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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15c8afa61c309aac2fac17d6061450716ecffe73
| 261,022
|
py
|
Python
|
instances/passenger_demand/pas-20210422-1717-int16e/9.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-int16e/9.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-int16e/9.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 30585
passenger_arriving = (
(6, 7, 5, 12, 5, 4, 3, 0, 3, 0, 0, 1, 0, 12, 9, 9, 3, 5, 2, 3, 2, 3, 0, 1, 2, 0), # 0
(8, 12, 2, 2, 8, 3, 3, 1, 2, 2, 1, 0, 0, 14, 10, 7, 6, 5, 4, 4, 3, 6, 3, 2, 2, 0), # 1
(7, 11, 10, 8, 6, 2, 4, 4, 5, 1, 2, 1, 0, 10, 7, 6, 9, 7, 5, 3, 4, 4, 4, 0, 1, 0), # 2
(7, 6, 11, 15, 6, 4, 4, 5, 3, 2, 2, 2, 0, 10, 10, 5, 5, 12, 3, 7, 2, 2, 4, 0, 0, 0), # 3
(10, 13, 11, 9, 8, 6, 5, 2, 1, 2, 0, 0, 0, 9, 3, 2, 6, 8, 8, 5, 6, 4, 3, 4, 0, 0), # 4
(14, 10, 13, 8, 8, 5, 7, 4, 4, 1, 1, 2, 0, 9, 10, 7, 4, 10, 6, 2, 4, 3, 1, 1, 0, 0), # 5
(16, 7, 7, 8, 9, 7, 6, 1, 5, 1, 2, 1, 0, 8, 9, 7, 6, 4, 4, 4, 5, 5, 3, 1, 0, 0), # 6
(21, 23, 5, 12, 8, 6, 4, 5, 2, 1, 2, 1, 0, 10, 7, 3, 8, 10, 5, 10, 5, 4, 6, 2, 0, 0), # 7
(14, 16, 9, 13, 4, 5, 4, 5, 7, 2, 3, 0, 0, 18, 13, 8, 9, 10, 6, 2, 6, 3, 3, 2, 0, 0), # 8
(15, 18, 14, 13, 12, 7, 4, 6, 8, 1, 0, 0, 0, 16, 6, 13, 7, 3, 8, 4, 2, 8, 6, 1, 0, 0), # 9
(17, 15, 10, 12, 4, 2, 5, 5, 4, 6, 4, 2, 0, 13, 11, 6, 10, 11, 6, 9, 3, 6, 4, 4, 3, 0), # 10
(12, 13, 12, 15, 19, 6, 6, 7, 8, 2, 1, 0, 0, 23, 8, 10, 12, 12, 5, 5, 3, 3, 5, 0, 1, 0), # 11
(10, 19, 7, 10, 10, 5, 5, 7, 6, 3, 1, 0, 0, 14, 10, 11, 5, 16, 4, 7, 9, 3, 6, 2, 0, 0), # 12
(15, 11, 15, 7, 10, 4, 8, 5, 4, 2, 2, 1, 0, 11, 14, 8, 9, 8, 7, 7, 4, 7, 2, 1, 1, 0), # 13
(15, 8, 13, 18, 10, 6, 9, 6, 7, 2, 5, 1, 0, 10, 18, 9, 7, 11, 9, 7, 3, 7, 4, 2, 1, 0), # 14
(10, 19, 6, 14, 12, 8, 8, 13, 9, 3, 3, 3, 0, 12, 6, 9, 9, 10, 7, 10, 3, 6, 3, 0, 0, 0), # 15
(7, 22, 12, 12, 9, 4, 8, 0, 4, 5, 2, 2, 0, 18, 9, 17, 9, 14, 12, 5, 6, 8, 3, 3, 1, 0), # 16
(18, 5, 12, 15, 9, 2, 10, 4, 7, 2, 5, 0, 0, 15, 13, 14, 14, 16, 9, 12, 6, 2, 8, 1, 2, 0), # 17
(22, 15, 12, 16, 11, 10, 3, 4, 8, 5, 2, 2, 0, 11, 13, 13, 6, 16, 7, 10, 4, 2, 4, 2, 1, 0), # 18
(15, 12, 8, 15, 9, 5, 7, 4, 6, 3, 2, 0, 0, 17, 15, 14, 8, 8, 5, 6, 3, 5, 2, 1, 0, 0), # 19
(14, 14, 11, 15, 15, 9, 5, 8, 6, 3, 2, 1, 0, 12, 16, 10, 4, 18, 10, 7, 3, 4, 5, 3, 0, 0), # 20
(18, 23, 12, 17, 18, 5, 6, 4, 5, 4, 0, 1, 0, 18, 14, 5, 11, 10, 11, 8, 4, 3, 6, 6, 0, 0), # 21
(10, 15, 12, 19, 14, 4, 4, 6, 6, 4, 5, 4, 0, 19, 17, 7, 13, 11, 8, 4, 2, 5, 7, 3, 0, 0), # 22
(13, 19, 14, 19, 11, 5, 9, 7, 5, 2, 3, 0, 0, 15, 15, 6, 9, 11, 8, 3, 0, 5, 7, 2, 0, 0), # 23
(14, 9, 9, 11, 13, 4, 5, 5, 8, 1, 2, 3, 0, 15, 17, 9, 8, 15, 15, 7, 3, 4, 4, 1, 0, 0), # 24
(18, 18, 20, 8, 11, 11, 6, 7, 2, 4, 1, 1, 0, 16, 20, 13, 11, 21, 9, 7, 3, 8, 4, 1, 0, 0), # 25
(24, 14, 15, 12, 12, 6, 11, 5, 7, 3, 5, 0, 0, 16, 18, 8, 10, 15, 6, 4, 4, 7, 3, 3, 2, 0), # 26
(15, 14, 11, 15, 15, 6, 2, 5, 7, 4, 2, 1, 0, 17, 11, 17, 12, 12, 6, 7, 1, 3, 3, 3, 2, 0), # 27
(10, 15, 18, 17, 16, 5, 5, 5, 8, 3, 3, 1, 0, 17, 19, 11, 10, 8, 10, 15, 2, 3, 6, 4, 2, 0), # 28
(14, 9, 21, 13, 12, 10, 4, 10, 6, 3, 2, 2, 0, 21, 8, 10, 15, 11, 3, 8, 3, 5, 1, 5, 1, 0), # 29
(13, 14, 11, 25, 15, 4, 8, 4, 6, 2, 2, 0, 0, 12, 18, 13, 13, 12, 7, 8, 2, 7, 3, 3, 2, 0), # 30
(16, 18, 15, 15, 14, 6, 3, 5, 12, 2, 3, 1, 0, 15, 12, 11, 10, 12, 10, 4, 6, 5, 6, 0, 2, 0), # 31
(20, 19, 18, 13, 9, 5, 6, 7, 4, 2, 2, 0, 0, 30, 21, 14, 12, 11, 7, 5, 8, 6, 4, 0, 2, 0), # 32
(21, 18, 10, 10, 11, 6, 6, 1, 4, 4, 2, 1, 0, 25, 18, 8, 11, 9, 7, 3, 2, 3, 5, 2, 0, 0), # 33
(18, 16, 17, 18, 14, 7, 7, 8, 7, 4, 1, 0, 0, 16, 14, 16, 7, 9, 8, 11, 3, 7, 4, 2, 4, 0), # 34
(21, 12, 12, 17, 9, 7, 6, 2, 7, 7, 1, 2, 0, 17, 15, 7, 7, 17, 6, 7, 3, 7, 5, 2, 0, 0), # 35
(15, 10, 9, 11, 9, 4, 10, 5, 6, 1, 1, 2, 0, 7, 17, 14, 9, 9, 4, 6, 7, 7, 10, 4, 2, 0), # 36
(10, 21, 19, 19, 13, 4, 7, 6, 3, 2, 2, 2, 0, 19, 14, 6, 8, 11, 5, 8, 0, 4, 5, 4, 2, 0), # 37
(20, 16, 22, 11, 16, 9, 10, 6, 5, 7, 1, 1, 0, 17, 9, 10, 9, 13, 9, 9, 3, 5, 5, 3, 1, 0), # 38
(15, 13, 14, 17, 12, 6, 8, 7, 4, 2, 2, 1, 0, 17, 14, 16, 11, 15, 2, 6, 5, 8, 4, 4, 2, 0), # 39
(17, 10, 23, 16, 11, 3, 13, 3, 4, 1, 2, 4, 0, 16, 16, 8, 15, 16, 11, 6, 8, 2, 5, 4, 0, 0), # 40
(12, 10, 6, 10, 10, 8, 10, 7, 4, 5, 1, 1, 0, 15, 21, 8, 14, 16, 11, 7, 3, 8, 5, 4, 0, 0), # 41
(21, 14, 15, 20, 10, 9, 6, 4, 6, 6, 2, 1, 0, 11, 19, 9, 10, 17, 7, 8, 2, 5, 7, 3, 1, 0), # 42
(23, 14, 12, 7, 9, 7, 6, 5, 2, 4, 0, 1, 0, 23, 18, 12, 11, 16, 9, 6, 5, 4, 5, 2, 4, 0), # 43
(16, 15, 17, 12, 5, 6, 4, 2, 4, 2, 0, 3, 0, 21, 19, 11, 12, 16, 9, 5, 5, 4, 5, 1, 0, 0), # 44
(12, 14, 19, 14, 11, 4, 9, 5, 8, 7, 1, 1, 0, 19, 14, 12, 7, 15, 2, 4, 6, 9, 3, 3, 3, 0), # 45
(9, 14, 11, 12, 10, 3, 6, 6, 6, 1, 5, 1, 0, 15, 14, 12, 10, 13, 4, 9, 3, 9, 1, 0, 1, 0), # 46
(21, 13, 9, 14, 21, 5, 5, 3, 11, 2, 4, 4, 0, 12, 15, 14, 10, 17, 7, 10, 4, 5, 6, 0, 1, 0), # 47
(12, 16, 16, 15, 12, 8, 9, 7, 6, 3, 2, 3, 0, 10, 17, 11, 5, 11, 13, 4, 3, 4, 6, 4, 1, 0), # 48
(14, 18, 12, 18, 10, 6, 8, 2, 5, 2, 3, 3, 0, 17, 12, 6, 7, 23, 5, 3, 7, 6, 7, 3, 0, 0), # 49
(20, 18, 10, 14, 9, 2, 5, 6, 6, 3, 3, 2, 0, 22, 16, 5, 5, 13, 8, 4, 8, 7, 4, 1, 1, 0), # 50
(15, 13, 5, 15, 14, 4, 10, 8, 6, 4, 2, 0, 0, 18, 9, 12, 12, 12, 11, 6, 5, 4, 2, 3, 0, 0), # 51
(19, 16, 12, 17, 7, 6, 10, 4, 4, 5, 2, 1, 0, 13, 18, 11, 7, 17, 12, 7, 1, 7, 6, 2, 2, 0), # 52
(11, 17, 20, 9, 9, 7, 5, 2, 6, 1, 2, 1, 0, 15, 20, 16, 9, 11, 6, 7, 2, 5, 7, 1, 0, 0), # 53
(16, 13, 15, 16, 11, 7, 6, 7, 2, 3, 5, 1, 0, 13, 12, 7, 13, 11, 7, 6, 4, 1, 11, 1, 1, 0), # 54
(19, 21, 19, 17, 9, 7, 4, 6, 7, 0, 0, 3, 0, 17, 10, 11, 6, 19, 5, 6, 5, 7, 7, 1, 2, 0), # 55
(21, 14, 13, 17, 12, 5, 8, 9, 6, 1, 1, 1, 0, 20, 7, 7, 8, 12, 7, 9, 2, 8, 8, 3, 1, 0), # 56
(17, 14, 11, 17, 7, 3, 8, 3, 6, 2, 4, 0, 0, 20, 17, 10, 7, 6, 8, 7, 6, 6, 6, 2, 3, 0), # 57
(13, 16, 9, 7, 18, 7, 6, 9, 9, 3, 2, 2, 0, 17, 12, 15, 14, 7, 6, 6, 8, 5, 5, 3, 3, 0), # 58
(20, 14, 10, 17, 13, 7, 7, 4, 8, 5, 2, 1, 0, 20, 16, 7, 10, 12, 4, 6, 4, 6, 4, 4, 2, 0), # 59
(21, 12, 15, 13, 13, 9, 12, 6, 7, 3, 2, 2, 0, 14, 12, 12, 6, 13, 8, 2, 7, 8, 6, 1, 0, 0), # 60
(20, 14, 17, 13, 15, 5, 3, 7, 6, 4, 1, 2, 0, 9, 10, 13, 6, 10, 4, 9, 4, 9, 7, 3, 2, 0), # 61
(7, 18, 15, 12, 17, 6, 5, 4, 5, 4, 0, 0, 0, 12, 15, 13, 12, 19, 6, 5, 5, 9, 1, 1, 2, 0), # 62
(21, 14, 14, 17, 15, 5, 12, 4, 10, 2, 4, 0, 0, 16, 12, 6, 11, 14, 2, 8, 5, 9, 8, 6, 3, 0), # 63
(26, 17, 18, 20, 7, 8, 10, 5, 8, 2, 0, 2, 0, 20, 19, 6, 9, 9, 8, 4, 4, 6, 3, 3, 3, 0), # 64
(19, 14, 15, 21, 10, 8, 5, 2, 8, 4, 2, 1, 0, 15, 18, 13, 9, 11, 7, 8, 1, 3, 5, 1, 1, 0), # 65
(19, 16, 10, 15, 8, 4, 7, 6, 8, 3, 2, 1, 0, 30, 13, 8, 9, 10, 13, 4, 5, 4, 4, 6, 1, 0), # 66
(9, 9, 13, 21, 13, 11, 5, 3, 4, 1, 2, 2, 0, 13, 19, 14, 7, 13, 8, 4, 3, 8, 3, 2, 2, 0), # 67
(9, 15, 12, 23, 12, 2, 5, 12, 9, 4, 1, 1, 0, 14, 12, 9, 8, 12, 8, 3, 4, 0, 5, 3, 1, 0), # 68
(16, 14, 9, 13, 16, 6, 6, 4, 5, 6, 2, 1, 0, 10, 12, 6, 8, 4, 5, 4, 4, 5, 1, 0, 0, 0), # 69
(12, 22, 17, 7, 16, 7, 4, 3, 7, 2, 3, 1, 0, 13, 16, 14, 3, 12, 6, 5, 6, 4, 8, 3, 4, 0), # 70
(16, 11, 13, 15, 12, 3, 6, 5, 6, 4, 2, 0, 0, 13, 9, 5, 8, 12, 4, 5, 5, 7, 6, 2, 0, 0), # 71
(13, 13, 17, 15, 10, 4, 7, 3, 4, 0, 3, 0, 0, 15, 11, 7, 9, 9, 4, 5, 4, 7, 8, 2, 1, 0), # 72
(16, 17, 12, 20, 22, 6, 11, 4, 6, 5, 3, 0, 0, 19, 9, 8, 11, 16, 4, 8, 7, 5, 4, 3, 0, 0), # 73
(17, 6, 14, 11, 10, 10, 6, 6, 6, 1, 3, 2, 0, 21, 10, 13, 11, 11, 6, 6, 1, 7, 4, 2, 4, 0), # 74
(12, 10, 13, 21, 13, 6, 8, 8, 12, 2, 2, 0, 0, 15, 12, 5, 4, 14, 6, 5, 2, 8, 6, 2, 3, 0), # 75
(19, 12, 17, 16, 13, 7, 7, 10, 7, 4, 0, 1, 0, 12, 15, 8, 5, 16, 5, 2, 2, 8, 1, 0, 1, 0), # 76
(18, 10, 15, 12, 8, 3, 6, 2, 4, 2, 2, 1, 0, 20, 8, 12, 7, 10, 4, 6, 6, 9, 2, 2, 3, 0), # 77
(8, 8, 13, 16, 14, 4, 7, 3, 2, 2, 4, 0, 0, 13, 10, 11, 7, 19, 4, 10, 6, 6, 1, 4, 1, 0), # 78
(15, 13, 12, 11, 11, 3, 2, 4, 5, 7, 2, 1, 0, 12, 16, 3, 9, 15, 7, 4, 8, 11, 4, 6, 1, 0), # 79
(16, 15, 18, 16, 9, 2, 7, 4, 10, 7, 1, 0, 0, 16, 16, 12, 6, 18, 5, 3, 3, 6, 4, 4, 0, 0), # 80
(15, 16, 4, 19, 10, 5, 4, 4, 5, 4, 3, 3, 0, 21, 11, 12, 7, 8, 2, 5, 2, 2, 7, 2, 2, 0), # 81
(21, 11, 14, 15, 15, 8, 6, 5, 3, 3, 2, 2, 0, 19, 10, 6, 7, 8, 6, 1, 6, 3, 2, 6, 1, 0), # 82
(11, 16, 8, 14, 9, 5, 3, 4, 3, 2, 0, 1, 0, 17, 9, 13, 7, 12, 7, 5, 9, 8, 7, 3, 0, 0), # 83
(13, 20, 13, 13, 16, 3, 3, 5, 11, 4, 0, 4, 0, 25, 12, 10, 15, 16, 6, 4, 6, 4, 5, 2, 1, 0), # 84
(14, 8, 13, 12, 13, 4, 3, 2, 4, 2, 2, 1, 0, 12, 16, 12, 9, 19, 7, 7, 6, 7, 4, 1, 0, 0), # 85
(16, 18, 12, 20, 12, 5, 5, 1, 8, 1, 1, 1, 0, 19, 19, 9, 6, 11, 12, 4, 2, 8, 4, 2, 2, 0), # 86
(25, 19, 13, 16, 10, 4, 7, 2, 2, 1, 1, 0, 0, 26, 14, 10, 10, 14, 9, 3, 6, 8, 4, 1, 3, 0), # 87
(17, 12, 8, 16, 11, 6, 5, 6, 6, 4, 2, 0, 0, 13, 20, 12, 9, 11, 11, 10, 2, 8, 5, 1, 3, 0), # 88
(10, 10, 10, 15, 8, 10, 12, 4, 8, 3, 5, 0, 0, 18, 13, 14, 6, 12, 4, 13, 3, 6, 1, 4, 3, 0), # 89
(12, 9, 14, 11, 16, 8, 5, 3, 8, 0, 2, 1, 0, 9, 9, 13, 8, 12, 6, 3, 5, 5, 7, 0, 0, 0), # 90
(18, 11, 10, 13, 10, 3, 5, 4, 5, 1, 3, 1, 0, 24, 11, 13, 7, 10, 3, 5, 2, 5, 5, 3, 1, 0), # 91
(17, 16, 11, 17, 16, 8, 6, 2, 3, 4, 0, 2, 0, 21, 13, 16, 9, 14, 4, 4, 5, 4, 5, 2, 0, 0), # 92
(14, 14, 6, 16, 7, 8, 11, 9, 8, 2, 4, 0, 0, 17, 13, 10, 6, 13, 8, 5, 4, 5, 5, 3, 0, 0), # 93
(22, 8, 8, 12, 9, 3, 0, 0, 3, 2, 1, 2, 0, 15, 12, 3, 11, 9, 3, 7, 7, 8, 12, 3, 1, 0), # 94
(18, 7, 14, 9, 7, 6, 7, 2, 7, 4, 3, 2, 0, 17, 20, 8, 6, 11, 5, 1, 4, 12, 6, 4, 1, 0), # 95
(18, 10, 9, 12, 10, 8, 9, 4, 4, 3, 5, 2, 0, 24, 15, 5, 10, 12, 11, 5, 5, 8, 6, 2, 1, 0), # 96
(15, 10, 11, 16, 11, 3, 5, 4, 3, 2, 3, 3, 0, 13, 11, 12, 8, 12, 5, 4, 0, 10, 2, 3, 1, 0), # 97
(22, 9, 10, 9, 12, 6, 7, 6, 6, 3, 1, 0, 0, 15, 20, 13, 9, 10, 7, 6, 4, 8, 8, 3, 1, 0), # 98
(15, 17, 9, 13, 8, 6, 6, 0, 4, 0, 2, 2, 0, 18, 15, 5, 11, 14, 6, 7, 6, 4, 5, 1, 2, 0), # 99
(16, 14, 12, 11, 10, 4, 6, 3, 5, 3, 2, 0, 0, 13, 12, 8, 8, 12, 5, 4, 2, 5, 6, 7, 3, 0), # 100
(14, 7, 13, 16, 11, 5, 3, 0, 7, 3, 2, 2, 0, 19, 8, 9, 6, 15, 8, 7, 5, 6, 3, 0, 2, 0), # 101
(18, 13, 13, 21, 16, 11, 3, 5, 6, 3, 0, 2, 0, 15, 13, 10, 9, 15, 6, 5, 2, 5, 10, 2, 2, 0), # 102
(15, 13, 12, 16, 10, 3, 6, 2, 6, 2, 0, 0, 0, 13, 10, 15, 10, 14, 12, 4, 3, 3, 3, 2, 1, 0), # 103
(15, 6, 21, 12, 19, 6, 7, 4, 6, 1, 2, 3, 0, 11, 6, 11, 7, 14, 2, 8, 3, 4, 8, 3, 2, 0), # 104
(18, 13, 12, 15, 13, 6, 2, 9, 8, 2, 5, 1, 0, 15, 20, 10, 7, 9, 5, 5, 3, 5, 3, 2, 1, 0), # 105
(13, 14, 9, 14, 5, 12, 5, 3, 5, 3, 2, 0, 0, 14, 11, 8, 2, 13, 8, 7, 0, 4, 5, 0, 2, 0), # 106
(15, 13, 15, 15, 16, 4, 3, 5, 5, 4, 0, 1, 0, 15, 14, 15, 3, 13, 5, 4, 4, 6, 2, 3, 1, 0), # 107
(11, 12, 17, 9, 9, 4, 8, 9, 9, 3, 4, 1, 0, 19, 15, 8, 7, 8, 5, 4, 4, 1, 6, 2, 1, 0), # 108
(13, 14, 17, 10, 12, 2, 5, 8, 5, 1, 0, 2, 0, 13, 16, 7, 9, 6, 5, 6, 5, 9, 5, 1, 0, 0), # 109
(15, 13, 11, 8, 15, 3, 4, 6, 6, 2, 1, 0, 0, 17, 11, 2, 4, 8, 6, 4, 3, 4, 2, 0, 1, 0), # 110
(13, 11, 11, 15, 18, 2, 8, 2, 2, 2, 3, 1, 0, 13, 14, 10, 9, 8, 6, 2, 8, 11, 8, 0, 2, 0), # 111
(18, 10, 14, 10, 11, 6, 6, 3, 6, 2, 1, 1, 0, 16, 17, 9, 7, 13, 3, 5, 6, 5, 4, 2, 1, 0), # 112
(11, 8, 12, 9, 4, 2, 3, 9, 1, 3, 5, 3, 0, 15, 9, 7, 7, 10, 6, 9, 8, 6, 2, 1, 0, 0), # 113
(14, 9, 14, 18, 9, 3, 2, 4, 6, 5, 1, 2, 0, 16, 17, 10, 9, 11, 5, 4, 2, 10, 2, 4, 2, 0), # 114
(8, 5, 15, 12, 12, 7, 6, 5, 5, 7, 2, 0, 0, 15, 12, 8, 10, 15, 4, 2, 2, 7, 5, 3, 2, 0), # 115
(12, 7, 12, 16, 9, 8, 3, 5, 3, 5, 2, 1, 0, 11, 12, 18, 7, 13, 6, 7, 4, 5, 3, 1, 1, 0), # 116
(15, 9, 10, 14, 13, 5, 10, 6, 4, 1, 2, 2, 0, 9, 10, 11, 5, 14, 4, 3, 6, 4, 3, 5, 1, 0), # 117
(12, 7, 15, 10, 10, 7, 6, 6, 4, 3, 3, 1, 0, 10, 15, 10, 11, 13, 5, 4, 5, 8, 6, 8, 0, 0), # 118
(16, 8, 8, 18, 10, 5, 1, 5, 8, 2, 3, 0, 0, 11, 16, 9, 8, 12, 6, 3, 2, 3, 6, 1, 0, 0), # 119
(13, 19, 10, 10, 9, 5, 5, 4, 7, 4, 1, 1, 0, 11, 13, 9, 6, 14, 4, 3, 2, 5, 4, 0, 1, 0), # 120
(15, 11, 9, 15, 12, 6, 3, 4, 6, 4, 3, 1, 0, 14, 7, 7, 5, 11, 10, 5, 4, 4, 3, 0, 1, 0), # 121
(14, 9, 13, 11, 4, 5, 3, 6, 9, 2, 6, 0, 0, 21, 12, 5, 7, 15, 7, 6, 6, 5, 3, 5, 1, 0), # 122
(12, 12, 11, 15, 10, 8, 3, 4, 3, 2, 1, 2, 0, 17, 9, 14, 7, 10, 5, 4, 1, 8, 2, 2, 1, 0), # 123
(14, 11, 8, 12, 15, 4, 3, 8, 3, 2, 2, 0, 0, 16, 10, 16, 13, 7, 2, 2, 1, 6, 5, 1, 0, 0), # 124
(15, 11, 7, 11, 10, 4, 7, 3, 3, 2, 2, 1, 0, 14, 10, 9, 3, 10, 4, 8, 8, 5, 3, 4, 0, 0), # 125
(17, 10, 13, 12, 8, 6, 3, 2, 10, 5, 1, 0, 0, 8, 9, 8, 6, 16, 5, 3, 3, 5, 4, 2, 0, 0), # 126
(17, 8, 15, 16, 13, 5, 3, 4, 5, 0, 1, 1, 0, 18, 12, 13, 8, 9, 3, 3, 5, 5, 7, 3, 0, 0), # 127
(18, 11, 21, 8, 8, 4, 8, 3, 10, 5, 0, 1, 0, 11, 11, 4, 4, 10, 9, 4, 3, 3, 2, 1, 2, 0), # 128
(16, 13, 9, 17, 12, 4, 3, 3, 5, 2, 1, 0, 0, 19, 9, 9, 8, 7, 4, 2, 1, 8, 6, 2, 1, 0), # 129
(12, 10, 10, 11, 11, 4, 4, 4, 8, 1, 0, 0, 0, 18, 10, 5, 6, 12, 7, 6, 4, 9, 6, 2, 2, 0), # 130
(12, 7, 14, 11, 10, 4, 4, 4, 2, 1, 1, 1, 0, 8, 10, 7, 7, 13, 5, 2, 4, 5, 3, 4, 2, 0), # 131
(15, 11, 7, 16, 6, 2, 3, 3, 7, 0, 4, 2, 0, 20, 10, 9, 9, 12, 12, 9, 3, 1, 3, 2, 1, 0), # 132
(15, 11, 16, 19, 9, 6, 3, 2, 4, 2, 1, 0, 0, 16, 18, 11, 4, 13, 8, 1, 6, 6, 6, 1, 0, 0), # 133
(18, 8, 8, 13, 9, 8, 11, 3, 3, 2, 2, 2, 0, 13, 12, 14, 5, 12, 4, 2, 7, 7, 2, 0, 1, 0), # 134
(10, 19, 14, 10, 11, 3, 0, 5, 4, 2, 2, 0, 0, 14, 7, 8, 5, 16, 3, 7, 4, 5, 5, 1, 2, 0), # 135
(13, 15, 12, 6, 10, 7, 4, 3, 5, 0, 3, 0, 0, 15, 14, 9, 4, 10, 11, 9, 6, 5, 6, 1, 1, 0), # 136
(18, 9, 13, 6, 8, 3, 4, 4, 6, 0, 2, 2, 0, 18, 7, 6, 6, 8, 5, 3, 3, 7, 4, 3, 1, 0), # 137
(11, 12, 19, 10, 10, 6, 3, 7, 3, 1, 4, 0, 0, 15, 12, 8, 3, 10, 5, 3, 3, 10, 2, 1, 0, 0), # 138
(20, 10, 15, 11, 14, 2, 3, 6, 7, 1, 4, 2, 0, 10, 11, 8, 10, 11, 2, 9, 5, 5, 10, 2, 2, 0), # 139
(17, 11, 6, 10, 7, 7, 2, 6, 11, 2, 1, 0, 0, 13, 8, 11, 5, 12, 2, 5, 1, 2, 2, 1, 3, 0), # 140
(16, 12, 13, 12, 7, 5, 2, 6, 8, 1, 0, 2, 0, 13, 12, 7, 10, 9, 3, 5, 3, 5, 3, 3, 1, 0), # 141
(13, 10, 10, 6, 13, 4, 3, 4, 2, 5, 1, 1, 0, 13, 11, 1, 6, 15, 11, 2, 2, 7, 2, 3, 2, 0), # 142
(13, 6, 13, 22, 9, 4, 5, 2, 6, 2, 1, 1, 0, 12, 7, 9, 11, 7, 2, 6, 3, 4, 9, 2, 0, 0), # 143
(10, 7, 9, 9, 12, 4, 5, 6, 5, 3, 2, 0, 0, 14, 11, 11, 10, 8, 4, 6, 4, 5, 1, 1, 2, 0), # 144
(15, 8, 12, 19, 11, 4, 2, 5, 6, 6, 2, 1, 0, 12, 12, 10, 6, 8, 7, 3, 6, 11, 4, 2, 0, 0), # 145
(16, 10, 12, 10, 7, 5, 3, 4, 1, 0, 1, 2, 0, 19, 8, 9, 8, 10, 5, 3, 3, 11, 3, 3, 1, 0), # 146
(11, 13, 17, 8, 8, 6, 4, 4, 10, 5, 0, 2, 0, 13, 11, 9, 5, 8, 6, 2, 3, 14, 6, 1, 3, 0), # 147
(14, 13, 10, 12, 6, 7, 2, 4, 3, 3, 0, 2, 0, 13, 10, 12, 5, 7, 3, 2, 3, 5, 1, 0, 1, 0), # 148
(10, 6, 11, 9, 10, 8, 2, 4, 3, 0, 2, 1, 0, 15, 12, 7, 9, 12, 3, 1, 1, 5, 4, 5, 0, 0), # 149
(14, 9, 8, 16, 13, 3, 2, 3, 6, 2, 2, 1, 0, 21, 15, 7, 7, 16, 2, 6, 5, 2, 2, 0, 1, 0), # 150
(11, 12, 11, 12, 7, 2, 8, 6, 0, 3, 0, 1, 0, 11, 8, 5, 11, 13, 4, 3, 5, 5, 4, 2, 1, 0), # 151
(10, 12, 11, 15, 18, 2, 5, 2, 8, 2, 1, 2, 0, 17, 13, 4, 4, 13, 6, 4, 1, 3, 8, 1, 1, 0), # 152
(11, 12, 10, 8, 4, 6, 2, 9, 4, 4, 0, 0, 0, 15, 10, 8, 2, 7, 6, 3, 0, 1, 2, 3, 2, 0), # 153
(15, 9, 12, 12, 8, 8, 4, 5, 9, 1, 1, 2, 0, 16, 9, 6, 4, 12, 7, 3, 5, 7, 5, 3, 0, 0), # 154
(9, 14, 10, 13, 10, 7, 2, 6, 7, 1, 1, 1, 0, 12, 10, 3, 9, 11, 4, 5, 2, 3, 5, 3, 0, 0), # 155
(14, 10, 12, 12, 8, 7, 0, 3, 3, 4, 2, 1, 0, 20, 16, 8, 5, 4, 1, 4, 6, 9, 5, 2, 0, 0), # 156
(21, 6, 11, 20, 12, 2, 5, 2, 5, 1, 0, 1, 0, 14, 7, 7, 7, 9, 8, 6, 3, 4, 6, 2, 0, 0), # 157
(6, 9, 13, 11, 6, 4, 5, 3, 4, 4, 1, 1, 0, 12, 12, 9, 2, 13, 5, 5, 3, 1, 5, 1, 1, 0), # 158
(5, 8, 14, 3, 5, 4, 1, 2, 5, 0, 2, 1, 0, 17, 8, 8, 6, 6, 5, 6, 2, 5, 3, 3, 2, 0), # 159
(8, 13, 11, 12, 8, 4, 3, 8, 4, 0, 1, 1, 0, 10, 10, 6, 1, 9, 4, 1, 1, 6, 3, 1, 0, 0), # 160
(9, 6, 18, 12, 10, 6, 6, 2, 3, 1, 1, 1, 0, 11, 9, 12, 3, 13, 7, 4, 4, 5, 5, 2, 1, 0), # 161
(11, 8, 6, 7, 10, 1, 3, 7, 6, 3, 0, 1, 0, 8, 15, 6, 9, 16, 5, 1, 2, 5, 3, 1, 0, 0), # 162
(13, 6, 6, 7, 11, 9, 2, 2, 4, 0, 1, 0, 0, 13, 9, 8, 4, 11, 1, 2, 1, 3, 3, 3, 2, 0), # 163
(8, 5, 16, 13, 8, 8, 3, 0, 4, 0, 0, 2, 0, 12, 12, 8, 7, 10, 8, 6, 3, 8, 2, 2, 0, 0), # 164
(14, 6, 6, 7, 8, 4, 2, 1, 4, 2, 0, 2, 0, 10, 7, 7, 4, 7, 7, 3, 4, 3, 5, 1, 0, 0), # 165
(4, 13, 13, 10, 14, 4, 5, 2, 5, 5, 2, 0, 0, 15, 3, 12, 2, 16, 5, 3, 3, 1, 3, 1, 1, 0), # 166
(9, 8, 14, 12, 3, 2, 1, 4, 5, 3, 0, 2, 0, 10, 10, 9, 5, 12, 0, 4, 5, 8, 4, 0, 0, 0), # 167
(18, 6, 10, 8, 8, 6, 1, 4, 7, 3, 0, 2, 0, 11, 11, 9, 5, 6, 7, 1, 1, 5, 2, 1, 0, 0), # 168
(9, 10, 14, 3, 5, 5, 3, 4, 4, 1, 1, 2, 0, 8, 4, 8, 3, 6, 5, 2, 2, 4, 4, 3, 1, 0), # 169
(8, 11, 10, 6, 8, 0, 4, 4, 6, 3, 0, 2, 0, 13, 5, 7, 3, 1, 7, 3, 3, 8, 2, 1, 2, 0), # 170
(10, 3, 13, 8, 5, 5, 5, 4, 4, 1, 4, 1, 0, 13, 10, 4, 2, 9, 3, 0, 2, 7, 3, 5, 1, 0), # 171
(15, 3, 7, 7, 6, 4, 1, 2, 2, 1, 1, 1, 0, 9, 8, 5, 7, 7, 0, 1, 2, 4, 2, 2, 2, 0), # 172
(10, 2, 10, 9, 7, 5, 2, 5, 4, 0, 0, 0, 0, 10, 7, 5, 2, 10, 1, 3, 5, 5, 3, 0, 0, 0), # 173
(7, 3, 6, 7, 6, 2, 3, 6, 5, 2, 1, 0, 0, 8, 4, 7, 9, 7, 2, 3, 3, 4, 5, 1, 0, 0), # 174
(3, 5, 10, 7, 4, 3, 4, 3, 1, 1, 0, 0, 0, 10, 6, 7, 4, 7, 4, 0, 1, 1, 4, 1, 0, 0), # 175
(9, 4, 7, 5, 7, 1, 3, 3, 3, 3, 0, 0, 0, 5, 11, 5, 3, 4, 2, 2, 4, 3, 3, 1, 0, 0), # 176
(6, 3, 4, 12, 4, 4, 2, 2, 1, 0, 1, 0, 0, 7, 7, 5, 1, 14, 1, 4, 2, 2, 2, 1, 0, 0), # 177
(6, 2, 8, 3, 5, 2, 2, 2, 4, 0, 2, 0, 0, 8, 5, 1, 3, 6, 3, 3, 3, 2, 2, 3, 1, 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 = (
(8.033384925394829, 8.840461695509067, 8.33805316738001, 9.943468438181492, 8.887496972175379, 5.021847891259743, 6.6336569845982645, 7.445081876767077, 9.744158499468812, 6.332824024835792, 6.728424262216965, 7.836664125289878, 8.134208340125381), # 0
(8.566923443231959, 9.424097110631614, 8.888554546128244, 10.600230805242587, 9.475984539958779, 5.353573734468089, 7.07115030602191, 7.9352219566491335, 10.387592522132655, 6.75036910764344, 7.172953817529811, 8.353946657302968, 8.671666635903767), # 1
(9.09875681436757, 10.005416273425567, 9.436867656875862, 11.254380327463672, 10.062340757999591, 5.683976183219912, 7.506909612737127, 8.423400396647072, 11.028458891004078, 7.166262040032874, 7.615717038042101, 8.869172243284888, 9.206983725135505), # 2
(9.6268124690345, 10.582112803098315, 9.980817390911767, 11.903322252051318, 10.644258681603043, 6.011744996136181, 7.939205826636729, 8.907681851991212, 11.664216257473749, 7.578852317481889, 8.054957458923813, 9.380297095888738, 9.738036490006762), # 3
(10.149017837465571, 11.15188031885724, 10.518228639524859, 12.544461826212112, 11.219431366074389, 6.335569931837869, 8.366309869613534, 9.386130977911865, 12.292323272932332, 7.986489435468286, 8.48891861534492, 9.885277427767623, 10.262701812703709), # 4
(10.663300349893618, 11.712412439909741, 11.04692629400403, 13.17520429715263, 11.785551866718848, 6.654140748945943, 8.786492663560358, 9.856812429639348, 12.910238588770495, 8.387522889469862, 8.915844042475412, 10.382069451574637, 10.778856575412524), # 5
(11.167587436551466, 12.261402785463202, 11.564735245638186, 13.792954912079445, 12.34031323884167, 6.9661472060813825, 9.19802513037002, 10.317790862403982, 13.515420856378904, 8.780302174964413, 9.333977275485251, 10.868629379962893, 11.284377660319372), # 6
(11.65980652767195, 12.79654497472501, 12.069480385716217, 14.39511891819914, 12.881408537748086, 7.270279061865153, 9.599178191935335, 10.767130931436084, 14.105328727148231, 9.16317678742974, 9.74156184954443, 11.342913425585486, 11.777141949610431), # 7
(12.137885053487896, 13.31553262690256, 12.558986605527034, 14.979101562718284, 13.406530818743338, 7.565226074918224, 9.988222770149116, 11.20289729196596, 14.67742085246913, 9.53449622234364, 10.136841299822914, 11.802877801095525, 12.255026325471867), # 8
(12.599750444232136, 13.816059361203237, 13.031078796359527, 15.54230809284347, 13.913373137132655, 7.849678003861574, 10.363429786904192, 11.623154599223941, 15.229155883732279, 9.892609975183907, 10.518059161490685, 12.246478719146102, 12.71590767008986), # 9
(13.043330130137491, 14.295818796834425, 13.483581849502599, 16.08214375578126, 14.399628548221282, 8.122324607316171, 10.723070164093368, 12.025967508440338, 15.757992472328343, 10.235867541428343, 10.883458969717719, 12.671672392390324, 13.157662865650577), # 10
(13.466551541436809, 14.752504553003531, 13.914320656245145, 16.596013798738237, 14.862990107314454, 8.38185564390299, 11.065414823609466, 12.409400674845465, 16.26138926964799, 10.56261841655475, 11.231284259673998, 13.076415033481297, 13.57816879434018), # 11
(13.8673421083629, 15.183810248917917, 14.321120107876064, 17.08132346892098, 15.301150869717404, 8.626960872242991, 11.388734687345298, 12.771518753669634, 16.736804927081888, 10.871212096040916, 11.559778566529495, 13.45866285507211, 13.975302338344855), # 12
(14.243629261148602, 15.587429503784993, 14.701805095684259, 17.53547801353607, 15.711803890735363, 8.856330050957158, 11.69130067719369, 13.11038640014317, 17.181698096020693, 11.159998075364648, 11.86718542545419, 13.816372069815873, 14.346940379850777), # 13
(14.593340430026746, 15.961055936812143, 15.054200510958635, 17.95588267979007, 16.092642225673583, 9.068652938666455, 11.971383715047459, 13.424068269496395, 17.593527427855076, 11.427325850003735, 12.151748371618055, 14.147498890365696, 14.690959801044102), # 14
(14.914403045230168, 16.30238316720675, 15.376131244988068, 18.339942714889578, 16.441358929837293, 9.26261929399186, 12.227254722799401, 13.71062901695961, 17.96975157397571, 11.671544915435986, 12.411710940191071, 14.449999529374674, 15.00523748411101), # 15
(15.204744536991681, 16.609104814176213, 15.66542218906148, 18.685063366041145, 16.755647058531732, 9.436918875554335, 12.457184622342362, 13.968133297763139, 18.307829185773258, 11.891004767139194, 12.64531666634322, 14.721830199495905, 15.287650311237673), # 16
(15.46229233554412, 16.878914496927916, 15.919898234467764, 18.98864988045138, 17.033199667062142, 9.590241441974857, 12.659444335569138, 14.19464576713731, 18.605218914638375, 12.084054900591148, 12.850809085244478, 14.960947113382488, 15.536075164610265), # 17
(15.684973871120327, 17.10950583466924, 16.137384272495808, 19.248107505326846, 17.271709810733743, 9.721276751874406, 12.832304784372562, 14.388231080312417, 18.859379411961754, 12.249044811269659, 13.026431732064815, 15.165306483687544, 15.748388926414954), # 18
(15.870716573953118, 17.29857244660759, 16.315705194434525, 19.460841487874106, 17.468870544851786, 9.828714563873934, 12.974036890645431, 14.546953892518793, 19.067769329134048, 12.384323994652526, 13.170428141974206, 15.332864523064154, 15.922468478837914), # 19
(16.01744787427533, 17.44380795195034, 16.452685891572806, 19.624257075299766, 17.62237492472151, 9.91124463659443, 13.08291157628058, 14.668878858986748, 19.22784731754592, 12.488241946217535, 13.28104185014264, 15.461577444165426, 16.05619070406532), # 20
(16.123095202319785, 17.542905969904893, 16.54615125519955, 19.73575951481038, 17.729916005648143, 9.967556728656858, 13.157199763170816, 14.752070634946598, 19.337072028588036, 12.559148161442488, 13.356516391740096, 15.54940145964447, 16.147432484283325), # 21
(16.18558598831933, 17.59356011967863, 16.593926176603656, 19.79275405361254, 17.78918684293692, 9.996340598682188, 13.19517237320896, 14.794593875628664, 19.392902113651065, 12.595392135805188, 13.395095301936545, 15.594292782154383, 16.194070701678125), # 22
(16.208629381348224, 17.599557750342935, 16.599877091906723, 19.799889300411525, 17.804371289652156, 10.0, 13.199686403614942, 14.79919012345679, 19.399881975308645, 12.599667636031093, 13.399932859458785, 15.599836122542294, 16.2), # 23
(16.225619860854646, 17.59605925925926, 16.598903703703705, 19.799011111111113, 17.812972181783763, 10.0, 13.197206100217867, 14.7928, 19.398946666666667, 12.59704098765432, 13.39939932659933, 15.598538271604937, 16.2), # 24
(16.242251568338528, 17.589163237311386, 16.59698216735254, 19.797273662551444, 17.821383912951205, 10.0, 13.192318244170096, 14.78024691358025, 19.3970987654321, 12.591870141746686, 13.39834143908218, 15.595976223136716, 16.2), # 25
(16.258523230476854, 17.578975034293556, 16.594138820301787, 19.79469670781893, 17.82960618947377, 10.0, 13.185098749293955, 14.76176790123457, 19.39436197530864, 12.58424113397348, 13.396768774161368, 15.592185093735715, 16.2), # 26
(16.27443357394662, 17.5656, 16.5904, 19.7913, 17.837638717670742, 10.0, 13.175623529411766, 14.7376, 19.39076, 12.57424, 13.39469090909091, 15.587200000000003, 16.2), # 27
(16.2899813254248, 17.549143484224967, 16.585792043895747, 19.787103292181072, 17.845481203861443, 10.0, 13.163968498345842, 14.707980246913582, 19.386316543209876, 12.561952775491541, 13.39211742112483, 15.581056058527665, 16.2), # 28
(16.3051652115884, 17.52971083676269, 16.580341289437587, 19.78212633744856, 17.853133354365152, 10.0, 13.150209569918506, 14.673145679012345, 19.381055308641976, 12.547465496113398, 13.389057887517147, 15.57378838591678, 16.2), # 29
(16.319983959114396, 17.50740740740741, 16.574074074074073, 19.77638888888889, 17.860594875501178, 10.0, 13.13442265795207, 14.633333333333333, 19.375, 12.530864197530866, 13.385521885521886, 15.56543209876543, 16.2), # 30
(16.334436294679772, 17.482338545953365, 16.567016735253773, 19.76991069958848, 17.867865473588814, 10.0, 13.116683676268863, 14.588780246913581, 19.368174320987656, 12.512234915409238, 13.381518992393067, 15.556022313671699, 16.2), # 31
(16.34852094496153, 17.45460960219479, 16.55919561042524, 19.762711522633747, 17.874944854947355, 10.0, 13.097068538691198, 14.539723456790126, 19.360601975308644, 12.49166368541381, 13.377058785384712, 15.545594147233656, 16.2), # 32
(16.362236636636634, 17.424325925925924, 16.55063703703704, 19.75481111111111, 17.8818327258961, 10.0, 13.075653159041394, 14.486400000000001, 19.352306666666667, 12.469236543209878, 13.372150841750841, 15.534182716049381, 16.2), # 33
(16.375582096382097, 17.391592866941014, 16.541367352537723, 19.746229218106997, 17.888528792754347, 10.0, 13.052513451141776, 14.429046913580246, 19.343312098765438, 12.445039524462736, 13.36680473874548, 15.521823136716964, 16.2), # 34
(16.388556050874893, 17.356515775034293, 16.53141289437586, 19.736985596707818, 17.895032761841392, 10.0, 13.027725328814654, 14.367901234567903, 19.333641975308645, 12.419158664837678, 13.361030053622645, 15.508550525834478, 16.2), # 35
(16.40115722679201, 17.3192, 16.5208, 19.7271, 17.901344339476537, 10.0, 13.001364705882352, 14.303200000000002, 19.32332, 12.391680000000001, 13.354836363636364, 15.494400000000002, 16.2), # 36
(16.41338435081044, 17.27975089163237, 16.50955500685871, 19.71659218106996, 17.907463231979076, 10.0, 12.97350749616719, 14.23518024691358, 19.31236987654321, 12.362689565615, 13.348233246040657, 15.479406675811616, 16.2), # 37
(16.425236149607162, 17.238273799725654, 16.49770425240055, 19.70548189300412, 17.913389145668305, 10.0, 12.944229613491487, 14.164079012345681, 19.300815308641976, 12.332273397347967, 13.341230278089538, 15.4636056698674, 16.2), # 38
(16.436711349859177, 17.194874074074075, 16.485274074074077, 19.69378888888889, 17.919121786863524, 10.0, 12.913606971677561, 14.090133333333334, 19.288680000000003, 12.300517530864198, 13.333837037037037, 15.447032098765431, 16.2), # 39
(16.44780867824346, 17.149657064471878, 16.472290809327845, 19.6815329218107, 17.924660861884032, 10.0, 12.88171548454773, 14.013580246913584, 19.27598765432099, 12.267508001828991, 13.326063100137175, 15.429721079103798, 16.2), # 40
(16.458526861437004, 17.102728120713305, 16.458780795610426, 19.66873374485597, 17.930006077049125, 10.0, 12.848631065924312, 13.934656790123459, 19.262761975308642, 12.233330845907636, 13.317918044643973, 15.411707727480568, 16.2), # 41
(16.4688646261168, 17.054192592592596, 16.444770370370374, 19.655411111111114, 17.935157138678093, 10.0, 12.814429629629629, 13.8536, 19.24902666666667, 12.198072098765433, 13.30941144781145, 15.393027160493828, 16.2), # 42
(16.47882069895983, 17.00415582990398, 16.430285871056242, 19.641584773662554, 17.940113753090245, 10.0, 12.779187089486001, 13.770646913580249, 19.234805432098767, 12.161817796067673, 13.300552886893627, 15.373714494741657, 16.2), # 43
(16.488393806643085, 16.9527231824417, 16.4153536351166, 19.62727448559671, 17.944875626604873, 10.0, 12.742979359315743, 13.686034567901238, 19.220121975308643, 12.124653973479653, 13.291351939144532, 15.353804846822133, 16.2), # 44
(16.497582675843546, 16.900000000000002, 16.400000000000002, 19.6125, 17.949442465541274, 10.0, 12.705882352941178, 13.600000000000001, 19.205, 12.086666666666668, 13.281818181818181, 15.333333333333332, 16.2), # 45
(16.50638603323821, 16.846091632373113, 16.384251303155008, 19.59728106995885, 17.953813976218747, 10.0, 12.667971984184621, 13.512780246913582, 19.189463209876543, 12.04794191129401, 13.271961192168598, 15.312335070873344, 16.2), # 46
(16.514802605504055, 16.79110342935528, 16.36813388203018, 19.581637448559672, 17.957989864956588, 10.0, 12.629324166868395, 13.424612345679012, 19.173535308641977, 12.008565743026978, 13.261790547449806, 15.29084517604024, 16.2), # 47
(16.522831119318074, 16.735140740740743, 16.351674074074076, 19.565588888888893, 17.961969838074097, 10.0, 12.590014814814815, 13.335733333333335, 19.15724, 11.968624197530865, 13.251315824915824, 15.268898765432098, 16.2), # 48
(16.53047030135726, 16.67830891632373, 16.334898216735255, 19.549155144032923, 17.965753601890572, 10.0, 12.550119841846204, 13.246380246913581, 19.14060098765432, 11.928203310470966, 13.240546601820677, 15.246530955647007, 16.2), # 49
(16.537718878298588, 16.620713305898494, 16.31783264746228, 19.53235596707819, 17.969340862725304, 10.0, 12.50971516178488, 13.15679012345679, 19.12364197530864, 11.887389117512575, 13.22949245541838, 15.223776863283039, 16.2), # 50
(16.544575576819057, 16.56245925925926, 16.300503703703704, 19.515211111111114, 17.9727313268976, 10.0, 12.46887668845316, 13.0672, 19.10638666666667, 11.846267654320988, 13.218162962962964, 15.200671604938274, 16.2), # 51
(16.551039123595647, 16.503652126200276, 16.282937722908095, 19.497740329218107, 17.975924700726743, 10.0, 12.427680335673365, 12.977846913580246, 19.0888587654321, 11.8049249565615, 13.206567701708444, 15.177250297210794, 16.2), # 52
(16.55710824530535, 16.444397256515778, 16.26516104252401, 19.479963374485596, 17.978920690532046, 10.0, 12.386202017267813, 12.888967901234569, 19.071081975308644, 11.763447059899406, 13.194716248908842, 15.153548056698675, 16.2), # 53
(16.562781668625146, 16.384800000000002, 16.2472, 19.4619, 17.981719002632804, 10.0, 12.344517647058824, 12.800799999999999, 19.05308, 11.72192, 13.18261818181818, 15.1296, 16.2), # 54
(16.568058120232035, 16.324965706447188, 16.229080932784637, 19.443569958847736, 17.984319343348304, 10.0, 12.302703138868717, 12.71358024691358, 19.034876543209876, 11.68042981252858, 13.170283077690485, 15.10544124371285, 16.2), # 55
(16.572936326802996, 16.264999725651577, 16.210830178326475, 19.424993004115226, 17.986721418997856, 10.0, 12.26083440651981, 12.627545679012346, 19.016495308641975, 11.639062533150437, 13.157720513779774, 15.0811069044353, 16.2), # 56
(16.577415015015013, 16.205007407407408, 16.192474074074077, 19.40618888888889, 17.988924935900748, 10.0, 12.218987363834422, 12.542933333333336, 18.997960000000003, 11.597904197530866, 13.144940067340068, 15.056632098765432, 16.2), # 57
(16.581492911545087, 16.145094101508917, 16.174038957475997, 19.387177366255145, 17.99092960037628, 10.0, 12.177237924634875, 12.459980246913581, 18.979294320987655, 11.557040841335164, 13.131951315625393, 15.032051943301326, 16.2), # 58
(16.585168743070195, 16.085365157750342, 16.155551165980796, 19.367978189300413, 17.992735118743752, 10.0, 12.135662002743485, 12.378923456790124, 18.960521975308644, 11.516558500228626, 13.11876383588976, 15.007401554641062, 16.2), # 59
(16.588441236267325, 16.02592592592593, 16.137037037037036, 19.34861111111111, 17.99434119732246, 10.0, 12.094335511982571, 12.3, 18.94166666666667, 11.476543209876544, 13.105387205387206, 14.982716049382717, 16.2), # 60
(16.591309117813463, 15.966881755829906, 16.11852290809328, 19.329095884773665, 17.995747542431697, 10.0, 12.053334366174454, 12.223446913580247, 18.922752098765432, 11.437081005944217, 13.091831001371743, 14.958030544124373, 16.2), # 61
(16.593771114385607, 15.908337997256517, 16.100035116598082, 19.30945226337449, 17.996953860390775, 10.0, 12.01273447914145, 12.149501234567902, 18.903801975308642, 11.398257924096939, 13.078104801097394, 14.933380155464107, 16.2), # 62
(16.595825952660736, 15.8504, 16.0816, 19.289700000000003, 17.99795985751897, 10.0, 11.972611764705881, 12.078400000000002, 18.88484, 11.36016, 13.064218181818184, 14.9088, 16.2), # 63
(16.597472359315837, 15.793173113854596, 16.0632438957476, 19.26985884773663, 17.998765240135597, 10.0, 11.933042136690068, 12.010380246913583, 18.86588987654321, 11.322873269318702, 13.050180720788127, 14.884325194330135, 16.2), # 64
(16.5987090610279, 15.73676268861454, 16.04499314128944, 19.249948559670784, 17.999369714559947, 10.0, 11.894101508916325, 11.945679012345678, 18.846975308641976, 11.286483767718336, 13.036001995261257, 14.859990855052581, 16.2), # 65
(16.599534784473914, 15.681274074074077, 16.026874074074076, 19.22998888888889, 17.999772987111317, 10.0, 11.855865795206972, 11.884533333333335, 18.828120000000002, 11.251077530864197, 13.021691582491583, 14.835832098765435, 16.2), # 66
(16.59994825633087, 15.626812620027435, 16.00891303155007, 19.209999588477366, 17.99997476410901, 10.0, 11.81841090938433, 11.827180246913583, 18.809347654320987, 11.216740594421584, 13.007259059733137, 14.811884042066758, 16.2), # 67
(16.59966658316932, 15.573197822912517, 15.991049519890261, 19.189826784755773, 17.999804728475752, 9.99981441853376, 11.781624311727434, 11.77335016003658, 18.790540557841794, 11.183392706635466, 12.992457581664603, 14.788048035039589, 16.19980024005487), # 68
(16.597026731078905, 15.51879283154122, 15.97278148148148, 19.168453623188405, 17.99825708061002, 9.998347325102882, 11.744429090154583, 11.720158024691358, 18.770876543209877, 11.150090225127087, 12.975780542264753, 14.76355035737492, 16.198217592592595), # 69
(16.59181726009423, 15.463347935749368, 15.954029492455417, 19.14573939881911, 17.995198902606308, 9.995458009449779, 11.706656215298192, 11.667123914037496, 18.750244627343395, 11.116671239140375, 12.957038218441728, 14.738276418068494, 16.195091735253776), # 70
(16.584111457028687, 15.406896269746449, 15.93480013717421, 19.12171760601181, 17.990668926006617, 9.991193293705228, 11.668322655262381, 11.61426538637403, 18.728675537265662, 11.083136574948224, 12.936299793254179, 14.712244699540344, 16.190463820301783), # 71
(16.573982608695655, 15.349470967741935, 15.915099999999999, 19.096421739130435, 17.98470588235294, 9.985600000000002, 11.62944537815126, 11.5616, 18.706200000000003, 11.04948705882353, 12.913634449760767, 14.685473684210528, 16.184375), # 72
(16.561504001908514, 15.291105163945307, 15.894935665294923, 19.069885292538917, 17.977348503187283, 9.978724950464867, 11.590041352068948, 11.50914531321445, 18.682848742569732, 11.01572351703919, 12.889111371020142, 14.65798185449907, 16.1768664266118), # 73
(16.546748923480646, 15.231831992566043, 15.874313717421124, 19.04214176060118, 17.96863552005164, 9.970614967230606, 11.550127545119556, 11.456918884316416, 18.658652491998172, 10.9818467758681, 12.86279974009097, 14.629787692826028, 16.167979252400553), # 74
(16.52979066022544, 15.171684587813619, 15.85324074074074, 19.01322463768116, 17.95860566448802, 9.961316872427986, 11.509720925407201, 11.404938271604939, 18.63364197530864, 10.947857661583152, 12.834768740031897, 14.600909681611435, 16.157754629629633), # 75
(16.510702498956285, 15.11069608389752, 15.831723319615913, 18.98316741814278, 17.94729766803841, 9.950877488187778, 11.468838461035993, 11.353221033379059, 18.607847919524463, 10.913757000457247, 12.805087553901586, 14.571366303275333, 16.146233710562413), # 76
(16.48955772648655, 15.048899615027217, 15.809768038408777, 18.95200359634997, 17.934750262244815, 9.939343636640757, 11.427497120110047, 11.301784727937816, 18.581301051668955, 10.87954561876328, 12.7738253647587, 14.54117604023777, 16.13345764746228), # 77
(16.46642962962963, 14.98632831541219, 15.787381481481482, 18.919766666666668, 17.92100217864924, 9.926762139917695, 11.38571387073348, 11.250646913580248, 18.55403209876543, 10.845224342774147, 12.741051355661883, 14.510357374918781, 16.119467592592596), # 78
(16.441391495198904, 14.923015319261916, 15.76457023319616, 18.88649012345679, 17.906092148793675, 9.913179820149367, 11.343505681010402, 11.199825148605397, 18.52607178783722, 10.810793998762742, 12.706834709669796, 14.478928789738408, 16.104304698216733), # 79
(16.414516610007755, 14.858993760785877, 15.74134087791495, 18.852207461084273, 17.890058904220126, 9.898643499466544, 11.30088951904493, 11.149336991312301, 18.497450845907636, 10.776255413001962, 12.671244609841102, 14.446908767116696, 16.08801011659808), # 80
(16.385878260869568, 14.79429677419355, 15.7177, 18.816952173913048, 17.872941176470587, 9.8832, 11.257882352941177, 11.099200000000002, 18.4682, 10.741609411764706, 12.63435023923445, 14.414315789473685, 16.070625), # 81
(16.355549734597723, 14.728957493694413, 15.693654183813445, 18.780757756307032, 17.854777697087066, 9.866896143880508, 11.214501150803258, 11.049431732967536, 18.43834997713763, 10.706856821323866, 12.596220780908501, 14.381168339229419, 16.052190500685874), # 82
(16.323604318005607, 14.663009053497943, 15.669210013717422, 18.743657702630166, 17.835607197611555, 9.849778753238837, 11.170762880735285, 11.000049748513947, 18.40793150434385, 10.671998467952339, 12.55692541792191, 14.34748489880394, 16.03274777091907), # 83
(16.290115297906603, 14.59648458781362, 15.644374074074074, 18.70568550724638, 17.815468409586057, 9.831894650205761, 11.126684510841374, 10.95107160493827, 18.376975308641974, 10.637035177923023, 12.516533333333333, 14.313283950617285, 16.012337962962963), # 84
(16.255155961114095, 14.529417230850923, 15.61915294924554, 18.666874664519593, 17.794400064552573, 9.813290656912057, 11.08228300922564, 10.902514860539554, 18.345512117055325, 10.60196777750881, 12.47511371020143, 14.2785839770895, 15.991002229080934), # 85
(16.21879959444146, 14.46184011681933, 15.593553223593966, 18.627258668813745, 17.772440894053094, 9.794013595488494, 11.037575343992193, 10.854397073616827, 18.313572656607228, 10.566797092982599, 12.432735731584856, 14.24340346064063, 15.968781721536352), # 86
(16.18111948470209, 14.393786379928315, 15.567581481481481, 18.586871014492754, 17.749629629629634, 9.774110288065843, 10.99257848324515, 10.806735802469136, 18.28118765432099, 10.531523950617284, 12.389468580542264, 14.207760883690709, 15.945717592592594), # 87
(16.142188918709373, 14.325289154387361, 15.541244307270233, 18.54574519592056, 17.726005002824177, 9.753627556774882, 10.947309395088626, 10.75954860539552, 18.248387837219937, 10.496149176685762, 12.345381440132318, 14.171674728659784, 15.921850994513035), # 88
(16.102081183276677, 14.256381574405948, 15.51454828532236, 18.503914707461085, 17.701605745178732, 9.732612223746381, 10.901785047626733, 10.712853040695016, 18.21520393232739, 10.460673597460932, 12.30054349341367, 14.135163477967897, 15.897223079561043), # 89
(16.06086956521739, 14.187096774193549, 15.4875, 18.461413043478263, 17.676470588235297, 9.711111111111112, 10.856022408963586, 10.666666666666666, 18.18166666666667, 10.425098039215687, 12.255023923444977, 14.098245614035088, 15.871875000000001), # 90
(16.0186273513449, 14.117467887959643, 15.460106035665294, 18.41827369833602, 17.650638263535864, 9.689171040999847, 10.810038447203299, 10.621007041609511, 18.14780676726109, 10.389423328222922, 12.208891913284896, 14.060939619281399, 15.845847908093276), # 91
(15.975427828472597, 14.047528049913716, 15.432372976680384, 18.374530166398284, 17.624147502622446, 9.666838835543363, 10.763850130449988, 10.57589172382259, 18.113654961133975, 10.353650290755535, 12.162216645992086, 14.023263976126877, 15.819182956104251), # 92
(15.931344283413848, 13.977310394265235, 15.404307407407408, 18.33021594202899, 17.597037037037037, 9.644161316872427, 10.717474426807762, 10.53133827160494, 18.079241975308644, 10.31777975308642, 12.1150673046252, 13.985237166991553, 15.791921296296294), # 93
(15.886450002982048, 13.906848055223684, 15.375915912208507, 18.285364519592058, 17.569345598321632, 9.621185307117818, 10.670928304380737, 10.487364243255604, 18.044598536808415, 10.281812541488476, 12.067513072242896, 13.946877674295479, 15.764104080932785), # 94
(15.840818273990577, 13.836174166998541, 15.347205075445817, 18.240009393451423, 17.541111918018238, 9.597957628410304, 10.62422873127303, 10.443987197073618, 18.00975537265661, 10.245749482234594, 12.019623131903835, 13.908203980458689, 15.735772462277092), # 95
(15.79452238325282, 13.765321863799286, 15.318181481481483, 18.194184057971015, 17.512374727668846, 9.574525102880658, 10.577392675588754, 10.401224691358026, 17.974743209876543, 10.209591401597677, 11.971466666666668, 13.869234567901238, 15.706967592592594), # 96
(15.747635617582157, 13.694324279835394, 15.28885171467764, 18.14792200751476, 17.483172758815464, 9.550934552659655, 10.530437105432021, 10.359094284407867, 17.939592775491544, 10.173339125850616, 11.923112859590052, 13.829987919043152, 15.677730624142663), # 97
(15.700231263791975, 13.623214549316343, 15.259222359396432, 18.101256736446594, 17.453544743000084, 9.52723279987807, 10.48337898890695, 10.317613534522177, 17.904334796524918, 10.136993481266307, 11.87463089373265, 13.790482516304477, 15.648102709190674), # 98
(15.652382608695653, 13.552025806451613, 15.229300000000002, 18.054221739130437, 17.423529411764708, 9.503466666666666, 10.43623529411765, 10.276800000000001, 17.869, 10.100555294117648, 11.826089952153112, 13.750736842105264, 15.618125000000001), # 99
(15.60416293910658, 13.480791185450682, 15.19909122085048, 18.00685050993022, 17.393165496651335, 9.479682975156226, 10.389022989168232, 10.236671239140376, 17.833619112940102, 10.064025390677534, 11.777559217910095, 13.710769378865548, 15.58783864883402), # 100
(15.555645541838135, 13.409543820523034, 15.168602606310015, 17.959176543209878, 17.36249172920197, 9.455928547477518, 10.34175904216282, 10.19724481024234, 17.798222862368544, 10.027404597218862, 11.72910787406226, 13.670598609005365, 15.557284807956103), # 101
(15.506903703703706, 13.338316845878138, 15.13784074074074, 17.911233333333335, 17.331546840958605, 9.432250205761319, 10.294460421205521, 10.15853827160494, 17.762841975308643, 9.990693740014526, 11.680805103668263, 13.63024301494477, 15.526504629629631), # 102
(15.458010711516671, 13.267143395725476, 15.1068122085048, 17.86305437466452, 17.300369563463246, 9.408694772138395, 10.247144094400449, 10.120569181527207, 17.72750717878372, 9.953893645337423, 11.632720089786758, 13.589721079103796, 15.495539266117968), # 103
(15.409039852090416, 13.196056604274526, 15.075523593964334, 17.814673161567367, 17.268998628257886, 9.385309068739522, 10.199827029851722, 10.083355098308186, 17.692249199817102, 9.91700513946045, 11.584922015476401, 13.549051283902486, 15.464429869684501), # 104
(15.360064412238325, 13.125089605734766, 15.043981481481481, 17.766123188405796, 17.237472766884533, 9.362139917695474, 10.152526195663453, 10.046913580246915, 17.6570987654321, 9.880029048656501, 11.537480063795854, 13.508252111760886, 15.433217592592593), # 105
(15.311157678773782, 13.054275534315678, 15.012192455418381, 17.717437949543747, 17.205830710885177, 9.339234141137021, 10.105258559939752, 10.011262185642433, 17.622086602652033, 9.842966199198472, 11.490463417803769, 13.46734204509903, 15.401943587105624), # 106
(15.26239293851017, 12.983647524226738, 14.980163100137176, 17.66865093934514, 17.174111191801824, 9.31663856119494, 10.058041090784739, 9.976418472793783, 17.58724343850023, 9.805817417359263, 11.443941260558804, 13.426339566336967, 15.370649005486968), # 107
(15.21384347826087, 12.913238709677422, 14.947900000000002, 17.619795652173917, 17.14235294117647, 9.294400000000001, 10.010890756302521, 9.942400000000001, 17.5526, 9.768583529411766, 11.397982775119617, 13.38526315789474, 15.339375000000002), # 108
(15.16558258483927, 12.843082224877207, 14.915409739369, 17.570905582393987, 17.11059469055112, 9.272565279682976, 9.96382452459722, 9.90922432556013, 17.518187014174668, 9.731265361628877, 11.352657144544864, 13.34413130219238, 15.308162722908094), # 109
(15.117683545058746, 12.77321120403558, 14.882698902606315, 17.522014224369297, 17.078875171467768, 9.251181222374639, 9.916859363772943, 9.876909007773206, 17.484035208047555, 9.693863740283494, 11.308033551893201, 13.302962481649942, 15.277053326474624), # 110
(15.07021964573269, 12.703658781362009, 14.849774074074077, 17.47315507246377, 17.047233115468412, 9.230294650205762, 9.87001224193381, 9.845471604938272, 17.450175308641978, 9.656379491648512, 11.264181180223286, 13.261775178687461, 15.246087962962964), # 111
(15.02326417367448, 12.634458091065975, 14.816641838134434, 17.42436162104133, 17.015707254095055, 9.209952385307119, 9.823300127183934, 9.814929675354367, 17.41663804298125, 9.618813441996826, 11.221169212593775, 13.220587875724977, 15.215307784636488), # 112
(14.976806757924871, 12.565757790057525, 14.78338852520331, 17.375734211987265, 16.98428108827793, 9.190191630743222, 9.776841541850832, 9.78536411004897, 17.383540498013794, 9.581287578580367, 11.179078249844586, 13.179508698407085, 15.184710241349155), # 113
(14.930369436640104, 12.498235493640857, 14.75047308003459, 17.327663074043738, 16.952629367306123, 9.170967373647843, 9.731229133456928, 9.757138015208191, 17.351390457140898, 9.544504268660452, 11.137990939381115, 13.13905947538076, 15.154040662656056), # 114
(14.883815844806392, 12.431915517892875, 14.717915092331708, 17.280135208290847, 16.920652284621763, 9.152229619998023, 9.6864954403065, 9.730244246845935, 17.320199965870064, 9.508520524780923, 11.09784721828335, 13.099260132094162, 15.123210610656603), # 115
(14.837087797180216, 12.366701250066724, 14.685651503974197, 17.233065840426246, 16.888301642214046, 9.133934203659356, 9.64256770804463, 9.70460850063839, 17.28989014276453, 9.473269373519276, 11.05856949003437, 13.060037115979753, 15.092171615609425), # 116
(14.790127108518035, 12.302496077415555, 14.653619256841578, 17.18637019614759, 16.855529242072176, 9.116036958497425, 9.599373182316404, 9.680156472261736, 17.260382106387524, 9.438683841453006, 11.020080158117253, 13.021316874470001, 15.06087520777316), # 117
(14.742875593576338, 12.239203387192518, 14.621755292813388, 17.139963501152533, 16.82228688618535, 9.098493718377823, 9.556839108766905, 9.656813857392155, 17.231596975302296, 9.404696955159615, 10.98230162601508, 12.98302585499736, 15.02927291740644), # 118
(14.695275067111588, 12.176726566650768, 14.589996553769158, 17.09376098113873, 16.788526376542755, 9.081260317166132, 9.51489273304121, 9.634506351705832, 17.20345586807207, 9.371241741216595, 10.945156297210925, 12.945090504994296, 14.997316274767892), # 119
(14.647267343880259, 12.114969003043454, 14.55827998158842, 17.04767786180383, 16.754199515133596, 9.064292588727945, 9.473461300784406, 9.613159650878949, 17.175879903260093, 9.338251226201448, 10.908566575187866, 12.907437271893276, 14.964956810116156), # 120
(14.59879423863883, 12.053834083623727, 14.5265425181507, 17.001629368845496, 16.71925810394707, 9.047546366928849, 9.432472057641569, 9.592699450587691, 17.148790199429598, 9.305658436691674, 10.872454863428986, 12.869992603126756, 14.932146053709857), # 121
(14.549797566143766, 11.993225195644738, 14.494721105335538, 16.95553072796137, 16.683653944972374, 9.03097748563443, 9.391852249257788, 9.573051446508238, 17.122107875143822, 9.273396399264763, 10.836743565417363, 12.832682946127202, 14.898835535807633), # 122
(14.50021914115155, 11.933045726359639, 14.462752685022458, 16.90929716484911, 16.647338840198707, 9.01454177871028, 9.351529121278142, 9.554141334316773, 17.095754048966008, 9.24139814049822, 10.801355084636072, 12.795434748327075, 14.864976786668116), # 123
(14.450000778418648, 11.87319906302158, 14.430574199090993, 16.86284390520638, 16.61026459161526, 8.998195080021983, 9.311429919347711, 9.535894809689482, 17.069649839459384, 9.209596686969538, 10.766211824568192, 12.758174457158841, 14.830521336549939), # 124
(14.399084292701534, 11.813588592883713, 14.398122589420678, 16.816086174730817, 16.572383001211236, 8.98189322343513, 9.271481889111582, 9.518237568302546, 17.04371636518719, 9.177925065256215, 10.731236188696803, 12.720828520054958, 14.795420715711726), # 125
(14.347411498756685, 11.754117703199192, 14.365334797891038, 16.768939199120087, 16.53364587097583, 8.965592042815308, 9.231612276214832, 9.501095305832148, 17.017874744712667, 9.146316301935748, 10.696350580504982, 12.683323384447895, 14.759626454412127), # 126
(14.294924211340579, 11.69468978122116, 14.332147766381608, 16.72131820407184, 16.494005002898238, 8.949247372028104, 9.19174832630255, 9.484393717954474, 16.99204609659905, 9.114703423585638, 10.661477403475807, 12.645585497770107, 14.723090082909758), # 127
(14.241564245209673, 11.635208214202777, 14.29849843677192, 16.67313841528373, 16.453412198967666, 8.93281504493911, 9.151817285019812, 9.4680585003457, 16.966151539409577, 9.083019456783381, 10.626539061092359, 12.607541307454062, 14.68576313146326), # 128
(14.187273415120451, 11.575576389397186, 14.264323750941504, 16.624315058453412, 16.4118192611733, 8.916250895413912, 9.111746398011702, 9.452015348682016, 16.94011219170748, 9.051197428106473, 10.591457956837715, 12.569117260932218, 14.647597130331262), # 129
(14.131993535829388, 11.515697694057547, 14.229560650769887, 16.57476335927854, 16.36917799150434, 8.899510757318094, 9.0714629109233, 9.4361899586396, 16.913849172056, 9.019170364132412, 10.556156494194951, 12.530239805637045, 14.608543609772397), # 130
(14.07566642209295, 11.455475515437003, 14.19414607813661, 16.524398543456762, 16.32544019194999, 8.88255046451725, 9.030894069399695, 9.42050802589464, 16.887283599018378, 8.986871291438696, 10.52055707664715, 12.490835389000999, 14.568554100045299), # 131
(14.018233888667616, 11.39481324078871, 14.158016974921194, 16.47313583668574, 16.280557664499447, 8.865325850876964, 8.98996711908596, 9.404895246123317, 16.860336591157846, 8.954233236602823, 10.484582107677383, 12.450830458456547, 14.527580131408602), # 132
(13.959637750309861, 11.333614257365817, 14.121110283003175, 16.420890464663124, 16.2344822111419, 8.847792750262826, 8.948609305627183, 9.389277315001811, 16.832929267037642, 8.921189226202292, 10.448153990768738, 12.410151461436149, 14.485573234120938), # 133
(13.899819821776152, 11.271781952421478, 14.083362944262086, 16.367577653086567, 16.18716563386655, 8.829906996540425, 8.906747874668445, 9.37357992820631, 16.804982745221007, 8.887672286814597, 10.411195129404286, 12.368724845372267, 14.442484938440934), # 134
(13.838721917822966, 11.209219713208839, 14.044711900577454, 16.313112627653727, 16.138559734662593, 8.811624423575347, 8.86431007185483, 9.357728781412993, 16.77641814427117, 8.853615445017242, 10.373627927067108, 12.326477057697364, 14.398266774627231), # 135
(13.776285853206776, 11.145830926981056, 14.005094093828815, 16.25741061406225, 16.08861631551923, 8.792900865233184, 8.821223142831416, 9.341649570298044, 16.74715658275137, 8.818951727387716, 10.335374787240283, 12.283334545843907, 14.352870272938459), # 136
(13.712453442684055, 11.081518980991277, 13.964446465895698, 16.200386838009802, 16.037287178425654, 8.773692155379518, 8.77741433324329, 9.325267990537647, 16.717119179224852, 8.783614160503523, 10.296358113406889, 12.239223757244352, 14.306246963633242), # 137
(13.647166501011277, 11.016187262492654, 13.922705958657628, 16.141956525194022, 15.98452412537107, 8.753954127879942, 8.732810888735527, 9.308509737807984, 16.68622705225485, 8.747535770942156, 10.256500309050004, 12.194071139331164, 14.258348376970226), # 138
(13.58036684294491, 10.949739158738339, 13.879809513994145, 16.082034901312575, 15.930278958344665, 8.733642616600042, 8.687340054953216, 9.29130050778524, 16.654401320404595, 8.710649585281116, 10.215723777652705, 12.14780313953681, 14.20912604320803), # 139
(13.511996283241437, 10.88207805698148, 13.83569407378478, 16.020537192063113, 15.874503479335647, 8.712713455405407, 8.640929077541434, 9.273565996145594, 16.62156310223733, 8.672888630097898, 10.17395092269807, 12.100346205293746, 14.158531492605304), # 140
(13.44199663665733, 10.813107344475235, 13.790296579909057, 15.957378623143285, 15.817149490333206, 8.691122478161624, 8.593505202145272, 9.255231898565233, 16.587633516316288, 8.634185931970002, 10.131104147669182, 12.05162678403444, 14.106516255420662), # 141
(13.37030971794905, 10.742730408472745, 13.743553974246513, 15.892474420250753, 15.75816879332654, 8.668825518734284, 8.544995674409803, 9.236223910720339, 16.552533681204707, 8.594474517474925, 10.087105856049115, 12.001571323191351, 14.053031861912746), # 142
(13.29687734187308, 10.67085063622717, 13.695403198676681, 15.82573980908316, 15.697513190304846, 8.64577841098897, 8.49532773998011, 9.21646772828709, 16.516184715465837, 8.553687413190165, 10.04187845132095, 11.950106270196944, 13.998029842340188), # 143
(13.221641323185896, 10.597371414991658, 13.645781195079085, 15.757090015338171, 15.635134483257326, 8.621936988791274, 8.444428644501278, 9.195889046941678, 16.478507737662895, 8.511757645693216, 9.995344336967761, 11.897158072483679, 13.941461726961624), # 144
(13.144543476643964, 10.52219613201936, 13.594624905333262, 15.686440264713433, 15.570984474173173, 8.597257086006785, 8.39222563361839, 9.174413562360282, 16.439423866359128, 8.46861824156158, 9.947425916472632, 11.842653177484022, 13.88327904603568), # 145
(13.065525617003761, 10.445228174563427, 13.541871271318747, 15.613705782906601, 15.505014965041589, 8.57169453650109, 8.338645952976528, 9.151966970219084, 16.39885422011777, 8.424202227372753, 9.898045593318638, 11.786518032630433, 13.82343332982099), # 146
(12.98452955902176, 10.366370929877009, 13.487457234915055, 15.538801795615328, 15.437177757851764, 8.545205174139772, 8.28361684822077, 9.128474966194265, 16.356719917502065, 8.378442629704233, 9.847125770988859, 11.728679085355378, 13.761876108576189), # 147
(12.901497117454435, 10.285527785213262, 13.431319738001733, 15.461643528537275, 15.367424654592899, 8.517744832788429, 8.227065564996202, 9.103863245962012, 16.312942077075245, 8.331272475133515, 9.794588852966372, 11.669062783091313, 13.698558912559907), # 148
(12.81637010705826, 10.20260212782533, 13.37339572245831, 15.382146207370084, 15.295707457254194, 8.48926934631264, 8.168919348947906, 9.078057505198506, 16.26744181740054, 8.282624790238101, 9.740357242734255, 11.607595573270707, 13.63343327203078), # 149
(12.729090342589704, 10.117497344966367, 13.313622130164312, 15.30022505781142, 15.221977967824841, 8.459734548577998, 8.109105445720962, 9.05098343957993, 16.220140257041205, 8.232432601595482, 9.684353343775589, 11.544203903326022, 13.566450717247434), # 150
(12.63959963880524, 10.030116823889527, 13.251935902999268, 15.215795305558927, 15.146187988294043, 8.429096273450089, 8.047551100960453, 9.02256674478247, 16.170958514560464, 8.180628935783165, 9.626499559573448, 11.478814220689715, 13.49756277846851), # 151
(12.54783981046135, 9.940363951847957, 13.188273982842723, 15.128772176310271, 15.06828932065099, 8.397310354794502, 7.984183560311464, 8.992733116482306, 16.119817708521552, 8.12714681937864, 9.566718293610915, 11.411352972794255, 13.426720985952636), # 152
(12.453752672314497, 9.848142116094811, 13.12257331157419, 15.039070895763093, 14.988233766884889, 8.364332626476825, 7.918930069419071, 8.96140825035562, 16.06663895748772, 8.071919278959406, 9.504931949371066, 11.341746607072103, 13.353876869958444), # 153
(12.357280039121166, 9.75335470388324, 13.054770831073213, 14.946606689615056, 14.905973128984929, 8.330118922362647, 7.851717873928365, 8.928517842078596, 16.011343380022186, 8.014879341102965, 9.44106293033698, 11.26992157095572, 13.278981960744572), # 154
(12.258363725637818, 9.655905102466392, 12.984803483219322, 14.851294783563805, 14.821459208940315, 8.294625076317555, 7.782474219484418, 8.893987587327418, 15.953852094688205, 7.955960032386807, 9.375033639991733, 11.195804311877572, 13.201987788569642), # 155
(12.15694554662093, 9.555696699097421, 12.912608209892042, 14.753050403307, 14.734643808740238, 8.257806922207138, 7.71112635173232, 8.85774318177827, 15.894086220049003, 7.8950943793884365, 9.306766481818407, 11.119321277270117, 13.122845883692296), # 156
(12.05296731682698, 9.452632881029478, 12.838121952970909, 14.6517887745423, 14.645478730373895, 8.219620293896982, 7.637601516317151, 8.819710321107332, 15.831966874667822, 7.832215408685347, 9.236183859300079, 11.04039891456582, 13.041507776371162), # 157
(11.943489514248384, 9.344724993235614, 12.75774712624377, 14.54363133064199, 14.549889769393596, 8.177639162107376, 7.560170753484572, 8.777275123758995, 15.762659346558557, 7.76538546606583, 9.160953204062308, 10.956159302710944, 12.954377375064553), # 158
(11.811658827165445, 9.220904511359164, 12.65078050944478, 14.406363454061527, 14.424306095650605, 8.117903436811366, 7.469140421417146, 8.715541652423012, 15.658283617955432, 7.683649590557993, 9.06786709699039, 10.850180037892974, 12.840684235072311), # 159
(11.655795351846896, 9.080154765665142, 12.515073532729422, 14.237724016654177, 14.266272210154874, 8.038946073676295, 7.363589997414055, 8.632958703243755, 15.515880363565842, 7.58592904298063, 8.955615213775264, 10.720803118220555, 12.69827297422973), # 160
(11.477155287337537, 8.92339338892875, 12.352075155056495, 14.039316006010765, 14.077428998851381, 7.941723586512502, 7.244290313611002, 8.530560852975649, 15.337327627198428, 7.473053109073501, 8.825186647359532, 10.569227950252113, 12.528598471710556), # 161
(11.27699483268217, 8.751538013925183, 12.163234335384793, 13.812742409722123, 13.859417347685127, 7.827192489130329, 7.112012202143695, 8.409382678373124, 15.12450345266182, 7.3458510745763705, 8.677570490685794, 10.39665394054607, 12.333115606688533), # 162
(11.056570186925597, 8.565506273429639, 11.950000032673124, 13.559606215379095, 13.613878142601102, 7.696309295340116, 6.967526495147841, 8.2704587561906, 14.87928588376465, 7.205152225229, 8.513755836696653, 10.204280495660853, 12.113279258337407), # 163
(10.817137549112616, 8.366215800217313, 11.713821205880283, 13.281510410572508, 13.342452269544303, 7.550030518952207, 6.811604024759146, 8.114823663182511, 14.603552964315558, 7.05178584677115, 8.334731778334714, 9.993307022154886, 11.870544305830926), # 164
(10.559953118288028, 8.154584227063411, 11.45614681396507, 12.980057982893204, 13.046780614459719, 7.389312673776939, 6.6450156231133155, 7.943511976103274, 14.299182738123168, 6.8865812249425815, 8.141487408542579, 9.764932926586592, 11.606365628342832), # 165
(10.286273093496636, 7.931529186743127, 11.178425815886285, 12.656851919932002, 12.728504063292343, 7.215112273624654, 6.468532122346058, 7.757558271707324, 13.968053248996117, 6.71036764548306, 7.935011820262847, 9.520357615514403, 11.322198105046873), # 166
(9.997353673783238, 7.6979683120316595, 10.882107170602728, 12.31349520927975, 12.389263501987168, 7.028385832305694, 6.28292435459308, 7.557997126749083, 13.61204254074304, 6.523974394132343, 7.716294106438124, 9.260780495496734, 11.019496615116793), # 167
(9.694451058192634, 7.454819235704206, 10.568639837073198, 11.951590838527274, 12.030699816489188, 6.830089863630398, 6.088963151990087, 7.345863117982976, 13.233028657172568, 6.328230756630195, 7.48632336001101, 8.987400973092019, 10.69971603772634), # 168
(9.378821445769624, 7.202999590535967, 10.239472774256495, 11.572741795265413, 11.654453892743392, 6.621180881409112, 5.887419346672787, 7.122190822163432, 12.832889642093342, 6.123966018716379, 7.24608867392411, 8.701418454858675, 10.364311252049257), # 169
(9.051721035559014, 6.94342700930214, 9.896054941111416, 11.178551067084992, 11.262166616694774, 6.402615399452171, 5.679063770776885, 6.888014816044876, 12.413503539313982, 5.912009466130653, 6.996579141120026, 8.404032347355134, 10.014737137259289), # 170
(8.7144060266056, 6.677019124777921, 9.539835296596765, 10.770621641576858, 10.85547887428833, 6.175349931569918, 5.464667256438089, 6.644369676381733, 11.976748392643131, 5.693190384612782, 6.738783854541357, 8.096442057139818, 9.652448572530185), # 171
(8.368132617954185, 6.4046935697385114, 9.172262799671339, 10.350556506331834, 10.436031551469046, 5.940340991572694, 5.245000635792105, 6.392289979928433, 11.524502245889417, 5.468338059902528, 6.473691907130711, 7.779846990771154, 9.278900437035686), # 172
(8.014157008649567, 6.127367976959108, 8.79478640929394, 9.919958648940762, 10.005465534181923, 5.69854509327084, 5.02083474097464, 6.132810303439398, 11.058643142861477, 5.238281777739651, 6.202292391830685, 7.45544655480756, 8.89554760994954), # 173
(7.6537353977365505, 5.845959979214909, 8.408855084423363, 9.480431056994465, 9.565421708371947, 5.450918750474696, 4.792940404121401, 5.866965223669057, 10.581049127367942, 5.003850823863915, 5.9255744015838845, 7.124440155807469, 8.503844970445494), # 174
(7.288123984259929, 5.561387209281111, 8.015917784018413, 9.033576718083788, 9.11754095998411, 5.198418476994606, 4.562088457368093, 5.595789317371834, 10.09359824321745, 4.765874484015079, 5.644527029332911, 6.788027200329303, 8.105247397697292), # 175
(6.91857896726451, 5.274567299932917, 7.617423467037885, 8.58099861979956, 8.663464174963408, 4.942000786640907, 4.329049732850424, 5.3203171613021585, 9.598168534218628, 4.525182043932907, 5.360139368020368, 6.447407094931487, 7.701209770878679), # 176
(6.546356545795092, 4.986417883945522, 7.214821092440582, 8.124299749732613, 8.204832239254838, 4.682622193223941, 4.094595062704101, 5.0415833322144525, 9.096638044180112, 4.282602789357159, 5.073400510588858, 6.103779246172446, 7.2931869691634), # 177
(6.172712918896475, 4.697856594094126, 6.809559619185302, 7.665083095473786, 7.743286038803382, 4.421239210554052, 3.859495279064828, 4.760622406863145, 8.590884816910537, 4.0389660060276, 4.78529954998098, 5.758343060610604, 6.882633871725203), # 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 = (
(6, 7, 5, 12, 5, 4, 3, 0, 3, 0, 0, 1, 0, 12, 9, 9, 3, 5, 2, 3, 2, 3, 0, 1, 2, 0), # 0
(14, 19, 7, 14, 13, 7, 6, 1, 5, 2, 1, 1, 0, 26, 19, 16, 9, 10, 6, 7, 5, 9, 3, 3, 4, 0), # 1
(21, 30, 17, 22, 19, 9, 10, 5, 10, 3, 3, 2, 0, 36, 26, 22, 18, 17, 11, 10, 9, 13, 7, 3, 5, 0), # 2
(28, 36, 28, 37, 25, 13, 14, 10, 13, 5, 5, 4, 0, 46, 36, 27, 23, 29, 14, 17, 11, 15, 11, 3, 5, 0), # 3
(38, 49, 39, 46, 33, 19, 19, 12, 14, 7, 5, 4, 0, 55, 39, 29, 29, 37, 22, 22, 17, 19, 14, 7, 5, 0), # 4
(52, 59, 52, 54, 41, 24, 26, 16, 18, 8, 6, 6, 0, 64, 49, 36, 33, 47, 28, 24, 21, 22, 15, 8, 5, 0), # 5
(68, 66, 59, 62, 50, 31, 32, 17, 23, 9, 8, 7, 0, 72, 58, 43, 39, 51, 32, 28, 26, 27, 18, 9, 5, 0), # 6
(89, 89, 64, 74, 58, 37, 36, 22, 25, 10, 10, 8, 0, 82, 65, 46, 47, 61, 37, 38, 31, 31, 24, 11, 5, 0), # 7
(103, 105, 73, 87, 62, 42, 40, 27, 32, 12, 13, 8, 0, 100, 78, 54, 56, 71, 43, 40, 37, 34, 27, 13, 5, 0), # 8
(118, 123, 87, 100, 74, 49, 44, 33, 40, 13, 13, 8, 0, 116, 84, 67, 63, 74, 51, 44, 39, 42, 33, 14, 5, 0), # 9
(135, 138, 97, 112, 78, 51, 49, 38, 44, 19, 17, 10, 0, 129, 95, 73, 73, 85, 57, 53, 42, 48, 37, 18, 8, 0), # 10
(147, 151, 109, 127, 97, 57, 55, 45, 52, 21, 18, 10, 0, 152, 103, 83, 85, 97, 62, 58, 45, 51, 42, 18, 9, 0), # 11
(157, 170, 116, 137, 107, 62, 60, 52, 58, 24, 19, 10, 0, 166, 113, 94, 90, 113, 66, 65, 54, 54, 48, 20, 9, 0), # 12
(172, 181, 131, 144, 117, 66, 68, 57, 62, 26, 21, 11, 0, 177, 127, 102, 99, 121, 73, 72, 58, 61, 50, 21, 10, 0), # 13
(187, 189, 144, 162, 127, 72, 77, 63, 69, 28, 26, 12, 0, 187, 145, 111, 106, 132, 82, 79, 61, 68, 54, 23, 11, 0), # 14
(197, 208, 150, 176, 139, 80, 85, 76, 78, 31, 29, 15, 0, 199, 151, 120, 115, 142, 89, 89, 64, 74, 57, 23, 11, 0), # 15
(204, 230, 162, 188, 148, 84, 93, 76, 82, 36, 31, 17, 0, 217, 160, 137, 124, 156, 101, 94, 70, 82, 60, 26, 12, 0), # 16
(222, 235, 174, 203, 157, 86, 103, 80, 89, 38, 36, 17, 0, 232, 173, 151, 138, 172, 110, 106, 76, 84, 68, 27, 14, 0), # 17
(244, 250, 186, 219, 168, 96, 106, 84, 97, 43, 38, 19, 0, 243, 186, 164, 144, 188, 117, 116, 80, 86, 72, 29, 15, 0), # 18
(259, 262, 194, 234, 177, 101, 113, 88, 103, 46, 40, 19, 0, 260, 201, 178, 152, 196, 122, 122, 83, 91, 74, 30, 15, 0), # 19
(273, 276, 205, 249, 192, 110, 118, 96, 109, 49, 42, 20, 0, 272, 217, 188, 156, 214, 132, 129, 86, 95, 79, 33, 15, 0), # 20
(291, 299, 217, 266, 210, 115, 124, 100, 114, 53, 42, 21, 0, 290, 231, 193, 167, 224, 143, 137, 90, 98, 85, 39, 15, 0), # 21
(301, 314, 229, 285, 224, 119, 128, 106, 120, 57, 47, 25, 0, 309, 248, 200, 180, 235, 151, 141, 92, 103, 92, 42, 15, 0), # 22
(314, 333, 243, 304, 235, 124, 137, 113, 125, 59, 50, 25, 0, 324, 263, 206, 189, 246, 159, 144, 92, 108, 99, 44, 15, 0), # 23
(328, 342, 252, 315, 248, 128, 142, 118, 133, 60, 52, 28, 0, 339, 280, 215, 197, 261, 174, 151, 95, 112, 103, 45, 15, 0), # 24
(346, 360, 272, 323, 259, 139, 148, 125, 135, 64, 53, 29, 0, 355, 300, 228, 208, 282, 183, 158, 98, 120, 107, 46, 15, 0), # 25
(370, 374, 287, 335, 271, 145, 159, 130, 142, 67, 58, 29, 0, 371, 318, 236, 218, 297, 189, 162, 102, 127, 110, 49, 17, 0), # 26
(385, 388, 298, 350, 286, 151, 161, 135, 149, 71, 60, 30, 0, 388, 329, 253, 230, 309, 195, 169, 103, 130, 113, 52, 19, 0), # 27
(395, 403, 316, 367, 302, 156, 166, 140, 157, 74, 63, 31, 0, 405, 348, 264, 240, 317, 205, 184, 105, 133, 119, 56, 21, 0), # 28
(409, 412, 337, 380, 314, 166, 170, 150, 163, 77, 65, 33, 0, 426, 356, 274, 255, 328, 208, 192, 108, 138, 120, 61, 22, 0), # 29
(422, 426, 348, 405, 329, 170, 178, 154, 169, 79, 67, 33, 0, 438, 374, 287, 268, 340, 215, 200, 110, 145, 123, 64, 24, 0), # 30
(438, 444, 363, 420, 343, 176, 181, 159, 181, 81, 70, 34, 0, 453, 386, 298, 278, 352, 225, 204, 116, 150, 129, 64, 26, 0), # 31
(458, 463, 381, 433, 352, 181, 187, 166, 185, 83, 72, 34, 0, 483, 407, 312, 290, 363, 232, 209, 124, 156, 133, 64, 28, 0), # 32
(479, 481, 391, 443, 363, 187, 193, 167, 189, 87, 74, 35, 0, 508, 425, 320, 301, 372, 239, 212, 126, 159, 138, 66, 28, 0), # 33
(497, 497, 408, 461, 377, 194, 200, 175, 196, 91, 75, 35, 0, 524, 439, 336, 308, 381, 247, 223, 129, 166, 142, 68, 32, 0), # 34
(518, 509, 420, 478, 386, 201, 206, 177, 203, 98, 76, 37, 0, 541, 454, 343, 315, 398, 253, 230, 132, 173, 147, 70, 32, 0), # 35
(533, 519, 429, 489, 395, 205, 216, 182, 209, 99, 77, 39, 0, 548, 471, 357, 324, 407, 257, 236, 139, 180, 157, 74, 34, 0), # 36
(543, 540, 448, 508, 408, 209, 223, 188, 212, 101, 79, 41, 0, 567, 485, 363, 332, 418, 262, 244, 139, 184, 162, 78, 36, 0), # 37
(563, 556, 470, 519, 424, 218, 233, 194, 217, 108, 80, 42, 0, 584, 494, 373, 341, 431, 271, 253, 142, 189, 167, 81, 37, 0), # 38
(578, 569, 484, 536, 436, 224, 241, 201, 221, 110, 82, 43, 0, 601, 508, 389, 352, 446, 273, 259, 147, 197, 171, 85, 39, 0), # 39
(595, 579, 507, 552, 447, 227, 254, 204, 225, 111, 84, 47, 0, 617, 524, 397, 367, 462, 284, 265, 155, 199, 176, 89, 39, 0), # 40
(607, 589, 513, 562, 457, 235, 264, 211, 229, 116, 85, 48, 0, 632, 545, 405, 381, 478, 295, 272, 158, 207, 181, 93, 39, 0), # 41
(628, 603, 528, 582, 467, 244, 270, 215, 235, 122, 87, 49, 0, 643, 564, 414, 391, 495, 302, 280, 160, 212, 188, 96, 40, 0), # 42
(651, 617, 540, 589, 476, 251, 276, 220, 237, 126, 87, 50, 0, 666, 582, 426, 402, 511, 311, 286, 165, 216, 193, 98, 44, 0), # 43
(667, 632, 557, 601, 481, 257, 280, 222, 241, 128, 87, 53, 0, 687, 601, 437, 414, 527, 320, 291, 170, 220, 198, 99, 44, 0), # 44
(679, 646, 576, 615, 492, 261, 289, 227, 249, 135, 88, 54, 0, 706, 615, 449, 421, 542, 322, 295, 176, 229, 201, 102, 47, 0), # 45
(688, 660, 587, 627, 502, 264, 295, 233, 255, 136, 93, 55, 0, 721, 629, 461, 431, 555, 326, 304, 179, 238, 202, 102, 48, 0), # 46
(709, 673, 596, 641, 523, 269, 300, 236, 266, 138, 97, 59, 0, 733, 644, 475, 441, 572, 333, 314, 183, 243, 208, 102, 49, 0), # 47
(721, 689, 612, 656, 535, 277, 309, 243, 272, 141, 99, 62, 0, 743, 661, 486, 446, 583, 346, 318, 186, 247, 214, 106, 50, 0), # 48
(735, 707, 624, 674, 545, 283, 317, 245, 277, 143, 102, 65, 0, 760, 673, 492, 453, 606, 351, 321, 193, 253, 221, 109, 50, 0), # 49
(755, 725, 634, 688, 554, 285, 322, 251, 283, 146, 105, 67, 0, 782, 689, 497, 458, 619, 359, 325, 201, 260, 225, 110, 51, 0), # 50
(770, 738, 639, 703, 568, 289, 332, 259, 289, 150, 107, 67, 0, 800, 698, 509, 470, 631, 370, 331, 206, 264, 227, 113, 51, 0), # 51
(789, 754, 651, 720, 575, 295, 342, 263, 293, 155, 109, 68, 0, 813, 716, 520, 477, 648, 382, 338, 207, 271, 233, 115, 53, 0), # 52
(800, 771, 671, 729, 584, 302, 347, 265, 299, 156, 111, 69, 0, 828, 736, 536, 486, 659, 388, 345, 209, 276, 240, 116, 53, 0), # 53
(816, 784, 686, 745, 595, 309, 353, 272, 301, 159, 116, 70, 0, 841, 748, 543, 499, 670, 395, 351, 213, 277, 251, 117, 54, 0), # 54
(835, 805, 705, 762, 604, 316, 357, 278, 308, 159, 116, 73, 0, 858, 758, 554, 505, 689, 400, 357, 218, 284, 258, 118, 56, 0), # 55
(856, 819, 718, 779, 616, 321, 365, 287, 314, 160, 117, 74, 0, 878, 765, 561, 513, 701, 407, 366, 220, 292, 266, 121, 57, 0), # 56
(873, 833, 729, 796, 623, 324, 373, 290, 320, 162, 121, 74, 0, 898, 782, 571, 520, 707, 415, 373, 226, 298, 272, 123, 60, 0), # 57
(886, 849, 738, 803, 641, 331, 379, 299, 329, 165, 123, 76, 0, 915, 794, 586, 534, 714, 421, 379, 234, 303, 277, 126, 63, 0), # 58
(906, 863, 748, 820, 654, 338, 386, 303, 337, 170, 125, 77, 0, 935, 810, 593, 544, 726, 425, 385, 238, 309, 281, 130, 65, 0), # 59
(927, 875, 763, 833, 667, 347, 398, 309, 344, 173, 127, 79, 0, 949, 822, 605, 550, 739, 433, 387, 245, 317, 287, 131, 65, 0), # 60
(947, 889, 780, 846, 682, 352, 401, 316, 350, 177, 128, 81, 0, 958, 832, 618, 556, 749, 437, 396, 249, 326, 294, 134, 67, 0), # 61
(954, 907, 795, 858, 699, 358, 406, 320, 355, 181, 128, 81, 0, 970, 847, 631, 568, 768, 443, 401, 254, 335, 295, 135, 69, 0), # 62
(975, 921, 809, 875, 714, 363, 418, 324, 365, 183, 132, 81, 0, 986, 859, 637, 579, 782, 445, 409, 259, 344, 303, 141, 72, 0), # 63
(1001, 938, 827, 895, 721, 371, 428, 329, 373, 185, 132, 83, 0, 1006, 878, 643, 588, 791, 453, 413, 263, 350, 306, 144, 75, 0), # 64
(1020, 952, 842, 916, 731, 379, 433, 331, 381, 189, 134, 84, 0, 1021, 896, 656, 597, 802, 460, 421, 264, 353, 311, 145, 76, 0), # 65
(1039, 968, 852, 931, 739, 383, 440, 337, 389, 192, 136, 85, 0, 1051, 909, 664, 606, 812, 473, 425, 269, 357, 315, 151, 77, 0), # 66
(1048, 977, 865, 952, 752, 394, 445, 340, 393, 193, 138, 87, 0, 1064, 928, 678, 613, 825, 481, 429, 272, 365, 318, 153, 79, 0), # 67
(1057, 992, 877, 975, 764, 396, 450, 352, 402, 197, 139, 88, 0, 1078, 940, 687, 621, 837, 489, 432, 276, 365, 323, 156, 80, 0), # 68
(1073, 1006, 886, 988, 780, 402, 456, 356, 407, 203, 141, 89, 0, 1088, 952, 693, 629, 841, 494, 436, 280, 370, 324, 156, 80, 0), # 69
(1085, 1028, 903, 995, 796, 409, 460, 359, 414, 205, 144, 90, 0, 1101, 968, 707, 632, 853, 500, 441, 286, 374, 332, 159, 84, 0), # 70
(1101, 1039, 916, 1010, 808, 412, 466, 364, 420, 209, 146, 90, 0, 1114, 977, 712, 640, 865, 504, 446, 291, 381, 338, 161, 84, 0), # 71
(1114, 1052, 933, 1025, 818, 416, 473, 367, 424, 209, 149, 90, 0, 1129, 988, 719, 649, 874, 508, 451, 295, 388, 346, 163, 85, 0), # 72
(1130, 1069, 945, 1045, 840, 422, 484, 371, 430, 214, 152, 90, 0, 1148, 997, 727, 660, 890, 512, 459, 302, 393, 350, 166, 85, 0), # 73
(1147, 1075, 959, 1056, 850, 432, 490, 377, 436, 215, 155, 92, 0, 1169, 1007, 740, 671, 901, 518, 465, 303, 400, 354, 168, 89, 0), # 74
(1159, 1085, 972, 1077, 863, 438, 498, 385, 448, 217, 157, 92, 0, 1184, 1019, 745, 675, 915, 524, 470, 305, 408, 360, 170, 92, 0), # 75
(1178, 1097, 989, 1093, 876, 445, 505, 395, 455, 221, 157, 93, 0, 1196, 1034, 753, 680, 931, 529, 472, 307, 416, 361, 170, 93, 0), # 76
(1196, 1107, 1004, 1105, 884, 448, 511, 397, 459, 223, 159, 94, 0, 1216, 1042, 765, 687, 941, 533, 478, 313, 425, 363, 172, 96, 0), # 77
(1204, 1115, 1017, 1121, 898, 452, 518, 400, 461, 225, 163, 94, 0, 1229, 1052, 776, 694, 960, 537, 488, 319, 431, 364, 176, 97, 0), # 78
(1219, 1128, 1029, 1132, 909, 455, 520, 404, 466, 232, 165, 95, 0, 1241, 1068, 779, 703, 975, 544, 492, 327, 442, 368, 182, 98, 0), # 79
(1235, 1143, 1047, 1148, 918, 457, 527, 408, 476, 239, 166, 95, 0, 1257, 1084, 791, 709, 993, 549, 495, 330, 448, 372, 186, 98, 0), # 80
(1250, 1159, 1051, 1167, 928, 462, 531, 412, 481, 243, 169, 98, 0, 1278, 1095, 803, 716, 1001, 551, 500, 332, 450, 379, 188, 100, 0), # 81
(1271, 1170, 1065, 1182, 943, 470, 537, 417, 484, 246, 171, 100, 0, 1297, 1105, 809, 723, 1009, 557, 501, 338, 453, 381, 194, 101, 0), # 82
(1282, 1186, 1073, 1196, 952, 475, 540, 421, 487, 248, 171, 101, 0, 1314, 1114, 822, 730, 1021, 564, 506, 347, 461, 388, 197, 101, 0), # 83
(1295, 1206, 1086, 1209, 968, 478, 543, 426, 498, 252, 171, 105, 0, 1339, 1126, 832, 745, 1037, 570, 510, 353, 465, 393, 199, 102, 0), # 84
(1309, 1214, 1099, 1221, 981, 482, 546, 428, 502, 254, 173, 106, 0, 1351, 1142, 844, 754, 1056, 577, 517, 359, 472, 397, 200, 102, 0), # 85
(1325, 1232, 1111, 1241, 993, 487, 551, 429, 510, 255, 174, 107, 0, 1370, 1161, 853, 760, 1067, 589, 521, 361, 480, 401, 202, 104, 0), # 86
(1350, 1251, 1124, 1257, 1003, 491, 558, 431, 512, 256, 175, 107, 0, 1396, 1175, 863, 770, 1081, 598, 524, 367, 488, 405, 203, 107, 0), # 87
(1367, 1263, 1132, 1273, 1014, 497, 563, 437, 518, 260, 177, 107, 0, 1409, 1195, 875, 779, 1092, 609, 534, 369, 496, 410, 204, 110, 0), # 88
(1377, 1273, 1142, 1288, 1022, 507, 575, 441, 526, 263, 182, 107, 0, 1427, 1208, 889, 785, 1104, 613, 547, 372, 502, 411, 208, 113, 0), # 89
(1389, 1282, 1156, 1299, 1038, 515, 580, 444, 534, 263, 184, 108, 0, 1436, 1217, 902, 793, 1116, 619, 550, 377, 507, 418, 208, 113, 0), # 90
(1407, 1293, 1166, 1312, 1048, 518, 585, 448, 539, 264, 187, 109, 0, 1460, 1228, 915, 800, 1126, 622, 555, 379, 512, 423, 211, 114, 0), # 91
(1424, 1309, 1177, 1329, 1064, 526, 591, 450, 542, 268, 187, 111, 0, 1481, 1241, 931, 809, 1140, 626, 559, 384, 516, 428, 213, 114, 0), # 92
(1438, 1323, 1183, 1345, 1071, 534, 602, 459, 550, 270, 191, 111, 0, 1498, 1254, 941, 815, 1153, 634, 564, 388, 521, 433, 216, 114, 0), # 93
(1460, 1331, 1191, 1357, 1080, 537, 602, 459, 553, 272, 192, 113, 0, 1513, 1266, 944, 826, 1162, 637, 571, 395, 529, 445, 219, 115, 0), # 94
(1478, 1338, 1205, 1366, 1087, 543, 609, 461, 560, 276, 195, 115, 0, 1530, 1286, 952, 832, 1173, 642, 572, 399, 541, 451, 223, 116, 0), # 95
(1496, 1348, 1214, 1378, 1097, 551, 618, 465, 564, 279, 200, 117, 0, 1554, 1301, 957, 842, 1185, 653, 577, 404, 549, 457, 225, 117, 0), # 96
(1511, 1358, 1225, 1394, 1108, 554, 623, 469, 567, 281, 203, 120, 0, 1567, 1312, 969, 850, 1197, 658, 581, 404, 559, 459, 228, 118, 0), # 97
(1533, 1367, 1235, 1403, 1120, 560, 630, 475, 573, 284, 204, 120, 0, 1582, 1332, 982, 859, 1207, 665, 587, 408, 567, 467, 231, 119, 0), # 98
(1548, 1384, 1244, 1416, 1128, 566, 636, 475, 577, 284, 206, 122, 0, 1600, 1347, 987, 870, 1221, 671, 594, 414, 571, 472, 232, 121, 0), # 99
(1564, 1398, 1256, 1427, 1138, 570, 642, 478, 582, 287, 208, 122, 0, 1613, 1359, 995, 878, 1233, 676, 598, 416, 576, 478, 239, 124, 0), # 100
(1578, 1405, 1269, 1443, 1149, 575, 645, 478, 589, 290, 210, 124, 0, 1632, 1367, 1004, 884, 1248, 684, 605, 421, 582, 481, 239, 126, 0), # 101
(1596, 1418, 1282, 1464, 1165, 586, 648, 483, 595, 293, 210, 126, 0, 1647, 1380, 1014, 893, 1263, 690, 610, 423, 587, 491, 241, 128, 0), # 102
(1611, 1431, 1294, 1480, 1175, 589, 654, 485, 601, 295, 210, 126, 0, 1660, 1390, 1029, 903, 1277, 702, 614, 426, 590, 494, 243, 129, 0), # 103
(1626, 1437, 1315, 1492, 1194, 595, 661, 489, 607, 296, 212, 129, 0, 1671, 1396, 1040, 910, 1291, 704, 622, 429, 594, 502, 246, 131, 0), # 104
(1644, 1450, 1327, 1507, 1207, 601, 663, 498, 615, 298, 217, 130, 0, 1686, 1416, 1050, 917, 1300, 709, 627, 432, 599, 505, 248, 132, 0), # 105
(1657, 1464, 1336, 1521, 1212, 613, 668, 501, 620, 301, 219, 130, 0, 1700, 1427, 1058, 919, 1313, 717, 634, 432, 603, 510, 248, 134, 0), # 106
(1672, 1477, 1351, 1536, 1228, 617, 671, 506, 625, 305, 219, 131, 0, 1715, 1441, 1073, 922, 1326, 722, 638, 436, 609, 512, 251, 135, 0), # 107
(1683, 1489, 1368, 1545, 1237, 621, 679, 515, 634, 308, 223, 132, 0, 1734, 1456, 1081, 929, 1334, 727, 642, 440, 610, 518, 253, 136, 0), # 108
(1696, 1503, 1385, 1555, 1249, 623, 684, 523, 639, 309, 223, 134, 0, 1747, 1472, 1088, 938, 1340, 732, 648, 445, 619, 523, 254, 136, 0), # 109
(1711, 1516, 1396, 1563, 1264, 626, 688, 529, 645, 311, 224, 134, 0, 1764, 1483, 1090, 942, 1348, 738, 652, 448, 623, 525, 254, 137, 0), # 110
(1724, 1527, 1407, 1578, 1282, 628, 696, 531, 647, 313, 227, 135, 0, 1777, 1497, 1100, 951, 1356, 744, 654, 456, 634, 533, 254, 139, 0), # 111
(1742, 1537, 1421, 1588, 1293, 634, 702, 534, 653, 315, 228, 136, 0, 1793, 1514, 1109, 958, 1369, 747, 659, 462, 639, 537, 256, 140, 0), # 112
(1753, 1545, 1433, 1597, 1297, 636, 705, 543, 654, 318, 233, 139, 0, 1808, 1523, 1116, 965, 1379, 753, 668, 470, 645, 539, 257, 140, 0), # 113
(1767, 1554, 1447, 1615, 1306, 639, 707, 547, 660, 323, 234, 141, 0, 1824, 1540, 1126, 974, 1390, 758, 672, 472, 655, 541, 261, 142, 0), # 114
(1775, 1559, 1462, 1627, 1318, 646, 713, 552, 665, 330, 236, 141, 0, 1839, 1552, 1134, 984, 1405, 762, 674, 474, 662, 546, 264, 144, 0), # 115
(1787, 1566, 1474, 1643, 1327, 654, 716, 557, 668, 335, 238, 142, 0, 1850, 1564, 1152, 991, 1418, 768, 681, 478, 667, 549, 265, 145, 0), # 116
(1802, 1575, 1484, 1657, 1340, 659, 726, 563, 672, 336, 240, 144, 0, 1859, 1574, 1163, 996, 1432, 772, 684, 484, 671, 552, 270, 146, 0), # 117
(1814, 1582, 1499, 1667, 1350, 666, 732, 569, 676, 339, 243, 145, 0, 1869, 1589, 1173, 1007, 1445, 777, 688, 489, 679, 558, 278, 146, 0), # 118
(1830, 1590, 1507, 1685, 1360, 671, 733, 574, 684, 341, 246, 145, 0, 1880, 1605, 1182, 1015, 1457, 783, 691, 491, 682, 564, 279, 146, 0), # 119
(1843, 1609, 1517, 1695, 1369, 676, 738, 578, 691, 345, 247, 146, 0, 1891, 1618, 1191, 1021, 1471, 787, 694, 493, 687, 568, 279, 147, 0), # 120
(1858, 1620, 1526, 1710, 1381, 682, 741, 582, 697, 349, 250, 147, 0, 1905, 1625, 1198, 1026, 1482, 797, 699, 497, 691, 571, 279, 148, 0), # 121
(1872, 1629, 1539, 1721, 1385, 687, 744, 588, 706, 351, 256, 147, 0, 1926, 1637, 1203, 1033, 1497, 804, 705, 503, 696, 574, 284, 149, 0), # 122
(1884, 1641, 1550, 1736, 1395, 695, 747, 592, 709, 353, 257, 149, 0, 1943, 1646, 1217, 1040, 1507, 809, 709, 504, 704, 576, 286, 150, 0), # 123
(1898, 1652, 1558, 1748, 1410, 699, 750, 600, 712, 355, 259, 149, 0, 1959, 1656, 1233, 1053, 1514, 811, 711, 505, 710, 581, 287, 150, 0), # 124
(1913, 1663, 1565, 1759, 1420, 703, 757, 603, 715, 357, 261, 150, 0, 1973, 1666, 1242, 1056, 1524, 815, 719, 513, 715, 584, 291, 150, 0), # 125
(1930, 1673, 1578, 1771, 1428, 709, 760, 605, 725, 362, 262, 150, 0, 1981, 1675, 1250, 1062, 1540, 820, 722, 516, 720, 588, 293, 150, 0), # 126
(1947, 1681, 1593, 1787, 1441, 714, 763, 609, 730, 362, 263, 151, 0, 1999, 1687, 1263, 1070, 1549, 823, 725, 521, 725, 595, 296, 150, 0), # 127
(1965, 1692, 1614, 1795, 1449, 718, 771, 612, 740, 367, 263, 152, 0, 2010, 1698, 1267, 1074, 1559, 832, 729, 524, 728, 597, 297, 152, 0), # 128
(1981, 1705, 1623, 1812, 1461, 722, 774, 615, 745, 369, 264, 152, 0, 2029, 1707, 1276, 1082, 1566, 836, 731, 525, 736, 603, 299, 153, 0), # 129
(1993, 1715, 1633, 1823, 1472, 726, 778, 619, 753, 370, 264, 152, 0, 2047, 1717, 1281, 1088, 1578, 843, 737, 529, 745, 609, 301, 155, 0), # 130
(2005, 1722, 1647, 1834, 1482, 730, 782, 623, 755, 371, 265, 153, 0, 2055, 1727, 1288, 1095, 1591, 848, 739, 533, 750, 612, 305, 157, 0), # 131
(2020, 1733, 1654, 1850, 1488, 732, 785, 626, 762, 371, 269, 155, 0, 2075, 1737, 1297, 1104, 1603, 860, 748, 536, 751, 615, 307, 158, 0), # 132
(2035, 1744, 1670, 1869, 1497, 738, 788, 628, 766, 373, 270, 155, 0, 2091, 1755, 1308, 1108, 1616, 868, 749, 542, 757, 621, 308, 158, 0), # 133
(2053, 1752, 1678, 1882, 1506, 746, 799, 631, 769, 375, 272, 157, 0, 2104, 1767, 1322, 1113, 1628, 872, 751, 549, 764, 623, 308, 159, 0), # 134
(2063, 1771, 1692, 1892, 1517, 749, 799, 636, 773, 377, 274, 157, 0, 2118, 1774, 1330, 1118, 1644, 875, 758, 553, 769, 628, 309, 161, 0), # 135
(2076, 1786, 1704, 1898, 1527, 756, 803, 639, 778, 377, 277, 157, 0, 2133, 1788, 1339, 1122, 1654, 886, 767, 559, 774, 634, 310, 162, 0), # 136
(2094, 1795, 1717, 1904, 1535, 759, 807, 643, 784, 377, 279, 159, 0, 2151, 1795, 1345, 1128, 1662, 891, 770, 562, 781, 638, 313, 163, 0), # 137
(2105, 1807, 1736, 1914, 1545, 765, 810, 650, 787, 378, 283, 159, 0, 2166, 1807, 1353, 1131, 1672, 896, 773, 565, 791, 640, 314, 163, 0), # 138
(2125, 1817, 1751, 1925, 1559, 767, 813, 656, 794, 379, 287, 161, 0, 2176, 1818, 1361, 1141, 1683, 898, 782, 570, 796, 650, 316, 165, 0), # 139
(2142, 1828, 1757, 1935, 1566, 774, 815, 662, 805, 381, 288, 161, 0, 2189, 1826, 1372, 1146, 1695, 900, 787, 571, 798, 652, 317, 168, 0), # 140
(2158, 1840, 1770, 1947, 1573, 779, 817, 668, 813, 382, 288, 163, 0, 2202, 1838, 1379, 1156, 1704, 903, 792, 574, 803, 655, 320, 169, 0), # 141
(2171, 1850, 1780, 1953, 1586, 783, 820, 672, 815, 387, 289, 164, 0, 2215, 1849, 1380, 1162, 1719, 914, 794, 576, 810, 657, 323, 171, 0), # 142
(2184, 1856, 1793, 1975, 1595, 787, 825, 674, 821, 389, 290, 165, 0, 2227, 1856, 1389, 1173, 1726, 916, 800, 579, 814, 666, 325, 171, 0), # 143
(2194, 1863, 1802, 1984, 1607, 791, 830, 680, 826, 392, 292, 165, 0, 2241, 1867, 1400, 1183, 1734, 920, 806, 583, 819, 667, 326, 173, 0), # 144
(2209, 1871, 1814, 2003, 1618, 795, 832, 685, 832, 398, 294, 166, 0, 2253, 1879, 1410, 1189, 1742, 927, 809, 589, 830, 671, 328, 173, 0), # 145
(2225, 1881, 1826, 2013, 1625, 800, 835, 689, 833, 398, 295, 168, 0, 2272, 1887, 1419, 1197, 1752, 932, 812, 592, 841, 674, 331, 174, 0), # 146
(2236, 1894, 1843, 2021, 1633, 806, 839, 693, 843, 403, 295, 170, 0, 2285, 1898, 1428, 1202, 1760, 938, 814, 595, 855, 680, 332, 177, 0), # 147
(2250, 1907, 1853, 2033, 1639, 813, 841, 697, 846, 406, 295, 172, 0, 2298, 1908, 1440, 1207, 1767, 941, 816, 598, 860, 681, 332, 178, 0), # 148
(2260, 1913, 1864, 2042, 1649, 821, 843, 701, 849, 406, 297, 173, 0, 2313, 1920, 1447, 1216, 1779, 944, 817, 599, 865, 685, 337, 178, 0), # 149
(2274, 1922, 1872, 2058, 1662, 824, 845, 704, 855, 408, 299, 174, 0, 2334, 1935, 1454, 1223, 1795, 946, 823, 604, 867, 687, 337, 179, 0), # 150
(2285, 1934, 1883, 2070, 1669, 826, 853, 710, 855, 411, 299, 175, 0, 2345, 1943, 1459, 1234, 1808, 950, 826, 609, 872, 691, 339, 180, 0), # 151
(2295, 1946, 1894, 2085, 1687, 828, 858, 712, 863, 413, 300, 177, 0, 2362, 1956, 1463, 1238, 1821, 956, 830, 610, 875, 699, 340, 181, 0), # 152
(2306, 1958, 1904, 2093, 1691, 834, 860, 721, 867, 417, 300, 177, 0, 2377, 1966, 1471, 1240, 1828, 962, 833, 610, 876, 701, 343, 183, 0), # 153
(2321, 1967, 1916, 2105, 1699, 842, 864, 726, 876, 418, 301, 179, 0, 2393, 1975, 1477, 1244, 1840, 969, 836, 615, 883, 706, 346, 183, 0), # 154
(2330, 1981, 1926, 2118, 1709, 849, 866, 732, 883, 419, 302, 180, 0, 2405, 1985, 1480, 1253, 1851, 973, 841, 617, 886, 711, 349, 183, 0), # 155
(2344, 1991, 1938, 2130, 1717, 856, 866, 735, 886, 423, 304, 181, 0, 2425, 2001, 1488, 1258, 1855, 974, 845, 623, 895, 716, 351, 183, 0), # 156
(2365, 1997, 1949, 2150, 1729, 858, 871, 737, 891, 424, 304, 182, 0, 2439, 2008, 1495, 1265, 1864, 982, 851, 626, 899, 722, 353, 183, 0), # 157
(2371, 2006, 1962, 2161, 1735, 862, 876, 740, 895, 428, 305, 183, 0, 2451, 2020, 1504, 1267, 1877, 987, 856, 629, 900, 727, 354, 184, 0), # 158
(2376, 2014, 1976, 2164, 1740, 866, 877, 742, 900, 428, 307, 184, 0, 2468, 2028, 1512, 1273, 1883, 992, 862, 631, 905, 730, 357, 186, 0), # 159
(2384, 2027, 1987, 2176, 1748, 870, 880, 750, 904, 428, 308, 185, 0, 2478, 2038, 1518, 1274, 1892, 996, 863, 632, 911, 733, 358, 186, 0), # 160
(2393, 2033, 2005, 2188, 1758, 876, 886, 752, 907, 429, 309, 186, 0, 2489, 2047, 1530, 1277, 1905, 1003, 867, 636, 916, 738, 360, 187, 0), # 161
(2404, 2041, 2011, 2195, 1768, 877, 889, 759, 913, 432, 309, 187, 0, 2497, 2062, 1536, 1286, 1921, 1008, 868, 638, 921, 741, 361, 187, 0), # 162
(2417, 2047, 2017, 2202, 1779, 886, 891, 761, 917, 432, 310, 187, 0, 2510, 2071, 1544, 1290, 1932, 1009, 870, 639, 924, 744, 364, 189, 0), # 163
(2425, 2052, 2033, 2215, 1787, 894, 894, 761, 921, 432, 310, 189, 0, 2522, 2083, 1552, 1297, 1942, 1017, 876, 642, 932, 746, 366, 189, 0), # 164
(2439, 2058, 2039, 2222, 1795, 898, 896, 762, 925, 434, 310, 191, 0, 2532, 2090, 1559, 1301, 1949, 1024, 879, 646, 935, 751, 367, 189, 0), # 165
(2443, 2071, 2052, 2232, 1809, 902, 901, 764, 930, 439, 312, 191, 0, 2547, 2093, 1571, 1303, 1965, 1029, 882, 649, 936, 754, 368, 190, 0), # 166
(2452, 2079, 2066, 2244, 1812, 904, 902, 768, 935, 442, 312, 193, 0, 2557, 2103, 1580, 1308, 1977, 1029, 886, 654, 944, 758, 368, 190, 0), # 167
(2470, 2085, 2076, 2252, 1820, 910, 903, 772, 942, 445, 312, 195, 0, 2568, 2114, 1589, 1313, 1983, 1036, 887, 655, 949, 760, 369, 190, 0), # 168
(2479, 2095, 2090, 2255, 1825, 915, 906, 776, 946, 446, 313, 197, 0, 2576, 2118, 1597, 1316, 1989, 1041, 889, 657, 953, 764, 372, 191, 0), # 169
(2487, 2106, 2100, 2261, 1833, 915, 910, 780, 952, 449, 313, 199, 0, 2589, 2123, 1604, 1319, 1990, 1048, 892, 660, 961, 766, 373, 193, 0), # 170
(2497, 2109, 2113, 2269, 1838, 920, 915, 784, 956, 450, 317, 200, 0, 2602, 2133, 1608, 1321, 1999, 1051, 892, 662, 968, 769, 378, 194, 0), # 171
(2512, 2112, 2120, 2276, 1844, 924, 916, 786, 958, 451, 318, 201, 0, 2611, 2141, 1613, 1328, 2006, 1051, 893, 664, 972, 771, 380, 196, 0), # 172
(2522, 2114, 2130, 2285, 1851, 929, 918, 791, 962, 451, 318, 201, 0, 2621, 2148, 1618, 1330, 2016, 1052, 896, 669, 977, 774, 380, 196, 0), # 173
(2529, 2117, 2136, 2292, 1857, 931, 921, 797, 967, 453, 319, 201, 0, 2629, 2152, 1625, 1339, 2023, 1054, 899, 672, 981, 779, 381, 196, 0), # 174
(2532, 2122, 2146, 2299, 1861, 934, 925, 800, 968, 454, 319, 201, 0, 2639, 2158, 1632, 1343, 2030, 1058, 899, 673, 982, 783, 382, 196, 0), # 175
(2541, 2126, 2153, 2304, 1868, 935, 928, 803, 971, 457, 319, 201, 0, 2644, 2169, 1637, 1346, 2034, 1060, 901, 677, 985, 786, 383, 196, 0), # 176
(2547, 2129, 2157, 2316, 1872, 939, 930, 805, 972, 457, 320, 201, 0, 2651, 2176, 1642, 1347, 2048, 1061, 905, 679, 987, 788, 384, 196, 0), # 177
(2553, 2131, 2165, 2319, 1877, 941, 932, 807, 976, 457, 322, 201, 0, 2659, 2181, 1643, 1350, 2054, 1064, 908, 682, 989, 790, 387, 197, 0), # 178
(2553, 2131, 2165, 2319, 1877, 941, 932, 807, 976, 457, 322, 201, 0, 2659, 2181, 1643, 1350, 2054, 1064, 908, 682, 989, 790, 387, 197, 0), # 179
)
passenger_arriving_rate = (
(8.033384925394829, 8.103756554216645, 6.9483776394833425, 7.45760132863612, 5.924997981450252, 2.9294112699015167, 3.3168284922991322, 3.102117448652949, 3.2480528331562706, 1.5832060062089484, 1.1214040437028276, 0.6530553437741565, 0.0, 8.134208340125381, 7.183608781515721, 5.607020218514138, 4.749618018626844, 6.496105666312541, 4.342964428114128, 3.3168284922991322, 2.0924366213582264, 2.962498990725126, 2.4858671095453735, 1.3896755278966686, 0.7367051412924223, 0.0), # 0
(8.566923443231959, 8.638755684745645, 7.407128788440204, 7.95017310393194, 6.317323026639185, 3.122918011773052, 3.535575153010955, 3.306342481937139, 3.462530840710885, 1.6875922769108604, 1.1954923029216353, 0.6961622214419141, 0.0, 8.671666635903767, 7.657784435861053, 5.9774615146081755, 5.06277683073258, 6.92506168142177, 4.628879474711995, 3.535575153010955, 2.230655722695037, 3.1586615133195926, 2.650057701310647, 1.4814257576880407, 0.7853414258859679, 0.0), # 1
(9.09875681436757, 9.171631583973436, 7.864056380729885, 8.440785245597754, 6.708227171999727, 3.3156527735449486, 3.7534548063685635, 3.5097501652696135, 3.676152963668026, 1.7915655100082188, 1.269286173007017, 0.7390976869404075, 0.0, 9.206983725135505, 8.13007455634448, 6.346430865035084, 5.374696530024655, 7.352305927336052, 4.913650231377459, 3.7534548063685635, 2.3683234096749635, 3.3541135859998636, 2.8135950818659183, 1.5728112761459772, 0.8337846894521307, 0.0), # 2
(9.6268124690345, 9.70027006950679, 8.317347825759807, 8.927491689038488, 7.096172454402028, 3.5068512477461056, 3.9696029133183646, 3.7115341049963386, 3.8880720858245827, 1.8947130793704727, 1.3424929098206355, 0.7816914246573948, 0.0, 9.738036490006762, 8.598605671231342, 6.712464549103178, 5.684139238111417, 7.7761441716491655, 5.196147746994874, 3.9696029133183646, 2.5048937483900753, 3.548086227201014, 2.97583056301283, 1.6634695651519613, 0.8818427335915264, 0.0), # 3
(10.149017837465571, 10.222556958952469, 8.765190532937382, 9.408346369659084, 7.479620910716259, 3.6957491269054237, 4.183154934806767, 3.910887907463277, 4.097441090977444, 1.996622358867072, 1.4148197692241535, 0.8237731189806353, 0.0, 10.262701812703709, 9.061504308786986, 7.074098846120767, 5.9898670766012145, 8.194882181954888, 5.475243070448588, 4.183154934806767, 2.6398208049324454, 3.7398104553581293, 3.136115456553029, 1.7530381065874767, 0.9293233599047701, 0.0), # 4
(10.663300349893618, 10.736378069917262, 9.205771911670025, 9.881403222864472, 7.8570345778125645, 3.8815821035518008, 4.393246331780179, 4.1070051790163955, 4.303412862923498, 2.096880722367466, 1.4859740070792353, 0.8651724542978865, 0.0, 10.778856575412524, 9.51689699727675, 7.429870035396177, 6.290642167102396, 8.606825725846996, 5.749807250622953, 4.393246331780179, 2.772558645394143, 3.9285172889062823, 3.2938010742881585, 1.841154382334005, 0.9760343699924785, 0.0), # 5
(11.167587436551466, 11.239619220007935, 9.637279371365155, 10.344716184059584, 8.226875492561113, 4.06358587021414, 4.59901256518501, 4.299079526001659, 4.5051402854596345, 2.195075543741104, 1.555662879247542, 0.9057191149969079, 0.0, 11.284377660319372, 9.962910264965986, 7.77831439623771, 6.5852266312233105, 9.010280570919269, 6.018711336402323, 4.59901256518501, 2.902561335867243, 4.113437746280557, 3.448238728019862, 1.9274558742730312, 1.021783565455267, 0.0), # 6
(11.65980652767195, 11.73016622683126, 10.05790032143018, 10.796339188649355, 8.587605691832056, 4.2409961194213395, 4.799589095967668, 4.486304554765035, 4.701776242382744, 2.2907941968574352, 1.6235936415907386, 0.9452427854654573, 0.0, 11.777141949610431, 10.397670640120028, 8.117968207953693, 6.872382590572304, 9.403552484765488, 6.280826376671049, 4.799589095967668, 3.029282942443814, 4.293802845916028, 3.598779729549786, 2.0115800642860364, 1.066378747893751, 0.0), # 7
(12.137885053487896, 12.205904907994013, 10.465822171272528, 11.234326172038713, 8.937687212495558, 4.413048543702297, 4.994111385074558, 4.667873871652484, 4.89247361748971, 2.3836240555859103, 1.6894735499704858, 0.9835731500912939, 0.0, 12.255026325471867, 10.81930465100423, 8.447367749852429, 7.150872166757729, 9.78494723497942, 6.535023420313477, 4.994111385074558, 3.152177531215927, 4.468843606247779, 3.744775390679572, 2.093164434254506, 1.1096277189085468, 0.0), # 8
(12.599750444232136, 12.664721081102966, 10.859232330299607, 11.656731069632603, 9.27558209142177, 4.578978835585919, 5.181714893452096, 4.842981083009976, 5.076385294577426, 2.4731524937959772, 1.7530098602484476, 1.0205398932621754, 0.0, 12.71590767008986, 11.225938825883926, 8.765049301242238, 7.41945748138793, 10.152770589154851, 6.780173516213966, 5.181714893452096, 3.270699168275656, 4.637791045710885, 3.8855770232108684, 2.1718464660599213, 1.1513382801002698, 0.0), # 9
(13.043330130137491, 13.104500563764889, 11.236318207918833, 12.061607816835945, 9.599752365480853, 4.7380226876011005, 5.361535082046684, 5.010819795183474, 5.252664157442781, 2.558966885357086, 1.8139098282862867, 1.0559726993658605, 0.0, 13.157662865650577, 11.615699693024464, 9.069549141431432, 7.676900656071258, 10.505328314885562, 7.015147713256865, 5.361535082046684, 3.3843019197150714, 4.799876182740427, 4.020535938945316, 2.247263641583767, 1.1913182330695355, 0.0), # 10
(13.466551541436809, 13.52312917358657, 11.595267213537621, 12.447010349053677, 9.908660071542968, 4.889415792276744, 5.532707411804733, 5.170583614518944, 5.420463089882663, 2.640654604138688, 1.8718807099456667, 1.0897012527901082, 0.0, 13.57816879434018, 11.986713780691188, 9.359403549728333, 7.921963812416063, 10.840926179765326, 7.238817060326522, 5.532707411804733, 3.4924398516262456, 4.954330035771484, 4.14900344968456, 2.3190534427075247, 1.229375379416961, 0.0), # 11
(13.8673421083629, 13.918492728174757, 11.934266756563387, 12.810992601690735, 10.200767246478268, 5.032393842141746, 5.694367343672649, 5.321466147362347, 5.578934975693962, 2.7178030240102293, 1.9266297610882495, 1.1215552379226759, 0.0, 13.975302338344855, 12.337107617149433, 9.633148805441246, 8.153409072030687, 11.157869951387925, 7.4500526063072865, 5.694367343672649, 3.5945670301012465, 5.100383623239134, 4.270330867230246, 2.3868533513126775, 1.26531752074316, 0.0), # 12
(14.243629261148602, 14.288477045136244, 12.251504246403549, 13.151608510152053, 10.474535927156907, 5.166192529725009, 5.845650338596845, 5.462661000059654, 5.727232698673564, 2.7899995188411624, 1.9778642375756985, 1.1513643391513229, 0.0, 14.346940379850777, 12.66500773066455, 9.889321187878492, 8.369998556523486, 11.454465397347128, 7.647725400083517, 5.845650338596845, 3.6901375212321494, 5.237267963578454, 4.383869503384019, 2.45030084928071, 1.2989524586487495, 0.0), # 13
(14.593340430026746, 14.630967942077797, 12.54516709246553, 13.466912009842552, 10.728428150449055, 5.2900475475554325, 5.9856918575237295, 5.593361778956831, 5.864509142618358, 2.856831462500934, 2.0252913952696763, 1.1789582408638082, 0.0, 14.690959801044102, 12.968540649501888, 10.12645697634838, 8.570494387502801, 11.729018285236716, 7.830706490539565, 5.9856918575237295, 3.778605391111023, 5.3642140752245275, 4.488970669947518, 2.509033418493106, 1.3300879947343454, 0.0), # 14
(14.914403045230168, 14.943851236606186, 12.813442704156724, 13.754957036167184, 10.960905953224861, 5.403194588161918, 6.1136273613997005, 5.7127620903998375, 5.989917191325237, 2.917886228858997, 2.0686184900318456, 1.2041666274478897, 0.0, 15.00523748411101, 13.245832901926784, 10.343092450159226, 8.753658686576989, 11.979834382650473, 7.997866926559773, 6.1136273613997005, 3.8594247058299413, 5.480452976612431, 4.584985678722395, 2.562688540831345, 1.3585319306005625, 0.0), # 15
(15.204744536991681, 15.225012746328195, 13.054518490884568, 14.013797524530858, 11.170431372354487, 5.504869344073363, 6.228592311171181, 5.820055540734641, 6.102609728591085, 2.972751191784799, 2.1075527777238703, 1.2268191832913256, 0.0, 15.287650311237673, 13.495011016204579, 10.53776388861935, 8.918253575354395, 12.20521945718217, 8.148077757028497, 6.228592311171181, 3.932049531480973, 5.585215686177244, 4.671265841510287, 2.6109036981769136, 1.384092067848018, 0.0), # 16
(15.46229233554412, 15.472338288850588, 13.266581862056471, 14.241487410338536, 11.355466444708094, 5.594307507818667, 6.329722167784569, 5.914435736307213, 6.201739638212791, 3.021013725147788, 2.141801514207413, 1.2467455927818742, 0.0, 15.536075164610265, 13.714201520600614, 10.709007571037066, 9.063041175443361, 12.403479276425582, 8.280210030830098, 6.329722167784569, 3.9959339341561906, 5.677733222354047, 4.747162470112846, 2.6533163724112945, 1.4065762080773265, 0.0), # 17
(15.684973871120327, 15.683713681780135, 13.447820227079841, 14.436080628995136, 11.514473207155827, 5.670744771926737, 6.416152392186281, 5.995096283463507, 6.286459803987251, 3.0622612028174157, 2.171071955344136, 1.2637755403072954, 0.0, 15.748388926414954, 13.901530943380248, 10.855359776720679, 9.186783608452245, 12.572919607974502, 8.39313479684891, 6.416152392186281, 4.050531979947669, 5.757236603577914, 4.812026876331712, 2.689564045415968, 1.4257921528891033, 0.0), # 18
(15.870716573953118, 15.857024742723624, 13.596420995362104, 14.59563111590558, 11.645913696567856, 5.733416828926462, 6.4870184453227155, 6.061230788549498, 6.355923109711349, 3.0960809986631324, 2.1950713569957014, 1.2777387102553464, 0.0, 15.922468478837914, 14.055125812808807, 10.975356784978505, 9.288242995989394, 12.711846219422698, 8.485723103969297, 6.4870184453227155, 4.095297734947473, 5.822956848283928, 4.865210371968527, 2.7192841990724212, 1.441547703883966, 0.0), # 19
(16.01744787427533, 15.990157289287811, 13.710571576310672, 14.718192806474825, 11.748249949814339, 5.781559371346751, 6.54145578814029, 6.112032857911145, 6.409282439181973, 3.1220604865543846, 2.213506975023774, 1.2884647870137858, 0.0, 16.05619070406532, 14.17311265715164, 11.067534875118868, 9.366181459663151, 12.818564878363945, 8.556846001075604, 6.54145578814029, 4.129685265247679, 5.874124974907169, 4.9060642688249425, 2.7421143152621346, 1.4536506626625285, 0.0), # 20
(16.123095202319785, 16.080997139079486, 13.78845937933296, 14.801819636107783, 11.819944003765428, 5.8144080917165, 6.578599881585408, 6.1466960978944165, 6.445690676196012, 3.139787040360623, 2.226086065290016, 1.2957834549703726, 0.0, 16.147432484283325, 14.253618004674097, 11.13043032645008, 9.419361121081867, 12.891381352392024, 8.605374537052183, 6.578599881585408, 4.153148636940357, 5.909972001882714, 4.933939878702596, 2.757691875866592, 1.461908830825408, 0.0), # 21
(16.18558598831933, 16.12743010970541, 13.82827181383638, 14.844565540209405, 11.85945789529128, 5.83119868256461, 6.59758618660448, 6.164414114845277, 6.464300704550355, 3.148848033951298, 2.232515883656091, 1.2995243985128655, 0.0, 16.194070701678125, 14.294768383641518, 11.162579418280455, 9.446544101853892, 12.92860140910071, 8.630179760783388, 6.59758618660448, 4.1651419161175784, 5.92972894764564, 4.948188513403136, 2.7656543627672763, 1.4661300099732195, 0.0), # 22
(16.208629381348224, 16.132927937814358, 13.83323090992227, 14.849916975308645, 11.869580859768103, 5.833333333333334, 6.599843201807471, 6.166329218106997, 6.466627325102881, 3.149916909007774, 2.233322143243131, 1.2999863435451913, 0.0, 16.2, 14.299849778997103, 11.166610716215654, 9.44975072702332, 12.933254650205763, 8.632860905349796, 6.599843201807471, 4.166666666666667, 5.9347904298840515, 4.949972325102882, 2.7666461819844543, 1.4666298125285782, 0.0), # 23
(16.225619860854646, 16.12972098765432, 13.832419753086421, 14.849258333333335, 11.875314787855842, 5.833333333333334, 6.598603050108934, 6.163666666666667, 6.466315555555555, 3.149260246913581, 2.2332332210998884, 1.2998781893004117, 0.0, 16.2, 14.298660082304526, 11.166166105499443, 9.44778074074074, 12.93263111111111, 8.629133333333334, 6.598603050108934, 4.166666666666667, 5.937657393927921, 4.949752777777779, 2.7664839506172845, 1.4663382716049385, 0.0), # 24
(16.242251568338528, 16.1233996342021, 13.830818472793784, 14.847955246913582, 11.880922608634137, 5.833333333333334, 6.596159122085048, 6.158436213991771, 6.465699588477367, 3.1479675354366723, 2.233056906513697, 1.2996646852613931, 0.0, 16.2, 14.296311537875322, 11.165284532568485, 9.443902606310015, 12.931399176954734, 8.62181069958848, 6.596159122085048, 4.166666666666667, 5.940461304317068, 4.949318415637862, 2.766163694558757, 1.4657636031092822, 0.0), # 25
(16.258523230476854, 16.114060448102425, 13.828449016918157, 14.846022530864197, 11.886404126315846, 5.833333333333334, 6.592549374646977, 6.150736625514405, 6.46478732510288, 3.146060283493371, 2.2327947956935614, 1.2993487578113097, 0.0, 16.2, 14.292836335924404, 11.163973978467807, 9.43818085048011, 12.92957465020576, 8.611031275720167, 6.592549374646977, 4.166666666666667, 5.943202063157923, 4.948674176954733, 2.7656898033836312, 1.46491458619113, 0.0), # 26
(16.27443357394662, 16.1018, 13.825333333333333, 14.843475, 11.891759145113827, 5.833333333333334, 6.587811764705883, 6.140666666666667, 6.463586666666666, 3.143560000000001, 2.232448484848485, 1.2989333333333337, 0.0, 16.2, 14.288266666666669, 11.162242424242425, 9.430679999999999, 12.927173333333332, 8.596933333333334, 6.587811764705883, 4.166666666666667, 5.945879572556914, 4.947825000000001, 2.765066666666667, 1.4638000000000002, 0.0), # 27
(16.2899813254248, 16.08671486053955, 13.821493369913123, 14.840327469135804, 11.896987469240962, 5.833333333333334, 6.581984249172921, 6.12832510288066, 6.462105514403292, 3.140488193872886, 2.232019570187472, 1.2984213382106389, 0.0, 16.2, 14.282634720317025, 11.160097850937358, 9.421464581618656, 12.924211028806583, 8.579655144032923, 6.581984249172921, 4.166666666666667, 5.948493734620481, 4.946775823045269, 2.764298673982625, 1.462428623685414, 0.0), # 28
(16.3051652115884, 16.0689016003658, 13.816951074531323, 14.83659475308642, 11.902088902910101, 5.833333333333334, 6.575104784959253, 6.113810699588477, 6.460351769547325, 3.1368663740283504, 2.2315096479195247, 1.2978156988263985, 0.0, 16.2, 14.27597268709038, 11.157548239597624, 9.41059912208505, 12.92070353909465, 8.559334979423868, 6.575104784959253, 4.166666666666667, 5.951044451455051, 4.945531584362141, 2.763390214906265, 1.460809236396891, 0.0), # 29
(16.319983959114396, 16.04845679012346, 13.811728395061728, 14.832291666666666, 11.907063250334119, 5.833333333333334, 6.567211328976035, 6.097222222222222, 6.458333333333333, 3.1327160493827173, 2.230920314253648, 1.297119341563786, 0.0, 16.2, 14.268312757201645, 11.15460157126824, 9.398148148148149, 12.916666666666666, 8.536111111111111, 6.567211328976035, 4.166666666666667, 5.953531625167059, 4.944097222222223, 2.7623456790123457, 1.458950617283951, 0.0), # 30
(16.334436294679772, 16.02547700045725, 13.805847279378145, 14.82743302469136, 11.911910315725876, 5.833333333333334, 6.558341838134432, 6.078658436213992, 6.456058106995885, 3.1280587288523103, 2.2302531653988447, 1.296335192805975, 0.0, 16.2, 14.259687120865724, 11.151265826994223, 9.384176186556928, 12.91211621399177, 8.510121810699589, 6.558341838134432, 4.166666666666667, 5.955955157862938, 4.942477674897121, 2.761169455875629, 1.4568615454961138, 0.0), # 31
(16.34852094496153, 16.00005880201189, 13.799329675354366, 14.82203364197531, 11.916629903298237, 5.833333333333334, 6.548534269345599, 6.058218106995886, 6.453533991769548, 3.1229159213534534, 2.229509797564119, 1.2954661789361381, 0.0, 16.2, 14.250127968297518, 11.147548987820594, 9.368747764060357, 12.907067983539095, 8.48150534979424, 6.548534269345599, 4.166666666666667, 5.958314951649118, 4.940677880658438, 2.759865935070873, 1.4545508001828993, 0.0), # 32
(16.362236636636634, 15.972298765432097, 13.792197530864199, 14.816108333333332, 11.921221817264065, 5.833333333333334, 6.537826579520697, 6.0360000000000005, 6.450768888888889, 3.1173091358024703, 2.228691806958474, 1.2945152263374486, 0.0, 16.2, 14.239667489711932, 11.143459034792368, 9.351927407407409, 12.901537777777778, 8.450400000000002, 6.537826579520697, 4.166666666666667, 5.960610908632033, 4.938702777777778, 2.75843950617284, 1.452027160493827, 0.0), # 33
(16.375582096382097, 15.942293461362596, 13.784472793781436, 14.809671913580248, 11.92568586183623, 5.833333333333334, 6.526256725570888, 6.012102880658436, 6.447770699588479, 3.111259881115685, 2.2278007897909133, 1.2934852613930805, 0.0, 16.2, 14.228337875323884, 11.139003948954567, 9.333779643347052, 12.895541399176958, 8.41694403292181, 6.526256725570888, 4.166666666666667, 5.962842930918115, 4.93655730452675, 2.7568945587562874, 1.449299405578418, 0.0), # 34
(16.388556050874893, 15.9101394604481, 13.776177411979882, 14.802739197530864, 11.930021841227594, 5.833333333333334, 6.513862664407327, 5.986625514403293, 6.4445473251028815, 3.1047896662094203, 2.226838342270441, 1.2923792104862066, 0.0, 16.2, 14.216171315348271, 11.134191711352205, 9.314368998628257, 12.889094650205763, 8.381275720164611, 6.513862664407327, 4.166666666666667, 5.965010920613797, 4.934246399176955, 2.755235482395977, 1.4463763145861912, 0.0), # 35
(16.40115722679201, 15.87593333333333, 13.767333333333335, 14.795325, 11.934229559651024, 5.833333333333334, 6.500682352941176, 5.959666666666668, 6.441106666666666, 3.097920000000001, 2.225806060606061, 1.2912000000000003, 0.0, 16.2, 14.203200000000002, 11.129030303030303, 9.29376, 12.882213333333333, 8.343533333333335, 6.500682352941176, 4.166666666666667, 5.967114779825512, 4.931775000000001, 2.753466666666667, 1.4432666666666667, 0.0), # 36
(16.41338435081044, 15.839771650663007, 13.757962505715593, 14.78744413580247, 11.938308821319383, 5.833333333333334, 6.486753748083595, 5.931325102880659, 6.437456625514404, 3.090672391403751, 2.2247055410067764, 1.2899505563176348, 0.0, 16.2, 14.18945611949398, 11.123527705033881, 9.27201717421125, 12.874913251028808, 8.303855144032923, 6.486753748083595, 4.166666666666667, 5.969154410659692, 4.929148045267491, 2.751592501143119, 1.4399792409693644, 0.0), # 37
(16.425236149607162, 15.801750983081849, 13.748086877000459, 14.77911141975309, 11.942259430445535, 5.833333333333334, 6.4721148067457435, 5.901699588477367, 6.433605102880659, 3.0830683493369926, 2.22353837968159, 1.2886338058222835, 0.0, 16.2, 14.174971864045116, 11.11769189840795, 9.249205048010975, 12.867210205761317, 8.262379423868314, 6.4721148067457435, 4.166666666666667, 5.971129715222768, 4.926370473251031, 2.7496173754000917, 1.4365228166438047, 0.0), # 38
(16.436711349859177, 15.761967901234568, 13.737728395061731, 14.770341666666667, 11.94608119124235, 5.833333333333334, 6.456803485838781, 5.8708888888888895, 6.42956, 3.0751293827160504, 2.2223061728395064, 1.2872526748971194, 0.0, 16.2, 14.159779423868311, 11.111530864197531, 9.225388148148149, 12.85912, 8.219244444444445, 6.456803485838781, 4.166666666666667, 5.973040595621175, 4.923447222222223, 2.7475456790123465, 1.4329061728395065, 0.0), # 39
(16.44780867824346, 15.720518975765888, 13.726909007773205, 14.761149691358025, 11.949773907922687, 5.833333333333334, 6.440857742273865, 5.838991769547327, 6.425329218106996, 3.0668770004572488, 2.2210105166895295, 1.2858100899253166, 0.0, 16.2, 14.143910989178481, 11.105052583447646, 9.200631001371743, 12.850658436213992, 8.174588477366258, 6.440857742273865, 4.166666666666667, 5.974886953961343, 4.920383230452676, 2.745381801554641, 1.42913808870599, 0.0), # 40
(16.458526861437004, 15.677500777320528, 13.71565066300869, 14.751550308641978, 11.953337384699417, 5.833333333333334, 6.424315532962156, 5.806106995884774, 6.420920658436214, 3.05833271147691, 2.2196530074406624, 1.2843089772900476, 0.0, 16.2, 14.12739875019052, 11.09826503720331, 9.174998134430727, 12.841841316872427, 8.128549794238685, 6.424315532962156, 4.166666666666667, 5.976668692349708, 4.9171834362139935, 2.743130132601738, 1.4252273433927756, 0.0), # 41
(16.4688646261168, 15.633009876543213, 13.70397530864198, 14.741558333333336, 11.956771425785394, 5.833333333333334, 6.4072148148148145, 5.772333333333334, 6.416342222222223, 3.049518024691359, 2.2182352413019086, 1.282752263374486, 0.0, 16.2, 14.110274897119341, 11.091176206509541, 9.148554074074074, 12.832684444444446, 8.081266666666668, 6.4072148148148145, 4.166666666666667, 5.978385712892697, 4.913852777777779, 2.740795061728396, 1.421182716049383, 0.0), # 42
(16.47882069895983, 15.587142844078647, 13.69190489254687, 14.731188580246915, 11.960075835393496, 5.833333333333334, 6.389593544743001, 5.737769547325104, 6.4116018106995885, 3.040454449016919, 2.2167588144822714, 1.281142874561805, 0.0, 16.2, 14.092571620179852, 11.083794072411356, 9.121363347050755, 12.823203621399177, 8.032877366255146, 6.389593544743001, 4.166666666666667, 5.980037917696748, 4.9103961934156395, 2.738380978509374, 1.4170129858253318, 0.0), # 43
(16.488393806643085, 15.539996250571559, 13.679461362597166, 14.720455864197532, 11.963250417736582, 5.833333333333334, 6.371489679657872, 5.702514403292183, 6.4067073251028805, 3.031163493369914, 2.2152253231907557, 1.279483737235178, 0.0, 16.2, 14.074321109586954, 11.076126615953777, 9.09349048010974, 12.813414650205761, 7.983520164609057, 6.371489679657872, 4.166666666666667, 5.981625208868291, 4.906818621399179, 2.7358922725194335, 1.4127269318701419, 0.0), # 44
(16.497582675843546, 15.491666666666667, 13.66666666666667, 14.709375000000001, 11.966294977027516, 5.833333333333334, 6.352941176470589, 5.666666666666668, 6.4016666666666655, 3.021666666666668, 2.213636363636364, 1.277777777777778, 0.0, 16.2, 14.055555555555554, 11.068181818181818, 9.065000000000001, 12.803333333333331, 7.9333333333333345, 6.352941176470589, 4.166666666666667, 5.983147488513758, 4.903125000000001, 2.733333333333334, 1.4083333333333337, 0.0), # 45
(16.50638603323821, 15.442250663008686, 13.653542752629173, 14.697960802469137, 11.969209317479164, 5.833333333333334, 6.333985992092311, 5.63032510288066, 6.396487736625514, 3.0119854778235036, 2.2119935320281, 1.2760279225727789, 0.0, 16.2, 14.036307148300564, 11.059967660140499, 9.035956433470508, 12.792975473251028, 7.882455144032924, 6.333985992092311, 4.166666666666667, 5.984604658739582, 4.899320267489713, 2.730708550525835, 1.4038409693644263, 0.0), # 46
(16.514802605504055, 15.391844810242342, 13.640111568358483, 14.686228086419753, 11.971993243304391, 5.833333333333334, 6.3146620834341975, 5.593588477366255, 6.391178436213992, 3.0021414357567453, 2.210298424574968, 1.2742370980033535, 0.0, 16.2, 14.016608078036885, 11.051492122874839, 9.006424307270233, 12.782356872427984, 7.831023868312758, 6.3146620834341975, 4.166666666666667, 5.985996621652196, 4.895409362139919, 2.728022313671697, 1.3992586191129404, 0.0), # 47
(16.522831119318074, 15.340545679012347, 13.626395061728397, 14.674191666666669, 11.974646558716064, 5.833333333333334, 6.295007407407407, 5.556555555555557, 6.385746666666667, 2.9921560493827166, 2.208552637485971, 1.272408230452675, 0.0, 16.2, 13.996490534979422, 11.042763187429854, 8.976468148148149, 12.771493333333334, 7.77917777777778, 6.295007407407407, 4.166666666666667, 5.987323279358032, 4.891397222222224, 2.7252790123456796, 1.3945950617283953, 0.0), # 48
(16.53047030135726, 15.288449839963418, 13.612415180612713, 14.661866358024692, 11.977169067927047, 5.833333333333334, 6.275059920923102, 5.519325102880659, 6.380200329218106, 2.982050827617742, 2.2067577669701133, 1.2705442463039174, 0.0, 16.2, 13.97598670934309, 11.033788834850565, 8.946152482853226, 12.760400658436213, 7.727055144032923, 6.275059920923102, 4.166666666666667, 5.9885845339635235, 4.887288786008232, 2.7224830361225427, 1.389859076360311, 0.0), # 49
(16.537718878298588, 15.235653863740286, 13.598193872885233, 14.649266975308642, 11.979560575150202, 5.833333333333334, 6.25485758089244, 5.481995884773663, 6.3745473251028795, 2.971847279378144, 2.204915409236397, 1.2686480719402533, 0.0, 16.2, 13.955128791342785, 11.024577046181985, 8.91554183813443, 12.749094650205759, 7.674794238683129, 6.25485758089244, 4.166666666666667, 5.989780287575101, 4.883088991769548, 2.7196387745770467, 1.385059442158208, 0.0), # 50
(16.544575576819057, 15.182254320987655, 13.583753086419755, 14.636408333333335, 11.981820884598399, 5.833333333333334, 6.23443834422658, 5.4446666666666665, 6.368795555555556, 2.9615669135802474, 2.2030271604938276, 1.2667226337448563, 0.0, 16.2, 13.933948971193416, 11.015135802469137, 8.88470074074074, 12.737591111111112, 7.622533333333334, 6.23443834422658, 4.166666666666667, 5.9909104422991994, 4.878802777777779, 2.716750617283951, 1.380204938271605, 0.0), # 51
(16.551039123595647, 15.128347782350252, 13.56911476909008, 14.623305246913581, 11.983949800484496, 5.833333333333334, 6.213840167836683, 5.407436213991769, 6.3629529218107, 2.9512312391403754, 2.2010946169514076, 1.2647708581008996, 0.0, 16.2, 13.912479439109894, 11.005473084757037, 8.853693717421125, 12.7259058436214, 7.570410699588477, 6.213840167836683, 4.166666666666667, 5.991974900242248, 4.874435082304528, 2.713822953818016, 1.3753043438500232, 0.0), # 52
(16.55710824530535, 15.074030818472796, 13.554300868770008, 14.609972530864198, 11.985947127021364, 5.833333333333334, 6.1931010086339064, 5.370403292181071, 6.357027325102881, 2.940861764974852, 2.1991193748181406, 1.2627956713915565, 0.0, 16.2, 13.890752385307119, 10.995596874090701, 8.822585294924554, 12.714054650205762, 7.518564609053499, 6.1931010086339064, 4.166666666666667, 5.992973563510682, 4.8699908436214, 2.710860173754002, 1.3703664380429816, 0.0), # 53
(16.562781668625146, 15.019400000000001, 13.539333333333333, 14.596425, 11.987812668421869, 5.833333333333334, 6.172258823529412, 5.333666666666667, 6.351026666666667, 2.9304800000000006, 2.19710303030303, 1.2608000000000001, 0.0, 16.2, 13.8688, 10.98551515151515, 8.791440000000001, 12.702053333333334, 7.467133333333333, 6.172258823529412, 4.166666666666667, 5.993906334210934, 4.865475000000001, 2.707866666666667, 1.3654000000000004, 0.0), # 54
(16.568058120232035, 14.964551897576587, 13.524234110653865, 14.582677469135803, 11.989546228898869, 5.833333333333334, 6.151351569434358, 5.2973251028806585, 6.344958847736625, 2.9201074531321454, 2.1950471796150812, 1.2587867703094042, 0.0, 16.2, 13.846654473403445, 10.975235898075404, 8.760322359396435, 12.68991769547325, 7.416255144032922, 6.151351569434358, 4.166666666666667, 5.994773114449434, 4.860892489711935, 2.704846822130773, 1.360413808870599, 0.0), # 55
(16.572936326802996, 14.909583081847279, 13.509025148605396, 14.56874475308642, 11.991147612665237, 5.833333333333334, 6.130417203259905, 5.261477366255145, 6.338831769547324, 2.9097656332876096, 2.1929534189632958, 1.2567589087029418, 0.0, 16.2, 13.824347995732358, 10.964767094816478, 8.729296899862828, 12.677663539094649, 7.366068312757203, 6.130417203259905, 4.166666666666667, 5.995573806332619, 4.856248251028807, 2.7018050297210796, 1.3554166438042983, 0.0), # 56
(16.577415015015013, 14.85459012345679, 13.493728395061732, 14.554641666666669, 11.99261662393383, 5.833333333333334, 6.109493681917211, 5.226222222222224, 6.332653333333334, 2.899476049382717, 2.1908233445566783, 1.254719341563786, 0.0, 16.2, 13.801912757201645, 10.95411672278339, 8.69842814814815, 12.665306666666668, 7.316711111111113, 6.109493681917211, 4.166666666666667, 5.996308311966915, 4.851547222222224, 2.6987456790123465, 1.3504172839506174, 0.0), # 57
(16.581492911545087, 14.79966959304984, 13.478365797896664, 14.540383024691359, 11.99395306691752, 5.833333333333334, 6.088618962317438, 5.191658436213992, 6.326431440329218, 2.8892602103337914, 2.1886585526042324, 1.2526709952751107, 0.0, 16.2, 13.779380948026215, 10.943292763021162, 8.667780631001373, 12.652862880658436, 7.2683218106995895, 6.088618962317438, 4.166666666666667, 5.99697653345876, 4.846794341563787, 2.695673159579333, 1.3454245084590766, 0.0), # 58
(16.585168743070195, 14.744918061271147, 13.462959304983997, 14.525983641975309, 11.995156745829167, 5.833333333333334, 6.067831001371743, 5.157884773662552, 6.320173991769548, 2.879139625057157, 2.1864606393149604, 1.2506167962200887, 0.0, 16.2, 13.756784758420972, 10.9323031965748, 8.63741887517147, 12.640347983539096, 7.221038683127573, 6.067831001371743, 4.166666666666667, 5.9975783729145835, 4.841994547325104, 2.6925918609968, 1.3404470964791952, 0.0), # 59
(16.588441236267325, 14.690432098765434, 13.44753086419753, 14.511458333333334, 11.996227464881638, 5.833333333333334, 6.0471677559912855, 5.125000000000001, 6.31388888888889, 2.8691358024691365, 2.184231200897868, 1.2485596707818931, 0.0, 16.2, 13.734156378600822, 10.921156004489339, 8.607407407407408, 12.62777777777778, 7.175000000000001, 6.0471677559912855, 4.166666666666667, 5.998113732440819, 4.837152777777779, 2.6895061728395064, 1.3354938271604941, 0.0), # 60
(16.591309117813463, 14.636308276177413, 13.432102423411067, 14.496821913580249, 11.997165028287798, 5.833333333333334, 6.026667183087227, 5.093102880658437, 6.3075840329218105, 2.8592702514860546, 2.1819718335619576, 1.246502545343698, 0.0, 16.2, 13.711527998780674, 10.909859167809786, 8.577810754458163, 12.615168065843621, 7.130344032921811, 6.026667183087227, 4.166666666666667, 5.998582514143899, 4.832273971193417, 2.6864204846822135, 1.3305734796524924, 0.0), # 61
(16.593771114385607, 14.582643164151806, 13.416695930498403, 14.482089197530867, 11.997969240260517, 5.833333333333334, 6.006367239570725, 5.062292181069959, 6.301267325102881, 2.849564481024235, 2.1796841335162327, 1.2444483462886757, 0.0, 16.2, 13.68893180917543, 10.898420667581162, 8.548693443072704, 12.602534650205762, 7.0872090534979435, 6.006367239570725, 4.166666666666667, 5.998984620130258, 4.827363065843623, 2.6833391860996807, 1.3256948331047098, 0.0), # 62
(16.595825952660736, 14.529533333333333, 13.401333333333335, 14.467275000000003, 11.998639905012647, 5.833333333333334, 5.986305882352941, 5.0326666666666675, 6.294946666666666, 2.8400400000000006, 2.1773696969696976, 1.2424000000000002, 0.0, 16.2, 13.6664, 10.886848484848487, 8.52012, 12.589893333333332, 7.045733333333335, 5.986305882352941, 4.166666666666667, 5.999319952506323, 4.822425000000002, 2.6802666666666672, 1.3208666666666669, 0.0), # 63
(16.597472359315837, 14.477075354366713, 13.386036579789668, 14.452394135802471, 11.999176826757065, 5.833333333333334, 5.966521068345034, 5.004325102880659, 6.288629958847737, 2.830718317329676, 2.1750301201313547, 1.2403604328608446, 0.0, 16.2, 13.64396476146929, 10.875150600656774, 8.492154951989026, 12.577259917695473, 7.006055144032923, 5.966521068345034, 4.166666666666667, 5.999588413378532, 4.817464711934158, 2.6772073159579337, 1.316097759487883, 0.0), # 64
(16.5987090610279, 14.425365797896662, 13.370827617741199, 14.437461419753088, 11.999579809706631, 5.833333333333334, 5.947050754458163, 4.977366255144033, 6.282325102880659, 2.8216209419295843, 2.1726669992102097, 1.238332571254382, 0.0, 16.2, 13.6216582837982, 10.863334996051048, 8.464862825788751, 12.564650205761318, 6.968312757201646, 5.947050754458163, 4.166666666666667, 5.999789904853316, 4.812487139917697, 2.67416552354824, 1.3113968907178786, 0.0), # 65
(16.599534784473914, 14.374501234567903, 13.35572839506173, 14.422491666666668, 11.99984865807421, 5.833333333333334, 5.927932897603486, 4.95188888888889, 6.27604, 2.81276938271605, 2.170281930415264, 1.2363193415637863, 0.0, 16.2, 13.599512757201648, 10.851409652076319, 8.438308148148149, 12.55208, 6.932644444444446, 5.927932897603486, 4.166666666666667, 5.999924329037105, 4.807497222222223, 2.6711456790123465, 1.3067728395061733, 0.0), # 66
(16.59994825633087, 14.324578235025148, 13.340760859625059, 14.407499691358025, 11.999983176072671, 5.833333333333334, 5.909205454692165, 4.927991769547327, 6.269782551440329, 2.8041851486053964, 2.1678765099555233, 1.23432367017223, 0.0, 16.2, 13.577560371894528, 10.839382549777614, 8.412555445816189, 12.539565102880658, 6.899188477366257, 5.909205454692165, 4.166666666666667, 5.999991588036336, 4.802499897119342, 2.6681521719250116, 1.3022343850022864, 0.0), # 67
(16.59966658316932, 14.275431337669806, 13.325874599908552, 14.39237008856683, 11.999869818983834, 5.833225077478026, 5.890812155863717, 4.905562566681908, 6.263513519280598, 2.795848176658867, 2.1654095969441007, 1.2323373362532992, 0.0, 16.19980024005487, 13.555710698786289, 10.827047984720503, 8.3875445299766, 12.527027038561195, 6.867787593354672, 5.890812155863717, 4.166589341055733, 5.999934909491917, 4.797456696188944, 2.6651749199817103, 1.29776648524271, 0.0), # 68
(16.597026731078905, 14.22556009557945, 13.310651234567901, 14.376340217391304, 11.998838053740013, 5.832369272976682, 5.872214545077291, 4.8833991769547325, 6.256958847736625, 2.7875225562817723, 2.162630090377459, 1.2302958631145768, 0.0, 16.198217592592595, 13.533254494260342, 10.813150451887294, 8.362567668845315, 12.51391769547325, 6.8367588477366255, 5.872214545077291, 4.165978052126201, 5.999419026870006, 4.792113405797102, 2.66213024691358, 1.2932327359617684, 0.0), # 69
(16.59181726009423, 14.174735607770254, 13.295024577046181, 14.359304549114333, 11.996799268404205, 5.8306838388457045, 5.853328107649096, 4.861301630848957, 6.2500815424477985, 2.7791678097850943, 2.159506369740288, 1.228189701505708, 0.0, 16.195091735253776, 13.510086716562785, 10.797531848701441, 8.337503429355282, 12.500163084895597, 6.80582228318854, 5.853328107649096, 4.164774170604074, 5.998399634202102, 4.786434849704778, 2.6590049154092363, 1.2886123279791142, 0.0), # 70
(16.584111457028687, 14.122988247267578, 13.279000114311843, 14.341288204508857, 11.993779284004411, 5.828196087994717, 5.8341613276311906, 4.8392772443225125, 6.242891845755221, 2.7707841437370564, 2.1560499655423633, 1.226020391628362, 0.0, 16.190463820301783, 13.486224307911982, 10.780249827711817, 8.312352431211167, 12.485783691510441, 6.774988142051518, 5.8341613276311906, 4.162997205710512, 5.9968896420022055, 4.780429401502953, 2.6558000228623686, 1.2839080224788708, 0.0), # 71
(16.573982608695655, 14.070348387096773, 13.262583333333334, 14.322316304347826, 11.989803921568626, 5.824933333333335, 5.81472268907563, 4.817333333333334, 6.2354, 2.762371764705883, 2.1522724082934617, 1.2237894736842108, 0.0, 16.184375, 13.461684210526316, 10.761362041467306, 8.287115294117648, 12.4708, 6.744266666666667, 5.81472268907563, 4.160666666666668, 5.994901960784313, 4.7741054347826095, 2.6525166666666666, 1.2791225806451614, 0.0), # 72
(16.561504001908514, 14.016846400283198, 13.245779721079103, 14.302413969404189, 11.984899002124855, 5.820922887771173, 5.795020676034474, 4.795477213839354, 6.227616247523244, 2.753930879259798, 2.1481852285033574, 1.2214984878749227, 0.0, 16.1768664266118, 13.436483366624147, 10.740926142516786, 8.261792637779392, 12.455232495046488, 6.713668099375096, 5.795020676034474, 4.157802062693695, 5.992449501062428, 4.76747132313473, 2.649155944215821, 1.274258763662109, 0.0), # 73
(16.546748923480646, 13.962512659852205, 13.228594764517604, 14.281606320450884, 11.979090346701094, 5.816192064217854, 5.775063772559778, 4.773716201798507, 6.219550830666057, 2.7454616939670253, 2.143799956681829, 1.219148974402169, 0.0, 16.167979252400553, 13.410638718423858, 10.718999783409142, 8.236385081901075, 12.439101661332113, 6.683202682517909, 5.775063772559778, 4.154422903012753, 5.989545173350547, 4.760535440150296, 2.645718952903521, 1.269319332713837, 0.0), # 74
(16.52979066022544, 13.90737753882915, 13.211033950617283, 14.259918478260868, 11.972403776325345, 5.810768175582992, 5.754860462703601, 4.752057613168724, 6.211213991769547, 2.7369644153957884, 2.13912812333865, 1.2167424734676198, 0.0, 16.157754629629633, 13.384167208143815, 10.695640616693249, 8.210893246187364, 12.422427983539094, 6.652880658436215, 5.754860462703601, 4.150548696844995, 5.986201888162673, 4.7533061594202906, 2.6422067901234567, 1.2643070489844683, 0.0), # 75
(16.510702498956285, 13.851471410239393, 13.193102766346595, 14.237375563607085, 11.964865112025606, 5.804678534776205, 5.734419230517997, 4.730508763907942, 6.2026159731748205, 2.728439250114312, 2.134181258983598, 1.2142805252729445, 0.0, 16.146233710562413, 13.357085778002387, 10.67090629491799, 8.185317750342936, 12.405231946349641, 6.622712269471118, 5.734419230517997, 4.146198953411575, 5.982432556012803, 4.745791854535696, 2.638620553269319, 1.259224673658127, 0.0), # 76
(16.48955772648655, 13.794824647108282, 13.174806698673981, 14.21400269726248, 11.956500174829877, 5.797950454707109, 5.7137485600550235, 4.70907696997409, 6.193767017222985, 2.7198864046908207, 2.1289708941264505, 1.2117646700198144, 0.0, 16.13345764746228, 13.329411370217956, 10.64485447063225, 8.15965921407246, 12.38753403444597, 6.592707757963726, 5.7137485600550235, 4.141393181933649, 5.9782500874149385, 4.738000899087494, 2.6349613397347964, 1.254074967918935, 0.0), # 77
(16.46642962962963, 13.737467622461173, 13.156151234567902, 14.189825, 11.94733478576616, 5.790611248285322, 5.69285693536674, 4.687769547325104, 6.184677366255142, 2.711306085693537, 2.123508559276981, 1.2091964479098987, 0.0, 16.119467592592596, 13.301160927008882, 10.617542796384903, 8.13391825708061, 12.369354732510285, 6.562877366255145, 5.69285693536674, 4.136150891632373, 5.97366739288308, 4.729941666666668, 2.6312302469135807, 1.248860692951016, 0.0), # 78
(16.441391495198904, 13.679430709323423, 13.1371418609968, 14.164867592592593, 11.93739476586245, 5.782688228420464, 5.671752840505201, 4.666593811918916, 6.1753572626124065, 2.702698499690686, 2.117805784944966, 1.2065773991448674, 0.0, 16.104304698216733, 13.27235139059354, 10.58902892472483, 8.108095499072057, 12.350714525224813, 6.533231336686482, 5.671752840505201, 4.130491591728903, 5.968697382931225, 4.721622530864199, 2.6274283721993603, 1.243584609938493, 0.0), # 79
(16.414516610007755, 13.620744280720386, 13.117784064929126, 14.139155595813204, 11.92670593614675, 5.774208708022151, 5.650444759522465, 4.645557079713459, 6.165816948635879, 2.694063853250491, 2.111874101640184, 1.2039090639263914, 0.0, 16.08801011659808, 13.242999703190304, 10.559370508200919, 8.082191559751472, 12.331633897271757, 6.503779911598843, 5.650444759522465, 4.1244347914443935, 5.963352968073375, 4.713051865271069, 2.6235568129858255, 1.23824948006549, 0.0), # 80
(16.385878260869568, 13.56143870967742, 13.098083333333335, 14.112714130434785, 11.915294117647058, 5.765200000000001, 5.628941176470589, 4.624666666666667, 6.156066666666666, 2.685402352941177, 2.1057250398724086, 1.2011929824561405, 0.0, 16.070625, 13.213122807017545, 10.528625199362043, 8.05620705882353, 12.312133333333332, 6.474533333333334, 5.628941176470589, 4.118, 5.957647058823529, 4.704238043478263, 2.619616666666667, 1.2328580645161293, 0.0), # 81
(16.355549734597723, 13.501544369219879, 13.078045153177872, 14.085568317230274, 11.903185131391377, 5.75568941726363, 5.607250575401629, 4.603929888736474, 6.146116659045877, 2.676714205330967, 2.099370130151417, 1.198430694935785, 0.0, 16.052190500685874, 13.182737644293633, 10.496850650757084, 8.030142615992899, 12.292233318091753, 6.445501844231063, 5.607250575401629, 4.111206726616879, 5.951592565695688, 4.695189439076759, 2.6156090306355746, 1.2274131244745345, 0.0), # 82
(16.323604318005607, 13.441091632373114, 13.057675011431185, 14.057743276972625, 11.890404798407703, 5.745704272722655, 5.585381440367643, 4.5833540618808115, 6.135977168114616, 2.667999616988085, 2.0928209029869853, 1.195623741566995, 0.0, 16.03274777091907, 13.151861157236944, 10.464104514934926, 8.003998850964255, 12.271954336229232, 6.416695686633136, 5.585381440367643, 4.104074480516182, 5.945202399203851, 4.6859144256575425, 2.6115350022862374, 1.2219174211248287, 0.0), # 83
(16.290115297906603, 13.380110872162485, 13.036978395061729, 14.029264130434784, 11.876978939724037, 5.735271879286694, 5.563342255420687, 4.562946502057613, 6.125658436213991, 2.659258794480756, 2.0860888888888893, 1.1927736625514405, 0.0, 16.012337962962963, 13.120510288065844, 10.430444444444445, 7.977776383442267, 12.251316872427982, 6.388125102880658, 5.563342255420687, 4.096622770919067, 5.938489469862018, 4.676421376811596, 2.607395679012346, 1.2163737156511352, 0.0), # 84
(16.255155961114095, 13.318632461613346, 13.015960791037951, 14.000155998389694, 11.862933376368382, 5.724419549865368, 5.54114150461282, 4.542714525224815, 6.115170705685108, 2.650491944377203, 2.0791856183669055, 1.1898819980907918, 0.0, 15.991002229080934, 13.088701978998708, 10.395928091834525, 7.951475833131607, 12.230341411370215, 6.35980033531474, 5.54114150461282, 4.088871107046691, 5.931466688184191, 4.666718666129899, 2.6031921582075905, 1.210784769237577, 0.0), # 85
(16.21879959444146, 13.256686773751051, 12.994627686328306, 13.970444001610309, 11.84829392936873, 5.713174597368289, 5.518787671996097, 4.522665447340345, 6.104524218869075, 2.64169927324565, 2.0721226219308098, 1.1869502883867193, 0.0, 15.968781721536352, 13.05645317225391, 10.360613109654047, 7.9250978197369495, 12.20904843773815, 6.331731626276483, 5.518787671996097, 4.080838998120206, 5.924146964684365, 4.656814667203437, 2.5989255372656612, 1.2051533430682777, 0.0), # 86
(16.18111948470209, 13.194304181600955, 12.972984567901234, 13.940153260869565, 11.833086419753089, 5.7015643347050755, 5.496289241622575, 4.5028065843621405, 6.093729218106997, 2.6328809876543215, 2.0649114300903775, 1.1839800736408925, 0.0, 15.945717592592594, 13.023780810049816, 10.324557150451888, 7.898642962962963, 12.187458436213994, 6.303929218106997, 5.496289241622575, 4.072545953360768, 5.9165432098765445, 4.646717753623189, 2.594596913580247, 1.1994821983273598, 0.0), # 87
(16.142188918709373, 13.131515058188414, 12.951036922725194, 13.90930889694042, 11.817336668549451, 5.689616074785349, 5.473654697544313, 4.483145252248133, 6.082795945739979, 2.624037294171441, 2.0575635733553868, 1.1809728940549822, 0.0, 15.921850994513035, 12.990701834604803, 10.287817866776932, 7.8721118825143215, 12.165591891479957, 6.276403353147386, 5.473654697544313, 4.064011481989534, 5.908668334274726, 4.636436298980141, 2.5902073845450393, 1.193774096198947, 0.0), # 88
(16.102081183276677, 13.068349776538785, 12.928790237768634, 13.877936030595814, 11.80107049678582, 5.677357130518723, 5.4508925238133665, 4.463688766956257, 6.07173464410913, 2.6151683993652335, 2.050090582235612, 1.1779302898306583, 0.0, 15.897223079561043, 12.957233188137238, 10.250452911178058, 7.845505198095699, 12.14346928821826, 6.24916427373876, 5.4508925238133665, 4.055255093227659, 5.90053524839291, 4.625978676865272, 2.585758047553727, 1.1880317978671624, 0.0), # 89
(16.06086956521739, 13.004838709677419, 12.906250000000002, 13.846059782608698, 11.784313725490197, 5.664814814814815, 5.428011204481793, 4.444444444444445, 6.060555555555556, 2.606274509803922, 2.04250398724083, 1.1748538011695908, 0.0, 15.871875000000001, 12.923391812865496, 10.212519936204147, 7.818823529411765, 12.121111111111112, 6.222222222222222, 5.428011204481793, 4.046296296296297, 5.892156862745098, 4.615353260869567, 2.5812500000000003, 1.1822580645161291, 0.0), # 90
(16.0186273513449, 12.941012230629672, 12.883421696387746, 13.813705273752014, 11.767092175690575, 5.652016440583244, 5.405019223601649, 4.4254196006706294, 6.049268922420364, 2.597355832055731, 2.0348153188808165, 1.17174496827345, 0.0, 15.845847908093276, 12.889194651007948, 10.174076594404081, 7.792067496167191, 12.098537844840727, 6.195587440938882, 5.405019223601649, 4.037154600416603, 5.883546087845287, 4.604568424584006, 2.5766843392775494, 1.1764556573299705, 0.0), # 91
(15.975427828472597, 12.876900712420905, 12.86031081390032, 13.780897624798712, 11.749431668414964, 5.638989320733629, 5.381925065224994, 4.406621551592746, 6.037884987044658, 2.5884125726888843, 2.027036107665348, 1.1686053313439067, 0.0, 15.819182956104251, 12.85465864478297, 10.135180538326738, 7.765237718066651, 12.075769974089315, 6.169270172229845, 5.381925065224994, 4.027849514809735, 5.874715834207482, 4.593632541599572, 2.5720621627800644, 1.1706273374928098, 0.0), # 92
(15.931344283413848, 12.812534528076466, 12.836922839506174, 13.747661956521743, 11.731358024691357, 5.625760768175583, 5.358737213403881, 4.388057613168725, 6.026413991769548, 2.5794449382716054, 2.0191778841042, 1.1654364305826295, 0.0, 15.791921296296294, 12.819800736408922, 10.095889420521, 7.738334814814815, 12.052827983539096, 6.143280658436215, 5.358737213403881, 4.018400548696845, 5.865679012345678, 4.582553985507248, 2.567384567901235, 1.1647758661887697, 0.0), # 93
(15.886450002982048, 12.74794405062171, 12.813263260173755, 13.714023389694043, 11.712897065547754, 5.612358095818728, 5.335464152190369, 4.369735101356501, 6.014866178936138, 2.5704531353721194, 2.01125217870715, 1.16223980619129, 0.0, 15.764104080932785, 12.784637868104188, 10.056260893535747, 7.711359406116356, 12.029732357872277, 6.117629141899102, 5.335464152190369, 4.008827211299091, 5.856448532773877, 4.571341129898015, 2.5626526520347515, 1.1589040046019738, 0.0), # 94
(15.840818273990577, 12.683159653081995, 12.789337562871514, 13.680007045088567, 11.694074612012159, 5.598808616572678, 5.312114365636515, 4.351661332114007, 6.003251790885536, 2.561437370558649, 2.0032705219839726, 1.1590169983715575, 0.0, 15.735772462277092, 12.749186982087132, 10.016352609919863, 7.684312111675945, 12.006503581771073, 6.09232586495961, 5.312114365636515, 3.999149011837627, 5.847037306006079, 4.560002348362857, 2.5578675125743033, 1.1530145139165453, 0.0), # 95
(15.79452238325282, 12.61821170848268, 12.765151234567902, 13.645638043478261, 11.674916485112563, 5.585139643347051, 5.288696337794377, 4.333843621399177, 5.991581069958848, 2.55239785039942, 1.9952444444444448, 1.1557695473251033, 0.0, 15.706967592592594, 12.713465020576134, 9.976222222222225, 7.657193551198258, 11.983162139917695, 6.067381069958849, 5.288696337794377, 3.9893854595336076, 5.8374582425562815, 4.5485460144927545, 2.553030246913581, 1.1471101553166074, 0.0), # 96
(15.747635617582157, 12.553130589849111, 12.740709762231369, 13.61094150563607, 11.655448505876976, 5.571378489051465, 5.265218552716011, 4.316289285169945, 5.979864258497181, 2.5433347814626543, 1.9871854765983423, 1.152498993253596, 0.0, 15.677730624142663, 12.677488925789556, 9.93592738299171, 7.630004344387961, 11.959728516994362, 6.042804999237923, 5.265218552716011, 3.9795560636081895, 5.827724252938488, 4.536980501878691, 2.5481419524462736, 1.141193689986283, 0.0), # 97
(15.700231263791975, 12.487946670206647, 12.71601863283036, 13.575942552334945, 11.635696495333388, 5.557552466595541, 5.241689494453475, 4.299005639384241, 5.968111598841639, 2.5342483703165772, 1.9791051489554419, 1.1492068763587067, 0.0, 15.648102709190674, 12.64127563994577, 9.89552574477721, 7.60274511094973, 11.936223197683278, 6.018607895137937, 5.241689494453475, 3.969680333282529, 5.817848247666694, 4.525314184111649, 2.5432037265660723, 1.1352678791096953, 0.0), # 98
(15.652382608695653, 12.422690322580646, 12.691083333333335, 13.540666304347827, 11.615686274509805, 5.543688888888889, 5.218117647058825, 4.282000000000001, 5.956333333333333, 2.5251388235294123, 1.9710149920255189, 1.1458947368421055, 0.0, 15.618125000000001, 12.604842105263158, 9.855074960127594, 7.575416470588236, 11.912666666666667, 5.9948000000000015, 5.218117647058825, 3.9597777777777776, 5.807843137254903, 4.51355543478261, 2.5382166666666675, 1.129335483870968, 0.0), # 99
(15.60416293910658, 12.357391919996457, 12.665909350708734, 13.505137882447666, 11.595443664434223, 5.529815068841132, 5.194511494584116, 4.265279682975157, 5.944539704313367, 2.516006347669384, 1.9629265363183495, 1.1425641149054624, 0.0, 15.58783864883402, 12.568205263960085, 9.814632681591746, 7.54801904300815, 11.889079408626735, 5.97139155616522, 5.194511494584116, 3.9498679063150943, 5.797721832217111, 4.501712627482556, 2.533181870141747, 1.1233992654542237, 0.0), # 100
(15.555645541838135, 12.292081835479447, 12.640502171925013, 13.469382407407409, 11.574994486134646, 5.515958319361886, 5.17087952108141, 4.248852004267642, 5.932740954122847, 2.506851149304716, 1.9548513123437101, 1.1392165507504473, 0.0, 15.557284807956103, 12.531382058254918, 9.77425656171855, 7.520553447914146, 11.865481908245695, 5.948392805974699, 5.17087952108141, 3.9399702281156324, 5.787497243067323, 4.48979413580247, 2.528100434385003, 1.1174619850435863, 0.0), # 101
(15.506903703703706, 12.22679044205496, 12.614867283950618, 13.433425000000002, 11.554364560639069, 5.5021459533607695, 5.1472302106027605, 4.2327242798353915, 5.920947325102881, 2.497673435003632, 1.9468008506113774, 1.135853584578731, 0.0, 15.526504629629631, 12.49438943036604, 9.734004253056886, 7.493020305010894, 11.841894650205761, 5.925813991769548, 5.1472302106027605, 3.93010425240055, 5.7771822803195345, 4.477808333333335, 2.522973456790124, 1.1115264038231782, 0.0), # 102
(15.458010711516671, 12.161548112748353, 12.589010173754001, 13.397290780998391, 11.533579708975497, 5.488405283747397, 5.123572047200224, 4.2169038256363365, 5.909169059594573, 2.4884734113343563, 1.9387866816311266, 1.132476756591983, 0.0, 15.495539266117968, 12.457244322511812, 9.693933408155633, 7.4654202340030675, 11.818338119189146, 5.903665355890872, 5.123572047200224, 3.920289488390998, 5.766789854487748, 4.465763593666131, 2.5178020347508006, 1.1055952829771232, 0.0), # 103
(15.409039852090416, 12.096385220584981, 12.562936328303612, 13.361004871175524, 11.512665752171923, 5.474763623431389, 5.099913514925861, 4.201397957628411, 5.897416399939034, 2.479251284865113, 1.9308203359127338, 1.129087606991874, 0.0, 15.464429869684501, 12.419963676910612, 9.654101679563668, 7.437753854595337, 11.794832799878067, 5.881957140679775, 5.099913514925861, 3.9105454453081343, 5.756332876085962, 4.4536682903918425, 2.5125872656607227, 1.099671383689544, 0.0), # 104
(15.360064412238325, 12.031332138590201, 12.536651234567902, 13.324592391304346, 11.491648511256354, 5.461248285322361, 5.076263097831727, 4.186213991769549, 5.885699588477366, 2.470007262164126, 1.922913343965976, 1.125687675980074, 0.0, 15.433217592592593, 12.382564435780811, 9.61456671982988, 7.410021786492376, 11.771399176954732, 5.860699588477368, 5.076263097831727, 3.9008916323731144, 5.745824255628177, 4.44153079710145, 2.5073302469135803, 1.093757467144564, 0.0), # 105
(15.311157678773782, 11.96641923978937, 12.510160379515318, 13.28807846215781, 11.470553807256785, 5.44788658232993, 5.052629279969876, 4.1713592440176805, 5.8740288675506775, 2.4607415497996183, 1.9150772363006283, 1.1222785037582528, 0.0, 15.401943587105624, 12.345063541340778, 9.575386181503141, 7.382224649398854, 11.748057735101355, 5.839902941624753, 5.052629279969876, 3.8913475588070923, 5.735276903628392, 4.429359487385938, 2.5020320759030636, 1.0878562945263066, 0.0), # 106
(15.26239293851017, 11.901676897207842, 12.483469250114315, 13.251488204508856, 11.449407461201215, 5.434705827363715, 5.0290205453923695, 4.156841030330743, 5.862414479500076, 2.451454354339816, 1.9073235434264675, 1.1188616305280807, 0.0, 15.370649005486968, 12.307477935808887, 9.536617717132337, 7.354363063019447, 11.724828959000153, 5.819577442463041, 5.0290205453923695, 3.8819327338312255, 5.724703730600607, 4.417162734836286, 2.496693850022863, 1.081970627018895, 0.0), # 107
(15.21384347826087, 11.83713548387097, 12.456583333333336, 13.214846739130437, 11.428235294117645, 5.421733333333335, 5.0054453781512604, 4.142666666666667, 5.850866666666667, 2.442145882352942, 1.8996637958532698, 1.1154385964912283, 0.0, 15.339375000000002, 12.26982456140351, 9.498318979266347, 7.326437647058825, 11.701733333333333, 5.799733333333334, 5.0054453781512604, 3.8726666666666674, 5.714117647058822, 4.40494891304348, 2.4913166666666675, 1.076103225806452, 0.0), # 108
(15.16558258483927, 11.772825372804107, 12.429508116140834, 13.17817918679549, 11.40706312703408, 5.408996413148403, 4.98191226229861, 4.128843468983388, 5.839395671391555, 2.4328163404072196, 1.8921095240908108, 1.112010941849365, 0.0, 15.308162722908094, 12.232120360343014, 9.460547620454054, 7.298449021221657, 11.67879134278311, 5.780380856576743, 4.98191226229861, 3.8635688665345733, 5.70353156351704, 4.392726395598498, 2.485901623228167, 1.0702568520731008, 0.0), # 109
(15.117683545058746, 11.708776937032614, 12.402249085505263, 13.141510668276972, 11.385916780978512, 5.396522379718539, 4.9584296818864715, 4.1153787532388355, 5.828011736015851, 2.423465935070874, 1.8846722586488671, 1.108580206804162, 0.0, 15.277053326474624, 12.194382274845779, 9.423361293244335, 7.27039780521262, 11.656023472031702, 5.76153025453437, 4.9584296818864715, 3.8546588426560997, 5.692958390489256, 4.380503556092325, 2.4804498171010527, 1.0644342670029652, 0.0), # 110
(15.07021964573269, 11.64502054958184, 12.374811728395064, 13.104866304347826, 11.36482207697894, 5.384338545953361, 4.935006120966905, 4.102279835390947, 5.816725102880659, 2.4140948729121283, 1.8773635300372145, 1.1051479315572885, 0.0, 15.246087962962964, 12.156627247130173, 9.386817650186073, 7.242284618736384, 11.633450205761317, 5.743191769547326, 4.935006120966905, 3.845956104252401, 5.68241103848947, 4.368288768115943, 2.474962345679013, 1.0586382317801675, 0.0), # 111
(15.02326417367448, 11.581586583477144, 12.347201531778696, 13.068271215781, 11.34380483606337, 5.372472224762486, 4.911650063591967, 4.089554031397653, 5.805546014327083, 2.404703360499207, 1.8701948687656293, 1.101715656310415, 0.0, 15.215307784636488, 12.118872219414563, 9.350974343828147, 7.214110081497619, 11.611092028654166, 5.725375643956714, 4.911650063591967, 3.837480160544633, 5.671902418031685, 4.356090405260334, 2.469440306355739, 1.0528715075888313, 0.0), # 112
(14.976806757924871, 11.51861130755273, 12.319490437669426, 13.031800658990448, 11.322854058851952, 5.3609451179335466, 4.888420770925416, 4.077235045853738, 5.794513499337931, 2.3953218946450923, 1.8631797083074313, 1.098292391533924, 0.0, 15.184710241349155, 12.081216306873161, 9.315898541537155, 7.185965683935276, 11.589026998675863, 5.708129064195233, 4.888420770925416, 3.829246512809676, 5.661427029425976, 4.343933552996817, 2.4638980875338854, 1.0471464825047938, 0.0), # 113
(14.930369436640104, 11.456715869170786, 12.292060900028826, 12.995747305532802, 11.301752911537415, 5.349730967961242, 4.865614566728464, 4.065474173003413, 5.783796819046966, 2.3861260671651134, 1.8563318232301862, 1.094921622948397, 0.0, 15.154040662656056, 12.044137852432362, 9.28165911615093, 7.1583782014953385, 11.567593638093932, 5.691663842204779, 4.865614566728464, 3.821236405686601, 5.6508764557687075, 4.331915768510935, 2.4584121800057654, 1.0415196244700715, 0.0), # 114
(14.883815844806392, 11.395922558068468, 12.264929243609757, 12.960101406218136, 11.280434856414509, 5.338800611665514, 4.84324772015325, 4.054268436185806, 5.773399988623354, 2.3771301311952313, 1.8496412030472253, 1.091605011007847, 0.0, 15.123210610656603, 12.007655121086316, 9.248206015236125, 7.131390393585693, 11.546799977246708, 5.675975810660129, 4.84324772015325, 3.8134290083325095, 5.640217428207254, 4.320033802072713, 2.452985848721952, 1.0359929598244064, 0.0), # 115
(14.837087797180216, 11.336142812561162, 12.238042919978499, 12.924799380319683, 11.25886776147603, 5.328128285467958, 4.821283854022315, 4.043586875265996, 5.763296714254843, 2.3683173433798195, 1.8430949150057288, 1.0883364263316462, 0.0, 15.092171615609425, 11.971700689648106, 9.215474575028642, 7.104952030139457, 11.526593428509686, 5.661021625372395, 4.821283854022315, 3.8058059181913984, 5.629433880738015, 4.308266460106562, 2.4476085839957, 1.0305584375055605, 0.0), # 116
(14.790127108518035, 11.277288070964257, 12.211349380701316, 12.88977764711069, 11.237019494714783, 5.317688225790165, 4.799686591158202, 4.033398530109057, 5.753460702129175, 2.359670960363252, 1.8366800263528757, 1.085109739539167, 0.0, 15.06087520777316, 11.936207134930834, 9.183400131764378, 7.079012881089755, 11.50692140425835, 5.6467579421526795, 4.799686591158202, 3.7983487327072605, 5.6185097473573915, 4.296592549036898, 2.4422698761402635, 1.0252080064512963, 0.0), # 117
(14.742875593576338, 11.21926977159314, 12.18479607734449, 12.854972625864399, 11.214857924123566, 5.3074546690537305, 4.7784195543834524, 4.023672440580065, 5.743865658434098, 2.351174238789904, 1.8303836043358468, 1.0819188212497801, 0.0, 15.02927291740644, 11.901107033747579, 9.151918021679233, 7.053522716369711, 11.487731316868196, 5.633141416812091, 4.7784195543834524, 3.791039049324093, 5.607428962061783, 4.284990875288134, 2.436959215468898, 1.0199336155993766, 0.0), # 118
(14.695275067111588, 11.161999352763203, 12.158330461474298, 12.820320735854047, 11.192350917695169, 5.297401851680244, 4.757446366520605, 4.014377646544097, 5.734485289357356, 2.3428104353041492, 1.824192716201821, 1.0787575420828581, 0.0, 14.997316274767892, 11.866332962911438, 9.120963581009105, 7.028431305912447, 11.468970578714712, 5.620128705161736, 4.757446366520605, 3.7838584654858884, 5.5961754588475845, 4.273440245284683, 2.43166609229486, 1.014727213887564, 0.0), # 119
(14.647267343880259, 11.105388252789831, 12.131899984657018, 12.785758396352872, 11.169466343422396, 5.287504010091301, 4.736730650392203, 4.005483187866229, 5.7252933010866975, 2.3345628065503625, 1.818094429197978, 1.0756197726577732, 0.0, 14.964956810116156, 11.831817499235502, 9.090472145989889, 7.003688419651086, 11.450586602173395, 5.60767646301272, 4.736730650392203, 3.7767885786366437, 5.584733171711198, 4.2619194654509585, 2.4263799969314035, 1.0095807502536214, 0.0), # 120
(14.59879423863883, 11.049347909988416, 12.105452098458917, 12.751222026634121, 11.146172069298046, 5.277735380708496, 4.716236028820784, 3.9969581044115383, 5.716263399809866, 2.326414609172919, 1.812075810571498, 1.0724993835938965, 0.0, 14.932146053709857, 11.797493219532859, 9.060379052857488, 6.979243827518756, 11.432526799619732, 5.595741346176154, 4.716236028820784, 3.769810986220354, 5.573086034649023, 4.250407342211375, 2.4210904196917835, 1.0044861736353108, 0.0), # 121
(14.549797566143766, 10.993789762674343, 12.078934254446281, 12.716648045971027, 11.122435963314915, 5.268070199953418, 4.695926124628894, 3.9887714360450994, 5.707369291714607, 2.3183490998161913, 1.8061239275695606, 1.0693902455106004, 0.0, 14.898835535807633, 11.763292700616601, 9.030619637847803, 6.955047299448573, 11.414738583429214, 5.584280010463139, 4.695926124628894, 3.762907285681013, 5.561217981657458, 4.238882681990344, 2.4157868508892566, 0.9994354329703949, 0.0), # 122
(14.50021914115155, 10.938625249163001, 12.052293904185383, 12.681972873636834, 11.098225893465804, 5.258482704247664, 4.675764560639071, 3.9808922226319887, 5.698584682988669, 2.3103495351245553, 1.8002258474393456, 1.0662862290272563, 0.0, 14.864976786668116, 11.729148519299818, 9.001129237196727, 6.931048605373665, 11.397169365977337, 5.573249111684785, 4.675764560639071, 3.7560590744626166, 5.549112946732902, 4.227324291212279, 2.4104587808370765, 0.9944204771966367, 0.0), # 123
(14.450000778418648, 10.883765807769782, 12.025478499242494, 12.647132928904785, 11.073509727743506, 5.248947130012824, 4.655714959673856, 3.9732895040372846, 5.689883279819794, 2.302399171742385, 1.794368637428032, 1.063181204763237, 0.0, 14.830521336549939, 11.694993252395603, 8.971843187140161, 6.907197515227153, 11.379766559639588, 5.562605305652198, 4.655714959673856, 3.74924795000916, 5.536754863871753, 4.215710976301596, 2.405095699848499, 0.9894332552517985, 0.0), # 124
(14.399084292701534, 10.82912287681007, 11.9984354911839, 12.612064631048113, 11.048255334140823, 5.239437713670492, 4.635740944555791, 3.965932320126061, 5.68123878839573, 2.294481266314054, 1.7885393647828007, 1.0600690433379134, 0.0, 14.795420715711726, 11.660759476717045, 8.942696823914003, 6.883443798942161, 11.36247757679146, 5.552305248176485, 4.635740944555791, 3.7424555097646373, 5.524127667070411, 4.204021543682705, 2.39968709823678, 0.9844657160736429, 0.0), # 125
(14.347411498756685, 10.774607894599258, 11.971112331575865, 12.576704399340066, 11.022430580650552, 5.229928691642264, 4.615806138107416, 3.958789710763395, 5.6726249149042225, 2.2865790754839375, 1.7827250967508306, 1.0569436153706582, 0.0, 14.759626454412127, 11.626379769077237, 8.913625483754151, 6.859737226451811, 11.345249829808445, 5.542305595068753, 4.615806138107416, 3.735663351173045, 5.511215290325276, 4.192234799780023, 2.394222466315173, 0.9795098085999328, 0.0), # 126
(14.294924211340579, 10.720132299452729, 11.943456471984673, 12.54098865305388, 10.996003335265492, 5.220394300349728, 4.595874163151275, 3.951830715814364, 5.664015365533016, 2.27867585589641, 1.7769129005793014, 1.0537987914808424, 0.0, 14.723090082909758, 11.591786706289264, 8.884564502896506, 6.836027567689229, 11.328030731066033, 5.53256300214011, 4.595874163151275, 3.728853071678377, 5.498001667632746, 4.1803295510179606, 2.388691294396935, 0.97455748176843, 0.0), # 127
(14.241564245209673, 10.665607529685879, 11.915415363976601, 12.504853811462798, 10.968941465978443, 5.210808776214481, 4.575908642509906, 3.9450243751440417, 5.655383846469858, 2.2707548641958457, 1.7710898435153934, 1.0506284422878387, 0.0, 14.68576313146326, 11.556912865166222, 8.855449217576966, 6.812264592587535, 11.310767692939717, 5.523034125201659, 4.575908642509906, 3.722006268724629, 5.484470732989221, 4.168284603820934, 2.3830830727953205, 0.9696006845168982, 0.0), # 128
(14.187273415120451, 10.610945023614088, 11.886936459117921, 12.468236293840059, 10.9412128407822, 5.201146355658116, 4.555873199005851, 3.938339728617507, 5.646704063902494, 2.2627993570266187, 1.765242992806286, 1.0474264384110183, 0.0, 14.647597130331262, 11.5216908225212, 8.82621496403143, 6.788398071079855, 11.293408127804987, 5.51367562006451, 4.555873199005851, 3.7151045397557967, 5.4706064203911, 4.156078764613354, 2.377387291823584, 0.9646313657830989, 0.0), # 129
(14.131993535829388, 10.556056219552751, 11.857967208974907, 12.431072519458905, 10.91278532766956, 5.191381275102222, 4.53573145546165, 3.9317458160998338, 5.637949724018666, 2.2547925910331035, 1.7593594156991588, 1.044186650469754, 0.0, 14.608543609772397, 11.48605315516729, 8.796797078495793, 6.764377773099309, 11.275899448037332, 5.504444142539767, 4.53573145546165, 3.7081294822158726, 5.45639266383478, 4.1436908398196355, 2.3715934417949813, 0.9596414745047956, 0.0), # 130
(14.07566642209295, 10.500852555817252, 11.828455065113841, 12.393298907592571, 10.883626794633326, 5.181487770968396, 4.515447034699847, 3.9252116774560997, 5.629094533006126, 2.2467178228596745, 1.7534261794411918, 1.0409029490834167, 0.0, 14.568554100045299, 11.449932439917582, 8.767130897205957, 6.740153468579022, 11.258189066012251, 5.49529634843854, 4.515447034699847, 3.701062693548854, 5.441813397316663, 4.131099635864191, 2.3656910130227686, 0.9546229596197504, 0.0), # 131
(14.018233888667616, 10.445245470722984, 11.798347479100995, 12.354851877514303, 10.853705109666297, 5.171440079678229, 4.49498355954298, 3.918706352551382, 5.620112197052615, 2.238558309150706, 1.7474303512795641, 1.0375692048713792, 0.0, 14.527580131408602, 11.413261253585167, 8.73715175639782, 6.715674927452117, 11.24022439410523, 5.486188893571935, 4.49498355954298, 3.693885771198735, 5.4268525548331485, 4.1182839591714355, 2.3596694958201994, 0.949567770065726, 0.0), # 132
(13.959637750309861, 10.38914640258533, 11.767591902502646, 12.315667848497343, 10.822988140761264, 5.161212437653315, 4.474304652813592, 3.9121988812507547, 5.61097642234588, 2.2302973065505736, 1.7413589984614566, 1.0341792884530125, 0.0, 14.485573234120938, 11.375972172983136, 8.706794992307282, 6.690891919651719, 11.22195284469176, 5.477078433751057, 4.474304652813592, 3.686580312609511, 5.411494070380632, 4.105222616165782, 2.3535183805005295, 0.9444678547804848, 0.0), # 133
(13.899819821776152, 10.332466789719687, 11.736135786885072, 12.275683239814924, 10.791443755911033, 5.150779081315248, 4.453373937334223, 3.9056583034192958, 5.601660915073669, 2.2219180717036497, 1.7351991882340478, 1.030727070447689, 0.0, 14.442484938440934, 11.337997774924577, 8.675995941170239, 6.6657542151109475, 11.203321830147338, 5.467921624787015, 4.453373937334223, 3.6791279152251772, 5.395721877955516, 4.091894413271643, 2.3472271573770147, 0.9393151627017899, 0.0), # 134
(13.838721917822966, 10.275118070441435, 11.703926583814546, 12.234834470740296, 10.759039823108395, 5.14011424708562, 4.432155035927415, 3.8990536589220803, 5.592139381423722, 2.213403861254311, 1.7289379878445184, 1.0272064214747805, 0.0, 14.398266774627231, 11.299270636222584, 8.64468993922259, 6.640211583762932, 11.184278762847445, 5.458675122490913, 4.432155035927415, 3.671510176489728, 5.379519911554198, 4.0782781569134325, 2.340785316762909, 0.9341016427674034, 0.0), # 135
(13.776285853206776, 10.217011683065968, 11.670911744857346, 12.193057960546685, 10.725744210346152, 5.129192171386024, 4.410611571415708, 3.892353987624185, 5.5823855275837895, 2.2047379318469296, 1.7225624645400475, 1.0236112121536591, 0.0, 14.352870272938459, 11.259723333690248, 8.612812322700236, 6.614213795540787, 11.164771055167579, 5.44929558267386, 4.410611571415708, 3.6637086938471604, 5.362872105173076, 4.064352653515563, 2.3341823489714693, 0.9288192439150881, 0.0), # 136
(13.712453442684055, 10.15805906590867, 11.63703872157975, 12.15029012850735, 10.691524785617101, 5.117987090638052, 4.388707166621645, 3.885528329390686, 5.572373059741617, 2.1959035401258813, 1.716059685567815, 1.0199353131036961, 0.0, 14.306246963633242, 11.219288444140656, 8.580298427839075, 6.587710620377642, 11.144746119483234, 5.439739661146961, 4.388707166621645, 3.6557050647414657, 5.345762392808551, 4.050096709502451, 2.3274077443159498, 0.9234599150826065, 0.0), # 137
(13.647166501011277, 10.098171657284933, 11.602254965548024, 12.106467393895517, 10.656349416914047, 5.106473241263299, 4.366405444367763, 3.8785457240866603, 5.56207568408495, 2.1868839427355393, 1.7094167181750008, 1.016172594944264, 0.0, 14.258348376970226, 11.1778985443869, 8.547083590875005, 6.560651828206616, 11.1241513681699, 5.4299640137213245, 4.366405444367763, 3.6474808866166426, 5.3281747084570235, 4.035489131298506, 2.320450993109605, 0.9180156052077213, 0.0), # 138
(13.58036684294491, 10.037260895510144, 11.566507928328454, 12.061526175984431, 10.620185972229777, 5.094624859683358, 4.343670027476608, 3.8713752115771833, 5.551467106801532, 2.1776623963202795, 1.7026206296087845, 1.0123169282947344, 0.0, 14.20912604320803, 11.135486211242075, 8.513103148043921, 6.532987188960837, 11.102934213603064, 5.419925296208056, 4.343670027476608, 3.6390177569166844, 5.3100929861148884, 4.020508725328145, 2.313301585665691, 0.912478263228195, 0.0), # 139
(13.511996283241437, 9.97523821889969, 11.529745061487317, 12.015402894047334, 10.583002319557098, 5.082416182319821, 4.320464538770717, 3.863985831727331, 5.54052103407911, 2.168222157524475, 1.6956584871163454, 1.008362183774479, 0.0, 14.158531492605304, 11.091984021519266, 8.478292435581725, 6.504666472573423, 11.08104206815822, 5.409580164418264, 4.320464538770717, 3.6302972730855863, 5.291501159778549, 4.005134298015779, 2.3059490122974635, 0.9068398380817901, 0.0), # 140
(13.44199663665733, 9.912015065768964, 11.491913816590882, 11.968033967357464, 10.544766326888803, 5.069821445594281, 4.296752601072636, 3.8563466244021805, 5.529211172105429, 2.158546482992501, 1.688517357944864, 1.00430223200287, 0.0, 14.106516255420662, 11.047324552031569, 8.442586789724318, 6.4756394489775015, 11.058422344210857, 5.398885274163053, 4.296752601072636, 3.6213010325673434, 5.272383163444402, 3.989344655785822, 2.2983827633181764, 0.9010922787062696, 0.0), # 141
(13.37030971794905, 9.84750287443335, 11.452961645205429, 11.919355815188066, 10.505445862217693, 5.056814885928333, 4.272497837204901, 3.848426629466808, 5.517511227068235, 2.1486186293687317, 1.6811843093415195, 1.0001309435992793, 0.0, 14.053031861912746, 11.001440379592072, 8.405921546707596, 6.445855888106194, 11.03502245413647, 5.3877972812535315, 4.272497837204901, 3.612010632805952, 5.252722931108846, 3.973118605062689, 2.2905923290410857, 0.8952275340393956, 0.0), # 142
(13.29687734187308, 9.781613083208239, 11.412835998897235, 11.86930485681237, 10.465008793536564, 5.043370739743566, 4.247663869990055, 3.840194886786288, 5.505394905155279, 2.1384218532975416, 1.6736464085534917, 0.9958421891830788, 0.0, 13.998029842340188, 10.954264081013864, 8.368232042767458, 6.415265559892624, 11.010789810310557, 5.376272841500803, 4.247663869990055, 3.6024076712454045, 5.232504396768282, 3.956434952270791, 2.282567199779447, 0.8892375530189309, 0.0), # 143
(13.221641323185896, 9.714257130409019, 11.37148432923257, 11.817817511503629, 10.423422988838217, 5.029463243461577, 4.222214322250639, 3.8316204362256996, 5.492835912554298, 2.1279394114233043, 1.6658907228279605, 0.99142983937364, 0.0, 13.941461726961624, 10.905728233110038, 8.329453614139801, 6.383818234269912, 10.985671825108597, 5.364268610715979, 4.222214322250639, 3.592473745329698, 5.2117114944191085, 3.9392725038345437, 2.2742968658465146, 0.8831142845826383, 0.0), # 144
(13.144543476643964, 9.64534645435108, 11.328854087777719, 11.764830198535075, 10.380656316115449, 5.015066633503958, 4.196112816809195, 3.8226723176501176, 5.479807955453042, 2.1171545603903956, 1.6579043194121055, 0.9868877647903354, 0.0, 13.88327904603568, 10.855765412693687, 8.289521597060528, 6.351463681171186, 10.959615910906084, 5.351741244710165, 4.196112816809195, 3.582190452502827, 5.190328158057724, 3.921610066178359, 2.265770817555544, 0.8768496776682801, 0.0), # 145
(13.065525617003761, 9.574792493349808, 11.284892726098956, 11.710279337179951, 10.33667664336106, 5.000155146292303, 4.169322976488264, 3.813319570924618, 5.4662847400392565, 2.1060505568431886, 1.6496742655531065, 0.9822098360525362, 0.0, 13.82343332982099, 10.804308196577896, 8.248371327765533, 6.318151670529565, 10.932569480078513, 5.338647399294466, 4.169322976488264, 3.5715393902087875, 5.16833832168053, 3.903426445726651, 2.2569785452197917, 0.870435681213619, 0.0), # 146
(12.98452955902176, 9.502506685720592, 11.239547695762546, 11.654101346711496, 10.291451838567841, 4.984703018248201, 4.141808424110385, 3.803531235914277, 5.4522399725006885, 2.094610657426059, 1.6411876284981433, 0.9773899237796149, 0.0, 13.761876108576189, 10.751289161575762, 8.205938142490716, 6.2838319722781755, 10.904479945001377, 5.324943730279988, 4.141808424110385, 3.5605021558915717, 5.145725919283921, 3.884700448903833, 2.2479095391525097, 0.8638642441564175, 0.0), # 147
(12.901497117454435, 9.428400469778822, 11.192766448334778, 11.596232646402957, 10.2449497697286, 4.968684485793251, 4.113532782498101, 3.7932763524841717, 5.437647359025082, 2.082818118783379, 1.6324314754943956, 0.9724218985909429, 0.0, 13.698558912559907, 10.69664088450037, 8.162157377471978, 6.248454356350136, 10.875294718050164, 5.310586893477841, 4.113532782498101, 3.5490603469951787, 5.1224748848643, 3.8654108821343196, 2.2385532896669558, 0.8571273154344385, 0.0), # 148
(12.81637010705826, 9.352385283839885, 11.144496435381926, 11.536609655527563, 10.197138304836129, 4.9520737853490395, 4.084459674473953, 3.7825239604993777, 5.42248060580018, 2.0706561975595257, 1.6233928737890426, 0.9672996311058923, 0.0, 13.63343327203078, 10.640295942164814, 8.116964368945213, 6.211968592678575, 10.84496121160036, 5.295533544699129, 4.084459674473953, 3.5371955609635997, 5.098569152418064, 3.845536551842522, 2.2288992870763855, 0.8502168439854443, 0.0), # 149
(12.729090342589704, 9.274372566219169, 11.09468510847026, 11.475168793358566, 10.147985311883227, 4.934845153337166, 4.054552722860481, 3.771243099824971, 5.406713419013735, 2.058108150398871, 1.614058890629265, 0.9620169919438353, 0.0, 13.566450717247434, 10.582186911382186, 8.070294453146325, 6.174324451196611, 10.81342683802747, 5.27974033975496, 4.054552722860481, 3.524889395240833, 5.0739926559416135, 3.825056264452856, 2.2189370216940523, 0.8431247787471974, 0.0), # 150
(12.63959963880524, 9.194273755232066, 11.043279919166057, 11.411846479169196, 10.097458658862696, 4.916972826179219, 4.023775550480226, 3.759402810326029, 5.390319504853488, 2.0451572339457917, 1.6044165932622414, 0.956567851724143, 0.0, 13.49756277846851, 10.522246368965572, 8.022082966311206, 6.135471701837374, 10.780639009706976, 5.263163934456441, 4.023775550480226, 3.5121234472708704, 5.048729329431348, 3.8039488263897328, 2.2086559838332116, 0.8358430686574607, 0.0), # 151
(12.54783981046135, 9.11200028919396, 10.990228319035603, 11.346579132232703, 10.045526213767326, 4.898431040296793, 3.992091780155732, 3.7469721318676275, 5.373272569507184, 2.0317867048446603, 1.5944530489351527, 0.950946081066188, 0.0, 13.426720985952636, 10.460406891728066, 7.9722652446757625, 6.09536011453398, 10.746545139014367, 5.245760984614678, 3.992091780155732, 3.4988793144977093, 5.022763106883663, 3.7821930440775686, 2.198045663807121, 0.8283636626539964, 0.0), # 152
(12.453752672314497, 9.027463606420243, 10.935477759645158, 11.27930317182232, 9.992155844589925, 4.8791940321114815, 3.9594650347095355, 3.7339201043148416, 5.355546319162572, 2.017979819739852, 1.5841553248951779, 0.945145550589342, 0.0, 13.353876869958444, 10.39660105648276, 7.920776624475889, 6.053939459219555, 10.711092638325145, 5.227488146040779, 3.9594650347095355, 3.485138594365344, 4.996077922294963, 3.759767723940774, 2.187095551929032, 0.8206785096745677, 0.0), # 153
(12.357280039121166, 8.940575145226303, 10.878975692561012, 11.209955017211293, 9.937315419323285, 4.859236038044878, 3.9258589369641825, 3.7202157675327485, 5.337114460007395, 2.0037198352757417, 1.5735104883894968, 0.9391601309129768, 0.0, 13.278981960744572, 10.330761440042743, 7.867552441947483, 6.011159505827224, 10.67422892001479, 5.208302074545848, 3.9258589369641825, 3.4708828843177697, 4.968657709661643, 3.736651672403765, 2.1757951385122025, 0.8127795586569367, 0.0), # 154
(12.258363725637818, 8.851246343927524, 10.820669569349436, 11.138471087672855, 9.880972805960209, 4.838531294518574, 3.891237109742209, 3.705828161386424, 5.317950698229401, 1.9889900080967022, 1.562505606665289, 0.9329836926564644, 0.0, 13.201987788569642, 10.262820619221108, 7.812528033326444, 5.966970024290106, 10.635901396458802, 5.188159425940994, 3.891237109742209, 3.456093781798981, 4.940486402980104, 3.712823695890952, 2.1641339138698874, 0.804658758538866, 0.0), # 155
(12.15694554662093, 8.759388640839303, 10.760506841576703, 11.06478780248025, 9.823095872493491, 4.817054037954164, 3.85556317586616, 3.690726325740946, 5.298028740016334, 1.9737735948471096, 1.5511277469697347, 0.9266101064391765, 0.0, 13.122845883692296, 10.19271117083094, 7.755638734848673, 5.921320784541328, 10.596057480032668, 5.167016856037325, 3.85556317586616, 3.440752884252974, 4.911547936246746, 3.688262600826751, 2.1521013683153405, 0.7963080582581185, 0.0), # 156
(12.05296731682698, 8.664913474277022, 10.698434960809092, 10.988841580906726, 9.76365248691593, 4.79477850477324, 3.8188007581585754, 3.6748793004613884, 5.27732229155594, 1.958053852171337, 1.5393639765500133, 0.9200332428804852, 0.0, 13.041507776371162, 10.120365671685335, 7.696819882750066, 5.87416155651401, 10.55464458311188, 5.1448310206459436, 3.8188007581585754, 3.4248417891237426, 4.881826243457965, 3.662947193635576, 2.1396869921618182, 0.7877194067524566, 0.0), # 157
(11.943489514248384, 8.56599791046598, 10.631455938536474, 10.907723497981493, 9.699926512929064, 4.7702895112293024, 3.780085376742286, 3.6571979682329148, 5.254219782186185, 1.9413463665164579, 1.5268255340103847, 0.9130132752259121, 0.0, 12.954377375064553, 10.043146027485031, 7.634127670051924, 5.824039099549372, 10.50843956437237, 5.120077155526081, 3.780085376742286, 3.407349650878073, 4.849963256464532, 3.6359078326604983, 2.126291187707295, 0.7787270827696345, 0.0), # 158
(11.811658827165445, 8.452495802079234, 10.542317091203984, 10.804772590546145, 9.61620406376707, 4.7354436714732975, 3.734570210708573, 3.6314756885095885, 5.21942787265181, 1.9209123976394986, 1.5113111828317318, 0.9041816698244146, 0.0, 12.840684235072311, 9.94599836806856, 7.556555914158659, 5.762737192918495, 10.43885574530362, 5.084065963913424, 3.734570210708573, 3.3824597653380692, 4.808102031883535, 3.6015908635153826, 2.108463418240797, 0.7684087092799304, 0.0), # 159
(11.655795351846896, 8.323475201859713, 10.429227943941186, 10.678293012490633, 9.51084814010325, 4.689385209644506, 3.6817949987070273, 3.5970661263515646, 5.171960121188613, 1.896482260745158, 1.4926025356292107, 0.893400259851713, 0.0, 12.69827297422973, 9.827402858368842, 7.463012678146054, 5.689446782235472, 10.343920242377227, 5.0358925768921905, 3.6817949987070273, 3.3495608640317895, 4.755424070051625, 3.559431004163545, 2.0858455887882372, 0.7566795638054286, 0.0), # 160
(11.477155287337537, 8.179777273184687, 10.293395962547079, 10.529487004508074, 9.38495266590092, 4.632672092132293, 3.622145156805501, 3.5544003554065204, 5.112442542399476, 1.8682632772683756, 1.4708644412265888, 0.8807689958543429, 0.0, 12.528598471710556, 9.68845895439777, 7.354322206132943, 5.6047898318051255, 10.224885084798952, 4.976160497569129, 3.622145156805501, 3.3090514943802094, 4.69247633295046, 3.509829001502692, 2.058679192509416, 0.7436161157440625, 0.0), # 161
(11.27699483268217, 8.022243179431417, 10.136028612820661, 10.359556807291593, 9.239611565123418, 4.565862285326026, 3.5560061010718473, 3.503909449322135, 5.041501150887273, 1.836462768644093, 1.4462617484476323, 0.8663878283788393, 0.0, 12.333115606688533, 9.530266112167231, 7.231308742238162, 5.509388305932278, 10.083002301774545, 4.9054732290509895, 3.5560061010718473, 3.261330203804304, 4.619805782561709, 3.4531856024305316, 2.0272057225641325, 0.7292948344937653, 0.0), # 162
(11.056570186925597, 7.851714083977169, 9.958333360560937, 10.169704661534322, 9.075918761734068, 4.489513755615068, 3.4837632475739206, 3.4460244817460834, 4.959761961254883, 1.8012880563072504, 1.418959306116109, 0.8503567079717379, 0.0, 12.113279258337407, 9.353923787689116, 7.0947965305805445, 5.40386416892175, 9.919523922509766, 4.824434274444517, 3.4837632475739206, 3.2067955397250487, 4.537959380867034, 3.3899015538447745, 1.9916666721121876, 0.71379218945247, 0.0), # 163
(10.817137549112616, 7.669031150199204, 9.761517671566903, 9.961132807929381, 8.894968179696201, 4.404184469388787, 3.405802012379573, 3.3811765263260463, 4.867850988105186, 1.762946461692788, 1.3891219630557858, 0.8327755851795738, 0.0, 11.870544305830926, 9.160531436975312, 6.945609815278928, 5.288839385078362, 9.735701976210372, 4.733647136856465, 3.405802012379573, 3.1458460495634197, 4.447484089848101, 3.320377602643128, 1.9523035343133808, 0.6971846500181095, 0.0), # 164
(10.559953118288028, 7.475035541474793, 9.546789011637559, 9.735043487169904, 8.697853742973145, 4.310432393036548, 3.3225078115566578, 3.3097966567096977, 4.766394246041056, 1.7216453062356458, 1.35691456809043, 0.8137444105488828, 0.0, 11.606365628342832, 8.951188516037709, 6.7845728404521495, 5.164935918706936, 9.532788492082112, 4.633715319393577, 3.3225078115566578, 3.078880280740391, 4.348926871486572, 3.245014495723302, 1.909357802327512, 0.6795486855886177, 0.0), # 165
(10.286273093496636, 7.270568421181199, 9.315354846571905, 9.492638939949002, 8.485669375528229, 4.208815492947715, 3.234266061173029, 3.2323159465447184, 4.656017749665372, 1.6775919113707654, 1.322501970043808, 0.7933631346262003, 0.0, 11.322198105046873, 8.726994480888202, 6.612509850219039, 5.0327757341122945, 9.312035499330744, 4.525242325162606, 3.234266061173029, 3.0062967806769394, 4.242834687764114, 3.1642129799830014, 1.8630709693143812, 0.6609607655619273, 0.0), # 166
(9.997353673783238, 7.056470952695688, 9.06842264216894, 9.235121406959813, 8.259509001324778, 4.099891735511655, 3.14146217729654, 3.1491654694787847, 4.537347513581013, 1.6309935985330861, 1.2860490177396875, 0.7717317079580612, 0.0, 11.019496615116793, 8.489048787538673, 6.430245088698436, 4.892980795599257, 9.074695027162026, 4.408831657270299, 3.14146217729654, 2.928494096794039, 4.129754500662389, 3.0783738023199385, 1.8136845284337881, 0.6414973593359717, 0.0), # 167
(9.694451058192634, 6.833584299395522, 8.807199864227664, 8.963693128895455, 8.020466544326124, 3.9842190871177325, 3.0444815759950434, 3.0607762991595733, 4.411009552390856, 1.5820576891575493, 1.247720560001835, 0.7489500810910016, 0.0, 10.69971603772634, 8.238450892001017, 6.2386028000091756, 4.746173067472647, 8.822019104781711, 4.285086818823403, 3.0444815759950434, 2.8458707765126663, 4.010233272163062, 2.987897709631819, 1.7614399728455332, 0.6212349363086839, 0.0), # 168
(9.378821445769624, 6.602749624657969, 8.53289397854708, 8.67955634644906, 7.769635928495594, 3.8623555141553156, 2.9437096733363934, 2.9675795092347634, 4.277629880697781, 1.5309915046790952, 1.2076814456540184, 0.7251182045715564, 0.0, 10.364311252049257, 7.976300250287119, 6.038407228270092, 4.592974514037284, 8.555259761395561, 4.154611312928669, 2.9437096733363934, 2.7588253672537966, 3.884817964247797, 2.8931854488163538, 1.706578795709416, 0.6002499658779973, 0.0), # 169
(9.051721035559014, 6.3648080918602945, 8.24671245092618, 8.383913300313743, 7.508111077796515, 3.7348589830137664, 2.8395318853884426, 2.870006173352032, 4.137834513104661, 1.4780023665326634, 1.1660965235200045, 0.7003360289462612, 0.0, 10.014737137259289, 7.7036963184088725, 5.830482617600023, 4.43400709959799, 8.275669026209322, 4.018008642692845, 2.8395318853884426, 2.6677564164384044, 3.7540555388982577, 2.7946377667712485, 1.649342490185236, 0.5786189174418451, 0.0), # 170
(8.7144060266056, 6.12060086437976, 7.949862747163971, 8.077966231182643, 7.23698591619222, 3.602287460082452, 2.7323336282190445, 2.7684873651590554, 3.992249464214377, 1.4232975961531957, 1.1231306424235596, 0.6747035047616515, 0.0, 9.652448572530185, 7.421738552378166, 5.615653212117798, 4.269892788459586, 7.984498928428754, 3.8758823112226777, 2.7323336282190445, 2.5730624714874657, 3.61849295809611, 2.692655410394215, 1.5899725494327943, 0.5564182603981601, 0.0), # 171
(8.368132617954185, 5.870969105593635, 7.643552333059449, 7.762917379748876, 6.9573543676460305, 3.4651989117507385, 2.6225003178960526, 2.663454158303514, 3.8415007486298056, 1.3670845149756323, 1.0789486511884518, 0.648320582564263, 0.0, 9.278900437035686, 7.1315264082068905, 5.3947432559422595, 4.101253544926896, 7.683001497259611, 3.7288358216249198, 2.6225003178960526, 2.475142079821956, 3.4786771838230153, 2.587639126582959, 1.52871046661189, 0.5337244641448761, 0.0), # 172
(8.014157008649567, 5.616753978879182, 7.328988674411616, 7.439968986705571, 6.6703103561212815, 3.3241513044079904, 2.51041737048732, 2.5553376264330825, 3.6862143809538255, 1.309570444434913, 1.0337153986384477, 0.62128721290063, 0.0, 8.89554760994954, 6.83415934190693, 5.168576993192238, 3.9287113333047383, 7.372428761907651, 3.5774726770063157, 2.51041737048732, 2.37439378886285, 3.3351551780606408, 2.479989662235191, 1.4657977348823235, 0.5106139980799257, 0.0), # 173
(7.6537353977365505, 5.358796647613667, 7.00737923701947, 7.110323292745849, 6.376947805581297, 3.179702604443573, 2.3964702020607005, 2.4445688431954404, 3.527016375789314, 1.250962705965979, 0.9875957335973142, 0.5937033463172892, 0.0, 8.503844970445494, 6.53073680949018, 4.93797866798657, 3.7528881178979363, 7.054032751578628, 3.4223963804736166, 2.3964702020607005, 2.2712161460311235, 3.1884739027906486, 2.370107764248617, 1.401475847403894, 0.4871633316012425, 0.0), # 174
(7.288123984259929, 5.097938275174352, 6.679931486682011, 6.7751825385628415, 6.078360639989406, 3.0324107782468537, 2.2810442286840464, 2.331578882238264, 3.36453274773915, 1.19146862100377, 0.9407545048888186, 0.5656689333607753, 0.0, 8.105247397697292, 6.222358266968527, 4.703772524444093, 3.574405863011309, 6.7290654954783, 3.26421043513357, 2.2810442286840464, 2.1660076987477526, 3.039180319994703, 2.2583941795209475, 1.3359862973364023, 0.46344893410675936, 0.0), # 175
(6.91857896726451, 4.835020024938507, 6.347852889198238, 6.435748964849671, 5.775642783308939, 2.882833792207196, 2.164524866425212, 2.216798817209233, 3.199389511406209, 1.131295510983227, 0.8933565613367281, 0.537283924577624, 0.0, 7.701209770878679, 5.910123170353863, 4.46678280668364, 3.39388653294968, 6.398779022812418, 3.103518344092926, 2.164524866425212, 2.0591669944337117, 2.8878213916544695, 2.1452496549498905, 1.2695705778396478, 0.4395472749944098, 0.0), # 176
(6.546356545795092, 4.570883060283395, 6.012350910367152, 6.093224812299459, 5.469888159503225, 2.731529612713966, 2.0472975313520503, 2.100659721756022, 3.0322126813933705, 1.07065069733929, 0.8455667517648098, 0.5086482705143706, 0.0, 7.2931869691634, 5.595130975658075, 4.227833758824048, 3.211952092017869, 6.064425362786741, 2.9409236104584306, 2.0472975313520503, 1.9510925805099755, 2.7349440797516125, 2.0310749374331536, 1.2024701820734305, 0.4155348236621269, 0.0), # 177
(6.172712918896475, 4.306368544586282, 5.6746330159877525, 5.74881232160534, 5.162190692535588, 2.5790562061565305, 1.929747639532414, 1.9835926695263104, 2.863628272303512, 1.0097415015069002, 0.7975499249968301, 0.4798619217175504, 0.0, 6.882633871725203, 5.278481138893053, 3.98774962498415, 3.0292245045207, 5.727256544607024, 2.7770297373368344, 1.929747639532414, 1.8421830043975218, 2.581095346267794, 1.916270773868447, 1.1349266031975505, 0.3914880495078438, 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
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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), # 125
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 132
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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), # 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
8, # 1
)
| 279.167914
| 491
| 0.771958
| 32,987
| 261,022
| 6.108073
| 0.230788
| 0.35377
| 0.339476
| 0.643218
| 0.365607
| 0.360058
| 0.359428
| 0.359189
| 0.359189
| 0.359189
| 0
| 0.851537
| 0.094758
| 261,022
| 934
| 492
| 279.466809
| 0.001181
| 0.015367
| 0
| 0.200873
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.005459
| 0
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| null | 1
| 1
| 1
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| 0
| 1
| 1
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| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
15d735edd72925c795af24fd466ad3d8a82af601
| 69
|
py
|
Python
|
backend/recipes/permissions.py
|
drodrz/Brewable
|
3d2de013a4b3e0da01b07b2859c0f855bdf83d84
|
[
"MIT"
] | 1
|
2019-03-26T19:44:07.000Z
|
2019-03-26T19:44:07.000Z
|
backend/recipes/permissions.py
|
drodrz/Brewabl
|
3d2de013a4b3e0da01b07b2859c0f855bdf83d84
|
[
"MIT"
] | null | null | null |
backend/recipes/permissions.py
|
drodrz/Brewabl
|
3d2de013a4b3e0da01b07b2859c0f855bdf83d84
|
[
"MIT"
] | null | null | null |
from rest_framework import permissions
#TODO: Introduce permissions
| 17.25
| 38
| 0.855072
| 8
| 69
| 7.25
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115942
| 69
| 4
| 39
| 17.25
| 0.95082
| 0.391304
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 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
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c61b5c20cc5009941414c5446f4f6167d49b240a
| 81
|
py
|
Python
|
sdc_api_py/_types/__init__.py
|
SadBoy228/sdc-api.py
|
fb00b3f10d9bb88e46a4c48d5737da70bc9b7143
|
[
"MIT"
] | 4
|
2021-05-20T07:14:30.000Z
|
2021-12-17T10:37:26.000Z
|
sdc_api_py/_types/__init__.py
|
SadBoy228/sdc-api.py
|
fb00b3f10d9bb88e46a4c48d5737da70bc9b7143
|
[
"MIT"
] | 1
|
2021-05-21T12:39:09.000Z
|
2021-05-21T12:39:09.000Z
|
sdc_api_py/_types/__init__.py
|
SadBoy228/sdc-api.py
|
fb00b3f10d9bb88e46a4c48d5737da70bc9b7143
|
[
"MIT"
] | 4
|
2021-05-18T19:35:47.000Z
|
2021-07-14T12:59:27.000Z
|
from .Guild import *
from .Raw import *
from .User import *
from .Warns import *
| 16.2
| 20
| 0.703704
| 12
| 81
| 4.75
| 0.5
| 0.526316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.197531
| 81
| 4
| 21
| 20.25
| 0.876923
| 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
|
c646f58836ec410e338ae3cdd0ef9d4a4b215fc5
| 122
|
py
|
Python
|
kinoml/datasets/kinomescan/core.py
|
t-kimber/kinoml
|
dc28fdbd416d1a53bb33131d6c6fcc05914b15cc
|
[
"MIT"
] | 36
|
2019-08-26T03:44:50.000Z
|
2022-03-17T12:58:11.000Z
|
kinoml/datasets/kinomescan/core.py
|
t-kimber/kinoml
|
dc28fdbd416d1a53bb33131d6c6fcc05914b15cc
|
[
"MIT"
] | 92
|
2019-08-28T11:53:28.000Z
|
2022-03-30T10:15:44.000Z
|
kinoml/datasets/kinomescan/core.py
|
t-kimber/kinoml
|
dc28fdbd416d1a53bb33131d6c6fcc05914b15cc
|
[
"MIT"
] | 18
|
2019-08-24T03:22:28.000Z
|
2021-08-12T12:37:47.000Z
|
from ..core import ProteinLigandDatasetProvider
class KinomeScanDatasetProvider(ProteinLigandDatasetProvider):
pass
| 20.333333
| 62
| 0.852459
| 8
| 122
| 13
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.106557
| 122
| 5
| 63
| 24.4
| 0.954128
| 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
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 6
|
c658122de2bf7fa060e81faed6c255c5c32c5a0f
| 136
|
py
|
Python
|
trainers/__init__.py
|
Frognar/Super-Resolution
|
406b909d71e156aa11ee589698744e3ad9abfee7
|
[
"MIT"
] | 1
|
2020-11-13T12:04:38.000Z
|
2020-11-13T12:04:38.000Z
|
trainers/__init__.py
|
Frognar/Super-Resolution
|
406b909d71e156aa11ee589698744e3ad9abfee7
|
[
"MIT"
] | null | null | null |
trainers/__init__.py
|
Frognar/Super-Resolution
|
406b909d71e156aa11ee589698744e3ad9abfee7
|
[
"MIT"
] | null | null | null |
from trainers.gan_trainer import GANTrainer
from trainers.net_trainer import NetTrainer
from trainers.regan_trainer import ReGANTrainer
| 34
| 47
| 0.889706
| 18
| 136
| 6.555556
| 0.555556
| 0.305085
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088235
| 136
| 3
| 48
| 45.333333
| 0.951613
| 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
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d69b821155504657db7a66a24d8a1eaf5bff68b6
| 2,318
|
py
|
Python
|
run_downloaded.py
|
tobinjo96/CPFcluster
|
af7c794c8527f543250b571d6aa03b7df9a58519
|
[
"MIT"
] | 2
|
2021-01-06T08:24:41.000Z
|
2021-11-02T02:02:30.000Z
|
run_downloaded.py
|
tobinjo96/CPFcluster
|
af7c794c8527f543250b571d6aa03b7df9a58519
|
[
"MIT"
] | 1
|
2021-08-14T12:43:00.000Z
|
2021-08-15T09:03:37.000Z
|
run_downloaded.py
|
tobinjo96/CPFcluster
|
af7c794c8527f543250b571d6aa03b7df9a58519
|
[
"MIT"
] | null | null | null |
import numpy as np
from CPFcluster import CPFcluster
import warnings
from sklearn import cluster, datasets, metrics
import csv
Data = np.load("Dermatology.npy")
X = Data[:, range(X.shape[1] - 1)]
y = Data[:, X.shape[1] - 1]
# normalize dataset for easier parameter selection
model = CPFcluster(k = 13, rho = 0.3, alpha = 1)
model.fit(X)
ami = metrics.adjusted_mutual_info_score(y.astype(int), model.memberships.astype(int))
#ARI
ari = metrics.adjusted_rand_score(y.astype(int), model.memberships.astype(int))
with open("CPF_Results.csv", 'a') as fd:
writer = csv.writer(fd)
writer.writerow(["DCF", "Dermatology", k, rho, len(np.unique(model.memberships)), ari,ami])
Data = np.load("Ecoli.npy")
X = Data[:, range(X.shape[1] - 1)]
y = Data[:, X.shape[1] - 1]
# normalize dataset for easier parameter selection
model = CPFcluster(k = 13, rho = 0.6, alpha = 1)
model.fit(X)
ami = metrics.adjusted_mutual_info_score(y.astype(int), model.memberships.astype(int))
#ARI
ari = metrics.adjusted_rand_score(y.astype(int), model.memberships.astype(int))
with open("CPF_Results.csv", 'a') as fd:
writer = csv.writer(fd)
writer.writerow(["DCF", "Ecoli", k, rho, len(np.unique(model.memberships)), ari,ami])
Data = np.load("Glass.npy")
X = Data[:, range(X.shape[1] - 1)]
y = Data[:, X.shape[1] - 1]
# normalize dataset for easier parameter selection
model = CPFcluster(k = 13, rho = 0.5, alpha = 1)
model.fit(X)
ami = metrics.adjusted_mutual_info_score(y.astype(int), model.memberships.astype(int))
#ARI
ari = metrics.adjusted_rand_score(y.astype(int), model.memberships.astype(int))
with open("CPF_Results.csv", 'a') as fd:
writer = csv.writer(fd)
writer.writerow(["DCF", "Glass", k, rho, len(np.unique(model.memberships)), ari,ami])
Data = np.load("Letter-Recognition.npy")
X = Data[:, range(X.shape[1] - 1)]
y = Data[:, X.shape[1] - 1]
# normalize dataset for easier parameter selection
model = CPFcluster(k = 25, rho = 0.9, alpha = 1)
model.fit(X)
ami = metrics.adjusted_mutual_info_score(y.astype(int), model.memberships.astype(int))
#ARI
ari = metrics.adjusted_rand_score(y.astype(int), model.memberships.astype(int))
with open("CPF_Results.csv", 'a') as fd:
writer = csv.writer(fd)
writer.writerow(["DCF", "Letter-Recognition", k, rho, len(np.unique(model.memberships)), ari,ami])
| 39.288136
| 103
| 0.701898
| 368
| 2,318
| 4.355978
| 0.173913
| 0.089832
| 0.034935
| 0.039925
| 0.873362
| 0.873362
| 0.873362
| 0.873362
| 0.873362
| 0.850281
| 0
| 0.01776
| 0.125539
| 2,318
| 58
| 104
| 39.965517
| 0.773064
| 0.089301
| 0
| 0.622222
| 0
| 0
| 0.080875
| 0.010466
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.111111
| 0
| 0.111111
| 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
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
ba3b278498afd28dcfd71c07b6f8d8a2f84b4fc7
| 62
|
py
|
Python
|
src/test_main.py
|
javimontiel98/P7JenkinsEj2
|
75acb318b7d686c1129d7df910f1ba10b51ccfb2
|
[
"MIT"
] | null | null | null |
src/test_main.py
|
javimontiel98/P7JenkinsEj2
|
75acb318b7d686c1129d7df910f1ba10b51ccfb2
|
[
"MIT"
] | null | null | null |
src/test_main.py
|
javimontiel98/P7JenkinsEj2
|
75acb318b7d686c1129d7df910f1ba10b51ccfb2
|
[
"MIT"
] | null | null | null |
from main import *
def test_suma():
assert suma(3,2) == 5
| 15.5
| 25
| 0.629032
| 11
| 62
| 3.454545
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0625
| 0.225806
| 62
| 3
| 26
| 20.666667
| 0.729167
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 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
|
baacc987d8c271b12d2ebb49db5ef09b4640b6e9
| 94
|
py
|
Python
|
gym_simplifiedtetris/__init__.py
|
OliverOverend/gym-simplifiedtetristemp
|
832a0e99b52ec0c13bad1badc0dc1ba6453a6981
|
[
"MIT"
] | 3
|
2021-10-04T19:38:14.000Z
|
2022-03-15T09:15:09.000Z
|
gym_simplifiedtetris/__init__.py
|
OliverOverend/gym-simplifiedtetristemp
|
832a0e99b52ec0c13bad1badc0dc1ba6453a6981
|
[
"MIT"
] | 2
|
2021-10-05T18:19:29.000Z
|
2021-10-05T18:29:37.000Z
|
gym_simplifiedtetris/__init__.py
|
OliverOverend/gym-simplifiedtetristemp
|
832a0e99b52ec0c13bad1badc0dc1ba6453a6981
|
[
"MIT"
] | 3
|
2021-11-19T20:50:07.000Z
|
2022-03-24T16:37:37.000Z
|
"""Import the envs module so that the envs register themselves in Gym."""
from .envs import *
| 31.333333
| 73
| 0.734043
| 15
| 94
| 4.6
| 0.733333
| 0.202899
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170213
| 94
| 2
| 74
| 47
| 0.884615
| 0.712766
| 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
|
bab99c4e42d07684794e579757bb403281bd1816
| 7,380
|
py
|
Python
|
VAPr/tests/test_filtering.py
|
ucsd-ccbb/VAPr
|
69b001e894bfc6a19077976ed3cd1dd3c88d21c9
|
[
"MIT"
] | 30
|
2017-01-19T23:16:04.000Z
|
2022-03-07T04:42:50.000Z
|
VAPr/tests/test_filtering.py
|
ucsd-ccbb/VAPr
|
69b001e894bfc6a19077976ed3cd1dd3c88d21c9
|
[
"MIT"
] | 24
|
2017-06-07T23:32:36.000Z
|
2021-06-22T20:31:05.000Z
|
VAPr/tests/test_filtering.py
|
ucsd-ccbb/VAPr
|
69b001e894bfc6a19077976ed3cd1dd3c88d21c9
|
[
"MIT"
] | 3
|
2018-08-07T22:18:09.000Z
|
2021-01-30T19:11:15.000Z
|
# standard libraries
import unittest
# project-specific libraries
import VAPr.filtering as ns_test
class TestFunctions(unittest.TestCase):
def test_get_sample_id_filter(self):
expected_output = {'samples.sample_id': "testname"}
real_output = ns_test.get_sample_id_filter("testname")
self.assertEqual(expected_output, real_output)
def test_get_any_of_sample_ids_filter(self):
expected_output = {'samples.sample_id': {'$in': ["testname1", "testname2"]}}
real_output = ns_test.get_any_of_sample_ids_filter(["testname1", "testname2"])
self.assertEqual(expected_output, real_output)
def test_make_rare_deleterious_variants_filter_w_samples(self):
expected_output = {
"$and":
[
{
"$or":
[
{"cadd.esp.af": {"$lt": 0.051}},
{"cadd.esp.af": {"$exists": False}}
]
},
{
"$or":
[
{"func_knowngene": "exonic"},
{"func_knowngene": "splicing"}
]
},
{"cadd.phred": {"$gte": 10}},
{"exonicfunc_knowngene": {"$ne": "synonymous SNV"}},
{"1000g2015aug_all": {"$lt": 0.051}},
{'samples.sample_id': {"$in":["testname1", "testname2"]}}
]
}
real_output = ns_test.make_rare_deleterious_variants_filter(["testname1", "testname2"])
self.assertEqual(expected_output, real_output)
def test_make_rare_deleterious_variants_filter_wo_samples(self):
expected_output = {
"$and":
[
{
"$or":
[
{"cadd.esp.af": {"$lt": 0.051}},
{"cadd.esp.af": {"$exists": False}}
]
},
{
"$or":
[
{"func_knowngene": "exonic"},
{"func_knowngene": "splicing"}
]
},
{"cadd.phred": {"$gte": 10}},
{"exonicfunc_knowngene": {"$ne": "synonymous SNV"}},
{"1000g2015aug_all": {"$lt": 0.051}}
]
}
real_output = ns_test.make_rare_deleterious_variants_filter()
self.assertEqual(expected_output, real_output)
def test_make_known_disease_variants_filter_w_samples(self):
expected_output = {
"$and":
[
{"$or":
[
{
"$and":
[
{"clinvar.rcv.accession": {"$exists": True}},
{"clinvar.rcv.clinical_significance": {"$nin": ["Benign", "Likely benign"]}}
]
},
{"cosmic.cosmic_id": {"$exists": True}}
]},
{'samples.sample_id': {"$in": ["testname1", "testname2"]}}
]
}
real_output = ns_test.make_known_disease_variants_filter(["testname1", "testname2"])
self.assertEqual(expected_output, real_output)
def test_make_known_disease_variants_filter_wo_samples(self):
expected_output = {
"$or":
[
{
"$and":
[
{"clinvar.rcv.accession": {"$exists": True}},
{"clinvar.rcv.clinical_significance": {"$nin": ["Benign", "Likely benign"]}}
]
},
{"cosmic.cosmic_id": {"$exists": True}}
]
}
real_output = ns_test.make_known_disease_variants_filter()
self.assertEqual(expected_output, real_output)
def test_make_deleterious_compound_heterozygote_variants_filter_w_samples(self):
expected_output = {
"$and":
[
{"genotype_subclass_by_class.heterozygous": "compound"},
{"cadd.phred": {"$gte": 10}},
{'samples.sample_id': {"$in": ["testname1", "testname2"]}}
]
}
real_output = ns_test.make_deleterious_compound_heterozygous_variants_filter(["testname1", "testname2"])
self.assertEqual(expected_output, real_output)
def test_make_deleterious_compound_heterozygote_variants_filter_wo_samples(self):
expected_output = {
"$and":
[
{"genotype_subclass_by_class.heterozygous": "compound"},
{"cadd.phred": {"$gte": 10}}
]
}
real_output = ns_test.make_deleterious_compound_heterozygous_variants_filter()
self.assertEqual(expected_output, real_output)
def test_make_de_novo_variants_filter(self):
expected_output = {
"$and":
[
{'samples.sample_id': "sampleA"},
{
"$and":
[
{'samples.sample_id': {"$ne": "sampleB"}},
{'samples.sample_id': {"$ne": "sampleC"}}
]
}
]
}
real_output = ns_test.make_de_novo_variants_filter("sampleA", "sampleB", "sampleC")
self.assertEqual(expected_output, real_output)
def test__append_sample_id_constraint_if_needed_is_needed(self):
input_list = [
{"genotype_subclass_by_class.heterozygous": "compound"},
{"cadd.phred": {"$gte": 10}}
]
expected_output = {
"$and":
[
{"genotype_subclass_by_class.heterozygous": "compound"},
{"cadd.phred": {"$gte": 10}},
{'samples.sample_id': {"$in": ["testname1", "testname2"]}}
]
}
real_output = ns_test._append_sample_id_constraint_if_needed(input_list, ["testname1", "testname2"])
self.assertDictEqual(expected_output, real_output)
def test__append_sample_id_constraint_if_needed_is_not_needed(self):
input_list = [
{"genotype_subclass_by_class.heterozygous": "compound"},
{"cadd.phred": {"$gte": 10}}
]
expected_output = {
"$and":
[
{"genotype_subclass_by_class.heterozygous": "compound"},
{"cadd.phred": {"$gte": 10}}
]
}
real_output = ns_test._append_sample_id_constraint_if_needed(input_list, None)
self.assertDictEqual(expected_output, real_output)
| 40.549451
| 116
| 0.449187
| 563
| 7,380
| 5.484902
| 0.166963
| 0.099741
| 0.042746
| 0.056995
| 0.910946
| 0.891516
| 0.855894
| 0.815738
| 0.794365
| 0.75421
| 0
| 0.01628
| 0.434011
| 7,380
| 181
| 117
| 40.773481
| 0.723007
| 0.006098
| 0
| 0.439024
| 0
| 0
| 0.183033
| 0.046645
| 0
| 0
| 0
| 0
| 0.067073
| 1
| 0.067073
| false
| 0
| 0.012195
| 0
| 0.085366
| 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
|
bacbedd4fe742dc4391401dc499d0566a70572b4
| 33
|
py
|
Python
|
pyobs/comm/dummy/__init__.py
|
pyobs/pyobs-core
|
e3401e63eb31587c2bc535f7346b7e4ef69d64ab
|
[
"MIT"
] | 4
|
2020-02-14T10:50:03.000Z
|
2022-03-25T04:15:06.000Z
|
pyobs/comm/dummy/__init__.py
|
pyobs/pyobs-core
|
e3401e63eb31587c2bc535f7346b7e4ef69d64ab
|
[
"MIT"
] | 60
|
2020-09-14T09:10:20.000Z
|
2022-03-25T17:51:42.000Z
|
pyobs/comm/dummy/__init__.py
|
pyobs/pyobs-core
|
e3401e63eb31587c2bc535f7346b7e4ef69d64ab
|
[
"MIT"
] | 2
|
2020-10-14T09:34:57.000Z
|
2021-04-27T09:35:57.000Z
|
from .dummycomm import DummyComm
| 16.5
| 32
| 0.848485
| 4
| 33
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.965517
| 0
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| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| true
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| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
79e5f5379c6cba80df2ca60b02f425abe5c175cd
| 89
|
py
|
Python
|
chapter02/first_project1/movie/views.py
|
Tomtao626/django
|
fe945063593b4bfe82d74842f728b854b501a294
|
[
"Apache-2.0"
] | null | null | null |
chapter02/first_project1/movie/views.py
|
Tomtao626/django
|
fe945063593b4bfe82d74842f728b854b501a294
|
[
"Apache-2.0"
] | null | null | null |
chapter02/first_project1/movie/views.py
|
Tomtao626/django
|
fe945063593b4bfe82d74842f728b854b501a294
|
[
"Apache-2.0"
] | null | null | null |
from django.http import HttpResponse
def movie(request):
return HttpResponse("电影首页")
| 22.25
| 36
| 0.775281
| 11
| 89
| 6.272727
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134831
| 89
| 4
| 37
| 22.25
| 0.896104
| 0
| 0
| 0
| 0
| 0
| 0.044444
| 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
|
030b711338a4f7e7ebd54591df76d78f496e9e34
| 336
|
py
|
Python
|
Cracking the Coding Interview/ctci-solutions-master/ch-06-math-and-logic-puzzles/03-dominoes.py
|
nikku1234/Code-Practise
|
94eb6680ea36efd10856c377000219285f77e5a4
|
[
"Apache-2.0"
] | 9
|
2020-07-02T06:06:17.000Z
|
2022-02-26T11:08:09.000Z
|
Cracking the Coding Interview/ctci-solutions-master/ch-06-math-and-logic-puzzles/03-dominoes.py
|
nikku1234/Code-Practise
|
94eb6680ea36efd10856c377000219285f77e5a4
|
[
"Apache-2.0"
] | 1
|
2021-11-04T17:26:36.000Z
|
2021-11-04T17:26:36.000Z
|
Cracking the Coding Interview/ctci-solutions-master/ch-06-math-and-logic-puzzles/03-dominoes.py
|
nikku1234/Code-Practise
|
94eb6680ea36efd10856c377000219285f77e5a4
|
[
"Apache-2.0"
] | 8
|
2021-01-31T10:31:12.000Z
|
2022-03-13T09:15:55.000Z
|
# Can 31 dominoes cover all but two opposite corners of a chess board?
def dominoes():
# Each dominoe must cover one black and one white square.
# But there are different numbers of black and white squares.
# Read up on such parity arguments here:
# http://ihxrelation.blogspot.com/2015/10/tiling-problems.html
return False
| 33.6
| 70
| 0.75
| 53
| 336
| 4.754717
| 0.849057
| 0.063492
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029197
| 0.184524
| 336
| 9
| 71
| 37.333333
| 0.890511
| 0.845238
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0.5
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
0344f6adf82ffbb6ea59852196b9981ecb2c833f
| 271
|
py
|
Python
|
rpython/jit/backend/x86/test/test_rawmem.py
|
nanjekyejoannah/pypy
|
e80079fe13c29eda7b2a6b4cd4557051f975a2d9
|
[
"Apache-2.0",
"OpenSSL"
] | 381
|
2018-08-18T03:37:22.000Z
|
2022-02-06T23:57:36.000Z
|
rpython/jit/backend/x86/test/test_rawmem.py
|
nanjekyejoannah/pypy
|
e80079fe13c29eda7b2a6b4cd4557051f975a2d9
|
[
"Apache-2.0",
"OpenSSL"
] | 16
|
2018-09-22T18:12:47.000Z
|
2022-02-22T20:03:59.000Z
|
rpython/jit/backend/x86/test/test_rawmem.py
|
nanjekyejoannah/pypy
|
e80079fe13c29eda7b2a6b4cd4557051f975a2d9
|
[
"Apache-2.0",
"OpenSSL"
] | 55
|
2015-08-16T02:41:30.000Z
|
2022-03-20T20:33:35.000Z
|
from rpython.jit.backend.x86.test.test_basic import Jit386Mixin
from rpython.jit.metainterp.test.test_rawmem import RawMemTests
class TestRawMem(Jit386Mixin, RawMemTests):
# for the individual tests see
# ====> ../../../metainterp/test/test_rawmem.py
pass
| 27.1
| 63
| 0.749077
| 34
| 271
| 5.882353
| 0.617647
| 0.12
| 0.14
| 0.24
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034043
| 0.132841
| 271
| 9
| 64
| 30.111111
| 0.817021
| 0.273063
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
0351ff4487e06c2d164570159538e9ce55a4aedf
| 119
|
py
|
Python
|
crispy/gui/__init__.py
|
thegreathippo/crispy
|
e648a25ff8ec24a3fac3931ba28660b8e22f3020
|
[
"MIT"
] | null | null | null |
crispy/gui/__init__.py
|
thegreathippo/crispy
|
e648a25ff8ec24a3fac3931ba28660b8e22f3020
|
[
"MIT"
] | null | null | null |
crispy/gui/__init__.py
|
thegreathippo/crispy
|
e648a25ff8ec24a3fac3931ba28660b8e22f3020
|
[
"MIT"
] | null | null | null |
"""
TODO:
* Fix click interface to lock on center of tiles rather than corner of tiles?
"""
from .core import app
| 17
| 81
| 0.689076
| 19
| 119
| 4.315789
| 0.894737
| 0.170732
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.226891
| 119
| 6
| 82
| 19.833333
| 0.891304
| 0.731092
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 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
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0356c79db92f3f5fa4678e5b406019aa80bf534c
| 88
|
py
|
Python
|
FastAPIRedisRQ/app/__init__.py
|
scionoftech/FastAPI-Full-Stack-Samples
|
e7d42661ed59324ff20f419d05c6cd1e7dab7e97
|
[
"MIT"
] | 29
|
2021-03-31T02:42:59.000Z
|
2022-03-12T16:20:05.000Z
|
FastAPIRedisRQ/app/__init__.py
|
scionoftech/FastAPI-Full-Stack-Samples
|
e7d42661ed59324ff20f419d05c6cd1e7dab7e97
|
[
"MIT"
] | null | null | null |
FastAPIRedisRQ/app/__init__.py
|
scionoftech/FastAPI-Full-Stack-Samples
|
e7d42661ed59324ff20f419d05c6cd1e7dab7e97
|
[
"MIT"
] | 4
|
2021-08-21T01:02:00.000Z
|
2022-01-09T15:33:51.000Z
|
from . import conf
from . import controller
from . import routes
from . import util
| 17.6
| 25
| 0.727273
| 12
| 88
| 5.333333
| 0.5
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.227273
| 88
| 4
| 26
| 22
| 0.941176
| 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
|
03698d9b759a77299af39788d6d302cd87e574fa
| 40
|
py
|
Python
|
chrome_trex_gym/envs/__init__.py
|
BadrYoubiIdrissi/chrome-trex-gym
|
236f113ebfe837607291e32ba7a774358971cc4e
|
[
"MIT"
] | 1
|
2020-04-27T11:38:57.000Z
|
2020-04-27T11:38:57.000Z
|
chrome_trex_gym/envs/__init__.py
|
BadrYoubiIdrissi/chrome-trex-gym
|
236f113ebfe837607291e32ba7a774358971cc4e
|
[
"MIT"
] | null | null | null |
chrome_trex_gym/envs/__init__.py
|
BadrYoubiIdrissi/chrome-trex-gym
|
236f113ebfe837607291e32ba7a774358971cc4e
|
[
"MIT"
] | 1
|
2019-04-18T22:44:18.000Z
|
2019-04-18T22:44:18.000Z
|
from .ChromeTrexEnv import ChromeTrexEnv
| 40
| 40
| 0.9
| 4
| 40
| 9
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075
| 40
| 1
| 40
| 40
| 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
|
cee48a8bda94b09d267ae3792d6d37e4ac718efa
| 45
|
py
|
Python
|
src/cbopensource/constant.py
|
tony163163/cb-threatconnect-connector
|
b1d3753d11a4532d9ebb41a693fa5640d5d329c5
|
[
"MIT"
] | 9
|
2015-12-17T19:35:58.000Z
|
2021-08-04T03:51:35.000Z
|
src/cbopensource/constant.py
|
tony163163/cb-threatconnect-connector
|
b1d3753d11a4532d9ebb41a693fa5640d5d329c5
|
[
"MIT"
] | 4
|
2015-11-11T15:13:36.000Z
|
2019-11-06T22:49:28.000Z
|
src/cbopensource/constant.py
|
tony163163/cb-threatconnect-connector
|
b1d3753d11a4532d9ebb41a693fa5640d5d329c5
|
[
"MIT"
] | 9
|
2015-11-10T21:51:10.000Z
|
2021-08-04T03:51:25.000Z
|
KiB = 1024
MiB = 1024 * KiB
GiB = 1024 * MiB
| 11.25
| 16
| 0.6
| 8
| 45
| 3.375
| 0.5
| 0.518519
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.375
| 0.288889
| 45
| 3
| 17
| 15
| 0.46875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
306896e585188544abd6fb560e9e884d2743c0e8
| 237
|
py
|
Python
|
scripts/item/consume_2433251.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 54
|
2019-04-16T23:24:48.000Z
|
2021-12-18T11:41:50.000Z
|
scripts/item/consume_2433251.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 3
|
2019-05-19T15:19:41.000Z
|
2020-04-27T16:29:16.000Z
|
scripts/item/consume_2433251.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 49
|
2020-11-25T23:29:16.000Z
|
2022-03-26T16:20:24.000Z
|
# Created by MechAviv
# Violetta's Charming Damage Skin | (2433251)
if sm.addDamageSkin(2433251):
sm.chat("'Violetta's Charming Damage Skin' Damage Skin has been added to your account's damage skin collection.")
sm.consumeItem()
| 47.4
| 118
| 0.746835
| 34
| 237
| 5.205882
| 0.617647
| 0.225989
| 0.19209
| 0.259887
| 0.305085
| 0
| 0
| 0
| 0
| 0
| 0
| 0.07
| 0.156118
| 237
| 5
| 119
| 47.4
| 0.815
| 0.265823
| 0
| 0
| 0
| 0.333333
| 0.598837
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
067800e121b943e4df819d002cdfade84a57f71e
| 82
|
py
|
Python
|
test/run/t258.py
|
timmartin/skulpt
|
2e3a3fbbaccc12baa29094a717ceec491a8a6750
|
[
"MIT"
] | 2,671
|
2015-01-03T08:23:25.000Z
|
2022-03-31T06:15:48.000Z
|
test/run/t258.py
|
csev/skulpt
|
9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f
|
[
"MIT"
] | 972
|
2015-01-05T08:11:00.000Z
|
2022-03-29T13:47:15.000Z
|
test/run/t258.py
|
csev/skulpt
|
9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f
|
[
"MIT"
] | 845
|
2015-01-03T19:53:36.000Z
|
2022-03-29T18:34:22.000Z
|
print [x for x in range(1,10) if False] or ["hello" for x in range(1,10) if True]
| 41
| 81
| 0.658537
| 20
| 82
| 2.7
| 0.6
| 0.148148
| 0.222222
| 0.407407
| 0.592593
| 0.592593
| 0.592593
| 0
| 0
| 0
| 0
| 0.090909
| 0.195122
| 82
| 1
| 82
| 82
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0.060976
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
ebf2d8be0f70fc2db4d19feb2abadcf3132ab85d
| 19
|
py
|
Python
|
BTS/__init__.py
|
BillyGTCarlyle/BTS
|
67e06af77ed41b9c7aef53f38bf3eedb7865d1af
|
[
"MIT"
] | null | null | null |
BTS/__init__.py
|
BillyGTCarlyle/BTS
|
67e06af77ed41b9c7aef53f38bf3eedb7865d1af
|
[
"MIT"
] | null | null | null |
BTS/__init__.py
|
BillyGTCarlyle/BTS
|
67e06af77ed41b9c7aef53f38bf3eedb7865d1af
|
[
"MIT"
] | null | null | null |
from .BTS import *
| 9.5
| 18
| 0.684211
| 3
| 19
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.210526
| 19
| 1
| 19
| 19
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ebf42677704a78f0cbe6765f91ebe8e8b88990c0
| 12,089
|
py
|
Python
|
compiler/services/filemanager/tests/test_filemanager.py
|
cul-it/arxiv-compiler
|
b4aaca17a08a752d8b5c12224edabd011a8920f7
|
[
"MIT"
] | 5
|
2019-05-26T22:47:35.000Z
|
2021-11-05T12:30:07.000Z
|
compiler/services/filemanager/tests/test_filemanager.py
|
arXiv/arxiv-compiler
|
b4aaca17a08a752d8b5c12224edabd011a8920f7
|
[
"MIT"
] | 16
|
2019-02-12T23:25:04.000Z
|
2021-04-30T15:04:48.000Z
|
compiler/services/filemanager/tests/test_filemanager.py
|
cul-it/arxiv-compiler
|
b4aaca17a08a752d8b5c12224edabd011a8920f7
|
[
"MIT"
] | 3
|
2019-01-10T22:01:50.000Z
|
2020-12-06T16:29:51.000Z
|
"""Tests for :mod:`compiler.services.filemanager`."""
from unittest import TestCase, mock
import json
import os
import requests
from flask import Flask
from arxiv.integration.api import exceptions, status
from .. import FileManager
from .... import domain, util
CONFIG = {
'FILEMANAGER_ENDPOINT': 'http://fooendpoint:1234',
'FILEMANAGER_VERIFY': False
}
mock_app = Flask('foo')
mock_app.config.update(CONFIG)
class TestServiceStatus(TestCase):
"""Test :func:`.FileManager.get_status`."""
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_status(self, mock_Session):
"""Get the status of the file manager service sucessfully."""
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(
return_value=mock.MagicMock(
status_code=status.OK,
json=mock.MagicMock(return_value={'iam': 'ok'})
)
)
)
self.assertEqual(FileManager.get_status(), {'iam': 'ok'},
"Gets the response content from the status enpoint")
class TestGetUploadInfo(TestCase):
""":func:`FileManager.get_upload_info` returns the current ETag."""
def session(self, status_code=status.OK, method="get", json={},
content="", headers={}):
"""Make a mock session."""
return mock.MagicMock(**{
method: mock.MagicMock(
return_value=mock.MagicMock(
status_code=status_code,
json=mock.MagicMock(
return_value=json
),
content=content,
headers=headers
)
)
})
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info(self, mock_Session):
"""Get info for an upload workspace that exists."""
etag = 'asdf12345checksum'
source_id = '123456'
mock_Session.return_value = self.session(headers={'ETag': etag})
info = FileManager.get_upload_info(source_id, 'footoken')
self.assertIsInstance(info, domain.SourcePackageInfo)
self.assertEqual(info.etag, etag)
self.assertEqual(info.source_id, source_id)
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_nonexistant(self, mock_Session):
"""Get info for an upload workspace that does not exist."""
source_id = '123456'
mock_Session.return_value = self.session(status.NOT_FOUND)
with self.assertRaises(exceptions.NotFound):
FileManager.get_upload_info(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_bad_request(self, mock_Session):
"""We made a bad request."""
source_id = '123456'
mock_Session.return_value = self.session(status.BAD_REQUEST)
with self.assertRaises(exceptions.BadRequest):
FileManager.get_upload_info(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_unauthorized(self, mock_Session):
"""We made an unauthorized request."""
source_id = '123456'
mock_Session.return_value = self.session(status.UNAUTHORIZED)
with self.assertRaises(exceptions.RequestUnauthorized):
FileManager.get_upload_info(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_forbidden(self, mock_Session):
"""We made a forbidden request."""
source_id = '123456'
mock_Session.return_value = self.session(status.FORBIDDEN)
with self.assertRaises(exceptions.RequestForbidden):
FileManager.get_upload_info(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_error(self, mock_Session):
"""FM service replied 500 Internal Server Error."""
source_id = '123456'
mock_Session.return_value = self.session(
status.INTERNAL_SERVER_ERROR
)
with self.assertRaises(exceptions.RequestFailed):
FileManager.get_upload_info(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_bad_json(self, mock_Session):
"""FM service reurns bad JSON."""
source_id = '123456'
def raise_JSONDecodeError(*a, **k):
raise json.decoder.JSONDecodeError('nope', 'nope', 0)
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(
return_value=mock.MagicMock(
status_code=status.OK,
json=mock.MagicMock(side_effect=raise_JSONDecodeError)
)
)
)
with self.assertRaises(exceptions.BadResponse):
FileManager.get_upload_info(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_bad_ssl(self, mock_Session):
"""FM service has bad TLS."""
source_id = '123456'
def raise_ssl_error(*a, **k):
raise requests.exceptions.SSLError('danger fill bobinson')
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(side_effect=raise_ssl_error)
)
with self.assertRaises(exceptions.SecurityException):
FileManager.get_upload_info(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_info_no_connection(self, mock_Session):
"""FM service cannot connect."""
source_id = '123456'
def raise_connection_error(*a, **k):
raise requests.exceptions.ConnectionError('where r u')
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(side_effect=raise_connection_error)
)
with self.assertRaises(exceptions.ConnectionFailed):
FileManager.get_upload_info(source_id, 'footoken')
class TestGetUpload(TestCase):
""":func:`FileManager.get_upload` returns the upload content."""
def session(self, status_code=status.OK, method="get", json={},
content="", headers={}):
"""Make a mock session."""
return mock.MagicMock(**{
method: mock.MagicMock(
return_value=mock.MagicMock(
status_code=status_code,
json=mock.MagicMock(
return_value=json
),
content=content,
headers=headers
)
)
})
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload(self, mock_Session):
"""Get upload that exists."""
etag = 'asdf12345checksum'
source_id = '123456'
content = b'foocontent'
mock_iter_content = mock.MagicMock(return_value=[content])
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(
return_value=mock.MagicMock(
status_code=status.OK,
iter_content=mock_iter_content,
headers={'ETag': etag}
)
)
)
info = FileManager.get_source_content(source_id, 'footoken')
self.assertIsInstance(info, domain.SourcePackage)
self.assertEqual(info.etag, etag)
self.assertEqual(info.source_id, source_id)
self.assertIsInstance(info.path, str)
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_with_filename(self, mock_Session):
"""Get upload with an explicit filename in ``content-disposition``."""
etag = 'asdf12345checksum'
source_id = '123456'
content = b'foocontent'
mock_iter_content = mock.MagicMock(return_value=[content])
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(
return_value=mock.MagicMock(
status_code=status.OK,
iter_content=mock_iter_content,
headers={'ETag': etag,
'content-disposition': 'filename=foo.tar.gz'}
)
)
)
info = FileManager.get_source_content(source_id, 'footoken')
self.assertIsInstance(info, domain.SourcePackage)
self.assertEqual(info.etag, etag)
self.assertEqual(info.source_id, source_id)
self.assertIsInstance(info.path, str)
self.assertEqual(info.path, '/tmp/foo.tar.gz')
self.assertTrue(os.path.exists(info.path))
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_with_suspicious_filename(self, mock_Session):
"""Get upload with a suspicious filename in ``content-disposition``."""
etag = 'asdf12345checksum'
source_id = '123456'
content = b'foocontent'
mock_iter_content = mock.MagicMock(return_value=[content])
filename = '../whereDoesThisGetWritten.txt'
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(
return_value=mock.MagicMock(
status_code=status.OK,
iter_content=mock_iter_content,
headers={'ETag': etag,
'content-disposition': f'filename={filename}'}
)
)
)
with self.assertRaises(RuntimeError):
FileManager.get_source_content(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_with_malicious_filename(self, mock_Session):
"""Get upload with a malicious filename in ``content-disposition``."""
etag = 'asdf12345checksum'
source_id = '123456'
content = b'foocontent'
mock_iter_content = mock.MagicMock(return_value=[content])
filename = '//bin/bash'
mock_Session.return_value = mock.MagicMock(
get=mock.MagicMock(
return_value=mock.MagicMock(
status_code=status.OK,
iter_content=mock_iter_content,
headers={'ETag': etag,
'content-disposition': f'filename={filename}'}
)
)
)
with self.assertRaises(RuntimeError):
FileManager.get_source_content(source_id, 'footoken')
@mock.patch('arxiv.integration.api.service.current_app', mock_app)
@mock.patch('arxiv.integration.api.service.requests.Session')
def test_get_upload_nonexistant(self, mock_Session):
"""Get info for an upload workspace that does not exist."""
source_id = '123456'
mock_Session.return_value = self.session(status.NOT_FOUND)
with self.assertRaises(exceptions.NotFound):
FileManager.get_source_content(source_id, 'footoken')
| 41.259386
| 79
| 0.63595
| 1,311
| 12,089
| 5.658276
| 0.116705
| 0.06309
| 0.079401
| 0.101105
| 0.818145
| 0.763953
| 0.747371
| 0.718792
| 0.708816
| 0.70248
| 0
| 0.012958
| 0.253123
| 12,089
| 292
| 80
| 41.400685
| 0.808617
| 0.073455
| 0
| 0.617021
| 0
| 0
| 0.178549
| 0.120324
| 0
| 0
| 0
| 0
| 0.106383
| 1
| 0.085106
| false
| 0
| 0.034043
| 0
| 0.140426
| 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
|
2315e2b5a6866183836a10ab88c483f07834ebd8
| 29
|
py
|
Python
|
hello.py
|
andreeapintoiu/session1
|
c0119d3267617d94b58cd4a71986f86f7e285cd1
|
[
"MIT"
] | null | null | null |
hello.py
|
andreeapintoiu/session1
|
c0119d3267617d94b58cd4a71986f86f7e285cd1
|
[
"MIT"
] | null | null | null |
hello.py
|
andreeapintoiu/session1
|
c0119d3267617d94b58cd4a71986f86f7e285cd1
|
[
"MIT"
] | null | null | null |
print('Hello world hgjhgjd')
| 14.5
| 28
| 0.758621
| 4
| 29
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.103448
| 29
| 1
| 29
| 29
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0.655172
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
23424cd42e7de96fef2dc7a1d2a83ab52b693bf3
| 207
|
py
|
Python
|
05.04.2022/listas/metodosDaLista/metLista.py
|
N0N4T0/python-codes
|
ac2b884f86749a8b179ff972cdb316ec4e005b32
|
[
"MIT"
] | null | null | null |
05.04.2022/listas/metodosDaLista/metLista.py
|
N0N4T0/python-codes
|
ac2b884f86749a8b179ff972cdb316ec4e005b32
|
[
"MIT"
] | null | null | null |
05.04.2022/listas/metodosDaLista/metLista.py
|
N0N4T0/python-codes
|
ac2b884f86749a8b179ff972cdb316ec4e005b32
|
[
"MIT"
] | null | null | null |
# Exibindo tamanho da lista nomes_paises
nomes_paises = ['Brasil', 'Argentina', 'China', 'Canadá', 'Japão']
tamanho_nomes_paises = len(nomes_paises)
print(len(nomes_paises))
print(type(len(nomes_paises)))
| 25.875
| 66
| 0.753623
| 28
| 207
| 5.321429
| 0.5
| 0.442953
| 0.281879
| 0.255034
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096618
| 207
| 7
| 67
| 29.571429
| 0.796791
| 0.183575
| 0
| 0
| 0
| 0
| 0.185629
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 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
|
88cabc8c04003e378a9d33ab1c04ba1205526462
| 562
|
py
|
Python
|
Builder/abstract_objects.py
|
HOWZ1T/learning-design-patterns
|
73a844f9d8ea00bae711fb0d90b93ca652b2b039
|
[
"MIT"
] | 1
|
2018-09-24T12:05:06.000Z
|
2018-09-24T12:05:06.000Z
|
Builder/abstract_objects.py
|
HOWZ1T/learning-design-patterns
|
73a844f9d8ea00bae711fb0d90b93ca652b2b039
|
[
"MIT"
] | null | null | null |
Builder/abstract_objects.py
|
HOWZ1T/learning-design-patterns
|
73a844f9d8ea00bae711fb0d90b93ca652b2b039
|
[
"MIT"
] | null | null | null |
from Builder import packing_objects
from Builder import interfaces
import abc
class Burger(interfaces.Item):
def packing(self):
return packing_objects.Wrapper()
@abc.abstractmethod
def price(self):
raise NotImplementedError("users must implement the method price to use this base class")
class ColdDrink(interfaces.Item):
def packing(self):
return packing_objects.Bottle()
@abc.abstractmethod
def price(self):
raise NotImplementedError("users must implement the method price to use this base class")
| 25.545455
| 97
| 0.729537
| 69
| 562
| 5.898551
| 0.42029
| 0.103194
| 0.083538
| 0.117936
| 0.742015
| 0.742015
| 0.742015
| 0.742015
| 0.506143
| 0.506143
| 0
| 0
| 0.202847
| 562
| 21
| 98
| 26.761905
| 0.908482
| 0
| 0
| 0.533333
| 0
| 0
| 0.213523
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.266667
| false
| 0
| 0.2
| 0.133333
| 0.733333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
0042ca9d8d7e019760d900cf948cd855da37cb8d
| 228
|
py
|
Python
|
Geometry/EcalAlgo/python/EcalGeometry_cfi.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 852
|
2015-01-11T21:03:51.000Z
|
2022-03-25T21:14:00.000Z
|
Geometry/EcalAlgo/python/EcalGeometry_cfi.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 30,371
|
2015-01-02T00:14:40.000Z
|
2022-03-31T23:26:05.000Z
|
Geometry/EcalAlgo/python/EcalGeometry_cfi.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 3,240
|
2015-01-02T05:53:18.000Z
|
2022-03-31T17:24:21.000Z
|
from Geometry.EcalAlgo.EcalEndcapGeometry_cfi import EcalEndcapGeometryEP
from Geometry.EcalAlgo.EcalPreshowerGeometry_cfi import EcalPreshowerGeometryEP
from Geometry.EcalAlgo.EcalBarrelGeometry_cfi import EcalBarrelGeometryEP
| 57
| 79
| 0.921053
| 21
| 228
| 9.857143
| 0.52381
| 0.173913
| 0.289855
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 228
| 3
| 80
| 76
| 0.958333
| 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
| 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
|
004f710668bfc6d6f15c273367df6dcd814481f2
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/pip/_internal/cli/parser.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/pip/_internal/cli/parser.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/pip/_internal/cli/parser.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/1b/1c/1b/0b60ea216c5910e8762985838271da34ade2ed9d8f614e1c201cf6b8d2
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.427083
| 0
| 96
| 1
| 96
| 96
| 0.46875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
cc7a6c1cfcc63d66fa717bffbde8bff33d48c9ef
| 1,570
|
py
|
Python
|
kdeploy/utils/http_util.py
|
kooksee/kdeploy
|
16057de236a866c4888d724677890e8d2e27cafe
|
[
"MIT"
] | 1
|
2017-12-24T07:59:25.000Z
|
2017-12-24T07:59:25.000Z
|
kdeploy/utils/http_util.py
|
kooksee/kdeploy
|
16057de236a866c4888d724677890e8d2e27cafe
|
[
"MIT"
] | null | null | null |
kdeploy/utils/http_util.py
|
kooksee/kdeploy
|
16057de236a866c4888d724677890e8d2e27cafe
|
[
"MIT"
] | null | null | null |
import aiohttp
async def __request_get(url, timeout=1, **kwargs):
async with aiohttp.ClientSession() as session:
try:
with aiohttp.Timeout(timeout):
async with session.get(url, **kwargs) as response:
return 1, await response.text()
except Exception as e:
return 0, str(e)
async def request_get(url, timeout=1, num_retry=3, **kwargs):
i = num_retry
err_msg = []
while i > 0:
st, res = await __request_get(url, timeout=timeout, **kwargs)
if not st:
i -= 1
err_msg.append(res)
continue
return 1, res
else:
return 0, err_msg
async def __request_post(url, data, timeout=1, **kwargs):
async with aiohttp.ClientSession() as session:
try:
with aiohttp.Timeout(timeout):
async with session.post(url, data=data, **kwargs) as response:
return 1, await response.text()
except Exception as e:
return 0, str(e)
async def request_post(url, data, timeout=1, num_retry=3, **kwargs):
i = num_retry
err_msg = []
while i > 0:
st, res = await __request_post(url, data, timeout=timeout, **kwargs)
if not st:
i -= 1
err_msg.append(res)
continue
return 1, res
else:
return 0, err_msg
async def main():
st, ct = await request_get('http://www.baidu.com/')
print(st)
print(ct)
if __name__ == '__main__':
import paco
paco.run(main())
| 24.153846
| 78
| 0.561146
| 203
| 1,570
| 4.17734
| 0.246305
| 0.042453
| 0.070755
| 0.070755
| 0.854953
| 0.841981
| 0.841981
| 0.810142
| 0.775943
| 0.775943
| 0
| 0.017192
| 0.333121
| 1,570
| 64
| 79
| 24.53125
| 0.792741
| 0
| 0
| 0.666667
| 0
| 0
| 0.018471
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.041667
| 0
| 0.208333
| 0.041667
| 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
|
cccfef2d2960486bcfa0ddd9c87db5ab250933f2
| 41
|
py
|
Python
|
diesel/util/patches/__init__.py
|
msabramo/diesel
|
a1ed7ed0358d0fec8592e23aafc3b7ec167ab649
|
[
"BSD-3-Clause"
] | 224
|
2015-01-03T06:33:05.000Z
|
2021-11-22T03:19:02.000Z
|
diesel/util/patches/__init__.py
|
dowski/diesel
|
d9824e467805caf40e0ba21b88a27db38e64c352
|
[
"BSD-3-Clause"
] | 12
|
2015-01-01T03:35:15.000Z
|
2021-05-22T23:37:28.000Z
|
diesel/util/patches/__init__.py
|
dowski/diesel
|
d9824e467805caf40e0ba21b88a27db38e64c352
|
[
"BSD-3-Clause"
] | 37
|
2015-01-04T01:47:55.000Z
|
2022-03-03T02:04:15.000Z
|
from requests_lib import enable_requests
| 20.5
| 40
| 0.902439
| 6
| 41
| 5.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097561
| 41
| 1
| 41
| 41
| 0.945946
| 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
|
aee90c09a9fcdeb6151c4210ed2485231338cff1
| 4,551
|
py
|
Python
|
artoo/artoo_api/migrations/0001_initial.py
|
phillipjhl/ARTOO
|
4fdd365ad71934514f69025c32ba1103d0a1b43b
|
[
"MIT"
] | 1
|
2020-10-08T20:48:44.000Z
|
2020-10-08T20:48:44.000Z
|
artoo/artoo_api/migrations/0001_initial.py
|
phillipjhl/ARTOO
|
4fdd365ad71934514f69025c32ba1103d0a1b43b
|
[
"MIT"
] | 6
|
2021-06-02T03:52:36.000Z
|
2021-12-15T19:01:45.000Z
|
artoo/artoo_api/migrations/0001_initial.py
|
phillipjhl/ARTOO
|
4fdd365ad71934514f69025c32ba1103d0a1b43b
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.1 on 2020-08-19 05:27
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Device',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=60)),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='DeviceType',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=60)),
('model_num', models.CharField(max_length=60, null=True)),
('wifi_enabled', models.BooleanField()),
('z_wave_enabled', models.BooleanField()),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='Location',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('location_name', models.CharField(max_length=50)),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='Sensor',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=60)),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
('device_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='artoo_api.device')),
],
),
migrations.CreateModel(
name='SensorType',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=60)),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='SensorData',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=60)),
('values', models.CharField(max_length=100)),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
('sensor_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='artoo_api.sensor')),
],
),
migrations.AddField(
model_name='sensor',
name='sensor_type_id',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='artoo_api.sensortype'),
),
migrations.CreateModel(
name='Hostname',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('ip_address', models.CharField(max_length=45)),
('hostname', models.CharField(max_length=45, null=True)),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
('device_id', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='artoo_api.device')),
],
),
migrations.AddField(
model_name='device',
name='device_type_id',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='artoo_api.devicetype'),
),
migrations.AddField(
model_name='device',
name='location_id',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='artoo_api.location'),
),
]
| 44.617647
| 120
| 0.575478
| 457
| 4,551
| 5.522976
| 0.164114
| 0.044374
| 0.116482
| 0.138669
| 0.787242
| 0.745246
| 0.719889
| 0.719889
| 0.719889
| 0.719889
| 0
| 0.010769
| 0.285871
| 4,551
| 101
| 121
| 45.059406
| 0.765846
| 0.009448
| 0
| 0.659574
| 1
| 0
| 0.112739
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.021277
| 0
| 0.06383
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
aefad90e5357eaaca960396847405794a759bf20
| 101
|
py
|
Python
|
pyhive/__init__.py
|
songyanho/PyHive
|
1f39ab88d1a1de97e9d98b96894d0c59db34d6a5
|
[
"Apache-2.0"
] | 1
|
2018-04-02T18:40:24.000Z
|
2018-04-02T18:40:24.000Z
|
pyhive/__init__.py
|
songyanho/PyHive
|
1f39ab88d1a1de97e9d98b96894d0c59db34d6a5
|
[
"Apache-2.0"
] | 1
|
2017-01-04T21:36:42.000Z
|
2017-01-04T21:36:42.000Z
|
pyhive/__init__.py
|
songyanho/PyHive
|
1f39ab88d1a1de97e9d98b96894d0c59db34d6a5
|
[
"Apache-2.0"
] | 3
|
2018-11-11T00:35:17.000Z
|
2020-12-04T17:52:37.000Z
|
from __future__ import absolute_import
from __future__ import unicode_literals
__version__ = '0.2.1'
| 25.25
| 39
| 0.841584
| 14
| 101
| 5.071429
| 0.714286
| 0.28169
| 0.450704
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.033333
| 0.108911
| 101
| 3
| 40
| 33.666667
| 0.755556
| 0
| 0
| 0
| 0
| 0
| 0.049505
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9db0fa8f054473a0aee584aba3fba0c02975acf9
| 29
|
py
|
Python
|
cyclegan/metrics/__init__.py
|
narumiruna/cyclegan-pytorch
|
11e28b7d9681e9cd40ecf9ee6d0fc93076d69365
|
[
"MIT"
] | 1
|
2020-03-19T07:38:42.000Z
|
2020-03-19T07:38:42.000Z
|
cyclegan/metrics/__init__.py
|
narumiruna/cyclegan-pytorch
|
11e28b7d9681e9cd40ecf9ee6d0fc93076d69365
|
[
"MIT"
] | null | null | null |
cyclegan/metrics/__init__.py
|
narumiruna/cyclegan-pytorch
|
11e28b7d9681e9cd40ecf9ee6d0fc93076d69365
|
[
"MIT"
] | null | null | null |
from .average import Average
| 14.5
| 28
| 0.827586
| 4
| 29
| 6
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 1
| 29
| 29
| 0.96
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9dc9277bfc1b98766dd9c672ca7b56b8aee8cf6f
| 30
|
py
|
Python
|
bayesian_sensor/__init__.py
|
lwestenberg/hass-bayesian-sensor-notebook
|
4f9150c5aec2c7657b44d497881e279f4f24e88d
|
[
"MIT"
] | 3
|
2019-09-08T17:17:21.000Z
|
2021-06-30T13:41:16.000Z
|
bayesian_sensor/__init__.py
|
westenberg/hass-bayesian-sensor-notebook
|
4f9150c5aec2c7657b44d497881e279f4f24e88d
|
[
"MIT"
] | 1
|
2021-11-19T17:31:11.000Z
|
2021-12-01T09:01:53.000Z
|
bayesian_sensor/__init__.py
|
westenberg/hass-bayesian-sensor-notebook
|
4f9150c5aec2c7657b44d497881e279f4f24e88d
|
[
"MIT"
] | null | null | null |
from .bayesian import Bayesian
| 30
| 30
| 0.866667
| 4
| 30
| 6.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 30
| 1
| 30
| 30
| 0.962963
| 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
|
9ddee085b5f96f234b50360c7cabe7fa0a5e1f16
| 323
|
py
|
Python
|
sentence_transformers/cross_encoder/evaluation/__init__.py
|
ccolas/sentence-transformers
|
d7235076a663114c5267b093d5c28e1fc0272f76
|
[
"Apache-2.0"
] | 7,566
|
2019-07-25T07:45:17.000Z
|
2022-03-31T22:15:35.000Z
|
sentence_transformers/cross_encoder/evaluation/__init__.py
|
ccolas/sentence-transformers
|
d7235076a663114c5267b093d5c28e1fc0272f76
|
[
"Apache-2.0"
] | 1,444
|
2019-07-25T11:53:48.000Z
|
2022-03-31T15:13:32.000Z
|
sentence_transformers/cross_encoder/evaluation/__init__.py
|
ccolas/sentence-transformers
|
d7235076a663114c5267b093d5c28e1fc0272f76
|
[
"Apache-2.0"
] | 1,567
|
2019-07-26T15:19:28.000Z
|
2022-03-31T19:57:35.000Z
|
from .CEBinaryAccuracyEvaluator import CEBinaryAccuracyEvaluator
from .CEBinaryClassificationEvaluator import CEBinaryClassificationEvaluator
from .CECorrelationEvaluator import CECorrelationEvaluator
from .CESoftmaxAccuracyEvaluator import CESoftmaxAccuracyEvaluator
from .CERerankingEvaluator import CERerankingEvaluator
| 53.833333
| 76
| 0.922601
| 20
| 323
| 14.9
| 0.35
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06192
| 323
| 5
| 77
| 64.6
| 0.983498
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d1d9965ce7ec9aabd5da3cb5356d31e39f4ca9f3
| 1,072
|
py
|
Python
|
benchmarks/energy_radius/__init__.py
|
MTD-group/amlt
|
568a37b06f2dd289d2b62c960406e3044195fb14
|
[
"MIT"
] | 2
|
2020-07-15T20:11:41.000Z
|
2022-03-31T17:47:38.000Z
|
benchmarks/energy_radius/__init__.py
|
MTD-group/amlt
|
568a37b06f2dd289d2b62c960406e3044195fb14
|
[
"MIT"
] | null | null | null |
benchmarks/energy_radius/__init__.py
|
MTD-group/amlt
|
568a37b06f2dd289d2b62c960406e3044195fb14
|
[
"MIT"
] | 1
|
2019-05-02T22:05:37.000Z
|
2019-05-02T22:05:37.000Z
|
from ase import io
import numpy as np
import os
def read_energy_radius_traj(file_name):
traj = io.Trajectory(os.path.abspath(file_name),'r')
#data = [ (im.get_volume()/len(im), im.get_potential_energy(force_consistent = True)/len(im)) for im in traj]
data = [ (np.linalg.norm(im[0].position - im[1].position),
im.get_potential_energy()/len(im)) for im in traj]
traj.close()
data = np.asarray(data).T
smap = np.argsort(data[0])
return np.array([data[0][smap],data[1][smap]] )
def read_force_radius_traj(file_name):
traj = io.Trajectory(os.path.abspath(file_name),'r')
#data = [ (im.get_volume()/len(im), im.get_potential_energy(force_consistent = True)/len(im)) for im in traj]
data = [ (np.linalg.norm(im[0].position - im[1].position),
np.linalg.norm(im.get_forces()[0]) ) for im in traj]
traj.close()
data = np.asarray(data).T
smap = np.argsort(data[0])
return np.array([data[0][smap],data[1][smap]] )
| 34.580645
| 117
| 0.601679
| 163
| 1,072
| 3.828221
| 0.263804
| 0.048077
| 0.044872
| 0.070513
| 0.828526
| 0.828526
| 0.820513
| 0.820513
| 0.820513
| 0.820513
| 0
| 0.013447
| 0.23694
| 1,072
| 30
| 118
| 35.733333
| 0.749389
| 0.201493
| 0
| 0.631579
| 0
| 0
| 0.002345
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.105263
| false
| 0
| 0.157895
| 0
| 0.368421
| 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
|
8839f469b7fa5ef86ceef1b119a0a3d738b01844
| 138
|
py
|
Python
|
surfstat/python/need_not_convert/SurfStatView.py
|
rudimeier/BrainStat
|
a5ef474ffd70300ecf5fa464fff4a41e71f4b7a1
|
[
"BSD-3-Clause"
] | null | null | null |
surfstat/python/need_not_convert/SurfStatView.py
|
rudimeier/BrainStat
|
a5ef474ffd70300ecf5fa464fff4a41e71f4b7a1
|
[
"BSD-3-Clause"
] | null | null | null |
surfstat/python/need_not_convert/SurfStatView.py
|
rudimeier/BrainStat
|
a5ef474ffd70300ecf5fa464fff4a41e71f4b7a1
|
[
"BSD-3-Clause"
] | null | null | null |
import numpy as np
def py_SurfStatView(struct, surf, title, background):
sys.exit("Function py_SurfStatView is not implemented yet")
| 27.6
| 63
| 0.775362
| 20
| 138
| 5.25
| 0.9
| 0.266667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.144928
| 138
| 4
| 64
| 34.5
| 0.889831
| 0
| 0
| 0
| 0
| 0
| 0.34058
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 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
| 1
| 0
| 1
| 0
|
0
| 6
|
88819fa96b9547b6ad5e37fb9e3a3c45d6dbbadb
| 180
|
py
|
Python
|
randomdataset/__main__.py
|
KCL-BMEIS/RandomDataset
|
5e58e767dc017cbaa35666e3860e4cc3b8793c78
|
[
"MIT"
] | 1
|
2021-03-26T10:04:51.000Z
|
2021-03-26T10:04:51.000Z
|
randomdataset/__main__.py
|
KCL-BMEIS/RandomDataset
|
5e58e767dc017cbaa35666e3860e4cc3b8793c78
|
[
"MIT"
] | 6
|
2021-06-15T23:33:22.000Z
|
2022-02-20T15:38:27.000Z
|
randomdataset/__main__.py
|
ericspod/RandomDataset
|
5e58e767dc017cbaa35666e3860e4cc3b8793c78
|
[
"MIT"
] | null | null | null |
# RandomDataset
# Copyright (c) 2021 Eric Kerfoot, KCL, see LICENSE file
if __name__ == "__main__":
from .application import generate_dataset
generate_dataset()
| 20
| 57
| 0.7
| 20
| 180
| 5.8
| 0.9
| 0.258621
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.028571
| 0.222222
| 180
| 8
| 58
| 22.5
| 0.8
| 0.377778
| 0
| 0
| 1
| 0
| 0.079208
| 0
| 0
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
31f8e84c6a582a40fb8119d3ebd065b7d7cce473
| 127
|
py
|
Python
|
parser/team26/G26/imports.py
|
Sedge77/tytus
|
898d5790c0c3b350ad85dd216d03c595c225df10
|
[
"MIT"
] | null | null | null |
parser/team26/G26/imports.py
|
Sedge77/tytus
|
898d5790c0c3b350ad85dd216d03c595c225df10
|
[
"MIT"
] | null | null | null |
parser/team26/G26/imports.py
|
Sedge77/tytus
|
898d5790c0c3b350ad85dd216d03c595c225df10
|
[
"MIT"
] | null | null | null |
import Instrucciones.DDL.create as create
import Expresiones.Condicionales as condicion
import Expresiones.Aritmeticas as arit
| 31.75
| 45
| 0.874016
| 16
| 127
| 6.9375
| 0.625
| 0.306306
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094488
| 127
| 3
| 46
| 42.333333
| 0.965217
| 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
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| null | 0
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| 1
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|
0
| 6
|
ee1ad24fb242a679dd140ca69cc14c50a3b674a1
| 45
|
py
|
Python
|
tests/test_files/test_nested_module_structure/test_nested_module.py
|
lowitea/flake8-fine-pytest
|
5f5b6a98abbc98e5a74c4ac8bd03890332828070
|
[
"MIT"
] | 4
|
2021-01-06T02:53:06.000Z
|
2022-02-24T14:11:23.000Z
|
tests/test_files/test_nested_module_structure/test_nested_module.py
|
lowitea/flake8-fine-pytest
|
5f5b6a98abbc98e5a74c4ac8bd03890332828070
|
[
"MIT"
] | 7
|
2020-05-12T06:49:25.000Z
|
2022-03-05T05:03:25.000Z
|
tests/test_files/test_nested_module_structure/test_nested_module.py
|
lowitea/flake8-fine-pytest
|
5f5b6a98abbc98e5a74c4ac8bd03890332828070
|
[
"MIT"
] | 6
|
2020-06-30T14:10:33.000Z
|
2020-12-21T10:19:01.000Z
|
def test_nested_module_structure():
pass
| 15
| 35
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0
| 6
|
a006d294a644d11c7558117e8c137c0650ae412b
| 24,821
|
py
|
Python
|
autoarray/util/binning_util.py
|
Sketos/PyAutoArray
|
72dc7e8d1c38786915f82a7e7284239e5ce87624
|
[
"MIT"
] | null | null | null |
autoarray/util/binning_util.py
|
Sketos/PyAutoArray
|
72dc7e8d1c38786915f82a7e7284239e5ce87624
|
[
"MIT"
] | null | null | null |
autoarray/util/binning_util.py
|
Sketos/PyAutoArray
|
72dc7e8d1c38786915f82a7e7284239e5ce87624
|
[
"MIT"
] | null | null | null |
from autoarray import decorator_util
import numpy as np
from autoarray.util import array_util, mask_util
@decorator_util.jit()
def padded_binning_shape_2d_from_shape_2d_and_bin_up_factor(shape_2d, bin_up_factor):
shape_remainder = (shape_2d[0] % bin_up_factor, shape_2d[1] % bin_up_factor)
if shape_remainder[0] != 0 and shape_remainder[1] != 0:
shape_pad = (
bin_up_factor - shape_remainder[0],
bin_up_factor - shape_remainder[1],
)
elif shape_remainder[0] != 0 and shape_remainder[1] == 0:
shape_pad = (bin_up_factor - shape_remainder[0], 0)
elif shape_remainder[0] == 0 and shape_remainder[1] != 0:
shape_pad = (0, bin_up_factor - shape_remainder[1])
else:
shape_pad = (0, 0)
return (shape_2d[0] + shape_pad[0], shape_2d[1] + shape_pad[1])
@decorator_util.jit()
def padded_binning_array_2d_from_array_2d(array_2d, bin_up_factor, pad_value=0.0):
"""If an array is to be binned up, but the dimensions are not divisible by the bin-up factor, this routine pads \
the array to make it divisible.
For example, if the array is shape (5,5) and the bin_up_factor is 2, this routine will pad the array to shape \
(6,6).
Parameters
----------
array_2d : ndarray
The 2D array that is padded.
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
pad_value : float
If the array is padded, the value the padded edge values are filled in using.
Returns
-------
ndarray
The 2D array that is padded before binning up.
Examples
--------
array_2d = np.ones((5,5))
padded_array_2d = padded_array_2d_for_binning_up_with_bin_up_factor( \
array_2d=array_2d, bin_up_factor=2, pad_value=0.0)
"""
padded_binning_shape_2d = padded_binning_shape_2d_from_shape_2d_and_bin_up_factor(
shape_2d=array_2d.shape, bin_up_factor=bin_up_factor
)
return array_util.resized_array_2d_from_array_2d(
array_2d=array_2d, resized_shape=padded_binning_shape_2d, pad_value=pad_value
)
@decorator_util.jit()
def bin_array_2d_via_mean(array_2d, bin_up_factor):
"""Bin up an array to coarser resolution, by binning up groups of pixels and using their mean value to determine \
the value of the new pixel.
If an array of shape (8,8) is input and the bin up size is 2, this would return a new array of size (4,4) where \
every pixel was the mean of each collection of 2x2 pixels on the (8,8) array.
If binning up the array leads to an edge being cut (e.g. a (9,9) array binned up by 2), the array is first \
padded to make the division work. One must be careful of edge effects in this case.
Parameters
----------
array_2d : ndarray
The 2D array that is binned up.
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
Returns
-------
ndarray
The binned up 2D array from the input 2D array.
Examples
--------
array_2d = np.ones((5,5))
resize_array = bin_up_array_2d_using_mean(array_2d=array_2d, bin_up_factor=2)
"""
padded_binning_array_2d = padded_binning_array_2d_from_array_2d(
array_2d=array_2d, bin_up_factor=bin_up_factor
)
binned_array_2d = np.zeros(
shape=(
padded_binning_array_2d.shape[0] // bin_up_factor,
padded_binning_array_2d.shape[1] // bin_up_factor,
)
)
for y in range(binned_array_2d.shape[0]):
for x in range(binned_array_2d.shape[1]):
value = 0.0
for y1 in range(bin_up_factor):
for x1 in range(bin_up_factor):
padded_y = y * bin_up_factor + y1
padded_x = x * bin_up_factor + x1
value += padded_binning_array_2d[padded_y, padded_x]
binned_array_2d[y, x] = value / (bin_up_factor ** 2.0)
return binned_array_2d
@decorator_util.jit()
def bin_array_2d_via_quadrature(array_2d, bin_up_factor):
"""Bin up an array to coarser resolution, by binning up groups of pixels and using their quadrature value to \
determine the value of the new pixel.
If an array of shape (8,8) is input and the bin up size is 2, this would return a new array of size (4,4) where \
every pixel was the quadrature of each collection of 2x2 pixels on the (8,8) array.
If binning up the array leads to an edge being cut (e.g. a (9,9) array binned up by 2), the array is first \
padded to make the division work. One must be careful of edge effects in this case.
Parameters
----------
array_2d : ndarray
The 2D array that is binned up.
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
Returns
-------
ndarray
The binned up 2D array from the input 2D array.
Examples
--------
array_2d = np.ones((5,5))
resize_array = bin_up_array_2d_using_quadrature(array_2d=array_2d, bin_up_factor=2)
"""
padded_binning_array_2d = padded_binning_array_2d_from_array_2d(
array_2d=array_2d, bin_up_factor=bin_up_factor
)
binned_array_2d = np.zeros(
shape=(
padded_binning_array_2d.shape[0] // bin_up_factor,
padded_binning_array_2d.shape[1] // bin_up_factor,
)
)
for y in range(binned_array_2d.shape[0]):
for x in range(binned_array_2d.shape[1]):
value = 0.0
for y1 in range(bin_up_factor):
for x1 in range(bin_up_factor):
padded_y = y * bin_up_factor + y1
padded_x = x * bin_up_factor + x1
value += padded_binning_array_2d[padded_y, padded_x] ** 2.0
binned_array_2d[y, x] = np.sqrt(value) / (bin_up_factor ** 2.0)
return binned_array_2d
@decorator_util.jit()
def bin_array_2d_via_sum(array_2d, bin_up_factor):
"""Bin up an array to coarser resolution, by binning up groups of pixels and using their sum value to determine \
the value of the new pixel.
If an array of shape (8,8) is input and the bin up size is 2, this would return a new array of size (4,4) where \
every pixel was the sum of each collection of 2x2 pixels on the (8,8) array.
If binning up the array leads to an edge being cut (e.g. a (9,9) array binned up by 2), the array is first \
padded to make the division work. One must be careful of edge effects in this case.
Parameters
----------
array_2d : ndarray
The 2D array that is binned up.
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
Returns
-------
ndarray
The binned up 2D array from the input 2D array.
Examples
--------
array_2d = np.ones((5,5))
resize_array = bin_up_array_2d_using_sum(array_2d=array_2d, bin_up_factor=2)
"""
padded_binning_array_2d = padded_binning_array_2d_from_array_2d(
array_2d=array_2d, bin_up_factor=bin_up_factor
)
binned_array_2d = np.zeros(
shape=(
padded_binning_array_2d.shape[0] // bin_up_factor,
padded_binning_array_2d.shape[1] // bin_up_factor,
)
)
for y in range(binned_array_2d.shape[0]):
for x in range(binned_array_2d.shape[1]):
value = 0.0
for y1 in range(bin_up_factor):
for x1 in range(bin_up_factor):
padded_y = y * bin_up_factor + y1
padded_x = x * bin_up_factor + x1
value += padded_binning_array_2d[padded_y, padded_x]
binned_array_2d[y, x] = value
return binned_array_2d
@decorator_util.jit()
def bin_mask_2d(mask_2d, bin_up_factor):
"""Bin up an array to coarser resolution, by binning up groups of pixels and using their sum value to determine \
the value of the new pixel.
If an array of shape (8,8) is input and the bin up size is 2, this would return a new array of size (4,4) where \
every pixel was the sum of each collection of 2x2 pixels on the (8,8) array.
If binning up the array leads to an edge being cut (e.g. a (9,9) array binned up by 2), an array is first \
extracted around the centre of that array.
Parameters
----------
mask_2d : ndarray
The 2D array that is resized.
new_shape : (int, int)
The (y,x) new pixel dimension of the trimmed array.
origin : (int, int)
The oigin of the resized array, e.g. the central pixel around which the array is extracted.
Returns
-------
ndarray
The resized 2D array from the input 2D array.
Examples
--------
array_2d = np.ones((5,5))
resize_array = resize_array_2d(array_2d=array_2d, new_shape=(2,2), origin=(2, 2))
"""
padded_mask_2d = padded_binning_array_2d_from_array_2d(
array_2d=mask_2d, bin_up_factor=bin_up_factor, pad_value=True
)
binned_mask = np.zeros(
shape=(
padded_mask_2d.shape[0] // bin_up_factor,
padded_mask_2d.shape[1] // bin_up_factor,
)
)
for y in range(binned_mask.shape[0]):
for x in range(binned_mask.shape[1]):
value = True
for y1 in range(bin_up_factor):
for x1 in range(bin_up_factor):
padded_y = y * bin_up_factor + y1
padded_x = x * bin_up_factor + x1
if padded_mask_2d[padded_y, padded_x] == False:
value = False
binned_mask[y, x] = value
return binned_mask
@decorator_util.jit()
def mask_1d_index_for_padded_mask_2d_index_via_mask_2d(mask_2d, bin_up_factor):
"""Create a 2D array which maps every False entry of a 2D mask to its 1D mask array index 2D binned mask. Every \
True entry is given a value -1.
This uses the function *mask_1d_index_for_padded_mask_2d_index*, see this method for a more detailed description of the \
util.
This function first pads the mask using the same padding when computed a binned up mask.
Parameters
----------
mask_2d : ndarray
The 2D mask that the util array is created for.
Returns
-------
ndarray
The 2D array util padded 2D mask entries to their 1D masked array indexes.
Examples
--------
mask_2d = np.full(fill_value=False, shape=(9,9))
mask_2d_to_mask_1d_index = mask_1d_index_for_padded_mask_2d_index_from_mask_2d(mask_2d=mask_2d)
"""
padded_mask_2d = padded_binning_array_2d_from_array_2d(
array_2d=mask_2d, bin_up_factor=bin_up_factor, pad_value=True
)
return mask_util.sub_mask_1d_index_for_sub_mask_2d_index_from_sub_mask_2d(
sub_mask_2d=padded_mask_2d
)
@decorator_util.jit()
def binned_mask_1d_index_for_padded_mask_2d_index_via_mask_2d(mask_2d, bin_up_factor):
"""Create a 2D array which maps every False entry of a 2D mask to its 1D binned mask index (created using the \
*binned_upmask_from_mask_2d_and_bin_up_factor* method).
We create an array the same shape as the 2D mask (after padding for the binnning up procedure), where each entry \
gives the binned up mask's 1D masked array index.
This is used as a convenience tool for creating structures util between different grids and structures.
For example, if we had a 4x4 mask:
[[False, False, False, False],
[False, False, False, False],
[ True, True, False, False],
[ True, True, False, False]]
For a bin_up_factor of 2, the resulting binned up mask is as follows (noting there is no padding in this example):
[[False, False],
[True, False]
The mask_2d_to_binned_mask_1d_index is therefore:
[[ 0, 0, 1, 1],
[ 0, 0, 1, 1],
[-1, -1, 2, 2],
[-1, -1, 2, 2]]
Parameters
----------
mask_2d : ndarray
The 2D mask that the binned mask 1d indexes are computing using
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
Returns
-------
ndarray
The 2D array util 2D mask entries to their 1D binned masked array indexes.
Examples
--------
mask_2d = np.full(fill_value=False, shape=(9,9))
mask_to_binned_mask =
mask_2d_to_binned_mask_1d_index_from_mask_2d_and_bin_up_factor(mask_2d=mask_2d, bin_up_factor=3)
"""
padded_mask_2d = padded_binning_array_2d_from_array_2d(
array_2d=mask_2d, bin_up_factor=bin_up_factor, pad_value=True
)
binned_mask = bin_mask_2d(mask_2d=mask_2d, bin_up_factor=bin_up_factor)
binned_mask_1d_index_for_padded_mask_2d_index = np.full(
fill_value=-1, shape=padded_mask_2d.shape
)
binned_mask_1d_index = 0
for bin_y in range(binned_mask.shape[0]):
for bin_x in range(binned_mask.shape[1]):
if binned_mask[bin_y, bin_x] == False:
for bin_y1 in range(bin_up_factor):
for bin_x1 in range(bin_up_factor):
mask_y = bin_y * bin_up_factor + bin_y1
mask_x = bin_x * bin_up_factor + bin_x1
if padded_mask_2d[mask_y, mask_x] == False:
binned_mask_1d_index_for_padded_mask_2d_index[
mask_y, mask_x
] = binned_mask_1d_index
binned_mask_1d_index += 1
return binned_mask_1d_index_for_padded_mask_2d_index
@decorator_util.jit()
def binned_masked_array_1d_for_masked_array_1d_via_mask_2d(mask_2d, bin_up_factor):
"""Create a 1D array which maps every (padded) masked index to its corresponding 1D index in the binned 1D \
mask.
This uses the convenience tools *padded_mask_2d_to_mask_1d* and *padded_mask_2d_to_binned_mask_1d* to \
make the calculation simpler.
For example, if we had a 4x4 mask:
[[False, False, False, False],
[False, False, False, False],
[ True, True, False, False],
[ True, True, False, False]]
For a bin_up_factor of 2, the resulting binned up mask is as follows (noting there is no padding in this example):
[[False, False],
[True, False]
The mask_2d_to_mask_1d_index is therefore:
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[-1, -1, 8, 9],
[-1, -1, 10, 11]]
And the mask_2d_to_binned_mask_1d_index is therefore:
[[ 0, 0, 1, 1],
[ 0, 0, 1, 1],
[-1, -1, 2, 2],
[-1, -1, 2, 2]]
Therefore, the masked_array_1d_for_binned_masked_array_1d would be:
[0, 0, 1, 1, 0, 0, 1, 1, 2, 2, 2, 2]
This tells us that:
- The first mask pixel maps to the first binned masked pixel (e.g. the 1D index of mask_2d after binning up).
- The second mask pixel maps to the first binned masked pixel (e.g. the 1D index of mask_2d after binning up)
- The third mask pixel maps to the second masked pixel (e.g. the 1D index of mask_2d after binning up)
Parameters
----------
mask_2d : ndarray
The 2D mask that the binned mask 1d index mappings are computed using
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
Returns
-------
ndarray
The 1D array util 1D binned mask entries to their corresponding 1D masked array index.
Examples
--------
mask_2d = np.full(fill_value=False, shape=(9,9))
mask_to_binned_mask =
masked_array_1d_for_binned_masked_array_1d_from_mask_2d_and_bin_up_factor(mask_2d=mask_2d, bin_up_factor=3)
"""
padded_mask_2d = padded_binning_array_2d_from_array_2d(
array_2d=mask_2d, bin_up_factor=bin_up_factor, pad_value=True
)
total_masked_pixels = mask_util.total_pixels_from_mask_2d(mask_2d=padded_mask_2d)
binned_masked_array_1d_for_masked_array_1d = np.zeros(shape=total_masked_pixels)
mask_1d_index_for_padded_mask_2d_index = mask_1d_index_for_padded_mask_2d_index_via_mask_2d(
mask_2d=mask_2d, bin_up_factor=bin_up_factor
)
binned_mask_1d_index_for_padded_mask_2d_index = binned_mask_1d_index_for_padded_mask_2d_index_via_mask_2d(
mask_2d=mask_2d, bin_up_factor=bin_up_factor
)
for mask_y in range(mask_1d_index_for_padded_mask_2d_index.shape[0]):
for mask_x in range(mask_1d_index_for_padded_mask_2d_index.shape[1]):
if mask_1d_index_for_padded_mask_2d_index[mask_y, mask_x] >= 0:
padded_mask_index = mask_1d_index_for_padded_mask_2d_index[
mask_y, mask_x
]
binned_mask_1d_index = binned_mask_1d_index_for_padded_mask_2d_index[
mask_y, mask_x
]
binned_masked_array_1d_for_masked_array_1d[
padded_mask_index
] = binned_mask_1d_index
return binned_masked_array_1d_for_masked_array_1d
@decorator_util.jit()
def masked_array_1d_for_binned_masked_array_1d_via_mask_2d(mask_2d, bin_up_factor):
"""Create a 1D array which maps every (padded) binned masked index to its correspond 1D index in the original 2D \
mask that was binned up.
Array indexing starts from the top-left and goes rightwards and downwards. The top-left pixel of each mask is \
used before binning up.
This uses the convenience tools *padded_mask_2d_to_mask_1d* and *padded_mask_2d_to_binned_mask_1d* to \
make the calculation simpler.
For example, if we had a 4x4 mask:
[[False, False, False, False],
[False, False, False, False],
[ True, True, False, False],
[ True, True, False, False]]
For a bin_up_factor of 2, the resulting binned up mask is as follows (noting there is no padding in this example):
[[False, False],
[True, False]
The mask_2d_to_mask_1d_index is therefore:
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[-1, -1, 8, 9],
[-1, -1, 10, 11]]
And the mask_2d_to_binned_mask_1d_index is therefore:
[[ 0, 0, 1, 1],
[ 0, 0, 1, 1],
[-1, -1, 2, 2],
[-1, -1, 2, 2]]
Therefore, the masked_array_1d_for_binned_masked_array_1d would be:
[0, 2, 8]
This tells us that:
- The first binned mask pixel maps to the first masked pixel (e.g. the 1D index of mask_2d).
- The second binned mask pixel maps to the third masked pixel (e.g. the 1D index of mask_2d)
- The third binned mask pixel maps to the ninth masked pixel (e.g. the 1D index of mask_2d)
Parameters
----------
mask_2d : ndarray
The 2D mask that the binned mask 1d index mappings are computed using
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
Returns
-------
ndarray
The 1D array util 1D binned mask entries to their corresponding 1D masked array index.
Examples
--------
mask_2d = np.full(fill_value=False, shape=(9,9))
mask_to_binned_mask =
masked_array_1d_for_binned_masked_array_1d_from_mask_2d_and_bin_up_factor(mask_2d=mask_2d, bin_up_factor=3)
"""
binned_upmask = bin_mask_2d(mask_2d=mask_2d, bin_up_factor=bin_up_factor)
total_binned_masked_pixels = mask_util.total_pixels_from_mask_2d(
mask_2d=binned_upmask
)
masked_array_1d_for_binned_masked_array_1d = -1 * np.ones(
total_binned_masked_pixels
)
mask_1d_index_for_padded_mask_2d_index = mask_1d_index_for_padded_mask_2d_index_via_mask_2d(
mask_2d=mask_2d, bin_up_factor=bin_up_factor
)
binned_mask_1d_index_for_padded_mask_2d_index = binned_mask_1d_index_for_padded_mask_2d_index_via_mask_2d(
mask_2d=mask_2d, bin_up_factor=bin_up_factor
)
for mask_y in range(mask_1d_index_for_padded_mask_2d_index.shape[0]):
for mask_x in range(mask_1d_index_for_padded_mask_2d_index.shape[1]):
if mask_1d_index_for_padded_mask_2d_index[mask_y, mask_x] >= 0:
binned_mask_index = binned_mask_1d_index_for_padded_mask_2d_index[
mask_y, mask_x
]
if masked_array_1d_for_binned_masked_array_1d[binned_mask_index] == -1:
padded_mask_index = mask_1d_index_for_padded_mask_2d_index[
mask_y, mask_x
]
masked_array_1d_for_binned_masked_array_1d[
binned_mask_index
] = padded_mask_index
return masked_array_1d_for_binned_masked_array_1d
@decorator_util.jit()
def masked_array_1d_for_binned_masked_array_1d_all_via_mask_2d(mask_2d, bin_up_factor):
"""Create a 2D array which maps every (padded) binned masked index to all of the corresponding 1D indexes of the \
the original 2D mask that was binned up.
Array indexing starts from the top-left and goes rightwards and downwards. The top-left pixel of each mask is \
used before binning up. Minus one's are used for util which go to masked values with True.
This uses the convenience tools *padded_mask_2d_to_mask_1d* and *padded_mask_2d_to_binned_mask_1d* to \
make the calculation simpler.
For example, if we had a 4x4 mask:
[[False, False, False, False],
[False, False, False, False],
[ True, True, False, False],
[ True, True, True, False]]
For a bin_up_factor of 2, the resulting binned up mask is as follows (noting there is no padding in this example):
[[False, False],
[True, False]
The mask_2d_to_mask_1d_index is therefore:
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[-1, -1, 8, 9],
[-1, -1, -1, 10]]
And the mask_2d_to_binned_mask_1d_index is therefore:
[[ 0, 0, 1, 1],
[ 0, 0, 1, 1],
[-1, -1, 2, 2],
[-1, -1, 2, 2]]
Therefore, the masked_array_1d_for_binned_masked_array_1d_all would be:
[[0, 1, 4, 5],
[2, 3, 6, 7]]
[8, 9, 10, -1]]
This tells us that:
- The first binned mask pixel maps to the first, second, fifth and sixth masked pixels.
- The second binned mask pixel maps to the third, fourth, seventh and eighth masked pixels
- The third binned mask pixel maps to the ninth, tenth and eleventh masked pixels (The fourth masked pixel it \
maps to is a *True* value and therefore masked.)
Parameters
----------
mask_2d : ndarray
The 2D mask that the binned mask 1d index mappings are computed using
bin_up_factor : int
The factor which the array is binned up by (e.g. a value of 2 bins every 2 x 2 pixels into one pixel).
Returns
-------
ndarray
The 1D array util 1D binned mask entries to their corresponding 1D masked array index.
Examples
--------
mask_2d = np.full(fill_value=False, shape=(9,9))
mask_to_binned_mask =
masked_array_1d_for_binned_masked_array_1d_from_mask_2d_and_bin_up_factor(mask_2d=mask_2d, bin_up_factor=3)
"""
binned_upmask = bin_mask_2d(mask_2d=mask_2d, bin_up_factor=bin_up_factor)
total_binned_masked_pixels = mask_util.total_pixels_from_mask_2d(
mask_2d=binned_upmask
)
masked_array_1d_for_binned_masked_array_1d_all = -1 * np.ones(
(total_binned_masked_pixels, bin_up_factor ** 2)
)
binned_masked_array_1d_sizes = np.zeros(total_binned_masked_pixels)
mask_1d_index_for_padded_mask_2d_index = mask_1d_index_for_padded_mask_2d_index_via_mask_2d(
mask_2d=mask_2d, bin_up_factor=bin_up_factor
)
binned_mask_1d_index_for_padded_mask_2d_index = binned_mask_1d_index_for_padded_mask_2d_index_via_mask_2d(
mask_2d=mask_2d, bin_up_factor=bin_up_factor
)
for mask_y in range(mask_1d_index_for_padded_mask_2d_index.shape[0]):
for mask_x in range(mask_1d_index_for_padded_mask_2d_index.shape[1]):
if mask_1d_index_for_padded_mask_2d_index[mask_y, mask_x] >= 0:
binned_mask_index = binned_mask_1d_index_for_padded_mask_2d_index[
mask_y, mask_x
]
binned_mask_count = int(binned_masked_array_1d_sizes[binned_mask_index])
padded_mask_index = mask_1d_index_for_padded_mask_2d_index[
mask_y, mask_x
]
masked_array_1d_for_binned_masked_array_1d_all[
binned_mask_index, binned_mask_count
] = padded_mask_index
binned_masked_array_1d_sizes[binned_mask_index] += 1
return masked_array_1d_for_binned_masked_array_1d_all, binned_masked_array_1d_sizes
| 35.560172
| 125
| 0.665122
| 4,044
| 24,821
| 3.758408
| 0.052918
| 0.057241
| 0.081058
| 0.029936
| 0.858083
| 0.837489
| 0.819199
| 0.795973
| 0.774788
| 0.741628
| 0
| 0.041762
| 0.257161
| 24,821
| 697
| 126
| 35.611191
| 0.782569
| 0.506547
| 0
| 0.486842
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| 0
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| 0
| 0
| 0
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| 0
| 0
| 1
| 0.048246
| false
| 0
| 0.013158
| 0
| 0.109649
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| null | 0
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| 0
| 0
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| 0
| 0
|
0
| 6
|
4e630ec4e125ea19493b3cda0906ca982a83ed00
| 6,843
|
py
|
Python
|
ODE_Isotropic_Spectrum.py
|
pedrodedin/Neutrino-Collective-Effects
|
d91c3f910a6407afe39d4c8f90c6d0765c0fc44c
|
[
"MIT"
] | null | null | null |
ODE_Isotropic_Spectrum.py
|
pedrodedin/Neutrino-Collective-Effects
|
d91c3f910a6407afe39d4c8f90c6d0765c0fc44c
|
[
"MIT"
] | null | null | null |
ODE_Isotropic_Spectrum.py
|
pedrodedin/Neutrino-Collective-Effects
|
d91c3f910a6407afe39d4c8f90c6d0765c0fc44c
|
[
"MIT"
] | null | null | null |
from Auxiliar_Functions import *
from scipy.integrate import odeint
def initiate(nu_types,r_i,r_f,E_i,E_f,E_step,E_0,Amplitude):
y0=[] #Initial state
omega=[]
flavor_sign=1
E_vec=np.arange(E_i,E_f,E_step)
n_E=len(E_vec)
n_f=len(nu_types)
n_dim=(n_f**2)-1
for i in range(n_E):
omega.append(delta_m2_31/(2*E_vec[i]*10**6)) #eV
for j in range(n_f):
if nu_types[j]=="nu_x":
flavor_sign=-1
if nu_types[j]=="nu_e":
flavor_sign=1
#nu
nu_spec=Amplitude[n_f*j]*phi_vec(E_vec[i],E_0[n_f*j],2.3)*E_step
y0.append(0)
y0.append(0)
y0.append(flavor_sign*nu_spec)
#nubar
nu_spec=Amplitude[n_f*j+1]*phi_vec(E_vec[i],E_0[n_f*j+1],2.3)*E_step
y0.append(0)
y0.append(0)
y0.append(flavor_sign*nu_spec)
#mu
mu_0=(10)*max(omega)
#r array
r_step = (2*np.pi/max(omega))/20 #eV⁻¹
r_i = r_i*from_eV_to_1_over_km #eV⁻¹
r_f = r_f*from_eV_to_1_over_km #eV⁻¹
r = np.arange(r_i,r_f,r_step) #eV⁻¹
return y0,omega,E_vec,r,mu_0,n_f,n_dim,n_E
def func_Collective_nu(y, time, params):
omega,mu_opt,mu_0,n_f,n_dim,n_E= params # unpack parameters
B=np.array(B_vec(n_dim,theta_31))
L=np.array(L_vec(n_dim))
r=time/from_eV_to_1_over_km #From eV⁻¹ to km
mu=mu_supernova(r,mu_opt,mu_0)
lamb=lambda_supernova(r,"no",0)
derivs=[]
nu, nubar = [],[]
num_diff_nu_compnents=2*n_f*n_dim
#Filling [Energy bin][Nu_types][3components]
for i in range(n_E):
nu.append([])
nubar.append([])
for j in range(n_f):
nu[i].append([])
nubar[i].append([])
for k in range(n_dim):
#nu
nu_index=(i*num_diff_nu_compnents)+k+2*j*n_dim
nu[i][j].append(y[nu_index])
#nubar
nubar_index=(i*num_diff_nu_compnents)+(k+n_dim)+2*j*n_dim
nubar[i][j].append(y[nubar_index])
#Summed nu and nubar components
nu_sum, nubar_sum=[],[]
nu_aux=np.transpose(nu,(2,0,1))
nubar_aux=np.transpose(nubar,(2,0,1))
for i in range(n_dim):
nu_sum.append(sum(map(sum,nu_aux[i])))
nubar_sum.append(sum(map(sum,nubar_aux[i])))
B=np.array(B)
nu_sum=np.array(nu_sum)
nubar_sum=np.array(nubar_sum)
# list of dy/dt=f functions
for i in range(n_E):
for j in range(n_f):
#nu
P_aux= cross_prod(nu[i][j],(B*omega[i]+L*lamb-mu*((nu_sum-nu[i][j])-nubar_sum)))
#P_aux= cross_prod(nu[i][j],(B*omega[i]+L*lamb-mu*(nu_sum-nubar_sum)))
for k in range(n_dim):
derivs.append(P_aux[k])
#nubar
P_aux= cross_prod(nubar[i][j],(-1*B*omega[i]+L*lamb-mu*(nu_sum-(nubar_sum-nubar[i][j]))))
#P_aux= cross_prod(nubar[i][j],(-1*B*omega[i]+L*lamb-mu*(nu_sum-nubar_sum)))
for k in range(n_dim):
derivs.append(P_aux[k])
return derivs
def solver_two_families(nu_types,r_i,r_f,E_i,E_f,E_step,E_0,Amplitude,mass_ord):
y0,omega,E_vec,r,mu_0,n_f,n_dim,n_E=initiate(nu_types,r_i,r_f,E_i,E_f,E_step,E_0,Amplitude)
if mass_ord=="NH":
params=np.array(omega),"SN",mu_0,n_f,n_dim,n_E
elif mass_ord=="IH":
params=-1*np.array(omega),"SN",mu_0,n_f,n_dim,n_E
else:
print("Not a mass ordering option!")
return 0
psoln= odeint(func_Collective_nu, y0, r, args=(params,))
nu, nubar= read_output(psoln,(n_f,n_dim,n_E))
nu_e_time,nubar_e_time,nu_x_time,nubar_x_time=read_two_flavor_v1(nu, nubar)
r=r/from_eV_to_1_over_km #From eV⁻¹ to km
#return nu_e_time,nubar_e_time, nu_x_time,nubar_x_time
return E_vec,r,mu_0,nu_e_time,nubar_e_time, nu_x_time,nubar_x_time, nu, nubar
################################ Second Implementation #################################
def initiate_v2(nu_types,t_bins,E_i,E_f,E_step,E_0,Amplitude):
y0=[] #Initial state
omega=[]
flavor_sign=1
E_vec=np.arange(E_i,E_f,E_step)
n_E=len(E_vec)
n_f=len(nu_types)
n_dim=(n_f**2)-1
for i in range(n_E):
omega.append(delta_m2_31/(2*E_vec[i]*10**6)) #eV
#nu
nu_e_spec=Amplitude[0]*phi_vec(E_vec[i],E_0[0],2.3)*E_step
nu_x_spec=Amplitude[2]*phi_vec(E_vec[i],E_0[2],2.3)*E_step
#Pz=(nu_e_spec-nu_x_spec)/(nu_e_spec+nu_x_spec)
Pz=(nu_e_spec-nu_x_spec)
y0.append(0)
y0.append(0)
y0.append(Pz)
#nubar
nu_e_spec=Amplitude[1]*phi_vec(E_vec[i],E_0[1],2.3)*E_step
nu_x_spec=Amplitude[3]*phi_vec(E_vec[i],E_0[3],2.3)*E_step
#Pz=(nu_e_spec-nu_x_spec)/(nu_e_spec+nu_x_spec)
Pz=(nu_e_spec-nu_x_spec)
y0.append(0)
y0.append(0)
y0.append(Pz)
#mu
mu_0=(10)*max(omega)
#time
#t_max = 4*(2*np.pi/min(omega)) #eV⁻¹
w_max=max(omega)
t_step = (2*np.pi/w_max)/100 #eV⁻¹
t_vec = np.arange(0., t_bins*t_step , t_step) #eV⁻¹
return y0,omega,E_vec,t_vec,mu_0,n_f,n_dim,n_E
def func_Collective_nu_v2(y, time, params):
omega,mu_0,n_f,n_dim,n_E= params # unpack parameters
B=np.array(B_vec(n_dim))
L=np.array(L_vec(n_dim))
r=time/from_eV_to_1_over_km #From eV⁻¹ to km
mu=mu_supernova_vec(r,mu_0)
lamb=lambda_supernova(r,"no",0)
derivs=[]
nu, nubar = [],[]
num_diff_nu_compnents=2*n_dim
#Filling [Energy bin][3components]
for i in range(n_E):
nu.append([])
nubar.append([])
for k in range(n_dim):
#nu
nu_index=(i*num_diff_nu_compnents)+k
nu[i].append(y[nu_index])
#nubar
nubar_index=(i*num_diff_nu_compnents)+(k+n_dim)
nubar[i].append(y[nubar_index])
#Summed nu and nubar components
nu_sum, nubar_sum=[],[]
nu_aux=np.transpose(nu,(1,0))
nubar_aux=np.transpose(nubar,(1,0))
for i in range(n_dim):
#print(sum(nu_aux[i]))
#print(sum(nubar_aux[i]))
nu_sum.append(sum(nu_aux[i]))
nubar_sum.append(sum(nubar_aux[i]))
B=np.array(B)
nu_sum=np.array(nu_sum)
nubar_sum=np.array(nubar_sum)
# list of dy/dt=f functions
for i in range(n_E):
#nu
P_aux= cross_prod(nu[i],(B*omega[i]+L*lamb-mu*(nu_sum-nubar_sum)))
for k in range(n_dim):
derivs.append(P_aux[k])
#nubar
P_aux= cross_prod(nubar[i],(-1*B*omega[i]+L*lamb-mu*(nu_sum-nubar_sum)))
for k in range(n_dim):
derivs.append(P_aux[k])
return derivs
def solver_two_families_v2(nu_types,t_bins,E_i,E_f,E_step,E_0,Amplitude,mass_ord):
#E_vec=np.arange(E_i,E_f,E_step)
y0,omega,E_vec,t_vec,mu_0,n_f,n_dim,n_E=initiate(nu_types,t_bins,E_i,E_f,E_step,E_0,Amplitude)
if mass_ord=="NH":
params=np.array(omega),mu_0,n_f,n_dim,n_E
elif mass_ord=="IH":
params=-1*np.array(omega),mu_0,n_f,n_dim,n_E
else:
print("Not a mass ordering option!")
return 0
psoln= odeint(func_Collective_nu, y0, t_vec, args=(params,))
#return nu_e_time,nubar_e_time, nu_x_time,nubar_x_time
return E_vec,t_vec,nu_e_time,nubar_e_time, nu_x_time,nubar_x_time, nu, nubar
| 28.752101
| 97
| 0.64007
| 1,421
| 6,843
| 2.787474
| 0.090077
| 0.031305
| 0.034335
| 0.018177
| 0.86872
| 0.826811
| 0.797021
| 0.775814
| 0.748043
| 0.739712
| 0
| 0.028561
| 0.186468
| 6,843
| 237
| 98
| 28.873418
| 0.681157
| 0.128891
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| 1
| 0.038217
| false
| 0
| 0.012739
| 0
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| 0.012739
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| 0
| null | 0
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| 1
| 1
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| 1
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| null | 0
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|
0
| 6
|
4e6a860b6180becb47879d1671527495498a1e99
| 3,715
|
py
|
Python
|
drives/migrations/0002_auto_20191201_2016.py
|
frostdpr/uva-rideon
|
3146c9ad5b12fa6bc9605b4f045b5dfd791ce8d1
|
[
"Apache-2.0"
] | null | null | null |
drives/migrations/0002_auto_20191201_2016.py
|
frostdpr/uva-rideon
|
3146c9ad5b12fa6bc9605b4f045b5dfd791ce8d1
|
[
"Apache-2.0"
] | null | null | null |
drives/migrations/0002_auto_20191201_2016.py
|
frostdpr/uva-rideon
|
3146c9ad5b12fa6bc9605b4f045b5dfd791ce8d1
|
[
"Apache-2.0"
] | 1
|
2020-02-09T18:09:02.000Z
|
2020-02-09T18:09:02.000Z
|
# Generated by Django 2.2.7 on 2019-12-01 20:16
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('drives', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.AddField(
model_name='riderreview',
name='by',
field=models.ForeignKey(default=-1, on_delete=django.db.models.deletion.CASCADE, related_name='rider_by', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='riderreview',
name='drive',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='drives.Drive'),
),
migrations.AddField(
model_name='riderreview',
name='of',
field=models.ForeignKey(default=-1, on_delete=django.db.models.deletion.CASCADE, related_name='rider_of', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='rideapplication',
name='user',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='user', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='rideapplication',
name='waypoint',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='waypoint', to='drives.Location'),
),
migrations.AddField(
model_name='driverreview',
name='by',
field=models.ForeignKey(default=-1, on_delete=django.db.models.deletion.CASCADE, related_name='driver_by', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='driverreview',
name='drive',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='drives.Drive'),
),
migrations.AddField(
model_name='driverreview',
name='of',
field=models.ForeignKey(default=-1, on_delete=django.db.models.deletion.CASCADE, related_name='driver_of', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='drive',
name='driver',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='driver', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='drive',
name='end_location',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='end_location', to='drives.Location'),
),
migrations.AddField(
model_name='drive',
name='passengers',
field=models.ManyToManyField(blank=True, related_name='passengers', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='drive',
name='requestList',
field=models.ManyToManyField(blank=True, related_name='requestList', to='drives.RideApplication'),
),
migrations.AddField(
model_name='drive',
name='start_location',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='start_location', to='drives.Location'),
),
migrations.AddField(
model_name='drive',
name='waypointList',
field=models.ManyToManyField(blank=True, related_name='waypointList', to='drives.Location'),
),
]
| 41.741573
| 157
| 0.629341
| 396
| 3,715
| 5.737374
| 0.15404
| 0.110915
| 0.141725
| 0.166373
| 0.817782
| 0.817782
| 0.759683
| 0.680018
| 0.680018
| 0.680018
| 0
| 0.008208
| 0.24576
| 3,715
| 88
| 158
| 42.215909
| 0.802641
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
14cc0e64f3344f962ebe25c5d0b35476e01b3321
| 45
|
py
|
Python
|
editor/__init__.py
|
mikhel1984/termit
|
721562cebdcca4ca743a3afd62c41ed0d3c631e6
|
[
"MIT"
] | 1
|
2022-01-20T16:57:55.000Z
|
2022-01-20T16:57:55.000Z
|
editor/__init__.py
|
mikhel1984/termit
|
721562cebdcca4ca743a3afd62c41ed0d3c631e6
|
[
"MIT"
] | 5
|
2021-12-26T09:25:56.000Z
|
2022-01-08T12:43:44.000Z
|
editor/__init__.py
|
mikhel1984/termit
|
721562cebdcca4ca743a3afd62c41ed0d3c631e6
|
[
"MIT"
] | null | null | null |
# 2021, S.Mikhel
from .editor import Editor
| 11.25
| 26
| 0.733333
| 7
| 45
| 4.714286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 0.177778
| 45
| 3
| 27
| 15
| 0.783784
| 0.311111
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
eed713c9a1420ce575389d60c91fd3bddd776237
| 45
|
py
|
Python
|
flask_auth0/__init__.py
|
djoek/Flask-Auth0
|
84f0f8986e77406d07f7ff0332d4599c90f1447a
|
[
"MIT"
] | null | null | null |
flask_auth0/__init__.py
|
djoek/Flask-Auth0
|
84f0f8986e77406d07f7ff0332d4599c90f1447a
|
[
"MIT"
] | null | null | null |
flask_auth0/__init__.py
|
djoek/Flask-Auth0
|
84f0f8986e77406d07f7ff0332d4599c90f1447a
|
[
"MIT"
] | null | null | null |
from .auth0_ext import AuthorizationCodeFlow
| 22.5
| 44
| 0.888889
| 5
| 45
| 7.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02439
| 0.088889
| 45
| 1
| 45
| 45
| 0.926829
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
eee70abaad333975eb5d520d6e4c2b5988fa1e72
| 5,748
|
py
|
Python
|
src/cubic_splines.py
|
LuluDavid/Polynomial_Interpolation
|
d6d212615dbd4ce20a0120b249fe35373bfa3b71
|
[
"MIT"
] | null | null | null |
src/cubic_splines.py
|
LuluDavid/Polynomial_Interpolation
|
d6d212615dbd4ce20a0120b249fe35373bfa3b71
|
[
"MIT"
] | null | null | null |
src/cubic_splines.py
|
LuluDavid/Polynomial_Interpolation
|
d6d212615dbd4ce20a0120b249fe35373bfa3b71
|
[
"MIT"
] | null | null | null |
# Cubic splines
import time
from scipy import misc
from polynomes import *
import polynomes
# linear complexity
def cubic_splines(f, a, b, n):
xcoords = [a + k * (b - a) / n for k in range(n + 1)]
ycoords = [f(k) for k in xcoords]
dycoords = [misc.derivative(f, k) for k in xcoords]
for k in range(n):
a0 = ycoords[k + 1] / (xcoords[k + 1] - xcoords[k])
a1 = ycoords[k] / (xcoords[k] - xcoords[k + 1])
a2 = (dycoords[k] - a0 - a1) / ((xcoords[k] - xcoords[k + 1]) ** 2)
a3 = (dycoords[k + 1] - a0 - a1) / ((xcoords[k + 1] - xcoords[k]) ** 2)
nu0 = -a0 * xcoords[k] - a1 * xcoords[k + 1] - a2 * xcoords[k] * xcoords[k + 1] ** 2 - a3 * xcoords[k] ** 2 * \
xcoords[k + 1]
nu1 = a0 + a1 + a2 * (xcoords[k + 1] ** 2 + 2 * xcoords[k + 1] * xcoords[k]) + a3 * (
xcoords[k] ** 2 + 2 * xcoords[k] * xcoords[k + 1])
nu2 = -a2 * (xcoords[k] + 2 * xcoords[k + 1]) - a3 * (xcoords[k + 1] + 2 * xcoords[k])
nu3 = a2 + a3
P = [nu0, nu1, nu2, nu3]
polynomial_graph(P, xcoords[k], xcoords[k + 1])
coordsx = np.linspace(a, b, 1000)
coordsy = [f(x) for x in coordsx]
plt.plot(coordsx, coordsy, "b-", label="original function")
plt.legend(loc="best")
# same complexity
def cubic_splines2(f, a, b, n):
xcoords = [a + k * (b - a) / n for k in range(n + 1)]
ycoords = [f(k) for k in xcoords]
dycoords = [misc.derivative(f, k) for k in xcoords]
for k in range(n):
M = np.array([(1, xcoords[k], xcoords[k] ** 2, xcoords[k] ** 3),
(1, xcoords[k + 1], xcoords[k + 1] ** 2, xcoords[k + 1] ** 3),
(0, 1, 2 * xcoords[k], 3 * xcoords[k] ** 2), (0, 1, 2 * xcoords[k + 1], 3 * xcoords[k + 1] ** 2)])
N = np.array([(ycoords[k]), (ycoords[k + 1]), (dycoords[k]), (dycoords[k + 1])])
O = np.dot(np.linalg.inv(M), N)
a0, a1, a2, a3 = O[0], O[1], O[2], O[3]
P = [a0, a1, a2, a3]
polynomial_graph(P, xcoords[k], xcoords[k + 1])
coordsx = np.linspace(a, b, 1000)
coordsy = [f(x) for x in coordsx]
plt.plot(coordsx, coordsy, "b-", label="original function")
plt.legend(loc="best")
# Integral approximation with cubic splines
# linear complexity
def interp_splines(f, a, b, n):
I = 0
xcoords = [a + k * (b - a) / n for k in range(n + 1)]
ycoords = [f(k) for k in xcoords]
dycoords = [misc.derivative(f, k) for k in xcoords]
for k in range(n):
a0 = ycoords[k + 1] / (xcoords[k + 1] - xcoords[k])
a1 = ycoords[k] / (xcoords[k] - xcoords[k + 1])
a2 = (dycoords[k] - a0 - a1) / ((xcoords[k] - xcoords[k + 1]) ** 2)
a3 = (dycoords[k + 1] - a0 - a1) / ((xcoords[k + 1] - xcoords[k]) ** 2)
nu0 = -a0 * xcoords[k] - a1 * xcoords[k + 1] - a2 * xcoords[k] * xcoords[k + 1] ** 2 - a3 * xcoords[k] ** 2 * \
xcoords[k + 1]
nu1 = a0 + a1 + a2 * (xcoords[k + 1] ** 2 + 2 * xcoords[k + 1] * xcoords[k]) + a3 * (
xcoords[k] ** 2 + 2 * xcoords[k] * xcoords[k + 1])
nu2 = -a2 * (xcoords[k] + 2 * xcoords[k + 1]) - a3 * (xcoords[k + 1] + 2 * xcoords[k])
nu3 = a2 + a3
P = [nu0, nu1, nu2, nu3]
I += polynomial_integral(P, xcoords[k], xcoords[k + 1])
return I
# Comparing the temporal efficiency of both methods on common functions
def cubic_splines_test(a, b, n):
res_f_ref = [np.sin, np.cos, np.tan, np.exp, np.cosh, np.sinh, np.tanh]
time1, time2 = [], []
p = len(res_f_ref)
for i in range(p):
# method 1
f = res_f_ref[i]
start1 = time.time()
xcoords = [a + k * (b - a) / n for k in range(n + 1)]
ycoords = [f(k) for k in xcoords]
dycoords = [misc.derivative(f, k) for k in xcoords]
for k in range(n):
a0 = ycoords[k + 1] / (xcoords[k + 1] - xcoords[k])
a1 = ycoords[k] / (xcoords[k] - xcoords[k + 1])
a2 = (dycoords[k] - a0 - a1) / ((xcoords[k] - xcoords[k + 1]) ** 2)
a3 = (dycoords[k + 1] - a0 - a1) / ((xcoords[k + 1] - xcoords[k]) ** 2)
nu0 = -a0 * xcoords[k] - a1 * xcoords[k + 1] - a2 * xcoords[k] * xcoords[k + 1] ** 2 - a3 * xcoords[
k] ** 2 * xcoords[k + 1]
nu1 = a0 + a1 + a2 * (xcoords[k + 1] ** 2 + 2 * xcoords[k + 1] * xcoords[k]) + a3 * (
xcoords[k] ** 2 + 2 * xcoords[k] * xcoords[k + 1])
nu2 = -a2 * (xcoords[k] + 2 * xcoords[k + 1]) - a3 * (xcoords[k + 1] + 2 * xcoords[k])
nu3 = a2 + a3
P = [nu0, nu1, nu2, nu3]
end1 = time.time()
time1 += [end1 - start1]
# method 2
start2 = time.time()
xcoords = [a + k * (b - a) / n for k in range(n + 1)]
ycoords = [f(k) for k in xcoords]
dycoords = [misc.derivative(f, k) for k in xcoords]
for k in range(n):
M = np.array([(1, xcoords[k], xcoords[k] ** 2, xcoords[k] ** 3),
(1, xcoords[k + 1], xcoords[k + 1] ** 2, xcoords[k + 1] ** 3),
(0, 1, 2 * xcoords[k], 3 * xcoords[k] ** 2),
(0, 1, 2 * xcoords[k + 1], 3 * xcoords[k + 1] ** 2)])
N = np.array([(ycoords[k]), (ycoords[k + 1]), (dycoords[k]), (dycoords[k + 1])])
O = np.dot(np.linalg.inv(M), N)
a0, a1, a2, a3 = O[0], O[1], O[2], O[3]
P = [a0, a1, a2, a3]
end2 = time.time()
time2 += [end2 - start2]
plt.clf()
sigma1, sigma2 = 0, 0
n = len(time1)
print(time1, time2)
for k in range(n):
sigma1 += time1[k]
sigma2 += time2[k]
print(sigma1 / n, sigma2 / n)
# First method is a bit faster on reference functions
| 44.215385
| 120
| 0.483125
| 890
| 5,748
| 3.104494
| 0.11573
| 0.28375
| 0.159609
| 0.098444
| 0.783569
| 0.772711
| 0.766196
| 0.766196
| 0.766196
| 0.766196
| 0
| 0.070189
| 0.328288
| 5,748
| 129
| 121
| 44.55814
| 0.645429
| 0.042971
| 0
| 0.685185
| 0
| 0
| 0.008377
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.037037
| false
| 0
| 0.037037
| 0
| 0.083333
| 0.018519
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
013c01b4e51a8f310d553c5020ab688bf2779022
| 22
|
py
|
Python
|
func/__init__.py
|
igroykt/letsencrypt-nic
|
67fb823f1435be1f109e3bab1f09579452814cb0
|
[
"BSD-3-Clause"
] | 4
|
2021-11-13T15:22:48.000Z
|
2022-02-25T04:01:38.000Z
|
func/__init__.py
|
igroykt/letsencrypt-nic
|
67fb823f1435be1f109e3bab1f09579452814cb0
|
[
"BSD-3-Clause"
] | 1
|
2022-02-08T09:02:03.000Z
|
2022-02-15T07:06:43.000Z
|
func/__init__.py
|
igroykt/letsencrypt-nic
|
67fb823f1435be1f109e3bab1f09579452814cb0
|
[
"BSD-3-Clause"
] | 2
|
2021-01-11T16:58:35.000Z
|
2022-01-14T12:26:51.000Z
|
from .func import Func
| 22
| 22
| 0.818182
| 4
| 22
| 4.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 22
| 1
| 22
| 22
| 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
|
018a7736136d6f2f8972576d1ed33c37ee61cea2
| 86
|
py
|
Python
|
tests/__init__.py
|
valleygtc/flask-sampleproject
|
1dcd0d4ebb38c269b1f2367e3fe9f73bd4f85fd7
|
[
"BSD-3-Clause"
] | null | null | null |
tests/__init__.py
|
valleygtc/flask-sampleproject
|
1dcd0d4ebb38c269b1f2367e3fe9f73bd4f85fd7
|
[
"BSD-3-Clause"
] | null | null | null |
tests/__init__.py
|
valleygtc/flask-sampleproject
|
1dcd0d4ebb38c269b1f2367e3fe9f73bd4f85fd7
|
[
"BSD-3-Clause"
] | null | null | null |
from app import create_app
test_app = create_app()
test_app.config['TESTING'] = True
| 17.2
| 33
| 0.767442
| 14
| 86
| 4.428571
| 0.571429
| 0.290323
| 0.419355
| 0.516129
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.127907
| 86
| 4
| 34
| 21.5
| 0.826667
| 0
| 0
| 0
| 0
| 0
| 0.081395
| 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
|
0196ea863ed60864e30f37c85ccd60520ee71c6c
| 34
|
py
|
Python
|
CodeWars/8 Kyu/get character from ASCII Value.py
|
anubhab-code/Competitive-Programming
|
de28cb7d44044b9e7d8bdb475da61e37c018ac35
|
[
"MIT"
] | null | null | null |
CodeWars/8 Kyu/get character from ASCII Value.py
|
anubhab-code/Competitive-Programming
|
de28cb7d44044b9e7d8bdb475da61e37c018ac35
|
[
"MIT"
] | null | null | null |
CodeWars/8 Kyu/get character from ASCII Value.py
|
anubhab-code/Competitive-Programming
|
de28cb7d44044b9e7d8bdb475da61e37c018ac35
|
[
"MIT"
] | null | null | null |
def get_char(c):
return chr(c)
| 17
| 17
| 0.647059
| 7
| 34
| 3
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205882
| 34
| 2
| 17
| 17
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
6d9c6232ebdf6f139bb0430191694d375684e554
| 389
|
py
|
Python
|
test.py
|
noncepool/gapcoin-hash-python
|
e557d3a551ceb15b7009898ba9f64bff9db4d346
|
[
"MIT"
] | null | null | null |
test.py
|
noncepool/gapcoin-hash-python
|
e557d3a551ceb15b7009898ba9f64bff9db4d346
|
[
"MIT"
] | null | null | null |
test.py
|
noncepool/gapcoin-hash-python
|
e557d3a551ceb15b7009898ba9f64bff9db4d346
|
[
"MIT"
] | null | null | null |
def _test():
import gapcoin_hash
header_hex = '02000000cce93da7214414192b753a52a6603f9dd9d910f78e3bff966cefc181f1c397d80fb872152769598c341f9139b14d63d2e95712e50cf8c70e97268d5f7692b775c0c1545400000000000001000e0000001400df7d07'
hash_int = gapcoin_hash.getpowdiff(header_hex)
print hash_int # hash_int = 3421862101076924
if __name__ == '__main__':
_test()
| 35.363636
| 197
| 0.807198
| 25
| 389
| 11.88
| 0.6
| 0.070707
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.444444
| 0.143959
| 389
| 10
| 198
| 38.9
| 0.447447
| 0.069409
| 0
| 0
| 0
| 0
| 0.516667
| 0.494444
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.142857
| null | null | 0.142857
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
0969d7e85394f3787b7adaf792bb6f81b3dad3aa
| 81
|
py
|
Python
|
Proyecto1/cloud_function/dummy-test.py
|
jasago/SOA2022-1
|
39f142f786887e50eae85e9f90b4a6194164bdc1
|
[
"MIT"
] | null | null | null |
Proyecto1/cloud_function/dummy-test.py
|
jasago/SOA2022-1
|
39f142f786887e50eae85e9f90b4a6194164bdc1
|
[
"MIT"
] | null | null | null |
Proyecto1/cloud_function/dummy-test.py
|
jasago/SOA2022-1
|
39f142f786887e50eae85e9f90b4a6194164bdc1
|
[
"MIT"
] | 11
|
2022-02-22T21:38:08.000Z
|
2022-03-02T04:52:35.000Z
|
from google.cloud import storage
import pytest
def dummy_test():
assert 3!=4
| 16.2
| 32
| 0.753086
| 13
| 81
| 4.615385
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029851
| 0.17284
| 81
| 5
| 33
| 16.2
| 0.865672
| 0
| 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
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
09bfc09f974d88fccc4b496ca4cda7f5a016efc8
| 3,552
|
py
|
Python
|
TD/src/acrobot_gridsearch.py
|
lucasgit/rl
|
1c4bbfad0b11c040ece2b9a384f3781de2c729ca
|
[
"MIT"
] | 1
|
2022-01-21T13:52:50.000Z
|
2022-01-21T13:52:50.000Z
|
TD/src/acrobot_gridsearch.py
|
lucaslehnert/pgq
|
1c4bbfad0b11c040ece2b9a384f3781de2c729ca
|
[
"MIT"
] | null | null | null |
TD/src/acrobot_gridsearch.py
|
lucaslehnert/pgq
|
1c4bbfad0b11c040ece2b9a384f3781de2c729ca
|
[
"MIT"
] | null | null | null |
'''
Created on Apr 24, 2016
@author: Lucas Lehnert (lucas.lehnert@mail.mcgill.ca)
Script to generate configurations for acrobot experiment.
'''
import json
import os
experimentDir = '../experiment/acrobot'
if not os.path.exists( experimentDir ):
os.makedirs( experimentDir )
def create_ac_all():
config = {}
params = {}
params['-e'] = [100]
params['-i'] = [1500]
params['-R'] = [20]
params['-a'] = [.1]
params['-b'] = [.005]
params['-A'] = ['Q', 'GQ', 'GQ2', 'PGQ', 'PGQ2']
params['--behaviorTemperature'] = [1.1]
params['--targetTemperature'] = [.5]
config['parameter'] = params
config['name'] = 'ac_all'
config['script'] = 'acrobot.py'
config['resultDir'] = experimentDir
config['logDir'] = experimentDir
with open( experimentDir + '/acrobot_experiment_config_ac_all.json', 'wb' ) as fp:
json.dump( config, fp )
def create_ac_all2():
config = {}
params = {}
params['-e'] = [100]
params['-i'] = [1500]
params['-R'] = [20]
params['-a'] = [.005, .01, .05, .1]
params['-b'] = [.005, .01, .05, .1]
params['-A'] = ['Q', 'GQ', 'GQ2', 'PGQ', 'PGQ2']
params['--behaviorTemperature'] = [1.5]
params['--targetTemperature'] = [.2]
config['parameter'] = params
config['name'] = 'ac_all'
config['script'] = 'acrobot.py'
config['resultDir'] = experimentDir
config['logDir'] = experimentDir
with open( experimentDir + '/acrobot_experiment_config_ac_all2.json', 'wb' ) as fp:
json.dump( config, fp )
def create_ac_GQ2_PGQ2():
config = {}
params = {}
params['-e'] = [100]
params['-i'] = [1500]
params['-R'] = [20]
params['-a'] = [.001, .005, .01, .05, .1, .2]
params['-b'] = [.001, .005, .01, .05, .1, .2]
params['-A'] = ['GQ2', 'PGQ2']
params['--behaviorTemperature'] = [1.1]
params['--targetTemperature'] = [.5]
config['parameter'] = params
config['name'] = 'ac_GQ2_PGQ2'
config['script'] = 'acrobot.py'
config['resultDir'] = experimentDir
config['logDir'] = experimentDir
with open( experimentDir + '/acrobot_experiment_config_ac_GQ2_PGQ2.json', 'wb' ) as fp:
json.dump( config, fp )
def create_ac_all_500():
config = {}
params = {}
params['-e'] = [500]
params['-i'] = [1500]
params['-R'] = [1] * 20
params['-a'] = [.1]
params['-b'] = [.005]
params['-A'] = ['Q', 'GQ', 'PGQ']
params['--behaviorTemperature'] = [1.1]
params['--targetTemperature'] = [.5]
config['parameter'] = params
config['name'] = 'ac_all_500'
config['script'] = 'acrobot.py'
config['resultDir'] = experimentDir
config['logDir'] = experimentDir
with open( experimentDir + '/acrobot_experiment_config_ac_all_500.json', 'wb' ) as fp:
json.dump( config, fp )
def create_ac_all_1000():
config = {}
params = {}
params['-e'] = [1000]
params['-i'] = [1500]
params['-R'] = [1] * 20
params['-a'] = [.1]
params['-b'] = [.005]
params['-A'] = ['Q', 'GQ', 'PGQ']
params['--behaviorTemperature'] = [1.1]
params['--targetTemperature'] = [.5]
config['parameter'] = params
config['name'] = 'ac_all_1000'
config['script'] = 'acrobot.py'
config['resultDir'] = experimentDir
config['logDir'] = experimentDir
with open( experimentDir + '/acrobot_experiment_config_ac_all_1000.json', 'wb' ) as fp:
json.dump( config, fp )
create_ac_all()
create_ac_all2()
create_ac_GQ2_PGQ2()
create_ac_all_500()
create_ac_all_1000()
| 24.666667
| 91
| 0.572917
| 414
| 3,552
| 4.777778
| 0.166667
| 0.032861
| 0.033367
| 0.048028
| 0.788675
| 0.777553
| 0.777553
| 0.759353
| 0.746208
| 0.746208
| 0
| 0.059734
| 0.217624
| 3,552
| 143
| 92
| 24.839161
| 0.652033
| 0.03857
| 0
| 0.68
| 1
| 0
| 0.236486
| 0.097239
| 0
| 0
| 0
| 0
| 0
| 1
| 0.05
| false
| 0
| 0.02
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| 0
| null | 0
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| 1
| 1
| 1
| 1
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| 0
| 1
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
09d444e39b57e1c7128be29aad421aa436a5bf8f
| 96
|
py
|
Python
|
Regularization/__init__.py
|
Kthyeon/micronet_neurips_challenge
|
9f71fb752e8fbd5abca07be530f7fb19e164125c
|
[
"MIT"
] | 19
|
2019-11-27T07:18:35.000Z
|
2021-08-20T14:16:17.000Z
|
Regularization/__init__.py
|
3outeille/KAIST-AI-NeurIPS2019-MicroNet-2nd-place-solution
|
9f71fb752e8fbd5abca07be530f7fb19e164125c
|
[
"MIT"
] | null | null | null |
Regularization/__init__.py
|
3outeille/KAIST-AI-NeurIPS2019-MicroNet-2nd-place-solution
|
9f71fb752e8fbd5abca07be530f7fb19e164125c
|
[
"MIT"
] | 6
|
2019-12-18T02:09:54.000Z
|
2021-06-21T11:34:36.000Z
|
from .orthogonal_weight import *
from .label_regularize import *
from .input_regularize import *
| 32
| 32
| 0.822917
| 12
| 96
| 6.333333
| 0.583333
| 0.263158
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114583
| 96
| 3
| 33
| 32
| 0.894118
| 0
| 0
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| 0
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| 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
|
61f9766c4ba0c9cdbba1921372905c540527fe8f
| 37
|
py
|
Python
|
schwa/learning/__init__.py
|
SBST-DPG/schwa
|
d09660e4b5bb665114c35ebe291e5620e59f4c4c
|
[
"MIT"
] | 9
|
2015-05-21T10:13:27.000Z
|
2020-11-06T22:21:03.000Z
|
schwa/learning/__init__.py
|
XiaoxueRenS/schwa
|
d09660e4b5bb665114c35ebe291e5620e59f4c4c
|
[
"MIT"
] | 5
|
2021-01-12T09:57:36.000Z
|
2021-07-20T08:29:16.000Z
|
schwa/learning/__init__.py
|
XiaoxueRenS/schwa
|
d09660e4b5bb665114c35ebe291e5620e59f4c4c
|
[
"MIT"
] | 9
|
2015-05-14T09:31:15.000Z
|
2021-02-07T02:53:17.000Z
|
from .feature_weight_learner import *
| 37
| 37
| 0.864865
| 5
| 37
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081081
| 37
| 1
| 37
| 37
| 0.882353
| 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
|
1113fa6ab359f66cafeff3a079843324c3dccab2
| 128
|
py
|
Python
|
m2data/__init__.py
|
Mindful/m2data
|
c1f6f978ed44d622bdcce30d6098131919d60a99
|
[
"MIT"
] | null | null | null |
m2data/__init__.py
|
Mindful/m2data
|
c1f6f978ed44d622bdcce30d6098131919d60a99
|
[
"MIT"
] | null | null | null |
m2data/__init__.py
|
Mindful/m2data
|
c1f6f978ed44d622bdcce30d6098131919d60a99
|
[
"MIT"
] | null | null | null |
from m2data.example import Example
from m2data.reader import Reader, M2ReaderException
from m2data.correction import Correction
| 32
| 51
| 0.867188
| 16
| 128
| 6.9375
| 0.4375
| 0.27027
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034783
| 0.101563
| 128
| 3
| 52
| 42.666667
| 0.930435
| 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
|
1114af6cac7c9733214a6506f351cbb6c7f858f9
| 8,897
|
py
|
Python
|
tests/legacy/functional/test_fill_pdf.py
|
DEVANATH45/PyPDFForm
|
924954410e071f54763cdd5e2a5641ae0ab7341b
|
[
"MIT"
] | null | null | null |
tests/legacy/functional/test_fill_pdf.py
|
DEVANATH45/PyPDFForm
|
924954410e071f54763cdd5e2a5641ae0ab7341b
|
[
"MIT"
] | null | null | null |
tests/legacy/functional/test_fill_pdf.py
|
DEVANATH45/PyPDFForm
|
924954410e071f54763cdd5e2a5641ae0ab7341b
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import os
import pytest
from PyPDFForm.legacy import PyPDFForm
@pytest.fixture
def pdf_samples():
return os.path.join(os.path.dirname(__file__), "../..", "..", "pdf_samples")
@pytest.fixture
def template_stream(pdf_samples):
with open(os.path.join(pdf_samples, "sample_template.pdf"), "rb+") as f:
return f.read()
@pytest.fixture
def comparing_size():
return 32767
def test_fill_simple_mode(template_stream, pdf_samples, comparing_size):
with open(os.path.join(pdf_samples, "sample_filled_simple_mode.pdf"), "rb+") as f:
obj = PyPDFForm(template_stream).fill(
{
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
},
)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
def test_fill_font_20(template_stream, pdf_samples, comparing_size):
with open(os.path.join(pdf_samples, "sample_filled_font_20.pdf"), "rb+") as f:
data_dict = {
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
}
obj = PyPDFForm(template_stream, simple_mode=False).fill(
data_dict,
font_size=20,
)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
for k, v in obj.elements.items():
assert k in data_dict
assert v.name in data_dict
assert v.value == data_dict[k]
if v.type == "text":
assert v.font_size == 20
assert v.font_color == (0, 0, 0)
assert v.text_x_offset == 0
assert v.text_y_offset == 0
assert v.text_wrap_length == 100
def test_fill_font_color_red(template_stream, pdf_samples, comparing_size):
with open(
os.path.join(pdf_samples, "sample_filled_font_color_red.pdf"), "rb+"
) as f:
data_dict = {
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
}
obj = PyPDFForm(template_stream, simple_mode=False).fill(
data_dict,
font_color=(1, 0, 0),
)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
for k, v in obj.elements.items():
assert k in data_dict
assert v.name in data_dict
assert v.value == data_dict[k]
if v.type == "text":
assert v.font_size == 12
assert v.font_color == (1, 0, 0)
assert v.text_x_offset == 0
assert v.text_y_offset == 0
assert v.text_wrap_length == 100
def test_fill_text_wrap_2(template_stream, pdf_samples, comparing_size):
with open(os.path.join(pdf_samples, "sample_filled_text_wrap_2.pdf"), "rb+") as f:
data_dict = {
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
}
obj = PyPDFForm(template_stream, simple_mode=False).fill(
data_dict,
text_wrap_length=2,
)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
for k, v in obj.elements.items():
assert k in data_dict
assert v.name in data_dict
assert v.value == data_dict[k]
if v.type == "text":
assert v.font_size == 12
assert v.font_color == (0, 0, 0)
assert v.text_x_offset == 0
assert v.text_y_offset == 0
assert v.text_wrap_length == 2
def test_fill_offset_100(template_stream, pdf_samples, comparing_size):
with open(os.path.join(pdf_samples, "sample_filled_offset_100.pdf"), "rb+") as f:
data_dict = {
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
}
obj = PyPDFForm(template_stream, simple_mode=False).fill(
data_dict,
text_x_offset=100,
text_y_offset=-100,
)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
for k, v in obj.elements.items():
assert k in data_dict
assert v.name in data_dict
assert v.value == data_dict[k]
if v.type == "text":
assert v.font_size == 12
assert v.font_color == (0, 0, 0)
assert v.text_x_offset == 100
assert v.text_y_offset == -100
assert v.text_wrap_length == 100
def test_fill_editable(template_stream, pdf_samples, comparing_size):
with open(os.path.join(pdf_samples, "sample_filled_editable.pdf"), "rb+") as f:
obj = PyPDFForm(template_stream, simple_mode=True).fill(
{
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
},
editable=True,
)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
def test_fill_with_customized_elements(template_stream, pdf_samples, comparing_size):
with open(
os.path.join(pdf_samples, "sample_filled_customized_elements.pdf"), "rb+"
) as f:
data_dict = {
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
}
obj = PyPDFForm(template_stream, simple_mode=False)
obj.elements["test"].font_size = 20
obj.elements["test_2"].text_x_offset = 50
obj.elements["test_2"].text_y_offset = -50
obj.elements["test_2"].text_wrap_length = 1
obj.elements["test_3"].text_wrap_length = 2
obj.fill(data_dict)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
for k, v in obj.elements.items():
assert k in data_dict
assert v.name in data_dict
assert v.value == data_dict[k]
assert obj.elements["test"].font_size == 20
assert obj.elements["test"].text_x_offset == 0
assert obj.elements["test"].text_y_offset == 0
assert obj.elements["test"].text_wrap_length == 100
assert obj.elements["test_2"].font_size == 12
assert obj.elements["test_2"].text_x_offset == 50
assert obj.elements["test_2"].text_y_offset == -50
assert obj.elements["test_2"].text_wrap_length == 1
assert obj.elements["test_3"].font_size == 12
assert obj.elements["test_3"].text_x_offset == 0
assert obj.elements["test_3"].text_y_offset == 0
assert obj.elements["test_3"].text_wrap_length == 2
def test_fill_with_customized_colors(template_stream, pdf_samples, comparing_size):
with open(
os.path.join(pdf_samples, "sample_filled_customized_colors.pdf"), "rb+"
) as f:
data_dict = {
"test": "test_1",
"check": True,
"test_2": "test_2",
"check_2": False,
"test_3": "test_3",
"check_3": True,
}
obj = PyPDFForm(template_stream, simple_mode=False)
obj.elements["test"].font_color = (1, 0, 0)
obj.elements["test_2"].font_color = (0, 1, 0)
obj.elements["test_3"].font_color = (0, 0, 1)
obj.fill(data_dict)
expected = f.read()
assert len(obj.stream) == len(expected)
assert obj.stream[:comparing_size] == expected[:comparing_size]
for k, v in obj.elements.items():
assert k in data_dict
assert v.name in data_dict
assert v.value == data_dict[k]
assert obj.elements["test"].font_color == (1, 0, 0)
assert obj.elements["test_2"].font_color == (0, 1, 0)
assert obj.elements["test_3"].font_color == (0, 0, 1)
| 31
| 86
| 0.553333
| 1,140
| 8,897
| 4.051754
| 0.064912
| 0.048495
| 0.074691
| 0.068197
| 0.903226
| 0.88569
| 0.86837
| 0.852349
| 0.746698
| 0.714873
| 0
| 0.031333
| 0.322019
| 8,897
| 286
| 87
| 31.108392
| 0.734416
| 0.00236
| 0
| 0.645455
| 0
| 0
| 0.097927
| 0.027158
| 0
| 0
| 0
| 0
| 0.313636
| 1
| 0.05
| false
| 0
| 0.013636
| 0.009091
| 0.077273
| 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
|
1125c81f8aa38672dc5328aacac9a6521635c09d
| 98
|
py
|
Python
|
src/starkware/contracts/upgrade/contracts.py
|
starkware-libs/starkgate-contracts
|
28f4032b101003b2c6682d753ea61c86b732012c
|
[
"Apache-2.0"
] | 9
|
2022-01-27T20:20:06.000Z
|
2022-03-29T12:05:57.000Z
|
src/starkware/contracts/upgrade/contracts.py
|
starkware-libs/starkgate-contracts
|
28f4032b101003b2c6682d753ea61c86b732012c
|
[
"Apache-2.0"
] | 2
|
2022-02-16T17:05:56.000Z
|
2022-02-16T17:06:54.000Z
|
src/starkware/contracts/upgrade/contracts.py
|
starkware-libs/starkgate-contracts
|
28f4032b101003b2c6682d753ea61c86b732012c
|
[
"Apache-2.0"
] | 1
|
2022-02-03T13:39:44.000Z
|
2022-02-03T13:39:44.000Z
|
from starkware.contracts.utils import load_nearby_contract
Proxy = load_nearby_contract("Proxy")
| 24.5
| 58
| 0.846939
| 13
| 98
| 6.076923
| 0.692308
| 0.253165
| 0.455696
| 0.582278
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081633
| 98
| 3
| 59
| 32.666667
| 0.877778
| 0
| 0
| 0
| 0
| 0
| 0.05102
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
|
11270ca53421685eba17b65e8b86c85abf352b66
| 41,382
|
py
|
Python
|
accountant/core/tests/test_api/test_transfer_share.py
|
XeryusTC/18xx-accountant
|
5dc70fb96042807ceaaadb51cea3108da4f40d85
|
[
"MIT"
] | null | null | null |
accountant/core/tests/test_api/test_transfer_share.py
|
XeryusTC/18xx-accountant
|
5dc70fb96042807ceaaadb51cea3108da4f40d85
|
[
"MIT"
] | 7
|
2017-03-29T18:52:44.000Z
|
2017-09-05T19:06:29.000Z
|
accountant/core/tests/test_api/test_transfer_share.py
|
XeryusTC/18xx-accountant
|
5dc70fb96042807ceaaadb51cea3108da4f40d85
|
[
"MIT"
] | 1
|
2019-12-16T22:27:07.000Z
|
2019-12-16T22:27:07.000Z
|
# -*- coding: utf-8 -*-
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase
from unittest import mock
from ... import factories
from ... import models
from ... import utils
from ... import views
class ShareTransactionTests(APITestCase):
def setUp(self):
self.game = factories.GameFactory()
self.url = reverse('transfer_share')
self.player = factories.PlayerFactory(game=self.game, cash=100)
# Company to buy shares from
self.source_company = factories.CompanyFactory(game=self.game,
cash=100)
# Company to buy shares with
self.buy_company = factories.CompanyFactory(game=self.game, cash=100)
# Company to buy shares in
self.share_company = factories.CompanyFactory(game=self.game, cash=0,
ipo_shares=5, bank_shares=5)
self.data = {'share': self.share_company.pk, 'amount': 1}
def test_buying_from_ipo_includes_game_instance_in_response(self):
self.data.update({'source_type': 'ipo', 'price': 10,
'buyer_type': 'player', 'player_buyer': self.player.pk})
response = self.client.post(self.url, self.data)
self.assertEqual(response.data['game']['uuid'], str(self.game.pk))
self.assertEqual(response.data['game']['cash'], 12010)
def test_buying_from_bank_includes_game_instance_in_response(self):
self.data.update({'source_type': 'bank', 'price': 20,
'buyer_type': 'player', 'player_buyer': self.player.pk})
response = self.client.post(self.url, self.data)
self.assertEqual(response.data['game']['uuid'], str(self.game.pk))
self.assertEqual(response.data['game']['cash'], 12020)
def test_ipo_buying_share_includes_game_instance_in_response(self):
self.data.update({'buyer_type': 'ipo', 'price': 30,
'source_type': 'player', 'player_source': self.player.pk})
response = self.client.post(self.url, self.data)
self.assertEqual(response.data['game']['uuid'], str(self.game.pk))
self.assertEqual(response.data['game']['cash'], 11970)
def test_bank_buying_share_includes_game_instance_in_response(self):
self.data.update({'buyer_type': 'bank', 'price': 40,
'source_type': 'player', 'player_source': self.player.pk})
response = self.client.post(self.url, self.data)
self.assertEqual(response.data['game']['uuid'], str(self.game.pk))
self.assertEqual(response.data['game']['cash'], 11960)
def test_company_whos_share_is_being_bought_is_always_in_response(self):
self.data.update({'source_type': 'ipo', 'price': 50,
'buyer_type': 'player', 'player_buyer': self.player.pk})
response = self.client.post(self.url, self.data)
self.assertEqual(len(response.data['companies']), 1)
self.assertEqual(str(self.share_company.pk),
response.data['companies'][0]['uuid'])
def test_company_buying_share_is_in_response(self):
self.data.update({'source_type': 'ipo', 'price': 60,
'buyer_type': 'company', 'company_buyer': self.buy_company.pk})
response = self.client.post(self.url, self.data)
self.buy_company.refresh_from_db()
self.assertIn(str(self.buy_company.pk),
[c['uuid'] for c in response.data['companies']])
self.assertEqual(self.buy_company.cash, 40)
self.assertIn(40, [c['cash'] for c in response.data['companies']])
def test_company_buying_itself_is_not_in_response_twice(self):
self.data.update({'source_type': 'ipo', 'price': 0,
'buyer_type': 'company', 'company_buyer': self.share_company.pk})
response = self.client.post(self.url, self.data)
self.share_company.refresh_from_db()
self.assertEqual(len(response.data['companies']), 1)
self.assertEqual(response.data['companies'][0]['uuid'],
str(self.share_company.pk))
self.assertEqual(response.data['companies'][0]['ipo_shares'], 4)
def test_company_selling_itself_is_not_in_response_twice(self):
factories.CompanyShareFactory(owner=self.share_company,
company=self.share_company, shares=5)
self.data.update({'price': 1, 'source_type': 'company',
'company_source': self.share_company.pk, 'buyer_type': 'ipo',
'share': self.share_company.pk})
response = self.client.post(self.url, self.data)
self.share_company.refresh_from_db()
self.assertEqual(len(response.data['companies']), 1)
self.assertEqual(str(self.share_company.pk),
response.data['companies'][0]['uuid'])
self.assertEqual(response.data['companies'][0]['ipo_shares'], 6)
def test_player_buying_share_is_in_response(self):
self.data.update({'source_type': 'ipo', 'price': 70,
'buyer_type': 'player', 'player_buyer': self.player.pk})
response = self.client.post(self.url, self.data)
self.player.refresh_from_db()
self.assertEqual(str(self.player.pk),
response.data['players'][0]['uuid'])
self.assertEqual(response.data['players'][0]['cash'], 30)
self.assertEqual(self.player.cash, 30)
def test_company_selling_share_is_in_response(self):
factories.CompanyShareFactory(owner=self.source_company,
company=self.share_company)
self.data.update({'buyer_type': 'bank', 'price': 80,
'source_type': 'company',
'company_source': self.source_company.pk})
response = self.client.post(self.url, self.data)
self.source_company.refresh_from_db()
self.assertIn(str(self.source_company.pk),
[c['uuid'] for c in response.data['companies']])
self.assertEqual(self.source_company.cash, 180)
self.assertIn(180, [c['cash'] for c in response.data['companies']])
def test_player_selling_share_is_in_response(self):
self.data.update({'buyer_type': 'bank', 'price': 90,
'source_type': 'player', 'player_source': self.player.pk})
response = self.client.post(self.url, self.data)
self.player.refresh_from_db()
self.assertEqual(str(self.player.pk),
response.data['players'][0]['uuid'])
self.assertEqual(response.data['players'][0]['cash'], 190)
self.assertEqual(self.player.cash, 190)
def test_when_player_buys_share_the_share_instance_is_in_response(self):
self.data.update({'price': 100, 'buyer_type': 'player',
'player_buyer': self.player.pk, 'source_type': 'ipo'})
response = self.client.post(self.url, self.data)
self.assertEqual(len(response.data['shares']), 1)
self.assertEqual(str(self.player.share_set.first().pk),
response.data['shares'][0]['uuid'])
def test_game_not_in_response_when_bank_or_ipo_not_involved(self):
factories.CompanyShareFactory(owner=self.share_company,
company=self.share_company)
self.data.update({'price': 105, 'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'company',
'company_source': self.share_company.pk})
response = self.client.post(self.url, self.data)
self.assertNotIn('game', response.data)
def test_players_key_not_in_response_when_no_player_involved(self):
factories.CompanyShareFactory(owner=self.share_company,
company=self.share_company)
self.data.update({'price': 105, 'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'company',
'company_source': self.share_company.pk})
response = self.client.post(self.url, self.data)
self.assertNotIn('players', response.data)
def test_when_company_buys_share_the_share_instance_is_in_response(self):
self.data.update({'price': 110, 'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'ipo'})
response = self.client.post(self.url, self.data)
self.assertEqual(len(response.data['shares']), 1)
self.assertEqual(str(self.buy_company.share_set.first().pk),
response.data['shares'][0]['uuid'])
def test_when_player_sells_share_the_share_instance_is_in_response(self):
factories.PlayerShareFactory(owner=self.player,
company=self.share_company, shares=3)
self.data.update({'price': 120, 'buyer_type': 'bank',
'source_type': 'player', 'player_source': self.player.pk})
response = self.client.post(self.url, self.data)
self.assertEqual(len(response.data['shares']), 1)
self.assertEqual(str(self.player.share_set.first().pk),
response.data['shares'][0]['uuid'])
self.assertEqual(response.data['shares'][0]['shares'], 2)
def test_when_company_sells_share_the_share_instance_is_in_response(self):
factories.CompanyShareFactory(owner=self.buy_company,
company=self.share_company, shares=4)
self.data.update({'price': 130, 'buyer_type': 'bank',
'source_type': 'company', 'company_source': self.buy_company.pk})
response = self.client.post(self.url, self.data)
self.assertEqual(len(response.data['shares']), 1)
self.assertEqual(str(self.buy_company.share_set.first().pk),
response.data['shares'][0]['uuid'])
self.assertEqual(response.data['shares'][0]['shares'], 3)
def test_share_company_is_up_to_date_in_response(self):
self.data.update({'price': -14, 'buyer_type': 'company',
'company_buyer': self.share_company.pk, 'source_type': 'ipo'})
response = self.client.post(self.url, self.data)
self.share_company.refresh_from_db()
self.assertEqual(self.share_company.cash,
response.data['companies'][0]['cash'])
self.assertEqual(self.share_company.cash, 14)
@mock.patch.object(utils, 'buy_share')
class ShareTransactionWithMockTests(APITestCase):
def setUp(self):
self.game = factories.GameFactory(cash=1000)
self.url = reverse('transfer_share')
self.player = factories.PlayerFactory(game=self.game, cash=100)
self.source_company = factories.CompanyFactory(game=self.game, cash=0)
self.buy_company = factories.CompanyFactory(game=self.game, cash=0)
factories.PlayerShareFactory(owner=self.player,
company=self.source_company, shares=5)
factories.CompanyShareFactory(owner=self.source_company,
company=self.source_company)
factories.CompanyShareFactory(owner=self.buy_company,
company=self.source_company)
factories.CompanyShareFactory(owner=self.buy_company,
company=self.buy_company)
def test_GET_request_is_empty(self, mock):
"""GET is for debug (and doc) purposes only"""
response = self.client.get(self.url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertIsNone(response.data)
def test_player_can_buy_from_ipo(self, mock_buy_share):
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'ipo', 'share': self.source_company.pk, 'price': 1}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.player,
self.source_company, utils.Share.IPO, 1, 1)
def test_player_can_buy_from_bank_pool(self, mock_buy_share):
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'bank', 'share': self.source_company.pk, 'price': 2}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.player,
self.source_company, utils.Share.BANK, 2, 1)
def test_player_can_buy_from_company_treasury(self, mock_buy_share):
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'company', 'company_source': self.source_company.pk,
'share': self.source_company.pk, 'price': 3}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.player,
self.source_company, self.source_company, 3, 1)
def test_player_can_sell_to_ipo(self, mock_buy_share):
data = {'buyer_type': 'ipo', 'source_type': 'player',
'player_source': self.player.pk, 'share': self.source_company.pk,
'price': 4}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(utils.Share.IPO,
self.source_company, self.player, 4, 1)
def test_player_can_sell_to_bank_pool(self, mock_buy_share):
data = {'buyer_type': 'bank', 'source_type': 'player',
'player_source': self.player.pk, 'share': self.source_company.pk,
'price': 5}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(utils.Share.BANK,
self.source_company, self.player, 5, 1)
def test_company_can_buy_own_share_from_ipo(self, mock_buy_share):
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'ipo', 'share': self.buy_company.pk,
'price': 6}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.buy_company,
self.buy_company, utils.Share.IPO, 6, 1)
def test_company_can_buy_own_share_from_bank_pool(self, mock_buy_share):
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'bank', 'share': self.buy_company.pk,
'price': 7}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.buy_company,
self.buy_company, utils.Share.BANK, 7, 1)
def test_company_can_buy_from_other_company_ipo(self, mock_buy_share):
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'ipo', 'share': self.source_company.pk,
'price': 8}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.buy_company,
self.source_company, utils.Share.IPO, 8, 1)
def test_company_can_buy_from_other_company_bank_pool(self,
mock_buy_share):
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'bank', 'share': self.source_company.pk,
'price': 9}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.buy_company,
self.source_company, utils.Share.BANK, 9, 1)
def test_company_can_buy_from_other_company_treasury(self, mock_buy_share):
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'company', 'company_source': self.source_company.pk,
'share': self.source_company.pk, 'price': 10}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.buy_company,
self.source_company, self.source_company, 10, 1)
def test_company_can_sell_to_ipo(self, mock_buy_share):
data = {'buyer_type': 'ipo', 'source_type': 'company',
'company_source': self.buy_company.pk,
'share': self.source_company.pk, 'price': 11}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(utils.Share.IPO,
self.source_company, self.buy_company, 11, 1)
def test_company_can_sell_to_bank_pool(self, mock_buy_share):
data = {'buyer_type': 'bank', 'source_type': 'company',
'company_source': self.buy_company.pk,
'share': self.source_company.pk, 'price': 12}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(utils.Share.BANK,
self.source_company, self.buy_company, 12, 1)
def test_player_cannot_buy_from_ipo_if_it_has_no_shares(self,
mock_buy_share):
self.source_company.ipo_shares = 0
self.source_company.save()
mock_buy_share.side_effect = utils.InvalidShareTransaction
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'ipo', 'share': self.source_company.pk, 'price': 13}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_player_cannot_buy_from_bank_pool_if_it_has_no_shares(self,
mock_buy_share):
mock_buy_share.side_effect = utils.InvalidShareTransaction
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'bank', 'share': self.source_company.pk,
'price': 14}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_player_cannot_buy_from_company_if_it_has_no_shares(self,
mock_buy_share):
mock_buy_share.side_effect = utils.InvalidShareTransaction
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'company', 'company_source': self.buy_company.pk,
'share': self.source_company.pk, 'price': 15}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_company_cannot_buy_from_ipo_if_it_has_no_shares(self,
mock_buy_share):
self.source_company.ipo_shares = 0
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'ipo', 'share': self.source_company.pk, 'price': 18}
mock_buy_share.side_effect = utils.InvalidShareTransaction
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_company_cannot_buy_from_bank_pool_if_it_has_no_shares(self,
mock_buy_share):
self.source_company.bank_shares = 0
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'bank', 'share': self.source_company.pk,
'price': 19}
mock_buy_share.side_effect = utils.InvalidShareTransaction
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_company_cannot_buy_from_other_company_if_it_has_no_shares(self,
mock_buy_share):
mock_buy_share.side_effect = utils.InvalidShareTransaction
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'company', 'company_source': self.source_company.pk,
'share': self.source_company.pk, 'price': 20}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_company_cannot_sell_to_ipo_if_it_has_no_shares(self,
mock_buy_share):
mock_buy_share.side_effect = utils.InvalidShareTransaction
data = {'buyer_type': 'ipo', 'source_type': 'company',
'company_source': self.buy_company.pk,
'share': self.source_company.pk, 'price': 21}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_company_cannot_sell_to_bank_pool_if_it_has_no_shares(self,
mock_buy_share):
mock_buy_share.side_effect = utils.InvalidShareTransaction
data = {'buyer_type': 'bank', 'source_type': 'company',
'company_source': self.buy_company.pk,
'share': self.source_company.pk, 'price': 22}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(views.NO_AVAILABLE_SHARES_ERROR,
response.data['non_field_errors'])
def test_player_buying_negative_shares_is_not_changed(self,
mock_buy_share):
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'ipo', 'share': self.source_company.pk, 'price': 23,
'amount': -2}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.player,
self.source_company, utils.Share.IPO, 23, -2)
def test_company_buying_negative_shares_is_not_changed(self,
mock_buy_share):
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'ipo', 'share': self.source_company.pk, 'price': 25,
'amount': -3}
response = self.client.post(self.url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
mock_buy_share.assert_called_once_with(self.buy_company,
self.source_company, utils.Share.IPO, 25, -3)
def test_gives_error_if_request_is_invalid(self, mock_buy_share):
response = self.client.post(self.url, {})
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_handles_invalid_transaction(self, mock_buy_share):
mock_buy_share.side_effect = utils.InvalidShareTransaction
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'ipo', 'share': self.source_company.pk,
'price': 29, 'amount': 1}
response = self.client.post(self.url, data)
self.assertEqual(response.data['non_field_errors'],
[views.NO_AVAILABLE_SHARES_ERROR])
def test_handles_different_game_exception(self, mock_buy_share):
factories.CompanyFactory()
player = factories.PlayerFactory()
mock_buy_share.side_effect = utils.DifferentGameException
data = {'buyer_type': 'player', 'player_buyer': player.pk,
'source_type': 'ipo', 'amount': 1,
'price': 30, 'share': self.source_company.pk}
response = self.client.post(self.url, data)
self.assertEqual(response.data['non_field_errors'],
[views.DIFFERENT_GAME_ERROR])
def test_does_not_handle_other_exceptions(self, mock_buy_share):
mock_buy_share.side_effect = Exception
data = {'buyer_type': 'company', 'company_buyer': self.buy_company.pk,
'source_type': 'ipo', 'share': self.source_company.pk,
'price': 31, 'amount': 1}
with self.assertRaises(Exception):
self.client.post(self.url, data)
@mock.patch.object(utils, 'buy_share')
class ShareTransactionLogTests(APITestCase):
def setUp(self):
self.game = factories.GameFactory(cash=10000)
self.url = reverse('transfer_share')
self.player = factories.PlayerFactory(game=self.game, cash=100)
self.share_company = factories.CompanyFactory(game=self.game, cash=0)
self.buy_company = factories.CompanyFactory(game=self.game, cash=0)
factories.PlayerShareFactory(owner=self.player,
company=self.share_company, shares=0)
factories.CompanyShareFactory(owner=self.buy_company,
company=self.share_company, shares=0)
self.data = {'share': self.share_company.pk}
def make_request(self):
response = self.client.post(self.url, self.data)
self.game.refresh_from_db()
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(1,
models.LogEntry.objects.filter(game=self.game).count())
return response
def test_transfering_includes_log_entry_in_response(self, mock):
data = {'buyer_type': 'player', 'player_buyer': self.player.pk,
'source_type': 'ipo', 'share': self.share_company.pk, 'price': 1}
response = self.client.post(self.url, data)
self.game.refresh_from_db()
self.assertEqual(response.data['log']['uuid'],
str(self.game.log.last().pk))
self.assertEqual(self.game.log_cursor, self.game.log.last())
def test_player_buying_share_from_ipo_creates_log_entry(self, mock):
self.data.update({'buyer_type': 'player',
'player_buyer': self.player.pk, 'source_type': 'ipo', 'price': 2,
'amount': 3})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 3 shares {} from the IPO for 2 each'.format(
self.player.name, self.share_company.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 3)
self.assertEqual(entry.price, 2)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'ipo')
self.assertEqual(entry.company, self.share_company)
def test_player_buying_share_from_bank_creates_log_entry(self, mock):
self.data.update({'buyer_type': 'player', 'source_type': 'bank',
'player_buyer': self.player.pk, 'price': 3, 'amount': 2})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 2 shares {} from the bank for 3 each'.format(
self.player.name, self.share_company.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 2)
self.assertEqual(entry.price, 3)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'bank')
self.assertEqual(entry.company, self.share_company)
def test_player_buying_share_from_company_creates_log_entry(self, mock):
self.data.update({'buyer_type': 'player', 'source_type': 'company',
'player_buyer': self.player.pk,
'company_source': self.buy_company.pk, 'price': 4})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 1 shares {} from {} for 4 each'.format(
self.player.name, self.share_company.name,
self.buy_company.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 1)
self.assertEqual(entry.price, 4)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'company')
self.assertEqual(entry.company_source, self.buy_company)
self.assertEqual(entry.company, self.share_company)
def test_player_buying_share_from_player_creates_log_entry(self, mock):
extra_player = factories.PlayerFactory(game=self.game)
factories.PlayerShareFactory(owner=extra_player,
company=self.share_company, shares=0)
self.data.update({'buyer_type': 'player', 'source_type': 'player',
'player_buyer': self.player.pk, 'player_source': extra_player.pk,
'price': 8, 'amount': 2})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 2 shares {} from {} for 8 each'.format(
self.player.name, self.share_company.name,
extra_player.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 2)
self.assertEqual(entry.price, 8)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'player')
self.assertEqual(entry.player_source, extra_player)
self.assertEqual(entry.company, self.share_company)
def test_company_buying_share_from_ipo_creates_log_entry(self, mock):
self.data.update({'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'ipo',
'price': 5, 'amount': 4})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 4 shares {} from the IPO for 5 each'.format(
self.buy_company.name, self.share_company.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 4)
self.assertEqual(entry.price, 5)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'ipo')
self.assertEqual(entry.company, self.share_company)
def test_company_buying_share_from_bank_creates_log_entry(self, mock):
self.data.update({'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'bank',
'price': 6, 'amount': 7})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 7 shares {} from the bank for 6 each'.format(
self.buy_company.name, self.share_company.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 7)
self.assertEqual(entry.price, 6)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'bank')
self.assertEqual(entry.company, self.share_company)
def test_company_buying_share_from_company_creates_log_entry(self, mock):
extra_company = factories.CompanyFactory(game=self.game, cash=0)
factories.CompanyShareFactory(owner=extra_company,
company=self.share_company, shares=1)
self.data.update({'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'company',
'company_source': extra_company.pk, 'price': 7})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 1 shares {} from {} for 7 each'.format(
self.buy_company.name, self.share_company.name,
extra_company.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 1)
self.assertEqual(entry.price, 7)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'company')
self.assertEqual(entry.company_source, extra_company)
self.assertEqual(entry.company, self.share_company)
def test_company_buying_share_from_player_creates_log_entry(self, mock):
self.data.update({'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'player',
'player_source': self.player.pk, 'price': 9})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} bought 1 shares {} from {} for 9 each'.format(
self.buy_company.name, self.share_company.name,
self.player.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, 1)
self.assertEqual(entry.price, 9)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'player')
self.assertEqual(entry.player_source, self.player)
self.assertEqual(entry.company, self.share_company)
def test_player_selling_share_to_ipo_creates_log_entry(self, mock):
self.data.update({'price': 10, 'buyer_type': 'player',
'player_buyer': self.player.pk, 'source_type': 'ipo',
'amount': -2})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 2 shares {} to the IPO for 10 each'.format(
self.player.name, self.share_company.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -2)
self.assertEqual(entry.price, 10)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'ipo')
self.assertEqual(entry.company, self.share_company)
def test_player_selling_share_to_bank_creates_log_entry(self, mock):
self.data.update({'price': 11, 'buyer_type': 'player',
'player_buyer': self.player.pk, 'source_type': 'bank',
'amount': -3})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 3 shares {} to the bank for 11 each'.format(
self.player.name, self.share_company.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -3)
self.assertEqual(entry.price, 11)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'bank')
self.assertEqual(entry.company, self.share_company)
def test_player_selling_share_to_player_creates_log_entry(self, mock):
extra_player = factories.PlayerFactory(game=self.game)
factories.PlayerShareFactory(owner=extra_player,
company=self.share_company, shares=0)
self.data.update({'price': 12, 'buyer_type': 'player',
'player_buyer': self.player.pk, 'source_type': 'player',
'player_source': extra_player.pk, 'amount': -1})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 1 shares {} to {} for 12 each'.format(
self.player.name, self.share_company.name,
extra_player.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -1)
self.assertEqual(entry.price, 12)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'player')
self.assertEqual(entry.player_source, extra_player)
self.assertEqual(entry.company, self.share_company)
def test_player_selling_share_to_company_creates_log_entry(self, mock):
self.data.update({'price': 13, 'buyer_type': 'player',
'player_buyer': self.player.pk, 'source_type': 'company',
'company_source': self.buy_company.pk, 'amount': -1})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 1 shares {} to {} for 13 each'.format(
self.player.name, self.share_company.name,
self.buy_company.name))
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -1)
self.assertEqual(entry.price, 13)
self.assertEqual(entry.buyer, 'player')
self.assertEqual(entry.player_buyer, self.player)
self.assertEqual(entry.source, 'company')
self.assertEqual(entry.company_source, self.buy_company)
self.assertEqual(entry.company, self.share_company)
def test_company_selling_share_to_ipo_creates_log_entry(self, mock):
self.data.update({'price': 14, 'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'ipo',
'amount': -2})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 2 shares {} to the IPO for 14 each'.format(
self.buy_company.name, self.share_company.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -2)
self.assertEqual(entry.price, 14)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'ipo')
self.assertEqual(entry.company, self.share_company)
def test_company_selling_share_to_bank_creates_log_entry(self, mock):
self.data.update({'price': 15, 'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'bank',
'amount': -3})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 3 shares {} to the bank for 15 each'.format(
self.buy_company.name, self.share_company.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -3)
self.assertEqual(entry.price, 15)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'bank')
self.assertEqual(entry.company, self.share_company)
def test_company_selling_share_to_player_creates_log_entry(self, mock):
self.data.update({'price': 16, 'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'player',
'player_source': self.player.pk, 'amount': -1})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 1 shares {} to {} for 16 each'.format(
self.buy_company.name, self.share_company.name,
self.player.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -1)
self.assertEqual(entry.price, 16)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'player')
self.assertEqual(entry.player_source, self.player)
self.assertEqual(entry.company, self.share_company)
def test_company_selling_share_to_company_creates_log_entry(self, mock):
extra_company = factories.CompanyFactory(game=self.game, cash=0)
factories.CompanyShareFactory(owner=extra_company,
company=self.share_company, shares=1)
self.data.update({'price': 17, 'buyer_type': 'company',
'company_buyer': self.buy_company.pk, 'source_type': 'company',
'company_source': extra_company.pk, 'amount': -1})
self.make_request()
entry = self.game.log.last()
self.assertEqual(entry.text,
'{} sold 1 shares {} to {} for 17 each'.format(
self.buy_company.name, self.share_company.name,
extra_company.name))
self.assertEqual(entry.acting_company, self.buy_company)
self.assertEqual(entry.action, models.LogEntry.TRANSFER_SHARE)
self.assertEqual(entry.shares, -1)
self.assertEqual(entry.price, 17)
self.assertEqual(entry.buyer, 'company')
self.assertEqual(entry.company_buyer, self.buy_company)
self.assertEqual(entry.source, 'company')
self.assertEqual(entry.company_source, extra_company)
self.assertEqual(entry.company, self.share_company)
| 51.406211
| 79
| 0.665603
| 5,209
| 41,382
| 5.040507
| 0.040507
| 0.119973
| 0.109689
| 0.031536
| 0.927826
| 0.915334
| 0.900137
| 0.885855
| 0.868944
| 0.840265
| 0
| 0.013107
| 0.207216
| 41,382
| 804
| 80
| 51.470149
| 0.78721
| 0.003431
| 0
| 0.619178
| 0
| 0
| 0.117994
| 0
| 0
| 0
| 0
| 0
| 0.328767
| 1
| 0.090411
| false
| 0
| 0.010959
| 0
| 0.106849
| 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
|
1139265496d05aa0db67800a45661c51ac57dd0b
| 42
|
py
|
Python
|
django_test/hmdb/tests.py
|
wolframowy/mgr
|
9d61cef8d135e255f724f57ba55a0dc8c4269219
|
[
"MIT"
] | null | null | null |
django_test/hmdb/tests.py
|
wolframowy/mgr
|
9d61cef8d135e255f724f57ba55a0dc8c4269219
|
[
"MIT"
] | null | null | null |
django_test/hmdb/tests.py
|
wolframowy/mgr
|
9d61cef8d135e255f724f57ba55a0dc8c4269219
|
[
"MIT"
] | null | null | null |
from .test_cases.test_reg_param import *
| 14
| 40
| 0.809524
| 7
| 42
| 4.428571
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119048
| 42
| 2
| 41
| 21
| 0.837838
| 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
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| 0
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| 0
| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
11473a722466231976f57d7188df7756a572e7cf
| 37
|
py
|
Python
|
src/lib/multiprocessing/dummy/__init__.py
|
DTenore/skulpt
|
098d20acfb088d6db85535132c324b7ac2f2d212
|
[
"MIT"
] | 2,671
|
2015-01-03T08:23:25.000Z
|
2022-03-31T06:15:48.000Z
|
src/lib/multiprocessing/dummy/__init__.py
|
wakeupmuyunhe/skulpt
|
a8fb11a80fb6d7c016bab5dfe3712517a350b347
|
[
"MIT"
] | 972
|
2015-01-05T08:11:00.000Z
|
2022-03-29T13:47:15.000Z
|
src/lib/multiprocessing/dummy/__init__.py
|
wakeupmuyunhe/skulpt
|
a8fb11a80fb6d7c016bab5dfe3712517a350b347
|
[
"MIT"
] | 845
|
2015-01-03T19:53:36.000Z
|
2022-03-29T18:34:22.000Z
|
import _sk_fail; _sk_fail._("dummy")
| 18.5
| 36
| 0.756757
| 6
| 37
| 3.833333
| 0.666667
| 0.521739
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.081081
| 37
| 1
| 37
| 37
| 0.676471
| 0
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| 0.135135
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| true
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| null | 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
fedb99384fbff9e1d848fc89d557a8fc6f169901
| 2,684
|
py
|
Python
|
tests/unittests/test_nlp_text_prediction.py
|
aaronkl/autogluon
|
1ca52059003b1b5fc9f6b40db1c847f219728c9d
|
[
"Apache-2.0"
] | null | null | null |
tests/unittests/test_nlp_text_prediction.py
|
aaronkl/autogluon
|
1ca52059003b1b5fc9f6b40db1c847f219728c9d
|
[
"Apache-2.0"
] | null | null | null |
tests/unittests/test_nlp_text_prediction.py
|
aaronkl/autogluon
|
1ca52059003b1b5fc9f6b40db1c847f219728c9d
|
[
"Apache-2.0"
] | null | null | null |
from autogluon import TextPrediction as task
from autogluon.utils.tabular.utils.loaders import load_pd
test_hyperparameters = {
'models': {
'BertForTextPredictionBasic': {
'search_space': {
'optimization.num_train_epochs': 1
}
}
}
}
def test_sst():
train_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sst/train.parquet')
dev_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sst/dev.parquet')
train_data = train_data.iloc[:100]
dev_data = dev_data.iloc[:10]
predictor = task.fit(train_data, hyperparameters=test_hyperparameters,
label='label', num_trials=1,
ngpus_per_trial=0,
verbosity=4,
output_directory='./sst',
plot_results=False)
dev_acc = predictor.evaluate(dev_data, metrics=['acc'])
dev_prediction = predictor.predict(dev_data)
dev_pred_prob = predictor.predict_proba(dev_data)
def test_mrpc():
train_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/train.parquet')
dev_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/dev.parquet')
train_data = train_data.iloc[:100]
dev_data = dev_data.iloc[:10]
predictor = task.fit(train_data, hyperparameters=test_hyperparameters,
label='label', num_trials=1,
verbosity=4,
ngpus_per_trial=1,
output_directory='./mrpc',
plot_results=False)
dev_acc = predictor.evaluate(dev_data, metrics=['acc'])
dev_prediction = predictor.predict(dev_data)
dev_pred_prob = predictor.predict_proba(dev_data)
def test_sts():
train_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/glue/sts/train.parquet')
dev_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/glue/sts/dev.parquet')
train_data = train_data.iloc[:100]
dev_data = dev_data.iloc[:10]
predictor = task.fit(train_data, hyperparameters=test_hyperparameters,
label='score', num_trials=1,
verbosity=4,
ngpus_per_trial=1,
output_directory='./sts',
plot_results=False)
dev_rmse = predictor.evaluate(dev_data, metrics=['rmse'])
dev_prediction = predictor.predict(dev_data)
| 40.059701
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| 0.598361
| 300
| 2,684
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| 0.800391
| 0.776908
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| 2,684
| 66
| 86
| 40.666667
| 0.788451
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|
0
| 6
|
fee67ce1d8c744cfee822102d70c16e27c580aba
| 115
|
py
|
Python
|
photo/admin.py
|
Firexd2/social-network
|
8f7799aa54871843f55aed578e2c89a964c97ecc
|
[
"MIT"
] | 2
|
2018-12-28T19:21:55.000Z
|
2019-05-15T14:37:12.000Z
|
photo/admin.py
|
Firexd2/social-network
|
8f7799aa54871843f55aed578e2c89a964c97ecc
|
[
"MIT"
] | null | null | null |
photo/admin.py
|
Firexd2/social-network
|
8f7799aa54871843f55aed578e2c89a964c97ecc
|
[
"MIT"
] | 2
|
2019-10-16T08:01:04.000Z
|
2021-07-13T06:02:15.000Z
|
from django.contrib import admin
from .models import *
admin.site.register(PhotoAlbum)
admin.site.register(Photo)
| 19.166667
| 32
| 0.808696
| 16
| 115
| 5.8125
| 0.625
| 0.236559
| 0.365591
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| 115
| 5
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| 23
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|
0
| 6
|
3a15f34f55842b018a41442e8ded7a6ac3f8a230
| 10,364
|
py
|
Python
|
tests/rest/flask_rest_test.py
|
estuaryoss/estuary-discovery
|
9615a9d544670570f14f4c72ca20f57a0cd9bba4
|
[
"Apache-2.0"
] | null | null | null |
tests/rest/flask_rest_test.py
|
estuaryoss/estuary-discovery
|
9615a9d544670570f14f4c72ca20f57a0cd9bba4
|
[
"Apache-2.0"
] | null | null | null |
tests/rest/flask_rest_test.py
|
estuaryoss/estuary-discovery
|
9615a9d544670570f14f4c72ca20f57a0cd9bba4
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python3
import os
import unittest
import requests
import yaml
from flask import json
from parameterized import parameterized
from requests_toolbelt.utils import dump
from rest.api.constants.api_constants import ApiCode
from rest.api.responsehelpers.error_codes import ErrorMessage
class FlaskServerTestCase(unittest.TestCase):
service = "http://localhost:8080"
# server = "http://" + os.environ.get('SERVER')
service_name = "Estuary-Discovery"
username = "admin"
password = "estuaryoss123!"
expected_version = "4.2.4"
def test_env_endpoint(self):
response = requests.get(self.service + "/env", auth=(self.username, self.password))
body = json.loads(response.text)
self.assertEqual(response.status_code, 200)
self.assertGreaterEqual(len(body.get('description')), 7)
# self.assertIsNotNone(body.get('description)["VARS_DIR"])
# self.assertIsNotNone(body.get('description)["TEMPLATES_DIR"])
self.assertEqual(body.get('message'), ErrorMessage.HTTP_CODE.get(ApiCode.SUCCESS.value))
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
def test_ping_endpoint(self):
response = requests.get(self.service + "/ping", auth=(self.username, self.password))
body = json.loads(response.text)
headers = response.headers
self.assertEqual(response.status_code, 200)
self.assertEqual(body.get('description'), "pong")
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
self.assertEqual(len(headers.get('X-Request-ID')), 16)
def test_getenv_endpoint_p(self):
env_var = "PATH"
response = requests.get(self.service + "/env/{}".format(env_var), auth=(self.username, self.password))
body = response.json()
self.assertEqual(response.status_code, 200)
self.assertEqual(body.get('message'), ErrorMessage.HTTP_CODE.get(ApiCode.SUCCESS.value))
self.assertIsNotNone(body.get('description'))
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
@parameterized.expand([
("FOO1", "BAR1")
])
@unittest.skipIf(os.environ.get('SKIP_ON_CENTOS') == "true", "skip on centos docker")
def test_env_load_from_props(self, env_var, expected_value):
response = requests.get(self.service + "/env/" + env_var, auth=(self.username, self.password))
body = response.json()
self.assertEqual(response.status_code, 200)
self.assertEqual(body.get("message"), ErrorMessage.HTTP_CODE.get(ApiCode.SUCCESS.value))
self.assertEqual(body.get('description'), expected_value)
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
def test_setenv_endpoint_json_with_values(self):
payload = {"a": "b", "FOO1": "BAR1"}
headers = {'Content-type': 'application/json'}
response = requests.post(self.service + f"/env", data=json.dumps(payload),
headers=headers, auth=(self.username, self.password))
body = response.json()
self.assertEqual(response.status_code, 200)
self.assertEqual(body.get('description'), payload)
self.assertEqual(body.get("message"), ErrorMessage.HTTP_CODE.get(ApiCode.SUCCESS.value))
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
def test_getenv_endpoint_n(self):
env_var = "alabalaportocala"
response = requests.get(self.service + "/env/{}".format(env_var), auth=(self.username, self.password))
body = response.json()
headers = response.headers
self.assertEqual(response.status_code, 200)
self.assertEqual(body.get('message'),
ErrorMessage.HTTP_CODE.get(ApiCode.SUCCESS.value))
self.assertEqual(body.get('description'), None)
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
self.assertEqual(len(headers.get('X-Request-ID')), 16)
def test_about_endpoint(self):
response = requests.get(self.service + "/about", auth=(self.username, self.password))
body = response.json()
headers = response.headers
self.assertEqual(response.status_code, 200)
self.assertIsInstance(body.get('description'), dict)
self.assertEqual(body.get('name'), self.service_name)
self.assertEqual(body.get('message'), ErrorMessage.HTTP_CODE.get(ApiCode.SUCCESS.value))
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
self.assertEqual(len(headers.get('X-Request-ID')), 16)
def test_about_endpoint_xid_set_by_client_is_same(self):
xid = "whatever"
headers = {
'X-Request-ID': xid
}
response = requests.get(self.service + "/about", headers=headers, auth=(self.username, self.password))
body = response.json()
headers = response.headers
self.assertEqual(response.status_code, 200)
self.assertIsInstance(body.get('description'), dict)
self.assertEqual(body.get('name'), self.service_name)
self.assertEqual(body.get('message'), ErrorMessage.HTTP_CODE.get(ApiCode.SUCCESS.value))
self.assertEqual(body.get('version'), self.expected_version)
self.assertEqual(body.get('code'), ApiCode.SUCCESS.value)
self.assertIsNotNone(body.get('timestamp'))
self.assertIsNotNone(body.get('path'))
self.assertEqual(headers.get('X-Request-ID'), xid)
def test_about_endpoint_unauthorized(self):
headers = {}
response = requests.get(self.service + "/about", headers=headers, auth=(self.username, "invalidPasswd"))
body = response.text
headers = response.headers
self.assertEqual(response.status_code, 401)
self.assertIn("Unauthorized", body)
self.assertEqual(len(headers.get('X-Request-ID')), 16)
def test_about_endpoint_options_must_be_auth(self):
headers = {}
response = requests.options(self.service + "/about", headers=headers, auth=(self.username, "invalidPasswd"))
headers = response.headers
self.assertEqual(response.status_code, 200)
self.assertEqual(len(headers.get('X-Request-ID')), 16)
def test_about_endpoint_unauthorized_xid_by_client_remains_the_same(self):
xid = "whatever"
headers = {
'X-Request-ID': xid
}
response = requests.get(self.service + "/about", headers=headers, auth=(self.username, "invalidPasswd"))
body = response.text
headers = response.headers
self.assertEqual(response.status_code, 401)
self.assertIn("Unauthorized", body)
self.assertEqual(headers.get('X-Request-ID'), xid)
def test_swagger_endpoint(self):
response = requests.get(self.service + "/apidocs", auth=(self.username, self.password))
body = response.text
self.assertEqual(response.status_code, 200)
self.assertTrue(body.find("html") >= 0)
@parameterized.expand([
("json.j2", "json.json"),
("yml.j2", "yml.yml")
])
def test_rend_endpoint_p(self, template, variables):
response = requests.get(self.service + "/render/{}/{}".format(template, variables), auth=(self.username, self.password))
body = yaml.safe_load(response.text)
self.assertEqual(response.status_code, 200)
self.assertEqual(len(body), 3)
@parameterized.expand([
("json.j2", "doesnotexists.json"),
("yml.j2", "doesnotexists.yml")
])
def test_rend_endpoint_no_such_variables_file_n(self, template, variables):
expected = "Exception"
response = requests.get(self.service + "/render/{}/{}".format(template, variables), auth=(self.username, self.password))
body = response.json()
self.assertEqual(response.status_code, 500)
# self.assertEqual(expected, body.get("description"))
self.assertIn(expected, body.get("description"))
@parameterized.expand([
("doesnotexists.j2", "json.json"),
("doesnotexists.j2", "yml.yml")
])
def test_rend_endpoint_no_such_template_file_n(self, template, variables):
expected = f"Exception"
response = requests.get(self.service + "/render/{}/{}".format(template, variables), auth=(self.username, self.password))
body = response.json()
self.assertEqual(response.status_code, 500)
self.assertIn(expected, body.get("description"))
@parameterized.expand([
("standalone.yml", "variables.yml")
])
def test_rendwithenv_endpoint(self, template, variables):
payload = {'DATABASE': 'mysql56', 'IMAGE': 'latest'}
headers = {'Content-type': 'application/json'}
response = requests.post(self.service + f"/render/{template}/{variables}", data=json.dumps(payload),
headers=headers, auth=(self.username, self.password))
print(dump.dump_response(response))
self.assertEqual(response.status_code, 200)
body = yaml.safe_load(response.text)
self.assertEqual(len(body.get("services")), 2)
self.assertEqual(int(body.get("version")), 3)
if __name__ == '__main__':
unittest.main()
| 44.102128
| 128
| 0.66345
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|
0
| 6
|
3a397320c90b23c1aa57d30d2d1a375c92b46ad9
| 118
|
py
|
Python
|
tests/test_config.py
|
donghak-shin/dp-tornado
|
095bb293661af35cce5f917d8a2228d273489496
|
[
"MIT"
] | 18
|
2015-04-07T14:28:39.000Z
|
2020-02-08T14:03:38.000Z
|
tests/test_config.py
|
donghak-shin/dp-tornado
|
095bb293661af35cce5f917d8a2228d273489496
|
[
"MIT"
] | 7
|
2016-10-05T05:14:06.000Z
|
2021-05-20T02:07:22.000Z
|
tests/test_config.py
|
donghak-shin/dp-tornado
|
095bb293661af35cce5f917d8a2228d273489496
|
[
"MIT"
] | 11
|
2015-12-15T09:49:39.000Z
|
2021-09-06T18:38:21.000Z
|
# -*- coding: utf-8 -*-
from . import utils
def config():
utils.expecting_text('get', '/config', 'done', 200)
| 13.111111
| 55
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0
| 6
|
3a3ab4d22dbc98df3fb8cc1d3186e6a76203c043
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/poetry/core/_vendor/jsonschema/benchmarks/__init__.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/poetry/core/_vendor/jsonschema/benchmarks/__init__.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/poetry/core/_vendor/jsonschema/benchmarks/__init__.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/93/5f/a5/fde3953587e3a754621a72bcb164071fcb494bc83da52c33d0c0dfc572
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|
0
| 6
|
28abce2654cf400596114bc6d4ba69154424d50e
| 198
|
py
|
Python
|
src/djask/admin/__init__.py
|
z-t-y/Djask
|
d9867b3b91e2c50a07c701b4e2ef51a0c583c82c
|
[
"MIT"
] | 19
|
2021-11-10T07:26:56.000Z
|
2022-02-07T08:45:48.000Z
|
src/djask/admin/__init__.py
|
z-t-y/Djask
|
d9867b3b91e2c50a07c701b4e2ef51a0c583c82c
|
[
"MIT"
] | 2
|
2021-11-10T07:25:19.000Z
|
2021-11-30T14:23:53.000Z
|
src/djask/admin/__init__.py
|
z-t-y/Djask
|
d9867b3b91e2c50a07c701b4e2ef51a0c583c82c
|
[
"MIT"
] | 1
|
2021-11-10T13:06:57.000Z
|
2021-11-10T13:06:57.000Z
|
from .ext import Admin
from .ui.decorators import admin_required
from .api.decorators import admin_required_api
from . import cli
__all__ = ["Admin", "admin_required", "admin_required_api", "cli"]
| 28.285714
| 66
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0
| 6
|
e9192ec10904da727a3581309af63ce9bca76c67
| 48
|
py
|
Python
|
tests/__init__.py
|
Harut/chakert
|
b06db748d1e316f3c433b08cee46725b0a45f17e
|
[
"MIT"
] | 34
|
2015-01-19T14:40:00.000Z
|
2021-12-04T06:38:52.000Z
|
tests/__init__.py
|
SmartTeleMax/chakert
|
f385cf5652fbb3aec3f8a27e55681669483d02b3
|
[
"MIT"
] | 6
|
2016-07-16T18:09:48.000Z
|
2016-07-20T15:57:37.000Z
|
tests/__init__.py
|
Harut/chakert
|
b06db748d1e316f3c433b08cee46725b0a45f17e
|
[
"MIT"
] | 5
|
2015-01-22T08:23:34.000Z
|
2017-09-15T15:47:36.000Z
|
from .ru import RuTests
from .en import EnTests
| 16
| 23
| 0.791667
| 8
| 48
| 4.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 48
| 2
| 24
| 24
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
3a5e27850f440fba136be83080bf8dc88b5bd36e
| 116
|
py
|
Python
|
src/kaa/nodes.py
|
mmicek/kaa
|
3583edf19b0e453c7de6c316a08d9eda72a1fcfc
|
[
"MIT"
] | 17
|
2019-07-10T12:24:53.000Z
|
2022-02-19T21:39:19.000Z
|
src/kaa/nodes.py
|
mmicek/kaa
|
3583edf19b0e453c7de6c316a08d9eda72a1fcfc
|
[
"MIT"
] | 29
|
2019-07-10T12:30:58.000Z
|
2021-12-30T15:33:44.000Z
|
src/kaa/nodes.py
|
mmicek/kaa
|
3583edf19b0e453c7de6c316a08d9eda72a1fcfc
|
[
"MIT"
] | 8
|
2019-03-26T23:08:40.000Z
|
2022-01-10T03:39:59.000Z
|
from ._kaa import Node, SpaceNode, BodyNode, HitboxNode
__all__ = ('Node', 'SpaceNode', 'BodyNode', 'HitboxNode')
| 23.2
| 57
| 0.715517
| 12
| 116
| 6.5
| 0.666667
| 0.333333
| 0.538462
| 0.794872
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12931
| 116
| 4
| 58
| 29
| 0.772277
| 0
| 0
| 0
| 0
| 0
| 0.267241
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
|
3adfe345d5e8c327c7d34081e8da79fc55dd5bb6
| 255
|
py
|
Python
|
python_runtime/exonum_runtime/runtime/runtime_schema.py
|
alekseysidorov/exonum-python-backend
|
fae38042acba4c7fd9ca05f6afa1e9bec54dd86d
|
[
"Apache-2.0"
] | 2
|
2019-10-06T17:23:08.000Z
|
2019-10-07T09:35:59.000Z
|
python_runtime/exonum_runtime/runtime/runtime_schema.py
|
alekseysidorov/exonum-python-backend
|
fae38042acba4c7fd9ca05f6afa1e9bec54dd86d
|
[
"Apache-2.0"
] | null | null | null |
python_runtime/exonum_runtime/runtime/runtime_schema.py
|
alekseysidorov/exonum-python-backend
|
fae38042acba4c7fd9ca05f6afa1e9bec54dd86d
|
[
"Apache-2.0"
] | 1
|
2020-01-18T09:29:30.000Z
|
2020-01-18T09:29:30.000Z
|
"""Python runtime schema"""
from exonum_runtime.merkledb.schema import Schema
# from exonum_runtime.merkledb.indices import ProofMapIndex
class PythonRuntimeSchema(Schema):
"""Python runtime schema implementation"""
# services: ProofMapIndex
| 21.25
| 59
| 0.780392
| 26
| 255
| 7.576923
| 0.5
| 0.13198
| 0.192893
| 0.233503
| 0.314721
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 255
| 11
| 60
| 23.181818
| 0.891403
| 0.552941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c90a2a61b5c4378f5da716f1fd04f0ba56655c19
| 29
|
py
|
Python
|
mp3downloader/__init__.py
|
Kerono4ka/MP3downloader
|
68cd0412e2ee7c3a5f27dab89bd7b7014c1d5f39
|
[
"MIT"
] | 1
|
2018-04-03T18:31:10.000Z
|
2018-04-03T18:31:10.000Z
|
mp3downloader/__init__.py
|
Kerono4ka/MP3downloader
|
68cd0412e2ee7c3a5f27dab89bd7b7014c1d5f39
|
[
"MIT"
] | 2
|
2021-03-31T18:44:49.000Z
|
2021-06-01T22:06:24.000Z
|
mp3downloader/__init__.py
|
Kerono4ka/MP3downloader
|
68cd0412e2ee7c3a5f27dab89bd7b7014c1d5f39
|
[
"MIT"
] | 1
|
2018-04-01T20:08:12.000Z
|
2018-04-01T20:08:12.000Z
|
from .mp3downloader import *
| 14.5
| 28
| 0.793103
| 3
| 29
| 7.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04
| 0.137931
| 29
| 1
| 29
| 29
| 0.88
| 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
|
a30bf8589e93b0d0b1624f003945ab4fa50c08ec
| 190
|
py
|
Python
|
s3utils.py
|
rayhu-osu/vcube
|
ff1af048adb8a9f1007368150a78b309b4d821af
|
[
"MIT"
] | 1
|
2019-02-20T18:47:04.000Z
|
2019-02-20T18:47:04.000Z
|
s3utils.py
|
rayhu-osu/vcube
|
ff1af048adb8a9f1007368150a78b309b4d821af
|
[
"MIT"
] | null | null | null |
s3utils.py
|
rayhu-osu/vcube
|
ff1af048adb8a9f1007368150a78b309b4d821af
|
[
"MIT"
] | null | null | null |
from storages.backends.s3boto3 import S3Boto3Storage
StaticRootS3Boto3Storage = lambda: S3Boto3Storage(location='static')
MediaRootS3Boto3Storage = lambda: S3Boto3Storage(location='media')
| 47.5
| 68
| 0.847368
| 16
| 190
| 10.0625
| 0.75
| 0.248447
| 0.347826
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.067797
| 0.068421
| 190
| 4
| 69
| 47.5
| 0.841808
| 0
| 0
| 0
| 0
| 0
| 0.057592
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
a36f9029c0b96ca836a186e5c06e793047fccbdf
| 211
|
py
|
Python
|
src/CanvasBackend/board_exceptions.py
|
HenryBlairG/CanvasClient
|
945fc4d45bd103f10e7a9b95d2947e8a1214818c
|
[
"MIT"
] | 2
|
2020-05-05T22:53:07.000Z
|
2020-09-04T20:58:18.000Z
|
src/CanvasBackend/board_exceptions.py
|
HenryBlairG/CanvasClient
|
945fc4d45bd103f10e7a9b95d2947e8a1214818c
|
[
"MIT"
] | null | null | null |
src/CanvasBackend/board_exceptions.py
|
HenryBlairG/CanvasClient
|
945fc4d45bd103f10e7a9b95d2947e8a1214818c
|
[
"MIT"
] | null | null | null |
class NoTokenError(Exception):
def __init__(self, *a, **kw):
super().__init__(*a, **kw)
class GetContentError(Exception):
def __init__(self, *a, **kw):
super().__init__(*a, **kw)
| 19.181818
| 34
| 0.582938
| 24
| 211
| 4.458333
| 0.416667
| 0.11215
| 0.299065
| 0.373832
| 0.654206
| 0.654206
| 0.654206
| 0.654206
| 0.654206
| 0.654206
| 0
| 0
| 0.227488
| 211
| 10
| 35
| 21.1
| 0.656442
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 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
|
6e7381b9eb7044ef58c2154632117026b73c1480
| 116
|
py
|
Python
|
saml2idp/tests/__init__.py
|
anentropic/django-saml2-idp
|
a2810de839b26cf740a7b1ad3e00658498ce4d22
|
[
"MIT"
] | null | null | null |
saml2idp/tests/__init__.py
|
anentropic/django-saml2-idp
|
a2810de839b26cf740a7b1ad3e00658498ce4d22
|
[
"MIT"
] | 1
|
2016-11-09T13:32:44.000Z
|
2019-01-31T19:06:05.000Z
|
saml2idp/tests/__init__.py
|
anentropic/django-saml2-idp
|
a2810de839b26cf740a7b1ad3e00658498ce4d22
|
[
"MIT"
] | null | null | null |
from deeplink import *
from google_apps import *
from salesforce import *
from signing import *
from views import *
| 19.333333
| 25
| 0.784483
| 16
| 116
| 5.625
| 0.5
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.172414
| 116
| 5
| 26
| 23.2
| 0.9375
| 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
|
6edac5d0aa32274e43ec77cb8e5f185420a4cde6
| 104
|
py
|
Python
|
vedastr/models/bodies/feature_extractors/decoders/__init__.py
|
csmasters/vedastr
|
7513384ab503f15dc574c7d92b75ff2092354757
|
[
"Apache-2.0"
] | 475
|
2020-03-17T01:46:32.000Z
|
2022-03-29T23:30:15.000Z
|
vedastr/models/bodies/feature_extractors/decoders/__init__.py
|
csmasters/vedastr
|
7513384ab503f15dc574c7d92b75ff2092354757
|
[
"Apache-2.0"
] | 71
|
2020-04-01T04:17:47.000Z
|
2021-11-18T06:55:14.000Z
|
vedastr/models/bodies/feature_extractors/decoders/__init__.py
|
csmasters/vedastr
|
7513384ab503f15dc574c7d92b75ff2092354757
|
[
"Apache-2.0"
] | 108
|
2020-02-21T10:30:37.000Z
|
2022-03-21T12:03:30.000Z
|
from .gfpn import GFPN
from .bricks import build_brick, build_bricks
from .builder import build_decoder
| 26
| 45
| 0.836538
| 16
| 104
| 5.25
| 0.5
| 0.261905
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 104
| 3
| 46
| 34.666667
| 0.923077
| 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
|
42f61eb63ac1edbe50b04476ccef8fb39e8b3fe7
| 159
|
py
|
Python
|
python/8kyu/quarter_of_the_year.py
|
Sigmanificient/codewars
|
b34df4bf55460d312b7ddf121b46a707b549387a
|
[
"MIT"
] | 3
|
2021-06-08T01:57:13.000Z
|
2021-06-26T10:52:47.000Z
|
python/8kyu/quarter_of_the_year.py
|
Sigmanificient/codewars
|
b34df4bf55460d312b7ddf121b46a707b549387a
|
[
"MIT"
] | null | null | null |
python/8kyu/quarter_of_the_year.py
|
Sigmanificient/codewars
|
b34df4bf55460d312b7ddf121b46a707b549387a
|
[
"MIT"
] | 2
|
2021-06-10T21:20:13.000Z
|
2021-06-30T10:13:26.000Z
|
"""Kata url: https://www.codewars.com/kata/5ce9c1000bab0b001134f5af."""
from math import ceil
def quarter_of(month: int) -> int:
return ceil(month / 3)
| 19.875
| 71
| 0.704403
| 22
| 159
| 5.045455
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.110294
| 0.144654
| 159
| 7
| 72
| 22.714286
| 0.705882
| 0.408805
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
2807ec420367ab2b2758fc8631f84b4b4ec096be
| 17,027
|
py
|
Python
|
jsparagus/parse_pgen_generated.py
|
est31/jsparagus
|
90a413065857840ea439c1dbf68e89b9f5e8f1bc
|
[
"Apache-2.0",
"MIT-0",
"MIT"
] | null | null | null |
jsparagus/parse_pgen_generated.py
|
est31/jsparagus
|
90a413065857840ea439c1dbf68e89b9f5e8f1bc
|
[
"Apache-2.0",
"MIT-0",
"MIT"
] | null | null | null |
jsparagus/parse_pgen_generated.py
|
est31/jsparagus
|
90a413065857840ea439c1dbf68e89b9f5e8f1bc
|
[
"Apache-2.0",
"MIT-0",
"MIT"
] | null | null | null |
# type: ignore
from jsparagus import runtime
from jsparagus.runtime import (Nt, InitNt, End, ErrorToken, StateTermValue,
ShiftError, ShiftAccept)
def state_43_actions(parser, lexer):
value = None
value = parser.stack[-1].value
replay = [StateTermValue(0, Nt(InitNt(goal=Nt('grammar'))), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_44_actions(parser, lexer):
value = None
value = parser.methods.nt_defs_single(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('nt_defs'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_45_actions(parser, lexer):
value = None
value = parser.methods.single(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('token_defs'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_46_actions(parser, lexer):
value = None
value = parser.methods.nt_defs_append(parser.stack[-2].value, parser.stack[-1].value)
replay = [StateTermValue(0, Nt('nt_defs'), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_47_actions(parser, lexer):
value = None
value = parser.methods.append(parser.stack[-2].value, parser.stack[-1].value)
replay = [StateTermValue(0, Nt('token_defs'), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_48_actions(parser, lexer):
value = None
raise ShiftAccept()
replay = [StateTermValue(0, Nt(InitNt(goal=Nt('grammar'))), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_49_actions(parser, lexer):
value = None
value = parser.methods.nt_def(None, None, parser.stack[-3].value, None)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-4:]
parser.shift_list(replay, lexer)
return
def state_50_actions(parser, lexer):
value = None
value = parser.methods.single(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('prods'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_51_actions(parser, lexer):
value = None
value = parser.methods.single(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('terms'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_52_actions(parser, lexer):
value = None
value = parser.methods.ident(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('symbol'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_53_actions(parser, lexer):
value = None
value = parser.methods.str(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('symbol'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_54_actions(parser, lexer):
value = None
value = parser.methods.empty(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('prods'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_55_actions(parser, lexer):
value = None
value = parser.methods.var_token(parser.stack[-2].value)
replay = [StateTermValue(0, Nt('token_def'), value, False)]
del parser.stack[-4:]
parser.shift_list(replay, lexer)
return
def state_56_actions(parser, lexer):
value = None
value = parser.methods.nt_def(None, None, parser.stack[-4].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-5:]
parser.shift_list(replay, lexer)
return
def state_57_actions(parser, lexer):
value = None
value = parser.methods.append(parser.stack[-2].value, parser.stack[-1].value)
replay = [StateTermValue(0, Nt('prods'), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_58_actions(parser, lexer):
value = None
value = parser.methods.prod(parser.stack[-2].value, None)
replay = [StateTermValue(0, Nt('prod'), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_59_actions(parser, lexer):
value = None
value = parser.methods.append(parser.stack[-2].value, parser.stack[-1].value)
replay = [StateTermValue(0, Nt('terms'), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_60_actions(parser, lexer):
value = None
value = parser.methods.optional(parser.stack[-2].value)
replay = [StateTermValue(0, Nt('term'), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_61_actions(parser, lexer):
value = None
value = parser.methods.nt_def(parser.stack[-5].value, None, parser.stack[-3].value, None)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-5:]
parser.shift_list(replay, lexer)
return
def state_62_actions(parser, lexer):
value = None
value = parser.methods.nt_def(None, parser.stack[-5].value, parser.stack[-3].value, None)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-5:]
parser.shift_list(replay, lexer)
return
def state_63_actions(parser, lexer):
value = None
value = parser.methods.const_token(parser.stack[-4].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('token_def'), value, False)]
del parser.stack[-5:]
parser.shift_list(replay, lexer)
return
def state_64_actions(parser, lexer):
value = None
value = parser.methods.prod(parser.stack[-3].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('prod'), value, False)]
del parser.stack[-3:]
parser.shift_list(replay, lexer)
return
def state_65_actions(parser, lexer):
value = None
value = parser.stack[-1].value
replay = [StateTermValue(0, Nt('reducer'), value, False)]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_66_actions(parser, lexer):
value = None
value = parser.methods.expr_match(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('expr'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_67_actions(parser, lexer):
value = None
value = parser.methods.expr_none()
replay = [StateTermValue(0, Nt('expr'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_68_actions(parser, lexer):
value = None
value = parser.methods.nt_def(parser.stack[-6].value, None, parser.stack[-4].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-6:]
parser.shift_list(replay, lexer)
return
def state_69_actions(parser, lexer):
value = None
value = parser.methods.nt_def(parser.stack[-6].value, parser.stack[-5].value, parser.stack[-3].value, None)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-6:]
parser.shift_list(replay, lexer)
return
def state_70_actions(parser, lexer):
value = None
value = parser.methods.nt_def(None, parser.stack[-6].value, parser.stack[-4].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-6:]
parser.shift_list(replay, lexer)
return
def state_71_actions(parser, lexer):
value = None
value = parser.methods.nt_def(parser.stack[-7].value, parser.stack[-6].value, parser.stack[-4].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('nt_def'), value, False)]
del parser.stack[-7:]
parser.shift_list(replay, lexer)
return
def state_72_actions(parser, lexer):
value = None
value = parser.methods.expr_call(parser.stack[-3].value, None)
replay = [StateTermValue(0, Nt('expr'), value, False)]
del parser.stack[-3:]
parser.shift_list(replay, lexer)
return
def state_73_actions(parser, lexer):
value = None
value = parser.methods.args_single(parser.stack[-1].value)
replay = [StateTermValue(0, Nt('expr_args'), value, False)]
del parser.stack[-1:]
parser.shift_list(replay, lexer)
return
def state_74_actions(parser, lexer):
value = None
value = parser.methods.expr_call(parser.stack[-4].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('expr'), value, False)]
del parser.stack[-4:]
parser.shift_list(replay, lexer)
return
def state_75_actions(parser, lexer):
value = None
value = parser.methods.expr_some(parser.stack[-2].value)
replay = [StateTermValue(0, Nt('expr'), value, False)]
del parser.stack[-4:]
parser.shift_list(replay, lexer)
return
def state_76_actions(parser, lexer):
value = None
value = parser.methods.args_append(parser.stack[-3].value, parser.stack[-1].value)
replay = [StateTermValue(0, Nt('expr_args'), value, False)]
del parser.stack[-3:]
parser.shift_list(replay, lexer)
return
def state_77_actions(parser, lexer):
value = None
value = parser.methods.grammar(None, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('grammar'), value, False)]
replay = replay + parser.stack[-1:]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
def state_78_actions(parser, lexer):
value = None
value = parser.methods.grammar(parser.stack[-3].value, parser.stack[-2].value)
replay = [StateTermValue(0, Nt('grammar'), value, False)]
replay = replay + parser.stack[-1:]
del parser.stack[-3:]
parser.shift_list(replay, lexer)
return
def state_79_actions(parser, lexer):
value = None
value = parser.stack[-2].value
replay = [StateTermValue(0, Nt('term'), value, False)]
replay = replay + parser.stack[-1:]
del parser.stack[-2:]
parser.shift_list(replay, lexer)
return
actions = [
# 0.
{'nt': 2, 'COMMENT': 3, 'goal': 4, 'token': 6, 'var': 7, Nt('grammar'): 43, Nt('nt_defs'): 1, Nt('nt_def'): 44, Nt('token_defs'): 5, Nt('token_def'): 45, Nt(InitNt(goal=Nt('grammar'))): 8},
# 1.
{End(): 77, 'goal': 4, 'COMMENT': 3, 'nt': 2, Nt('nt_def'): 46},
# 2.
{'IDENT': 10},
# 3.
{'nt': 11, 'goal': 12},
# 4.
{'nt': 13},
# 5.
{'nt': 2, 'COMMENT': 3, 'goal': 4, 'token': 6, 'var': 7, Nt('nt_defs'): 14, Nt('nt_def'): 44, Nt('token_def'): 47},
# 6.
{'IDENT': 15},
# 7.
{'token': 16},
# 8.
{End(): 48},
# 9.
{},
# 10.
{'{': 17},
# 11.
{'IDENT': 18},
# 12.
{'nt': 19},
# 13.
{'IDENT': 20},
# 14.
{End(): 78, 'goal': 4, 'COMMENT': 3, 'nt': 2, Nt('nt_def'): 46},
# 15.
{'=': 21},
# 16.
{'IDENT': 22},
# 17.
{'}': 49, 'IDENT': 52, 'STR': 53, 'COMMENT': 54, Nt('prods'): 23, Nt('prod'): 50, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 18.
{'{': 26},
# 19.
{'IDENT': 27},
# 20.
{'{': 28},
# 21.
{'STR': 29},
# 22.
{';': 55},
# 23.
{'}': 56, 'IDENT': 52, 'STR': 53, Nt('prod'): 57, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 24.
{';': 58, 'IDENT': 52, 'STR': 53, '=>': 31, Nt('term'): 59, Nt('symbol'): 25, Nt('reducer'): 30},
# 25.
{'=>': 79, 'STR': 79, 'IDENT': 79, ';': 79, '?': 60, Nt('reducer'): 79, Nt('symbol'): 79, Nt('term'): 79},
# 26.
{'}': 61, 'IDENT': 52, 'STR': 53, 'COMMENT': 54, Nt('prods'): 32, Nt('prod'): 50, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 27.
{'{': 33},
# 28.
{'}': 62, 'IDENT': 52, 'STR': 53, 'COMMENT': 54, Nt('prods'): 34, Nt('prod'): 50, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 29.
{';': 63},
# 30.
{';': 64},
# 31.
{'MATCH': 66, 'IDENT': 35, 'Some': 36, 'None': 67, Nt('expr'): 65},
# 32.
{'}': 68, 'IDENT': 52, 'STR': 53, Nt('prod'): 57, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 33.
{'}': 69, 'IDENT': 52, 'STR': 53, 'COMMENT': 54, Nt('prods'): 37, Nt('prod'): 50, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 34.
{'}': 70, 'IDENT': 52, 'STR': 53, Nt('prod'): 57, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 35.
{'(': 38},
# 36.
{'(': 39},
# 37.
{'}': 71, 'IDENT': 52, 'STR': 53, Nt('prod'): 57, Nt('terms'): 24, Nt('term'): 51, Nt('symbol'): 25},
# 38.
{')': 72, 'MATCH': 66, 'IDENT': 35, 'Some': 36, 'None': 67, Nt('expr_args'): 40, Nt('expr'): 73},
# 39.
{'MATCH': 66, 'IDENT': 35, 'Some': 36, 'None': 67, Nt('expr'): 41},
# 40.
{')': 74, ',': 42},
# 41.
{')': 75},
# 42.
{'MATCH': 66, 'IDENT': 35, 'Some': 36, 'None': 67, Nt('expr'): 76},
# 43.
state_43_actions,
# 44.
state_44_actions,
# 45.
state_45_actions,
# 46.
state_46_actions,
# 47.
state_47_actions,
# 48.
state_48_actions,
# 49.
state_49_actions,
# 50.
state_50_actions,
# 51.
state_51_actions,
# 52.
state_52_actions,
# 53.
state_53_actions,
# 54.
state_54_actions,
# 55.
state_55_actions,
# 56.
state_56_actions,
# 57.
state_57_actions,
# 58.
state_58_actions,
# 59.
state_59_actions,
# 60.
state_60_actions,
# 61.
state_61_actions,
# 62.
state_62_actions,
# 63.
state_63_actions,
# 64.
state_64_actions,
# 65.
state_65_actions,
# 66.
state_66_actions,
# 67.
state_67_actions,
# 68.
state_68_actions,
# 69.
state_69_actions,
# 70.
state_70_actions,
# 71.
state_71_actions,
# 72.
state_72_actions,
# 73.
state_73_actions,
# 74.
state_74_actions,
# 75.
state_75_actions,
# 76.
state_76_actions,
# 77.
state_77_actions,
# 78.
state_78_actions,
# 79.
state_79_actions,
]
error_codes = [
None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None,
]
goal_nt_to_init_state = {'grammar': 0}
class DefaultMethods:
def nt_defs_single(self, x0):
return ('nt_defs_single', x0)
def single(self, x0):
return ('single', x0)
def nt_defs_append(self, x0, x1):
return ('nt_defs_append', x0, x1)
def append(self, x0, x1):
return ('append', x0, x1)
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def ident(self, x0):
return ('ident', x0)
def str(self, x0):
return ('str', x0)
def empty(self, x0):
return ('empty', x0)
def var_token(self, x0):
return ('var_token', x0)
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def prod(self, x0, x1):
return ('prod', x0, x1)
def optional(self, x0):
return ('optional', x0)
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def const_token(self, x0, x1):
return ('const_token', x0, x1)
def prod(self, x0, x1):
return ('prod', x0, x1)
def expr_match(self, x0):
return ('expr_match', x0)
def expr_none(self, ):
return ('expr_none', )
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def nt_def(self, x0, x1, x2, x3):
return ('nt_def', x0, x1, x2, x3)
def expr_call(self, x0, x1):
return ('expr_call', x0, x1)
def args_single(self, x0):
return ('args_single', x0)
def expr_call(self, x0, x1):
return ('expr_call', x0, x1)
def expr_some(self, x0):
return ('expr_some', x0)
def args_append(self, x0, x1):
return ('args_append', x0, x1)
def grammar(self, x0, x1):
return ('grammar', x0, x1)
def grammar(self, x0, x1):
return ('grammar', x0, x1)
class Parser(runtime.Parser):
def __init__(self, goal='grammar', builder=None):
if builder is None:
builder = DefaultMethods()
super().__init__(actions, error_codes, goal_nt_to_init_state[goal], builder)
| 22.947439
| 193
| 0.595995
| 2,355
| 17,027
| 4.185138
| 0.062845
| 0.107143
| 0.094968
| 0.125
| 0.811485
| 0.797078
| 0.7917
| 0.7917
| 0.746144
| 0.706676
| 0
| 0.064587
| 0.227991
| 17,027
| 741
| 194
| 22.978408
| 0.685204
| 0.018911
| 0
| 0.541766
| 0
| 0
| 0.065877
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.159905
| false
| 0
| 0.004773
| 0.069212
| 0.326969
| 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
|
285f6235574681a07951403c29bcda0653c3ac05
| 212
|
py
|
Python
|
blog/django_blog/tools/views.py
|
bmaelum/django_public
|
dff3e9ab409c5815ac4b303ef73b02390afae722
|
[
"MIT"
] | null | null | null |
blog/django_blog/tools/views.py
|
bmaelum/django_public
|
dff3e9ab409c5815ac4b303ef73b02390afae722
|
[
"MIT"
] | 8
|
2019-10-21T19:51:56.000Z
|
2022-03-11T23:50:09.000Z
|
blog/django_blog/tools/views.py
|
bmaelum/django_public
|
dff3e9ab409c5815ac4b303ef73b02390afae722
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
from django.http import HttpResponse
# Create your views here.
def tools_list(request):
#return HttpResponse('tools')
return render(request, 'tools/tools_list.html')
| 23.555556
| 51
| 0.768868
| 28
| 212
| 5.75
| 0.607143
| 0.124224
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141509
| 212
| 8
| 52
| 26.5
| 0.884615
| 0.240566
| 0
| 0
| 0
| 0
| 0.132911
| 0.132911
| 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
|
2876c0ce5a2cae9977674c7f788d14ee18e15c9b
| 39
|
py
|
Python
|
footprint_airflow/dags/utils/__init__.py
|
footprint-network/footprint-analytics
|
5de4932ce1c21860785edcce90ffdf097b6f9921
|
[
"MIT"
] | null | null | null |
footprint_airflow/dags/utils/__init__.py
|
footprint-network/footprint-analytics
|
5de4932ce1c21860785edcce90ffdf097b6f9921
|
[
"MIT"
] | null | null | null |
footprint_airflow/dags/utils/__init__.py
|
footprint-network/footprint-analytics
|
5de4932ce1c21860785edcce90ffdf097b6f9921
|
[
"MIT"
] | 1
|
2021-09-20T22:31:20.000Z
|
2021-09-20T22:31:20.000Z
|
from utils import constant as Constant
| 19.5
| 38
| 0.846154
| 6
| 39
| 5.5
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 39
| 1
| 39
| 39
| 1
| 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
|
953f4acc27d82209af8e220dc492e0b57c9a95ac
| 20
|
py
|
Python
|
fpl/__init__.py
|
david-macleod/fpl
|
9bdf1c7e1c3333fcf7d0d7cf5e08551bb64f030d
|
[
"MIT"
] | null | null | null |
fpl/__init__.py
|
david-macleod/fpl
|
9bdf1c7e1c3333fcf7d0d7cf5e08551bb64f030d
|
[
"MIT"
] | null | null | null |
fpl/__init__.py
|
david-macleod/fpl
|
9bdf1c7e1c3333fcf7d0d7cf5e08551bb64f030d
|
[
"MIT"
] | null | null | null |
from .fpl import FPL
| 20
| 20
| 0.8
| 4
| 20
| 4
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 20
| 1
| 20
| 20
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
953fa1588f8360ecde679c3eef714a2abb1316a0
| 288
|
py
|
Python
|
fugue/workflow/__init__.py
|
kvnkho/fugue
|
5f3fe8f1fb72632e5b5987d720c1d1ef546e4682
|
[
"Apache-2.0"
] | 547
|
2020-09-22T08:30:14.000Z
|
2022-03-30T23:11:05.000Z
|
fugue/workflow/__init__.py
|
kvnkho/fugue
|
5f3fe8f1fb72632e5b5987d720c1d1ef546e4682
|
[
"Apache-2.0"
] | 196
|
2020-09-22T23:08:26.000Z
|
2022-03-26T21:22:48.000Z
|
fugue/workflow/__init__.py
|
kvnkho/fugue
|
5f3fe8f1fb72632e5b5987d720c1d1ef546e4682
|
[
"Apache-2.0"
] | 37
|
2020-09-23T17:05:00.000Z
|
2022-03-29T18:26:52.000Z
|
# flake8: noqa
from fugue.workflow._workflow_context import FugueWorkflowContext
from fugue.workflow.module import module
from fugue.workflow.utils import register_raw_df_type, is_acceptable_raw_df
from fugue.workflow.workflow import FugueWorkflow, WorkflowDataFrame, WorkflowDataFrames
| 41.142857
| 88
| 0.875
| 36
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|
0
| 6
|
954936fd1b0c1ca11feec15fa61204c80b0e2305
| 131
|
py
|
Python
|
posts/admin.py
|
robotgear/robotgear
|
15361aef197071e6cf23fca0e574fddeef97152c
|
[
"MIT"
] | null | null | null |
posts/admin.py
|
robotgear/robotgear
|
15361aef197071e6cf23fca0e574fddeef97152c
|
[
"MIT"
] | 13
|
2020-03-15T03:44:47.000Z
|
2022-03-11T23:48:01.000Z
|
posts/admin.py
|
robotgear/robotgear
|
15361aef197071e6cf23fca0e574fddeef97152c
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from posts.models import Post
@admin.register(Post)
class PostAdmin(admin.ModelAdmin):
pass
| 16.375
| 34
| 0.78626
| 18
| 131
| 5.722222
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| 131
| 7
| 35
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| 0
|
0
| 6
|
95870be9e7dbdb4f9e5d4c2f12d23f73b7fede98
| 6,331
|
py
|
Python
|
rdfframes/test_queries/test_join.py
|
qcri/RDFframe
|
2a50105479051c134cc5eddc9e20d55b755ef765
|
[
"MIT"
] | 13
|
2019-07-06T00:10:11.000Z
|
2022-02-20T02:14:16.000Z
|
rdfframes/test_queries/test_join.py
|
qcri/RDFrame
|
2a50105479051c134cc5eddc9e20d55b755ef765
|
[
"MIT"
] | 1
|
2019-05-20T08:51:42.000Z
|
2019-05-20T08:51:42.000Z
|
rdfframes/test_queries/test_join.py
|
qcri/RDFframe
|
2a50105479051c134cc5eddc9e20d55b755ef765
|
[
"MIT"
] | 3
|
2020-04-17T10:50:37.000Z
|
2022-03-23T01:30:16.000Z
|
from time import time
from rdfframes.knowledge_graph import KnowledgeGraph
from rdfframes.client.http_client import HttpClientDataFormat, HttpClient
from rdfframes.client.sparql_endpoint_client import SPARQLEndpointClient
from rdfframes.utils.constants import JoinType
__author__ = "Ghadeer"
endpoint = 'http://10.161.202.101:8890/sparql/'
port = 8890
output_format = HttpClientDataFormat.PANDAS_DF
max_rows = 1000000
timeout = 12000
"""
client = HttpClient(endpoint_url=endpoint,
port=port,
return_format=output_format,
timeout=timeout,
max_rows=max_rows
)
"""
client = SPARQLEndpointClient(endpoint)
graph = KnowledgeGraph(graph_name='dbpedia')
def expand_groupby_join(join_type):
basketball_palyer = graph.entities('dbpo:BasketballPlayer', entities_col_name='player')\
.expand('player', [('dbpp:team', 'team')])\
.group_by(['team']).count('player', 'count_basketball_players', True)
basketball_team = graph.entities('dbpo:BasketballTeam', entities_col_name='team')\
.expand('team', [('dbpp:president', 'president'), ('dbpp:sponsor', 'sponsor'), ('dbpp:name', 'name')])
basketball_palyer_team = basketball_team.join(basketball_palyer,'team', join_type=join_type)
print("SPARQL QUERY FOR JOIN TYPE {} \n{}\n".format(join_type, basketball_palyer_team.to_sparql()))
#df = basketball_palyer_team.execute(client)
#print(basketball_palyer_team.to_sparql())
#df = dataset.execute(client, return_format=output_format)
#print(df.shape)
def groupby_expand_join(join_type):
basketball_palyer = graph.entities('dbpo:BasketballPlayer', entities_col_name='player')\
.expand('player', [('dbpp:team', 'team')])\
.group_by(['team']).count('player', 'count_basketball_players', True)
basketball_team = graph.entities('dbpo:BasketballTeam', entities_col_name='team')\
.expand('team', [('dbpp:president', 'president'), ('dbpp:sponsor', 'sponsor'), ('dbpp:name', 'name')])
basketball_palyer_team = basketball_palyer.join(basketball_team,'team', join_type=join_type)
print("SPARQL QUERY FOR JOIN TYPE {} \n{}\n".format(join_type, basketball_palyer_team.to_sparql()))
def expand_join(join_type):
basketball_palyer = graph.entities('dbpo:BasketballPlayer', entities_col_name='player')\
.expand('player', [('dbpp:nationality', 'nationality') ,('dbpp:birthPlace', 'place')\
,('dbpp:birthDate','birthDate'),('dbpp:team', 'team')])
basketball_team = graph.entities('dbpo:BasketballTeam', entities_col_name='team')\
.expand('team', [('dbpp:president', 'president'), ('dbpp:sponsor', 'sponsor'), ('dbpp:name', 'name')])
basketball_palyer_team = basketball_team.join(basketball_palyer,'team', join_type=join_type)
print(basketball_palyer_team.to_sparql())
#df = basketball_palyer_team.execute(client)
def group_join(join_type):
basket_ball = graph.entities('dbpo:BasketballPlayer', entities_col_name='player') \
.expand('player', [('dbpp:birthPlace', 'place')]) \
.group_by(['place']).count('player', 'count_basketball_players', True)
tennis = graph.entities('dbpo:TennisPlayer', entities_col_name='player') \
.expand('player', [('dbpp:birthPlace', 'place')]) \
.group_by(['place']).count('player', 'count_tennis_players', True)
teams = basket_ball.join(tennis, 'place', join_type=join_type)
print(teams.to_sparql())
start = time()
expand_groupby_join(JoinType.InnerJoin)
duration = time()-start
print("Duration of Inner join on expandable grouped datasets = {} sec".format(duration))
start = time()
groupby_expand_join(JoinType.InnerJoin)
duration = time()-start
print("Duration of Inner join on grouped expandable datasets = {} sec".format(duration))
start = time()
expand_groupby_join(JoinType.LeftOuterJoin) ## change the type here.
duration = time()-start
print("Duration of LeftOuter Join on expandable grouped datasets = {} sec".format(duration))
start = time()
groupby_expand_join(JoinType.LeftOuterJoin) ## change the type here.
duration = time()-start
print("Duration of LeftOuter Join on grouped expandable datasets = {} sec".format(duration))
start = time()
expand_groupby_join(JoinType.RightOuterJoin) ## change the type here.
duration = time()-start
print("Duration of RightOuter Join on expandable grouped datasets = {} sec".format(duration))
start = time()
groupby_expand_join(JoinType.RightOuterJoin) ## change the type here.
duration = time()-start
print("Duration of RightOuter Join on grouped expandable datasets = {} sec".format(duration))
start = time()
expand_groupby_join(JoinType.OuterJoin) ## change the type here.
duration = time()-start
print("Duration of Outer join on expandable grouped datasets = {} sec".format(duration))
start = time()
groupby_expand_join(JoinType.OuterJoin) ## change the type here.
duration = time()-start
print("Duration of Outer join on grouped expandable datasets = {} sec".format(duration))
start = time()
expand_join(JoinType.InnerJoin) ## change the type here.
duration = time()-start
print("Duration of Inner join on expandable datasets = {} sec".format(duration))
start = time()
expand_join(JoinType.LeftOuterJoin) ## change the type here.
duration = time()-start
print("Duration of LeftOuter Join on expandable datasets = {} sec".format(duration))
start = time()
expand_join(JoinType.RightOuterJoin) ## change the type here.
duration = time()-start
print("Duration ofRightOuter Join on expandable datasets = {} sec".format(duration))
start = time()
expand_join(JoinType.OuterJoin) ## change the type here.
duration = time()-start
print("Duration of Outer join on expandable datasets = {} sec".format(duration))
start = time()
group_join(JoinType.InnerJoin) ## change the type here.
duration = time()-start
print("Duration of Inner join on expandable datasets = {} sec".format(duration))
start = time()
group_join(JoinType.LeftOuterJoin) ## change the type here.
duration = time()-start
print("Duration of LeftOuter Join on expandable datasets = {} sec".format(duration))
start = time()
group_join(JoinType.RightOuterJoin) ## change the type here.
duration = time()-start
print("Duration ofRightOuter Join on expandable datasets = {} sec".format(duration))
start = time()
group_join(JoinType.OuterJoin) ## change the type here.
duration = time()-start
print("Duration of Outer join on expandable datasets = {} sec".format(duration))
| 36.595376
| 104
| 0.743642
| 793
| 6,331
| 5.775536
| 0.12232
| 0.027948
| 0.059389
| 0.076856
| 0.818996
| 0.81441
| 0.805022
| 0.79738
| 0.79738
| 0.79738
| 0
| 0.005518
| 0.11262
| 6,331
| 172
| 105
| 36.80814
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| 0
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| 0
| 0.316711
| 0.027586
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| 0.036364
| false
| 0
| 0.045455
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| 0.081818
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| null | 0
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| 0
|
0
| 6
|
95a2c6545c4a96fa0bc95597b609537b3892dcd2
| 222
|
py
|
Python
|
yadlt/core/__init__.py
|
akmeraki/deep-learning-
|
ddeb1f2848da7b7bee166ad2152b4afc46bb2086
|
[
"MIT"
] | 1,093
|
2016-03-07T23:32:27.000Z
|
2019-09-19T12:40:30.000Z
|
Deep-Learning-TensorFlow/yadlt/core/__init__.py
|
zhwhong/awesome-deep-learning
|
ba4302a8d65ac8b63627bcfa8e3b23871fa2c390
|
[
"CC0-1.0"
] | 68
|
2016-03-18T15:44:15.000Z
|
2019-05-13T03:04:21.000Z
|
Deep-Learning-TensorFlow/yadlt/core/__init__.py
|
zhwhong/awesome-deep-learning
|
ba4302a8d65ac8b63627bcfa8e3b23871fa2c390
|
[
"CC0-1.0"
] | 459
|
2016-03-18T05:49:01.000Z
|
2019-09-13T14:14:11.000Z
|
"""Yadlt core package."""
from __future__ import absolute_import
from .config import *
from .layers import *
from .trainers import *
from .model import *
from .supervised_model import *
from .unsupervised_model import *
| 20.181818
| 38
| 0.765766
| 28
| 222
| 5.821429
| 0.464286
| 0.368098
| 0.184049
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| 0
| 0.148649
| 222
| 10
| 39
| 22.2
| 0.862434
| 0.085586
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| true
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| null | 1
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
95e55ffadb7ee85ce16d1d74e622208eb342f948
| 15,939
|
py
|
Python
|
contrib/FetchIoT/resources-Registration-CT.py
|
Ramzi04/aiocoap
|
8c5a10513ca4fb8000c476eab6c9831f9de1e4ed
|
[
"MIT"
] | null | null | null |
contrib/FetchIoT/resources-Registration-CT.py
|
Ramzi04/aiocoap
|
8c5a10513ca4fb8000c476eab6c9831f9de1e4ed
|
[
"MIT"
] | null | null | null |
contrib/FetchIoT/resources-Registration-CT.py
|
Ramzi04/aiocoap
|
8c5a10513ca4fb8000c476eab6c9831f9de1e4ed
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
import logging
import asyncio
from random import randint
from aiocoap import *
# </10359/0/50>;rt="ipso.act.lck";if="core.a";ct=40'
# POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=sl1&d=bldg1.fl2.off3")
logging.basicConfig(level=logging.DEBUG)
async def main():
############ Smart city resources ##########################
###################################################
index = 0 #used in IPSO objects instances
for indexBuilding in range(1,2): #1->11
############ Building resources ###########################
###################################################
for indexFloor in range(1,2): #1->4
############ floor resources #############################
###################################################
for indexOffice in range(1,2): #1->11
domain = "bldg"+str(indexBuilding)+".fl"+str(indexFloor)+".off"+str(indexOffice)
############ office resources #############################
###################################################
# Luminary1 and Luminary1 nodes
for indexLum in range(1,3):
context = await Context.create_client_context()
payload = b'</3392/%s/404/>;rt="ipso.sen.lt";if="core.s";ct=40;qos=%s;lt=%s;sz=%s;sec=osc;man=ecobee,</3311/%s/5851>;rt="ipso.act.lt.dim";if="core.a";ct=40;qos=%s;lt=%s;sz=%s;sec=osc;man=ecobee' % (str(index).encode('ascii'),str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'),str(randint(1,100)).encode('ascii'),str(index).encode('ascii'), str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'),str(randint(1,100)).encode('ascii') )
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=lm00"+str(indexLum)+"&d="+domain)
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
# Power Strip
context = await Context.create_client_context()
payload = b'</3312/0/5850>;rt="ipso.act.pwr.rel";if="core.a";ct=50;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest,</3312/1/5850>;rt="ipso.act.pwr.rel";if="core.a";ct=50;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest,</3312/2/5850>;rt="ipso.act.pwr.rel";if="core.a";ct=50;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest,</3312/3/5850>;rt="ipso.act.pwr.rel";if="core.a";ct=50;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest' % (str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'),str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'))
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=ps001&d="+domain)
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
# PIR sensor
context = await Context.create_client_context()
payload = b'</3302/%s/5500>;rt="ipso.sen.pres";if="core.s";ct=40;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest;obs' % (str(index).encode('ascii'), str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'))
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=pir1&d="+domain)
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
# HT sensor
context = await Context.create_client_context()
payload = b'</3303/%s/5700>;rt="ipso.sen.temp";if="core.s";ct=40;qos=%s;lt=%s;sz=%s;sec=osc;man=ecobee;obs,</3304/%s/5700>;rt="ipso.sen.hum";if="core.s";ct=40;qos=%s;lt=%s;sz=%s;sec=osc;man=ecobee;obs' % (str(index).encode('ascii'),str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'),str(randint(1,100)).encode('ascii') , str(index).encode('ascii'), str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'),str(randint(1,100)).encode('ascii'))
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=ht1&d="+domain)
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
# CO2 sensor
context = await Context.create_client_context()
payload = b'</6047/%s/5700>;rt="ipso.sen.co2";if="core.s";ct=40;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest;obs' % (str(index).encode('ascii'),str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'))
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=am1&d="+domain)
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
# office door smart lock (sl) actuator
context = await Context.create_client_context()
payload = b'</10359/%s/50>;rt="ipso.act.lck";if="core.a";ct=40;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest,</10359/%s/100>;rt="ipso.act.lck";if="core.a";ct=40;qos=%s;lt=%s;sz=%s;sec=dtls;man=nest' % (str(index).encode('ascii'),str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'),str(randint(1,100)).encode('ascii') ,str(index).encode('ascii'), str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'),str(randint(1,100)).encode('ascii'))
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=sl1&d="+domain)
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
# office SMART Thermostat
context = await Context.create_client_context()
payload = b'</12300/%s/5209>;rt="ipso.act.thrms";if="core.a";ct=40;qos=%s;lt=%s;sz=%s;sec=osc;man=ecobee' % (str(index).encode('ascii'),str(randint(1,100)).encode('ascii'), str(1000*randint(1,100)).encode('ascii'), str(randint(1,100)).encode('ascii'))
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=th1&d="+domain)
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
index += 1
################Groups####################################
index = 0
indexGroup = 0
payloadCO2 = payloadPIR = payloadHT = payloadLock = payloadTherms = b''
for indexBuilding in range(1,2): #1->11
for indexFloor in range(1,2): #1->4
for indexOffice in range(1,2): #1->11
payloadCO2 += b'</6047/%s/5700>;rt="co2";if="core.s";ct=40;qos=30;lt=80000;sz=88;sec=osc;man=ecobee;obs' %(str(index).encode('ascii'))
payloadPIR += b'</3302/%s/5500>;rt="pir";if="core.s";ct=40;qos=55;lt=85000;sz=15;sec=dtls;man=nest;obs' %(str(index).encode('ascii'))
payloadHT += b'</3303/%s/5700>;rt="ht";if="core.s";ct=40;qos=41;lt=25000;sz=22;sec=osc;man=ring;obs,</3304/%s/5700>;rt="ht";if="core.s";ct=40;qos=88;lt=55000;sz=15;sec=osc;man=ring;obs' %(str(index).encode('ascii'), str(index).encode('ascii'))
payloadLock += b'</10359/%s/50>;rt="sl";if="core.a";ct=40;qos=41;lt=12000;sz=23;sec=dtls;man=ecobee,</10359/%s/100>;rt="sl";if="core.a";ct=40;qos=90;lt=47000;sz=22;sec=dtls;man=ecobee' %(str(index).encode('ascii'), str(index).encode('ascii'))
payloadTherms += b'</12300/%s/5209>;rt="th";if="core.a";ct=40;qos=75;lt=99000;sz=17;sec=osc;man=nest' %(str(index).encode('ascii'))
index += 1
# Group co2 sensor for Buidings %i
context = await Context.create_client_context()
request = Message(code=POST, payload=payloadCO2, uri="coap://localhost/resourcedirectory/?ep=group%s&gr=co2.bldg%s&d=bldg%s&if=core.gp&bl=all.bldg%s" %(str(indexGroup),str(indexBuilding),str(indexBuilding),str(indexBuilding)))
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexGroup += 1
# Group PIR sensor for Buidings %i
context = await Context.create_client_context()
request = Message(code=POST, payload=payloadPIR, uri="coap://localhost/resourcedirectory/?ep=group%s&gr=pir.bldg%s&d=bldg%s&if=core.gp&bl=all.bldg%s" %(str(indexGroup),str(indexBuilding),str(indexBuilding) , str(indexBuilding)) )
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexGroup += 1
# Group HT sensor for Buidings %i
context = await Context.create_client_context()
request = Message(code=POST, payload=payloadHT, uri="coap://localhost/resourcedirectory/?ep=group%s&gr=ht.bldg%s&d=bldg%s&if=core.gp&bl=all.bldg%s" %(str(indexGroup),str(indexBuilding), str(indexBuilding), str(indexBuilding)))
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexGroup += 1
# Group SMART Lock for Buidings %i
context = await Context.create_client_context()
request = Message(code=POST, payload=payloadLock, uri="coap://localhost/resourcedirectory/?ep=group%s&gr=sl.bldg%s&d=bldg%s&if=core.gp&bl=all.bldg%s" %(str(indexGroup),str(indexBuilding),str(indexBuilding), str(indexBuilding) ))
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexGroup += 1
# Group Thermostat for Buidings %i
context = await Context.create_client_context()
request = Message(code=POST, payload=payloadTherms, uri="coap://localhost/resourcedirectory/?ep=group%s&gr=th.bldg%s&d=bldg%s&if=core.gp&bl=all.bldg%s" %(str(indexGroup),str(indexBuilding),str(indexBuilding) , str(indexBuilding)))
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexGroup += 1
# Group ALL for Buidings %i
payload = payloadCO2 + payloadPIR + payloadHT + payloadLock + payloadTherms
context = await Context.create_client_context()
request = Message(code=POST, payload=payload, uri="coap://localhost/resourcedirectory/?ep=group%s&gr=all.bldg%s&d=bldg%s&if=core.gp&bl=all" %(str(indexGroup),str(indexBuilding),str(indexBuilding) ))
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexGroup += 1
################Collections####################################
index = 0
indexCollection = 0
sensors_collection = actuators_collection = b''
for indexBuilding in range(1,2): #1->11
for indexFloor in range(1,2): #1->4
for indexOffice in range(1,2): #1->11
sensors_collection += b'</6047/%s/5700>;rt="ipso.sen.co2";if="core.s";ct=40;qos=30;lt=80000;sz=88;obs' %(str(index).encode('ascii'))
sensors_collection += b'</3392/%s/404>;rt="ipso.sen.lt";if="core.s";ct=40;qos=30;lt=80000;sz=88;obs' %(str(index).encode('ascii'))
sensors_collection += b'</3302/%s/5500>;rt="ipso.sen.pres";if="core.s";ct=40;qos=55;lt=85000;sz=15;obs' %(str(index).encode('ascii'))
sensors_collection += b'</3303/%s/5700>;rt="ipso.sen.temp";if="core.s";ct=40;qos=41;lt=25000;sz=22;obs,</3304/%s/5700>;rt="ipso.sen.hum";if="core.s";ct=40;qos=88;lt=55000;sz=15;obs' %(str(index).encode('ascii'), str(index).encode('ascii'))
actuators_collection += b'</10359/%s/50>;rt="ipso.act.lck";if="core.a";ct=40;qos=41;lt=12000;sz=23,</10359/%s/100>;rt="ipso.act.lck";if="core.a";ct=40;qos=90;lt=47000;sz=22' %(str(index).encode('ascii'), str(index).encode('ascii'))
actuators_collection += b'</12300/%s/5209>;rt="ipso.act.thrms";if="core.a";ct=40;qos=75;lt=99000;sz=17' %(str(index).encode('ascii'))
actuators_collection += b'</3311/%s/5851>;rt="ipso.lt.dim";if="core.a";ct=40;qos=55;lt=39000;sz=19' %(str(index).encode('ascii'))
actuators_collection += b'</3312/0/5850>;rt="ipso.pwr.rel";if="core.a";ct=50;qos=85;lt=31000;sz=11,</3312/1/5850>;rt="ipso.pwr.rel";if="core.a";ct=50;qos=15;lt=81000;sz=44,</3312/2/5850>;rt="ipso.pwr.rel";if="core.a";ct=50;qos=44;lt=74000;sz=15,</3312/3/5850>;rt="ipso.pwr.rel";if="core.a";ct=50;qos=47;lt=88000;sz=17'
index += 1
# Collection of sensors for Buidings %i
context = await Context.create_client_context()
request = Message(code=POST, payload=sensors_collection, uri="coap://localhost/resourcedirectory/?ep=col%s&if=core.ll&cn=col.sens.bldg%s" %(str(indexCollection), str(indexBuilding)))
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexCollection += 1
# Collection of actuators for Buidings %i
context = await Context.create_client_context()
request = Message(code=POST, payload=actuators_collection, uri="coap://localhost/resourcedirectory/?ep=col%s&if=core.ll&cn=col.acts.bldg%s" %(str(indexCollection), str(indexBuilding)))
request.opt.content_format = 40
try:
await context.request(request).response
except Exception as e:
print(e)
indexCollection += 1
if __name__ == "__main__":
asyncio.get_event_loop().run_until_complete(main())
| 71.475336
| 874
| 0.547023
| 2,055
| 15,939
| 4.209246
| 0.098783
| 0.081387
| 0.072832
| 0.076647
| 0.889249
| 0.861272
| 0.855607
| 0.845549
| 0.777225
| 0.746012
| 0
| 0.071966
| 0.258109
| 15,939
| 222
| 875
| 71.797297
| 0.659535
| 0.045925
| 0
| 0.693252
| 0
| 0.171779
| 0.279296
| 0.256049
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.02454
| 0
| 0.02454
| 0.092025
| 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
|
2504f0d0d9eb55329413b22e1cd96d93c0af69cf
| 66
|
py
|
Python
|
iiif_prezi3/base.py
|
rbturnbull/iiif-prezi3
|
0e66bc41438772c75e064c20964ed01aff1f3709
|
[
"Apache-2.0"
] | null | null | null |
iiif_prezi3/base.py
|
rbturnbull/iiif-prezi3
|
0e66bc41438772c75e064c20964ed01aff1f3709
|
[
"Apache-2.0"
] | null | null | null |
iiif_prezi3/base.py
|
rbturnbull/iiif-prezi3
|
0e66bc41438772c75e064c20964ed01aff1f3709
|
[
"Apache-2.0"
] | null | null | null |
from pydantic import BaseModel
class Base(BaseModel):
pass
| 9.428571
| 30
| 0.742424
| 8
| 66
| 6.125
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.212121
| 66
| 6
| 31
| 11
| 0.942308
| 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
|
2564e5b943dc0ff92de2ca0df38b229fad92fa19
| 45
|
py
|
Python
|
templatext/__init__.py
|
jaimeteb/templatext
|
eb484f33a7fd330115b0d2b458f8d4b5840775c4
|
[
"MIT"
] | null | null | null |
templatext/__init__.py
|
jaimeteb/templatext
|
eb484f33a7fd330115b0d2b458f8d4b5840775c4
|
[
"MIT"
] | null | null | null |
templatext/__init__.py
|
jaimeteb/templatext
|
eb484f33a7fd330115b0d2b458f8d4b5840775c4
|
[
"MIT"
] | null | null | null |
from templatext.templatext import Templatext
| 22.5
| 44
| 0.888889
| 5
| 45
| 8
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088889
| 45
| 1
| 45
| 45
| 0.97561
| 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
|
c2d0318877ef33d5bbfe4ed165f625346369bb7a
| 158
|
py
|
Python
|
tests/expectations/econ-gender-x-ideology-weighted-row-prop-moe.py
|
Crunch-io/crunch-cube
|
80986d5b2106c774f05176fb6c6a5ea0d840f09d
|
[
"MIT"
] | 3
|
2021-01-22T20:42:31.000Z
|
2021-06-02T17:53:19.000Z
|
tests/expectations/econ-gender-x-ideology-weighted-row-prop-moe.py
|
Crunch-io/crunch-cube
|
80986d5b2106c774f05176fb6c6a5ea0d840f09d
|
[
"MIT"
] | 331
|
2017-11-13T22:41:56.000Z
|
2021-12-02T21:59:43.000Z
|
tests/expectations/econ-gender-x-ideology-weighted-row-prop-moe.py
|
Crunch-io/crunch-cube
|
80986d5b2106c774f05176fb6c6a5ea0d840f09d
|
[
"MIT"
] | 1
|
2021-02-19T02:49:00.000Z
|
2021-02-19T02:49:00.000Z
|
[
[0.02175933, 0.03332428, 0.04187784, 0.03716728, 0.0308031, 0.01415677],
[0.02346025, 0.03427124, 0.04290557, 0.0354381, 0.02346025, 0.01953654],
]
| 31.6
| 76
| 0.670886
| 24
| 158
| 4.416667
| 0.5
| 0.169811
| 0.188679
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.779412
| 0.139241
| 158
| 4
| 77
| 39.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
c2d7625d3339fc61d7e51d0738618b0f8eca5c73
| 441
|
py
|
Python
|
release/scripts/presets/cloth/rubber.py
|
wycivil08/blendocv
|
f6cce83e1f149fef39afa8043aade9c64378f33e
|
[
"Unlicense"
] | 30
|
2015-01-29T14:06:05.000Z
|
2022-01-10T07:47:29.000Z
|
release/scripts/presets/cloth/rubber.py
|
ttagu99/blendocv
|
f6cce83e1f149fef39afa8043aade9c64378f33e
|
[
"Unlicense"
] | 1
|
2017-02-20T20:57:48.000Z
|
2018-12-19T23:44:38.000Z
|
release/scripts/presets/cloth/rubber.py
|
ttagu99/blendocv
|
f6cce83e1f149fef39afa8043aade9c64378f33e
|
[
"Unlicense"
] | 15
|
2015-04-23T02:38:36.000Z
|
2021-03-01T20:09:39.000Z
|
import bpy
bpy.context.active_object.modifiers['Cloth'].settings.quality = 7
bpy.context.active_object.modifiers['Cloth'].settings.mass = 3
bpy.context.active_object.modifiers['Cloth'].settings.structural_stiffness = 15
bpy.context.active_object.modifiers['Cloth'].settings.bending_stiffness = 25
bpy.context.active_object.modifiers['Cloth'].settings.spring_damping = 25
bpy.context.active_object.modifiers['Cloth'].settings.air_damping = 1
| 55.125
| 79
| 0.818594
| 60
| 441
| 5.85
| 0.35
| 0.17094
| 0.273504
| 0.376068
| 0.763533
| 0.763533
| 0.763533
| 0.262108
| 0
| 0
| 0
| 0.021378
| 0.045351
| 441
| 7
| 80
| 63
| 0.812352
| 0
| 0
| 0
| 0
| 0
| 0.068027
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.142857
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
6c2c14c30f0edc56988c1ec3234e0ca4ff59ba32
| 3,299
|
py
|
Python
|
tests/contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/test_michelson_coding_KT1T6C.py
|
juztin/pytezos-1
|
7e608ff599d934bdcf129e47db43dbdb8fef9027
|
[
"MIT"
] | 1
|
2021-05-20T16:52:08.000Z
|
2021-05-20T16:52:08.000Z
|
tests/contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/test_michelson_coding_KT1T6C.py
|
juztin/pytezos-1
|
7e608ff599d934bdcf129e47db43dbdb8fef9027
|
[
"MIT"
] | 1
|
2020-12-30T16:44:56.000Z
|
2020-12-30T16:44:56.000Z
|
tests/contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/test_michelson_coding_KT1T6C.py
|
tqtezos/pytezos
|
a4ac0b022d35d4c9f3062609d8ce09d584b5faa8
|
[
"MIT"
] | 1
|
2022-03-20T19:01:00.000Z
|
2022-03-20T19:01:00.000Z
|
from unittest import TestCase
from tests import get_data
from pytezos.michelson.micheline import michelson_to_micheline
from pytezos.michelson.formatter import micheline_to_michelson
class MichelsonCodingTestKT1T6C(TestCase):
def setUp(self):
self.maxDiff = None
def test_michelson_parse_code_KT1T6C(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/code_KT1T6C.json')
actual = michelson_to_micheline(get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/code_KT1T6C.tz'))
self.assertEqual(expected, actual)
def test_michelson_format_code_KT1T6C(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/code_KT1T6C.tz')
actual = micheline_to_michelson(get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/code_KT1T6C.json'),
inline=True)
self.assertEqual(expected, actual)
def test_michelson_inverse_code_KT1T6C(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/code_KT1T6C.json')
actual = michelson_to_micheline(micheline_to_michelson(expected))
self.assertEqual(expected, actual)
def test_michelson_parse_storage_KT1T6C(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/storage_KT1T6C.json')
actual = michelson_to_micheline(get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/storage_KT1T6C.tz'))
self.assertEqual(expected, actual)
def test_michelson_format_storage_KT1T6C(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/storage_KT1T6C.tz')
actual = micheline_to_michelson(get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/storage_KT1T6C.json'),
inline=True)
self.assertEqual(expected, actual)
def test_michelson_inverse_storage_KT1T6C(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/storage_KT1T6C.json')
actual = michelson_to_micheline(micheline_to_michelson(expected))
self.assertEqual(expected, actual)
def test_michelson_parse_parameter_ooQKSw(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/parameter_ooQKSw.json')
actual = michelson_to_micheline(get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/parameter_ooQKSw.tz'))
self.assertEqual(expected, actual)
def test_michelson_format_parameter_ooQKSw(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/parameter_ooQKSw.tz')
actual = micheline_to_michelson(get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/parameter_ooQKSw.json'),
inline=True)
self.assertEqual(expected, actual)
def test_michelson_inverse_parameter_ooQKSw(self):
expected = get_data(
path='contracts/KT1T6CDRQLRiFU5dszBZWWQwQc8aCbfwX3Mg/parameter_ooQKSw.json')
actual = michelson_to_micheline(micheline_to_michelson(expected))
self.assertEqual(expected, actual)
| 43.986667
| 90
| 0.735071
| 316
| 3,299
| 7.373418
| 0.120253
| 0.048069
| 0.070815
| 0.128755
| 0.895279
| 0.895279
| 0.895279
| 0.895279
| 0.872961
| 0.850215
| 0
| 0.040855
| 0.19127
| 3,299
| 74
| 91
| 44.581081
| 0.832459
| 0
| 0
| 0.590164
| 0
| 0
| 0.294938
| 0.294938
| 0
| 0
| 0
| 0
| 0.147541
| 1
| 0.163934
| false
| 0
| 0.065574
| 0
| 0.245902
| 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
|
6c3ed50642e57dcc9dc4711952b0ce7dce6e7745
| 241
|
py
|
Python
|
cgi-bin/specialCharacter.py
|
fanuware/pybrowser
|
910cebaee45524248c18d86605ba9e7f1b862c47
|
[
"MIT"
] | null | null | null |
cgi-bin/specialCharacter.py
|
fanuware/pybrowser
|
910cebaee45524248c18d86605ba9e7f1b862c47
|
[
"MIT"
] | null | null | null |
cgi-bin/specialCharacter.py
|
fanuware/pybrowser
|
910cebaee45524248c18d86605ba9e7f1b862c47
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
def printTextarea(textcontent):
import sys, codecs
#sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach())
print('<textarea id="textcontent" name="textcontent" form="usrform">'+textcontent+'</textarea>')
| 34.428571
| 100
| 0.701245
| 28
| 241
| 6.035714
| 0.714286
| 0.106509
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009302
| 0.107884
| 241
| 6
| 101
| 40.166667
| 0.776744
| 0.315353
| 0
| 0
| 0
| 0
| 0.441718
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0.666667
| 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
| 0
| 1
| 1
|
0
| 6
|
6c5a4c0745ea57884ef62f5181ff1a3a2b8c6e9c
| 18,966
|
py
|
Python
|
source/lib/cloudformation.py
|
pankajagrawal16/aws-control-tower-customizations
|
e4752bf19a1c8f0a597195982d63a1a2c2dd799a
|
[
"Apache-2.0"
] | 1
|
2020-02-11T16:34:09.000Z
|
2020-02-11T16:34:09.000Z
|
source/lib/cloudformation.py
|
pankajagrawal16/aws-control-tower-customizations
|
e4752bf19a1c8f0a597195982d63a1a2c2dd799a
|
[
"Apache-2.0"
] | null | null | null |
source/lib/cloudformation.py
|
pankajagrawal16/aws-control-tower-customizations
|
e4752bf19a1c8f0a597195982d63a1a2c2dd799a
|
[
"Apache-2.0"
] | null | null | null |
######################################################################################################################
# Copyright 2012-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License. #
######################################################################################################################
#!/bin/python
import boto3
import inspect
import os
from botocore.exceptions import ClientError
from lib.decorator import try_except_retry
cfn_client = boto3.client('cloudformation')
class StackSet(object):
def __init__(self, logger):
self.logger = logger
def describe_stack_set(self, stack_set_name):
try:
response = cfn_client.describe_stack_set(
StackSetName=stack_set_name
)
return response
except Exception:
pass
def describe_stack_set_operation(self, stack_set_name, operation_id):
try:
response = cfn_client.describe_stack_set_operation(
StackSetName=stack_set_name,
OperationId=operation_id
)
return response
except Exception as e:
self.logger.error("'{}' StackSet Operation ID: {} not found.".format(stack_set_name, operation_id))
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def list_stack_instances(self, **kwargs):
try:
response = cfn_client.list_stack_instances(**kwargs)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def list_stack_instances_per_account(self, stack_name, account_id, max_results=20):
try:
response = cfn_client.list_stack_instances(
StackSetName=stack_name,
StackInstanceAccount=account_id,
MaxResults=max_results
)
stack_instance_list = response.get('Summaries', [])
next_token = response.get('NextToken', None)
while next_token is not None:
self.logger.info("Next Token Returned: {}".format(next_token))
cfn_client.list_stack_instances(
StackSetName=stack_name,
StackInstanceAccount=account_id,
MaxResults=max_results,
NextToken=next_token
)
self.logger.info("Extending Stack Instance List")
stack_instance_list.extend(response.get('Summaries', []))
next_token = response.get('NextToken', None)
return stack_instance_list
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def create_stack_set(self, stack_set_name, template_url, cf_params, capabilities):
try:
parameters = []
d = {}
for key, value in cf_params.items():
'''This condition checks if the value is a List and convert it into a Comma-delimited string.
Note: Remember to change the parameter type from 'List<AWS::EC2::*::*>'
(Supported AWS-Specific Parameter Types) to 'CommaDelimitedList' in the template.'''
if type(value) == list:
value = ",".join(map(str, value))
d['ParameterKey'] = key
d['ParameterValue'] = value
parameters.append(d.copy())
response = cfn_client.create_stack_set(
StackSetName=stack_set_name,
TemplateURL=template_url,
Parameters=parameters,
Capabilities=[capabilities],
Tags=[
{
'Key': 'AWS_Solutions',
'Value': 'CustomControlTowerStackSet'
},
],
AdministrationRoleARN=os.environ.get('administration_role_arn'),
ExecutionRoleName=os.environ.get('execution_role_name')
)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def create_stack_instances(self, stack_set_name, account_list, region_list,
failed_tolerance_percent=0, max_concurrent_percent=100):
try:
response = cfn_client.create_stack_instances(
StackSetName=stack_set_name,
Accounts=account_list,
Regions=region_list,
OperationPreferences={
'FailureTolerancePercentage': failed_tolerance_percent,
'MaxConcurrentPercentage': max_concurrent_percent
}
)
return response
except ClientError as e:
if e.response['Error']['Code'] == 'OperationInProgressException':
self.logger.info("Caught exception 'OperationInProgressException', handling the exception...")
return {"OperationId": "OperationInProgressException"}
else:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def create_stack_instances_with_override_params(self, stack_set_name, account_list, region_list, override_params,
failed_tolerance_percent=0, max_concurrent_percent=100):
try:
parameters = []
d = {}
for key, value in override_params.items():
'''This condition checks if the value is a List and convert it into a Comma-delimited string.
Note: Remember to change the parameter type from 'List<AWS::EC2::*::*>'
(Supported AWS-Specific Parameter Types) to 'CommaDelimitedList' in the template.'''
if type(value) == list:
value = ",".join(map(str, value))
d['ParameterKey'] = key
d['ParameterValue'] = value
parameters.append(d.copy())
response = cfn_client.create_stack_instances(
StackSetName=stack_set_name,
Accounts=account_list,
Regions=region_list,
ParameterOverrides=parameters,
OperationPreferences={
'FailureTolerancePercentage': failed_tolerance_percent,
'MaxConcurrentPercentage': max_concurrent_percent
}
)
return response
except ClientError as e:
if e.response['Error']['Code'] == 'OperationInProgressException':
self.logger.info("Caught exception 'OperationInProgressException', handling the exception...")
return {"OperationId": "OperationInProgressException"}
else:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def update_stack_instances(self, stack_set_name, account_list, region_list, override_params,
failed_tolerance_percent=0, max_concurrent_percent=100):
try:
parameters = []
d = {}
for key, value in override_params.items():
'''This condition checks if the value is a List and convert it into a Comma-delimited string.
Note: Remember to change the parameter type from 'List<AWS::EC2::*::*>'
(Supported AWS-Specific Parameter Types) to 'CommaDelimitedList' in the template.'''
if type(value) == list:
value = ",".join(map(str, value))
d['ParameterKey'] = key
d['ParameterValue'] = value
parameters.append(d.copy())
response = cfn_client.update_stack_instances(
StackSetName=stack_set_name,
Accounts=account_list,
Regions=region_list,
ParameterOverrides=parameters,
OperationPreferences={
'FailureTolerancePercentage': failed_tolerance_percent,
'MaxConcurrentPercentage': max_concurrent_percent
}
)
return response
except ClientError as e:
if e.response['Error']['Code'] == 'OperationInProgressException':
self.logger.info("Caught exception 'OperationInProgressException', handling the exception...")
return {"OperationId": "OperationInProgressException"}
else:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def update_stack_set(self, stack_set_name, parameter, template_url, capabilities, failed_tolerance_percent=0,
max_concurrent_percent=100):
try:
parameters = []
d = {}
for key, value in parameter.items():
'''This condition checks if the value is a List and convert it into a Comma-delimited string.
Note: Remember to change the parameter type from 'List<AWS::EC2::*::*>'
(Supported AWS-Specific Parameter Types) to 'CommaDelimitedList' in the template.'''
if type(value) == list:
value = ",".join(map(str, value))
d['ParameterKey'] = key
d['ParameterValue'] = value
parameters.append(d.copy())
response = cfn_client.update_stack_set(
StackSetName=stack_set_name,
TemplateURL=template_url,
Parameters=parameters,
Capabilities=[capabilities],
AdministrationRoleARN=os.environ.get('administration_role_arn'),
ExecutionRoleName=os.environ.get('execution_role_name'),
OperationPreferences={
'FailureTolerancePercentage': failed_tolerance_percent,
'MaxConcurrentPercentage': max_concurrent_percent
}
)
return response
except ClientError as e:
if e.response['Error']['Code'] == 'OperationInProgressException':
self.logger.info("Caught exception 'OperationInProgressException', handling the exception...")
return {"OperationId": "OperationInProgressException"}
else:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def delete_stack_set(self, stack_set_name):
try:
response = cfn_client.delete_stack_set(
StackSetName=stack_set_name,
)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def delete_stack_instances(self, stack_set_name, account_list, region_list, retain_condition=False,
failed_tolerance_percent=0, max_concurrent_percent=100):
try:
response = cfn_client.delete_stack_instances(
StackSetName=stack_set_name,
Accounts=account_list,
Regions=region_list,
RetainStacks=retain_condition,
OperationPreferences={
'FailureTolerancePercentage': failed_tolerance_percent,
'MaxConcurrentPercentage': max_concurrent_percent
}
)
return response
except ClientError as e:
if e.response['Error']['Code'] == 'OperationInProgressException':
self.logger.info("Caught exception 'OperationInProgressException', handling the exception...")
return {"OperationId": "OperationInProgressException"}
else:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def describe_stack_instance(self, stack_set_name, account_id, region):
try:
response = cfn_client.describe_stack_instance(
StackSetName=stack_set_name,
StackInstanceAccount=account_id,
StackInstanceRegion=region
)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def list_stack_set_operations(self, **kwargs):
try:
response = cfn_client.list_stack_set_operations(**kwargs)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
class Stacks(object):
def __init__(self, logger, **kwargs):
self.logger = logger
if kwargs is not None:
if kwargs.get('credentials') is None:
logger.debug("Setting up CFN BOTO3 Client with default credentials")
self.cfn_client = boto3.client('cloudformation')
else:
logger.debug("Setting up CFN BOTO3 Client with ASSUMED ROLE credentials")
cred = kwargs.get('credentials')
region = kwargs.get('region', None)
if region:
self.cfn_client = boto3.client('cloudformation', region_name=region,
aws_access_key_id=cred.get('AccessKeyId'),
aws_secret_access_key=cred.get('SecretAccessKey'),
aws_session_token=cred.get('SessionToken')
)
else:
self.cfn_client = boto3.client('cloudformation',
aws_access_key_id=cred.get('AccessKeyId'),
aws_secret_access_key=cred.get('SecretAccessKey'),
aws_session_token=cred.get('SessionToken')
)
@try_except_retry()
def describe_stacks(self, stack_name):
try:
response = self.cfn_client.describe_stacks(
StackName=stack_name
)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
@try_except_retry()
def get_stack_summary(self, stack_name):
try:
response = self.cfn_client.get_template_summary(StackName=stack_name)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
@try_except_retry()
def get_template_summary(self, template_url):
try:
response = self.cfn_client.get_template_summary(TemplateURL=template_url)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
@try_except_retry()
def update_stack(self, stack_name, template_url, capabilities):
try:
response = self.cfn_client.update_stack(StackName=stack_name,
TemplateURL=template_url,
Capabilities=capabilities)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
def update_stack(self, stack_name, parameters, template_url, capabilities):
try:
response = cfn_client.update_stack(
StackName=stack_name,
TemplateURL=template_url,
Parameters=parameters,
Capabilities=capabilities
)
return response
except Exception as e:
message = {'FILE': __file__.split('/')[-1], 'CLASS': self.__class__.__name__,
'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}
self.logger.exception(message)
raise
| 45.922518
| 124
| 0.548508
| 1,741
| 18,966
| 5.689833
| 0.12579
| 0.026651
| 0.025439
| 0.034323
| 0.836967
| 0.814658
| 0.787805
| 0.778619
| 0.747527
| 0.736826
| 0
| 0.007687
| 0.341506
| 18,966
| 412
| 125
| 46.033981
| 0.785491
| 0.033639
| 0
| 0.69341
| 0
| 0
| 0.121364
| 0.044368
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057307
| false
| 0.002865
| 0.014327
| 0
| 0.143266
| 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
|
6c6354530eeb62dfaf174c832efdbc8c6332aa8f
| 17,640
|
py
|
Python
|
open_words/format_data.py
|
MT-GoCode/whitaker_microservice
|
a8bd81ed750294149235bf5d7814f52a52b1855a
|
[
"MIT"
] | 27
|
2018-01-10T10:57:54.000Z
|
2022-03-12T15:51:32.000Z
|
open_words/format_data.py
|
MT-GoCode/whitaker_microservice
|
a8bd81ed750294149235bf5d7814f52a52b1855a
|
[
"MIT"
] | 9
|
2017-11-12T17:07:27.000Z
|
2020-07-27T14:19:32.000Z
|
open_words/format_data.py
|
MT-GoCode/whitaker_microservice
|
a8bd81ed750294149235bf5d7814f52a52b1855a
|
[
"MIT"
] | 7
|
2018-04-27T19:32:40.000Z
|
2020-10-26T18:27:44.000Z
|
"""
format_data.py
Format the data from the input files from Whitaker's Words
"""
import json
def import_dicts():
data = []
with open('data/DICTLINE.GEN', encoding="ISO-8859-1") as f:
for i, line in enumerate( f ):
orth = line[0:19].replace("zzz", "-").strip()
parts = [orth]
if len( line[19:38].strip() ) > 0:
parts.append( line[19:38].replace("zzz", "-").strip() )
if len( line[38:57].strip() ) > 0:
parts.append( line[38:57].replace("zzz", "-").strip() )
if len( line[57:76].strip() ) > 0:
parts.append( line[57:76].replace("zzz", "-").strip() )
if len( line[83:87].strip() ) > 0:
n = line[83:87].strip().split(" ")
for n_i, v in enumerate(n):
try:
n[n_i] = int(v)
except ValueError:
pass
senses = line[109:].strip().split(";")
new_senses = []
for sense in senses:
sense = sense.strip()
if len( sense ):
new_senses.append(sense)
data.append({
'id' : i + 1,
'orth' : orth,
'parts' : parts,
'pos' : line[76:83].strip(),
'form' : line[83:100].strip(),
'n' : n,
'senses' : new_senses
})
with open('data/data.json', 'w') as out:
json.dump(data, out)
return
def import_stems():
data = []
with open('data/STEMLIST.GEN') as f:
for line in f:
if len( line[26:30].strip() ) > 0:
n = line[26:30].strip().split(" ")
for i, v in enumerate(n):
try:
n[i] = int(v)
except ValueError:
pass
data.append({
'orth' : line[0:19].strip(),
'pos' : line[19:26].strip(),
'form' : line[26:45].strip(),
'n' : n,
'wid' : int(line[50:].strip())
})
with open('data/data.json', 'w') as out:
json.dump(data, out)
return
def import_suffixes():
with open('data/suffixes.txt') as f:
i = 0
obj = {}
data = []
for line in f:
if i == 0:
obj['orth'] = line.replace("PREFIX", "").replace("SUFFIX", "").strip()
elif i == 1:
obj['pos'] = line[0].strip()
obj['form'] = line[0:].strip()
elif i == 2:
obj['senses'] = [line.strip()]
i = i + 1
if i == 3:
data.append(obj)
obj = {}
i = 0
with open('data/data.json', 'w') as out:
json.dump(data, out)
return
def import_prefixes():
with open('data/prefixes.txt') as f:
i = 0
obj = {}
data = []
for line in f:
if i == 0:
obj['orth'] = line.replace("PREFIX", "").strip()
elif i == 1:
obj['pos'] = line[0].strip()
obj['form'] = line[0:].strip()
elif i == 2:
obj['senses'] = [line.strip()]
i = i + 1
if i == 3:
data.append(obj)
obj = {}
i = 0
with open('data/data.json', 'w') as out:
json.dump(data, out)
return
def import_uniques():
with open('UNIQUES.LAT') as f:
i = 0
obj = {}
data = []
for line in f:
if i == 0:
obj['orth'] = line.strip()
elif i == 1:
obj['pos'] = line[0].strip()
obj['form'] = line[1:52].strip()
elif i == 2:
obj['senses'] = [line.strip()]
i = i + 1
if i == 3:
data.append(obj)
obj = {}
i = 0
with open('data.json', 'w') as out:
json.dump(data, out)
return
def import_inflects():
with open('INFLECTS.LAT') as f:
i = 0
obj = {}
data = []
for i, line in enumerate(f):
if len(line.strip()) > 0 and not line.strip().startswith("--"):
n = []
# Nouns
# No ending
if i in range(26,40):
n = parse_infl_type(line[7:21])
data.append({
'ending' : "",
'n' : n,
'note' : "",
'pos' : line[0:7].strip(),
'form' : line[7:21].strip()
})
# 1st declension
elif i in range(63, 85):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# 1st declension Greek
elif i in range(93, 99):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(103, 113):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(118, 127):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Second declension
elif i in range(139, 159):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(166, 175):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Second declension er
elif i in range(183, 186):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "er",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Second declension ius / ium
elif i in range(194, 201):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "ius/ium",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Second declension ius / ium
elif i in range(209, 214):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "ius/ium",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Second declension greek
elif i in range(220, 229):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(236, 245):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(250, 254):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(261, 265):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Third declension
elif i in range(279, 299):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(305, 313):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "stem_ends_in_cons",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(322, 332):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "i-stems_m-f",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(339, 347):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "i-stems_n",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Third declension greek
elif i in range(353, 359):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(363, 373):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(380, 393):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(403, 420):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Fourth declension
elif i in range(427, 449):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Fourth delcension u
elif i in range(454, 463):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "u",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(468, 474):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "jesus_jesu",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Fifth declension
elif i in range(479, 498):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Fifth declension
elif i in range(479, 498):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Abbreviations
elif i in range(501, 502):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "abbreviation",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Not declined
elif i in range(504, 505):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "not_declined",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Adjective
# First declension
elif i in range(515, 552):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(558, 581):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(588, 625):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(633, 646):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "first_and_second",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(650, 693):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "nullus_alius",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(701, 765):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Third declension adj
elif i in range(701, 765):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(773, 795):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(801, 812):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "two_endings",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(817, 828):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "three_endings",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
elif i in range(834, 846):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[23:33].strip(),
'n' : n,
'note' : "greek",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Verbs
# First conjugation
elif i in range(857, 1021):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# Second conjugation
elif i in range(1037, 1159):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# Third conjugation
elif i in range(1173, 1301):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
elif i in range(1311, 1450):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "irregular",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# Fourth conjugation
elif i in range(1459, 1558):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# Esse
elif i in range(1569, 1678):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "like_to_be",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# ire
elif i in range(1690, 1856):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "eo_ire",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# volere`
elif i in range(1869, 1936):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "volere",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# defective
elif i in range(1951, 2083):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "defective",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# kludge
elif i in range(2097, 2137):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[38:52].strip(),
'n' : n,
'note' : "",
'pos' : line[0:6].strip(),
'form' : line[10:34].strip()
})
# Participles / Supine
# participles 1-3
elif i in range(2144, 2618):
n = parse_infl_type(line[5:8])
data.append({
'ending' : line[38:51].strip(),
'n' : n,
'note' : "participles",
'pos' : line[0:5].strip(),
'form' : line[9:34].strip()
})
# supine
elif i in range(2627, 2630):
n = parse_infl_type(line[7:10])
data.append({
'ending' : line[24:30].strip(),
'n' : n,
'note' : "supine",
'pos' : line[0:7].strip(),
'form' : line[11:20].strip()
})
# Pronouns
elif i in range(2686, 2965):
n = parse_infl_type(line[6:9])
data.append({
'ending' : line[24:52].strip(),
'n' : n,
'note' : "pronoun",
'pos' : line[0:6].strip(),
'form' : line[10:17].strip()
})
# Numerals
elif i in range(2971, 3941):
n = parse_infl_type(line[7:10])
data.append({
'ending' : line[32:52].strip(),
'n' : n,
'note' : "numeral",
'pos' : line[0:7].strip(),
'form' : line[11:19].strip()
})
with open('data.json', 'w') as out:
json.dump(data, out)
return
def parse_infl_type(s):
if len( s.strip() ) > 0:
n = s.strip().split(" ")
for i, v in enumerate(n):
try:
n[i] = int(v)
except ValueError:
pass
return n
| 21.151079
| 74
| 0.471542
| 2,447
| 17,640
| 3.343277
| 0.106661
| 0.037282
| 0.055739
| 0.092409
| 0.797091
| 0.755653
| 0.731818
| 0.72375
| 0.715316
| 0.715316
| 0
| 0.09193
| 0.318594
| 17,640
| 833
| 75
| 21.176471
| 0.588686
| 0.034524
| 0
| 0.783439
| 0
| 0
| 0.09183
| 0
| 0
| 0
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| 0
| 0
| 1
| 0.011147
| false
| 0.004777
| 0.011147
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| 0.033439
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| 0
| 0
| 0
| 0
|
0
| 6
|
665f387e8e69293feb81d7b9ad74e49c4a4cb0ef
| 79
|
py
|
Python
|
pyexamples/__init__.py
|
carhartt21/PlotNeuralNet
|
09f820e771d0640e418ceebb292efee09422b7d2
|
[
"MIT"
] | 1
|
2020-01-24T09:53:43.000Z
|
2020-01-24T09:53:43.000Z
|
pyexamples/__init__.py
|
carhartt21/PlotNeuralNet
|
09f820e771d0640e418ceebb292efee09422b7d2
|
[
"MIT"
] | null | null | null |
pyexamples/__init__.py
|
carhartt21/PlotNeuralNet
|
09f820e771d0640e418ceebb292efee09422b7d2
|
[
"MIT"
] | null | null | null |
import pyexamples.yolo_5l
import pyexamples.HRNet
import pyexamples.outside30k
| 19.75
| 28
| 0.886076
| 10
| 79
| 6.9
| 0.6
| 0.695652
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| 0.041096
| 0.075949
| 79
| 3
| 29
| 26.333333
| 0.90411
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|
0
| 6
|
666ed2e8d6e72b48017b75625a5c68ac1a04cc21
| 5,844
|
py
|
Python
|
Functions/GlobCover_Converter.py
|
HesamZamanpour/wapor
|
553981e78164e7fd326be5f65a46bdd1dc80288a
|
[
"Apache-2.0"
] | 1
|
2021-05-24T08:12:03.000Z
|
2021-05-24T08:12:03.000Z
|
Functions/GlobCover_Converter.py
|
HesamZamanpour/wapor
|
553981e78164e7fd326be5f65a46bdd1dc80288a
|
[
"Apache-2.0"
] | 2
|
2020-06-25T08:27:55.000Z
|
2020-08-28T07:38:17.000Z
|
Functions/GlobCover_Converter.py
|
HesamZamanpour/wapor
|
553981e78164e7fd326be5f65a46bdd1dc80288a
|
[
"Apache-2.0"
] | 4
|
2020-09-23T09:51:59.000Z
|
2021-08-10T08:59:14.000Z
|
# -*- coding: utf-8 -*-
"""
WaterSat
author: Tim Martijn Hessels
Created on Tue Feb 26 08:17:10 2019
"""
def Globcover_LM(version = '1.0'):
ETlook_LM = {
11: 1, #Post-flooding or irrigated croplands (or aquatic)
14: 1, #Rainfed croplands
20: 1, #Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)
30: 1, #Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)
40: 1, #Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)
50: 1, #Closed (>40%) broadleaved deciduous forest (>5m)
60: 1, #Open (15-40%) broadleaved deciduous forest/woodland (>5m)
70: 1, #Closed (>40%) needleleaved evergreen forest (>5m)
90: 1, #Open (15-40%) needleleaved deciduous or evergreen forest (>5m)
100: 1, #Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)
110: 1, #Mosaic forest or shrubland (50-70%) / grassland (20-50%)
120: 1, #Mosaic grassland (50-70%) / forest or shrubland (20-50%)
130: 1, #Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)
140: 1, #Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
150: 1, #Sparse (<15%) vegetation
160: 1, #Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water
170: 1, #Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water
180: 1, #Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish or saline water
190: 3, #Artificial surfaces and associated areas (Urban areas >50%)
200: 1, #Bare areas
210: 2, #Water bodies
220: 1, #Permanent snow and ice
230: 0 #No data (burnt areas, clouds,…)
}
Classes_LM =dict()
Classes_LM['1.0'] = ETlook_LM
return Classes_LM[version]
def Globcover_MaxObs(version = '1.0'):
ETlook_Classes = {
11: 4.0, #Post-flooding or irrigated croplands (or aquatic)
14: 4.0, #Rainfed croplands
20: 2.0, #Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)
30: 3.5, #Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)
40: 0.1, #Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)
50: 0.6, #Closed (>40%) broadleaved deciduous forest (>5m)
60: 1.2, #Open (15-40%) broadleaved deciduous forest/woodland (>5m)
70: 2.0, #Closed (>40%) needleleaved evergreen forest (>5m)
90: 5.0, #Open (15-40%) needleleaved deciduous or evergreen forest (>5m)
100: 8.0, #Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)
110: 2.0, #Mosaic forest or shrubland (50-70%) / grassland (20-50%)
120: 8.0, #Mosaic grassland (50-70%) / forest or shrubland (20-50%)
130: 4.0, #Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)
140: 2.0, #Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
150: 1.0, #Sparse (<15%) vegetation
160: 0.3, #Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water
170: 6.0, #Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water
180: 10, #Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish or saline water
190: 0.1, #Artificial surfaces and associated areas (Urban areas >50%)
200: 10, #Bare areas
210: 0.1, #Water bodies
220: 0.1, #Permanent snow and ice
230: 0 #No data (burnt areas, clouds,…)
}
Classes_MaxObs =dict()
Classes_MaxObs['1.0'] = ETlook_Classes
return Classes_MaxObs[version]
def Globcover_Bulk(version = '1.0'):
ETlook_Classes = {
11: 200, #Post-flooding or irrigated croplands (or aquatic)
14: 200, #Rainfed croplands
20: 150, #Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)
30: 150, #Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)
40: 100, #Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)
50: 120, #Closed (>40%) broadleaved deciduous forest (>5m)
60: 100, #Open (15-40%) broadleaved deciduous forest/woodland (>5m)
70: 150, #Closed (>40%) needleleaved evergreen forest (>5m)
90: 180, #Open (15-40%) needleleaved deciduous or evergreen forest (>5m)
100: 175, #Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)
110: 150, #Mosaic forest or shrubland (50-70%) / grassland (20-50%)
120: 350, #Mosaic grassland (50-70%) / forest or shrubland (20-50%)
130: 175, #Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)
140: 250, #Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
150: 150, #Sparse (<15%) vegetation
160: 250, #Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water
170: 200, #Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water
180: 300, #Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish or saline water
190: 100, #Artificial surfaces and associated areas (Urban areas >50%)
200: 100, #Bare areas
210: 100, #Water bodies
220: 100, #Permanent snow and ice
230: 0 #No data (burnt areas, clouds,…)
}
Classes_Bulk =dict()
Classes_Bulk['1.0'] = ETlook_Classes
return Classes_Bulk[version]
| 54.111111
| 143
| 0.662902
| 814
| 5,844
| 4.748157
| 0.144963
| 0.037257
| 0.055886
| 0.065201
| 0.858473
| 0.853816
| 0.826391
| 0.786287
| 0.73273
| 0.657439
| 0
| 0.118861
| 0.212526
| 5,844
| 107
| 144
| 54.616822
| 0.719035
| 0.712868
| 0
| 0.057471
| 0
| 0
| 0.011264
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.034483
| false
| 0
| 0
| 0
| 0.068966
| 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
|
66b3d5eb8907aa62709c2943aea4ed24bf5af187
| 23
|
py
|
Python
|
ImageNet/models/__init__.py
|
wd-doylle/NeuronClustering
|
910bae6a9c7c445dc0428b2102e9f2ecbdbad6f0
|
[
"BSD-3-Clause"
] | 40
|
2018-03-15T02:49:08.000Z
|
2021-12-20T14:01:14.000Z
|
ImageNet/models/__init__.py
|
wd-doylle/NeuronClustering
|
910bae6a9c7c445dc0428b2102e9f2ecbdbad6f0
|
[
"BSD-3-Clause"
] | 2
|
2018-05-10T05:15:04.000Z
|
2018-11-06T12:41:04.000Z
|
ImageNet/models/__init__.py
|
wd-doylle/NeuronClustering
|
910bae6a9c7c445dc0428b2102e9f2ecbdbad6f0
|
[
"BSD-3-Clause"
] | 17
|
2018-03-14T21:24:01.000Z
|
2021-07-04T00:27:21.000Z
|
from .AlexNet import *
| 11.5
| 22
| 0.73913
| 3
| 23
| 5.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 23
| 1
| 23
| 23
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
66da2e0626d5f662c9930eb9bbf2ecef6210b7c7
| 96
|
py
|
Python
|
timeandmoneypy/time/time_interval.py
|
ilyaGotfryd/timeandmoneypy
|
e357e8d7df77c6414ad1e533d03274b156fdd282
|
[
"MIT"
] | null | null | null |
timeandmoneypy/time/time_interval.py
|
ilyaGotfryd/timeandmoneypy
|
e357e8d7df77c6414ad1e533d03274b156fdd282
|
[
"MIT"
] | null | null | null |
timeandmoneypy/time/time_interval.py
|
ilyaGotfryd/timeandmoneypy
|
e357e8d7df77c6414ad1e533d03274b156fdd282
|
[
"MIT"
] | null | null | null |
from timeandmoneypy.intervals.interval import Interval
class TimeInterval(Interval):
pass
| 16
| 54
| 0.8125
| 10
| 96
| 7.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135417
| 96
| 5
| 55
| 19.2
| 0.939759
| 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
|
dd0649eb31b2d800da0a8206605673cb72d0d37a
| 86
|
py
|
Python
|
MIT_CourseWare/computationalThinkingAndDataScience/clustering/testCluster.py
|
sum-coderepo/HadoopApp
|
0e8d48c5d541b5935c9054fb1335d829d67d7b59
|
[
"Apache-2.0"
] | 2
|
2020-05-26T23:58:32.000Z
|
2020-11-01T20:45:30.000Z
|
MIT_CourseWare/computationalThinkingAndDataScience/clustering/testCluster.py
|
sum-coderepo/HadoopApp
|
0e8d48c5d541b5935c9054fb1335d829d67d7b59
|
[
"Apache-2.0"
] | null | null | null |
MIT_CourseWare/computationalThinkingAndDataScience/clustering/testCluster.py
|
sum-coderepo/HadoopApp
|
0e8d48c5d541b5935c9054fb1335d829d67d7b59
|
[
"Apache-2.0"
] | null | null | null |
import lect12
print(lect12.scaleAttrs[12,32,10,9,38,78,36,7,76,736])
lect12.getData()
| 21.5
| 54
| 0.755814
| 17
| 86
| 3.823529
| 0.882353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.304878
| 0.046512
| 86
| 4
| 55
| 21.5
| 0.487805
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
dd0c960f27c2aa73dac426aee86932f960b46afb
| 852
|
py
|
Python
|
compound_types/type_checks.py
|
vahndi/compound-types
|
cda4f49651b4bfbcd9fe199de276be472620cfad
|
[
"MIT"
] | null | null | null |
compound_types/type_checks.py
|
vahndi/compound-types
|
cda4f49651b4bfbcd9fe199de276be472620cfad
|
[
"MIT"
] | null | null | null |
compound_types/type_checks.py
|
vahndi/compound-types
|
cda4f49651b4bfbcd9fe199de276be472620cfad
|
[
"MIT"
] | null | null | null |
def all_are_none(*args) -> bool:
"""
Return True if all args are None.
"""
return all([arg is None for arg in args])
def none_are_none(*args) -> bool:
"""
Return True if no args are None.
"""
return not any([arg is None for arg in args])
def any_are_not_none(*args) -> bool:
"""
Return True if any arg is not None.
"""
return any([arg is not None for arg in args])
def any_are_none(*args) -> bool:
"""
Return True if any arg is None.
"""
return any([arg is None for arg in args])
def one_is_none(*args) -> bool:
"""
Return True if exactly one arg is None.
"""
return sum([arg is None for arg in args]) == 1
def one_is_not_none(*args) -> bool:
"""
Return True if exactly one arg is not None.
"""
return sum([arg is not None for arg in args]) == 1
| 20.780488
| 54
| 0.590376
| 143
| 852
| 3.41958
| 0.13986
| 0.102249
| 0.147239
| 0.220859
| 0.860941
| 0.748466
| 0.744376
| 0.578732
| 0.400818
| 0.159509
| 0
| 0.003284
| 0.285211
| 852
| 40
| 55
| 21.3
| 0.799672
| 0.255869
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dd2a23bc9e4a0ee7f8e15ba7f6ff3ec21ba5e1bc
| 271
|
py
|
Python
|
django_rt_cdn/imagegenerators.py
|
reef-technologies/django-rt-cdn
|
753025c93edcc1cfe985722d51308640e0ab0169
|
[
"BSD-3-Clause"
] | null | null | null |
django_rt_cdn/imagegenerators.py
|
reef-technologies/django-rt-cdn
|
753025c93edcc1cfe985722d51308640e0ab0169
|
[
"BSD-3-Clause"
] | null | null | null |
django_rt_cdn/imagegenerators.py
|
reef-technologies/django-rt-cdn
|
753025c93edcc1cfe985722d51308640e0ab0169
|
[
"BSD-3-Clause"
] | null | null | null |
try:
from imagekit import register
from .contrib.imagekit.generatorlibrary import OriginResolution, Thumbnail
except ImportError:
pass
else:
register.generator('cdn:thumbnail', Thumbnail)
register.generator('cdn:origin_resolution', OriginResolution)
| 27.1
| 78
| 0.778598
| 27
| 271
| 7.777778
| 0.62963
| 0.161905
| 0.190476
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.143911
| 271
| 9
| 79
| 30.111111
| 0.905172
| 0
| 0
| 0
| 0
| 0
| 0.125461
| 0.077491
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.125
| 0.375
| 0
| 0.375
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 6
|
dd80f38e56654593698babb4d6fee25337e19bb7
| 205
|
py
|
Python
|
base/admin.py
|
AlexWanyoike/Neighborhood-Api
|
a3ebc72421c0602a44e8817ff2b283683a5ae93d
|
[
"MIT"
] | null | null | null |
base/admin.py
|
AlexWanyoike/Neighborhood-Api
|
a3ebc72421c0602a44e8817ff2b283683a5ae93d
|
[
"MIT"
] | null | null | null |
base/admin.py
|
AlexWanyoike/Neighborhood-Api
|
a3ebc72421c0602a44e8817ff2b283683a5ae93d
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Neighborhood, User, Business, Post
admin.site.register(Neighborhood)
admin.site.register(User)
admin.site.register(Business)
admin.site.register(Post)
| 29.285714
| 55
| 0.819512
| 28
| 205
| 6
| 0.428571
| 0.214286
| 0.404762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078049
| 205
| 7
| 56
| 29.285714
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 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
|
06cc60aaa0549da3ecb2b2a29f472ac95cd62448
| 161
|
py
|
Python
|
pyFBS/__init__.py
|
anantagrg/FBS_Substructuring
|
66555a1f80208c7bac16355822ac12fd195f5f68
|
[
"MIT"
] | null | null | null |
pyFBS/__init__.py
|
anantagrg/FBS_Substructuring
|
66555a1f80208c7bac16355822ac12fd195f5f68
|
[
"MIT"
] | null | null | null |
pyFBS/__init__.py
|
anantagrg/FBS_Substructuring
|
66555a1f80208c7bac16355822ac12fd195f5f68
|
[
"MIT"
] | null | null | null |
# import everything
from .IO import *
from .utility import *
from .VPT import *
from .display import *
from .SEMM import *
from .MCK import *
from .SVT import *
| 17.888889
| 22
| 0.714286
| 23
| 161
| 5
| 0.434783
| 0.521739
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.192547
| 161
| 8
| 23
| 20.125
| 0.884615
| 0.10559
| 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
|
06dd02e760870e12bafdf4055cae32729f23f956
| 142
|
py
|
Python
|
codevision/blog/admin.py
|
nabin0/codevision
|
658d153129a62e85316e543cef30656cc5e09a09
|
[
"MIT"
] | null | null | null |
codevision/blog/admin.py
|
nabin0/codevision
|
658d153129a62e85316e543cef30656cc5e09a09
|
[
"MIT"
] | null | null | null |
codevision/blog/admin.py
|
nabin0/codevision
|
658d153129a62e85316e543cef30656cc5e09a09
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Post, PostComments
# Register your models here.
admin.site.register((Post, PostComments))
| 35.5
| 41
| 0.809859
| 19
| 142
| 6.052632
| 0.631579
| 0.278261
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105634
| 142
| 4
| 41
| 35.5
| 0.905512
| 0.183099
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 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
|
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