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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
173d95490a42510e2c597222248a1c543e75fe0d | 503 | py | Python | src/masonite/providers/AuthenticationProvider.py | cercos/masonite | f7f220efa7fae833683e9f07ce13c3795a87d3b8 | [
"MIT"
] | 1,816 | 2018-02-14T01:59:51.000Z | 2022-03-31T17:09:20.000Z | src/masonite/providers/AuthenticationProvider.py | cercos/masonite | f7f220efa7fae833683e9f07ce13c3795a87d3b8 | [
"MIT"
] | 340 | 2018-02-11T00:27:26.000Z | 2022-03-21T12:00:24.000Z | src/masonite/providers/AuthenticationProvider.py | cercos/masonite | f7f220efa7fae833683e9f07ce13c3795a87d3b8 | [
"MIT"
] | 144 | 2018-03-18T00:08:16.000Z | 2022-02-26T01:51:58.000Z | from ..authentication import Auth
from ..authentication.guards import WebGuard
from ..configuration import config
from .Provider import Provider
class AuthenticationProvider(Provider):
def __init__(self, application):
self.application = application
def register(self):
auth = Auth(self.application).set_configuration(config("auth.guards"))
auth.add_guard("web", WebGuard(self.application))
self.application.bind("auth", auth)
def boot(self):
pass
| 27.944444 | 78 | 0.715706 | 55 | 503 | 6.436364 | 0.418182 | 0.211864 | 0.107345 | 0.169492 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.182903 | 503 | 17 | 79 | 29.588235 | 0.861314 | 0 | 0 | 0 | 0 | 0 | 0.035785 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.230769 | false | 0.076923 | 0.307692 | 0 | 0.615385 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
e5246e1e0f1e535692e216ba800693123f7d1f66 | 48 | py | Python | operations.py | grahamas/py-control | 75cf011bfd0acbd1f9f33d42502797c2e6931da0 | [
"Apache-2.0"
] | 1 | 2015-10-17T00:26:41.000Z | 2015-10-17T00:26:41.000Z | operations.py | grahamas/py-control | 75cf011bfd0acbd1f9f33d42502797c2e6931da0 | [
"Apache-2.0"
] | null | null | null | operations.py | grahamas/py-control | 75cf011bfd0acbd1f9f33d42502797c2e6931da0 | [
"Apache-2.0"
] | null | null | null | from email.parser import Parser as EmailParser
| 16 | 46 | 0.833333 | 7 | 48 | 5.714286 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.145833 | 48 | 2 | 47 | 24 | 0.97561 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e52a203c2ccf61ab88b8482f73c6465d2268defa | 3,187 | py | Python | listshuffler-be/tests/unit/test_get_instance.py | csiztom/listshuffler | d4fea7da3d506e93cca4a8e79c6f0ba98aebbac5 | [
"MIT"
] | null | null | null | listshuffler-be/tests/unit/test_get_instance.py | csiztom/listshuffler | d4fea7da3d506e93cca4a8e79c6f0ba98aebbac5 | [
"MIT"
] | null | null | null | listshuffler-be/tests/unit/test_get_instance.py | csiztom/listshuffler | d4fea7da3d506e93cca4a8e79c6f0ba98aebbac5 | [
"MIT"
] | null | null | null | import json
import datetime
from unittest import TestCase, mock
from src.get_instance import app
def good_api_event():
return {
"body": '{ "adminID": "thisnthat"}',
"queryStringParameters": None
}
def bad_api_event():
return {
"body": None,
"queryStringParameters": None
}
class TestGetInstance(TestCase):
def test_bad_api_call(self):
assert app.handler(bad_api_event(), "")['statusCode'] == 400
@mock.patch('src.helpers.rds_config.pymysql', autospec=True)
def test_non_existing_instance(self, mock_pymysql):
mock_cursor = mock.MagicMock()
mock_cursor.fetchone.return_value = None
mock_pymysql.connect.return_value.cursor.return_value.__enter__.return_value = mock_cursor
assert app.handler(good_api_event(), "")['statusCode'] == 404
@mock.patch('src.helpers.rds_config.pymysql', autospec=True)
def test_empty_instance(self, mock_pymysql):
mock_cursor = mock.MagicMock()
mock_cursor.fetchall.return_value = []
mock_cursor.fetchone.return_value = [
'id', 0, None, True, None, datetime.datetime(2020, 5, 17)]
mock_pymysql.connect.return_value.cursor.return_value.__enter__.return_value = mock_cursor
res = app.handler(good_api_event(), "")
assert res['statusCode'] == 200
assert json.loads(res['body'])['lists'] == []
assert json.loads(res['body'])['shuffled'] == 0
assert json.loads(res['body'])['shuffledID'] == None
assert json.loads(res['body'])['uniqueInMul'] == True
assert json.loads(res['body'])['preset'] == None
assert json.loads(res['body'])['shuffleTime'] == '2020-05-17'
@mock.patch('src.helpers.rds_config.pymysql', autospec=True)
def test_one_list_instance(self, mock_pymysql):
mock_cursor = mock.MagicMock()
mock_cursor.fetchall.return_value = [['id', 'name', 1]]
mock_cursor.fetchone.return_value = [
'id', 0, 'id2', False, None, None]
mock_pymysql.connect.return_value.cursor.return_value.__enter__.return_value = mock_cursor
res = app.handler(good_api_event(), "")
assert res['statusCode'] == 200
assert len(json.loads(res['body'])['lists']) == 1
assert json.loads(res['body'])['shuffled'] == 0
assert json.loads(res['body'])['shuffledID'] == 'id2'
assert json.loads(res['body'])['uniqueInMul'] == False
assert json.loads(res['body'])['preset'] == None
assert json.loads(res['body'])['shuffleTime'] == None
@mock.patch('src.helpers.rds_config.pymysql', autospec=True)
def test_more_list_instance(self, mock_pymysql):
mock_cursor = mock.MagicMock()
mock_cursor.fetchall.return_value = [
['id', 'name', 1], ['id2', 'name2', 1]]
mock_cursor.fetchone.return_value = [
'id', 0, 'id2', True, None, datetime.datetime(2020, 5, 17)]
mock_pymysql.connect.return_value.cursor.return_value.__enter__.return_value = mock_cursor
res = app.handler(good_api_event(), "")
assert res['statusCode'] == 200
assert len(json.loads(res['body'])['lists']) == 2
| 40.858974 | 98 | 0.641356 | 389 | 3,187 | 5.020566 | 0.190231 | 0.107015 | 0.079877 | 0.106503 | 0.806452 | 0.774706 | 0.738863 | 0.722478 | 0.722478 | 0.686124 | 0 | 0.020505 | 0.204267 | 3,187 | 77 | 99 | 41.38961 | 0.749606 | 0 | 0 | 0.453125 | 0 | 0 | 0.140885 | 0.050832 | 0 | 0 | 0 | 0 | 0.28125 | 1 | 0.109375 | false | 0 | 0.0625 | 0.03125 | 0.21875 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e574996bc0202b49abe400b283ca369147ff8b82 | 16 | py | Python | tmp/testdata/pkg/b.py | KGerring/importlab | bda6e8146b6b8eda3bb8f208944b30bcf340f92c | [
"Apache-2.0"
] | 130 | 2018-03-12T13:20:17.000Z | 2022-03-31T17:15:14.000Z | tmp/testdata/pkg/b.py | KGerring/importlab | bda6e8146b6b8eda3bb8f208944b30bcf340f92c | [
"Apache-2.0"
] | 33 | 2018-05-02T22:52:07.000Z | 2022-01-07T20:27:20.000Z | tmp/testdata/pkg/b.py | KGerring/importlab | bda6e8146b6b8eda3bb8f208944b30bcf340f92c | [
"Apache-2.0"
] | 20 | 2018-03-01T08:35:42.000Z | 2022-01-07T05:32:41.000Z | from . import c
| 8 | 15 | 0.6875 | 3 | 16 | 3.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 16 | 1 | 16 | 16 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e5ed5de6c346b76eddb462b214028d40178f5d14 | 35 | py | Python | appicon/ios/__init__.py | oxcug/appicon | 33aaadd24d2bf9a7bf05f4977f27e93e453c2b52 | [
"MIT"
] | 2 | 2021-10-03T09:37:42.000Z | 2021-11-17T03:45:02.000Z | appicon/ios/__init__.py | oxcug/appicon | 33aaadd24d2bf9a7bf05f4977f27e93e453c2b52 | [
"MIT"
] | 1 | 2021-11-17T03:44:53.000Z | 2021-11-24T07:28:50.000Z | appicon/ios/__init__.py | oxcug/appicon | 33aaadd24d2bf9a7bf05f4977f27e93e453c2b52 | [
"MIT"
] | 1 | 2021-11-23T17:26:30.000Z | 2021-11-23T17:26:30.000Z | from .iosicongen import IOSIconGen
| 17.5 | 34 | 0.857143 | 4 | 35 | 7.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 1 | 35 | 35 | 0.967742 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e5f576f5a31b4fe7217f43ebd802c8ffdbedc147 | 452 | py | Python | exercises/concept/ghost-gobble-arcade-game/.meta/exemplar.py | gsilvapt/python | d675468b2437d4c09c358d023ef998a05a781f58 | [
"MIT"
] | 200 | 2019-12-12T13:50:59.000Z | 2022-02-20T22:38:42.000Z | exercises/concept/ghost-gobble-arcade-game/.meta/exemplar.py | gsilvapt/python | d675468b2437d4c09c358d023ef998a05a781f58 | [
"MIT"
] | 1,938 | 2019-12-12T08:07:10.000Z | 2021-01-29T12:56:13.000Z | exercises/concept/ghost-gobble-arcade-game/.meta/exemplar.py | gsilvapt/python | d675468b2437d4c09c358d023ef998a05a781f58 | [
"MIT"
] | 239 | 2019-12-12T14:09:08.000Z | 2022-03-18T00:04:07.000Z | def eat_ghost(power_pellet_active, touching_ghost):
return power_pellet_active and touching_ghost
def score(touching_power_pellet, touching_dot):
return touching_power_pellet or touching_dot
def lose(power_pellet_active, touching_ghost):
return not power_pellet_active and touching_ghost
def win(has_eaten_all_dots, power_pellet_active, touching_ghost):
return has_eaten_all_dots and not lose(power_pellet_active, touching_ghost)
| 30.133333 | 79 | 0.836283 | 68 | 452 | 5.102941 | 0.279412 | 0.253602 | 0.293948 | 0.288184 | 0.628242 | 0.628242 | 0.207493 | 0 | 0 | 0 | 0 | 0 | 0.119469 | 452 | 14 | 80 | 32.285714 | 0.871859 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
005ee26e767e4bc7dca34ebcd3b53d1e560c6609 | 169 | py | Python | geometry/admin.py | rankrh/soli | 8f19945a175106064591d09a53d07fcbfa26b7da | [
"MIT"
] | null | null | null | geometry/admin.py | rankrh/soli | 8f19945a175106064591d09a53d07fcbfa26b7da | [
"MIT"
] | null | null | null | geometry/admin.py | rankrh/soli | 8f19945a175106064591d09a53d07fcbfa26b7da | [
"MIT"
] | 2 | 2019-09-07T15:10:14.000Z | 2020-09-04T01:51:19.000Z | from django.contrib import admin
from geometry.models.point import Point
from geometry.models.shape import Shape
admin.site.register(Point)
admin.site.register(Shape)
| 21.125 | 39 | 0.828402 | 25 | 169 | 5.6 | 0.44 | 0.171429 | 0.257143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.094675 | 169 | 7 | 40 | 24.142857 | 0.915033 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.6 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
0071c08d45e3e8b1de67e8ce7f78ed225823a3ec | 80 | py | Python | src/hugtools/_version.py | adam-huganir/hugtools | 5aa84f0d99b50146239db5567ecc2af57403b27d | [
"Apache-2.0"
] | null | null | null | src/hugtools/_version.py | adam-huganir/hugtools | 5aa84f0d99b50146239db5567ecc2af57403b27d | [
"Apache-2.0"
] | 1 | 2021-12-01T20:55:19.000Z | 2021-12-01T20:55:19.000Z | src/hugtools/_version.py | adam-huganir/hugtools | 5aa84f0d99b50146239db5567ecc2af57403b27d | [
"Apache-2.0"
] | null | null | null | import importlib.metadata
__version__ = importlib.metadata.version("hugtools")
| 20 | 52 | 0.825 | 8 | 80 | 7.75 | 0.625 | 0.548387 | 0.774194 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.075 | 80 | 3 | 53 | 26.666667 | 0.837838 | 0 | 0 | 0 | 0 | 0 | 0.1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
00b5cd966755f628b193bfb01431cf448e75a645 | 109 | py | Python | ds4ml/__init__.py | SebastianWolf-SAP/data-synthesis-for-machine-learning | b622739776cedf57a906d7304a96aa31f767340c | [
"Apache-2.0"
] | 12 | 2019-10-24T08:52:41.000Z | 2021-12-20T21:54:09.000Z | ds4ml/__init__.py | SebastianWolf-SAP/data-synthesis-for-machine-learning | b622739776cedf57a906d7304a96aa31f767340c | [
"Apache-2.0"
] | 7 | 2020-01-07T23:02:42.000Z | 2022-02-17T21:36:19.000Z | ds4ml/__init__.py | SebastianWolf-SAP/data-synthesis-for-machine-learning | b622739776cedf57a906d7304a96aa31f767340c | [
"Apache-2.0"
] | 9 | 2019-12-16T19:51:48.000Z | 2022-02-27T18:40:40.000Z |
from ds4ml.dataset import DataSet
from ds4ml.attribute import Attribute
from ds4ml.evaluator import BiFrame
| 21.8 | 37 | 0.853211 | 15 | 109 | 6.2 | 0.466667 | 0.290323 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.03125 | 0.119266 | 109 | 4 | 38 | 27.25 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
00d32dc81dc01f16a6e50f888e34ac4df6f30081 | 48 | py | Python | system/t10_task/__init__.py | kchida/aptly | 07165efc9d4bcd7018031787f27e70c2d8ecb8b9 | [
"MIT"
] | 16 | 2015-02-10T16:32:43.000Z | 2021-08-10T18:59:10.000Z | system/t10_task/__init__.py | kchida/aptly | 07165efc9d4bcd7018031787f27e70c2d8ecb8b9 | [
"MIT"
] | null | null | null | system/t10_task/__init__.py | kchida/aptly | 07165efc9d4bcd7018031787f27e70c2d8ecb8b9 | [
"MIT"
] | 8 | 2015-02-28T23:21:55.000Z | 2020-11-24T11:29:30.000Z | """
Test aptly task run
"""
from .run import *
| 8 | 19 | 0.604167 | 7 | 48 | 4.142857 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.229167 | 48 | 5 | 20 | 9.6 | 0.783784 | 0.395833 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
00d75005f806879ad5517a2afd14f553a6b136e4 | 46 | py | Python | deepdraken/image_generation/__init__.py | DevavratSinghBisht/deepdraken | 66671bee2d677d3e900077c5d1c66c0b1eff2cee | [
"Apache-2.0"
] | null | null | null | deepdraken/image_generation/__init__.py | DevavratSinghBisht/deepdraken | 66671bee2d677d3e900077c5d1c66c0b1eff2cee | [
"Apache-2.0"
] | null | null | null | deepdraken/image_generation/__init__.py | DevavratSinghBisht/deepdraken | 66671bee2d677d3e900077c5d1c66c0b1eff2cee | [
"Apache-2.0"
] | null | null | null | from deepdraken.image_generation.gans import * | 46 | 46 | 0.869565 | 6 | 46 | 6.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.065217 | 46 | 1 | 46 | 46 | 0.906977 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
00e102f25593b40d8de301614018fb8cd2e98fbd | 652 | py | Python | bdd/contact_scenario.py | apzhad/test | c82746d1d934974d8c789972b1871b6eeccdfae3 | [
"Apache-2.0"
] | null | null | null | bdd/contact_scenario.py | apzhad/test | c82746d1d934974d8c789972b1871b6eeccdfae3 | [
"Apache-2.0"
] | null | null | null | bdd/contact_scenario.py | apzhad/test | c82746d1d934974d8c789972b1871b6eeccdfae3 | [
"Apache-2.0"
] | null | null | null | from pytest_bdd import scenario
from .contact_steps import *
@scenario('contact.feature', 'Add new contact')
def test_add_new_contact():
pass
@scenario('contact.feature', 'Modify contact')
def test_modify_contact():
pass
@scenario('contact.feature', 'Delete contact')
def test_delete_contact():
pass
@scenario('contact.feature', 'Cancel delete contact')
def test_calcel_delete_contact():
pass
@scenario('contact.feature', 'Delete all contacts')
def test_delete_all_contact():
pass
@scenario('contact.feature', 'Modify contact from detail')
def test_modify_contact_from_detail():
pass
| 19.757576 | 59 | 0.707055 | 79 | 652 | 5.594937 | 0.253165 | 0.20362 | 0.298643 | 0.294118 | 0.486425 | 0.486425 | 0.208145 | 0 | 0 | 0 | 0 | 0 | 0.179448 | 652 | 32 | 60 | 20.375 | 0.826168 | 0 | 0 | 0.3 | 0 | 0 | 0.320968 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.3 | true | 0.3 | 0.1 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
daff5e1c27f79e9b3f667f4459b34028cc623888 | 173 | py | Python | part_1_players/NeuralNetTest.py | mattmahowald/NFL-Draft-Regression | 59e58581f07c1a5209e18116176ea491b6f3ee0f | [
"MIT"
] | null | null | null | part_1_players/NeuralNetTest.py | mattmahowald/NFL-Draft-Regression | 59e58581f07c1a5209e18116176ea491b6f3ee0f | [
"MIT"
] | null | null | null | part_1_players/NeuralNetTest.py | mattmahowald/NFL-Draft-Regression | 59e58581f07c1a5209e18116176ea491b6f3ee0f | [
"MIT"
] | null | null | null | from Regressor import Regressor
regressor = Regressor()
outputLR = regressor.fit_and_predict(2015)
outputNN = regressor.fit_and_predict(2015)
print outputLR
print outputNN | 21.625 | 42 | 0.83237 | 22 | 173 | 6.363636 | 0.454545 | 0.257143 | 0.214286 | 0.314286 | 0.371429 | 0 | 0 | 0 | 0 | 0 | 0 | 0.051613 | 0.104046 | 173 | 8 | 43 | 21.625 | 0.851613 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.166667 | null | null | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
979ae30f6d701be60a9d0554886510f6ed353c59 | 152 | py | Python | cnmodel/data/__init__.py | asasmal/cnmodel | ca834ae05e3c6487a441c9a6608eeacd46dae6aa | [
"BSD-3-Clause"
] | 1 | 2020-01-26T12:46:58.000Z | 2020-01-26T12:46:58.000Z | cnmodel/data/__init__.py | asasmal/cnmodel | ca834ae05e3c6487a441c9a6608eeacd46dae6aa | [
"BSD-3-Clause"
] | null | null | null | cnmodel/data/__init__.py | asasmal/cnmodel | ca834ae05e3c6487a441c9a6608eeacd46dae6aa | [
"BSD-3-Clause"
] | 1 | 2020-01-26T12:47:01.000Z | 2020-01-26T12:47:01.000Z | from ._db import get, get_source, add_table_data
from . import connectivity
from . import synapses
from . import populations
from . import ionchannels
| 21.714286 | 48 | 0.802632 | 21 | 152 | 5.619048 | 0.571429 | 0.338983 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151316 | 152 | 6 | 49 | 25.333333 | 0.914729 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
c126aed837ba9fedd9f0050b0250c174cc25aa68 | 16,266 | py | Python | script_federated_training.py | ameldocena/StratifiedAggregation | 0031fea120bff00c739eb6c3d654a5c6d3f094bb | [
"MIT"
] | null | null | null | script_federated_training.py | ameldocena/StratifiedAggregation | 0031fea120bff00c739eb6c3d654a5c6d3f094bb | [
"MIT"
] | null | null | null | script_federated_training.py | ameldocena/StratifiedAggregation | 0031fea120bff00c739eb6c3d654a5c6d3f094bb | [
"MIT"
] | null | null | null | from data_distribution import replace_N_with_M
from utilities import RandomSelectionStrategy
from saving_utils import get_class_label_from_num
import random as rd
import numpy as np
from server_core import run_exp
#START_EXP_IDX
exper = dict()
#exper['index'] = [956, 957, 958, 959, 960, 961] #MKrum-Horded-Unif, Median-Horded-Unif, TMean-Horded-Unif
#exper['pois'] = [0, 10, 10]
# This block can be set as constant
#exper['data'] = ['CIFAR10']
#UPNEXT: UNSUPERVISED STRATIFICATION
#Keyword arguments
kw_srsFedAvg = {"NUM_WORKERS_PER_ROUND" : 25,
"SELECTION_STRATEGY": "Simple_RS",
"strata_weights": None}
kw_strsFedAvg = {"NUM_WORKERS_PER_ROUND" : 25, #4, #25
"NUM_KRUM": 1, # Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
#Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4), # We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
kw_strsMKrumHorded = {"NUM_WORKERS_PER_ROUND" : 25, #4, #25
"NUM_KRUM": 3, # Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
#Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4), # We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
kw_strsMedianHorded = {"NUM_WORKERS_PER_ROUND" : 25, #4, #25
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
"NUM_KRUM": 1,
#Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
kw_strsTMeanHorded = {"NUM_WORKERS_PER_ROUND" : 25, #4, #25
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
"TRIM_PROPORTION": 1/5,
"NUM_KRUM": 1,
#Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
# Poisoned data - Krum
kw_srsKrum = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 1, # Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Simple_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
kw_strsKrum = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 1,
# Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
# Poisoned data - MKrum
kw_srsMKrum = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 3,
# Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Simple_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
kw_strsMKrum = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 3,
# Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
# Poisoned data - Median
kw_srsMed = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 1, # Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Simple_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
kw_strsMed = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 1, # Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
# Poisoned data - Trimmed Mean
kw_srsTMean = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 1,
# Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Simple_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
"TRIM_PROPORTION": 1 / 5,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'median', # stratification technique: 'median', 'kmeans'
"nclusters": 4, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
kw_strsTMean = {"NUM_WORKERS_PER_ROUND": 25,
"ASSUMED_POISONED_WORKERS_PER_ROUND" : 5,
"NUM_KRUM": 1,
# Number of gradients to average. If NUM_KRUM = 1, basic Krum; if NUM_KRUM > 1, multi-Krum
"SELECTION_STRATEGY": "Stratified_RS", # sample selection: Simple_RS or Stratified_RS
"nlabels": 10,
"TRIM_PROPORTION": 1 / 5,
# Stratified kwargs
"strata_weights": {'strata1': 0.25, 'strata2': 0.25, 'strata3': 0.25, 'strata4': 0.25},
# stratum weights for weighted aggregation
"conceal_pois_class": True, # Poisoned worker either conceals poisoned class
"influence": None, # 'strata1', # stratum to influence: "strata" + stratum number, default None
"stratify": 'kmeans', # stratification technique: 'median', 'kmeans'
"nclusters": 3, # Number of clusters for the unsupervised stratification technique
"assumed_workers_per_round_stratum": int(25 / 4),
# We divide NUM_WORKERS_PER_ROUND by the number of clusters
"assumed_poisoned_per_round_stratum": int((10 / 50) * (25 / 4))
}
#CIFAR-10, Stratified FedAvg, Clean and Poisoned, 3 label-flipped trials
exper['index'] = [953, 954, 955, 959, 960, 961]
exper['pois'] = [0, 0, 0, 10, 10, 10]
exper['source'] = [0, 1, 5,
0, 1, 5]
exper['target'] = [2, 9, 3,
2, 9, 3]
exper['agg'] = ['StratFedAvg', 'StratFedAvg', 'StratFedAvg',
'StratFedAvg', 'StratFedAvg', 'StratFedAvg']
exper['kwargs'] = [kw_strsFedAvg, kw_strsFedAvg, kw_strsFedAvg,
kw_strsFedAvg, kw_strsFedAvg, kw_strsFedAvg]
if __name__ == '__main__':
for num in range(len(exper['index'])):
START_EXP_IDX = exper['index'][num] # Change here
NUM_EXP = 1 # We can make this multiple experiments.
NUM_POISONED_WORKERS = exper['pois'][num] # The total number of poisoned workers/clients
REPLACEMENT_METHOD = replace_N_with_M
PARAMETERS_UPLOADED = 1.0
PARAMETERS_DOWNLOADED = 1.0 # set to 1.0 for unrestricted parameter sharing
KWARGS = exper['kwargs'][num]
DATASET = "CIFAR10" #
TARGET = exper['target'][num] # The target label as the poisoning/flipping
SOURCE = exper['source'][num] # poisoned class
AGG = exper['agg'][num] # StratKrum" #"StratFedAvg" #MultiKrum" #"StratTrimMean" #"StratKrum" #Change here
INIT = "Randomized" # "Default"
DISTRIBUTION = "Non-IID_v2"
count = 0
for experiment_id in range(START_EXP_IDX, START_EXP_IDX + NUM_EXP):
count += 1 #experiment count
rd.seed(1234 + 100 * count)
np.random.seed(1234 + 100 * (count - 1))
with open("experiment_param_notes.txt", 'a') as f:
f.write(f"{experiment_id}, {DATASET}, {NUM_POISONED_WORKERS}, {SOURCE}, {TARGET}, {get_class_label_from_num(DATASET, SOURCE)}, {get_class_label_from_num(DATASET, TARGET)}, {PARAMETERS_DOWNLOADED}, {PARAMETERS_UPLOADED}, {AGG}, {INIT}, {DISTRIBUTION}\n")
print(f"Exp ID: {experiment_id}, Num pois: {NUM_POISONED_WORKERS}, Uploaded: {PARAMETERS_UPLOADED}")
run_exp(REPLACEMENT_METHOD, NUM_POISONED_WORKERS, KWARGS, RandomSelectionStrategy(), experiment_id,
PARAMETERS_UPLOADED, PARAMETERS_DOWNLOADED, DATASET, INIT, DISTRIBUTION, SOURCE, TARGET, AGG)
| 57.073684 | 269 | 0.631809 | 1,934 | 16,266 | 5.102378 | 0.098759 | 0.04621 | 0.068403 | 0.045602 | 0.820835 | 0.805634 | 0.800162 | 0.800162 | 0.800162 | 0.795501 | 0 | 0.044513 | 0.25833 | 16,266 | 284 | 270 | 57.274648 | 0.773458 | 0.387741 | 0 | 0.65 | 0 | 0.005 | 0.369049 | 0.15863 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.03 | 0 | 0.03 | 0.005 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c15349ea8722695845337fa3f7655e9282355cf0 | 124,524 | py | Python | py_stringmatching/tests/test_simfunctions.py | kevalii/py_stringmatching | 6edeea7ca3035e4a170bc84a5b57b81e2b45b016 | [
"BSD-3-Clause"
] | 115 | 2016-04-20T06:59:28.000Z | 2022-02-12T03:32:59.000Z | py_stringmatching/tests/test_simfunctions.py | kevalii/py_stringmatching | 6edeea7ca3035e4a170bc84a5b57b81e2b45b016 | [
"BSD-3-Clause"
] | 59 | 2016-04-20T06:56:11.000Z | 2022-01-25T08:16:58.000Z | py_stringmatching/tests/test_simfunctions.py | kevalii/py_stringmatching | 6edeea7ca3035e4a170bc84a5b57b81e2b45b016 | [
"BSD-3-Clause"
] | 17 | 2016-04-20T06:59:46.000Z | 2022-01-18T17:45:48.000Z |
# coding=utf-8
from __future__ import unicode_literals
import math
import unittest
from nose.tools import *
# sequence based similarity measures
from py_stringmatching.similarity_measure.affine import Affine
from py_stringmatching.similarity_measure.bag_distance import BagDistance
from py_stringmatching.similarity_measure.editex import Editex
from py_stringmatching.similarity_measure.hamming_distance import HammingDistance
from py_stringmatching.similarity_measure.jaro import Jaro
from py_stringmatching.similarity_measure.jaro_winkler import JaroWinkler
from py_stringmatching.similarity_measure.levenshtein import Levenshtein
from py_stringmatching.similarity_measure.needleman_wunsch import NeedlemanWunsch
from py_stringmatching.similarity_measure.smith_waterman import SmithWaterman
# token based similarity measures
from py_stringmatching.similarity_measure.cosine import Cosine
from py_stringmatching.similarity_measure.dice import Dice
from py_stringmatching.similarity_measure.jaccard import Jaccard
from py_stringmatching.similarity_measure.overlap_coefficient import OverlapCoefficient
from py_stringmatching.similarity_measure.soft_tfidf import SoftTfIdf
from py_stringmatching.similarity_measure.tfidf import TfIdf
from py_stringmatching.similarity_measure.tversky_index import TverskyIndex
# hybrid similarity measures
from py_stringmatching.similarity_measure.generalized_jaccard import GeneralizedJaccard
from py_stringmatching.similarity_measure.monge_elkan import MongeElkan
#phonetic similarity measures
from py_stringmatching.similarity_measure.soundex import Soundex
#fuzzywuzzy similarity measures
from py_stringmatching.similarity_measure.partial_ratio import PartialRatio
from py_stringmatching.similarity_measure.ratio import Ratio
from py_stringmatching.similarity_measure.partial_token_sort import PartialTokenSort
from py_stringmatching.similarity_measure.token_sort import TokenSort
NUMBER_OF_DECIMAL_PLACES = 5
# ---------------------- sequence based similarity measures ----------------------
class AffineTestCases(unittest.TestCase):
def setUp(self):
self.affine = Affine()
self.affine_with_params1 = Affine(gap_start=2, gap_continuation=0.5)
self.sim_func = lambda s1, s2: (int(1 if s1 == s2 else 0))
self.affine_with_params2 = Affine(gap_continuation=0.2, sim_func=self.sim_func)
def test_valid_input(self):
self.assertAlmostEqual(self.affine.get_raw_score('dva', 'deeva'), 1.5)
self.assertAlmostEqual(self.affine_with_params1.get_raw_score('dva', 'deeve'), -0.5)
self.assertAlmostEqual(round(self.affine_with_params2.get_raw_score('AAAGAATTCA',
'AAATCA'),NUMBER_OF_DECIMAL_PLACES), 4.4)
self.assertAlmostEqual(self.affine_with_params2.get_raw_score(' ', ' '), 1)
self.assertEqual(self.affine.get_raw_score('', 'deeva'), 0)
def test_valid_input_non_ascii(self):
self.assertAlmostEqual(self.affine.get_raw_score(u'dva', u'dáóva'), 1.5)
self.assertAlmostEqual(self.affine.get_raw_score('dva', 'dáóva'), 1.5)
self.assertAlmostEqual(self.affine.get_raw_score('dva', b'd\xc3\xa1\xc3\xb3va'), 1.5)
def test_get_gap_start(self):
self.assertEqual(self.affine_with_params1.get_gap_start(), 2)
def test_get_gap_continuation(self):
self.assertEqual(self.affine_with_params2.get_gap_continuation(), 0.2)
def test_get_sim_func(self):
self.assertEqual(self.affine_with_params2.get_sim_func(), self.sim_func)
def test_set_gap_start(self):
af = Affine(gap_start=1)
self.assertEqual(af.get_gap_start(), 1)
self.assertAlmostEqual(af.get_raw_score('dva', 'deeva'), 1.5)
self.assertEqual(af.set_gap_start(2), True)
self.assertEqual(af.get_gap_start(), 2)
self.assertAlmostEqual(af.get_raw_score('dva', 'deeva'), 0.5)
def test_set_gap_continuation(self):
af = Affine(gap_continuation=0.3)
self.assertEqual(af.get_gap_continuation(), 0.3)
self.assertAlmostEqual(af.get_raw_score('dva', 'deeva'), 1.7)
self.assertEqual(af.set_gap_continuation(0.7), True)
self.assertEqual(af.get_gap_continuation(), 0.7)
self.assertAlmostEqual(af.get_raw_score('dva', 'deeva'), 1.3)
def test_set_sim_func(self):
fn1 = lambda s1, s2: (int(1 if s1 == s2 else 0))
fn2 = lambda s1, s2: (int(2 if s1 == s2 else -1))
af = Affine(sim_func=fn1)
self.assertEqual(af.get_sim_func(), fn1)
self.assertAlmostEqual(af.get_raw_score('dva', 'deeva'), 1.5)
self.assertEqual(af.set_sim_func(fn2), True)
self.assertEqual(af.get_sim_func(), fn2)
self.assertAlmostEqual(af.get_raw_score('dva', 'deeva'), 4.5)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.affine.get_raw_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.affine.get_raw_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.affine.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.affine.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.affine.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.affine.get_raw_score(12.90, 12.90)
class BagDistanceTestCases(unittest.TestCase):
def setUp(self):
self.bd = BagDistance()
def test_valid_input_raw_score(self):
self.assertEqual(self.bd.get_raw_score('a', ''), 1)
self.assertEqual(self.bd.get_raw_score('', 'a'), 1)
self.assertEqual(self.bd.get_raw_score('abc', ''), 3)
self.assertEqual(self.bd.get_raw_score('', 'abc'), 3)
self.assertEqual(self.bd.get_raw_score('', ''), 0)
self.assertEqual(self.bd.get_raw_score('a', 'a'), 0)
self.assertEqual(self.bd.get_raw_score('abc', 'abc'), 0)
self.assertEqual(self.bd.get_raw_score('a', 'ab'), 1)
self.assertEqual(self.bd.get_raw_score('b', 'ab'), 1)
self.assertEqual(self.bd.get_raw_score('ac', 'abc'), 1)
self.assertEqual(self.bd.get_raw_score('abcdefg', 'xabxcdxxefxgx'), 6)
self.assertEqual(self.bd.get_raw_score('ab', 'a'), 1)
self.assertEqual(self.bd.get_raw_score('ab', 'b'), 1)
self.assertEqual(self.bd.get_raw_score('abc', 'ac'), 1)
self.assertEqual(self.bd.get_raw_score('xabxcdxxefxgx', 'abcdefg'), 6)
self.assertEqual(self.bd.get_raw_score('a', 'b'), 1)
self.assertEqual(self.bd.get_raw_score('ab', 'ac'), 1)
self.assertEqual(self.bd.get_raw_score('ac', 'bc'), 1)
self.assertEqual(self.bd.get_raw_score('abc', 'axc'), 1)
self.assertEqual(self.bd.get_raw_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 6)
self.assertEqual(self.bd.get_raw_score('example', 'samples'), 2)
self.assertEqual(self.bd.get_raw_score('sturgeon', 'urgently'), 2)
self.assertEqual(self.bd.get_raw_score('bag_distance', 'frankenstein'), 6)
self.assertEqual(self.bd.get_raw_score('distance', 'difference'), 5)
self.assertEqual(self.bd.get_raw_score('java was neat', 'scala is great'), 6)
def test_valid_input_sim_score(self):
self.assertEqual(self.bd.get_sim_score('a', ''), 0.0)
self.assertEqual(self.bd.get_sim_score('', 'a'), 0.0)
self.assertEqual(self.bd.get_sim_score('abc', ''), 0.0)
self.assertEqual(self.bd.get_sim_score('', 'abc'), 0.0)
self.assertEqual(self.bd.get_sim_score('', ''), 1.0)
self.assertEqual(self.bd.get_sim_score('a', 'a'), 1.0)
self.assertEqual(self.bd.get_sim_score('abc', 'abc'), 1.0)
self.assertEqual(self.bd.get_sim_score('a', 'ab'), 1.0 - (1.0/2.0))
self.assertEqual(self.bd.get_sim_score('b', 'ab'), 1.0 - (1.0/2.0))
self.assertEqual(self.bd.get_sim_score('ac', 'abc'), 1.0 - (1.0/3.0))
self.assertEqual(self.bd.get_sim_score('abcdefg', 'xabxcdxxefxgx'), 1.0 - (6.0/13.0))
self.assertEqual(self.bd.get_sim_score('ab', 'a'), 1.0 - (1.0/2.0))
self.assertEqual(self.bd.get_sim_score('ab', 'b'), 1.0 - (1.0/2.0))
self.assertEqual(self.bd.get_sim_score('abc', 'ac'), 1.0 - (1.0/3.0))
self.assertEqual(self.bd.get_sim_score('xabxcdxxefxgx', 'abcdefg'), 1.0 - (6.0/13.0))
self.assertEqual(self.bd.get_sim_score('a', 'b'), 0.0)
self.assertEqual(self.bd.get_sim_score('ab', 'ac'), 1.0 - (1.0/2.0))
self.assertEqual(self.bd.get_sim_score('ac', 'bc'), 1.0 - (1.0/2.0))
self.assertEqual(self.bd.get_sim_score('abc', 'axc'), 1.0 - (1.0/3.0))
self.assertEqual(self.bd.get_sim_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 1.0 - (6.0/13.0))
self.assertEqual(self.bd.get_sim_score('example', 'samples'), 1.0 - (2.0/7.0))
self.assertEqual(self.bd.get_sim_score('sturgeon', 'urgently'), 1.0 - (2.0/8.0))
self.assertEqual(self.bd.get_sim_score('bag_distance', 'frankenstein'), 1.0 - (6.0/12.0))
self.assertEqual(self.bd.get_sim_score('distance', 'difference'), 1.0 - (5.0/10.0))
self.assertEqual(self.bd.get_sim_score('java was neat', 'scala is great'), 1.0 - (6.0/14.0))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.bd.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.bd.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.bd.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.bd.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.bd.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.bd.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.bd.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.bd.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.bd.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.bd.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.bd.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.bd.get_sim_score(12.90, 12.90)
class EditexTestCases(unittest.TestCase):
def setUp(self):
self.ed = Editex()
self.ed_with_params1 = Editex(match_cost=2)
self.ed_with_params2 = Editex(mismatch_cost=2)
self.ed_with_params3 = Editex(mismatch_cost=1)
self.ed_with_params4 = Editex(mismatch_cost=3, group_cost=2)
self.ed_with_params5 = Editex(mismatch_cost=3, group_cost=2, local=True)
self.ed_with_params6 = Editex(local=True)
def test_get_match_cost(self):
self.assertEqual(self.ed_with_params1.get_match_cost(), 2)
def test_get_group_cost(self):
self.assertEqual(self.ed_with_params4.get_group_cost(), 2)
def test_get_mismatch_cost(self):
self.assertEqual(self.ed_with_params4.get_mismatch_cost(), 3)
def test_get_local(self):
self.assertEqual(self.ed_with_params5.get_local(), True)
def test_set_match_cost(self):
ed = Editex(match_cost=2)
self.assertEqual(ed.get_match_cost(), 2)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 12)
self.assertEqual(ed.set_match_cost(4), True)
self.assertEqual(ed.get_match_cost(), 4)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 14)
def test_set_group_cost(self):
ed = Editex(group_cost=1)
self.assertEqual(ed.get_group_cost(), 1)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 3)
self.assertEqual(ed.set_group_cost(2), True)
self.assertEqual(ed.get_group_cost(), 2)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 4)
def test_set_mismatch_cost(self):
ed = Editex(mismatch_cost=2)
self.assertEqual(ed.get_mismatch_cost(), 2)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 3)
self.assertEqual(ed.set_mismatch_cost(4), True)
self.assertEqual(ed.get_mismatch_cost(), 4)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 5)
def test_set_local(self):
ed = Editex(local=False)
self.assertEqual(ed.get_local(), False)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 3)
self.assertEqual(ed.set_local(True), True)
self.assertEqual(ed.get_local(), True)
self.assertAlmostEqual(ed.get_raw_score('MARTHA', 'MARHTA'), 3)
def test_valid_input_raw_score(self):
self.assertEqual(self.ed.get_raw_score('MARTHA', 'MARTHA'), 0)
self.assertEqual(self.ed.get_raw_score('MARTHA', 'MARHTA'), 3)
self.assertEqual(self.ed.get_raw_score('ALIE', 'ALI'), 1)
self.assertEqual(self.ed_with_params1.get_raw_score('ALIE', 'ALI'), 7)
self.assertEqual(self.ed_with_params2.get_raw_score('ALIE', 'ALIF'), 2)
self.assertEqual(self.ed_with_params3.get_raw_score('ALIE', 'ALIF'), 1)
self.assertEqual(self.ed_with_params4.get_raw_score('ALIP', 'ALIF'), 2)
self.assertEqual(self.ed_with_params4.get_raw_score('ALIe', 'ALIF'), 3)
self.assertEqual(self.ed_with_params5.get_raw_score('WALIW', 'HALIH'), 6)
self.assertEqual(self.ed_with_params6.get_raw_score('niall', 'nihal'), 2)
self.assertEqual(self.ed_with_params6.get_raw_score('nihal', 'niall'), 2)
self.assertEqual(self.ed_with_params6.get_raw_score('neal', 'nihl'), 3)
self.assertEqual(self.ed_with_params6.get_raw_score('nihl', 'neal'), 3)
self.assertEqual(self.ed.get_raw_score('', ''), 0)
self.assertEqual(self.ed.get_raw_score('', 'MARTHA'), 12)
self.assertEqual(self.ed.get_raw_score('MARTHA', ''), 12)
def test_valid_input_sim_score(self):
self.assertEqual(self.ed.get_sim_score('MARTHA', 'MARTHA'), 1.0)
self.assertEqual(self.ed.get_sim_score('MARTHA', 'MARHTA'), 1.0 - (3.0/12.0))
self.assertEqual(self.ed.get_sim_score('ALIE', 'ALI'), 1.0 - (1.0/8.0))
self.assertEqual(self.ed_with_params1.get_sim_score('ALIE', 'ALI'), 1.0 - (7.0/8.0))
self.assertEqual(self.ed_with_params2.get_sim_score('ALIE', 'ALIF'), 1.0 - (2.0/8.0))
self.assertEqual(self.ed_with_params3.get_sim_score('ALIE', 'ALIF'), 1.0 - (1.0/4.0))
self.assertEqual(self.ed_with_params4.get_sim_score('ALIP', 'ALIF'), 1.0 - (2.0/12.0))
self.assertEqual(self.ed_with_params4.get_sim_score('ALIe', 'ALIF'), 1.0 - (3.0/12.0))
self.assertEqual(self.ed_with_params5.get_sim_score('WALIW', 'HALIH'), 1.0 - (6.0/15.0))
self.assertEqual(self.ed_with_params6.get_sim_score('niall', 'nihal'), 1.0 - (2.0/10.0))
self.assertEqual(self.ed_with_params6.get_sim_score('nihal', 'niall'), 1.0 - (2.0/10.0))
self.assertEqual(self.ed_with_params6.get_sim_score('neal', 'nihl'), 1.0 - (3.0/8.0))
self.assertEqual(self.ed_with_params6.get_sim_score('nihl', 'neal'), 1.0 - (3.0/8.0))
self.assertEqual(self.ed.get_sim_score('', ''), 1.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.ed.get_raw_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.ed.get_raw_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.ed.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.ed.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.ed.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.ed.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.ed.get_sim_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.ed.get_sim_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.ed.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.ed.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.ed.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.ed.get_sim_score(12.90, 12.90)
class JaroTestCases(unittest.TestCase):
def setUp(self):
self.jaro = Jaro()
def test_valid_input_raw_score(self):
# https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance
self.assertAlmostEqual(self.jaro.get_raw_score('MARTHA', 'MARHTA'),
0.9444444444444445)
self.assertAlmostEqual(self.jaro.get_raw_score('DWAYNE', 'DUANE'),
0.8222222222222223)
self.assertAlmostEqual(self.jaro.get_raw_score('DIXON', 'DICKSONX'),
0.7666666666666666)
self.assertEqual(self.jaro.get_raw_score('', 'deeva'), 0)
def test_valid_input_sim_score(self):
self.assertAlmostEqual(self.jaro.get_sim_score('MARTHA', 'MARHTA'),
0.9444444444444445)
self.assertAlmostEqual(self.jaro.get_sim_score('DWAYNE', 'DUANE'),
0.8222222222222223)
self.assertAlmostEqual(self.jaro.get_sim_score('DIXON', 'DICKSONX'),
0.7666666666666666)
self.assertEqual(self.jaro.get_sim_score('', 'deeva'), 0)
def test_non_ascii_input_raw_score(self):
self.assertAlmostEqual(self.jaro.get_raw_score(u'MARTHA', u'MARHTA'),
0.9444444444444445)
self.assertAlmostEqual(self.jaro.get_raw_score(u'László', u'Lsáló'),
0.8777777777777779)
self.assertAlmostEqual(self.jaro.get_raw_score('László', 'Lsáló'),
0.8777777777777779)
self.assertAlmostEqual(self.jaro.get_raw_score(b'L\xc3\xa1szl\xc3\xb3',
b'Ls\xc3\xa1l\xc3\xb3'),
0.8777777777777779)
def test_non_ascii_input_sim_score(self):
self.assertAlmostEqual(self.jaro.get_sim_score(u'MARTHA', u'MARHTA'),
0.9444444444444445)
self.assertAlmostEqual(self.jaro.get_sim_score(u'László', u'Lsáló'),
0.8777777777777779)
self.assertAlmostEqual(self.jaro.get_sim_score('László', 'Lsáló'),
0.8777777777777779)
self.assertAlmostEqual(self.jaro.get_sim_score(b'L\xc3\xa1szl\xc3\xb3',
b'Ls\xc3\xa1l\xc3\xb3'),
0.8777777777777779)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.jaro.get_raw_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.jaro.get_raw_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.jaro.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.jaro.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.jaro.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.jaro.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.jaro.get_sim_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.jaro.get_sim_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.jaro.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.jaro.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.jaro.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.jaro.get_sim_score(12.90, 12.90)
class JaroWinklerTestCases(unittest.TestCase):
def setUp(self):
self.jw = JaroWinkler()
def test_get_prefix_weight(self):
self.assertEqual(self.jw.get_prefix_weight(), 0.1)
def test_set_prefix_weight(self):
jw = JaroWinkler(prefix_weight=0.15)
self.assertEqual(jw.get_prefix_weight(), 0.15)
self.assertAlmostEqual(jw.get_raw_score('MARTHA', 'MARHTA'), 0.9694444444444444)
self.assertEqual(jw.set_prefix_weight(0.25), True)
self.assertEqual(jw.get_prefix_weight(), 0.25)
self.assertAlmostEqual(jw.get_raw_score('MARTHA', 'MARHTA'), 0.9861111111111112)
def test_valid_input_raw_score(self):
# https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance
self.assertAlmostEqual(self.jw.get_raw_score('MARTHA', 'MARHTA'),
0.9611111111111111)
self.assertAlmostEqual(self.jw.get_raw_score('DWAYNE', 'DUANE'), 0.84)
self.assertAlmostEqual(self.jw.get_raw_score('DIXON', 'DICKSONX'),
0.8133333333333332)
def test_valid_input_sim_score(self):
self.assertAlmostEqual(self.jw.get_sim_score('MARTHA', 'MARHTA'),
0.9611111111111111)
self.assertAlmostEqual(self.jw.get_sim_score('DWAYNE', 'DUANE'), 0.84)
self.assertAlmostEqual(self.jw.get_sim_score('DIXON', 'DICKSONX'),
0.8133333333333332)
def test_non_ascii_input_raw_score(self):
self.assertAlmostEqual(self.jw.get_raw_score(u'MARTHA', u'MARHTA'),
0.9611111111111111)
self.assertAlmostEqual(self.jw.get_raw_score(u'László', u'Lsáló'),
0.8900000000000001)
self.assertAlmostEqual(self.jw.get_raw_score('László', 'Lsáló'),
0.8900000000000001)
self.assertAlmostEqual(self.jw.get_raw_score(b'L\xc3\xa1szl\xc3\xb3',
b'Ls\xc3\xa1l\xc3\xb3'),
0.8900000000000001)
def test_non_ascii_input_sim_score(self):
self.assertAlmostEqual(self.jw.get_sim_score(u'MARTHA', u'MARHTA'),
0.9611111111111111)
self.assertAlmostEqual(self.jw.get_sim_score(u'László', u'Lsáló'),
0.8900000000000001)
self.assertAlmostEqual(self.jw.get_sim_score('László', 'Lsáló'),
0.8900000000000001)
self.assertAlmostEqual(self.jw.get_sim_score(b'L\xc3\xa1szl\xc3\xb3',
b'Ls\xc3\xa1l\xc3\xb3'),
0.8900000000000001)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.jw.get_raw_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.jw.get_raw_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.jw.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.jw.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.jw.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.jw.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.jw.get_sim_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.jw.get_sim_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.jw.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.jw.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.jw.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.jw.get_sim_score(12.90, 12.90)
class LevenshteinTestCases(unittest.TestCase):
def setUp(self):
self.lev = Levenshtein()
def test_valid_input_raw_score(self):
# http://oldfashionedsoftware.com/tag/levenshtein-distance/
self.assertEqual(self.lev.get_raw_score('a', ''), 1)
self.assertEqual(self.lev.get_raw_score('', 'a'), 1)
self.assertEqual(self.lev.get_raw_score('abc', ''), 3)
self.assertEqual(self.lev.get_raw_score('', 'abc'), 3)
self.assertEqual(self.lev.get_raw_score('', ''), 0)
self.assertEqual(self.lev.get_raw_score('a', 'a'), 0)
self.assertEqual(self.lev.get_raw_score('abc', 'abc'), 0)
self.assertEqual(self.lev.get_raw_score('a', 'ab'), 1)
self.assertEqual(self.lev.get_raw_score('b', 'ab'), 1)
self.assertEqual(self.lev.get_raw_score('ac', 'abc'), 1)
self.assertEqual(self.lev.get_raw_score('abcdefg', 'xabxcdxxefxgx'), 6)
self.assertEqual(self.lev.get_raw_score('ab', 'a'), 1)
self.assertEqual(self.lev.get_raw_score('ab', 'b'), 1)
self.assertEqual(self.lev.get_raw_score('abc', 'ac'), 1)
self.assertEqual(self.lev.get_raw_score('xabxcdxxefxgx', 'abcdefg'), 6)
self.assertEqual(self.lev.get_raw_score('a', 'b'), 1)
self.assertEqual(self.lev.get_raw_score('ab', 'ac'), 1)
self.assertEqual(self.lev.get_raw_score('ac', 'bc'), 1)
self.assertEqual(self.lev.get_raw_score('abc', 'axc'), 1)
self.assertEqual(self.lev.get_raw_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 6)
self.assertEqual(self.lev.get_raw_score('example', 'samples'), 3)
self.assertEqual(self.lev.get_raw_score('sturgeon', 'urgently'), 6)
self.assertEqual(self.lev.get_raw_score('levenshtein', 'frankenstein'), 6)
self.assertEqual(self.lev.get_raw_score('distance', 'difference'), 5)
self.assertEqual(self.lev.get_raw_score('java was neat', 'scala is great'), 7)
def test_valid_input_sim_score(self):
self.assertEqual(self.lev.get_sim_score('a', ''), 1.0 - (1.0/1.0))
self.assertEqual(self.lev.get_sim_score('', 'a'), 1.0 - (1.0/1.0))
self.assertEqual(self.lev.get_sim_score('abc', ''), 1.0 - (3.0/3.0))
self.assertEqual(self.lev.get_sim_score('', 'abc'), 1.0 - (3.0/3.0))
self.assertEqual(self.lev.get_sim_score('', ''), 1.0)
self.assertEqual(self.lev.get_sim_score('a', 'a'), 1.0)
self.assertEqual(self.lev.get_sim_score('abc', 'abc'), 1.0)
self.assertEqual(self.lev.get_sim_score('a', 'ab'), 1.0 - (1.0/2.0))
self.assertEqual(self.lev.get_sim_score('b', 'ab'), 1.0 - (1.0/2.0))
self.assertEqual(self.lev.get_sim_score('ac', 'abc'), 1.0 - (1.0/3.0))
self.assertEqual(self.lev.get_sim_score('abcdefg', 'xabxcdxxefxgx'), 1.0 - (6.0/13.0))
self.assertEqual(self.lev.get_sim_score('ab', 'a'), 1.0 - (1.0/2.0))
self.assertEqual(self.lev.get_sim_score('ab', 'b'), 1.0 - (1.0/2.0))
self.assertEqual(self.lev.get_sim_score('abc', 'ac'), 1.0 - (1.0/3.0))
self.assertEqual(self.lev.get_sim_score('xabxcdxxefxgx', 'abcdefg'), 1.0 - (6.0/13.0))
self.assertEqual(self.lev.get_sim_score('a', 'b'), 1.0 - (1.0/1.0))
self.assertEqual(self.lev.get_sim_score('ab', 'ac'), 1.0 - (1.0/2.0))
self.assertEqual(self.lev.get_sim_score('ac', 'bc'), 1.0 - (1.0/2.0))
self.assertEqual(self.lev.get_sim_score('abc', 'axc'), 1.0 - (1.0/3.0))
self.assertEqual(self.lev.get_sim_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 1.0 - (6.0/13.0))
self.assertEqual(self.lev.get_sim_score('example', 'samples'), 1.0 - (3.0/7.0))
self.assertEqual(self.lev.get_sim_score('sturgeon', 'urgently'), 1.0 - (6.0/8.0))
self.assertEqual(self.lev.get_sim_score('levenshtein', 'frankenstein'), 1.0 - (6.0/12.0))
self.assertEqual(self.lev.get_sim_score('distance', 'difference'), 1.0 - (5.0/10.0))
self.assertEqual(self.lev.get_sim_score('java was neat', 'scala is great'),
1.0 - (7.0/14.0))
def test_valid_input_non_ascii_raw_score(self):
self.assertEqual(self.lev.get_raw_score('ác', 'áóc'), 1)
self.assertEqual(self.lev.get_raw_score(u'ác', u'áóc'), 1)
self.assertEqual(self.lev.get_raw_score(b'\xc3\xa1c', b'\xc3\xa1\xc3\xb3c'), 1)
def test_valid_input_non_ascii_sim_score(self):
self.assertEqual(self.lev.get_sim_score('ác', 'áóc'), 1.0 - (1.0/3.0))
self.assertEqual(self.lev.get_sim_score(u'ác', u'áóc'),
1.0 - (1.0/3.0))
self.assertEqual(self.lev.get_sim_score(b'\xc3\xa1c',
b'\xc3\xa1\xc3\xb3c'), 1.0 - (1.0/3.0))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.lev.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.lev.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.lev.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.lev.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.lev.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.lev.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.lev.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.lev.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.lev.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.lev.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.lev.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.lev.get_sim_score(12.90, 12.90)
class HammingDistanceTestCases(unittest.TestCase):
def setUp(self):
self.hd = HammingDistance()
def test_valid_input_raw_score(self):
self.assertEqual(self.hd.get_raw_score('-789', 'john'), 4)
self.assertEqual(self.hd.get_raw_score('a', '*'), 1)
self.assertEqual(self.hd.get_raw_score('b', 'a'), 1)
self.assertEqual(self.hd.get_raw_score('abc', 'p q'), 3)
self.assertEqual(self.hd.get_raw_score('karolin', 'kathrin'), 3)
self.assertEqual(self.hd.get_raw_score('KARI', 'kari'), 4)
self.assertEqual(self.hd.get_raw_score('', ''), 0)
def test_valid_input_sim_score(self):
self.assertEqual(self.hd.get_sim_score('-789', 'john'), 1.0 - (4.0/4.0))
self.assertEqual(self.hd.get_sim_score('a', '*'), 1.0 - (1.0/1.0))
self.assertEqual(self.hd.get_sim_score('b', 'a'), 1.0 - (1.0/1.0))
self.assertEqual(self.hd.get_sim_score('abc', 'p q'), 1.0 - (3.0/3.0))
self.assertEqual(self.hd.get_sim_score('karolin', 'kathrin'), 1.0 - (3.0/7.0))
self.assertEqual(self.hd.get_sim_score('KARI', 'kari'), 1.0 - (4.0/4.0))
self.assertEqual(self.hd.get_sim_score('', ''), 1.0)
def test_valid_input_compatibility_raw_score(self):
self.assertEqual(self.hd.get_raw_score(u'karolin', u'kathrin'), 3)
self.assertEqual(self.hd.get_raw_score(u'', u''), 0)
# str_1 = u'foo'.encode(encoding='UTF-8', errors='strict')
# str_2 = u'bar'.encode(encoding='UTF-8', errors='strict')
# self.assertEqual(self.hd.get_raw_score(str_1, str_2), 3) # check with Ali - python 3 returns type error
# self.assertEqual(self.hd.get_raw_score(str_1, str_1), 0) # check with Ali - python 3 returns type error
def test_valid_input_compatibility_sim_score(self):
self.assertEqual(self.hd.get_sim_score(u'karolin', u'kathrin'), 1.0 - (3.0/7.0))
self.assertEqual(self.hd.get_sim_score(u'', u''), 1.0)
def test_valid_input_non_ascii_raw_score(self):
self.assertEqual(self.hd.get_raw_score(u'ábó', u'áóó'), 1)
self.assertEqual(self.hd.get_raw_score('ábó', 'áóó'), 1)
self.assertEqual(self.hd.get_raw_score(b'\xc3\xa1b\xc3\xb3',
b'\xc3\xa1\xc3\xb3\xc3\xb3'),
1)
def test_valid_input_non_ascii_sim_score(self):
self.assertEqual(self.hd.get_sim_score(u'ábó', u'áóó'), 1.0 - (1.0/3.0))
self.assertEqual(self.hd.get_sim_score('ábó', 'áóó'), 1.0 - (1.0/3.0))
self.assertEqual(self.hd.get_sim_score(b'\xc3\xa1b\xc3\xb3',
b'\xc3\xa1\xc3\xb3\xc3\xb3'),
1.0 - (1.0/3.0))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.hd.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.hd.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.hd.get_raw_score(None, None)
@raises(ValueError)
def test_invalid_input4_raw_score(self):
self.hd.get_raw_score('a', '')
@raises(ValueError)
def test_invalid_input5_raw_score(self):
self.hd.get_raw_score('', 'This is a long string')
@raises(ValueError)
def test_invalid_input6_raw_score(self):
self.hd.get_raw_score('ali', 'alex')
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.hd.get_raw_score('MA', 12)
@raises(TypeError)
def test_invalid_input8_raw_score(self):
self.hd.get_raw_score(12, 'MA')
@raises(TypeError)
def test_invalid_input9_raw_score(self):
self.hd.get_raw_score(12, 12)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.hd.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.hd.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.hd.get_sim_score(None, None)
@raises(ValueError)
def test_invalid_input4_sim_score(self):
self.hd.get_sim_score('a', '')
@raises(ValueError)
def test_invalid_input5_sim_score(self):
self.hd.get_sim_score('', 'This is a long string')
@raises(ValueError)
def test_invalid_input6_sim_score(self):
self.hd.get_sim_score('ali', 'alex')
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.hd.get_sim_score('MA', 12)
@raises(TypeError)
def test_invalid_input8_sim_score(self):
self.hd.get_sim_score(12, 'MA')
@raises(TypeError)
def test_invalid_input9_sim_score(self):
self.hd.get_sim_score(12, 12)
class NeedlemanWunschTestCases(unittest.TestCase):
def setUp(self):
self.nw = NeedlemanWunsch()
self.nw_with_params1 = NeedlemanWunsch(0.0)
self.nw_with_params2 = NeedlemanWunsch(1.0, sim_func=lambda s1, s2: (2 if s1 == s2 else -1))
self.sim_func = lambda s1, s2: (1 if s1 == s2 else -1)
self.nw_with_params3 = NeedlemanWunsch(gap_cost=0.5, sim_func=self.sim_func)
def test_get_gap_cost(self):
self.assertEqual(self.nw_with_params3.get_gap_cost(), 0.5)
def test_get_sim_func(self):
self.assertEqual(self.nw_with_params3.get_sim_func(), self.sim_func)
def test_set_gap_cost(self):
nw = NeedlemanWunsch(gap_cost=0.5)
self.assertEqual(nw.get_gap_cost(), 0.5)
self.assertAlmostEqual(nw.get_raw_score('dva', 'deeva'), 2.0)
self.assertEqual(nw.set_gap_cost(0.7), True)
self.assertEqual(nw.get_gap_cost(), 0.7)
self.assertAlmostEqual(nw.get_raw_score('dva', 'deeva'), 1.6000000000000001)
def test_set_sim_func(self):
fn1 = lambda s1, s2: (int(1 if s1 == s2 else 0))
fn2 = lambda s1, s2: (int(2 if s1 == s2 else -1))
nw = NeedlemanWunsch(sim_func=fn1)
self.assertEqual(nw.get_sim_func(), fn1)
self.assertAlmostEqual(nw.get_raw_score('dva', 'deeva'), 1.0)
self.assertEqual(nw.set_sim_func(fn2), True)
self.assertEqual(nw.get_sim_func(), fn2)
self.assertAlmostEqual(nw.get_raw_score('dva', 'deeva'), 4.0)
def test_valid_input(self):
self.assertEqual(self.nw.get_raw_score('dva', 'deeva'), 1.0)
self.assertEqual(self.nw_with_params1.get_raw_score('dva', 'deeve'), 2.0)
self.assertEqual(self.nw_with_params2.get_raw_score('dva', 'deeve'), 1.0)
self.assertEqual(self.nw_with_params3.get_raw_score('GCATGCUA', 'GATTACA'),
2.5)
def test_valid_input_non_ascii(self):
self.assertEqual(self.nw.get_raw_score(u'dva', u'dáóva'), 1.0)
self.assertEqual(self.nw.get_raw_score('dva', 'dáóva'), 1.0)
self.assertEqual(self.nw.get_raw_score('dva', b'd\xc3\xa1\xc3\xb3va'), 1.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.nw.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.nw.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.nw.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.nw.get_raw_score(['a'], 'b')
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.nw.get_raw_score('a', ['b'])
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.nw.get_raw_score(['a'], ['b'])
class SmithWatermanTestCases(unittest.TestCase):
def setUp(self):
self.sw = SmithWaterman()
self.sw_with_params1 = SmithWaterman(2.2)
self.sw_with_params2 = SmithWaterman(1, sim_func=lambda s1, s2:(2 if s1 == s2 else -1))
self.sw_with_params3 = SmithWaterman(gap_cost=1, sim_func=lambda s1, s2:(int(1 if s1 == s2 else -1)))
self.sim_func = lambda s1, s2: (1.5 if s1 == s2 else 0.5)
self.sw_with_params4 = SmithWaterman(gap_cost=1.4, sim_func=self.sim_func)
def test_get_gap_cost(self):
self.assertEqual(self.sw_with_params4.get_gap_cost(), 1.4)
def test_get_sim_func(self):
self.assertEqual(self.sw_with_params4.get_sim_func(), self.sim_func)
def test_set_gap_cost(self):
sw = SmithWaterman(gap_cost=0.3)
self.assertEqual(sw.get_gap_cost(), 0.3)
self.assertAlmostEqual(sw.get_raw_score('dva', 'deeva'), 2.3999999999999999)
self.assertEqual(sw.set_gap_cost(0.7), True)
self.assertEqual(sw.get_gap_cost(), 0.7)
self.assertAlmostEqual(sw.get_raw_score('dva', 'deeva'), 2.0)
def test_set_sim_func(self):
fn1 = lambda s1, s2: (int(1 if s1 == s2 else 0))
fn2 = lambda s1, s2: (int(2 if s1 == s2 else -1))
sw = SmithWaterman(sim_func=fn1)
self.assertEqual(sw.get_sim_func(), fn1)
self.assertAlmostEqual(sw.get_raw_score('dva', 'deeva'), 2.0)
self.assertEqual(sw.set_sim_func(fn2), True)
self.assertEqual(sw.get_sim_func(), fn2)
self.assertAlmostEqual(sw.get_raw_score('dva', 'deeva'), 4.0)
def test_valid_input(self):
self.assertEqual(self.sw.get_raw_score('cat', 'hat'), 2.0)
self.assertEqual(self.sw_with_params1.get_raw_score('dva', 'deeve'), 1.0)
self.assertEqual(self.sw_with_params2.get_raw_score('dva', 'deeve'), 2.0)
self.assertEqual(self.sw_with_params3.get_raw_score('GCATGCU', 'GATTACA'),
2.0)
self.assertEqual(self.sw_with_params4.get_raw_score('GCATAGCU', 'GATTACA'),
6.5)
def test_valid_input_non_ascii(self):
self.assertEqual(self.sw.get_raw_score(u'óát', u'cát'), 2.0)
self.assertEqual(self.sw.get_raw_score('óát', 'cát'), 2.0)
self.assertEqual(self.sw.get_raw_score(b'\xc3\xb3\xc3\xa1t', b'c\xc3\xa1t'),
2.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.sw.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.sw.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.sw.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.sw.get_raw_score('MARHTA', 12)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.sw.get_raw_score(12, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.sw.get_raw_score(12, 12)
class SoundexTestCases(unittest.TestCase):
def setUp(self):
self.sdx = Soundex()
def test_valid_input_raw_score(self):
self.assertEqual(self.sdx.get_raw_score('Robert', 'Rupert'), 1)
self.assertEqual(self.sdx.get_raw_score('Sue', 'S'), 1)
self.assertEqual(self.sdx.get_raw_score('robert', 'rupert'), 1)
self.assertEqual(self.sdx.get_raw_score('Gough', 'goff'), 0)
self.assertEqual(self.sdx.get_raw_score('gough', 'Goff'), 0)
self.assertEqual(self.sdx.get_raw_score('ali', 'a,,,li'), 1)
self.assertEqual(self.sdx.get_raw_score('Jawornicki', 'Yavornitzky'), 0)
self.assertEqual(self.sdx.get_raw_score('Robert', 'Robert'), 1)
def test_valid_input_sim_score(self):
self.assertEqual(self.sdx.get_sim_score('Robert', 'Rupert'), 1)
self.assertEqual(self.sdx.get_sim_score('Sue', 'S'), 1)
self.assertEqual(self.sdx.get_sim_score('robert', 'rupert'), 1)
self.assertEqual(self.sdx.get_sim_score('Gough', 'goff'), 0)
self.assertEqual(self.sdx.get_sim_score('gough', 'Goff'), 0)
self.assertEqual(self.sdx.get_sim_score('ali', 'a,,,li'), 1)
self.assertEqual(self.sdx.get_sim_score('Jawornicki', 'Yavornitzky'), 0)
self.assertEqual(self.sdx.get_sim_score('Robert', 'Robert'), 1)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.sdx.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.sdx.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.sdx.get_raw_score(None, None)
@raises(ValueError)
def test_invalid_input4_raw_score(self):
self.sdx.get_raw_score('a', '')
@raises(ValueError)
def test_invalid_input5_raw_score(self):
self.sdx.get_raw_score('', 'This is a long string')
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.sdx.get_raw_score('xyz', [''])
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.sdx.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.sdx.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.sdx.get_sim_score(None, None)
@raises(ValueError)
def test_invalid_input4_sim_score(self):
self.sdx.get_sim_score('a', '')
@raises(ValueError)
def test_invalid_input5_sim_score(self):
self.sdx.get_sim_score('', 'This is a long string')
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.sdx.get_sim_score('xyz', [''])
# ---------------------- token based similarity measures ----------------------
# ---------------------- set based similarity measures ----------------------
class OverlapCoefficientTestCases(unittest.TestCase):
def setUp(self):
self.oc = OverlapCoefficient()
def test_valid_input_raw_score(self):
self.assertEqual(self.oc.get_raw_score([], []), 1.0)
self.assertEqual(self.oc.get_raw_score(['data', 'science'], ['data']),
1.0 / min(2.0, 1.0))
self.assertEqual(self.oc.get_raw_score(['data', 'science'],
['science', 'good']), 1.0 / min(2.0, 3.0))
self.assertEqual(self.oc.get_raw_score([], ['data']), 0)
self.assertEqual(self.oc.get_raw_score(['data', 'data', 'science'],
['data', 'management']), 1.0 / min(3.0, 2.0))
def test_valid_input_raw_score_set_inp(self):
self.assertEqual(self.oc.get_raw_score(set(['data', 'science']), set(['data'])),
1.0 / min(2.0, 1.0))
def test_valid_input_sim_score(self):
self.assertEqual(self.oc.get_sim_score([], []), 1.0)
self.assertEqual(self.oc.get_sim_score(['data', 'science'], ['data']),
1.0 / min(2.0, 1.0))
self.assertEqual(self.oc.get_sim_score(['data', 'science'],
['science', 'good']), 1.0 / min(2.0, 3.0))
self.assertEqual(self.oc.get_sim_score([], ['data']), 0)
self.assertEqual(self.oc.get_sim_score(['data', 'data', 'science'],
['data', 'management']), 1.0 / min(3.0, 2.0))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.oc.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.oc.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.oc.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.oc.get_raw_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.oc.get_raw_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.oc.get_raw_score('MARTHA', 'MARTHA')
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.oc.get_sim_score(['a'], None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.oc.get_sim_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.oc.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.oc.get_sim_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.oc.get_sim_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.oc.get_sim_score('MARTHA', 'MARTHA')
class DiceTestCases(unittest.TestCase):
def setUp(self):
self.dice = Dice()
def test_valid_input_raw_score(self):
self.assertEqual(self.dice.get_raw_score(['data', 'science'], ['data']),
2 * 1.0 / 3.0)
self.assertEqual(self.dice.get_raw_score(['data', 'science'], ['science', 'good']),
2 * 1.0 / 4.0)
self.assertEqual(self.dice.get_raw_score([], ['data']), 0)
self.assertEqual(self.dice.get_raw_score(['data', 'data', 'science'],
['data', 'management']), 2 * 1.0 / 4.0)
self.assertEqual(self.dice.get_raw_score(['data', 'management'],
['data', 'data', 'science']), 2 * 1.0 / 4.0)
self.assertEqual(self.dice.get_raw_score([], []), 1.0)
self.assertEqual(self.dice.get_raw_score(['a', 'b'], ['b', 'a']), 1.0)
self.assertEqual(self.dice.get_raw_score(set([]), set([])), 1.0)
self.assertEqual(self.dice.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
2 * 3.0 / 11.0)
def test_valid_input_sim_score(self):
self.assertEqual(self.dice.get_sim_score(['data', 'science'], ['data']),
2 * 1.0 / 3.0)
self.assertEqual(self.dice.get_sim_score(['data', 'science'], ['science', 'good']),
2 * 1.0 / 4.0)
self.assertEqual(self.dice.get_sim_score([], ['data']), 0)
self.assertEqual(self.dice.get_sim_score(['data', 'data', 'science'],
['data', 'management']), 2 * 1.0 / 4.0)
self.assertEqual(self.dice.get_sim_score(['data', 'management'],
['data', 'data', 'science']), 2 * 1.0 / 4.0)
self.assertEqual(self.dice.get_sim_score([], []), 1.0)
self.assertEqual(self.dice.get_sim_score(['a', 'b'], ['b', 'a']), 1.0)
self.assertEqual(self.dice.get_sim_score(set([]), set([])), 1.0)
self.assertEqual(self.dice.get_sim_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
2 * 3.0 / 11.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.dice.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.dice.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.dice.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.dice.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.dice.get_raw_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.dice.get_raw_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.dice.get_raw_score('MARHTA', 'MARTHA')
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.dice.get_sim_score(1, 1)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.dice.get_sim_score(['a'], None)
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.dice.get_sim_score(None, ['b'])
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.dice.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.dice.get_sim_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.dice.get_sim_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.dice.get_sim_score('MARHTA', 'MARTHA')
class JaccardTestCases(unittest.TestCase):
def setUp(self):
self.jac = Jaccard()
def test_valid_input_raw_score(self):
self.assertEqual(self.jac.get_raw_score(['data', 'science'], ['data']),
1.0 / 2.0)
self.assertEqual(self.jac.get_raw_score(['data', 'science'],
['science', 'good']), 1.0 / 3.0)
self.assertEqual(self.jac.get_raw_score([], ['data']), 0)
self.assertEqual(self.jac.get_raw_score(['data', 'data', 'science'],
['data', 'management']), 1.0 / 3.0)
self.assertEqual(self.jac.get_raw_score(['data', 'management'],
['data', 'data', 'science']), 1.0 / 3.0)
self.assertEqual(self.jac.get_raw_score([], []), 1.0)
self.assertEqual(self.jac.get_raw_score(set([]),
set([])), 1.0)
self.assertEqual(self.jac.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}), 3.0 / 8.0)
def test_valid_input_sim_score(self):
self.assertEqual(self.jac.get_sim_score(['data', 'science'], ['data']),
1.0 / 2.0)
self.assertEqual(self.jac.get_sim_score(['data', 'science'],
['science', 'good']), 1.0 / 3.0)
self.assertEqual(self.jac.get_sim_score([], ['data']), 0)
self.assertEqual(self.jac.get_sim_score(['data', 'data', 'science'],
['data', 'management']), 1.0 / 3.0)
self.assertEqual(self.jac.get_sim_score(['data', 'management'],
['data', 'data', 'science']), 1.0 / 3.0)
self.assertEqual(self.jac.get_sim_score([], []), 1.0)
self.assertEqual(self.jac.get_sim_score(set([]), set([])), 1.0)
self.assertEqual(self.jac.get_sim_score({1, 1, 2, 3, 4},
{2, 3, 4, 5, 6, 7, 7, 8}), 3.0 / 8.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.jac.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.jac.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.jac.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.jac.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.jac.get_raw_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.jac.get_raw_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.jac.get_raw_score('MARTHA', 'MARTHA')
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.jac.get_sim_score(1, 1)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.jac.get_sim_score(['a'], None)
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.jac.get_sim_score(None, ['b'])
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.jac.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.jac.get_sim_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.jac.get_sim_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.jac.get_sim_score('MARTHA', 'MARTHA')
# Modified test cases to overcome the decimal points matching
class GeneralizedJaccardTestCases(unittest.TestCase):
def setUp(self):
self.gen_jac = GeneralizedJaccard()
self.jw_fn = JaroWinkler().get_raw_score
self.gen_jac_with_jw = GeneralizedJaccard(sim_func=self.jw_fn)
self.gen_jac_with_jw_08 = GeneralizedJaccard(sim_func=self.jw_fn,
threshold=0.8)
self.gen_jac_invalid = GeneralizedJaccard(sim_func=NeedlemanWunsch().get_raw_score,
threshold=0.8)
def test_get_sim_func(self):
self.assertEqual(self.gen_jac_with_jw_08.get_sim_func(), self.jw_fn)
def test_get_threshold(self):
self.assertEqual(self.gen_jac_with_jw_08.get_threshold(), 0.8)
def test_set_threshold(self):
gj = GeneralizedJaccard(threshold=0.8)
self.assertEqual(gj.get_threshold(), 0.8)
self.assertAlmostEqual(round(gj.get_raw_score(['Niall'], ['Neal', 'Njall']), NUMBER_OF_DECIMAL_PLACES),
round(0.43333333333333335, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(gj.set_threshold(0.9), True)
self.assertEqual(gj.get_threshold(), 0.9)
self.assertAlmostEqual(gj.get_raw_score(['Niall'], ['Neal', 'Njall']), 0.0)
def test_set_sim_func(self):
fn1 = JaroWinkler().get_raw_score
fn2 = Jaro().get_raw_score
gj = GeneralizedJaccard(sim_func=fn1)
self.assertEqual(gj.get_sim_func(), fn1)
self.assertAlmostEqual(gj.get_raw_score(['Niall'], ['Neal', 'Njall']), 0.44)
self.assertEqual(gj.set_sim_func(fn2), True)
self.assertEqual(gj.get_sim_func(), fn2)
self.assertAlmostEqual(round(gj.get_raw_score(['Niall'], ['Neal', 'Njall']), NUMBER_OF_DECIMAL_PLACES),
round(0.43333333333333335, NUMBER_OF_DECIMAL_PLACES))
def test_valid_input_raw_score(self):
self.assertEqual(self.gen_jac.get_raw_score([''], ['']), 1.0) # need to check this
self.assertEqual(self.gen_jac.get_raw_score([''], ['a']), 0.0)
self.assertEqual(self.gen_jac.get_raw_score(['a'], ['a']), 1.0)
self.assertEqual(self.gen_jac.get_raw_score([], ['Nigel']), 0.0)
self.assertEqual(round(self.gen_jac.get_raw_score(['Niall'], ['Neal']), NUMBER_OF_DECIMAL_PLACES),
round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_raw_score(['Niall'], ['Njall', 'Neal']), NUMBER_OF_DECIMAL_PLACES),
round(0.43333333333333335, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_raw_score(['Niall'], ['Neal', 'Njall']), NUMBER_OF_DECIMAL_PLACES),
round(0.43333333333333335, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_raw_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.6800468975468975, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac_with_jw.get_raw_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.7220003607503608, NUMBER_OF_DECIMAL_PLACES ))
self.assertEqual(round(self.gen_jac_with_jw.get_raw_score(
['Comp', 'Sci.', 'and', 'Engr', 'Dept.,', 'Universty', 'of', 'Cal,', 'San', 'Deigo'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.7075277777777778, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac_with_jw_08.get_raw_score(
['Comp', 'Sci.', 'and', 'Engr', 'Dept.,', 'Universty', 'of', 'Cal,', 'San', 'Deigo'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.45810185185185187, NUMBER_OF_DECIMAL_PLACES))
def test_valid_input_sim_score(self):
self.assertEqual(self.gen_jac.get_sim_score([''], ['']), 1.0) # need to check this
self.assertEqual(self.gen_jac.get_sim_score([''], ['a']), 0.0)
self.assertEqual(self.gen_jac.get_sim_score(['a'], ['a']), 1.0)
self.assertEqual(self.gen_jac.get_sim_score([], ['Nigel']), 0.0)
self.assertEqual(round(self.gen_jac.get_sim_score(['Niall'], ['Neal']), NUMBER_OF_DECIMAL_PLACES),
round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_sim_score(['Niall'], ['Njall', 'Neal']), NUMBER_OF_DECIMAL_PLACES),
round(0.43333333333333335, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_sim_score(['Niall'], ['Neal', 'Njall']), NUMBER_OF_DECIMAL_PLACES),
round(0.43333333333333335, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_sim_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.6800468975468975, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac_with_jw.get_sim_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES),round(0.7220003607503608, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac_with_jw.get_sim_score(
['Comp', 'Sci.', 'and', 'Engr', 'Dept.,', 'Universty', 'of', 'Cal,', 'San', 'Deigo'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.7075277777777778, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac_with_jw_08.get_sim_score(
['Comp', 'Sci.', 'and', 'Engr', 'Dept.,', 'Universty', 'of', 'Cal,', 'San', 'Deigo'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.45810185185185187, NUMBER_OF_DECIMAL_PLACES))
def test_valid_input_non_ascii_raw_score(self):
self.assertEqual(round(self.gen_jac.get_raw_score([u'Nóáll'], [u'Neál']), NUMBER_OF_DECIMAL_PLACES),
round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_raw_score(['Nóáll'], ['Neál']), NUMBER_OF_DECIMAL_PLACES),
round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_raw_score([b'N\xc3\xb3\xc3\xa1ll'], [b'Ne\xc3\xa1l']),
NUMBER_OF_DECIMAL_PLACES), round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
def test_valid_input_non_ascii_sim_score(self):
self.assertEqual(round(self.gen_jac.get_sim_score([u'Nóáll'], [u'Neál']), NUMBER_OF_DECIMAL_PLACES),
round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_sim_score(['Nóáll'], ['Neál']), NUMBER_OF_DECIMAL_PLACES),
round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.gen_jac.get_sim_score([b'N\xc3\xb3\xc3\xa1ll'], [b'Ne\xc3\xa1l']),
NUMBER_OF_DECIMAL_PLACES), round(0.7833333333333333, NUMBER_OF_DECIMAL_PLACES))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.gen_jac.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.gen_jac.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.gen_jac.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.gen_jac.get_raw_score("temp", "temp")
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.gen_jac.get_raw_score(['temp'], 'temp')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.gen_jac.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.gen_jac.get_raw_score('temp', ['temp'])
@raises(ValueError)
def test_invalid_sim_measure(self):
self.gen_jac_invalid.get_raw_score(
['Comp', 'Sci.', 'and', 'Engr', 'Dept.,', 'Universty', 'of', 'Cal,', 'San', 'Deigo'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego'])
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.gen_jac.get_sim_score(1, 1)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.gen_jac.get_sim_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.gen_jac.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.gen_jac.get_sim_score("temp", "temp")
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.gen_jac.get_sim_score(['temp'], 'temp')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.gen_jac.get_sim_score(['a'], None)
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.gen_jac.get_sim_score('temp', ['temp'])
@raises(ValueError)
def test_invalid_sim_measure_sim_score(self):
self.gen_jac_invalid.get_sim_score(
['Comp', 'Sci.', 'and', 'Engr', 'Dept.,', 'Universty', 'of', 'Cal,', 'San', 'Deigo'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego'])
class CosineTestCases(unittest.TestCase):
def setUp(self):
self.cos = Cosine()
def test_valid_input_raw_score(self):
self.assertEqual(self.cos.get_raw_score(['data', 'science'], ['data']), 1.0 / (math.sqrt(2) * math.sqrt(1)))
self.assertEqual(self.cos.get_raw_score(['data', 'science'], ['science', 'good']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(self.cos.get_raw_score([], ['data']), 0.0)
self.assertEqual(self.cos.get_raw_score(['data', 'data', 'science'], ['data', 'management']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(self.cos.get_raw_score(['data', 'management'], ['data', 'data', 'science']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(self.cos.get_raw_score([], []), 1.0)
self.assertEqual(self.cos.get_raw_score(set([]), set([])), 1.0)
self.assertEqual(self.cos.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
3.0 / (math.sqrt(4) * math.sqrt(7)))
def test_valid_input_sim_score(self):
self.assertEqual(self.cos.get_sim_score(['data', 'science'], ['data']), 1.0 / (math.sqrt(2) * math.sqrt(1)))
self.assertEqual(self.cos.get_sim_score(['data', 'science'], ['science', 'good']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(self.cos.get_sim_score([], ['data']), 0.0)
self.assertEqual(self.cos.get_sim_score(['data', 'data', 'science'], ['data', 'management']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(self.cos.get_sim_score(['data', 'management'], ['data', 'data', 'science']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(self.cos.get_sim_score([], []), 1.0)
self.assertEqual(self.cos.get_sim_score(set([]), set([])), 1.0)
self.assertEqual(self.cos.get_sim_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
3.0 / (math.sqrt(4) * math.sqrt(7)))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.cos.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.cos.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.cos.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.cos.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.cos.get_raw_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.cos.get_raw_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.cos.get_raw_score('MARTHA', 'MARTHA')
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.cos.get_sim_score(1, 1)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.cos.get_sim_score(['a'], None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.cos.get_sim_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.cos.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.cos.get_sim_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.cos.get_sim_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.cos.get_sim_score('MARTHA', 'MARTHA')
class TfidfTestCases(unittest.TestCase):
def setUp(self):
self.tfidf = TfIdf()
self.corpus = [['a', 'b', 'a'], ['a', 'c'], ['a'], ['b']]
self.tfidf_with_params1 = TfIdf(self.corpus, True)
self.tfidf_with_params2 = TfIdf([['a', 'b', 'a'], ['a', 'c'], ['a']])
self.tfidf_with_params3 = TfIdf([['x', 'y'], ['w'], ['q']])
def test_get_corpus_list(self):
self.assertEqual(self.tfidf_with_params1.get_corpus_list(), self.corpus)
def test_get_dampen(self):
self.assertEqual(self.tfidf_with_params1.get_dampen(), True)
def test_set_corpus_list(self):
corpus1 = [['a', 'b', 'a'], ['a', 'c'], ['a'], ['b']]
corpus2 = [['a', 'b', 'a'], ['a', 'c'], ['a'], ['b'], ['c', 'a', 'b']]
tfidf = TfIdf(corpus_list=corpus1)
self.assertEqual(tfidf.get_corpus_list(), corpus1)
self.assertAlmostEqual(tfidf.get_raw_score(['a', 'b', 'a'], ['a']), 0.5495722661728765)
self.assertEqual(tfidf.set_corpus_list(corpus2), True)
self.assertEqual(tfidf.get_corpus_list(), corpus2)
self.assertAlmostEqual(tfidf.get_raw_score(['a', 'b', 'a'], ['a']), 0.5692378887901467)
def test_set_dampen(self):
tfidf = TfIdf(self.corpus, dampen=False)
self.assertEqual(tfidf.get_dampen(), False)
self.assertAlmostEqual(tfidf.get_raw_score(['a', 'b', 'a'], ['a']), 0.7999999999999999)
self.assertEqual(tfidf.set_dampen(True), True)
self.assertEqual(tfidf.get_dampen(), True)
self.assertAlmostEqual(tfidf.get_raw_score(['a', 'b', 'a'], ['a']), 0.5495722661728765)
def test_valid_input_raw_score(self):
self.assertEqual(self.tfidf_with_params1.get_raw_score(['a', 'b', 'a'], ['a', 'c']),
0.11166746710505392)
self.assertEqual(self.tfidf_with_params2.get_raw_score(['a', 'b', 'a'], ['a', 'c']),
0.0)
self.assertEqual(self.tfidf_with_params2.get_raw_score(['a', 'b', 'a'], ['a']),
0.0)
self.assertEqual(self.tfidf.get_raw_score(['a', 'b', 'a'], ['a']), 0.0)
self.assertEqual(self.tfidf_with_params3.get_raw_score(['a', 'b', 'a'], ['a']), 0.0)
self.assertEqual(self.tfidf.get_raw_score(['a', 'b', 'a'], ['a']), 0.0)
self.assertEqual(self.tfidf.get_raw_score(['a', 'b', 'a'], ['a', 'b', 'a']), 1.0)
self.assertEqual(self.tfidf.get_raw_score([], ['a', 'b', 'a']), 0.0)
def test_valid_input_sim_score(self):
self.assertEqual(self.tfidf_with_params1.get_sim_score(['a', 'b', 'a'], ['a', 'c']),
0.11166746710505392)
self.assertEqual(self.tfidf_with_params2.get_sim_score(['a', 'b', 'a'], ['a', 'c']),
0.0)
self.assertEqual(self.tfidf_with_params2.get_sim_score(['a', 'b', 'a'], ['a']),
0.0)
self.assertEqual(self.tfidf.get_sim_score(['a', 'b', 'a'], ['a']), 0.0)
self.assertEqual(self.tfidf_with_params3.get_sim_score(['a', 'b', 'a'], ['a']), 0.0)
self.assertEqual(self.tfidf.get_sim_score(['a', 'b', 'a'], ['a']), 0.0)
self.assertEqual(self.tfidf.get_sim_score(['a', 'b', 'a'], ['a', 'b', 'a']), 1.0)
self.assertEqual(self.tfidf.get_sim_score([], ['a', 'b', 'a']), 0.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.tfidf.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.tfidf.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.tfidf.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.tfidf.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.tfidf.get_raw_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.tfidf.get_raw_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.tfidf.get_raw_score('MARTHA', 'MARTHA')
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.tfidf.get_sim_score(1, 1)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.tfidf.get_sim_score(['a'], None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.tfidf.get_sim_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.tfidf.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.tfidf.get_sim_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.tfidf.get_sim_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.tfidf.get_sim_score('MARTHA', 'MARTHA')
class TverskyIndexTestCases(unittest.TestCase):
def setUp(self):
self.tvi = TverskyIndex()
self.tvi_with_params1 = TverskyIndex(0.5, 0.5)
self.tvi_with_params2 = TverskyIndex(0.7, 0.8)
self.tvi_with_params3 = TverskyIndex(0.2, 0.4)
self.tvi_with_params4 = TverskyIndex(0.9, 0.8)
self.tvi_with_params5 = TverskyIndex(0.45, 0.85)
self.tvi_with_params6 = TverskyIndex(0, 0.6)
def test_get_alpha(self):
self.assertEqual(self.tvi_with_params5.get_alpha(), 0.45)
def test_get_beta(self):
self.assertEqual(self.tvi_with_params5.get_beta(), 0.85)
def test_set_alpha(self):
tvi = TverskyIndex(alpha=0.3)
self.assertEqual(tvi.get_alpha(), 0.3)
self.assertAlmostEqual(tvi.get_raw_score(['data', 'science'], ['data']),
0.7692307692307692)
self.assertEqual(tvi.set_alpha(0.7), True)
self.assertEqual(tvi.get_alpha(), 0.7)
self.assertAlmostEqual(tvi.get_raw_score(['data', 'science'], ['data']),
0.5882352941176471)
def test_set_beta(self):
tvi = TverskyIndex(beta=0.3)
self.assertEqual(tvi.get_beta(), 0.3)
self.assertAlmostEqual(tvi.get_raw_score(['data', 'science'], ['science', 'good']),
0.5555555555555556)
self.assertEqual(tvi.set_beta(0.7), True)
self.assertEqual(tvi.get_beta(), 0.7)
self.assertAlmostEqual(tvi.get_raw_score(['data', 'science'], ['science', 'good']),
0.45454545454545453)
def test_valid_input_raw_score(self):
self.assertEqual(self.tvi_with_params1.get_raw_score(['data', 'science'], ['data']),
1.0 / (1.0 + 0.5*1 + 0.5*0))
self.assertEqual(self.tvi.get_raw_score(['data', 'science'], ['science', 'good']),
1.0 / (1.0 + 0.5*1 + 0.5*1))
self.assertEqual(self.tvi.get_raw_score([], ['data']), 0)
self.assertEqual(self.tvi.get_raw_score(['data'], []), 0)
self.assertEqual(self.tvi_with_params2.get_raw_score(['data', 'data', 'science'],
['data', 'management']),
1.0 / (1.0 + 0.7*1 + 0.8*1))
self.assertEqual(self.tvi_with_params3.get_raw_score(['data', 'management', 'science'],
['data', 'data', 'science']),
2.0 / (2.0 + 0.2*1 + 0))
self.assertEqual(self.tvi.get_raw_score([], []), 1.0)
self.assertEqual(self.tvi_with_params4.get_raw_score(['a', 'b'], ['b', 'a']), 1.0)
self.assertEqual(self.tvi.get_raw_score(['a', 'b'], ['b', 'a']), 1.0)
self.assertEqual(self.tvi.get_raw_score(set([]), set([])), 1.0)
self.assertEqual(self.tvi_with_params5.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
3.0 / (3.0 + 0.45*1 + 0.85*4))
self.assertEqual(self.tvi_with_params6.get_raw_score(['data', 'science'],
['data', 'data', 'management', 'science']),
2.0 / (2.0 + 0 + 0.6*1))
def test_valid_input_sim_score(self):
self.assertEqual(self.tvi_with_params1.get_sim_score(['data', 'science'], ['data']),
1.0 / (1.0 + 0.5*1 + 0.5*0))
self.assertEqual(self.tvi.get_sim_score(['data', 'science'], ['science', 'good']),
1.0 / (1.0 + 0.5*1 + 0.5*1))
self.assertEqual(self.tvi.get_sim_score([], ['data']), 0)
self.assertEqual(self.tvi.get_sim_score(['data'], []), 0)
self.assertEqual(self.tvi_with_params2.get_sim_score(['data', 'data', 'science'],
['data', 'management']),
1.0 / (1.0 + 0.7*1 + 0.8*1))
self.assertEqual(self.tvi_with_params3.get_sim_score(['data', 'management', 'science'],
['data', 'data', 'science']),
2.0 / (2.0 + 0.2*1 + 0))
self.assertEqual(self.tvi.get_sim_score([], []), 1.0)
self.assertEqual(self.tvi_with_params4.get_sim_score(['a', 'b'], ['b', 'a']), 1.0)
self.assertEqual(self.tvi.get_sim_score(['a', 'b'], ['b', 'a']), 1.0)
self.assertEqual(self.tvi.get_sim_score(set([]), set([])), 1.0)
self.assertEqual(self.tvi_with_params5.get_sim_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
3.0 / (3.0 + 0.45*1 + 0.85*4))
self.assertEqual(self.tvi_with_params6.get_sim_score(['data', 'science'],
['data', 'data', 'management', 'science']),
2.0 / (2.0 + 0 + 0.6*1))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.tvi.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.tvi.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.tvi.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.tvi.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.tvi.get_raw_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.tvi.get_raw_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.tvi.get_raw_score('MARHTA', 'MARTHA')
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.tvi.get_sim_score(1, 1)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.tvi.get_sim_score(['a'], None)
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.tvi.get_sim_score(None, ['b'])
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.tvi.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.tvi.get_sim_score(None, 'MARHTA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.tvi.get_sim_score('MARHTA', None)
@raises(TypeError)
def test_invalid_input7_sim_score(self):
self.tvi.get_sim_score('MARHTA', 'MARTHA')
@raises(ValueError)
def test_invalid_input8(self):
tvi_invalid = TverskyIndex(0.5, -0.9)
@raises(ValueError)
def test_invalid_input9(self):
tvi_invalid = TverskyIndex(-0.5, 0.9)
@raises(ValueError)
def test_invalid_input10(self):
tvi_invalid = TverskyIndex(-0.5, -0.9)
# ---------------------- bag based similarity measures ----------------------
# class CosineTestCases(unittest.TestCase):
# def test_valid_input(self):
# NONQ_FROM = 'The quick brown fox jumped over the lazy dog.'
# NONQ_TO = 'That brown dog jumped over the fox.'
# self.assertEqual(cosine([], []), 1) # check-- done. both simmetrics, abydos return 1.
# self.assertEqual(cosine(['the', 'quick'], []), 0)
# self.assertEqual(cosine([], ['the', 'quick']), 0)
# self.assertAlmostEqual(cosine(whitespace(NONQ_TO), whitespace(NONQ_FROM)),
# 4/math.sqrt(9*7))
#
# @raises(TypeError)
# def test_invalid_input1_raw_score(self):
# cosine(['a'], None)
# @raises(TypeError)
# def test_invalid_input2_raw_score(self):
# cosine(None, ['b'])
# @raises(TypeError)
# def test_invalid_input3_raw_score(self):
# cosine(None, None)
# ---------------------- hybrid similarity measure ----------------------
class Soft_TfidfTestCases(unittest.TestCase):
def setUp(self):
self.soft_tfidf = SoftTfIdf()
self.corpus = [['a', 'b', 'a'], ['a', 'c'], ['a']]
self.non_ascii_corpus = [['á', 'b', 'á'], ['á', 'c'], ['á']]
self.soft_tfidf_with_params1 = SoftTfIdf(self.corpus,
sim_func=Jaro().get_raw_score,
threshold=0.8)
self.soft_tfidf_with_params2 = SoftTfIdf(self.corpus,
threshold=0.9)
self.soft_tfidf_with_params3 = SoftTfIdf([['x', 'y'], ['w'], ['q']])
self.affine_fn = Affine().get_raw_score
self.soft_tfidf_with_params4 = SoftTfIdf(sim_func=self.affine_fn, threshold=0.6)
self.soft_tfidf_non_ascii = SoftTfIdf(self.non_ascii_corpus,
sim_func=Jaro().get_raw_score,
threshold=0.8)
def test_get_corpus_list(self):
self.assertEqual(self.soft_tfidf_with_params1.get_corpus_list(), self.corpus)
def test_get_sim_func(self):
self.assertEqual(self.soft_tfidf_with_params4.get_sim_func(), self.affine_fn)
def test_get_threshold(self):
self.assertEqual(self.soft_tfidf_with_params4.get_threshold(), 0.6)
def test_set_corpus_list(self):
corpus1 = [['a', 'b', 'a'], ['a', 'c'], ['a'], ['b']]
corpus2 = [['a', 'b', 'a'], ['a', 'c'], ['a'], ['b'], ['c', 'a', 'b']]
soft_tfidf = SoftTfIdf(corpus_list=corpus1)
self.assertEqual(soft_tfidf.get_corpus_list(), corpus1)
self.assertAlmostEqual(soft_tfidf.get_raw_score(['a', 'b', 'a'], ['a']),
0.7999999999999999)
self.assertEqual(soft_tfidf.set_corpus_list(corpus2), True)
self.assertEqual(soft_tfidf.get_corpus_list(), corpus2)
self.assertAlmostEqual(soft_tfidf.get_raw_score(['a', 'b', 'a'], ['a']),
0.8320502943378437)
def test_set_threshold(self):
soft_tfidf = SoftTfIdf(threshold=0.5)
self.assertEqual(soft_tfidf.get_threshold(), 0.5)
self.assertAlmostEqual(soft_tfidf.get_raw_score(['ar', 'bfff', 'ab'], ['abcd']), 0.8179128813519699)
self.assertEqual(soft_tfidf.set_threshold(0.7), True)
self.assertEqual(soft_tfidf.get_threshold(), 0.7)
self.assertAlmostEqual(soft_tfidf.get_raw_score(['ar', 'bfff', 'ab'], ['abcd']), 0.4811252243246882)
def test_set_sim_func(self):
fn1 = JaroWinkler().get_raw_score
fn2 = Jaro().get_raw_score
soft_tfidf = SoftTfIdf(sim_func=fn1)
self.assertEqual(soft_tfidf.get_sim_func(), fn1)
self.assertAlmostEqual(soft_tfidf.get_raw_score(['ar', 'bfff', 'ab'], ['abcd']), 0.8612141515411919)
self.assertEqual(soft_tfidf.set_sim_func(fn2), True)
self.assertEqual(soft_tfidf.get_sim_func(), fn2)
self.assertAlmostEqual(soft_tfidf.get_raw_score(['ar', 'bfff', 'ab'], ['abcd']), 0.8179128813519699)
def test_valid_input_raw_score(self):
self.assertEqual(self.soft_tfidf_with_params1.get_raw_score(
['a', 'b', 'a'], ['a', 'c']), 0.17541160386140586)
self.assertEqual(self.soft_tfidf_with_params2.get_raw_score(
['a', 'b', 'a'], ['a']), 0.5547001962252291)
self.assertEqual(self.soft_tfidf_with_params3.get_raw_score(
['a', 'b', 'a'], ['a']), 0.0)
self.assertEqual(self.soft_tfidf_with_params4.get_raw_score(
['aa', 'bb', 'a'], ['ab', 'ba']),
0.81649658092772592)
self.assertEqual(self.soft_tfidf.get_raw_score(
['a', 'b', 'a'], ['a', 'b', 'a']), 1.0)
self.assertEqual(self.soft_tfidf.get_raw_score([], ['a', 'b', 'a']), 0.0)
def test_valid_input_non_ascii_raw_score(self):
self.assertEqual(self.soft_tfidf_non_ascii.get_raw_score(
[u'á', u'b', u'á'], [u'á', u'c']), 0.17541160386140586)
self.assertEqual(self.soft_tfidf_non_ascii.get_raw_score(
['á', 'b', 'á'], ['á', 'c']), 0.17541160386140586)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.soft_tfidf.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.soft_tfidf.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.soft_tfidf.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.soft_tfidf.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.soft_tfidf.get_raw_score(['MARHTA'], 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.soft_tfidf.get_raw_score('MARHTA', ['MARTHA'])
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.soft_tfidf.get_raw_score('MARTHA', 'MARTHA')
# Modified test cases to overcome the decimal points matching
class MongeElkanTestCases(unittest.TestCase):
def setUp(self):
self.me = MongeElkan()
self.me_with_nw = MongeElkan(NeedlemanWunsch().get_raw_score)
self.affine_fn = Affine().get_raw_score
self.me_with_affine = MongeElkan(self.affine_fn)
def test_get_sim_func(self):
self.assertEqual(self.me_with_affine.get_sim_func(), self.affine_fn)
def test_set_sim_func(self):
fn1 = JaroWinkler().get_raw_score
fn2 = NeedlemanWunsch().get_raw_score
me = MongeElkan(sim_func=fn1)
self.assertEqual(me.get_sim_func(), fn1)
self.assertAlmostEqual(round(me.get_raw_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.8364448051948052, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(me.set_sim_func(fn2), True)
self.assertEqual(me.get_sim_func(), fn2)
self.assertAlmostEqual(me.get_raw_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
2.0)
def test_valid_input(self):
self.assertEqual(self.me.get_raw_score([''], ['']), 1.0) # need to check this
self.assertEqual(self.me.get_raw_score([''], ['a']), 0.0)
self.assertEqual(self.me.get_raw_score(['a'], ['a']), 1.0)
self.assertEqual(round(self.me.get_raw_score(['Niall'], ['Neal']), NUMBER_OF_DECIMAL_PLACES),
round(0.8049999999999999, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.me.get_raw_score(['Niall'], ['Njall']), NUMBER_OF_DECIMAL_PLACES), 0.88)
self.assertEqual(round(self.me.get_raw_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
NUMBER_OF_DECIMAL_PLACES), round(0.8364448051948052, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(self.me_with_nw.get_raw_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
2.0)
self.assertEqual(self.me_with_affine.get_raw_score(
['Comput.', 'Sci.', 'and', 'Eng.', 'Dept.,', 'University', 'of', 'California,', 'San', 'Diego'],
['Department', 'of', 'Computer', 'Science,', 'Univ.', 'Calif.,', 'San', 'Diego']),
2.25)
self.assertEqual(round(self.me.get_raw_score(['Niall'], ['Niel']), NUMBER_OF_DECIMAL_PLACES),
round(0.8266666666666667, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.me.get_raw_score(['Niall'], ['Nigel']), NUMBER_OF_DECIMAL_PLACES),
round(0.7866666666666667, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(self.me.get_raw_score([], ['Nigel']), 0.0)
def test_valid_input_non_ascii(self):
self.assertEqual(round(self.me.get_raw_score([u'Nóáll'], [u'Neál']), NUMBER_OF_DECIMAL_PLACES),
round(0.8049999999999999, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.me.get_raw_score(['Nóáll'], ['Neál']), NUMBER_OF_DECIMAL_PLACES),
round(0.8049999999999999, NUMBER_OF_DECIMAL_PLACES))
self.assertEqual(round(self.me.get_raw_score([b'N\xc3\xb3\xc3\xa1ll'], [b'Ne\xc3\xa1l']),
NUMBER_OF_DECIMAL_PLACES), round(0.8049999999999999, NUMBER_OF_DECIMAL_PLACES))
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.me.get_raw_score(1, 1)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.me.get_raw_score(None, ['b'])
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.me.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.me.get_raw_score("temp", "temp")
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.me.get_raw_score(['temp'], 'temp')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.me.get_raw_score(['a'], None)
@raises(TypeError)
def test_invalid_input7_raw_score(self):
self.me.get_raw_score('temp', ['temp'])
# ---------------------- fuzzywuzzy similarity measure ----------------------
class PartialRatioTestCases(unittest.TestCase):
def setUp(self):
self.ratio = PartialRatio()
def test_valid_input_raw_score(self):
self.assertEqual(self.ratio.get_raw_score('a', ''), 0)
self.assertEqual(self.ratio.get_raw_score('', 'a'), 0)
self.assertEqual(self.ratio.get_raw_score('abc', ''), 0)
self.assertEqual(self.ratio.get_raw_score('', 'abc'), 0)
self.assertEqual(self.ratio.get_raw_score('', ''), 0)
self.assertEqual(self.ratio.get_raw_score('a', 'a'), 100)
self.assertEqual(self.ratio.get_raw_score('abc', 'abc'), 100)
self.assertEqual(self.ratio.get_raw_score('a', 'ab'), 100)
self.assertEqual(self.ratio.get_raw_score('b', 'ab'), 100)
self.assertEqual(self.ratio.get_raw_score(' ac', 'abc'), 67)
self.assertEqual(self.ratio.get_raw_score('abcdefg', 'xabxcdxxefxgx'), 57)
self.assertEqual(self.ratio.get_raw_score('ab', 'a'), 100)
self.assertEqual(self.ratio.get_raw_score('ab', 'A'), 0)
self.assertEqual(self.ratio.get_raw_score('Ab', 'a'), 0)
self.assertEqual(self.ratio.get_raw_score('Ab', 'A'), 100)
self.assertEqual(self.ratio.get_raw_score('Ab', 'b'), 100)
self.assertEqual(self.ratio.get_raw_score('ab', 'b'), 100)
self.assertEqual(self.ratio.get_raw_score('abc', 'ac'), 50)
self.assertEqual(self.ratio.get_raw_score('xabxcdxxefxgx', 'abcdefg'), 57)
self.assertEqual(self.ratio.get_raw_score('a', 'b'), 0)
self.assertEqual(self.ratio.get_raw_score('ab', 'ac'), 50)
self.assertEqual(self.ratio.get_raw_score('ac', 'bc'), 50)
self.assertEqual(self.ratio.get_raw_score('abc', 'axc'), 67)
self.assertEqual(self.ratio.get_raw_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 54)
self.assertEqual(self.ratio.get_raw_score('example', 'samples'), 71)
self.assertEqual(self.ratio.get_raw_score('bag_distance', 'frankenstein'), 36)
self.assertEqual(self.ratio.get_raw_score('distance', 'difference'), 38)
self.assertEqual(self.ratio.get_raw_score('java was neat', 'scala is great'), 62)
self.assertEqual(self.ratio.get_raw_score('java wAs nEat', 'scala is great'), 54)
self.assertEqual(self.ratio.get_raw_score('c++ was neat', 'java was neat'), 75)
def test_valid_input_sim_score(self):
self.assertAlmostEqual(self.ratio.get_sim_score('a', ''), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('', 'a'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', ''), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('', 'abc'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('', ''), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('a', 'a'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', 'abc'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('a', 'ab'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('b', 'ab'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score(' ac', 'abc'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('abcdefg', 'xabxcdxxefxgx'), 0.57)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'a'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'A'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('Ab', 'a'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('Ab', 'A'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('Ab', 'b'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'b'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', 'ac'), 0.50)
self.assertAlmostEqual(self.ratio.get_sim_score('xabxcdxxefxgx', 'abcdefg'), 0.57)
self.assertAlmostEqual(self.ratio.get_sim_score('a', 'b'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'ac'), 0.50)
self.assertAlmostEqual(self.ratio.get_sim_score('ac', 'bc'), 0.50)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', 'axc'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 0.54)
self.assertAlmostEqual(self.ratio.get_sim_score('example', 'samples'), 0.71)
self.assertAlmostEqual(self.ratio.get_sim_score('bag_distance', 'frankenstein'), 0.36)
self.assertAlmostEqual(self.ratio.get_sim_score('distance', 'difference'), 0.38)
self.assertAlmostEqual(self.ratio.get_sim_score('java was neat', 'scala is great'), 0.62)
self.assertAlmostEqual(self.ratio.get_sim_score('java wAs nEat', 'scala is great'), 0.54)
self.assertAlmostEqual(self.ratio.get_sim_score('c++ was neat', 'java was neat'), 0.75)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.ratio.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.ratio.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.ratio.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.ratio.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.ratio.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.ratio.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.ratio.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.ratio.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.ratio.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.ratio.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.ratio.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.ratio.get_sim_score(12.90, 12.90)
class RatioTestCases(unittest.TestCase):
def setUp(self):
self.ratio = Ratio()
def test_valid_input_raw_score(self):
self.assertEqual(self.ratio.get_raw_score('a', ''), 0)
self.assertEqual(self.ratio.get_raw_score('', 'a'), 0)
self.assertEqual(self.ratio.get_raw_score('abc', ''), 0)
self.assertEqual(self.ratio.get_raw_score('', 'abc'), 0)
self.assertEqual(self.ratio.get_raw_score('', ''), 0)
self.assertEqual(self.ratio.get_raw_score('a', 'a'), 100)
self.assertEqual(self.ratio.get_raw_score('abc', 'abc'), 100)
self.assertEqual(self.ratio.get_raw_score('a', 'ab'), 67)
self.assertEqual(self.ratio.get_raw_score('b', 'ab'), 67)
self.assertEqual(self.ratio.get_raw_score(' ac', 'abc'), 67)
self.assertEqual(self.ratio.get_raw_score('abcdefg', 'xabxcdxxefxgx'), 70)
self.assertEqual(self.ratio.get_raw_score('ab', 'a'), 67)
self.assertEqual(self.ratio.get_raw_score('ab', 'A'), 0)
self.assertEqual(self.ratio.get_raw_score('Ab', 'a'), 0)
self.assertEqual(self.ratio.get_raw_score('Ab', 'A'), 67)
self.assertEqual(self.ratio.get_raw_score('Ab', 'b'), 67)
self.assertEqual(self.ratio.get_raw_score('ab', 'b'), 67)
self.assertEqual(self.ratio.get_raw_score('abc', 'ac'), 80)
self.assertEqual(self.ratio.get_raw_score('xabxcdxxefxgx', 'abcdefg'), 70)
self.assertEqual(self.ratio.get_raw_score('a', 'b'), 0)
self.assertEqual(self.ratio.get_raw_score('ab', 'ac'), 50)
self.assertEqual(self.ratio.get_raw_score('ac', 'bc'), 50)
self.assertEqual(self.ratio.get_raw_score('abc', 'axc'), 67)
self.assertEqual(self.ratio.get_raw_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 54)
self.assertEqual(self.ratio.get_raw_score('example', 'samples'), 71)
self.assertEqual(self.ratio.get_raw_score('bag_distance', 'frankenstein'), 33)
self.assertEqual(self.ratio.get_raw_score('distance', 'difference'), 56)
self.assertEqual(self.ratio.get_raw_score('java was neat', 'scala is great'), 59)
self.assertEqual(self.ratio.get_raw_score('java wAs nEat', 'scala is great'), 52)
self.assertEqual(self.ratio.get_raw_score('scaLA is greAT', 'java wAs nEat'), 30)
def test_valid_input_sim_score(self):
self.assertAlmostEqual(self.ratio.get_sim_score('a', ''), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('', 'a'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', ''), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('', 'abc'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('', ''), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('a', 'a'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', 'abc'), 1.0)
self.assertAlmostEqual(self.ratio.get_sim_score('a', 'ab'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('b', 'ab'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score(' ac', 'abc'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('abcdefg', 'xabxcdxxefxgx'), 0.70)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'a'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'A'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('Ab', 'a'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('Ab', 'A'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('Ab', 'b'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'b'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', 'ac'), 0.80)
self.assertAlmostEqual(self.ratio.get_sim_score('xabxcdxxefxgx', 'abcdefg'), 0.70)
self.assertAlmostEqual(self.ratio.get_sim_score('a', 'b'), 0.0)
self.assertAlmostEqual(self.ratio.get_sim_score('ab', 'ac'), 0.50)
self.assertAlmostEqual(self.ratio.get_sim_score('ac', 'bc'), 0.50)
self.assertAlmostEqual(self.ratio.get_sim_score('abc', 'axc'), 0.67)
self.assertAlmostEqual(self.ratio.get_sim_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 0.54)
self.assertAlmostEqual(self.ratio.get_sim_score('example', 'samples'), 0.71)
self.assertAlmostEqual(self.ratio.get_sim_score('bag_distance', 'frankenstein'), 0.33)
self.assertAlmostEqual(self.ratio.get_sim_score('distance', 'difference'), 0.56)
self.assertAlmostEqual(self.ratio.get_sim_score('java was neat', 'scala is great'), 0.59)
self.assertAlmostEqual(self.ratio.get_sim_score('java wAs nEat', 'scala is great'), 0.52)
self.assertAlmostEqual(self.ratio.get_sim_score('scaLA is greAT', 'java wAs nEat'), 0.30)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.ratio.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.ratio.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.ratio.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.ratio.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.ratio.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.ratio.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.ratio.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.ratio.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.ratio.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.ratio.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.ratio.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.ratio.get_sim_score(12.90, 12.90)
class PartialTokenSortTestCases(unittest.TestCase):
def setUp(self):
self.partialTokenSort = PartialTokenSort()
def test_valid_input_raw_score(self):
self.assertEqual(self.partialTokenSort.get_raw_score('a', ''), 0)
self.assertEqual(self.partialTokenSort.get_raw_score('', 'a'), 0)
self.assertEqual(self.partialTokenSort.get_raw_score('abc', ''), 0)
self.assertEqual(self.partialTokenSort.get_raw_score('', 'abc'), 0)
self.assertEqual(self.partialTokenSort.get_raw_score('', ''), 0)
self.assertEqual(self.partialTokenSort.get_raw_score('a', 'a'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('abc', 'abc'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('a', 'ab'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('b', 'ab'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score(' ac', 'abc'), 50)
self.assertEqual(self.partialTokenSort.get_raw_score('abcdefg', 'xabxcdxxefxgx'), 57)
self.assertEqual(self.partialTokenSort.get_raw_score('ab', 'a'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('ab', 'A'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('Ab', 'a'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('Ab', 'A'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('Ab', 'b'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('ab', 'b'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('abc', 'ac'), 50)
self.assertEqual(self.partialTokenSort.get_raw_score('xabxcdxxefxgx', 'abcdefg'), 57)
self.assertEqual(self.partialTokenSort.get_raw_score('a', 'b'), 0)
self.assertEqual(self.partialTokenSort.get_raw_score('ab', 'ac'), 50)
self.assertEqual(self.partialTokenSort.get_raw_score('ac', 'bc'), 50)
self.assertEqual(self.partialTokenSort.get_raw_score('abc', 'axc'), 67)
self.assertEqual(self.partialTokenSort.get_raw_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 54)
self.assertEqual(self.partialTokenSort.get_raw_score('example', 'samples'), 71)
self.assertEqual(self.partialTokenSort.get_raw_score('bag_distance', 'frankenstein'), 36)
self.assertEqual(self.partialTokenSort.get_raw_score('distance', 'difference'), 38)
self.assertEqual(self.partialTokenSort.get_raw_score('java was neat', 'scala is great'), 38)
self.assertEqual(self.partialTokenSort.get_raw_score('java wAs nEat', 'scala is great'), 38)
self.assertEqual(self.partialTokenSort.get_raw_score('great is scala', 'java is great'), 77)
self.assertEqual(self.partialTokenSort.get_raw_score('Wisconsin Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('C++ and Java', 'Java and Python'), 80)
self.assertEqual(self.partialTokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++'), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=True), 100)
self.assertEqual(self.partialTokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=False), 100)
self.assertLess(self.partialTokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=False), 100)
self.assertLess(self.partialTokenSort.get_raw_score('Java C++', 'C++\u00C1 Java\u00C2', full_process=False), 100)
self.assertLess(self.partialTokenSort.get_raw_score('Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 100)
self.assertLess(self.partialTokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 100)
self.assertEqual(self.partialTokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 100)
self.assertLess(self.partialTokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=True, full_process=False), 100)
self.assertEqual(self.partialTokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 100)
def test_valid_input_sim_score(self):
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('a', ''), 0.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('', 'a'), 0.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('abc', ''), 0.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('', 'abc'), 0.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('', ''), 0.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('a', 'a'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('abc', 'abc'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('a', 'ab'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('b', 'ab'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score(' ac', 'abc'), 0.50)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('abcdefg', 'xabxcdxxefxgx'), 0.57)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('ab', 'a'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('ab', 'A'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('Ab', 'a'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('Ab', 'A'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('Ab', 'b'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('ab', 'b'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('abc', 'ac'), 0.50)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('xabxcdxxefxgx', 'abcdefg'), 0.57)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('a', 'b'), 0.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('ab', 'ac'), 0.50)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('ac', 'bc'), 0.50)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('abc', 'axc'), 0.67)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 0.54)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('example', 'samples'), 0.71)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('bag_distance', 'frankenstein'), 0.36)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('distance', 'difference'), 0.38)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('java was neat', 'scala is great'), 0.38)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('java wAs nEat', 'scala is great'), 0.38)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('great is scala', 'java is great'), 0.77)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('Wisconsin Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('C++ and Java', 'Java and Python'), 0.8)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++'), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=True), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=False), 1.0)
self.assertLess(self.partialTokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=False), 1.0)
self.assertLess(self.partialTokenSort.get_sim_score('Java C++', 'C++\u00C1 Java\u00C2', full_process=False), 100)
self.assertLess(self.partialTokenSort.get_sim_score('Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 1.0)
self.assertLess(self.partialTokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 1.0)
self.assertLess(self.partialTokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=True, full_process=False), 1.0)
self.assertAlmostEqual(self.partialTokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 1.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.partialTokenSort.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.partialTokenSort.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.partialTokenSort.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.partialTokenSort.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.partialTokenSort.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.partialTokenSort.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.partialTokenSort.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.partialTokenSort.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.partialTokenSort.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.partialTokenSort.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.partialTokenSort.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.partialTokenSort.get_sim_score(12.90, 12.90)
class TokenSortTestCases(unittest.TestCase):
def setUp(self):
self.tokenSort = TokenSort()
def test_valid_input_raw_score(self):
self.assertEqual(self.tokenSort.get_raw_score('a', ''), 0)
self.assertEqual(self.tokenSort.get_raw_score('', 'a'), 0)
self.assertEqual(self.tokenSort.get_raw_score('abc', ''), 0)
self.assertEqual(self.tokenSort.get_raw_score('', 'abc'), 0)
self.assertEqual(self.tokenSort.get_raw_score('', ''), 0)
self.assertEqual(self.tokenSort.get_raw_score('a', 'a'), 100)
self.assertEqual(self.tokenSort.get_raw_score('abc', 'abc'), 100)
self.assertEqual(self.tokenSort.get_raw_score('a', 'ab'), 67)
self.assertEqual(self.tokenSort.get_raw_score('b', 'ab'), 67)
self.assertEqual(self.tokenSort.get_raw_score(' ac', 'abc'), 80)
self.assertEqual(self.tokenSort.get_raw_score('abcdefg', 'xabxcdxxefxgx'), 70)
self.assertEqual(self.tokenSort.get_raw_score('ab', 'a'), 67)
self.assertEqual(self.tokenSort.get_raw_score('ab', 'A'), 67)
self.assertEqual(self.tokenSort.get_raw_score('Ab', 'a'), 67)
self.assertEqual(self.tokenSort.get_raw_score('Ab', 'A'), 67)
self.assertEqual(self.tokenSort.get_raw_score('Ab', 'b'), 67)
self.assertEqual(self.tokenSort.get_raw_score('ab', 'b'), 67)
self.assertEqual(self.tokenSort.get_raw_score('abc', 'ac'), 80)
self.assertEqual(self.tokenSort.get_raw_score('xabxcdxxefxgx', 'abcdefg'), 70)
self.assertEqual(self.tokenSort.get_raw_score('a', 'b'), 0)
self.assertEqual(self.tokenSort.get_raw_score('ab', 'ac'), 50)
self.assertEqual(self.tokenSort.get_raw_score('ac', 'bc'), 50)
self.assertEqual(self.tokenSort.get_raw_score('abc', 'axc'), 67)
self.assertEqual(self.tokenSort.get_raw_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 54)
self.assertEqual(self.tokenSort.get_raw_score('example', 'samples'), 71)
self.assertEqual(self.tokenSort.get_raw_score('bag_distance', 'frankenstein'), 33)
self.assertEqual(self.tokenSort.get_raw_score('distance', 'difference'), 56)
self.assertEqual(self.tokenSort.get_raw_score('java was neat', 'scala is great'), 37)
self.assertEqual(self.tokenSort.get_raw_score('java wAs nEat', 'scala is great'), 37)
self.assertEqual(self.tokenSort.get_raw_score('great is scala', 'java is great'), 81)
self.assertEqual(self.tokenSort.get_raw_score('Wisconsin Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 100)
self.assertEqual(self.tokenSort.get_raw_score('Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 83)
self.assertEqual(self.tokenSort.get_raw_score('C++ and Java', 'Java and Python'), 64)
self.assertEqual(self.tokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++'), 100)
self.assertEqual(self.tokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 100)
self.assertEqual(self.tokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 100)
self.assertEqual(self.tokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=True), 100)
self.assertLess(self.tokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=False), 100)
self.assertLess(self.tokenSort.get_raw_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=False), 100)
self.assertLess(self.tokenSort.get_raw_score('Java C++', 'C++\u00C1 Java\u00C2', full_process=False), 100)
self.assertLess(self.tokenSort.get_raw_score('Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 100)
self.assertLess(self.tokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 100)
self.assertLess(self.tokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 100)
self.assertLess(self.tokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=True, full_process=False), 100)
self.assertLess(self.tokenSort.get_raw_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 100)
def test_valid_input_sim_score(self):
self.assertAlmostEqual(self.tokenSort.get_sim_score('a', ''), 0.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('', 'a'), 0.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('abc', ''), 0.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('', 'abc'), 0.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('', ''), 0.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('a', 'a'), 1.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('abc', 'abc'), 1.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('a', 'ab'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('b', 'ab'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score(' ac', 'abc'), 0.80)
self.assertAlmostEqual(self.tokenSort.get_sim_score('abcdefg', 'xabxcdxxefxgx'), 0.70)
self.assertAlmostEqual(self.tokenSort.get_sim_score('ab', 'a'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('ab', 'A'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('Ab', 'a'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('Ab', 'A'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('Ab', 'b'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('ab', 'b'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('abc', 'ac'), 0.80)
self.assertAlmostEqual(self.tokenSort.get_sim_score('xabxcdxxefxgx', 'abcdefg'), 0.70)
self.assertAlmostEqual(self.tokenSort.get_sim_score('a', 'b'), 0.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('ab', 'ac'), 0.50)
self.assertAlmostEqual(self.tokenSort.get_sim_score('ac', 'bc'), 0.50)
self.assertAlmostEqual(self.tokenSort.get_sim_score('abc', 'axc'), 0.67)
self.assertAlmostEqual(self.tokenSort.get_sim_score('xabxcdxxefxgx', '1ab2cd34ef5g6'), 0.54)
self.assertAlmostEqual(self.tokenSort.get_sim_score('example', 'samples'), 0.71)
self.assertAlmostEqual(self.tokenSort.get_sim_score('bag_distance', 'frankenstein'), 0.33)
self.assertAlmostEqual(self.tokenSort.get_sim_score('distance', 'difference'), 0.56)
self.assertAlmostEqual(self.tokenSort.get_sim_score('java was neat', 'scala is great'), 0.37)
self.assertAlmostEqual(self.tokenSort.get_sim_score('java wAs nEat', 'scala is great'), 0.37)
self.assertAlmostEqual(self.tokenSort.get_sim_score('great is scala', 'java is great'), 0.81)
self.assertAlmostEqual(self.tokenSort.get_sim_score('Wisconsin Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 1.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('Badgers vs Chicago Bears', 'Chicago Bears vs Wisconsin Badgers'), 0.83)
self.assertAlmostEqual(self.tokenSort.get_sim_score('C++ and Java', 'Java and Python'), 0.64)
self.assertAlmostEqual(self.tokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++'), 1.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 1.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=True), 1.0)
self.assertAlmostEqual(self.tokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=True), 1.0)
self.assertLess(self.tokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', force_ascii=False), 1.0)
self.assertLess(self.tokenSort.get_sim_score('C++\u00C1 Java\u00C2', 'Java C++', full_process=False), 1.0)
self.assertLess(self.tokenSort.get_sim_score('Java C++', 'C++\u00C1 Java\u00C2', full_process=False), 100)
self.assertLess(self.tokenSort.get_sim_score('Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 1.0)
self.assertLess(self.tokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=False), 1.0)
self.assertLess(self.tokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 1.0)
self.assertLess(self.tokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=True, full_process=False), 1.0)
self.assertLess(self.tokenSort.get_sim_score(' Java C++', 'C++\u00C1 Java\u00C2', force_ascii=False, full_process=True), 1.0)
@raises(TypeError)
def test_invalid_input1_raw_score(self):
self.tokenSort.get_raw_score('a', None)
@raises(TypeError)
def test_invalid_input2_raw_score(self):
self.tokenSort.get_raw_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_raw_score(self):
self.tokenSort.get_raw_score(None, None)
@raises(TypeError)
def test_invalid_input4_raw_score(self):
self.tokenSort.get_raw_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_raw_score(self):
self.tokenSort.get_raw_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_raw_score(self):
self.tokenSort.get_raw_score(12.90, 12.90)
@raises(TypeError)
def test_invalid_input1_sim_score(self):
self.tokenSort.get_sim_score('a', None)
@raises(TypeError)
def test_invalid_input2_sim_score(self):
self.tokenSort.get_sim_score(None, 'b')
@raises(TypeError)
def test_invalid_input3_sim_score(self):
self.tokenSort.get_sim_score(None, None)
@raises(TypeError)
def test_invalid_input4_sim_score(self):
self.tokenSort.get_sim_score('MARHTA', 12.90)
@raises(TypeError)
def test_invalid_input5_sim_score(self):
self.tokenSort.get_sim_score(12.90, 'MARTHA')
@raises(TypeError)
def test_invalid_input6_sim_score(self):
self.tokenSort.get_sim_score(12.90, 12.90)
| 48.190402 | 149 | 0.64602 | 17,051 | 124,524 | 4.461498 | 0.026626 | 0.079082 | 0.082855 | 0.074902 | 0.936903 | 0.911861 | 0.886727 | 0.852918 | 0.816321 | 0.728655 | 0 | 0.047624 | 0.19634 | 124,524 | 2,583 | 150 | 48.209059 | 0.712536 | 0.017105 | 0 | 0.455763 | 0 | 0 | 0.088863 | 0.000392 | 0 | 0 | 0 | 0 | 0.388331 | 1 | 0.189861 | false | 0 | 0.012912 | 0 | 0.213773 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c159c39cd994ca79ff0311fceb831a58e520312d | 3,770 | py | Python | tests/func/goodreads/complement_file/test_comp_file_favourites.py | josealobato/go-over | ebc012a4d74a81fc729419f4ea670b9d6b4271bb | [
"MIT"
] | null | null | null | tests/func/goodreads/complement_file/test_comp_file_favourites.py | josealobato/go-over | ebc012a4d74a81fc729419f4ea670b9d6b4271bb | [
"MIT"
] | 7 | 2022-02-13T09:21:55.000Z | 2022-03-02T07:56:31.000Z | tests/func/goodreads/complement_file/test_comp_file_favourites.py | josealobato/go-over | ebc012a4d74a81fc729419f4ea670b9d6b4271bb | [
"MIT"
] | null | null | null | from typing import Dict
import json
import pytest
from ...func_test_tools import load_result, load_result_from_path, exist_in_path
from ...constants import *
# Favourites fixtures
@pytest.fixture(scope='function', name='complemetary_data_with_favourite_true')
def json_complement_file_with_favourites_true(tmpdir_factory):
""" Fixture to create complemtary with favourite equal true. """
file = tmpdir_factory.mktemp(SOURCE_DATA_PATH).join(JSON_COMPLEMENT_FILE)
book = {
"id": "57343730",
"is_favourite": True
}
to_dump = {"books": [book]}
with open(file, "w") as f:
json.dump(to_dump, f, indent=4)
return file
@pytest.fixture(scope='function', name='complemetary_data_with_favourite_false')
def json_complement_file_with_favourites_false(tmpdir_factory):
""" Fixture to create complemtary with favourite equal false. """
file = tmpdir_factory.mktemp(SOURCE_DATA_PATH).join(JSON_COMPLEMENT_FILE)
book = {
"id": "57343730",
"is_favourite": False
}
to_dump = {"books": [book]}
with open(file, "w") as f:
json.dump(to_dump, f, indent=4)
return file
@pytest.fixture(scope='function', name='complemetary_data_without_favourite')
def json_complement_file_without_favourites(tmpdir_factory):
""" Fixture to create complemtary without favourite. """
file = tmpdir_factory.mktemp(SOURCE_DATA_PATH).join(JSON_COMPLEMENT_FILE)
book = {
"id": "57343730"
}
to_dump = {"books": [book]}
with open(file, "w") as f:
json.dump(to_dump, f, indent=4)
return file
# Under test
from go_over.goodreads.processor import process
def test_no_favourites_generation_when_no_favourite(csv_one_book, complemetary_data_without_favourite, results_path):
""" When no favourites is given to the configuration file not favourites file is generated. """
# GIVEN A origina CSV and a complementari file that does not contain favourites.
# WHEN Genertate
process(csv_one_book, complemetary_data_without_favourite, results_path, {})
# THEN no favourites file is generated
assert not exist_in_path("books_favourites.json", results_path)
def test_no_favourites_generation_when_favourite_false(csv_one_book, complemetary_data_with_favourite_false, results_path):
""" When favourites is given with false value to the configuration file not favourites file is generated. """
# GIVEN A origina CSV and a complementari file that does not contain favourites.
# WHEN Genertate
process(csv_one_book, complemetary_data_with_favourite_false, results_path, {})
# THEN no favourites file is generated
assert not exist_in_path("books_favourites.json", results_path)
def test_favourites_generation(csv_one_book, complemetary_data_with_favourite_true, results_path):
""" When a favourites is given with true value to the configuration file favourites file is generated. """
# GIVEN A origina CSV and a complementari file that contains favourites.
# WHEN Genertate
process(csv_one_book, complemetary_data_with_favourite_true, results_path, {})
# THEN favourites file is generated
assert exist_in_path("books_favourites.json", results_path)
def test_favourites_generation_content(csv_one_book, complemetary_data_with_favourite_true, results_path):
""" When a favourites is given to the configuration file favourites file is generated. """
# GIVEN A origina CSV and a complementari file that contains favourites.
# WHEN Genertate
process(csv_one_book, complemetary_data_with_favourite_true, results_path, {})
# THEN favourites file is generated
results = load_result("books_favourites.json", results_path)
book = results['books'][0]
assert book["is_favourite"] == True | 45.421687 | 123 | 0.752785 | 508 | 3,770 | 5.297244 | 0.165354 | 0.049052 | 0.059457 | 0.086213 | 0.842809 | 0.822371 | 0.759941 | 0.753623 | 0.753623 | 0.658863 | 0 | 0.008869 | 0.162599 | 3,770 | 83 | 124 | 45.421687 | 0.843522 | 0.285411 | 0 | 0.461538 | 0 | 0 | 0.116332 | 0.073513 | 0 | 0 | 0 | 0 | 0.076923 | 1 | 0.134615 | false | 0 | 0.115385 | 0 | 0.307692 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c181729bb25f4361f5f37e30f6971bf86808fcb7 | 20 | py | Python | spynoza/conversion/__init__.py | spinoza-centre/spynoza | d71d69e3ea60c9544f4e63940f053a2d1b3ac65f | [
"MIT"
] | 7 | 2016-06-21T11:51:07.000Z | 2018-08-10T15:41:37.000Z | spynoza/conversion/__init__.py | spinoza-centre/spynoza | d71d69e3ea60c9544f4e63940f053a2d1b3ac65f | [
"MIT"
] | 12 | 2017-07-05T09:14:31.000Z | 2018-09-13T12:19:14.000Z | spynoza/conversion/__init__.py | spinoza-centre/spynoza | d71d69e3ea60c9544f4e63940f053a2d1b3ac65f | [
"MIT"
] | 8 | 2016-09-26T12:35:59.000Z | 2021-06-05T05:50:23.000Z | from . import nodes
| 10 | 19 | 0.75 | 3 | 20 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 20 | 1 | 20 | 20 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
c1a808acfe0dc3048b2e6b4aa4185fe31fab66da | 133 | py | Python | podpac/core/compositor/__init__.py | creare-com/podpac | 7feb5c957513c146ce73ba1c36c630284f513a6e | [
"Apache-2.0"
] | 46 | 2018-04-06T19:54:32.000Z | 2022-02-08T02:00:02.000Z | podpac/core/compositor/__init__.py | creare-com/podpac | 7feb5c957513c146ce73ba1c36c630284f513a6e | [
"Apache-2.0"
] | 474 | 2018-04-05T22:21:09.000Z | 2022-02-24T14:21:16.000Z | podpac/core/compositor/__init__.py | creare-com/podpac | 7feb5c957513c146ce73ba1c36c630284f513a6e | [
"Apache-2.0"
] | 4 | 2019-04-11T17:49:53.000Z | 2020-11-29T22:36:53.000Z | from .compositor import BaseCompositor
from .ordered_compositor import OrderedCompositor
from .tile_compositor import TileCompositor
| 33.25 | 49 | 0.887218 | 14 | 133 | 8.285714 | 0.571429 | 0.413793 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090226 | 133 | 3 | 50 | 44.333333 | 0.958678 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
c1b1ec8402e9cb39400ee25265bc948fd6f938b5 | 105 | py | Python | creator/ingest_runs/models/__init__.py | kids-first/kf-api-study-creator | 93a79b108b6474f9b4135ace06c89ddcf63dd257 | [
"Apache-2.0"
] | 3 | 2019-05-04T02:07:28.000Z | 2020-10-16T17:47:44.000Z | creator/ingest_runs/models/__init__.py | kids-first/kf-api-study-creator | 93a79b108b6474f9b4135ace06c89ddcf63dd257 | [
"Apache-2.0"
] | 604 | 2019-02-21T18:14:51.000Z | 2022-02-10T08:13:54.000Z | creator/ingest_runs/models/__init__.py | kids-first/kf-api-study-creator | 93a79b108b6474f9b4135ace06c89ddcf63dd257 | [
"Apache-2.0"
] | null | null | null | from .ingest_run import *
from .validation_run import *
from ..common.model import State, CANCEL_SOURCES
| 26.25 | 48 | 0.8 | 15 | 105 | 5.4 | 0.666667 | 0.222222 | 0.320988 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12381 | 105 | 3 | 49 | 35 | 0.880435 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
c1eeed1fc6225f2ae37c0bea3552166b6186ccd4 | 78 | py | Python | pyvi/identification/__init__.py | d-bouvier/pyvi | 6b38bfaed75f84f6bf2ef43b11535510ee1c0490 | [
"BSD-3-Clause"
] | 16 | 2018-06-24T03:42:56.000Z | 2022-03-31T08:31:01.000Z | pyvi/identification/__init__.py | d-bouvier/pyvi | 6b38bfaed75f84f6bf2ef43b11535510ee1c0490 | [
"BSD-3-Clause"
] | null | null | null | pyvi/identification/__init__.py | d-bouvier/pyvi | 6b38bfaed75f84f6bf2ef43b11535510ee1c0490 | [
"BSD-3-Clause"
] | 3 | 2019-03-21T01:18:39.000Z | 2021-12-02T00:50:20.000Z | from .methods import __doc__
from .methods import *
__all__ = methods.__all__
| 19.5 | 28 | 0.794872 | 10 | 78 | 5 | 0.5 | 0.44 | 0.68 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.141026 | 78 | 3 | 29 | 26 | 0.746269 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
de0882c410fb2e767d06c97855b465a0d7420ac5 | 8,553 | py | Python | tests/test_metrics.py | TomMonks/basecast | 312c2c2a80a4cd15257be4b53ced87d5d5fa5ec7 | [
"MIT"
] | 2 | 2020-08-01T20:52:41.000Z | 2021-01-05T14:53:24.000Z | tests/test_metrics.py | TomMonks/basecast | 312c2c2a80a4cd15257be4b53ced87d5d5fa5ec7 | [
"MIT"
] | 24 | 2020-05-09T20:24:48.000Z | 2022-02-04T10:06:06.000Z | tests/test_metrics.py | TomMonks/basecast | 312c2c2a80a4cd15257be4b53ced87d5d5fa5ec7 | [
"MIT"
] | 1 | 2020-10-30T17:09:48.000Z | 2020-10-30T17:09:48.000Z | '''
Unit test for forecast error functions (point and coverage)
in the metrics module
'''
import pytest
import numpy as np
from forecast_tools import metrics as m
@pytest.mark.parametrize("y_true, y_pred, metrics, expected",
[([1], [1], 'all', 6),
([1], [1], ['mae'], 1),
([1], [1], ['mae', 'me'], 2),
([1], [1], ['mae', 'me', 'smape'], 3),
([1], [1], ['mae', 'me', 'smape', 'mse',
'rmse', 'mape'], 6)])
def test_forecast_error_return_length(y_true, y_pred, metrics, expected):
'''
test the correct number of error metric functions are returned.
'''
funcs_dict = m.forecast_errors(y_true, y_pred, metrics)
assert len(funcs_dict) == expected
@pytest.mark.parametrize("y_true, y_pred, metrics, expected",
[([1], [1], 'all', ['me', 'mae', 'mse', 'rmse',
'mape', 'smape']),
([1], [1], ['mae'], ['mae']),
([1], [1], ['mae', 'me'], ['mae', 'me']),
([1], [1], ['mae', 'me', 'smape'], ['mae', 'me',
'smape'])])
def test_forecast_error_return_funcs(y_true, y_pred, metrics, expected):
'''
test the correct error functions are returned
'''
funcs_dict = m.forecast_errors(y_true, y_pred, metrics)
assert list(funcs_dict.keys()) == expected
@pytest.mark.parametrize("y_pred, y_true, expected",
[([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], 0.0),
([1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12], 6.0),
([103, 130, 132, 124, 124, 108],
[129, 111, 122, 129, 110, 141], 17.833333),
([103, 130, 132, 124, 124, 108, 160, 160],
[129, 111, 122, 129, 110, 141, 142, 143], 17.75)])
def test_mean_absolute_error(y_true, y_pred, expected):
'''
test mean absolute error calculation
'''
error = m.mean_absolute_error(y_true, y_pred)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_pred, y_true, expected",
[([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], 0.0),
([1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12], 6.0),
([103, 130, 132, 124, 124, 108],
[129, 111, 122, 129, 110, 141], 3.5),
([103, 130, 132, 124, 124, 108, 160, 160],
[129, 111, 122, 129, 110, 141, 142, 143], -1.75)])
def test_mean_error(y_true, y_pred, expected):
'''
test mean error calculation
'''
error = m.mean_error(y_true, y_pred)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_pred, y_true, expected",
[([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], 0.0),
([1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
65.3210678210678),
([103, 130, 132, 124, 124, 108],
[129, 111, 122, 129, 110, 141],
14.2460623711587),
([103, 130, 132, 124, 124, 108, 160, 160],
[129, 111, 122, 129, 110, 141, 142, 143],
13.7550678066365)])
def test_mean_absolute_percentage_error(y_true, y_pred, expected):
'''
test mean error calculation
'''
error = m.mean_absolute_percentage_error(y_true, y_pred)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_pred, y_true, expected",
[([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], 0.0),
([1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
36.0),
([103, 130, 132, 124, 124, 108],
[129, 111, 122, 129, 110, 141],
407.833333333333),
([103, 130, 132, 124, 124, 108, 160, 160],
[129, 111, 122, 129, 110, 141, 142, 143],
382.50)])
def test_mean_squared_error(y_true, y_pred, expected):
'''
test mean squared error calculation
'''
error = m.mean_squared_error(y_true, y_pred)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_pred, y_true, expected",
[([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], 0.0),
([1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
6.0),
([103, 130, 132, 124, 124, 108],
[129, 111, 122, 129, 110, 141],
20.1948838405506),
([103, 130, 132, 124, 124, 108, 160, 160],
[129, 111, 122, 129, 110, 141, 142, 143],
19.5576072156079)])
def test_root_mean_squared_error(y_true, y_pred, expected):
'''
test root mean squared error calculation
'''
error = m.root_mean_squared_error(y_true, y_pred)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_pred, y_true, expected",
[([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], 0.0),
([1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
99.5634920634921),
([103, 130, 132, 124, 124, 108],
[129, 111, 122, 129, 110, 141],
14.7466414897349),
([103, 130, 132, 124, 124, 108, 160, 160],
[129, 111, 122, 129, 110, 141, 142, 143],
13.9526876064932)])
def test_symmetric_mape(y_true, y_pred, expected):
'''
test symmetric mean absolute percentage error calculation
'''
error = m.symmetric_mean_absolute_percentage_error(y_true, y_pred)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_true, y_intervals, expected",
[([10, 20, 30, 40, 50],
[[5, 15, 25, 35, 45],
[15, 25, 35, 45, 55]],
1.0),
([20, 20, 30, 40, 50],
[[5, 15, 25, 35, 45],
[15, 25, 35, 45, 55]],
0.8),
([20, 30, 30, 40, 50],
[[5, 15, 25, 35, 45],
[15, 25, 35, 45, 55]],
0.6),
([20, 20, 30, 40, 30],
[[5, 15, 25, 35, 45],
[15, 25, 35, 45, 55]],
0.6),
([100, 100, 100, 100, 100],
[[5, 15, 25, 35, 45],
[15, 25, 35, 45, 55]],
0.0)])
def test_coverage(y_true, y_intervals, expected):
'''
test prediction interval coverage
'''
y_intervals = np.array(y_intervals).T
error = m.coverage(y_true, y_intervals)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_train, y_pred, y_true, expected",
[(np.arange(10), [1, 2, 3, 4, 5, 6],
[1, 2, 3, 4, 5, 6], 0.0),
(np.arange(1, 21), np.arange(21, 26),
np.full(5, 10), 13)])
def test_mase_naive(y_train, y_true, y_pred, expected):
'''
test mean absolute scaled error calculation using naive as scaler.
test calcs produced using libre office calc.
'''
error = m.mean_absolute_scaled_error(y_true, y_pred, y_train, period=None)
assert pytest.approx(expected) == error
@pytest.mark.parametrize("y_train, y_pred, y_true, expected",
[(np.arange(1, 21), [1, 2, 3, 4, 5, 6],
[1, 2, 3, 4, 5, 6], 0.0),
(np.arange(1, 21), np.arange(21, 26),
np.full(5, 10), 1.85714286)])
def test_mase_snaive(y_train, y_true, y_pred, expected):
'''
test mean absolute scaled error calculation using SNaive as scaler
test calcs produced using libre office calc.
'''
error = m.mean_absolute_scaled_error(y_true, y_pred, y_train, period=7)
assert pytest.approx(expected) == error
| 42.133005 | 78 | 0.440313 | 1,057 | 8,553 | 3.437086 | 0.130558 | 0.045417 | 0.041288 | 0.060556 | 0.817781 | 0.746491 | 0.714836 | 0.70823 | 0.688137 | 0.645747 | 0 | 0.215257 | 0.406875 | 8,553 | 202 | 79 | 42.341584 | 0.500887 | 0.079387 | 0 | 0.507353 | 0 | 0 | 0.052905 | 0 | 0 | 0 | 0 | 0 | 0.080882 | 1 | 0.080882 | false | 0 | 0.022059 | 0 | 0.102941 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e700947920c447b701228e5faf198f323b5e2f51 | 8,333 | py | Python | twiliotutorial/tests/unit/test_beer.py | Telmediq/twiliotutorial | e3ef3514f38ba7aca905ce262e9bb0a33dfc0741 | [
"MIT"
] | null | null | null | twiliotutorial/tests/unit/test_beer.py | Telmediq/twiliotutorial | e3ef3514f38ba7aca905ce262e9bb0a33dfc0741 | [
"MIT"
] | 6 | 2020-02-12T01:21:56.000Z | 2021-06-10T18:42:23.000Z | twiliotutorial/tests/unit/test_beer.py | Telmediq/twiliotutorial | e3ef3514f38ba7aca905ce262e9bb0a33dfc0741 | [
"MIT"
] | null | null | null | import json
from unittest import mock
from unittest.mock import call
from django.conf import settings
from django.test import SimpleTestCase
from twiliotutorial.beer import Beer, BeerFact
class BeerTestCase(SimpleTestCase):
"""Test Beer class methods. Since we dont have a Database, use SimpleTestCase"""
def test__get_random_beer_url__returns_url(self):
expected_url = 'https://sandbox-api.brewerydb.com/v2/beer/random'
beer = Beer()
url = beer.get_random_beer_url()
self.assertEqual(url, expected_url)
def test__get_beer_with_id_url__returns_url(self):
expected_url = 'https://sandbox-api.brewerydb.com/v2/beers'
beer = Beer()
url = beer.get_beer_with_id_url()
self.assertEqual(url, expected_url)
@mock.patch('twiliotutorial.beer.requests')
def test__get_beer_fact_from_api__returns_beer_fact(self, mock_requests):
expected_result = json.dumps({
'data': {
'name': 'test',
'id': 'test_id',
'abv': '1.0',
'ibu': 99,
'style': {'description': 'test_description'}
}
})
mock_result = mock.Mock()
mock_result.status_code = 200
mock_result.content = expected_result
mock_requests.get.return_value = mock_result
beer = Beer()
result = beer.get_beer_fact_from_api('http://some/url', {'some': 'param'})
self.assertEqual(result, json.loads(expected_result))
@mock.patch('twiliotutorial.beer.requests')
def test__get_beer_fact_from_api__status_non_200__returns_empty_result(self, mock_requests):
mock_result = mock.Mock()
mock_result.status_code = 404
mock_requests.get.return_value = mock_result
beer = Beer()
result = beer.get_beer_fact_from_api('http://some/url', {'some': 'param'})
self.assertEqual(result, {})
@mock.patch('twiliotutorial.beer.requests')
def test__get_beer_fact_from_api__content_not_json__returns_empty_result(self, mock_requests):
bad_json = 'foo: bar, [waz, foop]'
mock_result = mock.Mock()
mock_result.status_code = 200
mock_result.content = bad_json
mock_requests.get.return_value = mock_result
beer = Beer()
result = beer.get_beer_fact_from_api('http://some/url', {'some': 'param'})
self.assertEqual(result, {})
def test__convert_result_to_beer_fact__paginated__returns_first_result_as_beer_fact(self):
mock_api_response_data = {
'currentPage': 1,
'data': [{
'name': 'test',
'id': 'test_id',
'abv': '1.0',
'ibu': 99,
'style': {'description': 'test_description'}
}, ]
}
data = mock_api_response_data['data'][0]
expected_beer_fact = BeerFact(id=data.get('id'), name=data.get('name'), abv=data.get('abv'),
style=data.get('style'),
ibu=data.get('ibu'))
beer = Beer()
result = beer.convert_result_to_beer_fact(mock_api_response_data)
self.assertEqual(result, expected_beer_fact)
def test__convert_result_to_beer_fact__nonpaginated__returns_result_as_beer_fact(self):
mock_api_response_data = {
'data': {
'name': 'test',
'id': 'test_id',
'abv': '1.0',
'ibu': 99,
'style': {'description': 'test_description'}
}
}
data = mock_api_response_data['data']
expected_beer_fact = BeerFact(id=data.get('id'), name=data.get('name'), abv=data.get('abv'),
style=data.get('style'),
ibu=data.get('ibu'))
beer = Beer()
result = beer.convert_result_to_beer_fact(mock_api_response_data)
self.assertEqual(result, expected_beer_fact)
def test__convert_result_to_beer_fact__no_data__returns_empty_beer_fact(self):
expected_beer_fact = BeerFact(id=None, name=None, abv=None, style=None, ibu=None)
mock_api_response_data = {}
beer = Beer()
result = beer.convert_result_to_beer_fact(mock_api_response_data)
self.assertEqual(result, expected_beer_fact)
def test__convert_result_to_beer_fact__data_empty__returns_empty_beer_fact(self):
expected_beer_fact = BeerFact(id=None, name=None, abv=None, style=None, ibu=None)
mock_api_response_data = {
'data': None
}
beer = Beer()
result = beer.convert_result_to_beer_fact(mock_api_response_data)
self.assertEqual(result, expected_beer_fact)
def test__convert_result_to_beer_fact__no_id__returns_empty_beer_fact(self):
expected_beer_fact = BeerFact(id=None, name=None, abv=None, style=None, ibu=None)
mock_api_response_data = {
'data': {'name': 'test_name'}
}
beer = Beer()
result = beer.convert_result_to_beer_fact(mock_api_response_data)
self.assertEqual(result, expected_beer_fact)
def test__convert_result_to_beer_fact__no_name__returns_empty_beer_fact(self):
expected_beer_fact = BeerFact(id=None, name=None, abv=None, style=None, ibu=None)
mock_api_response_data = {
'data': {'id': 'test_id'}
}
beer = Beer()
result = beer.convert_result_to_beer_fact(mock_api_response_data)
self.assertEqual(result, expected_beer_fact)
@mock.patch('twiliotutorial.beer.Beer.get_random_beer_url')
@mock.patch('twiliotutorial.beer.Beer.get_beer_fact_from_api')
def test__get_random_beer_fact__calls_get_beer_fact_from_api_with_params__returns_expected_beer_fact(self,
mock_get,
mock_url):
expected_data = {
'data': {
'name': 'test',
'id': 'test_id',
'abv': '1.0',
'ibu': 99,
'style': {'description': 'test_description'}
}
}
data = expected_data.get('data')
expected_beer_fact = BeerFact(id=data.get('id'), name=data.get('name'), abv=data.get('abv'),
style=data.get('style'),
ibu=data.get('ibu'))
mock_get.return_value = expected_data
mock_url.return_value = 'http:/useless.org'
beer = Beer()
beer_fact = beer.get_random_beer_fact()
self.assertEqual(beer_fact, expected_beer_fact)
self.assertEqual(mock_get.call_args_list[0], call(url=mock_url(), params={'key': settings.BEER_API_KEY}))
@mock.patch('twiliotutorial.beer.Beer.get_beer_with_id_url')
@mock.patch('twiliotutorial.beer.Beer.get_beer_fact_from_api')
def test__get_beer_by_id__calls_get_beer_fact_from_api_with_params__returns_expected_beer_fact(self,
mock_get,
mock_url):
expected_data = {
'currentPage': 1,
'data': [{
'name': 'test',
'id': 'test_id',
'abv': '1.0',
'ibu': 99,
'style': {'description': 'test_description'}
}, ]
}
data = expected_data.get('data')[0]
expected_beer_fact = BeerFact(id=data.get('id'), name=data.get('name'), abv=data.get('abv'),
style=data.get('style'),
ibu=data.get('ibu'))
mock_get.return_value = expected_data
mock_url.return_value = 'http:/useless.org'
beer = Beer()
beer_fact = beer.get_beer_by_id('test_id')
self.assertEqual(beer_fact, expected_beer_fact)
self.assertEqual(mock_get.call_args_list[0],
call(url=mock_url(), params={'key': settings.BEER_API_KEY, 'ids': 'test_id'}))
| 40.0625 | 115 | 0.579383 | 966 | 8,333 | 4.587992 | 0.096273 | 0.095668 | 0.064982 | 0.060018 | 0.878384 | 0.857626 | 0.814982 | 0.797834 | 0.78926 | 0.773466 | 0 | 0.006958 | 0.310092 | 8,333 | 207 | 116 | 40.256039 | 0.763959 | 0.00888 | 0 | 0.666667 | 0 | 0 | 0.115958 | 0.032352 | 0 | 0 | 0 | 0 | 0.089286 | 1 | 0.077381 | false | 0 | 0.035714 | 0 | 0.119048 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e76d038a0c563a2aabc5139fe5f6def48e7226b3 | 42 | py | Python | database/models/__init__.py | ai404/esafe-platform | 2e29ab2d3deb81fd999b74a2f6844c54a836c6d8 | [
"MIT"
] | null | null | null | database/models/__init__.py | ai404/esafe-platform | 2e29ab2d3deb81fd999b74a2f6844c54a836c6d8 | [
"MIT"
] | null | null | null | database/models/__init__.py | ai404/esafe-platform | 2e29ab2d3deb81fd999b74a2f6844c54a836c6d8 | [
"MIT"
] | null | null | null | from .account import *
from .main import * | 21 | 22 | 0.738095 | 6 | 42 | 5.166667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 42 | 2 | 23 | 21 | 0.885714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e7b43b052276fdf6a913ba420462bd2b9031c9f6 | 138 | py | Python | extract_leaked_messages_to_csv_and_sqlite.py | DavidDarlingKhepryOrg/patriot-coalition-leaked-message-analysis | 00533276abb9aa3a7f1b6562eb675ac2c7a60c92 | [
"Apache-2.0"
] | null | null | null | extract_leaked_messages_to_csv_and_sqlite.py | DavidDarlingKhepryOrg/patriot-coalition-leaked-message-analysis | 00533276abb9aa3a7f1b6562eb675ac2c7a60c92 | [
"Apache-2.0"
] | 2 | 2020-10-09T05:20:13.000Z | 2020-10-09T05:28:16.000Z | extract_leaked_messages_to_csv_and_sqlite.py | DavidDarlingKhepryOrg/patriot-coalition-leaked-message-analysis | 00533276abb9aa3a7f1b6562eb675ac2c7a60c92 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
from leaked_message_extractor import extract_leaked_messages
if __name__ == "__main__":
extract_leaked_messages()
| 19.714286 | 60 | 0.797101 | 17 | 138 | 5.647059 | 0.764706 | 0.270833 | 0.4375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115942 | 138 | 6 | 61 | 23 | 0.786885 | 0.115942 | 0 | 0 | 0 | 0 | 0.066116 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
99d0b5c144a4b958fe93acd7abdc08f1f20f0c5a | 87 | py | Python | aluno/models.py | latreta/dvestagio | d18f7c7184748c7b88e335ae9ffd2bdcc197d14f | [
"MIT"
] | null | null | null | aluno/models.py | latreta/dvestagio | d18f7c7184748c7b88e335ae9ffd2bdcc197d14f | [
"MIT"
] | null | null | null | aluno/models.py | latreta/dvestagio | d18f7c7184748c7b88e335ae9ffd2bdcc197d14f | [
"MIT"
] | null | null | null | from django.contrib.auth.models import AbstractUser
class Aluno(AbstractSet):
pass | 21.75 | 51 | 0.804598 | 11 | 87 | 6.363636 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126437 | 87 | 4 | 52 | 21.75 | 0.921053 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
99d58b1f25371a6eb9ba62d50293307f7182fbdd | 16,857 | py | Python | named-entity-recognition/modeling.py | minstar/biobert-pytorch | 344e16e0e1afa508f4959eeabf12072e0da07ed3 | [
"Apache-2.0"
] | null | null | null | named-entity-recognition/modeling.py | minstar/biobert-pytorch | 344e16e0e1afa508f4959eeabf12072e0da07ed3 | [
"Apache-2.0"
] | null | null | null | named-entity-recognition/modeling.py | minstar/biobert-pytorch | 344e16e0e1afa508f4959eeabf12072e0da07ed3 | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
import os
import pdb
import torch
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from transformers import (
BertConfig,
BertModel,
BertForTokenClassification,
BertTokenizer,
RobertaConfig,
RobertaForTokenClassification,
RobertaTokenizer
)
from torchcrf import CRF
class BioMultiNER(BertForTokenClassification):
def __init__(self, config, num_labels=3):
super(BioMultiNER, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier_1 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.classifier_2 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.classifier_3 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.classifier_4 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.classifier_5 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.classifier_6 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.classifier_7 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.classifier_8 = torch.nn.Linear(config.hidden_size, self.num_labels)
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, \
ent_ids_1=None, ent_ids_2=None, ent_ids_3=None, ent_ids_4=None, \
ent_ids_5=None, ent_ids_6=None, ent_ids_7=None, ent_ids_8=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size,max_len,feat_dim = sequence_output.shape
sequence_output = self.dropout(sequence_output)
# logits = self.classifier(sequence_output)
### NCBI-disease ###
d_logits = self.classifier_1(sequence_output)
ent_ids_1 = torch.unsqueeze(ent_ids_1, 2)
d_logits = ent_ids_1 * d_logits
# ### BC5CDR-disease ###
bd_logits = self.classifier_2(sequence_output)
ent_ids_2 = torch.unsqueeze(ent_ids_2, 2)
bd_logits = ent_ids_2 * bd_logits
### BC5CDR-chem ###
bc_logits = self.classifier_3(sequence_output)
ent_ids_3 = torch.unsqueeze(ent_ids_3, 2)
bc_logits = ent_ids_3 * bc_logits
### BC4CHEMD ###
c_logits = self.classifier_4(sequence_output)
ent_ids_4 = torch.unsqueeze(ent_ids_4, 2)
c_logits = ent_ids_4 * c_logits
# ### BC2GM ###
g_logits = self.classifier_5(sequence_output)
ent_ids_5 = torch.unsqueeze(ent_ids_5, 2)
g_logits = ent_ids_5 * g_logits
### JNLPBA 2 ###
jn_logits = self.classifier_6(sequence_output)
ent_ids_6 = torch.unsqueeze(ent_ids_6, 2)
jn_logits = ent_ids_6 * jn_logits
### linnaeus ###
li_logits = self.classifier_7(sequence_output)
ent_ids_7 = torch.unsqueeze(ent_ids_7, 2)
li_logits = ent_ids_7 * li_logits
### s800 ###
s8_logits = self.classifier_8(sequence_output)
ent_ids_8 = torch.unsqueeze(ent_ids_8, 2)
s8_logits = ent_ids_8 * s8_logits
logits = d_logits + g_logits + c_logits + bd_logits + bc_logits + jn_logits + li_logits + s8_logits
outputs = (logits, sequence_output)
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
# active_loss = attention_mask.view(-1) == 1
# active_logits = logits.view(-1, self.num_labels)[active_loss]
# active_labels = labels.view(-1)[active_loss]
# loss = loss_fct(active_logits, active_labels)
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
return ((loss,) + outputs)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return loss
else:
return logits
class BioUniNER(BertForTokenClassification):
def __init__(self, config, num_labels=9):
super(BioUniNER, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier = torch.nn.Linear(config.hidden_size, self.num_labels)
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size,max_len,feat_dim = sequence_output.shape
sequence_output = self.dropout(sequence_output)
# logits = self.classifier(sequence_output)
### NCBI-disease ###
logits = self.classifier(sequence_output)
outputs = (logits, sequence_output)
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
# active_loss = attention_mask.view(-1) == 1
# active_logits = logits.view(-1, self.num_labels)[active_loss]
# active_labels = labels.view(-1)[active_loss]
# loss = loss_fct(active_logits, active_labels)
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
return ((loss,) + outputs)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return loss
else:
return logits
class CRFNER(BertForTokenClassification):
def __init__(self, config, num_labels=4):
super(CRFNER, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier = torch.nn.Linear(config.hidden_size, self.num_labels)
self.crf = CRF(num_tags=self.num_labels, batch_first=True)
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size, max_len, feat_dim = sequence_output.shape
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
outputs = (logits, sequence_output)
if labels is not None:
if attention_mask is not None:
# active_loss = attention_mask.view(-1) == 1
# active_logits = logits.view(-1, self.num_labels)
# active_labels = torch.where(
# active_loss, labels.view(-1), torch.tensor(3).type_as(labels)
# )
# active_logits = active_logits.view(batch_size, max_len, self.num_labels)
# active_labels = active_labels.view(batch_size, max_len)
attention_mask = attention_mask.type(torch.uint8)
log_likelihood, sequence_of_tags = self.crf(logits, labels, mask=attention_mask, reduction='mean'), self.crf.decode(logits)
return ((-1 * log_likelihood,) + outputs)
else:
log_likelihood = self.crf(logits, labels, reduction='mean')
return -1 * log_likelihood
else:
return logits
class BiLSTMCRFNER(BertForTokenClassification):
def __init__(self, config, num_labels=4):
super(BiLSTMCRFNER, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier = torch.nn.Linear(config.hidden_size, self.num_labels)
self.crf = CRF(num_tags=self.num_labels, batch_first=True)
self.bilstm = torch.nn.LSTM(config.hidden_size, (config.hidden_size) // 2, dropout=config.hidden_dropout_prob, batch_first=True, bidirectional=True)
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size, max_len, feat_dim = sequence_output.shape
sequence_output = self.dropout(sequence_output)
sequence_output, hc = self.bilstm(sequence_output)
logits = self.classifier(sequence_output)
outputs = (logits, sequence_output)
if labels is not None:
if attention_mask is not None:
attention_mask = attention_mask.type(torch.uint8)
log_likelihood, sequence_of_tags = self.crf(logits, labels, mask=attention_mask, reduction='mean'), self.crf.decode(logits)
return ((-1 * log_likelihood, ) + outputs)
else:
log_likelihood = self.crf(logits, labels, reduction='mean')
return -1 * log_likelihood
else:
return logits
class CRFNER_MASKSIM(BertForTokenClassification):
def __init__(self, config, num_labels=4, alpha=0):
super(CRFNER_MASKSIM, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier = torch.nn.Linear(config.hidden_size, self.num_labels)
self.crf = CRF(num_tags=self.num_labels, batch_first=True)
self.alpha = alpha
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size, max_len, feat_dim = sequence_output.shape
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
outputs = (logits, sequence_output)
sep_index = attention_mask.sum(dim=1) - token_type_ids.sum(dim=1)
final_index = attention_mask.sum(dim=1)
get_bef_embeds, get_aft_embeds = [], []
for batch_idx in range(batch_size):
bef_out = torch.mean(sequence_output[batch_idx][1:sep_index[batch_idx]], dim=0)
aft_out = torch.mean(sequence_output[batch_idx][sep_index[batch_idx]+1:final_index[batch_idx]], dim=0)
bef_out = bef_out.unsqueeze(dim=0)
aft_out = aft_out.unsqueeze(dim=0)
get_bef_embeds.append(bef_out)
get_aft_embeds.append(aft_out)
bef_embeddings = torch.cat(get_bef_embeds) # (batch, feat_dim)
aft_embeddings = torch.cat(get_aft_embeds) # (batch, feat_dim)
if labels is not None:
if attention_mask is not None:
attention_mask = attention_mask.type(torch.uint8)
log_likelihood, sequence_of_tags = self.crf(logits, labels, mask=attention_mask, reduction='mean'), self.crf.decode(logits)
cossim = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
cossim_loss = torch.mean(1. - cossim(bef_embeddings, aft_embeddings))
return (((1-self.alpha) * -1 * log_likelihood + self.alpha * cossim_loss,) + outputs)
else:
log_likelihood = self.crf(logits, labels, reduction='mean')
cossim = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
cossim_loss = torch.mean(1. - cossim(bef_embeddings, aft_embeddings))
return (1-self.alpha) * -1 * log_likelihood + self.alpha * cossim_loss
else:
return logits
class BioNER(BertForTokenClassification):
def __init__(self, config, num_labels=3, random_bias=False, freq_bias=False, pmi_bias=True):
super(BioNER, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier = torch.nn.Linear(config.hidden_size, self.num_labels)
self.random_bias = random_bias
self.freq_bias = freq_bias
self.pmi_bias = pmi_bias
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, bias_tensor=None, data_type=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size,max_len,feat_dim = sequence_output.shape
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
if data_type[0][0].item() == 1:
if self.random_bias:
rand_logits = torch.rand(batch_size, max_len, self.num_labels).cuda()
logits = logits + rand_logits
elif self.freq_bias or self.pmi_bias:
logits = logits + bias_tensor
outputs = (logits, sequence_output)
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
# active_loss = attention_mask.view(-1) == 1
# active_logits = logits.view(-1, self.num_labels)[active_loss]
# active_labels = labels.view(-1)[active_loss]
# loss = loss_fct(active_logits, active_labels)
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
return ((loss,) + outputs)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return loss
else:
return logits
class GeneralNER(BertForTokenClassification):
def __init__(self, config, num_labels=9, random_bias=False, freq_bias=False, pmi_bias=True):
super(GeneralNER, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
self.classifier = torch.nn.Linear(config.hidden_size, self.num_labels)
self.random_bias = random_bias
self.freq_bias = freq_bias
self.pmi_bias = pmi_bias
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, bias_tensor=None, data_type=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size,max_len,feat_dim = sequence_output.shape
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
if data_type[0][0].item() == 1:
if self.random_bias:
rand_logits = torch.rand(batch_size, max_len, self.num_labels).cuda()
logits = logits + rand_logits
elif self.freq_bias or self.pmi_bias:
logits = logits + bias_tensor
outputs = (logits, sequence_output)
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
# active_loss = attention_mask.view(-1) == 1
# active_logits = logits.view(-1, self.num_labels)[active_loss]
# active_labels = labels.view(-1)[active_loss]
# loss = loss_fct(active_logits, active_labels)
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
return ((loss,) + outputs)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return loss
else:
return logits | 46.31044 | 156 | 0.635048 | 2,117 | 16,857 | 4.757676 | 0.073217 | 0.07645 | 0.051628 | 0.02641 | 0.823173 | 0.810068 | 0.805103 | 0.795572 | 0.769261 | 0.757744 | 0 | 0.013296 | 0.268257 | 16,857 | 364 | 157 | 46.31044 | 0.803243 | 0.08756 | 0 | 0.67029 | 0 | 0 | 0.001569 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.050725 | false | 0 | 0.025362 | 0 | 0.177536 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
821650f3a8ecee7fcfadd17c136b1f722c9a5562 | 4,853 | py | Python | code/model/encoder/mpnn_encoder.py | ZJU-Fangyin/KCL | 1d1002aeee785e4eb1dfc121d6cbb9cefa4e985c | [
"MIT"
] | 24 | 2021-12-04T13:44:22.000Z | 2022-03-19T08:10:19.000Z | code/model/encoder/mpnn_encoder.py | Fangyin1994/KCL | 004f5681b77e4e75c791c909696fdb8a208501a2 | [
"MIT"
] | 3 | 2021-12-20T08:14:06.000Z | 2022-03-28T08:03:09.000Z | code/model/encoder/mpnn_encoder.py | Fangyin1994/KCL | 004f5681b77e4e75c791c909696fdb8a208501a2 | [
"MIT"
] | 1 | 2021-12-22T09:29:55.000Z | 2021-12-22T09:29:55.000Z |
import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
from dgl.nn.pytorch import NNConv
from ..layer.kmpnn import KMPNN
class MPNNGNN(nn.Module):
def __init__(self, args):
super(MPNNGNN, self).__init__()
self.project_node_feats = nn.Sequential(
nn.Linear(args['node_indim'], args['node_hidden_feats']),
nn.ReLU()
)
self.num_step_message_passing = args['num_step_message_passing']
edge_network = nn.Sequential(
nn.Linear(args['edge_indim'], args['edge_hidden_feats']),
nn.ReLU(),
nn.Linear(args['edge_hidden_feats'], args['node_hidden_feats'] * args['node_hidden_feats'])
)
self.gnn_layer = NNConv(
in_feats=args['node_hidden_feats'],
out_feats=args['node_hidden_feats'],
edge_func=edge_network,
aggregator_type='sum'
)
self.gru = nn.GRU(args['node_hidden_feats'], args['node_hidden_feats'])
self.out_dim = args['node_hidden_feats']
self.node_emb = nn.Embedding(343, args['node_indim'])
self.edge_emb = nn.Embedding(21, args['edge_indim'])
def reset_parameters(self):
"""Reinitialize model parameters."""
self.project_node_feats[0].reset_parameters()
self.gnn_layer.reset_parameters()
for layer in self.gnn_layer.edge_func:
if isinstance(layer, nn.Linear):
layer.reset_parameters()
self.gru.reset_parameters()
def forward(self, g):
node_feats = self.node_emb(g.ndata['h'])
edge_feats = self.edge_emb(g.edata['e'])
node_feats = self.project_node_feats(node_feats) # (V, node_out_feats)
hidden_feats = node_feats.unsqueeze(0) # (1, V, node_out_feats)
for _ in range(self.num_step_message_passing):
node_feats = F.relu(self.gnn_layer(g, node_feats, edge_feats))
node_feats, hidden_feats = self.gru(node_feats.unsqueeze(0), hidden_feats)
node_feats = node_feats.squeeze(0)
return node_feats
class KMPNNGNN(nn.Module):
def __init__(self, args, entity_emb, relation_emb):
super(KMPNNGNN, self).__init__()
self.project_node_feats = nn.Sequential(
nn.Linear(args['node_indim'], args['node_hidden_feats']),
nn.ReLU()
)
self.num_step_message_passing = args['num_step_message_passing']
attn_fc = nn.Linear(2 * args['node_hidden_feats'], 1, bias=False)
edge_network1 = nn.Sequential(
nn.Linear(args['edge_indim'], args['edge_hidden_feats']),
nn.ReLU(),
nn.Linear(args['edge_hidden_feats'], args['node_hidden_feats'] * args['node_hidden_feats'])
)
edge_network2 = nn.Sequential(
nn.Linear(args['edge_indim'], args['edge_hidden_feats']),
nn.ReLU(),
nn.Linear(args['edge_hidden_feats'], args['node_hidden_feats'] * args['node_hidden_feats'])
)
self.gnn_layer = KMPNN(
in_feats=args['node_hidden_feats'],
out_feats=args['node_hidden_feats'],
attn_fc=attn_fc,
edge_func1=edge_network1,
edge_func2=edge_network2,
aggregator_type='sum'
)
self.gru = nn.GRU(args['node_hidden_feats'], args['node_hidden_feats'])
self.out_dim = args['node_hidden_feats']
# self.node_emb = nn.Embedding(343, args['node_indim'])
# self.edge_emb = nn.Embedding(21, args['edge_indim'])
atom_emb = torch.randn((118, args['node_indim']))
node_emb = torch.cat((atom_emb, entity_emb),0)
bond_emb = torch.randn((4,args['edge_indim']))
edge_emb = torch.cat((bond_emb, relation_emb),0)
self.node_emb = nn.Embedding.from_pretrained(node_emb, freeze=False)
self.edge_emb = nn.Embedding.from_pretrained(edge_emb, freeze=False)
def reset_parameters(self):
"""Reinitialize model parameters."""
self.project_node_feats[0].reset_parameters()
self.gnn_layer.reset_parameters()
for layer in self.gnn_layer.edge_func:
if isinstance(layer, nn.Linear):
layer.reset_parameters()
self.gru.reset_parameters()
def forward(self, g):
node_feats = self.node_emb(g.ndata['h'])
edge_feats = self.edge_emb(g.edata['e'])
node_feats = self.project_node_feats(node_feats) # (V, node_out_feats)
hidden_feats = node_feats.unsqueeze(0) # (1, V, node_out_feats)
for _ in range(self.num_step_message_passing):
node_feats = F.relu(self.gnn_layer(g, node_feats, edge_feats))
node_feats, hidden_feats = self.gru(node_feats.unsqueeze(0), hidden_feats)
node_feats = node_feats.squeeze(0)
return node_feats
| 39.137097 | 103 | 0.630744 | 637 | 4,853 | 4.470958 | 0.138148 | 0.119733 | 0.093399 | 0.126756 | 0.82198 | 0.796699 | 0.777739 | 0.777739 | 0.777739 | 0.777739 | 0 | 0.009287 | 0.245621 | 4,853 | 123 | 104 | 39.455285 | 0.768642 | 0.052545 | 0 | 0.622449 | 0 | 0 | 0.125136 | 0.010483 | 0 | 0 | 0 | 0 | 0 | 1 | 0.061224 | false | 0.040816 | 0.061224 | 0 | 0.163265 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
82372c1aa5eed2d3b2988372440512d2bc376ebe | 201 | py | Python | Python/money_calculator.py | janvi16/-HACKTOBERFEST2K20 | aa9c8b6f7feb245793c0a003ba6fbea3fca9ca22 | [
"Apache-2.0"
] | null | null | null | Python/money_calculator.py | janvi16/-HACKTOBERFEST2K20 | aa9c8b6f7feb245793c0a003ba6fbea3fca9ca22 | [
"Apache-2.0"
] | null | null | null | Python/money_calculator.py | janvi16/-HACKTOBERFEST2K20 | aa9c8b6f7feb245793c0a003ba6fbea3fca9ca22 | [
"Apache-2.0"
] | null | null | null | def money_calculator(montante, taxa_juros_simples_mensal, dias_corridos):
#Calculates the money if you make an investment
return montante + montante*(taxa_juros_simples_mensal/30)*dias_corridos | 67 | 75 | 0.825871 | 28 | 201 | 5.607143 | 0.678571 | 0.152866 | 0.216561 | 0.305732 | 0.382166 | 0 | 0 | 0 | 0 | 0 | 0 | 0.011236 | 0.114428 | 201 | 3 | 75 | 67 | 0.870787 | 0.228856 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
41493a791b2cee75d4115b2fcbfd5340303fc657 | 249 | py | Python | CircuitQuantifier/critics.py | utkarsh7236/SCILLA | e11e4d753823ad522a1b3168283b6e6ffe3ea393 | [
"Apache-2.0"
] | 17 | 2019-12-09T19:09:07.000Z | 2021-08-29T01:11:13.000Z | CircuitQuantifier/critics.py | utkarsh7236/SCILLA | e11e4d753823ad522a1b3168283b6e6ffe3ea393 | [
"Apache-2.0"
] | 1 | 2021-04-14T15:08:18.000Z | 2021-04-14T15:08:18.000Z | CircuitQuantifier/critics.py | utkarsh7236/SCILLA | e11e4d753823ad522a1b3168283b6e6ffe3ea393 | [
"Apache-2.0"
] | 2 | 2020-06-05T03:01:06.000Z | 2020-07-09T07:13:12.000Z |
from CircuitQuantifier.critic_double_well import merit_DoubleWell
from CircuitQuantifier.critic_example_multi_evaluations import merit_TwoEvalExample
from CircuitQuantifier.critic_target_spectrum import merit_TargetSpectrum
| 49.8 | 83 | 0.839357 | 25 | 249 | 7.96 | 0.6 | 0.316583 | 0.407035 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148594 | 249 | 4 | 84 | 62.25 | 0.938679 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
4189aab0db9345e40ecf39d8a707768eb97cc0e9 | 35,366 | py | Python | src/xspline/core.py | zhengp0/bspline | 2302f11419e8b13305c93850ecd6df9045390dd6 | [
"BSD-2-Clause"
] | 4 | 2019-12-05T22:52:56.000Z | 2021-02-16T07:47:55.000Z | src/xspline/core.py | zhengp0/bspline | 2302f11419e8b13305c93850ecd6df9045390dd6 | [
"BSD-2-Clause"
] | null | null | null | src/xspline/core.py | zhengp0/bspline | 2302f11419e8b13305c93850ecd6df9045390dd6 | [
"BSD-2-Clause"
] | 1 | 2020-06-25T22:05:42.000Z | 2020-06-25T22:05:42.000Z | # -*- coding: utf-8 -*-
"""
core
~~~~
core module contains main functions and classes.
"""
import numpy as np
from . import utils
def bspline_domain(knots, degree, idx, l_extra=False, r_extra=False):
r"""Compute the support for the spline basis, knots degree and the index of
the basis.
Args:
knots (numpy.ndarray):
1D array that stores the knots of the splines.
degree (int):
A non-negative integer that indicates the degree of the polynomial.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
numpy.ndarray:
1D array with two elements represents that left and right end of the
support of the spline basis.
"""
num_knots = knots.size
num_intervals = num_knots - 1
num_splines = num_intervals + degree
if idx == -1:
idx = num_splines - 1
lb = knots[max(idx - degree, 0)]
ub = knots[min(idx + 1, num_intervals)]
if idx == 0 and l_extra:
lb = -np.inf
if idx == num_splines - 1 and r_extra:
ub = np.inf
return np.array([lb, ub])
def bspline_fun(x, knots, degree, idx, l_extra=False, r_extra=False):
r"""Compute the spline basis.
Args:
x (float | numpy.ndarray):
Scalar or numpy array that store the independent variables.
knots (numpy.ndarray):
1D array that stores the knots of the splines.
degree (int):
A non-negative integer that indicates the degree of the polynomial.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
float | numpy.ndarray:
Function values of the corresponding spline bases.
"""
num_knots = knots.size
num_intervals = num_knots - 1
num_splines = num_intervals + degree
if idx == -1:
idx = num_splines - 1
b = bspline_domain(knots, degree, idx, l_extra=l_extra, r_extra=r_extra)
if degree == 0:
f = utils.indicator_f(x, b, r_close=(idx == num_splines - 1))
return f
if idx == 0:
b_effect = bspline_domain(knots, degree, idx)
y = utils.indicator_f(x, b)
z = utils.linear_rf(x, b_effect)
return y*(z**degree)
if idx == num_splines - 1:
b_effect = bspline_domain(knots, degree, idx)
y = utils.indicator_f(x, b, r_close=True)
z = utils.linear_lf(x, b_effect)
return y*(z**degree)
lf = bspline_fun(x, knots, degree - 1, idx - 1,
l_extra=l_extra, r_extra=r_extra)
lf *= utils.linear_lf(x, bspline_domain(knots, degree - 1, idx - 1))
rf = bspline_fun(x, knots, degree - 1, idx,
l_extra=l_extra, r_extra=r_extra)
rf *= utils.linear_rf(x, bspline_domain(knots, degree - 1, idx))
return lf + rf
def bspline_dfun(x, knots, degree, order, idx, l_extra=False, r_extra=False):
r"""Compute the derivative of the spline basis.
Args:
x (float | numpy.ndarray):
Scalar or numpy array that store the independent variables.
knots (numpy.ndarray):
1D array that stores the knots of the splines.
degree (int):
A non-negative integer that indicates the degree of the polynomial.
order (int):
A non-negative integer that indicates the order of differentiation.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
float | numpy.ndarray:
Derivative values of the corresponding spline bases.
"""
num_knots = knots.size
num_intervals = num_knots - 1
num_splines = num_intervals + degree
if idx == -1:
idx = num_splines - 1
if order == 0:
return bspline_fun(x, knots, degree, idx,
l_extra=l_extra, r_extra=r_extra)
if order > degree:
if np.isscalar(x):
return 0.0
else:
return np.zeros(len(x))
if idx == 0:
rdf = 0.0
else:
b = bspline_domain(knots, degree - 1, idx - 1)
d = b[1] - b[0]
f = (x - b[0])/d
rdf = f*bspline_dfun(x, knots, degree - 1, order, idx - 1,
l_extra=l_extra, r_extra=r_extra)
rdf += order*bspline_dfun(x, knots, degree - 1, order - 1, idx - 1,
l_extra=l_extra, r_extra=r_extra)/d
if idx == num_splines - 1:
ldf = 0.0
else:
b = bspline_domain(knots, degree - 1, idx)
d = b[0] - b[1]
f = (x - b[1])/d
ldf = f*bspline_dfun(x, knots, degree - 1, order, idx,
l_extra=l_extra, r_extra=r_extra)
ldf += order*bspline_dfun(x, knots, degree - 1, order - 1, idx,
l_extra=l_extra, r_extra=r_extra)/d
return ldf + rdf
def bspline_ifun(a, x, knots, degree, order, idx, l_extra=False, r_extra=False):
r"""Compute the integral of the spline basis.
Args:
a (float | numpy.ndarray):
Scalar or numpy array that store the starting point of the integration.
x (float | numpy.ndarray):
Scalar or numpy array that store the ending point of the integration.
knots (numpy.ndarray):
1D array that stores the knots of the splines.
degree (int):
A non-negative integer that indicates the degree of the polynomial.
order (int):
A non-negative integer that indicates the order of integration.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
float | numpy.ndarray:
Integral values of the corresponding spline bases.
"""
num_knots = knots.size
num_intervals = num_knots - 1
num_splines = num_intervals + degree
if idx == -1:
idx = num_splines - 1
if order == 0:
return bspline_fun(x, knots, degree, idx,
l_extra=l_extra, r_extra=r_extra)
if degree == 0:
b = bspline_domain(knots, degree, idx, l_extra=l_extra, r_extra=r_extra)
return utils.indicator_if(a, x, order, b)
if idx == 0:
rif = 0.0
else:
b = bspline_domain(knots, degree - 1, idx - 1)
d = b[1] - b[0]
f = (x - b[0]) / d
rif = f*bspline_ifun(a, x, knots, degree - 1, order, idx - 1,
l_extra=l_extra, r_extra=r_extra)
rif -= order*bspline_ifun(a, x, knots, degree - 1, order + 1, idx - 1,
l_extra=l_extra, r_extra=r_extra)/d
if idx == num_splines - 1:
lif = 0.0
else:
b = bspline_domain(knots, degree - 1, idx)
d = b[0] - b[1]
f = (x - b[1]) / d
lif = f*bspline_ifun(a, x, knots, degree - 1, order, idx,
l_extra=l_extra, r_extra=r_extra)
lif -= order*bspline_ifun(a, x, knots, degree - 1, order + 1, idx,
l_extra=l_extra, r_extra=r_extra)/d
return lif + rif
class XSpline:
"""XSpline main class of the package.
"""
def __init__(self,
knots,
degree,
l_linear=False,
r_linear=False,
include_first_basis: bool = True):
r"""Constructor of the XSpline class.
knots (numpy.ndarray):
1D numpy array that store the knots, must including that boundary knots.
degree (int):
A non-negative integer that indicates the degree of the spline.
l_linear (bool, optional):
A bool variable, that if using the linear tail at left end.
r_linear (bool, optional):
A bool variable, that if using the linear tail at right end.
"""
# pre-process the knots vector
knots = list(set(knots))
knots = np.sort(np.array(knots))
self.knots = knots
self.degree = degree
self.l_linear = l_linear
self.r_linear = r_linear
self.basis_start = int(not include_first_basis)
# dimensions
self.num_knots = knots.size
self.num_intervals = knots.size - 1
# check inputs
int_l_linear = int(l_linear)
int_r_linear = int(r_linear)
assert self.num_intervals >= 1 + int_l_linear + int_r_linear
assert isinstance(self.degree, int) and self.degree >= 0
# create inner knots
self.inner_knots = self.knots[int_l_linear:
self.num_knots - int_r_linear]
self.lb = self.knots[0]
self.ub = self.knots[-1]
self.inner_lb = self.inner_knots[0]
self.inner_ub = self.inner_knots[-1]
self.num_spline_bases = self.inner_knots.size - 1 + self.degree - self.basis_start
def domain(self, idx, l_extra=False, r_extra=False):
"""Return the support of the XSpline.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
numpy.ndarray:
1D array with two elements represents that left and right end of the
support of the spline basis.
"""
inner_domain = bspline_domain(self.inner_knots,
self.degree,
idx,
l_extra=l_extra,
r_extra=r_extra)
lb = inner_domain[0]
ub = inner_domain[1]
lb = self.lb if inner_domain[0] == self.inner_lb else lb
ub = self.ub if inner_domain[1] == self.inner_ub else ub
return np.array([lb, ub])
def fun(self, x, idx, l_extra=False, r_extra=False):
r"""Compute the spline basis.
Args:
x (float | numpy.ndarray):
Scalar or numpy array that store the independent variables.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
float | numpy.ndarray:
Function values of the corresponding spline bases.
"""
if not self.l_linear and not self.r_linear:
return bspline_fun(x,
self.inner_knots,
self.degree,
idx,
l_extra=l_extra,
r_extra=r_extra)
x_is_scalar = np.isscalar(x)
if x_is_scalar:
x = np.array([x])
f = np.zeros(x.size)
m_idx = np.array([True] * x.size)
if self.l_linear:
l_idx = (x < self.inner_lb) & ((x >= self.lb) | l_extra)
m_idx &= (x >= self.inner_lb)
inner_lb_yun = bspline_fun(self.inner_lb,
self.inner_knots,
self.degree,
idx)
inner_lb_dfun = bspline_dfun(self.inner_lb,
self.inner_knots,
self.degree,
1, idx)
f[l_idx] = inner_lb_yun + inner_lb_dfun * (x[l_idx] - self.inner_lb)
if self.r_linear:
u_idx = (x > self.inner_ub) & ((x <= self.ub) | r_extra)
m_idx &= (x <= self.inner_ub)
inner_ub_yun = bspline_fun(self.inner_ub,
self.inner_knots,
self.degree,
idx)
inner_ub_dfun = bspline_dfun(self.inner_ub,
self.inner_knots,
self.degree,
1, idx)
f[u_idx] = inner_ub_yun + inner_ub_dfun * (x[u_idx] - self.inner_ub)
f[m_idx] = bspline_fun(x[m_idx],
self.inner_knots,
self.degree,
idx,
l_extra=l_extra,
r_extra=r_extra)
if x_is_scalar:
return f[0]
else:
return f
def dfun(self, x, order, idx, l_extra=False, r_extra=False):
r"""Compute the derivative of the spline basis.
Args:
x (float | numpy.ndarray):
Scalar or numpy array that store the independent variables.
order (int):
A non-negative integer that indicates the order of differentiation.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
float | numpy.ndarray:
Derivative values of the corresponding spline bases.
"""
if order == 0:
return self.fun(x, idx, l_extra=l_extra, r_extra=r_extra)
if (not self.l_linear) and (not self.r_linear):
return bspline_dfun(x,
self.knots,
self.degree,
order,
idx,
l_extra=l_extra,
r_extra=r_extra)
x_is_scalar = np.isscalar(x)
if x_is_scalar:
x = np.array([x])
dy = np.zeros(x.size)
m_idx = np.array([True] * x.size)
if self.l_linear:
l_idx = (x < self.inner_lb) & ((x >= self.lb) | l_extra)
m_idx &= (x >= self.inner_lb)
if order == 1:
inner_lb_dy = bspline_dfun(self.inner_lb,
self.inner_knots,
self.degree,
order, idx)
dy[l_idx] = np.repeat(inner_lb_dy, np.sum(l_idx))
if self.r_linear:
u_idx = (x > self.inner_ub) & ((x <= self.ub) | r_extra)
m_idx &= (x <= self.inner_ub)
if order == 1:
inner_ub_dy = bspline_dfun(self.inner_ub,
self.inner_knots,
self.degree,
order, idx)
dy[u_idx] = np.repeat(inner_ub_dy, np.sum(u_idx))
dy[m_idx] = bspline_dfun(x[m_idx],
self.inner_knots,
self.degree,
order,
idx,
l_extra=l_extra,
r_extra=r_extra)
if x_is_scalar:
return dy[0]
else:
return dy
def ifun(self, a, x, order, idx, l_extra=False, r_extra=False):
r"""Compute the integral of the spline basis.
Args:
a (float | numpy.ndarray):
Scalar or numpy array that store the starting point of the
integration.
x (float | numpy.ndarray):
Scalar or numpy array that store the ending point of the
integration.
order (int):
A non-negative integer that indicates the order of integration.
idx (int):
A non-negative integer that indicates the index in the spline bases
list.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
float | numpy.ndarray:
Integral values of the corresponding spline bases.
"""
if order == 0:
return self.fun(x, idx, l_extra=l_extra, r_extra=r_extra)
if (not self.l_linear) and (not self.r_linear):
return bspline_ifun(a, x,
self.knots,
self.degree,
order,
idx,
l_extra=l_extra,
r_extra=r_extra)
# verify the inputs
assert np.all(a <= x)
# function and derivative values at inner lb and inner rb
inner_lb_y = bspline_fun(self.inner_lb,
self.inner_knots,
self.degree,
idx)
inner_ub_y = bspline_fun(self.inner_ub,
self.inner_knots,
self.degree,
idx)
inner_lb_dy = bspline_dfun(self.inner_lb,
self.inner_knots,
self.degree,
1, idx)
inner_ub_dy = bspline_dfun(self.inner_ub,
self.inner_knots,
self.degree,
1, idx)
# there are in total 5 pieces functions
def l_piece(a, x, order):
return utils.linear_if(a, x, order,
self.inner_lb, inner_lb_y, inner_lb_dy)
def m_piece(a, x, order):
return bspline_ifun(a, x,
self.inner_knots,
self.degree,
order, idx,
l_extra=l_extra, r_extra=r_extra)
def r_piece(a, x, order):
return utils.linear_if(a, x, order,
self.inner_ub, inner_ub_y, inner_ub_dy)
def zero_piece(a, x, order):
if np.isscalar(a) and np.isscalar(x):
return 0.0
elif np.isscalar(a):
return np.zeros(x.size)
else:
return np.zeros(a.size)
funcs = []
knots = []
if not l_extra:
funcs.append(zero_piece)
if self.l_linear:
funcs.append(l_piece)
funcs.append(m_piece)
if self.r_linear:
funcs.append(r_piece)
if not r_extra:
funcs.append(zero_piece)
if not l_extra:
knots.append(self.lb)
knots.append(self.inner_lb)
if self.l_linear:
knots.append(self.inner_lb)
if self.r_linear:
knots.append(self.inner_ub)
if not r_extra:
knots.append(self.inner_ub)
knots.append(self.ub)
knots = np.sort(list(set(knots)))
return utils.pieces_if(a, x, order, funcs, knots)
def design_mat(self, x, l_extra=False, r_extra=False):
r"""Compute the design matrix of spline basis.
Args:
x (float | numpy.ndarray):
Scalar or numpy array that store the independent variables.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
numpy.ndarray:
Return design matrix.
"""
mat = np.vstack([
self.fun(x, idx, l_extra=l_extra, r_extra=r_extra)
for idx in range(self.basis_start, self.num_spline_bases + self.basis_start)
]).T
return mat
def design_dmat(self, x, order, l_extra=False, r_extra=False):
r"""Compute the design matrix of spline basis derivatives.
Args:
x (float | numpy.ndarray):
Scalar or numpy array that store the independent variables.
order (int):
A non-negative integer that indicates the order of differentiation.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
numpy.ndarray:
Return design matrix.
"""
dmat = np.vstack([
self.dfun(x, order, idx, l_extra=l_extra, r_extra=r_extra)
for idx in range(self.basis_start, self.num_spline_bases + self.basis_start)
]).T
return dmat
def design_imat(self, a, x, order, l_extra=False, r_extra=False):
r"""Compute the design matrix of the integrals of the spline bases.
Args:
a (float | numpy.ndarray):
Scalar or numpy array that store the starting point of the
integration.
x (float | numpy.ndarray):
Scalar or numpy array that store the ending point of the
integration.
order (int):
A non-negative integer that indicates the order of integration.
l_extra (bool, optional):
A optional bool variable indicates that if extrapolate at left end.
Default to be False.
r_extra (bool, optional):
A optional bool variable indicates that if extrapolate at right end.
Default to be False.
Returns:
numpy.ndarray:
Return design matrix.
"""
imat = np.vstack([
self.ifun(a, x, order, idx, l_extra=l_extra, r_extra=r_extra)
for idx in range(self.basis_start, self.num_spline_bases + self.basis_start)
]).T
return imat
def last_dmat(self):
"""Compute highest order of derivative in domain.
Returns:
numpy.ndarray:
1D array that contains highest order of derivative for intervals.
"""
# compute the last dmat for the inner domain
dmat = self.design_dmat(self.inner_knots[:-1], self.degree)
if self.l_linear:
dmat = np.vstack((self.design_dmat(np.array([self.inner_lb]), 1),
dmat))
if self.r_linear:
dmat = np.vstack((dmat,
self.design_dmat(np.array([self.inner_ub]), 1)))
return dmat
class NDXSpline:
"""Multi-dimensional xspline.
"""
def __init__(self, ndim, knots_list, degree_list,
l_linear_list=None,
r_linear_list=None,
include_first_basis_list=True):
"""Constructor of ndXSpline class
Args:
ndim (int):
Number of dimension.
knots_list (list{numpy.ndarray}):
List of knots for every dimension.
degree_list (list{int}):
List of degree for every dimension.
l_linear_list (list{bool} | None, optional):
List of indicator of if have left linear tail for each
dimension.
r_linear_list (list{bool} | None, optional):
List of indicator of if have right linear tail for each
dimension.
"""
self.ndim = ndim
self.knots_list = knots_list
self.degree_list = degree_list
self.l_linear_list = utils.option_to_list(l_linear_list, self.ndim)
self.r_linear_list = utils.option_to_list(r_linear_list, self.ndim)
self.include_first_basis_list = utils.option_to_list(include_first_basis_list, self.ndim)
self.spline_list = [
XSpline(self.knots_list[i], self.degree_list[i],
l_linear=self.l_linear_list[i],
r_linear=self.r_linear_list[i],
include_first_basis=self.include_first_basis_list[i])
for i in range(self.ndim)
]
self.num_knots_list = np.array([
spline.num_knots for spline in self.spline_list])
self.num_intervals_list = np.array([
spline.num_intervals for spline in self.spline_list])
self.num_spline_bases_list = np.array([
spline.num_spline_bases for spline in self.spline_list])
self.num_knots = self.num_knots_list.prod()
self.num_intervals = self.num_intervals_list.prod()
self.num_spline_bases = self.num_spline_bases_list.prod()
def design_mat(self, x_list,
is_grid=True,
l_extra_list=None,
r_extra_list=None):
"""Design matrix of the spline basis.
Args:
x_list (list{numpy.ndarray}):
A list of coordinates for each dimension, they should have the
same dimension or come in matrix form.
is_grid (bool, optional):
If `True` treat the coordinates from `x_list` as the grid points
and compute the mesh grid from it, otherwise, treat each group
of the coordinates independent.
l_extra_list (list{bool} | None, optional):
Indicators of if extrapolate in the left side for each
dimension.
r_extra_list (list{bool} | None, optional):
Indicators of if extrapolate in the right side for each
dimension.
Returns:
numpy.ndarray:
Design matrix.
"""
l_extra_list = utils.option_to_list(l_extra_list, self.ndim)
r_extra_list = utils.option_to_list(r_extra_list, self.ndim)
assert len(x_list) == self.ndim
assert len(l_extra_list) == self.ndim
assert len(r_extra_list) == self.ndim
mat_list = [spline.design_mat(x_list[i],
l_extra=l_extra_list[i],
r_extra=r_extra_list[i])
for i, spline in enumerate(self.spline_list)]
if is_grid:
mat = []
for i in range(self.num_spline_bases):
index_list = utils.order_to_index(i, self.num_spline_bases_list)
bases_list = [mat_list[j][:, index_list[j]]
for j in range(self.ndim)]
mat.append(utils.outer_flatten(*bases_list))
else:
num_points = x_list[0].size
assert np.all([x_list[i].size == num_points
for i in range(self.ndim)])
mat = []
for i in range(self.num_spline_bases):
index_list = utils.order_to_index(i, self.num_spline_bases_list)
bases_list = [mat_list[j][:, index_list[j]]
for j in range(self.ndim)]
mat.append(np.prod(bases_list, axis=0))
return np.ascontiguousarray(np.vstack(mat).T)
def design_dmat(self, x_list, n_list,
is_grid=True,
l_extra_list=None,
r_extra_list=None):
"""Design matrix of the derivatives of spline basis.
Args:
x_list (list{numpy.ndarray}):
A list of coordinates for each dimension, they should have the
same dimension or come in matrix form.
n_list (list{int}):
A list of integers indicates the order of differentiation for
each dimension.
is_grid (bool, optional):
If `True` treat the coordinates from `x_list` as the grid points
and compute the mesh grid from it, otherwise, treat each group
of the coordinates independent.
l_extra_list (list{bool} | None, optional):
Indicators of if extrapolate in the left side for each
dimension.
r_extra_list (list{bool} | None, optional):
Indicators of if extrapolate in the right side for each
dimension.
Returns:
numpy.ndarray:
Differentiation design matrix.
"""
l_extra_list = utils.option_to_list(l_extra_list, self.ndim)
r_extra_list = utils.option_to_list(r_extra_list, self.ndim)
assert len(x_list) == self.ndim
assert len(n_list) == self.ndim
assert len(l_extra_list) == self.ndim
assert len(r_extra_list) == self.ndim
dmat_list = [spline.design_dmat(x_list[i], n_list[i],
l_extra=l_extra_list[i],
r_extra=r_extra_list[i])
for i, spline in enumerate(self.spline_list)]
if is_grid:
dmat = []
for i in range(self.num_spline_bases):
index_list = utils.order_to_index(i, self.num_spline_bases_list)
bases_list = [dmat_list[j][:, index_list[j]]
for j in range(self.ndim)]
dmat.append(utils.outer_flatten(*bases_list))
else:
num_points = x_list[0].size
assert np.all([x_list[i].size == num_points
for i in range(self.ndim)])
dmat = []
for i in range(self.num_spline_bases):
index_list = utils.order_to_index(i, self.num_spline_bases_list)
bases_list = [dmat_list[j][:, index_list[j]]
for j in range(self.ndim)]
dmat.append(np.prod(bases_list, axis=0))
return np.ascontiguousarray(np.vstack(dmat).T)
def design_imat(self, a_list, x_list, n_list,
is_grid=True,
l_extra_list=None,
r_extra_list=None):
"""Design matrix of the spline basis.
Args:
a_list (list{numpy.ndarray}):
Start of integration of coordinates for each dimension.
x_list (list{numpy.ndarray}):
A list of coordinates for each dimension, they should have the
same dimension or come in matrix form.
n_list (list{int}):
A list of integers indicates the order of integration for
each dimension.
is_grid (bool, optional):
If `True` treat the coordinates from `x_list` as the grid points
and compute the mesh grid from it, otherwise, treat each group
of the coordinates independent.
l_extra_list (list{bool} | None, optional):
Indicators of if extrapolate in the left side for each
dimension.
r_extra_list (list{bool} | None, optional):
Indicators of if extrapolate in the right side for each
dimension.
Returns:
numpy.ndarray:
Integration design matrix.
"""
l_extra_list = utils.option_to_list(l_extra_list, self.ndim)
r_extra_list = utils.option_to_list(r_extra_list, self.ndim)
assert len(a_list) == self.ndim
assert len(x_list) == self.ndim
assert len(n_list) == self.ndim
assert len(l_extra_list) == self.ndim
assert len(r_extra_list) == self.ndim
imat_list = [spline.design_imat(a_list[i], x_list[i], n_list[i],
l_extra=l_extra_list[i],
r_extra=r_extra_list[i])
for i, spline in enumerate(self.spline_list)]
if is_grid:
imat = []
for i in range(self.num_spline_bases):
index_list = utils.order_to_index(i, self.num_spline_bases_list)
bases_list = [imat_list[j][:, index_list[j]]
for j in range(self.ndim)]
imat.append(utils.outer_flatten(*bases_list))
else:
num_points = x_list[0].size
assert np.all([x_list[i].size == num_points
for i in range(self.ndim)])
imat = []
for i in range(self.num_spline_bases):
index_list = utils.order_to_index(i, self.num_spline_bases_list)
bases_list = [imat_list[j][:, index_list[j]]
for j in range(self.ndim)]
imat.append(np.prod(bases_list, axis=0))
return np.ascontiguousarray(np.vstack(imat).T)
def last_dmat(self):
"""Highest order of derivative matrix.
Returns:
numpy.ndarray:
Design matrix contain the highest order of derivative.
"""
mat_list = [spline.last_dmat() for spline in self.spline_list]
mat = []
for i in range(self.num_spline_bases):
index_list = utils.order_to_index(i, self.num_spline_bases_list)
bases_list = [mat_list[j][:, index_list[j]]
for j in range(self.ndim)]
mat.append(utils.outer_flatten(*bases_list))
return np.ascontiguousarray(np.vstack(mat).T)
# TODO:
# 1. bspline function pass in too many default every time
# 2. name of f, df and if
# 3. the way to deal with the scalar vs array.
# 4. keep the naming scheme consistent.
| 35.508032 | 97 | 0.53707 | 4,476 | 35,366 | 4.068141 | 0.052055 | 0.032951 | 0.033225 | 0.019111 | 0.817288 | 0.777143 | 0.757263 | 0.7359 | 0.727937 | 0.726619 | 0 | 0.005687 | 0.383419 | 35,366 | 995 | 98 | 35.543719 | 0.829359 | 0.334191 | 0 | 0.6125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001005 | 0.0375 | 1 | 0.045833 | false | 0 | 0.004167 | 0.00625 | 0.13125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
41bc6f5b895ce3ac4d12876ea2e8b04cd378df59 | 1,183 | py | Python | src/snpgetter.py | nikbaya/msprime_sim | 8f71574f272c963dba29964439a229caaf883c63 | [
"MIT"
] | null | null | null | src/snpgetter.py | nikbaya/msprime_sim | 8f71574f272c963dba29964439a229caaf883c63 | [
"MIT"
] | null | null | null | src/snpgetter.py | nikbaya/msprime_sim | 8f71574f272c963dba29964439a229caaf883c63 | [
"MIT"
] | null | null | null | from __future__ import division
import numpy as np
def nextSNP(variant, index=None):
if index is None:
var_tmp = np.array(variant.genotypes[0::2].astype(int)) + np.array(variant.genotypes[1::2].astype(int))
else:
var_tmp = np.array(variant.genotypes[0::2][index].astype(int)) + np.array(variant.genotypes[1::2][index].astype(int))
n = len(var_tmp)
# Additive term.
mean_X = np.mean(var_tmp)
p = mean_X / 2
# Evaluate the mean and then sd to normalise.
X_A = (var_tmp - mean_X) / np.std(var_tmp)
# Dominance term.
X_D = np.ones(n)
X_D[var_tmp == 0] = - p / (1 - p)
X_D[var_tmp == 2] = - (1 - p) / p
# Evaluate the mean and then sd to normalise.
X_D = (X_D - np.mean(X_D)) / np.std(X_D)
return X_A, X_D
def nextSNP_add(variant, index=None):
if index is None:
var_tmp = np.array(variant.genotypes[0::2].astype(int)) + np.array(variant.genotypes[1::2].astype(int))
else:
var_tmp = np.array(variant.genotypes[0::2][index].astype(int)) + np.array(variant.genotypes[1::2][index].astype(int))
# Additive term.
mean_X = np.mean(var_tmp)
p = mean_X / 2
# Evaluate the mean and then sd to normalise.
X_A = (var_tmp - mean_X) / np.std(var_tmp)
return X_A
| 31.131579 | 119 | 0.674556 | 220 | 1,183 | 3.463636 | 0.204545 | 0.102362 | 0.146982 | 0.24147 | 0.7979 | 0.7979 | 0.7979 | 0.7979 | 0.7979 | 0.750656 | 0 | 0.022111 | 0.158918 | 1,183 | 38 | 120 | 31.131579 | 0.743719 | 0.14962 | 0 | 0.56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.08 | false | 0 | 0.08 | 0 | 0.24 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
41bfdabd1c239e7df25d19039e23e35114c3ee9e | 253 | py | Python | pt/snippets/admin.py | kevinqqnj/baozhong | aba1824d9d6a6d7c77a7c69d55a21174f2fac221 | [
"MIT"
] | null | null | null | pt/snippets/admin.py | kevinqqnj/baozhong | aba1824d9d6a6d7c77a7c69d55a21174f2fac221 | [
"MIT"
] | null | null | null | pt/snippets/admin.py | kevinqqnj/baozhong | aba1824d9d6a6d7c77a7c69d55a21174f2fac221 | [
"MIT"
] | null | null | null | from django import forms
from django.contrib import admin
from django.contrib.auth.admin import UserAdmin as AuthUserAdmin
from django.contrib.auth.forms import UserChangeForm, UserCreationForm
from .models import Snippet
admin.site.register(Snippet)
| 28.111111 | 70 | 0.84585 | 34 | 253 | 6.294118 | 0.470588 | 0.186916 | 0.238318 | 0.196262 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102767 | 253 | 8 | 71 | 31.625 | 0.942731 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.833333 | 0 | 0.833333 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
68c1c3e1740cfb6eeed0123a0d5c1efad4c30700 | 2,959 | py | Python | Global/migrations/0005_auto_20210527_1201.py | Muhammet-Yildiz/school-website-example | fc3f36fcadd0d03e6691efbacf200027f1afce2a | [
"MIT"
] | 2 | 2021-05-30T14:15:33.000Z | 2021-07-02T12:22:01.000Z | Global/migrations/0005_auto_20210527_1201.py | Muhammet-Yildiz/school-website-example | fc3f36fcadd0d03e6691efbacf200027f1afce2a | [
"MIT"
] | null | null | null | Global/migrations/0005_auto_20210527_1201.py | Muhammet-Yildiz/school-website-example | fc3f36fcadd0d03e6691efbacf200027f1afce2a | [
"MIT"
] | null | null | null | # Generated by Django 3.1.4 on 2021-05-27 09:01
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Global', '0004_auto_20210526_2012'),
]
operations = [
migrations.CreateModel(
name='Advertisements',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100, verbose_name='İlan Başlıgı ')),
('content', models.TextField(max_length=1000)),
('image', models.ImageField(blank=True, null=True, upload_to='advertisements_imgs/')),
('see', models.DecimalField(decimal_places=2, max_digits=7, verbose_name='İlanı Gören Kişi Sayısı')),
('slug', models.SlugField(blank=True, max_length=250, null=True)),
('created_date', models.DateTimeField(auto_now_add=True)),
],
options={
'verbose_name_plural': 'İlanlar',
},
),
migrations.CreateModel(
name='Automatıons',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100, verbose_name='Otomasyon Başlıgı ')),
('content', models.TextField(max_length=1000)),
('image', models.ImageField(blank=True, null=True, upload_to='news_imgs/')),
('see', models.DecimalField(decimal_places=2, max_digits=7, verbose_name='Otomasyonu Gören Kişi Sayısı')),
('slug', models.SlugField(blank=True, max_length=250, null=True)),
('created_date', models.DateTimeField(auto_now_add=True)),
],
options={
'verbose_name_plural': 'Otomasyonlar',
},
),
migrations.CreateModel(
name='Students',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100, verbose_name='Ögrenci Haber Başlıgı ')),
('content', models.TextField(max_length=1000)),
('image', models.ImageField(blank=True, null=True, upload_to='news_imgs/')),
('see', models.DecimalField(decimal_places=2, max_digits=7, verbose_name='Ögrenci Haberi Gören Kişi Sayısı')),
('slug', models.SlugField(blank=True, max_length=250, null=True)),
('created_date', models.DateTimeField(auto_now_add=True)),
],
options={
'verbose_name_plural': 'Ögrenci Haberleri',
},
),
migrations.AlterField(
model_name='announcements',
name='content',
field=models.TextField(max_length=1000),
),
]
| 46.234375 | 126 | 0.578574 | 303 | 2,959 | 5.475248 | 0.306931 | 0.079566 | 0.0434 | 0.057866 | 0.746835 | 0.729958 | 0.729958 | 0.729958 | 0.729958 | 0.729958 | 0 | 0.033506 | 0.28388 | 2,959 | 63 | 127 | 46.968254 | 0.747994 | 0.015208 | 0 | 0.526316 | 1 | 0 | 0.161745 | 0.007898 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.017544 | 0 | 0.070175 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
68df0256c11a0d431741fc04b4a4e7927f9140ad | 373 | py | Python | QARealtimeCollector/collectors/__init__.py | tbmilk/QUANTAXIS_RealtimeCollector | 1705b7261d4ffacd8fc0dcc8d6bed3ce651fa6ec | [
"MIT"
] | 47 | 2019-08-17T05:48:01.000Z | 2022-02-22T21:28:52.000Z | QARealtimeCollector/collectors/__init__.py | tbmilk/QUANTAXIS_RealtimeCollector | 1705b7261d4ffacd8fc0dcc8d6bed3ce651fa6ec | [
"MIT"
] | 4 | 2019-08-30T03:33:14.000Z | 2021-04-22T01:17:23.000Z | QARealtimeCollector/collectors/__init__.py | tbmilk/QUANTAXIS_RealtimeCollector | 1705b7261d4ffacd8fc0dcc8d6bed3ce651fa6ec | [
"MIT"
] | 42 | 2019-08-01T10:59:00.000Z | 2022-02-14T08:09:41.000Z | from QARealtimeCollector.collectors.ctpbeecollector import QARTC_CtpBeeCollector
from QARealtimeCollector.collectors.wscollector import QARTC_WsCollector
from QARealtimeCollector.collectors.stockcollector import QARTC_Stock
from QARealtimeCollector.collectors.simmarket import QARTC_RandomTick
from QARealtimeCollector.collectors.simcollector import QARTC_CTPTickCollector | 74.6 | 80 | 0.919571 | 35 | 373 | 9.657143 | 0.371429 | 0.340237 | 0.488166 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.053619 | 373 | 5 | 81 | 74.6 | 0.957507 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ec0e736b2a4e0dd6d413bbafff812e2d612afe1c | 277 | py | Python | src/kgmk/dsa/linked_list/doubly/__init__.py | kagemeka/python | 486ce39d97360b61029527bacf00a87fdbcf552c | [
"MIT"
] | null | null | null | src/kgmk/dsa/linked_list/doubly/__init__.py | kagemeka/python | 486ce39d97360b61029527bacf00a87fdbcf552c | [
"MIT"
] | null | null | null | src/kgmk/dsa/linked_list/doubly/__init__.py | kagemeka/python | 486ce39d97360b61029527bacf00a87fdbcf552c | [
"MIT"
] | null | null | null | from __future__ import annotations
import typing
import dataclasses
@dataclasses.dataclass
class DoublyLinkedListNode():
value: typing.Optional[typing.Any] = None
left: typing.Optional[DoublyLinkedListNode] = None
right: typing.Optional[DoublyLinkedListNode] = None
| 23.083333 | 53 | 0.801444 | 28 | 277 | 7.785714 | 0.535714 | 0.192661 | 0.311927 | 0.348624 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122744 | 277 | 11 | 54 | 25.181818 | 0.897119 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.375 | 0 | 0.875 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6b8572bd8a5830bf09b7c02824c76d01969d8c58 | 40 | py | Python | main.py | 343GuiltySpark-04/-cautious-tribble- | 4a98e0770afa5fe1da9103857067cdc54cf2a9a7 | [
"MIT"
] | null | null | null | main.py | 343GuiltySpark-04/-cautious-tribble- | 4a98e0770afa5fe1da9103857067cdc54cf2a9a7 | [
"MIT"
] | null | null | null | main.py | 343GuiltySpark-04/-cautious-tribble- | 4a98e0770afa5fe1da9103857067cdc54cf2a9a7 | [
"MIT"
] | null | null | null | import menu
import core
menu.menu()
| 5 | 11 | 0.7 | 6 | 40 | 4.666667 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.225 | 40 | 7 | 12 | 5.714286 | 0.903226 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6b9be1d0771928ce4f52ef22ad22d0ee64128fdf | 2,930 | py | Python | sprites.py | Bxvu/TopDownGame | 1f1f93a54cd6190142cc3a129abd6358e016966c | [
"MIT"
] | null | null | null | sprites.py | Bxvu/TopDownGame | 1f1f93a54cd6190142cc3a129abd6358e016966c | [
"MIT"
] | null | null | null | sprites.py | Bxvu/TopDownGame | 1f1f93a54cd6190142cc3a129abd6358e016966c | [
"MIT"
] | null | null | null | #made by Benthan Vu, sprite classes for game
#sources: goo.gl/2KMivS
import pygame as pg
import random
from pygame.sprite import Sprite
from settings import *
import pygame.math
class Player(Sprite):
def __init__(self):
Sprite.__init__(self)
self.image = pg.Surface((30,40))
self.image.fill(BLACK)
self.rect = self.image.get_rect()
self.rect.center = (WIDTH / 2, HEIGHT / 2)
self.vx = 0
self.vy = 0
self.rect.x, self.rect.y = (WIDTH / 2, HEIGHT / 2)
self.ang = 0
def update(self):
self.vx = 0
self.vy = 0
# self.ang += 1
#checks if a button is pressed
# self.image = pg.Surface((30,40))
# self.rect = self.image.get_rect()
# self.rect.center = (WIDTH / 2, HEIGHT / 2)
# self.image = pg.transform.rotate(self.image,self.ang)
# keys = pg.key.get_pressed()
# if keys[pg.K_LEFT]:
# self.vx = -5
# if keys[pg.K_RIGHT]:
# self.vx = 5
# if keys[pg.K_UP]:
# self.vy = -5
# if keys[pg.K_DOWN]:
# self.vy = 5
self.rect.x += self.vx
self.rect.y += self.vy
class Wall(Sprite):
def __init__(self):
Sprite.__init__(self)
self.image = pg.Surface((100,40))
self.image.fill(WHITE)
self.rect = self.image.get_rect()
self.rect.center = (WIDTH / 2, HEIGHT / 2)
self.vx = 0
self.vy = 0
self.rect.x = 0
self.rect.y = 100
def update(self):
self.vx = 0
self.vy = 0
self.moving = "no"
#checks if a button is pressed
keys = pg.key.get_pressed()
if keys[pg.K_a]:
self.vx = 5
self.moving = "left"
if keys[pg.K_d]:
self.vx = -5
self.moving = "right"
if keys[pg.K_w]:
self.vy = 5
self.moving = "up"
if keys[pg.K_s]:
self.vy = -5
self.moving = "down"
self.rect.x += self.vx
self.rect.y += self.vy
class Enemy(Sprite):
def __init__(self):
Sprite.__init__(self)
self.image = pg.Surface((30,40))
self.image.fill(BLACK)
self.rect = self.image.get_rect()
self.rect.center = (WIDTH / 2, HEIGHT / 2)
self.vx = 0
self.vy = 0
def update(self):
self.vx = 0
self.vy = 0
self.moving = "no"
keys = pg.key.get_pressed()
if keys[pg.K_a]:
self.vx = 5
self.moving = "left"
if keys[pg.K_d]:
self.vx = -5
self.moving = "right"
if keys[pg.K_w]:
self.vy = 5
self.moving = "up"
if keys[pg.K_s]:
self.vy = -5
self.moving = "down"
self.rect.x += self.vx
self.rect.y += self.vy
| 26.880734 | 63 | 0.48942 | 405 | 2,930 | 3.434568 | 0.17284 | 0.103523 | 0.069015 | 0.077642 | 0.797987 | 0.778577 | 0.744069 | 0.705248 | 0.705248 | 0.685119 | 0 | 0.031991 | 0.381229 | 2,930 | 108 | 64 | 27.12963 | 0.735245 | 0.164505 | 0 | 0.822785 | 0 | 0 | 0.013992 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.075949 | false | 0 | 0.063291 | 0 | 0.177215 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
6baa87c37439895ea64018d3a865cf2c6eb1d05c | 1,854 | py | Python | code/fe/16spacy_w2v.py | okotaku/pet_finder | 380e4f19172e06e92b5b752f59e2902efa6aee1f | [
"MIT"
] | 34 | 2019-07-31T01:17:18.000Z | 2020-11-15T20:01:30.000Z | code/fe/16spacy_w2v.py | okotaku/pet_finder | 380e4f19172e06e92b5b752f59e2902efa6aee1f | [
"MIT"
] | null | null | null | code/fe/16spacy_w2v.py | okotaku/pet_finder | 380e4f19172e06e92b5b752f59e2902efa6aee1f | [
"MIT"
] | 6 | 2019-07-31T07:21:35.000Z | 2021-05-21T12:46:06.000Z | import spacy
from utils import *
def spacy_d2v(train_text):
nlp = spacy.load('en_core_web_md')
result = np.array([nlp(text).vector for text in train["Description"].values])
d2v_cols = ["spacy_d2v_md{}".format(i) for i in range(1, result.shape[1] + 1)]
result = pd.DataFrame(result)
result.columns = d2v_cols
return result
def spacy_d2v(train_text):
nlp = spacy.load('en_core_web_sm')
result = np.zeros((len(train_text), 384))
for i, text in enumerate(train["Description"].values):
d2v = nlp(text).vector
if len(d2v) != 0:
result[i] = d2v
d2v_cols = ["spacy_d2v_sm{}".format(i) for i in range(1, result.shape[1] + 1)]
result = pd.DataFrame(result)
result.columns = d2v_cols
return result
def spacy_d2v(train_text):
nlp = spacy.load('en_core_web_lg')
result = np.array([nlp(text).vector for text in train["Description"].values])
d2v_cols = ["spacy_d2v_lg{}".format(i) for i in range(1, result.shape[1] + 1)]
result = pd.DataFrame(result)
result.columns = d2v_cols
return result
def spacy_d2v(train_text):
nlp = spacy.load('en_vectors_web_lg')
result = np.array([nlp(text).vector for text in train["Description"].values])
d2v_cols = ["spacy_d2v_vlg{}".format(i) for i in range(1, result.shape[1] + 1)]
result = pd.DataFrame(result)
result.columns = d2v_cols
return result
if __name__ == '__main__':
result = spacy_d2v(train["Description"])
result.to_feather("../feature/spacy_d2v_md.feather")
result = spacy_d2v(train["Description"])
result.to_feather("../feature/spacy_d2v_sm.feather")
result = spacy_d2v(train["Description"])
result.to_feather("../feature/spacy_d2v_lg.feather")
result = spacy_d2v(train["Description"])
result.to_feather("../feature/spacy_d2v_vlg.feather")
| 28.523077 | 83 | 0.669364 | 274 | 1,854 | 4.306569 | 0.171533 | 0.108475 | 0.088136 | 0.054237 | 0.838136 | 0.838136 | 0.838136 | 0.838136 | 0.838136 | 0.838136 | 0 | 0.028383 | 0.182848 | 1,854 | 64 | 84 | 28.96875 | 0.750495 | 0 | 0 | 0.534884 | 0 | 0 | 0.181769 | 0.067422 | 0 | 0 | 0 | 0 | 0 | 1 | 0.093023 | false | 0 | 0.046512 | 0 | 0.232558 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d404398fb042cd434b0f9843f79b98159c31386b | 115 | py | Python | server/apps/bot/dispatcher/callbacks/__init__.py | LowerDeez/movies_finder | 3763bfe4c0d1cfe36e081c45a9cc9cdaa85e0ee4 | [
"MIT"
] | null | null | null | server/apps/bot/dispatcher/callbacks/__init__.py | LowerDeez/movies_finder | 3763bfe4c0d1cfe36e081c45a9cc9cdaa85e0ee4 | [
"MIT"
] | null | null | null | server/apps/bot/dispatcher/callbacks/__init__.py | LowerDeez/movies_finder | 3763bfe4c0d1cfe36e081c45a9cc9cdaa85e0ee4 | [
"MIT"
] | null | null | null | from .discover_movies import *
from .entry_points import *
from .list_movies import *
from .search_movies import *
| 23 | 30 | 0.791304 | 16 | 115 | 5.4375 | 0.5 | 0.413793 | 0.367816 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.13913 | 115 | 4 | 31 | 28.75 | 0.878788 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
d43940ed640b724b580071671727fb35d6936dd1 | 11,303 | py | Python | pirates/leveleditor/worldData/SwampTestIslandB.py | itsyaboyrocket/pirates | 6ca1e7d571c670b0d976f65e608235707b5737e3 | [
"BSD-3-Clause"
] | 3 | 2021-02-25T06:38:13.000Z | 2022-03-22T07:00:15.000Z | pirates/leveleditor/worldData/SwampTestIslandB.py | itsyaboyrocket/pirates | 6ca1e7d571c670b0d976f65e608235707b5737e3 | [
"BSD-3-Clause"
] | null | null | null | pirates/leveleditor/worldData/SwampTestIslandB.py | itsyaboyrocket/pirates | 6ca1e7d571c670b0d976f65e608235707b5737e3 | [
"BSD-3-Clause"
] | 1 | 2021-02-25T06:38:17.000Z | 2021-02-25T06:38:17.000Z | # uncompyle6 version 3.2.0
# Python bytecode 2.4 (62061)
# Decompiled from: Python 2.7.14 (v2.7.14:84471935ed, Sep 16 2017, 20:19:30) [MSC v.1500 32 bit (Intel)]
# Embedded file name: pirates.leveleditor.worldData.SwampTestIslandB
from pandac.PandaModules import Point3, VBase3
objectStruct = {'Locator Links': [['1153868315.8sdnaik0', '1152910060.11sdnaik', 'Bi-directional'], ['1153868315.8sdnaik1', '1152910301.05sdnaik0', 'Bi-directional'], ['1153868634.75sdnaik0', '1152910060.11sdnaik0', 'Bi-directional'], ['1152910307.13sdnaik', '1156281363.2sdnaik1', 'Bi-directional'], ['1156281161.64sdnaik0', '1156281363.2sdnaik0', 'Bi-directional'], ['1153868634.75sdnaik1', '1156302222.63sdnaik', 'Bi-directional']], 'Objects': {'1152909972.77sdnaik': {'Type': 'Island', 'Name': 'SwampTestIslandB', 'File': '', 'Objects': {'1152910060.11sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_1', 'Hpr': VBase3(-18.331, 0.0, 0.0), 'Pos': Point3(-219.917, -319.235, 0.595), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1152910060.11sdnaik0': {'Type': 'Locator Node', 'Name': 'portal_exterior_2', 'Hpr': VBase3(68.97, 0.0, 0.0), 'Pos': Point3(-285.103, -58.817, 44.049), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1152910301.05sdnaik': {'Type': 'Island Game Area', 'File': 'SwampTemplateB', 'Hpr': VBase3(120.19, 0.0, 0.0), 'Objects': {'1152910301.05sdnaik0': {'Type': 'Locator Node', 'Name': 'portal_interior_1', 'GridPos': Point3(-606.498, -425.911, 232.255), 'Hpr': VBase3(-161.778, 0.0, -180.0), 'Pos': Point3(-236.144, -43.732, 21.034), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1152910307.13sdnaik': {'Type': 'Locator Node', 'Name': 'portal_interior_2', 'GridPos': Point3(-27.183, -186.116, 232.255), 'Hpr': VBase3(26.445, 0.0, -180.0), 'Pos': Point3(453.452, 255.559, 12.06), 'Scale': VBase3(1.0, 1.0, 1.0)}}, 'Pos': Point3(-1143.784, -1199.552, 81.761), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/swamps/swampB'}}, '1153868315.8sdnaik': {'Type': 'Connector Tunnel', 'File': '', 'Hpr': VBase3(29.967, 0.0, 0.0), 'Objects': {'1153868315.8sdnaik0': {'Type': 'Locator Node', 'Name': 'portal_connector_1', 'Hpr': VBase3(126.22, 0.0, 0.0), 'Pos': Point3(465.537, 517.058, 2.343), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1153868315.8sdnaik1': {'Type': 'Locator Node', 'Name': 'portal_connector_2', 'GridPos': Point3(-155.156, -163.935, 227.03), 'Hpr': VBase3(-148.231, 0.0, 0.0), 'Pos': Point3(453.452, 255.559, 12.06), 'Scale': VBase3(1.0, 1.0, 1.0)}}, 'Pos': Point3(-432.389, -1775.729, 86.948), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/tunnels/tunnel_swamp_cave'}}, '1153868634.75sdnaik': {'Type': 'Connector Tunnel', 'File': '', 'Hpr': VBase3(-153.313, 0.0, 0.0), 'Objects': {'1153868634.75sdnaik0': {'Type': 'Locator Node', 'Name': 'portal_connector_1', 'Hpr': VBase3(126.22, 0.0, 0.0), 'Pos': Point3(465.537, 517.058, 2.343), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1153868634.75sdnaik1': {'Type': 'Locator Node', 'Name': 'portal_connector_2', 'GridPos': Point3(-291.911, 214.833, 0.664), 'Hpr': VBase3(-148.231, 0.0, 0.0), 'Pos': Point3(453.452, 255.559, 12.06), 'Scale': VBase3(1.0, 1.0, 1.0)}}, 'Pos': Point3(-1091.077, 1336.074, 129.211), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/tunnels/tunnel_swamp_cave'}}, '1155864372.34sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_1', 'Hpr': VBase3(-18.331, 0.0, 0.0), 'Pos': Point3(-219.917, -319.235, 0.595), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1155864374.63sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_2', 'Hpr': VBase3(68.97, 0.0, 0.0), 'Pos': Point3(-285.103, -58.817, 44.049), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1155864384.91sdnaik': {'Type': 'Cell Portal Area', 'Name': 'cell_spanish_town', 'Hpr': Point3(0.0, 0.0, 0.0), 'Objects': {'1155866758.05sdnaik': {'Type': 'Building Exterior', 'File': 'bilgewater_guildhall_interior_a', 'ExtUid': '1155866758.05sdnaik0', 'Hpr': VBase3(68.18, 0.0, 0.0), 'Pos': Point3(506.389, 141.755, 45.292), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Name': 'English A', 'Door': 'models/buildings/shanty_guildhall_door', 'Interior': 'models/buildings/interior_shanty_guildhall', 'Model': 'models/buildings/english_corner_a'}}, '1158184464.98sdnaik': {'Type': 'Building Exterior', 'File': 'rambleshack_building_int_tavern', 'ExtUid': '1158184464.98sdnaik0', 'Hpr': VBase3(-43.794, 0.0, 0.0), 'Pos': Point3(560.901, 106.555, 41.918), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Name': '', 'Door': 'models/buildings/shanty_guildhall_door', 'Interior': 'models/buildings/interior_tavern', 'Model': 'models/buildings/shanty_tavern_exterior'}}, '1158184594.03sdnaik': {'Type': 'Building Exterior', 'File': 'swamptest_interior_1', 'ExtUid': '1158184594.03sdnaik0', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pos': Point3(409.067, 155.856, 44.575), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Name': '', 'Door': 'models/buildings/shanty_guildhall_door', 'Interior': 'models/buildings/interior_shanty_guildhall', 'Model': 'models/buildings/english_a'}}}, 'Pos': Point3(0.0, 0.0, 0.0), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1155864824.89sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_1', 'Hpr': VBase3(-18.331, 0.0, 0.0), 'Pos': Point3(-219.917, -319.235, 0.595), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1155864827.11sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_2', 'Hpr': VBase3(68.97, 0.0, 0.0), 'Pos': Point3(-285.103, -58.817, 44.049), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1156280826.23sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_1', 'Hpr': VBase3(-18.331, 0.0, 0.0), 'Pos': Point3(-219.917, -319.235, 0.595), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1156280828.67sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_2', 'Hpr': VBase3(68.97, 0.0, 0.0), 'Pos': Point3(-285.103, -58.817, 44.049), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1156281161.64sdnaik': {'Type': 'Island Game Area', 'File': 'SwampTemplateC', 'Hpr': VBase3(-36.598, 0.0, 0.0), 'Objects': {'1156281161.64sdnaik0': {'Type': 'Locator Node', 'Name': 'portal_interior_1', 'GridPos': Point3(-113.557, -119.557, 123.863), 'Hpr': VBase3(81.569, 0.0, 0.0), 'Pos': Point3(-383.486, 124.706, 14.047), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1156302222.63sdnaik': {'Type': 'Locator Node', 'Name': 'portal_interior_2', 'GridPos': Point3(-2121.404, -709.755, 122.18), 'Hpr': VBase3(135.469, 0.0, 0.0), 'Pos': Point3(557.708, 254.891, 12.365), 'Scale': VBase3(1.0, 1.0, 1.0)}}, 'Pos': Point3(-2816.118, 1584.312, 635.326), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/swamps/swampC'}}, '1156281363.2sdnaik': {'Type': 'Connector Tunnel', 'File': '', 'Hpr': VBase3(-94.487, 0.0, 0.0), 'Objects': {'1156281363.2sdnaik0': {'Type': 'Locator Node', 'Name': 'portal_connector_1', 'GridPos': Point3(-808.963, -680.48, 73.384), 'Hpr': VBase3(-88.748, 0.0, 0.0), 'Pos': Point3(-3.613, 0.304, 4.651), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1156281363.2sdnaik1': {'Type': 'Locator Node', 'Name': 'portal_connector_2', 'GridPos': Point3(-684.414, -557.419, 68.431), 'Hpr': VBase3(72.65, -1.426, -0.516), 'Pos': Point3(-103.188, 135.024, 3.777), 'Scale': VBase3(1.0, 1.0, 1.0)}}, 'Pos': Point3(-2717.734, -50.514, 446.686), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/tunnels/tunnel_swamp'}}, '1158184411.67sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_1', 'Hpr': VBase3(-18.331, 0.0, 0.0), 'Pos': Point3(-219.917, -319.235, 0.595), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1158184420.17sdnaik': {'Type': 'Locator Node', 'Name': 'portal_exterior_2', 'Hpr': VBase3(68.97, 0.0, 0.0), 'Pos': Point3(-285.103, -58.817, 44.049), 'Scale': VBase3(1.0, 1.0, 1.0)}}, 'Visual': {'Model': 'models/islands/bilgewater_zero'}}}, 'Node Links': [], 'Layers': {}, 'ObjectIds': {'1152909972.77sdnaik': '["Objects"]["1152909972.77sdnaik"]', '1152910060.11sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1152910060.11sdnaik"]', '1152910060.11sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1152910060.11sdnaik0"]', '1152910301.05sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1152910301.05sdnaik"]', '1152910301.05sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1152910301.05sdnaik"]["Objects"]["1152910301.05sdnaik0"]', '1152910307.13sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1152910301.05sdnaik"]["Objects"]["1152910307.13sdnaik"]', '1153868315.8sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1153868315.8sdnaik"]', '1153868315.8sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1153868315.8sdnaik"]["Objects"]["1153868315.8sdnaik0"]', '1153868315.8sdnaik1': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1153868315.8sdnaik"]["Objects"]["1153868315.8sdnaik1"]', '1153868634.75sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1153868634.75sdnaik"]', '1153868634.75sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1153868634.75sdnaik"]["Objects"]["1153868634.75sdnaik0"]', '1153868634.75sdnaik1': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1153868634.75sdnaik"]["Objects"]["1153868634.75sdnaik1"]', '1155864372.34sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864372.34sdnaik"]', '1155864374.63sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864374.63sdnaik"]', '1155864384.91sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864384.91sdnaik"]', '1155864824.89sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864824.89sdnaik"]', '1155864827.11sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864827.11sdnaik"]', '1155866758.05sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864384.91sdnaik"]["Objects"]["1155866758.05sdnaik"]', '1155866758.05sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864384.91sdnaik"]["Objects"]["1155866758.05sdnaik"]', '1156280826.23sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156280826.23sdnaik"]', '1156280828.67sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156280828.67sdnaik"]', '1156281161.64sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156281161.64sdnaik"]', '1156281161.64sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156281161.64sdnaik"]["Objects"]["1156281161.64sdnaik0"]', '1156281363.2sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156281363.2sdnaik"]', '1156281363.2sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156281363.2sdnaik"]["Objects"]["1156281363.2sdnaik0"]', '1156281363.2sdnaik1': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156281363.2sdnaik"]["Objects"]["1156281363.2sdnaik1"]', '1156302222.63sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1156281161.64sdnaik"]["Objects"]["1156302222.63sdnaik"]', '1158184411.67sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1158184411.67sdnaik"]', '1158184420.17sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1158184420.17sdnaik"]', '1158184464.98sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864384.91sdnaik"]["Objects"]["1158184464.98sdnaik"]', '1158184464.98sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864384.91sdnaik"]["Objects"]["1158184464.98sdnaik"]', '1158184594.03sdnaik': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864384.91sdnaik"]["Objects"]["1158184594.03sdnaik"]', '1158184594.03sdnaik0': '["Objects"]["1152909972.77sdnaik"]["Objects"]["1155864384.91sdnaik"]["Objects"]["1158184594.03sdnaik"]'}} | 1,883.833333 | 11,025 | 0.668141 | 1,530 | 11,303 | 4.888889 | 0.195425 | 0.023797 | 0.024064 | 0.031016 | 0.600401 | 0.546925 | 0.479545 | 0.438503 | 0.372193 | 0.267647 | 0 | 0.296531 | 0.066443 | 11,303 | 6 | 11,025 | 1,883.833333 | 0.412339 | 0.019641 | 0 | 0 | 0 | 0 | 0.595288 | 0.294665 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 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 | 1 | 0 | 0 | 0 | 0 | 6 |
d441d0f10107cab66d0f4a2deb933fc5ba7c7cef | 188 | py | Python | pyreisejl/utility/tests/test_call.py | danielolsen/REISE.jl | 4c6b39a48ab5f37ddfdfcd39a7d8fdf8e2c934c2 | [
"MIT"
] | 15 | 2021-03-02T11:54:51.000Z | 2022-02-01T05:52:33.000Z | pyreisejl/utility/tests/test_call.py | danielolsen/REISE.jl | 4c6b39a48ab5f37ddfdfcd39a7d8fdf8e2c934c2 | [
"MIT"
] | 51 | 2021-01-23T00:53:54.000Z | 2022-03-28T22:05:16.000Z | pyreisejl/utility/tests/test_call.py | danielolsen/REISE.jl | 4c6b39a48ab5f37ddfdfcd39a7d8fdf8e2c934c2 | [
"MIT"
] | 14 | 2021-02-01T21:19:34.000Z | 2022-02-11T13:15:10.000Z | import pytest
@pytest.mark.skip(reason="Need to run on the server")
def test():
from pyreisejl.utility.call import launch_scenario_performance
launch_scenario_performance("87")
| 20.888889 | 66 | 0.771277 | 26 | 188 | 5.423077 | 0.807692 | 0.198582 | 0.35461 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012346 | 0.138298 | 188 | 8 | 67 | 23.5 | 0.858025 | 0 | 0 | 0 | 0 | 0 | 0.143617 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0 | 0.4 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d44b7317b582d336d568326763565932b29c4a1b | 52 | py | Python | elichika/testtools/__init__.py | disktnk/chainer-compiler | 5cfd027b40ea6e4abf73eb42be70b4fba74d1cde | [
"MIT"
] | null | null | null | elichika/testtools/__init__.py | disktnk/chainer-compiler | 5cfd027b40ea6e4abf73eb42be70b4fba74d1cde | [
"MIT"
] | null | null | null | elichika/testtools/__init__.py | disktnk/chainer-compiler | 5cfd027b40ea6e4abf73eb42be70b4fba74d1cde | [
"MIT"
] | null | null | null | from testtools.testcasegen import generate_testcase
| 26 | 51 | 0.903846 | 6 | 52 | 7.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076923 | 52 | 1 | 52 | 52 | 0.958333 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d47bee747056713fc7c99fdda69a51335f35155b | 19 | py | Python | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/constantfolding/data/in_7_bool_simplification_recursion.py | JetBrains-Research/ast-transformations | 0ab408af3275b520cc87a473f418c4b4dfcb0284 | [
"MIT"
] | 8 | 2021-01-19T21:15:54.000Z | 2022-02-23T19:16:25.000Z | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/constantfolding/data/in_7_bool_simplification_recursion.py | JetBrains-Research/ast-transformations | 0ab408af3275b520cc87a473f418c4b4dfcb0284 | [
"MIT"
] | 4 | 2020-11-17T14:28:25.000Z | 2022-02-24T07:54:28.000Z | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/constantfolding/data/in_7_bool_simplification_recursion.py | nbirillo/ast-transformations | 717706765a2da29087a0de768fc851698886dd65 | [
"MIT"
] | 1 | 2022-02-23T19:16:30.000Z | 2022-02-23T19:16:30.000Z | x = 1 + 2 and 3 + 4 | 19 | 19 | 0.421053 | 6 | 19 | 1.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.363636 | 0.421053 | 19 | 1 | 19 | 19 | 0.363636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d47c5af836362948731859158ee95f8683c82ea5 | 171 | py | Python | tucat/core/tasks.py | natoinet/tucat | 635de353789662b555c54cad36b34303ba154869 | [
"BSD-3-Clause"
] | 2 | 2018-10-25T20:58:06.000Z | 2020-04-28T08:17:22.000Z | tucat/core/tasks.py | natoinet/tucat | 635de353789662b555c54cad36b34303ba154869 | [
"BSD-3-Clause"
] | 1 | 2018-10-21T21:16:28.000Z | 2018-10-26T14:59:55.000Z | tucat/core/tasks.py | natoinet/tucat | 635de353789662b555c54cad36b34303ba154869 | [
"BSD-3-Clause"
] | null | null | null | from __future__ import absolute_import
import time
from celery import shared_task
from celery.app.task import Task
from celery.signals import task_success, worker_init
| 19 | 52 | 0.847953 | 26 | 171 | 5.269231 | 0.5 | 0.218978 | 0.20438 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.128655 | 171 | 8 | 53 | 21.375 | 0.919463 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
d483b2a6d7c62e26cd1bab91e7b85e7a68604969 | 30 | py | Python | custom/sim_robot.py | herougan/TradeHunter | 1270a1d9807d1f2107db6bc78b98b584431840cc | [
"MIT"
] | null | null | null | custom/sim_robot.py | herougan/TradeHunter | 1270a1d9807d1f2107db6bc78b98b584431840cc | [
"MIT"
] | null | null | null | custom/sim_robot.py | herougan/TradeHunter | 1270a1d9807d1f2107db6bc78b98b584431840cc | [
"MIT"
] | 1 | 2022-02-09T08:45:05.000Z | 2022-02-09T08:45:05.000Z | from robot import FMACDRobot
| 10 | 28 | 0.833333 | 4 | 30 | 6.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 30 | 2 | 29 | 15 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2e332edae9be7b5c401744ef6add89f578ceebf5 | 59 | py | Python | spikeforest/spikeforest/spikewidgets/tables/sortingcomparisontable/__init__.py | mhhennig/spikeforest | 5b4507ead724af3de0be5d48a3b23aaedb0be170 | [
"Apache-2.0"
] | 1 | 2021-09-23T01:07:19.000Z | 2021-09-23T01:07:19.000Z | spikeforest/spikeforest/spikewidgets/tables/sortingcomparisontable/__init__.py | mhhennig/spikeforest | 5b4507ead724af3de0be5d48a3b23aaedb0be170 | [
"Apache-2.0"
] | null | null | null | spikeforest/spikeforest/spikewidgets/tables/sortingcomparisontable/__init__.py | mhhennig/spikeforest | 5b4507ead724af3de0be5d48a3b23aaedb0be170 | [
"Apache-2.0"
] | 1 | 2021-09-23T01:07:21.000Z | 2021-09-23T01:07:21.000Z | from .sortingcomparisontable import SortingComparisonTable
| 29.5 | 58 | 0.915254 | 4 | 59 | 13.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.067797 | 59 | 1 | 59 | 59 | 0.981818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2e371f63b0f1a7d0f9e6f2e9a7eeabe4457d277b | 17 | py | Python | python/testData/psi/DictLiteral.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 2 | 2018-12-29T09:53:39.000Z | 2018-12-29T09:53:42.000Z | python/testData/psi/DictLiteral.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 173 | 2018-07-05T13:59:39.000Z | 2018-08-09T01:12:03.000Z | python/testData/psi/DictLiteral.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 2 | 2020-03-15T08:57:37.000Z | 2020-04-07T04:48:14.000Z | {'a': 1, 'b': 2}
| 8.5 | 16 | 0.235294 | 4 | 17 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 0.235294 | 17 | 1 | 17 | 17 | 0.153846 | 0 | 0 | 0 | 0 | 0 | 0.117647 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cf007fa1e74177d3365d8175e7797acd2e643173 | 238 | py | Python | LoginHandler.py | futurechallenger/pytornado | 89e04192b4d142abf6d1d23b6bd876a54c31edd1 | [
"Artistic-2.0"
] | 2 | 2015-03-30T14:25:54.000Z | 2018-04-03T02:28:04.000Z | LoginHandler.py | futurechallenger/pytornado | 89e04192b4d142abf6d1d23b6bd876a54c31edd1 | [
"Artistic-2.0"
] | null | null | null | LoginHandler.py | futurechallenger/pytornado | 89e04192b4d142abf6d1d23b6bd876a54c31edd1 | [
"Artistic-2.0"
] | null | null | null |
# import tornado
# import tornado.web
# from tornado import gen
# from tornado import define, options, parse_command_line
import BaseHandler
class LoginHandler(BaseHandler.BaseHandler):
def get(self):
pass
| 18.307692 | 61 | 0.701681 | 27 | 238 | 6.111111 | 0.62963 | 0.236364 | 0.206061 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.239496 | 238 | 12 | 62 | 19.833333 | 0.911602 | 0.47479 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0.25 | 0.25 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
cf128f9a81914384c45a37c1692236803d432164 | 35 | py | Python | Testing/SingleTests/Environment/HaveLOOPY.py | illinois-ceesd/teesd | 764b27a0ca2f3aba1e00d8cf6bab8869d7ba1a59 | [
"MIT"
] | 1 | 2020-08-18T16:31:18.000Z | 2020-08-18T16:31:18.000Z | Testing/SingleTests/Environment/HaveLOOPY.py | illinois-ceesd/teesd | 764b27a0ca2f3aba1e00d8cf6bab8869d7ba1a59 | [
"MIT"
] | null | null | null | Testing/SingleTests/Environment/HaveLOOPY.py | illinois-ceesd/teesd | 764b27a0ca2f3aba1e00d8cf6bab8869d7ba1a59 | [
"MIT"
] | null | null | null | import loopy
print(loopy.version)
| 8.75 | 20 | 0.8 | 5 | 35 | 5.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 3 | 21 | 11.666667 | 0.903226 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
d8dff48e38b61653eb0c5a0ffaf4714d995f820e | 231 | py | Python | credentials.py | oscarkioge94/mpesa | 59c86cca60508b90305a2825a62ba80ef1d8f4ce | [
"MIT"
] | null | null | null | credentials.py | oscarkioge94/mpesa | 59c86cca60508b90305a2825a62ba80ef1d8f4ce | [
"MIT"
] | null | null | null | credentials.py | oscarkioge94/mpesa | 59c86cca60508b90305a2825a62ba80ef1d8f4ce | [
"MIT"
] | null | null | null | business_shortCode = "174379"
phone_number="254710830759"
lipa_na_mpesa_passkey = "bfb279f9aa9bdbcf158e97dd71a467cd2e0c893059b10f78e6b72ada1ed2c919"
consumer_key="zL2vYJyOZAiFM0A3C2bVXUQIFVNzyj77"
consumer_secret="ctxSXM5AGeqdA6Jm" | 46.2 | 90 | 0.900433 | 17 | 231 | 11.823529 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.273543 | 0.034632 | 231 | 5 | 91 | 46.2 | 0.627803 | 0 | 0 | 0 | 0 | 0 | 0.560345 | 0.413793 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
d8f19e35d353340543769063ad2aee47c14f0e3a | 167 | py | Python | info/modules/news/views.py | rs31-zy/flask_new_info | c48307ee319b29ac60bdee65f507a2d3bd4238a6 | [
"Apache-2.0"
] | null | null | null | info/modules/news/views.py | rs31-zy/flask_new_info | c48307ee319b29ac60bdee65f507a2d3bd4238a6 | [
"Apache-2.0"
] | null | null | null | info/modules/news/views.py | rs31-zy/flask_new_info | c48307ee319b29ac60bdee65f507a2d3bd4238a6 | [
"Apache-2.0"
] | null | null | null | from flask import render_template
from . import news_blu
@news_blu.route('/<int:news_id>')
def news_detail(news_id):
return render_template('news/detail.html') | 20.875 | 47 | 0.760479 | 26 | 167 | 4.615385 | 0.538462 | 0.233333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.11976 | 167 | 8 | 47 | 20.875 | 0.816327 | 0 | 0 | 0 | 0 | 0 | 0.178571 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.4 | 0.2 | 0.8 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
2b3db85a8a62a536768a2647a91600f65e72f575 | 32 | py | Python | Start.py | chuah007/HttpProxyServerAndControlPanel | d5e8625a9e7a3c53ef65a9dae399f9532b8346ba | [
"Apache-2.0"
] | null | null | null | Start.py | chuah007/HttpProxyServerAndControlPanel | d5e8625a9e7a3c53ef65a9dae399f9532b8346ba | [
"Apache-2.0"
] | null | null | null | Start.py | chuah007/HttpProxyServerAndControlPanel | d5e8625a9e7a3c53ef65a9dae399f9532b8346ba | [
"Apache-2.0"
] | null | null | null | print("this is my first commit") | 32 | 32 | 0.75 | 6 | 32 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 32 | 1 | 32 | 32 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0.69697 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
2b440c9951f561070a2a382f55ae52069bdcb7a9 | 324 | py | Python | src/spaceone/identity/connector/__init__.py | Jeoungseungho/identity | 8fa1c8d21952fb7b313624e632d98e99e5bf0def | [
"Apache-2.0"
] | null | null | null | src/spaceone/identity/connector/__init__.py | Jeoungseungho/identity | 8fa1c8d21952fb7b313624e632d98e99e5bf0def | [
"Apache-2.0"
] | null | null | null | src/spaceone/identity/connector/__init__.py | Jeoungseungho/identity | 8fa1c8d21952fb7b313624e632d98e99e5bf0def | [
"Apache-2.0"
] | null | null | null | from spaceone.identity.connector.plugin_service_connector import PluginServiceConnector
from spaceone.identity.connector.auth_plugin_connector import AuthPluginConnector
from spaceone.identity.connector.secret_connector import SecretConnector
from spaceone.identity.connector.repository_connector import RepositoryConnector
| 64.8 | 87 | 0.91358 | 34 | 324 | 8.529412 | 0.411765 | 0.165517 | 0.275862 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.049383 | 324 | 4 | 88 | 81 | 0.941558 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
991512ef79889a86436db062d3f6aae94c192fe7 | 69,638 | py | Python | scripts/other/Ui_main_qt4.py | SamKaiYang/ros_modbus_nex | b698cc73df65853866112f7501432a8509a2545c | [
"BSD-2-Clause"
] | null | null | null | scripts/other/Ui_main_qt4.py | SamKaiYang/ros_modbus_nex | b698cc73df65853866112f7501432a8509a2545c | [
"BSD-2-Clause"
] | null | null | null | scripts/other/Ui_main_qt4.py | SamKaiYang/ros_modbus_nex | b698cc73df65853866112f7501432a8509a2545c | [
"BSD-2-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'main.ui'
#
# Created by: PyQt4 UI code generator 4.12.1
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName(_fromUtf8("MainWindow"))
MainWindow.resize(929, 566)
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
MainWindow.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans Mono"))
MainWindow.setFont(font)
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap(_fromUtf8("src/modbus/modbus/picture/teco_icon.png")), QtGui.QIcon.Normal, QtGui.QIcon.Off)
MainWindow.setWindowIcon(icon)
MainWindow.setToolButtonStyle(QtCore.Qt.ToolButtonIconOnly)
self.centralwidget = QtGui.QWidget(MainWindow)
self.centralwidget.setObjectName(_fromUtf8("centralwidget"))
self.verticalLayoutWidget = QtGui.QWidget(self.centralwidget)
self.verticalLayoutWidget.setGeometry(QtCore.QRect(20, 110, 181, 381))
self.verticalLayoutWidget.setObjectName(_fromUtf8("verticalLayoutWidget"))
self.verticalLayout = QtGui.QVBoxLayout(self.verticalLayoutWidget)
self.verticalLayout.setMargin(0)
self.verticalLayout.setObjectName(_fromUtf8("verticalLayout"))
self.btn_reset = QtGui.QPushButton(self.verticalLayoutWidget)
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_reset.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.btn_reset.setFont(font)
self.btn_reset.setStyleSheet(_fromUtf8("background-color:#da7700;color:white;border-color: black;"))
self.btn_reset.setObjectName(_fromUtf8("btn_reset"))
self.verticalLayout.addWidget(self.btn_reset)
self.btn_enable = QtGui.QPushButton(self.verticalLayoutWidget)
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 210, 26))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_enable.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.btn_enable.setFont(font)
self.btn_enable.setStyleSheet(_fromUtf8("background-color:#00d21a;color:white;border-color: black;"))
self.btn_enable.setObjectName(_fromUtf8("btn_enable"))
self.verticalLayout.addWidget(self.btn_enable)
self.btn_disable = QtGui.QPushButton(self.verticalLayoutWidget)
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(177, 0, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_disable.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.btn_disable.setFont(font)
self.btn_disable.setStyleSheet(_fromUtf8("background-color:#b10011;color:white;border-color: black;"))
self.btn_disable.setObjectName(_fromUtf8("btn_disable"))
self.verticalLayout.addWidget(self.btn_disable)
self.onBtn = QtGui.QPushButton(self.verticalLayoutWidget)
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.onBtn.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.onBtn.setFont(font)
self.onBtn.setStyleSheet(_fromUtf8("background-color:#da7700;color:white;border-color: black;"))
self.onBtn.setObjectName(_fromUtf8("onBtn"))
self.verticalLayout.addWidget(self.onBtn)
self.offBtn = QtGui.QPushButton(self.verticalLayoutWidget)
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.offBtn.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.offBtn.setFont(font)
self.offBtn.setStyleSheet(_fromUtf8("background-color:#da7700;color:white;border-color: black;"))
self.offBtn.setObjectName(_fromUtf8("offBtn"))
self.verticalLayout.addWidget(self.offBtn)
self.gridLayoutWidget_2 = QtGui.QWidget(self.centralwidget)
self.gridLayoutWidget_2.setGeometry(QtCore.QRect(10, 10, 201, 94))
self.gridLayoutWidget_2.setObjectName(_fromUtf8("gridLayoutWidget_2"))
self.gridLayout_2 = QtGui.QGridLayout(self.gridLayoutWidget_2)
self.gridLayout_2.setMargin(0)
self.gridLayout_2.setObjectName(_fromUtf8("gridLayout_2"))
self.lineEdit_ip = QtGui.QLineEdit(self.gridLayoutWidget_2)
self.lineEdit_ip.setObjectName(_fromUtf8("lineEdit_ip"))
self.gridLayout_2.addWidget(self.lineEdit_ip, 2, 0, 1, 1)
self.label_ip = QtGui.QLabel(self.gridLayoutWidget_2)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setBold(True)
font.setWeight(75)
self.label_ip.setFont(font)
self.label_ip.setObjectName(_fromUtf8("label_ip"))
self.gridLayout_2.addWidget(self.label_ip, 0, 0, 1, 1)
self.btn_ip_set = QtGui.QPushButton(self.gridLayoutWidget_2)
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 86, 239))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_ip_set.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.btn_ip_set.setFont(font)
self.btn_ip_set.setStyleSheet(_fromUtf8("background-color:#0056ef;color:white;border-color: black;"))
self.btn_ip_set.setObjectName(_fromUtf8("btn_ip_set"))
self.gridLayout_2.addWidget(self.btn_ip_set, 3, 0, 1, 1)
self.tabWidget = QtGui.QTabWidget(self.centralwidget)
self.tabWidget.setGeometry(QtCore.QRect(220, 10, 691, 501))
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans Mono"))
font.setBold(True)
font.setWeight(75)
self.tabWidget.setFont(font)
self.tabWidget.setTabShape(QtGui.QTabWidget.Triangular)
self.tabWidget.setObjectName(_fromUtf8("tabWidget"))
self.tab_arm = QtGui.QWidget()
self.tab_arm.setObjectName(_fromUtf8("tab_arm"))
self.verticalLayoutWidget_3 = QtGui.QWidget(self.tab_arm)
self.verticalLayoutWidget_3.setGeometry(QtCore.QRect(10, 10, 671, 431))
self.verticalLayoutWidget_3.setObjectName(_fromUtf8("verticalLayoutWidget_3"))
self.verticalLayout_3 = QtGui.QVBoxLayout(self.verticalLayoutWidget_3)
self.verticalLayout_3.setMargin(0)
self.verticalLayout_3.setObjectName(_fromUtf8("verticalLayout_3"))
self.label_acs_command_list = QtGui.QLabel(self.verticalLayoutWidget_3)
font = QtGui.QFont()
font.setPointSize(15)
font.setBold(True)
font.setWeight(75)
self.label_acs_command_list.setFont(font)
self.label_acs_command_list.setAlignment(QtCore.Qt.AlignCenter)
self.label_acs_command_list.setObjectName(_fromUtf8("label_acs_command_list"))
self.verticalLayout_3.addWidget(self.label_acs_command_list)
self.tableView_joint = QtGui.QTableView(self.verticalLayoutWidget_3)
font = QtGui.QFont()
font.setPointSize(13)
self.tableView_joint.setFont(font)
self.tableView_joint.setSelectionMode(QtGui.QAbstractItemView.MultiSelection)
self.tableView_joint.setSelectionBehavior(QtGui.QAbstractItemView.SelectRows)
self.tableView_joint.setObjectName(_fromUtf8("tableView_joint"))
self.verticalLayout_3.addWidget(self.tableView_joint)
self.label_pcs_command_list = QtGui.QLabel(self.verticalLayoutWidget_3)
font = QtGui.QFont()
font.setPointSize(15)
font.setBold(True)
font.setWeight(75)
self.label_pcs_command_list.setFont(font)
self.label_pcs_command_list.setAlignment(QtCore.Qt.AlignCenter)
self.label_pcs_command_list.setObjectName(_fromUtf8("label_pcs_command_list"))
self.verticalLayout_3.addWidget(self.label_pcs_command_list)
self.tableView_pos = QtGui.QTableView(self.verticalLayoutWidget_3)
font = QtGui.QFont()
font.setPointSize(13)
self.tableView_pos.setFont(font)
self.tableView_pos.setSelectionMode(QtGui.QAbstractItemView.MultiSelection)
self.tableView_pos.setSelectionBehavior(QtGui.QAbstractItemView.SelectRows)
self.tableView_pos.setObjectName(_fromUtf8("tableView_pos"))
self.verticalLayout_3.addWidget(self.tableView_pos)
self.tabWidget.addTab(self.tab_arm, _fromUtf8(""))
self.tab_mission = QtGui.QWidget()
self.tab_mission.setObjectName(_fromUtf8("tab_mission"))
self.verticalLayoutWidget_2 = QtGui.QWidget(self.tab_mission)
self.verticalLayoutWidget_2.setGeometry(QtCore.QRect(550, 300, 131, 151))
self.verticalLayoutWidget_2.setObjectName(_fromUtf8("verticalLayoutWidget_2"))
self.verticalLayout_2 = QtGui.QVBoxLayout(self.verticalLayoutWidget_2)
self.verticalLayout_2.setMargin(0)
self.verticalLayout_2.setObjectName(_fromUtf8("verticalLayout_2"))
self.lcdNumber = QtGui.QLCDNumber(self.verticalLayoutWidget_2)
self.lcdNumber.setObjectName(_fromUtf8("lcdNumber"))
self.verticalLayout_2.addWidget(self.lcdNumber)
self.btn_start_time = QtGui.QPushButton(self.verticalLayoutWidget_2)
self.btn_start_time.setObjectName(_fromUtf8("btn_start_time"))
self.verticalLayout_2.addWidget(self.btn_start_time)
self.btn_stop_time = QtGui.QPushButton(self.verticalLayoutWidget_2)
self.btn_stop_time.setObjectName(_fromUtf8("btn_stop_time"))
self.verticalLayout_2.addWidget(self.btn_stop_time)
self.btn_reset_time = QtGui.QPushButton(self.verticalLayoutWidget_2)
self.btn_reset_time.setObjectName(_fromUtf8("btn_reset_time"))
self.verticalLayout_2.addWidget(self.btn_reset_time)
self.gridLayoutWidget = QtGui.QWidget(self.tab_mission)
self.gridLayoutWidget.setGeometry(QtCore.QRect(20, 260, 321, 131))
self.gridLayoutWidget.setObjectName(_fromUtf8("gridLayoutWidget"))
self.gridLayout = QtGui.QGridLayout(self.gridLayoutWidget)
self.gridLayout.setMargin(0)
self.gridLayout.setObjectName(_fromUtf8("gridLayout"))
self.label_mission_case_show = QtGui.QLabel(self.gridLayoutWidget)
self.label_mission_case_show.setObjectName(_fromUtf8("label_mission_case_show"))
self.gridLayout.addWidget(self.label_mission_case_show, 2, 1, 1, 1)
self.comboBox = QtGui.QComboBox(self.gridLayoutWidget)
self.comboBox.setObjectName(_fromUtf8("comboBox"))
self.gridLayout.addWidget(self.comboBox, 0, 0, 1, 1)
self.label_mission_case = QtGui.QLabel(self.gridLayoutWidget)
self.label_mission_case.setObjectName(_fromUtf8("label_mission_case"))
self.gridLayout.addWidget(self.label_mission_case, 2, 0, 1, 1)
self.btn_start_program = QtGui.QPushButton(self.gridLayoutWidget)
font = QtGui.QFont()
font.setPointSize(13)
self.btn_start_program.setFont(font)
self.btn_start_program.setStyleSheet(_fromUtf8("background-color:#0300fc;color:white;border-color: black;"))
self.btn_start_program.setObjectName(_fromUtf8("btn_start_program"))
self.gridLayout.addWidget(self.btn_start_program, 0, 1, 1, 1)
self.btn_stop_program = QtGui.QPushButton(self.gridLayoutWidget)
self.btn_stop_program.setStyleSheet(_fromUtf8("background-color:#005b62;color:white;border-color: black;"))
self.btn_stop_program.setObjectName(_fromUtf8("btn_stop_program"))
self.gridLayout.addWidget(self.btn_stop_program, 1, 1, 1, 1)
self.groupBox_speed = QtGui.QGroupBox(self.tab_mission)
self.groupBox_speed.setGeometry(QtCore.QRect(10, 110, 371, 131))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(173, 127, 168))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 125, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(173, 127, 168))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 125, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(173, 127, 168))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(238, 238, 236))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 125, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 125, 17))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.groupBox_speed.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(13)
self.groupBox_speed.setFont(font)
self.groupBox_speed.setAlignment(QtCore.Qt.AlignCenter)
self.groupBox_speed.setObjectName(_fromUtf8("groupBox_speed"))
self.horizontalSlider_acc = QtGui.QSlider(self.groupBox_speed)
self.horizontalSlider_acc.setGeometry(QtCore.QRect(10, 100, 160, 16))
self.horizontalSlider_acc.setStyleSheet(_fromUtf8(""))
self.horizontalSlider_acc.setMaximum(100)
self.horizontalSlider_acc.setOrientation(QtCore.Qt.Horizontal)
self.horizontalSlider_acc.setObjectName(_fromUtf8("horizontalSlider_acc"))
self.btn_acc_set = QtGui.QPushButton(self.groupBox_speed)
self.btn_acc_set.setGeometry(QtCore.QRect(270, 90, 81, 25))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_acc_set.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(13)
self.btn_acc_set.setFont(font)
self.btn_acc_set.setStyleSheet(_fromUtf8("background-color:#da7700;color:white;border-color: black;"))
self.btn_acc_set.setObjectName(_fromUtf8("btn_acc_set"))
self.lineEdit_acc = QtGui.QLineEdit(self.groupBox_speed)
self.lineEdit_acc.setGeometry(QtCore.QRect(180, 90, 81, 25))
self.lineEdit_acc.setObjectName(_fromUtf8("lineEdit_acc"))
self.btn_vel_set = QtGui.QPushButton(self.groupBox_speed)
self.btn_vel_set.setGeometry(QtCore.QRect(270, 40, 81, 25))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_vel_set.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(13)
self.btn_vel_set.setFont(font)
self.btn_vel_set.setStyleSheet(_fromUtf8("background-color:#da7700;color:white;border-color: black;"))
self.btn_vel_set.setObjectName(_fromUtf8("btn_vel_set"))
self.lineEdit_vel = QtGui.QLineEdit(self.groupBox_speed)
self.lineEdit_vel.setGeometry(QtCore.QRect(180, 40, 81, 25))
self.lineEdit_vel.setObjectName(_fromUtf8("lineEdit_vel"))
self.horizontalSlider_vel = QtGui.QSlider(self.groupBox_speed)
self.horizontalSlider_vel.setGeometry(QtCore.QRect(10, 50, 160, 16))
self.horizontalSlider_vel.setStyleSheet(_fromUtf8(""))
self.horizontalSlider_vel.setMaximum(100)
self.horizontalSlider_vel.setOrientation(QtCore.Qt.Horizontal)
self.horizontalSlider_vel.setObjectName(_fromUtf8("horizontalSlider_vel"))
self.label_velocity = QtGui.QLabel(self.groupBox_speed)
self.label_velocity.setGeometry(QtCore.QRect(20, 30, 121, 17))
self.label_velocity.setAlignment(QtCore.Qt.AlignCenter)
self.label_velocity.setObjectName(_fromUtf8("label_velocity"))
self.label_acceleration = QtGui.QLabel(self.groupBox_speed)
self.label_acceleration.setGeometry(QtCore.QRect(20, 80, 141, 17))
self.label_acceleration.setAlignment(QtCore.Qt.AlignCenter)
self.label_acceleration.setObjectName(_fromUtf8("label_acceleration"))
self.label_project_name = QtGui.QLabel(self.tab_mission)
self.label_project_name.setGeometry(QtCore.QRect(20, 20, 351, 17))
font = QtGui.QFont()
font.setPointSize(13)
self.label_project_name.setFont(font)
self.label_project_name.setAlignment(QtCore.Qt.AlignCenter)
self.label_project_name.setObjectName(_fromUtf8("label_project_name"))
self.lineEdit_project_name_select = QtGui.QLineEdit(self.tab_mission)
self.lineEdit_project_name_select.setGeometry(QtCore.QRect(20, 50, 251, 25))
self.lineEdit_project_name_select.setObjectName(_fromUtf8("lineEdit_project_name_select"))
self.btn_project_name_select = QtGui.QPushButton(self.tab_mission)
self.btn_project_name_select.setGeometry(QtCore.QRect(370, 50, 81, 25))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_project_name_select.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(13)
self.btn_project_name_select.setFont(font)
self.btn_project_name_select.setStyleSheet(_fromUtf8("background-color:#da7700;color:white;border-color: black;"))
self.btn_project_name_select.setObjectName(_fromUtf8("btn_project_name_select"))
self.btn_project_name_read = QtGui.QPushButton(self.tab_mission)
self.btn_project_name_read.setGeometry(QtCore.QRect(280, 50, 81, 25))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(218, 119, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.btn_project_name_read.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(13)
self.btn_project_name_read.setFont(font)
self.btn_project_name_read.setStyleSheet(_fromUtf8("background-color:#da7700;color:white;border-color: black;"))
self.btn_project_name_read.setObjectName(_fromUtf8("btn_project_name_read"))
self.btn_dance_program = QtGui.QPushButton(self.tab_mission)
self.btn_dance_program.setGeometry(QtCore.QRect(490, 120, 156, 29))
font = QtGui.QFont()
font.setPointSize(13)
self.btn_dance_program.setFont(font)
self.btn_dance_program.setStyleSheet(_fromUtf8("background-color:#0300fc;color:white;border-color: black;"))
self.btn_dance_program.setObjectName(_fromUtf8("btn_dance_program"))
self.btn_bow_program = QtGui.QPushButton(self.tab_mission)
self.btn_bow_program.setGeometry(QtCore.QRect(490, 170, 156, 29))
font = QtGui.QFont()
font.setPointSize(13)
self.btn_bow_program.setFont(font)
self.btn_bow_program.setStyleSheet(_fromUtf8("background-color:#0300fc;color:white;border-color: black;"))
self.btn_bow_program.setObjectName(_fromUtf8("btn_bow_program"))
self.btn_smile_program = QtGui.QPushButton(self.tab_mission)
self.btn_smile_program.setGeometry(QtCore.QRect(490, 220, 156, 29))
font = QtGui.QFont()
font.setPointSize(13)
self.btn_smile_program.setFont(font)
self.btn_smile_program.setStyleSheet(_fromUtf8("background-color:#0300fc;color:white;border-color: black;"))
self.btn_smile_program.setObjectName(_fromUtf8("btn_smile_program"))
self.btn_show_program = QtGui.QPushButton(self.tab_mission)
self.btn_show_program.setGeometry(QtCore.QRect(490, 30, 156, 29))
font = QtGui.QFont()
font.setPointSize(13)
self.btn_show_program.setFont(font)
self.btn_show_program.setStyleSheet(_fromUtf8("background-color:#0300fc;color:white;border-color: black;"))
self.btn_show_program.setObjectName(_fromUtf8("btn_show_program"))
self.tabWidget.addTab(self.tab_mission, _fromUtf8(""))
self.tab_state = QtGui.QWidget()
self.tab_state.setObjectName(_fromUtf8("tab_state"))
self.verticalLayoutWidget_4 = QtGui.QWidget(self.tab_state)
self.verticalLayoutWidget_4.setGeometry(QtCore.QRect(10, 10, 351, 431))
self.verticalLayoutWidget_4.setObjectName(_fromUtf8("verticalLayoutWidget_4"))
self.verticalLayout_4 = QtGui.QVBoxLayout(self.verticalLayoutWidget_4)
self.verticalLayout_4.setMargin(0)
self.verticalLayout_4.setObjectName(_fromUtf8("verticalLayout_4"))
self.label_rostopic_pub_list = QtGui.QLabel(self.verticalLayoutWidget_4)
font = QtGui.QFont()
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.label_rostopic_pub_list.setFont(font)
self.label_rostopic_pub_list.setObjectName(_fromUtf8("label_rostopic_pub_list"))
self.verticalLayout_4.addWidget(self.label_rostopic_pub_list)
self.tableView_pub = QtGui.QTableView(self.verticalLayoutWidget_4)
font = QtGui.QFont()
font.setPointSize(13)
self.tableView_pub.setFont(font)
self.tableView_pub.setSelectionMode(QtGui.QAbstractItemView.MultiSelection)
self.tableView_pub.setSelectionBehavior(QtGui.QAbstractItemView.SelectRows)
self.tableView_pub.setObjectName(_fromUtf8("tableView_pub"))
self.verticalLayout_4.addWidget(self.tableView_pub)
self.label_rostopic_echo_list = QtGui.QLabel(self.verticalLayoutWidget_4)
font = QtGui.QFont()
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.label_rostopic_echo_list.setFont(font)
self.label_rostopic_echo_list.setObjectName(_fromUtf8("label_rostopic_echo_list"))
self.verticalLayout_4.addWidget(self.label_rostopic_echo_list)
self.tableView_echo = QtGui.QTableView(self.verticalLayoutWidget_4)
font = QtGui.QFont()
font.setPointSize(13)
self.tableView_echo.setFont(font)
self.tableView_echo.setSelectionMode(QtGui.QAbstractItemView.MultiSelection)
self.tableView_echo.setSelectionBehavior(QtGui.QAbstractItemView.SelectRows)
self.tableView_echo.setObjectName(_fromUtf8("tableView_echo"))
self.verticalLayout_4.addWidget(self.tableView_echo)
self.tabWidget.addTab(self.tab_state, _fromUtf8(""))
self.tab_other = QtGui.QWidget()
self.tab_other.setObjectName(_fromUtf8("tab_other"))
self.label_arm_picture = QtGui.QLabel(self.tab_other)
self.label_arm_picture.setGeometry(QtCore.QRect(20, 80, 151, 141))
self.label_arm_picture.setText(_fromUtf8(""))
self.label_arm_picture.setPixmap(QtGui.QPixmap(_fromUtf8("../picture/teco_arm.png")))
self.label_arm_picture.setScaledContents(True)
self.label_arm_picture.setObjectName(_fromUtf8("label_arm_picture"))
self.label_Coordinate_configuration = QtGui.QLabel(self.tab_other)
self.label_Coordinate_configuration.setGeometry(QtCore.QRect(190, 40, 331, 431))
self.label_Coordinate_configuration.setText(_fromUtf8(""))
self.label_Coordinate_configuration.setPixmap(QtGui.QPixmap(_fromUtf8("../picture/Coordinate_system_configuration_2.jpg")))
self.label_Coordinate_configuration.setScaledContents(True)
self.label_Coordinate_configuration.setObjectName(_fromUtf8("label_Coordinate_configuration"))
self.label_other_armshow = QtGui.QLabel(self.tab_other)
self.label_other_armshow.setGeometry(QtCore.QRect(10, 0, 411, 41))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(143, 89, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(143, 89, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(143, 89, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(143, 89, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(143, 89, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(143, 89, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(190, 190, 190))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(190, 190, 190))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(190, 190, 190))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
self.label_other_armshow.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("DejaVu Sans Mono"))
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.label_other_armshow.setFont(font)
self.label_other_armshow.setObjectName(_fromUtf8("label_other_armshow"))
self.label_arm_gif = QtGui.QLabel(self.tab_other)
self.label_arm_gif.setGeometry(QtCore.QRect(30, 280, 151, 141))
self.label_arm_gif.setText(_fromUtf8(""))
self.label_arm_gif.setPixmap(QtGui.QPixmap(_fromUtf8("src/modbus/modbus/picture/teco_arm.png")))
self.label_arm_gif.setScaledContents(True)
self.label_arm_gif.setObjectName(_fromUtf8("label_arm_gif"))
self.tabWidget.addTab(self.tab_other, _fromUtf8(""))
self.tab = QtGui.QWidget()
self.tab.setObjectName(_fromUtf8("tab"))
self.btn_test = QtGui.QPushButton(self.tab)
self.btn_test.setGeometry(QtCore.QRect(140, 100, 179, 33))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(233, 185, 110))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(233, 185, 110))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(190, 190, 190))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(233, 185, 110))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
self.btn_test.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.btn_test.setFont(font)
self.btn_test.setObjectName(_fromUtf8("btn_test"))
self.btn_test2 = QtGui.QPushButton(self.tab)
self.btn_test2.setGeometry(QtCore.QRect(140, 200, 179, 33))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(233, 185, 110))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(233, 185, 110))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(190, 190, 190))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(233, 185, 110))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
self.btn_test2.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Bitstream Vera Sans"))
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.btn_test2.setFont(font)
self.btn_test2.setObjectName(_fromUtf8("btn_test2"))
self.tabWidget.addTab(self.tab, _fromUtf8(""))
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtGui.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 929, 23))
self.menubar.setObjectName(_fromUtf8("menubar"))
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtGui.QStatusBar(MainWindow)
self.statusbar.setObjectName(_fromUtf8("statusbar"))
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
self.tabWidget.setCurrentIndex(1)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
MainWindow.setWindowTitle(_translate("MainWindow", "TECO Arm Modbus Control Window", None))
self.btn_reset.setText(_translate("MainWindow", "Reset", None))
self.btn_enable.setText(_translate("MainWindow", "Enable", None))
self.btn_disable.setText(_translate("MainWindow", "Disable", None))
self.onBtn.setText(_translate("MainWindow", "Read state", None))
self.offBtn.setText(_translate("MainWindow", "Read off", None))
self.lineEdit_ip.setText(_translate("MainWindow", "192.168.0.6", None))
self.label_ip.setText(_translate("MainWindow", "Preset IP:192.168.0.6", None))
self.btn_ip_set.setText(_translate("MainWindow", "Connect", None))
self.tabWidget.setWhatsThis(_translate("MainWindow", "<html><head/><body><p>dd</p></body></html>", None))
self.label_acs_command_list.setText(_translate("MainWindow", "Joint Space (ACS)", None))
self.label_pcs_command_list.setText(_translate("MainWindow", "Cartesian Space (PCS)", None))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab_arm), _translate("MainWindow", "Arm", None))
self.btn_start_time.setText(_translate("MainWindow", "Start", None))
self.btn_stop_time.setText(_translate("MainWindow", "Stop", None))
self.btn_reset_time.setText(_translate("MainWindow", "Reset", None))
self.label_mission_case_show.setText(_translate("MainWindow", "???", None))
self.label_mission_case.setText(_translate("MainWindow", "You choose ?", None))
self.btn_start_program.setText(_translate("MainWindow", "Start task", None))
self.btn_stop_program.setText(_translate("MainWindow", "Stop", None))
self.groupBox_speed.setTitle(_translate("MainWindow", "Overall speed setting", None))
self.btn_acc_set.setText(_translate("MainWindow", "Set", None))
self.btn_vel_set.setText(_translate("MainWindow", "Set", None))
self.label_velocity.setText(_translate("MainWindow", "Velocity", None))
self.label_acceleration.setText(_translate("MainWindow", "Acceleration", None))
self.label_project_name.setText(_translate("MainWindow", "Project Name:", None))
self.btn_project_name_select.setText(_translate("MainWindow", "Select", None))
self.btn_project_name_read.setText(_translate("MainWindow", "Read", None))
self.btn_dance_program.setText(_translate("MainWindow", "dance", None))
self.btn_bow_program.setText(_translate("MainWindow", "bow", None))
self.btn_smile_program.setText(_translate("MainWindow", "smile", None))
self.btn_show_program.setText(_translate("MainWindow", "show", None))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab_mission), _translate("MainWindow", "Mission", None))
self.label_rostopic_pub_list.setText(_translate("MainWindow", "rostopic /reply_external_comm", None))
self.label_rostopic_echo_list.setText(_translate("MainWindow", "rostopic /write_external_comm", None))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab_state), _translate("MainWindow", "State", None))
self.label_other_armshow.setText(_translate("MainWindow", "Arm coordinate system configuration", None))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab_other), _translate("MainWindow", "Other", None))
self.btn_test.setText(_translate("MainWindow", "test", None))
self.btn_test2.setText(_translate("MainWindow", "test2", None))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab), _translate("MainWindow", "Page", None))
| 59.877902 | 131 | 0.704443 | 8,181 | 69,638 | 5.899401 | 0.039604 | 0.123635 | 0.073597 | 0.096596 | 0.820566 | 0.760375 | 0.72654 | 0.707333 | 0.683629 | 0.674782 | 0 | 0.042951 | 0.174861 | 69,638 | 1,162 | 132 | 59.929432 | 0.796975 | 0.002556 | 0 | 0.680314 | 1 | 0 | 0.045614 | 0.018315 | 0 | 0 | 0 | 0 | 0 | 1 | 0.004355 | false | 0 | 0.000871 | 0.002613 | 0.008711 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
9916ac8b64fb0ce2ba42f4fffcf5a4b0342930cc | 26 | py | Python | SevenSeg/__init__.py | medtrab/SevenSeg | de3eace776b170998450c2b52b7d3b4a29b58939 | [
"MIT"
] | null | null | null | SevenSeg/__init__.py | medtrab/SevenSeg | de3eace776b170998450c2b52b7d3b4a29b58939 | [
"MIT"
] | null | null | null | SevenSeg/__init__.py | medtrab/SevenSeg | de3eace776b170998450c2b52b7d3b4a29b58939 | [
"MIT"
] | null | null | null | from SevenSeg.Seg import * | 26 | 26 | 0.807692 | 4 | 26 | 5.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 26 | 1 | 26 | 26 | 0.913043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
992249a54c982ad8838a82c85fbdd1ebf14cd03e | 70 | py | Python | base_astro_bot/rsi_data/__init__.py | Mirdalan/base_astro_bot | 656ebd55c0f57fc18bf95227af9e20a4c1392489 | [
"MIT"
] | 2 | 2018-11-16T11:31:53.000Z | 2019-05-19T03:07:15.000Z | base_astro_bot/rsi_data/__init__.py | Mirdalan/base_astro_bot | 656ebd55c0f57fc18bf95227af9e20a4c1392489 | [
"MIT"
] | null | null | null | base_astro_bot/rsi_data/__init__.py | Mirdalan/base_astro_bot | 656ebd55c0f57fc18bf95227af9e20a4c1392489 | [
"MIT"
] | null | null | null | from .rsi_parser import RsiDataParser
from .rsi_mixin import RsiMixin
| 23.333333 | 37 | 0.857143 | 10 | 70 | 5.8 | 0.7 | 0.241379 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 70 | 2 | 38 | 35 | 0.935484 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
41dd986385ba59dd61ecf691c3e2287043f97e33 | 70 | py | Python | AoC20/day_05/a.py | a-recknagel/AoC20 | 7aa0013dc745bdc0ad357e1168b212bd065fd092 | [
"MIT"
] | null | null | null | AoC20/day_05/a.py | a-recknagel/AoC20 | 7aa0013dc745bdc0ad357e1168b212bd065fd092 | [
"MIT"
] | null | null | null | AoC20/day_05/a.py | a-recknagel/AoC20 | 7aa0013dc745bdc0ad357e1168b212bd065fd092 | [
"MIT"
] | null | null | null | from AoC20.day_5 import parse, data as data
print(max(parse(data)))
| 14 | 43 | 0.742857 | 13 | 70 | 3.923077 | 0.769231 | 0.352941 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0.142857 | 70 | 4 | 44 | 17.5 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 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 | 1 | 0 | 6 |
51174e0206d0f48a012f81239d6cb254a4eb5c46 | 2,861 | py | Python | program/testy/test_ModeSingle.py | peter2141/IBT | 8e6b1ac68680152ad744007aaf2b9e0a6d070d80 | [
"Apache-2.0"
] | null | null | null | program/testy/test_ModeSingle.py | peter2141/IBT | 8e6b1ac68680152ad744007aaf2b9e0a6d070d80 | [
"Apache-2.0"
] | null | null | null | program/testy/test_ModeSingle.py | peter2141/IBT | 8e6b1ac68680152ad744007aaf2b9e0a6d070d80 | [
"Apache-2.0"
] | null | null | null | import unittest
import sys
sys.path.append('..')
import modes
import xml.etree.cElementTree
import os
import global_var
os.system("tshark -r xml/smtp.pcap -T pdml > tmp.pdml")
class TestModeSingle(unittest.TestCase):
def test_expression_false(self):
result = None
for event, elem in xml.etree.cElementTree.iterparse('tmp.pdml', events=('start', 'end')):
if event == 'start':
if elem.tag == 'field':
if elem.get('name') is not None and elem.get('show') is not None:
global_var.xmlfields.append({elem.get('name'): elem.get('show')})
if event == 'end':
if elem.tag == 'packet': # ak koniec paketu tak nastavime flag
result = modes.modeSingle(['AVG(fake.attr) == 5'])
break
self.assertEqual(result, False)
def test_syntax_error(self):
result = None
for event, elem in xml.etree.cElementTree.iterparse('tmp.pdml', events=('start', 'end')):
if event == 'start':
if elem.tag == 'field':
if elem.get('name') is not None and elem.get('show') is not None:
global_var.xmlfields.append({elem.get('name'): elem.get('show')})
if event == 'end':
if elem.tag == 'packet': # ak koniec paketu tak nastavime flag
result = modes.modeSingle(['fake.attr == 5 ='])
break
self.assertEqual(result, False)
def test_no_values(self):
result = None
for event, elem in xml.etree.cElementTree.iterparse('tmp.pdml', events=('start', 'end')):
if event == 'start':
if elem.tag == 'field':
if elem.get('name') is not None and elem.get('show') is not None:
global_var.xmlfields.append({elem.get('name'): elem.get('show')})
if event == 'end':
if elem.tag == 'packet': # ak koniec paketu tak nastavime flag
result = modes.modeSingle(['fake.attr == 5'])
break
self.assertEqual(result, False)
def test_OK(self):
result = None
for event, elem in xml.etree.cElementTree.iterparse('tmp.pdml', events=('start', 'end')):
if event == 'start':
if elem.tag == 'field':
if elem.get('name') is not None and elem.get('show') is not None:
global_var.xmlfields.append({elem.get('name'): elem.get('show')})
if event == 'end':
if elem.tag == 'packet': # ak koniec paketu tak nastavime flag
result = modes.modeSingle(['dns.flags == "asd"'])
break
self.assertEqual(result, True)
if __name__ == '__main__':
unittest.main()
| 37.644737 | 97 | 0.532331 | 338 | 2,861 | 4.446746 | 0.213018 | 0.074518 | 0.047904 | 0.045243 | 0.815037 | 0.815037 | 0.815037 | 0.815037 | 0.815037 | 0.815037 | 0 | 0.001565 | 0.329955 | 2,861 | 75 | 98 | 38.146667 | 0.782473 | 0.049983 | 0 | 0.661017 | 0 | 0 | 0.119056 | 0 | 0 | 0 | 0 | 0 | 0.067797 | 1 | 0.067797 | false | 0 | 0.101695 | 0 | 0.186441 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5122b44e68af9cc18564ee7a911c1f61efb4bf86 | 163 | py | Python | server/tests/test_utils.py | amansrivastava17/embedding-as-sevice | 8cd4087cbd39ecb57ee732c029dea465ad364a5e | [
"MIT"
] | 173 | 2019-07-26T12:48:12.000Z | 2022-03-03T16:01:18.000Z | server/tests/test_utils.py | amansrivastava17/embedding-as-sevice | 8cd4087cbd39ecb57ee732c029dea465ad364a5e | [
"MIT"
] | 35 | 2019-05-31T13:02:48.000Z | 2022-02-28T10:54:14.000Z | server/tests/test_utils.py | amansrivastava17/embedding-as-sevice | 8cd4087cbd39ecb57ee732c029dea465ad364a5e | [
"MIT"
] | 25 | 2019-07-26T07:15:58.000Z | 2021-11-20T20:27:59.000Z | from embedding_as_service.utils import any2unicode
def test_any2unicode():
assert any2unicode("hello") == "hello"
assert any2unicode(b"hello") == "hello"
| 27.166667 | 50 | 0.736196 | 19 | 163 | 6.157895 | 0.631579 | 0.290598 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.028571 | 0.141104 | 163 | 5 | 51 | 32.6 | 0.807143 | 0 | 0 | 0 | 0 | 0 | 0.122699 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0.25 | true | 0 | 0.25 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5a9143735d26c2d48be18d9cfe0b4595bb271194 | 120 | py | Python | __init__.py | ruslankhayrut/russianCVparser | 47dec213b323b72cd3cdc8a2dee0f54091c5dea8 | [
"MIT"
] | null | null | null | __init__.py | ruslankhayrut/russianCVparser | 47dec213b323b72cd3cdc8a2dee0f54091c5dea8 | [
"MIT"
] | null | null | null | __init__.py | ruslankhayrut/russianCVparser | 47dec213b323b72cd3cdc8a2dee0f54091c5dea8 | [
"MIT"
] | null | null | null | from .cvparser.helpers import show_json
from .cvparser.nlparser import CVparser
from .cvparser.document import Document
| 30 | 39 | 0.85 | 16 | 120 | 6.3125 | 0.5 | 0.356436 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 120 | 3 | 40 | 40 | 0.935185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5ab2a5067d31c281fbd01e42ded746be7e8eb5b8 | 48 | py | Python | anyway/widgets/all_locations_widgets/__init__.py | MichalOren/anyway | 79cc25501dc1d643fc80b59b29f010b804acebb8 | [
"MIT"
] | null | null | null | anyway/widgets/all_locations_widgets/__init__.py | MichalOren/anyway | 79cc25501dc1d643fc80b59b29f010b804acebb8 | [
"MIT"
] | null | null | null | anyway/widgets/all_locations_widgets/__init__.py | MichalOren/anyway | 79cc25501dc1d643fc80b59b29f010b804acebb8 | [
"MIT"
] | null | null | null | from . import accident_count_by_severity_widget
| 24 | 47 | 0.895833 | 7 | 48 | 5.571429 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 48 | 1 | 48 | 48 | 0.886364 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
51a642dbc0374e98078c714a603bdde9cbd805ba | 141 | py | Python | login/login.py | kwens/third-login | adaf42cf6dc1ae69bab86f187a717f74035dd8cc | [
"MIT"
] | null | null | null | login/login.py | kwens/third-login | adaf42cf6dc1ae69bab86f187a717f74035dd8cc | [
"MIT"
] | 1 | 2021-06-01T23:29:29.000Z | 2021-06-01T23:29:29.000Z | login/login.py | kwens/third-login | adaf42cf6dc1ae69bab86f187a717f74035dd8cc | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
class LoginApi(object):
def __init__(self, login_type: str = 'dingding'):
self.login_type = login_type
| 20.142857 | 53 | 0.631206 | 18 | 141 | 4.555556 | 0.722222 | 0.329268 | 0.317073 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009009 | 0.212766 | 141 | 6 | 54 | 23.5 | 0.72973 | 0.148936 | 0 | 0 | 0 | 0 | 0.067797 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
51a6db074723c72d4ed471fa431d3f83d40d6390 | 61 | py | Python | python/testData/refactoring/introduceVariable/substringContainsEscapes.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 2 | 2019-04-28T07:48:50.000Z | 2020-12-11T14:18:08.000Z | python/testData/refactoring/introduceVariable/substringContainsEscapes.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 173 | 2018-07-05T13:59:39.000Z | 2018-08-09T01:12:03.000Z | python/testData/refactoring/introduceVariable/substringContainsEscapes.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 2 | 2020-03-15T08:57:37.000Z | 2020-04-07T04:48:14.000Z | print(u"Hel<selection>lo \u00d6sterreich\\!\n</selection>\n") | 61 | 61 | 0.737705 | 9 | 61 | 5 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0.016393 | 61 | 1 | 61 | 61 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0.822581 | 0.548387 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 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 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
51c86aa44117e67bcbcb49519adffd7c0cd500be | 93 | py | Python | tests/gcloud/test_GCloudStreaming.py | urbandataanalytics/SwissKnife | 46e2266744ff2a6e95c05817182b80d864e422a9 | [
"MIT"
] | 3 | 2020-04-27T15:28:40.000Z | 2020-05-27T10:33:16.000Z | tests/gcloud/test_GCloudStreaming.py | urbandataanalytics/SwissKnife | 46e2266744ff2a6e95c05817182b80d864e422a9 | [
"MIT"
] | 7 | 2020-04-30T09:47:14.000Z | 2021-04-05T13:07:12.000Z | tests/gcloud/test_GCloudStreaming.py | urbandataanalytics/SwissKnife | 46e2266744ff2a6e95c05817182b80d864e422a9 | [
"MIT"
] | null | null | null | import unittest
# How to test this?
class test_GCloudStreaming(unittest.TestCase):
pass | 15.5 | 46 | 0.774194 | 12 | 93 | 5.916667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16129 | 93 | 6 | 47 | 15.5 | 0.910256 | 0.182796 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
cfa5322342c4fb8570cde3b4faf5d8045d76a004 | 119,560 | py | Python | pirates/leveleditor/worldData/port_royal_area_cave_b_1.py | Willy5s/Pirates-Online-Rewritten | 7434cf98d9b7c837d57c181e5dabd02ddf98acb7 | [
"BSD-3-Clause"
] | 81 | 2018-04-08T18:14:24.000Z | 2022-01-11T07:22:15.000Z | pirates/leveleditor/worldData/port_royal_area_cave_b_1.py | Willy5s/Pirates-Online-Rewritten | 7434cf98d9b7c837d57c181e5dabd02ddf98acb7 | [
"BSD-3-Clause"
] | 4 | 2018-09-13T20:41:22.000Z | 2022-01-08T06:57:00.000Z | pirates/leveleditor/worldData/port_royal_area_cave_b_1.py | Willy5s/Pirates-Online-Rewritten | 7434cf98d9b7c837d57c181e5dabd02ddf98acb7 | [
"BSD-3-Clause"
] | 26 | 2018-05-26T12:49:27.000Z | 2021-09-11T09:11:59.000Z | from pandac.PandaModules import Point3, VBase3, Vec4
objectStruct = {'Interact Links': [['1176159360.0dxschafe', '1165019476.34Shochet', 'Bi-directional'], ['1176159104.0dxschafe0', '1176159104.0dxschafe', 'Bi-directional'], ['1176158080.0dxschafe', '1165019328.28Shochet', 'Bi-directional']],'Objects': {'1165001772.05sdnaik': {'Type': 'Island Game Area','Name': 'port_royal_area_cave_b_1','File': '','Environment': 'Cave','Instanced': True,'Minimap': False,'Objects': {'1165001975.75sdnaik': {'Type': 'Locator Node','Name': 'portal_interior_1','Hpr': VBase3(-98.823, 0.0, 0.0),'Pos': Point3(407.795, 202.769, 1.938),'Scale': VBase3(1.0, 1.0, 1.0)},'1165001975.77sdnaik': {'Type': 'Locator Node','Name': 'portal_interior_2','Hpr': VBase3(-5.579, 0.0, 0.0),'Pos': Point3(-535.718, 237.303, 77.641),'Scale': VBase3(1.0, 1.0, 1.0)},'1165019328.28Shochet': {'Type': 'Object Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(-43.12, -160.704, 32.334),'Priority': '1','Scale': VBase3(1.0, 1.0, 1.0),'SpawnDelay': '300','Spawnables': 'Buried Treasure','Visual': {'Color': (0.8, 0.2, 0.65, 1),'Model': 'models/misc/smiley'},'startingDepth': '12'},'1165019476.34Shochet': {'Type': 'Spawn Node','Aggro Radius': '12.0000','AnimSet': 'default','Hpr': VBase3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(65.172, 19.812, 27.497),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Bat T2','Start State': 'Ambush','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0.0, 0.0, 0.65, 1.0),'Model': 'models/misc/smiley'}},'1165019501.84Shochet': {'Type': 'Spawn Node','Aggro Radius': '6.0241','AnimSet': 'gp_chant_b','Hpr': VBase3(-22.353, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(29.027, -179.993, 28.77),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Skel T2','Start State': 'Idle','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1165019770.53Shochet': {'Type': 'Rope','DisableCollision': False,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(99.322, -114.135, 23.919),'Scale': VBase3(1.404, 1.404, 1.404),'Visual': {'Model': 'models/props/rope_pile'}},'1165197827.77Shochet': {'Type': 'Spawn Node','Aggro Radius': '12.0000','AnimSet': 'default','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': '100','Pause Duration': '30','Pos': Point3(-219.829, -109.567, 55.796),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Bat T2','Start State': 'Patrol','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1175127779.71kmuller': {'Type': 'Tunnel Cap','DisableCollision': False,'Hpr': VBase3(91.976, 0.0, 0.0),'Pos': Point3(-534.106, 237.425, 79.975),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/tunnels/tunnelcap_cave_interior'}},'1175127913.08kmuller': {'Type': 'Tunnel Cap','DisableCollision': False,'Hpr': VBase3(-16.185, 0.0, 0.0),'Pos': Point3(404.179, 196.552, 3.342),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/tunnels/tunnelcap_cave_interior'}},'1176158080.0dxschafe': {'Type': 'Spawn Node','Aggro Radius': '4.2169','AnimSet': 'gp_chant_b','Hpr': VBase3(-73.885, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(21.138, -169.644, 29.474),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Skel T2','Start State': 'Idle','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176158208.0dxschafe': {'Type': 'Spawn Node','Aggro Radius': '4.5181','AnimSet': 'gp_jump','Hpr': VBase3(-145.121, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(24.083, -152.619, 28.92),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Skel T2','Start State': 'Idle','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176158976.0dxschafe': {'Type': 'Spawn Node','Aggro Radius': '12.0000','AnimSet': 'default','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': '0','Pause Duration': '5','Pos': Point3(452.012, -157.684, 2.175),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Bat T2','Start State': 'Patrol','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176159104.0dxschafe': {'Type': 'Object Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(212.821, -253.894, 12.337),'Priority': '1','Scale': VBase3(1.0, 1.0, 1.0),'SpawnDelay': '20','Spawnables': 'Buried Treasure','Visual': {'Color': (0.8, 0.2, 0.65, 1),'Model': 'models/misc/smiley'},'startingDepth': '12'},'1176159104.0dxschafe0': {'Type': 'Spawn Node','AnimSet': 'default','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(181.049, -227.371, 17.005),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Bat T2','Start State': 'Ambush','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176159232.0dxschafe': {'Type': 'Spawn Node','Aggro Radius': '12.0000','AnimSet': 'gp_searching','Hpr': VBase3(94.892, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(-165.66, -15.591, 57.078),'PoseAnim': '','PoseFrame': '','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Skel T2','Start State': 'Idle','StartFrame': '0','Team': '1','TrailFX': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe': {'Type': 'Object Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(114.081, -30.115, 25.324),'Priority': '1','Scale': VBase3(1.0, 1.0, 1.0),'SpawnDelay': '20','Spawnables': 'Buried Treasure','Visual': {'Color': (0.8, 0.2, 0.65, 1),'Model': 'models/misc/smiley'},'startingDepth': '12'},'1176159360.0dxschafe0': {'Type': 'Player Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Index': -1,'Pos': Point3(53.311, -39.351, 28.03),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe1': {'Type': 'Player Spawn Node','Hpr': VBase3(-148.284, 0.0, 0.0),'Index': -1,'Pos': Point3(-35.011, 131.798, 31.948),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe10': {'Type': 'Player Spawn Node','Hpr': VBase3(-44.592, 0.0, 0.0),'Index': -1,'Pos': Point3(280.801, 63.668, 2.175),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe2': {'Type': 'Player Spawn Node','Hpr': VBase3(52.227, 0.0, 0.0),'Index': -1,'Pos': Point3(-303.428, 54.994, 63.492),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe3': {'Type': 'Player Spawn Node','Hpr': VBase3(31.799, 0.0, 0.0),'Index': -1,'Pos': Point3(-238.203, -61.675, 55.796),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe4': {'Type': 'Player Spawn Node','Hpr': VBase3(70.486, 0.0, 0.0),'Index': -1,'Pos': Point3(-376.883, 178.419, 72.713),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe5': {'Type': 'Player Spawn Node','Hpr': VBase3(-121.675, 0.0, 0.0),'Index': -1,'Pos': Point3(-471.14, 200.606, 78.064),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe6': {'Type': 'Player Spawn Node','Hpr': VBase3(-63.038, 0.0, 0.0),'Index': -1,'Pos': Point3(-65.347, 8.507, 33.309),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe7': {'Type': 'Player Spawn Node','Hpr': VBase3(27.133, 0.0, 0.0),'Index': -1,'Pos': Point3(67.029, -215.335, 27.435),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe8': {'Type': 'Player Spawn Node','Hpr': VBase3(86.406, 0.0, 0.0),'Index': -1,'Pos': Point3(179.483, -182.58, 17.311),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159360.0dxschafe9': {'Type': 'Player Spawn Node','Hpr': VBase3(-18.497, 0.0, 0.0),'Index': -1,'Pos': Point3(299.135, -133.258, 2.175),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159488.0dxschafe': {'Type': 'Player Spawn Node','Hpr': VBase3(157.518, 0.0, 0.0),'Index': -1,'Pos': Point3(393.404, 153.79, 2.175),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159488.0dxschafe0': {'Type': 'Player Spawn Node','Hpr': VBase3(91.238, 0.0, 0.0),'Index': -1,'Pos': Point3(395.981, -169.326, 2.175),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159488.0dxschafe1': {'Type': 'Player Spawn Node','Hpr': VBase3(128.459, 0.0, 0.0),'Index': -1,'Pos': Point3(235.776, -105.144, 6.165),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1176159488.0dxschafe2': {'Type': 'Player Spawn Node','Hpr': VBase3(-89.342, 0.0, 0.0),'Index': -1,'Pos': Point3(-211.451, 9.695, 55.796),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}},'1185388672.0dxschafe003': {'Type': 'Enemy_Props','DisableCollision': False,'Hpr': VBase3(12.899, 0.0, 0.0),'Pos': Point3(78.013, 15.294, 28.345),'Scale': VBase3(0.699, 0.699, 0.699),'Visual': {'Model': 'models/props/pir_m_prp_bon_pile_01'}},'1185388672.0dxschafe004': {'Type': 'Enemy_Props','DisableCollision': False,'Hpr': VBase3(32.503, 0.0, 0.0),'Pos': Point3(-133.238, 174.148, 38.334),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_bon_pile_01'}},'1185388672.0dxschafe006': {'Type': 'Enemy_Props','DisableCollision': False,'Hpr': VBase3(32.133, 4.364, 9.679),'Pos': Point3(204.27, -118.338, 14.387),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_bon_pile_01'}},'1185388672.0dxschafe007': {'Type': 'Enemy_Props','DisableCollision': False,'Hpr': VBase3(-21.535, 0.0, 0.0),'Pos': Point3(100.272, -134.885, 27.977),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_bon_pile_01'}},'1185388672.0dxschafe008': {'Type': 'Enemy_Props','DisableCollision': False,'Hpr': VBase3(75.316, 0.0, 0.0),'Pos': Point3(33.036, -166.186, 30.099),'Scale': VBase3(0.975, 0.975, 0.975),'Visual': {'Model': 'models/props/pir_m_prp_bon_pile_01'}},'1185388672.0dxschafe010': {'Type': 'Enemy_Props','DisableCollision': False,'Hpr': VBase3(-9.25, 0.0, 0.0),'Pos': Point3(-39.647, 92.595, 34.444),'Scale': VBase3(1.125, 1.125, 1.125),'Visual': {'Model': 'models/props/pir_m_prp_bon_pile_01'}},'1185388672.0dxschafe03': {'Type': 'Enemy_Props','DisableCollision': False,'Hpr': VBase3(-118.045, 0.0, -32.883),'Pos': Point3(76.471, 16.057, 28.728),'Scale': VBase3(0.699, 0.699, 0.699),'Visual': {'Model': 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extraInfo = {'camPos': Point3(-62.9301, 33.2035, 254.241),'camHpr': VBase3(-64.5687, -72.0393, 0),'focalLength': 1.39999997616,'skyState': -2,'fog': 0} | 39,853.333333 | 119,355 | 0.671454 | 17,062 | 119,560 | 4.647521 | 0.080178 | 0.028728 | 0.029094 | 0.022902 | 0.725648 | 0.550293 | 0.524667 | 0.473744 | 0.412228 | 0.401281 | 0 | 0.259864 | 0.053053 | 119,560 | 3 | 119,356 | 39,853.333333 | 0.440526 | 0 | 0 | 0 | 0 | 0 | 0.585182 | 0.311205 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 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 | 1 | 0 | 0 | 0 | 0 | 6 |
cfdbc843b8b2386a6630b8fc140707dbc18b4b18 | 29 | py | Python | basic/__init__.py | Harlen520/NLPTrainer | 392bc1d49885d5fef6afc75630a0ba5269f9ff69 | [
"Apache-2.0"
] | 2 | 2021-05-27T08:26:57.000Z | 2022-03-09T06:06:32.000Z | basic/__init__.py | Harlen520/NLPTrainer | 392bc1d49885d5fef6afc75630a0ba5269f9ff69 | [
"Apache-2.0"
] | null | null | null | basic/__init__.py | Harlen520/NLPTrainer | 392bc1d49885d5fef6afc75630a0ba5269f9ff69 | [
"Apache-2.0"
] | null | null | null |
from basic import basic_task | 14.5 | 28 | 0.862069 | 5 | 29 | 4.8 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 29 | 2 | 28 | 14.5 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
cfe5be7d79318887069f06e5b55a43b3159c45b4 | 112 | py | Python | afs/service/DBsServiceError.py | chanke/afspy | 525e7b3b53e58be515f11b83cc59ddb0765ef8e5 | [
"BSD-2-Clause"
] | null | null | null | afs/service/DBsServiceError.py | chanke/afspy | 525e7b3b53e58be515f11b83cc59ddb0765ef8e5 | [
"BSD-2-Clause"
] | null | null | null | afs/service/DBsServiceError.py | chanke/afspy | 525e7b3b53e58be515f11b83cc59ddb0765ef8e5 | [
"BSD-2-Clause"
] | null | null | null | from afs.util.AFSError import AFSError
class DBsServiceError(AFSError):
# No specific Method now
pass
| 16 | 38 | 0.75 | 14 | 112 | 6 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.196429 | 112 | 6 | 39 | 18.666667 | 0.933333 | 0.196429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
5c87e5705afc2410f1763282d684947021777eaf | 79 | py | Python | test/regression/features/floats/floats.py | bjpop/blip | 3d9105a44d1afb7bd007da3742fb19dc69372e10 | [
"BSD-3-Clause"
] | 137 | 2015-02-13T21:03:23.000Z | 2021-11-24T03:53:55.000Z | test/regression/features/floats/floats.py | bjpop/blip | 3d9105a44d1afb7bd007da3742fb19dc69372e10 | [
"BSD-3-Clause"
] | 2 | 2015-03-07T14:08:33.000Z | 2015-10-13T02:00:40.000Z | test/regression/features/floats/floats.py | bjpop/blip | 3d9105a44d1afb7bd007da3742fb19dc69372e10 | [
"BSD-3-Clause"
] | 4 | 2015-05-03T22:07:27.000Z | 2018-09-10T08:55:03.000Z | print(0.0)
print(-0.0)
print(1.3)
print(-1.3)
print(type(1.3))
print(float(1))
| 11.285714 | 16 | 0.632911 | 19 | 79 | 2.631579 | 0.315789 | 0.12 | 0.42 | 0.48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150685 | 0.075949 | 79 | 6 | 17 | 13.166667 | 0.534247 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
5cc813fcbd20c5d09af1d51dfc2edfdce79a161c | 182 | py | Python | src/brouwers/albums/tests/factory_models.py | modelbrouwers/modelbrouwers | e0ba4819bf726d6144c0a648fdd4731cdc098a52 | [
"MIT"
] | 6 | 2015-03-03T13:23:07.000Z | 2021-12-19T18:12:41.000Z | src/brouwers/albums/tests/factory_models.py | modelbrouwers/modelbrouwers | e0ba4819bf726d6144c0a648fdd4731cdc098a52 | [
"MIT"
] | 95 | 2015-02-07T00:55:39.000Z | 2022-02-08T20:22:05.000Z | src/brouwers/albums/tests/factory_models.py | modelbrouwers/modelbrouwers | e0ba4819bf726d6144c0a648fdd4731cdc098a52 | [
"MIT"
] | 2 | 2016-03-22T16:53:26.000Z | 2019-02-09T22:46:04.000Z | import warnings
from .factories import *
warnings.warn('Import from albums.tests.factories, the factory_models '
'module will be removed', PendingDeprecationWarning)
| 26 | 71 | 0.747253 | 20 | 182 | 6.75 | 0.75 | 0.207407 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181319 | 182 | 6 | 72 | 30.333333 | 0.90604 | 0 | 0 | 0 | 0 | 0 | 0.423077 | 0.126374 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 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 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7a29b7aae422f9012daca8f65eda3b510c2c5141 | 242 | py | Python | src/fastapi_aad_auth/_base/validators/__init__.py | oerpli/fastapi_aad_auth | 25a25406d8d8b29cf4457b9b9e7e256ac3c81633 | [
"MIT"
] | null | null | null | src/fastapi_aad_auth/_base/validators/__init__.py | oerpli/fastapi_aad_auth | 25a25406d8d8b29cf4457b9b9e7e256ac3c81633 | [
"MIT"
] | null | null | null | src/fastapi_aad_auth/_base/validators/__init__.py | oerpli/fastapi_aad_auth | 25a25406d8d8b29cf4457b9b9e7e256ac3c81633 | [
"MIT"
] | null | null | null |
from fastapi_aad_auth._base.validators.base import Validator # noqa: F401
from fastapi_aad_auth._base.validators.session import SessionValidator # noqa: F401
from fastapi_aad_auth._base.validators.token import TokenValidator # noqa: F401
| 48.4 | 84 | 0.834711 | 33 | 242 | 5.848485 | 0.424242 | 0.170984 | 0.217617 | 0.279793 | 0.580311 | 0.580311 | 0.414508 | 0.414508 | 0 | 0 | 0 | 0.041475 | 0.103306 | 242 | 4 | 85 | 60.5 | 0.847926 | 0.132231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7a35f3f815d1ad957610a4eebcbcb2fb5be45b29 | 4,329 | py | Python | tests/Argo_spike_test_validation.py | BillMills/AutoQC | cb56fa5bb2115170ec204edd84e2d69ce84be820 | [
"MIT"
] | 17 | 2015-01-31T00:35:58.000Z | 2020-10-26T19:01:46.000Z | tests/Argo_spike_test_validation.py | castelao/AutoQC | eb85422c1a6a5ff965a1ef96b3cb29240a66b506 | [
"MIT"
] | 163 | 2015-01-21T03:44:42.000Z | 2022-01-09T22:03:12.000Z | tests/Argo_spike_test_validation.py | BillMills/AutoQC | cb56fa5bb2115170ec204edd84e2d69ce84be820 | [
"MIT"
] | 11 | 2015-06-04T14:32:22.000Z | 2021-04-11T05:18:09.000Z | import qctests.Argo_spike_test
import util.testingProfile
import numpy
from util import obs_utils
##### Argo_spike_test ---------------------------------------------------
def test_Argo_spike_test_temperature_shallow():
'''
Make sure AST is flagging postive and negative temperature spikes at shallow depths
'''
###
# shallow - depth < 500 m
###
# pass a marginal positive spike (criteria exactly 6 C):
p = util.testingProfile.fakeProfile([5,11,5], [100,200,300], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
assert numpy.array_equal(qc, truth), 'incorrectly flagging a positive spike exactly at threshold (shallow).'
# pass a marginal negative spike (criteria exactly 6 C):
p = util.testingProfile.fakeProfile([5,-1,5], [100,200,300], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
assert numpy.array_equal(qc, truth), 'incorrectly flagging a negative spike exactly at threshold (shallow).'
# fail a marginal positive spike (criteria > 6 C):
p = util.testingProfile.fakeProfile([5,11.0001,5], [100,200,300], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
truth[1] = True
assert numpy.array_equal(qc, truth), 'failing to flag a positive spike just above threshold (shallow).'
# fail a marginal negative spike (criteria > 6 C):
p = util.testingProfile.fakeProfile([5,-1.0001,5], [100,200,300], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
truth[1] = True
assert numpy.array_equal(qc, truth), 'failing to flag a negative spike just above threshold (shallow).'
def test_Argo_spike_test_temperature_deep():
'''
Make sure AST is flagging postive and negative temperature spikes at deep depths
'''
###
# deep - depth > 500 m
###
# pass a marginal positive spike (criteria exactly 2 C):
p = util.testingProfile.fakeProfile([5,7,5], [1000,2000,3000], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
assert numpy.array_equal(qc, truth), 'incorrectly flagging a positive spike exactly at threshold. (deep)'
# pass a marginal negative spike (criteria exactly 2 C):
p = util.testingProfile.fakeProfile([5,3,5], [1000,2000,3000], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
assert numpy.array_equal(qc, truth), 'incorrectly flagging a negative spike exactly at threshold. (deep)'
# fail a marginal positive spike (criteria > 2 C):
p = util.testingProfile.fakeProfile([5,7.0001,5], [1000,2000,3000], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
truth[1] = True
assert numpy.array_equal(qc, truth), 'failing to flag a positive spike just above threshold. (deep)'
# fail a marginal negative spike (criteria > 2 C):
p = util.testingProfile.fakeProfile([5,2.999,5], [1000,2000,3000], latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
truth[1] = True
assert numpy.array_equal(qc, truth), 'failing to flag a negative spike just above threshold. (deep)'
def test_Argo_spike_test_temperature_threshold():
'''
check AST temperature behavior exactly at depth threshold (500m)
'''
# middle value should fail the deep check but pass the shallow check;
# at threshold, use deep criteria
p = util.testingProfile.fakeProfile([5,7.0001,5], obs_utils.pressure_to_depth([400,500,600], 0.0), latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
truth[1] = True
assert numpy.array_equal(qc, truth), 'failing to flag a positive spike just above threshold. (threshold)'
# as above, but passes just above 500m
p = util.testingProfile.fakeProfile([5,7.0001,5], obs_utils.pressure_to_depth([400,499,600], 0.0), latitude=0.0)
qc = qctests.Argo_spike_test.test(p, None)
truth = numpy.zeros(3, dtype=bool)
assert numpy.array_equal(qc, truth), 'flagged a spike using deep criteria when shallow should have been used. (threshold)'
| 45.09375 | 127 | 0.683761 | 638 | 4,329 | 4.551724 | 0.147335 | 0.046488 | 0.067149 | 0.075758 | 0.86157 | 0.846419 | 0.785468 | 0.771006 | 0.766873 | 0.703512 | 0 | 0.06119 | 0.184569 | 4,329 | 95 | 128 | 45.568421 | 0.761473 | 0.206976 | 0 | 0.480769 | 0 | 0 | 0.199226 | 0 | 0 | 0 | 0 | 0 | 0.192308 | 1 | 0.057692 | false | 0 | 0.076923 | 0 | 0.134615 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8feb8d08d2143b7c50a42eee7e60adf674cc28e5 | 2,752 | py | Python | actions/user_ops.py | capjamesg/cinnamon | 82c84e53854987e184198e334d5f23fe86fccf41 | [
"MIT"
] | null | null | null | actions/user_ops.py | capjamesg/cinnamon | 82c84e53854987e184198e334d5f23fe86fccf41 | [
"MIT"
] | 1 | 2022-02-09T14:58:42.000Z | 2022-02-09T14:58:42.000Z | actions/user_ops.py | capjamesg/cinnamon | 82c84e53854987e184198e334d5f23fe86fccf41 | [
"MIT"
] | null | null | null | import sqlite3
from flask import jsonify, request
def get_muted(request: request) -> dict:
connection = sqlite3.connect("microsub.db")
with connection:
cursor = connection.cursor()
cursor.execute(
"SELECT * FROM following WHERE muted = 1 AND channel = ?",
(request.args.get("channel"),),
)
return cursor.fetchall()
def mute(request: request) -> dict:
connection = sqlite3.connect("microsub.db")
with connection:
cursor = connection.cursor()
cursor.execute(
"UPDATE following SET muted = 1 WHERE url = ?", (request.form.get("url"),)
)
get_url = cursor.execute(
"SELECT url FROM following WHERE url = ?", (request.form.get("url"),)
).fetchone()
if get_url:
return jsonify({"url": get_url[0], "type": "mute"}), 200
else:
return jsonify({"error": "You are not following this feed."}), 400
def block(request: request) -> dict:
connection = sqlite3.connect("microsub.db")
with connection:
cursor = connection.cursor()
cursor.execute(
"UPDATE following SET blocked = 1 WHERE url = ?", (request.form.get("url"),)
)
get_url = cursor.execute(
"SELECT url FROM following WHERE url = ?", (request.form.get("url"),)
).fetchone()
if get_url:
return jsonify({"url": get_url[0], "type": "block"}), 200
else:
return jsonify({"error": "You are not following this feed."}), 400
def unblock(request: request) -> dict:
connection = sqlite3.connect("microsub.db")
with connection:
cursor = connection.cursor()
cursor.execute(
"UPDATE following SET blocked = 0 WHERE url = ?", (request.form.get("url"),)
)
get_url = cursor.execute(
"SELECT url FROM following WHERE url = ?", (request.form.get("url"),)
).fetchone()
if get_url:
return jsonify({"url": get_url[0], "type": "unblock"}), 200
else:
return jsonify({"error": "You are not following this feed."}), 400
def unmute(request: request) -> dict:
connection = sqlite3.connect("microsub.db")
with connection:
cursor = connection.cursor()
cursor.execute(
"UPDATE following SET muted = 0 WHERE url = ?", (request.form.get("url"),)
)
get_url = cursor.execute(
"SELECT url FROM following WHERE url = ?", (request.form.get("url"),)
).fetchone()
if get_url:
return jsonify({"url": get_url[0], "type": "unmute"}), 200
else:
return jsonify({"error": "You are not following this feed."}), 400
| 28.371134 | 88 | 0.56686 | 304 | 2,752 | 5.088816 | 0.157895 | 0.077569 | 0.077569 | 0.098255 | 0.882999 | 0.882999 | 0.882999 | 0.882999 | 0.882999 | 0.882999 | 0 | 0.020041 | 0.292878 | 2,752 | 96 | 89 | 28.666667 | 0.774923 | 0 | 0 | 0.656716 | 0 | 0 | 0.245276 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.074627 | false | 0 | 0.029851 | 0 | 0.238806 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8fecb20b8da1651c10bedf069a0bc9c3feba96ac | 87 | py | Python | type_sense/client.py | jkoestinger/django-typesense | b1472872ca7c3deab68704bb7bf4b103daed8bcd | [
"MIT"
] | 7 | 2020-12-10T15:02:24.000Z | 2022-03-23T21:47:25.000Z | type_sense/client.py | jkoestinger/django-typesense | b1472872ca7c3deab68704bb7bf4b103daed8bcd | [
"MIT"
] | null | null | null | type_sense/client.py | jkoestinger/django-typesense | b1472872ca7c3deab68704bb7bf4b103daed8bcd | [
"MIT"
] | null | null | null | import typesense
from settings import TYPESENSE
client = typesense.Client(TYPESENSE)
| 14.5 | 36 | 0.827586 | 10 | 87 | 7.2 | 0.5 | 0.416667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126437 | 87 | 5 | 37 | 17.4 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8feda7192a7b34ee5d691a3671e264bd4dcac19f | 76 | py | Python | core/views.py | sillygod/awesome-game-interview | 08a82e70c5fb150779f1f189798e1a38ca772184 | [
"MIT"
] | 1 | 2020-02-19T09:03:01.000Z | 2020-02-19T09:03:01.000Z | core/views.py | sillygod/django-as-pure-api-server | 40f9993b4e2eff99d3a55e21ad4f4ac1f0daff95 | [
"MIT"
] | 23 | 2017-07-15T08:06:21.000Z | 2022-03-11T23:26:00.000Z | core/views.py | sillygod/awesome-game-interview | 08a82e70c5fb150779f1f189798e1a38ca772184 | [
"MIT"
] | null | null | null | from django import http
def health(request):
return http.HttpResponse() | 19 | 30 | 0.763158 | 10 | 76 | 5.8 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.157895 | 76 | 4 | 30 | 19 | 0.90625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
56ae0c11d7d26d67d0e2b061c165f2146449064f | 273 | py | Python | 3.1forfunrage.py | dcaeben/pythonbasic | 243806ca8e1947d23ca4e64ab77341c44eae93a9 | [
"MIT"
] | null | null | null | 3.1forfunrage.py | dcaeben/pythonbasic | 243806ca8e1947d23ca4e64ab77341c44eae93a9 | [
"MIT"
] | null | null | null | 3.1forfunrage.py | dcaeben/pythonbasic | 243806ca8e1947d23ca4e64ab77341c44eae93a9 | [
"MIT"
] | null | null | null | #for i in range(5): #itera sobre un valor dado
# print(i)
#for i in range(5, 10): #itera sobre un valor dado
# print(i)
#for i in range(0, 10, 3): #itera sobre un valor dado
# print(i)
for i in range(-10, -100, -30): #itera sobre un valor dado
print(i)
| 19.5 | 59 | 0.611722 | 53 | 273 | 3.150943 | 0.301887 | 0.095808 | 0.143713 | 0.263473 | 0.922156 | 0.844311 | 0.844311 | 0.682635 | 0.682635 | 0.682635 | 0 | 0.073171 | 0.249084 | 273 | 13 | 60 | 21 | 0.741463 | 0.750916 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.5 | 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 | 1 | 0 | 6 |
56b98ca0970b382cd2e3ff19586d225f3f4fe94f | 105 | py | Python | bitmovin_api_sdk/encoding/filters/enhanced_watermark/customdata/__init__.py | jaythecaesarean/bitmovin-api-sdk-python | 48166511fcb9082041c552ace55a9b66cc59b794 | [
"MIT"
] | 11 | 2019-07-03T10:41:16.000Z | 2022-02-25T21:48:06.000Z | bitmovin_api_sdk/encoding/filters/enhanced_watermark/customdata/__init__.py | jaythecaesarean/bitmovin-api-sdk-python | 48166511fcb9082041c552ace55a9b66cc59b794 | [
"MIT"
] | 8 | 2019-11-23T00:01:25.000Z | 2021-04-29T12:30:31.000Z | bitmovin_api_sdk/encoding/filters/enhanced_watermark/customdata/__init__.py | jaythecaesarean/bitmovin-api-sdk-python | 48166511fcb9082041c552ace55a9b66cc59b794 | [
"MIT"
] | 13 | 2020-01-02T14:58:18.000Z | 2022-03-26T12:10:30.000Z | from bitmovin_api_sdk.encoding.filters.enhanced_watermark.customdata.customdata_api import CustomdataApi
| 52.5 | 104 | 0.914286 | 13 | 105 | 7.076923 | 0.846154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.038095 | 105 | 1 | 105 | 105 | 0.910891 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
56cb1ce39351e362735b9356f831584ffcb73f2c | 36,796 | py | Python | haproxy_test.py | immobiliare/collectd-haproxy-plugin | e321a176e2b61f6ecf113568a1885987b632bfb8 | [
"MIT"
] | 23 | 2021-11-08T11:04:17.000Z | 2022-01-21T12:26:31.000Z | haproxy_test.py | immobiliare/collectd-haproxy-plugin | e321a176e2b61f6ecf113568a1885987b632bfb8 | [
"MIT"
] | null | null | null | haproxy_test.py | immobiliare/collectd-haproxy-plugin | e321a176e2b61f6ecf113568a1885987b632bfb8 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
#
# Copyright (c) 2020 Immobiliare Labs <opensource@immobiliare.it>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import print_function
import collections
import sys
from mock import call
from mock import MagicMock
from mock import Mock
from mock import patch
class MockCollectd(MagicMock):
"""
Mocks the functions and objects provided by the collectd module
"""
@staticmethod
def log(log_str):
print(log_str)
debug = log
info = log
warning = log
error = log
class MockHAProxySocketSimple:
def __init__(self, sockets=["whatever"]):
self.sockets = sockets
def get_resolvers(self):
return {}
def get_server_info(self):
return {
'ConnRate': '3',
'CumReq': '5',
'Idle_pct': '78'
}
def get_server_stats(self):
return [{
'bin': '3120628',
'lastchg': '',
'lbt': '',
'weight': '',
'wretr': '',
'slim': '50',
'pid': '1',
'wredis': '',
'dresp': '0',
'ereq': '0',
'pxname': 'sample_proxy',
'stot': '39728',
'sid': '0',
'bout': '188112702395',
'qlimit': '',
'status': 'OPEN',
'smax': '2',
'dreq': '0',
'econ': '',
'iid': '2',
'chkfail': '',
'downtime': '',
'qcur': '',
'eresp': '',
'throttle': '',
'scur': '0',
'bck': '',
'qmax': '',
'act': '',
'chkdown': '',
'svname': 'FRONTEND'
}]
class MockHAProxySocketComplex:
def __init__(self, socket_file="whatever"):
self.socket_file = socket_file
def get_resolvers(self):
return {
'dns1': {
'sent': '8',
'snd_error': '0',
'valid': '4',
'update': '0',
'cname': '0',
'cname_error': '4',
'any_err': '0',
'nx': '0',
'timeout': '0',
'refused': '0',
'other': '0',
'invalid': '0',
'too_big': '0',
'truncated': '0',
'outdated': '0'
}, 'dns2': {
'sent': '0',
'snd_error': '0',
'valid': '0',
'update': '0',
'cname': '0',
'cname_error': '0',
'any_err': '0',
'nx': '0',
'timeout': '0',
'refused': '0',
'other': '0',
'invalid': '0',
'too_big': '0',
'truncated': '0',
'outdated': '0'
}
}
def get_server_info(self):
return {
'ConnRate': '3',
'CumReq': '5',
'Idle_pct': '78'
}
def get_server_stats(self):
return [{
'lastchg': '321093',
'agent_health': '',
'check_desc': 'Layer7 check passed',
'smax': '2',
'agent_rise': '',
'req_rate': '',
'check_status': 'L7OK',
'wredis': '0',
'comp_out': '',
'conn_rate': '',
'cli_abrt': '0',
'pxname': 'elasticsearch_backend',
'check_code': '0',
'check_health': '4',
'check_fall': '3',
'qlimit': '',
'bin': '0',
'conn_rate_max': '',
'hrsp_5xx': '',
'stot': '344777',
'econ': '0',
'iid': '3',
'hrsp_4xx': '',
'hanafail': '',
'downtime': '0',
'eresp': '0',
'bout': '0',
'dses': '',
'qtime': '0',
'srv_abrt': '0',
'throttle': '',
'ctime': '0',
'scur': '0',
'type': '2',
'check_rise': '2',
'intercepted': '',
'hrsp_2xx': '',
'mode': 'tcp',
'agent_code': '',
'qmax': '0',
'agent_desc': '',
'weight': '1',
'slim': '',
'pid': '1',
'comp_byp': '',
'lastsess': '0',
'comp_rsp': '',
'agent_status': '',
'check_duration': '0',
'rate': '2',
'rate_max': '9',
'dresp': '0',
'ereq': '',
'addr': '192.168.1.1:6379',
'comp_in': '',
'dcon': '',
'last_chk': '(tcp-check)',
'sid': '1',
'ttime': '18',
'hrsp_1xx': '',
'agent_duration': '',
'hrsp_other': '',
'status': 'UP',
'wretr': '0',
'lbtot': '344777',
'dreq': '',
'req_rate_max': '',
'conn_tot': '',
'chkfail': '0',
'cookie': '',
'qcur': '0',
'tracked': '',
'rtime': '0',
'last_agt': '',
'bck': '0',
'req_tot': '',
'rate_lim': '',
'hrsp_3xx': '',
'algo': '',
'act': '1',
'chkdown': '0',
'svname': 'elasticache',
'agent_fall': ''
}, {
'lastchg': '321093',
'agent_health': '',
'check_desc': '',
'smax': '2',
'agent_rise': '',
'req_rate': '',
'check_status': '',
'wredis': '0',
'comp_out': '0',
'conn_rate': '',
'cli_abrt': '0',
'pxname': 'elasticsearch_backend',
'check_code': '',
'check_health': '',
'check_fall': '',
'qlimit': '',
'bin': '0',
'conn_rate_max': '',
'hrsp_5xx': '',
'stot': '515751',
'econ': '0',
'iid': '3',
'hrsp_4xx': '',
'hanafail': '',
'downtime': '0',
'eresp': '0',
'bout': '0',
'dses': '',
'qtime': '0',
'srv_abrt': '0',
'throttle': '',
'ctime': '0',
'scur': '0',
'type': '1',
'check_rise': '',
'intercepted': '',
'hrsp_2xx': '',
'mode': 'tcp',
'agent_code': '',
'qmax': '0',
'agent_desc': '',
'weight': '1',
'slim': '800',
'pid': '1',
'comp_byp': '0',
'lastsess': '0',
'comp_rsp': '0',
'agent_status': '',
'check_duration': '',
'rate': '3',
'rate_max': '9',
'dresp': '0',
'ereq': '',
'addr': '',
'comp_in': '0',
'dcon': '',
'last_chk': '',
'sid': '0',
'ttime': '18',
'hrsp_1xx': '',
'agent_duration': '',
'hrsp_other': '',
'status': 'UP',
'wretr': '0',
'lbtot': '344777',
'dreq': '0',
'req_rate_max': '',
'conn_tot': '',
'chkfail': '',
'cookie': '',
'qcur': '0',
'tracked': '',
'rtime': '0',
'last_agt': '',
'bck': '0',
'req_tot': '',
'rate_lim': '',
'hrsp_3xx': '',
'algo': 'roundrobin',
'act': '1',
'chkdown': '0',
'svname': 'BACKEND',
'agent_fall': ''
}, {
'lastchg': '',
'agent_health': None,
'check_desc': None,
'smax': '0',
'agent_rise': None,
'req_rate': '0',
'check_status': '',
'wredis': '',
'comp_out': None,
'conn_rate': None,
'cli_abrt': None,
'pxname': 'sensu_frontend',
'check_code': '',
'check_health': None,
'check_fall': None,
'qlimit': '',
'bin': '0',
'conn_rate_max': None,
'hrsp_5xx': '',
'stot': '0',
'econ': '',
'iid': '4',
'hrsp_4xx': '',
'hanafail': '',
'downtime': '',
'eresp': '',
'bout': '0',
'dses': None,
'qtime': None,
'srv_abrt': None,
'throttle': '',
'ctime': None,
'scur': '0',
'type': '0',
'check_rise': None,
'intercepted': None,
'hrsp_2xx': '',
'mode': None,
'agent_code': None,
'qmax': '',
'agent_desc': None,
'weight': '',
'slim': '8000',
'pid': '1',
'comp_byp': None,
'lastsess': None,
'comp_rsp': None,
'agent_status': None,
'check_duration': '',
'rate': '0',
'rate_max': '10',
'dresp': '0',
'ereq': '0',
'addr': None,
'comp_in': None,
'dcon': None,
'last_chk': None,
'sid': '0',
'ttime': None,
'hrsp_1xx': '',
'agent_duration': None,
'hrsp_other': '',
'status': 'OPEN',
'wretr': '',
'lbtot': '',
'dreq': '0',
'req_rate_max': '0',
'conn_tot': None,
'chkfail': '',
'cookie': None,
'qcur': '',
'tracked': '',
'rtime': None,
'last_agt': None,
'bck': '',
'req_tot': '',
'rate_lim': '0',
'hrsp_3xx': '',
'algo': None,
'act': '',
'chkdown': '',
'svname': 'FRONTEND',
}]
# don't move the block below
sys.modules['collectd'] = MockCollectd()
import haproxy # nopep8
ConfigOption = collections.namedtuple('ConfigOption', ('key', 'values'))
mock_config_default_values = Mock()
mock_config_default_values.children = [
ConfigOption('Testing', ('True',))
]
def test_default_config():
module_config = haproxy.config(mock_config_default_values)
assert module_config['sockets'] == ['/var/run/haproxy.sock']
assert module_config['proxy_monitors'] == ['server', 'frontend', 'backend']
assert module_config['testing']
@patch('haproxy.HAProxySocket', MockHAProxySocketComplex)
def test_metrics_submitted_for_frontend_with_correct_names():
haproxy.submit_metrics = MagicMock()
mock_config = Mock()
mock_config.children = [
ConfigOption('ProxyMonitor', ('frontend',)),
ConfigOption('EnhancedMetrics', ('True',)),
ConfigOption('Testing', ('True',))
]
haproxy.collect_metrics(haproxy.config(mock_config))
haproxy.submit_metrics.assert_has_calls([
call({
'values': (3,),
'type_instance': 'connrate',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (5,),
'type_instance': 'cumreq',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (78,),
'type_instance': 'idle_pct',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'smax',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'rate',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'req_rate',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'dresp',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'ereq',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'dreq',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'bin',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'stot',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'req_rate_max',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (8000,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'slim',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'rate_lim',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'bout',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'scur',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (10,),
'plugin_instance': 'frontend.sensu_frontend',
'type_instance': 'rate_max',
'type': 'gauge',
'plugin': 'haproxy'
})
], any_order=True)
@patch('haproxy.HAProxySocket', MockHAProxySocketComplex)
def test_metrics_submitted_for_backend_and_server_with_correct_names():
haproxy.submit_metrics = MagicMock()
mock_config = Mock()
mock_config.children = [
ConfigOption('ProxyMonitor', ('backend',)),
ConfigOption('EnhancedMetrics', ('True',)),
ConfigOption('Testing', ('True',))
]
haproxy.collect_metrics(haproxy.config(mock_config))
haproxy.submit_metrics.assert_has_calls([
call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'rtime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (2,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'smax',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'lastsess',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'check_duration',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (2,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'rate',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'wredis',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'eresp',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'dresp',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'cli_abrt',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'bin',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (344777,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'lbtot',
'type': 'counter',
'plugin': 'haproxy'
}), call({
'values': (344777,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'stot',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'econ',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (18,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'ttime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'downtime',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'qcur',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'wretr',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'qtime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'srv_abrt',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'bout',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'ctime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'scur',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'bck',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'qmax',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (9,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'rate_max',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (1,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'act',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend.elasticache',
'type_instance': 'chkfail',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'rtime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (2,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'smax',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'comp_byp',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'lastsess',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (3,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'rate',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'wredis',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'comp_out',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'eresp',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'dresp',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'comp_in',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'dreq',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'cli_abrt',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'bin',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (344777,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'lbtot',
'type': 'counter',
'plugin': 'haproxy'
}), call({
'values': (515751,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'stot',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'econ',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (18,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'ttime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (800,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'slim',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'downtime',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'qcur',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'comp_rsp',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'wretr',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'qtime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'srv_abrt',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'bout',
'type': 'derive',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'ctime',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'scur',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'bck',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'qmax',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (9,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'rate_max',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (1,),
'plugin_instance': 'backend.elasticsearch_backend',
'type_instance': 'act',
'type': 'gauge',
'plugin': 'haproxy'
})
], any_order=True)
@patch('haproxy.HAProxySocket', MockHAProxySocketComplex)
def test_metrics_submitted_for_resolvers():
haproxy.submit_metrics = MagicMock()
mock_config = Mock()
mock_config.children = [
ConfigOption('Testing', ('True',))
]
haproxy.collect_metrics(haproxy.config(mock_config))
haproxy.submit_metrics.assert_has_calls([
call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'cname_error',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'truncated',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'update',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'refused',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'any_err',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'cname',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'outdated',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'too_big',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'invalid',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'snd_error',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'nx',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'valid',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'timeout',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'other',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns2',
'type_instance': 'sent',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (4,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'cname_error',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'truncated',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'update',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'refused',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'any_err',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance':
'cname',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'outdated',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'too_big',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'invalid',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'snd_error',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'nx',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (4,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'valid',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'timeout',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (0,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'other',
'type': 'gauge',
'plugin': 'haproxy'
}), call({
'values': (8,),
'plugin_instance': 'nameserver.dns1',
'type_instance': 'sent',
'type': 'gauge',
'plugin': 'haproxy'
})
], any_order=True)
def test_resolver_stats_can_be_parsed():
haproxy_socket = haproxy.HAProxySocket(MagicMock())
haproxy_socket.communicate = MagicMock(
return_value=["""Resolvers section mydns
nameserver dns1:
sent: 8
snd_error: 0
valid: 4
update: 0
cname: 0
cname_error: 4
any_err: 0
nx: 0
timeout: 0
refused: 0
other: 0
invalid: 0
too_big: 0
truncated: 0
outdated: 0
Resolvers section mydns2
nameserver dns2:
sent: 0
snd_error: 0
valid: 0
update: 0
cname: 0
cname_error: 0
any_err: 0
nx: 0
timeout: 0
refused: 0
other: 0
invalid: 0
too_big: 0
truncated: 0
outdated: 0"""])
assert haproxy_socket.get_resolvers() == {
'dns1': {
'sent': '8',
'snd_error': '0',
'valid': '4',
'update': '0',
'cname': '0',
'cname_error': '4',
'any_err': '0',
'nx': '0',
'timeout': '0',
'refused': '0',
'other': '0',
'invalid': '0',
'too_big': '0',
'truncated': '0',
'outdated': '0'
}, 'dns2': {
'sent': '0',
'snd_error': '0',
'valid': '0',
'update': '0',
'cname': '0',
'cname_error': '0',
'any_err': '0',
'nx': '0',
'timeout': '0',
'refused': '0',
'other': '0',
'invalid': '0',
'too_big': '0',
'truncated': '0',
'outdated': '0'
}}
| 31.666093 | 79 | 0.432737 | 2,885 | 36,796 | 5.329289 | 0.111612 | 0.068293 | 0.11278 | 0.152585 | 0.783285 | 0.77665 | 0.761821 | 0.746667 | 0.731837 | 0.70478 | 0 | 0.021128 | 0.396728 | 36,796 | 1,161 | 80 | 31.693368 | 0.671502 | 0.032694 | 0 | 0.839964 | 0 | 0 | 0.363043 | 0.06902 | 0 | 0 | 0 | 0 | 0.006329 | 1 | 0.012658 | false | 0.000904 | 0.007233 | 0.005425 | 0.031646 | 0.001808 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
56e7bc4c5df15cbd7b8c0306e7115577cd24ab11 | 428 | py | Python | holobot/discord/sdk/exceptions/__init__.py | rexor12/holobot | 89b7b416403d13ccfeee117ef942426b08d3651d | [
"MIT"
] | 1 | 2021-05-24T00:17:46.000Z | 2021-05-24T00:17:46.000Z | holobot/discord/sdk/exceptions/__init__.py | rexor12/holobot | 89b7b416403d13ccfeee117ef942426b08d3651d | [
"MIT"
] | 41 | 2021-03-24T22:50:09.000Z | 2021-12-17T12:15:13.000Z | holobot/discord/sdk/exceptions/__init__.py | rexor12/holobot | 89b7b416403d13ccfeee117ef942426b08d3651d | [
"MIT"
] | null | null | null | from .channel_not_found_error import ChannelNotFoundError
from .forbidden_error import ForbiddenError
from .message_not_found_error import MessageNotFoundError
from .permission_error import PermissionError
from .role_already_exists_error import RoleAlreadyExistsError
from .role_not_found_error import RoleNotFoundError
from .server_not_found_error import ServerNotFoundError
from .user_not_found_error import UserNotFoundError
| 47.555556 | 61 | 0.906542 | 52 | 428 | 7.076923 | 0.423077 | 0.23913 | 0.17663 | 0.258152 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.074766 | 428 | 8 | 62 | 53.5 | 0.929293 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 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 | 6 |
56efe84b9cbc2cd06a5fff7d17df75a664ab5530 | 13,497 | py | Python | eeauditor/tests/test_AWS_CodeArtifact_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 442 | 2020-03-15T20:56:36.000Z | 2022-03-31T22:13:07.000Z | eeauditor/tests/test_AWS_CodeArtifact_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 57 | 2020-03-15T22:09:56.000Z | 2022-03-31T13:17:06.000Z | eeauditor/tests/test_AWS_CodeArtifact_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 59 | 2020-03-15T21:19:10.000Z | 2022-03-31T15:01:31.000Z | #This file is part of ElectricEye.
#SPDX-License-Identifier: Apache-2.0
#Licensed to the Apache Software Foundation (ASF) under one
#or more contributor license agreements. See the NOTICE file
#distributed with this work for additional information
#regarding copyright ownership. The ASF licenses this file
#to you under the Apache License, Version 2.0 (the
#"License"); you may not use this file except in compliance
#with the License. You may obtain a copy of the License at
#http://www.apache.org/licenses/LICENSE-2.0
#Unless required by applicable law or agreed to in writing,
#software distributed under the License is distributed on an
#"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
#KIND, either express or implied. See the License for the
#specific language governing permissions and limitations
#under the License.
import datetime
import os
import pytest
import sys
from botocore.stub import Stubber, ANY
from . import context
from auditors.aws.AWS_CodeArtifact_Auditor import (
codeartifact_repo_policy_check,
codeartifact_domain_policy_check,
codeartifact
)
list_repositories = {
"repositories": [
{
"name": "npm-store",
"administratorAccount": "111122223333",
"domainName": "my-domain",
"domainOwner": "111122223333",
"arn": "arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store",
"description": "Provides npm artifacts from npm, Inc."
}
]
}
list_domains = {
'domains': [
{'name': 'eg-domain',
'owner': '111122223333',
'status': 'Active',
'encryptionKey': 'arn:aws:kms:ap-southeast-2:111122223333:key/abcdef-123456'
}
]
}
get_repository_permissions_policy_root_list_star = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": \
{"AWS":"arn:aws:iam::111122223333:root"}, \
"Action":"codeartifact:List*", \
"Resource":"arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store"}]}'
}
}
get_repository_permissions_policy_root_star = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": \
{"AWS":"arn:aws:iam::111122223333:root"}, \
"Action":"codeartifact:*", \
"Resource":"arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store"}]}'
}
}
get_repository_permissions_policy_star_update = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": "*", \
"Action":"codeartifact:PutRepositoryPermissionsPolicy", \
"Resource":"arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store"}]}'
}
}
get_repository_permissions_policy_star_star = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": "*", \
"Action":"*", \
"Resource":"arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store"}]}'
}
}
get_repository_permissions_policy_star_star_condition = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:repository/my-domain/npm-store",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": "*", \
"Action":"*", \
"Resource":"*", \
"Condition":{ \
"StringEquals":{ \
"aws:sourceVpce":"vpce-1a2b3c4d"}}}]}'}
}
get_domain_permissions_policy_star_delete = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:domain/eg-domain",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": "*", \
"Action":"codeartifact:DeleteDomainPermissionsPolicy", \
"Resource":"*"}]}'
}
}
get_domain_permissions_policy_star_list = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:domain/eg-domain",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": "*", \
"Action":"codeartifact:List*", \
"Resource":"*"}]}'
}
}
get_domain_permissions_policy_star_star_condition = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:domain/eg-domain",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": "*", \
"Action":"*", \
"Resource":"*", \
"Condition":{ \
"StringEquals":{ \
"aws:sourceVpce":"vpce-1a2b3c4d"}}}]}'}
}
get_domain_permissions_policy_root_star = {
"policy": {
"resourceArn": "arn:aws:codeartifact:us-west-2:111122223333:domain/eg-domain",
'revision': '1.0',
"document": '{"Version":"2008-10-17","Id":"__default_policy_ID", \
"Statement": \
[{"Sid":"__owner_statement", \
"Effect":"Allow", \
"Principal": \
{"AWS":"arn:aws:iam::111122223333:root"}, \
"Action":"codeartifact:*", \
"Resource":"arn:aws:codeartifact:us-west-2:111122223333:domain/eg-domain"}]}'
}
}
@pytest.fixture(scope="function")
def codeartifact_stubber():
codeartifact_stubber = Stubber(codeartifact)
codeartifact_stubber.activate()
yield codeartifact_stubber
codeartifact_stubber.deactivate()
def test_policy_star_list(codeartifact_stubber):
codeartifact_stubber.add_response("list_repositories", list_repositories)
codeartifact_stubber.add_response("get_repository_permissions_policy", get_repository_permissions_policy_root_list_star)
results = codeartifact_repo_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "npm-store" in result["Id"]:
assert result["RecordState"] == "ARCHIVED"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_policy_root_user(codeartifact_stubber):
codeartifact_stubber.add_response("list_repositories", list_repositories)
codeartifact_stubber.add_response("get_repository_permissions_policy", get_repository_permissions_policy_root_star)
results = codeartifact_repo_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "npm-store" in result["Id"]:
assert result["RecordState"] == "ACTIVE"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_policy_star_update(codeartifact_stubber):
codeartifact_stubber.add_response("list_repositories", list_repositories)
codeartifact_stubber.add_response("get_repository_permissions_policy", get_repository_permissions_policy_star_update)
results = codeartifact_repo_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "npm-store" in result["Id"]:
assert result["RecordState"] == "ACTIVE"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_policy_star_star(codeartifact_stubber):
codeartifact_stubber.add_response("list_repositories", list_repositories)
codeartifact_stubber.add_response("get_repository_permissions_policy", get_repository_permissions_policy_star_star)
results = codeartifact_repo_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "npm-store" in result["Id"]:
assert result["RecordState"] == "ACTIVE"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_policy_star_star_condition(codeartifact_stubber):
codeartifact_stubber.add_response("list_repositories", list_repositories)
codeartifact_stubber.add_response("get_repository_permissions_policy", get_repository_permissions_policy_star_star_condition)
results = codeartifact_repo_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "npm-store" in result["Id"]:
assert result["RecordState"] == "ARCHIVED"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_policy_no_policy(codeartifact_stubber):
codeartifact_stubber.add_response("list_repositories", list_repositories)
codeartifact_stubber.add_client_error("get_repository_permissions_policy", "ResourceNotFoundException")
results = codeartifact_repo_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "npm-store" in result["Id"]:
assert result["RecordState"] == "ACTIVE"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_domain_no_policy(codeartifact_stubber):
codeartifact_stubber.add_response("list_domains", list_domains)
codeartifact_stubber.add_client_error("get_domain_permissions_policy", "ResourceNotFoundException")
results = codeartifact_domain_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "eg-domain" in result["Id"]:
assert result["RecordState"] == "ACTIVE"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_domain_star_delete(codeartifact_stubber):
codeartifact_stubber.add_response("list_domains", list_domains)
codeartifact_stubber.add_response("get_domain_permissions_policy", get_domain_permissions_policy_star_delete)
results = codeartifact_domain_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "eg-domain" in result["Id"]:
assert result["RecordState"] == "ACTIVE"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_domain_star_list(codeartifact_stubber):
codeartifact_stubber.add_response("list_domains", list_domains)
codeartifact_stubber.add_response("get_domain_permissions_policy", get_domain_permissions_policy_star_list)
results = codeartifact_domain_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "eg-domain" in result["Id"]:
assert result["RecordState"] == "ARCHIVED"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_domain_star_delete(codeartifact_stubber):
codeartifact_stubber.add_response("list_domains", list_domains)
codeartifact_stubber.add_response("get_domain_permissions_policy", get_domain_permissions_policy_star_star_condition)
results = codeartifact_domain_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "eg-domain" in result["Id"]:
assert result["RecordState"] == "ARCHIVED"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
def test_domain_root_star(codeartifact_stubber):
codeartifact_stubber.add_response("list_domains", list_domains)
codeartifact_stubber.add_response("get_domain_permissions_policy", get_domain_permissions_policy_root_star)
results = codeartifact_domain_policy_check(
cache={}, awsAccountId="111122223333", awsRegion="us-east-1", awsPartition="aws"
)
for result in results:
if "eg-domain" in result["Id"]:
assert result["RecordState"] == "ACTIVE"
else:
assert False
codeartifact_stubber.assert_no_pending_responses()
| 38.452991 | 129 | 0.664073 | 1,423 | 13,497 | 6.019677 | 0.128602 | 0.108686 | 0.056502 | 0.070044 | 0.832827 | 0.823021 | 0.809246 | 0.800724 | 0.800257 | 0.796988 | 0 | 0.049526 | 0.202638 | 13,497 | 351 | 130 | 38.452991 | 0.746423 | 0.059643 | 0 | 0.670068 | 0 | 0.037415 | 0.318845 | 0.13134 | 0 | 0 | 0 | 0 | 0.112245 | 1 | 0.040816 | false | 0 | 0.02381 | 0 | 0.064626 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
71166a91222c404933806ea94a46a4ef679b63ed | 29,718 | py | Python | dbBackup.py | wholesomegarden/Challenge18 | 5aeac0f130fd69f9e29b3cf83d730e8e1fddd00d | [
"MIT"
] | 1 | 2021-05-04T10:19:51.000Z | 2021-05-04T10:19:51.000Z | dbBackup.py | wholesomegarden/Challenge18 | 5aeac0f130fd69f9e29b3cf83d730e8e1fddd00d | [
"MIT"
] | null | null | null | dbBackup.py | wholesomegarden/Challenge18 | 5aeac0f130fd69f9e29b3cf83d730e8e1fddd00d | [
"MIT"
] | null | null | null | {"users": {"972547932000@c.us": {"days": {"1": 1,"2": 5,"12": 1,"-1": 2,"13": 1,"3": 1,"-4": 0,"0": 0,"4": 1,"17": 10,"11": 5,"8": 8,"5": 0,"14": 1,"7": 8,"10": 0,"30": 3,"31": 1,"32": 5,"46": 0,"6": 3,"15": 1,"19": 10,"9998": 6,"500": 1,"502": 4,"505": 0,"507": 1,"503": 1,"2": 0},"score": 354,"username": "tami"},"972559721123@c.us": {"days": {"1": 20,"12": 0,"2": 3,"-2": 1,"-1": 1,"13": 86,"0": 0,"5": 24,"7": 32,"4": 3,"8": 0,"9": 0,"3": 0,"6": 1,"14": 2,"15": 0,"10": 0,"16": 747,"11": 3,"20": 5,"32": 3,"22": 4,"18": 32,"19": 23,"21": 16,"23": 8,"24": 0,"27": 6,"28": 9,"10.5": 9,"39": 90,"16.5": 4,"17": 47,"42": 5,"44": 3,"50": 3,"25": 2,"26": 0,"48": 3,"29": 0},"score": 0},"972526629728@c.us": {"days": {"-1": 3,"5": 1,"1": 1,"3": 1,"4": 1,"1": 3},"score": 11,"username": "Zohar"},"4915126273449@c.us": {},"919019151988@c.us": {},"12016151849@c.us": {"days": {"2": 6,"3": 9,"5": 12,"7": 11,"10": 2,"11": 12},"score": 61},"60173489486@c.us": {"days": {"1": 0,"8": 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| 14,859 | 29,717 | 0.507134 | 5,349 | 29,718 | 2.817349 | 0.112731 | 0.046782 | 0.092435 | 0.072196 | 0.338155 | 0.302256 | 0.254015 | 0.200133 | 0.059124 | 0.035236 | 0 | 0.364072 | 0.087018 | 29,718 | 1 | 29,718 | 29,718 | 0.191361 | 0 | 0 | 0 | 0 | 0 | 0.432095 | 0.060401 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
85427dbaefa2e4bb05bb05f37f555138411b37ac | 242 | py | Python | pyramda/function/apply_test.py | sergiors/pyramda | 5bf200888809b1bc946e813e29460f204bccd13e | [
"MIT"
] | 124 | 2015-07-30T21:34:25.000Z | 2022-02-19T08:45:50.000Z | pyramda/function/apply_test.py | sergiors/pyramda | 5bf200888809b1bc946e813e29460f204bccd13e | [
"MIT"
] | 37 | 2015-08-31T23:02:20.000Z | 2022-02-04T04:45:28.000Z | pyramda/function/apply_test.py | sergiors/pyramda | 5bf200888809b1bc946e813e29460f204bccd13e | [
"MIT"
] | 20 | 2015-08-04T18:59:09.000Z | 2021-12-13T08:08:59.000Z | from .apply import apply
from pyramda.private.asserts import assert_equal
def add(x, y):
return x + y
def apply_nocurry_test():
assert_equal(apply(add, [1, 2]), 3)
def apply_curry_test():
assert_equal(apply(add)([1, 2]), 3)
| 16.133333 | 48 | 0.681818 | 40 | 242 | 3.95 | 0.475 | 0.208861 | 0.189873 | 0.253165 | 0.329114 | 0.329114 | 0.329114 | 0.329114 | 0 | 0 | 0 | 0.030303 | 0.181818 | 242 | 14 | 49 | 17.285714 | 0.767677 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.375 | 1 | 0.375 | false | 0 | 0.25 | 0.125 | 0.75 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
85438c14907c3b4d11e5253ddac8bb1eb4345fc1 | 6,226 | py | Python | graph_as923_datarate.py | tanupoo/lorawan_toa | 57ff520583cd3c06c6918a2763471c1edba4dc47 | [
"MIT"
] | 22 | 2018-01-03T05:45:19.000Z | 2021-04-08T02:27:26.000Z | graph_as923_datarate.py | radiojitter/lorawan_toa | fb1ed3b47b3b5cc3452d10a03b65f150f42009fb | [
"MIT"
] | 2 | 2019-05-05T10:33:12.000Z | 2019-05-10T08:10:24.000Z | graph_as923_datarate.py | radiojitter/lorawan_toa | fb1ed3b47b3b5cc3452d10a03b65f150f42009fb | [
"MIT"
] | 17 | 2017-09-30T13:48:28.000Z | 2021-06-22T21:37:31.000Z |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import sys
import matplotlib.pyplot as plt
import numpy as np
from lorawan_toa import *
####
def get_y_toa(data_size, n_sf, n_bw=125):
if type(data_size) == list:
return [ get_y_toa(i, n_sf, n_bw=n_bw) for i in data_size ]
else:
return get_toa(data_size, n_sf, n_bw=n_bw)["t_packet"]
def get_y_br(data_size, n_sf, n_bw=125):
return [ (i*8)/(get_toa(i, n_sf, n_bw=n_bw)["t_packet"]/1000.)
for i in data_size ]
def get_y_br1(data_size, n_sf, n_bw=125,
enable_auto_ldro=True, enable_ldro=False):
if type(data_size) == list:
ret = []
for i in data_size:
ret.append(get_y_br1(i, n_sf, n_bw=n_bw,
enable_auto_ldro=enable_auto_ldro,
enable_ldro=enable_ldro))
return ret
else:
toa0 = get_toa(0, n_sf, n_bw=n_bw,
enable_auto_ldro=enable_auto_ldro,
enable_ldro=enable_ldro)["t_packet"]
toa = get_toa(data_size, n_sf, n_bw=n_bw,
enable_auto_ldro=enable_auto_ldro,
enable_ldro=enable_ldro)["t_packet"]
if toa == toa0:
return 0
else:
return (data_size*8)/((toa - toa0)/1000.)
########
#
x_nb_bytes = range(0, 40)
fig = plt.figure(facecolor='w', edgecolor='k')
ax = fig.add_subplot(1,1,1)
ax.set_title("LoRa Data Rate (BW=125kHz, AS923)")
ax.set_xlabel("PHY payload size (B)")
ax.set_ylabel("Bitrate (bps)")
ax.set_xlim(0, 40)
#ax.set_ylim(0, 700)
#ax2.set_ylim(0, 7000)
lines = []
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 12), "b-", label="SF12 DE=1")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 11), "g-", label="SF11 DE=1")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 10), "k-", label="SF10 DE=0")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 9), "c-", label="SF 9 DE=0")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 8), "m-", label="SF 8 DE=0")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 7), "y-", label="SF 7 DE=0")
#ax.plot(x_nb_bytes, [ 292.97 for i in x_nb_bytes ], "b--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 250.00 for i in x_nb_bytes ], "b--", lw=2, alpha=0.5)
#ax.plot(x_nb_bytes, [ 537.11 for i in x_nb_bytes ], "g--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 440.00 for i in x_nb_bytes ], "g--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 976.56 for i in x_nb_bytes ], "k--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 1757.81 for i in x_nb_bytes ], "c--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 3125.00 for i in x_nb_bytes ], "m--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 5468.75 for i in x_nb_bytes ], "y--", lw=2, alpha=0.5)
ax.axvline(12, color='k', linestyle='--', alpha=0.7)
ax.scatter(16, get_y_br1(16, 7), s=80, facecolors='none', edgecolors='r')
ax.scatter(19, get_y_br1(19, 7), s=80, facecolors='none', edgecolors='r')
ax.scatter(26, get_y_br1(26, 7), s=80, facecolors='none', edgecolors='r')
ax.grid(which="both")
ax.legend(lines, [i.get_label() for i in lines],
loc="upper right", prop={'size': 10})
fig.tight_layout()
plt.show()
fig.savefig("image/lorawan-dr-all-50b.png")
########
#
x_nb_bytes = range(0, 255)
fig = plt.figure(facecolor='w', edgecolor='k')
ax = fig.add_subplot(1,1,1)
ax.set_title("LoRa Data Rate (BW=125kHz, AS923)")
ax.set_xlabel("PHY payload size (B)")
ax.set_ylabel("Bitrate (bps)")
ax.set_xlim(0, 260)
#ax.set_ylim(0, 700)
#ax2.set_ylim(0, 7000)
lines = []
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 12), "b-", label="SF12 DE=1")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 11), "g-", label="SF11 DE=1")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 10), "k-", label="SF10 DE=0")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 9), "c-", label="SF 9 DE=0")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 8), "m-", label="SF 8 DE=0")
lines += ax.plot(x_nb_bytes, get_y_br1(x_nb_bytes, 7), "y-", label="SF 7 DE=0")
#ax.plot(x_nb_bytes, [ 292.97 for i in x_nb_bytes ], "b--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 250.00 for i in x_nb_bytes ], "b--", lw=2, alpha=0.5)
#ax.plot(x_nb_bytes, [ 537.11 for i in x_nb_bytes ], "g--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 440.00 for i in x_nb_bytes ], "g--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 976.56 for i in x_nb_bytes ], "k--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 1757.81 for i in x_nb_bytes ], "c--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 3125.00 for i in x_nb_bytes ], "m--", lw=2, alpha=0.5)
ax.plot(x_nb_bytes, [ 5468.75 for i in x_nb_bytes ], "y--", lw=2, alpha=0.5)
ax.axvline(12, color='k', linestyle='--', alpha=0.7)
ax.grid(which="both")
ax.legend(lines, [i.get_label() for i in lines],
loc="upper right", prop={'size': 10})
fig.tight_layout()
plt.show()
fig.savefig("image/lorawan-dr-all.png")
########
#
x_nb_bytes = range(0, 255)
fig = plt.figure(facecolor='w', edgecolor='k')
ax = fig.add_subplot(1,1,1)
ax.set_title("LoRa Data Rate (SF7, BW=125kHz [AS923 DR5])")
ax.set_xlabel("PHY payload size (B)")
ax.set_ylabel("Time on Air (ms)")
ax2 = ax.twinx()
ax2.set_ylabel("Bitrate (bps)")
ax.set_xlim(0, 260)
ax.set_ylim(0, 800)
ax2.set_ylim(0, 8000)
lines = []
n_sf = 7
lines += ax.plot(x_nb_bytes, get_y_toa(x_nb_bytes, n_sf), "k-", label="ToA")
lines += ax2.plot(x_nb_bytes, [ 5468.75 for i in x_nb_bytes ],
"r-", label="Equivalent BR.", linewidth=2)
lines += ax2.plot(x_nb_bytes, get_y_br(x_nb_bytes, n_sf),
"b-", label="Simple BR of PHY_PL/ToA")
lines += ax2.plot(x_nb_bytes, get_y_br1(x_nb_bytes, n_sf),
"y-", label="BR. PHY_PL/FixedToA auto LDRO")
lines += ax2.plot(x_nb_bytes, get_y_br1(x_nb_bytes, n_sf,
enable_auto_ldro=False,
enable_ldro=True),
"c-", label="BR. PHY_PL/FixedToA DE=1")
ax2.axvline(12, color='k', linestyle='--', alpha=0.7)
ax.grid(which="both")
ax.legend(lines, [i.get_label() for i in lines],
loc="lower right", prop={'size': 10})
fig.tight_layout()
plt.show()
fig.savefig("image/lorawan-dr-sf7-base.png")
| 35.175141 | 80 | 0.621587 | 1,205 | 6,226 | 2.966805 | 0.137759 | 0.057902 | 0.154406 | 0.110769 | 0.845594 | 0.802517 | 0.802517 | 0.783217 | 0.750769 | 0.72951 | 0 | 0.07222 | 0.188243 | 6,226 | 176 | 81 | 35.375 | 0.63514 | 0.068423 | 0 | 0.578512 | 0 | 0 | 0.120723 | 0.01407 | 0 | 0 | 0 | 0 | 0 | 1 | 0.024793 | false | 0 | 0.041322 | 0.008264 | 0.115702 | 0.008264 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8550b0041c6048d31576df4c6913c0846efc2b47 | 331 | py | Python | senseTk/tracking/__init__.py | Helicopt/senseToolkit | 1630ec3f03368980a13f448b3be554efe44ec7cb | [
"MIT"
] | 2 | 2018-07-30T03:54:58.000Z | 2018-12-17T16:09:06.000Z | senseTk/tracking/__init__.py | Helicopt/senseToolkit | 1630ec3f03368980a13f448b3be554efe44ec7cb | [
"MIT"
] | null | null | null | senseTk/tracking/__init__.py | Helicopt/senseToolkit | 1630ec3f03368980a13f448b3be554efe44ec7cb | [
"MIT"
] | null | null | null | #!/usr/bin/python
# -*- coding: utf8 -*-
#########################################################################
# File Name: tracking/__init__.py
# Author: Toka
# mail: fengweitao@sensetime.com
# Created Time: 2018年07月27日 星期五 12时31分00秒
#########################################################################
from . import mot
| 33.1 | 73 | 0.39577 | 24 | 331 | 5.291667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.049505 | 0.084592 | 331 | 9 | 74 | 36.777778 | 0.369637 | 0.462236 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8574990b58c1b8d0380ad3090337b3865e364794 | 14,417 | py | Python | Latest/venv/Lib/site-packages/pyface/tests/test_confirmation_dialog.py | adamcvj/SatelliteTracker | 49a8f26804422fdad6f330a5548e9f283d84a55d | [
"Apache-2.0"
] | 1 | 2022-01-09T20:04:31.000Z | 2022-01-09T20:04:31.000Z | Latest/venv/Lib/site-packages/pyface/tests/test_confirmation_dialog.py | adamcvj/SatelliteTracker | 49a8f26804422fdad6f330a5548e9f283d84a55d | [
"Apache-2.0"
] | 1 | 2022-02-15T12:01:57.000Z | 2022-03-24T19:48:47.000Z | Latest/venv/Lib/site-packages/pyface/tests/test_confirmation_dialog.py | adamcvj/SatelliteTracker | 49a8f26804422fdad6f330a5548e9f283d84a55d | [
"Apache-2.0"
] | null | null | null | from __future__ import absolute_import
import platform
import unittest
from ..confirmation_dialog import ConfirmationDialog, confirm
from ..constant import YES, NO, OK, CANCEL
from ..image_resource import ImageResource
from ..toolkit import toolkit_object
from ..window import Window
is_qt = toolkit_object.toolkit == 'qt4'
if is_qt:
from pyface.qt import qt_api
GuiTestAssistant = toolkit_object('util.gui_test_assistant:GuiTestAssistant')
no_gui_test_assistant = (GuiTestAssistant.__name__ == 'Unimplemented')
ModalDialogTester = toolkit_object(
'util.modal_dialog_tester:ModalDialogTester'
)
no_modal_dialog_tester = (ModalDialogTester.__name__ == 'Unimplemented')
is_pyqt5 = (is_qt and qt_api == 'pyqt5')
is_pyqt4_linux = (is_qt and qt_api == 'pyqt' and platform.system() == 'Linux')
@unittest.skipIf(no_gui_test_assistant, 'No GuiTestAssistant')
class TestConfirmationDialog(unittest.TestCase, GuiTestAssistant):
def setUp(self):
GuiTestAssistant.setUp(self)
self.dialog = ConfirmationDialog()
def tearDown(self):
if self.dialog.control is not None:
with self.delete_widget(self.dialog.control):
self.dialog.destroy()
self.dialog = None
GuiTestAssistant.tearDown(self)
def test_create(self):
# test that creation and destruction works as expected
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_destroy(self):
# test that destroy works even when no control
with self.event_loop():
self.dialog.destroy()
def test_size(self):
# test that size works as expected
self.dialog.size = (100, 100)
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_position(self):
# test that position works as expected
self.dialog.position = (100, 100)
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_create_parent(self):
# test that creation and destruction works as expected with a parent
with self.event_loop():
parent = Window()
self.dialog.parent = parent.control
parent._create()
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
with self.event_loop():
parent.destroy()
def test_create_yes_renamed(self):
# test that creation and destruction works as expected with ok_label
self.dialog.yes_label = u"Sure"
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_create_no_renamed(self):
# test that creation and destruction works as expected with ok_label
self.dialog.no_label = u"No Way"
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_create_yes_default(self):
# test that creation and destruction works as expected with ok_label
self.dialog.default = YES
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_create_cancel(self):
# test that creation and destruction works with cancel button
self.dialog.cancel = True
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_create_cancel_renamed(self):
# test that creation and destruction works with cancel button
self.dialog.cancel = True
self.dialog.cancel_label = "Back"
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_create_cancel_default(self):
# test that creation and destruction works as expected with ok_label
self.dialog.cancel = True
self.dialog.default = CANCEL
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
def test_create_image(self):
# test that creation and destruction works with a non-standard image
self.dialog.image = ImageResource('core')
with self.event_loop():
self.dialog._create()
with self.event_loop():
self.dialog.destroy()
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_close(self):
# test that closing works as expected
# XXX duplicate of Dialog test, not needed?
tester = ModalDialogTester(self.dialog.open)
tester.open_and_run(when_opened=lambda x: self.dialog.close())
self.assertEqual(tester.result, NO)
self.assertEqual(self.dialog.return_code, NO)
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_close_with_cancel(self):
# test that closing works as expected
self.dialog.cancel = True
tester = ModalDialogTester(self.dialog.open)
tester.open_and_run(when_opened=lambda x: self.dialog.close())
self.assertEqual(tester.result, CANCEL)
self.assertEqual(self.dialog.return_code, CANCEL)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_yes(self):
# test that Yes works as expected
tester = ModalDialogTester(self.dialog.open)
tester.open_and_wait(when_opened=lambda x: x.click_button(YES))
self.assertEqual(tester.result, YES)
self.assertEqual(self.dialog.return_code, YES)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_renamed_yes(self):
self.dialog.yes_label = u"Sure"
# test that Yes works as expected if renamed
tester = ModalDialogTester(self.dialog.open)
tester.open_and_wait(when_opened=lambda x: x.click_widget(u"Sure"))
self.assertEqual(tester.result, YES)
self.assertEqual(self.dialog.return_code, YES)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_no(self):
# test that No works as expected
tester = ModalDialogTester(self.dialog.open)
tester.open_and_wait(when_opened=lambda x: x.click_button(NO))
self.assertEqual(tester.result, NO)
self.assertEqual(self.dialog.return_code, NO)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_renamed_no(self):
self.dialog.no_label = u"No way"
# test that No works as expected if renamed
tester = ModalDialogTester(self.dialog.open)
tester.open_and_wait(when_opened=lambda x: x.click_widget(u"No way"))
self.assertEqual(tester.result, NO)
self.assertEqual(self.dialog.return_code, NO)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_cancel(self):
self.dialog.cancel = True
# test that Cancel works as expected
tester = ModalDialogTester(self.dialog.open)
tester.open_and_wait(when_opened=lambda x: x.click_button(CANCEL))
self.assertEqual(tester.result, CANCEL)
self.assertEqual(self.dialog.return_code, CANCEL)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_cancel_renamed(self):
self.dialog.cancel = True
self.dialog.cancel_label = u"Back"
# test that Cancel works as expected
tester = ModalDialogTester(self.dialog.open)
tester.open_and_wait(when_opened=lambda x: x.click_widget(u"Back"))
self.assertEqual(tester.result, CANCEL)
self.assertEqual(self.dialog.return_code, CANCEL)
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_parent(self):
# test that lifecycle works with a parent
parent = Window()
self.dialog.parent = parent.control
with self.event_loop():
parent.open()
tester = ModalDialogTester(self.dialog.open)
tester.open_and_run(when_opened=lambda x: x.close(accept=True))
with self.event_loop():
parent.close()
self.assertEqual(tester.result, OK)
self.assertEqual(self.dialog.return_code, OK)
@unittest.skipIf(no_gui_test_assistant, 'No GuiTestAssistant')
class TestConfirm(unittest.TestCase, GuiTestAssistant):
def setUp(self):
GuiTestAssistant.setUp(self)
def tearDown(self):
GuiTestAssistant.tearDown(self)
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_reject(self):
# test that cancel works as expected
tester = ModalDialogTester(
lambda: confirm(None, "message", cancel=True)
)
tester.open_and_run(when_opened=lambda x: x.close(accept=False))
self.assertEqual(tester.result, CANCEL)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_yes(self):
# test that yes works as expected
tester = ModalDialogTester(lambda: confirm(None, "message"))
tester.open_and_wait(when_opened=lambda x: x.click_button(YES))
self.assertEqual(tester.result, YES)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_no(self):
# test that yes works as expected
tester = ModalDialogTester(lambda: confirm(None, "message"))
tester.open_and_wait(when_opened=lambda x: x.click_button(NO))
self.assertEqual(tester.result, NO)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_cancel(self):
# test that cancel works as expected
tester = ModalDialogTester(
lambda: confirm(None, "message", cancel=True)
)
tester.open_and_wait(when_opened=lambda x: x.click_button(CANCEL))
self.assertEqual(tester.result, CANCEL)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_title(self):
# test that title works as expected
tester = ModalDialogTester(
lambda: confirm(None, "message", title='Title')
)
tester.open_and_run(when_opened=lambda x: x.click_button(NO))
self.assertEqual(tester.result, NO)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_default_yes(self):
# test that default works as expected
tester = ModalDialogTester(
lambda: confirm(None, "message", default=YES)
)
tester.open_and_run(when_opened=lambda x: x.click_button(YES))
self.assertEqual(tester.result, YES)
@unittest.skipIf(
is_pyqt5, "Confirmation dialog click tests don't work on pyqt5."
) # noqa
@unittest.skipIf(
is_pyqt4_linux,
"Confirmation dialog click tests don't work reliably on linux. Issue #282."
) # noqa
@unittest.skipIf(no_modal_dialog_tester, 'ModalDialogTester unavailable')
def test_default_cancel(self):
# test that default works as expected
tester = ModalDialogTester(
lambda: confirm(None, "message", cancel=True, default=YES)
)
tester.open_and_run(when_opened=lambda x: x.click_button(CANCEL))
self.assertEqual(tester.result, CANCEL)
| 36.778061 | 84 | 0.665464 | 1,758 | 14,417 | 5.288965 | 0.068259 | 0.072059 | 0.03775 | 0.049365 | 0.872015 | 0.839858 | 0.827597 | 0.806948 | 0.794902 | 0.757152 | 0 | 0.008059 | 0.24263 | 14,417 | 391 | 85 | 36.872123 | 0.843484 | 0.098564 | 0 | 0.666667 | 0 | 0 | 0.173003 | 0.006336 | 0 | 0 | 0 | 0 | 0.081699 | 1 | 0.104575 | false | 0 | 0.029412 | 0 | 0.140523 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a40de844c1639657a991509f5144210af3d95670 | 247 | py | Python | vsp-banckend/virtualStoreApp/serializers/__init__.py | DannielF/virtual-store-project | a8600b6acdabf33cdc4d7ae5c744edd064fe0a1b | [
"MIT"
] | null | null | null | vsp-banckend/virtualStoreApp/serializers/__init__.py | DannielF/virtual-store-project | a8600b6acdabf33cdc4d7ae5c744edd064fe0a1b | [
"MIT"
] | null | null | null | vsp-banckend/virtualStoreApp/serializers/__init__.py | DannielF/virtual-store-project | a8600b6acdabf33cdc4d7ae5c744edd064fe0a1b | [
"MIT"
] | null | null | null | from .orderProductSerializer import OrderProductSerializer
from .orderSerializer import OrderSerializer
from .userSerializer import UserSerializer
from .productSerializer import ProductSerializer
from .providerSerializer import ProviderSerializer
| 41.166667 | 58 | 0.898785 | 20 | 247 | 11.1 | 0.35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.080972 | 247 | 5 | 59 | 49.4 | 0.977974 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a462f6d2c54a73dc484b9dc95eda5c6e34a67999 | 5,761 | py | Python | F3DASM-2/fenics_SolidMechanics-main/materials/hyperelastic.py | gawelk/F3DAS | 4a4e7233add608820de9ee0fd1c369c2fa1d24c1 | [
"BSD-3-Clause"
] | 45 | 2019-10-15T06:08:23.000Z | 2020-08-01T03:15:11.000Z | F3DASM-2/fenics_SolidMechanics-main/materials/hyperelastic.py | gawelk/F3DAS | 4a4e7233add608820de9ee0fd1c369c2fa1d24c1 | [
"BSD-3-Clause"
] | 19 | 2021-02-28T16:06:30.000Z | 2022-03-12T01:02:29.000Z | F3DASM-2/fenics_SolidMechanics-main/materials/hyperelastic.py | gawelk/F3DAS | 4a4e7233add608820de9ee0fd1c369c2fa1d24c1 | [
"BSD-3-Clause"
] | 10 | 2020-01-10T09:42:58.000Z | 2020-07-20T19:57:15.000Z | from ..src.continuum import *
class NeoHookean(Material):
"""
Neo-Hookean material model implementation
"""
def __init__(self, u, F_macro, E=None, nu=None, mu=None, lmbda=None):
""" Initialize """
################################
# Initialize material properties
################################
if E is not None and nu is not None:
self.mu, self.lmbda = Lame(E,nu)
else:
self.mu, self.lmbda = mu, lmbda
self.mu = Constant(self.mu)
self.K = Constant(self.lmbda + 2./3. * self.mu)
self.C1 = self.mu/2.
self.D1 = self.K /2.
Material.__init__(self,u=u, F_macro=F_macro) # Initialize base-class
def Energy(self):
""" Method: Implement energy density function """
#psi = self.mu/2*(tr(self.b)-3-2*ln(self.J))+self.K/2*(self.J-1)**2
psi = (self.mu/2)*(tr(self.C)- 3) - self.mu*ln(self.J) + (self.lmbda/2)*(ln(self.J))**2
return psi
class SVenantKirchhoff(Material):
"""
Neo-Hookean material model implementation
"""
def __init__(self, u, F_macro, E=None, nu=None, mu=None, lmbda=None):
""" Initialize """
################################
# Initialize material properties
################################
if E is not None and nu is not None:
self.mu, self.lmbda = Lame(E,nu)
else:
self.mu, self.lmbda = mu, lmbda
self.mu = Constant(self.mu)
self.K = Constant(self.lmbda + 2./3. * self.mu)
self.C1 = self.mu/2.
self.D1 = self.K /2.
Material.__init__(self,u=u, F_macro=F_macro) # Initialize base-class
def Energy(self):
""" Method: Implement energy density function """
psi = self.lmbda/2*(tr(self.E))**2 + self.mu*tr(self.E*self.E.T)
return psi
class ArrudaBoyce(Material):
"""
Arruda-Boyce Material Model
"""
def __init__(self, u, F_macro, E=None, nu=None, mu=None, lmbda=None, lmbda_m=2.8):
""" Initialize """
################################
# Initialize material properties
################################
if E is not None and nu is not None:
self.mu, self.lmbda = Lame(E,nu)
else:
self.mu, self.lmbda = mu, lmbda
self.K = self.lmbda + 2./3. * self.mu
#print(self.K)
self.C1 = self.mu/2.
self.D1 = self.K /2.
self.lmbda_m = lmbda_m
self.a = [0.5 , 1./20., 11./1050., 19./7000., 519./673750.]
self.a1 = [1 , 3./5., 99./175., 513./875.,42039./67375.]
self.mu_m = self.mu / sum([1,3./(5*self.lmbda_m**2),99./(175*self.lmbda_m**4),513./(875*self.lmbda_m**6),42039./(67375*self.lmbda_m**8)])
self.beta = 1./self.lmbda_m**2
Material.__init__(self,u=u, F_macro=F_macro)
def Energy(self):
""" Method: Implement energy density function """
#self.mu = self.mu* 1/2*(sum([i*self.a1[i-1]*self.beta**(i-1) for i in range(1,6)]))
#psi_C = self.mu/2* sum([self.a[i-1]*self.beta**(i-1)*(((tr(self.C))**(i)-3**i)) for i in range(1,6)])
psi_J = self.K/2. * ((self.J**2-1)/2.-ln(self.J))
#psi_J = self.K/2. * (ln(self.J**(1/2)))**2
lm_ = self.lmbda_m
psi_C = self.mu*(1/2*((tr(self.C)*self.J**(-2/3))-3)+1./(20.*lm_**2)*((tr(self.C)*self.J**(-2/3))**2-3**2)+\
11/(1050*lm_**2)*((tr(self.C)*self.J**(-2/3))**3-3**3)+19/(7000*lm_**2)*((tr(self.C)*self.J**(-2/3))**4-3**4)+\
519/(673750*lm_**2)*((tr(self.C)*self.J**(-2/3))**5-3**5))
#psi_C = self.mu*(1/2*(tr(self.C)-3) +1./(20.*lm_**2)*(tr(self.C)**2-3**2)+\
# 11/(1050*lm_**2)*(tr(self.C)**3-3**3)+19/(7000*lm_**2)*(tr(self.C)**4-3**4)+\
# 519/(673750*lm_**2)*(tr(self.C)**5-3**5))
return psi_C+psi_J
class MooneyRivlin(Material):
"""
Mooney-Rivlin Material Model
"""
def __init__(self, u, F_macro, E=None, nu=None, mu=None, lmbda=None):
""" Initialize """
################################
# Initialize material properties
################################
if E is not None and nu is not None:
self.mu, self.lmbda = Lame(E,nu)
else:
self.mu, self.lmbda = mu, lmbda
self.K = self.lmbda + 2./3. * self.mu
self.C1 = self.mu/2.
self.D1 = self.K /2.
self.c01 = -1.5
self.c10 = 3.4
Material.__init__(self,u=u, F_macro=F_macro)
def Energy(self):
""" Method: Implement energy density function """
psi = self.c10*(self.I1_C-3) + self.c01*(self.I2_C-3) + self.K/2*(self.J-1)**2 - 2*(self.c10+self.c01)*ln(self.J)
return psi
class Gent(Material):
"""
Gent Material Model
"""
def __init__(self, u, F_macro, E=None, nu=None, mu=None, lmbda=None, Jm=80):
""" Initialize """
################################
# Initialize material properties
################################
if E is not None and nu is not None:
self.mu, self.lmbda = Lame(E,nu)
else:
self.mu, self.lmbda = mu, lmbda
self.K = self.lmbda + 2./3. * self.mu
self.C1 = self.mu/2.
self.D1 = self.K /2.
self.Jm = Jm
Material.__init__(self,u=u, F_macro=F_macro)
def Energy(self):
""" Method: Implement energy density function """
psi_C = -self.mu/2*self.Jm*ln(1-(tr(self.C)-3-2*ln(self.J))/self.Jm)
psi_J = self.K/2. * ((self.J**2-1)/2.-ln(self.J))
return psi_C + psi_J
| 30.807487 | 145 | 0.486895 | 856 | 5,761 | 3.175234 | 0.109813 | 0.07947 | 0.062546 | 0.032377 | 0.790287 | 0.760486 | 0.734364 | 0.714128 | 0.707873 | 0.676968 | 0 | 0.066474 | 0.279292 | 5,761 | 186 | 146 | 30.973118 | 0.58815 | 0.199445 | 0 | 0.703704 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.123457 | false | 0 | 0.012346 | 0 | 0.259259 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a464496d8accf1dc7adf95b1cbf27d7f1d925c67 | 38 | py | Python | coref_client/__init__.py | tugas-akhir-nlp/coreference-resolution-cnn-v2 | b112893b3bd7b893e3830e183aa79acff8af9896 | [
"MIT"
] | null | null | null | coref_client/__init__.py | tugas-akhir-nlp/coreference-resolution-cnn-v2 | b112893b3bd7b893e3830e183aa79acff8af9896 | [
"MIT"
] | null | null | null | coref_client/__init__.py | tugas-akhir-nlp/coreference-resolution-cnn-v2 | b112893b3bd7b893e3830e183aa79acff8af9896 | [
"MIT"
] | null | null | null | from .coref_client import CorefClient
| 19 | 37 | 0.868421 | 5 | 38 | 6.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.105263 | 38 | 1 | 38 | 38 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f13d1a42ea1d282096e317b96651b03b06458947 | 760 | py | Python | temboo/core/Library/Amazon/CloudDrive/Account/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | 7 | 2016-03-07T02:07:21.000Z | 2022-01-21T02:22:41.000Z | temboo/core/Library/Amazon/CloudDrive/Account/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | null | null | null | temboo/core/Library/Amazon/CloudDrive/Account/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | 8 | 2016-06-14T06:01:11.000Z | 2020-04-22T09:21:44.000Z | from temboo.Library.Amazon.CloudDrive.Account.GetAccountInfo import GetAccountInfo, GetAccountInfoInputSet, GetAccountInfoResultSet, GetAccountInfoChoreographyExecution
from temboo.Library.Amazon.CloudDrive.Account.GetEndpoint import GetEndpoint, GetEndpointInputSet, GetEndpointResultSet, GetEndpointChoreographyExecution
from temboo.Library.Amazon.CloudDrive.Account.GetQuota import GetQuota, GetQuotaInputSet, GetQuotaResultSet, GetQuotaChoreographyExecution
from temboo.Library.Amazon.CloudDrive.Account.GetUsage import GetUsage, GetUsageInputSet, GetUsageResultSet, GetUsageChoreographyExecution
from temboo.Library.Amazon.CloudDrive.Account.SetupAccount import SetupAccount, SetupAccountInputSet, SetupAccountResultSet, SetupAccountChoreographyExecution
| 126.666667 | 168 | 0.901316 | 60 | 760 | 11.416667 | 0.45 | 0.072993 | 0.124088 | 0.167883 | 0.291971 | 0.291971 | 0 | 0 | 0 | 0 | 0 | 0 | 0.046053 | 760 | 5 | 169 | 152 | 0.944828 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
f1572fb36ed401725229fc5d8f6436e0e3f09179 | 273 | py | Python | dash_app/layout/colors.py | meoke/pangviz | bf9c43c103c25752053052b602c7390f6f7b12a8 | [
"MIT"
] | 1 | 2020-08-18T18:27:12.000Z | 2020-08-18T18:27:12.000Z | dash_app/layout/colors.py | meoke/pangviz | bf9c43c103c25752053052b602c7390f6f7b12a8 | [
"MIT"
] | 3 | 2019-08-29T14:39:16.000Z | 2020-05-05T13:44:11.000Z | dash_app/layout/colors.py | meoke/pangviz | bf9c43c103c25752053052b602c7390f6f7b12a8 | [
"MIT"
] | 1 | 2020-04-24T07:13:02.000Z | 2020-04-24T07:13:02.000Z | colors = {'dark_background': '#275972',
'accent': 'rgb(255, 137, 48)',
'page_element': 'rgb(80, 133, 165)',
'light_background': 'rgb(221, 226, 233)',
'transparent': 'rgba(255,255,255,0)',
'background': 'rgb(221, 226, 233)'} | 45.5 | 51 | 0.520147 | 32 | 273 | 4.34375 | 0.65625 | 0.18705 | 0.230216 | 0.273381 | 0.316547 | 0 | 0 | 0 | 0 | 0 | 0 | 0.247525 | 0.260073 | 273 | 6 | 52 | 45.5 | 0.440594 | 0 | 0 | 0 | 0 | 0 | 0.605839 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f15a3248bcec2eb38527429bccf13dfec57ace56 | 260,284 | py | Python | instances/passenger_demand/pas-20210422-1717-int18e/46.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int18e/46.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int18e/46.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 34558
passenger_arriving = (
(11, 6, 8, 8, 12, 3, 6, 3, 2, 2, 3, 0, 0, 12, 8, 1, 8, 12, 5, 4, 2, 1, 3, 0, 0, 0), # 0
(14, 13, 8, 13, 10, 3, 3, 3, 8, 0, 1, 2, 0, 16, 7, 9, 6, 3, 7, 4, 3, 4, 3, 0, 0, 0), # 1
(8, 7, 5, 11, 8, 8, 6, 2, 4, 1, 3, 1, 0, 8, 8, 5, 7, 11, 7, 9, 1, 6, 3, 2, 1, 0), # 2
(4, 8, 8, 14, 9, 5, 5, 4, 1, 1, 3, 1, 0, 11, 5, 7, 10, 13, 6, 3, 4, 4, 5, 3, 3, 0), # 3
(14, 12, 13, 16, 13, 2, 4, 3, 6, 1, 0, 0, 0, 12, 11, 7, 3, 10, 6, 6, 3, 5, 1, 1, 1, 0), # 4
(13, 19, 11, 9, 8, 6, 6, 0, 5, 3, 4, 1, 0, 24, 14, 9, 10, 2, 4, 5, 6, 1, 3, 2, 1, 0), # 5
(16, 15, 4, 17, 11, 4, 4, 5, 6, 3, 1, 1, 0, 8, 12, 11, 7, 7, 5, 3, 2, 6, 5, 1, 1, 0), # 6
(14, 11, 10, 11, 14, 4, 1, 6, 7, 2, 3, 1, 0, 11, 15, 10, 8, 11, 5, 5, 1, 5, 4, 7, 2, 0), # 7
(21, 14, 12, 13, 15, 3, 9, 10, 3, 5, 2, 0, 0, 8, 8, 12, 9, 14, 11, 6, 2, 5, 4, 1, 1, 0), # 8
(17, 14, 18, 14, 4, 9, 9, 11, 4, 0, 1, 0, 0, 8, 14, 5, 13, 11, 8, 2, 3, 3, 3, 1, 1, 0), # 9
(14, 21, 10, 12, 9, 0, 1, 8, 3, 3, 6, 1, 0, 16, 19, 7, 10, 13, 3, 10, 4, 5, 3, 4, 0, 0), # 10
(14, 11, 11, 10, 9, 7, 7, 2, 6, 2, 3, 2, 0, 15, 11, 13, 8, 10, 4, 7, 0, 3, 4, 4, 1, 0), # 11
(14, 14, 11, 14, 17, 3, 8, 6, 4, 2, 2, 0, 0, 11, 11, 7, 9, 10, 7, 7, 5, 2, 3, 3, 0, 0), # 12
(12, 20, 10, 16, 10, 6, 3, 9, 10, 4, 1, 1, 0, 16, 16, 12, 9, 15, 13, 6, 2, 9, 5, 4, 1, 0), # 13
(14, 15, 15, 12, 12, 5, 6, 9, 11, 5, 2, 0, 0, 8, 14, 12, 10, 10, 5, 5, 4, 3, 3, 5, 0, 0), # 14
(16, 16, 16, 19, 10, 11, 12, 6, 5, 0, 0, 3, 0, 18, 10, 11, 8, 20, 11, 4, 4, 8, 6, 5, 0, 0), # 15
(26, 6, 10, 18, 12, 4, 7, 4, 10, 2, 2, 1, 0, 12, 12, 11, 13, 21, 6, 5, 9, 6, 3, 3, 2, 0), # 16
(26, 18, 16, 17, 12, 7, 7, 7, 8, 0, 2, 3, 0, 21, 18, 11, 9, 10, 11, 5, 6, 5, 3, 0, 3, 0), # 17
(16, 28, 14, 29, 15, 10, 11, 8, 4, 2, 1, 1, 0, 20, 15, 11, 9, 10, 9, 12, 3, 13, 7, 4, 2, 0), # 18
(16, 17, 21, 23, 16, 9, 8, 5, 5, 3, 3, 2, 0, 16, 11, 8, 5, 11, 9, 10, 2, 3, 7, 3, 0, 0), # 19
(24, 19, 15, 17, 12, 9, 6, 9, 3, 4, 7, 3, 0, 24, 13, 16, 7, 13, 7, 13, 4, 8, 3, 5, 2, 0), # 20
(20, 20, 15, 19, 6, 2, 8, 5, 6, 5, 1, 0, 0, 17, 19, 10, 10, 14, 7, 8, 3, 6, 8, 2, 1, 0), # 21
(17, 16, 12, 10, 19, 5, 7, 4, 4, 2, 2, 3, 0, 18, 16, 17, 8, 9, 4, 14, 5, 10, 4, 2, 1, 0), # 22
(27, 26, 15, 18, 16, 7, 5, 4, 10, 3, 1, 1, 0, 19, 12, 15, 13, 23, 9, 7, 7, 11, 3, 1, 1, 0), # 23
(15, 19, 12, 12, 15, 9, 6, 4, 4, 2, 3, 4, 0, 12, 17, 10, 12, 15, 12, 8, 6, 8, 5, 2, 0, 0), # 24
(22, 20, 14, 19, 12, 10, 6, 5, 8, 4, 7, 2, 0, 27, 17, 12, 13, 19, 11, 10, 5, 7, 4, 2, 3, 0), # 25
(27, 18, 16, 22, 16, 6, 5, 5, 7, 2, 3, 3, 0, 19, 19, 21, 9, 13, 11, 15, 4, 6, 9, 6, 0, 0), # 26
(16, 17, 13, 21, 8, 6, 6, 2, 9, 7, 0, 0, 0, 18, 11, 13, 9, 13, 13, 12, 6, 11, 8, 6, 4, 0), # 27
(19, 18, 15, 19, 16, 6, 9, 6, 11, 4, 1, 3, 0, 25, 19, 14, 6, 19, 8, 6, 2, 6, 8, 2, 5, 0), # 28
(20, 23, 14, 25, 16, 2, 11, 10, 7, 6, 1, 3, 0, 12, 15, 13, 10, 7, 15, 5, 3, 9, 4, 2, 3, 0), # 29
(19, 21, 15, 26, 14, 7, 3, 9, 8, 8, 4, 0, 0, 20, 19, 12, 5, 15, 5, 11, 5, 8, 8, 6, 2, 0), # 30
(20, 19, 9, 18, 10, 7, 8, 9, 10, 3, 1, 0, 0, 16, 16, 17, 5, 16, 7, 11, 5, 10, 6, 4, 4, 0), # 31
(18, 20, 12, 23, 13, 9, 12, 5, 9, 8, 3, 1, 0, 18, 19, 9, 10, 17, 8, 5, 3, 6, 8, 5, 1, 0), # 32
(23, 24, 13, 8, 10, 4, 9, 5, 7, 7, 6, 4, 0, 20, 12, 11, 10, 24, 10, 8, 4, 4, 4, 3, 0, 0), # 33
(20, 19, 12, 14, 14, 4, 7, 6, 9, 5, 5, 1, 0, 22, 18, 9, 4, 20, 9, 5, 6, 8, 8, 4, 0, 0), # 34
(20, 15, 12, 20, 17, 4, 10, 9, 5, 3, 4, 1, 0, 19, 14, 14, 14, 17, 11, 7, 2, 5, 6, 3, 1, 0), # 35
(20, 17, 6, 13, 9, 10, 9, 9, 6, 3, 2, 5, 0, 20, 14, 18, 11, 16, 8, 5, 5, 8, 6, 2, 0, 0), # 36
(12, 17, 18, 16, 16, 7, 14, 12, 4, 5, 5, 1, 0, 14, 11, 14, 10, 15, 11, 6, 3, 0, 5, 2, 3, 0), # 37
(15, 16, 15, 18, 13, 4, 1, 7, 10, 1, 3, 3, 0, 19, 25, 14, 7, 14, 11, 7, 4, 3, 4, 5, 0, 0), # 38
(19, 17, 18, 19, 17, 6, 11, 4, 7, 4, 2, 1, 0, 18, 16, 14, 6, 14, 10, 3, 3, 6, 14, 0, 2, 0), # 39
(20, 19, 10, 12, 8, 5, 6, 5, 6, 7, 2, 1, 0, 18, 13, 10, 11, 15, 12, 9, 4, 3, 2, 4, 3, 0), # 40
(13, 12, 16, 21, 15, 4, 7, 7, 3, 3, 5, 3, 0, 16, 16, 16, 6, 17, 6, 11, 6, 5, 4, 3, 3, 0), # 41
(18, 21, 14, 15, 11, 8, 5, 4, 9, 2, 3, 3, 0, 13, 15, 15, 16, 17, 8, 4, 5, 9, 6, 8, 1, 0), # 42
(13, 23, 15, 18, 9, 9, 5, 4, 4, 3, 3, 1, 0, 20, 21, 15, 17, 13, 5, 5, 3, 11, 2, 4, 1, 0), # 43
(16, 18, 12, 21, 10, 9, 2, 7, 8, 5, 1, 1, 0, 13, 18, 6, 9, 20, 6, 7, 5, 8, 4, 1, 1, 0), # 44
(20, 15, 14, 11, 17, 6, 4, 5, 7, 7, 7, 0, 0, 24, 13, 18, 7, 12, 3, 6, 1, 3, 4, 3, 2, 0), # 45
(26, 16, 24, 23, 15, 10, 5, 14, 13, 4, 0, 2, 0, 27, 16, 13, 10, 18, 8, 8, 7, 9, 1, 1, 0, 0), # 46
(16, 11, 15, 15, 13, 6, 4, 3, 4, 0, 3, 0, 0, 17, 16, 16, 11, 15, 12, 8, 5, 9, 4, 3, 2, 0), # 47
(16, 14, 15, 26, 21, 7, 4, 2, 5, 4, 1, 1, 0, 15, 16, 11, 12, 19, 11, 7, 2, 8, 8, 5, 1, 0), # 48
(18, 20, 18, 9, 18, 3, 6, 5, 8, 3, 2, 3, 0, 11, 18, 10, 8, 11, 10, 3, 1, 7, 8, 3, 1, 0), # 49
(18, 18, 24, 15, 13, 3, 7, 0, 10, 2, 1, 1, 0, 23, 14, 13, 16, 11, 13, 6, 6, 4, 7, 3, 2, 0), # 50
(14, 19, 18, 8, 10, 6, 4, 7, 9, 3, 1, 0, 0, 22, 23, 15, 15, 17, 11, 4, 5, 10, 2, 2, 0, 0), # 51
(18, 16, 10, 20, 20, 6, 7, 6, 10, 4, 7, 1, 0, 14, 15, 13, 10, 21, 15, 6, 6, 6, 9, 1, 1, 0), # 52
(19, 23, 14, 15, 10, 9, 11, 4, 13, 2, 1, 5, 0, 19, 14, 14, 14, 12, 9, 11, 2, 5, 8, 2, 3, 0), # 53
(12, 11, 19, 19, 13, 7, 6, 9, 8, 5, 4, 2, 0, 19, 11, 10, 7, 11, 9, 8, 5, 6, 5, 4, 4, 0), # 54
(20, 10, 10, 29, 16, 2, 12, 4, 6, 1, 1, 3, 0, 17, 19, 11, 10, 10, 5, 9, 4, 5, 8, 2, 2, 0), # 55
(11, 24, 20, 17, 13, 8, 3, 7, 5, 5, 5, 4, 0, 22, 13, 15, 10, 14, 8, 6, 3, 7, 2, 1, 1, 0), # 56
(18, 14, 11, 16, 14, 13, 8, 4, 10, 4, 3, 0, 0, 22, 9, 6, 12, 17, 10, 6, 6, 3, 4, 5, 0, 0), # 57
(13, 14, 10, 15, 14, 6, 9, 4, 5, 1, 2, 1, 0, 25, 13, 16, 11, 14, 7, 11, 5, 6, 7, 5, 1, 0), # 58
(20, 21, 13, 13, 22, 4, 7, 3, 5, 5, 2, 1, 0, 14, 18, 7, 15, 6, 9, 8, 6, 5, 4, 1, 1, 0), # 59
(20, 16, 19, 20, 15, 6, 2, 2, 13, 5, 3, 1, 0, 18, 12, 14, 9, 10, 7, 6, 6, 5, 12, 2, 3, 0), # 60
(24, 19, 10, 18, 11, 7, 4, 8, 8, 6, 2, 2, 0, 13, 12, 13, 3, 17, 13, 11, 3, 12, 9, 1, 1, 0), # 61
(18, 10, 15, 14, 12, 10, 9, 7, 6, 2, 1, 0, 0, 15, 13, 14, 12, 13, 9, 9, 3, 6, 2, 1, 1, 0), # 62
(26, 13, 19, 13, 9, 12, 9, 6, 5, 0, 3, 2, 0, 10, 10, 19, 7, 10, 12, 6, 3, 9, 4, 3, 3, 0), # 63
(21, 24, 15, 19, 15, 2, 9, 5, 8, 4, 3, 2, 0, 18, 10, 11, 11, 18, 11, 5, 6, 8, 5, 3, 0, 0), # 64
(9, 14, 15, 15, 12, 4, 4, 7, 3, 7, 3, 0, 0, 22, 18, 13, 14, 18, 5, 1, 7, 6, 4, 3, 0, 0), # 65
(13, 15, 25, 17, 11, 7, 6, 2, 4, 2, 3, 1, 0, 16, 9, 6, 13, 10, 6, 2, 5, 8, 6, 3, 1, 0), # 66
(17, 15, 15, 18, 11, 5, 6, 6, 9, 3, 1, 1, 0, 18, 18, 10, 8, 11, 12, 3, 6, 7, 3, 3, 3, 0), # 67
(16, 15, 14, 12, 15, 6, 8, 10, 12, 1, 4, 1, 0, 19, 12, 11, 16, 14, 12, 8, 5, 7, 4, 0, 1, 0), # 68
(18, 23, 12, 19, 18, 6, 5, 6, 8, 2, 0, 3, 0, 20, 15, 18, 6, 9, 12, 3, 6, 6, 4, 4, 1, 0), # 69
(11, 14, 15, 18, 9, 9, 1, 7, 7, 5, 5, 0, 0, 21, 7, 10, 13, 16, 4, 4, 5, 6, 5, 2, 4, 0), # 70
(21, 15, 20, 18, 6, 6, 6, 3, 9, 1, 4, 6, 0, 12, 17, 9, 5, 16, 8, 6, 5, 11, 3, 7, 1, 0), # 71
(22, 14, 10, 23, 12, 5, 6, 3, 8, 1, 1, 1, 0, 17, 4, 14, 7, 11, 8, 6, 4, 7, 5, 4, 1, 0), # 72
(17, 21, 17, 17, 11, 5, 7, 5, 2, 4, 2, 0, 0, 16, 11, 6, 12, 18, 7, 7, 4, 8, 9, 0, 1, 0), # 73
(21, 13, 9, 22, 17, 11, 8, 4, 6, 1, 6, 3, 0, 11, 16, 8, 7, 13, 6, 5, 2, 6, 6, 0, 3, 0), # 74
(15, 23, 14, 16, 12, 7, 3, 3, 10, 3, 4, 0, 0, 15, 19, 12, 7, 13, 8, 5, 3, 4, 11, 2, 1, 0), # 75
(18, 23, 19, 18, 16, 5, 9, 8, 5, 0, 4, 0, 0, 13, 16, 12, 12, 13, 6, 8, 2, 9, 7, 4, 2, 0), # 76
(10, 15, 18, 12, 9, 8, 10, 6, 3, 4, 2, 1, 0, 17, 15, 12, 11, 15, 9, 4, 3, 5, 10, 5, 1, 0), # 77
(17, 16, 11, 12, 17, 5, 8, 3, 9, 4, 1, 2, 0, 25, 20, 14, 6, 12, 4, 10, 3, 10, 6, 6, 3, 0), # 78
(19, 17, 16, 15, 16, 8, 9, 6, 5, 6, 5, 2, 0, 15, 17, 6, 13, 17, 9, 10, 4, 5, 5, 2, 1, 0), # 79
(21, 11, 19, 8, 23, 5, 8, 4, 8, 1, 0, 3, 0, 16, 18, 13, 10, 18, 12, 8, 5, 13, 7, 5, 1, 0), # 80
(16, 13, 12, 21, 12, 4, 3, 6, 9, 4, 4, 1, 0, 26, 14, 4, 11, 13, 5, 6, 4, 1, 3, 3, 1, 0), # 81
(21, 16, 13, 20, 15, 4, 9, 1, 9, 1, 5, 2, 0, 20, 13, 13, 11, 12, 5, 5, 2, 4, 4, 3, 2, 0), # 82
(32, 17, 15, 15, 9, 5, 6, 5, 8, 3, 2, 0, 0, 12, 17, 9, 9, 11, 7, 8, 3, 12, 6, 4, 2, 0), # 83
(14, 14, 17, 25, 11, 6, 7, 4, 1, 3, 4, 1, 0, 20, 14, 7, 8, 16, 6, 8, 5, 8, 3, 0, 1, 0), # 84
(16, 11, 8, 21, 7, 9, 11, 2, 5, 6, 2, 1, 0, 21, 14, 11, 7, 17, 7, 5, 4, 6, 6, 1, 1, 0), # 85
(18, 18, 10, 17, 12, 9, 2, 3, 6, 5, 2, 2, 0, 25, 19, 8, 6, 9, 8, 7, 2, 7, 3, 3, 2, 0), # 86
(21, 12, 16, 12, 20, 2, 7, 5, 6, 2, 3, 2, 0, 17, 14, 11, 10, 12, 2, 4, 5, 9, 6, 8, 1, 0), # 87
(14, 19, 12, 9, 20, 10, 7, 5, 6, 6, 6, 0, 0, 17, 14, 14, 7, 9, 11, 8, 5, 5, 5, 1, 1, 0), # 88
(18, 14, 12, 19, 17, 6, 6, 7, 2, 2, 2, 1, 0, 20, 13, 11, 4, 15, 4, 8, 7, 9, 5, 4, 4, 0), # 89
(19, 27, 14, 25, 15, 7, 4, 4, 2, 3, 3, 1, 0, 14, 17, 12, 14, 10, 7, 5, 1, 5, 7, 4, 0, 0), # 90
(25, 15, 18, 11, 8, 12, 5, 8, 9, 4, 3, 0, 0, 21, 19, 13, 5, 8, 4, 10, 3, 6, 11, 2, 2, 0), # 91
(27, 13, 18, 20, 20, 9, 9, 5, 5, 2, 5, 1, 0, 14, 19, 14, 8, 17, 8, 9, 3, 7, 4, 6, 2, 0), # 92
(27, 23, 12, 22, 17, 6, 6, 5, 8, 1, 2, 1, 0, 14, 18, 9, 15, 15, 6, 5, 7, 7, 8, 0, 3, 0), # 93
(18, 13, 13, 12, 15, 10, 6, 4, 10, 0, 2, 0, 0, 21, 10, 8, 3, 14, 8, 8, 2, 8, 1, 1, 2, 0), # 94
(14, 17, 17, 12, 12, 10, 5, 8, 4, 2, 0, 3, 0, 25, 9, 13, 10, 15, 5, 5, 5, 1, 4, 2, 0, 0), # 95
(27, 16, 11, 14, 18, 5, 4, 2, 8, 3, 4, 5, 0, 17, 12, 7, 16, 12, 8, 8, 9, 5, 3, 3, 1, 0), # 96
(21, 20, 20, 21, 9, 6, 5, 2, 6, 1, 0, 0, 0, 14, 10, 7, 5, 18, 8, 6, 3, 5, 10, 3, 0, 0), # 97
(17, 13, 15, 17, 11, 5, 9, 3, 3, 3, 1, 2, 0, 17, 11, 6, 5, 12, 9, 9, 3, 6, 5, 1, 2, 0), # 98
(14, 20, 14, 16, 15, 5, 6, 3, 7, 3, 4, 2, 0, 13, 14, 14, 11, 12, 14, 7, 5, 10, 3, 2, 0, 0), # 99
(21, 18, 19, 14, 8, 7, 8, 3, 6, 3, 3, 1, 0, 17, 18, 8, 9, 8, 7, 2, 4, 4, 3, 1, 1, 0), # 100
(11, 14, 8, 11, 16, 5, 10, 4, 6, 6, 2, 1, 0, 10, 16, 7, 9, 8, 4, 7, 3, 8, 5, 7, 0, 0), # 101
(21, 21, 19, 14, 6, 5, 7, 5, 7, 2, 4, 2, 0, 16, 8, 7, 5, 13, 4, 4, 7, 14, 4, 2, 1, 0), # 102
(21, 15, 11, 19, 17, 7, 6, 2, 6, 5, 2, 1, 0, 25, 22, 13, 11, 10, 2, 8, 4, 7, 3, 2, 3, 0), # 103
(29, 7, 8, 16, 10, 4, 4, 3, 5, 3, 2, 1, 0, 14, 14, 11, 11, 15, 6, 1, 2, 3, 3, 3, 1, 0), # 104
(14, 11, 13, 9, 18, 5, 5, 3, 9, 2, 0, 0, 0, 17, 9, 11, 14, 17, 5, 12, 5, 6, 4, 3, 0, 0), # 105
(14, 13, 16, 15, 10, 6, 8, 5, 4, 5, 3, 1, 0, 23, 12, 13, 9, 6, 9, 6, 5, 6, 3, 3, 1, 0), # 106
(18, 15, 13, 15, 13, 6, 6, 4, 3, 2, 1, 1, 0, 16, 15, 12, 3, 18, 3, 5, 5, 6, 4, 1, 1, 0), # 107
(24, 14, 13, 15, 9, 10, 3, 5, 6, 3, 5, 3, 0, 15, 17, 12, 7, 16, 4, 6, 2, 9, 4, 2, 2, 0), # 108
(9, 18, 10, 18, 13, 3, 6, 2, 7, 2, 2, 2, 0, 25, 18, 13, 8, 9, 6, 8, 3, 4, 5, 2, 0, 0), # 109
(36, 13, 11, 21, 10, 4, 7, 8, 5, 4, 2, 0, 0, 19, 19, 13, 14, 10, 3, 6, 2, 6, 8, 0, 0, 0), # 110
(13, 13, 16, 12, 12, 10, 6, 3, 4, 3, 4, 1, 0, 16, 13, 13, 7, 8, 3, 5, 9, 10, 5, 1, 0, 0), # 111
(13, 10, 11, 21, 8, 3, 5, 2, 4, 2, 1, 1, 0, 21, 19, 17, 4, 15, 5, 4, 6, 7, 3, 2, 1, 0), # 112
(12, 13, 16, 16, 17, 7, 7, 5, 8, 2, 4, 2, 0, 22, 14, 10, 6, 12, 3, 15, 3, 3, 4, 0, 0, 0), # 113
(18, 7, 15, 10, 10, 8, 8, 0, 7, 1, 1, 0, 0, 17, 15, 12, 9, 11, 4, 4, 3, 7, 9, 2, 0, 0), # 114
(30, 15, 14, 12, 15, 4, 2, 4, 7, 2, 1, 0, 0, 23, 9, 13, 8, 13, 8, 8, 9, 5, 5, 0, 2, 0), # 115
(18, 14, 13, 16, 8, 8, 9, 7, 3, 5, 1, 0, 0, 17, 13, 14, 11, 15, 10, 6, 6, 3, 4, 3, 0, 0), # 116
(29, 21, 11, 7, 14, 6, 8, 2, 10, 0, 1, 1, 0, 12, 15, 10, 8, 15, 4, 5, 8, 6, 5, 3, 1, 0), # 117
(13, 12, 13, 26, 14, 9, 4, 4, 8, 5, 1, 0, 0, 11, 16, 15, 11, 14, 10, 3, 6, 9, 4, 0, 0, 0), # 118
(16, 10, 19, 15, 8, 7, 1, 2, 6, 2, 1, 1, 0, 14, 16, 14, 5, 11, 11, 3, 8, 5, 5, 2, 0, 0), # 119
(16, 13, 16, 15, 12, 8, 4, 3, 3, 2, 2, 0, 0, 19, 8, 9, 4, 19, 4, 3, 0, 4, 5, 3, 1, 0), # 120
(22, 16, 9, 19, 11, 10, 8, 4, 9, 3, 5, 1, 0, 14, 10, 14, 9, 11, 7, 3, 6, 7, 3, 4, 2, 0), # 121
(19, 9, 9, 9, 14, 9, 5, 6, 4, 3, 1, 1, 0, 18, 12, 12, 6, 11, 5, 2, 4, 3, 3, 3, 1, 0), # 122
(16, 10, 16, 17, 12, 9, 4, 1, 9, 2, 3, 2, 0, 15, 17, 7, 11, 7, 4, 4, 4, 10, 5, 3, 5, 0), # 123
(19, 16, 15, 11, 6, 3, 9, 4, 3, 3, 3, 0, 0, 15, 9, 13, 5, 11, 5, 3, 5, 5, 4, 2, 3, 0), # 124
(15, 7, 14, 9, 12, 8, 1, 2, 14, 4, 2, 1, 0, 23, 19, 16, 12, 9, 8, 4, 3, 5, 3, 5, 0, 0), # 125
(19, 12, 17, 14, 10, 5, 4, 3, 7, 5, 3, 0, 0, 15, 13, 7, 4, 15, 9, 10, 6, 7, 5, 3, 1, 0), # 126
(17, 12, 16, 20, 13, 5, 2, 5, 5, 2, 1, 2, 0, 20, 8, 15, 6, 14, 12, 4, 4, 7, 4, 1, 0, 0), # 127
(21, 14, 13, 19, 7, 4, 6, 2, 5, 2, 3, 0, 0, 19, 20, 11, 4, 11, 10, 5, 3, 4, 4, 2, 1, 0), # 128
(19, 10, 7, 16, 12, 2, 5, 1, 2, 1, 2, 0, 0, 20, 17, 8, 8, 10, 5, 7, 3, 8, 2, 5, 0, 0), # 129
(13, 9, 15, 15, 14, 11, 4, 3, 13, 1, 1, 4, 0, 11, 16, 9, 8, 13, 10, 6, 5, 2, 9, 3, 3, 0), # 130
(15, 11, 13, 14, 15, 9, 8, 7, 1, 2, 5, 0, 0, 12, 9, 13, 6, 19, 3, 8, 10, 8, 6, 3, 2, 0), # 131
(15, 11, 19, 10, 15, 3, 4, 8, 5, 3, 0, 2, 0, 20, 12, 7, 7, 15, 6, 4, 4, 8, 1, 2, 3, 0), # 132
(15, 9, 12, 12, 20, 5, 9, 4, 4, 1, 3, 0, 0, 14, 12, 7, 4, 9, 6, 1, 4, 8, 7, 0, 0, 0), # 133
(15, 12, 12, 19, 11, 11, 4, 3, 6, 1, 1, 0, 0, 14, 18, 7, 17, 4, 4, 4, 6, 7, 4, 2, 3, 0), # 134
(9, 14, 15, 11, 7, 4, 8, 3, 4, 2, 2, 0, 0, 23, 15, 8, 9, 14, 3, 3, 6, 3, 2, 2, 2, 0), # 135
(12, 9, 11, 12, 16, 10, 5, 2, 3, 4, 1, 0, 0, 17, 17, 14, 4, 17, 5, 6, 4, 6, 3, 4, 0, 0), # 136
(13, 22, 13, 20, 9, 7, 1, 1, 10, 2, 2, 1, 0, 15, 13, 7, 4, 18, 6, 6, 3, 3, 4, 1, 2, 0), # 137
(10, 14, 15, 15, 8, 6, 6, 6, 7, 2, 2, 0, 0, 27, 8, 10, 9, 11, 8, 7, 3, 7, 4, 2, 2, 0), # 138
(17, 19, 18, 15, 14, 5, 0, 3, 10, 2, 2, 1, 0, 16, 12, 15, 8, 6, 4, 8, 2, 5, 7, 0, 2, 0), # 139
(22, 13, 11, 14, 16, 6, 1, 4, 8, 5, 0, 0, 0, 20, 6, 10, 6, 14, 6, 9, 6, 5, 7, 5, 1, 0), # 140
(10, 10, 16, 22, 5, 4, 5, 1, 8, 1, 3, 1, 0, 17, 15, 4, 12, 6, 7, 8, 4, 4, 1, 4, 2, 0), # 141
(13, 9, 11, 9, 19, 2, 4, 4, 6, 3, 1, 0, 0, 20, 7, 6, 9, 18, 5, 5, 1, 7, 6, 2, 1, 0), # 142
(16, 11, 13, 12, 17, 8, 4, 1, 4, 3, 1, 1, 0, 15, 9, 5, 10, 21, 6, 2, 5, 4, 5, 1, 0, 0), # 143
(11, 8, 14, 19, 8, 4, 4, 3, 4, 6, 3, 0, 0, 21, 20, 10, 8, 8, 2, 3, 3, 3, 3, 3, 1, 0), # 144
(16, 14, 14, 14, 11, 6, 5, 1, 7, 1, 2, 1, 0, 17, 16, 8, 10, 11, 5, 4, 4, 3, 3, 2, 0, 0), # 145
(16, 10, 6, 16, 11, 10, 1, 6, 11, 2, 1, 3, 0, 8, 9, 8, 7, 13, 5, 5, 5, 2, 2, 5, 2, 0), # 146
(29, 16, 16, 6, 6, 7, 5, 6, 4, 3, 2, 1, 0, 21, 13, 3, 5, 11, 7, 6, 4, 4, 5, 6, 1, 0), # 147
(12, 6, 11, 16, 11, 5, 3, 2, 4, 3, 1, 2, 0, 17, 7, 8, 8, 18, 5, 4, 4, 5, 10, 2, 4, 0), # 148
(18, 9, 9, 11, 18, 8, 4, 2, 4, 2, 1, 0, 0, 15, 8, 12, 4, 19, 2, 6, 5, 3, 7, 3, 0, 0), # 149
(12, 10, 8, 13, 11, 3, 3, 2, 4, 2, 1, 3, 0, 14, 5, 6, 12, 8, 5, 8, 1, 4, 1, 3, 2, 0), # 150
(10, 7, 13, 11, 15, 5, 7, 5, 4, 2, 2, 1, 0, 23, 17, 4, 12, 15, 5, 2, 1, 6, 3, 2, 3, 0), # 151
(12, 13, 16, 21, 15, 6, 6, 3, 6, 0, 2, 1, 0, 17, 5, 6, 4, 13, 5, 2, 2, 4, 5, 3, 1, 0), # 152
(8, 8, 13, 14, 8, 4, 6, 5, 5, 4, 2, 2, 0, 14, 8, 2, 5, 9, 7, 1, 2, 2, 4, 1, 0, 0), # 153
(12, 8, 16, 10, 10, 3, 4, 8, 7, 6, 2, 2, 0, 14, 11, 2, 7, 13, 9, 4, 1, 9, 10, 2, 0, 0), # 154
(12, 5, 10, 12, 6, 6, 6, 7, 6, 1, 1, 2, 0, 16, 13, 11, 5, 11, 9, 4, 5, 6, 5, 1, 0, 0), # 155
(18, 12, 11, 13, 8, 5, 3, 4, 4, 3, 1, 2, 0, 9, 12, 10, 5, 10, 8, 6, 4, 7, 5, 0, 0, 0), # 156
(12, 10, 11, 9, 7, 4, 7, 5, 5, 1, 1, 2, 0, 14, 11, 11, 11, 11, 9, 4, 4, 4, 1, 4, 2, 0), # 157
(14, 6, 17, 8, 10, 7, 2, 3, 3, 4, 3, 2, 0, 11, 11, 6, 9, 9, 3, 6, 3, 6, 3, 1, 2, 0), # 158
(18, 18, 8, 9, 7, 4, 3, 2, 9, 4, 2, 2, 0, 14, 11, 8, 9, 12, 5, 4, 6, 5, 2, 1, 1, 0), # 159
(15, 9, 13, 13, 12, 3, 6, 2, 9, 2, 2, 1, 0, 13, 9, 5, 5, 10, 5, 4, 5, 9, 4, 1, 0, 0), # 160
(19, 8, 10, 16, 7, 3, 2, 4, 4, 1, 3, 1, 0, 12, 12, 6, 7, 13, 6, 2, 4, 5, 6, 1, 0, 0), # 161
(17, 8, 15, 6, 15, 5, 5, 2, 7, 1, 1, 1, 0, 16, 17, 8, 6, 12, 7, 5, 1, 3, 7, 1, 0, 0), # 162
(13, 12, 9, 7, 13, 7, 5, 6, 3, 3, 2, 0, 0, 12, 8, 3, 7, 10, 5, 7, 7, 4, 7, 1, 4, 0), # 163
(12, 8, 10, 8, 15, 5, 4, 7, 5, 1, 2, 0, 0, 10, 12, 14, 5, 7, 4, 3, 3, 11, 5, 3, 1, 0), # 164
(9, 10, 7, 10, 15, 6, 4, 7, 10, 3, 4, 1, 0, 11, 13, 8, 6, 11, 2, 3, 5, 3, 3, 2, 2, 0), # 165
(13, 7, 13, 11, 12, 7, 2, 2, 7, 0, 3, 1, 0, 16, 6, 9, 2, 12, 10, 3, 4, 9, 3, 2, 0, 0), # 166
(4, 7, 10, 9, 7, 2, 3, 2, 7, 1, 2, 3, 0, 16, 6, 9, 5, 11, 4, 4, 6, 6, 4, 0, 0, 0), # 167
(8, 9, 8, 12, 11, 2, 3, 1, 3, 6, 0, 0, 0, 7, 7, 10, 3, 9, 6, 6, 1, 1, 6, 2, 1, 0), # 168
(8, 17, 7, 12, 12, 5, 5, 3, 6, 2, 1, 2, 0, 17, 17, 5, 7, 8, 3, 0, 2, 5, 2, 0, 2, 0), # 169
(10, 5, 11, 11, 5, 6, 4, 2, 7, 2, 0, 2, 0, 14, 3, 13, 3, 16, 7, 2, 4, 7, 3, 0, 0, 0), # 170
(3, 4, 9, 11, 10, 3, 2, 2, 6, 0, 1, 1, 0, 10, 8, 16, 2, 10, 1, 2, 6, 4, 1, 3, 0, 0), # 171
(17, 6, 8, 8, 11, 4, 2, 3, 3, 1, 2, 1, 0, 10, 9, 8, 7, 9, 5, 5, 5, 3, 5, 1, 0, 0), # 172
(9, 5, 4, 9, 9, 2, 0, 2, 6, 1, 0, 0, 0, 10, 3, 8, 4, 14, 2, 3, 4, 0, 0, 1, 0, 0), # 173
(8, 9, 7, 6, 8, 3, 2, 3, 8, 0, 0, 1, 0, 10, 5, 6, 3, 8, 4, 1, 0, 7, 3, 3, 0, 0), # 174
(7, 5, 9, 11, 5, 1, 4, 3, 4, 0, 2, 2, 0, 5, 3, 9, 5, 8, 2, 5, 2, 5, 4, 0, 1, 0), # 175
(10, 5, 5, 7, 8, 1, 4, 2, 4, 1, 2, 1, 0, 11, 7, 7, 3, 16, 2, 3, 2, 4, 2, 0, 0, 0), # 176
(7, 2, 1, 12, 6, 2, 2, 2, 3, 4, 0, 0, 0, 8, 6, 4, 6, 8, 2, 1, 1, 5, 2, 1, 0, 0), # 177
(4, 8, 5, 5, 8, 0, 3, 1, 1, 3, 1, 1, 0, 8, 6, 6, 2, 8, 7, 1, 2, 5, 2, 5, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(9.037558041069182, 9.9455194074477, 9.380309813302512, 11.18640199295418, 9.998434093697302, 5.64957887766721, 7.462864107673047, 8.375717111362961, 10.962178311902413, 7.124427027940266, 7.569477294994085, 8.816247140951113, 9.150984382641052), # 0
(9.637788873635953, 10.602109249460566, 9.999623864394273, 11.925259655897909, 10.660482607453627, 6.0227704512766005, 7.955044094274649, 8.927124701230275, 11.686041587399236, 7.59416524609887, 8.069573044721038, 9.398189989465838, 9.755624965391739), # 1
(10.236101416163518, 11.256093307603763, 10.616476113985344, 12.66117786839663, 11.320133352749538, 6.3944732061224006, 8.445273314329269, 9.476325446227955, 12.407016252379588, 8.062044795036982, 8.567681667797364, 9.9778187736955, 10.357856690777442), # 2
(10.830164027663812, 11.904876903485604, 11.228419564775738, 13.391237533557733, 11.974791016803424, 6.763213120653203, 8.93160655496632, 10.021142083490112, 13.122243289657968, 8.526208857167125, 9.061827141289289, 10.55283423287483, 10.955291051257605), # 3
(11.417645067148767, 12.545865358714394, 11.833007219465467, 14.112519554488625, 12.621860286833686, 7.127516173317602, 9.412098603315226, 10.559397350150848, 13.828863682048873, 8.984800614901822, 9.550033442263036, 11.120937106238575, 11.54553953929167), # 4
(11.996212893630318, 13.176463994898459, 12.427792080754532, 14.822104834296708, 13.258745850058704, 7.485908342564186, 9.884804246505404, 11.088913983344266, 14.524018412366805, 9.435963250653593, 10.030324547784838, 11.679828133021466, 12.126213647339089), # 5
(12.5635358661204, 13.794078133646101, 13.010327151342958, 15.517074276089375, 13.882852393696878, 7.836915606841555, 10.347778271666273, 11.60751472020448, 15.204848463426268, 9.877839946834966, 10.500724434920908, 12.227208052458254, 12.694924867859292), # 6
(13.117282343630944, 14.396113096565637, 13.578165433930742, 16.194508782974033, 14.491584604966597, 8.179063944598298, 10.799075465927253, 12.113022297865593, 15.868494818041759, 10.308573885858456, 10.959257080737483, 12.760777603783673, 13.249284693311735), # 7
(13.655120685173882, 14.979974205265378, 14.128859931217914, 16.85148925805807, 15.082347171086255, 8.510879334283002, 11.236750616417757, 12.603259453461705, 16.512098459027772, 10.726308250136594, 11.403946462300778, 13.278237526232465, 13.786904616155851), # 8
(14.174719249761154, 15.543066781353641, 14.659963645904467, 17.485096604448906, 15.652544779274237, 8.830887754344271, 11.658858510267216, 13.076048924126933, 17.132800369198815, 11.129186222081895, 11.83281655667702, 13.777288559039365, 14.305396128851092), # 9
(14.673746396404677, 16.082796146438728, 15.169029580690424, 18.092411725253918, 16.199582116748942, 9.137615183230693, 12.063453934605038, 13.52921344699538, 17.727741531369386, 11.515350984106886, 12.243891340932432, 14.255631441439114, 14.802370723856898), # 10
(15.149870484116411, 16.596567622128973, 15.653610738275788, 18.670515523580516, 16.72086387072876, 9.429587599390864, 12.44859167656065, 13.960575759201147, 18.294062928353988, 11.882945718624095, 12.635194792133248, 14.710966912666459, 15.2754398936327), # 11
(15.600759871908263, 17.081786530032655, 16.111260121360573, 19.216488902536103, 17.21379472843208, 9.705330981273365, 12.812326523263462, 14.367958597878339, 18.82890554296712, 12.23011360804603, 13.004750887345683, 15.140995711956123, 15.722215130637963), # 12
(16.02408291879218, 17.535858191758116, 16.539530732644792, 19.727412765228078, 17.675779377077284, 9.963371307326803, 13.152713261842901, 14.749184700161067, 19.329410358023278, 12.554997834785228, 13.350583603635965, 15.543418578542857, 16.140307927332124), # 13
(16.41750798378009, 17.95618792891366, 16.935975574828465, 20.20036801476383, 18.10422250388278, 10.202234555999762, 13.46780667942839, 15.102076803183444, 19.79271835633696, 12.855741581254202, 13.670716918070312, 15.915936251661408, 16.527329776174614), # 14
(16.77870342588394, 18.34018106310759, 17.298147650611575, 20.632435554250776, 18.496528796066954, 10.420446705740842, 13.755661563149326, 15.424457644079562, 20.215970520722674, 13.130488029865482, 13.963174807714955, 16.256249470546507, 16.880892169624886), # 15
(17.10533760411564, 18.685242915948237, 17.623599962694165, 21.02069628679629, 18.8501029408482, 10.616533734998628, 14.014332700135158, 15.71414995998353, 20.596307833994917, 13.377380363031593, 14.225981249636122, 16.56205897443289, 17.198606600142384), # 16
(17.395078877487137, 18.988778809043904, 17.909885513776235, 21.362231115507804, 19.162349625444907, 10.789021622221714, 14.24187487751528, 15.968976488029472, 20.930871278968173, 13.594561763165041, 14.457160220900038, 16.8310655025553, 17.47808456018655), # 17
(17.645595605010367, 19.248194064002895, 18.154557306557784, 21.654120943492703, 19.43067353707546, 10.936436345858706, 14.436342882419133, 16.18675996535147, 21.216801838456973, 13.780175412678366, 14.654735698572916, 17.060969794148487, 17.716937542216822), # 18
(17.85455614569726, 19.46089400243354, 18.355168343738843, 21.893446673858367, 19.65247936295826, 11.057303884358175, 14.59579150197611, 16.36532312908364, 21.4512404952758, 13.93236449398409, 14.81673165972098, 17.249472588447173, 17.912777038692653), # 19
(18.01962885855975, 19.624283945944132, 18.509271628019405, 22.077289209712237, 19.8251717903117, 11.150150216168733, 14.718275523315652, 16.50248871636009, 21.631328232239156, 14.049272189494726, 14.94117208141047, 17.394274624686105, 18.063214542073485), # 20
(18.13848210260976, 19.735769216143005, 18.614420162099496, 22.202729454161673, 19.94615550635416, 11.213501319738963, 14.801849733567167, 16.596079464314922, 21.754206032161537, 14.1290416816228, 15.026080940707608, 17.49307664210003, 18.165861544818743), # 21
(18.20878423685924, 19.792755134638462, 18.668166948679115, 22.266848310314106, 20.012835198304035, 11.245883173517461, 14.844568919860079, 16.643918110082247, 21.81701487785745, 14.169816152780836, 15.069482214678613, 17.54357937992368, 18.218329539387888), # 22
(18.23470805401675, 19.799502469135803, 18.674861728395065, 22.274875462962967, 20.029917700858675, 11.25, 14.84964720406681, 16.64908888888889, 21.824867222222224, 14.17462609053498, 15.074924466891131, 17.549815637860082, 18.225), # 23
(18.253822343461476, 19.79556666666667, 18.673766666666666, 22.273887500000004, 20.039593704506736, 11.25, 14.8468568627451, 16.6419, 21.823815, 14.17167111111111, 15.074324242424245, 17.548355555555556, 18.225), # 24
(18.272533014380844, 19.78780864197531, 18.671604938271606, 22.27193287037037, 20.049056902070106, 11.25, 14.841358024691358, 16.62777777777778, 21.82173611111111, 14.16585390946502, 15.073134118967452, 17.545473251028806, 18.225), # 25
(18.290838634286462, 19.776346913580248, 18.668406172839507, 22.269033796296295, 20.05830696315799, 11.25, 14.833236092955698, 16.60698888888889, 21.81865722222222, 14.157271275720165, 15.07136487093154, 17.54120823045268, 18.225), # 26
(18.308737770689945, 19.7613, 18.6642, 22.265212499999997, 20.067343557379587, 11.25, 14.822576470588237, 16.579800000000002, 21.814605, 14.146019999999998, 15.069027272727272, 17.535600000000002, 18.225), # 27
(18.3262289911029, 19.742786419753084, 18.659016049382718, 22.260491203703705, 20.076166354344124, 11.25, 14.809464560639071, 16.54647777777778, 21.809606111111112, 14.132196872427985, 15.066132098765433, 17.528688065843625, 18.225), # 28
(18.34331086303695, 19.720924691358025, 18.652883950617287, 22.25489212962963, 20.084775023660796, 11.25, 14.793985766158318, 16.507288888888887, 21.803687222222223, 14.115898683127574, 15.06269012345679, 17.520511934156378, 18.225), # 29
(18.359981954003697, 19.695833333333333, 18.645833333333332, 22.2484375, 20.093169234938827, 11.25, 14.776225490196078, 16.4625, 21.796875, 14.097222222222223, 15.058712121212121, 17.51111111111111, 18.225), # 30
(18.376240831514746, 19.667630864197534, 18.637893827160497, 22.241149537037035, 20.101348657787415, 11.25, 14.756269135802471, 16.412377777777778, 21.78919611111111, 14.07626427983539, 15.054208866442199, 17.500525102880662, 18.225), # 31
(18.392086063081717, 19.636435802469137, 18.629095061728393, 22.233050462962964, 20.10931296181577, 11.25, 14.734202106027599, 16.357188888888892, 21.780677222222224, 14.053121646090535, 15.0491911335578, 17.48879341563786, 18.225), # 32
(18.407516216216216, 19.602366666666665, 18.619466666666668, 22.2241625, 20.117061816633115, 11.25, 14.710109803921569, 16.2972, 21.771345, 14.027891111111112, 15.043669696969696, 17.475955555555554, 18.225), # 33
(18.422529858429858, 19.56554197530864, 18.609038271604938, 22.21450787037037, 20.12459489184864, 11.25, 14.684077632534496, 16.232677777777777, 21.761226111111114, 14.000669465020577, 15.037655331088663, 17.462051028806584, 18.225), # 34
(18.437125557234253, 19.52608024691358, 18.597839506172843, 22.204108796296293, 20.131911857071568, 11.25, 14.656190994916486, 16.163888888888888, 21.750347222222224, 13.971553497942386, 15.031158810325476, 17.447119341563788, 18.225), # 35
(18.45130188014101, 19.484099999999998, 18.5859, 22.192987499999997, 20.139012381911105, 11.25, 14.626535294117646, 16.0911, 21.738735, 13.94064, 15.024190909090908, 17.431200000000004, 18.225), # 36
(18.46505739466174, 19.43971975308642, 18.57324938271605, 22.181166203703704, 20.145896135976457, 11.25, 14.595195933188089, 16.014577777777777, 21.72641611111111, 13.908025761316873, 15.016762401795738, 17.414332510288066, 18.225), # 37
(18.47839066830806, 19.39305802469136, 18.559917283950615, 22.168667129629632, 20.152562788876843, 11.25, 14.562258315177923, 15.934588888888891, 21.713417222222223, 13.873807572016462, 15.00888406285073, 17.396556378600824, 18.225), # 38
(18.491300268591576, 19.34423333333333, 18.545933333333334, 22.1555125, 20.159012010221467, 11.25, 14.527807843137257, 15.8514, 21.699765000000003, 13.838082222222223, 15.000566666666668, 17.37791111111111, 18.225), # 39
(18.503784763023894, 19.293364197530863, 18.531327160493827, 22.14172453703704, 20.165243469619533, 11.25, 14.491929920116196, 15.765277777777781, 21.685486111111114, 13.800946502057615, 14.99182098765432, 17.358436213991773, 18.225), # 40
(18.51584271911663, 19.24056913580247, 18.51612839506173, 22.127325462962965, 20.171256836680264, 11.25, 14.454709949164851, 15.67648888888889, 21.67060722222222, 13.76249720164609, 14.982657800224468, 17.338171193415636, 18.225), # 41
(18.527472704381402, 19.18596666666667, 18.500366666666668, 22.112337500000002, 20.177051781012857, 11.25, 14.416233333333333, 15.5853, 21.655155000000004, 13.72283111111111, 14.97308787878788, 17.317155555555555, 18.225), # 42
(18.538673286329807, 19.12967530864198, 18.484071604938272, 22.096782870370372, 20.182627972226527, 11.25, 14.37658547567175, 15.491977777777779, 21.63915611111111, 13.682045020576133, 14.96312199775533, 17.295428806584365, 18.225), # 43
(18.54944303247347, 19.071813580246914, 18.467272839506176, 22.0806837962963, 20.18798507993048, 11.25, 14.335851779230211, 15.396788888888892, 21.62263722222222, 13.64023572016461, 14.952770931537597, 17.2730304526749, 18.225), # 44
(18.55978051032399, 19.0125, 18.45, 22.064062500000002, 20.193122773733933, 11.25, 14.294117647058824, 15.3, 21.605625, 13.597500000000002, 14.942045454545454, 17.25, 18.225), # 45
(18.569684287392985, 18.951853086419753, 18.432282716049382, 22.046941203703703, 20.198040723246088, 11.25, 14.251468482207699, 15.20187777777778, 21.588146111111108, 13.553934650205761, 14.930956341189674, 17.226376954732512, 18.225), # 46
(18.579152931192063, 18.88999135802469, 18.41415061728395, 22.02934212962963, 20.202738598076163, 11.25, 14.207989687726945, 15.102688888888888, 21.570227222222226, 13.50963646090535, 14.919514365881032, 17.20220082304527, 18.225), # 47
(18.588185009232834, 18.827033333333333, 18.395633333333333, 22.0112875, 20.20721606783336, 11.25, 14.163766666666668, 15.0027, 21.551895000000002, 13.464702222222222, 14.907730303030302, 17.177511111111112, 18.225), # 48
(18.596779089026917, 18.763097530864197, 18.376760493827163, 21.99279953703704, 20.211472802126895, 11.25, 14.118884822076978, 14.902177777777778, 21.53317611111111, 13.419228724279836, 14.895614927048262, 17.152347325102884, 18.225), # 49
(18.604933738085908, 18.698302469135808, 18.357561728395066, 21.973900462962963, 20.21550847056597, 11.25, 14.073429557007989, 14.801388888888889, 21.514097222222222, 13.373312757201646, 14.883179012345678, 17.126748971193418, 18.225), # 50
(18.61264752392144, 18.63276666666667, 18.338066666666666, 21.9546125, 20.219322742759797, 11.25, 14.027486274509805, 14.7006, 21.494685000000004, 13.32705111111111, 14.870433333333335, 17.10075555555556, 18.225), # 51
(18.619919014045102, 18.56660864197531, 18.318304938271606, 21.934957870370372, 20.222915288317584, 11.25, 13.981140377632535, 14.600077777777777, 21.47496611111111, 13.280540576131688, 14.857388664421999, 17.074406584362144, 18.225), # 52
(18.626746775968517, 18.49994691358025, 18.29830617283951, 21.914958796296297, 20.226285776848552, 11.25, 13.93447726942629, 14.50008888888889, 21.454967222222226, 13.233877942386831, 14.844055780022448, 17.04774156378601, 18.225), # 53
(18.63312937720329, 18.432900000000004, 18.2781, 21.8946375, 20.229433877961906, 11.25, 13.887582352941177, 14.400899999999998, 21.434715, 13.18716, 14.830445454545453, 17.0208, 18.225), # 54
(18.63906538526104, 18.365586419753086, 18.25771604938272, 21.874016203703704, 20.232359261266843, 11.25, 13.840541031227307, 14.302777777777777, 21.414236111111112, 13.140483539094651, 14.816568462401795, 16.993621399176956, 18.225), # 55
(18.64455336765337, 18.298124691358026, 18.237183950617286, 21.85311712962963, 20.235061596372585, 11.25, 13.793438707334786, 14.20598888888889, 21.393557222222224, 13.09394534979424, 14.802435578002246, 16.96624526748971, 18.225), # 56
(18.649591891891887, 18.230633333333333, 18.216533333333334, 21.8319625, 20.23754055288834, 11.25, 13.746360784313726, 14.110800000000001, 21.372705, 13.047642222222223, 14.788057575757577, 16.93871111111111, 18.225), # 57
(18.654179525488225, 18.163230864197534, 18.195793827160493, 21.810574537037034, 20.239795800423316, 11.25, 13.699392665214235, 14.017477777777778, 21.35170611111111, 13.001670946502058, 14.773445230078567, 16.91105843621399, 18.225), # 58
(18.658314835953966, 18.096035802469135, 18.174995061728396, 21.788975462962963, 20.24182700858672, 11.25, 13.65261975308642, 13.92628888888889, 21.330587222222224, 12.956128312757203, 14.758609315375981, 16.883326748971193, 18.225), # 59
(18.661996390800738, 18.02916666666667, 18.154166666666665, 21.767187500000002, 20.243633846987766, 11.25, 13.606127450980392, 13.8375, 21.309375000000003, 12.911111111111111, 14.743560606060607, 16.855555555555558, 18.225), # 60
(18.665222757540146, 17.962741975308646, 18.13333827160494, 21.74523287037037, 20.24521598523566, 11.25, 13.560001161946259, 13.751377777777778, 21.288096111111113, 12.866716131687244, 14.728309876543209, 16.82778436213992, 18.225), # 61
(18.66799250368381, 17.89688024691358, 18.112539506172844, 21.7231337962963, 20.246573092939624, 11.25, 13.514326289034132, 13.66818888888889, 21.266777222222224, 12.823040164609054, 14.712867901234567, 16.80005267489712, 18.225), # 62
(18.670304196743327, 17.831699999999998, 18.0918, 21.7009125, 20.24770483970884, 11.25, 13.469188235294117, 13.5882, 21.245445, 12.78018, 14.697245454545456, 16.7724, 18.225), # 63
(18.672156404230314, 17.767319753086422, 18.071149382716047, 21.678591203703704, 20.24861089515255, 11.25, 13.424672403776325, 13.511677777777779, 21.22412611111111, 12.738232427983538, 14.681453310886642, 16.7448658436214, 18.225), # 64
(18.67354769365639, 17.703858024691357, 18.05061728395062, 21.65619212962963, 20.24929092887994, 11.25, 13.380864197530865, 13.438888888888888, 21.202847222222225, 12.697294238683126, 14.665502244668913, 16.717489711934153, 18.225), # 65
(18.674476632533153, 17.641433333333335, 18.030233333333335, 21.6337375, 20.249744610500233, 11.25, 13.337849019607843, 13.3701, 21.181635000000004, 12.657462222222222, 14.649403030303029, 16.690311111111114, 18.225), # 66
(18.674941788372227, 17.580164197530863, 18.010027160493827, 21.611249537037036, 20.249971609622634, 11.25, 13.29571227305737, 13.30557777777778, 21.16051611111111, 12.618833168724281, 14.633166442199778, 16.6633695473251, 18.225), # 67
(18.674624906065485, 17.519847550776582, 17.989930709876543, 21.588555132850242, 20.249780319535223, 11.24979122085048, 13.254327350693364, 13.245018930041153, 21.13935812757202, 12.5813167949649, 14.616514779372677, 16.636554039419536, 18.22477527006173), # 68
(18.671655072463768, 17.458641935483872, 17.969379166666666, 21.564510326086953, 20.248039215686273, 11.248140740740741, 13.212482726423904, 13.185177777777778, 21.11723611111111, 12.543851503267971, 14.597753110047847, 16.608994152046783, 18.222994791666668), # 69
(18.665794417606012, 17.39626642771804, 17.948283179012343, 21.538956823671498, 20.244598765432098, 11.244890260631001, 13.169988242210465, 13.125514403292183, 21.09402520576132, 12.506255144032922, 14.576667995746943, 16.580560970327056, 18.219478202160495), # 70
(18.657125389157272, 17.332758303464754, 17.92665015432099, 21.51193230676329, 20.239502541757446, 11.240092455418381, 13.12686298717018, 13.066048559670783, 21.06975997942387, 12.46852864681675, 14.553337267410951, 16.551275286982886, 18.21427179783951), # 71
(18.64573043478261, 17.268154838709677, 17.9044875, 21.48347445652174, 20.23279411764706, 11.2338, 13.083126050420168, 13.0068, 21.044475000000002, 12.43067294117647, 14.527838755980863, 16.52115789473684, 18.207421875), # 72
(18.631692002147076, 17.20249330943847, 17.88180262345679, 21.45362095410628, 20.224517066085692, 11.226065569272976, 13.038796521077565, 12.947788477366256, 21.01820483539095, 12.392688956669087, 14.50025029239766, 16.490229586311454, 18.198974729938275), # 73
(18.61509253891573, 17.1358109916368, 17.858602932098762, 21.42240948067633, 20.214714960058096, 11.216941838134431, 12.9938934882595, 12.889033744855967, 20.990984053497943, 12.354577622851611, 14.470649707602341, 16.45851115442928, 18.18897665895062), # 74
(18.59601449275362, 17.06814516129032, 17.83489583333333, 21.389877717391304, 20.203431372549023, 11.206481481481482, 12.9484360410831, 12.830555555555556, 20.96284722222222, 12.316339869281046, 14.439114832535884, 16.426023391812866, 18.177473958333334), # 75
(18.57454031132582, 16.99953309438471, 17.8106887345679, 21.35606334541063, 20.19070987654321, 11.19473717421125, 12.902443268665492, 12.772373662551441, 20.93382890946502, 12.277976625514404, 14.405723498139285, 16.392787091184747, 18.164512924382716), # 76
(18.55075244229737, 16.93001206690562, 17.785989043209874, 21.32100404589372, 20.176594045025414, 11.18176159122085, 12.855934260123803, 12.714507818930043, 20.90396368312757, 12.239488821108692, 14.370553535353537, 16.358823045267492, 18.150139853395064), # 77
(18.524733333333334, 16.859619354838713, 17.760804166666667, 21.2847375, 20.16112745098039, 11.167607407407406, 12.808928104575164, 12.65697777777778, 20.87328611111111, 12.200877385620915, 14.333682775119618, 16.324152046783627, 18.134401041666667), # 78
(18.496565432098766, 16.788392234169656, 17.735141512345677, 21.24730138888889, 20.144353667392885, 11.152327297668037, 12.761443891136702, 12.59980329218107, 20.84183076131687, 12.162143248608086, 14.29518904837852, 16.28879488845571, 18.117342785493825), # 79
(18.466331186258724, 16.71636798088411, 17.70900848765432, 21.208733393719807, 20.126316267247642, 11.135973936899862, 12.713500708925546, 12.543004115226339, 20.809632201646092, 12.123287339627208, 14.255150186071239, 16.252772363006283, 18.09901138117284), # 80
(18.434113043478263, 16.643583870967742, 17.682412499999998, 21.169071195652176, 20.10705882352941, 11.118599999999999, 12.665117647058823, 12.486600000000001, 20.776725, 12.084310588235295, 14.213644019138757, 16.216105263157896, 18.079453124999997), # 81
(18.399993451422436, 16.570077180406216, 17.655360956790126, 21.12835247584541, 20.086624909222948, 11.10025816186557, 12.616313794653665, 12.430610699588478, 20.743143724279836, 12.045213923989348, 14.170748378522063, 16.178814381633096, 18.058714313271608), # 82
(18.364054857756308, 16.495885185185184, 17.6278612654321, 21.086614915458934, 20.065058097313, 11.08100109739369, 12.567108240827196, 12.37505596707819, 20.70892294238683, 12.00599827644638, 14.12654109516215, 16.14092051115443, 18.036841242283952), # 83
(18.326379710144927, 16.421045161290323, 17.599920833333332, 21.043896195652174, 20.042401960784314, 11.060881481481482, 12.517520074696545, 12.319955555555556, 20.674097222222223, 11.9666645751634, 14.0811, 16.102444444444444, 18.013880208333333), # 84
(18.287050456253354, 16.345594384707287, 17.571547067901232, 21.000233997584544, 20.01870007262164, 11.039951989026063, 12.467568385378843, 12.265329218106997, 20.63870113168724, 11.92721374969741, 14.034502923976609, 16.06340697422569, 17.989877507716052), # 85
(18.246149543746643, 16.269570131421744, 17.54274737654321, 20.955666002415462, 19.99399600580973, 11.018265294924555, 12.417272261991217, 12.21119670781893, 20.60276923868313, 11.887646729605423, 13.986827698032961, 16.02382889322071, 17.964879436728395), # 86
(18.203759420289852, 16.193009677419354, 17.513529166666665, 20.910229891304347, 19.968333333333337, 10.995874074074074, 12.366650793650793, 12.157577777777778, 20.566336111111116, 11.847964444444443, 13.938152153110048, 15.983730994152046, 17.938932291666667), # 87
(18.159962533548043, 16.11595029868578, 17.483899845679012, 20.86396334541063, 19.941755628177198, 10.972831001371743, 12.315723069474704, 12.104492181069958, 20.52943631687243, 11.808167823771482, 13.888554120148857, 15.943134069742257, 17.912082368827164), # 88
(18.11484133118626, 16.03842927120669, 17.453866820987656, 20.81690404589372, 19.91430646332607, 10.94918875171468, 12.264508178580074, 12.051959670781894, 20.492104423868312, 11.76825779714355, 13.838111430090379, 15.902058912713883, 17.884375964506173), # 89
(18.068478260869565, 15.960483870967742, 17.423437500000002, 20.769089673913047, 19.886029411764707, 10.925, 12.213025210084034, 12.0, 20.454375000000002, 11.728235294117647, 13.786901913875598, 15.860526315789475, 17.855859375), # 90
(18.020955770263015, 15.8821513739546, 17.392619290123456, 20.720557910628024, 19.85696804647785, 10.900317421124829, 12.161293253103711, 11.9486329218107, 20.41628261316873, 11.688101244250786, 13.735003402445509, 15.818557071691574, 17.826578896604936), # 91
(17.97235630703167, 15.80346905615293, 17.361419598765433, 20.671346437198068, 19.827165940450254, 10.875193689986283, 12.109331396756236, 11.897878189300412, 20.377861831275723, 11.647856577099976, 13.682493726741095, 15.776171973142736, 17.796580825617283), # 92
(17.92276231884058, 15.724474193548389, 17.329845833333334, 20.621492934782612, 19.796666666666667, 10.84968148148148, 12.057158730158731, 11.847755555555556, 20.339147222222223, 11.607502222222221, 13.62945071770335, 15.733391812865497, 17.76591145833333), # 93
(17.872256253354806, 15.645204062126643, 17.29790540123457, 20.571035084541062, 19.765513798111837, 10.823833470507545, 12.00479434242833, 11.798284773662553, 20.300173353909464, 11.567039109174534, 13.575952206273259, 15.690237383582414, 17.734617091049383), # 94
(17.820920558239397, 15.56569593787336, 17.265605709876546, 20.52001056763285, 19.733750907770517, 10.797702331961592, 11.95225732268216, 11.749485596707821, 20.260974794238685, 11.526468167513919, 13.522076023391813, 15.646729478016026, 17.70274402006173), # 95
(17.76883768115942, 15.485987096774197, 17.23295416666667, 20.468457065217393, 19.701421568627453, 10.77134074074074, 11.899566760037347, 11.701377777777779, 20.221586111111108, 11.485790326797385, 13.4679, 15.602888888888891, 17.67033854166667), # 96
(17.716090069779927, 15.406114814814819, 17.199958179012345, 20.416412258454105, 19.668569353667394, 10.744801371742112, 11.846741743611025, 11.65398106995885, 20.182041872427984, 11.445006516581941, 13.413501967038808, 15.558736408923545, 17.637446952160495), # 97
(17.66276017176597, 15.326116367980884, 17.166625154320986, 20.363913828502415, 19.635237835875095, 10.718136899862827, 11.793801362520316, 11.607315226337448, 20.142376646090533, 11.404117666424595, 13.35895975544923, 15.514292830842535, 17.604115547839505), # 98
(17.608930434782607, 15.246029032258065, 17.1329625, 20.31099945652174, 19.601470588235298, 10.6914, 11.740764705882354, 11.5614, 20.102625, 11.363124705882353, 13.304351196172249, 15.469578947368422, 17.570390625), # 99
(17.5546833064949, 15.165890083632016, 17.09897762345679, 20.257706823671498, 19.567311183732752, 10.664643347050754, 11.687650862814262, 11.516255144032922, 20.062821502057616, 11.322028564512225, 13.249754120148857, 15.42461555122374, 17.536318479938274), # 100
(17.500101234567904, 15.085736798088412, 17.064677932098768, 20.204073611111113, 19.532803195352216, 10.637919615912208, 11.634478922433171, 11.471900411522633, 20.02300072016461, 11.280830171871218, 13.195246358320043, 15.379423435131034, 17.501945408950615), # 101
(17.44526666666667, 15.005606451612904, 17.030070833333333, 20.1501375, 19.497990196078433, 10.611281481481482, 11.58126797385621, 11.428355555555555, 19.98319722222222, 11.239530457516341, 13.140905741626794, 15.334023391812867, 17.467317708333336), # 102
(17.390262050456254, 14.92553632019116, 16.9951637345679, 20.095936171497584, 19.462915758896152, 10.584781618655693, 11.528037106200506, 11.385640329218107, 19.943445576131687, 11.1981303510046, 13.086810101010101, 15.28843621399177, 17.432481674382714), # 103
(17.335169833601718, 14.845563679808842, 16.959964043209876, 20.041507306763286, 19.427623456790123, 10.558472702331962, 11.474805408583187, 11.343774485596708, 19.90378034979424, 11.156630781893005, 13.03303726741095, 15.242682694390297, 17.397483603395063), # 104
(17.280072463768114, 14.765725806451613, 16.924479166666668, 19.98688858695652, 19.392156862745097, 10.532407407407408, 11.421591970121383, 11.302777777777779, 19.86423611111111, 11.115032679738563, 12.979665071770334, 15.196783625730996, 17.362369791666666), # 105
(17.225052388620504, 14.686059976105138, 16.888716512345678, 19.932117693236716, 19.356559549745825, 10.50663840877915, 11.36841587993222, 11.262669958847736, 19.82484742798354, 11.07333697409828, 12.92677134502924, 15.15075980073641, 17.327186535493826), # 106
(17.17019205582394, 14.606603464755079, 16.852683487654325, 19.877232306763286, 19.32087509077705, 10.48121838134431, 11.31529622713283, 11.223470781893006, 19.78564886831276, 11.03154459452917, 12.874433918128654, 15.104632012129088, 17.29198013117284), # 107
(17.11557391304348, 14.5273935483871, 16.8163875, 19.822270108695655, 19.28514705882353, 10.4562, 11.262252100840335, 11.185200000000002, 19.746675000000003, 10.989656470588237, 12.82273062200957, 15.05842105263158, 17.256796875000003), # 108
(17.061280407944178, 14.448467502986858, 16.779835956790127, 19.767268780193234, 19.249419026870008, 10.431635939643346, 11.209302590171871, 11.147877366255145, 19.707960390946504, 10.947673531832486, 12.771739287612972, 15.012147714966428, 17.221683063271605), # 109
(17.007393988191087, 14.369862604540026, 16.743036265432103, 19.71226600241546, 19.213734567901238, 10.407578875171467, 11.15646678424456, 11.111522633744855, 19.669539609053498, 10.90559670781893, 12.72153774587985, 14.965832791856185, 17.18668499228395), # 110
(16.953997101449275, 14.29161612903226, 16.705995833333336, 19.65729945652174, 19.178137254901962, 10.384081481481482, 11.103763772175537, 11.076155555555555, 19.631447222222224, 10.863426928104575, 12.672203827751195, 14.919497076023394, 17.151848958333336), # 111
(16.90117219538379, 14.213765352449222, 16.66872206790124, 19.602406823671497, 19.142670660856936, 10.361196433470509, 11.051212643081925, 11.041795884773663, 19.593717798353907, 10.821165122246429, 12.623815364167996, 14.873161360190599, 17.11722125771605), # 112
(16.84890760266548, 14.136477513814715, 16.631312090853726, 19.547700988485673, 19.10731622431267, 10.338965584586125, 10.998946734582185, 11.00853462380509, 19.556483060265517, 10.778948525902914, 12.57646303107516, 14.826947285707972, 17.0827990215178), # 113
(16.796665616220118, 14.060514930345965, 16.594282215038913, 19.493620958299207, 19.071708038219388, 10.317338295353823, 10.947632775139043, 10.976780267109216, 19.52031426428351, 10.73756730224301, 12.530239806803754, 14.781441909803354, 17.048295745488062), # 114
(16.744292825407193, 13.985904957629483, 16.55765447887317, 19.440152109327204, 19.035733820199482, 10.296258322497776, 10.89730737034481, 10.946524777701677, 19.485224961603823, 10.697085590378538, 12.485078120568769, 14.736667648605932, 17.013611936988678), # 115
(16.691723771827743, 13.912538906325063, 16.521357941970972, 19.38719907047953, 18.999339347490803, 10.275675979116777, 10.847888671550209, 10.917684563218188, 19.451126410610094, 10.657428045209185, 12.440890676288666, 14.692541755477222, 16.978693067560602), # 116
(16.63889299708279, 13.840308087092497, 16.485321663946774, 19.33466647066604, 18.9624703973312, 10.255541578309604, 10.799294830105955, 10.890176031294454, 19.417929869685967, 10.618519321634633, 12.39759017788191, 14.64898148377875, 16.943484608744804), # 117
(16.58573504277338, 13.769103810591583, 16.44947470441506, 19.2824589387966, 18.925072746958516, 10.235805433175049, 10.751443997362767, 10.863915589566174, 19.385546597215082, 10.580284074554568, 12.355089329266963, 14.60590408687203, 16.907932032082243), # 118
(16.532184450500534, 13.698817387482112, 16.413746122990304, 19.23048110378107, 18.887092173610597, 10.2164178568119, 10.70425432467136, 10.838819645669062, 19.353887851581078, 10.54264695886867, 12.31330083436229, 14.563226818118581, 16.87198080911388), # 119
(16.47817576186529, 13.629340128423884, 16.37806497928697, 19.17863759452931, 18.848474454525295, 10.197329162318939, 10.657643963382455, 10.814804607238818, 19.322864891167605, 10.50553262947663, 12.272137397086349, 14.520866930879935, 16.835576411380675), # 120
(16.423643518468683, 13.560563344076693, 16.342360332919537, 19.12683303995118, 18.809165366940455, 10.178489662794956, 10.611531064846766, 10.791786881911152, 19.2923889743583, 10.468865741278133, 12.23151172135761, 14.4787416785176, 16.79866431042359), # 121
(16.36852226191174, 13.49237834510033, 16.30656124350248, 19.07497206895654, 18.76911068809392, 10.159849671338735, 10.565833780415012, 10.769682877321769, 19.2623713595368, 10.43257094917286, 12.191336511094532, 14.436768314393102, 16.761189977783587), # 122
(16.312746533795494, 13.424676442154594, 16.270596770650265, 19.02295931045525, 18.728256195223544, 10.141359501049065, 10.52047026143791, 10.74840900110637, 19.232723305086758, 10.396572908060497, 12.151524470215579, 14.394864091867959, 16.72309888500163), # 123
(16.256250875720976, 13.357348945899277, 16.234395973977367, 18.970699393357176, 18.68654766556717, 10.12296946502473, 10.475358659266176, 10.727881660900668, 19.20335606939181, 10.36079627284073, 12.111988302639215, 14.352946264303695, 16.68433650361868), # 124
(16.198969829289226, 13.290287166994178, 16.197887913098263, 18.91809694657217, 18.643930876362642, 10.104629876364521, 10.43041712525053, 10.708017264340365, 19.174180910835588, 10.32516569841324, 12.072640712283903, 14.310932085061827, 16.644848305175692), # 125
(16.14083793610127, 13.22338241609909, 16.16100164762742, 18.8650565990101, 18.60035160484781, 10.086291048167222, 10.385563810741687, 10.688732219061166, 19.145109087801753, 10.289605839677717, 12.033394403068103, 14.268738807503881, 16.604579761213643), # 126
(16.08178973775815, 13.156526003873804, 16.123666237179307, 18.81148297958082, 18.555755628260517, 10.067903293531618, 10.34071686709037, 10.669942932698781, 19.116051858673934, 10.254041351533843, 11.994162078910282, 14.226283684991369, 16.56347634327348), # 127
(16.021759775860883, 13.089609240978122, 16.08581074136841, 18.7572807171942, 18.51008872383862, 10.0494169255565, 10.295794445647289, 10.651565812888913, 19.086920481835772, 10.218396888881303, 11.954856443728904, 14.183483970885819, 16.521483522896165), # 128
(15.960682592010507, 13.022523438071834, 16.047364219809193, 18.702354440760086, 18.46329666881996, 10.03078225734065, 10.250714697763163, 10.633517267267269, 19.057626215670915, 10.182597106619781, 11.915390201442428, 14.140256918548745, 16.478546771622668), # 129
(15.89849272780806, 12.955159905814739, 16.008255732116123, 18.646608779188355, 18.415325240442385, 10.011949601982854, 10.205395774788713, 10.61571370346955, 19.028080318563003, 10.146566659648963, 11.87567605596932, 14.096519781341675, 16.434611560993947), # 130
(15.83512472485457, 12.887409954866628, 15.968414337903685, 18.589948361388856, 18.36612021594374, 9.992869272581904, 10.159755828074656, 10.59807152913147, 18.998194048895677, 10.110230202868534, 11.835626711228041, 14.052189812626125, 16.38962336255096), # 131
(15.770513124751067, 12.8191648958873, 15.927769096786342, 18.532277816271456, 18.315627372561877, 9.973491582236585, 10.113713008971706, 10.580507151888732, 18.967878665052577, 10.073512391178177, 11.795154871137056, 14.007184265763614, 16.343527647834676), # 132
(15.704592469098595, 12.750316039536544, 15.88624906837857, 18.473501772746012, 18.263792487534637, 9.95376684404568, 10.06718546883058, 10.562936979377039, 18.93704542541735, 10.036337879477578, 11.754173239614829, 13.961420394115667, 16.296269888386057), # 133
(15.63729729949817, 12.68075469647416, 15.843783312294848, 18.413524859722386, 18.210561338099865, 9.933645371107978, 10.020091359002002, 10.545277419232098, 18.905605588373632, 9.998631322666423, 11.712594520579822, 13.914815451043799, 16.24779555574605), # 134
(15.568562157550836, 12.610372177359944, 15.800300888149636, 18.352251706110444, 18.15587970149542, 9.913077476522266, 9.972348830836681, 10.527444879089616, 18.873470412305064, 9.960317375644397, 11.670331417950496, 13.867286689909534, 16.198050121455637), # 135
(15.498321584857623, 12.539059792853687, 15.755730855557415, 18.28958694082003, 18.09969335495913, 9.892013473387332, 9.923876035685343, 10.509355766585298, 18.840551155595293, 9.92132069331118, 11.627296635645319, 13.818751364074394, 16.146979057055766), # 136
(15.426510123019561, 12.466708853615184, 15.710002274132659, 18.225435192761026, 18.04194807572886, 9.870403674801956, 9.8745911248987, 10.490926489354854, 18.80675907662796, 9.881565930566463, 11.583402877582751, 13.769126726899895, 16.094527834087398), # 137
(15.353062313637686, 12.393210670304235, 15.66304420348983, 18.159701090843274, 17.982589641042455, 9.848198393864935, 9.824412249827468, 10.472073455033982, 18.772005433786706, 9.840977742309924, 11.538562847681254, 13.718330031747561, 16.040641924091503), # 138
(15.277912698313022, 12.31845655358063, 15.614785703243411, 18.092289263976646, 17.921563828137746, 9.825347943675048, 9.773257561822367, 10.452713071258394, 18.73620148545517, 9.799480783441254, 11.492689249859293, 13.66627853197891, 15.985266798609034), # 139
(15.200995818646616, 12.242337814104165, 15.565155833007877, 18.023104341071, 17.858816414252605, 9.801802637331082, 9.721045212234115, 10.432761745663793, 18.699258490016998, 9.756999708860134, 11.445694788035329, 13.612889480955465, 15.928347929180966), # 140
(15.122246216239494, 12.164745762534638, 15.514083652397689, 17.952050951036195, 17.794293176624855, 9.777512787931828, 9.667693352413432, 10.412135885885887, 18.661087705855824, 9.713459173466253, 11.39749216612783, 13.558080132038745, 15.869830787348244), # 141
(15.041598432692682, 12.08557170953184, 15.461498221027327, 17.879033722782097, 17.727939892492355, 9.752428708576069, 9.613120133711027, 10.39075189956038, 18.621600391355297, 9.66878383215929, 11.347994088055255, 13.50176773859027, 15.80966084465184), # 142
(14.958987009607215, 12.004706965755565, 15.407328598511267, 17.803957285218555, 17.659702339092952, 9.726500712362592, 9.557243707477623, 10.368526194322978, 18.580707804899063, 9.622898339838935, 11.297113257736068, 13.443869553971561, 15.747783572632711), # 143
(14.874346488584132, 11.922042841865615, 15.35150384446397, 17.72672626725544, 17.58952629366449, 9.699679112390184, 9.499982225063938, 10.34537517780939, 18.53832120487076, 9.575727351404868, 11.244762379088732, 13.384302831544138, 15.684144442831826), # 144
(14.787611411224459, 11.837470648521778, 15.29395301849992, 17.64724529780261, 17.51735753344482, 9.671914221757634, 9.441253837820689, 10.321215257655316, 18.494351849654016, 9.527195521756779, 11.190854156031712, 13.322984824669524, 15.618688926790139), # 145
(14.69871631912923, 11.750881696383855, 15.23460518023359, 17.565419005769925, 17.443141835671785, 9.643156353563725, 9.380976697098594, 10.295962841496468, 18.448710997632492, 9.477227505794348, 11.135301292483467, 13.259832786709236, 15.551362496048613), # 146
(14.607595753899481, 11.662167296111635, 15.173389389279437, 17.481152020067245, 17.36682497758323, 9.613355820907245, 9.319068954248365, 10.269534336968547, 18.401309907189823, 9.425747958417263, 11.078016492362465, 13.194763971024798, 15.482110622148213), # 147
(14.51418425713624, 11.571218758364918, 15.11023470525195, 17.394348969604433, 17.28835273641701, 9.582462936886982, 9.255448760620729, 10.241846151707264, 18.352059836709653, 9.372681534525205, 11.018912459587169, 13.127695630977726, 15.410878776629895), # 148
(14.418416370440541, 11.477927393803494, 15.045070187765598, 17.304914483291345, 17.207670889410966, 9.550428014601719, 9.190034267566393, 10.21281469334832, 18.30087204457561, 9.317952889017864, 10.957901898076038, 13.058545019929545, 15.337612431034628), # 149
(14.320226635413416, 11.382184513087163, 14.97782489643485, 17.212753190037848, 17.124725213802947, 9.517201367150248, 9.122743626436081, 10.182356369527422, 18.247657789171353, 9.261486676794918, 10.894897511747537, 12.987229391241772, 15.262257056903364), # 150
(14.219549593655895, 11.283881426875716, 14.908427890874176, 17.117769718753795, 17.0394614868308, 9.48273330763135, 9.05349498858051, 10.150387587880278, 18.19232832888052, 9.20320755275606, 10.829812004520129, 12.91366599827593, 15.184758125777073), # 151
(14.116319786769019, 11.182909445828951, 14.836808230698063, 17.019868698349054, 16.951825485732364, 9.446974149143815, 8.982206505350396, 10.116824756042595, 18.134794922086748, 9.143040171800969, 10.762558080312278, 12.837772094393538, 15.105061109196717), # 152
(14.010471756353809, 11.079159880606662, 14.762894975520963, 16.91895475773348, 16.8617629877455, 9.409874204786428, 8.908796328096455, 10.081584281650072, 18.07496882717368, 9.080909188829333, 10.693048443042448, 12.759464932956115, 15.02311147870325), # 153
(13.901940044011312, 10.972524041868644, 14.686617184957365, 16.81493252581694, 16.769219770108045, 9.371383787657978, 8.83318260816941, 10.044582572338422, 18.01276130252496, 9.016739258740834, 10.6211957966291, 12.678661767325185, 14.938854705837642), # 154
(13.790659191342543, 10.86289324027469, 14.607903918621735, 16.707706631509282, 16.674141610057855, 9.331453210857248, 8.75528349691997, 10.005736035743345, 17.948083606524232, 8.950455036435159, 10.5469128449907, 12.595279850862267, 14.852236262140847), # 155
(13.676563739948545, 10.750158786484597, 14.526684236128547, 16.597181703720377, 16.576474284832766, 9.29003278748303, 8.67501714569886, 9.964961079500554, 17.88084699755513, 8.88198117681199, 10.470112292045709, 12.50923643692888, 14.763201619153833), # 156
(13.559588231430352, 10.634211991158162, 14.442887197092272, 16.483262371360087, 16.476163571670632, 9.247072830634105, 8.592301705856794, 9.922174111245749, 17.8109627340013, 8.811242334771014, 10.39070684171259, 12.420448778886547, 14.671696248417557), # 157
(13.43642570352943, 10.512815617390064, 14.352465517024239, 16.36158524697224, 16.368625990567796, 9.199844057370798, 8.505192097670143, 9.87443451422887, 17.732991764878374, 8.73605864932406, 10.306072354570096, 12.32567921554981, 14.573674546947622), # 158
(13.288116180561124, 10.37351757527906, 14.232128073125379, 16.207158885819215, 16.22734435760693, 9.132641366412786, 8.40278297409429, 9.804984358975888, 17.61556907019986, 8.644105789377742, 10.20135048411419, 12.206452542629595, 14.445769764456351), # 159
(13.112769770827757, 10.215174111373285, 14.0794577243206, 16.017439518735948, 16.04955623642423, 9.043814332885832, 8.284038747090811, 9.712078541149223, 17.455365409011574, 8.534170173353209, 10.075067115497172, 12.060903507998123, 14.285557096008445), # 160
(12.911799698254727, 10.038817562544844, 13.896084549438555, 15.79423050676211, 15.837107623707803, 8.934439034826566, 8.149826602812377, 9.596880959597605, 17.254493580598233, 8.407184747707687, 9.928334978279473, 11.890381444033627, 14.094673280674375), # 161
(12.686619186767443, 9.84548026566583, 13.683638627307893, 15.539335210937388, 15.591844516145768, 8.80559155027162, 8.001013727411657, 9.460555513169764, 17.015066384244545, 8.264082458898416, 9.762266802021516, 11.696235683114327, 13.874755057524599), # 162
(12.438641460291295, 9.636194557608343, 13.443750036757264, 15.254556992301481, 15.315612910426239, 8.65834795725763, 7.838467307041322, 9.304266100714425, 16.73919661923523, 8.105796253382625, 9.577975316283736, 11.479815557618458, 13.627439165629584), # 163
(12.16927974275169, 9.411992775244478, 13.178048856615318, 14.941699211894072, 15.01025880323734, 8.493784333821234, 7.663054527854039, 9.129176621080324, 16.428997084855002, 7.933259077617543, 9.376573250626553, 11.242470399924246, 13.35436234405979), # 164
(11.879947258074031, 9.173907255446338, 12.888165165710705, 14.602565230754854, 14.677628191267182, 8.312976757999055, 7.475642576002479, 8.936450973116184, 16.086580580388564, 7.747403878060404, 9.1591733346104, 10.985549542409915, 13.057161331885686), # 165
(11.572057230183715, 8.922970335086019, 12.57572904287207, 14.238958409923503, 14.319567071203886, 8.117001307827735, 7.277098637639315, 8.727253055670738, 15.714059905120632, 7.549163601168441, 8.926888297795703, 10.710402317453703, 12.737472868177733), # 166
(11.24702288300614, 8.660214351035616, 12.242370566928068, 13.852682110439718, 13.937921439735565, 7.906934061343905, 7.0682898989172145, 8.502746767592717, 15.31354785833592, 7.339471193398886, 8.680830869742888, 10.418378057433825, 12.396933692006392), # 167
(10.906257440466712, 8.386671640167231, 11.889719816707347, 13.445539693343184, 13.534537293550335, 7.683851096584198, 6.850083545988848, 8.264096007730847, 14.887157239319139, 7.11925960120897, 8.422113780012385, 10.11082609472852, 12.037180542442131), # 168
(10.551174126490828, 8.103374539352963, 11.519406871038555, 13.019334519673588, 13.111260629336316, 7.4488284915852505, 6.623346765006885, 8.012464674933861, 14.437000847355009, 6.889461771055926, 8.151849758164623, 9.78909576171601, 11.659850158555415), # 169
(10.18318616500389, 7.811355385464907, 11.133061808750343, 12.575869950470615, 12.66993744378162, 7.2029423243836925, 6.388946742123995, 7.749016668050485, 13.96519148172823, 6.6510106493969845, 7.871151533760029, 9.454536390774527, 11.2665792794167), # 170
(9.8037067799313, 7.511646515375161, 10.73231470867136, 12.116949346773964, 12.21241373357437, 6.947268673016157, 6.147750663492849, 7.47491588592945, 13.47384194172352, 6.404839182689379, 7.581131836359027, 9.108497314282296, 10.859004644096458), # 171
(9.414149195198457, 7.205280265955825, 10.318795649630257, 11.644376069623315, 11.740535495402677, 6.682883615519281, 5.900625715266118, 7.191326227419487, 12.965065026625595, 6.151880317390344, 7.282903395522049, 8.752327864617548, 10.438762991665145), # 172
(9.015926634730764, 6.893288974078996, 9.894134710455681, 11.159953480058356, 11.256148725954663, 6.410863229929695, 5.64843908359647, 6.899411591369322, 12.440973535719161, 5.893066999957107, 6.97757894080952, 8.387377374158506, 10.007491061193234), # 173
(8.610452322453618, 6.576704976616772, 9.459961969976282, 10.665484939118773, 10.76109942191844, 6.132283594284034, 5.3920579546365754, 6.600335876627689, 11.903680268288936, 5.629332176846904, 6.66627120178187, 8.014995175283403, 9.566825591751181), # 174
(8.19913948229242, 6.256560610441251, 9.017907507020714, 10.162773807844262, 10.257233579982124, 5.848220786618931, 5.132349514539104, 6.295262982043313, 11.35529802361963, 5.361608794516964, 6.3500929079995245, 7.636530600370466, 9.118403322409455), # 175
(7.783401338172574, 5.933888212424531, 8.569601400417621, 9.653623447274505, 9.746397196833835, 5.55975088497102, 4.870180949456727, 5.985356806464928, 10.797939600995955, 5.090829799424521, 6.0301567890229135, 7.253332981797922, 8.663860992238513), # 176
(7.364651114019479, 5.6097201194387125, 8.116673728995655, 9.13983721844919, 9.230436269161691, 5.267949967376934, 4.606419445542112, 5.671781248741259, 10.233717799702626, 4.817928138026804, 5.7075755744124645, 6.866751651944002, 8.204835340308824), # 177
(6.944302033758534, 5.285088668355891, 7.660754571583465, 8.623218482408008, 8.711196793653805, 4.973894111873309, 4.341932188947932, 5.355700207721038, 9.664745419024355, 4.54383675678105, 5.383461993728603, 6.478135943186929, 7.742963105690853), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(11, 6, 8, 8, 12, 3, 6, 3, 2, 2, 3, 0, 0, 12, 8, 1, 8, 12, 5, 4, 2, 1, 3, 0, 0, 0), # 0
(25, 19, 16, 21, 22, 6, 9, 6, 10, 2, 4, 2, 0, 28, 15, 10, 14, 15, 12, 8, 5, 5, 6, 0, 0, 0), # 1
(33, 26, 21, 32, 30, 14, 15, 8, 14, 3, 7, 3, 0, 36, 23, 15, 21, 26, 19, 17, 6, 11, 9, 2, 1, 0), # 2
(37, 34, 29, 46, 39, 19, 20, 12, 15, 4, 10, 4, 0, 47, 28, 22, 31, 39, 25, 20, 10, 15, 14, 5, 4, 0), # 3
(51, 46, 42, 62, 52, 21, 24, 15, 21, 5, 10, 4, 0, 59, 39, 29, 34, 49, 31, 26, 13, 20, 15, 6, 5, 0), # 4
(64, 65, 53, 71, 60, 27, 30, 15, 26, 8, 14, 5, 0, 83, 53, 38, 44, 51, 35, 31, 19, 21, 18, 8, 6, 0), # 5
(80, 80, 57, 88, 71, 31, 34, 20, 32, 11, 15, 6, 0, 91, 65, 49, 51, 58, 40, 34, 21, 27, 23, 9, 7, 0), # 6
(94, 91, 67, 99, 85, 35, 35, 26, 39, 13, 18, 7, 0, 102, 80, 59, 59, 69, 45, 39, 22, 32, 27, 16, 9, 0), # 7
(115, 105, 79, 112, 100, 38, 44, 36, 42, 18, 20, 7, 0, 110, 88, 71, 68, 83, 56, 45, 24, 37, 31, 17, 10, 0), # 8
(132, 119, 97, 126, 104, 47, 53, 47, 46, 18, 21, 7, 0, 118, 102, 76, 81, 94, 64, 47, 27, 40, 34, 18, 11, 0), # 9
(146, 140, 107, 138, 113, 47, 54, 55, 49, 21, 27, 8, 0, 134, 121, 83, 91, 107, 67, 57, 31, 45, 37, 22, 11, 0), # 10
(160, 151, 118, 148, 122, 54, 61, 57, 55, 23, 30, 10, 0, 149, 132, 96, 99, 117, 71, 64, 31, 48, 41, 26, 12, 0), # 11
(174, 165, 129, 162, 139, 57, 69, 63, 59, 25, 32, 10, 0, 160, 143, 103, 108, 127, 78, 71, 36, 50, 44, 29, 12, 0), # 12
(186, 185, 139, 178, 149, 63, 72, 72, 69, 29, 33, 11, 0, 176, 159, 115, 117, 142, 91, 77, 38, 59, 49, 33, 13, 0), # 13
(200, 200, 154, 190, 161, 68, 78, 81, 80, 34, 35, 11, 0, 184, 173, 127, 127, 152, 96, 82, 42, 62, 52, 38, 13, 0), # 14
(216, 216, 170, 209, 171, 79, 90, 87, 85, 34, 35, 14, 0, 202, 183, 138, 135, 172, 107, 86, 46, 70, 58, 43, 13, 0), # 15
(242, 222, 180, 227, 183, 83, 97, 91, 95, 36, 37, 15, 0, 214, 195, 149, 148, 193, 113, 91, 55, 76, 61, 46, 15, 0), # 16
(268, 240, 196, 244, 195, 90, 104, 98, 103, 36, 39, 18, 0, 235, 213, 160, 157, 203, 124, 96, 61, 81, 64, 46, 18, 0), # 17
(284, 268, 210, 273, 210, 100, 115, 106, 107, 38, 40, 19, 0, 255, 228, 171, 166, 213, 133, 108, 64, 94, 71, 50, 20, 0), # 18
(300, 285, 231, 296, 226, 109, 123, 111, 112, 41, 43, 21, 0, 271, 239, 179, 171, 224, 142, 118, 66, 97, 78, 53, 20, 0), # 19
(324, 304, 246, 313, 238, 118, 129, 120, 115, 45, 50, 24, 0, 295, 252, 195, 178, 237, 149, 131, 70, 105, 81, 58, 22, 0), # 20
(344, 324, 261, 332, 244, 120, 137, 125, 121, 50, 51, 24, 0, 312, 271, 205, 188, 251, 156, 139, 73, 111, 89, 60, 23, 0), # 21
(361, 340, 273, 342, 263, 125, 144, 129, 125, 52, 53, 27, 0, 330, 287, 222, 196, 260, 160, 153, 78, 121, 93, 62, 24, 0), # 22
(388, 366, 288, 360, 279, 132, 149, 133, 135, 55, 54, 28, 0, 349, 299, 237, 209, 283, 169, 160, 85, 132, 96, 63, 25, 0), # 23
(403, 385, 300, 372, 294, 141, 155, 137, 139, 57, 57, 32, 0, 361, 316, 247, 221, 298, 181, 168, 91, 140, 101, 65, 25, 0), # 24
(425, 405, 314, 391, 306, 151, 161, 142, 147, 61, 64, 34, 0, 388, 333, 259, 234, 317, 192, 178, 96, 147, 105, 67, 28, 0), # 25
(452, 423, 330, 413, 322, 157, 166, 147, 154, 63, 67, 37, 0, 407, 352, 280, 243, 330, 203, 193, 100, 153, 114, 73, 28, 0), # 26
(468, 440, 343, 434, 330, 163, 172, 149, 163, 70, 67, 37, 0, 425, 363, 293, 252, 343, 216, 205, 106, 164, 122, 79, 32, 0), # 27
(487, 458, 358, 453, 346, 169, 181, 155, 174, 74, 68, 40, 0, 450, 382, 307, 258, 362, 224, 211, 108, 170, 130, 81, 37, 0), # 28
(507, 481, 372, 478, 362, 171, 192, 165, 181, 80, 69, 43, 0, 462, 397, 320, 268, 369, 239, 216, 111, 179, 134, 83, 40, 0), # 29
(526, 502, 387, 504, 376, 178, 195, 174, 189, 88, 73, 43, 0, 482, 416, 332, 273, 384, 244, 227, 116, 187, 142, 89, 42, 0), # 30
(546, 521, 396, 522, 386, 185, 203, 183, 199, 91, 74, 43, 0, 498, 432, 349, 278, 400, 251, 238, 121, 197, 148, 93, 46, 0), # 31
(564, 541, 408, 545, 399, 194, 215, 188, 208, 99, 77, 44, 0, 516, 451, 358, 288, 417, 259, 243, 124, 203, 156, 98, 47, 0), # 32
(587, 565, 421, 553, 409, 198, 224, 193, 215, 106, 83, 48, 0, 536, 463, 369, 298, 441, 269, 251, 128, 207, 160, 101, 47, 0), # 33
(607, 584, 433, 567, 423, 202, 231, 199, 224, 111, 88, 49, 0, 558, 481, 378, 302, 461, 278, 256, 134, 215, 168, 105, 47, 0), # 34
(627, 599, 445, 587, 440, 206, 241, 208, 229, 114, 92, 50, 0, 577, 495, 392, 316, 478, 289, 263, 136, 220, 174, 108, 48, 0), # 35
(647, 616, 451, 600, 449, 216, 250, 217, 235, 117, 94, 55, 0, 597, 509, 410, 327, 494, 297, 268, 141, 228, 180, 110, 48, 0), # 36
(659, 633, 469, 616, 465, 223, 264, 229, 239, 122, 99, 56, 0, 611, 520, 424, 337, 509, 308, 274, 144, 228, 185, 112, 51, 0), # 37
(674, 649, 484, 634, 478, 227, 265, 236, 249, 123, 102, 59, 0, 630, 545, 438, 344, 523, 319, 281, 148, 231, 189, 117, 51, 0), # 38
(693, 666, 502, 653, 495, 233, 276, 240, 256, 127, 104, 60, 0, 648, 561, 452, 350, 537, 329, 284, 151, 237, 203, 117, 53, 0), # 39
(713, 685, 512, 665, 503, 238, 282, 245, 262, 134, 106, 61, 0, 666, 574, 462, 361, 552, 341, 293, 155, 240, 205, 121, 56, 0), # 40
(726, 697, 528, 686, 518, 242, 289, 252, 265, 137, 111, 64, 0, 682, 590, 478, 367, 569, 347, 304, 161, 245, 209, 124, 59, 0), # 41
(744, 718, 542, 701, 529, 250, 294, 256, 274, 139, 114, 67, 0, 695, 605, 493, 383, 586, 355, 308, 166, 254, 215, 132, 60, 0), # 42
(757, 741, 557, 719, 538, 259, 299, 260, 278, 142, 117, 68, 0, 715, 626, 508, 400, 599, 360, 313, 169, 265, 217, 136, 61, 0), # 43
(773, 759, 569, 740, 548, 268, 301, 267, 286, 147, 118, 69, 0, 728, 644, 514, 409, 619, 366, 320, 174, 273, 221, 137, 62, 0), # 44
(793, 774, 583, 751, 565, 274, 305, 272, 293, 154, 125, 69, 0, 752, 657, 532, 416, 631, 369, 326, 175, 276, 225, 140, 64, 0), # 45
(819, 790, 607, 774, 580, 284, 310, 286, 306, 158, 125, 71, 0, 779, 673, 545, 426, 649, 377, 334, 182, 285, 226, 141, 64, 0), # 46
(835, 801, 622, 789, 593, 290, 314, 289, 310, 158, 128, 71, 0, 796, 689, 561, 437, 664, 389, 342, 187, 294, 230, 144, 66, 0), # 47
(851, 815, 637, 815, 614, 297, 318, 291, 315, 162, 129, 72, 0, 811, 705, 572, 449, 683, 400, 349, 189, 302, 238, 149, 67, 0), # 48
(869, 835, 655, 824, 632, 300, 324, 296, 323, 165, 131, 75, 0, 822, 723, 582, 457, 694, 410, 352, 190, 309, 246, 152, 68, 0), # 49
(887, 853, 679, 839, 645, 303, 331, 296, 333, 167, 132, 76, 0, 845, 737, 595, 473, 705, 423, 358, 196, 313, 253, 155, 70, 0), # 50
(901, 872, 697, 847, 655, 309, 335, 303, 342, 170, 133, 76, 0, 867, 760, 610, 488, 722, 434, 362, 201, 323, 255, 157, 70, 0), # 51
(919, 888, 707, 867, 675, 315, 342, 309, 352, 174, 140, 77, 0, 881, 775, 623, 498, 743, 449, 368, 207, 329, 264, 158, 71, 0), # 52
(938, 911, 721, 882, 685, 324, 353, 313, 365, 176, 141, 82, 0, 900, 789, 637, 512, 755, 458, 379, 209, 334, 272, 160, 74, 0), # 53
(950, 922, 740, 901, 698, 331, 359, 322, 373, 181, 145, 84, 0, 919, 800, 647, 519, 766, 467, 387, 214, 340, 277, 164, 78, 0), # 54
(970, 932, 750, 930, 714, 333, 371, 326, 379, 182, 146, 87, 0, 936, 819, 658, 529, 776, 472, 396, 218, 345, 285, 166, 80, 0), # 55
(981, 956, 770, 947, 727, 341, 374, 333, 384, 187, 151, 91, 0, 958, 832, 673, 539, 790, 480, 402, 221, 352, 287, 167, 81, 0), # 56
(999, 970, 781, 963, 741, 354, 382, 337, 394, 191, 154, 91, 0, 980, 841, 679, 551, 807, 490, 408, 227, 355, 291, 172, 81, 0), # 57
(1012, 984, 791, 978, 755, 360, 391, 341, 399, 192, 156, 92, 0, 1005, 854, 695, 562, 821, 497, 419, 232, 361, 298, 177, 82, 0), # 58
(1032, 1005, 804, 991, 777, 364, 398, 344, 404, 197, 158, 93, 0, 1019, 872, 702, 577, 827, 506, 427, 238, 366, 302, 178, 83, 0), # 59
(1052, 1021, 823, 1011, 792, 370, 400, 346, 417, 202, 161, 94, 0, 1037, 884, 716, 586, 837, 513, 433, 244, 371, 314, 180, 86, 0), # 60
(1076, 1040, 833, 1029, 803, 377, 404, 354, 425, 208, 163, 96, 0, 1050, 896, 729, 589, 854, 526, 444, 247, 383, 323, 181, 87, 0), # 61
(1094, 1050, 848, 1043, 815, 387, 413, 361, 431, 210, 164, 96, 0, 1065, 909, 743, 601, 867, 535, 453, 250, 389, 325, 182, 88, 0), # 62
(1120, 1063, 867, 1056, 824, 399, 422, 367, 436, 210, 167, 98, 0, 1075, 919, 762, 608, 877, 547, 459, 253, 398, 329, 185, 91, 0), # 63
(1141, 1087, 882, 1075, 839, 401, 431, 372, 444, 214, 170, 100, 0, 1093, 929, 773, 619, 895, 558, 464, 259, 406, 334, 188, 91, 0), # 64
(1150, 1101, 897, 1090, 851, 405, 435, 379, 447, 221, 173, 100, 0, 1115, 947, 786, 633, 913, 563, 465, 266, 412, 338, 191, 91, 0), # 65
(1163, 1116, 922, 1107, 862, 412, 441, 381, 451, 223, 176, 101, 0, 1131, 956, 792, 646, 923, 569, 467, 271, 420, 344, 194, 92, 0), # 66
(1180, 1131, 937, 1125, 873, 417, 447, 387, 460, 226, 177, 102, 0, 1149, 974, 802, 654, 934, 581, 470, 277, 427, 347, 197, 95, 0), # 67
(1196, 1146, 951, 1137, 888, 423, 455, 397, 472, 227, 181, 103, 0, 1168, 986, 813, 670, 948, 593, 478, 282, 434, 351, 197, 96, 0), # 68
(1214, 1169, 963, 1156, 906, 429, 460, 403, 480, 229, 181, 106, 0, 1188, 1001, 831, 676, 957, 605, 481, 288, 440, 355, 201, 97, 0), # 69
(1225, 1183, 978, 1174, 915, 438, 461, 410, 487, 234, 186, 106, 0, 1209, 1008, 841, 689, 973, 609, 485, 293, 446, 360, 203, 101, 0), # 70
(1246, 1198, 998, 1192, 921, 444, 467, 413, 496, 235, 190, 112, 0, 1221, 1025, 850, 694, 989, 617, 491, 298, 457, 363, 210, 102, 0), # 71
(1268, 1212, 1008, 1215, 933, 449, 473, 416, 504, 236, 191, 113, 0, 1238, 1029, 864, 701, 1000, 625, 497, 302, 464, 368, 214, 103, 0), # 72
(1285, 1233, 1025, 1232, 944, 454, 480, 421, 506, 240, 193, 113, 0, 1254, 1040, 870, 713, 1018, 632, 504, 306, 472, 377, 214, 104, 0), # 73
(1306, 1246, 1034, 1254, 961, 465, 488, 425, 512, 241, 199, 116, 0, 1265, 1056, 878, 720, 1031, 638, 509, 308, 478, 383, 214, 107, 0), # 74
(1321, 1269, 1048, 1270, 973, 472, 491, 428, 522, 244, 203, 116, 0, 1280, 1075, 890, 727, 1044, 646, 514, 311, 482, 394, 216, 108, 0), # 75
(1339, 1292, 1067, 1288, 989, 477, 500, 436, 527, 244, 207, 116, 0, 1293, 1091, 902, 739, 1057, 652, 522, 313, 491, 401, 220, 110, 0), # 76
(1349, 1307, 1085, 1300, 998, 485, 510, 442, 530, 248, 209, 117, 0, 1310, 1106, 914, 750, 1072, 661, 526, 316, 496, 411, 225, 111, 0), # 77
(1366, 1323, 1096, 1312, 1015, 490, 518, 445, 539, 252, 210, 119, 0, 1335, 1126, 928, 756, 1084, 665, 536, 319, 506, 417, 231, 114, 0), # 78
(1385, 1340, 1112, 1327, 1031, 498, 527, 451, 544, 258, 215, 121, 0, 1350, 1143, 934, 769, 1101, 674, 546, 323, 511, 422, 233, 115, 0), # 79
(1406, 1351, 1131, 1335, 1054, 503, 535, 455, 552, 259, 215, 124, 0, 1366, 1161, 947, 779, 1119, 686, 554, 328, 524, 429, 238, 116, 0), # 80
(1422, 1364, 1143, 1356, 1066, 507, 538, 461, 561, 263, 219, 125, 0, 1392, 1175, 951, 790, 1132, 691, 560, 332, 525, 432, 241, 117, 0), # 81
(1443, 1380, 1156, 1376, 1081, 511, 547, 462, 570, 264, 224, 127, 0, 1412, 1188, 964, 801, 1144, 696, 565, 334, 529, 436, 244, 119, 0), # 82
(1475, 1397, 1171, 1391, 1090, 516, 553, 467, 578, 267, 226, 127, 0, 1424, 1205, 973, 810, 1155, 703, 573, 337, 541, 442, 248, 121, 0), # 83
(1489, 1411, 1188, 1416, 1101, 522, 560, 471, 579, 270, 230, 128, 0, 1444, 1219, 980, 818, 1171, 709, 581, 342, 549, 445, 248, 122, 0), # 84
(1505, 1422, 1196, 1437, 1108, 531, 571, 473, 584, 276, 232, 129, 0, 1465, 1233, 991, 825, 1188, 716, 586, 346, 555, 451, 249, 123, 0), # 85
(1523, 1440, 1206, 1454, 1120, 540, 573, 476, 590, 281, 234, 131, 0, 1490, 1252, 999, 831, 1197, 724, 593, 348, 562, 454, 252, 125, 0), # 86
(1544, 1452, 1222, 1466, 1140, 542, 580, 481, 596, 283, 237, 133, 0, 1507, 1266, 1010, 841, 1209, 726, 597, 353, 571, 460, 260, 126, 0), # 87
(1558, 1471, 1234, 1475, 1160, 552, 587, 486, 602, 289, 243, 133, 0, 1524, 1280, 1024, 848, 1218, 737, 605, 358, 576, 465, 261, 127, 0), # 88
(1576, 1485, 1246, 1494, 1177, 558, 593, 493, 604, 291, 245, 134, 0, 1544, 1293, 1035, 852, 1233, 741, 613, 365, 585, 470, 265, 131, 0), # 89
(1595, 1512, 1260, 1519, 1192, 565, 597, 497, 606, 294, 248, 135, 0, 1558, 1310, 1047, 866, 1243, 748, 618, 366, 590, 477, 269, 131, 0), # 90
(1620, 1527, 1278, 1530, 1200, 577, 602, 505, 615, 298, 251, 135, 0, 1579, 1329, 1060, 871, 1251, 752, 628, 369, 596, 488, 271, 133, 0), # 91
(1647, 1540, 1296, 1550, 1220, 586, 611, 510, 620, 300, 256, 136, 0, 1593, 1348, 1074, 879, 1268, 760, 637, 372, 603, 492, 277, 135, 0), # 92
(1674, 1563, 1308, 1572, 1237, 592, 617, 515, 628, 301, 258, 137, 0, 1607, 1366, 1083, 894, 1283, 766, 642, 379, 610, 500, 277, 138, 0), # 93
(1692, 1576, 1321, 1584, 1252, 602, 623, 519, 638, 301, 260, 137, 0, 1628, 1376, 1091, 897, 1297, 774, 650, 381, 618, 501, 278, 140, 0), # 94
(1706, 1593, 1338, 1596, 1264, 612, 628, 527, 642, 303, 260, 140, 0, 1653, 1385, 1104, 907, 1312, 779, 655, 386, 619, 505, 280, 140, 0), # 95
(1733, 1609, 1349, 1610, 1282, 617, 632, 529, 650, 306, 264, 145, 0, 1670, 1397, 1111, 923, 1324, 787, 663, 395, 624, 508, 283, 141, 0), # 96
(1754, 1629, 1369, 1631, 1291, 623, 637, 531, 656, 307, 264, 145, 0, 1684, 1407, 1118, 928, 1342, 795, 669, 398, 629, 518, 286, 141, 0), # 97
(1771, 1642, 1384, 1648, 1302, 628, 646, 534, 659, 310, 265, 147, 0, 1701, 1418, 1124, 933, 1354, 804, 678, 401, 635, 523, 287, 143, 0), # 98
(1785, 1662, 1398, 1664, 1317, 633, 652, 537, 666, 313, 269, 149, 0, 1714, 1432, 1138, 944, 1366, 818, 685, 406, 645, 526, 289, 143, 0), # 99
(1806, 1680, 1417, 1678, 1325, 640, 660, 540, 672, 316, 272, 150, 0, 1731, 1450, 1146, 953, 1374, 825, 687, 410, 649, 529, 290, 144, 0), # 100
(1817, 1694, 1425, 1689, 1341, 645, 670, 544, 678, 322, 274, 151, 0, 1741, 1466, 1153, 962, 1382, 829, 694, 413, 657, 534, 297, 144, 0), # 101
(1838, 1715, 1444, 1703, 1347, 650, 677, 549, 685, 324, 278, 153, 0, 1757, 1474, 1160, 967, 1395, 833, 698, 420, 671, 538, 299, 145, 0), # 102
(1859, 1730, 1455, 1722, 1364, 657, 683, 551, 691, 329, 280, 154, 0, 1782, 1496, 1173, 978, 1405, 835, 706, 424, 678, 541, 301, 148, 0), # 103
(1888, 1737, 1463, 1738, 1374, 661, 687, 554, 696, 332, 282, 155, 0, 1796, 1510, 1184, 989, 1420, 841, 707, 426, 681, 544, 304, 149, 0), # 104
(1902, 1748, 1476, 1747, 1392, 666, 692, 557, 705, 334, 282, 155, 0, 1813, 1519, 1195, 1003, 1437, 846, 719, 431, 687, 548, 307, 149, 0), # 105
(1916, 1761, 1492, 1762, 1402, 672, 700, 562, 709, 339, 285, 156, 0, 1836, 1531, 1208, 1012, 1443, 855, 725, 436, 693, 551, 310, 150, 0), # 106
(1934, 1776, 1505, 1777, 1415, 678, 706, 566, 712, 341, 286, 157, 0, 1852, 1546, 1220, 1015, 1461, 858, 730, 441, 699, 555, 311, 151, 0), # 107
(1958, 1790, 1518, 1792, 1424, 688, 709, 571, 718, 344, 291, 160, 0, 1867, 1563, 1232, 1022, 1477, 862, 736, 443, 708, 559, 313, 153, 0), # 108
(1967, 1808, 1528, 1810, 1437, 691, 715, 573, 725, 346, 293, 162, 0, 1892, 1581, 1245, 1030, 1486, 868, 744, 446, 712, 564, 315, 153, 0), # 109
(2003, 1821, 1539, 1831, 1447, 695, 722, 581, 730, 350, 295, 162, 0, 1911, 1600, 1258, 1044, 1496, 871, 750, 448, 718, 572, 315, 153, 0), # 110
(2016, 1834, 1555, 1843, 1459, 705, 728, 584, 734, 353, 299, 163, 0, 1927, 1613, 1271, 1051, 1504, 874, 755, 457, 728, 577, 316, 153, 0), # 111
(2029, 1844, 1566, 1864, 1467, 708, 733, 586, 738, 355, 300, 164, 0, 1948, 1632, 1288, 1055, 1519, 879, 759, 463, 735, 580, 318, 154, 0), # 112
(2041, 1857, 1582, 1880, 1484, 715, 740, 591, 746, 357, 304, 166, 0, 1970, 1646, 1298, 1061, 1531, 882, 774, 466, 738, 584, 318, 154, 0), # 113
(2059, 1864, 1597, 1890, 1494, 723, 748, 591, 753, 358, 305, 166, 0, 1987, 1661, 1310, 1070, 1542, 886, 778, 469, 745, 593, 320, 154, 0), # 114
(2089, 1879, 1611, 1902, 1509, 727, 750, 595, 760, 360, 306, 166, 0, 2010, 1670, 1323, 1078, 1555, 894, 786, 478, 750, 598, 320, 156, 0), # 115
(2107, 1893, 1624, 1918, 1517, 735, 759, 602, 763, 365, 307, 166, 0, 2027, 1683, 1337, 1089, 1570, 904, 792, 484, 753, 602, 323, 156, 0), # 116
(2136, 1914, 1635, 1925, 1531, 741, 767, 604, 773, 365, 308, 167, 0, 2039, 1698, 1347, 1097, 1585, 908, 797, 492, 759, 607, 326, 157, 0), # 117
(2149, 1926, 1648, 1951, 1545, 750, 771, 608, 781, 370, 309, 167, 0, 2050, 1714, 1362, 1108, 1599, 918, 800, 498, 768, 611, 326, 157, 0), # 118
(2165, 1936, 1667, 1966, 1553, 757, 772, 610, 787, 372, 310, 168, 0, 2064, 1730, 1376, 1113, 1610, 929, 803, 506, 773, 616, 328, 157, 0), # 119
(2181, 1949, 1683, 1981, 1565, 765, 776, 613, 790, 374, 312, 168, 0, 2083, 1738, 1385, 1117, 1629, 933, 806, 506, 777, 621, 331, 158, 0), # 120
(2203, 1965, 1692, 2000, 1576, 775, 784, 617, 799, 377, 317, 169, 0, 2097, 1748, 1399, 1126, 1640, 940, 809, 512, 784, 624, 335, 160, 0), # 121
(2222, 1974, 1701, 2009, 1590, 784, 789, 623, 803, 380, 318, 170, 0, 2115, 1760, 1411, 1132, 1651, 945, 811, 516, 787, 627, 338, 161, 0), # 122
(2238, 1984, 1717, 2026, 1602, 793, 793, 624, 812, 382, 321, 172, 0, 2130, 1777, 1418, 1143, 1658, 949, 815, 520, 797, 632, 341, 166, 0), # 123
(2257, 2000, 1732, 2037, 1608, 796, 802, 628, 815, 385, 324, 172, 0, 2145, 1786, 1431, 1148, 1669, 954, 818, 525, 802, 636, 343, 169, 0), # 124
(2272, 2007, 1746, 2046, 1620, 804, 803, 630, 829, 389, 326, 173, 0, 2168, 1805, 1447, 1160, 1678, 962, 822, 528, 807, 639, 348, 169, 0), # 125
(2291, 2019, 1763, 2060, 1630, 809, 807, 633, 836, 394, 329, 173, 0, 2183, 1818, 1454, 1164, 1693, 971, 832, 534, 814, 644, 351, 170, 0), # 126
(2308, 2031, 1779, 2080, 1643, 814, 809, 638, 841, 396, 330, 175, 0, 2203, 1826, 1469, 1170, 1707, 983, 836, 538, 821, 648, 352, 170, 0), # 127
(2329, 2045, 1792, 2099, 1650, 818, 815, 640, 846, 398, 333, 175, 0, 2222, 1846, 1480, 1174, 1718, 993, 841, 541, 825, 652, 354, 171, 0), # 128
(2348, 2055, 1799, 2115, 1662, 820, 820, 641, 848, 399, 335, 175, 0, 2242, 1863, 1488, 1182, 1728, 998, 848, 544, 833, 654, 359, 171, 0), # 129
(2361, 2064, 1814, 2130, 1676, 831, 824, 644, 861, 400, 336, 179, 0, 2253, 1879, 1497, 1190, 1741, 1008, 854, 549, 835, 663, 362, 174, 0), # 130
(2376, 2075, 1827, 2144, 1691, 840, 832, 651, 862, 402, 341, 179, 0, 2265, 1888, 1510, 1196, 1760, 1011, 862, 559, 843, 669, 365, 176, 0), # 131
(2391, 2086, 1846, 2154, 1706, 843, 836, 659, 867, 405, 341, 181, 0, 2285, 1900, 1517, 1203, 1775, 1017, 866, 563, 851, 670, 367, 179, 0), # 132
(2406, 2095, 1858, 2166, 1726, 848, 845, 663, 871, 406, 344, 181, 0, 2299, 1912, 1524, 1207, 1784, 1023, 867, 567, 859, 677, 367, 179, 0), # 133
(2421, 2107, 1870, 2185, 1737, 859, 849, 666, 877, 407, 345, 181, 0, 2313, 1930, 1531, 1224, 1788, 1027, 871, 573, 866, 681, 369, 182, 0), # 134
(2430, 2121, 1885, 2196, 1744, 863, 857, 669, 881, 409, 347, 181, 0, 2336, 1945, 1539, 1233, 1802, 1030, 874, 579, 869, 683, 371, 184, 0), # 135
(2442, 2130, 1896, 2208, 1760, 873, 862, 671, 884, 413, 348, 181, 0, 2353, 1962, 1553, 1237, 1819, 1035, 880, 583, 875, 686, 375, 184, 0), # 136
(2455, 2152, 1909, 2228, 1769, 880, 863, 672, 894, 415, 350, 182, 0, 2368, 1975, 1560, 1241, 1837, 1041, 886, 586, 878, 690, 376, 186, 0), # 137
(2465, 2166, 1924, 2243, 1777, 886, 869, 678, 901, 417, 352, 182, 0, 2395, 1983, 1570, 1250, 1848, 1049, 893, 589, 885, 694, 378, 188, 0), # 138
(2482, 2185, 1942, 2258, 1791, 891, 869, 681, 911, 419, 354, 183, 0, 2411, 1995, 1585, 1258, 1854, 1053, 901, 591, 890, 701, 378, 190, 0), # 139
(2504, 2198, 1953, 2272, 1807, 897, 870, 685, 919, 424, 354, 183, 0, 2431, 2001, 1595, 1264, 1868, 1059, 910, 597, 895, 708, 383, 191, 0), # 140
(2514, 2208, 1969, 2294, 1812, 901, 875, 686, 927, 425, 357, 184, 0, 2448, 2016, 1599, 1276, 1874, 1066, 918, 601, 899, 709, 387, 193, 0), # 141
(2527, 2217, 1980, 2303, 1831, 903, 879, 690, 933, 428, 358, 184, 0, 2468, 2023, 1605, 1285, 1892, 1071, 923, 602, 906, 715, 389, 194, 0), # 142
(2543, 2228, 1993, 2315, 1848, 911, 883, 691, 937, 431, 359, 185, 0, 2483, 2032, 1610, 1295, 1913, 1077, 925, 607, 910, 720, 390, 194, 0), # 143
(2554, 2236, 2007, 2334, 1856, 915, 887, 694, 941, 437, 362, 185, 0, 2504, 2052, 1620, 1303, 1921, 1079, 928, 610, 913, 723, 393, 195, 0), # 144
(2570, 2250, 2021, 2348, 1867, 921, 892, 695, 948, 438, 364, 186, 0, 2521, 2068, 1628, 1313, 1932, 1084, 932, 614, 916, 726, 395, 195, 0), # 145
(2586, 2260, 2027, 2364, 1878, 931, 893, 701, 959, 440, 365, 189, 0, 2529, 2077, 1636, 1320, 1945, 1089, 937, 619, 918, 728, 400, 197, 0), # 146
(2615, 2276, 2043, 2370, 1884, 938, 898, 707, 963, 443, 367, 190, 0, 2550, 2090, 1639, 1325, 1956, 1096, 943, 623, 922, 733, 406, 198, 0), # 147
(2627, 2282, 2054, 2386, 1895, 943, 901, 709, 967, 446, 368, 192, 0, 2567, 2097, 1647, 1333, 1974, 1101, 947, 627, 927, 743, 408, 202, 0), # 148
(2645, 2291, 2063, 2397, 1913, 951, 905, 711, 971, 448, 369, 192, 0, 2582, 2105, 1659, 1337, 1993, 1103, 953, 632, 930, 750, 411, 202, 0), # 149
(2657, 2301, 2071, 2410, 1924, 954, 908, 713, 975, 450, 370, 195, 0, 2596, 2110, 1665, 1349, 2001, 1108, 961, 633, 934, 751, 414, 204, 0), # 150
(2667, 2308, 2084, 2421, 1939, 959, 915, 718, 979, 452, 372, 196, 0, 2619, 2127, 1669, 1361, 2016, 1113, 963, 634, 940, 754, 416, 207, 0), # 151
(2679, 2321, 2100, 2442, 1954, 965, 921, 721, 985, 452, 374, 197, 0, 2636, 2132, 1675, 1365, 2029, 1118, 965, 636, 944, 759, 419, 208, 0), # 152
(2687, 2329, 2113, 2456, 1962, 969, 927, 726, 990, 456, 376, 199, 0, 2650, 2140, 1677, 1370, 2038, 1125, 966, 638, 946, 763, 420, 208, 0), # 153
(2699, 2337, 2129, 2466, 1972, 972, 931, 734, 997, 462, 378, 201, 0, 2664, 2151, 1679, 1377, 2051, 1134, 970, 639, 955, 773, 422, 208, 0), # 154
(2711, 2342, 2139, 2478, 1978, 978, 937, 741, 1003, 463, 379, 203, 0, 2680, 2164, 1690, 1382, 2062, 1143, 974, 644, 961, 778, 423, 208, 0), # 155
(2729, 2354, 2150, 2491, 1986, 983, 940, 745, 1007, 466, 380, 205, 0, 2689, 2176, 1700, 1387, 2072, 1151, 980, 648, 968, 783, 423, 208, 0), # 156
(2741, 2364, 2161, 2500, 1993, 987, 947, 750, 1012, 467, 381, 207, 0, 2703, 2187, 1711, 1398, 2083, 1160, 984, 652, 972, 784, 427, 210, 0), # 157
(2755, 2370, 2178, 2508, 2003, 994, 949, 753, 1015, 471, 384, 209, 0, 2714, 2198, 1717, 1407, 2092, 1163, 990, 655, 978, 787, 428, 212, 0), # 158
(2773, 2388, 2186, 2517, 2010, 998, 952, 755, 1024, 475, 386, 211, 0, 2728, 2209, 1725, 1416, 2104, 1168, 994, 661, 983, 789, 429, 213, 0), # 159
(2788, 2397, 2199, 2530, 2022, 1001, 958, 757, 1033, 477, 388, 212, 0, 2741, 2218, 1730, 1421, 2114, 1173, 998, 666, 992, 793, 430, 213, 0), # 160
(2807, 2405, 2209, 2546, 2029, 1004, 960, 761, 1037, 478, 391, 213, 0, 2753, 2230, 1736, 1428, 2127, 1179, 1000, 670, 997, 799, 431, 213, 0), # 161
(2824, 2413, 2224, 2552, 2044, 1009, 965, 763, 1044, 479, 392, 214, 0, 2769, 2247, 1744, 1434, 2139, 1186, 1005, 671, 1000, 806, 432, 213, 0), # 162
(2837, 2425, 2233, 2559, 2057, 1016, 970, 769, 1047, 482, 394, 214, 0, 2781, 2255, 1747, 1441, 2149, 1191, 1012, 678, 1004, 813, 433, 217, 0), # 163
(2849, 2433, 2243, 2567, 2072, 1021, 974, 776, 1052, 483, 396, 214, 0, 2791, 2267, 1761, 1446, 2156, 1195, 1015, 681, 1015, 818, 436, 218, 0), # 164
(2858, 2443, 2250, 2577, 2087, 1027, 978, 783, 1062, 486, 400, 215, 0, 2802, 2280, 1769, 1452, 2167, 1197, 1018, 686, 1018, 821, 438, 220, 0), # 165
(2871, 2450, 2263, 2588, 2099, 1034, 980, 785, 1069, 486, 403, 216, 0, 2818, 2286, 1778, 1454, 2179, 1207, 1021, 690, 1027, 824, 440, 220, 0), # 166
(2875, 2457, 2273, 2597, 2106, 1036, 983, 787, 1076, 487, 405, 219, 0, 2834, 2292, 1787, 1459, 2190, 1211, 1025, 696, 1033, 828, 440, 220, 0), # 167
(2883, 2466, 2281, 2609, 2117, 1038, 986, 788, 1079, 493, 405, 219, 0, 2841, 2299, 1797, 1462, 2199, 1217, 1031, 697, 1034, 834, 442, 221, 0), # 168
(2891, 2483, 2288, 2621, 2129, 1043, 991, 791, 1085, 495, 406, 221, 0, 2858, 2316, 1802, 1469, 2207, 1220, 1031, 699, 1039, 836, 442, 223, 0), # 169
(2901, 2488, 2299, 2632, 2134, 1049, 995, 793, 1092, 497, 406, 223, 0, 2872, 2319, 1815, 1472, 2223, 1227, 1033, 703, 1046, 839, 442, 223, 0), # 170
(2904, 2492, 2308, 2643, 2144, 1052, 997, 795, 1098, 497, 407, 224, 0, 2882, 2327, 1831, 1474, 2233, 1228, 1035, 709, 1050, 840, 445, 223, 0), # 171
(2921, 2498, 2316, 2651, 2155, 1056, 999, 798, 1101, 498, 409, 225, 0, 2892, 2336, 1839, 1481, 2242, 1233, 1040, 714, 1053, 845, 446, 223, 0), # 172
(2930, 2503, 2320, 2660, 2164, 1058, 999, 800, 1107, 499, 409, 225, 0, 2902, 2339, 1847, 1485, 2256, 1235, 1043, 718, 1053, 845, 447, 223, 0), # 173
(2938, 2512, 2327, 2666, 2172, 1061, 1001, 803, 1115, 499, 409, 226, 0, 2912, 2344, 1853, 1488, 2264, 1239, 1044, 718, 1060, 848, 450, 223, 0), # 174
(2945, 2517, 2336, 2677, 2177, 1062, 1005, 806, 1119, 499, 411, 228, 0, 2917, 2347, 1862, 1493, 2272, 1241, 1049, 720, 1065, 852, 450, 224, 0), # 175
(2955, 2522, 2341, 2684, 2185, 1063, 1009, 808, 1123, 500, 413, 229, 0, 2928, 2354, 1869, 1496, 2288, 1243, 1052, 722, 1069, 854, 450, 224, 0), # 176
(2962, 2524, 2342, 2696, 2191, 1065, 1011, 810, 1126, 504, 413, 229, 0, 2936, 2360, 1873, 1502, 2296, 1245, 1053, 723, 1074, 856, 451, 224, 0), # 177
(2966, 2532, 2347, 2701, 2199, 1065, 1014, 811, 1127, 507, 414, 230, 0, 2944, 2366, 1879, 1504, 2304, 1252, 1054, 725, 1079, 858, 456, 224, 0), # 178
(2966, 2532, 2347, 2701, 2199, 1065, 1014, 811, 1127, 507, 414, 230, 0, 2944, 2366, 1879, 1504, 2304, 1252, 1054, 725, 1079, 858, 456, 224, 0), # 179
)
passenger_arriving_rate = (
(9.037558041069182, 9.116726123493724, 7.81692484441876, 8.389801494715634, 6.665622729131535, 3.295587678639206, 3.7314320538365235, 3.4898821297345672, 3.654059437300804, 1.781106756985067, 1.261579549165681, 0.7346872617459261, 0.0, 9.150984382641052, 8.081559879205185, 6.307897745828405, 5.3433202709552, 7.308118874601608, 4.885834981628395, 3.7314320538365235, 2.3539911990280045, 3.3328113645657673, 2.7966004982385453, 1.5633849688837522, 0.828793283953975, 0.0), # 0
(9.637788873635953, 9.718600145338852, 8.333019886995228, 8.943944741923431, 7.106988404969084, 3.5132827632446837, 3.9775220471373247, 3.7196352921792815, 3.8953471957997454, 1.8985413115247178, 1.3449288407868398, 0.7831824991221532, 0.0, 9.755624965391739, 8.615007490343684, 6.724644203934198, 5.695623934574153, 7.790694391599491, 5.207489409050994, 3.9775220471373247, 2.509487688031917, 3.553494202484542, 2.9813149139744777, 1.6666039773990458, 0.883509104121714, 0.0), # 1
(10.236101416163518, 10.318085531970116, 8.847063428321121, 9.495883401297473, 7.546755568499692, 3.7301093702380674, 4.222636657164634, 3.948468935928315, 4.135672084126529, 2.015511198759246, 1.4279469446328943, 0.8314848978079584, 0.0, 10.357856690777442, 9.14633387588754, 7.13973472316447, 6.046533596277737, 8.271344168253059, 5.527856510299641, 4.222636657164634, 2.6643638358843336, 3.773377784249846, 3.1652944670991583, 1.7694126856642243, 0.938007775633647, 0.0), # 2
(10.830164027663812, 10.912803828195138, 9.357016303979782, 10.0434281501683, 7.983194011202283, 3.9452076537143688, 4.46580327748316, 4.175475868120881, 4.374081096552656, 2.1315522142917818, 1.5103045235482149, 0.8794028527395692, 0.0, 10.955291051257605, 9.67343138013526, 7.551522617741075, 6.3946566428753435, 8.748162193105312, 5.845666215369232, 4.46580327748316, 2.818005466938835, 3.9915970056011414, 3.3478093833894342, 1.8714032607959565, 0.9920730752904672, 0.0), # 3
(11.417645067148767, 11.500376578821527, 9.860839349554556, 10.584389665866468, 8.41457352455579, 4.1577177677686015, 4.706049301657613, 4.399748895896186, 4.609621227349624, 2.246200153725456, 1.5916722403771728, 0.9267447588532147, 0.0, 11.54553953929167, 10.19419234738536, 7.958361201885864, 6.738600461176366, 9.219242454699248, 6.159648454254661, 4.706049301657613, 2.969798405549001, 4.207286762277895, 3.528129888622157, 1.9721678699109113, 1.0454887798928663, 0.0), # 4
(11.996212893630318, 12.07842532865692, 10.356493400628777, 11.11657862572253, 8.839163900039136, 4.366779866495776, 4.942402123252702, 4.620380826393444, 4.841339470788935, 2.3589908126633987, 1.67172075796414, 0.9733190110851223, 0.0, 12.126213647339089, 10.706509121936344, 8.358603789820698, 7.076972437990195, 9.68267894157787, 6.468533156950822, 4.942402123252702, 3.119128476068411, 4.419581950019568, 3.705526208574178, 2.071298680125756, 1.0980386662415385, 0.0), # 5
(12.5635358661204, 12.644571622508925, 10.8419392927858, 11.63780570706703, 9.255234929131252, 4.571534103990907, 5.173889135833137, 4.836464466751867, 5.068282821142089, 2.469459986708742, 1.750120739153485, 1.0189340043715214, 0.0, 12.694924867859292, 11.208274048086732, 8.750603695767424, 7.408379960126224, 10.136565642284179, 6.771050253452613, 5.173889135833137, 3.265381502850648, 4.627617464565626, 3.8792685690223445, 2.16838785855716, 1.1495065111371752, 0.0), # 6
(13.117282343630944, 13.196437005185167, 11.315137861608953, 12.145881587230525, 9.661056403311065, 4.771120634349007, 5.399537732963626, 5.047092624110664, 5.289498272680586, 2.5771434714646144, 1.8265428467895808, 1.0633981336486396, 0.0, 13.249284693311735, 11.697379470135033, 9.132714233947903, 7.7314304143938415, 10.578996545361171, 7.06592967375493, 5.399537732963626, 3.4079433102492906, 4.830528201655532, 4.048627195743509, 2.2630275723217905, 1.1996760913804698, 0.0), # 7
(13.655120685173882, 13.731643021493262, 11.774049942681595, 12.638616943543553, 10.054898114057503, 4.964679611665085, 5.618375308208878, 5.251358105609044, 5.504032819675924, 2.681577062534149, 1.9006577437167966, 1.1065197938527056, 0.0, 13.786904616155851, 12.171717732379758, 9.503288718583983, 8.044731187602444, 11.008065639351848, 7.351901347852662, 5.618375308208878, 3.5461997226179176, 5.027449057028751, 4.212872314514518, 2.3548099885363194, 1.248331183772115, 0.0), # 8
(14.174719249761154, 14.247811216240837, 12.216636371587056, 13.11382245333668, 10.43502985284949, 5.151351190034158, 5.829429255133608, 5.4483537183862225, 5.710933456399605, 2.782296555520474, 1.9721360927795035, 1.1481073799199473, 0.0, 14.305396128851092, 12.629181179119417, 9.860680463897518, 8.34688966656142, 11.42186691279921, 7.627695205740712, 5.829429255133608, 3.679536564310113, 5.217514926424745, 4.371274151112227, 2.4433272743174115, 1.2952555651128035, 0.0), # 9
(14.673746396404677, 14.7425631342355, 12.640857983908687, 13.569308793940438, 10.799721411165962, 5.330275523551238, 6.031726967302519, 5.637172269581408, 5.909247177123128, 2.878837746026722, 2.0406485568220725, 1.187969286786593, 0.0, 14.802370723856898, 13.06766215465252, 10.20324278411036, 8.636513238080164, 11.818494354246257, 7.892041177413972, 6.031726967302519, 3.8073396596794558, 5.399860705582981, 4.52310293131348, 2.5281715967817378, 1.3402330122032275, 0.0), # 10
(15.149870484116411, 15.213520320284891, 13.044675615229824, 14.002886642685386, 11.14724258048584, 5.500592766311337, 6.224295838280325, 5.816906566333811, 6.098020976117995, 2.970736429656024, 2.105865798688875, 1.2259139093888718, 0.0, 15.2754398936327, 13.485053003277587, 10.529328993444373, 8.912209288968072, 12.19604195223599, 8.143669192867335, 6.224295838280325, 3.9289948330795266, 5.57362129024292, 4.66762888089513, 2.6089351230459648, 1.3830473018440812, 0.0), # 11
(15.600759871908263, 15.6583043191966, 13.42605010113381, 14.412366676902078, 11.475863152288053, 5.6614430724094635, 6.406163261631731, 5.986649415782641, 6.276301847655707, 3.0575284020115086, 2.1674584812242808, 1.2617496426630104, 0.0, 15.722215130637963, 13.879246069293112, 10.837292406121403, 9.172585206034523, 12.552603695311413, 8.381309182095698, 6.406163261631731, 4.043887908863902, 5.737931576144026, 4.804122225634027, 2.6852100202267626, 1.4234822108360548, 0.0), # 12
(16.02408291879218, 16.074536675778273, 13.782942277203993, 14.795559573921057, 11.783852918051522, 5.8119665959406355, 6.576356630921451, 6.145493625067111, 6.443136786007759, 3.138749458696308, 2.225097267272661, 1.2952848815452382, 0.0, 16.140307927332124, 14.248133696997618, 11.125486336363304, 9.416248376088921, 12.886273572015519, 8.603691075093955, 6.576356630921451, 4.151404711386168, 5.891926459025761, 4.93185319130702, 2.756588455440799, 1.4613215159798432, 0.0), # 13
(16.41750798378009, 16.45983893483752, 14.113312979023721, 15.150276011072872, 12.069481669255186, 5.9513034909998614, 6.733903339714195, 6.292532001326435, 6.597572785445653, 3.2139353953135514, 2.2784528196783858, 1.3263280209717843, 0.0, 16.527329776174614, 14.589608230689624, 11.392264098391927, 9.641806185940652, 13.195145570891306, 8.80954480185701, 6.733903339714195, 4.250931064999901, 6.034740834627593, 5.050092003690958, 2.8226625958047444, 1.4963489940761385, 0.0), # 14
(16.77870342588394, 16.811832641181958, 14.415123042176313, 15.474326665688082, 12.33101919737797, 6.078593911682158, 6.877830781574663, 6.426857351699818, 6.738656840240891, 3.2826220074663714, 2.3271958012858263, 1.3546874558788757, 0.0, 16.880892169624886, 14.90156201466763, 11.63597900642913, 9.847866022399112, 13.477313680481782, 8.997600292379746, 6.877830781574663, 4.341852794058684, 6.165509598688985, 5.158108888562695, 2.883024608435263, 1.5283484219256327, 0.0), # 15
(17.10533760411564, 17.128139339619217, 14.686333302245139, 15.765522215097217, 12.566735293898798, 6.192978012082533, 7.007166350067579, 6.547562483326471, 6.865435944664972, 3.344345090757899, 2.370996874939354, 1.380171581202741, 0.0, 17.198606600142384, 15.181887393230149, 11.85498437469677, 10.033035272273695, 13.730871889329944, 9.16658747665706, 7.007166350067579, 4.423555722916095, 6.283367646949399, 5.255174071699074, 2.9372666604490276, 1.55710357632902, 0.0), # 16
(17.395078877487137, 17.406380574956913, 14.92490459481353, 16.021673336630855, 12.774899750296605, 6.2935959462960005, 7.12093743875764, 6.653740203345614, 6.976957092989391, 3.398640440791261, 2.40952670348334, 1.4025887918796085, 0.0, 17.47808456018655, 15.428476710675692, 12.047633517416699, 10.195921322373781, 13.953914185978782, 9.31523628468386, 7.12093743875764, 4.4954256759257145, 6.387449875148302, 5.340557778876952, 2.984980918962706, 1.5823982340869922, 0.0), # 17
(17.645595605010367, 17.644177892002652, 15.12879775546482, 16.24059070761953, 12.953782358050306, 6.379587868417579, 7.2181714412095666, 6.744483318896446, 7.072267279485658, 3.4450438531695924, 2.4424559497621527, 1.4217474828457075, 0.0, 17.716937542216822, 15.63922231130278, 12.212279748810763, 10.335131559508774, 14.144534558971316, 9.442276646455024, 7.2181714412095666, 4.556848477441128, 6.476891179025153, 5.413530235873177, 3.0257595510929645, 1.6040161720002415, 0.0), # 18
(17.85455614569726, 17.83915283556408, 15.29597361978237, 16.420085005393776, 13.10165290863884, 6.450093932542269, 7.297895750988055, 6.818884637118185, 7.150413498425267, 3.4830911234960236, 2.4694552766201636, 1.4374560490372645, 0.0, 17.912777038692653, 15.812016539409907, 12.347276383100818, 10.449273370488068, 14.300826996850533, 9.546438491965459, 7.297895750988055, 4.607209951815906, 6.55082645431942, 5.473361668464593, 3.059194723956474, 1.621741166869462, 0.0), # 19
(18.01962885855975, 17.988926950448786, 15.424393023349506, 16.55796690728418, 13.216781193541133, 6.504254292765094, 7.359137761657826, 6.876036965150038, 7.210442744079718, 3.5123180473736824, 2.490195346901745, 1.4495228853905089, 0.0, 18.063214542073485, 15.944751739295596, 12.450976734508725, 10.536954142121044, 14.420885488159437, 9.626451751210054, 7.359137761657826, 4.645895923403639, 6.608390596770566, 5.51932230242806, 3.084878604669901, 1.6353569954953444, 0.0), # 20
(18.13848210260976, 18.09112178146442, 15.51201680174958, 16.652047090621256, 13.297437004236105, 6.541209103181062, 7.400924866783583, 6.915033110131218, 7.251402010720512, 3.532260420405701, 2.5043468234512685, 1.4577563868416692, 0.0, 18.165861544818743, 16.03532025525836, 12.52173411725634, 10.5967812612171, 14.502804021441024, 9.681046354183705, 7.400924866783583, 4.672292216557902, 6.648718502118053, 5.550682363540419, 3.1024033603499164, 1.644647434678584, 0.0), # 21
(18.20878423685924, 18.143358873418588, 15.55680579056593, 16.70013623273558, 13.341890132202689, 6.560098517885186, 7.422284459930039, 6.934965879200936, 7.27233829261915, 3.54245403819521, 2.5115803691131027, 1.4619649483269737, 0.0, 18.218329539387888, 16.08161443159671, 12.557901845565512, 10.627362114585626, 14.5446765852383, 9.70895223088131, 7.422284459930039, 4.6857846556322755, 6.6709450661013445, 5.5667120775785275, 3.111361158113186, 1.649396261219872, 0.0), # 22
(18.23470805401675, 18.14954393004115, 15.562384773662554, 16.706156597222225, 13.353278467239116, 6.5625, 7.424823602033405, 6.937120370370371, 7.274955740740741, 3.543656522633746, 2.512487411148522, 1.4624846364883404, 0.0, 18.225, 16.08733100137174, 12.56243705574261, 10.630969567901236, 14.549911481481482, 9.71196851851852, 7.424823602033405, 4.6875, 6.676639233619558, 5.568718865740743, 3.1124769547325113, 1.6499585390946503, 0.0), # 23
(18.253822343461476, 18.145936111111112, 15.561472222222221, 16.705415625000004, 13.359729136337823, 6.5625, 7.42342843137255, 6.934125, 7.274604999999999, 3.5429177777777783, 2.5123873737373743, 1.462362962962963, 0.0, 18.225, 16.085992592592593, 12.561936868686871, 10.628753333333332, 14.549209999999999, 9.707775, 7.42342843137255, 4.6875, 6.679864568168911, 5.568471875000002, 3.1122944444444447, 1.649630555555556, 0.0), # 24
(18.272533014380844, 18.138824588477366, 15.559670781893006, 16.70394965277778, 13.366037934713404, 6.5625, 7.420679012345679, 6.928240740740742, 7.273912037037037, 3.541463477366256, 2.512189019827909, 1.4621227709190674, 0.0, 18.225, 16.08335048010974, 12.560945099139545, 10.624390432098766, 14.547824074074073, 9.69953703703704, 7.420679012345679, 4.6875, 6.683018967356702, 5.567983217592594, 3.1119341563786014, 1.6489840534979427, 0.0), # 25
(18.290838634286462, 18.128318004115226, 15.557005144032923, 16.70177534722222, 13.372204642105325, 6.5625, 7.416618046477849, 6.919578703703704, 7.27288574074074, 3.539317818930042, 2.511894145155257, 1.4617673525377233, 0.0, 18.225, 16.079440877914955, 12.559470725776283, 10.617953456790124, 14.54577148148148, 9.687410185185186, 7.416618046477849, 4.6875, 6.686102321052663, 5.567258449074075, 3.111401028806585, 1.648028909465021, 0.0), # 26
(18.308737770689945, 18.114524999999997, 15.553500000000001, 16.698909375, 13.378229038253057, 6.5625, 7.411288235294118, 6.908250000000002, 7.271535, 3.5365050000000005, 2.5115045454545455, 1.4613000000000003, 0.0, 18.225, 16.0743, 12.557522727272728, 10.609514999999998, 14.54307, 9.671550000000002, 7.411288235294118, 4.6875, 6.689114519126528, 5.566303125, 3.1107000000000005, 1.646775, 0.0), # 27
(18.3262289911029, 18.097554218106993, 15.549180041152265, 16.695368402777778, 13.384110902896083, 6.5625, 7.404732280319536, 6.894365740740742, 7.269868703703704, 3.533049218106997, 2.5110220164609056, 1.4607240054869688, 0.0, 18.225, 16.067964060356655, 12.555110082304529, 10.599147654320989, 14.539737407407408, 9.652112037037039, 7.404732280319536, 4.6875, 6.6920554514480415, 5.565122800925927, 3.1098360082304533, 1.6452322016460905, 0.0), # 28
(18.34331086303695, 18.077514300411522, 15.54406995884774, 16.69116909722222, 13.389850015773863, 6.5625, 7.396992883079159, 6.8780370370370365, 7.267895740740741, 3.5289746707818943, 2.510448353909465, 1.4600426611796984, 0.0, 18.225, 16.06046927297668, 12.552241769547326, 10.58692401234568, 14.535791481481482, 9.629251851851851, 7.396992883079159, 4.6875, 6.694925007886932, 5.563723032407409, 3.1088139917695483, 1.6434103909465023, 0.0), # 29
(18.359981954003697, 18.054513888888888, 15.538194444444445, 16.686328125000003, 13.395446156625884, 6.5625, 7.388112745098039, 6.859375, 7.265625, 3.5243055555555567, 2.509785353535354, 1.4592592592592593, 0.0, 18.225, 16.05185185185185, 12.548926767676768, 10.572916666666668, 14.53125, 9.603125, 7.388112745098039, 4.6875, 6.697723078312942, 5.562109375000001, 3.107638888888889, 1.6413194444444446, 0.0), # 30
(18.376240831514746, 18.028661625514406, 15.531578189300415, 16.680862152777777, 13.400899105191609, 6.5625, 7.378134567901236, 6.838490740740741, 7.26306537037037, 3.5190660699588485, 2.5090348110737, 1.458377091906722, 0.0, 18.225, 16.04214801097394, 12.5451740553685, 10.557198209876542, 14.52613074074074, 9.573887037037037, 7.378134567901236, 4.6875, 6.7004495525958045, 5.56028738425926, 3.106315637860083, 1.638969238683128, 0.0), # 31
(18.392086063081717, 18.000066152263376, 15.524245884773661, 16.674787847222223, 13.406208641210513, 6.5625, 7.3671010530137995, 6.815495370370372, 7.260225740740741, 3.5132804115226346, 2.5081985222596335, 1.4573994513031552, 0.0, 18.225, 16.031393964334704, 12.540992611298167, 10.539841234567902, 14.520451481481482, 9.541693518518521, 7.3671010530137995, 4.6875, 6.703104320605257, 5.558262615740742, 3.1048491769547324, 1.6363696502057616, 0.0), # 32
(18.407516216216216, 17.96883611111111, 15.516222222222224, 16.668121874999997, 13.411374544422076, 6.5625, 7.355054901960784, 6.790500000000001, 7.257115, 3.506972777777779, 2.507278282828283, 1.4563296296296298, 0.0, 18.225, 16.019625925925926, 12.536391414141413, 10.520918333333334, 14.51423, 9.5067, 7.355054901960784, 4.6875, 6.705687272211038, 5.5560406250000005, 3.103244444444445, 1.6335305555555555, 0.0), # 33
(18.422529858429858, 17.93508014403292, 15.507531893004115, 16.660880902777777, 13.41639659456576, 6.5625, 7.342038816267248, 6.7636157407407405, 7.253742037037037, 3.500167366255145, 2.5062758885147773, 1.4551709190672155, 0.0, 18.225, 16.006880109739367, 12.531379442573886, 10.500502098765432, 14.507484074074075, 9.469062037037038, 7.342038816267248, 4.6875, 6.70819829728288, 5.553626967592593, 3.1015063786008232, 1.6304618312757202, 0.0), # 34
(18.437125557234253, 17.898906893004114, 15.49819958847737, 16.65308159722222, 13.421274571381044, 6.5625, 7.328095497458243, 6.734953703703703, 7.250115740740741, 3.4928883744855974, 2.5051931350542462, 1.4539266117969825, 0.0, 18.225, 15.993192729766804, 12.52596567527123, 10.47866512345679, 14.500231481481482, 9.428935185185185, 7.328095497458243, 4.6875, 6.710637285690522, 5.551027199074074, 3.099639917695474, 1.627173353909465, 0.0), # 35
(18.45130188014101, 17.860424999999996, 15.488249999999999, 16.644740624999997, 13.426008254607403, 6.5625, 7.313267647058823, 6.704625000000001, 7.246244999999999, 3.485160000000001, 2.504031818181818, 1.4526000000000006, 0.0, 18.225, 15.978600000000004, 12.520159090909091, 10.45548, 14.492489999999998, 9.386475, 7.313267647058823, 4.6875, 6.7130041273037016, 5.548246875, 3.0976500000000002, 1.623675, 0.0), # 36
(18.46505739466174, 17.819743106995883, 15.477707818930043, 16.63587465277778, 13.430597423984304, 6.5625, 7.2975979665940445, 6.672740740740741, 7.242138703703703, 3.477006440329219, 2.502793733632623, 1.451194375857339, 0.0, 18.225, 15.963138134430727, 12.513968668163116, 10.431019320987655, 14.484277407407406, 9.341837037037038, 7.2975979665940445, 4.6875, 6.715298711992152, 5.545291550925927, 3.0955415637860084, 1.619976646090535, 0.0), # 37
(18.47839066830806, 17.776969855967078, 15.466597736625513, 16.626500347222226, 13.435041859251228, 6.5625, 7.281129157588961, 6.639412037037038, 7.237805740740741, 3.4684518930041164, 2.5014806771417883, 1.4497130315500688, 0.0, 18.225, 15.946843347050754, 12.507403385708942, 10.405355679012347, 14.475611481481481, 9.295176851851854, 7.281129157588961, 4.6875, 6.717520929625614, 5.542166782407409, 3.0933195473251027, 1.61608816872428, 0.0), # 38
(18.491300268591576, 17.732213888888886, 15.454944444444445, 16.616634375, 13.439341340147644, 6.5625, 7.2639039215686285, 6.60475, 7.233255000000001, 3.4595205555555566, 2.500094444444445, 1.4481592592592594, 0.0, 18.225, 15.92975185185185, 12.500472222222223, 10.378561666666666, 14.466510000000001, 9.24665, 7.2639039215686285, 4.6875, 6.719670670073822, 5.538878125000001, 3.0909888888888895, 1.6120194444444444, 0.0), # 39
(18.503784763023894, 17.685583847736623, 15.442772633744857, 16.60629340277778, 13.443495646413021, 6.5625, 7.245964960058098, 6.568865740740742, 7.228495370370371, 3.4502366255144046, 2.49863683127572, 1.4465363511659812, 0.0, 18.225, 15.911899862825791, 12.4931841563786, 10.350709876543212, 14.456990740740743, 9.196412037037039, 7.245964960058098, 4.6875, 6.721747823206511, 5.535431134259261, 3.0885545267489714, 1.6077803497942387, 0.0), # 40
(18.51584271911663, 17.637188374485596, 15.430106995884776, 16.595494097222222, 13.447504557786843, 6.5625, 7.2273549745824255, 6.531870370370371, 7.22353574074074, 3.4406243004115233, 2.4971096333707448, 1.4448475994513033, 0.0, 18.225, 15.893323593964332, 12.485548166853723, 10.321872901234567, 14.44707148148148, 9.14461851851852, 7.2273549745824255, 4.6875, 6.723752278893421, 5.531831365740742, 3.0860213991769556, 1.6033807613168727, 0.0), # 41
(18.527472704381402, 17.587136111111114, 15.416972222222224, 16.584253125000004, 13.45136785400857, 6.5625, 7.208116666666666, 6.493875, 7.218385000000001, 3.4307077777777786, 2.4955146464646467, 1.4430962962962963, 0.0, 18.225, 15.874059259259258, 12.477573232323234, 10.292123333333333, 14.436770000000003, 9.091425000000001, 7.208116666666666, 4.6875, 6.725683927004285, 5.5280843750000015, 3.083394444444445, 1.598830555555556, 0.0), # 42
(18.538673286329807, 17.53553569958848, 15.403393004115227, 16.57258715277778, 13.455085314817683, 6.5625, 7.188292737835875, 6.454990740740741, 7.213052037037036, 3.420511255144034, 2.4938536662925554, 1.4412857338820306, 0.0, 18.225, 15.854143072702334, 12.469268331462775, 10.2615337654321, 14.426104074074072, 9.036987037037038, 7.188292737835875, 4.6875, 6.727542657408842, 5.524195717592594, 3.080678600823046, 1.5941396090534983, 0.0), # 43
(18.54944303247347, 17.482495781893004, 15.389394032921814, 16.560512847222224, 13.458656719953654, 6.5625, 7.1679258896151055, 6.415328703703706, 7.2075457407407395, 3.4100589300411532, 2.4921284885895996, 1.439419204389575, 0.0, 18.225, 15.833611248285322, 12.460642442947998, 10.230176790123457, 14.415091481481479, 8.981460185185188, 7.1679258896151055, 4.6875, 6.729328359976827, 5.520170949074076, 3.077878806584363, 1.5893177983539097, 0.0), # 44
(18.55978051032399, 17.428124999999998, 15.375, 16.548046875, 13.462081849155954, 6.5625, 7.147058823529412, 6.375000000000001, 7.201874999999999, 3.3993750000000014, 2.4903409090909094, 1.4375000000000002, 0.0, 18.225, 15.8125, 12.451704545454545, 10.198125000000001, 14.403749999999999, 8.925, 7.147058823529412, 4.6875, 6.731040924577977, 5.516015625000001, 3.075, 1.584375, 0.0), # 45
(18.569684287392985, 17.372531995884774, 15.360235596707819, 16.535205902777776, 13.465360482164058, 6.5625, 7.125734241103849, 6.334115740740741, 7.196048703703703, 3.388483662551441, 2.4884927235316128, 1.4355314128943761, 0.0, 18.225, 15.790845541838134, 12.442463617658062, 10.16545098765432, 14.392097407407405, 8.86776203703704, 7.125734241103849, 4.6875, 6.732680241082029, 5.511735300925927, 3.072047119341564, 1.5793210905349795, 0.0), # 46
(18.579152931192063, 17.31582541152263, 15.345125514403293, 16.522006597222223, 13.46849239871744, 6.5625, 7.103994843863473, 6.292787037037037, 7.190075740740742, 3.3774091152263384, 2.486585727646839, 1.4335167352537728, 0.0, 18.225, 15.768684087791497, 12.432928638234193, 10.132227345679013, 14.380151481481484, 8.809901851851851, 7.103994843863473, 4.6875, 6.73424619935872, 5.507335532407408, 3.069025102880659, 1.5741659465020577, 0.0), # 47
(18.588185009232834, 17.258113888888886, 15.329694444444444, 16.508465625, 13.471477378555573, 6.5625, 7.081883333333334, 6.251125000000001, 7.183965000000001, 3.3661755555555564, 2.4846217171717173, 1.4314592592592594, 0.0, 18.225, 15.746051851851853, 12.423108585858586, 10.098526666666666, 14.367930000000001, 8.751575, 7.081883333333334, 4.6875, 6.735738689277786, 5.502821875000001, 3.065938888888889, 1.5689194444444445, 0.0), # 48
(18.596779089026917, 17.199506069958847, 15.313967078189304, 16.49459965277778, 13.47431520141793, 6.5625, 7.059442411038489, 6.209240740740741, 7.17772537037037, 3.35480718106996, 2.4826024878413775, 1.4293622770919072, 0.0, 18.225, 15.722985048010976, 12.413012439206886, 10.064421543209878, 14.35545074074074, 8.692937037037037, 7.059442411038489, 4.6875, 6.737157600708965, 5.498199884259261, 3.0627934156378607, 1.5635914609053498, 0.0), # 49
(18.604933738085908, 17.140110596707824, 15.297968106995889, 16.480425347222223, 13.477005647043978, 6.5625, 7.0367147785039945, 6.16724537037037, 7.1713657407407405, 3.3433281893004123, 2.480529835390947, 1.427229080932785, 0.0, 18.225, 15.699519890260632, 12.402649176954732, 10.029984567901234, 14.342731481481481, 8.634143518518519, 7.0367147785039945, 4.6875, 6.738502823521989, 5.4934751157407415, 3.059593621399178, 1.5581918724279842, 0.0), # 50
(18.61264752392144, 17.080036111111113, 15.281722222222223, 16.465959375, 13.479548495173198, 6.5625, 7.013743137254902, 6.12525, 7.164895000000001, 3.3317627777777785, 2.478405555555556, 1.4250629629629634, 0.0, 18.225, 15.675692592592595, 12.392027777777779, 9.995288333333333, 14.329790000000003, 8.57535, 7.013743137254902, 4.6875, 6.739774247586599, 5.488653125000001, 3.0563444444444445, 1.552730555555556, 0.0), # 51
(18.619919014045102, 17.019391255144033, 15.26525411522634, 16.45121840277778, 13.481943525545056, 6.5625, 6.9905701888162675, 6.08336574074074, 7.158322037037037, 3.320135144032923, 2.4762314440703332, 1.4228672153635122, 0.0, 18.225, 15.651539368998632, 12.381157220351666, 9.960405432098767, 14.316644074074073, 8.516712037037037, 6.9905701888162675, 4.6875, 6.740971762772528, 5.483739467592594, 3.0530508230452678, 1.547217386831276, 0.0), # 52
(18.626746775968517, 16.958284670781893, 15.248588477366258, 16.43621909722222, 13.484190517899034, 6.5625, 6.967238634713145, 6.041703703703704, 7.1516557407407415, 3.3084694855967087, 2.4740092966704084, 1.4206451303155008, 0.0, 18.225, 15.627096433470507, 12.37004648335204, 9.925408456790123, 14.303311481481483, 8.458385185185186, 6.967238634713145, 4.6875, 6.742095258949517, 5.478739699074075, 3.049717695473252, 1.5416622427983542, 0.0), # 53
(18.63312937720329, 16.896825000000003, 15.23175, 16.420978125, 13.486289251974604, 6.5625, 6.943791176470588, 6.000374999999999, 7.144905, 3.296790000000001, 2.4717409090909093, 1.4184000000000003, 0.0, 18.225, 15.602400000000001, 12.358704545454545, 9.89037, 14.28981, 8.400525, 6.943791176470588, 4.6875, 6.743144625987302, 5.473659375000001, 3.04635, 1.5360750000000005, 0.0), # 54
(18.63906538526104, 16.835120884773662, 15.2147633744856, 16.405512152777778, 13.488239507511228, 6.5625, 6.9202705156136535, 5.9594907407407405, 7.1380787037037035, 3.2851208847736637, 2.4694280770669663, 1.4161351165980798, 0.0, 18.225, 15.577486282578874, 12.34714038533483, 9.855362654320988, 14.276157407407407, 8.343287037037037, 6.9202705156136535, 4.6875, 6.744119753755614, 5.468504050925927, 3.04295267489712, 1.530465534979424, 0.0), # 55
(18.64455336765337, 16.77328096707819, 15.197653292181073, 16.389837847222225, 13.49004106424839, 6.5625, 6.896719353667393, 5.9191620370370375, 7.131185740740741, 3.2734863374485608, 2.467072596333708, 1.4138537722908093, 0.0, 18.225, 15.5523914951989, 12.335362981668538, 9.82045901234568, 14.262371481481482, 8.286826851851853, 6.896719353667393, 4.6875, 6.745020532124195, 5.463279282407409, 3.0395306584362145, 1.5248437242798356, 0.0), # 56
(18.649591891891887, 16.711413888888888, 15.180444444444445, 16.373971875, 13.49169370192556, 6.5625, 6.873180392156863, 5.879500000000001, 7.124235, 3.2619105555555565, 2.4646762626262633, 1.4115592592592594, 0.0, 18.225, 15.527151851851851, 12.323381313131314, 9.785731666666667, 14.24847, 8.231300000000001, 6.873180392156863, 4.6875, 6.74584685096278, 5.457990625000001, 3.0360888888888895, 1.5192194444444447, 0.0), # 57
(18.654179525488225, 16.64962829218107, 15.163161522633745, 16.357930902777774, 13.49319720028221, 6.5625, 6.849696332607118, 5.840615740740741, 7.11723537037037, 3.2504177366255154, 2.4622408716797612, 1.4092548696844995, 0.0, 18.225, 15.501803566529492, 12.311204358398806, 9.751253209876543, 14.23447074074074, 8.176862037037038, 6.849696332607118, 4.6875, 6.746598600141105, 5.4526436342592595, 3.032632304526749, 1.5136025720164612, 0.0), # 58
(18.658314835953966, 16.58803281893004, 15.145829218106996, 16.34173159722222, 13.494551339057814, 6.5625, 6.82630987654321, 5.802620370370371, 7.110195740740741, 3.2390320781893016, 2.4597682192293306, 1.4069438957475995, 0.0, 18.225, 15.476382853223592, 12.298841096146651, 9.717096234567903, 14.220391481481482, 8.12366851851852, 6.82630987654321, 4.6875, 6.747275669528907, 5.447243865740742, 3.0291658436213997, 1.5080029835390947, 0.0), # 59
(18.661996390800738, 16.526736111111113, 15.128472222222221, 16.325390625, 13.495755897991843, 6.5625, 6.803063725490196, 5.765625, 7.103125, 3.2277777777777787, 2.4572601010101014, 1.40462962962963, 0.0, 18.225, 15.450925925925928, 12.286300505050505, 9.683333333333334, 14.20625, 8.071875, 6.803063725490196, 4.6875, 6.747877948995922, 5.441796875000001, 3.0256944444444445, 1.502430555555556, 0.0), # 60
(18.665222757540146, 16.465846810699592, 15.111115226337452, 16.308924652777776, 13.496810656823772, 6.5625, 6.780000580973129, 5.729740740740741, 7.0960320370370376, 3.216679032921812, 2.4547183127572016, 1.40231536351166, 0.0, 18.225, 15.425468998628258, 12.273591563786008, 9.650037098765434, 14.192064074074075, 8.021637037037038, 6.780000580973129, 4.6875, 6.748405328411886, 5.436308217592593, 3.0222230452674905, 1.496895164609054, 0.0), # 61
(18.66799250368381, 16.40547355967078, 15.093782921810703, 16.292350347222225, 13.497715395293081, 6.5625, 6.757163144517066, 5.695078703703705, 7.088925740740741, 3.2057600411522644, 2.4521446502057613, 1.4000043895747603, 0.0, 18.225, 15.40004828532236, 12.260723251028807, 9.61728012345679, 14.177851481481483, 7.973110185185186, 6.757163144517066, 4.6875, 6.748857697646541, 5.430783449074076, 3.018756584362141, 1.4914066872427985, 0.0), # 62
(18.670304196743327, 16.345724999999998, 15.0765, 16.275684375, 13.498469893139227, 6.5625, 6.734594117647059, 5.6617500000000005, 7.081815, 3.195045000000001, 2.4495409090909095, 1.3977000000000002, 0.0, 18.225, 15.3747, 12.247704545454548, 9.585135, 14.16363, 7.926450000000001, 6.734594117647059, 4.6875, 6.749234946569613, 5.425228125000001, 3.0153000000000003, 1.485975, 0.0), # 63
(18.672156404230314, 16.286709773662555, 15.059291152263373, 16.258943402777778, 13.499073930101698, 6.5625, 6.712336201888163, 5.629865740740741, 7.0747087037037035, 3.1845581069958855, 2.446908885147774, 1.3954054869684502, 0.0, 18.225, 15.34946035665295, 12.23454442573887, 9.553674320987653, 14.149417407407407, 7.881812037037038, 6.712336201888163, 4.6875, 6.749536965050849, 5.419647800925927, 3.011858230452675, 1.4806099794238687, 0.0), # 64
(18.67354769365639, 16.228536522633743, 15.042181069958849, 16.242144097222223, 13.49952728591996, 6.5625, 6.690432098765433, 5.599537037037037, 7.067615740740742, 3.1743235596707824, 2.4442503741114856, 1.3931241426611796, 0.0, 18.225, 15.324365569272972, 12.221251870557428, 9.522970679012344, 14.135231481481483, 7.839351851851852, 6.690432098765433, 4.6875, 6.74976364295998, 5.4140480324074085, 3.00843621399177, 1.4753215020576131, 0.0), # 65
(18.674476632533153, 16.17131388888889, 15.025194444444447, 16.225303125, 13.499829740333489, 6.5625, 6.668924509803921, 5.570875000000001, 7.060545000000001, 3.1643655555555563, 2.4415671717171716, 1.3908592592592597, 0.0, 18.225, 15.299451851851854, 12.207835858585858, 9.493096666666666, 14.121090000000002, 7.799225000000001, 6.668924509803921, 4.6875, 6.749914870166744, 5.408434375000001, 3.0050388888888895, 1.4701194444444448, 0.0), # 66
(18.674941788372227, 16.11515051440329, 15.00835596707819, 16.208437152777776, 13.499981073081756, 6.5625, 6.647856136528685, 5.543990740740742, 7.05350537037037, 3.154708292181071, 2.438861073699963, 1.3886141289437586, 0.0, 18.225, 15.274755418381341, 12.194305368499816, 9.464124876543211, 14.10701074074074, 7.761587037037039, 6.647856136528685, 4.6875, 6.749990536540878, 5.40281238425926, 3.001671193415638, 1.465013683127572, 0.0), # 67
(18.674624906065485, 16.059860254878533, 14.99160892489712, 16.19141634963768, 13.499853546356814, 6.56237821216278, 6.627163675346682, 5.518757887517148, 7.046452709190673, 3.145329198741226, 2.436085796562113, 1.3863795032849615, 0.0, 18.22477527006173, 15.250174536134574, 12.180428982810565, 9.435987596223676, 14.092905418381346, 7.726261042524007, 6.627163675346682, 4.6874130086877, 6.749926773178407, 5.3971387832125615, 2.998321784979424, 1.4599872958980487, 0.0), # 68
(18.671655072463768, 16.00375510752688, 14.974482638888889, 16.173382744565217, 13.498692810457515, 6.561415432098766, 6.606241363211952, 5.493824074074074, 7.039078703703703, 3.1359628758169937, 2.4329588516746417, 1.3840828460038987, 0.0, 18.222994791666668, 15.224911306042884, 12.164794258373206, 9.407888627450978, 14.078157407407407, 7.6913537037037045, 6.606241363211952, 4.686725308641976, 6.749346405228757, 5.391127581521739, 2.994896527777778, 1.4548868279569895, 0.0), # 69
(18.665794417606012, 15.946577558741536, 14.956902649176953, 16.154217617753623, 13.496399176954732, 6.559519318701418, 6.5849941211052325, 5.468964334705077, 7.031341735253773, 3.1265637860082314, 2.429444665957824, 1.3817134141939216, 0.0, 18.219478202160495, 15.198847556133135, 12.147223329789119, 9.379691358024692, 14.062683470507546, 7.656550068587107, 6.5849941211052325, 4.685370941929584, 6.748199588477366, 5.384739205917875, 2.9913805298353906, 1.4496888689765035, 0.0), # 70
(18.657125389157272, 15.888361778176023, 14.938875128600824, 16.133949230072467, 13.493001694504963, 6.556720598994056, 6.56343149358509, 5.444186899862826, 7.023253326474624, 3.1171321617041885, 2.425556211235159, 1.3792729405819073, 0.0, 18.21427179783951, 15.172002346400978, 12.127781056175793, 9.351396485112563, 14.046506652949247, 7.621861659807958, 6.56343149358509, 4.683371856424325, 6.746500847252482, 5.377983076690823, 2.987775025720165, 1.4443965252887296, 0.0), # 71
(18.64573043478261, 15.82914193548387, 14.92040625, 16.112605842391304, 13.488529411764706, 6.553050000000001, 6.541563025210084, 5.4195, 7.014825, 3.1076682352941183, 2.421306459330144, 1.376763157894737, 0.0, 18.207421875, 15.144394736842104, 12.10653229665072, 9.323004705882353, 14.02965, 7.587300000000001, 6.541563025210084, 4.680750000000001, 6.744264705882353, 5.370868614130436, 2.98408125, 1.4390129032258066, 0.0), # 72
(18.631692002147076, 15.768952200318596, 14.90150218621399, 16.09021571557971, 13.483011377390461, 6.548538248742569, 6.519398260538782, 5.394911865569274, 7.006068278463649, 3.0981722391672726, 2.4167083820662767, 1.374185798859288, 0.0, 18.198974729938275, 15.116043787452165, 12.083541910331384, 9.294516717501814, 14.012136556927299, 7.552876611796983, 6.519398260538782, 4.677527320530407, 6.741505688695231, 5.363405238526571, 2.9803004372427986, 1.4335411091198726, 0.0), # 73
(18.61509253891573, 15.707826742333731, 14.882169110082302, 16.06680711050725, 13.47647664003873, 6.543216072245086, 6.49694674412975, 5.37043072702332, 6.996994684499314, 3.0886444057129037, 2.411774951267057, 1.3715425962024403, 0.0, 18.18897665895062, 15.086968558226841, 12.058874756335285, 9.26593321713871, 13.993989368998628, 7.518603017832648, 6.49694674412975, 4.673725765889347, 6.738238320019365, 5.355602370169083, 2.976433822016461, 1.4279842493030668, 0.0), # 74
(18.59601449275362, 15.645799731182793, 14.862413194444443, 16.04240828804348, 13.468954248366014, 6.537114197530865, 6.47421802054155, 5.346064814814815, 6.98761574074074, 3.0790849673202625, 2.406519138755981, 1.3688352826510723, 0.0, 18.177473958333334, 15.057188109161793, 12.032595693779903, 9.237254901960785, 13.97523148148148, 7.484490740740742, 6.47421802054155, 4.669367283950618, 6.734477124183007, 5.347469429347827, 2.9724826388888888, 1.422345430107527, 0.0), # 75
(18.57454031132582, 15.582905336519316, 14.842240612139918, 16.01704750905797, 13.460473251028805, 6.53026335162323, 6.451221634332746, 5.321822359396434, 6.977942969821673, 3.069494156378602, 2.400953916356548, 1.3660655909320625, 0.0, 18.164512924382716, 15.026721500252684, 12.004769581782737, 9.208482469135802, 13.955885939643347, 7.450551303155008, 6.451221634332746, 4.664473822588021, 6.730236625514403, 5.339015836352658, 2.9684481224279837, 1.4166277578653925, 0.0), # 76
(18.55075244229737, 15.519177727996816, 14.821657536008228, 15.99075303442029, 13.451062696683609, 6.522694261545496, 6.4279671300619015, 5.2977115912208514, 6.967987894375857, 3.059872205277174, 2.3950922558922563, 1.3632352537722912, 0.0, 18.150139853395064, 14.9955877914952, 11.975461279461282, 9.179616615831518, 13.935975788751714, 7.416796227709193, 6.4279671300619015, 4.659067329675354, 6.725531348341804, 5.330251011473431, 2.964331507201646, 1.4108343389088016, 0.0), # 77
(18.524733333333334, 15.45465107526882, 14.80067013888889, 15.963553124999999, 13.440751633986928, 6.514437654320987, 6.404464052287582, 5.273740740740742, 6.957762037037036, 3.0502193464052296, 2.388947129186603, 1.3603460038986357, 0.0, 18.134401041666667, 14.963806042884991, 11.944735645933015, 9.150658039215687, 13.915524074074073, 7.383237037037039, 6.404464052287582, 4.653169753086419, 6.720375816993464, 5.3211843750000005, 2.960134027777778, 1.404968279569893, 0.0), # 78
(18.496565432098766, 15.389359547988851, 14.779284593621398, 15.935476041666668, 13.429569111595256, 6.505524256973022, 6.380721945568351, 5.249918038408779, 6.947276920438957, 3.0405358121520223, 2.382531508063087, 1.3573995740379758, 0.0, 18.117342785493825, 14.931395314417731, 11.912657540315433, 9.121607436456063, 13.894553840877913, 7.349885253772292, 6.380721945568351, 4.646803040695016, 6.714784555797628, 5.311825347222223, 2.95585691872428, 1.399032686180805, 0.0), # 79
(18.466331186258724, 15.323337315810434, 14.757507073045266, 15.906550045289855, 13.417544178165095, 6.49598479652492, 6.356750354462773, 5.226251714677641, 6.9365440672153635, 3.030821834906803, 2.375858364345207, 1.3543976969171905, 0.0, 18.09901138117284, 14.898374666089092, 11.879291821726033, 9.092465504720405, 13.873088134430727, 7.316752400548698, 6.356750354462773, 4.639989140374943, 6.708772089082547, 5.302183348429953, 2.9515014146090537, 1.3930306650736761, 0.0), # 80
(18.434113043478263, 15.256618548387095, 14.735343749999998, 15.876803396739131, 13.404705882352939, 6.48585, 6.3325588235294115, 5.202750000000001, 6.925574999999999, 3.0210776470588248, 2.36894066985646, 1.3513421052631582, 0.0, 18.079453124999997, 14.864763157894737, 11.844703349282298, 9.063232941176471, 13.851149999999999, 7.283850000000001, 6.3325588235294115, 4.63275, 6.7023529411764695, 5.292267798913045, 2.94706875, 1.3869653225806453, 0.0), # 81
(18.399993451422436, 15.189237415372364, 14.712800797325105, 15.846264356884058, 13.391083272815298, 6.475150594421583, 6.308156897326833, 5.179421124828533, 6.914381241426612, 3.011303480997338, 2.3617913964203443, 1.3482345318027582, 0.0, 18.058714313271608, 14.830579849830338, 11.80895698210172, 9.03391044299201, 13.828762482853223, 7.2511895747599455, 6.308156897326833, 4.625107567443988, 6.695541636407649, 5.2820881189613536, 2.9425601594650215, 1.3808397650338515, 0.0), # 82
(18.364054857756308, 15.121228086419752, 14.689884387860083, 15.8149611865942, 13.376705398208665, 6.463917306812986, 6.283554120413598, 5.156273319615913, 6.902974314128944, 3.001499569111596, 2.3544235158603586, 1.3450767092628693, 0.0, 18.036841242283952, 14.79584380189156, 11.772117579301792, 9.004498707334786, 13.805948628257887, 7.218782647462278, 6.283554120413598, 4.617083790580704, 6.688352699104333, 5.2716537288647345, 2.9379768775720168, 1.374657098765432, 0.0), # 83
(18.326379710144927, 15.052624731182796, 14.666600694444444, 15.78292214673913, 13.361601307189542, 6.452180864197532, 6.258760037348273, 5.133314814814815, 6.89136574074074, 2.9916661437908503, 2.3468500000000003, 1.3418703703703705, 0.0, 18.013880208333333, 14.760574074074073, 11.73425, 8.97499843137255, 13.78273148148148, 7.186640740740741, 6.258760037348273, 4.608700617283951, 6.680800653594771, 5.260974048913044, 2.933320138888889, 1.3684204301075271, 0.0), # 84
(18.287050456253354, 14.983461519315012, 14.642955889917694, 15.750175498188408, 13.345800048414427, 6.439971993598538, 6.233784192689422, 5.110553840877915, 6.879567043895747, 2.981803437424353, 2.3390838206627684, 1.338617247852141, 0.0, 17.989877507716052, 14.724789726373547, 11.69541910331384, 8.945410312273058, 13.759134087791494, 7.154775377229082, 6.233784192689422, 4.5999799954275264, 6.672900024207213, 5.250058499396137, 2.928591177983539, 1.362132865392274, 0.0), # 85
(18.246149543746643, 14.913772620469931, 14.618956147119343, 15.716749501811597, 13.32933067053982, 6.427321422039324, 6.208636130995608, 5.087998628257887, 6.86758974622771, 2.9719116824013563, 2.3311379496721605, 1.3353190744350594, 0.0, 17.964879436728395, 14.68850981878565, 11.655689748360802, 8.915735047204068, 13.73517949245542, 7.123198079561043, 6.208636130995608, 4.590943872885232, 6.66466533526991, 5.2389165006038665, 2.923791229423869, 1.3557975109518121, 0.0), # 86
(18.203759420289852, 14.843592204301075, 14.594607638888888, 15.68267241847826, 13.312222222222225, 6.41425987654321, 6.1833253968253965, 5.065657407407408, 6.855445370370372, 2.9619911111111112, 2.323025358851675, 1.3319775828460039, 0.0, 17.938932291666667, 14.651753411306041, 11.615126794258373, 8.885973333333332, 13.710890740740744, 7.091920370370371, 6.1833253968253965, 4.581614197530865, 6.656111111111112, 5.227557472826088, 2.9189215277777776, 1.3494174731182798, 0.0), # 87
(18.159962533548043, 14.772954440461966, 14.569916538065844, 15.647972509057974, 13.294503752118132, 6.400818084133517, 6.157861534737352, 5.043538408779149, 6.843145438957476, 2.952041955942871, 2.31475902002481, 1.328594505811855, 0.0, 17.912082368827164, 14.614539563930402, 11.573795100124048, 8.856125867828611, 13.686290877914953, 7.06095377229081, 6.157861534737352, 4.572012917238227, 6.647251876059066, 5.215990836352659, 2.913983307613169, 1.3429958582238153, 0.0), # 88
(18.11484133118626, 14.701893498606132, 14.544889017489714, 15.612678034420288, 13.276204308884047, 6.387026771833563, 6.132254089290037, 5.0216498628257895, 6.830701474622771, 2.942064449285888, 2.3063519050150636, 1.3251715760594904, 0.0, 17.884375964506173, 14.576887336654393, 11.531759525075316, 8.826193347857663, 13.661402949245542, 7.0303098079561055, 6.132254089290037, 4.562161979881116, 6.638102154442024, 5.2042260114734304, 2.908977803497943, 1.3365357726005578, 0.0), # 89
(18.068478260869565, 14.630443548387097, 14.519531250000002, 15.576817255434786, 13.257352941176471, 6.372916666666668, 6.106512605042017, 5.0, 6.818125, 2.9320588235294123, 2.2978169856459334, 1.3217105263157898, 0.0, 17.855859375, 14.538815789473684, 11.489084928229666, 8.796176470588236, 13.63625, 7.0, 6.106512605042017, 4.552083333333334, 6.6286764705882355, 5.192272418478263, 2.903906250000001, 1.3300403225806454, 0.0), # 90
(18.020955770263015, 14.558638759458383, 14.493849408436214, 15.540418432971018, 13.237978697651899, 6.35851849565615, 6.0806466265518555, 4.978597050754459, 6.80542753772291, 2.922025311062697, 2.2891672337409186, 1.3182130893076314, 0.0, 17.826578896604936, 14.500343982383942, 11.445836168704592, 8.76607593318809, 13.61085507544582, 6.9700358710562424, 6.0806466265518555, 4.541798925468679, 6.6189893488259495, 5.180139477657007, 2.898769881687243, 1.3235126144962168, 0.0), # 91
(17.97235630703167, 14.486513301473519, 14.467849665637862, 15.50350982789855, 13.218110626966835, 6.343862985825332, 6.054665698378118, 4.957449245541839, 6.7926206104252405, 2.9119641442749944, 2.2804156211235163, 1.3146809977618947, 0.0, 17.796580825617283, 14.46149097538084, 11.40207810561758, 8.735892432824983, 13.585241220850481, 6.940428943758574, 6.054665698378118, 4.531330704160951, 6.609055313483418, 5.167836609299518, 2.8935699331275724, 1.3169557546794108, 0.0), # 92
(17.92276231884058, 14.414101344086022, 14.441538194444446, 15.46611970108696, 13.197777777777777, 6.328980864197531, 6.0285793650793655, 4.936564814814815, 6.779715740740741, 2.9018755555555558, 2.2715751196172254, 1.3111159844054583, 0.0, 17.76591145833333, 14.422275828460037, 11.357875598086125, 8.705626666666666, 13.559431481481482, 6.911190740740742, 6.0285793650793655, 4.520700617283951, 6.598888888888888, 5.155373233695654, 2.888307638888889, 1.3103728494623659, 0.0), # 93
(17.872256253354806, 14.341437056949422, 14.414921167695475, 15.428276313405796, 13.177009198741224, 6.313902857796068, 6.002397171214165, 4.915951989026064, 6.766724451303155, 2.891759777293634, 2.2626587010455435, 1.3075197819652014, 0.0, 17.734617091049383, 14.382717601617212, 11.313293505227715, 8.675279331880901, 13.53344890260631, 6.88233278463649, 6.002397171214165, 4.509930612711477, 6.588504599370612, 5.1427587711352665, 2.882984233539095, 1.3037670051772203, 0.0), # 94
(17.820920558239397, 14.268554609717246, 14.388004758230455, 15.390007925724635, 13.155833938513677, 6.298659693644262, 5.97612866134108, 4.895618998628259, 6.753658264746228, 2.88161704187848, 2.253679337231969, 1.3038941231680024, 0.0, 17.70274402006173, 14.342835354848022, 11.268396686159845, 8.644851125635439, 13.507316529492456, 6.853866598079563, 5.97612866134108, 4.49904263831733, 6.577916969256838, 5.130002641908213, 2.8776009516460914, 1.2971413281561135, 0.0), # 95
(17.76883768115942, 14.195488172043014, 14.360795138888891, 15.351342798913045, 13.134281045751635, 6.283282098765432, 5.9497833800186735, 4.875574074074075, 6.740528703703703, 2.8714475816993468, 2.2446500000000005, 1.300240740740741, 0.0, 17.67033854166667, 14.30264814814815, 11.22325, 8.614342745098039, 13.481057407407405, 6.825803703703705, 5.9497833800186735, 4.488058641975309, 6.5671405228758175, 5.117114266304349, 2.8721590277777787, 1.2904989247311833, 0.0), # 96
(17.716090069779927, 14.12227191358025, 14.333298482510289, 15.31230919384058, 13.112379569111596, 6.267800800182899, 5.9233708718055125, 4.855825445816188, 6.727347290809328, 2.8612516291454857, 2.235583661173135, 1.2965613674102956, 0.0, 17.637446952160495, 14.262175041513249, 11.177918305865674, 8.583754887436456, 13.454694581618655, 6.798155624142662, 5.9233708718055125, 4.477000571559214, 6.556189784555798, 5.104103064613527, 2.8666596965020577, 1.2838429012345685, 0.0), # 97
(17.66276017176597, 14.048940003982477, 14.305520961934155, 15.27293537137681, 13.090158557250064, 6.252246524919983, 5.896900681260158, 4.83638134430727, 6.714125548696844, 2.851029416606149, 2.226493292574872, 1.2928577359035447, 0.0, 17.604115547839505, 14.22143509493899, 11.13246646287436, 8.553088249818446, 13.428251097393687, 6.770933882030178, 5.896900681260158, 4.465890374942845, 6.545079278625032, 5.090978457125605, 2.8611041923868314, 1.277176363998407, 0.0), # 98
(17.608930434782607, 13.975526612903225, 14.277468750000002, 15.233249592391303, 13.067647058823532, 6.23665, 5.870382352941177, 4.8172500000000005, 6.700875, 2.8407811764705886, 2.2173918660287084, 1.2891315789473687, 0.0, 17.570390625, 14.180447368421053, 11.086959330143541, 8.522343529411764, 13.40175, 6.744150000000001, 5.870382352941177, 4.45475, 6.533823529411766, 5.0777498641304355, 2.8554937500000004, 1.2705024193548389, 0.0), # 99
(17.5546833064949, 13.902065909996015, 14.249148019547325, 15.193280117753623, 13.044874122488501, 6.2210419524462734, 5.843825431407131, 4.798439643347051, 6.687607167352539, 2.8305071411280567, 2.2082923533581433, 1.285384629268645, 0.0, 17.536318479938274, 14.139230921955095, 11.041461766790714, 8.49152142338417, 13.375214334705078, 6.717815500685871, 5.843825431407131, 4.443601394604481, 6.522437061244251, 5.064426705917875, 2.8498296039094653, 1.2638241736360014, 0.0), # 100
(17.500101234567904, 13.828592064914377, 14.22056494341564, 15.153055208333335, 13.021868796901476, 6.205453109282122, 5.817239461216586, 4.7799585048010975, 6.674333573388203, 2.820207542967805, 2.1992077263866743, 1.281618619594253, 0.0, 17.501945408950615, 14.097804815536781, 10.99603863193337, 8.460622628903414, 13.348667146776407, 6.691941906721536, 5.817239461216586, 4.432466506630087, 6.510934398450738, 5.051018402777779, 2.8441129886831282, 1.2571447331740344, 0.0), # 101
(17.44526666666667, 13.755139247311828, 14.191725694444445, 15.112603125, 12.998660130718955, 6.189914197530865, 5.790633986928105, 4.761814814814815, 6.66106574074074, 2.809882614379086, 2.1901509569377993, 1.2778352826510724, 0.0, 17.467317708333336, 14.056188109161795, 10.950754784688995, 8.429647843137257, 13.32213148148148, 6.666540740740741, 5.790633986928105, 4.421367283950618, 6.499330065359477, 5.037534375000001, 2.838345138888889, 1.2504672043010754, 0.0), # 102
(17.390262050456254, 13.681741626841896, 14.16263644547325, 15.071952128623188, 12.975277172597433, 6.174455944215821, 5.764018553100253, 4.7440168038408785, 6.647815192043895, 2.7995325877511505, 2.181135016835017, 1.2740363511659811, 0.0, 17.432481674382714, 14.014399862825789, 10.905675084175085, 8.39859776325345, 13.29563038408779, 6.64162352537723, 5.764018553100253, 4.410325674439872, 6.487638586298717, 5.023984042874397, 2.8325272890946502, 1.2437946933492634, 0.0), # 103
(17.335169833601718, 13.608433373158105, 14.133303369341563, 15.031130480072465, 12.951748971193414, 6.159109076360311, 5.737402704291593, 4.7265727023319615, 6.634593449931413, 2.7891576954732518, 2.1721728779018252, 1.2702235578658583, 0.0, 17.397483603395063, 13.972459136524439, 10.860864389509127, 8.367473086419754, 13.269186899862826, 6.617201783264746, 5.737402704291593, 4.399363625971651, 6.475874485596707, 5.010376826690822, 2.826660673868313, 1.237130306650737, 0.0), # 104
(17.280072463768114, 13.535248655913978, 14.103732638888891, 14.99016644021739, 12.928104575163397, 6.143904320987655, 5.710795985060692, 4.709490740740741, 6.621412037037037, 2.7787581699346413, 2.1632775119617227, 1.2663986354775831, 0.0, 17.362369791666666, 13.930384990253412, 10.816387559808613, 8.336274509803923, 13.242824074074074, 6.5932870370370384, 5.710795985060692, 4.388503086419754, 6.464052287581699, 4.996722146739131, 2.820746527777778, 1.2304771505376346, 0.0), # 105
(17.225052388620504, 13.462221644763043, 14.073930426954732, 14.949088269927536, 12.904373033163882, 6.128872405121171, 5.68420793996611, 4.6927791495198905, 6.608282475994512, 2.7683342435245706, 2.1544618908382067, 1.2625633167280343, 0.0, 17.327186535493826, 13.888196484008375, 10.772309454191033, 8.30500273057371, 13.216564951989024, 6.5698908093278465, 5.68420793996611, 4.377766003657979, 6.452186516581941, 4.98302942330918, 2.8147860853909465, 1.223838331342095, 0.0), # 106
(17.17019205582394, 13.389386509358822, 14.043902906378605, 14.907924230072464, 12.880583393851367, 6.114044055784181, 5.657648113566415, 4.6764461591220865, 6.595216289437586, 2.7578861486322928, 2.145738986354776, 1.2587193343440908, 0.0, 17.29198013117284, 13.845912677784996, 10.728694931773878, 8.273658445896878, 13.190432578875171, 6.547024622770921, 5.657648113566415, 4.367174325560129, 6.440291696925684, 4.969308076690822, 2.808780581275721, 1.2172169553962566, 0.0), # 107
(17.11557391304348, 13.31677741935484, 14.013656250000002, 14.866702581521741, 12.856764705882352, 6.099450000000001, 5.631126050420168, 4.660500000000001, 6.582225000000001, 2.7474141176470597, 2.1371217703349283, 1.2548684210526317, 0.0, 17.256796875000003, 13.803552631578947, 10.685608851674642, 8.242242352941178, 13.164450000000002, 6.524700000000001, 5.631126050420168, 4.356750000000001, 6.428382352941176, 4.955567527173915, 2.8027312500000003, 1.2106161290322583, 0.0), # 108
(17.061280407944178, 13.24442854440462, 13.983196630658439, 14.825451585144926, 12.832946017913338, 6.085120964791952, 5.604651295085936, 4.644948902606311, 6.569320130315501, 2.736918382958122, 2.1286232146021624, 1.2510123095805359, 0.0, 17.221683063271605, 13.761135405385891, 10.64311607301081, 8.210755148874364, 13.138640260631002, 6.502928463648835, 5.604651295085936, 4.346514974851394, 6.416473008956669, 4.941817195048309, 2.796639326131688, 1.2040389585822384, 0.0), # 109
(17.007393988191087, 13.17237405416169, 13.95253022119342, 14.784199501811596, 12.809156378600825, 6.071087677183356, 5.57823339212228, 4.62980109739369, 6.556513203017833, 2.726399176954733, 2.120256290979975, 1.2471527326546823, 0.0, 17.18668499228395, 13.718680059201501, 10.601281454899876, 8.179197530864197, 13.113026406035665, 6.4817215363511655, 5.57823339212228, 4.336491197988112, 6.404578189300413, 4.928066500603866, 2.790506044238684, 1.1974885503783357, 0.0), # 110
(16.953997101449275, 13.10064811827957, 13.921663194444447, 14.742974592391306, 12.785424836601308, 6.0573808641975315, 5.551881886087768, 4.615064814814815, 6.543815740740741, 2.715856732026144, 2.1120339712918663, 1.2432914230019496, 0.0, 17.151848958333336, 13.676205653021444, 10.56016985645933, 8.147570196078432, 13.087631481481482, 6.461090740740741, 5.551881886087768, 4.326700617283951, 6.392712418300654, 4.914324864130436, 2.78433263888889, 1.1909680107526885, 0.0), # 111
(16.90117219538379, 13.029284906411787, 13.890601723251033, 14.701805117753622, 12.76178044057129, 6.044031252857797, 5.5256063215409625, 4.60074828532236, 6.531239266117969, 2.7052912805616076, 2.103969227361333, 1.2394301133492167, 0.0, 17.11722125771605, 13.633731246841382, 10.519846136806663, 8.115873841684822, 13.062478532235938, 6.441047599451304, 5.5256063215409625, 4.3171651806127125, 6.380890220285645, 4.900601705917875, 2.778120344650207, 1.1844804460374354, 0.0), # 112
(16.84890760266548, 12.958437720996821, 13.859426742378105, 14.660775741364255, 12.738210816208445, 6.03106325767524, 5.499473367291093, 4.586889426585454, 6.518827686755172, 2.694737131475729, 2.0960771718458604, 1.2355789404756645, 0.0, 17.0827990215178, 13.591368345232306, 10.480385859229301, 8.084211394427186, 13.037655373510344, 6.421645197219636, 5.499473367291093, 4.307902326910885, 6.369105408104223, 4.886925247121419, 2.7718853484756214, 1.178039792817893, 0.0), # 113
(16.796665616220118, 12.888805352817133, 13.828568512532428, 14.620215718724406, 12.71447202547959, 6.018447338956397, 5.473816387569522, 4.57365844462884, 6.506771421427836, 2.684391825560753, 2.0883733011339594, 1.2317868258169462, 0.0, 17.048295745488062, 13.549655083986407, 10.441866505669795, 8.053175476682258, 13.013542842855673, 6.403121822480377, 5.473816387569522, 4.298890956397426, 6.357236012739795, 4.873405239574803, 2.7657137025064857, 1.1717095775288306, 0.0), # 114
(16.744292825407193, 12.820412877827026, 13.798045399060976, 14.580114081995404, 12.690489213466321, 6.006150688123703, 5.448653685172405, 4.561051990709032, 6.495074987201274, 2.674271397594635, 2.0808463534281283, 1.2280556373838278, 0.0, 17.013611936988678, 13.508612011222104, 10.404231767140642, 8.022814192783905, 12.990149974402549, 6.385472786992645, 5.448653685172405, 4.290107634374073, 6.345244606733161, 4.860038027331802, 2.7596090798121957, 1.165492079802457, 0.0), # 115
(16.691723771827743, 12.753160664131308, 13.767798284975811, 14.540399302859647, 12.666226231660534, 5.994144321151453, 5.423944335775104, 4.549035234674245, 6.483708803536698, 2.6643570113022967, 2.0734817793814444, 1.224378479623102, 0.0, 16.978693067560602, 13.46816327585412, 10.367408896907222, 7.9930710339068884, 12.967417607073395, 6.368649328543944, 5.423944335775104, 4.281531657965324, 6.333113115830267, 4.846799767619883, 2.7535596569951624, 1.1593782421937553, 0.0), # 116
(16.63889299708279, 12.686949079834788, 13.73776805328898, 14.50099985299953, 12.641646931554131, 5.982399254013936, 5.399647415052978, 4.537573346372689, 6.472643289895322, 2.6546298304086586, 2.0662650296469853, 1.2207484569815625, 0.0, 16.943484608744804, 13.428233026797187, 10.331325148234924, 7.963889491225975, 12.945286579790643, 6.352602684921765, 5.399647415052978, 4.2731423242956685, 6.320823465777066, 4.833666617666511, 2.747553610657796, 1.1533590072577082, 0.0), # 117
(16.58573504277338, 12.621678493042284, 13.707895587012551, 14.461844204097451, 12.616715164639011, 5.970886502685445, 5.375721998681383, 4.526631495652572, 6.461848865738361, 2.6450710186386424, 2.0591815548778274, 1.2171586739060027, 0.0, 16.907932032082243, 13.388745412966028, 10.295907774389137, 7.935213055915925, 12.923697731476722, 6.337284093913602, 5.375721998681383, 4.264918930489604, 6.3083575823195055, 4.820614734699151, 2.74157911740251, 1.1474253175492988, 0.0), # 118
(16.532184450500534, 12.557249271858602, 13.678121769158587, 14.422860827835802, 12.591394782407065, 5.9595770831402755, 5.35212716233568, 4.516174852362109, 6.451295950527026, 2.6356617397171678, 2.0522168057270487, 1.2136022348432152, 0.0, 16.87198080911388, 13.349624583275366, 10.261084028635242, 7.906985219151502, 12.902591901054052, 6.322644793306953, 5.35212716233568, 4.256840773671625, 6.295697391203532, 4.807620275945268, 2.7356243538317178, 1.1415681156235096, 0.0), # 119
(16.47817576186529, 12.49356178438856, 13.648387482739144, 14.383978195896983, 12.565649636350196, 5.948442011352714, 5.3288219816912274, 4.506168586349507, 6.440954963722534, 2.626383157369158, 2.045356232847725, 1.2100722442399947, 0.0, 16.835576411380675, 13.31079468663994, 10.226781164238623, 7.879149472107472, 12.881909927445069, 6.308636020889311, 5.3288219816912274, 4.248887150966224, 6.282824818175098, 4.794659398632328, 2.7296774965478288, 1.1357783440353237, 0.0), # 120
(16.423643518468683, 12.430516398736968, 13.618633610766281, 14.345124779963385, 12.539443577960302, 5.937452303297058, 5.305765532423383, 4.49657786746298, 6.430796324786099, 2.6172164353195337, 2.038585286892935, 1.2065618065431336, 0.0, 16.79866431042359, 13.272179871974467, 10.192926434464676, 7.8516493059586, 12.861592649572199, 6.295209014448172, 5.305765532423383, 4.2410373594978985, 6.269721788980151, 4.781708259987796, 2.7237267221532564, 1.1300469453397246, 0.0), # 121
(16.36852226191174, 12.368013483008635, 13.588801036252066, 14.306229051717406, 12.51274045872928, 5.926578974947596, 5.282916890207506, 4.487367865550737, 6.420790453178933, 2.6081427372932153, 2.0318894185157554, 1.2030640261994254, 0.0, 16.761189977783587, 13.233704288193676, 10.159447092578777, 7.824428211879645, 12.841580906357866, 6.282315011771032, 5.282916890207506, 4.2332706963911395, 6.25637022936464, 4.768743017239136, 2.7177602072504135, 1.1243648620916942, 0.0), # 122
(16.312746533795494, 12.305953405308378, 13.558830642208555, 14.267219482841437, 12.485504130149028, 5.915793042278621, 5.260235130718955, 4.478503750460988, 6.410907768362252, 2.5991432270151247, 2.0252540783692634, 1.1995720076556633, 0.0, 16.72309888500163, 13.195292084212294, 10.126270391846315, 7.797429681045372, 12.821815536724504, 6.269905250645383, 5.260235130718955, 4.225566458770444, 6.242752065074514, 4.755739827613813, 2.711766128441711, 1.1187230368462162, 0.0), # 123
(16.256250875720976, 12.244236533741004, 13.528663311647806, 14.228024545017881, 12.457698443711445, 5.905065521264426, 5.237679329633088, 4.469950692041945, 6.401118689797269, 2.590199068210183, 2.018664717106536, 1.1960788553586414, 0.0, 16.68433650361868, 13.156867408945052, 10.09332358553268, 7.770597204630548, 12.802237379594539, 6.257930968858723, 5.237679329633088, 4.217903943760304, 6.2288492218557225, 4.742674848339295, 2.7057326623295617, 1.1131124121582732, 0.0), # 124
(16.198969829289226, 12.18276323641133, 13.498239927581887, 14.188572709929128, 12.429287250908427, 5.894367427879304, 5.215208562625265, 4.461673860141818, 6.391393636945196, 2.5812914246033105, 2.012106785380651, 1.1925776737551523, 0.0, 16.644848305175692, 13.118354411306674, 10.060533926903252, 7.74387427380993, 12.782787273890392, 6.246343404198546, 5.215208562625265, 4.210262448485217, 6.2146436254542134, 4.7295242366430434, 2.6996479855163775, 1.1075239305828484, 0.0), # 125
(16.14083793610127, 12.121433881424165, 13.46750137302285, 14.148792449257574, 12.400234403231872, 5.883669778097547, 5.192781905370843, 4.453638424608819, 6.381703029267251, 2.57240145991943, 2.005565733844684, 1.1890615672919902, 0.0, 16.604579761213643, 13.079677240211891, 10.02782866922342, 7.717204379758288, 12.763406058534501, 6.235093794452347, 5.192781905370843, 4.202621270069677, 6.200117201615936, 4.716264149752526, 2.69350027460457, 1.1019485346749243, 0.0), # 126
(16.08178973775815, 12.06014883688432, 13.436388530982757, 14.108612234685616, 12.370503752173677, 5.872943587893444, 5.170358433545185, 4.445809555291159, 6.3720172862246445, 2.563510337883461, 1.9990270131517138, 1.1855236404159475, 0.0, 16.56347634327348, 13.040760044575421, 9.99513506575857, 7.690531013650382, 12.744034572449289, 6.224133377407623, 5.170358433545185, 4.194959705638174, 6.185251876086839, 4.702870744895206, 2.6872777061965514, 1.0963771669894837, 0.0), # 127
(16.021759775860883, 11.998808470896611, 13.404842284473675, 14.06796053789565, 12.340059149225747, 5.862159873241292, 5.147897222823644, 4.438152422037048, 6.362306827278591, 2.554599222220326, 1.9924760739548175, 1.1819569975738184, 0.0, 16.521483522896165, 13.001526973312, 9.962380369774086, 7.663797666660978, 12.724613654557182, 6.2134133908518665, 5.147897222823644, 4.187257052315209, 6.170029574612873, 4.689320179298551, 2.680968456894735, 1.0908007700815103, 0.0), # 128
(15.960682592010507, 11.937313151565847, 13.37280351650766, 14.026765830570064, 12.308864445879973, 5.85128965011538, 5.125357348881582, 4.430632194694696, 6.352542071890305, 2.5456492766549457, 1.9858983669070716, 1.1783547432123955, 0.0, 16.478546771622668, 12.96190217533635, 9.929491834535357, 7.636947829964836, 12.70508414378061, 6.202885072572574, 5.125357348881582, 4.179492607225272, 6.154432222939986, 4.675588610190022, 2.6745607033015326, 1.0852102865059863, 0.0), # 129
(15.89849272780806, 11.875563246996844, 13.34021311009677, 13.984956584391266, 12.276883493628256, 5.840303934489999, 5.102697887394356, 4.423214043112313, 6.342693439521001, 2.536641664912241, 1.9792793426615536, 1.174709981778473, 0.0, 16.434611560993947, 12.921809799563201, 9.896396713307768, 7.609924994736723, 12.685386879042001, 6.192499660357238, 5.102697887394356, 4.171645667492856, 6.138441746814128, 4.66165219479709, 2.668042622019354, 1.0795966588178951, 0.0), # 130
(15.83512472485457, 11.81345912529441, 13.307011948253072, 13.942461271041642, 12.244080143962494, 5.829173742339445, 5.079877914037328, 4.415863137138113, 6.332731349631892, 2.527557550717134, 1.9726044518713404, 1.1710158177188439, 0.0, 16.38962336255096, 12.88117399490728, 9.863022259356702, 7.5826726521514, 12.665462699263784, 6.182208391993358, 5.079877914037328, 4.16369553024246, 6.122040071981247, 4.647487090347215, 2.6614023896506143, 1.073950829572219, 0.0), # 131
(15.770513124751067, 11.750901154563357, 13.27314091398862, 13.899208362203591, 12.210418248374584, 5.817870089638008, 5.056856504485853, 4.408544646620305, 6.322626221684192, 2.5183780977945447, 1.9658591451895095, 1.1672653554803014, 0.0, 16.343527647834676, 12.839918910283313, 9.829295725947548, 7.555134293383633, 12.645252443368385, 6.171962505268427, 5.056856504485853, 4.155621492598577, 6.105209124187292, 4.633069454067865, 2.654628182797724, 1.0682637413239418, 0.0), # 132
(15.704592469098595, 11.687789702908498, 13.238540890315475, 13.855126329559509, 12.175861658356425, 5.80636399235998, 5.03359273441529, 4.4012237414071, 6.312348475139116, 2.509084469869395, 1.9590288732691383, 1.1634516995096391, 0.0, 16.296269888386057, 12.797968694606027, 9.795144366345692, 7.527253409608184, 12.624696950278231, 6.1617132379699395, 5.03359273441529, 4.1474028516857, 6.087930829178212, 4.618375443186504, 2.647708178063095, 1.0625263366280455, 0.0), # 133
(15.63729729949817, 11.624025138434646, 13.203152760245707, 13.81014364479179, 12.14037422539991, 5.794626466479654, 5.010045679501001, 4.3938655913467075, 6.301868529457877, 2.499657830666606, 1.952099086763304, 1.1595679542536501, 0.0, 16.24779555574605, 12.755247496790147, 9.76049543381652, 7.498973491999817, 12.603737058915755, 6.151411827885391, 5.010045679501001, 4.139018904628324, 6.070187112699955, 4.6033812149305975, 2.6406305520491418, 1.0567295580395135, 0.0), # 134
(15.568562157550836, 11.559507829246614, 13.166917406791363, 13.764188779582833, 12.103919800996945, 5.7826285279713225, 4.986174415418341, 4.3864353662873405, 6.291156804101687, 2.4900793439110998, 1.945055236325083, 1.155607224159128, 0.0, 16.198050121455637, 12.711679465750406, 9.725276181625414, 7.470238031733298, 12.582313608203375, 6.141009512802277, 4.986174415418341, 4.130448948550945, 6.051959900498472, 4.588062926527612, 2.633383481358273, 1.0508643481133288, 0.0), # 135
(15.498321584857623, 11.494138143449213, 13.129775712964513, 13.717190205615022, 12.066462236639419, 5.770341192809277, 4.961938017842671, 4.378898236077208, 6.280183718531764, 2.4803301733277956, 1.9378827726075534, 1.1515626136728663, 0.0, 16.146979057055766, 12.667188750401527, 9.689413863037766, 7.4409905199833855, 12.560367437063528, 6.130457530508091, 4.961938017842671, 4.121672280578055, 6.033231118319709, 4.572396735205008, 2.6259551425929026, 1.044921649404474, 0.0), # 136
(15.426510123019561, 11.427816449147253, 13.091668561777217, 13.66907639457077, 12.02796538381924, 5.757735476967808, 4.93729556244935, 4.371219370564522, 6.2689196922093195, 2.4703914826416162, 1.930567146263792, 1.1474272272416581, 0.0, 16.094527834087398, 12.621699499658236, 9.652835731318959, 7.411174447924847, 12.537839384418639, 6.119707118790331, 4.93729556244935, 4.112668197834148, 6.01398269190962, 4.556358798190257, 2.6183337123554433, 1.0388924044679322, 0.0), # 137
(15.353062313637686, 11.360443114445548, 13.052536836241526, 13.619775818132457, 11.988393094028304, 5.744782396421213, 4.912206124913734, 4.363363939597493, 6.257335144595569, 2.4602444355774815, 1.9230938079468758, 1.143194169312297, 0.0, 16.040641924091503, 12.575135862435264, 9.615469039734378, 7.380733306732443, 12.514670289191137, 6.10870951543649, 4.912206124913734, 4.103415997443723, 5.994196547014152, 4.5399252727108195, 2.6105073672483052, 1.0327675558586864, 0.0), # 138
(15.277912698313022, 11.29191850744891, 13.01232141936951, 13.569216947982484, 11.947709218758497, 5.731452967143778, 4.886628780911184, 4.355297113024331, 6.245400495151722, 2.449870195860314, 1.9154482083098823, 1.1388565443315761, 0.0, 15.985266798609034, 12.527421987647335, 9.577241041549412, 7.3496105875809405, 12.490800990303445, 6.0974159582340635, 4.886628780911184, 4.093894976531271, 5.973854609379249, 4.523072315994162, 2.602464283873902, 1.0265380461317193, 0.0), # 139
(15.200995818646616, 11.22214299626215, 12.970963194173232, 13.51732825580325, 11.905877609501736, 5.717718205109798, 4.860522606117057, 4.346984060693248, 6.233086163338999, 2.439249927215034, 1.9076157980058883, 1.134407456746289, 0.0, 15.928347929180966, 12.478482024209175, 9.538078990029442, 7.3177497816451, 12.466172326677999, 6.085777684970546, 4.860522606117057, 4.084084432221284, 5.952938804750868, 4.505776085267751, 2.5941926388346466, 1.020194817842014, 0.0), # 140
(15.122246216239494, 11.151016948990085, 12.92840304366474, 13.464038213277146, 11.862862117749902, 5.7035491262935665, 4.833846676206716, 4.338389952452453, 6.220362568618608, 2.4283647933665637, 1.8995820276879718, 1.129840011003229, 0.0, 15.869830787348244, 12.428240121035515, 9.497910138439858, 7.2850943800996895, 12.440725137237216, 6.073745933433434, 4.833846676206716, 4.0739636616382615, 5.931431058874951, 4.48801273775905, 2.5856806087329485, 1.0137288135445532, 0.0), # 141
(15.041598432692682, 11.07844073373752, 12.884581850856106, 13.409275292086573, 11.818626594994903, 5.688916746669374, 4.806560066855513, 4.329479958150158, 6.207200130451765, 2.417195958039823, 1.8913323480092095, 1.1251473115491895, 0.0, 15.80966084465184, 12.37662042704108, 9.456661740046046, 7.251587874119467, 12.41440026090353, 6.061271941410222, 4.806560066855513, 4.063511961906696, 5.909313297497452, 4.469758430695525, 2.5769163701712214, 1.00713097579432, 0.0), # 142
(14.958987009607215, 11.004314718609267, 12.839440498759389, 13.352967963913915, 11.773134892728635, 5.673792082211512, 4.778621853738811, 4.320219247634575, 6.1935692682996875, 2.405724584959734, 1.8828522096226783, 1.1203224628309636, 0.0, 15.747783572632711, 12.323547091140597, 9.41426104811339, 7.217173754879202, 12.387138536599375, 6.048306946688404, 4.778621853738811, 4.05270863015108, 5.886567446364317, 4.45098932130464, 2.5678880997518783, 1.0003922471462972, 0.0), # 143
(14.874346488584132, 10.928539271710147, 12.792919870386642, 13.29504470044158, 11.726350862442994, 5.658146148894274, 4.749991112531969, 4.310572990753912, 6.1794404016235855, 2.3939318378512175, 1.8741270631814555, 1.115358569295345, 0.0, 15.684144442831826, 12.268944262248793, 9.370635315907277, 7.181795513553651, 12.358880803247171, 6.034802187055478, 4.749991112531969, 4.04153296349591, 5.863175431221497, 4.431681566813861, 2.5585839740773286, 0.993503570155468, 0.0), # 144
(14.787611411224459, 10.851014761144963, 12.744960848749933, 13.235433973351956, 11.67823835562988, 5.641949962691953, 4.7206269189103445, 4.300506357356382, 6.164783949884672, 2.381798880439195, 1.865142359338619, 1.110248735389127, 0.0, 15.618688926790139, 12.212736089280396, 9.325711796693094, 7.145396641317584, 12.329567899769344, 6.020708900298935, 4.7206269189103445, 4.029964259065681, 5.83911917781494, 4.411811324450653, 2.548992169749987, 0.986455887376815, 0.0), # 145
(14.69871631912923, 10.771641555018533, 12.695504316861326, 13.174064254327444, 11.62876122378119, 5.62517453957884, 4.690488348549297, 4.289984517290195, 6.1495703325441635, 2.3693068764485874, 1.8558835487472447, 1.104986065559103, 0.0, 15.551362496048613, 12.154846721150133, 9.279417743736223, 7.107920629345761, 12.299140665088327, 6.005978324206273, 4.690488348549297, 4.0179818139848855, 5.814380611890595, 4.391354751442482, 2.539100863372265, 0.9792401413653213, 0.0), # 146
(14.607595753899481, 10.690320021435666, 12.644491157732865, 13.110864015050435, 11.577883318388821, 5.607790895529226, 4.659534477124183, 4.278972640403562, 6.133769969063274, 2.3564369896043162, 1.846336082060411, 1.0995636642520668, 0.0, 15.482110622148213, 12.095200306772732, 9.231680410302054, 7.069310968812948, 12.267539938126548, 5.990561696564987, 4.659534477124183, 4.005564925378019, 5.7889416591944105, 4.370288005016812, 2.5288982315465733, 0.9718472746759697, 0.0), # 147
(14.51418425713624, 10.606950528501175, 12.591862254376625, 13.045761727203324, 11.525568490944673, 5.5897700465174065, 4.627724380310364, 4.2674358965446935, 6.1173532789032175, 2.3431703836313016, 1.836485409931195, 1.0939746359148106, 0.0, 15.410878776629895, 12.033720995062914, 9.182427049655974, 7.029511150893903, 12.234706557806435, 5.974410255162571, 4.627724380310364, 3.9926928903695758, 5.762784245472337, 4.348587242401109, 2.5183724508753254, 0.9642682298637433, 0.0), # 148
(14.418416370440541, 10.52143344431987, 12.537558489804665, 12.97868586246851, 11.471780592940643, 5.57108300851767, 4.595017133783196, 4.255339455561801, 6.100290681525203, 2.3294882222544664, 1.8263169830126733, 1.0882120849941288, 0.0, 15.337612431034628, 11.970332934935415, 9.131584915063366, 6.988464666763398, 12.200581363050405, 5.957475237786521, 4.595017133783196, 3.9793450060840496, 5.735890296470322, 4.326228620822837, 2.507511697960933, 0.9564939494836247, 0.0), # 149
(14.320226635413416, 10.433669136996565, 12.481520747029043, 12.909564892528387, 11.416483475868631, 5.551700797504312, 4.561371813218041, 4.242648487303093, 6.0825525963904505, 2.31537166919873, 1.815816251957923, 1.0822691159368145, 0.0, 15.262257056903364, 11.904960275304958, 9.079081259789614, 6.946115007596189, 12.165105192780901, 5.93970788222433, 4.561371813218041, 3.9655005696459367, 5.7082417379343156, 4.303188297509463, 2.4963041494058085, 0.948515376090597, 0.0), # 150
(14.219549593655895, 10.343557974636072, 12.423689909061814, 12.838327289065347, 11.359640991220532, 5.531594429451621, 4.526747494290255, 4.229328161616783, 6.064109442960174, 2.3008018881890155, 1.8049686674200216, 1.0761388331896609, 0.0, 15.184758125777073, 11.837527165086268, 9.024843337100108, 6.902405664567045, 12.128218885920347, 5.921059426263496, 4.526747494290255, 3.951138878179729, 5.679820495610266, 4.27944242968845, 2.484737981812363, 0.9403234522396431, 0.0), # 151
(14.116319786769019, 10.251000325343204, 12.364006858915053, 12.76490152376179, 11.301216990488243, 5.510734920333892, 4.491103252675198, 4.215343648351081, 6.044931640695582, 2.2857600429502427, 1.7937596800520466, 1.0698143411994616, 0.0, 15.105061109196717, 11.767957753194075, 8.968798400260232, 6.857280128850727, 12.089863281391164, 5.901481107691514, 4.491103252675198, 3.936239228809923, 5.650608495244121, 4.254967174587264, 2.4728013717830106, 0.931909120485746, 0.0), # 152
(14.010471756353809, 10.155896557222773, 12.302412479600802, 12.68921606830011, 11.241175325163667, 5.489093286125417, 4.454398164048228, 4.200660117354197, 6.024989609057894, 2.2702272972073336, 1.782174740507075, 1.0632887444130097, 0.0, 15.02311147870325, 11.696176188543106, 8.910873702535374, 6.810681891622, 12.049979218115787, 5.880924164295876, 4.454398164048228, 3.920780918661012, 5.620587662581833, 4.229738689433371, 2.4604824959201608, 0.9232633233838886, 0.0), # 153
(13.901940044011312, 10.05814703837959, 12.238847654131138, 12.611199394362703, 11.179479846738696, 5.466640542800487, 4.416591304084705, 4.185242738474343, 6.00425376750832, 2.254184814685209, 1.7701992994381837, 1.0565551472770989, 0.0, 14.938854705837642, 11.622106620048086, 8.850996497190918, 6.762554444055626, 12.00850753501664, 5.85933983386408, 4.416591304084705, 3.904743244857491, 5.589739923369348, 4.203733131454236, 2.447769530826228, 0.9143770034890537, 0.0), # 154
(13.790659191342543, 9.957652136918465, 12.173253265518113, 12.530779973631962, 11.116094406705237, 5.443347706333395, 4.377641748459985, 4.169056681559727, 5.982694535508077, 2.23761375910879, 1.7578188074984502, 1.0496066542385225, 0.0, 14.852236262140847, 11.545673196623744, 8.789094037492251, 6.712841277326369, 11.965389071016155, 5.836679354183619, 4.377641748459985, 3.8881055045238533, 5.5580472033526185, 4.176926657877321, 2.4346506531036227, 0.9052411033562243, 0.0), # 155
(13.676563739948545, 9.854312220944214, 12.10557019677379, 12.447886277790282, 11.050982856555176, 5.419185792698435, 4.33750857284943, 4.152067116458564, 5.960282332518376, 2.220495294202998, 1.7450187153409518, 1.0424363697440735, 0.0, 14.763201619153833, 11.466800067184806, 8.725093576704758, 6.661485882608993, 11.920564665036752, 5.81289396304199, 4.33750857284943, 3.870846994784596, 5.525491428277588, 4.149295425930095, 2.4211140393547583, 0.8958465655403832, 0.0), # 156
(13.559588231430352, 9.748027658561648, 12.035739330910227, 12.362446778520066, 10.984109047780422, 5.394125817869895, 4.296150852928397, 4.134239213019062, 5.9369875780004335, 2.202810583692754, 1.731784473618765, 1.0350373982405456, 0.0, 14.671696248417557, 11.385411380646001, 8.658922368093824, 6.60843175107826, 11.873975156000867, 5.787934898226687, 4.296150852928397, 3.8529470127642105, 5.492054523890211, 4.120815592840023, 2.407147866182046, 0.8861843325965136, 0.0), # 157
(13.43642570352943, 9.636747649274225, 11.960387930853534, 12.27118893522918, 10.912417327045198, 5.366575700132966, 4.252596048835072, 4.1143477142620295, 5.910997254959458, 2.1840146623310153, 1.717678725761683, 1.027139934629151, 0.0, 14.573674546947622, 11.298539280920659, 8.588393628808413, 6.552043986993045, 11.821994509918916, 5.7600867999668415, 4.252596048835072, 3.833268357237833, 5.456208663522599, 4.090396311743061, 2.3920775861707066, 0.8760679681158388, 0.0), # 158
(13.288116180561124, 9.509057777339137, 11.860106727604483, 12.155369164364412, 10.818229571737954, 5.327374130407459, 4.201391487047145, 4.085410149573287, 5.871856356733287, 2.161026447344436, 1.7002250806856987, 1.0172043785524665, 0.0, 14.445769764456351, 11.189248164077128, 8.501125403428492, 6.483079342033307, 11.743712713466573, 5.719574209402602, 4.201391487047145, 3.8052672360053275, 5.409114785868977, 4.051789721454805, 2.372021345520897, 0.8644597979399218, 0.0), # 159
(13.112769770827757, 9.363909602092178, 11.732881436933834, 12.013079639051961, 10.699704157616154, 5.275558360850069, 4.142019373545406, 4.04669939214551, 5.818455136337191, 2.1335425433383026, 1.6791778525828622, 1.0050752923331772, 0.0, 14.285557096008445, 11.055828215664945, 8.39588926291431, 6.400627630014906, 11.636910272674381, 5.665379149003714, 4.142019373545406, 3.7682559720357633, 5.349852078808077, 4.004359879683988, 2.346576287386767, 0.8512645092811072, 0.0), # 160
(12.911799698254727, 9.202249432332774, 11.580070457865464, 11.845672880071582, 10.558071749138534, 5.21175610364883, 4.0749133014061885, 3.9987003998323356, 5.751497860199411, 2.101796186926922, 1.6547224963799123, 0.9908651203361357, 0.0, 14.094673280674375, 10.899516323697492, 8.273612481899562, 6.305388560780765, 11.502995720398822, 5.59818055976527, 4.0749133014061885, 3.722682931177736, 5.279035874569267, 3.9485576266905285, 2.3160140915730927, 0.8365681302120704, 0.0), # 161
(12.686619186767443, 9.025023576860344, 11.403032189423245, 11.654501408203041, 10.394563010763845, 5.1365950709917785, 4.000506863705828, 3.941898130487402, 5.6716887947481816, 2.0660206147246045, 1.6270444670035862, 0.9746863069261941, 0.0, 13.874755057524599, 10.721549376188133, 8.13522233501793, 6.198061844173813, 11.343377589496363, 5.518657382682362, 4.000506863705828, 3.668996479279842, 5.197281505381922, 3.884833802734348, 2.280606437884649, 0.8204566888054858, 0.0), # 162
(12.438641460291295, 8.833178344474314, 11.203125030631053, 11.44091774422611, 10.210408606950825, 5.050702975066952, 3.919233653520661, 3.876777541964344, 5.579732206411743, 2.0264490633456567, 1.5963292193806227, 0.956651296468205, 0.0, 13.627439165629584, 10.523164261150253, 7.9816460969031136, 6.079347190036969, 11.159464412823485, 5.427488558750082, 3.919233653520661, 3.6076449821906795, 5.105204303475412, 3.813639248075371, 2.2406250061262107, 0.8030162131340287, 0.0), # 163
(12.16927974275169, 8.627660043974105, 10.981707380512765, 11.206274408920553, 10.006839202158226, 4.954707528062387, 3.8315272639270197, 3.8038235921168018, 5.476332361618334, 1.9833147694043862, 1.562762208437759, 0.9368725333270206, 0.0, 13.35436234405979, 10.305597866597225, 7.813811042188794, 5.949944308213158, 10.952664723236667, 5.325353028963523, 3.8315272639270197, 3.5390768057588473, 5.003419601079113, 3.735424802973519, 2.1963414761025533, 0.7843327312703733, 0.0), # 164
(11.879947258074031, 8.409414984159142, 10.740137638092254, 10.95192392306614, 9.785085460844787, 4.849236442166116, 3.7378212880012396, 3.7235212387984102, 5.3621935267961875, 1.9368509695151015, 1.5265288891017337, 0.915462461867493, 0.0, 13.057161331885686, 10.070087080542422, 7.632644445508667, 5.810552908545303, 10.724387053592375, 5.2129297343177745, 3.7378212880012396, 3.4637403158329394, 4.892542730422393, 3.6506413076887143, 2.148027527618451, 0.7644922712871949, 0.0), # 165
(11.572057230183715, 8.17938947382885, 10.479774202393392, 10.679218807442627, 9.546378047469258, 4.734917429566179, 3.6385493188196576, 3.636355439862808, 5.2380199683735436, 1.8872909002921108, 1.4878147162992839, 0.8925335264544754, 0.0, 12.737472868177733, 9.817868790999228, 7.4390735814964195, 5.661872700876331, 10.476039936747087, 5.090897615807931, 3.6385493188196576, 3.3820838782615565, 4.773189023734629, 3.5597396024808767, 2.0959548404786785, 0.7435808612571683, 0.0), # 166
(11.24702288300614, 7.938529821782648, 10.201975472440058, 10.389511582829789, 9.291947626490376, 4.6123782024506115, 3.5341449494586072, 3.542811153163632, 5.104515952778639, 1.834867798349722, 1.4468051449571482, 0.8681981714528189, 0.0, 12.396933692006392, 9.550179885981006, 7.23402572478574, 5.504603395049164, 10.209031905557278, 4.959935614429085, 3.5341449494586072, 3.2945558588932937, 4.645973813245188, 3.4631705276099303, 2.040395094488012, 0.7216845292529681, 0.0), # 167
(10.906257440466712, 7.687782336819962, 9.908099847256123, 10.084154770007387, 9.023024862366888, 4.482246473007449, 3.425041772994424, 3.44337333655452, 4.962385746439713, 1.779814900302243, 1.4036856300020644, 0.8425688412273767, 0.0, 12.037180542442131, 9.268257253501142, 7.018428150010321, 5.339444700906728, 9.924771492879426, 4.820722671176328, 3.425041772994424, 3.2016046235767495, 4.511512431183444, 3.361384923335797, 1.9816199694512246, 0.6988893033472693, 0.0), # 168
(10.551174126490828, 7.428093327740216, 9.599505725865463, 9.76450088975519, 8.740840419557543, 4.3451499534247295, 3.3116733825034426, 3.338526947889109, 4.812333615785002, 1.7223654427639818, 1.3586416263607706, 0.8157579801430009, 0.0, 11.659850158555415, 8.97333778157301, 6.793208131803853, 5.167096328291944, 9.624667231570005, 4.673937727044753, 3.3116733825034426, 3.103678538160521, 4.370420209778771, 3.254833629918398, 1.9199011451730927, 0.675281211612747, 0.0), # 169
(10.18318616500389, 7.160409103342831, 9.277551507291953, 9.43190246285296, 8.44662496252108, 4.201716355890488, 3.1944733710619975, 3.228756945021036, 4.655063827242743, 1.6627526623492466, 1.311858588960005, 0.7878780325645439, 0.0, 11.2665792794167, 8.666658358209983, 6.559292944800025, 4.988257987047739, 9.310127654485486, 4.52025972302945, 3.1944733710619975, 3.0012259684932054, 4.22331248126054, 3.1439674876176547, 1.8555103014583907, 0.6509462821220756, 0.0), # 170
(9.8037067799313, 6.88567597242723, 8.943595590559468, 9.087712010080473, 8.141609155716246, 4.052573392592758, 3.0738753317464247, 3.1145482858039375, 4.491280647241173, 1.6012097956723452, 1.2635219727265048, 0.759041442856858, 0.0, 10.859004644096458, 8.349455871425437, 6.317609863632523, 4.803629387017034, 8.982561294482347, 4.360367600125513, 3.0738753317464247, 2.8946952804233987, 4.070804577858123, 3.029237336693492, 1.7887191181118935, 0.6259705429479302, 0.0), # 171
(9.414149195198457, 6.604840243792839, 8.59899637469188, 8.733282052217486, 7.827023663601784, 3.898348775719581, 2.950312857633059, 2.996385928091453, 4.321688342208532, 1.5379700793475863, 1.2138172325870082, 0.7293606553847958, 0.0, 10.438762991665145, 8.022967209232752, 6.069086162935041, 4.613910238042758, 8.643376684417063, 4.194940299328034, 2.950312857633059, 2.7845348397997007, 3.913511831800892, 2.911094017405829, 1.7197992749383764, 0.6004400221629854, 0.0), # 172
(9.015926634730764, 6.31884822623908, 8.245112258713068, 8.369965110043767, 7.504099150636442, 3.739670217458989, 2.824219541798235, 2.874754829737218, 4.146991178573053, 1.4732667499892769, 1.1629298234682535, 0.6989481145132089, 0.0, 10.007491061193234, 7.6884292596452966, 5.8146491173412675, 4.41980024996783, 8.293982357146106, 4.024656761632105, 2.824219541798235, 2.6711930124707064, 3.752049575318221, 2.7899883700145893, 1.6490224517426137, 0.5744407478399164, 0.0), # 173
(8.610452322453618, 6.028646228565374, 7.883301641646902, 7.99911370433908, 7.174066281278959, 3.57716542999902, 2.6960289773182877, 2.7501399485948705, 3.9678934227629785, 1.4073330442117262, 1.1110452002969786, 0.6679162646069503, 0.0, 9.566825591751181, 7.347078910676452, 5.555226001484892, 4.221999132635178, 7.935786845525957, 3.850195928032819, 2.6960289773182877, 2.5551181642850143, 3.5870331406394795, 2.6663712347796937, 1.5766603283293805, 0.5480587480513978, 0.0), # 174
(8.19913948229242, 5.7351805595711465, 7.514922922517262, 7.622080355883197, 6.838155719988082, 3.41146212552771, 2.566174757269552, 2.623026242518047, 3.7850993412065432, 1.3404021986292411, 1.058348817999921, 0.6363775500308723, 0.0, 9.118403322409455, 7.000153050339593, 5.291744089999604, 4.021206595887723, 7.5701986824130865, 3.6722367395252657, 2.566174757269552, 2.4367586610912215, 3.419077859994041, 2.540693451961066, 1.5029845845034526, 0.5213800508701043, 0.0), # 175
(7.783401338172574, 5.43939752805582, 7.141334500348018, 7.240217585455879, 6.497598131222556, 3.2431880162330953, 2.4350904747283635, 2.493898669360387, 3.5993132003319848, 1.2727074498561304, 1.0050261315038191, 0.6044444151498269, 0.0, 8.663860992238513, 6.648888566648095, 5.025130657519095, 3.8181223495683905, 7.1986264006639695, 3.4914581371045417, 2.4350904747283635, 2.3165628687379254, 3.248799065611278, 2.4134058618186267, 1.4282669000696038, 0.49449068436871096, 0.0), # 176
(7.364651114019479, 5.1422434428188195, 6.763894774163046, 6.8548779138368925, 6.1536241794411275, 3.0729708143032117, 2.303209722771056, 2.3632421869755245, 3.411239266567542, 1.2044820345067013, 0.9512625957354108, 0.5722293043286669, 0.0, 8.204835340308824, 6.2945223476153345, 4.756312978677054, 3.6134461035201033, 6.822478533135084, 3.3085390617657344, 2.303209722771056, 2.1949791530737226, 3.0768120897205637, 2.284959304612298, 1.3527789548326095, 0.4674766766198928, 0.0), # 177
(6.944302033758534, 4.8446646126595665, 6.383962142986221, 6.467413861806007, 5.807464529102536, 2.901438231926097, 2.170966094473966, 2.2315417532170994, 3.2215818063414514, 1.1359591891952627, 0.897243665621434, 0.5398446619322442, 0.0, 7.742963105690853, 5.938291281254685, 4.486218328107169, 3.4078775675857873, 6.443163612682903, 3.1241584545039394, 2.170966094473966, 2.072455879947212, 2.903732264551268, 2.1558046206020025, 1.2767924285972443, 0.44042405569632426, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
45, # 1
)
| 278.37861 | 490 | 0.771311 | 32,987 | 260,284 | 6.0857 | 0.234426 | 0.35507 | 0.340724 | 0.645582 | 0.366114 | 0.361018 | 0.360659 | 0.36059 | 0.36059 | 0.36059 | 0 | 0.851072 | 0.095027 | 260,284 | 934 | 491 | 278.67666 | 0.001184 | 0.01541 | 0 | 0.200873 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.005459 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
74f8f90ab4981fab9b9cfa63236687afa4091c0b | 154 | py | Python | backend/users/models.py | th3n3xtg3n3ration/kubernetes-api-controller | c77fa8069ab247f793bf19e6bed8e480da6f775a | [
"MIT"
] | null | null | null | backend/users/models.py | th3n3xtg3n3ration/kubernetes-api-controller | c77fa8069ab247f793bf19e6bed8e480da6f775a | [
"MIT"
] | null | null | null | backend/users/models.py | th3n3xtg3n3ration/kubernetes-api-controller | c77fa8069ab247f793bf19e6bed8e480da6f775a | [
"MIT"
] | null | null | null | from django.contrib.auth.models import AbstractUser
from django.db import models
from common.models import BaseModel
class User(AbstractUser):
pass
| 19.25 | 51 | 0.811688 | 21 | 154 | 5.952381 | 0.619048 | 0.16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136364 | 154 | 7 | 52 | 22 | 0.93985 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.6 | 0 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
7438caca4c4e9ef833a9b6bafa0f7c705c4e7342 | 633 | py | Python | atests/local/libraries/other/other.py | nokia/crl-doc | fee1c26e93f9492ad7b8681c0e27d2048c968cdd | [
"BSD-3-Clause"
] | null | null | null | atests/local/libraries/other/other.py | nokia/crl-doc | fee1c26e93f9492ad7b8681c0e27d2048c968cdd | [
"BSD-3-Clause"
] | 5 | 2019-08-30T12:13:25.000Z | 2019-09-06T08:00:12.000Z | atests/local/libraries/other/other.py | nokia/crl-doc | fee1c26e93f9492ad7b8681c0e27d2048c968cdd | [
"BSD-3-Clause"
] | 2 | 2019-08-30T12:11:10.000Z | 2020-01-23T20:50:29.000Z | class Other(object):
@staticmethod
def other_example():
"""Other example library prints argument passed to the library
initialization.
Example:
+-----------------------------+---------------+-----------------+
| Library | Other | |
+-----------------------------+---------------+-----------------+
| Other.Other | | |
+-----------------------------+---------------+-----------------+
Returns:
other_example
"""
return 'other_example'
| 30.142857 | 73 | 0.279621 | 28 | 633 | 6.214286 | 0.535714 | 0.275862 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.369668 | 633 | 20 | 74 | 31.65 | 0.43609 | 0.696682 | 0 | 0 | 0 | 0 | 0.12381 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
77951bbdd7dc3a5929f46c05aec47389661ebe64 | 96 | py | Python | venv/lib/python3.8/site-packages/importlib_metadata/_itertools.py | Retraces/UkraineBot | 3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/importlib_metadata/_itertools.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | venv/lib/python3.8/site-packages/importlib_metadata/_itertools.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/e5/35/23/fc03c91eade9be39f4e219cfda860179b3f6368ec798d1ff864386c0b4 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.40625 | 0 | 96 | 1 | 96 | 96 | 0.489583 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7aeb52c00be025afd444bfa95a177b8efb605437 | 44,276 | py | Python | tinyquery/compiler_test.py | graingert/tinyquery | f26940a2ad240911e278ef7c82e3f14e0f4c5e4e | [
"MIT"
] | 104 | 2015-02-21T22:54:15.000Z | 2022-03-21T11:08:02.000Z | tinyquery/compiler_test.py | graingert/tinyquery | f26940a2ad240911e278ef7c82e3f14e0f4c5e4e | [
"MIT"
] | 14 | 2018-01-30T16:32:09.000Z | 2022-03-02T12:57:11.000Z | tinyquery/compiler_test.py | graingert/tinyquery | f26940a2ad240911e278ef7c82e3f14e0f4c5e4e | [
"MIT"
] | 28 | 2015-09-16T22:42:44.000Z | 2022-01-15T11:51:45.000Z | # TODO(colin): fix these lint errors (http://pep8.readthedocs.io/en/release-1.7.x/intro.html#error-codes)
# pep8-disable:E122
from __future__ import absolute_import
import collections
import datetime
import unittest
from tinyquery import exceptions
from tinyquery import compiler
from tinyquery import context
from tinyquery import runtime
from tinyquery import tinyquery
from tinyquery import tq_ast
from tinyquery import tq_modes
from tinyquery import tq_types
from tinyquery import type_context
from tinyquery import typed_ast
class CompilerTest(unittest.TestCase):
def setUp(self):
self.table1 = tinyquery.Table(
'table1',
0,
collections.OrderedDict([
('value', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE, values=[])),
('value2', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE, values=[]))
]))
self.table1_type_ctx = self.make_type_context(
[('table1', 'value', tq_types.INT),
('table1', 'value2', tq_types.INT)]
)
self.table2 = tinyquery.Table(
'table2',
0,
collections.OrderedDict([
('value', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE, values=[])),
('value3', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE, values=[]))
])
)
self.table2_type_ctx = self.make_type_context(
[('table2', 'value', tq_types.INT),
('table2', 'value3', tq_types.INT)]
)
self.table3 = tinyquery.Table(
'table3',
0,
collections.OrderedDict([
('value', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE, values=[])),
])
)
self.table3_type_ctx = self.make_type_context(
[('table3', 'value', tq_types.INT)]
)
self.rainbow_table = tinyquery.Table(
'rainbow_table',
3,
collections.OrderedDict([
('ints', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE,
values=[-2147483649, -0, 2147483648])),
('floats', context.Column(type=tq_types.FLOAT,
mode=tq_modes.NULLABLE,
values=[1.41, 2.72,
float('infinity')])),
('bools', context.Column(type=tq_types.BOOL,
mode=tq_modes.NULLABLE,
values=[True, False, True])),
('strings', context.Column(type=tq_types.STRING,
mode=tq_modes.NULLABLE,
values=["infrared", "indigo",
"ultraviolet"])),
('times', context.Column(type=tq_types.TIMESTAMP,
mode=tq_modes.NULLABLE,
values=[
datetime.datetime(1969, 12, 31,
23, 59, 59),
datetime.datetime(1999, 12, 31,
23, 59, 59),
datetime.datetime(2038, 1, 19,
3, 14, 8)]))]))
self.rainbow_table_type_ctx = self.make_type_context(
[('rainbow_table', 'ints', tq_types.INT),
('rainbow_table', 'floats', tq_types.FLOAT),
('rainbow_table', 'bools', tq_types.BOOL),
('rainbow_table', 'strings', tq_types.STRING),
('rainbow_table', 'times', tq_types.TIMESTAMP)]
)
self.record_table = tinyquery.Table(
'record_table',
0,
collections.OrderedDict([
('r1.i', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE, values=[])),
('r1.s', context.Column(type=tq_types.STRING,
mode=tq_modes.NULLABLE, values=[])),
('r2.i', context.Column(type=tq_types.INT,
mode=tq_modes.NULLABLE, values=[])),
])
)
self.record_table_type_ctx = self.make_type_context(
[('record_table', 'r1.i', tq_types.INT),
('record_table', 'r1.s', tq_types.STRING),
('record_table', 'r2.i', tq_types.INT)]
)
self.tables_by_name = {
'table1': self.table1,
'table2': self.table2,
'table3': self.table3,
'rainbow_table': self.rainbow_table,
'record_table': self.record_table,
}
def assert_compiled_select(self, text, expected_ast):
ast = compiler.compile_text(text, self.tables_by_name)
self.assertEqual(expected_ast, ast)
def assert_compile_error(self, text):
self.assertRaises(exceptions.CompileError, compiler.compile_text,
text, self.tables_by_name)
def make_type_context(self, table_column_type_triples,
implicit_column_context=None):
return type_context.TypeContext.from_full_columns(
collections.OrderedDict(
((table, column), col_type)
for table, column, col_type in table_column_type_triples
), implicit_column_context)
def test_compile_simple_select(self):
self.assert_compiled_select(
'SELECT value FROM table1',
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'value', tq_types.INT)],
self.make_type_context([('table1', 'value', tq_types.INT)])
))
)
def test_unary_operator(self):
self.assert_compiled_select(
'SELECT -5',
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_unary_op('-'),
[typed_ast.Literal(5, tq_types.INT)],
tq_types.INT),
'f0_', None
)],
typed_ast.NoTable(),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'f0_', tq_types.INT)],
self.make_type_context([]))
)
)
def test_mistyped_unary_operator(self):
with self.assertRaises(exceptions.CompileError) as context:
compiler.compile_text('SELECT -strings FROM rainbow_table',
self.tables_by_name)
self.assertTrue('Invalid type for operator' in str(context.exception))
def test_strange_arithmetic(self):
try:
compiler.compile_text('SELECT times + ints + floats + bools FROM '
'rainbow_table', self.tables_by_name)
except exceptions.CompileError:
self.fail('Compiler exception on arithmetic across all numeric '
'types.')
def test_mistyped_binary_operator(self):
with self.assertRaises(exceptions.CompileError) as context:
compiler.compile_text('SELECT ints CONTAINS strings FROM '
'rainbow_table',
self.tables_by_name)
self.assertTrue('Invalid types for operator' in str(context.exception))
def test_function_calls(self):
self.assert_compiled_select(
'SELECT ABS(-3), POW(2, 3), NOW()',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_func('abs'),
[typed_ast.FunctionCall(
runtime.get_unary_op('-'),
[typed_ast.Literal(3, tq_types.INT)],
tq_types.INT
)],
tq_types.INT),
'f0_', None),
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_func('pow'), [
typed_ast.Literal(2, tq_types.INT),
typed_ast.Literal(3, tq_types.INT)],
tq_types.INT
),
'f1_', None
),
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_func('now'), [], tq_types.INT
),
'f2_', None
)],
typed_ast.NoTable(),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context([
(None, 'f0_', tq_types.INT), (None, 'f1_', tq_types.INT),
(None, 'f2_', tq_types.INT)],
self.make_type_context([]))
)
)
def test_mistyped_function_call(self):
with self.assertRaises(exceptions.CompileError) as context:
compiler.compile_text('SELECT SUM(strings) FROM rainbow_table',
self.tables_by_name)
self.assertTrue('Invalid types for function' in str(context.exception))
def test_case(self):
self.assert_compiled_select(
'SELECT CASE WHEN TRUE THEN 1 WHEN FALSE THEN 2 END',
typed_ast.Select(
select_fields=[
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_func('if'),
[
typed_ast.Literal(True, tq_types.BOOL),
typed_ast.Literal(1, tq_types.INT),
typed_ast.FunctionCall(
runtime.get_func('if'),
[
typed_ast.Literal(False, tq_types.BOOL),
typed_ast.Literal(2, tq_types.INT),
typed_ast.Literal(None, tq_types.NONETYPE),
],
tq_types.INT)
],
tq_types.INT),
'f0_', None)
],
table=typed_ast.NoTable(),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=None,
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=None,
limit=None,
type_ctx=self.make_type_context(
[(None, 'f0_', tq_types.INT)],
self.make_type_context([]))))
def test_where(self):
self.assert_compiled_select(
'SELECT value FROM table1 WHERE value > 3',
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.FunctionCall(
runtime.get_binary_op('>'),
[typed_ast.ColumnRef('table1', 'value', tq_types.INT),
typed_ast.Literal(3, tq_types.INT)],
tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'value', tq_types.INT)],
self.make_type_context(
[('table1', 'value', tq_types.INT)]))
)
)
def test_having(self):
self.assert_compiled_select(
'SELECT value FROM table1 HAVING value > 3',
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.FunctionCall(
runtime.get_binary_op('>'),
[typed_ast.ColumnRef(None, 'value', tq_types.INT),
typed_ast.Literal(3, tq_types.INT)],
tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'value', tq_types.INT)],
self.make_type_context(
[('table1', 'value', tq_types.INT)]))
)
)
def test_multiple_select(self):
self.assert_compiled_select(
'SELECT value * 3 AS foo, value, value + 1, value bar, value - 1 '
'FROM table1',
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_binary_op('*'),
[typed_ast.ColumnRef('table1', 'value', tq_types.INT),
typed_ast.Literal(3, tq_types.INT)],
tq_types.INT),
'foo', None),
typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None),
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_binary_op('+'),
[typed_ast.ColumnRef('table1', 'value', tq_types.INT),
typed_ast.Literal(1, tq_types.INT)],
tq_types.INT),
'f0_', None),
typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'bar', None),
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_binary_op('-'),
[typed_ast.ColumnRef('table1', 'value', tq_types.INT),
typed_ast.Literal(1, tq_types.INT)],
tq_types.INT),
'f1_', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context([
(None, 'foo', tq_types.INT),
(None, 'value', tq_types.INT),
(None, 'f0_', tq_types.INT), (None, 'bar', tq_types.INT),
(None, 'f1_', tq_types.INT)],
self.make_type_context(
[('table1', 'value', tq_types.INT)]
))
)
)
def test_duplicate_aliases_not_allowed(self):
self.assert_compile_error(
'SELECT 0 AS foo, value foo FROM table1')
def test_aggregates(self):
self.assert_compiled_select(
'SELECT MAX(value), MIN(value) FROM table1',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.AggregateFunctionCall(
runtime.get_func('max'),
[typed_ast.ColumnRef('table1', 'value', tq_types.INT)],
tq_types.INT
),
'f0_', None),
typed_ast.SelectField(
typed_ast.AggregateFunctionCall(
runtime.get_func('min'),
[typed_ast.ColumnRef('table1', 'value', tq_types.INT)],
tq_types.INT
),
'f1_', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
typed_ast.GroupSet(set(), []),
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context([
(None, 'f0_', tq_types.INT),
(None, 'f1_', tq_types.INT)],
self.make_type_context([]))))
def mixed_aggregate_non_aggregate_not_allowed(self):
self.assert_compile_error(
'SELECT value, SUM(value) FROM table1')
def mixed_aggregate_non_aggregate_single_field_not_allowed(self):
self.assert_compile_error(
'SELECT value + SUM(value) FROM table1')
def test_group_by_alias(self):
self.assert_compiled_select(
'SELECT 0 AS foo FROM table1 GROUP BY foo',
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.Literal(0, tq_types.INT), 'foo', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
typed_ast.GroupSet(
alias_groups={'foo'},
field_groups=[]
),
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'foo', tq_types.INT)],
self.make_type_context([]))
)
)
def test_group_by_field(self):
self.assert_compiled_select(
'SELECT SUM(value) FROM table1 GROUP BY value2',
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_func('sum'),
[typed_ast.ColumnRef('table1', 'value', tq_types.INT)],
tq_types.INT
),
'f0_', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
typed_ast.GroupSet(
alias_groups=set(),
field_groups=[
typed_ast.ColumnRef('table1', 'value2', tq_types.INT)]
),
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'f0_', tq_types.INT)],
self.make_type_context([]))
))
def test_order_by_field(self):
self.assert_compiled_select(
'SELECT value FROM table1 ORDER BY value2 DESC',
typed_ast.Select(
select_fields=[typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None)],
table=typed_ast.Table('table1', self.table1_type_ctx),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=None,
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=[tq_ast.Ordering(tq_ast.ColumnId('value2'), False)],
limit=None,
type_ctx=self.make_type_context(
[(None, 'value', tq_types.INT)],
self.make_type_context([('table1', 'value',
tq_types.INT)]))
))
def test_order_by_multiple_fields(self):
self.assert_compiled_select(
'SELECT value FROM table1 ORDER BY value2, value DESC',
typed_ast.Select(
select_fields=[typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None)],
table=typed_ast.Table('table1', self.table1_type_ctx),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=None,
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=[tq_ast.Ordering(tq_ast.ColumnId('value2'), True),
tq_ast.Ordering(tq_ast.ColumnId('value'), False)],
limit=None,
type_ctx=self.make_type_context(
[(None, 'value', tq_types.INT)],
self.make_type_context([('table1', 'value', tq_types.INT)
]))
))
def test_select_grouped_and_non_grouped_fields(self):
self.assert_compiled_select(
'SELECT value, SUM(value2) FROM table1 GROUP BY value',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None),
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_func('sum'),
[typed_ast.ColumnRef('table1', 'value2',
tq_types.INT)],
tq_types.INT),
'f0_', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
typed_ast.GroupSet(
alias_groups={'value'},
field_groups=[]
),
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'value', tq_types.INT),
(None, 'f0_', tq_types.INT)],
self.make_type_context(
[('table1', 'value', tq_types.INT)]))
)
)
def test_grouped_fields_require_aggregates(self):
self.assert_compile_error(
'SELECT value + 1 AS foo, foo FROM table1 GROUP BY foo')
def test_select_multiple_tables(self):
# Union of columns should be taken, with no aliases.
unioned_type_ctx = self.make_type_context(
[(None, 'value', tq_types.INT), (None, 'value2', tq_types.INT),
(None, 'value3', tq_types.INT)])
self.assert_compiled_select(
'SELECT value, value2, value3 FROM table1, table2',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef(None, 'value', tq_types.INT),
'value', None),
typed_ast.SelectField(
typed_ast.ColumnRef(None, 'value2', tq_types.INT),
'value2', None),
typed_ast.SelectField(
typed_ast.ColumnRef(None, 'value3', tq_types.INT),
'value3', None)],
typed_ast.TableUnion([
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Table('table2', self.table2_type_ctx)],
unioned_type_ctx
),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'value', tq_types.INT),
(None, 'value2', tq_types.INT),
(None, 'value3', tq_types.INT)],
self.make_type_context(
[(None, 'value', tq_types.INT),
(None, 'value2', tq_types.INT),
(None, 'value3', tq_types.INT)]))
)
)
def test_subquery(self):
self.assert_compiled_select(
'SELECT foo, foo + 1 FROM (SELECT value + 1 AS foo FROM table1)',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef(None, 'foo', tq_types.INT), 'foo',
None),
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_binary_op('+'), [
typed_ast.ColumnRef(None, 'foo', tq_types.INT),
typed_ast.Literal(1, tq_types.INT)],
tq_types.INT),
'f0_', None
)],
typed_ast.Select(
[typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_binary_op('+'), [
typed_ast.ColumnRef('table1', 'value',
tq_types.INT),
typed_ast.Literal(1, tq_types.INT)],
tq_types.INT),
'foo', None
)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'foo', tq_types.INT)],
self.make_type_context(
[('table1', 'value', tq_types.INT)]
))
),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'foo', tq_types.INT), (None, 'f0_', tq_types.INT)],
self.make_type_context([(None, 'foo', tq_types.INT)]))
)
)
def test_table_aliases(self):
self.assert_compiled_select(
'SELECT t.value FROM table1 t',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef('t', 'value', tq_types.INT),
't.value', None)],
typed_ast.Table('table1', self.make_type_context(
[('t', 'value', tq_types.INT),
('t', 'value2', tq_types.INT)])),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 't.value', tq_types.INT)],
self.make_type_context(
[('t', 'value', tq_types.INT)]
))
)
)
def test_implicitly_accessed_column(self):
self.assert_compiled_select(
'SELECT table1.value FROM (SELECT value + 1 AS foo FROM table1)',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'table1.value', None)],
typed_ast.Select([
typed_ast.SelectField(
typed_ast.FunctionCall(
runtime.get_binary_op('+'), [
typed_ast.ColumnRef('table1', 'value',
tq_types.INT),
typed_ast.Literal(1, tq_types.INT)
],
tq_types.INT
),
'foo', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'foo', tq_types.INT)],
self.make_type_context(
[('table1', 'value', tq_types.INT)]))),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'table1.value', tq_types.INT)],
self.make_type_context(
[('table1', 'value', tq_types.INT)]
)))
)
def test_subquery_aliases(self):
self.assert_compiled_select(
'SELECT t.value FROM (SELECT value FROM table1) t',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef('t', 'value', tq_types.INT),
't.value', None)],
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'value', tq_types.INT)],
self.make_type_context(
[('t', 'value', tq_types.INT)]))
),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 't.value', tq_types.INT)],
self.make_type_context(
[('t', 'value', tq_types.INT)]))
)
)
def test_simple_join(self):
self.assert_compiled_select(
'SELECT value2 '
'FROM table1 t1 JOIN table2 t2 ON t1.value = t2.value',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef('t1', 'value2', tq_types.INT),
'value2', None
)],
typed_ast.Join(
typed_ast.Table('table1',
self.make_type_context([
('t1', 'value', tq_types.INT),
('t1', 'value2', tq_types.INT),
])),
[(typed_ast.Table('table2',
self.make_type_context([
('t2', 'value', tq_types.INT),
('t2', 'value3', tq_types.INT),
])),
tq_ast.JoinType.INNER)],
[[typed_ast.JoinFields(
typed_ast.ColumnRef('t1', 'value', tq_types.INT),
typed_ast.ColumnRef('t2', 'value', tq_types.INT)
)]],
self.make_type_context([
('t1', 'value', tq_types.INT),
('t1', 'value2', tq_types.INT),
('t2', 'value', tq_types.INT),
('t2', 'value3', tq_types.INT),
])
),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context(
[(None, 'value2', tq_types.INT)],
self.make_type_context([('t1', 'value2', tq_types.INT)])
)
)
)
def test_join_multiple_fields(self):
self.assert_compiled_select(
'SELECT 0 '
'FROM table1 t1 JOIN table2 t2 '
'ON t1.value == t2.value AND t2.value3 = t1.value2',
typed_ast.Select(
select_fields=[
typed_ast.SelectField(
typed_ast.Literal(0, tq_types.INT), 'f0_', None)],
table=typed_ast.Join(
base=typed_ast.Table('table1',
self.make_type_context([
('t1', 'value', tq_types.INT),
('t1', 'value2', tq_types.INT),
])),
tables=[
(typed_ast.Table(
'table2',
self.make_type_context([
('t2', 'value', tq_types.INT),
('t2', 'value3', tq_types.INT),
])),
tq_ast.JoinType.INNER)],
conditions=[[
typed_ast.JoinFields(
typed_ast.ColumnRef('t1', 'value', tq_types.INT),
typed_ast.ColumnRef('t2', 'value', tq_types.INT)
), typed_ast.JoinFields(
typed_ast.ColumnRef('t1', 'value2', tq_types.INT),
typed_ast.ColumnRef('t2', 'value3', tq_types.INT)
)]],
type_ctx=self.make_type_context([
('t1', 'value', tq_types.INT),
('t1', 'value2', tq_types.INT),
('t2', 'value', tq_types.INT),
('t2', 'value3', tq_types.INT),
])
),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=None,
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=None,
limit=None,
type_ctx=self.make_type_context(
[(None, 'f0_', tq_types.INT)],
self.make_type_context([]))
)
)
def test_multi_way_join(self):
self.assert_compiled_select(
'SELECT 0 '
'FROM table1 t1 JOIN table2 t2 ON t1.value = t2.value '
'LEFT JOIN table3 t3 ON t2.value3 = t3.value',
typed_ast.Select(
select_fields=[
typed_ast.SelectField(
typed_ast.Literal(0, tq_types.INT), 'f0_', None)],
table=typed_ast.Join(
base=typed_ast.Table('table1',
self.make_type_context([
('t1', 'value', tq_types.INT),
('t1', 'value2', tq_types.INT),
])),
tables=[
(typed_ast.Table(
'table2',
self.make_type_context([
('t2', 'value', tq_types.INT),
('t2', 'value3', tq_types.INT),
])),
tq_ast.JoinType.INNER),
(typed_ast.Table(
'table3',
self.make_type_context([
('t3', 'value', tq_types.INT)
])),
tq_ast.JoinType.LEFT_OUTER
)],
conditions=[
[typed_ast.JoinFields(
typed_ast.ColumnRef('t1', 'value', tq_types.INT),
typed_ast.ColumnRef('t2', 'value', tq_types.INT)
)], [typed_ast.JoinFields(
typed_ast.ColumnRef('t2', 'value3', tq_types.INT),
typed_ast.ColumnRef('t3', 'value', tq_types.INT)
)]],
type_ctx=self.make_type_context([
('t1', 'value', tq_types.INT),
('t1', 'value2', tq_types.INT),
('t2', 'value', tq_types.INT),
('t2', 'value3', tq_types.INT),
('t3', 'value', tq_types.INT),
])
),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=None,
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=None,
limit=None,
type_ctx=self.make_type_context(
[(None, 'f0_', tq_types.INT)],
self.make_type_context([]))
)
)
def test_select_star(self):
self.assert_compiled_select(
'SELECT * FROM table1',
typed_ast.Select([
typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value', tq_types.INT),
'value', None),
typed_ast.SelectField(
typed_ast.ColumnRef('table1', 'value2', tq_types.INT),
'value2', None)],
typed_ast.Table('table1', self.table1_type_ctx),
typed_ast.Literal(True, tq_types.BOOL),
None,
typed_ast.Literal(True, tq_types.BOOL),
None,
None,
self.make_type_context([
(None, 'value', tq_types.INT),
(None, 'value2', tq_types.INT)],
self.make_type_context([
('table1', 'value', tq_types.INT),
('table1', 'value2', tq_types.INT)]))))
def test_select_record(self):
self.assert_compiled_select(
'SELECT r1.s FROM record_table',
typed_ast.Select(
select_fields=[
typed_ast.SelectField(
typed_ast.ColumnRef('record_table', 'r1.s',
tq_types.STRING),
'r1.s', None)],
table=typed_ast.Table('record_table',
self.record_table_type_ctx),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=None,
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=None,
limit=None,
type_ctx=self.make_type_context(
[(None, 'r1.s', tq_types.STRING)],
self.make_type_context([
('record_table', 'r1.s', tq_types.STRING)]))))
def test_record_star(self):
self.assert_compiled_select(
'SELECT r1.* FROM record_table',
typed_ast.Select(
select_fields=[
typed_ast.SelectField(
typed_ast.ColumnRef('record_table', 'r1.i',
tq_types.INT),
'r1.i', None),
typed_ast.SelectField(
typed_ast.ColumnRef('record_table', 'r1.s',
tq_types.STRING),
'r1.s', None),
],
table=typed_ast.Table('record_table',
self.record_table_type_ctx),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=None,
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=None,
limit=None,
type_ctx=self.make_type_context(
[(None, 'r1.i', tq_types.INT),
(None, 'r1.s', tq_types.STRING)],
self.make_type_context([
('record_table', 'r1.i', tq_types.INT),
('record_table', 'r1.s', tq_types.STRING)]))))
def test_within_record(self):
self.assert_compiled_select(
'SELECT r1.s, COUNT(r1.s) WITHIN RECORD AS num_s_in_r1 '
'FROM record_table',
typed_ast.Select(
select_fields=[
typed_ast.SelectField(
typed_ast.ColumnRef('record_table', 'r1.s',
tq_types.STRING),
'r1.s', None),
typed_ast.SelectField(typed_ast.FunctionCall(
runtime.get_func('count'),
[typed_ast.ColumnRef('record_table', 'r1.s',
tq_types.STRING)],
tq_types.INT
), 'num_s_in_r1', 'RECORD')],
table=typed_ast.Table('record_table',
self.record_table_type_ctx),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=typed_ast.GroupSet(set(), []),
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=None,
limit=None,
type_ctx=self.make_type_context(
[(None, 'r1.s', tq_types.STRING),
(None, 'num_s_in_r1', tq_types.INT)],
self.make_type_context([]))))
def test_within_clause(self):
self.assert_compiled_select(
'SELECT r1.s, COUNT(r1.s) WITHIN r1 AS num_s_in_r1 '
'FROM record_table',
typed_ast.Select(
select_fields=[
typed_ast.SelectField(
typed_ast.ColumnRef('record_table', 'r1.s',
tq_types.STRING),
'r1.s', None),
typed_ast.SelectField(typed_ast.FunctionCall(
runtime.get_func('count'),
[typed_ast.ColumnRef('record_table', 'r1.s',
tq_types.STRING)],
tq_types.INT
), 'num_s_in_r1', 'r1')],
table=typed_ast.Table('record_table',
self.record_table_type_ctx),
where_expr=typed_ast.Literal(True, tq_types.BOOL),
group_set=typed_ast.GroupSet(set(), []),
having_expr=typed_ast.Literal(True, tq_types.BOOL),
orderings=None,
limit=None,
type_ctx=self.make_type_context(
[(None, 'r1.s', tq_types.STRING),
(None, 'num_s_in_r1', tq_types.INT)],
self.make_type_context([]))))
def test_within_clause_error(self):
with self.assertRaises(exceptions.CompileError) as context:
compiler.compile_text(
'SELECT r1.s, COUNT(r1.s) WITHIN r2 AS '
'num_s_in_r1 FROM record_table',
self.tables_by_name)
self.assertTrue('WITHIN clause syntax error' in
str(context.exception))
| 43.664694 | 105 | 0.449544 | 4,096 | 44,276 | 4.578125 | 0.056641 | 0.101163 | 0.099723 | 0.062393 | 0.862415 | 0.844603 | 0.815593 | 0.789409 | 0.75256 | 0.731442 | 0 | 0.017069 | 0.445569 | 44,276 | 1,013 | 106 | 43.707799 | 0.746823 | 0.003862 | 0 | 0.670478 | 0 | 0 | 0.086871 | 0 | 0 | 0 | 0 | 0.000987 | 0.043659 | 1 | 0.040541 | false | 0 | 0.014553 | 0.00104 | 0.057173 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
bb3925504ec44dac18201f00f6c79125396d2e27 | 148 | py | Python | source/pdv/partners/admin.py | DanielBacci/geodjango | b8d163e862255834f6ba495019f63fff51aecb7e | [
"MIT"
] | null | null | null | source/pdv/partners/admin.py | DanielBacci/geodjango | b8d163e862255834f6ba495019f63fff51aecb7e | [
"MIT"
] | 6 | 2019-12-04T23:50:03.000Z | 2021-09-22T17:56:51.000Z | source/pdv/partners/admin.py | DanielBacci/geodjango | b8d163e862255834f6ba495019f63fff51aecb7e | [
"MIT"
] | null | null | null | from django.contrib import admin
from pdv.partners.models import Partner
@admin.register(Partner)
class PartnerAdmin(admin.ModelAdmin):
pass
| 16.444444 | 39 | 0.797297 | 19 | 148 | 6.210526 | 0.736842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.128378 | 148 | 8 | 40 | 18.5 | 0.914729 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.4 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
bb4192d141a1db895bd0031d0895c139c59e79d0 | 115 | py | Python | example/code/hello.py | ahf/onion-tex | 49afc3e5c51e10cec206d961ef73d0fabe58974a | [
"BSD-2-Clause"
] | 9 | 2018-04-24T12:03:28.000Z | 2021-08-02T20:28:07.000Z | example/code/hello.py | dgoulet-tor/onion-tex | a53551210c14e0f24ba04d47fcc35297bb3b7338 | [
"BSD-2-Clause"
] | 1 | 2019-11-27T19:47:04.000Z | 2019-11-27T19:47:04.000Z | example/code/hello.py | dgoulet-tor/onion-tex | a53551210c14e0f24ba04d47fcc35297bb3b7338 | [
"BSD-2-Clause"
] | 1 | 2019-11-27T19:39:52.000Z | 2019-11-27T19:39:52.000Z | #!/usr/bin/env python
if __name__ == '__main__':
# Print "Hello world" to the user.
print("Hello world!")
| 19.166667 | 38 | 0.626087 | 16 | 115 | 4 | 0.8125 | 0.3125 | 0.46875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.208696 | 115 | 5 | 39 | 23 | 0.703297 | 0.46087 | 0 | 0 | 0 | 0 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 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 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
247b2ee72ea8c1f18e5cbf8bb526936384e7ebce | 23 | py | Python | src/archs/cluster/baselines/__init__.py | hendraet/IIC | a5bab915eda133b0ecfd42eaacd60c7b26807cb6 | [
"MIT"
] | 767 | 2019-03-28T00:22:53.000Z | 2022-03-31T09:27:01.000Z | src/archs/cluster/baselines/__init__.py | hendraet/IIC | a5bab915eda133b0ecfd42eaacd60c7b26807cb6 | [
"MIT"
] | 113 | 2019-03-30T20:44:58.000Z | 2022-03-22T04:46:55.000Z | src/archs/cluster/baselines/__init__.py | hendraet/IIC | a5bab915eda133b0ecfd42eaacd60c7b26807cb6 | [
"MIT"
] | 209 | 2019-03-28T16:06:04.000Z | 2022-03-29T15:08:47.000Z | from triplets import *
| 11.5 | 22 | 0.782609 | 3 | 23 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173913 | 23 | 1 | 23 | 23 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
701b0ef32b2859a9bef830e7a18f0537126ee0b5 | 25 | py | Python | training/__init__.py | Rekrau/PyGreentea | 457d7dc5be12b15c3c7663ceaf6d74301de56e43 | [
"BSD-2-Clause"
] | null | null | null | training/__init__.py | Rekrau/PyGreentea | 457d7dc5be12b15c3c7663ceaf6d74301de56e43 | [
"BSD-2-Clause"
] | 4 | 2016-04-22T15:39:21.000Z | 2016-11-15T21:23:58.000Z | training/__init__.py | Rekrau/PyGreentea | 457d7dc5be12b15c3c7663ceaf6d74301de56e43 | [
"BSD-2-Clause"
] | 4 | 2017-05-12T00:17:55.000Z | 2019-07-01T19:23:32.000Z | from monitoring import *
| 12.5 | 24 | 0.8 | 3 | 25 | 6.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 25 | 1 | 25 | 25 | 0.952381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
56319c9890f537ee301ff3d6c8be10ae334794bf | 13,019 | py | Python | modules/ESP32/romans.py | ccccmagicboy/MicroPython_fw | d2049bc19e3d5010f5d6d0d17aa13a8693914fbd | [
"MIT"
] | 23 | 2020-01-22T00:40:20.000Z | 2021-08-03T20:42:07.000Z | modules/ESP32/romans.py | ccccmagicboy/MicroPython_fw | d2049bc19e3d5010f5d6d0d17aa13a8693914fbd | [
"MIT"
] | 10 | 2020-02-18T09:57:04.000Z | 2020-03-04T11:39:17.000Z | modules/ESP32/romans.py | ccccmagicboy/MicroPython_fw | d2049bc19e3d5010f5d6d0d17aa13a8693914fbd | [
"MIT"
] | 5 | 2020-02-20T09:35:45.000Z | 2022-01-04T16:23:13.000Z | def glyphs():
return 96
_font =\
b'\x00\x4a\x5a\x08\x4d\x57\x52\x46\x52\x54\x20\x52\x52\x59\x51'\
b'\x5a\x52\x5b\x53\x5a\x52\x59\x05\x4a\x5a\x4e\x46\x4e\x4d\x20'\
b'\x52\x56\x46\x56\x4d\x0b\x48\x5d\x53\x42\x4c\x62\x20\x52\x59'\
b'\x42\x52\x62\x20\x52\x4c\x4f\x5a\x4f\x20\x52\x4b\x55\x59\x55'\
b'\x1a\x48\x5c\x50\x42\x50\x5f\x20\x52\x54\x42\x54\x5f\x20\x52'\
b'\x59\x49\x57\x47\x54\x46\x50\x46\x4d\x47\x4b\x49\x4b\x4b\x4c'\
b'\x4d\x4d\x4e\x4f\x4f\x55\x51\x57\x52\x58\x53\x59\x55\x59\x58'\
b'\x57\x5a\x54\x5b\x50\x5b\x4d\x5a\x4b\x58\x1f\x46\x5e\x5b\x46'\
b'\x49\x5b\x20\x52\x4e\x46\x50\x48\x50\x4a\x4f\x4c\x4d\x4d\x4b'\
b'\x4d\x49\x4b\x49\x49\x4a\x47\x4c\x46\x4e\x46\x50\x47\x53\x48'\
b'\x56\x48\x59\x47\x5b\x46\x20\x52\x57\x54\x55\x55\x54\x57\x54'\
b'\x59\x56\x5b\x58\x5b\x5a\x5a\x5b\x58\x5b\x56\x59\x54\x57\x54'\
b'\x22\x45\x5f\x5c\x4f\x5c\x4e\x5b\x4d\x5a\x4d\x59\x4e\x58\x50'\
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b'\x11\x48\x5c\x51\x46\x4e\x47\x4c\x4a\x4b\x4f\x4b\x52\x4c\x57'\
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b'\x46\x53\x5b\x0e\x48\x5c\x4c\x4b\x4c\x4a\x4d\x48\x4e\x47\x50'\
b'\x46\x54\x46\x56\x47\x57\x48\x58\x4a\x58\x4c\x57\x4e\x55\x51'\
b'\x4b\x5b\x59\x5b\x0f\x48\x5c\x4d\x46\x58\x46\x52\x4e\x55\x4e'\
b'\x57\x4f\x58\x50\x59\x53\x59\x55\x58\x58\x56\x5a\x53\x5b\x50'\
b'\x5b\x4d\x5a\x4c\x59\x4b\x57\x06\x48\x5c\x55\x46\x4b\x54\x5a'\
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b'\x48\x5c\x58\x49\x57\x47\x54\x46\x52\x46\x4f\x47\x4d\x4a\x4c'\
b'\x4f\x4c\x54\x4d\x58\x4f\x5a\x52\x5b\x53\x5b\x56\x5a\x58\x58'\
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b'\x4e\x52\x4c\x50\x4b\x4d\x4b\x4c\x4c\x49\x4e\x47\x51\x46\x52'\
b'\x46\x55\x47\x57\x49\x58\x4d\x58\x52\x57\x57\x55\x5a\x52\x5b'\
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b'\x52\x53\x57\x52\x58\x51\x57\x52\x56\x53\x57\x53\x59\x51\x5b'\
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b'\x4f\x20\x52\x49\x55\x5b\x55\x03\x46\x5e\x4a\x49\x5a\x52\x4a'\
b'\x5b\x14\x49\x5b\x4c\x4b\x4c\x4a\x4d\x48\x4e\x47\x50\x46\x54'\
b'\x46\x56\x47\x57\x48\x58\x4a\x58\x4c\x57\x4e\x56\x4f\x52\x51'\
b'\x52\x54\x20\x52\x52\x59\x51\x5a\x52\x5b\x53\x5a\x52\x59\x37'\
b'\x45\x60\x57\x4e\x56\x4c\x54\x4b\x51\x4b\x4f\x4c\x4e\x4d\x4d'\
b'\x50\x4d\x53\x4e\x55\x50\x56\x53\x56\x55\x55\x56\x53\x20\x52'\
b'\x51\x4b\x4f\x4d\x4e\x50\x4e\x53\x4f\x55\x50\x56\x20\x52\x57'\
b'\x4b\x56\x53\x56\x55\x58\x56\x5a\x56\x5c\x54\x5d\x51\x5d\x4f'\
b'\x5c\x4c\x5b\x4a\x59\x48\x57\x47\x54\x46\x51\x46\x4e\x47\x4c'\
b'\x48\x4a\x4a\x49\x4c\x48\x4f\x48\x52\x49\x55\x4a\x57\x4c\x59'\
b'\x4e\x5a\x51\x5b\x54\x5b\x57\x5a\x59\x59\x5a\x58\x20\x52\x58'\
b'\x4b\x57\x53\x57\x55\x58\x56\x08\x49\x5b\x52\x46\x4a\x5b\x20'\
b'\x52\x52\x46\x5a\x5b\x20\x52\x4d\x54\x57\x54\x17\x47\x5c\x4b'\
b'\x46\x4b\x5b\x20\x52\x4b\x46\x54\x46\x57\x47\x58\x48\x59\x4a'\
b'\x59\x4c\x58\x4e\x57\x4f\x54\x50\x20\x52\x4b\x50\x54\x50\x57'\
b'\x51\x58\x52\x59\x54\x59\x57\x58\x59\x57\x5a\x54\x5b\x4b\x5b'\
b'\x12\x48\x5d\x5a\x4b\x59\x49\x57\x47\x55\x46\x51\x46\x4f\x47'\
b'\x4d\x49\x4c\x4b\x4b\x4e\x4b\x53\x4c\x56\x4d\x58\x4f\x5a\x51'\
b'\x5b\x55\x5b\x57\x5a\x59\x58\x5a\x56\x0f\x47\x5c\x4b\x46\x4b'\
b'\x5b\x20\x52\x4b\x46\x52\x46\x55\x47\x57\x49\x58\x4b\x59\x4e'\
b'\x59\x53\x58\x56\x57\x58\x55\x5a\x52\x5b\x4b\x5b\x0b\x48\x5b'\
b'\x4c\x46\x4c\x5b\x20\x52\x4c\x46\x59\x46\x20\x52\x4c\x50\x54'\
b'\x50\x20\x52\x4c\x5b\x59\x5b\x08\x48\x5a\x4c\x46\x4c\x5b\x20'\
b'\x52\x4c\x46\x59\x46\x20\x52\x4c\x50\x54\x50\x16\x48\x5d\x5a'\
b'\x4b\x59\x49\x57\x47\x55\x46\x51\x46\x4f\x47\x4d\x49\x4c\x4b'\
b'\x4b\x4e\x4b\x53\x4c\x56\x4d\x58\x4f\x5a\x51\x5b\x55\x5b\x57'\
b'\x5a\x59\x58\x5a\x56\x5a\x53\x20\x52\x55\x53\x5a\x53\x08\x47'\
b'\x5d\x4b\x46\x4b\x5b\x20\x52\x59\x46\x59\x5b\x20\x52\x4b\x50'\
b'\x59\x50\x02\x4e\x56\x52\x46\x52\x5b\x0a\x4a\x5a\x56\x46\x56'\
b'\x56\x55\x59\x54\x5a\x52\x5b\x50\x5b\x4e\x5a\x4d\x59\x4c\x56'\
b'\x4c\x54\x08\x47\x5c\x4b\x46\x4b\x5b\x20\x52\x59\x46\x4b\x54'\
b'\x20\x52\x50\x4f\x59\x5b\x05\x48\x59\x4c\x46\x4c\x5b\x20\x52'\
b'\x4c\x5b\x58\x5b\x0b\x46\x5e\x4a\x46\x4a\x5b\x20\x52\x4a\x46'\
b'\x52\x5b\x20\x52\x5a\x46\x52\x5b\x20\x52\x5a\x46\x5a\x5b\x08'\
b'\x47\x5d\x4b\x46\x4b\x5b\x20\x52\x4b\x46\x59\x5b\x20\x52\x59'\
b'\x46\x59\x5b\x15\x47\x5d\x50\x46\x4e\x47\x4c\x49\x4b\x4b\x4a'\
b'\x4e\x4a\x53\x4b\x56\x4c\x58\x4e\x5a\x50\x5b\x54\x5b\x56\x5a'\
b'\x58\x58\x59\x56\x5a\x53\x5a\x4e\x59\x4b\x58\x49\x56\x47\x54'\
b'\x46\x50\x46\x0d\x47\x5c\x4b\x46\x4b\x5b\x20\x52\x4b\x46\x54'\
b'\x46\x57\x47\x58\x48\x59\x4a\x59\x4d\x58\x4f\x57\x50\x54\x51'\
b'\x4b\x51\x18\x47\x5d\x50\x46\x4e\x47\x4c\x49\x4b\x4b\x4a\x4e'\
b'\x4a\x53\x4b\x56\x4c\x58\x4e\x5a\x50\x5b\x54\x5b\x56\x5a\x58'\
b'\x58\x59\x56\x5a\x53\x5a\x4e\x59\x4b\x58\x49\x56\x47\x54\x46'\
b'\x50\x46\x20\x52\x53\x57\x59\x5d\x10\x47\x5c\x4b\x46\x4b\x5b'\
b'\x20\x52\x4b\x46\x54\x46\x57\x47\x58\x48\x59\x4a\x59\x4c\x58'\
b'\x4e\x57\x4f\x54\x50\x4b\x50\x20\x52\x52\x50\x59\x5b\x14\x48'\
b'\x5c\x59\x49\x57\x47\x54\x46\x50\x46\x4d\x47\x4b\x49\x4b\x4b'\
b'\x4c\x4d\x4d\x4e\x4f\x4f\x55\x51\x57\x52\x58\x53\x59\x55\x59'\
b'\x58\x57\x5a\x54\x5b\x50\x5b\x4d\x5a\x4b\x58\x05\x4a\x5a\x52'\
b'\x46\x52\x5b\x20\x52\x4b\x46\x59\x46\x0a\x47\x5d\x4b\x46\x4b'\
b'\x55\x4c\x58\x4e\x5a\x51\x5b\x53\x5b\x56\x5a\x58\x58\x59\x55'\
b'\x59\x46\x05\x49\x5b\x4a\x46\x52\x5b\x20\x52\x5a\x46\x52\x5b'\
b'\x0b\x46\x5e\x48\x46\x4d\x5b\x20\x52\x52\x46\x4d\x5b\x20\x52'\
b'\x52\x46\x57\x5b\x20\x52\x5c\x46\x57\x5b\x05\x48\x5c\x4b\x46'\
b'\x59\x5b\x20\x52\x59\x46\x4b\x5b\x06\x49\x5b\x4a\x46\x52\x50'\
b'\x52\x5b\x20\x52\x5a\x46\x52\x50\x08\x48\x5c\x59\x46\x4b\x5b'\
b'\x20\x52\x4b\x46\x59\x46\x20\x52\x4b\x5b\x59\x5b\x0b\x4b\x59'\
b'\x4f\x42\x4f\x62\x20\x52\x50\x42\x50\x62\x20\x52\x4f\x42\x56'\
b'\x42\x20\x52\x4f\x62\x56\x62\x02\x4b\x59\x4b\x46\x59\x5e\x0b'\
b'\x4b\x59\x54\x42\x54\x62\x20\x52\x55\x42\x55\x62\x20\x52\x4e'\
b'\x42\x55\x42\x20\x52\x4e\x62\x55\x62\x05\x4a\x5a\x52\x44\x4a'\
b'\x52\x20\x52\x52\x44\x5a\x52\x02\x49\x5b\x49\x62\x5b\x62\x07'\
b'\x4e\x56\x53\x4b\x51\x4d\x51\x4f\x52\x50\x53\x4f\x52\x4e\x51'\
b'\x4f\x11\x49\x5c\x58\x4d\x58\x5b\x20\x52\x58\x50\x56\x4e\x54'\
b'\x4d\x51\x4d\x4f\x4e\x4d\x50\x4c\x53\x4c\x55\x4d\x58\x4f\x5a'\
b'\x51\x5b\x54\x5b\x56\x5a\x58\x58\x11\x48\x5b\x4c\x46\x4c\x5b'\
b'\x20\x52\x4c\x50\x4e\x4e\x50\x4d\x53\x4d\x55\x4e\x57\x50\x58'\
b'\x53\x58\x55\x57\x58\x55\x5a\x53\x5b\x50\x5b\x4e\x5a\x4c\x58'\
b'\x0e\x49\x5b\x58\x50\x56\x4e\x54\x4d\x51\x4d\x4f\x4e\x4d\x50'\
b'\x4c\x53\x4c\x55\x4d\x58\x4f\x5a\x51\x5b\x54\x5b\x56\x5a\x58'\
b'\x58\x11\x49\x5c\x58\x46\x58\x5b\x20\x52\x58\x50\x56\x4e\x54'\
b'\x4d\x51\x4d\x4f\x4e\x4d\x50\x4c\x53\x4c\x55\x4d\x58\x4f\x5a'\
b'\x51\x5b\x54\x5b\x56\x5a\x58\x58\x11\x49\x5b\x4c\x53\x58\x53'\
b'\x58\x51\x57\x4f\x56\x4e\x54\x4d\x51\x4d\x4f\x4e\x4d\x50\x4c'\
b'\x53\x4c\x55\x4d\x58\x4f\x5a\x51\x5b\x54\x5b\x56\x5a\x58\x58'\
b'\x08\x4d\x59\x57\x46\x55\x46\x53\x47\x52\x4a\x52\x5b\x20\x52'\
b'\x4f\x4d\x56\x4d\x16\x49\x5c\x58\x4d\x58\x5d\x57\x60\x56\x61'\
b'\x54\x62\x51\x62\x4f\x61\x20\x52\x58\x50\x56\x4e\x54\x4d\x51'\
b'\x4d\x4f\x4e\x4d\x50\x4c\x53\x4c\x55\x4d\x58\x4f\x5a\x51\x5b'\
b'\x54\x5b\x56\x5a\x58\x58\x0a\x49\x5c\x4d\x46\x4d\x5b\x20\x52'\
b'\x4d\x51\x50\x4e\x52\x4d\x55\x4d\x57\x4e\x58\x51\x58\x5b\x08'\
b'\x4e\x56\x51\x46\x52\x47\x53\x46\x52\x45\x51\x46\x20\x52\x52'\
b'\x4d\x52\x5b\x0b\x4d\x57\x52\x46\x53\x47\x54\x46\x53\x45\x52'\
b'\x46\x20\x52\x53\x4d\x53\x5e\x52\x61\x50\x62\x4e\x62\x08\x49'\
b'\x5a\x4d\x46\x4d\x5b\x20\x52\x57\x4d\x4d\x57\x20\x52\x51\x53'\
b'\x58\x5b\x02\x4e\x56\x52\x46\x52\x5b\x12\x43\x61\x47\x4d\x47'\
b'\x5b\x20\x52\x47\x51\x4a\x4e\x4c\x4d\x4f\x4d\x51\x4e\x52\x51'\
b'\x52\x5b\x20\x52\x52\x51\x55\x4e\x57\x4d\x5a\x4d\x5c\x4e\x5d'\
b'\x51\x5d\x5b\x0a\x49\x5c\x4d\x4d\x4d\x5b\x20\x52\x4d\x51\x50'\
b'\x4e\x52\x4d\x55\x4d\x57\x4e\x58\x51\x58\x5b\x11\x49\x5c\x51'\
b'\x4d\x4f\x4e\x4d\x50\x4c\x53\x4c\x55\x4d\x58\x4f\x5a\x51\x5b'\
b'\x54\x5b\x56\x5a\x58\x58\x59\x55\x59\x53\x58\x50\x56\x4e\x54'\
b'\x4d\x51\x4d\x11\x48\x5b\x4c\x4d\x4c\x62\x20\x52\x4c\x50\x4e'\
b'\x4e\x50\x4d\x53\x4d\x55\x4e\x57\x50\x58\x53\x58\x55\x57\x58'\
b'\x55\x5a\x53\x5b\x50\x5b\x4e\x5a\x4c\x58\x11\x49\x5c\x58\x4d'\
b'\x58\x62\x20\x52\x58\x50\x56\x4e\x54\x4d\x51\x4d\x4f\x4e\x4d'\
b'\x50\x4c\x53\x4c\x55\x4d\x58\x4f\x5a\x51\x5b\x54\x5b\x56\x5a'\
b'\x58\x58\x08\x4b\x58\x4f\x4d\x4f\x5b\x20\x52\x4f\x53\x50\x50'\
b'\x52\x4e\x54\x4d\x57\x4d\x11\x4a\x5b\x58\x50\x57\x4e\x54\x4d'\
b'\x51\x4d\x4e\x4e\x4d\x50\x4e\x52\x50\x53\x55\x54\x57\x55\x58'\
b'\x57\x58\x58\x57\x5a\x54\x5b\x51\x5b\x4e\x5a\x4d\x58\x08\x4d'\
b'\x59\x52\x46\x52\x57\x53\x5a\x55\x5b\x57\x5b\x20\x52\x4f\x4d'\
b'\x56\x4d\x0a\x49\x5c\x4d\x4d\x4d\x57\x4e\x5a\x50\x5b\x53\x5b'\
b'\x55\x5a\x58\x57\x20\x52\x58\x4d\x58\x5b\x05\x4a\x5a\x4c\x4d'\
b'\x52\x5b\x20\x52\x58\x4d\x52\x5b\x0b\x47\x5d\x4a\x4d\x4e\x5b'\
b'\x20\x52\x52\x4d\x4e\x5b\x20\x52\x52\x4d\x56\x5b\x20\x52\x5a'\
b'\x4d\x56\x5b\x05\x4a\x5b\x4d\x4d\x58\x5b\x20\x52\x58\x4d\x4d'\
b'\x5b\x09\x4a\x5a\x4c\x4d\x52\x5b\x20\x52\x58\x4d\x52\x5b\x50'\
b'\x5f\x4e\x61\x4c\x62\x4b\x62\x08\x4a\x5b\x58\x4d\x4d\x5b\x20'\
b'\x52\x4d\x4d\x58\x4d\x20\x52\x4d\x5b\x58\x5b\x27\x4b\x59\x54'\
b'\x42\x52\x43\x51\x44\x50\x46\x50\x48\x51\x4a\x52\x4b\x53\x4d'\
b'\x53\x4f\x51\x51\x20\x52\x52\x43\x51\x45\x51\x47\x52\x49\x53'\
b'\x4a\x54\x4c\x54\x4e\x53\x50\x4f\x52\x53\x54\x54\x56\x54\x58'\
b'\x53\x5a\x52\x5b\x51\x5d\x51\x5f\x52\x61\x20\x52\x51\x53\x53'\
b'\x55\x53\x57\x52\x59\x51\x5a\x50\x5c\x50\x5e\x51\x60\x52\x61'\
b'\x54\x62\x02\x4e\x56\x52\x42\x52\x62\x27\x4b\x59\x50\x42\x52'\
b'\x43\x53\x44\x54\x46\x54\x48\x53\x4a\x52\x4b\x51\x4d\x51\x4f'\
b'\x53\x51\x20\x52\x52\x43\x53\x45\x53\x47\x52\x49\x51\x4a\x50'\
b'\x4c\x50\x4e\x51\x50\x55\x52\x51\x54\x50\x56\x50\x58\x51\x5a'\
b'\x52\x5b\x53\x5d\x53\x5f\x52\x61\x20\x52\x53\x53\x51\x55\x51'\
b'\x57\x52\x59\x53\x5a\x54\x5c\x54\x5e\x53\x60\x52\x61\x50\x62'\
b'\x17\x46\x5e\x49\x55\x49\x53\x4a\x50\x4c\x4f\x4e\x4f\x50\x50'\
b'\x54\x53\x56\x54\x58\x54\x5a\x53\x5b\x51\x20\x52\x49\x53\x4a'\
b'\x51\x4c\x50\x4e\x50\x50\x51\x54\x54\x56\x55\x58\x55\x5a\x54'\
b'\x5b\x51\x5b\x4f\x22\x4a\x5a\x4a\x46\x4a\x5b\x4b\x5b\x4b\x46'\
b'\x4c\x46\x4c\x5b\x4d\x5b\x4d\x46\x4e\x46\x4e\x5b\x4f\x5b\x4f'\
b'\x46\x50\x46\x50\x5b\x51\x5b\x51\x46\x52\x46\x52\x5b\x53\x5b'\
b'\x53\x46\x54\x46\x54\x5b\x55\x5b\x55\x46\x56\x46\x56\x5b\x57'\
b'\x5b\x57\x46\x58\x46\x58\x5b\x59\x5b\x59\x46\x5a\x46\x5a\x5b'\
b''
_index =\
b'\x00\x00\x03\x00\x16\x00\x23\x00\x3c\x00\x73\x00\xb4\x00\xfb'\
b'\x00\x0c\x01\x23\x01\x3a\x01\x4d\x01\x5a\x01\x6b\x01\x72\x01'\
b'\x7f\x01\x86\x01\xab\x01\xb6\x01\xd5\x01\xf6\x01\x05\x02\x2a'\
b'\x02\x5b\x02\x68\x02\xa5\x02\xd6\x02\xef\x02\x0c\x03\x15\x03'\
b'\x22\x03\x2b\x03\x56\x03\xc7\x03\xda\x03\x0b\x04\x32\x04\x53'\
b'\x04\x6c\x04\x7f\x04\xae\x04\xc1\x04\xc8\x04\xdf\x04\xf2\x04'\
b'\xff\x04\x18\x05\x2b\x05\x58\x05\x75\x05\xa8\x05\xcb\x05\xf6'\
b'\x05\x03\x06\x1a\x06\x27\x06\x40\x06\x4d\x06\x5c\x06\x6f\x06'\
b'\x88\x06\x8f\x06\xa8\x06\xb5\x06\xbc\x06\xcd\x06\xf2\x06\x17'\
b'\x07\x36\x07\x5b\x07\x80\x07\x93\x07\xc2\x07\xd9\x07\xec\x07'\
b'\x05\x08\x18\x08\x1f\x08\x46\x08\x5d\x08\x82\x08\xa7\x08\xcc'\
b'\x08\xdf\x08\x04\x09\x17\x09\x2e\x09\x3b\x09\x54\x09\x61\x09'\
b'\x76\x09\x89\x09\xda\x09\xe1\x09\x32\x0a\x63\x0a'
_mvfont = memoryview(_font)
def _chr_addr(ordch):
offset = 2 * (ordch - 32)
return int.from_bytes(_index[offset:offset + 2], 'little')
def get_ch(ordch):
offset = _chr_addr(ordch if 32 <= ordch <= 127 else ord('?'))
count = _font[offset]
return _mvfont[offset:offset+(count+2)*2-1]
| 60.273148 | 65 | 0.705815 | 3,169 | 13,019 | 2.895551 | 0.046071 | 0.057541 | 0.038252 | 0.013078 | 0.378814 | 0.315715 | 0.277136 | 0.246404 | 0.205972 | 0.182323 | 0 | 0.374294 | 0.020508 | 13,019 | 215 | 66 | 60.553488 | 0.345279 | 0 | 0 | 0.019231 | 0 | 0.9375 | 0.898441 | 0.897903 | 0 | 1 | 0 | 0 | 0 | 1 | 0.014423 | false | 0 | 0 | 0.004808 | 0.028846 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
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