | lucylow/audius-protocol | 5ef93462f9dc7df01a15877c02ca79b9a7d99236 | [
"Apache-2.0"
] | 1 | 2022-03-27T21:40:36.000Z | 2022-03-27T21:40:36.000Z | discovery-provider/integration_tests/tasks/test_index_aggregate_monthly_plays.py | abelxmendoza/audius-protocol | 33757e1b722a4be97960086b98b26ae3a75ee56b | [
"Apache-2.0"
] | null | null | null | discovery-provider/integration_tests/tasks/test_index_aggregate_monthly_plays.py | abelxmendoza/audius-protocol | 33757e1b722a4be97960086b98b26ae3a75ee56b | [
"Apache-2.0"
] | null | null | null | import logging
from datetime import date, timedelta
from typing import List
from integration_tests.utils import populate_mock_db
from src.models import AggregateMonthlyPlays, IndexingCheckpoints
from src.tasks.index_aggregate_monthly_plays import (
AGGREGATE_MONTHLY_PLAYS_TABLE_NAME,
_index_aggregate_monthly_plays,
)
from src.utils.db_session import get_db
logger = logging.getLogger(__name__)
CURRENT_TIMESTAMP = date.fromisoformat("2022-01-20")
LAST_MONTH_TIMESTAMP = CURRENT_TIMESTAMP - timedelta(weeks=4)
LAST_YEAR_TIMESTAMP = CURRENT_TIMESTAMP - timedelta(weeks=52)
# Tests
def test_index_aggregate_monthly_plays_populate(app):
"""Test populate plays from empty"""
# setup
with app.app_context():
db = get_db()
# run
entities = {
"tracks": [
{"track_id": 1, "title": "track 1"},
{"track_id": 2, "title": "track 2"},
{"track_id": 3, "title": "track 3"},
],
"plays": [
# Last year
{"item_id": 3, "created_at": LAST_YEAR_TIMESTAMP},
# Last month
{"item_id": 1, "created_at": LAST_MONTH_TIMESTAMP},
# This month
{"item_id": 2, "created_at": CURRENT_TIMESTAMP - timedelta(weeks=2)},
{"item_id": 2, "created_at": CURRENT_TIMESTAMP - timedelta(weeks=2)},
{"item_id": 1, "created_at": CURRENT_TIMESTAMP},
{"item_id": 3, "created_at": CURRENT_TIMESTAMP},
],
}
populate_mock_db(db, entities)
with db.scoped_session() as session:
_index_aggregate_monthly_plays(session)
results: List[AggregateMonthlyPlays] = (
session.query(AggregateMonthlyPlays)
.order_by(
AggregateMonthlyPlays.timestamp,
AggregateMonthlyPlays.play_item_id,
)
.all()
)
assert len(results) == 5
assert results[0].play_item_id == 3
assert results[0].timestamp == LAST_YEAR_TIMESTAMP.replace(day=1)
assert results[0].count == 1
assert results[1].play_item_id == 1
assert results[1].timestamp == LAST_MONTH_TIMESTAMP.replace(day=1)
assert results[1].count == 1
assert results[2].play_item_id == 1
assert results[2].timestamp == CURRENT_TIMESTAMP.replace(day=1)
assert results[2].count == 1
assert results[3].play_item_id == 2
assert results[3].timestamp == CURRENT_TIMESTAMP.replace(day=1)
assert results[3].count == 2
assert results[4].play_item_id == 3
assert results[4].timestamp == CURRENT_TIMESTAMP.replace(day=1)
assert results[4].count == 1
new_checkpoint: IndexingCheckpoints = (
session.query(IndexingCheckpoints.last_checkpoint)
.filter(IndexingCheckpoints.tablename == AGGREGATE_MONTHLY_PLAYS_TABLE_NAME)
.scalar()
)
assert new_checkpoint == 6
def test_index_aggregate_monthly_plays_update(app):
"""Test that we should insert new play counts and update existing"""
# setup
with app.app_context():
db = get_db()
# run
entities = {
"tracks": [
{"track_id": 1, "title": "track 1"},
{"track_id": 2, "title": "track 2"},
{"track_id": 3, "title": "track 3"},
],
"aggregate_monthly_plays": [
{
"play_item_id": 3,
"timestamp": LAST_YEAR_TIMESTAMP.replace(day=1),
"count": 2,
},
{
"play_item_id": 2,
"timestamp": LAST_MONTH_TIMESTAMP.replace(day=1),
"count": 1,
},
],
"plays": [
# Last year
{"id": 4, "item_id": 3, "created_at": LAST_YEAR_TIMESTAMP},
# Last month
{"id": 5, "item_id": 1, "created_at": LAST_MONTH_TIMESTAMP},
# This month
{
"id": 6,
"item_id": 2,
"created_at": CURRENT_TIMESTAMP - timedelta(weeks=2),
},
{
"id": 7,
"item_id": 2,
"created_at": CURRENT_TIMESTAMP - timedelta(weeks=2),
},
{"id": 8, "item_id": 1, "created_at": CURRENT_TIMESTAMP},
{"id": 9, "item_id": 3, "created_at": CURRENT_TIMESTAMP},
],
}
populate_mock_db(db, entities)
with db.scoped_session() as session:
_index_aggregate_monthly_plays(session)
results: List[AggregateMonthlyPlays] = (
session.query(AggregateMonthlyPlays)
.order_by(
AggregateMonthlyPlays.timestamp,
AggregateMonthlyPlays.play_item_id,
)
.all()
)
assert len(results) == 6
assert results[0].play_item_id == 3
assert results[0].timestamp == LAST_YEAR_TIMESTAMP.replace(day=1)
assert results[0].count == 3
assert results[1].play_item_id == 1
assert results[1].timestamp == LAST_MONTH_TIMESTAMP.replace(day=1)
assert results[1].count == 1
assert results[2].play_item_id == 2
assert results[2].timestamp == LAST_MONTH_TIMESTAMP.replace(day=1)
assert results[2].count == 1
assert results[3].play_item_id == 1
assert results[3].timestamp == CURRENT_TIMESTAMP.replace(day=1)
assert results[3].count == 1
assert results[4].play_item_id == 2
assert results[4].timestamp == CURRENT_TIMESTAMP.replace(day=1)
assert results[4].count == 2
assert results[5].play_item_id == 3
assert results[5].timestamp == CURRENT_TIMESTAMP.replace(day=1)
assert results[5].count == 1
new_checkpoint: IndexingCheckpoints = (
session.query(IndexingCheckpoints.last_checkpoint)
.filter(IndexingCheckpoints.tablename == AGGREGATE_MONTHLY_PLAYS_TABLE_NAME)
.scalar()
)
assert new_checkpoint == 9
def test_index_aggregate_monthly_plays_same_checkpoint(app):
"""Test that we should not update when last index is the same"""
# setup
with app.app_context():
db = get_db()
# run
entities = {
"tracks": [
{"track_id": 1, "title": "track 1"},
{"track_id": 2, "title": "track 2"},
{"track_id": 3, "title": "track 3"},
{"track_id": 4, "title": "track 4"},
],
"aggregate_monthly_plays": [
{
"play_item_id": 3,
"timestamp": LAST_YEAR_TIMESTAMP.replace(day=1),
"count": 2,
},
{
"play_item_id": 2,
"timestamp": LAST_MONTH_TIMESTAMP.replace(day=1),
"count": 1,
},
],
"indexing_checkpoints": [
{
"tablename": "aggregate_monthly_plays",
"last_checkpoint": 9,
}
],
"plays": [
# Current Plays
{"id": 9},
],
}
populate_mock_db(db, entities)
with db.scoped_session() as session:
_index_aggregate_monthly_plays(session)
results: List[AggregateMonthlyPlays] = (
session.query(AggregateMonthlyPlays)
.order_by(AggregateMonthlyPlays.play_item_id)
.all()
)
assert len(results) == 2
new_checkpoint: IndexingCheckpoints = (
session.query(IndexingCheckpoints.last_checkpoint)
.filter(IndexingCheckpoints.tablename == AGGREGATE_MONTHLY_PLAYS_TABLE_NAME)
.scalar()
)
assert new_checkpoint == 9
def test_index_aggregate_monthly_plays_no_plays(app):
"""Tests that aggregate_monthly_plays should skip indexing if there are no plays"""
# setup
with app.app_context():
db = get_db()
# run
entities = {"plays": []}
populate_mock_db(db, entities)
with db.scoped_session() as session:
_index_aggregate_monthly_plays(session)
| 32.257028 | 88 | 0.57744 | 873 | 8,032 | 5.050401 | 0.120275 | 0.097301 | 0.066682 | 0.068043 | 0.833296 | 0.800181 | 0.761624 | 0.748469 | 0.726922 | 0.72284 | 0 | 0.024969 | 0.306897 | 8,032 | 248 | 89 | 32.387097 | 0.76702 | 0.044198 | 0 | 0.580311 | 0 | 0 | 0.089386 | 0.00903 | 0 | 0 | 0 | 0 | 0.202073 | 1 | 0.020725 | false | 0 | 0.036269 | 0 | 0.056995 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7a334f6a4d692e6464a774d3c7dcbe4a5a410043 | 107 | py | Python | zenduty/configuration.py | Zenduty/zenduty-python-sdk | 4ae19f45bd9114aadd905a5dc885f5822fdf8098 | [
"MIT"
] | null | null | null | zenduty/configuration.py | Zenduty/zenduty-python-sdk | 4ae19f45bd9114aadd905a5dc885f5822fdf8098 | [
"MIT"
] | null | null | null | zenduty/configuration.py | Zenduty/zenduty-python-sdk | 4ae19f45bd9114aadd905a5dc885f5822fdf8098 | [
"MIT"
] | 4 | 2019-07-05T17:59:59.000Z | 2021-12-06T12:38:07.000Z | class Configuration(object):
def __init__(self,access_token):
self.access_token = access_token
| 26.75 | 40 | 0.738318 | 13 | 107 | 5.538462 | 0.615385 | 0.458333 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.17757 | 107 | 3 | 41 | 35.666667 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
7a4e8a9d03c4f02bc9bc58ed5182391c8378c7d4 | 206 | py | Python | whatisit/apps/main/context_processors.py | radinformatics/whatisit | 9cfc8924b722678d3b2ca7e5ad77b9254fcb93f3 | [
"MIT"
] | 1 | 2021-06-08T11:08:09.000Z | 2021-06-08T11:08:09.000Z | whatisit/apps/main/context_processors.py | radinformatics/whatisit | 9cfc8924b722678d3b2ca7e5ad77b9254fcb93f3 | [
"MIT"
] | 24 | 2016-10-21T00:55:30.000Z | 2017-01-05T03:13:57.000Z | whatisit/apps/main/context_processors.py | radinformatics/whatisit | 9cfc8924b722678d3b2ca7e5ad77b9254fcb93f3 | [
"MIT"
] | null | null | null | from whatisit.settings import (
DOMAIN_NAME,
DISQUS_NAME
)
def domain_processor(request):
return {'domain': DOMAIN_NAME}
def disqus_processor(request):
return {'DISQUS_NAME': DISQUS_NAME}
| 18.727273 | 39 | 0.73301 | 25 | 206 | 5.76 | 0.44 | 0.208333 | 0.194444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.169903 | 206 | 10 | 40 | 20.6 | 0.842105 | 0 | 0 | 0 | 0 | 0 | 0.082524 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.125 | 0.25 | 0.625 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
7a80eac6fd8d838604b59aca2d873e7328db5120 | 28,293 | py | Python | test/sampler_test.py | k-ship/bilby | 916d5c4ee4cdb102f1408bd20bc25fa250ab92f0 | [
"MIT"
] | null | null | null | test/sampler_test.py | k-ship/bilby | 916d5c4ee4cdb102f1408bd20bc25fa250ab92f0 | [
"MIT"
] | null | null | null | test/sampler_test.py | k-ship/bilby | 916d5c4ee4cdb102f1408bd20bc25fa250ab92f0 | [
"MIT"
] | null | null | null | from __future__ import absolute_import
import bilby
from bilby.core import prior
import unittest
from mock import MagicMock
import numpy as np
import os
import shutil
import copy
class TestSampler(unittest.TestCase):
def setUp(self):
likelihood = bilby.core.likelihood.Likelihood()
likelihood.parameters = dict(a=1, b=2, c=3)
delta_prior = prior.DeltaFunction(peak=0)
delta_prior.rescale = MagicMock(return_value=prior.DeltaFunction(peak=1))
delta_prior.prob = MagicMock(return_value=1)
delta_prior.sample = MagicMock(return_value=0)
uniform_prior = prior.Uniform(0, 1)
uniform_prior.rescale = MagicMock(return_value=prior.Uniform(0, 2))
uniform_prior.prob = MagicMock(return_value=1)
uniform_prior.sample = MagicMock(return_value=0.5)
priors = dict(a=delta_prior, b='string', c=uniform_prior)
likelihood.log_likelihood_ratio = MagicMock(return_value=1)
likelihood.log_likelihood = MagicMock(return_value=2)
test_directory = 'test_directory'
if os.path.isdir(test_directory):
os.rmdir(test_directory)
self.sampler = bilby.core.sampler.Sampler(
likelihood=likelihood, priors=priors,
outdir=test_directory, use_ratio=False,
skip_import_verification=True)
def tearDown(self):
del self.sampler
def test_search_parameter_keys(self):
expected_search_parameter_keys = ['c']
self.assertListEqual(self.sampler.search_parameter_keys, expected_search_parameter_keys)
def test_fixed_parameter_keys(self):
expected_fixed_parameter_keys = ['a']
self.assertListEqual(self.sampler.fixed_parameter_keys, expected_fixed_parameter_keys)
def test_ndim(self):
self.assertEqual(self.sampler.ndim, 1)
def test_kwargs(self):
self.assertDictEqual(self.sampler.kwargs, {})
def test_label(self):
self.assertEqual(self.sampler.label, 'label')
def test_prior_transform_transforms_search_parameter_keys(self):
self.sampler.prior_transform([0])
expected_prior = prior.Uniform(0, 1)
self.assertListEqual([self.sampler.priors['c'].minimum,
self.sampler.priors['c'].maximum],
[expected_prior.minimum,
expected_prior.maximum])
def test_prior_transform_does_not_transform_fixed_parameter_keys(self):
self.sampler.prior_transform([0])
self.assertEqual(self.sampler.priors['a'].peak,
prior.DeltaFunction(peak=0).peak)
def test_log_prior(self):
self.assertEqual(self.sampler.log_prior({1}), 0.0)
def test_log_likelihood_with_use_ratio(self):
self.sampler.use_ratio = True
self.assertEqual(self.sampler.log_likelihood([0]), 1)
def test_log_likelihood_without_use_ratio(self):
self.sampler.use_ratio = False
self.assertEqual(self.sampler.log_likelihood([0]), 2)
def test_log_likelihood_correctly_sets_parameters(self):
expected_dict = dict(a=0,
b=2,
c=0)
_ = self.sampler.log_likelihood([0])
self.assertDictEqual(self.sampler.likelihood.parameters, expected_dict)
def test_get_random_draw(self):
self.assertEqual(self.sampler.get_random_draw_from_prior(), np.array([0.5]))
def test_base_run_sampler(self):
sampler_copy = copy.copy(self.sampler)
self.sampler.run_sampler()
self.assertDictEqual(sampler_copy.__dict__, self.sampler.__dict__)
class TestCPNest(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.Cpnest(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(verbose=1, nthreads=1, nlive=500, maxmcmc=1000,
seed=None, poolsize=100, nhamiltonian=0, resume=True,
output='outdir/cpnest_label/', proposals=None,
n_periodic_checkpoint=8000)
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(verbose=1, nthreads=1, nlive=250, maxmcmc=1000,
seed=None, poolsize=100, nhamiltonian=0, resume=True,
output='outdir/cpnest_label/', proposals=None,
n_periodic_checkpoint=8000)
for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['nlive']
new_kwargs[equiv] = 250
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestDynesty(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.Dynesty(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(bound='multi', sample='rwalk', periodic=None, reflective=None, verbose=True,
check_point_delta_t=600, nlive=1000, first_update=None,
npdim=None, rstate=None, queue_size=None, pool=None,
use_pool=None, live_points=None, logl_args=None, logl_kwargs=None,
ptform_args=None, ptform_kwargs=None,
enlarge=1.5, bootstrap=None, vol_dec=0.5, vol_check=8.0,
facc=0.2, slices=5, dlogz=0.1, maxiter=None, maxcall=None,
logl_max=np.inf, add_live=True, print_progress=True, save_bounds=False,
walks=100, update_interval=600, print_func='func', n_effective=None,
maxmcmc=5000, nact=5)
self.sampler.kwargs['print_func'] = 'func' # set this manually as this is not testable otherwise
# DictEqual can't handle lists so we check these separately
self.assertEqual([], self.sampler.kwargs['periodic'])
self.assertEqual([], self.sampler.kwargs['reflective'])
self.sampler.kwargs['periodic'] = expected['periodic']
self.sampler.kwargs['reflective'] = expected['reflective']
for key in self.sampler.kwargs.keys():
print("key={}, expected={}, actual={}"
.format(key, expected[key], self.sampler.kwargs[key]))
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(bound='multi', sample='rwalk', periodic=[], reflective=[], verbose=True,
check_point_delta_t=600, nlive=1000, first_update=None,
npdim=None, rstate=None, queue_size=None, pool=None,
use_pool=None, live_points=None, logl_args=None, logl_kwargs=None,
ptform_args=None, ptform_kwargs=None,
enlarge=1.5, bootstrap=None, vol_dec=0.5, vol_check=8.0,
facc=0.2, slices=5, dlogz=0.1, maxiter=None, maxcall=None,
logl_max=np.inf, add_live=True, print_progress=True, save_bounds=False,
walks=100, update_interval=600, print_func='func', n_effective=None,
maxmcmc=5000, nact=5)
for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['nlive']
new_kwargs[equiv] = 1000
self.sampler.kwargs = new_kwargs
self.sampler.kwargs['print_func'] = 'func' # set this manually as this is not testable otherwise
self.assertDictEqual(expected, self.sampler.kwargs)
def test_prior_boundary(self):
self.priors['a'] = bilby.core.prior.Prior(boundary='periodic')
self.priors['b'] = bilby.core.prior.Prior(boundary='reflective')
self.priors['c'] = bilby.core.prior.Prior(boundary=None)
self.priors['d'] = bilby.core.prior.Prior(boundary='reflective')
self.priors['e'] = bilby.core.prior.Prior(boundary='periodic')
self.sampler = bilby.core.sampler.Dynesty(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
self.assertEqual([0, 4], self.sampler.kwargs["periodic"])
self.assertEqual(self.sampler._periodic, self.sampler.kwargs["periodic"])
self.assertEqual([1, 3], self.sampler.kwargs["reflective"])
self.assertEqual(self.sampler._reflective, self.sampler.kwargs["reflective"])
class TestEmcee(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.Emcee(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(nwalkers=500, a=2, args=[], kwargs={},
postargs=None, pool=None, live_dangerously=False,
runtime_sortingfn=None, lnprob0=None, rstate0=None,
blobs0=None, iterations=100, thin=1, storechain=True, mh_proposal=None
)
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(nwalkers=100, a=2, args=[], kwargs={},
postargs=None, pool=None, live_dangerously=False,
runtime_sortingfn=None, lnprob0=None, rstate0=None,
blobs0=None, iterations=100, thin=1, storechain=True, mh_proposal=None)
for equiv in bilby.core.sampler.base_sampler.MCMCSampler.nwalkers_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['nwalkers']
new_kwargs[equiv] = 100
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestKombine(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.Kombine(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(nwalkers=500, args=[], pool=None, transd=False,
lnpost0=None, blob0=None, iterations=500, storechain=True, processes=1, update_interval=None,
kde=None, kde_size=None, spaces=None, freeze_transd=False, test_steps=16, critical_pval=0.05,
max_steps=None, burnin_verbose=False)
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(nwalkers=400, args=[], pool=None, transd=False,
lnpost0=None, blob0=None, iterations=500, storechain=True, processes=1, update_interval=None,
kde=None, kde_size=None, spaces=None, freeze_transd=False, test_steps=16, critical_pval=0.05,
max_steps=None, burnin_verbose=False)
for equiv in bilby.core.sampler.base_sampler.MCMCSampler.nwalkers_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['nwalkers']
new_kwargs[equiv] = 400
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestNestle(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.Nestle(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True,
verbose=False)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(verbose=False, method='multi', npoints=500,
update_interval=None, npdim=None, maxiter=None,
maxcall=None, dlogz=None, decline_factor=None,
rstate=None, callback=None, steps=20, enlarge=1.2)
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(verbose=False, method='multi', npoints=345,
update_interval=None, npdim=None, maxiter=None,
maxcall=None, dlogz=None, decline_factor=None,
rstate=None, callback=None, steps=20, enlarge=1.2)
self.sampler.kwargs['npoints'] = 123
for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['npoints']
new_kwargs[equiv] = 345
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestPolyChord(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.PyPolyChord(self.likelihood, self.priors,
outdir='outdir', label='polychord',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(use_polychord_defaults=False, nlive=self.sampler.ndim*25, num_repeats=self.sampler.ndim*5,
nprior=-1, do_clustering=True, feedback=1, precision_criterion=0.001,
logzero=-1e30, max_ndead=-1, boost_posterior=0.0, posteriors=True,
equals=True, cluster_posteriors=True, write_resume=True,
write_paramnames=False, read_resume=True, write_stats=True,
write_live=True, write_dead=True, write_prior=True,
compression_factor=np.exp(-1), base_dir='outdir',
file_root='polychord', seed=-1, grade_dims=list([self.sampler.ndim]),
grade_frac=list([1.0]*len([self.sampler.ndim])), nlives={})
self.sampler._setup_dynamic_defaults()
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(use_polychord_defaults=False, nlive=123, num_repeats=self.sampler.ndim*5,
nprior=-1, do_clustering=True, feedback=1, precision_criterion=0.001,
logzero=-1e30, max_ndead=-1, boost_posterior=0.0, posteriors=True,
equals=True, cluster_posteriors=True, write_resume=True,
write_paramnames=False, read_resume=True, write_stats=True,
write_live=True, write_dead=True, write_prior=True,
compression_factor=np.exp(-1), base_dir='outdir',
file_root='polychord', seed=-1, grade_dims=list([self.sampler.ndim]),
grade_frac=list([1.0]*len([self.sampler.ndim])), nlives={})
self.sampler._setup_dynamic_defaults()
for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['nlive']
new_kwargs[equiv] = 123
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestPTEmcee(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.Ptemcee(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(ntemps=2, nwalkers=500,
Tmax=None, betas=None,
threads=1, pool=None, a=2.0,
loglargs=[], logpargs=[],
loglkwargs={}, logpkwargs={},
adaptation_lag=10000, adaptation_time=100,
random=None, iterations=100, thin=1,
storechain=True, adapt=True,
swap_ratios=False,
)
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(ntemps=2, nwalkers=150,
Tmax=None, betas=None,
threads=1, pool=None, a=2.0,
loglargs=[], logpargs=[],
loglkwargs={}, logpkwargs={},
adaptation_lag=10000, adaptation_time=100,
random=None, iterations=100, thin=1,
storechain=True, adapt=True,
swap_ratios=False,
)
for equiv in bilby.core.sampler.base_sampler.MCMCSampler.nwalkers_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['nwalkers']
new_kwargs[equiv] = 150
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestPyMC3(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.sampler = bilby.core.sampler.Pymc3(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(
draws=500, step=None, init='auto', n_init=200000, start=None, trace=None, chain_idx=0,
chains=2, cores=1, tune=500, nuts_kwargs=None, step_kwargs=None, progressbar=True,
model=None, random_seed=None, discard_tuned_samples=True,
compute_convergence_checks=True)
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(
draws=500, step=None, init='auto', n_init=200000, start=None, trace=None, chain_idx=0,
chains=2, cores=1, tune=500, nuts_kwargs=None, step_kwargs=None, progressbar=True,
model=None, random_seed=None, discard_tuned_samples=True,
compute_convergence_checks=True)
self.sampler.kwargs['draws'] = 123
for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['draws']
new_kwargs[equiv] = 500
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestPymultinest(unittest.TestCase):
def setUp(self):
self.likelihood = MagicMock()
self.priors = bilby.core.prior.PriorDict(
dict(a=bilby.core.prior.Uniform(0, 1),
b=bilby.core.prior.Uniform(0, 1)))
self.priors['a'] = bilby.core.prior.Prior(boundary='periodic')
self.priors['b'] = bilby.core.prior.Prior(boundary='reflective')
self.sampler = bilby.core.sampler.Pymultinest(self.likelihood, self.priors,
outdir='outdir', label='label',
use_ratio=False, plot=False,
skip_import_verification=True)
def tearDown(self):
del self.likelihood
del self.priors
del self.sampler
def test_default_kwargs(self):
expected = dict(importance_nested_sampling=False, resume=True,
verbose=True, sampling_efficiency='parameter',
outputfiles_basename='outdir/pm_label/',
n_live_points=500, n_params=2,
n_clustering_params=None, wrapped_params=None,
multimodal=True, const_efficiency_mode=False,
evidence_tolerance=0.5,
n_iter_before_update=100, null_log_evidence=-1e90,
max_modes=100, mode_tolerance=-1e90, seed=-1,
context=0, write_output=True, log_zero=-1e100,
max_iter=0, init_MPI=False, dump_callback=None)
self.assertListEqual([1, 0], self.sampler.kwargs['wrapped_params']) # Check this separately
self.sampler.kwargs['wrapped_params'] = None # The dict comparison can't handle lists
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
expected = dict(importance_nested_sampling=False, resume=True,
verbose=True, sampling_efficiency='parameter',
outputfiles_basename='outdir/pm_label/',
n_live_points=123, n_params=2,
n_clustering_params=None, wrapped_params=None,
multimodal=True, const_efficiency_mode=False,
evidence_tolerance=0.5,
n_iter_before_update=100, null_log_evidence=-1e90,
max_modes=100, mode_tolerance=-1e90, seed=-1,
context=0, write_output=True, log_zero=-1e100,
max_iter=0, init_MPI=False, dump_callback=None)
for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
new_kwargs = self.sampler.kwargs.copy()
del new_kwargs['n_live_points']
new_kwargs['wrapped_params'] = None # The dict comparison can't handle lists
new_kwargs[equiv] = 123
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
class TestRunningSamplers(unittest.TestCase):
def setUp(self):
np.random.seed(42)
bilby.core.utils.command_line_args.bilby_test_mode = False
self.x = np.linspace(0, 1, 11)
self.model = lambda x, m, c: m * x + c
self.injection_parameters = dict(m=0.5, c=0.2)
self.sigma = 0.1
self.y = self.model(self.x, **self.injection_parameters) +\
np.random.normal(0, self.sigma, len(self.x))
self.likelihood = bilby.likelihood.GaussianLikelihood(
self.x, self.y, self.model, self.sigma)
self.priors = bilby.core.prior.PriorDict()
self.priors['m'] = bilby.core.prior.Uniform(0, 5, boundary='reflective')
self.priors['c'] = bilby.core.prior.Uniform(-2, 2, boundary='reflective')
bilby.core.utils.check_directory_exists_and_if_not_mkdir('outdir')
def tearDown(self):
del self.likelihood
del self.priors
bilby.core.utils.command_line_args.bilby_test_mode = False
shutil.rmtree('outdir')
def test_run_cpnest(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='cpnest',
nlive=100, save=False, resume=False)
def test_run_dynesty(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='dynesty',
nlive=100, save=False)
def test_run_dynamic_dynesty(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='dynamic_dynesty',
nlive=100, save=False)
def test_run_emcee(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='emcee',
iterations=1000, nwalkers=10, save=False)
def test_run_kombine(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='kombine',
iterations=2500, nwalkers=100, save=False)
def test_run_nestle(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='nestle',
nlive=100, save=False)
def test_run_pypolychord(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors,
sampler='pypolychord', nlive=100, save=False)
def test_run_ptemcee(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='ptemcee',
nsteps=1000, nwalkers=10, ntemps=10, save=False)
def test_run_pymc3(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='pymc3',
draws=50, tune=50, n_init=1000, save=False)
def test_run_pymultinest(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors,
sampler='pymultinest', nlive=100, save=False)
def test_run_PTMCMCSampler(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors,
sampler='PTMCMCsampler', Niter=101, burn=2,
isave=100, save=False)
if __name__ == '__main__':
unittest.main()
| 47.076539 | 117 | 0.592938 | 3,155 | 28,293 | 5.149604 | 0.115372 | 0.071767 | 0.055456 | 0.017234 | 0.813627 | 0.780944 | 0.758725 | 0.738475 | 0.712747 | 0.698037 | 0 | 0.025302 | 0.302937 | 28,293 | 600 | 118 | 47.155 | 0.798499 | 0.009225 | 0 | 0.633136 | 0 | 0 | 0.027547 | 0 | 0 | 0 | 0 | 0 | 0.074951 | 1 | 0.128205 | false | 0 | 0.043393 | 0 | 0.193294 | 0.013807 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7a8fcee3f1d354f4324875d8cc4dd98fc8c204a7 | 37 | py | Python | python/stringcharactorcontinuelength.py | doublechoose/LPL | b231a14d74c2ed96ee2f2e832b388887a6700d7a | [
"MIT"
] | null | null | null | python/stringcharactorcontinuelength.py | doublechoose/LPL | b231a14d74c2ed96ee2f2e832b388887a6700d7a | [
"MIT"
] | null | null | null | python/stringcharactorcontinuelength.py | doublechoose/LPL | b231a14d74c2ed96ee2f2e832b388887a6700d7a | [
"MIT"
] | null | null | null | a = 'alsjglasjglsjgl33r3ljh3h3kvmm'
| 12.333333 | 35 | 0.810811 | 2 | 37 | 15 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151515 | 0.108108 | 37 | 2 | 36 | 18.5 | 0.757576 | 0 | 0 | 0 | 0 | 0 | 0.805556 | 0.805556 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7aa0979efb606e0c84c9bee90483e524a609688e | 165 | py | Python | Python_Network_Automation_II/chapter16_codes/twilio/credentials.py | yasser296/Python-Projects | eae3598e2d4faf08d9def92c8b417c2e7946c5f4 | [
"MIT"
] | null | null | null | Python_Network_Automation_II/chapter16_codes/twilio/credentials.py | yasser296/Python-Projects | eae3598e2d4faf08d9def92c8b417c2e7946c5f4 | [
"MIT"
] | null | null | null | Python_Network_Automation_II/chapter16_codes/twilio/credentials.py | yasser296/Python-Projects | eae3598e2d4faf08d9def92c8b417c2e7946c5f4 | [
"MIT"
] | null | null | null | account_sid = "ACe9c9ed26425723d4113f026bc87bd6e4"
auth_token = "2407186298268594f576d8fb72e1075b"
my_smartphone = "+201271653370"
twilio_trial = "+19034857225"
| 33 | 51 | 0.818182 | 12 | 165 | 10.916667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.469799 | 0.09697 | 165 | 4 | 52 | 41.25 | 0.409396 | 0 | 0 | 0 | 0 | 0 | 0.565217 | 0.409938 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8f87fd08034b5d828e122d7e296c6ffb12622bde | 64 | py | Python | eth/beacon/types/custody_challenges.py | Bhargavasomu/py-evm | ee8f72d5a70805575a967cde0a43942e1526264e | [
"MIT"
] | null | null | null | eth/beacon/types/custody_challenges.py | Bhargavasomu/py-evm | ee8f72d5a70805575a967cde0a43942e1526264e | [
"MIT"
] | null | null | null | eth/beacon/types/custody_challenges.py | Bhargavasomu/py-evm | ee8f72d5a70805575a967cde0a43942e1526264e | [
"MIT"
] | null | null | null | import rlp
class CustodyChallenge(rlp.Serializable):
pass
| 10.666667 | 41 | 0.765625 | 7 | 64 | 7 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.171875 | 64 | 5 | 42 | 12.8 | 0.924528 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
8f9e96c861817fcdf0f6278e81a81a0a8fb38eba | 1,167 | py | Python | valorantrpc/client_api.py | hackertr44/Valorant-Rpc | 7623bd143c239ed9cc1de01c1ce07dd5222d6856 | [
"MIT"
] | 1 | 2021-05-04T22:34:17.000Z | 2021-05-04T22:34:17.000Z | valorantrpc/client_api.py | Shmalle/valorant-rpc | e33aeec887a4e4a35c7aabb18e1b3afd8c1f43dd | [
"MIT"
] | null | null | null | valorantrpc/client_api.py | Shmalle/valorant-rpc | e33aeec887a4e4a35c7aabb18e1b3afd8c1f43dd | [
"MIT"
] | null | null | null | import re
import aiohttp
import asyncio
import requests
import json
import os
from .exceptions import AuthError
from valorantrpc import utils
def get_glz(endpoint,headers):
config = utils.get_config()
client_region = config["region"]
r = requests.get(f'https://glz-{client_region}-1.{client_region}.a.pvp.net{endpoint}', headers=headers)
data = json.loads(r.text)
return data
def get_pd(endpoint,headers):
config = utils.get_config()
client_region = config["region"]
r = requests.get(f'https://pd.{client_region}.a.pvp.net{endpoint}', headers=headers)
data = json.loads(r.text)
return data
def post_glz(endpoint,headers,data=None):
config = utils.get_config()
client_region = config["region"]
r = requests.post(f'https://glz-{client_region}-1.{client_region}.a.pvp.net{endpoint}', headers=headers, data=data)
data = json.loads(r.text)
return data
def post_pd(endpoint,headers,data=None):
config = utils.get_config()
client_region = config["region"]
r = requests.post(f'https://pd.{client_region}.a.pvp.net{endpoint}', headers=headers, data=data)
data = json.loads(r.text)
return data | 32.416667 | 119 | 0.710368 | 170 | 1,167 | 4.770588 | 0.2 | 0.147965 | 0.069051 | 0.098644 | 0.827374 | 0.827374 | 0.827374 | 0.827374 | 0.827374 | 0.81381 | 0 | 0.002012 | 0.148243 | 1,167 | 36 | 120 | 32.416667 | 0.813883 | 0 | 0 | 0.5 | 0 | 0 | 0.210616 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.125 | false | 0 | 0.25 | 0 | 0.5 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8fac8fa3582d432a38b0488e0bc47b9672f98f64 | 47 | py | Python | Vision.py | Isaac-the-Man/rcjbot2018 | 87f8487280edf9e9a49465e590111187c9cb7c43 | [
"Unlicense"
] | null | null | null | Vision.py | Isaac-the-Man/rcjbot2018 | 87f8487280edf9e9a49465e590111187c9cb7c43 | [
"Unlicense"
] | null | null | null | Vision.py | Isaac-the-Man/rcjbot2018 | 87f8487280edf9e9a49465e590111187c9cb7c43 | [
"Unlicense"
] | null | null | null | import cv2 as cv
print('Vision initialized')
| 9.4 | 27 | 0.744681 | 7 | 47 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025641 | 0.170213 | 47 | 4 | 28 | 11.75 | 0.871795 | 0 | 0 | 0 | 0 | 0 | 0.382979 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
8fc5753a7e1242b09baf868eb6081e2b47d88878 | 127 | py | Python | engine/tests/test_swarm_cluster.py | GoContainer/Sweady | 79fd7f0a14d50afbf4406bb57a6bdee082e4f3f5 | [
"MIT"
] | 2 | 2017-04-17T20:42:33.000Z | 2017-04-21T08:06:28.000Z | engine/tests/test_swarm_cluster.py | GoContainer/Sweady | 79fd7f0a14d50afbf4406bb57a6bdee082e4f3f5 | [
"MIT"
] | 10 | 2017-04-14T10:00:33.000Z | 2017-04-26T18:18:44.000Z | engine/tests/test_swarm_cluster.py | Sweady/Sweady | 79fd7f0a14d50afbf4406bb57a6bdee082e4f3f5 | [
"MIT"
] | 3 | 2017-03-21T13:54:45.000Z | 2017-04-26T12:20:35.000Z | # Test Docker Swarm
def test_docker_swarm_enabled(Command):
assert 'Swarm: active' in Command.check_output('docker info') | 25.4 | 65 | 0.771654 | 18 | 127 | 5.222222 | 0.666667 | 0.212766 | 0.319149 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133858 | 127 | 5 | 65 | 25.4 | 0.854545 | 0.133858 | 0 | 0 | 0 | 0 | 0.220183 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0.5 | false | 0 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
890bd26843a4334bb9050f11df10073c1f29064c | 87 | py | Python | a2c_ppo_acktr/algo/__init__.py | fgolemo/pytorch-a2c-ppo-acktr-gail | 366d22b7e6a049fb3de804619050cc6e61af86e2 | [
"MIT"
] | 1 | 2019-07-05T19:57:26.000Z | 2019-07-05T19:57:26.000Z | a2c_ppo_acktr/algo/__init__.py | fgolemo/pytorch-a2c-ppo-acktr-gail | 366d22b7e6a049fb3de804619050cc6e61af86e2 | [
"MIT"
] | 1 | 2020-09-16T13:00:16.000Z | 2020-09-16T13:00:16.000Z | a2c_ppo_acktr/algo/__init__.py | fgolemo/pytorch-a2c-ppo-acktr-gail | 366d22b7e6a049fb3de804619050cc6e61af86e2 | [
"MIT"
] | 3 | 2019-07-07T20:16:27.000Z | 2020-12-23T20:18:18.000Z | from .a2c_acktr import A2C_ACKTR
from .ppo import PPO
from .random import RANDOM_AGENT
| 21.75 | 32 | 0.827586 | 15 | 87 | 4.6 | 0.466667 | 0.231884 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.026667 | 0.137931 | 87 | 3 | 33 | 29 | 0.893333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
891aa2f5ab95d4a7160231a0e756c2738eb624df | 196 | py | Python | tests/unit/linestring/__init__.py | phuntimes/mongoshapes | f461c67343c32c6b97af8d67a269b4de492d1d71 | [
"MIT"
] | 1 | 2020-11-26T05:58:23.000Z | 2020-11-26T05:58:23.000Z | tests/unit/linestring/__init__.py | Sean-McVeigh/mongoshapes | f461c67343c32c6b97af8d67a269b4de492d1d71 | [
"MIT"
] | null | null | null | tests/unit/linestring/__init__.py | Sean-McVeigh/mongoshapes | f461c67343c32c6b97af8d67a269b4de492d1d71 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from mongoshapes import LineString as GeoShape
from mongoshapes import LineStringDict as GeoDict
from mongoengine import LineStringField as GeoField
| 28 | 51 | 0.795918 | 25 | 196 | 6.24 | 0.72 | 0.192308 | 0.269231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005882 | 0.132653 | 196 | 6 | 52 | 32.666667 | 0.911765 | 0.214286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
64ebd3c0163616de5a0ed19b61c06527339a9cbe | 40 | py | Python | mp-tracker/mod/__init__.py | zekroTJA/masterypoints-tracker | 9f68dc3e7d7b1fed17107377e13a40941d158f12 | [
"MIT"
] | null | null | null | mp-tracker/mod/__init__.py | zekroTJA/masterypoints-tracker | 9f68dc3e7d7b1fed17107377e13a40941d158f12 | [
"MIT"
] | null | null | null | mp-tracker/mod/__init__.py | zekroTJA/masterypoints-tracker | 9f68dc3e7d7b1fed17107377e13a40941d158f12 | [
"MIT"
] | null | null | null | # flake8: noqa
from .tracker import *
| 13.333333 | 23 | 0.675 | 5 | 40 | 5.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.032258 | 0.225 | 40 | 2 | 24 | 20 | 0.83871 | 0.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8f27f3a050fd6e19fb915ef0fb5a8ca200106442 | 107 | py | Python | sample/helpers.py | JynLeazy/samplemod | 77cbd3085267913220bc530b9b137aef111d0cd5 | [
"BSD-2-Clause"
] | null | null | null | sample/helpers.py | JynLeazy/samplemod | 77cbd3085267913220bc530b9b137aef111d0cd5 | [
"BSD-2-Clause"
] | null | null | null | sample/helpers.py | JynLeazy/samplemod | 77cbd3085267913220bc530b9b137aef111d0cd5 | [
"BSD-2-Clause"
] | null | null | null | """"This module return true if get_answer() """
def get_answer():
"""Get an answer."""
return True
| 21.4 | 47 | 0.616822 | 15 | 107 | 4.266667 | 0.6 | 0.3125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.205607 | 107 | 4 | 48 | 26.75 | 0.752941 | 0.514019 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
56fc217ecfd2f8cdeca41ed537007007865ddc72 | 121 | py | Python | setup.py | Berdugo1994/Tweeter-Search-Engine | ff80707d64b792288b877814d79e39c5b5ceb7ad | [
"MIT"
] | null | null | null | setup.py | Berdugo1994/Tweeter-Search-Engine | ff80707d64b792288b877814d79e39c5b5ceb7ad | [
"MIT"
] | null | null | null | setup.py | Berdugo1994/Tweeter-Search-Engine | ff80707d64b792288b877814d79e39c5b5ceb7ad | [
"MIT"
] | null | null | null | import nltk
import search_engine_best
nltk.download('stopwords')
nltk.download('punkt')
search_engine_best.main() | 17.285714 | 27 | 0.77686 | 16 | 121 | 5.625 | 0.5625 | 0.266667 | 0.355556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115702 | 121 | 7 | 28 | 17.285714 | 0.841122 | 0 | 0 | 0 | 0 | 0 | 0.12069 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
85566c7374f95a82046c84931e491ee9eaa1ba3f | 182 | py | Python | cvworkflow/cvfunctions.py | ktoth17/contrast-variation-rsoxs | 0fd4c9aa6c3415949f1835616bec177c6525155a | [
"BSD-3-Clause"
] | null | null | null | cvworkflow/cvfunctions.py | ktoth17/contrast-variation-rsoxs | 0fd4c9aa6c3415949f1835616bec177c6525155a | [
"BSD-3-Clause"
] | null | null | null | cvworkflow/cvfunctions.py | ktoth17/contrast-variation-rsoxs | 0fd4c9aa6c3415949f1835616bec177c6525155a | [
"BSD-3-Clause"
] | null | null | null | # cvworkflow/cvfunctions.py
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
import matplotlib.cm as cm
| 18.2 | 31 | 0.807692 | 29 | 182 | 5.068966 | 0.551724 | 0.217687 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 182 | 9 | 32 | 20.222222 | 0.954545 | 0.137363 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8594579b85ab7e1471f8c5919f21575813a92133 | 450 | py | Python | data_mine/utils/__init__.py | SebiSebi/DataMine | d2dd9ed7e2608918dd2908fa29238f600c768eb3 | [
"Apache-2.0"
] | 9 | 2020-07-01T21:53:36.000Z | 2020-12-15T08:49:08.000Z | data_mine/utils/__init__.py | ChewKokWah/DataMine | d2dd9ed7e2608918dd2908fa29238f600c768eb3 | [
"Apache-2.0"
] | 7 | 2020-04-04T19:30:16.000Z | 2020-06-26T12:18:10.000Z | data_mine/utils/__init__.py | ChewKokWah/DataMine | d2dd9ed7e2608918dd2908fa29238f600c768eb3 | [
"Apache-2.0"
] | 2 | 2020-03-21T13:55:27.000Z | 2020-07-01T21:53:38.000Z | from __future__ import absolute_import
from .archive_utils import is_archive
from .archive_utils import extract_archive
from .misc_utils import datamine_cache_dir
from .misc_utils import file_sha256
from .misc_utils import get_home_dir
from .misc_utils import is_integer
from .misc_utils import num_decimal_places
from .misc_utils import url_to_filename
from .requests_utils import download_file
from .requests_utils import download_file_if_missing
| 34.615385 | 52 | 0.877778 | 71 | 450 | 5.126761 | 0.380282 | 0.302198 | 0.214286 | 0.313187 | 0.313187 | 0.192308 | 0 | 0 | 0 | 0 | 0 | 0.007407 | 0.1 | 450 | 12 | 53 | 37.5 | 0.891358 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f1641c32ba5670ba8429deb98a3c96fad9600311 | 87 | py | Python | backend/app/rooms/__init__.py | vpaliy/react-chat | 883934b4983136380e4569e7f65722bf7e9fd628 | [
"MIT"
] | 1 | 2018-12-03T05:53:48.000Z | 2018-12-03T05:53:48.000Z | backend/app/rooms/__init__.py | vpaliy/react-chat | 883934b4983136380e4569e7f65722bf7e9fd628 | [
"MIT"
] | null | null | null | backend/app/rooms/__init__.py | vpaliy/react-chat | 883934b4983136380e4569e7f65722bf7e9fd628 | [
"MIT"
] | null | null | null | from flask import Blueprint
rooms = Blueprint('rooms', __name__)
from views import *
| 14.5 | 36 | 0.758621 | 11 | 87 | 5.636364 | 0.636364 | 0.451613 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16092 | 87 | 5 | 37 | 17.4 | 0.849315 | 0 | 0 | 0 | 0 | 0 | 0.057471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
f1695714d148f7f8c28a3a934f77987372dca985 | 30 | py | Python | data_spec_validator/spec/custom_spec/__init__.py | travisliu/data-spec-validator | 7ee0944ca9899d565ad04ed82ca26bb402970958 | [
"MIT"
] | 23 | 2021-08-11T08:53:15.000Z | 2022-02-14T04:44:13.000Z | data_spec_validator/spec/custom_spec/__init__.py | travisliu/data-spec-validator | 7ee0944ca9899d565ad04ed82ca26bb402970958 | [
"MIT"
] | 2 | 2021-09-11T08:59:12.000Z | 2022-03-29T00:40:42.000Z | data_spec_validator/spec/custom_spec/__init__.py | travisliu/data-spec-validator | 7ee0944ca9899d565ad04ed82ca26bb402970958 | [
"MIT"
] | 1 | 2022-01-04T07:45:22.000Z | 2022-01-04T07:45:22.000Z | from .defines import register
| 15 | 29 | 0.833333 | 4 | 30 | 6.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133333 | 30 | 1 | 30 | 30 | 0.961538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f1893f52b99e2281af9b357d11ecbbdf9d9b4d65 | 364 | py | Python | simulate/reporters/__init__.py | charles9li/simulate-openmm | cfc76294dd4b00147769fc83c7673fce5bd499cc | [
"MIT"
] | null | null | null | simulate/reporters/__init__.py | charles9li/simulate-openmm | cfc76294dd4b00147769fc83c7673fce5bd499cc | [
"MIT"
] | null | null | null | simulate/reporters/__init__.py | charles9li/simulate-openmm | cfc76294dd4b00147769fc83c7673fce5bd499cc | [
"MIT"
] | null | null | null | from .energyreporter import EnergyReporter, PotentialEnergyReporter, KineticEnergyReporter
from .radiusofgyrationreporter import RadiusOfGyrationReporter
from .endtoenddistancereporter import EndToEndDistanceReporter
from .rnemdreporter import RNEMDReporter
from .rnemdvelocityreporter import RNEMDVelocityReporter
from .savestatereporter import SaveStateReporter
| 52 | 90 | 0.906593 | 26 | 364 | 12.692308 | 0.384615 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.071429 | 364 | 6 | 91 | 60.666667 | 0.976331 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2d278d91d4921c71fdc86ab749731f5badb11a0f | 74 | py | Python | starter/cli.py | yoyota/python-starter | 5e85347dbbbc8b91603656383c37a6c5f2504fa1 | [
"MIT"
] | null | null | null | starter/cli.py | yoyota/python-starter | 5e85347dbbbc8b91603656383c37a6c5f2504fa1 | [
"MIT"
] | 2 | 2020-02-23T03:24:47.000Z | 2020-02-26T09:55:21.000Z | starter/cli.py | yoyota/python-starter | 5e85347dbbbc8b91603656383c37a6c5f2504fa1 | [
"MIT"
] | null | null | null | import fire
from starter.app import main
def cli():
fire.Fire(main)
| 10.571429 | 28 | 0.702703 | 12 | 74 | 4.333333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.202703 | 74 | 6 | 29 | 12.333333 | 0.881356 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7418e173afcd2a4d45b08eadbf57dd8a8034074e | 80 | py | Python | malort/__init__.py | CamDavidsonPilon/malort | bd463fe57fd7a15ccd24a1c7cfedefea4b31d3ac | [
"MIT"
] | 1 | 2021-03-06T13:17:33.000Z | 2021-03-06T13:17:33.000Z | malort/__init__.py | CamDavidsonPilon/malort | bd463fe57fd7a15ccd24a1c7cfedefea4b31d3ac | [
"MIT"
] | null | null | null | malort/__init__.py | CamDavidsonPilon/malort | bd463fe57fd7a15ccd24a1c7cfedefea4b31d3ac | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from malort import stats
from malort.core import analyze | 26.666667 | 31 | 0.725 | 12 | 80 | 4.833333 | 0.75 | 0.344828 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014706 | 0.15 | 80 | 3 | 31 | 26.666667 | 0.838235 | 0.2625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7485fca7d464be15acc7701f51bffa81f2578213 | 1,172 | py | Python | run.py | ironarmchad/depths_char_sheet | 542f969af951e9d249fe36bbd6dad147a69d9edb | [
"MIT"
] | null | null | null | run.py | ironarmchad/depths_char_sheet | 542f969af951e9d249fe36bbd6dad147a69d9edb | [
"MIT"
] | 1 | 2021-06-02T00:01:31.000Z | 2021-06-02T00:01:31.000Z | run.py | ironarmchad/depths_char_sheet | 542f969af951e9d249fe36bbd6dad147a69d9edb | [
"MIT"
] | 1 | 2019-08-01T16:55:54.000Z | 2019-08-01T16:55:54.000Z | from app import create_app, db
from app.auth.models import User
from app.game.models import Game
import sys
if __name__ == '__main__':
char_app = create_app('dev')
with char_app.app_context():
db.create_all()
user = User.query.filter_by(user_name='su_ironarmchad').first()
if not user:
user = User.create_user(user='su_ironarmchad',
password='PIANO@230jap',
role='SUPER')
if not Game.query.filter_by(name='No Game').first():
Game.create_game(game_name='No Game', game_lore='', game_summary="", st_id=user.id)
char_app.run()
else:
char_app = create_app('prod')
with char_app.app_context():
db.create_all()
user = User.query.filter_by(user_name='su_ironarmchad').first()
if not user:
user = User.create_user(user='su_ironarmchad',
password='PIANO@230jap',
role='SUPER')
if not Game.query.filter_by(name='No Game').first():
Game.create_game(game_name='No Game', game_lore='', game_summary="", st_id=user.id)
| 35.515152 | 95 | 0.581911 | 153 | 1,172 | 4.183007 | 0.248366 | 0.1 | 0.08125 | 0.05 | 0.765625 | 0.765625 | 0.765625 | 0.765625 | 0.765625 | 0.765625 | 0 | 0.007212 | 0.290102 | 1,172 | 32 | 96 | 36.625 | 0.762019 | 0 | 0 | 0.666667 | 0 | 0 | 0.113578 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.074074 | 0.148148 | 0 | 0.148148 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
7489e49e964ea97b42a4c19b50d1ffa517f0034b | 23 | py | Python | discord/ext/buttons/__init__.py | matcool/buttons | ffa9ba656bd3a907f2553e63f652b754a106c604 | [
"MIT"
] | 31 | 2019-09-24T03:35:30.000Z | 2022-01-11T08:32:10.000Z | discord/ext/buttons/__init__.py | UnrealFar/buttons | 12914e718474073b207a918da5d2bf5306af2aae | [
"MIT"
] | 1 | 2020-09-30T09:17:27.000Z | 2020-10-08T07:00:51.000Z | discord/ext/buttons/__init__.py | UnrealFar/buttons | 12914e718474073b207a918da5d2bf5306af2aae | [
"MIT"
] | 38 | 2019-09-25T08:10:56.000Z | 2022-01-06T07:44:21.000Z | from .buttons import *
| 11.5 | 22 | 0.73913 | 3 | 23 | 5.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173913 | 23 | 1 | 23 | 23 | 0.894737 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
749c55545d9fc0a06ed77254808364a890d51c93 | 138 | py | Python | erexplain/__init__.py | vincenzomartello/ExplainER | 4b208751796a7e831904c8987cf0f26f80e7af9a | [
"ECL-2.0",
"Apache-2.0"
] | 4 | 2020-06-05T18:24:56.000Z | 2021-07-02T09:03:39.000Z | erexplain/__init__.py | vincenzomartello/ExplainER | 4b208751796a7e831904c8987cf0f26f80e7af9a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | erexplain/__init__.py | vincenzomartello/ExplainER | 4b208751796a7e831904c8987cf0f26f80e7af9a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | from erexplain.triangles_method import explainSamples
from erexplain.pattern_discovery import getMaxFrequentPatterns,mineAssociationRules
| 46 | 83 | 0.92029 | 13 | 138 | 9.615385 | 0.769231 | 0.208 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.057971 | 138 | 2 | 84 | 69 | 0.961538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
77a719ba931bce6accf322aff228b74e9f4ba6b1 | 29 | py | Python | settings.py | aral/isvat | 6335f6625b5f883343bed64334022e39214d7ca5 | [
"MIT"
] | null | null | null | settings.py | aral/isvat | 6335f6625b5f883343bed64334022e39214d7ca5 | [
"MIT"
] | 1 | 2015-10-10T09:16:08.000Z | 2015-10-13T09:36:55.000Z | settings.py | aral/isvat | 6335f6625b5f883343bed64334022e39214d7ca5 | [
"MIT"
] | null | null | null | from isvat.settings import *
| 14.5 | 28 | 0.793103 | 4 | 29 | 5.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 29 | 1 | 29 | 29 | 0.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bb592204dbb9e64d78d346bb0c96c4039003283f | 49 | py | Python | cflow/models/cv/__init__.py | carefree0910/carefree-flow | 7035015a072cf8142074d01683889f90950d2939 | [
"MIT"
] | 11 | 2021-08-25T11:10:49.000Z | 2021-09-05T11:52:42.000Z | cflow/models/cv/__init__.py | carefree0910/carefree-flow | 7035015a072cf8142074d01683889f90950d2939 | [
"MIT"
] | null | null | null | cflow/models/cv/__init__.py | carefree0910/carefree-flow | 7035015a072cf8142074d01683889f90950d2939 | [
"MIT"
] | 2 | 2021-08-28T01:17:10.000Z | 2021-09-02T04:04:43.000Z | from .encoder import *
from .classifier import *
| 16.333333 | 25 | 0.755102 | 6 | 49 | 6.166667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.163265 | 49 | 2 | 26 | 24.5 | 0.902439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
24f5bb90d219451c46a763b099b99f6c544f9c2d | 30 | py | Python | python/testData/inspections/PyRelativeImportInspection/PlainDirectoryDottedImportFromTwoElementsWithAs/plainDirectory/script_after.py | Tasemo/intellij-community | 50aeaf729b7073e91c7c77487a1f155e0dfe3fcd | [
"Apache-2.0"
] | 2 | 2019-04-28T07:48:50.000Z | 2020-12-11T14:18:08.000Z | python/testData/inspections/PyRelativeImportInspection/PlainDirectoryDottedImportFromTwoElementsWithAs/plainDirectory/script_after.py | Tasemo/intellij-community | 50aeaf729b7073e91c7c77487a1f155e0dfe3fcd | [
"Apache-2.0"
] | null | null | null | python/testData/inspections/PyRelativeImportInspection/PlainDirectoryDottedImportFromTwoElementsWithAs/plainDirectory/script_after.py | Tasemo/intellij-community | 50aeaf729b7073e91c7c77487a1f155e0dfe3fcd | [
"Apache-2.0"
] | null | null | null | from util import foo, bar as b | 30 | 30 | 0.766667 | 7 | 30 | 3.285714 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 30 | 1 | 30 | 30 | 0.958333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7054fd2f24530ff1ca1d87b94a070b41731711fe | 26 | py | Python | rofi_mpd/__init__.py | Oro/Rofi_MPD | d9f597c58f37065625fc0095661b949ea138ab66 | [
"MIT"
] | 22 | 2018-09-13T21:06:50.000Z | 2022-03-10T16:49:58.000Z | rofi_mpd/__init__.py | Oro/Rofi_MPD | d9f597c58f37065625fc0095661b949ea138ab66 | [
"MIT"
] | 18 | 2018-10-05T07:20:32.000Z | 2020-07-10T10:21:24.000Z | rofi_mpd/__init__.py | Oro/Rofi_MPD | d9f597c58f37065625fc0095661b949ea138ab66 | [
"MIT"
] | 6 | 2018-10-05T07:42:53.000Z | 2021-06-04T13:37:40.000Z | from .rofi_mpd import run
| 13 | 25 | 0.807692 | 5 | 26 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 26 | 1 | 26 | 26 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
56170eb3c4322c52f3783c72d4ac7b021c28fcc9 | 118 | py | Python | models/__init__.py | Tawasta/account_followup_overdue_invoice | acc10c5f87f39331292e6f991c42fce878fadbec | [
"CC-BY-3.0"
] | 1 | 2017-03-02T12:34:58.000Z | 2017-03-02T12:34:58.000Z | models/__init__.py | Tawasta/account_followup_overdue_invoice | acc10c5f87f39331292e6f991c42fce878fadbec | [
"CC-BY-3.0"
] | null | null | null | models/__init__.py | Tawasta/account_followup_overdue_invoice | acc10c5f87f39331292e6f991c42fce878fadbec | [
"CC-BY-3.0"
] | null | null | null | import account_followup
import account_followup_line
import account_invoice
import account_journal
import res_partner
| 19.666667 | 28 | 0.915254 | 16 | 118 | 6.375 | 0.5 | 0.509804 | 0.411765 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.084746 | 118 | 5 | 29 | 23.6 | 0.944444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
56512d58fa018fbdc7615dbab157eb8d3d2e74cc | 6,131 | py | Python | loldib/getratings/models/NA/na_jax/na_jax_top.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_jax/na_jax_top.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_jax/na_jax_top.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | from getratings.models.ratings import Ratings
class NA_Jax_Top_Aatrox(Ratings):
pass
class NA_Jax_Top_Ahri(Ratings):
pass
class NA_Jax_Top_Akali(Ratings):
pass
class NA_Jax_Top_Alistar(Ratings):
pass
class NA_Jax_Top_Amumu(Ratings):
pass
class NA_Jax_Top_Anivia(Ratings):
pass
class NA_Jax_Top_Annie(Ratings):
pass
class NA_Jax_Top_Ashe(Ratings):
pass
class NA_Jax_Top_AurelionSol(Ratings):
pass
class NA_Jax_Top_Azir(Ratings):
pass
class NA_Jax_Top_Bard(Ratings):
pass
class NA_Jax_Top_Blitzcrank(Ratings):
pass
class NA_Jax_Top_Brand(Ratings):
pass
class NA_Jax_Top_Braum(Ratings):
pass
class NA_Jax_Top_Caitlyn(Ratings):
pass
class NA_Jax_Top_Camille(Ratings):
pass
class NA_Jax_Top_Cassiopeia(Ratings):
pass
class NA_Jax_Top_Chogath(Ratings):
pass
class NA_Jax_Top_Corki(Ratings):
pass
class NA_Jax_Top_Darius(Ratings):
pass
class NA_Jax_Top_Diana(Ratings):
pass
class NA_Jax_Top_Draven(Ratings):
pass
class NA_Jax_Top_DrMundo(Ratings):
pass
class NA_Jax_Top_Ekko(Ratings):
pass
class NA_Jax_Top_Elise(Ratings):
pass
class NA_Jax_Top_Evelynn(Ratings):
pass
class NA_Jax_Top_Ezreal(Ratings):
pass
class NA_Jax_Top_Fiddlesticks(Ratings):
pass
class NA_Jax_Top_Fiora(Ratings):
pass
class NA_Jax_Top_Fizz(Ratings):
pass
class NA_Jax_Top_Galio(Ratings):
pass
class NA_Jax_Top_Gangplank(Ratings):
pass
class NA_Jax_Top_Garen(Ratings):
pass
class NA_Jax_Top_Gnar(Ratings):
pass
class NA_Jax_Top_Gragas(Ratings):
pass
class NA_Jax_Top_Graves(Ratings):
pass
class NA_Jax_Top_Hecarim(Ratings):
pass
class NA_Jax_Top_Heimerdinger(Ratings):
pass
class NA_Jax_Top_Illaoi(Ratings):
pass
class NA_Jax_Top_Irelia(Ratings):
pass
class NA_Jax_Top_Ivern(Ratings):
pass
class NA_Jax_Top_Janna(Ratings):
pass
class NA_Jax_Top_JarvanIV(Ratings):
pass
class NA_Jax_Top_Jax(Ratings):
pass
class NA_Jax_Top_Jayce(Ratings):
pass
class NA_Jax_Top_Jhin(Ratings):
pass
class NA_Jax_Top_Jinx(Ratings):
pass
class NA_Jax_Top_Kalista(Ratings):
pass
class NA_Jax_Top_Karma(Ratings):
pass
class NA_Jax_Top_Karthus(Ratings):
pass
class NA_Jax_Top_Kassadin(Ratings):
pass
class NA_Jax_Top_Katarina(Ratings):
pass
class NA_Jax_Top_Kayle(Ratings):
pass
class NA_Jax_Top_Kayn(Ratings):
pass
class NA_Jax_Top_Kennen(Ratings):
pass
class NA_Jax_Top_Khazix(Ratings):
pass
class NA_Jax_Top_Kindred(Ratings):
pass
class NA_Jax_Top_Kled(Ratings):
pass
class NA_Jax_Top_KogMaw(Ratings):
pass
class NA_Jax_Top_Leblanc(Ratings):
pass
class NA_Jax_Top_LeeSin(Ratings):
pass
class NA_Jax_Top_Leona(Ratings):
pass
class NA_Jax_Top_Lissandra(Ratings):
pass
class NA_Jax_Top_Lucian(Ratings):
pass
class NA_Jax_Top_Lulu(Ratings):
pass
class NA_Jax_Top_Lux(Ratings):
pass
class NA_Jax_Top_Malphite(Ratings):
pass
class NA_Jax_Top_Malzahar(Ratings):
pass
class NA_Jax_Top_Maokai(Ratings):
pass
class NA_Jax_Top_MasterYi(Ratings):
pass
class NA_Jax_Top_MissFortune(Ratings):
pass
class NA_Jax_Top_MonkeyKing(Ratings):
pass
class NA_Jax_Top_Mordekaiser(Ratings):
pass
class NA_Jax_Top_Morgana(Ratings):
pass
class NA_Jax_Top_Nami(Ratings):
pass
class NA_Jax_Top_Nasus(Ratings):
pass
class NA_Jax_Top_Nautilus(Ratings):
pass
class NA_Jax_Top_Nidalee(Ratings):
pass
class NA_Jax_Top_Nocturne(Ratings):
pass
class NA_Jax_Top_Nunu(Ratings):
pass
class NA_Jax_Top_Olaf(Ratings):
pass
class NA_Jax_Top_Orianna(Ratings):
pass
class NA_Jax_Top_Ornn(Ratings):
pass
class NA_Jax_Top_Pantheon(Ratings):
pass
class NA_Jax_Top_Poppy(Ratings):
pass
class NA_Jax_Top_Quinn(Ratings):
pass
class NA_Jax_Top_Rakan(Ratings):
pass
class NA_Jax_Top_Rammus(Ratings):
pass
class NA_Jax_Top_RekSai(Ratings):
pass
class NA_Jax_Top_Renekton(Ratings):
pass
class NA_Jax_Top_Rengar(Ratings):
pass
class NA_Jax_Top_Riven(Ratings):
pass
class NA_Jax_Top_Rumble(Ratings):
pass
class NA_Jax_Top_Ryze(Ratings):
pass
class NA_Jax_Top_Sejuani(Ratings):
pass
class NA_Jax_Top_Shaco(Ratings):
pass
class NA_Jax_Top_Shen(Ratings):
pass
class NA_Jax_Top_Shyvana(Ratings):
pass
class NA_Jax_Top_Singed(Ratings):
pass
class NA_Jax_Top_Sion(Ratings):
pass
class NA_Jax_Top_Sivir(Ratings):
pass
class NA_Jax_Top_Skarner(Ratings):
pass
class NA_Jax_Top_Sona(Ratings):
pass
class NA_Jax_Top_Soraka(Ratings):
pass
class NA_Jax_Top_Swain(Ratings):
pass
class NA_Jax_Top_Syndra(Ratings):
pass
class NA_Jax_Top_TahmKench(Ratings):
pass
class NA_Jax_Top_Taliyah(Ratings):
pass
class NA_Jax_Top_Talon(Ratings):
pass
class NA_Jax_Top_Taric(Ratings):
pass
class NA_Jax_Top_Teemo(Ratings):
pass
class NA_Jax_Top_Thresh(Ratings):
pass
class NA_Jax_Top_Tristana(Ratings):
pass
class NA_Jax_Top_Trundle(Ratings):
pass
class NA_Jax_Top_Tryndamere(Ratings):
pass
class NA_Jax_Top_TwistedFate(Ratings):
pass
class NA_Jax_Top_Twitch(Ratings):
pass
class NA_Jax_Top_Udyr(Ratings):
pass
class NA_Jax_Top_Urgot(Ratings):
pass
class NA_Jax_Top_Varus(Ratings):
pass
class NA_Jax_Top_Vayne(Ratings):
pass
class NA_Jax_Top_Veigar(Ratings):
pass
class NA_Jax_Top_Velkoz(Ratings):
pass
class NA_Jax_Top_Vi(Ratings):
pass
class NA_Jax_Top_Viktor(Ratings):
pass
class NA_Jax_Top_Vladimir(Ratings):
pass
class NA_Jax_Top_Volibear(Ratings):
pass
class NA_Jax_Top_Warwick(Ratings):
pass
class NA_Jax_Top_Xayah(Ratings):
pass
class NA_Jax_Top_Xerath(Ratings):
pass
class NA_Jax_Top_XinZhao(Ratings):
pass
class NA_Jax_Top_Yasuo(Ratings):
pass
class NA_Jax_Top_Yorick(Ratings):
pass
class NA_Jax_Top_Zac(Ratings):
pass
class NA_Jax_Top_Zed(Ratings):
pass
class NA_Jax_Top_Ziggs(Ratings):
pass
class NA_Jax_Top_Zilean(Ratings):
pass
class NA_Jax_Top_Zyra(Ratings):
pass
| 14.702638 | 46 | 0.750938 | 972 | 6,131 | 4.3107 | 0.151235 | 0.230549 | 0.329356 | 0.428162 | 0.784726 | 0.784726 | 0 | 0 | 0 | 0 | 0 | 0 | 0.18121 | 6,131 | 416 | 47 | 14.737981 | 0.834661 | 0 | 0 | 0.498195 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.498195 | 0.00361 | 0 | 0.501805 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
5664dffc2f64784558323ecb5e805a50527ea1be | 4,133 | py | Python | coindeblend/models/decompnet.py | aboucaud/deblend | 59b950d7de82814a42671e22497f87f3653942f6 | [
"BSD-3-Clause"
] | 3 | 2021-09-03T10:10:03.000Z | 2021-09-03T20:01:03.000Z | coindeblend/models/decompnet.py | aboucaud/deblend | 59b950d7de82814a42671e22497f87f3653942f6 | [
"BSD-3-Clause"
] | 3 | 2021-08-25T15:47:28.000Z | 2022-02-10T00:19:44.000Z | coindeblend/models/decompnet.py | aboucaud/deblend | 59b950d7de82814a42671e22497f87f3653942f6 | [
"BSD-3-Clause"
] | 2 | 2020-09-28T18:35:59.000Z | 2020-10-01T14:08:10.000Z | from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import Conv2DTranspose
from keras.layers import Activation
from keras.layers import BatchNormalization
from keras.layers import SimpleRNN
from keras.layers import Reshape
from keras.layers import Flatten
from keras.backend import is_keras_tensor
from keras.engine.topology import get_source_inputs
__all__ = ['category_decomposition_net', 'instance_decomposition_net']
def category_decomposition_net(input_tensor=None, input_shape=None):
"""
"""
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# conv(1,32,5,2)
x = Conv2D(32, (5, 5), strides=2)(img_input)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# conv(32,32,5,2)
x = Conv2D(32, (5, 5), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# conv(32,64,3,2)
x = Conv2D(64, (3, 3), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# conv(64,64,3,1)
x = Conv2D(64, (3, 3), strides=1)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# conv(64,64,3,2)
x = Conv2D(64, (3, 3), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# conv(64,64,3,1)
x = Conv2D(64, (3, 3), strides=1)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# deconv(64,64,3,2)
x = Conv2DTranspose(64, (3, 3), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# deconv(64,64,3,2)
x = Conv2DTranspose(64, (3, 3), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# deconv(64,64,3,2)
x = Conv2DTranspose(64, (3, 3), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# deconv(64,1,1,1)
x = Conv2DTranspose(1, (1, 1), strides=1)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
model = Model(inputs, x, name="DecompNet_part1")
return model
def instance_decomposition_net(input_tensor=None, input_shape=None):
"""
"""
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# conv(1,32,5,2)-
x = Conv2D(32, (5, 5), strides=2)(img_input)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# conv(32,32,3,2)
x = Conv2D(32, (3, 3), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# conv(32,32,3,2)
x = Conv2D(32, (3, 3), strides=2)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# x = Reshape()(x)
# rnn-fc
x = SimpleRNN(2048)(x)
x = Activation('relu')(x)
# rnn-fc
x = SimpleRNN(2048)(x)
x = Activation('relu')(x)
# deconv(32,32,3,1)
x = Conv2DTranspose(32, (3, 3), strides=1)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# deconv(32,32,3,1)
x = Conv2DTranspose(64, (3, 3), strides=1)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# deconv(32,32,5,1)
x = Conv2DTranspose(64, (5, 5), strides=1)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
# deconv(64,3,1,1)
x = Conv2DTranspose(1, (1, 1), strides=1)(x)
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
model = Model(inputs, x, name="DecompNet_part2")
return model
| 30.389706 | 70 | 0.618679 | 615 | 4,133 | 4.065041 | 0.104065 | 0.028 | 0.0288 | 0.1216 | 0.776 | 0.776 | 0.776 | 0.776 | 0.776 | 0.776 | 0 | 0.07065 | 0.222599 | 4,133 | 135 | 71 | 30.614815 | 0.707439 | 0.097266 | 0 | 0.789474 | 0 | 0 | 0.042865 | 0.014107 | 0 | 0 | 0 | 0 | 0 | 1 | 0.021053 | false | 0 | 0.115789 | 0 | 0.157895 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
566563ef97a6d08bc7337878a654d9c1d60986c9 | 3,971 | py | Python | backend/wod_board/tests/crud/test_round.py | GuillaumeOj/P13-WOD-Board | 36df7979e63c354507edb56eabdfc548b1964d08 | [
"MIT"
] | null | null | null | backend/wod_board/tests/crud/test_round.py | GuillaumeOj/P13-WOD-Board | 36df7979e63c354507edb56eabdfc548b1964d08 | [
"MIT"
] | 82 | 2021-01-17T18:12:23.000Z | 2021-06-12T21:46:49.000Z | backend/wod_board/tests/crud/test_round.py | GuillaumeOj/WodBoard | 1ac12404f6094909c9bf116bcaf6ccd60e85bc00 | [
"MIT"
] | null | null | null | import pytest
from wod_board import exceptions
from wod_board.crud import round_crud
from wod_board.models import wod_round
from wod_board.schemas import round_schemas
def test_create_round(db, db_wod, db_user):
assert db.query(wod_round.Round).count() == 0
wanted_round = round_schemas.RoundCreate(
position=1,
duration_seconds=60,
repetition=5,
wod_id=db_wod.id,
)
round_crud.create_round(db, wanted_round, db_user.id)
assert db.query(wod_round.Round).count() == 1
with pytest.raises(exceptions.DuplicatedRoundPosition):
round_crud.create_round(db, wanted_round, db_user.id)
assert db.query(wod_round.Round).count() == 1
with pytest.raises(exceptions.UserIsNotAuthor):
round_crud.create_round(db, wanted_round, 2)
assert db.query(wod_round.Round).count() == 1
wanted_round = round_schemas.RoundCreate(
position=1,
duration_seconds=60,
repetition=5,
wod_id=2,
)
with pytest.raises(exceptions.UnknownWod):
round_crud.create_round(db, wanted_round, db_user.id)
assert db.query(wod_round.Round).count() == 1
def test_update_round(db, db_round, db_user):
assert db.query(wod_round.Round).count() == 1
round_schema = round_schemas.RoundCreate(
position=db_round.position,
duration_seconds=60,
repetition=5,
wod_id=db_round.wod_id,
)
assert db_round.duration_seconds != round_schema.duration_seconds
assert db_round.repetition != round_schema.repetition
round_crud.update_round(db, round_schema, db_round.id, db_user.id)
db_round = db.get(wod_round.Round, db_round.id)
assert db_round.duration_seconds == round_schema.duration_seconds
assert db_round.repetition == round_schema.repetition
assert db.query(wod_round.Round).count() == 1
with pytest.raises(exceptions.UnknownRound):
round_crud.update_round(db, round_schema, 2, db_user.id)
assert db.query(wod_round.Round).count() == 1
with pytest.raises(exceptions.UserIsNotAuthor):
round_crud.update_round(db, round_schema, db_round.id, 2)
assert db.query(wod_round.Round).count() == 1
round_schema = round_schemas.RoundCreate(
position=db_round.position,
duration_seconds=60,
repetition=5,
wod_id=2,
)
with pytest.raises(exceptions.UnknownWod):
round_crud.update_round(db, round_schema, db_round.id, db_user.id)
assert db.query(wod_round.Round).count() == 1
db.add(
wod_round.Round(
position=2, repetition=0, duration_seconds=0, wod_id=db_round.wod_id
)
)
db.commit()
assert db.query(wod_round.Round).count() == 2
round_schema = round_schemas.RoundCreate(
position=2,
duration_seconds=60,
repetition=5,
wod_id=db_round.wod_id,
)
with pytest.raises(exceptions.DuplicatedRoundPosition):
round_crud.update_round(db, round_schema, db_round.id, db_user.id)
assert db.query(wod_round.Round).count() == 2
def test_delete_round_by_id(db, db_round, db_user):
assert db.query(wod_round.Round).count() == 1
with pytest.raises(exceptions.UnknownRound):
round_crud.delete_round_by_id(db, 2, db_user.id)
assert db.query(wod_round.Round).count() == 1
with pytest.raises(exceptions.UserIsNotAuthor):
round_crud.delete_round_by_id(db, db_round.id, 2)
assert db.query(wod_round.Round).count() == 1
assert round_crud.delete_round_by_id(db, db_round.id, db_user.id)
assert db.query(wod_round.Round).count() == 0
def test_get_rounds_by_wod_id(db, db_round):
assert db.query(wod_round.Round).count() == 1
rounds = round_crud.get_rounds_by_wod_id(db, db_round.id)
assert len(rounds) == 1
assert db.query(wod_round.Round).count() == 1
rounds = round_crud.get_rounds_by_wod_id(db, 2)
assert len(rounds) == 0
assert db.query(wod_round.Round).count() == 1
| 33.091667 | 80 | 0.699572 | 578 | 3,971 | 4.532872 | 0.077855 | 0.069466 | 0.104198 | 0.116031 | 0.871756 | 0.870992 | 0.848473 | 0.790458 | 0.751908 | 0.725191 | 0 | 0.015528 | 0.189121 | 3,971 | 119 | 81 | 33.369748 | 0.798137 | 0 | 0 | 0.606383 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.276596 | 1 | 0.042553 | false | 0 | 0.053191 | 0 | 0.095745 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3b24934390e1da7222f8dc87495bc01bac638cde | 93 | py | Python | name_format.py | godontop/python-work | ea22e0df8b0b17605f5a434e556a388d1f75aa47 | [
"MIT"
] | null | null | null | name_format.py | godontop/python-work | ea22e0df8b0b17605f5a434e556a388d1f75aa47 | [
"MIT"
] | null | null | null | name_format.py | godontop/python-work | ea22e0df8b0b17605f5a434e556a388d1f75aa47 | [
"MIT"
] | null | null | null | name = "As Pros"
print(name.lower())
print(name.upper())
name = "as pros"
print(name.title()) | 18.6 | 19 | 0.666667 | 15 | 93 | 4.133333 | 0.466667 | 0.435484 | 0.322581 | 0.483871 | 0.612903 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107527 | 93 | 5 | 20 | 18.6 | 0.746988 | 0 | 0 | 0 | 0 | 0 | 0.148936 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.6 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
3b33e747a2d85ec244e2ab632721a6c198e1126d | 29 | py | Python | countop/__init__.py | ankur-gupta/countop | a10d4a0361477be5d5bc4957952dd47965f7787e | [
"MIT"
] | null | null | null | countop/__init__.py | ankur-gupta/countop | a10d4a0361477be5d5bc4957952dd47965f7787e | [
"MIT"
] | null | null | null | countop/__init__.py | ankur-gupta/countop | a10d4a0361477be5d5bc4957952dd47965f7787e | [
"MIT"
] | null | null | null | from .integer import Integer
| 14.5 | 28 | 0.827586 | 4 | 29 | 6 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 29 | 1 | 29 | 29 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3b5dba1a42286afce081b78cd6cc62b8085a1be7 | 15,572 | py | Python | Breathing/CNN_1D/utils.py | DresvyanskiyDenis/compare20_MB | ea5ecb1bad33af284006b5870f971cff4953aa7a | [
"RSA-MD"
] | 2 | 2021-05-11T22:31:45.000Z | 2022-02-07T14:28:56.000Z | Breathing/CNN_1D/utils.py | DresvyanskiyDenis/compare20_MB | ea5ecb1bad33af284006b5870f971cff4953aa7a | [
"RSA-MD"
] | 1 | 2020-10-15T19:32:10.000Z | 2020-10-15T19:32:10.000Z | Breathing/CNN_1D/utils.py | DresvyanskiyDenis/compare20_MB | ea5ecb1bad33af284006b5870f971cff4953aa7a | [
"RSA-MD"
] | 2 | 2020-09-28T00:07:05.000Z | 2021-01-23T05:56:40.000Z | import os
import tensorflow as tf
import pandas as pd
import numpy as np
import scipy
from keras import Sequential
from keras.layers import Conv1D, MaxPool1D, LSTM, Dense, Dropout, Flatten, TimeDistributed
from keras import backend as K
from matplotlib import pyplot as plt
from scipy.io import wavfile
from scipy.stats import pearsonr
from sklearn.preprocessing import StandardScaler
def load_data(path_to_data, path_to_labels, prefix):
# labels
labels=pd.read_csv(path_to_labels+'labels.csv', sep=',')
labels = labels.loc[labels['filename'].str.contains(prefix)]
if not prefix=='test':
labels['upper_belt']=labels['upper_belt'].astype('float32')
else:
labels['upper_belt']=0
labels['upper_belt']=labels['upper_belt'].astype('float32')
# data
fs, example = wavfile.read(path_to_data + labels.iloc[0, 0])
result_data = np.zeros(shape=(np.unique(labels['filename']).shape[0], example.shape[0]))
files=np.unique(labels['filename'])
filename_dict={}
for i in range(len(files)):
frame_rate, data = wavfile.read(path_to_data+files[i])
result_data[i]=data
filename_dict[i]=files[i]
return result_data, labels, filename_dict, frame_rate
def how_many_windows_do_i_need(length_sequence, window_size, step):
start_idx=0
counter=0
while True:
if start_idx+window_size>length_sequence:
break
start_idx+=step
counter+=1
if start_idx!=length_sequence:
counter+=1
return counter
def prepare_data(data, labels, class_to_filename_dict, frame_rate, size_window, step_for_window):
label_rate=25 # 25 Hz label rate
num_windows=how_many_windows_do_i_need(data.shape[1],size_window, step_for_window)
new_data=np.zeros(shape=(data.shape[0],int(num_windows),size_window))
length_of_label_window=int(size_window/frame_rate*label_rate)
step_of_label_window=int(length_of_label_window*(step_for_window/size_window))
new_labels=np.zeros(shape=(np.unique(labels['filename']).shape[0], int(num_windows),length_of_label_window ))
new_labels_timesteps=np.zeros(shape=new_labels.shape)
for instance_idx in range(data.shape[0]):
start_idx_data=0
start_idx_label=0
temp_labels=labels[labels['filename']==class_to_filename_dict[instance_idx]]
temp_labels=temp_labels.drop(columns=['filename'])
temp_labels=temp_labels.values
for windows_idx in range(num_windows-1):
new_data[instance_idx,windows_idx]=data[instance_idx,start_idx_data:start_idx_data+size_window]
new_labels[instance_idx,windows_idx]=temp_labels[start_idx_label:start_idx_label+length_of_label_window, 1]
new_labels_timesteps[instance_idx, windows_idx]=temp_labels[start_idx_label:start_idx_label+length_of_label_window, 0]
start_idx_data+=step_for_window
start_idx_label+=step_of_label_window
if start_idx_data+size_window>=data.shape[1]:
new_data[instance_idx,num_windows-1]=data[instance_idx, data.shape[1]-size_window:data.shape[1]]
new_labels[instance_idx, num_windows-1]=temp_labels[temp_labels.shape[0]-length_of_label_window:temp_labels.shape[0],1]
new_labels_timesteps[instance_idx, num_windows-1]=temp_labels[temp_labels.shape[0]-length_of_label_window:temp_labels.shape[0],0]
else:
new_data[instance_idx,num_windows-1]=data[instance_idx,start_idx_data:start_idx_data+size_window]
new_labels[instance_idx,num_windows-1]=temp_labels[start_idx_label:start_idx_label+length_of_label_window, 1]
new_labels_timesteps[instance_idx, num_windows-1]=temp_labels[start_idx_label:start_idx_label+length_of_label_window, 0]
start_idx_data+=step_for_window
start_idx_label+=step_of_label_window
return new_data, new_labels, new_labels_timesteps
def instance_normalization(data):
for instance_idx in range(data.shape[0]):
scaler=StandardScaler()
temp_data=data[instance_idx].reshape((-1,1))
temp_data=scaler.fit_transform(temp_data)
temp_data=temp_data.reshape((data.shape[1:]))
data[instance_idx]=temp_data
return data
def sample_standart_normalization(data, scaler=None):
tmp_shape=data.shape
tmp_data=data.reshape((-1,1))
if scaler==None:
scaler=StandardScaler()
tmp_data=scaler.fit_transform(tmp_data)
else:
tmp_data=scaler.transform(tmp_data)
data=tmp_data.reshape(tmp_shape)
return data
def sample_minmax_normalization(data, min=None, max=None):
result_shape=data.shape
tmp_data=data.reshape((-1))
if max==None:
max=np.max(tmp_data)
if min == None:
min=np.min(tmp_data)
tmp_data=2*(tmp_data-min)/(max-min)-1
data=tmp_data.reshape(result_shape)
return data, min, max
def create_model(input_shape):
model=tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(input_shape=input_shape, filters=64, kernel_size=10, strides=1, activation='relu', padding='same'))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.MaxPool1D(pool_size=10))
model.add(tf.keras.layers.Conv1D(filters=128, kernel_size=8, strides=1, activation='relu', padding='same'))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.MaxPool1D(pool_size=4))
model.add(tf.keras.layers.Conv1D(filters=256, kernel_size=6, strides=1, activation='relu', padding='same' ))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.MaxPool1D(pool_size=4))
model.add(tf.keras.layers.Conv1D(filters=256, kernel_size=5, strides=1, activation='relu', padding='same' ))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.AvgPool1D(pool_size=4))
model.add(tf.keras.layers.LSTM(256, return_sequences=True))
model.add(tf.keras.layers.LSTM(256, return_sequences=True))
model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1, activation='tanh')))
model.add(tf.keras.layers.Flatten())
print(model.summary())
return model
def identity_block(input_tensor, filters, block_number):
filter1, filter2, filter3 = filters
x = tf.keras.layers.Conv1D(filters=filter1, kernel_size=1, strides=1, activation=None, padding='same',
use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(1e-4))(input_tensor)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation(activation='relu')(x)
x = tf.keras.layers.Conv1D(filters=filter2, kernel_size=5, strides=1, activation=None, padding='same',
use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(1e-4))(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation(activation='relu')(x)
x = tf.keras.layers.Conv1D(filters=filter3, kernel_size=1, strides=1, activation=None, padding='same',
use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(1e-4))(x)
x = tf.keras.layers.BatchNormalization(name='last_identity_bn_block_' + str(block_number))(x)
x = tf.keras.layers.add([x, input_tensor])
x = tf.keras.layers.Activation('relu')(x)
return x
def conv_block(input_tensor, filters, block_number):
filter1, filter2, filter3 = filters
x = tf.keras.layers.Conv1D(filters=filter1, kernel_size=1, strides=1, activation=None, padding='same',
use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(1e-4))(input_tensor)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation(activation='relu')(x)
x = tf.keras.layers.Conv1D(filters=filter2, kernel_size=5, strides=1, activation=None, padding='same',
use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(1e-4))(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation(activation='relu')(x)
x = tf.keras.layers.Conv1D(filters=filter3, kernel_size=1, strides=1, activation=None, padding='same',
use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(1e-4))(x)
x = tf.keras.layers.BatchNormalization(name='last_conv_bn_block_' + str(block_number))(x)
shortcut = tf.keras.layers.Conv1D(filters=filter3, kernel_size=1, strides=1, activation=None,
use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(1e-4))(input_tensor)
shortcut = tf.keras.layers.BatchNormalization(name='shortcut_bn_block_' + str(block_number))(shortcut)
x = tf.keras.layers.add([x, shortcut])
x = tf.keras.layers.Activation('relu')(x)
return x
def create_complex_model(input_shape):
input = tf.keras.layers.Input(shape=input_shape)
x = tf.keras.layers.Conv1D(filters=128, kernel_size=8, strides=1, activation=None, padding='same',
kernel_regularizer=tf.keras.regularizers.l2(1e-4))(input)
x = tf.keras.layers.BatchNormalization(name='last_conv_bn_block_1')(x)
x = tf.keras.layers.Activation(activation='relu')(x)
output_block1 = tf.keras.layers.MaxPool1D(pool_size=10)(x)
x = conv_block(output_block1, [64, 64, 256], 2)
x = identity_block(x, [64, 64, 256], 'identity_1')
x = identity_block(x, [64, 64, 256], 'identity_2')
output_block2 = tf.keras.layers.AvgPool1D(pool_size=8)(x)
x = conv_block(output_block2, [128, 128, 512], 3)
x = identity_block(x, [128, 128, 512], 'identity_3')
x = identity_block(x, [128, 128, 512], 'identity_4')
output_block3 = tf.keras.layers.AvgPool1D(pool_size=8)(x)
x = tf.keras.layers.LSTM(512, return_sequences=True)(output_block3)
x = tf.keras.layers.Dropout(0.3)(x)
x = tf.keras.layers.LSTM(256, return_sequences=True)(x)
x = tf.keras.layers.TimeDistributed(
tf.keras.layers.Dense(1, activation='tanh', kernel_regularizer=tf.keras.regularizers.l2(1e-4)))(x)
x = tf.keras.layers.Flatten()(x)
model = tf.keras.Model(inputs=[input], outputs=[x])
print(model.summary())
return model
def correlation_coefficient_loss(y_true, y_pred):
x=y_true
y=y_pred
mx=K.mean(x, axis=1, keepdims=True)
my=K.mean(y, axis=1, keepdims=True)
xm,ym=x-mx,y-my
r_num=K.sum(tf.multiply(xm, ym), axis=1)
sum_square_x=K.sum(K.square(xm), axis=1)
sum_square_y = K.sum(K.square(ym), axis=1)
sqrt_x = tf.sqrt(sum_square_x)
sqrt_y = tf.sqrt(sum_square_y)
r_den=tf.multiply(sqrt_x, sqrt_y)
result=tf.divide(r_num, r_den)
#tf.print('result:', result)
result=K.mean(result)
#tf.print('mean result:', result)
return 1 - result
def pearson_coef(y_true, y_pred):
return scipy.stats.pearsonr(y_true, y_pred)
def concatenate_prediction(true_values, predicted_values, timesteps_labels, class_dict):
predicted_values=predicted_values.reshape(timesteps_labels.shape)
tmp=np.zeros(shape=(true_values.shape[0],3))
result_predicted_values=pd.DataFrame(data=tmp, columns=true_values.columns, dtype='float32')
result_predicted_values['filename']=result_predicted_values['filename'].astype('str')
index_temp=0
for instance_idx in range(predicted_values.shape[0]):
timesteps=np.unique(timesteps_labels[instance_idx])
for timestep in timesteps:
# assignment for filename and timestep
result_predicted_values.iloc[index_temp,0]=class_dict[instance_idx]
result_predicted_values.iloc[index_temp,1]=timestep
# calculate mean of windows
result_predicted_values.iloc[index_temp,2]=np.mean(predicted_values[instance_idx,timesteps_labels[instance_idx]==timestep])
index_temp+=1
#print('concatenation...instance:', instance_idx, ' done')
return result_predicted_values
def load_test_data(path_to_data, path_to_labels, prefix):
# labels
labels = pd.read_csv(path_to_labels + 'labels.csv', sep=',')
labels = labels.loc[labels['filename'].str.contains(prefix)]
#labels.drop(columns=['upper_belt'], inplace=True)
# data
fs, example = wavfile.read(path_to_data + labels.iloc[0, 0])
result_data = np.zeros(shape=(np.unique(labels['filename']).shape[0], example.shape[0]))
files = np.unique(labels['filename'])
filename_dict = {}
for i in range(len(files)):
frame_rate, data = wavfile.read(path_to_data + files[i])
result_data[i] = data
filename_dict[i] = files[i]
return result_data, labels, filename_dict, frame_rate
def prepare_test_data(data, labels, class_to_filename_dict, frame_rate, size_window, step_for_window):
label_rate=25 # 25 Hz label rate
num_windows=how_many_windows_do_i_need(data.shape[1],size_window, step_for_window)
new_data=np.zeros(shape=(data.shape[0],int(num_windows),size_window))
length_of_label_window=int(size_window/frame_rate*label_rate)
step_of_label_window=int(length_of_label_window*(step_for_window/size_window))
new_labels_timesteps=np.zeros(shape=(new_data.shape[0], int(num_windows),length_of_label_window ))
for instance_idx in range(data.shape[0]):
start_idx_data=0
start_idx_label=0
temp_labels=labels[labels['filename']==class_to_filename_dict[instance_idx]]
temp_labels=temp_labels.drop(columns=['filename','upper_belt'])
temp_labels=temp_labels.values.reshape((-1,1))
for windows_idx in range(num_windows-1):
new_data[instance_idx,windows_idx]=data[instance_idx,start_idx_data:start_idx_data+size_window]
new_labels_timesteps[instance_idx, windows_idx]=temp_labels[start_idx_label:start_idx_label+length_of_label_window, 0]
start_idx_data+=step_for_window
start_idx_label+=step_of_label_window
if start_idx_data+size_window>=data.shape[1]:
new_data[instance_idx,num_windows-1]=data[instance_idx, data.shape[1]-size_window:data.shape[1]]
new_labels_timesteps[instance_idx, num_windows-1]=temp_labels[temp_labels.shape[0]-length_of_label_window:temp_labels.shape[0],0]
else:
new_data[instance_idx,num_windows-1]=data[instance_idx,start_idx_data:start_idx_data+size_window]
new_labels_timesteps[instance_idx, num_windows-1]=temp_labels[start_idx_label:start_idx_label+length_of_label_window, 0]
start_idx_data+=step_for_window
start_idx_label+=step_of_label_window
return new_data, new_labels_timesteps
def concatenate_prediction_test(true_values, predicted_values, timesteps_labels, class_dict):
predicted_values=predicted_values.reshape(timesteps_labels.shape)
tmp=np.zeros(shape=(true_values.shape[0],3))
result_predicted_values=pd.DataFrame(data=tmp, columns=true_values.columns, dtype='float32')
result_predicted_values['filename']=result_predicted_values['filename'].astype('str')
index_temp=0
for instance_idx in range(predicted_values.shape[0]):
timesteps=np.unique(timesteps_labels[instance_idx])
for timestep in timesteps:
# assignment for filename and timestep
result_predicted_values.iloc[index_temp,0]=class_dict[instance_idx]
result_predicted_values.iloc[index_temp,1]=timestep
# calculate mean of windows
result_predicted_values.iloc[index_temp,2]=np.mean(predicted_values[instance_idx,timesteps_labels[instance_idx]==timestep])
index_temp+=1
#print('concatenation...instance:', instance_idx, ' done')
return result_predicted_values
| 50.558442 | 141 | 0.715965 | 2,291 | 15,572 | 4.601484 | 0.090354 | 0.041833 | 0.064124 | 0.037185 | 0.811136 | 0.794157 | 0.769019 | 0.765035 | 0.732024 | 0.700911 | 0 | 0.022995 | 0.159389 | 15,572 | 307 | 142 | 50.723127 | 0.782353 | 0.026137 | 0 | 0.545802 | 0 | 0 | 0.030103 | 0.001518 | 0 | 0 | 0 | 0 | 0 | 1 | 0.061069 | false | 0 | 0.045802 | 0.003817 | 0.167939 | 0.007634 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8e5cbb58b4a9b5ed4f0a70fe4145e96e7be7744e | 24 | py | Python | catkin_workspace/devel/lib/python2.7/dist-packages/project_weather_x/srv/__init__.py | NarendraPatwardhan/HuskyWeatherCast | 1ffadca23368a497ce7d3003806b548307bb7596 | [
"MIT"
] | null | null | null | catkin_workspace/devel/lib/python2.7/dist-packages/project_weather_x/srv/__init__.py | NarendraPatwardhan/HuskyWeatherCast | 1ffadca23368a497ce7d3003806b548307bb7596 | [
"MIT"
] | null | null | null | catkin_workspace/devel/lib/python2.7/dist-packages/project_weather_x/srv/__init__.py | NarendraPatwardhan/HuskyWeatherCast | 1ffadca23368a497ce7d3003806b548307bb7596 | [
"MIT"
] | null | null | null | from ._Weather import *
| 12 | 23 | 0.75 | 3 | 24 | 5.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 24 | 1 | 24 | 24 | 0.85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8e63e8ff085829e09acf95919794753db04b57ed | 48 | py | Python | spirl/configs/rl/kitchen/SAC/conf.py | kouroshHakha/fist | 328c098789239fd892e17edefd799fc1957ab637 | [
"BSD-3-Clause"
] | 8 | 2021-10-14T03:14:23.000Z | 2022-03-15T21:31:17.000Z | spirl/configs/rl/kitchen/SAC/conf.py | kouroshHakha/fist | 328c098789239fd892e17edefd799fc1957ab637 | [
"BSD-3-Clause"
] | null | null | null | spirl/configs/rl/kitchen/SAC/conf.py | kouroshHakha/fist | 328c098789239fd892e17edefd799fc1957ab637 | [
"BSD-3-Clause"
] | 1 | 2021-09-13T20:42:28.000Z | 2021-09-13T20:42:28.000Z | from spirl.configs.rl.kitchen.base_conf import * | 48 | 48 | 0.833333 | 8 | 48 | 4.875 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0625 | 48 | 1 | 48 | 48 | 0.866667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8eea466a3a63adce807bb2ba1e9dad76c53403e2 | 186 | py | Python | src/compas_fab/robots/configuration.py | yck011522/compas_fab | db7c8e54184dbbad9be5a818cf7ff814c95cf162 | [
"MIT"
] | 64 | 2019-08-07T07:19:06.000Z | 2022-03-22T16:48:23.000Z | src/compas_fab/robots/configuration.py | yck011522/compas_fab | db7c8e54184dbbad9be5a818cf7ff814c95cf162 | [
"MIT"
] | 228 | 2019-07-08T07:55:30.000Z | 2022-03-25T16:39:17.000Z | src/compas_fab/robots/configuration.py | yck011522/compas_fab | db7c8e54184dbbad9be5a818cf7ff814c95cf162 | [
"MIT"
] | 18 | 2019-08-04T16:42:37.000Z | 2022-01-12T18:36:06.000Z | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from compas.robots import Configuration
__all__ = [
'Configuration',
]
| 18.6 | 39 | 0.817204 | 21 | 186 | 6.380952 | 0.52381 | 0.223881 | 0.358209 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.145161 | 186 | 9 | 40 | 20.666667 | 0.842767 | 0 | 0 | 0 | 0 | 0 | 0.069892 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.571429 | 0 | 0.571429 | 0.142857 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
79d8b4355bc54d8df7ef53aeec89d2d71bfd2575 | 401 | py | Python | tests/socks_data.py | ccssrryy/libsocks | acc92f4a92ee2e07ba40d3d3055d36547b7cd2c0 | [
"MIT"
] | 1 | 2019-08-28T20:46:42.000Z | 2019-08-28T20:46:42.000Z | tests/socks_data.py | ccssrryy/libsocks | acc92f4a92ee2e07ba40d3d3055d36547b7cd2c0 | [
"MIT"
] | null | null | null | tests/socks_data.py | ccssrryy/libsocks | acc92f4a92ee2e07ba40d3d3055d36547b7cd2c0 | [
"MIT"
] | null | null | null | proxy_ip = "172.217.24.14"
proxy_port = 80
socks5_no_auth_resp = [b'\x05\x00',b'\x05\x00\x00', b'\x01',b'\x00\x00\x00\x00', b'\x06\xb5']
socks5_auth_resp = [b'\x05\x02',b'\x01\x00', b'\x05\x00\x00', b'\x01', b'\x00\x00\x00\x00', b'\x06\xb5']
socks5_auth_fail_resp = [b'\x05\x02',b'\x01\x01', b'\x05\x00\x00', b'\x01', b'\x00\x00\x00\x00', b'\x06\xb5']
socks4_resp = [b'\x00Z\x00\x00\x00\x00\x00\x00']
| 57.285714 | 109 | 0.640898 | 87 | 401 | 2.827586 | 0.252874 | 0.414634 | 0.365854 | 0.292683 | 0.715447 | 0.642276 | 0.520325 | 0.520325 | 0.520325 | 0.520325 | 0 | 0.286863 | 0.069825 | 401 | 6 | 110 | 66.833333 | 0.372654 | 0 | 0 | 0 | 0 | 0 | 0.503741 | 0.072319 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
79dbbef1d1b2a1efe991cd95e174b2983e05bb50 | 24 | py | Python | python/ROHSApy/ROHSApy/__init__.py | antoinemarchal/ROHSA | e36bc49b5bc60047634594d6c7ae42ef92291194 | [
"MIT"
] | 9 | 2018-11-01T15:46:41.000Z | 2020-11-12T19:02:27.000Z | python/ROHSApy/ROHSApy/__init__.py | antoinemarchal/ROHSA | e36bc49b5bc60047634594d6c7ae42ef92291194 | [
"MIT"
] | 2 | 2020-04-24T12:13:18.000Z | 2020-04-24T12:41:02.000Z | python/ROHSApy/ROHSApy/__init__.py | antoinemarchal/ROHSA | e36bc49b5bc60047634594d6c7ae42ef92291194 | [
"MIT"
] | 1 | 2019-10-08T07:58:34.000Z | 2019-10-08T07:58:34.000Z | from .core import ROHSA
| 12 | 23 | 0.791667 | 4 | 24 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 24 | 1 | 24 | 24 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8dac6dc6a96d84918be0a5d821349a7fc9777f85 | 296 | py | Python | tests/conftest.py | DanGroverUK/nukeuuid | 69d5804bebbbe60a8500337717013cef6baacf19 | [
"Apache-2.0"
] | 13 | 2017-03-16T10:56:52.000Z | 2021-02-14T06:33:19.000Z | tests/conftest.py | DanGroverUK/nukeuuid | 69d5804bebbbe60a8500337717013cef6baacf19 | [
"Apache-2.0"
] | 1 | 2018-10-29T19:01:38.000Z | 2018-10-30T09:57:23.000Z | tests/conftest.py | DanGroverUK/nukeuuid | 69d5804bebbbe60a8500337717013cef6baacf19 | [
"Apache-2.0"
] | 1 | 2018-10-23T09:32:56.000Z | 2018-10-23T09:32:56.000Z | # nukeuuid py.test configuration
import pytest
@pytest.fixture(scope='session')
def nuke():
import nuke
return nuke
@pytest.fixture(scope='session')
def nukeuuid():
import nukeuuid
return nukeuuid
@pytest.fixture(scope='session')
def uuid():
import uuid
return uuid
| 14.095238 | 32 | 0.699324 | 36 | 296 | 5.75 | 0.361111 | 0.188406 | 0.26087 | 0.362319 | 0.405797 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.192568 | 296 | 20 | 33 | 14.8 | 0.866109 | 0.101351 | 0 | 0.230769 | 0 | 0 | 0.079545 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.230769 | true | 0 | 0.307692 | 0 | 0.769231 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8dbedc376e2c41d113427eca306e82ac42fa233e | 130 | py | Python | creatorUtils/compat/os_.py | TheElementalOfDestruction/creatorUtils | 4f8b15cfc735069466337667c50f7af4f65dfbec | [
"MIT"
] | null | null | null | creatorUtils/compat/os_.py | TheElementalOfDestruction/creatorUtils | 4f8b15cfc735069466337667c50f7af4f65dfbec | [
"MIT"
] | null | null | null | creatorUtils/compat/os_.py | TheElementalOfDestruction/creatorUtils | 4f8b15cfc735069466337667c50f7af4f65dfbec | [
"MIT"
] | null | null | null | from os import *
import sys
if sys.version_info[0] >= 3:
if not hasattr(os, 'getcwdu'):
os.getcwdu = os.getcwd
| 18.571429 | 35 | 0.6 | 20 | 130 | 3.85 | 0.65 | 0.233766 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.021277 | 0.276923 | 130 | 6 | 36 | 21.666667 | 0.797872 | 0 | 0 | 0 | 0 | 0 | 0.056452 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
8dca04c496896841c3a9e35c136778f321575dac | 131 | py | Python | deuces3/__init__.py | MarshHawk/poker-with-bayes-service | f311bcdd77a748ef71b840248600fa973ea0ecc3 | [
"MIT"
] | null | null | null | deuces3/__init__.py | MarshHawk/poker-with-bayes-service | f311bcdd77a748ef71b840248600fa973ea0ecc3 | [
"MIT"
] | null | null | null | deuces3/__init__.py | MarshHawk/poker-with-bayes-service | f311bcdd77a748ef71b840248600fa973ea0ecc3 | [
"MIT"
] | null | null | null | from deuces3.card import Card
from deuces3.deck import Deck
from deuces3.evaluator import Evaluator
from deuces3.cards import cards | 32.75 | 39 | 0.854962 | 20 | 131 | 5.6 | 0.35 | 0.392857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.034483 | 0.114504 | 131 | 4 | 40 | 32.75 | 0.931034 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8dd0b8cd04b52e83c6c9614e1e6dafda5a823d00 | 27 | py | Python | Old Releases/Windows Releases/WINDOWS v1.1.0/Source Code/Windows Development/updateCheck.py | GokeyCoder/MandarinOS | 44a809292627480a295c3ae1dffa87804d6f8d83 | [
"MIT"
] | null | null | null | Old Releases/Windows Releases/WINDOWS v1.1.0/Source Code/Windows Development/updateCheck.py | GokeyCoder/MandarinOS | 44a809292627480a295c3ae1dffa87804d6f8d83 | [
"MIT"
] | null | null | null | Old Releases/Windows Releases/WINDOWS v1.1.0/Source Code/Windows Development/updateCheck.py | GokeyCoder/MandarinOS | 44a809292627480a295c3ae1dffa87804d6f8d83 | [
"MIT"
] | null | null | null | def checkForUpdate():
pass | 13.5 | 21 | 0.777778 | 3 | 27 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 27 | 2 | 22 | 13.5 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
8de074368e9d23a2217d305d920f83b63fb52d09 | 36 | py | Python | odooku/services/websocket/__init__.py | davejrv/import | 0dbca8f432d1a051a2bdb30c952cc26f1ffd74ae | [
"Apache-2.0"
] | 55 | 2017-09-11T06:48:39.000Z | 2022-03-31T18:14:46.000Z | odooku/services/websocket/__init__.py | davejrv/import | 0dbca8f432d1a051a2bdb30c952cc26f1ffd74ae | [
"Apache-2.0"
] | 4 | 2018-01-13T09:13:48.000Z | 2019-09-28T10:24:43.000Z | odooku/services/websocket/__init__.py | davejrv/import | 0dbca8f432d1a051a2bdb30c952cc26f1ffd74ae | [
"Apache-2.0"
] | 46 | 2017-12-30T22:31:45.000Z | 2022-02-17T05:35:55.000Z | from .server import WebSocketServer
| 18 | 35 | 0.861111 | 4 | 36 | 7.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 36 | 1 | 36 | 36 | 0.96875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5c0abccb2237680c7a1a7c7aa9bcf0ae1cf53e00 | 25 | py | Python | seaborn/flask/example/bindings/python_bindings/__init__.py | christensonb/Seaborn | adac2dfe60aabd754f93efd3b109213e5ee58772 | [
"MIT"
] | 9 | 2019-06-07T22:57:07.000Z | 2022-01-17T12:35:08.000Z | seaborn/flask/example/bindings/python_bindings/__init__.py | christensonb/Seaborn | adac2dfe60aabd754f93efd3b109213e5ee58772 | [
"MIT"
] | 4 | 2018-01-01T16:15:15.000Z | 2018-03-14T22:39:47.000Z | seaborn/flask/example/bindings/python_bindings/__init__.py | christensonb/Seaborn | adac2dfe60aabd754f93efd3b109213e5ee58772 | [
"MIT"
] | 4 | 2020-09-02T16:17:58.000Z | 2021-12-05T21:28:32.000Z | from .connection import * | 25 | 25 | 0.8 | 3 | 25 | 6.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12 | 25 | 1 | 25 | 25 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
308f0a4ff872086c0a171d9909b6e01b724a7a55 | 171 | py | Python | simp_py_examples/m5stack/ex012_hello.py | kcfkwok2003/Simp_py | f75e66da01b45dc8688dda602f8b33d4258f0c31 | [
"MIT"
] | null | null | null | simp_py_examples/m5stack/ex012_hello.py | kcfkwok2003/Simp_py | f75e66da01b45dc8688dda602f8b33d4258f0c31 | [
"MIT"
] | null | null | null | simp_py_examples/m5stack/ex012_hello.py | kcfkwok2003/Simp_py | f75e66da01b45dc8688dda602f8b33d4258f0c31 | [
"MIT"
] | null | null | null | from simp_py import tft
def hello():
global tft
tft.tft.clear()
tft.tft.text(0,0,'hello')
tft.tft.text(0,20,'world')
if __name__=='__main__':
hello()
| 17.1 | 30 | 0.619883 | 28 | 171 | 3.464286 | 0.571429 | 0.247423 | 0.206186 | 0.226804 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.036765 | 0.204678 | 171 | 9 | 31 | 19 | 0.676471 | 0 | 0 | 0 | 0 | 0 | 0.105263 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.125 | true | 0 | 0.125 | 0 | 0.25 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
30a7c58756bf7c536bf6e51c22c69b73b43ecdb3 | 255 | py | Python | cartography/__init__.py | babywyrm/cartography | ecec80a1db1fc926c4786b04830ca974a9af94de | [
"Apache-2.0"
] | null | null | null | cartography/__init__.py | babywyrm/cartography | ecec80a1db1fc926c4786b04830ca974a9af94de | [
"Apache-2.0"
] | null | null | null | cartography/__init__.py | babywyrm/cartography | ecec80a1db1fc926c4786b04830ca974a9af94de | [
"Apache-2.0"
] | null | null | null | __all__ = ['EXPERIMENTAL_NEO4J_4X_SUPPORT', 'patch_session_obj']
# experimental neo4j 4.x support
from cartography.experimental_neo4j_4x_support import EXPERIMENTAL_NEO4J_4X_SUPPORT
from cartography.experimental_neo4j_4x_support import patch_session_obj
| 42.5 | 83 | 0.882353 | 34 | 255 | 6.029412 | 0.382353 | 0.414634 | 0.370732 | 0.507317 | 0.526829 | 0.526829 | 0.526829 | 0.526829 | 0 | 0 | 0 | 0.042194 | 0.070588 | 255 | 5 | 84 | 51 | 0.822785 | 0.117647 | 0 | 0 | 0 | 0 | 0.206278 | 0.130045 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
30b10c0f223a593adfa4e70e8a4cec5e901ba0f3 | 49 | py | Python | bci_lib/Stages/Classification/__init__.py | SahandSadeghpour/bci_lib | 0fac693d6fae40956d9a716d466e1de0fdce8998 | [
"MIT"
] | null | null | null | bci_lib/Stages/Classification/__init__.py | SahandSadeghpour/bci_lib | 0fac693d6fae40956d9a716d466e1de0fdce8998 | [
"MIT"
] | null | null | null | bci_lib/Stages/Classification/__init__.py | SahandSadeghpour/bci_lib | 0fac693d6fae40956d9a716d466e1de0fdce8998 | [
"MIT"
] | null | null | null | from .ML import MLModel, CreateModel, Train, Test | 49 | 49 | 0.795918 | 7 | 49 | 5.571429 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122449 | 49 | 1 | 49 | 49 | 0.906977 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
30bd3712b804db7ed66b2f0339b9fb2eb5d60950 | 40 | py | Python | env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_scattergl.py | acrucetta/Chicago_COVI_WebApp | a37c9f492a20dcd625f8647067394617988de913 | [
"MIT",
"Unlicense"
] | 11,750 | 2015-10-12T07:03:39.000Z | 2022-03-31T20:43:15.000Z | env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_scattergl.py | acrucetta/Chicago_COVI_WebApp | a37c9f492a20dcd625f8647067394617988de913 | [
"MIT",
"Unlicense"
] | 2,951 | 2015-10-12T00:41:25.000Z | 2022-03-31T22:19:26.000Z | env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_scattergl.py | acrucetta/Chicago_COVI_WebApp | a37c9f492a20dcd625f8647067394617988de913 | [
"MIT",
"Unlicense"
] | 2,623 | 2015-10-15T14:40:27.000Z | 2022-03-28T16:05:50.000Z | from plotly.graph_objs import Scattergl
| 20 | 39 | 0.875 | 6 | 40 | 5.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 40 | 1 | 40 | 40 | 0.944444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
30da0ab6825195e41e31e051ee16a14360a97a2f | 28 | py | Python | cyclum/models/__init__.py | lshh125/cyclum | 4bd7f136680108d28e4d07e627cda7cd4a242e64 | [
"MIT"
] | 12 | 2020-03-01T09:15:45.000Z | 2021-10-03T07:58:48.000Z | cyclum/models/__init__.py | lshh125/cyclum | 4bd7f136680108d28e4d07e627cda7cd4a242e64 | [
"MIT"
] | 5 | 2020-11-13T18:38:18.000Z | 2021-12-17T18:47:32.000Z | cyclum/models/__init__.py | lshh125/cyclum | 4bd7f136680108d28e4d07e627cda7cd4a242e64 | [
"MIT"
] | 5 | 2020-03-21T01:51:44.000Z | 2022-03-15T11:08:59.000Z | from .ae import AutoEncoder
| 14 | 27 | 0.821429 | 4 | 28 | 5.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 28 | 1 | 28 | 28 | 0.958333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
30fe5d3ff0abef3674180d36acc644cf059ec300 | 120 | py | Python | torchseq/models/kl_divergence.py | tomhosking/torchseq | 1b08c16822a553ecb77b96289fb21eb0a13d9c6b | [
"Apache-2.0"
] | 17 | 2021-02-25T14:24:06.000Z | 2021-12-12T07:12:26.000Z | torchseq/models/kl_divergence.py | tomhosking/torchseq | 1b08c16822a553ecb77b96289fb21eb0a13d9c6b | [
"Apache-2.0"
] | null | null | null | torchseq/models/kl_divergence.py | tomhosking/torchseq | 1b08c16822a553ecb77b96289fb21eb0a13d9c6b | [
"Apache-2.0"
] | null | null | null | import torch
def gaussian_kl(mu, logvar):
return -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1)
| 20 | 75 | 0.633333 | 21 | 120 | 3.571429 | 0.761905 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05102 | 0.183333 | 120 | 5 | 76 | 24 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
30ff89036cd1373c79a6fec095302833b094e678 | 78 | py | Python | glosstools/__init__.py | Retr0327/glossing-tools | ab42b4941e1fe57d1b61ae5ff3c3df0138071e59 | [
"Apache-2.0"
] | null | null | null | glosstools/__init__.py | Retr0327/glossing-tools | ab42b4941e1fe57d1b61ae5ff3c3df0138071e59 | [
"Apache-2.0"
] | null | null | null | glosstools/__init__.py | Retr0327/glossing-tools | ab42b4941e1fe57d1b61ae5ff3c3df0138071e59 | [
"Apache-2.0"
] | null | null | null | from .gloss_adder import GlossAdder
from .gloss_replacer import GlossReplacer
| 26 | 41 | 0.871795 | 10 | 78 | 6.6 | 0.7 | 0.272727 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 78 | 2 | 42 | 39 | 0.942857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a50071ee485c03a9383601006535afc22434683d | 143 | py | Python | quetzal/app/redoc/__init__.py | OpenMindInnovation/quetzal | 3940dfe8e3d2a1060ec89ba4e575365563042bf9 | [
"BSD-3-Clause"
] | 2 | 2019-10-11T11:14:19.000Z | 2020-07-15T12:52:12.000Z | quetzal/app/redoc/__init__.py | OpenMindInnovation/quetzal | 3940dfe8e3d2a1060ec89ba4e575365563042bf9 | [
"BSD-3-Clause"
] | 5 | 2019-09-17T16:12:16.000Z | 2020-05-08T17:22:47.000Z | quetzal/app/redoc/__init__.py | OpenMindInnovation/quetzal | 3940dfe8e3d2a1060ec89ba4e575365563042bf9 | [
"BSD-3-Clause"
] | 1 | 2019-04-02T10:46:15.000Z | 2019-04-02T10:46:15.000Z | from flask import Blueprint
bp = Blueprint('redoc', __name__)
# Import routes to create them
from quetzal.app.redoc import routes # nopep8
| 17.875 | 46 | 0.762238 | 20 | 143 | 5.25 | 0.7 | 0.228571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008403 | 0.167832 | 143 | 7 | 47 | 20.428571 | 0.87395 | 0.244755 | 0 | 0 | 0 | 0 | 0.047619 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
6911e0d09c4c78426b306986be386fb0639c9f3a | 158 | py | Python | tests/test_data.py | mrghg/py12box | ab4a64d20d858f27ce8474d8d11c8af8d28f725e | [
"MIT"
] | 2 | 2021-03-15T09:21:48.000Z | 2021-03-17T11:37:54.000Z | tests/test_data.py | mrghg/py12box | ab4a64d20d858f27ce8474d8d11c8af8d28f725e | [
"MIT"
] | 15 | 2021-02-15T06:16:15.000Z | 2021-09-22T14:13:05.000Z | tests/test_data.py | mrghg/py12box | ab4a64d20d858f27ce8474d8d11c8af8d28f725e | [
"MIT"
] | null | null | null |
from py12box import get_data
from pathlib import Path
def test_data_path():
assert get_data("blah") == Path(__file__).parents[1] / "py12box/data/blah" | 19.75 | 78 | 0.734177 | 24 | 158 | 4.5 | 0.583333 | 0.12963 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.037037 | 0.14557 | 158 | 8 | 78 | 19.75 | 0.762963 | 0 | 0 | 0 | 0 | 0 | 0.133758 | 0 | 0 | 0 | 0 | 0 | 0.25 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6918f2efafb52cf650803a4c548df40cdb753fba | 132 | py | Python | powcoin/utils.py | jpthor/digital-cash | e81887ef490b8cdf65d9f75d253717aa8ea6dad3 | [
"MIT"
] | null | null | null | powcoin/utils.py | jpthor/digital-cash | e81887ef490b8cdf65d9f75d253717aa8ea6dad3 | [
"MIT"
] | null | null | null | powcoin/utils.py | jpthor/digital-cash | e81887ef490b8cdf65d9f75d253717aa8ea6dad3 | [
"MIT"
] | null | null | null | import pickle
def serialize(coin):
return pickle.dumps(coin)
def deserialize(serialized):
return pickle.loads(serialized)
| 16.5 | 35 | 0.757576 | 16 | 132 | 6.25 | 0.625 | 0.24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151515 | 132 | 7 | 36 | 18.857143 | 0.892857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0.2 | 0.4 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
69278ba35916e6acd129b8777efd1c1869b7e940 | 126 | py | Python | napari_points_io/__init__.py | maweigert/napari-points-io | f92d8d2bc7e2336f41259aca82db171169bb13be | [
"BSD-3-Clause"
] | null | null | null | napari_points_io/__init__.py | maweigert/napari-points-io | f92d8d2bc7e2336f41259aca82db171169bb13be | [
"BSD-3-Clause"
] | null | null | null | napari_points_io/__init__.py | maweigert/napari-points-io | f92d8d2bc7e2336f41259aca82db171169bb13be | [
"BSD-3-Clause"
] | null | null | null |
__version__ = "0.0.1"
from ._reader import napari_get_reader
from ._writer import napari_get_writer, napari_write_points
| 14 | 59 | 0.801587 | 19 | 126 | 4.684211 | 0.578947 | 0.269663 | 0.337079 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.027523 | 0.134921 | 126 | 8 | 60 | 15.75 | 0.788991 | 0 | 0 | 0 | 0 | 0 | 0.04065 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
694c2dcd787bc5fa929d15c0c58df3ed2789c338 | 87 | py | Python | pipely/engine/__init__.py | 5x12/pipely | 9d67bc35ede403c79cc624d2f51e15de7167754c | [
"MIT"
] | 9 | 2022-01-14T17:02:59.000Z | 2022-03-26T19:12:51.000Z | pipely/engine/__init__.py | 5x12/pipely | 9d67bc35ede403c79cc624d2f51e15de7167754c | [
"MIT"
] | 1 | 2022-03-11T19:42:32.000Z | 2022-03-24T16:36:23.000Z | pipely/engine/__init__.py | 5x12/pipely | 9d67bc35ede403c79cc624d2f51e15de7167754c | [
"MIT"
] | 2 | 2022-03-11T03:06:39.000Z | 2022-03-15T17:31:32.000Z | from .trigger_from_yaml import YamlTrigger
from .trigger_from_class import ClassTrigger | 43.5 | 44 | 0.896552 | 12 | 87 | 6.166667 | 0.583333 | 0.297297 | 0.405405 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08046 | 87 | 2 | 44 | 43.5 | 0.925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
15daaa8da7d212c74951bb8152dd7a13a6d0470a | 42 | py | Python | terra/preprocessing/__init__.py | VAW-SwissTerra/SwissTerra | c681ade9064fea6b035bb184280f33df5baebfad | [
"Apache-2.0"
] | 3 | 2020-11-14T22:31:25.000Z | 2022-02-20T19:21:57.000Z | terra/preprocessing/__init__.py | VAW-SwissTerra/SwissTerra | c681ade9064fea6b035bb184280f33df5baebfad | [
"Apache-2.0"
] | 1 | 2020-11-03T08:22:29.000Z | 2020-11-03T08:22:29.000Z | terra/preprocessing/__init__.py | VAW-SwissTerra/SwissTerra | c681ade9064fea6b035bb184280f33df5baebfad | [
"Apache-2.0"
] | null | null | null | from . import masks, image_meta, overview
| 21 | 41 | 0.785714 | 6 | 42 | 5.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 42 | 1 | 42 | 42 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
15e3e58e75ab0367b05f4b30df96050b08a86a96 | 24 | py | Python | igate/__init__.py | samwathegreat/igate_telem | 6185f6248a0ac2e30cbaf14927ab1ef1cedd4ac0 | [
"BSD-2-Clause"
] | 5 | 2020-08-26T08:18:48.000Z | 2022-02-06T16:37:59.000Z | igate/__init__.py | samwathegreat/igate_telem | 6185f6248a0ac2e30cbaf14927ab1ef1cedd4ac0 | [
"BSD-2-Clause"
] | 3 | 2022-02-05T23:04:28.000Z | 2022-02-07T04:58:48.000Z | igate/__init__.py | samwathegreat/igate_telem | 6185f6248a0ac2e30cbaf14927ab1ef1cedd4ac0 | [
"BSD-2-Clause"
] | 2 | 2021-11-04T14:23:37.000Z | 2022-02-06T00:09:27.000Z | from .telem import main
| 12 | 23 | 0.791667 | 4 | 24 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 24 | 1 | 24 | 24 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
15ea4d7f75900f7d75cb16225f86dae2cda514bc | 38 | py | Python | translator_train/worksheet.py | tooshort26/mt-kamayo-api | 75580d2225410d389486e4876a0d55dbb80bae55 | [
"MIT"
] | 12 | 2018-09-01T04:29:16.000Z | 2020-11-05T20:03:51.000Z | translator_train/worksheet.py | tooshort26/mt-kamayo-api | 75580d2225410d389486e4876a0d55dbb80bae55 | [
"MIT"
] | 6 | 2021-04-30T21:04:04.000Z | 2022-02-10T00:45:19.000Z | translator_train/worksheet.py | tooshort26/Py-MT-ML | 84330ebdfdbd7855c49854f9fb9af9ea3e90c234 | [
"MIT"
] | 5 | 2018-08-24T02:41:40.000Z | 2019-11-25T12:56:13.000Z | import keras
print(keras.__version__) | 12.666667 | 24 | 0.842105 | 5 | 38 | 5.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.078947 | 38 | 3 | 24 | 12.666667 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
c616b735fbf57bedebd76fc2dad20d2288cb008c | 22,565 | py | Python | tests/test_organization_client.py | bgroveben/launchkey-python | c102d76040221059e7b87d96496edb1be3824d3b | [
"MIT"
] | 1 | 2018-12-06T04:42:35.000Z | 2018-12-06T04:42:35.000Z | tests/test_organization_client.py | bgroveben/launchkey-python | c102d76040221059e7b87d96496edb1be3824d3b | [
"MIT"
] | 1 | 2018-12-11T22:31:03.000Z | 2018-12-11T22:31:03.000Z | tests/test_organization_client.py | bgroveben/launchkey-python | c102d76040221059e7b87d96496edb1be3824d3b | [
"MIT"
] | null | null | null | import unittest
from mock import MagicMock, ANY
from uuid import uuid4
from launchkey.clients import OrganizationClient
from launchkey.clients.organization import Directory
from launchkey.transports.base import APIResponse
from launchkey.exceptions import LaunchKeyAPIException, InvalidParameters, LastRemainingKey, PublicKeyDoesNotExist, \
InvalidPublicKey, PublicKeyAlreadyInUse, LastRemainingSDKKey, InvalidSDKKey, Forbidden
from datetime import datetime
import pytz
from .shared import SharedTests
from ddt import ddt, data
try:
from base64 import encodebytes as encodestring
except ImportError:
from base64 import encodestring
class TestOrganizationClient(SharedTests.Services):
def setUp(self):
client = OrganizationClient(uuid4(), MagicMock())
self.setup_client(client)
@ddt
class TestOrganizationClientDirectories(unittest.TestCase):
def setUp(self):
self._transport = MagicMock()
self._response = APIResponse({}, {}, 200)
self._transport.post.return_value = self._response
self._transport.get.return_value = self._response
self._transport.put.return_value = self._response
self._transport.delete.return_value = self._response
self._transport.patch.return_value = self._response
self._organization_client = OrganizationClient(uuid4(), self._transport)
def test_create_directory_success(self):
self._response.data = {"id": "2ee0bd0a-a493-4376-9ff3-5936bd7da67b"}
directory_id = self._organization_client.create_directory(ANY)
self.assertEqual(directory_id, "2ee0bd0a-a493-4376-9ff3-5936bd7da67b")
self._transport.post.assert_called_once()
def test_get_all_directories(self):
self._response.data = [
{
"id": "abe9ff82-b665-4dd5-97e6-06fc599bb9cc",
"service_ids": ["9d2038f8-8e46-4106-b4b7-94929e474ffc", "a2670521-d66c-4761-93c7-474537a6bd5f"],
"sdk_keys": ["a99db42d-0aab-4e7f-80a6-163b31ef9f31", "b9a949d1-ec5b-4395-b978-47fc7183ffce"],
"premium": True,
"name": "A Test Directory",
"android_key": "afb1ad83-09d5-427a-b097-e8aa982c4d6c",
"ios_certificate_fingerprint": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
"active": True
}
]
response = self._organization_client.get_all_directories()
self._transport.get.assert_called_once()
self.assertEqual(len(response), 1)
directory = response[0]
self.assertIsInstance(directory, Directory)
self.assertEqual(directory.id, "abe9ff82-b665-4dd5-97e6-06fc599bb9cc")
self.assertIsInstance(directory.service_ids, list)
self.assertIn("9d2038f8-8e46-4106-b4b7-94929e474ffc", directory.service_ids)
self.assertIn("a2670521-d66c-4761-93c7-474537a6bd5f", directory.service_ids)
self.assertIsInstance(directory.sdk_keys, list)
self.assertIn("a99db42d-0aab-4e7f-80a6-163b31ef9f31", directory.sdk_keys)
self.assertIn("b9a949d1-ec5b-4395-b978-47fc7183ffce", directory.sdk_keys)
self.assertEqual(directory.premium, True)
self.assertEqual(directory.name, "A Test Directory")
self.assertEqual(directory.android_key, "afb1ad83-09d5-427a-b097-e8aa982c4d6c")
self.assertEqual(directory.ios_certificate_fingerprint, "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz")
self.assertEqual(directory.active, True)
def test_get_directories(self):
self._response.data = [
{
"id": "abe9ff82-b665-4dd5-97e6-06fc599bb9cc",
"service_ids": ["9d2038f8-8e46-4106-b4b7-94929e474ffc", "a2670521-d66c-4761-93c7-474537a6bd5f"],
"sdk_keys": ["a99db42d-0aab-4e7f-80a6-163b31ef9f31", "b9a949d1-ec5b-4395-b978-47fc7183ffce"],
"premium": True,
"name": "A Test Directory",
"android_key": "afb1ad83-09d5-427a-b097-e8aa982c4d6c",
"ios_certificate_fingerprint": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
"active": True
}
]
response = self._organization_client.get_directories("760b2ae5-b44b-49ac-a83c-d3421b30936f")
self._transport.post.assert_called_once()
self.assertEqual(len(response), 1)
directory = response[0]
self.assertIsInstance(directory, Directory)
self.assertEqual(directory.id, "abe9ff82-b665-4dd5-97e6-06fc599bb9cc")
self.assertIsInstance(directory.service_ids, list)
self.assertIn("9d2038f8-8e46-4106-b4b7-94929e474ffc", directory.service_ids)
self.assertIn("a2670521-d66c-4761-93c7-474537a6bd5f", directory.service_ids)
self.assertIsInstance(directory.sdk_keys, list)
self.assertIn("a99db42d-0aab-4e7f-80a6-163b31ef9f31", directory.sdk_keys)
self.assertIn("b9a949d1-ec5b-4395-b978-47fc7183ffce", directory.sdk_keys)
self.assertEqual(directory.premium, True)
self.assertEqual(directory.name, "A Test Directory")
self.assertEqual(directory.android_key, "afb1ad83-09d5-427a-b097-e8aa982c4d6c")
self.assertEqual(directory.ios_certificate_fingerprint, "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz")
self.assertEqual(directory.active, True)
def test_get_directories_invalid_params(self):
self._transport.post.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.get_directories("c7d4ffcd-069d-4ea7-9994-03c25ce42bd8")
def test_get_directory(self):
self._response.data = [
{
"id": "abe9ff82-b665-4dd5-97e6-06fc599bb9cc",
"service_ids": ["9d2038f8-8e46-4106-b4b7-94929e474ffc", "a2670521-d66c-4761-93c7-474537a6bd5f"],
"sdk_keys": ["a99db42d-0aab-4e7f-80a6-163b31ef9f31", "b9a949d1-ec5b-4395-b978-47fc7183ffce"],
"premium": True,
"name": "A Test Directory",
"android_key": "afb1ad83-09d5-427a-b097-e8aa982c4d6c",
"ios_certificate_fingerprint": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
"active": True
}
]
directory = self._organization_client.get_directory(ANY)
self._transport.post.assert_called_once()
self.assertIsInstance(directory, Directory)
self.assertEqual(directory.id, "abe9ff82-b665-4dd5-97e6-06fc599bb9cc")
self.assertIsInstance(directory.service_ids, list)
self.assertIn("9d2038f8-8e46-4106-b4b7-94929e474ffc", directory.service_ids)
self.assertIn("a2670521-d66c-4761-93c7-474537a6bd5f", directory.service_ids)
self.assertIsInstance(directory.sdk_keys, list)
self.assertIn("a99db42d-0aab-4e7f-80a6-163b31ef9f31", directory.sdk_keys)
self.assertIn("b9a949d1-ec5b-4395-b978-47fc7183ffce", directory.sdk_keys)
self.assertEqual(directory.premium, True)
self.assertEqual(directory.name, "A Test Directory")
self.assertEqual(directory.android_key, "afb1ad83-09d5-427a-b097-e8aa982c4d6c")
self.assertEqual(directory.ios_certificate_fingerprint, "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz")
self.assertEqual(directory.active, True)
def test_get_directory_invalid_params(self):
self._transport.post.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.get_directory(ANY)
def test_update_directory_success(self):
self._organization_client.update_directory("683e9dea-5128-471e-8264-6f8f6ba522ab")
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "683e9dea-5128-471e-8264-6f8f6ba522ab"}, self._transport.patch.call_args)
def test_update_directory_ios_p12(self):
self._organization_client.update_directory("683e9dea-5128-471e-8264-6f8f6ba522ab", ios_p12=b'An iOS P12')
self._transport.patch.assert_called_once()
self.assertIn(
{
"directory_id": "683e9dea-5128-471e-8264-6f8f6ba522ab",
"ios_p12": encodestring(b'An iOS P12').decode('utf-8')
},
self._transport.patch.call_args
)
def test_update_directory_android_key(self):
self._organization_client.update_directory("683e9dea-5128-471e-8264-6f8f6ba522ab",
android_key="465e74df-13a0-4049-8f31-a9715cb8c12b")
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "683e9dea-5128-471e-8264-6f8f6ba522ab",
"android_key": "465e74df-13a0-4049-8f31-a9715cb8c12b"}, self._transport.patch.call_args)
def test_update_directory_active(self):
self._organization_client.update_directory("683e9dea-5128-471e-8264-6f8f6ba522ab", active=True)
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "683e9dea-5128-471e-8264-6f8f6ba522ab", "active": True},
self._transport.patch.call_args)
def test_update_directory_all(self):
self._organization_client.update_directory("683e9dea-5128-471e-8264-6f8f6ba522ab", ios_p12=b'An iOS P12',
android_key="465e74df-13a0-4049-8f31-a9715cb8c12b", active=True)
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "683e9dea-5128-471e-8264-6f8f6ba522ab",
"ios_p12": encodestring(b'An iOS P12').decode('utf-8'),
"android_key": "465e74df-13a0-4049-8f31-a9715cb8c12b",
"active": True},
self._transport.patch.call_args)
def test_update_directory_invalid_params(self):
self._transport.patch.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.update_directory(ANY)
def test_add_directory_public_key_success(self):
self._response.data = {"key_id": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz"}
key_id = self._organization_client.add_directory_public_key("5e49fc4c-ddcb-48db-8473-a5f996b85fbc",
"public-key")
self._transport.post.assert_called_once()
self.assertIn({"directory_id": "5e49fc4c-ddcb-48db-8473-a5f996b85fbc", "public_key": "public-key"},
self._transport.post.call_args)
self.assertEqual(key_id, "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz")
def test_add_directory_public_key_expires(self):
self._response.data = {"key_id": ANY}
self._organization_client.add_directory_public_key("5e49fc4c-ddcb-48db-8473-a5f996b85fbc", "public-key",
expires=datetime(year=2017, month=10, day=3, hour=22,
minute=50, second=15))
self._transport.post.assert_called_once()
self.assertIn({"directory_id": "5e49fc4c-ddcb-48db-8473-a5f996b85fbc", "public_key": "public-key",
"date_expires": "2017-10-03T22:50:15Z"},
self._transport.post.call_args)
def test_add_directory_public_key_active(self):
self._response.data = {"key_id": ANY}
self._organization_client.add_directory_public_key("5e49fc4c-ddcb-48db-8473-a5f996b85fbc", "public-key",
active=True)
self.assertIn({"directory_id": "5e49fc4c-ddcb-48db-8473-a5f996b85fbc", "public_key": "public-key",
"active": True}, self._transport.post.call_args)
def test_add_directory_public_key_all(self):
self._response.data = {"key_id": ANY}
self._organization_client.add_directory_public_key("5e49fc4c-ddcb-48db-8473-a5f996b85fbc", "public-key",
expires=datetime(year=2017, month=10, day=3, hour=22,
minute=50, second=15), active=True)
self._transport.post.assert_called_once()
self.assertIn({"directory_id": "5e49fc4c-ddcb-48db-8473-a5f996b85fbc", "public_key": "public-key",
"date_expires": "2017-10-03T22:50:15Z", "active": True},
self._transport.post.call_args)
def test_add_directory_public_key_invalid_params(self):
self._transport.post.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.add_directory_public_key(ANY, ANY)
def test_add_directory_public_key_invalid_public_key(self):
self._transport.post.side_effect = LaunchKeyAPIException({"error_code": "KEY-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidPublicKey):
self._organization_client.add_directory_public_key(ANY, ANY)
def test_add_directory_public_key_invalid_key_already_in_use(self):
self._transport.post.side_effect = LaunchKeyAPIException({"error_code": "KEY-002", "error_detail": ""}, 400)
with self.assertRaises(PublicKeyAlreadyInUse):
self._organization_client.add_directory_public_key(ANY, ANY)
def test_add_directory_public_key_forbidden(self):
self._transport.post.side_effect = LaunchKeyAPIException({}, 403)
with self.assertRaises(Forbidden):
self._organization_client.add_directory_public_key(ANY, ANY)
@data(True, False)
def test_get_directory_public_keys(self, active):
self._response.data = [
{
"id": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
"active": active,
"date_created": "2017-10-03T22:50:15Z",
"date_expires": "2018-10-03T22:50:15Z",
"public_key": "A Public Key"
}
]
public_keys = self._organization_client.get_directory_public_keys("a08eab76-4094-4d60-aca1-30efbab3179b")
self.assertEqual(len(public_keys), 1)
key = public_keys[0]
self.assertEqual(key.id, "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz")
self.assertEqual(key.active, active)
self.assertEqual(key.created, datetime(year=2017, month=10, day=3, hour=22, minute=50,
second=15, tzinfo=pytz.timezone("UTC")))
self.assertEqual(key.expires, datetime(year=2018, month=10, day=3, hour=22, minute=50,
second=15, tzinfo=pytz.timezone("UTC")))
self.assertEqual(key.public_key, "A Public Key")
def test_get_service_public_keys_invalid_params(self):
self._transport.post.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""},
400)
with self.assertRaises(InvalidParameters):
self._organization_client.get_directory_public_keys(ANY)
def test_get_service_public_keys_forbidden(self):
self._transport.post.side_effect = LaunchKeyAPIException({}, 403)
with self.assertRaises(Forbidden):
self._organization_client.get_directory_public_keys(ANY)
def test_remove_directory_public_key_success(self):
self._organization_client.remove_directory_public_key(ANY, ANY)
self._transport.delete.assert_called_once()
def test_remove_directory_invalid_params(self):
self._transport.delete.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.remove_directory_public_key(ANY, ANY)
def test_remove_directory_last_remaining_key(self):
self._transport.delete.side_effect = LaunchKeyAPIException({"error_code": "KEY-004", "error_detail": ""}, 400)
with self.assertRaises(LastRemainingKey):
self._organization_client.remove_directory_public_key(ANY, ANY)
def test_remove_directory_public_key_public_key_does_not_exist(self):
self._transport.delete.side_effect = LaunchKeyAPIException({"error_code": "KEY-003", "error_detail": ""},
400)
with self.assertRaises(PublicKeyDoesNotExist):
self._organization_client.remove_directory_public_key(ANY, ANY)
def test_remove_directory_public_key_public_key_forbidden(self):
self._transport.delete.side_effect = LaunchKeyAPIException({}, 403)
with self.assertRaises(Forbidden):
self._organization_client.remove_directory_public_key(ANY, ANY)
def test_update_directory_public_key(self):
self._organization_client.update_directory_public_key("1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz")
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"key_id": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz"},
self._transport.patch.call_args)
def test_update_directory_public_key_expires(self):
self._organization_client.update_directory_public_key("1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
expires=datetime(year=2017, month=10, day=3, hour=22,
minute=50, second=15))
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"key_id": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
"date_expires": "2017-10-03T22:50:15Z"},
self._transport.patch.call_args)
def test_update_directory_public_key_active(self):
self._organization_client.update_directory_public_key("1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
active=True)
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"key_id": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
"active": True},
self._transport.patch.call_args)
def test_update_directory_public_key_all(self):
self._organization_client.update_directory_public_key("1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
expires=datetime(year=2017, month=10, day=3, hour=22,
minute=50, second=15),
active=True)
self._transport.patch.assert_called_once()
self.assertIn({"directory_id": "1fa129ee-bb63-4705-a8cb-1c5be8000a0e",
"key_id": "ab:cd:ef:gh:ij:kl:mn:op:qr:st:uv:wx:yz",
"date_expires": "2017-10-03T22:50:15Z",
"active": True},
self._transport.patch.call_args)
def test_update_directory_public_key_public_key_invalid_params(self):
self._transport.patch.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.update_service_public_key(ANY, ANY)
def test_update_directory_public_key_public_key_does_not_exist(self):
self._transport.patch.side_effect = LaunchKeyAPIException({"error_code": "KEY-003", "error_detail": ""}, 400)
with self.assertRaises(PublicKeyDoesNotExist):
self._organization_client.update_service_public_key(ANY, ANY)
def test_update_directory_public_key_forbidden(self):
self._transport.patch.side_effect = LaunchKeyAPIException({}, 403)
with self.assertRaises(Forbidden):
self._organization_client.update_service_public_key(ANY, ANY)
def test_generate_and_add_directory_sdk_key_success(self):
self._response.data = {"sdk_key": "249b1df4-91f2-42e9-9599-da48f982404e"}
sdk_key = self._organization_client.generate_and_add_directory_sdk_key(
"b4ce1d35-63e3-4bd3-affc-dd073d391107"
)
self._transport.post.assert_called_once()
self.assertEqual(sdk_key, "249b1df4-91f2-42e9-9599-da48f982404e")
def test_generate_and_add_directory_sdk_key_invalid_params(self):
self._transport.post.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.generate_and_add_directory_sdk_key(ANY)
def test_remove_directory_sdk_key_success(self):
self._organization_client.remove_directory_sdk_key(ANY, ANY)
self._transport.delete.assert_called_once()
def test_remove_directory_sdk_key_invalid_params(self):
self._transport.delete.side_effect = LaunchKeyAPIException({"error_code": "ARG-001", "error_detail": ""}, 400)
with self.assertRaises(InvalidParameters):
self._organization_client.remove_directory_sdk_key(ANY, ANY)
def test_remove_directory_sdk_key_last_remaining_sdk_key(self):
self._transport.delete.side_effect = LaunchKeyAPIException({"error_code": "ORG-005", "error_detail": ""}, 400)
with self.assertRaises(LastRemainingSDKKey):
self._organization_client.remove_directory_sdk_key(ANY, ANY)
def test_remove_directory_sdk_key_invalid_sdk_key(self):
self._transport.delete.side_effect = LaunchKeyAPIException({"error_code": "ORG-006", "error_detail": ""}, 400)
with self.assertRaises(InvalidSDKKey):
self._organization_client.remove_directory_sdk_key(ANY, ANY)
| 57.417303 | 118 | 0.652781 | 2,551 | 22,565 | 5.508428 | 0.095649 | 0.054583 | 0.065756 | 0.010248 | 0.889126 | 0.874182 | 0.832124 | 0.807074 | 0.794976 | 0.768645 | 0 | 0.100953 | 0.232661 | 22,565 | 392 | 119 | 57.563776 | 0.710598 | 0 | 0 | 0.540698 | 0 | 0.052326 | 0.214093 | 0.151961 | 0 | 0 | 0 | 0 | 0.296512 | 1 | 0.125 | false | 0 | 0.040698 | 0 | 0.171512 | 0.017442 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c653d8f248d49c60c6fbb5cad4aa16caf2d0cf11 | 5,105 | py | Python | project/apps/main_site/migrations/0010_auto__add_field_sale_logo_thumb.py | buddyup/dashboard | 3c4b9ac32331b0a3bf0bb41acd31f5a4ce053dd8 | [
"BSD-2-Clause"
] | null | null | null | project/apps/main_site/migrations/0010_auto__add_field_sale_logo_thumb.py | buddyup/dashboard | 3c4b9ac32331b0a3bf0bb41acd31f5a4ce053dd8 | [
"BSD-2-Clause"
] | null | null | null | project/apps/main_site/migrations/0010_auto__add_field_sale_logo_thumb.py | buddyup/dashboard | 3c4b9ac32331b0a3bf0bb41acd31f5a4ce053dd8 | [
"BSD-2-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'Sale.logo_thumb'
db.add_column(u'main_site_sale', 'logo_thumb',
self.gf('django.db.models.fields.files.ImageField')(max_length=100, null=True, blank=True),
keep_default=False)
def backwards(self, orm):
# Deleting field 'Sale.logo_thumb'
db.delete_column(u'main_site_sale', 'logo_thumb')
models = {
u'main_site.datapoint': {
'Meta': {'object_name': 'DataPoint'},
'buddy_ratio': ('django.db.models.fields.FloatField', [], {}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'num_active_users': ('django.db.models.fields.IntegerField', [], {}),
'num_attended_one_event': ('django.db.models.fields.IntegerField', [], {}),
'num_authenticated': ('django.db.models.fields.IntegerField', [], {}),
'num_buddies': ('django.db.models.fields.IntegerField', [], {}),
'num_buddy_requests': ('django.db.models.fields.IntegerField', [], {}),
'num_filled_in_profile': ('django.db.models.fields.IntegerField', [], {}),
'num_hit_home_page': ('django.db.models.fields.IntegerField', [], {}),
'num_total_users': ('django.db.models.fields.IntegerField', [], {}),
'num_with_one_buddy': ('django.db.models.fields.IntegerField', [], {}),
'num_with_one_class': ('django.db.models.fields.IntegerField', [], {}),
'recorded_at': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2014, 11, 23, 0, 0)'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'main_site.milestone': {
'Meta': {'ordering': "('recorded_at',)", 'object_name': 'Milestone'},
'after_pic_1': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'after_pic_2': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'after_pic_3': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'after_pic_thumb_1': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'after_pic_thumb_2': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'after_pic_thumb_3': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'before_pic_1': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'before_pic_2': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'before_pic_3': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'before_pic_thumb_1': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'before_pic_thumb_2': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'before_pic_thumb_3': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'recorded_at': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2014, 11, 23, 0, 0)'}),
'type': ('django.db.models.fields.CharField', [], {'max_length': '20'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'main_site.sale': {
'Meta': {'object_name': 'Sale'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'logo': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'logo_thumb': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'recorded_at': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2014, 11, 23, 0, 0)'}),
'status': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
}
}
complete_apps = ['main_site'] | 70.902778 | 139 | 0.579432 | 579 | 5,105 | 4.924007 | 0.174439 | 0.112241 | 0.191512 | 0.273588 | 0.807436 | 0.782182 | 0.708523 | 0.64644 | 0.616977 | 0.616977 | 0 | 0.023942 | 0.19001 | 5,105 | 72 | 140 | 70.902778 | 0.665538 | 0.01665 | 0 | 0.180328 | 0 | 0 | 0.561292 | 0.310743 | 0 | 0 | 0 | 0 | 0 | 1 | 0.032787 | false | 0 | 0.065574 | 0 | 0.147541 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
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