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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
557b599b7cea9f42f3e8370380dfde22bbc3fa66 | 30 | py | Python | kelp/theano/__init__.py | KathrynJones1/kelp | 6c10c70cf1d9c5c59332a44d041d5790ae9b45a8 | [
"BSD-3-Clause"
] | null | null | null | kelp/theano/__init__.py | KathrynJones1/kelp | 6c10c70cf1d9c5c59332a44d041d5790ae9b45a8 | [
"BSD-3-Clause"
] | null | null | null | kelp/theano/__init__.py | KathrynJones1/kelp | 6c10c70cf1d9c5c59332a44d041d5790ae9b45a8 | [
"BSD-3-Clause"
] | null | null | null | from .theano import * # noqa
| 15 | 29 | 0.666667 | 4 | 30 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.233333 | 30 | 1 | 30 | 30 | 0.869565 | 0.133333 | 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 |
5585b5cb650ffd6eb7b70ba6cc05627cadf42bc4 | 39 | py | Python | qt_binder/qt/QtTest.py | Qt-Widgets/qt_binding-traits | 16c7a9ee439ff35d4d027c797ae1d05453a5fc06 | [
"BSD-3-Clause"
] | 15 | 2015-09-02T11:16:50.000Z | 2021-06-24T04:00:52.000Z | qt_binder/qt/QtTest.py | Qt-Widgets/qt_binding-traits | 16c7a9ee439ff35d4d027c797ae1d05453a5fc06 | [
"BSD-3-Clause"
] | 54 | 2015-09-02T10:45:49.000Z | 2020-11-30T13:01:05.000Z | qt_binder/qt/QtTest.py | Qt-Widgets/qt_binding-traits | 16c7a9ee439ff35d4d027c797ae1d05453a5fc06 | [
"BSD-3-Clause"
] | 3 | 2015-09-16T17:23:50.000Z | 2016-07-23T05:35:55.000Z | from pyface.qt.QtTest import * # noqa
| 19.5 | 38 | 0.717949 | 6 | 39 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.179487 | 39 | 1 | 39 | 39 | 0.875 | 0.102564 | 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 |
e98444049b2d96bdd606fa612dc7054e88cadb10 | 209 | py | Python | cognite/async_client/_api/assets.py | cognitedata/cognite-async | 256223b6b4f3e1630a8ab289ac4295a20d24437b | [
"Apache-2.0"
] | 1 | 2020-01-24T13:35:21.000Z | 2020-01-24T13:35:21.000Z | cognite/async_client/_api/assets.py | cognitedata/cognite-async | 256223b6b4f3e1630a8ab289ac4295a20d24437b | [
"Apache-2.0"
] | null | null | null | cognite/async_client/_api/assets.py | cognitedata/cognite-async | 256223b6b4f3e1630a8ab289ac4295a20d24437b | [
"Apache-2.0"
] | null | null | null | from cognite.async_client.utils import extends_class
from cognite.client._api.assets import AssetsAPI
@extends_class(extends=AssetsAPI)
class AssetsAPIExtensions:
"""Extensions to AssetsAPI"""
pass
| 20.9 | 52 | 0.799043 | 25 | 209 | 6.52 | 0.6 | 0.134969 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.124402 | 209 | 9 | 53 | 23.222222 | 0.89071 | 0.110048 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.4 | 0 | 0.6 | 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 |
75f754a8d0c820c0a0f7538bc761b9b61adb3b12 | 43,490 | py | Python | data/transcoder_evaluation_gfg/python/REMOVE_ARRAY_END_ELEMENT_MAXIMIZE_SUM_PRODUCT.py | mxl1n/CodeGen | e5101dd5c5e9c3720c70c80f78b18f13e118335a | [
"MIT"
] | 241 | 2021-07-20T08:35:20.000Z | 2022-03-31T02:39:08.000Z | data/transcoder_evaluation_gfg/python/REMOVE_ARRAY_END_ELEMENT_MAXIMIZE_SUM_PRODUCT.py | mxl1n/CodeGen | e5101dd5c5e9c3720c70c80f78b18f13e118335a | [
"MIT"
] | 49 | 2021-07-22T23:18:42.000Z | 2022-03-24T09:15:26.000Z | data/transcoder_evaluation_gfg/python/REMOVE_ARRAY_END_ELEMENT_MAXIMIZE_SUM_PRODUCT.py | mxl1n/CodeGen | e5101dd5c5e9c3720c70c80f78b18f13e118335a | [
"MIT"
] | 71 | 2021-07-21T05:17:52.000Z | 2022-03-29T23:49:28.000Z | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
def f_gold ( dp , a , low , high , turn ) :
if ( low == high ) :
return a [ low ] * turn
if ( dp [ low ] [ high ] != 0 ) :
return dp [ low ] [ high ]
dp [ low ] [ high ] = max ( a [ low ] * turn + f_gold ( dp , a , low + 1 , high , turn + 1 ) , a [ high ] * turn + f_gold ( dp , a , low , high - 1 , turn + 1 ) ) ;
return dp [ low ] [ high ]
#TOFILL
if __name__ == '__main__':
param = [
([[23, 37, 54, 57, 59, 75, 97], [9, 15, 34, 39, 80, 96, 99], [15, 25, 26, 31, 43, 47, 93], [22, 31, 37, 44, 54, 62, 91], [7, 19, 32, 56, 57, 70, 81], [16, 37, 49, 77, 81, 82, 85], [44, 48, 64, 74, 79, 89, 99]],[31, 50, 50, 68, 69, 80, 87],6,5,4,),
([[-24, -34, -86, -16, -34, 14, 76, 4, 18, 60, -4, -24, 46, -26, -74, 6, 50, -34, 8, -30, -62, 56, -78, -50, 76, -98, -64, -72, -76, 46, -70, 4, -92, -18, 10, -76, 78, -26], [-34, -30, -96, -4, 76, 48, -10, 96, -88, 96, 90, 40, -24, 68, -42, -48, -30, -32, -44, -50, -98, 34, -78, -52, -88, -38, 54, -64, -94, -48, -80, -2, -90, -14, -8, 90, -78, -36], [30, -80, -58, 48, -80, -78, 40, -48, -40, -84, 2, 44, 72, 6, 78, -84, -30, -70, 32, 86, 16, 68, 40, -36, 80, -46, 66, -92, 8, 72, -56, 58, -72, 44, 40, 6, 66, -66], [16, -72, -66, -30, 66, -94, 36, -34, -78, 14, -74, -54, 48, 6, -40, -40, -24, -6, 18, -20, -88, -8, 82, -56, -96, -32, 30, -22, 70, -4, 98, -40, -72, 66, -54, -60, 52, 66], [-28, -52, 90, -52, 12, 98, 42, -52, 38, -60, -28, 94, 86, -22, -62, 68, 12, 92, -82, 18, -2, 82, -28, 72, -78, -70, 40, -54, -24, -68, -74, -40, -32, 14, 88, 94, -46, -14], [-38, -30, 62, -52, 54, 56, 12, 32, -78, 24, 38, -82, 6, -64, -96, -56, -30, 62, -94, -26, -64, -38, 96, 72, 54, -56, 56, 82, 6, -30, 94, 80, -68, 18, 84, 58, -48, -34], [82, -44, 14, -26, -14, -92, 62, -48, -52, 26, -30, -76, -26, 28, 54, -16, -60, 16, -76, -90, 20, -8, 56, -86, 66, -84, 92, -52, 90, 30, 38, -2, 90, -50, 88, 44, -66, -80], [-22, 68, 62, -84, -76, -12, 82, 70, -58, 86, 20, -66, 96, -28, 6, 60, -90, 52, -28, 8, 34, 90, 38, 24, 76, -22, 6, 16, -46, -4, 84, -6, 6, 30, 50, 26, 8, -8], [34, -62, -26, 18, -14, 42, -50, 72, 16, -62, -76, 32, -20, 82, 8, 74, 60, -60, -16, -50, -38, -88, 68, -26, 66, -14, 64, 42, 98, 40, -56, 28, -96, 36, -82, -74, 38, -26], [-66, -58, -84, 16, -42, 4, -38, -6, -32, -98, 20, -96, 60, -38, 24, -8, -74, 52, 98, 52, -10, -24, -22, 78, -20, 58, -24, -98, -76, 0, -94, 6, 28, 42, 20, -36, 68, -68], [-20, 70, -80, 68, -26, -26, -22, 88, 86, 12, -98, -80, 2, -22, -2, 2, -52, -50, 30, 12, 74, 34, -14, -54, 70, 16, -76, -56, 16, -50, -14, -4, 30, 48, -14, 84, -34, 30], [68, 68, -86, 66, -64, 60, -28, 52, 14, -40, -98, 22, -30, 28, -48, -76, 66, 94, -28, 32, 88, 86, -76, -4, 68, -56, 76, -4, 36, 16, -4, 8, -44, -84, 74, 74, 96, -22], [-14, -88, -52, -72, -60, -50, 32, 86, 14, -26, 36, -84, 38, 80, -86, -64, 14, -96, 86, -52, -48, -16, -78, -66, -10, -24, 70, 22, 90, 46, -74, 36, -74, 2, 96, 6, 4, 34], [-34, 72, -40, 34, -30, 18, 54, -50, 0, 94, -62, 80, 4, 84, 10, 98, 56, -36, -94, 88, 10, -30, 90, -52, 14, -46, 30, 82, -66, 8, -98, 86, -90, 46, -44, -92, 22, 58], [70, -44, -28, -78, -62, -78, -96, -6, -92, -86, -82, 72, -50, 26, -4, 82, -42, 58, 28, -88, 98, -98, -14, 72, -24, 58, 72, -72, 6, -78, 34, -34, 58, -62, 78, -98, 0, 50], [84, 48, -82, -32, -22, 16, -34, -28, -76, 40, 26, 30, 70, 28, -64, -90, 70, -90, 82, 60, 10, -52, 0, 50, -38, -32, -18, 2, 48, 20, -96, 4, 62, -28, 28, -12, 50, -90], [64, 58, -34, 10, -44, -72, 62, 70, 60, 84, -12, -46, -82, -12, 80, 46, 44, -58, -18, 10, 44, 50, -60, -20, 82, -10, 18, -4, 48, 22, -14, 12, -76, -32, 8, -60, -54, -6], [22, -58, 58, -24, -58, -64, 62, -38, -36, 44, -82, 46, -78, 54, 96, 24, 84, 90, -2, -98, -74, 8, 44, -94, 84, 48, -2, 66, -44, 52, -42, -36, 34, -98, -6, 54, 26, 18], [-28, 30, -66, -14, -20, -44, -62, -20, 90, -92, -38, 64, 44, -60, 90, -60, -82, 36, -46, 52, -60, 26, 12, 80, -64, 92, -22, -68, -10, 54, -96, 44, 70, 10, 4, -4, -94, 66], [-70, 54, -32, 92, -26, -66, 28, -98, -14, -20, 40, -36, -6, -60, -36, -32, 26, 90, 34, -34, 82, 48, -82, -8, -86, -74, -58, -68, -68, -16, -26, -88, -6, -76, -12, -68, -98, -94], [14, -84, 90, 88, 80, -28, -46, 4, -4, 36, -56, -44, 68, 24, 24, 70, 36, -4, 58, -78, 14, -48, -46, 58, -44, -66, 72, -36, 84, 70, -26, 72, 76, 30, -30, 92, 4, -40], [-24, -28, 0, -44, -48, 74, 76, 50, -88, 36, -24, 62, -34, 82, -20, 38, -76, 16, -70, 92, -82, 56, 72, 78, 40, 52, -52, -38, 36, -44, 92, 46, -60, -54, 58, 96, 74, -18], [70, -30, -62, -74, -88, -92, 72, -42, 34, 76, -4, -94, 22, -82, 56, 2, 44, -64, -88, -20, 96, 0, -12, -20, -40, -56, -8, 18, -8, 18, 98, 28, 50, -14, 72, 50, 4, 38], [72, -66, 16, -44, 94, 10, 60, 96, 24, 12, 92, 30, 2, 64, 4, 58, 74, -24, -96, -52, 72, 10, -2, -18, -74, -2, 70, -6, -60, 72, -32, 34, -78, -10, -2, -30, 54, 42], [80, 92, 18, 54, -42, 50, -62, 76, 94, 38, 84, 78, 44, 98, 78, -54, -36, -80, 62, 14, 24, 86, 52, 4, 78, 96, 20, 58, -64, -42, 20, -98, -90, 6, 30, 56, -66, -34], [-86, 60, -62, -32, -12, 92, -54, -2, 8, 58, 68, 42, -46, -8, 82, -28, -96, 62, 46, -12, 0, 88, 82, -26, -42, 36, 46, -46, 54, -58, -58, -62, 32, -48, -60, 86, 4, 56], [88, -36, 4, 60, 24, 6, 72, 58, 44, 14, -40, -64, 36, -92, 36, -56, -78, 86, 80, 80, -94, 64, -4, -2, 86, 26, -98, -56, 62, -56, -18, 40, 12, 26, 50, 72, -16, -58], [-88, 4, -66, 64, 42, 94, 54, -38, 8, -18, -8, 88, 46, 42, 64, -88, 94, 58, -50, 26, 38, 92, -66, 82, 8, 38, -92, -32, 50, -44, -88, -6, 34, 12, 66, 54, -52, -86], [20, -90, 2, -94, -76, -28, -76, 44, -12, -56, 50, 4, 34, -88, 46, 2, 60, 52, 22, 98, -84, 6, -26, -90, -4, 48, -66, -42, 58, -22, 30, -22, -82, -50, 42, 84, 94, 4], [-56, 4, -4, 54, 64, 82, 88, 8, 50, 66, -2, 80, -4, 12, 24, -56, -52, 80, 66, 82, -66, 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62, 86, 66, 53, 64, 73, 86, 8, 20, 8, 3, 46, 51, 11, 53, 50, 57, 26, 93, 80, 41, 34, 70, 82, 82], [77, 1, 52, 73, 66, 59, 96, 50, 60, 63, 38, 44, 26, 95, 62, 99, 37, 34, 8, 44, 31, 10, 82, 75, 82, 92, 54, 61, 3, 52, 55, 7, 48, 22, 43, 71, 50, 96, 27, 56, 44, 66, 48, 25, 27, 22], [5, 86, 67, 70, 46, 7, 43, 52, 7, 19, 85, 27, 99, 55, 1, 48, 16, 41, 2, 47, 59, 51, 21, 19, 80, 80, 8, 41, 51, 75, 74, 49, 54, 48, 73, 15, 69, 24, 96, 19, 97, 23, 28, 90, 60, 50], [76, 65, 97, 12, 56, 95, 81, 27, 85, 46, 42, 56, 77, 90, 55, 6, 91, 41, 81, 93, 62, 83, 56, 21, 31, 28, 10, 28, 47, 92, 29, 85, 9, 30, 94, 62, 88, 86, 52, 16, 30, 50, 47, 1, 51, 11], [83, 14, 96, 9, 59, 84, 27, 94, 5, 20, 93, 58, 99, 71, 9, 5, 78, 38, 97, 97, 42, 88, 50, 51, 28, 64, 62, 32, 22, 50, 18, 57, 12, 61, 86, 72, 35, 53, 64, 42, 90, 32, 46, 84, 82, 65], [28, 99, 16, 15, 46, 15, 57, 32, 82, 90, 2, 29, 28, 8, 41, 33, 74, 61, 87, 64, 7, 51, 79, 30, 70, 33, 88, 9, 24, 7, 61, 22, 5, 12, 37, 19, 91, 38, 55, 23, 54, 62, 82, 8, 44, 73], [30, 77, 76, 3, 41, 88, 95, 36, 78, 76, 33, 86, 54, 38, 92, 36, 65, 99, 67, 8, 72, 33, 71, 88, 8, 63, 43, 89, 57, 6, 20, 74, 52, 50, 61, 66, 52, 3, 3, 60, 28, 6, 90, 51, 60, 15], [32, 86, 94, 46, 87, 40, 20, 75, 67, 86, 63, 63, 48, 42, 81, 69, 30, 11, 45, 18, 58, 68, 58, 79, 95, 51, 81, 1, 88, 58, 49, 75, 89, 60, 52, 24, 80, 70, 47, 17, 45, 94, 69, 17, 11, 97]],[19, 39, 31, 50, 61, 70, 43, 64, 45, 82, 49, 80, 21, 37, 96, 30, 42, 82, 96, 30, 22, 26, 42, 88, 57, 12, 59, 58, 83, 64, 66, 2, 37, 60, 5, 76, 20, 81, 10, 57, 70, 74, 65, 72, 15, 84],34,25,32,)
]
n_success = 0
for i, parameters_set in enumerate(param):
if f_filled(*parameters_set) == f_gold(*parameters_set):
n_success+=1
print("#Results: %i, %i" % (n_success, len(param))) | 1,242.571429 | 10,917 | 0.420327 | 11,233 | 43,490 | 1.625657 | 0.013354 | 0.14008 | 0.15722 | 0.168885 | 0.24807 | 0.214391 | 0.203932 | 0.203932 | 0.203932 | 0.203932 | 0 | 0.555057 | 0.261416 | 43,490 | 35 | 10,918 | 1,242.571429 | 0.013449 | 0.004254 | 0 | 0.08 | 0 | 0 | 0.000554 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.04 | false | 0 | 0 | 0 | 0.16 | 0.04 | 0 | 0 | 1 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 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 |
75ffabe74841749471c6964664714c8b52e67d19 | 141 | py | Python | pydataclust/UnscaleData.py | mattkjames7/pykmeans | df2cf01d985616d86b222cdaa57dc87567fe6751 | [
"MIT"
] | null | null | null | pydataclust/UnscaleData.py | mattkjames7/pykmeans | df2cf01d985616d86b222cdaa57dc87567fe6751 | [
"MIT"
] | null | null | null | pydataclust/UnscaleData.py | mattkjames7/pykmeans | df2cf01d985616d86b222cdaa57dc87567fe6751 | [
"MIT"
] | null | null | null | import numpy as np
def UnscaleData(data,scales,shifts):
'''
Returns data back to the original scales.
'''
return data*scales + shifts
| 15.666667 | 42 | 0.716312 | 20 | 141 | 5.05 | 0.75 | 0.19802 | 0.316832 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.184397 | 141 | 8 | 43 | 17.625 | 0.878261 | 0.29078 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 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 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f93908067f550a33a55465128bd613635a00e5f7 | 576 | py | Python | hagelslag/evaluation/__init__.py | stsaten6/hagelslag | 6b7d0779a0b0ac4bd26fbe4931b406fad1ef9f9e | [
"MIT"
] | 2 | 2018-04-16T07:12:48.000Z | 2019-04-09T11:57:08.000Z | hagelslag/evaluation/__init__.py | stsaten6/hagelslag | 6b7d0779a0b0ac4bd26fbe4931b406fad1ef9f9e | [
"MIT"
] | null | null | null | hagelslag/evaluation/__init__.py | stsaten6/hagelslag | 6b7d0779a0b0ac4bd26fbe4931b406fad1ef9f9e | [
"MIT"
] | null | null | null | from hagelslag.evaluation.ProbabilityMetrics import DistributedCRPS, DistributedReliability, DistributedROC
from hagelslag.evaluation.ContingencyTable import ContingencyTable
from hagelslag.evaluation.MetricPlotter import roc_curve, reliability_diagram, attributes_diagram, performance_diagram
from hagelslag.evaluation.GridEvaluator import GridEvaluator
from hagelslag.evaluation.NeighborEvaluator import NeighborEvaluator
from hagelslag.evaluation.ObjectEvaluator import ObjectEvaluator
from hagelslag.evaluation.MulticlassContingencyTable import MulticlassContingencyTable
| 72 | 118 | 0.909722 | 51 | 576 | 10.196078 | 0.392157 | 0.175 | 0.309615 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.057292 | 576 | 7 | 119 | 82.285714 | 0.957643 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 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 |
f940d20805cf2e5524660cf6fc23b6e6e8cba653 | 78 | py | Python | api/src/model/user/User.py | SamuelJansen/Courses | bc3c1c203cc7efb9bfec39dcca3bc33ea1150736 | [
"MIT"
] | null | null | null | api/src/model/user/User.py | SamuelJansen/Courses | bc3c1c203cc7efb9bfec39dcca3bc33ea1150736 | [
"MIT"
] | null | null | null | api/src/model/user/User.py | SamuelJansen/Courses | bc3c1c203cc7efb9bfec39dcca3bc33ea1150736 | [
"MIT"
] | null | null | null | from model.user import ApplicationUser
class User(ApplicationUser):
pass
| 15.6 | 38 | 0.794872 | 9 | 78 | 6.888889 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 78 | 4 | 39 | 19.5 | 0.939394 | 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 |
f9ba8776cacce0e7f37c7f22abc610b8f22d2b56 | 157 | py | Python | src/MeterReader/PythonClient/PythonClient.py | karthik-iyer/MeterReader | ff22e0466922b792109d5ba66e568bff8d2d3216 | [
"Apache-2.0"
] | null | null | null | src/MeterReader/PythonClient/PythonClient.py | karthik-iyer/MeterReader | ff22e0466922b792109d5ba66e568bff8d2d3216 | [
"Apache-2.0"
] | null | null | null | src/MeterReader/PythonClient/PythonClient.py | karthik-iyer/MeterReader | ff22e0466922b792109d5ba66e568bff8d2d3216 | [
"Apache-2.0"
] | null | null | null | import grpc
import MeterReader_pb2 as MeterReader
import MeterReader_pb2_grpc as MeterReaderService
def main():
print("Calling gRPC Service")
main()
| 14.272727 | 49 | 0.789809 | 20 | 157 | 6.05 | 0.55 | 0.280992 | 0.330579 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.015038 | 0.152866 | 157 | 10 | 50 | 15.7 | 0.894737 | 0 | 0 | 0 | 0 | 0 | 0.127389 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | true | 0 | 0.5 | 0 | 0.666667 | 0.166667 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ddb93a83b841e13aeac07cf9ca2f3c1162ce2187 | 40 | py | Python | app/bot/routes.py | NarayanAdithya/Portfolio2.0 | 691acbac1ad4220cb67c5e07a80bd401421f00d3 | [
"MIT"
] | null | null | null | app/bot/routes.py | NarayanAdithya/Portfolio2.0 | 691acbac1ad4220cb67c5e07a80bd401421f00d3 | [
"MIT"
] | null | null | null | app/bot/routes.py | NarayanAdithya/Portfolio2.0 | 691acbac1ad4220cb67c5e07a80bd401421f00d3 | [
"MIT"
] | null | null | null | from . import bot
from app import db_m
| 10 | 20 | 0.75 | 8 | 40 | 3.625 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.225 | 40 | 3 | 21 | 13.333333 | 0.935484 | 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 |
349aa87a62dc097de70b60330649192d355ddb6f | 232 | py | Python | core/models/__init__.py | zhuhanqing/ELight | 1d9e84d4a53b40c1b05119ff2912a01a69b32007 | [
"MIT"
] | null | null | null | core/models/__init__.py | zhuhanqing/ELight | 1d9e84d4a53b40c1b05119ff2912a01a69b32007 | [
"MIT"
] | null | null | null | core/models/__init__.py | zhuhanqing/ELight | 1d9e84d4a53b40c1b05119ff2912a01a69b32007 | [
"MIT"
] | null | null | null | '''
Author: Hanqing Zhu(hqzhu@utexas.edu)
Date: 2022-04-07 10:38:34
LastEditTime: 2022-04-08 23:57:19
LastEditors: Hanqing Zhu(hqzhu@utexas.edu)
Description:
FilePath: /projects/ELight/core/models/__init__.py
'''
from .vgg import * | 25.777778 | 50 | 0.75431 | 37 | 232 | 4.621622 | 0.810811 | 0.116959 | 0.175439 | 0.245614 | 0.280702 | 0 | 0 | 0 | 0 | 0 | 0 | 0.132701 | 0.090517 | 232 | 9 | 51 | 25.777778 | 0.677725 | 0.883621 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
34b00b8cf0ab01841ce4dc7d69eaa9baac2b2ea0 | 93 | py | Python | examples/base64-b64encode-standalone.py | lyvd/bandit4mal | b1ca9eb773ebed84d04cfeb589d028af532d1d11 | [
"Apache-2.0"
] | null | null | null | examples/base64-b64encode-standalone.py | lyvd/bandit4mal | b1ca9eb773ebed84d04cfeb589d028af532d1d11 | [
"Apache-2.0"
] | null | null | null | examples/base64-b64encode-standalone.py | lyvd/bandit4mal | b1ca9eb773ebed84d04cfeb589d028af532d1d11 | [
"Apache-2.0"
] | null | null | null | from base64 import b64encode
b64encode("=82cus2Ylh2YvQ3clVXclJ3Lw9GdukHelR2LvoDc0RHa"[::-1])
| 31 | 63 | 0.83871 | 7 | 93 | 11.142857 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 0.053763 | 93 | 2 | 64 | 46.5 | 0.704545 | 0 | 0 | 0 | 0 | 0 | 0.473118 | 0.473118 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 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 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
34e1dd5dff13a0ad17560bb006390d5f36e34532 | 177 | py | Python | class4pgm/service/__init__.py | mingen-pan/class4pgm | a4bbfef4bb70420c8ed35b2bef9b91ebabe28c3c | [
"MIT"
] | 1 | 2020-09-07T13:46:28.000Z | 2020-09-07T13:46:28.000Z | class4pgm/service/__init__.py | mingen-pan/class4pgm | a4bbfef4bb70420c8ed35b2bef9b91ebabe28c3c | [
"MIT"
] | null | null | null | class4pgm/service/__init__.py | mingen-pan/class4pgm | a4bbfef4bb70420c8ed35b2bef9b91ebabe28c3c | [
"MIT"
] | null | null | null | from .base_service import BaseService
from .neo4j_service import Neo4jService
from .redis_graph_service import RedisGraphService
from .service_generator import ServiceGenerator
| 35.4 | 50 | 0.887006 | 21 | 177 | 7.238095 | 0.571429 | 0.256579 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012422 | 0.090395 | 177 | 4 | 51 | 44.25 | 0.931677 | 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 |
9b95729afa34613a1d4884c9a846f7be2f425611 | 167 | py | Python | test.py | yumingwjobjob/Yuming_Wang_Yahoo_finance | 5c34ee73a87671c15a4cfb36210d9a986a1c511c | [
"MIT"
] | null | null | null | test.py | yumingwjobjob/Yuming_Wang_Yahoo_finance | 5c34ee73a87671c15a4cfb36210d9a986a1c511c | [
"MIT"
] | null | null | null | test.py | yumingwjobjob/Yuming_Wang_Yahoo_finance | 5c34ee73a87671c15a4cfb36210d9a986a1c511c | [
"MIT"
] | null | null | null | import notional_of_postion_calculation
my_test = notional_of_postion_calculation.notion_of_position_calculation()
print(my_test.get_notional_of_position(100,"TSLA"))
| 33.4 | 74 | 0.886228 | 24 | 167 | 5.583333 | 0.541667 | 0.223881 | 0.253731 | 0.41791 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.01875 | 0.041916 | 167 | 4 | 75 | 41.75 | 0.81875 | 0 | 0 | 0 | 0 | 0 | 0.023952 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0.333333 | 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 | 1 | 0 | 0 | 0 | 0 | 6 |
fd2e762af67ebc587ce9e0bf70370821e888cceb | 99 | py | Python | helpers/handle_creds.py | davidgibbons/Binance-volatility-trading-bot | ff03452f01f9de4ceb5a6ab1516a094356476449 | [
"MIT"
] | 1 | 2021-05-17T09:34:19.000Z | 2021-05-17T09:34:19.000Z | helpers/handle_creds.py | Tarantinooo/Binance-volatility-trading-bot | 314c48fbe2941b829e0ed6b989a34374ff03efff | [
"MIT"
] | null | null | null | helpers/handle_creds.py | Tarantinooo/Binance-volatility-trading-bot | 314c48fbe2941b829e0ed6b989a34374ff03efff | [
"MIT"
] | 1 | 2021-05-18T09:16:22.000Z | 2021-05-18T09:16:22.000Z | def load_correct_creds(creds):
return creds['prod']['access_key'], creds['prod']['secret_key']
| 33 | 67 | 0.707071 | 14 | 99 | 4.714286 | 0.642857 | 0.272727 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 99 | 2 | 68 | 49.5 | 0.733333 | 0 | 0 | 0 | 0 | 0 | 0.282828 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 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 |
fd30dc80f8b01f93a663e87d0674731926b19c74 | 19,958 | py | Python | pycle/bicycle-scrapes/epey-scrape/downLink8.py | fusuyfusuy/School-Projects | 8e38f19da90f63ac9c9ec91e550fc5aaab3d0234 | [
"MIT"
] | null | null | null | pycle/bicycle-scrapes/epey-scrape/downLink8.py | fusuyfusuy/School-Projects | 8e38f19da90f63ac9c9ec91e550fc5aaab3d0234 | [
"MIT"
] | null | null | null | pycle/bicycle-scrapes/epey-scrape/downLink8.py | fusuyfusuy/School-Projects | 8e38f19da90f63ac9c9ec91e550fc5aaab3d0234 | [
"MIT"
] | null | null | null |
from bs4 import BeautifulSoup
import os
import wget
from urllib.request import Request, urlopen
bicycles=[{'name': 'Corelli Sandy 6 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-sandy-6-0.html'}, {'name': 'Ghost Panamao X3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-panamao-x3.html'}, {'name': 'Kron XC100 29 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc100-29-hd.html'}, {'name': 'Bisan TRX 8200 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-trx-8200.html'}, {'name': 'Corelli Trivor 7 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-trivor-7-0.html'}, {'name': 'Corelli Snoop 5.3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-5-3.html'}, {'name': 'Sedona 720 Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-720.html'}, {'name': 'Bisan MTX 7100 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-mtx-7100-29.html'}, {'name': 'Salcano Antalya 700 Lady Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-antalya-700-lady.html'}, {'name': 'Carraro Speed 260 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-speed-260.html'}, {'name': 'Carraro CR-Race 062 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-cr-race-062.html'}, {'name': 'Mosso 24 Cavalier Tourney Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-24-cavalier-tourney.html'}, {'name': 'Peugeot M18-27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/peugeot-m18-27-5.html'}, {'name': 'Corelli Agile 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-agile-1-0.html'}, {'name': 'Mosso WildFire V 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-wildfire-v-27-5.html'}, {'name': 'Bianchi Star Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-star-24.html'}, {'name': 'Yuki E-Wild 50 Bisiklet', 'link': 'https://www.epey.com/bisiklet/yuki-e-wild-50.html'}, {'name': 'Peugeot M17-27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/peugeot-m17-27-5.html'}, {'name': 'Carraro Force 401 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-401.html'}, {'name': 'Corelli Strike 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-strike-1-0.html'}, {'name': 'Salcano NG650 24 Lady MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng650-24-lady-md.html'}, {'name': 'Kron XC100 26 Erkek V Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc100-erkek-v.html'}, {'name': 'Ümit 1625 Winx Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1625-winx.html'}, {'name': 'Bianchi Alto Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-alto-26.html'}, {'name': 'Bianchi Aspid 47 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-aspid-47.html'}, {'name': 'Yuki YD-EBX053DP Bisiklet', 'link': 'https://www.epey.com/bisiklet/yuki-yd-ebx053dp.html'}, {'name': 'Gitane Buddy Bisiklet', 'link': 'https://www.epey.com/bisiklet/gitane-buddy.html'}, {'name': 'Salcano Astro Lady V Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-astro-lady-v.html'}, {'name': 'Corelli Via Lady 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-via-lady-1-0.html'}, {'name': 'Kron Ares 4.0 24 V Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-ares-4-0-24-v.html'}, {'name': 'Bisan KDS 2400 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-kds-2400.html'}, {'name': 'Corelli Chronic 2.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-chronic-2-0.html'}, {'name': 'Corelli Snoop 3.3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-3-3.html'}, {'name': 'Carraro Force 610 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-610.html'}, {'name': 'Ümit 1464 Actress Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1464-actress.html'}, {'name': 'Ümit 2465 Mirage V Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2465-mirage-v.html'}, {'name': 'Kron XC50 16 V Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc50-16-v.html'}, {'name': 'Corelli Sandy 3.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-sandy-3-0.html'}, {'name': 'Corelli Chronic 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-chronic-1-0.html'}, {'name': 'Ghost Powerkid 12 Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-powerkid-12.html'}, {'name': 'Salcano Helen 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-helen-20.html'}, {'name': 'Corelli Grace 2.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-grace-2-0.html'}, {'name': 'Whistle Kanza 1682 Bisiklet', 'link': 'https://www.epey.com/bisiklet/whistle-kanza-1682.html'}, {'name': 'Ghost Kato 2 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-kato-2-29.html'}, {'name': 'Bianchi Milano Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-milano-26.html'}, {'name': 'Ümit 2810 Alanya Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2810-alanya.html'}, {'name': 'Salcano NG750 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng750-20.html'}, {'name': 'Salcano Mostar 700 Lady Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-mostar-700-lady.html'}, {'name': 'Carraro Sportive 230 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-sportive-230.html'}, {'name': 'Ümit 1401 Ponny Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1401-ponny.html'}, {'name': 'Ümit 2004 Transformers Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2004-transformers.html'}, {'name': 'Ümit 1648 Racer Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1648-racer.html'}, {'name': 'Salcano Cherry 14 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-cherry-14.html'}, {'name': 'Salcano 500 26 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-500-26-md.html'}, {'name': 'Salcano Lily 26 Lady V Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-lily-26-lady-v.html'}, {'name': 'Salcano Astro S Lady HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-astro-s-lady-hd.html'}, {'name': 'Carraro Force 751 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-751.html'}, {'name': 'Kron FD750 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-fd750.html'}, {'name': 'Kron FXC500 24 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-fxc500-24-md.html'}, {'name': 'Kron Ares 5.0 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-ares-5-0-27-5.html'}, {'name': 'Kron Ares 4.0 24 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-ares-4-0-24-md.html'}, {'name': 'Kron Anthea 4.0 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-anthea-4-0-26.html'}, {'name': 'Kron Ares 3.0 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-ares-3-0-24.html'}, {'name': 'Peugeot M11-27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/peugeot-m11-27-5.html'}, {'name': 'Peugeot JM248 Bisiklet', 'link': 'https://www.epey.com/bisiklet/peugeot-jm248.html'}, {'name': 'Corelli Pearl Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-pearl.html'}, {'name': 'Corelli Snoop 4.3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-4-3.html'}, {'name': 'Carraro Force 201 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-201.html'}, {'name': 'Carraro Big 2912 NX Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-big-2912-nx.html'}, {'name': 'Carraro Force 701 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-701.html'}, {'name': 'Berg Compact Sport Bisiklet', 'link': 'https://www.epey.com/bisiklet/berg-compact-sport.html'}, {'name': 'Berg Buzzy Bloom Bisiklet', 'link': 'https://www.epey.com/bisiklet/berg-buzzy-bloom.html'}, {'name': 'Ümit 1676 Lavida Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1676-lavida.html'}, {'name': 'Ümit 2626 Stitch 2D Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2626-stitch-2d.html'}, {'name': 'Ümit 2476 Lavida Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2476-lavida.html'}, {'name': 'Ümit 2957 Accrue HYD Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2957-accrue-hyd.html'}, {'name': 'Kron TX100 Lady V Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-tx100-lady-v.html'}, {'name': 'Sedona Gusto Lady Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-gusto-lady.html'}, {'name': 'Corelli Trivor 5.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-trivor-5-0.html'}, {'name': 'Corelli Snoop 2.3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-2-3.html'}, {'name': 'Corelli Teton 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-teton-1-0.html'}, {'name': 'Bianchi Aspid 26 Kadın Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-aspid-26-bayan.html'}, {'name': 'Scott Aspect 960 Bisiklet', 'link': 'https://www.epey.com/bisiklet/scott-aspect-960.html'}, {'name': 'Ghost Kato D4.4 AL K Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-kato-d4-4-al-k.html'}, {'name': 'Bianchi Cargo Junior Kız Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-cargo-junior-kiz-16.html'}, {'name': 'Salcano Astro S 27.5 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-astro-s-27-5-hd.html'}, {'name': 'Salcano NG650 26 Lady HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng650-26-lady-hd.html'}, {'name': 'Salcano NG750 24 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng750-24-md.html'}, {'name': 'Salcano NG850 24 Man Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng850-24-man.html'}, {'name': 'Salcano City Explorer 40 V R Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-city-explorer-40-v.html'}, {'name': 'Salcano City Life 20 Lady V Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-city-life-20-lady-v.html'}, {'name': 'Salcano Wolf 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-wolf-20.html'}, {'name': 'Soho Flex 7.1 TS Bisiklet', 'link': 'https://www.epey.com/bisiklet/soho-flex-7-1-ts.html'}, {'name': 'Corelli Pierre 7.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-pierre-7-0.html'}, {'name': 'Corelli Adonis 1.2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-adonis-1-2.html'}, {'name': 'Corelli Cyborg 3.2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-cyborg-3-2.html'}, {'name': 'Corelli Cyborg 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-cyborg-1-0.html'}, {'name': 'Corelli Scopri 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-scopri-1-0.html'}, {'name': 'Orbis Voltage 28 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-voltage-28.html'}, {'name': 'Orbis Tweety 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-tweety-20.html'}, {'name': 'Orbis Cool 16 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-cool-16.html'}, {'name': 'Orbis Jungle Monkey 16 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-jungle-monkey-16.html'}, {'name': 'Orbis Nikita 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-nikita-24.html'}, {'name': 'Orbis Sonic 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-sonic-20.html'}, {'name': 'Tern Verge 9PL Bisiklet', 'link': 'https://www.epey.com/bisiklet/tern-verge-9pl.html'}, {'name': 'Bisan XTY 5600 V Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-xty-5600-v.html'}, {'name': 'Kron FD 1500 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-fd-1500.html'}, {'name': 'Kron X1 29 Man HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-x1-29-man-hd.html'}, {'name': 'Kron XC300 27.5 Man HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc300-27-5-man-hd.html'}, {'name': 'Whistle Miwok 1727 Bisiklet', 'link': 'https://www.epey.com/bisiklet/whistle-miwok-1727.html'}, {'name': 'Mosso 20 Marine V Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-20-marine-v.html'}, {'name': 'Mosso Legarda 1721 LSM V Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-legarda-1721-lsm-v.html'}, {'name': 'Mosso 770TB3 SMD Deore Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-770tb3-smd-deore.html'}, {'name': 'Mosso 760CB Ultegra Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-760cb-ultegra.html'}, {'name': 'Mosso WildFire HYD 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-wildfire-hyd-29.html'}, {'name': 'Merida Speeder 400 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-speeder-400.html'}, {'name': 'Ghost Tacana 1 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-tacana-1-29.html'}, {'name': 'Carraro Daytona 2627 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-daytona-2627.html'}, {'name': 'Carraro Big 729 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-big-729.html'}, {'name': 'Carraro Sportive 124 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-sportive-124.html'}, {'name': 'Bianchi ARX 729 29 inç Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-arx-729-29.html'}, {'name': 'Bianchi Folding 6 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-folding-6v.html'}, {'name': 'Bianchi Rainbow Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-rainbow.html'}, {'name': 'Sedona 910 Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-910.html'}, {'name': 'Salcano NG555 29 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng555-29-hd.html'}, {'name': 'Salcano NG750 29 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng750-29-md.html'}, {'name': 'Salcano City Fun 60 Lady V Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-city-fun-60-lady-v.html'}, {'name': 'Ümit 1449 Monster High Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1449-monster-high.html'}, {'name': 'Cube SL Road Pro Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-sl-road-pro.html'}, {'name': 'Arbike 2011 Baloon Bisiklet', 'link': 'https://www.epey.com/bisiklet/arbike-2011-baloon.html'}, {'name': 'Tern Verge X10 Bisiklet', 'link': 'https://www.epey.com/bisiklet/tern-verge-x10.html'}, {'name': 'Salcano Fantasia 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-fantasia-24.html'}, {'name': 'Cube Nature Pro Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-nature-pro.html'}, {'name': 'Cube Tonopah Pro Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-tonopah-pro.html'}, {'name': 'Cube Attain SL Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-attain-sl.html'}, {'name': 'Cube Aim Allroad 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-aim-allroad-26.html'}, {'name': 'Cube LTD SL 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-ltd-sl-27-5.html'}, {'name': 'Coranna 2690 Castor Bisiklet', 'link': 'https://www.epey.com/bisiklet/coranna-2690-castor.html'}, {'name': 'Merida BIG.NINE 300 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-big-nine-300-29.html'}, {'name': 'Merida BIG.SEVEN 500 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-big-seven-500-27-5.html'}, {'name': 'Trek X-Caliber 9 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/trek-x-caliber-9-27-5.html'}, {'name': 'Corratec X Vert 0.4 29ER 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corratec-x-vert-0-4-29er-29.html'}, {'name': 'Corratec Revolution XT 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corratec-revolution-xt-27-5.html'}, {'name': 'Corratec C29ER Trekking Two Lady 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corratec-c29er-trekking-two-lady-29.html'}, {'name': 'Cannondale Quick 4 28 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cannondale-quick-4-28.html'}, {'name': 'Geotech Mode 26.2 22.YIL özel Seri 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/geotech-mode-26-2-22-yil-ozel-seri-26.html'}, {'name': 'Corratec X Vert Motion 650B 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corratec-x-vert-motion-650b-27-5.html'}, {'name': 'Look 795 Light Black Shimano Ultegra DI2 Mavic Aksium 28 Bisiklet', 'link': 'https://www.epey.com/bisiklet/look-795-light-black-shimano-ultegra-di2-mavic-aksium-28.html'}, {'name': 'Geotech Eliptik Bisiklet', 'link': 'https://www.epey.com/bisiklet/geotech-eliptik.html'}, {'name': 'Kron XC150 29 V Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc150-29.html'}, {'name': 'Kron SPX450 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-spx450-disc-28.html'}, {'name': 'Salcano Insomnia 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-insomnia-27-5.html'}, {'name': 'Salcano Üsküp 26 Lady Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-uskup-26.html'}, {'name': 'Salcano XRS001 Ultegra Di2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-xrs001-ultegra-di2.html'}, {'name': 'Salcano Double S Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-double-s.html'}, {'name': 'Salcano Fantom 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-fantom-20.html'}, {'name': 'Salcano Bosphorus I3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-bosphorus-i3.html'}, {'name': 'Salcano Assos 20 27.5 XT Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-assos-20-xt-27-5.html'}, {'name': 'Salcano Istanbul 27 Lady HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-istanbul-27-lady-hd.html'}, {'name': 'Scott Aspect 670 Bisiklet', 'link': 'https://www.epey.com/bisiklet/scott-aspect-670-26.html'}, {'name': 'Sedona 320 Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-320.html'}, {'name': 'Bianchi Touring 827 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-touring-827-28.html'}, {'name': 'Bianchi SLR 600 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-slr-600-28.html'}, {'name': 'Bianchi ARX 627 27.5 inç Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-arx-627-27-5.html'}, {'name': 'Bianchi Speed 9000 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-speed-9000-26.html'}, {'name': 'Bianchi Spider 400 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-spider-400-20.html'}, {'name': 'Carraro Sportive 227 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-sportive-227.html'}, {'name': 'Carraro Due Tandem Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-due-tandem.html'}, {'name': 'Carraro Big 927 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-big-927.html'}, {'name': 'Carraro Force 270 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-270-27-5.html'}, {'name': 'Sedona 820 Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-820.html'}, {'name': 'Ümit 2429 Blackmount Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2429-blackmount.html'}, {'name': 'Ümit 2457 Albatros V Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2457-albatros-v.html'}, {'name': 'Ümit 1445 Ninja Turtles Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1445-ninja-turtles-14.html'}, {'name': 'Ümit 1408 Princess Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1408-princess.html'}, {'name': 'Ümit 2608 Safiro Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2608-safiro.html'}, {'name': 'Ümit 2804 Flurry 28 Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2804-flurry-28.html'}, {'name': 'Ümit 2600 Colorado Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2600-colorado.html'}, {'name': 'Ümit Coranna 1639 Panter 16 Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-coranna-1639-panter-16.html'}, {'name': 'RKS XR6 Bisiklet', 'link': 'https://www.epey.com/bisiklet/rks-xr6.html'}, {'name': 'Salcano Cappadocia 3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-cappadocia-3.html'}]
for i in bicycles:
url = i['link']
try:
req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req).read()
except:
print("err in "+i['link'])
else:
print("Downloaded "+i['name']+" ", end="\r")
fileName = i['name'].replace('/','_')
f = open("./listItems/"+fileName+'.html', 'wb')
f.write(webpage)
f.close
| 907.181818 | 19,378 | 0.688897 | 3,078 | 19,958 | 4.466537 | 0.117284 | 0.157987 | 0.223814 | 0.263311 | 0.667443 | 0.60947 | 0.589322 | 0.569246 | 0.351106 | 0.137693 | 0 | 0.059588 | 0.078415 | 19,958 | 21 | 19,379 | 950.380952 | 0.68787 | 0 | 0 | 0 | 0 | 2.055556 | 0.815804 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.222222 | 0 | 0.222222 | 0.111111 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
fd52ddd554c0677a9890c81975328428f4843090 | 46 | py | Python | roboepics_client/__init__.py | RoboEpics/roboepics-client | 14cf06998259249497f08d48fe73af5098636c24 | [
"MIT"
] | null | null | null | roboepics_client/__init__.py | RoboEpics/roboepics-client | 14cf06998259249497f08d48fe73af5098636c24 | [
"MIT"
] | null | null | null | roboepics_client/__init__.py | RoboEpics/roboepics-client | 14cf06998259249497f08d48fe73af5098636c24 | [
"MIT"
] | null | null | null | from .roboepics_client import RoboEpicsClient
| 23 | 45 | 0.891304 | 5 | 46 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.086957 | 46 | 1 | 46 | 46 | 0.952381 | 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 |
b5fe3ae33554037d04aa5e5e8340822d41249986 | 23 | py | Python | atomicblock/data/path/__init__.py | AtomicSet/AtomicBlock | dd63b092fb8c30d6e4b1880986b4b7d2fd453f6f | [
"MIT"
] | null | null | null | atomicblock/data/path/__init__.py | AtomicSet/AtomicBlock | dd63b092fb8c30d6e4b1880986b4b7d2fd453f6f | [
"MIT"
] | null | null | null | atomicblock/data/path/__init__.py | AtomicSet/AtomicBlock | dd63b092fb8c30d6e4b1880986b4b7d2fd453f6f | [
"MIT"
] | null | null | null | from .main import path
| 11.5 | 22 | 0.782609 | 4 | 23 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173913 | 23 | 1 | 23 | 23 | 0.947368 | 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 |
bd49f9510199df0e7f7762119bb090362381cb23 | 178 | py | Python | ex0097.py | EwertonRosendo/PastaDeExercicios | 68d23194b87ce1c8405c70fcceb3378955815d7d | [
"MIT"
] | null | null | null | ex0097.py | EwertonRosendo/PastaDeExercicios | 68d23194b87ce1c8405c70fcceb3378955815d7d | [
"MIT"
] | null | null | null | ex0097.py | EwertonRosendo/PastaDeExercicios | 68d23194b87ce1c8405c70fcceb3378955815d7d | [
"MIT"
] | null | null | null | def escreva(texto):
print("~"*len(texto))
print(F" texto")
print("~"*len(texto))
escreva("Gustavo Guanabara")
escreva("Curso de Python no Youtube")
escreva("Cev")
| 17.8 | 37 | 0.640449 | 23 | 178 | 4.956522 | 0.608696 | 0.263158 | 0.22807 | 0.315789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.162921 | 178 | 9 | 38 | 19.777778 | 0.765101 | 0 | 0 | 0.285714 | 0 | 0 | 0.308989 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0 | 0 | 0 | 0.142857 | 0.428571 | 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 |
1fbf73390b557c4d62fbfd2bb2a0f95029445b64 | 729 | py | Python | srds/__init__.py | thrau/srds | 98468b73278c4e42be8ff2425f7fa7099bf3255d | [
"MIT"
] | 1 | 2020-02-26T18:24:01.000Z | 2020-02-26T18:24:01.000Z | srds/__init__.py | thrau/srds | 98468b73278c4e42be8ff2425f7fa7099bf3255d | [
"MIT"
] | null | null | null | srds/__init__.py | thrau/srds | 98468b73278c4e42be8ff2425f7fa7099bf3255d | [
"MIT"
] | null | null | null | from srds.srds import seed, randint, randfloat, coin_flip, random_id, choose, choose_index, random_walk_norm, logistic, \
RandomSampler, ConstantSampler, IntegerSampler, BoundRejectionSampler, BufferedSampler, PopulationSampler, \
IntegerTruncationSampler, ParameterizedDistribution, ScaledParetoSampler
name = "srds"
__all__ = [
'seed',
'randint',
'randfloat',
'coin_flip',
'random_id',
'choose',
'choose_index',
'random_walk_norm',
'logistic',
'RandomSampler',
'ConstantSampler',
'IntegerSampler',
'BoundRejectionSampler',
'BufferedSampler',
'PopulationSampler',
'IntegerTruncationSampler',
'ParameterizedDistribution',
'ScaledParetoSampler',
]
| 27 | 121 | 0.711934 | 53 | 729 | 9.528302 | 0.490566 | 0.043564 | 0.079208 | 0.09505 | 0.942574 | 0.942574 | 0.942574 | 0.942574 | 0.942574 | 0.942574 | 0 | 0 | 0.178326 | 729 | 26 | 122 | 28.038462 | 0.843072 | 0 | 0 | 0 | 0 | 0 | 0.33882 | 0.096022 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.041667 | 0 | 0.041667 | 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 |
1fda3c5ad75a470a7f27690b0a6b1fede1982fa9 | 168 | py | Python | academic/apps/people/context_processors.py | phretor/django-academic | 864452238056e07056990479396e8446a1bad086 | [
"BSD-3-Clause"
] | 2 | 2015-10-16T17:07:03.000Z | 2016-06-23T09:54:51.000Z | academic/apps/people/context_processors.py | phretor/django-academic | 864452238056e07056990479396e8446a1bad086 | [
"BSD-3-Clause"
] | null | null | null | academic/apps/people/context_processors.py | phretor/django-academic | 864452238056e07056990479396e8446a1bad086 | [
"BSD-3-Clause"
] | null | null | null | from academic import settings
def default_picture_url(context):
return {
'ACADEMIC_PEOPLE_DEFAULT_PICTURE':
settings.PEOPLE_DEFAULT_PICTURE, }
| 24 | 46 | 0.72619 | 18 | 168 | 6.388889 | 0.611111 | 0.365217 | 0.347826 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.214286 | 168 | 6 | 47 | 28 | 0.871212 | 0 | 0 | 0 | 0 | 0 | 0.184524 | 0.184524 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.2 | 0.2 | 0.6 | 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 | 0 | 1 | 1 | 0 | 0 | 6 |
1fe0bb2eb28da9983f8c75ee18798dabb9daaf79 | 23 | py | Python | hedge_hog/powerup/__init__.py | otivedani/hedge_hog | 62026e63b6bdc72cc4f0c984136712e6ee090f68 | [
"MIT"
] | null | null | null | hedge_hog/powerup/__init__.py | otivedani/hedge_hog | 62026e63b6bdc72cc4f0c984136712e6ee090f68 | [
"MIT"
] | null | null | null | hedge_hog/powerup/__init__.py | otivedani/hedge_hog | 62026e63b6bdc72cc4f0c984136712e6ee090f68 | [
"MIT"
] | null | null | null | from .indextra import * | 23 | 23 | 0.782609 | 3 | 23 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 23 | 1 | 23 | 23 | 0.9 | 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 |
1fe2d0f499ff3a0dd6077c9105ee0122056783dc | 8,625 | py | Python | anoncreds/test/test_single_prover_single_issuer.py | fabienpe/indy-anoncreds | 3173226ed1450d55f62ea679b930475c92b03c00 | [
"Apache-2.0"
] | 16 | 2017-07-17T04:51:30.000Z | 2019-03-18T02:21:33.000Z | anoncreds/test/test_single_prover_single_issuer.py | fabienpe/indy-anoncreds | 3173226ed1450d55f62ea679b930475c92b03c00 | [
"Apache-2.0"
] | 28 | 2017-06-29T18:00:00.000Z | 2018-09-05T09:57:41.000Z | anoncreds/test/test_single_prover_single_issuer.py | fabienpe/indy-anoncreds | 3173226ed1450d55f62ea679b930475c92b03c00 | [
"Apache-2.0"
] | 25 | 2016-10-17T13:15:02.000Z | 2017-06-19T11:54:20.000Z | import pytest
from anoncreds.protocol.types import ProofRequest, PredicateGE, Claims, \
ProofClaims, AttributeInfo
from anoncreds.test.conftest import presentProofAndVerify
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testPrimaryClaimOnlyEmpty(prover1, verifier, claimsProver1Gvt, nonce):
proofRequest = ProofRequest("proof1", "1.0", nonce)
claims, requestedProof = await prover1._findClaims(proofRequest)
claims = {schemaId: ProofClaims(
Claims(primaryClaim=proofClaim.claims.primaryClaim))
for schemaId, proofClaim in claims.items()}
proof = await prover1._prepareProof(claims, proofRequest.nonce, requestedProof)
assert await verifier.verify(proofRequest, proof)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testPrimaryClaimNoPredicates(prover1, verifier, claimsProver1Gvt,
nonce, schemaGvtId):
proofRequest = ProofRequest("proof1", "1.0", nonce,
verifiableAttributes={'uuid1': AttributeInfo(name='name')}, predicates={})
claims, requestedProof = await prover1._findClaims(proofRequest)
claims = {
schemaId: ProofClaims(
Claims(primaryClaim=proofClaim.claims.primaryClaim), [AttributeInfo(name='name')], [])
for schemaId, proofClaim in claims.items()}
proof = await prover1._prepareProof(claims, proofRequest.nonce, requestedProof)
assert await verifier.verify(proofRequest, proof)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testPrimaryClaimPredicatesOnly(prover1, verifier, claimsProver1Gvt,
nonce, schemaGvtId):
predicate = PredicateGE('age', 18)
proofRequest = ProofRequest("proof1", "1.0", nonce,
verifiableAttributes={},
predicates={'predicate_uuid1': predicate})
claims, requestedProof = await prover1._findClaims(proofRequest)
claims = {
schemaId: ProofClaims(
Claims(primaryClaim=proofClaim.claims.primaryClaim), predicates=[predicate])
for schemaId, proofClaim in claims.items()}
proof = await prover1._prepareProof(claims, proofRequest.nonce, requestedProof)
assert await verifier.verify(proofRequest, proof)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testEmpty(prover1, verifier, claimsProver1Gvt):
assert await presentProofAndVerify(verifier, ProofRequest("proof1", "1.0", verifier.generateNonce()), prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testNoPredicates(prover1, verifier, claimsProver1Gvt):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={'uuid': AttributeInfo(name='name')}, predicates={})
assert await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testMultipleRevealedAttrs(prover1, verifier, claimsProver1Gvt):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={'uuid1': AttributeInfo(name='name'),
'uuid2': AttributeInfo(name='sex')},
predicates={})
assert await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testGePredicate(prover1, verifier, claimsProver1Gvt):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={
'attr_uuid': AttributeInfo(name='name')},
predicates={'predicate_uuid': PredicateGE('age', 18)})
assert await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testGePredicateForEqual(prover1, verifier, claimsProver1Gvt):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={
'attr_uuid': AttributeInfo(name='name')},
predicates={'predicate_uuid': PredicateGE('age', 28)})
assert await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testGePredicateNegative(prover1, verifier, claimsProver1Gvt):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={
'attr_uuid': AttributeInfo(name='name')},
predicates={'predicate_uuid': PredicateGE('age', 29)})
with pytest.raises(ValueError):
await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testMultipleGePredicate(prover1, verifier, claimsProver1Gvt):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={
'attr_uuid': AttributeInfo(name='name')},
predicates={'predicate_uuid1': PredicateGE('age', 18),
'predicate_uuid2': PredicateGE('height', 170)})
assert await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testMultipleGePredicateNegative(prover1, verifier, claimsProver1Gvt):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={
'attr_uuid': AttributeInfo(name='name')},
predicates={'predicate_uuid1': PredicateGE('age', 18),
'predicate_uuid2': PredicateGE('height', 180)})
with pytest.raises(ValueError):
await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testNonceShouldBeSame(prover1, verifier, claimsProver1Gvt, nonce,
genNonce):
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={'attr_uuid': AttributeInfo(name='name')})
proof = await prover1.presentProof(proofRequest)
proofRequest = ProofRequest("proof1", "1.0", genNonce,
verifiableAttributes=proofRequest.verifiableAttributes,
predicates=proofRequest.predicates)
assert not await verifier.verify(proofRequest, proof)
@pytest.mark.skipif('sys.platform == "win32"', reason='SOV-86')
@pytest.mark.asyncio
async def testUParamShouldBeSame(prover1, verifier, issuerGvt, schemaGvtId,
attrsProver1Gvt, keysGvt,
issueAccumulatorGvt):
claimsReq = await prover1.createClaimRequest(schemaGvtId)
claimsReq = claimsReq._replace(U=claimsReq.U ** 2)
claim_signature, claim_attributes = await issuerGvt.issueClaim(schemaGvtId, claimsReq)
await prover1.processClaim(schemaGvtId, claim_attributes, claim_signature)
proofRequest = ProofRequest("proof1", "1.0", verifier.generateNonce(),
verifiableAttributes={
'attr_uuid': AttributeInfo(name='name')},
predicates={})
assert not await presentProofAndVerify(verifier, proofRequest, prover1)
@pytest.mark.asyncio
async def testUrParamShouldBeSame(prover1, issuerGvt, schemaGvtId,
attrsProver1Gvt, keysGvt,
issueAccumulatorGvt):
claimsReq = await prover1.createClaimRequest(schemaGvtId)
claimsReq = claimsReq._replace(Ur=claimsReq.Ur ** 2)
claim_signature, claim_attributes = await issuerGvt.issueClaim(schemaGvtId, claimsReq)
with pytest.raises(ValueError):
await prover1.processClaim(schemaGvtId, claim_attributes, claim_signature)
| 47.651934 | 114 | 0.646377 | 729 | 8,625 | 7.604938 | 0.134431 | 0.048701 | 0.042929 | 0.055556 | 0.835859 | 0.789141 | 0.769481 | 0.737554 | 0.714466 | 0.714466 | 0 | 0.02593 | 0.239884 | 8,625 | 180 | 115 | 47.916667 | 0.819707 | 0 | 0 | 0.666667 | 0 | 0 | 0.090319 | 0 | 0 | 0 | 0 | 0 | 0.07971 | 1 | 0 | false | 0 | 0.021739 | 0 | 0.021739 | 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 |
951690b935b4a9e5e1afdaf4c7081de9cf9568d0 | 281 | py | Python | tests/utils/python/test_checks.py | yzpang/jiant | 192d6b525c06f33010b59044df40cb86bbfba4ea | [
"MIT"
] | 1,108 | 2019-04-22T09:19:19.000Z | 2022-03-31T13:23:51.000Z | tests/utils/python/test_checks.py | yzpang/jiant | 192d6b525c06f33010b59044df40cb86bbfba4ea | [
"MIT"
] | 737 | 2019-04-22T14:30:36.000Z | 2022-03-31T22:22:17.000Z | tests/utils/python/test_checks.py | yzpang/jiant | 192d6b525c06f33010b59044df40cb86bbfba4ea | [
"MIT"
] | 273 | 2019-04-23T01:42:11.000Z | 2022-03-25T15:59:38.000Z | import jiant.utils.python.checks as py_checks
def test_dict_equal():
assert py_checks.dict_equal({1: 2}, {1: 2})
assert not py_checks.dict_equal({1: 2}, {1: 3})
assert not py_checks.dict_equal({1: 2}, {2: 2})
assert not py_checks.dict_equal({1: 2}, {2: 2, 1: 1})
| 31.222222 | 57 | 0.654804 | 53 | 281 | 3.264151 | 0.301887 | 0.231214 | 0.277457 | 0.393064 | 0.635838 | 0.635838 | 0.635838 | 0.514451 | 0.514451 | 0.346821 | 0 | 0.077586 | 0.174377 | 281 | 8 | 58 | 35.125 | 0.668103 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.666667 | 1 | 0.166667 | true | 0 | 0.166667 | 0 | 0.333333 | 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 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
1f10edc6a4e902e95c0d04478da4ae6f5eb2914c | 710 | py | Python | lib/conf_reader.py | mswilkhu1/ssense | 59987a3c492591c8217811e79a63f25652f0295c | [
"MIT"
] | null | null | null | lib/conf_reader.py | mswilkhu1/ssense | 59987a3c492591c8217811e79a63f25652f0295c | [
"MIT"
] | null | null | null | lib/conf_reader.py | mswilkhu1/ssense | 59987a3c492591c8217811e79a63f25652f0295c | [
"MIT"
] | null | null | null | import configparser
def read_config_data(section, key):
config = configparser.ConfigParser()
config.read('./config.cfg')
return config.get(section, key)
def fetch_login_page_elements_locators(section, key):
config = configparser.ConfigParser()
config.read('./selectors/login_page.cfg')
return config.get(section, key)
def fetch_home_page_elements_locators(section, key):
config = configparser.ConfigParser()
config.read('./selectors/home_page.cfg')
return config.get(section, key)
def fetch_checkout_pages_elements_locators(section, key):
config = configparser.ConfigParser()
config.read('./selectors/checkout_pages.cfg')
return config.get(section, key)
| 27.307692 | 57 | 0.74507 | 86 | 710 | 5.953488 | 0.232558 | 0.15625 | 0.125 | 0.21875 | 0.833984 | 0.833984 | 0.779297 | 0.681641 | 0.611328 | 0.455078 | 0 | 0 | 0.138028 | 710 | 25 | 58 | 28.4 | 0.836601 | 0 | 0 | 0.470588 | 0 | 0 | 0.130986 | 0.114085 | 0 | 0 | 0 | 0 | 0 | 1 | 0.235294 | false | 0 | 0.058824 | 0 | 0.529412 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 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 | 0 | 1 | 0 | 0 | 6 |
1f298adb8032d994e752f9ea8dc69a7897418cf8 | 226 | py | Python | backend/tests/wallet_tests/__init__.py | tanshuai/reference-wallet | e8efec4acc6af6e319cf075c10693ddf7754cc83 | [
"Apache-2.0"
] | 14 | 2020-12-17T08:03:51.000Z | 2022-03-26T04:21:18.000Z | backend/tests/wallet_tests/__init__.py | tanshuai/reference-wallet | e8efec4acc6af6e319cf075c10693ddf7754cc83 | [
"Apache-2.0"
] | 20 | 2020-12-15T12:02:56.000Z | 2021-05-19T23:37:34.000Z | backend/tests/wallet_tests/__init__.py | tanshuai/reference-wallet | e8efec4acc6af6e319cf075c10693ddf7754cc83 | [
"Apache-2.0"
] | 12 | 2020-12-10T16:35:27.000Z | 2022-02-01T04:06:10.000Z | # Copyright (c) The Diem Core Contributors
# SPDX-License-Identifier: Apache-2.0
ASSOC_ADDRESS: str = "0000000000000000000000000a550c18"
ASSOC_AUTHKEY: str = "3126dc954143b1282565e8829cd8cdfdc179db444f64b406dee28015fce7f392"
| 37.666667 | 87 | 0.840708 | 20 | 226 | 9.4 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.357488 | 0.084071 | 226 | 5 | 88 | 45.2 | 0.550725 | 0.336283 | 0 | 0 | 0 | 0 | 0.653061 | 0.653061 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
1f2ad7a844574a25efe6f7b8e8e083f2700aaaa5 | 186 | py | Python | generated-libraries/python/netapp/test/stringalias.py | radekg/netapp-ontap-lib-get | 6445ebb071ec147ea82a486fbe9f094c56c5c40d | [
"MIT"
] | 2 | 2017-03-28T15:31:26.000Z | 2018-08-16T22:15:18.000Z | generated-libraries/python/netapp/test/stringalias.py | radekg/netapp-ontap-lib-get | 6445ebb071ec147ea82a486fbe9f094c56c5c40d | [
"MIT"
] | null | null | null | generated-libraries/python/netapp/test/stringalias.py | radekg/netapp-ontap-lib-get | 6445ebb071ec147ea82a486fbe9f094c56c5c40d | [
"MIT"
] | null | null | null | class Stringalias(basestring):
"""
This kind of typedef is just an alias for a string.
"""
@staticmethod
def get_api_name():
return "stringalias"
| 18.6 | 55 | 0.591398 | 21 | 186 | 5.142857 | 0.952381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.322581 | 186 | 9 | 56 | 20.666667 | 0.857143 | 0.274194 | 0 | 0 | 0 | 0 | 0.092437 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0 | 0.25 | 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 | 0 | 1 | 1 | 0 | 0 | 6 |
1f57a92f8cef9c0180e24761395a286badae49eb | 3,053 | py | Python | src/dqn.py | suessmann/TLC-DQN | 5cf8047e4efcd348d0711c4fc7aff7dba5804a83 | [
"MIT"
] | 42 | 2020-06-26T08:51:32.000Z | 2022-03-06T02:29:38.000Z | src/dqn.py | AntonioAlgaida/intelligent_traffic_lights | 1cc750e505ece493d5d7c7797630bef01e421ba0 | [
"MIT"
] | 9 | 2020-07-10T00:45:58.000Z | 2022-02-23T10:38:32.000Z | src/dqn.py | AntonioAlgaida/intelligent_traffic_lights | 1cc750e505ece493d5d7c7797630bef01e421ba0 | [
"MIT"
] | 9 | 2020-10-23T17:41:04.000Z | 2022-01-10T12:22:05.000Z | import torch
import torch.nn as nn
import torch.nn.functional as F
import random
class DQNetwork(nn.Module):
def __init__(self):
super().__init__()
self.features1 = nn.Sequential(
nn.Conv2d(1, 16, 4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 32, 2, stride=1),
nn.ReLU()
)
self.features2 = nn.Sequential(
nn.Conv2d(1, 16, 4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 32, 2, stride=1),
nn.ReLU()
)
# self.features3 = nn.Linear(4, 4)
self.linear_relu1 = nn.Sequential(
nn.Linear(32*7*7 + 32*7*7 + 4, 128),
nn.ReLU(),
# nn.Dropout(0.2),
nn.Linear(128, 64),
nn.ReLU(),
# nn.Dropout(0.2),
nn.Linear(64, 32),
nn.ReLU(),
# nn.Dropout(0.2)
)
self.classifier = nn.Sequential(
nn.Linear(32, 4),
# nn.Softmax(dim=-1)
# nn.ReLU()
)
def forward(self, state):
x1, x2, x3 = state
x1 = self.features1(x1.view(x1.size(0), 1, -1, 16))
x2 = self.features2(x2.view(x1.size(0), 1, -1, 16))
# x3 = self.features3(x3)
x1 = x1.view(x1.size(0), -1)
x2 = x2.view(x2.size(0), -1)
x3 = x3.view(x3.size(0), -1)
x = torch.cat((x1, x2, x3), dim=1)
x = self.linear_relu1(x)
x = self.classifier(x)
return x
def predict(self, state, eps):
prob = random.random()
if prob < eps:
return random.randint(0, 3)
else:
act = self.forward(state)
return act.argmax().item()
class FCQNetwork(nn.Module):
def __init__(self):
super().__init__()
self.features1 = nn.Sequential(
nn.Linear(256, 256),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(256, 128),
nn.ReLU()
)
self.features2 = nn.Sequential(
nn.Linear(256, 256),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(256, 128),
nn.ReLU()
)
self.linear_q = nn.Linear(128 + 128 + 4, 4)
def forward(self, state):
x1, x2, x3 = state
x1 = self.features1(x1.view(x1.size(0), 1, 1, -1))
x2 = self.features2(x2.view(x1.size(0), 1, 1, -1))
# x3 = self.features3(x3)
x1 = x1.view(x1.size(0), -1)
x2 = x2.view(x2.size(0), -1)
x3 = x3.view(x3.size(0), -1)
x = torch.cat((x1, x2, x3), dim=1)
x = self.linear_q(x)
return x
def predict(self, state, eps):
prob = random.random()
if prob < eps:
return random.randint(0, 3)
else:
act = self.forward(state)
return act.argmax().item()
| 26.094017 | 59 | 0.443498 | 385 | 3,053 | 3.464935 | 0.155844 | 0.053973 | 0.044978 | 0.049475 | 0.829835 | 0.796852 | 0.784108 | 0.766117 | 0.728636 | 0.728636 | 0 | 0.106489 | 0.409433 | 3,053 | 116 | 60 | 26.318966 | 0.633389 | 0.05208 | 0 | 0.686747 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.072289 | false | 0 | 0.048193 | 0 | 0.216867 | 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 |
2f25abc779d93c35402343d510b0de08c447dd7d | 7,474 | py | Python | tests/tests.py | aurimas13/Calculator-program | d6151206ab98b4a0bd05298de9687c440ec3d5ca | [
"MIT"
] | 7 | 2021-11-27T08:11:01.000Z | 2021-12-04T01:22:25.000Z | tests/tests.py | JohnGun10/Calculator-program | c9d42bd448f22f75ae044418471c4da986193606 | [
"MIT"
] | null | null | null | tests/tests.py | JohnGun10/Calculator-program | c9d42bd448f22f75ae044418471c4da986193606 | [
"MIT"
] | 1 | 2021-11-27T08:40:23.000Z | 2021-11-27T08:40:23.000Z | # Tests file
# Calculator program | tests.py
#
# Created by Aurimas A. Nausedas on 05/15/21.
# Uploaded by Aurimas A. Nausedas on 05/31/21.
# Updated by Aurimas A. Nausedas on 11/05/21.
# Updated by Aurimas A. Nausedas on 11/05/21.
import pytest
import math
from calculator.calculator import Calculator
def test_addition_when_memory_value_equal_to_zero():
"""test of add method to see how it handles when you pass 2, 3.3222, 44 and class is initialized to zero"""
_calculator = Calculator()
additives = [2, 3.3222, 44]
products = [2, 5.3222000000000005, 49.3222]
for index, additive in enumerate(additives):
assert _calculator.add(additive) == products[index]
def test_addition_when_memory_value_equal_to_one_various_range():
"""test of add method to see how it handles when you pass 0, 10, -13 and class is initialized to one"""
_calculator = Calculator(1)
additives = [0, 10, -13]
products = [1, 11, -2]
for index, additive in enumerate(additives):
assert _calculator.add(additive) == products[index]
def test_addition_when_memory_value_equal_to_negative_one_various_range():
"""test of add method to see how it handles when you pass -77, 0, 184.55 and class is initialized to -73"""
_calculator = Calculator(-73)
additives = [0, -77, 184.55]
products = [-73, -150, 34.55000000000001]
for index, additive in enumerate(additives):
assert _calculator.add(additive) == products[index]
def test_subtraction_when_memory_value_equal_to_zero():
"""test of subtract method to see how it handles when you pass range from -1 to 1 and class is initialized to zero"""
_calculator = Calculator()
for a in range(-1, 1):
if a == 1:
assert _calculator.subtract(a) == 0
else:
assert _calculator.subtract(a) == 1
def test_subtraction_when_memory_value_equal_to_negative_one_positive_range():
"""test of subtract method to see how it handles when you pass -57, 0, 74.55 and class is initialized to negative one"""
_calculator = Calculator(-1)
subtractives = [-57, 0, 74.55]
products = [56, 56, -18.549999999999997]
for index, subtractive in enumerate(subtractives):
assert _calculator.subtract(subtractive) == products[index]
def test_subtraction_when_memory_value_equal_to_one_positive_range():
"""test of subtract method to see how it handles when you pass -17, 0, 41 and class is initialized to one"""
_calculator = Calculator(1)
subtractives = [-17, 0, 41]
products = [18, 18, -23]
for index, subtractive in enumerate(subtractives):
assert _calculator.subtract(subtractive) == products[index]
def test_multiplication_when_memory_value_equal_to_zero():
"""test of multiply method to see how it handles when you pass range from -1 to 1 and class is initialized to zero"""
_calculator = Calculator()
for a in range(-1, 1):
assert _calculator.multiply(a) == 0
def test_multiplication_when_memory_value_equal_to_negative_one_positive_range():
"""test of multiply method to see how it handles when you pass positive numbers and class is initialized to negative one"""
_calculator = Calculator(-1)
multipliers = [2, 3.4, 4]
products = [-2, -6.8, -27.2]
for index, multiplier in enumerate(multipliers):
assert _calculator.multiply(multiplier) == products[index]
def test_multiplication_when_memory_value_equal_to_negative_eight_various_inputs():
"""test of multiply method to see how it handles when you pass 8, -10, 0 and class is initialized to negative one"""
_calculator = Calculator(-8)
multipliers = [8.5, -10, 0]
products = [-68.0, 680, 0]
for index, multiplier in enumerate(multipliers):
assert _calculator.multiply(multiplier) == products[index]
def test_multiplication_when_memory_value_equal_to_one_positive_range():
"""test of multiply method to see how it handles when you pass positive numbers and class is initialized to one"""
_calculator = Calculator(1)
multipliers = [2, 3, 4]
products = [2, 6, 24]
for index, multiplier in enumerate(multipliers):
assert _calculator.multiply(multiplier) == products[index]
def test_multiplication_when_memory_value_equal_to_one_negative_range():
"""test of multiply method to see how it handles when you pass negative numbers and class is initialized to one"""
_calculator = Calculator(1)
multipliers = [-2, -3, -4]
products = [-2, 6, -24]
for index, multiplier in enumerate(multipliers):
assert _calculator.multiply(multiplier) == products[index]
def test_division_when_memory_value_equal_to_eight_and_root_is_zero():
"""test of divide method to see how it handles when you pass root as zero and class is initialized to 8"""
_calculator = Calculator(8)
with pytest.raises(ZeroDivisionError):
assert _calculator.divide(0)
def test_division_when_memory_value_equal_to_one_positive_range():
"""test of divide method to see how it handles when you pass -12, 2, 8 and class is initialized to 24"""
_calculator = Calculator(24)
divisors = [-12, 2, 8]
products = [-2, -1, -0.125]
for index, divisor in enumerate(divisors):
assert _calculator.divide(divisor) == products[index]
def test_division_when_memory_value_equal_to_one_negative_range():
"""test of divide method to see how it handles when you pass negative numbers and class is initialized to one"""
_calculator = Calculator(1)
divisors = [-2, -3, -4]
products = [-0.5, 0.16666666666666666, -0.041666666666666664]
for index, divisor in enumerate(divisors):
assert _calculator.divide(divisor) == products[index]
def test_division_when_memory_value_equal_to_negative_one_positive_range():
"""test of divide method to see how it handles when you pass -12, 0.5, -2 and class
is initialized to negative 24"""
_calculator = Calculator(-24)
divisors = [-12, 0.5, -2]
products = [2, 4, -2]
for index, divisor in enumerate(divisors):
assert _calculator.divide(divisor) == products[index]
def test_division_when_memory_value_equal_to_negative_one_negative_range():
"""test of divide method to see how it handles when you pass 0.125, -2, 4 and class
is initialized to negative one"""
_calculator = Calculator(-1)
divisors = [0.125, -2, 4]
products = [-8, 4, 1]
for index, divisor in enumerate(divisors):
assert _calculator.divide(divisor) == products[index]
def test_root_when_memory_value_equal_to_zero_when_root_is_negative_one():
"""test of root method to see how it handles when you pass zero root to negative one. This checks an interesting case"""
_calculator = Calculator(0)
assert math.isinf(float(_calculator.root(-1)))
def test_root_when_memory_value_equal_to_eight_root_is_positive():
"""test of root method to see how it handles when the class is initialized to eight and root is 3"""
_calculator = Calculator(8)
assert _calculator.root(3) == 2
def test_root_when_memory_value_equal_to_eight_root_is_negative():
"""test of root method to see how it handles when the class is initialized to eight and root is -3"""
_calculator = Calculator(8)
assert _calculator.root(-3) == 0.5
def test_root_when_memory_value_equal_to_one_negative_range():
"""test of root method to see how it handles when you pass negative numbers and class is initialized to one"""
_calculator = Calculator(1)
for a in range(-6, -1):
assert _calculator.root(a) == 1
def test_root_when_memory_value_equal_to_negative_one_negative_range():
"""test of root method to see how it handles negative roots when class is initialized to negative one"""
_calculator = Calculator(-1)
for a in range(-100, 0):
assert _calculator.root(a) == -1
| 38.725389 | 124 | 0.756757 | 1,153 | 7,474 | 4.697311 | 0.10928 | 0.081241 | 0.058161 | 0.077548 | 0.840473 | 0.814993 | 0.781942 | 0.780465 | 0.745938 | 0.698486 | 0 | 0.05438 | 0.14624 | 7,474 | 192 | 125 | 38.927083 | 0.79439 | 0.327669 | 0 | 0.389381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.19469 | 1 | 0.185841 | false | 0 | 0.026549 | 0 | 0.212389 | 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 |
2f949a92e17fc70157f395b744587cbb205ab81d | 8,097 | py | Python | tests/attributes/test_blobs.py | yaal-fr/sheraf | 9821a53d8b0ea0aba420175e4cfa81529262f88c | [
"MIT"
] | 1 | 2020-03-18T09:54:52.000Z | 2020-03-18T09:54:52.000Z | tests/attributes/test_blobs.py | yaal-fr/sheraf | 9821a53d8b0ea0aba420175e4cfa81529262f88c | [
"MIT"
] | null | null | null | tests/attributes/test_blobs.py | yaal-fr/sheraf | 9821a53d8b0ea0aba420175e4cfa81529262f88c | [
"MIT"
] | null | null | null | import io
import tests
import sheraf
class ModelWithBlob(tests.UUIDAutoModel):
attr = sheraf.SimpleAttribute()
blob = sheraf.BlobAttribute()
blobs = sheraf.SmallListAttribute(sheraf.BlobAttribute())
def check_blob(model, database, check_path):
assert bool(model.blob) and model.blob
f = model.blob.open()
assert b"ABCDEF" == f.read()
if check_path:
assert database.storage.blob_dir in f.name
assert model.blob.data == b"ABCDEF"
assert model.blob["data"] == b"ABCDEF"
assert model.blob.original_name == "image.png"
assert model.blob["original_name"] == "image.png"
assert model.blob.filename == f.name
assert model.blob["filename"] == f.name
assert model.blob.file_extension == "png"
assert model.blob["file_extension"] == "png"
assert str(model.blob) == "image.png"
assert 1 == len(model.blob)
f.close()
def check_blobs(model, database, check_path, nb_expected):
assert len(model.blobs) == nb_expected
for blob in model.blobs:
assert bool(blob) and blob
f = blob.open()
assert b"ABCDEF" == f.read()
if check_path:
assert database.storage.blob_dir in f.name
assert blob.data == b"ABCDEF"
assert blob["data"] == b"ABCDEF"
assert blob.original_name == "image.png"
assert blob["original_name"] == "image.png"
assert blob.filename == f.name
assert blob["filename"] == f.name
assert blob.file_extension == "png"
assert blob["file_extension"] == "png"
assert str(blob) == "image.png"
assert 1 == len(blob)
f.close()
def test_crud(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create()
assert not m.blob and not bool(m.blob)
m.blob = sheraf.Blob.create(b"ABCDEF", "image.png")
check_blob(m, sheraf_zeo_database, False)
with sheraf.connection():
check_blob(m, sheraf_zeo_database, True)
assert '<Blob filename="image.png">' == repr(m.blob)
with sheraf.connection():
m = ModelWithBlob.read(m.id)
check_blob(m, sheraf_zeo_database, True)
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
m.blob.delete()
with sheraf.connection():
m = ModelWithBlob.read(m.id)
assert not m.blob
assert not bool(m.blob)
def test_write_none(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create(blob=sheraf.Blob.create(b"ABCDEF", "image.png"))
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
m.blob = None
assert m.blob is None
with sheraf.connection():
m = ModelWithBlob.read(m.id)
assert m.blob is None
def test_create_dict(sheraf_zeo_database):
with sheraf.connection(commit=True):
d = dict(data=b"ABCDEF", filename="image.png")
m = ModelWithBlob.create(blob=d)
check_blob(m, sheraf_zeo_database, False)
with sheraf.connection():
m = ModelWithBlob.read(m.id)
check_blob(m, sheraf_zeo_database, True)
def test_blob_stream(sheraf_zeo_database):
with sheraf.connection(commit=True):
blob = sheraf.Blob.create(stream=io.BytesIO(b"ABCDEF"), filename="image.png")
m = ModelWithBlob.create(blob=blob)
check_blob(m, sheraf_zeo_database, False)
with sheraf.connection():
m = ModelWithBlob.read(m.id)
check_blob(m, sheraf_zeo_database, True)
def test_update_blob(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create()
m.edit({"blob": sheraf.Blob.create(b"ABCDEF", "image.png")})
check_blob(m, sheraf_zeo_database, False)
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
check_blob(m, sheraf_zeo_database, True)
m.edit({"blob": sheraf.Blob.create(b"ABCDEFG", "image2.png")})
assert m.blob.original_name == "image2.png"
with sheraf.connection():
m = ModelWithBlob.read(m.id)
assert m.blob.original_name == "image2.png"
def test_overwrite_with_empty_blob(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create(blob=sheraf.Blob.create(b"ABCDEF", "image.png"))
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
m.blob = sheraf.Blob.create(b"", None)
assert m.blob is None
with sheraf.connection():
m = ModelWithBlob.read(m.id)
assert m.blob is None
def test_blob_list_crud(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create()
assert not m.blobs and not bool(m.blobs)
m.blobs.append(sheraf.Blob.create(b"ABCDEF", "image.png"))
check_blobs(m, sheraf_zeo_database, False, 1)
with sheraf.connection():
check_blobs(m, sheraf_zeo_database, True, 1)
assert '<Blob filename="image.png">' == repr(m.blobs[0])
with sheraf.connection():
m = ModelWithBlob.read(m.id)
check_blobs(m, sheraf_zeo_database, True, 1)
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
m.blobs[0].delete()
with sheraf.connection():
m = ModelWithBlob.read(m.id)
assert not m.blobs[0]
assert not bool(m.blobs[0])
def test_update_blob_list(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create()
m.edit({"blobs": [sheraf.Blob.create(b"ABCDEF", "image.png")]})
check_blobs(m, sheraf_zeo_database, False, 1)
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
check_blobs(m, sheraf_zeo_database, True, 1)
m.edit({"blobs": [sheraf.Blob.create(b"ABCDEFG", "image2.png")]})
assert m.blobs[0].original_name == "image2.png"
with sheraf.connection():
m = ModelWithBlob.read(m.id)
assert m.blobs[0].original_name == "image2.png"
def test_overwrite_with_empty_blob_list(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create(blobs=[sheraf.Blob.create(b"ABCDEF", "image.png")])
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
m.blobs[0] = sheraf.Blob.create(b"", None)
assert m.blobs[0] is None
with sheraf.connection():
m = ModelWithBlob.read(m.id)
assert m.blobs[0] is None
def test_create_blob_list_dict(sheraf_zeo_database):
with sheraf.connection(commit=True):
d = dict(data=b"ABCDEF", filename="image.png")
m = ModelWithBlob.create(blobs=[d])
check_blobs(m, sheraf_zeo_database, False, 1)
with sheraf.connection():
m = ModelWithBlob.read(m.id)
check_blobs(m, sheraf_zeo_database, True, 1)
def test_remove_all_blobs(sheraf_zeo_database):
with sheraf.connection(commit=True):
m = ModelWithBlob.create(
blobs=[
sheraf.Blob.create(b"ABCDEF", "image.png"),
sheraf.Blob.create(b"ABCDEF", "image.png"),
]
)
check_blobs(m, sheraf_zeo_database, False, 2)
with sheraf.connection(commit=True):
m = ModelWithBlob.read(m.id)
m.blobs = None
with sheraf.connection():
m = ModelWithBlob.read(m.id)
check_blobs(m, sheraf_zeo_database, True, 0)
def test_shortcut(sheraf_zeo_database):
with sheraf.connection(commit=True):
class File:
def __init__(self, stream=None, filename=None, data=None):
self.stream = stream
self.filename = filename
self.data = data
m = ModelWithBlob.create()
assert not m.blob and not bool(m.blob)
m.blob = File(stream=io.BytesIO(b"ABCDEF"), filename="image.png")
check_blob(m, sheraf_zeo_database, False)
m = ModelWithBlob.create()
assert not m.blob and not bool(m.blob)
m.blob = File(data=b"ABCDEF", filename="image.png")
check_blob(m, sheraf_zeo_database, False)
| 31.628906 | 85 | 0.641472 | 1,080 | 8,097 | 4.677778 | 0.075 | 0.069279 | 0.138559 | 0.078979 | 0.860451 | 0.85669 | 0.834125 | 0.775534 | 0.728424 | 0.681512 | 0 | 0.004172 | 0.230332 | 8,097 | 255 | 86 | 31.752941 | 0.806483 | 0 | 0 | 0.560209 | 0 | 0 | 0.066197 | 0.005187 | 0 | 0 | 0 | 0 | 0.246073 | 1 | 0.078534 | false | 0 | 0.015707 | 0 | 0.120419 | 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 |
85dba997fc29eaa28cb9892c986c0250ffdffeac | 107 | py | Python | nlpiper/core/__init__.py | jfecunha/NLPiper | e0d6aeb52b9ea22825f70f8b60a9f09c5b74f096 | [
"MIT"
] | 11 | 2022-02-17T14:48:39.000Z | 2022-02-20T03:46:19.000Z | nlpiper/core/__init__.py | jfecunha/NLPiper | e0d6aeb52b9ea22825f70f8b60a9f09c5b74f096 | [
"MIT"
] | 21 | 2022-02-17T14:26:49.000Z | 2022-03-04T12:11:05.000Z | nlpiper/core/__init__.py | dlite-tools/NLPiper | 3610598ad831072f8ee7e44f0b827b074f92a612 | [
"MIT"
] | 1 | 2022-03-11T11:47:50.000Z | 2022-03-11T11:47:50.000Z | """Core Module."""
from nlpiper.core.document import Document
from nlpiper.core.composition import Compose
| 26.75 | 44 | 0.803738 | 14 | 107 | 6.142857 | 0.571429 | 0.255814 | 0.348837 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.093458 | 107 | 3 | 45 | 35.666667 | 0.886598 | 0.11215 | 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 |
c847c0fc5617266f9107287531509d236c64a149 | 122 | py | Python | Memento/Python 3/WorkspaceState.py | kuuhaku86/design-patterns | 9044ecbeb366fec97e27f1ec51e66d0fafdace07 | [
"MIT"
] | 11 | 2022-03-24T15:08:06.000Z | 2022-03-30T19:24:30.000Z | Memento/Python 3/WorkspaceState.py | kuuhaku86/design-patterns | 9044ecbeb366fec97e27f1ec51e66d0fafdace07 | [
"MIT"
] | null | null | null | Memento/Python 3/WorkspaceState.py | kuuhaku86/design-patterns | 9044ecbeb366fec97e27f1ec51e66d0fafdace07 | [
"MIT"
] | null | null | null | class WorkspaceState:
def __init__(self, state):
self.state = state
def get_state(self):
return self.state
| 17.428571 | 28 | 0.688525 | 16 | 122 | 4.9375 | 0.5 | 0.341772 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.221311 | 122 | 6 | 29 | 20.333333 | 0.831579 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0 | 0.2 | 0.8 | 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 |
c0876c166a8f2e3727f98790d0451d9a3a608423 | 230 | py | Python | inventory_management/inventory/admin.py | macungmacung/cruddjango | f4ba0e34aad11721a22cb0f20f37ac70eb49012c | [
"MIT"
] | null | null | null | inventory_management/inventory/admin.py | macungmacung/cruddjango | f4ba0e34aad11721a22cb0f20f37ac70eb49012c | [
"MIT"
] | null | null | null | inventory_management/inventory/admin.py | macungmacung/cruddjango | f4ba0e34aad11721a22cb0f20f37ac70eb49012c | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import *
# Register your models here.
from import_export.admin import ImportExportModelAdmin
@admin.register(Desktop, Laptop, Mobile)
class ViewAdmin(ImportExportModelAdmin):
pass | 28.75 | 54 | 0.817391 | 27 | 230 | 6.925926 | 0.62963 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.117391 | 230 | 8 | 55 | 28.75 | 0.921182 | 0.113043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.166667 | 0.666667 | 0 | 0.833333 | 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 |
c0b79ca5e56a4d2af8900a1c6ceeb58989241ab1 | 37 | py | Python | {{cookiecutter.project_slug}}/backend/app/app/data/manage.py | tonyf/full-stack-fastapi-postgresql | 2bd73227b14c55ea2c69af46ce169e25e3f4cd26 | [
"MIT"
] | null | null | null | {{cookiecutter.project_slug}}/backend/app/app/data/manage.py | tonyf/full-stack-fastapi-postgresql | 2bd73227b14c55ea2c69af46ce169e25e3f4cd26 | [
"MIT"
] | null | null | null | {{cookiecutter.project_slug}}/backend/app/app/data/manage.py | tonyf/full-stack-fastapi-postgresql | 2bd73227b14c55ea2c69af46ce169e25e3f4cd26 | [
"MIT"
] | null | null | null | from .user.manage import user # noqa | 37 | 37 | 0.756757 | 6 | 37 | 4.666667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.162162 | 37 | 1 | 37 | 37 | 0.903226 | 0.108108 | 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 |
8d099f01b5b7a6bbff2a721f486a4c907c77da78 | 34 | py | Python | arrowstack/__init__.py | effordsbeard/arrowstack | 33f2eff3be07cf65e38610f0701743e775c1bbc6 | [
"MIT"
] | 1 | 2018-10-12T11:43:07.000Z | 2018-10-12T11:43:07.000Z | arrowstack/release.py | effordsbeard/arrowstack | 33f2eff3be07cf65e38610f0701743e775c1bbc6 | [
"MIT"
] | null | null | null | arrowstack/release.py | effordsbeard/arrowstack | 33f2eff3be07cf65e38610f0701743e775c1bbc6 | [
"MIT"
] | null | null | null | def main():
print('asdafasd')
| 11.333333 | 21 | 0.588235 | 4 | 34 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.205882 | 34 | 2 | 22 | 17 | 0.740741 | 0 | 0 | 0 | 0 | 0 | 0.235294 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 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 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
23916d217c28f0d8e6cf58d940a9ab632fe75688 | 216 | py | Python | RemoteIocServer/__init__.py | GustavLero/EPICS-inst_servers | 4bcdd6a80f1d9e074de3f0f7c66968d506981988 | [
"BSD-3-Clause"
] | null | null | null | RemoteIocServer/__init__.py | GustavLero/EPICS-inst_servers | 4bcdd6a80f1d9e074de3f0f7c66968d506981988 | [
"BSD-3-Clause"
] | null | null | null | RemoteIocServer/__init__.py | GustavLero/EPICS-inst_servers | 4bcdd6a80f1d9e074de3f0f7c66968d506981988 | [
"BSD-3-Clause"
] | null | null | null | from __future__ import print_function, unicode_literals, division, absolute_import
from RemoteIocServer import config_monitor, gateway, pvdb, utilities
__all__ = ['config_monitor', 'gateway', 'pvdb', 'utilities']
| 30.857143 | 82 | 0.800926 | 24 | 216 | 6.666667 | 0.666667 | 0.1625 | 0.25 | 0.3 | 0.4125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.106481 | 216 | 6 | 83 | 36 | 0.829016 | 0 | 0 | 0 | 0 | 0 | 0.157407 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.333333 | 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 |
23feafdc0161f7458664c46c08363862883d9ea6 | 4,770 | py | Python | socialserver/tests/api/legacy/test_admin_usermod.py | niallasher/socialserver-neo | 7e7d25d939133d149b56ccd54fbfa62d75cabb73 | [
"MIT"
] | null | null | null | socialserver/tests/api/legacy/test_admin_usermod.py | niallasher/socialserver-neo | 7e7d25d939133d149b56ccd54fbfa62d75cabb73 | [
"MIT"
] | 11 | 2022-03-10T04:55:09.000Z | 2022-03-30T14:24:19.000Z | socialserver/tests/api/legacy/test_admin_usermod.py | niallasher/socialserver-neo | 7e7d25d939133d149b56ccd54fbfa62d75cabb73 | [
"MIT"
] | null | null | null | # Copyright (c) Niall Asher 2022
# noinspection PyUnresolvedReferences
from socialserver.util.test import (
test_db,
set_user_attributes_db,
create_user_with_request,
server_address,
)
from socialserver.constants import (
AccountAttributes,
LegacyErrorCodes,
LegacyAdminUserModTypes,
)
import requests
def test_toggle_verification_status_on_user_legacy(test_db, server_address):
# make test user an admin
set_user_attributes_db(
test_db.db, username="test", attributes=[AccountAttributes.ADMIN.value]
)
create_user_with_request(username="test2", password="password")
# verify the user
r = requests.post(
f"{server_address}/api/v1/admin/usermod",
json={
"session_token": test_db.access_token,
"modtype": LegacyAdminUserModTypes.VERIFICATION_STATUS.value,
"username": "test2",
},
)
assert r.status_code == 201
# get info and check they're verified now
r = requests.get(
f"{server_address}/api/v1/users",
json={"session_token": test_db.access_token, "username": "test2"},
)
assert r.json()["isVerified"] is True
assert r.status_code == 200
# now undo it!
r = requests.post(
f"{server_address}/api/v1/admin/usermod",
json={
"session_token": test_db.access_token,
"modtype": LegacyAdminUserModTypes.VERIFICATION_STATUS.value,
"username": "test2",
},
)
assert r.status_code == 201
# get info and check they're no longer verified
r = requests.get(
f"{server_address}/api/v1/users",
json={"session_token": test_db.access_token, "username": "test2"},
)
assert r.json()["isVerified"] is False
assert r.status_code == 200
def test_toggle_moderation_status_on_user_legacy(test_db, server_address):
# make test user an admin
set_user_attributes_db(
test_db.db, username="test", attributes=[AccountAttributes.ADMIN.value]
)
create_user_with_request(username="test2", password="password")
# verify the user
r = requests.post(
f"{server_address}/api/v1/admin/usermod",
json={
"session_token": test_db.access_token,
"modtype": LegacyAdminUserModTypes.MODERATOR_STATUS.value,
"username": "test2",
},
)
assert r.status_code == 201
# get info and check they're verified now
r = requests.get(
f"{server_address}/api/v1/users",
json={"session_token": test_db.access_token, "username": "test2"},
)
assert r.json()["isModerator"] is True
assert r.status_code == 200
# now undo it!
r = requests.post(
f"{server_address}/api/v1/admin/usermod",
json={
"session_token": test_db.access_token,
"modtype": LegacyAdminUserModTypes.MODERATOR_STATUS.value,
"username": "test2",
},
)
assert r.status_code == 201
# get info and check they're no longer verified
r = requests.get(
f"{server_address}/api/v1/users",
json={"session_token": test_db.access_token, "username": "test2"},
)
assert r.json()["isModerator"] is False
assert r.status_code == 200
def test_attempt_admin_mod_insufficient_perms_legacy(test_db, server_address):
r = requests.post(
f"{server_address}/api/v1/admin/usermod",
json={
"session_token": test_db.access_token,
"modtype": LegacyAdminUserModTypes.VERIFICATION_STATUS.value,
"username": test_db.username,
},
)
assert r.status_code == 401
assert r.json()["err"] == LegacyErrorCodes.USER_NOT_ADMIN.value
def test_attempt_admin_mod_invalid_username_legacy(test_db, server_address):
set_user_attributes_db(
test_db.db, test_db.username, [AccountAttributes.ADMIN.value]
)
r = requests.post(
f"{server_address}/api/v1/admin/usermod",
json={
"session_token": test_db.access_token,
"modtype": LegacyAdminUserModTypes.VERIFICATION_STATUS.value,
"username": "does_not_exist",
},
)
assert r.status_code == 404
def test_attempt_admin_mod_invalid_modtype_legacy(test_db, server_address):
set_user_attributes_db(
test_db.db, test_db.username, [AccountAttributes.ADMIN.value]
)
r = requests.post(
f"{server_address}/api/v1/admin/usermod",
json={
"session_token": test_db.access_token,
"modtype": "definitely_invalid",
"username": test_db.username,
},
)
# this is supposed to be 500. the old server returned 500,
# so the compatibility stuff requires this :(
assert r.status_code == 500
| 29.627329 | 79 | 0.646122 | 566 | 4,770 | 5.19788 | 0.174912 | 0.050986 | 0.052345 | 0.063562 | 0.805574 | 0.791978 | 0.772264 | 0.772264 | 0.772264 | 0.75051 | 0 | 0.017689 | 0.241509 | 4,770 | 160 | 80 | 29.8125 | 0.795467 | 0.093291 | 0 | 0.588235 | 0 | 0 | 0.18705 | 0.087027 | 0 | 0 | 0 | 0 | 0.134454 | 1 | 0.042017 | false | 0.016807 | 0.02521 | 0 | 0.067227 | 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 |
f1bd5c0aba107ad3315a57bd619e8e3e3c41e434 | 153 | py | Python | django_blog/config/views.py | 092113219/django-blog | 7bb907f51a5be8b1152e0b5117bc046894f993c3 | [
"Apache-2.0"
] | null | null | null | django_blog/config/views.py | 092113219/django-blog | 7bb907f51a5be8b1152e0b5117bc046894f993c3 | [
"Apache-2.0"
] | null | null | null | django_blog/config/views.py | 092113219/django-blog | 7bb907f51a5be8b1152e0b5117bc046894f993c3 | [
"Apache-2.0"
] | null | null | null | from django.shortcuts import render
from django.http import HttpResponse
# Create your views here.
def links(request):
return HttpResponse('links') | 21.857143 | 36 | 0.784314 | 20 | 153 | 6 | 0.75 | 0.166667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.143791 | 153 | 7 | 37 | 21.857143 | 0.916031 | 0.150327 | 0 | 0 | 0 | 0 | 0.03876 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.5 | 0.25 | 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 |
f1c33c1a386973a9ad083903a5cfdf2ba886ee03 | 15,642 | py | Python | python/graph_script.py | StocksandVagabonds/CS122-Booling4Soup | dc9f08853c81ccd65e58b89781b9a2d07ff428de | [
"Unlicense",
"MIT"
] | 1 | 2021-03-29T04:38:06.000Z | 2021-03-29T04:38:06.000Z | python/graph_script.py | StocksandVagabonds/CS122-Booling4Soup | dc9f08853c81ccd65e58b89781b9a2d07ff428de | [
"Unlicense",
"MIT"
] | null | null | null | python/graph_script.py | StocksandVagabonds/CS122-Booling4Soup | dc9f08853c81ccd65e58b89781b9a2d07ff428de | [
"Unlicense",
"MIT"
] | null | null | null | # General Imports
import bokeh.io
import geopandas as gpd
import pandas as pd
import json
import numpy as np
import requests
from urllib.request import urlopen
# Bokeh Imports
from bokeh.models import (CDSView, ColorBar, ColumnDataSource,
CustomJS, CustomJSFilter,
GeoJSONDataSource, HoverTool,
LinearColorMapper, Slider, CategoricalColorMapper)
from bokeh.layouts import column, row, widgetbox
from bokeh.palettes import brewer
from bokeh.plotting import figure
from bokeh.palettes import Spectral6
from bokeh.transform import factor_cmap
# Uploading dataframes
districts = gpd.read_file('generated_data/districts1.shp')
# Converting dataframes to GEOJSON
geosource_districts = GeoJSONDataSource(geojson=districts.to_json())
# Creating a green palette for the geomaps
green_palette = brewer['BuGn'][8][::-1]
# Creating a blue palette for the geomaps
blue_palette = brewer['Blues'][8][::-1]
# Creating a red palette for the geomaps
red_palette = brewer['OrRd'][8][::-1]
# Creating a diverging palette for the geomaps
diverging_palette = brewer['RdBu'][8][::-1]
def tweet_count(title):
"""
Creates the visualizations for the web application using the Bokeh framework
Args:
title ([string]): title of figure
Returns:
p (bokeh object): bokeh plot figure
"""
# Instantiate LinearColorMapper that linearly maps numbers in a range,
# into a sequence of colors.
color_mapper = LinearColorMapper(palette=green_palette, low=0, high=1500)
# Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
width=500, height=20,
border_line_color=None,
location=(0, 0),
orientation='horizontal')
# Create figure object.
p1 = figure(title=title,
plot_height=600,
plot_width=950,
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset")
# Hiding Axis Labels
p1.xaxis.major_tick_line_color = None # turn off x-axis major ticks
p1.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
p1.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p1.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
p1.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p1.yaxis.major_label_text_font_size = '0pt' # turn off y-axis tick labels
# Add patch renderer to figure.
states = p1.patches("xs", "ys", source=geosource_districts,
fill_color={'field': 'tweet_cnt_',
'transform': color_mapper},
line_color='gray',
line_width=0.75,
fill_alpha=1)
# Create hover tool
p1.add_tools(HoverTool(renderers=[states],
tooltips=[('State', '@state_name'),
('Party', '@party'),
('Name of Representative', '@name'),
('Vote Summary', '@vote_summa'),
('Tweet Keywords', '@keywords_l')
]))
# Specify layout
p1.add_layout(color_bar, 'below')
return p1
def user_count(title):
"""
Creates the visualizations for the web application using the Bokeh framework
Args:
title ([string]): title of figure
Returns:
p (bokeh object): bokeh plot figure
"""
# Instantiate LinearColorMapper that linearly maps numbers in a range,
# into a sequence of colors.
color_mapper = LinearColorMapper(palette=green_palette, low=0, high=300)
# Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
width=500, height=20,
border_line_color=None,
location=(0, 0),
orientation='horizontal')
# Create figure object.
p2 = figure(title=title,
plot_height=600,
plot_width=950,
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset")
# Hiding Axis Labels
p2.xaxis.major_tick_line_color = None # turn off x-axis major ticks
p2.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
p2.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p2.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
p2.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p2.yaxis.major_label_text_font_size = '0pt' # turn off y-axis tick labels
# Add patch renderer to figure.
states = p2.patches("xs", "ys", source=geosource_districts,
fill_color={'field': 'users_cnt_',
'transform': color_mapper},
line_color='gray',
line_width=0.75,
fill_alpha=1)
# Create hover tool
p2.add_tools(HoverTool(renderers=[states],
tooltips=[('State', '@state_name'),
('Party', '@party'),
('Name of Representative', '@name'),
('Vote Summary', '@vote_summa'),
('Tweet Keywords', '@keywords_l')
]))
# Specify layout
p2.add_layout(color_bar, 'below')
return p2
def vote_summary(title):
"""
Creates the visualizations for the web application using the Bokeh framework
Args:
title ([string]): title of figure
Returns:
p (bokeh object): bokeh plot figure
"""
color_mapper = CategoricalColorMapper(
palette=["#7cd274", "gray", "#f7a9a1"],
factors=['democractic', 'mixed', 'anti_democratic'])
# Create figure object.
p3 = figure(title=title,
plot_height=600,
plot_width=950,
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset")
# Hiding Axis Labels
p3.xaxis.major_tick_line_color = None # turn off x-axis major ticks
p3.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
p3.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p3.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
p3.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p3.yaxis.major_label_text_font_size = '0pt' # turn off y-axis tick labels
# Add patch renderer to figure.
states = p3.patches("xs", "ys", source=geosource_districts,
fill_color={'field': 'vote_summa',
'transform': color_mapper},
line_color='gray',
line_width=0.75,
fill_alpha=1)
# Create hover tool
p3.add_tools(HoverTool(renderers=[states],
tooltips=[('State', '@state_name'),
('Party', '@party'),
('Impeach Trump', '@vote_17'),
('Reject Arizona election results', '@vote_10'),
('Approve Pennsylvania election results', '@vote_11'),
('Name of Representative', '@name'),
('Vote Summary', '@vote_summa')
]))
return p3
def positive_reps(title):
"""
Creates the visualizations for the web application using the Bokeh framework
Args:
title ([string]): title of figure
Returns:
p (bokeh object): bokeh plot figure
"""
# Instantiate LinearColorMapper that linearly maps numbers in a range,
# into a sequence of colors.
color_mapper = LinearColorMapper(palette=blue_palette, low=0, high=max(districts['percent_po']))
# Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
width=500, height=20,
border_line_color=None,
location=(0, 0),
orientation='horizontal')
# Create figure object.
p4 = figure(title="Percent Positive for Reps",
plot_height=600,
plot_width=950,
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset")
# Hiding Axis Labels
p4.xaxis.major_tick_line_color = None # turn off x-axis major ticks
p4.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
p4.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p4.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
p4.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p4.yaxis.major_label_text_font_size = '0pt' # turn off y-axis tick labels
# Add patch renderer to figure.
states = p4.patches("xs", "ys", source=geosource_districts,
fill_color={'field': 'percent_po',
'transform': color_mapper},
line_color='gray',
line_width=0.75,
fill_alpha=1)
# Create hover tool
p4.add_tools(HoverTool(renderers=[states],
tooltips=[('State', '@state_name'),
('Party', '@party'),
('Percent Positive', '@percent_po'),
('Name of Representative', '@name'),
('Vote Summary', '@vote_summa'),
('Common Words', '@common_wor')
]))
# Specify layout
p4.add_layout(color_bar, 'below')
return p4
def negative_reps(title):
"""
Creates the visualizations for the web application using the Bokeh framework
Args:
title ([string]): title of figure
Returns:
p (bokeh object): bokeh plot figure
"""
# Instantiate LinearColorMapper that linearly maps numbers in a range,
# into a sequence of colors.
color_mapper = LinearColorMapper(palette=red_palette, low=0, high=max(districts['percent_ne']))
# Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
width=500, height=20,
border_line_color=None,
location=(0, 0),
orientation='horizontal')
# Create figure object.
p5 = figure(title="Percent Negative for Reps",
plot_height=600,
plot_width=950,
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset")
# Hiding Axis Labels
p5.xaxis.major_tick_line_color = None # turn off x-axis major ticks
p5.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
p5.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p5.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
p5.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p5.yaxis.major_label_text_font_size = '0pt' # turn off y-axis tick labels
# Add patch renderer to figure.
states = p5.patches("xs", "ys", source=geosource_districts,
fill_color={'field': 'percent_ne',
'transform': color_mapper},
line_color='gray',
line_width=0.75,
fill_alpha=1)
# Create hover tool
p5.add_tools(HoverTool(renderers=[states],
tooltips=[('State', '@state_name'),
('Party', '@party'),
('Percent Negative', '@percent_ne'),
('Name of Representative', '@name'),
('Vote Summary', '@vote_summa'),
('Common Words', '@common_wor')
]))
# Specify layout
p5.add_layout(color_bar, 'below')
return p5
def reps_vs_constituents(title):
"""
Creates the visualizations for the web application using the Bokeh framework
Args:
title ([string]): title of figure
Returns:
p (bokeh object): bokeh plot figure
"""
# Instantiate LinearColorMapper that linearly maps numbers in a range,
# into a sequence of colors.
color_mapper = LinearColorMapper(palette=diverging_palette, low=min(districts['mean_dif_s']), high=max(districts['mean_dif_s']))
# Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
width=500, height=20,
border_line_color=None,
location=(0, 0),
orientation='horizontal')
# Create figure object.
p6 = figure(title=title,
plot_height=600,
plot_width=950,
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset")
# Hiding Axis Labels
p6.xaxis.major_tick_line_color = None # turn off x-axis major ticks
p6.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
p6.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p6.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
p6.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
p6.yaxis.major_label_text_font_size = '0pt' # turn off y-axis tick labels
# Add patch renderer to figure.
states = p6.patches("xs", "ys", source=geosource_districts,
fill_color={'field': 'mean_dif_s',
'transform': color_mapper},
line_color='gray',
line_width=0.75,
fill_alpha=1)
# Create hover tool
p6.add_tools(HoverTool(renderers=[states],
tooltips=[('State', '@state_name'),
('Party', '@party'),
('Percent Positive', '@percent_po'),
('Name of Representative', '@name'),
('Vote Summary', '@vote_summa'),
('Constituent Common words', '@keywords_l'),
('Representative Common words', '@common_wor')
]))
# Specify layout
p6.add_layout(color_bar, 'below')
return p6 | 41.823529 | 133 | 0.53497 | 1,661 | 15,642 | 4.857917 | 0.131246 | 0.031231 | 0.046722 | 0.050564 | 0.817078 | 0.817078 | 0.795638 | 0.78721 | 0.781757 | 0.757467 | 0 | 0.02157 | 0.374632 | 15,642 | 374 | 134 | 41.823529 | 0.803312 | 0.221327 | 0 | 0.497797 | 0 | 0 | 0.132391 | 0.002514 | 0 | 0 | 0 | 0 | 0 | 1 | 0.026432 | false | 0 | 0.057269 | 0 | 0.110132 | 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 |
f1cbf073b07eaf0579db8d4a1a851d7b804ca14a | 42 | py | Python | src/spaceone/secret/error/__init__.py | ku524/secret | c5dad49f40ab1cbbaa0b8f01222de10ae73d1fb1 | [
"Apache-2.0"
] | 7 | 2020-06-04T23:01:12.000Z | 2021-01-31T08:41:29.000Z | src/spaceone/secret/error/__init__.py | ku524/secret | c5dad49f40ab1cbbaa0b8f01222de10ae73d1fb1 | [
"Apache-2.0"
] | 2 | 2020-08-05T13:31:53.000Z | 2021-03-07T15:15:14.000Z | src/spaceone/secret/error/__init__.py | ku524/secret | c5dad49f40ab1cbbaa0b8f01222de10ae73d1fb1 | [
"Apache-2.0"
] | 6 | 2020-06-10T01:59:35.000Z | 2021-11-25T06:30:35.000Z | from spaceone.secret.error.custom import * | 42 | 42 | 0.833333 | 6 | 42 | 5.833333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.071429 | 42 | 1 | 42 | 42 | 0.897436 | 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 |
f1d56daf12218ebc79de51f1f352bcdc0ee062e2 | 44 | py | Python | winregmgr/__init__.py | Luckykarter/winregmgr | c6c2a7963a8d22ac18cc7eddeef23a18c05a6dd8 | [
"MIT"
] | null | null | null | winregmgr/__init__.py | Luckykarter/winregmgr | c6c2a7963a8d22ac18cc7eddeef23a18c05a6dd8 | [
"MIT"
] | null | null | null | winregmgr/__init__.py | Luckykarter/winregmgr | c6c2a7963a8d22ac18cc7eddeef23a18c05a6dd8 | [
"MIT"
] | 1 | 2021-04-14T07:02:52.000Z | 2021-04-14T07:02:52.000Z | from winregmgr.winreg_manager import OpenKey | 44 | 44 | 0.909091 | 6 | 44 | 6.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.068182 | 44 | 1 | 44 | 44 | 0.95122 | 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 |
f1df2dd3811e32df5afc0c755bde102529492a8e | 42 | py | Python | nm_launch_api/config/local.py | markliederbach/nm-launch-api | e4e4519d4b23680f906f2efcea036112cb8729e3 | [
"ISC"
] | null | null | null | nm_launch_api/config/local.py | markliederbach/nm-launch-api | e4e4519d4b23680f906f2efcea036112cb8729e3 | [
"ISC"
] | null | null | null | nm_launch_api/config/local.py | markliederbach/nm-launch-api | e4e4519d4b23680f906f2efcea036112cb8729e3 | [
"ISC"
] | null | null | null | from nm_launch_api.config.common import *
| 21 | 41 | 0.833333 | 7 | 42 | 4.714286 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.095238 | 42 | 1 | 42 | 42 | 0.868421 | 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 |
9e80340cd0fe7dfab38c962d50f409a265e0a78e | 55 | py | Python | myproject/__init__.py | houghb/myproject | 3b913acae36df4fbddbe2fe684c512ae6e6d1805 | [
"BSD-2-Clause"
] | null | null | null | myproject/__init__.py | houghb/myproject | 3b913acae36df4fbddbe2fe684c512ae6e6d1805 | [
"BSD-2-Clause"
] | null | null | null | myproject/__init__.py | houghb/myproject | 3b913acae36df4fbddbe2fe684c512ae6e6d1805 | [
"BSD-2-Clause"
] | 1 | 2019-10-02T17:08:17.000Z | 2019-10-02T17:08:17.000Z | from . import pronto_utils_b
from . import basic_utils
| 18.333333 | 28 | 0.818182 | 9 | 55 | 4.666667 | 0.666667 | 0.47619 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.145455 | 55 | 2 | 29 | 27.5 | 0.893617 | 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 |
9ebc6d4c7babb00967a8259d65c5a4df5241101e | 29 | py | Python | src/ml_helper/__init__.py | akoury/MachineLearning | 69b41a6a613363eee568921ed35f750b32dac69f | [
"MIT"
] | 9 | 2019-04-15T08:00:03.000Z | 2021-04-29T14:52:33.000Z | src/ml_helper/__init__.py | akoury/MachineLearning | 69b41a6a613363eee568921ed35f750b32dac69f | [
"MIT"
] | null | null | null | src/ml_helper/__init__.py | akoury/MachineLearning | 69b41a6a613363eee568921ed35f750b32dac69f | [
"MIT"
] | null | null | null | from ml_helper import helper
| 14.5 | 28 | 0.862069 | 5 | 29 | 4.8 | 0.8 | 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 |
7b541877ae8a46815519c83697d83d01149bc958 | 46 | py | Python | lectures/lectures/python/02_variables_and_functions/my_package/foo_module.py | wilsonjefferson/DSSC_AP | 18010e3166e4477234958202edec1e3f63b418e3 | [
"MIT"
] | null | null | null | lectures/lectures/python/02_variables_and_functions/my_package/foo_module.py | wilsonjefferson/DSSC_AP | 18010e3166e4477234958202edec1e3f63b418e3 | [
"MIT"
] | null | null | null | lectures/lectures/python/02_variables_and_functions/my_package/foo_module.py | wilsonjefferson/DSSC_AP | 18010e3166e4477234958202edec1e3f63b418e3 | [
"MIT"
] | 1 | 2021-06-24T13:30:36.000Z | 2021-06-24T13:30:36.000Z | def foo():
print('Fooing from a package')
| 15.333333 | 34 | 0.630435 | 7 | 46 | 4.142857 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.217391 | 46 | 2 | 35 | 23 | 0.805556 | 0 | 0 | 0 | 0 | 0 | 0.456522 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 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 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
7b5dbcd6566bbac662e5df25c6fc15eff328ff83 | 26 | py | Python | modules/2.79/bpy/types/KeyingSet.py | cmbasnett/fake-bpy-module | acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55 | [
"MIT"
] | null | null | null | modules/2.79/bpy/types/KeyingSet.py | cmbasnett/fake-bpy-module | acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55 | [
"MIT"
] | null | null | null | modules/2.79/bpy/types/KeyingSet.py | cmbasnett/fake-bpy-module | acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55 | [
"MIT"
] | null | null | null | def refresh():
pass
| 5.2 | 14 | 0.538462 | 3 | 26 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.346154 | 26 | 4 | 15 | 6.5 | 0.823529 | 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 |
7b63a65d4f3001330e1752e90afa490890a08536 | 39,196 | py | Python | tests/unit/netapi/rest_tornado/test_saltnado.py | tomdoherty/salt | f87d5d7abbf9777773c4d91fdafecb8b1a728e76 | [
"Apache-2.0"
] | 9,425 | 2015-01-01T05:59:24.000Z | 2022-03-31T20:44:05.000Z | tests/unit/netapi/rest_tornado/test_saltnado.py | tomdoherty/salt | f87d5d7abbf9777773c4d91fdafecb8b1a728e76 | [
"Apache-2.0"
] | 33,507 | 2015-01-01T00:19:56.000Z | 2022-03-31T23:48:20.000Z | tests/unit/netapi/rest_tornado/test_saltnado.py | tomdoherty/salt | f87d5d7abbf9777773c4d91fdafecb8b1a728e76 | [
"Apache-2.0"
] | 5,810 | 2015-01-01T19:11:45.000Z | 2022-03-31T02:37:20.000Z | import salt.ext.tornado
import salt.ext.tornado.testing
import salt.netapi.rest_tornado.saltnado as saltnado
from tests.support.mock import MagicMock, patch
class TestJobNotRunning(salt.ext.tornado.testing.AsyncTestCase):
def setUp(self):
super().setUp()
self.mock = MagicMock()
self.mock.opts = {
"syndic_wait": 0.1,
"cachedir": "/tmp/testing/cachedir",
"sock_dir": "/tmp/testing/sock_drawer",
"transport": "zeromq",
"extension_modules": "/tmp/testing/moduuuuules",
"order_masters": False,
"gather_job_timeout": 10.001,
}
self.handler = saltnado.SaltAPIHandler(self.mock, self.mock)
self.handler._write_buffer = []
self.handler._transforms = []
self.handler.lowstate = []
self.handler.content_type = "text/plain"
self.handler.dumper = lambda x: x
f = salt.ext.tornado.gen.Future()
f.set_result({"jid": f, "minions": []})
self.handler.saltclients.update({"local": lambda *args, **kwargs: f})
@salt.ext.tornado.testing.gen_test
def test_when_disbatch_has_already_finished_then_writing_return_should_not_fail(
self,
):
self.handler.finish()
result = yield self.handler.disbatch()
# No assertion necessary, because we just want no failure here.
# Asserting that it doesn't raise anything is... the default behavior
# for a test.
@salt.ext.tornado.testing.gen_test
def test_when_disbatch_has_already_finished_then_finishing_should_not_fail(self):
self.handler.finish()
result = yield self.handler.disbatch()
# No assertion necessary, because we just want no failure here.
# Asserting that it doesn't raise anything is... the default behavior
# for a test.
@salt.ext.tornado.testing.gen_test
def test_when_event_times_out_and_minion_is_not_running_result_should_be_True(self):
fut = salt.ext.tornado.gen.Future()
fut.set_exception(saltnado.TimeoutException())
self.mock.event_listener.get_event.return_value = fut
wrong_future = salt.ext.tornado.gen.Future()
result = yield self.handler.job_not_running(
jid=42, tgt="*", tgt_type="glob", minions=[], is_finished=wrong_future
)
self.assertTrue(result)
@salt.ext.tornado.testing.gen_test
def test_when_event_times_out_and_minion_is_not_running_minion_data_should_not_be_set(
self,
):
fut = salt.ext.tornado.gen.Future()
fut.set_exception(saltnado.TimeoutException())
self.mock.event_listener.get_event.return_value = fut
wrong_future = salt.ext.tornado.gen.Future()
minions = {}
result = yield self.handler.job_not_running(
jid=42, tgt="*", tgt_type="glob", minions=minions, is_finished=wrong_future
)
assert not minions
@salt.ext.tornado.testing.gen_test
def test_when_event_finally_finishes_and_returned_minion_not_in_minions_it_should_be_set_to_False(
self,
):
expected_id = 42
no_data_event = salt.ext.tornado.gen.Future()
no_data_event.set_result({"data": {}})
empty_return_event = salt.ext.tornado.gen.Future()
empty_return_event.set_result({"data": {"return": {}}})
actual_return_event = salt.ext.tornado.gen.Future()
actual_return_event.set_result(
{"data": {"return": {"something happened here": "OK?"}, "id": expected_id}}
)
timed_out_event = salt.ext.tornado.gen.Future()
timed_out_event.set_exception(saltnado.TimeoutException())
self.mock.event_listener.get_event.side_effect = [
no_data_event,
empty_return_event,
actual_return_event,
timed_out_event,
timed_out_event,
]
minions = {}
yield self.handler.job_not_running(
jid=99,
tgt="*",
tgt_type="fnord",
minions=minions,
is_finished=salt.ext.tornado.gen.Future(),
)
self.assertFalse(minions[expected_id])
@salt.ext.tornado.testing.gen_test
def test_when_event_finally_finishes_and_returned_minion_already_in_minions_it_should_not_be_changed(
self,
):
expected_id = 42
expected_value = object()
minions = {expected_id: expected_value}
no_data_event = salt.ext.tornado.gen.Future()
no_data_event.set_result({"data": {}})
empty_return_event = salt.ext.tornado.gen.Future()
empty_return_event.set_result({"data": {"return": {}}})
actual_return_event = salt.ext.tornado.gen.Future()
actual_return_event.set_result(
{"data": {"return": {"something happened here": "OK?"}, "id": expected_id}}
)
timed_out_event = salt.ext.tornado.gen.Future()
timed_out_event.set_exception(saltnado.TimeoutException())
self.mock.event_listener.get_event.side_effect = [
no_data_event,
empty_return_event,
actual_return_event,
timed_out_event,
timed_out_event,
]
yield self.handler.job_not_running(
jid=99,
tgt="*",
tgt_type="fnord",
minions=minions,
is_finished=salt.ext.tornado.gen.Future(),
)
self.assertIs(minions[expected_id], expected_value)
@salt.ext.tornado.testing.gen_test
def test_when_event_returns_early_and_finally_times_out_result_should_be_True(self):
no_data_event = salt.ext.tornado.gen.Future()
no_data_event.set_result({"data": {}})
empty_return_event = salt.ext.tornado.gen.Future()
empty_return_event.set_result({"data": {"return": {}}})
actual_return_event = salt.ext.tornado.gen.Future()
actual_return_event.set_result(
{"data": {"return": {"something happened here": "OK?"}, "id": "fnord"}}
)
timed_out_event = salt.ext.tornado.gen.Future()
timed_out_event.set_exception(saltnado.TimeoutException())
self.mock.event_listener.get_event.side_effect = [
no_data_event,
empty_return_event,
actual_return_event,
timed_out_event,
timed_out_event,
]
result = yield self.handler.job_not_running(
jid=99,
tgt="*",
tgt_type="fnord",
minions={},
is_finished=salt.ext.tornado.gen.Future(),
)
self.assertTrue(result)
@salt.ext.tornado.testing.gen_test
def test_when_event_finishes_but_is_finished_is_done_then_result_should_be_True(
self,
):
expected_minion_id = "fnord"
expected_minion_value = object()
no_data_event = salt.ext.tornado.gen.Future()
no_data_event.set_result({"data": {}})
empty_return_event = salt.ext.tornado.gen.Future()
empty_return_event.set_result({"data": {"return": {}}})
actual_return_event = salt.ext.tornado.gen.Future()
actual_return_event.set_result(
{
"data": {
"return": {"something happened here": "OK?"},
"id": expected_minion_id,
}
}
)
is_finished = salt.ext.tornado.gen.Future()
def abort(*args, **kwargs):
yield actual_return_event
f = salt.ext.tornado.gen.Future()
f.set_exception(saltnado.TimeoutException())
is_finished.set_result("This is done")
yield f
assert False, "Never should make it here"
minions = {expected_minion_id: expected_minion_value}
self.mock.event_listener.get_event.side_effect = (x for x in abort())
result = yield self.handler.job_not_running(
jid=99,
tgt="*",
tgt_type="fnord",
minions=minions,
is_finished=is_finished,
)
self.assertTrue(result)
# These are failsafes to ensure nothing super sideways happened
self.assertTrue(len(minions) == 1, str(minions))
self.assertIs(minions[expected_minion_id], expected_minion_value)
@salt.ext.tornado.testing.gen_test
def test_when_is_finished_times_out_before_event_finishes_result_should_be_True(
self,
):
# Other test times out with event - this one should time out for is_finished
finished = salt.ext.tornado.gen.Future()
finished.set_exception(saltnado.TimeoutException())
wrong_future = salt.ext.tornado.gen.Future()
self.mock.event_listener.get_event.return_value = wrong_future
result = yield self.handler.job_not_running(
jid=42, tgt="*", tgt_type="glob", minions=[], is_finished=finished
)
self.assertTrue(result)
@salt.ext.tornado.testing.gen_test
def test_when_is_finished_times_out_before_event_finishes_event_should_have_result_set_to_None(
self,
):
finished = salt.ext.tornado.gen.Future()
finished.set_exception(saltnado.TimeoutException())
wrong_future = salt.ext.tornado.gen.Future()
self.mock.event_listener.get_event.return_value = wrong_future
result = yield self.handler.job_not_running(
jid=42, tgt="*", tgt_type="glob", minions=[], is_finished=finished
)
self.assertIsNone(wrong_future.result())
# TODO: I think we can extract seUp into a superclass -W. Werner, 2020-11-03
class TestGetMinionReturns(salt.ext.tornado.testing.AsyncTestCase):
def setUp(self):
super().setUp()
self.mock = MagicMock()
self.mock.opts = {
"syndic_wait": 0.1,
"cachedir": "/tmp/testing/cachedir",
"sock_dir": "/tmp/testing/sock_drawer",
"transport": "zeromq",
"extension_modules": "/tmp/testing/moduuuuules",
"order_masters": False,
"gather_job_timeout": 10.001,
}
self.handler = saltnado.SaltAPIHandler(self.mock, self.mock)
f = salt.ext.tornado.gen.Future()
f.set_result({"jid": f, "minions": []})
@salt.ext.tornado.testing.gen_test
def test_if_finished_before_any_events_return_then_result_should_be_empty_dictionary(
self,
):
expected_result = {}
xxx = salt.ext.tornado.gen.Future()
xxx.set_result(None)
is_finished = salt.ext.tornado.gen.Future()
is_finished.set_result(None)
actual_result = yield self.handler.get_minion_returns(
events=[],
is_finished=is_finished,
is_timed_out=salt.ext.tornado.gen.Future(),
min_wait_time=xxx,
minions={},
)
self.assertDictEqual(actual_result, expected_result)
# TODO: Copy above - test with timed out -W. Werner, 2020-11-05
@salt.ext.tornado.testing.gen_test
def test_if_is_finished_after_events_return_then_result_should_contain_event_result_data(
self,
):
expected_result = {
"minion1": {"fnord": "this is some fnordish data"},
"minion2": {"fnord": "this is some other fnordish data"},
}
xxx = salt.ext.tornado.gen.Future()
xxx.set_result(None)
is_finished = salt.ext.tornado.gen.Future()
# XXX what do I do here?
events = [
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
]
events[0].set_result(
{
"tag": "fnord",
"data": {"id": "minion1", "return": expected_result["minion1"]},
}
)
events[1].set_result(
{
"tag": "fnord",
"data": {"id": "minion2", "return": expected_result["minion2"]},
}
)
self.io_loop.call_later(0.2, lambda: is_finished.set_result(None))
actual_result = yield self.handler.get_minion_returns(
events=events,
is_finished=is_finished,
is_timed_out=salt.ext.tornado.gen.Future(),
min_wait_time=xxx,
minions={
"minion1": False,
"minion2": False,
"never returning minion": False,
},
)
assert actual_result == expected_result
@salt.ext.tornado.testing.gen_test
def test_if_timed_out_after_events_return_then_result_should_contain_event_result_data(
self,
):
expected_result = {
"minion1": {"fnord": "this is some fnordish data"},
"minion2": {"fnord": "this is some other fnordish data"},
}
xxx = salt.ext.tornado.gen.Future()
xxx.set_result(None)
is_timed_out = salt.ext.tornado.gen.Future()
# XXX what do I do here?
events = [
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
]
events[0].set_result(
{
"tag": "fnord",
"data": {"id": "minion1", "return": expected_result["minion1"]},
}
)
events[1].set_result(
{
"tag": "fnord",
"data": {"id": "minion2", "return": expected_result["minion2"]},
}
)
self.io_loop.call_later(0.2, lambda: is_timed_out.set_result(None))
actual_result = yield self.handler.get_minion_returns(
events=events,
is_finished=salt.ext.tornado.gen.Future(),
is_timed_out=is_timed_out,
min_wait_time=xxx,
minions={
"minion1": False,
"minion2": False,
"never returning minion": False,
},
)
assert actual_result == expected_result
@salt.ext.tornado.testing.gen_test
def test_if_wait_timer_is_not_done_even_though_results_are_then_data_should_not_yet_be_returned(
self,
):
expected_result = {
"one": {"fnordy one": "one has some data"},
"two": {"fnordy two": "two has some data"},
}
events = [salt.ext.tornado.gen.Future(), salt.ext.tornado.gen.Future()]
events[0].set_result(
{"tag": "fnord", "data": {"id": "one", "return": expected_result["one"]}}
)
events[1].set_result(
{"tag": "fnord", "data": {"id": "two", "return": expected_result["two"]}}
)
wait_timer = salt.ext.tornado.gen.Future()
fut = self.handler.get_minion_returns(
events=events,
is_finished=salt.ext.tornado.gen.Future(),
is_timed_out=salt.ext.tornado.gen.Future(),
min_wait_time=wait_timer,
minions={"one": False, "two": False},
)
def boop():
yield fut
self.io_loop.spawn_callback(boop)
yield salt.ext.tornado.gen.sleep(0.1)
assert not fut.done()
wait_timer.set_result(None)
actual_result = yield fut
assert actual_result == expected_result
@salt.ext.tornado.testing.gen_test
def test_when_is_finished_any_other_futures_should_be_canceled(self):
events = [
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
]
is_finished = salt.ext.tornado.gen.Future()
is_finished.set_result(None)
yield self.handler.get_minion_returns(
events=events,
is_finished=is_finished,
is_timed_out=salt.ext.tornado.gen.Future(),
min_wait_time=salt.ext.tornado.gen.Future(),
minions={"one": False, "two": False},
)
are_done = [event.done() for event in events]
assert all(are_done)
@salt.ext.tornado.testing.gen_test
def test_when_an_event_times_out_then_we_should_not_enter_an_infinite_loop(self):
# NOTE: this test will enter an infinite loop if the code is broken. I
# was not able to figure out a way to ensure that the test exits with
# failure rather than stalling forever. That is because the
# TimeoutException happens first and then tornado will never yield
# control to another coroutine. Like a coroutine to remove the future
# with the TimeoutException. It is also not possible to clear the
# TimeoutException.
events = [
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
]
# Arguably any event would work, but 3 isn't the first, so it
# gives us a little more confidence that this test is testing
# correctly
events[3].set_exception(saltnado.TimeoutException())
times_out_later = salt.ext.tornado.gen.Future()
# 0.5s should be long enough that the test gets through doing other
# things before hitting this timeout, which will cancel all the
# in-flight futures.
self.io_loop.call_later(0.5, lambda: times_out_later.set_result(None))
yield self.handler.get_minion_returns(
events=events,
is_finished=salt.ext.tornado.gen.Future(),
is_timed_out=times_out_later,
min_wait_time=salt.ext.tornado.gen.Future(),
minions={"one": False, "two": False},
)
# Technically we don't /need/ to check that all events are done,
# but it's incorrect to exit the function without ensuring all
# futures are canceled.
are_done = [event.done() for event in events]
assert all(are_done)
assert times_out_later.done()
@salt.ext.tornado.testing.gen_test
def test_when_is_timed_out_any_other_futures_should_be_canceled(self):
# There is some question about whether this test is or should be
# necessary. Or if it's meaningful. The code that this is testing
# should never actually be able to make it to this point -- because
# when all events have completed it should exit at a different branch.
# That being said, the worst case is that this is just a duplicate
# or irrelevant test, and can be removed.
events = [
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
]
is_timed_out = salt.ext.tornado.gen.Future()
is_timed_out.set_result(None)
yield self.handler.get_minion_returns(
events=events,
is_finished=salt.ext.tornado.gen.Future(),
is_timed_out=is_timed_out,
min_wait_time=salt.ext.tornado.gen.Future(),
minions={"one": False, "two": False},
)
are_done = [event.done() for event in events]
assert all(are_done)
@salt.ext.tornado.testing.gen_test
def test_when_min_wait_time_and_nothing_todo_any_other_futures_should_be_canceled(
self,
):
events = [
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
salt.ext.tornado.gen.Future(),
]
is_finished = salt.ext.tornado.gen.Future()
min_wait_time = salt.ext.tornado.gen.Future()
self.io_loop.call_later(0.2, lambda: min_wait_time.set_result(None))
yield self.handler.get_minion_returns(
events=events,
is_finished=is_finished,
is_timed_out=salt.ext.tornado.gen.Future(),
min_wait_time=min_wait_time,
minions={"one": True, "two": True},
)
are_done = [event.done() for event in events] + [is_finished.done()]
assert all(are_done)
@salt.ext.tornado.testing.gen_test
def test_when_is_finished_but_not_is_timed_out_then_timed_out_should_not_be_set_to_done(
self,
):
events = [salt.ext.tornado.gen.Future()]
is_timed_out = salt.ext.tornado.gen.Future()
is_finished = salt.ext.tornado.gen.Future()
is_finished.set_result(None)
yield self.handler.get_minion_returns(
events=events,
is_finished=is_finished,
is_timed_out=is_timed_out,
min_wait_time=salt.ext.tornado.gen.Future(),
minions={"one": False, "two": False},
)
assert not is_timed_out.done()
@salt.ext.tornado.testing.gen_test
def test_when_min_wait_time_and_all_completed_but_not_is_timed_out_then_timed_out_should_not_be_set_to_done(
self,
):
events = [salt.ext.tornado.gen.Future()]
is_timed_out = salt.ext.tornado.gen.Future()
min_wait_time = salt.ext.tornado.gen.Future()
self.io_loop.call_later(0.2, lambda: min_wait_time.set_result(None))
yield self.handler.get_minion_returns(
events=events,
is_finished=salt.ext.tornado.gen.Future(),
is_timed_out=is_timed_out,
min_wait_time=min_wait_time,
minions={"one": True},
)
assert not is_timed_out.done()
@salt.ext.tornado.testing.gen_test
def test_when_things_are_completed_but_not_timed_out_then_timed_out_event_should_not_be_done(
self,
):
events = [
salt.ext.tornado.gen.Future(),
]
events[0].set_result({"tag": "fnord", "data": {"id": "one", "return": {}}})
min_wait_time = salt.ext.tornado.gen.Future()
min_wait_time.set_result(None)
is_timed_out = salt.ext.tornado.gen.Future()
yield self.handler.get_minion_returns(
events=events,
is_finished=salt.ext.tornado.gen.Future(),
is_timed_out=is_timed_out,
min_wait_time=min_wait_time,
minions={"one": True},
)
assert not is_timed_out.done()
class TestDisbatchLocal(salt.ext.tornado.testing.AsyncTestCase):
def setUp(self):
super().setUp()
self.mock = MagicMock()
self.mock.opts = {
"syndic_wait": 0.1,
"cachedir": "/tmp/testing/cachedir",
"sock_dir": "/tmp/testing/sock_drawer",
"transport": "zeromq",
"extension_modules": "/tmp/testing/moduuuuules",
"order_masters": False,
"gather_job_timeout": 10.001,
}
self.handler = saltnado.SaltAPIHandler(self.mock, self.mock)
@salt.ext.tornado.testing.gen_test
def test_when_is_timed_out_is_set_before_other_events_are_completed_then_result_should_be_empty_dictionary(
self,
):
completed_event = salt.ext.tornado.gen.Future()
never_completed = salt.ext.tornado.gen.Future()
# TODO: We may need to tweak these values to get them close enough but not so far away -W. Werner, 2020-11-17
gather_timeout = 0.1
event_timeout = gather_timeout + 0.05
def fancy_get_event(*args, **kwargs):
if kwargs.get("tag").endswith("/ret"):
return never_completed
return completed_event
def completer():
completed_event.set_result(
{
"tag": "fnord",
"data": {
"return": "This should never be in chunk_ret",
"id": "fnord",
},
}
)
self.io_loop.call_later(event_timeout, completer)
f = salt.ext.tornado.gen.Future()
f.set_result({"jid": "42", "minions": []})
with patch.object(
self.handler.application.event_listener,
"get_event",
autospec=True,
side_effect=fancy_get_event,
), patch.dict(
self.handler.application.opts,
{"gather_job_timeout": gather_timeout, "timeout": 42},
), patch.dict(
self.handler.saltclients, {"local": lambda *args, **kwargs: f}
):
result = yield self.handler._disbatch_local(
chunk={"tgt": "*", "tgt_type": "glob", "fun": "test.ping"}
)
assert result == {}
@salt.ext.tornado.testing.gen_test
def test_when_is_finished_is_set_before_events_return_then_no_data_should_be_returned(
self,
):
completed_event = salt.ext.tornado.gen.Future()
never_completed = salt.ext.tornado.gen.Future()
gather_timeout = 2
event_timeout = gather_timeout - 1
def fancy_get_event(*args, **kwargs):
if kwargs.get("tag").endswith("/ret"):
return never_completed
return completed_event
def completer():
completed_event.set_result(
{
"tag": "fnord",
"data": {
"return": "This should never be in chunk_ret",
"id": "fnord",
},
}
)
self.io_loop.call_later(event_timeout, completer)
def toggle_is_finished(*args, **kwargs):
finished = kwargs.get("is_finished", args[4] if len(args) > 4 else None)
assert finished is not None
finished.set_result(42)
f = salt.ext.tornado.gen.Future()
f.set_result({"jid": "42", "minions": []})
with patch.object(
self.handler.application.event_listener,
"get_event",
autospec=True,
side_effect=fancy_get_event,
), patch.object(
self.handler,
"job_not_running",
autospec=True,
side_effect=toggle_is_finished,
), patch.dict(
self.handler.application.opts,
{"gather_job_timeout": gather_timeout, "timeout": 42},
), patch.dict(
self.handler.saltclients, {"local": lambda *args, **kwargs: f}
):
result = yield self.handler._disbatch_local(
chunk={"tgt": "*", "tgt_type": "glob", "fun": "test.ping"}
)
assert result == {}
@salt.ext.tornado.testing.gen_test
def test_when_is_finished_then_all_collected_data_should_be_returned(self):
completed_event = salt.ext.tornado.gen.Future()
never_completed = salt.ext.tornado.gen.Future()
# This timeout should never be reached
gather_timeout = 42
completed_events = [salt.ext.tornado.gen.Future() for _ in range(5)]
for i, event in enumerate(completed_events):
event.set_result(
{
"tag": "fnord",
"data": {
"return": "return from fnord {}".format(i),
"id": "fnord {}".format(i),
},
}
)
uncompleted_events = [salt.ext.tornado.gen.Future() for _ in range(5)]
events = iter(completed_events + uncompleted_events)
expected_result = {
"fnord 0": "return from fnord 0",
"fnord 1": "return from fnord 1",
"fnord 2": "return from fnord 2",
"fnord 3": "return from fnord 3",
"fnord 4": "return from fnord 4",
}
def fancy_get_event(*args, **kwargs):
if kwargs.get("tag").endswith("/ret"):
return never_completed
else:
return next(events)
def toggle_is_finished(*args, **kwargs):
finished = kwargs.get("is_finished", args[4] if len(args) > 4 else None)
assert finished is not None
finished.set_result(42)
f = salt.ext.tornado.gen.Future()
f.set_result({"jid": "42", "minions": ["non-existent minion"]})
with patch.object(
self.handler.application.event_listener,
"get_event",
autospec=True,
side_effect=fancy_get_event,
), patch.object(
self.handler,
"job_not_running",
autospec=True,
side_effect=toggle_is_finished,
), patch.dict(
self.handler.application.opts,
{"gather_job_timeout": gather_timeout, "timeout": 42},
), patch.dict(
self.handler.saltclients, {"local": lambda *args, **kwargs: f}
):
result = yield self.handler._disbatch_local(
chunk={"tgt": "*", "tgt_type": "glob", "fun": "test.ping"}
)
assert result == expected_result
@salt.ext.tornado.testing.gen_test
def test_when_is_timed_out_then_all_collected_data_should_be_returned(self):
completed_event = salt.ext.tornado.gen.Future()
never_completed = salt.ext.tornado.gen.Future()
# 2s is probably enough for any kind of computer to manage to
# do all the other processing. We could maybe reduce this - just
# depends on how slow of a system we're running on.
# TODO: Maybe we should have a test helper/fixture that benchmarks the system and gets a reasonable timeout? -W. Werner, 2020-11-19
gather_timeout = 2
completed_events = [salt.ext.tornado.gen.Future() for _ in range(5)]
for i, event in enumerate(completed_events):
event.set_result(
{
"tag": "fnord",
"data": {
"return": "return from fnord {}".format(i),
"id": "fnord {}".format(i),
},
}
)
uncompleted_events = [salt.ext.tornado.gen.Future() for _ in range(5)]
events = iter(completed_events + uncompleted_events)
expected_result = {
"fnord 0": "return from fnord 0",
"fnord 1": "return from fnord 1",
"fnord 2": "return from fnord 2",
"fnord 3": "return from fnord 3",
"fnord 4": "return from fnord 4",
}
def fancy_get_event(*args, **kwargs):
if kwargs.get("tag").endswith("/ret"):
return never_completed
else:
return next(events)
f = salt.ext.tornado.gen.Future()
f.set_result({"jid": "42", "minions": ["non-existent minion"]})
with patch.object(
self.handler.application.event_listener,
"get_event",
autospec=True,
side_effect=fancy_get_event,
), patch.dict(
self.handler.application.opts,
{"gather_job_timeout": gather_timeout, "timeout": 42},
), patch.dict(
self.handler.saltclients, {"local": lambda *args, **kwargs: f}
):
result = yield self.handler._disbatch_local(
chunk={"tgt": "*", "tgt_type": "glob", "fun": "test.ping"}
)
assert result == expected_result
@salt.ext.tornado.testing.gen_test
def test_when_minions_all_return_then_all_collected_data_should_be_returned(self):
completed_event = salt.ext.tornado.gen.Future()
never_completed = salt.ext.tornado.gen.Future()
# Timeout is something ridiculously high - it should never be reached
gather_timeout = 20
completed_events = [salt.ext.tornado.gen.Future() for _ in range(10)]
events_by_id = {}
for i, event in enumerate(completed_events):
id_ = "fnord {}".format(i)
events_by_id[id_] = event
event.set_result(
{
"tag": "fnord",
"data": {"return": "return from {}".format(id_), "id": id_},
}
)
expected_result = {
"fnord 0": "return from fnord 0",
"fnord 1": "return from fnord 1",
"fnord 2": "return from fnord 2",
"fnord 3": "return from fnord 3",
"fnord 4": "return from fnord 4",
"fnord 5": "return from fnord 5",
"fnord 6": "return from fnord 6",
"fnord 7": "return from fnord 7",
"fnord 8": "return from fnord 8",
"fnord 9": "return from fnord 9",
}
def fancy_get_event(*args, **kwargs):
tag = kwargs.get("tag", "").rpartition("/")[-1]
return events_by_id.get(tag, never_completed)
f = salt.ext.tornado.gen.Future()
f.set_result(
{
"jid": "42",
"minions": [e.result()["data"]["id"] for e in completed_events],
}
)
with patch.object(
self.handler.application.event_listener,
"get_event",
autospec=True,
side_effect=fancy_get_event,
), patch.dict(
self.handler.application.opts,
{"gather_job_timeout": gather_timeout, "timeout": 42},
), patch.dict(
self.handler.saltclients, {"local": lambda *args, **kwargs: f}
):
result = yield self.handler._disbatch_local(
chunk={"tgt": "*", "tgt_type": "glob", "fun": "test.ping"}
)
assert result == expected_result
@salt.ext.tornado.testing.gen_test
def test_when_min_wait_time_has_not_passed_then_disbatch_should_not_return_expected_data_until_time_has_passed(
self,
):
completed_event = salt.ext.tornado.gen.Future()
never_completed = salt.ext.tornado.gen.Future()
wait_timer = salt.ext.tornado.gen.Future()
gather_timeout = 20
completed_events = [salt.ext.tornado.gen.Future() for _ in range(10)]
events_by_id = {}
# Setup some real-enough looking return data
for i, event in enumerate(completed_events):
id_ = "fnord {}".format(i)
events_by_id[id_] = event
event.set_result(
{
"tag": "fnord",
"data": {"return": "return from {}".format(id_), "id": id_},
}
)
# Hard coded instead of dynamic to avoid potentially writing a test
# that does nothing
expected_result = {
"fnord 0": "return from fnord 0",
"fnord 1": "return from fnord 1",
"fnord 2": "return from fnord 2",
"fnord 3": "return from fnord 3",
"fnord 4": "return from fnord 4",
"fnord 5": "return from fnord 5",
"fnord 6": "return from fnord 6",
"fnord 7": "return from fnord 7",
"fnord 8": "return from fnord 8",
"fnord 9": "return from fnord 9",
}
# If this is one of our fnord events, return that future, otherwise
# they're bogus events that are irrelevant to our current testing.
# They get to wait for-ev-errrrr
def fancy_get_event(*args, **kwargs):
tag = kwargs.get("tag", "").rpartition("/")[-1]
return events_by_id.get(tag, never_completed)
minions = {}
def capture_minions(*args, **kwargs):
"""
Take minions that would be passed to a function, and
store them for later checking.
"""
nonlocal minions
minions = args[3]
# Needed to have both a fake sleep, as well as a *real* sleep.
# The fake sleep is necessary so that we can return our own
# min_wait_time future. The fakeo_timer object is how we signal
# which one we need to be returning.
orig_sleep = salt.ext.tornado.gen.sleep
fakeo_timer = object()
@salt.ext.tornado.gen.coroutine
def fake_sleep(timer):
# only return our fake min_wait_time future when the sentinel
# value is provided. Otherwise it's just a number.
if timer is fakeo_timer:
yield wait_timer
else:
yield orig_sleep(timer)
f = salt.ext.tornado.gen.Future()
f.set_result(
{
"jid": "42",
"minions": [e.result()["data"]["id"] for e in completed_events],
}
)
with patch.object(
self.handler.application.event_listener,
"get_event",
autospec=True,
side_effect=fancy_get_event,
), patch.object(
self.handler,
"job_not_running",
autospec=True,
side_effect=capture_minions,
), patch.dict(
self.handler.application.opts,
{
"gather_job_timeout": gather_timeout,
"timeout": 42,
"syndic_wait": fakeo_timer,
"order_masters": True,
},
), patch(
"salt.ext.tornado.gen.sleep",
autospec=True,
side_effect=fake_sleep,
), patch.dict(
self.handler.saltclients, {"local": lambda *args, **kwargs: f}
):
# Example timeline that we're testing:
#
# If there's a min wait time of 10s, and all the results come
# back in 5s, we still need to wait the full 10s.
#
# Here:
# t=0, all events are completed
# t=0.1, we check that all minions have been set to True, i.e. all
# events are completed. We also ensure that the future has
# not completed.
# t=0.1+, we complete our injected timer, and then ensure that all
# the correct data has been returned.
fut = self.handler._disbatch_local(
chunk={"tgt": "*", "tgt_type": "glob", "fun": "test.ping"}
)
def boop():
yield fut
self.io_loop.spawn_callback(boop)
yield salt.ext.tornado.gen.sleep(0.1)
# here, all the minions should be complete (i.e. "True")
assert all(minions[m_id] for m_id in minions)
# But _disbatch_local is not returned yet because min_wait_time has not passed
assert not fut.done()
wait_timer.set_result(None)
result = yield fut
assert result == expected_result
# Question: Currently, job_not_running can add to the minions dict, which
# affects the more_todo result. However, the events are never added to
# once we have entered the loop. I'm not sure if this is an oversight, or
# simply an implicit expectation. I am making the assumption that this
# behavior is correct and does not need extra testing. Otherwise, we should
# be testing that when minions are added within job_not_running, that it
# should affect the regular loop
# -W. Werner, 2020-11-19
| 37.833977 | 139 | 0.58284 | 4,674 | 39,196 | 4.657039 | 0.092854 | 0.050811 | 0.101622 | 0.098406 | 0.775256 | 0.758074 | 0.751505 | 0.737998 | 0.728993 | 0.719392 | 0 | 0.009238 | 0.306792 | 39,196 | 1,035 | 140 | 37.870531 | 0.791874 | 0.103888 | 0 | 0.752663 | 0 | 0 | 0.088928 | 0.006656 | 0 | 0 | 0 | 0.001932 | 0.040237 | 1 | 0.053254 | false | 0.001183 | 0.004734 | 0 | 0.073373 | 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 |
7b92dea6921d24d64189a1e25d70a7147dd1d83b | 10,586 | py | Python | src/models/DataLoader.py | prise6/lincoln-psg-challenge | 5810f51ade6a0d73d5b2ed6e9ff5143fe88701d8 | [
"MIT"
] | null | null | null | src/models/DataLoader.py | prise6/lincoln-psg-challenge | 5810f51ade6a0d73d5b2ed6e9ff5143fe88701d8 | [
"MIT"
] | 1 | 2021-03-25T23:11:26.000Z | 2021-03-25T23:11:26.000Z | src/models/DataLoader.py | prise6/lincoln-psg-challenge | 5810f51ade6a0d73d5b2ed6e9ff5143fe88701d8 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
class DataLoader:
def __init__(self, config, type_loader = 'simple'):
self.data = None
self.train_data = None
self.test_data = None
self.type_loader = type_loader
self.config = config
self.len_seq_train = self.config.get('data_loader')[self.type_loader]['len_seq_train']
self.len_seq_pred = self.config.get('data_loader')[self.type_loader]['len_seq_pred']
self.batch_size = self.config.get('data_loader')[self.type_loader]['batch_size']
self.output_dim = self.config.get('data_loader')[self.type_loader]['output_dim']
self.read_data()
self.split_train_test()
def read_data(self):
self.data = pd.read_csv(self.config.get('data_loader')[self.type_loader]['data_path'])
def split_train_test(self):
np.random.seed = 263131
liste_game_id = np.unique(self.data['game_id'])
np.random.shuffle(liste_game_id)
game_id_train = liste_game_id[0:120]
game_id_test = liste_game_id[120:]
self.train_data = self.data[self.data['game_id'].isin(game_id_train)]
self.test_data = self.data[self.data['game_id'].isin(game_id_test)]
def generate_seqs(self, games, len_seq_train = 10, len_seq_pred = 1):
# variables en entrée de la fonction
# games = games
# len_seq_train = 10
# len_seq_pred = 1
# récupérér len_seq_train + len_seq_pred au hasard
len_seq = len_seq_train + len_seq_pred
# restreindre le "games" en choisissant le game_id et la period_id
game_id_random = np.random.choice(np.unique(games['game_id']))
period_id_random = np.random.randint(1, 3)
seq = games[(games['game_id'] == game_id_random) & (games['event_period_id'] == period_id_random)]
event_order_max = np.max(seq['event_order'])
event_order_min = np.min(seq['event_order'])
start_seq = np.random.randint(event_order_min, event_order_max - len_seq)
end_seq = start_seq + len_seq
seq_sel = seq[(seq['event_order'] >= start_seq) & (seq['event_order'] < end_seq)]
seq_sel = seq_sel.sort_values(by = ['game_id', 'event_period_id', 'event_order'], inplace = False)
seq_train_coord = np.array(seq_sel.head(len_seq_train)[['event_x', 'event_y', 'features_change_team', 'features_seconds']])
seq_train_position = np.array(seq_sel.head(len_seq_train)[['position_type']]).flatten()
seq_train_event = np.array(seq_sel.head(len_seq_train)[['event_type_id_recoded']]).flatten()
seq_train_zone = np.array(seq_sel.head(len_seq_train)[['zone_name_id']]).flatten()
seq_pred = np.array(seq_sel.tail(len_seq_pred)[['event_x', 'event_y']])
return [seq_train_coord, seq_train_position, seq_train_event, seq_train_zone], seq_pred
def generator(self, type = "train"):
data = self.train_data if type == "train" else self.test_data
while True:
X_COORD = np.zeros((self.batch_size, self.len_seq_train, 4))
X_POSITION = np.zeros((self.batch_size, self.len_seq_train))
X_EVENT = np.zeros((self.batch_size, self.len_seq_train))
X_ZONE = np.zeros((self.batch_size, self.len_seq_train))
Y = np.zeros((self.batch_size, self.output_dim))
for i in range(self.batch_size):
X_train, Y_train = self.generate_seqs(data, self.len_seq_train, self.len_seq_pred)
X_COORD[i, : ] = X_train[0]
X_POSITION[i, : ] = X_train[1]
X_EVENT[i, : ] = X_train[2]
X_ZONE[i, : ] = X_train[3]
Y[i, : ] = Y_train
yield [X_COORD, X_POSITION, X_EVENT, X_ZONE], Y
class DataLoaderEvents(DataLoader):
def generator(self, type = "train"):
data = self.train_data if type == "train" else self.test_data
while True:
X_COORD = np.zeros((self.batch_size, self.len_seq_train, 4))
Y = np.zeros((self.batch_size, self.output_dim))
for i in range(self.batch_size):
X_train, Y_train = self.generate_seqs(data, self.len_seq_train, self.len_seq_pred)
X_COORD[i, : ] = X_train[0]
Y[i, : ] = Y_train
yield X_COORD, Y
class DataLoaderTeamChange(DataLoader):
def __init__(self, config, type_loader = 'simple'):
self.data = None
self.train_data = None
self.test_data = None
self.type_loader = type_loader
self.config = config
self.input_dim = self.config.get('data_loader')[self.type_loader]['input_dim']
self.batch_size = self.config.get('data_loader')[self.type_loader]['batch_size']
self.output_dim = self.config.get('data_loader')[self.type_loader]['output_dim']
self.read_data()
self.split_train_test()
# def read_data(self):
# data = pd.read_csv(self.config.get('data_loader')[self.type_loader]['data_path'])
# idx_a_predire = data.groupby(['game_id', 'event_period_id']).tail(1).index
# self.data = data.loc[~data.index.isin(idx_a_predire)]
def split_train_test(self):
np.random.seed = 3216321
self.train_data = self.data.sample(frac = .75)
self.test_data = self.data.loc[~self.data.index.isin(self.train_data.index)]
def generate_seqs(self, games):
# restreindre le "games" en choisissant le game_id et la period_id
np.random.seed = None
line_random = np.random.choice(np.unique(games.index))
line = games.loc[line_random]
cols_x = ['elapsed_time_since_possesion', 'last_chg_possesion', 'event_x',
'event_y', 'event_type_1', 'event_type_2', 'event_type_3',
'event_type_4', 'event_type_5', 'event_type_6', 'event_type_7',
'event_type_8', 'event_type_10', 'event_type_11', 'event_type_12',
'event_type_13', 'event_type_14', 'event_type_15', 'event_type_16',
'event_type_41', 'event_type_44', 'event_type_45', 'event_type_49',
'event_type_50', 'event_type_51', 'event_type_54', 'event_type_55',
'event_type_61', 'event_type_74', 'event_type_1_t1',
'event_type_2_t1', 'event_type_3_t1', 'event_type_4_t1',
'event_type_5_t1', 'event_type_6_t1', 'event_type_7_t1',
'event_type_8_t1', 'event_type_10_t1', 'event_type_11_t1',
'event_type_12_t1', 'event_type_13_t1', 'event_type_14_t1',
'event_type_15_t1', 'event_type_16_t1', 'event_type_41_t1',
'event_type_44_t1', 'event_type_45_t1', 'event_type_49_t1',
'event_type_50_t1', 'event_type_51_t1', 'event_type_54_t1',
'event_type_55_t1', 'event_type_61_t1', 'event_type_74_t1',
'event_x_t1', 'event_y_t1',
'event_type_id_special', 'event_team_id_special']
cols_x = ['elapsed_time_since_possesion', 'last_chg_possesion', 'event_x',
'event_y', 'event_type_id_special', 'event_team_id_special',
'event_type_1',
'event_type_12',
'event_type_1_t1', 'event_type_2_t1', 'event_type_3_t1',
'event_type_4_t1', 'event_type_5_t1', 'event_type_6_t1',
'event_type_7_t1', 'event_type_8_t1', 'event_type_10_t1',
'event_type_11_t1', 'event_type_12_t1', 'event_type_13_t1',
'event_type_14_t1', 'event_type_15_t1', 'event_type_16_t1',
'event_type_41_t1', 'event_type_44_t1', 'event_type_45_t1',
'event_type_49_t1', 'event_type_50_t1', 'event_type_51_t1',
'event_type_54_t1', 'event_type_55_t1', 'event_type_61_t1',
'event_type_74_t1', 'event_x_t1', 'event_y_t1', 'team_id_139',
'team_id_140', 'team_id_143', 'team_id_144', 'team_id_145',
'team_id_146', 'team_id_147', 'team_id_148', 'team_id_149',
'team_id_150', 'team_id_152', 'team_id_427', 'team_id_428',
'team_id_429', 'team_id_430', 'team_id_694', 'team_id_1028',
'team_id_1395', 'team_id_2128', 'team_id_2130']
# cols_x = ['elapsed_time_since_possesion', 'last_chg_possesion', 'event_x', 'event_y', 'event_x_t1', 'event_y_t1']
line_x = np.array(line[cols_x])
# line_x = np.array(line[['event_x', 'event_y']])
line_y = np.array(line[['changement_possesion']])
return line_x, line_y
def generator(self, type = "train"):
data = self.train_data if type == "train" else self.test_data
while True:
X = np.zeros((self.batch_size, self.input_dim))
Y = np.zeros((self.batch_size, self.output_dim))
for i in range(self.batch_size):
X_train, Y_train = self.generate_seqs(data)
X[i, : ] = X_train
Y[i, : ] = Y_train
yield X, Y
# class DataLoaderTeamChange(DataLoader):
# def generate_seqs(self, games, len_seq_train = 10, len_seq_pred = 1):
# # variables en entrée de la fonction
# # games = games
# # len_seq_train = 10
# # len_seq_pred = 1
# # récupérér len_seq_train + len_seq_pred au hasard
# len_seq = len_seq_train + len_seq_pred
# # restreindre le "games" en choisissant le game_id et la period_id
# game_id_random = np.random.choice(np.unique(games['game_id']))
# period_id_random = np.random.randint(1, 3)
# seq = games[(games['game_id'] == game_id_random) & (games['event_period_id'] == period_id_random)]
# event_order_max = np.max(seq['event_order'])
# event_order_min = np.min(seq['event_order'])
# start_seq = np.random.randint(event_order_min, event_order_max - len_seq)
# end_seq = start_seq + len_seq
# seq_sel = seq[(seq['event_order'] >= start_seq) & (seq['event_order'] < end_seq)]
# seq_sel = seq_sel.sort_values(by = ['game_id', 'event_period_id', 'event_order'], inplace = False)
# seq_train_coord = np.array(seq_sel.head(len_seq_train)[['event_x', 'event_y', 'features_change_team', 'features_seconds']])
# seq_train_position = np.array(seq_sel.head(len_seq_train)[['position_type']]).flatten()
# seq_train_event = np.array(seq_sel.head(len_seq_train)[['event_type_id_recoded']]).flatten()
# seq_train_zone = np.array(seq_sel.head(len_seq_train)[['zone_name_id']]).flatten()
# seq_pred = np.array(seq_sel.tail(len_seq_pred)[['features_change_team']])
# return [seq_train_coord, seq_train_position, seq_train_event, seq_train_zone], seq_pred | 42.344 | 133 | 0.642452 | 1,586 | 10,586 | 3.85309 | 0.107818 | 0.119293 | 0.088202 | 0.030601 | 0.836033 | 0.793978 | 0.78596 | 0.771232 | 0.750123 | 0.73785 | 0 | 0.036032 | 0.223975 | 10,586 | 250 | 134 | 42.344 | 0.707851 | 0.222464 | 0 | 0.347826 | 0 | 0 | 0.252107 | 0.019665 | 0 | 0 | 0 | 0 | 0 | 1 | 0.072464 | false | 0 | 0.014493 | 0 | 0.123188 | 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 |
7b97a300f30beeb6f0c27b2f840e24a10a8ce088 | 29 | py | Python | mini_game_004/__init__.py | CompassMentis/pyweek31 | ac4ebdfeb94ccaedbb231b7c9aa3a2400de1b7c7 | [
"MIT"
] | null | null | null | mini_game_004/__init__.py | CompassMentis/pyweek31 | ac4ebdfeb94ccaedbb231b7c9aa3a2400de1b7c7 | [
"MIT"
] | null | null | null | mini_game_004/__init__.py | CompassMentis/pyweek31 | ac4ebdfeb94ccaedbb231b7c9aa3a2400de1b7c7 | [
"MIT"
] | null | null | null | from .game_class import Game
| 14.5 | 28 | 0.827586 | 5 | 29 | 4.6 | 0.8 | 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 |
c8f98270b955ca6eb6c52065b9690c0b30f0d2b1 | 128 | py | Python | weather_bot/hello.py | Jelle-M/personal-discord-weatherbot | 4a9b13753fe6d05e2071057f697d3e780763615a | [
"MIT"
] | null | null | null | weather_bot/hello.py | Jelle-M/personal-discord-weatherbot | 4a9b13753fe6d05e2071057f697d3e780763615a | [
"MIT"
] | 7 | 2019-02-06T15:00:08.000Z | 2019-02-09T19:14:02.000Z | weather_bot/hello.py | MoskiMBA/personal-discord-weatherbot | 4a9b13753fe6d05e2071057f697d3e780763615a | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""Print Hello, World!."""
def main() -> str:
"""Print Hello world."""
return 'hello world!'
| 14.222222 | 28 | 0.515625 | 15 | 128 | 4.4 | 0.666667 | 0.454545 | 0.454545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010101 | 0.226563 | 128 | 8 | 29 | 16 | 0.656566 | 0.484375 | 0 | 0 | 0 | 0 | 0.218182 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 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 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cda45c73fcdec9cec8d30303277be446102276b8 | 29 | py | Python | lexicalbinary/__init__.py | skio-music/lexical-binary | 93ce53e2430af33ab83377e48fe351c96e693f5b | [
"MIT"
] | null | null | null | lexicalbinary/__init__.py | skio-music/lexical-binary | 93ce53e2430af33ab83377e48fe351c96e693f5b | [
"MIT"
] | null | null | null | lexicalbinary/__init__.py | skio-music/lexical-binary | 93ce53e2430af33ab83377e48fe351c96e693f5b | [
"MIT"
] | 1 | 2019-09-10T06:10:52.000Z | 2019-09-10T06:10:52.000Z | from .lexicalbinary import *
| 14.5 | 28 | 0.793103 | 3 | 29 | 7.666667 | 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 |
cdd9b109bcab171a289c0337cc75bed3f10404bd | 62 | py | Python | aur_deploy/__init__.py | codeswhite/aur-deploy | 20ba4ba66d606bdb2c23577bb092e2c6b8fc8469 | [
"MIT"
] | null | null | null | aur_deploy/__init__.py | codeswhite/aur-deploy | 20ba4ba66d606bdb2c23577bb092e2c6b8fc8469 | [
"MIT"
] | 5 | 2020-07-14T17:38:03.000Z | 2020-07-18T16:08:51.000Z | aur_deploy/__init__.py | codeswhite/aur-deploy | 20ba4ba66d606bdb2c23577bb092e2c6b8fc8469 | [
"MIT"
] | null | null | null | from .aur_deploy import aur_deploy
from .__main__ import main
| 20.666667 | 34 | 0.83871 | 10 | 62 | 4.6 | 0.5 | 0.391304 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 62 | 2 | 35 | 31 | 0.851852 | 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 |
cde965160ac6c8936b4d0bb2c7b7b3e4d10b9cc8 | 1,714 | py | Python | covid-survive-master/enemy.py | brunouni/HealthPub | 0da1ff714474023247bdcc8f36d3a7bc64b432b2 | [
"MIT"
] | null | null | null | covid-survive-master/enemy.py | brunouni/HealthPub | 0da1ff714474023247bdcc8f36d3a7bc64b432b2 | [
"MIT"
] | null | null | null | covid-survive-master/enemy.py | brunouni/HealthPub | 0da1ff714474023247bdcc8f36d3a7bc64b432b2 | [
"MIT"
] | null | null | null | import pygame
import random
class Enemy(pygame.sprite.Sprite):
def __init__(self, *groups):
super().__init__(*groups)
self.image = pygame.image.load("img/covid.png")
self.image = pygame.transform.scale(self.image, [35, 35])
self.rect = pygame.Rect(540, 360, 25, 25)
self.rect.x = 1080 + random.randint(1, 400)
self.rect.y = random.randint(150, 600)
self.speed = 1 + random.random() * 2
def update(self, *args):
self.rect.x -= self.speed
if self.rect.right < 0:
self.kill()
class Enemy2(pygame.sprite.Sprite):
def __init__(self, *groups):
super().__init__(*groups)
self.image = pygame.image.load("img/covid2.png")
self.image = pygame.transform.scale(self.image, [35, 35])
self.rect = pygame.Rect(540, 360, 25, 25)
self.rect.x = 0 - random.randint(1, 400)
self.rect.y = random.randint(150, 600)
self.speed = 2 + random.random() * 2
def update(self, *args):
self.rect.x += self.speed
if self.rect.left > 1080:
self.kill()
class Enemy3(pygame.sprite.Sprite):
def __init__(self, *groups):
super().__init__(*groups)
self.image = pygame.image.load("img/covid3.png")
self.image = pygame.transform.scale(self.image, [35, 35])
self.rect = pygame.Rect(540, 360, 25, 25)
self.rect.x = random.randint(150, 950)
self.rect.y = 720 + random.randint(1, 400)
self.speed = 3 + random.random() * 2
def update(self, *args):
self.rect.y -= self.speed
if self.rect.top < 0:
self.kill() | 29.050847 | 66 | 0.561844 | 226 | 1,714 | 4.154867 | 0.212389 | 0.127796 | 0.095847 | 0.067093 | 0.829606 | 0.787007 | 0.787007 | 0.787007 | 0.787007 | 0.746539 | 0 | 0.079012 | 0.291132 | 1,714 | 59 | 67 | 29.050847 | 0.693827 | 0 | 0 | 0.487805 | 0 | 0 | 0.024744 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.146341 | false | 0 | 0.04878 | 0 | 0.268293 | 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 |
cdfe373333214fd9f6088f5aa07d1143776f63d0 | 85 | py | Python | zolo/benchmarks/timeret.py | firefirer1983/zolo | 889409b491363eb54c2997e01333b77bc81e0c89 | [
"MIT"
] | 2 | 2021-05-06T12:10:02.000Z | 2021-08-15T09:25:31.000Z | zolo/benchmarks/timeret.py | firefirer1983/zolo | 889409b491363eb54c2997e01333b77bc81e0c89 | [
"MIT"
] | null | null | null | zolo/benchmarks/timeret.py | firefirer1983/zolo | 889409b491363eb54c2997e01333b77bc81e0c89 | [
"MIT"
] | null | null | null | from .base import Benchmark
class TimeReturn(Benchmark, alias="timeret"):
pass
| 14.166667 | 45 | 0.741176 | 10 | 85 | 6.3 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.164706 | 85 | 5 | 46 | 17 | 0.887324 | 0 | 0 | 0 | 0 | 0 | 0.082353 | 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 |
b534f2d8ff30c8ee80b48a812ed1c1d089826da4 | 25,278 | py | Python | test_autoarray/inversion/test_mappers.py | jonathanfrawley/PyAutoArray_copy | c21e8859bdb20737352147b9904797ac99985b73 | [
"MIT"
] | null | null | null | test_autoarray/inversion/test_mappers.py | jonathanfrawley/PyAutoArray_copy | c21e8859bdb20737352147b9904797ac99985b73 | [
"MIT"
] | null | null | null | test_autoarray/inversion/test_mappers.py | jonathanfrawley/PyAutoArray_copy | c21e8859bdb20737352147b9904797ac99985b73 | [
"MIT"
] | null | null | null | import numpy as np
import pytest
import autoarray as aa
def grid_to_pixel_pixels_via_nearest_neighbour(grid, pixel_centers):
def compute_squared_separation(coordinate1, coordinate2):
"""
Returns the squared separation of two grid (no square root for efficiency)"""
return (coordinate1[0] - coordinate2[0]) ** 2 + (
coordinate1[1] - coordinate2[1]
) ** 2
image_pixels = grid.shape[0]
image_to_pixelization = np.zeros((image_pixels,))
for image_index, image_coordinate in enumerate(grid):
distances = list(
map(
lambda centers: compute_squared_separation(image_coordinate, centers),
pixel_centers,
)
)
image_to_pixelization[image_index] = np.argmin(distances)
return image_to_pixelization
class TestRectangularMapper:
def test__sub_to_pix__various_grids__1_coordinate_per_square_pixel__in_centre_of_pixels(
self,
):
# _ _ _
# I_I_I_I Boundaries for pixels x = 0 and y = 0 -1.0 to -(1/3)
# I_I_I_I Boundaries for pixels x = 1 and y = 1 - (1/3) to (1/3)
# I_I_I_I Boundaries for pixels x = 2 and y = 2 - (1/3)" to 1.0"
grid = aa.Grid2D.manual_slim(
[
[1.0, -1.0],
[1.0, 0.0],
[1.0, 1.0],
[0.0, -1.0],
[0.0, 0.0],
[0.0, 1.0],
[-1.0, -1.0],
[-1.0, 0.0],
[-1.0, 1.0],
],
pixel_scales=1.0,
shape_native=(3, 3),
)
pixelization_grid = aa.Grid2DRectangular(
grid=np.ones((2, 2)), shape_native=(3, 3), pixel_scales=(1.0, 1.0)
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index
== np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
]
# _ _ _
# I_I_I_I Boundaries for pixels x = 0 and y = 0 -1.0 to -(1/3)
# I_I_I_I Boundaries for pixels x = 1 and y = 1 - (1/3) to (1/3)
# I_I_I_I Boundaries for pixels x = 2 and y = 2 - (1/3)" to 1.0"
grid = aa.Grid2D.manual_slim(
[
[1.0, -1.0],
[1.0, 0.0],
[1.0, 1.0],
[-0.32, -1.0],
[-0.32, 0.32],
[0.0, 1.0],
[-0.34, -0.34],
[-0.34, 0.325],
[-1.0, 1.0],
],
pixel_scales=1.0,
shape_native=(3, 3),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index
== np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
]
def test__sub_to_pix__3x3_grid_of_pixel_grid__add_multiple_grid_to_1_pixel(self):
# _ _ _
# -1.0 to -(1/3) I_I_I_I
# -(1/3) to (1/3) I_I_I_I
# (1/3) to 1.0 I_I_I_I
grid = aa.Grid2D.manual_slim(
[
[1.0, -1.0],
[0.0, 0.0],
[1.0, 1.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[-1.0, -1.0],
[0.0, 0.0],
[-1.0, 1.0],
],
pixel_scales=1.0,
shape_native=(3, 3),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index
== np.array([0, 4, 2, 4, 4, 4, 6, 4, 8])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[],
[2],
[],
[1, 3, 4, 5, 7],
[],
[6],
[],
[8],
]
def test__sub_to_pix__various_grids__1_coordinate_in_each_pixel(self):
# _ _ _
# I_I_I_I
# I_I_I_I
# I_I_I_I
# I_I_I_I
# Boundaries for column pixel 0 -1.0 to -(1/3)
# Boundaries for column pixel 1 -(1/3) to (1/3)
# Boundaries for column pixel 2 (1/3) to 1.0
# Bounadries for row pixel 0 -1.0 to -0.5
# Bounadries for row pixel 1 -0.5 to 0.0
# Bounadries for row pixel 2 0.0 to 0.5
# Bounadries for row pixel 3 0.5 to 1.0
grid = aa.Grid2D.manual_slim(
[
[1.0, -1.0],
[1.0, 0.0],
[1.0, 1.0],
[0.5, -1.0],
[-0.5, 1.0],
[-1.0, 1.0],
],
pixel_scales=1.0,
shape_native=(3, 2),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(4, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index
== np.array([0, 1, 2, 3, 8, 11])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[2],
[3],
[],
[],
[],
[],
[4],
[],
[],
[5],
]
# _ _ _ _
# I_I_I_I_I
# I_I_I_I_I
# I_I_I_I_I
# Boundaries for row pixel 0 -1.0 to -(1/3)
# Boundaries for row pixel 1 -(1/3) to (1/3)
# Boundaries for row pixel 2 (1/3) to 1.0
# Bounadries for column pixel 0 -1.0 to -0.5
# Bounadries for column pixel 1 -0.5 to 0.0
# Bounadries for column pixel 2 0.0 to 0.5
# Bounadries for column pixel 3 0.5 to 1.0
grid = aa.Grid2D.manual_slim(
[
[1.0, -1.0],
[1.0, -0.49],
[1.0, 0.01],
[0.32, 0.01],
[-0.34, -0.01],
[-1.0, 1.0],
],
pixel_scales=1.0,
shape_native=(2, 3),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 4), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index
== np.array([0, 1, 2, 6, 9, 11])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[2],
[],
[],
[],
[3],
[],
[],
[4],
[],
[5],
]
def test__sub_to_pix__3x3_grid__change_scaledond_dimensions_size__grid_adapts_accordingly(
self,
):
# _ _ _
# I_I_I_I Boundaries for pixels x = 0 and y = 0 -1.5 to -0.5
# I_I_I_I Boundaries for pixels x = 1 and y = 1 -0.5 to 0.5
# I_I_I_I Boundaries for pixels x = 2 and y = 2 0.5 to 1.5
grid = aa.Grid2D.manual_slim(
[[1.5, -1.5], [1.0, 0.0], [1.0, 0.6], [-1.4, 0.0], [-1.5, 1.5]],
pixel_scales=1.0,
shape_native=(5, 1),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index == np.array([0, 1, 2, 7, 8])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[2],
[],
[],
[],
[],
[3],
[4],
]
def test__sub_to_pix__various_grids__change_scaledond_dimensions__not_symmetric(
self,
):
# _ _ _
# I_I_I_I Boundaries for pixels x = 0 and y = 0 -1.5 to -0.5
# I_I_I_I Boundaries for pixels x = 1 and y = 1 -0.5 to 0.5
# I_I_I_I Boundaries for pixels x = 2 and y = 2 0.5 to 1.5
grid = aa.Grid2D.manual_slim(
[[1.0, -1.5], [1.0, -0.49], [0.32, -1.5], [0.32, 0.51], [-1.0, 1.5]],
pixel_scales=1.0,
shape_native=(5, 1),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index == np.array([0, 1, 3, 5, 8])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[],
[2],
[],
[3],
[],
[],
[4],
]
# _ _ _
# I_I_I_I
# I_I_I_I
# I_I_I_I
# I_I_I_I
grid = aa.Grid2D.manual_slim(
[[1.0, -1.5], [1.0, -0.49], [0.49, -1.5], [-0.6, 0.0], [-1.0, 1.5]],
pixel_scales=1.0,
shape_native=(5, 1),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(4, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index == np.array([0, 1, 3, 10, 11])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[],
[2],
[],
[],
[],
[],
[],
[],
[3],
[4],
]
# _ _ _ _
# I_I_I_I_I
# I_I_I_I_I
# I_I_I_I_I
grid = aa.Grid2D.manual_slim(
[[1.0, -1.5], [1.0, -0.49], [0.32, -1.5], [-0.34, 0.49], [-1.0, 1.5]],
pixel_scales=1.0,
shape_native=(5, 1),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 4), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index == np.array([0, 1, 4, 10, 11])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[],
[],
[2],
[],
[],
[],
[],
[],
[3],
[4],
]
def test__sub_to_pix__different_image_and_sub_grids(self):
# _ _ _
# -1.0 to -(1/3) I_I_I_I
# -(1/3) to (1/3) I_I_I_I
# (1/3) to 1.0 I_I_I_I
grid = aa.Grid2D.manual_slim(
[
[1.0, -1.0],
[1.0, 0.0],
[1.0, 1.0],
[0.0, -1.0],
[0.0, 0.0],
[0.0, 1.0],
[-1.0, -1.0],
[-1.0, 0.0],
[-1.0, 1.0],
],
pixel_scales=1.0,
shape_native=(3, 3),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index
== np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
]
def test__sub_to_pix__3x3_grid_of_pixel_grid___shift_coordinates_to_new_centre__centre_adjusts_based_on_grid(
self,
):
# _ _ _
# I_I_I_I Boundaries for pixels x = 0 and y = 0 -1.0 to -(1/3)
# I_I_I_I Boundaries for pixels x = 1 and y = 1 - (1/3) to (1/3)
# I_I_I_I Boundaries for pixels x = 2 and y = 2 - (1/3)" to 1.0"
grid = aa.Grid2D.manual_slim(
[
[2.0, 0.0],
[2.0, 1.0],
[2.0, 2.0],
[1.0, 0.0],
[1.0, 1.0],
[1.0, 2.0],
[0.0, 0.0],
[0.0, 1.0],
[0.0, 2.0],
],
pixel_scales=1.0,
shape_native=(3, 3),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index
== np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
]
def test__sub_to_pix__4x3_grid__non_symmetric_centre_shift(self):
# _ _ _
# I_I_I_I
# I_I_I_I
# I_I_I_I
# I_I_I_I
grid = aa.Grid2D.manual_slim(
[[3.0, -0.5], [3.0, 0.51], [2.49, -0.5], [1.4, 1.0], [1.0, 2.5]],
pixel_scales=1.0,
shape_native=(5, 1),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(4, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index == np.array([0, 1, 3, 10, 11])
).all()
assert mapper.all_sub_slim_indexes_for_pixelization_index == [
[0],
[1],
[],
[2],
[],
[],
[],
[],
[],
[],
[3],
[4],
]
def test__reconstructed_pixelization__3x3_pixelization__solution_vector_ascending(
self,
):
grid = aa.Grid2D.manual_slim(
[
[2.0, -1.0],
[2.0, 0.0],
[2.0, 1.0],
[0.0, -1.0],
[0.0, 0.0],
[0.0, 1.0],
[-2.0, -1.0],
[-2.0, 0.0],
[-2.0, 1.0],
],
pixel_scales=1.0,
shape_native=(3, 3),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
recon_pix = mapper.reconstruction_from(
solution_vector=np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])
)
assert (
recon_pix.native
== np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
).all()
assert recon_pix.pixel_scales == pytest.approx((4.0 / 3.0, 2.0 / 3.0), 1e-2)
assert recon_pix.origin == (0.0, 0.0)
def test__reconstructed_pixelization__compare_to_imaging_util(self):
grid = aa.Grid2D.manual_slim(
[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]],
pixel_scales=1.0,
shape_native=(2, 2),
)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(4, 3), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
solution = np.array(
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 1.0, 2.0, 3.0]
)
recon_pix = mapper.reconstruction_from(solution_vector=solution)
recon_pix_util = aa.util.array_2d.array_2d_native_from(
array_2d_slim=solution,
mask_2d=np.full(fill_value=False, shape=(4, 3)),
sub_size=1,
)
assert (recon_pix.native == recon_pix_util).all()
assert recon_pix.shape_native == (4, 3)
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 4), grid=grid
)
mapper = aa.Mapper(
source_grid_slim=grid, source_pixelization_grid=pixelization_grid
)
solution = np.array(
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 1.0, 2.0, 3.0]
)
recon_pix = mapper.reconstruction_from(solution_vector=solution)
recon_pix_util = aa.util.array_2d.array_2d_native_from(
array_2d_slim=solution,
mask_2d=np.full(fill_value=False, shape=(3, 4)),
sub_size=1,
)
assert (recon_pix.native == recon_pix_util).all()
assert recon_pix.shape_native == (3, 4)
def test__pixel_signals__compare_to_mapper_util(self, grid_2d_7x7, image_7x7):
pixelization_grid = aa.Grid2DRectangular.overlay_grid(
shape_native=(3, 3), grid=grid_2d_7x7
)
mapper = aa.Mapper(
source_grid_slim=grid_2d_7x7,
source_pixelization_grid=pixelization_grid,
hyper_data=image_7x7,
)
pixel_signals = mapper.pixel_signals_from_signal_scale(signal_scale=2.0)
pixel_signals_util = aa.util.mapper.adaptive_pixel_signals_from(
pixels=9,
signal_scale=2.0,
pixelization_index_for_sub_slim_index=mapper.pixelization_index_for_sub_slim_index,
slim_index_for_sub_slim_index=grid_2d_7x7.mask._slim_index_for_sub_slim_index,
hyper_image=image_7x7,
)
assert (pixel_signals == pixel_signals_util).all()
def test__image_from_source__different_types_of_lists_input(self, sub_grid_2d_7x7):
rectangular_pixelization_grid = aa.Grid2DRectangular.overlay_grid(
grid=sub_grid_2d_7x7, shape_native=(3, 3)
)
rectangular_mapper = aa.Mapper(
source_grid_slim=sub_grid_2d_7x7,
source_pixelization_grid=rectangular_pixelization_grid,
)
full_indexes = rectangular_mapper.slim_indexes_from_pixelization_indexes(
pixelization_indexes=[0, 1]
)
assert full_indexes == [0, 1, 2, 3, 4, 5, 6, 7]
full_indexes = rectangular_mapper.slim_indexes_from_pixelization_indexes(
pixelization_indexes=[[0], [4]]
)
assert full_indexes == [[0, 1, 2, 3], [16, 17, 18, 19]]
class TestVoronoiMapper:
def test__grid_to_pixel_pixels_via_nearest_neighbour__case1__correct_pairs(self):
pixel_centers = np.array([[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]])
grid = aa.Grid2D.manual_slim(
[[1.1, 1.1], [-1.1, 1.1], [-1.1, -1.1], [1.1, -1.1]],
shape_native=(2, 2),
pixel_scales=1.0,
)
sub_to_pix = grid_to_pixel_pixels_via_nearest_neighbour(grid, pixel_centers)
assert sub_to_pix[0] == 0
assert sub_to_pix[1] == 1
assert sub_to_pix[2] == 2
assert sub_to_pix[3] == 3
def test__grid_to_pixel_pixels_via_nearest_neighbour___case2__correct_pairs(self):
pixel_centers = np.array([[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]])
grid = aa.Grid2D.manual_slim(
[
[1.1, 1.1],
[-1.1, 1.1],
[-1.1, -1.1],
[1.1, -1.1],
[0.9, -0.9],
[-0.9, -0.9],
[-0.9, 0.9],
[0.9, 0.9],
],
shape_native=(3, 3),
pixel_scales=0.1,
)
sub_to_pix = grid_to_pixel_pixels_via_nearest_neighbour(grid, pixel_centers)
assert sub_to_pix[0] == 0
assert sub_to_pix[1] == 1
assert sub_to_pix[2] == 2
assert sub_to_pix[3] == 3
assert sub_to_pix[4] == 3
assert sub_to_pix[5] == 2
assert sub_to_pix[6] == 1
assert sub_to_pix[7] == 0
def test__grid_to_pixel_pixels_via_nearest_neighbour___case3__correct_pairs(self):
pixel_centers = np.array(
[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0], [0.0, 0.0], [2.0, 2.0]]
)
grid = aa.Grid2D.manual_slim(
[
[0.1, 0.1],
[-0.1, -0.1],
[0.49, 0.49],
[0.51, 0.51],
[1.01, 1.01],
[1.51, 1.51],
],
shape_native=(3, 2),
pixel_scales=1.0,
)
sub_to_pix = grid_to_pixel_pixels_via_nearest_neighbour(grid, pixel_centers)
assert sub_to_pix[0] == 4
assert sub_to_pix[1] == 4
assert sub_to_pix[2] == 4
assert sub_to_pix[3] == 0
assert sub_to_pix[4] == 0
assert sub_to_pix[5] == 5
def test__sub_to_pix_of_mapper_matches_nearest_neighbor_calculation(
self, grid_2d_7x7
):
pixelization_grid = aa.Grid2D.manual_slim(
[[0.1, 0.1], [1.1, 0.1], [2.1, 0.1], [0.1, 1.1], [1.1, 1.1], [2.1, 1.1]],
shape_native=(3, 2),
pixel_scales=1.0,
)
sub_to_pix_nearest_neighbour = grid_to_pixel_pixels_via_nearest_neighbour(
grid_2d_7x7, pixelization_grid
)
nearest_pixelization_index_for_slim_index = np.array(
[0, 0, 1, 0, 0, 1, 2, 2, 3]
)
pixelization_grid = aa.Grid2DVoronoi(
grid=pixelization_grid,
nearest_pixelization_index_for_slim_index=nearest_pixelization_index_for_slim_index,
)
mapper = aa.Mapper(
source_grid_slim=grid_2d_7x7, source_pixelization_grid=pixelization_grid
)
assert (
mapper.pixelization_index_for_sub_slim_index == sub_to_pix_nearest_neighbour
).all()
def test__pixel_scales___for_voronoi_mapper(self, grid_2d_7x7, image_7x7):
pixelization_grid = aa.Grid2D.manual_slim(
[[0.1, 0.1], [1.1, 0.1], [2.1, 0.1], [0.1, 1.1], [1.1, 1.1], [2.1, 1.1]],
shape_native=(3, 2),
pixel_scales=1.0,
)
nearest_pixelization_index_for_slim_index = np.array(
[0, 0, 1, 0, 0, 1, 2, 2, 3]
)
pixelization_grid = aa.Grid2DVoronoi(
grid=pixelization_grid,
nearest_pixelization_index_for_slim_index=nearest_pixelization_index_for_slim_index,
)
mapper = aa.Mapper(
source_grid_slim=grid_2d_7x7,
source_pixelization_grid=pixelization_grid,
hyper_data=image_7x7,
)
pixel_signals = mapper.pixel_signals_from_signal_scale(signal_scale=2.0)
pixel_signals_util = aa.util.mapper.adaptive_pixel_signals_from(
pixels=6,
signal_scale=2.0,
pixelization_index_for_sub_slim_index=mapper.pixelization_index_for_sub_slim_index,
slim_index_for_sub_slim_index=grid_2d_7x7.mask._slim_index_for_sub_slim_index,
hyper_image=image_7x7,
)
assert (pixel_signals == pixel_signals_util).all()
| 30.092857 | 114 | 0.470013 | 3,212 | 25,278 | 3.376712 | 0.049813 | 0.032454 | 0.030426 | 0.030979 | 0.868707 | 0.846211 | 0.829246 | 0.812004 | 0.803706 | 0.748479 | 0 | 0.092396 | 0.407429 | 25,278 | 839 | 115 | 30.128725 | 0.631684 | 0.080742 | 0 | 0.659906 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.084243 | 1 | 0.029641 | false | 0 | 0.00468 | 0 | 0.040562 | 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 |
b587332d970454bee5e4cd995ed7f7f60d3f0e7f | 123 | py | Python | algo/common.py | softarts/tradebook | bdce9206e94147e93c547edb2d1d2aebd17f2f9c | [
"Apache-2.0"
] | null | null | null | algo/common.py | softarts/tradebook | bdce9206e94147e93c547edb2d1d2aebd17f2f9c | [
"Apache-2.0"
] | null | null | null | algo/common.py | softarts/tradebook | bdce9206e94147e93c547edb2d1d2aebd17f2f9c | [
"Apache-2.0"
] | null | null | null | class AlgoBase(object):
def run_algo(self, ohlc):
pass
def post_algo(self, df, ohlc_dct):
return df | 24.6 | 38 | 0.617886 | 18 | 123 | 4.055556 | 0.722222 | 0.219178 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.284553 | 123 | 5 | 39 | 24.6 | 0.829545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0.2 | 0 | 0.2 | 0.8 | 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 | 1 | 0 | 1 | 0 | 0 | 0 | 6 |
b5ab90c34e998e52afe6bf1f701e07d24da60208 | 129 | py | Python | test/test_pytest.py | roger-lo/Filed | 0ad39a650fb4842e8388b0be2d826f1ef77ee347 | [
"Apache-2.0"
] | null | null | null | test/test_pytest.py | roger-lo/Filed | 0ad39a650fb4842e8388b0be2d826f1ef77ee347 | [
"Apache-2.0"
] | null | null | null | test/test_pytest.py | roger-lo/Filed | 0ad39a650fb4842e8388b0be2d826f1ef77ee347 | [
"Apache-2.0"
] | null | null | null | # Dummy test case
# TODO: remove this entire file when we have at least 1 proper test
def test_assert():
assert True == True
| 25.8 | 67 | 0.72093 | 22 | 129 | 4.181818 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009901 | 0.217054 | 129 | 4 | 68 | 32.25 | 0.90099 | 0.627907 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 1 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 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 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a9718b0e1c8302782e76d90ef74c3f3f1ca3afa2 | 128 | py | Python | sklearn_plus/neural_network/text_classification/__init__.py | liuxiaoan8008/sklearn-plus | 67258f6c9b833c82c2ffa2ec062fc2cc686b3004 | [
"MIT"
] | null | null | null | sklearn_plus/neural_network/text_classification/__init__.py | liuxiaoan8008/sklearn-plus | 67258f6c9b833c82c2ffa2ec062fc2cc686b3004 | [
"MIT"
] | null | null | null | sklearn_plus/neural_network/text_classification/__init__.py | liuxiaoan8008/sklearn-plus | 67258f6c9b833c82c2ffa2ec062fc2cc686b3004 | [
"MIT"
] | null | null | null | from __future__ import absolute_import
from .binary_classifier import BiClassifier
from .text_classifier import TextClassifier
| 25.6 | 43 | 0.882813 | 15 | 128 | 7.066667 | 0.6 | 0.301887 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.101563 | 128 | 4 | 44 | 32 | 0.921739 | 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 |
8d5d56dd417577a9464c424abf035d528c40ca4a | 89 | py | Python | src/pages/tensorflow.py | calabres97/ai-portfolio | a24e4f7307bd7fdfedd0ded1e08682c1fb9e75cf | [
"Apache-2.0"
] | null | null | null | src/pages/tensorflow.py | calabres97/ai-portfolio | a24e4f7307bd7fdfedd0ded1e08682c1fb9e75cf | [
"Apache-2.0"
] | null | null | null | src/pages/tensorflow.py | calabres97/ai-portfolio | a24e4f7307bd7fdfedd0ded1e08682c1fb9e75cf | [
"Apache-2.0"
] | null | null | null | import streamlit as st
def write():
st.title("Tensorflow Examples and Applications") | 22.25 | 52 | 0.752809 | 12 | 89 | 5.583333 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.157303 | 89 | 4 | 52 | 22.25 | 0.893333 | 0 | 0 | 0 | 0 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | 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 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8d75f4724d092ac90e8911f0a6f8179d0f0a232a | 341 | py | Python | week6/test_jogo_do_nim.py | jotavev/usp-computer-science-python | 82aaa410f245e339c25e4a2b1a61e410259d0f8c | [
"MIT"
] | 1 | 2020-06-10T03:39:07.000Z | 2020-06-10T03:39:07.000Z | week6/test_jogo_do_nim.py | jotavev/usp-computer-science-python | 82aaa410f245e339c25e4a2b1a61e410259d0f8c | [
"MIT"
] | null | null | null | week6/test_jogo_do_nim.py | jotavev/usp-computer-science-python | 82aaa410f245e339c25e4a2b1a61e410259d0f8c | [
"MIT"
] | null | null | null | from temp2 import computador_escolhe_jogada
from temp3 import usuario_escolhe_jogada
def test1_3():
assert computador_escolhe_jogada(1, 3) == 1
def test3_2():
assert computador_escolhe_jogada(2, 2) == 2
def test2_3():
assert computador_escolhe_jogada(2, 3) == 2
def test3_3():
assert computador_escolhe_jogada(3, 3) == 3
| 22.733333 | 47 | 0.741935 | 52 | 341 | 4.557692 | 0.307692 | 0.329114 | 0.485232 | 0.489451 | 0.510549 | 0 | 0 | 0 | 0 | 0 | 0 | 0.077193 | 0.164223 | 341 | 14 | 48 | 24.357143 | 0.754386 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4 | 1 | 0.4 | true | 0 | 0.2 | 0 | 0.6 | 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 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
8d7886eaf0cc75e5b79897575e89b4df699f90e0 | 208 | py | Python | data_utils/__init__.py | DerTreiber/GluonSeg | 30652018ee3b5fd45fc9ee52ae16c12f7919d02b | [
"MIT"
] | 9 | 2017-10-10T06:10:39.000Z | 2021-01-02T05:29:31.000Z | data_utils/__init__.py | DerTreiber/GluonSeg | 30652018ee3b5fd45fc9ee52ae16c12f7919d02b | [
"MIT"
] | 1 | 2018-01-12T05:25:36.000Z | 2018-01-12T05:25:36.000Z | data_utils/__init__.py | DerTreiber/GluonSeg | 30652018ee3b5fd45fc9ee52ae16c12f7919d02b | [
"MIT"
] | 2 | 2017-10-10T07:00:05.000Z | 2019-03-23T02:05:54.000Z | from __future__ import absolute_import
from data_utils.DataTransformer import *
from data_utils.SegDataLoaderBase import SegDataLoaderBase
from data_utils.Enqueuer import SequenceEnqueuer, GeneratorEnqueuer
| 34.666667 | 67 | 0.889423 | 23 | 208 | 7.695652 | 0.478261 | 0.135593 | 0.220339 | 0.214689 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.086538 | 208 | 5 | 68 | 41.6 | 0.931579 | 0 | 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 | 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 |
5750e18dfaf9e223416ea126e309dbe81b33ab06 | 36 | py | Python | python/20181228/Code/p2/p4/c3.py | Realize0917/career | b5d02ac53cfc3ce3a2ca38d11480c51560283e67 | [
"MIT"
] | 3 | 2019-01-17T05:50:51.000Z | 2019-03-15T10:10:07.000Z | python/20181228/Code/p2/p4/c3.py | Realize0917/career | b5d02ac53cfc3ce3a2ca38d11480c51560283e67 | [
"MIT"
] | 10 | 2019-01-17T06:07:03.000Z | 2019-02-19T05:55:25.000Z | python/20181228/Code/p2/p4/c3.py | Realize0917/career | b5d02ac53cfc3ce3a2ca38d11480c51560283e67 | [
"MIT"
] | 4 | 2018-12-22T07:32:55.000Z | 2019-03-06T09:13:48.000Z | print('casdf')
from .c4 import c4_a
| 12 | 20 | 0.722222 | 7 | 36 | 3.571429 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.064516 | 0.138889 | 36 | 2 | 21 | 18 | 0.741935 | 0 | 0 | 0 | 0 | 0 | 0.138889 | 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 |
5758b39fce8a36f0084c8a83b2ba8d6d159a1a04 | 6,206 | py | Python | disba/_dispersion.py | th-reb/disba | b364a40ebe511f8b8d1db75557f9467fa6f12490 | [
"BSD-3-Clause"
] | null | null | null | disba/_dispersion.py | th-reb/disba | b364a40ebe511f8b8d1db75557f9467fa6f12490 | [
"BSD-3-Clause"
] | null | null | null | disba/_dispersion.py | th-reb/disba | b364a40ebe511f8b8d1db75557f9467fa6f12490 | [
"BSD-3-Clause"
] | null | null | null | from collections import namedtuple
import numpy as np
from ._base import BaseDispersion
from ._common import ifunc
from ._surf96 import surf96
__all__ = [
"DispersionCurve",
"PhaseDispersion",
"GroupDispersion",
]
DispersionCurve = namedtuple(
"DispersionCurve", ("x", "velocity", "mode", "wave", "type", "x_axis_type")
)
_XAXIS = ["period", "frequency"]
class PhaseDispersion(BaseDispersion):
def __init__(
self, thickness, velocity_p, velocity_s, density, algorithm="dunkin", dc=0.005,
):
"""
Phase velocity dispersion class.
Parameters
----------
thickness : array_like
Layer thickness (in km).
velocity_p : array_like
Layer P-wave velocity (in km/s).
velocity_s : array_like
Layer S-wave velocity (in km/s).
density : array_like
Layer density (in g/cm3).
algorithm : str {'dunkin', 'fast-delta'}, optional, default 'dunkin'
Algorithm to use for computation of Rayleigh-wave dispersion:
- 'dunkin': Dunkin's matrix (adapted from surf96),
- 'fast-delta': fast delta matrix (after Buchen and Ben-Hador, 1996).
dc : scalar, optional, default 0.005
Phase velocity increment for root finding.
"""
super().__init__(thickness, velocity_p, velocity_s, density, algorithm, dc)
def __call__(self, t, mode=0, wave="rayleigh", x_axis="period"):
"""
Calculate phase velocities for input period axis.
Parameters
----------
t : array_like
Periods (in s).
mode : int, optional, default 0
Mode number (0 if fundamental).
wave : str {'love', 'rayleigh'}, optional, default 'rayleigh'
Wave type.
Returns
-------
namedtuple
Dispersion curve as a namedtuple (period, velocity, mode, wave, type).
Note
----
This function does not perform any check to reduce overhead in case this function is called multiple times (e.g. inversion).
"""
if x_axis not in _XAXIS:
raise ValueError("Incorrect x-axis specified. Please choose either 'frequency' or 'period' as x-axis.")
elif x_axis == "frequency":
#Makes sure frequency is sorted and convert to sorted periods
t = np.sort(t)
t = 1 / t[::-1]
c = surf96(
t,
self._thickness,
self._velocity_p,
self._velocity_s,
self._density,
mode,
ifunc[self._algorithm][wave],
self._dc,
)
idx = c > 0.0
t = t[idx]
c = c[idx]
if x_axis == "frequency":
t = 1 / t[::-1]
return DispersionCurve(t, c, mode, wave, "phase", x_axis_type=x_axis)
class GroupDispersion(BaseDispersion):
def __init__(
self,
thickness,
velocity_p,
velocity_s,
density,
algorithm="dunkin",
dc=0.005,
dt=0.025,
):
"""
Group velocity dispersion class.
Parameters
----------
thickness : array_like
Layer thickness (in km).
velocity_p : array_like
Layer P-wave velocity (in km/s).
velocity_s : array_like
Layer S-wave velocity (in km/s).
density : array_like
Layer density (in g/cm3).
algorithm : str {'dunkin', 'fast-delta'}, optional, default 'dunkin'
Algorithm to use for computation of Rayleigh-wave dispersion:
- 'dunkin': Dunkin's matrix (adapted from surf96),
- 'fast-delta': fast delta matrix (after Buchen and Ben-Hador, 1996).
dc : scalar, optional, default 0.005
Phase velocity increment for root finding.
dt : scalar, optional, default 0.025
Frequency increment (%) for calculating group velocity.
"""
super().__init__(thickness, velocity_p, velocity_s, density, algorithm, dc)
self._dt = dt
def __call__(self, t, mode=0, wave="rayleigh", x_axis="period"):
"""
Calculate group velocities for input period axis.
Parameters
----------
t : array_like
Periods (in s).
mode : int, optional, default 0
Mode number (0 if fundamental).
wave : str {'love', 'rayleigh'}, optional, default 'rayleigh'
Wave type.
Returns
-------
namedtuple
Dispersion curve as a namedtuple (period, velocity, mode, wave, type).
Note
----
This function does not perform any check to reduce overhead in case this function is called multiple times (e.g. inversion).
"""
if x_axis not in _XAXIS:
raise ValueError("Incorrect x-axis specified. Please choose either 'frequency' or 'period' as x-axis.")
elif x_axis == "frequency":
#Makes sure frequency is sorted and convert to sorted periods
t = np.sort(t)
t = 1 / t[::-1]
t1 = t / (1.0 + self._dt)
c = surf96(
t1,
self._thickness,
self._velocity_p,
self._velocity_s,
self._density,
mode,
ifunc[self._algorithm][wave],
self._dc,
)
idx = c > 0.0
t = t[idx]
c = c[idx]
t1 = t1[idx]
t2 = t / (1.0 - self._dt)
c2 = surf96(
t2,
self._thickness,
self._velocity_p,
self._velocity_s,
self._density,
mode,
ifunc[self._algorithm][wave],
self._dc,
)
idx = c2 > 0.0
t = t[idx]
t1 = 1.0 / t1[idx]
t2 = 1.0 / t2[idx]
c = (t1 - t2) / (t1 / c[idx] - t2 / c2[idx])
if x_axis == "frequency":
t = 1 / t[::-1]
return DispersionCurve(t, c, mode, wave, "group", x_axis)
@property
def dt(self):
"""Return frequency increment (%) for calculating group velocity."""
return self._dt
| 29.273585 | 132 | 0.538511 | 702 | 6,206 | 4.621083 | 0.183761 | 0.024661 | 0.034525 | 0.032059 | 0.848027 | 0.840321 | 0.812577 | 0.812577 | 0.812577 | 0.812577 | 0 | 0.023186 | 0.35369 | 6,206 | 211 | 133 | 29.412322 | 0.78559 | 0.407025 | 0 | 0.536082 | 0 | 0.020619 | 0.117129 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.051546 | false | 0 | 0.051546 | 0 | 0.154639 | 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 |
93a91f9614406efa3988e134bd78fdc960c1d65d | 5,168 | py | Python | 2018 Codegate Quals/babyRSA/RSAbaby.py | mathboy7/CTF | e279bf91ad6b22fc41087571c5fba9501832ab80 | [
"Apache-2.0"
] | 46 | 2017-11-07T05:30:26.000Z | 2021-11-24T09:33:41.000Z | 2018 Codegate Quals/babyRSA/RSAbaby.py | mathboy7/CTF | e279bf91ad6b22fc41087571c5fba9501832ab80 | [
"Apache-2.0"
] | null | null | null | 2018 Codegate Quals/babyRSA/RSAbaby.py | mathboy7/CTF | e279bf91ad6b22fc41087571c5fba9501832ab80 | [
"Apache-2.0"
] | 3 | 2017-11-14T14:45:28.000Z | 2020-08-30T07:59:04.000Z | g = 14511485561279877242490049924164262671564856980418706493772866848857612385453104346586350276227873984815502106112389832011566814347565705873657427101510533972939335373118027470906354834216983842099812965592939768854241417529908124711818216182341332507918374220901579987851767888710421089266081280013256600425746557269742268670300714949183260246617797156425767983027415373581836147225552931559016487193903056680274018867169067069164417868649729813464306199388375773268972224468436723728788928618254041886532217172217283880677562744928063668302190530092708676086756514664006766909499651097644447881334032649057611965077951245778537347658519214651268439995915614667939336569800565797702566887133370244643122543689011224353239395653153094885449557256699923700742653930928887024447374907536229536501931493386170594869542262576409686250950887746501725676758035668270309685358291271363775138099327895323451901829587908987436831617628346535627562925010698445652286450107659802164994355539623617745529876829000553355956755914526849056343372137493951531663650121127924626353148067965144997177441402726593083629261964699315644045714647617156724816370270635144953182744245498998992807987174252376199074131496163299914588620694929584594866873400406185502626180264465104468365933575409921644759774899908018217623256295871823903858740112075223018089096313796599554636163186830200265892525403238639070366999401808068998639590975305617369688731214141047568939908240058088089504343104889824160334560324387496383256518400827927341943755279126157377196722373876343583757261084975726106468397487366825775319965557539853162973895788663508023419482720093445137085452233528426725965549266605359644884153719762909553953900709890192728260024241748671796401590112629479273363064208874240854298225057415248756216847693518038319188675206377870041466557414694779134628404260587970
h = 200972731730097636976827049698214756107439330058946586294810837394189769656758467301378455256704981506024979360358854939307759891385801491668590432728409172325924823845795802068569504027458509726942683684845099685005724309372842055251251103232234279320256975662933177657993600463290652464246399357992101963313348397652939723188131041888535203383479379782750484175239116419074864386243581748425119257869351582631464696880797553969260415636591522791709442079709586828716914705946883433533874750682958642851920347897328709815665287336267018234850211541263570668304013958387590188226346947851729783080697306777656948546082
N = 523639805914061918270627443134741619704989339108811345591765650823383811679404400743730300288077320843234806116907796484315512386749183735427076044515394957782722144465236043561036957495670530886847413432636828661793513741180618385135095922719611444315861194066682307139969523206842728092440966461922557111209480112023032164065707216752568624317883094770784553451376502893748762652573604180632157059219119741129827017117558208565054860250853978397405747507844727903363351081745897472675235414693294079400158465019978970101063161094836772073302365997371679643083941089269169502839517043186914783290465318781726781533226599462066259256698885200843104424722505593942510854302401488139137362276492532699951880474157691347473741517183512613811731637427562990396497067805682564174185792379491573312640862381843195615293946630128509982267460922475624107750277459002662884836031305873522960659017891138316482378312004790485681371129328860344989214941450460756203906709954285455206483931555441550631622907560476932030275168094874500348941952385811045752980245084909805234648503736291123092594689494187215718382724496356220857628352007757197464098872772987476828030721472777531411032286344430474215475330008833588291692767417022829531866323051
sub = 0xdeadbeef
sumarr = []
mularr = []
for i in range(0, 1024):
for j in range(0, 1024):
s = ((i + j) ^ (i - j)) % 1024
m = i * (j-sub) % 1024
sumarr.append(s)
mularr.append(m)
hmod = h % 1024
gmod = g % 1024
vi = []
vj = []
for i in range(0, 1024*1024):
if sumarr[i] == hmod and mularr[i] == gmod:
print "i: " + str(i/1024)
print "j: " + str(i%1024)
vi.append(i / 1024)
vj.append(i % 1024)
print vi
print vj
#############################################################
for loop in xrange(409):
sumarr = []
mularr = []
for i in xrange(len(vi)):
sumarr.append([])
mularr.append([])
for i in xrange(len(vi)):
for j in range(0, 32):
for k in range(0, 32):
v1 = 2**(10+5*loop) * j + vi[i]
v2 = 2**(10+5*loop) * k + vj[i]
s = ((v1 + v2) ^ (v1 - v2)) % 2**(15+5*loop)
if(s < 0):
s += 2**(15+5*loop)
m = v1 * (v2 - sub) % 2**(15+5*loop)
if(m < 0):
m += 2**(15+5*loop)
sumarr[i].append(s)
mularr[i].append(m)
hmod = h % 2**(15+5*loop)
gmod = g % 2**(15+5*loop)
vi_ = []
vj_ = []
for i in range(len(vi)):
for j in range(0, 1024):
if sumarr[i][j] == hmod and mularr[i][j] == gmod:
vi_.append(vi[i]+2**(10+5*loop)*(j/32))
vj_.append(vj[i]+2**(10+5*loop)*(j%32))
print str(loop)
print len(vi_)
print len(vj_)
print "\n"
if loop >= 407:
for p in vj_:
if N % p == 0:
print "p found!"
print "p val: " + hex(p)
vi = vi_
vj = vj_
| 59.402299 | 1,854 | 0.853328 | 266 | 5,168 | 16.545113 | 0.169173 | 0.011361 | 0.010907 | 0.010907 | 0.047944 | 0.035901 | 0.013179 | 0 | 0 | 0 | 0 | 0.79954 | 0.074303 | 5,168 | 86 | 1,855 | 60.093023 | 0.120401 | 0 | 0 | 0.126984 | 0 | 0 | 0.004504 | 0 | 0 | 1 | 0.001958 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.15873 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
9e09b4a0fa354872acfaea585a4f6a0b9db09bbe | 45 | py | Python | tests/test_apps/cliapp/importerrorapp.py | flippersmcgee/flask | 6efaedd3394e8bd797ee38095d96753da4b1b90d | [
"BSD-3-Clause"
] | null | null | null | tests/test_apps/cliapp/importerrorapp.py | flippersmcgee/flask | 6efaedd3394e8bd797ee38095d96753da4b1b90d | [
"BSD-3-Clause"
] | 1 | 2020-07-13T09:45:50.000Z | 2020-07-13T09:45:50.000Z | tests/test_apps/cliapp/importerrorapp.py | flippersmcgee/flask | 6efaedd3394e8bd797ee38095d96753da4b1b90d | [
"BSD-3-Clause"
] | null | null | null | from flask import Flask
raise ImportError()
| 11.25 | 23 | 0.8 | 6 | 45 | 6 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.155556 | 45 | 3 | 24 | 15 | 0.947368 | 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 |
192440c3942a19bdbdb645c16b86e9b86e62454e | 175 | py | Python | simpbot/bottools/__init__.py | IsmaelRLG/simpbot | 7d4288334f637e0a7774ef1acda933a668c60181 | [
"MIT"
] | 3 | 2017-05-08T14:53:40.000Z | 2021-12-18T22:15:14.000Z | simpbot/bottools/__init__.py | IsmaelRLG/simpbot | 7d4288334f637e0a7774ef1acda933a668c60181 | [
"MIT"
] | null | null | null | simpbot/bottools/__init__.py | IsmaelRLG/simpbot | 7d4288334f637e0a7774ef1acda933a668c60181 | [
"MIT"
] | 1 | 2017-05-15T23:28:56.000Z | 2017-05-15T23:28:56.000Z | # -*- coding: utf-8 -*-
# Simple Bot (SimpBot)
# Copyright 2016-2017, Ismael Lugo (kwargs)
#lint:disable
from . import irc
from . import dummy
from . import text
#lint:enable | 19.444444 | 43 | 0.697143 | 25 | 175 | 4.88 | 0.8 | 0.245902 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.061644 | 0.165714 | 175 | 9 | 44 | 19.444444 | 0.773973 | 0.611429 | 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 |
1978e6e97ea61c1249cfc581e66642d9db81da6c | 20 | py | Python | clks/nnet/maml/__init__.py | cjliux/mdst.c2f | 5617624b25ddaa11ffbc07401d3fe0276ca220d5 | [
"BSD-3-Clause"
] | 2 | 2020-07-17T12:12:35.000Z | 2020-09-12T14:28:55.000Z | clks/nnet/maml/__init__.py | cjliux/mdst.c2f | 5617624b25ddaa11ffbc07401d3fe0276ca220d5 | [
"BSD-3-Clause"
] | null | null | null | clks/nnet/maml/__init__.py | cjliux/mdst.c2f | 5617624b25ddaa11ffbc07401d3fe0276ca220d5 | [
"BSD-3-Clause"
] | null | null | null | from .maml import *
| 10 | 19 | 0.7 | 3 | 20 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 20 | 1 | 20 | 20 | 0.875 | 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 |
197e8a1773556db790fbeb3157e5f6ad26c4d50d | 121 | py | Python | tests/test_src.py | darrenleeweber/conda_template | 11e6488f612ea1337d7e9dc185b497e0862e0a9a | [
"Apache-2.0"
] | 6 | 2020-05-18T10:21:39.000Z | 2022-03-14T07:47:51.000Z | tests/test_src.py | dazza-codes/conda_template | 11e6488f612ea1337d7e9dc185b497e0862e0a9a | [
"Apache-2.0"
] | 3 | 2019-02-13T02:55:29.000Z | 2019-02-21T03:14:01.000Z | tests/test_src.py | dazza-codes/conda_template | 11e6488f612ea1337d7e9dc185b497e0862e0a9a | [
"Apache-2.0"
] | 1 | 2020-05-17T18:46:50.000Z | 2020-05-17T18:46:50.000Z | """
Test the module
"""
from types import ModuleType
import src
def test_src():
assert isinstance(src, ModuleType)
| 12.1 | 38 | 0.719008 | 16 | 121 | 5.375 | 0.6875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 121 | 9 | 39 | 13.444444 | 0.868687 | 0.123967 | 0 | 0 | 0 | 0 | 0 | 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 |
198581e17a64011deccca5f927b3abaf6e810b3d | 49 | py | Python | enthought/util/refresh.py | enthought/etsproxy | 4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347 | [
"BSD-3-Clause"
] | 3 | 2016-12-09T06:05:18.000Z | 2018-03-01T13:00:29.000Z | enthought/util/refresh.py | enthought/etsproxy | 4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347 | [
"BSD-3-Clause"
] | 1 | 2020-12-02T00:51:32.000Z | 2020-12-02T08:48:55.000Z | enthought/util/refresh.py | enthought/etsproxy | 4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347 | [
"BSD-3-Clause"
] | null | null | null | # proxy module
from traits.util.refresh import *
| 16.333333 | 33 | 0.77551 | 7 | 49 | 5.428571 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 49 | 2 | 34 | 24.5 | 0.904762 | 0.244898 | 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 |
199e8ad294c46038f5cbe933385cc2ad015057b7 | 18,040 | py | Python | api/converse/tests/test_views.py | aberrier/exploud | b5184e10480a91ea5566fc54f337ac59d11de315 | [
"MIT"
] | null | null | null | api/converse/tests/test_views.py | aberrier/exploud | b5184e10480a91ea5566fc54f337ac59d11de315 | [
"MIT"
] | 4 | 2018-09-11T16:36:59.000Z | 2021-04-30T20:35:11.000Z | api/converse/tests/test_views.py | aberrier/exploud | b5184e10480a91ea5566fc54f337ac59d11de315 | [
"MIT"
] | 2 | 2018-07-04T08:00:07.000Z | 2019-06-04T19:46:57.000Z | import pytest
import json
import requests
from mock import patch, MagicMock, Mock
from flask import url_for
import io
from api.exceptions import BadParameterException, MissingParameterException, InvalidCredentialsException, \
ExternalAPIException, APIException, BadHeaderException, MissingHeaderException, OperationFailedException
from api.converse.views import get_crypto, get_news, get_weather
from api.converse.constants import AUDIO_FORMATS, SUPPORTED_FORMATS, TEXT_FORMATS, CUSTOM_MESSAGES, DEFAULT_INTENT
from api.converse.views import nlp, tts, stt
from api.speech_to_text.google.constants import LANGUAGES_CODE, SIMPLIFIED_LANGUAGES_CODE
# Ensure that Converse behaves correctly when provided correct information
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
@patch('api.converse.views.check_special_intent', autospec=True)
def test_converse_text_to_text_success(mock_check_special_intent, mock_recast_send_request_dialog, client,
converse_text_request, recast_answer_response):
mock_recast_send_request_dialog.return_value = recast_answer_response
mock_check_special_intent.return_value = None
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
assert res.status_code == 200
assert mock_recast_send_request_dialog.call_count == 1
assert mock_check_special_intent.call_count == 1
# Ensure that Converse behaves correctly when provided correct information
@patch.object(tts, 'ibm_send_request', autospec=True)
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
@patch('api.converse.views.check_special_intent', autospec=True)
def test_converse_text_to_audio_success(mock_check_special_intent, mock_recast_send_request_dialog,
mock_ibm_send_request, client, converse_text_request, converse_audio_response,
recast_answer_response):
mock_recast_send_request_dialog.return_value = recast_answer_response
mock_ibm_send_request.return_value = converse_audio_response['body']
mock_check_special_intent.return_value = None
res = client.post(
url_for('converse.conversation-audio'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
assert res.status_code == 200
assert mock_recast_send_request_dialog.call_count == 1
assert mock_check_special_intent.call_count == 1
assert res.headers.get('JSON')
assert mock_ibm_send_request.call_count == 1
# Ensure that Converse behaves correctly when provided correct information
@patch.object(stt, 'google_speech_send_request', autospec=True)
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
@patch('api.converse.views.check_special_intent', autospec=True)
def test_converse_audio_to_text_success(mock_check_special_intent, mock_recast_send_request_dialog,
mock_google_speech_send_request, client, converse_audio_request,
converse_text_request, recast_answer_response):
mock_recast_send_request_dialog.return_value = recast_answer_response
mock_google_speech_send_request.return_value = {'text': converse_text_request['text'], 'confidence': 0.99}
mock_check_special_intent.return_value = None
res = client.post(
url_for('converse.conversation-text'),
content_type='multipart/form-data',
data={
'audio': (io.BytesIO(converse_audio_request['audio']), 'audio.wav'),
'language': converse_audio_request['language'],
'user_id': converse_audio_request['user_id']
}
)
assert res.status_code == 200
assert mock_recast_send_request_dialog.call_count == 1
assert mock_check_special_intent.call_count == 1
# Ensure that Converse behaves correctly when provided correct information
@patch.object(tts, 'ibm_send_request', autospec=True)
@patch.object(stt, 'google_speech_send_request', autospec=True)
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
@patch('api.converse.views.check_special_intent', autospec=True)
def test_converse_audio_to_audio_success(mock_check_special_intent, mock_recast_send_request_dialog,
mock_google_speech_send_request, mock_ibm_send_request,
converse_audio_response, client, converse_audio_request,
converse_text_request, recast_answer_response):
mock_recast_send_request_dialog.return_value = recast_answer_response
mock_ibm_send_request.return_value = converse_audio_response['body']
mock_google_speech_send_request.return_value = {'text': converse_text_request['text'], 'confidence': 0.99}
mock_check_special_intent.return_value = None
res = client.post(
url_for('converse.conversation-text'),
content_type='multipart/form-data',
data={
'audio': (io.BytesIO(converse_audio_request['audio']), 'audio.wav'),
'language': converse_audio_request['language'],
'user_id': converse_audio_request['user_id']
}
)
assert res.status_code == 200
assert mock_recast_send_request_dialog.call_count == 1
assert mock_check_special_intent.call_count == 1
# Ensure that special intents work correctly
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
@patch('api.converse.views.get_weather', autospec=True)
@patch('api.converse.views.get_crypto', autospec=True)
@patch('api.converse.views.get_news', autospec=True)
def test_converse_special_intent_weather(mock_get_news, mock_get_crypto, mock_get_weather,
mock_recast_send_request_dialog, client,
converse_text_request, recast_answer_response, converse_weather_response,
converse_crypto_response, converse_news_response):
completed_recast_request = recast_answer_response
completed_recast_request['results']['nlp']['entities'] = {
"datetime": [
{
"formatted": "mercredi 01 août 2018 à 13h07m11s (+0000)",
"iso": "2018-08-01T13:07:11+00:00",
"raw": "demain"
}
],
"location": [
{
"formatted": "Paris, France",
"lat": 48.856614,
"lng": 2.3522219,
"raw": "Paris"
}
],
"cryptomonnaie": [
{
"confidence": 0.93,
"raw": "ethereum",
"value": "ethereum"
}
]
}
# Weather
completed_recast_request['results']['nlp']['intents'][0]['slug'] = 'get-weather'
mock_recast_send_request_dialog.return_value = completed_recast_request
mock_get_weather.return_value = converse_weather_response
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
assert res.status_code == 200
assert mock_get_weather.call_count == 1
# Crypto
completed_recast_request['results']['nlp']['intents'][0]['slug'] = 'cryptonews'
mock_recast_send_request_dialog.return_value = completed_recast_request
mock_get_crypto.return_value = converse_crypto_response, True
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
assert res.status_code == 200
assert mock_get_crypto.call_count == 1
# News
completed_recast_request['results']['nlp']['intents'][0]['slug'] = 'news'
mock_recast_send_request_dialog.return_value = completed_recast_request
mock_get_news.return_value = converse_news_response
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
assert res.status_code == 200
assert mock_get_news.call_count == 1
assert mock_recast_send_request_dialog.call_count == 3
# Ensure that Converse behaves correctly when language is missing
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
def test_converse_missing_language(mock_recast_send_request_dialog, client, converse_text_request,
converse_audio_request):
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps({
'text': converse_text_request['text'],
'user_id': converse_text_request['user_id']
})
)
expected_result = {'errors': [dict(MissingParameterException('language'))]}
assert res.status_code == 400
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
res = client.post(
url_for('converse.conversation-audio'),
content_type='multipart/form-data',
data={
'audio': (io.BytesIO(converse_audio_request['audio']), 'audio.wav'),
'user_id': converse_audio_request['user_id']
}
)
assert res.status_code == 400
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when language is not correct
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
def test_converse_bad_language(mock_recast_send_request_dialog, client, converse_text_request, converse_audio_request):
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps({
'text': converse_text_request['text'],
'user_id': converse_text_request['user_id'],
'language': 'xx-XX'
})
)
expected_result = {'errors': [dict(BadParameterException('language', valid_values=LANGUAGES_CODE))]}
print(res.data)
assert res.status_code == 422
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
res = client.post(
url_for('converse.conversation-audio'),
content_type='multipart/form-data',
data={
'audio': (io.BytesIO(converse_audio_request['audio']), 'audio.wav'),
'language': 'xx-XX',
'user_id': converse_audio_request['user_id']
}
)
assert res.status_code == 422
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when text is missing
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
def test_converse_missing_text(mock_recast_send_request_dialog, client, converse_text_request):
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps({
'user_id': converse_text_request['user_id'],
'language': converse_text_request['language']
})
)
expected_result = {'errors': [dict(MissingParameterException('text'))]}
assert res.status_code == 400
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when audio is missing
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
def test_converse_missing_audio(mock_recast_send_request_dialog, client, converse_audio_request):
res = client.post(
url_for('converse.conversation-audio'),
content_type='multipart/form-data',
data={
'language': converse_audio_request['language'],
'user_id': converse_audio_request['user_id']
}
)
expected_result = {'errors': [dict(MissingParameterException('audio'))]}
assert res.status_code == 400
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when header is not correct
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
def test_converse_bad_header(mock_recast_send_request_dialog, client, converse_text_request):
res = client.post(
url_for('converse.conversation-text'),
content_type='xxxxx/xxxx',
data=json.dumps(converse_text_request)
)
expected_result = {'errors': [dict(BadHeaderException('Content-Type', valid_values=SUPPORTED_FORMATS))]}
assert res.status_code == 400
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when header is missing
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
def test_converse_missing_header(mock_recast_send_request_dialog, client, converse_text_request):
res = client.post(
url_for('converse.conversation-text'),
data=json.dumps(converse_text_request)
)
expected_result = {'errors': [dict(MissingHeaderException('Content-Type'))]}
assert res.status_code == 400
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when google speech failed
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
@patch.object(stt, 'google_speech_send_request', autospec=True)
def test_converse_stt_fail(mock_google_speech_send_request, mock_recast_send_request_dialog, client, converse_audio_request):
mock_google_speech_send_request.side_effect = OperationFailedException()
res = client.post(
url_for('converse.conversation-text'),
content_type='multipart/form-data',
data={
'audio': (io.BytesIO(converse_audio_request['audio']), 'audio.wav'),
'language': converse_audio_request['language'],
'user_id': converse_audio_request['user_id']
}
)
dict_res = json.loads(res.data)
assert res.status_code == 200
assert dict_res['message'] == CUSTOM_MESSAGES[SIMPLIFIED_LANGUAGES_CODE[converse_audio_request['language']]]["not-heard"]
assert dict_res['intent'] == DEFAULT_INTENT
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when google speech stopped
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
@patch.object(stt, 'google_speech_send_request', autospec=True)
def test_converse_stt_stop(mock_google_speech_send_request, mock_recast_send_request_dialog, client, converse_audio_request):
mock_google_speech_send_request.side_effect = Exception()
res = client.post(
url_for('converse.conversation-text'),
content_type='multipart/form-data',
data={
'audio': (io.BytesIO(converse_audio_request['audio']), 'audio.wav'),
'language': converse_audio_request['language'],
'user_id': converse_audio_request['user_id']
}
)
expected_result = {'errors': [dict(ExternalAPIException('Google'))]}
assert res.status_code == 503
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_recast_send_request_dialog.call_count == 0
# Ensure that Converse behaves correctly when recast credentials are invalid
@patch.object(requests, 'post', autospec=True)
def test_converse_nlp_invalid_credentials(mock_post, client, converse_text_request):
mock_post.return_value = Mock(status_code=401)
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
expected_result = {'errors': [dict(InvalidCredentialsException(api_name='Recast'))]}
assert res.status_code == 401
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_post.call_count == 1
# Ensure that Converse behaves correctly when services are not working properly
@patch('api.converse.views.check_special_intent', autospec=True)
def test_converse_nlp_services_offline(mock_check_special_intent, client, converse_text_request):
mock_check_special_intent.side_effect = ExternalAPIException()
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
expected_result = {'errors': [dict(ExternalAPIException())]}
assert res.status_code == 503
assert sorted(json.loads(res.data).items()) == sorted(expected_result.items())
assert mock_check_special_intent.call_count == 1
# Ensure that Converse behaves correctly when checking special intents stopped
@patch('api.converse.views.check_special_intent', autospec=True)
def test_converse_nlp_special_intents_stop(mock_check_special_intent, client, converse_text_request):
mock_check_special_intent.side_effect = Exception()
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
assert res.status_code == 500
assert mock_check_special_intent.call_count == 1
# Ensure that Converse behaves correctly when nlp helper stopped
@patch.object(nlp, 'recast_send_request_dialog', autospec=True)
def test_converse_nlp_stop(mock_recast_send_request_dialog, client, converse_text_request):
mock_recast_send_request_dialog.side_effect = Exception()
res = client.post(
url_for('converse.conversation-text'),
content_type='application/json',
data=json.dumps(converse_text_request)
)
assert res.status_code == 500
assert mock_recast_send_request_dialog.call_count == 1 | 44.987531 | 125 | 0.712694 | 2,180 | 18,040 | 5.551376 | 0.081651 | 0.062717 | 0.070236 | 0.095026 | 0.843497 | 0.823748 | 0.811849 | 0.803999 | 0.775161 | 0.761444 | 0 | 0.010396 | 0.184202 | 18,040 | 401 | 126 | 44.987531 | 0.811918 | 0.064246 | 0 | 0.607784 | 0 | 0 | 0.153372 | 0.080837 | 0 | 0 | 0 | 0 | 0.179641 | 1 | 0.050898 | false | 0 | 0.032934 | 0 | 0.083832 | 0.002994 | 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 |
2733f35e76ab39607f5fdff1a06e1d3fe4cb146b | 21 | py | Python | data/__init__.py | saicoco/gluon-east | 9597bf4fe20a971940fbd5e72c221040ecacb5b7 | [
"MIT"
] | 2 | 2019-01-05T02:40:06.000Z | 2019-03-20T18:00:05.000Z | data/__init__.py | saicoco/gluon-east | 9597bf4fe20a971940fbd5e72c221040ecacb5b7 | [
"MIT"
] | null | null | null | data/__init__.py | saicoco/gluon-east | 9597bf4fe20a971940fbd5e72c221040ecacb5b7 | [
"MIT"
] | null | null | null | from ic_data import * | 21 | 21 | 0.809524 | 4 | 21 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 21 | 1 | 21 | 21 | 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 |
2750246e580f9167eef9027d8748f596aa8aa00c | 27 | py | Python | keras_htr/data_source/__init__.py | X-rayLaser/Keras-HTR | e27f717c2c8af75f4774c62a81f70f46c4e8eadf | [
"MIT"
] | 8 | 2020-06-02T06:14:07.000Z | 2022-02-14T14:58:33.000Z | keras_htr/data_source/__init__.py | X-rayLaser/Keras-HTR | e27f717c2c8af75f4774c62a81f70f46c4e8eadf | [
"MIT"
] | 1 | 2020-07-08T18:03:21.000Z | 2020-07-12T08:05:56.000Z | keras_htr/data_source/__init__.py | X-rayLaser/Keras-HTR | e27f717c2c8af75f4774c62a81f70f46c4e8eadf | [
"MIT"
] | 1 | 2020-12-29T09:39:14.000Z | 2020-12-29T09:39:14.000Z | from .iam import IAMSource
| 13.5 | 26 | 0.814815 | 4 | 27 | 5.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148148 | 27 | 1 | 27 | 27 | 0.956522 | 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 |
275898a3cfbb878bb9bb70e6c94a2789653961cb | 80 | py | Python | spa/models/recurrence.py | fergalmoran/dss | 684fb4030e33212c3ecde774ca86cb74a1ffc8ac | [
"BSD-2-Clause"
] | null | null | null | spa/models/recurrence.py | fergalmoran/dss | 684fb4030e33212c3ecde774ca86cb74a1ffc8ac | [
"BSD-2-Clause"
] | 3 | 2020-02-11T21:55:44.000Z | 2021-06-10T17:35:37.000Z | spa/models/recurrence.py | fergalmoran/dss | 684fb4030e33212c3ecde774ca86cb74a1ffc8ac | [
"BSD-2-Clause"
] | null | null | null | from spa.models._lookup import _Lookup
class Recurrence(_Lookup):
pass
| 16 | 39 | 0.7375 | 10 | 80 | 5.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 80 | 4 | 40 | 20 | 0.875 | 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 |
27740e0d7ae2f7ef2a968efe7d60b6b738ba2a74 | 227 | py | Python | class9/ex8/mytest/__init__.py | patrebert/pynet_cert | b82cce3ddb20d9e4abc89d74579ddeb3513bdf55 | [
"Apache-2.0"
] | null | null | null | class9/ex8/mytest/__init__.py | patrebert/pynet_cert | b82cce3ddb20d9e4abc89d74579ddeb3513bdf55 | [
"Apache-2.0"
] | null | null | null | class9/ex8/mytest/__init__.py | patrebert/pynet_cert | b82cce3ddb20d9e4abc89d74579ddeb3513bdf55 | [
"Apache-2.0"
] | null | null | null | from mytest.simple import func1
from mytest.whatever import func2
from mytest.world import func3
from mytest.world import testclass
from mytest.world import MyClass
__all__ = ['func1', 'func2', 'func3', 'testclass', 'MyClass']
| 32.428571 | 61 | 0.784141 | 31 | 227 | 5.612903 | 0.387097 | 0.287356 | 0.258621 | 0.362069 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.03 | 0.118943 | 227 | 6 | 62 | 37.833333 | 0.84 | 0 | 0 | 0 | 0 | 0 | 0.136564 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.833333 | 0 | 0.833333 | 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 |
27819c6dc8e05ccfe7c15ea46aa129b152618678 | 1,469 | py | Python | vector.py | nathanfohkens/theMLbook | e62817b1a67492f168ccd4197ded254bd8f329ee | [
"MIT"
] | 1,625 | 2019-01-14T14:04:36.000Z | 2022-03-28T21:14:20.000Z | vector.py | a272573094/theMLbook | 9236ec87b0cf563e0998d723be19e58155003a9d | [
"MIT"
] | 6 | 2019-04-17T17:20:48.000Z | 2020-12-17T19:50:26.000Z | vector.py | a272573094/theMLbook | 9236ec87b0cf563e0998d723be19e58155003a9d | [
"MIT"
] | 540 | 2019-01-14T14:07:54.000Z | 2022-03-28T21:14:23.000Z | import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
matplotlib.rcParams.update({'font.size': 18})
plt.figure(1)
plt.quiver([0, 0, 0], [0, 0, 0], [2, -2, 1], [3, 5, 0], color=['r','b','g'], angles='xy', scale_units='xy', scale=1)
plt.xlim(-3, 3)
plt.ylim(-1, 6)
plt.xlabel('$x^{(1)}$')
plt.ylabel('$x^{(2)}$')
fig1 = plt.gcf()
fig1.subplots_adjust(top = 0.98, bottom = 0.1, right = 0.98, left = 0.12, hspace = 0, wspace = 0)
fig1.savefig('../../Illustrations/vector-0.eps', format='eps', dpi=1000, bbox_inches = 'tight', pad_inches = 0.1)
fig1.savefig('../../Illustrations/vector-0.pdf', format='pdf', dpi=1000, bbox_inches = 'tight', pad_inches = 0.1)
fig1.savefig('../../Illustrations/vector-0.png', dpi=1000, bbox_inches = 'tight', pad_inches = 0.1)
plt.show()
plt.figure(2)
plt.scatter([2, -2, 1], [3, 5, 0], color=['r','b','g'])
plt.xlim(-3, 3)
plt.ylim(-1, 6)
plt.xlabel('$x^{(1)}$')
plt.ylabel('$x^{(2)}$')
fig1 = plt.gcf()
fig1.subplots_adjust(top = 0.98, bottom = 0.1, right = 0.98, left = 0.12, hspace = 0, wspace = 0)
fig1.savefig('../../Illustrations/vector-1.eps', format='eps', dpi=1000, bbox_inches = 'tight', pad_inches = 0.1)
fig1.savefig('../../Illustrations/vector-1.pdf', format='pdf', dpi=1000, bbox_inches = 'tight', pad_inches = 0.1)
fig1.savefig('../../Illustrations/vector-1.png', dpi=1000, bbox_inches = 'tight', pad_inches = 0.1)
plt.show()
| 43.205882 | 116 | 0.640572 | 244 | 1,469 | 3.795082 | 0.258197 | 0.017279 | 0.155508 | 0.194384 | 0.764579 | 0.75594 | 0.75594 | 0.75594 | 0.75594 | 0.75594 | 0 | 0.085736 | 0.102791 | 1,469 | 33 | 117 | 44.515152 | 0.616844 | 0 | 0 | 0.482759 | 0 | 0 | 0.225323 | 0.130701 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.068966 | 0 | 0.068966 | 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 |
27a09ea49f1b95031d89892e989a6a2cd7351194 | 77 | py | Python | tests/test_files/argv.py | tusharsadhwani/zxpy | aa15e438288614d5138d08319b7f51282990baf7 | [
"MIT"
] | 418 | 2021-05-08T11:46:29.000Z | 2022-03-31T07:28:37.000Z | tests/test_files/argv.py | tusharsadhwani/zxpy | aa15e438288614d5138d08319b7f51282990baf7 | [
"MIT"
] | 31 | 2021-05-10T07:58:57.000Z | 2022-03-07T20:05:32.000Z | tests/test_files/argv.py | tusharsadhwani/zxpy | aa15e438288614d5138d08319b7f51282990baf7 | [
"MIT"
] | 11 | 2021-05-12T12:20:55.000Z | 2022-03-17T22:02:34.000Z | import sys
assert len(sys.argv) == 1
assert sys.argv[0].endswith("argv.py")
| 15.4 | 38 | 0.701299 | 14 | 77 | 3.857143 | 0.642857 | 0.259259 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.029412 | 0.116883 | 77 | 4 | 39 | 19.25 | 0.764706 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 0 | 0 | 0 | 0 | 0 | 0.666667 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 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 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
27b74f563d64829ffa7b9bfff6777a3dbeb45711 | 66 | py | Python | corehq/apps/locations/tests/__init__.py | dslowikowski/commcare-hq | ad8885cf8dab69dc85cb64f37aeaf06106124797 | [
"BSD-3-Clause"
] | 1 | 2015-02-10T23:26:39.000Z | 2015-02-10T23:26:39.000Z | corehq/apps/locations/tests/__init__.py | SEL-Columbia/commcare-hq | 992ee34a679c37f063f86200e6df5a197d5e3ff6 | [
"BSD-3-Clause"
] | null | null | null | corehq/apps/locations/tests/__init__.py | SEL-Columbia/commcare-hq | 992ee34a679c37f063f86200e6df5a197d5e3ff6 | [
"BSD-3-Clause"
] | null | null | null | from .test_location_import import *
from .test_site_code import *
| 22 | 35 | 0.818182 | 10 | 66 | 5 | 0.6 | 0.32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 66 | 2 | 36 | 33 | 0.862069 | 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 |
27cad4c423764b14c963ed884703f8bebcb54cff | 2,137 | py | Python | api/user/migrations/0012_last_notified_fields.py | uktrade/market-access-api | 850a59880f8f62263784bcd9c6b3362e447dbc7a | [
"MIT"
] | null | null | null | api/user/migrations/0012_last_notified_fields.py | uktrade/market-access-api | 850a59880f8f62263784bcd9c6b3362e447dbc7a | [
"MIT"
] | 51 | 2018-05-31T12:16:31.000Z | 2022-03-08T09:36:48.000Z | api/user/migrations/0012_last_notified_fields.py | uktrade/market-access-api | 850a59880f8f62263784bcd9c6b3362e447dbc7a | [
"MIT"
] | 2 | 2019-12-24T09:47:42.000Z | 2021-02-09T09:36:51.000Z | # Generated by Django 2.2.11 on 2020-05-18 10:00
import django.contrib.postgres.fields
import django.utils.timezone
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("user", "0011_clean_filters"),
]
operations = [
migrations.AlterModelOptions(
name="savedsearch",
options={"ordering": ("name",)},
),
migrations.AddField(
model_name="mybarrierssavedsearch",
name="last_notified_barrier_ids",
field=django.contrib.postgres.fields.ArrayField(
base_field=models.UUIDField(), blank=True, default=list, size=None
),
),
migrations.AddField(
model_name="mybarrierssavedsearch",
name="last_notified_on",
field=models.DateTimeField(
auto_now_add=True, default=django.utils.timezone.now
),
preserve_default=False,
),
migrations.AddField(
model_name="savedsearch",
name="last_notified_barrier_ids",
field=django.contrib.postgres.fields.ArrayField(
base_field=models.UUIDField(), blank=True, default=list, size=None
),
),
migrations.AddField(
model_name="savedsearch",
name="last_notified_on",
field=models.DateTimeField(
auto_now_add=True, default=django.utils.timezone.now
),
preserve_default=False,
),
migrations.AddField(
model_name="teambarrierssavedsearch",
name="last_notified_barrier_ids",
field=django.contrib.postgres.fields.ArrayField(
base_field=models.UUIDField(), blank=True, default=list, size=None
),
),
migrations.AddField(
model_name="teambarrierssavedsearch",
name="last_notified_on",
field=models.DateTimeField(
auto_now_add=True, default=django.utils.timezone.now
),
preserve_default=False,
),
]
| 32.876923 | 82 | 0.582592 | 194 | 2,137 | 6.237113 | 0.298969 | 0.089256 | 0.11405 | 0.133884 | 0.782645 | 0.782645 | 0.782645 | 0.782645 | 0.669421 | 0.669421 | 0 | 0.013689 | 0.316331 | 2,137 | 64 | 83 | 33.390625 | 0.814511 | 0.021526 | 0 | 0.793103 | 1 | 0 | 0.133078 | 0.078028 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.051724 | 0 | 0.103448 | 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 |
27cbc8118c93c5665c811727ce03b44ce0165367 | 42 | py | Python | mat2vec/training/__init__.py | bowen-gao/mat2vec | aabbc45a5881d22756795a4cb6e71dd74946c34e | [
"MIT"
] | 593 | 2019-06-01T14:49:24.000Z | 2022-03-20T12:46:27.000Z | mat2vec/training/__init__.py | bowen-gao/mat2vec | aabbc45a5881d22756795a4cb6e71dd74946c34e | [
"MIT"
] | 22 | 2019-07-03T17:37:45.000Z | 2021-11-08T17:00:04.000Z | mat2vec/training/__init__.py | bowen-gao/mat2vec | aabbc45a5881d22756795a4cb6e71dd74946c34e | [
"MIT"
] | 166 | 2019-06-23T15:22:38.000Z | 2022-03-23T21:01:35.000Z | from mat2vec.training.helpers import utils | 42 | 42 | 0.880952 | 6 | 42 | 6.166667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025641 | 0.071429 | 42 | 1 | 42 | 42 | 0.923077 | 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 |
27d72a39dca56fcb628b8dbcc17c4ecfe91962d6 | 839 | py | Python | mobync/synchronizer.py | mobync/python-server | a65b183e2bc69b6b7ab649d4a1bffdb16bb4c8a6 | [
"MIT"
] | 3 | 2020-11-08T13:47:51.000Z | 2021-03-17T02:38:37.000Z | mobync/synchronizer.py | mobync/python-server | a65b183e2bc69b6b7ab649d4a1bffdb16bb4c8a6 | [
"MIT"
] | null | null | null | mobync/synchronizer.py | mobync/python-server | a65b183e2bc69b6b7ab649d4a1bffdb16bb4c8a6 | [
"MIT"
] | 1 | 2021-03-17T22:17:45.000Z | 2021-03-17T22:17:45.000Z | import abc
from typing import List
from mobync import ReadFilter
class Synchronizer(metaclass=abc.ABCMeta):
@abc.abstractmethod
def read(self, where: str, filters: List[ReadFilter]) -> str:
pass
@abc.abstractmethod
def update(self, where: str, data_json: str) -> None:
pass
@abc.abstractmethod
def validate_update(self, owner_id: str, **kwargs) -> bool:
pass
@abc.abstractmethod
def create(self, where: str, data_json: str) -> None: # todo: should receive dict?
pass
@abc.abstractmethod
def validate_create(self, owner_id: str, **kwargs) -> bool:
pass
@abc.abstractmethod
def delete(self, where: str, data_json: str) -> None:
pass
@abc.abstractmethod
def validate_delete(self, owner_id: str, **kwargs) -> bool:
pass
| 23.305556 | 87 | 0.646007 | 103 | 839 | 5.174757 | 0.330097 | 0.223265 | 0.262664 | 0.270169 | 0.564728 | 0.50469 | 0.50469 | 0.401501 | 0.401501 | 0.401501 | 0 | 0 | 0.246722 | 839 | 35 | 88 | 23.971429 | 0.843354 | 0.030989 | 0 | 0.56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.028571 | 0 | 1 | 0.28 | false | 0.28 | 0.12 | 0 | 0.44 | 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 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
7e06b1ec285ab55f4530715a5b73fe7c48570e43 | 110 | py | Python | bunq/sdk/exception/bad_request_exception.py | mwiekens/sdk_python | 9333636083bc63dca4353e8f497588f57617efec | [
"MIT"
] | 88 | 2017-08-01T18:39:46.000Z | 2022-02-21T12:34:16.000Z | bunq/sdk/exception/bad_request_exception.py | mwiekens/sdk_python | 9333636083bc63dca4353e8f497588f57617efec | [
"MIT"
] | 136 | 2017-08-02T13:54:41.000Z | 2021-04-25T20:31:08.000Z | bunq/sdk/exception/bad_request_exception.py | mwiekens/sdk_python | 9333636083bc63dca4353e8f497588f57617efec | [
"MIT"
] | 30 | 2017-08-15T09:35:42.000Z | 2021-05-06T12:42:06.000Z | from bunq.sdk.exception.api_exception import ApiException
class BadRequestException(ApiException):
pass
| 18.333333 | 57 | 0.827273 | 12 | 110 | 7.5 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.118182 | 110 | 5 | 58 | 22 | 0.927835 | 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 |
7e0b6bda598bdb5b9db5f19df1a6a3f494b420c3 | 256 | py | Python | Release/Tests/AnalysisTest/Python.VS.TestData/Grammar/FromImportStmt.py | rsumner33/PTVS | f5d67cff8c7bb32992dd4f77c0dfddaca6071250 | [
"Apache-2.0"
] | null | null | null | Release/Tests/AnalysisTest/Python.VS.TestData/Grammar/FromImportStmt.py | rsumner33/PTVS | f5d67cff8c7bb32992dd4f77c0dfddaca6071250 | [
"Apache-2.0"
] | null | null | null | Release/Tests/AnalysisTest/Python.VS.TestData/Grammar/FromImportStmt.py | rsumner33/PTVS | f5d67cff8c7bb32992dd4f77c0dfddaca6071250 | [
"Apache-2.0"
] | 1 | 2020-12-09T10:16:23.000Z | 2020-12-09T10:16:23.000Z | from sys import winver
from sys import winver as baz
from sys.foo import winver
from sys.foo import winver as baz
from ...foo import bar
from ....foo import bar
from ......foo import bar
from .......foo import bar
from foo import (foo as bar, baz as quox) | 28.444444 | 41 | 0.726563 | 48 | 256 | 3.895833 | 0.208333 | 0.336898 | 0.347594 | 0.342246 | 0.764706 | 0.411765 | 0.411765 | 0.411765 | 0.411765 | 0.411765 | 0 | 0 | 0.171875 | 256 | 9 | 41 | 28.444444 | 0.877358 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 1 | null | null | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
fd8ecbb4e87d8f38e448f2f9cdfda0028217d08c | 93 | py | Python | pgnlp/metrics.py | porfyriosg/pgnlp | d43b104f16dd8ca1fa7a988bcd0ba6f6183f3a4c | [
"MIT"
] | 4 | 2020-12-24T16:00:33.000Z | 2020-12-24T21:46:14.000Z | pgnlp/metrics.py | porfyriosg/pgnlp | d43b104f16dd8ca1fa7a988bcd0ba6f6183f3a4c | [
"MIT"
] | null | null | null | pgnlp/metrics.py | porfyriosg/pgnlp | d43b104f16dd8ca1fa7a988bcd0ba6f6183f3a4c | [
"MIT"
] | null | null | null | from .utils import *
def count_words(text:str=''):
return len(re.findall(r'\w+', text))
| 18.6 | 40 | 0.645161 | 15 | 93 | 3.933333 | 0.933333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150538 | 93 | 4 | 41 | 23.25 | 0.746835 | 0 | 0 | 0 | 0 | 0 | 0.032258 | 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 |
fdfe361b9d0330507d0722f27517256ca4bbfbea | 120 | py | Python | string.py | sshan0509/day3 | 21cb110f28346e5171252a1ecd37b48526e7afd6 | [
"Apache-2.0"
] | null | null | null | string.py | sshan0509/day3 | 21cb110f28346e5171252a1ecd37b48526e7afd6 | [
"Apache-2.0"
] | null | null | null | string.py | sshan0509/day3 | 21cb110f28346e5171252a1ecd37b48526e7afd6 | [
"Apache-2.0"
] | null | null | null | name = "Nick"
city = 'Seoul'
print(name)
print(city)
print(name, city)
print(name + " " + city)
print("Hello, " + name)
| 15 | 24 | 0.625 | 17 | 120 | 4.411765 | 0.352941 | 0.36 | 0.346667 | 0.453333 | 0.466667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 120 | 7 | 25 | 17.142857 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0.141667 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.714286 | 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 |
e3624c049cdd9380a1219082970de07021015841 | 76 | py | Python | esbo_etc/__init__.py | LukasK13/ESBO-ETC | d1db999f1670f2777c5227d79629d421f03e5393 | [
"Apache-2.0"
] | null | null | null | esbo_etc/__init__.py | LukasK13/ESBO-ETC | d1db999f1670f2777c5227d79629d421f03e5393 | [
"Apache-2.0"
] | null | null | null | esbo_etc/__init__.py | LukasK13/ESBO-ETC | d1db999f1670f2777c5227d79629d421f03e5393 | [
"Apache-2.0"
] | null | null | null | from esbo_etc.classes import *
from esbo_etc.lib import *
# __root__ = "."
| 15.2 | 30 | 0.710526 | 11 | 76 | 4.363636 | 0.636364 | 0.333333 | 0.458333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.171053 | 76 | 4 | 31 | 19 | 0.761905 | 0.184211 | 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 |
8b561d87ec5f65a292c7168beb617a9df3fff6db | 258,537 | py | Python | instances/passenger_demand/pas-20210422-1717-int4e-1/56.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-int4e-1/56.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-int4e-1/56.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 7738
passenger_arriving = (
(2, 0, 0, 2, 0, 2, 1, 0, 3, 0, 0, 0, 0, 2, 2, 2, 1, 1, 2, 0, 0, 1, 3, 0, 0, 0), # 0
(3, 3, 2, 3, 2, 3, 1, 1, 0, 0, 0, 0, 0, 4, 1, 2, 0, 2, 2, 1, 1, 0, 0, 1, 0, 0), # 1
(0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 2, 0, 3, 4, 2, 1, 2, 1, 0, 0, 0), # 2
(2, 7, 1, 2, 1, 4, 1, 0, 0, 0, 0, 0, 0, 2, 4, 2, 1, 0, 0, 2, 0, 2, 0, 2, 1, 0), # 3
(6, 2, 3, 1, 5, 0, 4, 3, 1, 2, 0, 0, 0, 2, 4, 2, 1, 2, 0, 2, 0, 1, 0, 0, 0, 0), # 4
(2, 2, 3, 2, 3, 4, 1, 0, 0, 0, 0, 0, 0, 4, 2, 1, 1, 3, 2, 1, 0, 1, 0, 1, 0, 0), # 5
(5, 6, 4, 2, 1, 2, 2, 1, 6, 1, 0, 0, 0, 3, 4, 1, 3, 1, 2, 2, 1, 1, 1, 0, 0, 0), # 6
(1, 6, 6, 1, 2, 0, 2, 2, 2, 1, 1, 0, 0, 4, 4, 3, 1, 4, 0, 1, 0, 1, 0, 1, 0, 0), # 7
(0, 3, 4, 2, 3, 0, 1, 3, 1, 1, 0, 1, 0, 8, 6, 0, 1, 3, 3, 2, 0, 0, 3, 0, 0, 0), # 8
(1, 4, 2, 3, 4, 0, 0, 0, 1, 1, 2, 1, 0, 1, 3, 3, 3, 1, 2, 2, 0, 1, 0, 2, 1, 0), # 9
(2, 2, 2, 4, 7, 0, 4, 0, 1, 2, 0, 0, 0, 3, 2, 2, 2, 3, 3, 2, 1, 0, 0, 0, 1, 0), # 10
(2, 2, 4, 5, 2, 2, 2, 1, 2, 0, 0, 2, 0, 3, 2, 1, 2, 2, 2, 3, 0, 1, 2, 0, 1, 0), # 11
(6, 4, 1, 4, 2, 1, 3, 0, 2, 1, 1, 0, 0, 3, 6, 3, 4, 1, 1, 0, 1, 1, 1, 1, 1, 0), # 12
(5, 4, 0, 3, 3, 1, 1, 1, 1, 1, 1, 0, 0, 5, 0, 2, 1, 1, 4, 1, 1, 0, 2, 0, 0, 0), # 13
(4, 2, 4, 2, 2, 1, 0, 1, 2, 2, 1, 1, 0, 4, 5, 3, 2, 2, 3, 1, 1, 0, 2, 0, 1, 0), # 14
(4, 5, 2, 1, 2, 1, 3, 1, 1, 0, 1, 1, 0, 3, 4, 1, 0, 3, 4, 1, 0, 3, 1, 0, 1, 0), # 15
(3, 1, 4, 5, 2, 0, 1, 1, 3, 0, 2, 0, 0, 3, 1, 1, 0, 6, 1, 1, 0, 1, 1, 0, 0, 0), # 16
(4, 7, 2, 0, 4, 3, 3, 2, 1, 1, 0, 0, 0, 2, 2, 1, 3, 3, 1, 3, 2, 1, 1, 1, 0, 0), # 17
(6, 1, 6, 2, 5, 2, 1, 3, 1, 1, 1, 1, 0, 2, 6, 3, 0, 2, 3, 2, 2, 1, 2, 0, 0, 0), # 18
(4, 0, 2, 3, 1, 2, 1, 1, 4, 1, 3, 0, 0, 4, 3, 2, 3, 3, 1, 1, 0, 2, 0, 0, 1, 0), # 19
(4, 7, 4, 1, 4, 2, 1, 1, 1, 1, 0, 1, 0, 6, 5, 4, 1, 2, 1, 2, 2, 2, 1, 0, 1, 0), # 20
(6, 7, 4, 4, 3, 1, 2, 2, 0, 0, 0, 0, 0, 4, 6, 1, 2, 3, 1, 1, 2, 0, 0, 0, 0, 0), # 21
(4, 7, 2, 2, 3, 2, 1, 1, 0, 3, 0, 1, 0, 3, 3, 5, 2, 5, 4, 0, 1, 3, 1, 2, 0, 0), # 22
(2, 5, 7, 3, 2, 3, 2, 0, 1, 0, 0, 0, 0, 2, 1, 2, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0), # 23
(5, 4, 6, 4, 3, 1, 2, 1, 2, 1, 1, 0, 0, 6, 3, 3, 1, 1, 2, 2, 1, 4, 0, 0, 1, 0), # 24
(4, 0, 3, 6, 3, 3, 1, 4, 2, 0, 1, 0, 0, 3, 3, 4, 4, 2, 4, 1, 1, 1, 3, 1, 0, 0), # 25
(5, 4, 6, 3, 2, 1, 0, 1, 3, 1, 1, 0, 0, 2, 3, 1, 3, 2, 0, 1, 1, 0, 1, 1, 0, 0), # 26
(4, 3, 3, 2, 3, 4, 2, 2, 1, 1, 1, 0, 0, 3, 4, 2, 2, 1, 5, 2, 2, 2, 2, 1, 0, 0), # 27
(5, 6, 5, 2, 2, 3, 0, 2, 4, 1, 0, 0, 0, 2, 3, 4, 3, 2, 4, 3, 0, 0, 1, 3, 0, 0), # 28
(3, 3, 2, 2, 1, 1, 1, 4, 1, 1, 0, 1, 0, 1, 4, 1, 1, 4, 3, 0, 1, 3, 3, 1, 1, 0), # 29
(7, 5, 2, 5, 5, 0, 2, 5, 1, 1, 0, 0, 0, 5, 3, 2, 1, 2, 1, 0, 1, 0, 1, 1, 1, 0), # 30
(5, 2, 2, 7, 3, 1, 1, 0, 1, 1, 0, 0, 0, 5, 4, 3, 0, 6, 5, 1, 2, 2, 2, 0, 0, 0), # 31
(3, 9, 3, 5, 3, 4, 2, 2, 1, 1, 0, 0, 0, 2, 4, 3, 1, 3, 2, 2, 1, 0, 1, 0, 0, 0), # 32
(7, 2, 4, 7, 3, 3, 1, 0, 0, 0, 0, 1, 0, 6, 6, 3, 3, 2, 2, 1, 2, 3, 0, 1, 0, 0), # 33
(1, 7, 2, 3, 3, 0, 3, 2, 1, 0, 0, 1, 0, 8, 3, 0, 2, 3, 2, 1, 0, 1, 2, 0, 0, 0), # 34
(5, 6, 3, 4, 2, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 2, 7, 0, 3, 4, 1, 1, 0, 0, 0), # 35
(4, 4, 5, 6, 0, 3, 3, 1, 0, 0, 0, 0, 0, 7, 4, 5, 8, 7, 2, 2, 1, 1, 3, 0, 0, 0), # 36
(1, 4, 3, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 6, 4, 4, 2, 4, 4, 1, 2, 2, 1, 1, 0), # 37
(4, 3, 1, 7, 2, 0, 3, 1, 5, 1, 2, 0, 0, 8, 3, 3, 0, 4, 1, 4, 0, 4, 0, 0, 0, 0), # 38
(2, 4, 1, 3, 0, 0, 1, 1, 0, 1, 3, 0, 0, 4, 4, 3, 3, 4, 4, 2, 1, 1, 0, 0, 2, 0), # 39
(2, 4, 6, 3, 3, 1, 0, 0, 2, 0, 0, 1, 0, 4, 1, 2, 4, 2, 4, 1, 1, 2, 2, 0, 0, 0), # 40
(6, 7, 4, 2, 8, 0, 2, 3, 0, 2, 0, 1, 0, 1, 7, 1, 2, 3, 0, 1, 1, 1, 1, 1, 1, 0), # 41
(2, 2, 5, 3, 6, 0, 3, 2, 0, 1, 2, 1, 0, 5, 2, 2, 3, 3, 1, 0, 2, 1, 0, 2, 0, 0), # 42
(10, 2, 4, 2, 5, 1, 2, 4, 2, 0, 0, 0, 0, 4, 1, 3, 2, 4, 1, 1, 1, 1, 2, 1, 1, 0), # 43
(6, 8, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 4, 3, 3, 2, 2, 2, 0, 1, 1, 1, 1, 0, 0), # 44
(0, 3, 2, 1, 2, 1, 3, 2, 3, 1, 0, 0, 0, 0, 7, 0, 1, 1, 5, 1, 0, 1, 1, 1, 0, 0), # 45
(2, 3, 8, 2, 5, 1, 1, 4, 1, 1, 1, 0, 0, 5, 3, 4, 1, 3, 2, 1, 1, 2, 0, 1, 0, 0), # 46
(3, 5, 3, 4, 3, 4, 3, 0, 1, 0, 0, 0, 0, 3, 6, 3, 3, 3, 1, 1, 1, 3, 1, 0, 1, 0), # 47
(1, 5, 4, 5, 2, 0, 1, 2, 2, 2, 0, 1, 0, 4, 4, 2, 2, 4, 2, 1, 0, 1, 1, 2, 0, 0), # 48
(7, 4, 2, 3, 2, 1, 0, 2, 1, 0, 0, 0, 0, 1, 2, 2, 1, 5, 0, 1, 0, 2, 1, 0, 1, 0), # 49
(3, 2, 2, 1, 1, 2, 2, 2, 3, 1, 0, 1, 0, 6, 3, 3, 2, 2, 4, 2, 0, 1, 2, 0, 0, 0), # 50
(7, 4, 2, 5, 4, 1, 1, 0, 3, 1, 0, 0, 0, 4, 5, 3, 3, 2, 2, 2, 1, 1, 4, 1, 0, 0), # 51
(5, 2, 9, 3, 6, 0, 0, 2, 3, 1, 1, 0, 0, 4, 3, 2, 2, 3, 3, 0, 1, 3, 2, 2, 1, 0), # 52
(3, 2, 2, 3, 5, 1, 3, 0, 2, 0, 1, 1, 0, 6, 2, 5, 3, 3, 2, 3, 0, 0, 1, 0, 0, 0), # 53
(7, 6, 3, 4, 3, 0, 1, 1, 2, 2, 0, 0, 0, 4, 2, 0, 1, 1, 0, 2, 0, 2, 3, 0, 1, 0), # 54
(4, 4, 1, 4, 3, 1, 2, 1, 2, 2, 0, 0, 0, 3, 2, 2, 1, 3, 2, 2, 1, 2, 0, 0, 0, 0), # 55
(5, 3, 3, 1, 3, 1, 0, 2, 1, 1, 0, 0, 0, 2, 3, 1, 3, 6, 1, 1, 2, 1, 2, 2, 0, 0), # 56
(2, 4, 6, 5, 3, 4, 3, 0, 1, 0, 0, 0, 0, 3, 2, 6, 3, 4, 1, 3, 1, 0, 0, 0, 0, 0), # 57
(3, 4, 3, 5, 1, 3, 1, 0, 5, 0, 2, 1, 0, 6, 5, 0, 0, 5, 4, 1, 3, 2, 0, 1, 0, 0), # 58
(5, 6, 4, 3, 5, 0, 2, 2, 2, 1, 2, 0, 0, 4, 3, 2, 2, 4, 4, 2, 3, 5, 0, 0, 2, 0), # 59
(0, 5, 3, 1, 2, 3, 2, 2, 1, 1, 0, 0, 0, 4, 3, 4, 6, 2, 3, 4, 0, 4, 4, 0, 0, 0), # 60
(2, 4, 0, 2, 7, 0, 2, 1, 1, 0, 2, 0, 0, 8, 4, 3, 1, 4, 1, 1, 0, 2, 1, 2, 1, 0), # 61
(5, 5, 6, 2, 3, 1, 1, 2, 2, 2, 0, 0, 0, 1, 2, 12, 2, 3, 1, 0, 0, 3, 1, 1, 1, 0), # 62
(5, 1, 3, 4, 2, 3, 3, 1, 1, 2, 1, 0, 0, 4, 3, 2, 1, 4, 1, 2, 1, 2, 0, 1, 0, 0), # 63
(1, 3, 8, 6, 4, 2, 0, 2, 0, 2, 0, 1, 0, 6, 3, 2, 2, 0, 1, 1, 2, 0, 0, 0, 0, 0), # 64
(4, 7, 5, 4, 3, 1, 0, 1, 3, 0, 0, 0, 0, 4, 5, 1, 2, 2, 2, 0, 0, 2, 0, 0, 0, 0), # 65
(3, 3, 1, 5, 5, 2, 0, 0, 3, 1, 0, 1, 0, 3, 4, 2, 3, 6, 2, 2, 0, 2, 1, 0, 0, 0), # 66
(6, 2, 1, 3, 5, 1, 1, 3, 2, 1, 0, 0, 0, 4, 2, 2, 6, 4, 2, 4, 2, 1, 3, 0, 0, 0), # 67
(4, 3, 5, 3, 1, 1, 3, 2, 2, 0, 0, 0, 0, 1, 4, 3, 1, 2, 0, 1, 1, 2, 4, 2, 2, 0), # 68
(3, 2, 6, 3, 0, 2, 0, 2, 1, 1, 0, 0, 0, 6, 4, 5, 2, 2, 1, 1, 0, 0, 1, 0, 0, 0), # 69
(5, 4, 6, 2, 1, 0, 0, 1, 1, 1, 1, 0, 0, 7, 4, 3, 2, 5, 0, 2, 3, 0, 4, 0, 1, 0), # 70
(4, 3, 3, 4, 0, 3, 1, 0, 0, 3, 1, 0, 0, 4, 5, 1, 4, 3, 2, 6, 1, 1, 1, 1, 1, 0), # 71
(2, 3, 4, 4, 3, 0, 1, 2, 2, 1, 0, 0, 0, 3, 4, 5, 3, 2, 2, 4, 0, 3, 3, 2, 0, 0), # 72
(2, 4, 2, 3, 4, 1, 6, 0, 4, 0, 0, 0, 0, 5, 0, 6, 1, 5, 2, 1, 1, 2, 1, 2, 1, 0), # 73
(3, 2, 2, 2, 2, 2, 1, 2, 2, 0, 1, 0, 0, 2, 2, 1, 3, 1, 0, 3, 2, 3, 1, 1, 1, 0), # 74
(1, 6, 4, 5, 6, 1, 1, 0, 1, 1, 1, 0, 0, 4, 2, 4, 2, 1, 1, 0, 2, 3, 0, 2, 0, 0), # 75
(3, 4, 5, 3, 3, 0, 1, 1, 4, 1, 1, 0, 0, 7, 3, 4, 2, 6, 3, 1, 1, 1, 3, 1, 0, 0), # 76
(2, 2, 3, 8, 1, 2, 5, 3, 1, 0, 0, 0, 0, 5, 6, 4, 2, 2, 1, 0, 0, 2, 2, 1, 2, 0), # 77
(4, 5, 4, 5, 5, 1, 1, 0, 2, 1, 0, 1, 0, 4, 5, 2, 2, 1, 3, 3, 0, 1, 1, 0, 1, 0), # 78
(10, 4, 4, 3, 2, 3, 2, 2, 0, 0, 0, 1, 0, 4, 5, 5, 5, 5, 2, 4, 1, 1, 2, 0, 0, 0), # 79
(7, 2, 3, 1, 1, 0, 1, 3, 1, 1, 1, 0, 0, 2, 1, 3, 4, 4, 1, 0, 0, 0, 1, 2, 0, 0), # 80
(6, 0, 4, 3, 3, 0, 0, 3, 4, 0, 0, 1, 0, 4, 3, 3, 0, 5, 2, 5, 1, 3, 0, 0, 1, 0), # 81
(4, 3, 5, 2, 2, 2, 1, 1, 1, 2, 2, 1, 0, 4, 3, 2, 2, 6, 1, 1, 1, 1, 0, 0, 0, 0), # 82
(4, 5, 3, 4, 1, 3, 0, 0, 0, 1, 0, 0, 0, 2, 5, 2, 0, 6, 0, 0, 2, 0, 3, 1, 0, 0), # 83
(0, 5, 5, 5, 2, 0, 1, 2, 1, 2, 1, 0, 0, 1, 4, 2, 2, 3, 4, 0, 1, 0, 1, 0, 0, 0), # 84
(7, 3, 3, 3, 3, 1, 2, 0, 0, 2, 0, 0, 0, 3, 5, 4, 2, 4, 3, 2, 0, 0, 0, 1, 0, 0), # 85
(3, 2, 2, 3, 2, 3, 0, 2, 2, 2, 1, 0, 0, 7, 5, 1, 0, 1, 3, 2, 1, 4, 2, 2, 0, 0), # 86
(3, 2, 2, 4, 5, 0, 2, 2, 3, 0, 1, 1, 0, 4, 2, 1, 2, 4, 2, 3, 1, 0, 1, 2, 0, 0), # 87
(2, 2, 3, 4, 6, 3, 1, 0, 1, 2, 0, 1, 0, 6, 10, 2, 0, 1, 3, 4, 0, 2, 0, 0, 1, 0), # 88
(4, 4, 5, 3, 2, 2, 1, 1, 0, 1, 1, 0, 0, 5, 3, 0, 1, 3, 2, 3, 0, 1, 2, 1, 0, 0), # 89
(3, 8, 4, 3, 4, 3, 0, 1, 2, 0, 1, 0, 0, 5, 1, 2, 1, 1, 2, 4, 1, 1, 0, 0, 1, 0), # 90
(5, 0, 4, 7, 2, 3, 0, 0, 1, 2, 0, 1, 0, 5, 2, 1, 3, 1, 0, 1, 1, 1, 2, 0, 0, 0), # 91
(3, 5, 2, 3, 5, 4, 2, 0, 1, 0, 0, 0, 0, 6, 4, 2, 1, 3, 2, 2, 0, 2, 2, 0, 1, 0), # 92
(5, 4, 2, 2, 1, 3, 2, 0, 2, 0, 1, 0, 0, 2, 3, 0, 2, 1, 3, 2, 1, 1, 3, 0, 0, 0), # 93
(3, 5, 4, 2, 2, 1, 0, 2, 0, 0, 2, 0, 0, 6, 3, 3, 3, 4, 0, 2, 0, 0, 3, 0, 0, 0), # 94
(8, 2, 7, 0, 3, 0, 1, 1, 5, 0, 0, 0, 0, 2, 2, 2, 1, 5, 2, 1, 0, 1, 1, 0, 0, 0), # 95
(3, 0, 0, 3, 6, 4, 2, 2, 1, 0, 0, 0, 0, 2, 1, 2, 2, 4, 2, 0, 1, 0, 0, 1, 0, 0), # 96
(3, 3, 0, 2, 4, 0, 1, 0, 1, 2, 1, 1, 0, 5, 5, 2, 3, 1, 2, 2, 1, 1, 1, 0, 0, 0), # 97
(7, 2, 3, 2, 4, 2, 0, 1, 6, 0, 0, 1, 0, 4, 3, 1, 0, 5, 1, 2, 1, 2, 0, 0, 0, 0), # 98
(5, 2, 6, 8, 4, 0, 0, 0, 0, 1, 2, 0, 0, 5, 3, 2, 0, 2, 2, 0, 2, 0, 3, 2, 0, 0), # 99
(8, 3, 4, 2, 4, 2, 3, 2, 5, 1, 0, 0, 0, 3, 4, 0, 2, 3, 1, 2, 0, 2, 4, 0, 0, 0), # 100
(7, 4, 2, 5, 3, 2, 2, 2, 1, 1, 1, 0, 0, 2, 7, 2, 2, 1, 1, 0, 0, 1, 3, 0, 0, 0), # 101
(6, 7, 4, 3, 2, 1, 2, 2, 1, 1, 0, 0, 0, 6, 5, 3, 3, 5, 0, 1, 0, 2, 0, 0, 0, 0), # 102
(3, 3, 2, 5, 1, 1, 3, 0, 2, 0, 0, 0, 0, 5, 2, 1, 2, 5, 2, 0, 2, 1, 3, 1, 0, 0), # 103
(1, 3, 3, 3, 3, 0, 1, 1, 2, 2, 0, 0, 0, 9, 3, 4, 2, 2, 1, 2, 2, 0, 1, 0, 0, 0), # 104
(3, 3, 5, 0, 3, 0, 0, 2, 1, 0, 1, 0, 0, 1, 3, 3, 4, 2, 3, 1, 2, 1, 1, 0, 0, 0), # 105
(5, 6, 3, 4, 2, 3, 1, 0, 1, 0, 2, 0, 0, 2, 5, 2, 0, 1, 2, 2, 2, 2, 0, 1, 0, 0), # 106
(1, 4, 1, 4, 2, 2, 0, 4, 0, 2, 1, 1, 0, 8, 7, 1, 2, 8, 2, 0, 0, 1, 2, 0, 0, 0), # 107
(1, 3, 0, 6, 2, 3, 3, 1, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 2, 0, 2, 1, 1, 1, 0, 0), # 108
(5, 3, 2, 2, 0, 0, 2, 2, 2, 0, 1, 2, 0, 5, 2, 5, 2, 7, 0, 2, 0, 2, 2, 0, 0, 0), # 109
(3, 2, 4, 3, 2, 1, 0, 1, 3, 0, 1, 0, 0, 3, 1, 1, 0, 3, 0, 1, 1, 0, 0, 2, 0, 0), # 110
(2, 3, 4, 3, 4, 1, 2, 0, 2, 0, 1, 1, 0, 3, 2, 4, 1, 3, 0, 0, 1, 0, 2, 2, 0, 0), # 111
(3, 2, 5, 7, 0, 2, 1, 0, 1, 1, 2, 0, 0, 6, 2, 0, 2, 5, 1, 1, 2, 1, 0, 0, 1, 0), # 112
(5, 3, 3, 7, 4, 1, 1, 0, 2, 0, 0, 0, 0, 2, 3, 2, 1, 2, 1, 2, 1, 0, 2, 0, 0, 0), # 113
(5, 2, 4, 3, 1, 1, 2, 2, 1, 0, 0, 0, 0, 4, 5, 1, 2, 2, 2, 1, 0, 3, 2, 0, 1, 0), # 114
(6, 2, 2, 4, 2, 1, 0, 1, 1, 1, 1, 0, 0, 4, 11, 0, 3, 3, 0, 2, 1, 2, 0, 1, 0, 0), # 115
(3, 3, 3, 2, 3, 2, 0, 4, 1, 1, 0, 0, 0, 1, 1, 0, 4, 3, 3, 1, 3, 0, 0, 0, 1, 0), # 116
(7, 4, 4, 4, 4, 1, 2, 0, 3, 1, 1, 0, 0, 1, 2, 1, 1, 3, 1, 0, 1, 3, 0, 0, 0, 0), # 117
(5, 3, 1, 1, 0, 1, 1, 0, 0, 2, 0, 0, 0, 1, 1, 1, 4, 3, 1, 2, 3, 0, 1, 1, 0, 0), # 118
(1, 2, 7, 3, 2, 2, 0, 0, 2, 0, 0, 0, 0, 7, 2, 4, 1, 2, 1, 1, 2, 1, 1, 0, 0, 0), # 119
(5, 4, 1, 1, 4, 2, 2, 0, 0, 0, 0, 1, 0, 2, 3, 7, 1, 2, 0, 2, 1, 2, 1, 0, 0, 0), # 120
(2, 2, 3, 1, 3, 0, 1, 0, 0, 1, 2, 0, 0, 4, 3, 1, 2, 3, 0, 2, 0, 2, 1, 2, 1, 0), # 121
(4, 1, 2, 3, 4, 0, 1, 0, 3, 0, 1, 0, 0, 6, 1, 2, 2, 4, 0, 3, 2, 1, 0, 1, 0, 0), # 122
(3, 5, 6, 3, 1, 1, 1, 1, 0, 1, 2, 0, 0, 5, 0, 3, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0), # 123
(1, 4, 6, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 3, 1, 5, 0, 5, 0, 3, 3, 0, 0, 0), # 124
(1, 0, 4, 3, 3, 0, 3, 0, 2, 0, 0, 0, 0, 4, 2, 2, 5, 7, 1, 0, 0, 2, 1, 3, 0, 0), # 125
(8, 1, 1, 9, 3, 2, 0, 2, 2, 1, 0, 0, 0, 2, 4, 5, 1, 3, 3, 1, 4, 3, 0, 0, 0, 0), # 126
(5, 2, 1, 3, 4, 1, 2, 1, 0, 0, 0, 1, 0, 5, 6, 2, 1, 3, 1, 0, 1, 0, 2, 1, 0, 0), # 127
(9, 3, 2, 0, 1, 4, 0, 2, 1, 0, 2, 0, 0, 3, 3, 4, 6, 0, 0, 0, 0, 1, 2, 2, 0, 0), # 128
(4, 1, 3, 4, 3, 2, 0, 0, 1, 0, 1, 1, 0, 4, 3, 4, 1, 3, 0, 2, 2, 1, 4, 1, 0, 0), # 129
(4, 5, 3, 1, 3, 0, 1, 0, 2, 0, 0, 0, 0, 2, 2, 2, 0, 3, 0, 1, 0, 2, 0, 0, 0, 0), # 130
(2, 2, 4, 6, 0, 3, 3, 0, 0, 0, 0, 2, 0, 3, 2, 2, 0, 6, 0, 0, 0, 1, 0, 1, 0, 0), # 131
(5, 2, 3, 7, 1, 1, 3, 0, 0, 0, 1, 1, 0, 5, 1, 1, 1, 3, 1, 2, 0, 0, 0, 0, 0, 0), # 132
(2, 3, 1, 3, 5, 2, 1, 0, 3, 1, 0, 0, 0, 6, 1, 3, 2, 4, 2, 1, 2, 0, 1, 0, 0, 0), # 133
(2, 1, 6, 2, 6, 1, 1, 2, 2, 1, 1, 0, 0, 4, 1, 4, 0, 4, 2, 1, 4, 1, 3, 0, 2, 0), # 134
(2, 0, 3, 3, 5, 3, 0, 0, 3, 2, 0, 0, 0, 2, 1, 4, 1, 3, 1, 0, 4, 0, 2, 1, 0, 0), # 135
(3, 3, 3, 4, 0, 1, 2, 0, 2, 1, 0, 1, 0, 2, 4, 1, 5, 3, 0, 2, 1, 3, 0, 1, 0, 0), # 136
(4, 3, 2, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 6, 2, 4, 2, 6, 2, 0, 1, 0, 0, 2, 1, 0), # 137
(0, 1, 5, 3, 4, 3, 1, 1, 1, 0, 0, 1, 0, 3, 10, 3, 0, 4, 0, 1, 0, 2, 1, 1, 1, 0), # 138
(7, 2, 4, 3, 0, 1, 1, 0, 1, 0, 0, 0, 0, 3, 0, 3, 1, 3, 3, 2, 2, 0, 3, 0, 0, 0), # 139
(3, 4, 4, 4, 1, 0, 0, 2, 1, 1, 3, 1, 0, 5, 3, 2, 0, 3, 1, 3, 0, 3, 1, 1, 0, 0), # 140
(5, 2, 1, 2, 1, 0, 1, 0, 1, 1, 0, 4, 0, 2, 4, 4, 1, 1, 1, 1, 1, 1, 3, 0, 0, 0), # 141
(4, 2, 1, 5, 0, 2, 1, 2, 2, 1, 0, 0, 0, 2, 3, 1, 1, 2, 1, 0, 2, 1, 0, 0, 0, 0), # 142
(3, 3, 2, 5, 2, 0, 0, 0, 0, 1, 1, 0, 0, 5, 0, 1, 2, 3, 2, 1, 1, 1, 0, 0, 0, 0), # 143
(2, 1, 4, 1, 2, 3, 0, 0, 2, 0, 0, 0, 0, 3, 5, 2, 0, 4, 3, 1, 2, 1, 1, 0, 1, 0), # 144
(3, 6, 2, 1, 1, 0, 1, 2, 1, 0, 0, 0, 0, 5, 5, 1, 2, 3, 1, 0, 0, 1, 0, 0, 0, 0), # 145
(3, 1, 5, 4, 3, 1, 1, 0, 2, 0, 0, 0, 0, 4, 4, 1, 1, 6, 0, 0, 1, 2, 0, 0, 0, 0), # 146
(3, 1, 5, 4, 1, 0, 1, 2, 1, 1, 0, 0, 0, 1, 3, 0, 1, 3, 0, 1, 1, 0, 0, 0, 0, 0), # 147
(3, 5, 2, 1, 2, 1, 1, 2, 3, 0, 1, 0, 0, 4, 1, 1, 1, 9, 0, 4, 0, 0, 0, 1, 0, 0), # 148
(2, 5, 3, 2, 2, 2, 0, 2, 1, 0, 1, 0, 0, 5, 3, 2, 1, 5, 1, 1, 1, 0, 0, 0, 0, 0), # 149
(5, 0, 5, 5, 1, 3, 0, 1, 0, 2, 1, 0, 0, 3, 2, 2, 1, 3, 0, 1, 1, 0, 2, 0, 0, 0), # 150
(1, 3, 0, 4, 3, 1, 3, 0, 1, 0, 0, 0, 0, 4, 7, 2, 1, 0, 1, 0, 0, 2, 0, 1, 0, 0), # 151
(1, 2, 1, 1, 3, 0, 1, 3, 0, 0, 0, 0, 0, 4, 4, 5, 2, 1, 0, 2, 0, 0, 1, 0, 0, 0), # 152
(1, 4, 5, 2, 3, 1, 1, 0, 2, 0, 1, 0, 0, 7, 0, 1, 1, 3, 4, 2, 2, 1, 1, 1, 0, 0), # 153
(2, 6, 1, 4, 1, 2, 3, 0, 0, 0, 1, 0, 0, 3, 0, 4, 1, 3, 2, 3, 1, 3, 0, 0, 1, 0), # 154
(5, 2, 3, 2, 2, 0, 3, 0, 2, 1, 0, 0, 0, 4, 0, 3, 1, 0, 0, 1, 0, 4, 2, 1, 0, 0), # 155
(2, 1, 0, 4, 1, 0, 0, 1, 1, 0, 2, 1, 0, 2, 1, 1, 1, 1, 0, 3, 0, 3, 2, 0, 0, 0), # 156
(5, 4, 6, 1, 1, 1, 1, 0, 2, 1, 0, 1, 0, 4, 3, 0, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0), # 157
(4, 4, 4, 2, 1, 0, 1, 0, 1, 0, 0, 0, 0, 4, 1, 1, 0, 4, 3, 1, 1, 1, 0, 1, 1, 0), # 158
(4, 5, 4, 8, 5, 3, 2, 1, 4, 1, 0, 1, 0, 1, 3, 1, 2, 4, 1, 1, 1, 2, 0, 1, 0, 0), # 159
(4, 1, 2, 1, 4, 3, 1, 1, 0, 0, 0, 0, 0, 3, 4, 2, 1, 2, 1, 2, 1, 2, 0, 0, 0, 0), # 160
(2, 3, 3, 0, 0, 4, 1, 5, 1, 0, 0, 0, 0, 6, 2, 2, 1, 4, 0, 1, 0, 3, 0, 0, 0, 0), # 161
(2, 1, 5, 3, 4, 4, 2, 0, 1, 1, 0, 0, 0, 2, 3, 2, 1, 3, 0, 1, 1, 3, 0, 1, 0, 0), # 162
(2, 4, 3, 6, 3, 2, 0, 1, 1, 0, 0, 0, 0, 4, 1, 0, 3, 2, 0, 1, 1, 0, 0, 0, 0, 0), # 163
(3, 3, 2, 6, 2, 0, 0, 0, 1, 2, 1, 1, 0, 5, 2, 4, 3, 4, 1, 2, 1, 0, 1, 0, 0, 0), # 164
(3, 1, 4, 1, 0, 0, 5, 0, 2, 0, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 0, 1, 0, 1, 0, 0), # 165
(2, 0, 2, 2, 2, 5, 2, 1, 1, 1, 0, 0, 0, 3, 4, 3, 4, 2, 0, 1, 0, 1, 1, 0, 1, 0), # 166
(3, 0, 5, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 1, 5, 2, 0, 6, 0, 1, 0, 0, 1, 1, 0, 0), # 167
(3, 2, 2, 4, 1, 1, 0, 1, 3, 0, 0, 0, 0, 4, 1, 0, 1, 0, 2, 0, 2, 1, 0, 0, 0, 0), # 168
(5, 1, 4, 1, 2, 2, 1, 2, 1, 1, 0, 1, 0, 3, 3, 0, 1, 0, 1, 2, 2, 3, 2, 1, 0, 0), # 169
(4, 0, 1, 4, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 3, 0, 4, 0, 2, 1, 2, 2, 0, 0, 0), # 170
(1, 2, 3, 1, 2, 2, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 2, 0), # 171
(1, 1, 0, 3, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 1, 2, 1, 4, 2, 0, 1, 0, 0, 0, 0, 0), # 172
(0, 2, 1, 1, 2, 1, 0, 1, 0, 0, 1, 0, 0, 2, 3, 1, 0, 2, 0, 0, 0, 1, 0, 1, 0, 0), # 173
(2, 0, 1, 2, 5, 2, 0, 0, 1, 0, 0, 1, 0, 4, 1, 2, 3, 1, 1, 2, 0, 0, 1, 0, 1, 0), # 174
(0, 3, 0, 1, 3, 4, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 1, 1, 0, 3, 0, 0, 0, 0), # 175
(3, 1, 2, 2, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0), # 176
(2, 3, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 2, 2, 2, 1, 0, 0, 0, 0, 0, 2, 1, 0, 0), # 177
(3, 3, 4, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 3, 2, 3, 3, 1, 0, 0, 1, 0, 0, 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 = (
(2.0083462313487073, 2.2101154238772667, 2.0845132918450027, 2.485867109545373, 2.2218742430438447, 1.2554619728149357, 1.6584142461495661, 1.8612704691917692, 2.436039624867203, 1.583206006208948, 1.6821060655542412, 1.9591660313224695, 2.0335520850313453), # 0
(2.1417308608079897, 2.3560242776579035, 2.222138636532061, 2.6500577013106468, 2.3689961349896946, 1.3383934336170222, 1.7677875765054776, 1.9838054891622834, 2.5968981305331638, 1.68759227691086, 1.7932384543824527, 2.088486664325742, 2.1679166589759418), # 1
(2.2746892035918926, 2.5013540683563917, 2.3592169142189654, 2.813595081865918, 2.5155851894998977, 1.420994045804978, 1.8767274031842818, 2.105850099161768, 2.7571147227510195, 1.7915655100082184, 1.9039292595105253, 2.217293060821222, 2.301745931283876), # 2
(2.406703117258625, 2.645528200774579, 2.4952043477279418, 2.9758305630128294, 2.661064670400761, 1.5029362490340452, 1.9848014566591823, 2.226920462997803, 2.916054064368437, 1.8947130793704723, 2.013739364730953, 2.3450742739721844, 2.4345091225016904), # 3
(2.537254459366393, 2.78797007971431, 2.6295571598812146, 3.136115456553028, 2.804857841518597, 1.5838924829594672, 2.0915774674033836, 2.3465327444779662, 3.073080818233083, 1.9966223588670715, 2.12222965383623, 2.4713193569419056, 2.565675453175927), # 4
(2.6658250874734044, 2.9281031099774353, 2.7617315735010073, 3.2938010742881576, 2.946387966679712, 1.6635351872364859, 2.1966231658900894, 2.464203107409837, 3.227559647192624, 2.0968807223674655, 2.228961010618853, 2.595517362893659, 2.694714143853131), # 5
(2.7918968591378666, 3.0653506963658006, 2.8911838114095465, 3.448238728019861, 3.0850783097104175, 1.7415368015203456, 2.299506282592505, 2.5794477156009954, 3.378855214094726, 2.1950755437411034, 2.333494318871313, 2.7171573449907234, 2.821094415079843), # 6
(2.9149516319179876, 3.1991362436812527, 3.017370096429054, 3.598779729549785, 3.2203521344370216, 1.8175697654662883, 2.399794547983834, 2.691782732859021, 3.526332181787058, 2.290794196857435, 2.4353904623861076, 2.8357283563963716, 2.944285487402608), # 7
(3.034471263371974, 3.32888315672564, 3.1397466513817585, 3.744775390679571, 3.3516327046858345, 1.891306518729556, 2.497055692537279, 2.80072432299149, 3.6693552131172824, 2.38362405558591, 2.5342103249557284, 2.950719450273881, 3.063756581367967), # 8
(3.149937611058034, 3.4540148403008093, 3.2577696990898817, 3.8855770232108675, 3.478343284283164, 1.9624195009653935, 2.590857446726048, 2.9057886498059853, 3.8072889709330697, 2.473152493795977, 2.629514790372671, 3.0616196797865256, 3.178976917522465), # 9
(3.2608325325343728, 3.573954699208606, 3.3708954623756497, 4.020535938945315, 3.5999071370553204, 2.030581151829043, 2.680767541023342, 3.0064918771100846, 3.939498118082086, 2.5589668853570857, 2.7208647424294297, 3.167918098097581, 3.2894157164126443), # 10
(3.3666378853592023, 3.6881261382508828, 3.4785801640612863, 4.149003449684559, 3.7157475268286135, 2.0954639109757474, 2.7663537059023664, 3.102350168711366, 4.065347317411997, 2.6406546041386876, 2.8078210649184996, 3.269103758370324, 3.394542198585045), # 11
(3.466835527090725, 3.795952562229479, 3.580280026969016, 4.270330867230245, 3.825287717429351, 2.156740218060748, 2.8471836718363246, 3.1928796884174084, 4.184201231770472, 2.717803024010229, 2.8899446416323737, 3.3646657137680274, 3.493825584586214), # 12
(3.5609073152871504, 3.896857375946248, 3.6754512739210647, 4.383869503384018, 3.9279509726838406, 2.2140825127392896, 2.9228251692984224, 3.2775966000357926, 4.295424524005173, 2.789999518841162, 2.9667963563635475, 3.4540930174539684, 3.586735094962694), # 13
(3.6483351075066865, 3.9902639842030356, 3.763550127739659, 4.488970669947517, 4.023160556418396, 2.2671632346666137, 2.9928459287618647, 3.3560170673740988, 4.398381856963769, 2.8568314625009337, 3.0379370929045137, 3.536874722591424, 3.6727399502610254), # 14
(3.728600761307542, 4.075595791801687, 3.844032811247017, 4.584985678722394, 4.110339732459323, 2.315654823497965, 3.0568136806998503, 3.4276572542399024, 4.4924378934939275, 2.9178862288589964, 3.1029277350477678, 3.6124998823436685, 3.7513093710277525), # 15
(3.8011861342479203, 4.152276203544053, 3.91635554726537, 4.671265841510286, 4.188911764632933, 2.359229718888584, 3.1142961555855906, 3.4920333244407846, 4.5769572964433145, 2.9727511917847984, 3.161329166585805, 3.680457549873976, 3.8219125778094183), # 16
(3.86557308388603, 4.219728624231979, 3.979974558616941, 4.747162470112845, 4.2582999167655355, 2.3975603604937143, 3.1648610838922844, 3.5486614417843274, 4.651304728659594, 3.021013725147787, 3.2127022713111195, 3.740236778345622, 3.8840187911525663), # 17
(3.921243467780082, 4.27737645866731, 4.034346068123952, 4.8120268763317116, 4.317927452683436, 2.4303191879686015, 3.2080761960931405, 3.5970577700781043, 4.7148448529904385, 3.0622612028174148, 3.2566079330162037, 3.791326620921886, 3.9370972316037385), # 18
(3.9676791434882794, 4.3246431116518975, 4.078926298608631, 4.8652103719685265, 4.3672176362129465, 2.4571786409684835, 3.2435092226613578, 3.6367384731296983, 4.766942332283512, 3.0960809986631315, 3.2926070354935515, 3.8332161307660386, 3.9806171197094784), # 19
(4.0043619685688325, 4.360951987987585, 4.113171472893201, 4.906064268824942, 4.405593731180377, 2.4778111591486076, 3.270727894070145, 3.667219714746687, 4.80696182938648, 3.1220604865543837, 3.32026046253566, 3.8653943610413566, 4.01404767601633), # 20
(4.030773800579946, 4.385726492476223, 4.136537813799888, 4.933939878702595, 4.432479001412036, 2.4918891821642144, 3.289299940792704, 3.6880176587366496, 4.834268007147009, 3.139787040360622, 3.339129097935024, 3.8873503649111174, 4.036858121070831), # 21
(4.046396497079832, 4.398390029919658, 4.148481544150914, 4.948188513403135, 4.44729671073423, 2.499085149670547, 3.29879309330224, 3.698648468907166, 4.848225528412766, 3.148848033951297, 3.348773825484136, 3.898573195538596, 4.048517675419531), # 22
(4.052157345337056, 4.399889437585734, 4.149969272976681, 4.949972325102881, 4.451092822413039, 2.5, 3.2999216009037355, 3.6997975308641977, 4.849970493827161, 3.149916909007773, 3.349983214864696, 3.8999590306355736, 4.05), # 23
(4.056404965213662, 4.399014814814815, 4.149725925925926, 4.949752777777778, 4.453243045445941, 2.5, 3.299301525054467, 3.6982, 4.849736666666667, 3.14926024691358, 3.3498498316498324, 3.8996345679012343, 4.05), # 24
(4.060562892084632, 4.397290809327846, 4.149245541838135, 4.949318415637861, 4.455345978237801, 2.5, 3.298079561042524, 3.6950617283950624, 4.849274691358025, 3.1479675354366714, 3.349585359770545, 3.898994055784179, 4.05), # 25
(4.0646308076192135, 4.394743758573389, 4.148534705075447, 4.948674176954732, 4.457401547368442, 2.5, 3.2962746873234887, 3.6904419753086426, 4.84859049382716, 3.14606028349337, 3.349192193540342, 3.8980462734339287, 4.05), # 26
(4.068608393486655, 4.3914, 4.1476, 4.947825, 4.459409679417686, 2.5, 3.2939058823529415, 3.6844, 4.84769, 3.14356, 3.3486727272727275, 3.8968000000000007, 4.05), # 27
(4.0724953313562, 4.387285871056242, 4.146448010973937, 4.946775823045268, 4.461370300965361, 2.5, 3.2909921245864604, 3.6769950617283955, 4.846579135802469, 3.1404881938728852, 3.3480293552812075, 3.895264014631916, 4.05), # 28
(4.0762913028971, 4.382427709190672, 4.145085322359397, 4.94553158436214, 4.463283338591288, 2.5, 3.2875523924796264, 3.6682864197530862, 4.845263827160494, 3.1368663740283496, 3.3472644718792868, 3.893447096479195, 4.05), # 29
(4.079995989778599, 4.376851851851852, 4.143518518518518, 4.944097222222222, 4.4651487188752945, 2.5, 3.2836056644880176, 3.658333333333333, 4.84375, 3.1327160493827164, 3.3463804713804715, 3.8913580246913577, 4.05), # 30
(4.083609073669943, 4.370584636488341, 4.141754183813443, 4.94247767489712, 4.466966368397204, 2.5, 3.279170919067216, 3.6471950617283952, 4.842043580246914, 3.1280587288523094, 3.3453797480982668, 3.8890055784179247, 4.05), # 31
(4.087130236240382, 4.363652400548697, 4.13979890260631, 4.940677880658437, 4.468736213736839, 2.5, 3.2742671346727996, 3.6349308641975315, 4.840150493827161, 3.1229159213534525, 3.344264696346178, 3.886398536808414, 4.05), # 32
(4.090559159159159, 4.356081481481481, 4.13765925925926, 4.938702777777777, 4.470458181474025, 2.5, 3.2689132897603486, 3.6216000000000004, 4.838076666666667, 3.1173091358024694, 3.3430377104377103, 3.8835456790123453, 4.05), # 33
(4.093895524095524, 4.347898216735254, 4.135341838134431, 4.936557304526749, 4.472132198188587, 2.5, 3.263128362785444, 3.6072617283950614, 4.835828024691359, 3.111259881115684, 3.34170118468637, 3.880455784179241, 4.05), # 34
(4.097139012718723, 4.339128943758573, 4.132853223593965, 4.9342463991769545, 4.473758190460348, 2.5, 3.2569313322036635, 3.5919753086419757, 4.833410493827161, 3.1047896662094194, 3.3402575134056613, 3.8771376314586194, 4.05), # 35
(4.100289306698002, 4.3298, 4.1302, 4.931775, 4.475336084869134, 2.5, 3.250341176470588, 3.5758000000000005, 4.83083, 3.0979200000000002, 3.338709090909091, 3.8736000000000006, 4.05), # 36
(4.10334608770261, 4.319937722908093, 4.127388751714678, 4.92914804526749, 4.476865807994769, 2.5, 3.2433768740417976, 3.558795061728395, 4.828092469135803, 3.09067239140375, 3.337058311510164, 3.869851668952904, 4.05), # 37
(4.1063090374017905, 4.3095684499314135, 4.124426063100137, 4.92637047325103, 4.478347286417076, 2.5, 3.2360574033728717, 3.5410197530864203, 4.825203827160494, 3.0830683493369917, 3.3353075695223846, 3.86590141746685, 4.05), # 38
(4.109177837464794, 4.298718518518519, 4.121318518518519, 4.923447222222222, 4.479780446715881, 2.5, 3.2284017429193903, 3.5225333333333335, 4.822170000000001, 3.0751293827160495, 3.3334592592592593, 3.861758024691358, 4.05), # 39
(4.111952169560865, 4.2874142661179695, 4.118072702331961, 4.920383230452675, 4.481165215471008, 2.5, 3.2204288711369324, 3.503395061728396, 4.818996913580247, 3.066877000457248, 3.3315157750342936, 3.8574302697759495, 4.05), # 40
(4.114631715359251, 4.275682030178326, 4.114695198902607, 4.917183436213993, 4.482501519262281, 2.5, 3.212157766481078, 3.4836641975308646, 4.8156904938271605, 3.058332711476909, 3.329479511160993, 3.852926931870142, 4.05), # 41
(4.1172161565292, 4.263548148148149, 4.111192592592594, 4.9138527777777785, 4.483789284669523, 2.5, 3.2036074074074072, 3.4634, 4.812256666666667, 3.0495180246913582, 3.3273528619528623, 3.848256790123457, 4.05), # 42
(4.119705174739957, 4.251038957475995, 4.1075714677640605, 4.910396193415639, 4.485028438272561, 2.5, 3.1947967723715003, 3.4426617283950622, 4.808701358024692, 3.0404544490169183, 3.3251382217234067, 3.8434286236854143, 4.05), # 43
(4.122098451660771, 4.238180795610425, 4.10383840877915, 4.906818621399178, 4.486218906651218, 2.5, 3.185744839828936, 3.4215086419753096, 4.805030493827161, 3.031163493369913, 3.322837984786133, 3.838451211705533, 4.05), # 44
(4.1243956689608865, 4.2250000000000005, 4.1000000000000005, 4.903125, 4.487360616385319, 2.5, 3.1764705882352944, 3.4000000000000004, 4.80125, 3.021666666666667, 3.3204545454545453, 3.833333333333333, 4.05), # 45
(4.126596508309553, 4.211522908093278, 4.096062825788752, 4.8993202674897125, 4.488453494054687, 2.5, 3.1669929960461554, 3.3781950617283956, 4.797365802469136, 3.0119854778235027, 3.3179902980421496, 3.828083767718336, 4.05), # 46
(4.128700651376014, 4.19777585733882, 4.092033470507545, 4.895409362139918, 4.489497466239147, 2.5, 3.1573310417170988, 3.356153086419753, 4.793383827160494, 3.0021414357567444, 3.3154476368624515, 3.82271129401006, 4.05), # 47
(4.130707779829518, 4.183785185185186, 4.087918518518519, 4.891397222222223, 4.490492459518524, 2.5, 3.1475037037037037, 3.333933333333334, 4.78931, 2.992156049382716, 3.312828956228956, 3.8172246913580246, 4.05), # 48
(4.132617575339315, 4.169577229080932, 4.083724554183814, 4.887288786008231, 4.491438400472643, 2.5, 3.137529960461551, 3.3115950617283954, 4.78515024691358, 2.9820508276177415, 3.3101366504551692, 3.8116327389117517, 4.05), # 49
(4.134429719574647, 4.155178326474624, 4.07945816186557, 4.883088991769547, 4.492335215681326, 2.5, 3.12742879044622, 3.2891975308641976, 4.78091049382716, 2.9718472793781436, 3.307373113854595, 3.8059442158207597, 4.05), # 50
(4.136143894204764, 4.140614814814815, 4.075125925925926, 4.8788027777777785, 4.4931828317244, 2.5, 3.11721917211329, 3.2668, 4.776596666666667, 2.961566913580247, 3.304540740740741, 3.8001679012345684, 4.05), # 51
(4.137759780898912, 4.125913031550069, 4.070734430727024, 4.874435082304527, 4.493981175181686, 2.5, 3.1069200839183413, 3.2444617283950614, 4.772214691358025, 2.951231239140375, 3.301641925427111, 3.7943125743026984, 4.05), # 52
(4.139277061326338, 4.1110993141289445, 4.0662902606310025, 4.869990843621399, 4.4947301726330116, 2.5, 3.0965505043169532, 3.222241975308642, 4.767770493827161, 2.9408617649748514, 3.2986790622272104, 3.7883870141746687, 4.05), # 53
(4.140695417156286, 4.0962000000000005, 4.0618, 4.865475, 4.495429750658201, 2.5, 3.086129411764706, 3.2001999999999997, 4.76327, 2.93048, 3.295654545454545, 3.7824, 4.05), # 54
(4.142014530058009, 4.081241426611797, 4.057270233196159, 4.860892489711934, 4.496079835837076, 2.5, 3.075675784717179, 3.178395061728395, 4.758719135802469, 2.920107453132145, 3.292570769422621, 3.7763603109282124, 4.05), # 55
(4.143234081700749, 4.066249931412894, 4.052707544581619, 4.8562482510288065, 4.496680354749464, 2.5, 3.0652086016299527, 3.1568864197530866, 4.754123827160494, 2.909765633287609, 3.2894301284449434, 3.770276726108825, 4.05), # 56
(4.144353753753753, 4.051251851851852, 4.048118518518519, 4.851547222222223, 4.497231233975187, 2.5, 3.0547468409586056, 3.135733333333334, 4.749490000000001, 2.8994760493827165, 3.286235016835017, 3.764158024691358, 4.05), # 57
(4.145373227886272, 4.036273525377229, 4.043509739368999, 4.846794341563786, 4.49773240009407, 2.5, 3.044309481158719, 3.1149950617283952, 4.744823580246914, 2.889260210333791, 3.2829878289063483, 3.7580129858253315, 4.05), # 58
(4.146292185767549, 4.0213412894375855, 4.038887791495199, 4.841994547325103, 4.498183779685938, 2.5, 3.0339155006858713, 3.094730864197531, 4.740130493827161, 2.8791396250571566, 3.27969095897244, 3.7518503886602654, 4.05), # 59
(4.147110309066831, 4.006481481481482, 4.034259259259259, 4.837152777777778, 4.498585299330615, 2.5, 3.0235838779956428, 3.075, 4.7354166666666675, 2.869135802469136, 3.2763468013468016, 3.745679012345679, 4.05), # 60
(4.147827279453366, 3.9917204389574765, 4.02963072702332, 4.832273971193416, 4.498936885607924, 2.5, 3.0133335915436135, 3.0558617283950618, 4.730688024691358, 2.859270251486054, 3.2729577503429357, 3.7395076360310933, 4.05), # 61
(4.148442778596402, 3.977084499314129, 4.025008779149521, 4.827363065843622, 4.499238465097694, 2.5, 3.0031836197853625, 3.0373753086419755, 4.725950493827161, 2.8495644810242347, 3.2695262002743486, 3.7333450388660268, 4.05), # 62
(4.148956488165184, 3.9626, 4.0204, 4.822425000000001, 4.499489964379743, 2.5, 2.9931529411764703, 3.0196000000000005, 4.72121, 2.84004, 3.266054545454546, 3.7272, 4.05), # 63
(4.149368089828959, 3.948293278463649, 4.0158109739369, 4.817464711934157, 4.499691310033899, 2.5, 2.983260534172517, 3.0025950617283956, 4.716472469135803, 2.8307183173296755, 3.2625451801970318, 3.7210812985825337, 4.05), # 64
(4.149677265256975, 3.934190672153635, 4.01124828532236, 4.812487139917696, 4.499842428639987, 2.5, 2.9735253772290813, 2.9864197530864196, 4.711743827160494, 2.821620941929584, 3.2590004988153143, 3.7149977137631454, 4.05), # 65
(4.149883696118478, 3.920318518518519, 4.006718518518519, 4.8074972222222225, 4.499943246777829, 2.5, 2.963966448801743, 2.971133333333334, 4.7070300000000005, 2.8127693827160494, 3.2554228956228957, 3.7089580246913587, 4.05), # 66
(4.149987064082717, 3.9067031550068587, 4.002228257887517, 4.8024998971193416, 4.499993691027252, 2.5, 2.9546027273460824, 2.956795061728396, 4.702336913580247, 2.804185148605396, 3.2518147649332843, 3.7029710105166895, 4.05), # 67
(4.14991664579233, 3.8932994557281293, 3.9977623799725652, 4.797456696188943, 4.499951182118938, 2.49995360463344, 2.9454060779318585, 2.943337540009145, 4.6976351394604485, 2.7958481766588665, 3.2481143954161507, 3.697012008759897, 4.0499500600137175), # 68
(4.149256682769726, 3.879698207885305, 3.99319537037037, 4.792113405797101, 4.499564270152505, 2.4995868312757206, 2.9361072725386457, 2.9300395061728395, 4.692719135802469, 2.787522556281772, 3.243945135566188, 3.69088758934373, 4.049554398148149), # 69
(4.147954315023558, 3.865836983937342, 3.9885073731138543, 4.7864348497047775, 4.498799725651577, 2.4988645023624447, 2.926664053824548, 2.916780978509374, 4.687561156835849, 2.779167809785094, 3.239259554610432, 3.6845691045171236, 4.048772933813444), # 70
(4.146027864257172, 3.851724067436612, 3.9837000342935527, 4.7804294015029525, 4.497667231501654, 2.497798323426307, 2.9170806638155953, 2.9035663465935073, 4.6821688843164155, 2.770784143737056, 3.2340749483135447, 3.678061174885086, 4.047615955075446), # 71
(4.143495652173914, 3.8373677419354837, 3.9787749999999997, 4.774105434782609, 4.496176470588235, 2.4964000000000004, 2.907361344537815, 2.8904, 4.676550000000001, 2.7623717647058825, 3.228408612440192, 3.671368421052632, 4.04609375), # 72
(4.140376000477128, 3.8227762909863268, 3.973733916323731, 4.767471323134729, 4.494337125796821, 2.494681237616217, 2.897510338017237, 2.8772863283036125, 4.670712185642433, 2.7539308792597974, 3.2222778427550356, 3.6644954636247675, 4.04421660665295), # 73
(4.136687230870161, 3.807957998141511, 3.968578429355281, 4.760535440150295, 4.49215888001291, 2.4926537418076515, 2.887531886279889, 2.864229721079104, 4.664663122999543, 2.745461693967025, 3.2156999350227427, 3.657446923206507, 4.041994813100138), # 74
(4.13244766505636, 3.7929211469534048, 3.963310185185185, 4.75330615942029, 4.489651416122005, 2.4903292181069965, 2.8774302313518003, 2.8512345679012348, 4.65841049382716, 2.736964415395788, 3.2086921850079744, 3.650227420402859, 4.039438657407408), # 75
(4.127675624739071, 3.77767402097438, 3.957930829903978, 4.745791854535695, 4.486824417009602, 2.4877193720469446, 2.8672096152589983, 2.8383052583447648, 4.651961979881116, 2.7284392501143118, 3.2012718884753966, 3.6428415758188333, 4.036558427640603), # 76
(4.122389431621637, 3.7622249037568043, 3.952442009602194, 4.738000899087493, 4.483687565561204, 2.4848359091601893, 2.8568742800275118, 2.825446181984454, 4.645325262917239, 2.71988640469082, 3.193456341189675, 3.6352940100594426, 4.03336441186557), # 77
(4.1166074074074075, 3.7465820788530473, 3.9468453703703705, 4.729941666666667, 4.48025054466231, 2.481690534979424, 2.84642846768337, 2.812661728395062, 4.638508024691357, 2.7113060856935367, 3.1852628389154707, 3.6275893437296953, 4.029866898148149), # 78
(4.110347873799726, 3.730753829815479, 3.94114255829904, 4.721622530864198, 4.476523037198419, 2.4782949550373417, 2.8358764202526006, 2.7999562871513493, 4.631517946959305, 2.7026984996906855, 3.176708677417449, 3.619732197434602, 4.026076174554183), # 79
(4.103629152501939, 3.714748440196469, 3.9353352194787377, 4.713051865271068, 4.4725147260550315, 2.474660874866636, 2.8252223797612324, 2.7873342478280754, 4.624362711476909, 2.6940638532504906, 3.1678111524602754, 3.611727191779174, 4.02200252914952), # 80
(4.096469565217392, 3.6985741935483873, 3.929425, 4.704238043478262, 4.468235294117647, 2.4708, 2.8144705882352943, 2.7748000000000004, 4.61705, 2.6854023529411766, 3.1585875598086126, 3.603578947368421, 4.01765625), # 81
(4.088887433649431, 3.682239373423603, 3.9234135459533612, 4.695189439076758, 4.463694424271766, 2.466724035970127, 2.8036252877008145, 2.762357933241884, 4.6095874942844075, 2.6767142053309665, 3.1490551952271253, 3.5952920848073546, 4.013047625171469), # 82
(4.080901079501402, 3.6657522633744857, 3.9173025034293554, 4.685914425657542, 4.458901799402889, 2.4624446883097093, 2.7926907201838214, 2.7500124371284866, 4.6019828760859625, 2.6679996169880846, 3.1392313544804775, 3.586871224700985, 4.008186942729767), # 83
(4.072528824476651, 3.649121146953405, 3.9110935185185185, 4.676421376811595, 4.453867102396514, 2.4579736625514403, 2.7816711277103434, 2.7377679012345677, 4.5942438271604935, 2.6592587944807557, 3.1291333333333333, 3.578320987654321, 4.003084490740741), # 84
(4.063788990278524, 3.6323543077127307, 3.904788237311385, 4.666718666129898, 4.448600016138143, 2.4533226642280144, 2.77057075230641, 2.7256287151348886, 4.586378029263831, 2.6504919443772024, 3.1187784275503576, 3.569645994272375, 3.9977505572702334), # 85
(4.054699898610365, 3.6154600292048324, 3.8983883058984916, 4.656814667203436, 4.443110223513274, 2.4485033988721234, 2.7593938359980483, 2.7135992684042067, 4.578393164151807, 2.6416992732456497, 3.108183932896214, 3.5608508651601576, 3.992195430384088), # 86
(4.045279871175523, 3.5984465949820788, 3.8918953703703703, 4.6467177536231885, 4.437407407407409, 2.443527572016461, 2.7481446208112876, 2.701683950617284, 4.570296913580248, 2.632880987654321, 3.097367145135566, 3.551940220922677, 3.9864293981481485), # 87
(4.035547229677343, 3.5813222885968403, 3.8853110768175583, 4.63643629898014, 4.431501250706044, 2.4384068891937205, 2.7368273487721564, 2.68988715134888, 4.562096959304984, 2.6240372941714405, 3.0863453600330795, 3.542918682164946, 3.9804627486282587), # 88
(4.025520295819169, 3.564095393601487, 3.87863707133059, 4.625978676865271, 4.425401436294683, 2.4331530559365953, 2.7254462619066833, 2.678213260173754, 4.553800983081848, 2.615168399365233, 3.0751358733534175, 3.533790869491974, 3.9743057698902606), # 89
(4.015217391304348, 3.546774193548387, 3.871875, 4.615353260869566, 4.419117647058824, 2.427777777777778, 2.7140056022408965, 2.6666666666666665, 4.545416666666667, 2.6062745098039217, 3.0637559808612442, 3.524561403508772, 3.9679687500000003), # 90
(4.004656837836225, 3.529366971989911, 3.8650265089163236, 4.604568424584005, 4.412659565883966, 2.4222927602499618, 2.7025096118008247, 2.6552517604023778, 4.536951691815273, 2.5973558320557304, 3.052222978321224, 3.5152349048203497, 3.961461977023319), # 91
(3.9938569571181493, 3.511882012478429, 3.858093244170096, 4.593632541599571, 4.406036875655611, 2.4167097088858407, 2.690962532612497, 2.6439729309556474, 4.528413740283494, 2.588412572688884, 3.0405541614980214, 3.5058159940317193, 3.954795739026063), # 92
(3.982836070853462, 3.4943275985663087, 3.851076851851852, 4.582553985507247, 4.399259259259259, 2.411040329218107, 2.6793686067019404, 2.632834567901235, 4.519810493827161, 2.579444938271605, 3.0287668261563, 3.496309291747888, 3.9479803240740736), # 93
(3.971612500745512, 3.476712013805921, 3.8439789780521267, 4.571341129898014, 4.392336399580408, 2.4052963267794545, 2.6677320760951844, 2.621841060813901, 4.511149634202104, 2.570453135372119, 3.016878268060724, 3.4867194185738697, 3.9410260202331964), # 94
(3.960204568497644, 3.4590435417496352, 3.8368012688614543, 4.560002348362856, 4.3852779795045596, 2.399489407102576, 2.6560571828182575, 2.6109967992684044, 4.502438843164152, 2.5614373705586484, 3.0049057829759587, 3.477050995114672, 3.933943115569273), # 95
(3.948630595813205, 3.4413304659498216, 3.829545370370371, 4.548546014492754, 4.378093681917211, 2.3936312757201645, 2.6443481688971886, 2.6003061728395065, 4.493685802469136, 2.5523978503994194, 2.992866666666667, 3.4673086419753094, 3.9267418981481486), # 96
(3.936908904395539, 3.4235810699588485, 3.82221292866941, 4.53698050187869, 4.370793189703866, 2.3877336381649137, 2.6326092763580053, 2.5897735711019667, 4.484898193872886, 2.543334781462654, 2.980778214897513, 3.457496979760788, 3.919432656035666), # 97
(3.925057815947994, 3.4058036373290856, 3.814805589849108, 4.525314184111648, 4.363386185750021, 2.3818081999695173, 2.6208447472267373, 2.5794033836305443, 4.476083699131229, 2.534248370316577, 2.9686577234331626, 3.4476206290761193, 3.9120256772976685), # 98
(3.9130956521739133, 3.3880064516129034, 3.8073250000000005, 4.513555434782609, 4.355882352941177, 2.3758666666666666, 2.6090588235294123, 2.5692000000000004, 4.46725, 2.525138823529412, 2.956522488038278, 3.437684210526316, 3.9045312500000002), # 99
(3.901040734776645, 3.3701977963626706, 3.79977280521262, 4.501712627482555, 4.348291374162834, 2.3699207437890566, 2.597255747292058, 2.559167809785094, 4.458404778235026, 2.5160063476693835, 2.944389804477524, 3.427692344716387, 3.896959662208505), # 100
(3.888911385459534, 3.3523859551307584, 3.792150651577504, 4.4897941358024696, 4.340622932300493, 2.3639821368693794, 2.585439760540705, 2.549311202560585, 4.449555715592136, 2.5068511493047154, 2.932276968515565, 3.4176496522513413, 3.8893212019890258), # 101
(3.8767259259259266, 3.3345792114695345, 3.784460185185185, 4.477808333333334, 4.332886710239651, 2.3580625514403297, 2.5736151053013803, 2.539634567901235, 4.440710493827161, 2.4976734350036316, 2.9202012759170657, 3.4075607537361927, 3.881626157407408), # 102
(3.864502677879168, 3.316785848931369, 3.7767030521262, 4.46576359366613, 4.325092390865811, 2.3521736930345987, 2.561786023600112, 2.530142295381802, 4.43187679469593, 2.488473411334356, 2.9081800224466896, 3.397430269775949, 3.873884816529492), # 103
(3.852259963022604, 3.2990141510686315, 3.7688808984910835, 4.453668290391842, 4.317249657064472, 2.3463272671848805, 2.5499567574629305, 2.5208387745770464, 4.4230622999542755, 2.4792512848651125, 2.8962305038691003, 3.3872628209756215, 3.8661074674211253), # 104
(3.840016103059581, 3.2812724014336916, 3.7609953703703702, 4.441530797101449, 4.309368191721133, 2.3405349794238686, 2.5381315489158633, 2.511728395061729, 4.414274691358025, 2.4700072621641254, 2.8843700159489636, 3.3770630279402214, 3.858304398148148), # 105
(3.8277894196934454, 3.2635688835789196, 3.7530481138545952, 4.429359487385937, 4.301457677721294, 2.3348085352842554, 2.526314639984938, 2.5028155464106083, 4.405521650663008, 2.460741549799618, 2.872615854450942, 3.3668355112747577, 3.850485896776406), # 106
(3.8155982346275423, 3.2459118810566845, 3.745040775034294, 4.417162734836285, 4.293527797950456, 2.329159640298735, 2.5145102726961848, 2.494104618198446, 4.396810859625058, 2.4514543543398157, 2.860985315139701, 3.356584891584242, 3.842662251371742), # 107
(3.8034608695652175, 3.2283096774193556, 3.7369750000000006, 4.404948913043479, 4.285588235294117, 2.3236000000000003, 2.5027226890756302, 2.4856000000000003, 4.38815, 2.4421458823529414, 2.8494956937799043, 3.346315789473685, 3.8348437500000006), # 108
(3.7913956462098173, 3.210770556219302, 3.72885243484225, 4.392726395598497, 4.27764867263778, 2.318141319920744, 2.490956131149305, 2.4773060813900325, 4.379546753543667, 2.432816340407219, 2.838164286136216, 3.336032825548095, 3.8270406807270234), # 109
(3.7794208862646865, 3.193302801008895, 3.7206747256515786, 4.380503556092324, 4.269718792866942, 2.3127953055936596, 2.4792148409432357, 2.4692272519433014, 4.371008802011889, 2.4234659350708734, 2.8270083879733003, 3.3257406204124855, 3.819263331618656), # 110
(3.7675549114331726, 3.175914695340502, 3.712443518518519, 4.368288768115942, 4.261808278867103, 2.3075736625514405, 2.4675030604834527, 2.461367901234568, 4.362543827160494, 2.414094872912128, 2.8160452950558215, 3.3154437946718653, 3.811521990740741), # 111
(3.75581604341862, 3.1586145227664937, 3.7041604595336084, 4.356090405260333, 4.253926813523764, 2.3024880963267798, 2.4558250317959835, 2.453732418838592, 4.354159510745313, 2.4047033604992065, 2.805292303148444, 3.3051469689312443, 3.803826946159122), # 112
(3.744201689481218, 3.141439447514381, 3.6958471313008276, 4.343933552996816, 4.246070272069482, 2.2975479076858054, 2.444210385462708, 2.4463410275122426, 4.345885124503448, 2.395321894645092, 2.7947695624611466, 3.2948771746017713, 3.7961775603372887), # 113
(3.732592359160026, 3.1245588734102143, 3.6876182700086475, 4.331915768510934, 4.238157341826531, 2.2927418434119606, 2.432807283364232, 2.439284503802048, 4.3378476142852245, 2.386126067165113, 2.784497734845279, 3.28476486884519, 3.788510165664014), # 114
(3.720953961201598, 3.107978879473219, 3.679478773082927, 4.320033802072712, 4.230163071155441, 2.2880574049995057, 2.421623860076625, 2.4325610617114837, 4.330049991467516, 2.377130131195231, 2.7744618045708376, 3.2748150330235406, 3.7808026526641507), # 115
(3.709271949295054, 3.091675312516681, 3.6714128759935494, 4.3082664601065614, 4.222075410553511, 2.283483550914839, 2.4106419270111576, 2.4261521251595974, 4.322472535691133, 2.368317343379819, 2.7646423725085927, 3.2650092789949383, 3.7730429039023563), # 116
(3.697531777129509, 3.0756240193538886, 3.6634048142103945, 4.296592549036897, 4.213882310518044, 2.279009239624356, 2.399843295579101, 2.420039118065434, 4.315095526596881, 2.3596709603632515, 2.755020039529313, 3.2553292186175002, 3.76521880194329), # 117
(3.6857188983940845, 3.0598008467981295, 3.655438823203347, 4.284990875288133, 4.205571721546337, 2.2746234295944556, 2.3892097771917262, 2.414203464348039, 4.307899243825574, 2.3511742387899037, 2.74557540650377, 3.24575646374934, 3.75731822935161), # 118
(3.673818766777897, 3.044181641662692, 3.6474991384422895, 4.273440245284682, 4.197131594135689, 2.270315079291533, 2.3787231832603024, 2.408626587926458, 4.300863967018017, 2.342810435304149, 2.7362890743027313, 3.236272626248574, 3.749329068691973), # 119
(3.6618168359700647, 3.0287422507608635, 3.639569995397105, 4.261919465450958, 4.188549878783399, 2.266073147181986, 2.3683653251961014, 2.403289912719737, 4.293969975815023, 2.334562806550362, 2.7271416437969664, 3.226859317973319, 3.741239202529039), # 120
(3.6496985596597074, 3.0134585209059317, 3.631635629537675, 4.250407342211374, 4.179814525986767, 2.261886591732212, 2.358118014410392, 2.398174862646923, 4.2871975498573995, 2.3264146091729185, 2.7181137158572466, 3.217498150781689, 3.7330365134274643), # 121
(3.6374493915359416, 2.9983062989111846, 3.6236802763338845, 4.238882681990343, 4.170913486243093, 2.2577443714086076, 2.347963062314447, 2.3932628616270595, 4.2805269687859555, 2.318349099816191, 2.7091858913543407, 3.2081707365318004, 3.7247088839519082), # 122
(3.6250547852878876, 2.9832614315899098, 3.6156881712556146, 4.227324291212278, 4.161834710049677, 2.25363544467757, 2.3378822803195356, 2.3885353335791932, 4.273938512241502, 2.310349535124555, 2.700338771159018, 3.198858687081769, 3.716244196667029), # 123
(3.612500194604662, 2.968299765755395, 3.607643549772748, 4.215710976301595, 4.152566147903815, 2.2495487700054957, 2.327857479836928, 2.3839737024223706, 4.267412459864846, 2.3023991717423846, 2.691552956142048, 3.1895436142897102, 3.7076303341374848), # 124
(3.5997710731753836, 2.9533971482209282, 3.5995306473551696, 4.204021543682704, 4.143095750302809, 2.2454733058587824, 2.3178704722778956, 2.3795593920756364, 4.260929091296798, 2.2944812663140537, 2.6828090471742008, 3.1802071300137396, 3.6988551789279316), # 125
(3.5868528746891712, 2.938529425799798, 3.5913336994727594, 4.192234799780022, 4.133411467743957, 2.241398010703827, 2.307903069053708, 2.375273826458037, 4.254468686178167, 2.286579075483937, 2.6740876451262454, 3.170830846111974, 3.6899066136030316), # 126
(3.5737310528351447, 2.92367244530529, 3.583036941595402, 4.18032955101796, 4.123501250724559, 2.237311843007026, 2.2979370815756375, 2.3710984294886184, 4.248011524149763, 2.2786758558964095, 2.6653693508689518, 3.1613963744425266, 3.6807725207274395), # 127
(3.5603910613024183, 2.908802053550694, 3.57462460919298, 4.168284603820933, 4.113353049741916, 2.2332037612347775, 2.287954321254953, 2.367014625086425, 4.241537884852394, 2.2707548641958453, 2.6566347652730897, 3.1518853268635154, 3.671440782865815), # 128
(3.546818353780113, 2.8938940973492966, 3.566080937735376, 4.156078764613353, 4.102954815293325, 2.229062723853478, 2.2779365995029255, 2.363003837170504, 4.23502804792687, 2.2627993570266183, 2.6478644892094287, 3.1422793152330546, 3.6618992825828154), # 129
(3.532998383957347, 2.8789244235143867, 3.5573901626924718, 4.143690839819635, 4.092294497876085, 2.2248776893295235, 2.267865727730825, 2.3590474896599, 4.228462293014, 2.254792591033103, 2.639039123548738, 3.1325599514092612, 3.6521359024430993), # 130
(3.5189166055232377, 2.863868878859251, 3.5485365195341525, 4.1310996358641905, 4.081360047987498, 2.2206376161293124, 2.2577235173499237, 2.35512700647366, 4.2218208997545945, 2.246717822859674, 2.6301392691617873, 3.1227088472502498, 3.6421385250113247), # 131
(3.504558472166904, 2.8487033101971777, 3.5395042437302986, 4.118283959171435, 4.070139416124862, 2.216331462719241, 2.24749177977149, 2.3512238115308293, 4.215084147789462, 2.2385583091507057, 2.6211455269193458, 3.112707614614137, 3.6318950328521504), # 132
(3.4899094375774653, 2.833403564341454, 3.5302775707507936, 4.105222616165781, 4.058620552785475, 2.2119481875657065, 2.237152326406796, 2.347319328750453, 4.2082323167594105, 2.230297306550573, 2.6120384976921844, 3.102537865359037, 3.6213933085302346), # 133
(3.474954955444038, 2.8179454881053694, 3.5208407360655216, 4.091894413271642, 4.046791408466637, 2.207476749135106, 2.2266869686671114, 2.3433949820515774, 4.201245686305252, 2.2219180717036493, 2.6027987823510714, 3.0921812113430667, 3.6106212346102335), # 134
(3.4596804794557414, 2.8023049283022097, 3.5111779751443635, 4.078278156913432, 4.034639933665648, 2.202906105893837, 2.2160775179637073, 2.339432195353248, 4.194104536067792, 2.2134038612543105, 2.593406981766777, 3.081619264424341, 3.599566693656808), # 135
(3.444071463301694, 2.786457731745264, 3.5012735234572037, 4.064352653515562, 4.022154078879807, 2.198225216308296, 2.205305785707854, 2.335412392574511, 4.186789145687842, 2.204737931846929, 2.583843696810071, 3.0708336364609767, 3.5882175682346147), # 136
(3.4281133606710137, 2.770379745247819, 3.4911116164739244, 4.0500967095024505, 4.0093217946064135, 2.1934230388448794, 2.1943535833108223, 2.3313169976344117, 4.179279794806213, 2.195903540125881, 2.5740895283517222, 3.059805939311088, 3.5765617409083106), # 137
(3.4117916252528193, 2.7540468156231634, 3.480676489664407, 4.0354891312985055, 3.9961310313427676, 2.1884885319699854, 2.1832027221838817, 2.327127434451996, 4.1715567630637125, 2.186883942735539, 2.564125077262501, 3.048517784832791, 3.5645870942425564), # 138
(3.3950917107362275, 2.7374347896845848, 3.469952378498536, 4.020508725328144, 3.9825697395861663, 2.1834106541500105, 2.171835013738304, 2.32282512694631, 4.163600330101149, 2.177662396320279, 2.5539309444131764, 3.0369507848842026, 3.5522815108020076), # 139
(3.3779990708103593, 2.72051951424537, 3.458923518446195, 4.005134298015778, 3.968625869833912, 2.1781783638513517, 2.1602322693853586, 2.3183914990363985, 4.155390775559333, 2.1682221575244744, 2.5434877306745176, 3.0250865513234366, 3.539632873151326), # 140
(3.3604991591643323, 2.7032768361188086, 3.4475741449772643, 3.989344655785821, 3.9542873725833014, 2.172780619540406, 2.148376300536318, 2.3138079746413083, 4.146908379079072, 2.1585464829925005, 2.5327760369172956, 3.01290669600861, 3.5266290638551654), # 141
(3.3425774294872626, 2.6856826021181863, 3.4358884935616283, 3.9731186050626883, 3.939542198331635, 2.167206379683571, 2.1362489186024507, 2.3090559776800847, 4.138133420301177, 2.1486186293687313, 2.521776464012279, 3.000392830797838, 3.5132579654781866), # 142
(3.32421933546827, 2.6677126590567926, 3.4238507996691703, 3.95643495227079, 3.9243782975762116, 2.1614446027472427, 2.1238319349950276, 2.3041169320717727, 4.129046178866459, 2.138421853297541, 2.5104696128302373, 2.987526567549236, 3.499507460585047), # 143
(3.305410330796474, 2.6493428537479145, 3.411445298769771, 3.939272503834543, 3.9087836208143316, 2.1554842471978186, 2.1111071611253194, 2.2989722617354196, 4.119626934415724, 2.127939411423304, 2.4988360842419404, 2.9742895181209197, 3.485365431740406), # 144
(3.286135869160991, 2.63054903300484, 3.3986562263333155, 3.921610066178358, 3.892746118543293, 2.149314271501696, 2.0980564084045974, 2.2936033905900706, 4.109855966589782, 2.117154560390395, 2.486856479118158, 2.9606632943710056, 3.47081976150892), # 145
(3.2663814042509403, 2.6113070436408568, 3.385467817829687, 3.9034264457266503, 3.8762537412603972, 2.1429236341252724, 2.084661488244132, 2.287991742554771, 4.099713555029442, 2.106050556843188, 2.4745113983296596, 2.946629508157608, 3.4558583324552474), # 146
(3.24613238975544, 2.5915927324692523, 3.371864308728764, 3.884700448903832, 3.859294439462941, 2.136301293534943, 2.0709042120551926, 2.282118741548566, 4.089179979375516, 2.0946106574260583, 2.4617814427472147, 2.9321697713388444, 3.4404690271440472), # 147
(3.2253742793636087, 2.5713819463033154, 3.357829934500433, 3.8654108821343187, 3.8418561636482247, 2.129436208197107, 2.0567663912490506, 2.275965811490503, 4.078235519268811, 2.0828181187833787, 2.448647213241593, 2.9172656957728282, 3.4246397281399767), # 148
(3.204092526764565, 2.5506505319563324, 3.3433489306145776, 3.845536551842521, 3.8239268643135484, 2.12231733657816, 2.0422298372369765, 2.2695143762996266, 4.066860454350135, 2.0706561975595252, 2.435089310683564, 2.901898893317677, 3.408358318007695), # 149
(3.182272585647426, 2.5293743362415917, 3.328405532541078, 3.825056264452855, 3.8054944919562104, 2.1149336371444996, 2.0272763614302405, 2.2627458598949826, 4.055035064260301, 2.0581081503988705, 2.4210883359438973, 2.8860509758315054, 3.3916126793118586), # 150
(3.15989990970131, 2.5075292059723817, 3.312983975749817, 3.803948826389732, 3.786546997073511, 2.107274068362522, 2.011887775240113, 2.2556416861956174, 4.042739628640116, 2.0451572339457913, 2.406624889893362, 2.869703555172429, 3.3743906946171274), # 151
(3.1369599526153373, 2.485090987961989, 3.297068495710681, 3.7821930440775677, 3.7670723301627476, 2.0993275886986256, 1.996045890077866, 2.2481832791205765, 4.029954427130388, 2.03178670484466, 2.3916795734027287, 2.8528382431985637, 3.356680246488159), # 152
(3.1134381680786243, 2.462035529023703, 3.2806433278935474, 3.759767723940773, 3.7470584417212223, 2.0910831566192063, 1.9797325173547677, 2.240352062588905, 4.01665973937193, 2.0179798197398515, 2.3762329873427666, 2.835436651768026, 3.338469217489611), # 153
(3.0893200097802915, 2.43833867597081, 3.2636927077683033, 3.736651672403764, 3.726493282246232, 2.082529730590662, 1.9629294684820913, 2.232129460519649, 4.002835845005547, 2.0037198352757413, 2.360265732584245, 2.81748039273893, 3.319745490186143), # 154
(3.0645909314094544, 2.413976275616598, 3.2462008708048304, 3.7128236958909513, 3.7053648022350787, 2.0736562690793887, 1.9456185548711045, 2.2234968968318545, 3.9884630236720513, 1.9889900080967018, 2.343758409997933, 2.798951077969393, 3.3004969471424106), # 155
(3.0392363866552325, 2.3889241747743553, 3.2281520524730105, 3.68826260082675, 3.6836609521850594, 2.0644517305517844, 1.92778158793308, 2.2144357954445675, 3.9735215550122507, 1.9737735948471091, 2.3266916204546018, 2.7798303193175293, 3.280711470923074), # 156
(3.013241829206745, 2.3631582202573695, 3.209530488242727, 3.662947193635575, 3.661369682593474, 2.0549050734742456, 1.9094003790792877, 2.204927580276833, 3.9579917186669555, 1.9580538521713367, 2.3090459648250197, 2.760099728641455, 3.2603769440927906), # 157
(2.985872378562096, 2.3361812483089035, 3.1894367815609423, 3.6359078326604974, 3.637472442348399, 2.044409790526844, 1.890042688371143, 2.194318780939749, 3.9406648366396393, 1.9413463665164574, 2.290238301015577, 2.739039825677736, 3.238594343766138), # 158
(2.9529147067913613, 2.305226127839791, 3.162695127361195, 3.6015908635153817, 3.6060765239126513, 2.0294758592028415, 1.8672851053542865, 2.178885413105753, 3.914570904488858, 1.9209123976394982, 2.2669667742475976, 2.7125450094732435, 3.210171058768078), # 159
(2.913948837961724, 2.2700386914162856, 3.1287683831823556, 3.559431004163544, 3.5665680525387184, 2.0097365184190736, 1.8408974993535137, 2.158239675810939, 3.8789700908914604, 1.8964822607451575, 2.238903803443816, 2.680200779555139, 3.1745682435574323), # 160
(2.869288821834384, 2.2308483472321874, 3.0880187887641237, 3.509829001502691, 3.5193572497128454, 1.9854308966281256, 1.8110725784027506, 2.132640213243912, 3.834331906799607, 1.8682632772683752, 2.206296661839883, 2.6423069875630283, 3.132149617927639), # 161
(2.8192487081705426, 2.1878845034812957, 3.0408085838461982, 3.4531856024305307, 3.464854336921282, 1.9567981222825823, 1.7780030505359237, 2.102345669593281, 3.781125863165455, 1.8364627686440926, 2.1693926226714484, 2.5991634851365175, 3.0832789016721334), # 162
(2.7641425467313994, 2.1413765683574097, 2.987500008168281, 3.3899015538447737, 3.4034695356502755, 1.924077323835029, 1.7418816237869603, 2.06761468904765, 3.7198214709411626, 1.80128805630725, 2.1284389591741633, 2.5510701239152134, 3.0283198145843517), # 163
(2.704284387278154, 2.0915539500543283, 2.9284553014700707, 3.320377602643127, 3.3356130673860758, 1.8875076297380518, 1.7029010061897865, 2.0287059157956278, 3.6508882410788894, 1.7629464616927875, 2.0836829445836784, 2.4983267555387214, 2.9676360764577314), # 164
(2.639988279572007, 2.038646056765853, 2.8640367034912675, 3.245014495723301, 3.2616951536149297, 1.8473281684442346, 1.6612539057783289, 1.9858779940258184, 3.574795684530792, 1.7216453062356454, 2.0353718521356448, 2.441233231646648, 2.901591407085708), # 165
(2.571568273374159, 1.9828822966857818, 2.7946064539715714, 3.1642129799830006, 3.1821260158230857, 1.8037780684061635, 1.6171330305865146, 1.939389567926831, 3.4920133122490293, 1.677591911370765, 1.9837529550657118, 2.3800894038786007, 2.830549526261718), # 166
(2.4993384184458094, 1.9244920780079149, 2.720526792650682, 3.0783738023199376, 3.097315875496792, 1.7570964580764235, 1.57073108864827, 1.8894992816872707, 3.40301063518576, 1.6309935985330857, 1.929073526609531, 2.3151951238741835, 2.7548741537791983), # 167
(2.4236127645481584, 1.8637048089260515, 2.6421599592682994, 2.9878977096318184, 3.007674954122297, 1.7075224659075996, 1.5222407879975217, 1.836465779495744, 3.308257164293142, 1.5820576891575489, 1.8715808400027525, 2.2468502432730046, 2.674929009431585), # 168
(2.344705361442406, 1.8007498976339917, 2.5598681935641237, 2.8931854488163533, 2.913613473185848, 1.655295220352278, 1.4718548366681967, 1.780547705540858, 3.2082224105233355, 1.5309915046790947, 1.8115221684810274, 2.175354613714669, 2.591077813012314), # 169
(2.2629302588897535, 1.735856752325535, 2.474013735277854, 2.794637766771248, 2.8155416541736935, 1.6006538498630427, 1.4197659426942213, 1.722003704011219, 3.1033758848284956, 1.4780023665326631, 1.7491447852800066, 2.1010080868387835, 2.503684284314822), # 170
(2.1786015066514, 1.6692547811944802, 2.3849588241491912, 2.6926554103942144, 2.7138697185720826, 1.5438374828924795, 1.3661668141095222, 1.6610924190954333, 2.9941870981607828, 1.4232975961531955, 1.6846959636353394, 2.0241105142849545, 2.413112143132546), # 171
(2.092033154488546, 1.6011733924346279, 2.2930656999178347, 2.5876391265829586, 2.6090078878672616, 1.4850852478931735, 1.3112501589480263, 1.5980724949821083, 2.8811255614723543, 1.367084514975632, 1.6184229767826777, 1.9449617476927885, 2.3197251092589215), # 172
(2.003539252162392, 1.531841994239777, 2.198696602323485, 2.4799896622351905, 2.5013663835454807, 1.42463627331771, 1.25520868524366, 1.5332025758598495, 2.7646607857153693, 1.3095704444349128, 1.5505730979576713, 1.86386163870189, 2.223886902487385), # 173
(1.9134338494341376, 1.4614899948037272, 2.102213771105841, 2.3701077642486164, 2.3913554270929867, 1.362729687618674, 1.1982351010303502, 1.4667413059172643, 2.6452622818419855, 1.2509627059659787, 1.4813936003959711, 1.7811100389518673, 2.1259612426113734), # 174
(1.8220309960649823, 1.3903468023202779, 2.003979446004603, 2.258394179520947, 2.2793852399960275, 1.2996046192486514, 1.1405221143420232, 1.3989473293429584, 2.5233995608043625, 1.1914686210037697, 1.4111317573332278, 1.6970068000823257, 2.026311849424323), # 175
(1.7296447418161276, 1.3186418249832292, 1.9043558667594713, 2.14524965494989, 2.165866043740852, 1.2355001966602268, 1.082262433212606, 1.3300792903255396, 2.399542133554657, 1.1312955109832268, 1.340034842005092, 1.6118517737328717, 1.9253024427196697), # 176
(1.636589136448773, 1.2466044709863806, 1.8037052731101455, 2.031074937433153, 2.0512080598137095, 1.1706555483059853, 1.0236487656760251, 1.2603958330536131, 2.274159511045028, 1.0706506973392897, 1.2683501276472144, 1.5259448115431116, 1.82329674229085), # 177
(1.5431782297241188, 1.1744641485235314, 1.7023899047963256, 1.9162707738684466, 1.9358215097008455, 1.105309802638513, 0.964873819766207, 1.1901556017157862, 2.147721204227634, 1.0097415015069, 1.196324887495245, 1.439585765152651, 1.7206584679313008), # 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, 0, 0, 2, 0, 2, 1, 0, 3, 0, 0, 0, 0, 2, 2, 2, 1, 1, 2, 0, 0, 1, 3, 0, 0, 0), # 0
(5, 3, 2, 5, 2, 5, 2, 1, 3, 0, 0, 0, 0, 6, 3, 4, 1, 3, 4, 1, 1, 1, 3, 1, 0, 0), # 1
(5, 7, 2, 6, 2, 5, 2, 1, 3, 0, 0, 0, 0, 10, 7, 6, 1, 6, 8, 3, 2, 3, 4, 1, 0, 0), # 2
(7, 14, 3, 8, 3, 9, 3, 1, 3, 0, 0, 0, 0, 12, 11, 8, 2, 6, 8, 5, 2, 5, 4, 3, 1, 0), # 3
(13, 16, 6, 9, 8, 9, 7, 4, 4, 2, 0, 0, 0, 14, 15, 10, 3, 8, 8, 7, 2, 6, 4, 3, 1, 0), # 4
(15, 18, 9, 11, 11, 13, 8, 4, 4, 2, 0, 0, 0, 18, 17, 11, 4, 11, 10, 8, 2, 7, 4, 4, 1, 0), # 5
(20, 24, 13, 13, 12, 15, 10, 5, 10, 3, 0, 0, 0, 21, 21, 12, 7, 12, 12, 10, 3, 8, 5, 4, 1, 0), # 6
(21, 30, 19, 14, 14, 15, 12, 7, 12, 4, 1, 0, 0, 25, 25, 15, 8, 16, 12, 11, 3, 9, 5, 5, 1, 0), # 7
(21, 33, 23, 16, 17, 15, 13, 10, 13, 5, 1, 1, 0, 33, 31, 15, 9, 19, 15, 13, 3, 9, 8, 5, 1, 0), # 8
(22, 37, 25, 19, 21, 15, 13, 10, 14, 6, 3, 2, 0, 34, 34, 18, 12, 20, 17, 15, 3, 10, 8, 7, 2, 0), # 9
(24, 39, 27, 23, 28, 15, 17, 10, 15, 8, 3, 2, 0, 37, 36, 20, 14, 23, 20, 17, 4, 10, 8, 7, 3, 0), # 10
(26, 41, 31, 28, 30, 17, 19, 11, 17, 8, 3, 4, 0, 40, 38, 21, 16, 25, 22, 20, 4, 11, 10, 7, 4, 0), # 11
(32, 45, 32, 32, 32, 18, 22, 11, 19, 9, 4, 4, 0, 43, 44, 24, 20, 26, 23, 20, 5, 12, 11, 8, 5, 0), # 12
(37, 49, 32, 35, 35, 19, 23, 12, 20, 10, 5, 4, 0, 48, 44, 26, 21, 27, 27, 21, 6, 12, 13, 8, 5, 0), # 13
(41, 51, 36, 37, 37, 20, 23, 13, 22, 12, 6, 5, 0, 52, 49, 29, 23, 29, 30, 22, 7, 12, 15, 8, 6, 0), # 14
(45, 56, 38, 38, 39, 21, 26, 14, 23, 12, 7, 6, 0, 55, 53, 30, 23, 32, 34, 23, 7, 15, 16, 8, 7, 0), # 15
(48, 57, 42, 43, 41, 21, 27, 15, 26, 12, 9, 6, 0, 58, 54, 31, 23, 38, 35, 24, 7, 16, 17, 8, 7, 0), # 16
(52, 64, 44, 43, 45, 24, 30, 17, 27, 13, 9, 6, 0, 60, 56, 32, 26, 41, 36, 27, 9, 17, 18, 9, 7, 0), # 17
(58, 65, 50, 45, 50, 26, 31, 20, 28, 14, 10, 7, 0, 62, 62, 35, 26, 43, 39, 29, 11, 18, 20, 9, 7, 0), # 18
(62, 65, 52, 48, 51, 28, 32, 21, 32, 15, 13, 7, 0, 66, 65, 37, 29, 46, 40, 30, 11, 20, 20, 9, 8, 0), # 19
(66, 72, 56, 49, 55, 30, 33, 22, 33, 16, 13, 8, 0, 72, 70, 41, 30, 48, 41, 32, 13, 22, 21, 9, 9, 0), # 20
(72, 79, 60, 53, 58, 31, 35, 24, 33, 16, 13, 8, 0, 76, 76, 42, 32, 51, 42, 33, 15, 22, 21, 9, 9, 0), # 21
(76, 86, 62, 55, 61, 33, 36, 25, 33, 19, 13, 9, 0, 79, 79, 47, 34, 56, 46, 33, 16, 25, 22, 11, 9, 0), # 22
(78, 91, 69, 58, 63, 36, 38, 25, 34, 19, 13, 9, 0, 81, 80, 49, 34, 60, 46, 33, 16, 26, 22, 11, 9, 0), # 23
(83, 95, 75, 62, 66, 37, 40, 26, 36, 20, 14, 9, 0, 87, 83, 52, 35, 61, 48, 35, 17, 30, 22, 11, 10, 0), # 24
(87, 95, 78, 68, 69, 40, 41, 30, 38, 20, 15, 9, 0, 90, 86, 56, 39, 63, 52, 36, 18, 31, 25, 12, 10, 0), # 25
(92, 99, 84, 71, 71, 41, 41, 31, 41, 21, 16, 9, 0, 92, 89, 57, 42, 65, 52, 37, 19, 31, 26, 13, 10, 0), # 26
(96, 102, 87, 73, 74, 45, 43, 33, 42, 22, 17, 9, 0, 95, 93, 59, 44, 66, 57, 39, 21, 33, 28, 14, 10, 0), # 27
(101, 108, 92, 75, 76, 48, 43, 35, 46, 23, 17, 9, 0, 97, 96, 63, 47, 68, 61, 42, 21, 33, 29, 17, 10, 0), # 28
(104, 111, 94, 77, 77, 49, 44, 39, 47, 24, 17, 10, 0, 98, 100, 64, 48, 72, 64, 42, 22, 36, 32, 18, 11, 0), # 29
(111, 116, 96, 82, 82, 49, 46, 44, 48, 25, 17, 10, 0, 103, 103, 66, 49, 74, 65, 42, 23, 36, 33, 19, 12, 0), # 30
(116, 118, 98, 89, 85, 50, 47, 44, 49, 26, 17, 10, 0, 108, 107, 69, 49, 80, 70, 43, 25, 38, 35, 19, 12, 0), # 31
(119, 127, 101, 94, 88, 54, 49, 46, 50, 27, 17, 10, 0, 110, 111, 72, 50, 83, 72, 45, 26, 38, 36, 19, 12, 0), # 32
(126, 129, 105, 101, 91, 57, 50, 46, 50, 27, 17, 11, 0, 116, 117, 75, 53, 85, 74, 46, 28, 41, 36, 20, 12, 0), # 33
(127, 136, 107, 104, 94, 57, 53, 48, 51, 27, 17, 12, 0, 124, 120, 75, 55, 88, 76, 47, 28, 42, 38, 20, 12, 0), # 34
(132, 142, 110, 108, 96, 58, 54, 48, 52, 27, 17, 13, 0, 124, 121, 76, 57, 95, 76, 50, 32, 43, 39, 20, 12, 0), # 35
(136, 146, 115, 114, 96, 61, 57, 49, 52, 27, 17, 13, 0, 131, 125, 81, 65, 102, 78, 52, 33, 44, 42, 20, 12, 0), # 36
(137, 150, 118, 116, 97, 63, 57, 49, 52, 27, 17, 13, 0, 134, 131, 85, 69, 104, 82, 56, 34, 46, 44, 21, 13, 0), # 37
(141, 153, 119, 123, 99, 63, 60, 50, 57, 28, 19, 13, 0, 142, 134, 88, 69, 108, 83, 60, 34, 50, 44, 21, 13, 0), # 38
(143, 157, 120, 126, 99, 63, 61, 51, 57, 29, 22, 13, 0, 146, 138, 91, 72, 112, 87, 62, 35, 51, 44, 21, 15, 0), # 39
(145, 161, 126, 129, 102, 64, 61, 51, 59, 29, 22, 14, 0, 150, 139, 93, 76, 114, 91, 63, 36, 53, 46, 21, 15, 0), # 40
(151, 168, 130, 131, 110, 64, 63, 54, 59, 31, 22, 15, 0, 151, 146, 94, 78, 117, 91, 64, 37, 54, 47, 22, 16, 0), # 41
(153, 170, 135, 134, 116, 64, 66, 56, 59, 32, 24, 16, 0, 156, 148, 96, 81, 120, 92, 64, 39, 55, 47, 24, 16, 0), # 42
(163, 172, 139, 136, 121, 65, 68, 60, 61, 32, 24, 16, 0, 160, 149, 99, 83, 124, 93, 65, 40, 56, 49, 25, 17, 0), # 43
(169, 180, 140, 137, 122, 66, 68, 61, 61, 33, 25, 16, 0, 164, 152, 102, 85, 126, 95, 65, 41, 57, 50, 26, 17, 0), # 44
(169, 183, 142, 138, 124, 67, 71, 63, 64, 34, 25, 16, 0, 164, 159, 102, 86, 127, 100, 66, 41, 58, 51, 27, 17, 0), # 45
(171, 186, 150, 140, 129, 68, 72, 67, 65, 35, 26, 16, 0, 169, 162, 106, 87, 130, 102, 67, 42, 60, 51, 28, 17, 0), # 46
(174, 191, 153, 144, 132, 72, 75, 67, 66, 35, 26, 16, 0, 172, 168, 109, 90, 133, 103, 68, 43, 63, 52, 28, 18, 0), # 47
(175, 196, 157, 149, 134, 72, 76, 69, 68, 37, 26, 17, 0, 176, 172, 111, 92, 137, 105, 69, 43, 64, 53, 30, 18, 0), # 48
(182, 200, 159, 152, 136, 73, 76, 71, 69, 37, 26, 17, 0, 177, 174, 113, 93, 142, 105, 70, 43, 66, 54, 30, 19, 0), # 49
(185, 202, 161, 153, 137, 75, 78, 73, 72, 38, 26, 18, 0, 183, 177, 116, 95, 144, 109, 72, 43, 67, 56, 30, 19, 0), # 50
(192, 206, 163, 158, 141, 76, 79, 73, 75, 39, 26, 18, 0, 187, 182, 119, 98, 146, 111, 74, 44, 68, 60, 31, 19, 0), # 51
(197, 208, 172, 161, 147, 76, 79, 75, 78, 40, 27, 18, 0, 191, 185, 121, 100, 149, 114, 74, 45, 71, 62, 33, 20, 0), # 52
(200, 210, 174, 164, 152, 77, 82, 75, 80, 40, 28, 19, 0, 197, 187, 126, 103, 152, 116, 77, 45, 71, 63, 33, 20, 0), # 53
(207, 216, 177, 168, 155, 77, 83, 76, 82, 42, 28, 19, 0, 201, 189, 126, 104, 153, 116, 79, 45, 73, 66, 33, 21, 0), # 54
(211, 220, 178, 172, 158, 78, 85, 77, 84, 44, 28, 19, 0, 204, 191, 128, 105, 156, 118, 81, 46, 75, 66, 33, 21, 0), # 55
(216, 223, 181, 173, 161, 79, 85, 79, 85, 45, 28, 19, 0, 206, 194, 129, 108, 162, 119, 82, 48, 76, 68, 35, 21, 0), # 56
(218, 227, 187, 178, 164, 83, 88, 79, 86, 45, 28, 19, 0, 209, 196, 135, 111, 166, 120, 85, 49, 76, 68, 35, 21, 0), # 57
(221, 231, 190, 183, 165, 86, 89, 79, 91, 45, 30, 20, 0, 215, 201, 135, 111, 171, 124, 86, 52, 78, 68, 36, 21, 0), # 58
(226, 237, 194, 186, 170, 86, 91, 81, 93, 46, 32, 20, 0, 219, 204, 137, 113, 175, 128, 88, 55, 83, 68, 36, 23, 0), # 59
(226, 242, 197, 187, 172, 89, 93, 83, 94, 47, 32, 20, 0, 223, 207, 141, 119, 177, 131, 92, 55, 87, 72, 36, 23, 0), # 60
(228, 246, 197, 189, 179, 89, 95, 84, 95, 47, 34, 20, 0, 231, 211, 144, 120, 181, 132, 93, 55, 89, 73, 38, 24, 0), # 61
(233, 251, 203, 191, 182, 90, 96, 86, 97, 49, 34, 20, 0, 232, 213, 156, 122, 184, 133, 93, 55, 92, 74, 39, 25, 0), # 62
(238, 252, 206, 195, 184, 93, 99, 87, 98, 51, 35, 20, 0, 236, 216, 158, 123, 188, 134, 95, 56, 94, 74, 40, 25, 0), # 63
(239, 255, 214, 201, 188, 95, 99, 89, 98, 53, 35, 21, 0, 242, 219, 160, 125, 188, 135, 96, 58, 94, 74, 40, 25, 0), # 64
(243, 262, 219, 205, 191, 96, 99, 90, 101, 53, 35, 21, 0, 246, 224, 161, 127, 190, 137, 96, 58, 96, 74, 40, 25, 0), # 65
(246, 265, 220, 210, 196, 98, 99, 90, 104, 54, 35, 22, 0, 249, 228, 163, 130, 196, 139, 98, 58, 98, 75, 40, 25, 0), # 66
(252, 267, 221, 213, 201, 99, 100, 93, 106, 55, 35, 22, 0, 253, 230, 165, 136, 200, 141, 102, 60, 99, 78, 40, 25, 0), # 67
(256, 270, 226, 216, 202, 100, 103, 95, 108, 55, 35, 22, 0, 254, 234, 168, 137, 202, 141, 103, 61, 101, 82, 42, 27, 0), # 68
(259, 272, 232, 219, 202, 102, 103, 97, 109, 56, 35, 22, 0, 260, 238, 173, 139, 204, 142, 104, 61, 101, 83, 42, 27, 0), # 69
(264, 276, 238, 221, 203, 102, 103, 98, 110, 57, 36, 22, 0, 267, 242, 176, 141, 209, 142, 106, 64, 101, 87, 42, 28, 0), # 70
(268, 279, 241, 225, 203, 105, 104, 98, 110, 60, 37, 22, 0, 271, 247, 177, 145, 212, 144, 112, 65, 102, 88, 43, 29, 0), # 71
(270, 282, 245, 229, 206, 105, 105, 100, 112, 61, 37, 22, 0, 274, 251, 182, 148, 214, 146, 116, 65, 105, 91, 45, 29, 0), # 72
(272, 286, 247, 232, 210, 106, 111, 100, 116, 61, 37, 22, 0, 279, 251, 188, 149, 219, 148, 117, 66, 107, 92, 47, 30, 0), # 73
(275, 288, 249, 234, 212, 108, 112, 102, 118, 61, 38, 22, 0, 281, 253, 189, 152, 220, 148, 120, 68, 110, 93, 48, 31, 0), # 74
(276, 294, 253, 239, 218, 109, 113, 102, 119, 62, 39, 22, 0, 285, 255, 193, 154, 221, 149, 120, 70, 113, 93, 50, 31, 0), # 75
(279, 298, 258, 242, 221, 109, 114, 103, 123, 63, 40, 22, 0, 292, 258, 197, 156, 227, 152, 121, 71, 114, 96, 51, 31, 0), # 76
(281, 300, 261, 250, 222, 111, 119, 106, 124, 63, 40, 22, 0, 297, 264, 201, 158, 229, 153, 121, 71, 116, 98, 52, 33, 0), # 77
(285, 305, 265, 255, 227, 112, 120, 106, 126, 64, 40, 23, 0, 301, 269, 203, 160, 230, 156, 124, 71, 117, 99, 52, 34, 0), # 78
(295, 309, 269, 258, 229, 115, 122, 108, 126, 64, 40, 24, 0, 305, 274, 208, 165, 235, 158, 128, 72, 118, 101, 52, 34, 0), # 79
(302, 311, 272, 259, 230, 115, 123, 111, 127, 65, 41, 24, 0, 307, 275, 211, 169, 239, 159, 128, 72, 118, 102, 54, 34, 0), # 80
(308, 311, 276, 262, 233, 115, 123, 114, 131, 65, 41, 25, 0, 311, 278, 214, 169, 244, 161, 133, 73, 121, 102, 54, 35, 0), # 81
(312, 314, 281, 264, 235, 117, 124, 115, 132, 67, 43, 26, 0, 315, 281, 216, 171, 250, 162, 134, 74, 122, 102, 54, 35, 0), # 82
(316, 319, 284, 268, 236, 120, 124, 115, 132, 68, 43, 26, 0, 317, 286, 218, 171, 256, 162, 134, 76, 122, 105, 55, 35, 0), # 83
(316, 324, 289, 273, 238, 120, 125, 117, 133, 70, 44, 26, 0, 318, 290, 220, 173, 259, 166, 134, 77, 122, 106, 55, 35, 0), # 84
(323, 327, 292, 276, 241, 121, 127, 117, 133, 72, 44, 26, 0, 321, 295, 224, 175, 263, 169, 136, 77, 122, 106, 56, 35, 0), # 85
(326, 329, 294, 279, 243, 124, 127, 119, 135, 74, 45, 26, 0, 328, 300, 225, 175, 264, 172, 138, 78, 126, 108, 58, 35, 0), # 86
(329, 331, 296, 283, 248, 124, 129, 121, 138, 74, 46, 27, 0, 332, 302, 226, 177, 268, 174, 141, 79, 126, 109, 60, 35, 0), # 87
(331, 333, 299, 287, 254, 127, 130, 121, 139, 76, 46, 28, 0, 338, 312, 228, 177, 269, 177, 145, 79, 128, 109, 60, 36, 0), # 88
(335, 337, 304, 290, 256, 129, 131, 122, 139, 77, 47, 28, 0, 343, 315, 228, 178, 272, 179, 148, 79, 129, 111, 61, 36, 0), # 89
(338, 345, 308, 293, 260, 132, 131, 123, 141, 77, 48, 28, 0, 348, 316, 230, 179, 273, 181, 152, 80, 130, 111, 61, 37, 0), # 90
(343, 345, 312, 300, 262, 135, 131, 123, 142, 79, 48, 29, 0, 353, 318, 231, 182, 274, 181, 153, 81, 131, 113, 61, 37, 0), # 91
(346, 350, 314, 303, 267, 139, 133, 123, 143, 79, 48, 29, 0, 359, 322, 233, 183, 277, 183, 155, 81, 133, 115, 61, 38, 0), # 92
(351, 354, 316, 305, 268, 142, 135, 123, 145, 79, 49, 29, 0, 361, 325, 233, 185, 278, 186, 157, 82, 134, 118, 61, 38, 0), # 93
(354, 359, 320, 307, 270, 143, 135, 125, 145, 79, 51, 29, 0, 367, 328, 236, 188, 282, 186, 159, 82, 134, 121, 61, 38, 0), # 94
(362, 361, 327, 307, 273, 143, 136, 126, 150, 79, 51, 29, 0, 369, 330, 238, 189, 287, 188, 160, 82, 135, 122, 61, 38, 0), # 95
(365, 361, 327, 310, 279, 147, 138, 128, 151, 79, 51, 29, 0, 371, 331, 240, 191, 291, 190, 160, 83, 135, 122, 62, 38, 0), # 96
(368, 364, 327, 312, 283, 147, 139, 128, 152, 81, 52, 30, 0, 376, 336, 242, 194, 292, 192, 162, 84, 136, 123, 62, 38, 0), # 97
(375, 366, 330, 314, 287, 149, 139, 129, 158, 81, 52, 31, 0, 380, 339, 243, 194, 297, 193, 164, 85, 138, 123, 62, 38, 0), # 98
(380, 368, 336, 322, 291, 149, 139, 129, 158, 82, 54, 31, 0, 385, 342, 245, 194, 299, 195, 164, 87, 138, 126, 64, 38, 0), # 99
(388, 371, 340, 324, 295, 151, 142, 131, 163, 83, 54, 31, 0, 388, 346, 245, 196, 302, 196, 166, 87, 140, 130, 64, 38, 0), # 100
(395, 375, 342, 329, 298, 153, 144, 133, 164, 84, 55, 31, 0, 390, 353, 247, 198, 303, 197, 166, 87, 141, 133, 64, 38, 0), # 101
(401, 382, 346, 332, 300, 154, 146, 135, 165, 85, 55, 31, 0, 396, 358, 250, 201, 308, 197, 167, 87, 143, 133, 64, 38, 0), # 102
(404, 385, 348, 337, 301, 155, 149, 135, 167, 85, 55, 31, 0, 401, 360, 251, 203, 313, 199, 167, 89, 144, 136, 65, 38, 0), # 103
(405, 388, 351, 340, 304, 155, 150, 136, 169, 87, 55, 31, 0, 410, 363, 255, 205, 315, 200, 169, 91, 144, 137, 65, 38, 0), # 104
(408, 391, 356, 340, 307, 155, 150, 138, 170, 87, 56, 31, 0, 411, 366, 258, 209, 317, 203, 170, 93, 145, 138, 65, 38, 0), # 105
(413, 397, 359, 344, 309, 158, 151, 138, 171, 87, 58, 31, 0, 413, 371, 260, 209, 318, 205, 172, 95, 147, 138, 66, 38, 0), # 106
(414, 401, 360, 348, 311, 160, 151, 142, 171, 89, 59, 32, 0, 421, 378, 261, 211, 326, 207, 172, 95, 148, 140, 66, 38, 0), # 107
(415, 404, 360, 354, 313, 163, 154, 143, 171, 89, 59, 32, 0, 426, 384, 261, 212, 328, 209, 172, 97, 149, 141, 67, 38, 0), # 108
(420, 407, 362, 356, 313, 163, 156, 145, 173, 89, 60, 34, 0, 431, 386, 266, 214, 335, 209, 174, 97, 151, 143, 67, 38, 0), # 109
(423, 409, 366, 359, 315, 164, 156, 146, 176, 89, 61, 34, 0, 434, 387, 267, 214, 338, 209, 175, 98, 151, 143, 69, 38, 0), # 110
(425, 412, 370, 362, 319, 165, 158, 146, 178, 89, 62, 35, 0, 437, 389, 271, 215, 341, 209, 175, 99, 151, 145, 71, 38, 0), # 111
(428, 414, 375, 369, 319, 167, 159, 146, 179, 90, 64, 35, 0, 443, 391, 271, 217, 346, 210, 176, 101, 152, 145, 71, 39, 0), # 112
(433, 417, 378, 376, 323, 168, 160, 146, 181, 90, 64, 35, 0, 445, 394, 273, 218, 348, 211, 178, 102, 152, 147, 71, 39, 0), # 113
(438, 419, 382, 379, 324, 169, 162, 148, 182, 90, 64, 35, 0, 449, 399, 274, 220, 350, 213, 179, 102, 155, 149, 71, 40, 0), # 114
(444, 421, 384, 383, 326, 170, 162, 149, 183, 91, 65, 35, 0, 453, 410, 274, 223, 353, 213, 181, 103, 157, 149, 72, 40, 0), # 115
(447, 424, 387, 385, 329, 172, 162, 153, 184, 92, 65, 35, 0, 454, 411, 274, 227, 356, 216, 182, 106, 157, 149, 72, 41, 0), # 116
(454, 428, 391, 389, 333, 173, 164, 153, 187, 93, 66, 35, 0, 455, 413, 275, 228, 359, 217, 182, 107, 160, 149, 72, 41, 0), # 117
(459, 431, 392, 390, 333, 174, 165, 153, 187, 95, 66, 35, 0, 456, 414, 276, 232, 362, 218, 184, 110, 160, 150, 73, 41, 0), # 118
(460, 433, 399, 393, 335, 176, 165, 153, 189, 95, 66, 35, 0, 463, 416, 280, 233, 364, 219, 185, 112, 161, 151, 73, 41, 0), # 119
(465, 437, 400, 394, 339, 178, 167, 153, 189, 95, 66, 36, 0, 465, 419, 287, 234, 366, 219, 187, 113, 163, 152, 73, 41, 0), # 120
(467, 439, 403, 395, 342, 178, 168, 153, 189, 96, 68, 36, 0, 469, 422, 288, 236, 369, 219, 189, 113, 165, 153, 75, 42, 0), # 121
(471, 440, 405, 398, 346, 178, 169, 153, 192, 96, 69, 36, 0, 475, 423, 290, 238, 373, 219, 192, 115, 166, 153, 76, 42, 0), # 122
(474, 445, 411, 401, 347, 179, 170, 154, 192, 97, 71, 36, 0, 480, 423, 293, 240, 375, 220, 193, 116, 167, 153, 76, 42, 0), # 123
(475, 449, 417, 402, 349, 181, 170, 154, 192, 97, 71, 36, 0, 485, 423, 296, 241, 380, 220, 198, 116, 170, 156, 76, 42, 0), # 124
(476, 449, 421, 405, 352, 181, 173, 154, 194, 97, 71, 36, 0, 489, 425, 298, 246, 387, 221, 198, 116, 172, 157, 79, 42, 0), # 125
(484, 450, 422, 414, 355, 183, 173, 156, 196, 98, 71, 36, 0, 491, 429, 303, 247, 390, 224, 199, 120, 175, 157, 79, 42, 0), # 126
(489, 452, 423, 417, 359, 184, 175, 157, 196, 98, 71, 37, 0, 496, 435, 305, 248, 393, 225, 199, 121, 175, 159, 80, 42, 0), # 127
(498, 455, 425, 417, 360, 188, 175, 159, 197, 98, 73, 37, 0, 499, 438, 309, 254, 393, 225, 199, 121, 176, 161, 82, 42, 0), # 128
(502, 456, 428, 421, 363, 190, 175, 159, 198, 98, 74, 38, 0, 503, 441, 313, 255, 396, 225, 201, 123, 177, 165, 83, 42, 0), # 129
(506, 461, 431, 422, 366, 190, 176, 159, 200, 98, 74, 38, 0, 505, 443, 315, 255, 399, 225, 202, 123, 179, 165, 83, 42, 0), # 130
(508, 463, 435, 428, 366, 193, 179, 159, 200, 98, 74, 40, 0, 508, 445, 317, 255, 405, 225, 202, 123, 180, 165, 84, 42, 0), # 131
(513, 465, 438, 435, 367, 194, 182, 159, 200, 98, 75, 41, 0, 513, 446, 318, 256, 408, 226, 204, 123, 180, 165, 84, 42, 0), # 132
(515, 468, 439, 438, 372, 196, 183, 159, 203, 99, 75, 41, 0, 519, 447, 321, 258, 412, 228, 205, 125, 180, 166, 84, 42, 0), # 133
(517, 469, 445, 440, 378, 197, 184, 161, 205, 100, 76, 41, 0, 523, 448, 325, 258, 416, 230, 206, 129, 181, 169, 84, 44, 0), # 134
(519, 469, 448, 443, 383, 200, 184, 161, 208, 102, 76, 41, 0, 525, 449, 329, 259, 419, 231, 206, 133, 181, 171, 85, 44, 0), # 135
(522, 472, 451, 447, 383, 201, 186, 161, 210, 103, 76, 42, 0, 527, 453, 330, 264, 422, 231, 208, 134, 184, 171, 86, 44, 0), # 136
(526, 475, 453, 449, 385, 201, 186, 161, 211, 103, 76, 42, 0, 533, 455, 334, 266, 428, 233, 208, 135, 184, 171, 88, 45, 0), # 137
(526, 476, 458, 452, 389, 204, 187, 162, 212, 103, 76, 43, 0, 536, 465, 337, 266, 432, 233, 209, 135, 186, 172, 89, 46, 0), # 138
(533, 478, 462, 455, 389, 205, 188, 162, 213, 103, 76, 43, 0, 539, 465, 340, 267, 435, 236, 211, 137, 186, 175, 89, 46, 0), # 139
(536, 482, 466, 459, 390, 205, 188, 164, 214, 104, 79, 44, 0, 544, 468, 342, 267, 438, 237, 214, 137, 189, 176, 90, 46, 0), # 140
(541, 484, 467, 461, 391, 205, 189, 164, 215, 105, 79, 48, 0, 546, 472, 346, 268, 439, 238, 215, 138, 190, 179, 90, 46, 0), # 141
(545, 486, 468, 466, 391, 207, 190, 166, 217, 106, 79, 48, 0, 548, 475, 347, 269, 441, 239, 215, 140, 191, 179, 90, 46, 0), # 142
(548, 489, 470, 471, 393, 207, 190, 166, 217, 107, 80, 48, 0, 553, 475, 348, 271, 444, 241, 216, 141, 192, 179, 90, 46, 0), # 143
(550, 490, 474, 472, 395, 210, 190, 166, 219, 107, 80, 48, 0, 556, 480, 350, 271, 448, 244, 217, 143, 193, 180, 90, 47, 0), # 144
(553, 496, 476, 473, 396, 210, 191, 168, 220, 107, 80, 48, 0, 561, 485, 351, 273, 451, 245, 217, 143, 194, 180, 90, 47, 0), # 145
(556, 497, 481, 477, 399, 211, 192, 168, 222, 107, 80, 48, 0, 565, 489, 352, 274, 457, 245, 217, 144, 196, 180, 90, 47, 0), # 146
(559, 498, 486, 481, 400, 211, 193, 170, 223, 108, 80, 48, 0, 566, 492, 352, 275, 460, 245, 218, 145, 196, 180, 90, 47, 0), # 147
(562, 503, 488, 482, 402, 212, 194, 172, 226, 108, 81, 48, 0, 570, 493, 353, 276, 469, 245, 222, 145, 196, 180, 91, 47, 0), # 148
(564, 508, 491, 484, 404, 214, 194, 174, 227, 108, 82, 48, 0, 575, 496, 355, 277, 474, 246, 223, 146, 196, 180, 91, 47, 0), # 149
(569, 508, 496, 489, 405, 217, 194, 175, 227, 110, 83, 48, 0, 578, 498, 357, 278, 477, 246, 224, 147, 196, 182, 91, 47, 0), # 150
(570, 511, 496, 493, 408, 218, 197, 175, 228, 110, 83, 48, 0, 582, 505, 359, 279, 477, 247, 224, 147, 198, 182, 92, 47, 0), # 151
(571, 513, 497, 494, 411, 218, 198, 178, 228, 110, 83, 48, 0, 586, 509, 364, 281, 478, 247, 226, 147, 198, 183, 92, 47, 0), # 152
(572, 517, 502, 496, 414, 219, 199, 178, 230, 110, 84, 48, 0, 593, 509, 365, 282, 481, 251, 228, 149, 199, 184, 93, 47, 0), # 153
(574, 523, 503, 500, 415, 221, 202, 178, 230, 110, 85, 48, 0, 596, 509, 369, 283, 484, 253, 231, 150, 202, 184, 93, 48, 0), # 154
(579, 525, 506, 502, 417, 221, 205, 178, 232, 111, 85, 48, 0, 600, 509, 372, 284, 484, 253, 232, 150, 206, 186, 94, 48, 0), # 155
(581, 526, 506, 506, 418, 221, 205, 179, 233, 111, 87, 49, 0, 602, 510, 373, 285, 485, 253, 235, 150, 209, 188, 94, 48, 0), # 156
(586, 530, 512, 507, 419, 222, 206, 179, 235, 112, 87, 50, 0, 606, 513, 373, 286, 488, 254, 235, 150, 209, 188, 94, 48, 0), # 157
(590, 534, 516, 509, 420, 222, 207, 179, 236, 112, 87, 50, 0, 610, 514, 374, 286, 492, 257, 236, 151, 210, 188, 95, 49, 0), # 158
(594, 539, 520, 517, 425, 225, 209, 180, 240, 113, 87, 51, 0, 611, 517, 375, 288, 496, 258, 237, 152, 212, 188, 96, 49, 0), # 159
(598, 540, 522, 518, 429, 228, 210, 181, 240, 113, 87, 51, 0, 614, 521, 377, 289, 498, 259, 239, 153, 214, 188, 96, 49, 0), # 160
(600, 543, 525, 518, 429, 232, 211, 186, 241, 113, 87, 51, 0, 620, 523, 379, 290, 502, 259, 240, 153, 217, 188, 96, 49, 0), # 161
(602, 544, 530, 521, 433, 236, 213, 186, 242, 114, 87, 51, 0, 622, 526, 381, 291, 505, 259, 241, 154, 220, 188, 97, 49, 0), # 162
(604, 548, 533, 527, 436, 238, 213, 187, 243, 114, 87, 51, 0, 626, 527, 381, 294, 507, 259, 242, 155, 220, 188, 97, 49, 0), # 163
(607, 551, 535, 533, 438, 238, 213, 187, 244, 116, 88, 52, 0, 631, 529, 385, 297, 511, 260, 244, 156, 220, 189, 97, 49, 0), # 164
(610, 552, 539, 534, 438, 238, 218, 187, 246, 116, 89, 52, 0, 633, 531, 387, 299, 512, 261, 244, 156, 221, 189, 98, 49, 0), # 165
(612, 552, 541, 536, 440, 243, 220, 188, 247, 117, 89, 52, 0, 636, 535, 390, 303, 514, 261, 245, 156, 222, 190, 98, 50, 0), # 166
(615, 552, 546, 538, 442, 243, 220, 188, 248, 117, 89, 52, 0, 637, 540, 392, 303, 520, 261, 246, 156, 222, 191, 99, 50, 0), # 167
(618, 554, 548, 542, 443, 244, 220, 189, 251, 117, 89, 52, 0, 641, 541, 392, 304, 520, 263, 246, 158, 223, 191, 99, 50, 0), # 168
(623, 555, 552, 543, 445, 246, 221, 191, 252, 118, 89, 53, 0, 644, 544, 392, 305, 520, 264, 248, 160, 226, 193, 100, 50, 0), # 169
(627, 555, 553, 547, 445, 247, 222, 191, 252, 118, 89, 53, 0, 646, 546, 395, 305, 524, 264, 250, 161, 228, 195, 100, 50, 0), # 170
(628, 557, 556, 548, 447, 249, 222, 191, 253, 118, 89, 53, 0, 647, 547, 395, 306, 525, 265, 250, 162, 229, 195, 101, 52, 0), # 171
(629, 558, 556, 551, 447, 249, 222, 192, 253, 118, 90, 53, 0, 649, 548, 397, 307, 529, 267, 250, 163, 229, 195, 101, 52, 0), # 172
(629, 560, 557, 552, 449, 250, 222, 193, 253, 118, 91, 53, 0, 651, 551, 398, 307, 531, 267, 250, 163, 230, 195, 102, 52, 0), # 173
(631, 560, 558, 554, 454, 252, 222, 193, 254, 118, 91, 54, 0, 655, 552, 400, 310, 532, 268, 252, 163, 230, 196, 102, 53, 0), # 174
(631, 563, 558, 555, 457, 256, 222, 193, 255, 118, 91, 54, 0, 655, 554, 400, 310, 533, 269, 253, 163, 233, 196, 102, 53, 0), # 175
(634, 564, 560, 557, 458, 257, 222, 193, 258, 118, 91, 54, 0, 655, 554, 400, 311, 534, 269, 253, 164, 233, 196, 103, 53, 0), # 176
(636, 567, 561, 557, 458, 258, 223, 193, 259, 119, 91, 54, 0, 657, 556, 402, 312, 534, 269, 253, 164, 233, 198, 104, 53, 0), # 177
(639, 570, 565, 558, 458, 259, 223, 193, 259, 119, 91, 54, 0, 659, 559, 404, 315, 537, 270, 253, 164, 234, 198, 104, 53, 0), # 178
(639, 570, 565, 558, 458, 259, 223, 193, 259, 119, 91, 54, 0, 659, 559, 404, 315, 537, 270, 253, 164, 234, 198, 104, 53, 0), # 179
)
passenger_arriving_rate = (
(2.0083462313487073, 2.025939138554161, 1.7370944098708356, 1.86440033215903, 1.481249495362563, 0.7323528174753792, 0.8292071230747831, 0.7755293621632372, 0.8120132082890676, 0.3958015015522371, 0.2803510109257069, 0.16326383594353913, 0.0, 2.0335520850313453, 1.7959021953789303, 1.4017550546285344, 1.187404504656711, 1.6240264165781353, 1.085741107028532, 0.8292071230747831, 0.5231091553395566, 0.7406247476812815, 0.6214667773863434, 0.34741888197416715, 0.18417628532310557, 0.0), # 0
(2.1417308608079897, 2.159688921186411, 1.851782197110051, 1.987543275982985, 1.5793307566597963, 0.780729502943263, 0.8838937882527388, 0.8265856204842847, 0.8656327101777213, 0.4218980692277151, 0.29887307573040883, 0.17404055536047852, 0.0, 2.1679166589759418, 1.9144461089652633, 1.4943653786520439, 1.265694207683145, 1.7312654203554425, 1.1572198686779986, 0.8838937882527388, 0.5576639306737593, 0.7896653783298981, 0.6625144253276618, 0.3703564394220102, 0.19633535647149197, 0.0), # 1
(2.2746892035918926, 2.292907895993359, 1.9660140951824712, 2.1101963113994384, 1.6770567929999318, 0.8289131933862371, 0.9383637015921409, 0.8774375413174034, 0.9190382409170065, 0.4478913775020547, 0.31732154325175427, 0.18477442173510186, 0.0, 2.301745931283876, 2.03251863908612, 1.586607716258771, 1.3436741325061639, 1.838076481834013, 1.2284125578443648, 0.9383637015921409, 0.5920808524187409, 0.8385283964999659, 0.7033987704664796, 0.3932028190364943, 0.20844617236303267, 0.0), # 2
(2.406703117258625, 2.4250675173766973, 2.0793369564399518, 2.231872922259622, 1.774043113600507, 0.8767128119365264, 0.9924007283295911, 0.9278835262490847, 0.9720180214561457, 0.4736782698426182, 0.3356232274551589, 0.1954228561643487, 0.0, 2.4345091225016904, 2.1496514178078354, 1.6781161372757945, 1.4210348095278542, 1.9440360429122914, 1.2990369367487185, 0.9924007283295911, 0.6262234370975188, 0.8870215568002535, 0.7439576407532075, 0.41586739128799033, 0.2204606833978816, 0.0), # 3
(2.537254459366393, 2.555639239738117, 2.1912976332343455, 2.352086592414771, 1.8699052276790646, 0.9239372817263559, 1.0457887337016918, 0.9777219768658193, 1.024360272744361, 0.499155589716768, 0.3537049423060384, 0.20594327974515883, 0.0, 2.565675453175927, 2.2653760771967466, 1.7685247115301916, 1.4974667691503036, 2.048720545488722, 1.368810767612147, 1.0457887337016918, 0.6599552012331114, 0.9349526138395323, 0.7840288641382572, 0.4382595266468692, 0.23233083997619253, 0.0), # 4
(2.6658250874734044, 2.6840945174793154, 2.3014429779175063, 2.470350805716118, 1.9642586444531411, 0.9703955258879502, 1.0983115829450447, 1.0267512947540989, 1.0758532157308744, 0.5242201805918665, 0.37149350176980883, 0.21629311357447162, 0.0, 2.694714143853131, 2.3792242493191873, 1.8574675088490442, 1.572660541775599, 2.151706431461749, 1.4374518126557383, 1.0983115829450447, 0.6931396613485358, 0.9821293222265706, 0.8234502685720396, 0.46028859558350127, 0.24400859249811963, 0.0), # 5
(2.7918968591378666, 2.8099048050019837, 2.4093198428412888, 2.586179046014896, 2.0567188731402783, 1.015896467553535, 1.1497531412962525, 1.0747698815004147, 1.1262850713649086, 0.548768885935276, 0.3889157198118855, 0.22642977874922698, 0.0, 2.821094415079843, 2.4907275662414965, 1.9445785990594275, 1.6463066578058276, 2.2525701427298173, 1.5046778341005806, 1.1497531412962525, 0.7256403339668107, 1.0283594365701392, 0.8620596820049655, 0.4818639685682578, 0.25544589136381673, 0.0), # 6
(2.9149516319179876, 2.932541556707815, 2.514475080357545, 2.699084797162339, 2.146901422958014, 1.0602490298553349, 1.199897273991917, 1.1215761386912588, 1.175444060595686, 0.5726985492143588, 0.40589841039768465, 0.23631069636636431, 0.0, 2.944285487402608, 2.599417660030007, 2.029492051988423, 1.718095647643076, 2.350888121191372, 1.5702065941677623, 1.199897273991917, 0.7573207356109535, 1.073450711479007, 0.8996949323874465, 0.5028950160715091, 0.26659468697343774, 0.0), # 7
(3.034471263371974, 3.051476226998503, 2.616455542818132, 2.8085815430096783, 2.2344218031238894, 1.1032621359255743, 1.2485278462686396, 1.166968467913121, 1.2231184043724275, 0.5959060138964776, 0.42236838749262146, 0.24589328752282347, 0.0, 3.063756581367967, 2.7048261627510577, 2.111841937463107, 1.7877180416894323, 2.446236808744855, 1.6337558550783693, 1.2485278462686396, 0.7880443828039817, 1.1172109015619447, 0.936193847669893, 0.5232911085636265, 0.2774069297271367, 0.0), # 8
(3.149937611058034, 3.1661802702757416, 2.7148080825749017, 2.9141827674081506, 2.3188955228554424, 1.1447447088964797, 1.295428723363024, 1.210745270752494, 1.2690963236443564, 0.6182881234489943, 0.4382524650621119, 0.25513497331554386, 0.0, 3.178976917522465, 2.8064847064709815, 2.1912623253105594, 1.8548643703469825, 2.538192647288713, 1.6950433790534916, 1.295428723363024, 0.817674792068914, 1.1594477614277212, 0.9713942558027171, 0.5429616165149803, 0.28783457002506746, 0.0), # 9
(3.2608325325343728, 3.276125140941222, 2.8090795519797083, 3.0154019542089863, 2.3999380913702133, 1.1845056719002751, 1.340383770511671, 1.2527049487958686, 1.3131660393606952, 0.6397417213392715, 0.45347745707157167, 0.2639931748414651, 0.0, 3.2894157164126443, 2.903924923256116, 2.267387285357858, 1.9192251640178144, 2.6263320787213904, 1.7537869283142162, 1.340383770511671, 0.8460754799287679, 1.1999690456851067, 1.005133984736329, 0.5618159103959417, 0.2978295582673839, 0.0), # 10
(3.3666378853592023, 3.3807822933966425, 2.8988168033844053, 3.1117525872634193, 2.477165017885742, 1.222353948069186, 1.3831768529511832, 1.292645903629736, 1.3551157724706657, 0.660163651034672, 0.46797017748641667, 0.27242531319752705, 0.0, 3.394542198585045, 2.996678445172797, 2.339850887432083, 1.9804909531040158, 2.7102315449413314, 1.8097042650816304, 1.3831768529511832, 0.8731099629065614, 1.238582508942871, 1.03725086242114, 0.5797633606768812, 0.30734384485424027, 0.0), # 11
(3.466835527090725, 3.479623182043689, 2.9835666891408468, 3.202748150422684, 2.550191811619567, 1.2580984605354364, 1.4235918359181623, 1.3303665368405868, 1.3947337439234906, 0.6794507560025573, 0.48165744027206236, 0.28038880948066897, 0.0, 3.493825584586214, 3.0842769042873583, 2.4082872013603116, 2.0383522680076718, 2.789467487846981, 1.8625131515768216, 1.4235918359181623, 0.8986417575253116, 1.2750959058097835, 1.0675827168075616, 0.5967133378281694, 0.31632938018579, 0.0), # 12
(3.5609073152871504, 3.572119261284061, 3.062876061600887, 3.2879021275380134, 2.618633981789227, 1.2915481324312523, 1.4614125846492112, 1.3656652500149136, 1.431808174668391, 0.6974998797102906, 0.49446605939392463, 0.2878410847878307, 0.0, 3.586735094962694, 3.1662519326661376, 2.472330296969623, 2.0924996391308714, 2.863616349336782, 1.9119313500208792, 1.4614125846492112, 0.9225343803080374, 1.3093169908946134, 1.0959673758460047, 0.6125752123201775, 0.3247381146621874, 0.0), # 13
(3.6483351075066865, 3.6577419855194493, 3.1362917731163824, 3.366728002460638, 2.6821070376122638, 1.3225118868888581, 1.4964229643809324, 1.3983404447392078, 1.4661272856545895, 0.7142078656252335, 0.5063228488174191, 0.29473956021595205, 0.0, 3.6727399502610254, 3.242135162375472, 2.531614244087095, 2.1426235968757004, 2.932254571309179, 1.9576766226348912, 1.4964229643809324, 0.9446513477777557, 1.3410535188061319, 1.1222426674868795, 0.6272583546232765, 0.33252199868358634, 0.0), # 14
(3.728600761307542, 3.7359628091515464, 3.203360676039181, 3.438739259041796, 2.7402264883062153, 1.3507986470404796, 1.5284068403499251, 1.4281905225999594, 1.4974792978313092, 0.7294715572147492, 0.5171546225079614, 0.30104165686197243, 0.0, 3.7513093710277525, 3.311458225481696, 2.5857731125398065, 2.188414671644247, 2.9949585956626184, 1.9994667316399433, 1.5284068403499251, 0.9648561764574853, 1.3701132441531076, 1.1462464196805988, 0.6406721352078363, 0.33963298265014064, 0.0), # 15
(3.8011861342479203, 3.806253186582049, 3.263629622721142, 3.5034493811327145, 2.792607843088622, 1.3762173360183407, 1.5571480777927953, 1.4550138851836603, 1.5256524321477714, 0.7431877979461997, 0.5268881944309676, 0.3067047958228314, 0.0, 3.8219125778094183, 3.3737527540511447, 2.6344409721548376, 2.229563393838599, 3.0513048642955427, 2.0370194392571244, 1.5571480777927953, 0.9830123828702433, 1.396303921544311, 1.1678164603775718, 0.6527259245442284, 0.3460230169620045, 0.0), # 16
(3.86557308388603, 3.868084572212647, 3.3166454655141178, 3.560371852584634, 2.8388666111770235, 1.3985768769546667, 1.5824305419461422, 1.4786089340768032, 1.5504349095531977, 0.755253431286947, 0.5354503785518533, 0.31168639819546856, 0.0, 3.8840187911525663, 3.4285503801501536, 2.6772518927592666, 2.2657602938608403, 3.1008698191063955, 2.0700525077075245, 1.5824305419461422, 0.9989834835390476, 1.4194333055885118, 1.1867906175282115, 0.6633290931028236, 0.35164405201933163, 0.0), # 17
(3.921243467780082, 3.920928420445034, 3.3619550567699603, 3.609020157248784, 2.878618301788957, 1.4176861929816842, 1.6040380980465703, 1.4987740708658768, 1.5716149509968127, 0.7655653007043539, 0.542767988836034, 0.31594388507682386, 0.0, 3.9370972316037385, 3.475382735845062, 2.7138399441801697, 2.2966959021130613, 3.1432299019936254, 2.0982836992122276, 1.6040380980465703, 1.0126329949869173, 1.4393091508944784, 1.203006719082928, 0.672391011353992, 0.35644803822227583, 0.0), # 18
(3.9676791434882794, 3.964256185680906, 3.399105248840526, 3.648907778976395, 2.911478424141964, 1.4333542072316154, 1.6217546113306789, 1.5153076971373745, 1.5889807774278373, 0.7740202496657831, 0.5487678392489254, 0.3194346775638366, 0.0, 3.9806171197094784, 3.513781453202202, 2.7438391962446262, 2.3220607489973486, 3.1779615548556746, 2.1214307759923243, 1.6217546113306789, 1.0238244337368683, 1.455739212070982, 1.2163025929921318, 0.6798210497681053, 0.3603869259709915, 0.0), # 19
(4.0043619685688325, 3.997539322321953, 3.427642894077668, 3.679548201618706, 2.9370624874535847, 1.4453898428366878, 1.6353639470350725, 1.5280082144777862, 1.6023206097954932, 0.7805151216385962, 0.5533767437559435, 0.32211619675344644, 0.0, 4.01404767601633, 3.54327816428791, 2.766883718779717, 2.341545364915788, 3.2046412195909864, 2.139211500268901, 1.6353639470350725, 1.0324213163119198, 1.4685312437267923, 1.2265160672062356, 0.6855285788155336, 0.36341266566563213, 0.0), # 20
(4.030773800579946, 4.020249284769871, 3.44711484483324, 3.700454909026946, 2.954986000941357, 1.453602022929125, 1.644649970396352, 1.5366740244736041, 1.611422669049003, 0.7849467600901557, 0.556521516322504, 0.32394586374259315, 0.0, 4.036858121070831, 3.5634045011685243, 2.78260758161252, 2.3548402802704667, 3.222845338098006, 2.151343634263046, 1.644649970396352, 1.0382871592350893, 1.4774930004706786, 1.233484969675649, 0.689422968966648, 0.365477207706352, 0.0), # 21
(4.046396497079832, 4.031857527426353, 3.457067953459095, 3.7111413850523514, 2.96486447382282, 1.4577996706411525, 1.64939654665112, 1.5411035287113193, 1.6160751761375887, 0.7872120084878245, 0.5581289709140228, 0.3248810996282164, 0.0, 4.048517675419531, 3.5736920959103795, 2.7906448545701137, 2.361636025463473, 3.2321503522751773, 2.157544940195847, 1.64939654665112, 1.0412854790293946, 1.48243223691141, 1.237047128350784, 0.6914135906918191, 0.3665325024933049, 0.0), # 22
(4.052157345337056, 4.0332319844535895, 3.4583077274805674, 3.712479243827161, 2.9673952149420257, 1.4583333333333335, 1.6499608004518678, 1.5415823045267492, 1.6166568312757204, 0.7874792272519435, 0.5583305358107827, 0.3249965858862978, 0.0, 4.05, 3.574962444749276, 2.7916526790539136, 2.36243768175583, 3.2333136625514407, 2.158215226337449, 1.6499608004518678, 1.0416666666666667, 1.4836976074710129, 1.2374930812757206, 0.6916615454961136, 0.36665745313214454, 0.0), # 23
(4.056404965213662, 4.03243024691358, 3.4581049382716054, 3.7123145833333338, 2.9688286969639606, 1.4583333333333335, 1.6496507625272334, 1.5409166666666667, 1.6165788888888888, 0.7873150617283953, 0.5583083052749721, 0.3249695473251029, 0.0, 4.05, 3.5746650205761314, 2.7915415263748606, 2.361945185185185, 3.2331577777777776, 2.1572833333333334, 1.6496507625272334, 1.0416666666666667, 1.4844143484819803, 1.2374381944444448, 0.6916209876543211, 0.3665845679012346, 0.0), # 24
(4.060562892084632, 4.030849908550525, 3.457704618198446, 3.7119888117283955, 2.970230652158534, 1.4583333333333335, 1.649039780521262, 1.5396090534979427, 1.6164248971193418, 0.7869918838591681, 0.5582642266284242, 0.3249161713153483, 0.0, 4.05, 3.5740778844688306, 2.7913211331421213, 2.3609756515775038, 3.2328497942386836, 2.15545267489712, 1.649039780521262, 1.0416666666666667, 1.485115326079267, 1.2373296039094654, 0.6915409236396892, 0.36644090077732056, 0.0), # 25
(4.0646308076192135, 4.028515112025606, 3.457112254229539, 3.711505632716049, 2.9716010315789614, 1.4583333333333335, 1.6481373436617444, 1.5376841563786012, 1.61619683127572, 0.7865150708733427, 0.5581986989233904, 0.3248371894528274, 0.0, 4.05, 3.573209083981101, 2.7909934946169517, 2.3595452126200276, 3.23239366255144, 2.152757818930042, 1.6481373436617444, 1.0416666666666667, 1.4858005157894807, 1.2371685442386833, 0.6914224508459078, 0.3662286465477825, 0.0), # 26
(4.068608393486655, 4.02545, 3.4563333333333333, 3.71086875, 2.972939786278457, 1.4583333333333335, 1.6469529411764707, 1.5351666666666668, 1.6158966666666665, 0.7858900000000002, 0.5581121212121213, 0.32473333333333343, 0.0, 4.05, 3.572066666666667, 2.7905606060606063, 2.3576699999999997, 3.231793333333333, 2.1492333333333336, 1.6469529411764707, 1.0416666666666667, 1.4864698931392284, 1.2369562500000002, 0.6912666666666667, 0.36595000000000005, 0.0), # 27
(4.0724953313562, 4.021678715134888, 3.4553733424782807, 3.710081867283951, 2.9742468673102405, 1.4583333333333335, 1.6454960622932302, 1.532081275720165, 1.615526378600823, 0.7851220484682215, 0.558004892546868, 0.3246053345526597, 0.0, 4.05, 3.5706586800792564, 2.7900244627343396, 2.355366145404664, 3.231052757201646, 2.144913786008231, 1.6454960622932302, 1.0416666666666667, 1.4871234336551202, 1.2366939557613172, 0.6910746684956562, 0.3656071559213535, 0.0), # 28
(4.0762913028971, 4.01722540009145, 3.4542377686328307, 3.709148688271605, 2.9755222257275253, 1.4583333333333335, 1.6437761962398132, 1.5284526748971192, 1.6150879423868312, 0.7842165935070876, 0.5578774119798812, 0.3244539247065996, 0.0, 4.05, 3.568993171772595, 2.789387059899406, 2.3526497805212623, 3.2301758847736624, 2.139833744855967, 1.6437761962398132, 1.0416666666666667, 1.4877611128637627, 1.2363828960905352, 0.6908475537265663, 0.36520230909922274, 0.0), # 29
(4.079995989778599, 4.012114197530865, 3.452932098765432, 3.7080729166666666, 2.9767658125835297, 1.4583333333333335, 1.6418028322440088, 1.5243055555555556, 1.6145833333333333, 0.7831790123456793, 0.557730078563412, 0.3242798353909465, 0.0, 4.05, 3.5670781893004113, 2.78865039281706, 2.349537037037037, 3.2291666666666665, 2.134027777777778, 1.6418028322440088, 1.0416666666666667, 1.4883829062917648, 1.2360243055555558, 0.6905864197530864, 0.36473765432098776, 0.0), # 30
(4.083609073669943, 4.006369250114313, 3.4514618198445364, 3.70685825617284, 2.977977578931469, 1.4583333333333335, 1.639585459533608, 1.519664609053498, 1.6140145267489712, 0.7820146822130776, 0.5575632913497112, 0.32408379820149374, 0.0, 4.05, 3.564921780216431, 2.7878164567485557, 2.346044046639232, 3.2280290534979423, 2.1275304526748973, 1.639585459533608, 1.0416666666666667, 1.4889887894657345, 1.2356194187242802, 0.6902923639689073, 0.36421538637402845, 0.0), # 31
(4.087130236240382, 4.000014700502972, 3.4498324188385916, 3.7055084104938274, 2.979157475824559, 1.4583333333333335, 1.6371335673363998, 1.5145545267489715, 1.613383497942387, 0.7807289803383634, 0.5573774493910297, 0.32386654473403453, 0.0, 4.05, 3.5625319920743794, 2.7868872469551484, 2.3421869410150893, 3.226766995884774, 2.12037633744856, 1.6371335673363998, 1.0416666666666667, 1.4895787379122796, 1.2351694701646094, 0.6899664837677183, 0.3636377000457248, 0.0), # 32
(4.090559159159159, 3.993074691358024, 3.4480493827160497, 3.704027083333333, 2.9803054543160163, 1.4583333333333335, 1.6344566448801743, 1.5090000000000001, 1.6126922222222222, 0.7793272839506176, 0.5571729517396184, 0.32362880658436216, 0.0, 4.05, 3.559916872427983, 2.785864758698092, 2.3379818518518523, 3.2253844444444444, 2.1126000000000005, 1.6344566448801743, 1.0416666666666667, 1.4901527271580082, 1.2346756944444446, 0.68960987654321, 0.36300679012345677, 0.0), # 33
(4.093895524095524, 3.985573365340649, 3.446118198445359, 3.702417978395062, 2.9814214654590576, 1.4583333333333335, 1.631564181392722, 1.503025720164609, 1.6119426748971197, 0.7778149702789212, 0.5569501974477283, 0.3233713153482701, 0.0, 4.05, 3.557084468830971, 2.784750987238642, 2.333444910836763, 3.2238853497942395, 2.1042360082304525, 1.631564181392722, 1.0416666666666667, 1.4907107327295288, 1.2341393261316875, 0.6892236396890719, 0.3623248513946045, 0.0), # 34
(4.097139012718723, 3.977534865112025, 3.4440443529949705, 3.700684799382716, 2.9825054603068986, 1.4583333333333335, 1.6284656661018317, 1.4966563786008233, 1.6111368312757204, 0.7761974165523551, 0.5567095855676103, 0.32309480262155166, 0.0, 4.05, 3.554042828837068, 2.7835479278380513, 2.3285922496570644, 3.2222736625514408, 2.0953189300411528, 1.6284656661018317, 1.0416666666666667, 1.4912527301534493, 1.2335615997942388, 0.6888088705989942, 0.3615940786465478, 0.0), # 35
(4.100289306698002, 3.9689833333333326, 3.4418333333333337, 3.69883125, 2.983557389912756, 1.4583333333333335, 1.625170588235294, 1.489916666666667, 1.6102766666666666, 0.7744800000000003, 0.5564515151515153, 0.3228000000000001, 0.0, 4.05, 3.5508000000000006, 2.782257575757576, 2.32344, 3.220553333333333, 2.0858833333333338, 1.625170588235294, 1.0416666666666667, 1.491778694956378, 1.2329437500000002, 0.6883666666666668, 0.3608166666666667, 0.0), # 36
(4.10334608770261, 3.9599429126657517, 3.4394906264288982, 3.6968610339506176, 2.984577205329846, 1.4583333333333335, 1.6216884370208988, 1.4828312757201647, 1.609364156378601, 0.7726680978509377, 0.5561763852516941, 0.3224876390794087, 0.0, 4.05, 3.547364029873495, 2.7808819262584703, 2.3180042935528125, 3.218728312757202, 2.0759637860082307, 1.6216884370208988, 1.0416666666666667, 1.492288602664923, 1.2322870113168727, 0.6878981252857798, 0.3599948102423411, 0.0), # 37
(4.1063090374017905, 3.9504377457704623, 3.4370217192501147, 3.6947778549382724, 2.985564857611384, 1.4583333333333335, 1.6180287016864359, 1.4754248971193418, 1.6084012757201647, 0.7707670873342481, 0.5558845949203975, 0.32215845145557087, 0.0, 4.05, 3.543742966011279, 2.7794229746019874, 2.3123012620027437, 3.2168025514403293, 2.0655948559670785, 1.6180287016864359, 1.0416666666666667, 1.492782428805692, 1.2315926183127577, 0.6874043438500229, 0.35913070416095116, 0.0), # 38
(4.109177837464794, 3.940491975308642, 3.434432098765433, 3.6925854166666667, 2.9865202978105874, 1.4583333333333335, 1.6142008714596952, 1.4677222222222224, 1.60739, 0.7687823456790126, 0.5555765432098766, 0.32181316872427984, 0.0, 4.05, 3.539944855967078, 2.777882716049383, 2.306347037037037, 3.21478, 2.0548111111111114, 1.6142008714596952, 1.0416666666666667, 1.4932601489052937, 1.2308618055555558, 0.6868864197530866, 0.3582265432098766, 0.0), # 39
(4.111952169560865, 3.930129743941472, 3.4317272519433013, 3.690287422839506, 2.9874434769806717, 1.4583333333333335, 1.6102144355684662, 1.4597479423868318, 1.606332304526749, 0.7667192501143122, 0.5552526291723824, 0.32145252248132916, 0.0, 4.05, 3.5359777472946203, 2.7762631458619116, 2.300157750342936, 3.212664609053498, 2.0436471193415646, 1.6102144355684662, 1.0416666666666667, 1.4937217384903358, 1.230095807613169, 0.6863454503886602, 0.3572845221764975, 0.0), # 40
(4.114631715359251, 3.919375194330132, 3.4289126657521725, 3.6878875771604944, 2.988334346174854, 1.4583333333333335, 1.606078883240539, 1.4515267489711936, 1.6052301646090534, 0.7645831778692275, 0.5549132518601656, 0.3210772443225119, 0.0, 4.05, 3.53184968754763, 2.7745662593008276, 2.2937495336076816, 3.210460329218107, 2.0321374485596713, 1.606078883240539, 1.0416666666666667, 1.494167173087427, 1.2292958590534984, 0.6857825331504345, 0.3563068358481939, 0.0), # 41
(4.1172161565292, 3.908252469135803, 3.425993827160495, 3.685389583333334, 2.9891928564463486, 1.4583333333333335, 1.6018037037037036, 1.4430833333333335, 1.6040855555555558, 0.7623795061728398, 0.5545588103254772, 0.3206880658436215, 0.0, 4.05, 3.5275687242798353, 2.7727940516273852, 2.2871385185185185, 3.2081711111111115, 2.020316666666667, 1.6018037037037036, 1.0416666666666667, 1.4945964282231743, 1.2284631944444449, 0.685198765432099, 0.35529567901234577, 0.0), # 42
(4.119705174739957, 3.8967857110196618, 3.4229762231367173, 3.6827971450617287, 2.990018958848374, 1.4583333333333335, 1.5973983861857501, 1.434442386831276, 1.6029004526748971, 0.7601136122542298, 0.5541897036205679, 0.32028571864045124, 0.0, 4.05, 3.523142905044963, 2.770948518102839, 2.2803408367626887, 3.2058009053497942, 2.0082193415637866, 1.5973983861857501, 1.0416666666666667, 1.495009479424187, 1.2275990483539099, 0.6845952446273434, 0.35425324645633294, 0.0), # 43
(4.122098451660771, 3.8849990626428896, 3.4198653406492916, 3.680113966049383, 2.9908126044341454, 1.4583333333333335, 1.592872419914468, 1.4256286008230457, 1.6016768312757201, 0.7577908733424785, 0.5538063307976889, 0.3198709343087945, 0.0, 4.05, 3.5185802773967385, 2.7690316539884443, 2.273372620027435, 3.2033536625514403, 1.9958800411522641, 1.592872419914468, 1.0416666666666667, 1.4954063022170727, 1.2267046553497947, 0.6839730681298584, 0.35318173296753547, 0.0), # 44
(4.1243956689608865, 3.872916666666667, 3.4166666666666674, 3.6773437500000004, 2.991573744256879, 1.4583333333333335, 1.5882352941176472, 1.416666666666667, 1.6004166666666664, 0.755416666666667, 0.553409090909091, 0.3194444444444445, 0.0, 4.05, 3.5138888888888884, 2.7670454545454546, 2.2662500000000003, 3.2008333333333328, 1.9833333333333336, 1.5882352941176472, 1.0416666666666667, 1.4957868721284395, 1.2257812500000003, 0.6833333333333335, 0.3520833333333334, 0.0), # 45
(4.126596508309553, 3.8605626657521714, 3.4133856881572933, 3.674490200617284, 2.992302329369791, 1.4583333333333335, 1.5834964980230777, 1.407581275720165, 1.5991219341563785, 0.7529963694558759, 0.552998383007025, 0.3190069806431947, 0.0, 4.05, 3.509076787075141, 2.7649919150351248, 2.258989108367627, 3.198243868312757, 1.970613786008231, 1.5834964980230777, 1.0416666666666667, 1.4961511646848955, 1.2248300668724283, 0.6826771376314588, 0.35096024234110657, 0.0), # 46
(4.128700651376014, 3.8479612025605854, 3.4100278920896208, 3.6715570216049382, 2.992998310826098, 1.4583333333333335, 1.5786655208585494, 1.3983971193415639, 1.597794609053498, 0.7505353589391863, 0.552574606143742, 0.3185592745008384, 0.0, 4.05, 3.5041520195092213, 2.7628730307187097, 2.2516060768175583, 3.195589218106996, 1.9577559670781894, 1.5786655208585494, 1.0416666666666667, 1.496499155413049, 1.2238523405349797, 0.6820055784179242, 0.3498146547782351, 0.0), # 47
(4.130707779829518, 3.835136419753087, 3.4065987654320993, 3.6685479166666672, 2.993661639679016, 1.4583333333333335, 1.5737518518518518, 1.3891388888888891, 1.5964366666666667, 0.7480390123456792, 0.5521381593714928, 0.31810205761316873, 0.0, 4.05, 3.4991226337448555, 2.7606907968574634, 2.244117037037037, 3.1928733333333335, 1.944794444444445, 1.5737518518518518, 1.0416666666666667, 1.496830819839508, 1.222849305555556, 0.6813197530864199, 0.34864876543209883, 0.0), # 48
(4.132617575339315, 3.8221124599908545, 3.403103795153178, 3.665466589506173, 2.9942922669817618, 1.4583333333333335, 1.5687649802307755, 1.3798312757201647, 1.5950500823045266, 0.7455127069044355, 0.5516894417425283, 0.31763606157597934, 0.0, 4.05, 3.4939966773357725, 2.7584472087126413, 2.2365381207133064, 3.190100164609053, 1.9317637860082308, 1.5687649802307755, 1.0416666666666667, 1.4971461334908809, 1.221822196502058, 0.6806207590306357, 0.34746476909007773, 0.0), # 49
(4.134429719574647, 3.8089134659350714, 3.399548468221308, 3.6623167438271604, 2.9948901437875506, 1.4583333333333335, 1.56371439522311, 1.3704989711934157, 1.5936368312757199, 0.742961819844536, 0.5512288523090992, 0.3171620179850633, 0.0, 4.05, 3.4887821978356963, 2.7561442615454963, 2.2288854595336076, 3.1872736625514397, 1.9186985596707822, 1.56371439522311, 1.0416666666666667, 1.4974450718937753, 1.220772247942387, 0.6799096936442617, 0.346264860539552, 0.0), # 50
(4.136143894204764, 3.7955635802469136, 3.3959382716049387, 3.659102083333334, 2.9954552211495997, 1.4583333333333335, 1.558609586056645, 1.3611666666666666, 1.592198888888889, 0.7403917283950618, 0.5507567901234569, 0.31668065843621407, 0.0, 4.05, 3.483487242798354, 2.7537839506172843, 2.221175185185185, 3.184397777777778, 1.9056333333333335, 1.558609586056645, 1.0416666666666667, 1.4977276105747999, 1.2197006944444448, 0.6791876543209877, 0.34505123456790127, 0.0), # 51
(4.137759780898912, 3.782086945587563, 3.39227869227252, 3.6558263117283953, 2.995987450121124, 1.4583333333333335, 1.5534600419591706, 1.3518590534979422, 1.590738230452675, 0.7378078097850939, 0.5502736542378519, 0.3161927145252249, 0.0, 4.05, 3.4781198597774736, 2.7513682711892593, 2.2134234293552812, 3.18147646090535, 1.8926026748971192, 1.5534600419591706, 1.0416666666666667, 1.497993725060562, 1.218608770576132, 0.678455738454504, 0.3438260859625058, 0.0), # 52
(4.139277061326338, 3.768507704618199, 3.388575217192502, 3.6524931327160495, 2.996486781755341, 1.4583333333333335, 1.5482752521584766, 1.3426008230452677, 1.5892568312757203, 0.735215441243713, 0.5497798437045351, 0.3156989178478891, 0.0, 4.05, 3.4726880963267797, 2.7488992185226753, 2.2056463237311386, 3.1785136625514405, 1.8796411522633747, 1.5482752521584766, 1.0416666666666667, 1.4982433908776704, 1.21749771090535, 0.6777150434385005, 0.3425916095107454, 0.0), # 53
(4.140695417156286, 3.7548500000000002, 3.3848333333333334, 3.64910625, 2.996953167105467, 1.4583333333333335, 1.543064705882353, 1.3334166666666667, 1.5877566666666667, 0.7326200000000002, 0.5492757575757575, 0.31520000000000004, 0.0, 4.05, 3.4672, 2.7463787878787875, 2.1978600000000004, 3.1755133333333334, 1.8667833333333332, 1.543064705882353, 1.0416666666666667, 1.4984765835527336, 1.2163687500000002, 0.6769666666666667, 0.3413500000000001, 0.0), # 54
(4.142014530058009, 3.741137974394147, 3.381058527663466, 3.6456693672839506, 2.997386557224717, 1.4583333333333335, 1.5378378923585896, 1.3243312757201646, 1.5862397119341562, 0.7300268632830363, 0.5487617949037703, 0.31469669257735106, 0.0, 4.05, 3.4616636183508613, 2.743808974518851, 2.1900805898491087, 3.1724794238683125, 1.8540637860082305, 1.5378378923585896, 1.0416666666666667, 1.4986932786123586, 1.2152231224279837, 0.6762117055326933, 0.34010345221764976, 0.0), # 55
(4.143234081700749, 3.7273957704618197, 3.377256287151349, 3.642186188271605, 2.9977869031663094, 1.4583333333333335, 1.5326043008149763, 1.3153693415637862, 1.584707942386831, 0.7274414083219024, 0.5482383547408239, 0.31418972717573546, 0.0, 4.05, 3.4560869989330896, 2.7411917737041196, 2.182324224965707, 3.169415884773662, 1.8415170781893007, 1.5326043008149763, 1.0416666666666667, 1.4988934515831547, 1.2140620627572019, 0.6754512574302699, 0.33885416095107457, 0.0), # 56
(4.144353753753753, 3.7136475308641974, 3.373432098765433, 3.638660416666667, 2.9981541559834577, 1.4583333333333335, 1.5273734204793028, 1.306555555555556, 1.5831633333333335, 0.7248690123456792, 0.5477058361391696, 0.3136798353909465, 0.0, 4.05, 3.4504781893004113, 2.7385291806958474, 2.1746070370370374, 3.166326666666667, 1.8291777777777782, 1.5273734204793028, 1.0416666666666667, 1.4990770779917288, 1.212886805555556, 0.6746864197530866, 0.33760432098765436, 0.0), # 57
(4.145373227886272, 3.69991739826246, 3.369591449474166, 3.6350957561728396, 2.99848826672938, 1.4583333333333335, 1.5221547405793594, 1.297914609053498, 1.5816078600823045, 0.7223150525834479, 0.5471646381510581, 0.3131677488187777, 0.0, 4.05, 3.444845237006554, 2.7358231907552906, 2.166945157750343, 3.163215720164609, 1.8170804526748974, 1.5221547405793594, 1.0416666666666667, 1.49924413336469, 1.2116985853909468, 0.6739182898948333, 0.33635612711476914, 0.0), # 58
(4.146292185767549, 3.6862295153177866, 3.365739826245999, 3.631495910493827, 2.9987891864572918, 1.4583333333333335, 1.5169577503429357, 1.289471193415638, 1.580043497942387, 0.7197849062642893, 0.5466151598287401, 0.3126541990550222, 0.0, 4.05, 3.439196189605243, 2.7330757991437, 2.1593547187928674, 3.160086995884774, 1.8052596707818933, 1.5169577503429357, 1.0416666666666667, 1.4993945932286459, 1.210498636831276, 0.6731479652492, 0.3351117741197988, 0.0), # 59
(4.147110309066831, 3.6726080246913586, 3.3618827160493825, 3.6278645833333334, 2.9990568662204096, 1.4583333333333335, 1.5117919389978214, 1.2812500000000002, 1.5784722222222225, 0.7172839506172841, 0.546057800224467, 0.3121399176954733, 0.0, 4.05, 3.4335390946502056, 2.7302890011223346, 2.151851851851852, 3.156944444444445, 1.7937500000000002, 1.5117919389978214, 1.0416666666666667, 1.4995284331102048, 1.2092881944444447, 0.6723765432098766, 0.33387345679012354, 0.0), # 60
(4.147827279453366, 3.6590770690443533, 3.3580256058527667, 3.624205478395062, 2.9992912570719494, 1.4583333333333335, 1.5066667957718067, 1.2732757201646092, 1.5768960082304526, 0.7148175628715137, 0.5454929583904894, 0.3116256363359245, 0.0, 4.05, 3.4278819996951686, 2.7274647919524466, 2.1444526886145407, 3.1537920164609052, 1.7825860082304528, 1.5066667957718067, 1.0416666666666667, 1.4996456285359747, 1.2080684927983543, 0.6716051211705534, 0.3326433699131231, 0.0), # 61
(4.148442778596402, 3.6456607910379515, 3.3541739826246006, 3.6205222993827166, 2.999492310065129, 1.4583333333333335, 1.5015918098926813, 1.2655730452674898, 1.5753168312757202, 0.7123911202560588, 0.5449210333790582, 0.31111208657216893, 0.0, 4.05, 3.4222329522938577, 2.7246051668952904, 2.137173360768176, 3.1506336625514404, 1.7718022633744859, 1.5015918098926813, 1.0416666666666667, 1.4997461550325646, 1.2068407664609058, 0.6708347965249202, 0.33142370827617745, 0.0), # 62
(4.148956488165184, 3.6323833333333333, 3.350333333333334, 3.6168187500000006, 2.9996599762531617, 1.4583333333333335, 1.4965764705882352, 1.2581666666666669, 1.5737366666666666, 0.7100100000000001, 0.5443424242424244, 0.31060000000000004, 0.0, 4.05, 3.4166, 2.721712121212122, 2.13003, 3.147473333333333, 1.7614333333333339, 1.4965764705882352, 1.0416666666666667, 1.4998299881265809, 1.2056062500000004, 0.6700666666666668, 0.3302166666666667, 0.0), # 63
(4.149368089828959, 3.6192688385916783, 3.346509144947417, 3.613098533950618, 2.999794206689266, 1.4583333333333335, 1.4916302670862585, 1.2510812757201648, 1.5721574897119341, 0.707679579332419, 0.5437575300328387, 0.31009010821521116, 0.0, 4.05, 3.4109911903673225, 2.7187876501641934, 2.1230387379972564, 3.1443149794238683, 1.7515137860082308, 1.4916302670862585, 1.0416666666666667, 1.499897103344633, 1.2043661779835395, 0.6693018289894834, 0.3290244398719708, 0.0), # 64
(4.149677265256975, 3.6063414494741655, 3.3427069044352997, 3.609365354938272, 2.999894952426658, 1.4583333333333335, 1.4867626886145406, 1.2443415637860082, 1.5705812757201647, 0.7054052354823961, 0.5431667498025524, 0.3095831428135955, 0.0, 4.05, 3.40541457094955, 2.715833749012762, 2.116215706447188, 3.1411625514403294, 1.7420781893004116, 1.4867626886145406, 1.0416666666666667, 1.499947476213329, 1.2031217849794242, 0.66854138088706, 0.32784922267946964, 0.0), # 65
(4.149883696118478, 3.593625308641976, 3.3389320987654325, 3.605622916666667, 2.9999621645185526, 1.4583333333333335, 1.4819832244008715, 1.2379722222222225, 1.56901, 0.7031923456790125, 0.542570482603816, 0.3090798353909466, 0.0, 4.05, 3.399878189300412, 2.7128524130190796, 2.1095770370370373, 3.13802, 1.7331611111111116, 1.4819832244008715, 1.0416666666666667, 1.4999810822592763, 1.2018743055555559, 0.6677864197530866, 0.3266932098765433, 0.0), # 66
(4.149987064082717, 3.581144558756287, 3.3351902149062647, 3.601874922839506, 2.999995794018168, 1.4583333333333335, 1.4773013636730412, 1.2319979423868317, 1.5674456378600823, 0.7010462871513491, 0.5419691274888808, 0.3085809175430575, 0.0, 4.05, 3.394390092973632, 2.7098456374444035, 2.103138861454047, 3.1348912757201646, 1.7247971193415643, 1.4773013636730412, 1.0416666666666667, 1.499997897009084, 1.2006249742798356, 0.6670380429812529, 0.3255585962505716, 0.0), # 67
(4.14991664579233, 3.5688578344174515, 3.331468649977138, 3.5980925221417075, 2.9999674547459585, 1.4583062693695066, 1.4727030389659292, 1.226390641670477, 1.5658783798201494, 0.6989620441647167, 0.5413523992360252, 0.3080843340633248, 0.0, 4.0499500600137175, 3.388927674696572, 2.7067619961801257, 2.09688613249415, 3.131756759640299, 1.716946898338668, 1.4727030389659292, 1.0416473352639333, 1.4999837273729792, 1.199364174047236, 0.6662937299954276, 0.3244416213106775, 0.0), # 68
(4.149256682769726, 3.5563900238948625, 3.3276628086419753, 3.594085054347826, 2.999709513435003, 1.4580923182441705, 1.4680536362693228, 1.2208497942386831, 1.5642397119341562, 0.6968806390704431, 0.5406575225943647, 0.3075739657786442, 0.0, 4.049554398148149, 3.3833136235650856, 2.7032876129718235, 2.090641917211329, 3.1284794238683125, 1.7091897119341564, 1.4680536362693228, 1.0414945130315503, 1.4998547567175016, 1.1980283514492756, 0.665532561728395, 0.3233081839904421, 0.0), # 69
(4.147954315023558, 3.5436839019425634, 3.3237561442615453, 3.589826137278583, 2.999199817101051, 1.4576709597114261, 1.463332026912274, 1.2153254077122393, 1.5625203856119496, 0.6947919524462736, 0.539876592435072, 0.307047425376427, 0.0, 4.048772933813444, 3.3775216791406963, 2.6993829621753602, 2.0843758573388205, 3.1250407712238992, 1.701455570797135, 1.463332026912274, 1.0411935426510186, 1.4995999085505256, 1.1966087124261946, 0.6647512288523091, 0.32215308199477855, 0.0), # 70
(4.146027864257172, 3.5307470618168946, 3.319750028577961, 3.585322051127214, 2.9984448210011028, 1.4570490219986791, 1.4585403319077976, 1.2098193110806281, 1.5607229614388052, 0.6926960359342641, 0.5390124913855908, 0.3065050979070905, 0.0, 4.047615955075446, 3.3715560769779955, 2.695062456927954, 2.0780881078027917, 3.1214459228776104, 1.6937470355128794, 1.4585403319077976, 1.040749301427628, 1.4992224105005514, 1.1951073503757383, 0.6639500057155922, 0.3209770056197177, 0.0), # 71
(4.143495652173914, 3.5175870967741933, 3.3156458333333334, 3.5805790760869565, 2.9974509803921565, 1.4562333333333337, 1.4536806722689075, 1.2043333333333335, 1.55885, 0.6905929411764707, 0.5380681020733654, 0.3059473684210527, 0.0, 4.04609375, 3.365421052631579, 2.6903405103668265, 2.071778823529412, 3.1177, 1.6860666666666668, 1.4536806722689075, 1.040166666666667, 1.4987254901960783, 1.1935263586956524, 0.6631291666666667, 0.31978064516129034, 0.0), # 72
(4.140376000477128, 3.5042116000707995, 3.3114449302697757, 3.575603492351047, 2.996224750531214, 1.4552307219427933, 1.4487551690086184, 1.1988693034598386, 1.556904061880811, 0.6884827198149495, 0.5370463071258393, 0.30537462196873066, 0.0, 4.04421660665295, 3.3591208416560367, 2.6852315356291965, 2.065448159444848, 3.113808123761622, 1.678417024843774, 1.4487551690086184, 1.0394505156734237, 1.498112375265607, 1.1918678307836825, 0.6622889860539553, 0.31856469091552725, 0.0), # 73
(4.136687230870161, 3.4906281649630513, 3.307148691129401, 3.570401580112721, 2.9947725866752735, 1.4540480160544635, 1.4437659431399446, 1.1934290504496268, 1.5548877076665142, 0.6863654234917563, 0.5359499891704572, 0.30478724360054227, 0.0, 4.041994813100138, 3.3526596796059644, 2.6797499458522855, 2.0590962704752687, 3.1097754153330284, 1.6708006706294773, 1.4437659431399446, 1.0386057257531882, 1.4973862933376367, 1.190133860037574, 0.6614297382258802, 0.31732983317845925, 0.0), # 74
(4.13244766505636, 3.4768443847072876, 3.302758487654321, 3.564979619565217, 2.9931009440813363, 1.452692043895748, 1.4387151156759002, 1.188014403292181, 1.5528034979423868, 0.6842411038489471, 0.5347820308346625, 0.30418561836690494, 0.0, 4.039438657407408, 3.3460418020359537, 2.673910154173312, 2.052723311546841, 3.1056069958847736, 1.6632201646090536, 1.4387151156759002, 1.0376371742112487, 1.4965504720406682, 1.1883265398550726, 0.6605516975308642, 0.3160767622461171, 0.0), # 75
(4.127675624739071, 3.462867852559848, 3.2982756915866487, 3.559343890901771, 2.9912162780064016, 1.4511696336940512, 1.4336048076294992, 1.1826271909769854, 1.5506539932937051, 0.682109812528578, 0.5335453147458995, 0.3035701313182361, 0.0, 4.036558427640603, 3.339271444500597, 2.6677265737294973, 2.046329437585734, 3.1013079865874102, 1.6556780673677796, 1.4336048076294992, 1.0365497383528937, 1.4956081390032008, 1.186447963633924, 0.6596551383173298, 0.31480616841453174, 0.0), # 76
(4.122389431621637, 3.4487061617770705, 3.2937016746684953, 3.55350067431562, 2.9891250437074692, 1.4494876136767771, 1.4284371400137559, 1.1772692424935225, 1.5484417543057463, 0.6799716011727052, 0.5322427235316126, 0.3029411675049536, 0.0, 4.03336441186557, 3.332352842554489, 2.6612136176580625, 2.039914803518115, 3.0968835086114925, 1.6481769394909316, 1.4284371400137559, 1.0353482954834123, 1.4945625218537346, 1.1845002247718734, 0.6587403349336991, 0.31351874197973373, 0.0), # 77
(4.1166074074074075, 3.4343669056152932, 3.2890378086419756, 3.54745625, 2.98683369644154, 1.4476528120713306, 1.423214233841685, 1.171942386831276, 1.5461693415637856, 0.6778265214233843, 0.5308771398192452, 0.30229911197747467, 0.0, 4.029866898148149, 3.3252902317522204, 2.654385699096226, 2.0334795642701526, 3.092338683127571, 1.6407193415637862, 1.423214233841685, 1.0340377229080933, 1.49341684822077, 1.182485416666667, 0.6578075617283952, 0.312215173237754, 0.0), # 78
(4.110347873799726, 3.4198576773308558, 3.2842854652492, 3.541216898148148, 2.9843486914656125, 1.445672057105116, 1.4179382101263003, 1.166648452979729, 1.5438393156531016, 0.6756746249226715, 0.5294514462362415, 0.30164434978621685, 0.0, 4.026076174554183, 3.318087847648385, 2.6472572311812077, 2.0270238747680143, 3.0876786313062032, 1.6333078341716205, 1.4179382101263003, 1.0326228979322258, 1.4921743457328063, 1.1804056327160497, 0.6568570930498401, 0.31089615248462327, 0.0), # 79
(4.103629152501939, 3.4051860701800964, 3.2794460162322814, 3.534788898953301, 2.9816764840366874, 1.4435521770055377, 1.4126111898806162, 1.1613892699283648, 1.5414542371589697, 0.6735159633126228, 0.527968525410046, 0.30097726598159785, 0.0, 4.02200252914952, 3.310749925797576, 2.6398426270502298, 2.020547889937868, 3.0829084743179394, 1.6259449778997108, 1.4126111898806162, 1.0311086978610984, 1.4908382420183437, 1.1782629663177673, 0.6558892032464564, 0.3095623700163725, 0.0), # 80
(4.096469565217392, 3.390359677419355, 3.2745208333333338, 3.5281785326086963, 2.9788235294117644, 1.4413000000000002, 1.4072352941176471, 1.1561666666666668, 1.5390166666666665, 0.6713505882352943, 0.5264312599681021, 0.3002982456140351, 0.0, 4.01765625, 3.303280701754386, 2.632156299840511, 2.0140517647058824, 3.078033333333333, 1.6186333333333336, 1.4072352941176471, 1.0295, 1.4894117647058822, 1.1760595108695657, 0.6549041666666667, 0.30821451612903233, 0.0), # 81
(4.088887433649431, 3.3753860923049697, 3.269511288294468, 3.5213920793075686, 2.975796282847844, 1.4389223543159075, 1.4018126438504073, 1.1509824721841184, 1.5365291647614692, 0.6691785513327417, 0.5248425325378543, 0.29960767373394626, 0.0, 4.013047625171469, 3.2956844110734083, 2.624212662689271, 2.0075356539982248, 3.0730583295229383, 1.6113754610577657, 1.4018126438504073, 1.0278016816542197, 1.487898141423922, 1.1737973597691898, 0.6539022576588936, 0.30685328111863364, 0.0), # 82
(4.080901079501402, 3.3602729080932785, 3.264418752857796, 3.514435819243156, 2.9726011996019257, 1.4364260681806638, 1.3963453600919107, 1.1458385154702029, 1.533994292028654, 0.6669999042470213, 0.5232052257467463, 0.29890593539174876, 0.0, 4.008186942729767, 3.287965289309236, 2.6160261287337314, 2.0009997127410637, 3.067988584057308, 1.604173921658284, 1.3963453600919107, 1.0260186201290455, 1.4863005998009629, 1.1714786064143856, 0.6528837505715593, 0.3054793552812072, 0.0), # 83
(4.072528824476651, 3.345027718040621, 3.259244598765432, 3.507316032608696, 2.969244734931009, 1.4338179698216735, 1.3908355638551717, 1.1407366255144034, 1.5314146090534977, 0.664814698620189, 0.5215222222222223, 0.2981934156378601, 0.0, 4.003084490740741, 3.280127572016461, 2.6076111111111113, 1.9944440958605667, 3.0628292181069954, 1.5970312757201646, 1.3908355638551717, 1.0241556927297668, 1.4846223674655046, 1.169105344202899, 0.6518489197530865, 0.3040934289127838, 0.0), # 84
(4.063788990278524, 3.3296581154033364, 3.253990197759488, 3.5000389995974235, 2.9657333440920954, 1.431104887466342, 1.385285376153205, 1.1356786313062037, 1.528792676421277, 0.6626229860943007, 0.5197964045917264, 0.29747049952269794, 0.0, 3.9977505572702334, 3.272175494749677, 2.5989820229586313, 1.9878689582829017, 3.057585352842554, 1.589950083828685, 1.385285376153205, 1.0222177767616727, 1.4828666720460477, 1.1666796665324748, 0.6507980395518976, 0.30269619230939426, 0.0), # 85
(4.054699898610365, 3.3141716934377627, 3.2486569215820764, 3.492611000402577, 2.9620734823421824, 1.4282936493420721, 1.3796969179990242, 1.1306663618350863, 1.5261310547172688, 0.6604248183114125, 0.5180306554827024, 0.29673757209667984, 0.0, 3.992195430384088, 3.2641132930634775, 2.5901532774135116, 1.9812744549342374, 3.0522621094345377, 1.5829329065691207, 1.3796969179990242, 1.0202097495300515, 1.4810367411710912, 1.1642036668008593, 0.6497313843164153, 0.3012883357670694, 0.0), # 86
(4.045279871175523, 3.298576045400239, 3.2432461419753085, 3.485038315217391, 2.9582716049382722, 1.4253910836762689, 1.3740723104056438, 1.1257016460905351, 1.5234323045267493, 0.6582202469135804, 0.5162278575225944, 0.29599501841022313, 0.0, 3.9864293981481485, 3.255945202512454, 2.581139287612972, 1.9746607407407408, 3.0468646090534985, 1.5759823045267491, 1.3740723104056438, 1.018136488340192, 1.4791358024691361, 1.1616794384057973, 0.6486492283950618, 0.29987054958183995, 0.0), # 87
(4.035547229677343, 3.2828787645471036, 3.2377592306812986, 3.477327224235105, 2.954334167137363, 1.4224040186963371, 1.3684136743860782, 1.1207863130620332, 1.5206989864349947, 0.6560093235428602, 0.5143908933388467, 0.29524322351374555, 0.0, 3.9804627486282587, 3.2476754586512007, 2.571954466694233, 1.9680279706285804, 3.0413979728699894, 1.5691008382868465, 1.3684136743860782, 1.0160028704973836, 1.4771670835686814, 1.1591090747450352, 0.6475518461362598, 0.29844352404973673, 0.0), # 88
(4.025520295819169, 3.267087444134696, 3.2321975594421586, 3.4694840076489535, 2.950267624196455, 1.4193392826296807, 1.3627231309533416, 1.1159221917390643, 1.5179336610272824, 0.6537920998413084, 0.512522645558903, 0.29448257245766457, 0.0, 3.9743057698902606, 3.2393082970343094, 2.5626132277945146, 1.9613762995239248, 3.035867322054565, 1.56229106843469, 1.3627231309533416, 1.0138137733069148, 1.4751338120982276, 1.156494669216318, 0.6464395118884317, 0.2970079494667906, 0.0), # 89
(4.015217391304348, 3.2512096774193546, 3.2265625000000004, 3.4615149456521745, 2.946078431372549, 1.4162037037037039, 1.3570028011204482, 1.1111111111111112, 1.515138888888889, 0.6515686274509805, 0.5106259968102075, 0.2937134502923977, 0.0, 3.9679687500000003, 3.230847953216374, 2.553129984051037, 1.9547058823529413, 3.030277777777778, 1.5555555555555556, 1.3570028011204482, 1.0115740740740742, 1.4730392156862746, 1.1538383152173917, 0.6453125000000001, 0.2955645161290323, 0.0), # 90
(4.004656837836225, 3.235253057657418, 3.2208554240969365, 3.4534263184380034, 2.9417730439226437, 1.413004110145811, 1.3512548059004124, 1.1063549001676574, 1.512317230605091, 0.6493389580139327, 0.5087038297202041, 0.2929362420683625, 0.0, 3.961461977023319, 3.222298662751987, 2.5435191486010202, 1.9480168740417978, 3.024634461210182, 1.5488968602347204, 1.3512548059004124, 1.0092886501041507, 1.4708865219613219, 1.1511421061460014, 0.6441710848193873, 0.2941139143324926, 0.0), # 91
(3.9938569571181493, 3.2192251781052263, 3.21507770347508, 3.445224406199678, 2.937357917103741, 1.4097473301834071, 1.3454812663062485, 1.1016553878981865, 1.5094712467611644, 0.6471031431722211, 0.506759026916337, 0.29215133283597666, 0.0, 3.954795739026063, 3.2136646611957427, 2.5337951345816845, 1.9413094295166629, 3.018942493522329, 1.5423175430574612, 1.3454812663062485, 1.0069623787024338, 1.4686789585518705, 1.148408135399893, 0.6430155406950161, 0.29265683437320245, 0.0), # 92
(3.982836070853462, 3.2031336320191164, 3.2092307098765436, 3.4369154891304357, 2.932839506172839, 1.4064401920438958, 1.3396843033509702, 1.0970144032921811, 1.506603497942387, 0.6448612345679013, 0.50479447102605, 0.29135910764565737, 0.0, 3.9479803240740736, 3.2049501841022305, 2.52397235513025, 1.9345837037037037, 3.013206995884774, 1.5358201646090537, 1.3396843033509702, 1.0046001371742113, 1.4664197530864196, 1.145638496376812, 0.6418461419753088, 0.29119396654719243, 0.0), # 93
(3.971612500745512, 3.1869860126554275, 3.203315815043439, 3.428505847423511, 2.9282242663869384, 1.403089523954682, 1.3338660380475922, 1.0924337753391253, 1.5037165447340346, 0.6426132838430298, 0.5028130446767874, 0.2905599515478225, 0.0, 3.9410260202331964, 3.196159467026047, 2.5140652233839367, 1.927839851529089, 3.007433089468069, 1.5294072854747756, 1.3338660380475922, 1.0022068028247728, 1.4641121331934692, 1.1428352824745038, 0.6406631630086879, 0.28972600115049346, 0.0), # 94
(3.960204568497644, 3.170789913270499, 3.1973343907178786, 3.420001761272142, 2.9235186530030397, 1.3997021541431696, 1.3280285914091288, 1.0879153330285019, 1.500812947721384, 0.6403593426396622, 0.5008176304959931, 0.28975424959288937, 0.0, 3.933943115569273, 3.187296745521783, 2.504088152479966, 1.9210780279189863, 3.001625895442768, 1.5230814662399026, 1.3280285914091288, 0.9997872529594067, 1.4617593265015199, 1.1400005870907142, 0.6394668781435758, 0.2882536284791363, 0.0), # 95
(3.948630595813205, 3.15455292712067, 3.1912878086419756, 3.4114095108695652, 2.9187291212781408, 1.3962849108367628, 1.3221740844485943, 1.0834609053497943, 1.497895267489712, 0.638099462599855, 0.4988111111111112, 0.2889423868312758, 0.0, 3.9267418981481486, 3.1783662551440335, 2.494055555555556, 1.9142983877995645, 2.995790534979424, 1.5168452674897122, 1.3221740844485943, 0.9973463648834019, 1.4593645606390704, 1.1371365036231886, 0.6382575617283952, 0.28677753882915186, 0.0), # 96
(3.936908904395539, 3.138282647462278, 3.185177440557842, 3.4027353764090176, 2.913862126469244, 1.3928446222628663, 1.3163046381790027, 1.0790723212924862, 1.4949660646242953, 0.6358336953656636, 0.4967963691495856, 0.288124748313399, 0.0, 3.919432656035666, 3.169372231447389, 2.4839818457479277, 1.9075010860969903, 2.9899321292485905, 1.5107012498094807, 1.3163046381790027, 0.9948890159020474, 1.456931063234622, 1.1342451254696728, 0.6370354881115684, 0.2852984224965707, 0.0), # 97
(3.925057815947994, 3.1219866675516617, 3.17900465820759, 3.3939856380837363, 2.908924123833347, 1.3893881166488853, 1.3104223736133687, 1.0747514098460602, 1.4920278997104097, 0.6335620925791443, 0.49477628723886047, 0.28730171908967667, 0.0, 3.9120256772976685, 3.1603189099864424, 2.4738814361943025, 1.9006862777374325, 2.9840557994208194, 1.5046519737844843, 1.3104223736133687, 0.9924200833206323, 1.4544620619166735, 1.1313285460279123, 0.6358009316415181, 0.2838169697774238, 0.0), # 98
(3.9130956521739133, 3.1056725806451615, 3.1727708333333338, 3.3851665760869567, 2.9039215686274513, 1.3859222222222223, 1.3045294117647062, 1.0705000000000002, 1.4890833333333333, 0.6312847058823531, 0.4927537480063797, 0.2864736842105264, 0.0, 3.9045312500000002, 3.1512105263157895, 2.4637687400318984, 1.893854117647059, 2.9781666666666666, 1.4987000000000004, 1.3045294117647062, 0.9899444444444444, 1.4519607843137257, 1.1283888586956525, 0.6345541666666669, 0.282333870967742, 0.0), # 99
(3.901040734776645, 3.0893479799991144, 3.1664773376771835, 3.3762844706119166, 2.8988609161085557, 1.382453767210283, 1.298627873646029, 1.0663199207437892, 1.4861349260783419, 0.629001586917346, 0.49073163407958736, 0.2856410287263656, 0.0, 3.896959662208505, 3.142051315990021, 2.4536581703979365, 1.8870047607520375, 2.9722698521566837, 1.492847889041305, 1.298627873646029, 0.9874669765787736, 1.4494304580542778, 1.125428156870639, 0.6332954675354368, 0.28084981636355594, 0.0), # 100
(3.888911385459534, 3.0730204588698617, 3.160125542981253, 3.367345601851852, 2.8937486215336614, 1.3789895798404714, 1.2927198802703526, 1.0622130010669104, 1.4831852385307118, 0.626712787326179, 0.48871282808592753, 0.2848041376876118, 0.0, 3.8893212019890258, 3.1328455145637295, 2.4435641404296375, 1.8801383619785366, 2.9663704770614236, 1.4870982014936747, 1.2927198802703526, 0.9849925570289081, 1.4468743107668307, 1.1224485339506176, 0.6320251085962507, 0.2793654962608966, 0.0), # 101
(3.8767259259259266, 3.05669761051374, 3.1537168209876545, 3.3583562500000004, 2.8885911401597673, 1.3755364883401924, 1.2868075526506901, 1.0581810699588479, 1.4802368312757201, 0.624418358750908, 0.48670021265284436, 0.28396339614468274, 0.0, 3.881626157407408, 3.12359735759151, 2.4335010632642216, 1.8732550762527236, 2.9604736625514403, 1.481453497942387, 1.2868075526506901, 0.9825260631001375, 1.4442955700798836, 1.1194520833333337, 0.630743364197531, 0.27788160095579456, 0.0), # 102
(3.864502677879168, 3.040387028187088, 3.1472525434385004, 3.349322695249598, 2.883394927243874, 1.3721013209368493, 1.280893011800056, 1.0542259564090841, 1.4772922648986433, 0.6221183528335891, 0.48469667040778164, 0.2831191891479958, 0.0, 3.873884816529492, 3.114311080627953, 2.4234833520389083, 1.8663550585007669, 2.9545845297972866, 1.475916338972718, 1.280893011800056, 0.9800723720977494, 1.441697463621937, 1.1164408984165328, 0.6294505086877001, 0.2763988207442808, 0.0), # 103
(3.852259963022604, 3.0240963051462453, 3.140734082075903, 3.340251217793881, 2.878166438042981, 1.3686909058578471, 1.2749783787314652, 1.0503494894071028, 1.4743540999847584, 0.6198128212162782, 0.48270508397818346, 0.2822719017479685, 0.0, 3.8661074674211253, 3.104990919227653, 2.413525419890917, 1.8594384636488344, 2.948708199969517, 1.4704892851699438, 1.2749783787314652, 0.9776363613270336, 1.4390832190214904, 1.1134170725979606, 0.6281468164151807, 0.274917845922386, 0.0), # 104
(3.840016103059581, 3.0078330346475504, 3.1341628086419755, 3.3311480978260866, 2.8729121278140886, 1.3653120713305902, 1.2690657744579317, 1.0465534979423872, 1.4714248971193415, 0.6175018155410315, 0.480728335991494, 0.2814219189950185, 0.0, 3.858304398148148, 3.0956411089452027, 2.40364167995747, 1.852505446623094, 2.942849794238683, 1.465174897119342, 1.2690657744579317, 0.9752229080932786, 1.4364560639070443, 1.1103826992753625, 0.6268325617283951, 0.273439366786141, 0.0), # 105
(3.8277894196934454, 2.9916048099473427, 3.1275400948788294, 3.3220196155394524, 2.867638451814196, 1.3619716455824824, 1.263157319992469, 1.0428398110044201, 1.4685072168876694, 0.6151853874499046, 0.4787693090751571, 0.2805696259395632, 0.0, 3.850485896776406, 3.0862658853351945, 2.3938465453757853, 1.8455561623497134, 2.9370144337753388, 1.4599757354061882, 1.263157319992469, 0.9728368897017731, 1.433819225907098, 1.1073398718464844, 0.6255080189757659, 0.27196407363157665, 0.0), # 106
(3.8155982346275423, 2.9754192243019606, 3.1208673125285786, 3.312872051127214, 2.8623518653003037, 1.3586764568409289, 1.2572551363480924, 1.0392102575826858, 1.465603619875019, 0.612863588584954, 0.47683088585661687, 0.2797154076320202, 0.0, 3.842662251371742, 3.0768694839522217, 2.3841544292830843, 1.8385907657548617, 2.931207239750038, 1.4548943606157603, 1.2572551363480924, 0.9704831834578064, 1.4311759326501519, 1.1042906837090716, 0.6241734625057157, 0.2704926567547237, 0.0), # 107
(3.8034608695652175, 2.9592838709677425, 3.114145833333334, 3.303711684782609, 2.857058823529411, 1.3554333333333337, 1.2513613445378151, 1.0356666666666667, 1.4627166666666667, 0.6105364705882355, 0.47491594896331746, 0.2788596491228071, 0.0, 3.8348437500000006, 3.0674561403508775, 2.374579744816587, 1.8316094117647062, 2.9254333333333333, 1.4499333333333335, 1.2513613445378151, 0.9681666666666668, 1.4285294117647056, 1.10123722826087, 0.6228291666666669, 0.269025806451613, 0.0), # 108
(3.7913956462098173, 2.9432063432010267, 3.1073770290352085, 3.2945447966988723, 2.85176578175852, 1.3522491032871007, 1.2454780655746525, 1.032210867245847, 1.4598489178478888, 0.6082040851018049, 0.4730273810227027, 0.2780027354623413, 0.0, 3.8270406807270234, 3.0580300900857535, 2.3651369051135136, 1.8246122553054143, 2.9196978356957777, 1.4450952141441857, 1.2454780655746525, 0.9658922166336433, 1.42588289087926, 1.0981815988996244, 0.6214754058070417, 0.2675642130182752, 0.0), # 109
(3.7794208862646865, 2.9271942342581534, 3.1005622713763157, 3.285377667069243, 2.846479195244628, 1.3491305949296348, 1.2396074204716179, 1.0288446883097089, 1.4570029340039627, 0.6058664837677185, 0.4711680646622168, 0.2771450517010405, 0.0, 3.819263331618656, 3.0485955687114448, 2.355840323311084, 1.817599451303155, 2.9140058680079255, 1.4403825636335925, 1.2396074204716179, 0.9636647106640249, 1.423239597622314, 1.0951258890230813, 0.6201124542752632, 0.2661085667507413, 0.0), # 110
(3.7675549114331726, 2.91125513739546, 3.093702932098766, 3.2762165760869566, 2.841205519244735, 1.3460846364883403, 1.2337515302417263, 1.0255699588477367, 1.4541812757201646, 0.6035237182280321, 0.4693408825093036, 0.27628698288932213, 0.0, 3.811521990740741, 3.039156811782543, 2.346704412546518, 1.810571154684096, 2.9083625514403293, 1.4357979423868314, 1.2337515302417263, 0.9614890260631003, 1.4206027596223676, 1.0920721920289858, 0.6187405864197533, 0.26465955794504187, 0.0), # 111
(3.75581604341862, 2.895396645869286, 3.086800382944674, 3.26706780394525, 2.8359512090158425, 1.3431180561906215, 1.2279125158979918, 1.0223885078494133, 1.4513865035817708, 0.6011758401248017, 0.46754871719140734, 0.2754289140776037, 0.0, 3.803826946159122, 3.0297180548536407, 2.337743585957037, 1.8035275203744048, 2.9027730071635416, 1.4313439109891786, 1.2279125158979918, 0.9593700401361582, 1.4179756045079213, 1.0890226013150834, 0.6173600765889348, 0.2632178768972078, 0.0), # 112
(3.744201689481218, 2.8796528268881825, 3.0798726094173565, 3.257950164747612, 2.830713514712988, 1.3402362794833866, 1.222105192731354, 1.0193087614634344, 1.4486283748344828, 0.5988304736612731, 0.4657949270768578, 0.274573097883481, 0.0, 3.7961775603372887, 3.0203040767182903, 2.328974635384289, 1.796491420983819, 2.8972567496689656, 1.4270322660488082, 1.222105192731354, 0.957311628202419, 1.415356757356494, 1.0859833882492043, 0.6159745218834713, 0.26178662062619845, 0.0), # 113
(3.732592359160026, 2.8641789672926965, 3.0730152250072065, 3.2489368263832006, 2.8254382278843537, 1.3374327419903105, 1.216403641682116, 1.0163685432508534, 1.4459492047617415, 0.5965315167912784, 0.46408295580754655, 0.2737304057370992, 0.0, 3.788510165664014, 3.0110344631080905, 2.3204147790377325, 1.7895945503738346, 2.891898409523483, 1.4229159605511947, 1.216403641682116, 0.9553091014216503, 1.4127191139421769, 1.0829789421277338, 0.6146030450014414, 0.2603799061175179, 0.0), # 114
(3.720953961201598, 2.848980639517117, 3.066232310902439, 3.240025351554534, 2.820108714103627, 1.3347001529163784, 1.2108119300383124, 1.0135671090464515, 1.4433499971558386, 0.5942825327988078, 0.46241030076180634, 0.27290125275196175, 0.0, 3.7808026526641507, 3.001913780271579, 2.3120515038090312, 1.7828475983964231, 2.886699994311677, 1.4189939526650321, 1.2108119300383124, 0.9533572520831274, 1.4100543570518136, 1.0800084505181782, 0.613246462180488, 0.2589982399561016, 0.0), # 115
(3.709271949295054, 2.8340357031402905, 3.0595107299946247, 3.231199845079921, 2.8147169403690073, 1.3320320713669895, 1.2053209635055788, 1.010896718816499, 1.4408241785637108, 0.5920793358449549, 0.4607737287514322, 0.27208410658291154, 0.0, 3.7730429039023563, 2.9929251724120265, 2.3038686437571605, 1.7762380075348643, 2.8816483571274216, 1.4152554063430987, 1.2053209635055788, 0.9514514795478496, 1.4073584701845037, 1.0770666150266406, 0.611902145998925, 0.25763960937639013, 0.0), # 116
(3.697531777129509, 2.8193220177410643, 3.052837345175329, 3.2224444117776727, 2.8092548736786958, 1.3294220564475412, 1.1999216477895505, 1.0083496325272643, 1.4383651755322937, 0.589917740090813, 0.45917000658821894, 0.2712774348847917, 0.0, 3.76521880194329, 2.9840517837327085, 2.2958500329410945, 1.7697532202724386, 2.8767303510645874, 1.4116894855381699, 1.1999216477895505, 0.9495871831768151, 1.4046274368393479, 1.0741481372592245, 0.6105674690350659, 0.25630200161282407, 0.0), # 117
(3.6857188983940845, 2.804817442898285, 3.0461990193361226, 3.2137431564660996, 2.8037144810308914, 1.3268636672634326, 1.1946048885958631, 1.0059181101450163, 1.4359664146085245, 0.587793559697476, 0.4575959010839617, 0.27047970531244503, 0.0, 3.75731822935161, 2.9752767584368947, 2.287979505419808, 1.7633806790924278, 2.871932829217049, 1.4082853542030227, 1.1946048885958631, 0.9477597623310232, 1.4018572405154457, 1.0712477188220335, 0.6092398038672245, 0.25498340389984414, 0.0), # 118
(3.673818766777897, 2.790499838190801, 3.0395826153685745, 3.2050801839635117, 2.7980877294237922, 1.324350462920061, 1.1893615916301512, 1.0035944116360243, 1.433621322339339, 0.5857026088260373, 0.45604817905045525, 0.26968938552071453, 0.0, 3.749329068691973, 2.9665832407278594, 2.2802408952522764, 1.7571078264781117, 2.867242644678678, 1.405032176290434, 1.1893615916301512, 0.9459646163714721, 1.3990438647118961, 1.0683600613211708, 0.607916523073715, 0.253681803471891, 0.0), # 119
(3.6618168359700647, 2.776347063197458, 3.0329749961642545, 3.196439599088218, 2.792366585855599, 1.3218760025228253, 1.1841826625980507, 1.0013707969665573, 1.4313233252716744, 0.5836407016375906, 0.4545236072994945, 0.2689049431644433, 0.0, 3.741239202529039, 2.9579543748088755, 2.272618036497472, 1.7509221049127714, 2.8626466505433488, 1.40191911575318, 1.1841826625980507, 0.9441971446591609, 1.3961832929277995, 1.0654798663627396, 0.6065949992328509, 0.25239518756340534, 0.0), # 120
(3.6496985596597074, 2.762336977497104, 3.0263630246147293, 3.1878055066585302, 2.7865430173245116, 1.319433845177124, 1.179059007205196, 0.9992395261028846, 1.4290658499524664, 0.5816036522932297, 0.4530189526428745, 0.26812484589847413, 0.0, 3.7330365134274643, 2.9493733048832147, 2.265094763214372, 1.744810956879689, 2.858131699904933, 1.3989353365440385, 1.179059007205196, 0.9424527465550885, 1.3932715086622558, 1.0626018355528437, 0.6052726049229459, 0.2511215434088277, 0.0), # 121
(3.6374493915359416, 2.7484474406685857, 3.0197335636115703, 3.179162011492757, 2.780608990828729, 1.3170175499883545, 1.1739815311572235, 0.9971928590112749, 1.4268423229286518, 0.5795872749540478, 0.45153098189239016, 0.2673475613776501, 0.0, 3.7247088839519082, 2.9408231751541503, 2.2576549094619507, 1.7387618248621433, 2.8536846458573035, 1.3960700026157848, 1.1739815311572235, 0.9407268214202532, 1.3903044954143644, 1.059720670497586, 0.6039467127223141, 0.24985885824259874, 0.0), # 122
(3.6250547852878876, 2.7346563122907503, 3.013073476046346, 3.1704932184092085, 2.774556473366451, 1.314620676061916, 1.1689411401597678, 0.9952230556579972, 1.4246461707471672, 0.5775873837811388, 0.4500564618598364, 0.2665715572568141, 0.0, 3.716244196667029, 2.9322871298249544, 2.2502823092991817, 1.7327621513434162, 2.8492923414943343, 1.3933122779211962, 1.1689411401597678, 0.9390147686156541, 1.3872782366832255, 1.0568310728030696, 0.6026146952092691, 0.24860511929915918, 0.0), # 123
(3.612500194604662, 2.7209414519424455, 3.0063696248106235, 3.1617832322261963, 2.7683774319358765, 1.312236782503206, 1.163928739918464, 0.9933223760093212, 1.4224708199549485, 0.5755997929355963, 0.448592159357008, 0.26579530119080924, 0.0, 3.7076303341374848, 2.923748313098901, 2.2429607967850402, 1.7267993788067884, 2.844941639909897, 1.3906513264130496, 1.163928739918464, 0.93731198750229, 1.3841887159679382, 1.053927744075399, 0.6012739249621247, 0.24735831381294962, 0.0), # 124
(3.5997710731753836, 2.7072807192025174, 2.999608872795975, 3.1530161577620284, 2.7620638335352057, 1.309859428417623, 1.1589352361389478, 0.9914830800315152, 1.4203096970989324, 0.5736203165785135, 0.4471348411957002, 0.26501726083447835, 0.0, 3.6988551789279316, 2.9151898691792613, 2.235674205978501, 1.7208609497355403, 2.840619394197865, 1.3880763120441213, 1.1589352361389478, 0.9356138774411593, 1.3810319167676028, 1.0510053859206763, 0.599921774559195, 0.24611642901841072, 0.0), # 125
(3.5868528746891712, 2.6936519736498146, 2.9927780828939663, 3.1441760998350166, 2.755607645162638, 1.307482172910566, 1.153951534526854, 0.9896974276908488, 1.4181562287260556, 0.5716447688709844, 0.44568127418770764, 0.26423590384266454, 0.0, 3.6899066136030316, 2.9065949422693094, 2.2284063709385378, 1.7149343066129528, 2.8363124574521112, 1.3855763987671883, 1.153951534526854, 0.9339158377932613, 1.377803822581319, 1.0480586999450057, 0.5985556165787933, 0.2448774521499832, 0.0), # 126
(3.5737310528351447, 2.680033074863182, 2.9858641179961682, 3.13524716326347, 2.749000833816373, 1.305098575087432, 1.1489685407878187, 0.987957678953591, 1.416003841383254, 0.5696689639741025, 0.44422822514482535, 0.2634496978702106, 0.0, 3.6807725207274395, 2.897946676572316, 2.2211411257241265, 1.7090068919223071, 2.832007682766508, 1.3831407505350275, 1.1489685407878187, 0.9322132679195942, 1.3745004169081865, 1.0450823877544901, 0.5971728235992337, 0.2436393704421075, 0.0), # 127
(3.5603910613024183, 2.6664018824214697, 2.9788538409941503, 3.1262134528656995, 2.7422353664946106, 1.3027021940536203, 1.1439771606274765, 0.9862560937860104, 1.4138459616174646, 0.5676887160489614, 0.44277246087884836, 0.2626571105719597, 0.0, 3.671440782865815, 2.8892282162915555, 2.2138623043942416, 1.7030661481468838, 2.827691923234929, 1.3807585313004147, 1.1439771606274765, 0.9305015671811573, 1.3711176832473053, 1.0420711509552334, 0.5957707681988301, 0.24240017112922455, 0.0), # 128
(3.546818353780113, 2.652736255903522, 2.9717341147794802, 3.1170590734600148, 2.73530321019555, 1.300286588914529, 1.1389682997514627, 0.9845849321543767, 1.4116760159756234, 0.5656998392566547, 0.4413107482015715, 0.2618566096027546, 0.0, 3.6618992825828154, 2.8804227056303, 2.2065537410078573, 1.6970995177699637, 2.823352031951247, 1.3784189050161275, 1.1389682997514627, 0.9287761349389492, 1.367651605097775, 1.0390196911533385, 0.594346822955896, 0.24115784144577473, 0.0), # 129
(3.532998383957347, 2.6390140548881877, 2.9644918022437268, 3.107768129864726, 2.72819633191739, 1.2978453187755554, 1.1339328638654125, 0.9829364540249584, 1.4094874310046666, 0.5636981477582759, 0.4398398539247897, 0.2610466626174385, 0.0, 3.6521359024430993, 2.8715132887918227, 2.199199269623948, 1.6910944432748272, 2.818974862009333, 1.3761110356349417, 1.1339328638654125, 0.9270323705539681, 1.364098165958695, 1.0359227099549089, 0.5928983604487453, 0.2399103686261989, 0.0), # 130
(3.5189166055232377, 2.625213138954313, 2.9571137662784603, 3.098324726898143, 2.7209066986583315, 1.295371942742099, 1.1288617586749619, 0.9813029193640249, 1.4072736332515314, 0.5616794557149186, 0.43835654486029796, 0.2602257372708542, 0.0, 3.6421385250113247, 2.8624831099793955, 2.1917827243014893, 1.6850383671447555, 2.814547266503063, 1.373824087109635, 1.1288617586749619, 0.9252656733872135, 1.3604533493291657, 1.0327749089660478, 0.5914227532556922, 0.2386557399049376, 0.0), # 131
(3.504558472166904, 2.611311367680746, 2.9495868697752488, 3.0887129693785758, 2.7134262774165743, 1.2928600199195572, 1.123745889885745, 0.9796765881378455, 1.4050280492631537, 0.5596395772876765, 0.43685758781989104, 0.2593923012178448, 0.0, 3.6318950328521504, 2.853315313396292, 2.184287939099455, 1.6789187318630292, 2.8100560985263074, 1.3715472233929837, 1.123745889885745, 0.9234714427996837, 1.3567131387082871, 1.0295709897928589, 0.5899173739550498, 0.2373919425164315, 0.0), # 132
(3.4899094375774653, 2.5972866006463327, 2.9418979756256616, 3.0789169621243357, 2.705747035190316, 1.2903031094133288, 1.118576163203398, 0.9780497203126887, 1.40274410558647, 0.5575743266376434, 0.43533974961536415, 0.25854482211325314, 0.0, 3.6213933085302346, 2.843993043245784, 2.1766987480768205, 1.6727229799129297, 2.80548821117294, 1.3692696084377642, 1.118576163203398, 0.9216450781523777, 1.352873517595158, 1.0263056540414455, 0.5883795951251324, 0.2361169636951212, 0.0), # 133
(3.474954955444038, 2.583116697429922, 2.934033946721268, 3.068920809953731, 2.697860938977758, 1.287694770328812, 1.1133434843335557, 0.9764145758548239, 1.4004152287684173, 0.5554795179259124, 0.43379979705851196, 0.25768176761192224, 0.0, 3.6106212346102335, 2.8344994437311444, 2.1689989852925597, 1.6664385537777369, 2.8008304575368346, 1.3669804061967537, 1.1133434843335557, 0.9197819788062943, 1.348930469488879, 1.0229736033179107, 0.5868067893442537, 0.23482879067544749, 0.0), # 134
(3.4596804794557414, 2.5687795176103587, 2.9259816459536365, 3.058708617685074, 2.689759955777099, 1.285028561771405, 1.1080387589818537, 0.9747634147305201, 1.3980348453559306, 0.5533509653135777, 0.4322344969611296, 0.2568016053686951, 0.0, 3.599566693656808, 2.824817659055646, 2.1611724848056477, 1.660052895940733, 2.796069690711861, 1.3646687806227282, 1.1080387589818537, 0.917877544122432, 1.3448799778885494, 1.0195695392283581, 0.5851963291907273, 0.23352541069185084, 0.0), # 135
(3.444071463301694, 2.554252920766492, 2.9177279362143365, 3.0482644901366713, 2.681436052586538, 1.282298042846506, 1.102652892853927, 0.9730884969060463, 1.3955963818959474, 0.5511844829617324, 0.43064061613501187, 0.2559028030384148, 0.0, 3.5882175682346147, 2.814930833422562, 2.153203080675059, 1.6535534488851968, 2.7911927637918947, 1.362323895668465, 1.102652892853927, 0.9159271734617901, 1.340718026293269, 1.0160881633788907, 0.5835455872428673, 0.23220481097877202, 0.0), # 136
(3.4281133606710137, 2.5395147664771676, 2.9092596803949373, 3.0375725321268376, 2.6728811964042754, 1.279496772659513, 1.0971767916554112, 0.9713820823476715, 1.3930932649354042, 0.5489758850314703, 0.42901492139195374, 0.25498382827592403, 0.0, 3.5765617409083106, 2.804822111035164, 2.145074606959769, 1.6469276550944105, 2.7861865298708084, 1.3599349152867402, 1.0971767916554112, 0.9139262661853664, 1.3364405982021377, 1.0125241773756128, 0.5818519360789874, 0.23086497877065162, 0.0), # 137
(3.4117916252528193, 2.5245429143212332, 2.900563741387006, 3.0266168484738794, 2.6640873542285117, 1.2766183103158248, 1.0916013610919408, 0.9696364310216651, 1.3905189210212374, 0.5467209856838848, 0.4273541795437502, 0.254043148736066, 0.0, 3.5645870942425564, 2.794474636096725, 2.136770897718751, 1.640162957051654, 2.781037842042475, 1.3574910034303311, 1.0916013610919408, 0.9118702216541607, 1.3320436771142559, 1.0088722828246266, 0.5801127482774012, 0.22950390130193032, 0.0), # 138
(3.3950917107362275, 2.509315223877536, 2.8916269820821134, 3.015381543996108, 2.6550464930574442, 1.2736562149208395, 1.085917506869152, 0.9678438028942958, 1.387866776700383, 0.5444155990800699, 0.4256551574021961, 0.2530792320736836, 0.0, 3.5522815108020076, 2.7838715528105187, 2.1282757870109803, 1.6332467972402092, 2.775733553400766, 1.354981324052014, 1.085917506869152, 0.9097544392291711, 1.3275232465287221, 1.0051271813320362, 0.5783253964164228, 0.22811956580704876, 0.0), # 139
(3.3779990708103593, 2.4938095547249226, 2.882436265371829, 3.0038507235118335, 2.6457505798892744, 1.2706040455799552, 1.0801161346926793, 0.9659964579318328, 1.3851302585197776, 0.5420555393811187, 0.42391462177908634, 0.25209054594361974, 0.0, 3.539632873151326, 2.7729960053798166, 2.1195731088954313, 1.6261666181433558, 2.770260517039555, 1.352395041104566, 1.0801161346926793, 0.9075743182713966, 1.3228752899446372, 1.0012835745039448, 0.5764872530743659, 0.22670995952044753, 0.0), # 140
(3.3604991591643323, 2.478003766442241, 2.8729784541477206, 2.992008491839366, 2.636191581722201, 1.2674553613985702, 1.074188150268159, 0.9640866561005451, 1.3823027930263572, 0.5396366207481252, 0.422129339486216, 0.2510755580007175, 0.0, 3.5266290638551654, 2.761831138007892, 2.1106466974310796, 1.6189098622443754, 2.7646055860527143, 1.3497213185407633, 1.074188150268159, 0.9053252581418358, 1.3180957908611004, 0.9973361639464555, 0.5745956908295441, 0.2252730696765674, 0.0), # 141
(3.3425774294872626, 2.4618757186083373, 2.863240411301357, 2.9798389537970165, 2.626361465554423, 1.2642037214820832, 1.0681244593012253, 0.962106657366702, 1.3793778067670588, 0.5371546573421829, 0.4202960773353799, 0.25003273589981984, 0.0, 3.5132579654781866, 2.750360094898018, 2.101480386676899, 1.6114639720265485, 2.7587556135341176, 1.3469493203133829, 1.0681244593012253, 0.903002658201488, 1.3131807327772116, 0.9932796512656723, 0.5726480822602714, 0.2238068835098489, 0.0), # 142
(3.32421933546827, 2.4454032708020597, 2.8532089997243086, 2.9673262142030925, 2.616252198384141, 1.2608426849358916, 1.0619159674975138, 0.960048721696572, 1.3763487262888197, 0.5346054633243854, 0.4184116021383729, 0.2489605472957697, 0.0, 3.499507460585047, 2.738566020253466, 2.0920580106918645, 1.603816389973156, 2.7526974525776393, 1.3440682103752009, 1.0619159674975138, 0.9006019178113511, 1.3081260991920705, 0.9891087380676977, 0.5706417999448617, 0.22230938825473273, 0.0), # 143
(3.305410330796474, 2.4285642826022547, 2.8428710823081427, 2.954454377875907, 2.6058557472095543, 1.2573658108653942, 1.0555535805626597, 0.9579051090564249, 1.3732089781385746, 0.5319848528558261, 0.4164726807069901, 0.24785745984341, 0.0, 3.485365431740406, 2.7264320582775095, 2.0823634035349503, 1.595954558567478, 2.746417956277149, 1.3410671526789948, 1.0555535805626597, 0.8981184363324245, 1.3029278736047771, 0.9848181259586359, 0.5685742164616286, 0.22077857114565957, 0.0), # 144
(3.286135869160991, 2.41133661358777, 2.8322135219444298, 2.9412075496337686, 2.595164079028862, 1.2537666583759894, 1.0490282042022987, 0.9556680794125294, 1.3699519888632605, 0.5292886400975989, 0.41447607985302637, 0.24672194119758384, 0.0, 3.47081976150892, 2.7139413531734218, 2.072380399265132, 1.5878659202927965, 2.739903977726521, 1.3379353111775412, 1.0490282042022987, 0.8955476131257067, 1.297582039514431, 0.9804025165445898, 0.566442704388886, 0.21921241941707004, 0.0), # 145
(3.2663814042509403, 2.393698123337452, 2.821223181524739, 2.927569834294988, 2.584169160840265, 1.2500387865730758, 1.042330744122066, 0.9533298927311545, 1.3665711850098141, 0.5265126392107972, 0.4124185663882766, 0.24555245901313405, 0.0, 3.4558583324552474, 2.701077049144474, 2.062092831941383, 1.5795379176323912, 2.7331423700196282, 1.3346618498236165, 1.042330744122066, 0.8928848475521969, 1.2920845804201324, 0.9758566114316628, 0.5642446363049479, 0.21760892030340476, 0.0), # 146
(3.24613238975544, 2.375626671430148, 2.8098869239406365, 2.913525336677874, 2.5728629596419603, 1.2461757545620502, 1.0354521060275963, 0.9508828089785692, 1.3630599931251721, 0.5236526643565147, 0.4102969071245358, 0.24434748094490372, 0.0, 3.4404690271440472, 2.6878222903939406, 2.051484535622679, 1.5709579930695439, 2.7261199862503442, 1.331235932569997, 1.0354521060275963, 0.8901255389728929, 1.2864314798209802, 0.9711751122259582, 0.5619773847881274, 0.21596606103910437, 0.0), # 147
(3.2253742793636087, 2.3571001174447055, 2.7981916120836945, 2.899058161600739, 2.56123744243215, 1.2421711214483127, 1.0283831956245253, 0.9483190881210429, 1.3594118397562704, 0.5207045296958448, 0.4081078688735989, 0.24310547464773571, 0.0, 3.4246397281399767, 2.6741602211250926, 2.0405393443679944, 1.562113589087534, 2.718823679512541, 1.3276467233694602, 1.0283831956245253, 0.8872650867487947, 1.280618721216075, 0.9663527205335799, 0.5596383224167389, 0.21428182885860964, 0.0), # 148
(3.204092526764565, 2.338096320959971, 2.7861241088454816, 2.884152413881891, 2.549284576209032, 1.2380184463372599, 1.0211149186184882, 0.9456309901248444, 1.355620151450045, 0.5176640493898814, 0.40584821844726066, 0.24182490777647309, 0.0, 3.408358318007695, 2.6600739855412034, 2.0292410922363033, 1.5529921481696438, 2.71124030290009, 1.3238833861747823, 1.0211149186184882, 0.8842988902408999, 1.274642288104516, 0.9613841379606305, 0.5572248217690964, 0.21255421099636107, 0.0), # 149
(3.182272585647426, 2.3185931415547922, 2.773671277117565, 2.8687921983396416, 2.5369963279708068, 1.2337112883342916, 1.0136381807151202, 0.9428107749562428, 1.3516783547534337, 0.5145270375997177, 0.40351472265731625, 0.24050424798595882, 0.0, 3.3916126793118586, 2.6455467278455465, 2.017573613286581, 1.5435811127991528, 2.7033567095068674, 1.31993508493874, 1.0136381807151202, 0.8812223488102082, 1.2684981639854034, 0.956264066113214, 0.5547342554235131, 0.21078119468679934, 0.0), # 150
(3.15989990970131, 2.2985684388080165, 2.7608199797915143, 2.852961619792299, 2.524364664715674, 1.2292432065448047, 1.0059438876200566, 0.9398507025815073, 1.347579876213372, 0.5112893084864479, 0.40110414831556035, 0.23914196293103576, 0.0, 3.3743906946171274, 2.630561592241393, 2.0055207415778016, 1.5338679254593435, 2.695159752426744, 1.3157909836141102, 1.0059438876200566, 0.8780308618177176, 1.262182332357837, 0.9509872065974332, 0.5521639959583029, 0.20896076716436518, 0.0), # 151
(3.1369599526153373, 2.27800007229849, 2.747557079758901, 2.836644783058176, 2.5113815534418316, 1.2246077600741982, 0.998022945038933, 0.9367430329669069, 1.343318142376796, 0.5079466762111651, 0.3986132622337882, 0.237736520266547, 0.0, 3.356680246488159, 2.6151017229320166, 1.9930663111689406, 1.523840028633495, 2.686636284753592, 1.3114402461536696, 0.998022945038933, 0.8747198286244273, 1.2556907767209158, 0.9455482610193922, 0.5495114159517802, 0.2070909156634991, 0.0), # 152
(3.1134381680786243, 2.2568659016050607, 2.7338694399112895, 2.81982579295558, 2.4980389611474814, 1.2197985080278704, 0.9898662586773839, 0.9334800260787104, 1.338886579790643, 0.504494954934963, 0.39603883122379446, 0.2362863876473355, 0.0, 3.338469217489611, 2.59915026412069, 1.9801941561189722, 1.5134848648048886, 2.677773159581286, 1.3068720365101947, 0.9898662586773839, 0.871284648591336, 1.2490194805737407, 0.9399419309851935, 0.546773887982258, 0.20516962741864192, 0.0), # 153
(3.0893200097802915, 2.2351437863065757, 2.719743923140253, 2.802488754302823, 2.4843288548308213, 1.2148090095112194, 0.9814647342410456, 0.9300539418831871, 1.3342786150018489, 0.5009299588189354, 0.3933776220973742, 0.2347900327282442, 0.0, 3.319745490186143, 2.5826903600106856, 1.9668881104868707, 1.502789876456806, 2.6685572300036977, 1.302075518636462, 0.9814647342410456, 0.8677207210794424, 1.2421644274154107, 0.9341629181009412, 0.5439487846280506, 0.20319488966423419, 0.0), # 154
(3.0645909314094544, 2.212811585981881, 2.705167392337359, 2.7846177719182137, 2.470243201490052, 1.2096328236296434, 0.9728092774355522, 0.926457040346606, 1.3294876745573503, 0.49724750202417556, 0.39062640166632223, 0.2332459231641161, 0.0, 3.3004969471424106, 2.565705154805277, 1.953132008331611, 1.4917425060725265, 2.6589753491147006, 1.2970398564852486, 0.9728092774355522, 0.8640234454497453, 1.235121600745026, 0.928205923972738, 0.5410334784674719, 0.2011646896347165, 0.0), # 155
(3.0392363866552325, 2.1898471602098257, 2.6901267103941757, 2.7661969506200625, 2.455773968123373, 1.204263509488541, 0.96389079396654, 0.9226815814352365, 1.3245071850040835, 0.4934433987117774, 0.38778193674243366, 0.23165252660979413, 0.0, 3.280711470923074, 2.548177792707735, 1.9389096837121682, 1.480330196135332, 2.649014370008167, 1.2917542140093312, 0.96389079396654, 0.8601882210632436, 1.2278869840616864, 0.9220656502066877, 0.5380253420788351, 0.19907701456452961, 0.0), # 156
(3.013241829206745, 2.1662283685692554, 2.674608740202273, 2.7472103952266815, 2.4409131217289826, 1.19869462619331, 0.9547001895396439, 0.9187198251153471, 1.319330572888985, 0.4895134630428343, 0.38484099413750333, 0.2300083107201213, 0.0, 3.2603769440927906, 2.5300914179213336, 1.9242049706875164, 1.4685403891285025, 2.63866114577797, 1.2862077551614859, 0.9547001895396439, 0.8562104472809356, 1.2204565608644913, 0.915736798408894, 0.5349217480404546, 0.19692985168811414, 0.0), # 157
(2.985872378562096, 2.141499477616495, 2.6578639846341185, 2.7269308744953733, 2.424981628232266, 1.1925723778073256, 0.9450213441855715, 0.9142994920582287, 1.3135549455465463, 0.48533659162911447, 0.38170638350259617, 0.22825331880647803, 0.0, 3.238594343766138, 2.510786506871258, 1.908531917512981, 1.456009774887343, 2.6271098910930926, 1.2800192888815203, 0.9450213441855715, 0.8518374127195183, 1.212490814116133, 0.9089769581651246, 0.5315727969268238, 0.19468177069240863, 0.0), # 158
(2.9529147067913613, 2.1131239505198085, 2.635579272800996, 2.7011931476365363, 2.4040510159417674, 1.1838609178683244, 0.9336425526771432, 0.9078689221273971, 1.3048569681629525, 0.48022809940987465, 0.37782779570793296, 0.22604541745610365, 0.0, 3.210171058768078, 2.48649959201714, 1.8891389785396648, 1.4406842982296237, 2.609713936325905, 1.271016490978356, 0.9336425526771432, 0.8456149413345173, 1.2020255079708837, 0.9003977158788457, 0.5271158545601993, 0.1921021773199826, 0.0), # 159
(2.913948837961724, 2.0808688004649283, 2.6073069859852964, 2.669573253122658, 2.3777120350258123, 1.1723463024111265, 0.9204487496767568, 0.8992665315878912, 1.2929900302971533, 0.4741205651862895, 0.3731506339073027, 0.22335006496292825, 0.0, 3.1745682435574323, 2.4568507145922105, 1.8657531695365135, 1.422361695558868, 2.5859800605943066, 1.2589731442230476, 0.9204487496767568, 0.8373902160079474, 1.1888560175129061, 0.8898577510408863, 0.5214613971970593, 0.18916989095135714, 0.0), # 160
(2.869288821834384, 2.0449443182961717, 2.5733489906367697, 2.6323717511270184, 2.34623816647523, 1.1581680230330733, 0.9055362892013753, 0.8886000888516301, 1.278110635599869, 0.4670658193170939, 0.3677161103066472, 0.22019224896358572, 0.0, 3.132149617927639, 2.4221147385994426, 1.8385805515332359, 1.4011974579512814, 2.556221271199738, 1.2440401243922823, 0.9055362892013753, 0.8272628735950524, 1.173119083237615, 0.877457250375673, 0.514669798127354, 0.18590402893601563, 0.0), # 161
(2.8192487081705426, 2.005560794857854, 2.5340071532051653, 2.5898892018228983, 2.3099028912808546, 1.1414655713315065, 0.8890015252679618, 0.8759773623305338, 1.2603752877218182, 0.45911569216102327, 0.3615654371119081, 0.21659695709470983, 0.0, 3.0832789016721334, 2.382566528041808, 1.8078271855595405, 1.3773470764830695, 2.5207505754436363, 1.2263683072627474, 0.8890015252679618, 0.815332550951076, 1.1549514456404273, 0.8632964006076329, 0.5068014306410331, 0.18232370862344133, 0.0), # 162
(2.7641425467313994, 1.9629285209942922, 2.4895833401402343, 2.5424261653835805, 2.268979690433517, 1.122378438903767, 0.8709408118934802, 0.8615061204365209, 1.2399404903137208, 0.4503220140768126, 0.35473982652902725, 0.21258917699293448, 0.0, 3.0283198145843517, 2.338480946922279, 1.7736991326451361, 1.3509660422304375, 2.4798809806274416, 1.2061085686111293, 0.8709408118934802, 0.8016988849312622, 1.1344898452167584, 0.8474753884611936, 0.4979166680280469, 0.1784480473631175, 0.0), # 163
(2.704284387278154, 1.917257787549801, 2.4403794178917257, 2.4902832019823453, 2.2237420449240504, 1.1010461173471968, 0.8514505030948932, 0.8452941315815116, 1.2169627470262965, 0.440736615423197, 0.34728049076394646, 0.20819389629489346, 0.0, 2.9676360764577314, 2.290132859243828, 1.736402453819732, 1.3222098462695906, 2.433925494052593, 1.1834117842141163, 0.8514505030948932, 0.7864615123908549, 1.1118710224620252, 0.830094400660782, 0.4880758835783452, 0.17429616250452737, 0.0), # 164
(2.639988279572007, 1.8687588853686983, 2.3866972529093897, 2.433760871792476, 2.174463435743286, 1.077608098259137, 0.8306269528891644, 0.8274491641774244, 1.191598561510264, 0.43041132655891146, 0.3392286420226075, 0.2034361026372207, 0.0, 2.901591407085708, 2.237797129009427, 1.6961432101130374, 1.291233979676734, 2.383197123020528, 1.1584288298483942, 0.8306269528891644, 0.7697200701850978, 1.087231717871643, 0.8112536239308255, 0.477339450581878, 0.16988717139715442, 0.0), # 165
(2.571568273374159, 1.8176421052952998, 2.3288387116429763, 2.3731597349872504, 2.121417343882057, 1.0522038732369288, 0.8085665152932573, 0.8080789866361796, 1.164004437416343, 0.41939797784269134, 0.330625492510952, 0.19834078365655008, 0.0, 2.830549526261718, 2.1817486202220504, 1.6531274625547598, 1.2581939335280736, 2.328008874832686, 1.1313105812906514, 0.8085665152932573, 0.7515741951692348, 1.0607086719410286, 0.7910532449957504, 0.4657677423285953, 0.16524019139048182, 0.0), # 166
(2.4993384184458094, 1.764117738173922, 2.267105660542235, 2.308780351739953, 2.0648772503311945, 1.0249729338779137, 0.785365544324135, 0.7872913673696962, 1.1343368783952532, 0.40774839963327153, 0.3215122544349219, 0.1929329269895153, 0.0, 2.7548741537791983, 2.122262196884668, 1.607561272174609, 1.2232451988998143, 2.2686737567905064, 1.1022079143175747, 0.785365544324135, 0.7321235241985098, 1.0324386251655973, 0.7695934505799846, 0.45342113210844703, 0.16037433983399293, 0.0), # 167
(2.4236127645481584, 1.7083960748488805, 2.201799966056916, 2.240923282223864, 2.005116636081531, 0.9960547717794331, 0.7611203939987609, 0.7651940747898933, 1.102752388097714, 0.3955144222893873, 0.31193014000045877, 0.1872375202727504, 0.0, 2.674929009431585, 2.0596127230002543, 1.5596507000022939, 1.1865432668681617, 2.205504776195428, 1.0712717047058506, 0.7611203939987609, 0.7114676941281666, 1.0025583180407656, 0.7469744274079547, 0.4403599932113833, 0.15530873407717097, 0.0), # 168
(2.344705361442406, 1.6506874061644923, 2.13322349463677, 2.169889086612265, 1.9424089821238986, 0.9655888785388289, 0.7359274183340984, 0.7418948773086909, 1.0694074701744452, 0.3827478761697738, 0.3019203614135046, 0.1812795511428891, 0.0, 2.591077813012314, 1.9940750625717798, 1.509601807067523, 1.148243628509321, 2.1388149403488903, 1.0386528282321672, 0.7359274183340984, 0.6897063418134491, 0.9712044910619493, 0.7232963622040884, 0.426644698927354, 0.15006249146949932, 0.0), # 169
(2.2629302588897535, 1.5912020229650736, 2.061678112731545, 2.095978325078436, 1.8770277694491289, 0.9337147457534416, 0.7098829713471106, 0.717501543338008, 1.0344586282761652, 0.36950059163316584, 0.2915241308800011, 0.1750840072365653, 0.0, 2.503684284314822, 1.9259240796022181, 1.4576206544000057, 1.1085017748994974, 2.0689172565523304, 1.0045021606732112, 0.7098829713471106, 0.6669391041096011, 0.9385138847245644, 0.6986594416928121, 0.412335622546309, 0.14465472936046128, 0.0), # 170
(2.1786015066514, 1.53015021609494, 1.9874656867909928, 2.0194915577956607, 1.809246479048055, 0.900571865020613, 0.6830834070547611, 0.6921218412897638, 0.9980623660535942, 0.35582439903829893, 0.2807826606058899, 0.16867587619041288, 0.0, 2.413112143132546, 1.8554346380945415, 1.4039133030294495, 1.0674731971148965, 1.9961247321071884, 0.9689705778056694, 0.6830834070547611, 0.6432656178718664, 0.9046232395240275, 0.6731638525985537, 0.3974931373581986, 0.13910456509954003, 0.0), # 171
(2.092033154488546, 1.4677422763984087, 1.9108880832648623, 1.940729344937219, 1.7393385919115076, 0.8662997279376846, 0.6556250794740132, 0.6658635395758785, 0.9603751871574514, 0.3417711287439081, 0.26973716279711296, 0.16208014564106574, 0.0, 2.3197251092589215, 1.7828816020517226, 1.3486858139855649, 1.025313386231724, 1.9207503743149028, 0.9322089554062299, 0.6556250794740132, 0.618785519955489, 0.8696692959557538, 0.6469097816457398, 0.3821776166529725, 0.13343111603621902, 0.0), # 172
(2.003539252162392, 1.4041884947197956, 1.832247168602904, 1.8599922466763927, 1.6675775890303204, 0.8310378261019976, 0.62760434262183, 0.6388344066082706, 0.9215535952384564, 0.32739261110872825, 0.25842884965961194, 0.1553218032251575, 0.0, 2.223886902487385, 1.7085398354767325, 1.2921442482980594, 0.9821778333261846, 1.8431071904769127, 0.8943681692515789, 0.62760434262183, 0.5935984472157125, 0.8337887945151602, 0.6199974155587977, 0.36644943372058086, 0.12765349951998142, 0.0), # 173
(1.9134338494341376, 1.3396991619034166, 1.7518448092548675, 1.7775808231864623, 1.5942369513953243, 0.7949256511108933, 0.5991175505151751, 0.6111422107988601, 0.8817540939473285, 0.31274067649149473, 0.24689893339932856, 0.1484258365793223, 0.0, 2.1259612426113734, 1.632684202372545, 1.2344946669966426, 0.9382220294744841, 1.763508187894657, 0.8555990951184042, 0.5991175505151751, 0.5678040365077809, 0.7971184756976621, 0.5925269410621542, 0.3503689618509735, 0.12179083290031062, 0.0), # 174
(1.8220309960649823, 1.274484568793588, 1.6699828716705027, 1.6937956346407104, 1.5195901599973516, 0.7581026945617134, 0.5702610571710116, 0.582894720559566, 0.8411331869347874, 0.2978671552509425, 0.23518862622220466, 0.14141723334019382, 0.0, 2.026311849424323, 1.5555895667421318, 1.1759431311110233, 0.8936014657528273, 1.682266373869575, 0.8160526087833925, 0.5702610571710116, 0.5415019246869381, 0.7597950799986758, 0.5645985448802369, 0.33399657433410057, 0.11586223352668984, 0.0), # 175
(1.7296447418161276, 1.2087550062346268, 1.5869632222995596, 1.6089372412124177, 1.4439106958272347, 0.720708448051799, 0.541131216606303, 0.5541997043023082, 0.7998473778515522, 0.28282387774580675, 0.22333914033418203, 0.134320981144406, 0.0, 1.9253024427196697, 1.4775307925884658, 1.11669570167091, 0.84847163323742, 1.5996947557031045, 0.7758795860232315, 0.541131216606303, 0.5147917486084279, 0.7219553479136174, 0.5363124137374726, 0.31739264445991194, 0.10988681874860246, 0.0), # 176
(1.636589136448773, 1.1427207650708489, 1.503087727591788, 1.5233062030748648, 1.3674720398758062, 0.6828824031784915, 0.5118243828380126, 0.5251649304390055, 0.7580531703483426, 0.2676626743348225, 0.21139168794120244, 0.12716206762859264, 0.0, 1.82329674229085, 1.3987827439145188, 1.056958439706012, 0.8029880230044673, 1.5161063406966853, 0.7352309026146077, 0.5118243828380126, 0.48777314512749387, 0.6837360199379031, 0.5077687343582884, 0.3006175455183576, 0.10388370591553173, 0.0), # 177
(1.5431782297241188, 1.0765921361465705, 1.4186582539969381, 1.437203080401335, 1.290547673133897, 0.6447640515391326, 0.4824369098831035, 0.4958981673815776, 0.715907068075878, 0.25243537537672506, 0.19938748124920752, 0.1199654804293876, 0.0, 1.7206584679313008, 1.3196202847232632, 0.9969374062460375, 0.757306126130175, 1.431814136151756, 0.6942574343342086, 0.4824369098831035, 0.46054575109938045, 0.6452738365669485, 0.47906769346711175, 0.2837316507993876, 0.09787201237696096, 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
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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), # 154
(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), # 155
(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
55, # 1
)
| 276.51016 | 500 | 0.769766 | 32,987 | 258,537 | 6.03274 | 0.198442 | 0.358187 | 0.343715 | 0.65125 | 0.387232 | 0.380474 | 0.374077 | 0.367775 | 0.365353 | 0.363926 | 0 | 0.849959 | 0.095669 | 258,537 | 934 | 501 | 276.80621 | 0.001193 | 0.015514 | 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 |
8b5acffc0e471fda19a1cdda932eb058d52e29b8 | 3,735 | py | Python | Board.py | FlubOtic/Tic-Tac-Toe | 2b51a1af589bb388c3e37bcc30c08dd1231b2d03 | [
"Unlicense"
] | null | null | null | Board.py | FlubOtic/Tic-Tac-Toe | 2b51a1af589bb388c3e37bcc30c08dd1231b2d03 | [
"Unlicense"
] | null | null | null | Board.py | FlubOtic/Tic-Tac-Toe | 2b51a1af589bb388c3e37bcc30c08dd1231b2d03 | [
"Unlicense"
] | null | null | null | class Board:
def __init__(self):
self.block = [["Empty" for x in range(3)] for y in range(3)]
self.won = False
def set_block(self, location, xo):
self.block[location[0]][location[1]] = xo
self.won = self.horizontal_check(location, xo)
def available_block(self, location):
if self.block[location[0]][location[1]] == "Empty":
return True
else:
return False
def draw(self):
for i in range(3):
for j in range(3):
if self.block[i][j] == "Empty":
return False
return True
def horizontal_check(self, location, xo):
empty = [False, False]
consecutive = 1
for i in range(2):
if -1 < location[0] + 1 + i < 3 and not empty[0]:
if self.block[location[0] + 1 + i][location[1]] == xo:
consecutive += 1
else:
empty[0] = True
if -1 < location[0] - 1 - i < 3 and not empty[1]:
if self.block[location[0] - 1 - i][location[1]] == xo:
consecutive += 1
else:
empty[1] = True
if consecutive >= 3:
return True
else:
return self.vertical_check(location, xo)
def vertical_check(self, location, xo):
empty = [False, False]
consecutive = 1
for i in range(2):
if -1 < location[1] + 1 + i < 3 and not empty[0]:
if self.block[location[0]][location[1] + 1 + i] == xo:
consecutive += 1
else:
empty[0] = True
if -1 < location[1] - 1 - i < 3 and not empty[1]:
if self.block[location[0]][location[1] - 1 - i] == xo:
consecutive += 1
else:
empty[1] = True
if consecutive >= 3:
return True
else:
return self.forward_diagonal_check(location, xo)
def forward_diagonal_check(self, location, xo):
empty = [False, False]
consecutive = 1
for i in range(2):
if -1 < location[0] + 1 + i < 3 and -1 < location[1] + 1 + i < 3 and not empty[0]:
if self.block[location[0] + 1 + i][location[1] + 1 + i] == xo:
consecutive += 1
else:
empty[0] = True
if -1 < location[0] - 1 - i < 3 and -1 < location[1] - 1 - i < 3 and not empty[1]:
if self.block[location[0] - 1 - i][location[1] - 1 - i] == xo:
consecutive += 1
else:
empty[1] = True
if consecutive >= 3:
return True
else:
return self.backward_diagonal_check(location, xo)
def backward_diagonal_check(self, location, xo):
empty = [False, False]
consecutive = 1
for i in range(2):
if -1 < location[0] - 1 - i < 3 and -1 < location[1] + 1 + i < 3 and not empty[0]:
if self.block[location[0] - 1 - i][location[1] + 1 + i] == xo:
consecutive += 1
else:
empty[0] = True
if -1 < location[0] + 1 + i < 3 and -1 < location[1] - 1 - i < 3 and not empty[1]:
if self.block[location[0] + 1 + i][location[1] - 1 - i] == xo:
consecutive += 1
else:
empty[1] = True
if consecutive >= 3:
return True
else:
return False
| 35.235849 | 94 | 0.43668 | 446 | 3,735 | 3.616592 | 0.085202 | 0.029758 | 0.074396 | 0.081835 | 0.802232 | 0.751395 | 0.734656 | 0.716677 | 0.716677 | 0.716677 | 0 | 0.05814 | 0.44739 | 3,735 | 106 | 95 | 35.235849 | 0.723353 | 0 | 0 | 0.6 | 0 | 0 | 0.004015 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.088889 | false | 0 | 0 | 0 | 0.233333 | 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 |
8b7afb448a0d19aabb57cbd1068272f9e3fecbf0 | 124 | py | Python | ncssl_api_client/api/commands/create_command.py | antonku/ncssl_api_client | c463b000960d50368d39bde2a180499f1da3a29a | [
"MIT"
] | 8 | 2017-11-28T11:05:52.000Z | 2021-11-16T13:52:45.000Z | ncssl_api_client/api/commands/create_command.py | antonku/ncssl_api_client | c463b000960d50368d39bde2a180499f1da3a29a | [
"MIT"
] | 4 | 2018-12-23T14:52:11.000Z | 2019-08-09T21:01:44.000Z | ncssl_api_client/api/commands/create_command.py | antonku/ncssl_api_client | c463b000960d50368d39bde2a180499f1da3a29a | [
"MIT"
] | 2 | 2017-11-28T14:38:24.000Z | 2017-11-29T09:03:20.000Z | from ncssl_api_client.api.commands.abstract_command import AbstractCommand
class CreateCommand(AbstractCommand):
pass
| 20.666667 | 74 | 0.846774 | 14 | 124 | 7.285714 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104839 | 124 | 5 | 75 | 24.8 | 0.918919 | 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 |
8bb4888998e2491bda692ec0cf1858e911259795 | 13,152 | py | Python | api/tests.py | mahbd/simplify-rest-framework | c270a215260c313134662fb5175a63fe716a9553 | [
"MIT"
] | null | null | null | api/tests.py | mahbd/simplify-rest-framework | c270a215260c313134662fb5175a63fe716a9553 | [
"MIT"
] | null | null | null | api/tests.py | mahbd/simplify-rest-framework | c270a215260c313134662fb5175a63fe716a9553 | [
"MIT"
] | null | null | null | from django.contrib.auth.models import User
from django.test import TestCase, Client
from api.models import Problem, Contest, TestCase as TestCaseModel
c = Client()
class UserTestCase(TestCase):
def setUp(self):
self.user = User.objects.create_user(username='testuser', password='12345', first_name='Test',
last_name='User', email='test@gmail.com')
self.api_url = '/api/user/'
def test_create_user(self):
response = c.post(self.api_url, {'username': 'testuser2', 'password': '12345', 'first_name': 'Test',
'last_name': 'User', 'email': 'test@gmail.com'})
self.assertEqual(response.status_code, 201)
data = response.json()
self.assertEqual(User.objects.get(username='testuser2').check_password('12345'), True)
self.assertEqual(data['username'], 'testuser2')
self.assertEqual(data['first_name'], 'Test')
self.assertEqual(data['last_name'], 'User')
self.assertEqual(data['email'], 'test@gmail.com')
def test_get_user_list(self):
response = c.get(self.api_url)
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(data[0]['username'], 'testuser')
self.assertEqual(len(data), User.objects.all().count())
def test_get_user(self):
response = c.get(self.api_url + '1/')
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(data['username'], 'testuser')
self.assertEqual(data['first_name'], 'Test')
self.assertEqual(data['last_name'], 'User')
self.assertEqual(data['email'], 'test@gmail.com')
class ProblemTestCase(TestCase):
def setUp(self):
self.user = User.objects.create_user(username='testuser', password='12345', first_name='Test',
last_name='User')
self.api_url = '/api/problem/'
c.force_login(self.user)
c.post(self.api_url, {'title': 'Test Problem first', 'description': 'Test Description second',
'input_terms': 'hello world bad',
'output_terms': 'nice world bad'})
c.logout()
def test_create_problem(self):
response = c.post(self.api_url, {'title': 'Test Problem', 'description': 'Test Description',
'input_terms': 'hello world',
'output_terms': 'nice world'})
self.assertEqual(response.status_code, 201, f'{response.content}')
data = response.json()
self.assertEqual(data['title'], 'Test Problem')
self.assertEqual(data['description'], 'Test Description')
self.assertEqual(data['input_terms'], 'hello world')
self.assertEqual(data['output_terms'], 'nice world')
def test_problem_low_difficulty(self):
c.force_login(self.user)
response = c.post(self.api_url, {'title': 'Test Problem', 'description': 'Test Description',
'input_terms': 'hello world',
'output_terms': 'nice world', 'difficulty': 100})
self.assertEqual(response.status_code, 400)
def test_get_problem_list(self):
response = c.get(self.api_url)
self.assertEqual(response.status_code, 200)
data = response.json()
def test_get_problem(self):
response = c.get(self.api_url + '1/')
self.assertEqual(response.status_code, 200)
def test_update_problem(self):
c.force_login(self.user)
response = c.put(self.api_url + '1/', data={'title': 'Test Problem', 'description': 'Test Description',
'input_terms': 'hello world',
'output_terms': 'nice world'}, content_type='application/json')
self.assertEqual(response.status_code, 200, f'{response.content}')
data = response.json()
self.assertEqual(data['title'], 'Test Problem')
self.assertEqual(data['description'], 'Test Description')
self.assertEqual(data['input_terms'], 'hello world')
self.assertEqual(data['output_terms'], 'nice world')
def test_delete_problem(self):
c.force_login(self.user)
response = c.delete(self.api_url + '1/')
self.assertEqual(response.status_code, 204)
self.assertEqual(Problem.objects.all().count(), 0)
def test_update_problem_anonymous(self):
response = c.put(self.api_url + '1/', data={'title': 'Test Problem', 'description': 'Test Description',
'input_terms': 'hello world',
'output_terms': 'nice world'}, content_type='application/json')
self.assertEqual(response.status_code, 403)
def test_delete_problem_anonymous(self):
response = c.delete(self.api_url + '1/')
self.assertEqual(response.status_code, 403)
class ContestTestCase(TestCase):
def setUp(self):
self.user = User.objects.create_user(username='testuser', password='12345', first_name='Test',
last_name='User')
self.api_url = '/api/contest/'
c.force_login(self.user)
c.post(self.api_url, {'title': 'Test Contest first', 'description': 'Test Description second',
'start_date': '2020-01-01', 'end_date': '2020-01-01'})
c.logout()
def test_create_contest(self):
c.force_login(self.user)
response = c.post(self.api_url, {'title': 'Test Contest', 'description': 'Test Description',
'start_date': '2020-01-01', 'end_date': '2020-01-01'})
self.assertEqual(response.status_code, 201, f'{response.content}')
def test_create_contest_anonymous(self):
response = c.post(self.api_url, {'title': 'Test Contest', 'description': 'Test Description',
'start_date': '2020-01-01', 'end_date': '2020-01-01'})
self.assertEqual(response.status_code, 403)
def test_get_contest_list(self):
response = c.get(self.api_url)
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(len(data), Contest.objects.all().count())
def test_get_contest_list_anon(self):
response = c.get(self.api_url)
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(len(data), Contest.objects.all().count())
def test_get_contest(self):
response = c.get(self.api_url + '1/')
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(data['title'], 'Test Contest first')
self.assertEqual(data['description'], 'Test Description second')
def test_get_contest_anon(self):
response = c.get(self.api_url + '1/')
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(data['title'], 'Test Contest first')
self.assertEqual(data['description'], 'Test Description second')
def test_update_contest(self):
c.force_login(self.user)
response = c.put(self.api_url + '1/', data={'title': 'Test Contest', 'description': 'Test Description'},
content_type='application/json')
self.assertEqual(response.status_code, 200, f'{response.content}')
data = response.json()
self.assertEqual(data['title'], 'Test Contest')
self.assertEqual(data['description'], 'Test Description')
def test_update_contest_anon(self):
response = c.put(self.api_url + '1/', data={'title': 'Test Contest', 'description': 'Test Description'})
self.assertEqual(response.status_code, 403)
def test_delete_contest(self):
c.force_login(self.user)
response = c.delete(self.api_url + '1/')
self.assertEqual(response.status_code, 204)
self.assertEqual(Contest.objects.all().count(), 0)
def test_delete_contest_anon(self):
response = c.delete(self.api_url + '1/')
self.assertEqual(response.status_code, 403)
def test_update_contest_non_permitted_user(self):
user = User.objects.create_user(username='testuser2', password='12345', first_name='Test')
c.force_login(user)
response = c.put(self.api_url + '1/', data={'title': 'Test Contest', 'description': 'Test Description'})
self.assertEqual(response.status_code, 403)
def test_update_contest_writers(self):
user = User.objects.create_user(username='testuser2', password='12345', first_name='Test')
c.force_login(user)
contest = Contest.objects.get(id=1)
contest.writers.add(user)
contest.save()
response = c.put(self.api_url + '1/', data={'title': 'Test Contest', 'description': 'Test Description'},
content_type='application/json')
self.assertEqual(response.status_code, 200)
contest.refresh_from_db()
self.assertEqual(Contest.objects.get(id=1).title, 'Test Contest')
self.assertEqual(Contest.objects.get(id=1).description, 'Test Description')
class TestCaseTestCase(TestCase):
def setUp(self) -> None:
self.user = User.objects.create_user(username='testuser', password='12345', first_name='Test')
self.problem = Problem.objects.create(title='Test Problem', description='Test Description', user=self.user)
self.test_case = TestCaseModel.objects.create(inputs='fsdaf', output='fdsjfkl', user=self.user,
problem=self.problem)
self.api = '/api/test-case/'
def test_create_test_case(self):
c.force_login(self.user)
response = c.post(self.api, data={'inputs': 'fsdaf', 'output': 'fdsjfkl', 'problem': self.problem.id},
content_type='application/json')
self.assertEqual(response.status_code, 201, f'{response.content}')
data = response.json()
self.assertEqual(data['inputs'], 'fsdaf')
self.assertEqual(data['output'], 'fdsjfkl')
self.assertEqual(TestCaseModel.objects.all().count(), 2)
def test_create_test_case_anon(self):
response = c.post(self.api, data={'inputs': 'fsdaf', 'output': 'fdsjfkl', 'problem': self.problem.id},
content_type='application/json')
self.assertEqual(response.status_code, 403)
def test_create_test_case_non_problem_writer(self):
user = User.objects.create_user(username='testuser2', password='12345', first_name='Test')
c.force_login(user)
response = c.post(self.api, data={'inputs': 'fsdaf', 'output': 'fdsjfkl', 'problem': self.problem.id},
content_type='application/json')
# ToDo: Fix this. This user should not be permitted to create test cases for this problem
self.assertEqual(response.status_code, 201)
def test_update_test_case(self):
c.force_login(self.user)
response = c.put(self.api + '1/', data={'inputs': 'fsdaf', 'output': 'fdsjfkl', 'problem': self.problem.id},
content_type='application/json')
self.assertEqual(response.status_code, 405)
def test_partial_update_test_case(self):
c.force_login(self.user)
response = c.patch(self.api + '1/', data={'inputs': 'fsdaf', 'output': 'fdsjfkl', 'problem': self.problem.id},
content_type='application/json')
self.assertEqual(response.status_code, 405)
def test_delete_test_case(self):
c.force_login(self.user)
response = c.delete(self.api + '1/')
self.assertEqual(response.status_code, 204)
def test_delete_test_case_anon(self):
response = c.delete(self.api + '1/')
self.assertEqual(response.status_code, 403)
def test_get_test_case_list(self):
c.force_login(self.user)
response = c.get(self.api)
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(len(data), TestCaseModel.objects.all().count())
def test_get_test_case_list_anon(self):
response = c.get(self.api)
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(len(data), TestCaseModel.objects.all().count())
def test_get_test_case_detail(self):
c.force_login(self.user)
response = c.get(self.api + '1/')
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(data['inputs'], 'fsdaf')
self.assertEqual(data['output'], 'fdsjfkl')
def test_get_test_case_detail_anon(self):
response = c.get(self.api + '1/')
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertEqual(data['inputs'], 'fsdaf')
self.assertEqual(data['output'], 'fdsjfkl')
| 47.139785 | 118 | 0.615648 | 1,536 | 13,152 | 5.113932 | 0.076172 | 0.141311 | 0.099554 | 0.125525 | 0.863144 | 0.820242 | 0.783068 | 0.765627 | 0.760789 | 0.747167 | 0 | 0.023216 | 0.243461 | 13,152 | 278 | 119 | 47.309353 | 0.766231 | 0.006615 | 0 | 0.665236 | 0 | 0 | 0.1714 | 0 | 0 | 0 | 0 | 0.003597 | 0.317597 | 1 | 0.16309 | false | 0.038627 | 0.012876 | 0 | 0.193133 | 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 |
47a3c741fd341fa75ff763da8ee5bced83259a9b | 108 | py | Python | myproject/__init__.py | rickliao18/myproject | 308c582d967a647c49543d68d352558474753861 | [
"BSD-2-Clause"
] | null | null | null | myproject/__init__.py | rickliao18/myproject | 308c582d967a647c49543d68d352558474753861 | [
"BSD-2-Clause"
] | null | null | null | myproject/__init__.py | rickliao18/myproject | 308c582d967a647c49543d68d352558474753861 | [
"BSD-2-Clause"
] | null | null | null | print('Hello from init.py')
from . import pronto_utils
from . import basic_utils
from .constants import pi
| 18 | 27 | 0.777778 | 17 | 108 | 4.823529 | 0.647059 | 0.243902 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148148 | 108 | 5 | 28 | 21.6 | 0.891304 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 0 | 0.75 | 0.25 | 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 |
9a21597eeba999a0c79f92a84e0b21b9823b694e | 72 | py | Python | publicdata/nlsy/__init__.py | CivicKnowledge/publicdata | 37210e3c3b89cf8068feb79a2f12923b3cb5c336 | [
"MIT"
] | 2 | 2017-10-10T18:53:40.000Z | 2020-05-28T21:49:01.000Z | publicdata/nlsy/__init__.py | CivicKnowledge/publicdata | 37210e3c3b89cf8068feb79a2f12923b3cb5c336 | [
"MIT"
] | 7 | 2018-10-02T15:53:22.000Z | 2019-01-27T23:06:32.000Z | publicdata/nlsy/__init__.py | CivicKnowledge/publicdata | 37210e3c3b89cf8068feb79a2f12923b3cb5c336 | [
"MIT"
] | 2 | 2018-08-31T15:46:52.000Z | 2019-09-18T05:31:28.000Z |
from .nlsy import NLSY97, NLSY79
class NlsyError(Exception):
pass | 12 | 32 | 0.736111 | 9 | 72 | 5.888889 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.068966 | 0.194444 | 72 | 6 | 33 | 12 | 0.844828 | 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 |
9a338476537832bef622cf8569c58c9838965a49 | 15,022 | py | Python | tests/modules/extra/rabbitmq/application/message/consumer/test_rabbitmq_message_consumer.py | alice-biometrics/petisco | b96e697cc875f67a28e60b4fc0d9ed9fc646cd86 | [
"MIT"
] | 19 | 2019-11-01T09:27:17.000Z | 2021-12-15T10:52:31.000Z | tests/modules/extra/rabbitmq/application/message/consumer/test_rabbitmq_message_consumer.py | alice-biometrics/petisco | b96e697cc875f67a28e60b4fc0d9ed9fc646cd86 | [
"MIT"
] | 68 | 2020-01-15T06:55:00.000Z | 2022-02-22T15:57:24.000Z | tests/modules/extra/rabbitmq/application/message/consumer/test_rabbitmq_message_consumer.py | alice-biometrics/petisco | b96e697cc875f67a28e60b4fc0d9ed9fc646cd86 | [
"MIT"
] | 2 | 2019-11-19T10:40:25.000Z | 2019-11-28T07:12:07.000Z | import copy
from time import sleep
import pytest
from meiga import BoolResult, isFailure, isSuccess
from petisco import DomainEvent
from tests.modules.extra.rabbitmq.mother.domain_event_user_created_mother import (
DomainEventUserCreatedMother,
)
from tests.modules.extra.rabbitmq.mother.message_subscriber_mother import (
MessageSubscriberMother,
)
from tests.modules.extra.rabbitmq.mother.rabbitmq_domain_event_bus_mother import (
RabbitMqDomainEventBusMother,
)
from tests.modules.extra.rabbitmq.mother.rabbitmq_message_configurer_mother import (
RabbitMqMessageConfigurerMother,
)
from tests.modules.extra.rabbitmq.mother.rabbitmq_message_consumer_mother import (
RabbitMqMessageConsumerMother,
)
from tests.modules.extra.rabbitmq.utils.spy_messages import SpyMessages
from tests.modules.extra.testing_decorators import testing_with_rabbitmq
@pytest.mark.integration
@testing_with_rabbitmq
@pytest.mark.parametrize(
"max_retries_allowed,expected_number_event_consumed,simulated_results",
[
(0, 1, [isFailure]),
(1, 2, [isFailure, isSuccess]),
(2, 3, [isFailure, isFailure, isSuccess]),
(3, 4, [isFailure, isFailure, isFailure, isSuccess]),
(4, 5, [isFailure, isFailure, isFailure, isFailure, isSuccess]),
],
)
def test_rabbitmq_message_consumer_should_publish_consume_and_retry_event_from_rabbitmq_when_fail_consumer(
max_retries_allowed, expected_number_event_consumed, simulated_results
):
spy = SpyMessages()
def assert_consumer(domain_event: DomainEvent) -> BoolResult:
spy.append(domain_event)
result = simulated_results.pop(0)
return result
domain_event = DomainEventUserCreatedMother.random()
subscribers = [
MessageSubscriberMother.domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer
)
]
configurer = RabbitMqMessageConfigurerMother.with_retry_ttl_10ms()
configurer.configure_subscribers(subscribers)
bus = RabbitMqDomainEventBusMother.default()
bus.publish(domain_event)
consumer = RabbitMqMessageConsumerMother.with_max_retries(max_retries_allowed)
consumer.add_subscribers(subscribers)
consumer.start()
sleep(1.0)
consumer.stop()
configurer.clear()
spy.assert_number_unique_messages(1)
spy.assert_first_message(domain_event)
spy.assert_last_message(domain_event)
spy.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed
)
@pytest.mark.integration
@testing_with_rabbitmq
def test_rabbitmq_message_consumer_publish_consume_and_retry_event_with_two_handlers_from_rabbitmq():
spy_consumer_1 = SpyMessages()
spy_consumer_2 = SpyMessages()
def assert_consumer_1(domain_event: DomainEvent) -> BoolResult:
spy_consumer_1.append(domain_event)
return isSuccess
def assert_consumer_2(domain_event: DomainEvent) -> BoolResult:
spy_consumer_2.append(domain_event)
return isSuccess
domain_event = DomainEventUserCreatedMother.random()
subscribers = [
MessageSubscriberMother.domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer_1
),
MessageSubscriberMother.other_domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer_2
),
]
configurer = RabbitMqMessageConfigurerMother.default()
configurer.configure_subscribers(subscribers)
bus = RabbitMqDomainEventBusMother.default()
bus.publish(domain_event)
consumer = RabbitMqMessageConsumerMother.default()
consumer.add_subscribers(subscribers)
consumer.start()
sleep(1.0)
consumer.stop()
configurer.clear()
spy_consumer_1.assert_number_unique_messages(1)
spy_consumer_1.assert_first_message(domain_event)
spy_consumer_1.assert_count_by_message_id(domain_event.message_id, 1)
spy_consumer_2.assert_number_unique_messages(1)
spy_consumer_2.assert_first_message(domain_event)
spy_consumer_2.assert_count_by_message_id(domain_event.message_id, 1)
@pytest.mark.integration
@testing_with_rabbitmq
@pytest.mark.parametrize(
"max_retries_allowed,expected_number_event_consumed,simulated_results",
[
(0, 1, [isFailure]),
(1, 2, [isFailure, isSuccess]),
(2, 3, [isFailure, isFailure, isSuccess]),
(3, 4, [isFailure, isFailure, isFailure, isSuccess]),
(4, 5, [isFailure, isFailure, isFailure, isFailure, isSuccess]),
],
)
def test_rabbitmq_message_consumer_publish_consume_and_retry_event_with_two_handlers_from_rabbitmq_when_fail_consumer(
max_retries_allowed, expected_number_event_consumed, simulated_results
):
spy_consumer_1 = SpyMessages()
spy_consumer_2 = SpyMessages()
simulated_results_1 = copy.deepcopy(simulated_results)
simulated_results_2 = copy.deepcopy(simulated_results)
def assert_consumer_1(domain_event: DomainEvent) -> BoolResult:
spy_consumer_1.append(domain_event)
result = simulated_results_1.pop(0)
return result
def assert_consumer_2(domain_event: DomainEvent) -> BoolResult:
spy_consumer_2.append(domain_event)
result = simulated_results_2.pop(0)
return result
domain_event = DomainEventUserCreatedMother.random()
subscribers = [
MessageSubscriberMother.domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer_1
),
MessageSubscriberMother.other_domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer_2
),
]
configurer = RabbitMqMessageConfigurerMother.with_retry_ttl_10ms()
configurer.configure_subscribers(subscribers)
bus = RabbitMqDomainEventBusMother.default()
bus.publish(domain_event)
consumer = RabbitMqMessageConsumerMother.with_max_retries(max_retries_allowed)
consumer.add_subscribers(subscribers)
consumer.start()
sleep(1.5)
consumer.stop()
configurer.clear()
print(f"num events: {len(spy_consumer_1.messages)} - {spy_consumer_1}")
print(f"num events: {len(spy_consumer_2.messages)} - {spy_consumer_2}")
spy_consumer_1.assert_number_unique_messages(1)
spy_consumer_1.assert_first_message(domain_event)
spy_consumer_1.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed
)
spy_consumer_2.assert_number_unique_messages(1)
spy_consumer_2.assert_first_message(domain_event)
spy_consumer_2.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed
)
@pytest.mark.integration
@testing_with_rabbitmq
@pytest.mark.parametrize(
"max_retries_allowed,expected_number_event_consumed,simulated_results",
[
(0, 1, [isFailure]),
(1, 2, [isFailure, isSuccess]),
(2, 3, [isFailure, isFailure, isSuccess]),
(3, 4, [isFailure, isFailure, isFailure, isSuccess]),
(4, 5, [isFailure, isFailure, isFailure, isFailure, isSuccess]),
],
)
def test_rabbitmq_message_consumer_should_publish_consume_and_retry_event_not_affecting_store_queue_from_rabbitmq_when_fail_handler_consumer(
max_retries_allowed, expected_number_event_consumed, simulated_results
):
spy_consumer_event_store = SpyMessages()
spy_consumer_handler = SpyMessages()
def assert_consumer_event_store(domain_event: DomainEvent) -> BoolResult:
spy_consumer_event_store.append(domain_event)
return isSuccess
def assert_consumer_handler(domain_event: DomainEvent) -> BoolResult:
spy_consumer_handler.append(domain_event)
result = simulated_results.pop(0)
return result
domain_event = DomainEventUserCreatedMother.random()
subscribers = [
MessageSubscriberMother.domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer_handler
),
MessageSubscriberMother.all_messages_subscriber(
handler=assert_consumer_event_store
),
]
configurer = RabbitMqMessageConfigurerMother.with_retry_ttl_10ms()
configurer.configure_subscribers(subscribers)
bus = RabbitMqDomainEventBusMother.default()
bus.publish(domain_event)
consumer = RabbitMqMessageConsumerMother.with_max_retries(max_retries_allowed)
consumer.add_subscribers(subscribers)
consumer.start()
sleep(1.0)
consumer.stop()
configurer.clear()
spy_consumer_event_store.assert_number_unique_messages(1)
spy_consumer_event_store.assert_first_message(domain_event)
spy_consumer_event_store.assert_count_by_message_id(domain_event.message_id, 1)
spy_consumer_handler.assert_number_unique_messages(1)
spy_consumer_handler.assert_first_message(domain_event)
spy_consumer_handler.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed
)
@pytest.mark.integration
@testing_with_rabbitmq
def test_rabbitmq_message_consumer_publish_consume_retry_and_send_to_dead_letter_event_from_rabbitmq_when_fail_consumer():
max_retries_allowed = 2
expected_number_event_consumed = 3
spy = SpyMessages()
spy_dead_letter = SpyMessages()
def assert_consumer(domain_event: DomainEvent) -> BoolResult:
spy.append(domain_event)
return isFailure
domain_event = DomainEventUserCreatedMother.random()
subscribers = [
MessageSubscriberMother.domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer
)
]
configurer = RabbitMqMessageConfigurerMother.with_retry_ttl_10ms()
configurer.configure_subscribers(subscribers)
bus = RabbitMqDomainEventBusMother.default()
bus.publish(domain_event)
consumer = RabbitMqMessageConsumerMother.with_max_retries(max_retries_allowed)
consumer.add_subscribers(subscribers)
def dead_letter_consumer(domain_event: DomainEvent) -> BoolResult:
spy_dead_letter.append(domain_event)
return isSuccess
dead_letter_message_subscriber = MessageSubscriberMother.domain_event_subscriber(
domain_event_type=type(domain_event), handler=dead_letter_consumer
)
consumer.add_subscriber_on_dead_letter(dead_letter_message_subscriber)
consumer.start()
sleep(2.5)
consumer.stop()
configurer.clear()
spy.assert_number_unique_messages(1)
spy.assert_first_message(domain_event)
spy.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed
)
spy_dead_letter.assert_number_unique_messages(1)
spy_dead_letter.assert_first_message(domain_event)
spy_dead_letter.assert_count_by_message_id(domain_event.message_id, 1)
@pytest.mark.integration
@testing_with_rabbitmq
@pytest.mark.parametrize(
"max_retries_allowed, expected_number_event_consumed_by_store, expected_number_event_consumed_by_handler_1, expected_number_event_consumed_by_handler_2,simulated_results_store, simulated_results_handler_1, simulated_results_handler_2",
[
(1, 2, 1, 1, [isFailure, isSuccess], [isSuccess], [isSuccess]),
(1, 1, 2, 1, [isSuccess], [isFailure, isSuccess], [isSuccess]),
(1, 1, 1, 2, [isSuccess], [isSuccess], [isFailure, isSuccess]),
(1, 2, 2, 1, [isFailure, isSuccess], [isFailure, isSuccess], [isSuccess]),
(2, 2, 1, 1, [isFailure, isSuccess], [isSuccess], [isSuccess]),
(2, 1, 2, 1, [isSuccess], [isFailure, isSuccess], [isSuccess]),
(2, 1, 1, 2, [isSuccess], [isSuccess], [isFailure, isSuccess]),
(2, 2, 2, 1, [isFailure, isSuccess], [isFailure, isSuccess], [isSuccess]),
(
2,
2,
2,
2,
[isFailure, isSuccess],
[isFailure, isSuccess],
[isFailure, isSuccess],
),
(3, 3, 1, 1, [isFailure, isFailure, isSuccess], [isSuccess], [isSuccess]),
(3, 1, 3, 1, [isSuccess], [isFailure, isFailure, isSuccess], [isSuccess]),
(3, 1, 1, 3, [isSuccess], [isSuccess], [isFailure, isFailure, isSuccess]),
],
)
def test_rabbitmq_message_consumer_should_publish_consume_and_retry_event_not_affecting_other_queue_including_store_queue_from_rabbitmq(
max_retries_allowed,
expected_number_event_consumed_by_store,
expected_number_event_consumed_by_handler_1,
expected_number_event_consumed_by_handler_2,
simulated_results_store,
simulated_results_handler_1,
simulated_results_handler_2,
):
spy_consumer_event_store = SpyMessages()
spy_consumer_handler_1 = SpyMessages()
spy_consumer_handler_2 = SpyMessages()
def assert_consumer_event_store(domain_event: DomainEvent) -> BoolResult:
spy_consumer_event_store.append(domain_event)
result = simulated_results_store.pop(0)
return result
def assert_consumer_handler_1(domain_event: DomainEvent) -> BoolResult:
spy_consumer_handler_1.append(domain_event)
result = simulated_results_handler_1.pop(0)
return result
def assert_consumer_handler_2(domain_event: DomainEvent) -> BoolResult:
spy_consumer_handler_2.append(domain_event)
result = simulated_results_handler_2.pop(0)
return result
domain_event = DomainEventUserCreatedMother.random()
subscribers = [
MessageSubscriberMother.domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer_handler_1
),
MessageSubscriberMother.other_domain_event_subscriber(
domain_event_type=type(domain_event), handler=assert_consumer_handler_2
),
MessageSubscriberMother.all_messages_subscriber(
handler=assert_consumer_event_store
),
]
configurer = RabbitMqMessageConfigurerMother.with_retry_ttl_10ms()
configurer.configure_subscribers(subscribers)
bus = RabbitMqDomainEventBusMother.default()
bus.publish(domain_event)
consumer = RabbitMqMessageConsumerMother.with_max_retries(max_retries_allowed)
consumer.add_subscribers(subscribers)
consumer.start()
sleep(1.0)
consumer.stop()
configurer.clear()
spy_consumer_event_store.assert_number_unique_messages(1)
spy_consumer_event_store.assert_first_message(domain_event)
spy_consumer_event_store.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed_by_store
)
spy_consumer_handler_1.assert_number_unique_messages(1)
spy_consumer_handler_1.assert_first_message(domain_event)
spy_consumer_handler_1.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed_by_handler_1
)
spy_consumer_handler_2.assert_number_unique_messages(1)
spy_consumer_handler_2.assert_first_message(domain_event)
spy_consumer_handler_2.assert_count_by_message_id(
domain_event.message_id, expected_number_event_consumed_by_handler_2
)
| 35.262911 | 239 | 0.755891 | 1,698 | 15,022 | 6.243816 | 0.063015 | 0.096491 | 0.037634 | 0.05348 | 0.888323 | 0.87408 | 0.85333 | 0.794284 | 0.72873 | 0.724014 | 0 | 0.014918 | 0.165557 | 15,022 | 425 | 240 | 35.345882 | 0.830874 | 0 | 0 | 0.618343 | 0 | 0 | 0.037146 | 0.031354 | 0 | 0 | 0 | 0 | 0.174556 | 1 | 0.053254 | false | 0 | 0.035503 | 0 | 0.12426 | 0.005917 | 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 |
7be8d4012f9ccea3889d2c8b026d1e875907cc92 | 38 | py | Python | addons/event_sale/wizard/__init__.py | jjiege/odoo | fd5b8ad387c1881f349d125cbd56433f4d49398f | [
"MIT"
] | null | null | null | addons/event_sale/wizard/__init__.py | jjiege/odoo | fd5b8ad387c1881f349d125cbd56433f4d49398f | [
"MIT"
] | null | null | null | addons/event_sale/wizard/__init__.py | jjiege/odoo | fd5b8ad387c1881f349d125cbd56433f4d49398f | [
"MIT"
] | null | null | null | from . import event_edit_registration
| 19 | 37 | 0.868421 | 5 | 38 | 6.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.105263 | 38 | 1 | 38 | 38 | 0.911765 | 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 |
7bff9efe1f63cfa62ff148848204ea81bd3fb123 | 94,071 | py | Python | envdsys/plots/apps/plot_app.py | NOAA-PMEL/envDataSystem | 4db4a3569d2329658799a3eef06ce36dd5c0597d | [
"Unlicense"
] | 1 | 2021-11-06T19:22:53.000Z | 2021-11-06T19:22:53.000Z | envdsys/plots/apps/plot_app.py | NOAA-PMEL/envDataSystem | 4db4a3569d2329658799a3eef06ce36dd5c0597d | [
"Unlicense"
] | 25 | 2019-06-18T20:40:36.000Z | 2021-07-23T20:56:48.000Z | envdsys/plots/apps/plot_app.py | NOAA-PMEL/envDataSystem | 4db4a3569d2329658799a3eef06ce36dd5c0597d | [
"Unlicense"
] | null | null | null | from plots.plot_buffer import PlotBufferManager, PlotBuffer
from asyncio.queues import Queue
# from collections import deque
# import asyncio
import abc
import copy
# import time
import math
# import utilities.util
# from datetime import datetime
from bokeh.models import Line, Circle, Legend
from bokeh.plotting import figure, ColumnDataSource
from bokeh.models.widgets import TextInput, Select, MultiSelect
from bokeh.layouts import layout, column
from bokeh.models import LinearAxis, Range1d, DataRange1d
from bokeh.models import DatetimeTickFormatter, ColorBar
from bokeh.models import HoverTool, PanTool, ResetTool, WheelZoomTool
from bokeh.palettes import Spectral6
from bokeh.transform import linear_cmap, LinearColorMapper
from bokeh.tile_providers import get_provider, Vendors
# from bokeh.palettes import Dark2_5 as palette_Dark2_5
# from bokeh.palettes import brewer as palette_brewer
# from bokeh.palettes import Category20_20 as palette
from bokeh.palettes import Category10_10 as palette
# import json
# import envdaq.util.util
from shared.utilities.util import string_to_dt
class PlotApp(abc.ABC):
def __init__(
self,
config,
plot_name='default',
app_name='/',
title=''):
self.config = config
self.source_config_map = dict()
self.name = app_name
self.plot_name = plot_name
self.title = title
self.source = None
self.sources = dict()
self.current_data = dict()
self.source_map = dict()
self.server_id = None
self.message_buffer = None
self.msg_buffer = Queue()
self.prefix = ''
self.prefix_map = dict()
# TODO make plotdata rollover runtime settable
# init to 2*60 minutes of data
self.rollover = 3600*2
# TODO Do I need this much buffer? Is a minute enough? 30sec?
# self.buf_size = 100
self.buf_size = 60
# PlotBufferManager.add_buffer(PlotBuffer('/', self.msg_buffer))
# print(f'init: {self.name}')
if self.config:
# print(f'plot_app: {config}')
self.setup()
else:
self.source = ColumnDataSource(
data=dict(x=[], y=[])
)
@abc.abstractmethod
def setup(self):
# config is now just plot_def for this app and
# we are mapping just the source config for each source
if self.config:
if 'source_map' in self.config:
for src_id, src_config in self.config['source_map'].items():
self.source_config_map[src_id] = src_config
if 'alias' in src_config:
self.prefix_map[src_id] = src_config['alias']['prefix']
else:
self.prefix_map[src_id] = ''
# if config:
# if 'ID' in config:
# id = config['ID']
# self.config_map[id] = config
# # source_id = 'default'
# # if 'plot_meta' in config:
# if 'alias' in config:
# self.prefix_map[id] = config['alias']['prefix']
# else:
# self.prefix_map[id] = ''
@abc.abstractmethod
def make_document(self, doc):
pass
def start(self, server_id):
self.server_id = server_id
PlotBufferManager.add_buffer(
PlotBuffer(
self.server_id,
self.name,
self.msg_buffer,
buf_size=self.buf_size
)
)
# print('here')
def stop(self, server_id):
PlotBufferManager.remove_buffer(server_id, self.name)
self.msg_buffer = None
async def update_data(self, msg):
# print(f'update data: {msg}')
self.update_main_source(msg)
# await self.main_buffer.put(msg)
await self.msg_buffer.put(msg)
def update_main_source(self, msg):
pass
def handle_main(self, msg):
pass
def encode_data_id(self, src_id, data_name):
return (src_id + '@' + data_name)
def decode_data_id(self, data_id):
parts = data_id.split('@')
return parts[0], parts[1]
# async def update_main_source(self):
class TimeSeries1D(PlotApp):
def __init__(
self,
config,
plot_name='default',
app_name='/ts_1d',
title='TimeSeries1D'
):
super().__init__(
config,
plot_name=plot_name,
app_name=app_name,
title=title
)
print(f'TimeSeries1D init: {app_name}')
# TODO: use config to define data source
# self.source = self.configure_data_source(config)
def setup(self):
super().setup()
# print(f'TS1D:setup: {self.config}')
# self.name = self.config['plot_meta']['name']
if self.config['app_type'] == 'TimeSeries1D':
self.current_data['TimeSeries1D'] = dict()
self.current_data['TimeSeries1D']['y_data'] = []
# if plot['app_type'] == 'TimeSeries1D':
# self.name = plot['app_name']
# if 'TimeSeries1D' in self.config['plot_meta']:
# ts1d_config = self.config['plot_meta']['TimeSeries1D']
ts1d_config = self.config
if 'source_map' not in ts1d_config:
print(f'no source map in plot {self.name}')
return
# build map
ts1d_map = dict()
for src_id, src in ts1d_config['source_map'].items():
prefix = ''
if len(self.prefix_map[src_id]) > 0:
prefix = self.prefix_map[src_id] + '_'
ts1d_map[src_id] = dict()
ts1d_map[src_id]['source'] = dict()
# for y in ts1d_config['y_data']:
for ydata_id, ydata in src['y_data'].items():
data = dict()
data['datetime'] = []
for y in ydata:
name = prefix + y
# if len(self.prefix) > 0:
# name = self.prefix + '_' + y
data[name] = []
cds = ColumnDataSource(data=data)
# added ydata_id to allow for different length
# source data
ts1d_map[src_id]['source'][ydata_id] = cds
# print(f'*&*&* data: {data}')
# self.source = ColumnDataSource(data=data)
# default_data = ts1d_config['default_y_data']
default_data = src['default_y_data']
# print(f'default data : {default_data}')
# self.current_data['TimeSeries1D'] = dict()
# new_default_data = []
for y in default_data:
y = prefix + y
# if len(self.prefix) > 0:
# y = self.prefix + '_' + y
# new_default_data.append((src_id, y))
# new_default_data.append(
# self.encode_data_id(src_id, y)
# )
self.current_data['TimeSeries1D']['y_data'].append(
self.encode_data_id(src_id, y)
)
# self.current_data['TimeSeries1D']['y_data'] = (
# new_default_data
# )
# for y in ts1d_config['y_data']:
info_map = dict()
for ydata_id, ydata in src['y_data'].items():
# for y in src['y_data']:
for y in ydata:
meas_config = self.get_measurement_config(src_id, y)
# print(f'meas config = {meas_config}')
if meas_config:
units = 'counts'
if 'units' in meas_config:
units = meas_config['units']
color = ''
if 'pref_color' in meas_config:
color = meas_config['pref_color']
y = prefix + y
# if len(self.prefix) > 0:
# y = self.prefix + '_' + y
# ts1d_map[y] = {
info_map[y] = {
'units': units,
'color': color,
}
ts1d_map[src_id]['info_map'] = info_map
self.source_map['TimeSeries1D'] = ts1d_map
# print(f'ts1d_setup source: {self.source}')
# print(f'ts1d_setup current: {self.current_data}')
# print(f'ts1d_setup map: {self.source_map}')
# if self.config:
# print(f'plotapp_configure: {self.config}')
def update_main_source(self, msg):
# while True:
# msg = await self.main_buffer.get()
# print(f'TS1D: update_main_source')
if msg:
src_id, data = self.handle_main(msg)
# print(f' {src_id}: {data}')
if data:
for y_id, y_data in data.items():
# print(f'data: {data}')
# self.source.stream(data, rollover=self.rollover)
# source = self.source_map['TimeSeries1D'][src_id][ydata_id]['source']
source = (
self.source_map['TimeSeries1D'][src_id]['source'][y_id]
)
# print(f'909090 source: {source.data}, {src_id}, {data}')
source.stream(y_data, rollover=self.rollover)
# self.source_map['TimeSeries1D'][src_id]['source'].stream(
# data,
# rollover=self.rollover
# )
# print(f'source: {source.data}')
# print(f'update_main_source: {self.source.data["datetime"]}')
def handle_main(self, msg):
data = None
src_id = None
# os.environ['TZ'] = 'UTC+0'
# time.tzset()
if 'message' in msg:
# print(f'here:1')
src_id = msg['message']['SENDER_ID']
body = msg['message']['BODY']
data = dict()
dt_string = body['DATA']['DATETIME']
# data['datetime'] = []
# data['datetime'].append(
# envdaq.util.util.string_to_dt(dt_string),
# # datetime.strptime(dt_string, '%Y-%m-%dT%H:%M:%SZ')
# # utilities.util.string_to_dt(dt_string)
# )
# print(data['datetime'])
# print(f'here:1')
# source_data = self.source_map['TimeSeries1D'][src_id]['source']
source_data = self.source_map['TimeSeries1D'][src_id]['source']
# print(f'here:2 {source_data}')
for name, meas in body['DATA']['MEASUREMENTS'].items():
if len(self.prefix_map[src_id]) > 0:
name = self.prefix_map[src_id] + '_' + name
# if name in self.source.data:
# print(f' {source_data.data}')
for y_id, ysrc in source_data.items():
# if name in source_data.data:
if name in ysrc.data:
# create src data for y_id
if y_id not in data:
data[y_id] = dict()
# add datetime to y_id just once
if 'datetime' not in data[y_id]:
data[y_id]['datetime'] = []
data[y_id]['datetime'].append(
string_to_dt(dt_string),
)
# print(f' {name}: {meas["VALUE"]}')
data[y_id][name] = []
data[y_id][name].append(meas['VALUE'])
# data[name] = meas['VALUE']
if len(data) == 0:
data = None
src_id = None
return src_id, data
def get_measurement_config(self, src_id, meas_name):
if (
src_id in self.source_config_map and
'measurement_meta' in self.source_config_map[src_id]
):
config = self.source_config_map[src_id]
for datatype, datamap in config['measurement_meta'].items():
if meas_name in datamap:
return datamap[meas_name]
else:
return dict()
def get_prefix_map(self):
return copy.deepcopy(self.prefix_map)
def get_prefix(self):
return self.prefix
def get_source_map(self):
return self.source_map
def get_source_data(self):
# print(f'source data: {self.source.data}')
# return json.loads(json.dumps(self.source.data))
return self.source.data
def get_source_meta(self):
return (
copy.deepcopy(self.current_data),
copy.deepcopy(self.source_map)
)
def get_rollover(self):
return self.rollover
def make_document(self, doc):
# self.source = ColumnDataSource({'x': [], 'y': [], 'color': []})
# id = self.name
# source = ColumnDataSource(
# data=dict(x=[], y=[], color=[])
# )
# source_map = self.get_source_map()
# source = ColumnDataSource(
# data=self.get_source_data()
# )
# TODO: instantiate ColDatSrc here
current_data, source_map = self.get_source_meta()
# replace ColumnDataSource in source_map with
# versions instantiated here. Works when deepcopy doesn't
for src_id, src in source_map['TimeSeries1D'].items():
for ydata_id, ysrc in src['source'].items():
source_map['TimeSeries1D'][src_id]['source'][ydata_id] = (
ColumnDataSource(
data=ysrc.data
)
)
# # if 'source' not in src:
# # continue
# source_data = ColumnDataSource(
# data=src['source'].data
# )
# if source_data:
# source_map['TimeSeries1D'][src_id]['source'] = source_data
# print(f'^^^^ {current_data}, {source_map}')
# print(f'plot init: {source.data}')
prefix_map = self.get_prefix_map()
# prefix = self.get_prefix()
rollover = self.get_rollover()
def encode_data_id(src_id, data_name):
return (src_id + '@' + data_name)
def decode_data_id(data_id):
parts = data_id.split('@')
return parts[0], parts[1]
def update_source():
# print('update_test')
plot_buffer = PlotBufferManager.get_buffer(
self.server_id,
self.name,
)
# print(f'plot buffer = {plot_buffer}, {self.server_id}, {self.name}')
if plot_buffer and plot_buffer.has_message():
# print(f'name: {id}, {self.name}')
# data_msg = plot_buffer.buffer
data_msg = plot_buffer.read()
src_id, data = handle(data_msg)
# print(f' update_source: {src_id}, {data}')
if data:
for y_id, y_data in data.items():
# print(f'data[datetime] = {data["datetime"]}')
# print(f'232323 data: {data}')
# source.stream(data, rollover=self.rollover)
# source = source_map['TimeSeries1D'][src_id]['source']
# print(f' {source.data}')
# source.stream(data, rollover=rollover)
# print(f' {source.data}')
sm = source_map['TimeSeries1D'][src_id]['source'][y_id]
sm.stream(
y_data,
rollover=rollover
)
# print(f' update test: {source_map["TimeSeries1D"][src_id]["source"].data}')
# print(f'update_test: {source.data["datetime"]}')
def handle(msg):
data = None
src_id = None
# os.environ['TZ'] = 'UTC+0'
# time.tzset()
if 'message' in msg:
src_id = msg['message']['SENDER_ID']
body = msg['message']['BODY']
data = dict()
dt_string = body['DATA']['DATETIME']
# print(f'*****pandas: {pd.to_datetime(dt_string, format=isofmt)}')
# data['datetime'] = []
# data['datetime'].append(
# # utilities.util.string_to_dt(dt_string).replace(tzinfo=None)
# envdaq.util.util.string_to_dt(dt_string)
# )
# print(data['datetime'])
# source_data = source_map['TimeSeries1D'][src_id]['source']
try:
source_data = source_map['TimeSeries1D'][src_id]['source']
# print(f'****** app update: {source_data.data}')
for name, meas in body['DATA']['MEASUREMENTS'].items():
# print(f' {name}: {meas}')
if len(prefix_map[src_id]) > 0:
name = prefix_map[src_id] + '_' + name
# print(f'{name} in {source_data.data}')
for y_id, ysrc in source_data.items():
if name in ysrc.data:
# create src data for y_id
if y_id not in data:
data[y_id] = dict()
# add datetime to y_id just once
if 'datetime' not in data[y_id]:
data[y_id]['datetime'] = []
data[y_id]['datetime'].append(
string_to_dt(dt_string),
)
# print(f' {name}: {meas["VALUE"]}')
data[y_id][name] = []
data[y_id][name].append(meas['VALUE'])
# data[name] = meas['VALUE']
# if name in source_data.data:
# data[name] = []
# data[name].append(meas['VALUE'])
if len(data) == 0:
data = None
src_id = None
except KeyError:
pass
return src_id, data
# def update():
# def update_axes(number):
# print(f'update_axes: {number}')
# doc.clear()
# fig = figure(title='Streaming Circle Plot!', #, sizing_mode='scale_width',
# x_range=[0, 1], y_range=[0, 1]
# )
# fig.circle(source=source, x='x', y='y', color='color', size=10)
# fig.yaxis.axis_label = 'one'
# if number==2:
# fig.extra_y_ranges = {"two": Range1d(start=0, end=10)}
# fig.circle(source=source, x='x', y='y', color='black', y_range_name="two")
# fig.add_layout(LinearAxis(y_range_name="two", axis_label='two'), 'left')
# l = layout([
# [traces],
# [fig],
# ])
# doc.add_root(l)
def build_plot():
# doc.clear()
TOOLTIPS = [
('name', '$name'),
# ("time", "$x"),
("value", "@$name"),
# ("value", "@y"),
# ("$name", "@$name"),
# ("(x,y)", "($x, $y)"),
# ("desc", "@desc"),
]
# hover_tool = HoverTool(
# tooltips=[
# ('name', '$name'),
# # ("time", "$x"),
# ("value", "@$name"),
# # ('col', '@x'),('row', '@y')
# ]
# )
# tools = [HoverTool(), WheelZoomTool(), PanTool(), ResetTool()]
tools = "pan,wheel_zoom,box_zoom,reset,hover,save"
fig = figure(
# title=self.title,
x_axis_label="DateTime",
x_axis_type="datetime",
plot_width=600,
plot_height=300,
toolbar_location='above',
tooltips=TOOLTIPS,
sizing_mode='stretch_width',
# tools=tools,
# tools=tools,
# y_range=DataRange1d()
# x_range=[0, 1],
# y_range=[0, 1],
)
axes_map = dict()
for trace in current_data['TimeSeries1D']['y_data']:
# src_id = trace[0]
# y_name = trace[1]
src_id, y_name = decode_data_id(trace)
# print(f'trace: {trace}, {src_id}, {y_name}')
sm_id = source_map['TimeSeries1D'][src_id]
# print(f"here1: {sm}")
if y_name in sm_id['info_map']:
# print("here2")
info_map = sm_id['info_map'][y_name]
# print("here3")
units = info_map['units']
# print("here4")
if units not in axes_map:
axes_map[units] = []
# print("here5")
axes_map[units].append(trace)
# print("here6")
first = True
legend_items = []
trace_cnt = 0
for axis, data in axes_map.items():
if first:
for id_y in data:
# print(f'id_y: {id_y}')
# src_id = id_y[0]
# y_data = id_y[1]
src_id, y_data = decode_data_id(id_y)
sm = source_map['TimeSeries1D'][src_id]['source']
for ydata_id, ysrc in sm.items():
if y_data in ysrc.data:
y_data_id = ydata_id
y_source = sm[y_data_id]
# print(f'y_source: {y_source.data}')
new_line = fig.line(
# source=source,
source=y_source,
x='datetime',
y=y_data,
# legend=y_data,
line_color=palette[trace_cnt],
# bounds='auto',
name=y_data,
)
legend_items.append((y_data, [new_line]))
trace_cnt += 1
# hover_tool.renderers.append(new_line)
fig.yaxis.axis_label = axis
fig.xaxis.formatter = DatetimeTickFormatter(
days="%F",
hours="%F %H:%M",
minutes="%F %H:%M",
minsec="%T",
seconds="%T"
)
else:
# renders = []
# for y_data in data:
for id_y in data:
# src_id = id_y[0]
# y_data = id_y[1]
src_id, y_data = decode_data_id(id_y)
sm = source_map['TimeSeries1D'][src_id]['source']
for ydata_id, ysrc in sm.items():
if y_data in ysrc.data:
y_data_id = ydata_id
y_source = sm[y_data_id]
# y_source = source_map['TimeSeries1D'][src_id]['source']
fig.extra_y_ranges[axis] = DataRange1d()
# axis: Range1d()}
new_line = Line(
x='datetime',
y=y_data,
line_color=palette[trace_cnt],
# bounds='auto',
name=y_data,
)
render = fig.add_glyph(
# source,
y_source,
new_line,
y_range_name=axis,
name=y_data,
)
fig.extra_y_ranges[axis].renderers.append(render)
legend_items.append((y_data, [render]))
trace_cnt += 1
# hover_tool.renderers.append(render)
# line = fig.line(
# source=source,
# x='datetime',
# y=y_data,
# # legend=y_data,
# y_range_name=axis
# )
# renders.append(line)
# fig.xaxis.axis_label = axis
fig.add_layout(LinearAxis(
y_range_name=axis, axis_label=axis), 'left')
first = False
# hover_tool = HoverTool(
# hover = fig.select(dict(type=HoverTool))
# hover.tooltips = [
# ('name', '$name'),
# # ("time", "$x"),
# ("value", "@$name"),
# # ('col', '@x'),('row', '@y')
# ]
# )
legend = Legend(
items=legend_items,
location='center',
# location=(0, -30)
)
fig.add_layout(legend, 'right')
# hover = fig.select(dict(type=HoverTool))
# hover.tooltips = [
# ]
return fig
def update_traces(attrname, old, new):
trace_list = traces.value
print(f'update_traces: {trace_list}')
current_data['TimeSeries1D']['y_data'] = traces.value
fig = build_plot()
# doc.title = self.title
# doc.add_periodic_callback(update_source, 1000)
doc_layout.children[1] = fig
# doc_layout= layout([
# [traces],
# [fig],
# ])
# doc.add_root(l)
# if 'two' in trace_list:
# print('two axes')
# update_axes(2)
# else:
# update_axes(1)
# doc.clear()
# ll = layout(
# [fig],
# )
# doc.add_root(ll)
# new_data = {'x': data['x'], 'y': data['y'], 'color': data['color']}
# new_data = dict(x=data['x'], y=data['y'], color=data['color'])
# print(f'new_data: {data}')
# new = {'x': [random.random()],
# 'y': [random.random()],
# 'color': [random.choice(['red', 'blue', 'green'])]}
# source.stream(new, rollover=10)
# try:
# with pull_session(url='http://localhost:5001/') as mysession:
# print(mysession)
# finally:
# pass
# if self.source is not None:
# print(f'stream: {self.source}')
# self.source.stream(data, rollover=10)
# TOOLTIPS = [
# ("index", "$index"),
# ("(x,y)", "($x, $y)"),
# ("desc", "@desc"),
# ]
# TOOLTIPS = [
# ('name', '$name'),
# # ("time", "$x"),
# ("value", "@$name")
# # ("(x,y)", "($x, $y)"),
# # ("desc", "@desc"),
# ]
# fig = figure(
# title=self.title,
# x_axis_label="DateTime",
# x_axis_type="datetime",
# plot_width=600,
# plot_height=300,
# # tooltips=TOOLTIPS,
# # , sizing_mode='scale_width',
# # x_range=[0, 1],
# # y_range=[0, 1]
# )
# for trace in current_data:
fig = build_plot()
# fig.line(
# source=source,
# x='datetime',
# y='test_concentration',
# # legend='concentration'
# ) # , color='color', size=10)
# # fig.circle(source=source, x='datetime', y='concentration')
# fig.xaxis.formatter = DatetimeTickFormatter(
# days="%F",
# hours="%F %H:%M",
# minutes="%F %H:%M",
# minsec="%T",
# seconds="%T"
# )
# add_line(trace)
doc.title = self.title
# doc.add_periodic_callback(update_source, 1000)
doc.add_periodic_callback(update_source, 250)
# new_data = TextInput(value='')
# new_data.on_change('value', update)
traces_options = []
# for name, val in source.data.items():
for src_id, src in source_map['TimeSeries1D'].items():
for ydata_id, ysrc in src['source'].items():
# for name, val in src['source'].data.items():
for name, val in ysrc.data.items():
if name != "datetime":
# traces_options.append(((src_id, name), name))
option_val = encode_data_id(src_id, name)
traces_options.append((option_val, name))
traces_current = current_data['TimeSeries1D']['y_data']
# traces_current = ['test_concentration']
# print(f'options, current: {traces_options}, {traces_current}')
traces = MultiSelect(
title='Select data to plot',
value=traces_current,
options=traces_options,
)
traces.on_change('value', update_traces)
doc_layout = layout(
[
[traces],
[fig],
],
sizing_mode="scale_width"
# width_policy="fit"
# sizing_mode="stretch_width"
)
doc.add_root(doc_layout)
# doc.add_root(fig)
class SizeDistribution(PlotApp):
def __init__(
self,
config,
plot_name='default',
app_name='/size_dist',
title='Size Distribution'
):
super().__init__(
config,
plot_name=plot_name,
app_name=app_name,
title=title
)
print(f'SizeDistribution init: {app_name}')
# TODO: use config to define data source
# self.source = self.configure_data_source(config)
self.buf_size = 10
def setup(self, ):
super().setup()
# print(f'SD:setup: {self.config}')
if self.config['app_type'] == 'SizeDistribution':
# if plot['app_type'] == 'SizeDistribution':
# self.name = plot['app_name']
# if 'TimeSeries1D' in self.config['plot_meta']:
# sd_config = self.config['plot_meta']['TimeSeries1D']
# sd_config = plot
sd_config = self.config
if 'source_map' not in sd_config:
print(f'no source map in plot {self.name}')
return
self.current_data['SizeDistribution'] = dict()
self.current_data['SizeDistribution']['y_data'] = []
# build map
sd_map = dict()
for src_id, src in sd_config['source_map'].items():
prefix = ''
if len(self.prefix_map[src_id]) > 0:
prefix = self.prefix_map[src_id] + '_'
sd_map[src_id] = dict()
sd_map[src_id]['source'] = dict()
# data['datetime'] = []
# for y in sd_config['y_data']:
for ydata_id, ydata in src['y_data'].items():
data = dict()
for y in ydata:
name = prefix + y
# if len(self.prefix) > 0:
# name = self.prefix + '_' + y
data[name] = []
cds = ColumnDataSource(data=data)
# added ydata_id to allow for different length
# source data
sd_map[src_id]['source'][ydata_id] = cds
# for y in src['y_data']:
# name = prefix + y
# # if len(self.prefix) > 0:
# # name = self.prefix + '_' + y
# data[name] = []
# # print(f'*&*&* data: {data}')
# # self.source = ColumnDataSource(data=data)
# cds = ColumnDataSource(data=data)
# sd_map[src_id]['source'] = cds
# default_data = sd_config['default_y_data']
default_data = src['default_y_data']
# print(f'default data : {default_data}')
# self.current_data['SizeDistribution'] = dict()
# new_default_data = []
for y in default_data:
# if len(self.prefix) > 0:
# y = self.prefix + '_' + y
# new_default_data.append(y)
y = prefix + y
# new_default_data.append((src_id, y))
# new_default_data.append(
# self.encode_data_id(src_id, y)
# )
self.current_data['SizeDistribution']['y_data'].append(
self.encode_data_id(src_id, y)
)
# print(f'new_default_data: {new_default_data}')
# self.current_data['SizeDistribution']['y_data'] = (
# new_default_data
# )
# print(f'21212121 current data: {self.current_data}')
# build map
# sd_map = dict()
info_map = dict()
# for y in sd_config['y_data']:
for ydata_id, ydata in src['y_data'].items():
# for y in src['y_data']:
for y in ydata:
# for y in src['y_data']:
meas_config = self.get_measurement_config(src_id, y)
# print(f'meas Config = {meas_config}')
if meas_config:
x_axis = 'diameter'
if (
'dimensions' in meas_config and
'axes' in meas_config
):
axes = meas_config['dimensions']['axes']
if (len(axes) > 1):
# assume x-axis is second dim
x_axis_dim = axes[1]
x_axis = meas_config['axes'][x_axis_dim]
x_axis = prefix + x_axis
units = 'counts'
if 'units' in meas_config:
units = meas_config['units']
color = ''
if 'pref_color' in meas_config:
color = meas_config['pref_color']
# if len(self.prefix) > 0:
# y = self.prefix + '_' + y
y = prefix + y
info_map[y] = {
'x_axis': x_axis,
'units': units,
'color': color,
}
sd_map[src_id]['info_map'] = info_map
self.source_map['SizeDistribution'] = sd_map
# print(f'sd_setup source: {self.source.data}')
# print(f'sd_setup current: {self.current_data}')
# print(f'sd_setup map: {self.source_map}')
# if self.config:
# print(f'plotapp_configure: {self.config}')
def update_main_source(self, msg):
# while True:
# msg = await self.main_buffer.get()
if msg:
src_id, data = self.handle_main(msg)
if data:
for y_id, y_data in data.items():
source = (
self.source_map['SizeDistribution'][src_id]['source'][y_id]
)
source.data = y_data
# print(f'data: {data}')
# self.source.stream(data, rollover=self.rollover)
# self.source.data = data
# self.source_map['SizeDistribution'][src_id]['source'].data = data
# self.source_map['SizeDistribution'][src_id]['source'].stream(
# data,
# rollover=self.rollover
# )
# print(f'update_main_source: {self.source.data["datetime"]}')
def handle_main(self, msg):
data = None
src_id = None
# os.environ['TZ'] = 'UTC+0'
# time.tzset()
if 'message' in msg:
# print(f'$$$$$$ @@@ {msg}')
src_id = msg['message']['SENDER_ID']
body = msg['message']['BODY']
data = dict()
# print(f' *** handle: {src_id}, {body}')
# dt_string = body['DATA']['DATETIME']
# data['datetime'] = []
# data['datetime'].append(
# envdaq.util.util.string_to_dt(dt_string),
# # datetime.strptime(dt_string, '%Y-%m-%dT%H:%M:%SZ')
# # utilities.util.string_to_dt(dt_string)
# )
# print(data['datetime'])
source_data = self.source_map['SizeDistribution'][src_id]['source']
# print(f' source_data: {source_data}')
for name, meas in body['DATA']['MEASUREMENTS'].items():
# print(f' {name}: {meas}')
if len(self.prefix_map[src_id]) > 0:
name = self.prefix_map[src_id] + '_' + name
# if name in self.source.data:
# print(f' {source_data.data}')
for y_id, ysrc in source_data.items():
if name in ysrc.data:
# create src data for y_id
if y_id not in data:
data[y_id] = dict()
data[y_id][name] = meas['VALUE']
# if name in source_data.data:
# # data[name] = []
# # data[name].append(meas['VALUE'])
# data[name] = meas['VALUE']
# if len(data) == 0:
# data = None
# src_id = None
return src_id, data
def get_measurement_config(self, src_id, meas_name):
# if 'measurement_meta' in self.config:
# for datatype, datamap in self.config['measurement_meta'].items():
# if meas_name in datamap:
# return datamap[meas_name]
# else:
# return dict()
if (
src_id in self.source_config_map and
'measurement_meta' in self.source_config_map[src_id]
):
config = self.source_config_map[src_id]
for datatype, datamap in config['measurement_meta'].items():
if meas_name in datamap:
return datamap[meas_name]
else:
return dict()
def get_prefix_map(self):
return self.prefix_map
def get_prefix(self):
return self.prefix
def get_source_map(self):
return self.source_map
def get_source_data(self):
# print(f'source data: {self.source.data}')
# return json.loads(json.dumps(self.source.data))
return self.source.data
def get_source_meta(self):
return (
copy.deepcopy(self.current_data),
copy.deepcopy(self.source_map)
)
def make_document(self, doc):
# self.source = ColumnDataSource({'x': [], 'y': [], 'color': []})
# id = self.name
# source = ColumnDataSource(
# data=dict(x=[], y=[], color=[])
# )
# source = ColumnDataSource(
# data=self.get_source_data()
# )
current_data, source_map = self.get_source_meta()
# print(f'plot init: {source.data}')
# replace ColumnDataSource in source_map with
# versions instantiated here. Works when deepcopy doesn't
for src_id, src in source_map['SizeDistribution'].items():
for ydata_id, ysrc in src['source'].items():
source_map['SizeDistribution'][src_id]['source'][ydata_id] = (
ColumnDataSource(
data=ysrc.data
)
)
# if 'source' not in src:
# continue
# source_data = ColumnDataSource(
# data=src['source'].data
# )
# if source_data:
# source_map['SizeDistribution'][src_id]['source'] = source_data
prefix_map = self.get_prefix_map()
# prefix = self.get_prefix()
def encode_data_id(src_id, data_name):
return (src_id + '@' + data_name)
def decode_data_id(data_id):
parts = data_id.split('@')
return parts[0], parts[1]
def update_source():
# print('update_test')
plot_buffer = PlotBufferManager.get_buffer(
self.server_id,
self.name,
)
# print(f'plot buffer = {plot_buffer}, {self.server_id}, {self.name}')
if plot_buffer and plot_buffer.has_message():
# print(f'name: {id}, {self.name}')
# print(
# f'plot_buffer: {len(plot_buffer.buffer)}, {plot_buffer.buffer}')
# data_msg = plot_buffer.buffer
data_msg = plot_buffer.read()
src_id, data = handle(data_msg)
# print(f' {src_id}: {data}')
if data:
for y_id, y_data in data.items():
source_map['SizeDistribution'][src_id]['source'][y_id].data = y_data
# sm.data = y_data
# print(f'66666 update source: {data}, {sm.data}')
# source.stream(data, rollover=self.rollover)
# source.stream(data, rollover=1)
# source.stream(data, rollover=len(data[next(iter(data))]))
# source.data = data
# source = source_map['SizeDistribution'][src_id]['source']
# print(f'source: {source}, {self.rollover}')
# source_map['SizeDistribution'][src_id]['source'].data = data
# source_map['SizeDistribution'][src_id]['source'].stream(
# data,
# rollover=self.rollover
# )
# source = ColumnDataSource(
# data=self.get_source_data()
# )
# print(f'999999 update: {source.data}')
# print(f'update_test: {source.data["datetime"]}')
def handle(msg):
data = None
src_id = None
# os.environ['TZ'] = 'UTC+0'
# time.tzset()
# print(f'handle: {msg}')
if 'message' in msg:
src_id = msg['message']['SENDER_ID']
body = msg['message']['BODY']
# print(f' *** handle: {src_id}, {body}')
data = dict()
# dt_string = body['DATA']['DATETIME']
# # print(f'*****pandas: {pd.to_datetime(dt_string, format=isofmt)}')
# data['datetime'] = []
# data['datetime'].append(
# # utilities.util.string_to_dt(dt_string).replace(tzinfo=None)
# envdaq.util.util.string_to_dt(dt_string)
# )
# print(data['datetime'])
try:
source_data = source_map['SizeDistribution'][src_id]['source']
for name, meas in body['DATA']['MEASUREMENTS'].items():
# print('here')
if len(prefix_map[src_id]) > 0:
name = prefix_map[src_id] + '_' + name
# if name in source.data:
# print(f' {name} in {source_data.data}')
for y_id, ysrc in source_data.items():
if name in ysrc.data:
# create src data for y_id
if y_id not in data:
data[y_id] = dict()
# # add datetime to y_id just once
# if 'datetime' not in data[y_id]:
# data[y_id]['datetime'] = []
# data[y_id]['datetime'].append(
# envdaq.util.util.string_to_dt(dt_string),
# )
# data[y_id][name] = []
# data[y_id][name].append(meas['VALUE'])
data[y_id][name] = meas['VALUE']
# if name in source_data.data:
# # print(f'22222222 data: {name}, {source.data}, {data}')
# # data[name] = []
# # data[name].append(meas['VALUE'])
# data[name] = meas['VALUE']
# # print(f'33333333 data: {name}, {source.data}, {data}')
if len(data) == 0:
data = None
src_id = None
except KeyError:
pass
return src_id, data
# def update():
# def update_axes(number):
# print(f'update_axes: {number}')
# doc.clear()
# fig = figure(title='Streaming Circle Plot!', #, sizing_mode='scale_width',
# x_range=[0, 1], y_range=[0, 1]
# )
# fig.circle(source=source, x='x', y='y', color='color', size=10)
# fig.yaxis.axis_label = 'one'
# if number==2:
# fig.extra_y_ranges = {"two": Range1d(start=0, end=10)}
# fig.circle(source=source, x='x', y='y', color='black', y_range_name="two")
# fig.add_layout(LinearAxis(y_range_name="two", axis_label='two'), 'left')
# l = layout([
# [traces],
# [fig],
# ])
# doc.add_root(l)
def build_plot():
# doc.clear()
TOOLTIPS = [
("name", "$name"),
# ("(x,y)", "($x, $y)"),
# ("desc", "@desc"),
("Dp", "$x um"),
("N", "$y cm-3"),
]
fig = figure(
# title=self.title,
x_axis_label="Diameter",
x_axis_type="log",
plot_width=600,
plot_height=300,
toolbar_location='above',
tooltips=TOOLTIPS,
sizing_mode='stretch_width',
# x_range=[0, 1],
# y_range=[0, 1],
)
axes_map = dict()
# print(f'49494 current_data: {current_data}')
for trace in current_data['SizeDistribution']['y_data']:
# src_id = trace[0]
# y_name = trace[1]
src_id, y_name = decode_data_id(trace)
sm_id = source_map['SizeDistribution'][src_id]
if y_name in sm_id['info_map']:
info_map = sm_id['info_map'][y_name]
units = info_map['units']
# units = source_map['SizeDistribution'][trace]['units']
if units not in axes_map:
axes_map[units] = []
axes_map[units].append(trace)
first = True
legend_items = []
trace_cnt = 0
# print(f'11111111111 source: {source.column_names}')
for axis, data in axes_map.items():
if first:
for id_y in data:
# print(f'y_data, data: {axis}, {y_data}, {data}, {source.data}')
# src_id = id_y[0]
# y_data = id_y[1]
src_id, y_data = decode_data_id(id_y)
sm = source_map['SizeDistribution'][src_id]
for ydata_id, ysrc in sm['source'].items():
if y_data in ysrc.data:
y_data_id = ydata_id
y_source = sm['source'][y_data_id]
# y_source = sm['source']
# print(f'1010101 sd: build: {y_source}, {y_data}, {sm["info_map"][y_data]["x_axis"]}')
new_line = fig.line(
source=y_source,
x=sm['info_map'][y_data]['x_axis'],
# x='msems_diameter',
# y='test_size_distribution',
y=y_data,
# legend=y_data,
line_color=palette[trace_cnt],
name=y_data,
)
new_circle = fig.circle(
source=y_source,
# x=source_map['SizeDistribution'][y_data]['x_axis'],
x=sm['info_map'][y_data]['x_axis'],
# x='msems_diameter',
# y='test_size_distribution',
y=y_data,
# legend=y_data,
color=palette[trace_cnt],
name=y_data,
)
legend_items.append((y_data, [new_line, new_circle]))
trace_cnt += 1
fig.yaxis.axis_label = axis
# fig.xaxis.formatter = DatetimeTickFormatter(
# days="%F",
# hours="%F %H:%M",
# minutes="%F %H:%M",
# minsec="%T",
# seconds="%T"
# )
else:
# renders = []
for id_y in data:
# print(f'y_data, data: {axis}, {y_data}, {data}, {source.data}')
# src_id = id_y[0]
# y_data = id_y[1]
src_id, y_data = decode_data_id(id_y)
sm = source_map['SizeDistribution'][src_id]
for ydata_id, ysrc in sm['source'].items():
if y_data in ysrc.data:
y_data_id = ydata_id
y_source = sm['source'][y_data_id]
# y_source = sm['source']
fig.extra_y_ranges[axis] = DataRange1d()
# axis: Range1d()}
new_line = Line(
x=sm['info_map'][y_data]['x_axis'],
# x=source_map['SizeDistribution'][y_data]['x_axis'],
y=y_data,
line_color=palette[trace_cnt],
name=y_data,
)
render_line = fig.add_glyph(
# source,
y_source,
new_line,
y_range_name=axis
)
fig.extra_y_ranges[axis].renderers.append(render_line)
new_circle = Circle(
x=sm['info_map'][y_data]['x_axis'],
# x=source_map['SizeDistribution'][y_data]['x_axis'],
y=y_data,
line_color=palette[trace_cnt],
name=y_data,
)
render_circle = fig.add_glyph(
# source,
y_source,
new_line,
y_range_name=axis
)
fig.extra_y_ranges[axis].renderers.append(
render_circle
)
legend_items.append(
(y_data, [render_line, render_circle])
)
# line = fig.line(
# source=source,
# x='datetime',
# y=y_data,
# # legend=y_data,
# y_range_name=axis
# )
# renders.append(line)
# fig.xaxis.axis_label = axis
fig.add_layout(LinearAxis(
y_range_name=axis, axis_label=axis), 'left')
first = False
legend = Legend(
items=legend_items,
location='center',
# location=(0, -30)
)
fig.add_layout(legend, 'right')
return fig
def update_traces(attrname, old, new):
trace_list = traces.value
print(f'update_traces: {trace_list}')
current_data['SizeDistribution']['y_data'] = traces.value
fig = build_plot()
# doc.title = self.title
# doc.add_periodic_callback(update_source, 1000)
doc_layout.children[1] = fig
# TOOLTIPS = [
# ("name", "@name"),
# # ("(x,y)", "($x, $y)"),
# # ("desc", "@desc"),
# ("Dp", "$x um"),
# ("N", "$y cm-3"),
# ]
fig = build_plot()
doc.title = self.title
# doc.add_periodic_callback(update_source, 1000)
doc.add_periodic_callback(update_source, 250)
traces_options = []
# for name, val in source.data.items():
# if name != "datetime":
# traces_options.append(name)
for src_id, src in source_map['SizeDistribution'].items():
for ydata_id, ysrc in src['source'].items():
# for name, val in src['source'].data.items():
for name, val in ysrc.data.items():
if name != "datetime":
# traces_options.append(((src_id, name), name))
option = (encode_data_id(src_id, name))
traces_options.append((option, name))
# traces_options.append(name)
traces_current = current_data['SizeDistribution']['y_data']
traces = MultiSelect(
title='Select data to plot',
value=traces_current,
options=traces_options,
)
traces.on_change('value', update_traces)
doc_layout = layout(
[
[traces],
[fig],
],
sizing_mode="scale_width"
# width_policy="fit"
# sizing_mode="stretch_width"
)
# time.sleep(0.5)
doc.add_root(doc_layout)
# doc.add_root(fig)
class GeoMapPlot(PlotApp):
def __init__(
self,
config,
plot_name='default',
app_name='/geomap',
title='GeoMapPlot'
):
super().__init__(
config,
plot_name=plot_name,
app_name=app_name,
title=title
)
print(f'GeoMapPlot init: {app_name}')
# TODO: use config to define data source
# self.source = self.configure_data_source(config)
def setup(self):
super().setup()
# print(f'GeoMap:setup: {self.config}')
if self.config['app_type'] == 'GeoMapPlot':
self.current_data['GeoMapPlot'] = dict()
self.current_data['GeoMapPlot']['z_data'] = []
# if plot['app_type'] == 'TimeSeries1D':
# self.name = plot['app_name']
# if 'TimeSeries1D' in self.config['plot_meta']:
# ts1d_config = self.config['plot_meta']['TimeSeries1D']
geo_config = self.config
if 'source_map' not in geo_config:
print(f'no source map in plot {self.name}')
return
# prefix = ''
# if len(self.prefix_map[id]) > 0:
# prefix = self.prefix_map[id] + '_'
# source_entry = dict()
# build map
geo_map = dict()
self.sync_buffer = dict()
self.sync_buffer['DATETIME'] = []
self.sync_buffer['GPS'] = dict()
self.sync_buffer['DATA'] = dict()
for src_id, src in geo_config['source_map'].items():
prefix = ''
if len(self.prefix_map[src_id]) > 0:
prefix = self.prefix_map[src_id] + '_'
geo_map[src_id] = dict()
geo_map[src_id]['source'] = dict()
# data['latitude'] = []
# data['longitude'] = []
# data['altitude'] = []
# for y in ts1d_config['y_data']:
for ydata_id, ydata in src['z_data'].items():
data = dict()
data['datetime'] = []
# for y in src['z_data']:
for y in ydata:
name = prefix + y
# if len(self.prefix) > 0:
# name = self.prefix + '_' + y
data[name] = []
# add lat/lon and x/y unit arrays
param_list = [
'latitude',
'latitude_y',
'longitude',
'longitude_x',
'altitude'
]
for par in param_list:
# par_name = prefix+par
if par not in data:
data[par] = []
# if 'latitude' not in data:
# data['latitude'] = []
# if 'latitude_y' not in data:
# data['latitude_y'] = []
# if 'longitude' not in data:
# data['longitude'] = []
# if 'longitude_x' not in data:
# data['longitude_x'] = []
# if 'altitude' not in data:
# data['altitude'] = []
cds = ColumnDataSource(data=data)
geo_map[src_id]['source'][ydata_id] = cds
default_data = src['default_z_data']
# print(f'default data : {default_data}')
# self.current_data['TimeSeries1D'] = dict()
# new_default_data = []
for y in default_data:
y = prefix + y
# if len(self.prefix) > 0:
# y = self.prefix + '_' + y
# new_default_data.append((src_id, y))
# new_default_data.append(
# self.encode_data_id(src_id, y)
# )
self.current_data['GeoMapPlot']['z_data'].append(
self.encode_data_id(src_id, y)
)
# self.current_data['TimeSeries1D']['y_data'] = (
# new_default_data
# )
# for y in ts1d_config['y_data']:
info_map = dict()
for ydata_id, ydata in src['z_data'].items():
# for y in src['z_data']:
for y in ydata:
meas_config = self.get_measurement_config(src_id, y)
# print(f'meas config = {meas_config}')
if meas_config:
units = 'counts'
if 'units' in meas_config:
units = meas_config['units']
color = ''
if 'pref_color' in meas_config:
color = meas_config['pref_color']
y = prefix + y
# if len(self.prefix) > 0:
# y = self.prefix + '_' + y
# ts1d_map[y] = {
info_map[y] = {
'units': units,
'color': color,
}
geo_map[src_id]['info_map'] = info_map
# setup sources to mate gps and other data
if 'primary_gps' in src:
# primary_gps = src_id
# self.sync_buffer['GPS'][src_id] = deque(maxlen=10)
geo_map[src_id]['primary_gps'] = src['primary_gps']
self.sync_buffer['GPS'][src_id] = dict()
else:
# self.sync_buffer['DATA'][src_id] = deque(maxlen=10)
self.sync_buffer['DATA'][src_id] = dict()
self.source_map['GeoMapPlot'] = geo_map
# print(f'geo_setup source: {self.source}')
# print(f'geo_setup current: {self.current_data}')
# print(f'geo_setup map: {self.source_map}')
# if self.config:
# print(f'plotapp_configure: {self.config}')
def update_main_source(self, msg):
# while True:
# msg = await self.main_buffer.get()
# print(f'TS1D: update_main_source')
if msg:
src_id, data = self.handle_main(msg)
if data:
for y_id, y_data in data.items():
# print(f'data: {data}')
# self.source.stream(data, rollover=self.rollover)
source = (
self.source_map['GeoMapPlot'][src_id]['source'][y_id]
)
# print(f'909090 source: {source.data}, {src_id}, {y_data}')
source.stream(y_data, rollover=self.rollover)
# self.source_map['TimeSeries1D'][src_id]['source'].stream(
# data,
# rollover=self.rollover
# )
# print(f'source: {source.data}')
# print(f'update_main_source: {self.source.data["datetime"]}')
def get_sync_data_main(self, src_id, dt_string, msg_body):
max_size = 120
# check to see if src_id is gps
if src_id in self.sync_buffer['GPS']:
self.sync_buffer['GPS'][src_id][dt_string] = msg_body
elif src_id in self.sync_buffer['DATA']:
self.sync_buffer['DATA'][src_id][dt_string] = msg_body
self.sync_buffer['DATETIME'].append(dt_string)
# msg_list = []
msg_map = dict()
gps_id = next(iter(self.sync_buffer['GPS']))
gps_data = self.sync_buffer['GPS'][gps_id]
for src_id, data in self.sync_buffer['DATA'].items():
if (
dt_string in data and
dt_string in gps_data
):
# msg_list.append(
# {
# dt_string: {
msg_map[dt_string] = {
'GPS': {
'src_id': gps_id,
'body': gps_data
},
'DATA': {
'src_id': src_id,
'body': data
}
}
# check for dt buffer length and trim if necessary
if len(self.sync_buffer['DATETIME']) > max_size:
dt = self.sync_buffer['DATETIME'][0]
gps_id = next(iter(self.sync_buffer['GPS']))
if dt in self.sync_buffer['GPS'][gps_id]:
self.sync_buffer['GPS'][gps_id].pop(dt)
for src_id, data in self.sync_buffer['DATA'].items():
if dt in data:
data.pop(dt)
self.sync_buffer['DATETIME'].pop(0)
# return msg_list
return msg_map
def handle_main(self, msg):
data = None
src_id = None
# os.environ['TZ'] = 'UTC+0'
# time.tzset()
if 'message' in msg:
# print(f'here:1')
src_id = msg['message']['SENDER_ID']
msg_body = msg['message']['BODY']
data = dict()
dt_string = msg_body['DATA']['DATETIME']
msg_map = self.get_sync_data_main(src_id, dt_string, msg_body)
# print(f'msg_list: {msg_list}')
if not msg_map:
return None, None
# data['datetime'] = []
# data['datetime'].append(
# envdaq.util.util.string_to_dt(dt_string),
# # datetime.strptime(dt_string, '%Y-%m-%dT%H:%M:%SZ')
# # utilities.util.string_to_dt(dt_string)
# )
# print(data['datetime'])
# print(f'here:1')
source_data = self.source_map['GeoMapPlot'][src_id]['source']
# print(f'here:2 {source_data}')
# add gps data
# gps_src_id = msg_map[dt_string]['GPS']['src_id']
gps_body = msg_map[dt_string]['GPS']['body']
lon = None
lat = None
alt = None
# lon_x_name = 'longitude_x'
# lat_y_name = 'latitude_y'
for name, meas in (
gps_body[dt_string]['DATA']['MEASUREMENTS'].items()
):
if name == 'longitude':
lon = meas['VALUE']
elif name == 'latitude':
lat = meas['VALUE']
elif name == 'altitude':
alt = meas['VALUE']
data_src_id = msg_map[dt_string]['DATA']['src_id']
data_body = msg_map[dt_string]['DATA']['body']
for name, meas in (
data_body[dt_string]['DATA']['MEASUREMENTS'].items()
):
if len(self.prefix_map[data_src_id]) > 0:
name = self.prefix_map[data_src_id] + '_' + name
for y_id, ysrc in source_data.items():
if name in ysrc.data:
# if name in source_data.data:
# create src data for y_id
if y_id not in data:
data[y_id] = dict()
# add datetime to y_id just once
if 'datetime' not in data[y_id]:
data[y_id]['datetime'] = []
data[y_id]['datetime'].append(
string_to_dt(dt_string),
)
# print(f' {name}: {meas["VALUE"]}')
data[y_id][name] = []
data[y_id][name].append(meas['VALUE'])
if lat and lon:
lon_x, lat_y = self.main_merc(lat, lon)
if 'latitude' not in data[y_id]:
data[y_id]['latitude'] = []
data[y_id]['latitude'].append(lat)
if 'latitude_y' not in data[y_id]:
data[y_id]['latitude_y'] = []
data[y_id]['latitude_y'].append(lat_y)
if 'longitude' not in data[y_id]:
data[y_id]['longitude'] = []
data[y_id]['longitude'].append(lon)
if 'longitude_y' not in data[y_id]:
data[y_id]['longitude_x'] = []
data[y_id]['longitude_x'].append(lon_x)
if 'altitude' not in data[y_id]:
data[y_id]['altitude'] = []
data[y_id]['altitude'].append(alt)
if len(data) == 0:
data = None
src_id = None
return src_id, data
def main_merc(self, lat, lon):
r_major = 6378137.000
x = r_major * math.radians(lon)
scale = x/lon
y = 180.0/math.pi * \
math.log(math.tan(math.pi/4.0 + lat *
(math.pi/180.0)/2.0)) * scale
return (x, y)
def get_measurement_config(self, src_id, meas_name):
if (
src_id in self.source_config_map and
'measurement_meta' in self.source_config_map[src_id]
):
config = self.source_config_map[src_id]
for datatype, datamap in config['measurement_meta'].items():
if meas_name in datamap:
return datamap[meas_name]
else:
return dict()
def get_prefix_map(self):
return copy.deepcopy(self.prefix_map)
def get_prefix(self):
return self.prefix
def get_source_map(self):
return self.source_map
def get_source_data(self):
# print(f'source data: {self.source.data}')
# return json.loads(json.dumps(self.source.data))
return self.source.data
def get_source_meta(self):
return (
copy.deepcopy(self.current_data),
copy.deepcopy(self.source_map)
)
def get_rollover(self):
return self.rollover
def make_document(self, doc):
# self.source = ColumnDataSource({'x': [], 'y': [], 'color': []})
# id = self.name
# source = ColumnDataSource(
# data=dict(x=[], y=[], color=[])
# )
# source_map = self.get_source_map()
# source = ColumnDataSource(
# data=self.get_source_data()
# )
sync_buffer = dict()
sync_buffer['DATETIME'] = []
sync_buffer['GPS'] = dict()
sync_buffer['DATA'] = dict()
# TODO: instantiate ColDatSrc here
current_data, source_map = self.get_source_meta()
# replace ColumnDataSource in source_map with
# versions instantiated here. Works when deepcopy doesn't
for src_id, src in source_map['GeoMapPlot'].items():
for ydata_id, ysrc in src['source'].items():
source_map['GeoMapPlot'][src_id]['source'][ydata_id] = (
ColumnDataSource(
data=ysrc.data
)
)
# setup sources to mate gps and other data
if 'primary_gps' in src:
# primary_gps = src_id
# self.sync_buffer['GPS'][src_id] = deque(maxlen=10)
sync_buffer['GPS'][src_id] = dict()
else:
# self.sync_buffer['DATA'][src_id] = deque(maxlen=10)
sync_buffer['DATA'][src_id] = dict()
# if 'source' not in src:
# continue
# source_data = ColumnDataSource(
# data=src['source'].data
# )
# if source_data:
# source_map['GeoMapPlot'][src_id]['source'] = source_data
# print(f'^^^^ {current_data}, {source_map}')
# print(f'plot init: {source.data}')
prefix_map = self.get_prefix_map()
# prefix = self.get_prefix()
rollover = self.get_rollover()
def encode_data_id(src_id, data_name):
return (src_id + '@' + data_name)
def decode_data_id(data_id):
parts = data_id.split('@')
return parts[0], parts[1]
def update_source():
# print('update_test')
plot_buffer = PlotBufferManager.get_buffer(
self.server_id,
self.name,
)
if plot_buffer and plot_buffer.has_message():
# print(f'name: {id}, {self.name}')
# data_msg = plot_buffer.buffer
data_msg = plot_buffer.read()
src_id, data = handle(data_msg)
# print(f' update_source: {src_id}, {data}')
if data:
for y_id, y_data in data.items():
# print(f'data[datetime] = {data["datetime"]}')
# print(f'232323 data: {data}')
# source.stream(data, rollover=self.rollover)
# source = source_map['TimeSeries1D'][src_id]['source']
# print(f' {source.data}')
# source.stream(data, rollover=rollover)
# print(f' {source.data}')
sm = source_map['GeoMapPlot'][src_id]['source'][y_id]
sm.stream(
y_data,
rollover=rollover
)
# source_map['GeoMapPlot'][src_id]['source'].stream(
# data,
# rollover=rollover
# )
# print(f'update_test: {source.data["datetime"]}')
def merc(lat, lon):
r_major = 6378137.000
x = r_major * math.radians(lon)
scale = x/lon
y = 180.0/math.pi * \
math.log(math.tan(math.pi/4.0 + lat *
(math.pi/180.0)/2.0)) * scale
return (x, y)
def get_sync_data(src_id, dt_string, msg_body):
max_size = 120
# check to see if src_id is gps
if src_id in sync_buffer['GPS']:
sync_buffer['GPS'][src_id][dt_string] = msg_body
elif src_id in sync_buffer['DATA']:
sync_buffer['DATA'][src_id][dt_string] = msg_body
sync_buffer['DATETIME'].append(dt_string)
# msg_list = []
msg_map = dict()
gps_id = next(iter(sync_buffer['GPS']))
gps_data = sync_buffer['GPS'][gps_id]
for src_id, data in sync_buffer['DATA'].items():
if (
dt_string in data and
dt_string in gps_data
):
# msg_list.append(
# {
# dt_string: {
msg_map[dt_string] = {
'GPS': {
'src_id': gps_id,
'body': gps_data
},
'DATA': {
'src_id': src_id,
'body': data
}
}
# check for dt buffer length and trim if necessary
if len(sync_buffer['DATETIME']) > max_size:
dt = sync_buffer['DATETIME'][0]
gps_id = next(iter(sync_buffer['GPS']))
if dt in sync_buffer['GPS'][gps_id]:
sync_buffer['GPS'][gps_id].pop(dt)
for src_id, data in sync_buffer['DATA'].items():
if dt in data:
data.pop(dt)
sync_buffer['DATETIME'].pop(0)
# return msg_list
return msg_map
def handle(msg):
data = None
src_id = None
# os.environ['TZ'] = 'UTC+0'
# time.tzset()
if 'message' in msg:
src_id = msg['message']['SENDER_ID']
msg_body = msg['message']['BODY']
data = dict()
dt_string = msg_body['DATA']['DATETIME']
msg_map = get_sync_data(src_id, dt_string, msg_body)
# print(f'msg_list: {msg_list}')
if not msg_map:
return None, None
try:
source_data = source_map['GeoMapPlot'][src_id]['source']
gps_body = msg_map[dt_string]['GPS']['body']
lon = None
lat = None
alt = None
for name, meas in (
gps_body[dt_string]['DATA']['MEASUREMENTS'].items()
):
if name == 'longitude':
lon = meas['VALUE']
elif name == 'latitude':
lat = meas['VALUE']
elif name == 'altitude':
alt = meas['VALUE']
data_src_id = msg_map[dt_string]['DATA']['src_id']
data_body = msg_map[dt_string]['DATA']['body']
for name, meas in (
data_body[dt_string]['DATA']['MEASUREMENTS'].items()
):
# print(f' {name}: {meas}')
if len(prefix_map[data_src_id]) > 0:
name = prefix_map[data_src_id] + '_' + name
for y_id, ysrc in source_data.items():
if name in ysrc.data:
# if name in source_data.data:
# create src data for y_id
if y_id not in data:
data[y_id] = dict()
# add datetime to y_id just once
if 'datetime' not in data[y_id]:
data[y_id]['datetime'] = []
data[y_id]['datetime'].append(
string_to_dt(dt_string),
)
data[y_id][name] = []
data[y_id][name].append(meas['VALUE'])
if lat and lon:
lon_x, lat_y = merc(lat, lon)
# print(f'lat: {lat}, {lat_y}, lon: {lon}, {lon_x}')
if 'latitude' not in data[y_id]:
data[y_id]['latitude'] = []
data[y_id]['latitude'].append(lat)
if 'latitude_y' not in data[y_id]:
data[y_id]['latitude_y'] = []
data[y_id]['latitude_y'].append(lat_y)
if 'longitude' not in data[y_id]:
data[y_id]['longitude'] = []
data[y_id]['longitude'].append(lon)
if 'longitude_x' not in data[y_id]:
data[y_id]['longitude_x'] = []
data[y_id]['longitude_x'].append(lon_x)
if 'altitude' not in data[y_id]:
data[y_id]['altitude'] = []
data[y_id]['altitude'].append(alt)
# # print(f'{name} in {source_data.data}')
# if name in source_data.data:
# data[name] = []
# data[name].append(meas['VALUE'])
# if len(data) == 0:
# data = None
# src_id = None
# data['datetime'] = []
# data['datetime'].append(
# # utilities.util.string_to_dt(dt_string).replace(tzinfo=None)
# string_to_dt(dt_string)
# )
# # print(data['datetime'])
# source_data = source_map['GeoMapPlot'][src_id]['source']
# # print(f'****** app update: {source_data.data}')
# for name, meas in body['DATA']['MEASUREMENTS'].items():
# # print(f' {name}: {meas}')
# if len(prefix_map[src_id]) > 0:
# name = prefix_map[src_id] + '_' + name
# # print(f'{name} in {source_data.data}')
# if name in source_data.data:
# data[name] = []
# data[name].append(meas['VALUE'])
if len(data) == 0:
data = None
src_id = None
except KeyError:
pass
return src_id, data
# def update():
# def update_axes(number):
# print(f'update_axes: {number}')
# doc.clear()
# fig = figure(title='Streaming Circle Plot!', #, sizing_mode='scale_width',
# x_range=[0, 1], y_range=[0, 1]
# )
# fig.circle(source=source, x='x', y='y', color='color', size=10)
# fig.yaxis.axis_label = 'one'
# if number==2:
# fig.extra_y_ranges = {"two": Range1d(start=0, end=10)}
# fig.circle(source=source, x='x', y='y', color='black', y_range_name="two")
# fig.add_layout(LinearAxis(y_range_name="two", axis_label='two'), 'left')
# l = layout([
# [traces],
# [fig],
# ])
# doc.add_root(l)
def build_plot():
# doc.clear()
tile_provider = get_provider(Vendors.CARTODBPOSITRON)
default_lat_range = (-85, 85)
default_lon_range = (-180, 180)
x_min, y_min = merc(default_lat_range[0], default_lon_range[0])
x_max, y_max = merc(default_lat_range[1], default_lon_range[1])
# fig = figure(
# # title=self.title,
# x_range=(x_min, x_max),
# y_range=(y_min, y_max),
# x_axis_type='mercator',
# y_axis_type='mercator',
# # x_axis_label="DateTime",
# # x_axis_type="datetime",
# # plot_width=500,
# plot_height=500,
# toolbar_location='above',
# tooltips=TOOLTIPS,
# # sizing_mode='stretch_width',
# # x_range=[0, 1],
# # y_range=[0, 1],
# )
# fig.add_tile(tile_provider)
axes_map = dict()
current_trace = ''
for trace in current_data['GeoMapPlot']['z_data']:
# src_id = trace[0]
# y_name = trace[1]
src_id, y_name = decode_data_id(trace)
current_trace = y_name
# print(f'trace: {trace}, {src_id}, {y_name}')
sm = source_map['GeoMapPlot'][src_id]
# print(f"here1: {sm}")
if y_name in sm['info_map']:
# print("here2")
info_map = sm['info_map'][y_name]
# print("here3")
units = info_map['units']
# print("here4")
if units not in axes_map:
axes_map[units] = []
# print("here5")
axes_map[units].append(trace)
# print("here6")
TOOLTIPS = [
# ("index", "$index"),
("(lat,lon)", "(@latitude, @longitude)"),
# ("desc", "@desc"),
(current_trace, f'@{current_trace}')
]
fig = figure(
# title=self.title,
x_range=(x_min, x_max),
y_range=(y_min, y_max),
x_axis_type='mercator',
y_axis_type='mercator',
# x_axis_label="DateTime",
# x_axis_type="datetime",
# plot_width=500,
plot_height=500,
toolbar_location='above',
tooltips=TOOLTIPS,
# sizing_mode='stretch_width',
# x_range=[0, 1],
# y_range=[0, 1],
)
fig.add_tile(tile_provider)
first = True
legend_items = []
color_bar = None
trace_cnt = 0
for axis, data in axes_map.items():
# if first:
for id_y in data:
# print(f'id_y: {id_y}')
# src_id = id_y[0]
# y_data = id_y[1]
src_id, z_data = decode_data_id(id_y)
sm = source_map['GeoMapPlot'][src_id]['source']
for zdata_id, zsrc in sm.items():
if z_data in zsrc.data:
z_data_id = zdata_id
z_source = sm[z_data_id]
low = 0
high = 1000
if z_source.data[z_data]:
low = min(z_source.data[z_data])
high = max(z_source.data[z_data])
mapper = linear_cmap(
field_name=z_data,
palette=Spectral6,
low=low,
high=high,
# low=0,
# high=1000,
)
# z_source = source_map['GeoMapPlot'][src_id]['source']
# print(f'y_source: {y_source.data}')
# new_line = fig.line(
# # source=source,
# source=z_source,
# x='longitude',
# y='latitude',
# line_color=mapper,
# # color=mapper,
# # size=z_data,
# # legend=y_data,
# )
# color_bar = ColorBar(
# color_mapper=mapper
# )
new_pt = fig.circle(
# source=source,
source=z_source,
x='longitude_x',
y='latitude_y',
# line_color=['black', ],
color=mapper,
# size=z_data,
# legend=y_data,
)
color_bar = ColorBar(
color_mapper=mapper['transform'],
)
# legend_items.append((z_data, [new_line]))
# legend_items.append((z_data, [new_line, new_pt]))
# # fig.yaxis.axis_label = axis
# # fig.xaxis.formatter = DatetimeTickFormatter(
# # days="%F",
# # hours="%F %H:%M",
# # minutes="%F %H:%M",
# # minsec="%T",
# # seconds="%T"
# # )
# # else:
# # # renders = []
# # # for y_data in data:
# # for id_y in data:
# # # src_id = id_y[0]
# # # y_data = id_y[1]
# # src_id, y_data = decode_data_id(id_y)
# # y_source = source_map['TimeSeries1D'][src_id]['source']
# # fig.extra_y_ranges[axis] = DataRange1d()
# # # axis: Range1d()}
# # new_line = Line(
# # x='datetime',
# # y=y_data,
# # )
# # render = fig.add_glyph(
# # # source,
# # y_source,
# # new_line,
# # y_range_name=axis
# # )
# # fig.extra_y_ranges[axis].renderers.append(render)
# # legend_items.append((y_data, [render]))
# # # line = fig.line(
# # # source=source,
# # # x='datetime',
# # # y=y_data,
# # # # legend=y_data,
# # # y_range_name=axis
# # # )
# # # renders.append(line)
# # # fig.xaxis.axis_label = axis
# # fig.add_layout(LinearAxis(
# # y_range_name=axis, axis_label=axis), 'left')
# first = False
legend = Legend(
items=legend_items,
location='center',
# location=(0, -30)
)
# color_bar = ColorBar(
# color_mapper=mapper
# )
# # colorbar = ColorBar(color_mapper=mapper['transform'], width=8, location=(0,0))
if color_bar:
fig.add_layout(color_bar, 'right')
# # fig.add_layout(legend, 'right')
return fig
def update_traces(attrname, old, new):
trace_list = traces.value
print(f'update_traces: {trace_list}')
if current_data['GeoMapPlot']['z_data']:
current_data['GeoMapPlot']['z_data'][0] = traces.value
else:
current_data['GeoMapPlot']['z_data'].append(traces.value)
fig = build_plot()
# doc.title = self.title
# doc.add_periodic_callback(update_source, 1000)
doc_layout.children[1] = fig
# doc_layout= layout([
# [traces],
# [fig],
# ])
# doc.add_root(l)
# if 'two' in trace_list:
# print('two axes')
# update_axes(2)
# else:
# update_axes(1)
# doc.clear()
# ll = layout(
# [fig],
# )
# doc.add_root(ll)
# new_data = {'x': data['x'], 'y': data['y'], 'color': data['color']}
# new_data = dict(x=data['x'], y=data['y'], color=data['color'])
# print(f'new_data: {data}')
# new = {'x': [random.random()],
# 'y': [random.random()],
# 'color': [random.choice(['red', 'blue', 'green'])]}
# source.stream(new, rollover=10)
# try:
# with pull_session(url='http://localhost:5001/') as mysession:
# print(mysession)
# finally:
# pass
# if self.source is not None:
# print(f'stream: {self.source}')
# self.source.stream(data, rollover=10)
# TOOLTIPS = [
# # ("index", "$index"),
# ("(lat,lon)", "(@latitude, @longitude)"),
# # ("desc", "@desc"),
# ("value", "@$name")
# ]
fig = build_plot()
doc.title = self.title
doc.add_periodic_callback(update_source, 250)
traces_options = []
for src_id, src in source_map['GeoMapPlot'].items():
for ydata_id, ysrc in src['source'].items():
for name, val in ysrc.data.items():
if (
name != "latitude" and
name != 'longitude' and
name != 'altitude' and
name != "latitude_y" and
name != 'longitude_x' and
name != 'datetime'
):
# traces_options.append(((src_id, name), name))
option_val = encode_data_id(src_id, name)
traces_options.append((option_val, name))
# traces_current = current_data['GeoMapPlot']['z_data']
trace_current = ''
if current_data['GeoMapPlot']['z_data']:
trace_current = current_data['GeoMapPlot']['z_data'][0]
# traces_current = ['test_concentration']
# print(f'options, current: {traces_options}, {traces_current}')
# traces = MultiSelect(
traces = Select(
title='Select data to plot',
# value=traces_current,
value=trace_current,
options=traces_options,
)
traces.on_change('value', update_traces)
doc_layout = layout(
[
[traces],
[fig],
],
# sizing_mode="stretch_width"
)
doc.add_root(doc_layout)
# doc.add_root(fig)
| 38.131739 | 111 | 0.429452 | 9,278 | 94,071 | 4.127829 | 0.044729 | 0.029767 | 0.011515 | 0.011907 | 0.869367 | 0.843099 | 0.812732 | 0.78132 | 0.749909 | 0.726121 | 0 | 0.010081 | 0.452722 | 94,071 | 2,466 | 112 | 38.147202 | 0.733815 | 0.296829 | 0 | 0.687023 | 0 | 0 | 0.057733 | 0.000615 | 0 | 0 | 0 | 0.000406 | 0 | 1 | 0.05598 | false | 0.005089 | 0.014419 | 0.017812 | 0.115352 | 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 |
d0e2f373421e5f70ac9447d2a7a88dfead439604 | 159 | py | Python | tool/class_names.py | WildflowerSchools/pytorch-YOLOv4 | 2a3eea9b84036afdc87ab4ed006b028646bb2945 | [
"Apache-2.0"
] | 3 | 2020-08-09T22:22:42.000Z | 2020-11-15T02:59:05.000Z | tool/class_names.py | WildflowerSchools/pytorch-YOLOv4 | 2a3eea9b84036afdc87ab4ed006b028646bb2945 | [
"Apache-2.0"
] | null | null | null | tool/class_names.py | WildflowerSchools/pytorch-YOLOv4 | 2a3eea9b84036afdc87ab4ed006b028646bb2945 | [
"Apache-2.0"
] | 1 | 2020-08-09T22:26:17.000Z | 2020-08-09T22:26:17.000Z | import os
COCO_NAMES = os.path.join(os.path.dirname(__file__), '../data/coco.names')
VOC_NAMES = os.path.join(os.path.dirname(__file__), '../data/voc.names')
| 31.8 | 74 | 0.716981 | 26 | 159 | 4 | 0.384615 | 0.230769 | 0.211538 | 0.288462 | 0.692308 | 0.692308 | 0.692308 | 0.692308 | 0.692308 | 0 | 0 | 0 | 0.069182 | 159 | 4 | 75 | 39.75 | 0.702703 | 0 | 0 | 0 | 0 | 0 | 0.220126 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 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 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
efb13acb8b6499082f02efac2b85cddce6507c2d | 10,229 | py | Python | app/authorization/tests/test_membership_mutations.py | getmetamapper/metamapper | 0b2f67eec03fbf7ece35ff9f58ea9bb2dde4d85f | [
"BSD-2-Clause"
] | 53 | 2020-07-01T23:11:59.000Z | 2022-03-31T19:10:28.000Z | app/authorization/tests/test_membership_mutations.py | metamapper-io/metamapper | 376716e72bcaca62f1ec09ca9a13a0346e5502f9 | [
"BSD-2-Clause"
] | 5 | 2020-11-25T19:48:57.000Z | 2022-02-27T23:50:18.000Z | app/authorization/tests/test_membership_mutations.py | metamapper-io/metamapper | 376716e72bcaca62f1ec09ca9a13a0346e5502f9 | [
"BSD-2-Clause"
] | 5 | 2020-08-29T16:43:59.000Z | 2022-01-17T19:05:30.000Z | # -*- coding: utf-8 -*-
import unittest.mock as mock
import testutils.cases as cases
import testutils.decorators as decorators
import app.authorization.models as models
class GrantMembershipTests(cases.GraphQLTestCase):
"""Test cases for granting membership to a workspace.
"""
operation = 'grantMembership'
statement = '''
mutation grantMembership($email: String!, $permissions: String!) {
grantMembership(input: {
email: $email,
permissions: $permissions,
}) {
ok
errors {
resource
field
code
}
}
}
'''
@mock.patch('app.authorization.emails.membership_granted')
@decorators.as_someone(['OWNER'])
def test_grant_new_membership(self, membership_granted):
variables = {
'permissions': 'MEMBER',
'email': self.users['OUTSIDER'].email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
membership_granted.assert_called_once()
membership_granted.assert_called_with(
variables['email'],
self.workspace,
variables['permissions'],
)
self.assertOk(response)
self.assertInstanceExists(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
permissions=variables['permissions'],
)
@mock.patch('app.authorization.emails.membership_granted')
@decorators.as_someone(['OWNER'])
def test_upgrade_membership(self, membership_granted):
variables = {
'permissions': 'MEMBER',
'email': self.users['READONLY'].email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
membership_granted.assert_called_once()
membership_granted.assert_called_with(
variables['email'],
self.workspace,
variables['permissions'],
)
self.assertOk(response)
self.assertInstanceExists(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
permissions=variables['permissions'],
)
@mock.patch('app.authorization.emails.membership_granted')
@decorators.as_someone(['OWNER'])
def test_downgrade_membership(self, membership_granted):
variables = {
'permissions': 'READONLY',
'email': self.users['MEMBER'].email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
membership_granted.assert_called_once()
membership_granted.assert_called_with(
variables['email'],
self.workspace,
variables['permissions'],
)
self.assertOk(response)
self.assertInstanceExists(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
permissions=variables['permissions'],
)
@mock.patch('app.authorization.emails.membership_granted')
@decorators.as_someone(['MEMBER', 'READONLY', 'OUTSIDER'])
def test_grant_membership_when_unauthorized(self, membership_granted):
owner = self.users['OWNER']
variables = {
'permissions': 'MEMBER',
'email': owner.email,
}
response = self.execute(variables=variables)
owner.refresh_from_db()
membership_granted.assert_not_called()
self.assertPermissionDenied(response)
self.assertTrue(owner.is_owner(self.workspace.pk))
@mock.patch('app.authorization.emails.membership_granted')
@decorators.as_someone(['OWNER'])
def test_grant_membership_invalid_permissions(self, membership_granted):
variables = {
'permissions': 'super-hero',
'email': self.users['READONLY'].email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
self.assertEqual(response, {
'ok': False,
'errors': [
{
'resource': 'Membership',
'field': 'permissions',
'code': 'invalid_choice',
},
],
})
membership_granted.assert_not_called()
self.assertInstanceDoesNotExist(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
permissions=variables['permissions'],
)
@mock.patch('app.authorization.emails.membership_granted')
@decorators.as_someone(['OWNER'])
def test_grant_membership_invalid_email(self, membership_granted):
variables = {
'permissions': 'MEMBER',
'email': 'thisisnotanemail',
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
self.assertEqual(response, {
'ok': False,
'errors': [
{
'resource': 'Membership',
'field': 'email',
'code': 'invalid',
},
],
})
membership_granted.assert_not_called()
self.assertInstanceDoesNotExist(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
)
@mock.patch('app.authorization.emails.membership_granted')
@decorators.as_someone(['OWNER'])
def test_grant_membership_on_self(self, membership_granted):
variables = {
'permissions': 'MEMBER',
'email': self.user.email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
self.assertEqual(response, {
'ok': False,
'errors': [
{
'resource': 'Membership',
'field': 'email',
'code': 'self_update',
},
],
})
membership_granted.assert_not_called()
self.user.refresh_from_db()
self.assertTrue(self.user.is_owner(self.workspace.id))
class RevokeMembershipTests(cases.GraphQLTestCase):
"""Test cases for revoking membership to a workspace.
"""
operation = 'revokeMembership'
statement = '''
mutation revokeMembership($email: String!) {
revokeMembership(input: {
email: $email
}) {
ok
errors {
resource
field
code
}
}
}
'''
@mock.patch('app.authorization.emails.membership_revoked')
@decorators.as_someone(['OWNER'])
def test_revoke_readonly(self, membership_revoked):
user = self.users['READONLY']
variables = {
'email': user.email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
membership_revoked.assert_called_once()
membership_revoked.assert_called_with(user.email, self.workspace)
self.assertOk(response)
self.assertInstanceDeleted(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
)
@mock.patch('app.authorization.emails.membership_revoked')
@decorators.as_someone(['OWNER'])
def test_revoke_member(self, membership_revoked):
user = self.users['MEMBER']
variables = {
'email': user.email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
membership_revoked.assert_called_once()
membership_revoked.assert_called_with(user.email, self.workspace)
self.assertOk(response)
self.assertInstanceDeleted(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
)
@mock.patch('app.authorization.emails.membership_revoked')
@decorators.as_someone(['OWNER'])
def test_revoke_owner(self, membership_revoked):
user = self.users['MEMBER']
self.workspace.grant_membership(user, models.Membership.OWNER)
variables = {
'email': user.email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
membership_revoked.assert_called_once()
membership_revoked.assert_called_with(user.email, self.workspace)
self.assertOk(response)
self.assertInstanceDeleted(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
)
@mock.patch('app.authorization.emails.membership_revoked')
@decorators.as_someone(['OWNER', 'MEMBER', 'READONLY'])
def test_revoke_self(self, membership_revoked):
variables = {
'email': self.user.email,
}
response = self.execute(variables=variables)
response = response['data'][self.operation]
membership_revoked.assert_called_once()
membership_revoked.assert_called_with(self.user.email, self.workspace)
self.assertOk(response)
self.assertInstanceDeleted(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
)
membership_revoked.reset_mock()
@mock.patch('app.authorization.emails.membership_revoked')
@decorators.as_someone(['MEMBER', 'READONLY', 'OUTSIDER'])
def test_revoke_membership_unauthorized(self, membership_revoked):
variables = {
'email': self.users['OWNER'].email,
}
response = self.execute(variables=variables)
membership_revoked.assert_not_called()
self.assertPermissionDenied(response)
self.assertInstanceExists(
model_class=models.Membership,
workspace=self.workspace,
user_id=variables['email'],
)
| 30.443452 | 78 | 0.599863 | 891 | 10,229 | 6.709315 | 0.102132 | 0.06825 | 0.024088 | 0.050184 | 0.836902 | 0.808966 | 0.774339 | 0.72081 | 0.703245 | 0.703245 | 0 | 0.000137 | 0.287418 | 10,229 | 335 | 79 | 30.534328 | 0.820003 | 0.0131 | 0 | 0.683824 | 0 | 0 | 0.174038 | 0.060294 | 0 | 0 | 0 | 0 | 0.158088 | 1 | 0.044118 | false | 0 | 0.014706 | 0 | 0.080882 | 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 |
efcf82fa58df0ba334dcc4cf237dc19596b078f3 | 79 | py | Python | app_with_import_error/settings.py | lsgunth/djappsettings | cf6f5126a8373da82797d14298320f100b1fb353 | [
"MIT"
] | 2 | 2015-02-02T17:17:09.000Z | 2021-08-28T09:09:47.000Z | app_with_import_error/settings.py | lsgunth/djappsettings | cf6f5126a8373da82797d14298320f100b1fb353 | [
"MIT"
] | 1 | 2015-05-25T19:21:59.000Z | 2015-05-25T19:21:59.000Z | app_with_import_error/settings.py | lsgunth/djappsettings | cf6f5126a8373da82797d14298320f100b1fb353 | [
"MIT"
] | 2 | 2018-12-05T23:14:44.000Z | 2019-01-10T17:38:48.000Z | import blah # noqa
# ^ Generate an ImportError to make sure we don't mask it
| 19.75 | 57 | 0.721519 | 14 | 79 | 4.071429 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.227848 | 79 | 3 | 58 | 26.333333 | 0.934426 | 0.759494 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
efd7228f967d4045c49c463086908e25f1c79bcb | 116 | py | Python | cursodjango/aperitivos/views.py | jona04/app-curso-django | 2080eb974b31beafa8ca4f07b801771f4d691ad8 | [
"MIT"
] | null | null | null | cursodjango/aperitivos/views.py | jona04/app-curso-django | 2080eb974b31beafa8ca4f07b801771f4d691ad8 | [
"MIT"
] | 39 | 2020-04-25T16:26:53.000Z | 2021-09-22T18:59:59.000Z | cursodjango/aperitivos/views.py | jona04/app-curso-django | 2080eb974b31beafa8ca4f07b801771f4d691ad8 | [
"MIT"
] | null | null | null | from django.shortcuts import render
def video(request, slug):
return render(request, 'aperitivos/video.html')
| 19.333333 | 51 | 0.758621 | 15 | 116 | 5.866667 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 116 | 5 | 52 | 23.2 | 0.88 | 0 | 0 | 0 | 0 | 0 | 0.181034 | 0.181034 | 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 |
eff20c51708f83c91360b1af030c62d0d284f79b | 137 | py | Python | src/pymane/error.py | Ostoic/pymane | 99d9efd17945058a8a46319605e231a0e185c98f | [
"MIT"
] | null | null | null | src/pymane/error.py | Ostoic/pymane | 99d9efd17945058a8a46319605e231a0e185c98f | [
"MIT"
] | null | null | null | src/pymane/error.py | Ostoic/pymane | 99d9efd17945058a8a46319605e231a0e185c98f | [
"MIT"
] | null | null | null | class CharacterNotFoundError(Exception):
pass
class UnhandledParseError(Exception):
pass
class CaptchaRequiredError(Exception):
pass | 17.125 | 40 | 0.839416 | 12 | 137 | 9.583333 | 0.5 | 0.33913 | 0.313043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.094891 | 137 | 8 | 41 | 17.125 | 0.927419 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 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 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
4bc9830a624d5a2d1552060ef3d20400a19eaf86 | 134 | py | Python | models/EBM/__init__.py | JunLi-Galios/flow-based-CoopNets | 3580c8ca242768be3cb24ae10cd522924dca52d3 | [
"MIT"
] | null | null | null | models/EBM/__init__.py | JunLi-Galios/flow-based-CoopNets | 3580c8ca242768be3cb24ae10cd522924dca52d3 | [
"MIT"
] | null | null | null | models/EBM/__init__.py | JunLi-Galios/flow-based-CoopNets | 3580c8ca242768be3cb24ae10cd522924dca52d3 | [
"MIT"
] | null | null | null | from models.EBM.ebm import F
from models.EBM.trainer import train_full, train_single_step, test
from models.EBM.sampler import sample
| 33.5 | 66 | 0.835821 | 23 | 134 | 4.73913 | 0.565217 | 0.275229 | 0.357798 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104478 | 134 | 3 | 67 | 44.666667 | 0.908333 | 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 |
4bed4d6b9a4d72f2361082b61f35820cd13ecaec | 41 | py | Python | pytlssniff/__init__.py | DNS-Privacy-Security/PyTLSSniff | 04ff3d61a47666e2be8295c19ffbd6334a39e388 | [
"MIT"
] | 1 | 2020-07-31T13:51:31.000Z | 2020-07-31T13:51:31.000Z | pytlssniff/__init__.py | DNS-Privacy-Security/PyTLSSniff | 04ff3d61a47666e2be8295c19ffbd6334a39e388 | [
"MIT"
] | null | null | null | pytlssniff/__init__.py | DNS-Privacy-Security/PyTLSSniff | 04ff3d61a47666e2be8295c19ffbd6334a39e388 | [
"MIT"
] | null | null | null | from .sniffer import TLSHandshakeSniffer
| 20.5 | 40 | 0.878049 | 4 | 41 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.097561 | 41 | 1 | 41 | 41 | 0.972973 | 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 |
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