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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
abc6c4a582e9119d60c6e319dbe627b1da591e9b | 140 | py | Python | energyreport/classes/save.py | t-maes/Energy | 257c6b4a58af1067880870c828e966ba4c6e7f5d | [
"MIT"
] | null | null | null | energyreport/classes/save.py | t-maes/Energy | 257c6b4a58af1067880870c828e966ba4c6e7f5d | [
"MIT"
] | 13 | 2020-09-17T13:11:22.000Z | 2021-10-16T15:15:47.000Z | energyreport/classes/save.py | t-maes/Energy | 257c6b4a58af1067880870c828e966ba4c6e7f5d | [
"MIT"
] | 2 | 2020-10-03T15:29:50.000Z | 2021-10-04T07:50:35.000Z | from .building import Building
class Save:
building: Building = None
@staticmethod
def reset():
Save.building = None
| 14 | 30 | 0.65 | 15 | 140 | 6.066667 | 0.6 | 0.263736 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.278571 | 140 | 9 | 31 | 15.555556 | 0.90099 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | true | 0 | 0.166667 | 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 | 0 | 0 | 1 | 0 | 0 | 5 |
abdf8e4e7fc6c1c0ea7192d08ad76a2e0d3bd5e6 | 41 | py | Python | commands/info.py | pieroproietti/penguins-eggs2 | 7c029cf1d180bd5d7ace856d547de8540b61c093 | [
"MIT"
] | null | null | null | commands/info.py | pieroproietti/penguins-eggs2 | 7c029cf1d180bd5d7ace856d547de8540b61c093 | [
"MIT"
] | null | null | null | commands/info.py | pieroproietti/penguins-eggs2 | 7c029cf1d180bd5d7ace856d547de8540b61c093 | [
"MIT"
] | null | null | null | def info(args):
print("eggs v.0.0.1")
| 13.666667 | 24 | 0.585366 | 9 | 41 | 2.666667 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 0.170732 | 41 | 2 | 25 | 20.5 | 0.617647 | 0 | 0 | 0 | 0 | 0 | 0.292683 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
abe5724740261ba45655ffd087e2f63437dc0c8e | 131 | py | Python | beagle/building/__init__.py | FernandoGaGu/beagle | b1c968ec84d560e9903a582413e6334fcf447735 | [
"BSD-3-Clause"
] | 1 | 2020-12-27T15:58:14.000Z | 2020-12-27T15:58:14.000Z | beagle/building/__init__.py | FernandoGaGu/beagle | b1c968ec84d560e9903a582413e6334fcf447735 | [
"BSD-3-Clause"
] | null | null | null | beagle/building/__init__.py | FernandoGaGu/beagle | b1c968ec84d560e9903a582413e6334fcf447735 | [
"BSD-3-Clause"
] | null | null | null | from .loader import use_algorithm
from .spea2 import spea2
from .nsga2 import nsga2
__all__ = ['use_algorithm', 'spea2', 'nsga2']
| 21.833333 | 45 | 0.755725 | 18 | 131 | 5.166667 | 0.444444 | 0.258065 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.053097 | 0.137405 | 131 | 5 | 46 | 26.2 | 0.769912 | 0 | 0 | 0 | 0 | 0 | 0.175573 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.75 | 0 | 0.75 | 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 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
abfe1f7960f33359a2f7ca8734cc7e9c18ae9312 | 80 | py | Python | deepatari/tools/__init__.py | cowhi/deepatari | 3b676ca4fc66266d766cd2366226f3e10213bc78 | [
"MIT"
] | 10 | 2016-06-10T01:13:44.000Z | 2017-10-15T10:47:09.000Z | deepatari/tools/__init__.py | cowhi/deepatari | 3b676ca4fc66266d766cd2366226f3e10213bc78 | [
"MIT"
] | null | null | null | deepatari/tools/__init__.py | cowhi/deepatari | 3b676ca4fc66266d766cd2366226f3e10213bc78 | [
"MIT"
] | 2 | 2016-06-10T14:38:08.000Z | 2020-08-29T03:11:06.000Z | from .arg_parser import str2bool, parse_args
from .statistics import Statistics
| 26.666667 | 44 | 0.85 | 11 | 80 | 6 | 0.727273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014085 | 0.1125 | 80 | 2 | 45 | 40 | 0.915493 | 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 | 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 | 5 |
abfe3ba9cfb330874b5408637a1d6ede3c3b94dd | 3,344 | py | Python | bob/kaldi/test/test_gmm.py | bioidiap/bob.kaldi | fe5f968a0aa114bd7dafc0c651366588b0383222 | [
"BSD-3-Clause"
] | 2 | 2020-09-15T07:25:18.000Z | 2021-09-16T02:13:26.000Z | bob/kaldi/test/test_gmm.py | bioidiap/bob.kaldi | fe5f968a0aa114bd7dafc0c651366588b0383222 | [
"BSD-3-Clause"
] | null | null | null | bob/kaldi/test/test_gmm.py | bioidiap/bob.kaldi | fe5f968a0aa114bd7dafc0c651366588b0383222 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
#
# Milos Cernak <milos.cernak@idiap.ch>
# March 1, 2017
#
"""Tests for Kaldi bindings"""
import os
import numpy as np
import pkg_resources
import bob.io.audio
import bob.io.base.test_utils
import bob.kaldi
def test_ubm_train():
temp_file = bob.io.base.test_utils.temporary_filename()
sample = pkg_resources.resource_filename(__name__, "data/sample16k.wav")
data = bob.io.audio.reader(sample)
# MFCC
array = bob.kaldi.mfcc(data.load()[0], data.rate, normalization=False)
# Train small diagonall GMM
dubm = bob.kaldi.ubm_train(
array, temp_file, num_gauss=2, num_gselect=2, num_iters=2
)
# assert os.path.exists(dubm)
assert dubm.find("DiagGMM")
def test_ubm_full_train():
temp_dubm_file = bob.io.base.test_utils.temporary_filename()
temp_fubm_file = bob.io.base.test_utils.temporary_filename()
sample = pkg_resources.resource_filename(__name__, "data/sample16k.wav")
data = bob.io.audio.reader(sample)
# MFCC
array = bob.kaldi.mfcc(data.load()[0], data.rate, normalization=False)
# Train small diagonal GMM
dubm = bob.kaldi.ubm_train(
array, temp_dubm_file, num_gauss=2, num_gselect=2, num_iters=2
)
# Train small full GMM
fubm = bob.kaldi.ubm_full_train(
array, dubm, temp_fubm_file, num_gselect=2, num_iters=2
)
assert fubm.find("FullGMM")
def test_ubm_enroll():
temp_dubm_file = bob.io.base.test_utils.temporary_filename()
sample = pkg_resources.resource_filename(__name__, "data/sample16k.wav")
data = bob.io.audio.reader(sample)
# MFCC
array = bob.kaldi.mfcc(data.load()[0], data.rate, normalization=False)
# Train small diagonal GMM
dubm = bob.kaldi.ubm_train(
array, temp_dubm_file, num_gauss=2, num_gselect=2, num_iters=2
)
# Perform MAP adaptation of the GMM
spk_model = bob.kaldi.ubm_enroll(array, dubm)
# assert os.path.exists(spk_model)
assert spk_model.find("DiagGMM")
def test_gmm_score():
temp_dubm_file = bob.io.base.test_utils.temporary_filename()
sample = pkg_resources.resource_filename(__name__, "data/sample16k.wav")
data = bob.io.audio.reader(sample)
# MFCC
array = bob.kaldi.mfcc(data.load()[0], data.rate, normalization=False)
# Train small diagonal GMM
dubm = bob.kaldi.ubm_train(
array, temp_dubm_file, num_gauss=2, num_gselect=2, num_iters=2
)
# Perform MAP adaptation of the GMM
spk_model = bob.kaldi.ubm_enroll(array, dubm)
# GMM scoring
score = bob.kaldi.gmm_score(array, spk_model, dubm)
np.testing.assert_allclose(score, [0.28698], 1e-03, 1e-05)
# def test_gmm_score_fast():
# temp_dubm_file = bob.io.base.test_utils.temporary_filename()
# sample = pkg_resources.resource_filename(__name__, "data/sample16k.wav")
# data = bob.io.audio.reader(sample)
# # MFCC
# array = bob.kaldi.mfcc(data.load()[0], data.rate, normalization=False)
# # Train small diagonal GMM
# dubm = bob.kaldi.ubm_train(
# array, temp_dubm_file, num_gauss=2, num_gselect=2, num_iters=2
# )
# # Perform MAP adaptation of the GMM
# spk_model = bob.kaldi.ubm_enroll(array, dubm)
# # GMM scoring
# score = bob.kaldi.gmm_score_fast(array, spk_model, dubm)
# np.testing.assert_allclose(score, [0.282168])
| 29.333333 | 78 | 0.694079 | 492 | 3,344 | 4.479675 | 0.172764 | 0.061706 | 0.044918 | 0.041289 | 0.798094 | 0.789927 | 0.789927 | 0.775408 | 0.760889 | 0.760889 | 0 | 0.02052 | 0.183911 | 3,344 | 113 | 79 | 29.59292 | 0.787102 | 0.307416 | 0 | 0.489796 | 0 | 0 | 0.040915 | 0 | 0 | 0 | 0 | 0 | 0.081633 | 1 | 0.081633 | false | 0 | 0.122449 | 0 | 0.204082 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
e61494617cc29128a881133cce3dddbf57595434 | 41 | py | Python | resources/ui/__init__.py | stylekilla/syncmrt | 816bb57d80d6595719b8b9d7f027f4f17d0a6c0a | [
"Apache-2.0"
] | null | null | null | resources/ui/__init__.py | stylekilla/syncmrt | 816bb57d80d6595719b8b9d7f027f4f17d0a6c0a | [
"Apache-2.0"
] | 25 | 2019-03-05T05:56:35.000Z | 2019-07-24T13:11:57.000Z | resources/ui/__init__.py | stylekilla/syncmrt | 816bb57d80d6595719b8b9d7f027f4f17d0a6c0a | [
"Apache-2.0"
] | 1 | 2019-11-27T05:10:47.000Z | 2019-11-27T05:10:47.000Z | from . import menubar, sidebar, workspace | 41 | 41 | 0.804878 | 5 | 41 | 6.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121951 | 41 | 1 | 41 | 41 | 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 | 0 | 0 | 0 | 5 |
e646d225a56238579dcf732db1ad1fe25b8840cc | 547 | py | Python | route_data.py | jiashunwang/Long-term-Motion-in-3D-Scenes | a86b484079c1873aaa98fd90c0b02adb6eb059ae | [
"Apache-2.0"
] | 63 | 2020-12-20T04:56:40.000Z | 2022-03-30T02:46:21.000Z | route_data.py | jiashunwang/Long-term-Motion-in-3D-Scenes | a86b484079c1873aaa98fd90c0b02adb6eb059ae | [
"Apache-2.0"
] | 7 | 2021-05-10T18:44:31.000Z | 2022-01-13T02:57:22.000Z | route_data.py | jiashunwang/Long-term-Motion-in-3D-Scenes | a86b484079c1873aaa98fd90c0b02adb6eb059ae | [
"Apache-2.0"
] | 4 | 2021-04-22T01:14:12.000Z | 2021-08-10T03:44:49.000Z | import torch.utils.data as data
import torch
import numpy as np
class ROUTEDATA(data.Dataset):
def __init__(self):
self.data=np.load('./data/routepose_training_data.npy',allow_pickle=True)
self.len=(len(self.data)//8)*8
def __getitem__(self, index):
return self.data[index][0],self.data[index][1],self.data[index][2],self.data[index][3],\
self.data[index][4],self.data[index][5],self.data[index][6],self.data[index][7],self.data[index][8]
def __len__(self):
return self.len | 42.076923 | 116 | 0.647166 | 85 | 547 | 3.988235 | 0.388235 | 0.259587 | 0.345133 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.024554 | 0.180987 | 547 | 13 | 117 | 42.076923 | 0.732143 | 0 | 0 | 0 | 0 | 0 | 0.063433 | 0.063433 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0.166667 | 0.75 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
0518c17323f48352cece1a82c85e29d6e870445b | 130 | py | Python | Documentation/GuidesFromPlosCompBioPaper/ExampleCaseC/AdditionalInputFiles/RestingCondition/LCxcoronaryRdController.py | carthurs/CRIMSONGUI | 1464df9c4d04cf3ba131ca90b91988a06845c68e | [
"BSD-3-Clause"
] | 10 | 2020-09-17T18:55:31.000Z | 2022-02-23T02:52:38.000Z | Documentation/GuidesFromPlosCompBioPaper/ExampleCaseC/AdditionalInputFiles/RestingCondition/LCxcoronaryRdController.py | carthurs/CRIMSONGUI | 1464df9c4d04cf3ba131ca90b91988a06845c68e | [
"BSD-3-Clause"
] | null | null | null | Documentation/GuidesFromPlosCompBioPaper/ExampleCaseC/AdditionalInputFiles/RestingCondition/LCxcoronaryRdController.py | carthurs/CRIMSONGUI | 1464df9c4d04cf3ba131ca90b91988a06845c68e | [
"BSD-3-Clause"
] | 3 | 2021-05-19T09:02:21.000Z | 2021-07-26T17:39:57.000Z | version https://git-lfs.github.com/spec/v1
oid sha256:cd1093c3277f49e869f718c6ffae7784919512668c6a275162d15badff97f1c1
size 11900
| 32.5 | 75 | 0.884615 | 13 | 130 | 8.846154 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.419355 | 0.046154 | 130 | 3 | 76 | 43.333333 | 0.508065 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
051a15fb3bfcc2fbdd2d01ad6bc665b2fb72aab7 | 73 | py | Python | ping/tasks.py | affan2/django-ping | 11c9e303e7e29ed6a74c8eb5952ba4a988b9ec34 | [
"MIT"
] | null | null | null | ping/tasks.py | affan2/django-ping | 11c9e303e7e29ed6a74c8eb5952ba4a988b9ec34 | [
"MIT"
] | null | null | null | ping/tasks.py | affan2/django-ping | 11c9e303e7e29ed6a74c8eb5952ba4a988b9ec34 | [
"MIT"
] | 1 | 2020-01-09T10:21:57.000Z | 2020-01-09T10:21:57.000Z | from celery.task import task
@task()
def sample_task():
return True | 12.166667 | 28 | 0.712329 | 11 | 73 | 4.636364 | 0.727273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.191781 | 73 | 6 | 29 | 12.166667 | 0.864407 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.25 | 0.25 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
052b7d4866d8715a6a79622c687628df8cf8c2e9 | 206 | py | Python | xastropy/igm/setup_package.py | bpholden/xastropy | 66aff0995a84c6829da65996d2379ba4c946dabe | [
"BSD-3-Clause"
] | 3 | 2015-08-23T00:32:58.000Z | 2020-12-31T02:37:52.000Z | xastropy/igm/setup_package.py | Kristall-WangShiwei/xastropy | 723fe56cb48d5a5c4cdded839082ee12ef8c6732 | [
"BSD-3-Clause"
] | 104 | 2015-07-17T18:31:54.000Z | 2018-06-29T17:04:09.000Z | xastropy/igm/setup_package.py | Kristall-WangShiwei/xastropy | 723fe56cb48d5a5c4cdded839082ee12ef8c6732 | [
"BSD-3-Clause"
] | 16 | 2015-07-17T15:50:37.000Z | 2019-04-21T03:42:47.000Z | def get_package_data():
# Installs the testing data files. Unable to get package_data
# to deal with a directory hierarchy of files, so just explicitly list.
return {'xastropy.igm': ['fN/*.p']}
| 41.2 | 75 | 0.703883 | 31 | 206 | 4.580645 | 0.806452 | 0.140845 | 0.197183 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.194175 | 206 | 4 | 76 | 51.5 | 0.855422 | 0.626214 | 0 | 0 | 0 | 0 | 0.243243 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 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 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
054265c2d133a02b08aac6f0d08044327fb41a4b | 175 | py | Python | booley/exceptions.py | kasappeal/booley | 87b4c350b0ce0d85d3b4642a25db2745c9e44c94 | [
"MIT"
] | null | null | null | booley/exceptions.py | kasappeal/booley | 87b4c350b0ce0d85d3b4642a25db2745c9e44c94 | [
"MIT"
] | null | null | null | booley/exceptions.py | kasappeal/booley | 87b4c350b0ce0d85d3b4642a25db2745c9e44c94 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
class VariableNotFound(BaseException):
pass
class UnknownOperation(BaseException):
pass
class BooleySyntaxError(BaseException):
pass
| 12.5 | 39 | 0.714286 | 15 | 175 | 8.333333 | 0.6 | 0.408 | 0.352 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006993 | 0.182857 | 175 | 13 | 40 | 13.461538 | 0.867133 | 0.12 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
05475bfdc6349b984c4e5f41a6f8730290fb6c86 | 570 | py | Python | toony/accounts.py | thewallacems/Toony | 01a6e74e5f05491e2efc365101dc3f0e5dce062c | [
"Unlicense"
] | 3 | 2021-05-17T04:11:23.000Z | 2022-01-26T21:17:20.000Z | toony/accounts.py | thewallacems/Toony | 01a6e74e5f05491e2efc365101dc3f0e5dce062c | [
"Unlicense"
] | null | null | null | toony/accounts.py | thewallacems/Toony | 01a6e74e5f05491e2efc365101dc3f0e5dce062c | [
"Unlicense"
] | 1 | 2021-02-18T18:18:14.000Z | 2021-02-18T18:18:14.000Z | import json
import os.path
__internal_json = json.load(open('accounts.json', 'r')) if os.path.exists('accounts.json') else {}
def create(username: str, password: str, toon: str):
__internal_json[username] = {'password': password, 'toon': toon}
json.dump(__internal_json, open('accounts.json', 'w'), indent=2)
def delete(username: str):
del __internal_json[username]
json.dump(__internal_json, open('accounts.json', 'w'), indent=2)
def exists(username: str):
return username in __internal_json
def load() -> dict:
return __internal_json
| 23.75 | 98 | 0.701754 | 78 | 570 | 4.858974 | 0.346154 | 0.221636 | 0.126649 | 0.105541 | 0.248021 | 0.248021 | 0.248021 | 0.248021 | 0.248021 | 0.248021 | 0 | 0.004124 | 0.149123 | 570 | 23 | 99 | 24.782609 | 0.77732 | 0 | 0 | 0.153846 | 0 | 0 | 0.117544 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.307692 | false | 0.153846 | 0.153846 | 0.153846 | 0.615385 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 5 |
0582ed34a157d8f79c66fcd1c3b1956425ed687c | 293 | py | Python | tests/__main__.py | mitiku1/All-In-One | 8d63877941ef3e935bcd395ccecb24b600e5d2b0 | [
"MIT"
] | null | null | null | tests/__main__.py | mitiku1/All-In-One | 8d63877941ef3e935bcd395ccecb24b600e5d2b0 | [
"MIT"
] | null | null | null | tests/__main__.py | mitiku1/All-In-One | 8d63877941ef3e935bcd395ccecb24b600e5d2b0 | [
"MIT"
] | 3 | 2018-05-02T09:13:35.000Z | 2018-11-14T05:39:30.000Z | from tests.preprocessors_test import TestBaseProcessor
import unittest
# if __name__ == '__main__':
# suite = unittest.TestLoader().loadTestsFromTestCase(TestBaseProcessor)
suite = unittest.TestLoader().loadTestsFromTestCase(TestBaseProcessor)
unittest.TextTestRunner(verbosity=2).run(suite)
| 36.625 | 72 | 0.832765 | 27 | 293 | 8.703704 | 0.62963 | 0.110638 | 0.195745 | 0.374468 | 0.519149 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003663 | 0.068259 | 293 | 7 | 73 | 41.857143 | 0.857143 | 0.331058 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 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 | 0 | 0 | 0 | 5 |
05836e1b44b65c3e4a215edb3a33ffba37259773 | 231 | py | Python | ROPgadget/ropgadget/loaders/__init__.py | crosssitescriptin/ROPgadget | b72934cd83ab20591945e2c4af795406f9443840 | [
"Apache-2.0"
] | 1 | 2020-12-15T05:56:11.000Z | 2020-12-15T05:56:11.000Z | ROPgadget/ropgadget/loaders/__init__.py | crosssitescriptin/ROPgadget | b72934cd83ab20591945e2c4af795406f9443840 | [
"Apache-2.0"
] | null | null | null | ROPgadget/ropgadget/loaders/__init__.py | crosssitescriptin/ROPgadget | b72934cd83ab20591945e2c4af795406f9443840 | [
"Apache-2.0"
] | null | null | null | ## -*- coding: utf-8 -*-
##
## incon - 2014-05-12 - ROPgadget tool
##
## http://twitter.com/Hexdumping
##
##
import ropgadget.loaders.elf
import ropgadget.loaders.macho
import ropgadget.loaders.pe
import ropgadget.loaders.raw
| 17.769231 | 39 | 0.69697 | 29 | 231 | 5.551724 | 0.655172 | 0.372671 | 0.546584 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.045 | 0.134199 | 231 | 12 | 40 | 19.25 | 0.76 | 0.385281 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
058894da1059979444ddb5348ed98e6d6ebdcf60 | 135 | py | Python | py/python/ifelse.py | dacanizares/IntroCS-ES | 1324b59a3bed86559117b01ad85384d593394d4a | [
"MIT"
] | 2 | 2020-03-21T19:12:10.000Z | 2020-03-27T03:59:41.000Z | py/python/ifelse.py | dacanizares/IntroCS-ES | 1324b59a3bed86559117b01ad85384d593394d4a | [
"MIT"
] | 13 | 2020-03-20T01:27:57.000Z | 2020-08-08T18:20:29.000Z | py/python/ifelse.py | dacanizares/IntroCS-ES | 1324b59a3bed86559117b01ad85384d593394d4a | [
"MIT"
] | null | null | null | a = int(input('Digite un nro '))
b = int(input('Digite un nro '))
if a > b:
print('El mayor es ', a)
else:
print('El mayor es ', b) | 22.5 | 32 | 0.577778 | 26 | 135 | 3 | 0.5 | 0.205128 | 0.358974 | 0.410256 | 0.487179 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.222222 | 135 | 6 | 33 | 22.5 | 0.742857 | 0 | 0 | 0 | 0 | 0 | 0.382353 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
555214b6f6674e52e1d8b2f6dd55828a26e7ac2a | 236 | bzl | Python | hugo/private/local_hugo_repository/TEMPLATE.defs.bzl | dwtj/dwtj_rules_hugo | 02eaf9946058c2a286c79d49da14a35caf574bea | [
"MIT"
] | 1 | 2021-05-28T15:42:00.000Z | 2021-05-28T15:42:00.000Z | hugo/private/local_hugo_repository/TEMPLATE.defs.bzl | dwtj/dwtj_rules_hugo | 02eaf9946058c2a286c79d49da14a35caf574bea | [
"MIT"
] | null | null | null | hugo/private/local_hugo_repository/TEMPLATE.defs.bzl | dwtj/dwtj_rules_hugo | 02eaf9946058c2a286c79d49da14a35caf574bea | [
"MIT"
] | null | null | null | # This file was instantiated from a template with the following substitutions:
#
# - REPOSITORY_NAME: {REPOSITORY_NAME}
def register_hugo_toolchain():
native.register_toolchains(
"@{REPOSITORY_NAME}//:hugo_toolchain",
) | 29.5 | 78 | 0.741525 | 26 | 236 | 6.461538 | 0.730769 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.161017 | 236 | 8 | 79 | 29.5 | 0.848485 | 0.478814 | 0 | 0 | 0 | 0 | 0.291667 | 0.291667 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0 | 0 | 0.25 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
557c8c550a9c9bb2055648db067e33d6fbb3299e | 26 | py | Python | mywebsite/subscribers/mailchimp.py | Zadigo/ecommerce_template | a4572c3faeaeb9cd399351c0fd1f19a4ef94de27 | [
"MIT"
] | 16 | 2020-07-01T03:42:40.000Z | 2022-02-21T21:02:27.000Z | mywebsite/subscribers/mailchimp.py | Zadigo/ecommerce_template | a4572c3faeaeb9cd399351c0fd1f19a4ef94de27 | [
"MIT"
] | 14 | 2020-11-19T18:55:28.000Z | 2022-02-01T22:08:23.000Z | mywebsite/subscribers/mailchimp.py | Zadigo/ecommerce_template | a4572c3faeaeb9cd399351c0fd1f19a4ef94de27 | [
"MIT"
] | 7 | 2020-06-30T23:55:36.000Z | 2021-11-12T00:06:40.000Z | class MailChimp:
pass
| 8.666667 | 16 | 0.692308 | 3 | 26 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.269231 | 26 | 2 | 17 | 13 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
5586ffb6982e18c58f62ecc7c9c66862f1dc8ff0 | 1,638 | py | Python | tests/test_database.py | open-austin/data-portal-analysis | b7c9e018324adefad1e6265dfd083e59fa14483a | [
"Unlicense"
] | 5 | 2015-12-01T16:26:30.000Z | 2021-11-18T04:48:21.000Z | tests/test_database.py | open-austin/data-portal-analysis | b7c9e018324adefad1e6265dfd083e59fa14483a | [
"Unlicense"
] | 38 | 2015-11-04T16:53:50.000Z | 2016-03-19T00:13:22.000Z | tests/test_database.py | open-austin/data-portal-analysis | b7c9e018324adefad1e6265dfd083e59fa14483a | [
"Unlicense"
] | 4 | 2015-12-20T20:58:55.000Z | 2021-11-18T04:47:56.000Z | import sys
import os
from nose.tools import assert_equals
import utilities
import json
import dataset
def test_add_view():
with open('tests/test_view_resource.json') as data_json:
json_str = data_json.read()
test_view = json.loads(json_str)
analyzer = utilities.ViewAnalyzer("sqlite:///tests/test.db")
analyzer.add_view(test_view)
with dataset.connect('sqlite:///tests/test.db') as db:
views_table = db['unnormalized']
for current_record in views_table.all():
assert_equals(current_record['last_modified'], 0)
assert_equals(current_record['view_name'], u'Test Dataset')
os.remove('tests/test.db')
def test_update_view():
with open('tests/test_view_resource.json') as data_json:
json_str = data_json.read()
test_view = json.loads(json_str)
with open('tests/newer_test_view_resource.json') as data_json:
json_str = data_json.read()
newer_test_view = json.loads(json_str)
analyzer = utilities.ViewAnalyzer("sqlite:///tests/test.db")
analyzer.add_view(test_view)
with dataset.connect('sqlite:///tests/test.db') as db:
views_table = db['unnormalized']
current_record = views_table.find_one(view_id = u'abcd-1234')
assert_equals(current_record['last_modified'], 0)
analyzer.add_view(newer_test_view)
with dataset.connect('sqlite:///tests/test.db') as db:
views_table = db['unnormalized']
current_record = views_table.find_one(view_id = u'abcd-1234')
assert_equals(current_record['last_modified'], 10)
os.remove('tests/test.db')
| 30.333333 | 71 | 0.68315 | 227 | 1,638 | 4.665198 | 0.22467 | 0.076487 | 0.07271 | 0.080264 | 0.782814 | 0.746931 | 0.746931 | 0.710104 | 0.710104 | 0.710104 | 0 | 0.009098 | 0.19475 | 1,638 | 53 | 72 | 30.90566 | 0.793783 | 0 | 0 | 0.621622 | 0 | 0 | 0.212454 | 0.126984 | 0 | 0 | 0 | 0 | 0.135135 | 1 | 0.054054 | false | 0 | 0.162162 | 0 | 0.216216 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
559ca16dede3b9cd5fed5080e042c16e2ff80d08 | 7,466 | py | Python | case/Test_Environment/Push/Test_push_sms_send.py | Four-sun/Requests_Load | 472f3f6d9bd407f1c4ed30a5557ec141e2434188 | [
"Apache-2.0"
] | null | null | null | case/Test_Environment/Push/Test_push_sms_send.py | Four-sun/Requests_Load | 472f3f6d9bd407f1c4ed30a5557ec141e2434188 | [
"Apache-2.0"
] | null | null | null | case/Test_Environment/Push/Test_push_sms_send.py | Four-sun/Requests_Load | 472f3f6d9bd407f1c4ed30a5557ec141e2434188 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created: on 2018-04-11
@author: Four
Project: case\send_message.py
URL: http://push-pc-qa.eslink.net.cn/push/sms/send
"""
import unittest
import os
import time
import sys
import requests
from common.Request_Package import send_requests
from common.Excel_readline import ExcelUtil
from common.log import Logger
# 获取demo_api.xlsx路径
path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))
testxlsx = os.path.join(path, "config")
reportxlsx = os.path.join(testxlsx, "push_send_message.xlsx")
Sheet_Name = "Sheet1"
logger_message = Logger()
#获取当前时间
send_time = time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime(time.time()))
class Test_Push_Sms_Send(unittest.TestCase):
def Loging_etbc(self):
u"""发送请求登陆etbc"""
try:
payload={
"loginName": "zhangyang1",
"loginPwd": "zj03030418"
}
logger_message.loginfo(u'%s\t入参%s\t' % (send_time,payload))
login_etbc = requests.post('http://etbc-qa.eslink.net.cn/user/login', data=payload)
json_result = login_etbc.json()
self.assertEqual(200,login_etbc.status_code,msg='失败原因：200 != %s' % (login_etbc.status_code))
self.assertTrue(json_result["success"],msg='失败原因：%s' % json_result["msg"])
logger_message.loginfo(u"%s\t方法名：%s\t请求地址：%s\t请求状态：%s\t返回内容：%s" % (send_time, sys._getframe().f_code.co_name, login_etbc.url, login_etbc.status_code, login_etbc.text))
return login_etbc
except AssertionError as Error:
logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s" % (send_time, sys._getframe().f_code.co_name, Error))
def test_ID_0(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c=requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 0
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
finally:
time.sleep(30)
def test_ID_1(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c=requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 1
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
def test_ID_2(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 2
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
finally:
time.sleep(30)
def test_ID_3(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 3
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
def test_ID_4(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 4
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
finally:
time.sleep(30)
def test_ID_5(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c=requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 5
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
def test_ID_6(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 6
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
def test_ID_7(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 7
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
def test_ID_8(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 8
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
def test_ID_9(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 9
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
def test_ID_010(self):
try:
data = ExcelUtil(reportxlsx,Sheet_Name).dict_data()
login_cookies=Test_Push_Sms_Send.Loging_etbc(self)
c = requests.utils.dict_from_cookiejar(login_cookies.cookies)
test_id = 10
s = requests.session()
res = send_requests(s, data[test_id], c)
self.assertTrue(res)
except Exception as Error:
logger_message.logwarning('%s\t%s\t' % (send_time,Error))
raise
if __name__ == "__main__":
unittest.main()
| 37.33 | 179 | 0.605411 | 951 | 7,466 | 4.499474 | 0.141956 | 0.046272 | 0.033419 | 0.042066 | 0.775649 | 0.758355 | 0.758355 | 0.758355 | 0.758355 | 0.743865 | 0 | 0.010467 | 0.283418 | 7,466 | 199 | 180 | 37.517588 | 0.789346 | 0.02344 | 0 | 0.684211 | 0 | 0 | 0.04398 | 0.008109 | 0 | 0 | 0 | 0 | 0.081871 | 1 | 0.070175 | false | 0 | 0.046784 | 0 | 0.128655 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
559e236e3dada62f2101bb939a2def0061b3d7a4 | 142 | py | Python | valid8/tests/helpers/math.py | smarie/python-validate | c8a10ccede1c0782355439b0966f532bf00dfcab | [
"BSD-3-Clause"
] | 26 | 2018-01-10T03:44:19.000Z | 2021-11-28T07:56:31.000Z | valid8/tests/helpers/math.py | smarie/python-validate | c8a10ccede1c0782355439b0966f532bf00dfcab | [
"BSD-3-Clause"
] | 55 | 2017-11-06T14:45:47.000Z | 2021-05-12T08:28:11.000Z | valid8/tests/helpers/math.py | smarie/python-valid8 | c8a10ccede1c0782355439b0966f532bf00dfcab | [
"BSD-3-Clause"
] | null | null | null | try:
from math import isfinite, inf
except ImportError:
inf = float('inf')
def isfinite(x):
return x != inf and x != -inf
| 20.285714 | 37 | 0.598592 | 20 | 142 | 4.25 | 0.65 | 0.094118 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.288732 | 142 | 6 | 38 | 23.666667 | 0.841584 | 0 | 0 | 0 | 0 | 0 | 0.021127 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0.333333 | 0.166667 | 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 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 5 |
55b0606ff465bb20355ec63f20297cf999d42bd4 | 7,118 | py | Python | penguins/tests/test_Functions_interp1D.py | cnickle/penquins | 3d09eeaa887f8ec56e65b7b6b87a51da824d3edf | [
"Apache-2.0"
] | null | null | null | penguins/tests/test_Functions_interp1D.py | cnickle/penquins | 3d09eeaa887f8ec56e65b7b6b87a51da824d3edf | [
"Apache-2.0"
] | 3 | 2021-07-06T00:23:39.000Z | 2021-11-20T14:59:33.000Z | penguins/tests/test_Functions_interp1D.py | cnickle/penquins | 3d09eeaa887f8ec56e65b7b6b87a51da824d3edf | [
"Apache-2.0"
] | null | null | null | from penguins.functions import tunnelmodel_singleLevel
from penguins.functions import averageBridgePopulation
from penguins.functions import MarcusETRates
from penguins.functions import interp1D
import numpy as np
import matplotlib.pyplot as plt
import time
import pandas as pd
#TODO I need to fix all of these interp1D functions
def test_SAM():
v = np.arange(-2,2,.001)
V=[]
for i in range(2):
V = np.append(V,v)
V = sorted(V)
# n gammaW gammaC deltaE eta sigma c vg T
args = [1.50e+02, 1.375e-05, 0.0352, 0.75, 5.32e-01, 0, 0, 0, 300]
start = time.time()
vecCur = np.vectorize(tunnelmodel_singleLevel)
y1 = vecCur(V, *args)
time1 = time.time()-start
start = time.time()
fast = interp1D(tunnelmodel_singleLevel)
y2 = fast(V, *args)
time2 = time.time()-start
print('Slow: %.2f\t\tFast: %.2f\t\tSpeed Increase: %.0f%%'%(time1,time2,time1/time2*100))
plt.figure()
plt.scatter(V,y1, color = 'black')
plt.plot(V,y2, color = 'red')
def test_SET():
v = np.arange(-.05,.05,.001)
V=[]
for i in range(2):
V = np.append(V,v)
V = sorted(V)
# n gammaW gammaC deltaE eta sigma c vg T
args = [1.50e+02, 1.375e-05, 0.0352, 0.03, 5.32e-01, 0, 0, 0, 300]
start = time.time()
vecCur = np.vectorize(tunnelmodel_singleLevel)
y1 = vecCur(V, *args)
time1 = time.time()-start
start = time.time()
fast = interp1D(tunnelmodel_singleLevel)
y2 = fast(V, *args)
time2 = time.time()-start
# print('Slow: %.2f\t\tFast: %.2f\t\tSpeed Increase: %.0f%%'%(time1,time2,time1/time2*100))
plt.figure()
plt.scatter(V,y1, color = 'black')
plt.plot(V,y2, color = 'red')
def test_Hysteric():
def HysteresisModel_Slow(vb, n, gammaL, gammaR, kappa, sigma, E_AB, E_AC, chi, eta,
gam, lam, P, u, c, vg, T):
volts = list(set(np.round(vb,2)))
#%% Calculate all currents:
calcDB = pd.DataFrame()
calcDB['V'] = sorted(volts)
eqSTL = interp1D(tunnelmodel_singleLevel)
calcDB['I_np'] = eqSTL(calcDB['V'], n, gammaL*gammaR, gammaL+gammaR, E_AB,
eta, sigma, c, vg, T)
calcDB['I_p'] = eqSTL(calcDB['V'], n, gammaL*gammaR*kappa**2,
(gammaL+gammaR)*kappa, E_AB+chi, eta, sigma, c, vg,
T)
eqETRates = interp1D(MarcusETRates)
calcDB['R_AC'], calcDB['R_CA'] = eqETRates(calcDB['V'], gam, lam, E_AC, T)
calcDB['R_BD'], calcDB['R_DB'] = eqETRates(calcDB['V'], gam*kappa, lam,
E_AC+chi, T)
eqBridge = interp1D(averageBridgePopulation)
calcDB['n_np'] = eqBridge(calcDB['V'], gammaL, gammaR, E_AB, eta, c, vg, T)
calcDB['n_p'] = eqBridge(calcDB['V'], gammaL*kappa, gammaR*kappa,
E_AB+chi, eta, c, vg, T)
calcDB['k_S0_S1'] = (1-calcDB['n_np'])*calcDB['R_AC'] + calcDB['n_np']*calcDB['R_BD']
calcDB['k_S1_S0'] = (1-calcDB['n_p'])*calcDB['R_CA'] + calcDB['n_p']*calcDB['R_DB']
delt = abs(vb[2]-vb[3])/u
I = []
Parray = []
delArray = []
for i,V in enumerate(vb):
V = np.round(V,2)
tempDf =calcDB[calcDB['V']==np.round(V,2)].reset_index()
calcs = dict(tempDf.iloc[0])
Parray += [P]
I += [((1-P)*calcs['I_np']+P*calcs['I_p'])]
dPdt = calcs['k_S0_S1']-P*(calcs['k_S0_S1']+calcs['k_S1_S0'])
delArray += [dPdt]
P = P+dPdt*delt
return I, Parray
def HysteresisModel_Fast(vb, n, gammaL, gammaR, kappa, sigma, E_AB, E_AC, chi, eta,
gam, lam, P, u, c, vg, T):
volts = list(set(np.round(vb,2)))
#%% Calculate all currents:
calcDB = pd.DataFrame()
calcDB['V'] = sorted(volts)
eqSTL = np.vectorize(tunnelmodel_singleLevel)
calcDB['I_np'] = eqSTL(calcDB['V'], n, gammaL*gammaR, gammaL+gammaR, E_AB,
eta, sigma, c, vg, T)
calcDB['I_p'] = eqSTL(calcDB['V'], n, gammaL*gammaR*kappa**2,
(gammaL+gammaR)*kappa, E_AB+chi, eta, sigma, c, vg,
T)
eqETRates = np.vectorize(MarcusETRates)
calcDB['R_AC'], calcDB['R_CA'] = eqETRates(calcDB['V'], gam, lam, E_AC, T)
calcDB['R_BD'], calcDB['R_DB'] = eqETRates(calcDB['V'], gam*kappa, lam,
E_AC+chi, T)
eqBridge = np.vectorize(averageBridgePopulation)
calcDB['n_np'] = eqBridge(calcDB['V'], gammaL, gammaR, E_AB, eta, c, vg, T)
calcDB['n_p'] = eqBridge(calcDB['V'], gammaL*kappa, gammaR*kappa,
E_AB+chi, eta, c, vg, T)
calcDB['k_S0_S1'] = (1-calcDB['n_np'])*calcDB['R_AC'] + calcDB['n_np']*calcDB['R_BD']
calcDB['k_S1_S0'] = (1-calcDB['n_p'])*calcDB['R_CA'] + calcDB['n_p']*calcDB['R_DB']
delt = abs(vb[2]-vb[3])/u
I = []
Parray = []
delArray = []
for i,V in enumerate(vb):
V = np.round(V,2)
tempDf =calcDB[calcDB['V']==np.round(V,2)].reset_index()
calcs = dict(tempDf.iloc[0])
Parray += [P]
I += [((1-P)*calcs['I_np']+P*calcs['I_p'])]
dPdt = calcs['k_S0_S1']-P*(calcs['k_S0_S1']+calcs['k_S1_S0'])
delArray += [dPdt]
P = P+dPdt*delt
return I, Parray
initpar = {
'n' :1.50e+02,
'gammaL' :5.52E-04,
'gammaR' :2.03E-02,
'kappa' :2.81,
'sigma' :0.00e+00,
'E_AB' :6.93e-01,
'E_AC' :-7.17e-01,
'chi' :1.58e+00,
'eta' :5.23e-01,
'gam' :7.12e-01,
'lam' :1.21e+00,
'P' :0.00e+00,
'u' :1.00e-02,
'c' :0.00e+00,
'vg' :0.00e+00,
'T' :3.00e+02
}
DataFile = 'Data\\AsymNeg_cont_Normalized.txt'
data = pd.read_csv(DataFile, delimiter = '\t')
colV = '-2.00V_1'
start = time.time()
y1,_ = HysteresisModel_Slow(data[colV],*list(initpar.values()))
time1 = time.time()-start
start = time.time()
y2,_ = HysteresisModel_Fast(data[colV],*list(initpar.values()))
time2 = time.time()-start
print('Slow: %.2f\t\tFast: %.2f\t\tSpeed Increase: %.0f%%'%(time1,time2,time1/time2*100))
plt.figure()
plt.scatter(data[colV],np.abs(y1), color = 'black')
plt.plot( data[colV], np.abs(y2), color = 'red')
plt.ylim(7.2e-10,2e-05)
plt.yscale('log')
| 35.949495 | 95 | 0.493257 | 940 | 7,118 | 3.632979 | 0.171277 | 0.032796 | 0.014056 | 0.019327 | 0.77306 | 0.752855 | 0.752855 | 0.743777 | 0.743777 | 0.743777 | 0 | 0.05514 | 0.342652 | 7,118 | 197 | 96 | 36.13198 | 0.674717 | 0.045799 | 0 | 0.66443 | 0 | 0 | 0.064702 | 0.004864 | 0 | 0 | 0 | 0.005076 | 0 | 1 | 0.033557 | false | 0 | 0.053691 | 0 | 0.100671 | 0.013423 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
55bc573ef9adcd1c419ec559780de184f86e4dd2 | 15,949 | py | Python | pykquery/KQueryListener.py | lahiri-phdworks/KLEE-KQueryParser | a573951188f99746cd12da2f3f60e78ba78244e0 | [
"Apache-2.0"
] | null | null | null | pykquery/KQueryListener.py | lahiri-phdworks/KLEE-KQueryParser | a573951188f99746cd12da2f3f60e78ba78244e0 | [
"Apache-2.0"
] | null | null | null | pykquery/KQueryListener.py | lahiri-phdworks/KLEE-KQueryParser | a573951188f99746cd12da2f3f60e78ba78244e0 | [
"Apache-2.0"
] | null | null | null | # Generated from KQuery.g4 by ANTLR 4.9.2
from antlr4 import *
if __name__ is not None and "." in __name__:
from .KQueryParser import KQueryParser
else:
from KQueryParser import KQueryParser
# This class defines a complete listener for a parse tree produced by KQueryParser.
class KQueryListener(ParseTreeListener):
# Enter a parse tree produced by KQueryParser#kqueryExpression.
def enterKqueryExpression(self, ctx:KQueryParser.KqueryExpressionContext):
pass
# Exit a parse tree produced by KQueryParser#kqueryExpression.
def exitKqueryExpression(self, ctx:KQueryParser.KqueryExpressionContext):
pass
# Enter a parse tree produced by KQueryParser#queryStatements.
def enterQueryStatements(self, ctx:KQueryParser.QueryStatementsContext):
pass
# Exit a parse tree produced by KQueryParser#queryStatements.
def exitQueryStatements(self, ctx:KQueryParser.QueryStatementsContext):
pass
# Enter a parse tree produced by KQueryParser#ktranslationUnit.
def enterKtranslationUnit(self, ctx:KQueryParser.KtranslationUnitContext):
pass
# Exit a parse tree produced by KQueryParser#ktranslationUnit.
def exitKtranslationUnit(self, ctx:KQueryParser.KtranslationUnitContext):
pass
# Enter a parse tree produced by KQueryParser#queryCommand.
def enterQueryCommand(self, ctx:KQueryParser.QueryCommandContext):
pass
# Exit a parse tree produced by KQueryParser#queryCommand.
def exitQueryCommand(self, ctx:KQueryParser.QueryCommandContext):
pass
# Enter a parse tree produced by KQueryParser#queryExpr.
def enterQueryExpr(self, ctx:KQueryParser.QueryExprContext):
pass
# Exit a parse tree produced by KQueryParser#queryExpr.
def exitQueryExpr(self, ctx:KQueryParser.QueryExprContext):
pass
# Enter a parse tree produced by KQueryParser#evalExprList.
def enterEvalExprList(self, ctx:KQueryParser.EvalExprListContext):
pass
# Exit a parse tree produced by KQueryParser#evalExprList.
def exitEvalExprList(self, ctx:KQueryParser.EvalExprListContext):
pass
# Enter a parse tree produced by KQueryParser#evalArrayList.
def enterEvalArrayList(self, ctx:KQueryParser.EvalArrayListContext):
pass
# Exit a parse tree produced by KQueryParser#evalArrayList.
def exitEvalArrayList(self, ctx:KQueryParser.EvalArrayListContext):
pass
# Enter a parse tree produced by KQueryParser#expressionList.
def enterExpressionList(self, ctx:KQueryParser.ExpressionListContext):
pass
# Exit a parse tree produced by KQueryParser#expressionList.
def exitExpressionList(self, ctx:KQueryParser.ExpressionListContext):
pass
# Enter a parse tree produced by KQueryParser#identifierList.
def enterIdentifierList(self, ctx:KQueryParser.IdentifierListContext):
pass
# Exit a parse tree produced by KQueryParser#identifierList.
def exitIdentifierList(self, ctx:KQueryParser.IdentifierListContext):
pass
# Enter a parse tree produced by KQueryParser#arrayDeclaration.
def enterArrayDeclaration(self, ctx:KQueryParser.ArrayDeclarationContext):
pass
# Exit a parse tree produced by KQueryParser#arrayDeclaration.
def exitArrayDeclaration(self, ctx:KQueryParser.ArrayDeclarationContext):
pass
# Enter a parse tree produced by KQueryParser#numArrayElements.
def enterNumArrayElements(self, ctx:KQueryParser.NumArrayElementsContext):
pass
# Exit a parse tree produced by KQueryParser#numArrayElements.
def exitNumArrayElements(self, ctx:KQueryParser.NumArrayElementsContext):
pass
# Enter a parse tree produced by KQueryParser#arrayInitializer.
def enterArrayInitializer(self, ctx:KQueryParser.ArrayInitializerContext):
pass
# Exit a parse tree produced by KQueryParser#arrayInitializer.
def exitArrayInitializer(self, ctx:KQueryParser.ArrayInitializerContext):
pass
# Enter a parse tree produced by KQueryParser#VariableName.
def enterVariableName(self, ctx:KQueryParser.VariableNameContext):
pass
# Exit a parse tree produced by KQueryParser#VariableName.
def exitVariableName(self, ctx:KQueryParser.VariableNameContext):
pass
# Enter a parse tree produced by KQueryParser#NamedAbbreviation.
def enterNamedAbbreviation(self, ctx:KQueryParser.NamedAbbreviationContext):
pass
# Exit a parse tree produced by KQueryParser#NamedAbbreviation.
def exitNamedAbbreviation(self, ctx:KQueryParser.NamedAbbreviationContext):
pass
# Enter a parse tree produced by KQueryParser#SizeQuery.
def enterSizeQuery(self, ctx:KQueryParser.SizeQueryContext):
pass
# Exit a parse tree produced by KQueryParser#SizeQuery.
def exitSizeQuery(self, ctx:KQueryParser.SizeQueryContext):
pass
# Enter a parse tree produced by KQueryParser#ArithExpr.
def enterArithExpr(self, ctx:KQueryParser.ArithExprContext):
pass
# Exit a parse tree produced by KQueryParser#ArithExpr.
def exitArithExpr(self, ctx:KQueryParser.ArithExprContext):
pass
# Enter a parse tree produced by KQueryParser#NotExprWidth.
def enterNotExprWidth(self, ctx:KQueryParser.NotExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#NotExprWidth.
def exitNotExprWidth(self, ctx:KQueryParser.NotExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#BitwExprWidth.
def enterBitwExprWidth(self, ctx:KQueryParser.BitwExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#BitwExprWidth.
def exitBitwExprWidth(self, ctx:KQueryParser.BitwExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#CompExprWidth.
def enterCompExprWidth(self, ctx:KQueryParser.CompExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#CompExprWidth.
def exitCompExprWidth(self, ctx:KQueryParser.CompExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#ConcatExprWidth.
def enterConcatExprWidth(self, ctx:KQueryParser.ConcatExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#ConcatExprWidth.
def exitConcatExprWidth(self, ctx:KQueryParser.ConcatExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#ArrExtractExprWidth.
def enterArrExtractExprWidth(self, ctx:KQueryParser.ArrExtractExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#ArrExtractExprWidth.
def exitArrExtractExprWidth(self, ctx:KQueryParser.ArrExtractExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#BitExtractExprWidth.
def enterBitExtractExprWidth(self, ctx:KQueryParser.BitExtractExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#BitExtractExprWidth.
def exitBitExtractExprWidth(self, ctx:KQueryParser.BitExtractExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#ReadExpresssionVersioned.
def enterReadExpresssionVersioned(self, ctx:KQueryParser.ReadExpresssionVersionedContext):
pass
# Exit a parse tree produced by KQueryParser#ReadExpresssionVersioned.
def exitReadExpresssionVersioned(self, ctx:KQueryParser.ReadExpresssionVersionedContext):
pass
# Enter a parse tree produced by KQueryParser#SelectExprWidth.
def enterSelectExprWidth(self, ctx:KQueryParser.SelectExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#SelectExprWidth.
def exitSelectExprWidth(self, ctx:KQueryParser.SelectExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#NegationExprWidth.
def enterNegationExprWidth(self, ctx:KQueryParser.NegationExprWidthContext):
pass
# Exit a parse tree produced by KQueryParser#NegationExprWidth.
def exitNegationExprWidth(self, ctx:KQueryParser.NegationExprWidthContext):
pass
# Enter a parse tree produced by KQueryParser#VersionExpr.
def enterVersionExpr(self, ctx:KQueryParser.VersionExprContext):
pass
# Exit a parse tree produced by KQueryParser#VersionExpr.
def exitVersionExpr(self, ctx:KQueryParser.VersionExprContext):
pass
# Enter a parse tree produced by KQueryParser#Singleton.
def enterSingleton(self, ctx:KQueryParser.SingletonContext):
pass
# Exit a parse tree produced by KQueryParser#Singleton.
def exitSingleton(self, ctx:KQueryParser.SingletonContext):
pass
# Enter a parse tree produced by KQueryParser#genericBitRead.
def enterGenericBitRead(self, ctx:KQueryParser.GenericBitReadContext):
pass
# Exit a parse tree produced by KQueryParser#genericBitRead.
def exitGenericBitRead(self, ctx:KQueryParser.GenericBitReadContext):
pass
# Enter a parse tree produced by KQueryParser#bitExtractExpr.
def enterBitExtractExpr(self, ctx:KQueryParser.BitExtractExprContext):
pass
# Exit a parse tree produced by KQueryParser#bitExtractExpr.
def exitBitExtractExpr(self, ctx:KQueryParser.BitExtractExprContext):
pass
# Enter a parse tree produced by KQueryParser#VersionVariableName.
def enterVersionVariableName(self, ctx:KQueryParser.VersionVariableNameContext):
pass
# Exit a parse tree produced by KQueryParser#VersionVariableName.
def exitVersionVariableName(self, ctx:KQueryParser.VersionVariableNameContext):
pass
# Enter a parse tree produced by KQueryParser#UpdationList.
def enterUpdationList(self, ctx:KQueryParser.UpdationListContext):
pass
# Exit a parse tree produced by KQueryParser#UpdationList.
def exitUpdationList(self, ctx:KQueryParser.UpdationListContext):
pass
# Enter a parse tree produced by KQueryParser#notExpr.
def enterNotExpr(self, ctx:KQueryParser.NotExprContext):
pass
# Exit a parse tree produced by KQueryParser#notExpr.
def exitNotExpr(self, ctx:KQueryParser.NotExprContext):
pass
# Enter a parse tree produced by KQueryParser#concatExpr.
def enterConcatExpr(self, ctx:KQueryParser.ConcatExprContext):
pass
# Exit a parse tree produced by KQueryParser#concatExpr.
def exitConcatExpr(self, ctx:KQueryParser.ConcatExprContext):
pass
# Enter a parse tree produced by KQueryParser#exprNegation.
def enterExprNegation(self, ctx:KQueryParser.ExprNegationContext):
pass
# Exit a parse tree produced by KQueryParser#exprNegation.
def exitExprNegation(self, ctx:KQueryParser.ExprNegationContext):
pass
# Enter a parse tree produced by KQueryParser#selectExpr.
def enterSelectExpr(self, ctx:KQueryParser.SelectExprContext):
pass
# Exit a parse tree produced by KQueryParser#selectExpr.
def exitSelectExpr(self, ctx:KQueryParser.SelectExprContext):
pass
# Enter a parse tree produced by KQueryParser#arrExtractExpr.
def enterArrExtractExpr(self, ctx:KQueryParser.ArrExtractExprContext):
pass
# Exit a parse tree produced by KQueryParser#arrExtractExpr.
def exitArrExtractExpr(self, ctx:KQueryParser.ArrExtractExprContext):
pass
# Enter a parse tree produced by KQueryParser#varName.
def enterVarName(self, ctx:KQueryParser.VarNameContext):
pass
# Exit a parse tree produced by KQueryParser#varName.
def exitVarName(self, ctx:KQueryParser.VarNameContext):
pass
# Enter a parse tree produced by KQueryParser#leftExpr.
def enterLeftExpr(self, ctx:KQueryParser.LeftExprContext):
pass
# Exit a parse tree produced by KQueryParser#leftExpr.
def exitLeftExpr(self, ctx:KQueryParser.LeftExprContext):
pass
# Enter a parse tree produced by KQueryParser#rightExpr.
def enterRightExpr(self, ctx:KQueryParser.RightExprContext):
pass
# Exit a parse tree produced by KQueryParser#rightExpr.
def exitRightExpr(self, ctx:KQueryParser.RightExprContext):
pass
# Enter a parse tree produced by KQueryParser#updateList.
def enterUpdateList(self, ctx:KQueryParser.UpdateListContext):
pass
# Exit a parse tree produced by KQueryParser#updateList.
def exitUpdateList(self, ctx:KQueryParser.UpdateListContext):
pass
# Enter a parse tree produced by KQueryParser#bitwiseExpr.
def enterBitwiseExpr(self, ctx:KQueryParser.BitwiseExprContext):
pass
# Exit a parse tree produced by KQueryParser#bitwiseExpr.
def exitBitwiseExpr(self, ctx:KQueryParser.BitwiseExprContext):
pass
# Enter a parse tree produced by KQueryParser#comparisonExpr.
def enterComparisonExpr(self, ctx:KQueryParser.ComparisonExprContext):
pass
# Exit a parse tree produced by KQueryParser#comparisonExpr.
def exitComparisonExpr(self, ctx:KQueryParser.ComparisonExprContext):
pass
# Enter a parse tree produced by KQueryParser#arithmeticExpr.
def enterArithmeticExpr(self, ctx:KQueryParser.ArithmeticExprContext):
pass
# Exit a parse tree produced by KQueryParser#arithmeticExpr.
def exitArithmeticExpr(self, ctx:KQueryParser.ArithmeticExprContext):
pass
# Enter a parse tree produced by KQueryParser#domain.
def enterDomain(self, ctx:KQueryParser.DomainContext):
pass
# Exit a parse tree produced by KQueryParser#domain.
def exitDomain(self, ctx:KQueryParser.DomainContext):
pass
# Enter a parse tree produced by KQueryParser#rangeLimit.
def enterRangeLimit(self, ctx:KQueryParser.RangeLimitContext):
pass
# Exit a parse tree produced by KQueryParser#rangeLimit.
def exitRangeLimit(self, ctx:KQueryParser.RangeLimitContext):
pass
# Enter a parse tree produced by KQueryParser#arrName.
def enterArrName(self, ctx:KQueryParser.ArrNameContext):
pass
# Exit a parse tree produced by KQueryParser#arrName.
def exitArrName(self, ctx:KQueryParser.ArrNameContext):
pass
# Enter a parse tree produced by KQueryParser#numberList.
def enterNumberList(self, ctx:KQueryParser.NumberListContext):
pass
# Exit a parse tree produced by KQueryParser#numberList.
def exitNumberList(self, ctx:KQueryParser.NumberListContext):
pass
# Enter a parse tree produced by KQueryParser#number.
def enterNumber(self, ctx:KQueryParser.NumberContext):
pass
# Exit a parse tree produced by KQueryParser#number.
def exitNumber(self, ctx:KQueryParser.NumberContext):
pass
# Enter a parse tree produced by KQueryParser#constant.
def enterConstant(self, ctx:KQueryParser.ConstantContext):
pass
# Exit a parse tree produced by KQueryParser#constant.
def exitConstant(self, ctx:KQueryParser.ConstantContext):
pass
# Enter a parse tree produced by KQueryParser#boolnum.
def enterBoolnum(self, ctx:KQueryParser.BoolnumContext):
pass
# Exit a parse tree produced by KQueryParser#boolnum.
def exitBoolnum(self, ctx:KQueryParser.BoolnumContext):
pass
# Enter a parse tree produced by KQueryParser#signedConstant.
def enterSignedConstant(self, ctx:KQueryParser.SignedConstantContext):
pass
# Exit a parse tree produced by KQueryParser#signedConstant.
def exitSignedConstant(self, ctx:KQueryParser.SignedConstantContext):
pass
# Enter a parse tree produced by KQueryParser#widthOrSizeExpr.
def enterWidthOrSizeExpr(self, ctx:KQueryParser.WidthOrSizeExprContext):
pass
# Exit a parse tree produced by KQueryParser#widthOrSizeExpr.
def exitWidthOrSizeExpr(self, ctx:KQueryParser.WidthOrSizeExprContext):
pass
del KQueryParser | 33.227083 | 94 | 0.744373 | 1,607 | 15,949 | 7.382701 | 0.153703 | 0.053102 | 0.088503 | 0.159305 | 0.831423 | 0.493425 | 0.490728 | 0.490307 | 0 | 0 | 0 | 0.00039 | 0.195937 | 15,949 | 480 | 95 | 33.227083 | 0.92475 | 0.379397 | 0 | 0.483721 | 1 | 0 | 0.000103 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.483721 | false | 0.483721 | 0.013953 | 0 | 0.502326 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
e9549e5d03a89d30d774f4c0eeb3a1aac6dcaf3b | 132 | py | Python | page/admin.py | Jeonghun-Ban/likelion-mju.com | d3bcc9b088e9c7ee4f27329a3b67e599e6ae6de4 | [
"MIT"
] | 4 | 2020-03-27T03:37:46.000Z | 2020-07-17T11:47:13.000Z | page/admin.py | Jeonghun-Ban/likelion-mju.com | d3bcc9b088e9c7ee4f27329a3b67e599e6ae6de4 | [
"MIT"
] | 8 | 2021-03-30T12:47:50.000Z | 2022-01-13T02:16:51.000Z | page/admin.py | Jeonghun-Ban/likelion-mju.com | d3bcc9b088e9c7ee4f27329a3b67e599e6ae6de4 | [
"MIT"
] | 5 | 2020-03-09T07:34:45.000Z | 2021-05-26T05:37:38.000Z | from django.contrib import admin
from page.models import Application
# Register your models here.
admin.site.register(Application) | 22 | 35 | 0.825758 | 18 | 132 | 6.055556 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.113636 | 132 | 6 | 36 | 22 | 0.931624 | 0.19697 | 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 | 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 | 5 |
e9777ea1457cd6eb47b2692d3e293c29ad9aa780 | 4,684 | py | Python | days/10-12-pytest/guess/test_guess.py | pruty20/100daysofcode-with-python-course | f928e209a94024c1fd4e120c41580fed7ab2b90a | [
"MIT"
] | null | null | null | days/10-12-pytest/guess/test_guess.py | pruty20/100daysofcode-with-python-course | f928e209a94024c1fd4e120c41580fed7ab2b90a | [
"MIT"
] | null | null | null | days/10-12-pytest/guess/test_guess.py | pruty20/100daysofcode-with-python-course | f928e209a94024c1fd4e120c41580fed7ab2b90a | [
"MIT"
] | null | null | null | """
command line: pytest --> will run the tests
command line: pytest --cov-report term-missing --cov='.'
--> will check the coverage for how much code is being tested
"""
from unittest.mock import patch
import random, pytest
from guess import get_random_number, Game
## this allows us everytime when random module is called, to return 17
@patch.object(random, 'randint')
def test_get_random_number(m):
m.return_value = 17
assert get_random_number() == 17
"""
Mocking user_input
"""
@patch("builtins.input", side_effect=[11, '12', 'Bob', 12, 5, -1, 21, 7, None])
def test_guess(inp):
game = Game()
# good
assert game.guess() == 11
assert game.guess() == 12
# not a number
with pytest.raises(ValueError):
game.guess()
# already guessed
with pytest.raises(ValueError):
game.guess()
# good
assert game.guess() == 5
# out of range values
with pytest.raises(ValueError):
game.guess()
with pytest.raises(ValueError):
game.guess()
# good
assert game.guess() == 7
# user hit enter
with pytest.raises(ValueError):
game.guess()
"""
Testing a program's stdout with capfd
"""
def test_validate_guess(capfd):
game = Game()
game._answer = 2
assert not game._validate_guess(1)
out, _ = capfd.readouterr()
# print(out) # run with pytest -s test_guess.py --> its not capturing the output but it prints it to the console // this can be run only once per assertion if run on multiple assertions it will fail the test
assert out.rstrip() == '1 is too low' # run without -s to just check the percentage for passing
assert not game._validate_guess(3)
out, _ = capfd.readouterr()
assert out.rstrip() == '3 is too high'
assert game._validate_guess(2)
out, _ = capfd.readouterr()
assert out.rstrip() == '2 is correct!'
# assert not game._validate_guess(3)
# assert game._validate_guess(2)
@patch("builtins.input", side_effect=[4, 22, 9, 4, 6])
def test_game_win(inp, capfd):
"""
Modify variable in Game class back from self._answer = 6
to self._answer = get_random_number() when done
"""
game = Game()
game._answer = 6
game()
assert game._win is True
out = capfd.readouterr()[0]
expected = ['4 is too low', 'Number not in range', '9 is too high',
'Already guessed', '6 is correct!', 'It took you 3 guesses']
output = [line.strip() for line in out.split('\n') if line.strip()]
for line, exp in zip(output, expected):
assert line == exp
@patch("builtins.input", side_effect=[None, 5, 9, 14, 11, 12])
def test_game_lose(inp, capfd):
game = Game()
game._answer = 13
game()
assert game._win is False
#
#
# @patch("builtins.input", side_effect=[11, '12', 'Bob', 12, 5,
# -1, 21, 7, None])
# def test_guess(inp):
# game = Game()
# # good
# assert game.guess() == 11
# assert game.guess() == 12
# # not a number
# with pytest.raises(ValueError):
# game.guess()
# # already guessed 12
# with pytest.raises(ValueError):
# game.guess()
# # good
# assert game.guess() == 5
# # out of range values
# with pytest.raises(ValueError):
# game.guess()
# with pytest.raises(ValueError):
# game.guess()
# # good
# assert game.guess() == 7
# # user hit enter
# with pytest.raises(ValueError):
# game.guess()
#
#
# def test_validate_guess(capfd):
# game = Game()
# game._answer = 2
#
# assert not game._validate_guess(1)
# out, _ = capfd.readouterr()
# assert out.rstrip() == '1 is too low'
#
# assert not game._validate_guess(3)
# out, _ = capfd.readouterr()
# assert out.rstrip() == '3 is too high'
#
# assert game._validate_guess(2)
# out, _ = capfd.readouterr()
# assert out.rstrip() == '2 is correct!'
#
#
# @patch("builtins.input", side_effect=[4, 22, 9, 4, 6])
# def test_game_win(inp, capfd):
# game = Game()
# game._answer = 6
#
# game()
# assert game._win is True
#
# out = capfd.readouterr()[0]
# expected = ['4 is too low', 'Number not in range',
# '9 is too high', 'Already guessed',
# '6 is correct!', 'It took you 3 guesses']
#
# output = [line.strip() for line
# in out.split('\n') if line.strip()]
# for line, exp in zip(output, expected):
# assert line == exp
#
#
# @patch("builtins.input", side_effect=[None, 5, 9, 14, 11, 12])
# def test_game_lose(inp, capfd):
# game = Game()
# game._answer = 13
#
# game()
# assert game._win is False
| 26.765714 | 212 | 0.601836 | 643 | 4,684 | 4.281493 | 0.22084 | 0.058845 | 0.058118 | 0.094442 | 0.758082 | 0.749364 | 0.739557 | 0.722121 | 0.722121 | 0.722121 | 0 | 0.029623 | 0.257686 | 4,684 | 174 | 213 | 26.91954 | 0.762151 | 0.538642 | 0 | 0.351852 | 0 | 0 | 0.094876 | 0 | 0 | 0 | 0 | 0 | 0.259259 | 1 | 0.092593 | false | 0 | 0.055556 | 0 | 0.148148 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
e99009aa504cd19dedf103512a11a30560ce07c7 | 375 | py | Python | holobot/discord/sdk/exceptions/permission_error.py | rexor12/holobot | 89b7b416403d13ccfeee117ef942426b08d3651d | [
"MIT"
] | 1 | 2021-05-24T00:17:46.000Z | 2021-05-24T00:17:46.000Z | holobot/discord/sdk/exceptions/permission_error.py | rexor12/holobot | 89b7b416403d13ccfeee117ef942426b08d3651d | [
"MIT"
] | 41 | 2021-03-24T22:50:09.000Z | 2021-12-17T12:15:13.000Z | holobot/discord/sdk/exceptions/permission_error.py | rexor12/holobot | 89b7b416403d13ccfeee117ef942426b08d3651d | [
"MIT"
] | null | null | null | from ..enums import Permission
from typing import Optional
class PermissionError(Exception):
def __init__(self, permissions: Optional[Permission], *args: object) -> None:
super().__init__(*args)
self.__permissions: Optional[Permission] = permissions
@property
def permissions(self) -> Optional[Permission]:
return self.__permissions
| 31.25 | 81 | 0.709333 | 37 | 375 | 6.864865 | 0.513514 | 0.177165 | 0.181102 | 0.259843 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.192 | 375 | 11 | 82 | 34.090909 | 0.838284 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0.222222 | 0.111111 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
e9a9b7f19998716e834b7844bcd0f7342ef81143 | 77 | py | Python | tests/settings.py | Berndzz/lexrank | 07bdd1579c408cf73cc822da303734d0a70cf3f7 | [
"MIT"
] | 99 | 2018-11-01T08:05:48.000Z | 2022-03-09T17:45:07.000Z | tests/settings.py | Berndzz/lexrank | 07bdd1579c408cf73cc822da303734d0a70cf3f7 | [
"MIT"
] | 4 | 2020-02-27T14:16:25.000Z | 2022-02-16T14:38:49.000Z | tests/settings.py | Berndzz/lexrank | 07bdd1579c408cf73cc822da303734d0a70cf3f7 | [
"MIT"
] | 33 | 2018-12-19T05:08:34.000Z | 2022-02-09T17:29:52.000Z | from lexrank.utils.package import get_folder
DATA_ROOT = get_folder('data')
| 19.25 | 44 | 0.805195 | 12 | 77 | 4.916667 | 0.75 | 0.305085 | 0.440678 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103896 | 77 | 3 | 45 | 25.666667 | 0.855072 | 0 | 0 | 0 | 0 | 0 | 0.051948 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
e9b53623ecbacc43501922a9f3d626dd6eb1dea2 | 66 | py | Python | social/backends/douban.py | raccoongang/python-social-auth | 81c0a542d158772bd3486d31834c10af5d5f08b0 | [
"BSD-3-Clause"
] | 1,987 | 2015-01-01T16:12:45.000Z | 2022-03-29T14:24:25.000Z | social/backends/douban.py | raccoongang/python-social-auth | 81c0a542d158772bd3486d31834c10af5d5f08b0 | [
"BSD-3-Clause"
] | 731 | 2015-01-01T22:55:25.000Z | 2022-03-10T15:07:51.000Z | virtual/lib/python3.6/site-packages/social/backends/douban.py | dennismwaniki67/awards | 80ed10541f5f751aee5f8285ab1ad54cfecba95f | [
"MIT"
] | 1,082 | 2015-01-01T16:27:26.000Z | 2022-03-22T21:18:33.000Z | from social_core.backends.douban import DoubanOAuth, DoubanOAuth2
| 33 | 65 | 0.878788 | 8 | 66 | 7.125 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016393 | 0.075758 | 66 | 1 | 66 | 66 | 0.918033 | 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 | 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 | 5 |
e9bbc90b70e8b5e1530c93aa942a2ae0636d3248 | 315 | py | Python | testing_suite/talon_label_reads/test_compute_frac_As.py | kopardev/TALON | 8014faed5f982e5e106ec05239e47d65878e76c3 | [
"MIT"
] | 47 | 2020-03-31T19:56:11.000Z | 2022-03-31T18:00:21.000Z | testing_suite/talon_label_reads/test_compute_frac_As.py | kopardev/TALON | 8014faed5f982e5e106ec05239e47d65878e76c3 | [
"MIT"
] | 44 | 2020-03-23T02:15:08.000Z | 2022-03-30T17:27:26.000Z | testing_suite/talon_label_reads/test_compute_frac_As.py | kopardev/TALON | 8014faed5f982e5e106ec05239e47d65878e76c3 | [
"MIT"
] | 11 | 2020-05-13T18:41:23.000Z | 2021-12-28T07:48:58.000Z | from talon import talon_label_reads as tlr
def test_frac_as():
""" Compute the fraction of As in the sequence, making sure we don't have
int rounding """
assert tlr.compute_frac_As("AAAAAA") == 1
assert tlr.compute_frac_As("AATG") == 0.5
assert tlr.compute_frac_As("ACTGACTGG") == 2.0/9.0
| 31.5 | 77 | 0.685714 | 53 | 315 | 3.886792 | 0.622642 | 0.116505 | 0.23301 | 0.291262 | 0.320388 | 0 | 0 | 0 | 0 | 0 | 0 | 0.027778 | 0.2 | 315 | 9 | 78 | 35 | 0.789683 | 0.260317 | 0 | 0 | 0 | 0 | 0.087558 | 0 | 0 | 0 | 0 | 0 | 0.6 | 1 | 0.2 | true | 0 | 0.2 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
e9bbda6ff941eee73b168ad2b7fb2e65f55434a7 | 57 | py | Python | refract/__init__.py | joshbenner/python-refract | ed257a715f98671e05fa1e1b86e8cde4ddd6114b | [
"MIT"
] | 1 | 2016-10-04T18:40:08.000Z | 2016-10-04T18:40:08.000Z | refract/__init__.py | joshbenner/python-refract | ed257a715f98671e05fa1e1b86e8cde4ddd6114b | [
"MIT"
] | null | null | null | refract/__init__.py | joshbenner/python-refract | ed257a715f98671e05fa1e1b86e8cde4ddd6114b | [
"MIT"
] | null | null | null | from .elements import *
from .namespace import Namespace
| 19 | 32 | 0.807018 | 7 | 57 | 6.571429 | 0.571429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.140351 | 57 | 2 | 33 | 28.5 | 0.938776 | 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 | 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 | 5 |
e9cc4db08d8cf4838f92fbf95916622a593eea95 | 67 | py | Python | openapi_core/deserializing/parameters/util.py | Yarn-e/openapi-core | fda9fbd3bc1c0879818e00445e1ad0731f80b065 | [
"BSD-3-Clause"
] | 160 | 2017-11-20T13:39:04.000Z | 2022-03-31T14:48:27.000Z | openapi_core/deserializing/parameters/util.py | Yarn-e/openapi-core | fda9fbd3bc1c0879818e00445e1ad0731f80b065 | [
"BSD-3-Clause"
] | 384 | 2017-09-21T12:42:31.000Z | 2022-03-21T17:21:05.000Z | openapi_core/deserializing/parameters/util.py | Yarn-e/openapi-core | fda9fbd3bc1c0879818e00445e1ad0731f80b065 | [
"BSD-3-Clause"
] | 100 | 2017-11-21T08:07:01.000Z | 2022-01-20T20:32:52.000Z | def split(value, separator=","):
return value.split(separator)
| 22.333333 | 33 | 0.701493 | 8 | 67 | 5.875 | 0.625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.134328 | 67 | 2 | 34 | 33.5 | 0.810345 | 0 | 0 | 0 | 0 | 0 | 0.014925 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 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 | 0 | 1 | 1 | 0 | 0 | 5 |
757f71d195d17f0b222249ef6780d2e162186523 | 88 | py | Python | third_party/numpy_array_operations.py | DahlitzFlorian/python-snippets | 212f63f820b6f5842f74913ed08da18d41dfe7a4 | [
"MIT"
] | 29 | 2019-03-25T09:35:12.000Z | 2022-01-08T22:09:03.000Z | third_party/numpy_array_operations.py | DahlitzFlorian/python-snippets | 212f63f820b6f5842f74913ed08da18d41dfe7a4 | [
"MIT"
] | null | null | null | third_party/numpy_array_operations.py | DahlitzFlorian/python-snippets | 212f63f820b6f5842f74913ed08da18d41dfe7a4 | [
"MIT"
] | 4 | 2020-05-19T21:18:12.000Z | 2021-05-18T12:49:21.000Z | import numpy as np
x = np.array([1, 2, 3, 4, 5])
print(f"{x * 2}")
print(f"{x * x}")
| 11 | 29 | 0.5 | 20 | 88 | 2.2 | 0.65 | 0.272727 | 0.318182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 0.227273 | 88 | 7 | 30 | 12.571429 | 0.558824 | 0 | 0 | 0 | 0 | 0 | 0.159091 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0.5 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
f99620b010ea8e8feb3481d231403ea39965ee6e | 64 | py | Python | serve.py | javifernandez/csswg-test | 39c274a17ffdc6d1a57fd61f46e5a9be0f02b4a8 | [
"BSD-3-Clause"
] | 777 | 2017-08-29T15:15:32.000Z | 2022-03-21T05:29:41.000Z | third_party/WebKit/LayoutTests/external/csswg-test/serve.py | harrymarkovskiy/WebARonARCore | 2441c86a5fd975f09a6c30cddb57dfb7fc239699 | [
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | 66 | 2017-08-30T18:31:18.000Z | 2021-08-02T10:59:35.000Z | third_party/WebKit/LayoutTests/external/csswg-test/serve.py | harrymarkovskiy/WebARonARCore | 2441c86a5fd975f09a6c30cddb57dfb7fc239699 | [
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | 123 | 2017-08-30T01:19:34.000Z | 2022-03-17T22:55:31.000Z | from wpt_tools.serve import serve
def main():
serve.main()
| 12.8 | 33 | 0.703125 | 10 | 64 | 4.4 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1875 | 64 | 4 | 34 | 16 | 0.846154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
f99a6feb2ef27c3bebe5b280a21f676cf333450b | 105 | py | Python | larkspur/__init__.py | Feathr/larkspur | 32a97f8689c285014474cabd0aa234bec1319200 | [
"MIT"
] | null | null | null | larkspur/__init__.py | Feathr/larkspur | 32a97f8689c285014474cabd0aa234bec1319200 | [
"MIT"
] | null | null | null | larkspur/__init__.py | Feathr/larkspur | 32a97f8689c285014474cabd0aa234bec1319200 | [
"MIT"
] | null | null | null | from .larkspur import BloomFilter, ScalableBloomFilter
__all__ = ['BloomFilter', 'ScalableBloomFilter']
| 26.25 | 54 | 0.809524 | 8 | 105 | 10.125 | 0.75 | 0.740741 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.095238 | 105 | 3 | 55 | 35 | 0.852632 | 0 | 0 | 0 | 0 | 0 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
f9c7fa44be26107a4b1c90bb2baba6b805d4a195 | 145 | py | Python | stocks/analyze/__init__.py | FriendlyUser/price-prediction | 4be17ac250c8cb079cc9f8cacdc92a91e146ee9a | [
"Apache-2.0"
] | 1 | 2021-02-19T04:12:53.000Z | 2021-02-19T04:12:53.000Z | stocks/analyze/__init__.py | FriendlyUser/price-prediction | 4be17ac250c8cb079cc9f8cacdc92a91e146ee9a | [
"Apache-2.0"
] | 4 | 2020-06-17T03:29:23.000Z | 2020-08-12T15:45:46.000Z | stocks/analyze/__init__.py | FriendlyUser/price-prediction | 4be17ac250c8cb079cc9f8cacdc92a91e146ee9a | [
"Apache-2.0"
] | 1 | 2021-10-02T20:24:12.000Z | 2021-10-02T20:24:12.000Z | from stocks.analyze.allocate import generate_performance, \
generate_risk_stats, generate_estimated_returns, \
generate_portfolio_allocations | 48.333333 | 59 | 0.868966 | 16 | 145 | 7.4375 | 0.8125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.082759 | 145 | 3 | 60 | 48.333333 | 0.894737 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 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 | 0 | 0 | 0 | 5 |
fb0543b07dbbbbc4b808004ec47ccd19330f259e | 54 | py | Python | example_agents/random/trainer.py | dimitrios-ath/agentos | e01d13447c52cdcecef6a1ecaadcf6160df1d104 | [
"Apache-2.0"
] | null | null | null | example_agents/random/trainer.py | dimitrios-ath/agentos | e01d13447c52cdcecef6a1ecaadcf6160df1d104 | [
"Apache-2.0"
] | null | null | null | example_agents/random/trainer.py | dimitrios-ath/agentos | e01d13447c52cdcecef6a1ecaadcf6160df1d104 | [
"Apache-2.0"
] | null | null | null | class BasicTrainer:
def train(self):
pass
| 13.5 | 20 | 0.611111 | 6 | 54 | 5.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.314815 | 54 | 3 | 21 | 18 | 0.891892 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0.333333 | 0 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
348443300a20a0c48e74564c52d63460cd4ffcdb | 73 | py | Python | odin/strategy/indicators/__init__.py | gsamarakoon/Odin | e2e9d638c68947d24f1260d35a3527dd84c2523f | [
"MIT"
] | 103 | 2017-01-14T19:38:14.000Z | 2022-03-10T12:52:09.000Z | odin/strategy/indicators/__init__.py | gsamarakoon/Odin | e2e9d638c68947d24f1260d35a3527dd84c2523f | [
"MIT"
] | 6 | 2017-01-19T01:38:53.000Z | 2020-03-09T19:03:18.000Z | odin/strategy/indicators/__init__.py | JamesBrofos/Odin | e2e9d638c68947d24f1260d35a3527dd84c2523f | [
"MIT"
] | 33 | 2017-02-05T21:51:17.000Z | 2021-12-22T20:38:30.000Z | from .moving_average import MovingAverage
from .williams import Williams
| 24.333333 | 41 | 0.863014 | 9 | 73 | 6.888889 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.109589 | 73 | 2 | 42 | 36.5 | 0.953846 | 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 | 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 | 5 |
34a3e28948ab2d0273469f5710783608f6cb859c | 111 | py | Python | enthought/type_manager/abstract_type_system.py | enthought/etsproxy | 4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347 | [
"BSD-3-Clause"
] | 3 | 2016-12-09T06:05:18.000Z | 2018-03-01T13:00:29.000Z | enthought/type_manager/abstract_type_system.py | enthought/etsproxy | 4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347 | [
"BSD-3-Clause"
] | 1 | 2020-12-02T00:51:32.000Z | 2020-12-02T08:48:55.000Z | enthought/type_manager/abstract_type_system.py | enthought/etsproxy | 4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347 | [
"BSD-3-Clause"
] | null | null | null | # proxy module
from __future__ import absolute_import
from apptools.type_manager.abstract_type_system import *
| 27.75 | 56 | 0.864865 | 15 | 111 | 5.866667 | 0.733333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.099099 | 111 | 3 | 57 | 37 | 0.88 | 0.108108 | 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 | 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 | 5 |
34abf5688cd4a8347b2f83c784e11ac3f45fe5b8 | 41 | py | Python | test.py | scapodicasa/raspi-dashboard | b5f5e61d265b5316072fbd3a89fca300274eb38b | [
"MIT"
] | null | null | null | test.py | scapodicasa/raspi-dashboard | b5f5e61d265b5316072fbd3a89fca300274eb38b | [
"MIT"
] | null | null | null | test.py | scapodicasa/raspi-dashboard | b5f5e61d265b5316072fbd3a89fca300274eb38b | [
"MIT"
] | null | null | null | import raspi_dashboard as rd
rd.start()
| 10.25 | 28 | 0.780488 | 7 | 41 | 4.428571 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.146341 | 41 | 3 | 29 | 13.666667 | 0.885714 | 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 | 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 | 0 | 0 | 5 |
34c006be1945546aef1a38c53481ed2e0a3b855f | 1,346 | py | Python | nn/mlp.py | stegben/play-reinforcement-learning | 676122cb211122baff0241a004c4f32e919cafd1 | [
"MIT"
] | null | null | null | nn/mlp.py | stegben/play-reinforcement-learning | 676122cb211122baff0241a004c4f32e919cafd1 | [
"MIT"
] | null | null | null | nn/mlp.py | stegben/play-reinforcement-learning | 676122cb211122baff0241a004c4f32e919cafd1 | [
"MIT"
] | null | null | null | import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class MLP(nn.Module):
def __init__(self, input_dim, hidden_structure, output_dim):
super(MLP, self).__init__()
all_layers_dim = [input_dim] + hidden_structure + [output_dim]
layers = []
for idx in range(len(all_layers_dim) - 1):
n_in = all_layers_dim[idx]
n_out = all_layers_dim[idx + 1]
layers.append(nn.Linear(n_in, n_out))
layers.append(nn.ReLU())
layers.append(nn.Dropout(0.5))
self.model = nn.Sequential(*layers)
def forward(self, x):
return F.softmax(self.model.forward(x))
class LinearOutputMLP(nn.Module):
def __init__(self, input_dim, hidden_structure, output_dim):
super(LinearOutputMLP, self).__init__()
all_layers_dim = [input_dim] + hidden_structure
layers = []
for idx in range(len(all_layers_dim) - 1):
n_in = all_layers_dim[idx]
n_out = all_layers_dim[idx + 1]
layers.append(nn.Linear(n_in, n_out))
layers.append(nn.ReLU())
layers.append(nn.Dropout(0.5))
layers.append(nn.Linear(n_out, output_dim))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
| 29.26087 | 70 | 0.618128 | 189 | 1,346 | 4.121693 | 0.227513 | 0.092426 | 0.123235 | 0.1181 | 0.77792 | 0.750963 | 0.73941 | 0.73941 | 0.73941 | 0.629012 | 0 | 0.008097 | 0.265973 | 1,346 | 45 | 71 | 29.911111 | 0.780364 | 0 | 0 | 0.606061 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.121212 | false | 0 | 0.121212 | 0.060606 | 0.363636 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
34e4adf37e9a1d3de74ff8444251b95198faeed0 | 187 | py | Python | pyfrechet/__init__.py | compgeomTU/frechetForCurves | 625bfe32a45d23b194226b4ac7713ded09bd2825 | [
"MIT"
] | null | null | null | pyfrechet/__init__.py | compgeomTU/frechetForCurves | 625bfe32a45d23b194226b4ac7713ded09bd2825 | [
"MIT"
] | null | null | null | pyfrechet/__init__.py | compgeomTU/frechetForCurves | 625bfe32a45d23b194226b4ac7713ded09bd2825 | [
"MIT"
] | null | null | null | ## @package pyfrechet
# Init file for package.
from .distance import Distance, StrongDistance, WeakDistance
from .optimise import BinarySearch
from .visualize import FreeSpaceDiagram
| 20.777778 | 60 | 0.807487 | 20 | 187 | 7.55 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.139037 | 187 | 8 | 61 | 23.375 | 0.937888 | 0.224599 | 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 | 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 | 5 |
34eb4135860e7e10ffcf0f91b201572c2c15a64c | 68 | py | Python | getnet/services/customers/__init__.py | rafagonc/getnet-py | d2a5278b497408b5245d5d0fecd2e424f4ddb0d5 | [
"MIT"
] | 2 | 2021-04-09T20:17:41.000Z | 2021-04-09T20:18:06.000Z | getnet/services/customers/__init__.py | rafagonc/getnet-py | d2a5278b497408b5245d5d0fecd2e424f4ddb0d5 | [
"MIT"
] | 5 | 2019-11-24T16:24:11.000Z | 2021-02-22T16:10:05.000Z | getnet/services/customers/__init__.py | rafagonc/getnet-py | d2a5278b497408b5245d5d0fecd2e424f4ddb0d5 | [
"MIT"
] | 3 | 2020-07-25T23:00:59.000Z | 2022-02-15T02:37:27.000Z | from .service import Service, Customer
from .address import Address
| 22.666667 | 38 | 0.823529 | 9 | 68 | 6.222222 | 0.555556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.132353 | 68 | 2 | 39 | 34 | 0.949153 | 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 | 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 | 5 |
9b6dc2e37d3bfbfe772a21624560d9d6a046745f | 47 | py | Python | augmenters/__init__.py | ml4ai/mliis | f40352e734f77609bcd5c4ad330ea73a897a217d | [
"MIT"
] | 23 | 2020-06-01T09:21:58.000Z | 2022-03-01T07:36:25.000Z | augmenters/__init__.py | ml4ai/mliis | f40352e734f77609bcd5c4ad330ea73a897a217d | [
"MIT"
] | 6 | 2020-05-20T05:57:06.000Z | 2022-03-14T09:44:35.000Z | augmenters/__init__.py | ml4ai/mliis | f40352e734f77609bcd5c4ad330ea73a897a217d | [
"MIT"
] | 5 | 2020-05-07T23:34:25.000Z | 2022-03-11T11:10:30.000Z | """Image segmentation augmentation functions""" | 47 | 47 | 0.808511 | 4 | 47 | 9.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06383 | 47 | 1 | 47 | 47 | 0.863636 | 0.87234 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
9b76ed7336572082efc53afadb3a3f42558e933e | 105 | py | Python | mysql_commando/__init__.py | c4s4/mysql_commando | def44e111e4e6438d7bc4e0f407c38af86e98880 | [
"Apache-2.0"
] | 2 | 2016-07-27T12:59:47.000Z | 2019-11-30T14:24:56.000Z | mysql_commando/__init__.py | c4s4/mysql_commando | def44e111e4e6438d7bc4e0f407c38af86e98880 | [
"Apache-2.0"
] | null | null | null | mysql_commando/__init__.py | c4s4/mysql_commando | def44e111e4e6438d7bc4e0f407c38af86e98880 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# encoding: UTF-8
#pylint: disable=W0403
from mysql_commando import MysqlCommando
| 17.5 | 40 | 0.780952 | 15 | 105 | 5.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.053763 | 0.114286 | 105 | 5 | 41 | 21 | 0.817204 | 0.542857 | 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 | 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 | 5 |
9bb44265a28c48462a98343ea4270932a9405c32 | 73 | py | Python | tests/__init__.py | ReeceHoffmann/virtool | f9befad060fe16fa29fb80124e674ac5a9c4f538 | [
"MIT"
] | 39 | 2016-10-31T23:28:59.000Z | 2022-01-15T00:00:42.000Z | tests/__init__.py | ReeceHoffmann/virtool | f9befad060fe16fa29fb80124e674ac5a9c4f538 | [
"MIT"
] | 1,690 | 2017-02-07T23:39:48.000Z | 2022-03-31T22:30:44.000Z | tests/__init__.py | ReeceHoffmann/virtool | f9befad060fe16fa29fb80124e674ac5a9c4f538 | [
"MIT"
] | 25 | 2017-02-08T18:25:31.000Z | 2021-09-20T22:55:25.000Z | import pytest
pytest.register_assert_rewrite("tests.fixtures.response")
| 18.25 | 57 | 0.849315 | 9 | 73 | 6.666667 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.054795 | 73 | 3 | 58 | 24.333333 | 0.869565 | 0 | 0 | 0 | 0 | 0 | 0.315068 | 0.315068 | 0 | 0 | 0 | 0 | 0.5 | 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 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
32e929adccfe4fefa7bb0e1d6692cd329e41ad05 | 42 | py | Python | flask_api/app/common/appifaceprog.py | brennanhfredericks/network-monitor-server | 7c811d7851aee5d069569306c46dff39d8d52400 | [
"MIT"
] | null | null | null | flask_api/app/common/appifaceprog.py | brennanhfredericks/network-monitor-server | 7c811d7851aee5d069569306c46dff39d8d52400 | [
"MIT"
] | null | null | null | flask_api/app/common/appifaceprog.py | brennanhfredericks/network-monitor-server | 7c811d7851aee5d069569306c46dff39d8d52400 | [
"MIT"
] | null | null | null | from flask_restful import Api
api = Api() | 14 | 29 | 0.761905 | 7 | 42 | 4.428571 | 0.714286 | 0.387097 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 42 | 3 | 30 | 14 | 0.885714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 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 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
fd24209bdabacff5473d53f39c2da6662aed0da4 | 44 | py | Python | mwpersistence/errors.py | mdamien/python-mwpersistence | 2b98847fb8acaca38b3cbf94bde3fd7e27d2b67d | [
"MIT"
] | 3 | 2018-09-17T11:09:34.000Z | 2019-05-25T12:38:49.000Z | mwpersistence/errors.py | mdamien/python-mwpersistence | 2b98847fb8acaca38b3cbf94bde3fd7e27d2b67d | [
"MIT"
] | 5 | 2015-09-18T14:27:38.000Z | 2018-10-10T04:10:35.000Z | mwpersistence/errors.py | mdamien/python-mwpersistence | 2b98847fb8acaca38b3cbf94bde3fd7e27d2b67d | [
"MIT"
] | 3 | 2019-01-16T10:19:32.000Z | 2021-07-15T13:36:02.000Z | class FileTypeError(RuntimeError):
pass
| 14.666667 | 34 | 0.772727 | 4 | 44 | 8.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.159091 | 44 | 2 | 35 | 22 | 0.918919 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
b5f2769f12a142a0617e5e73bdf19d286089882e | 23,073 | py | Python | agents.py | pathway/alphaxos | 562e227d51e019c4e3432382866d8bb7314325c8 | [
"MIT"
] | 11 | 2018-04-10T17:51:59.000Z | 2021-12-08T05:29:17.000Z | agents.py | pathway/alphaxos | 562e227d51e019c4e3432382866d8bb7314325c8 | [
"MIT"
] | 1 | 2018-04-11T05:08:56.000Z | 2018-04-11T05:08:56.000Z | agents.py | pathway/alphaxos | 562e227d51e019c4e3432382866d8bb7314325c8 | [
"MIT"
] | 4 | 2018-04-12T15:08:38.000Z | 2020-12-04T05:02:55.000Z |
from params import *
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Convolution2D, Permute, Add, Lambda
from keras.optimizers import Adam
import keras.backend as K
from rl.agents.dqn import DQNAgent
from rl.policy import LinearAnnealedPolicy, BoltzmannQPolicy, EpsGreedyQPolicy
from rl.memory import SequentialMemory
from rl.core import Processor
from rl.callbacks import FileLogger, ModelIntervalCheckpoint
from keras.layers import Dense, Input, GlobalMaxPooling1D, InputLayer, Conv2D, Conv1D, Reshape,MaxPooling1D, MaxPooling2D, BatchNormalization, Subtract
from keras import backend as K
import numpy as np
import random
class BaseAgent(object):
agent_label=str(random.randint(1000000,9999999))
def reload(self):
pass
def save(self):
pass
class RandomAgent(BaseAgent):
'''
RandomAgent:
- pick a random action
- if its not valid, keep picking new random until valid one found
- return first valid action found
'''
env = None
def __init__(self, action_space):
self.action_space = action_space
#print(self.action_space)
def forward(self,observation):
return self.act(observation)
def act(self, observation):
rm = random.randint(0, self.action_space.n - 1)
while not self.env.action_is_valid(rm):
rm = random.randint(0, self.action_space.n - 1)
return rm
class CountChoculaAgent(BaseAgent):
'''
Always pick the first valid action (starting from smallest)
'''
env = None
def __init__(self, action_space):
self.action_space = action_space
# print(self.action_space)
def forward(self, observation):
return self.act(observation)
def act(self, observation):
for i in range(0, self.action_space.n):
if self.env.action_is_valid(i):
return i
class HumanAgent(BaseAgent):
'''
Take in keyboard input to select square.
'''
def __init__(self, action_space):
self.action_space = action_space
#print(self.action_space)
def forward(self,observation):
return self.act(observation,None,None)
def act(self, observation):
print(observation)
mx = self.action_space.n
rm = input("Move 1 to %s: " % str(mx))
rm = int(rm)-int('1')
return rm
class WrapperAgent(BaseAgent):
'''
It wraps "smart_agent" (eg. a DQN or other agent).
smart_agent should have a load_weights()
'''
smart_agent = None
env = None
modelfile = None
def __init__(self, smart_agent, action_space, random_ratio=0.03):
self.action_space = action_space
self.smart_agent = smart_agent
def reload(self):
self.smart_agent.reload()
def save(self):
self.smart_agent.save()
def load_replay(self, memoryfile):
pass
def load_from_disk(model, agent_info):
path = '/SRC/pathway/alphaxos/'
modelfile = path + agent_info['modelfile']
memorypath = path + agent_info['memoryfile']
load_memory(model, memorypath)
print('loaded: ', memorypath)
# print(self.action_space)
def forward(self, observation):
return self.act(observation)
def act(self, observation):
a = self.smart_agent.forward(observation)
self.q_values = self.smart_agent.q_values
return a
class ChaosDqnAgent(BaseAgent):
'''
This agent does Epsilon-greedy-ish rollouts.
The purpose is to ensure that competing DQNs do not get trapped in mutual local minima.
Adding a random element prevents competely closing off any state pathways.
Maybe more importantly, it also is an example of composing agents from others.
It wraps "smart_agent" (eg. a DQN or other agent).
Note it is not precisely Epsilon-Greedy, but rather Valid-Epsilon-Greedy,
its choice of random move are limited to valid moves given the current board state.
'''
smart_agent = None
env = None
def __init__(self, smart_agent, action_space, random_ratio=0.03):
self.action_space = action_space
self.random_ratio = random_ratio
self.smart_agent = smart_agent
# print(self.action_space)
def forward(self, observation):
return self.act(observation)
def act(self, observation):
if random.random() < self.random_ratio:
return self.random_act(observation)
else:
a = self.smart_agent.forward(observation)
self.q_values = self.smart_agent.q_values
return a
def random_act(self, observation):
rm = random.randint(0, self.action_space.n - 1)
while not self.env.action_is_valid(rm):
rm = random.randint(0, self.action_space.n - 1)
return rm
class DeltaChaosDqnAgent(BaseAgent):
'''
This agent does "Delta-Epsilon-greedy"-ish rollouts.
Which I should change.
TODO: change
'''
smart_agent = None
env = None
def __init__(self, smart_agent, action_space, random_ratio=0.03, delta_window=0.1):
self.action_space = action_space
self.random_ratio = random_ratio
self.delta_window = delta_window
self.smart_agent = smart_agent
# print(self.action_space)
def forward(self, observation):
return self.act(observation, None, None)
def act(self, observation ):
if random.random() < self.random_ratio:
# implement epsilon exploration
return self.random_act(observation )
else:
a = self.smart_agent.forward(observation)
# implement delta exploration
self.q_values = self.smart_agent.q_values
qq = self.q_values
# find best; limit to within self.delta_window of best
maxq = np.max(qq)
threshq = maxq - self.delta_window
# sort q values
allowed_mask = qq > threshq
indexes = np.arange(len(qq))
allowed_indexes = indexes[allowed_mask]
selected_index = np.random.choice(allowed_indexes, 1)
a = selected_index[0]
# set action
self.smart_agent.recent_action = a
return a
def random_act(self, observation ):
rm = random.randint(0, self.action_space.n - 1)
while not self.env.action_is_valid(rm):
rm = random.randint(0, self.action_space.n - 1)
return rm
'''
class TensorForceAgent(BaseAgent):
' ''
It wraps "smart_agent" tensorforce ag (eg. a DQN or other agent).
smart_agent should have a load_weights()
' ''
smart_agent = None
env = None
modelfile = None
def __init__(self, smart_agent, action_space, random_ratio=0.03):
self.action_space = action_space
self.smart_agent = smart_agent
def reload(self):
self.smart_agent.reload()
def save(self):
self.smart_agent.save()
def load_replay(self, memoryfile):
pass
def load_from_disk(model, agent_info):
path = '/SRC/pathway/alphaxos/'
modelfile = path + agent_info['modelfile']
memorypath = path + agent_info['memoryfile']
load_memory(model, memorypath)
print('loaded: ', memorypath)
# print(self.action_space)
def forward(self, observation):
return self.act(observation, None, None)
def act(self, observation, reward, done):
a = self.smart_agent.forward(observation)
self.q_values = self.smart_agent.q_values
return a
'''
class Agency(object):
agents={}
def add_agent(self,agent_kind,agent):
self.agents[(agent_kind,agent.agent_label)]=agent
def find_agent(self,agent_kind,agent_label):
return self.agents[(agent_kind,agent_label)]
def find_agent_kind(self,agent_kind):
l=self.list_agents()
matches = [ (a[0],a[1]) for a in l if a[0]==agent_kind ]
if not matches:
return None
return self.find_agent(matches[0][0],matches[0][1])
def list_agents(self):
k = self.agents.keys()
return k
def get_qfunction_approximator_xos0(out_width,side_normalization_factor):
input_shape = (3,3)
input_x = Input(shape= (1,) + input_shape)
x = Reshape( ( 3,3,1) )(input_x)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Flatten()(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos(out_width,side_normalization_factor):
'''
prepare a fresh agent.
side=-1: if you are opponent (within the env)
opponent cannot learn.
'''
input_shape = (3,3)
#input_x = Input(shape=(1,) + input_shape)
input_x = Input(shape= (1,) + input_shape)
#x = input_x
x = Reshape( ( 3,3,1) )(input_x)
#x = Conv2D(64, kernel_size=(2, 2), strides=(1, 1), activation='relu')(x)
#x = Conv2D(64, kernel_size=(3, 3), strides=(1, 1), activation='relu')(x)
#x = MaxPooling2D(pool_size=(2, 2) )(x)
#x = Conv2D(32, kernel_size=(2, 2), strides=(1, 1), activation='relu')(x)
#x = Conv2D(32, kernel_size=(2, 2), strides=(1, 1), activation='relu')(x)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Flatten()(x)
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(x)
input_x_sidenorm_square = Lambda(lambda z: K.square(z))(input_x_sidenorm)
x = Dense(500)(input_x_sidenorm)
x = BatchNormalization()(x)
x= Activation('relu')(x)
x = Dense(100)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x= keras.layers.concatenate([x,input_x_sidenorm_square]) # highway
x = Dense(out_width)(x)
#pre_predictions = Activation('linear')(x)
predictions = Activation('linear')(x)
#subtract_layer = Lambda(lambda inputs: inputs[0] - inputs[1], output_shape=lambda shapes: shapes[0])
#predictions = Subtract()( [pre_predictions,input_x_sidenorm_square] )
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos2(out_width,side_normalization_factor):
'''
prepare a fresh agent.
side=-1: if you are opponent (within the env)
opponent cannot learn.
'''
input_shape = (3,3)
input_x = Input(shape= (1,) + input_shape)
x = Reshape( ( 3,3,1) )(input_x)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Flatten()(x)
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(x)
x = Dense(500)(input_x_sidenorm)
x = BatchNormalization()(x)
x= Activation('relu')(x)
x = Dense(100)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
#subtract_layer = Lambda(lambda inputs: inputs[0] - inputs[1], output_shape=lambda shapes: shapes[0])
#predictions = Subtract()( [pre_predictions,input_x_sidenorm_square] )
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos3(out_width,side_normalization_factor):
'''
prepare a fresh agent.
side=-1: if you are opponent (within the env)
opponent cannot learn.
'''
input_shape = (3,3)
input_x = Input(shape= (1,) + input_shape)
x = Reshape( ( 3,3,1) )(input_x)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Flatten()(x)
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(x)
x = Dense(81)(input_x_sidenorm)
x = BatchNormalization()(x)
x= Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
#subtract_layer = Lambda(lambda inputs: inputs[0] - inputs[1], output_shape=lambda shapes: shapes[0])
#predictions = Subtract()( [pre_predictions,input_x_sidenorm_square] )
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos4(out_width,side_normalization_factor):
'''
prepare a fresh agent.
side=-1: if you are opponent (within the env)
opponent cannot learn.
'''
input_shape = (3,3)
#input_x = Input(shape=(1,) + input_shape)
input_x = Input(shape= (1,) + input_shape)
# skip connection
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(input_x)
input_x_sidenorm_flat = Flatten()(input_x_sidenorm )
input_x_sidenorm_square = Lambda(lambda z: K.square(z))(input_x_sidenorm_flat)
#x = input_x
x = Reshape( ( 3,3,1) )(input_x_sidenorm )
x = Conv2D(64, kernel_size=(2, 2), strides=(1, 1), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2) )(x)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Flatten()(x)
x = Dense(27)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x= keras.layers.concatenate([x,input_x_sidenorm_square]) # highway
x = Dense(out_width)(x)
#pre_predictions = Activation('linear')(x)
predictions = Activation('linear')(x)
#subtract_layer = Lambda(lambda inputs: inputs[0] - inputs[1], output_shape=lambda shapes: shapes[0])
#predictions = Subtract()( [pre_predictions,input_x_sidenorm_square] )
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos5(out_width,side_normalization_factor):
input_shape = (3,3)
input_x = Input(shape= (1,) + input_shape)
# skip connection
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(input_x)
input_x_sidenorm_flat = Flatten()(input_x_sidenorm )
input_x_sidenorm_square = Lambda(lambda z: K.square(z))(input_x_sidenorm_flat)
x = Reshape( ( 3,3,1) )(input_x_sidenorm )
x = Conv2D(64, kernel_size=(2, 2), strides=(1, 1), activation='relu')(x)
x = Flatten()(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(27)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x= keras.layers.concatenate([x,input_x_sidenorm_square]) # highway
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos6(out_width,side_normalization_factor):
input_shape = (3,3)
input_x = Input(shape= (1,) + input_shape)
# skip connection
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(input_x)
input_x_sidenorm_flat = Flatten()(input_x_sidenorm )
input_x_sidenorm_square = Lambda(lambda z: K.square(z))(input_x_sidenorm_flat)
x = Reshape( ( 3,3,1) )(input_x_sidenorm )
x1 = Conv2D(9, kernel_size=(3, 1), strides=(1, 1), activation='relu')(x)
x1 = Flatten()(x1)
x2 = Conv2D(9, kernel_size=(1, 3), strides=(1, 1), activation='relu')(x)
x2 = Flatten()(x2)
x= keras.layers.concatenate([x1,x2,input_x_sidenorm_flat])
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(27)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x= keras.layers.concatenate([x,input_x_sidenorm_square]) # highway
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos7(out_width,side_normalization_factor):
input_shape = (3,3)
input_x = Input(shape= (1,) + input_shape)
# skip connection
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(input_x)
input_x_sidenorm_flat = Flatten()(input_x_sidenorm )
flatten = Reshape( (9,1) )(input_x_sidenorm_flat)
input_x_sidenorm_square = Lambda(lambda z: K.square(z))(input_x_sidenorm_flat)
x1 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=1, activation='relu')(flatten ))
x2 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=2, activation='relu')(flatten ))
x3 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=3, activation='relu')(flatten ))
x4 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=4, activation='relu')(flatten ))
x= keras.layers.concatenate([x1,x2,x3,x4]) # ,flatten ?
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_xos8(out_width,side_normalization_factor):
input_shape = (3,3)
input_x = Input(shape= (1,) + input_shape)
# skip connection
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(input_x)
input_x_sidenorm_flat = Flatten()(input_x_sidenorm )
flatten = Reshape( (9,1) )(input_x_sidenorm_flat)
input_x_sidenorm_square = Lambda(lambda z: K.square(z))(input_x_sidenorm_flat)
x1 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=1, activation='relu')(flatten ))
x2 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=2, activation='relu')(flatten ))
x3 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=3, activation='relu')(flatten ))
x4 = Flatten()(Conv1D(9, kernel_size=3, strides=1, dilation_rate=4, activation='relu')(flatten ))
x= keras.layers.concatenate([x1,x2,x3,x4,input_x_sidenorm_flat])
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_c4_1(out_width,side_normalization_factor):
input_shape = (6,7)
input_x = Input(shape= (1,) + input_shape)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Flatten()(input_x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_c4_2(out_width,side_normalization_factor):
input_shape = (6,7)
input_x = Input(shape= (1,) + input_shape)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Conv2D(64, kernel_size=(4, 4), strides=(1, 1), activation='relu')(input_x)
x = Flatten()(x)
x = Dense(27)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_c4_3(out_width,side_normalization_factor):
input_shape = (6,7)
input_x = Input(shape= (1,) + input_shape)
# When instantiating agent network, multiply board
# by -1 or +1 depending on which side agent is playing.
# This allows agent to otherwise be ambivalent to side.
x = Reshape( ( 6,7,1) )(input_x )
x = Conv2D(32, kernel_size=(4, 4), strides=(1, 1), activation='relu')(x)
x = MaxPooling2D(pool_size=(1,1) )(x)
x = Flatten()(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
def get_qfunction_approximator_c4_4(out_width,side_normalization_factor):
input_shape = (6,7)
input_x = Input(shape= (1,) + input_shape)
# skip connection
input_x_sidenorm = Lambda(lambda z: z * side_normalization_factor)(input_x)
input_x_sidenorm_flat = Flatten()(input_x_sidenorm )
flatten = Reshape( (42,1) )(input_x_sidenorm_flat)
input_x_sidenorm_square = Lambda(lambda z: K.square(z))(input_x_sidenorm_flat)
conv_count = 27
x1 = Flatten()( MaxPooling1D(pool_size=(1) )(Conv1D(conv_count, kernel_size=4, strides=1, dilation_rate=1, activation='relu')(flatten )) )
x2 = Flatten()(MaxPooling1D(pool_size=(1) )(Conv1D(conv_count, kernel_size=4, strides=1, dilation_rate=6, activation='relu')(flatten )) )
x3 = Flatten()(MaxPooling1D(pool_size=(1) )(Conv1D(conv_count, kernel_size=4, strides=1, dilation_rate=7, activation='relu')(flatten )) )
x4 = Flatten()(MaxPooling1D(pool_size=(1) )(Conv1D(conv_count, kernel_size=4, strides=1, dilation_rate=8, activation='relu')(flatten )) )
x= keras.layers.concatenate([x1,x2,x3,x4,input_x_sidenorm_flat])
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(out_width)(x)
predictions = Activation('linear')(x)
model = keras.models.Model(inputs=input_x, outputs=predictions )
print(model.summary())
return model
dqn_agents ={
'dqn0':{ 'qfn': get_qfunction_approximator_xos0, 'modelfile': 'xos33_dqn0.hd5', 'memoryfile': 'xos33_dqn0.mem', },
'dqn2':{ 'qfn': get_qfunction_approximator_xos2, 'modelfile': 'xos33_dqn2.hd5', 'memoryfile': 'xos33_dqn2.mem', },
'dqn3': {'qfn': get_qfunction_approximator_xos3, 'modelfile': 'xos33_dqn3.hd5', 'memoryfile': 'xos33_dqn3.mem', },
'dqn4': {'qfn': get_qfunction_approximator_xos4, 'modelfile': 'xos33_dqn4.hd5', 'memoryfile': 'xos33_dqn4.mem', },
'dqn5': {'qfn': get_qfunction_approximator_xos5, 'modelfile': 'xos33_dqn5.hd5', 'memoryfile': 'xos33_dqn5.mem', },
'dqn6': {'qfn': get_qfunction_approximator_xos6, 'modelfile': 'xos33_dqn6.hd5', 'memoryfile': 'xos33_dqn6.mem', },
'dqn7': {'qfn': get_qfunction_approximator_xos7, 'modelfile': 'xos33_dqn7.hd5', 'memoryfile': 'xos33_dqn7.mem', },
'dqn8': {'qfn': get_qfunction_approximator_xos8, 'modelfile': 'xos33_dqn8.hd5', 'memoryfile': 'xos33_dqn8.mem', },
#'dqn5': {'qfn': get_qfunction_approximator_xos5, 'modelfile': 'xos33_dqn5.hd5', 'memoryfile': 'xos33_dqn5.mem', },
'c4_dqn1': {'qfn': get_qfunction_approximator_c4_1, 'modelfile': 'c4_dqn1.hd5', 'memoryfile': 'c4_dqn1.mem', },
'c4_dqn2': {'qfn': get_qfunction_approximator_c4_2, 'modelfile': 'c4_dqn2.hd5', 'memoryfile': 'c4_dqn2.mem', },
'c4_dqn3': {'qfn': get_qfunction_approximator_c4_3, 'modelfile': 'c4_dqn3.hd5', 'memoryfile': 'c4_dqn3.mem', },
'c4_dqn4': {'qfn': get_qfunction_approximator_c4_4, 'modelfile': 'c4_dqn4.hd5', 'memoryfile': 'c4_dqn4.mem', },
}
def get_dqn_agent(env,dqn_agent_subtype,folder='/SRC/pathway/alphaxos/models/',load=False,load_mem=True,side_normalization_factor=1.0):
agent_info = dqn_agents[dqn_agent_subtype]
model = agent_info['qfn'](out_width=env.action_space.n,side_normalization_factor=side_normalization_factor)
# see https://github.com/keras-rl/keras-rl/blob/master/examples/duel_dqn_cartpole.py
memory = SequentialMemory(limit=100000, window_length=1)
'''
test_policy = ValidGreedyQPolicy()
test_policy = ValidGreedyQPolicy()
test_policy.env=env
policy = ValidEpsGreedyQPolicy(0.1)
policy.env=env
'''
policy = EpsGreedyQPolicy(regime_params['epsilon-train'])
#policy = ValidEpsGreedyQPolicy(0.1)
policy.env=env
test_policy=None
dqn = DQNAgent(model=model, batch_size=regime_params['memory_batch_size'], gamma=regime_params['gamma'], nb_actions=env.action_space.n, memory=memory, nb_steps_warmup=regime_params['steps_warmup'],
target_model_update=regime_params['steps_target_model_update'], policy=policy, test_policy=test_policy, enable_double_dqn=True)
dqn.compile(Adam(lr=regime_params['learning_rate']), metrics=['mae'])
dqn.modelfile = folder+agent_info['modelfile']
dqn.memoryfile = folder+agent_info['memoryfile']
if load:
dqn.reload()
if load_mem:
dqn.reload_memory()
#dqn.env=env
return dqn
| 27.865942 | 198 | 0.728384 | 3,367 | 23,073 | 4.787051 | 0.104247 | 0.036481 | 0.047773 | 0.021839 | 0.768892 | 0.733342 | 0.728192 | 0.721492 | 0.719506 | 0.71659 | 0 | 0.025344 | 0.136393 | 23,073 | 827 | 199 | 27.899637 | 0.783549 | 0.190569 | 0 | 0.670051 | 0 | 0 | 0.060625 | 0.004376 | 0 | 0 | 0 | 0.001209 | 0 | 1 | 0.111675 | false | 0.007614 | 0.038071 | 0.017766 | 0.284264 | 0.038071 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
bd4da718ffd14f14e6d42c4f0049eee94949248f | 3,665 | py | Python | runtests.py | kingsaffair/django-ucamwebauth | 3e5d1f1fa67f7b4145fe03355fde80eb03398c54 | [
"MIT"
] | null | null | null | runtests.py | kingsaffair/django-ucamwebauth | 3e5d1f1fa67f7b4145fe03355fde80eb03398c54 | [
"MIT"
] | null | null | null | runtests.py | kingsaffair/django-ucamwebauth | 3e5d1f1fa67f7b4145fe03355fde80eb03398c54 | [
"MIT"
] | null | null | null | import logging
from django.core.management import execute_from_command_line
from django.conf import settings
settings.configure(
DEBUG=False,
DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': 'test.db', }},
TIME_ZONE='Europe/London',
USE_TZ=True,
SITE_ID=1,
ROOT_URLCONF='ucamwebauth.urls',
INSTALLED_APPS=(
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'django.contrib.messages',
'django.contrib.staticfiles',
'ucamwebauth',
),
AUTHENTICATION_BACKENDS=('ucamwebauth.backends.RavenAuthBackend', ),
MIDDLEWARE_CLASSES=(
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
),
UCAMWEBAUTH_LOGIN_URL='https://demo.raven.cam.ac.uk/auth/authenticate.html',
UCAMWEBAUTH_LOGOUT_URL='https://demo.raven.cam.ac.uk/auth/logout.html',
UCAMWEBAUTH_CERTS={901: """-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----
"""},
UCAMWEBAUTH_TIMEOUT=60,
TEMPLATES=[
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [
# insert your TEMPLATE_DIRS here
],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
# Insert your TEMPLATE_CONTEXT_PROCESSORS here or use this
# list if you haven't customized them:
'django.contrib.auth.context_processors.auth',
'django.template.context_processors.debug',
'django.template.context_processors.i18n',
'django.template.context_processors.media',
'django.template.context_processors.static',
'django.template.context_processors.tz',
'django.contrib.messages.context_processors.messages',
],
},
},
]
)
logging.basicConfig()
execute_from_command_line(['', 'test'])
| 44.156627 | 89 | 0.74925 | 246 | 3,665 | 11.036585 | 0.54878 | 0.05267 | 0.055249 | 0.05709 | 0.020626 | 0.020626 | 0.020626 | 0.020626 | 0 | 0 | 0 | 0.044175 | 0.166166 | 3,665 | 82 | 90 | 44.695122 | 0.844241 | 0.033834 | 0 | 0.051948 | 0 | 0 | 0.684478 | 0.593441 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.038961 | 0 | 0.038961 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
1f996492543d0a86470eb453fc4832390d0d7013 | 156 | py | Python | src/hostvirtmgr.py | retspen/hostvirtmgr | 833394571d37f41097a4a983e029ffb036cfb49f | [
"Apache-2.0"
] | 4 | 2021-02-15T08:39:35.000Z | 2021-02-20T22:37:17.000Z | src/hostvirtmgr.py | retspen/hostvirtmgr | 833394571d37f41097a4a983e029ffb036cfb49f | [
"Apache-2.0"
] | null | null | null | src/hostvirtmgr.py | retspen/hostvirtmgr | 833394571d37f41097a4a983e029ffb036cfb49f | [
"Apache-2.0"
] | null | null | null | import main
import uvicorn
from settings import HOST, PORT
if __name__ == "__main__":
uvicorn.run("main:app", host=HOST, port=PORT, log_level="info")
| 19.5 | 67 | 0.724359 | 23 | 156 | 4.521739 | 0.608696 | 0.153846 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.147436 | 156 | 7 | 68 | 22.285714 | 0.781955 | 0 | 0 | 0 | 0 | 0 | 0.128205 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.6 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
1fad5d2d6d4c597137caf2db21b57db2687a8281 | 97 | py | Python | src/utils/return_total_amount.py | lucas54neves/financial-manager | 89be34ad34a33490dc7ebb421b794dcb2f20d9b1 | [
"MIT"
] | null | null | null | src/utils/return_total_amount.py | lucas54neves/financial-manager | 89be34ad34a33490dc7ebb421b794dcb2f20d9b1 | [
"MIT"
] | null | null | null | src/utils/return_total_amount.py | lucas54neves/financial-manager | 89be34ad34a33490dc7ebb421b794dcb2f20d9b1 | [
"MIT"
] | null | null | null | def return_total_amount(transactions):
return transactions.sum()["column_installment_value"]
| 32.333333 | 57 | 0.814433 | 11 | 97 | 6.818182 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.082474 | 97 | 2 | 58 | 48.5 | 0.842697 | 0 | 0 | 0 | 0 | 0 | 0.247423 | 0.247423 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 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 | 0 | 1 | 1 | 0 | 0 | 5 |
1faeba1c0d4b88f2f495049aad5b1eac453e18f6 | 35 | py | Python | test1.py | Dean2411/lesson1a | 989bd16d0f3eb87516c4892f7dc95fa0f551bcdd | [
"Apache-2.0"
] | null | null | null | test1.py | Dean2411/lesson1a | 989bd16d0f3eb87516c4892f7dc95fa0f551bcdd | [
"Apache-2.0"
] | null | null | null | test1.py | Dean2411/lesson1a | 989bd16d0f3eb87516c4892f7dc95fa0f551bcdd | [
"Apache-2.0"
] | null | null | null | print "its getting late" {sure is}
| 17.5 | 34 | 0.714286 | 6 | 35 | 4.166667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.171429 | 35 | 1 | 35 | 35 | 0.862069 | 0 | 0 | 0 | 0 | 0 | 0.457143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
1fb266bb71bcb8d9106bcf621a4b3410838dca6d | 72 | py | Python | examples_tts/tacotron2/__init__.py | MODAK27/tts-replica | 4fef1b2b415c23d74296196f39560f4308f91447 | [
"Apache-2.0"
] | null | null | null | examples_tts/tacotron2/__init__.py | MODAK27/tts-replica | 4fef1b2b415c23d74296196f39560f4308f91447 | [
"Apache-2.0"
] | null | null | null | examples_tts/tacotron2/__init__.py | MODAK27/tts-replica | 4fef1b2b415c23d74296196f39560f4308f91447 | [
"Apache-2.0"
] | null | null | null | from examples_tts.tacotron2.tacotron_dataset import CharactorMelDataset
| 36 | 71 | 0.916667 | 8 | 72 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014706 | 0.055556 | 72 | 1 | 72 | 72 | 0.926471 | 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 | 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 | 5 |
9507480c050d7c4aafbc4fa8635b8e80ebdef219 | 128 | py | Python | Per-Con/Frontend/Backend/admin.py | yeniv/Best-Web-Development-Resources | ca36205226b86825b44d6f4c8367e3ac31bda86c | [
"MIT"
] | 43 | 2020-11-18T04:40:36.000Z | 2022-03-20T18:27:33.000Z | Per-Con/Frontend/Backend/admin.py | yeniv/Best-Web-Development-Resources | ca36205226b86825b44d6f4c8367e3ac31bda86c | [
"MIT"
] | 5 | 2020-11-20T15:37:18.000Z | 2022-01-31T14:49:46.000Z | Per-Con/Frontend/Backend/admin.py | yeniv/Best-Web-Development-Resources | ca36205226b86825b44d6f4c8367e3ac31bda86c | [
"MIT"
] | 16 | 2020-11-18T17:03:48.000Z | 2022-01-31T12:33:14.000Z | from django.contrib import admin
from Backend.models import Contact
# Register your models here.
admin.site.register(Contact) | 32 | 35 | 0.8125 | 18 | 128 | 5.777778 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 128 | 4 | 36 | 32 | 0.928571 | 0.203125 | 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 | 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 | 5 |
9509a068193d1f8c10620a9efc35076932f10f24 | 20 | py | Python | dbt/adapters/synapse/__version__.py | swanjson/dbt-synapse | 38f96116b6b89921e6083ac3850f3cabbb1f2c05 | [
"MIT"
] | null | null | null | dbt/adapters/synapse/__version__.py | swanjson/dbt-synapse | 38f96116b6b89921e6083ac3850f3cabbb1f2c05 | [
"MIT"
] | null | null | null | dbt/adapters/synapse/__version__.py | swanjson/dbt-synapse | 38f96116b6b89921e6083ac3850f3cabbb1f2c05 | [
"MIT"
] | null | null | null | version = '1.0.2b1'
| 10 | 19 | 0.6 | 4 | 20 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.235294 | 0.15 | 20 | 1 | 20 | 20 | 0.470588 | 0 | 0 | 0 | 0 | 0 | 0.35 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
1f3a19d356eeef95a96d8af2b285a5e4e664e8a3 | 147 | py | Python | mir3/modules/unsupervised_submodule.py | pymir3/pymir3 | c1bcca66a5ef1ff0ebd6373e3820e72dee6b0b70 | [
"MIT"
] | 12 | 2015-08-03T12:41:11.000Z | 2020-08-18T07:55:23.000Z | mir3/modules/unsupervised_submodule.py | pymir3/pymir3 | c1bcca66a5ef1ff0ebd6373e3820e72dee6b0b70 | [
"MIT"
] | 1 | 2015-05-27T18:47:20.000Z | 2015-05-27T18:47:20.000Z | mir3/modules/unsupervised_submodule.py | pymir3/pymir3 | c1bcca66a5ef1ff0ebd6373e3820e72dee6b0b70 | [
"MIT"
] | 3 | 2016-03-18T03:30:02.000Z | 2018-07-05T02:29:16.000Z | import mir3.module
class Unsupervised(mir3.module.Module):
def get_help(self):
return """unsupervised trackers and their utilities"""
| 24.5 | 62 | 0.727891 | 18 | 147 | 5.888889 | 0.777778 | 0.188679 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016393 | 0.170068 | 147 | 5 | 63 | 29.4 | 0.852459 | 0 | 0 | 0 | 0 | 0 | 0.278912 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0.25 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
1f54a5d8df7e7d78d15505702c9986fa86077408 | 140 | py | Python | fetching/__init__.py | nthparty/fetching | df7ffee65aae8fc6f572fdaa0bff3d4a414f8f97 | [
"MIT"
] | null | null | null | fetching/__init__.py | nthparty/fetching | df7ffee65aae8fc6f572fdaa0bff3d4a414f8f97 | [
"MIT"
] | null | null | null | fetching/__init__.py | nthparty/fetching | df7ffee65aae8fc6f572fdaa0bff3d4a414f8f97 | [
"MIT"
] | null | null | null | from fetching.fetching import Fetching
def fetch(targets: list, token: str = None):
return Fetching().fetch_and_build(targets, token)
| 23.333333 | 53 | 0.757143 | 19 | 140 | 5.473684 | 0.684211 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 140 | 5 | 54 | 28 | 0.866667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 5 |
1f54e43b3479c63f5feca5fce019ea355160cfc5 | 4,218 | py | Python | dbhelper.py | rorro/music-player | e0427c0a604f0dcd37e1ac53c35742a6fb4b0ddf | [
"MIT"
] | 1 | 2020-05-27T14:04:25.000Z | 2020-05-27T14:04:25.000Z | dbhelper.py | rorro/music-player | e0427c0a604f0dcd37e1ac53c35742a6fb4b0ddf | [
"MIT"
] | 7 | 2020-06-12T10:25:25.000Z | 2020-10-09T13:04:55.000Z | dbhelper.py | rorro/music-player | e0427c0a604f0dcd37e1ac53c35742a6fb4b0ddf | [
"MIT"
] | null | null | null | import sqlite3
from urllib.parse import quote, unquote
DATABASE = "database.db"
def get_votes(link):
db = sqlite3.connect(DATABASE)
c = db.cursor()
c.execute('''select upvotes, downvotes from songs where link = ? ''', (link,))
res = c.fetchall()
if res:
return res[0]
return None
db.close()
def has_voted(token, link):
db = sqlite3.connect(DATABASE)
c = db.cursor()
c.execute(''' select * from user_votes where token = ? and link = ?''', (token, link))
res = c.fetchall()
if res:
return True
else:
return False
db.close()
def get_vote_type(token, link):
db = sqlite3.connect(DATABASE)
c = db.cursor()
c.execute(''' select vote from user_votes where token = ? and link = ?''', (token, link))
res = c.fetchall()
if res:
return res[0][0]
else:
return None
db.close()
def upvote(token, link):
vote_type = get_vote_type(token, link)
votes = get_votes(link)
voted = has_voted(token, link)
db = sqlite3.connect(DATABASE)
c = db.cursor()
if votes:
if not voted:
c.execute(''' update songs set upvotes = ? where link = ? ''', (votes[0]+1, link))
c.execute(''' insert into user_votes (token, link, vote)
values (?, ?, ?) ''', (token, link, 1))
else:
if vote_type == 1:
c.execute(''' delete from user_votes where token = ? and link = ? ''', (token, link))
c.execute(''' update songs set upvotes = ? where link = ? ''', (votes[0]-1, link))
else:
c.execute(''' update user_votes set vote = ? where token = ? and link = ? ''', (1, token, link))
c.execute(''' update songs set upvotes = ?, downvotes = ?
where link = ? ''', (votes[0]+1, votes[1]-1, link))
else:
c.execute(''' insert into songs values (?, ?, ?) ''', (link, 1, 0))
c.execute(''' insert into user_votes (token, link, vote)
values (?, ?, ?) ''', (token, link, 1))
db.commit()
db.close()
def downvote(token, link):
vote_type = get_vote_type(token, link)
votes = get_votes(link)
voted = has_voted(token, link)
db = sqlite3.connect(DATABASE)
c = db.cursor()
if votes:
if not voted:
c.execute(''' update songs set downvotes = ? where link = ? ''', (votes[1]+1, link))
c.execute(''' insert into user_votes (token, link, vote)
values (?, ?, ?) ''', (token, link, 0))
else:
if vote_type == 0:
c.execute(''' delete from user_votes where token = ? and link = ? ''', (token, link))
c.execute(''' update songs set downvotes = ? where link = ? ''', (votes[1]-1, link))
else:
c.execute(''' update user_votes set vote = ? where token = ? and link = ? ''', (0, token, link))
c.execute(''' update songs set upvotes = ?, downvotes = ?
where link = ? ''', (votes[0]-1, votes[1]+1, link))
else:
c.execute(''' insert into songs values (?, ?, ?) ''', (link, 0, 1))
c.execute(''' insert into user_votes (token, link, vote)
values (?, ?, ?) ''', (token, link, 0))
db.commit()
db.close()
def highlight_votes(upvotes, downvotes):
db = sqlite3.connect(DATABASE)
c = db.cursor()
upvoted, downvoted = [], []
if upvotes == "true":
c.execute(''' select link from songs where upvotes > 0 and downvotes = 0 ''')
upvoted = [link[0] for link in c.fetchall()]
if downvotes == "true":
c.execute(''' select link from songs where upvotes = 0 and downvotes > 0 ''')
downvoted = [link[0] for link in c.fetchall()]
res = {"upvoted": upvoted, "downvoted": downvoted}
return res
db.close()
def get_upvotes():
upvotes = highlight_votes("true", "false")["upvoted"]
with open("upvotes.txt", "a") as f:
for link in upvotes:
fixed_link = unquote(link)
fixed_link = "/".join(fixed_link.split("/")[1:])
f.write(fixed_link+"\n")
| 30.345324 | 112 | 0.528924 | 513 | 4,218 | 4.2846 | 0.128655 | 0.090082 | 0.050955 | 0.065514 | 0.78071 | 0.741128 | 0.741128 | 0.705187 | 0.705187 | 0.689263 | 0 | 0.015464 | 0.3101 | 4,218 | 138 | 113 | 30.565217 | 0.739863 | 0 | 0 | 0.568627 | 0 | 0 | 0.321081 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.068627 | false | 0 | 0.019608 | 0 | 0.156863 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
1f86165bcf2bfa4daa08b26a1cb7c5b501ed56eb | 339 | py | Python | code/1/main.py | pwang13/AutomatedSE_Coursework | b416672d9756fcc60367143b989d29b0c905cfc3 | [
"Unlicense"
] | null | null | null | code/1/main.py | pwang13/AutomatedSE_Coursework | b416672d9756fcc60367143b989d29b0c905cfc3 | [
"Unlicense"
] | null | null | null | code/1/main.py | pwang13/AutomatedSE_Coursework | b416672d9756fcc60367143b989d29b0c905cfc3 | [
"Unlicense"
] | null | null | null | #/usr/bin/python2
import utest
import sys
sys.dont_write_bytecode=true
print "\nLoading and testing Timmons\n\n"
import timmons
utest.oks()
print "\nLoading and testing laurel\n\n"
import laurel
utest.oks()
print "\nLoading and testing wang\n\n"
import wang
utest.oks()
print "\nLoading and testing goff\n\n"
import goff
utest.oks()
| 14.73913 | 41 | 0.758112 | 56 | 339 | 4.553571 | 0.357143 | 0.203922 | 0.25098 | 0.360784 | 0.364706 | 0.364706 | 0 | 0 | 0 | 0 | 0 | 0.00339 | 0.129794 | 339 | 22 | 42 | 15.409091 | 0.861017 | 0.047198 | 0 | 0.266667 | 0 | 0 | 0.388199 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.4 | null | null | 0.266667 | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
2f56ef973e16486b06ab8b92cac0602d1253ae47 | 219 | py | Python | risk_manage/__init__.py | william1209/algo_design_4ca | 8031aa52c0c5cabdc67358babaaba4831b00fec0 | [
"MIT"
] | null | null | null | risk_manage/__init__.py | william1209/algo_design_4ca | 8031aa52c0c5cabdc67358babaaba4831b00fec0 | [
"MIT"
] | 2 | 2021-04-15T10:10:45.000Z | 2021-04-28T07:04:54.000Z | risk_manage/__init__.py | william1209/algo_design_4ca | 8031aa52c0c5cabdc67358babaaba4831b00fec0 | [
"MIT"
] | 1 | 2021-04-20T08:24:37.000Z | 2021-04-20T08:24:37.000Z | from risk_manage.Data_Prepare import Data_Prepare
from risk_manage.model_data_parse import model_data_parse
from risk_manage.cluster_model import cluster_model
from risk_manage.Decision_Boundary import Decision_Boundary | 54.75 | 59 | 0.913242 | 34 | 219 | 5.470588 | 0.323529 | 0.172043 | 0.301075 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.068493 | 219 | 4 | 59 | 54.75 | 0.911765 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 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 | 5 |
2f61c2f917a5ef7058966ed72180574cb9a1d4e8 | 79 | py | Python | twitchbot/api/__init__.py | cvangheem/Twitchbot | 48bb065951e88e4d2e9ef8d0c1a3afb0150a5eb5 | [
"MIT"
] | 87 | 2018-05-22T18:30:42.000Z | 2022-03-12T19:31:52.000Z | twitchbot/api/__init__.py | cvangheem/Twitchbot | 48bb065951e88e4d2e9ef8d0c1a3afb0150a5eb5 | [
"MIT"
] | 32 | 2019-04-01T20:07:33.000Z | 2022-01-14T03:00:58.000Z | twitchbot/api/__init__.py | cvangheem/Twitchbot | 48bb065951e88e4d2e9ef8d0c1a3afb0150a5eb5 | [
"MIT"
] | 43 | 2018-08-29T04:59:47.000Z | 2022-03-09T16:47:14.000Z | from .streaminfoapi import *
from .userinfoapi import *
from .baseapi import *
| 19.75 | 28 | 0.772152 | 9 | 79 | 6.777778 | 0.555556 | 0.327869 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151899 | 79 | 3 | 29 | 26.333333 | 0.910448 | 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 | 0 | 0 | 0 | 5 |
c824c118104b7b6e8cf03ae33c640da629c85c3c | 301 | py | Python | pytest_splunk_addon/standard_lib/sample_generation/__init__.py | monishshah18/pytest-splunk-addon | 1600f2c7d30ec304e9855642e63511780556b406 | [
"Apache-2.0"
] | null | null | null | pytest_splunk_addon/standard_lib/sample_generation/__init__.py | monishshah18/pytest-splunk-addon | 1600f2c7d30ec304e9855642e63511780556b406 | [
"Apache-2.0"
] | null | null | null | pytest_splunk_addon/standard_lib/sample_generation/__init__.py | monishshah18/pytest-splunk-addon | 1600f2c7d30ec304e9855642e63511780556b406 | [
"Apache-2.0"
] | null | null | null | from .sample_event import SampleEvent
from .rule import Rule, raise_warning
from .sample_stanza import SampleStanza
from .eventgen_parser import EventgenParser
from .sample_event import SampleEvent
from .sample_generator import SampleGenerator
from .sample_xdist_generator import SampleXdistGenerator
| 37.625 | 56 | 0.877076 | 37 | 301 | 6.918919 | 0.459459 | 0.195313 | 0.117188 | 0.164063 | 0.28125 | 0.28125 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096346 | 301 | 7 | 57 | 43 | 0.941176 | 0 | 0 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
c092b107f06445ea328eadd12c9f01323dc2de84 | 117 | py | Python | tbot/rest_api/__init__.py | TheDreamPort/tbot | a58fde6ebe80f14a5d504d9191705dc186837a37 | [
"MIT"
] | null | null | null | tbot/rest_api/__init__.py | TheDreamPort/tbot | a58fde6ebe80f14a5d504d9191705dc186837a37 | [
"MIT"
] | null | null | null | tbot/rest_api/__init__.py | TheDreamPort/tbot | a58fde6ebe80f14a5d504d9191705dc186837a37 | [
"MIT"
] | null | null | null | from __future__ import absolute_import
from rest_api.celeryconf import app as celery_app
__all__ = ['celery_app']
| 16.714286 | 49 | 0.811966 | 17 | 117 | 4.882353 | 0.647059 | 0.216867 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136752 | 117 | 6 | 50 | 19.5 | 0.821782 | 0 | 0 | 0 | 0 | 0 | 0.086207 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 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 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
c0ab6fcc6faf09240984703f2297ad23a1502410 | 108 | py | Python | graph_io/classes/cypher_query.py | Octavian-ai/synthetic-graph-data | b327cfb06d420d216a5377f2ce953355089e0e6b | [
"MIT"
] | 16 | 2018-09-06T09:27:03.000Z | 2021-05-28T01:35:44.000Z | graph_io/classes/cypher_query.py | Octavian-ai/generate-data | b327cfb06d420d216a5377f2ce953355089e0e6b | [
"MIT"
] | 1 | 2021-02-10T00:02:43.000Z | 2021-02-10T00:02:43.000Z | graph_io/classes/cypher_query.py | Octavian-ai/generate-data | b327cfb06d420d216a5377f2ce953355089e0e6b | [
"MIT"
] | 7 | 2018-07-23T08:39:54.000Z | 2021-02-08T16:24:54.000Z | class CypherQuery(object):
def __init__(self, value: str):
self.value = value.replace('\t', ' ') | 36 | 45 | 0.62037 | 13 | 108 | 4.846154 | 0.769231 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.203704 | 108 | 3 | 45 | 36 | 0.732558 | 0 | 0 | 0 | 0 | 0 | 0.027523 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
c0ae0c180bf69c22194df0cc9a31b34f864504eb | 74 | py | Python | tapis_cli/clients/services/__init__.py | bpachev/tapis-cli | c3128fb5b63ef74e06b737bbd95ef28fb24f0d32 | [
"BSD-3-Clause"
] | 8 | 2020-10-18T22:48:23.000Z | 2022-01-10T09:16:14.000Z | tapis_cli/clients/services/__init__.py | bpachev/tapis-cli | c3128fb5b63ef74e06b737bbd95ef28fb24f0d32 | [
"BSD-3-Clause"
] | 238 | 2019-09-04T14:37:54.000Z | 2020-04-15T16:24:24.000Z | tapis_cli/clients/services/__init__.py | bpachev/tapis-cli | c3128fb5b63ef74e06b737bbd95ef28fb24f0d32 | [
"BSD-3-Clause"
] | 5 | 2019-09-20T04:23:49.000Z | 2020-01-16T17:45:14.000Z | """Implementation of service-specific clients
"""
from .taccapis import *
| 18.5 | 45 | 0.756757 | 8 | 74 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121622 | 74 | 3 | 46 | 24.666667 | 0.861538 | 0.567568 | 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 | 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 | 5 |
c0cd286b6e88d940985f4b18acfb19cf1f182b42 | 214 | py | Python | assets/clear_program.py | DamianB-BitFlipper/algopytest-tutorial | 1c078b8f033f4557fdddb84375ad46aec72f0105 | [
"MIT"
] | null | null | null | assets/clear_program.py | DamianB-BitFlipper/algopytest-tutorial | 1c078b8f033f4557fdddb84375ad46aec72f0105 | [
"MIT"
] | null | null | null | assets/clear_program.py | DamianB-BitFlipper/algopytest-tutorial | 1c078b8f033f4557fdddb84375ad46aec72f0105 | [
"MIT"
] | null | null | null | from pyteal import *
def clear_program():
"""A clear program that always approves."""
return Return(Int(1))
if __name__ == "__main__":
print(compileTeal(clear_program(), Mode.Application, version=5))
| 23.777778 | 68 | 0.696262 | 27 | 214 | 5.148148 | 0.814815 | 0.258993 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.011173 | 0.163551 | 214 | 8 | 69 | 26.75 | 0.765363 | 0.172897 | 0 | 0 | 0 | 0 | 0.046784 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0 | 0.2 | 0 | 0.6 | 0.2 | 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 | 0 | 0 | 1 | 0 | 0 | 5 |
c0fdeff081dc9242bbc5533cf55643cb323d6cfc | 102 | py | Python | distributions/__init__.py | SJaffa/streamlit-tutorial | ed645466e788ccdf0a1ac5111cb741ea1739eca2 | [
"MIT"
] | null | null | null | distributions/__init__.py | SJaffa/streamlit-tutorial | ed645466e788ccdf0a1ac5111cb741ea1739eca2 | [
"MIT"
] | null | null | null | distributions/__init__.py | SJaffa/streamlit-tutorial | ed645466e788ccdf0a1ac5111cb741ea1739eca2 | [
"MIT"
] | null | null | null | from .normal import normal_distribution
from .widgets import fun_widgets
from .basics import basics
| 17 | 39 | 0.833333 | 14 | 102 | 5.928571 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137255 | 102 | 5 | 40 | 20.4 | 0.943182 | 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 | 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 | 5 |
23ba8aca889cde01949058fe31d4a7eb3cf399d5 | 44 | py | Python | main.py | LyricLy/Cled | 81c969bbb2206124ca286ba240d8c908e656a020 | [
"CC0-1.0"
] | 1 | 2020-04-08T10:05:01.000Z | 2020-04-08T10:05:01.000Z | main.py | LyricLy/Cled | 81c969bbb2206124ca286ba240d8c908e656a020 | [
"CC0-1.0"
] | null | null | null | main.py | LyricLy/Cled | 81c969bbb2206124ca286ba240d8c908e656a020 | [
"CC0-1.0"
] | null | null | null | async def loop(client):
print("Ready!")
| 14.666667 | 23 | 0.636364 | 6 | 44 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 44 | 2 | 24 | 22 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0.136364 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 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 | 0 | 0 | 0 | 1 | 0 | 5 |
23c726f4254fd9b15eedfc4b2727511e4b27e0dc | 37 | py | Python | run.py | muxuezi/flaskblog | 880f7c97e7b8391b0b58ce06ffc9928b52bfd57e | [
"Apache-2.0"
] | null | null | null | run.py | muxuezi/flaskblog | 880f7c97e7b8391b0b58ce06ffc9928b52bfd57e | [
"Apache-2.0"
] | 6 | 2019-05-02T10:35:38.000Z | 2019-06-02T10:05:16.000Z | run.py | muxuezi/flaskblog | 880f7c97e7b8391b0b58ce06ffc9928b52bfd57e | [
"Apache-2.0"
] | null | null | null | from app.server import app
app.run() | 12.333333 | 26 | 0.756757 | 7 | 37 | 4 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135135 | 37 | 3 | 27 | 12.333333 | 0.875 | 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 | 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 | 0 | 0 | 5 |
23d44b0fc990b339e4accffc3ca3f03cb136254e | 221 | py | Python | grumpyforms/fields/__init__.py | FelixSchwarz/grumpywidgets | 10fe8349b6dec4116850160e92da7f2de3e5f713 | [
"MIT"
] | null | null | null | grumpyforms/fields/__init__.py | FelixSchwarz/grumpywidgets | 10fe8349b6dec4116850160e92da7f2de3e5f713 | [
"MIT"
] | null | null | null | grumpyforms/fields/__init__.py | FelixSchwarz/grumpywidgets | 10fe8349b6dec4116850160e92da7f2de3e5f713 | [
"MIT"
] | 1 | 2021-09-09T08:41:23.000Z | 2021-09-09T08:41:23.000Z |
from __future__ import absolute_import
from .buttons import *
from .checkbox import *
from .inputfields import *
from .list_field import *
from .radiobutton import *
from .select_field import *
from .textarea import *
| 18.416667 | 38 | 0.778281 | 28 | 221 | 5.892857 | 0.428571 | 0.424242 | 0.181818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.158371 | 221 | 11 | 39 | 20.090909 | 0.887097 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
f1bfab39d39e2bedf774e8015c7b389de711c095 | 114 | py | Python | apps/calendar/api/serializers.py | mobius-labs/app | bdf8226d8b16cea609a7af01be51c9bd4b867ab3 | [
"MIT"
] | 1 | 2021-11-13T10:52:08.000Z | 2021-11-13T10:52:08.000Z | apps/calendar/api/serializers.py | mobius-labs/app | bdf8226d8b16cea609a7af01be51c9bd4b867ab3 | [
"MIT"
] | 1 | 2021-11-13T04:25:00.000Z | 2021-11-13T04:25:00.000Z | apps/calendar/api/serializers.py | mobius-labs/app | bdf8226d8b16cea609a7af01be51c9bd4b867ab3 | [
"MIT"
] | null | null | null | from rest_framework import serializers
# from .models import model1, model2
# Create your ModelSerializers here
| 19 | 38 | 0.815789 | 14 | 114 | 6.571429 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.020619 | 0.149123 | 114 | 5 | 39 | 22.8 | 0.927835 | 0.596491 | 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 | 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 | 5 |
f1bfbdfb0e61682006be0d856a466d55f95e8433 | 98 | py | Python | app/ticket/__init__.py | AniaPeszek/ReclamationAndTicketSystem | 42551732dcc9af42dc7401fbc13b8fdb6e3c132f | [
"MIT"
] | null | null | null | app/ticket/__init__.py | AniaPeszek/ReclamationAndTicketSystem | 42551732dcc9af42dc7401fbc13b8fdb6e3c132f | [
"MIT"
] | null | null | null | app/ticket/__init__.py | AniaPeszek/ReclamationAndTicketSystem | 42551732dcc9af42dc7401fbc13b8fdb6e3c132f | [
"MIT"
] | null | null | null | from flask import Blueprint
bp = Blueprint("ticket_bp", __name__)
from app.ticket import routes
| 16.333333 | 37 | 0.785714 | 14 | 98 | 5.142857 | 0.642857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 98 | 5 | 38 | 19.6 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0.091837 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 5 |
f1ce7b0ab62f383c862530b3cce44bf42eac4e35 | 220,589 | py | Python | mysite/patterns/79.py | BioinfoNet/prepub | e19c48cabf8bd22736dcef9308a5e196cfd8119a | [
"MIT"
] | 19 | 2016-06-17T23:36:27.000Z | 2020-01-13T16:41:55.000Z | mysite/patterns/79.py | BioinfoNet/prepub | e19c48cabf8bd22736dcef9308a5e196cfd8119a | [
"MIT"
] | 13 | 2016-06-06T12:57:05.000Z | 2019-02-05T02:21:00.000Z | patterns/79.py | OmnesRes/GRIMMER | 173c99ebdb6a9edb1242d24a791d0c5d778ff643 | [
"MIT"
] | 7 | 2017-03-28T18:12:22.000Z | 2021-06-16T09:32:59.000Z | pattern_zero=[0.0, 0.0124979971, 0.0246755328, 0.0253164557, 0.036532607, 0.0378144528, 0.0480692197, 0.0499919885, 0.0506329114, 0.0592853709, 0.0618490627, 0.0631309085, 0.0701810607, 0.0733856754, 0.0753084442, 0.0759493671, 0.0807562891, 0.0846018266, 0.0871655183, 0.0884473642, 0.0910110559, 0.0954975164, 0.0987021311, 0.1006248999, 0.1009453613, 0.1012658228, 0.1060727448, 0.1099182823, 0.1105592053, 0.112481974, 0.1137638199, 0.1163275116, 0.1198525877, 0.1208139721, 0.1240185868, 0.1259413556, 0.126261817, 0.1265822785, 0.1288255087, 0.1313892004, 0.135234738, 0.135875661, 0.1374779683, 0.1377984297, 0.1390802756, 0.1416439673, 0.1451690434, 0.1458099664, 0.1461304278, 0.1493350425, 0.1512578112, 0.1515782727, 0.1518987342, 0.153821503, 0.1541419644, 0.1567056561, 0.1605511937, 0.1611921166, 0.1615125781, 0.162794424, 0.1631148854, 0.1643967313, 0.166960423, 0.1688831918, 0.1704854991, 0.171126422, 0.1714468835, 0.1746514982, 0.175933344, 0.1765742669, 0.1768947284, 0.1772151899, 0.1791379587, 0.1794584201, 0.1820221118, 0.1826630348, 0.1858676494, 0.1865085723, 0.1868290338, 0.1881108797, 0.1884313411, 0.1890722641, 0.189713187, 0.1922768787, 0.1941996475, 0.1951610319, 0.1958019548, 0.1964428777, 0.1967633392, 0.1999679539, 0.2009293382, 0.2012497997, 0.2018907226, 0.2022111841, 0.2025316456, 0.2044544144, 0.2047748758, 0.2063771831, 0.2073385675, 0.2079794905, 0.2111841051, 0.2115045666, 0.211825028, 0.2121454895, 0.2134273354, 0.2137477968, 0.2143887198, 0.2150296427, 0.2163114885, 0.2175933344, 0.2195161032, 0.2204774876, 0.220797949, 0.2211184105, 0.2217593334, 0.2220797949, 0.2249639481, 0.2252844096, 0.2262457939, 0.2265662554, 0.2272071783, 0.2275276398, 0.2278481013, 0.2288094857, 0.2297708701, 0.2300913315, 0.2316936388, 0.2323345618, 0.2326550232, 0.2332959462, 0.2355391764, 0.2365005608, 0.2368210223, 0.2371414837, 0.2374619452, 0.2384233296, 0.2387437911, 0.2390642525, 0.2397051755, 0.2403460984, 0.2409870213, 0.2416279442, 0.2429097901, 0.2432302516, 0.2448325589, 0.2451530203, 0.2457939433, 0.2461144047, 0.2464348662, 0.2467553277, 0.2470757891, 0.2473962506, 0.2480371735, 0.2489985579, 0.2496394809, 0.2499599423, 0.2502804038, 0.2506008652, 0.2515622496, 0.2518827111, 0.252523634, 0.2528440955, 0.253164557, 0.2541259414, 0.2550873257, 0.2554077872, 0.2570100945, 0.2576510175, 0.2579714789, 0.2586124019, 0.2608556321, 0.2618170165, 0.262137478, 0.2624579394, 0.2627784009, 0.2637397853, 0.2640602468, 0.2643807082, 0.2650216311, 0.2656625541, 0.266303477, 0.2669443999, 0.2682262458, 0.2685467073, 0.2701490146, 0.270469476, 0.271110399, 0.2714308604, 0.2717513219, 0.2720717834, 0.2723922448, 0.2727127063, 0.2733536292, 0.2743150136, 0.2749559365, 0.275276398, 0.2755968595, 0.2759173209, 0.2768787053, 0.2771991668, 0.2778400897, 0.2781605512, 0.2784810127, 0.2794423971, 0.2804037814, 0.2807242429, 0.2823265502, 0.2829674732, 0.2832879346, 0.2839288576, 0.2861720878, 0.2871334722, 0.2874539337, 0.2877743951, 0.2880948566, 0.289056241, 0.2893767025, 0.2896971639, 0.2903380868, 0.2909790098, 0.2916199327, 0.2922608556, 0.2935427015, 0.293863163, 0.2954654703, 0.2957859317, 0.2964268547, 0.2967473161, 0.2970677776, 0.2973882391, 0.2977087005, 0.298029162, 0.2986700849, 0.2996314693, 0.3002723922, 0.3005928537, 0.3009133152, 0.3012337766, 0.302195161, 0.3025156225, 0.3031565454, 0.3034770069, 0.3037974684, 0.3047588527, 0.3057202371, 0.3060406986, 0.3076430059, 0.3082839289, 0.3086043903, 0.3092453133, 0.3114885435, 0.3124499279, 0.3127703894, 0.3130908508, 0.3134113123, 0.3143726967, 0.3146931581, 0.3150136196, 0.3156545425, 0.3162954655, 0.3169363884, 0.3175773113, 0.3188591572, 0.3191796187, 0.320781926, 0.3211023874, 0.3217433104, 0.3220637718, 0.3223842333, 0.3227046948, 0.3230251562, 0.3233456177, 0.3239865406, 0.324947925, 0.3255888479, 0.3259093094, 0.3262297709, 0.3265502323, 0.3275116167, 0.3278320782, 0.3284730011, 0.3287934626, 0.3291139241, 0.3300753084, 0.3310366928, 0.3313571543, 0.3329594616, 0.3336003846, 0.333920846, 0.3345617689, 0.3368049992, 0.3377663836, 0.3380868451, 0.3384073065, 0.338727768, 0.3396891524, 0.3400096138, 0.3403300753, 0.3409709982, 0.3416119212, 0.3422528441, 0.342893767, 0.3441756129, 0.3444960743, 0.3460983817, 0.3464188431, 0.3470597661, 0.3473802275, 0.347700689, 0.3480211505, 0.3483416119, 0.3486620734, 0.3493029963, 0.3502643807, 0.3509053036, 0.3512257651, 0.3515462266, 0.351866688, 0.3528280724, 0.3531485339, 0.3537894568, 0.3541099183, 0.3544303797, 0.3553917641, 0.3563531485, 0.35667361, 0.3582759173, 0.3589168403, 0.3592373017, 0.3598782246, 0.3621214549, 0.3630828393, 0.3634033008, 0.3637237622, 0.3640442237, 0.3650056081, 0.3653260695, 0.365646531, 0.3662874539, 0.3669283769, 0.3675692998, 0.3682102227, 0.3694920686, 0.36981253, 0.3714148374, 0.3717352988, 0.3723762218, 0.3726966832, 0.3730171447, 0.3733376062, 0.3736580676, 0.3739785291, 0.374619452, 0.3755808364, 0.3762217593, 0.3765422208, 0.3768626823, 0.3771831437, 0.3781445281, 0.3784649896, 0.3791059125, 0.379426374, 0.3797468354, 0.3807082198, 0.3816696042, 0.3819900657, 0.383592373, 0.3842332959, 0.3845537574, 0.3851946803, 0.3874379106, 0.388399295, 0.3887197564, 0.3890402179, 0.3893606794, 0.3903220638, 0.3906425252, 0.3909629867, 0.3916039096, 0.3922448326, 0.3928857555, 0.3935266784, 0.3948085243, 0.3951289857, 0.3967312931, 0.3970517545, 0.3976926775, 0.3980131389, 0.3983336004, 0.3986540618, 0.3989745233, 0.3992949848, 0.3999359077, 0.4008972921, 0.401538215, 0.4018586765, 0.402179138, 0.4024995994, 0.4034609838, 0.4037814453, 0.4044223682, 0.4047428297, 0.4050632911, 0.4060246755, 0.4069860599, 0.4073065214, 0.4089088287, 0.4095497516, 0.4098702131, 0.410511136, 0.4127543663, 0.4137157507, 0.4140362121, 0.4143566736, 0.4146771351, 0.4156385195, 0.4159589809, 0.4162794424, 0.4169203653, 0.4175612883, 0.4182022112, 0.4188431341, 0.42012498, 0.4204454414, 0.4220477488, 0.4223682102, 0.4230091332, 0.4233295946, 0.4236500561, 0.4239705175, 0.424290979, 0.4246114405, 0.4252523634, 0.4262137478, 0.4268546707, 0.4271751322, 0.4274955937, 0.4278160551, 0.4287774395, 0.429097901, 0.4297388239, 0.4300592854, 0.4303797468, 0.4313411312, 0.4323025156, 0.4326229771, 0.4342252844, 0.4348662073, 0.4351866688, 0.4358275917, 0.438070822, 0.4390322064, 0.4393526678, 0.4396731293, 0.4399935908, 0.4409549752, 0.4412754366, 0.4415958981, 0.442236821, 0.442877744, 0.4435186669, 0.4441595898, 0.4454414357, 0.4457618971, 0.4473642045, 0.4476846659, 0.4483255888, 0.4486460503, 0.4489665118, 0.4492869732, 0.4496074347, 0.4499278962, 0.4505688191, 0.4515302035, 0.4521711264, 0.4524915879, 0.4528120494, 0.4531325108, 0.4540938952, 0.4544143567, 0.4550552796, 0.4553757411, 0.4556962025, 0.4566575869, 0.4576189713, 0.4579394328, 0.4595417401, 0.460182663, 0.4605031245, 0.4611440474, 0.4633872777, 0.4643486621, 0.4646691235, 0.464989585, 0.4653100465, 0.4662714309, 0.4665918923, 0.4669123538, 0.4675532767, 0.4681941996, 0.4688351226, 0.4694760455, 0.4707578914, 0.4710783528, 0.4726806602, 0.4730011216, 0.4736420445, 0.473962506, 0.4742829675, 0.4746034289, 0.4749238904, 0.4752443519, 0.4758852748, 0.4768466592, 0.4774875821, 0.4778080436, 0.478128505, 0.4784489665, 0.4794103509, 0.4797308124, 0.4803717353, 0.4806921968, 0.4810126582, 0.4819740426, 0.482935427, 0.4832558885, 0.4848581958, 0.4854991187, 0.4858195802, 0.4864605031, 0.4887037334, 0.4896651178, 0.4899855792, 0.4903060407, 0.4906265022, 0.4915878866, 0.491908348, 0.4922288095, 0.4928697324, 0.4935106553, 0.4941515783, 0.4947925012, 0.4960743471, 0.4963948085, 0.4979971158, 0.4983175773, 0.4989585002, 0.4992789617, 0.4995994232, 0.4999198846, 0.5002403461, 0.5005608076, 0.5012017305, 0.5021631149, 0.5028040378, 0.5031244993, 0.5034449607, 0.5037654222, 0.5047268066, 0.5050472681, 0.505688191, 0.5060086525, 0.5063291139, 0.5072904983, 0.5082518827, 0.5085723442, 0.5101746515, 0.5108155744, 0.5111360359, 0.5117769588, 0.5140201891, 0.5149815735, 0.5153020349, 0.5156224964, 0.5159429579, 0.5169043423, 0.5172248037, 0.5175452652, 0.5181861881, 0.518827111, 0.519468034, 0.5201089569, 0.5213908028, 0.5217112642, 0.5233135715, 0.523634033, 0.5242749559, 0.5245954174, 0.5249158789, 0.5252363403, 0.5255568018, 0.5258772633, 0.5265181862, 0.5274795706, 0.5281204935, 0.528440955, 0.5287614164, 0.5290818779, 0.5300432623, 0.5303637238, 0.5310046467, 0.5313251082, 0.5316455696, 0.532606954, 0.5335683384, 0.5338887999, 0.5354911072, 0.5361320301, 0.5364524916, 0.5370934145, 0.5393366448, 0.5402980292, 0.5406184906, 0.5409389521, 0.5412594136, 0.5422207979, 0.5425412594, 0.5428617209, 0.5435026438, 0.5441435667, 0.5447844897, 0.5454254126, 0.5467072585, 0.5470277199, 0.5486300272, 0.5489504887, 0.5495914116, 0.5499118731, 0.5502323346, 0.550552796, 0.5508732575, 0.551193719, 0.5518346419, 0.5527960263, 0.5534369492, 0.5537574107, 0.5540778721, 0.5543983336, 0.555359718, 0.5556801795, 0.5563211024, 0.5566415639, 0.5569620253, 0.5579234097, 0.5588847941, 0.5592052556, 0.5608075629, 0.5614484858, 0.5617689473, 0.5624098702, 0.5646531005, 0.5656144849, 0.5659349463, 0.5662554078, 0.5665758693, 0.5675372536, 0.5678577151, 0.5681781766, 0.5688190995, 0.5694600224, 0.5701009454, 0.5707418683, 0.5720237141, 0.5723441756, 0.5739464829, 0.5742669444, 0.5749078673, 0.5752283288, 0.5755487903, 0.5758692517, 0.5761897132, 0.5765101747, 0.5771510976, 0.578112482, 0.5787534049, 0.5790738664, 0.5793943278, 0.5797147893, 0.5806761737, 0.5809966352, 0.5816375581, 0.5819580195, 0.582278481, 0.5832398654, 0.5842012498, 0.5845217113, 0.5861240186, 0.5867649415, 0.587085403, 0.5877263259, 0.5899695562, 0.5909309406, 0.591251402, 0.5915718635, 0.5918923249, 0.5928537093, 0.5931741708, 0.5934946323, 0.5941355552, 0.5947764781, 0.5954174011, 0.596058324, 0.5973401698, 0.5976606313, 0.5992629386, 0.5995834001, 0.600224323, 0.6005447845, 0.600865246, 0.6011857074, 0.6015061689, 0.6018266303, 0.6024675533, 0.6034289377, 0.6040698606, 0.6043903221, 0.6047107835, 0.605031245, 0.6059926294, 0.6063130909, 0.6069540138, 0.6072744752, 0.6075949367, 0.6085563211, 0.6095177055, 0.609838167, 0.6114404743, 0.6120813972, 0.6124018587, 0.6130427816, 0.6152860119, 0.6162473963, 0.6165678577, 0.6168883192, 0.6172087806, 0.618170165, 0.6184906265, 0.618811088, 0.6194520109, 0.6200929338, 0.6207338568, 0.6213747797, 0.6226566255, 0.622977087, 0.6245793943, 0.6248998558, 0.6255407787, 0.6258612402, 0.6261817017, 0.6265021631, 0.6268226246, 0.627143086, 0.627784009, 0.6287453934, 0.6293863163, 0.6297067778, 0.6300272392, 0.6303477007, 0.6313090851, 0.6316295465, 0.6322704695, 0.6325909309, 0.6329113924, 0.6338727768, 0.6348341612, 0.6351546227, 0.63675693, 0.6373978529, 0.6377183144, 0.6383592373, 0.6406024676, 0.6415638519, 0.6418843134, 0.6422047749, 0.6425252363, 0.6434866207, 0.6438070822, 0.6441275437, 0.6447684666, 0.6454093895, 0.6460503125, 0.6466912354, 0.6479730812, 0.6482935427, 0.64989585, 0.6502163115, 0.6508572344, 0.6511776959, 0.6514981573, 0.6518186188, 0.6521390803, 0.6524595417, 0.6531004647, 0.6540618491, 0.654702772, 0.6550232335, 0.6553436949, 0.6556641564, 0.6566255408, 0.6569460022, 0.6575869252, 0.6579073866, 0.6582278481, 0.6591892325, 0.6601506169, 0.6604710784, 0.6620733857, 0.6627143086, 0.6630347701, 0.663675693, 0.6659189232, 0.6668803076, 0.6672007691, 0.6675212306, 0.667841692, 0.6688030764, 0.6691235379, 0.6694439994, 0.6700849223, 0.6707258452, 0.6713667681, 0.6720076911, 0.6732895369, 0.6736099984, 0.6752123057, 0.6755327672, 0.6761736901, 0.6764941516, 0.676814613, 0.6771350745, 0.677455536, 0.6777759974, 0.6784169204, 0.6793783048, 0.6800192277, 0.6803396892, 0.6806601506, 0.6809806121, 0.6819419965, 0.6822624579, 0.6829033809, 0.6832238423, 0.6835443038, 0.6845056882, 0.6854670726, 0.685787534, 0.6873898414, 0.6880307643, 0.6883512258, 0.6889921487, 0.6912353789, 0.6921967633, 0.6925172248, 0.6928376863, 0.6931581477, 0.6941195321, 0.6944399936, 0.6947604551, 0.695401378, 0.6960423009, 0.6966832238, 0.6973241468, 0.6986059926, 0.6989264541, 0.7005287614, 0.7008492229, 0.7014901458, 0.7018106073, 0.7021310687, 0.7024515302, 0.7027719917, 0.7030924531, 0.7037333761, 0.7046947605, 0.7053356834, 0.7056561448, 0.7059766063, 0.7062970678, 0.7072584522, 0.7075789136, 0.7082198366, 0.708540298, 0.7088607595, 0.7098221439, 0.7107835283, 0.7111039897, 0.7127062971, 0.71334722, 0.7136676815, 0.7143086044, 0.7165518346, 0.717513219, 0.7178336805, 0.718154142, 0.7184746034, 0.7194359878, 0.7197564493, 0.7200769108, 0.7207178337, 0.7213587566, 0.7219996795, 0.7226406025, 0.7239224483, 0.7242429098, 0.7258452171, 0.7261656786, 0.7268066015, 0.727127063, 0.7274475244, 0.7277679859, 0.7280884474, 0.7284089088, 0.7290498318, 0.7300112162, 0.7306521391, 0.7309726005, 0.731293062, 0.7316135235, 0.7325749079, 0.7328953693, 0.7335362923, 0.7338567537, 0.7341772152, 0.7351385996, 0.736099984, 0.7364204454, 0.7380227528, 0.7386636757, 0.7389841372, 0.7396250601, 0.7418682903, 0.7428296747, 0.7431501362, 0.7434705977, 0.7437910591, 0.7447524435, 0.745072905, 0.7453933664, 0.7460342894, 0.7466752123, 0.7473161352, 0.7479570582, 0.749238904, 0.7495593655, 0.7511616728, 0.7514821343, 0.7521230572, 0.7524435187, 0.7527639801, 0.7530844416, 0.7534049031, 0.7537253645, 0.7543662875, 0.7553276718, 0.7559685948, 0.7562890562, 0.7566095177, 0.7569299792, 0.7578913636, 0.758211825, 0.758852748, 0.7591732094, 0.7594936709, 0.7604550553, 0.7614164397, 0.7617369011, 0.7633392085, 0.7639801314, 0.7643005929, 0.7649415158, 0.767184746, 0.7681461304, 0.7684665919, 0.7687870534, 0.7691075148, 0.7700688992, 0.7703893607, 0.7707098221, 0.7713507451, 0.771991668, 0.7726325909, 0.7732735139, 0.7745553597, 0.7748758212, 0.7764781285, 0.77679859, 0.7774395129, 0.7777599744, 0.7780804358, 0.7784008973, 0.7787213588, 0.7790418202, 0.7796827432, 0.7806441275, 0.7812850505, 0.7816055119, 0.7819259734, 0.7822464349, 0.7832078193, 0.7835282807, 0.7841692037, 0.7844896651, 0.7848101266, 0.785771511, 0.7867328954, 0.7870533568, 0.7886556642, 0.7892965871, 0.7896170486, 0.7902579715, 0.7925012017, 0.7934625861, 0.7937830476, 0.7941035091, 0.7944239705, 0.7953853549, 0.7957058164, 0.7960262778, 0.7966672008, 0.7973081237, 0.7979490466, 0.7985899696, 0.7998718154, 0.8001922769, 0.8017945842, 0.8021150457, 0.8027559686, 0.8030764301, 0.8033968915, 0.803717353, 0.8040378145, 0.8043582759, 0.8049991988, 0.8059605832, 0.8066015062, 0.8069219676, 0.8072424291, 0.8075628906, 0.808524275, 0.8088447364, 0.8094856593, 0.8098061208, 0.8101265823, 0.8110879667, 0.8120493511, 0.8123698125, 0.8139721199, 0.8146130428, 0.8149335042, 0.8155744272, 0.8178176574, 0.8187790418, 0.8190995033, 0.8194199647, 0.8197404262, 0.8207018106, 0.8210222721, 0.8213427335, 0.8219836565, 0.8226245794, 0.8232655023, 0.8239064253, 0.8251882711, 0.8255087326, 0.8271110399, 0.8274315014, 0.8280724243, 0.8283928858, 0.8287133472, 0.8290338087, 0.8293542701, 0.8296747316, 0.8303156545, 0.8312770389, 0.8319179619, 0.8322384233, 0.8325588848, 0.8328793463, 0.8338407307, 0.8341611921, 0.834802115, 0.8351225765, 0.835443038, 0.8364044224, 0.8373658068, 0.8376862682, 0.8392885755, 0.8399294985, 0.8402499599, 0.8408908829, 0.8431341131, 0.8440954975, 0.844415959, 0.8447364204, 0.8450568819, 0.8460182663, 0.8463387278, 0.8466591892, 0.8473001122, 0.8479410351, 0.848581958, 0.8492228809, 0.8505047268, 0.8508251883, 0.8524274956, 0.8527479571, 0.85338888, 0.8537093415, 0.8540298029, 0.8543502644, 0.8546707258, 0.8549911873, 0.8556321102, 0.8565934946, 0.8572344176, 0.857554879, 0.8578753405, 0.858195802, 0.8591571863, 0.8594776478, 0.8601185707, 0.8604390322, 0.8607594937, 0.8617208781, 0.8626822625, 0.8630027239, 0.8646050312, 0.8652459542, 0.8655664156, 0.8662073386, 0.8684505688, 0.8694119532, 0.8697324147, 0.8700528761, 0.8703733376, 0.871334722, 0.8716551835, 0.8719756449, 0.8726165679, 0.8732574908, 0.8738984137, 0.8745393366, 0.8758211825, 0.876141644, 0.8777439513, 0.8780644128, 0.8787053357, 0.8790257971, 0.8793462586, 0.8796667201, 0.8799871815, 0.880307643, 0.8809485659, 0.8819099503, 0.8825508733, 0.8828713347, 0.8831917962, 0.8835122577, 0.884473642, 0.8847941035, 0.8854350264, 0.8857554879, 0.8860759494, 0.8870373338, 0.8879987182, 0.8883191796, 0.8899214869, 0.8905624099, 0.8908828713, 0.8915237943, 0.8937670245, 0.8947284089, 0.8950488704, 0.8953693318, 0.8956897933, 0.8966511777, 0.8969716392, 0.8972921006, 0.8979330236, 0.8985739465, 0.8992148694, 0.8998557923, 0.9011376382, 0.9014580997, 0.903060407, 0.9033808685, 0.9040217914, 0.9043422528, 0.9046627143, 0.9049831758, 0.9053036372, 0.9056240987, 0.9062650216, 0.907226406, 0.907867329, 0.9081877904, 0.9085082519, 0.9088287133, 0.9097900977, 0.9101105592, 0.9107514821, 0.9110719436, 0.9113924051, 0.9123537895, 0.9133151739, 0.9136356353, 0.9152379426, 0.9158788656, 0.916199327, 0.91684025, 0.9190834802, 0.9200448646, 0.9203653261, 0.9206857875, 0.921006249, 0.9219676334, 0.9222880949, 0.9226085563, 0.9232494793, 0.9238904022, 0.9245313251, 0.925172248, 0.9264540939, 0.9267745554, 0.9283768627, 0.9286973241, 0.9293382471, 0.9296587085, 0.92997917, 0.9302996315, 0.9306200929, 0.9309405544, 0.9315814773, 0.9325428617, 0.9331837847, 0.9335042461, 0.9338247076, 0.934145169, 0.9351065534, 0.9354270149, 0.9360679378, 0.9363883993, 0.9367088608, 0.9376702452, 0.9386316295, 0.938952091, 0.9405543983, 0.9411953213, 0.9415157827, 0.9421567057, 0.9443999359, 0.9453613203, 0.9456817818, 0.9460022432, 0.9463227047, 0.9472840891, 0.9476045506, 0.947925012, 0.9485659349, 0.9492068579, 0.9498477808, 0.9504887037, 0.9517705496, 0.9520910111, 0.9536933184, 0.9540137798, 0.9546547028, 0.9549751642, 0.9552956257, 0.9556160872, 0.9559365486, 0.9562570101, 0.956897933, 0.9578593174, 0.9585002403, 0.9588207018, 0.9591411633, 0.9594616247, 0.9604230091, 0.9607434706, 0.9613843935, 0.961704855, 0.9620253165, 0.9629867008, 0.9639480852, 0.9642685467, 0.965870854, 0.966511777, 0.9668322384, 0.9674731614, 0.9697163916, 0.970677776, 0.9709982375, 0.9713186989, 0.9716391604, 0.9726005448, 0.9729210062, 0.9732414677, 0.9738823906, 0.9745233136, 0.9751642365, 0.9758051594, 0.9770870053, 0.9774074668, 0.9790097741, 0.9793302355, 0.9799711585, 0.9802916199, 0.9806120814, 0.9809325429, 0.9812530043, 0.9815734658, 0.9822143887, 0.9831757731, 0.983816696, 0.9841371575, 0.984457619, 0.9847780804, 0.9857394648, 0.9860599263, 0.9867008492, 0.9870213107, 0.9873417722, 0.9883031565, 0.9892645409, 0.9895850024, 0.9911873097, 0.9918282327, 0.9921486941, 0.992789617, 0.9950328473, 0.9959942317, 0.9963146932, 0.9966351546, 0.9969556161, 0.9979170005, 0.9982374619, 0.9985579234, 0.9991988463, 0.9998397693]
pattern_odd=[0.0004806922, 0.00112161513, 0.00240346098, 0.00272392245, 0.00432622977, 0.00464669124, 0.00528761416, 0.00560807563, 0.00592853709, 0.00624899856, 0.00656946002, 0.00688992149, 0.00753084442, 0.00849222881, 0.00913315174, 0.0094536132, 0.00977407467, 0.01009453613, 0.01105592053, 0.01137638199, 0.01201730492, 0.01233776638, 0.01265822785, 0.01361961224, 0.01458099664, 0.0149014581, 0.01650376542, 0.01714468835, 0.01746514982, 0.01810607275, 0.020349303, 0.02131068739, 0.02163114885, 0.02195161032, 0.02227207178, 0.02323345618, 0.02355391764, 0.02387437911, 0.02451530204, 0.02515622496, 0.02579714789, 0.02643807082, 0.02771991668, 0.02804037815, 0.02964268547, 0.02996314693, 0.03060406986, 0.03092453133, 0.03124499279, 0.03156545425, 0.03188591572, 0.03220637718, 0.03284730011, 0.03380868451, 0.03444960744, 0.0347700689, 0.03509053036, 0.03541099183, 0.03637237622, 0.03669283769, 0.03733376062, 0.03765422208, 0.03797468354, 0.03893606794, 0.03989745233, 0.0402179138, 0.04182022112, 0.04246114405, 0.04278160551, 0.04342252844, 0.04566575869, 0.04662714309, 0.04694760455, 0.04726806602, 0.04758852748, 0.04854991187, 0.04887037334, 0.0491908348, 0.04983175773, 0.05047268066, 0.05111360359, 0.05175452652, 0.05303637238, 0.05335683384, 0.05495914116, 0.05527960263, 0.05592052556, 0.05624098702, 0.05656144849, 0.05688190995, 0.05720237142, 0.05752283288, 0.05816375581, 0.0591251402, 0.05976606313, 0.0600865246, 0.06040698606, 0.06072744752, 0.06168883192, 0.06200929338, 0.06265021631, 0.06297067778, 0.06329113924, 0.06425252363, 0.06521390803, 0.06553436949, 0.06713667682, 0.06777759974, 0.06809806121, 0.06873898414, 0.07098221439, 0.07194359878, 0.07226406025, 0.07258452171, 0.07290498318, 0.07386636757, 0.07418682903, 0.0745072905, 0.07514821343, 0.07578913636, 0.07643005929, 0.07707098221, 0.07835282807, 0.07867328954, 0.08027559686, 0.08059605832, 0.08123698125, 0.08155744272, 0.08187790418, 0.08219836565, 0.08251882711, 0.08283928858, 0.08348021151, 0.0844415959, 0.08508251883, 0.08540298029, 0.08572344176, 0.08604390322, 0.08700528761, 0.08732574908, 0.08796667201, 0.08828713347, 0.08860759494, 0.08956897933, 0.09053036372, 0.09085082519, 0.09245313251, 0.09309405544, 0.0934145169, 0.09405543983, 0.09629867009, 0.09726005448, 0.09758051594, 0.09790097741, 0.09822143887, 0.09918282327, 0.09950328473, 0.0998237462, 0.10046466912, 0.10110559205, 0.10174651498, 0.10238743791, 0.10366928377, 0.10398974523, 0.10559205256, 0.10591251402, 0.10655343695, 0.10687389841, 0.10719435988, 0.10751482134, 0.10783528281, 0.10815574427, 0.1087966672, 0.10975805159, 0.11039897452, 0.11071943599, 0.11103989745, 0.11136035892, 0.11232174331, 0.11264220478, 0.1132831277, 0.11360358917, 0.11392405063, 0.11488543503, 0.11584681942, 0.11616728088, 0.11776958821, 0.11841051114, 0.1187309726, 0.11937189553, 0.12161512578, 0.12257651018, 0.12289697164, 0.1232174331, 0.12353789457, 0.12449927896, 0.12481974043, 0.12514020189, 0.12578112482, 0.12642204775, 0.12706297068, 0.12770389361, 0.12898573947, 0.12930620093, 0.13090850825, 0.13122896972, 0.13186989265, 0.13219035411, 0.13251081557, 0.13283127704, 0.1331517385, 0.13347219997, 0.1341131229, 0.13507450729, 0.13571543022, 0.13603589168, 0.13635635315, 0.13667681461, 0.13763819901, 0.13795866047, 0.1385995834, 0.13892004487, 0.13924050633, 0.14020189072, 0.14116327512, 0.14148373658, 0.1430860439, 0.14372696683, 0.1440474283, 0.14468835123, 0.14693158148, 0.14789296587, 0.14821342734, 0.1485338888, 0.14885435026, 0.14981573466, 0.15013619612, 0.15045665759, 0.15109758052, 0.15173850345, 0.15237942637, 0.1530203493, 0.15430219516, 0.15462265663, 0.15622496395, 0.15654542541, 0.15718634834, 0.15750680981, 0.15782727127, 0.15814773274, 0.1584681942, 0.15878865566, 0.15942957859, 0.16039096299, 0.16103188592, 0.16135234738, 0.16167280885, 0.16199327031, 0.1629546547, 0.16327511617, 0.1639160391, 0.16423650056, 0.16455696203, 0.16551834642, 0.16647973081, 0.16680019228, 0.1684024996, 0.16904342253, 0.16936388399, 0.17000480692, 0.17224803717, 0.17320942157, 0.17352988303, 0.1738503445, 0.17417080596, 0.17513219035, 0.17545265182, 0.17577311328, 0.17641403621, 0.17705495914, 0.17769588207, 0.178336805, 0.17961865086, 0.17993911232, 0.18154141964, 0.18186188111, 0.18250280404, 0.1828232655, 0.18314372697, 0.18346418843, 0.1837846499, 0.18410511136, 0.18474603429, 0.18570741868, 0.18634834161, 0.18666880308, 0.18698926454, 0.18730972601, 0.1882711104, 0.18859157186, 0.18923249479, 0.18955295626, 0.18987341772, 0.19083480212, 0.19179618651, 0.19211664797, 0.1937189553, 0.19435987823, 0.19468033969, 0.19532126262, 0.19756449287, 0.19852587726, 0.19884633873, 0.19916680019, 0.19948726166, 0.20044864605, 0.20076910752, 0.20108956898, 0.20173049191, 0.20237141484, 0.20301233777, 0.2036532607, 0.20493510655, 0.20525556802, 0.20685787534, 0.20717833681, 0.20781925973, 0.2081397212, 0.20846018266, 0.20878064413, 0.20910110559, 0.20942156706, 0.21006248999, 0.21102387438, 0.21166479731, 0.21198525877, 0.21230572024, 0.2126261817, 0.2135875661, 0.21390802756, 0.21454895049, 0.21486941195, 0.21518987342, 0.21615125781, 0.21711264221, 0.21743310367, 0.21903541099, 0.21967633392, 0.21999679539, 0.22063771831, 0.22288094857, 0.22384233296, 0.22416279442, 0.22448325589, 0.22480371735, 0.22576510175, 0.22608556321, 0.22640602468, 0.22704694761, 0.22768787053, 0.22832879346, 0.22896971639, 0.23025156225, 0.23057202371, 0.23217433104, 0.2324947925, 0.23313571543, 0.2334561769, 0.23377663836, 0.23409709982, 0.23441756129, 0.23473802275, 0.23537894568, 0.23634033008, 0.236981253, 0.23730171447, 0.23762217593, 0.2379426374, 0.23890402179, 0.23922448326, 0.23986540619, 0.24018586765, 0.24050632911, 0.24146771351, 0.2424290979, 0.24274955937, 0.24435186669, 0.24499278962, 0.24531325108, 0.24595417401, 0.24819740426, 0.24915878866, 0.24947925012, 0.24979971159, 0.25012017305, 0.25108155744, 0.25140201891, 0.25172248037, 0.2523634033, 0.25300432623, 0.25364524916, 0.25428617209, 0.25556801795, 0.25588847941, 0.25749078673, 0.2578112482, 0.25845217113, 0.25877263259, 0.25909309406, 0.25941355552, 0.25973401698, 0.26005447845, 0.26069540138, 0.26165678577, 0.2622977087, 0.26261817017, 0.26293863163, 0.26325909309, 0.26422047749, 0.26454093895, 0.26518186188, 0.26550232335, 0.26582278481, 0.2667841692, 0.2677455536, 0.26806601506, 0.26966832238, 0.27030924531, 0.27062970678, 0.27127062971, 0.27351385996, 0.27447524435, 0.27479570582, 0.27511616728, 0.27543662875, 0.27639801314, 0.2767184746, 0.27703893607, 0.277679859, 0.27832078193, 0.27896170486, 0.27960262778, 0.28088447364, 0.28120493511, 0.28280724243, 0.28312770389, 0.28376862682, 0.28408908829, 0.28440954975, 0.28473001122, 0.28505047268, 0.28537093415, 0.28601185707, 0.28697324147, 0.2876141644, 0.28793462586, 0.28825508733, 0.28857554879, 0.28953693318, 0.28985739465, 0.29049831758, 0.29081877904, 0.29113924051, 0.2921006249, 0.29306200929, 0.29338247076, 0.29498477808, 0.29562570101, 0.29594616247, 0.2965870854, 0.29883031566, 0.29979170005, 0.30011216151, 0.30043262298, 0.30075308444, 0.30171446884, 0.3020349303, 0.30235539176, 0.30299631469, 0.30363723762, 0.30427816055, 0.30491908348, 0.30620092934, 0.3065213908, 0.30812369813, 0.30844415959, 0.30908508252, 0.30940554398, 0.30972600545, 0.31004646691, 0.31036692838, 0.31068738984, 0.31132831277, 0.31228969716, 0.31293062009, 0.31325108156, 0.31357154302, 0.31389200449, 0.31485338888, 0.31517385034, 0.31581477327, 0.31613523474, 0.3164556962, 0.3174170806, 0.31837846499, 0.31869892645, 0.32030123378, 0.32094215671, 0.32126261817, 0.3219035411, 0.32414677135, 0.32510815574, 0.32542861721, 0.32574907867, 0.32606954014, 0.32703092453, 0.327351386, 0.32767184746, 0.32831277039, 0.32895369332, 0.32959461625, 0.33023553918, 0.33151738503, 0.3318378465, 0.33344015382, 0.33376061529, 0.33440153822, 0.33472199968, 0.33504246114, 0.33536292261, 0.33568338407, 0.33600384554, 0.33664476847, 0.33760615286, 0.33824707579, 0.33856753725, 0.33888799872, 0.33920846018, 0.34016984458, 0.34049030604, 0.34113122897, 0.34145169043, 0.3417721519, 0.34273353629, 0.34369492069, 0.34401538215, 0.34561768947, 0.3462586124, 0.34657907387, 0.3472199968, 0.34946322705, 0.35042461144, 0.35074507291, 0.35106553437, 0.35138599583, 0.35234738023, 0.35266784169, 0.35298830316, 0.35362922609, 0.35427014902, 0.35491107194, 0.35555199487, 0.35683384073, 0.3571543022, 0.35875660952, 0.35907707098, 0.35971799391, 0.36003845538, 0.36035891684, 0.36067937831, 0.36099983977, 0.36132030123, 0.36196122416, 0.36292260856, 0.36356353149, 0.36388399295, 0.36420445441, 0.36452491588, 0.36548630027, 0.36580676174, 0.36644768467, 0.36676814613, 0.3670886076, 0.36804999199, 0.36901137638, 0.36933183785, 0.37093414517, 0.3715750681, 0.37189552956, 0.37253645249, 0.37477968274, 0.37574106714, 0.3760615286, 0.37638199007, 0.37670245153, 0.37766383592, 0.37798429739, 0.37830475885, 0.37894568178, 0.37958660471, 0.38022752764, 0.38086845057, 0.38215029643, 0.38247075789, 0.38407306521, 0.38439352668, 0.38503444961, 0.38535491107, 0.38567537254, 0.385995834, 0.38631629547, 0.38663675693, 0.38727767986, 0.38823906425, 0.38887998718, 0.38920044865, 0.38952091011, 0.38984137158, 0.39080275597, 0.39112321743, 0.39176414036, 0.39208460183, 0.39240506329, 0.39336644769, 0.39432783208, 0.39464829354, 0.39625060087, 0.39689152379, 0.39721198526, 0.39785290819, 0.40009613844, 0.40105752283, 0.4013779843, 0.40169844576, 0.40201890723, 0.40298029162, 0.40330075308, 0.40362121455, 0.40426213748, 0.40490306041, 0.40554398334, 0.40618490627, 0.40746675212, 0.40778721359, 0.40938952091, 0.40970998238, 0.4103509053, 0.41067136677, 0.41099182823, 0.4113122897, 0.41163275116, 0.41195321263, 0.41259413556, 0.41355551995, 0.41419644288, 0.41451690434, 0.41483736581, 0.41515782727, 0.41611921167, 0.41643967313, 0.41708059606, 0.41740105752, 0.41772151899, 0.41868290338, 0.41964428777, 0.41996474924, 0.42156705656, 0.42220797949, 0.42252844096, 0.42316936388, 0.42541259414, 0.42637397853, 0.42669443999, 0.42701490146, 0.42733536292, 0.42829674732, 0.42861720878, 0.42893767025, 0.42957859317, 0.4302195161, 0.43086043903, 0.43150136196, 0.43278320782, 0.43310366928, 0.43470597661, 0.43502643807, 0.435667361, 0.43598782246, 0.43630828393, 0.43662874539, 0.43694920686, 0.43726966832, 0.43791059125, 0.43887197565, 0.43951289857, 0.43983336004, 0.4401538215, 0.44047428297, 0.44143566736, 0.44175612883, 0.44239705176, 0.44271751322, 0.44303797468, 0.44399935908, 0.44496074347, 0.44528120494, 0.44688351226, 0.44752443519, 0.44784489665, 0.44848581958, 0.45072904983, 0.45169043423, 0.45201089569, 0.45233135715, 0.45265181862, 0.45361320301, 0.45393366448, 0.45425412594, 0.45489504887, 0.4555359718, 0.45617689473, 0.45681781766, 0.45809966352, 0.45842012498, 0.4600224323, 0.46034289377, 0.4609838167, 0.46130427816, 0.46162473963, 0.46194520109, 0.46226566255, 0.46258612402, 0.46322704695, 0.46418843134, 0.46482935427, 0.46514981574, 0.4654702772, 0.46579073866, 0.46675212306, 0.46707258452, 0.46771350745, 0.46803396892, 0.46835443038, 0.46931581477, 0.47027719917, 0.47059766063, 0.47219996795, 0.47284089088, 0.47316135235, 0.47380227528, 0.47604550553, 0.47700688992, 0.47732735139, 0.47764781285, 0.47796827432, 0.47892965871, 0.47925012017, 0.47957058164, 0.48021150457, 0.4808524275, 0.48149335043, 0.48213427335, 0.48341611921, 0.48373658068, 0.485338888, 0.48565934946, 0.48630027239, 0.48662073386, 0.48694119532, 0.48726165679, 0.48758211825, 0.48790257972, 0.48854350264, 0.48950488704, 0.49014580997, 0.49046627143, 0.4907867329, 0.49110719436, 0.49206857875, 0.49238904022, 0.49302996315, 0.49335042461, 0.49367088608, 0.49463227047, 0.49559365486, 0.49591411633, 0.49751642365, 0.49815734658, 0.49847780804, 0.49911873097, 0.50136196122, 0.50232334562, 0.50264380708, 0.50296426855, 0.50328473001, 0.50424611441, 0.50456657587, 0.50488703733, 0.50552796026, 0.50616888319, 0.50680980612, 0.50745072905, 0.50873257491, 0.50905303637, 0.5106553437, 0.51097580516, 0.51161672809, 0.51193718955, 0.51225765102, 0.51257811248, 0.51289857395, 0.51321903541, 0.51385995834, 0.51482134273, 0.51546226566, 0.51578272713, 0.51610318859, 0.51642365006, 0.51738503445, 0.51770549591, 0.51834641884, 0.51866688031, 0.51898734177, 0.51994872617, 0.52091011056, 0.52123057202, 0.52283287935, 0.52347380228, 0.52379426374, 0.52443518667, 0.52667841692, 0.52763980131, 0.52796026278, 0.52828072424, 0.52860118571, 0.5295625701, 0.52988303157, 0.53020349303, 0.53084441596, 0.53148533889, 0.53212626182, 0.53276718475, 0.5340490306, 0.53436949207, 0.53597179939, 0.53629226086, 0.53693318379, 0.53725364525, 0.53757410671, 0.53789456818, 0.53821502964, 0.53853549111, 0.53917641404, 0.54013779843, 0.54077872136, 0.54109918282, 0.54141964429, 0.54174010575, 0.54270149015, 0.54302195161, 0.54366287454, 0.543983336, 0.54430379747, 0.54526518186, 0.54622656626, 0.54654702772, 0.54814933504, 0.54879025797, 0.54911071944, 0.54975164237, 0.55199487262, 0.55295625701, 0.55327671848, 0.55359717994, 0.5539176414, 0.5548790258, 0.55519948726, 0.55551994873, 0.55616087166, 0.55680179458, 0.55744271751, 0.55808364044, 0.5593654863, 0.55968594777, 0.56128825509, 0.56160871655, 0.56224963948, 0.56257010095, 0.56289056241, 0.56321102387, 0.56353148534, 0.5638519468, 0.56449286973, 0.56545425413, 0.56609517706, 0.56641563852, 0.56673609998, 0.56705656145, 0.56801794584, 0.56833840731, 0.56897933024, 0.5692997917, 0.56962025317, 0.57058163756, 0.57154302195, 0.57186348342, 0.57346579074, 0.57410671367, 0.57442717513, 0.57506809806, 0.57731132831, 0.57827271271, 0.57859317417, 0.57891363564, 0.5792340971, 0.58019548149, 0.58051594296, 0.58083640442, 0.58147732735, 0.58211825028, 0.58275917321, 0.58340009614, 0.584681942, 0.58500240346, 0.58660471078, 0.58692517225, 0.58756609518, 0.58788655664, 0.58820701811, 0.58852747957, 0.58884794104, 0.5891684025, 0.58980932543, 0.59077070982, 0.59141163275, 0.59173209422, 0.59205255568, 0.59237301715, 0.59333440154, 0.593654863, 0.59429578593, 0.5946162474, 0.59493670886, 0.59589809325, 0.59685947765, 0.59717993911, 0.59878224644, 0.59942316936, 0.59974363083, 0.60038455376, 0.60262778401, 0.6035891684, 0.60390962987, 0.60423009133, 0.6045505528, 0.60551193719, 0.60583239865, 0.60615286012, 0.60679378305, 0.60743470598, 0.60807562891, 0.60871655184, 0.60999839769, 0.61031885916, 0.61192116648, 0.61224162794, 0.61288255087, 0.61320301234, 0.6135234738, 0.61384393527, 0.61416439673, 0.6144848582, 0.61512578113, 0.61608716552, 0.61672808845, 0.61704854991, 0.61736901138, 0.61768947284, 0.61865085723, 0.6189713187, 0.61961224163, 0.61993270309, 0.62025316456, 0.62121454895, 0.62217593334, 0.62249639481, 0.62409870213, 0.62473962506, 0.62506008653, 0.62570100945, 0.62794423971, 0.6289056241, 0.62922608556, 0.62954654703, 0.62986700849, 0.63082839289, 0.63114885435, 0.63146931582, 0.63211023874, 0.63275116167, 0.6333920846, 0.63403300753, 0.63531485339, 0.63563531485, 0.63723762218, 0.63755808364, 0.63819900657, 0.63851946803, 0.6388399295, 0.63916039096, 0.63948085243, 0.63980131389, 0.64044223682, 0.64140362122, 0.64204454414, 0.64236500561, 0.64268546707, 0.64300592854, 0.64396731293, 0.6442877744, 0.64492869732, 0.64524915879, 0.64556962025, 0.64653100465, 0.64749238904, 0.64781285051, 0.64941515783, 0.65005608076, 0.65037654222, 0.65101746515, 0.6532606954, 0.6542220798, 0.65454254126, 0.65486300272, 0.65518346419, 0.65614484858, 0.65646531005, 0.65678577151, 0.65742669444, 0.65806761737, 0.6587085403, 0.65934946323, 0.66063130909, 0.66095177055, 0.66255407787, 0.66287453934, 0.66351546227, 0.66383592373, 0.6641563852, 0.66447684666, 0.66479730812, 0.66511776959, 0.66575869252, 0.66672007691, 0.66736099984, 0.6676814613, 0.66800192277, 0.66832238423, 0.66928376863, 0.66960423009, 0.67024515302, 0.67056561449, 0.67088607595, 0.67184746034, 0.67280884474, 0.6731293062, 0.67473161352, 0.67537253645, 0.67569299792, 0.67633392085, 0.6785771511, 0.67953853549, 0.67985899696, 0.68017945842, 0.68049991989, 0.68146130428, 0.68178176574, 0.68210222721, 0.68274315014, 0.68338407307, 0.68402499599, 0.68466591892, 0.68594776478, 0.68626822625, 0.68787053357, 0.68819099503, 0.68883191796, 0.68915237943, 0.68947284089, 0.68979330236, 0.69011376382, 0.69043422528, 0.69107514821, 0.69203653261, 0.69267745554, 0.692997917, 0.69331837847, 0.69363883993, 0.69460022432, 0.69492068579, 0.69556160872, 0.69588207018, 0.69620253165, 0.69716391604, 0.69812530043, 0.6984457619, 0.70004806922, 0.70068899215, 0.70100945361, 0.70165037654, 0.70389360679, 0.70485499119, 0.70517545265, 0.70549591412, 0.70581637558, 0.70677775997, 0.70709822144, 0.7074186829, 0.70805960583, 0.70870052876, 0.70934145169, 0.70998237462, 0.71126422048, 0.71158468194, 0.71318698927, 0.71350745073, 0.71414837366, 0.71446883512, 0.71478929659, 0.71510975805, 0.71543021952, 0.71575068098, 0.71639160391, 0.7173529883, 0.71799391123, 0.7183143727, 0.71863483416, 0.71895529563, 0.71991668002, 0.72023714148, 0.72087806441, 0.72119852588, 0.72151898734, 0.72248037174, 0.72344175613, 0.72376221759, 0.72536452492, 0.72600544785, 0.72632590931, 0.72696683224, 0.72921006249, 0.73017144688, 0.73049190835, 0.73081236981, 0.73113283128, 0.73209421567, 0.73241467714, 0.7327351386, 0.73337606153, 0.73401698446, 0.73465790739, 0.73529883032, 0.73658067617, 0.73690113764, 0.73850344496, 0.73882390643, 0.73946482935, 0.73978529082, 0.74010575228, 0.74042621375, 0.74074667521, 0.74106713668, 0.74170805961, 0.742669444, 0.74331036693, 0.74363082839, 0.74395128986, 0.74427175132, 0.74523313572, 0.74555359718, 0.74619452011, 0.74651498157, 0.74683544304, 0.74779682743, 0.74875821183, 0.74907867329, 0.75068098061, 0.75132190354, 0.75164236501, 0.75228328794, 0.75452651819, 0.75548790258, 0.75580836404, 0.75612882551, 0.75644928697, 0.75741067137, 0.75773113283, 0.7580515943, 0.75869251723, 0.75933344015, 0.75997436308, 0.76061528601, 0.76189713187, 0.76221759333, 0.76381990066, 0.76414036212, 0.76478128505, 0.76510174652, 0.76542220798, 0.76574266944, 0.76606313091, 0.76638359237, 0.7670245153, 0.7679858997, 0.76862682263, 0.76894728409, 0.76926774555, 0.76958820702, 0.77054959141, 0.77087005288, 0.77151097581, 0.77183143727, 0.77215189873, 0.77311328313, 0.77407466752, 0.77439512899, 0.77599743631, 0.77663835924, 0.7769588207, 0.77759974363, 0.77984297388, 0.78080435828, 0.78112481974, 0.78144528121, 0.78176574267, 0.78272712706, 0.78304758853, 0.78336804999, 0.78400897292, 0.78464989585, 0.78529081878, 0.78593174171, 0.78721358757, 0.78753404903, 0.78913635635, 0.78945681782, 0.79009774075, 0.79041820221, 0.79073866368, 0.79105912514, 0.79137958661, 0.79170004807, 0.792340971, 0.79330235539, 0.79394327832, 0.79426373979, 0.79458420125, 0.79490466271, 0.79586604711, 0.79618650857, 0.7968274315, 0.79714789297, 0.79746835443, 0.79842973882, 0.79939112322, 0.79971158468, 0.801313892, 0.80195481493, 0.8022752764, 0.80291619933, 0.80515942958, 0.80612081397, 0.80644127544, 0.8067617369, 0.80708219837, 0.80804358276, 0.80836404422, 0.80868450569, 0.80932542862, 0.80996635155, 0.81060727448, 0.8112481974, 0.81253004326, 0.81285050473, 0.81445281205, 0.81477327351, 0.81541419644, 0.81573465791, 0.81605511937, 0.81637558084, 0.8166960423, 0.81701650377, 0.81765742669, 0.81861881109, 0.81925973402, 0.81958019548, 0.81990065695, 0.82022111841, 0.8211825028, 0.82150296427, 0.8221438872, 0.82246434866, 0.82278481013, 0.82374619452, 0.82470757891, 0.82502804038, 0.8266303477, 0.82727127063, 0.82759173209, 0.82823265502, 0.83047588528, 0.83143726967, 0.83175773113, 0.8320781926, 0.83239865406, 0.83336003846, 0.83368049992, 0.83400096138, 0.83464188431, 0.83528280724, 0.83592373017, 0.8365646531, 0.83784649896, 0.83816696042, 0.83976926775, 0.84008972921, 0.84073065214, 0.8410511136, 0.84137157507, 0.84169203653, 0.842012498, 0.84233295946, 0.84297388239, 0.84393526678, 0.84457618971, 0.84489665118, 0.84521711264, 0.84553757411, 0.8464989585, 0.84681941997, 0.84746034289, 0.84778080436, 0.84810126582, 0.84906265022, 0.85002403461, 0.85034449607, 0.8519468034, 0.85258772633, 0.85290818779, 0.85354911072, 0.85579234097, 0.85675372537, 0.85707418683, 0.85739464829, 0.85771510976, 0.85867649415, 0.85899695562, 0.85931741708, 0.85995834001, 0.86059926294, 0.86124018587, 0.8618811088, 0.86316295466, 0.86348341612, 0.86508572344, 0.86540618491, 0.86604710784, 0.8663675693, 0.86668803076, 0.86700849223, 0.86732895369, 0.86764941516, 0.86829033809, 0.86925172248, 0.86989264541, 0.87021310687, 0.87053356834, 0.8708540298, 0.8718154142, 0.87213587566, 0.87277679859, 0.87309726005, 0.87341772152, 0.87437910591, 0.87534049031, 0.87566095177, 0.87726325909, 0.87790418202, 0.87822464349, 0.87886556642, 0.88110879667, 0.88207018106, 0.88239064253, 0.88271110399, 0.88303156545, 0.88399294985, 0.88431341131, 0.88463387278, 0.88527479571, 0.88591571864, 0.88655664156, 0.88719756449, 0.88847941035, 0.88879987182, 0.89040217914, 0.8907226406, 0.89136356353, 0.891684025, 0.89200448646, 0.89232494793, 0.89264540939, 0.89296587085, 0.89360679378, 0.89456817818, 0.89520910111, 0.89552956257, 0.89585002404, 0.8961704855, 0.89713186989, 0.89745233136, 0.89809325429, 0.89841371575, 0.89873417722, 0.89969556161, 0.900656946, 0.90097740747, 0.90257971479, 0.90322063772, 0.90354109918, 0.90418202211, 0.90642525236, 0.90738663676, 0.90770709822, 0.90802755969, 0.90834802115, 0.90930940554, 0.90962986701, 0.90995032847, 0.9105912514, 0.91123217433, 0.91187309726, 0.91251402019, 0.91379586605, 0.91411632751, 0.91571863483, 0.9160390963, 0.91668001923, 0.91700048069, 0.91732094216, 0.91764140362, 0.91796186509, 0.91828232655, 0.91892324948, 0.91988463387, 0.9205255568, 0.92084601827, 0.92116647973, 0.9214869412, 0.92244832559, 0.92276878705, 0.92340970998, 0.92373017145, 0.92405063291, 0.92501201731, 0.9259734017, 0.92629386316, 0.92789617049, 0.92853709342, 0.92885755488, 0.92949847781, 0.93174170806, 0.93270309245, 0.93302355392, 0.93334401538, 0.93366447685, 0.93462586124, 0.93494632271, 0.93526678417, 0.9359077071, 0.93654863003, 0.93718955296, 0.93783047589, 0.93911232174, 0.93943278321, 0.94103509053, 0.941355552, 0.94199647492, 0.94231693639, 0.94263739785, 0.94295785932, 0.94327832078, 0.94359878225, 0.94423970518, 0.94520108957, 0.9458420125, 0.94616247396, 0.94648293543, 0.94680339689, 0.94776478129, 0.94808524275, 0.94872616568, 0.94904662714, 0.94936708861, 0.950328473, 0.9512898574, 0.95161031886, 0.95321262618, 0.95385354911, 0.95417401058, 0.9548149335, 0.95705816376, 0.95801954815, 0.95834000961, 0.95866047108, 0.95898093254, 0.95994231694, 0.9602627784, 0.96058323987, 0.96122416279, 0.96186508572, 0.96250600865, 0.96314693158, 0.96442877744, 0.9647492389, 0.96635154623, 0.96667200769, 0.96731293062, 0.96763339209, 0.96795385355, 0.96827431501, 0.96859477648, 0.96891523794, 0.96955616087, 0.97051754527, 0.97115846819, 0.97147892966, 0.97179939112, 0.97211985259, 0.97308123698, 0.97340169845, 0.97404262138, 0.97436308284, 0.9746835443, 0.9756449287, 0.97660631309, 0.97692677456, 0.97852908188, 0.97917000481, 0.97949046627, 0.9801313892, 0.98237461945, 0.98333600385, 0.98365646531, 0.98397692678, 0.98429738824, 0.98525877263, 0.9855792341, 0.98589969556, 0.98654061849, 0.98718154142, 0.98782246435, 0.98846338728, 0.98974523314, 0.9900656946, 0.99166800192, 0.99198846339, 0.99262938632, 0.99294984778, 0.99327030925, 0.99359077071, 0.99391123217, 0.99423169364, 0.99487261657, 0.99583400096, 0.99647492389, 0.99679538536, 0.99711584682, 0.99743630828, 0.99839769268, 0.99871815414, 0.99935907707, 0.99967953854]
pattern_even=[0.0, 0.0009613844, 0.0019227688, 0.0022432303, 0.0038455376, 0.0044864605, 0.004806922, 0.0054478449, 0.0076910751, 0.0086524595, 0.008972921, 0.0092933825, 0.0096138439, 0.0105752283, 0.0108956898, 0.0112161513, 0.0118570742, 0.0124979971, 0.01313892, 0.013779843, 0.0150616888, 0.0153821503, 0.0169844576, 0.0173049191, 0.017945842, 0.0182663035, 0.0185867649, 0.0189072264, 0.0192276879, 0.0195481493, 0.0201890723, 0.0211504567, 0.0217913796, 0.0221118411, 0.0224323025, 0.022752764, 0.0237141484, 0.0240346098, 0.0246755328, 0.0249959942, 0.0253164557, 0.0262778401, 0.0272392245, 0.0275596859, 0.0291619933, 0.0298029162, 0.0301233777, 0.0307643006, 0.0330075308, 0.0339689152, 0.0342893767, 0.0346098382, 0.0349302996, 0.035891684, 0.0362121455, 0.036532607, 0.0371735299, 0.0378144528, 0.0384553757, 0.0390962987, 0.0403781445, 0.040698606, 0.0423009133, 0.0426213748, 0.0432622977, 0.0435827592, 0.0439032206, 0.0442236821, 0.0445441436, 0.044864605, 0.045505528, 0.0464669124, 0.0471078353, 0.0474282967, 0.0477487582, 0.0480692197, 0.0490306041, 0.0493510655, 0.0499919885, 0.0503124499, 0.0506329114, 0.0515942958, 0.0525556802, 0.0528761416, 0.054478449, 0.0551193719, 0.0554398334, 0.0560807563, 0.0583239865, 0.0592853709, 0.0596058324, 0.0599262939, 0.0602467553, 0.0612081397, 0.0615286012, 0.0618490627, 0.0624899856, 0.0631309085, 0.0637718314, 0.0644127544, 0.0656946002, 0.0660150617, 0.067617369, 0.0679378305, 0.0685787534, 0.0688992149, 0.0692196763, 0.0695401378, 0.0698605993, 0.0701810607, 0.0708219837, 0.0717833681, 0.072424291, 0.0727447524, 0.0730652139, 0.0733856754, 0.0743470598, 0.0746675212, 0.0753084442, 0.0756289056, 0.0759493671, 0.0769107515, 0.0778721359, 0.0781925973, 0.0797949047, 0.0804358276, 0.0807562891, 0.081397212, 0.0836404422, 0.0846018266, 0.0849222881, 0.0852427496, 0.085563211, 0.0865245954, 0.0868450569, 0.0871655183, 0.0878064413, 0.0884473642, 0.0890882871, 0.0897292101, 0.0910110559, 0.0913315174, 0.0929338247, 0.0932542862, 0.0938952091, 0.0942156706, 0.094536132, 0.0948565935, 0.095177055, 0.0954975164, 0.0961384394, 0.0970998237, 0.0977407467, 0.0980612081, 0.0983816696, 0.0987021311, 0.0996635155, 0.0999839769, 0.1006248999, 0.1009453613, 0.1012658228, 0.1022272072, 0.1031885916, 0.103509053, 0.1051113604, 0.1057522833, 0.1060727448, 0.1067136677, 0.1089568979, 0.1099182823, 0.1102387438, 0.1105592053, 0.1108796667, 0.1118410511, 0.1121615126, 0.112481974, 0.113122897, 0.1137638199, 0.1144047428, 0.1150456658, 0.1163275116, 0.1166479731, 0.1182502804, 0.1185707419, 0.1192116648, 0.1195321263, 0.1198525877, 0.1201730492, 0.1204935107, 0.1208139721, 0.121454895, 0.1224162794, 0.1230572024, 0.1233776638, 0.1236981253, 0.1240185868, 0.1249799712, 0.1253004326, 0.1259413556, 0.126261817, 0.1265822785, 0.1275436629, 0.1285050473, 0.1288255087, 0.1304278161, 0.131068739, 0.1313892004, 0.1320301234, 0.1342733536, 0.135234738, 0.1355551995, 0.135875661, 0.1361961224, 0.1371575068, 0.1374779683, 0.1377984297, 0.1384393527, 0.1390802756, 0.1397211985, 0.1403621215, 0.1416439673, 0.1419644288, 0.1435667361, 0.1438871976, 0.1445281205, 0.144848582, 0.1451690434, 0.1454895049, 0.1458099664, 0.1461304278, 0.1467713507, 0.1477327351, 0.1483736581, 0.1486941195, 0.149014581, 0.1493350425, 0.1502964269, 0.1506168883, 0.1512578112, 0.1515782727, 0.1518987342, 0.1528601186, 0.153821503, 0.1541419644, 0.1557442718, 0.1563851947, 0.1567056561, 0.1573465791, 0.1595898093, 0.1605511937, 0.1608716552, 0.1611921166, 0.1615125781, 0.1624739625, 0.162794424, 0.1631148854, 0.1637558084, 0.1643967313, 0.1650376542, 0.1656785772, 0.166960423, 0.1672808845, 0.1688831918, 0.1692036533, 0.1698445762, 0.1701650377, 0.1704854991, 0.1708059606, 0.171126422, 0.1714468835, 0.1720878064, 0.1730491908, 0.1736901138, 0.1740105752, 0.1743310367, 0.1746514982, 0.1756128826, 0.175933344, 0.1765742669, 0.1768947284, 0.1772151899, 0.1781765743, 0.1791379587, 0.1794584201, 0.1810607274, 0.1817016504, 0.1820221118, 0.1826630348, 0.184906265, 0.1858676494, 0.1861881109, 0.1865085723, 0.1868290338, 0.1877904182, 0.1881108797, 0.1884313411, 0.1890722641, 0.189713187, 0.1903541099, 0.1909950328, 0.1922768787, 0.1925973402, 0.1941996475, 0.194520109, 0.1951610319, 0.1954814934, 0.1958019548, 0.1961224163, 0.1964428777, 0.1967633392, 0.1974042621, 0.1983656465, 0.1990065695, 0.1993270309, 0.1996474924, 0.1999679539, 0.2009293382, 0.2012497997, 0.2018907226, 0.2022111841, 0.2025316456, 0.20349303, 0.2044544144, 0.2047748758, 0.2063771831, 0.2070181061, 0.2073385675, 0.2079794905, 0.2102227207, 0.2111841051, 0.2115045666, 0.211825028, 0.2121454895, 0.2131068739, 0.2134273354, 0.2137477968, 0.2143887198, 0.2150296427, 0.2156705656, 0.2163114885, 0.2175933344, 0.2179137959, 0.2195161032, 0.2198365647, 0.2204774876, 0.220797949, 0.2211184105, 0.221438872, 0.2217593334, 0.2220797949, 0.2227207178, 0.2236821022, 0.2243230252, 0.2246434866, 0.2249639481, 0.2252844096, 0.2262457939, 0.2265662554, 0.2272071783, 0.2275276398, 0.2278481013, 0.2288094857, 0.2297708701, 0.2300913315, 0.2316936388, 0.2323345618, 0.2326550232, 0.2332959462, 0.2355391764, 0.2365005608, 0.2368210223, 0.2371414837, 0.2374619452, 0.2384233296, 0.2387437911, 0.2390642525, 0.2397051755, 0.2403460984, 0.2409870213, 0.2416279442, 0.2429097901, 0.2432302516, 0.2448325589, 0.2451530203, 0.2457939433, 0.2461144047, 0.2464348662, 0.2467553277, 0.2470757891, 0.2473962506, 0.2480371735, 0.2489985579, 0.2496394809, 0.2499599423, 0.2502804038, 0.2506008652, 0.2515622496, 0.2518827111, 0.252523634, 0.2528440955, 0.253164557, 0.2541259414, 0.2550873257, 0.2554077872, 0.2570100945, 0.2576510175, 0.2579714789, 0.2586124019, 0.2608556321, 0.2618170165, 0.262137478, 0.2624579394, 0.2627784009, 0.2637397853, 0.2640602468, 0.2643807082, 0.2650216311, 0.2656625541, 0.266303477, 0.2669443999, 0.2682262458, 0.2685467073, 0.2701490146, 0.270469476, 0.271110399, 0.2714308604, 0.2717513219, 0.2720717834, 0.2723922448, 0.2727127063, 0.2733536292, 0.2743150136, 0.2749559365, 0.275276398, 0.2755968595, 0.2759173209, 0.2768787053, 0.2771991668, 0.2778400897, 0.2781605512, 0.2784810127, 0.2794423971, 0.2804037814, 0.2807242429, 0.2823265502, 0.2829674732, 0.2832879346, 0.2839288576, 0.2861720878, 0.2871334722, 0.2874539337, 0.2877743951, 0.2880948566, 0.289056241, 0.2893767025, 0.2896971639, 0.2903380868, 0.2909790098, 0.2916199327, 0.2922608556, 0.2935427015, 0.293863163, 0.2954654703, 0.2957859317, 0.2964268547, 0.2967473161, 0.2970677776, 0.2973882391, 0.2977087005, 0.298029162, 0.2986700849, 0.2996314693, 0.3002723922, 0.3005928537, 0.3009133152, 0.3012337766, 0.302195161, 0.3025156225, 0.3031565454, 0.3034770069, 0.3037974684, 0.3047588527, 0.3057202371, 0.3060406986, 0.3076430059, 0.3082839289, 0.3086043903, 0.3092453133, 0.3114885435, 0.3124499279, 0.3127703894, 0.3130908508, 0.3134113123, 0.3143726967, 0.3146931581, 0.3150136196, 0.3156545425, 0.3162954655, 0.3169363884, 0.3175773113, 0.3188591572, 0.3191796187, 0.320781926, 0.3211023874, 0.3217433104, 0.3220637718, 0.3223842333, 0.3227046948, 0.3230251562, 0.3233456177, 0.3239865406, 0.324947925, 0.3255888479, 0.3259093094, 0.3262297709, 0.3265502323, 0.3275116167, 0.3278320782, 0.3284730011, 0.3287934626, 0.3291139241, 0.3300753084, 0.3310366928, 0.3313571543, 0.3329594616, 0.3336003846, 0.333920846, 0.3345617689, 0.3368049992, 0.3377663836, 0.3380868451, 0.3384073065, 0.338727768, 0.3396891524, 0.3400096138, 0.3403300753, 0.3409709982, 0.3416119212, 0.3422528441, 0.342893767, 0.3441756129, 0.3444960743, 0.3460983817, 0.3464188431, 0.3470597661, 0.3473802275, 0.347700689, 0.3480211505, 0.3483416119, 0.3486620734, 0.3493029963, 0.3502643807, 0.3509053036, 0.3512257651, 0.3515462266, 0.351866688, 0.3528280724, 0.3531485339, 0.3537894568, 0.3541099183, 0.3544303797, 0.3553917641, 0.3563531485, 0.35667361, 0.3582759173, 0.3589168403, 0.3592373017, 0.3598782246, 0.3621214549, 0.3630828393, 0.3634033008, 0.3637237622, 0.3640442237, 0.3650056081, 0.3653260695, 0.365646531, 0.3662874539, 0.3669283769, 0.3675692998, 0.3682102227, 0.3694920686, 0.36981253, 0.3714148374, 0.3717352988, 0.3723762218, 0.3726966832, 0.3730171447, 0.3733376062, 0.3736580676, 0.3739785291, 0.374619452, 0.3755808364, 0.3762217593, 0.3765422208, 0.3768626823, 0.3771831437, 0.3781445281, 0.3784649896, 0.3791059125, 0.379426374, 0.3797468354, 0.3807082198, 0.3816696042, 0.3819900657, 0.383592373, 0.3842332959, 0.3845537574, 0.3851946803, 0.3874379106, 0.388399295, 0.3887197564, 0.3890402179, 0.3893606794, 0.3903220638, 0.3906425252, 0.3909629867, 0.3916039096, 0.3922448326, 0.3928857555, 0.3935266784, 0.3948085243, 0.3951289857, 0.3967312931, 0.3970517545, 0.3976926775, 0.3980131389, 0.3983336004, 0.3986540618, 0.3989745233, 0.3992949848, 0.3999359077, 0.4008972921, 0.401538215, 0.4018586765, 0.402179138, 0.4024995994, 0.4034609838, 0.4037814453, 0.4044223682, 0.4047428297, 0.4050632911, 0.4060246755, 0.4069860599, 0.4073065214, 0.4089088287, 0.4095497516, 0.4098702131, 0.410511136, 0.4127543663, 0.4137157507, 0.4140362121, 0.4143566736, 0.4146771351, 0.4156385195, 0.4159589809, 0.4162794424, 0.4169203653, 0.4175612883, 0.4182022112, 0.4188431341, 0.42012498, 0.4204454414, 0.4220477488, 0.4223682102, 0.4230091332, 0.4233295946, 0.4236500561, 0.4239705175, 0.424290979, 0.4246114405, 0.4252523634, 0.4262137478, 0.4268546707, 0.4271751322, 0.4274955937, 0.4278160551, 0.4287774395, 0.429097901, 0.4297388239, 0.4300592854, 0.4303797468, 0.4313411312, 0.4323025156, 0.4326229771, 0.4342252844, 0.4348662073, 0.4351866688, 0.4358275917, 0.438070822, 0.4390322064, 0.4393526678, 0.4396731293, 0.4399935908, 0.4409549752, 0.4412754366, 0.4415958981, 0.442236821, 0.442877744, 0.4435186669, 0.4441595898, 0.4454414357, 0.4457618971, 0.4473642045, 0.4476846659, 0.4483255888, 0.4486460503, 0.4489665118, 0.4492869732, 0.4496074347, 0.4499278962, 0.4505688191, 0.4515302035, 0.4521711264, 0.4524915879, 0.4528120494, 0.4531325108, 0.4540938952, 0.4544143567, 0.4550552796, 0.4553757411, 0.4556962025, 0.4566575869, 0.4576189713, 0.4579394328, 0.4595417401, 0.460182663, 0.4605031245, 0.4611440474, 0.4633872777, 0.4643486621, 0.4646691235, 0.464989585, 0.4653100465, 0.4662714309, 0.4665918923, 0.4669123538, 0.4675532767, 0.4681941996, 0.4688351226, 0.4694760455, 0.4707578914, 0.4710783528, 0.4726806602, 0.4730011216, 0.4736420445, 0.473962506, 0.4742829675, 0.4746034289, 0.4749238904, 0.4752443519, 0.4758852748, 0.4768466592, 0.4774875821, 0.4778080436, 0.478128505, 0.4784489665, 0.4794103509, 0.4797308124, 0.4803717353, 0.4806921968, 0.4810126582, 0.4819740426, 0.482935427, 0.4832558885, 0.4848581958, 0.4854991187, 0.4858195802, 0.4864605031, 0.4887037334, 0.4896651178, 0.4899855792, 0.4903060407, 0.4906265022, 0.4915878866, 0.491908348, 0.4922288095, 0.4928697324, 0.4935106553, 0.4941515783, 0.4947925012, 0.4960743471, 0.4963948085, 0.4979971158, 0.4983175773, 0.4989585002, 0.4992789617, 0.4995994232, 0.4999198846, 0.5002403461, 0.5005608076, 0.5012017305, 0.5021631149, 0.5028040378, 0.5031244993, 0.5034449607, 0.5037654222, 0.5047268066, 0.5050472681, 0.505688191, 0.5060086525, 0.5063291139, 0.5072904983, 0.5082518827, 0.5085723442, 0.5101746515, 0.5108155744, 0.5111360359, 0.5117769588, 0.5140201891, 0.5149815735, 0.5153020349, 0.5156224964, 0.5159429579, 0.5169043423, 0.5172248037, 0.5175452652, 0.5181861881, 0.518827111, 0.519468034, 0.5201089569, 0.5213908028, 0.5217112642, 0.5233135715, 0.523634033, 0.5242749559, 0.5245954174, 0.5249158789, 0.5252363403, 0.5255568018, 0.5258772633, 0.5265181862, 0.5274795706, 0.5281204935, 0.528440955, 0.5287614164, 0.5290818779, 0.5300432623, 0.5303637238, 0.5310046467, 0.5313251082, 0.5316455696, 0.532606954, 0.5335683384, 0.5338887999, 0.5354911072, 0.5361320301, 0.5364524916, 0.5370934145, 0.5393366448, 0.5402980292, 0.5406184906, 0.5409389521, 0.5412594136, 0.5422207979, 0.5425412594, 0.5428617209, 0.5435026438, 0.5441435667, 0.5447844897, 0.5454254126, 0.5467072585, 0.5470277199, 0.5486300272, 0.5489504887, 0.5495914116, 0.5499118731, 0.5502323346, 0.550552796, 0.5508732575, 0.551193719, 0.5518346419, 0.5527960263, 0.5534369492, 0.5537574107, 0.5540778721, 0.5543983336, 0.555359718, 0.5556801795, 0.5563211024, 0.5566415639, 0.5569620253, 0.5579234097, 0.5588847941, 0.5592052556, 0.5608075629, 0.5614484858, 0.5617689473, 0.5624098702, 0.5646531005, 0.5656144849, 0.5659349463, 0.5662554078, 0.5665758693, 0.5675372536, 0.5678577151, 0.5681781766, 0.5688190995, 0.5694600224, 0.5701009454, 0.5707418683, 0.5720237141, 0.5723441756, 0.5739464829, 0.5742669444, 0.5749078673, 0.5752283288, 0.5755487903, 0.5758692517, 0.5761897132, 0.5765101747, 0.5771510976, 0.578112482, 0.5787534049, 0.5790738664, 0.5793943278, 0.5797147893, 0.5806761737, 0.5809966352, 0.5816375581, 0.5819580195, 0.582278481, 0.5832398654, 0.5842012498, 0.5845217113, 0.5861240186, 0.5867649415, 0.587085403, 0.5877263259, 0.5899695562, 0.5909309406, 0.591251402, 0.5915718635, 0.5918923249, 0.5928537093, 0.5931741708, 0.5934946323, 0.5941355552, 0.5947764781, 0.5954174011, 0.596058324, 0.5973401698, 0.5976606313, 0.5992629386, 0.5995834001, 0.600224323, 0.6005447845, 0.600865246, 0.6011857074, 0.6015061689, 0.6018266303, 0.6024675533, 0.6034289377, 0.6040698606, 0.6043903221, 0.6047107835, 0.605031245, 0.6059926294, 0.6063130909, 0.6069540138, 0.6072744752, 0.6075949367, 0.6085563211, 0.6095177055, 0.609838167, 0.6114404743, 0.6120813972, 0.6124018587, 0.6130427816, 0.6152860119, 0.6162473963, 0.6165678577, 0.6168883192, 0.6172087806, 0.618170165, 0.6184906265, 0.618811088, 0.6194520109, 0.6200929338, 0.6207338568, 0.6213747797, 0.6226566255, 0.622977087, 0.6245793943, 0.6248998558, 0.6255407787, 0.6258612402, 0.6261817017, 0.6265021631, 0.6268226246, 0.627143086, 0.627784009, 0.6287453934, 0.6293863163, 0.6297067778, 0.6300272392, 0.6303477007, 0.6313090851, 0.6316295465, 0.6322704695, 0.6325909309, 0.6329113924, 0.6338727768, 0.6348341612, 0.6351546227, 0.63675693, 0.6373978529, 0.6377183144, 0.6383592373, 0.6406024676, 0.6415638519, 0.6418843134, 0.6422047749, 0.6425252363, 0.6434866207, 0.6438070822, 0.6441275437, 0.6447684666, 0.6454093895, 0.6460503125, 0.6466912354, 0.6479730812, 0.6482935427, 0.64989585, 0.6502163115, 0.6508572344, 0.6511776959, 0.6514981573, 0.6518186188, 0.6521390803, 0.6524595417, 0.6531004647, 0.6540618491, 0.654702772, 0.6550232335, 0.6553436949, 0.6556641564, 0.6566255408, 0.6569460022, 0.6575869252, 0.6579073866, 0.6582278481, 0.6591892325, 0.6601506169, 0.6604710784, 0.6620733857, 0.6627143086, 0.6630347701, 0.663675693, 0.6659189232, 0.6668803076, 0.6672007691, 0.6675212306, 0.667841692, 0.6688030764, 0.6691235379, 0.6694439994, 0.6700849223, 0.6707258452, 0.6713667681, 0.6720076911, 0.6732895369, 0.6736099984, 0.6752123057, 0.6755327672, 0.6761736901, 0.6764941516, 0.676814613, 0.6771350745, 0.677455536, 0.6777759974, 0.6784169204, 0.6793783048, 0.6800192277, 0.6803396892, 0.6806601506, 0.6809806121, 0.6819419965, 0.6822624579, 0.6829033809, 0.6832238423, 0.6835443038, 0.6845056882, 0.6854670726, 0.685787534, 0.6873898414, 0.6880307643, 0.6883512258, 0.6889921487, 0.6912353789, 0.6921967633, 0.6925172248, 0.6928376863, 0.6931581477, 0.6941195321, 0.6944399936, 0.6947604551, 0.695401378, 0.6960423009, 0.6966832238, 0.6973241468, 0.6986059926, 0.6989264541, 0.7005287614, 0.7008492229, 0.7014901458, 0.7018106073, 0.7021310687, 0.7024515302, 0.7027719917, 0.7030924531, 0.7037333761, 0.7046947605, 0.7053356834, 0.7056561448, 0.7059766063, 0.7062970678, 0.7072584522, 0.7075789136, 0.7082198366, 0.708540298, 0.7088607595, 0.7098221439, 0.7107835283, 0.7111039897, 0.7127062971, 0.71334722, 0.7136676815, 0.7143086044, 0.7165518346, 0.717513219, 0.7178336805, 0.718154142, 0.7184746034, 0.7194359878, 0.7197564493, 0.7200769108, 0.7207178337, 0.7213587566, 0.7219996795, 0.7226406025, 0.7239224483, 0.7242429098, 0.7258452171, 0.7261656786, 0.7268066015, 0.727127063, 0.7274475244, 0.7277679859, 0.7280884474, 0.7284089088, 0.7290498318, 0.7300112162, 0.7306521391, 0.7309726005, 0.731293062, 0.7316135235, 0.7325749079, 0.7328953693, 0.7335362923, 0.7338567537, 0.7341772152, 0.7351385996, 0.736099984, 0.7364204454, 0.7380227528, 0.7386636757, 0.7389841372, 0.7396250601, 0.7418682903, 0.7428296747, 0.7431501362, 0.7434705977, 0.7437910591, 0.7447524435, 0.745072905, 0.7453933664, 0.7460342894, 0.7466752123, 0.7473161352, 0.7479570582, 0.749238904, 0.7495593655, 0.7511616728, 0.7514821343, 0.7521230572, 0.7524435187, 0.7527639801, 0.7530844416, 0.7534049031, 0.7537253645, 0.7543662875, 0.7553276718, 0.7559685948, 0.7562890562, 0.7566095177, 0.7569299792, 0.7578913636, 0.758211825, 0.758852748, 0.7591732094, 0.7594936709, 0.7604550553, 0.7614164397, 0.7617369011, 0.7633392085, 0.7639801314, 0.7643005929, 0.7649415158, 0.767184746, 0.7681461304, 0.7684665919, 0.7687870534, 0.7691075148, 0.7700688992, 0.7703893607, 0.7707098221, 0.7713507451, 0.771991668, 0.7726325909, 0.7732735139, 0.7745553597, 0.7748758212, 0.7764781285, 0.77679859, 0.7774395129, 0.7777599744, 0.7780804358, 0.7784008973, 0.7787213588, 0.7790418202, 0.7796827432, 0.7806441275, 0.7812850505, 0.7816055119, 0.7819259734, 0.7822464349, 0.7832078193, 0.7835282807, 0.7841692037, 0.7844896651, 0.7848101266, 0.785771511, 0.7867328954, 0.7870533568, 0.7886556642, 0.7892965871, 0.7896170486, 0.7902579715, 0.7925012017, 0.7934625861, 0.7937830476, 0.7941035091, 0.7944239705, 0.7953853549, 0.7957058164, 0.7960262778, 0.7966672008, 0.7973081237, 0.7979490466, 0.7985899696, 0.7998718154, 0.8001922769, 0.8017945842, 0.8021150457, 0.8027559686, 0.8030764301, 0.8033968915, 0.803717353, 0.8040378145, 0.8043582759, 0.8049991988, 0.8059605832, 0.8066015062, 0.8069219676, 0.8072424291, 0.8075628906, 0.808524275, 0.8088447364, 0.8094856593, 0.8098061208, 0.8101265823, 0.8110879667, 0.8120493511, 0.8123698125, 0.8139721199, 0.8146130428, 0.8149335042, 0.8155744272, 0.8178176574, 0.8187790418, 0.8190995033, 0.8194199647, 0.8197404262, 0.8207018106, 0.8210222721, 0.8213427335, 0.8219836565, 0.8226245794, 0.8232655023, 0.8239064253, 0.8251882711, 0.8255087326, 0.8271110399, 0.8274315014, 0.8280724243, 0.8283928858, 0.8287133472, 0.8290338087, 0.8293542701, 0.8296747316, 0.8303156545, 0.8312770389, 0.8319179619, 0.8322384233, 0.8325588848, 0.8328793463, 0.8338407307, 0.8341611921, 0.834802115, 0.8351225765, 0.835443038, 0.8364044224, 0.8373658068, 0.8376862682, 0.8392885755, 0.8399294985, 0.8402499599, 0.8408908829, 0.8431341131, 0.8440954975, 0.844415959, 0.8447364204, 0.8450568819, 0.8460182663, 0.8463387278, 0.8466591892, 0.8473001122, 0.8479410351, 0.848581958, 0.8492228809, 0.8505047268, 0.8508251883, 0.8524274956, 0.8527479571, 0.85338888, 0.8537093415, 0.8540298029, 0.8543502644, 0.8546707258, 0.8549911873, 0.8556321102, 0.8565934946, 0.8572344176, 0.857554879, 0.8578753405, 0.858195802, 0.8591571863, 0.8594776478, 0.8601185707, 0.8604390322, 0.8607594937, 0.8617208781, 0.8626822625, 0.8630027239, 0.8646050312, 0.8652459542, 0.8655664156, 0.8662073386, 0.8684505688, 0.8694119532, 0.8697324147, 0.8700528761, 0.8703733376, 0.871334722, 0.8716551835, 0.8719756449, 0.8726165679, 0.8732574908, 0.8738984137, 0.8745393366, 0.8758211825, 0.876141644, 0.8777439513, 0.8780644128, 0.8787053357, 0.8790257971, 0.8793462586, 0.8796667201, 0.8799871815, 0.880307643, 0.8809485659, 0.8819099503, 0.8825508733, 0.8828713347, 0.8831917962, 0.8835122577, 0.884473642, 0.8847941035, 0.8854350264, 0.8857554879, 0.8860759494, 0.8870373338, 0.8879987182, 0.8883191796, 0.8899214869, 0.8905624099, 0.8908828713, 0.8915237943, 0.8937670245, 0.8947284089, 0.8950488704, 0.8953693318, 0.8956897933, 0.8966511777, 0.8969716392, 0.8972921006, 0.8979330236, 0.8985739465, 0.8992148694, 0.8998557923, 0.9011376382, 0.9014580997, 0.903060407, 0.9033808685, 0.9040217914, 0.9043422528, 0.9046627143, 0.9049831758, 0.9053036372, 0.9056240987, 0.9062650216, 0.907226406, 0.907867329, 0.9081877904, 0.9085082519, 0.9088287133, 0.9097900977, 0.9101105592, 0.9107514821, 0.9110719436, 0.9113924051, 0.9123537895, 0.9133151739, 0.9136356353, 0.9152379426, 0.9158788656, 0.916199327, 0.91684025, 0.9190834802, 0.9200448646, 0.9203653261, 0.9206857875, 0.921006249, 0.9219676334, 0.9222880949, 0.9226085563, 0.9232494793, 0.9238904022, 0.9245313251, 0.925172248, 0.9264540939, 0.9267745554, 0.9283768627, 0.9286973241, 0.9293382471, 0.9296587085, 0.92997917, 0.9302996315, 0.9306200929, 0.9309405544, 0.9315814773, 0.9325428617, 0.9331837847, 0.9335042461, 0.9338247076, 0.934145169, 0.9351065534, 0.9354270149, 0.9360679378, 0.9363883993, 0.9367088608, 0.9376702452, 0.9386316295, 0.938952091, 0.9405543983, 0.9411953213, 0.9415157827, 0.9421567057, 0.9443999359, 0.9453613203, 0.9456817818, 0.9460022432, 0.9463227047, 0.9472840891, 0.9476045506, 0.947925012, 0.9485659349, 0.9492068579, 0.9498477808, 0.9504887037, 0.9517705496, 0.9520910111, 0.9536933184, 0.9540137798, 0.9546547028, 0.9549751642, 0.9552956257, 0.9556160872, 0.9559365486, 0.9562570101, 0.956897933, 0.9578593174, 0.9585002403, 0.9588207018, 0.9591411633, 0.9594616247, 0.9604230091, 0.9607434706, 0.9613843935, 0.961704855, 0.9620253165, 0.9629867008, 0.9639480852, 0.9642685467, 0.965870854, 0.966511777, 0.9668322384, 0.9674731614, 0.9697163916, 0.970677776, 0.9709982375, 0.9713186989, 0.9716391604, 0.9726005448, 0.9729210062, 0.9732414677, 0.9738823906, 0.9745233136, 0.9751642365, 0.9758051594, 0.9770870053, 0.9774074668, 0.9790097741, 0.9793302355, 0.9799711585, 0.9802916199, 0.9806120814, 0.9809325429, 0.9812530043, 0.9815734658, 0.9822143887, 0.9831757731, 0.983816696, 0.9841371575, 0.984457619, 0.9847780804, 0.9857394648, 0.9860599263, 0.9867008492, 0.9870213107, 0.9873417722, 0.9883031565, 0.9892645409, 0.9895850024, 0.9911873097, 0.9918282327, 0.9921486941, 0.992789617, 0.9950328473, 0.9959942317, 0.9963146932, 0.9966351546, 0.9969556161, 0.9979170005, 0.9982374619, 0.9985579234, 0.9991988463, 0.9998397693]
averages_even={0.0: [0.0], 0.1903541099: [0.4050632911392, 0.5949367088608], 0.8694119532: [0.9367088607595, 0.0632911392405], 0.1720878064: [0.5443037974684, 0.4556962025316], 0.1051113604: [0.7088607594937, 0.2911392405063], 0.6761736901: [0.7341772151899, 0.2658227848101], 0.2195161032: [0.7848101265823, 0.2151898734177], 0.5002403461: [0.1772151898734, 0.8227848101266], 0.184906265: [0.620253164557, 0.379746835443], 0.401538215: [0.4810126582278, 0.5189873417722], 0.2515622496: [0.2784810126582, 0.7215189873418], 0.5031244993: [0.5063291139241, 0.4936708860759], 0.3807082198: [0.3544303797468, 0.6455696202532], 0.92997917: [0.8607594936709, 0.1392405063291], 0.5060086525: [0.8860759493671, 0.1139240506329], 0.9918282327: [0.6329113924051, 0.3670886075949], 0.3730171447: [0.1392405063291, 0.8607594936709], 0.6168883192: [0.873417721519, 0.126582278481], 0.6226566255: [0.1012658227848, 0.8987341772152], 0.0781925973: [0.8481012658228, 0.1518987341772], 0.1563851947: [0.6329113924051, 0.3670886075949], 0.7335362923: [0.0253164556962, 0.9746835443038], 0.3156545425: [0.253164556962, 0.746835443038], 0.3409709982: [0.253164556962, 0.746835443038], 0.7453933664: [0.9620253164557, 0.0379746835443], 0.745072905: [0.1645569620253, 0.8354430379747], 0.0852427496: [0.873417721519, 0.126582278481], 0.3739785291: [0.9240506329114, 0.0759493670886], 0.6165678577: [0.6962025316456, 0.3037974683544], 0.9991988463: [0.253164556962, 0.746835443038], 0.1884313411: [0.9620253164557, 0.0379746835443], 0.0301233777: [0.0886075949367, 0.9113924050633], 0.8617208781: [0.3544303797468, 0.6455696202532], 0.1435667361: [0.7848101265823, 0.2151898734177], 0.4323025156: [0.8101265822785, 0.1898734177215], 0.0092933825: [0.873417721519, 0.126582278481], 0.2175933344: [0.1012658227848, 0.8987341772152], 0.8732574908: [0.0126582278481, 0.9873417721519], 0.4906265022: [0.7974683544304, 0.2025316455696], 0.6034289377: [0.4683544303797, 0.5316455696203], 0.9841371575: [0.5063291139241, 0.4936708860759], 0.1253004326: [0.7721518987342, 0.2278481012658], 0.0275596859: [0.8481012658228, 0.1518987341772], 0.1233776638: [0.5063291139241, 0.4936708860759], 0.2467553277: [0.5569620253165, 0.4430379746835], 0.6120813972: [0.6329113924051, 0.3670886075949], 0.4073065214: [0.8481012658228, 0.1518987341772], 0.1313892004: [0.0886075949367, 0.9113924050633], 0.4963948085: [0.5822784810127, 0.4177215189873], 0.4390322064: [0.9367088607595, 0.0632911392405], 0.9415157827: [0.0886075949367, 0.9113924050633], 0.4992789617: [0.3291139240506, 0.6708860759494], 0.6207338568: [0.4050632911392, 0.5949367088608], 0.7316135235: [0.9493670886076, 0.0506329113924], 0.6819419965: [0.2784810126582, 0.7215189873418], 0.8809485659: [0.5443037974684, 0.4556962025316], 0.9443999359: [0.620253164557, 0.379746835443], 0.7431501362: [0.6962025316456, 0.3037974683544], 0.4095497516: [0.6329113924051, 0.3670886075949], 0.1865085723: [0.873417721519, 0.126582278481], 0.1416439673: [0.1012658227848, 0.8987341772152], 0.3150136196: [0.9620253164557, 0.0379746835443], 0.8540298029: [0.8607594936709, 0.1392405063291], 0.3992949848: [0.9240506329114, 0.0759493670886], 0.7569299792: [0.9493670886076, 0.0506329113924], 0.2156705656: [0.4050632911392, 0.5949367088608], 0.8626822625: [0.8101265822785, 0.1898734177215], 0.8655664156: [0.0886075949367, 0.9113924050633], 0.4342252844: [0.7088607594937, 0.2911392405063], 0.7380227528: [0.7088607594937, 0.2911392405063], 0.8325588848: [0.6582278481013, 0.3417721518987], 0.1890722641: [0.253164556962, 0.746835443038], 0.0612081397: [0.3924050632911, 0.6075949367089], 0.6015061689: [0.1772151898734, 0.8227848101266], 0.9822143887: [0.5443037974684, 0.4556962025316], 0.6043903221: [0.5063291139241, 0.4936708860759], 0.6072744752: [0.8860759493671, 0.1139240506329], 0.338727768: [0.7974683544304, 0.2025316455696], 0.6130427816: [0.2405063291139, 0.7594936708861], 0.8043582759: [0.9240506329114, 0.0759493670886], 0.1605511937: [0.9367088607595, 0.0632911392405], 0.253164557: [0.0], 0.7239224483: [0.1012658227848, 0.8987341772152], 0.7268066015: [0.7341772151899, 0.2658227848101], 0.1817016504: [0.6329113924051, 0.3670886075949], 0.5534369492: [0.4810126582278, 0.5189873417722], 0.3662874539: [0.253164556962, 0.746835443038], 0.9549751642: [0.3291139240506, 0.6708860759494], 0.8463387278: [0.1645569620253, 0.8354430379747], 0.8492228809: [0.3164556962025, 0.6835443037975], 0.5563211024: [0.0253164556962, 0.9746835443038], 0.2137477968: [0.9620253164557, 0.0379746835443], 0.4274955937: [0.6582278481013, 0.3417721518987], 0.1708059606: [0.5569620253165, 0.4430379746835], 0.8578753405: [0.6582278481013, 0.3417721518987], 0.8607594937: [0.0], 0.2954654703: [0.7848101265823, 0.2151898734177], 0.121454895: [0.5443037974684, 0.4556962025316], 0.2429097901: [0.1012658227848, 0.8987341772152], 0.9745233136: [0.0126582278481, 0.9873417721519], 0.4887037334: [0.620253164557, 0.379746835443], 0.5995834001: [0.4303797468354, 0.5696202531646], 0.9802916199: [0.3291139240506, 0.6708860759494], 0.1506168883: [0.7721518987342, 0.2278481012658], 0.0753084442: [0.0253164556962, 0.9746835443038], 0.0221118411: [0.5063291139241, 0.4936708860759], 0.9831757731: [0.4683544303797, 0.5316455696203], 0.2457939433: [0.7341772151899, 0.2658227848101], 0.35667361: [0.8481012658228, 0.1518987341772], 0.6255407787: [0.7341772151899, 0.2658227848101], 0.3444960743: [0.5822784810127, 0.4177215189873], 0.1881108797: [0.1645569620253, 0.8354430379747], 0.1099182823: [0.9367088607595, 0.0632911392405], 0.7277679859: [0.5569620253165, 0.4430379746835], 0.0153821503: [0.5822784810127, 0.4177215189873], 0.3031565454: [0.0253164556962, 0.9746835443038], 0.0195481493: [0.9240506329114, 0.0759493670886], 0.3653260695: [0.1645569620253, 0.8354430379747], 0.0913315174: [0.5822784810127, 0.4177215189873], 0.844415959: [0.6962025316456, 0.3037974683544], 0.1643967313: [0.0126582278481, 0.9873417721519], 0.211825028: [0.873417721519, 0.126582278481], 0.9238904022: [0.0126582278481, 0.9873417721519], 0.2916199327: [0.4050632911392, 0.5949367088608], 0.1458099664: [0.1772151898734, 0.8227848101266], 0.2409870213: [0.4050632911392, 0.5949367088608], 0.1204935107: [0.1772151898734, 0.8227848101266], 0.3682102227: [0.3164556962025, 0.6835443037975], 0.9668322384: [0.0886075949367, 0.9113924050633], 0.3473802275: [0.3291139240506, 0.6708860759494], 0.1477327351: [0.4683544303797, 0.5316455696203], 0.4848581958: [0.7088607594937, 0.2911392405063], 0.5918923249: [0.7974683544304, 0.2025316455696], 0.9267745554: [0.5822784810127, 0.4177215189873], 0.9726005448: [0.3924050632911, 0.6075949367089], 0.9639480852: [0.8101265822785, 0.1898734177215], 0.1486941195: [0.5063291139241, 0.4936708860759], 0.0371735299: [0.253164556962, 0.746835443038], 0.5976606313: [0.5822784810127, 0.4177215189873], 0.6005447845: [0.3291139240506, 0.6708860759494], 0.708540298: [0.8860759493671, 0.1139240506329], 0.7143086044: [0.2405063291139, 0.7594936708861], 0.7200769108: [0.9620253164557, 0.0379746835443], 0.2070181061: [0.6329113924051, 0.3670886075949], 0.0637718314: [0.4050632911392, 0.5949367088608], 0.7258452171: [0.2151898734177, 0.7848101265823], 0.4169203653: [0.253164556962, 0.746835443038], 0.8338407307: [0.2784810126582, 0.7215189873418], 0.9325428617: [0.4683544303797, 0.5316455696203], 0.4979971158: [0.2151898734177, 0.7848101265823], 0.2755968595: [0.6582278481013, 0.3417721518987], 0.1361961224: [0.7974683544304, 0.2025316455696], 0.0038455376: [0.7088607594937, 0.2911392405063], 0.4752443519: [0.9240506329114, 0.0759493670886], 0.5755487903: [0.1392405063291, 0.8607594936709], 0.1438871976: [0.4303797468354, 0.5696202531646], 0.2390642525: [0.9620253164557, 0.0379746835443], 0.1195321263: [0.3291139240506, 0.6708860759494], 0.9591411633: [0.6582278481013, 0.3417721518987], 0.9354270149: [0.7721518987342, 0.2278481012658], 0.482935427: [0.8101265822785, 0.1898734177215], 0.9620253165: [0.0], 0.5842012498: [0.8101265822785, 0.1898734177215], 0.1467713507: [0.5443037974684, 0.4556962025316], 0.0733856754: [0.9493670886076, 0.0506329113924], 0.171126422: [0.1772151898734, 0.8227848101266], 0.5899695562: [0.620253164557, 0.379746835443], 0.3422528441: [0.4050632911392, 0.5949367088608], 0.1810607274: [0.7088607594937, 0.2911392405063], 0.7479570582: [0.3164556962025, 0.6835443037975], 0.2964268547: [0.7341772151899, 0.2658227848101], 0.7008492229: [0.4303797468354, 0.5696202531646], 0.351866688: [0.9493670886076, 0.0506329113924], 0.2384233296: [0.3924050632911, 0.6075949367089], 0.1182502804: [0.7848101265823, 0.2151898734177], 0.7088607595: [0.0], 0.9158788656: [0.6329113924051, 0.3670886075949], 0.718154142: [0.873417721519, 0.126582278481], 0.4050632911: [0.0], 0.8290338087: [0.5569620253165, 0.4430379746835], 0.2079794905: [0.2405063291139, 0.7594936708861], 0.1567056561: [0.0886075949367, 0.9113924050633], 0.3976926775: [0.7341772151899, 0.2658227848101], 0.7537253645: [0.9240506329114, 0.0759493670886], 0.9222880949: [0.1645569620253, 0.8354430379747], 0.2464348662: [0.8607594936709, 0.1392405063291], 0.9456817818: [0.6962025316456, 0.3037974683544], 0.1419644288: [0.5822784810127, 0.4177215189873], 0.1185707419: [0.4303797468354, 0.5696202531646], 0.5707418683: [0.3164556962025, 0.6835443037975], 0.6713667681: [0.4050632911392, 0.5949367088608], 0.5765101747: [0.9240506329114, 0.0759493670886], 0.5793943278: [0.6582278481013, 0.3417721518987], 0.072424291: [0.4810126582278, 0.5189873417722], 0.582278481: [0.0], 0.9629867008: [0.3544303797468, 0.6455696202532], 0.9203653261: [0.6962025316456, 0.3037974683544], 0.6931581477: [0.7974683544304, 0.2025316455696], 0.1528601186: [0.3544303797468, 0.6455696202532], 0.1740105752: [0.5063291139241, 0.4936708860759], 0.6989264541: [0.5822784810127, 0.4177215189873], 0.3509053036: [0.4810126582278, 0.5189873417722], 0.7018106073: [0.3291139240506, 0.6708860759494], 0.8646050312: [0.7088607594937, 0.2911392405063], 0.7046947605: [0.4683544303797, 0.5316455696203], 0.0884473642: [0.0126582278481, 0.9873417721519], 0.749238904: [0.1012658227848, 0.8987341772152], 0.8155744272: [0.2405063291139, 0.7594936708861], 0.8213427335: [0.9620253164557, 0.0379746835443], 0.1403621215: [0.3164556962025, 0.6835443037975], 0.0342893767: [0.6962025316456, 0.3037974683544], 0.5742669444: [0.4303797468354, 0.5696202531646], 0.8271110399: [0.2151898734177, 0.7848101265823], 0.8328793463: [0.9493670886076, 0.0506329113924], 0.9306200929: [0.1772151898734, 0.8227848101266], 0.1541419644: [0.8481012658228, 0.1518987341772], 0.3400096138: [0.1645569620253, 0.8354430379747], 0.3127703894: [0.6962025316456, 0.3037974683544], 0.1858676494: [0.9367088607595, 0.0632911392405], 0.8069219676: [0.5063291139241, 0.4936708860759], 0.5688190995: [0.253164556962, 0.746835443038], 0.4351866688: [0.0886075949367, 0.9113924050633], 0.9338247076: [0.6582278481013, 0.3417721518987], 0.0211504567: [0.4683544303797, 0.5316455696203], 0.9552956257: [0.8607594936709, 0.1392405063291], 0.6854670726: [0.8101265822785, 0.1898734177215], 0.3441756129: [0.1012658227848, 0.8987341772152], 0.0602467553: [0.7974683544304, 0.2025316455696], 0.6912353789: [0.620253164557, 0.379746835443], 0.6941195321: [0.3924050632911, 0.6075949367089], 0.3470597661: [0.7341772151899, 0.2658227848101], 0.0185867649: [0.8607594936709, 0.1392405063291], 0.1006248999: [0.0253164556962, 0.9746835443038], 0.2794423971: [0.3544303797468, 0.6455696202532], 0.7027719917: [0.1772151898734, 0.8227848101266], 0.685787534: [0.8481012658228, 0.1518987341772], 0.374619452: [0.5443037974684, 0.4556962025316], 0.3528280724: [0.2784810126582, 0.7215189873418], 0.438070822: [0.620253164557, 0.379746835443], 0.8194199647: [0.873417721519, 0.126582278481], 0.7832078193: [0.2784810126582, 0.7215189873418], 0.8251882711: [0.1012658227848, 0.8987341772152], 0.103509053: [0.8481012658228, 0.1518987341772], 0.4140362121: [0.6962025316456, 0.3037974683544], 0.0291619933: [0.7088607594937, 0.2911392405063], 0.555359718: [0.2784810126582, 0.7215189873418], 0.9360679378: [0.0253164556962, 0.9746835443038], 0.6114404743: [0.7088607594937, 0.2911392405063], 0.4694760455: [0.3164556962025, 0.6835443037975], 0.2714308604: [0.3291139240506, 0.6708860759494], 0.2506008652: [0.9493670886076, 0.0506329113924], 0.0599262939: [0.873417721519, 0.126582278481], 0.3983336004: [0.1392405063291, 0.8607594936709], 0.9504887037: [0.3164556962025, 0.6835443037975], 0.4300592854: [0.8860759493671, 0.1139240506329], 0.4935106553: [0.0126582278481, 0.9873417721519], 0.3403300753: [0.9620253164557, 0.0379746835443], 0.004806922: [0.0886075949367, 0.9113924050633], 0.6835443038: [0.0], 0.0865245954: [0.3924050632911, 0.6075949367089], 0.3493029963: [0.5443037974684, 0.4556962025316], 0.1730491908: [0.4683544303797, 0.5316455696203], 0.0849222881: [0.6962025316456, 0.3037974683544], 0.8030764301: [0.3291139240506, 0.6708860759494], 0.8059605832: [0.4683544303797, 0.5316455696203], 0.5486300272: [0.2151898734177, 0.7848101265823], 0.7691075148: [0.7974683544304, 0.2025316455696], 0.8088447364: [0.7721518987342, 0.2278481012658], 0.1009453613: [0.8860759493671, 0.1139240506329], 0.6755327672: [0.4303797468354, 0.5696202531646], 0.7389841372: [0.0886075949367, 0.9113924050633], 0.8146130428: [0.6329113924051, 0.3670886075949], 0.4960743471: [0.1012658227848, 0.8987341772152], 0.5050472681: [0.7721518987342, 0.2278481012658], 0.992789617: [0.2405063291139, 0.7594936708861], 0.9283768627: [0.2151898734177, 0.7848101265823], 0.550552796: [0.5569620253165, 0.4430379746835], 0.3211023874: [0.4303797468354, 0.5696202531646], 0.934145169: [0.9493670886076, 0.0506329113924], 0.1390802756: [0.0126582278481, 0.9873417721519], 0.916199327: [0.0886075949367, 0.9113924050633], 0.5592052556: [0.8481012658228, 0.1518987341772], 0.884473642: [0.2784810126582, 0.7215189873418], 0.2771991668: [0.7721518987342, 0.2278481012658], 0.5678577151: [0.1645569620253, 0.8354430379747], 0.0592853709: [0.9367088607595, 0.0632911392405], 0.3723762218: [0.7341772151899, 0.2658227848101], 0.3819900657: [0.8481012658228, 0.1518987341772], 0.2640602468: [0.1645569620253, 0.8354430379747], 0.4358275917: [0.2405063291139, 0.7594936708861], 0.9351065534: [0.2784810126582, 0.7215189873418], 0.085563211: [0.7974683544304, 0.2025316455696], 0.6845056882: [0.3544303797468, 0.6455696202532], 0.0246755328: [0.0253164556962, 0.9746835443038], 0.9985579234: [0.9620253164557, 0.0379746835443], 0.6873898414: [0.7088607594937, 0.2911392405063], 0.7953853549: [0.3924050632911, 0.6075949367089], 0.1608716552: [0.6962025316456, 0.3037974683544], 0.144848582: [0.3291139240506, 0.6708860759494], 0.8110879667: [0.3544303797468, 0.6455696202532], 0.8040378145: [0.1772151898734, 0.8227848101266], 0.4034609838: [0.2784810126582, 0.7215189873418], 0.8098061208: [0.8860759493671, 0.1139240506329], 0.4396731293: [0.873417721519, 0.126582278481], 0.5992629386: [0.2151898734177, 0.7848101265823], 0.9206857875: [0.873417721519, 0.126582278481], 0.5428617209: [0.9620253164557, 0.0379746835443], 0.4278160551: [0.9493670886076, 0.0506329113924], 0.9190834802: [0.620253164557, 0.379746835443], 0.2297708701: [0.8101265822785, 0.1898734177215], 0.9264540939: [0.1012658227848, 0.8987341772152], 0.0685787534: [0.7341772151899, 0.2658227848101], 0.4646691235: [0.6962025316456, 0.3037974683544], 0.189713187: [0.0126582278481, 0.9873417721519], 0.5543983336: [0.9493670886076, 0.0506329113924], 0.4675532767: [0.253164556962, 0.746835443038], 0.4146771351: [0.7974683544304, 0.2025316455696], 0.1012658228: [0.0], 0.0022432303: [0.8481012658228, 0.1518987341772], 0.5441435667: [0.0126582278481, 0.9873417721519], 0.0423009133: [0.7848101265823, 0.2151898734177], 0.3384073065: [0.873417721519, 0.126582278481], 0.1518987342: [0.0], 0.7979490466: [0.4050632911392, 0.5949367088608], 0.9883031565: [0.3544303797468, 0.6455696202532], 0.1983656465: [0.4683544303797, 0.5316455696203], 0.3967312931: [0.7848101265823, 0.2151898734177], 0.0150616888: [0.1012658227848, 0.8987341772152], 0.1236981253: [0.6582278481013, 0.3417721518987], 0.8021150457: [0.4303797468354, 0.5696202531646], 0.0503124499: [0.8860759493671, 0.1139240506329], 0.4024995994: [0.9493670886076, 0.0506329113924], 0.4579394328: [0.8481012658228, 0.1518987341772], 0.270469476: [0.4303797468354, 0.5696202531646], 0.5409389521: [0.873417721519, 0.126582278481], 0.5499118731: [0.3291139240506, 0.6708860759494], 0.0201890723: [0.5443037974684, 0.4556962025316], 0.0846018266: [0.9367088607595, 0.0632911392405], 0.1692036533: [0.4303797468354, 0.5696202531646], 0.9245313251: [0.4050632911392, 0.5949367088608], 0.2733536292: [0.5443037974684, 0.4556962025316], 0.803717353: [0.5569620253165, 0.4430379746835], 0.5495914116: [0.7341772151899, 0.2658227848101], 0.1163275116: [0.1012658227848, 0.8987341772152], 0.3287934626: [0.8860759493671, 0.1139240506329], 0.0583239865: [0.620253164557, 0.379746835443], 0.6604710784: [0.8481012658228, 0.1518987341772], 0.9588207018: [0.5063291139241, 0.4936708860759], 0.7870533568: [0.8481012658228, 0.1518987341772], 0.2022111841: [0.8860759493671, 0.1139240506329], 0.3345617689: [0.2405063291139, 0.7594936708861], 0.1672808845: [0.5822784810127, 0.4177215189873], 0.6720076911: [0.3164556962025, 0.6835443037975], 0.6777759974: [0.9240506329114, 0.0759493670886], 0.5556801795: [0.7721518987342, 0.2278481012658], 0.3928857555: [0.4050632911392, 0.5949367088608], 0.7242429098: [0.5822784810127, 0.4177215189873], 0.7886556642: [0.7088607594937, 0.2911392405063], 0.7460342894: [0.253164556962, 0.746835443038], 0.965870854: [0.7088607594937, 0.2911392405063], 0.7944239705: [0.7974683544304, 0.2025316455696], 0.3986540618: [0.5569620253165, 0.4430379746835], 0.7973081237: [0.0126582278481, 0.9873417721519], 0.8001922769: [0.5822784810127, 0.4177215189873], 0.5274795706: [0.4683544303797, 0.5316455696203], 0.4540938952: [0.2784810126582, 0.7215189873418], 0.5303637238: [0.7721518987342, 0.2278481012658], 0.9110719436: [0.8860759493671, 0.1139240506329], 0.5361320301: [0.6329113924051, 0.3670886075949], 0.91684025: [0.2405063291139, 0.7594936708861], 0.9226085563: [0.9620253164557, 0.0379746835443], 0.5447844897: [0.4050632911392, 0.5949367088608], 0.7300112162: [0.4683544303797, 0.5316455696203], 0.8524274956: [0.2151898734177, 0.7848101265823], 0.7934625861: [0.9367088607595, 0.0632911392405], 0.6556641564: [0.9493670886076, 0.0506329113924], 0.2300913315: [0.8481012658228, 0.1518987341772], 0.983816696: [0.4810126582278, 0.5189873417722], 0.6672007691: [0.6962025316456, 0.3037974683544], 0.6700849223: [0.253164556962, 0.746835443038], 0.3890402179: [0.873417721519, 0.126582278481], 0.194520109: [0.4303797468354, 0.5696202531646], 0.6524595417: [0.9240506329114, 0.0759493670886], 0.7867328954: [0.8101265822785, 0.1898734177215], 0.7960262778: [0.9620253164557, 0.0379746835443], 0.0493510655: [0.7721518987342, 0.2278481012658], 0.4473642045: [0.2151898734177, 0.7848101265823], 0.7896170486: [0.0886075949367, 0.9113924050633], 0.7925012017: [0.620253164557, 0.379746835443], 0.4313411312: [0.3544303797468, 0.6455696202532], 0.4236500561: [0.1392405063291, 0.8607594936709], 0.2627784009: [0.7974683544304, 0.2025316455696], 0.3537894568: [0.0253164556962, 0.9746835443038], 0.2265662554: [0.7721518987342, 0.2278481012658], 0.528440955: [0.5063291139241, 0.4936708860759], 0.2656625541: [0.0126582278481, 0.9873417721519], 0.1342733536: [0.620253164557, 0.379746835443], 0.2685467073: [0.5822784810127, 0.4177215189873], 0.3239865406: [0.5443037974684, 0.4556962025316], 0.2996314693: [0.4683544303797, 0.5316455696203], 0.6508572344: [0.7341772151899, 0.2658227848101], 0.4271751322: [0.5063291139241, 0.4936708860759], 0.0237141484: [0.2784810126582, 0.7215189873418], 0.6566255408: [0.2784810126582, 0.7215189873418], 0.2275276398: [0.8860759493671, 0.1139240506329], 0.2012497997: [0.7721518987342, 0.2278481012658], 0.8684505688: [0.620253164557, 0.379746835443], 0.1925973402: [0.5822784810127, 0.4177215189873], 0.7732735139: [0.3164556962025, 0.6835443037975], 0.7790418202: [0.9240506329114, 0.0759493670886], 0.0105752283: [0.3924050632911, 0.6075949367089], 0.3909629867: [0.9620253164557, 0.0379746835443], 0.0554398334: [0.0886075949367, 0.9113924050633], 0.7848101266: [0.0], 0.5149815735: [0.9367088607595, 0.0632911392405], 0.8956897933: [0.7974683544304, 0.2025316455696], 0.6620733857: [0.7088607594937, 0.2911392405063], 0.2246434866: [0.5063291139241, 0.4936708860759], 0.9014580997: [0.5822784810127, 0.4177215189873], 0.2618170165: [0.9367088607595, 0.0632911392405], 0.4521711264: [0.4810126582278, 0.5189873417722], 0.5265181862: [0.5443037974684, 0.4556962025316], 0.1224162794: [0.4683544303797, 0.5316455696203], 0.907226406: [0.4683544303797, 0.5316455696203], 0.6373978529: [0.6329113924051, 0.3670886075949], 0.8049991988: [0.5443037974684, 0.4556962025316], 0.0403781445: [0.1012658227848, 0.8987341772152], 0.1615125781: [0.7974683544304, 0.2025316455696], 0.0807562891: [0.0886075949367, 0.9113924050633], 0.3259093094: [0.5063291139241, 0.4936708860759], 0.135234738: [0.9367088607595, 0.0632911392405], 0.654702772: [0.4810126582278, 0.5189873417722], 0.302195161: [0.2784810126582, 0.7215189873418], 0.166960423: [0.1012658227848, 0.8987341772152], 0.731293062: [0.6582278481013, 0.3417721518987], 0.3842332959: [0.6329113924051, 0.3670886075949], 0.7684665919: [0.6962025316456, 0.3037974683544], 0.7197564493: [0.1645569620253, 0.8354430379747], 0.429097901: [0.7721518987342, 0.2278481012658], 0.7713507451: [0.253164556962, 0.746835443038], 0.2198365647: [0.4303797468354, 0.5696202531646], 0.0688992149: [0.3291139240506, 0.6708860759494], 0.8985739465: [0.0126582278481, 0.9873417721519], 0.5072904983: [0.3544303797468, 0.6455696202532], 0.8879987182: [0.8101265822785, 0.1898734177215], 0.5101746515: [0.7088607594937, 0.2911392405063], 0.0192276879: [0.1772151898734, 0.8227848101266], 0.0769107515: [0.3544303797468, 0.6455696202532], 0.2227207178: [0.5443037974684, 0.4556962025316], 0.8937670245: [0.620253164557, 0.379746835443], 0.2579714789: [0.0886075949367, 0.9113924050633], 0.4483255888: [0.7341772151899, 0.2658227848101], 0.8966511777: [0.3924050632911, 0.6075949367089], 0.518827111: [0.0126582278481, 0.9873417721519], 0.2143887198: [0.253164556962, 0.746835443038], 0.5467072585: [0.1012658227848, 0.8987341772152], 0.2608556321: [0.620253164557, 0.379746835443], 0.1304278161: [0.7088607594937, 0.2911392405063], 0.8703733376: [0.7974683544304, 0.2025316455696], 0.5245954174: [0.3291139240506, 0.6708860759494], 0.6736099984: [0.5822784810127, 0.4177215189873], 0.6325909309: [0.8860759493671, 0.1139240506329], 0.3162954655: [0.0126582278481, 0.9873417721519], 0.3191796187: [0.5822784810127, 0.4177215189873], 0.0464669124: [0.4683544303797, 0.5316455696203], 0.289056241: [0.3924050632911, 0.6075949367089], 0.3220637718: [0.3291139240506, 0.6708860759494], 0.7521230572: [0.7341772151899, 0.2658227848101], 0.324947925: [0.4683544303797, 0.5316455696203], 0.9085082519: [0.6582278481013, 0.3417721518987], 0.7578913636: [0.2784810126582, 0.7215189873418], 0.1573465791: [0.2405063291139, 0.7594936708861], 0.054478449: [0.7088607594937, 0.2911392405063], 0.6575869252: [0.0253164556962, 0.9746835443038], 0.1961224163: [0.5569620253165, 0.4430379746835], 0.7428296747: [0.9367088607595, 0.0632911392405], 0.9113924051: [0.0], 0.880307643: [0.9240506329114, 0.0759493670886], 0.1288255087: [0.8481012658228, 0.1518987341772], 0.220797949: [0.3291139240506, 0.6708860759494], 0.4415958981: [0.9620253164557, 0.0379746835443], 0.8716551835: [0.1645569620253, 0.8354430379747], 0.8860759494: [0.0], 0.2541259414: [0.3544303797468, 0.6455696202532], 0.5111360359: [0.0886075949367, 0.9113924050633], 0.1861881109: [0.6962025316456, 0.3037974683544], 0.2570100945: [0.7088607594937, 0.2911392405063], 0.1285050473: [0.8101265822785, 0.1898734177215], 0.2499599423: [0.5063291139241, 0.4936708860759], 0.2236821022: [0.4683544303797, 0.5316455696203], 0.5169043423: [0.3924050632911, 0.6075949367089], 0.3124499279: [0.9367088607595, 0.0632911392405], 0.627784009: [0.5443037974684, 0.4556962025316], 0.5364524916: [0.0886075949367, 0.9113924050633], 0.3634033008: [0.6962025316456, 0.3037974683544], 0.3951289857: [0.5822784810127, 0.4177215189873], 0.2134273354: [0.1645569620253, 0.8354430379747], 0.1736901138: [0.4810126582278, 0.5189873417722], 0.2451530203: [0.4303797468354, 0.5696202531646], 0.6422047749: [0.873417721519, 0.126582278481], 0.8793462586: [0.8607594936709, 0.1392405063291], 0.7075789136: [0.7721518987342, 0.2278481012658], 0.3765422208: [0.5063291139241, 0.4936708860759], 0.7559685948: [0.4810126582278, 0.5189873417722], 0.0474282967: [0.5063291139241, 0.4936708860759], 0.758852748: [0.0253164556962, 0.9746835443038], 0.379426374: [0.8860759493671, 0.1139240506329], 0.7617369011: [0.8481012658228, 0.1518987341772], 0.1451690434: [0.8607594936709, 0.1392405063291], 0.4348662073: [0.6329113924051, 0.3670886075949], 0.8726165679: [0.253164556962, 0.746835443038], 0.271110399: [0.7341772151899, 0.2658227848101], 0.0836404422: [0.620253164557, 0.379746835443], 0.2025316456: [0.0], 0.1611921166: [0.873417721519, 0.126582278481], 0.7325749079: [0.2784810126582, 0.7215189873418], 0.2502804038: [0.6582278481013, 0.3417721518987], 0.5034449607: [0.6582278481013, 0.3417721518987], 0.4297388239: [0.0253164556962, 0.9746835443038], 0.1265822785: [0.0], 0.9921486941: [0.0886075949367, 0.9113924050633], 0.1108796667: [0.7974683544304, 0.2025316455696], 0.9950328473: [0.620253164557, 0.379746835443], 0.6172087806: [0.7974683544304, 0.2025316455696], 0.4989585002: [0.7341772151899, 0.2658227848101], 0.6200929338: [0.0126582278481, 0.9873417721519], 0.6293863163: [0.4810126582278, 0.5189873417722], 0.3114885435: [0.620253164557, 0.379746835443], 0.6258612402: [0.3291139240506, 0.6708860759494], 0.3143726967: [0.3924050632911, 0.6075949367089], 0.6316295465: [0.7721518987342, 0.2278481012658], 0.36981253: [0.5822784810127, 0.4177215189873], 0.1791379587: [0.8101265822785, 0.1898734177215], 0.3726966832: [0.3291139240506, 0.6708860759494], 0.4008972921: [0.4683544303797, 0.5316455696203], 0.0938952091: [0.7341772151899, 0.2658227848101], 0.3784649896: [0.7721518987342, 0.2278481012658], 0.2977087005: [0.1772151898734, 0.8227848101266], 0.4922288095: [0.9620253164557, 0.0379746835443], 0.0615286012: [0.1645569620253, 0.8354430379747], 0.785771511: [0.3544303797468, 0.6455696202532], 0.9873417722: [0.0], 0.6095177055: [0.8101265822785, 0.1898734177215], 0.4246114405: [0.9240506329114, 0.0759493670886], 0.6124018587: [0.0886075949367, 0.9113924050633], 0.3076430059: [0.7088607594937, 0.2911392405063], 0.2489985579: [0.4683544303797, 0.5316455696203], 0.9959942317: [0.9367088607595, 0.0632911392405], 0.2063771831: [0.7088607594937, 0.2911392405063], 0.618170165: [0.3924050632911, 0.6075949367089], 0.6268226246: [0.1772151898734, 0.8227848101266], 0.3736580676: [0.1772151898734, 0.8227848101266], 0.8101265823: [0.0], 0.6383592373: [0.2405063291139, 0.7594936708861], 0.7434705977: [0.873417721519, 0.126582278481], 0.6011857074: [0.5569620253165, 0.4430379746835], 0.0910110559: [0.1012658227848, 0.8987341772152], 0.3640442237: [0.7974683544304, 0.2025316455696], 0.9536933184: [0.2151898734177, 0.7848101265823], 0.8543502644: [0.5569620253165, 0.4430379746835], 0.8549911873: [0.9240506329114, 0.0759493670886], 0.022752764: [0.9493670886076, 0.0506329113924], 0.8572344176: [0.4810126582278, 0.5189873417722], 0.8601185707: [0.0253164556962, 0.9746835443038], 0.2150296427: [0.0126582278481, 0.9873417721519], 0.1240185868: [0.9493670886076, 0.0506329113924], 0.8630027239: [0.8481012658228, 0.1518987341772], 0.1089568979: [0.620253164557, 0.379746835443], 0.2179137959: [0.5822784810127, 0.4177215189873], 0.8745393366: [0.3164556962025, 0.6835443037975], 0.9607434706: [0.7721518987342, 0.2278481012658], 0.3009133152: [0.6582278481013, 0.3417721518987], 0.0169844576: [0.7848101265823, 0.2151898734177], 0.6047107835: [0.6582278481013, 0.3417721518987], 0.3037974684: [0.0], 0.0679378305: [0.4303797468354, 0.5696202531646], 0.2470757891: [0.1772151898734, 0.8227848101266], 0.4941515783: [0.4050632911392, 0.5949367088608], 0.9911873097: [0.7088607594937, 0.2911392405063], 0.7338567537: [0.8860759493671, 0.1139240506329], 0.1993270309: [0.5063291139241, 0.4936708860759], 0.1941996475: [0.7848101265823, 0.2151898734177], 0.6162473963: [0.9367088607595, 0.0632911392405], 0.4303797468: [0.0], 0.9969556161: [0.7974683544304, 0.2025316455696], 0.3621214549: [0.620253164557, 0.379746835443], 0.727127063: [0.3291139240506, 0.6708860759494], 0.3650056081: [0.3924050632911, 0.6075949367089], 0.7328953693: [0.7721518987342, 0.2278481012658], 0.9053036372: [0.1772151898734, 0.8227848101266], 0.7386636757: [0.6329113924051, 0.3670886075949], 0.2637397853: [0.3924050632911, 0.6075949367089], 0.5909309406: [0.9367088607595, 0.0632911392405], 0.4262137478: [0.4683544303797, 0.5316455696203], 0.1794584201: [0.8481012658228, 0.1518987341772], 0.3060406986: [0.8481012658228, 0.1518987341772], 0.0897292101: [0.3164556962025, 0.6835443037975], 0.8998557923: [0.3164556962025, 0.6835443037975], 0.7812850505: [0.4810126582278, 0.5189873417722], 0.4223682102: [0.4303797468354, 0.5696202531646], 0.0044864605: [0.6329113924051, 0.3670886075949], 0.858195802: [0.9493670886076, 0.0506329113924], 0.9081877904: [0.5063291139241, 0.4936708860759], 0.4858195802: [0.0886075949367, 0.9113924050633], 0.4239705175: [0.5569620253165, 0.4430379746835], 0.1371575068: [0.3924050632911, 0.6075949367089], 0.4903060407: [0.873417721519, 0.126582278481], 0.6085563211: [0.3544303797468, 0.6455696202532], 0.9892645409: [0.8101265822785, 0.1898734177215], 0.3057202371: [0.8101265822785, 0.1898734177215], 0.1650376542: [0.4050632911392, 0.5949367088608], 0.2480371735: [0.5443037974684, 0.4556962025316], 0.7194359878: [0.3924050632911, 0.6075949367089], 0.0983816696: [0.6582278481013, 0.3417721518987], 0.4252523634: [0.5443037974684, 0.4556962025316], 0.9774074668: [0.5822784810127, 0.4177215189873], 0.1820221118: [0.0886075949367, 0.9113924050633], 0.7280884474: [0.1772151898734, 0.8227848101266], 0.045505528: [0.5443037974684, 0.4556962025316], 0.7309726005: [0.5063291139241, 0.4936708860759], 0.3669283769: [0.0126582278481, 0.9873417721519], 0.8447364204: [0.873417721519, 0.126582278481], 0.8505047268: [0.1012658227848, 0.8987341772152], 0.85338888: [0.7341772151899, 0.2658227848101], 0.3012337766: [0.9493670886076, 0.0506329113924], 0.9485659349: [0.253164556962, 0.746835443038], 0.6024675533: [0.5443037974684, 0.4556962025316], 0.3329594616: [0.7088607594937, 0.2911392405063], 0.8591571863: [0.2784810126582, 0.7215189873418], 0.4864605031: [0.2405063291139, 0.7594936708861], 0.2432302516: [0.5822784810127, 0.4177215189873], 0.4915878866: [0.3924050632911, 0.6075949367089], 0.9758051594: [0.3164556962025, 0.6835443037975], 0.8280724243: [0.7341772151899, 0.2658227848101], 0.600865246: [0.1392405063291, 0.8607594936709], 0.9815734658: [0.9240506329114, 0.0759493670886], 0.1772151899: [0.0], 0.2397051755: [0.253164556962, 0.746835443038], 0.2461144047: [0.3291139240506, 0.6708860759494], 0.9642685467: [0.8481012658228, 0.1518987341772], 0.0624899856: [0.253164556962, 0.746835443038], 0.3047588527: [0.3544303797468, 0.6455696202532], 0.717513219: [0.9367088607595, 0.0632911392405], 0.3999359077: [0.5443037974684, 0.4556962025316], 0.0384553757: [0.4050632911392, 0.5949367088608], 0.2723922448: [0.1772151898734, 0.8227848101266], 0.153821503: [0.8101265822785, 0.1898734177215], 0.388399295: [0.9367088607595, 0.0632911392405], 0.42012498: [0.1012658227848, 0.8987341772152], 0.7261656786: [0.4303797468354, 0.5696202531646], 0.8341611921: [0.7721518987342, 0.2278481012658], 0.7290498318: [0.5443037974684, 0.4556962025316], 0.013779843: [0.3164556962025, 0.6835443037975], 0.8399294985: [0.6329113924051, 0.3670886075949], 0.3396891524: [0.3924050632911, 0.6075949367089], 0.6063130909: [0.7721518987342, 0.2278481012658], 0.424290979: [0.1772151898734, 0.8227848101266], 0.9857394648: [0.2784810126582, 0.7215189873418], 0.3717352988: [0.4303797468354, 0.5696202531646], 0.9594616247: [0.9493670886076, 0.0506329113924], 0.4797308124: [0.7721518987342, 0.2278481012658], 0.5816375581: [0.0253164556962, 0.9746835443038], 0.5287614164: [0.6582278481013, 0.3417721518987], 0.0730652139: [0.6582278481013, 0.3417721518987], 0.0182663035: [0.3291139240506, 0.6708860759494], 0.3278320782: [0.7721518987342, 0.2278481012658], 0.9870213107: [0.8860759493671, 0.1139240506329], 0.4854991187: [0.6329113924051, 0.3670886075949], 0.4230091332: [0.7341772151899, 0.2658227848101], 0.5931741708: [0.1645569620253, 0.8354430379747], 0.9729210062: [0.1645569620253, 0.8354430379747], 0.9738823906: [0.253164556962, 0.746835443038], 0.298029162: [0.9240506329114, 0.0759493670886], 0.1137638199: [0.0126582278481, 0.9873417721519], 0.6018266303: [0.9240506329114, 0.0759493670886], 0.1057522833: [0.6329113924051, 0.3670886075949], 0.1701650377: [0.3291139240506, 0.6708860759494], 0.7098221439: [0.3544303797468, 0.6455696202532], 0.3563531485: [0.8101265822785, 0.1898734177215], 0.0445441436: [0.1772151898734, 0.8227848101266], 0.7184746034: [0.7974683544304, 0.2025316455696], 0.7213587566: [0.0126582278481, 0.9873417721519], 0.1201730492: [0.5569620253165, 0.4430379746835], 0.2073385675: [0.0886075949367, 0.9113924050633], 0.8322384233: [0.5063291139241, 0.4936708860759], 0.8351225765: [0.8860759493671, 0.1139240506329], 0.4576189713: [0.8101265822785, 0.1898734177215], 0.4044223682: [0.0253164556962, 0.9746835443038], 0.4204454414: [0.5822784810127, 0.4177215189873], 0.0262778401: [0.3544303797468, 0.6455696202532], 0.4758852748: [0.5443037974684, 0.4556962025316], 0.8466591892: [0.9620253164557, 0.0379746835443], 0.9546547028: [0.7341772151899, 0.2658227848101], 0.5797147893: [0.9493670886076, 0.0506329113924], 0.9604230091: [0.2784810126582, 0.7215189873418], 0.8780644128: [0.4303797468354, 0.5696202531646], 0.6152860119: [0.620253164557, 0.379746835443], 0.5402980292: [0.9367088607595, 0.0632911392405], 0.591251402: [0.6962025316456, 0.3037974683544], 0.3336003846: [0.6329113924051, 0.3670886075949], 0.3762217593: [0.4810126582278, 0.5189873417722], 0.587085403: [0.0886075949367, 0.9113924050633], 0.5941355552: [0.253164556962, 0.746835443038], 0.1826630348: [0.2405063291139, 0.7594936708861], 0.3970517545: [0.4303797468354, 0.5696202531646], 0.4287774395: [0.2784810126582, 0.7215189873418], 0.7021310687: [0.8607594936709, 0.1392405063291], 0.921006249: [0.7974683544304, 0.2025316455696], 0.0996635155: [0.2784810126582, 0.7215189873418], 0.0490306041: [0.2784810126582, 0.7215189873418], 0.7107835283: [0.8101265822785, 0.1898734177215], 0.7136676815: [0.0886075949367, 0.9113924050633], 0.7165518346: [0.620253164557, 0.379746835443], 0.7306521391: [0.4810126582278, 0.5189873417722], 0.5588847941: [0.8101265822785, 0.1898734177215], 0.3512257651: [0.5063291139241, 0.4936708860759], 0.8274315014: [0.4303797468354, 0.5696202531646], 0.8303156545: [0.5443037974684, 0.4556962025316], 0.262137478: [0.6962025316456, 0.3037974683544], 0.293863163: [0.5822784810127, 0.4177215189873], 0.6511776959: [0.3291139240506, 0.6708860759494], 0.162794424: [0.1645569620253, 0.8354430379747], 0.0124979971: [0.0126582278481, 0.9873417721519], 0.7780804358: [0.8607594936709, 0.1392405063291], 0.4524915879: [0.5063291139241, 0.4936708860759], 0.4947925012: [0.3164556962025, 0.6835443037975], 0.081397212: [0.2405063291139, 0.7594936708861], 0.2909790098: [0.0126582278481, 0.9873417721519], 0.4749238904: [0.1772151898734, 0.8227848101266], 0.5659349463: [0.6962025316456, 0.3037974683544], 0.5720237141: [0.1012658227848, 0.8987341772152], 0.5749078673: [0.7341772151899, 0.2658227848101], 0.6540618491: [0.4683544303797, 0.5316455696203], 0.4778080436: [0.5063291139241, 0.4936708860759], 0.4089088287: [0.7088607594937, 0.2911392405063], 0.9585002403: [0.4810126582278, 0.5189873417722], 0.3230251562: [0.1772151898734, 0.8227848101266], 0.2903380868: [0.253164556962, 0.746835443038], 0.2403460984: [0.0126582278481, 0.9873417721519], 0.8883191796: [0.8481012658228, 0.1518987341772], 0.6944399936: [0.1645569620253, 0.8354430379747], 0.5300432623: [0.2784810126582, 0.7215189873418], 0.0871655183: [0.9620253164557, 0.0379746835443], 0.3486620734: [0.9240506329114, 0.0759493670886], 0.0108956898: [0.1645569620253, 0.8354430379747], 0.1483736581: [0.4810126582278, 0.5189873417722], 0.6569460022: [0.7721518987342, 0.2278481012658], 0.7030924531: [0.9240506329114, 0.0759493670886], 0.8473001122: [0.253164556962, 0.746835443038], 0.7059766063: [0.6582278481013, 0.3417721518987], 0.9107514821: [0.0253164556962, 0.9746835443038], 0.4069860599: [0.8101265822785, 0.1898734177215], 0.8197404262: [0.7974683544304, 0.2025316455696], 0.8226245794: [0.0126582278481, 0.9873417721519], 0.0096138439: [0.7974683544304, 0.2025316455696], 0.8255087326: [0.5822784810127, 0.4177215189873], 0.8283928858: [0.3291139240506, 0.6708860759494], 0.596058324: [0.3164556962025, 0.6835443037975], 0.266303477: [0.4050632911392, 0.5949367088608], 0.8312770389: [0.4683544303797, 0.5316455696203], 0.4156385195: [0.3924050632911, 0.6075949367089], 0.2807242429: [0.8481012658228, 0.1518987341772], 0.4710783528: [0.5822784810127, 0.4177215189873], 0.7748758212: [0.5822784810127, 0.4177215189873], 0.1656785772: [0.3164556962025, 0.6835443037975], 0.3630828393: [0.9367088607595, 0.0632911392405], 0.1974042621: [0.5443037974684, 0.4556962025316], 0.473962506: [0.3291139240506, 0.6708860759494], 0.5701009454: [0.4050632911392, 0.5949367088608], 0.9799711585: [0.7341772151899, 0.2658227848101], 0.4768466592: [0.4683544303797, 0.5316455696203], 0.5758692517: [0.5569620253165, 0.4430379746835], 0.5787534049: [0.4810126582278, 0.5189873417722], 0.6925172248: [0.6962025316456, 0.3037974683544], 0.695401378: [0.253164556962, 0.746835443038], 0.5832398654: [0.3544303797468, 0.6455696202532], 0.6466912354: [0.3164556962025, 0.6835443037975], 0.0756289056: [0.8860759493671, 0.1139240506329], 0.7219996795: [0.4050632911392, 0.5949367088608], 0.3025156225: [0.7721518987342, 0.2278481012658], 0.8120493511: [0.8101265822785, 0.1898734177215], 0.8972921006: [0.9620253164557, 0.0379746835443], 0.4819740426: [0.3544303797468, 0.6455696202532], 0.8149335042: [0.0886075949367, 0.9113924050633], 0.8178176574: [0.620253164557, 0.379746835443], 0.8207018106: [0.3924050632911, 0.6075949367089], 0.5861240186: [0.7088607594937, 0.2911392405063], 0.4643486621: [0.9367088607595, 0.0632911392405], 0.4742829675: [0.1392405063291, 0.8607594936709], 0.3310366928: [0.8101265822785, 0.1898734177215], 0.9315814773: [0.5443037974684, 0.4556962025316], 0.0954975164: [0.9240506329114, 0.0759493670886], 0.5537574107: [0.5063291139241, 0.4936708860759], 0.0173049191: [0.4303797468354, 0.5696202531646], 0.5566415639: [0.8860759493671, 0.1139240506329], 0.9033808685: [0.4303797468354, 0.5696202531646], 0.5255568018: [0.1772151898734, 0.8227848101266], 0.3262297709: [0.6582278481013, 0.3417721518987], 0.4730011216: [0.4303797468354, 0.5696202531646], 0.5681781766: [0.9620253164557, 0.0379746835443], 0.3380868451: [0.6962025316456, 0.3037974683544], 0.4531325108: [0.9493670886076, 0.0506329113924], 0.5739464829: [0.2151898734177, 0.7848101265823], 0.9697163916: [0.620253164557, 0.379746835443], 0.1704854991: [0.8607594936709, 0.1392405063291], 0.0426213748: [0.4303797468354, 0.5696202531646], 0.9556160872: [0.5569620253165, 0.4430379746835], 0.1996474924: [0.6582278481013, 0.3417721518987], 0.4175612883: [0.0126582278481, 0.9873417721519], 0.2743150136: [0.4683544303797, 0.5316455696203], 0.9460022432: [0.873417721519, 0.126582278481], 0.402179138: [0.6582278481013, 0.3417721518987], 0.8072424291: [0.6582278481013, 0.3417721518987], 0.5313251082: [0.8860759493671, 0.1139240506329], 0.5947764781: [0.0126582278481, 0.9873417721519], 0.0506329114: [0.0], 0.4496074347: [0.1772151898734, 0.8227848101266], 0.2973882391: [0.5569620253165, 0.4430379746835], 0.3291139241: [0.0], 0.2121454895: [0.7974683544304, 0.2025316455696], 0.8187790418: [0.9367088607595, 0.0632911392405], 0.2316936388: [0.7088607594937, 0.2911392405063], 0.4633872777: [0.620253164557, 0.379746835443], 0.5489504887: [0.4303797468354, 0.5696202531646], 0.9296587085: [0.3291139240506, 0.6708860759494], 0.2759173209: [0.9493670886076, 0.0506329113924], 0.4662714309: [0.3924050632911, 0.6075949367089], 0.3034770069: [0.8860759493671, 0.1139240506329], 0.9062650216: [0.5443037974684, 0.4556962025316], 0.9411953213: [0.6329113924051, 0.3670886075949], 0.5662554078: [0.873417721519, 0.126582278481], 0.5370934145: [0.2405063291139, 0.7594936708861], 0.3002723922: [0.4810126582278, 0.5189873417722], 0.6771350745: [0.5569620253165, 0.4430379746835], 0.0961384394: [0.5443037974684, 0.4556962025316], 0.7274475244: [0.8607594936709, 0.1392405063291], 0.6829033809: [0.0253164556962, 0.9746835443038], 0.1502964269: [0.2784810126582, 0.7215189873418], 0.1714468835: [0.9240506329114, 0.0759493670886], 0.7937830476: [0.6962025316456, 0.3037974683544], 0.7966672008: [0.253164556962, 0.746835443038], 0.0551193719: [0.6329113924051, 0.3670886075949], 0.6668803076: [0.9367088607595, 0.0632911392405], 0.9998397693: [0.0126582278481, 0.9873417721519], 0.5845217113: [0.8481012658228, 0.1518987341772], 0.9716391604: [0.7974683544304, 0.2025316455696], 0.4595417401: [0.7088607594937, 0.2911392405063], 0.6479730812: [0.1012658227848, 0.8987341772152], 0.5412594136: [0.7974683544304, 0.2025316455696], 0.036532607: [0.9620253164557, 0.0379746835443], 0.1951610319: [0.7341772151899, 0.2658227848101], 0.9219676334: [0.3924050632911, 0.6075949367089], 0.1377984297: [0.9620253164557, 0.0379746835443], 0.2720717834: [0.5569620253165, 0.4430379746835], 0.5470277199: [0.5822784810127, 0.4177215189873], 0.1374779683: [0.1645569620253, 0.8354430379747], 0.2326550232: [0.0886075949367, 0.9113924050633], 0.4653100465: [0.7974683544304, 0.2025316455696], 0.2111841051: [0.9367088607595, 0.0632911392405], 0.5527960263: [0.4683544303797, 0.5316455696203], 0.9335042461: [0.5063291139241, 0.4936708860759], 0.2778400897: [0.0253164556962, 0.9746835443038], 0.4681941996: [0.0126582278481, 0.9873417721519], 0.663675693: [0.2405063291139, 0.7594936708861], 0.0701810607: [0.9240506329114, 0.0759493670886], 0.9046627143: [0.8607594936709, 0.1392405063291], 0.6694439994: [0.9620253164557, 0.0379746835443], 0.9152379426: [0.7088607594937, 0.2911392405063], 0.6793783048: [0.4683544303797, 0.5316455696203], 0.6752123057: [0.2151898734177, 0.7848101265823], 0.6809806121: [0.9493670886076, 0.0506329113924], 0.7806441275: [0.4683544303797, 0.5316455696203], 0.0118570742: [0.253164556962, 0.746835443038], 0.8440954975: [0.9367088607595, 0.0632911392405], 0.9709982375: [0.6962025316456, 0.3037974683544], 0.9498477808: [0.4050632911392, 0.5949367088608], 0.8033968915: [0.8607594936709, 0.1392405063291], 0.4556962025: [0.0], 0.2278481013: [0.0], 0.5335683384: [0.8101265822785, 0.1898734177215], 0.2682262458: [0.1012658227848, 0.8987341772152], 0.5393366448: [0.620253164557, 0.379746835443], 0.9200448646: [0.9367088607595, 0.0632911392405], 0.1355551995: [0.6962025316456, 0.3037974683544], 0.0929338247: [0.7848101265823, 0.2151898734177], 0.149014581: [0.6582278481013, 0.3417721518987], 0.9286973241: [0.4303797468354, 0.5696202531646], 0.4774875821: [0.4810126582278, 0.5189873417722], 0.5508732575: [0.1772151898734, 0.8227848101266], 0.9806120814: [0.8607594936709, 0.1392405063291], 0.0692196763: [0.8607594936709, 0.1392405063291], 0.2768787053: [0.2784810126582, 0.7215189873418], 0.5354911072: [0.7088607594937, 0.2911392405063], 0.6675212306: [0.873417721519, 0.126582278481], 0.01313892: [0.4050632911392, 0.5949367088608], 0.6732895369: [0.1012658227848, 0.8987341772152], 0.3906425252: [0.1645569620253, 0.8354430379747], 0.7841692037: [0.0253164556962, 0.9746835443038], 0.0804358276: [0.6329113924051, 0.3670886075949], 0.3935266784: [0.3164556962025, 0.6835443037975], 0.8992148694: [0.4050632911392, 0.5949367088608], 0.3851946803: [0.2405063291139, 0.7594936708861], 0.4486460503: [0.3291139240506, 0.6708860759494], 0.7957058164: [0.1645569620253, 0.8354430379747], 0.5518346419: [0.5443037974684, 0.4556962025316], 0.5258772633: [0.9240506329114, 0.0759493670886], 0.2643807082: [0.9620253164557, 0.0379746835443], 0.5316455696: [0.0], 0.9123537895: [0.3544303797468, 0.6455696202532], 0.1144047428: [0.4050632911392, 0.5949367088608], 0.2288094857: [0.3544303797468, 0.6455696202532], 0.2701490146: [0.7848101265823, 0.2151898734177], 0.4605031245: [0.0886075949367, 0.9113924050633], 0.6482935427: [0.5822784810127, 0.4177215189873], 0.3255888479: [0.4810126582278, 0.5189873417722], 0.040698606: [0.5822784810127, 0.4177215189873], 0.1060727448: [0.0886075949367, 0.9113924050633], 0.7127062971: [0.7088607594937, 0.2911392405063], 0.1781765743: [0.3544303797468, 0.6455696202532], 0.3284730011: [0.0253164556962, 0.9746835443038], 0.7024515302: [0.5569620253165, 0.4430379746835], 0.0009613844: [0.3544303797468, 0.6455696202532], 0.903060407: [0.2151898734177, 0.7848101265823], 0.4832558885: [0.8481012658228, 0.1518987341772], 0.6627143086: [0.6329113924051, 0.3670886075949], 0.7764781285: [0.2151898734177, 0.7848101265823], 0.9895850024: [0.8481012658228, 0.1518987341772], 0.0019227688: [0.8101265822785, 0.1898734177215], 0.2877743951: [0.873417721519, 0.126582278481], 0.7822464349: [0.9493670886076, 0.0506329113924], 0.0644127544: [0.3164556962025, 0.6835443037975], 0.1320301234: [0.2405063291139, 0.7594936708861], 0.5915718635: [0.873417721519, 0.126582278481], 0.5181861881: [0.253164556962, 0.746835443038], 0.6550232335: [0.5063291139241, 0.4936708860759], 0.3592373017: [0.0886075949367, 0.9113924050633], 0.7819259734: [0.6582278481013, 0.3417721518987], 0.9613843935: [0.0253164556962, 0.9746835443038], 0.1954814934: [0.3291139240506, 0.6708860759494], 0.2272071783: [0.0253164556962, 0.9746835443038], 0.532606954: [0.3544303797468, 0.6455696202532], 0.4566575869: [0.3544303797468, 0.6455696202532], 0.6406024676: [0.620253164557, 0.379746835443], 0.3217433104: [0.7341772151899, 0.2658227848101], 0.8899214869: [0.7088607594937, 0.2911392405063], 0.876141644: [0.5822784810127, 0.4177215189873], 0.6521390803: [0.1772151898734, 0.8227848101266], 0.3275116167: [0.2784810126582, 0.7215189873418], 0.1637558084: [0.253164556962, 0.746835443038], 0.5973401698: [0.1012658227848, 0.8987341772152], 0.6579073866: [0.8860759493671, 0.1139240506329], 0.1756128826: [0.2784810126582, 0.7215189873418], 0.8293542701: [0.1772151898734, 0.8227848101266], 0.7396250601: [0.2405063291139, 0.7594936708861], 0.478128505: [0.6582278481013, 0.3417721518987], 0.7745553597: [0.1012658227848, 0.8987341772152], 0.7774395129: [0.7341772151899, 0.2658227848101], 0.9966351546: [0.873417721519, 0.126582278481], 0.7687870534: [0.873417721519, 0.126582278481], 0.4441595898: [0.3164556962025, 0.6835443037975], 0.1461304278: [0.9240506329114, 0.0759493670886], 0.8969716392: [0.1645569620253, 0.8354430379747], 0.6447684666: [0.253164556962, 0.746835443038], 0.112481974: [0.9620253164557, 0.0379746835443], 0.2249639481: [0.6582278481013, 0.3417721518987], 0.6832238423: [0.8860759493671, 0.1139240506329], 0.7082198366: [0.0253164556962, 0.9746835443038], 0.2624579394: [0.873417721519, 0.126582278481], 0.20349303: [0.3544303797468, 0.6455696202532], 0.6514981573: [0.1392405063291, 0.8607594936709], 0.4528120494: [0.6582278481013, 0.3417721518987], 0.4492869732: [0.5569620253165, 0.4430379746835], 0.4810126582: [0.0], 0.6415638519: [0.9367088607595, 0.0632911392405], 0.6502163115: [0.4303797468354, 0.5696202531646], 0.3791059125: [0.0253164556962, 0.9746835443038], 0.3265502323: [0.9493670886076, 0.0506329113924], 0.9133151739: [0.8101265822785, 0.1898734177215], 0.0086524595: [0.9367088607595, 0.0632911392405], 0.523634033: [0.4303797468354, 0.5696202531646], 0.2935427015: [0.1012658227848, 0.8987341772152], 0.2323345618: [0.6329113924051, 0.3670886075949], 0.0477487582: [0.6582278481013, 0.3417721518987], 0.3887197564: [0.6962025316456, 0.3037974683544], 0.2102227207: [0.620253164557, 0.379746835443], 0.7726325909: [0.4050632911392, 0.5949367088608], 0.0525556802: [0.8101265822785, 0.1898734177215], 0.9043422528: [0.3291139240506, 0.6708860759494], 0.8835122577: [0.9493670886076, 0.0506329113924], 0.2528440955: [0.8860759493671, 0.1139240506329], 0.5085723442: [0.8481012658228, 0.1518987341772], 0.1743310367: [0.6582278481013, 0.3417721518987], 0.8950488704: [0.6962025316456, 0.3037974683544], 0.5172248037: [0.1645569620253, 0.8354430379747], 0.970677776: [0.9367088607595, 0.0632911392405], 0.4489665118: [0.1392405063291, 0.8607594936709], 0.5201089569: [0.3164556962025, 0.6835443037975], 0.017945842: [0.7341772151899, 0.2658227848101], 0.5928537093: [0.3924050632911, 0.6075949367089], 0.6338727768: [0.3544303797468, 0.6455696202532], 0.3916039096: [0.253164556962, 0.746835443038], 0.9517705496: [0.1012658227848, 0.8987341772152], 0.63675693: [0.7088607594937, 0.2911392405063], 0.4233295946: [0.3291139240506, 0.6708860759494], 0.6425252363: [0.7974683544304, 0.2025316455696], 0.6454093895: [0.0126582278481, 0.9873417721519], 0.7534049031: [0.1772151898734, 0.8227848101266], 0.6591892325: [0.3544303797468, 0.6455696202532], 0.094536132: [0.8607594936709, 0.1392405063291], 0.3781445281: [0.2784810126582, 0.7215189873418], 0.7591732094: [0.8860759493671, 0.1139240506329], 0.7649415158: [0.2405063291139, 0.7594936708861], 0.4435186669: [0.4050632911392, 0.5949367088608], 0.7037333761: [0.5443037974684, 0.4556962025316], 0.8758211825: [0.1012658227848, 0.8987341772152], 0.7707098221: [0.9620253164557, 0.0379746835443], 0.383592373: [0.7088607594937, 0.2911392405063], 0.4393526678: [0.6962025316456, 0.3037974683544], 0.5037654222: [0.9493670886076, 0.0506329113924], 0.1259413556: [0.0253164556962, 0.9746835443038], 0.442236821: [0.253164556962, 0.746835443038], 0.0618490627: [0.9620253164557, 0.0379746835443], 0.3377663836: [0.9367088607595, 0.0632911392405], 0.9982374619: [0.1645569620253, 0.8354430379747], 0.5153020349: [0.6962025316456, 0.3037974683544], 0.275276398: [0.5063291139241, 0.4936708860759], 0.1022272072: [0.3544303797468, 0.6455696202532], 0.3130908508: [0.873417721519, 0.126582278481], 0.6261817017: [0.8607594936709, 0.1392405063291], 0.9293382471: [0.7341772151899, 0.2658227848101], 0.6460503125: [0.4050632911392, 0.5949367088608], 0.6348341612: [0.8101265822785, 0.1898734177215], 0.3714148374: [0.7848101265823, 0.2151898734177], 0.3188591572: [0.1012658227848, 0.8987341772152], 0.8364044224: [0.3544303797468, 0.6455696202532], 0.4499278962: [0.9240506329114, 0.0759493670886], 0.0076910751: [0.620253164557, 0.379746835443], 0.7514821343: [0.4303797468354, 0.5696202531646], 0.0890882871: [0.4050632911392, 0.5949367088608], 0.3771831437: [0.9493670886076, 0.0506329113924], 0.7543662875: [0.5443037974684, 0.4556962025316], 0.7639801314: [0.6329113924051, 0.3670886075949], 0.0307643006: [0.2405063291139, 0.7594936708861], 0.4326229771: [0.8481012658228, 0.1518987341772], 0.2669443999: [0.3164556962025, 0.6835443037975], 0.7364204454: [0.8481012658228, 0.1518987341772], 0.8738984137: [0.4050632911392, 0.5949367088608], 0.5249158789: [0.1392405063291, 0.8607594936709], 0.8402499599: [0.0886075949367, 0.9113924050633], 0.6518186188: [0.5569620253165, 0.4430379746835], 0.8796667201: [0.5569620253165, 0.4430379746835], 0.4412754366: [0.1645569620253, 0.8354430379747], 0.3893606794: [0.7974683544304, 0.2025316455696], 0.5047268066: [0.2784810126582, 0.7215189873418], 0.8854350264: [0.0253164556962, 0.9746835443038], 0.9056240987: [0.9240506329114, 0.0759493670886], 0.9963146932: [0.6962025316456, 0.3037974683544], 0.6184906265: [0.1645569620253, 0.8354430379747], 0.3092453133: [0.2405063291139, 0.7594936708861], 0.4995994232: [0.1392405063291, 0.8607594936709], 0.966511777: [0.6329113924051, 0.3670886075949], 0.6213747797: [0.3164556962025, 0.6835443037975], 0.8232655023: [0.4050632911392, 0.5949367088608], 0.627143086: [0.9240506329114, 0.0759493670886], 0.6300272392: [0.6582278481013, 0.3417721518987], 0.6329113924: [0.0], 0.622977087: [0.5822784810127, 0.4177215189873], 0.5723441756: [0.5822784810127, 0.4177215189873], 0.2970677776: [0.8607594936709, 0.1392405063291], 0.7437910591: [0.7974683544304, 0.2025316455696], 0.7466752123: [0.0126582278481, 0.9873417721519], 0.4550552796: [0.0253164556962, 0.9746835443038], 0.7495593655: [0.5822784810127, 0.4177215189873], 0.2243230252: [0.4810126582278, 0.5189873417722], 0.5819580195: [0.8860759493671, 0.1139240506329], 0.7524435187: [0.3291139240506, 0.6708860759494], 0.8604390322: [0.8860759493671, 0.1139240506329], 0.7553276718: [0.4683544303797, 0.5316455696203], 0.8662073386: [0.2405063291139, 0.7594936708861], 0.8719756449: [0.9620253164557, 0.0379746835443], 0.1958019548: [0.8607594936709, 0.1392405063291], 0.8777439513: [0.2151898734177, 0.7848101265823], 0.578112482: [0.4683544303797, 0.5316455696203], 0.4928697324: [0.253164556962, 0.746835443038], 0.320781926: [0.7848101265823, 0.2151898734177], 0.5028040378: [0.4810126582278, 0.5189873417722], 0.3948085243: [0.1012658227848, 0.8987341772152], 0.4159589809: [0.1645569620253, 0.8354430379747], 0.8953693318: [0.873417721519, 0.126582278481], 0.4794103509: [0.2784810126582, 0.7215189873418], 0.3082839289: [0.6329113924051, 0.3670886075949], 0.6194520109: [0.253164556962, 0.746835443038], 0.2550873257: [0.8101265822785, 0.1898734177215], 0.8319179619: [0.4810126582278, 0.5189873417722], 0.834802115: [0.0253164556962, 0.9746835443038], 0.736099984: [0.8101265822785, 0.1898734177215], 0.3694920686: [0.1012658227848, 0.8987341772152], 0.7418682903: [0.620253164557, 0.379746835443], 0.7447524435: [0.3924050632911, 0.6075949367089], 0.8527479571: [0.4303797468354, 0.5696202531646], 0.3553917641: [0.3544303797468, 0.6455696202532], 0.8556321102: [0.5443037974684, 0.4556962025316], 0.4188431341: [0.3164556962025, 0.6835443037975], 0.9011376382: [0.1012658227848, 0.8987341772152], 0.9751642365: [0.4050632911392, 0.5949367088608], 0.8700528761: [0.873417721519, 0.126582278481], 0.1493350425: [0.9493670886076, 0.0506329113924], 0.126261817: [0.8860759493671, 0.1139240506329], 0.9860599263: [0.7721518987342, 0.2278481012658], 0.8287133472: [0.8607594936709, 0.1392405063291], 0.600224323: [0.7341772151899, 0.2658227848101], 0.9809325429: [0.5569620253165, 0.4430379746835], 0.491908348: [0.1645569620253, 0.8354430379747], 0.6059926294: [0.2784810126582, 0.7215189873418], 0.2839288576: [0.2405063291139, 0.7594936708861], 0.9867008492: [0.0253164556962, 0.9746835443038], 0.0695401378: [0.5569620253165, 0.4430379746835], 0.6947604551: [0.9620253164557, 0.0379746835443], 0.2473962506: [0.9240506329114, 0.0759493670886], 0.758211825: [0.7721518987342, 0.2278481012658], 0.6531004647: [0.5443037974684, 0.4556962025316], 0.3582759173: [0.7088607594937, 0.2911392405063], 0.7226406025: [0.3164556962025, 0.6835443037975], 0.7284089088: [0.9240506329114, 0.0759493670886], 0.3134113123: [0.7974683544304, 0.2025316455696], 0.365646531: [0.9620253164557, 0.0379746835443], 0.7341772152: [0.0], 0.8450568819: [0.7974683544304, 0.2025316455696], 0.8479410351: [0.0126582278481, 0.9873417721519], 0.1275436629: [0.3544303797468, 0.6455696202532], 0.8508251883: [0.5822784810127, 0.4177215189873], 0.8408908829: [0.2405063291139, 0.7594936708861], 0.4268546707: [0.4810126582278, 0.5189873417722], 0.8537093415: [0.3291139240506, 0.6708860759494], 0.0054478449: [0.2405063291139, 0.7594936708861], 0.7005287614: [0.2151898734177, 0.7848101265823], 0.8565934946: [0.4683544303797, 0.5316455696203], 0.1909950328: [0.3164556962025, 0.6835443037975], 0.8594776478: [0.7721518987342, 0.2278481012658], 0.4137157507: [0.9367088607595, 0.0632911392405], 0.4454414357: [0.1012658227848, 0.8987341772152], 0.9674731614: [0.2405063291139, 0.7594936708861], 0.6691235379: [0.1645569620253, 0.8354430379747], 0.2163114885: [0.3164556962025, 0.6835443037975], 0.1999679539: [0.9493670886076, 0.0506329113924], 0.9732414677: [0.9620253164557, 0.0379746835443], 0.5954174011: [0.4050632911392, 0.5949367088608], 0.9790097741: [0.2151898734177, 0.7848101265823], 0.3005928537: [0.5063291139241, 0.4936708860759], 0.6040698606: [0.4810126582278, 0.5189873417722], 0.9847780804: [0.9493670886076, 0.0506329113924], 0.6069540138: [0.0253164556962, 0.9746835443038], 0.609838167: [0.8481012658228, 0.1518987341772], 0.3460983817: [0.7848101265823, 0.2151898734177], 0.5159429579: [0.7974683544304, 0.2025316455696], 0.2896971639: [0.9620253164557, 0.0379746835443], 0.7178336805: [0.6962025316456, 0.3037974683544], 0.3589168403: [0.6329113924051, 0.3670886075949], 0.0362121455: [0.1645569620253, 0.8354430379747], 0.7207178337: [0.253164556962, 0.746835443038], 0.3531485339: [0.7721518987342, 0.2278481012658], 0.2448325589: [0.7848101265823, 0.2151898734177], 0.2922608556: [0.3164556962025, 0.6835443037975], 0.0298029162: [0.6329113924051, 0.3670886075949], 0.0189072264: [0.5569620253165, 0.4430379746835], 0.8373658068: [0.8101265822785, 0.1898734177215], 0.7351385996: [0.3544303797468, 0.6455696202532], 0.8431341131: [0.620253164557, 0.379746835443], 0.8460182663: [0.3924050632911, 0.6075949367089], 0.2115045666: [0.6962025316456, 0.3037974683544], 0.8546707258: [0.1772151898734, 0.8227848101266], 0.5217112642: [0.5822784810127, 0.4177215189873], 0.857554879: [0.5063291139241, 0.4936708860759], 0.7062970678: [0.9493670886076, 0.0506329113924], 0.0471078353: [0.4810126582278, 0.5189873417722], 0.2893767025: [0.1645569620253, 0.8354430379747], 0.5877263259: [0.2405063291139, 0.7594936708861], 0.9713186989: [0.873417721519, 0.126582278481], 0.2967473161: [0.3291139240506, 0.6708860759494], 0.5934946323: [0.9620253164557, 0.0379746835443], 0.1877904182: [0.3924050632911, 0.6075949367089], 0.9770870053: [0.1012658227848, 0.8987341772152], 0.6297067778: [0.5063291139241, 0.4936708860759], 0.4899855792: [0.6962025316456, 0.3037974683544], 0.7566095177: [0.6582278481013, 0.3417721518987], 0.0378144528: [0.0126582278481, 0.9873417721519], 0.605031245: [0.9493670886076, 0.0506329113924], 0.71334722: [0.6329113924051, 0.3670886075949], 0.64989585: [0.2151898734177, 0.7848101265823], 0.505688191: [0.0253164556962, 0.9746835443038], 0.3480211505: [0.5569620253165, 0.4430379746835], 0.0224323025: [0.6582278481013, 0.3417721518987], 0.3637237622: [0.873417721519, 0.126582278481], 0.835443038: [0.0], 0.2374619452: [0.7974683544304, 0.2025316455696], 0.4220477488: [0.2151898734177, 0.7848101265823], 0.0717833681: [0.4683544303797, 0.5316455696203], 0.2387437911: [0.1645569620253, 0.8354430379747], 0.9578593174: [0.4683544303797, 0.5316455696203], 0.4803717353: [0.0253164556962, 0.9746835443038], 0.2874539337: [0.6962025316456, 0.3037974683544], 0.1595898093: [0.620253164557, 0.379746835443], 0.1208139721: [0.9240506329114, 0.0759493670886], 0.9793302355: [0.4303797468354, 0.5696202531646], 0.0797949047: [0.7088607594937, 0.2911392405063], 0.2957859317: [0.4303797468354, 0.5696202531646], 0.0746675212: [0.7721518987342, 0.2278481012658], 0.0878064413: [0.253164556962, 0.746835443038], 0.3544303797: [0.0], 0.7053356834: [0.4810126582278, 0.5189873417722], 0.3541099183: [0.8860759493671, 0.1139240506329], 0.0942156706: [0.3291139240506, 0.6708860759494], 0.7111039897: [0.8481012658228, 0.1518987341772], 0.5435026438: [0.253164556962, 0.746835443038], 0.3598782246: [0.2405063291139, 0.7594936708861], 0.2586124019: [0.2405063291139, 0.7594936708861], 0.5806761737: [0.2784810126582, 0.7215189873418], 0.1631148854: [0.9620253164557, 0.0379746835443], 0.6441275437: [0.9620253164557, 0.0379746835443], 0.1768947284: [0.8860759493671, 0.1139240506329], 0.4182022112: [0.4050632911392, 0.5949367088608], 0.8392885755: [0.7088607594937, 0.2911392405063], 0.0708219837: [0.5443037974684, 0.4556962025316], 0.4806921968: [0.8860759493671, 0.1139240506329], 0.2368210223: [0.6962025316456, 0.3037974683544], 0.2861720878: [0.620253164557, 0.379746835443], 0.5752283288: [0.3291139240506, 0.6708860759494], 0.9559365486: [0.1772151898734, 0.8227848101266], 0.1445281205: [0.7341772151899, 0.2658227848101], 0.1166479731: [0.5822784810127, 0.4177215189873], 0.1198525877: [0.8607594936709, 0.1392405063291], 0.5809966352: [0.7721518987342, 0.2278481012658], 0.961704855: [0.8860759493671, 0.1139240506329], 0.8376862682: [0.8481012658228, 0.1518987341772], 0.5867649415: [0.6329113924051, 0.3670886075949], 0.0868450569: [0.1645569620253, 0.8354430379747], 0.1624739625: [0.3924050632911, 0.6075949367089], 0.3502643807: [0.4683544303797, 0.5316455696203], 0.1102387438: [0.6962025316456, 0.3037974683544], 0.1765742669: [0.0253164556962, 0.9746835443038], 0.6434866207: [0.3924050632911, 0.6075949367089], 0.044864605: [0.9240506329114, 0.0759493670886], 0.4143566736: [0.873417721519, 0.126582278481], 0.2220797949: [0.9240506329114, 0.0759493670886], 0.3169363884: [0.4050632911392, 0.5949367088608], 0.6973241468: [0.3164556962025, 0.6835443037975], 0.0435827592: [0.3291139240506, 0.6708860759494], 0.5646531005: [0.620253164557, 0.379746835443], 0.9453613203: [0.9367088607595, 0.0632911392405], 0.4726806602: [0.2151898734177, 0.7848101265823], 0.5675372536: [0.3924050632911, 0.6075949367089], 0.0528761416: [0.8481012658228, 0.1518987341772], 0.9540137798: [0.4303797468354, 0.5696202531646], 0.2880948566: [0.7974683544304, 0.2025316455696], 0.4784489665: [0.9493670886076, 0.0506329113924], 0.5790738664: [0.5063291139241, 0.4936708860759], 0.9101105592: [0.7721518987342, 0.2278481012658], 0.1454895049: [0.5569620253165, 0.4430379746835], 0.067617369: [0.7848101265823, 0.2151898734177], 0.3464188431: [0.4303797468354, 0.5696202531646], 0.6928376863: [0.873417721519, 0.126582278481], 0.6986059926: [0.1012658227848, 0.8987341772152], 0.5761897132: [0.1772151898734, 0.8227848101266], 0.7014901458: [0.7341772151899, 0.2658227848101], 0.3515462266: [0.6582278481013, 0.3417721518987], 0.4047428297: [0.8860759493671, 0.1139240506329], 0.8819099503: [0.4683544303797, 0.5316455696203], 0.0272392245: [0.8101265822785, 0.1898734177215], 0.8123698125: [0.8481012658228, 0.1518987341772], 0.7072584522: [0.2784810126582, 0.7215189873418], 0.956897933: [0.5443037974684, 0.4556962025316], 0.1249799712: [0.2784810126582, 0.7215189873418], 0.8210222721: [0.1645569620253, 0.8354430379747], 0.8239064253: [0.3164556962025, 0.6835443037975], 0.8296747316: [0.9240506329114, 0.0759493670886], 0.2784810127: [0.0], 0.4688351226: [0.4050632911392, 0.5949367088608], 0.0442236821: [0.5569620253165, 0.4430379746835], 0.9405543983: [0.7088607594937, 0.2911392405063], 0.0727447524: [0.5063291139241, 0.4936708860759], 0.5656144849: [0.9367088607595, 0.0632911392405], 0.3227046948: [0.5569620253165, 0.4430379746835], 0.9463227047: [0.7974683544304, 0.2025316455696], 0.8139721199: [0.7088607594937, 0.2911392405063], 0.4746034289: [0.5569620253165, 0.4430379746835], 0.9520910111: [0.5822784810127, 0.4177215189873], 0.008972921: [0.6962025316456, 0.3037974683544], 0.2871334722: [0.9367088607595, 0.0632911392405], 0.6822624579: [0.7721518987342, 0.2278481012658], 0.5771510976: [0.5443037974684, 0.4556962025316], 0.6880307643: [0.6329113924051, 0.3670886075949], 0.9472840891: [0.3924050632911, 0.6075949367089], 0.3483416119: [0.1772151898734, 0.8227848101266], 0.2829674732: [0.6329113924051, 0.3670886075949], 0.2018907226: [0.0253164556962, 0.9746835443038], 0.4037814453: [0.7721518987342, 0.2278481012658], 0.7562890562: [0.5063291139241, 0.4936708860759], 0.4098702131: [0.0886075949367, 0.9113924050633], 0.8831917962: [0.6582278481013, 0.3417721518987], 0.7473161352: [0.4050632911392, 0.5949367088608], 0.2047748758: [0.8481012658228, 0.1518987341772], 0.8219836565: [0.253164556962, 0.746835443038], 0.8908828713: [0.0886075949367, 0.9113924050633], 0.464989585: [0.873417721519, 0.126582278481], 0.0759493671: [0.0], 0.1512578112: [0.0253164556962, 0.9746835443038], 0.5579234097: [0.3544303797468, 0.6455696202532], 0.9386316295: [0.8101265822785, 0.1898734177215], 0.2804037814: [0.8101265822785, 0.1898734177215], 0.5608075629: [0.7088607594937, 0.2911392405063], 0.3675692998: [0.4050632911392, 0.5949367088608], 0.4707578914: [0.1012658227848, 0.8987341772152], 0.5082518827: [0.8101265822785, 0.1898734177215], 0.2832879346: [0.0886075949367, 0.9113924050633], 0.3175773113: [0.3164556962025, 0.6835443037975], 0.4736420445: [0.7341772151899, 0.2658227848101], 0.5694600224: [0.0126582278481, 0.9873417721519], 0.1746514982: [0.9493670886076, 0.0506329113924], 0.677455536: [0.1772151898734, 0.8227848101266], 0.1031885916: [0.8101265822785, 0.1898734177215], 0.6803396892: [0.5063291139241, 0.4936708860759], 0.8857554879: [0.8860759493671, 0.1139240506329], 0.3416119212: [0.0126582278481, 0.9873417721519], 0.984457619: [0.6582278481013, 0.3417721518987], 0.6889921487: [0.2405063291139, 0.7594936708861], 0.0999839769: [0.7721518987342, 0.2278481012658], 0.7998718154: [0.1012658227848, 0.8987341772152], 0.0499919885: [0.0253164556962, 0.9746835443038], 0.8027559686: [0.7341772151899, 0.2658227848101], 0.808524275: [0.2784810126582, 0.7215189873418], 0.5140201891: [0.620253164557, 0.379746835443], 0.9562570101: [0.9240506329114, 0.0759493670886], 0.0932542862: [0.4303797468354, 0.5696202531646], 0.4611440474: [0.2405063291139, 0.7594936708861], 0.9363883993: [0.8860759493671, 0.1139240506329], 0.4999198846: [0.5569620253165, 0.4430379746835], 0.925172248: [0.3164556962025, 0.6835443037975], 0.1118410511: [0.3924050632911, 0.6075949367089], 0.342893767: [0.3164556962025, 0.6835443037975], 0.5502323346: [0.1392405063291, 0.8607594936709], 0.9309405544: [0.9240506329114, 0.0759493670886], 0.4669123538: [0.9620253164557, 0.0379746835443], 0.4896651178: [0.9367088607595, 0.0632911392405], 0.9367088608: [0.0], 0.0349302996: [0.7974683544304, 0.2025316455696], 0.1397211985: [0.4050632911392, 0.5949367088608], 0.0698605993: [0.1772151898734, 0.8227848101266], 0.5617689473: [0.0886075949367, 0.9113924050633], 0.2823265502: [0.7088607594937, 0.2911392405063], 0.5614484858: [0.6329113924051, 0.3670886075949], 0.6248998558: [0.4303797468354, 0.5696202531646], 0.1688831918: [0.7848101265823, 0.2151898734177], 0.1557442718: [0.7088607594937, 0.2911392405063], 0.6883512258: [0.0886075949367, 0.9113924050633], 0.6784169204: [0.5443037974684, 0.4556962025316], 0.8787053357: [0.7341772151899, 0.2658227848101], 0.7892965871: [0.6329113924051, 0.3670886075949], 0.2355391764: [0.620253164557, 0.379746835443], 0.0977407467: [0.4810126582278, 0.5189873417722], 0.3989745233: [0.1772151898734, 0.8227848101266], 0.2009293382: [0.2784810126582, 0.7215189873418], 0.4018586765: [0.5063291139241, 0.4936708860759], 0.8066015062: [0.4810126582278, 0.5189873417722], 0.3755808364: [0.4683544303797, 0.5316455696203], 0.8094856593: [0.0253164556962, 0.9746835443038], 0.2518827111: [0.7721518987342, 0.2278481012658], 0.460182663: [0.6329113924051, 0.3670886075949], 0.1150456658: [0.3164556962025, 0.6835443037975], 0.5425412594: [0.1645569620253, 0.8354430379747], 0.9232494793: [0.253164556962, 0.746835443038], 0.2727127063: [0.9240506329114, 0.0759493670886], 0.410511136: [0.2405063291139, 0.7594936708861], 0.0987021311: [0.9493670886076, 0.0506329113924], 0.2211184105: [0.8607594936709, 0.1392405063291], 0.947925012: [0.9620253164557, 0.0379746835443], 0.1105592053: [0.873417721519, 0.126582278481], 0.551193719: [0.9240506329114, 0.0759493670886], 0.5540778721: [0.6582278481013, 0.3417721518987], 0.1067136677: [0.2405063291139, 0.7594936708861], 0.5569620253: [0.0], 0.333920846: [0.0886075949367, 0.9113924050633], 0.6707258452: [0.0126582278481, 0.9873417721519], 0.3368049992: [0.620253164557, 0.379746835443], 0.6764941516: [0.3291139240506, 0.6708860759494], 0.7844896651: [0.8860759493671, 0.1139240506329], 0.1698445762: [0.7341772151899, 0.2658227848101], 0.9088287133: [0.9493670886076, 0.0506329113924], 0.7902579715: [0.2405063291139, 0.7594936708861], 0.5012017305: [0.5443037974684, 0.4556962025316], 0.1192116648: [0.7341772151899, 0.2658227848101], 0.3980131389: [0.3291139240506, 0.6708860759494], 0.1990065695: [0.4810126582278, 0.5189873417722], 0.2371414837: [0.873417721519, 0.126582278481], 0.6800192277: [0.4810126582278, 0.5189873417722], 0.8017945842: [0.2151898734177, 0.7848101265823], 0.9097900977: [0.2784810126582, 0.7215189873418], 0.5406184906: [0.6962025316456, 0.3037974683544], 0.0339689152: [0.9367088607595, 0.0632911392405], 0.2576510175: [0.6329113924051, 0.3670886075949], 0.135875661: [0.873417721519, 0.126582278481], 0.0240346098: [0.7721518987342, 0.2278481012658], 0.1868290338: [0.7974683544304, 0.2025316455696], 0.7056561448: [0.5063291139241, 0.4936708860759], 0.9376702452: [0.3544303797468, 0.6455696202532], 0.3300753084: [0.3544303797468, 0.6455696202532], 0.6601506169: [0.8101265822785, 0.1898734177215], 0.6630347701: [0.0886075949367, 0.9113924050633], 0.6659189232: [0.620253164557, 0.379746835443], 0.6688030764: [0.3924050632911, 0.6075949367089], 0.77679859: [0.4303797468354, 0.5696202531646], 0.5665758693: [0.7974683544304, 0.2025316455696], 0.4409549752: [0.3924050632911, 0.6075949367089], 0.7796827432: [0.5443037974684, 0.4556962025316], 0.175933344: [0.7721518987342, 0.2278481012658], 0.0656946002: [0.1012658227848, 0.8987341772152], 0.2332959462: [0.2405063291139, 0.7594936708861], 0.7530844416: [0.5569620253165, 0.4430379746835], 0.7941035091: [0.873417721519, 0.126582278481], 0.8799871815: [0.1772151898734, 0.8227848101266], 0.9812530043: [0.1772151898734, 0.8227848101266], 0.5242749559: [0.7341772151899, 0.2658227848101], 0.131068739: [0.6329113924051, 0.3670886075949], 0.0249959942: [0.8860759493671, 0.1139240506329], 0.2262457939: [0.2784810126582, 0.7215189873418], 0.113122897: [0.253164556962, 0.746835443038], 0.1230572024: [0.4810126582278, 0.5189873417722], 0.907867329: [0.4810126582278, 0.5189873417722], 0.2650216311: [0.253164556962, 0.746835443038], 0.2717513219: [0.8607594936709, 0.1392405063291], 0.4553757411: [0.8860759493671, 0.1139240506329], 0.4127543663: [0.620253164557, 0.379746835443], 0.5454254126: [0.3164556962025, 0.6835443037975], 0.9136356353: [0.8481012658228, 0.1518987341772], 0.3233456177: [0.9240506329114, 0.0759493670886], 0.0631309085: [0.0126582278481, 0.9873417721519], 0.0432622977: [0.7341772151899, 0.2658227848101], 0.6553436949: [0.6582278481013, 0.3417721518987], 0.347700689: [0.8607594936709, 0.1392405063291], 0.0948565935: [0.5569620253165, 0.4430379746835], 0.6582278481: [0.0], 0.4162794424: [0.9620253164557, 0.0379746835443], 0.1922768787: [0.1012658227848, 0.8987341772152], 0.221438872: [0.5569620253165, 0.4430379746835], 0.9492068579: [0.0126582278481, 0.9873417721519], 0.3845537574: [0.0886075949367, 0.9113924050633], 0.0480692197: [0.9493670886076, 0.0506329113924], 0.771991668: [0.0126582278481, 0.9873417721519], 0.2986700849: [0.5443037974684, 0.4556962025316], 0.6960423009: [0.0126582278481, 0.9873417721519], 0.3874379106: [0.620253164557, 0.379746835443], 0.7777599744: [0.3291139240506, 0.6708860759494], 0.3903220638: [0.3924050632911, 0.6075949367089], 0.9302996315: [0.5569620253165, 0.4430379746835], 0.7835282807: [0.7721518987342, 0.2278481012658], 0.8915237943: [0.2405063291139, 0.7594936708861], 0.035891684: [0.3924050632911, 0.6075949367089], 0.2554077872: [0.8481012658228, 0.1518987341772], 0.0560807563: [0.2405063291139, 0.7594936708861], 0.519468034: [0.4050632911392, 0.5949367088608], 0.667841692: [0.7974683544304, 0.2025316455696], 0.6377183144: [0.0886075949367, 0.9113924050633], 0.8697324147: [0.6962025316456, 0.3037974683544], 0.4515302035: [0.4683544303797, 0.5316455696203], 0.5252363403: [0.5569620253165, 0.4430379746835], 0.4665918923: [0.1645569620253, 0.8354430379747], 0.4457618971: [0.5822784810127, 0.4177215189873], 0.0330075308: [0.620253164557, 0.379746835443], 0.5281204935: [0.4810126582278, 0.5189873417722], 0.0660150617: [0.5822784810127, 0.4177215189873], 0.4544143567: [0.7721518987342, 0.2278481012658], 0.5310046467: [0.0253164556962, 0.9746835443038], 0.5338887999: [0.8481012658228, 0.1518987341772], 0.6418843134: [0.6962025316456, 0.3037974683544], 0.3223842333: [0.8607594936709, 0.1392405063291], 0.8979330236: [0.253164556962, 0.746835443038], 0.9331837847: [0.4810126582278, 0.5189873417722], 0.7614164397: [0.8101265822785, 0.1898734177215], 0.095177055: [0.1772151898734, 0.8227848101266], 0.7643005929: [0.0886075949367, 0.9113924050633], 0.4060246755: [0.3544303797468, 0.6455696202532], 0.9049831758: [0.5569620253165, 0.4430379746835], 0.767184746: [0.620253164557, 0.379746835443], 0.938952091: [0.8481012658228, 0.1518987341772], 0.7700688992: [0.3924050632911, 0.6075949367089], 0.1121615126: [0.1645569620253, 0.8354430379747], 0.7787213588: [0.1772151898734, 0.8227848101266], 0.7816055119: [0.5063291139241, 0.4936708860759], 0.0980612081: [0.5063291139241, 0.4936708860759], 0.5117769588: [0.2405063291139, 0.7594936708861], 0.4476846659: [0.4303797468354, 0.5696202531646], 0.3922448326: [0.0126582278481, 0.9873417721519], 0.5175452652: [0.9620253164557, 0.0379746835443], 0.1515782727: [0.8860759493671, 0.1139240506329], 0.5005608076: [0.9240506329114, 0.0759493670886], 0.0253164557: [0.0], 0.0390962987: [0.3164556962025, 0.6835443037975], 0.848581958: [0.4050632911392, 0.5949367088608], 0.4505688191: [0.5443037974684, 0.4556962025316], 0.2252844096: [0.9493670886076, 0.0506329113924], 0.5233135715: [0.2151898734177, 0.7848101265823], 0.9040217914: [0.7341772151899, 0.2658227848101], 0.6313090851: [0.2784810126582, 0.7215189873418], 0.2044544144: [0.8101265822785, 0.1898734177215], 0.5290818779: [0.9493670886076, 0.0506329113924], 0.676814613: [0.8607594936709, 0.1392405063291], 0.3768626823: [0.6582278481013, 0.3417721518987], 0.1964428777: [0.1772151898734, 0.8227848101266], 0.2781605512: [0.8860759493671, 0.1139240506329], 0.3797468354: [0.0], 0.7594936709: [0.0], 0.0596058324: [0.6962025316456, 0.3037974683544], 0.5063291139: [0.0], 0.7985899696: [0.3164556962025, 0.6835443037975], 0.6966832238: [0.4050632911392, 0.5949367088608], 0.7681461304: [0.9367088607595, 0.0632911392405], 0.0743470598: [0.2784810126582, 0.7215189873418], 0.8652459542: [0.6329113924051, 0.3670886075949], 0.9421567057: [0.2405063291139, 0.7594936708861], 0.7703893607: [0.1645569620253, 0.8354430379747], 0.8790257971: [0.3291139240506, 0.6708860759494], 0.2217593334: [0.1772151898734, 0.8227848101266], 0.0970998237: [0.4683544303797, 0.5316455696203], 0.2204774876: [0.7341772151899, 0.2658227848101], 0.8847941035: [0.7721518987342, 0.2278481012658], 0.8905624099: [0.6329113924051, 0.3670886075949], 0.2496394809: [0.4810126582278, 0.5189873417722], 0.5156224964: [0.873417721519, 0.126582278481], 0.0217913796: [0.4810126582278, 0.5189873417722], 0.1967633392: [0.9240506329114, 0.0759493670886], 0.6265021631: [0.5569620253165, 0.4430379746835], 0.5213908028: [0.1012658227848, 0.8987341772152], 0.1384393527: [0.253164556962, 0.746835443038], 0.3086043903: [0.0886075949367, 0.9113924050633], 0.3146931581: [0.1645569620253, 0.8354430379747], 0.0346098382: [0.873417721519, 0.126582278481], 0.6806601506: [0.6582278481013, 0.3417721518987], 0.6322704695: [0.0253164556962, 0.9746835443038], 0.0439032206: [0.8607594936709, 0.1392405063291], 0.8075628906: [0.9493670886076, 0.0506329113924], 0.6351546227: [0.8481012658228, 0.1518987341772], 0.3313571543: [0.8481012658228, 0.1518987341772], 0.0515942958: [0.3544303797468, 0.6455696202532], 0.9979170005: [0.3924050632911, 0.6075949367089], 0.3733376062: [0.5569620253165, 0.4430379746835], 0.6438070822: [0.1645569620253, 0.8354430379747], 0.5422207979: [0.3924050632911, 0.6075949367089], 0.8870373338: [0.3544303797468, 0.6455696202532], 0.2749559365: [0.4810126582278, 0.5189873417722], 0.7784008973: [0.5569620253165, 0.4430379746835], 0.7604550553: [0.3544303797468, 0.6455696202532], 0.3816696042: [0.8101265822785, 0.1898734177215], 0.7633392085: [0.7088607594937, 0.2911392405063], 0.871334722: [0.3924050632911, 0.6075949367089], 0.2416279442: [0.3164556962025, 0.6835443037975], 0.0778721359: [0.8101265822785, 0.1898734177215], 0.4399935908: [0.7974683544304, 0.2025316455696], 0.5021631149: [0.4683544303797, 0.5316455696203], 0.8828713347: [0.5063291139241, 0.4936708860759], 0.252523634: [0.0253164556962, 0.9746835443038], 0.2131068739: [0.3924050632911, 0.6075949367089], 0.442877744: [0.0126582278481, 0.9873417721519], 0.9476045506: [0.1645569620253, 0.8354430379747], 0.5624098702: [0.2405063291139, 0.7594936708861], 0.5108155744: [0.6329113924051, 0.3670886075949], 0.4983175773: [0.4303797468354, 0.5696202531646], 0.618811088: [0.9620253164557, 0.0379746835443], 0.6245793943: [0.2151898734177, 0.7848101265823], 0.6303477007: [0.9493670886076, 0.0506329113924], 0.6075949367: [0.0], 0.6287453934: [0.4683544303797, 0.5316455696203], 0.6921967633: [0.9367088607595, 0.0632911392405], 0.7511616728: [0.2151898734177, 0.7848101265823], 0.8190995033: [0.6962025316456, 0.3037974683544], 0.8825508733: [0.4810126582278, 0.5189873417722], 0.2365005608: [0.9367088607595, 0.0632911392405], 0.0112161513: [0.9620253164557, 0.0379746835443], 0.8947284089: [0.9367088607595, 0.0632911392405], 0.7527639801: [0.1392405063291, 0.8607594936709]}
averages_odd={0.62217593334: [0.8101265822785, 0.1898734177215], 0.43470597661: [0.7848101265823, 0.2151898734177], 0.46835443038: [0.0], 0.10975805159: [0.4683544303797, 0.5316455696203], 0.25012017305: [0.7974683544304, 0.2025316455696], 0.49302996315: [0.0253164556962, 0.9746835443038], 0.44047428297: [0.9493670886076, 0.0506329113924], 0.12578112482: [0.253164556962, 0.746835443038], 0.25300432623: [0.0126582278481, 0.9873417721519], 0.93783047589: [0.3164556962025, 0.6835443037975], 0.49591411633: [0.8481012658228, 0.1518987341772], 0.56289056241: [0.8607594936709, 0.1392405063291], 0.30844415959: [0.4303797468354, 0.5696202531646], 0.94359878225: [0.9240506329114, 0.0759493670886], 0.31132831277: [0.5443037974684, 0.4556962025316], 0.00977407467: [0.6582278481013, 0.3417721518987], 0.67953853549: [0.9367088607595, 0.0632911392405], 0.36676814613: [0.8860759493671, 0.1139240506329], 0.49815734658: [0.6329113924051, 0.3670886075949], 0.18410511136: [0.9240506329114, 0.0759493670886], 0.15782727127: [0.8607594936709, 0.1392405063291], 0.68819099503: [0.4303797468354, 0.5696202531646], 0.79618650857: [0.7721518987342, 0.2278481012658], 0.69107514821: [0.5443037974684, 0.4556962025316], 0.37253645249: [0.2405063291139, 0.7594936708861], 0.60871655184: [0.3164556962025, 0.6835443037975], 0.18698926454: [0.6582278481013, 0.3417721518987], 0.80195481493: [0.6329113924051, 0.3670886075949], 0.6144848582: [0.9240506329114, 0.0759493670886], 0.02355391764: [0.1645569620253, 0.8354430379747], 0.61736901138: [0.6582278481013, 0.3417721518987], 0.43086043903: [0.4050632911392, 0.5949367088608], 0.62025316456: [0.0], 0.92629386316: [0.8481012658228, 0.1518987341772], 0.21615125781: [0.3544303797468, 0.6455696202532], 0.73113283128: [0.7974683544304, 0.2025316455696], 0.00464669124: [0.4303797468354, 0.5696202531646], 0.1087966672: [0.5443037974684, 0.4556962025316], 0.73401698446: [0.0126582278481, 0.9873417721519], 0.43662874539: [0.5569620253165, 0.4430379746835], 0.73690113764: [0.5822784810127, 0.4177215189873], 0.21903541099: [0.7088607594937, 0.2911392405063], 0.69716391604: [0.3544303797468, 0.6455696202532], 0.49206857875: [0.2784810126582, 0.7215189873418], 0.742669444: [0.4683544303797, 0.5316455696203], 0.06265021631: [0.0253164556962, 0.9746835443038], 0.06168883192: [0.2784810126582, 0.7215189873418], 0.74555359718: [0.7721518987342, 0.2278481012658], 0.85354911072: [0.2405063291139, 0.7594936708861], 0.1530203493: [0.3164556962025, 0.6835443037975], 0.24819740426: [0.620253164557, 0.379746835443], 0.85931741708: [0.9620253164557, 0.0379746835443], 0.12481974043: [0.1645569620253, 0.8354430379747], 0.31036692838: [0.1772151898734, 0.8227848101266], 0.86508572344: [0.7848101265823, 0.2151898734177], 0.36580676174: [0.7721518987342, 0.2278481012658], 0.68049991989: [0.7974683544304, 0.2025316455696], 0.8708540298: [0.9493670886076, 0.0506329113924], 0.68338407307: [0.0126582278481, 0.9873417721519], 0.79137958661: [0.1772151898734, 0.8227848101266], 0.79426373979: [0.5063291139241, 0.4936708860759], 0.3715750681: [0.6329113924051, 0.3670886075949], 0.79714789297: [0.8860759493671, 0.1139240506329], 0.93494632271: [0.1645569620253, 0.8354430379747], 0.04662714309: [0.9367088607595, 0.0632911392405], 0.42701490146: [0.873417721519, 0.126582278481], 0.80291619933: [0.2405063291139, 0.7594936708861], 0.91379586605: [0.1012658227848, 0.8987341772152], 0.80868450569: [0.9620253164557, 0.0379746835443], 0.53597179939: [0.7848101265823, 0.2151898734177], 0.10783528281: [0.1772151898734, 0.8227848101266], 0.91668001923: [0.7341772151899, 0.2658227848101], 0.43278320782: [0.1012658227848, 0.8987341772152], 0.54174010575: [0.9493670886076, 0.0506329113924], 0.21711264221: [0.8101265822785, 0.1898734177215], 0.36901137638: [0.8101265822785, 0.1898734177215], 0.03060406986: [0.7341772151899, 0.2658227848101], 0.30075308444: [0.7974683544304, 0.2025316455696], 0.49110719436: [0.9493670886076, 0.0506329113924], 0.15109758052: [0.253164556962, 0.746835443038], 0.30363723762: [0.0126582278481, 0.9873417721519], 0.3065213908: [0.5822784810127, 0.4177215189873], 0.36196122416: [0.5443037974684, 0.4556962025316], 0.86316295466: [0.1012658227848, 0.8987341772152], 0.09085082519: [0.8481012658228, 0.1518987341772], 0.86604710784: [0.7341772151899, 0.2658227848101], 0.59333440154: [0.2784810126582, 0.7215189873418], 0.97404262138: [0.0253164556962, 0.9746835443038], 0.18314372697: [0.8607594936709, 0.1392405063291], 0.97692677456: [0.8481012658228, 0.1518987341772], 0.42316936388: [0.2405063291139, 0.7594936708861], 0.9855792341: [0.1645569620253, 0.8354430379747], 0.21230572024: [0.6582278481013, 0.3417721518987], 0.98846338728: [0.3164556962025, 0.6835443037975], 0.86124018587: [0.4050632911392, 0.5949367088608], 0.10687389841: [0.3291139240506, 0.6708860759494], 0.6135234738: [0.8607594936709, 0.1392405063291], 0.42893767025: [0.9620253164557, 0.0379746835443], 0.72151898734: [0.0], 0.21518987342: [0.0], 0.53693318379: [0.7341772151899, 0.2658227848101], 0.07386636757: [0.3924050632911, 0.6075949367089], 0.06072744752: [0.9493670886076, 0.0506329113924], 0.73017144688: [0.9367088607595, 0.0632911392405], 0.54270149015: [0.2784810126582, 0.7215189873418], 0.48726165679: [0.5569620253165, 0.4430379746835], 0.24435186669: [0.7088607594937, 0.2911392405063], 0.8410511136: [0.3291139240506, 0.6708860759494], 0.29979170005: [0.9367088607595, 0.0632911392405], 0.49014580997: [0.4810126582278, 0.5189873417722], 0.84393526678: [0.4683544303797, 0.5316455696203], 0.03765422208: [0.8860759493671, 0.1139240506329], 0.65646531005: [0.1645569620253, 0.8354430379747], 0.12289697164: [0.6962025316456, 0.3037974683544], 0.65934946323: [0.3164556962025, 0.6835443037975], 0.178336805: [0.3164556962025, 0.6835443037975], 0.66511776959: [0.9240506329114, 0.0759493670886], 0.66800192277: [0.6582278481013, 0.3417721518987], 0.36099983977: [0.1772151898734, 0.8227848101266], 0.77599743631: [0.7088607594937, 0.2911392405063], 0.36388399295: [0.5063291139241, 0.4936708860759], 0.78176574267: [0.7974683544304, 0.2025316455696], 0.04566575869: [0.620253164557, 0.379746835443], 0.78464989585: [0.0126582278481, 0.9873417721519], 0.78753404903: [0.5822784810127, 0.4177215189873], 0.79041820221: [0.3291139240506, 0.6708860759494], 0.42220797949: [0.6329113924051, 0.3670886075949], 0.89841371575: [0.8860759493671, 0.1139240506329], 0.79330235539: [0.4683544303797, 0.5316455696203], 0.10591251402: [0.4303797468354, 0.5696202531646], 0.46194520109: [0.5569620253165, 0.4430379746835], 0.49367088608: [0.0], 0.90418202211: [0.2405063291139, 0.7594936708861], 0.07290498318: [0.7974683544304, 0.2025316455696], 0.90995032847: [0.9620253164557, 0.0379746835443], 0.00753084442: [0.5443037974684, 0.4556962025316], 0.72248037174: [0.3544303797468, 0.6455696202532], 0.29306200929: [0.8101265822785, 0.1898734177215], 0.48341611921: [0.1012658227848, 0.8987341772152], 0.72536452492: [0.7088607594937, 0.2911392405063], 0.91571863483: [0.7848101265823, 0.2151898734177], 0.2424290979: [0.8101265822785, 0.1898734177215], 0.83336003846: [0.3924050632911, 0.6075949367089], 0.29594616247: [0.0886075949367, 0.9113924050633], 0.48630027239: [0.7341772151899, 0.2658227848101], 0.29883031566: [0.620253164557, 0.379746835443], 0.842012498: [0.1772151898734, 0.8227848101266], 0.15013619612: [0.1645569620253, 0.8354430379747], 0.84489665118: [0.5063291139241, 0.4936708860759], 0.35427014902: [0.0126582278481, 0.9873417721519], 0.30171446884: [0.3924050632911, 0.6075949367089], 0.84778080436: [0.8860759493671, 0.1139240506329], 0.3571543022: [0.5822784810127, 0.4177215189873], 0.95866047108: [0.873417721519, 0.126582278481], 0.58083640442: [0.9620253164557, 0.0379746835443], 0.36003845538: [0.3291139240506, 0.6708860759494], 0.96442877744: [0.1012658227848, 0.8987341772152], 0.05175452652: [0.3164556962025, 0.6835443037975], 0.58660471078: [0.7848101265823, 0.2151898734177], 0.96731293062: [0.7341772151899, 0.2658227848101], 0.36292260856: [0.4683544303797, 0.5316455696203], 0.20846018266: [0.8607594936709, 0.1392405063291], 0.97308123698: [0.2784810126582, 0.7215189873418], 0.51578272713: [0.5063291139241, 0.4936708860759], 0.0004806922: [0.4050632911392, 0.5949367088608], 0.51866688031: [0.8860759493671, 0.1139240506329], 0.52443518667: [0.2405063291139, 0.7594936708861], 0.05976606313: [0.4810126582278, 0.5189873417722], 0.47957058164: [0.9620253164557, 0.0379746835443], 0.90802755969: [0.873417721519, 0.126582278481], 0.53020349303: [0.9620253164557, 0.0379746835443], 0.24050632911: [0.0], 0.2921006249: [0.3544303797468, 0.6455696202532], 0.63819900657: [0.7341772151899, 0.2658227848101], 0.03669283769: [0.7721518987342, 0.2278481012658], 0.29498477808: [0.7088607594937, 0.2911392405063], 0.64396731293: [0.2784810126582, 0.7215189873418], 0.485338888: [0.7848101265823, 0.2151898734177], 0.14821342734: [0.6962025316456, 0.3037974683544], 0.35042461144: [0.9367088607595, 0.0632911392405], 0.08796667201: [0.0253164556962, 0.9746835443038], 0.76061528601: [0.3164556962025, 0.6835443037975], 0.76638359237: [0.9240506329114, 0.0759493670886], 0.57891363564: [0.873417721519, 0.126582278481], 0.76926774555: [0.6582278481013, 0.3417721518987], 0.35907707098: [0.4303797468354, 0.5696202531646], 0.77215189873: [0.0], 0.584681942: [0.1012658227848, 0.8987341772152], 0.41451690434: [0.5063291139241, 0.4936708860759], 0.58756609518: [0.7341772151899, 0.2658227848101], 0.10398974523: [0.5822784810127, 0.4177215189873], 0.69556160872: [0.0253164556962, 0.9746835443038], 0.41740105752: [0.8860759493671, 0.1139240506329], 0.6984457619: [0.8481012658228, 0.1518987341772], 0.20942156706: [0.9240506329114, 0.0759493670886], 0.47284089088: [0.6329113924051, 0.3670886075949], 0.02964268547: [0.2151898734177, 0.7848101265823], 0.70709822144: [0.1645569620253, 0.8354430379747], 0.28537093415: [0.9240506329114, 0.0759493670886], 0.70998237462: [0.3164556962025, 0.6835443037975], 0.33568338407: [0.1772151898734, 0.8227848101266], 0.28825508733: [0.6582278481013, 0.3417721518987], 0.71575068098: [0.9240506329114, 0.0759493670886], 0.01810607275: [0.2405063291139, 0.7594936708861], 0.82374619452: [0.3544303797468, 0.6455696202532], 0.29113924051: [0.0], 0.48149335043: [0.4050632911392, 0.5949367088608], 0.8266303477: [0.7088607594937, 0.2911392405063], 0.34657907387: [0.0886075949367, 0.9113924050633], 0.83239865406: [0.7974683544304, 0.2025316455696], 0.08700528761: [0.2784810126582, 0.7215189873418], 0.83528280724: [0.0126582278481, 0.9873417721519], 0.34946322705: [0.620253164557, 0.379746835443], 0.64781285051: [0.8481012658228, 0.1518987341772], 0.83816696042: [0.5822784810127, 0.4177215189873], 0.17545265182: [0.1645569620253, 0.8354430379747], 0.94616247396: [0.5063291139241, 0.4936708860759], 0.35234738023: [0.3924050632911, 0.6075949367089], 0.75869251723: [0.253164556962, 0.746835443038], 0.94904662714: [0.8860759493671, 0.1139240506329], 0.01105592053: [0.2784810126582, 0.7215189873418], 0.40778721359: [0.5822784810127, 0.4177215189873], 0.9548149335: [0.2405063291139, 0.7594936708861], 0.41067136677: [0.3291139240506, 0.6708860759494], 0.77311328313: [0.3544303797468, 0.6455696202532], 0.41355551995: [0.4683544303797, 0.5316455696203], 0.88110879667: [0.620253164557, 0.379746835443], 0.50328473001: [0.7974683544304, 0.2025316455696], 0.79458420125: [0.6582278481013, 0.3417721518987], 0.88399294985: [0.3924050632911, 0.6075949367089], 0.50616888319: [0.0126582278481, 0.9873417721519], 0.41643967313: [0.7721518987342, 0.2278481012658], 0.50905303637: [0.5822784810127, 0.4177215189873], 0.51193718955: [0.3291139240506, 0.6708860759494], 0.14148373658: [0.8481012658228, 0.1518987341772], 0.89264540939: [0.1772151898734, 0.8227848101266], 0.51482134273: [0.4683544303797, 0.5316455696203], 0.28440954975: [0.8607594936709, 0.1392405063291], 0.89552956257: [0.5063291139241, 0.4936708860759], 0.51770549591: [0.7721518987342, 0.2278481012658], 0.62570100945: [0.2405063291139, 0.7594936708861], 0.47764781285: [0.873417721519, 0.126582278481], 0.34273353629: [0.3544303797468, 0.6455696202532], 0.08604390322: [0.9493670886076, 0.0506329113924], 0.34561768947: [0.7088607594937, 0.2911392405063], 0.83047588528: [0.620253164557, 0.379746835443], 0.17352988303: [0.6962025316456, 0.3037974683544], 0.941355552: [0.4303797468354, 0.5696202531646], 0.56353148534: [0.1772151898734, 0.8227848101266], 0.94423970518: [0.5443037974684, 0.4556962025316], 0.35138599583: [0.7974683544304, 0.2025316455696], 0.56641563852: [0.5063291139241, 0.4936708860759], 0.2135875661: [0.2784810126582, 0.7215189873418], 0.17641403621: [0.253164556962, 0.746835443038], 0.5692997917: [0.8860759493671, 0.1139240506329], 0.24531325108: [0.0886075949367, 0.9113924050633], 0.57506809806: [0.2405063291139, 0.7594936708861], 0.73978529082: [0.3291139240506, 0.6708860759494], 0.40970998238: [0.4303797468354, 0.5696202531646], 0.68594776478: [0.1012658227848, 0.8987341772152], 0.99359077071: [0.5569620253165, 0.4430379746835], 0.41259413556: [0.5443037974684, 0.4556962025316], 0.68883191796: [0.7341772151899, 0.2658227848101], 0.01458099664: [0.8101265822785, 0.1898734177215], 0.277679859: [0.253164556962, 0.746835443038], 0.46803396892: [0.8860759493671, 0.1139240506329], 0.69460022432: [0.2784810126582, 0.7215189873418], 0.23473802275: [0.9240506329114, 0.0759493670886], 0.07578913636: [0.0126582278481, 0.9873417721519], 0.51289857395: [0.1772151898734, 0.8227848101266], 0.80836404422: [0.1645569620253, 0.8354430379747], 0.47380227528: [0.2405063291139, 0.7594936708861], 0.8112481974: [0.3164556962025, 0.6835443037975], 0.23762217593: [0.6582278481013, 0.3417721518987], 0.08508251883: [0.4810126582278, 0.5189873417722], 0.62954654703: [0.873417721519, 0.126582278481], 0.3417721519: [0.0], 0.63531485339: [0.1012658227848, 0.8987341772152], 0.71126422048: [0.1012658227848, 0.8987341772152], 0.74331036693: [0.4810126582278, 0.5189873417722], 0.02163114885: [0.6962025316456, 0.3037974683544], 0.74619452011: [0.0253164556962, 0.9746835443038], 0.74907867329: [0.8481012658228, 0.1518987341772], 0.20076910752: [0.1645569620253, 0.8354430379747], 0.40298029162: [0.3924050632911, 0.6075949367089], 0.10110559205: [0.0126582278481, 0.9873417721519], 0.75773113283: [0.1645569620253, 0.8354430379747], 0.2036532607: [0.3164556962025, 0.6835443037975], 0.87437910591: [0.3544303797468, 0.6455696202532], 0.46418843134: [0.4683544303797, 0.5316455696203], 0.13763819901: [0.2784810126582, 0.7215189873418], 0.87726325909: [0.7088607594937, 0.2911392405063], 0.68979330236: [0.5569620253165, 0.4430379746835], 0.2767184746: [0.1645569620253, 0.8354430379747], 0.98525877263: [0.3924050632911, 0.6075949367089], 0.46707258452: [0.7721518987342, 0.2278481012658], 0.69267745554: [0.4810126582278, 0.5189873417722], 0.0347700689: [0.5063291139241, 0.4936708860759], 0.88303156545: [0.7974683544304, 0.2025316455696], 0.27960262778: [0.3164556962025, 0.6835443037975], 0.99391123217: [0.1772151898734, 0.8227848101266], 0.80644127544: [0.6962025316456, 0.3037974683544], 0.80932542862: [0.253164556962, 0.746835443038], 0.07098221439: [0.620253164557, 0.379746835443], 0.54526518186: [0.3544303797468, 0.6455696202532], 0.9259734017: [0.8101265822785, 0.1898734177215], 0.04278160551: [0.0886075949367, 0.9113924050633], 0.54814933504: [0.7088607594937, 0.2911392405063], 0.92885755488: [0.0886075949367, 0.9113924050633], 0.34369492069: [0.8101265822785, 0.1898734177215], 0.93174170806: [0.620253164557, 0.379746835443], 0.19884633873: [0.6962025316456, 0.3037974683544], 0.5539176414: [0.7974683544304, 0.2025316455696], 0.93462586124: [0.3924050632911, 0.6075949367089], 0.2379426374: [0.9493670886076, 0.0506329113924], 0.55680179458: [0.0126582278481, 0.9873417721519], 0.45809966352: [0.1012658227848, 0.8987341772152], 0.40201890723: [0.7974683544304, 0.2025316455696], 0.6676814613: [0.5063291139241, 0.4936708860759], 0.20173049191: [0.253164556962, 0.746835443038], 0.40490306041: [0.0126582278481, 0.9873417721519], 0.46034289377: [0.4303797468354, 0.5696202531646], 0.13571543022: [0.4810126582278, 0.5189873417722], 0.46322704695: [0.5443037974684, 0.4556962025316], 0.01714468835: [0.6329113924051, 0.3670886075949], 0.87534049031: [0.8101265822785, 0.1898734177215], 0.60262778401: [0.620253164557, 0.379746835443], 0.11616728088: [0.8481012658228, 0.1518987341772], 0.98333600385: [0.9367088607595, 0.0632911392405], 0.60551193719: [0.3924050632911, 0.6075949367089], 0.1385995834: [0.0253164556962, 0.9746835443038], 0.23377663836: [0.8607594936709, 0.1392405063291], 0.99198846339: [0.4303797468354, 0.5696202531646], 0.61416439673: [0.1772151898734, 0.8227848101266], 0.99487261657: [0.5443037974684, 0.4556962025316], 0.61704854991: [0.5063291139241, 0.4936708860759], 0.61993270309: [0.8860759493671, 0.1139240506329], 0.00528761416: [0.7341772151899, 0.2658227848101], 0.73081236981: [0.873417721519, 0.126582278481], 0.54622656626: [0.8101265822785, 0.1898734177215], 0.73658067617: [0.1012658227848, 0.8987341772152], 0.54911071944: [0.0886075949367, 0.9113924050633], 0.73946482935: [0.7341772151899, 0.2658227848101], 0.09918282327: [0.3924050632911, 0.6075949367089], 0.55199487262: [0.620253164557, 0.379746835443], 0.8519468034: [0.7088607594937, 0.2911392405063], 0.5548790258: [0.3924050632911, 0.6075949367089], 0.85034449607: [0.8481012658228, 0.1518987341772], 0.66287453934: [0.4303797468354, 0.5696202531646], 0.40105752283: [0.9367088607595, 0.0632911392405], 0.66575869252: [0.5443037974684, 0.4556962025316], 0.02515622496: [0.0126582278481, 0.9873417721519], 0.22896971639: [0.3164556962025, 0.6835443037975], 0.03380868451: [0.4683544303797, 0.5316455696203], 0.68017945842: [0.873417721519, 0.126582278481], 0.46226566255: [0.1772151898734, 0.8227848101266], 0.13667681461: [0.9493670886076, 0.0506329113924], 0.79105912514: [0.5569620253165, 0.4430379746835], 0.27479570582: [0.6962025316456, 0.3037974683544], 0.46514981574: [0.5063291139241, 0.4936708860759], 0.79394327832: [0.4810126582278, 0.5189873417722], 0.08219836565: [0.5569620253165, 0.4430379746835], 0.7968274315: [0.0253164556962, 0.9746835443038], 0.33023553918: [0.3164556962025, 0.6835443037975], 0.79971158468: [0.8481012658228, 0.1518987341772], 0.90770709822: [0.6962025316456, 0.3037974683544], 0.61512578113: [0.5443037974684, 0.4556962025316], 0.9105912514: [0.253164556962, 0.746835443038], 0.04182022112: [0.7088607594937, 0.2911392405063], 0.33600384554: [0.9240506329114, 0.0759493670886], 0.72600544785: [0.6329113924051, 0.3670886075949], 0.33888799872: [0.6582278481013, 0.3417721518987], 0.09822143887: [0.7974683544304, 0.2025316455696], 0.73465790739: [0.4050632911392, 0.5949367088608], 0.39432783208: [0.8101265822785, 0.1898734177215], 0.84553757411: [0.9493670886076, 0.0506329113924], 0.39721198526: [0.0886075949367, 0.9113924050633], 0.04983175773: [0.253164556962, 0.746835443038], 0.45265181862: [0.7974683544304, 0.2025316455696], 0.13186989265: [0.7341772151899, 0.2658227848101], 0.22704694761: [0.253164556962, 0.746835443038], 0.26518186188: [0.0253164556962, 0.9746835443038], 0.85707418683: [0.6962025316456, 0.3037974683544], 0.4555359718: [0.0126582278481, 0.9873417721519], 0.85995834001: [0.253164556962, 0.746835443038], 0.26806601506: [0.8481012658228, 0.1518987341772], 0.96795385355: [0.8607594936709, 0.1392405063291], 0.45842012498: [0.5822784810127, 0.4177215189873], 0.59589809325: [0.3544303797468, 0.6455696202532], 0.46130427816: [0.3291139240506, 0.6708860759494], 0.06809806121: [0.0886075949367, 0.9113924050633], 0.97949046627: [0.0886075949367, 0.9113924050633], 0.1639160391: [0.0253164556962, 0.9746835443038], 0.98237461945: [0.620253164557, 0.379746835443], 0.52796026278: [0.6962025316456, 0.3037974683544], 0.16680019228: [0.8481012658228, 0.1518987341772], 0.53084441596: [0.253164556962, 0.746835443038], 0.33504246114: [0.8607594936709, 0.1392405063291], 0.36644768467: [0.0253164556962, 0.9746835443038], 0.09726005448: [0.9367088607595, 0.0632911392405], 0.91732094216: [0.8607594936709, 0.1392405063291], 0.64749238904: [0.8101265822785, 0.1898734177215], 0.39336644769: [0.3544303797468, 0.6455696202532], 0.65037654222: [0.0886075949367, 0.9113924050633], 0.01233776638: [0.8860759493671, 0.1139240506329], 0.6532606954: [0.620253164557, 0.379746835443], 0.39625060087: [0.7088607594937, 0.2911392405063], 0.65614484858: [0.3924050632911, 0.6075949367089], 0.45169043423: [0.9367088607595, 0.0632911392405], 0.03284730011: [0.5443037974684, 0.4556962025316], 0.1132831277: [0.0253164556962, 0.9746835443038], 0.66479730812: [0.1772151898734, 0.8227848101266], 0.26422047749: [0.2784810126582, 0.7215189873418], 0.13283127704: [0.5569620253165, 0.4430379746835], 0.58820701811: [0.8607594936709, 0.1392405063291], 0.06713667682: [0.7088607594937, 0.2911392405063], 0.16199327031: [0.9493670886076, 0.0506329113924], 0.59685947765: [0.8101265822785, 0.1898734177215], 0.32542861721: [0.6962025316456, 0.3037974683544], 0.70485499119: [0.9367088607595, 0.0632911392405], 0.59974363083: [0.0886075949367, 0.9113924050633], 0.32831277039: [0.253164556962, 0.746835443038], 0.05688190995: [0.5569620253165, 0.4430379746835], 0.71350745073: [0.4303797468354, 0.5696202531646], 0.71639160391: [0.5443037974684, 0.4556962025316], 0.09629867009: [0.620253164557, 0.379746835443], 0.38663675693: [0.9240506329114, 0.0759493670886], 0.92244832559: [0.2784810126582, 0.7215189873418], 0.38952091011: [0.6582278481013, 0.3417721518987], 0.04887037334: [0.1645569620253, 0.8354430379747], 0.83592373017: [0.4050632911392, 0.5949367088608], 0.39240506329: [0.0], 0.25749078673: [0.7848101265823, 0.2151898734177], 0.84169203653: [0.5569620253165, 0.4430379746835], 0.44784489665: [0.0886075949367, 0.9113924050633], 0.6542220798: [0.9367088607595, 0.0632911392405], 0.84457618971: [0.4810126582278, 0.5189873417722], 0.13924050633: [0.0], 0.11232174331: [0.2784810126582, 0.7215189873418], 0.84746034289: [0.0253164556962, 0.9746835443038], 0.45072904983: [0.620253164557, 0.379746835443], 0.76510174652: [0.3291139240506, 0.6708860759494], 0.13090850825: [0.7848101265823, 0.2151898734177], 0.22608556321: [0.1645569620253, 0.8354430379747], 0.7679858997: [0.4683544303797, 0.5316455696203], 0.26325909309: [0.9493670886076, 0.0506329113924], 0.45361320301: [0.3924050632911, 0.6075949367089], 0.77087005288: [0.7721518987342, 0.2278481012658], 0.96122416279: [0.253164556962, 0.746835443038], 0.31869892645: [0.8481012658228, 0.1518987341772], 0.77663835924: [0.6329113924051, 0.3670886075949], 0.01009453613: [0.9493670886076, 0.0506329113924], 0.89040217914: [0.7848101265823, 0.2151898734177], 0.51257811248: [0.5569620253165, 0.4430379746835], 0.1629546547: [0.2784810126582, 0.7215189873418], 0.51546226566: [0.4810126582278, 0.5189873417722], 0.8961704855: [0.9493670886076, 0.0506329113924], 0.327351386: [0.1645569620253, 0.8354430379747], 0.51834641884: [0.0253164556962, 0.9746835443038], 0.52123057202: [0.8481012658228, 0.1518987341772], 0.19211664797: [0.8481012658228, 0.1518987341772], 0.38567537254: [0.8607594936709, 0.1392405063291], 0.78529081878: [0.4050632911392, 0.5949367088608], 0.25364524916: [0.4050632911392, 0.5949367088608], 0.44399935908: [0.3544303797468, 0.6455696202532], 0.03188591572: [0.1772151898734, 0.8227848101266], 0.11136035892: [0.9493670886076, 0.0506329113924], 0.83976926775: [0.7848101265823, 0.2151898734177], 0.44688351226: [0.7088607594937, 0.2911392405063], 0.12898573947: [0.1012658227848, 0.8987341772152], 0.22416279442: [0.6962025316456, 0.3037974683544], 0.25941355552: [0.5569620253165, 0.4430379746835], 0.06521390803: [0.8101265822785, 0.1898734177215], 0.2622977087: [0.4810126582278, 0.5189873417722], 0.57859317417: [0.6962025316456, 0.3037974683544], 0.15814773274: [0.5569620253165, 0.4430379746835], 0.58147732735: [0.253164556962, 0.746835443038], 0.03989745233: [0.8101265822785, 0.1898734177215], 0.16103188592: [0.4810126582278, 0.5189873417722], 0.69812530043: [0.8101265822785, 0.1898734177215], 0.3760615286: [0.6962025316456, 0.3037974683544], 0.5106553437: [0.7848101265823, 0.2151898734177], 0.70100945361: [0.0886075949367, 0.9113924050633], 0.08123698125: [0.7341772151899, 0.2658227848101], 0.70389360679: [0.620253164557, 0.379746835443], 0.37894568178: [0.253164556962, 0.746835443038], 0.51642365006: [0.9493670886076, 0.0506329113924], 0.70677775997: [0.3924050632911, 0.6075949367089], 0.81477327351: [0.4303797468354, 0.5696202531646], 0.81765742669: [0.5443037974684, 0.4556962025316], 0.04246114405: [0.6329113924051, 0.3670886075949], 0.18570741868: [0.4683544303797, 0.5316455696203], 0.4401538215: [0.6582278481013, 0.3417721518987], 0.6388399295: [0.8607594936709, 0.1392405063291], 0.11039897452: [0.4810126582278, 0.5189873417722], 0.44303797468: [0.0], 0.12706297068: [0.4050632911392, 0.5949367088608], 0.25556801795: [0.1012658227848, 0.8987341772152], 0.75548790258: [0.9367088607595, 0.0632911392405], 0.06425252363: [0.3544303797468, 0.6455696202532], 0.05592052556: [0.7341772151899, 0.2658227848101], 0.25845217113: [0.7341772151899, 0.2658227848101], 0.15622496395: [0.7848101265823, 0.2151898734177], 0.76414036212: [0.4303797468354, 0.5696202531646], 0.31389200449: [0.9493670886076, 0.0506329113924], 0.87213587566: [0.7721518987342, 0.2278481012658], 0.7670245153: [0.5443037974684, 0.4556962025316], 0.96186508572: [0.0126582278481, 0.9873417721519], 0.87790418202: [0.6329113924051, 0.3670886075949], 0.12257651018: [0.9367088607595, 0.0632911392405], 0.69331837847: [0.6582278481013, 0.3417721518987], 0.08027559686: [0.2151898734177, 0.7848101265823], 0.69620253165: [0.0], 0.88655664156: [0.4050632911392, 0.5949367088608], 0.1882711104: [0.2784810126582, 0.7215189873418], 0.80708219837: [0.7974683544304, 0.2025316455696], 0.99743630828: [0.9493670886076, 0.0506329113924], 0.37798429739: [0.1645569620253, 0.8354430379747], 0.80996635155: [0.0126582278481, 0.9873417721519], 0.00592853709: [0.8607594936709, 0.1392405063291], 0.81285050473: [0.5822784810127, 0.4177215189873], 0.38086845057: [0.3164556962025, 0.6835443037975], 0.81573465791: [0.3291139240506, 0.6708860759494], 0.21743310367: [0.8481012658228, 0.1518987341772], 0.81861881109: [0.4683544303797, 0.5316455696203], 0.43630828393: [0.8607594936709, 0.1392405063291], 0.82150296427: [0.7721518987342, 0.2278481012658], 0.92949847781: [0.2405063291139, 0.7594936708861], 0.12514020189: [0.9620253164557, 0.0379746835443], 0.82727127063: [0.6329113924051, 0.3670886075949], 0.25172248037: [0.9620253164557, 0.0379746835443], 0.93526678417: [0.9620253164557, 0.0379746835443], 0.55744271751: [0.4050632911392, 0.5949367088608], 0.06329113924: [0.0], 0.06200929338: [0.7721518987342, 0.2278481012658], 0.02771991668: [0.1012658227848, 0.8987341772152], 0.94103509053: [0.7848101265823, 0.2151898734177], 0.49751642365: [0.7088607594937, 0.2911392405063], 0.15430219516: [0.1012658227848, 0.8987341772152], 0.24947925012: [0.6962025316456, 0.3037974683544], 0.31004646691: [0.5569620253165, 0.4430379746835], 0.94680339689: [0.9493670886076, 0.0506329113924], 0.03893606794: [0.3544303797468, 0.6455696202532], 0.31293062009: [0.4810126582278, 0.5189873417722], 0.15718634834: [0.7341772151899, 0.2658227848101], 0.31581477327: [0.0253164556962, 0.9746835443038], 0.09245313251: [0.7088607594937, 0.2911392405063], 0.87886556642: [0.2405063291139, 0.7594936708861], 0.18634834161: [0.4810126582278, 0.5189873417722], 0.98974523314: [0.1012658227848, 0.8987341772152], 0.88463387278: [0.9620253164557, 0.0379746835443], 0.61192116648: [0.7848101265823, 0.2151898734177], 0.50680980612: [0.4050632911392, 0.5949367088608], 0.99262938632: [0.7341772151899, 0.2658227848101], 0.04694760455: [0.6962025316456, 0.3037974683544], 0.61768947284: [0.9493670886076, 0.0506329113924], 0.99839769268: [0.2784810126582, 0.7215189873418], 0.18923249479: [0.0253164556962, 0.9746835443038], 0.62922608556: [0.6962025316456, 0.3037974683544], 0.63211023874: [0.253164556962, 0.746835443038], 0.54975164237: [0.2405063291139, 0.7594936708861], 0.74010575228: [0.8607594936709, 0.1392405063291], 0.05495914116: [0.2151898734177, 0.7848101265823], 0.55551994873: [0.9620253164557, 0.0379746835443], 0.15237942637: [0.4050632911392, 0.5949367088608], 0.30620092934: [0.1012658227848, 0.8987341772152], 0.56128825509: [0.7848101265823, 0.2151898734177], 0.12449927896: [0.3924050632911, 0.6075949367089], 0.30908508252: [0.7341772151899, 0.2658227848101], 0.56705656145: [0.9493670886076, 0.0506329113924], 0.07835282807: [0.1012658227848, 0.8987341772152], 0.68947284089: [0.8607594936709, 0.1392405063291], 0.02323345618: [0.3924050632911, 0.6075949367089], 0.79746835443: [0.0], 0.18730972601: [0.9493670886076, 0.0506329113924], 0.42861720878: [0.1645569620253, 0.8354430379747], 0.80612081397: [0.9367088607595, 0.0632911392405], 0.71863483416: [0.6582278481013, 0.3417721518987], 0.10751482134: [0.5569620253165, 0.4430379746835], 0.43150136196: [0.3164556962025, 0.6835443037975], 0.91700048069: [0.3291139240506, 0.6708860759494], 0.91988463387: [0.4683544303797, 0.5316455696203], 0.73241467714: [0.1645569620253, 0.8354430379747], 0.92276878705: [0.7721518987342, 0.2278481012658], 0.01361961224: [0.3544303797468, 0.6455696202532], 0.73529883032: [0.3164556962025, 0.6835443037975], 0.43726966832: [0.9240506329114, 0.0759493670886], 0.15045665759: [0.9620253164557, 0.0379746835443], 0.74106713668: [0.9240506329114, 0.0759493670886], 0.30235539176: [0.9620253164557, 0.0379746835443], 0.74395128986: [0.6582278481013, 0.3417721518987], 0.03797468354: [0.0], 0.12353789457: [0.7974683544304, 0.2025316455696], 0.74683544304: [0.0], 0.49559365486: [0.8101265822785, 0.1898734177215], 0.85771510976: [0.7974683544304, 0.2025316455696], 0.30812369813: [0.7848101265823, 0.2151898734177], 0.24146771351: [0.3544303797468, 0.6455696202532], 0.49847780804: [0.0886075949367, 0.9113924050633], 0.86059926294: [0.0126582278481, 0.9873417721519], 0.86348341612: [0.5822784810127, 0.4177215189873], 0.36356353149: [0.4810126582278, 0.5189873417722], 0.8663675693: [0.3291139240506, 0.6708860759494], 0.18250280404: [0.7341772151899, 0.2658227848101], 0.97436308284: [0.8860759493671, 0.1139240506329], 0.86925172248: [0.4683544303797, 0.5316455696203], 0.38920044865: [0.5063291139241, 0.4936708860759], 0.59942316936: [0.6329113924051, 0.3670886075949], 0.40009613844: [0.620253164557, 0.379746835443], 0.9801313892: [0.2405063291139, 0.7594936708861], 0.36933183785: [0.8481012658228, 0.1518987341772], 0.98589969556: [0.9620253164557, 0.0379746835443], 0.10655343695: [0.7341772151899, 0.2658227848101], 0.99166800192: [0.7848101265823, 0.2151898734177], 0.21454895049: [0.0253164556962, 0.9746835443038], 0.53436949207: [0.5822784810127, 0.4177215189873], 0.53725364525: [0.3291139240506, 0.6708860759494], 0.91796186509: [0.1772151898734, 0.8227848101266], 0.54013779843: [0.4683544303797, 0.5316455696203], 0.92084601827: [0.5063291139241, 0.4936708860759], 0.54302195161: [0.7721518987342, 0.2278481012658], 0.92373017145: [0.8860759493671, 0.1139240506329], 0.54879025797: [0.6329113924051, 0.3670886075949], 0.65678577151: [0.9620253164557, 0.0379746835443], 0.30427816055: [0.4050632911392, 0.5949367088608], 0.66255407787: [0.7848101265823, 0.2151898734177], 0.49463227047: [0.3544303797468, 0.6455696202532], 0.07643005929: [0.4050632911392, 0.5949367088608], 0.35971799391: [0.7341772151899, 0.2658227848101], 0.66832238423: [0.9493670886076, 0.0506329113924], 0.01137638199: [0.7721518987342, 0.2278481012658], 0.59173209422: [0.5063291139241, 0.4936708860759], 0.36548630027: [0.2784810126582, 0.7215189873418], 0.5946162474: [0.8860759493671, 0.1139240506329], 0.18346418843: [0.5569620253165, 0.4430379746835], 0.60038455376: [0.2405063291139, 0.7594936708861], 0.10559205256: [0.7848101265823, 0.2151898734177], 0.98397692678: [0.873417721519, 0.126582278481], 0.60615286012: [0.9620253164557, 0.0379746835443], 0.2126261817: [0.9493670886076, 0.0506329113924], 0.71414837366: [0.7341772151899, 0.2658227848101], 0.42669443999: [0.6962025316456, 0.3037974683544], 0.71991668002: [0.2784810126582, 0.7215189873418], 0.42957859317: [0.253164556962, 0.746835443038], 0.12161512578: [0.620253164557, 0.379746835443], 0.48790257972: [0.9240506329114, 0.0759493670886], 0.84233295946: [0.9240506329114, 0.0759493670886], 0.30043262298: [0.873417721519, 0.126582278481], 0.4907867329: [0.6582278481013, 0.3417721518987], 0.84521711264: [0.6582278481013, 0.3417721518987], 0.84810126582: [0.0], 0.48213427335: [0.3164556962025, 0.6835443037975], 0.66063130909: [0.1012658227848, 0.8987341772152], 0.66351546227: [0.7341772151899, 0.2658227848101], 0.35875660952: [0.7848101265823, 0.2151898734177], 0.00240346098: [0.1012658227848, 0.8987341772152], 0.97660631309: [0.8101265822785, 0.1898734177215], 0.77439512899: [0.8481012658228, 0.1518987341772], 0.66928376863: [0.2784810126582, 0.7215189873418], 0.9647492389: [0.5822784810127, 0.4177215189873], 0.21006248999: [0.5443037974684, 0.4556962025316], 0.18154141964: [0.2151898734177, 0.7848101265823], 0.41708059606: [0.0253164556962, 0.9746835443038], 0.36452491588: [0.9493670886076, 0.0506329113924], 0.78304758853: [0.1645569620253, 0.8354430379747], 0.78593174171: [0.3164556962025, 0.6835443037975], 0.41996474924: [0.8481012658228, 0.1518987341772], 0.79170004807: [0.9240506329114, 0.0759493670886], 0.89969556161: [0.3544303797468, 0.6455696202532], 0.05303637238: [0.1012658227848, 0.8987341772152], 0.90257971479: [0.7088607594937, 0.2911392405063], 0.52763980131: [0.9367088607595, 0.0632911392405], 0.23986540619: [0.0253164556962, 0.9746835443038], 0.29081877904: [0.8860759493671, 0.1139240506329], 0.90834802115: [0.7974683544304, 0.2025316455696], 0.00913315174: [0.4810126582278, 0.5189873417722], 0.91411632751: [0.5822784810127, 0.4177215189873], 0.24274955937: [0.8481012658228, 0.1518987341772], 0.2965870854: [0.2405063291139, 0.7594936708861], 0.48694119532: [0.8607594936709, 0.1392405063291], 0.0745072905: [0.9620253164557, 0.0379746835443], 0.35491107194: [0.4050632911392, 0.5949367088608], 0.84906265022: [0.3544303797468, 0.6455696202532], 0.02227207178: [0.7974683544304, 0.2025316455696], 0.95705816376: [0.620253164557, 0.379746835443], 0.5792340971: [0.7974683544304, 0.2025316455696], 0.95994231694: [0.3924050632911, 0.6075949367089], 0.17961865086: [0.1012658227848, 0.8987341772152], 0.67088607595: [0.0], 0.36067937831: [0.5569620253165, 0.4430379746835], 0.58500240346: [0.5822784810127, 0.4177215189873], 0.09053036372: [0.8101265822785, 0.1898734177215], 0.58788655664: [0.3291139240506, 0.6708860759494], 0.96859477648: [0.1772151898734, 0.8227848101266], 0.41611921167: [0.2784810126582, 0.7215189873418], 0.59077070982: [0.4683544303797, 0.5316455696203], 0.97147892966: [0.5063291139241, 0.4936708860759], 0.20878064413: [0.5569620253165, 0.4430379746835], 0.593654863: [0.7721518987342, 0.2278481012658], 0.70165037654: [0.2405063291139, 0.7594936708861], 0.00656946002: [0.1772151898734, 0.8227848101266], 0.7074186829: [0.9620253164557, 0.0379746835443], 0.51994872617: [0.3544303797468, 0.6455696202532], 0.21166479731: [0.4810126582278, 0.5189873417722], 0.28697324147: [0.4683544303797, 0.5316455696203], 0.52283287935: [0.7088607594937, 0.2911392405063], 0.28985739465: [0.7721518987342, 0.2278481012658], 0.52860118571: [0.7974683544304, 0.2025316455696], 0.48021150457: [0.253164556962, 0.746835443038], 0.53148533889: [0.0126582278481, 0.9873417721519], 0.63948085243: [0.1772151898734, 0.8227848101266], 0.64236500561: [0.5063291139241, 0.4936708860759], 0.29562570101: [0.6329113924051, 0.3670886075949], 0.64524915879: [0.8860759493671, 0.1139240506329], 0.1485338888: [0.873417721519, 0.126582278481], 0.22480371735: [0.7974683544304, 0.2025316455696], 0.35106553437: [0.873417721519, 0.126582278481], 0.65101746515: [0.2405063291139, 0.7594936708861], 0.76189713187: [0.1012658227848, 0.8987341772152], 0.17769588207: [0.4050632911392, 0.5949367088608], 0.76478128505: [0.7341772151899, 0.2658227848101], 0.35683384073: [0.1012658227848, 0.8987341772152], 0.08956897933: [0.3544303797468, 0.6455696202532], 0.77054959141: [0.2784810126582, 0.7215189873418], 0.20685787534: [0.7848101265823, 0.2151898734177], 0.41515782727: [0.9493670886076, 0.0506329113924], 0.58884794104: [0.1772151898734, 0.8227848101266], 0.88431341131: [0.1645569620253, 0.8354430379747], 0.88719756449: [0.3164556962025, 0.6835443037975], 0.89296587085: [0.9240506329114, 0.0759493670886], 0.70549591412: [0.873417721519, 0.126582278481], 0.1187309726: [0.0886075949367, 0.9113924050633], 0.28601185707: [0.5443037974684, 0.4556962025316], 0.81637558084: [0.5569620253165, 0.4430379746835], 0.14372696683: [0.6329113924051, 0.3670886075949], 0.23890402179: [0.2784810126582, 0.7215189873418], 0.81925973402: [0.4810126582278, 0.5189873417722], 0.47925012017: [0.1645569620253, 0.8354430379747], 0.8221438872: [0.0253164556962, 0.9746835443038], 0.07258452171: [0.873417721519, 0.126582278481], 0.0600865246: [0.5063291139241, 0.4936708860759], 0.82502804038: [0.8481012658228, 0.1518987341772], 0.92789617049: [0.7088607594937, 0.2911392405063], 0.3472199968: [0.2405063291139, 0.7594936708861], 0.83368049992: [0.1645569620253, 0.8354430379747], 0.00272392245: [0.5822784810127, 0.4177215189873], 0.8365646531: [0.3164556962025, 0.6835443037975], 0.5638519468: [0.9240506329114, 0.0759493670886], 0.17577311328: [0.9620253164557, 0.0379746835443], 0.56673609998: [0.6582278481013, 0.3417721518987], 0.35298830316: [0.9620253164557, 0.0379746835443], 0.950328473: [0.3544303797468, 0.6455696202532], 0.08860759494: [0.0], 0.95321262618: [0.7088607594937, 0.2911392405063], 0.20493510655: [0.1012658227848, 0.8987341772152], 0.76862682263: [0.4810126582278, 0.5189873417722], 0.95898093254: [0.7974683544304, 0.2025316455696], 0.4113122897: [0.5569620253165, 0.4430379746835], 0.77151097581: [0.0253164556962, 0.9746835443038], 0.02579714789: [0.4050632911392, 0.5949367088608], 0.41419644288: [0.4810126582278, 0.5189873417722], 0.88239064253: [0.6962025316456, 0.3037974683544], 0.50456657587: [0.1645569620253, 0.8354430379747], 0.20781925973: [0.7341772151899, 0.2658227848101], 0.88527479571: [0.253164556962, 0.746835443038], 0.50745072905: [0.3164556962025, 0.6835443037975], 0.03509053036: [0.6582278481013, 0.3417721518987], 0.11776958821: [0.7088607594937, 0.2911392405063], 0.51321903541: [0.9240506329114, 0.0759493670886], 0.62121454895: [0.3544303797468, 0.6455696202532], 0.51610318859: [0.6582278481013, 0.3417721518987], 0.28505047268: [0.1772151898734, 0.8227848101266], 0.62409870213: [0.7088607594937, 0.2911392405063], 0.0149014581: [0.8481012658228, 0.1518987341772], 0.28793462586: [0.5063291139241, 0.4936708860759], 0.62986700849: [0.7974683544304, 0.2025316455696], 0.14468835123: [0.2405063291139, 0.7594936708861], 0.63275116167: [0.0126582278481, 0.9873417721519], 0.63563531485: [0.5822784810127, 0.4177215189873], 0.63851946803: [0.3291139240506, 0.6708860759494], 0.3462586124: [0.6329113924051, 0.3670886075949], 0.55616087166: [0.253164556962, 0.746835443038], 0.74651498157: [0.8860759493671, 0.1139240506329], 0.1738503445: [0.873417721519, 0.126582278481], 0.20301233777: [0.4050632911392, 0.5949367088608], 0.57058163756: [0.3544303797468, 0.6455696202532], 0.9512898574: [0.8101265822785, 0.1898734177215], 0.40746675212: [0.1012658227848, 0.8987341772152], 0.57346579074: [0.7088607594937, 0.2911392405063], 0.05111360359: [0.4050632911392, 0.5949367088608], 0.68146130428: [0.3924050632911, 0.6075949367089], 0.4103509053: [0.7341772151899, 0.2658227848101], 0.23217433104: [0.7848101265823, 0.2151898734177], 0.46579073866: [0.9493670886076, 0.0506329113924], 0.69011376382: [0.1772151898734, 0.8227848101266], 0.692997917: [0.5063291139241, 0.4936708860759], 0.27832078193: [0.0126582278481, 0.9873417721519], 0.69588207018: [0.8860759493671, 0.1139240506329], 0.28120493511: [0.5822784810127, 0.4177215189873], 0.8067617369: [0.873417721519, 0.126582278481], 0.0591251402: [0.4683544303797, 0.5316455696203], 0.28408908829: [0.3291139240506, 0.6708860759494], 0.81253004326: [0.1012658227848, 0.8987341772152], 0.16904342253: [0.6329113924051, 0.3670886075949], 0.62506008653: [0.0886075949367, 0.9113924050633], 0.81541419644: [0.7341772151899, 0.2658227848101], 0.62794423971: [0.620253164557, 0.379746835443], 0.02131068739: [0.9367088607595, 0.0632911392405], 0.63082839289: [0.3924050632911, 0.6075949367089], 0.8211825028: [0.2784810126582, 0.7215189873418], 0.73882390643: [0.4303797468354, 0.5696202531646], 0.9602627784: [0.1645569620253, 0.8354430379747], 0.74170805961: [0.5443037974684, 0.4556962025316], 0.0998237462: [0.9620253164557, 0.0379746835443], 0.60423009133: [0.873417721519, 0.126582278481], 0.20108956898: [0.9620253164557, 0.0379746835443], 0.40362121455: [0.9620253164557, 0.0379746835443], 0.75612882551: [0.873417721519, 0.126582278481], 0.01265822785: [0.0], 0.86700849223: [0.5569620253165, 0.4430379746835], 0.86989264541: [0.4810126582278, 0.5189873417722], 0.40938952091: [0.7848101265823, 0.2151898734177], 0.87277679859: [0.0253164556962, 0.9746835443038], 0.11584681942: [0.8101265822785, 0.1898734177215], 0.27447524435: [0.9367088607595, 0.0632911392405], 0.87566095177: [0.8481012658228, 0.1518987341772], 0.46482935427: [0.4810126582278, 0.5189873417722], 0.13795866047: [0.7721518987342, 0.2278481012658], 0.98365646531: [0.6962025316456, 0.3037974683544], 0.23313571543: [0.7341772151899, 0.2658227848101], 0.98654061849: [0.253164556962, 0.746835443038], 0.46771350745: [0.0253164556962, 0.9746835443038], 0.47059766063: [0.8481012658228, 0.1518987341772], 0.28312770389: [0.4303797468354, 0.5696202531646], 0.81060727448: [0.4050632911392, 0.5949367088608], 0.33856753725: [0.5063291139241, 0.4936708860759], 0.9214869412: [0.9493670886076, 0.0506329113924], 0.17000480692: [0.2405063291139, 0.7594936708861], 0.54366287454: [0.0253164556962, 0.9746835443038], 0.34145169043: [0.8860759493671, 0.1139240506329], 0.54654702772: [0.8481012658228, 0.1518987341772], 0.08572344176: [0.6582278481013, 0.3417721518987], 0.39689152379: [0.6329113924051, 0.3670886075949], 0.93302355392: [0.6962025316456, 0.3037974683544], 0.19916680019: [0.873417721519, 0.126582278481], 0.55519948726: [0.1645569620253, 0.8354430379747], 0.9359077071: [0.253164556962, 0.746835443038], 0.55808364044: [0.3164556962025, 0.6835443037975], 0.67184746034: [0.3544303797468, 0.6455696202532], 0.40554398334: [0.4050632911392, 0.5949367088608], 0.67473161352: [0.7088607594937, 0.2911392405063], 0.91123217433: [0.0126582278481, 0.9873417721519], 0.11488543503: [0.3544303797468, 0.6455696202532], 0.27062970678: [0.0886075949367, 0.9113924050633], 0.4609838167: [0.7341772151899, 0.2658227848101], 0.13603589168: [0.5063291139241, 0.4936708860759], 0.27351385996: [0.620253164557, 0.379746835443], 0.06873898414: [0.2405063291139, 0.7594936708861], 0.60390962987: [0.6962025316456, 0.3037974683544], 0.05816375581: [0.5443037974684, 0.4556962025316], 0.27639801314: [0.3924050632911, 0.6075949367089], 0.89232494793: [0.5569620253165, 0.4430379746835], 0.60679378305: [0.253164556962, 0.746835443038], 0.46675212306: [0.2784810126582, 0.7215189873418], 0.13892004487: [0.8860759493671, 0.1139240506329], 0.3318378465: [0.5822784810127, 0.4177215189873], 0.99327030925: [0.8607594936709, 0.1392405063291], 0.33472199968: [0.3291139240506, 0.6708860759494], 0.72344175613: [0.8101265822785, 0.1898734177215], 0.72632590931: [0.0886075949367, 0.9113924050633], 0.33760615286: [0.4683544303797, 0.5316455696203], 0.72921006249: [0.620253164557, 0.379746835443], 0.73209421567: [0.3924050632911, 0.6075949367089], 0.34049030604: [0.7721518987342, 0.2278481012658], 0.53917641404: [0.5443037974684, 0.4556962025316], 0.74074667521: [0.1772151898734, 0.8227848101266], 0.55327671848: [0.6962025316456, 0.3037974683544], 0.74363082839: [0.5063291139241, 0.4936708860759], 0.6641563852: [0.8607594936709, 0.1392405063291], 0.45425412594: [0.9620253164557, 0.0379746835443], 0.40169844576: [0.873417721519, 0.126582278481], 0.85739464829: [0.873417721519, 0.126582278481], 0.11392405063: [0.0], 0.2667841692: [0.3544303797468, 0.6455696202532], 0.67280884474: [0.8101265822785, 0.1898734177215], 0.1341131229: [0.5443037974684, 0.4556962025316], 0.67569299792: [0.0886075949367, 0.9113924050633], 0.26966832238: [0.7088607594937, 0.2911392405063], 0.97115846819: [0.4810126582278, 0.5189873417722], 0.4600224323: [0.7848101265823, 0.2151898734177], 0.6785771511: [0.620253164557, 0.379746835443], 0.06777759974: [0.6329113924051, 0.3670886075949], 0.78945681782: [0.4303797468354, 0.5696202531646], 0.792340971: [0.5443037974684, 0.4556962025316], 0.27543662875: [0.7974683544304, 0.2025316455696], 0.00432622977: [0.2151898734177, 0.7848101265823], 0.33376061529: [0.4303797468354, 0.5696202531646], 0.91187309726: [0.4050632911392, 0.5949367088608], 0.5340490306: [0.1012658227848, 0.8987341772152], 0.33664476847: [0.5443037974684, 0.4556962025316], 0.91764140362: [0.5569620253165, 0.4430379746835], 0.19532126262: [0.2405063291139, 0.7594936708861], 0.9205255568: [0.4810126582278, 0.5189873417722], 0.39208460183: [0.8860759493671, 0.1139240506329], 0.92340970998: [0.0253164556962, 0.9746835443038], 0.0491908348: [0.9620253164557, 0.0379746835443], 0.84681941997: [0.7721518987342, 0.2278481012658], 0.59237301715: [0.9493670886076, 0.0506329113924], 0.39785290819: [0.2405063291139, 0.7594936708861], 0.26293863163: [0.6582278481013, 0.3417721518987], 0.85258772633: [0.6329113924051, 0.3670886075949], 0.13219035411: [0.3291139240506, 0.6708860759494], 0.26582278481: [0.0], 0.45617689473: [0.4050632911392, 0.5949367088608], 0.96635154623: [0.7848101265823, 0.2151898734177], 0.58852747957: [0.5569620253165, 0.4430379746835], 0.05720237142: [0.1772151898734, 0.8227848101266], 0.87053356834: [0.6582278481013, 0.3417721518987], 0.59141163275: [0.4810126582278, 0.5189873417722], 0.13507450729: [0.4683544303797, 0.5316455696203], 0.97211985259: [0.9493670886076, 0.0506329113924], 0.59429578593: [0.0253164556962, 0.9746835443038], 0.23025156225: [0.1012658227848, 0.8987341772152], 0.32414677135: [0.620253164557, 0.379746835443], 0.59717993911: [0.8481012658228, 0.1518987341772], 0.020349303: [0.620253164557, 0.379746835443], 0.95417401058: [0.0886075949367, 0.9113924050633], 0.32703092453: [0.3924050632911, 0.6075949367089], 0.16423650056: [0.8860759493671, 0.1139240506329], 0.60583239865: [0.1645569620253, 0.8354430379747], 0.52347380228: [0.6329113924051, 0.3670886075949], 0.08283928858: [0.9240506329114, 0.0759493670886], 0.38535491107: [0.3291139240506, 0.6708860759494], 0.53212626182: [0.4050632911392, 0.5949367088608], 0.38823906425: [0.4683544303797, 0.5316455696203], 0.53789456818: [0.5569620253165, 0.4430379746835], 0.39112321743: [0.7721518987342, 0.2278481012658], 0.54077872136: [0.4810126582278, 0.5189873417722], 0.65454254126: [0.6962025316456, 0.3037974683544], 0.25909309406: [0.8607594936709, 0.1392405063291], 0.65742669444: [0.253164556962, 0.746835443038], 0.58211825028: [0.0126582278481, 0.9873417721519], 0.45233135715: [0.873417721519, 0.126582278481], 0.77407466752: [0.8101265822785, 0.1898734177215], 0.1331517385: [0.1772151898734, 0.8227848101266], 0.22832879346: [0.4050632911392, 0.5949367088608], 0.7769588207: [0.0886075949367, 0.9113924050633], 0.2677455536: [0.8101265822785, 0.1898734177215], 0.77984297388: [0.620253164557, 0.379746835443], 0.0402179138: [0.8481012658228, 0.1518987341772], 0.44496074347: [0.8101265822785, 0.1898734177215], 0.78272712706: [0.3924050632911, 0.6075949367089], 0.8907226406: [0.4303797468354, 0.5696202531646], 0.89360679378: [0.5443037974684, 0.4556962025316], 0.32606954014: [0.7974683544304, 0.2025316455696], 0.08187790418: [0.8607594936709, 0.1392405063291], 0.32895369332: [0.0126582278481, 0.9873417721519], 0.10366928377: [0.1012658227848, 0.8987341772152], 0.71478929659: [0.8607594936709, 0.1392405063291], 0.38439352668: [0.4303797468354, 0.5696202531646], 0.38727767986: [0.5443037974684, 0.4556962025316], 0.19435987823: [0.6329113924051, 0.3670886075949], 0.83143726967: [0.9367088607595, 0.0632911392405], 0.11103989745: [0.6582278481013, 0.3417721518987], 0.84008972921: [0.4303797468354, 0.5696202531646], 0.94808524275: [0.7721518987342, 0.2278481012658], 0.84297388239: [0.5443037974684, 0.4556962025316], 0.44848581958: [0.2405063291139, 0.7594936708861], 0.05624098702: [0.3291139240506, 0.6708860759494], 0.95385354911: [0.6329113924051, 0.3670886075949], 0.13122896972: [0.4303797468354, 0.5696202531646], 0.22640602468: [0.9620253164557, 0.0379746835443], 0.96250600865: [0.4050632911392, 0.5949367088608], 0.16039096299: [0.4683544303797, 0.5316455696203], 0.96827431501: [0.5569620253165, 0.4430379746835], 0.78080435828: [0.9367088607595, 0.0632911392405], 0.88879987182: [0.5822784810127, 0.4177215189873], 0.51097580516: [0.4303797468354, 0.5696202531646], 0.891684025: [0.3291139240506, 0.6708860759494], 0.32510815574: [0.9367088607595, 0.0632911392405], 0.51385995834: [0.5443037974684, 0.4556962025316], 0.89456817818: [0.4683544303797, 0.5316455696203], 0.18955295626: [0.8860759493671, 0.1139240506329], 0.16327511617: [0.7721518987342, 0.2278481012658], 0.89745233136: [0.7721518987342, 0.2278481012658], 0.02387437911: [0.9620253164557, 0.0379746835443], 0.90322063772: [0.6329113924051, 0.3670886075949], 0.6333920846: [0.4050632911392, 0.5949367088608], 0.38631629547: [0.1772151898734, 0.8227848101266], 0.63916039096: [0.5569620253165, 0.4430379746835], 0.44175612883: [0.7721518987342, 0.2278481012658], 0.64204454414: [0.4810126582278, 0.5189873417722], 0.12642204775: [0.0126582278481, 0.9873417721519], 0.95834000961: [0.6962025316456, 0.3037974683544], 0.64492869732: [0.0253164556962, 0.9746835443038], 0.25428617209: [0.3164556962025, 0.6835443037975], 0.56545425413: [0.4683544303797, 0.5316455696203], 0.75580836404: [0.6962025316456, 0.3037974683544], 0.56833840731: [0.7721518987342, 0.2278481012658], 0.44752443519: [0.6329113924051, 0.3670886075949], 0.12930620093: [0.5822784810127, 0.4177215189873], 0.14981573466: [0.3924050632911, 0.6075949367089], 0.22448325589: [0.873417721519, 0.126582278481], 0.26005447845: [0.9240506329114, 0.0759493670886], 0.57410671367: [0.6329113924051, 0.3670886075949], 0.1584681942: [0.1772151898734, 0.8227848101266], 0.96058323987: [0.9620253164557, 0.0379746835443], 0.09790097741: [0.873417721519, 0.126582278481], 0.58275917321: [0.4050632911392, 0.5949367088608], 0.31837846499: [0.8101265822785, 0.1898734177215], 0.69363883993: [0.9493670886076, 0.0506329113924], 0.32126261817: [0.0886075949367, 0.9113924050633], 0.16135234738: [0.5063291139241, 0.4936708860759], 0.37670245153: [0.7974683544304, 0.2025316455696], 0.32959461625: [0.4050632911392, 0.5949367088608], 0.04726806602: [0.873417721519, 0.126582278481], 0.70517545265: [0.6962025316456, 0.3037974683544], 0.47732735139: [0.6962025316456, 0.3037974683544], 0.37958660471: [0.0126582278481, 0.9873417721519], 0.70805960583: [0.253164556962, 0.746835443038], 0.81605511937: [0.8607594936709, 0.1392405063291], 0.38247075789: [0.5822784810127, 0.4177215189873], 0.63146931582: [0.9620253164557, 0.0379746835443], 0.43791059125: [0.5443037974684, 0.4556962025316], 0.82470757891: [0.8101265822785, 0.1898734177215], 0.21967633392: [0.6329113924051, 0.3670886075949], 0.63723762218: [0.7848101265823, 0.2151898734177], 0.82759173209: [0.0886075949367, 0.9113924050633], 0.06297067778: [0.8860759493671, 0.1139240506329], 0.05527960263: [0.4303797468354, 0.5696202531646], 0.64300592854: [0.9493670886076, 0.0506329113924], 0.49911873097: [0.2405063291139, 0.7594936708861], 0.03220637718: [0.9240506329114, 0.0759493670886], 0.15654542541: [0.4303797468354, 0.5696202531646], 0.76542220798: [0.8607594936709, 0.1392405063291], 0.87341772152: [0.0], 0.3174170806: [0.3544303797468, 0.6455696202532], 0.15942957859: [0.5443037974684, 0.4556962025316], 0.50136196122: [0.620253164557, 0.379746835443], 0.88207018106: [0.9367088607595, 0.0632911392405], 0.32030123378: [0.7088607594937, 0.2911392405063], 0.94776478129: [0.2784810126582, 0.7215189873418], 0.59493670886: [0.0], 0.99294984778: [0.3291139240506, 0.6708860759494], 0.37574106714: [0.9367088607595, 0.0632911392405], 0.99583400096: [0.4683544303797, 0.5316455696203], 0.18859157186: [0.7721518987342, 0.2278481012658], 0.99871815414: [0.7721518987342, 0.2278481012658], 0.81701650377: [0.9240506329114, 0.0759493670886], 0.81990065695: [0.6582278481013, 0.3417721518987], 0.43694920686: [0.1772151898734, 0.8227848101266], 0.82278481013: [0.0], 0.55295625701: [0.9367088607595, 0.0632911392405], 0.43983336004: [0.5063291139241, 0.4936708860759], 0.93366447685: [0.7974683544304, 0.2025316455696], 0.22063771831: [0.2405063291139, 0.7594936708861], 0.2523634033: [0.253164556962, 0.746835443038], 0.93654863003: [0.0126582278481, 0.9873417721519], 0.44271751322: [0.8860759493671, 0.1139240506329], 0.93943278321: [0.5822784810127, 0.4177215189873], 0.56160871655: [0.4303797468354, 0.5696202531646], 0.94231693639: [0.3291139240506, 0.6708860759494], 0.56449286973: [0.5443037974684, 0.4556962025316], 0.15462265663: [0.5822784810127, 0.4177215189873], 0.94520108957: [0.4683544303797, 0.5316455696203], 0.24979971159: [0.873417721519, 0.126582278481], 0.31068738984: [0.9240506329114, 0.0759493670886], 0.67537253645: [0.6329113924051, 0.3670886075949], 0.41163275116: [0.1772151898734, 0.8227848101266], 0.31357154302: [0.6582278481013, 0.3417721518987], 0.15750680981: [0.3291139240506, 0.6708860759494], 0.68402499599: [0.4050632911392, 0.5949367088608], 0.3164556962: [0.0], 0.37189552956: [0.0886075949367, 0.9113924050633], 0.50232334562: [0.9367088607595, 0.0632911392405], 0.18666880308: [0.5063291139241, 0.4936708860759], 0.61031885916: [0.5822784810127, 0.4177215189873], 0.37477968274: [0.620253164557, 0.379746835443], 0.61320301234: [0.3291139240506, 0.6708860759494], 0.09405543983: [0.2405063291139, 0.7594936708861], 0.61608716552: [0.4683544303797, 0.5316455696203], 0.99679538536: [0.5063291139241, 0.4936708860759], 0.4302195161: [0.0126582278481, 0.9873417721519], 0.37766383592: [0.3924050632911, 0.6075949367089], 0.6189713187: [0.7721518987342, 0.2278481012658], 0.99967953854: [0.8860759493671, 0.1139240506329], 0.43310366928: [0.5822784810127, 0.4177215189873], 0.62473962506: [0.6329113924051, 0.3670886075949], 0.7327351386: [0.9620253164557, 0.0379746835443], 0.43598782246: [0.3291139240506, 0.6708860759494], 0.73850344496: [0.7848101265823, 0.2151898734177], 0.43887197565: [0.4683544303797, 0.5316455696203], 0.1232174331: [0.873417721519, 0.126582278481], 0.74427175132: [0.9493670886076, 0.0506329113924], 0.25140201891: [0.1645569620253, 0.8354430379747], 0.55968594777: [0.5822784810127, 0.4177215189873], 0.56257010095: [0.3291139240506, 0.6708860759494], 0.07707098221: [0.3164556962025, 0.6835443037975], 0.67056561449: [0.8860759493671, 0.1139240506329], 0.30972600545: [0.8607594936709, 0.1392405063291], 0.88591571864: [0.0126582278481, 0.9873417721519], 0.94936708861: [0.0], 0.67633392085: [0.2405063291139, 0.7594936708861], 0.86668803076: [0.8607594936709, 0.1392405063291], 0.68210222721: [0.9620253164557, 0.0379746835443], 0.36804999199: [0.3544303797468, 0.6455696202532], 0.18474603429: [0.5443037974684, 0.4556962025316], 0.68787053357: [0.7848101265823, 0.2151898734177], 0.37093414517: [0.7088607594937, 0.2911392405063], 0.79586604711: [0.2784810126582, 0.7215189873418], 0.00112161513: [0.3164556962025, 0.6835443037975], 0.09309405544: [0.6329113924051, 0.3670886075949], 0.42637397853: [0.9367088607595, 0.0632911392405], 0.21390802756: [0.7721518987342, 0.2278481012658], 0.91828232655: [0.9240506329114, 0.0759493670886], 0.92116647973: [0.6582278481013, 0.3417721518987], 0.48758211825: [0.1772151898734, 0.8227848101266], 0.43502643807: [0.4303797468354, 0.5696202531646], 0.92405063291: [0.0], 0.30011216151: [0.6962025316456, 0.3037974683544], 0.49046627143: [0.5063291139241, 0.4936708860759], 0.24595417401: [0.2405063291139, 0.7594936708861], 0.30299631469: [0.253164556962, 0.746835443038], 0.93270309245: [0.9367088607595, 0.0632911392405], 0.49335042461: [0.8860759493671, 0.1139240506329], 0.74523313572: [0.2784810126582, 0.7215189873418], 0.03092453133: [0.3291139240506, 0.6708860759494], 0.85899695562: [0.1645569620253, 0.8354430379747], 0.36132030123: [0.9240506329114, 0.0759493670886], 0.8618811088: [0.3164556962025, 0.6835443037975], 0.36420445441: [0.6582278481013, 0.3417721518987], 0.86764941516: [0.9240506329114, 0.0759493670886], 0.1828232655: [0.3291139240506, 0.6708860759494], 0.9756449287: [0.3544303797468, 0.6455696202532], 0.3670886076: [0.0], 0.97852908188: [0.7088607594937, 0.2911392405063], 0.42252844096: [0.0886075949367, 0.9113924050633], 0.6035891684: [0.9367088607595, 0.0632911392405], 0.98429738824: [0.7974683544304, 0.2025316455696], 0.21198525877: [0.5063291139241, 0.4936708860759], 0.98718154142: [0.0126582278481, 0.9873417721519], 0.42541259414: [0.620253164557, 0.379746835443], 0.9900656946: [0.5822784810127, 0.4177215189873], 0.05335683384: [0.5822784810127, 0.4177215189873], 0.61224162794: [0.4303797468354, 0.5696202531646], 0.42829674732: [0.3924050632911, 0.6075949367089], 0.72023714148: [0.7721518987342, 0.2278481012658], 0.53276718475: [0.3164556962025, 0.6835443037975], 0.21486941195: [0.8860759493671, 0.1139240506329], 0.48373658068: [0.5822784810127, 0.4177215189873], 0.53853549111: [0.9240506329114, 0.0759493670886], 0.10815574427: [0.9240506329114, 0.0759493670886], 0.54141964429: [0.6582278481013, 0.3417721518987], 0.48662073386: [0.3291139240506, 0.6708860759494], 0.14885435026: [0.7974683544304, 0.2025316455696], 0.54430379747: [0.0], 0.92501201731: [0.3544303797468, 0.6455696202532], 0.48950488704: [0.4683544303797, 0.5316455696203], 0.07514821343: [0.253164556962, 0.746835443038], 0.65518346419: [0.7974683544304, 0.2025316455696], 0.3020349303: [0.1645569620253, 0.8354430379747], 0.65806761737: [0.0126582278481, 0.9873417721519], 0.49238904022: [0.7721518987342, 0.2278481012658], 0.15173850345: [0.0126582278481, 0.9873417721519], 0.66095177055: [0.5822784810127, 0.4177215189873], 0.30491908348: [0.3164556962025, 0.6835443037975], 0.66383592373: [0.3291139240506, 0.6708860759494], 0.00560807563: [0.3291139240506, 0.6708860759494], 0.66672007691: [0.4683544303797, 0.5316455696203], 0.36035891684: [0.8607594936709, 0.1392405063291], 0.66960423009: [0.7721518987342, 0.2278481012658], 0.77759974363: [0.2405063291139, 0.7594936708861], 0.52828072424: [0.873417721519, 0.126582278481], 0.78336804999: [0.9620253164557, 0.0379746835443], 0.1837846499: [0.1772151898734, 0.8227848101266], 0.59878224644: [0.7088607594937, 0.2911392405063], 0.78913635635: [0.7848101265823, 0.2151898734177], 0.42156705656: [0.7088607594937, 0.2911392405063], 0.02643807082: [0.3164556962025, 0.6835443037975], 0.6045505528: [0.7974683544304, 0.2025316455696], 0.79490466271: [0.9493670886076, 0.0506329113924], 0.60743470598: [0.0126582278481, 0.9873417721519], 0.71543021952: [0.1772151898734, 0.8227848101266], 0.42733536292: [0.7974683544304, 0.2025316455696], 0.7183143727: [0.5063291139241, 0.4936708860759], 0.10719435988: [0.8607594936709, 0.1392405063291], 0.72119852588: [0.8860759493671, 0.1139240506329], 0.14693158148: [0.620253164557, 0.379746835443], 0.72696683224: [0.2405063291139, 0.7594936708861], 0.48565934946: [0.4303797468354, 0.5696202531646], 0.07418682903: [0.1645569620253, 0.8354430379747], 0.83784649896: [0.1012658227848, 0.8987341772152], 0.48854350264: [0.5443037974684, 0.4556962025316], 0.84073065214: [0.7341772151899, 0.2658227848101], 0.31485338888: [0.2784810126582, 0.7215189873418], 0.24499278962: [0.6329113924051, 0.3670886075949], 0.37830475885: [0.9620253164557, 0.0379746835443], 0.8464989585: [0.2784810126582, 0.7215189873418], 0.0094536132: [0.5063291139241, 0.4936708860759], 0.64268546707: [0.6582278481013, 0.3417721518987], 0.96314693158: [0.3164556962025, 0.6835443037975], 0.96891523794: [0.9240506329114, 0.0759493670886], 0.18186188111: [0.4303797468354, 0.5696202531646], 0.78144528121: [0.873417721519, 0.126582278481], 0.97179939112: [0.6582278481013, 0.3417721518987], 0.41772151899: [0.0], 0.9746835443: [0.0], 0.78721358757: [0.1012658227848, 0.8987341772152], 0.79009774075: [0.7341772151899, 0.2658227848101], 0.51738503445: [0.2784810126582, 0.7215189873418], 0.21102387438: [0.4683544303797, 0.5316455696203], 0.89809325429: [0.0253164556962, 0.9746835443038], 0.90097740747: [0.8481012658228, 0.1518987341772], 0.11937189553: [0.2405063291139, 0.7594936708861], 0.47892965871: [0.3924050632911, 0.6075949367089], 0.24018586765: [0.8860759493671, 0.1139240506329], 0.90962986701: [0.1645569620253, 0.8354430379747], 0.94327832078: [0.1772151898734, 0.8227848101266], 0.91251402019: [0.3164556962025, 0.6835443037975], 0.63980131389: [0.9240506329114, 0.0759493670886], 0.06040698606: [0.6582278481013, 0.3417721518987], 0.53757410671: [0.8607594936709, 0.1392405063291], 0.14789296587: [0.9367088607595, 0.0632911392405], 0.64556962025: [0.0], 0.02195161032: [0.873417721519, 0.126582278481], 0.03733376062: [0.0253164556962, 0.9746835443038], 0.35266784169: [0.1645569620253, 0.8354430379747], 0.17705495914: [0.0126582278481, 0.9873417721519], 0.35555199487: [0.3164556962025, 0.6835443037975], 0.58051594296: [0.1645569620253, 0.8354430379747], 0.17993911232: [0.5822784810127, 0.4177215189873], 0.58340009614: [0.3164556962025, 0.6835443037975], 0.5891684025: [0.9240506329114, 0.0759493670886], 0.59205255568: [0.6582278481013, 0.3417721518987], 0.20910110559: [0.1772151898734, 0.8227848101266], 0.70004806922: [0.7088607594937, 0.2911392405063], 0.41964428777: [0.8101265822785, 0.1898734177215], 0.11841051114: [0.6329113924051, 0.3670886075949], 0.70581637558: [0.7974683544304, 0.2025316455696], 0.70870052876: [0.0126582278481, 0.9873417721519], 0.1430860439: [0.7088607594937, 0.2911392405063], 0.71158468194: [0.5822784810127, 0.4177215189873], 0.2876141644: [0.4810126582278, 0.5189873417722], 0.47796827432: [0.7974683544304, 0.2025316455696], 0.71446883512: [0.3291139240506, 0.6708860759494], 0.07226406025: [0.6962025316456, 0.3037974683544], 0.02996314693: [0.4303797468354, 0.5696202531646], 0.82246434866: [0.8860759493671, 0.1139240506329], 0.7173529883: [0.4683544303797, 0.5316455696203], 0.29049831758: [0.0253164556962, 0.9746835443038], 0.52988303157: [0.1645569620253, 0.8354430379747], 0.4808524275: [0.0126582278481, 0.9873417721519], 0.82823265502: [0.2405063291139, 0.7594936708861], 0.29338247076: [0.8481012658228, 0.1518987341772], 0.04342252844: [0.2405063291139, 0.7594936708861], 0.83400096138: [0.9620253164557, 0.0379746835443], 0.64653100465: [0.3544303797468, 0.6455696202532], 0.17513219035: [0.3924050632911, 0.6075949367089], 0.64941515783: [0.7088607594937, 0.2911392405063], 0.75741067137: [0.3924050632911, 0.6075949367089], 0.08828713347: [0.8860759493671, 0.1139240506329], 0.76606313091: [0.1772151898734, 0.8227848101266], 0.76894728409: [0.5063291139241, 0.4936708860759], 0.77183143727: [0.8860759493671, 0.1139240506329], 0.2081397212: [0.3291139240506, 0.6708860759494], 0.20717833681: [0.4303797468354, 0.5696202531646], 0.88271110399: [0.873417721519, 0.126582278481], 0.50488703733: [0.9620253164557, 0.0379746835443], 0.88847941035: [0.1012658227848, 0.8987341772152], 0.41868290338: [0.3544303797468, 0.6455696202532], 0.14116327512: [0.8101265822785, 0.1898734177215], 0.89136356353: [0.7341772151899, 0.2658227848101], 0.23634033008: [0.4683544303797, 0.5316455696203], 0.28376862682: [0.7341772151899, 0.2658227848101], 0.89520910111: [0.4810126582278, 0.5189873417722], 0.89713186989: [0.2784810126582, 0.7215189873418], 0.87822464349: [0.0886075949367, 0.9113924050633], 0.47700688992: [0.9367088607595, 0.0632911392405], 0.1440474283: [0.0886075949367, 0.9113924050633], 0.23922448326: [0.7721518987342, 0.2278481012658], 0.28953693318: [0.2784810126582, 0.7215189873418], 0.03637237622: [0.2784810126582, 0.7215189873418], 0.17320942157: [0.9367088607595, 0.0632911392405], 0.8320781926: [0.873417721519, 0.126582278481], 0.08732574908: [0.7721518987342, 0.2278481012658], 0.56224963948: [0.7341772151899, 0.2658227848101], 0.94295785932: [0.5569620253165, 0.4430379746835], 0.35074507291: [0.6962025316456, 0.3037974683544], 0.9458420125: [0.4810126582278, 0.5189873417722], 0.56801794584: [0.2784810126582, 0.7215189873418], 0.94872616568: [0.0253164556962, 0.9746835443038], 0.35362922609: [0.253164556962, 0.746835443038], 0.95161031886: [0.8481012658228, 0.1518987341772], 0.20525556802: [0.5822784810127, 0.4177215189873], 0.68466591892: [0.3164556962025, 0.6835443037975], 0.41195321263: [0.9240506329114, 0.0759493670886], 0.69043422528: [0.9240506329114, 0.0759493670886], 0.50296426855: [0.873417721519, 0.126582278481], 0.41483736581: [0.6582278481013, 0.3417721518987], 0.23441756129: [0.1772151898734, 0.8227848101266], 0.50873257491: [0.1012658227848, 0.8987341772152], 0.47027719917: [0.8101265822785, 0.1898734177215], 0.51161672809: [0.7341772151899, 0.2658227848101], 0.28280724243: [0.7848101265823, 0.2151898734177], 0.61961224163: [0.0253164556962, 0.9746835443038], 0.47316135235: [0.0886075949367, 0.9113924050633], 0.10174651498: [0.4050632911392, 0.5949367088608], 0.62249639481: [0.8481012658228, 0.1518987341772], 0.23730171447: [0.5063291139241, 0.4936708860759], 0.47604550553: [0.620253164557, 0.379746835443], 0.28857554879: [0.9493670886076, 0.0506329113924], 0.63114885435: [0.1645569620253, 0.8354430379747], 0.63403300753: [0.3164556962025, 0.6835443037975], 0.34401538215: [0.8481012658228, 0.1518987341772], 0.74779682743: [0.3544303797468, 0.6455696202532], 0.17417080596: [0.7974683544304, 0.2025316455696], 0.75068098061: [0.7088607594937, 0.2911392405063], 0.05047268066: [0.0126582278481, 0.9873417721519], 0.56609517706: [0.4810126582278, 0.5189873417722], 0.75644928697: [0.7974683544304, 0.2025316455696], 0.56897933024: [0.0253164556962, 0.9746835443038], 0.75933344015: [0.0126582278481, 0.9873417721519], 0.57186348342: [0.8481012658228, 0.1518987341772], 0.76221759333: [0.5822784810127, 0.4177215189873], 0.67985899696: [0.6962025316456, 0.3037974683544], 0.87021310687: [0.5063291139241, 0.4936708860759], 0.10238743791: [0.3164556962025, 0.6835443037975], 0.68274315014: [0.253164556962, 0.746835443038], 0.87309726005: [0.8860759493671, 0.1139240506329], 0.41099182823: [0.8607594936709, 0.1392405063291], 0.93718955296: [0.4050632911392, 0.5949367088608], 0.2324947925: [0.4303797468354, 0.5696202531646], 0.27896170486: [0.4050632911392, 0.5949367088608], 0.46931581477: [0.3544303797468, 0.6455696202532], 0.8022752764: [0.0886075949367, 0.9113924050633], 0.14020189072: [0.3544303797468, 0.6455696202532], 0.23537894568: [0.5443037974684, 0.4556962025316], 0.80515942958: [0.620253164557, 0.379746835443], 0.47219996795: [0.7088607594937, 0.2911392405063], 0.80804358276: [0.3924050632911, 0.6075949367089], 0.03541099183: [0.9493670886076, 0.0506329113924], 0.28473001122: [0.5569620253165, 0.4430379746835], 0.16936388399: [0.0886075949367, 0.9113924050633], 0.8166960423: [0.1772151898734, 0.8227848101266], 0.34016984458: [0.2784810126582, 0.7215189873418], 0.81958019548: [0.5063291139241, 0.4936708860759], 0.08540298029: [0.5063291139241, 0.4936708860759], 0.17224803717: [0.620253164557, 0.379746835443], 0.93334401538: [0.873417721519, 0.126582278481], 0.00624899856: [0.5569620253165, 0.4430379746835], 0.74875821183: [0.8101265822785, 0.1898734177215], 0.93911232174: [0.1012658227848, 0.8987341772152], 0.4013779843: [0.6962025316456, 0.3037974683544], 0.75164236501: [0.0886075949367, 0.9113924050633], 0.94199647492: [0.7341772151899, 0.2658227848101], 0.75452651819: [0.620253164557, 0.379746835443], 0.40426213748: [0.253164556962, 0.746835443038], 0.86540618491: [0.4303797468354, 0.5696202531646], 0.86829033809: [0.5443037974684, 0.4556962025316], 0.23057202371: [0.5822784810127, 0.4177215189873], 0.23409709982: [0.5569620253165, 0.4430379746835], 0.46258612402: [0.9240506329114, 0.0759493670886], 0.03124499279: [0.8607594936709, 0.1392405063291], 0.27511616728: [0.873417721519, 0.126582278481], 0.4654702772: [0.6582278481013, 0.3417721518987], 0.2334561769: [0.3291139240506, 0.6708860759494], 0.98782246435: [0.4050632911392, 0.5949367088608], 0.60999839769: [0.1012658227848, 0.8987341772152], 0.40618490627: [0.3164556962025, 0.6835443037975], 0.01746514982: [0.0886075949367, 0.9113924050633], 0.61288255087: [0.7341772151899, 0.2658227848101], 0.33344015382: [0.7848101265823, 0.2151898734177], 0.28088447364: [0.1012658227848, 0.8987341772152], 0.99647492389: [0.4810126582278, 0.5189873417722], 0.61865085723: [0.2784810126582, 0.7215189873418], 0.99935907707: [0.0253164556962, 0.9746835443038], 0.0844415959: [0.4683544303797, 0.5316455696203], 0.33920846018: [0.9493670886076, 0.0506329113924], 0.39464829354: [0.8481012658228, 0.1518987341772], 0.92853709342: [0.6329113924051, 0.3670886075949], 0.55359717994: [0.873417721519, 0.126582278481], 0.19948726166: [0.7974683544304, 0.2025316455696], 0.66447684666: [0.5569620253165, 0.4430379746835], 0.5593654863: [0.1012658227848, 0.8987341772152], 0.10046466912: [0.253164556962, 0.746835443038], 0.66736099984: [0.4810126582278, 0.5189873417722], 0.40330075308: [0.1645569620253, 0.8354430379747], 0.67024515302: [0.0253164556962, 0.9746835443038], 0.20237141484: [0.0126582278481, 0.9873417721519], 0.6731293062: [0.8481012658228, 0.1518987341772], 0.05752283288: [0.9240506329114, 0.0759493670886], 0.27127062971: [0.2405063291139, 0.7594936708861], 0.46162473963: [0.8607594936709, 0.1392405063291], 0.68178176574: [0.1645569620253, 0.8354430379747], 0.13635635315: [0.6582278481013, 0.3417721518987], 0.236981253: [0.4810126582278, 0.5189873417722], 0.03444960744: [0.4810126582278, 0.5189873417722], 0.27703893607: [0.9620253164557, 0.0379746835443], 0.60807562891: [0.4050632911392, 0.5949367088608], 0.79842973882: [0.3544303797468, 0.6455696202532], 0.16551834642: [0.3544303797468, 0.6455696202532], 0.801313892: [0.7088607594937, 0.2911392405063], 0.61384393527: [0.5569620253165, 0.4430379746835], 0.90930940554: [0.3924050632911, 0.6075949367089], 0.08348021151: [0.5443037974684, 0.4556962025316], 0.61672808845: [0.4810126582278, 0.5189873417722], 0.51898734177: [0.0], 0.33536292261: [0.5569620253165, 0.4430379746835], 0.1684024996: [0.7088607594937, 0.2911392405063], 0.33824707579: [0.4810126582278, 0.5189873417722], 0.73049190835: [0.6962025316456, 0.3037974683544], 0.02451530204: [0.253164556962, 0.746835443038], 0.73337606153: [0.253164556962, 0.746835443038], 0.19756449287: [0.620253164557, 0.379746835443], 0.09950328473: [0.1645569620253, 0.8354430379747], 0.85002403461: [0.8101265822785, 0.1898734177215], 0.85290818779: [0.0886075949367, 0.9113924050633], 0.20044864605: [0.3924050632911, 0.6075949367089], 0.85579234097: [0.620253164557, 0.379746835443], 0.45489504887: [0.253164556962, 0.746835443038], 0.85867649415: [0.3924050632911, 0.6075949367089], 0.96667200769: [0.4303797468354, 0.5696202531646], 0.96955616087: [0.5443037974684, 0.4556962025316], 0.27030924531: [0.6329113924051, 0.3670886075949], 0.00849222881: [0.4683544303797, 0.5316455696203], 0.0934145169: [0.0886075949367, 0.9113924050633], 0.79073866368: [0.8607594936709, 0.1392405063291], 0.08251882711: [0.1772151898734, 0.8227848101266], 0.79939112322: [0.8101265822785, 0.1898734177215], 0.33151738503: [0.1012658227848, 0.8987341772152], 0.52667841692: [0.620253164557, 0.379746835443], 0.90738663676: [0.9367088607595, 0.0632911392405], 0.16647973081: [0.8101265822785, 0.1898734177215], 0.5295625701: [0.3924050632911, 0.6075949367089], 0.33440153822: [0.7341772151899, 0.2658227848101], 0.04854991187: [0.3924050632911, 0.6075949367089], 0.9160390963: [0.4303797468354, 0.5696202531646], 0.38984137158: [0.9493670886076, 0.0506329113924], 0.53821502964: [0.1772151898734, 0.8227848101266], 0.91892324948: [0.5443037974684, 0.4556962025316], 0.54109918282: [0.5063291139241, 0.4936708860759], 0.543983336: [0.8860759493671, 0.1139240506329], 0.65486300272: [0.873417721519, 0.126582278481], 0.19852587726: [0.9367088607595, 0.0632911392405], 0.06553436949: [0.8481012658228, 0.1518987341772], 0.05656144849: [0.8607594936709, 0.1392405063291], 0.45393366448: [0.1645569620253, 0.8354430379747], 0.13251081557: [0.8607594936709, 0.1392405063291], 0.85675372537: [0.9367088607595, 0.0632911392405], 0.22768787053: [0.0126582278481, 0.9873417721519], 0.58692517225: [0.4303797468354, 0.5696202531646], 0.45681781766: [0.3164556962025, 0.6835443037975], 0.96763339209: [0.3291139240506, 0.6708860759494], 0.58980932543: [0.5443037974684, 0.4556962025316], 0.97051754527: [0.4683544303797, 0.5316455696203], 0.16167280885: [0.6582278481013, 0.3417721518987], 0.97340169845: [0.7721518987342, 0.2278481012658], 0.03156545425: [0.5569620253165, 0.4430379746835], 0.08155744272: [0.3291139240506, 0.6708860759494], 0.97917000481: [0.6329113924051, 0.3670886075949], 0.32767184746: [0.9620253164557, 0.0379746835443], 0.70934145169: [0.4050632911392, 0.5949367088608], 0.16455696203: [0.0], 0.71510975805: [0.5569620253165, 0.4430379746835], 0.01201730492: [0.0253164556962, 0.9746835443038], 0.71799391123: [0.4810126582278, 0.5189873417722], 0.385995834: [0.5569620253165, 0.4430379746835], 0.72087806441: [0.0253164556962, 0.9746835443038], 0.1937189553: [0.7088607594937, 0.2911392405063], 0.72376221759: [0.8481012658228, 0.1518987341772], 0.38887998718: [0.4810126582278, 0.5189873417722], 0.53629226086: [0.4303797468354, 0.5696202531646], 0.83175773113: [0.6962025316456, 0.3037974683544], 0.09758051594: [0.6962025316456, 0.3037974683544], 0.6442877744: [0.7721518987342, 0.2278481012658], 0.83464188431: [0.253164556962, 0.746835443038], 0.39176414036: [0.0253164556962, 0.9746835443038], 0.22288094857: [0.620253164557, 0.379746835443], 0.65005608076: [0.6329113924051, 0.3670886075949], 0.94263739785: [0.8607594936709, 0.1392405063291], 0.02804037815: [0.5822784810127, 0.4177215189873], 0.25973401698: [0.1772151898734, 0.8227848101266], 0.6587085403: [0.4050632911392, 0.5949367088608], 0.22576510175: [0.3924050632911, 0.6075949367089], 0.26261817017: [0.5063291139241, 0.4936708860759], 0.76958820702: [0.9493670886076, 0.0506329113924], 0.01650376542: [0.7088607594937, 0.2911392405063], 0.11360358917: [0.8860759493671, 0.1139240506329], 0.26550232335: [0.8860759493671, 0.1139240506329], 0.13347219997: [0.9240506329114, 0.0759493670886], 0.34113122897: [0.0253164556962, 0.9746835443038], 0.32094215671: [0.6329113924051, 0.3670886075949], 0.78112481974: [0.6962025316456, 0.3037974683544], 0.08059605832: [0.4303797468354, 0.5696202531646], 0.50424611441: [0.3924050632911, 0.6075949367089], 0.78400897292: [0.253164556962, 0.746835443038], 0.89200448646: [0.8607594936709, 0.1392405063291], 0.04758852748: [0.7974683544304, 0.2025316455696], 0.900656946: [0.8101265822785, 0.1898734177215], 0.38215029643: [0.1012658227848, 0.8987341772152], 0.71318698927: [0.7848101265823, 0.2151898734177], 0.90354109918: [0.0886075949367, 0.9113924050633], 0.19179618651: [0.8101265822785, 0.1898734177215], 0.90642525236: [0.620253164557, 0.379746835443], 0.38503444961: [0.7341772151899, 0.2658227848101], 0.71895529563: [0.9493670886076, 0.0506329113924], 0.19468033969: [0.0886075949367, 0.9113924050633], 0.39080275597: [0.2784810126582, 0.7215189873418], 0.25588847941: [0.5822784810127, 0.4177215189873], 0.94648293543: [0.6582278481013, 0.3417721518987], 0.84137157507: [0.8607594936709, 0.1392405063291], 0.22384233296: [0.9367088607595, 0.0632911392405], 0.25877263259: [0.3291139240506, 0.6708860759494], 0.57154302195: [0.8101265822785, 0.1898734177215], 0.57442717513: [0.0886075949367, 0.9113924050633], 0.11264220478: [0.7721518987342, 0.2278481012658], 0.26165678577: [0.4683544303797, 0.5316455696203], 0.57731132831: [0.620253164557, 0.379746835443], 0.45201089569: [0.6962025316456, 0.3037974683544], 0.95801954815: [0.9367088607595, 0.0632911392405], 0.58019548149: [0.3924050632911, 0.6075949367089], 0.26454093895: [0.7721518987342, 0.2278481012658], 0.51225765102: [0.8607594936709, 0.1392405063291], 0.32574907867: [0.873417721519, 0.126582278481], 0.89585002404: [0.6582278481013, 0.3417721518987], 0.18987341772: [0.0], 0.89873417722: [0.0], 0.52091011056: [0.8101265822785, 0.1898734177215], 0.6289056241: [0.9367088607595, 0.0632911392405], 0.52379426374: [0.0886075949367, 0.9113924050633], 0.38407306521: [0.7848101265823, 0.2151898734177], 0.43951289857: [0.4810126582278, 0.5189873417722], 0.63755808364: [0.4303797468354, 0.5696202531646], 0.00688992149: [0.9240506329114, 0.0759493670886], 0.64044223682: [0.5443037974684, 0.4556962025316], 0.44239705176: [0.0253164556962, 0.9746835443038], 0.75132190354: [0.6329113924051, 0.3670886075949], 0.44528120494: [0.8481012658228, 0.1518987341772], 0.2578112482: [0.4303797468354, 0.5696202531646], 0.56321102387: [0.5569620253165, 0.4430379746835], 0.56962025317: [0.0], 0.75997436308: [0.4050632911392, 0.5949367088608], 0.31325108156: [0.5063291139241, 0.4936708860759], 0.26069540138: [0.5443037974684, 0.4556962025316], 0.76574266944: [0.5569620253165, 0.4430379746835], 0.07867328954: [0.5822784810127, 0.4177215189873], 0.57827271271: [0.9367088607595, 0.0632911392405], 0.07194359878: [0.9367088607595, 0.0632911392405], 0.31613523474: [0.8860759493671, 0.1139240506329], 0.68626822625: [0.5822784810127, 0.4177215189873], 0.15878865566: [0.9240506329114, 0.0759493670886], 0.68915237943: [0.3291139240506, 0.6708860759494], 0.69203653261: [0.4683544303797, 0.5316455696203], 0.69492068579: [0.7721518987342, 0.2278481012658], 0.3219035411: [0.2405063291139, 0.7594936708861], 0.70068899215: [0.6329113924051, 0.3670886075949], 0.38022752764: [0.4050632911392, 0.5949367088608], 0.81445281205: [0.7848101265823, 0.2151898734177], 0.19083480212: [0.3544303797468, 0.6455696202532], 0.435667361: [0.7341772151899, 0.2658227848101], 0.82022111841: [0.9493670886076, 0.0506329113924], 0.21999679539: [0.0886075949367, 0.9113924050633], 0.25108155744: [0.3924050632911, 0.6075949367089], 0.44143566736: [0.2784810126582, 0.7215189873418], 0.64140362122: [0.4683544303797, 0.5316455696203], 0.74042621375: [0.5569620253165, 0.4430379746835], 0.11071943599: [0.5063291139241, 0.4936708860759], 0.86732895369: [0.1772151898734, 0.8227848101266], 0.75228328794: [0.2405063291139, 0.7594936708861], 0.12770389361: [0.3164556962025, 0.6835443037975], 0.24915878866: [0.9367088607595, 0.0632911392405], 0.30940554398: [0.3291139240506, 0.6708860759494], 0.7580515943: [0.9620253164557, 0.0379746835443], 0.31228969716: [0.4683544303797, 0.5316455696203], 0.76381990066: [0.7848101265823, 0.2151898734177], 0.8718154142: [0.2784810126582, 0.7215189873418], 0.31517385034: [0.7721518987342, 0.2278481012658], 0.50264380708: [0.6962025316456, 0.3037974683544], 0.50552796026: [0.253164556962, 0.746835443038], 0.99423169364: [0.9240506329114, 0.0759493670886], 0.37638199007: [0.873417721519, 0.126582278481], 0.99711584682: [0.6582278481013, 0.3417721518987]} | 44,117.8 | 79,146 | 0.782256 | 27,812 | 220,589 | 6.204228 | 0.116604 | 0.001901 | 0.001443 | 0.012819 | 0.677419 | 0.159326 | 0.159326 | 0.157384 | 0.157384 | 0.155339 | 0 | 0.834573 | 0.063013 | 220,589 | 5 | 79,146 | 44,117.8 | 0.000266 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
f1fdacbac504a4766f5e380a95f5ef466fd2a412 | 249 | py | Python | core/calls/admin.py | Nikita-Filonov/lama_logger | 7b3f474ddf35685e6949ab00d7272d16c630295c | [
"Apache-2.0"
] | null | null | null | core/calls/admin.py | Nikita-Filonov/lama_logger | 7b3f474ddf35685e6949ab00d7272d16c630295c | [
"Apache-2.0"
] | null | null | null | core/calls/admin.py | Nikita-Filonov/lama_logger | 7b3f474ddf35685e6949ab00d7272d16c630295c | [
"Apache-2.0"
] | 1 | 2021-12-21T09:39:02.000Z | 2021-12-21T09:39:02.000Z | from django.contrib import admin
# Register your models here.
from core.calls.models import Request, RequestsFilter, CustomRequestsHistory
admin.site.register(Request)
admin.site.register(RequestsFilter)
admin.site.register(CustomRequestsHistory)
| 27.666667 | 76 | 0.843373 | 29 | 249 | 7.241379 | 0.517241 | 0.128571 | 0.242857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.080321 | 249 | 8 | 77 | 31.125 | 0.917031 | 0.104418 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 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 | 0 | 0 | 0 | 5 |
9e651ef3643068a67e9b7692433cded1cb7eaf1b | 75 | py | Python | pymonad/either/__init__.py | Wildhoney/Pymonad | 177989b3d0f362c3bf3af962d89306309ff000c3 | [
"MIT"
] | null | null | null | pymonad/either/__init__.py | Wildhoney/Pymonad | 177989b3d0f362c3bf3af962d89306309ff000c3 | [
"MIT"
] | null | null | null | pymonad/either/__init__.py | Wildhoney/Pymonad | 177989b3d0f362c3bf3af962d89306309ff000c3 | [
"MIT"
] | null | null | null | from .either import Either
from .left import Left
from .right import Right
| 18.75 | 26 | 0.8 | 12 | 75 | 5 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 75 | 3 | 27 | 25 | 0.952381 | 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 | 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 | 5 |
9e66638fef07a95e9e81f8638d8b074ed4744a1f | 30 | py | Python | _init_.py | SabaFadhl/saba_board_package_python | 01baa604d6a313294b864544e373e64de5d34780 | [
"MIT"
] | null | null | null | _init_.py | SabaFadhl/saba_board_package_python | 01baa604d6a313294b864544e373e64de5d34780 | [
"MIT"
] | null | null | null | _init_.py | SabaFadhl/saba_board_package_python | 01baa604d6a313294b864544e373e64de5d34780 | [
"MIT"
] | null | null | null | from saba_board.cells import * | 30 | 30 | 0.833333 | 5 | 30 | 4.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 30 | 1 | 30 | 30 | 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 | 0 | 0 | 0 | 5 |
9ea04f6c4fd96802f613d09361f247a6b2ad1fbb | 160 | py | Python | boa3_test/test_sc/function_test/ReturnIfExpression.py | hal0x2328/neo3-boa | 6825a3533384cb01660773050719402a9703065b | [
"Apache-2.0"
] | 25 | 2020-07-22T19:37:43.000Z | 2022-03-08T03:23:55.000Z | boa3_test/test_sc/function_test/ReturnIfExpression.py | hal0x2328/neo3-boa | 6825a3533384cb01660773050719402a9703065b | [
"Apache-2.0"
] | 419 | 2020-04-23T17:48:14.000Z | 2022-03-31T13:17:45.000Z | boa3_test/test_sc/function_test/ReturnIfExpression.py | hal0x2328/neo3-boa | 6825a3533384cb01660773050719402a9703065b | [
"Apache-2.0"
] | 15 | 2020-05-21T21:54:24.000Z | 2021-11-18T06:17:24.000Z | from boa3.builtin import public
@public
def Main(condition: bool) -> int:
# the function has a return to each condition
return 5 if condition else 10
| 20 | 49 | 0.725 | 25 | 160 | 4.64 | 0.84 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.032 | 0.21875 | 160 | 7 | 50 | 22.857143 | 0.896 | 0.26875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0.25 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
9ea9375a57c4079282daaced657684726a9720ce | 267 | py | Python | appraise/beta16/admin.py | yuyang-huang/Appraise | 05b956e90ec47d79125e71e2f7acacbec37ff4b3 | [
"BSD-3-Clause"
] | 68 | 2015-03-20T15:39:30.000Z | 2022-03-03T13:44:31.000Z | appraise/beta16/admin.py | yuyang-huang/Appraise | 05b956e90ec47d79125e71e2f7acacbec37ff4b3 | [
"BSD-3-Clause"
] | 30 | 2015-04-12T13:14:51.000Z | 2021-05-06T11:42:18.000Z | appraise/beta16/admin.py | yuyang-huang/Appraise | 05b956e90ec47d79125e71e2f7acacbec37ff4b3 | [
"BSD-3-Clause"
] | 24 | 2016-03-15T09:38:08.000Z | 2021-01-06T02:52:43.000Z | from django.contrib import admin
from appraise.beta16.models import AbsoluteScoringTask, AbsoluteScoringData
from appraise.beta16.models import MetaData
admin.site.register(AbsoluteScoringTask)
admin.site.register(AbsoluteScoringData)
admin.site.register(MetaData)
| 29.666667 | 75 | 0.865169 | 30 | 267 | 7.7 | 0.433333 | 0.116883 | 0.220779 | 0.207792 | 0.25974 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016064 | 0.067416 | 267 | 8 | 76 | 33.375 | 0.911647 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
7b6cebf195388c192715a2af66bd63ee7bd0bc71 | 233 | py | Python | sandbox/ex2/parallel_trpo/simple_container.py | sokol1412/rllab_hierarchical_rl | 6d46c02e32c3d7e9ac55d753d6a3823ff86c5a57 | [
"MIT"
] | null | null | null | sandbox/ex2/parallel_trpo/simple_container.py | sokol1412/rllab_hierarchical_rl | 6d46c02e32c3d7e9ac55d753d6a3823ff86c5a57 | [
"MIT"
] | null | null | null | sandbox/ex2/parallel_trpo/simple_container.py | sokol1412/rllab_hierarchical_rl | 6d46c02e32c3d7e9ac55d753d6a3823ff86c5a57 | [
"MIT"
] | null | null | null | class SimpleContainer(object):
"""
Container for convenient references.
"""
def __init__(self, **kwargs):
self.__dict__.update(**kwargs)
def append(self, **kwargs):
self.__dict__.update(**kwargs) | 23.3 | 40 | 0.626609 | 23 | 233 | 5.826087 | 0.608696 | 0.149254 | 0.208955 | 0.268657 | 0.447761 | 0.447761 | 0 | 0 | 0 | 0 | 0 | 0 | 0.227468 | 233 | 10 | 41 | 23.3 | 0.744444 | 0.154506 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
7bc83557e04b2e647f4f095315af0dcbb15a7845 | 194 | py | Python | src/demos/greedy/huffman1.py | DavidLlorens/algoritmia | 40ca0a89ea6de9b633fa5f697f0a28cae70816a2 | [
"MIT"
] | 6 | 2018-09-15T15:09:10.000Z | 2022-02-27T01:23:11.000Z | src/demos/greedy/huffman1.py | JeromeIllgner/algoritmia | 406afe7206f2411557859bf03480c16db7dcce0d | [
"MIT"
] | null | null | null | src/demos/greedy/huffman1.py | JeromeIllgner/algoritmia | 406afe7206f2411557859bf03480c16db7dcce0d | [
"MIT"
] | 5 | 2018-07-10T20:19:55.000Z | 2021-03-31T03:32:22.000Z | #coding: latin1
#< full
from algoritmia.problems.compression.huffman1 import HuffmanCodeBuilder1
print(HuffmanCodeBuilder1().build_code({'a':30, 'b':25, 'c':15, 'd':20, 'e':10}))
#> full | 27.714286 | 82 | 0.695876 | 25 | 194 | 5.36 | 0.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.081395 | 0.113402 | 194 | 7 | 83 | 27.714286 | 0.697674 | 0.134021 | 0 | 0 | 0 | 0 | 0.03125 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 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 | 0 | 1 | 0 | 5 |
c8b59ec7e95984b3b0de874a2a27b7bc5d05fe35 | 20,357 | py | Python | test/crystal/test_utils.py | unkcpz/sagar | 097a9e77200d79e40c45c2741c9c1e61a1013b22 | [
"MIT"
] | 2 | 2018-09-05T10:40:01.000Z | 2018-09-18T01:09:20.000Z | test/crystal/test_utils.py | unkcpz/sagar | 097a9e77200d79e40c45c2741c9c1e61a1013b22 | [
"MIT"
] | 6 | 2018-10-17T07:48:27.000Z | 2019-11-04T13:39:07.000Z | test/crystal/test_utils.py | unkcpz/pyyabc | 097a9e77200d79e40c45c2741c9c1e61a1013b22 | [
"MIT"
] | 3 | 2018-05-03T08:15:42.000Z | 2018-08-28T05:45:33.000Z | # -*- coding: utf-8 -*-
import unittest
import numpy
import copy
from sagar.crystal.structure import Cell
from sagar.crystal.utils import non_dup_hnfs, _is_hnf_dup, _hnfs
from sagar.crystal.utils import IntMat3x3, snf
from sagar.toolkit.mathtool import extended_gcd
class TestHnf(unittest.TestCase):
def test(self):
pass
def setUp(self):
# BCC
bcc_latt = [1, 1, -1,
-1, 1, 1,
1, -1, 1]
bcc_pos = [(0, 0, 0)]
bcc_atoms = [0]
self.bcc_pcell = Cell(bcc_latt, bcc_pos, bcc_atoms)
# FCC
fcc_latt = [0, 5, 5,
5, 0, 5,
5, 5, 0]
fcc_pos = [(0, 0, 0)]
fcc_atoms = [0]
self.fcc_pcell = Cell(fcc_latt, fcc_pos, fcc_atoms)
# HCP
hcp_b = [2.51900005, 0., 0.,
-1.25950003, 2.18151804, 0.,
0., 0., 4.09100008]
hcp_positions = [(0.33333334, 0.66666669, 0.25),
(0.66666663, 0.33333331, 0.75)]
hcp_numbers = [0, 0]
self.hcp_pcell = Cell(hcp_b, hcp_positions, hcp_numbers)
def test_hnf_cells(self):
# Results from <PHYSICAL REVIEW B 80, 014120 (2009)>
# BCC
wanted = [1, 2, 3, 7, 5, 10, 7]
got = [len(non_dup_hnfs(self.bcc_pcell, i))
for i in range(1, 8)]
# for h in non_dup_hnfs(self.bcc_pcell, 7):
# print(h)
self.assertEqual(got, wanted)
# FCC
wanted = [1, 2, 3, 7, 5, 10, 7]
got = [len(non_dup_hnfs(self.fcc_pcell, i))
for i in range(1, 8)]
self.assertEqual(got, wanted)
# HCP
wanted = [1, 3, 5, 11, 7, 19, 11, 34]
got = [len(non_dup_hnfs(self.hcp_pcell, i))
for i in range(1, 9)]
self.assertEqual(got, wanted)
def test_is_hnf_dup(self):
hnf_x = numpy.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 2]])
hnf_y = numpy.array([[1, 0, 0],
[0, 2, 0],
[0, 0, 1]])
rot_syms = self.bcc_pcell.get_rotations(1e-3)
is_dup = _is_hnf_dup(hnf_x, hnf_y, rot_syms, prec=1e-3)
self.assertTrue(is_dup)
# debug for compare method
# numpy.mod problem!
hnf_x = numpy.array([[1, 0, 0],
[0, 1, 2],
[0, 0, 5]])
hnf_y = numpy.array([[1, 0, 3],
[0, 1, 3],
[0, 0, 5]])
rot_syms = self.bcc_pcell.get_rotations(1e-3)
is_dup = _is_hnf_dup(hnf_x, hnf_y, rot_syms, prec=1e-5)
self.assertTrue(is_dup)
# debug for compare method
# numpy.astype problem!
hnf_x = numpy.array([[1, 0, 6],
[0, 1, 6],
[0, 0, 7]])
hnf_y = numpy.array([[1, 0, 3],
[0, 1, 6],
[0, 0, 7]])
rot_syms = self.bcc_pcell.get_rotations(1e-3)
is_dup = _is_hnf_dup(hnf_x, hnf_y, rot_syms, prec=1e-5)
self.assertTrue(is_dup)
class TestMat3x3(unittest.TestCase):
def setUp(self):
self.mat = IntMat3x3([0, 1, 2,
3, 4, 5,
6, 7, 8])
self.realmat = IntMat3x3([2, 4, 4,
-6, 6, 12,
10, -4, -16])
def test_get_snf(self):
mat = copy.copy(self.realmat)
snf_D, snf_S, snf_T = mat.get_snf()
SAT = numpy.matmul(snf_S, numpy.matmul(self.realmat.mat, snf_T))
wanted_mat = numpy.array([2, 0, 0,
0, 6, 0,
0, 0, 12]).reshape((3, 3))
numpy.testing.assert_almost_equal(SAT, wanted_mat)
numpy.testing.assert_almost_equal(snf_D, wanted_mat)
def test_bugs_get_snf(self):
mat = IntMat3x3([-1, 1, 1,
1, -1, 1,
1, 1, -1])
ori_mat = copy.copy(mat)
snf_D, snf_S, snf_T = mat.get_snf()
SAT = numpy.matmul(snf_S, numpy.matmul(ori_mat.mat, snf_T))
wanted_mat = numpy.array([1, 0, 0,
0, 2, 0,
0, 0, 2]).reshape((3, 3))
numpy.testing.assert_almost_equal(SAT, wanted_mat)
numpy.testing.assert_almost_equal(snf_D, wanted_mat)
def test_dead_loop_bug(self):
mat = IntMat3x3([1, 0, 0,
1, 1, 0,
0, 0, 7])
# import pdb; pdb.set_trace()
ori_mat = copy.copy(mat)
snf_D, snf_S, snf_T = mat.get_snf()
SAT = numpy.matmul(snf_S, numpy.matmul(ori_mat.mat, snf_T))
wanted_mat = numpy.array([1, 0, 0,
0, 1, 0,
0, 0, 7]).reshape((3, 3))
numpy.testing.assert_almost_equal(SAT, wanted_mat)
numpy.testing.assert_almost_equal(snf_D, wanted_mat)
def test_diag_increment_bug(self):
mat = IntMat3x3([1, 0, 0,
0, 2, 0,
0, 0, 1])
ori_mat = copy.copy(mat)
snf_D, snf_S, snf_T = mat.get_snf()
SAT = numpy.matmul(snf_S, numpy.matmul(ori_mat.mat, snf_T))
wanted_mat = numpy.array([1, 0, 0,
0, 1, 0,
0, 0, 2]).reshape((3, 3))
numpy.testing.assert_almost_equal(SAT, wanted_mat)
numpy.testing.assert_almost_equal(snf_D, wanted_mat)
def test_snf_random(self):
for i in range(100):
mat = self._get_random_mat()
mat = IntMat3x3(mat)
ori_mat = copy.copy(mat)
snf_D, snf_S, snf_T = mat.get_snf()
SAT = numpy.matmul(snf_S, numpy.matmul(ori_mat.mat, snf_T))
# print("mat", ori_mat.mat)
# print("snf_D", snf_D)
# print("snf_S", snf_S)
# print("snf_T", snf_T)
# print("det S", numpy.linalg.det(snf_S))
# print("det T", numpy.linalg.det(snf_T))
numpy.testing.assert_almost_equal(SAT, snf_D)
numpy.testing.assert_almost_equal(numpy.linalg.det(snf_S), 1)
numpy.testing.assert_almost_equal(numpy.linalg.det(snf_T), 1)
def _get_random_mat(self):
k = 15
mat = numpy.random.randint(k, size=(3, 3)) - k // 2
if numpy.linalg.det(mat) < 0.5:
mat = self._get_random_mat()
return mat
def test_det_1_bug(self):
"""
需要保证左乘和右乘矩阵的行列式为1
"""
mat = IntMat3x3([7, -5, -3,
3, 1, 6,
-5, -5, 5])
ori_mat = copy.copy(mat)
# import pdb; pdb.set_trace()
snf_D, snf_S, snf_T = mat.get_snf()
SAT = numpy.matmul(snf_S, numpy.matmul(ori_mat.mat, snf_T))
wanted_mat = numpy.array([1, 0, 0,
0, 1, 0,
0, 0, 500]).reshape((3, 3))
numpy.testing.assert_almost_equal(SAT, wanted_mat)
numpy.testing.assert_almost_equal(snf_D, wanted_mat)
numpy.testing.assert_almost_equal(numpy.linalg.det(snf_S), 1)
numpy.testing.assert_almost_equal(numpy.linalg.det(snf_T), 1)
def test_snf_diag(self):
for i in range(100):
mat = numpy.random.randint(100, size=9).reshape((3, 3))
mat = IntMat3x3(mat)
snf_D, _, _ = mat.get_snf()
self.assertTrue(self.is_diag(snf_D))
def is_diag(self, mat):
return numpy.all(mat == numpy.diag(numpy.diagonal(mat)))
def test_snf_diag_positive(self):
for i in range(10):
mat = numpy.random.randint(100, size=9).reshape((3, 3))
mat = IntMat3x3(mat)
self.assertTrue(numpy.all(mat.mat >= numpy.zeros_like(mat.mat)))
def test_snf_diag_incremental(self):
for i in range(10):
mat = numpy.random.randint(100, size=9).reshape((3, 3))
mat = IntMat3x3(mat)
list_diag = numpy.diagonal(mat.mat).tolist()
self.assertTrue(sorted(list_diag), list_diag)
def test_search_first_pivot(self):
self.assertEqual(self.mat.search_first_pivot(), 1)
def test_swap_rows(self):
mat = copy.copy(self.mat)
mat.swap_rows(0, 1)
wanted_mat = numpy.array([3, 4, 5,
0, 1, 2,
6, 7, 8]).reshape((3, 3))
wanted_op = numpy.array([0, 1, 0,
1, 0, 0,
0, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted_ori_mat = self.mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted_ori_mat)
def test_flip_sign_row(self):
mat = copy.copy(self.mat)
mat.flip_sign_row(1)
wanted_mat = numpy.array([0, 1, 2,
-3, -4, -5,
6, 7, 8]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
0, -1, 0,
0, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(self.mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_set_zero(self):
mat = IntMat3x3([3, 4, 5,
0, 1, 2,
6, 7, 8])
ori_mat = copy.copy(mat)
r, s, t = extended_gcd(mat.mat[0, 0], mat.mat[2, 0])
mat._set_zero(0, 2, mat.mat[0, 0], mat.mat[2, 0], r, s, t)
wanted_mat = numpy.array([3, 4, 5,
0, 1, 2,
0, -1, -2]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
0, 1, 0,
-2, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(ori_mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_zero_first_column(self):
mat = copy.copy(self.realmat)
mat._zero_first_column()
wanted_mat = numpy.array([2, 4, 4,
0, 18, 24,
0, -24, -36]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
3, 1, 0,
-5, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(self.realmat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_zero_first_ele_in_row_i(self):
mat = copy.copy(self.realmat)
mat._zero_first_ele_in_row_i(1)
wanted_mat = numpy.array([2, 4, 4,
0, -18, -24,
10, -4, -16]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
-3, -1, 0,
0, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(self.realmat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_first_exact_division(self):
mat = IntMat3x3([1, 0, 0,
1, 1, 0,
0, 0, 7])
ori_mat = copy.copy(mat)
mat._first_exact_division()
wanted_mat = numpy.array([1, 0, 0,
0, 1, 0,
0, 0, 7]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
-1, 1, 0,
0, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(ori_mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_zero_first_row(self):
mat = copy.copy(self.realmat)
mat._zero_first_row()
wanted_mat = numpy.array([2, 0, 0,
-6, 18, 24,
10, -24, -36]).reshape((3, 3))
wanted_op = numpy.array([1, -2, -2,
0, 1, 0,
0, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opR, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(self.realmat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_zero_second_column(self):
mat = IntMat3x3([2, 0, 0,
0, 6, 12,
0, 18, 24])
ori_mat = copy.copy(mat)
mat._zero_second_column()
wanted_mat = numpy.array([2, 0, 0,
0, 6, 12,
0, 0, -12]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
0, 1, 0,
0, -3, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(ori_mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_second_exact_division(self):
mat = IntMat3x3([1, 0, 0,
0, 1, 0,
0, 1, 7])
ori_mat = copy.copy(mat)
mat._second_exact_division()
wanted_mat = numpy.array([1, 0, 0,
0, 1, 0,
0, 0, 7]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
0, 1, 0,
0, -1, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(ori_mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_zero_second_row(self):
mat = IntMat3x3([2, 0, 0,
0, 6, 12,
0, 0, -12])
ori_mat = copy.copy(mat)
mat._zero_second_row()
wanted_mat = numpy.array([2, 0, 0,
0, 6, 0,
0, 0, -12]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
0, 1, -2,
0, 0, 1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opR, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(ori_mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_positive_diag(self):
mat = IntMat3x3([2, 0, 0,
0, 6, 0,
0, 0, -12])
ori_mat = copy.copy(mat)
mat._positive_diag()
wanted_mat = numpy.array([2, 0, 0,
0, 6, 0,
0, 0, 12]).reshape((3, 3))
wanted_op = numpy.array([1, 0, 0,
0, 1, 0,
0, 0, -1]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_op)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(ori_mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
def test_sort_diag(self):
mat = IntMat3x3([1, 0, 0,
0, 2, 0,
0, 0, 1])
ori_mat = copy.copy(mat)
mat._sort_diag()
wanted_mat = numpy.array([1, 0, 0,
0, 1, 0,
0, 0, 2]).reshape((3, 3))
wanted_opL = numpy.array([1, 0, 0,
0, 0, 1,
0, 1, 0]).reshape((3, 3))
wanted_opR = numpy.array([1, 0, 0,
0, 0, 1,
0, 1, 0]).reshape((3, 3))
numpy.testing.assert_almost_equal(mat.mat, wanted_mat)
numpy.testing.assert_almost_equal(mat.opL, wanted_opL)
numpy.testing.assert_almost_equal(mat.opR, wanted_opR)
# make sure operation is right, which can restore origin matrix
wanted = mat.mat
got = numpy.matmul(mat.opL, numpy.matmul(ori_mat.mat, mat.opR))
numpy.testing.assert_almost_equal(got, wanted)
class TestSnfHnf(unittest.TestCase):
def setUp(self):
# BCC
bcc_latt = [0.5, 0.5, -0.5,
-0.5, 0.5, 0.5,
0.5, -0.5, 0.5]
bcc_pos = [(0, 0, 0)]
bcc_atoms = [0]
self.bcc_pcell = Cell(bcc_latt, bcc_pos, bcc_atoms)
# FCC
fcc_latt = [0, 5, 5,
5, 0, 5,
5, 5, 0]
fcc_pos = [(0, 0, 0)]
fcc_atoms = [0]
self.fcc_pcell = Cell(fcc_latt, fcc_pos, fcc_atoms)
def test_hart_forcade_2008_table_III(self):
"""
TAKE CARE! The second line of table is snfs of hnfs which are non-redundant
"""
wanted_a = [1, 7, 13, 35, 31, 91, 57, 155,
130, 217, 133, 455, 183, 399, 403, 651]
# 此为hnf去除旋转对称性后在做snf的结果！
wanted_b = [1, 1, 1, 2, 1, 1, 1, 3, 2, 1, 1, 2, 1, 1, 1, 4]
wanted_b_quick = [1, 1, 1, 2, 1, 2, 1, 3, 2, 2, 1, 4, 1, 2, 2, 4]
# duplicated hnfs produce test
a = []
for i in range(1, 17):
len_volume = len([h for h in _hnfs(i)])
a.append(len_volume)
self.assertEqual(a, wanted_a)
# non-duplicated snfs: b slow test 100+s
# b = []
# for i in range(1, 17):
# s_set = set()
# for h in non_dup_hnfs(self.fcc_pcell, volume=i):
# snf_D, _, _ = snf(h)
# s_flat_tuple = tuple(numpy.diagonal(snf_D).tolist())
# s_set.add(s_flat_tuple)
# b.append(len(s_set))
# self.assertEqual(b, wanted_b)
# duplicated snfs: b quick test
b = []
for i in range(1, 17):
s_set = set()
for h in _hnfs(i):
snf_D, _, _ = snf(h)
s_flat_tuple = tuple(numpy.diagonal(snf_D).tolist())
s_set.add(s_flat_tuple)
b.append(len(s_set))
self.assertEqual(b, wanted_b_quick)
| 38.701521 | 83 | 0.491674 | 2,744 | 20,357 | 3.461006 | 0.078353 | 0.02843 | 0.018637 | 0.13141 | 0.810045 | 0.773086 | 0.749395 | 0.725703 | 0.699695 | 0.658734 | 0 | 0.073202 | 0.384634 | 20,357 | 525 | 84 | 38.775238 | 0.684921 | 0.084295 | 0 | 0.619403 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.159204 | 1 | 0.077114 | false | 0.002488 | 0.017413 | 0.002488 | 0.106965 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 5 |
c8ce9166739978461dadbfde903a79746822824d | 73 | py | Python | mlsurvey/workflows/tasks/__init__.py | jlaumonier/mlsurvey | 373598d067c7f0930ba13fe8da9756ce26eecbaf | [
"MIT"
] | null | null | null | mlsurvey/workflows/tasks/__init__.py | jlaumonier/mlsurvey | 373598d067c7f0930ba13fe8da9756ce26eecbaf | [
"MIT"
] | null | null | null | mlsurvey/workflows/tasks/__init__.py | jlaumonier/mlsurvey | 373598d067c7f0930ba13fe8da9756ce26eecbaf | [
"MIT"
] | null | null | null | from .base_task import BaseTask
from .load_data_task import LoadDataTask
| 24.333333 | 40 | 0.863014 | 11 | 73 | 5.454545 | 0.727273 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.109589 | 73 | 2 | 41 | 36.5 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
c8f9289cbcbe0eee1d0bb4cb7e24e036026217e4 | 2,586 | py | Python | tests/test_mimetypes.py | mohd-akram/webhelpers | 3f702f02474130e3c9ad608ed6116b39013cdb3d | [
"BSD-3-Clause"
] | null | null | null | tests/test_mimetypes.py | mohd-akram/webhelpers | 3f702f02474130e3c9ad608ed6116b39013cdb3d | [
"BSD-3-Clause"
] | null | null | null | tests/test_mimetypes.py | mohd-akram/webhelpers | 3f702f02474130e3c9ad608ed6116b39013cdb3d | [
"BSD-3-Clause"
] | 1 | 2019-07-31T11:00:05.000Z | 2019-07-31T11:00:05.000Z | import mimetypes
from nose.plugins.skip import SkipTest
from nose.tools import eq_
from webhelpers.mimehelper import MIMETypes
from util import test_environ
def _check_webob_dependency():
try:
import webob
except ImportError:
raise SkipTest("WebOb not installed; skipping test")
def setup():
MIMETypes.init()
mimetypes.add_type('application/xml', '.xml', True)
def test_register_alias():
MIMETypes.add_alias('html', 'text/html')
eq_(MIMETypes.aliases['html'], 'text/html')
def test_usage():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/test.html'
m = MIMETypes(environ)
eq_(m.mimetype('html'), 'text/html')
def test_root_path():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/'
environ['HTTP_ACCEPT'] = 'text/html, application/xml'
m = MIMETypes(environ)
eq_(m.mimetype('text/html'), 'text/html')
def test_with_extension():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/test.xml'
environ['HTTP_ACCEPT'] = 'text/html, application/xml'
m = MIMETypes(environ)
eq_(m.mimetype('text/html'), False)
eq_(m.mimetype('application/xml'), 'application/xml')
def test_with_unregistered_extention():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/test.iscool'
environ['HTTP_ACCEPT'] = 'application/xml'
m = MIMETypes(environ)
eq_(m.mimetype('text/html'), False)
eq_(m.mimetype('application/xml'), 'application/xml')
def test_with_no_extention():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/test'
environ['HTTP_ACCEPT'] = 'application/xml'
m = MIMETypes(environ)
eq_(m.mimetype('text/html'), False)
eq_(m.mimetype('application/xml'), 'application/xml')
def test_with_no_extention_and_no_accept():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/test'
m = MIMETypes(environ)
eq_(m.mimetype('html'), 'text/html')
def test_with_text_star_accept():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/test.iscool'
environ['HTTP_ACCEPT'] = 'text/*'
m = MIMETypes(environ)
eq_(m.mimetype('text/html'), 'text/html')
def test_with_star_star_accept():
_check_webob_dependency()
environ = test_environ()
environ['PATH_INFO'] = '/test.iscool'
environ['HTTP_ACCEPT'] = '*/*'
m = MIMETypes(environ)
eq_(m.mimetype('application/xml'), 'application/xml')
| 29.724138 | 60 | 0.680974 | 318 | 2,586 | 5.248428 | 0.169811 | 0.109047 | 0.072499 | 0.129419 | 0.753146 | 0.741762 | 0.729179 | 0.705812 | 0.705812 | 0.674056 | 0 | 0 | 0.169374 | 2,586 | 86 | 61 | 30.069767 | 0.777002 | 0 | 0 | 0.60274 | 0 | 0 | 0.225445 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.150685 | false | 0 | 0.09589 | 0 | 0.246575 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
cdd32a5fa9c4393f08252908f5b1ee014208097a | 131 | py | Python | test.py | Erope/BaiduSmartCar | 7483d5737c3588b8440fd740b72e6b44718b0511 | [
"MIT"
] | 2 | 2021-11-03T11:55:17.000Z | 2022-01-12T09:35:34.000Z | test.py | liu-yunjie/BaiduSmartCar | 7483d5737c3588b8440fd740b72e6b44718b0511 | [
"MIT"
] | null | null | null | test.py | liu-yunjie/BaiduSmartCar | 7483d5737c3588b8440fd740b72e6b44718b0511 | [
"MIT"
] | 6 | 2021-07-31T04:04:39.000Z | 2022-01-12T09:35:33.000Z | from motor.i2c import motor
import time
motor_dev = motor()
motor_dev.run([20, 20, 20, 20])
time.sleep(5)
motor_dev.run([0,0,0,0]) | 18.714286 | 31 | 0.709924 | 27 | 131 | 3.333333 | 0.407407 | 0.266667 | 0.244444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12069 | 0.114504 | 131 | 7 | 32 | 18.714286 | 0.655172 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 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 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
a80033908c12bf7910b8da3f4e437ad8b3bd407c | 308 | py | Python | contas/models/pessoas.py | ricmedeiroos/AC9-atualizado | e7ff317830c6429629498a0a5cc63a9d62320c0f | [
"Apache-2.0"
] | null | null | null | contas/models/pessoas.py | ricmedeiroos/AC9-atualizado | e7ff317830c6429629498a0a5cc63a9d62320c0f | [
"Apache-2.0"
] | 6 | 2020-06-05T20:57:34.000Z | 2022-03-11T23:47:43.000Z | contas/models/pessoas.py | ricmedeiroos/AC9-atualizado | e7ff317830c6429629498a0a5cc63a9d62320c0f | [
"Apache-2.0"
] | null | null | null | from django.db import models
class Pessoa(models.Model):
nome = models.CharField(max_length=255)
email = models.CharField(max_length=255, unique=True)
celular = models.CharField(max_length=20, unique=True)
def __str__(self):
return self.nome
class Meta:
abstract = True | 25.666667 | 58 | 0.694805 | 41 | 308 | 5.04878 | 0.585366 | 0.217391 | 0.26087 | 0.347826 | 0.26087 | 0 | 0 | 0 | 0 | 0 | 0 | 0.032787 | 0.207792 | 308 | 12 | 59 | 25.666667 | 0.815574 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.111111 | false | 0 | 0.111111 | 0.111111 | 0.888889 | 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 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
a82097a9ded8637d6068e7d32bc400a296cc0cd2 | 55 | py | Python | d/__init__.py | desireevl/astro-pointer | 5dbd0502001f954a8fed06d449e8fd47c39ff4db | [
"MIT"
] | 2 | 2017-10-24T07:20:18.000Z | 2021-11-02T18:53:36.000Z | d/__init__.py | desireevl/astro-pointer | 5dbd0502001f954a8fed06d449e8fd47c39ff4db | [
"MIT"
] | 2 | 2017-07-15T13:23:06.000Z | 2017-08-27T06:03:37.000Z | d/__init__.py | desireevl/astro-pointer | 5dbd0502001f954a8fed06d449e8fd47c39ff4db | [
"MIT"
] | null | null | null | from .driver import rotate_to_azimuth, turn_to_altitude | 55 | 55 | 0.890909 | 9 | 55 | 5 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.072727 | 55 | 1 | 55 | 55 | 0.882353 | 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 | 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 | 5 |
b55ca5fdcaf30c2c46f512f2a08113217fc71f54 | 86 | py | Python | notebooks/config.py | kbren/uwnet | aac01e243c19686b10c214b1c56b0bb7b7e06a07 | [
"MIT"
] | 1 | 2020-06-22T19:36:34.000Z | 2020-06-22T19:36:34.000Z | notebooks/config.py | kbren/uwnet | aac01e243c19686b10c214b1c56b0bb7b7e06a07 | [
"MIT"
] | null | null | null | notebooks/config.py | kbren/uwnet | aac01e243c19686b10c214b1c56b0bb7b7e06a07 | [
"MIT"
] | 2 | 2021-01-05T10:57:32.000Z | 2022-02-07T19:01:53.000Z | sam = "/Users/noah/workspace/models/SAMUWgh/"
nextflow_workdir = "/Users/noah/Data/0/" | 43 | 45 | 0.744186 | 12 | 86 | 5.25 | 0.833333 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012346 | 0.05814 | 86 | 2 | 46 | 43 | 0.765432 | 0 | 0 | 0 | 0 | 0 | 0.643678 | 0.425287 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 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 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
b599d3364ccb1effbaacf5dad9527df9c6b28645 | 873 | py | Python | downsample.py | KartikaySrivadtava/dl-for-har-ea1e9babb2b178cc338dbc72db974325c193c781 | f4fa436000a46df80ec083c8e3692cd21787e5b3 | [
"MIT"
] | null | null | null | downsample.py | KartikaySrivadtava/dl-for-har-ea1e9babb2b178cc338dbc72db974325c193c781 | f4fa436000a46df80ec083c8e3692cd21787e5b3 | [
"MIT"
] | null | null | null | downsample.py | KartikaySrivadtava/dl-for-har-ea1e9babb2b178cc338dbc72db974325c193c781 | f4fa436000a46df80ec083c8e3692cd21787e5b3 | [
"MIT"
] | null | null | null | import numpy as np
import pandas as pd
import os
data = pd.read_csv(os.path.join('C:/Users/karti/PycharmProjects/HAR/data/rwhar_data.csv'), sep=',')
df2 = data[data.index % 2 == 0] # Selects every 4th raw starting from 0
print(df2.shape[0])
df2.to_csv('C:/Users/karti/PycharmProjects/HAR/data/rwhar_data_25.csv', index=False)
df2 = data[data.index % 4 == 0] # Selects every 4th raw starting from 0
print(df2.shape[0])
df2.to_csv('C:/Users/karti/PycharmProjects/HAR/data/rwhar_data_12.csv', index=False)
df2 = data[data.index % 8 == 0] # Selects every 4th raw starting from 0
print(df2.shape[0])
df2.to_csv('C:/Users/karti/PycharmProjects/HAR/data/rwhar_data_6.csv', index=False)
df2 = data[data.index % 16 == 0] # Selects every 4th raw starting from 0
print(df2.shape[0])
df2.to_csv('C:/Users/karti/PycharmProjects/HAR/data/rwhar_data_3.csv', index=False)
| 29.1 | 99 | 0.727377 | 155 | 873 | 4.006452 | 0.258065 | 0.048309 | 0.088567 | 0.20934 | 0.826087 | 0.826087 | 0.826087 | 0.68599 | 0.618357 | 0.618357 | 0 | 0.050649 | 0.117984 | 873 | 29 | 100 | 30.103448 | 0.755844 | 0.172967 | 0 | 0.25 | 0 | 0 | 0.391911 | 0.390516 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.1875 | 0 | 0.1875 | 0.25 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
b5a822c51c5966fd3d706570760de64c774b06d5 | 145 | py | Python | weight convertor.py | Inventor-Eon/Projectbasedlearn_1 | 9603f3dd442996f5b1b794920cd8441096686b37 | [
"MIT"
] | 1 | 2021-07-23T16:54:28.000Z | 2021-07-23T16:54:28.000Z | weight convertor.py | Inventor-Eon/Projectbasedlearn_1 | 9603f3dd442996f5b1b794920cd8441096686b37 | [
"MIT"
] | null | null | null | weight convertor.py | Inventor-Eon/Projectbasedlearn_1 | 9603f3dd442996f5b1b794920cd8441096686b37 | [
"MIT"
] | null | null | null | weight=int(input())
unit=(input())
if unit.upper=="l":
converted=weight*0.45
print(converted)
else:
converted=(weight//0.45)
print(converted) | 18.125 | 25 | 0.710345 | 22 | 145 | 4.681818 | 0.545455 | 0.291262 | 0.31068 | 0.349515 | 0.621359 | 0.621359 | 0 | 0 | 0 | 0 | 0 | 0.045113 | 0.082759 | 145 | 8 | 26 | 18.125 | 0.729323 | 0 | 0 | 0.25 | 0 | 0 | 0.006849 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.25 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
a946943190a543a4011a124e06263d1c3b7fd4fe | 96 | py | Python | inference/rsml/__init__.py | Benjamin-deLaverny/RootNav-2.0 | 14b6d7353687acf640e5efbd224a35d9131e7275 | [
"BSD-3-Clause"
] | 23 | 2019-07-25T10:15:20.000Z | 2022-01-26T03:28:56.000Z | inference/rsml/__init__.py | rootnav2/RootNav-2.0 | 3e973c0f7fc34b3938a2294e858d1a0de76e9f0f | [
"BSD-3-Clause"
] | 7 | 2019-08-07T15:56:26.000Z | 2022-01-13T01:28:22.000Z | inference/rsml/__init__.py | rootnav2/RootNav-2.0 | 3e973c0f7fc34b3938a2294e858d1a0de76e9f0f | [
"BSD-3-Clause"
] | 11 | 2019-07-25T10:15:25.000Z | 2022-02-15T09:14:49.000Z | from .rsmlwriter import RSMLWriter
from .plants import Root, Plant
from .splines import Spline | 32 | 35 | 0.8125 | 13 | 96 | 6 | 0.615385 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.145833 | 96 | 3 | 36 | 32 | 0.95122 | 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 | 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 | 5 |
a961989d3af787dce13a22dbc57116ab760d42c6 | 118 | py | Python | stubs/jwcrypto/jwk/__init__.py | rboixaderg/guillotina | fcae65c2185222272f3b8fee4bc2754e81e0e983 | [
"BSD-2-Clause"
] | 173 | 2017-03-10T18:26:12.000Z | 2022-03-03T06:48:56.000Z | stubs/jwcrypto/jwk/__init__.py | rboixaderg/guillotina | fcae65c2185222272f3b8fee4bc2754e81e0e983 | [
"BSD-2-Clause"
] | 921 | 2017-03-08T14:04:43.000Z | 2022-03-30T10:28:56.000Z | stubs/jwcrypto/jwk/__init__.py | rboixaderg/guillotina | fcae65c2185222272f3b8fee4bc2754e81e0e983 | [
"BSD-2-Clause"
] | 60 | 2017-03-16T19:59:44.000Z | 2022-03-03T06:48:59.000Z | from typing import Dict
class JWK:
def generate(self, kty: str, size: int = 256) -> Dict[str, str]:
...
| 16.857143 | 68 | 0.584746 | 17 | 118 | 4.058824 | 0.823529 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.034884 | 0.271186 | 118 | 6 | 69 | 19.666667 | 0.767442 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
a9710bce0778a52a3bff595ab3f8d42e4a189f84 | 172 | py | Python | exercises/exc_A5.py | dataXcode/IPP | c9b94ad2d7dc14b01e6657a4fa555507bbc7e93b | [
"MIT"
] | null | null | null | exercises/exc_A5.py | dataXcode/IPP | c9b94ad2d7dc14b01e6657a4fa555507bbc7e93b | [
"MIT"
] | null | null | null | exercises/exc_A5.py | dataXcode/IPP | c9b94ad2d7dc14b01e6657a4fa555507bbc7e93b | [
"MIT"
] | null | null | null | #1 Create variable savings
_____________
#2 Create variable factor
_____________
#3 Calculate the result
_____________________________
#4 Print out the result
_____________ | 21.5 | 29 | 0.860465 | 17 | 172 | 4.705882 | 0.764706 | 0.35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.026316 | 0.116279 | 172 | 8 | 30 | 21.5 | 0.5 | 0.540698 | 0 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
a98bd8280e314dcc03673da27aa9bd60b3b62715 | 1,005 | py | Python | test/cmd/at/test_cmd_at_get_imei.py | jochenparm/moler | 0253d677e0ef150206758c7991197ba5687d0965 | [
"BSD-3-Clause"
] | 57 | 2018-02-20T08:16:47.000Z | 2022-03-28T10:36:57.000Z | test/cmd/at/test_cmd_at_get_imei.py | jochenparm/moler | 0253d677e0ef150206758c7991197ba5687d0965 | [
"BSD-3-Clause"
] | 377 | 2018-07-19T11:56:27.000Z | 2021-07-09T13:08:12.000Z | test/cmd/at/test_cmd_at_get_imei.py | jochenparm/moler | 0253d677e0ef150206758c7991197ba5687d0965 | [
"BSD-3-Clause"
] | 24 | 2018-04-14T20:49:40.000Z | 2022-03-29T10:44:26.000Z | # -*- coding: utf-8 -*-
"""
Testing GetImei command.
"""
__author__ = 'Grzegorz Latuszek'
__copyright__ = 'Copyright (C) 2020, Nokia'
__email__ = 'grzegorz.latuszek@nokia.com'
def test_calling_at_cmd_get_imei_returns_expected_result(buffer_connection):
from moler.cmd.at import get_imei
at_cmd_get_imsi = get_imei.GetImei(connection=buffer_connection.moler_connection)
buffer_connection.remote_inject_response([get_imei.COMMAND_OUTPUT_ver_default])
result = at_cmd_get_imsi()
assert result == get_imei.COMMAND_RESULT_ver_default
def test_calling_at_cmd_get_imei_ver_imei_returns_expected_result(buffer_connection):
from moler.cmd.at import get_imei
at_cmd_get_imsi = get_imei.GetImei(connection=buffer_connection.moler_connection,
**get_imei.COMMAND_KWARGS_ver_imei)
buffer_connection.remote_inject_response([get_imei.COMMAND_OUTPUT_ver_imei])
result = at_cmd_get_imsi()
assert result == get_imei.COMMAND_RESULT_ver_imei
| 38.653846 | 85 | 0.778109 | 137 | 1,005 | 5.138686 | 0.284672 | 0.109375 | 0.068182 | 0.068182 | 0.769886 | 0.769886 | 0.769886 | 0.701705 | 0.701705 | 0.701705 | 0 | 0.005794 | 0.141294 | 1,005 | 25 | 86 | 40.2 | 0.809965 | 0.046766 | 0 | 0.25 | 0 | 0 | 0.072632 | 0.028421 | 0 | 0 | 0 | 0 | 0.125 | 1 | 0.125 | false | 0 | 0.125 | 0 | 0.25 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
8d12ab41e09980bc6e02adf2a4a21b8bb87c07fb | 50 | py | Python | camp/FileIO/__init__.py | blakezim/CAMP | a42a407dc62151ab8a7eb4be3aee1318b984502c | [
"MIT"
] | 4 | 2021-03-02T05:18:06.000Z | 2021-11-29T16:06:39.000Z | camp/FileIO/__init__.py | blakezim/CAMP | a42a407dc62151ab8a7eb4be3aee1318b984502c | [
"MIT"
] | null | null | null | camp/FileIO/__init__.py | blakezim/CAMP | a42a407dc62151ab8a7eb4be3aee1318b984502c | [
"MIT"
] | 1 | 2021-03-26T20:38:11.000Z | 2021-03-26T20:38:11.000Z | from .ITKFileIO import *
from .OBJFileIO import *
| 16.666667 | 24 | 0.76 | 6 | 50 | 6.333333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 50 | 2 | 25 | 25 | 0.904762 | 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 | 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 | 5 |
8d1f8d3ae7d36cb37ccc387a65a7eda46637ac01 | 327 | py | Python | validator/views/general.py | s-scherrer/qa4sm | 99fa62d5e42e5a2b81c5bad1553c8137fe4259e7 | [
"MIT"
] | 10 | 2019-02-27T15:05:15.000Z | 2022-03-10T21:13:40.000Z | validator/views/general.py | s-scherrer/qa4sm | 99fa62d5e42e5a2b81c5bad1553c8137fe4259e7 | [
"MIT"
] | 69 | 2019-07-04T23:20:17.000Z | 2022-03-29T06:34:06.000Z | validator/views/general.py | s-scherrer/qa4sm | 99fa62d5e42e5a2b81c5bad1553c8137fe4259e7 | [
"MIT"
] | 10 | 2019-03-14T11:46:58.000Z | 2022-03-25T13:06:16.000Z | from django.shortcuts import render
from validator.models import Settings
def home(request):
return render(request, 'validator/index.html', {'news_text': Settings.load().news})
def alpha(request):
return render(request, 'validator/alpha.html')
def terms(request):
return render(request, 'validator/terms.html')
| 25.153846 | 87 | 0.746177 | 42 | 327 | 5.785714 | 0.452381 | 0.160494 | 0.234568 | 0.320988 | 0.432099 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125382 | 327 | 12 | 88 | 27.25 | 0.84965 | 0 | 0 | 0 | 0 | 0 | 0.211009 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.375 | false | 0 | 0.25 | 0.375 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
8d9fc7d9d88ea7de0116077d86091c65e693bdc8 | 32 | py | Python | phyper/__init__.py | LucaMarconato/phyper | 065f41dbdce93b95cd2f8a16ad72a1cf57826c66 | [
"MIT"
] | 1 | 2020-08-14T07:40:18.000Z | 2020-08-14T07:40:18.000Z | phyper/__init__.py | LucaMarconato/phyper | 065f41dbdce93b95cd2f8a16ad72a1cf57826c66 | [
"MIT"
] | null | null | null | phyper/__init__.py | LucaMarconato/phyper | 065f41dbdce93b95cd2f8a16ad72a1cf57826c66 | [
"MIT"
] | null | null | null | from phyper.phyper import Parser | 32 | 32 | 0.875 | 5 | 32 | 5.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.09375 | 32 | 1 | 32 | 32 | 0.965517 | 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 | 0 | 0 | 0 | 5 |
8da4d6bba4b4dadfb698884108a96b47e2d3eaf9 | 540 | py | Python | src/rastervision/core/labels.py | nholeman/raster-vision | f3e1e26c555feed6fa018183c3fa04d7858d91bd | [
"Apache-2.0"
] | null | null | null | src/rastervision/core/labels.py | nholeman/raster-vision | f3e1e26c555feed6fa018183c3fa04d7858d91bd | [
"Apache-2.0"
] | null | null | null | src/rastervision/core/labels.py | nholeman/raster-vision | f3e1e26c555feed6fa018183c3fa04d7858d91bd | [
"Apache-2.0"
] | null | null | null | from abc import ABC
class Labels(ABC):
"""A set of spatially referenced labels.
A set of labels predicted by a model or provided by human labelers for the
sake of training. Every label is associated with a spatial location and a
class. For object detection, a label is a bounding box surrounding an
object and the associated class. For classification, a label is a bounding
box representing a cell/chip within a spatial grid and its class.
For segmentation, a label is a pixel and its class.
"""
pass
| 36 | 78 | 0.731481 | 88 | 540 | 4.488636 | 0.511364 | 0.070886 | 0.060759 | 0.068354 | 0.101266 | 0.101266 | 0 | 0 | 0 | 0 | 0 | 0 | 0.233333 | 540 | 14 | 79 | 38.571429 | 0.954106 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 5 |
a5c1b6be6857a9cc116c8aea349bf603d491a92d | 432 | py | Python | {{cookiecutter.app_name}}/backend/apps/accounts/management/commands/createsu.py | RyanShahidi/Django-Nuxt-Docker-AWS-Cookiecutter | 2d549ee220648dbb469a08752eb6aa2ec2bb091e | [
"MIT"
] | 2 | 2021-08-04T17:51:36.000Z | 2022-01-08T17:40:16.000Z | {{cookiecutter.app_name}}/backend/apps/accounts/management/commands/createsu.py | RyanShahidi/Django-Nuxt-Docker-AWS-Cookiecutter | 2d549ee220648dbb469a08752eb6aa2ec2bb091e | [
"MIT"
] | 1 | 2021-07-31T12:08:44.000Z | 2021-07-31T12:13:35.000Z | {{cookiecutter.app_name}}/backend/apps/accounts/management/commands/createsu.py | RyanShahidi/Django-Nuxt-Docker-AWS-Cookiecutter | 2d549ee220648dbb469a08752eb6aa2ec2bb091e | [
"MIT"
] | 1 | 2021-09-08T23:25:35.000Z | 2021-09-08T23:25:35.000Z | from django.core.management.base import BaseCommand
from apps.accounts.models import CustomUser
import os
class Command(BaseCommand):
def handle(self, *args, **options):
if not CustomUser.objects.filter(username=os.environ.get("SUPERUSER_USERNAME")).exists():
CustomUser.objects.create_superuser(os.environ.get("SUPERUSER_USERNAME"), os.environ.get("SUPERUSER_EMAIL"), os.environ.get("SUPERUSER_PASSWORD")) | 48 | 158 | 0.763889 | 54 | 432 | 6.018519 | 0.574074 | 0.110769 | 0.147692 | 0.258462 | 0.267692 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108796 | 432 | 9 | 158 | 48 | 0.844156 | 0 | 0 | 0 | 0 | 0 | 0.159353 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0.142857 | 0.428571 | 0 | 0.714286 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 5 |
57308c001755996b56faf64cb58ff80b59f1c49f | 220 | py | Python | ssd/modeling/box_head/__init__.py | BeibinLi/SSD | 2cd30f02c21b0a8731a34dca2a89d6e099ca3442 | [
"MIT"
] | null | null | null | ssd/modeling/box_head/__init__.py | BeibinLi/SSD | 2cd30f02c21b0a8731a34dca2a89d6e099ca3442 | [
"MIT"
] | null | null | null | ssd/modeling/box_head/__init__.py | BeibinLi/SSD | 2cd30f02c21b0a8731a34dca2a89d6e099ca3442 | [
"MIT"
] | null | null | null | from ssd.modeling import registry
from .box_head import SSDBoxHead
__all__ = ['build_box_head', 'SSDBoxHead']
def build_box_head(cfg):
# TODO: make it more general
return registry.BOX_HEADS['SSDBoxHead'](cfg)
| 22 | 48 | 0.75 | 31 | 220 | 5 | 0.612903 | 0.135484 | 0.154839 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 220 | 9 | 49 | 24.444444 | 0.828877 | 0.118182 | 0 | 0 | 0 | 0 | 0.177083 | 0 | 0 | 0 | 0 | 0.111111 | 0 | 1 | 0.2 | false | 0 | 0.4 | 0.2 | 0.8 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 5 |
57338edb4624fb6f84f253ae5c4f36251835b6d9 | 172 | py | Python | app/api/ping.py | duckbytes/bloodbike-api | c6867160dd899a90aa7315125ac04e4cb71e7b79 | [
"Apache-2.0"
] | 2 | 2021-06-27T09:01:26.000Z | 2021-07-04T22:07:42.000Z | app/api/ping.py | duckbytes/bloodbike-api | c6867160dd899a90aa7315125ac04e4cb71e7b79 | [
"Apache-2.0"
] | 1 | 2021-07-20T21:10:19.000Z | 2021-07-20T21:10:19.000Z | app/api/ping.py | duckbytes/bloodbike-api | c6867160dd899a90aa7315125ac04e4cb71e7b79 | [
"Apache-2.0"
] | null | null | null | from app import root_ns as ns
from flask_restx import Resource
@ns.route('/ping', endpoint="api_ping")
class Ping(Resource):
def get(self):
return "pong", 200 | 21.5 | 39 | 0.697674 | 27 | 172 | 4.333333 | 0.740741 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.021429 | 0.186047 | 172 | 8 | 40 | 21.5 | 0.814286 | 0 | 0 | 0 | 0 | 0 | 0.098266 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0.333333 | 0.166667 | 0.833333 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 5 |
93b3f55a4b273d86037fdaca9d03a2993bc9b2d7 | 2,165 | py | Python | signalpy/jslib.py | Ksengine/SignalPy | bca374def747241263e7cb67abc10f3a42334b63 | [
"MIT"
] | 6 | 2020-07-26T09:18:43.000Z | 2021-12-29T14:54:34.000Z | signalpy/jslib.py | Ksengine/SignalPy | bca374def747241263e7cb67abc10f3a42334b63 | [
"MIT"
] | 2 | 2020-10-18T03:36:44.000Z | 2020-10-31T15:30:32.000Z | signalpy/jslib.py | Ksengine/SignalPy | bca374def747241263e7cb67abc10f3a42334b63 | [
"MIT"
] | 1 | 2020-10-16T20:00:44.000Z | 2020-10-16T20:00:44.000Z | data = 'function SignalPy(url,secure){\n if (secure === undefined) {\n secure = \'://\';\n } \n if (secure === true) {\n secure = \'s://\';\n } \n if (secure === undefined) {\n secure = \'://\';\n }\n if (\'WebSocket\' in window){\n url=\'ws\'+secure+url;\n w = new WebSocket(url);\n return w;\n }\n else{\n url=\'http\'+secure+url;\n obj = new signalpyajax();\n obj.url=url;\n obj.receive();\n return obj;\n }\n}\nsignalpyajax={\n id:"",\n url:"",\n onopen:function(){\n this.state=\'open\'\n },\n onerror:function(obj){},\n onmessage:function(msg){},\n _open:function(msg){\n if(this.id===\'\'){\n this.id=msg;this.onopen()\n }else{\n arr=JSON.parse(msg)\n for(message in arr){\n this.onmessage({data:message})\n }\n }\n this.receive();\n },\n receive:function () {\n _this=this;\n var request = this.return_ajax();\n request.open(\'POST\',this.url+\'?id=\'+this.id);\n request.onreadystatechange = function() {\n if (this.readyState == 4 && this.status == 200) {\n _this._open(this.responseText)\n }\n };\n request.onerror = function() {\n _this.onerror({})\n };\n request.send();\n},\n send:function (msg) {\n _this=this;\n var request = this.return_ajax()\n request.open(\'POST\',this.url+this.id);\n request.onerror = function() {\n _this.onerror({message:msg})\n };\n request.send(msg);\n},\n\n return_ajax:function (){\n try{\n // Opera 8.0+, Firefox, Safari (1st attempt)\n xhttp = new XMLHttpRequest();\n return xhttp;\n }catch (e){\n // IE browser (2nd attempt)\n try{\n xhttp = new ActiveXObject("Msxml2.XMLHTTP");\n return xhttp;\n }catch (e) {\n try{\n // 3rd attempt\n xhttp = new ActiveXObject("Microsoft.XMLHTTP");\n return xhttp;\n }catch (e){\n return false;\n }\n }\n}\n }}\n'
| 1,082.5 | 2,164 | 0.502079 | 284 | 2,165 | 3.792254 | 0.239437 | 0.03714 | 0.016713 | 0.036212 | 0.293408 | 0.293408 | 0.293408 | 0.209842 | 0.159703 | 0.10585 | 0 | 0.006566 | 0.296536 | 2,165 | 1 | 2,165 | 2,165 | 0.700591 | 0 | 0 | 0 | 0 | 6 | 0.961201 | 0.20739 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 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 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.