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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
038ef99b85316638a984dc40c5ae5b2e3a1c26ce | 20 | py | Python | pyasf/__init__.py | blanzer/pyasf | 8363b410788701938d76008a78928a324e724a94 | [
"MIT"
] | null | null | null | pyasf/__init__.py | blanzer/pyasf | 8363b410788701938d76008a78928a324e724a94 | [
"MIT"
] | null | null | null | pyasf/__init__.py | blanzer/pyasf | 8363b410788701938d76008a78928a324e724a94 | [
"MIT"
] | null | null | null | from .pyasf import * | 20 | 20 | 0.75 | 3 | 20 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 20 | 1 | 20 | 20 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
03ba64b89c279fa392b252596f6790fb70fea065 | 31 | py | Python | src/eye_tools/__init__.py | jpcurrea/eye_tools | 004c8ab774a6b27c021a628ae8f7fe8dc45e5e1e | [
"MIT"
] | null | null | null | src/eye_tools/__init__.py | jpcurrea/eye_tools | 004c8ab774a6b27c021a628ae8f7fe8dc45e5e1e | [
"MIT"
] | null | null | null | src/eye_tools/__init__.py | jpcurrea/eye_tools | 004c8ab774a6b27c021a628ae8f7fe8dc45e5e1e | [
"MIT"
] | null | null | null | from .analysis_tools import *
| 10.333333 | 29 | 0.774194 | 4 | 31 | 5.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16129 | 31 | 2 | 30 | 15.5 | 0.884615 | 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 |
03ec683e444b42deb0a1705026a3a8000713a847 | 72 | py | Python | tests/samples/project/vendor/fooba/experiments/start.py | machinable-org/machinable | 9d96e942dde05d68699bc7bc0c3d062ee18652ad | [
"MIT"
] | 23 | 2020-02-28T14:29:04.000Z | 2021-12-23T20:50:54.000Z | tests/samples/project/vendor/fooba/experiments/start.py | machinable-org/machinable | 9d96e942dde05d68699bc7bc0c3d062ee18652ad | [
"MIT"
] | 172 | 2020-02-24T12:12:11.000Z | 2022-03-29T03:08:24.000Z | tests/samples/project/vendor/fooba/experiments/start.py | machinable-org/machinable | 9d96e942dde05d68699bc7bc0c3d062ee18652ad | [
"MIT"
] | 1 | 2020-11-23T22:42:20.000Z | 2020-11-23T22:42:20.000Z | from machinable import Component
class TestNode(Component):
pass
| 10.285714 | 32 | 0.763889 | 8 | 72 | 6.875 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.194444 | 72 | 6 | 33 | 12 | 0.948276 | 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 |
03ec722f0056221e6c01f870196ed0de49a38e25 | 37 | py | Python | src/fate_of_dice/system/__init__.py | bonczeq/FateOfDice | ce1704ac490f55bc600c0963958d4175104e85e5 | [
"MIT"
] | null | null | null | src/fate_of_dice/system/__init__.py | bonczeq/FateOfDice | ce1704ac490f55bc600c0963958d4175104e85e5 | [
"MIT"
] | null | null | null | src/fate_of_dice/system/__init__.py | bonczeq/FateOfDice | ce1704ac490f55bc600c0963958d4175104e85e5 | [
"MIT"
] | null | null | null | from .basic_result import DiceResult
| 18.5 | 36 | 0.864865 | 5 | 37 | 6.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 37 | 1 | 37 | 37 | 0.939394 | 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 |
03fbee0dff32602e0b0af605e1a44fa00a2dc027 | 3,474 | py | Python | seismicpro/src/utils/normalization.py | janwillembuist/SeismicPro | 5431bf800c06a44fd5b3c0553d98040147ecb176 | [
"Apache-2.0"
] | 97 | 2019-09-17T08:49:32.000Z | 2022-03-20T02:11:16.000Z | seismicpro/src/utils/normalization.py | janwillembuist/SeismicPro | 5431bf800c06a44fd5b3c0553d98040147ecb176 | [
"Apache-2.0"
] | 59 | 2019-09-09T20:42:07.000Z | 2022-03-31T09:41:49.000Z | seismicpro/src/utils/normalization.py | janwillembuist/SeismicPro | 5431bf800c06a44fd5b3c0553d98040147ecb176 | [
"Apache-2.0"
] | 45 | 2019-10-17T07:56:24.000Z | 2022-03-23T16:18:03.000Z | """Implements optimized functions for various gather normalizations"""
import numpy as np
from numba import njit
from . import general_utils
@njit(nogil=True)
def scale_standard(data, mean, std, eps):
r"""Scale `data` using the following formula:
:math:`S = \frac{data - mean}{std + eps}`
Parameters
----------
data : np.ndarray
Data to scale.
mean : float or np.ndarray
Mean value. Must be broadcastable to `data.shape`.
std : float or np.ndarray
Standard deviation. Must be broadcastable to `data.shape`.
eps : float
A constant to be added to the denominator to avoid division by zero.
Returns
-------
data : np.ndarray
Scaled data with unchanged shape.
"""
data = (data - mean) / (std + eps)
return data
@njit(nogil=True)
def scale_maxabs(data, min_value, max_value, clip, eps):
r"""Scale `data` using the following formula:
:math:`S = \frac{data}{max(|min_value|, |max_value|) + eps}`
Parameters
----------
data : 2d np.ndarray
Data to scale.
min_value : int, float, 1d or 2d array-like
Minimum value. Dummy trailing axes are added to the array to have at least 2 dimensions, the result must be
broadcastable to `data.shape`.
max_value : int, float, 1d or 2d array-like
Maximum value. Dummy trailing axes are added to the array to have at least 2 dimensions, the result must be
broadcastable to `data.shape`.
clip : bool
Whether to clip scaled data to the [-1, 1] range.
eps : float
A constant to be added to the denominator to avoid division by zero.
Returns
-------
data : np.ndarray
Scaled data with unchanged shape.
"""
max_abs = np.maximum(np.abs(min_value), np.abs(max_value))
# Use np.atleast_2d(array).T to make the array 2-dimentional by adding dummy trailing axes
# for further broadcasting to work tracewise
data /= np.atleast_2d(np.asarray(max_abs)).T + eps
if clip:
data = general_utils.clip(data, np.float32(-1), np.float32(1))
return data
@njit(nogil=True)
def scale_minmax(data, min_value, max_value, clip, eps):
r"""Scale `data` using the following formula:
:math:`S = \frac{data - min_value}{max_value - min_value + eps}`
Parameters
----------
data : 2d np.ndarray
Data to scale.
min_value : int, float, 1d or 2d array-like
Minimum value. Dummy trailing axes are added to the array to have at least 2 dimensions, the result must be
broadcastable to `data.shape`.
max_value : int, float, 1d or 2d array-like
Maximum value. Dummy trailing axes are added to the array to have at least 2 dimensions, the result must be
broadcastable to `data.shape`.
clip : bool
Whether to clip scaled data to the [0, 1] range.
eps : float
A constant to be added to the denominator to avoid division by zero.
Returns
-------
data : np.ndarray
Scaled data with unchanged shape.
"""
# Use np.atleast_2d(array).T to make the array 2-dimentional by adding dummy trailing axes
# for further broadcasting to work tracewise
min_value = np.atleast_2d(np.asarray(min_value)).T
max_value = np.atleast_2d(np.asarray(max_value)).T
data = (data - min_value) / (max_value - min_value + eps)
if clip:
data = general_utils.clip(data, np.float32(0), np.float32(1))
return data
| 33.403846 | 115 | 0.651123 | 515 | 3,474 | 4.324272 | 0.190291 | 0.043107 | 0.031432 | 0.056578 | 0.845532 | 0.813651 | 0.752582 | 0.724742 | 0.703637 | 0.703637 | 0 | 0.014198 | 0.249856 | 3,474 | 103 | 116 | 33.728155 | 0.840368 | 0.677029 | 0 | 0.32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.12 | false | 0 | 0.12 | 0 | 0.36 | 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 |
03fe20b8c0bc22bd5edb566c11970ead6a47beca | 358 | py | Python | models/__init__.py | StephenCurry-LH/ecg | f6dffeb108515d7307773112482d4d8f81ba9442 | [
"Apache-2.0"
] | null | null | null | models/__init__.py | StephenCurry-LH/ecg | f6dffeb108515d7307773112482d4d8f81ba9442 | [
"Apache-2.0"
] | null | null | null | models/__init__.py | StephenCurry-LH/ecg | f6dffeb108515d7307773112482d4d8f81ba9442 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
'''
@time: 2019/9/8 20:13
@ author: javis
'''
from .resnet import resnet34,resnet18
# from .resnet import resnet34, resnet50, resnet101, resnet152
from .ResNext import ResNeXt50_2x16d, ResNeXt50_2x32d, ResNeXt50_4x64d
from .ResNext import ResNeXt101_2x64d, ResNeXt101_4x64d
from .ResNext import ResNeXt152_2x64d, ResNeXt152_4x64d
| 25.571429 | 71 | 0.773743 | 46 | 358 | 5.869565 | 0.565217 | 0.122222 | 0.188889 | 0.177778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.204473 | 0.125698 | 358 | 13 | 72 | 27.538462 | 0.658147 | 0.340782 | 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 | 1 | 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 |
ff03d7bbce770765b24a0f0985e393abfbc3a328 | 99 | py | Python | kubectlfr/__init__.py | theophanevie/kubectlfr | 4705182b85991db4f008eedd5604e72fe4dfc045 | [
"MIT"
] | 7 | 2022-01-21T20:40:51.000Z | 2022-01-22T08:46:17.000Z | kubectlfr/__init__.py | theophanevie/kubectlfr | 4705182b85991db4f008eedd5604e72fe4dfc045 | [
"MIT"
] | 1 | 2022-01-22T15:07:50.000Z | 2022-01-22T15:07:50.000Z | kubectlfr/__init__.py | theophanevie/kubectlfr | 4705182b85991db4f008eedd5604e72fe4dfc045 | [
"MIT"
] | 1 | 2022-01-22T00:19:13.000Z | 2022-01-22T00:19:13.000Z | import sys
from kubectlfr.main import kubectlfr
def main() -> None:
kubectlfr(sys.argv[1:])
| 12.375 | 36 | 0.69697 | 14 | 99 | 4.928571 | 0.642857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012346 | 0.181818 | 99 | 7 | 37 | 14.142857 | 0.839506 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ff0c299dbab4e5cc2d241e52ffb7a9e88d1da4c3 | 90 | py | Python | mmorpg/old/Model/Direction/CardinalDirection/cardinaldirection.py | InnovAnon-Inc/MAiZE | 6b7b266d85f8932557013e3c32bcc728c53f616f | [
"Unlicense"
] | null | null | null | mmorpg/old/Model/Direction/CardinalDirection/cardinaldirection.py | InnovAnon-Inc/MAiZE | 6b7b266d85f8932557013e3c32bcc728c53f616f | [
"Unlicense"
] | null | null | null | mmorpg/old/Model/Direction/CardinalDirection/cardinaldirection.py | InnovAnon-Inc/MAiZE | 6b7b266d85f8932557013e3c32bcc728c53f616f | [
"Unlicense"
] | null | null | null | from Model.Direction.direction import Direction
class CardinalDirection (Direction): pass | 30 | 47 | 0.855556 | 10 | 90 | 7.7 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088889 | 90 | 3 | 48 | 30 | 0.939024 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0.5 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
45849f0e73cff4de058afc952b55b6eca429e343 | 95 | py | Python | 992020.py | veolex123/John2020 | 892c4476e7a786e8f1691d0d092fcb8fba0761f2 | [
"MIT"
] | null | null | null | 992020.py | veolex123/John2020 | 892c4476e7a786e8f1691d0d092fcb8fba0761f2 | [
"MIT"
] | null | null | null | 992020.py | veolex123/John2020 | 892c4476e7a786e8f1691d0d092fcb8fba0761f2 | [
"MIT"
] | null | null | null | for i in range(9,0, -1):
for j in range(9,0, -1):
print(i*j, end=' ')
print(' ')
| 15.833333 | 29 | 0.452632 | 19 | 95 | 2.263158 | 0.526316 | 0.325581 | 0.372093 | 0.418605 | 0.465116 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 0.305263 | 95 | 5 | 30 | 19 | 0.560606 | 0 | 0 | 0 | 0 | 0 | 0.021277 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
45b0924cd7a1b71a14678e72725f8f28d7b6953a | 32 | py | Python | src/stateful/__init__.py | DataAsCode/stateful | 7c461589090ca9fabfbb97d3d17d34a6a2c7a185 | [
"MIT"
] | null | null | null | src/stateful/__init__.py | DataAsCode/stateful | 7c461589090ca9fabfbb97d3d17d34a6a2c7a185 | [
"MIT"
] | null | null | null | src/stateful/__init__.py | DataAsCode/stateful | 7c461589090ca9fabfbb97d3d17d34a6a2c7a185 | [
"MIT"
] | 1 | 2020-11-24T12:32:48.000Z | 2020-11-24T12:32:48.000Z | from stateful.state import State | 32 | 32 | 0.875 | 5 | 32 | 5.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.09375 | 32 | 1 | 32 | 32 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
45c44cfe2e2fb35b7c6ebf8c2c63b5e586fa844f | 96 | py | Python | venv/lib/python3.8/site-packages/pyflakes/test/test_other.py | Retraces/UkraineBot | 3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/pyflakes/test/test_other.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/pyflakes/test/test_other.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/16/69/cf/58bf4e618e97dbda6f7079f2c1356d63520f1b32bca29056c48b486566 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.447917 | 0 | 96 | 1 | 96 | 96 | 0.447917 | 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 |
45cafc27dbbcaabf1011aedd832b2441db304380 | 41 | py | Python | Programs/RandomNumber.py | GopinathBalaji/Basic-Programs-Python | 8993e73428f1b6d4e2e601983c9c0f1bd0f92935 | [
"MIT"
] | 5 | 2021-07-20T08:12:29.000Z | 2022-01-18T20:00:50.000Z | Programs/random_number.py | Janhavi-2001/Basic-Programs-Python | 1bba988d77e962ddd4c78fb1beb9bf00798423c9 | [
"MIT"
] | 26 | 2020-12-26T14:42:05.000Z | 2021-12-04T09:23:41.000Z | Programs/random_number.py | Janhavi-2001/Basic-Programs-Python | 1bba988d77e962ddd4c78fb1beb9bf00798423c9 | [
"MIT"
] | 14 | 2021-04-01T19:24:35.000Z | 2022-01-10T11:29:28.000Z | import random
print(random.randint(0,9)) | 13.666667 | 26 | 0.780488 | 7 | 41 | 4.571429 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.052632 | 0.073171 | 41 | 3 | 26 | 13.666667 | 0.789474 | 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 |
45d74eb0e1a07748d97c065151b2d241aa15656f | 18 | py | Python | pylsd/__init__.py | cshields143/pylsd | 921873a4f4ccbb96859ebb80dbe7f6d99839529e | [
"BSD-2-Clause"
] | null | null | null | pylsd/__init__.py | cshields143/pylsd | 921873a4f4ccbb96859ebb80dbe7f6d99839529e | [
"BSD-2-Clause"
] | null | null | null | pylsd/__init__.py | cshields143/pylsd | 921873a4f4ccbb96859ebb80dbe7f6d99839529e | [
"BSD-2-Clause"
] | null | null | null | from . import lsd
| 9 | 17 | 0.722222 | 3 | 18 | 4.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.222222 | 18 | 1 | 18 | 18 | 0.928571 | 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 |
affcd9beff3bce518ae956aed818bc824a90d989 | 189 | py | Python | 3rd/mujoco/python/gym-baxter/gym_baxter/envs/__init__.py | Tadinu/my_arm | ac4fb295ddad7c7ee999a03d2e7d229802b64226 | [
"BSD-3-Clause"
] | 4 | 2021-02-20T15:59:42.000Z | 2022-03-25T04:04:21.000Z | 3rd/mujoco/python/gym-baxter/gym_baxter/envs/__init__.py | Tadinu/my_arm | ac4fb295ddad7c7ee999a03d2e7d229802b64226 | [
"BSD-3-Clause"
] | 1 | 2021-04-14T04:12:48.000Z | 2021-04-14T04:12:48.000Z | 3rd/mujoco/python/gym-baxter/gym_baxter/envs/__init__.py | Tadinu/my_arm | ac4fb295ddad7c7ee999a03d2e7d229802b64226 | [
"BSD-3-Clause"
] | 2 | 2019-10-29T12:41:16.000Z | 2021-03-22T16:38:27.000Z | from gym_baxter.envs.baxter_env import BaxterEnv
#from gym_baxter.envs.soccer_empty_goal import SoccerEmptyGoalEnv
#from gym_baxter.envs.soccer_against_keeper import SoccerAgainstKeeperEnv
| 47.25 | 73 | 0.89418 | 26 | 189 | 6.192308 | 0.538462 | 0.130435 | 0.242236 | 0.31677 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.063492 | 189 | 3 | 74 | 63 | 0.909605 | 0.719577 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
b30d2dee6480d58c395b798d67f63af39bf93877 | 40 | py | Python | src/vox/linty/__init__.py | Peilonrayz/vox | 026a82bb3c0d47988cd20d18639bcb0e249ee211 | [
"MIT"
] | null | null | null | src/vox/linty/__init__.py | Peilonrayz/vox | 026a82bb3c0d47988cd20d18639bcb0e249ee211 | [
"MIT"
] | null | null | null | src/vox/linty/__init__.py | Peilonrayz/vox | 026a82bb3c0d47988cd20d18639bcb0e249ee211 | [
"MIT"
] | null | null | null | from . import display, from_str, to_str
| 20 | 39 | 0.775 | 7 | 40 | 4.142857 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 40 | 1 | 40 | 40 | 0.852941 | 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 |
b31f348931552bf0dde374a25cc878db5614511c | 8,723 | py | Python | trackeval/baselines/pascal_colormap.py | AlexanderSing/TrackEval | 373e643f8989445f0253af6748e9e247d6ae6322 | [
"MIT"
] | 325 | 2021-02-25T19:00:23.000Z | 2022-03-31T14:30:42.000Z | trackeval/baselines/pascal_colormap.py | AlexanderSing/TrackEval | 373e643f8989445f0253af6748e9e247d6ae6322 | [
"MIT"
] | 49 | 2021-03-26T14:40:28.000Z | 2022-03-27T17:33:13.000Z | trackeval/baselines/pascal_colormap.py | AlexanderSing/TrackEval | 373e643f8989445f0253af6748e9e247d6ae6322 | [
"MIT"
] | 93 | 2021-02-26T09:05:37.000Z | 2022-03-30T11:44:01.000Z | pascal_colormap = [
0 , 0, 0,
0.5020, 0, 0,
0, 0.5020, 0,
0.5020, 0.5020, 0,
0, 0, 0.5020,
0.5020, 0, 0.5020,
0, 0.5020, 0.5020,
0.5020, 0.5020, 0.5020,
0.2510, 0, 0,
0.7529, 0, 0,
0.2510, 0.5020, 0,
0.7529, 0.5020, 0,
0.2510, 0, 0.5020,
0.7529, 0, 0.5020,
0.2510, 0.5020, 0.5020,
0.7529, 0.5020, 0.5020,
0, 0.2510, 0,
0.5020, 0.2510, 0,
0, 0.7529, 0,
0.5020, 0.7529, 0,
0, 0.2510, 0.5020,
0.5020, 0.2510, 0.5020,
0, 0.7529, 0.5020,
0.5020, 0.7529, 0.5020,
0.2510, 0.2510, 0,
0.7529, 0.2510, 0,
0.2510, 0.7529, 0,
0.7529, 0.7529, 0,
0.2510, 0.2510, 0.5020,
0.7529, 0.2510, 0.5020,
0.2510, 0.7529, 0.5020,
0.7529, 0.7529, 0.5020,
0, 0, 0.2510,
0.5020, 0, 0.2510,
0, 0.5020, 0.2510,
0.5020, 0.5020, 0.2510,
0, 0, 0.7529,
0.5020, 0, 0.7529,
0, 0.5020, 0.7529,
0.5020, 0.5020, 0.7529,
0.2510, 0, 0.2510,
0.7529, 0, 0.2510,
0.2510, 0.5020, 0.2510,
0.7529, 0.5020, 0.2510,
0.2510, 0, 0.7529,
0.7529, 0, 0.7529,
0.2510, 0.5020, 0.7529,
0.7529, 0.5020, 0.7529,
0, 0.2510, 0.2510,
0.5020, 0.2510, 0.2510,
0, 0.7529, 0.2510,
0.5020, 0.7529, 0.2510,
0, 0.2510, 0.7529,
0.5020, 0.2510, 0.7529,
0, 0.7529, 0.7529,
0.5020, 0.7529, 0.7529,
0.2510, 0.2510, 0.2510,
0.7529, 0.2510, 0.2510,
0.2510, 0.7529, 0.2510,
0.7529, 0.7529, 0.2510,
0.2510, 0.2510, 0.7529,
0.7529, 0.2510, 0.7529,
0.2510, 0.7529, 0.7529,
0.7529, 0.7529, 0.7529,
0.1255, 0, 0,
0.6275, 0, 0,
0.1255, 0.5020, 0,
0.6275, 0.5020, 0,
0.1255, 0, 0.5020,
0.6275, 0, 0.5020,
0.1255, 0.5020, 0.5020,
0.6275, 0.5020, 0.5020,
0.3765, 0, 0,
0.8784, 0, 0,
0.3765, 0.5020, 0,
0.8784, 0.5020, 0,
0.3765, 0, 0.5020,
0.8784, 0, 0.5020,
0.3765, 0.5020, 0.5020,
0.8784, 0.5020, 0.5020,
0.1255, 0.2510, 0,
0.6275, 0.2510, 0,
0.1255, 0.7529, 0,
0.6275, 0.7529, 0,
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0.6275, 0.2510, 0.5020,
0.1255, 0.7529, 0.5020,
0.6275, 0.7529, 0.5020,
0.3765, 0.2510, 0,
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0.3765, 0.7529, 0,
0.8784, 0.7529, 0,
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0.8784, 0.2510, 0.5020,
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0.1255, 0, 0.2510,
0.6275, 0, 0.2510,
0.1255, 0.5020, 0.2510,
0.6275, 0.5020, 0.2510,
0.1255, 0, 0.7529,
0.6275, 0, 0.7529,
0.1255, 0.5020, 0.7529,
0.6275, 0.5020, 0.7529,
0.3765, 0, 0.2510,
0.8784, 0, 0.2510,
0.3765, 0.5020, 0.2510,
0.8784, 0.5020, 0.2510,
0.3765, 0, 0.7529,
0.8784, 0, 0.7529,
0.3765, 0.5020, 0.7529,
0.8784, 0.5020, 0.7529,
0.1255, 0.2510, 0.2510,
0.6275, 0.2510, 0.2510,
0.1255, 0.7529, 0.2510,
0.6275, 0.7529, 0.2510,
0.1255, 0.2510, 0.7529,
0.6275, 0.2510, 0.7529,
0.1255, 0.7529, 0.7529,
0.6275, 0.7529, 0.7529,
0.3765, 0.2510, 0.2510,
0.8784, 0.2510, 0.2510,
0.3765, 0.7529, 0.2510,
0.8784, 0.7529, 0.2510,
0.3765, 0.2510, 0.7529,
0.8784, 0.2510, 0.7529,
0.3765, 0.7529, 0.7529,
0.8784, 0.7529, 0.7529,
0, 0.1255, 0,
0.5020, 0.1255, 0,
0, 0.6275, 0,
0.5020, 0.6275, 0,
0, 0.1255, 0.5020,
0.5020, 0.1255, 0.5020,
0, 0.6275, 0.5020,
0.5020, 0.6275, 0.5020,
0.2510, 0.1255, 0,
0.7529, 0.1255, 0,
0.2510, 0.6275, 0,
0.7529, 0.6275, 0,
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0.7529, 0.1255, 0.5020,
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0.7529, 0.6275, 0.5020,
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0.5020, 0.3765, 0,
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0.5020, 0.8784, 0,
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0.5020, 0.3765, 0.5020,
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0.2510, 0.3765, 0,
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0.7529, 0.8784, 0.5020,
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0.5020, 0.6275, 0.2510,
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0.5020, 0.6275, 0.7529,
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0.7529, 0.1255, 0.7529,
0.2510, 0.6275, 0.7529,
0.7529, 0.6275, 0.7529,
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0, 0.3765, 0.7529,
0.5020, 0.3765, 0.7529,
0, 0.8784, 0.7529,
0.5020, 0.8784, 0.7529,
0.2510, 0.3765, 0.2510,
0.7529, 0.3765, 0.2510,
0.2510, 0.8784, 0.2510,
0.7529, 0.8784, 0.2510,
0.2510, 0.3765, 0.7529,
0.7529, 0.3765, 0.7529,
0.2510, 0.8784, 0.7529,
0.7529, 0.8784, 0.7529,
0.1255, 0.1255, 0,
0.6275, 0.1255, 0,
0.1255, 0.6275, 0,
0.6275, 0.6275, 0,
0.1255, 0.1255, 0.5020,
0.6275, 0.1255, 0.5020,
0.1255, 0.6275, 0.5020,
0.6275, 0.6275, 0.5020,
0.3765, 0.1255, 0,
0.8784, 0.1255, 0,
0.3765, 0.6275, 0,
0.8784, 0.6275, 0,
0.3765, 0.1255, 0.5020,
0.8784, 0.1255, 0.5020,
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0.8784, 0.6275, 0.5020,
0.1255, 0.3765, 0,
0.6275, 0.3765, 0,
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0.6275, 0.8784, 0,
0.1255, 0.3765, 0.5020,
0.6275, 0.3765, 0.5020,
0.1255, 0.8784, 0.5020,
0.6275, 0.8784, 0.5020,
0.3765, 0.3765, 0,
0.8784, 0.3765, 0,
0.3765, 0.8784, 0,
0.8784, 0.8784, 0,
0.3765, 0.3765, 0.5020,
0.8784, 0.3765, 0.5020,
0.3765, 0.8784, 0.5020,
0.8784, 0.8784, 0.5020,
0.1255, 0.1255, 0.2510,
0.6275, 0.1255, 0.2510,
0.1255, 0.6275, 0.2510,
0.6275, 0.6275, 0.2510,
0.1255, 0.1255, 0.7529,
0.6275, 0.1255, 0.7529,
0.1255, 0.6275, 0.7529,
0.6275, 0.6275, 0.7529,
0.3765, 0.1255, 0.2510,
0.8784, 0.1255, 0.2510,
0.3765, 0.6275, 0.2510,
0.8784, 0.6275, 0.2510,
0.3765, 0.1255, 0.7529,
0.8784, 0.1255, 0.7529,
0.3765, 0.6275, 0.7529,
0.8784, 0.6275, 0.7529,
0.1255, 0.3765, 0.2510,
0.6275, 0.3765, 0.2510,
0.1255, 0.8784, 0.2510,
0.6275, 0.8784, 0.2510,
0.1255, 0.3765, 0.7529,
0.6275, 0.3765, 0.7529,
0.1255, 0.8784, 0.7529,
0.6275, 0.8784, 0.7529,
0.3765, 0.3765, 0.2510,
0.8784, 0.3765, 0.2510,
0.3765, 0.8784, 0.2510,
0.8784, 0.8784, 0.2510,
0.3765, 0.3765, 0.7529,
0.8784, 0.3765, 0.7529,
0.3765, 0.8784, 0.7529,
0.8784, 0.8784, 0.7529] | 33.941634 | 33 | 0.38324 | 1,410 | 8,723 | 2.370213 | 0.007092 | 0.076601 | 0.229803 | 0.041891 | 0.994614 | 0.728007 | 0.728007 | 0.669659 | 0.652902 | 0.486535 | 0 | 0.700189 | 0.455119 | 8,723 | 257 | 34 | 33.941634 | 0.002946 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 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 |
b3752c432aadccc554ead7ac68bb6dbd18cb3da5 | 35 | py | Python | __init__.py | lbk0116/Inventory | ad9ff0b5ddf8550a0375971a34d6c820252121fd | [
"Apache-2.0"
] | 3 | 2018-11-22T11:38:56.000Z | 2022-03-22T03:55:57.000Z | __init__.py | lbk0116/Inventory | ad9ff0b5ddf8550a0375971a34d6c820252121fd | [
"Apache-2.0"
] | null | null | null | __init__.py | lbk0116/Inventory | ad9ff0b5ddf8550a0375971a34d6c820252121fd | [
"Apache-2.0"
] | 3 | 2016-11-14T06:58:15.000Z | 2020-03-12T12:49:06.000Z |
from . import models
import wizard | 11.666667 | 20 | 0.8 | 5 | 35 | 5.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.171429 | 35 | 3 | 21 | 11.666667 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2fea72589bfbaa29e9fc970aba6af3301f9f5fc2 | 20 | py | Python | geolearn/__init__.py | Guoyinzh/geolearn | 564a103246c1fd326f8b2b7d8d8c88ab391e2450 | [
"Apache-2.0"
] | 1 | 2020-07-06T17:32:44.000Z | 2020-07-06T17:32:44.000Z | geolearn/__init__.py | Guoyinzh/geolearn | 564a103246c1fd326f8b2b7d8d8c88ab391e2450 | [
"Apache-2.0"
] | null | null | null | geolearn/__init__.py | Guoyinzh/geolearn | 564a103246c1fd326f8b2b7d8d8c88ab391e2450 | [
"Apache-2.0"
] | null | null | null |
from . import test
| 6.666667 | 18 | 0.7 | 3 | 20 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 20 | 2 | 19 | 10 | 0.933333 | 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 |
2ff3d1cc544686c2e39737e3bc1efd77dd54010c | 163 | py | Python | app/location/admin.py | maro99/yapen | 0de7aa9d4b152aadd18511be6e536e89645452d9 | [
"MIT"
] | 1 | 2019-04-28T12:21:51.000Z | 2019-04-28T12:21:51.000Z | app/location/admin.py | maro99/yapen | 0de7aa9d4b152aadd18511be6e536e89645452d9 | [
"MIT"
] | 5 | 2018-07-30T05:44:44.000Z | 2020-06-05T18:56:41.000Z | app/location/admin.py | maro99/yapen | 0de7aa9d4b152aadd18511be6e536e89645452d9 | [
"MIT"
] | 5 | 2018-07-23T05:21:41.000Z | 2018-08-08T05:00:42.000Z | from django.contrib import admin
from .models import Location, Pension, Room
admin.site.register(Location)
admin.site.register(Pension)
admin.site.register(Room) | 23.285714 | 43 | 0.815951 | 23 | 163 | 5.782609 | 0.478261 | 0.203008 | 0.383459 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08589 | 163 | 7 | 44 | 23.285714 | 0.892617 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 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 |
ff4c68831d8defaece370c6ad91a7c475163247e | 270 | py | Python | {{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/frontend_templates/skeleton/api.py | chrishavlin/dxlcookiecuttertest | b297760506d65e42f546a2051c3b8d2f1e7167b7 | [
"BSD-3-Clause"
] | null | null | null | {{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/frontend_templates/skeleton/api.py | chrishavlin/dxlcookiecuttertest | b297760506d65e42f546a2051c3b8d2f1e7167b7 | [
"BSD-3-Clause"
] | 1 | 2022-03-23T23:22:54.000Z | 2022-03-23T23:22:54.000Z | {{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/frontend_templates/skeleton/api.py | chrishavlin/dxlcookiecuttertest | b297760506d65e42f546a2051c3b8d2f1e7167b7 | [
"BSD-3-Clause"
] | 1 | 2021-10-20T19:37:13.000Z | 2021-10-20T19:37:13.000Z | from .data_structures import {{ cookiecutter.frontend_name }}Dataset, {{ cookiecutter.frontend_name }}Grid, {{ cookiecutter.frontend_name }}Hierarchy
from .fields import {{ cookiecutter.frontend_name }}FieldInfo
from .io import {{ cookiecutter.frontend_name }}IOHandler
| 67.5 | 149 | 0.8 | 30 | 270 | 7 | 0.466667 | 0.47619 | 0.571429 | 0.428571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088889 | 270 | 3 | 150 | 90 | 0.853659 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 1 | null | null | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
ff896dcbaaf6e9f26d02be8d236e46952d348d93 | 872 | py | Python | 2017/day05.py | ardavast/AdventOfCode | 2c5062e182122a08c10491a5a149286b90ae8688 | [
"MIT"
] | null | null | null | 2017/day05.py | ardavast/AdventOfCode | 2c5062e182122a08c10491a5a149286b90ae8688 | [
"MIT"
] | null | null | null | 2017/day05.py | ardavast/AdventOfCode | 2c5062e182122a08c10491a5a149286b90ae8688 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
def part1(filename):
l = []
ip = 0
count = 0
with open(filename) as f:
for line in f:
l.append(int(line))
while True:
try:
oldIp = ip
ip += l[ip]
l[oldIp] += 1
count += 1
except IndexError:
print(count)
break
def part2(filename):
l = []
ip = 0
count = 0
with open(filename) as f:
for line in f:
l.append(int(line))
while True:
try:
oldIp = ip
ip += l[ip]
if l[oldIp] >= 3:
l[oldIp] -= 1
else:
l[oldIp] += 1
count += 1
except IndexError:
print(count)
break
if __name__ == '__main__':
part1('day05input.txt')
part2('day05input.txt') | 17.44 | 31 | 0.417431 | 99 | 872 | 3.59596 | 0.373737 | 0.033708 | 0.058989 | 0.067416 | 0.724719 | 0.724719 | 0.724719 | 0.724719 | 0.724719 | 0.724719 | 0 | 0.041304 | 0.472477 | 872 | 50 | 32 | 17.44 | 0.732609 | 0.024083 | 0 | 0.789474 | 0 | 0 | 0.042303 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.052632 | false | 0 | 0 | 0 | 0.052632 | 0.052632 | 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 |
ffa93a5b82410c0b0e6a689d63d713327a494ed1 | 122 | py | Python | sample_prj/__main__.py | stdtom/sample_prj | 18070bc2a21244854b692fb9e048cba71a17f98e | [
"Apache-2.0"
] | null | null | null | sample_prj/__main__.py | stdtom/sample_prj | 18070bc2a21244854b692fb9e048cba71a17f98e | [
"Apache-2.0"
] | null | null | null | sample_prj/__main__.py | stdtom/sample_prj | 18070bc2a21244854b692fb9e048cba71a17f98e | [
"Apache-2.0"
] | null | null | null | import sys
if __name__ == "__main__":
import sample_prj.cli
sys.exit(sample_prj.cli.main()) # pragma: no cover
| 17.428571 | 55 | 0.680328 | 18 | 122 | 4.055556 | 0.666667 | 0.246575 | 0.328767 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.196721 | 122 | 6 | 56 | 20.333333 | 0.744898 | 0.131148 | 0 | 0 | 0 | 0 | 0.076923 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
ffb113153c13d33cca3df295a01ea6e53111a559 | 23 | py | Python | er1_robot/devel/lib/python2.7/dist-packages/er1_motor_driver/msg/__init__.py | arvindpereira/clover_hack_day | f8f49d7401b21c3932bd09dd58d7f2b9ba33ea6b | [
"MIT"
] | 2 | 2015-10-13T18:12:30.000Z | 2015-10-24T19:03:21.000Z | er1_robot/devel/lib/python2.7/dist-packages/er1_motor_driver/msg/__init__.py | arvindpereira/clover_hack_day | f8f49d7401b21c3932bd09dd58d7f2b9ba33ea6b | [
"MIT"
] | null | null | null | er1_robot/devel/lib/python2.7/dist-packages/er1_motor_driver/msg/__init__.py | arvindpereira/clover_hack_day | f8f49d7401b21c3932bd09dd58d7f2b9ba33ea6b | [
"MIT"
] | 1 | 2020-05-08T23:13:28.000Z | 2020-05-08T23:13:28.000Z | from ._Motors import *
| 11.5 | 22 | 0.73913 | 3 | 23 | 5.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173913 | 23 | 1 | 23 | 23 | 0.842105 | 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 |
442e6ef248fb4debacebd00e87b8518578ca93e2 | 4,247 | py | Python | tests/components/shelly/test_button.py | MrDelik/core | 93a66cc357b226389967668441000498a10453bb | [
"Apache-2.0"
] | 30,023 | 2016-04-13T10:17:53.000Z | 2020-03-02T12:56:31.000Z | tests/components/shelly/test_button.py | MrDelik/core | 93a66cc357b226389967668441000498a10453bb | [
"Apache-2.0"
] | 31,101 | 2020-03-02T13:00:16.000Z | 2022-03-31T23:57:36.000Z | tests/components/shelly/test_button.py | MrDelik/core | 93a66cc357b226389967668441000498a10453bb | [
"Apache-2.0"
] | 11,956 | 2016-04-13T18:42:31.000Z | 2020-03-02T09:32:12.000Z | """Tests for Shelly button platform."""
from homeassistant.components.button import DOMAIN as BUTTON_DOMAIN
from homeassistant.components.button.const import SERVICE_PRESS
from homeassistant.components.shelly.const import DOMAIN
from homeassistant.const import ATTR_ENTITY_ID, STATE_UNKNOWN
from homeassistant.core import HomeAssistant
from homeassistant.helpers.entity_registry import async_get
async def test_block_button(hass: HomeAssistant, coap_wrapper):
"""Test block device OTA button."""
assert coap_wrapper
entity_registry = async_get(hass)
entity_registry.async_get_or_create(
BUTTON_DOMAIN,
DOMAIN,
"test_name_ota_update_beta",
suggested_object_id="test_name_ota_update_beta",
disabled_by=None,
)
hass.async_create_task(
hass.config_entries.async_forward_entry_setup(coap_wrapper.entry, BUTTON_DOMAIN)
)
await hass.async_block_till_done()
# stable channel button
state = hass.states.get("button.test_name_ota_update")
assert state
assert state.state == STATE_UNKNOWN
await hass.services.async_call(
BUTTON_DOMAIN,
SERVICE_PRESS,
{ATTR_ENTITY_ID: "button.test_name_ota_update"},
blocking=True,
)
await hass.async_block_till_done()
assert coap_wrapper.device.trigger_ota_update.call_count == 1
coap_wrapper.device.trigger_ota_update.assert_called_with(beta=False)
# beta channel button
state = hass.states.get("button.test_name_ota_update_beta")
assert state
assert state.state == STATE_UNKNOWN
await hass.services.async_call(
BUTTON_DOMAIN,
SERVICE_PRESS,
{ATTR_ENTITY_ID: "button.test_name_ota_update_beta"},
blocking=True,
)
await hass.async_block_till_done()
assert coap_wrapper.device.trigger_ota_update.call_count == 2
coap_wrapper.device.trigger_ota_update.assert_called_with(beta=True)
# reboot button
state = hass.states.get("button.test_name_reboot")
assert state
assert state.state == STATE_UNKNOWN
await hass.services.async_call(
BUTTON_DOMAIN,
SERVICE_PRESS,
{ATTR_ENTITY_ID: "button.test_name_reboot"},
blocking=True,
)
await hass.async_block_till_done()
assert coap_wrapper.device.trigger_reboot.call_count == 1
async def test_rpc_button(hass: HomeAssistant, rpc_wrapper):
"""Test rpc device OTA button."""
assert rpc_wrapper
entity_registry = async_get(hass)
entity_registry.async_get_or_create(
BUTTON_DOMAIN,
DOMAIN,
"test_name_ota_update_beta",
suggested_object_id="test_name_ota_update_beta",
disabled_by=None,
)
hass.async_create_task(
hass.config_entries.async_forward_entry_setup(rpc_wrapper.entry, BUTTON_DOMAIN)
)
await hass.async_block_till_done()
# stable channel button
state = hass.states.get("button.test_name_ota_update")
assert state
assert state.state == STATE_UNKNOWN
await hass.services.async_call(
BUTTON_DOMAIN,
SERVICE_PRESS,
{ATTR_ENTITY_ID: "button.test_name_ota_update"},
blocking=True,
)
await hass.async_block_till_done()
assert rpc_wrapper.device.trigger_ota_update.call_count == 1
rpc_wrapper.device.trigger_ota_update.assert_called_with(beta=False)
# beta channel button
state = hass.states.get("button.test_name_ota_update_beta")
assert state
assert state.state == STATE_UNKNOWN
await hass.services.async_call(
BUTTON_DOMAIN,
SERVICE_PRESS,
{ATTR_ENTITY_ID: "button.test_name_ota_update_beta"},
blocking=True,
)
await hass.async_block_till_done()
assert rpc_wrapper.device.trigger_ota_update.call_count == 2
rpc_wrapper.device.trigger_ota_update.assert_called_with(beta=True)
# reboot button
state = hass.states.get("button.test_name_reboot")
assert state
assert state.state == STATE_UNKNOWN
await hass.services.async_call(
BUTTON_DOMAIN,
SERVICE_PRESS,
{ATTR_ENTITY_ID: "button.test_name_reboot"},
blocking=True,
)
await hass.async_block_till_done()
assert rpc_wrapper.device.trigger_reboot.call_count == 1
| 31 | 88 | 0.724276 | 553 | 4,247 | 5.191682 | 0.122966 | 0.062696 | 0.045977 | 0.071055 | 0.823058 | 0.823058 | 0.823058 | 0.811912 | 0.810519 | 0.810519 | 0 | 0.001759 | 0.196609 | 4,247 | 136 | 89 | 31.227941 | 0.839683 | 0.034377 | 0 | 0.692308 | 0 | 0 | 0.106415 | 0.106415 | 0 | 0 | 0 | 0 | 0.230769 | 1 | 0 | false | 0 | 0.057692 | 0 | 0.057692 | 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 |
448bc3f126a69a907adcc735a65603294cbbec79 | 185 | py | Python | books/models.py | f4ww4z/my_library_django_emberjs | 1cf563bdcdbbe585c1716c79f87d803119bbc840 | [
"MIT"
] | null | null | null | books/models.py | f4ww4z/my_library_django_emberjs | 1cf563bdcdbbe585c1716c79f87d803119bbc840 | [
"MIT"
] | null | null | null | books/models.py | f4ww4z/my_library_django_emberjs | 1cf563bdcdbbe585c1716c79f87d803119bbc840 | [
"MIT"
] | null | null | null | from django.db import models
class Book(models.Model):
title = models.CharField(max_length=500)
author = models.CharField(max_length=100)
description = models.TextField()
| 23.125 | 45 | 0.740541 | 24 | 185 | 5.625 | 0.708333 | 0.222222 | 0.266667 | 0.355556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.038462 | 0.156757 | 185 | 7 | 46 | 26.428571 | 0.826923 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.2 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
92643cd1a7b4ee7a4afe054d0f5cdb695fbb4009 | 5,976 | py | Python | test/test_generate_buildspec.py | exasol/script-languages-container-ci-setup | f08a692c2f79a071df3f9240b2754279dc7edaba | [
"MIT"
] | null | null | null | test/test_generate_buildspec.py | exasol/script-languages-container-ci-setup | f08a692c2f79a071df3f9240b2754279dc7edaba | [
"MIT"
] | 2 | 2022-03-16T19:43:11.000Z | 2022-03-18T06:31:26.000Z | test/test_generate_buildspec.py | exasol/script-languages-container-ci-setup | f08a692c2f79a071df3f9240b2754279dc7edaba | [
"MIT"
] | null | null | null | import json
import jsonschema
import pytest
from exasol_script_languages_container_ci_setup.lib.render_template import render_template
from exasol_script_languages_container_ci_setup.lib.run_generate_buildspec import run_generate_buildspec, \
get_config_file_parameter
from exasol_script_languages_container_ci_setup.lib.run_generate_release_buildspec import run_generate_release_buildspec
expected_result_root_buildspec = """version: 0.2
# ---- AUTOMATICALLY GENERATED FILE --------
# ---- DO NOT EDIT MANUALLY, BUT USE PYTHON MODULE "script-languages-container-ci-setup" TO UPDATE ---
batch:
fast-fail: false
build-graph:
- identifier: build_test_flavor
env:
variables:
FLAVOR: test-flavor
buildspec: {location}/build_buildspec.yaml
privileged-mode: true
type: BUILD_GENERAL1_MEDIUM
"""
def test_buildspec(tmp_path):
"""
Run run_generate_buildspec() for one flavor and compare result!
"""
root_path = tmp_path / "flavors"
test_flavor = root_path / "test-flavor"
test_flavor.mkdir(parents=True, exist_ok=False)
out_path = tmp_path / "out"
out_path.mkdir(parents=False, exist_ok=False)
run_generate_buildspec((str(root_path),), str(out_path.absolute()), config_file=None)
with open(out_path / "buildspec.yaml", "r") as res_file:
res = res_file.read()
assert res == expected_result_root_buildspec.format(location=str(out_path))
with open(out_path / "build_buildspec.yaml", "r") as res_file:
res = res_file.read()
# For build_buildspec.yaml we re-use the template for testing
expected_result_build_buildspec = render_template("build_buildspec.yaml", config_file_parameter="")
assert res == expected_result_build_buildspec
def test_release_buildspec(tmp_path):
"""
Run run_generate_release_buildspec() for one flavor and compare result!
"""
root_path = tmp_path / "flavors"
test_flavor = root_path / "test-flavor"
test_flavor.mkdir(parents=True, exist_ok=False)
out_path = tmp_path / "out"
out_path.mkdir(parents=False, exist_ok=False)
run_generate_release_buildspec((str(root_path),), str(out_path.absolute()), config_file=None)
with open(out_path / "buildspec.yaml", "r") as res_file:
res = res_file.read()
assert res == expected_result_root_buildspec.format(location=str(out_path))
with open(out_path / "build_buildspec.yaml", "r") as res_file:
res = res_file.read()
# For build_buildspec.yaml we re-use the template for testing
expected_result_build_buildspec = render_template("release_build_buildspec.yaml", config_file_parameter="")
assert res == expected_result_build_buildspec
def test_buildspec_with_valid_config_file(tmp_path):
"""
Run run_generate_buildspec() for one flavor with a valid config file and compare result!
"""
root_path = tmp_path / "flavors"
test_flavor = root_path / "test-flavor"
test_flavor.mkdir(parents=True, exist_ok=False)
out_path = tmp_path / "out"
out_path.mkdir(parents=False, exist_ok=False)
a_folder = tmp_path / "a_folder"
a_folder.mkdir(parents=False, exist_ok=False)
config_file_path = tmp_path / "build_config.json"
config = {"build_ignore": {"ignored_paths": [str(a_folder)]}}
with open(config_file_path, "w") as f:
json.dump(config, f)
run_generate_buildspec((str(root_path),), str(out_path.absolute()),
config_file=str(config_file_path.absolute()))
with open(out_path / "buildspec.yaml", "r") as res_file:
res = res_file.read()
assert res == expected_result_root_buildspec.format(location=str(out_path))
with open(out_path / "build_buildspec.yaml", "r") as res_file:
res = res_file.read()
# For build_buildspec.yaml we re-use the template for testing
expected_result_build_buildspec = render_template("build_buildspec.yaml",
config_file_parameter=
get_config_file_parameter(config_file_path))
assert res == expected_result_build_buildspec
def test_buildspec_with_invalid_config_file(tmp_path):
"""
Run run_generate_buildspec() for one flavor with an invalid config file and check for correct exception!
"""
root_path = tmp_path / "flavors"
test_flavor = root_path / "test-flavor"
test_flavor.mkdir(parents=True, exist_ok=False)
out_path = tmp_path / "out"
out_path.mkdir(parents=False, exist_ok=False)
config_file_path = tmp_path / "build_config.json"
# Incorrect config ('ignored_path' instead of 'ignored_paths')
config = {"build_ignore": {"ignored_path": ["a_folder"]}}
with open(config_file_path, "w") as f:
json.dump(config, f)
with pytest.raises(jsonschema.exceptions.ValidationError):
run_generate_buildspec((str(root_path),), str(out_path.absolute()),
config_file=str(config_file_path.absolute()))
def test_buildspec_with_invalid_folder(tmp_path):
"""
Run run_generate_buildspec() for one flavor with a valid config file,
but invalid content and check for correct exception!
"""
root_path = tmp_path / "flavors"
test_flavor = root_path / "test-flavor"
test_flavor.mkdir(parents=True, exist_ok=False)
out_path = tmp_path / "out"
out_path.mkdir(parents=False, exist_ok=False)
config_file_path = tmp_path / "build_config.json"
a_folder = tmp_path / "a_folder"
# Incorrect config (tmp_path/a_folder does not exists)
config = {"build_ignore": {"ignored_paths": [str(a_folder)]}}
with open(config_file_path, "w") as f:
json.dump(config, f)
with pytest.raises(ValueError):
run_generate_buildspec((str(root_path),), str(out_path.absolute()),
config_file=str(config_file_path.absolute()))
| 37.822785 | 120 | 0.695281 | 800 | 5,976 | 4.86375 | 0.13875 | 0.064251 | 0.036751 | 0.033924 | 0.813159 | 0.796453 | 0.775636 | 0.775636 | 0.764328 | 0.758931 | 0 | 0.000629 | 0.201305 | 5,976 | 157 | 121 | 38.063694 | 0.814582 | 0.125 | 0 | 0.626263 | 0 | 0.010101 | 0.164043 | 0.022741 | 0 | 0 | 0 | 0 | 0.060606 | 1 | 0.050505 | false | 0 | 0.060606 | 0 | 0.111111 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2b983d86724398a1a40618c665f018d5f35181b4 | 4,533 | py | Python | tests/why_test.py | DataCanvasIO/YLearn | d65b5afb83deed154c710de9096317165d95014a | [
"Apache-2.0"
] | 3 | 2022-03-28T07:41:28.000Z | 2022-03-29T06:24:52.000Z | tests/why_test.py | DataCanvasIO/YLearn | d65b5afb83deed154c710de9096317165d95014a | [
"Apache-2.0"
] | null | null | null | tests/why_test.py | DataCanvasIO/YLearn | d65b5afb83deed154c710de9096317165d95014a | [
"Apache-2.0"
] | null | null | null | import numpy as np
import pytest
from ylearn import Why
from . import _dgp
def _validate_it(cc, test_data):
print('-' * 30)
e = cc.causal_effect()
print('causal effect:', e, sep='\n')
print('-' * 30)
e = cc.cohort_causal_effect(test_data)
print('cohort causal effect:', e, sep='\n')
print('-' * 30)
e = cc.local_causal_effect(test_data)
print('local causal effect:', e, sep='\n')
if cc.scorers_ is not None:
score = cc.score()
print("score:", score)
def test_basis():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x2b_y1()
cc = Why()
cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
_validate_it(cc, test_data)
def test_identify_treatment():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x2b_y1()
cc = Why()
# cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
cc.fit(data, outcome[0], treatment=None, adjustment=adjustment, covariate=covariate)
_validate_it(cc, test_data)
def test_whatif_discrete():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x2b_y1()
cc = Why()
cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
new_value = np.ones_like(test_data[treatment[0]])
new_y = cc.whatif(test_data, new_value, treatment[0])
assert new_y is not None
print(new_y.shape, new_y)
def test_whatif_continuous():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x1m_y1()
data[treatment] = data[treatment].astype('float32')
test_data[treatment] = test_data[treatment].astype('float32')
cc = Why()
cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
new_value = np.ones_like(test_data[treatment[0]])
new_y = cc.whatif(test_data, new_value, treatment[0])
assert new_y is not None
print(new_y.shape, new_y)
def test_policy_tree():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x1m_y1()
# data[treatment] = data[treatment].astype('float32')
# test_data[treatment] = test_data[treatment].astype('float32')
cc = Why()
cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
ptree = cc.policy_tree(test_data)
assert ptree is not None
def test_policy_tree_dml():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x1m_y1()
# data[treatment] = data[treatment].astype('float32')
# test_data[treatment] = test_data[treatment].astype('float32')
cc = Why(estimator='dml')
# cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
cc.fit(data, treatment[0], treatment=outcome, adjustment=adjustment, covariate=covariate)
ptree = cc.policy_tree(test_data)
assert ptree is not None
def test_policy_interpreter():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x1m_y1()
# data[treatment] = data[treatment].astype('float32')
# test_data[treatment] = test_data[treatment].astype('float32')
cc = Why()
cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
pi = cc.policy_interpreter(test_data)
assert pi is not None
@pytest.mark.xfail(reason='to be fixed')
def test_discovery_treatment():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x2b_y1()
cc = Why(identify='discovery')
# cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
cc.fit(data, outcome[0], treatment=None, adjustment=adjustment, covariate=covariate)
_validate_it(cc, test_data)
@pytest.mark.xfail(reason='to be fixed')
def test_discovery_taci():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x2b_y1()
cc = Why(identify='discovery')
# cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
cc.fit(data, outcome[0])
_validate_it(cc, test_data)
@pytest.mark.xfail(reason='to be fixed')
def test_score():
data, test_data, outcome, treatment, adjustment, covariate = _dgp.generate_data_x2b_y1()
cc = Why(scorer='auto')
cc.fit(data, outcome[0], treatment=treatment, adjustment=adjustment, covariate=covariate)
_validate_it(cc, test_data)
| 35.414063 | 95 | 0.716744 | 607 | 4,533 | 5.14168 | 0.116969 | 0.084588 | 0.040372 | 0.066645 | 0.891381 | 0.863505 | 0.863505 | 0.863505 | 0.863505 | 0.846203 | 0 | 0.015649 | 0.154203 | 4,533 | 127 | 96 | 35.692913 | 0.798383 | 0.154644 | 0 | 0.604938 | 0 | 0 | 0.037163 | 0 | 0 | 0 | 0 | 0 | 0.061728 | 1 | 0.135802 | false | 0 | 0.049383 | 0 | 0.185185 | 0.111111 | 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 |
2bbcdcdc6de71119b96227a5b497b519f08a3c45 | 214 | py | Python | conftest.py | gis-ops/pyvroom | b7d23e405b8734c5672eecf6f394b6364103cbf1 | [
"BSD-2-Clause"
] | 13 | 2021-12-28T13:04:45.000Z | 2022-01-06T22:05:51.000Z | conftest.py | gis-ops/pyvroom | b7d23e405b8734c5672eecf6f394b6364103cbf1 | [
"BSD-2-Clause"
] | 26 | 2022-01-06T09:36:45.000Z | 2022-03-26T11:43:14.000Z | conftest.py | gis-ops/pyvroom | b7d23e405b8734c5672eecf6f394b6364103cbf1 | [
"BSD-2-Clause"
] | 4 | 2022-01-06T14:34:56.000Z | 2022-03-29T11:53:48.000Z | """Global configuration."""
import pytest
import vroom
@pytest.fixture(autouse=True)
def global_setup(doctest_namespace, monkeypatch):
"""Global configuration setup."""
doctest_namespace["vroom"] = vroom
| 21.4 | 49 | 0.747664 | 23 | 214 | 6.826087 | 0.565217 | 0.242038 | 0.267516 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121495 | 214 | 9 | 50 | 23.777778 | 0.835106 | 0.228972 | 0 | 0 | 0 | 0 | 0.032468 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.4 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2bc343abf90757353d1b70330baa2d21aeff8153 | 77 | py | Python | katas/beta/strange_strings_parser.py | the-zebulan/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | 40 | 2016-03-09T12:26:20.000Z | 2022-03-23T08:44:51.000Z | katas/beta/strange_strings_parser.py | akalynych/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | null | null | null | katas/beta/strange_strings_parser.py | akalynych/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | 36 | 2016-11-07T19:59:58.000Z | 2022-03-31T11:18:27.000Z | import re
def parser(strng):
return re.split(r'[!#%&*+:;=>?|]', strng)
| 12.833333 | 45 | 0.532468 | 10 | 77 | 4.1 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.168831 | 77 | 5 | 46 | 15.4 | 0.640625 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 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 |
2bca8a2db048e3f61a09f989ebd1167e223f41e3 | 5,002 | py | Python | wdae/wdae/family_api/tests/test_family_api.py | iossifovlab/gpf | e556243d29666179dbcb72859845b4d6c011af2b | [
"MIT"
] | null | null | null | wdae/wdae/family_api/tests/test_family_api.py | iossifovlab/gpf | e556243d29666179dbcb72859845b4d6c011af2b | [
"MIT"
] | 82 | 2019-07-22T11:44:23.000Z | 2022-01-13T15:27:33.000Z | wdae/wdae/family_api/tests/test_family_api.py | iossifovlab/gpf | e556243d29666179dbcb72859845b4d6c011af2b | [
"MIT"
] | null | null | null | import pytest
from rest_framework import status
from dae.variants.attributes import Sex, Role, Status
pytestmark = pytest.mark.usefixtures(
"wdae_gpf_instance", "dae_calc_gene_sets")
def test_list_families_view(admin_client):
url = "/api/v3/families/Study1"
response = admin_client.get(url)
assert response.status_code == status.HTTP_200_OK
assert list(response.data) == [
"f1", "f3", "f4", "f5", "f6", "f7", "f8", "f9", "f10", "f11"
]
def test_list_families_view_nonexistent(admin_client):
url = "/api/v3/families/Study123123123"
response = admin_client.get(url)
assert response.status_code == status.HTTP_404_NOT_FOUND
def test_family_details_view(admin_client):
url = "/api/v3/families/Study1/f6"
response = admin_client.get(url)
assert response.status_code == status.HTTP_200_OK
assert response.data == {
"family_id": "f6",
"family_type": "TRIO",
"person_ids": ["mom6", "dad6", "ch6"],
"samples_index": None
}
def test_family_details_view_nonexistent(admin_client):
url = "/api/v3/families/Study1/f654654654"
response = admin_client.get(url)
assert response.status_code == status.HTTP_404_NOT_FOUND
def test_list_members_view(admin_client):
url = "/api/v3/families/Study1/f6/members"
response = admin_client.get(url)
assert response.status_code == status.HTTP_200_OK
assert response.data == ["mom6", "dad6", "ch6"]
def test_list_members_view_nonexistent(admin_client):
url = "/api/v3/families/Study1/f654654654/members"
response = admin_client.get(url)
assert response.status_code == status.HTTP_404_NOT_FOUND
def test_member_details_view(admin_client):
url = "/api/v3/families/Study1/f6/members/ch6"
response = admin_client.get(url)
assert response.status_code == status.HTTP_200_OK
assert response.data == {
"person_id": "ch6",
"family_id": "f6",
"dad_id": "dad6",
"mom_id": "mom6",
"sample_id": "ch6",
"index": 2,
"sex": str(Sex.male),
"role": str(Role.prb),
"status": str(Status.affected),
"layout": None,
"generated": None,
"family_bin": None,
"not_sequenced": None,
"missing": False,
}
def test_member_details_view_nonexistent(admin_client):
url = "/api/v3/families/Study1/f6/members/ch456456"
response = admin_client.get(url)
assert response.status_code == status.HTTP_404_NOT_FOUND
def test_full_family_details_view(admin_client):
url = "/api/v3/families/Study1/f6/members/all"
response = admin_client.get(url)
assert response.status_code == status.HTTP_200_OK
assert len(response.data) == 3
assert response.data[0] == {
"person_id": "mom6",
"family_id": "f6",
"dad_id": None,
"mom_id": None,
"sample_id": "mom6",
"index": 0,
"sex": str(Sex.female),
"role": str(Role.mom),
"status": str(Status.unaffected),
"layout": None,
"generated": None,
"family_bin": None,
"not_sequenced": None,
"missing": False,
}
assert response.data[1] == {
"person_id": "dad6",
"family_id": "f6",
"dad_id": None,
"mom_id": None,
"sample_id": "dad6",
"index": 1,
"sex": str(Sex.male),
"role": str(Role.dad),
"status": str(Status.unaffected),
"layout": None,
"generated": None,
"family_bin": None,
"not_sequenced": None,
"missing": False,
}
assert response.data[2] == {
"person_id": "ch6",
"family_id": "f6",
"dad_id": "dad6",
"mom_id": "mom6",
"sample_id": "ch6",
"index": 2,
"sex": str(Sex.male),
"role": str(Role.prb),
"status": str(Status.affected),
"layout": None,
"generated": None,
"family_bin": None,
"not_sequenced": None,
"missing": False,
}
def test_full_study_families_view(admin_client):
url = "/api/v3/families/Study1/all"
response = admin_client.get(url)
assert response.status_code == status.HTTP_200_OK
assert len(response.data) == 10
f6_idx = -1
for idx, fam in enumerate(response.data):
if fam["family_id"] == "f6":
f6_idx = idx
break
f6 = response.data[f6_idx]
assert f6["family_id"] == "f6"
assert f6["family_type"] == "TRIO"
assert f6["person_ids"] == ["mom6", "dad6", "ch6"]
assert len(f6["members"]) == 3
assert f6["members"][2] == {
"person_id": "ch6",
"family_id": "f6",
"dad_id": "dad6",
"mom_id": "mom6",
"sample_id": "ch6",
"index": 2,
"sex": str(Sex.male),
"role": str(Role.prb),
"status": str(Status.affected),
"layout": None,
"generated": None,
"family_bin": None,
"not_sequenced": None,
"missing": False,
}
| 29.251462 | 68 | 0.595162 | 619 | 5,002 | 4.579968 | 0.164782 | 0.077601 | 0.049383 | 0.059965 | 0.809877 | 0.766138 | 0.766138 | 0.757672 | 0.742857 | 0.707937 | 0 | 0.041667 | 0.251499 | 5,002 | 170 | 69 | 29.423529 | 0.715545 | 0 | 0 | 0.571429 | 0 | 0 | 0.222711 | 0.067173 | 0 | 0 | 0 | 0 | 0.163265 | 1 | 0.068027 | false | 0 | 0.020408 | 0 | 0.088435 | 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 |
2bd216c676218c14da6852516f534f704dff9f8f | 95 | py | Python | tg-launchbot/permissions.py | 499602D2/tg-launchbot | 9a947590b3095dece9171bc8e15fd857f4e3fccb | [
"MIT"
] | 13 | 2020-11-05T12:53:31.000Z | 2022-02-21T14:27:51.000Z | tg-launchbot/permissions.py | 499602D2/tg-launchbot | 9a947590b3095dece9171bc8e15fd857f4e3fccb | [
"MIT"
] | 3 | 2021-03-03T20:46:47.000Z | 2022-02-11T17:25:50.000Z | tg-launchbot/permissions.py | 499602D2/tg-launchbot | 9a947590b3095dece9171bc8e15fd857f4e3fccb | [
"MIT"
] | 4 | 2020-11-05T14:07:04.000Z | 2022-02-21T14:27:53.000Z | # load the current status of the permissions into memory
def load_permissions_status():
return | 31.666667 | 56 | 0.821053 | 14 | 95 | 5.428571 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136842 | 95 | 3 | 57 | 31.666667 | 0.926829 | 0.568421 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
921d5bbf4c214e03fc1118b76c9c57d483e2f065 | 6,527 | py | Python | tests/test_transform.py | tulibraries/tulflow | 652957d079c481a84b3602932ed86f3b2b21e3e9 | [
"Apache-2.0"
] | 1 | 2022-03-04T18:27:06.000Z | 2022-03-04T18:27:06.000Z | tests/test_transform.py | tulibraries/tulflow | 652957d079c481a84b3602932ed86f3b2b21e3e9 | [
"Apache-2.0"
] | 117 | 2019-08-29T21:34:53.000Z | 2022-03-31T22:11:58.000Z | tests/test_transform.py | tulibraries/tulflow | 652957d079c481a84b3602932ed86f3b2b21e3e9 | [
"Apache-2.0"
] | 1 | 2021-09-22T20:40:12.000Z | 2021-09-22T20:40:12.000Z | """Tests suite for tulflow.transform (functions for transforming XML or JSON in Airflow Tasks)."""
import unittest
import boto3
from lxml import etree
from moto import mock_s3
from tulflow import transform
import logging
from mock import patch
class TestXSLTransform(unittest.TestCase):
"""Test Class for functions that transform XML from S3 with XSL."""
maxDiff = None
kwargs = {
"source_prefix": "dpla_test/new-updated-filtered",
"destination_prefix": "dpla_test/transformed",
"bucket": "tulib-airflow-test",
"schematron_filename": "transforms/dplah.xsl",
"access_id": "kittens",
"access_secret": "puppies"
}
def setUp(self):
transform.prepare_saxon_engine()
@mock_s3
@patch('subprocess.check_output')
def test_transform_s3_xml_simple(self, mocked_subprocess):
"""Test Pulling S3 XML, Transforming with XSLT, & Writing to S3."""
# setup kwargs for test runs
access_id = self.kwargs.get("access_id")
access_secret = self.kwargs.get("access_secret")
bucket = self.kwargs.get("bucket")
test_key = self.kwargs.get("source_prefix") + "/xsl-sample.xml"
# create expected mocked s3 resources
conn = boto3.client("s3", aws_access_key_id=access_id, aws_secret_access_key=access_secret)
conn.create_bucket(Bucket=bucket)
conn.put_object(Bucket=bucket, Key=test_key, Body=open("tests/fixtures/xsl-sample.xml").read())
test_content_exists = conn.get_object(Bucket=bucket, Key=test_key)
test_object_exists = conn.list_objects(Bucket=bucket)
self.assertEqual(test_content_exists["Body"].read(), open("tests/fixtures/xsl-sample.xml", "rb").read())
self.assertEqual(test_content_exists["ResponseMetadata"]["HTTPStatusCode"], 200)
self.assertEqual(test_object_exists["Contents"][0]["Key"], test_key)
# setup mocked subprocess result
mocked_subprocess.side_effect = [
open("tests/fixtures/xsl-sample-simple-output-record1.xml", "rb").read(),
open("tests/fixtures/xsl-sample-simple-output-record2.xml", "rb").read(),
open("tests/fixtures/xsl-sample-simple-output-record3.xml", "rb").read()
]
# run tests
with self.assertLogs() as log:
transform.transform_s3_xsl(**self.kwargs)
self.assertIn("INFO:root:Transforming File dpla_test/new-updated-filtered/xsl-sample.xml", log.output)
self.assertIn("INFO:root:Transforming Record oai:digital.library.villanova.edu:vudl:293113", log.output)
self.assertIn("INFO:root:Transforming Record oai:digital.library.villanova.edu:vudl:469533", log.output)
self.assertIn("INFO:root:Transforming Record oai:digital.library.villanova.edu:vudl:469545", log.output)
test_output_objects = conn.list_objects(Bucket=bucket, Prefix=self.kwargs.get("destination_prefix"))
self.assertEqual(test_output_objects["ResponseMetadata"]["HTTPStatusCode"], 200)
test_output_objects_ar = [object.get("Key") for object in test_output_objects["Contents"]]
self.assertEqual(test_output_objects_ar, ["dpla_test/transformed/xsl-sample.xml"])
test_output_content = etree.fromstring(conn.get_object(Bucket=bucket, Key="dpla_test/transformed/xsl-sample.xml")["Body"].read())
should_match_output = etree.fromstring(open("tests/fixtures/xsl-sample-simple-output-all.xml", "rb").read())
self.assertEqual(
etree.tostring(test_output_content, pretty_print=True),
etree.tostring(should_match_output, pretty_print=True)
)
@mock_s3
@patch('subprocess.check_output')
def test_transform_s3_xml_complex(self, mocked_subprocess):
"""Test Pulling S3 XML, Transforming with Complex XSLT, & Writing to S3."""
# setup kwargs for test runs
access_id = self.kwargs.get("access_id")
access_secret = self.kwargs.get("access_secret")
bucket = self.kwargs.get("bucket")
test_key = self.kwargs.get("source_prefix") + "/xsl-sample.xml"
# create expected mocked s3 resources
conn = boto3.client("s3", aws_access_key_id=access_id, aws_secret_access_key=access_secret)
conn.create_bucket(Bucket=bucket)
conn.put_object(Bucket=bucket, Key=test_key, Body=open("tests/fixtures/xsl-sample.xml").read())
test_content_exists = conn.get_object(Bucket=bucket, Key=test_key)
test_object_exists = conn.list_objects(Bucket=bucket)
self.assertEqual(test_content_exists["Body"].read(), open("tests/fixtures/xsl-sample.xml", "rb").read())
self.assertEqual(test_content_exists["ResponseMetadata"]["HTTPStatusCode"], 200)
self.assertEqual(test_object_exists["Contents"][0]["Key"], test_key)
# setup mocked subprocess result
mocked_subprocess.side_effect = [
open("tests/fixtures/xsl-sample-complex-output-record1.xml", "rb").read(),
open("tests/fixtures/xsl-sample-complex-output-record2.xml", "rb").read(),
open("tests/fixtures/xsl-sample-complex-output-record3.xml", "rb").read()
]
# run tests
with self.assertLogs() as log:
transform.transform_s3_xsl(**self.kwargs)
self.assertIn("INFO:root:Transforming File dpla_test/new-updated-filtered/xsl-sample.xml", log.output)
self.assertIn("INFO:root:Transforming Record oai:digital.library.villanova.edu:vudl:293113", log.output)
self.assertIn("INFO:root:Transforming Record oai:digital.library.villanova.edu:vudl:469533", log.output)
self.assertIn("INFO:root:Transforming Record oai:digital.library.villanova.edu:vudl:469545", log.output)
test_output_objects = conn.list_objects(Bucket=bucket, Prefix=self.kwargs.get("destination_prefix"))
self.assertEqual(test_output_objects["ResponseMetadata"]["HTTPStatusCode"], 200)
test_output_objects_ar = [object.get("Key") for object in test_output_objects["Contents"]]
self.assertEqual(test_output_objects_ar, ["dpla_test/transformed/xsl-sample.xml"])
test_output_content = etree.fromstring(conn.get_object(Bucket=bucket, Key="dpla_test/transformed/xsl-sample.xml")["Body"].read())
should_match_output = etree.fromstring(open("tests/fixtures/xsl-sample-complex-output-all.xml", "rb").read())
self.assertEqual(
etree.tostring(test_output_content, pretty_print=True),
etree.tostring(should_match_output, pretty_print=True)
)
| 56.756522 | 137 | 0.700322 | 828 | 6,527 | 5.339372 | 0.155797 | 0.040715 | 0.032572 | 0.054286 | 0.886677 | 0.880796 | 0.880796 | 0.874915 | 0.874915 | 0.851391 | 0 | 0.01383 | 0.169144 | 6,527 | 114 | 138 | 57.254386 | 0.801401 | 0.075839 | 0 | 0.637363 | 0 | 0 | 0.306296 | 0.216023 | 0 | 0 | 0 | 0 | 0.241758 | 1 | 0.032967 | false | 0 | 0.076923 | 0 | 0.142857 | 0.043956 | 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 |
a65dcd00bf345f07f44980c401a3295f844e34fd | 154 | py | Python | Scalability_Security/security/xss/views.py | fangyiyu/CS50_web_programming | 8d6f304772ae8bd8cd373f17545d507c6e55768e | [
"MIT"
] | 2 | 2021-04-05T15:29:08.000Z | 2022-03-08T11:07:21.000Z | Lecture 8 : Scalability and Security/src8/security/xss/views.py | Sumanth-Talluri/CS50-Web-Programming-with-Python-and-JavaScript | 8d5f83f4354f1f27138a2a9c40317d358f3b2f9a | [
"MIT"
] | null | null | null | Lecture 8 : Scalability and Security/src8/security/xss/views.py | Sumanth-Talluri/CS50-Web-Programming-with-Python-and-JavaScript | 8d5f83f4354f1f27138a2a9c40317d358f3b2f9a | [
"MIT"
] | null | null | null | from django.shortcuts import HttpResponse, render
# Create your views here.
def index(request, path):
return HttpResponse(f"Requested Path: {path}")
| 25.666667 | 50 | 0.75974 | 20 | 154 | 5.85 | 0.85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 154 | 5 | 51 | 30.8 | 0.886364 | 0.149351 | 0 | 0 | 0 | 0 | 0.170543 | 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 |
a66fe52c8e7fb866a303fd8d8eff87cd588894fa | 163 | py | Python | geneblocks/DiffBlocks/__init__.py | Edinburgh-Genome-Foundry/Geneblocks | 87a8df33fab2295e357884fa2742d3e03e98d9a5 | [
"MIT"
] | 26 | 2018-02-12T13:14:14.000Z | 2021-08-06T16:51:46.000Z | geneblocks/DiffBlocks/__init__.py | Edinburgh-Genome-Foundry/Geneblocks | 87a8df33fab2295e357884fa2742d3e03e98d9a5 | [
"MIT"
] | 6 | 2020-05-20T20:26:08.000Z | 2022-02-15T11:39:35.000Z | geneblocks/DiffBlocks/__init__.py | Edinburgh-Genome-Foundry/Geneblocks | 87a8df33fab2295e357884fa2742d3e03e98d9a5 | [
"MIT"
] | 3 | 2019-11-04T23:00:17.000Z | 2021-10-06T23:45:25.000Z | from .DiffBlocks import DiffBlocks, DiffBlock
from .DiffRecordTranslator import DiffRecordTranslator
__all__ = ['DiffBlocks', 'DiffBlock', 'DiffRecordTranslator'] | 40.75 | 61 | 0.828221 | 13 | 163 | 10.076923 | 0.461538 | 0.290076 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08589 | 163 | 4 | 61 | 40.75 | 0.879195 | 0 | 0 | 0 | 0 | 0 | 0.237805 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a67781273c77691913764977aa04f24ea1da6fb0 | 32 | py | Python | clash-of-code/shortest/mens_wifes_kids.py | jonasnic/codingame | f1a7fe8007b9ca63bdf30cd72f4d6ac41a5ac721 | [
"MIT"
] | 30 | 2016-04-30T01:56:05.000Z | 2022-03-09T22:19:12.000Z | clash-of-code/shortest/mens_wifes_kids.py | jonasnic/codingame | f1a7fe8007b9ca63bdf30cd72f4d6ac41a5ac721 | [
"MIT"
] | 1 | 2021-05-19T19:36:45.000Z | 2021-05-19T19:36:45.000Z | clash-of-code/shortest/mens_wifes_kids.py | jonasnic/codingame | f1a7fe8007b9ca63bdf30cd72f4d6ac41a5ac721 | [
"MIT"
] | 17 | 2020-01-28T13:54:06.000Z | 2022-03-26T09:49:27.000Z | x=int(input())
print(x+x*x+x**3) | 16 | 17 | 0.59375 | 9 | 32 | 2.111111 | 0.555556 | 0.315789 | 0.315789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.032258 | 0.03125 | 32 | 2 | 17 | 16 | 0.580645 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.5 | 1 | 1 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
a67f2918d8953fae3b2e515dd8382a32e71a5cd5 | 9,508 | py | Python | repro_eval/test/test_path_param.py | irgroup/repro_eval | 35a4cf083dbb5f4b29d6ef602a604f0686a537c9 | [
"MIT"
] | 8 | 2020-10-27T02:11:53.000Z | 2022-03-02T11:00:10.000Z | repro_eval/test/test_path_param.py | irgroup/repro_eval | 35a4cf083dbb5f4b29d6ef602a604f0686a537c9 | [
"MIT"
] | 2 | 2021-01-25T19:59:39.000Z | 2021-12-07T09:29:01.000Z | repro_eval/test/test_path_param.py | irgroup/repro_eval | 35a4cf083dbb5f4b29d6ef602a604f0686a537c9 | [
"MIT"
] | 1 | 2021-04-16T16:21:16.000Z | 2021-04-16T16:21:16.000Z | import pytest
from repro_eval.Evaluator import RpdEvaluator, RplEvaluator
import numpy as np
rpd_eval = RpdEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt',
run_b_rep_path='./example/rpd_b.txt',
run_a_rep_path='./example/rpd_a.txt')
rpd_eval.trim()
rpd_eval.evaluate()
def test_ktu_path_param():
ktu = rpd_eval.ktau_union()
assert 'baseline' in ktu.keys()
assert 'advanced' in ktu.keys()
_rpd_eval = RpdEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt')
_rpd_eval.trim()
_rpd_eval.evaluate()
_ktu = _rpd_eval.ktau_union(run_b_path='./example/rpd_b.txt')
assert 'baseline' in _ktu.keys()
assert ktu.get('baseline') == _ktu.get('baseline')
_ktu = _rpd_eval.ktau_union(run_b_path='./example/rpd_b.txt', run_a_path='./example/rpd_a.txt')
assert 'advanced' in _ktu.keys()
assert ktu.get('advanced') == _ktu.get('advanced')
def test_rbo_path_param():
rbo = rpd_eval.rbo()
assert 'baseline' in rbo.keys()
assert 'advanced' in rbo.keys()
_rpd_eval = RpdEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt')
_rpd_eval.trim()
_rpd_eval.evaluate()
_rbo = _rpd_eval.rbo(run_b_path='./example/rpd_b.txt')
assert 'baseline' in _rbo.keys()
assert rbo.get('baseline') == _rbo.get('baseline')
_rbo = _rpd_eval.rbo(run_b_path='./example/rpd_b.txt', run_a_path='./example/rpd_a.txt')
assert 'advanced' in _rbo.keys()
assert rbo.get('advanced') == _rbo.get('advanced')
def test_rmse_path_param():
rmse = rpd_eval.rmse()
assert 'baseline' in rmse.keys()
assert 'advanced' in rmse.keys()
_rpd_eval = RpdEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt')
_rpd_eval.trim()
_rpd_eval.evaluate()
_rmse = _rpd_eval.rmse(run_b_path='./example/rpd_b.txt')
assert 'baseline' in _rmse.keys()
assert rmse.get('baseline') == _rmse.get('baseline')
_rmse = _rpd_eval.rmse(run_b_path='./example/rpd_b.txt', run_a_path='./example/rpd_a.txt')
assert 'advanced' in _rmse.keys()
assert rmse.get('advanced') == _rmse.get('advanced')
def test_rpd_ttest_path_param():
pval = rpd_eval.ttest()
assert 'baseline' in pval.keys()
assert 'advanced' in pval.keys()
_rpd_eval = RpdEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt')
_rpd_eval.trim()
_rpd_eval.evaluate()
_pval = _rpd_eval.ttest(run_b_path='./example/rpd_b.txt')
assert 'baseline' in _pval.keys()
# pick a few samples here since nan comparisons cause problems in combination with assert
assert pval.get('baseline').get('ndcg') == _pval.get('baseline').get('ndcg')
assert pval.get('baseline').get('P_10') == _pval.get('baseline').get('P_10')
assert pval.get('baseline').get('map') == _pval.get('baseline').get('map')
_pval = _rpd_eval.ttest(run_b_path='./example/rpd_b.txt', run_a_path='./example/rpd_a.txt')
assert 'advanced' in _pval.keys()
# pick a few samples here since nan comparisons cause problems in combination with assert
assert pval.get('advanced').get('ndcg') == _pval.get('advanced').get('ndcg')
assert pval.get('advanced').get('P_10') == _pval.get('advanced').get('P_10')
assert pval.get('advanced').get('map') == _pval.get('advanced').get('map')
def test_rpl_ttest_path_param():
rpl_eval = RplEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt',
run_b_rep_path='./example/rpl_b.txt',
run_a_rep_path='./example/rpl_a.txt',
qrel_rpl_path='./example/data/qrels/core18.txt')
rpl_eval.trim()
rpl_eval.evaluate()
pval = rpl_eval.ttest()
assert 'baseline' in pval.keys()
assert 'advanced' in pval.keys()
_rpl_eval = RplEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt',
qrel_rpl_path='./example/data/qrels/core18.txt')
_rpl_eval.trim()
_rpl_eval.evaluate()
_pval = _rpl_eval.ttest(run_b_path='./example/rpl_b.txt')
assert 'baseline' in _pval.keys()
# pick a few samples here since nan comparisons cause problems in combination with assert
assert pval.get('baseline').get('ndcg') == _pval.get('baseline').get('ndcg')
assert pval.get('baseline').get('P_10') == _pval.get('baseline').get('P_10')
assert pval.get('baseline').get('map') == _pval.get('baseline').get('map')
_pval = _rpl_eval.ttest(run_b_path='./example/rpl_b.txt', run_a_path='./example/rpl_a.txt')
assert 'advanced' in _pval.keys()
# pick a few samples here since nan comparisons cause problems in combination with assert
assert pval.get('advanced').get('ndcg') == _pval.get('advanced').get('ndcg')
assert pval.get('advanced').get('P_10') == _pval.get('advanced').get('P_10')
assert pval.get('advanced').get('map') == _pval.get('advanced').get('map')
def test_rpd_er_path_param():
er = rpd_eval.er()
_rpd_eval = RpdEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt')
_rpd_eval.trim()
_rpd_eval.evaluate()
_er = _rpd_eval.er(run_b_path='./example/rpd_b.txt', run_a_path='./example/rpd_a.txt')
# pick a few samples here since nan comparisons cause problems in combination with assert
assert er.get('ndcg') == _er.get('ndcg')
assert er.get('P_10') == _er.get('P_10')
assert er.get('map') == _er.get('map')
def test_rpl_er_path_param():
rpl_eval = RplEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt',
run_b_rep_path='./example/rpl_b.txt',
run_a_rep_path='./example/rpl_a.txt',
qrel_rpl_path='./example/data/qrels/core18.txt')
rpl_eval.trim()
rpl_eval.evaluate()
er = rpl_eval.er()
_rpl_eval = RplEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt',
qrel_rpl_path='./example/data/qrels/core18.txt')
_rpl_eval.trim()
_rpl_eval.evaluate()
_er = _rpl_eval.er(run_b_path='./example/rpl_b.txt', run_a_path='./example/rpl_a.txt')
# pick a few samples here since nan comparisons cause problems in combination with assert
assert er.get('ndcg') == _er.get('ndcg')
assert er.get('P_10') == _er.get('P_10')
assert er.get('map') == _er.get('map')
def test_rpd_dri_path_param():
dri = rpd_eval.dri()
_rpd_eval = RpdEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt')
_rpd_eval.trim()
_rpd_eval.evaluate()
_dri = _rpd_eval.dri(run_b_path='./example/rpd_b.txt', run_a_path='./example/rpd_a.txt')
# pick a few samples here since nan comparisons cause problems in combination with assert
assert dri.get('ndcg') == _dri.get('ndcg')
assert dri.get('P_10') == _dri.get('P_10')
assert dri.get('map') == _dri.get('map')
def test_rpl_dri_path_param():
rpl_eval = RplEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt',
run_b_rep_path='./example/rpl_b.txt',
run_a_rep_path='./example/rpl_a.txt',
qrel_rpl_path='./example/data/qrels/core18.txt')
rpl_eval.trim()
rpl_eval.evaluate()
dri = rpl_eval.dri()
_rpl_eval = RplEvaluator(qrel_orig_path='./example/data/qrels/core17.txt',
run_b_orig_path='./example/orig_b.txt',
run_a_orig_path='./example/orig_a.txt',
qrel_rpl_path='./example/data/qrels/core18.txt')
_rpl_eval.trim()
_rpl_eval.evaluate()
_dri = _rpl_eval.dri(run_b_path='./example/rpl_b.txt', run_a_path='./example/rpl_a.txt')
# pick a few samples here since nan comparisons cause problems in combination with assert
assert dri.get('ndcg') == _dri.get('ndcg')
assert dri.get('P_10') == _dri.get('P_10')
assert dri.get('map') == _dri.get('map')
| 42.070796 | 99 | 0.619163 | 1,347 | 9,508 | 4.03415 | 0.045286 | 0.153846 | 0.107656 | 0.090909 | 0.926757 | 0.911299 | 0.870997 | 0.866397 | 0.860876 | 0.860876 | 0 | 0.009547 | 0.22886 | 9,508 | 225 | 100 | 42.257778 | 0.731588 | 0.073938 | 0 | 0.646341 | 0 | 0 | 0.263984 | 0.066962 | 0 | 0 | 0 | 0 | 0.304878 | 1 | 0.054878 | false | 0 | 0.018293 | 0 | 0.073171 | 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 |
a6b2a6846ae0a40cf789c750b9c56a17601f744b | 20,301 | py | Python | controller/CutCaptcha.py | wudinaonao/FlaskMark12306Captcha | c03550dc5583b435192f220f871c71cabadd3e39 | [
"Apache-2.0"
] | 1 | 2020-07-21T06:41:07.000Z | 2020-07-21T06:41:07.000Z | controller/CutCaptcha.py | wudinaonao/FlaskMark12306Captcha | c03550dc5583b435192f220f871c71cabadd3e39 | [
"Apache-2.0"
] | 6 | 2020-11-13T18:45:19.000Z | 2022-03-12T00:22:18.000Z | controller/CutCaptcha.py | wudinaonao/FlaskMark12306Captcha | c03550dc5583b435192f220f871c71cabadd3e39 | [
"Apache-2.0"
] | null | null | null | from io import BytesIO
from PIL import Image
from entities import ResultCutCaptcha
from controller.utils import Base64
from typing import Tuple
from typing import Any
class CutCaptcha(object):
@classmethod
def _imageToBytes(cls, image: Image) -> bytes:
imageIO = BytesIO()
image.save(imageIO, format="PNG")
return imageIO.getvalue()
@classmethod
def _cutLabel(cls, imageByte: bytes) -> bytes:
"""return Image object"""
label = Image.open(BytesIO(imageByte)).convert("RGB")
x = 117
y = 0
w = 180
h = 30
label = label.crop((x, y, w, h))
return cls._imageToBytes(label)
@classmethod
def _cutImage(cls, imageByte: bytes) -> Tuple[bytes, bytes, bytes, bytes, bytes, bytes, bytes, bytes]:
"""return Image object tuple"""
image = Image.open(BytesIO(imageByte)).convert("RGB")
space = 67 + 5
x0, y0, w0, h0 = 0 * space + 5, 0 * space + 41, 1 * space, 0 * space + 41 + 67
x1, y1, w1, h1 = 0 * space + 5, 1 * space + 41, 1 * space, 1 * space + 41 + 67
x2, y2, w2, h2 = 1 * space + 5, 0 * space + 41, 2 * space, 0 * space + 41 + 67
x3, y3, w3, h3 = 1 * space + 5, 1 * space + 41, 2 * space, 1 * space + 41 + 67
x4, y4, w4, h4 = 2 * space + 5, 0 * space + 41, 3 * space, 0 * space + 41 + 67
x5, y5, w5, h5 = 2 * space + 5, 1 * space + 41, 3 * space, 1 * space + 41 + 67
x6, y6, w6, h6 = 3 * space + 5, 0 * space + 41, 4 * space, 0 * space + 41 + 67
x7, y7, w7, h7 = 3 * space + 5, 1 * space + 41, 4 * space, 1 * space + 41 + 67
image0 = image.crop((x0, y0, w0, h0))
image1 = image.crop((x1, y1, w1, h1))
image2 = image.crop((x2, y2, w2, h2))
image3 = image.crop((x3, y3, w3, h3))
image4 = image.crop((x4, y4, w4, h4))
image5 = image.crop((x5, y5, w5, h5))
image6 = image.crop((x6, y6, w6, h6))
image7 = image.crop((x7, y7, w7, h7))
return (cls._imageToBytes(image0),
cls._imageToBytes(image1),
cls._imageToBytes(image2),
cls._imageToBytes(image3),
cls._imageToBytes(image4),
cls._imageToBytes(image5),
cls._imageToBytes(image6),
cls._imageToBytes(image7))
@classmethod
def cut(cls, imageByte: bytes) -> ResultCutCaptcha:
return ResultCutCaptcha(
label=cls._cutLabel(imageByte),
images=cls._cutImage(imageByte)
)
if __name__ == '__main__':
base64_str = 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"
result = CutCaptcha.cut(Base64.convertToBytes(base64_str))
print(result)
| 285.929577 | 17,631 | 0.906753 | 996 | 20,301 | 18.457831 | 0.683735 | 0.006092 | 0.003481 | 0.00544 | 0.027633 | 0.01262 | 0.008812 | 0.006636 | 0.006636 | 0.006636 | 0 | 0.154918 | 0.041968 | 20,301 | 70 | 17,632 | 290.014286 | 0.790323 | 0.002217 | 0 | 0.067797 | 0 | 0.016949 | 0.870783 | 0.869943 | 0 | 1 | 0 | 0 | 0 | 1 | 0.067797 | false | 0 | 0.101695 | 0.016949 | 0.254237 | 0.016949 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5b60d5bfcd3dfd26a7686efecee6577269dc8e42 | 159 | py | Python | news/admin.py | ArRosid/djangorestframework-newsapi | 2abf69fe2e7e91b3b0434c1e1f5f2e921da9802f | [
"MIT"
] | null | null | null | news/admin.py | ArRosid/djangorestframework-newsapi | 2abf69fe2e7e91b3b0434c1e1f5f2e921da9802f | [
"MIT"
] | 6 | 2020-06-05T22:39:47.000Z | 2022-02-10T08:22:14.000Z | news/admin.py | ArRosid/djangorestframework-newsapi | 2abf69fe2e7e91b3b0434c1e1f5f2e921da9802f | [
"MIT"
] | 1 | 2022-02-19T20:44:21.000Z | 2022-02-19T20:44:21.000Z | from django.contrib import admin
from . import models
admin.site.register(models.Article)
admin.site.register(models.Journalist)
# Register your models here.
| 22.714286 | 38 | 0.811321 | 22 | 159 | 5.863636 | 0.545455 | 0.139535 | 0.263566 | 0.356589 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.100629 | 159 | 6 | 39 | 26.5 | 0.902098 | 0.163522 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
5b77158ebdcf10a010ded8ad1303ed94f607cc96 | 5,959 | py | Python | API/main.py | BigDataArchitecture/Assignment-3-4 | f4c87dadf443273ed532a8f9ea3c364b9fda75eb | [
"MIT"
] | null | null | null | API/main.py | BigDataArchitecture/Assignment-3-4 | f4c87dadf443273ed532a8f9ea3c364b9fda75eb | [
"MIT"
] | null | null | null | API/main.py | BigDataArchitecture/Assignment-3-4 | f4c87dadf443273ed532a8f9ea3c364b9fda75eb | [
"MIT"
] | null | null | null | from typing import Optional
import uvicorn
from fastapi import FastAPI, File,HTTPException
from pydantic import BaseModel
import tensorflow as tf
import input_main
# from Functions import make_gif
import os
import h5py
import io
from fastapi.responses import FileResponse
from starlette.responses import StreamingResponse
import numpy as np
import glob
from PIL import Image
from matplotlib import pyplot as plt
import json
app = FastAPI()
@app.get("/")
def welcome():
return FileResponse('/Users/parthshah/Downloads/international-Container-Cargo-ship-in-the-ocean.jpg')
# Api root or home endpoint
@app.get('/nowcast_results/backtest/')
def nowcast_backtest_function(begin_location,begin_yearmonth:int,begin_day:int,begin_time:int,model: Optional[str] = None,index: Optional[str] = None):
output = {}
if model== "":
model = "gan_generator"
if index == "":
index = 24
output['Model'] = model
output['Index'] = index
print("model",model)
try:
path,describe,y_pred = input_main.input(begin_location,begin_yearmonth,begin_day,begin_time,model,int(index))
if path == 1:
raise HTTPException(status_code=404, detail="Event not found")
else:
for i in range(12):
output[i] = y_pred[:,:,:,i].tolist()
return output
# return {"Model": model, "Index":index,"Main":path, "Y Preds":"/Prediction/Array/Y_Pred.h5","Y Preds Analyse Image":"/Prediction/Image/Prediction.png","describe":describe}
except IndexError as error:
print(error)
raise HTTPException(status_code=404, detail=str(error))
except UnboundLocalError as error:
raise HTTPException(status_code=406, detail="No such Model Please select any of following ['gan_generator','mse_and_style','style','mse_file']")
@app.get('/nowcast_results/forecast/')
def nowcast_forecast_function(begin_location,begin_yearmonth:int,begin_day:int,begin_time:int,model: Optional[str] = None,index: Optional[str] = None):
print("index",index)
if model== "":
model = "gan_generator"
if index == "":
index = 24
output = {}
try:
output['Model'] = model
output['Index'] = index
path,describe,y_pred = input_main.input(begin_location,begin_yearmonth,begin_day,begin_time,model,int(index))
if path == 1:
raise HTTPException(status_code=404, detail="Event not found")
else:
for i in range(12):
output[i] = y_pred[:,:,:,i].tolist()
return output
# {"Y Pred":y_pred.shape,"Model": model, "Index":index,"Main":path, "Y Preds":"/Prediction/Array/Y_Pred.h5","Y Preds 12 Image":"/Prediction/Image/12Images/","Describe":describe}
except IndexError as error:
print(error)
raise HTTPException(status_code=404, detail=str(error))
except UnboundLocalError as error:
raise HTTPException(status_code=406, detail="No such Model Please select any of following ['gan_generator','mse_and_style','style','mse_file']")
@app.get('/nowcast_results/backtest/latlong/')
def nowcast_backtest_analysis_function(lat:float,lon:float,distance:int,model: Optional[str] = None,index: Optional[str] = None):
output = {}
if model== "":
model = "gan_generator"
if index == "":
index = 1
output['Model'] = model
output['Index'] = index
try:
path,describe,y_pred = input_main.input_latlong(lat,lon,distance,model,index)
if path == 1:
raise HTTPException(status_code=404, detail="Event not found")
else:
for i in range(12):
output[i] = y_pred[:,:,:,i].tolist()
return output
# return {"Model": model, "Index":index,"Main":path, "Y Preds":"/Prediction/Array/Y_Pred.h5","Y Preds Analyse Image":"/Prediction/Image/Prediction.png","describe":describe}
except IndexError as error:
print(error)
raise HTTPException(status_code=404, detail=str(error))
except UnboundLocalError as error:
raise HTTPException(status_code=406, detail="No such Model Please select any of following ['gan_generator','mse_and_style','style','mse_file']")
@app.get('/nowcast_results/forecast/latlong/')
def nowcast_forecast_gif_function(lat:float,lon:float,distance:int,model: Optional[str] = None,index: Optional[str] = None):
output = {}
if model== "":
model = "gan_generator"
if index == "":
index = 24
print("model",model)
output['Model'] = model
output['Index'] = index
try:
path,describe,y_pred = input_main.input_latlong(lat,lon,distance,model,index)
if path == 1:
raise HTTPException(status_code=404, detail="Event not found")
else:
for i in range(12):
output[i] = y_pred[:,:,:,i].tolist()
return output
# return {"Model": model, "Index":index,"Main":path, "Y Preds":"/Prediction/Array/Y_Pred.h5","Y Preds 12 Image":"/Prediction/Image/12Images/","Describe":describe}
except IndexError as error:
print(error)
output["Error"] = str(error)
raise HTTPException(status_code=404, detail=str(error))
except UnboundLocalError as error:
raise HTTPException(status_code=406, detail="No such Model Please select any of following ['gan_generator','mse_and_style','style','mse_file']")
# @app.get('/nowcast_results/try/')
# def nowcast_forecast_gif_function1():
# a = h5py.File('/Users/parthshah/Documents/Northeastern/Spring2022/BigDataAnalytics/Assignment3/API/Intermediate_Files/694474/Prediction/Array/Y_Pred.h5','r')
# dict1 = {}
# dict1[1] = a['Pred'][:,:,:,11]
# print(type(dict1))
# str1 = str(dict1)
# print(json.dumps(dict1[1].tolist()))
# return dict1[1].tolist()
if __name__ == '__main__':
uvicorn.run("main:app", host="127.0.0.1", port=8001, reload=True)
| 41.964789 | 189 | 0.659003 | 765 | 5,959 | 5.006536 | 0.196078 | 0.019582 | 0.075196 | 0.087728 | 0.751958 | 0.736815 | 0.721671 | 0.721671 | 0.720888 | 0.720888 | 0 | 0.021886 | 0.202551 | 5,959 | 141 | 190 | 42.262411 | 0.784091 | 0.1903 | 0 | 0.710526 | 0 | 0.008772 | 0.163028 | 0.084425 | 0 | 0 | 0 | 0 | 0 | 1 | 0.04386 | false | 0 | 0.140351 | 0.008772 | 0.22807 | 0.061404 | 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 |
7508bc2cb094b04485ac93c65383a13a0840f001 | 195 | py | Python | {{cookiecutter.project_name}}/{{cookiecutter.app_name}}/models/{{cookiecutter.domain_name}}.py | kristianmandrup/cookiecutter-flask-restful | 2ac8b429ea35e849455d231b2fac45fe642ff10d | [
"MIT"
] | null | null | null | {{cookiecutter.project_name}}/{{cookiecutter.app_name}}/models/{{cookiecutter.domain_name}}.py | kristianmandrup/cookiecutter-flask-restful | 2ac8b429ea35e849455d231b2fac45fe642ff10d | [
"MIT"
] | null | null | null | {{cookiecutter.project_name}}/{{cookiecutter.app_name}}/models/{{cookiecutter.domain_name}}.py | kristianmandrup/cookiecutter-flask-restful | 2ac8b429ea35e849455d231b2fac45fe642ff10d | [
"MIT"
] | null | null | null | class {{cookiecutter.domain_name|title}}():
"""Basic {{cookiecutter.domain_name|title}} model"""
def __repr__(self):
return "<{{cookiecutter.domain_name|title}} %s>" % self.name
| 32.5 | 68 | 0.661538 | 22 | 195 | 5.545455 | 0.545455 | 0.442623 | 0.540984 | 0.663934 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148718 | 195 | 5 | 69 | 39 | 0.73494 | 0 | 0 | 0 | 0 | 0 | 0.272727 | 0.244755 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
75136d175ae5866c4329c9646df3ed928bff57e4 | 33 | py | Python | snuggle/scores/__init__.py | halfak/snuggle | 384818aaf8a783013b076ada3c74226f10e5dc18 | [
"MIT"
] | 2 | 2021-04-26T20:34:25.000Z | 2021-11-12T11:26:57.000Z | snuggle/scores/__init__.py | halfak/snuggle | 384818aaf8a783013b076ada3c74226f10e5dc18 | [
"MIT"
] | null | null | null | snuggle/scores/__init__.py | halfak/snuggle | 384818aaf8a783013b076ada3c74226f10e5dc18 | [
"MIT"
] | null | null | null | from .stiki import STiki, NoScore | 33 | 33 | 0.818182 | 5 | 33 | 5.4 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 1 | 33 | 33 | 0.931034 | 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 |
f33483f3125c6e6d2ef9d0bbf29c10d835994e8b | 64 | py | Python | judge/__init__.py | despawnerer/judge | e7f0a8ec8346bca67f5c01fe5ac0447a75bf9a23 | [
"MIT"
] | 1 | 2016-05-18T17:05:12.000Z | 2016-05-18T17:05:12.000Z | judge/__init__.py | despawnerer/judge | e7f0a8ec8346bca67f5c01fe5ac0447a75bf9a23 | [
"MIT"
] | null | null | null | judge/__init__.py | despawnerer/judge | e7f0a8ec8346bca67f5c01fe5ac0447a75bf9a23 | [
"MIT"
] | null | null | null | from .decide import * # noqa
from .predicates import * # noqa
| 21.333333 | 33 | 0.6875 | 8 | 64 | 5.5 | 0.625 | 0.454545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.21875 | 64 | 2 | 34 | 32 | 0.88 | 0.140625 | 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 |
f3a3e5805eff5286e6c1fd4563c3ba2c6fc770cc | 130 | py | Python | gcn/graph/__init__.py | icoxfog417/graph-convolution-nlp | 2f15da072e401528d9faf76985d05afce336798f | [
"MIT"
] | 233 | 2018-09-27T15:43:56.000Z | 2022-02-22T16:57:50.000Z | gcn/graph/__init__.py | dubeyakshat07/graph-convolution-nlp | 2f15da072e401528d9faf76985d05afce336798f | [
"MIT"
] | 7 | 2019-12-16T21:10:24.000Z | 2022-02-10T00:17:05.000Z | gcn/graph/__init__.py | dubeyakshat07/graph-convolution-nlp | 2f15da072e401528d9faf76985d05afce336798f | [
"MIT"
] | 40 | 2019-01-21T03:05:19.000Z | 2021-10-05T20:15:14.000Z | from .similarity_graph import SimilarityGraph
from .dependency_graph import DependencyGraph
from .static_graph import StaticGraph
| 32.5 | 45 | 0.884615 | 15 | 130 | 7.466667 | 0.6 | 0.294643 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.092308 | 130 | 3 | 46 | 43.333333 | 0.949153 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 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 |
3437196767acddf185451a0ddfdc7aaaa76c2ee8 | 67 | py | Python | networks/__init__.py | marsggbo/CovidNet3D | 0aeca91a775f938a0e568dd88d8162473dacf3ce | [
"MIT"
] | 5 | 2021-02-23T06:43:31.000Z | 2021-07-05T15:24:05.000Z | networks/__init__.py | etherx-dev/CovidNet3D | b107d7d965cad07f1890ee492857273f3468cc01 | [
"MIT"
] | 1 | 2021-06-08T21:06:10.000Z | 2021-06-08T21:06:10.000Z | networks/__init__.py | etherx-dev/CovidNet3D | b107d7d965cad07f1890ee492857273f3468cc01 | [
"MIT"
] | 4 | 2021-02-01T03:29:16.000Z | 2021-08-05T09:13:37.000Z | from .build import *
from .ops import *
from .mobile3d_net import * | 22.333333 | 27 | 0.746269 | 10 | 67 | 4.9 | 0.6 | 0.408163 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017857 | 0.164179 | 67 | 3 | 27 | 22.333333 | 0.857143 | 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 |
34792013794787bf1b5517d6cc66a5bf031f8125 | 24,635 | py | Python | cottonformation/res/managedblockchain.py | gitter-badger/cottonformation-project | 354f1dce7ea106e209af2d5d818b6033a27c193c | [
"BSD-2-Clause"
] | null | null | null | cottonformation/res/managedblockchain.py | gitter-badger/cottonformation-project | 354f1dce7ea106e209af2d5d818b6033a27c193c | [
"BSD-2-Clause"
] | null | null | null | cottonformation/res/managedblockchain.py | gitter-badger/cottonformation-project | 354f1dce7ea106e209af2d5d818b6033a27c193c | [
"BSD-2-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""
This module
"""
import attr
import typing
from ..core.model import (
Property, Resource, Tag, GetAtt, TypeHint, TypeCheck,
)
from ..core.constant import AttrMeta
#--- Property declaration ---
@attr.s
class NodeNodeConfiguration(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Node.NodeConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-node-nodeconfiguration.html
Property Document:
- ``rp_AvailabilityZone``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-node-nodeconfiguration.html#cfn-managedblockchain-node-nodeconfiguration-availabilityzone
- ``rp_InstanceType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-node-nodeconfiguration.html#cfn-managedblockchain-node-nodeconfiguration-instancetype
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Node.NodeConfiguration"
rp_AvailabilityZone: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "AvailabilityZone"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-node-nodeconfiguration.html#cfn-managedblockchain-node-nodeconfiguration-availabilityzone"""
rp_InstanceType: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "InstanceType"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-node-nodeconfiguration.html#cfn-managedblockchain-node-nodeconfiguration-instancetype"""
@attr.s
class MemberNetworkFabricConfiguration(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.NetworkFabricConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkfabricconfiguration.html
Property Document:
- ``rp_Edition``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkfabricconfiguration.html#cfn-managedblockchain-member-networkfabricconfiguration-edition
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.NetworkFabricConfiguration"
rp_Edition: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Edition"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkfabricconfiguration.html#cfn-managedblockchain-member-networkfabricconfiguration-edition"""
@attr.s
class MemberApprovalThresholdPolicy(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.ApprovalThresholdPolicy"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-approvalthresholdpolicy.html
Property Document:
- ``p_ProposalDurationInHours``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-approvalthresholdpolicy.html#cfn-managedblockchain-member-approvalthresholdpolicy-proposaldurationinhours
- ``p_ThresholdComparator``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-approvalthresholdpolicy.html#cfn-managedblockchain-member-approvalthresholdpolicy-thresholdcomparator
- ``p_ThresholdPercentage``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-approvalthresholdpolicy.html#cfn-managedblockchain-member-approvalthresholdpolicy-thresholdpercentage
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.ApprovalThresholdPolicy"
p_ProposalDurationInHours: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "ProposalDurationInHours"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-approvalthresholdpolicy.html#cfn-managedblockchain-member-approvalthresholdpolicy-proposaldurationinhours"""
p_ThresholdComparator: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ThresholdComparator"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-approvalthresholdpolicy.html#cfn-managedblockchain-member-approvalthresholdpolicy-thresholdcomparator"""
p_ThresholdPercentage: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "ThresholdPercentage"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-approvalthresholdpolicy.html#cfn-managedblockchain-member-approvalthresholdpolicy-thresholdpercentage"""
@attr.s
class MemberVotingPolicy(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.VotingPolicy"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-votingpolicy.html
Property Document:
- ``p_ApprovalThresholdPolicy``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-votingpolicy.html#cfn-managedblockchain-member-votingpolicy-approvalthresholdpolicy
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.VotingPolicy"
p_ApprovalThresholdPolicy: typing.Union['MemberApprovalThresholdPolicy', dict] = attr.ib(
default=None,
converter=MemberApprovalThresholdPolicy.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(MemberApprovalThresholdPolicy)),
metadata={AttrMeta.PROPERTY_NAME: "ApprovalThresholdPolicy"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-votingpolicy.html#cfn-managedblockchain-member-votingpolicy-approvalthresholdpolicy"""
@attr.s
class MemberMemberFabricConfiguration(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.MemberFabricConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberfabricconfiguration.html
Property Document:
- ``rp_AdminPassword``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberfabricconfiguration.html#cfn-managedblockchain-member-memberfabricconfiguration-adminpassword
- ``rp_AdminUsername``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberfabricconfiguration.html#cfn-managedblockchain-member-memberfabricconfiguration-adminusername
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.MemberFabricConfiguration"
rp_AdminPassword: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "AdminPassword"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberfabricconfiguration.html#cfn-managedblockchain-member-memberfabricconfiguration-adminpassword"""
rp_AdminUsername: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "AdminUsername"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberfabricconfiguration.html#cfn-managedblockchain-member-memberfabricconfiguration-adminusername"""
@attr.s
class MemberNetworkFrameworkConfiguration(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.NetworkFrameworkConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkframeworkconfiguration.html
Property Document:
- ``p_NetworkFabricConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkframeworkconfiguration.html#cfn-managedblockchain-member-networkframeworkconfiguration-networkfabricconfiguration
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.NetworkFrameworkConfiguration"
p_NetworkFabricConfiguration: typing.Union['MemberNetworkFabricConfiguration', dict] = attr.ib(
default=None,
converter=MemberNetworkFabricConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(MemberNetworkFabricConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "NetworkFabricConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkframeworkconfiguration.html#cfn-managedblockchain-member-networkframeworkconfiguration-networkfabricconfiguration"""
@attr.s
class MemberNetworkConfiguration(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.NetworkConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html
Property Document:
- ``rp_Framework``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-framework
- ``rp_FrameworkVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-frameworkversion
- ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-name
- ``rp_VotingPolicy``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-votingpolicy
- ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-description
- ``p_NetworkFrameworkConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-networkframeworkconfiguration
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.NetworkConfiguration"
rp_Framework: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Framework"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-framework"""
rp_FrameworkVersion: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "FrameworkVersion"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-frameworkversion"""
rp_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-name"""
rp_VotingPolicy: typing.Union['MemberVotingPolicy', dict] = attr.ib(
default=None,
converter=MemberVotingPolicy.from_dict,
validator=attr.validators.instance_of(MemberVotingPolicy),
metadata={AttrMeta.PROPERTY_NAME: "VotingPolicy"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-votingpolicy"""
p_Description: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Description"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-description"""
p_NetworkFrameworkConfiguration: typing.Union['MemberNetworkFrameworkConfiguration', dict] = attr.ib(
default=None,
converter=MemberNetworkFrameworkConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(MemberNetworkFrameworkConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "NetworkFrameworkConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-networkconfiguration.html#cfn-managedblockchain-member-networkconfiguration-networkframeworkconfiguration"""
@attr.s
class MemberMemberFrameworkConfiguration(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.MemberFrameworkConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberframeworkconfiguration.html
Property Document:
- ``p_MemberFabricConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberframeworkconfiguration.html#cfn-managedblockchain-member-memberframeworkconfiguration-memberfabricconfiguration
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.MemberFrameworkConfiguration"
p_MemberFabricConfiguration: typing.Union['MemberMemberFabricConfiguration', dict] = attr.ib(
default=None,
converter=MemberMemberFabricConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(MemberMemberFabricConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "MemberFabricConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberframeworkconfiguration.html#cfn-managedblockchain-member-memberframeworkconfiguration-memberfabricconfiguration"""
@attr.s
class MemberMemberConfiguration(Property):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member.MemberConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberconfiguration.html
Property Document:
- ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberconfiguration.html#cfn-managedblockchain-member-memberconfiguration-name
- ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberconfiguration.html#cfn-managedblockchain-member-memberconfiguration-description
- ``p_MemberFrameworkConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberconfiguration.html#cfn-managedblockchain-member-memberconfiguration-memberframeworkconfiguration
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member.MemberConfiguration"
rp_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberconfiguration.html#cfn-managedblockchain-member-memberconfiguration-name"""
p_Description: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Description"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberconfiguration.html#cfn-managedblockchain-member-memberconfiguration-description"""
p_MemberFrameworkConfiguration: typing.Union['MemberMemberFrameworkConfiguration', dict] = attr.ib(
default=None,
converter=MemberMemberFrameworkConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(MemberMemberFrameworkConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "MemberFrameworkConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-managedblockchain-member-memberconfiguration.html#cfn-managedblockchain-member-memberconfiguration-memberframeworkconfiguration"""
#--- Resource declaration ---
@attr.s
class Member(Resource):
"""
AWS Object Type = "AWS::ManagedBlockchain::Member"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html
Property Document:
- ``rp_MemberConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-memberconfiguration
- ``p_InvitationId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-invitationid
- ``p_NetworkConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-networkconfiguration
- ``p_NetworkId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-networkid
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Member"
rp_MemberConfiguration: typing.Union['MemberMemberConfiguration', dict] = attr.ib(
default=None,
converter=MemberMemberConfiguration.from_dict,
validator=attr.validators.instance_of(MemberMemberConfiguration),
metadata={AttrMeta.PROPERTY_NAME: "MemberConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-memberconfiguration"""
p_InvitationId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "InvitationId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-invitationid"""
p_NetworkConfiguration: typing.Union['MemberNetworkConfiguration', dict] = attr.ib(
default=None,
converter=MemberNetworkConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(MemberNetworkConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "NetworkConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-networkconfiguration"""
p_NetworkId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "NetworkId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#cfn-managedblockchain-member-networkid"""
@property
def rv_MemberId(self) -> GetAtt:
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#aws-resource-managedblockchain-member-return-values"""
return GetAtt(resource=self, attr_name="MemberId")
@property
def rv_NetworkId(self) -> GetAtt:
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-member.html#aws-resource-managedblockchain-member-return-values"""
return GetAtt(resource=self, attr_name="NetworkId")
@attr.s
class Node(Resource):
"""
AWS Object Type = "AWS::ManagedBlockchain::Node"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html
Property Document:
- ``rp_NetworkId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#cfn-managedblockchain-node-networkid
- ``rp_NodeConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#cfn-managedblockchain-node-nodeconfiguration
- ``p_MemberId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#cfn-managedblockchain-node-memberid
"""
AWS_OBJECT_TYPE = "AWS::ManagedBlockchain::Node"
rp_NetworkId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "NetworkId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#cfn-managedblockchain-node-networkid"""
rp_NodeConfiguration: typing.Union['NodeNodeConfiguration', dict] = attr.ib(
default=None,
converter=NodeNodeConfiguration.from_dict,
validator=attr.validators.instance_of(NodeNodeConfiguration),
metadata={AttrMeta.PROPERTY_NAME: "NodeConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#cfn-managedblockchain-node-nodeconfiguration"""
p_MemberId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "MemberId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#cfn-managedblockchain-node-memberid"""
@property
def rv_MemberId(self) -> GetAtt:
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#aws-resource-managedblockchain-node-return-values"""
return GetAtt(resource=self, attr_name="MemberId")
@property
def rv_NodeId(self) -> GetAtt:
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#aws-resource-managedblockchain-node-return-values"""
return GetAtt(resource=self, attr_name="NodeId")
@property
def rv_Arn(self) -> GetAtt:
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#aws-resource-managedblockchain-node-return-values"""
return GetAtt(resource=self, attr_name="Arn")
@property
def rv_NetworkId(self) -> GetAtt:
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-managedblockchain-node.html#aws-resource-managedblockchain-node-return-values"""
return GetAtt(resource=self, attr_name="NetworkId")
| 62.209596 | 262 | 0.782951 | 2,377 | 24,635 | 8.030711 | 0.040808 | 0.143381 | 0.040914 | 0.06323 | 0.86521 | 0.86521 | 0.841846 | 0.774635 | 0.774635 | 0.773692 | 0 | 0.000045 | 0.09933 | 24,635 | 395 | 263 | 62.367089 | 0.860285 | 0.377715 | 0 | 0.39801 | 0 | 0 | 0.128758 | 0.09548 | 0 | 0 | 0 | 0 | 0 | 1 | 0.029851 | false | 0.00995 | 0.019901 | 0 | 0.323383 | 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 |
cae123dd1aba9de60a95bd77b8df87f3ec9aabad | 35 | py | Python | users/models/__init__.py | sharif-42/Personal_Website | 7c385bec272ec7b5c816eab92e3b5bfb8cd80016 | [
"MIT"
] | null | null | null | users/models/__init__.py | sharif-42/Personal_Website | 7c385bec272ec7b5c816eab92e3b5bfb8cd80016 | [
"MIT"
] | 9 | 2021-03-30T13:41:09.000Z | 2022-03-12T00:32:50.000Z | users/models/__init__.py | abheist/goldenSwan-backend | 153e16bb829f113fb429131436324631f15ae064 | [
"MIT"
] | null | null | null | from users.models.user import User
| 17.5 | 34 | 0.828571 | 6 | 35 | 4.833333 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 1 | 35 | 35 | 0.935484 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
cae16d560475d9885ce8c229f41e64b052bd67bd | 124 | py | Python | src/domain/errors/unable_to_convert_image_to_grayscale_failure.py | OzielFilho/ProjetoFinalPdi | c9e6fe415f1a985d6eeac204580d3ab623026665 | [
"MIT"
] | null | null | null | src/domain/errors/unable_to_convert_image_to_grayscale_failure.py | OzielFilho/ProjetoFinalPdi | c9e6fe415f1a985d6eeac204580d3ab623026665 | [
"MIT"
] | null | null | null | src/domain/errors/unable_to_convert_image_to_grayscale_failure.py | OzielFilho/ProjetoFinalPdi | c9e6fe415f1a985d6eeac204580d3ab623026665 | [
"MIT"
] | null | null | null | from domain.errors.image_failure import ImageFailure
class UnableToConvertImageToGrayscaleFailure(ImageFailure):
pass
| 20.666667 | 59 | 0.854839 | 11 | 124 | 9.545455 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104839 | 124 | 5 | 60 | 24.8 | 0.945946 | 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 |
1b1742827867f1f6cbdba562ecb2b003c0353235 | 27 | py | Python | taattack/_datasets/ag_news/__init__.py | linerxliner/ValCAT | e62985c6c64f6415bb2bb4716bd02d9686badd47 | [
"MIT"
] | null | null | null | taattack/_datasets/ag_news/__init__.py | linerxliner/ValCAT | e62985c6c64f6415bb2bb4716bd02d9686badd47 | [
"MIT"
] | null | null | null | taattack/_datasets/ag_news/__init__.py | linerxliner/ValCAT | e62985c6c64f6415bb2bb4716bd02d9686badd47 | [
"MIT"
] | null | null | null | from .ag_news import AgNews | 27 | 27 | 0.851852 | 5 | 27 | 4.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 27 | 1 | 27 | 27 | 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 |
1b393b5eae4b55fa61b83e57ade5a59893eb4d5d | 9,874 | py | Python | Tests/test_rest_api.py | James-Chapman/Python_REST_API | d15d39f1c3fd07f18651a94325efb250290c2601 | [
"BSD-3-Clause"
] | null | null | null | Tests/test_rest_api.py | James-Chapman/Python_REST_API | d15d39f1c3fd07f18651a94325efb250290c2601 | [
"BSD-3-Clause"
] | null | null | null | Tests/test_rest_api.py | James-Chapman/Python_REST_API | d15d39f1c3fd07f18651a94325efb250290c2601 | [
"BSD-3-Clause"
] | null | null | null | import http.client
import json
import threading
import time
import pytest
from RESTfulHTTPRequestHandler import RESTfulHTTPRequestHandler
@pytest.fixture(scope="module", autouse=True)
def start_rest_service():
print("Starting server")
SERVER_IP = "0.0.0.0"
SERVER_PORT = 8080
SERVER = http.server.ThreadingHTTPServer((SERVER_IP, SERVER_PORT), RESTfulHTTPRequestHandler)
thread1 = threading.Thread(target=SERVER.serve_forever, args=())
thread1.daemon = True
thread1.start()
time.sleep(5) # Give server a chance to start
yield SERVER
def test_POST_api_job_start():
testData = {"command": "ping -n 5 127.0.0.1"}
jsonString = json.dumps(testData)
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': len(jsonString)}
# Create 101 jobs
for i in range(101):
restConn.connect()
restConn.request('POST', '/api/jobs', jsonString, headers)
resp = restConn.getresponse()
restConn.close()
assert(resp.status == 200)
def test_PUT_api_job_stop():
time.sleep(1) # Sleep while server starts
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
headers = {'Content-type': 'application/json;charset=utf-8'}
restConn.connect()
restConn.request("PUT", "/api/jobs/100/stop", headers=headers)
resp = restConn.getresponse()
assert(resp.status == 200)
resp.close()
restConn.close()
# Now check that the job has been stopped.
def test_GET_api_job_100():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
headers = {'Content-type': 'application/json;charset=utf-8'}
restConn.connect()
restConn.request("GET", "/api/jobs/100", headers=headers)
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (data["id"] == 100)
assert (data["status"] == "stopped")
assert (data["command"] == "ping -n 5 127.0.0.1")
def test_GET_api_job_99():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('GET', '/api/jobs/99', "", headers)
resp = restConn.getresponse()
assert (resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (data["id"] == 99)
assert (data["status"] == "running")
assert (data["command"] == "ping -n 5 127.0.0.1")
def test_GET_api_job_0():
time.sleep(4) # Give jobs time to complete
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
restConn.putrequest("GET", "/api/jobs/0")
restConn.putheader("content-type", "application/json;charset=utf-8")
restConn.endheaders()
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (data["id"] == 0)
assert (data["status"] == "completed")
assert (data["command"] == "ping -n 5 127.0.0.1")
assert(data["stdout"] != "")
def test_GET_api_job_5():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('GET', '/api/jobs/5', "", headers)
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (data["id"] == 5)
assert (data["status"] == "completed")
assert (data["command"] == "ping -n 5 127.0.0.1")
assert (data["stdout"] != "")
def test_GET_api_job_9999():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('GET', '/api/jobs/9999', "", headers)
resp = restConn.getresponse()
assert(resp.status == 404)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert(data["id"] == -1)
def test_GET_api_jobs():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
restConn.putrequest("GET", "/api/jobs")
restConn.putheader("content-type", "application/json;charset=utf-8")
restConn.endheaders()
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (len(data["jobs"]) > 0)
def test_GET_api_jobs_running():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
restConn.putrequest("GET", "/api/jobs?status=running")
restConn.putheader("content-type", "application/json;charset=utf-8")
restConn.endheaders()
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (len(data["jobs"]) > 0)
def test_GET_api_jobs_stopped():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
restConn.putrequest("GET", "/api/jobs?status=stopped")
restConn.putheader("content-type", "application/json;charset=utf-8")
restConn.endheaders()
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (len(data["jobs"]) > 0)
def test_GET_api_jobs_completed():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
restConn.putrequest("GET", "/api/jobs?status=completed")
restConn.putheader("content-type", "application/json;charset=utf-8")
restConn.endheaders()
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn.close()
assert (len(data["jobs"]) > 0)
def test_POST_api_job_start_empty_command():
testData = {"command": ""}
jsonString = json.dumps(testData)
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': len(jsonString)}
restConn.connect()
restConn.request('POST', '/api/jobs', jsonString, headers)
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
restConn.close()
data = json.loads(bytes.decode(bytedata, "utf-8"))
assert(data["id"] == -1)
def test_rest_api_with_garbage_command():
testData = {"command": "this_command_doesnt_exist even with args"}
jsonString = json.dumps(testData)
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': len(jsonString)}
restConn.connect()
restConn.request('POST', '/api/jobs', jsonString, headers)
resp = restConn.getresponse()
assert(resp.status == 200)
bytedata = resp.read()
restConn.close()
data = json.loads(bytes.decode(bytedata, "utf-8"))
time.sleep(1) # server needs time to work out the command is garbage
restConn1 = http.client.HTTPConnection("127.0.0.1", 8080)
headers = {'Content-type': 'application/json;charset=utf-8'}
restConn1.connect()
path = "/api/jobs/{}".format(data["id"])
restConn1.request("GET", path, headers=headers)
resp = restConn1.getresponse()
assert (resp.status == 200)
bytedata = resp.read()
data = json.loads(bytes.decode(bytedata, "utf-8"))
restConn1.close()
assert(data["status"] == "stopped")
def test_rest_api_false_path_GET():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('GET', '/api/doesnt-exist', "", headers)
resp = restConn.getresponse()
assert (resp.status == 404)
def test_rest_api_false_path_POST():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('POST', '/api/rubbish', "", headers)
resp = restConn.getresponse()
assert (resp.status == 404)
def test_rest_api_false_path_PUT():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('PUT', '/api/rubbish', "", headers)
resp = restConn.getresponse()
assert (resp.status == 404)
def test_rest_api_false_path_DELETE():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('DELETE', '/api/rubbish', "", headers)
resp = restConn.getresponse()
assert (resp.status == 404)
def test_rest_api_OPTIONS():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('OPTIONS', '/path/doesnt/matter', "", headers)
resp = restConn.getresponse()
assert (resp.status == 200)
def test_rest_api_false_path_NON_EXISTANT():
restConn = http.client.HTTPConnection("127.0.0.1", 8080)
restConn.connect()
headers = {'Content-type': 'application/json;charset=utf-8', 'Content-length': 0}
restConn.request('NON_EXISTANT', '/api/rubbish', "", headers)
resp = restConn.getresponse()
assert (resp.status == 501)
| 35.517986 | 99 | 0.664371 | 1,255 | 9,874 | 5.152191 | 0.104382 | 0.019796 | 0.019332 | 0.023198 | 0.826013 | 0.820445 | 0.799258 | 0.799258 | 0.781163 | 0.762759 | 0 | 0.04855 | 0.165586 | 9,874 | 277 | 100 | 35.646209 | 0.736254 | 0.019445 | 0 | 0.698276 | 0 | 0 | 0.200103 | 0.072248 | 0 | 0 | 0 | 0 | 0.176724 | 1 | 0.086207 | false | 0 | 0.025862 | 0 | 0.112069 | 0.00431 | 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 |
1b67b132fc1368f011ffbedb258e5d0877bb1539 | 36 | py | Python | simple.py | javakung/masahiro | bf107b8a75103258c44fd5adde78043399d3216c | [
"MIT"
] | null | null | null | simple.py | javakung/masahiro | bf107b8a75103258c44fd5adde78043399d3216c | [
"MIT"
] | null | null | null | simple.py | javakung/masahiro | bf107b8a75103258c44fd5adde78043399d3216c | [
"MIT"
] | null | null | null | def say(text):
print('say',text) | 18 | 21 | 0.611111 | 6 | 36 | 3.666667 | 0.666667 | 0.636364 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 36 | 2 | 21 | 18 | 0.733333 | 0 | 0 | 0 | 0 | 0 | 0.081081 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
1b6a2ea5c1d11720d01b9c747c696b1eb195433d | 28 | py | Python | nba_ss_db/scrape/__init__.py | jsonchin/nba_stats_scraper_db_storage | 33f33d89c5c76db9625f4973db8afdbbd7045263 | [
"Apache-2.0"
] | 4 | 2017-11-04T05:03:57.000Z | 2022-01-30T13:24:15.000Z | nba_ss_db/scrape/__init__.py | jsonchin/nba_stats_scraper_db_storage | 33f33d89c5c76db9625f4973db8afdbbd7045263 | [
"Apache-2.0"
] | 1 | 2021-06-01T22:05:19.000Z | 2021-06-01T22:05:19.000Z | nba_ss_db/scrape/__init__.py | jsonchin/nba_stats_scraper_db_storage | 33f33d89c5c76db9625f4973db8afdbbd7045263 | [
"Apache-2.0"
] | 2 | 2017-11-26T18:59:59.000Z | 2018-07-05T18:05:09.000Z | from . import scraper, utils | 28 | 28 | 0.785714 | 4 | 28 | 5.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 28 | 1 | 28 | 28 | 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 |
1b98e8fc5e5f8bd6d9e2a5fd5c1f9e6b7068b730 | 858 | py | Python | Search/Modes.py | jbzdarkid/TwitchLink | c7bae13b46c7e6af7dc74539fdbca9cbb01f4778 | [
"MIT"
] | 26 | 2021-02-04T00:29:21.000Z | 2022-03-25T17:14:43.000Z | Search/Modes.py | jbzdarkid/TwitchLink | c7bae13b46c7e6af7dc74539fdbca9cbb01f4778 | [
"MIT"
] | 19 | 2021-02-04T01:27:07.000Z | 2022-03-19T16:22:46.000Z | Search/Modes.py | jbzdarkid/TwitchLink | c7bae13b46c7e6af7dc74539fdbca9cbb01f4778 | [
"MIT"
] | 10 | 2021-06-08T17:41:40.000Z | 2022-03-28T22:38:40.000Z | class SearchModes:
class MODES:
CHANNEL = "channel"
VIDEO = "video"
CLIP = "clip"
URL = "url"
CHANNEL = lambda: SearchModes(SearchModes.MODES.CHANNEL)
VIDEO = lambda: SearchModes(SearchModes.MODES.VIDEO)
CLIP = lambda: SearchModes(SearchModes.MODES.CLIP)
URL = lambda: SearchModes(SearchModes.MODES.URL)
def __init__(self, searchMode):
self.setMode(searchMode)
def setMode(self, searchMode):
self._searchMode = searchMode
def getMode(self):
return self._searchMode
def isChannel(self):
return self._searchMode == self.MODES.CHANNEL
def isVideo(self):
return self._searchMode == self.MODES.VIDEO
def isClip(self):
return self._searchMode == self.MODES.CLIP
def isUrl(self):
return self._searchMode == self.MODES.URL | 26.8125 | 60 | 0.65035 | 92 | 858 | 5.956522 | 0.206522 | 0.20438 | 0.19708 | 0.218978 | 0.240876 | 0.240876 | 0 | 0 | 0 | 0 | 0 | 0 | 0.248252 | 858 | 32 | 61 | 26.8125 | 0.849612 | 0 | 0 | 0 | 0 | 0 | 0.022119 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.291667 | false | 0 | 0 | 0.208333 | 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 |
1ba715107051878654d614c0563409a55f9d55d0 | 117 | py | Python | expects/factory.py | danibaena/expects | 296203a3fb07cf3061b8f7b348136c9208195d93 | [
"Apache-2.0"
] | 189 | 2015-01-05T13:26:40.000Z | 2021-09-27T12:44:48.000Z | expects/factory.py | danibaena/expects | 296203a3fb07cf3061b8f7b348136c9208195d93 | [
"Apache-2.0"
] | 38 | 2015-02-13T16:08:23.000Z | 2022-02-14T12:14:28.000Z | expects/factory.py | danibaena/expects | 296203a3fb07cf3061b8f7b348136c9208195d93 | [
"Apache-2.0"
] | 32 | 2015-03-12T08:06:47.000Z | 2022-03-08T18:16:28.000Z | # -*- coding: utf-8 -*
from .expectations import Expectation
def expect(subject):
return Expectation(subject)
| 14.625 | 37 | 0.709402 | 13 | 117 | 6.384615 | 0.846154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010309 | 0.17094 | 117 | 7 | 38 | 16.714286 | 0.845361 | 0.17094 | 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 |
1bc0dee6512dee5dd396b980936f3c7be2910e24 | 21 | py | Python | tasks/snli/third_party/models/__init__.py | etri-edgeai/nn-comp-discblock | 6e00a019c223508797ca91a7d5ffec7917b12c6d | [
"Apache-2.0"
] | 10 | 2021-11-19T06:24:51.000Z | 2022-02-09T15:44:00.000Z | tasks/snli/third_party/models/__init__.py | etri-edgeai/nn-comp-discblock | 6e00a019c223508797ca91a7d5ffec7917b12c6d | [
"Apache-2.0"
] | 9 | 2021-10-01T11:06:27.000Z | 2021-12-23T02:10:52.000Z | tasks/snli/third_party/models/__init__.py | etri-edgeai/nn-comp-discblock | 6e00a019c223508797ca91a7d5ffec7917b12c6d | [
"Apache-2.0"
] | 2 | 2021-09-14T04:08:36.000Z | 2021-11-19T06:24:54.000Z | from .bilstm import * | 21 | 21 | 0.761905 | 3 | 21 | 5.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 21 | 1 | 21 | 21 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1bc765756d526736cf6a3bc250962a905c74a2fe | 96 | py | Python | venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/search.py | Retraces/UkraineBot | 3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/search.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/search.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/36/e5/28/ca853b94c668be26e06488e44cab51b31595b98dc54587ce26270cefe9 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4375 | 0 | 96 | 1 | 96 | 96 | 0.458333 | 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 |
8473e79ce1dc30c2604853fa5480c0867b6a4485 | 325 | py | Python | alphatwirl/selection/factories/LambdaStrFromDictFactory.py | benkrikler/alphatwirl | cda7d12fec21291ea33af23234fc08be19430934 | [
"BSD-3-Clause"
] | null | null | null | alphatwirl/selection/factories/LambdaStrFromDictFactory.py | benkrikler/alphatwirl | cda7d12fec21291ea33af23234fc08be19430934 | [
"BSD-3-Clause"
] | 7 | 2018-02-26T10:32:26.000Z | 2018-03-19T12:27:12.000Z | alphatwirl/selection/factories/LambdaStrFromDictFactory.py | benkrikler/alphatwirl | cda7d12fec21291ea33af23234fc08be19430934 | [
"BSD-3-Clause"
] | null | null | null | # Tai Sakuma <tai.sakuma@gmail.com>
##__________________________________________________________________||
def LambdaStrFromDictFactory(key, **kargs):
return kargs['LambdaStrClass'](lambda_str = kargs['aliasDict'][key].format(**kargs), name = key)
##__________________________________________________________________||
| 40.625 | 100 | 0.809231 | 22 | 325 | 5.909091 | 0.681818 | 0.138462 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.067692 | 325 | 7 | 101 | 46.428571 | 0.429043 | 0.52 | 0 | 0 | 0 | 0 | 0.153333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
8478fa34761867d4aaa7f45e3eb24d07c46e332b | 26 | py | Python | feeder/__init__.py | niais/mv-ignet | 903dd4e48971b2c165269820fa8679b354dd41a2 | [
"BSD-2-Clause"
] | 18 | 2021-01-07T12:38:58.000Z | 2021-09-26T11:36:03.000Z | feeder/__init__.py | niais/mv-ignet | 903dd4e48971b2c165269820fa8679b354dd41a2 | [
"BSD-2-Clause"
] | 8 | 2021-04-16T11:55:44.000Z | 2022-01-10T11:52:07.000Z | feeder/__init__.py | niais/mv-ignet | 903dd4e48971b2c165269820fa8679b354dd41a2 | [
"BSD-2-Clause"
] | 1 | 2021-01-20T07:33:03.000Z | 2021-01-20T07:33:03.000Z | from . import NTUDatasets
| 13 | 25 | 0.807692 | 3 | 26 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 26 | 1 | 26 | 26 | 0.954545 | 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 |
8491a870b91029a54a432db42dfaf63a43ed2b93 | 41 | py | Python | src/workflowtools/scripts/rmbmenuhook/__init__.py | bohdon/maya-workflowtools | 11587464a4f253eb4d8ab5d034fc93676d726414 | [
"MIT"
] | null | null | null | src/workflowtools/scripts/rmbmenuhook/__init__.py | bohdon/maya-workflowtools | 11587464a4f253eb4d8ab5d034fc93676d726414 | [
"MIT"
] | null | null | null | src/workflowtools/scripts/rmbmenuhook/__init__.py | bohdon/maya-workflowtools | 11587464a4f253eb4d8ab5d034fc93676d726414 | [
"MIT"
] | null | null | null |
from .core import *
from .menu import *
| 10.25 | 19 | 0.682927 | 6 | 41 | 4.666667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.219512 | 41 | 3 | 20 | 13.666667 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
84920797588f00f8d38dad60680dd7cd78500eed | 26 | py | Python | kpireport/tests/test_view.py | diurnalist/kpireporter | b3ce9ca52567405557ea12f45c1a7fda076d746a | [
"BlueOak-1.0.0",
"Apache-2.0"
] | 9 | 2021-05-17T05:32:46.000Z | 2022-03-16T22:49:26.000Z | kpireport/tests/test_view.py | diurnalist/kpireporter | b3ce9ca52567405557ea12f45c1a7fda076d746a | [
"BlueOak-1.0.0",
"Apache-2.0"
] | 4 | 2020-10-10T23:38:20.000Z | 2020-11-08T22:41:24.000Z | kpireport/tests/test_view.py | diurnalist/kpireporter | b3ce9ca52567405557ea12f45c1a7fda076d746a | [
"BlueOak-1.0.0",
"Apache-2.0"
] | 1 | 2021-01-12T02:49:04.000Z | 2021-01-12T02:49:04.000Z | def test_view():
pass
| 8.666667 | 16 | 0.615385 | 4 | 26 | 3.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.269231 | 26 | 2 | 17 | 13 | 0.789474 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
849b8cb8e6ac933479473dc8dde8c9dbfe4f015c | 19 | py | Python | src/bread/cli/__init__.py | ninivert/bread | 9f8502574312d702fee9910130cffe3d876efced | [
"MIT"
] | null | null | null | src/bread/cli/__init__.py | ninivert/bread | 9f8502574312d702fee9910130cffe3d876efced | [
"MIT"
] | null | null | null | src/bread/cli/__init__.py | ninivert/bread | 9f8502574312d702fee9910130cffe3d876efced | [
"MIT"
] | null | null | null | from ._cli import * | 19 | 19 | 0.736842 | 3 | 19 | 4.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.157895 | 19 | 1 | 19 | 19 | 0.8125 | 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 |
170381d9e841911c723fa6202b20829ff06d7d9a | 1,783 | py | Python | PB_Conversores.py | angatu0/python_level0 | 365bb78d71e212b51d985edfd71c342d1fddca18 | [
"MIT"
] | 1 | 2019-07-24T02:38:50.000Z | 2019-07-24T02:38:50.000Z | PB_Conversores.py | angatu0/python_level0 | 365bb78d71e212b51d985edfd71c342d1fddca18 | [
"MIT"
] | null | null | null | PB_Conversores.py | angatu0/python_level0 | 365bb78d71e212b51d985edfd71c342d1fddca18 | [
"MIT"
] | null | null | null | # The goal is load menu with two options for choose which convert mode.
# In addition to humanize more the interaction.
print('Hi, How to help you?')
menu = input('Choose the convert mode:: \n [A] Fahrenheit > Celsius. \n [B] Seconds > Hours.\n Enter your option: ')
if menu == 'A': # Choose temperature, ask the value.
F = float(input('Entering the temperature in Fahrenheit for convert in Celsius: '))
C = (F - 32) * 5 / 9
print('The temperature is {:.2f}ºC'.format(C))
elif menu == 'B':
segt = int(input('What is the total time in seconds: '))
h = segt // 3600
sr = segt % 3600
min = sr // 60
srf = sr % 60
if h == 0:
print('{}min {}s'.format(min,srf))
else:
print('{}h {}min {}s'.format(h,min,srf))
print('Hope this helps. I see you later.')
else:
while menu != 'A' and 'B':
print('Invalid option. Entering "A" or "B".')
menu = input('Choose what you would like to convert:'
'\n [A] Fahrenheit > Celsius.'
'\n [B] Seconds > Hours.'
'\n Entering thr option here: ')
if menu == 'A': # Choose temperature, ask the value.
F = float(input('Entering the temperature in Fahrenheit for convert in Celsius: '))
C = (F - 32) * 5 / 9
print('The temperature is {:.2f}ºC'.format(C))
elif menu == 'B':
segt = int(input('What is the total time in seconds: '))
h = segt // 3600
sr = segt % 3600
min = sr // 60
srf = sr % 60
if h == 0:
print('{}min {}s'.format(min, srf))
else:
print('{}h {}min {}s'.format(h, min, srf))
print('Hope this helps. I see you later.') | 43.487805 | 116 | 0.523836 | 247 | 1,783 | 3.781377 | 0.315789 | 0.059957 | 0.042827 | 0.040685 | 0.715203 | 0.715203 | 0.715203 | 0.715203 | 0.715203 | 0.642398 | 0 | 0.030227 | 0.332025 | 1,783 | 41 | 117 | 43.487805 | 0.753988 | 0.103758 | 0 | 0.794872 | 0 | 0.025641 | 0.401506 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.25641 | 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 |
ca247fe08a53942f8642aef605773c5c41a6be1c | 143 | py | Python | cfgov/v1/admin.py | m3brown/cfgov-refresh | 9582dccc97498a27fcf78a70bb50ef06efa2ce74 | [
"CC0-1.0"
] | null | null | null | cfgov/v1/admin.py | m3brown/cfgov-refresh | 9582dccc97498a27fcf78a70bb50ef06efa2ce74 | [
"CC0-1.0"
] | null | null | null | cfgov/v1/admin.py | m3brown/cfgov-refresh | 9582dccc97498a27fcf78a70bb50ef06efa2ce74 | [
"CC0-1.0"
] | null | null | null | from django.contrib import admin
from models.snippets import Contact
@admin.register(Contact)
class ContactAdmin(admin.ModelAdmin):
pass
| 17.875 | 37 | 0.804196 | 18 | 143 | 6.388889 | 0.722222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125874 | 143 | 7 | 38 | 20.428571 | 0.92 | 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 |
ca5a0852a6312a3e102d090203e4ab6039779046 | 29,392 | py | Python | nas_bench/cell.py | ntienvu/TW_NAS | 72a6d3c933978663c583661eee765bc316f66572 | [
"Apache-2.0"
] | 4 | 2021-11-01T14:01:39.000Z | 2022-02-28T03:04:27.000Z | nas_bench/cell.py | ntienvu/TW_NAS | 72a6d3c933978663c583661eee765bc316f66572 | [
"Apache-2.0"
] | null | null | null | nas_bench/cell.py | ntienvu/TW_NAS | 72a6d3c933978663c583661eee765bc316f66572 | [
"Apache-2.0"
] | 2 | 2021-06-08T09:13:03.000Z | 2021-11-01T14:01:45.000Z | import sys
sys.path.insert(0,'..')
sys.path.insert(0,'../../')
import numpy as np
import copy
import random
import ot
from nasbench import api as nb101_api
#import time
from scipy.sparse.csgraph import shortest_path
from nas_201_api import NASBench201API as API
from tw_2g_v2b import TW_2G_NB201,TW_2G_NB101,TW_NASBENCH201,TW_NASBENCH101
#from tw_2g_v2b import TW_Operations_NB101, TW_InDegrees_NASBENCH,TW_OutDegrees_NASBENCH
class Cell:
def __init__(self, matrix, ops):
self.matrix = matrix
self.ops = ops
self.get_infor()
def get_infor(self):
self.INPUT = 'input'
self.OUTPUT = 'output'
self.CONV3X3 = 'conv3x3-bn-relu'
self.CONV1X1 = 'conv1x1-bn-relu'
self.MAXPOOL3X3 = 'maxpool3x3'
self.OPS = [self.CONV3X3, self.CONV1X1, self.MAXPOOL3X3]
self.OPS_2Gram=[]
self.NUM_VERTICES = 7
self.OP_SPOTS = self.NUM_VERTICES - 2
self.MAX_EDGES = 9
def serialize(self):
return {
'matrix': self.matrix,
'ops': self.ops
}
def modelspec(self):
return nb101_api.ModelSpec(matrix=self.matrix, ops=self.ops)
@classmethod
def random_cell(cls, nasbench):
"""
From the NASBench repository
https://github.com/google-research/nasbench
"""
INPUT = 'input'
OUTPUT = 'output'
CONV3X3 = 'conv3x3-bn-relu'
CONV1X1 = 'conv1x1-bn-relu'
MAXPOOL3X3 = 'maxpool3x3'
OPS = [CONV3X3, CONV1X1, MAXPOOL3X3]
#OPS_2Gram=[]
NUM_VERTICES = 7
#OP_SPOTS = NUM_VERTICES - 2
#MAX_EDGES = 9
while True:
matrix = np.random.choice(
[0, 1], size=(NUM_VERTICES, NUM_VERTICES))
matrix = np.triu(matrix, 1)
ops = np.random.choice(OPS, size=NUM_VERTICES).tolist()
ops[0] = INPUT
ops[-1] = OUTPUT
spec = nb101_api.ModelSpec(matrix=matrix, ops=ops)
if nasbench.is_valid(spec):
return {
'matrix': matrix,
'ops': ops
}
def get_val_loss(self, nasbench, deterministic=1, patience=50):
if not deterministic:
# output one of the three validation accuracies at random
return (100*(1 - nasbench.query(nb101_api.ModelSpec(matrix=self.matrix, ops=self.ops))['validation_accuracy']))
else:
# query the api until we see all three accuracies, then average them
# a few architectures only have two accuracies, so we use patience to avoid an infinite loop
accs = []
while len(accs) < 3 and patience > 0:
patience -= 1
acc = nasbench.query(nb101_api.ModelSpec(matrix=self.matrix, ops=self.ops))['validation_accuracy']
if acc not in accs:
accs.append(acc)
return round(100*(1-np.mean(accs)), 3)
def get_test_loss(self, nasbench, patience=50):
"""
query the api until we see all three accuracies, then average them
a few architectures only have two accuracies, so we use patience to avoid an infinite loop
"""
accs = []
while len(accs) < 3 and patience > 0:
patience -= 1
acc = nasbench.query(nb101_api.ModelSpec(matrix=self.matrix, ops=self.ops))['test_accuracy']
if acc not in accs:
accs.append(acc)
return round(100*(1-np.mean(accs)), 3)
def perturb(self, nasbench, edits=1):
"""
create new perturbed cell
inspird by https://github.com/google-research/nasbench
"""
new_matrix = copy.deepcopy(self.matrix)
new_ops = copy.deepcopy(self.ops)
for _ in range(edits):
while True:
if np.random.random() < 0.5:
for src in range(0, self.NUM_VERTICES - 1):
for dst in range(src+1, self.NUM_VERTICES):
new_matrix[src][dst] = 1 - new_matrix[src][dst]
else:
for ind in range(1, self.NUM_VERTICES - 1):
available = [op for op in self.OPS if op != new_ops[ind]]
new_ops[ind] = np.random.choice(available)
new_spec = nb101_api.ModelSpec(new_matrix, new_ops)
if nasbench.is_valid(new_spec):
break
return {
'matrix': new_matrix,
'ops': new_ops
}
def mutate(self, nasbench, mutation_rate=1.0):
"""
similar to perturb. A stochastic approach to perturbing the cell
inspird by https://github.com/google-research/nasbench
"""
while True:
new_matrix = copy.deepcopy(self.matrix)
new_ops = copy.deepcopy(self.ops)
edge_mutation_prob = mutation_rate / self.NUM_VERTICES
for src in range(0, self.NUM_VERTICES - 1):
for dst in range(src + 1, self.NUM_VERTICES):
if random.random() < edge_mutation_prob:
new_matrix[src, dst] = 1 - new_matrix[src, dst]
op_mutation_prob = mutation_rate / self.OP_SPOTS
for ind in range(1, self.OP_SPOTS + 1):
if random.random() < op_mutation_prob:
available = [o for o in self.OPS if o != new_ops[ind]]
new_ops[ind] = random.choice(available)
new_spec = nb101_api.ModelSpec(new_matrix, new_ops)
if nasbench.is_valid(new_spec):
return {
'matrix': new_matrix,
'ops': new_ops
}
def encode_cell(self):
"""
compute the "standard" encoding,
i.e. adjacency matrix + op list encoding
"""
encoding_length = (self.NUM_VERTICES ** 2 - self.NUM_VERTICES) // 2 + self.OP_SPOTS
encoding = np.zeros((encoding_length))
dic = {self.CONV1X1: 0., self.CONV3X3: 0.5, self.MAXPOOL3X3: 1.0}
n = 0
for i in range(self.NUM_VERTICES - 1):
for j in range(i+1, self.NUM_VERTICES):
encoding[n] = self.matrix[i][j]
n += 1
for i in range(1, self.NUM_VERTICES - 1):
encoding[-i] = dic[self.ops[i]]
return tuple(encoding)
def get_paths(self):
"""
return all paths from input to output
"""
paths = []
for j in range(0, self.NUM_VERTICES):
paths.append([[]]) if self.matrix[0][j] else paths.append([])
# create paths sequentially
for i in range(1, self.NUM_VERTICES - 1):
for j in range(1, self.NUM_VERTICES):
if self.matrix[i][j]:
for path in paths[i]:
paths[j].append([*path, self.ops[i]])
return paths[-1]
def get_path_indices(self):
"""
compute the index of each path
There are 3^0 + ... + 3^5 paths total.
(Paths can be length 0 to 5, and for each path, for each node, there
are three choices for the operation.)
"""
paths = self.get_paths()
mapping = {self.CONV3X3: 0, self.CONV1X1: 1, self.MAXPOOL3X3: 2}
path_indices = []
for path in paths:
index = 0
for i in range(self.NUM_VERTICES - 1):
if i == len(path):
path_indices.append(index)
break
else:
index += len(self.OPS) ** i * (mapping[path[i]] + 1)
return tuple(path_indices)
def encode_paths(self):
""" output one-hot encoding of paths """
num_paths = sum([len(self.OPS) ** i for i in range(self.OP_SPOTS + 1)])
path_indices = self.get_path_indices()
path_encoding = np.zeros(num_paths)
for index in path_indices:
path_encoding[index] = 1
return path_encoding
def path_distance(self, other):
"""
compute the distance between two architectures
by comparing their path encodings
"""
return np.sum(np.array(self.encode_paths() != np.array(other.encode_paths())))
def ot_distance(self, other):
# distance based on OTMANN distance adapted to cell-based search spaces
# see our arxiv paper for more details
MAXVAL = 10000;
MX=self.matrix
MY=other.matrix
opX=self.get_1gram_count_vector(MX,self.ops,self.OPS)
opY=self.get_1gram_count_vector(MY,other.ops,self.OPS)
Mcost = np.asarray([[0,0.2,MAXVAL],[0.2,0,MAXVAL],[MAXVAL,MAXVAL,0]])
# from Table 1 in https://arxiv.org/pdf/1802.07191.pdf
Wd=ot.emd2(opX,opY,Mcost)
return Wd
def gwot_distance(self, other):
# distance based on OTMANN distance adapted to cell-based search spaces
# see our arxiv paper for more details
row_sums = sorted(np.array(self.matrix).sum(axis=0))
col_sums = sorted(np.array(self.matrix).sum(axis=1))
other_row_sums = sorted(np.array(other.matrix).sum(axis=0))
other_col_sums = sorted(np.array(other.matrix).sum(axis=1))
row_dist = np.sum(np.abs(np.subtract(row_sums, other_row_sums)))
col_dist = np.sum(np.abs(np.subtract(col_sums, other_col_sums)))
counts = [self.ops.count(op) for op in self.OPS]
other_counts = [other.ops.count(op) for op in self.OPS]
ops_dist = np.sum(np.abs(np.subtract(counts, other_counts)))
n=self.matrix.shape[0]
p = ot.unif(n)
q = ot.unif(n)
C1=self.matrix
C2=other.matrix
C1=C1+1e-8
C2=C2+1e-8
C1 /= C1.max()
C2 /= C2.max()
#start = time.time()
#gw, log = ot.gromov.entropic_gromov_wasserstein(
#C1, C2, p, q, 'kl_loss', epsilon=1e-3, log=True, verbose=False)
gw, log = ot.gromov.gromov_wasserstein(
C1, C2, p, q, 'square_loss', log=True, verbose=False)
dist1=(row_dist + col_dist + ops_dist)/(np.sum(self.matrix)+np.sum(other.matrix))
#end = time.time()
#print(end - start)
dist2=(log['gw_dist']-0.05)/0.4
return dist1+dist2
def gw_distance(self, other):
# George Andrew D Briggs
# 0.48 - 0.08
n=self.matrix.shape[0]
p = ot.unif(n)
q = ot.unif(n)
C1=self.matrix
C2=other.matrix
C1=C1+1e-8
C2=C2+1e-8
C1 /= C1.max()
C2 /= C2.max()
#gw, log = ot.gromov.entropic_gromov_wasserstein(
#C1, C2, p, q, 'square_loss', epsilon=1e-3, log=True, verbose=False)
gw, log = ot.gromov.gromov_wasserstein(
C1, C2, p, q, 'square_loss', log=True, verbose=False)
#dist=(log['gw_dist']-0.05)/0.4
dist=(log['gw_dist'])
return dist
def get_1gram_count_vector(self,MX,ops,OPS):
tempX=np.sum(MX,axis=1)
idxRow= set(np.argwhere(tempX).ravel())
#idxRow=set(np.argwhere(tempX==0))
countX=np.sum(MX,axis=0)
idxCol= set(np.argwhere(countX).ravel())
idx= list(idxRow.union(idxCol))
myops=[ops[ii] for ii in idx]
opX = [myops.count(op) for op in OPS]
return opX
def tw_distance(self, other,lamb=0.5):
MX=self.matrix
MY=other.matrix
#Xops=self.ops[1:-1]
#Yops=other.ops[1:-1]
#MX=MX[1:-1,1:-1] # crop 7x7 to 5x5
#MY=MY[1:-1,1:-1] # crop 7x7 to 5x5
# remove empty row and empty col
opX=self.get_1gram_count_vector(MX,self.ops,self.OPS)
opY=self.get_1gram_count_vector(MY,other.ops,self.OPS)
# get layer order using shortest path
layerX=shortest_path(MX,method="D")
layerX[layerX==np.inf]=-1
layerX=layerX[0,:]
#layerXOut=layerX[:,0]
layerY=shortest_path(MY,method="D")
layerY[layerY==np.inf]=-1
layerY=layerY[0,:]
#layerYOut=layerY[:,0] #opX = [self.ops.count(op) for op in OPS]
#opY = [other.ops.count(op) for op in OPS]
return TW_NASBENCH101(MX,MY,opX,opY,layerX,layerY)
def mapping_operation(self,opsrow,opscol,OPS):
if opsrow==OPS[0]:# cov3x3
uu=1
if opsrow==OPS[1]:# cov 1x1
uu=2
if opsrow==OPS[2]:# max pooling
uu=3
if opscol==OPS[0]:# cov3x3
index=3*uu
return index
if opscol==OPS[1]:#cov 1x1
index=3*uu+1
return index
if opscol==OPS[2]:#max pooling
index=3*uu+2
return index
return -1
def count_operation_2gram(self,MX,ops):
count=[0]*12
# first three dimension 1gram
count[:3]=self.get_1gram_count_vector(MX,ops,self.OPS)
MX=MX[1:-1,1:-1] # crop 7x7 to 5x5
ops=ops[1:-1] # remove INPUT, OUTPUT
# process 9 remaining dimension
for ii in range(MX.shape[0]): # each row
for jj in range(MX.shape[1]): # each column
if MX[ii,jj]>0:
index=self.mapping_operation(ops[ii],ops[jj],self.OPS)
count[index]+=1
return count
def tw_2g_distance(self,other):
MX=self.matrix
MY=other.matrix
# remove empty row
#tempX=np.sum(MX,axis=1)
#idx= np.argwhere(tempX).ravel()
#opX=[self.ops[ii] for ii in idx]
#tempY=np.sum(MY,axis=1)
#idx= np.argwhere(tempY).ravel()
#opY=[other.ops[ii] for ii in idx]
opX = self.count_operation_2gram(MX,self.ops)
opY = self.count_operation_2gram(MY,other.ops)
layerX=shortest_path(MX,method="D")
layerX[layerX==np.inf]=-1
layerX=layerX[0,:]
#layerXOut=layerX[:,0]
layerY=shortest_path(MY,method="D")
layerY[layerY==np.inf]=-1
layerY=layerY[0,:]
#print(opX,opY)
#print(dd)
return TW_2G_NB101(MX,MY,opX,opY,layerX,layerY) # return 3 elements
dataset_nasbench201 = 'to_be_specified'
class Cell_NB201(Cell):
def set_dataset(dataset_nb201):
global dataset_nasbench201
dataset_nasbench201=dataset_nb201
print('dataset for nasbench201 is ',dataset_nasbench201)
def __init__(self, matrix, ops):
#self.dataset='cifar100'
#self.dataset='ImageNet16-120'
self.dataset=dataset_nasbench201
self.matrix = matrix
self.ops = ops
self.matrix = matrix
self.ops = ops
self.INPUT = 'input'
self.OUTPUT = 'output'
self.CONV3X3 = 'nor_conv_3x3'
self.CONV1X1 = 'nor_conv_1x1'
self.AVEPOOL3X3='avg_pool_3x3'
self.SKIPCONNECT='skip_connect'
self.NONE='none'
self.OPS = [self.CONV3X3, self.CONV1X1, self.AVEPOOL3X3,self.SKIPCONNECT,self.NONE]
self.OPS_TW = [self.CONV3X3, self.CONV1X1, self.AVEPOOL3X3,self.SKIPCONNECT]
#self.OPS = [self.CONV3X3, self.CONV1X1, self.AVEPOOL3X3,self.SKIPCONNECT]
self.OPS_2Gram=[]
self.NUM_VERTICES = 8
self.OP_SPOTS = self.NUM_VERTICES - 2
self.MAX_EDGES = 10
def serialize(self):
return {
'matrix': self.matrix,
'ops': self.ops
}
def modelspec(self):
print("not implemented")
return API.ModelSpec(matrix=self.matrix, ops=self.ops)
def Nas201_String_To_OpsMatrix(self,mystr):
tokenCell = mystr.split("+")
nOperation=8
listOperation=[0]*int(nOperation)
#listOperation = cell(length(tokenCell)*(length(tokenCell)+1)/2, 1);
curID = 0
listOperation[0] = 'input';
for ii in range(len(tokenCell)):
tmpCell = tokenCell[ii].split('|')
strimTmpCell = tmpCell[1:-1]
for jj in range(len(strimTmpCell)):
opTmpCell = strimTmpCell[jj].split('~')
curID = curID + 1;
listOperation[curID] = opTmpCell[0];
curID = curID + 1
listOperation[curID] = 'output'
adjacencyMatrix = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0]])
adjacencyMatrix=adjacencyMatrix.T
return listOperation,adjacencyMatrix
def Nas201_OpsMatrix_To_String(self,listOperation,Mat):
ss = "|" + listOperation[1] + "~0|";
ss = ss + "+|" + listOperation[2] + "~0|" + listOperation[3] + "~1|";
ss = ss + "+|" + listOperation[4] + "~0|" + listOperation[5] + "~1|" + listOperation[6] + "~2|";
return ss
def is_valid(self):
return 1
@classmethod
def random_cell(cls, nasbench):
"""
From the NASBench repository
https://github.com/google-research/nasbench
"""
INPUT = 'input'
OUTPUT = 'output'
CONV3X3 = 'nor_conv_3x3'
CONV1X1 = 'nor_conv_1x1'
AVEPOOL3X3='avg_pool_3x3'
SKIPCONNECT='skip_connect'
NONE='none'
OPS = [CONV3X3, CONV1X1, AVEPOOL3X3,SKIPCONNECT,NONE]
#OPS_2Gram=[]
NUM_VERTICES = 8
#OP_SPOTS = NUM_VERTICES - 2
#MAX_EDGES = 10
matrix = np.random.choice(
[0, 1], size=(NUM_VERTICES, NUM_VERTICES))
matrix = np.triu(matrix, 1)
ops = np.random.choice(OPS, size=NUM_VERTICES).tolist()
ops[0] = INPUT
ops[-1] = OUTPUT
return { 'matrix': matrix,
'ops': ops}
def get_val_loss(self, nasbench):
# output one of the three validation accuracies at random
#return (100*(1 - nasbench.query(api.ModelSpec(matrix=self.matrix, ops=self.ops))['validation_accuracy']))
# get index based on the matrix and operation
ss_query=self.Nas201_OpsMatrix_To_String(self.ops,self.matrix)
index = nasbench.query_index_by_arch(ss_query)
#mystr="|avg_pool_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|none~0|nor_conv_3x3~1|none~2|"
#index = nasbench.query_index_by_arch(mystr)
results = nasbench.query_by_index(index, self.dataset) # a dict of all trials for 1st net on cifar100, where the key is the seed
#results=results[888]
results=results[111]
try:
accuracy=np.round(results.get_eval('x-valid')['accuracy'],decimals=4)
except:
#print(ss_query)
accuracy=np.round(results.get_eval('ori-test')['accuracy'],decimals=4)
return 100-accuracy
def get_test_loss(self, nasbench, patience=50):
"""
query the api until we see all three accuracies, then average them
a few architectures only have two accuracies, so we use patience to avoid an infinite loop
"""
ss_query=self.Nas201_OpsMatrix_To_String(self.ops,self.matrix)
index = nasbench.query_index_by_arch(ss_query)
results = nasbench.query_by_index(index, self.dataset) # a dict of all trials for 1st net on cifar100, where the key is the seed
#results=results[888]
results=results[111]
try:
accuracy=np.round(results.get_eval('x-test')['accuracy'],decimals=4)
except:
#print(ss_query)
accuracy=np.round(results.get_eval('ori-test')['accuracy'],decimals=4)
return 100-accuracy
def perturb(self, nasbench, edits=1):
"""
create new perturbed cell
inspird by https://github.com/google-research/nasbench
"""
new_matrix = copy.deepcopy(self.matrix)
new_ops = copy.deepcopy(self.ops)
for _ in range(edits):
while True:
if np.random.random() < 0.5:
for src in range(0, self.NUM_VERTICES - 1):
for dst in range(src+1, self.NUM_VERTICES):
new_matrix[src][dst] = 1 - new_matrix[src][dst]
else:
for ind in range(1, self.NUM_VERTICES - 1):
available = [op for op in self.OPS if op != new_ops[ind]]
new_ops[ind] = np.random.choice(available)
new_spec = API.ModelSpec(new_matrix, new_ops)
if nasbench.is_valid(new_spec):
break
return {
'matrix': new_matrix,
'ops': new_ops
}
def mutate(self, nasbench, mutation_rate=1.0):
"""
similar to perturb. A stochastic approach to perturbing the cell
inspird by https://github.com/google-research/nasbench
"""
while True:
new_matrix = copy.deepcopy(self.matrix)
new_ops = copy.deepcopy(self.ops)
edge_mutation_prob = mutation_rate / self.NUM_VERTICES
for src in range(0, self.NUM_VERTICES - 1):
for dst in range(src + 1, self.NUM_VERTICES):
if random.random() < edge_mutation_prob:
new_matrix[src, dst] = 1 - new_matrix[src, dst]
op_mutation_prob = mutation_rate / self.OP_SPOTS
for ind in range(1, self.OP_SPOTS + 1):
if random.random() < op_mutation_prob:
available = [o for o in self.OPS if o != new_ops[ind]]
new_ops[ind] = random.choice(available)
return {
'matrix': new_matrix,
'ops': new_ops
}
def encode_cell(self):
"""
compute the "standard" encoding,
i.e. adjacency matrix + op list encoding
"""
encoding_length = (self.NUM_VERTICES ** 2 - self.NUM_VERTICES) // 2 + self.OP_SPOTS
encoding = np.zeros((encoding_length))
dic = {self.CONV3X3: 0, self.CONV1X1: 1, self.AVEPOOL3X3: 2, self.SKIPCONNECT:3, self.NONE:4}
n = 0
for i in range(self.NUM_VERTICES - 1):
for j in range(i+1, self.NUM_VERTICES):
encoding[n] = self.matrix[i][j]
n += 1
for i in range(1, self.NUM_VERTICES - 1):
encoding[-i] = dic[self.ops[i]]
return tuple(encoding)
def get_paths(self):
"""
return all paths from input to output
"""
paths = []
for j in range(0, self.NUM_VERTICES):
paths.append([[]]) if self.matrix[0][j] else paths.append([])
# create paths sequentially
for i in range(1, self.NUM_VERTICES - 1):
for j in range(1, self.NUM_VERTICES):
if self.matrix[i][j]:
for path in paths[i]:
paths[j].append([*path, self.ops[i]])
return paths[-1]
def get_path_indices(self):
"""
compute the index of each path
There are 3^0 + ... + 3^5 paths total.
(Paths can be length 0 to 5, and for each path, for each node, there
are three choices for the operation.)
"""
paths = self.get_paths()
mapping = {self.CONV3X3: 0, self.CONV1X1: 1, self.AVEPOOL3X3: 2, self.SKIPCONNECT:3,self.NONE:4}
path_indices = []
for path in paths:
index = 0
for i in range(self.NUM_VERTICES - 1):
if i == len(path):
path_indices.append(index)
break
else:
index += len(self.OPS) ** i * (mapping[path[i]] + 1)
return tuple(path_indices)
def encode_paths(self):
""" output one-hot encoding of paths """
num_paths = sum([len(self.OPS) ** i for i in range(self.OP_SPOTS + 1)])
path_indices = self.get_path_indices()
path_encoding = np.zeros(num_paths)
try:
for index in path_indices:
path_encoding[index] = 1
except:
print("bug")
for index in path_indices:
path_encoding[index] = 1
return path_encoding
def path_distance(self, other):
"""
compute the distance between two architectures
by comparing their path encodings
"""
return np.sum(np.array(self.encode_paths() != np.array(other.encode_paths())))
def edit_distance(self, other):
return super(Cell_NB201, self).edit_distance(other)
def nasbot_distance(self, other):
return super(Cell_NB201, self).nasbot_distance(other)
def ot_distance(self, other):
# distance based on OTMANN distance adapted to cell-based search spaces
# see our arxiv paper for more details
MAXVAL = 10000;
MX=self.matrix
MY=other.matrix
opX=super(Cell_NB201, self).get_1gram_count_vector(MX,self.ops,self.OPS_TW)
opY=super(Cell_NB201, self).get_1gram_count_vector(MY,other.ops,self.OPS_TW)
Mcost = np.asarray([[0,0.2,MAXVAL],[0.2,0,MAXVAL],[MAXVAL,MAXVAL,0]])
# from Table 1 in https://arxiv.org/pdf/1802.07191.pdf
Wd=ot.emd2(opX,opY,Mcost)
return Wd
def gw_distance(self, other):
return super(Cell_NB201, self).gw_distance(other)
#def get_1gram_count_vector(self,MX,ops):
#return super(Cell_NB201, self).get_1gram_count_vector(MX,ops)
def tw_distance(self, other,lamb=0.5):
MX=self.matrix
MY=other.matrix
#Xops=self.ops[1:-1]
#Yops=other.ops[1:-1]
#MX=MX[1:-1,1:-1] # crop 7x7 to 5x5
#MY=MY[1:-1,1:-1] # crop 7x7 to 5x5
# remove empty row and empty col
opX=self.get_1gram_count_vector(MX,self.ops,self.OPS_TW)
opY=self.get_1gram_count_vector(MY,other.ops,self.OPS_TW)
#opX = [self.ops.count(op) for op in OPS]
#opY = [other.ops.count(op) for op in OPS]
layerX=shortest_path(MX,method="D")
layerX[layerX==np.inf]=-1
layerX=layerX[0,:]
#layerXOut=layerX[:,0]
layerY=shortest_path(MY,method="D")
layerY[layerY==np.inf]=-1
layerY=layerY[0,:]
return TW_NASBENCH201(MX, MY, opX, opY, layerX,layerY)
def mapping_operation(self,opsrow,opscol,OPS):
if opsrow==OPS[0]:# cov3x3
uu=1
if opsrow==OPS[1]:# cov 1x1
uu=2
if opsrow==OPS[2]:# ave pooling
uu=3
if opsrow==OPS[3]:# skip connect
uu=4
if opscol==OPS[0]:# cov3x3
index=4*uu
return index
if opscol==OPS[1]:#cov 1x1
index=4*uu+1
return index
if opscol==OPS[2]:#ave pooling
index=4*uu+2
return index
if opscol==OPS[3]:#skip connect
index=4*uu+3
return index
return -1
def count_operation_2gram(self,MX,ops):
count=[0]*20
# first three dimension 1gram
count[:4]=self.get_1gram_count_vector(MX,ops,self.OPS_TW) # remove None
MX=MX[1:-1,1:-1] # crop 7x7 to 5x5
ops=ops[1:-1] # remove INPUT, OUTPUT
idx=[ii for ii, val in enumerate(ops) if val in self.OPS_TW]
temp=MX[idx,:]
MX=temp[:,idx]
ops = [ops[ii] for ii in idx]
#ops=ops[idx]
# process 9 remaining dimension
for ii in range(MX.shape[0]): # each row
for jj in range(MX.shape[1]): # each column
if MX[ii,jj]>0:
index=self.mapping_operation(ops[ii],ops[jj],self.OPS_TW)
count[index]+=1
return count
# def tw_2gram_distance(self,other,lamb=0.5):
# return super(Cell_NB201, self).tw_2gram_distance(other,lamb)
def tw_2g_distance(self,other):
MX=self.matrix
MY=other.matrix
# remove empty row
#tempX=np.sum(MX,axis=1)
#idx= np.argwhere(tempX).ravel()
#opX=[self.ops[ii] for ii in idx]
#tempY=np.sum(MY,axis=1)
#idx= np.argwhere(tempY).ravel()
#opY=[other.ops[ii] for ii in idx]
opX = self.count_operation_2gram(MX,self.ops)
opY = self.count_operation_2gram(MY,other.ops)
layerX=shortest_path(MX,method="D")
layerX[layerX==np.inf]=-1
layerX=layerX[0,:]
#layerXOut=layerX[:,0]
layerY=shortest_path(MY,method="D")
layerY[layerY==np.inf]=-1
layerY=layerY[0,:]
return TW_2G_NB201(MX,MY,opX,opY,layerX,layerY) # return 3 elements
| 33.629291 | 136 | 0.547462 | 3,829 | 29,392 | 4.087229 | 0.095586 | 0.028179 | 0.007476 | 0.008179 | 0.835911 | 0.815911 | 0.797508 | 0.776997 | 0.737061 | 0.719233 | 0 | 0.043028 | 0.335806 | 29,392 | 874 | 137 | 33.629291 | 0.758631 | 0.181036 | 0 | 0.696629 | 0 | 0 | 0.023957 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.088015 | false | 0 | 0.016854 | 0.013109 | 0.20412 | 0.005618 | 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 |
04ab597c11ecf11c46bef12dc7c165f7ca2b4157 | 277 | py | Python | TradovatePy/config.py | antonio-hickey/TradovatePy | fe7a917a49d291bd42585f4cd0268fe223923ecf | [
"MIT"
] | 2 | 2022-01-17T03:20:41.000Z | 2022-03-23T02:21:52.000Z | TradovatePy/config.py | antonio-hickey/TradovatePy | fe7a917a49d291bd42585f4cd0268fe223923ecf | [
"MIT"
] | null | null | null | TradovatePy/config.py | antonio-hickey/TradovatePy | fe7a917a49d291bd42585f4cd0268fe223923ecf | [
"MIT"
] | null | null | null | URLs = {
"DEMO": "https://demo.tradovateapi.com/v1",
"LIVE": "https://live.tradovateapi.com/v1",
"MD": "wss://md.tradovateapi.com/v1/websocket",
"WS_DEMO": "wss://demo.tradovateapi.com/v1/websocket",
"WS_LIVE": "wss://live.tradovateapi.com/v1/websocket",
}
| 34.625 | 58 | 0.638989 | 36 | 277 | 4.861111 | 0.305556 | 0.428571 | 0.485714 | 0.445714 | 0.32 | 0 | 0 | 0 | 0 | 0 | 0 | 0.020576 | 0.122744 | 277 | 7 | 59 | 39.571429 | 0.699588 | 0 | 0 | 0 | 0 | 0 | 0.743682 | 0.425993 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
04b5594286e21ce09c40aadef5825b36606c4165 | 27 | py | Python | Author/tests/__init__.py | CMPUT404-Fa21-Organization/CMPUT404-Project-Social-Distribution | 63c0ba2a03f0b462e3673ce7a4bf6bae7999440c | [
"Apache-2.0"
] | 3 | 2021-12-11T13:43:56.000Z | 2022-03-31T02:36:05.000Z | Author/tests/__init__.py | CMPUT404-Fa21-Organization/CMPUT404-Project-Social-Distribution | 63c0ba2a03f0b462e3673ce7a4bf6bae7999440c | [
"Apache-2.0"
] | 9 | 2021-10-01T22:46:57.000Z | 2021-12-16T18:01:31.000Z | Author/tests/__init__.py | CMPUT404-Fa21-Organization/CMPUT404-Project-Social-Distribution | 63c0ba2a03f0b462e3673ce7a4bf6bae7999440c | [
"Apache-2.0"
] | 2 | 2021-12-16T16:37:10.000Z | 2021-12-16T20:30:12.000Z | from .test_author import *
| 13.5 | 26 | 0.777778 | 4 | 27 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148148 | 27 | 1 | 27 | 27 | 0.869565 | 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 |
6d35d2449e02d409eeff6e3e01beb2778ffe0222 | 120 | py | Python | pybankreader/formats/__init__.py | baldman/pybankreader | 3a96d6c89e408a315ccb4f7e7a3c63325c347d2d | [
"BSD-3-Clause"
] | 1 | 2022-03-29T14:09:41.000Z | 2022-03-29T14:09:41.000Z | pybankreader/formats/__init__.py | baldman/pybankreader | 3a96d6c89e408a315ccb4f7e7a3c63325c347d2d | [
"BSD-3-Clause"
] | null | null | null | pybankreader/formats/__init__.py | baldman/pybankreader | 3a96d6c89e408a315ccb4f7e7a3c63325c347d2d | [
"BSD-3-Clause"
] | null | null | null | from .bbf.reports import AdvmulReport as BBFAdvmul # NOQA
from .gpc.reports import AccountReport as GPCAccount # NOQA
| 40 | 60 | 0.8 | 16 | 120 | 6 | 0.6875 | 0.270833 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 120 | 2 | 61 | 60 | 0.941176 | 0.075 | 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 |
6d602b53f1d0aa49db96d15dfc23e57df4676a19 | 128 | py | Python | base/model/__init__.py | stevenchen521/quant_ml | f7d5efc49c934724f97fcafacc560f4a35b24551 | [
"MIT"
] | 5 | 2019-02-14T03:12:22.000Z | 2022-01-24T18:43:07.000Z | base/model/__init__.py | stevenchen521/quant_ml | f7d5efc49c934724f97fcafacc560f4a35b24551 | [
"MIT"
] | null | null | null | base/model/__init__.py | stevenchen521/quant_ml | f7d5efc49c934724f97fcafacc560f4a35b24551 | [
"MIT"
] | 2 | 2019-11-13T18:56:13.000Z | 2021-12-31T01:25:22.000Z | import mongoengine
mongoengine.connect(db="doricapital", host="localhost")
# mongoengine.connect(db="Mongo", host="doricapital") | 42.666667 | 55 | 0.789063 | 14 | 128 | 7.214286 | 0.571429 | 0.356436 | 0.39604 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.046875 | 128 | 3 | 56 | 42.666667 | 0.827869 | 0.398438 | 0 | 0 | 0 | 0 | 0.263158 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
eda07dec9154f18254f30d2fcac734310c5a71db | 23,175 | py | Python | alembic/versions/6a16d2aa6b6a_add_huawei_4g_managedobjects.py | bodastage/bts-database | 96df7915621dd46daf55016eedf5cfc84dd0e3a2 | [
"Apache-2.0"
] | 1 | 2019-08-30T01:20:14.000Z | 2019-08-30T01:20:14.000Z | alembic/versions/6a16d2aa6b6a_add_huawei_4g_managedobjects.py | bodastage/bts-database | 96df7915621dd46daf55016eedf5cfc84dd0e3a2 | [
"Apache-2.0"
] | 1 | 2018-05-30T09:29:24.000Z | 2018-05-30T10:04:37.000Z | alembic/versions/6a16d2aa6b6a_add_huawei_4g_managedobjects.py | bodastage/bts-database | 96df7915621dd46daf55016eedf5cfc84dd0e3a2 | [
"Apache-2.0"
] | 3 | 2018-03-10T23:29:30.000Z | 2019-02-19T22:11:09.000Z | """Add Huawei 4G managedobjects
Revision ID: 6a16d2aa6b6a
Revises: 805d9d91ef77
Create Date: 2018-02-13 01:44:09.030000
"""
from alembic import op
import sqlalchemy as sa
import datetime
# revision identifiers, used by Alembic.
revision = '6a16d2aa6b6a'
down_revision = '805d9d91ef77'
branch_labels = None
depends_on = None
def upgrade():
managedobjects = sa.sql.table(
'managedobjects',
sa.Column('pk', sa.Integer, sa.Sequence('seq_managedobjects_pk', ), primary_key=True, nullable=False),
sa.Column('name', sa.String(50), nullable=False),
sa.Column('notes', sa.Text),
sa.Column('label', sa.String(200)),
sa.Column('parent_pk', sa.Integer),
sa.Column('tech_pk', sa.Integer),
sa.Column('vendor_pk', sa.Integer),
sa.Column('modified_by', sa.Integer),
sa.Column('added_by', sa.Integer),
sa.Column('date_added', sa.TIMESTAMP, default=datetime.datetime.utcnow, onupdate=datetime.datetime.utcnow),
sa.Column('date_modified', sa.TIMESTAMP, default=datetime.datetime.utcnow)
)
op.bulk_insert(managedobjects, [
{'name': 'ALGODEFAULTPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ANR', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'APPCERT', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'BASEBANDEQM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'BASEBANDEQMBOARDREF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'BCCHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'BFMIMOADAPTIVEPARACFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0,
'added_by': 0},
{'name': 'CAMGTCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLACBAR', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLACCESS', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLALGOSWITCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLBF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLBFMIMOPARACFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLCHPWRCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLCSPCPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLCOMPALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLICIC', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLICICMCPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLPCPDCCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLPCPDSCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLPCPDSCHPA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLPCPHICH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDLSCHALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDRXPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDSS', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLDYNACBARALGOPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLHOPARACFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLIDPRDUPT', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLLOWPOWER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLMBMSCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLMCPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLMIMOPARACFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLMLB', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLMLBHO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLMRO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLNOACCESSALMPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLOP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLPCALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLPDCCHALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLPUCCHALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLRACHALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLRACTHD', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLRESEL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLRESELGERAN', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLRESELUTRAN', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLRFSHUTDOWN', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLRICALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLSEL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLSERVICEDIFFCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLSHUTDOWN', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLSIMAP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLSTANDARDQCI', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLULCOMPALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLULICIC', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLULICICMCPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLULPCCOMM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLULPCDEDIC', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CELLULSCHALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CERTCHKTSK', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CERTDEPLOY', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CERTMK', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CERTREQ', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CNOPERATOR', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CNOPERATORHOCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CNOPERATORIPPATH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CNOPERATORSTANDARDQCI', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0,
'added_by': 0},
{'name': 'CNOPERATORTA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'COUNTERCHECKPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CPBEARER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CQIADAPTIVECFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CRLPOLICY', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CSFALLBACKBLINDHOCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CSFALLBACKHO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CSFALLBACKPOLICYCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'CSPCALGOPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'DEVIP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'DHCPRELAYSWITCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'DIFPRI', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'DISTBASEDHO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'DRX', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'DRXPARAGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'DSCPMAP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'EMC', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBALGOSWITCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBAUTOPOWEROFF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBCIPHERCAP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBCONNSTATETIMER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBFUNCTION', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBINTEGRITYCAP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBMLB', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBPATH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ENODEBSHARINGMODE', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'EPGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'ETHPORT', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'EUCELLSECTOREQM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'EUCOSCHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'EUTRANEXTERNALCELL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'EUTRANINTRAFREQNCELL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'FDDRESMODE', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'filefooter', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GERANEXTERNALCELL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GERANINTERFCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GERANNCELL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GERANNFREQGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GERANNFREQGROUPARFCN', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GLOBALPROCSWITCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GTPU', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'GTRANSPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'HOMEASCOMM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'IKECFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTERFREQHOGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTERRATCELLSHUTDOWN', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTERRATHOCDMA1XRTTGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0,
'added_by': 0},
{'name': 'INTERRATHOCDMAHRPDGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0,
'added_by': 0},
{'name': 'INTERRATHOCOMM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTERRATHOCOMMGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTERRATHOGERANGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTERRATHOUTRANGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTERRATPOLICYCFGGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0,
'added_by': 0},
{'name': 'INTRAFREQHOGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'INTRARATHOCOMM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'IPGUARD', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'IPPATH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'IPRT', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'LOCATION', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'MIMOADAPTIVEPARACFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'MMEFEATURECFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'MRO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'NE', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'NODE', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'OMCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'PCCHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'PDCPROHCPARA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'PDSCHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'PHICHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'PUCCHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'PUSCHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'PUSCHPARAM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'RACHCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'RET', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'RETDEVICEDATA', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'RETSUBUNIT', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'RLCPDCPPARAGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'RRCCONNSTATETIMER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'RRUJOINTCALPARACFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'S1', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'S1INTERFACE', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'S1REESTTIMER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SCTPHOST', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SCTPHOSTREF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SCTPLNK', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SCTPPEER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SCTPPEERREF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SECTOR', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SECTORANTENNAREF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SECTOREQM', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SECTOREQMANTENNAREF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SERVICEIFDLEARFCNGRP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SERVICEIFHOCFGGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SERVICEIRHOCFGGROUP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SIMULOAD', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SRSADAPTIVECFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SRSCFG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'STANDARDQCI', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'SUBSESSION_NE', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TACALG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TCEIPMAPPING', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TCPACKCTRLALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TCPACKLIMITALG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TCPMSSCTRL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TDDFRAMEOFFSET', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TDDRESMODESWITCH', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TIMEALIGNMENTTIMER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TOLCALG', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TPEALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TRUSTCERT', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'TYPDRBBSR', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'UDT', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'UDTPARAGRP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'UETIMERCONST', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'USERPLANEHOST', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'USERPLANEHOSTREF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'USERPLANEPEER', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'USERPLANEPEERREF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'UTRANEXTERNALCELL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'UTRANNCELL', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'UTRANNFREQ', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'VLANMAP', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'VQMALGO', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'VRF', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'X2', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'X2BLACKWHITELIST', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
{'name': 'X2INTERFACE', 'parent_pk': 0, 'vendor_pk': 2, 'tech_pk': 3, 'modified_by': 0, 'added_by': 0},
])
def downgrade():
op.execute("""DELETE FROM managedobjects WHERE vendor_pk = {0} AND tech_pk = {1}""".format(2, 3))
| 96.161826 | 120 | 0.578943 | 3,460 | 23,175 | 3.592486 | 0.078324 | 0.093644 | 0.140467 | 0.234111 | 0.763234 | 0.755591 | 0.749155 | 0.749155 | 0.749155 | 0.749155 | 0 | 0.054391 | 0.1789 | 23,175 | 240 | 121 | 96.5625 | 0.598823 | 0.006775 | 0 | 0.022222 | 0 | 0 | 0.513255 | 0.005737 | 0 | 0 | 0 | 0 | 0 | 1 | 0.008889 | false | 0 | 0.013333 | 0 | 0.022222 | 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 |
edf4523f8f3aecff7f3e36d6a0922689ade1ff3a | 167 | py | Python | Chapter 10/10-1.py | lzhang1/BeginningPygame | c239925041a6fa361386f65316ef4bea12c3b482 | [
"MIT"
] | 43 | 2015-09-20T02:05:48.000Z | 2022-03-01T22:00:43.000Z | Chapter 10/10-1.py | lzhang1/BeginningPygame | c239925041a6fa361386f65316ef4bea12c3b482 | [
"MIT"
] | null | null | null | Chapter 10/10-1.py | lzhang1/BeginningPygame | c239925041a6fa361386f65316ef4bea12c3b482 | [
"MIT"
] | 40 | 2015-05-19T06:51:13.000Z | 2022-03-27T18:11:16.000Z | def stereo_pan(x_coord, screen_width):
right_volume = float(x_coord) / screen_width
left_volume = 1.0 - right_volume
return (left_volume, right_volume)
| 23.857143 | 49 | 0.730539 | 25 | 167 | 4.48 | 0.56 | 0.294643 | 0.214286 | 0.303571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014706 | 0.185629 | 167 | 6 | 50 | 27.833333 | 0.808824 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
edfa7d04143dd4c7c060f92b8cca61144c0d8ed3 | 16,960 | py | Python | tests/chemplot_visualize_plot_unittest.py | mcsorkun/ChemPlot | bb5002558c47b15a4d501f839a6de0de9a44586d | [
"BSD-3-Clause"
] | 32 | 2021-07-14T16:31:42.000Z | 2022-03-30T09:19:10.000Z | tests/chemplot_visualize_plot_unittest.py | ergroup/ChemPlot | d5d439ef877f6b1fe6b8245efe7c69a4c206bb56 | [
"BSD-3-Clause"
] | 1 | 2021-12-07T17:06:00.000Z | 2022-01-14T03:26:45.000Z | tests/chemplot_visualize_plot_unittest.py | ergroup/ChemPlot | d5d439ef877f6b1fe6b8245efe7c69a4c206bb56 | [
"BSD-3-Clause"
] | 7 | 2021-07-15T14:02:39.000Z | 2022-03-31T15:44:49.000Z | import unittest
from unittest.mock import patch
from chemplot import Plotter
import pandas as pd
import numpy as np
import os
from scipy import stats
from matplotlib import pyplot
from io import StringIO
class TestVisualizePlot(unittest.TestCase):
@classmethod
def setUpClass(cls):
file_LOGS = os.path.join('test_data', 'R_1291_LOGS.csv')
cls.data_LOGS = pd.read_csv(file_LOGS)
file_BBBP = os.path.join('test_data', 'C_2039_BBBP_2.csv')
cls.data_BBBP = pd.read_csv(file_BBBP)
cls.plotter_pca_LOGS = Plotter.from_smiles(cls.data_LOGS["smiles"], target=cls.data_LOGS["target"], target_type="R", sim_type="tailored")
cls.plotter_pca_BBBP = Plotter.from_smiles(cls.data_BBBP["smiles"], target=cls.data_BBBP["target"], target_type="C", sim_type="tailored")
cls.plotter_pca_LOGS.pca()
cls.plotter_pca_BBBP.pca()
def test_default_kind_none(self):
"""
1. Test checks if default kind is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.get_label(), "scatter")
pyplot.close()
def test_default_kind(self):
"""
2. Test checks if default kind is assigned with anytext
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='anytext', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.get_label(), "scatter")
pyplot.close()
@patch('sys.stdout', new_callable=StringIO)
def test_INFO_kind_with_anytext(self, mock_stdout):
"""
3. Test checks if user is informed about kind
"""
self.plotter_pca_LOGS.visualize_plot(kind='anytext', size=20, remove_outliers=False, is_colored=True, colorbar=False)
assert str('kind indicates which type of plot must be visualized. Currently supported static visualization are:\n'+
'-scatter plot (scatter)\n'+
'-hexagon plot (hex)\n'+
'-kernel density estimation plot (kde)\n'+
'Please input one between scatter, hex or kde for parameter kind.\n'+
'As default scatter has been taken.') in mock_stdout.getvalue()
pyplot.close()
def test_default_is_colored(self):
"""
4. Test checks if default is_colored is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, colorbar=False)
self.assertTrue(len(result.collections)>1)
pyplot.close()
def test_default_remove_outliers(self):
"""
5. Test checks if default remove_outliers is assigned
"""
self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, is_colored=True, colorbar=False)
x = self.plotter_pca_LOGS._Plotter__df_2_components.columns[0]
y = self.plotter_pca_LOGS._Plotter__df_2_components.columns[1]
self.assertTrue(self.plotter_pca_LOGS.df_plot_xy.equals(self.plotter_pca_LOGS._Plotter__df_2_components[[x,y]]))
pyplot.close()
def test_default_size(self):
"""
6. Test checks if default size is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.figure.get_size_inches()[0], 20)
self.assertEqual(result.figure.get_size_inches()[1], 20)
pyplot.close()
def test_kind_scatter(self):
"""
7. Test checks if kind is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.get_label(), "scatter")
pyplot.close()
def test_is_colored_true_scatter(self):
"""
8. Test checks if is_colored is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertTrue(len(result.collections)>1)
pyplot.close()
def test_is_colored_false_scatter(self):
"""
9. Test checks if is_colored is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=False, colorbar=False)
self.assertTrue(len(result.collections) == 1)
pyplot.close()
def test_remove_outliers_false_scatter(self):
"""
10. Test checks if remove_outliers is assigned
"""
self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=False)
x = self.plotter_pca_LOGS._Plotter__df_2_components.columns[0]
y = self.plotter_pca_LOGS._Plotter__df_2_components.columns[1]
self.assertTrue(self.plotter_pca_LOGS.df_plot_xy.equals(self.plotter_pca_LOGS._Plotter__df_2_components[[x,y]]))
pyplot.close()
def test_remove_outliers_true_scatter(self):
"""
11. Test checks if remove_outliers is assigned
"""
self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=True, is_colored=True, colorbar=False)
df_no_outliers = self.plotter_pca_LOGS._Plotter__df_2_components.copy()
x = self.plotter_pca_LOGS._Plotter__df_2_components.columns[0]
y = self.plotter_pca_LOGS._Plotter__df_2_components.columns[1]
df_no_outliers = df_no_outliers[[x,y]]
df_no_outliers= df_no_outliers[(np.abs(stats.zscore(df_no_outliers))<3).all(axis=1)]
self.assertTrue(self.plotter_pca_LOGS.df_plot_xy.equals(df_no_outliers))
pyplot.close()
def test_size_scatter(self):
"""
12. Test checks if size is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.figure.get_size_inches()[0], 20)
self.assertEqual(result.figure.get_size_inches()[1], 20)
pyplot.close()
def test_kind_hex(self):
"""
13. Test checks if kind is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='hex', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.get_label(), "hex")
pyplot.close()
def test_remove_outliers_false_hex(self):
"""
14. Test checks if remove_outliers is assigned
"""
self.plotter_pca_LOGS.visualize_plot(kind='hex', size=20, remove_outliers=False, is_colored=True, colorbar=False)
x = self.plotter_pca_LOGS._Plotter__df_2_components.columns[0]
y = self.plotter_pca_LOGS._Plotter__df_2_components.columns[1]
self.assertTrue(self.plotter_pca_LOGS.df_plot_xy.equals(self.plotter_pca_LOGS._Plotter__df_2_components[[x,y]]))
pyplot.close()
def test_remove_outliers_true_hex(self):
"""
15. Test checks if remove_outliers is assigned
"""
self.plotter_pca_LOGS.visualize_plot(kind='hex', size=20, remove_outliers=True, is_colored=True, colorbar=False)
df_no_outliers = self.plotter_pca_LOGS._Plotter__df_2_components.copy()
x = self.plotter_pca_LOGS._Plotter__df_2_components.columns[0]
y = self.plotter_pca_LOGS._Plotter__df_2_components.columns[1]
df_no_outliers = df_no_outliers[[x,y]]
df_no_outliers= df_no_outliers[(np.abs(stats.zscore(df_no_outliers))<3).all(axis=1)]
self.assertTrue(self.plotter_pca_LOGS.df_plot_xy.equals(df_no_outliers))
pyplot.close()
def test_size_hex(self):
"""
16. Test checks if size is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='hex', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.figure.get_size_inches()[0], 20)
self.assertEqual(result.figure.get_size_inches()[1], 20)
pyplot.close()
def test_kind_kde(self):
"""
17. Test checks if kind is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='kde', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.get_label(), "kde")
pyplot.close()
def test_remove_outliers_false_kde(self):
"""
18. Test checks if remove_outliers is assigned
"""
self.plotter_pca_LOGS.visualize_plot(kind='kde', size=20, remove_outliers=False, is_colored=True, colorbar=False)
x = self.plotter_pca_LOGS._Plotter__df_2_components.columns[0]
y = self.plotter_pca_LOGS._Plotter__df_2_components.columns[1]
self.assertTrue(self.plotter_pca_LOGS.df_plot_xy.equals(self.plotter_pca_LOGS._Plotter__df_2_components[[x,y]]))
pyplot.close()
def test_remove_outliers_true_kde(self):
"""
19. Test checks if remove_outliers is assigned
"""
self.plotter_pca_LOGS.visualize_plot(kind='kde', size=20, remove_outliers=True, is_colored=True, colorbar=False)
df_no_outliers = self.plotter_pca_LOGS._Plotter__df_2_components.copy()
x = self.plotter_pca_LOGS._Plotter__df_2_components.columns[0]
y = self.plotter_pca_LOGS._Plotter__df_2_components.columns[1]
df_no_outliers = df_no_outliers[[x,y]]
df_no_outliers= df_no_outliers[(np.abs(stats.zscore(df_no_outliers))<3).all(axis=1)]
self.assertTrue(self.plotter_pca_LOGS.df_plot_xy.equals(df_no_outliers))
pyplot.close()
def test_size_kde(self):
"""
20. Test checks if size is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='kde', size=20, remove_outliers=False, is_colored=True, colorbar=False)
self.assertEqual(result.figure.get_size_inches()[0], 20)
self.assertEqual(result.figure.get_size_inches()[1], 20)
pyplot.close()
def test_default_colorbar(self):
"""
21. Test checks if default value of colorbar is assigned
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True)
self.assertNotIsInstance(result.get_legend(), type(None))
self.assertEqual(len(result.figure.axes), 1)
pyplot.close()
def test_colorbar_R_remove_legend(self):
"""
22. Test checks if colorbar is assigned when target type is R and therefore legend removed
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=True)
self.assertIsInstance(result.get_legend(), type(None))
pyplot.close()
def test_colorbar_C_keep_legend(self):
"""
23. Test checks if colorbar is ignored when target type is C and therefore legend kept
"""
result = self.plotter_pca_BBBP.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=True)
self.assertNotIsInstance(result.get_legend(), type(None))
pyplot.close()
def test_colorbar_R_add_colorbar(self):
"""
24. Test checks if colorbar is assigned when target type is R
"""
result = self.plotter_pca_LOGS.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=True)
self.assertTrue(len(result.figure.axes)>=1)
pyplot.close()
def test_colorbar_C_ignore_colorbar(self):
"""
25. Test checks if colorbar is ignored when target type is C
"""
result = self.plotter_pca_BBBP.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=True)
self.assertTrue(len(result.figure.axes)==1)
pyplot.close()
def test_default_title(self):
"""
26. Test checks if the default title is assigned
"""
result = self.plotter_pca_BBBP.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=True)
self.assertEqual(result.get_title(), self.plotter_pca_BBBP._Plotter__plot_title)
pyplot.close()
def test_assigned_title(self):
"""
27. Test checks if title is assigned
"""
result = self.plotter_pca_BBBP.visualize_plot(kind='scatter', size=20, remove_outliers=False, is_colored=True, colorbar=True, title="title")
self.assertTrue(result.get_title()=="title")
pyplot.close()
@patch('sys.stdout', new_callable=StringIO)
def test_INFO_call_without_reduction(self, mock_stdout):
"""
28. Test checks if user is informed a plot cannot be created without reducing the dimensions first
"""
file_SAMPL = os.path.join('test_data', 'R_642_SAMPL.csv')
data_SAMPL = pd.read_csv(file_SAMPL)
cp = Plotter.from_smiles(data_SAMPL["smiles"], target=data_SAMPL["target"], target_type="R", sim_type="tailored")
result = cp.visualize_plot()
assert result is None
assert 'Reduce the dimensions of your molecules before creating a plot.' in mock_stdout.getvalue()
def test_default_filename_scatter(self):
"""
29. Test checks if the default value of filename is assigned with scatter
"""
try:
os.remove("scatter_test.png")
except FileNotFoundError:
pass
expected = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.plotter_pca_BBBP.visualize_plot(kind='scatter')
result = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.assertEqual(expected, result)
pyplot.close()
def test_filename_scatter(self):
"""
30. Test checks if the value of filename is assigned with scatter
"""
try:
os.remove("scatter_test.png")
except FileNotFoundError:
pass
expected = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.plotter_pca_BBBP.visualize_plot(kind='scatter', filename="scatter_test.png")
result = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.assertEqual(expected, result - 1)
os.remove("scatter_test.png")
pyplot.close()
def test_default_filename_hex(self):
"""
31. Test checks if the default value of filename is assigned with hex
"""
try:
os.remove("hex_test.png")
except FileNotFoundError:
pass
expected = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.plotter_pca_BBBP.visualize_plot(kind='hex')
result = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.assertEqual(expected, result)
pyplot.close()
def test_filename_hex(self):
"""
32. Test checks if the value of filename is assigned with hex
"""
try:
os.remove("hex_test.png")
except FileNotFoundError:
pass
expected = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.plotter_pca_BBBP.visualize_plot(kind='hex', filename="hex_test.png")
result = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.assertEqual(expected, result - 1)
os.remove("hex_test.png")
pyplot.close()
def test_default_filename_kde(self):
"""
33. Test checks if the default value of filename is assigned with kde
"""
try:
os.remove("kde_test.png")
except FileNotFoundError:
pass
expected = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.plotter_pca_BBBP.visualize_plot(kind='kde')
result = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.assertEqual(expected, result)
pyplot.close()
def test_filename_kde(self):
"""
34. Test checks if the value of filename is assigned with kde
"""
try:
os.remove("kde_test.png")
except FileNotFoundError:
pass
expected = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.plotter_pca_BBBP.visualize_plot(kind='kde', filename="kde_test.png")
result = len([name for name in os.listdir('.') if os.path.isfile(name)])
self.assertEqual(expected, result - 1)
os.remove("kde_test.png")
pyplot.close()
if __name__ == '__main__':
unittest.main()
| 44.631579 | 148 | 0.653774 | 2,240 | 16,960 | 4.689286 | 0.092411 | 0.062833 | 0.082635 | 0.087395 | 0.821592 | 0.806931 | 0.791032 | 0.771135 | 0.756759 | 0.756759 | 0 | 0.015204 | 0.236026 | 16,960 | 379 | 149 | 44.74934 | 0.795477 | 0.107134 | 0 | 0.582979 | 0 | 0 | 0.062604 | 0 | 0 | 0 | 0 | 0 | 0.170213 | 1 | 0.148936 | false | 0.025532 | 0.038298 | 0 | 0.191489 | 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 |
612d7a01c79b0f454bfe10f8b7ebc46682bb874b | 58 | py | Python | arkfbp/state/app_state.py | arkfbp/arkfbp-py | 2444736462e8b4f09ae1ffe56779d9f515deb39f | [
"MIT"
] | 2 | 2020-09-11T09:26:43.000Z | 2020-12-17T07:32:38.000Z | arkfbp/state/app_state.py | arkfbp/arkfbp-py | 2444736462e8b4f09ae1ffe56779d9f515deb39f | [
"MIT"
] | 4 | 2020-12-02T03:42:38.000Z | 2020-12-14T07:56:06.000Z | arkfbp/state/app_state.py | arkfbp/arkfbp-py | 2444736462e8b4f09ae1ffe56779d9f515deb39f | [
"MIT"
] | 2 | 2020-12-08T01:11:54.000Z | 2021-01-25T04:29:15.000Z | from .base import State
class AppState(State):
pass
| 9.666667 | 23 | 0.706897 | 8 | 58 | 5.125 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.224138 | 58 | 5 | 24 | 11.6 | 0.911111 | 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 |
b628bb783b94c3cb2f0789db1db071b46283f7b7 | 124 | py | Python | packages/python/readme_metrics/__init__.py | mderazon/metrics-sdks | ea2ee94af06ee1b01a2c2ac8f69bd97d2ce1956a | [
"ISC"
] | null | null | null | packages/python/readme_metrics/__init__.py | mderazon/metrics-sdks | ea2ee94af06ee1b01a2c2ac8f69bd97d2ce1956a | [
"ISC"
] | null | null | null | packages/python/readme_metrics/__init__.py | mderazon/metrics-sdks | ea2ee94af06ee1b01a2c2ac8f69bd97d2ce1956a | [
"ISC"
] | null | null | null | from readme_metrics.MetricsApiConfig import MetricsApiConfig
from readme_metrics.MetricsMiddleware import MetricsMiddleware
| 41.333333 | 62 | 0.919355 | 12 | 124 | 9.333333 | 0.5 | 0.178571 | 0.303571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.064516 | 124 | 2 | 63 | 62 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b62e4f59e1d8bada115ab401007610bf728370d5 | 107 | py | Python | _modules/utils/cache/add_never_cache_headers/views.py | looking-for-a-job/django-examples | dfafa450668cac5c0351f6c7238b8886511229bf | [
"Unlicense"
] | null | null | null | _modules/utils/cache/add_never_cache_headers/views.py | looking-for-a-job/django-examples | dfafa450668cac5c0351f6c7238b8886511229bf | [
"Unlicense"
] | null | null | null | _modules/utils/cache/add_never_cache_headers/views.py | looking-for-a-job/django-examples | dfafa450668cac5c0351f6c7238b8886511229bf | [
"Unlicense"
] | null | null | null | from django.http import HttpResponse
def my_view(request):
return HttpResponse("return this string")
| 17.833333 | 45 | 0.775701 | 14 | 107 | 5.857143 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.149533 | 107 | 5 | 46 | 21.4 | 0.901099 | 0 | 0 | 0 | 0 | 0 | 0.168224 | 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 |
b630a931514cf715f2e798d4bc551facd74cdc79 | 40 | py | Python | rcnn/modeling/fpn/__init__.py | rs9899/Parsing-R-CNN | a0c9ed8850abe740eedf8bfc6e1577cc0aa3fc7b | [
"MIT"
] | 289 | 2018-10-25T09:42:57.000Z | 2022-03-30T08:31:50.000Z | rcnn/modeling/fpn/__init__.py | qzane/Parsing-R-CNN | 8c4d940dcd322bf7a8671f8b0faaabb3259bd384 | [
"MIT"
] | 28 | 2019-01-07T02:39:49.000Z | 2022-01-25T08:54:36.000Z | rcnn/modeling/fpn/__init__.py | qzane/Parsing-R-CNN | 8c4d940dcd322bf7a8671f8b0faaabb3259bd384 | [
"MIT"
] | 44 | 2018-12-20T07:36:46.000Z | 2022-03-16T14:30:20.000Z | from .FPN import *
from .HRFPN import *
| 13.333333 | 20 | 0.7 | 6 | 40 | 4.666667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 40 | 2 | 21 | 20 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b688896a480940b8e027f9d12bd81a451a05da85 | 44 | py | Python | authlib/specs/rfc6749/grants.py | tk193192/authlib | 4c60a628f64c6d385a06ea55e416092726b94d07 | [
"BSD-3-Clause"
] | 2 | 2021-04-26T18:17:37.000Z | 2021-04-28T21:39:45.000Z | authlib/specs/rfc6749/grants.py | tk193192/authlib | 4c60a628f64c6d385a06ea55e416092726b94d07 | [
"BSD-3-Clause"
] | 4 | 2021-03-19T08:17:59.000Z | 2021-06-10T19:34:36.000Z | authlib/specs/rfc6749/grants.py | tk193192/authlib | 4c60a628f64c6d385a06ea55e416092726b94d07 | [
"BSD-3-Clause"
] | 2 | 2021-05-24T20:34:12.000Z | 2022-03-26T07:46:17.000Z | from authlib.oauth2.rfc6749.grants import *
| 22 | 43 | 0.818182 | 6 | 44 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 0.090909 | 44 | 1 | 44 | 44 | 0.775 | 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 |
b6a7bc989a31733384f3a7535fc6e100b741cee6 | 168 | py | Python | skil/__init__.py | farizrahman4u/skil-python | 1e9d411c70e20b3748a184e80d17a5ef98e83260 | [
"Apache-2.0"
] | 1 | 2020-08-12T22:52:07.000Z | 2020-08-12T22:52:07.000Z | skil/__init__.py | farizrahman4u/skil-python | 1e9d411c70e20b3748a184e80d17a5ef98e83260 | [
"Apache-2.0"
] | null | null | null | skil/__init__.py | farizrahman4u/skil-python | 1e9d411c70e20b3748a184e80d17a5ef98e83260 | [
"Apache-2.0"
] | null | null | null | from skil.base import *
from skil.deployments import *
from skil.experiments import *
from skil.models import *
from skil.workspaces import *
from skil.context import * | 28 | 30 | 0.791667 | 24 | 168 | 5.541667 | 0.375 | 0.360902 | 0.526316 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136905 | 168 | 6 | 31 | 28 | 0.917241 | 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 |
fcb7ef7c102863d83ffcb6e3c6f6c171d0d64d3c | 236 | py | Python | ai/domain_adaptation/utils/system.py | aayushkafle/implicit_alignment | 4835a8a5acc4b30daf7e1c95195f160e76306cd1 | [
"Apache-2.0"
] | null | null | null | ai/domain_adaptation/utils/system.py | aayushkafle/implicit_alignment | 4835a8a5acc4b30daf7e1c95195f160e76306cd1 | [
"Apache-2.0"
] | null | null | null | ai/domain_adaptation/utils/system.py | aayushkafle/implicit_alignment | 4835a8a5acc4b30daf7e1c95195f160e76306cd1 | [
"Apache-2.0"
] | 1 | 2021-04-15T13:29:34.000Z | 2021-04-15T13:29:34.000Z | import warnings
def filter_deprecation_warning():
print('Caution: deprecation warnings are filtered!')
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)
| 29.5 | 66 | 0.788136 | 22 | 236 | 8.363636 | 0.681818 | 0.23913 | 0.304348 | 0.391304 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114407 | 236 | 7 | 67 | 33.714286 | 0.880383 | 0 | 0 | 0 | 0 | 0 | 0.233051 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0 | 0.2 | 0 | 0.4 | 0.2 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
1e09aab77a57cc78115698f09d148fdc98208e35 | 28,116 | py | Python | apps/events/tests/view_tests.py | Nicolaad/onlineweb4 | 5942eaf907d6824d5384147627def9edefdb9946 | [
"MIT"
] | null | null | null | apps/events/tests/view_tests.py | Nicolaad/onlineweb4 | 5942eaf907d6824d5384147627def9edefdb9946 | [
"MIT"
] | null | null | null | apps/events/tests/view_tests.py | Nicolaad/onlineweb4 | 5942eaf907d6824d5384147627def9edefdb9946 | [
"MIT"
] | null | null | null | from datetime import timedelta
from unittest.mock import patch
from captcha.client import RecaptchaResponse
from django.contrib.auth.models import Group
from django.core import mail
from django.test import TestCase
from django.urls import reverse
from django.utils import timezone
from django_dynamic_fixture import G
from freezegun import freeze_time
from rest_framework import status
from apps.authentication.models import AllowedUsername, OnlineGroup
from apps.marks.models import MarkRuleSet
from apps.payment.models import PaymentDelay, PaymentPrice
from ..models import TYPE_CHOICES, AttendanceEvent, Event, Extras, GroupRestriction
from .utils import (
add_payment_delay,
add_to_arrkom,
add_to_bedkom,
add_to_trikom,
attend_user_to_event,
generate_attendee,
generate_event,
generate_payment,
generate_user,
pay_for_event,
)
class EventsTestMixin:
def setUp(self):
G(Group, pk=1, name="arrKom")
G(Group, pk=3, name="bedKom")
G(Group, pk=8, name="triKom")
G(Group, pk=12, name="Komiteer")
self.user = generate_user("test")
self.client.force_login(self.user)
self.mark_rule_set = G(MarkRuleSet)
self.event = generate_event()
self.event_url = reverse(
"events_details", args=(self.event.id, self.event.slug)
)
def assertInMessages(self, message_text, response):
messages = [str(message) for message in response.context["messages"]]
self.assertIn(message_text, messages)
class EventsDetailRestricted(EventsTestMixin, TestCase):
def test_ok(self):
response = self.client.get(self.event_url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_404(self):
event = generate_event()
url = reverse("events_details", args=(event.id + 10, event.slug))
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_group_restricted_access(self):
add_to_trikom(self.user)
trikom = Group.objects.get(name__iexact="trikom")
G(GroupRestriction, event=self.event, groups=[trikom])
response = self.client.get(self.event_url)
messages = list(response.context["messages"])
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(len(messages), 0)
def test_group_restricted_no_access(self):
add_to_trikom(self.user)
arrkom = Group.objects.get(name__iexact="arrkom")
G(GroupRestriction, event=self.event, groups=[arrkom])
response = self.client.get(self.event_url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertInMessages("Du har ikke tilgang til dette arrangementet.", response)
def test_group_hidden_no_access(self):
self.event = G(Event, visible=False)
self.event_url = reverse(
"events_details", args=(self.event.id, self.event.slug)
)
response = self.client.get(self.event_url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertInMessages("Du har ikke tilgang til dette arrangementet.", response)
class EventsDetailPayment(EventsTestMixin, TestCase):
def test_payment_logged_out(self):
payment = generate_payment(self.event)
self.client.logout()
response = self.client.get(self.event_url)
context = response.context
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(context["payment"], payment)
self.assertEqual(context["user_paid"], False)
self.assertEqual(context["payment_delay"], None)
self.assertEqual(context["payment_relation_id"], None)
def test_payment_not_attended(self):
payment = generate_payment(self.event)
response = self.client.get(self.event_url)
context = response.context
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(context["payment"], payment)
self.assertEqual(context["user_attending"], False)
self.assertEqual(context["user_paid"], False)
self.assertEqual(context["payment_delay"], None)
self.assertEqual(context["payment_relation_id"], None)
def test_payment_attended(self):
payment = generate_payment(self.event)
attend_user_to_event(self.event, self.user)
response = self.client.get(self.event_url)
context = response.context
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(context["payment"], payment)
self.assertEqual(context["user_attending"], True)
self.assertEqual(context["user_paid"], False)
self.assertEqual(context["payment_delay"], None)
self.assertEqual(context["payment_relation_id"], None)
def test_payment_paid(self):
payment = generate_payment(self.event)
attend_user_to_event(self.event, self.user)
payment_relation = pay_for_event(self.event, self.user)
response = self.client.get(self.event_url)
context = response.context
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(context["payment"], payment)
self.assertEqual(context["user_attending"], True)
self.assertEqual(context["user_paid"], True)
self.assertEqual(context["payment_delay"], None)
self.assertEqual(context["payment_relation_id"], payment_relation.id)
def test_payment_attended_with_delay(self):
payment = generate_payment(self.event)
payment_delay = add_payment_delay(payment, self.user)
attend_user_to_event(self.event, self.user)
response = self.client.get(self.event_url)
context = response.context
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(context["payment"], payment)
self.assertEqual(context["user_attending"], True)
self.assertEqual(context["user_paid"], False)
self.assertEqual(context["payment_delay"], payment_delay)
self.assertEqual(context["payment_relation_id"], None)
class EventsDetailExtras(EventsTestMixin, TestCase):
def extras_post(self, event_url, extras_id):
return self.client.post(
event_url,
{"action": "extras", "extras_id": extras_id},
HTTP_X_REQUESTED_WITH="XMLHttpRequest",
)
def test_extras_on_non_attendance_event(self):
event = G(Event)
extras = G(Extras)
event_url = reverse("events_details", args=(event.id, event.slug))
response = self.extras_post(event_url, extras.id)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
response.json()["message"], "Dette er ikke et påmeldingsarrangement."
)
def test_extras_on_not_attended_event(self):
extras = G(Extras)
response = self.extras_post(self.event_url, extras.id)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
response.json()["message"], "Du er ikke påmeldt dette arrangementet."
)
def test_invalid_extras(self):
attend_user_to_event(self.event, self.user)
response = self.extras_post(self.event_url, 1000)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()["message"], "Ugyldig valg")
def test_extras_success(self):
extras = G(Extras)
event = G(Event)
G(AttendanceEvent, event=event, extras=[extras])
attend_user_to_event(event, self.user)
event_url = reverse("events_details", args=(event.id, event.slug))
response = self.extras_post(event_url, extras.id)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()["message"], "Lagret ditt valg")
class EventsAttend(EventsTestMixin, TestCase):
def test_attend_404(self):
url = reverse("attend_event", args=(1000,))
response = self.client.post(url)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_attend_not_attendance_event(self):
event = G(Event)
url = reverse("attend_event", args=(event.id,))
response = self.client.post(url, follow=True)
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Dette er ikke et påmeldingsarrangement.", response)
def test_attend_get(self):
url = reverse("attend_event", args=(self.event.id,))
response = self.client.get(url, follow=True)
self.assertRedirects(response, self.event.get_absolute_url())
self.assertInMessages("Vennligst fyll ut skjemaet.", response)
def test_attend_missing_note(self):
form_params = {"g-recaptcha-response": "PASSED"}
url = reverse("attend_event", args=(self.event.id,))
response = self.client.post(url, form_params, follow=True)
self.assertRedirects(response, self.event.get_absolute_url())
self.assertInMessages("Du må fylle inn et notat!", response)
def test_attend_not_accepted_rules(self):
form_params = {"g-recaptcha-response": "PASSED"}
url = reverse("attend_event", args=(self.event.id,))
G(
AllowedUsername,
username=self.user.ntnu_username,
expiration_date=timezone.now() + timedelta(days=1),
)
response = self.client.post(url, form_params, follow=True)
self.assertRedirects(response, self.event.get_absolute_url())
self.assertInMessages("Du må godta prikkereglene!", response)
@patch("captcha.fields.client.submit")
def test_attend_invalid_captcha(self, mocked_submit):
mocked_submit.return_value = RecaptchaResponse(is_valid=False)
url = reverse("attend_event", args=(self.event.id,))
form_params = {"g-recaptcha-response": "WRONG"}
G(
AllowedUsername,
username=self.user.ntnu_username,
expiration_date=timezone.now() + timedelta(days=1),
)
MarkRuleSet.accept_mark_rules(self.user)
response = self.client.post(url, form_params, follow=True)
self.assertRedirects(response, self.event.get_absolute_url())
self.assertInMessages("Du klarte ikke captchaen! Er du en bot?", response)
@patch("captcha.fields.client.submit")
def test_attend_before_registration_start(self, mocked_submit):
mocked_submit.return_value = RecaptchaResponse(is_valid=True)
event = G(Event)
G(
AttendanceEvent,
event=event,
registration_start=timezone.now() + timedelta(days=1),
registration_end=timezone.now() + timedelta(days=2),
)
url = reverse("attend_event", args=(event.id,))
form_params = {"g-recaptcha-response": "PASSED"}
G(
AllowedUsername,
username=self.user.ntnu_username,
expiration_date=timezone.now() + timedelta(days=1),
)
MarkRuleSet.accept_mark_rules(self.user)
response = self.client.post(url, form_params, follow=True)
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Påmeldingen har ikke åpnet enda.", response)
@patch("captcha.fields.client.submit")
def test_attend_successfully(self, mocked_submit):
mocked_submit.return_value = RecaptchaResponse(is_valid=True)
event = G(Event)
G(
AttendanceEvent,
event=event,
registration_start=timezone.now() - timedelta(days=1),
registration_end=timezone.now() + timedelta(days=1),
)
url = reverse("attend_event", args=(event.id,))
form_params = {"g-recaptcha-response": "PASSED"}
G(
AllowedUsername,
username=self.user.ntnu_username,
expiration_date=timezone.now() + timedelta(days=1),
)
MarkRuleSet.accept_mark_rules(self.user)
response = self.client.post(url, form_params, follow=True)
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Du er nå meldt på arrangementet.", response)
@patch("captcha.fields.client.submit")
def test_attend_twice(self, mocked_submit):
mocked_submit.return_value = RecaptchaResponse(is_valid=True)
event = G(Event)
G(
AttendanceEvent,
event=event,
registration_start=timezone.now() - timedelta(days=1),
registration_end=timezone.now() + timedelta(days=1),
)
url = reverse("attend_event", args=(event.id,))
form_params = {"g-recaptcha-response": "PASSED"}
G(
AllowedUsername,
username=self.user.ntnu_username,
expiration_date=timezone.now() + timedelta(days=1),
)
MarkRuleSet.accept_mark_rules(self.user)
self.client.post(url, form_params, follow=True)
response = self.client.post(url, form_params, follow=True)
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Du er allerede meldt på dette arrangementet.", response)
@patch("captcha.fields.client.submit")
def test_attend_with_payment_creates_paymentdelay(self, mocked_submit):
mocked_submit.return_value = RecaptchaResponse(is_valid=True)
event = G(Event)
G(
AttendanceEvent,
event=event,
registration_start=timezone.now() - timedelta(days=1),
registration_end=timezone.now() + timedelta(days=1),
)
self.event_payment = generate_payment(
event, payment_type=3, delay=timedelta(days=2)
)
G(PaymentPrice, price=200, payment=self.event_payment)
url = reverse("attend_event", args=(event.id,))
form_params = {"g-recaptcha-response": "PASSED"}
G(
AllowedUsername,
username=self.user.ntnu_username,
expiration_date=timezone.now() + timedelta(days=1),
)
MarkRuleSet.accept_mark_rules(self.user)
self.client.post(url, form_params, follow=True)
self.assertTrue(PaymentDelay.objects.filter(user=self.user).exists())
class EventsUnattend(EventsTestMixin, TestCase):
def test_unattend_not_attended(self):
url = reverse("unattend_event", args=(self.event.id,))
response = self.client.post(url, follow=True)
self.assertRedirects(response, self.event.get_absolute_url())
self.assertInMessages("Du er ikke påmeldt dette arrangementet.", response)
def test_unattend_not_attendance_event(self):
event = G(Event)
url = reverse("unattend_event", args=(event.id,))
response = self.client.post(url, follow=True)
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Dette er ikke et påmeldingsarrangement.", response)
def test_unattend_deadline_yesterday(self):
event = G(Event)
G(
AttendanceEvent,
event=event,
unattend_deadline=timezone.now() - timedelta(days=1),
)
attend_user_to_event(event, self.user)
url = reverse("unattend_event", args=(event.id,))
response = self.client.post(url, follow=True)
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages(
"Avmeldingsfristen for dette arrangementet har utløpt.", response
)
def test_unattend_event_started(self):
event = G(Event, event_start=timezone.now() - timedelta(days=1))
G(
AttendanceEvent,
event=event,
unattend_deadline=timezone.now() + timedelta(days=1),
)
attend_user_to_event(event, self.user)
url = reverse("unattend_event", args=(event.id,))
response = self.client.post(url, follow=True)
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Dette arrangementet har allerede startet.", response)
def test_unattend_successfully(self):
event = G(Event, event_start=timezone.now() + timedelta(days=1))
G(
AttendanceEvent,
event=event,
unattend_deadline=timezone.now() + timedelta(days=1),
)
attend_user_to_event(event, self.user)
url = reverse("unattend_event", args=(event.id,))
response = self.client.post(url, follow=True, HTTP_HOST="example.com")
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Du ble meldt av arrangementet.", response)
def test_unattend_payment_not_refunded(self):
event = G(Event, event_start=timezone.now() + timedelta(days=1))
G(
AttendanceEvent,
event=event,
unattend_deadline=timezone.now() + timedelta(days=1),
)
attend_user_to_event(event, self.user)
generate_payment(event)
pay_for_event(event, self.user)
url = reverse("unattend_event", args=(event.id,))
response = self.client.post(url, follow=True, HTTP_HOST="example.com")
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages(
"Du har betalt for arrangementet og må refundere før du kan melde deg av",
response,
)
def test_unattend_payment_removes_payment_delays(self):
event = G(Event, event_start=timezone.now() + timedelta(days=1))
G(
AttendanceEvent,
event=event,
unattend_deadline=timezone.now() + timedelta(days=1),
)
attend_user_to_event(event, self.user)
payment = generate_payment(event)
pay_for_event(event, self.user, refunded=True)
payment_delay = add_payment_delay(payment, self.user)
url = reverse("unattend_event", args=(event.id,))
response = self.client.post(url, follow=True, HTTP_HOST="example.com")
self.assertRedirects(response, event.get_absolute_url())
self.assertInMessages("Du ble meldt av arrangementet.", response)
self.assertEqual(PaymentDelay.objects.filter(id=payment_delay.id).count(), 0)
class EventsUnattendWaitlist(TestCase):
def setUp(self):
self.event = G(Event, event_start=timezone.now() + timedelta(days=1))
G(
AttendanceEvent,
event=self.event,
unattend_deadline=timezone.now() + timedelta(days=1),
max_capacity=2,
waitlist=True,
)
self.user = generate_user("test")
self.client.force_login(self.user)
self.other_user = generate_user("other")
self.url = reverse("unattend_event", args=(self.event.id,))
def test_unattend_notifies_waitlist_when_attending(self):
generate_attendee(self.event, "user1")
attend_user_to_event(self.event, self.user)
generate_attendee(self.event, "user2")
generate_attendee(self.event, "user3")
self.client.post(self.url, follow=True, HTTP_HOST="example.com")
self.assertEqual(len(mail.outbox), 1)
self.assertIn("Du har fått plass på", mail.outbox[0].subject)
def test_unattend_does_not_notify_waitlist_when_on_waitlist(self):
generate_attendee(self.event, "user1")
generate_attendee(self.event, "user2")
attend_user_to_event(self.event, self.user)
generate_attendee(self.event, "user3")
self.client.post(self.url, follow=True, HTTP_HOST="example.com")
self.assertEqual(len(mail.outbox), 0)
@freeze_time("2017-01-01 12:00")
def test_payment_type_instant_uses_extended(self):
generate_payment(self.event, payment_type=1)
generate_attendee(self.event, "user1")
attend_user_to_event(self.event, self.user)
attend_user_to_event(self.event, self.other_user)
generate_attendee(self.event, "user3")
payment_delay_time = timedelta(days=2)
self.client.post(self.url, follow=True, HTTP_HOST="example.com")
self.assertEqual(len(mail.outbox), 1)
self.assertIn("Du har fått plass på", mail.outbox[0].subject)
payment_delay = PaymentDelay.objects.get(user=self.other_user)
self.assertEqual(payment_delay.valid_to, timezone.now() + payment_delay_time)
def test_payment_delay_is_not_created_if_deadline_over_48_hours(self):
generate_payment(
self.event, payment_type=2, deadline=timezone.now() + timedelta(days=3)
)
generate_attendee(self.event, "user1")
attend_user_to_event(self.event, self.user)
attend_user_to_event(self.event, self.other_user)
generate_attendee(self.event, "user3")
self.client.post(self.url, follow=True, HTTP_HOST="example.com")
self.assertEqual(len(mail.outbox), 1)
self.assertIn("Du har fått plass på", mail.outbox[0].subject)
payment_delay = PaymentDelay.objects.filter(user=self.other_user)
self.assertFalse(payment_delay.exists())
@freeze_time("2017-01-01 12:00")
def test_payment_delay_is_created_if_deadline_under_48_hours(self):
generate_payment(
self.event, payment_type=2, deadline=timezone.now() + timedelta(hours=47)
)
generate_attendee(self.event, "user1")
attend_user_to_event(self.event, self.user)
attend_user_to_event(self.event, self.other_user)
generate_attendee(self.event, "user3")
payment_delay_time = timedelta(days=2)
self.client.post(self.url, follow=True, HTTP_HOST="example.com")
self.assertEqual(len(mail.outbox), 1)
self.assertIn("Du har fått plass på", mail.outbox[0].subject)
payment_delay = PaymentDelay.objects.get(user=self.other_user)
self.assertEqual(payment_delay.valid_to, timezone.now() + payment_delay_time)
@freeze_time("2017-01-01 12:00")
def test_payment_type_delay_uses_payment_delay(self):
delay_days = 4
payment_delay_time = timedelta(days=delay_days)
generate_payment(self.event, payment_type=3, delay=payment_delay_time)
generate_attendee(self.event, "user1")
attend_user_to_event(self.event, self.user)
attend_user_to_event(self.event, self.other_user)
generate_attendee(self.event, "user3")
self.client.post(self.url, follow=True, HTTP_HOST="example.com")
self.assertEqual(len(mail.outbox), 1)
self.assertIn("Du har fått plass på", mail.outbox[0].subject)
payment_delay = PaymentDelay.objects.get(user=self.other_user)
self.assertEqual(payment_delay.valid_to, timezone.now() + payment_delay_time)
class EventMailParticipates(EventsTestMixin, TestCase):
def setUp(self):
super().setUp()
self.mail_url = reverse("event_mail_participants", args=(self.event.id,))
def test_not_attendance_event(self):
event = G(Event)
url = reverse("event_mail_participants", args=(event.id,))
response = self.client.get(url, follow=True)
self.assertInMessages("Dette er ikke et påmeldingsarrangement.", response)
self.assertEqual(len(mail.outbox), 0)
def test_missing_access(self):
response = self.client.get(self.mail_url, follow=True)
self.assertInMessages("Du har ikke tilgang til å vise denne siden.", response)
self.assertEqual(len(mail.outbox), 0)
def test_get_own_social_event_as_bedkom(self):
add_to_bedkom(self.user)
bedkom = Group.objects.get(name__iexact="bedkom")
event = generate_event(TYPE_CHOICES[0][0], organizer=bedkom)
url = reverse("event_mail_participants", args=(event.id,))
response = self.client.get(url)
self.assertEqual(response.context["event"], event)
self.assertEqual(len(mail.outbox), 0)
def test_get_as_arrkom(self):
add_to_arrkom(self.user)
event = generate_event(TYPE_CHOICES[0][0])
url = reverse("event_mail_participants", args=(event.id,))
response = self.client.get(url)
self.assertEqual(response.context["event"], event)
self.assertEqual(len(mail.outbox), 0)
def test_post_as_arrkom_missing_data(self):
add_to_arrkom(self.user)
event = generate_event(TYPE_CHOICES[0][0])
url = reverse("event_mail_participants", args=(event.id,))
response = self.client.post(url)
self.assertEqual(response.context["event"], event)
self.assertInMessages(
"Vi klarte ikke å sende mailene dine. Prøv igjen", response
)
self.assertEqual(len(mail.outbox), 0)
def test_post_as_arrkom_successfully(self):
organizer_email = "arrkom@online.ntnu.no"
add_to_arrkom(self.user)
event = generate_event(TYPE_CHOICES[0][0])
G(OnlineGroup, email=organizer_email, group=event.organizer)
url = reverse("event_mail_participants", args=(event.id,))
response = self.client.post(
url, {"to_email": "1", "subject": "Test", "message": "Test message"}
)
self.assertEqual(response.context["event"], event)
self.assertInMessages("Mailen ble sendt", response)
self.assertEqual(mail.outbox[0].from_email, "arrkom@online.ntnu.no")
self.assertEqual(mail.outbox[0].subject, "Test")
self.assertIn("Test message", mail.outbox[0].body)
def test_post_as_arrkom_invalid_from_email_defaults_to_kontakt(self):
add_to_arrkom(self.user)
event = generate_event(TYPE_CHOICES[0][0])
G(OnlineGroup, email="", group=event.organizer)
url = reverse("event_mail_participants", args=(event.id,))
response = self.client.post(
url, {"to_email": "1", "subject": "Test", "message": "Test message"}
)
self.assertEqual(response.context["event"], event)
self.assertInMessages("Mailen ble sendt", response)
self.assertEqual(len(mail.outbox), 1)
self.assertEqual(mail.outbox[0].from_email, "kontakt@online.ntnu.no")
self.assertEqual(mail.outbox[0].subject, "Test")
self.assertIn("Test message", mail.outbox[0].body)
def test_post_as_arrkom_invalid_to_email(self):
add_to_arrkom(self.user)
event = generate_event(TYPE_CHOICES[0][0])
url = reverse("event_mail_participants", args=(event.id,))
response = self.client.post(
url, {"to_email": "1000", "subject": "Test", "message": "Test message"}
)
self.assertEqual(response.context["event"], event)
self.assertInMessages(
"Vi klarte ikke å sende mailene dine. Prøv igjen", response
)
self.assertEqual(len(mail.outbox), 0)
class EventsArchive(TestCase):
def test_events_index_empty(self):
url = reverse("events_index")
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_events_index_exists(self):
generate_event()
url = reverse("events_index")
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
class EventsSearch(TestCase):
def test_search_events(self):
query = ""
_url_pre_get_param = reverse("search_events")
url = _url_pre_get_param + "?query=%s" % query
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
class EventsCalendar(TestCase):
def test_events_ics_all(self):
url = reverse("events_ics")
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_events_ics_specific_event(self):
event = generate_event()
url = reverse("event_ics", args=(event.id,))
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
| 37.289125 | 87 | 0.668303 | 3,377 | 28,116 | 5.356233 | 0.090613 | 0.043288 | 0.03881 | 0.034498 | 0.807331 | 0.782287 | 0.754976 | 0.724403 | 0.709863 | 0.68211 | 0 | 0.01 | 0.217492 | 28,116 | 753 | 88 | 37.338645 | 0.812145 | 0 | 0 | 0.625 | 0 | 0 | 0.100868 | 0.01693 | 0 | 0 | 0 | 0 | 0.217014 | 1 | 0.095486 | false | 0.010417 | 0.027778 | 0.001736 | 0.144097 | 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 |
1e5af2e842580f1e1bcdbbfe8f75509632242dbb | 118 | py | Python | Boatman_webserver/lights/tests.py | sjefferson99/Boatman-webserver | 5a0416c1835fe1a8b7119d7e36a42e02c4cbf6d2 | [
"MIT"
] | null | null | null | Boatman_webserver/lights/tests.py | sjefferson99/Boatman-webserver | 5a0416c1835fe1a8b7119d7e36a42e02c4cbf6d2 | [
"MIT"
] | 1 | 2022-02-20T12:32:51.000Z | 2022-02-20T12:32:51.000Z | Boatman_webserver/lights/tests.py | sjefferson99/Boatman-webserver | 5a0416c1835fe1a8b7119d7e36a42e02c4cbf6d2 | [
"MIT"
] | null | null | null | from django.test import TestCase
from .models import light, group
from django.core.exceptions import ValidationError
| 23.6 | 50 | 0.838983 | 16 | 118 | 6.1875 | 0.6875 | 0.20202 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.118644 | 118 | 4 | 51 | 29.5 | 0.951923 | 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 |
1e76557735b7d3d154f0f84ac5bcc02689e6bdf7 | 25 | py | Python | quick_easy/__init__.py | araile/anki-quick-easy | 53fbbb22491aca2b7dd863c620de1deea7a07f60 | [
"MIT"
] | 3 | 2018-07-01T20:14:12.000Z | 2018-07-16T03:47:20.000Z | quick_easy/__init__.py | araile/anki-quick-easy | 53fbbb22491aca2b7dd863c620de1deea7a07f60 | [
"MIT"
] | 1 | 2018-04-05T10:28:16.000Z | 2018-04-05T10:28:16.000Z | quick_easy/__init__.py | araile/anki-quick-easy | 53fbbb22491aca2b7dd863c620de1deea7a07f60 | [
"MIT"
] | 3 | 2018-02-27T02:08:35.000Z | 2019-05-11T13:51:13.000Z | from . import quick_easy
| 12.5 | 24 | 0.8 | 4 | 25 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 25 | 1 | 25 | 25 | 0.904762 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 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 |
1eb4d01cafe8f5a3c5a9759dabb638851f372c39 | 257,344 | py | Python | instances/passenger_demand/pas-20210422-1717-int8e-1/51.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-int8e-1/51.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-int8e-1/51.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 15308
passenger_arriving = (
(3, 5, 6, 5, 3, 2, 3, 1, 0, 0, 1, 0, 0, 5, 2, 0, 3, 1, 0, 2, 3, 2, 1, 1, 1, 0), # 0
(3, 6, 4, 1, 1, 4, 5, 2, 2, 2, 0, 0, 0, 2, 2, 1, 2, 3, 1, 1, 0, 0, 3, 0, 1, 0), # 1
(3, 3, 3, 6, 5, 0, 1, 2, 4, 1, 2, 0, 0, 9, 3, 5, 1, 1, 2, 4, 1, 1, 3, 0, 0, 0), # 2
(3, 6, 9, 5, 5, 3, 0, 3, 0, 2, 1, 0, 0, 3, 4, 6, 6, 1, 2, 0, 4, 2, 2, 1, 1, 0), # 3
(2, 3, 3, 2, 6, 4, 2, 3, 1, 2, 2, 1, 0, 8, 4, 2, 3, 10, 2, 1, 1, 2, 0, 1, 0, 0), # 4
(8, 7, 6, 6, 3, 1, 2, 0, 3, 0, 2, 0, 0, 7, 4, 4, 3, 4, 3, 3, 3, 2, 4, 2, 0, 0), # 5
(7, 7, 2, 8, 0, 1, 4, 2, 0, 1, 4, 0, 0, 5, 2, 3, 5, 5, 3, 1, 1, 2, 4, 1, 1, 0), # 6
(3, 8, 3, 4, 7, 1, 2, 3, 1, 2, 3, 0, 0, 2, 3, 2, 1, 4, 3, 1, 0, 1, 3, 2, 0, 0), # 7
(5, 6, 5, 4, 3, 3, 5, 3, 6, 1, 1, 0, 0, 7, 8, 1, 5, 1, 3, 5, 2, 2, 3, 3, 0, 0), # 8
(6, 8, 8, 9, 1, 3, 5, 2, 2, 0, 1, 2, 0, 11, 6, 5, 4, 3, 0, 5, 1, 1, 3, 1, 0, 0), # 9
(3, 5, 5, 5, 4, 0, 2, 2, 3, 0, 1, 0, 0, 7, 4, 4, 2, 5, 6, 2, 3, 3, 2, 4, 4, 0), # 10
(7, 5, 6, 5, 6, 6, 0, 1, 1, 1, 1, 0, 0, 8, 5, 5, 3, 8, 3, 0, 2, 1, 0, 0, 0, 0), # 11
(12, 6, 5, 4, 8, 1, 3, 2, 5, 1, 1, 1, 0, 7, 4, 8, 2, 6, 4, 3, 1, 1, 2, 2, 1, 0), # 12
(7, 10, 7, 3, 8, 4, 5, 1, 5, 2, 3, 0, 0, 11, 5, 6, 2, 7, 3, 0, 3, 2, 0, 1, 0, 0), # 13
(7, 7, 6, 5, 3, 3, 2, 1, 4, 4, 2, 0, 0, 9, 5, 4, 3, 9, 4, 2, 1, 3, 4, 3, 1, 0), # 14
(6, 10, 1, 10, 4, 3, 1, 2, 2, 4, 1, 0, 0, 6, 10, 2, 3, 5, 4, 4, 0, 3, 2, 2, 2, 0), # 15
(10, 11, 10, 6, 9, 5, 3, 1, 1, 1, 2, 1, 0, 11, 1, 4, 6, 9, 4, 4, 1, 0, 2, 2, 0, 0), # 16
(8, 8, 7, 10, 5, 1, 1, 6, 3, 1, 0, 1, 0, 7, 5, 3, 6, 6, 6, 2, 4, 2, 1, 2, 0, 0), # 17
(5, 10, 10, 6, 2, 4, 2, 1, 1, 4, 2, 1, 0, 12, 8, 5, 5, 6, 5, 4, 0, 5, 3, 1, 1, 0), # 18
(10, 11, 3, 7, 4, 1, 5, 5, 2, 5, 2, 0, 0, 7, 9, 8, 7, 4, 5, 2, 2, 5, 3, 1, 0, 0), # 19
(10, 10, 9, 6, 4, 3, 0, 2, 6, 1, 1, 0, 0, 7, 5, 5, 3, 4, 3, 3, 0, 4, 1, 3, 0, 0), # 20
(11, 11, 4, 8, 10, 2, 1, 5, 5, 1, 2, 1, 0, 4, 5, 2, 6, 6, 8, 3, 1, 4, 4, 1, 0, 0), # 21
(11, 12, 5, 10, 5, 4, 7, 4, 2, 2, 0, 1, 0, 6, 7, 8, 4, 8, 4, 5, 3, 2, 0, 1, 1, 0), # 22
(8, 6, 10, 5, 5, 4, 2, 1, 7, 5, 0, 0, 0, 15, 3, 5, 7, 1, 1, 5, 3, 4, 4, 0, 0, 0), # 23
(8, 6, 12, 9, 8, 0, 4, 1, 4, 3, 0, 1, 0, 7, 9, 4, 3, 5, 8, 7, 2, 2, 5, 0, 2, 0), # 24
(10, 8, 8, 7, 4, 4, 1, 3, 2, 1, 0, 0, 0, 8, 12, 3, 7, 6, 3, 2, 1, 3, 1, 1, 1, 0), # 25
(5, 7, 11, 9, 4, 3, 1, 2, 1, 2, 0, 2, 0, 9, 6, 4, 2, 5, 6, 4, 8, 5, 1, 2, 1, 0), # 26
(14, 11, 9, 4, 7, 2, 3, 3, 2, 2, 2, 1, 0, 5, 5, 3, 4, 4, 5, 4, 2, 3, 3, 1, 0, 0), # 27
(13, 9, 7, 8, 4, 2, 4, 2, 4, 4, 1, 0, 0, 11, 11, 6, 5, 5, 4, 8, 3, 3, 3, 0, 0, 0), # 28
(4, 8, 13, 9, 4, 5, 1, 2, 2, 1, 0, 1, 0, 6, 8, 6, 6, 4, 5, 6, 3, 4, 1, 2, 2, 0), # 29
(7, 6, 0, 5, 2, 4, 3, 1, 7, 3, 0, 0, 0, 11, 8, 10, 5, 9, 1, 3, 2, 5, 3, 2, 2, 0), # 30
(13, 12, 5, 4, 1, 3, 2, 3, 2, 5, 1, 2, 0, 8, 10, 8, 4, 8, 5, 5, 1, 1, 2, 2, 1, 0), # 31
(8, 7, 5, 11, 8, 1, 4, 4, 3, 1, 1, 3, 0, 8, 3, 4, 2, 7, 5, 1, 0, 2, 2, 2, 0, 0), # 32
(8, 9, 7, 5, 1, 2, 2, 2, 6, 3, 0, 1, 0, 4, 9, 9, 1, 6, 4, 3, 2, 2, 7, 2, 0, 0), # 33
(8, 15, 4, 8, 3, 2, 6, 4, 4, 2, 4, 0, 0, 12, 6, 8, 8, 7, 6, 2, 5, 1, 2, 1, 0, 0), # 34
(8, 8, 6, 7, 5, 4, 3, 2, 2, 1, 0, 1, 0, 8, 7, 4, 5, 6, 4, 6, 6, 2, 4, 1, 1, 0), # 35
(8, 6, 6, 9, 6, 4, 6, 7, 0, 1, 0, 2, 0, 5, 9, 5, 4, 8, 5, 3, 3, 4, 0, 1, 3, 0), # 36
(10, 9, 9, 6, 3, 1, 6, 0, 3, 3, 0, 3, 0, 8, 8, 4, 7, 5, 5, 5, 3, 4, 2, 2, 0, 0), # 37
(8, 7, 6, 6, 6, 2, 5, 2, 5, 2, 1, 0, 0, 8, 6, 5, 5, 6, 3, 4, 5, 2, 1, 3, 1, 0), # 38
(12, 10, 7, 7, 4, 2, 5, 6, 4, 0, 0, 1, 0, 5, 6, 5, 8, 4, 3, 3, 0, 2, 0, 2, 1, 0), # 39
(11, 8, 5, 8, 7, 6, 2, 1, 2, 2, 1, 1, 0, 8, 6, 2, 3, 9, 4, 1, 0, 2, 1, 4, 3, 0), # 40
(4, 7, 10, 7, 6, 5, 2, 7, 3, 3, 0, 0, 0, 6, 13, 2, 4, 2, 5, 9, 2, 1, 0, 1, 1, 0), # 41
(12, 11, 1, 6, 3, 2, 2, 4, 4, 0, 0, 1, 0, 11, 8, 2, 5, 6, 0, 2, 4, 3, 4, 1, 1, 0), # 42
(6, 8, 10, 6, 8, 4, 3, 2, 5, 4, 0, 0, 0, 15, 5, 6, 1, 4, 3, 1, 2, 5, 5, 3, 0, 0), # 43
(11, 6, 8, 8, 2, 1, 2, 4, 2, 0, 0, 1, 0, 6, 4, 5, 5, 9, 8, 2, 3, 1, 0, 0, 2, 0), # 44
(11, 8, 9, 8, 9, 2, 2, 4, 1, 0, 1, 0, 0, 6, 8, 10, 4, 10, 4, 4, 4, 1, 2, 1, 3, 0), # 45
(5, 5, 9, 10, 2, 2, 4, 4, 1, 2, 2, 0, 0, 12, 9, 5, 8, 5, 3, 3, 0, 3, 3, 4, 1, 0), # 46
(9, 8, 5, 9, 3, 2, 2, 2, 7, 4, 1, 1, 0, 11, 5, 4, 5, 5, 3, 6, 1, 6, 6, 2, 2, 0), # 47
(3, 8, 4, 4, 4, 2, 1, 2, 4, 2, 2, 1, 0, 7, 3, 3, 3, 5, 4, 4, 1, 1, 0, 0, 0, 0), # 48
(9, 10, 6, 7, 4, 0, 6, 2, 5, 2, 2, 1, 0, 11, 7, 5, 3, 10, 5, 4, 2, 2, 3, 1, 0, 0), # 49
(11, 3, 15, 5, 10, 4, 3, 4, 0, 0, 2, 0, 0, 8, 10, 3, 4, 4, 5, 0, 2, 3, 2, 1, 0, 0), # 50
(9, 6, 6, 8, 6, 6, 4, 3, 1, 1, 1, 0, 0, 5, 4, 11, 6, 6, 3, 1, 4, 1, 2, 1, 1, 0), # 51
(11, 6, 6, 10, 6, 6, 1, 0, 3, 1, 4, 1, 0, 10, 9, 5, 2, 11, 3, 3, 0, 4, 2, 2, 0, 0), # 52
(9, 9, 5, 7, 7, 4, 7, 1, 2, 2, 1, 2, 0, 13, 7, 5, 5, 3, 8, 2, 3, 5, 2, 0, 1, 0), # 53
(5, 7, 8, 3, 6, 3, 4, 4, 4, 1, 2, 0, 0, 7, 8, 10, 4, 11, 2, 6, 3, 3, 2, 3, 1, 0), # 54
(5, 10, 6, 8, 7, 3, 2, 2, 2, 1, 2, 0, 0, 13, 3, 7, 8, 8, 4, 1, 1, 2, 1, 1, 2, 0), # 55
(9, 10, 6, 9, 4, 4, 1, 0, 4, 3, 0, 0, 0, 6, 9, 8, 1, 9, 3, 1, 1, 2, 2, 1, 1, 0), # 56
(11, 11, 8, 9, 8, 5, 7, 5, 5, 1, 2, 0, 0, 8, 6, 2, 6, 7, 4, 2, 3, 2, 4, 2, 1, 0), # 57
(7, 4, 6, 8, 4, 1, 1, 2, 2, 1, 0, 2, 0, 6, 9, 7, 4, 2, 4, 3, 0, 5, 1, 2, 0, 0), # 58
(11, 7, 6, 4, 6, 1, 5, 2, 3, 1, 1, 0, 0, 5, 5, 7, 5, 6, 5, 2, 2, 3, 4, 1, 0, 0), # 59
(12, 6, 1, 5, 6, 2, 3, 4, 2, 1, 1, 1, 0, 6, 5, 3, 5, 9, 2, 7, 1, 1, 1, 0, 0, 0), # 60
(7, 7, 6, 11, 5, 0, 4, 5, 2, 3, 1, 0, 0, 7, 7, 8, 5, 11, 4, 3, 1, 3, 2, 6, 0, 0), # 61
(14, 11, 7, 10, 5, 0, 4, 3, 6, 3, 1, 1, 0, 7, 8, 5, 3, 11, 6, 4, 2, 1, 3, 2, 0, 0), # 62
(4, 9, 1, 5, 12, 4, 2, 3, 5, 0, 0, 0, 0, 13, 8, 11, 6, 10, 2, 3, 1, 5, 3, 1, 0, 0), # 63
(11, 3, 5, 6, 4, 4, 3, 3, 3, 2, 1, 0, 0, 8, 8, 1, 0, 8, 3, 3, 2, 2, 2, 1, 2, 0), # 64
(12, 10, 3, 7, 13, 3, 4, 0, 2, 1, 0, 0, 0, 5, 5, 9, 7, 4, 3, 2, 2, 4, 2, 0, 0, 0), # 65
(13, 8, 7, 7, 9, 5, 0, 1, 6, 1, 1, 0, 0, 8, 3, 3, 8, 4, 2, 2, 1, 3, 2, 3, 1, 0), # 66
(10, 8, 3, 5, 11, 1, 4, 3, 4, 0, 0, 0, 0, 5, 15, 7, 5, 11, 3, 2, 3, 4, 2, 1, 0, 0), # 67
(15, 10, 14, 14, 11, 2, 3, 6, 8, 2, 0, 0, 0, 8, 10, 6, 5, 4, 3, 3, 4, 0, 3, 4, 0, 0), # 68
(9, 7, 6, 7, 4, 0, 1, 3, 0, 2, 0, 0, 0, 9, 4, 2, 5, 6, 4, 4, 4, 4, 3, 2, 0, 0), # 69
(8, 9, 7, 4, 8, 1, 7, 4, 3, 1, 0, 0, 0, 11, 9, 6, 4, 7, 1, 2, 1, 1, 1, 2, 0, 0), # 70
(7, 8, 7, 4, 5, 2, 2, 1, 2, 3, 0, 1, 0, 8, 5, 1, 4, 7, 2, 0, 1, 2, 2, 1, 0, 0), # 71
(6, 6, 6, 4, 6, 6, 3, 2, 5, 0, 0, 0, 0, 8, 7, 8, 4, 5, 1, 5, 0, 4, 9, 1, 0, 0), # 72
(6, 4, 8, 10, 5, 4, 3, 3, 0, 1, 0, 1, 0, 6, 7, 5, 4, 6, 3, 3, 3, 5, 0, 0, 1, 0), # 73
(10, 11, 11, 4, 6, 3, 4, 4, 2, 1, 2, 0, 0, 5, 1, 2, 3, 9, 3, 5, 4, 1, 2, 2, 0, 0), # 74
(7, 3, 9, 10, 7, 4, 0, 0, 1, 1, 0, 0, 0, 8, 5, 5, 4, 7, 2, 2, 5, 0, 3, 1, 0, 0), # 75
(11, 10, 5, 14, 8, 4, 5, 2, 2, 0, 2, 1, 0, 8, 2, 8, 5, 10, 4, 0, 1, 2, 2, 3, 1, 0), # 76
(8, 4, 5, 9, 7, 6, 3, 0, 3, 3, 0, 0, 0, 7, 3, 9, 0, 4, 4, 3, 1, 2, 1, 4, 1, 0), # 77
(6, 1, 4, 7, 4, 5, 2, 6, 2, 0, 3, 0, 0, 9, 7, 6, 2, 8, 5, 2, 1, 4, 3, 2, 3, 0), # 78
(12, 5, 8, 12, 3, 3, 9, 4, 5, 1, 1, 1, 0, 5, 11, 4, 4, 6, 3, 3, 3, 6, 1, 2, 1, 0), # 79
(4, 4, 8, 11, 7, 5, 0, 2, 5, 2, 1, 1, 0, 3, 10, 2, 10, 9, 3, 0, 3, 2, 1, 2, 0, 0), # 80
(10, 4, 9, 10, 10, 0, 2, 3, 4, 1, 1, 0, 0, 6, 3, 6, 2, 4, 4, 1, 3, 5, 3, 1, 1, 0), # 81
(12, 11, 7, 6, 2, 1, 2, 3, 1, 3, 3, 1, 0, 8, 12, 2, 3, 2, 2, 2, 0, 1, 2, 1, 2, 0), # 82
(11, 8, 7, 6, 9, 5, 2, 0, 4, 1, 1, 2, 0, 7, 5, 7, 3, 6, 5, 4, 2, 2, 3, 3, 1, 0), # 83
(6, 10, 2, 7, 2, 2, 2, 3, 2, 3, 1, 1, 0, 13, 8, 8, 5, 7, 3, 4, 1, 6, 3, 0, 1, 0), # 84
(10, 6, 10, 6, 5, 2, 2, 6, 5, 2, 1, 1, 0, 12, 6, 4, 1, 6, 1, 1, 1, 3, 3, 2, 0, 0), # 85
(10, 6, 5, 5, 7, 3, 2, 5, 5, 1, 1, 0, 0, 4, 9, 4, 5, 3, 8, 3, 1, 5, 4, 1, 1, 0), # 86
(14, 3, 7, 8, 7, 2, 3, 2, 2, 1, 0, 1, 0, 7, 4, 8, 3, 11, 2, 2, 1, 2, 2, 3, 1, 0), # 87
(10, 7, 6, 9, 4, 5, 2, 4, 4, 1, 0, 1, 0, 8, 11, 8, 6, 5, 3, 4, 0, 1, 1, 1, 0, 0), # 88
(7, 8, 4, 3, 9, 1, 1, 3, 4, 0, 0, 0, 0, 11, 6, 10, 3, 2, 2, 1, 0, 2, 2, 2, 0, 0), # 89
(9, 6, 8, 6, 4, 2, 1, 7, 3, 0, 0, 1, 0, 11, 5, 5, 4, 7, 3, 3, 0, 0, 5, 1, 2, 0), # 90
(7, 3, 4, 5, 5, 4, 2, 1, 3, 1, 1, 0, 0, 8, 8, 6, 1, 9, 4, 2, 3, 4, 3, 1, 2, 0), # 91
(6, 5, 4, 4, 4, 2, 4, 3, 1, 0, 0, 1, 0, 12, 2, 4, 2, 6, 4, 1, 2, 4, 3, 0, 2, 0), # 92
(6, 2, 3, 8, 7, 2, 7, 3, 2, 0, 1, 1, 0, 7, 1, 9, 3, 5, 3, 0, 2, 2, 1, 2, 0, 0), # 93
(12, 8, 3, 3, 7, 2, 2, 0, 1, 0, 0, 0, 0, 9, 4, 3, 6, 5, 3, 0, 1, 3, 4, 1, 1, 0), # 94
(11, 5, 8, 6, 5, 1, 2, 2, 2, 2, 3, 1, 0, 9, 8, 6, 3, 5, 4, 4, 3, 3, 1, 3, 0, 0), # 95
(4, 7, 6, 9, 2, 7, 3, 6, 4, 5, 1, 0, 0, 8, 7, 5, 6, 5, 2, 3, 2, 3, 5, 0, 0, 0), # 96
(10, 4, 8, 7, 6, 3, 1, 4, 1, 0, 1, 0, 0, 11, 6, 3, 5, 6, 7, 3, 3, 6, 2, 0, 0, 0), # 97
(3, 7, 6, 6, 12, 2, 2, 2, 6, 3, 0, 0, 0, 11, 6, 6, 3, 5, 2, 4, 2, 1, 4, 1, 3, 0), # 98
(17, 6, 1, 13, 5, 5, 3, 0, 3, 1, 1, 0, 0, 10, 10, 6, 3, 3, 5, 4, 0, 2, 2, 0, 0, 0), # 99
(8, 6, 5, 9, 5, 5, 4, 2, 2, 3, 1, 0, 0, 6, 6, 7, 4, 6, 4, 1, 5, 2, 2, 4, 0, 0), # 100
(5, 6, 6, 5, 5, 0, 3, 0, 2, 2, 1, 2, 0, 17, 3, 5, 1, 5, 6, 6, 2, 3, 2, 1, 2, 0), # 101
(5, 5, 5, 4, 9, 1, 2, 4, 1, 1, 1, 1, 0, 8, 5, 6, 4, 7, 3, 4, 2, 3, 3, 1, 0, 0), # 102
(4, 6, 5, 7, 10, 3, 2, 3, 4, 0, 0, 0, 0, 4, 4, 7, 3, 3, 3, 2, 1, 1, 1, 0, 0, 0), # 103
(7, 8, 6, 7, 5, 0, 4, 1, 0, 0, 0, 0, 0, 5, 4, 6, 3, 8, 4, 3, 4, 3, 1, 2, 1, 0), # 104
(9, 7, 4, 5, 4, 4, 0, 5, 1, 3, 0, 0, 0, 7, 6, 5, 4, 7, 2, 1, 2, 2, 0, 0, 1, 0), # 105
(7, 2, 5, 5, 6, 2, 2, 1, 2, 1, 0, 1, 0, 10, 5, 3, 5, 9, 3, 2, 1, 3, 3, 2, 1, 0), # 106
(6, 10, 6, 8, 5, 2, 3, 2, 6, 1, 0, 0, 0, 9, 6, 8, 3, 9, 4, 3, 3, 2, 1, 3, 0, 0), # 107
(4, 6, 7, 7, 6, 4, 2, 2, 4, 1, 1, 0, 0, 10, 3, 2, 3, 5, 6, 1, 2, 2, 1, 2, 0, 0), # 108
(8, 6, 2, 9, 4, 4, 2, 5, 1, 1, 1, 0, 0, 9, 6, 6, 1, 3, 1, 4, 2, 3, 2, 1, 1, 0), # 109
(4, 8, 9, 4, 6, 0, 6, 1, 3, 1, 1, 2, 0, 10, 6, 7, 5, 6, 3, 4, 4, 3, 2, 1, 1, 0), # 110
(8, 3, 8, 7, 7, 3, 6, 3, 3, 1, 1, 0, 0, 11, 8, 3, 7, 9, 5, 5, 1, 5, 1, 3, 0, 0), # 111
(7, 3, 7, 7, 3, 3, 2, 2, 3, 0, 0, 0, 0, 9, 5, 4, 5, 11, 4, 3, 2, 4, 0, 1, 0, 0), # 112
(5, 4, 5, 4, 4, 4, 4, 1, 3, 2, 0, 1, 0, 5, 5, 3, 7, 3, 5, 4, 2, 5, 2, 1, 0, 0), # 113
(2, 4, 5, 5, 3, 1, 2, 1, 0, 1, 2, 0, 0, 11, 6, 9, 3, 10, 2, 4, 4, 4, 3, 2, 1, 0), # 114
(8, 11, 11, 8, 5, 4, 3, 0, 5, 2, 2, 2, 0, 8, 8, 3, 6, 5, 2, 6, 1, 1, 5, 0, 0, 0), # 115
(5, 5, 8, 4, 4, 1, 1, 6, 5, 3, 0, 1, 0, 5, 6, 6, 4, 6, 2, 2, 1, 4, 2, 1, 0, 0), # 116
(12, 5, 4, 5, 1, 8, 3, 0, 6, 2, 0, 0, 0, 8, 8, 2, 1, 6, 3, 2, 2, 4, 2, 1, 0, 0), # 117
(6, 8, 4, 10, 5, 1, 0, 1, 4, 2, 2, 0, 0, 7, 6, 6, 2, 10, 1, 3, 3, 2, 1, 2, 0, 0), # 118
(13, 8, 3, 5, 8, 2, 2, 2, 3, 0, 0, 0, 0, 12, 3, 1, 6, 12, 3, 1, 7, 3, 2, 0, 0, 0), # 119
(7, 5, 6, 11, 4, 2, 3, 4, 2, 3, 0, 0, 0, 8, 8, 5, 5, 3, 4, 3, 2, 2, 2, 0, 2, 0), # 120
(8, 2, 9, 8, 8, 3, 4, 2, 2, 1, 2, 0, 0, 7, 4, 3, 2, 7, 1, 3, 0, 1, 3, 0, 0, 0), # 121
(4, 4, 9, 2, 6, 3, 1, 2, 6, 0, 0, 0, 0, 6, 10, 6, 1, 6, 3, 1, 0, 3, 1, 0, 0, 0), # 122
(8, 5, 3, 6, 4, 4, 2, 1, 2, 3, 1, 0, 0, 5, 5, 4, 1, 6, 1, 3, 0, 2, 2, 1, 0, 0), # 123
(9, 4, 2, 11, 8, 0, 1, 1, 1, 3, 0, 0, 0, 7, 6, 3, 3, 7, 4, 1, 3, 3, 1, 2, 1, 0), # 124
(3, 7, 3, 8, 6, 2, 0, 1, 4, 2, 2, 1, 0, 9, 3, 3, 5, 4, 1, 2, 1, 6, 2, 2, 1, 0), # 125
(8, 2, 5, 8, 5, 1, 2, 0, 5, 4, 0, 0, 0, 6, 6, 4, 1, 5, 3, 3, 1, 2, 2, 1, 0, 0), # 126
(5, 7, 4, 9, 7, 1, 1, 2, 2, 2, 4, 0, 0, 7, 1, 3, 1, 8, 1, 2, 1, 8, 3, 0, 0, 0), # 127
(6, 8, 9, 8, 5, 0, 2, 1, 2, 2, 1, 0, 0, 7, 9, 2, 2, 7, 2, 0, 1, 3, 2, 2, 1, 0), # 128
(7, 8, 5, 5, 4, 3, 5, 2, 3, 3, 1, 0, 0, 10, 6, 2, 2, 3, 5, 1, 0, 3, 2, 0, 1, 0), # 129
(5, 5, 5, 11, 4, 2, 2, 0, 2, 0, 1, 1, 0, 11, 3, 3, 4, 4, 2, 1, 1, 3, 1, 2, 0, 0), # 130
(9, 2, 5, 9, 7, 1, 0, 0, 0, 2, 0, 1, 0, 5, 6, 4, 1, 7, 3, 2, 1, 3, 2, 1, 0, 0), # 131
(3, 2, 10, 4, 12, 1, 1, 3, 3, 1, 1, 0, 0, 3, 7, 6, 4, 4, 5, 0, 4, 3, 1, 2, 0, 0), # 132
(9, 4, 5, 7, 5, 2, 3, 0, 5, 1, 1, 0, 0, 12, 2, 8, 3, 6, 4, 2, 0, 4, 2, 3, 1, 0), # 133
(8, 3, 6, 4, 0, 3, 3, 1, 3, 1, 0, 0, 0, 4, 6, 5, 4, 7, 1, 4, 3, 1, 2, 2, 0, 0), # 134
(6, 2, 8, 4, 2, 5, 0, 0, 3, 5, 0, 0, 0, 7, 6, 1, 3, 7, 3, 1, 3, 3, 1, 1, 0, 0), # 135
(3, 6, 6, 6, 5, 3, 2, 0, 3, 0, 1, 0, 0, 7, 5, 5, 2, 3, 0, 2, 2, 3, 1, 1, 0, 0), # 136
(5, 6, 3, 6, 0, 2, 2, 2, 3, 1, 3, 0, 0, 7, 6, 3, 0, 2, 2, 0, 2, 1, 4, 1, 0, 0), # 137
(4, 8, 5, 5, 4, 2, 3, 2, 4, 3, 0, 1, 0, 12, 7, 4, 3, 3, 1, 5, 2, 3, 0, 1, 0, 0), # 138
(5, 5, 10, 9, 6, 2, 1, 2, 0, 1, 0, 1, 0, 7, 5, 3, 2, 3, 1, 2, 2, 0, 2, 0, 0, 0), # 139
(5, 3, 2, 10, 1, 1, 3, 1, 4, 0, 0, 2, 0, 8, 8, 7, 5, 4, 0, 4, 1, 1, 0, 0, 0, 0), # 140
(6, 8, 9, 11, 4, 4, 3, 0, 1, 0, 1, 1, 0, 8, 5, 5, 3, 6, 3, 0, 1, 2, 3, 1, 0, 0), # 141
(8, 6, 7, 8, 6, 4, 1, 3, 3, 2, 2, 0, 0, 7, 10, 6, 7, 7, 0, 1, 0, 1, 4, 2, 1, 0), # 142
(5, 8, 7, 3, 4, 2, 1, 3, 1, 2, 0, 0, 0, 11, 9, 2, 1, 6, 1, 1, 1, 0, 3, 1, 0, 0), # 143
(9, 1, 5, 5, 5, 2, 1, 3, 3, 0, 0, 1, 0, 10, 4, 5, 5, 5, 2, 2, 1, 2, 6, 0, 1, 0), # 144
(10, 3, 12, 10, 4, 2, 0, 1, 2, 1, 0, 0, 0, 3, 4, 3, 4, 2, 2, 2, 1, 3, 2, 0, 1, 0), # 145
(6, 7, 5, 14, 4, 2, 2, 2, 5, 0, 0, 0, 0, 5, 6, 4, 1, 3, 5, 2, 1, 1, 3, 0, 0, 0), # 146
(3, 5, 4, 6, 5, 2, 3, 2, 3, 2, 2, 0, 0, 8, 3, 7, 0, 8, 2, 5, 0, 2, 3, 3, 1, 0), # 147
(6, 5, 9, 7, 4, 1, 5, 2, 5, 1, 1, 0, 0, 4, 4, 4, 5, 8, 1, 5, 1, 2, 3, 2, 0, 0), # 148
(8, 4, 8, 5, 4, 1, 2, 0, 4, 1, 1, 0, 0, 5, 7, 5, 0, 4, 5, 2, 3, 3, 1, 1, 0, 0), # 149
(4, 5, 5, 5, 3, 1, 0, 4, 4, 1, 0, 0, 0, 9, 4, 4, 2, 9, 3, 1, 1, 3, 3, 1, 0, 0), # 150
(7, 8, 5, 6, 2, 2, 3, 3, 2, 1, 1, 0, 0, 8, 8, 5, 8, 7, 4, 2, 0, 3, 4, 4, 0, 0), # 151
(11, 5, 5, 6, 5, 2, 0, 2, 5, 1, 2, 0, 0, 7, 1, 5, 4, 1, 2, 1, 2, 1, 2, 1, 0, 0), # 152
(4, 7, 5, 7, 6, 1, 0, 2, 1, 1, 0, 0, 0, 8, 4, 5, 5, 10, 5, 1, 2, 5, 1, 1, 0, 0), # 153
(7, 8, 5, 3, 4, 2, 1, 1, 3, 1, 0, 0, 0, 6, 7, 4, 2, 10, 4, 3, 0, 3, 3, 2, 0, 0), # 154
(6, 3, 3, 5, 3, 2, 0, 3, 4, 0, 0, 0, 0, 14, 5, 5, 1, 6, 5, 1, 1, 1, 4, 1, 0, 0), # 155
(9, 4, 4, 2, 4, 1, 2, 2, 3, 1, 0, 0, 0, 4, 5, 4, 1, 5, 4, 2, 2, 4, 5, 3, 1, 0), # 156
(2, 6, 7, 4, 7, 5, 3, 1, 0, 3, 1, 0, 0, 7, 8, 6, 2, 2, 2, 1, 2, 1, 3, 1, 0, 0), # 157
(3, 5, 3, 3, 5, 0, 2, 1, 5, 2, 0, 0, 0, 2, 6, 8, 6, 3, 1, 3, 2, 0, 1, 4, 0, 0), # 158
(4, 5, 11, 9, 5, 2, 1, 4, 4, 1, 1, 1, 0, 10, 6, 4, 1, 7, 4, 2, 4, 0, 2, 2, 1, 0), # 159
(3, 6, 5, 4, 3, 1, 1, 4, 0, 0, 1, 2, 0, 10, 7, 3, 1, 3, 2, 2, 2, 3, 2, 0, 1, 0), # 160
(4, 3, 5, 8, 6, 2, 3, 0, 1, 1, 0, 1, 0, 11, 3, 3, 4, 5, 4, 1, 2, 4, 1, 0, 0, 0), # 161
(2, 5, 3, 6, 3, 3, 0, 3, 1, 3, 1, 0, 0, 9, 6, 6, 3, 4, 5, 0, 1, 5, 2, 0, 0, 0), # 162
(6, 3, 9, 2, 5, 1, 1, 0, 3, 0, 0, 1, 0, 5, 4, 2, 1, 4, 3, 2, 1, 6, 0, 2, 0, 0), # 163
(3, 7, 5, 6, 7, 1, 1, 2, 4, 1, 2, 1, 0, 3, 3, 5, 0, 2, 2, 2, 0, 2, 2, 2, 1, 0), # 164
(8, 2, 3, 5, 3, 1, 1, 1, 2, 1, 0, 1, 0, 6, 6, 5, 2, 7, 5, 2, 2, 1, 1, 0, 0, 0), # 165
(1, 2, 7, 8, 7, 3, 1, 4, 0, 1, 0, 0, 0, 6, 5, 7, 3, 8, 1, 2, 4, 3, 0, 0, 0, 0), # 166
(7, 3, 5, 9, 6, 0, 2, 2, 2, 1, 0, 0, 0, 4, 8, 2, 2, 3, 1, 3, 1, 3, 1, 2, 0, 0), # 167
(5, 3, 4, 5, 3, 2, 1, 0, 0, 0, 0, 1, 0, 7, 2, 5, 3, 7, 4, 0, 3, 4, 1, 0, 0, 0), # 168
(4, 2, 5, 5, 2, 3, 2, 0, 4, 1, 2, 3, 0, 4, 5, 4, 1, 5, 1, 0, 3, 3, 0, 1, 0, 0), # 169
(2, 1, 3, 4, 6, 3, 1, 1, 2, 1, 0, 0, 0, 8, 0, 2, 2, 4, 3, 0, 0, 4, 2, 0, 0, 0), # 170
(5, 1, 3, 5, 2, 0, 3, 0, 0, 1, 1, 0, 0, 3, 7, 4, 2, 2, 1, 1, 2, 0, 3, 0, 0, 0), # 171
(3, 2, 5, 3, 2, 1, 1, 1, 2, 1, 0, 0, 0, 1, 5, 0, 2, 3, 1, 1, 0, 2, 0, 0, 0, 0), # 172
(2, 3, 4, 4, 2, 6, 2, 0, 1, 1, 0, 0, 0, 3, 5, 3, 1, 3, 0, 1, 1, 1, 1, 0, 0, 0), # 173
(2, 0, 2, 2, 3, 1, 2, 1, 2, 0, 2, 0, 0, 5, 2, 1, 1, 5, 2, 1, 0, 3, 0, 1, 0, 0), # 174
(2, 1, 2, 3, 4, 2, 2, 1, 2, 0, 0, 0, 0, 5, 2, 2, 0, 4, 2, 0, 1, 1, 0, 2, 0, 0), # 175
(2, 2, 4, 3, 1, 2, 1, 1, 1, 0, 0, 0, 0, 4, 3, 1, 1, 2, 2, 1, 2, 3, 2, 2, 0, 0), # 176
(1, 1, 1, 5, 2, 1, 0, 0, 4, 0, 0, 0, 0, 3, 5, 1, 0, 3, 1, 0, 0, 2, 2, 0, 0, 0), # 177
(1, 1, 4, 1, 2, 1, 1, 1, 1, 0, 1, 0, 0, 9, 2, 3, 1, 4, 0, 1, 2, 2, 1, 0, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(4.0166924626974145, 4.420230847754533, 4.169026583690005, 4.971734219090746, 4.4437484860876895, 2.5109239456298713, 3.3168284922991322, 3.7225409383835384, 4.872079249734406, 3.166412012417896, 3.3642121311084825, 3.918332062644939, 4.067104170062691), # 0
(4.283461721615979, 4.712048555315807, 4.444277273064122, 5.3001154026212935, 4.737992269979389, 2.6767868672340445, 3.535575153010955, 3.9676109783245668, 5.1937962610663275, 3.37518455382172, 3.5864769087649053, 4.176973328651484, 4.3358333179518835), # 1
(4.549378407183785, 5.0027081367127835, 4.718433828437931, 5.627190163731836, 5.0311703789997955, 2.841988091609956, 3.7534548063685635, 4.211700198323536, 5.514229445502039, 3.583131020016437, 3.8078585190210505, 4.434586121642444, 4.603491862567752), # 2
(4.81340623451725, 5.291056401549158, 4.9904086954558835, 5.951661126025659, 5.322129340801522, 3.0058724980680904, 3.9696029133183646, 4.453840925995606, 5.832108128736874, 3.7894261587409446, 4.027478729461906, 4.690148547944369, 4.869018245003381), # 3
(5.074508918732786, 5.57594015942862, 5.259114319762429, 6.272230913106056, 5.609715683037194, 3.1677849659189343, 4.183154934806767, 4.6930654889559325, 6.146161636466166, 3.993244717734143, 4.24445930767246, 4.942638713883811, 5.131350906351854), # 4
(5.331650174946809, 5.856206219954871, 5.523463147002015, 6.587602148576315, 5.892775933359424, 3.3270703744729717, 4.393246331780179, 4.928406214819674, 6.455119294385248, 4.193761444734931, 4.457922021237706, 5.191034725787318, 5.389428287706262), # 5
(5.583793718275733, 6.130701392731601, 5.782367622819093, 6.896477456039722, 6.170156619420835, 3.4830736030406912, 4.59901256518501, 5.158895431201991, 6.757710428189452, 4.390151087482207, 4.666988637742626, 5.434314689981447, 5.642188830159686), # 6
(5.829903263835975, 6.398272487362505, 6.034740192858108, 7.19755945909957, 6.440704268874043, 3.6351395309325767, 4.799589095967668, 5.383565465718042, 7.052664363574116, 4.58158839371487, 4.870780924772215, 5.671456712792743, 5.888570974805216), # 7
(6.068942526743948, 6.65776631345128, 6.279493302763517, 7.489550781359142, 6.703265409371669, 3.782613037459112, 4.994111385074558, 5.60144864598298, 7.338710426234565, 4.76724811117182, 5.068420649911457, 5.901438900547762, 6.127513162735934), # 8
(6.299875222116068, 6.908029680601619, 6.515539398179763, 7.771154046421735, 6.956686568566328, 3.924839001930787, 5.181714893452096, 5.811577299611971, 7.6145779418661395, 4.946304987591954, 5.259029580745342, 6.123239359573051, 6.35795383504493), # 9
(6.5216650650687455, 7.147909398417212, 6.7417909247512995, 8.04107187789063, 7.199814274110641, 4.061162303658086, 5.361535082046684, 6.012983754220169, 7.878996236164172, 5.117933770714171, 5.441729484858859, 6.335836196195162, 6.578831432825289), # 10
(6.7332757707184046, 7.3762522765017655, 6.957160328122573, 8.298006899369119, 7.431495053657227, 4.190927821951495, 5.532707411804733, 6.204700337422732, 8.130694634823994, 5.281309208277375, 5.615642129836999, 6.538207516740648, 6.78908439717009), # 11
(6.93367105418145, 7.591905124458958, 7.160560053938032, 8.54066173446049, 7.650575434858702, 4.313480436121496, 5.694367343672649, 6.385759376834817, 8.368402463540944, 5.435606048020458, 5.7798892832647475, 6.729331427536055, 6.987651169172428), # 12
(7.121814630574301, 7.793714751892496, 7.3509025478421295, 8.767739006768036, 7.855901945367681, 4.428165025478579, 5.845650338596845, 6.555193200071585, 8.590849048010346, 5.579999037682324, 5.933592712727095, 6.908186034907937, 7.173470189925388), # 13
(7.296670215013373, 7.980527968406071, 7.527100255479318, 8.977941339895034, 8.046321112836791, 4.5343264693332275, 5.9856918575237295, 6.7120341347481975, 8.796763713927538, 5.713662925001867, 6.0758741858090275, 7.073749445182848, 7.345479900522051), # 14
(7.457201522615084, 8.151191583603374, 7.688065622494034, 9.169971357444789, 8.220679464918646, 4.63130964699593, 6.1136273613997005, 6.855314508479805, 8.984875786987855, 5.835772457717993, 6.2058554700955355, 7.224999764687337, 7.502618742055505), # 15
(7.602372268495841, 8.304552407088106, 7.83271109453074, 9.342531683020573, 8.377823529265866, 4.718459437777168, 6.228592311171181, 6.984066648881569, 9.153914592886629, 5.945502383569597, 6.32265833317161, 7.360915099747952, 7.643825155618837), # 16
(7.73114616777206, 8.439457248463958, 7.959949117233882, 9.49432494022569, 8.516599833531071, 4.795120720987429, 6.329722167784569, 7.097322883568655, 9.302609457319187, 6.042027450295574, 6.425404542622239, 7.480473556691244, 7.768037582305133), # 17
(7.842486935560164, 8.55475291733462, 8.068692136247904, 9.624053752663423, 8.635854905366871, 4.860638375937203, 6.416152392186281, 7.194115540156209, 9.429689705980877, 6.1245224056348295, 6.513215866032407, 7.582653241843772, 7.874194463207477), # 18
(7.935358286976559, 8.649286223303795, 8.157852597217262, 9.730420743937053, 8.734435272425893, 4.914357281936967, 6.4870184453227155, 7.273476946259397, 9.533884664567024, 6.192161997326263, 6.585214070987103, 7.666432261532077, 7.961234239418957), # 19
(8.008723937137665, 8.72190397597517, 8.226342945786403, 9.812128537649883, 8.811187462360754, 4.955622318297215, 6.54145578814029, 7.334439429493374, 9.61392365877296, 6.2441209731087675, 6.64052092507132, 7.730788722082713, 8.02809535203266), # 20
(8.061547601159893, 8.771452984952447, 8.273075627599775, 9.86787975740519, 8.864958002824071, 4.983778364328429, 6.578599881585408, 7.376035317473299, 9.668536014294018, 6.279574080721244, 6.678258195870048, 7.774700729822235, 8.073716242141662), # 21
(8.092792994159664, 8.796780059839316, 8.296963088301828, 9.89637702680627, 8.89459342146846, 4.998170299341094, 6.59758618660448, 7.397296937814332, 9.696451056825532, 6.297696067902594, 6.697547650968272, 7.797146391077192, 8.097035350839063), # 22
(8.104314690674112, 8.799778875171468, 8.299938545953362, 9.899944650205763, 8.902185644826078, 5.0, 6.599843201807471, 7.399595061728395, 9.699940987654323, 6.299833818015546, 6.699966429729392, 7.799918061271147, 8.1), # 23
(8.112809930427323, 8.79802962962963, 8.299451851851853, 9.899505555555557, 8.906486090891882, 5.0, 6.598603050108934, 7.3964, 9.699473333333334, 6.29852049382716, 6.699699663299665, 7.799269135802469, 8.1), # 24
(8.121125784169264, 8.794581618655693, 8.29849108367627, 9.898636831275722, 8.910691956475603, 5.0, 6.596159122085048, 7.390123456790125, 9.69854938271605, 6.295935070873343, 6.69917071954109, 7.797988111568358, 8.1), # 25
(8.129261615238427, 8.789487517146778, 8.297069410150893, 9.897348353909464, 8.914803094736884, 5.0, 6.592549374646977, 7.380883950617285, 9.69718098765432, 6.29212056698674, 6.698384387080684, 7.7960925468678575, 8.1), # 26
(8.13721678697331, 8.7828, 8.2952, 9.89565, 8.918819358835371, 5.0, 6.587811764705883, 7.3688, 9.69538, 6.28712, 6.697345454545455, 7.793600000000001, 8.1), # 27
(8.1449906627124, 8.774571742112483, 8.292896021947874, 9.893551646090536, 8.922740601930721, 5.0, 6.581984249172921, 7.353990123456791, 9.693158271604938, 6.2809763877457705, 6.696058710562415, 7.790528029263832, 8.1), # 28
(8.1525826057942, 8.764855418381345, 8.290170644718794, 9.89106316872428, 8.926566677182576, 5.0, 6.575104784959253, 7.3365728395061724, 9.690527654320988, 6.273732748056699, 6.6945289437585735, 7.78689419295839, 8.1), # 29
(8.159991979557198, 8.753703703703705, 8.287037037037036, 9.888194444444444, 8.930297437750589, 5.0, 6.567211328976035, 7.316666666666666, 9.6875, 6.265432098765433, 6.692760942760943, 7.782716049382715, 8.1), # 30
(8.167218147339886, 8.741169272976682, 8.283508367626887, 9.88495534979424, 8.933932736794407, 5.0, 6.558341838134432, 7.2943901234567905, 9.684087160493828, 6.256117457704619, 6.6907594961965335, 7.778011156835849, 8.1), # 31
(8.174260472480764, 8.727304801097395, 8.27959780521262, 9.881355761316874, 8.937472427473677, 5.0, 6.548534269345599, 7.269861728395063, 9.680300987654322, 6.245831842706905, 6.688529392692356, 7.772797073616828, 8.1), # 32
(8.181118318318317, 8.712162962962962, 8.27531851851852, 9.877405555555555, 8.94091636294805, 5.0, 6.537826579520697, 7.243200000000001, 9.676153333333334, 6.234618271604939, 6.6860754208754205, 7.7670913580246905, 8.1), # 33
(8.187791048191048, 8.695796433470507, 8.270683676268861, 9.873114609053498, 8.944264396377173, 5.0, 6.526256725570888, 7.214523456790123, 9.671656049382719, 6.222519762231368, 6.68340236937274, 7.760911568358482, 8.1), # 34
(8.194278025437447, 8.678257887517146, 8.26570644718793, 9.868492798353909, 8.947516380920696, 5.0, 6.513862664407327, 7.183950617283951, 9.666820987654322, 6.209579332418839, 6.680515026811323, 7.754275262917239, 8.1), # 35
(8.200578613396004, 8.6596, 8.2604, 9.86355, 8.950672169738269, 5.0, 6.500682352941176, 7.151600000000001, 9.66166, 6.1958400000000005, 6.677418181818182, 7.747200000000001, 8.1), # 36
(8.20669217540522, 8.639875445816186, 8.254777503429356, 9.85829609053498, 8.953731615989538, 5.0, 6.486753748083595, 7.11759012345679, 9.656184938271606, 6.1813447828075, 6.674116623020328, 7.739703337905808, 8.1), # 37
(8.212618074803581, 8.619136899862827, 8.248852126200275, 9.85274094650206, 8.956694572834152, 5.0, 6.4721148067457435, 7.0820395061728405, 9.650407654320988, 6.166136698673983, 6.670615139044769, 7.7318028349337, 8.1), # 38
(8.218355674929589, 8.597437037037038, 8.242637037037039, 9.846894444444445, 8.959560893431762, 5.0, 6.456803485838781, 7.045066666666667, 9.644340000000001, 6.150258765432099, 6.666918518518519, 7.723516049382716, 8.1), # 39
(8.22390433912173, 8.574828532235939, 8.236145404663922, 9.84076646090535, 8.962330430942016, 5.0, 6.440857742273865, 7.006790123456792, 9.637993827160495, 6.133754000914496, 6.663031550068587, 7.714860539551899, 8.1), # 40
(8.229263430718502, 8.551364060356653, 8.229390397805213, 9.834366872427985, 8.965003038524562, 5.0, 6.424315532962156, 6.967328395061729, 9.631380987654321, 6.116665422953818, 6.658959022321986, 7.705853863740284, 8.1), # 41
(8.2344323130584, 8.527096296296298, 8.222385185185187, 9.827705555555557, 8.967578569339047, 5.0, 6.4072148148148145, 6.9268, 9.624513333333335, 6.0990360493827165, 6.654705723905725, 7.696513580246914, 8.1), # 42
(8.239410349479915, 8.50207791495199, 8.215142935528121, 9.820792386831277, 8.970056876545122, 5.0, 6.389593544743001, 6.8853234567901245, 9.617402716049384, 6.080908898033837, 6.650276443446813, 7.6868572473708285, 8.1), # 43
(8.244196903321543, 8.47636159122085, 8.2076768175583, 9.813637242798356, 8.972437813302436, 5.0, 6.371489679657872, 6.843017283950619, 9.610060987654322, 6.062326986739826, 6.645675969572266, 7.676902423411066, 8.1), # 44
(8.248791337921773, 8.450000000000001, 8.200000000000001, 9.80625, 8.974721232770637, 5.0, 6.352941176470589, 6.800000000000001, 9.6025, 6.043333333333334, 6.640909090909091, 7.666666666666666, 8.1), # 45
(8.253193016619106, 8.423045816186557, 8.192125651577504, 9.798640534979425, 8.976906988109373, 5.0, 6.333985992092311, 6.756390123456791, 9.594731604938271, 6.023970955647005, 6.635980596084299, 7.656167535436672, 8.1), # 46
(8.257401302752028, 8.39555171467764, 8.18406694101509, 9.790818724279836, 8.978994932478294, 5.0, 6.3146620834341975, 6.712306172839506, 9.586767654320989, 6.004282871513489, 6.630895273724903, 7.64542258802012, 8.1), # 47
(8.261415559659037, 8.367570370370371, 8.175837037037038, 9.782794444444447, 8.980984919037049, 5.0, 6.295007407407407, 6.667866666666668, 9.57862, 5.984312098765432, 6.625657912457912, 7.634449382716049, 8.1), # 48
(8.26523515067863, 8.339154458161865, 8.167449108367627, 9.774577572016462, 8.982876800945286, 5.0, 6.275059920923102, 6.623190123456791, 9.57030049382716, 5.964101655235483, 6.6202733009103385, 7.623265477823503, 8.1), # 49
(8.268859439149294, 8.310356652949247, 8.15891632373114, 9.766177983539094, 8.984670431362652, 5.0, 6.25485758089244, 6.578395061728395, 9.56182098765432, 5.943694558756287, 6.61474622770919, 7.611888431641519, 8.1), # 50
(8.272287788409528, 8.28122962962963, 8.150251851851852, 9.757605555555557, 8.9863656634488, 5.0, 6.23443834422658, 6.5336, 9.553193333333335, 5.923133827160494, 6.609081481481482, 7.600335802469137, 8.1), # 51
(8.275519561797823, 8.251826063100138, 8.141468861454047, 9.748870164609054, 8.987962350363372, 5.0, 6.213840167836683, 6.488923456790123, 9.54442938271605, 5.90246247828075, 6.603283850854222, 7.588625148605397, 8.1), # 52
(8.278554122652675, 8.222198628257889, 8.132580521262005, 9.739981687242798, 8.989460345266023, 5.0, 6.1931010086339064, 6.444483950617284, 9.535540987654322, 5.881723529949703, 6.597358124454421, 7.576774028349337, 8.1), # 53
(8.281390834312573, 8.192400000000001, 8.1236, 9.73095, 8.990859501316402, 5.0, 6.172258823529412, 6.400399999999999, 9.52654, 5.86096, 6.59130909090909, 7.5648, 8.1), # 54
(8.284029060116017, 8.162482853223594, 8.114540466392318, 9.721784979423868, 8.992159671674152, 5.0, 6.151351569434358, 6.35679012345679, 9.517438271604938, 5.84021490626429, 6.585141538845242, 7.552720621856425, 8.1), # 55
(8.286468163401498, 8.132499862825789, 8.105415089163237, 9.712496502057613, 8.993360709498928, 5.0, 6.130417203259905, 6.313772839506173, 9.508247654320988, 5.819531266575218, 6.578860256889887, 7.54055345221765, 8.1), # 56
(8.288707507507507, 8.102503703703704, 8.096237037037039, 9.703094444444446, 8.994462467950374, 5.0, 6.109493681917211, 6.271466666666668, 9.498980000000001, 5.798952098765433, 6.572470033670034, 7.528316049382716, 8.1), # 57
(8.290746455772544, 8.072547050754459, 8.087019478737998, 9.693588683127572, 8.99546480018814, 5.0, 6.088618962317438, 6.2299901234567905, 9.489647160493828, 5.778520420667582, 6.565975657812697, 7.516025971650663, 8.1), # 58
(8.292584371535098, 8.042682578875171, 8.077775582990398, 9.683989094650206, 8.996367559371876, 5.0, 6.067831001371743, 6.189461728395062, 9.480260987654322, 5.758279250114313, 6.55938191794488, 7.503700777320531, 8.1), # 59
(8.294220618133663, 8.012962962962964, 8.068518518518518, 9.674305555555556, 8.99717059866123, 5.0, 6.0471677559912855, 6.15, 9.470833333333335, 5.738271604938272, 6.552693602693603, 7.491358024691358, 8.1), # 60
(8.295654558906731, 7.983440877914953, 8.05926145404664, 9.664547942386832, 8.997873771215849, 5.0, 6.026667183087227, 6.1117234567901235, 9.461376049382716, 5.718540502972108, 6.545915500685871, 7.4790152720621865, 8.1), # 61
(8.296885557192804, 7.954168998628258, 8.050017558299041, 9.654726131687244, 8.998476930195388, 5.0, 6.006367239570725, 6.074750617283951, 9.451900987654321, 5.699128962048469, 6.539052400548697, 7.4666900777320535, 8.1), # 62
(8.297912976330368, 7.9252, 8.0408, 9.644850000000002, 8.998979928759486, 5.0, 5.986305882352941, 6.039200000000001, 9.44242, 5.68008, 6.532109090909092, 7.4544, 8.1), # 63
(8.298736179657919, 7.896586556927298, 8.0316219478738, 9.634929423868314, 8.999382620067799, 5.0, 5.966521068345034, 6.005190123456791, 9.432944938271605, 5.661436634659351, 6.5250903603940635, 7.442162597165067, 8.1), # 64
(8.29935453051395, 7.86838134430727, 8.02249657064472, 9.624974279835392, 8.999684857279973, 5.0, 5.947050754458163, 5.972839506172839, 9.423487654320988, 5.643241883859168, 6.518000997630629, 7.429995427526291, 8.1), # 65
(8.299767392236957, 7.840637037037038, 8.013437037037038, 9.614994444444445, 8.999886493555659, 5.0, 5.927932897603486, 5.942266666666668, 9.414060000000001, 5.625538765432099, 6.510845791245791, 7.417916049382717, 8.1), # 66
(8.299974128165434, 7.813406310013717, 8.004456515775034, 9.604999794238683, 8.999987382054504, 5.0, 5.909205454692165, 5.913590123456792, 9.404673827160494, 5.608370297210792, 6.5036295298665685, 7.405942021033379, 8.1), # 67
(8.29983329158466, 7.786598911456259, 7.9955247599451305, 9.594913392377887, 8.999902364237876, 4.99990720926688, 5.890812155863717, 5.88667508001829, 9.395270278920897, 5.591696353317733, 6.496228790832301, 7.394024017519794, 8.099900120027435), # 68
(8.298513365539453, 7.75939641577061, 7.98639074074074, 9.584226811594203, 8.99912854030501, 4.999173662551441, 5.872214545077291, 5.860079012345679, 9.385438271604938, 5.575045112563544, 6.487890271132376, 7.38177517868746, 8.099108796296298), # 69
(8.295908630047116, 7.731673967874684, 7.977014746227709, 9.572869699409555, 8.997599451303154, 4.9977290047248895, 5.853328107649096, 5.833561957018748, 9.375122313671698, 5.558335619570188, 6.478519109220864, 7.369138209034247, 8.097545867626888), # 70
(8.292055728514343, 7.703448134873224, 7.967400068587105, 9.560858803005905, 8.995334463003308, 4.995596646852614, 5.8341613276311906, 5.807132693187015, 9.364337768632831, 5.541568287474112, 6.468149896627089, 7.356122349770172, 8.095231910150892), # 71
(8.286991304347827, 7.674735483870967, 7.9575499999999995, 9.548210869565217, 8.99235294117647, 4.992800000000001, 5.81472268907563, 5.7808, 9.353100000000001, 5.524743529411765, 6.456817224880384, 7.342736842105264, 8.0921875), # 72
(8.280752000954257, 7.6455525819726535, 7.947467832647462, 9.534942646269458, 8.988674251593642, 4.989362475232434, 5.795020676034474, 5.754572656607225, 9.341424371284866, 5.507861758519595, 6.444555685510071, 7.328990927249535, 8.0884332133059), # 73
(8.273374461740323, 7.615915996283022, 7.937156858710562, 9.52107088030059, 8.98431776002582, 4.985307483615303, 5.775063772559778, 5.728459442158208, 9.329326245999086, 5.49092338793405, 6.431399870045485, 7.314893846413014, 8.083989626200276), # 74
(8.26489533011272, 7.5858422939068095, 7.92662037037037, 9.50661231884058, 8.97930283224401, 4.980658436213993, 5.754860462703601, 5.7024691358024695, 9.31682098765432, 5.473928830791576, 6.417384370015949, 7.300454840805718, 8.078877314814816), # 75
(8.255351249478142, 7.55534804194876, 7.915861659807956, 9.49158370907139, 8.973648834019205, 4.975438744093889, 5.734419230517997, 5.6766105166895295, 9.303923959762232, 5.4568785002286235, 6.402543776950793, 7.2856831516376666, 8.073116855281206), # 76
(8.244778863243274, 7.524449807513609, 7.904884019204388, 9.476001798174986, 8.967375131122408, 4.9696718183203785, 5.7137485600550235, 5.650892363968908, 9.290650525834478, 5.43977280938164, 6.38691268237935, 7.270588020118885, 8.06672882373114), # 77
(8.233214814814815, 7.493164157706095, 7.893690740740741, 9.459883333333334, 8.96050108932462, 4.963381069958848, 5.69285693536674, 5.625323456790124, 9.277016049382715, 5.422612171387073, 6.370525677830941, 7.255178687459391, 8.059733796296298), # 78
(8.220695747599452, 7.461507659630958, 7.88228511659808, 9.443245061728396, 8.953046074396838, 4.956589910074683, 5.671752840505201, 5.5999125743026985, 9.26303589391861, 5.405396999381371, 6.353417354834898, 7.239464394869204, 8.052152349108367), # 79
(8.207258305003878, 7.429496880392938, 7.870670438957475, 9.426103730542136, 8.945029452110063, 4.949321749733272, 5.650444759522465, 5.574668495656151, 9.248725422953818, 5.388127706500981, 6.335622304920551, 7.223454383558348, 8.04400505829904), # 80
(8.192939130434784, 7.397148387096775, 7.85885, 9.408476086956524, 8.936470588235293, 4.9416, 5.628941176470589, 5.549600000000001, 9.2341, 5.370804705882353, 6.317175119617225, 7.207157894736842, 8.0353125), # 81
(8.177774867298861, 7.364478746847206, 7.8468270919067225, 9.390378878153516, 8.927388848543533, 4.933448071940254, 5.607250575401629, 5.524715866483768, 9.219174988568815, 5.353428410661933, 6.298110390454251, 7.190584169614709, 8.026095250342937), # 82
(8.161802159002804, 7.331504526748971, 7.834605006858711, 9.371828851315083, 8.917803598805778, 4.924889376619419, 5.585381440367643, 5.500024874256973, 9.203965752171925, 5.335999233976169, 6.278462708960955, 7.17374244940197, 8.016373885459535), # 83
(8.145057648953301, 7.29824229390681, 7.822187037037037, 9.35284275362319, 8.907734204793028, 4.915947325102881, 5.563342255420687, 5.475535802469135, 9.188487654320987, 5.3185175889615115, 6.258266666666667, 7.156641975308642, 8.006168981481482), # 84
(8.127577980557048, 7.264708615425461, 7.80957647462277, 9.333437332259797, 8.897200032276286, 4.906645328456029, 5.54114150461282, 5.451257430269777, 9.172756058527662, 5.300983888754405, 6.237556855100715, 7.13929198854475, 7.995501114540467), # 85
(8.10939979722073, 7.230920058409665, 7.796776611796983, 9.313629334406873, 8.886220447026547, 4.897006797744247, 5.518787671996097, 5.4271985368084135, 9.156786328303614, 5.283398546491299, 6.216367865792428, 7.121701730320315, 7.984390860768176), # 86
(8.090559742351045, 7.1968931899641575, 7.7837907407407405, 9.293435507246377, 8.874814814814817, 4.887055144032922, 5.496289241622575, 5.403367901234568, 9.140593827160496, 5.265761975308642, 6.194734290271132, 7.103880441845354, 7.972858796296297), # 87
(8.071094459354686, 7.162644577193681, 7.7706221536351165, 9.27287259796028, 8.863002501412089, 4.876813778387441, 5.473654697544313, 5.37977430269776, 9.124193918609969, 5.248074588342881, 6.172690720066159, 7.085837364329892, 7.960925497256517), # 88
(8.051040591638339, 7.128190787202974, 7.75727414266118, 9.251957353730543, 8.850802872589366, 4.8663061118731905, 5.4508925238133665, 5.356426520347508, 9.107601966163696, 5.230336798730466, 6.150271746706835, 7.067581738983948, 7.948611539780521), # 89
(8.030434782608696, 7.093548387096774, 7.74375, 9.230706521739132, 8.838235294117649, 4.855555555555556, 5.428011204481793, 5.333333333333333, 9.090833333333334, 5.2125490196078434, 6.1275119617224885, 7.049122807017544, 7.9359375000000005), # 90
(8.00931367567245, 7.058733943979822, 7.730053017832647, 9.20913684916801, 8.825319131767932, 4.8445855204999235, 5.405019223601649, 5.3105035208047555, 9.073903383630546, 5.194711664111461, 6.104445956642448, 7.0304698096406995, 7.922923954046638), # 91
(7.9877139142362985, 7.023764024956858, 7.716186488340192, 9.187265083199142, 8.812073751311223, 4.833419417771681, 5.381925065224994, 5.287945861911295, 9.056827480566987, 5.176825145377768, 6.081108322996043, 7.011631988063439, 7.909591478052126), # 92
(7.965672141706924, 6.988655197132617, 7.702153703703704, 9.165107971014494, 8.798518518518518, 4.822080658436214, 5.358737213403881, 5.26566913580247, 9.039620987654322, 5.15888987654321, 6.0575336523126, 6.992618583495776, 7.895960648148147), # 93
(7.943225001491024, 6.953424027611842, 7.6879579561042535, 9.142682259796029, 8.784672799160816, 4.810592653558909, 5.335464152190369, 5.243682121627802, 9.022299268404208, 5.140906270744238, 6.033756536121448, 6.973438837147739, 7.882052040466393), # 94
(7.920409136995288, 6.9180870834992705, 7.673602537722909, 9.120004696725712, 8.770555959009119, 4.798978814205152, 5.312114365636515, 5.221993598536809, 9.004877686328305, 5.122874741117297, 6.009811565951917, 6.954101990229344, 7.867886231138546), # 95
(7.89726119162641, 6.882660931899643, 7.659090740740742, 9.097092028985507, 8.756187363834423, 4.787262551440329, 5.288696337794377, 5.200612345679013, 8.987371604938271, 5.104795700798839, 5.985733333333334, 6.934617283950619, 7.853483796296297), # 96
(7.873817808791078, 6.847162139917697, 7.64442585733882, 9.07396100375738, 8.741586379407732, 4.775467276329827, 5.265218552716011, 5.179547142203933, 8.969796387745772, 5.086669562925308, 5.961556429795026, 6.914993959521576, 7.838865312071332), # 97
(7.850115631895988, 6.811607274658171, 7.629611179698216, 9.050628368223297, 8.726772371500042, 4.763616399939035, 5.241689494453475, 5.158806767261089, 8.952167398262459, 5.068496740633154, 5.937315446866325, 6.895241258152239, 7.824051354595337), # 98
(7.826191304347827, 6.776012903225807, 7.614650000000001, 9.027110869565218, 8.711764705882354, 4.751733333333333, 5.218117647058825, 5.138400000000001, 8.9345, 5.050277647058824, 5.913044976076556, 6.875368421052632, 7.8090625000000005), # 99
(7.80208146955329, 6.740395592725341, 7.59954561042524, 9.00342525496511, 8.696582748325667, 4.739841487578113, 5.194511494584116, 5.118335619570188, 8.916809556470051, 5.032012695338767, 5.888779608955048, 6.855384689432774, 7.79391932441701), # 100
(7.777822770919068, 6.704771910261517, 7.584301303155008, 8.979588271604939, 8.681245864600985, 4.727964273738759, 5.17087952108141, 5.09862240512117, 8.899111431184272, 5.013702298609431, 5.86455393703113, 6.835299304502683, 7.7786424039780515), # 101
(7.753451851851853, 6.669158422939069, 7.56892037037037, 8.955616666666668, 8.665773420479303, 4.7161251028806594, 5.1472302106027605, 5.07926913580247, 8.881420987654321, 4.995346870007263, 5.840402551834131, 6.815121507472385, 7.763252314814816), # 102
(7.729005355758336, 6.633571697862738, 7.5534061042524, 8.93152718733226, 8.650184781731623, 4.704347386069197, 5.123572047200224, 5.060284590763604, 8.86375358939186, 4.976946822668712, 5.816360044893379, 6.794860539551898, 7.747769633058984), # 103
(7.704519926045208, 6.598028302137263, 7.537761796982167, 8.907336580783683, 8.634499314128943, 4.692654534369761, 5.099913514925861, 5.041677549154093, 8.846124599908551, 4.958502569730225, 5.792461007738201, 6.774525641951243, 7.732214934842251), # 104
(7.680032206119162, 6.562544802867383, 7.5219907407407405, 8.883061594202898, 8.618736383442267, 4.681069958847737, 5.076263097831727, 5.023456790123458, 8.82854938271605, 4.940014524328251, 5.768740031897927, 6.754126055880443, 7.716608796296296), # 105
(7.655578839386891, 6.527137767157839, 7.5060962277091905, 8.858718974771874, 8.602915355442589, 4.669617070568511, 5.052629279969876, 5.005631092821217, 8.811043301326016, 4.921483099599236, 5.745231708901884, 6.733671022549515, 7.700971793552812), # 106
(7.631196469255085, 6.491823762113369, 7.490081550068588, 8.83432546967257, 8.587055595900912, 4.65831928059747, 5.0290205453923695, 4.988209236396892, 8.793621719250115, 4.9029087086796315, 5.721970630279402, 6.713169783168484, 7.685324502743484), # 107
(7.606921739130435, 6.456619354838711, 7.473950000000001, 8.809897826086958, 8.571176470588235, 4.647200000000001, 5.0054453781512604, 4.9712000000000005, 8.7763, 4.884291764705883, 5.698991387559809, 6.69263157894737, 7.669687500000001), # 108
(7.582791292419635, 6.421541112438604, 7.4577048696845, 8.785452791196994, 8.55529734527556, 4.636282639841488, 4.98191226229861, 4.954612162780065, 8.759093507087334, 4.865632680814438, 5.676328572272432, 6.67206565109619, 7.654081361454047), # 109
(7.558841772529373, 6.38660560201779, 7.441349451303157, 8.761007112184648, 8.539437585733884, 4.625590611187319, 4.9584296818864715, 4.938454503886603, 8.742017604023777, 4.846931870141747, 5.654016775946601, 6.651481240824971, 7.638526663237312), # 110
(7.535109822866345, 6.351829390681004, 7.424887037037038, 8.736577536231884, 8.523616557734206, 4.615147325102881, 4.935006120966905, 4.922735802469136, 8.725087654320989, 4.828189745824256, 5.632090590111643, 6.630887589343731, 7.623043981481482), # 111
(7.51163208683724, 6.317229045532987, 7.408320919067217, 8.712180810520666, 8.507853627047528, 4.6049761926535595, 4.911650063591967, 4.907464837677184, 8.708319021490626, 4.809406720998413, 5.610584606296888, 6.6102939378624885, 7.607653892318244), # 112
(7.488403378962436, 6.282878895028762, 7.391694262601655, 8.687867105993632, 8.492140544138964, 4.595095815371611, 4.888420770925416, 4.892682055024485, 8.691770249006897, 4.790643789290184, 5.589539124922293, 6.589754349203543, 7.592355120674577), # 113
(7.465184718320052, 6.249117746820429, 7.375236540017295, 8.663831537021869, 8.476314683653062, 4.585483686823921, 4.865614566728464, 4.878569007604096, 8.675695228570449, 4.772252134330226, 5.568995469690558, 6.56952973769038, 7.577020331328028), # 114
(7.441907922403196, 6.215957758946438, 7.358957546165854, 8.640067604145424, 8.460326142310882, 4.576114809999011, 4.84324772015325, 4.865122123422967, 8.660099982935032, 4.754260262390462, 5.548923609141675, 6.549630066047081, 7.561605305328301), # 115
(7.418543898590108, 6.183350625033362, 7.342825751987099, 8.616532920213123, 8.444150821107023, 4.566967101829678, 4.821283854022315, 4.852304250319195, 8.644945071382265, 4.736634686759638, 5.529284745017185, 6.530018557989877, 7.546085807804713), # 116
(7.395063554259018, 6.151248038707777, 7.326809628420789, 8.593185098073794, 8.427764621036088, 4.558018479248712, 4.799686591158202, 4.840078236130868, 8.630191053193762, 4.719341920726503, 5.510040079058626, 6.5106584372350005, 7.53043760388658), # 117
(7.371437796788169, 6.119601693596259, 7.310877646406694, 8.569981750576266, 8.411143443092675, 4.549246859188911, 4.7784195543834524, 4.828406928696078, 8.615798487651148, 4.7023484775798075, 5.49115081300754, 6.49151292749868, 7.51463645870322), # 118
(7.347637533555794, 6.088363283325384, 7.294998276884579, 8.546880490569364, 8.394263188271378, 4.540630158583066, 4.757446366520605, 4.817253175852916, 8.601727934036035, 4.685620870608298, 5.4725781486054625, 6.472545252497148, 7.498658137383946), # 119
(7.323633671940129, 6.057484501521727, 7.27913999079421, 8.523838930901915, 8.377099757566798, 4.532146294363972, 4.736730650392203, 4.806579825439474, 8.587939951630046, 4.669125613100724, 5.454283287593933, 6.453718635946638, 7.482478405058078), # 120
(7.299397119319415, 6.026917041811863, 7.26327125907535, 8.500814684422748, 8.359629051973535, 4.523773183464424, 4.716236028820784, 4.796349725293846, 8.574395099714799, 4.652829218345837, 5.436227431714493, 6.434996301563378, 7.466073026854929), # 121
(7.274898783071883, 5.996612597822369, 7.247360552667769, 8.477765363980685, 8.341826972486187, 4.515488742817215, 4.695926124628894, 4.786525723254119, 8.561053937571911, 4.636698199632382, 5.4183717827086815, 6.416341473063601, 7.4494177679038165), # 122
(7.250109570575775, 5.9665228631798195, 7.231376342511229, 8.454648582424555, 8.323669420099353, 4.50727088935514, 4.675764560639071, 4.7770706671583865, 8.547877024483004, 4.62069907024911, 5.400677542318036, 6.397717374163538, 7.432488393334058), # 123
(7.225000389209324, 5.93659953151079, 7.215287099545496, 8.43142195260319, 8.30513229580763, 4.499097540010991, 4.655714959673856, 4.767947404844741, 8.534824919729692, 4.604798343484769, 5.383105912284096, 6.3790872285794205, 7.4152606682749695), # 124
(7.199542146350767, 5.9067942964418565, 7.199061294710339, 8.408043087365408, 8.286191500605618, 4.490946611717565, 4.635740944555791, 4.759118784151273, 8.521858182593595, 4.588962532628107, 5.3656180943484015, 6.360414260027479, 7.397710357855863), # 125
(7.1737057493783425, 5.877058851599596, 7.182667398945519, 8.384469599560044, 8.266822935487914, 4.482796021407654, 4.615806138107416, 4.750547652916074, 8.508937372356334, 4.573158150967874, 5.348175290252491, 6.341661692223948, 7.379813227206063), # 126
(7.147462105670289, 5.84734489061058, 7.166073883190804, 8.36065910203592, 8.247002501449119, 4.474623686014052, 4.595874163151275, 4.742196858977237, 8.496023048299525, 4.557351711792819, 5.3307387017379035, 6.322792748885053, 7.361545041454879), # 127
(7.120782122604837, 5.817604107101388, 7.14924921838596, 8.336569207641865, 8.226706099483833, 4.466407522469555, 4.575908642509906, 4.73402925017285, 8.483075769704788, 4.5415097283916905, 5.3132695305461795, 6.303770653727031, 7.34288156573163), # 128
(7.093636707560226, 5.787788194698593, 7.132161875470752, 8.312157529226706, 8.20590963058665, 4.458125447706956, 4.555873199005851, 4.726007674341008, 8.47005609585374, 4.5255987140532365, 5.2957289784188575, 6.284558630466109, 7.323798565165631), # 129
(7.065996767914694, 5.757848847028773, 7.1147803253849435, 8.28738167963927, 8.18458899575217, 4.449755378659047, 4.53573145546165, 4.7180949793198, 8.456924586028, 4.509585182066206, 5.278078247097476, 6.2651199028185225, 7.3042718048861985), # 130
(7.037833211046475, 5.727737757718502, 7.097073039068305, 8.262199271728381, 8.162720095974995, 4.441275232258625, 4.515447034699847, 4.71025401294732, 8.443641799509189, 4.493435645719348, 5.260278538323575, 6.2454176945004996, 7.2842770500226495), # 131
(7.009116944333808, 5.697406620394355, 7.079008487460597, 8.23656791834287, 8.140278832249724, 4.432662925438482, 4.49498355954298, 4.7024476230616585, 8.430168295578923, 4.4771166183014115, 5.2422910538386915, 6.225415229228274, 7.263790065704301), # 132
(6.979818875154931, 5.666807128682908, 7.060555141501587, 8.210445232331562, 8.11724110557095, 4.423896375131413, 4.474304652813592, 4.694638657500906, 8.416464633518821, 4.460594613101146, 5.224076995384369, 6.205075730718074, 7.242786617060469), # 133
(6.949909910888076, 5.635890976210739, 7.041681472131043, 8.183788826543283, 8.093582816933274, 4.414953498270212, 4.453373937334223, 4.686789964103155, 8.402491372610504, 4.443836143407299, 5.205597564702143, 6.184362422686133, 7.221242469220467), # 134
(6.919360958911483, 5.604609856604419, 7.022355950288727, 8.156556313826863, 8.069279867331296, 4.405812211787674, 4.432155035927415, 4.678864390706496, 8.388209072135584, 4.426807722508621, 5.186813963533554, 6.163238528848682, 7.199133387313616), # 135
(6.888142926603388, 5.572915463490528, 7.002547046914407, 8.128705307031124, 8.044308157759614, 4.396450432616592, 4.410611571415708, 4.670824785149022, 8.373578291375685, 4.409475863693858, 5.167687393620142, 6.1416672729219535, 7.176435136469229), # 136
(6.856226721342027, 5.540759490495638, 6.982223232947849, 8.100193419004901, 8.018643589212827, 4.386846077689759, 4.388707166621645, 4.662633995268823, 8.358559589612426, 4.391807080251762, 5.1481790567034444, 6.119611878622176, 7.153123481816621), # 137
(6.823583250505639, 5.508093631246327, 6.961352979328814, 8.070978262597011, 7.992262062685535, 4.376977063939971, 4.366405444367763, 4.654254868903992, 8.343113526127425, 4.373767885471078, 5.128250154525002, 6.097035569665582, 7.129174188485113), # 138
(6.790183421472455, 5.4748695793691695, 6.939904756997072, 8.041017450656287, 7.965139479172333, 4.366821308300021, 4.343670027476608, 4.64565025389262, 8.327200660202298, 4.355324792640558, 5.107861888826353, 6.073901569768405, 7.104563021604015), # 139
(6.755998141620719, 5.44103902849074, 6.91784703689239, 8.010268596031556, 7.937251739667824, 4.356356727702703, 4.320464538770717, 4.636782998072797, 8.310781551118666, 4.336444315048949, 5.086975461349035, 6.050173102646873, 7.079265746302652), # 140
(6.720998318328665, 5.406553672237617, 6.895148289954529, 7.978689311571642, 7.908574745166603, 4.345561239080812, 4.296752601072636, 4.6276159492826165, 8.293816758158144, 4.317092965985001, 5.065552073834591, 6.02581339201722, 7.053258127710331), # 141
(6.685154858974525, 5.371365204236373, 6.871776987123257, 7.946237210125377, 7.87908439666327, 4.334412759367142, 4.272497837204901, 4.6181119553601695, 8.276266840602354, 4.2972372587374625, 5.043552928024558, 6.000785661595676, 7.026515930956373), # 142
(6.64843867093654, 5.335425318113585, 6.8477015993383406, 7.91286990454158, 7.848756595152423, 4.322889205494485, 4.247663869990055, 4.608233864143545, 8.258092357732918, 4.276843706595082, 5.020939225660475, 5.975053135098472, 6.999014921170094), # 143
(6.610820661592948, 5.298685707495829, 6.822890597539542, 7.878545007669086, 7.817567241628663, 4.310968494395637, 4.222214322250639, 4.597944523470839, 8.239253868831447, 4.255878822846608, 4.997672168483881, 5.948579036241839, 6.970730863480812), # 144
(6.572271738321982, 5.26109806600968, 6.797312452666631, 7.843220132356716, 7.785492237086586, 4.298628543003392, 4.196112816809195, 4.587206781180141, 8.219711933179564, 4.23430912078079, 4.973712958236316, 5.921326588742011, 6.94163952301784), # 145
(6.5327628085018805, 5.2226140872817135, 6.770935635659374, 7.806852891453301, 7.7525074825207945, 4.285847268250545, 4.169322976488264, 4.575983485109542, 8.199427110058885, 4.212101113686376, 4.949022796659319, 5.893259016315216, 6.911716664910495), # 146
(6.49226477951088, 5.1831854649385045, 6.743728617457528, 7.769400897807664, 7.718588878925882, 4.272602587069886, 4.141808424110385, 4.564237483097132, 8.178359958751033, 4.189221314852117, 4.923562885494429, 5.864339542677689, 6.8809380542880945), # 147
(6.450748558727217, 5.142763892606631, 6.715659869000866, 7.730821764268637, 7.683712327296449, 4.258872416394214, 4.113532782498101, 4.551931622981006, 8.156471038537623, 4.1656362375667575, 4.897294426483186, 5.8345313915456565, 6.8492794562799535), # 148
(6.40818505352913, 5.101301063912665, 6.686697861229155, 7.691073103685042, 7.647853728627097, 4.24463467315632, 4.084459674473953, 4.539028752599253, 8.13372090870027, 4.1413123951190505, 4.870178621367128, 5.803797786635354, 6.81671663601539), # 149
(6.364545171294852, 5.058748672483183, 6.656811065082156, 7.65011252890571, 7.610988983912421, 4.229867274288999, 4.054552722860481, 4.525491719789965, 8.110070128520602, 4.116216300797741, 4.8421766718877945, 5.772101951663011, 6.783225358623717), # 150
(6.31979981940262, 5.015058411944763, 6.625967951499634, 7.607897652779464, 7.573093994147022, 4.214548136725044, 4.023775550480226, 4.511283372391235, 8.085479257280232, 4.090314467891583, 4.813249779786724, 5.739407110344858, 6.748781389234255), # 151
(6.273919905230675, 4.970181975923978, 6.594136991421362, 7.5643860881551355, 7.534144660325495, 4.198655177397251, 3.992091780155732, 4.496366558241153, 8.059908854260776, 4.06357340968932, 4.7833591468054575, 5.705676486397127, 6.713360492976318), # 152
(6.226876336157249, 4.924071058047406, 6.561286655787095, 7.519535447881546, 7.4941168834424445, 4.182166313238413, 3.9594650347095355, 4.48070412517781, 8.03331947874386, 4.035959639479703, 4.752465974685533, 5.670873303536052, 6.676938434979222), # 153
(6.178640019560583, 4.87667735194162, 6.527385415536607, 7.473303344807528, 7.452986564492464, 4.165059461181324, 3.9258589369641825, 4.464258921039298, 8.005671690011093, 4.0074396705514825, 4.72053146516849, 5.63496078547786, 6.639490980372286), # 154
(6.129181862818909, 4.827952551233196, 6.492401741609661, 7.425647391781903, 7.410729604470157, 4.147312538158777, 3.891237109742209, 4.446993793663709, 7.976926047344103, 3.9779800161934036, 4.687516819995866, 5.597902155938786, 6.600993894284821), # 155
(6.078472773310465, 4.7778483495487105, 6.456304104946021, 7.3765252016535, 7.367321904370119, 4.128903461103569, 3.85556317586616, 4.428871590889135, 7.947043110024501, 3.9475471896942183, 4.6533832409092035, 5.559660638635059, 6.561422941846148), # 156
(6.02648365841349, 4.726316440514739, 6.419060976485454, 7.32589438727115, 7.322739365186948, 4.109810146948491, 3.8188007581585754, 4.409855160553666, 7.915983437333911, 3.9161077043426733, 4.618091929650039, 5.52019945728291, 6.520753888185581), # 157
(5.971744757124192, 4.672362496617807, 6.378873563121885, 7.271815665320995, 7.274944884696798, 4.088819581053688, 3.780085376742286, 4.388637561879498, 7.881329673279279, 3.882692733032915, 4.580476602031154, 5.478079651355472, 6.477188687532276), # 158
(5.9058294135827225, 4.610452255679582, 6.32539025472239, 7.203181727030763, 7.212153047825303, 4.058951718405683, 3.734570210708573, 4.357770826211506, 7.829141808977716, 3.8418247952789963, 4.533933548495195, 5.425090018946487, 6.420342117536156), # 159
(5.827897675923448, 4.540077382832571, 6.257536766364711, 7.118862008327088, 7.133136105077437, 4.019473036838147, 3.6817949987070273, 4.316479351621878, 7.757940181782921, 3.792964521490315, 4.477807606887632, 5.360401559110278, 6.349136487114865), # 160
(5.738577643668768, 4.461696694464375, 6.1760375775282474, 7.019658003005382, 7.038714499425691, 3.970861793256251, 3.622145156805501, 4.265280426487824, 7.668663813599214, 3.7365265545367503, 4.412593323679766, 5.284613975126057, 6.264299235855278), # 161
(5.638497416341085, 4.375769006962591, 6.0816171676923965, 6.9063712048610615, 6.929708673842564, 3.9135962445651646, 3.5560061010718473, 4.204691339186562, 7.56225172633091, 3.6729255372881853, 4.338785245342897, 5.198326970273035, 6.166557803344267), # 162
(5.528285093462799, 4.2827531367148195, 5.975000016336562, 6.779803107689547, 6.806939071300551, 3.848154647670058, 3.4837632475739206, 4.1352293780953, 7.439642941882325, 3.6025761126145, 4.2568779183483265, 5.102140247830427, 6.0566396291687035), # 163
(5.408568774556308, 4.183107900108657, 5.856910602940141, 6.640755205286254, 6.6712261347721515, 3.7750152594761035, 3.405802012379573, 4.0574118315912555, 7.301776482157779, 3.525892923385575, 4.167365889167357, 4.996653511077443, 5.935272152915463), # 164
(5.279976559144014, 4.077292113531706, 5.728073406982535, 6.490028991446602, 6.523390307229859, 3.6946563368884693, 3.3225078115566578, 3.971755988051637, 7.149591369061584, 3.4432906124712908, 4.0707437042712895, 4.882466463293296, 5.803182814171416), # 165
(5.143136546748318, 3.9657645933715635, 5.589212907943143, 6.328425959966001, 6.3642520316461715, 3.607556136812327, 3.234266061173029, 3.878779135853662, 6.984026624498059, 3.35518382274153, 3.9675059101314236, 4.760178807757201, 5.661099052523436), # 166
(4.998676836891619, 3.8489841560158298, 5.441053585301364, 6.156747604639875, 6.194631750993584, 3.514192916152847, 3.14146217729654, 3.7789985633745413, 6.80602127037152, 3.2619871970661714, 3.858147053219062, 4.630390247748367, 5.509748307558397), # 167
(4.847225529096317, 3.727409617852103, 5.284319918536599, 5.975795419263637, 6.015349908244594, 3.415044931815199, 3.0444815759950434, 3.672931558991488, 6.616514328586284, 3.1641153783150977, 3.743161680005505, 4.493700486546009, 5.34985801886317), # 168
(4.689410722884812, 3.6014997952679835, 5.119736387128247, 5.786370897632707, 5.827226946371696, 3.310590440704556, 2.9437096733363934, 3.561095411081716, 6.416444821046671, 3.0619830093581895, 3.623044336962055, 4.350709227429338, 5.182155626024628), # 169
(4.525860517779507, 3.47171350465107, 4.948027470555708, 5.589275533542496, 5.631083308347387, 3.2013076997260854, 2.8395318853884426, 3.444007408022438, 6.206751769656991, 2.9560047330653263, 3.498289570560013, 4.202016173677567, 5.007368568629644), # 170
(4.3572030133028, 3.3385095623889605, 4.7699176482983825, 5.385310820788429, 5.427739437144165, 3.087674965784959, 2.7323336282190445, 3.3221848381908665, 5.9883741963215655, 2.846595192306391, 3.3693919272706787, 4.048221028569909, 4.826224286265092), # 171
(4.184066308977092, 3.2023467848692557, 4.586131399835669, 5.175278253165917, 5.218015775734523, 2.970170495786347, 2.6225003178960526, 3.1961449899642167, 5.762251122944709, 2.734169029951264, 3.2368459535653553, 3.889923495385577, 4.639450218517843), # 172
(4.007078504324784, 3.063683988479554, 4.39739320464697, 4.959979324470381, 5.002732767090961, 2.84927254663542, 2.51041737048732, 3.066405151719699, 5.529321571430739, 2.6191408888698255, 3.1011461959153426, 3.72772327740378, 4.44777380497477), # 173
(3.8268676988682753, 2.9229799896074544, 4.204427542211682, 4.740215528497233, 4.782710854185973, 2.725459375237348, 2.3964702020607005, 2.9334826118345285, 5.290524563683971, 2.5019254119319574, 2.9627872007919422, 3.5622200779037345, 4.251922485222747), # 174
(3.6440619921299646, 2.7806936046405557, 4.007958892009206, 4.516788359041894, 4.558770479992055, 2.599209238497303, 2.2810442286840464, 2.797894658685917, 5.046799121608725, 2.3829372420075394, 2.8222635146664556, 3.3940136001646515, 4.052623698848646), # 175
(3.459289483632255, 2.6372836499664585, 3.8087117335189427, 4.29049930989978, 4.331732087481704, 2.4710003933204536, 2.164524866425212, 2.6601585806510792, 4.799084267109314, 2.2625910219664536, 2.680069684010184, 3.2237035474657434, 3.8506048854393393), # 176
(3.273178272897546, 2.493208941972761, 3.607410546220291, 4.062149874866306, 4.102416119627419, 2.3413110966119706, 2.0472975313520503, 2.5207916661072263, 4.548319022090056, 2.1413013946785795, 2.536700255294429, 3.051889623086223, 3.6465934845817), # 177
(3.0863564594482376, 2.348928297047063, 3.404779809592651, 3.832541547736893, 3.871643019401691, 2.210619605277026, 1.929747639532414, 2.3803112034315723, 4.295442408455268, 2.0194830030138, 2.39264977499049, 2.879171530305302, 3.4413169358626017), # 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 = (
(3, 5, 6, 5, 3, 2, 3, 1, 0, 0, 1, 0, 0, 5, 2, 0, 3, 1, 0, 2, 3, 2, 1, 1, 1, 0), # 0
(6, 11, 10, 6, 4, 6, 8, 3, 2, 2, 1, 0, 0, 7, 4, 1, 5, 4, 1, 3, 3, 2, 4, 1, 2, 0), # 1
(9, 14, 13, 12, 9, 6, 9, 5, 6, 3, 3, 0, 0, 16, 7, 6, 6, 5, 3, 7, 4, 3, 7, 1, 2, 0), # 2
(12, 20, 22, 17, 14, 9, 9, 8, 6, 5, 4, 0, 0, 19, 11, 12, 12, 6, 5, 7, 8, 5, 9, 2, 3, 0), # 3
(14, 23, 25, 19, 20, 13, 11, 11, 7, 7, 6, 1, 0, 27, 15, 14, 15, 16, 7, 8, 9, 7, 9, 3, 3, 0), # 4
(22, 30, 31, 25, 23, 14, 13, 11, 10, 7, 8, 1, 0, 34, 19, 18, 18, 20, 10, 11, 12, 9, 13, 5, 3, 0), # 5
(29, 37, 33, 33, 23, 15, 17, 13, 10, 8, 12, 1, 0, 39, 21, 21, 23, 25, 13, 12, 13, 11, 17, 6, 4, 0), # 6
(32, 45, 36, 37, 30, 16, 19, 16, 11, 10, 15, 1, 0, 41, 24, 23, 24, 29, 16, 13, 13, 12, 20, 8, 4, 0), # 7
(37, 51, 41, 41, 33, 19, 24, 19, 17, 11, 16, 1, 0, 48, 32, 24, 29, 30, 19, 18, 15, 14, 23, 11, 4, 0), # 8
(43, 59, 49, 50, 34, 22, 29, 21, 19, 11, 17, 3, 0, 59, 38, 29, 33, 33, 19, 23, 16, 15, 26, 12, 4, 0), # 9
(46, 64, 54, 55, 38, 22, 31, 23, 22, 11, 18, 3, 0, 66, 42, 33, 35, 38, 25, 25, 19, 18, 28, 16, 8, 0), # 10
(53, 69, 60, 60, 44, 28, 31, 24, 23, 12, 19, 3, 0, 74, 47, 38, 38, 46, 28, 25, 21, 19, 28, 16, 8, 0), # 11
(65, 75, 65, 64, 52, 29, 34, 26, 28, 13, 20, 4, 0, 81, 51, 46, 40, 52, 32, 28, 22, 20, 30, 18, 9, 0), # 12
(72, 85, 72, 67, 60, 33, 39, 27, 33, 15, 23, 4, 0, 92, 56, 52, 42, 59, 35, 28, 25, 22, 30, 19, 9, 0), # 13
(79, 92, 78, 72, 63, 36, 41, 28, 37, 19, 25, 4, 0, 101, 61, 56, 45, 68, 39, 30, 26, 25, 34, 22, 10, 0), # 14
(85, 102, 79, 82, 67, 39, 42, 30, 39, 23, 26, 4, 0, 107, 71, 58, 48, 73, 43, 34, 26, 28, 36, 24, 12, 0), # 15
(95, 113, 89, 88, 76, 44, 45, 31, 40, 24, 28, 5, 0, 118, 72, 62, 54, 82, 47, 38, 27, 28, 38, 26, 12, 0), # 16
(103, 121, 96, 98, 81, 45, 46, 37, 43, 25, 28, 6, 0, 125, 77, 65, 60, 88, 53, 40, 31, 30, 39, 28, 12, 0), # 17
(108, 131, 106, 104, 83, 49, 48, 38, 44, 29, 30, 7, 0, 137, 85, 70, 65, 94, 58, 44, 31, 35, 42, 29, 13, 0), # 18
(118, 142, 109, 111, 87, 50, 53, 43, 46, 34, 32, 7, 0, 144, 94, 78, 72, 98, 63, 46, 33, 40, 45, 30, 13, 0), # 19
(128, 152, 118, 117, 91, 53, 53, 45, 52, 35, 33, 7, 0, 151, 99, 83, 75, 102, 66, 49, 33, 44, 46, 33, 13, 0), # 20
(139, 163, 122, 125, 101, 55, 54, 50, 57, 36, 35, 8, 0, 155, 104, 85, 81, 108, 74, 52, 34, 48, 50, 34, 13, 0), # 21
(150, 175, 127, 135, 106, 59, 61, 54, 59, 38, 35, 9, 0, 161, 111, 93, 85, 116, 78, 57, 37, 50, 50, 35, 14, 0), # 22
(158, 181, 137, 140, 111, 63, 63, 55, 66, 43, 35, 9, 0, 176, 114, 98, 92, 117, 79, 62, 40, 54, 54, 35, 14, 0), # 23
(166, 187, 149, 149, 119, 63, 67, 56, 70, 46, 35, 10, 0, 183, 123, 102, 95, 122, 87, 69, 42, 56, 59, 35, 16, 0), # 24
(176, 195, 157, 156, 123, 67, 68, 59, 72, 47, 35, 10, 0, 191, 135, 105, 102, 128, 90, 71, 43, 59, 60, 36, 17, 0), # 25
(181, 202, 168, 165, 127, 70, 69, 61, 73, 49, 35, 12, 0, 200, 141, 109, 104, 133, 96, 75, 51, 64, 61, 38, 18, 0), # 26
(195, 213, 177, 169, 134, 72, 72, 64, 75, 51, 37, 13, 0, 205, 146, 112, 108, 137, 101, 79, 53, 67, 64, 39, 18, 0), # 27
(208, 222, 184, 177, 138, 74, 76, 66, 79, 55, 38, 13, 0, 216, 157, 118, 113, 142, 105, 87, 56, 70, 67, 39, 18, 0), # 28
(212, 230, 197, 186, 142, 79, 77, 68, 81, 56, 38, 14, 0, 222, 165, 124, 119, 146, 110, 93, 59, 74, 68, 41, 20, 0), # 29
(219, 236, 197, 191, 144, 83, 80, 69, 88, 59, 38, 14, 0, 233, 173, 134, 124, 155, 111, 96, 61, 79, 71, 43, 22, 0), # 30
(232, 248, 202, 195, 145, 86, 82, 72, 90, 64, 39, 16, 0, 241, 183, 142, 128, 163, 116, 101, 62, 80, 73, 45, 23, 0), # 31
(240, 255, 207, 206, 153, 87, 86, 76, 93, 65, 40, 19, 0, 249, 186, 146, 130, 170, 121, 102, 62, 82, 75, 47, 23, 0), # 32
(248, 264, 214, 211, 154, 89, 88, 78, 99, 68, 40, 20, 0, 253, 195, 155, 131, 176, 125, 105, 64, 84, 82, 49, 23, 0), # 33
(256, 279, 218, 219, 157, 91, 94, 82, 103, 70, 44, 20, 0, 265, 201, 163, 139, 183, 131, 107, 69, 85, 84, 50, 23, 0), # 34
(264, 287, 224, 226, 162, 95, 97, 84, 105, 71, 44, 21, 0, 273, 208, 167, 144, 189, 135, 113, 75, 87, 88, 51, 24, 0), # 35
(272, 293, 230, 235, 168, 99, 103, 91, 105, 72, 44, 23, 0, 278, 217, 172, 148, 197, 140, 116, 78, 91, 88, 52, 27, 0), # 36
(282, 302, 239, 241, 171, 100, 109, 91, 108, 75, 44, 26, 0, 286, 225, 176, 155, 202, 145, 121, 81, 95, 90, 54, 27, 0), # 37
(290, 309, 245, 247, 177, 102, 114, 93, 113, 77, 45, 26, 0, 294, 231, 181, 160, 208, 148, 125, 86, 97, 91, 57, 28, 0), # 38
(302, 319, 252, 254, 181, 104, 119, 99, 117, 77, 45, 27, 0, 299, 237, 186, 168, 212, 151, 128, 86, 99, 91, 59, 29, 0), # 39
(313, 327, 257, 262, 188, 110, 121, 100, 119, 79, 46, 28, 0, 307, 243, 188, 171, 221, 155, 129, 86, 101, 92, 63, 32, 0), # 40
(317, 334, 267, 269, 194, 115, 123, 107, 122, 82, 46, 28, 0, 313, 256, 190, 175, 223, 160, 138, 88, 102, 92, 64, 33, 0), # 41
(329, 345, 268, 275, 197, 117, 125, 111, 126, 82, 46, 29, 0, 324, 264, 192, 180, 229, 160, 140, 92, 105, 96, 65, 34, 0), # 42
(335, 353, 278, 281, 205, 121, 128, 113, 131, 86, 46, 29, 0, 339, 269, 198, 181, 233, 163, 141, 94, 110, 101, 68, 34, 0), # 43
(346, 359, 286, 289, 207, 122, 130, 117, 133, 86, 46, 30, 0, 345, 273, 203, 186, 242, 171, 143, 97, 111, 101, 68, 36, 0), # 44
(357, 367, 295, 297, 216, 124, 132, 121, 134, 86, 47, 30, 0, 351, 281, 213, 190, 252, 175, 147, 101, 112, 103, 69, 39, 0), # 45
(362, 372, 304, 307, 218, 126, 136, 125, 135, 88, 49, 30, 0, 363, 290, 218, 198, 257, 178, 150, 101, 115, 106, 73, 40, 0), # 46
(371, 380, 309, 316, 221, 128, 138, 127, 142, 92, 50, 31, 0, 374, 295, 222, 203, 262, 181, 156, 102, 121, 112, 75, 42, 0), # 47
(374, 388, 313, 320, 225, 130, 139, 129, 146, 94, 52, 32, 0, 381, 298, 225, 206, 267, 185, 160, 103, 122, 112, 75, 42, 0), # 48
(383, 398, 319, 327, 229, 130, 145, 131, 151, 96, 54, 33, 0, 392, 305, 230, 209, 277, 190, 164, 105, 124, 115, 76, 42, 0), # 49
(394, 401, 334, 332, 239, 134, 148, 135, 151, 96, 56, 33, 0, 400, 315, 233, 213, 281, 195, 164, 107, 127, 117, 77, 42, 0), # 50
(403, 407, 340, 340, 245, 140, 152, 138, 152, 97, 57, 33, 0, 405, 319, 244, 219, 287, 198, 165, 111, 128, 119, 78, 43, 0), # 51
(414, 413, 346, 350, 251, 146, 153, 138, 155, 98, 61, 34, 0, 415, 328, 249, 221, 298, 201, 168, 111, 132, 121, 80, 43, 0), # 52
(423, 422, 351, 357, 258, 150, 160, 139, 157, 100, 62, 36, 0, 428, 335, 254, 226, 301, 209, 170, 114, 137, 123, 80, 44, 0), # 53
(428, 429, 359, 360, 264, 153, 164, 143, 161, 101, 64, 36, 0, 435, 343, 264, 230, 312, 211, 176, 117, 140, 125, 83, 45, 0), # 54
(433, 439, 365, 368, 271, 156, 166, 145, 163, 102, 66, 36, 0, 448, 346, 271, 238, 320, 215, 177, 118, 142, 126, 84, 47, 0), # 55
(442, 449, 371, 377, 275, 160, 167, 145, 167, 105, 66, 36, 0, 454, 355, 279, 239, 329, 218, 178, 119, 144, 128, 85, 48, 0), # 56
(453, 460, 379, 386, 283, 165, 174, 150, 172, 106, 68, 36, 0, 462, 361, 281, 245, 336, 222, 180, 122, 146, 132, 87, 49, 0), # 57
(460, 464, 385, 394, 287, 166, 175, 152, 174, 107, 68, 38, 0, 468, 370, 288, 249, 338, 226, 183, 122, 151, 133, 89, 49, 0), # 58
(471, 471, 391, 398, 293, 167, 180, 154, 177, 108, 69, 38, 0, 473, 375, 295, 254, 344, 231, 185, 124, 154, 137, 90, 49, 0), # 59
(483, 477, 392, 403, 299, 169, 183, 158, 179, 109, 70, 39, 0, 479, 380, 298, 259, 353, 233, 192, 125, 155, 138, 90, 49, 0), # 60
(490, 484, 398, 414, 304, 169, 187, 163, 181, 112, 71, 39, 0, 486, 387, 306, 264, 364, 237, 195, 126, 158, 140, 96, 49, 0), # 61
(504, 495, 405, 424, 309, 169, 191, 166, 187, 115, 72, 40, 0, 493, 395, 311, 267, 375, 243, 199, 128, 159, 143, 98, 49, 0), # 62
(508, 504, 406, 429, 321, 173, 193, 169, 192, 115, 72, 40, 0, 506, 403, 322, 273, 385, 245, 202, 129, 164, 146, 99, 49, 0), # 63
(519, 507, 411, 435, 325, 177, 196, 172, 195, 117, 73, 40, 0, 514, 411, 323, 273, 393, 248, 205, 131, 166, 148, 100, 51, 0), # 64
(531, 517, 414, 442, 338, 180, 200, 172, 197, 118, 73, 40, 0, 519, 416, 332, 280, 397, 251, 207, 133, 170, 150, 100, 51, 0), # 65
(544, 525, 421, 449, 347, 185, 200, 173, 203, 119, 74, 40, 0, 527, 419, 335, 288, 401, 253, 209, 134, 173, 152, 103, 52, 0), # 66
(554, 533, 424, 454, 358, 186, 204, 176, 207, 119, 74, 40, 0, 532, 434, 342, 293, 412, 256, 211, 137, 177, 154, 104, 52, 0), # 67
(569, 543, 438, 468, 369, 188, 207, 182, 215, 121, 74, 40, 0, 540, 444, 348, 298, 416, 259, 214, 141, 177, 157, 108, 52, 0), # 68
(578, 550, 444, 475, 373, 188, 208, 185, 215, 123, 74, 40, 0, 549, 448, 350, 303, 422, 263, 218, 145, 181, 160, 110, 52, 0), # 69
(586, 559, 451, 479, 381, 189, 215, 189, 218, 124, 74, 40, 0, 560, 457, 356, 307, 429, 264, 220, 146, 182, 161, 112, 52, 0), # 70
(593, 567, 458, 483, 386, 191, 217, 190, 220, 127, 74, 41, 0, 568, 462, 357, 311, 436, 266, 220, 147, 184, 163, 113, 52, 0), # 71
(599, 573, 464, 487, 392, 197, 220, 192, 225, 127, 74, 41, 0, 576, 469, 365, 315, 441, 267, 225, 147, 188, 172, 114, 52, 0), # 72
(605, 577, 472, 497, 397, 201, 223, 195, 225, 128, 74, 42, 0, 582, 476, 370, 319, 447, 270, 228, 150, 193, 172, 114, 53, 0), # 73
(615, 588, 483, 501, 403, 204, 227, 199, 227, 129, 76, 42, 0, 587, 477, 372, 322, 456, 273, 233, 154, 194, 174, 116, 53, 0), # 74
(622, 591, 492, 511, 410, 208, 227, 199, 228, 130, 76, 42, 0, 595, 482, 377, 326, 463, 275, 235, 159, 194, 177, 117, 53, 0), # 75
(633, 601, 497, 525, 418, 212, 232, 201, 230, 130, 78, 43, 0, 603, 484, 385, 331, 473, 279, 235, 160, 196, 179, 120, 54, 0), # 76
(641, 605, 502, 534, 425, 218, 235, 201, 233, 133, 78, 43, 0, 610, 487, 394, 331, 477, 283, 238, 161, 198, 180, 124, 55, 0), # 77
(647, 606, 506, 541, 429, 223, 237, 207, 235, 133, 81, 43, 0, 619, 494, 400, 333, 485, 288, 240, 162, 202, 183, 126, 58, 0), # 78
(659, 611, 514, 553, 432, 226, 246, 211, 240, 134, 82, 44, 0, 624, 505, 404, 337, 491, 291, 243, 165, 208, 184, 128, 59, 0), # 79
(663, 615, 522, 564, 439, 231, 246, 213, 245, 136, 83, 45, 0, 627, 515, 406, 347, 500, 294, 243, 168, 210, 185, 130, 59, 0), # 80
(673, 619, 531, 574, 449, 231, 248, 216, 249, 137, 84, 45, 0, 633, 518, 412, 349, 504, 298, 244, 171, 215, 188, 131, 60, 0), # 81
(685, 630, 538, 580, 451, 232, 250, 219, 250, 140, 87, 46, 0, 641, 530, 414, 352, 506, 300, 246, 171, 216, 190, 132, 62, 0), # 82
(696, 638, 545, 586, 460, 237, 252, 219, 254, 141, 88, 48, 0, 648, 535, 421, 355, 512, 305, 250, 173, 218, 193, 135, 63, 0), # 83
(702, 648, 547, 593, 462, 239, 254, 222, 256, 144, 89, 49, 0, 661, 543, 429, 360, 519, 308, 254, 174, 224, 196, 135, 64, 0), # 84
(712, 654, 557, 599, 467, 241, 256, 228, 261, 146, 90, 50, 0, 673, 549, 433, 361, 525, 309, 255, 175, 227, 199, 137, 64, 0), # 85
(722, 660, 562, 604, 474, 244, 258, 233, 266, 147, 91, 50, 0, 677, 558, 437, 366, 528, 317, 258, 176, 232, 203, 138, 65, 0), # 86
(736, 663, 569, 612, 481, 246, 261, 235, 268, 148, 91, 51, 0, 684, 562, 445, 369, 539, 319, 260, 177, 234, 205, 141, 66, 0), # 87
(746, 670, 575, 621, 485, 251, 263, 239, 272, 149, 91, 52, 0, 692, 573, 453, 375, 544, 322, 264, 177, 235, 206, 142, 66, 0), # 88
(753, 678, 579, 624, 494, 252, 264, 242, 276, 149, 91, 52, 0, 703, 579, 463, 378, 546, 324, 265, 177, 237, 208, 144, 66, 0), # 89
(762, 684, 587, 630, 498, 254, 265, 249, 279, 149, 91, 53, 0, 714, 584, 468, 382, 553, 327, 268, 177, 237, 213, 145, 68, 0), # 90
(769, 687, 591, 635, 503, 258, 267, 250, 282, 150, 92, 53, 0, 722, 592, 474, 383, 562, 331, 270, 180, 241, 216, 146, 70, 0), # 91
(775, 692, 595, 639, 507, 260, 271, 253, 283, 150, 92, 54, 0, 734, 594, 478, 385, 568, 335, 271, 182, 245, 219, 146, 72, 0), # 92
(781, 694, 598, 647, 514, 262, 278, 256, 285, 150, 93, 55, 0, 741, 595, 487, 388, 573, 338, 271, 184, 247, 220, 148, 72, 0), # 93
(793, 702, 601, 650, 521, 264, 280, 256, 286, 150, 93, 55, 0, 750, 599, 490, 394, 578, 341, 271, 185, 250, 224, 149, 73, 0), # 94
(804, 707, 609, 656, 526, 265, 282, 258, 288, 152, 96, 56, 0, 759, 607, 496, 397, 583, 345, 275, 188, 253, 225, 152, 73, 0), # 95
(808, 714, 615, 665, 528, 272, 285, 264, 292, 157, 97, 56, 0, 767, 614, 501, 403, 588, 347, 278, 190, 256, 230, 152, 73, 0), # 96
(818, 718, 623, 672, 534, 275, 286, 268, 293, 157, 98, 56, 0, 778, 620, 504, 408, 594, 354, 281, 193, 262, 232, 152, 73, 0), # 97
(821, 725, 629, 678, 546, 277, 288, 270, 299, 160, 98, 56, 0, 789, 626, 510, 411, 599, 356, 285, 195, 263, 236, 153, 76, 0), # 98
(838, 731, 630, 691, 551, 282, 291, 270, 302, 161, 99, 56, 0, 799, 636, 516, 414, 602, 361, 289, 195, 265, 238, 153, 76, 0), # 99
(846, 737, 635, 700, 556, 287, 295, 272, 304, 164, 100, 56, 0, 805, 642, 523, 418, 608, 365, 290, 200, 267, 240, 157, 76, 0), # 100
(851, 743, 641, 705, 561, 287, 298, 272, 306, 166, 101, 58, 0, 822, 645, 528, 419, 613, 371, 296, 202, 270, 242, 158, 78, 0), # 101
(856, 748, 646, 709, 570, 288, 300, 276, 307, 167, 102, 59, 0, 830, 650, 534, 423, 620, 374, 300, 204, 273, 245, 159, 78, 0), # 102
(860, 754, 651, 716, 580, 291, 302, 279, 311, 167, 102, 59, 0, 834, 654, 541, 426, 623, 377, 302, 205, 274, 246, 159, 78, 0), # 103
(867, 762, 657, 723, 585, 291, 306, 280, 311, 167, 102, 59, 0, 839, 658, 547, 429, 631, 381, 305, 209, 277, 247, 161, 79, 0), # 104
(876, 769, 661, 728, 589, 295, 306, 285, 312, 170, 102, 59, 0, 846, 664, 552, 433, 638, 383, 306, 211, 279, 247, 161, 80, 0), # 105
(883, 771, 666, 733, 595, 297, 308, 286, 314, 171, 102, 60, 0, 856, 669, 555, 438, 647, 386, 308, 212, 282, 250, 163, 81, 0), # 106
(889, 781, 672, 741, 600, 299, 311, 288, 320, 172, 102, 60, 0, 865, 675, 563, 441, 656, 390, 311, 215, 284, 251, 166, 81, 0), # 107
(893, 787, 679, 748, 606, 303, 313, 290, 324, 173, 103, 60, 0, 875, 678, 565, 444, 661, 396, 312, 217, 286, 252, 168, 81, 0), # 108
(901, 793, 681, 757, 610, 307, 315, 295, 325, 174, 104, 60, 0, 884, 684, 571, 445, 664, 397, 316, 219, 289, 254, 169, 82, 0), # 109
(905, 801, 690, 761, 616, 307, 321, 296, 328, 175, 105, 62, 0, 894, 690, 578, 450, 670, 400, 320, 223, 292, 256, 170, 83, 0), # 110
(913, 804, 698, 768, 623, 310, 327, 299, 331, 176, 106, 62, 0, 905, 698, 581, 457, 679, 405, 325, 224, 297, 257, 173, 83, 0), # 111
(920, 807, 705, 775, 626, 313, 329, 301, 334, 176, 106, 62, 0, 914, 703, 585, 462, 690, 409, 328, 226, 301, 257, 174, 83, 0), # 112
(925, 811, 710, 779, 630, 317, 333, 302, 337, 178, 106, 63, 0, 919, 708, 588, 469, 693, 414, 332, 228, 306, 259, 175, 83, 0), # 113
(927, 815, 715, 784, 633, 318, 335, 303, 337, 179, 108, 63, 0, 930, 714, 597, 472, 703, 416, 336, 232, 310, 262, 177, 84, 0), # 114
(935, 826, 726, 792, 638, 322, 338, 303, 342, 181, 110, 65, 0, 938, 722, 600, 478, 708, 418, 342, 233, 311, 267, 177, 84, 0), # 115
(940, 831, 734, 796, 642, 323, 339, 309, 347, 184, 110, 66, 0, 943, 728, 606, 482, 714, 420, 344, 234, 315, 269, 178, 84, 0), # 116
(952, 836, 738, 801, 643, 331, 342, 309, 353, 186, 110, 66, 0, 951, 736, 608, 483, 720, 423, 346, 236, 319, 271, 179, 84, 0), # 117
(958, 844, 742, 811, 648, 332, 342, 310, 357, 188, 112, 66, 0, 958, 742, 614, 485, 730, 424, 349, 239, 321, 272, 181, 84, 0), # 118
(971, 852, 745, 816, 656, 334, 344, 312, 360, 188, 112, 66, 0, 970, 745, 615, 491, 742, 427, 350, 246, 324, 274, 181, 84, 0), # 119
(978, 857, 751, 827, 660, 336, 347, 316, 362, 191, 112, 66, 0, 978, 753, 620, 496, 745, 431, 353, 248, 326, 276, 181, 86, 0), # 120
(986, 859, 760, 835, 668, 339, 351, 318, 364, 192, 114, 66, 0, 985, 757, 623, 498, 752, 432, 356, 248, 327, 279, 181, 86, 0), # 121
(990, 863, 769, 837, 674, 342, 352, 320, 370, 192, 114, 66, 0, 991, 767, 629, 499, 758, 435, 357, 248, 330, 280, 181, 86, 0), # 122
(998, 868, 772, 843, 678, 346, 354, 321, 372, 195, 115, 66, 0, 996, 772, 633, 500, 764, 436, 360, 248, 332, 282, 182, 86, 0), # 123
(1007, 872, 774, 854, 686, 346, 355, 322, 373, 198, 115, 66, 0, 1003, 778, 636, 503, 771, 440, 361, 251, 335, 283, 184, 87, 0), # 124
(1010, 879, 777, 862, 692, 348, 355, 323, 377, 200, 117, 67, 0, 1012, 781, 639, 508, 775, 441, 363, 252, 341, 285, 186, 88, 0), # 125
(1018, 881, 782, 870, 697, 349, 357, 323, 382, 204, 117, 67, 0, 1018, 787, 643, 509, 780, 444, 366, 253, 343, 287, 187, 88, 0), # 126
(1023, 888, 786, 879, 704, 350, 358, 325, 384, 206, 121, 67, 0, 1025, 788, 646, 510, 788, 445, 368, 254, 351, 290, 187, 88, 0), # 127
(1029, 896, 795, 887, 709, 350, 360, 326, 386, 208, 122, 67, 0, 1032, 797, 648, 512, 795, 447, 368, 255, 354, 292, 189, 89, 0), # 128
(1036, 904, 800, 892, 713, 353, 365, 328, 389, 211, 123, 67, 0, 1042, 803, 650, 514, 798, 452, 369, 255, 357, 294, 189, 90, 0), # 129
(1041, 909, 805, 903, 717, 355, 367, 328, 391, 211, 124, 68, 0, 1053, 806, 653, 518, 802, 454, 370, 256, 360, 295, 191, 90, 0), # 130
(1050, 911, 810, 912, 724, 356, 367, 328, 391, 213, 124, 69, 0, 1058, 812, 657, 519, 809, 457, 372, 257, 363, 297, 192, 90, 0), # 131
(1053, 913, 820, 916, 736, 357, 368, 331, 394, 214, 125, 69, 0, 1061, 819, 663, 523, 813, 462, 372, 261, 366, 298, 194, 90, 0), # 132
(1062, 917, 825, 923, 741, 359, 371, 331, 399, 215, 126, 69, 0, 1073, 821, 671, 526, 819, 466, 374, 261, 370, 300, 197, 91, 0), # 133
(1070, 920, 831, 927, 741, 362, 374, 332, 402, 216, 126, 69, 0, 1077, 827, 676, 530, 826, 467, 378, 264, 371, 302, 199, 91, 0), # 134
(1076, 922, 839, 931, 743, 367, 374, 332, 405, 221, 126, 69, 0, 1084, 833, 677, 533, 833, 470, 379, 267, 374, 303, 200, 91, 0), # 135
(1079, 928, 845, 937, 748, 370, 376, 332, 408, 221, 127, 69, 0, 1091, 838, 682, 535, 836, 470, 381, 269, 377, 304, 201, 91, 0), # 136
(1084, 934, 848, 943, 748, 372, 378, 334, 411, 222, 130, 69, 0, 1098, 844, 685, 535, 838, 472, 381, 271, 378, 308, 202, 91, 0), # 137
(1088, 942, 853, 948, 752, 374, 381, 336, 415, 225, 130, 70, 0, 1110, 851, 689, 538, 841, 473, 386, 273, 381, 308, 203, 91, 0), # 138
(1093, 947, 863, 957, 758, 376, 382, 338, 415, 226, 130, 71, 0, 1117, 856, 692, 540, 844, 474, 388, 275, 381, 310, 203, 91, 0), # 139
(1098, 950, 865, 967, 759, 377, 385, 339, 419, 226, 130, 73, 0, 1125, 864, 699, 545, 848, 474, 392, 276, 382, 310, 203, 91, 0), # 140
(1104, 958, 874, 978, 763, 381, 388, 339, 420, 226, 131, 74, 0, 1133, 869, 704, 548, 854, 477, 392, 277, 384, 313, 204, 91, 0), # 141
(1112, 964, 881, 986, 769, 385, 389, 342, 423, 228, 133, 74, 0, 1140, 879, 710, 555, 861, 477, 393, 277, 385, 317, 206, 92, 0), # 142
(1117, 972, 888, 989, 773, 387, 390, 345, 424, 230, 133, 74, 0, 1151, 888, 712, 556, 867, 478, 394, 278, 385, 320, 207, 92, 0), # 143
(1126, 973, 893, 994, 778, 389, 391, 348, 427, 230, 133, 75, 0, 1161, 892, 717, 561, 872, 480, 396, 279, 387, 326, 207, 93, 0), # 144
(1136, 976, 905, 1004, 782, 391, 391, 349, 429, 231, 133, 75, 0, 1164, 896, 720, 565, 874, 482, 398, 280, 390, 328, 207, 94, 0), # 145
(1142, 983, 910, 1018, 786, 393, 393, 351, 434, 231, 133, 75, 0, 1169, 902, 724, 566, 877, 487, 400, 281, 391, 331, 207, 94, 0), # 146
(1145, 988, 914, 1024, 791, 395, 396, 353, 437, 233, 135, 75, 0, 1177, 905, 731, 566, 885, 489, 405, 281, 393, 334, 210, 95, 0), # 147
(1151, 993, 923, 1031, 795, 396, 401, 355, 442, 234, 136, 75, 0, 1181, 909, 735, 571, 893, 490, 410, 282, 395, 337, 212, 95, 0), # 148
(1159, 997, 931, 1036, 799, 397, 403, 355, 446, 235, 137, 75, 0, 1186, 916, 740, 571, 897, 495, 412, 285, 398, 338, 213, 95, 0), # 149
(1163, 1002, 936, 1041, 802, 398, 403, 359, 450, 236, 137, 75, 0, 1195, 920, 744, 573, 906, 498, 413, 286, 401, 341, 214, 95, 0), # 150
(1170, 1010, 941, 1047, 804, 400, 406, 362, 452, 237, 138, 75, 0, 1203, 928, 749, 581, 913, 502, 415, 286, 404, 345, 218, 95, 0), # 151
(1181, 1015, 946, 1053, 809, 402, 406, 364, 457, 238, 140, 75, 0, 1210, 929, 754, 585, 914, 504, 416, 288, 405, 347, 219, 95, 0), # 152
(1185, 1022, 951, 1060, 815, 403, 406, 366, 458, 239, 140, 75, 0, 1218, 933, 759, 590, 924, 509, 417, 290, 410, 348, 220, 95, 0), # 153
(1192, 1030, 956, 1063, 819, 405, 407, 367, 461, 240, 140, 75, 0, 1224, 940, 763, 592, 934, 513, 420, 290, 413, 351, 222, 95, 0), # 154
(1198, 1033, 959, 1068, 822, 407, 407, 370, 465, 240, 140, 75, 0, 1238, 945, 768, 593, 940, 518, 421, 291, 414, 355, 223, 95, 0), # 155
(1207, 1037, 963, 1070, 826, 408, 409, 372, 468, 241, 140, 75, 0, 1242, 950, 772, 594, 945, 522, 423, 293, 418, 360, 226, 96, 0), # 156
(1209, 1043, 970, 1074, 833, 413, 412, 373, 468, 244, 141, 75, 0, 1249, 958, 778, 596, 947, 524, 424, 295, 419, 363, 227, 96, 0), # 157
(1212, 1048, 973, 1077, 838, 413, 414, 374, 473, 246, 141, 75, 0, 1251, 964, 786, 602, 950, 525, 427, 297, 419, 364, 231, 96, 0), # 158
(1216, 1053, 984, 1086, 843, 415, 415, 378, 477, 247, 142, 76, 0, 1261, 970, 790, 603, 957, 529, 429, 301, 419, 366, 233, 97, 0), # 159
(1219, 1059, 989, 1090, 846, 416, 416, 382, 477, 247, 143, 78, 0, 1271, 977, 793, 604, 960, 531, 431, 303, 422, 368, 233, 98, 0), # 160
(1223, 1062, 994, 1098, 852, 418, 419, 382, 478, 248, 143, 79, 0, 1282, 980, 796, 608, 965, 535, 432, 305, 426, 369, 233, 98, 0), # 161
(1225, 1067, 997, 1104, 855, 421, 419, 385, 479, 251, 144, 79, 0, 1291, 986, 802, 611, 969, 540, 432, 306, 431, 371, 233, 98, 0), # 162
(1231, 1070, 1006, 1106, 860, 422, 420, 385, 482, 251, 144, 80, 0, 1296, 990, 804, 612, 973, 543, 434, 307, 437, 371, 235, 98, 0), # 163
(1234, 1077, 1011, 1112, 867, 423, 421, 387, 486, 252, 146, 81, 0, 1299, 993, 809, 612, 975, 545, 436, 307, 439, 373, 237, 99, 0), # 164
(1242, 1079, 1014, 1117, 870, 424, 422, 388, 488, 253, 146, 82, 0, 1305, 999, 814, 614, 982, 550, 438, 309, 440, 374, 237, 99, 0), # 165
(1243, 1081, 1021, 1125, 877, 427, 423, 392, 488, 254, 146, 82, 0, 1311, 1004, 821, 617, 990, 551, 440, 313, 443, 374, 237, 99, 0), # 166
(1250, 1084, 1026, 1134, 883, 427, 425, 394, 490, 255, 146, 82, 0, 1315, 1012, 823, 619, 993, 552, 443, 314, 446, 375, 239, 99, 0), # 167
(1255, 1087, 1030, 1139, 886, 429, 426, 394, 490, 255, 146, 83, 0, 1322, 1014, 828, 622, 1000, 556, 443, 317, 450, 376, 239, 99, 0), # 168
(1259, 1089, 1035, 1144, 888, 432, 428, 394, 494, 256, 148, 86, 0, 1326, 1019, 832, 623, 1005, 557, 443, 320, 453, 376, 240, 99, 0), # 169
(1261, 1090, 1038, 1148, 894, 435, 429, 395, 496, 257, 148, 86, 0, 1334, 1019, 834, 625, 1009, 560, 443, 320, 457, 378, 240, 99, 0), # 170
(1266, 1091, 1041, 1153, 896, 435, 432, 395, 496, 258, 149, 86, 0, 1337, 1026, 838, 627, 1011, 561, 444, 322, 457, 381, 240, 99, 0), # 171
(1269, 1093, 1046, 1156, 898, 436, 433, 396, 498, 259, 149, 86, 0, 1338, 1031, 838, 629, 1014, 562, 445, 322, 459, 381, 240, 99, 0), # 172
(1271, 1096, 1050, 1160, 900, 442, 435, 396, 499, 260, 149, 86, 0, 1341, 1036, 841, 630, 1017, 562, 446, 323, 460, 382, 240, 99, 0), # 173
(1273, 1096, 1052, 1162, 903, 443, 437, 397, 501, 260, 151, 86, 0, 1346, 1038, 842, 631, 1022, 564, 447, 323, 463, 382, 241, 99, 0), # 174
(1275, 1097, 1054, 1165, 907, 445, 439, 398, 503, 260, 151, 86, 0, 1351, 1040, 844, 631, 1026, 566, 447, 324, 464, 382, 243, 99, 0), # 175
(1277, 1099, 1058, 1168, 908, 447, 440, 399, 504, 260, 151, 86, 0, 1355, 1043, 845, 632, 1028, 568, 448, 326, 467, 384, 245, 99, 0), # 176
(1278, 1100, 1059, 1173, 910, 448, 440, 399, 508, 260, 151, 86, 0, 1358, 1048, 846, 632, 1031, 569, 448, 326, 469, 386, 245, 99, 0), # 177
(1279, 1101, 1063, 1174, 912, 449, 441, 400, 509, 260, 152, 86, 0, 1367, 1050, 849, 633, 1035, 569, 449, 328, 471, 387, 245, 99, 0), # 178
(1279, 1101, 1063, 1174, 912, 449, 441, 400, 509, 260, 152, 86, 0, 1367, 1050, 849, 633, 1035, 569, 449, 328, 471, 387, 245, 99, 0), # 179
)
passenger_arriving_rate = (
(4.0166924626974145, 4.051878277108322, 3.4741888197416713, 3.72880066431806, 2.962498990725126, 1.4647056349507583, 1.6584142461495661, 1.5510587243264744, 1.6240264165781353, 0.7916030031044742, 0.5607020218514138, 0.32652767188707826, 0.0, 4.067104170062691, 3.5918043907578605, 2.803510109257069, 2.374809009313422, 3.2480528331562706, 2.171482214057064, 1.6584142461495661, 1.0462183106791132, 1.481249495362563, 1.2429335547726867, 0.6948377639483343, 0.36835257064621113, 0.0), # 0
(4.283461721615979, 4.319377842372822, 3.703564394220102, 3.97508655196597, 3.1586615133195926, 1.561459005886526, 1.7677875765054776, 1.6531712409685695, 1.7312654203554425, 0.8437961384554302, 0.5977461514608177, 0.34808111072095704, 0.0, 4.3358333179518835, 3.8288922179305267, 2.9887307573040878, 2.53138841536629, 3.462530840710885, 2.3144397373559973, 1.7677875765054776, 1.1153278613475186, 1.5793307566597963, 1.3250288506553236, 0.7407128788440204, 0.39267071294298395, 0.0), # 1
(4.549378407183785, 4.585815791986718, 3.9320281903649423, 4.220392622798877, 3.3541135859998636, 1.6578263867724743, 1.8767274031842818, 1.7548750826348067, 1.838076481834013, 0.8957827550041094, 0.6346430865035085, 0.3695488434702037, 0.0, 4.603491862567752, 4.06503727817224, 3.173215432517542, 2.6873482650123277, 3.676152963668026, 2.4568251156887295, 1.8767274031842818, 1.1841617048374817, 1.6770567929999318, 1.4067975409329592, 0.7864056380729886, 0.41689234472606534, 0.0), # 2
(4.81340623451725, 4.850135034753395, 4.1586739128799035, 4.463745844519244, 3.548086227201014, 1.7534256238730528, 1.9848014566591823, 1.8557670524981693, 1.9440360429122914, 0.9473565396852364, 0.6712464549103178, 0.3908457123286974, 0.0, 4.869018245003381, 4.299302835615671, 3.356232274551589, 2.8420696190557084, 3.8880720858245827, 2.598073873497437, 1.9848014566591823, 1.2524468741950376, 1.774043113600507, 1.487915281506415, 0.8317347825759807, 0.4409213667957632, 0.0), # 3
(5.074508918732786, 5.111278479476234, 4.382595266468691, 4.704173184829542, 3.7398104553581293, 1.8478745634527118, 2.0915774674033836, 1.9554439537316386, 2.048720545488722, 0.998311179433536, 0.7074098846120768, 0.41188655949031766, 0.0, 5.131350906351854, 4.530752154393493, 3.5370494230603833, 2.9949335383006073, 4.097441090977444, 2.737621535224294, 2.0915774674033836, 1.3199104024662227, 1.8699052276790646, 1.5680577282765145, 0.8765190532937384, 0.46466167995238505, 0.0), # 4
(5.331650174946809, 5.368189034958631, 4.602885955835013, 4.940701611432236, 3.9285172889062823, 1.9407910517759004, 2.1966231658900894, 2.0535025895081978, 2.151706431461749, 1.048440361183733, 0.7429870035396177, 0.43258622714894324, 0.0, 5.389428287706262, 4.758448498638375, 3.7149350176980884, 3.145321083551198, 4.303412862923498, 2.8749036253114766, 2.1966231658900894, 1.3862793226970715, 1.9642586444531411, 1.6469005371440792, 0.9205771911670025, 0.48801718499623925, 0.0), # 5
(5.583793718275733, 5.619809610003967, 4.8186396856825775, 5.172358092029792, 4.113437746280557, 2.03179293510707, 2.299506282592505, 2.1495397630008295, 2.2525701427298173, 1.097537771870552, 0.777831439623771, 0.45285955749845397, 0.0, 5.642188830159686, 4.981455132482993, 3.889157198118855, 3.2926133156116553, 4.5051402854596345, 3.0093556682011613, 2.299506282592505, 1.4512806679336214, 2.0567188731402783, 1.724119364009931, 0.9637279371365156, 0.5108917827276335, 0.0), # 6
(5.829903263835975, 5.86508311341563, 5.02895016071509, 5.398169594324678, 4.293802845916028, 2.1204980597106697, 2.399794547983834, 2.2431522773825177, 2.350888121191372, 1.1453970984287176, 0.8117968207953693, 0.47262139273272863, 0.0, 5.888570974805216, 5.198835320060014, 4.058984103976846, 3.436191295286152, 4.701776242382744, 3.1404131883355246, 2.399794547983834, 1.514641471221907, 2.146901422958014, 1.799389864774893, 1.0057900321430182, 0.5331893739468755, 0.0), # 7
(6.068942526743948, 6.102952453997006, 5.232911085636264, 5.617163086019357, 4.468843606247779, 2.2065242718511486, 2.497055692537279, 2.333936935826242, 2.446236808744855, 1.1918120277929551, 0.8447367749852429, 0.49178657504564693, 0.0, 6.127513162735934, 5.409652325502115, 4.223683874926214, 3.5754360833788645, 4.89247361748971, 3.2675117101567386, 2.497055692537279, 1.5760887656079634, 2.2344218031238894, 1.872387695339786, 1.046582217127253, 0.5548138594542734, 0.0), # 8
(6.299875222116068, 6.332360540551483, 5.429616165149803, 5.828365534816301, 4.637791045710885, 2.2894894177929594, 2.590857446726048, 2.421490541504988, 2.538192647288713, 1.2365762468979886, 0.8765049301242238, 0.5102699466310877, 0.0, 6.35795383504493, 5.612969412941963, 4.382524650621119, 3.709728740693965, 5.076385294577426, 3.390086758106983, 2.590857446726048, 1.635349584137828, 2.3188955228554424, 1.9427885116054342, 1.0859232330299606, 0.5756691400501349, 0.0), # 9
(6.5216650650687455, 6.552250281882444, 5.6181591039594165, 6.0308039084179725, 4.799876182740427, 2.3690113438005502, 2.680767541023342, 2.505409897591737, 2.6263320787213904, 1.279483442678543, 0.9069549141431433, 0.5279863496829302, 0.0, 6.578831432825289, 5.807849846512232, 4.534774570715716, 3.838450328035629, 5.252664157442781, 3.5075738566284325, 2.680767541023342, 1.6921509598575357, 2.3999380913702133, 2.010267969472658, 1.1236318207918834, 0.5956591165347678, 0.0), # 10
(6.7332757707184046, 6.761564586793285, 5.797633606768811, 6.223505174526839, 4.954330035771484, 2.444707896138372, 2.7663537059023664, 2.585291807259472, 2.7102315449413314, 1.320327302069344, 0.9359403549728333, 0.5448506263950541, 0.0, 6.78908439717009, 5.993356890345594, 4.679701774864166, 3.9609819062080316, 5.420463089882663, 3.619408530163261, 2.7663537059023664, 1.7462199258131228, 2.477165017885742, 2.07450172484228, 1.1595267213537623, 0.6146876897084805, 0.0), # 11
(6.93367105418145, 6.959246364087378, 5.9671333782816935, 6.405496300845368, 5.100383623239134, 2.516196921070873, 2.8471836718363246, 2.6607330736811736, 2.789467487846981, 1.3589015120051147, 0.9633148805441247, 0.5607776189613379, 0.0, 6.987651169172428, 6.168553808574717, 4.816574402720623, 4.0767045360153435, 5.578934975693962, 3.7250263031536432, 2.8471836718363246, 1.7972835150506232, 2.550191811619567, 2.135165433615123, 1.1934266756563388, 0.63265876037158, 0.0), # 12
(7.121814630574301, 7.144238522568122, 6.125752123201774, 6.575804255076027, 5.237267963578454, 2.5830962648625047, 2.9228251692984224, 2.731330500029827, 2.863616349336782, 1.3949997594205812, 0.9889321187878493, 0.5756821695756614, 0.0, 7.173470189925388, 6.332503865332275, 4.944660593939246, 4.184999278261743, 5.727232698673564, 3.8238627000417584, 2.9228251692984224, 1.8450687606160747, 2.618633981789227, 2.1919347516920094, 1.225150424640355, 0.6494762293243748, 0.0), # 13
(7.296670215013373, 7.315483971038899, 6.272583546232765, 6.733456004921276, 5.3642140752245275, 2.6450237737777162, 2.9928459287618647, 2.7966808894784156, 2.932254571309179, 1.428415731250467, 1.0126456976348381, 0.5894791204319041, 0.0, 7.345479900522051, 6.484270324750944, 5.06322848817419, 4.285247193751401, 5.864509142618358, 3.9153532452697823, 2.9928459287618647, 1.8893026955555114, 2.6821070376122638, 2.244485334973759, 1.254516709246553, 0.6650439973671727, 0.0), # 14
(7.457201522615084, 7.471925618303093, 6.406721352078362, 6.877478518083592, 5.480452976612431, 2.701597294080959, 3.0568136806998503, 2.8563810451999188, 2.9949585956626184, 1.4589431144294984, 1.0343092450159228, 0.6020833137239449, 0.0, 7.502618742055505, 6.622916450963392, 5.171546225079613, 4.376829343288494, 5.989917191325237, 3.9989334632798865, 3.0568136806998503, 1.9297123529149707, 2.7402264883062153, 2.2924928393611976, 1.2813442704156726, 0.6792659653002813, 0.0), # 15
(7.602372268495841, 7.612506373164098, 6.527259245442284, 7.006898762265429, 5.585215686177244, 2.7524346720366815, 3.1142961555855906, 2.9100277703673205, 3.0513048642955427, 1.4863755958923994, 1.0537763888619351, 0.6134095916456628, 0.0, 7.643825155618837, 6.747505508102289, 5.268881944309675, 4.459126787677198, 6.102609728591085, 4.074038878514249, 3.1142961555855906, 1.9660247657404866, 2.792607843088622, 2.3356329207551436, 1.3054518490884568, 0.692046033924009, 0.0), # 16
(7.73114616777206, 7.736169144425294, 6.6332909310282355, 7.120743705169268, 5.677733222354047, 2.7971537539093334, 3.1648610838922844, 2.9572178681536063, 3.1008698191063955, 1.510506862573894, 1.0709007571037066, 0.6233727963909371, 0.0, 7.768037582305133, 6.857100760300307, 5.354503785518533, 4.531520587721681, 6.201739638212791, 4.140105015415049, 3.1648610838922844, 1.9979669670780953, 2.8388666111770235, 2.373581235056423, 1.3266581862056472, 0.7032881040386633, 0.0), # 17
(7.842486935560164, 7.841856840890068, 6.723910113539921, 7.218040314497568, 5.757236603577914, 2.8353723859633684, 3.2080761960931405, 2.9975481417317535, 3.1432299019936254, 1.5311306014087078, 1.085535977672068, 0.6318877701536477, 0.0, 7.874194463207477, 6.950765471690124, 5.427679888360339, 4.593391804226123, 6.286459803987251, 4.196567398424455, 3.2080761960931405, 2.0252659899738346, 2.878618301788957, 2.406013438165856, 1.344782022707984, 0.7128960764445517, 0.0), # 18
(7.935358286976559, 7.928512371361812, 6.798210497681052, 7.29781555795279, 5.822956848283928, 2.866708414463231, 3.2435092226613578, 3.030615394274749, 3.1779615548556746, 1.5480404993315662, 1.0975356784978507, 0.6388693551276732, 0.0, 7.961234239418957, 7.027562906404404, 5.4876783924892525, 4.644121497994697, 6.355923109711349, 4.242861551984649, 3.2435092226613578, 2.0476488674737365, 2.911478424141964, 2.4326051859842637, 1.3596420995362106, 0.720773851941983, 0.0), # 19
(8.008723937137665, 7.995078644643906, 6.855285788155336, 7.359096403237412, 5.874124974907169, 2.8907796856733756, 3.270727894070145, 3.0560164289555725, 3.2046412195909864, 1.5610302432771923, 1.106753487511887, 0.6442323935068929, 0.0, 8.02809535203266, 7.08655632857582, 5.533767437559434, 4.683090729831576, 6.409282439181973, 4.278423000537802, 3.270727894070145, 2.0648426326238396, 2.9370624874535847, 2.4530321344124713, 1.3710571576310673, 0.7268253313312643, 0.0), # 20
(8.061547601159893, 8.040498569539743, 6.89422968966648, 7.400909818053892, 5.909972001882714, 2.90720404585825, 3.289299940792704, 3.0733480489472083, 3.222845338098006, 1.5698935201803115, 1.113043032645008, 0.6478917274851863, 0.0, 8.073716242141662, 7.1268090023370485, 5.56521516322504, 4.709680560540933, 6.445690676196012, 4.302687268526092, 3.289299940792704, 2.0765743184701786, 2.954986000941357, 2.466969939351298, 1.378845937933296, 0.730954415412704, 0.0), # 21
(8.092792994159664, 8.063715054852706, 6.91413590691819, 7.422282770104703, 5.92972894764564, 2.915599341282305, 3.29879309330224, 3.0822070574226386, 3.2321503522751773, 1.574424016975649, 1.1162579418280456, 0.6497621992564327, 0.0, 8.097035350839063, 7.147384191820759, 5.581289709140227, 4.723272050926946, 6.464300704550355, 4.315089880391694, 3.29879309330224, 2.0825709580587892, 2.96486447382282, 2.474094256701568, 1.3828271813836381, 0.7330650049866098, 0.0), # 22
(8.104314690674112, 8.066463968907179, 6.916615454961135, 7.424958487654322, 5.9347904298840515, 2.916666666666667, 3.2999216009037355, 3.0831646090534983, 3.2333136625514407, 1.574958454503887, 1.1166610716215655, 0.6499931717725956, 0.0, 8.1, 7.149924889498552, 5.583305358107827, 4.72487536351166, 6.466627325102881, 4.316430452674898, 3.2999216009037355, 2.0833333333333335, 2.9673952149420257, 2.474986162551441, 1.3833230909922272, 0.7333149062642891, 0.0), # 23
(8.112809930427323, 8.06486049382716, 6.916209876543211, 7.4246291666666675, 5.937657393927921, 2.916666666666667, 3.299301525054467, 3.0818333333333334, 3.2331577777777776, 1.5746301234567905, 1.1166166105499442, 0.6499390946502058, 0.0, 8.1, 7.149330041152263, 5.583083052749721, 4.72389037037037, 6.466315555555555, 4.314566666666667, 3.299301525054467, 2.0833333333333335, 2.9688286969639606, 2.4748763888888896, 1.3832419753086422, 0.7331691358024692, 0.0), # 24
(8.121125784169264, 8.06169981710105, 6.915409236396892, 7.423977623456791, 5.940461304317068, 2.916666666666667, 3.298079561042524, 3.0792181069958855, 3.2328497942386836, 1.5739837677183361, 1.1165284532568485, 0.6498323426306966, 0.0, 8.1, 7.148155768937661, 5.5826422662842425, 4.7219513031550076, 6.465699588477367, 4.31090534979424, 3.298079561042524, 2.0833333333333335, 2.970230652158534, 2.474659207818931, 1.3830818472793784, 0.7328818015546411, 0.0), # 25
(8.129261615238427, 8.057030224051212, 6.914224508459078, 7.423011265432098, 5.943202063157923, 2.916666666666667, 3.2962746873234887, 3.0753683127572025, 3.23239366255144, 1.5730301417466854, 1.1163973978467807, 0.6496743789056548, 0.0, 8.1, 7.146418167962202, 5.581986989233903, 4.719090425240055, 6.46478732510288, 4.305515637860084, 3.2962746873234887, 2.0833333333333335, 2.9716010315789614, 2.4743370884773666, 1.3828449016918156, 0.732457293095565, 0.0), # 26
(8.13721678697331, 8.0509, 6.9126666666666665, 7.4217375, 5.945879572556914, 2.916666666666667, 3.2939058823529415, 3.0703333333333336, 3.231793333333333, 1.5717800000000004, 1.1162242424242426, 0.6494666666666669, 0.0, 8.1, 7.144133333333334, 5.581121212121213, 4.715339999999999, 6.463586666666666, 4.298466666666667, 3.2939058823529415, 2.0833333333333335, 2.972939786278457, 2.4739125000000004, 1.3825333333333334, 0.7319000000000001, 0.0), # 27
(8.1449906627124, 8.043357430269776, 6.910746684956561, 7.420163734567902, 5.948493734620481, 2.916666666666667, 3.2909921245864604, 3.06416255144033, 3.231052757201646, 1.570244096936443, 1.116009785093736, 0.6492106691053194, 0.0, 8.1, 7.141317360158513, 5.580048925468679, 4.710732290809328, 6.462105514403292, 4.289827572016462, 3.2909921245864604, 2.0833333333333335, 2.9742468673102405, 2.4733879115226345, 1.3821493369913125, 0.731214311842707, 0.0), # 28
(8.1525826057942, 8.0344508001829, 6.908475537265661, 7.41829737654321, 5.951044451455051, 2.916666666666667, 3.2875523924796264, 3.0569053497942384, 3.2301758847736624, 1.5684331870141752, 1.1157548239597623, 0.6489078494131992, 0.0, 8.1, 7.13798634354519, 5.578774119798812, 4.705299561042525, 6.460351769547325, 4.279667489711934, 3.2875523924796264, 2.0833333333333335, 2.9755222257275253, 2.4727657921810704, 1.3816951074531325, 0.7304046181984455, 0.0), # 29
(8.159991979557198, 8.02422839506173, 6.905864197530864, 7.416145833333333, 5.953531625167059, 2.916666666666667, 3.2836056644880176, 3.048611111111111, 3.2291666666666665, 1.5663580246913587, 1.115460157126824, 0.648559670781893, 0.0, 8.1, 7.134156378600823, 5.57730078563412, 4.699074074074074, 6.458333333333333, 4.268055555555556, 3.2836056644880176, 2.0833333333333335, 2.9767658125835297, 2.4720486111111115, 1.3811728395061729, 0.7294753086419755, 0.0), # 30
(8.167218147339886, 8.012738500228625, 6.902923639689073, 7.41371651234568, 5.955955157862938, 2.916666666666667, 3.279170919067216, 3.039329218106996, 3.2280290534979423, 1.5640293644261551, 1.1151265826994223, 0.6481675964029875, 0.0, 8.1, 7.129843560432862, 5.575632913497111, 4.692088093278464, 6.456058106995885, 4.2550609053497945, 3.279170919067216, 2.0833333333333335, 2.977977578931469, 2.4712388374485603, 1.3805847279378145, 0.7284307727480569, 0.0), # 31
(8.174260472480764, 8.000029401005945, 6.899664837677183, 7.411016820987655, 5.958314951649118, 2.916666666666667, 3.2742671346727996, 3.029109053497943, 3.226766995884774, 1.5614579606767267, 1.1147548987820595, 0.6477330894680691, 0.0, 8.1, 7.125063984148759, 5.573774493910297, 4.684373882030179, 6.453533991769548, 4.24075267489712, 3.2742671346727996, 2.0833333333333335, 2.979157475824559, 2.470338940329219, 1.3799329675354366, 0.7272754000914496, 0.0), # 32
(8.181118318318317, 7.986149382716048, 6.896098765432099, 7.408054166666666, 5.960610908632033, 2.916666666666667, 3.2689132897603486, 3.0180000000000002, 3.2253844444444444, 1.5586545679012351, 1.114345903479237, 0.6472576131687243, 0.0, 8.1, 7.119833744855966, 5.571729517396184, 4.6759637037037045, 6.450768888888889, 4.225200000000001, 3.2689132897603486, 2.0833333333333335, 2.9803054543160163, 2.469351388888889, 1.37921975308642, 0.7260135802469135, 0.0), # 33
(8.187791048191048, 7.971146730681298, 6.892236396890718, 7.404835956790124, 5.962842930918115, 2.916666666666667, 3.263128362785444, 3.006051440329218, 3.2238853497942395, 1.5556299405578424, 1.1139003948954567, 0.6467426306965403, 0.0, 8.1, 7.114168937661942, 5.569501974477284, 4.666889821673526, 6.447770699588479, 4.208472016460905, 3.263128362785444, 2.0833333333333335, 2.9814214654590576, 2.468278652263375, 1.3784472793781437, 0.724649702789209, 0.0), # 34
(8.194278025437447, 7.95506973022405, 6.888088705989941, 7.401369598765432, 5.965010920613797, 2.916666666666667, 3.2569313322036635, 2.9933127572016467, 3.2222736625514408, 1.5523948331047102, 1.1134191711352206, 0.6461896052431033, 0.0, 8.1, 7.108085657674136, 5.5670958556761025, 4.657184499314129, 6.4445473251028815, 4.1906378600823055, 3.2569313322036635, 2.0833333333333335, 2.9825054603068986, 2.4671231995884777, 1.3776177411979884, 0.7231881572930956, 0.0), # 35
(8.200578613396004, 7.937966666666665, 6.8836666666666675, 7.3976625, 5.967114779825512, 2.916666666666667, 3.250341176470588, 2.979833333333334, 3.220553333333333, 1.5489600000000006, 1.1129030303030305, 0.6456000000000002, 0.0, 8.1, 7.101600000000001, 5.564515151515152, 4.64688, 6.441106666666666, 4.1717666666666675, 3.250341176470588, 2.0833333333333335, 2.983557389912756, 2.4658875000000005, 1.3767333333333336, 0.7216333333333333, 0.0), # 36
(8.20669217540522, 7.919885825331503, 6.8789812528577965, 7.393722067901235, 5.969154410659692, 2.916666666666667, 3.2433768740417976, 2.9656625514403294, 3.218728312757202, 1.5453361957018754, 1.1123527705033882, 0.6449752781588174, 0.0, 8.1, 7.09472805974699, 5.561763852516941, 4.636008587105625, 6.437456625514404, 4.1519275720164615, 3.2433768740417976, 2.0833333333333335, 2.984577205329846, 2.4645740226337454, 1.3757962505715595, 0.7199896204846822, 0.0), # 37
(8.212618074803581, 7.9008754915409245, 6.874043438500229, 7.389555709876545, 5.971129715222768, 2.916666666666667, 3.2360574033728717, 2.9508497942386835, 3.2168025514403293, 1.5415341746684963, 1.111769189840795, 0.6443169029111417, 0.0, 8.1, 7.087485932022558, 5.558845949203975, 4.624602524005487, 6.433605102880659, 4.131189711934157, 3.2360574033728717, 2.0833333333333335, 2.985564857611384, 2.4631852366255154, 1.3748086877000458, 0.7182614083219023, 0.0), # 38
(8.218355674929589, 7.880983950617284, 6.868864197530866, 7.3851708333333335, 5.973040595621175, 2.916666666666667, 3.2284017429193903, 2.9354444444444447, 3.21478, 1.5375646913580252, 1.1111530864197532, 0.6436263374485597, 0.0, 8.1, 7.079889711934156, 5.555765432098766, 4.612694074074074, 6.42956, 4.109622222222223, 3.2284017429193903, 2.0833333333333335, 2.9865202978105874, 2.4617236111111116, 1.3737728395061732, 0.7164530864197532, 0.0), # 39
(8.22390433912173, 7.860259487882944, 6.863454503886603, 7.380574845679012, 5.974886953961343, 2.916666666666667, 3.2204288711369324, 2.9194958847736636, 3.212664609053498, 1.5334385002286244, 1.1105052583447648, 0.6429050449626583, 0.0, 8.1, 7.071955494589241, 5.552526291723823, 4.600315500685872, 6.425329218106996, 4.087294238683129, 3.2204288711369324, 2.0833333333333335, 2.9874434769806717, 2.460191615226338, 1.3726909007773205, 0.714569044352995, 0.0), # 40
(8.229263430718502, 7.838750388660264, 6.857825331504345, 7.375775154320989, 5.976668692349708, 2.916666666666667, 3.212157766481078, 2.903053497942387, 3.210460329218107, 1.529166355738455, 1.1098265037203312, 0.6421544886450238, 0.0, 8.1, 7.06369937509526, 5.549132518601655, 4.587499067215363, 6.420920658436214, 4.0642748971193425, 3.212157766481078, 2.0833333333333335, 2.988334346174854, 2.4585917181069967, 1.371565066300869, 0.7126136716963878, 0.0), # 41
(8.2344323130584, 7.816504938271606, 6.85198765432099, 7.370779166666668, 5.978385712892697, 2.916666666666667, 3.2036074074074072, 2.886166666666667, 3.2081711111111115, 1.5247590123456796, 1.1091176206509543, 0.641376131687243, 0.0, 8.1, 7.0551374485596705, 5.5455881032547705, 4.574277037037037, 6.416342222222223, 4.040633333333334, 3.2036074074074072, 2.0833333333333335, 2.9891928564463486, 2.4569263888888897, 1.370397530864198, 0.7105913580246915, 0.0), # 42
(8.239410349479915, 7.7935714220393235, 6.845952446273435, 7.3655942901234575, 5.980037917696748, 2.916666666666667, 3.1947967723715003, 2.868884773662552, 3.2058009053497942, 1.5202272245084596, 1.1083794072411357, 0.6405714372809025, 0.0, 8.1, 7.046285810089926, 5.541897036205678, 4.5606816735253775, 6.4116018106995885, 4.016438683127573, 3.1947967723715003, 2.0833333333333335, 2.990018958848374, 2.4551980967078197, 1.369190489254687, 0.7085064929126659, 0.0), # 43
(8.244196903321543, 7.769998125285779, 6.839730681298583, 7.360227932098766, 5.981625208868291, 2.916666666666667, 3.185744839828936, 2.8512572016460913, 3.2033536625514403, 1.515581746684957, 1.1076126615953779, 0.639741868617589, 0.0, 8.1, 7.037160554793477, 5.538063307976889, 4.54674524005487, 6.4067073251028805, 3.9917600823045283, 3.185744839828936, 2.0833333333333335, 2.9908126044341454, 2.4534093106995893, 1.3679461362597167, 0.7063634659350709, 0.0), # 44
(8.248791337921773, 7.745833333333334, 6.833333333333335, 7.354687500000001, 5.983147488513758, 2.916666666666667, 3.1764705882352944, 2.833333333333334, 3.2008333333333328, 1.510833333333334, 1.106818181818182, 0.638888888888889, 0.0, 8.1, 7.027777777777777, 5.534090909090909, 4.532500000000001, 6.4016666666666655, 3.9666666666666672, 3.1764705882352944, 2.0833333333333335, 2.991573744256879, 2.4515625000000005, 1.366666666666667, 0.7041666666666668, 0.0), # 45
(8.253193016619106, 7.721125331504343, 6.8267713763145865, 7.348980401234568, 5.984604658739582, 2.916666666666667, 3.1669929960461554, 2.81516255144033, 3.198243868312757, 1.5059927389117518, 1.10599676601405, 0.6380139612863894, 0.0, 8.1, 7.018153574150282, 5.5299838300702495, 4.517978216735254, 6.396487736625514, 3.941227572016462, 3.1669929960461554, 2.0833333333333335, 2.992302329369791, 2.4496601337448567, 1.3653542752629175, 0.7019204846822131, 0.0), # 46
(8.257401302752028, 7.695922405121171, 6.8200557841792415, 7.3431140432098765, 5.985996621652196, 2.916666666666667, 3.1573310417170988, 2.7967942386831277, 3.195589218106996, 1.5010707178783727, 1.105149212287484, 0.6371185490016767, 0.0, 8.1, 7.008304039018443, 5.525746061437419, 4.503212153635117, 6.391178436213992, 3.915511934156379, 3.1573310417170988, 2.0833333333333335, 2.992998310826098, 2.4477046810699594, 1.3640111568358484, 0.6996293095564702, 0.0), # 47
(8.261415559659037, 7.670272839506174, 6.8131975308641985, 7.3370958333333345, 5.987323279358032, 2.916666666666667, 3.1475037037037037, 2.7782777777777783, 3.1928733333333335, 1.4960780246913583, 1.1042763187429856, 0.6362041152263375, 0.0, 8.1, 6.998245267489711, 5.521381593714927, 4.488234074074074, 6.385746666666667, 3.88958888888889, 3.1475037037037037, 2.0833333333333335, 2.993661639679016, 2.445698611111112, 1.3626395061728398, 0.6972975308641977, 0.0), # 48
(8.26523515067863, 7.644224919981709, 6.806207590306356, 7.330933179012346, 5.9885845339635235, 2.916666666666667, 3.137529960461551, 2.7596625514403295, 3.190100164609053, 1.491025413808871, 1.1033788834850566, 0.6352721231519587, 0.0, 8.1, 6.987993354671545, 5.5168944174252825, 4.473076241426613, 6.380200329218106, 3.8635275720164617, 3.137529960461551, 2.0833333333333335, 2.9942922669817618, 2.443644393004116, 1.3612415180612714, 0.6949295381801555, 0.0), # 49
(8.268859439149294, 7.617826931870143, 6.799096936442616, 7.324633487654321, 5.989780287575101, 2.916666666666667, 3.12742879044622, 2.7409979423868314, 3.1872736625514397, 1.485923639689072, 1.1024577046181985, 0.6343240359701267, 0.0, 8.1, 6.977564395671393, 5.512288523090993, 4.457770919067215, 6.3745473251028795, 3.8373971193415644, 3.12742879044622, 2.0833333333333335, 2.9948901437875506, 2.441544495884774, 1.3598193872885234, 0.692529721079104, 0.0), # 50
(8.272287788409528, 7.591127160493827, 6.791876543209877, 7.318204166666668, 5.9909104422991994, 2.916666666666667, 3.11721917211329, 2.7223333333333333, 3.184397777777778, 1.4807834567901237, 1.1015135802469138, 0.6333613168724281, 0.0, 8.1, 6.966974485596708, 5.507567901234569, 4.44235037037037, 6.368795555555556, 3.811266666666667, 3.11721917211329, 2.0833333333333335, 2.9954552211495997, 2.4394013888888897, 1.3583753086419754, 0.6901024691358025, 0.0), # 51
(8.275519561797823, 7.564173891175126, 6.78455738454504, 7.311652623456791, 5.991974900242248, 2.916666666666667, 3.1069200839183413, 2.7037181069958844, 3.18147646090535, 1.4756156195701877, 1.1005473084757038, 0.6323854290504498, 0.0, 8.1, 6.956239719554947, 5.502736542378519, 4.4268468587105625, 6.3629529218107, 3.7852053497942384, 3.1069200839183413, 2.0833333333333335, 2.995987450121124, 2.437217541152264, 1.356911476909008, 0.6876521719250116, 0.0), # 52
(8.278554122652675, 7.537015409236398, 6.777150434385004, 7.304986265432099, 5.992973563510682, 2.916666666666667, 3.0965505043169532, 2.6852016460905355, 3.1785136625514405, 1.470430882487426, 1.0995596874090703, 0.6313978356957782, 0.0, 8.1, 6.945376192653559, 5.4977984370453505, 4.411292647462277, 6.357027325102881, 3.7592823045267494, 3.0965505043169532, 2.0833333333333335, 2.996486781755341, 2.4349954218107, 1.355430086877001, 0.6851832190214908, 0.0), # 53
(8.281390834312573, 7.5097000000000005, 6.769666666666667, 7.2982125, 5.993906334210934, 2.916666666666667, 3.086129411764706, 2.6668333333333334, 3.1755133333333334, 1.4652400000000003, 1.098551515151515, 0.6304000000000001, 0.0, 8.1, 6.9344, 5.492757575757575, 4.395720000000001, 6.351026666666667, 3.7335666666666665, 3.086129411764706, 2.0833333333333335, 2.996953167105467, 2.4327375000000004, 1.3539333333333334, 0.6827000000000002, 0.0), # 54
(8.284029060116017, 7.482275948788294, 6.762117055326932, 7.291338734567901, 5.994773114449434, 2.916666666666667, 3.075675784717179, 2.6486625514403292, 3.1724794238683125, 1.4600537265660727, 1.0975235898075406, 0.6293933851547021, 0.0, 8.1, 6.923327236701723, 5.487617949037702, 4.380161179698217, 6.344958847736625, 3.708127572016461, 3.075675784717179, 2.0833333333333335, 2.997386557224717, 2.4304462448559674, 1.3524234110653865, 0.6802069044352995, 0.0), # 55
(8.286468163401498, 7.454791540923639, 6.754512574302698, 7.28437237654321, 5.995573806332619, 2.916666666666667, 3.0652086016299527, 2.6307386831275723, 3.169415884773662, 1.4548828166438048, 1.0964767094816479, 0.6283794543514709, 0.0, 8.1, 6.912173997866179, 5.482383547408239, 4.364648449931414, 6.338831769547324, 3.6830341563786013, 3.0652086016299527, 2.0833333333333335, 2.9977869031663094, 2.4281241255144037, 1.3509025148605398, 0.6777083219021491, 0.0), # 56
(8.288707507507507, 7.427295061728395, 6.746864197530866, 7.277320833333334, 5.996308311966915, 2.916666666666667, 3.0547468409586056, 2.613111111111112, 3.166326666666667, 1.4497380246913585, 1.0954116722783391, 0.627359670781893, 0.0, 8.1, 6.900956378600823, 5.477058361391695, 4.349214074074075, 6.332653333333334, 3.6583555555555565, 3.0547468409586056, 2.0833333333333335, 2.9981541559834577, 2.425773611111112, 1.3493728395061733, 0.6752086419753087, 0.0), # 57
(8.290746455772544, 7.39983479652492, 6.739182898948332, 7.270191512345679, 5.99697653345876, 2.916666666666667, 3.044309481158719, 2.595829218106996, 3.163215720164609, 1.4446301051668957, 1.0943292763021162, 0.6263354976375554, 0.0, 8.1, 6.889690474013108, 5.471646381510581, 4.333890315500686, 6.326431440329218, 3.6341609053497947, 3.044309481158719, 2.0833333333333335, 2.99848826672938, 2.4233971707818935, 1.3478365797896665, 0.6727122542295383, 0.0), # 58
(8.292584371535098, 7.372459030635573, 6.731479652491998, 7.262991820987654, 5.9975783729145835, 2.916666666666667, 3.0339155006858713, 2.578942386831276, 3.160086995884774, 1.4395698125285785, 1.0932303196574802, 0.6253083981100444, 0.0, 8.1, 6.878392379210486, 5.4661515982874, 4.318709437585735, 6.320173991769548, 3.6105193415637866, 3.0339155006858713, 2.0833333333333335, 2.9987891864572918, 2.420997273662552, 1.3462959304984, 0.6702235482395976, 0.0), # 59
(8.294220618133663, 7.345216049382717, 6.723765432098765, 7.255729166666667, 5.998113732440819, 2.916666666666667, 3.0235838779956428, 2.5625000000000004, 3.156944444444445, 1.4345679012345682, 1.092115600448934, 0.6242798353909466, 0.0, 8.1, 6.867078189300411, 5.460578002244669, 4.303703703703704, 6.31388888888889, 3.5875000000000004, 3.0235838779956428, 2.0833333333333335, 2.9990568662204096, 2.4185763888888894, 1.3447530864197532, 0.6677469135802471, 0.0), # 60
(8.295654558906731, 7.3181541380887065, 6.716051211705533, 7.248410956790124, 5.998582514143899, 2.916666666666667, 3.0133335915436135, 2.5465514403292184, 3.1537920164609052, 1.4296351257430273, 1.0909859167809788, 0.623251272671849, 0.0, 8.1, 6.855763999390337, 5.454929583904893, 4.2889053772290815, 6.3075840329218105, 3.5651720164609055, 3.0133335915436135, 2.0833333333333335, 2.9992912570719494, 2.4161369855967085, 1.3432102423411068, 0.6652867398262462, 0.0), # 61
(8.296885557192804, 7.291321582075903, 6.708347965249201, 7.241044598765433, 5.998984620130258, 2.916666666666667, 3.0031836197853625, 2.5311460905349796, 3.1506336625514404, 1.4247822405121175, 1.0898420667581163, 0.6222241731443379, 0.0, 8.1, 6.844465904587715, 5.449210333790581, 4.274346721536352, 6.301267325102881, 3.5436045267489718, 3.0031836197853625, 2.0833333333333335, 2.999492310065129, 2.4136815329218115, 1.3416695930498403, 0.6628474165523549, 0.0), # 62
(8.297912976330368, 7.264766666666667, 6.700666666666668, 7.233637500000001, 5.999319952506323, 2.916666666666667, 2.9931529411764703, 2.5163333333333338, 3.147473333333333, 1.4200200000000003, 1.0886848484848488, 0.6212000000000001, 0.0, 8.1, 6.8332, 5.443424242424244, 4.26006, 6.294946666666666, 3.5228666666666677, 2.9931529411764703, 2.0833333333333335, 2.9996599762531617, 2.411212500000001, 1.3401333333333336, 0.6604333333333334, 0.0), # 63
(8.298736179657919, 7.2385376771833565, 6.693018289894834, 7.226197067901236, 5.999588413378532, 2.916666666666667, 2.983260534172517, 2.5021625514403296, 3.1443149794238683, 1.415359158664838, 1.0875150600656773, 0.6201802164304223, 0.0, 8.1, 6.821982380734645, 5.437575300328387, 4.246077475994513, 6.288629958847737, 3.5030275720164616, 2.983260534172517, 2.0833333333333335, 2.999794206689266, 2.408732355967079, 1.3386036579789669, 0.6580488797439416, 0.0), # 64
(8.29935453051395, 7.212682898948331, 6.685413808870599, 7.218730709876544, 5.999789904853316, 2.916666666666667, 2.9735253772290813, 2.4886831275720165, 3.1411625514403294, 1.4108104709647922, 1.0863334996051048, 0.619166285627191, 0.0, 8.1, 6.8108291418991, 5.431667498025524, 4.232431412894376, 6.282325102880659, 3.484156378600823, 2.9735253772290813, 2.0833333333333335, 2.999894952426658, 2.4062435699588485, 1.33708276177412, 0.6556984453589393, 0.0), # 65
(8.299767392236957, 7.187250617283952, 6.677864197530865, 7.211245833333334, 5.999924329037105, 2.916666666666667, 2.963966448801743, 2.475944444444445, 3.13802, 1.406384691358025, 1.085140965207632, 0.6181596707818932, 0.0, 8.1, 6.799756378600824, 5.425704826038159, 4.2191540740740745, 6.27604, 3.466322222222223, 2.963966448801743, 2.0833333333333335, 2.9999621645185526, 2.4037486111111117, 1.3355728395061732, 0.6533864197530866, 0.0), # 66
(8.299974128165434, 7.162289117512574, 6.670380429812529, 7.203749845679012, 5.999991588036336, 2.916666666666667, 2.9546027273460824, 2.4639958847736634, 3.1348912757201646, 1.4020925743026982, 1.0839382549777616, 0.617161835086115, 0.0, 8.1, 6.788780185947264, 5.419691274888807, 4.206277722908094, 6.269782551440329, 3.4495942386831286, 2.9546027273460824, 2.0833333333333335, 2.999995794018168, 2.401249948559671, 1.3340760859625058, 0.6511171925011432, 0.0), # 67
(8.29983329158466, 7.137715668834903, 6.662937299954276, 7.196185044283415, 5.999934909491917, 2.916612538739013, 2.9454060779318585, 2.452781283340954, 3.131756759640299, 1.3979240883294335, 1.0827047984720504, 0.6161686681266496, 0.0, 8.099900120027435, 6.777855349393144, 5.413523992360251, 4.1937722649883, 6.263513519280598, 3.433893796677336, 2.9454060779318585, 2.0832946705278665, 2.9999674547459585, 2.398728348094472, 1.3325874599908551, 0.648883242621355, 0.0), # 68
(8.298513365539453, 7.112780047789725, 6.655325617283951, 7.188170108695652, 5.999419026870006, 2.916184636488341, 2.9361072725386457, 2.4416995884773662, 3.1284794238683125, 1.3937612781408861, 1.0813150451887295, 0.6151479315572884, 0.0, 8.099108796296298, 6.766627247130171, 5.406575225943647, 4.181283834422658, 6.256958847736625, 3.4183794238683127, 2.9361072725386457, 2.0829890260631005, 2.999709513435003, 2.396056702898551, 1.33106512345679, 0.6466163679808842, 0.0), # 69
(8.295908630047116, 7.087367803885127, 6.647512288523091, 7.179652274557166, 5.998399634202102, 2.9153419194228523, 2.926664053824548, 2.4306508154244786, 3.1250407712238992, 1.3895839048925471, 1.079753184870144, 0.614094850752854, 0.0, 8.097545867626888, 6.755043358281393, 5.3987659243507204, 4.168751714677641, 6.2500815424477985, 3.40291114159427, 2.926664053824548, 2.082387085302037, 2.999199817101051, 2.393217424852389, 1.3295024577046182, 0.6443061639895571, 0.0), # 70
(8.292055728514343, 7.061494123633789, 6.639500057155922, 7.170644102254428, 5.9968896420022055, 2.9140980439973583, 2.9170806638155953, 2.4196386221612562, 3.1214459228776104, 1.3853920718685282, 1.0780249827711816, 0.613010195814181, 0.0, 8.095231910150892, 6.743112153955991, 5.390124913855908, 4.1561762156055835, 6.242891845755221, 3.387494071025759, 2.9170806638155953, 2.081498602855256, 2.9984448210011028, 2.3902147007514767, 1.3279000114311843, 0.6419540112394354, 0.0), # 71
(8.286991304347827, 7.035174193548387, 6.631291666666667, 7.161158152173913, 5.994901960784313, 2.9124666666666674, 2.907361344537815, 2.408666666666667, 3.1177, 1.3811858823529415, 1.0761362041467308, 0.6118947368421054, 0.0, 8.0921875, 6.730842105263158, 5.380681020733653, 4.143557647058824, 6.2354, 3.3721333333333336, 2.907361344537815, 2.080333333333334, 2.9974509803921565, 2.3870527173913048, 1.3262583333333333, 0.6395612903225807, 0.0), # 72
(8.280752000954257, 7.008423200141599, 6.622889860539551, 7.151206984702094, 5.992449501062428, 2.9104614438855867, 2.897510338017237, 2.397738606919677, 3.113808123761622, 1.376965439629899, 1.0740926142516787, 0.6107492439374613, 0.0, 8.0884332133059, 6.7182416833120735, 5.370463071258393, 4.130896318889696, 6.227616247523244, 3.356834049687548, 2.897510338017237, 2.0789010313468475, 2.996224750531214, 2.383735661567365, 1.3245779721079105, 0.6371293818310545, 0.0), # 73
(8.273374461740323, 6.981256329926103, 6.614297382258802, 7.140803160225442, 5.989545173350547, 2.908096032108927, 2.887531886279889, 2.3868581008992535, 3.1097754153330284, 1.3727308469835127, 1.0718999783409144, 0.6095744872010845, 0.0, 8.083989626200276, 6.705319359211929, 5.359499891704571, 4.118192540950537, 6.219550830666057, 3.3416013412589547, 2.887531886279889, 2.0772114515063764, 2.9947725866752735, 2.380267720075148, 1.3228594764517605, 0.6346596663569185, 0.0), # 74
(8.26489533011272, 6.953688769414575, 6.605516975308642, 7.129959239130434, 5.986201888162673, 2.905384087791496, 2.8774302313518003, 2.376028806584362, 3.1056069958847736, 1.3684822076978942, 1.069564061669325, 0.6083712367338099, 0.0, 8.078877314814816, 6.692083604071907, 5.347820308346624, 4.105446623093682, 6.211213991769547, 3.3264403292181073, 2.8774302313518003, 2.0752743484224974, 2.9931009440813363, 2.3766530797101453, 1.3211033950617284, 0.6321535244922342, 0.0), # 75
(8.255351249478142, 6.925735705119696, 6.596551383173297, 7.118687781803542, 5.982432556012803, 2.9023392673881023, 2.8672096152589983, 2.365254381953971, 3.1013079865874102, 1.364219625057156, 1.067090629491799, 0.6071402626364722, 0.0, 8.073116855281206, 6.678542889001194, 5.335453147458995, 4.092658875171468, 6.2026159731748205, 3.311356134735559, 2.8672096152589983, 2.0730994767057873, 2.9912162780064016, 2.372895927267848, 1.3193102766346596, 0.6296123368290635, 0.0), # 76
(8.244778863243274, 6.897412323554141, 6.587403349336991, 7.10700134863124, 5.9782500874149385, 2.8989752273535543, 2.8568742800275118, 2.354538484987045, 3.0968835086114925, 1.3599432023454103, 1.0644854470632252, 0.6058823350099072, 0.0, 8.06672882373114, 6.664705685108978, 5.322427235316125, 4.07982960703623, 6.193767017222985, 3.296353878981863, 2.8568742800275118, 2.0706965909668247, 2.9891250437074692, 2.369000449543747, 1.3174806698673982, 0.6270374839594675, 0.0), # 77
(8.233214814814815, 6.8687338112305865, 6.578075617283951, 7.0949125, 5.97366739288308, 2.895305624142661, 2.84642846768337, 2.343884773662552, 3.092338683127571, 1.3556530428467686, 1.0617542796384905, 0.6045982239549493, 0.0, 8.059733796296298, 6.650580463504441, 5.308771398192452, 4.066959128540305, 6.184677366255142, 3.2814386831275724, 2.84642846768337, 2.0680754458161865, 2.98683369644154, 2.364970833333334, 1.3156151234567903, 0.624430346475508, 0.0), # 78
(8.220695747599452, 6.8397153546617115, 6.5685709304984, 7.082433796296296, 5.968697382931225, 2.891344114210232, 2.8358764202526006, 2.333296905959458, 3.0876786313062032, 1.351349249845343, 1.058902892472483, 0.6032886995724337, 0.0, 8.052152349108367, 6.63617569529677, 5.294514462362415, 4.0540477495360285, 6.1753572626124065, 3.266615668343241, 2.8358764202526006, 2.0652457958644517, 2.9843486914656125, 2.3608112654320994, 1.3137141860996802, 0.6217923049692465, 0.0), # 79
(8.207258305003878, 6.810372140360193, 6.558892032464563, 7.069577797906602, 5.963352968073375, 2.8871043540110755, 2.8252223797612324, 2.3227785398567296, 3.0829084743179394, 1.3470319266252455, 1.055937050820092, 0.6019545319631957, 0.0, 8.04400505829904, 6.621499851595152, 5.2796852541004595, 4.041095779875736, 6.165816948635879, 3.2518899557994216, 2.8252223797612324, 2.0622173957221968, 2.9816764840366874, 2.3565259326355346, 1.3117784064929128, 0.619124740032745, 0.0), # 80
(8.192939130434784, 6.78071935483871, 6.5490416666666675, 7.056357065217393, 5.957647058823529, 2.8826000000000005, 2.8144705882352943, 2.3123333333333336, 3.078033333333333, 1.3427011764705885, 1.0528625199362043, 0.6005964912280702, 0.0, 8.0353125, 6.606561403508772, 5.264312599681022, 4.028103529411765, 6.156066666666666, 3.237266666666667, 2.8144705882352943, 2.059, 2.9788235294117644, 2.3521190217391315, 1.3098083333333335, 0.6164290322580647, 0.0), # 81
(8.177774867298861, 6.750772184609939, 6.539022576588936, 7.042784158615137, 5.951592565695688, 2.877844708631815, 2.8036252877008145, 2.301964944368237, 3.0730583295229383, 1.3383571026654835, 1.0496850650757086, 0.5992153474678925, 0.0, 8.026095250342937, 6.5913688221468165, 5.248425325378542, 4.0150713079964495, 6.146116659045877, 3.2227509221155315, 2.8036252877008145, 2.0556033633084394, 2.975796282847844, 2.3475947195383795, 1.3078045153177873, 0.6137065622372673, 0.0), # 82
(8.161802159002804, 6.720545816186557, 6.528837505715592, 7.028871638486312, 5.945202399203851, 2.8728521363613275, 2.7926907201838214, 2.2916770309404058, 3.067988584057308, 1.3339998084940425, 1.0464104514934927, 0.5978118707834975, 0.0, 8.016373885459535, 6.575930578618472, 5.232052257467463, 4.001999425482127, 6.135977168114616, 3.208347843316568, 2.7926907201838214, 2.052037240258091, 2.9726011996019257, 2.3429572128287712, 1.3057675011431187, 0.6109587105624144, 0.0), # 83
(8.145057648953301, 6.690055436081242, 6.518489197530864, 7.014632065217392, 5.938489469862018, 2.867635939643347, 2.7816711277103434, 2.2814732510288067, 3.0628292181069954, 1.329629397240378, 1.0430444444444447, 0.5963868312757202, 0.0, 8.006168981481482, 6.560255144032922, 5.215222222222223, 3.9888881917211334, 6.125658436213991, 3.194062551440329, 2.7816711277103434, 2.0483113854595336, 2.969244734931009, 2.338210688405798, 1.303697839506173, 0.6081868578255676, 0.0), # 84
(8.127577980557048, 6.659316230806673, 6.507980395518976, 7.000077999194847, 5.931466688184191, 2.862209774932684, 2.77057075230641, 2.2713572626124074, 3.057585352842554, 1.3252459721886014, 1.0395928091834528, 0.5949409990453959, 0.0, 7.995501114540467, 6.544350989499354, 5.197964045917263, 3.9757379165658033, 6.115170705685108, 3.17990016765737, 2.77057075230641, 2.0444355535233454, 2.9657333440920954, 2.3333593330649496, 1.3015960791037953, 0.6053923846187885, 0.0), # 85
(8.10939979722073, 6.6283433868755255, 6.497313843164153, 6.985222000805154, 5.924146964684365, 2.8565872986841443, 2.7593938359980483, 2.2613327236701726, 3.0522621094345377, 1.320849636622825, 1.0360613109654049, 0.5934751441933597, 0.0, 7.984390860768176, 6.528226586126955, 5.180306554827023, 3.9625489098684747, 6.104524218869075, 3.1658658131382413, 2.7593938359980483, 2.040419499060103, 2.9620734823421824, 2.3284073336017186, 1.2994627686328306, 0.6025766715341389, 0.0), # 86
(8.090559742351045, 6.597152090800478, 6.486492283950617, 6.970076630434782, 5.9165432098765445, 2.8507821673525378, 2.7481446208112876, 2.2514032921810703, 3.0468646090534985, 1.3164404938271608, 1.0324557150451887, 0.5919900368204463, 0.0, 7.972858796296297, 6.511890405024908, 5.162278575225944, 3.9493214814814817, 6.093729218106997, 3.1519646090534983, 2.7481446208112876, 2.036272976680384, 2.9582716049382722, 2.3233588768115947, 1.2972984567901236, 0.5997410991636799, 0.0), # 87
(8.071094459354686, 6.565757529094207, 6.475518461362597, 6.95465444847021, 5.908668334274726, 2.8448080373926743, 2.7368273487721564, 2.2415726261240665, 3.0413979728699894, 1.3120186470857205, 1.0287817866776934, 0.5904864470274911, 0.0, 7.960925497256517, 6.495350917302401, 5.143908933388466, 3.9360559412571607, 6.082795945739979, 3.138201676573693, 2.7368273487721564, 2.032005740994767, 2.954334167137363, 2.3182181494900704, 1.2951036922725196, 0.5968870480994735, 0.0), # 88
(8.051040591638339, 6.534174888269392, 6.464395118884317, 6.938968015297907, 5.90053524839291, 2.8386785652593614, 2.7254462619066833, 2.2318443834781285, 3.035867322054565, 1.3075841996826167, 1.025045291117806, 0.5889651449153291, 0.0, 7.948611539780521, 6.478616594068619, 5.125226455589029, 3.9227525990478496, 6.07173464410913, 3.12458213686938, 2.7254462619066833, 2.0276275466138296, 2.950267624196455, 2.312989338432636, 1.2928790237768635, 0.5940158989335812, 0.0), # 89
(8.030434782608696, 6.502419354838709, 6.453125000000001, 6.923029891304349, 5.892156862745098, 2.8324074074074077, 2.7140056022408965, 2.2222222222222223, 3.030277777777778, 1.303137254901961, 1.021251993620415, 0.5874269005847954, 0.0, 7.9359375000000005, 6.461695906432748, 5.106259968102074, 3.9094117647058826, 6.060555555555556, 3.111111111111111, 2.7140056022408965, 2.0231481481481484, 2.946078431372549, 2.3076766304347833, 1.2906250000000001, 0.5911290322580646, 0.0), # 90
(8.00931367567245, 6.470506115314836, 6.441710848193873, 6.906852636876007, 5.883546087845287, 2.826008220291622, 2.7025096118008247, 2.2127098003353147, 3.024634461210182, 1.2986779160278654, 1.0174076594404082, 0.585872484136725, 0.0, 7.922923954046638, 6.444597325503974, 5.0870382972020405, 3.8960337480835956, 6.049268922420364, 3.097793720469441, 2.7025096118008247, 2.0185773002083014, 2.9417730439226437, 2.302284212292003, 1.2883421696387747, 0.5882278286649852, 0.0), # 91
(7.9877139142362985, 6.438450356210453, 6.43015540695016, 6.890448812399356, 5.874715834207482, 2.8194946603668143, 2.690962532612497, 2.203310775796373, 3.018942493522329, 1.2942062863444421, 1.013518053832674, 0.5843026656719533, 0.0, 7.909591478052126, 6.427329322391485, 5.067590269163369, 3.8826188590333257, 6.037884987044658, 3.0846350861149223, 2.690962532612497, 2.0139247574048675, 2.937357917103741, 2.296816270799786, 1.2860310813900322, 0.5853136687464049, 0.0), # 92
(7.965672141706924, 6.406267264038233, 6.418461419753087, 6.873830978260871, 5.865679012345678, 2.8128803840877916, 2.6793686067019404, 2.1940288065843623, 3.013206995884774, 1.2897224691358027, 1.0095889420521, 0.5827182152913147, 0.0, 7.895960648148147, 6.409900368204461, 5.0479447102605, 3.8691674074074074, 6.026413991769548, 3.0716403292181074, 2.6793686067019404, 2.0092002743484225, 2.932839506172839, 2.291276992753624, 1.2836922839506175, 0.5823879330943849, 0.0), # 93
(7.943225001491024, 6.373972025310855, 6.406631630086878, 6.857011694847022, 5.856448532773877, 2.806179047909364, 2.6677320760951844, 2.1848675506782507, 3.007433089468069, 1.2852265676860597, 1.005626089353575, 0.581119903095645, 0.0, 7.882052040466393, 6.392318934052094, 5.028130446767873, 3.855679703058178, 6.014866178936138, 3.058814570949551, 2.6677320760951844, 2.0044136056495456, 2.9282242663869384, 2.2856705649490077, 1.2813263260173757, 0.5794520023009869, 0.0), # 94
(7.920409136995288, 6.341579826540998, 6.394668781435757, 6.840003522544284, 5.847037306006079, 2.799404308286339, 2.6560571828182575, 2.1758306660570037, 3.001625895442768, 1.2807186852793244, 1.0016352609919863, 0.5795084991857787, 0.0, 7.867886231138546, 6.374593491043566, 5.008176304959932, 3.8421560558379726, 6.003251790885536, 3.046162932479805, 2.6560571828182575, 1.9995745059188135, 2.9235186530030397, 2.2800011741814283, 1.2789337562871517, 0.5765072569582727, 0.0), # 95
(7.89726119162641, 6.30910585424134, 6.382575617283951, 6.8228190217391305, 5.8374582425562815, 2.7925698216735255, 2.6443481688971886, 2.1669218106995887, 2.995790534979424, 1.27619892519971, 0.9976222222222224, 0.5778847736625516, 0.0, 7.853483796296297, 6.356732510288067, 4.988111111111112, 3.828596775599129, 5.991581069958848, 3.0336905349794243, 2.6443481688971886, 1.9946927297668038, 2.9187291212781408, 2.2742730072463773, 1.2765151234567904, 0.5735550776583037, 0.0), # 96
(7.873817808791078, 6.276565294924556, 6.370354881115684, 6.805470752818035, 5.827724252938488, 2.7856892445257326, 2.6326092763580053, 2.1581446425849724, 2.9899321292485905, 1.2716673907313272, 0.9935927382991712, 0.576249496626798, 0.0, 7.838865312071332, 6.338744462894778, 4.967963691495855, 3.8150021721939806, 5.979864258497181, 3.0214024996189615, 2.6326092763580053, 1.9897780318040947, 2.913862126469244, 2.2684902509393456, 1.2740709762231368, 0.5705968449931414, 0.0), # 97
(7.850115631895988, 6.243973335103323, 6.35800931641518, 6.787971276167473, 5.817848247666694, 2.7787762332977706, 2.6208447472267373, 2.1495028196921204, 2.9840557994208194, 1.2671241851582886, 0.9895525744777209, 0.5746034381793533, 0.0, 7.824051354595337, 6.320637819972885, 4.947762872388605, 3.801372555474865, 5.968111598841639, 3.0093039475689687, 2.6208447472267373, 1.9848401666412645, 2.908924123833347, 2.2626570920558247, 1.2716018632830361, 0.5676339395548476, 0.0), # 98
(7.826191304347827, 6.211345161290323, 6.3455416666666675, 6.770333152173913, 5.807843137254903, 2.7718444444444446, 2.6090588235294123, 2.1410000000000005, 2.9781666666666666, 1.2625694117647062, 0.9855074960127594, 0.5729473684210528, 0.0, 7.8090625000000005, 6.302421052631579, 4.927537480063797, 3.787708235294118, 5.956333333333333, 2.9974000000000007, 2.6090588235294123, 1.9798888888888888, 2.9039215686274513, 2.256777717391305, 1.2691083333333337, 0.564667741935484, 0.0), # 99
(7.80208146955329, 6.178695959998229, 6.332954675354367, 6.752568941223833, 5.797721832217111, 2.764907534420566, 2.597255747292058, 2.1326398414875785, 2.9722698521566837, 1.258003173834692, 0.9814632681591747, 0.5712820574527312, 0.0, 7.79391932441701, 6.284102631980042, 4.907316340795873, 3.774009521504075, 5.944539704313367, 2.98569577808261, 2.597255747292058, 1.9749339531575472, 2.8988609161085557, 2.250856313741278, 1.2665909350708735, 0.5616996327271119, 0.0), # 100
(7.777822770919068, 6.1460409177397235, 6.320251085962506, 6.734691203703704, 5.787497243067323, 2.757979159680943, 2.585439760540705, 2.124426002133821, 2.9663704770614236, 1.253425574652358, 0.9774256561718551, 0.5696082753752236, 0.0, 7.7786424039780515, 6.265691029127459, 4.887128280859275, 3.760276723957073, 5.932740954122847, 2.9741964029873493, 2.585439760540705, 1.9699851140578162, 2.8937486215336614, 2.244897067901235, 1.2640502171925014, 0.5587309925217931, 0.0), # 101
(7.753451851851853, 6.11339522102748, 6.307433641975309, 6.716712500000001, 5.7771822803195345, 2.7510729766803848, 2.5736151053013803, 2.1163621399176957, 2.9604736625514403, 1.248836717501816, 0.9734004253056887, 0.5679267922893655, 0.0, 7.763252314814816, 6.24719471518302, 4.867002126528443, 3.746510152505447, 5.920947325102881, 2.962906995884774, 2.5736151053013803, 1.965052126200275, 2.8885911401597673, 2.2389041666666674, 1.261486728395062, 0.5557632019115891, 0.0), # 102
(7.729005355758336, 6.080774056374176, 6.294505086877001, 6.698645390499196, 5.766789854487748, 2.7442026418736987, 2.561786023600112, 2.1084519128181682, 2.9545845297972866, 1.2442367056671781, 0.9693933408155633, 0.5662383782959916, 0.0, 7.747769633058984, 6.228622161255906, 4.846966704077817, 3.7327101170015338, 5.909169059594573, 2.951832677945436, 2.561786023600112, 1.960144744195499, 2.883394927243874, 2.2328817968330656, 1.2589010173754003, 0.5527976414885616, 0.0), # 103
(7.704519926045208, 6.048192610292491, 6.281468164151806, 6.680502435587762, 5.756332876085962, 2.7373818117156943, 2.5499567574629305, 2.1006989788142056, 2.948708199969517, 1.2396256424325565, 0.9654101679563669, 0.564543803495937, 0.0, 7.732214934842251, 6.209981838455306, 4.827050839781834, 3.7188769272976687, 5.897416399939034, 2.9409785703398876, 2.5499567574629305, 1.9552727226540672, 2.878166438042981, 2.2268341451959213, 1.2562936328303613, 0.549835691844772, 0.0), # 104
(7.680032206119162, 6.015666069295101, 6.268325617283951, 6.662296195652173, 5.745824255628177, 2.7306241426611804, 2.5381315489158633, 2.0931069958847743, 2.942849794238683, 1.235003631082063, 0.961456671982988, 0.562843837990037, 0.0, 7.716608796296296, 6.1912822178904054, 4.80728335991494, 3.705010893246188, 5.885699588477366, 2.930349794238684, 2.5381315489158633, 1.9504458161865572, 2.8729121278140886, 2.220765398550725, 1.2536651234567902, 0.546878733572282, 0.0), # 105
(7.655578839386891, 5.983209619894685, 6.255080189757659, 6.644039231078905, 5.735276903628392, 2.723943291164965, 2.526314639984938, 2.0856796220088403, 2.9370144337753388, 1.2303707748998092, 0.9575386181503142, 0.5611392518791264, 0.0, 7.700971793552812, 6.172531770670389, 4.787693090751571, 3.691112324699427, 5.8740288675506775, 2.9199514708123764, 2.526314639984938, 1.9456737794035461, 2.867638451814196, 2.214679743692969, 1.2510160379515318, 0.5439281472631533, 0.0), # 106
(7.631196469255085, 5.950838448603921, 6.241734625057157, 6.625744102254428, 5.724703730600607, 2.7173529136818577, 2.5145102726961848, 2.0784205151653716, 2.931207239750038, 1.225727177169908, 0.9536617717132337, 0.5594308152640404, 0.0, 7.685324502743484, 6.153738967904443, 4.768308858566169, 3.6771815315097234, 5.862414479500076, 2.9097887212315205, 2.5145102726961848, 1.9409663669156128, 2.8623518653003037, 2.208581367418143, 1.2483469250114314, 0.5409853135094475, 0.0), # 107
(7.606921739130435, 5.918567741935485, 6.228291666666668, 6.607423369565218, 5.714117647058822, 2.7108666666666674, 2.5027226890756302, 2.0713333333333335, 2.9254333333333333, 1.221072941176471, 0.9498318979266349, 0.5577192982456142, 0.0, 7.669687500000001, 6.134912280701755, 4.749159489633174, 3.6632188235294123, 5.850866666666667, 2.899866666666667, 2.5027226890756302, 1.9363333333333337, 2.857058823529411, 2.20247445652174, 1.2456583333333338, 0.538051612903226, 0.0), # 108
(7.582791292419635, 5.886412686402053, 6.214754058070417, 6.589089593397745, 5.70353156351704, 2.7044982065742014, 2.490956131149305, 2.064421734491694, 2.9196978356957777, 1.2164081702036098, 0.9460547620454054, 0.5560054709246826, 0.0, 7.654081361454047, 6.116060180171507, 4.730273810227027, 3.6492245106108285, 5.839395671391555, 2.8901904282883715, 2.490956131149305, 1.9317844332672867, 2.85176578175852, 2.196363197799249, 1.2429508116140835, 0.5351284260365504, 0.0), # 109
(7.558841772529373, 5.854388468516307, 6.201124542752631, 6.570755334138486, 5.692958390489256, 2.6982611898592697, 2.4792148409432357, 2.0576893766194178, 2.9140058680079255, 1.211732967535437, 0.9423361293244336, 0.554290103402081, 0.0, 7.638526663237312, 6.0971911374228895, 4.711680646622168, 3.63519890260631, 5.828011736015851, 2.880765127267185, 2.4792148409432357, 1.9273294213280499, 2.846479195244628, 2.1902517780461626, 1.2402249085505264, 0.5322171335014826, 0.0), # 110
(7.535109822866345, 5.82251027479092, 6.187405864197532, 6.552433152173913, 5.68241103848947, 2.6921692729766806, 2.4675030604834527, 2.0511399176954734, 2.9083625514403293, 1.2070474364560642, 0.9386817650186072, 0.5525739657786443, 0.0, 7.623043981481482, 6.078313623565086, 4.693408825093036, 3.621142309368192, 5.816725102880659, 2.871595884773663, 2.4675030604834527, 1.9229780521262005, 2.841205519244735, 2.1841443840579715, 1.2374811728395065, 0.5293191158900837, 0.0), # 111
(7.51163208683724, 5.790793291738572, 6.173600765889348, 6.5341356078905, 5.671902418031685, 2.686236112381243, 2.4558250317959835, 2.0447770156988265, 2.9027730071635416, 1.2023516802496035, 0.9350974343828147, 0.5508578281552075, 0.0, 7.607653892318244, 6.059436109707281, 4.675487171914074, 3.6070550407488096, 5.805546014327083, 2.862687821978357, 2.4558250317959835, 1.9187400802723165, 2.8359512090158425, 2.178045202630167, 1.2347201531778695, 0.5264357537944157, 0.0), # 112
(7.488403378962436, 5.759305653776365, 6.159745218834713, 6.515900329495224, 5.661427029425976, 2.6804725589667733, 2.444210385462708, 2.038617522926869, 2.8972567496689656, 1.1976609473225461, 0.9315898541537156, 0.549146195766962, 0.0, 7.592355120674577, 6.0406081534365805, 4.657949270768578, 3.592982841967638, 5.794513499337931, 2.8540645320976163, 2.444210385462708, 1.914623256404838, 2.830713514712988, 2.1719667764984085, 1.2319490437669427, 0.5235732412523969, 0.0), # 113
(7.465184718320052, 5.728357934585393, 6.146030450014413, 6.497873652766401, 5.6508764557687075, 2.674865483980621, 2.432807283364232, 2.0327370865017067, 2.891898409523483, 1.1930630335825567, 0.9281659116150931, 0.5474608114741984, 0.0, 7.577020331328028, 6.022068926216181, 4.640829558075465, 3.5791891007476693, 5.783796819046966, 2.8458319211023895, 2.432807283364232, 1.9106182028433005, 2.8254382278843537, 2.1659578842554676, 1.2292060900028827, 0.5207598122350358, 0.0), # 114
(7.441907922403196, 5.697961279034234, 6.132464621804878, 6.480050703109068, 5.640217428207254, 2.669400305832757, 2.421623860076625, 2.027134218092903, 2.886699994311677, 1.1885650655976157, 0.9248206015236127, 0.5458025055039235, 0.0, 7.561605305328301, 6.003827560543158, 4.6241030076180625, 3.5656951967928463, 5.773399988623354, 2.8379879053300643, 2.421623860076625, 1.9067145041662548, 2.820108714103627, 2.1600169010363564, 1.226492924360976, 0.5179964799122032, 0.0), # 115
(7.418543898590108, 5.668071406280581, 6.119021459989249, 6.462399690159842, 5.629433880738015, 2.664064142733979, 2.4106419270111576, 2.021793437632998, 2.8816483571274216, 1.1841586716899097, 0.9215474575028644, 0.5441682131658231, 0.0, 7.546085807804713, 5.985850344824053, 4.607737287514321, 3.5524760150697285, 5.763296714254843, 2.8305108126861973, 2.4106419270111576, 1.9029029590956992, 2.8147169403690073, 2.154133230053281, 1.22380429199785, 0.5152792187527803, 0.0), # 116
(7.395063554259018, 5.638644035482129, 6.105674690350658, 6.444888823555345, 5.6185097473573915, 2.6588441128950824, 2.399843295579101, 2.0166992650545286, 2.8767303510645874, 1.179835480181626, 0.9183400131764379, 0.5425548697695834, 0.0, 7.53043760388658, 5.968103567465417, 4.591700065882189, 3.5395064405448773, 5.753460702129175, 2.8233789710763397, 2.399843295579101, 1.8991743663536302, 2.8092548736786958, 2.148296274518449, 1.2211349380701317, 0.5126040032256481, 0.0), # 117
(7.371437796788169, 5.60963488579657, 6.092398038672245, 6.427486312932199, 5.607428962061783, 2.6537273345268653, 2.3892097771917262, 2.0118362202900326, 2.871932829217049, 1.175587119394952, 0.9151918021679234, 0.5409594106248901, 0.0, 7.51463645870322, 5.950553516873789, 4.575959010839616, 3.5267613581848556, 5.743865658434098, 2.8165707084060454, 2.3892097771917262, 1.8955195246620464, 2.8037144810308914, 2.142495437644067, 1.218479607734449, 0.5099668077996883, 0.0), # 118
(7.347637533555794, 5.580999676381602, 6.079165230737149, 6.410160367927023, 5.5961754588475845, 2.648700925840122, 2.3787231832603024, 2.0071888232720485, 2.867242644678678, 1.1714052176520746, 0.9120963581009105, 0.5393787710414291, 0.0, 7.498658137383946, 5.933166481455719, 4.560481790504553, 3.5142156529562234, 5.734485289357356, 2.810064352580868, 2.3787231832603024, 1.8919292327429442, 2.7980877294237922, 2.1367201226423416, 1.21583304614743, 0.507363606943782, 0.0), # 119
(7.323633671940129, 5.552694126394916, 6.065949992328509, 6.392879198176436, 5.584733171711198, 2.6437520050456507, 2.3683653251961014, 2.0027415939331146, 2.8626466505433488, 1.1672814032751813, 0.909047214598989, 0.5378098863288866, 0.0, 7.482478405058078, 5.915908749617751, 4.545236072994944, 3.501844209825543, 5.7252933010866975, 2.80383823150636, 2.3683653251961014, 1.8883942893183219, 2.792366585855599, 2.1309597327254792, 1.2131899984657017, 0.5047903751268107, 0.0), # 120
(7.299397119319415, 5.524673954994208, 6.052726049229459, 6.3756110133170605, 5.573086034649023, 2.638867690354248, 2.358118014410392, 1.9984790522057692, 2.858131699904933, 1.1632073045864595, 0.906037905285749, 0.5362496917969483, 0.0, 7.466073026854929, 5.898746609766429, 4.530189526428744, 3.489621913759378, 5.716263399809866, 2.797870673088077, 2.358118014410392, 1.884905493110177, 2.7865430173245116, 2.1252036711056874, 1.2105452098458918, 0.5022430868176554, 0.0), # 121
(7.274898783071883, 5.496894881337171, 6.039467127223141, 6.358324022985514, 5.561217981657458, 2.634035099976709, 2.347963062314447, 1.9943857180225497, 2.8536846458573035, 1.1591745499080957, 0.9030619637847803, 0.5346951227553002, 0.0, 7.4494177679038165, 5.8816463503083005, 4.515309818923901, 3.4775236497242865, 5.707369291714607, 2.7921400052315697, 2.347963062314447, 1.8814536428405064, 2.780608990828729, 2.119441340995172, 1.2078934254446283, 0.49971771648519747, 0.0), # 122
(7.250109570575775, 5.469312624581501, 6.026146952092692, 6.340986436818417, 5.549112946732902, 2.629241352123832, 2.3378822803195356, 1.9904461113159944, 2.8492923414943343, 1.1551747675622777, 0.9001129237196728, 0.5331431145136282, 0.0, 7.432488393334058, 5.864574259649909, 4.500564618598363, 3.4655243026868323, 5.698584682988669, 2.7866245558423923, 2.3378822803195356, 1.8780295372313083, 2.774556473366451, 2.1136621456061393, 1.2052293904185383, 0.49721023859831837, 0.0), # 123
(7.225000389209324, 5.441882903884891, 6.012739249621247, 6.323566464452393, 5.536754863871753, 2.624473565006412, 2.327857479836928, 1.9866447520186423, 2.844941639909897, 1.1511995858711925, 0.897184318714016, 0.5315906023816185, 0.0, 7.4152606682749695, 5.847496626197802, 4.4859215935700805, 3.4535987576135767, 5.689883279819794, 2.781302652826099, 2.327857479836928, 1.87462397500458, 2.7683774319358765, 2.107855488150798, 1.2025478499242495, 0.49471662762589924, 0.0), # 124
(7.199542146350767, 5.414561438405035, 5.99921774559195, 6.306032315524057, 5.524127667070411, 2.619718856835246, 2.3178704722778956, 1.9829661600630304, 2.840619394197865, 1.147240633157027, 0.8942696823914004, 0.5300345216689567, 0.0, 7.397710357855863, 5.8303797383585225, 4.471348411957002, 3.4417218994710805, 5.68123878839573, 2.7761526240882426, 2.3178704722778956, 1.8712277548823186, 2.7620638335352057, 2.1020107718413525, 1.19984354911839, 0.49223285803682143, 0.0), # 125
(7.1737057493783425, 5.387303947299629, 5.985556165787933, 6.288352199670033, 5.511215290325276, 2.614964345821132, 2.307903069053708, 1.9793948553816976, 2.8363124574521112, 1.1432895377419687, 0.8913625483754153, 0.5284718076853291, 0.0, 7.379813227206063, 5.813189884538619, 4.4568127418770755, 3.4298686132259055, 5.6726249149042225, 2.7711527975343766, 2.307903069053708, 1.8678316755865225, 2.755607645162638, 2.0961173998900113, 1.1971112331575866, 0.4897549042999664, 0.0), # 126
(7.147462105670289, 5.360066149726364, 5.9717282359923365, 6.27049432652694, 5.498001667632746, 2.610197150174864, 2.2979370815756375, 1.975915357907182, 2.832007682766508, 1.139337927948205, 0.8884564502896507, 0.5268993957404212, 0.0, 7.361545041454879, 5.795893353144632, 4.442282251448253, 3.4180137838446143, 5.664015365533016, 2.766281501070055, 2.2979370815756375, 1.8644265358391885, 2.749000833816373, 2.0901647755089803, 1.1943456471984675, 0.487278740884215, 0.0), # 127
(7.120782122604837, 5.332803764842939, 5.957707681988301, 6.252426905731399, 5.484470732989221, 2.6054043881072406, 2.287954321254953, 1.9725121875720208, 2.827691923234929, 1.1353774320979229, 0.8855449217576967, 0.5253142211439193, 0.0, 7.34288156573163, 5.778456432583111, 4.427724608788483, 3.4061322962937677, 5.655383846469858, 2.7615170626008294, 2.287954321254953, 1.8610031343623146, 2.7422353664946106, 2.084142301910467, 1.1915415363976603, 0.4848003422584491, 0.0), # 128
(7.093636707560226, 5.305472511807044, 5.9434682295589605, 6.2341181469200295, 5.4706064203911, 2.600573177829058, 2.2779365995029255, 1.9691698643087534, 2.823352031951247, 1.1313996785133094, 0.882621496403143, 0.5237132192055092, 0.0, 7.323798565165631, 5.7608454112606, 4.413107482015715, 3.3941990355399274, 5.646704063902494, 2.756837810032255, 2.2779365995029255, 1.8575522698778983, 2.73530321019555, 2.078039382306677, 1.188693645911792, 0.48231568289154947, 0.0), # 129
(7.065996767914694, 5.2780281097763755, 5.9289836044874535, 6.215536259729452, 5.45639266383478, 2.595690637551111, 2.267865727730825, 1.9658729080499169, 2.818974862009333, 1.1273962955165517, 0.8796797078495794, 0.522093325234877, 0.0, 7.3042718048861985, 5.743026577583645, 4.398398539247896, 3.3821888865496543, 5.637949724018666, 2.7522220712698835, 2.267865727730825, 1.8540647411079363, 2.72819633191739, 2.0718454199098177, 1.1857967208974907, 0.4798207372523978, 0.0), # 130
(7.037833211046475, 5.250426277908626, 5.914227532556921, 6.196649453796286, 5.441813397316663, 2.590743885484198, 2.2577235173499237, 1.9626058387280498, 2.814547266503063, 1.1233589114298372, 0.8767130897205959, 0.5204514745417084, 0.0, 7.2842770500226495, 5.724966219958791, 4.383565448602979, 3.370076734289511, 5.629094533006126, 2.74764817421927, 2.2577235173499237, 1.850531346774427, 2.7209066986583315, 2.0655498179320957, 1.1828455065113843, 0.4773114798098752, 0.0), # 131
(7.009116944333808, 5.222622735361492, 5.8991737395504975, 6.1774259387571515, 5.4268525548331485, 2.5857200398391145, 2.24749177977149, 1.959353176275691, 2.8100560985263074, 1.119279154575353, 0.8737151756397821, 0.5187846024356896, 0.0, 7.263790065704301, 5.706630626792584, 4.36857587819891, 3.3578374637260584, 5.620112197052615, 2.7430944467859675, 2.24749177977149, 1.8469428855993675, 2.7134262774165743, 2.0591419795857178, 1.1798347479100997, 0.474783885032863, 0.0), # 132
(6.979818875154931, 5.194573201292665, 5.883795951251323, 6.1578339242486715, 5.411494070380632, 2.5806062188266576, 2.237152326406796, 1.9560994406253773, 2.80548821117294, 1.1151486532752868, 0.8706794992307283, 0.5170896442265063, 0.0, 7.242786617060469, 5.687986086491568, 4.353397496153641, 3.3454459598258595, 5.61097642234588, 2.7385392168755285, 2.237152326406796, 1.8432901563047555, 2.705747035190316, 2.052611308082891, 1.1767591902502648, 0.4722339273902424, 0.0), # 133
(6.949909910888076, 5.166233394859844, 5.868067893442536, 6.137841619907462, 5.395721877955516, 2.575389540657624, 2.2266869686671114, 1.9528291517096479, 2.8008304575368346, 1.1109590358518249, 0.8675995941170239, 0.5153635352238445, 0.0, 7.221242469220467, 5.668998887462289, 4.3379979705851195, 3.3328771075554737, 5.601660915073669, 2.7339608123935073, 2.2266869686671114, 1.8395639576125886, 2.697860938977758, 2.0459472066358213, 1.1736135786885074, 0.46965758135089497, 0.0), # 134
(6.919360958911483, 5.137559035220717, 5.851963291907273, 6.117417235370148, 5.379519911554198, 2.57005712354281, 2.2160775179637073, 1.9495268294610402, 2.796069690711861, 1.1067019306271555, 0.8644689939222592, 0.5136032107373902, 0.0, 7.199133387313616, 5.649635318111292, 4.322344969611295, 3.320105791881466, 5.592139381423722, 2.7293375612454565, 2.2160775179637073, 1.835755088244864, 2.689759955777099, 2.0391390784567163, 1.1703926583814546, 0.4670508213837017, 0.0), # 135
(6.888142926603388, 5.108505841532984, 5.835455872428673, 6.096528980273343, 5.362872105173076, 2.564596085693012, 2.205305785707854, 1.9461769938120925, 2.7911927637918947, 1.1023689659234648, 0.8612812322700237, 0.5118056060768296, 0.0, 7.176435136469229, 5.629861666845124, 4.306406161350118, 3.3071068977703937, 5.5823855275837895, 2.72464779133693, 2.205305785707854, 1.8318543469235802, 2.681436052586538, 2.0321763267577815, 1.1670911744857346, 0.46440962195754404, 0.0), # 136
(6.856226721342027, 5.079029532954335, 5.818519360789875, 6.075145064253675, 5.345762392808551, 2.558993545319026, 2.1943535833108223, 1.942764164695343, 2.7861865298708084, 1.0979517700629406, 0.8580298427839075, 0.5099676565518481, 0.0, 7.153123481816621, 5.609644222070328, 4.290149213919538, 3.293855310188821, 5.572373059741617, 2.7198698305734803, 2.1943535833108223, 1.8278525323707329, 2.6728811964042754, 2.0250483547512257, 1.1637038721579749, 0.46172995754130325, 0.0), # 137
(6.823583250505639, 5.0490858286424665, 5.801127482774012, 6.053233696947759, 5.3281747084570235, 2.5532366206316497, 2.1832027221838817, 1.9392728620433302, 2.781037842042475, 1.0934419713677697, 0.8547083590875004, 0.508086297472132, 0.0, 7.129174188485113, 5.58894927219345, 4.273541795437502, 3.280325914103308, 5.56207568408495, 2.7149820068606623, 2.1832027221838817, 1.8237404433083213, 2.6640873542285117, 2.017744565649253, 1.1602254965548024, 0.45900780260386065, 0.0), # 138
(6.790183421472455, 5.018630447755072, 5.783253964164227, 6.030763087992216, 5.3100929861148884, 2.547312429841679, 2.171835013738304, 1.9356876057885917, 2.775733553400766, 1.0888311981601397, 0.8513103148043922, 0.5061584641473672, 0.0, 7.104563021604015, 5.567743105621037, 4.256551574021961, 3.2664935944804183, 5.551467106801532, 2.709962648104028, 2.171835013738304, 1.8195088784583422, 2.6550464930574442, 2.0102543626640723, 1.1566507928328456, 0.4562391316140975, 0.0), # 139
(6.755998141620719, 4.987619109449845, 5.764872530743658, 6.007701447023667, 5.291501159778549, 2.5412080911599104, 2.1602322693853586, 1.9319929158636655, 2.770260517039555, 1.0841110787622374, 0.8478292435581727, 0.5041810918872395, 0.0, 7.079265746302652, 5.545992010759633, 4.2391462177908625, 3.2523332362867117, 5.54052103407911, 2.704790082209132, 2.1602322693853586, 1.8151486365427931, 2.6457505798892744, 2.0025671490078896, 1.1529745061487318, 0.45341991904089507, 0.0), # 140
(6.720998318328665, 4.956007532884482, 5.745956908295441, 5.984016983678732, 5.272383163444402, 2.5349107227971404, 2.148376300536318, 1.9281733122010902, 2.7646055860527143, 1.0792732414962505, 0.844258678972432, 0.502151116001435, 0.0, 7.053258127710331, 5.523662276015784, 4.221293394862159, 3.2378197244887508, 5.529211172105429, 2.6994426370815265, 2.148376300536318, 1.8106505162836717, 2.636191581722201, 1.994672327892911, 1.1491913816590882, 0.4505461393531348, 0.0), # 141
(6.685154858974525, 4.923751437216675, 5.726480822602714, 5.959677907594033, 5.252722931108846, 2.5284074429641663, 2.1362489186024507, 1.924213314733404, 2.7587556135341176, 1.0743093146843659, 0.8405921546707598, 0.5000654717996397, 0.0, 7.026515930956373, 5.500720189796036, 4.202960773353798, 3.222927944053097, 5.517511227068235, 2.6938986406267658, 2.1362489186024507, 1.806005316402976, 2.626361465554423, 1.9865593025313446, 1.1452961645205428, 0.4476137670196978, 0.0), # 142
(6.64843867093654, 4.890806541604119, 5.706417999448617, 5.934652428406185, 5.232504396768282, 2.521685369871783, 2.1238319349950276, 1.920097443393144, 2.7526974525776393, 1.0692109266487708, 0.8368232042767458, 0.4979210945915394, 0.0, 6.999014921170094, 5.477132040506932, 4.184116021383729, 3.207632779946312, 5.505394905155279, 2.6881364207504017, 2.1238319349950276, 1.8012038356227023, 2.616252198384141, 1.9782174761353954, 1.1412835998897235, 0.44461877650946546, 0.0), # 143
(6.610820661592948, 4.857128565204509, 5.685742164616285, 5.908908755751814, 5.2117114944191085, 2.5147316217307885, 2.1111071611253194, 1.9158102181128498, 2.746417956277149, 1.0639697057116522, 0.8329453614139802, 0.49571491968682, 0.0, 6.970730863480812, 5.452864116555019, 4.164726807069901, 3.191909117134956, 5.492835912554298, 2.6821343053579896, 2.1111071611253194, 1.796236872664849, 2.6058557472095543, 1.9696362519172719, 1.1371484329232573, 0.44155714229131915, 0.0), # 144
(6.572271738321982, 4.82267322717554, 5.6644270438888595, 5.882415099267537, 5.190328158057724, 2.507533316751979, 2.0980564084045974, 1.9113361588250588, 2.739903977726521, 1.0585772801951978, 0.8289521597060527, 0.4934438823951677, 0.0, 6.94163952301784, 5.4278827063468436, 4.144760798530264, 3.175731840585593, 5.479807955453042, 2.6758706223550823, 2.0980564084045974, 1.7910952262514135, 2.595164079028862, 1.9608050330891795, 1.132885408777772, 0.4384248388341401, 0.0), # 145
(6.5327628085018805, 4.787396246674904, 5.642446363049478, 5.855139668589976, 5.16833832168053, 2.5000775731461515, 2.084661488244132, 1.906659785462309, 2.7331423700196282, 1.0530252784215943, 0.8248371327765532, 0.4911049180262681, 0.0, 6.911716664910495, 5.402154098288948, 4.124185663882766, 3.1590758352647823, 5.4662847400392565, 2.669323699647233, 2.084661488244132, 1.7857696951043938, 2.584169160840265, 1.9517132228633256, 1.1284892726098958, 0.4352178406068095, 0.0), # 146
(6.49226477951088, 4.751253342860296, 5.619773847881273, 5.827050673355748, 5.145725919283921, 2.4923515091241004, 2.0709042120551926, 1.9017656179571385, 2.7261199862503442, 1.0473053287130294, 0.8205938142490716, 0.48869496188980743, 0.0, 6.8809380542880945, 5.375644580787881, 4.102969071245358, 3.1419159861390877, 5.4522399725006885, 2.662471865139994, 2.0709042120551926, 1.7802510779457859, 2.5728629596419603, 1.9423502244519164, 1.1239547695762548, 0.43193212207820875, 0.0), # 147
(6.450748558727217, 4.714200234889411, 5.596383224167389, 5.798116323201478, 5.1224748848643, 2.4843422428966253, 2.0567663912490506, 1.8966381762420859, 2.718823679512541, 1.0414090593916896, 0.8162157377471978, 0.48621094929547143, 0.0, 6.8492794562799535, 5.348320442250185, 4.081078688735989, 3.124227178175068, 5.437647359025082, 2.6552934467389204, 2.0567663912490506, 1.7745301734975893, 2.56123744243215, 1.9327054410671598, 1.1192766448334779, 0.42856365771721927, 0.0), # 148
(6.40818505352913, 4.676192641919942, 5.572248217690963, 5.768304827763782, 5.098569152418064, 2.4760368926745198, 2.0422298372369765, 1.8912619802496888, 2.71124030290009, 1.0353280987797628, 0.8116964368945213, 0.48364981555294617, 0.0, 6.81671663601539, 5.320147971082407, 4.058482184472607, 3.1059842963392876, 5.42248060580018, 2.6477667723495646, 2.0422298372369765, 1.7685977804817998, 2.549284576209032, 1.922768275921261, 1.1144496435381928, 0.42510842199272214, 0.0), # 149
(6.364545171294852, 4.6371862831095845, 5.54734255423513, 5.737584396679283, 5.0739926559416135, 2.467422576668583, 2.0272763614302405, 1.8856215499124855, 2.7033567095068674, 1.0290540751994355, 0.8070294453146325, 0.48100849597191764, 0.0, 6.783225358623717, 5.291093455691093, 4.035147226573162, 3.0871622255983056, 5.406713419013735, 2.63987016987748, 2.0272763614302405, 1.7624446976204164, 2.5369963279708068, 1.912528132226428, 1.1094685108470261, 0.4215623893735987, 0.0), # 150
(6.31979981940262, 4.597136877616033, 5.521639959583029, 5.705923239584598, 5.048729329431348, 2.4584864130896094, 2.011887775240113, 1.8797014051630145, 2.695159752426744, 1.0225786169728959, 0.8022082966311207, 0.4782839258620715, 0.0, 6.748781389234255, 5.261123184482786, 4.011041483155603, 3.067735850918687, 5.390319504853488, 2.6315819672282204, 2.011887775240113, 1.7560617236354352, 2.524364664715674, 1.9019744131948664, 1.1043279919166058, 0.41792153432873036, 0.0), # 151
(6.273919905230675, 4.55600014459698, 5.495114159517802, 5.673289566116352, 5.022763106883663, 2.4492155201483965, 1.996045890077866, 1.8734860659338137, 2.686636284753592, 1.0158933524223301, 0.7972265244675764, 0.475473040533094, 0.0, 6.713360492976318, 5.230203445864033, 3.9861326223378812, 3.04768005726699, 5.373272569507184, 2.622880492307339, 1.996045890077866, 1.7494396572488546, 2.5113815534418316, 1.8910965220387843, 1.0990228319035604, 0.4141818313269982, 0.0), # 152
(6.226876336157249, 4.5137318032101215, 5.467738879822579, 5.63965158591116, 4.996077922294963, 2.4395970160557408, 1.9797325173547677, 1.8669600521574208, 2.677773159581286, 1.008989909869926, 0.7920776624475889, 0.472572775294671, 0.0, 6.676938434979222, 5.19830052824138, 3.9603883122379444, 3.0269697296097773, 5.355546319162572, 2.6137440730203894, 1.9797325173547677, 1.742569297182672, 2.4980389611474814, 1.879883861970387, 1.093547775964516, 0.41033925483728384, 0.0), # 153
(6.178640019560583, 4.4702875726131515, 5.439487846280506, 5.604977508605646, 4.968657709661643, 2.429618019022439, 1.9629294684820913, 1.8601078837663743, 2.6685572300036977, 1.0018599176378709, 0.7867552441947484, 0.4695800654564884, 0.0, 6.639490980372286, 5.165380720021371, 3.9337762209737415, 3.005579752913612, 5.337114460007395, 2.604151037272924, 1.9629294684820913, 1.7354414421588849, 2.4843288548308213, 1.8683258362018824, 1.0878975692561013, 0.40638977932846837, 0.0), # 154
(6.129181862818909, 4.425623171963762, 5.410334784674718, 5.569235543836427, 4.940486402980104, 2.419265647259287, 1.9456185548711045, 1.852914080693212, 2.6589753491147006, 0.9944950040483511, 0.7812528033326445, 0.4664918463282322, 0.0, 6.600993894284821, 5.131410309610554, 3.906264016663222, 2.983485012145053, 5.317950698229401, 2.594079712970497, 1.9456185548711045, 1.7280468908994906, 2.470243201490052, 1.856411847945476, 1.0820669569349437, 0.402329379269433, 0.0), # 155
(6.078472773310465, 4.3796943204196515, 5.3802534207883514, 5.532393901240125, 4.911547936246746, 2.408527018977082, 1.92778158793308, 1.845363162870473, 2.649014370008167, 0.9868867974235548, 0.7755638734848673, 0.46330505321958826, 0.0, 6.561422941846148, 5.09635558541547, 3.8778193674243364, 2.960660392270664, 5.298028740016334, 2.5835084280186624, 1.92778158793308, 1.720376442126487, 2.455773968123373, 1.8441313004133755, 1.0760506841576702, 0.39815402912905923, 0.0), # 156
(6.02648365841349, 4.332456737138511, 5.349217480404546, 5.494420790453363, 4.881826243457965, 2.39738925238662, 1.9094003790792877, 1.8374396502306942, 2.63866114577797, 0.9790269260856685, 0.7696819882750067, 0.4600166214402426, 0.0, 6.520753888185581, 5.060182835842667, 3.848409941375033, 2.937080778257005, 5.27732229155594, 2.5724155103229718, 1.9094003790792877, 1.7124208945618713, 2.4409131217289826, 1.831473596817788, 1.0698434960809091, 0.3938597033762283, 0.0), # 157
(5.971744757124192, 4.28299895523299, 5.315727969268237, 5.453861748990747, 4.849963256464532, 2.3851447556146512, 1.890042688371143, 1.8285989841164574, 2.6271098910930926, 0.9706731832582289, 0.7634127670051923, 0.45650663761295607, 0.0, 6.477188687532276, 5.021573013742516, 3.817063835025962, 2.912019549774686, 5.254219782186185, 2.5600385777630406, 1.890042688371143, 1.7036748254390366, 2.424981628232266, 1.8179539163302492, 1.0631455938536476, 0.38936354138481727, 0.0), # 158
(5.9058294135827225, 4.226247901039617, 5.271158545601992, 5.402386295273073, 4.808102031883535, 2.3677218357366487, 1.8672851053542865, 1.8157378442547942, 2.609713936325905, 0.9604561988197493, 0.7556555914158659, 0.4520908349122073, 0.0, 6.420342117536156, 4.97299918403428, 3.7782779570793297, 2.8813685964592475, 5.21942787265181, 2.542032981956712, 1.8672851053542865, 1.6912298826690346, 2.4040510159417674, 1.8007954317576913, 1.0542317091203985, 0.3842043546399652, 0.0), # 159
(5.827897675923448, 4.161737600929857, 5.214613971970593, 5.339146506245316, 4.755424070051625, 2.344692604822253, 1.8408974993535137, 1.7985330631757823, 2.5859800605943066, 0.948241130372579, 0.7463012678146054, 0.4467001299258565, 0.0, 6.349136487114865, 4.913701429184421, 3.731506339073027, 2.844723391117736, 5.171960121188613, 2.5179462884460952, 1.8408974993535137, 1.6747804320158948, 2.3777120350258123, 1.7797155020817725, 1.0429227943941186, 0.3783397819027143, 0.0), # 160
(5.738577643668768, 4.0898886365923435, 5.146697981273539, 5.264743502254037, 4.69247633295046, 2.3163360460661466, 1.8110725784027506, 1.7772001777032602, 2.556221271199738, 0.9341316386341878, 0.7354322206132944, 0.44038449792717144, 0.0, 6.264299235855278, 4.844229477198885, 3.6771611030664717, 2.8023949159025627, 5.112442542399476, 2.4880802487845646, 1.8110725784027506, 1.6545257471901047, 2.34623816647523, 1.754914500751346, 1.029339596254708, 0.37180805787203125, 0.0), # 161
(5.638497416341085, 4.011121589715708, 5.068014306410331, 5.179778403645797, 4.619805782561709, 2.282931142663013, 1.7780030505359237, 1.7519547246610676, 2.5207505754436363, 0.9182313843220465, 0.7231308742238162, 0.43319391418941966, 0.0, 6.166557803344267, 4.765133056083616, 3.615654371119081, 2.754694152966139, 5.041501150887273, 2.4527366145254947, 1.7780030505359237, 1.630665101902152, 2.3099028912808546, 1.7265928012152658, 1.0136028612820662, 0.36464741724688265, 0.0), # 162
(5.528285093462799, 3.9258570419885843, 4.979166680280469, 5.084852330767161, 4.537959380867034, 2.244756877807534, 1.7418816237869603, 1.7230122408730417, 2.4798809806274416, 0.9006440281536252, 0.7094796530580545, 0.42517835398586895, 0.0, 6.0566396291687035, 4.676961893844558, 3.5473982652902722, 2.701932084460875, 4.959761961254883, 2.4122171372222585, 1.7418816237869603, 1.6033977698625244, 2.268979690433517, 1.6949507769223873, 0.9958333360560938, 0.356896094726235, 0.0), # 163
(5.408568774556308, 3.834515575099602, 4.8807588357834515, 4.980566403964691, 4.447484089848101, 2.2020922346943936, 1.7029010061897865, 1.6905882631630231, 2.433925494052593, 0.881473230846394, 0.6945609815278929, 0.4163877925897869, 0.0, 5.935272152915463, 4.580265718487656, 3.472804907639464, 2.644419692539181, 4.867850988105186, 2.3668235684282326, 1.7029010061897865, 1.5729230247817099, 2.2237420449240504, 1.660188801321564, 0.9761517671566904, 0.34859232500905474, 0.0), # 164
(5.279976559144014, 3.7375177707373965, 4.773394505818779, 4.867521743584952, 4.348926871486572, 2.155216196518274, 1.6612539057783289, 1.6548983283548488, 2.383197123020528, 0.8608226531178229, 0.678457284045215, 0.4068722052744414, 0.0, 5.803182814171416, 4.475594258018854, 3.3922864202260747, 2.582467959353468, 4.766394246041056, 2.3168576596967885, 1.6612539057783289, 1.5394401403701956, 2.174463435743286, 1.622507247861651, 0.954678901163756, 0.33977434279430885, 0.0), # 165
(5.143136546748318, 3.6352842105905996, 4.657677423285953, 4.746319469974501, 4.242834687764114, 2.1044077464738575, 1.6171330305865146, 1.6161579732723592, 2.328008874832686, 0.8387959556853827, 0.661250985021904, 0.39668156731310017, 0.0, 5.661099052523436, 4.363497240444101, 3.3062549251095197, 2.5163878670561473, 4.656017749665372, 2.262621162581303, 1.6171330305865146, 1.5031483903384697, 2.121417343882057, 1.5821064899915007, 0.9315354846571906, 0.33048038278096364, 0.0), # 166
(4.998676836891619, 3.528235476347844, 4.53421132108447, 4.617560703479906, 4.129754500662389, 2.0499458677558273, 1.57073108864827, 1.5745827347393924, 2.2686737567905064, 0.8154967992665431, 0.6430245088698437, 0.3858658539790306, 0.0, 5.509748307558397, 4.244524393769336, 3.215122544349218, 2.4464903977996286, 4.537347513581013, 2.2044158286351494, 1.57073108864827, 1.4642470483970196, 2.0648772503311945, 1.5391869011599693, 0.9068422642168941, 0.32074867966798587, 0.0), # 167
(4.847225529096317, 3.416792149697761, 4.403599932113832, 4.481846564447728, 4.010233272163062, 1.9921095435588663, 1.5222407879975217, 1.5303881495797866, 2.205504776195428, 0.7910288445787746, 0.6238602800009175, 0.3744750405455008, 0.0, 5.34985801886317, 4.119225446000509, 3.1193014000045878, 2.3730865337363234, 4.411009552390856, 2.1425434094117013, 1.5222407879975217, 1.4229353882563331, 2.005116636081531, 1.4939488548159094, 0.8807199864227666, 0.31061746815434194, 0.0), # 168
(4.689410722884812, 3.3013748123289846, 4.26644698927354, 4.33977817322453, 3.884817964247797, 1.9311777570776578, 1.4718548366681967, 1.4837897546173817, 2.1388149403488903, 0.7654957523395476, 0.6038407228270092, 0.3625591022857782, 0.0, 5.182155626024628, 3.9881501251435596, 3.019203614135046, 2.296487257018642, 4.277629880697781, 2.0773056564643344, 1.4718548366681967, 1.3794126836268983, 1.9424089821238986, 1.4465927244081769, 0.853289397854708, 0.30012498293899864, 0.0), # 169
(4.525860517779507, 3.1824040459301473, 4.12335622546309, 4.191956650156872, 3.7540555388982577, 1.8674294915068832, 1.4197659426942213, 1.435003086676016, 2.0689172565523304, 0.7390011832663317, 0.5830482617600022, 0.3501680144731306, 0.0, 5.007368568629644, 3.8518481592044362, 2.9152413088000113, 2.217003549798995, 4.137834513104661, 2.0090043213464224, 1.4197659426942213, 1.3338782082192022, 1.8770277694491289, 1.3973188833856243, 0.824671245092618, 0.28930945872092256, 0.0), # 170
(4.3572030133028, 3.06030043218988, 3.9749313735819856, 4.038983115591321, 3.61849295809611, 1.801143730041226, 1.3661668141095222, 1.3842436825795277, 1.9961247321071884, 0.7116487980765979, 0.5615653212117798, 0.33735175238082576, 0.0, 4.826224286265092, 3.710869276189083, 2.807826606058899, 2.134946394229793, 3.992249464214377, 1.9379411556113388, 1.3661668141095222, 1.2865312357437328, 1.809246479048055, 1.3463277051971074, 0.7949862747163972, 0.27820913019908006, 0.0), # 171
(4.184066308977092, 2.9354845527968174, 3.8217761665297245, 3.881458689874438, 3.4786771838230153, 1.7325994558753692, 1.3112501589480263, 1.331727079151757, 1.9207503743149028, 0.6835422574878162, 0.5394743255942259, 0.3241602912821315, 0.0, 4.639450218517843, 3.5657632041034453, 2.6973716279711297, 2.050626772463448, 3.8415007486298056, 1.8644179108124599, 1.3112501589480263, 1.237571039910978, 1.7393385919115076, 1.2938195632914795, 0.764355233305945, 0.26686223207243803, 0.0), # 172
(4.007078504324784, 2.808376989439591, 3.664494337205808, 3.7199844933527855, 3.3351551780606408, 1.6620756522039952, 1.25520868524366, 1.2776688132165412, 1.8431071904769127, 0.6547852222174565, 0.5168576993192239, 0.310643606450315, 0.0, 4.44777380497477, 3.417079670953465, 2.584288496596119, 1.9643556666523692, 3.6862143809538255, 1.7887363385031578, 1.25520868524366, 1.187196894431425, 1.6675775890303204, 1.2399948311175955, 0.7328988674411617, 0.25530699903996285, 0.0), # 173
(3.8268676988682753, 2.6793983238068333, 3.503689618509735, 3.5551616463729245, 3.1884739027906486, 1.5898513022217866, 1.1982351010303502, 1.2222844215977202, 1.763508187894657, 0.6254813529829895, 0.4937978667986571, 0.2968516731586446, 0.0, 4.251922485222747, 3.26536840474509, 2.468989333993285, 1.8764440589489682, 3.527016375789314, 1.7111981902368083, 1.1982351010303502, 1.1356080730155618, 1.5942369513953243, 1.1850538821243084, 0.700737923701947, 0.24358166580062124, 0.0), # 174
(3.6440619921299646, 2.548969137587176, 3.3399657433410055, 3.3875912692814207, 3.039180319994703, 1.5162053891234268, 1.1405221143420232, 1.165789441119132, 1.682266373869575, 0.595734310501885, 0.4703772524444093, 0.28283446668038764, 0.0, 4.052623698848646, 3.1111791334842636, 2.3518862622220467, 1.7872029315056546, 3.36453274773915, 1.632105217566785, 1.1405221143420232, 1.0830038493738763, 1.5195901599973516, 1.1291970897604737, 0.6679931486682011, 0.23172446705337968, 0.0), # 175
(3.459289483632255, 2.4175100124692537, 3.173926444599119, 3.2178744824248353, 2.8878213916544695, 1.441416896103598, 1.082262433212606, 1.1083994086046165, 1.5996947557031045, 0.5656477554916135, 0.44667828066836407, 0.268641962288812, 0.0, 3.8506048854393393, 2.9550615851769315, 2.23339140334182, 1.69694326647484, 3.199389511406209, 1.551759172046463, 1.082262433212606, 1.0295834972168558, 1.4439106958272347, 1.0726248274749453, 0.6347852889198239, 0.2197736374972049, 0.0), # 176
(3.273178272897546, 2.2854415301416977, 3.006175455183576, 3.0466124061497295, 2.7349440797516125, 1.365764806356983, 1.0236487656760251, 1.050329860878011, 1.5161063406966853, 0.535325348669645, 0.4227833758824049, 0.2543241352571853, 0.0, 3.6465934845817, 2.7975654878290377, 2.113916879412024, 1.6059760460089345, 3.0322126813933705, 1.4704618052292153, 1.0236487656760251, 0.9755462902549877, 1.3674720398758062, 1.0155374687165768, 0.6012350910367152, 0.20776741183106345, 0.0), # 177
(3.0863564594482376, 2.153184272293141, 2.8373165079938762, 2.87440616080267, 2.581095346267794, 1.2895281030782653, 0.964873819766207, 0.9917963347631552, 1.431814136151756, 0.5048707507534501, 0.39877496249841504, 0.2399309608587752, 0.0, 3.4413169358626017, 2.6392405694465264, 1.993874812492075, 1.51461225226035, 2.863628272303512, 1.3885148686684172, 0.964873819766207, 0.9210915021987609, 1.290547673133897, 0.9581353869342235, 0.5674633015987752, 0.1957440247539219, 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
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
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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), # 134
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 135
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 136
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 137
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 138
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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), # 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
50, # 1
)
| 275.234225 | 493 | 0.768699 | 32,987 | 257,344 | 5.996574 | 0.21272 | 0.360348 | 0.345788 | 0.655177 | 0.380301 | 0.371181 | 0.367253 | 0.366121 | 0.365797 | 0.365797 | 0 | 0.84919 | 0.096113 | 257,344 | 934 | 494 | 275.528908 | 0.001199 | 0.015586 | 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 |
1eb95c11db6f813d4386b94dc770092d96a0a284 | 489 | py | Python | util/__init__.py | sabbirahm3d/ds5k-capstone-dataset | d6d5ed5a1043de87b90e3e4b1737e6ffc563eeaf | [
"MIT"
] | null | null | null | util/__init__.py | sabbirahm3d/ds5k-capstone-dataset | d6d5ed5a1043de87b90e3e4b1737e6ffc563eeaf | [
"MIT"
] | 1 | 2021-06-01T22:50:17.000Z | 2021-06-01T22:50:17.000Z | util/__init__.py | ribbas/ds5k-capstone-dataset | d6d5ed5a1043de87b90e3e4b1737e6ffc563eeaf | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from datetime import datetime
from termcolor import cprint
def __arg_fmt(*args):
return datetime.now().strftime("%H:%M:%S | ") + \
("{}" * len(args)).format(*args)
def eprint(*ostream):
cprint(__arg_fmt(*ostream), "red")
def wprint(*ostream):
cprint(__arg_fmt(*ostream), "yellow")
def sprint(*ostream):
cprint(__arg_fmt(*ostream), "green")
def iprint(*ostream):
cprint(__arg_fmt(*ostream), "blue")
| 14.818182 | 53 | 0.627812 | 62 | 489 | 4.709677 | 0.532258 | 0.10274 | 0.219178 | 0.260274 | 0.356164 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002488 | 0.177914 | 489 | 32 | 54 | 15.28125 | 0.723881 | 0.08589 | 0 | 0 | 0 | 0 | 0.069663 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.384615 | true | 0 | 0.153846 | 0.076923 | 0.615385 | 0.692308 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 6 |
1edd74ade2f07c4eab8b5dc45039a0297e1f387a | 96 | py | Python | venv/lib/python3.8/site-packages/libfuturize/fixes/fix_basestring.py | Retraces/UkraineBot | 3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/libfuturize/fixes/fix_basestring.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/libfuturize/fixes/fix_basestring.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/6c/79/0a/b8cce1af914c5f08d794c3a3b287784b7ad091575bce1a16578fad9776 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.416667 | 0 | 96 | 1 | 96 | 96 | 0.479167 | 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 |
94c20b5ff60d57ac703d79b462aee0406e97f756 | 27,849 | py | Python | baseline_modify/utils/model.py | lxchtan/DSTC9-Track1 | 26c3d36df1ab13a766767989434b79894b5317c5 | [
"Apache-2.0"
] | 7 | 2021-04-20T09:04:59.000Z | 2022-03-07T03:42:09.000Z | baseline_modify/utils/model.py | lxchtan/DSTC9-Track1 | 26c3d36df1ab13a766767989434b79894b5317c5 | [
"Apache-2.0"
] | null | null | null | baseline_modify/utils/model.py | lxchtan/DSTC9-Track1 | 26c3d36df1ab13a766767989434b79894b5317c5 | [
"Apache-2.0"
] | null | null | null | import torch
import torch.nn.functional as F
import logging
import math
import numpy as np
from torch.nn import KLDivLoss, MSELoss
from .metrics import ROUGE_list
from .auxiliary import top_filtering
logger = logging.getLogger(__name__)
def run_batch_generation_for_latentCopy(args, model, batch):
batch = tuple(input_tensor.to(args.device) for input_tensor in batch)
input_ids, token_type_ids, lm_labels, input_masks, input_masks_with_knowledge, knowledgeROIs = batch
ori_model = model.module if hasattr(model, "module") else model
ori_model.model_stage = 0
model_outputs = model(input_ids=input_ids, token_type_ids=None, labels=lm_labels, attention_mask=None)
z, z_distribution = model_outputs[:2]
ori_model.model_stage = 1
model_outputs = model(input_ids=input_ids, token_type_ids=None, labels=lm_labels, attention_mask=input_masks)
z_prior, z_prior_distribution = model_outputs[:2]
ori_model.model_stage = 2
model_outputs = model(input_ids=input_ids, token_type_ids=None, labels=lm_labels,
attention_mask=input_masks_with_knowledge, z_hidden_embeds=z, knowledgeROIs=knowledgeROIs)
KLDiv_Loss = KLDivLoss(reduction='batchmean')
kld_loss = KLDiv_Loss(z_prior_distribution.log(), z_distribution) if getattr(args, "latent_modify", '') != 'real' \
else KLDiv_Loss(z_distribution.log(), z_prior_distribution)
lm_loss, bow_loss, norm_loss, lm_logits = model_outputs[:4]
return lm_loss, lm_logits, (bow_loss, norm_loss), kld_loss
def run_batch_generation_eval_for_latentCopy(args, model, batch):
batch = tuple(input_tensor.to(args.device) for input_tensor in batch)
input_ids, token_type_ids, lm_labels, input_masks, input_masks_with_knowledge, knowledgeROIs = batch
ori_model = model.module if hasattr(model, "module") else model
ori_model.model_stage = 1
model_outputs = model(input_ids=input_ids, token_type_ids=None, labels=lm_labels, attention_mask=input_masks)
z_prior, z_prior_distribution = model_outputs[:2]
ori_model.model_stage = 2
model_outputs = model(input_ids=input_ids, token_type_ids=None, labels=lm_labels,
attention_mask=input_masks_with_knowledge, z_hidden_embeds=z_prior, knowledgeROIs=knowledgeROIs)
lm_loss, bow_loss, norm_loss, lm_logits = model_outputs[:4]
return lm_loss, lm_logits, (bow_loss, norm_loss), torch.tensor([])
def run_batch_generation_greedy_sample_for_latentCopy(args, model, batch, dataset):
special_tokens_ids = args.tokenizer.convert_tokens_to_ids(dataset.SPECIAL_TOKENS_VALUES)
current_output = []
another_data = []
example = batch[0]
knowledge, history = example["knowledge"], example["history"]
response_text = example["response_text"]
dialog_id = example["dialog_id"]
instance, sequence = dataset.build_input_from_segments(
knowledge, history, current_output, with_eos=False
)
input_ids = torch.tensor(instance["input_ids"], device=args.device).unsqueeze(0)
input_masks = torch.tensor(instance["input_masks"], device=args.device).unsqueeze(0)
ori_model = model.module if hasattr(model, "module") else model
ori_model.model_stage = 1
model_outputs = model(input_ids=input_ids, token_type_ids=None, attention_mask=input_masks)
z_post, z_post_distribution = model_outputs[:2]
ori_model.model_stage = 2
for i in range(args.max_length):
instance, sequence = dataset.build_input_from_segments(
knowledge, history, current_output, with_eos=False
)
input_ids = torch.tensor(instance["input_ids"], device=args.device).unsqueeze(0)
# input_masks = torch.tensor(instance["input_masks"], device=args.device).unsqueeze(0)
input_masks_with_knowledge = torch.tensor(instance["input_masks_with_knowledge"], device=args.device).unsqueeze(0)
knowledgeROIs = torch.tensor(instance["knowledgeROIs"], device=args.device).unsqueeze(0)
model_outputs = model(input_ids=input_ids, token_type_ids=None, attention_mask=input_masks_with_knowledge,
z_hidden_embeds=z_post, knowledgeROIs=knowledgeROIs)
logits, attention_dist, p_gen = model_outputs[:3]
logits = logits[0, -1, :] / args.temperature
logits = top_filtering(logits, top_k=args.top_k, top_p=args.top_p)
probs = F.softmax(logits, dim=-1)
prev = torch.topk(probs, 1)[1] if args.no_sample else torch.multinomial(probs, 1)
if i < args.min_length and prev.item() in special_tokens_ids:
while prev.item() in special_tokens_ids:
if probs.max().item() == 1:
logger.warning("Warning: model generating special token with probability 1! Breaking...")
break
prev = torch.topk(probs, 1)[1] if args.no_sample else torch.multinomial(probs, 1)
if prev.item() in special_tokens_ids:
break
if type(p_gen) != float:
p_gen = p_gen[0, -1, 0]
# logger.info(p_gen)
attention_dist = attention_dist[0, -1, :]
probs *= p_gen
attention_dist *= (1 - p_gen)
probs = probs.scatter_add(0, input_ids.squeeze(0), attention_dist)
prev = torch.topk(probs, 1)[1] if args.no_sample else torch.multinomial(probs, 1)
if i < args.min_length and prev.item() in special_tokens_ids:
while prev.item() in special_tokens_ids:
if probs.max().item() == 1:
logger.warning("Warning: model generating special token with probability 1! Breaking...")
break
prev = torch.multinomial(probs, num_samples=1)
if prev.item() in special_tokens_ids:
break
current_output.append(prev.item())
if type(p_gen) != float:
another_data.append(format(p_gen.item(), ".4f"))
return (current_output, another_data), response_text, dialog_id
# Auxiliary for Beam Search
def get_initial_values(args, model, dataset, history, knowledge, model_pre=lambda outputs, **kwargs: outputs,
prob_postprocess=lambda outputs, probs, **kwargs: (outputs, probs)):
outputs = ()
GFM = True # args.GFM
current_output = []
sub_beam_size = args.sub_beam_size
group_num = args.group_num
whole_beam_size = sub_beam_size * group_num
instance, sequence = dataset.build_input_from_segments(
knowledge, history, current_output, with_eos=False
)
input_ids = torch.tensor(instance["input_ids"], device=args.device).unsqueeze(0)
input_masks = torch.tensor(instance["input_masks"], device=args.device).unsqueeze(0)
model_args = {
'input_ids': input_ids,
'token_type_ids': None,
'attention_mask': input_masks
}
outputs = model_pre(outputs, args=args, model=model, model_args=model_args, instance=instance,
whole_beam_size=whole_beam_size)
model_outputs = model(**model_args)
logits = model_outputs[0]
logits = logits[0, -1, :] / args.temperature
probs = F.softmax(logits, dim=-1)
outputs, probs = prob_postprocess(outputs, probs, input_ids=input_ids, model_outputs=model_outputs,
whole_beam_size=whole_beam_size)
_indices = torch.topk(probs, whole_beam_size)[1] \
if args.no_sample else torch.multinomial(probs, whole_beam_size) # torch.multinomial(probs, whole_beam_size)
_values = torch.index_select(torch.log(probs), 0, index=_indices)
if GFM:
_index = []
for i in range(group_num):
_index.extend([i + group_num * j for j in range(sub_beam_size)])
_values = _values[_index]
_indices = _indices[_index]
score = _values.unsqueeze(-1)
current_output = _indices.unsqueeze(-1)
outputs = (current_output, score) + outputs
return outputs # (current_output, score, z_post, p_gen_tensors)
def build_inputs(args, current_output, dataset, history, knowledge, whole_beam_size):
input_ids = []
input_masks = []
current_output_list = current_output.cpu().numpy().tolist()
for j in range(whole_beam_size):
instance, sequence = dataset.build_input_from_segments(
knowledge, history, current_output_list[j], with_eos=False
)
input_ids.append(torch.tensor(instance["input_ids"], device=args.device))
input_masks.append(torch.tensor(instance["input_masks"], device=args.device))
input_ids = torch.stack(input_ids, dim=0)
input_masks = torch.stack(input_masks, dim=0)
output = (input_ids, input_masks)
return output
def cal_next_word(args, score, probs, current_output, indices_shift, special_tokens_ids,
place_hold_index, final_output, final_score, finish_index, finish_output_sign):
sub_beam_size = args.sub_beam_size
group_num = args.group_num
tmp_new_scores = torch.log(probs)
tmp_score = score.repeat((1, tmp_new_scores.size(-1))) + tmp_new_scores
tmp_score = tmp_score.reshape((group_num, -1))
tmp_indices = torch.topk(tmp_score, sub_beam_size)[1] if args.no_sample \
else torch.multinomial(F.softmax(tmp_score, dim=-1), sub_beam_size)
tmp_score = tmp_score.gather(dim=-1, index=tmp_indices).view(-1, 1)
tmp_indices = tmp_indices.view(-1, 1)
last_indices = tmp_indices // tmp_new_scores.size(-1) + indices_shift
new_indices = tmp_indices % tmp_new_scores.size(-1)
tmp_current_output = torch.cat([current_output[last_indices.view(-1)], new_indices], dim=-1)
gain_finish_sentences(args, tmp_score, tmp_current_output, final_output, final_score, finish_index,
finish_output_sign,
place_hold_index, special_tokens_ids, new_indices, score, last_indices)
return tmp_current_output, tmp_score
def gain_finish_sentences(args, tmp_score, tmp_current_output, final_output, final_score, finish_index,
finish_output_sign,
place_hold_index, special_tokens_ids, new_indices=None, score=None, last_indices=None):
sub_beam_size = args.sub_beam_size
for j in finish_index.copy():
if new_indices is None or new_indices[j] in special_tokens_ids:
group_id = j // sub_beam_size
group_start = group_id * sub_beam_size
finish_output_sign[group_id] -= 1
if finish_output_sign[group_id] == 0:
for k in range(group_start, group_start + sub_beam_size):
finish_index.remove(k)
# Less than zero since finish_index.copy() will not be deleted at this time
elif finish_output_sign[group_id] < 0:
finish_output_sign[group_id] = 0
continue
place_hold = group_start + sub_beam_size - 1 - finish_output_sign[group_id]
place_hold_index.append(place_hold)
final_score[place_hold] = tmp_score[j].item()
final_output[place_hold] = tmp_current_output[j].cpu().numpy().tolist()
if last_indices is not None:
score[
last_indices.view(-1)[j]] = -10000 # The score wouldn't be reorder, so we need to used the last indices.
def get_final_response(args, knowledge, final_score, final_output):
select_method = getattr(args, "response_select_method", "final_score")
output_index = int(np.argmax(final_score))
if select_method == "rouge_score":
metric = ROUGE_list()
rouge_score = []
for sentence in final_output:
metric.update((sentence, knowledge))
rouge_score.append(metric.compute())
output_index = int(np.argmax(rouge_score))
real_output = final_output[output_index]
return real_output
def run_batch_generation_beam_sample_for_latentCopy(args, model, batch, dataset):
def prob_postprocess_latentCopy(outputs, probs, **kwargs):
input_ids = kwargs.get('input_ids')
model_outputs = kwargs.get('model_outputs')
whole_beam_size = kwargs.get('whole_beam_size')
attention_dist, p_gen = model_outputs[1:3]
if not isinstance(p_gen, float):
p_gen = p_gen[0, -1, 0]
attention_dist = attention_dist[0, -1, :]
probs *= p_gen
attention_dist *= (1 - p_gen)
probs = probs.scatter_add(0, input_ids.squeeze(0), attention_dist)
p_gen_tensors = p_gen.repeat((whole_beam_size, 1))
outputs += (p_gen_tensors,)
return outputs, probs
def model_pre_latentCopy(outputs, **kwargs):
args = kwargs.get('args')
model = kwargs.get('model')
model_args = kwargs.get('model_args')
instance = kwargs.get('instance')
whole_beam_size = kwargs.get('whole_beam_size')
model.model_stage = 1
model_outputs = model(**model_args)
z_post, z_post_distribution = model_outputs[:2]
model.model_stage = 2
input_masks_with_knowledge = torch.tensor(instance["input_masks_with_knowledge"], device=args.device).unsqueeze(0)
knowledgeROIs = torch.tensor(instance["knowledgeROIs"], device=args.device).unsqueeze(0)
model_args.update({
'attention_mask': input_masks_with_knowledge,
'z_hidden_embeds': z_post,
'knowledgeROIs': knowledgeROIs
})
z_post = z_post.expand((whole_beam_size,) + z_post.size()[1:])
return outputs + (z_post,)
def build_inputs_knowledge(args, current_output, dataset, history, knowledge, whole_beam_size):
input_ids = []
input_masks = []
knowledgeROIs = []
current_output_list = current_output.cpu().numpy().tolist()
for j in range(whole_beam_size):
instance, sequence = dataset.build_input_from_segments(
knowledge, history, current_output_list[j], with_eos=False
)
input_ids.append(torch.tensor(instance["input_ids"], device=args.device))
input_masks.append(torch.tensor(instance["input_masks_with_knowledge"], device=args.device))
knowledgeROIs.append(torch.tensor(instance["knowledgeROIs"], device=args.device))
input_ids = torch.stack(input_ids, dim=0)
input_masks = torch.stack(input_masks, dim=0)
knowledgeROIs = torch.stack(knowledgeROIs, dim=0)
return input_ids, input_masks, knowledgeROIs
build_inputs = build_inputs_knowledge
# Initial
sub_beam_size = args.sub_beam_size
group_num = args.group_num
whole_beam_size = sub_beam_size * group_num
special_tokens_ids = args.tokenizer.convert_tokens_to_ids(dataset.SPECIAL_TOKENS_VALUES)
finish_index = [i for i in range(whole_beam_size)]
finish_output_sign = [sub_beam_size] * group_num
final_score = [-1] * whole_beam_size
final_output = [None] * whole_beam_size
place_hold_index = []
indices_shift = torch.tensor(range(0, whole_beam_size, sub_beam_size), dtype=torch.int64, device=args.device) \
.unsqueeze(-1).repeat(1, sub_beam_size).view(-1).unsqueeze(-1)
example = batch[0]
knowledge, history = example["knowledge"], example["history"]
response_text = example["response_text"]
dialog_id = example["dialog_id"]
current_output, score, z_post, p_gen_tensors = get_initial_values(args, model, dataset, history, knowledge,
model_pre=model_pre_latentCopy,
prob_postprocess=prob_postprocess_latentCopy)
for i in range(1, args.max_length):
input_ids, input_masks_with_knowledge, knowledgeROIs = build_inputs(args, current_output, dataset, history,
knowledge, whole_beam_size)
model_outputs = model(input_ids=input_ids, token_type_ids=None, attention_mask=input_masks_with_knowledge,
z_hidden_embeds=z_post, knowledgeROIs=knowledgeROIs)
logits, attention_dist, p_gen = model_outputs[:3]
logits = logits[:, -1, :] / args.temperature
probs = F.softmax(logits, dim=-1)
# Jump out with lm score
cal_next_word(args, score, probs, current_output, indices_shift, special_tokens_ids,
place_hold_index, final_output, final_score, finish_index, finish_output_sign)
if len(finish_index) == 0: break
# Real calculation
if type(p_gen) != float:
p_gen = p_gen[:, -1, :]
p_gen_tensors = torch.cat([p_gen_tensors, p_gen], dim=-1)
attention_dist = attention_dist[:, -1, :]
probs *= p_gen
attention_dist *= (1 - p_gen)
probs = probs.scatter_add(1, input_ids, attention_dist)
current_output, score = cal_next_word(args, score, probs, current_output, indices_shift, special_tokens_ids,
place_hold_index, final_output, final_score, finish_index, finish_output_sign)
if len(finish_index) == 0: break
# Remain
gain_finish_sentences(args, score, current_output, final_output, final_score, finish_index, finish_output_sign,
place_hold_index, special_tokens_ids)
# End
real_output = get_final_response(args, knowledge, final_score, final_output)
return (real_output,
("Beam Result", final_output, final_score, p_gen_tensors.cpu().numpy().tolist())), response_text, dialog_id
# TODO: reformat
def run_batch_generation_diversity_beam_sample_for_latentCopy(args, model, batch, dataset):
GFM = True # args.GFM
sub_beam_size = args.sub_beam_size
group_num = args.group_num
whole_beam_size = sub_beam_size * group_num
penalty_lambda = getattr(args, "penalty_lambda", 0.6)
special_tokens_ids = args.tokenizer.convert_tokens_to_ids(dataset.SPECIAL_TOKENS_VALUES)
current_output = []
example = batch[0]
knowledge, history = example["knowledge"], example["history"]
response_text = example["response_text"]
dialog_id = example["dialog_id"]
# Initial
indices_shift = torch.tensor(range(0, whole_beam_size, sub_beam_size), dtype=torch.int64, device=args.device) \
.unsqueeze(-1).repeat(1, sub_beam_size).view(-1).unsqueeze(-1)
finish_index = [i for i in range(whole_beam_size)]
place_hold_index = []
finish_output_sign = [sub_beam_size] * group_num
final_score = [-1] * whole_beam_size
final_output = [None] * whole_beam_size
instance, sequence = dataset.build_input_from_segments(
knowledge, history, current_output, with_eos=False
)
input_ids = torch.tensor(instance["input_ids"], device=args.device).unsqueeze(0)
input_masks = torch.tensor(instance["input_masks"], device=args.device).unsqueeze(0)
input_masks_with_knowledge = torch.tensor(instance["input_masks_with_knowledge"], device=args.device).unsqueeze(0)
knowledgeROIs = torch.tensor(instance["knowledgeROIs"], device=args.device).unsqueeze(0)
model.model_stage = 1
model_outputs = model(input_ids=input_ids, token_type_ids=None, attention_mask=input_masks)
z_post, z_post_distribution = model_outputs[:2]
model.model_stage = 2
model_outputs = model(input_ids=input_ids, token_type_ids=None, attention_mask=input_masks_with_knowledge,
z_hidden_embeds=z_post, knowledgeROIs=knowledgeROIs)
logits, attention_dist, p_gen = model_outputs[:3]
logits = logits[0, -1, :] / args.temperature
probs = F.softmax(logits, dim=-1)
assert type(p_gen) != float
p_gen = p_gen[0, -1, 0]
attention_dist = attention_dist[0, -1, :]
probs *= p_gen
attention_dist *= (1 - p_gen)
probs = probs.scatter_add(0, input_ids.squeeze(0), attention_dist)
new_scores = torch.log(probs)
new_indices_list = []
for _ in range(group_num):
sub_new_indices = torch.topk(new_scores, sub_beam_size)[1] if args.no_sample \
else torch.multinomial(F.softmax(new_scores, dim=-1), sub_beam_size)
new_indices_list.append(sub_new_indices)
new_scores[sub_new_indices] -= penalty_lambda
new_indices = torch.cat(new_indices_list, dim=0)
scores = new_scores[new_indices].unsqueeze(1)
current_output = new_indices.unsqueeze(1)
p_gen_tensors = p_gen.repeat((whole_beam_size, 1))
z_post = z_post.expand((whole_beam_size,) + z_post.size()[1:])
for i in range(1, args.max_length):
# Build input
input_ids = []
input_masks_with_knowledge = []
knowledgeROIs = []
current_output_list = current_output.cpu().numpy().tolist()
for j in range(whole_beam_size):
instance, sequence = dataset.build_input_from_segments(
knowledge, history, current_output_list[j], with_eos=False
)
input_ids.append(torch.tensor(instance["input_ids"], device=args.device))
input_masks_with_knowledge.append(torch.tensor(instance["input_masks_with_knowledge"], device=args.device))
knowledgeROIs.append(torch.tensor(instance["knowledgeROIs"], device=args.device))
input_ids = torch.stack(input_ids, dim=0)
input_masks_with_knowledge = torch.stack(input_masks_with_knowledge, dim=0)
knowledgeROIs = torch.stack(knowledgeROIs, dim=0)
model_outputs = model(input_ids=input_ids, token_type_ids=None, attention_mask=input_masks_with_knowledge,
z_hidden_embeds=z_post, knowledgeROIs=knowledgeROIs)
logits, attention_dist, p_gen = model_outputs[:3]
logits = logits[:, -1, :] / args.temperature
probs = F.softmax(logits, dim=-1)
# Jump out with lm score
tmp_new_scores = torch.log(probs)
new_indices_list = []
last_indices_list = []
for i in range(1, group_num + 1):
sub_new_scores = scores[(i - 1) * sub_beam_size: i * sub_beam_size, ...] \
+ tmp_new_scores[(i - 1) * sub_beam_size: i * sub_beam_size, ...]
sub_indices = torch.topk(sub_new_scores.view(-1), sub_beam_size)[1] if args.no_sample \
else torch.multinomial(F.softmax(sub_new_scores.view(-1), dim=-1), sub_beam_size)
sub_last_indices = sub_indices // sub_new_scores.size(-1) + (i - 1) * sub_beam_size
sub_new_indices = sub_indices % sub_new_scores.size(-1)
new_indices_list.append(sub_new_indices)
last_indices_list.append(sub_last_indices)
tmp_new_scores[i * sub_beam_size:, sub_new_indices] -= penalty_lambda
new_indices = torch.cat(new_indices_list, dim=0)
last_indices = torch.cat(last_indices_list, dim=0)
tmp_scores = scores[last_indices] + tmp_new_scores.gather(dim=1, index=new_indices.unsqueeze(1))
for j in finish_index.copy():
if new_indices[j] in special_tokens_ids:
group_id = j // sub_beam_size
group_start = group_id * sub_beam_size
finish_output_sign[group_id] -= 1
if finish_output_sign[group_id] == 0:
for k in range(group_start, group_start + sub_beam_size): finish_index.remove(k)
elif finish_output_sign[group_id] < 0:
continue
place_hold = group_start + sub_beam_size - 1 - finish_output_sign[group_id]
# place_hold_index.append(place_hold)
final_score[place_hold] = tmp_scores[j].item()
final_output[place_hold] = current_output[j].cpu().numpy().tolist()
scores[j] = -10000
if len(finish_index) == 0: break
# End with Jump out
# Cal Real Score
if type(p_gen) != float:
p_gen = p_gen[:, -1, :]
attention_dist = attention_dist[:, -1, :]
probs *= p_gen
attention_dist *= (1 - p_gen)
probs = probs.scatter_add(1, input_ids, attention_dist)
new_scores = torch.log(probs)
new_indices_list = []
last_indices_list = []
for i in range(1, group_num + 1):
sub_new_scores = scores[(i - 1) * sub_beam_size: i * sub_beam_size, ...] \
+ new_scores[(i - 1) * sub_beam_size: i * sub_beam_size, ...]
sub_indices = torch.topk(sub_new_scores.view(-1), sub_beam_size)[1] if args.no_sample \
else torch.multinomial(F.softmax(sub_new_scores.view(-1), dim=-1), sub_beam_size)
sub_last_indices = sub_indices // sub_new_scores.size(-1) + (i - 1) * sub_beam_size
sub_new_indices = sub_indices % sub_new_scores.size(-1)
new_indices_list.append(sub_new_indices)
last_indices_list.append(sub_last_indices)
new_scores[i * sub_beam_size:, sub_new_indices] -= penalty_lambda
new_indices = torch.cat(new_indices_list, dim=0)
last_indices = torch.cat(last_indices_list, dim=0)
scores = scores[last_indices] + new_scores.gather(dim=1, index=new_indices.unsqueeze(1))
# Break Out
for j in finish_index.copy():
if new_indices[j] in special_tokens_ids:
group_id = j // sub_beam_size
group_start = group_id * sub_beam_size
finish_output_sign[group_id] -= 1
if finish_output_sign[group_id] == 0:
for k in range(group_start, group_start + sub_beam_size): finish_index.remove(k)
elif finish_output_sign[group_id] < 0:
continue
place_hold = group_start + sub_beam_size - 1 - finish_output_sign[group_id]
# place_hold_index.append(place_hold)
final_score[place_hold] = scores[j].item()
final_output[place_hold] = current_output[j].cpu().numpy().tolist()
scores[j] = -10000
if len(finish_index) == 0:
break
# End Break Out
current_output = torch.cat([current_output[last_indices], new_indices.unsqueeze(1)], dim=-1)
p_gen_tensors = torch.cat([p_gen_tensors, p_gen], dim=-1)
# Deal with residue
for j in finish_index:
group_id = j // sub_beam_size
group_start = group_id * sub_beam_size
finish_output_sign[group_id] -= 1
place_hold = group_start + sub_beam_size - 1 - finish_output_sign[group_id]
final_score[place_hold] = scores[j].item()
final_output[place_hold] = current_output[j].cpu().numpy().tolist()
output_index = int(np.argmax(final_score))
select_method = getattr(args, "response_select_method", "final_score")
if select_method == "rouge_score":
metric = ROUGE_list()
rouge_score = []
for sentence in final_output:
metric.update((sentence, knowledge))
rouge_score.append(metric.compute())
output_index = int(np.argmax(rouge_score))
real_output = final_output[output_index]
return (real_output,
("Beam Result", final_output, final_score, p_gen_tensors.cpu().numpy().tolist())), response_text, dialog_id
def run_batch_selection_train(args, model, batch):
batch = tuple(input_tensor.to(args.device) for input_tensor in batch if isinstance(input_tensor, torch.Tensor))
input_ids, token_type_ids, mc_token_ids, lm_labels, mc_labels = batch
model_outputs = model(
input_ids=input_ids, token_type_ids=token_type_ids,
mc_token_ids=mc_token_ids, mc_labels=mc_labels
)
mc_loss = model_outputs[0]
lm_logits, mc_logits = model_outputs[1], model_outputs[2]
return mc_loss, lm_logits, mc_logits, mc_labels
def run_batch_selection_eval(args, model, batch):
candidates_per_forward = args.max_candidates_per_forward_eval * (
args.n_gpu if isinstance(model, torch.nn.DataParallel) else 1)
batch = tuple(input_tensor.to(args.device) for input_tensor in batch if isinstance(input_tensor, torch.Tensor))
input_ids, token_type_ids, mc_token_ids, _, mc_labels = batch
all_mc_logits = []
for index in range(0, input_ids.size(1), candidates_per_forward):
model_outputs = model(
input_ids=input_ids[0, index:index + candidates_per_forward].unsqueeze(1),
token_type_ids=token_type_ids[0, index:index + candidates_per_forward].unsqueeze(1),
mc_token_ids=mc_token_ids[0, index:index + candidates_per_forward].unsqueeze(1)
)
mc_logits = model_outputs[1]
all_mc_logits.append(mc_logits.detach())
all_mc_logits = torch.cat(all_mc_logits, dim=0).unsqueeze(0)
return torch.tensor(0.0), torch.tensor([]), all_mc_logits, mc_labels
def run_batch_detection(args, model, batch):
batch = tuple(input_tensor.to(args.device) for input_tensor in batch if isinstance(input_tensor, torch.Tensor))
input_ids, token_type_ids, mc_token_ids, lm_labels, labels = batch
model_outputs = model(
input_ids=input_ids, token_type_ids=token_type_ids,
mc_token_ids=mc_token_ids, labels=labels
)
cls_loss = model_outputs[0]
lm_logits, cls_logits = model_outputs[1], model_outputs[2]
return cls_loss, lm_logits, cls_logits, labels
def run_batch_generation(args, model, batch):
model_name = f"run_batch_generation_for_{args.model_type}"
return eval(model_name)(args, model, batch)
def run_batch_generation_sample(args, model, batch, dataset):
middle_name = "beam" if args.beam_search else "greedy"
diversity = getattr(args, "diversity_beam_search", False)
return eval(f"run_batch_generation{'_diversity' if diversity else ''}_{middle_name}_sample_for_{args.model_type}")(
args, model, batch, dataset)
| 45.504902 | 120 | 0.7192 | 4,014 | 27,849 | 4.64001 | 0.063279 | 0.037369 | 0.032483 | 0.017718 | 0.814443 | 0.802792 | 0.780617 | 0.759624 | 0.726336 | 0.706792 | 0 | 0.010717 | 0.172394 | 27,849 | 611 | 121 | 45.579378 | 0.797379 | 0.023053 | 0 | 0.636183 | 0 | 0 | 0.038924 | 0.011626 | 0 | 0 | 0 | 0.001637 | 0.001988 | 1 | 0.035785 | false | 0 | 0.015905 | 0 | 0.085487 | 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 |
a202acc8c5643962327b2e18e9f3ad2d9f380a4c | 201 | py | Python | 003_LargestPrimeFactor.py | joetache4/ProjectEuler | f101e927d73dbafa11af1b208992bf0d830c88b1 | [
"MIT"
] | null | null | null | 003_LargestPrimeFactor.py | joetache4/ProjectEuler | f101e927d73dbafa11af1b208992bf0d830c88b1 | [
"MIT"
] | null | null | null | 003_LargestPrimeFactor.py | joetache4/ProjectEuler | f101e927d73dbafa11af1b208992bf0d830c88b1 | [
"MIT"
] | null | null | null | '''
Joe Walter
difficulty: 5%
run time: 0:00
answer: 6857
***
003 Largest Prime Factor
Largest prime factor of 600851475143
'''
from lib.num import factor
print(max(factor(600851475143)))
| 11.166667 | 36 | 0.701493 | 28 | 201 | 5.035714 | 0.785714 | 0.170213 | 0.255319 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.216049 | 0.19403 | 201 | 17 | 37 | 11.823529 | 0.654321 | 0.681592 | 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 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
a204af6f655d1f7fbe2b1fd188ddc9fd3b880401 | 30 | py | Python | VTree/__init__.py | MarcoMuellner/VTree | c4bd509daeb80652075df1937b5861fa3e281dff | [
"MIT"
] | null | null | null | VTree/__init__.py | MarcoMuellner/VTree | c4bd509daeb80652075df1937b5861fa3e281dff | [
"MIT"
] | null | null | null | VTree/__init__.py | MarcoMuellner/VTree | c4bd509daeb80652075df1937b5861fa3e281dff | [
"MIT"
] | null | null | null | from VTree.vtree import VTree
| 15 | 29 | 0.833333 | 5 | 30 | 5 | 0.6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133333 | 30 | 1 | 30 | 30 | 0.961538 | 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 |
bf5c7c9f63a96c942b79f08278c9d7d73453139a | 1,420 | py | Python | sudoku/question.py | eudika/puzzle | ede8c33c322f63b92736c2ec3135127322718733 | [
"MIT"
] | null | null | null | sudoku/question.py | eudika/puzzle | ede8c33c322f63b92736c2ec3135127322718733 | [
"MIT"
] | null | null | null | sudoku/question.py | eudika/puzzle | ede8c33c322f63b92736c2ec3135127322718733 | [
"MIT"
] | 1 | 2021-02-01T12:30:16.000Z | 2021-02-01T12:30:16.000Z | # use 0 for blank cell
questions = [
[
[5, 3, 0, 0, 7, 0, 0, 0, 0],
[6, 0, 0, 1, 9, 5, 0, 0, 0],
[0, 9, 8, 0, 0, 0, 0, 6, 0],
[8, 0, 0, 0, 6, 0, 0, 0, 3],
[4, 0, 0, 8, 0, 3, 0, 0, 1],
[7, 0, 0, 0, 2, 0, 0, 0, 6],
[0, 6, 0, 0, 0, 0, 2, 8, 0],
[0, 0, 0, 4, 1, 9, 0, 0, 5],
[0, 0, 0, 0, 8, 0, 0, 7, 9]
],
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 5, 8, 0, 0, 0, 3, 9, 0],
[0, 1, 7, 6, 0, 3, 2, 4, 0],
[0, 0, 2, 3, 0, 1, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 3, 5, 0, 4, 8, 0, 0],
[0, 2, 4, 9, 0, 6, 7, 1, 0],
[0, 7, 1, 0, 0, 0, 4, 3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]
],
[
[8, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 3, 6, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 9, 0, 2, 0, 0],
[0, 5, 0, 0, 0, 7, 0, 0, 0],
[0, 0, 0, 0, 4, 5, 7, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 3, 0],
[0, 0, 1, 0, 0, 0, 0, 6, 8],
[0, 0, 8, 5, 0, 0, 0, 1, 0],
[0, 9, 0, 0, 0, 0, 4, 0, 0]
],
[
[4, 9, 0, 0, 0, 0, 0, 6, 8],
[8, 0, 0, 0, 4, 0, 0, 0, 3],
[0, 0, 0, 6, 0, 1, 0, 0, 0],
[0, 0, 5, 0, 6, 0, 4, 0, 0],
[0, 4, 0, 5, 0, 3, 0, 1, 0],
[0, 0, 1, 0, 2, 0, 3, 0, 0],
[0, 0, 0, 4, 0, 2, 0, 0, 0],
[5, 0, 0, 0, 1, 0, 0, 0, 9],
[9, 2, 0, 0, 0, 0, 0, 8, 1]
],
]
| 28.979592 | 36 | 0.245775 | 330 | 1,420 | 1.057576 | 0.045455 | 0.848138 | 0.825215 | 0.641834 | 0.69341 | 0.567335 | 0.363897 | 0.180516 | 0.180516 | 0.12894 | 0 | 0.429326 | 0.466901 | 1,420 | 48 | 37 | 29.583333 | 0.031704 | 0.014085 | 0 | 0.130435 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 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 |
bfd027c4b523608bcae21d254032d38a95c54c3a | 44,176 | py | Python | code/transitemcee.py | mrtommyb/GP_model_Kepler_data | a51ba4b6ab325484b47b2e594539f537cacdbb62 | [
"MIT"
] | null | null | null | code/transitemcee.py | mrtommyb/GP_model_Kepler_data | a51ba4b6ab325484b47b2e594539f537cacdbb62 | [
"MIT"
] | 1 | 2018-12-19T10:46:59.000Z | 2018-12-20T14:36:03.000Z | code/transitemcee.py | mrtommyb/GP_model_Kepler_data | a51ba4b6ab325484b47b2e594539f537cacdbb62 | [
"MIT"
] | 1 | 2018-12-18T16:46:13.000Z | 2018-12-18T16:46:13.000Z | import sys
import numpy as np
#import matplotlib.pyplot as plt
import emcee
import tmodtom as tmod
import time as thetime
from scipy.stats import truncnorm
from claretquadpy import claretquad
from claret4ppy import claretlimb4p
from copy import deepcopy
from numpy import random
#from bilin_interp import ld_quad
class transitemcee(object):
def __init__(self,nplanets,cadence=1625.3,
ldfileloc='/Users/tom/svn_code/tom_code/',
codedir='/Users/tom/svn_code/tom_code/'):
sys.path.append(codedir)
self.nplanets = nplanets
nmax = 1500000 #from the fortran
self._ntt = np.zeros(nplanets)
self._tobs = np.empty([self.nplanets,nmax])
self._omc = np.empty([self.nplanets,nmax])
self.cadence = cadence / 86400.
self.allow_ecc_orbit = False
self.ldfileloc = ldfileloc
self.onlytransits = False
self.tregion = 500
def get_stellar(self,teff,logg,FeH,n_ldparams=4):
"""
read in stellar parameters
inputs
teff : float
The effective temperature of the star
logg : float
the surface gravity of the star in log cgs
FeH : float
the metalicity of the star in log solar
optional
n_ldparams : int
"""
self.Teff = teff
self.logg = logg
self.FeH = FeH
if n_ldparams == 2:
#if teff < 3500 and logg >= 3.5:
if False:
#this block should never run
ldfile = self.ldfileloc + 'claret-quad-phoenix.txt'
self.ld1,self.ld2 = ld_quad(ldfile,
self.Teff,self.logg)
self.ld3 = 0.0
self.ld4 = 0.0
#elif logg < 3.5 or teff >= 3500:
if True:
ldfile = self.ldfileloc + 'claret-limb-quad.txt'
self.ld1,self.ld2 = claretquad(ldfile,
self.Teff,self.logg,self.FeH)
self.ld3 = 0.0
self.ld4 = 0.0
elif n_ldparams == 4:
ldfile = self.ldfileloc + 'claret-limb.txt'
self.ld1,self.ld2,self.ld3,self.ld4 = claretlimb4p(ldfile,
self.Teff,self.logg,self.FeH)
def open_lightcurve(self,filename,timeoffset=0.0,
normalize=False):
t = np.genfromtxt(filename).T
time = t[0] - timeoffset
if normalize:
flux = t[1] / np.median(t[1])
err = t[2] / np.median(t[1])
else:
flux = t[1]
err = t[2]
self.time = time
self.flux = flux
self.err = err
self.npt = len(time)
self._itime = np.zeros(self.npt) + self.cadence
self._datatype = np.zeros(self.npt)
def already_open(self,t1,f1,e1,timeoffset=0.0,normalize=False):
time = t1 - timeoffset
if normalize:
flux = f1 / np.median(f1)
err = e1 / np.median(f1)
else:
flux = f1
err = e1
self.time = time
self.flux = flux
self.err = err
self.npt = len(time)
self._itime = np.zeros(self.npt) + self.cadence
self._datatype = np.zeros(self.npt)
def get_rho(self,rho_vals,prior=False,rho_start=0.0,
rho_stop = 30.):
"""
inputs
rho_vals : array_like
Two parameter array with value
rho, rho_unc
prior : bool, optional
should this rho be used as a prior?
"""
self.rho_0 = rho_vals[0]
self.rho_0_unc = rho_vals[1]
self.rho_0_start = rho_start
self.rho_0_stop = rho_stop
if prior:
self.rho_prior = True
else:
self.rho_prior = False
def get_zpt(self,zpt_0):
self.zpt_0 = zpt_0
if self.zpt_0 == 0.0:
self.zpt_0 = 1.E-10
def get_sol(self,*args,**kwargs):
"""
reads the guess transit fit solution
There are 6 args for every planet
T0, period, impact paramter, rp/rs, ecosw and esinw
optional keywords, these are kept fixed (for now)
dil : float, optional
dilution
veloffset : float, optional
velocity zeropoint
rvamp : float, optional
radial velocity amplitude from doppler beaming
occ : float, optional
occultation depth
ell : float, optional
amplitude of ellipsoidal variations
alb : float, optional
geometric albedo of the planet
"""
assert len(args) == self.nplanets * 6
if 'dil' in kwargs.keys():
dil = kwargs['dil']
print ' running with dil = %s' %(dil)
else:
dil = 0.0
if 'veloffset' in kwargs.keys():
veloffset = kwargs['veloffset']
else:
veloffset = 0.0
if 'rvamp' in kwargs.keys():
rvamp = kwargs['rvamp']
else:
rvamp = 0.0
if 'occ' in kwargs.keys():
occ = kwargs['occ']
else:
occ = 0.0
if 'ell' in kwargs.keys():
ell = kwargs['ell']
else:
ell = 0.0
if 'alb' in kwargs.keys():
alb = kwargs['alb']
else:
alb = 0.0
try:
if self.zpt_0 == 0.:
self.zpt_0 = 1.E-10
except AttributeError:
self.zpt_0 = 1.E-10
self.zpt_0_unc = 1.E-6
fit_sol = np.array([self.rho_0,self.zpt_0])
for i in xrange(self.nplanets):
T0_0 = args[i*6]
per_0 = args[i*6 +1]
b_0 = args[i*6 +2]
rprs_0 = args[i*6 +3]
ecosw_0 = args[i*6 +4]
esinw_0 = args[i*6 +5]
new_params = np.array([T0_0,per_0,
b_0,rprs_0,ecosw_0,esinw_0])
fit_sol = np.r_[fit_sol,new_params]
self.fit_sol = fit_sol
self.fit_sol_0 = deepcopy(self.fit_sol)
self.fixed_sol = np.array([self.ld1,self.ld2,
self.ld3,self.ld4,
dil,veloffset,rvamp,
occ,ell,alb])
def cut_non_transit(self,ntdur=10):
#make a mask for each planet candidate
self.onlytransits = True
tregion = np.zeros(self.nplanets)
maskdat = np.zeros([self.npt,self.nplanets],dtype=bool)
for i in xrange(self.nplanets):
T0 = self.fit_sol[i*6 + 2]
per = self.fit_sol[i*6 + 3]
rho = self.fit_sol[0]
ars = self.get_ar(rho,per)
tdur_dys = (1./ars) * per * (1./np.pi)
#this is buggy because T0 is not nessessarily time of first transit
#but time of a transit. So fudge.
#subtract make T0 the first transit
time0 = np.copy(T0)
while True:
if time0 - per < self.time[0]:
break
else:
time0 = time0 - per
ntransits = int((self.time[-1] - self.time[0]) / per) + 1
t_times = np.arange(ntransits)*per + T0
#make sure the first and last transit are not excluded even if
#partially in the data
t_times = np.r_[t_times,t_times[0] - per,t_times[-1] + per]
for j in t_times:
maskdat[:,i] = np.logical_or(maskdat[:,i],
np.logical_and(
self.time < j +tdur_dys*ntdur,
self.time > j - tdur_dys*ntdur) )
tregion[i] = ntdur*tdur_dys
#create a final mask that is the OR of the
#individual masks
finmask = np.zeros(self.npt)
for i in xrange(self.nplanets):
finmask = np.logical_or(finmask,maskdat[:,i])
self.time = self.time[finmask]
self.flux = self.flux[finmask]
self.err = self.err[finmask]
self._itime = self._itime[finmask]
self._datatype = self._datatype[finmask]
self.tregion = tregion
def get_ar(self,rho,period):
""" gets a/R* from period and mean stellar density"""
G = 6.67E-11
rho_SI = rho * 1000.
tpi = 3. * np.pi
period_s = period * 86400.
part1 = period_s**2 * G * rho_SI
ar = (part1 / tpi)**(1./3.)
return ar
# def calc_model(self,fitsol):
# sol = np.zeros([8 + 10*self.nplanets])
# rho = fitsol[0]
# zpt = fitsol[1]
# ld1,ld2,ld3,ld4 = self.fixed_sol[0:4]
# dil = self.fixed_sol[4]
# veloffset = self.fixed_sol[5]
# fixed_stuff = self.fixed_sol[6:10]
# sol[0:8] = np.array([rho,ld1,ld2,ld3,ld4,
# dil,veloffset,zpt])
# for i in xrange(self.nplanets):
# sol[8+(i*10):8+(i*10)+10] = np.r_[fitsol[2+i*6:8+i*6],fixed_stuff]
# tmodout = tmod.transitmodel(self.nplanets,sol,self.time,self._itime,
# self._ntt,self._tobs,self._omc,self._datatype)
# return tmodout - 1.
# def logchi2(self,fitsol):
# rho = fitsol[0]
# if rho < 0.001 or rho > 30.:
# return -np.inf
# rprs = fitsol[np.arange(self.nplanets)*6 + 5]
# if np.any(rprs < 0.) or np.any(rprs > 0.5):
# return -np.inf
# ecosw = fitsol[np.arange(self.nplanets)*6 + 6]
# if np.any(ecosw < -1.0) or np.any(ecosw > 1.0):
# return -np.inf
# esinw = fitsol[np.arange(self.nplanets)*6 + 7]
# if np.any(esinw < -1.0) or np.any(esinw > 1.0):
# return -np.inf
# b = fitsol[np.arange(self.nplanets)*6 + 4]
# if np.any(b < 0.) or np.any(b > 1.0 + rprs):
# return -np.inf
# model_lc = self.calc_model(fitsol)
# if self.rho_prior:
# chi2prior = (self.rho_0 - rho)**2 / self.rho_0_unc**2
# else:
# chi2prior = 0.0
# chi2val = np.sum((model_lc - self.flux)**2 / self.err**2)
# chi2tot = chi2val + chi2prior
# logp = -chi2tot / 2.
# return logp
# def do_emcee(self,nwalkers,threads=16,burnin=100,fullrun=1000):
# l_var = 8
# p0 = self.get_guess(nwalkers)
# sampler = emcee.EnsembleSampler(nwalkers, l_var, self.logchi2,
# threads=threads)
# time1 = thetime.time()
# pos, prob, state = sampler.run_mcmc(p0, burnin)
# sampler.reset()
# time2 = thetime.time()
# print 'burn-in took ' + str((time2 - time1)/60.) + ' min'
# time1 = thetime.time()
# sampler.run_mcmc(pos, fullrun)
# time2 = thetime.time()
# print 'MCMC run took ' + str((time2 - time1)/60.) + ' min'
# print
# print("Mean acceptance: "
# + str(np.mean(sampler.acceptance_fraction)))
# print
# try:
# print("Autocorrelation times sampled:", fullrun / sampler.acor)
# except RuntimeError:
# print("No Autocorrelation")
# return sampler, (time2 - time1)/60.
def get_guess(self,nwalkers):
"""
pick sensible starting ranges for the guess parameters
T0, period, impact paramter, rp/rs, ecosw and esinw
"""
rho_unc = 0.001
zpt_unc = 1.E-8
T0_unc = 0.0002
per_unc = 0.00005
b_unc = 0.001
rprs_unc = 0.0001
ecosw_unc = 0.001
esinw_unc = 0.001
p0 = np.zeros([nwalkers,2+self.nplanets*6])
rho = self.fit_sol[0]
zpt = self.fit_sol[1]
start,stop = (0.0001 - rho) / rho_unc, (30.0 - rho) / rho_unc
p0[...,0] = truncnorm.rvs(start,stop
,loc=rho,scale=rho_unc,size=nwalkers)
p0[...,1] = np.random.normal(loc=zpt,scale=zpt,size=nwalkers)
for i in xrange(self.nplanets):
T0,per,b,rprs,ecosw,esinw = self.fit_sol[i*6+2:i*6 + 8]
b = 0.0
ecosw = 0.0
esinw = 0.0
p0[...,i*6+2] = np.random.normal(T0,T0_unc,size=nwalkers)
p0[...,i*6+3] = np.random.normal(per,per_unc,size=nwalkers)
start,stop = (0.0 - b) / b_unc, (0.5 - b) / b_unc
p0[...,i*6+4] = truncnorm.rvs(start,stop
,loc=b,scale=b_unc,size=nwalkers)
start,stop = (0.0 - rprs) / rprs_unc, (0.5 - rprs) / rprs_unc
p0[...,i*6+5] = truncnorm.rvs(start,stop
,loc=rprs,scale=rprs_unc,size=nwalkers)
start,stop = (0.0 - ecosw) / ecosw_unc, (0.5 - ecosw) / ecosw_unc
p0[...,i*6+6] = truncnorm.rvs(start,stop
,loc=ecosw,scale=ecosw_unc,size=nwalkers)
start,stop = (0.0 - esinw) / esinw_unc, (0.5 - esinw) / esinw_unc
p0[...,i*6+7] = truncnorm.rvs(start,stop
,loc=esinw,scale=esinw_unc,size=nwalkers)
return p0
class transitemcee_paramprior(transitemcee):
def __init__(self,nplanets,cadence=1626.3,
ldfileloc='/Users/tom/svn_code/tom_code/'):
transitemcee.__init__(self,nplanets,cadence,ldfileloc)
def get_stellar(self,teff,teff_unc,logg,logg_unc,FeH,FeH_unc,
n_ldparams=2):
"""
read in stellar parameters
inputs
teff : float
The effective temperature of the star
logg : float
the surface gravity of the star in log cgs
FeH : float
the metalicity of the star in log solar
optional
n_ldparams : int
"""
self.Teff = teff
self.Teff_unc = teff_unc
self.logg = logg
self.logg_unc = logg_unc
self.FeH = FeH
self.FeH_unc = FeH_unc
self.n_ldparams = n_ldparams
def get_sol(self,*args,**kwargs):
"""
reads the guess transit fit solution
There are 6 args for every planet
T0, period, impact paramter, rp/rs, ecosw and esinw
optional keywords, these are kept fixed (for now)
dil : float, optional
dilution
veloffset : float, optional
velocity zeropoint
rvamp : float, optional
radial velocity amplitude from doppler beaming
occ : float, optional
occultation depth
ell : float, optional
amplitude of ellipsoidal variations
alb : float, optional
geometric albedo of the planet
"""
assert len(args) == self.nplanets * 6
if 'dil' in kwargs.keys():
dil = kwargs['dil']
print ' running with dil = %s' %(dil)
else:
dil = 0.0
if 'veloffset' in kwargs.keys():
veloffset = kwargs['veloffset']
else:
veloffset = 0.0
if 'rvamp' in kwargs.keys():
rvamp = kwargs['rvamp']
else:
rvamp = 0.0
if 'occ' in kwargs.keys():
occ = kwargs['occ']
else:
occ = 0.0
if 'ell' in kwargs.keys():
ell = kwargs['ell']
else:
ell = 0.0
if 'alb' in kwargs.keys():
alb = kwargs['alb']
else:
alb = 0.0
try:
if self.zpt_0 == 0.:
self.zpt_0 = 1.E-10
except AttributeError:
self.zpt_0 = 1.E-10
self.zpt_0_unc = 1.E-6
fit_sol = np.array([self.rho_0,self.zpt_0,self.Teff,self.logg,self.FeH])
for i in xrange(self.nplanets):
T0_0 = args[i*6]
per_0 = args[i*6 +1]
b_0 = args[i*6 +2]
rprs_0 = args[i*6 +3]
ecosw_0 = args[i*6 +4]
esinw_0 = args[i*6 +5]
new_params = np.array([T0_0,per_0,
b_0,rprs_0,ecosw_0,esinw_0])
fit_sol = np.r_[fit_sol,new_params]
self.fit_sol = fit_sol
self.fit_sol_0 = deepcopy(self.fit_sol)
self.fixed_sol = np.array([
dil,veloffset,rvamp,
occ,ell,alb])
def get_guess(self,nwalkers):
"""
pick sensible starting ranges for the guess parameters
T0, period, impact paramter, rp/rs, ecosw and esinw
"""
rho_unc = 0.001
zpt_unc = 1.E-8
teff_unc = 10
logg_unc = 0.01
feh_unc = 0.01
T0_unc = 0.0002
per_unc = 0.00005
b_unc = 0.001
rprs_unc = 0.0001
ecosw_unc = 0.001
esinw_unc = 0.001
p0 = np.zeros([nwalkers,5+self.nplanets*6])
rho = self.fit_sol[0]
zpt = self.fit_sol[1]
teff = self.fit_sol[2]
logg = self.fit_sol[3]
feh = self.fit_sol[4]
start,stop = (0.0001 - rho) / rho_unc, (30.0 - rho) / rho_unc
p0[...,0] = truncnorm.rvs(start,stop
,loc=rho,scale=rho_unc,size=nwalkers)
p0[...,1] = np.random.normal(loc=zpt,scale=zpt,size=nwalkers)
start,stop = (3500. - teff) / teff_unc, (50000. - teff) / teff_unc
p0[...,2] = truncnorm.rvs(start,stop
,loc=teff,scale=teff_unc,size=nwalkers)
start,stop = (0.0 - logg) / logg_unc, (5. - logg) / logg_unc
p0[...,3] = truncnorm.rvs(start,stop
,loc=logg,scale=logg_unc,size=nwalkers)
start,stop = (-5.0 - feh) / feh_unc, (1.0 - feh) / feh_unc
p0[...,4] = truncnorm.rvs(start,stop
,loc=feh,scale=feh_unc,size=nwalkers)
for i in xrange(self.nplanets):
T0,per,b,rprs,ecosw,esinw = self.fit_sol[i*6+5:i*6 + 11]
b = 0.0
ecosw = 0.0
esinw = 0.0
p0[...,i*6+5] = np.random.normal(T0,T0_unc,size=nwalkers)
p0[...,i*6+6] = np.random.normal(per,per_unc,size=nwalkers)
start,stop = (0.0 - b) / b_unc, (0.5 - b) / b_unc
p0[...,i*6+7] = truncnorm.rvs(start,stop
,loc=b,scale=b_unc,size=nwalkers)
start,stop = (0.0 - rprs) / rprs_unc, (0.5 - rprs) / rprs_unc
p0[...,i*6+8] = truncnorm.rvs(start,stop
,loc=rprs,scale=rprs_unc,size=nwalkers)
start,stop = (0.0 - ecosw) / ecosw_unc, (0.5 - ecosw) / ecosw_unc
p0[...,i*6+9] = truncnorm.rvs(start,stop
,loc=ecosw,scale=ecosw_unc,size=nwalkers)
start,stop = (0.0 - esinw) / esinw_unc, (0.5 - esinw) / esinw_unc
p0[...,i*6+10] = truncnorm.rvs(start,stop
,loc=esinw,scale=esinw_unc,size=nwalkers)
return p0
def cut_non_transit(self,ntdur=10):
#make a mask for each planet candidate
self.onlytransits = True
tregion = np.zeros(self.nplanets)
maskdat = np.zeros([self.npt,self.nplanets],dtype=bool)
for i in xrange(self.nplanets):
T0 = self.fit_sol[i*6 + 5]
per = self.fit_sol[i*6 + 6]
rho = self.fit_sol[0]
ars = self.get_ar(rho,per)
tdur_dys = (1./ars) * per * (1./np.pi)
#this is buggy because T0 is not nessessarily time of first transit
#but time of a transit. So fudge.
#subtract make T0 the first transit
time0 = np.copy(T0)
while True:
if time0 - per < self.time[0]:
break
else:
time0 = time0 - per
ntransits = int((self.time[-1] - self.time[0]) / per) + 1
t_times = np.arange(ntransits)*per + T0
#make sure the first and last transit are not excluded even if
#partially in the data
t_times = np.r_[t_times,t_times[0] - per,t_times[-1] + per]
for j in t_times:
maskdat[:,i] = np.logical_or(maskdat[:,i],
np.logical_and(
self.time < j +tdur_dys*ntdur,
self.time > j - tdur_dys*ntdur) )
tregion[i] = ntdur*tdur_dys
#create a final mask that is the OR of the
#individual masks
finmask = np.zeros(self.npt)
for i in xrange(self.nplanets):
finmask = np.logical_or(finmask,maskdat[:,i])
self.time = self.time[finmask]
self.flux = self.flux[finmask]
self.err = self.err[finmask]
self._itime = self._itime[finmask]
self._datatype = self._datatype[finmask]
self.tregion = tregion
class transitemcee_paramprior_occ(transitemcee_paramprior):
pass
class transitemcee_fitldp(transitemcee):
def __init__(self,nplanets,cadence=1626.3,
ldfileloc='/Users/tom/svn_code/tom_code/',
codedir='/Users/tom/svn_code/tom_code/'):
transitemcee.__init__(self,nplanets,cadence,ldfileloc,codedir)
def get_stellar(self,teff,logg,FeH,
n_ldparams=2,ldp_prior=True):
"""
read in stellar parameters
inputs
teff : float
The effective temperature of the star
logg : float
the surface gravity of the star in log cgs
FeH : float
the metalicity of the star in log solar
optional
n_ldparams : int
"""
self.Teff = teff
self.logg = logg
self.FeH = FeH
self.ld1_unc = 0.1
self.ld2_unc = 0.1
self.ld3_unc = 0.1
self.ld4_unc = 0.1
if teff < 3500:
teff = 3500
self.ld1_unc = 0.2
self.ld2_unc = 0.2
if logg < 0.0:
logg = 0.0
self.ld1_unc = 0.05
self.ld2_unc = 0.05
if logg > 5.0:
logg = 5.0
self.ld1_unc = 0.05
self.ld2_unc = 0.05
if FeH < -5.0:
FeH = -5.0
self.ld1_unc = 0.05
self.ld2_unc = 0.05
if FeH > 1.0:
FeH = 1.0
self.ld1_unc = 0.05
self.ld2_unc = 0.05
if n_ldparams == 2:
ldfile = self.ldfileloc + 'claret-limb-quad.txt'
self.ld1,self.ld2 = claretquad(ldfile,
teff,logg,FeH)
self.ld3 = 0.0
self.ld4 = 0.0
if teff < 3500:
self.ld1,self.ld2 = claretquad(ldfile,
3500.,logg,FeH)
elif n_ldparams == 4:
ldfile = self.ldfileloc + 'claret-limb.txt'
self.ld1,self.ld2,self.ld3,self.ld4 = claretlimb4p(
ldfile,
self.Teff,self.logg,self.FeH)
self.ldp_prior = ldp_prior
self.n_ldparams = n_ldparams
def get_sol(self,*args,**kwargs):
"""
reads the guess transit fit solution
There are 6 args for every planet
T0, period, impact paramter, rp/rs, ecosw and esinw
optional keywords, these are kept fixed (for now)
dil : float, optional
dilution
veloffset : float, optional
velocity zeropoint
rvamp : float, optional
radial velocity amplitude from doppler beaming
occ : float, optional
occultation depth
ell : float, optional
amplitude of ellipsoidal variations
alb : float, optional
geometric albedo of the planet
"""
assert len(args) == self.nplanets * 6
if 'dil' in kwargs.keys():
dil = kwargs['dil']
print ' running with dil = %s' %(dil)
else:
dil = 0.0
if 'veloffset' in kwargs.keys():
veloffset = kwargs['veloffset']
else:
veloffset = 0.0
if 'rvamp' in kwargs.keys():
rvamp = kwargs['rvamp']
else:
rvamp = 0.0
if 'occ' in kwargs.keys():
occ = kwargs['occ']
else:
occ = 0.0
if 'ell' in kwargs.keys():
ell = kwargs['ell']
else:
ell = 0.0
if 'alb' in kwargs.keys():
alb = kwargs['alb']
else:
alb = 0.0
try:
if self.zpt_0 == 0.:
self.zpt_0 = 1.E-10
except AttributeError:
self.zpt_0 = 1.E-10
self.zpt_0_unc = 1.E-6
if self.n_ldparams == 2:
fit_sol = np.array([self.rho_0,self.zpt_0,
self.ld1,self.ld2])
elif self.n_ldparams == 4:
fit_sol = np.array([self.rho_0,self.zpt_0,
self.ld1,self.ld2,self.ld3, self.ld4])
for i in xrange(self.nplanets):
T0_0 = args[i*6]
per_0 = args[i*6 +1]
b_0 = args[i*6 +2]
rprs_0 = args[i*6 +3]
ecosw_0 = args[i*6 +4]
esinw_0 = args[i*6 +5]
new_params = np.array([T0_0,per_0,
b_0,rprs_0,ecosw_0,esinw_0])
fit_sol = np.r_[fit_sol,new_params]
self.fit_sol = fit_sol
self.fit_sol_0 = deepcopy(self.fit_sol)
self.fixed_sol = np.array([
dil,veloffset,rvamp,
occ,ell,alb])
def get_guess(self,nwalkers):
"""
pick sensible starting ranges for the guess parameters
T0, period, impact paramter, rp/rs, ecosw and esinw
"""
rho_unc = 0.1
zpt_unc = 1.E-8
ld1_unc = 0.05
ld2_unc = 0.05
ld3_unc = 0.05
ld4_unc = 0.05
T0_unc = 0.0002
per_unc = 0.00005
b_unc = 0.001
rprs_unc = 0.0001
ecosw_unc = 0.001
esinw_unc = 0.001
#p0 = np.zeros([nwalkers,4+self.nplanets*6])
if self.n_ldparams == 2:
p0 = np.zeros([nwalkers,4+self.nplanets*6+1])
elif self.n_ldparams == 4:
p0 = np.zeros([nwalkers,6+self.nplanets*6+1])
rho = self.fit_sol[0]
zpt = self.fit_sol[1]
ld1 = self.fit_sol[2]
ld2 = self.fit_sol[3]
if self.n_ldparams == 4:
ld3 = self.fit_sol[4]
ld4 = self.fit_sol[5]
addval = 2
start,stop = (0.0 - ld3) / ld3_unc, (1.0 - ld3) / ld3_unc
p0[...,4] = truncnorm.rvs(start,stop
,loc=ld3,scale=ld3_unc,size=nwalkers)
start,stop = (0.0 - ld4) / ld4_unc, (1.0 - ld4) / ld4_unc
p0[...,5] = truncnorm.rvs(start,stop
,loc=ld4,scale=ld4_unc,size=nwalkers)
else:
addval = 0
start,stop = (0.0001 - rho) / rho_unc, (30.0 - rho) / rho_unc
p0[...,0] = truncnorm.rvs(start,stop
,loc=rho,scale=rho_unc,size=nwalkers)
p0[...,1] = np.random.normal(loc=zpt,scale=zpt,size=nwalkers)
start,stop = (0.0 - ld1) / ld1_unc, (1.0 - ld1) / ld1_unc
p0[...,2] = truncnorm.rvs(start,stop
,loc=ld1,scale=ld1_unc,size=nwalkers)
start,stop = (0.0 - ld2) / ld2_unc, (1.0 - ld2) / ld2_unc
p0[...,3] = truncnorm.rvs(start,stop
,loc=ld2,scale=ld2_unc,size=nwalkers)
for i in xrange(self.nplanets):
(T0,per,b,rprs,ecosw,
esinw) = self.fit_sol[i*6+4+addval:i*6 + 10+addval]
b = 0.2
ecosw = 0.0
esinw = 0.0
p0[...,i*6+4+addval] = np.random.normal(
T0,T0_unc,size=nwalkers)
p0[...,i*6+5+addval] = np.random.normal(
per,per_unc,size=nwalkers)
start,stop = (0.0 - b) / b_unc, (0.5 - b) / b_unc
p0[...,i*6+6+addval] = truncnorm.rvs(
start,stop
,loc=b,scale=b_unc,size=nwalkers)
start,stop = (0.0 - rprs) / rprs_unc, (0.5 - rprs) / rprs_unc
p0[...,i*6+7+addval] = truncnorm.rvs(
start,stop
,loc=rprs,scale=rprs_unc,size=nwalkers)
start,stop = (0.0 - ecosw) / ecosw_unc, (0.5 - ecosw) / ecosw_unc
p0[...,i*6+8+addval] = truncnorm.rvs(
start,stop
,loc=ecosw,scale=ecosw_unc,size=nwalkers)
start,stop = (0.0 - esinw) / esinw_unc, (0.5 - esinw) / esinw_unc
p0[...,i*6+9+addval] = truncnorm.rvs(
start,stop
,loc=esinw,scale=esinw_unc,size=nwalkers)
#this is the jitter term
#make it like self.err
errterm = np.median(self.err)
start,stop = 0.0,10.
p0[...,-1] = truncnorm.rvs(start,stop,
loc=0.0,scale=0.1*errterm,size=nwalkers)
return p0
def cut_non_transit(self,ntdur=10):
#make a mask for each planet candidate
self.onlytransits = True
tregion = np.zeros(self.nplanets)
maskdat = np.zeros([self.npt,self.nplanets],dtype=bool)
if self.n_ldparams == 2:
addval = 0
elif self.n_ldparams == 4:
addval = 2
for i in xrange(self.nplanets):
T0 = self.fit_sol[i*6 + 4+addval]
per = self.fit_sol[i*6 + 5+addval]
rho = self.fit_sol[0]
ars = self.get_ar(rho,per)
tdur_dys = (1./ars) * per * (1./np.pi)
#this is buggy because T0 is not nessessarily time of first transit
#but time of a transit. So fudge.
#subtract make T0 the first transit
time0 = np.copy(T0)
while True:
if time0 - per < self.time[0]:
break
else:
time0 = time0 - per
ntransits = int((self.time[-1] - self.time[0]) / per) + 1
t_times = np.arange(ntransits)*per + T0
#make sure the first and last transit are not excluded even if
#partially in the data
t_times = np.r_[t_times,t_times[0] - per,t_times[-1] + per]
for j in t_times:
maskdat[:,i] = np.logical_or(maskdat[:,i],
np.logical_and(
self.time < j +tdur_dys*ntdur,
self.time > j - tdur_dys*ntdur) )
tregion[i] = ntdur*tdur_dys
#create a final mask that is the OR of the
#individual masks
finmask = np.zeros(self.npt)
for i in xrange(self.nplanets):
finmask = np.logical_or(finmask,maskdat[:,i])
self.time = self.time[finmask]
self.flux = self.flux[finmask]
self.err = self.err[finmask]
self._itime = self._itime[finmask]
self._datatype = self._datatype[finmask]
self.tregion = tregion
def get_ar(rho,period):
""" gets a/R* from period and mean stellar density"""
G = 6.67E-11
rho_SI = rho * 1000.
tpi = 3. * np.pi
period_s = period * 86400.
part1 = period_s**2 * G * rho_SI
ar = (part1 / tpi)**(1./3.)
return ar
def logchi2(fitsol,nplanets,rho_0,rho_0_unc,rho_prior,
flux,err,fixed_sol,time,itime,ntt,tobs,omc,datatype,
onlytransits=False,tregion=0.0):
#here are some priors to keep values sensible
rho = fitsol[0]
if rho < 0.0001 or rho > 100.:
return -np.inf
rprs = fitsol[np.arange(nplanets)*6 + 5]
if np.any(rprs < 0.) or np.any(rprs > 0.5):
return -np.inf
ecosw = fitsol[np.arange(nplanets)*6 + 6]
if np.any(ecosw < -1.0) or np.any(ecosw > 1.0):
return -np.inf
esinw = fitsol[np.arange(nplanets)*6 + 7]
if np.any(esinw < -1.0) or np.any(esinw > 1.0):
return -np.inf
#avoid parabolic orbits
ecc = np.sqrt(esinw**2 + ecosw**2)
if np.any(ecc > 1.0):
return -np.inf
#avoid orbits where the planet enters the star
per = fitsol[np.arange(nplanets)*6 + 3]
ar = get_ar(rho,per)
if np.any(ecc > (1.-(1./ar))):
return -np.inf
b = fitsol[np.arange(nplanets)*6 + 4]
if np.any(b < 0.) or np.any(b > 1.0 + rprs):
return -np.inf
if onlytransits:
T0 = fitsol[np.arange(nplanets)*6 + 2]
if np.any(T0 < T0 - tregion) or np.any(T0 > T0 + tregion):
return -np.inf
model_lc = calc_model(fitsol,nplanets,fixed_sol,
time,itime,ntt,tobs,omc,datatype)
if rho_prior:
chi2prior = (rho_0 - rho)**2 / rho_0_unc**2
else:
chi2prior = 0.0
ecc[ecc == 0.0] = 1.E-10
chi2ecc = np.log(1. / ecc)
chi2val = np.sum((model_lc - flux)**2 / err**2)
chi2const = np.log(1. / (np.sqrt(2.*np.pi) * np.mean(err)))
chi2tot = (-chi2val/2.) + chi2prior
#include eccentricity in the prior
#having np.log(chi2ecc) -> e**(-chi2/2) / ecc
logp = chi2tot + np.sum(chi2ecc)
return logp
def calc_model(fitsol,nplanets,fixed_sol,time,itime,ntt,tobs,omc,datatype):
sol = np.zeros([8 + 10*nplanets])
rho = fitsol[0]
zpt = fitsol[1]
ld1,ld2,ld3,ld4 = fixed_sol[0:4]
dil = fixed_sol[4]
veloffset = fixed_sol[5]
fixed_stuff = fixed_sol[6:10]
sol[0:8] = np.array([rho,ld1,ld2,ld3,ld4,
dil,veloffset,zpt])
for i in xrange(nplanets):
sol[8+(i*10):8+(i*10)+10] = np.r_[fitsol[2+i*6:8+i*6],fixed_stuff]
tmodout = tmod.transitmodel(nplanets,sol,time,itime,
ntt,tobs,omc,datatype)
return tmodout - 1.
def logchi2_paramprior(fitsol,nplanets,rho_0,rho_0_unc,rho_prior,
teff_0,teff_0_unc,logg_0,logg_0_unc,feh_0,feh_0_unc,
flux,err,fixed_sol,time,itime,ntt,tobs,omc,datatype,
n_ldparams=2,ldfileloc='/Users/tom/svn_code/tom_code/',
onlytransits=False,tregion=0.0):
minf = -np.inf
#here are some priors to keep values sensible
rho = fitsol[0]
if rho < 1.E-6 or rho > 100.:
return minf
teff = fitsol[2]
if teff < 3500 or teff > 50000.:
return minf
logg = fitsol[3]
if logg < 0.0 or logg > 5.:
return minf
feh = fitsol[4]
if feh < -5. or feh > 1.:
return minf
rprs = fitsol[np.arange(nplanets)*6 + 8]
if np.any(rprs < 0.) or np.any(rprs > 0.5):
return minf
ecosw = fitsol[np.arange(nplanets)*6 + 9]
if np.any(ecosw < -1.0) or np.any(ecosw > 1.0):
return minf
esinw = fitsol[np.arange(nplanets)*6 + 10]
if np.any(esinw < -1.0) or np.any(esinw > 1.0):
return minf
#avoid parabolic orbits
ecc = np.sqrt(esinw**2 + ecosw**2)
if np.any(ecc > 1.0):
return minf
#avoid orbits where the planet enters the star
per = fitsol[np.arange(nplanets)*6 + 6]
ar = get_ar(rho,per)
if np.any(ecc > (1.-(1./ar))):
return minf
b = fitsol[np.arange(nplanets)*6 + 7]
if np.any(b < 0.) or np.any(b > 1.0 + rprs):
return minf
if onlytransits:
T0 = fitsol[np.arange(nplanets)*6 + 5]
if np.any(T0 < T0 - tregion) or np.any(T0 > T0 + tregion):
return minf
#calc thing limb darkening here
if n_ldparams == 2:
#if teff < 3500 and logg >= 3.5:
if False:
#this block should never run
ldfile = ldfileloc + 'claret-quad-phoenix.txt'
ld1,ld2 = ld_quad(ldfile,
teff,logg)
ld3 = 0.0
ld4 = 0.0
#elif logg < 3.5 or teff >= 3500:
if True:
ldfile = ldfileloc + 'claret-limb-quad.txt'
ld1,ld2 = claretquad(ldfile,
teff,logg,feh)
ld3 = 0.0
ld4 = 0.0
elif n_ldparams == 4:
ldfile = ldfileloc + 'claret-limb.txt'
ld1,ld2,ld3,ld4 = claretlimb4p(ldfile,
teff,logg,feh)
lds = np.array([ld1,ld2,ld3,ld4])
fitsol_model_calc = np.r_[fitsol[0:2],fitsol[5:]]
fixed_sol_model_calc = np.r_[lds,fixed_sol]
model_lc = calc_model(fitsol_model_calc,nplanets,fixed_sol_model_calc,
time,itime,ntt,tobs,omc,datatype)
if rho_prior:
rho_prior = (rho_0 - rho)**2 / rho_0_unc**2
#teff_prior = (teff_0 - teff)**2 / teff_0_unc**2
#logg_prior = (logg_0 - logg)**2 / logg_0_unc**2
#feh_prior = (feh_0 - feh)**2 / feh_0_unc**2
#chi2prior = rho_prior+teff_prior+logg_prior+feh_prior
else:
rho_prior = 0.0
teff_prior = (teff_0 - teff)**2 / teff_0_unc**2
logg_prior = (logg_0 - logg)**2 / logg_0_unc**2
feh_prior = (feh_0 - feh)**2 / feh_0_unc**2
chi2prior = -0.5*(rho_prior+teff_prior+logg_prior+feh_prior)
ecc[ecc == 0.0] = 1.E-10
chi2ecc = np.log(1. / ecc)
chi2val = -0.5*np.sum(((model_lc - flux)* (model_lc - flux))
/ (err*err))
#chi2const = np.log(np.sum(1./(np.sqrt(2.*np.pi)*err)))
chi2const = 0.0
chi2tot = chi2const + chi2val + chi2prior
#include eccentricity in the prior
#having np.log(chi2ecc) -> e**(-chi2/2) / ecc
logp = chi2tot + np.sum(chi2ecc)
return logp
def logchi2_fitldp(fitsol,nplanets,rho_0,rho_0_unc,rho_prior,
ld1_0,ld1_0_unc,ld2_0,ld2_0_unc,ldp_prior,
flux,err,fixed_sol,time,itime,ntt,tobs,omc,datatype,
n_ldparams=2,ldfileloc='/Users/tom/svn_code/tom_code/',
onlytransits=False,tregion=0.0):
minf = -np.inf
#here are some priors to keep values sensible
rho = fitsol[0]
if rho < 1.E-6 or rho > 100.:
return minf
ld1 = fitsol[2]
ld2 = fitsol[3]
#some lind darkening constraints
#from Burke et al. 2008 (XO-2b)
if ld1 < 0.0:
return minf
if ld1 + ld2 > 1.0:
return minf
if ld1 + 2.*ld2 < 0.0:
return minf
if ld2 < -0.8:
return minf
if n_ldparams == 2:
ld3, ld4 = 0.0,0.0
addval = 0
elif n_ldparams == 4:
ld3 = fitsol[4]
ld4 = fitsol[5]
addval = 2
rprs = fitsol[np.arange(nplanets)*6 + 7 + addval]
if np.any(rprs < 0.) or np.any(rprs > 0.5):
return minf
ecosw = fitsol[np.arange(nplanets)*6 + 8+addval]
if np.any(ecosw < -1.0) or np.any(ecosw > 1.0):
return minf
esinw = fitsol[np.arange(nplanets)*6 + 9+addval]
if np.any(esinw < -1.0) or np.any(esinw > 1.0):
return minf
#avoid parabolic orbits
ecc = np.sqrt(esinw**2 + ecosw**2)
if np.any(ecc > 1.0):
return minf
#avoid orbits where the planet enters the star
per = fitsol[np.arange(nplanets)*6 + 5+addval]
ar = get_ar(rho,per)
if np.any(ecc > (1.-(1./ar))):
return minf
b = fitsol[np.arange(nplanets)*6 + 6+addval]
if np.any(b < 0.) or np.any(b > 1.0 + rprs):
return minf
if onlytransits:
T0 = fitsol[np.arange(nplanets)*6 + 4+addval]
if np.any(T0 < T0 - tregion) or np.any(T0 > T0 + tregion):
return minf
jitter = fitsol[-1]
if jitter < 0.0:
return minf
err_jit = np.sqrt(err**2 + jitter**2)
err_jit2 = err**2 + jitter**2
lds = np.array([ld1,ld2,ld3,ld4])
fitsol_model_calc = np.r_[fitsol[0:2],fitsol[4:]]
fixed_sol_model_calc = np.r_[lds,fixed_sol]
model_lc = calc_model(fitsol_model_calc,nplanets,fixed_sol_model_calc,
time,itime,ntt,tobs,omc,datatype)
# if rho_prior:
# rhoprior = (rho_0 - rho)**2 / rho_0_unc**2
# else:
# rhoprior = 0.0
# if ldp_prior:
# ldprior1 = (ld1_0 - ld1)*(ld1_0 - ld1) / ld1_0_unc**2
# ldprior2 = (ld2_0 - ld2)*(ld2_0 - ld2) / ld2_0_unc**2
# ldprior = ldprior1 + ldprior2
# else:
# ldprior = 0.0
# chi2prior = -0.5*(rhoprior+ldprior)
ecc[ecc == 0.0] = 1.E-10
#chi2ecc = np.log(1. / ecc)
# chi2val = -0.5*np.sum(((model_lc - flux)* (model_lc - flux))
# / (err_jit*err_jit))
# chi2const = -1.0*np.sum(np.log(err_jit))
# #chi2const = 0.0
# chi2tot = chi2const + chi2val + chi2prior
# #include eccentricity in the prior
# #having np.log(chi2ecc) -> e**(-chi2/2) / ecc
# logp = chi2tot + np.sum(chi2ecc)
npt_lc = len(err_jit)
loglc = (
- (npt_lc/2.)*np.log(2.*np.pi)
- 0.5 * np.sum(np.log(err_jit2))
- 0.5 * np.sum((model_lc - flux)**2 / err_jit2)
)
if rho_prior:
logrho = (
- 0.5 * np.log(2.*np.pi)
- 0.5 * np.log(rho_0_unc**2)
- 0.5 * (rho_0 - rho)**2 / rho_0_unc**2
)
else:
rho_prior = 0.0
if ldp_prior:
logld1 = (
- 0.5 * np.log(2.*np.pi)
- 0.5 * np.log(ld1_0_unc**2)
- 0.5 * (ld1_0 - ld1)**2 / ld1_0_unc**2
)
logld2 = (
- 0.5 * np.log(2.*np.pi)
- 0.5 * np.log(ld2_0_unc**2)
- 0.5 * (ld2_0 - ld2)**2 / ld2_0_unc**2
)
logldp = logld1 + logld2
else:
logldp = 0.0
logecc = - np.sum(np.log(ecc))
logLtot = loglc + logrho + logldp + logecc
return logLtot
# def calc_model_paramprior(fitsol,nplanets,fixed_sol,time,itime,ntt,tobs,omc,datatype):
# sol = np.zeros([8 + 10*nplanets])
# rho = fitsol[0]
# zpt = fitsol[1]
# ld1,ld2,ld3,ld4 = fixed_sol[0:4]
# dil = fixed_sol[4]
# veloffset = fixed_sol[5]
# fixed_stuff = fixed_sol[6:10]
# sol[0:8] = np.array([rho,ld1,ld2,ld3,ld4,
# dil,veloffset,zpt])
# for i in xrange(nplanets):
# sol[8+(i*10):8+(i*10)+10] = np.r_[fitsol[2+i*6:8+i*6],fixed_stuff]
# tmodout = tmod.transitmodel(nplanets,sol,time,itime,
# ntt,tobs,omc,datatype)
# return tmodout - 1.
def get_stats(par_arr,noprint=False):
par_arr
onesig = (1. - 0.682689492) / 2.
twosig = (1. - 0.954499736) / 2.
threesig = (1. - 0.997300204) / 2.
med = np.median(par_arr)
stdev = np.std(par_arr)
sort_arr = np.sort(par_arr)
nval = len(par_arr)
m1 = med - sort_arr[np.floor(onesig * nval)]
p1 = sort_arr[np.floor(nval - (onesig * nval))] - med
m2 = med - sort_arr[np.floor(twosig * nval)]
p2 = sort_arr[np.floor(nval - (twosig * nval))] - med
m3 = med - sort_arr[np.floor(threesig * nval)]
p3 = sort_arr[np.floor(nval - (threesig * nval))] - med
ninefivelow = sort_arr[np.floor(0.025*nval)]
ninefivehigh = sort_arr[np.floor(0.975*nval)]
if not noprint:
print '95percent credible interval = %s - %s' %(ninefivelow,ninefivehigh)
return np.array([med,stdev,p1,m1,p2,m2,p3,m3])
def model_real_paramprior(rho,zpt,teff,logg,feh,T0,
per,b,rprs,ecosw,esinw,
time,itime,ntt,tobs,omc,datatype,
n_ldparams=2,
ldfileloc='/Users/tom/svn_code/tom_code/'):
ldfile = ldfileloc + 'claret-limb-quad.txt'
ld1,ld2 = claretquad(ldfile,teff,logg,feh)
ld3 = 0.0
ld4 = 0.0
dil=0.0
veloffset = 0.0
rvamp = 0.0
occ = 0.0
ell = 0.0
alb = 0.0
nplanets = 1
sol = np.array([rho,ld1,ld2,ld3,ld4,
dil,veloffset,zpt,T0,per,b,rprs,ecosw,esinw,
rvamp,occ,ell,alb])
tmodout = tmod.transitmodel(nplanets,sol,time,itime,
ntt,tobs,omc,datatype) - 1.0
return tmodout
def testtom(t,num):
rho,zpt,teff,logg,feh,T0,per,b,rprs,ecosw,esinw = (t[...,num])
mod = model_real_paramprior(rho,zpt,teff,logg,feh,T0,per,b,rprs,ecosw,
esinw,M.time,M._itime,M._ntt,M._tobs,M._omc,M._datatype,
n_ldparams=2,ldfileloc='/Users/tom/svn_code/tom_code/')
q,f = get_qf(M.time,a,per,T0)
plt.plot(q,f,alpha=0.5)
def run_crap(t):
for num in random.choice(np.arange(len(t[1])),size=10):
testtom(t,num)
q,f = get_qf(M.time,M.flux,per,T0)
plt.scatter(q,f,s=1,color='k',alpha=0.2)
def get_qf(time,flux,period,epoch):
date1 = (time - epoch) + 0.5*period
phi1 = (((date1 / period) - np.floor(date1/period)) * 24. * period) - 12*period
q1 = np.sort(phi1)
f1 = (flux[np.argsort(phi1)]) * 1.E6
return q1, f1
| 31.919075 | 88 | 0.524267 | 6,194 | 44,176 | 3.629642 | 0.070552 | 0.008451 | 0.015123 | 0.021484 | 0.797305 | 0.763411 | 0.740059 | 0.729206 | 0.707143 | 0.692599 | 0 | 0.060029 | 0.349126 | 44,176 | 1,383 | 89 | 31.942155 | 0.721873 | 0.127015 | 0 | 0.64123 | 0 | 0 | 0.019903 | 0.00883 | 0 | 0 | 0 | 0 | 0.003417 | 0 | null | null | 0.001139 | 0.01139 | null | null | 0.006834 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
781ab1b68d7175d472282dd2c34637ddada37ab7 | 23,855 | py | Python | tests/test_outputs_handler_matsim_xml_writer.py | arup-group/genet | 24bfbee31da6d7951598adb29ddf17d3a08ed5e6 | [
"MIT"
] | 22 | 2020-12-22T11:11:44.000Z | 2022-03-07T16:25:35.000Z | tests/test_outputs_handler_matsim_xml_writer.py | tkahng/genet | d5c29ed9e44408b60f55d8de889d7430debc9f04 | [
"MIT"
] | 27 | 2020-12-22T09:45:35.000Z | 2022-03-03T14:52:24.000Z | tests/test_outputs_handler_matsim_xml_writer.py | tkahng/genet | d5c29ed9e44408b60f55d8de889d7430debc9f04 | [
"MIT"
] | 7 | 2021-01-02T10:00:05.000Z | 2022-01-06T03:53:43.000Z | import os, sys
import pytest
import lxml
from copy import deepcopy
from shapely.geometry import LineString
from tests.fixtures import network_object_from_test_data, full_fat_default_config_path, assert_semantically_equal
from tests import xml_diff
from genet.outputs_handler import matsim_xml_writer
from genet.core import Network
from genet.schedule_elements import read_vehicle_types
from genet.inputs_handler import read
import xml.etree.cElementTree as ET
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
pt2matsim_network_test_file = os.path.abspath(
os.path.join(os.path.dirname(__file__), "test_data", "matsim", "network.xml"))
pt2matsim_schedule_file = os.path.abspath(
os.path.join(os.path.dirname(__file__), "test_data", "matsim", "schedule.xml"))
pt2matsim_vehicles_file = os.path.abspath(
os.path.join(os.path.dirname(__file__), "test_data", "matsim", "vehicles.xml"))
@pytest.fixture
def network_dtd():
dtd_path = os.path.abspath(os.path.join(os.path.dirname(__file__),
"test_data", "dtd", "matsim", "network_v2.dtd"))
yield lxml.etree.DTD(dtd_path)
@pytest.fixture
def schedule_dtd():
dtd_path = os.path.abspath(os.path.join(os.path.dirname(__file__),
"test_data", "dtd", "matsim", "transitSchedule_v2.dtd"))
yield lxml.etree.DTD(dtd_path)
@pytest.fixture
def vehicles_xsd():
xsd_path = os.path.abspath(os.path.join(os.path.dirname(__file__),
"test_data", "dtd", "matsim", "vehicleDefinitions_v1.0.xsd"))
xml_schema_doc = lxml.etree.parse(xsd_path)
yield lxml.etree.XMLSchema(xml_schema_doc)
@pytest.fixture
def vehicle_types():
vehicle_types_config = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'genet',
"configs", "vehicles", "vehicle_definitions.yml"))
return read_vehicle_types(vehicle_types_config)
def test_generates_valid_matsim_network_xml_file(network_object_from_test_data, network_dtd, tmpdir):
matsim_xml_writer.write_matsim_network(tmpdir, network_object_from_test_data)
generated_network_file_path = os.path.join(tmpdir, 'network.xml')
xml_obj = lxml.etree.parse(generated_network_file_path)
assert network_dtd.validate(xml_obj), \
'Doc generated at {} is not valid against DTD due to {}'.format(generated_network_file_path,
network_dtd.error_log.filter_from_errors())
def test_network_from_test_osm_data_produces_valid_matsim_network_xml_file(full_fat_default_config_path, network_dtd,
tmpdir):
osm_test_file = os.path.abspath(
os.path.join(os.path.dirname(__file__), "test_data", "osm", "osm.xml"))
network = read.read_osm(osm_test_file, full_fat_default_config_path, 1, 'epsg:27700')
network.write_to_matsim(tmpdir)
generated_network_file_path = os.path.join(tmpdir, 'network.xml')
xml_obj = lxml.etree.parse(generated_network_file_path)
assert network_dtd.validate(xml_obj), \
'Doc generated at {} is not valid against DTD due to {}'.format(generated_network_file_path,
network_dtd.error_log.filter_from_errors())
def test_network_with_extra_attribs_produces_valid_matsim_network_xml_file(tmpdir, network_dtd):
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
network.add_link('0', '0', '1', attribs={'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'extra_Special_attrib': 12})
network.write_to_matsim(tmpdir)
generated_network_file_path = os.path.join(tmpdir, 'network.xml')
xml_obj = lxml.etree.parse(generated_network_file_path)
assert network_dtd.validate(xml_obj), \
'Doc generated at {} is not valid against DTD due to {}'.format(generated_network_file_path,
network_dtd.error_log.filter_from_errors())
_network_from_file = read.read_matsim(path_to_network=generated_network_file_path, epsg='epsg:27700')
assert_semantically_equal(dict(_network_from_file.nodes()), {
'0': {'id': '0', 'x': 1.0, 'y': 2.0, 'lon': -7.557148039524952, 'lat': 49.766825803756994,
's2_id': 5205973754090365183},
'1': {'id': '1', 'x': 2.0, 'y': 2.0, 'lon': -7.557134218911724, 'lat': 49.766826468710484,
's2_id': 5205973754090480551}})
assert_semantically_equal(dict(_network_from_file.links()), {
'0': {'id': '0', 'from': '0', 'to': '1', 'freespeed': 1.0, 'capacity': 20.0, 'permlanes': 1.0, 'oneway': '1',
'modes': {'car'}, 's2_from': 5205973754090365183, 's2_to': 5205973754090480551, 'length': 1.0}})
def test_tolerates_networks_with_no_oneway_flag_on_links(tmpdir, network_dtd):
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
network.add_link('0', '0', '1', attribs={
'id': '0',
'from': '0', 'to': '1',
'length': 1,
'freespeed': 1,
'capacity': 20,
'permlanes': 1,
'modes': ['car']
})
network.write_to_matsim(tmpdir)
generated_network_file_path = os.path.join(tmpdir, 'network.xml')
xml_obj = lxml.etree.parse(generated_network_file_path)
assert network_dtd.validate(xml_obj), \
'Doc generated at {} is not valid against DTD due to {}'.format(generated_network_file_path,
network_dtd.error_log.filter_from_errors())
_network_from_file = read.read_matsim(path_to_network=generated_network_file_path, epsg='epsg:27700')
assert_semantically_equal(dict(_network_from_file.nodes()), {
'0': {'id': '0', 'x': 1.0, 'y': 2.0, 'lon': -7.557148039524952, 'lat': 49.766825803756994,
's2_id': 5205973754090365183},
'1': {'id': '1', 'x': 2.0, 'y': 2.0, 'lon': -7.557134218911724, 'lat': 49.766826468710484,
's2_id': 5205973754090480551}})
assert_semantically_equal(dict(_network_from_file.links()), {
'0': {
'id': '0',
'from': '0',
'to': '1',
'freespeed': 1.0,
'capacity': 20.0,
'permlanes': 1.0,
'modes': {'car'},
's2_from': 5205973754090365183,
's2_to': 5205973754090480551,
'length': 1.0
}
})
def test_network_with_attribs_doesnt_loose_any_attributes_after_saving(tmpdir):
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
network.add_link('0', '0', '1', attribs={'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'extra_Special_attrib': 12})
network.add_link('0', '0', '1', attribs={'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'attributes': {
'osm:way:lanes': {'name': 'osm:way:lanes',
'class': 'java.lang.String',
'text': '3'}}})
link_attributes = deepcopy(dict(network.links()))
node_attributes = deepcopy(dict(network.nodes()))
network.write_to_matsim(tmpdir)
link_attributes_post_save = dict(network.links())
node_attributes_post_save = dict(network.nodes())
assert_semantically_equal(link_attributes_post_save, link_attributes)
assert_semantically_equal(node_attributes_post_save, node_attributes)
def test_saving_network_with_geometry_doesnt_change_data_on_the_network(tmpdir):
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
network.add_link('0', '0', '1', attribs={'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1,2), (2,3), (3,4)]),
'extra_Special_attrib': 12})
network.add_link('0', '0', '1', attribs={'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1,2), (2,3), (3,4)]),
'attributes': {
'osm:way:lanes': {'name': 'osm:way:lanes',
'class': 'java.lang.String',
'text': '3'}}})
link_attributes = deepcopy(dict(network.links()))
node_attributes = deepcopy(dict(network.nodes()))
network.write_to_matsim(tmpdir)
link_attributes_post_save = dict(network.links())
node_attributes_post_save = dict(network.nodes())
assert_semantically_equal(link_attributes_post_save, link_attributes)
assert_semantically_equal(node_attributes_post_save, node_attributes)
def test_saving_network_with_geometry_produces_correct_polyline_in_link_attributes(tmpdir):
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
network.add_link('0', '0', '1', attribs={'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1,2), (2,3), (3,4)]),
'extra_Special_attrib': 12})
network.write_to_matsim(tmpdir)
found_geometry_attrib = False
for event, elem in ET.iterparse(os.path.join(tmpdir, 'network.xml'), events=('start', 'end')):
if event == 'start':
if elem.tag == 'attribute':
if elem.attrib['name'] == 'geometry':
assert elem.text == '_ibE_seK_ibE_ibE_ibE_ibE'
found_geometry_attrib = True
assert found_geometry_attrib
def test_saving_network_with_wrongly_formatted_attributes_with_geometry(tmpdir):
# attributes are assumed to be a nested dictionary of very specific format. Due to the fact that user can
# do virtually anything to edge attributes, or due to calculation error, this may not be the case. If it's not
# of correct format, we don't expect it to get saved to the matsim network.xml
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
link_attribs = {'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1,2), (2,3), (3,4)]),
'attributes': {'heyo': 'whoop'}
}
network.add_link('0', '0', '1', attribs=link_attribs)
network.write_to_matsim(tmpdir)
assert_semantically_equal(dict(network.links()), {'0': link_attribs})
assert_semantically_equal(matsim_xml_writer.check_link_attributes(link_attribs),
{'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1, 2), (2, 3), (3, 4)])
}
)
found_geometry_attrib = False
for event, elem in ET.iterparse(os.path.join(tmpdir, 'network.xml'), events=('start', 'end')):
if event == 'start':
if elem.tag == 'attribute':
if elem.attrib['name'] == 'geometry':
assert elem.text == '_ibE_seK_ibE_ibE_ibE_ibE'
found_geometry_attrib = True
assert found_geometry_attrib
def test_saving_network_with_bonkers_attributes_with_geometry(tmpdir):
# attributes are assumed to be a nested dictionary of very specific format. Due to the fact that user can
# do virtually anything to edge attributes, or due to calculation error, this may not be the case. If it's not
# of correct format, we don't expect it to get saved to the matsim network.xml
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
link_attribs = {'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1,2), (2,3), (3,4)]),
'attributes': float('nan')
}
network.add_link('0', '0', '1', attribs=link_attribs)
network.write_to_matsim(tmpdir)
assert_semantically_equal(dict(network.links()), {'0': link_attribs})
assert_semantically_equal(matsim_xml_writer.check_link_attributes(link_attribs),
{'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1, 2), (2, 3), (3, 4)])
}
)
found_geometry_attrib = False
for event, elem in ET.iterparse(os.path.join(tmpdir, 'network.xml'), events=('start', 'end')):
if event == 'start':
if elem.tag == 'attribute':
if elem.attrib['name'] == 'geometry':
assert elem.text == '_ibE_seK_ibE_ibE_ibE_ibE'
found_geometry_attrib = True
assert found_geometry_attrib
def test_saving_network_with_correct_attributes_and_geometry(tmpdir):
# attributes are assumed to be a nested dictionary of very specific format. Due to the fact that user can
# do virtually anything to edge attributes, or due to calculation error, this may not be the case. If it's not
# of correct format, we don't expect it to get saved to the matsim network.xml
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
link_attribs = {'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1,2), (2,3), (3,4)]),
'attributes': {
'osm:way:lanes': {'name': 'osm:way:lanes',
'class': 'java.lang.String',
'text': '3'}
}
}
network.add_link('0', '0', '1', attribs=link_attribs)
network.write_to_matsim(tmpdir)
assert_semantically_equal(dict(network.links()), {'0': link_attribs})
assert_semantically_equal(matsim_xml_writer.check_link_attributes(link_attribs), link_attribs)
found_geometry_attrib = False
for event, elem in ET.iterparse(os.path.join(tmpdir, 'network.xml'), events=('start', 'end')):
if event == 'start':
if elem.tag == 'attribute':
if elem.attrib['name'] == 'geometry':
assert elem.text == '_ibE_seK_ibE_ibE_ibE_ibE'
found_geometry_attrib = True
assert found_geometry_attrib
def test_saving_network_with_geometry_produces_polyline_if_link_already_has_other_attributes(tmpdir):
network = Network('epsg:27700')
network.add_node('0', attribs={'id': '0', 'x': 1, 'y': 2, 'lat': 1, 'lon': 2})
network.add_node('1', attribs={'id': '1', 'x': 2, 'y': 2, 'lat': 2, 'lon': 2})
network.add_link('0', '0', '1', attribs={'id': '0', 'from': '0', 'to': '1', 'length': 1, 'freespeed': 1,
'capacity': 20, 'permlanes': 1, 'oneway': '1', 'modes': ['car'],
'geometry': LineString([(1,2), (2,3), (3,4)]),
'attributes': {
'osm:way:lanes': {'name': 'osm:way:lanes',
'class': 'java.lang.String',
'text': '3'}}})
network.write_to_matsim(tmpdir)
found_geometry_attrib = False
for event, elem in ET.iterparse(os.path.join(tmpdir, 'network.xml'), events=('start', 'end')):
if event == 'start':
if elem.tag == 'attribute':
if elem.attrib['name'] == 'geometry':
assert elem.text == '_ibE_seK_ibE_ibE_ibE_ibE'
found_geometry_attrib = True
assert found_geometry_attrib
def test_write_matsim_network_produces_semantically_equal_xml_to_input_matsim_xml(network_object_from_test_data,
tmpdir):
matsim_xml_writer.write_matsim_network(tmpdir, network_object_from_test_data)
xml_diff.assert_semantically_equal(os.path.join(tmpdir, 'network.xml'), pt2matsim_network_test_file)
def test_generates_valid_matsim_schedule_xml_file(network_object_from_test_data, schedule_dtd, tmpdir):
matsim_xml_writer.write_matsim_schedule(tmpdir, network_object_from_test_data.schedule)
generated_file_path = os.path.join(tmpdir, 'schedule.xml')
xml_obj = lxml.etree.parse(generated_file_path)
assert schedule_dtd.validate(xml_obj), \
'Doc generated at {} is not valid against DTD due to {} errors - first error {}' \
.format(generated_file_path,
len(schedule_dtd.error_log.filter_from_errors()),
schedule_dtd.error_log.filter_from_errors()[0])
def test_write_matsim_schedule_produces_semantically_equal_xml_to_input_matsim_xml(network_object_from_test_data,
tmpdir):
matsim_xml_writer.write_matsim_schedule(tmpdir, network_object_from_test_data.schedule)
xml_diff.assert_semantically_equal(os.path.join(tmpdir, 'schedule.xml'), pt2matsim_schedule_file)
def test_write_matsim_schedule_produces_semantically_equal_xml_to_input_matsim_xml_if_stops_need_to_reprojected(
network_object_from_test_data, tmpdir):
# we change all the stops in the one service and one route that exists in the test data
network_object_from_test_data.schedule.route('VJbd8660f05fe6f744e58a66ae12bd66acbca88b98').reproject('epsg:3035')
matsim_xml_writer.write_matsim_schedule(tmpdir, network_object_from_test_data.schedule)
xml_diff.assert_semantically_equal(os.path.join(tmpdir, 'schedule.xml'), pt2matsim_schedule_file)
def test_generates_valid_matsim_vehicles_xml_file(tmpdir, vehicles_xsd, vehicle_types):
vehicle_dict = {
'veh_1': {'type': 'bus'},
'veh_2': {'type': 'bus'},
'veh_3': {'type': 'bus'},
'veh_4': {'type': 'tram'},
'veh_5': {'type': 'rail'},
'veh_6': {'type': 'subway'}
}
matsim_xml_writer.write_vehicles(tmpdir, vehicle_dict, vehicle_types)
generated_file_path = os.path.join(tmpdir, 'vehicles.xml')
xml_obj = lxml.etree.parse(generated_file_path)
vehicles_xsd.assertValid(xml_obj)
def test_generates_matsim_vehicles_xml_file_containing_expected_vehicle_types(tmpdir, vehicle_types):
vehicle_dict = {
'veh_1': {'type': 'bus'},
'veh_2': {'type': 'bus'},
'veh_3': {'type': 'bus'},
'veh_4': {'type': 'tram'},
'veh_5': {'type': 'rail'},
'veh_6': {'type': 'subway'}
}
matsim_xml_writer.write_vehicles(tmpdir, vehicle_dict, vehicle_types)
generated_file_path = os.path.join(tmpdir, 'vehicles.xml')
xml_obj = lxml.etree.parse(generated_file_path)
vehicle_types = xml_obj.findall('{http://www.matsim.org/files/dtd}vehicleType')
expected_vehicle_types = {v['type'] for k,v in vehicle_dict.items()}
actual_vehicle_types = set()
for vehicle_type in vehicle_types:
actual_vehicle_types.add(vehicle_type.get('id'))
assert expected_vehicle_types == actual_vehicle_types
def test_generates_matsim_vehicles_xml_file_containing_expected_vehicles(tmpdir, vehicle_types):
vehicle_dict = {
'veh_1': {'type': 'bus'},
'veh_2': {'type': 'bus'},
'veh_3': {'type': 'bus'},
'veh_4': {'type': 'tram'},
'veh_5': {'type': 'rail'},
'veh_6': {'type': 'subway'}
}
matsim_xml_writer.write_vehicles(tmpdir, vehicle_dict, vehicle_types)
generated_file_path = os.path.join(tmpdir, 'vehicles.xml')
xml_obj = lxml.etree.parse(generated_file_path)
vehicles = xml_obj.findall('{http://www.matsim.org/files/dtd}vehicle')
assert len(vehicles) == len(vehicle_dict)
for vehicle in vehicles:
assert vehicle_dict[vehicle.get('id')]['type'] == vehicle.get('type')
def test_throws_exception_when_generating_vehicles_xml_from_unrecognised_vehicle_types(tmpdir, vehicle_types):
vehicle_dict = {
'veh_1': {'type': 'bus'},
'veh_4': {'type': 'tram'},
'veh_5': {'type': 'rocket ship'},
}
with pytest.raises(NotImplementedError) as e:
matsim_xml_writer.write_vehicles(tmpdir, vehicle_dict, vehicle_types)
assert 'No Vehicle Type info available for mode rocket ship' in str(e.value)
def test_write_matsim_vehicles_produces_semantically_equal_xml_to_input_matsim_xml(network_object_from_test_data,
tmpdir):
network = network_object_from_test_data
matsim_xml_writer.write_matsim_schedule(tmpdir, network.schedule)
matsim_xml_writer.write_vehicles(tmpdir, network.schedule.vehicles, network.schedule.vehicle_types)
xml_diff.assert_semantically_equal(os.path.join(tmpdir, 'vehicles.xml'), pt2matsim_vehicles_file)
| 51.634199 | 117 | 0.57686 | 2,892 | 23,855 | 4.48029 | 0.090595 | 0.020375 | 0.020066 | 0.020993 | 0.856062 | 0.838466 | 0.815003 | 0.799799 | 0.796172 | 0.770472 | 0 | 0.045749 | 0.276127 | 23,855 | 461 | 118 | 51.746204 | 0.704598 | 0.040034 | 0 | 0.671309 | 0 | 0 | 0.143693 | 0.010223 | 0 | 0 | 0 | 0 | 0.108635 | 1 | 0.066852 | false | 0 | 0.033426 | 0 | 0.103064 | 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 |
7832eca570081a73826d0d22a031b56d9580e22d | 96 | py | Python | venv/lib/python3.8/site-packages/poetry/console/commands/command.py | Retraces/UkraineBot | 3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/poetry/console/commands/command.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/poetry/console/commands/command.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/56/84/72/17e2777b4dde572c90f35acc44886554c20a643ee1fa9fd8f6eed92f51 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.427083 | 0 | 96 | 1 | 96 | 96 | 0.46875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
785960acbbf4bbe39ad3a68b7bb79d5725079424 | 33 | py | Python | src/ltkit/panel/client/__init__.py | ptr-yudai/ltkit | a0f82712c7391a2ed2d06d2a80be982256cae5fa | [
"MIT"
] | 1 | 2016-05-05T17:05:54.000Z | 2016-05-05T17:05:54.000Z | src/ltkit/panel/server/__init__.py | ptr-yudai/ltkit | a0f82712c7391a2ed2d06d2a80be982256cae5fa | [
"MIT"
] | 1 | 2016-05-05T17:31:35.000Z | 2016-05-06T08:37:32.000Z | src/ltkit/panel/server/__init__.py | ptr-yudai/ltkit | a0f82712c7391a2ed2d06d2a80be982256cae5fa | [
"MIT"
] | null | null | null | import post
import questionnaire
| 11 | 20 | 0.878788 | 4 | 33 | 7.25 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 2 | 21 | 16.5 | 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 |
78afcbe24d632ccfd031ae7b5af3bf52effecd49 | 85,344 | py | Python | peoples_advisor/api/oanda/oanda_api.py | Wumphlett/Peoples-Advisor | a965a7547a546a48656832975bbb45c0c6e44f78 | [
"MIT"
] | null | null | null | peoples_advisor/api/oanda/oanda_api.py | Wumphlett/Peoples-Advisor | a965a7547a546a48656832975bbb45c0c6e44f78 | [
"MIT"
] | null | null | null | peoples_advisor/api/oanda/oanda_api.py | Wumphlett/Peoples-Advisor | a965a7547a546a48656832975bbb45c0c6e44f78 | [
"MIT"
] | null | null | null | import abc
import json
from datetime import datetime
from typing import List, Optional, Union
import requests
api_version = "v3"
practice_url = "https://api-fxpractice.oanda.com"
live_url = "https://api-fxtrade.oanda.com"
practice_stream_url = "https://stream-fxpractice.oanda.com"
live_stream_url = "https://stream-fxtrade.oanda.com"
def _conditional_update(store_dict, condition, key, value):
if condition is not None and (condition is not bool or condition):
store_dict.update({key: value})
class OandaError(Exception):
def __init__(self, message="Null Message"):
super().__init__(message)
class ClientExtensions:
"""
Define client extensions for a given operation (DO NOT INTERACT WITH IF YOUR ACCOUNT IS ASSOCIATED WITH MT4)
"""
def __init__(self, client_id: str, client_tag: str, client_comment: str):
"""
Create client extensions
Args:
client_id (str): A client specified id string
client_tag (str): A client specified tag string
client_comment (str): A client specified comment
"""
self.id = client_id
self.tag = client_tag
self.comment = client_comment
def as_dict(self):
return {"id": self.id, "tag": self.tag, "comment": self.comment}
class TakeProfitDetails:
"""
Define the details of a take profit order to be created
"""
def __init__(
self,
price: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Create take profit details
Args:
price (float): The price that the take profit order will be triggered at
see PriceValue in oanda_guide.txt
time_in_force (str, optional): The time in force for the created take profit order
NOTE: May only be 'GTC', 'GTD', or 'GFD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the take profit order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
client_extensions (ClientExtensions, optional): The client extensions to add to the take profit order
"""
self.price = price
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.client_extensions = client_extensions.as_dict() if client_extensions is not None else None
def as_dict(self):
tpd_dict = {"price": str(self.price), "timeInForce": self.time_in_force}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(tpd_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
tpd_dict.update(self.client_extensions if self.client_extensions else {})
return tpd_dict
class StopLossDetails:
"""
Define the details of a stop loss order to be created
"""
def __init__(
self,
price: float = None,
distance: float = None,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Create stop loss details
Args:
price (float): The price that the take profit order will be triggered at
NOTE: Only price or distance may be specified
see PriceValue in oanda_guide.txt
distance (float): The distance (in price units) from the trade's open price to use as the stop loss
order price
NOTE: Only price or distance may be specified
time_in_force (str, optional): The time in force for the created take profit order
NOTE: May only be 'GTC', 'GTD', or 'GFD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the take profit order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
client_extensions (ClientExtensions, optional): The client extensions to add to the take profit order
"""
if (price is None and distance is None) or (price and distance):
raise OandaError("Only price or distance may be specified")
self.price = price
self.distance = distance
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.client_extensions = client_extensions.as_dict() if client_extensions is not None else None
def as_dict(self):
sl_dict = {"timeInForce": self.time_in_force}
sl_dict.update({"price": str(self.price)} if self.price else {})
sl_dict.update({"distance": str(self.distance)} if self.distance else {})
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(sl_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
sl_dict.update(self.client_extensions if self.client_extensions else {})
return sl_dict
class GuaranteedStopLossDetails:
"""
Define the details of a guaranteed stop loss order to be created
"""
def __init__(
self,
price: float = None,
distance: float = None,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Create stop loss details
Args:
price (float): The price that the take profit order will be triggered at
NOTE: Only price or distance may be specified
see PriceValue in oanda_guide.txt
distance (float): The distance (in price units) from the trade's open price to use as the stop loss
order price
NOTE: Only price or distance may be specified
time_in_force (str, optional): The time in force for the created take profit order
NOTE: May only be 'GTC', 'GTD', or 'GFD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the take profit order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
client_extensions (ClientExtensions, optional): The client extensions to add to the take profit order
"""
if (price is None and distance is None) or (price and distance):
raise OandaError("Only price or distance may be specified")
self.price = price
self.distance = distance
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.client_extensions = client_extensions.as_dict() if client_extensions is not None else None
def as_dict(self):
gsl_dict = {"timeInForce": self.time_in_force}
gsl_dict.update({"price": str(self.price)} if self.price else {})
gsl_dict.update({"distance": str(self.distance)} if self.distance else {})
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(gsl_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
gsl_dict.update(self.client_extensions if self.client_extensions else {})
return gsl_dict
class TrailingStopLossDetails:
"""
Define the details of a stop loss order to be created
"""
def __init__(
self,
distance: float = None,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Create stop loss details
Args:
distance (float): The distance (in price units) from the trade's open price to use as the stop loss
order price
time_in_force (str, optional): The time in force for the created take profit order
NOTE: May only be 'GTC', 'GTD', or 'GFD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the take profit order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
client_extensions (ClientExtensions, optional): The client extensions to add to the take profit order
"""
self.distance = distance
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.client_extensions = client_extensions.as_dict() if client_extensions is not None else None
def as_dict(self):
tsl_dict = {"timeInForce": self.time_in_force}
tsl_dict.update({"distance": str(self.distance)} if self.distance else {})
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(tsl_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
tsl_dict.update(self.client_extensions if self.client_extensions else {})
return tsl_dict
class OrderRequest:
__metaclass__ = abc.ABCMeta
def __init__(self, order_type: str):
self.type = order_type
@abc.abstractmethod
def as_dict(self):
return
class MarketOrderRequest(OrderRequest):
def __init__(
self,
instrument: str,
units: float,
time_in_force: Optional[str] = "FOK",
position_fill: Optional[str] = "DEFAULT",
price_floor: Optional[float] = None,
take_profit_on_fill: Optional[TakeProfitDetails] = None,
stop_loss_on_fill: Optional[StopLossDetails] = None,
guaranteed_stop_loss_on_fill: Optional[GuaranteedStopLossDetails] = None,
trailing_stop_loss_on_fill: Optional[TrailingStopLossDetails] = None,
client_extensions: Optional[ClientExtensions] = None,
trade_client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a market order request
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
units (float): The quantity requested to be filled by the market order
NOTE: A positive number creates a long order, negative number creates a short order
time_in_force (str, optional): The time in force for the requested market order
NOTE: May only be 'FOK', 'IOC'
see TimeInForce in oanda_guide.txt
position_fill (str, optional): Specify how positions in the account are modified when the order is filled
see OrderPositionFill in oanda_guide.txt
price_floor (float, optional): The worst price you're willing to have the market order filled at
see PriceValue in oanda_guide.txt
take_profit_on_fill (TakeProfitDetails, optional): Specify the details of a take profit order to be created
This can happen when a filled order opens a trade requiring a take profit, or when a trade's dependent
take profit order is modified directly through the trade
stop_loss_on_fill (StopLossDetails, optional): Specify the details of a stop loss order to be created
This can happen when a filled order opens a trade requiring a stop loss, or when a trade's dependent
stop loss order is modified directly through the trade
guaranteed_stop_loss_on_fill (GuaranteedStopLossDetails, optional): Specify the details of a guaranteed
stop loss order to be created
This can happen when a filled order opens a trade requiring a guaranteed stop loss, or when a trade's
dependent guaranteed stop loss order is modified directly through the trade
trailing_stop_loss_on_fill (TrailingStopLossDetails, optional): Specify the details of a trailing stop
loss order to be created
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
client_extensions (ClientExtensions, optional): The client extensions to add to the market order
trade_client_extensions (ClientExtensions, optional): The client extensions to add to the trade created
when the order is filled
"""
super().__init__("MARKET")
self.instrument = instrument
self.units = units
self.time_in_force = time_in_force
self.position_fill = position_fill
self.price_floor = price_floor
self.take_profit_on_fill = take_profit_on_fill
self.stop_loss_on_fill = stop_loss_on_fill
self.guaranteed_stop_loss_on_fill = guaranteed_stop_loss_on_fill
self.trailing_stop_loss_on_fill = trailing_stop_loss_on_fill
self.client_extensions = client_extensions
self.trade_client_extensions = trade_client_extensions
def as_dict(self):
mor_dict = {
"type": self.type,
"instrument": self.instrument,
"units": str(self.units),
"timeInForce": self.time_in_force,
"positionFill": self.position_fill,
}
_conditional_update(mor_dict, self.price_floor, "priceBound", str(self.price_floor))
_conditional_update(
mor_dict,
self.take_profit_on_fill,
"takeProfitOnFill",
self.take_profit_on_fill.as_dict(),
)
_conditional_update(
mor_dict,
self.stop_loss_on_fill,
"stopLossOnFill",
self.stop_loss_on_fill.as_dict(),
)
_conditional_update(
mor_dict,
self.guaranteed_stop_loss_on_fill,
"guaranteedStopLossOnFill",
self.guaranteed_stop_loss_on_fill.as_dict(),
)
_conditional_update(
mor_dict,
self.trailing_stop_loss_on_fill,
"trailingStopLossOnFill",
self.trailing_stop_loss_on_fill.as_dict(),
)
_conditional_update(
mor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
_conditional_update(
mor_dict,
self.trade_client_extensions,
"tradeClientExtensions",
self.trade_client_extensions.as_dict(),
)
return mor_dict
class LimitOrderRequest(OrderRequest):
def __init__(
self,
instrument: str,
units: float,
price: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
position_fill: Optional[str] = "DEFAULT",
trigger_condition: Optional[str] = "DEFAULT",
take_profit_on_fill: Optional[TakeProfitDetails] = None,
stop_loss_on_fill: Optional[StopLossDetails] = None,
guaranteed_stop_loss_on_fill: Optional[GuaranteedStopLossDetails] = None,
trailing_stop_loss_on_fill: Optional[TrailingStopLossDetails] = None,
client_extensions: Optional[ClientExtensions] = None,
trade_client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a limit order request
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
units (float): The quantity requested to be filled by the limit order
NOTE: A positive number creates a long order, negative number creates a short order
price (float): The price threshold for the limit order (the order will only be filled by a market price
equal to or greater than this price)
time_in_force (str, optional): The time in force for the requested limit order
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the limit order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
position_fill (str, optional): Specify how positions in the account are modified when the order is filled
see OrderPositionFill in oanda_guide.txt
trigger_condition (str, optional): Specify which price component should be used when determining if an
order should be triggered and filled
see OrderTriggerCondition in oanda_guide.txt
take_profit_on_fill (TakeProfitDetails, optional): Specify the details of a take profit order to be created
This can happen when a filled order opens a trade requiring a take profit, or when a trade's dependent
take profit order is modified directly through the trade
stop_loss_on_fill (StopLossDetails, optional): Specify the details of a stop loss order to be created
This can happen when a filled order opens a trade requiring a stop loss, or when a trade's dependent
stop loss order is modified directly through the trade
guaranteed_stop_loss_on_fill (GuaranteedStopLossDetails, optional): Specify the details of a guaranteed
stop loss order to be created
This can happen when a filled order opens a trade requiring a guaranteed stop loss, or when a trade's
dependent guaranteed stop loss order is modified directly through the trade
trailing_stop_loss_on_fill (TrailingStopLossDetails, optional): Specify the details of a trailing stop
loss order to be created
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
client_extensions (ClientExtensions, optional): The client extensions to add to the limit order
trade_client_extensions (ClientExtensions, optional): The client extensions to add to the trade created
when the order is filled
"""
super().__init__("LIMIT")
self.instrument = instrument
self.units = units
self.price = price
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.position_fill = position_fill
self.trigger_condition = trigger_condition
self.take_profit_on_fill = take_profit_on_fill
self.stop_loss_on_fill = stop_loss_on_fill
self.guaranteed_stop_loss_on_fill = guaranteed_stop_loss_on_fill
self.trailing_stop_loss_on_fill = trailing_stop_loss_on_fill
self.client_extensions = client_extensions
self.trade_client_extensions = trade_client_extensions
def as_dict(self):
lor_dict = {
"type": self.type,
"instrument": self.instrument,
"units": str(self.units),
"price": str(self.price),
"timeInForce": self.time_in_force,
"positionFill": self.position_fill,
"triggerCondition": self.trigger_condition,
}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(lor_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
_conditional_update(
lor_dict,
self.take_profit_on_fill,
"takeProfitOnFill",
self.take_profit_on_fill.as_dict(),
)
_conditional_update(
lor_dict,
self.stop_loss_on_fill,
"stopLossOnFill",
self.stop_loss_on_fill.as_dict(),
)
_conditional_update(
lor_dict,
self.guaranteed_stop_loss_on_fill,
"guaranteedStopLossOnFill",
self.guaranteed_stop_loss_on_fill.as_dict(),
)
_conditional_update(
lor_dict,
self.trailing_stop_loss_on_fill,
"trailingStopLossOnFill",
self.trailing_stop_loss_on_fill.as_dict(),
)
_conditional_update(
lor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
_conditional_update(
lor_dict,
self.trade_client_extensions,
"tradeClientExtensions",
self.trade_client_extensions.as_dict(),
)
return lor_dict
class StopOrderRequest(OrderRequest):
def __init__(
self,
instrument: str,
units: float,
price: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
position_fill: Optional[str] = "DEFAULT",
trigger_condition: Optional[str] = "DEFAULT",
price_floor: Optional[float] = None,
take_profit_on_fill: Optional[TakeProfitDetails] = None,
stop_loss_on_fill: Optional[StopLossDetails] = None,
guaranteed_stop_loss_on_fill: Optional[GuaranteedStopLossDetails] = None,
trailing_stop_loss_on_fill: Optional[TrailingStopLossDetails] = None,
client_extensions: Optional[ClientExtensions] = None,
trade_client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a stop order request
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
units (float): The quantity requested to be filled by the stop order
NOTE: A positive number creates a long order, negative number creates a short order
price (float): The price threshold for the stop order (the order will only be filled by a market price
equal to or greater than this price)
time_in_force (str, optional): The time in force for the requested stop order
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the stop order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
position_fill (str, optional): Specify how positions in the account are modified when the order is filled
see OrderPositionFill in oanda_guide.txt
trigger_condition (str, optional): Specify which price component should be used when determining if an
order should be triggered and filled
see OrderTriggerCondition in oanda_guide.txt
price_floor (float, optional): The worst price you're willing to have the stop order filled at
see PriceValue in oanda_guide.txt
take_profit_on_fill (TakeProfitDetails, optional): Specify the details of a take profit order to be created
This can happen when a filled order opens a trade requiring a take profit, or when a trade's dependent
take profit order is modified directly through the trade
stop_loss_on_fill (StopLossDetails, optional): Specify the details of a stop loss order to be created
This can happen when a filled order opens a trade requiring a stop loss, or when a trade's dependent
stop loss order is modified directly through the trade
guaranteed_stop_loss_on_fill (GuaranteedStopLossDetails, optional): Specify the details of a guaranteed
stop loss order to be created
This can happen when a filled order opens a trade requiring a guaranteed stop loss, or when a trade's
dependent guaranteed stop loss order is modified directly through the trade
trailing_stop_loss_on_fill (TrailingStopLossDetails, optional): Specify the details of a trailing stop
loss order to be created
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
client_extensions (ClientExtensions, optional): The client extensions to add to the stop order
trade_client_extensions (ClientExtensions, optional): The client extensions to add to the trade created
when the order is filled
"""
super().__init__("STOP")
self.instrument = instrument
self.units = units
self.price = price
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.position_fill = position_fill
self.trigger_condition = trigger_condition
self.price_floor = price_floor
self.take_profit_on_fill = take_profit_on_fill
self.stop_loss_on_fill = stop_loss_on_fill
self.guaranteed_stop_loss_on_fill = guaranteed_stop_loss_on_fill
self.trailing_stop_loss_on_fill = trailing_stop_loss_on_fill
self.client_extensions = client_extensions
self.trade_client_extensions = trade_client_extensions
def as_dict(self):
sor_dict = {
"type": self.type,
"instrument": self.instrument,
"units": str(self.units),
"price": str(self.price),
"timeInForce": self.time_in_force,
"positionFill": self.position_fill,
"triggerCondition": self.trigger_condition,
}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(sor_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
_conditional_update(sor_dict, self.price_floor, "priceBound", str(self.price_floor))
_conditional_update(
sor_dict,
self.take_profit_on_fill,
"takeProfitOnFill",
self.take_profit_on_fill.as_dict(),
)
_conditional_update(
sor_dict,
self.stop_loss_on_fill,
"stopLossOnFill",
self.stop_loss_on_fill.as_dict(),
)
_conditional_update(
sor_dict,
self.guaranteed_stop_loss_on_fill,
"guaranteedStopLossOnFill",
self.guaranteed_stop_loss_on_fill.as_dict(),
)
_conditional_update(
sor_dict,
self.trailing_stop_loss_on_fill,
"trailingStopLossOnFill",
self.trailing_stop_loss_on_fill.as_dict(),
)
_conditional_update(
sor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
_conditional_update(
sor_dict,
self.trade_client_extensions,
"tradeClientExtensions",
self.trade_client_extensions.as_dict(),
)
return sor_dict
class MarketIfTouchedOrderRequest(OrderRequest):
def __init__(
self,
instrument: str,
units: float,
price: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
position_fill: Optional[str] = "DEFAULT",
trigger_condition: Optional[str] = "DEFAULT",
price_floor: Optional[float] = None,
take_profit_on_fill: Optional[TakeProfitDetails] = None,
stop_loss_on_fill: Optional[StopLossDetails] = None,
guaranteed_stop_loss_on_fill: Optional[GuaranteedStopLossDetails] = None,
trailing_stop_loss_on_fill: Optional[TrailingStopLossDetails] = None,
client_extensions: Optional[ClientExtensions] = None,
trade_client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a market if touched order request
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
units (float): The quantity requested to be filled by the market if touched order
NOTE: A positive number creates a long order, negative number creates a short order
price (float): The price threshold for the market if touched order (the order will only be filled by a
market price equal to or greater than this price)
time_in_force (str, optional): The time in force for the requested market if touched order
NOTE: May only be 'GTC', 'GFD', 'GTD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the market if touched order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
position_fill (str, optional): Specify how positions in the account are modified when the order is filled
see OrderPositionFill in oanda_guide.txt
trigger_condition (str, optional): Specify which price component should be used when determining if an
order should be triggered and filled
see OrderTriggerCondition in oanda_guide.txt
price_floor (float, optional): The worst price you're willing to have the market if touched order filled at
see PriceValue in oanda_guide.txt
take_profit_on_fill (TakeProfitDetails, optional): Specify the details of a take profit order to be created
This can happen when a filled order opens a trade requiring a take profit, or when a trade's dependent
take profit order is modified directly through the trade
stop_loss_on_fill (StopLossDetails, optional): Specify the details of a stop loss order to be created
This can happen when a filled order opens a trade requiring a stop loss, or when a trade's dependent
stop loss order is modified directly through the trade
guaranteed_stop_loss_on_fill (GuaranteedStopLossDetails, optional): Specify the details of a guaranteed
stop loss order to be created
This can happen when a filled order opens a trade requiring a guaranteed stop loss, or when a trade's
dependent guaranteed stop loss order is modified directly through the trade
trailing_stop_loss_on_fill (TrailingStopLossDetails, optional): Specify the details of a trailing stop
loss order to be created
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
client_extensions (ClientExtensions, optional): The client extensions to add to the market if touched order
trade_client_extensions (ClientExtensions, optional): The client extensions to add to the trade created
when the order is filled
"""
super().__init__("MARKET_IF_TOUCHED")
self.instrument = instrument
self.units = units
self.price = price
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.position_fill = position_fill
self.trigger_condition = trigger_condition
self.price_floor = price_floor
self.take_profit_on_fill = take_profit_on_fill
self.stop_loss_on_fill = stop_loss_on_fill
self.guaranteed_stop_loss_on_fill = guaranteed_stop_loss_on_fill
self.trailing_stop_loss_on_fill = trailing_stop_loss_on_fill
self.client_extensions = client_extensions
self.trade_client_extensions = trade_client_extensions
def as_dict(self):
motor_dict = {
"type": self.type,
"instrument": self.instrument,
"units": str(self.units),
"price": str(self.price),
"timeInForce": self.time_in_force,
"positionFill": self.position_fill,
"triggerCondition": self.trigger_condition,
}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(motor_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
_conditional_update(motor_dict, self.price_floor, "priceBound", str(self.price_floor))
_conditional_update(
motor_dict,
self.take_profit_on_fill,
"takeProfitOnFill",
self.take_profit_on_fill.as_dict(),
)
_conditional_update(
motor_dict,
self.stop_loss_on_fill,
"stopLossOnFill",
self.stop_loss_on_fill.as_dict(),
)
_conditional_update(
motor_dict,
self.guaranteed_stop_loss_on_fill,
"guaranteedStopLossOnFill",
self.guaranteed_stop_loss_on_fill.as_dict(),
)
_conditional_update(
motor_dict,
self.trailing_stop_loss_on_fill,
"trailingStopLossOnFill",
self.trailing_stop_loss_on_fill.as_dict(),
)
_conditional_update(
motor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
_conditional_update(
motor_dict,
self.trade_client_extensions,
"tradeClientExtensions",
self.trade_client_extensions.as_dict(),
)
return motor_dict
class TakeProfitOrderRequest(OrderRequest):
def __init__(
self,
trade_id: int,
price: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
trigger_condition: Optional[str] = "DEFAULT",
client_trade_id: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a take profit order request
Args:
trade_id (int): The id of the trade to close when the price threshold is breached
price (float): The price threshold for the take profit order (the order will only be filled by a market
price equal to or greater than this price)
time_in_force (str, optional): The time in force for the requested take profit order
NOTE: May only be 'GTC', 'GFD', 'GTD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the take profit order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
trigger_condition (str, optional): Specify which price component should be used when determining if an
order should be triggered and filled
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
see OrderTriggerCondition in oanda_guide.txt
client_trade_id (str, optional): The client trade id of the order to close when the price
threshold is reached
client_extensions (ClientExtensions, optional): The client extensions to add to the take profit order
"""
super().__init__("TAKE_PROFIT")
self.trade_id = trade_id
self.price = price
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.trigger_condition = trigger_condition
self.client_trade_id = client_trade_id
self.client_extensions = client_extensions
def as_dict(self):
tpor_dict = {
"type": self.type,
"tradeID": self.trade_id,
"price": str(self.price),
"timeInForce": self.time_in_force,
"triggerCondition": self.trigger_condition,
}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
_conditional_update(tpor_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
_conditional_update(tpor_dict, self.client_trade_id, "clientTradeID", self.client_trade_id)
_conditional_update(
tpor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
return tpor_dict
class StopLossOrderRequest(OrderRequest):
def __init__(
self,
trade_id: int,
price: float,
distance: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
trigger_condition: Optional[str] = "DEFAULT",
client_trade_id: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a stop loss order request
Args:
trade_id (int): The id of the trade to close when the price threshold is breached
price (float): The price threshold for the stop loss order (the order will only be filled by a market price
equal to or greater than this price)
NOTE: Only price or distance may be specified
distance (float): The distance (in price units) from the trade's open price to use as the stop loss
order price
NOTE: Only price or distance may be specified
NOTE: If the trade is short, the instrument's bid price is used, if long, the ask is used
time_in_force (str, optional): The time in force for the requested stop loss order
NOTE: May only be 'GTC', 'GFD', 'GTD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the stop loss order will be canceled on if time_in_force is 'GTD'
see DateTime in oanda_guide.txt
trigger_condition (str, optional): Specify which price component should be used when determining if an
order should be triggered and filled
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
see OrderTriggerCondition in oanda_guide.txt
client_trade_id (str, optional): The client trade id of the order to close when the price
threshold is reached
client_extensions (ClientExtensions, optional): The client extensions to add to the stop loss order
"""
super().__init__("STOP_LOSS")
if (price is None and distance is None) or (price and distance):
raise OandaError("Only price or distance may be specified")
self.trade_id = trade_id
self.price = price
self.distance = distance
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.trigger_condition = trigger_condition
self.client_trade_id = client_trade_id
self.client_extensions = client_extensions
def as_dict(self):
slor_dict = {
"type": self.type,
"tradeID": self.trade_id,
"timeInForce": self.time_in_force,
"triggerCondition": self.trigger_condition,
}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
slor_dict.update({"price": str(self.price)} if self.price else {})
slor_dict.update({"distance": str(self.distance)} if self.distance else {})
_conditional_update(slor_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
_conditional_update(slor_dict, self.client_trade_id, "clientTradeID", self.client_trade_id)
_conditional_update(
slor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
return slor_dict
class GuaranteedStopLossOrderRequest(OrderRequest):
def __init__(
self,
trade_id: int,
price: float,
distance: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
trigger_condition: Optional[str] = "DEFAULT",
client_trade_id: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a guaranteed stop loss order request
Args:
trade_id (int): The id of the trade to close when the price threshold is breached
price (float): The price threshold for the guaranteed stop loss order (the order will only be
filled by a market price equal to or greater than this price)
NOTE: Only price or distance may be specified
distance (float): The distance (in price units) from the trade's open price to use as the stop loss
order price
NOTE: Only price or distance may be specified
NOTE: If the trade is short, the instrument's bid price is used, if long, the ask is used
time_in_force (str, optional): The time in force for the requested guaranteed stop loss order
NOTE: May only be 'GTC', 'GFD', 'GTD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the guaranteed stop loss order will be canceled on if
time_in_force is 'GTD'
see DateTime in oanda_guide.txt
trigger_condition (str, optional): Specify which price component should be used when determining if an
order should be triggered and filled
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
see OrderTriggerCondition in oanda_guide.txt
client_trade_id (str, optional): The client trade id of the order to close when the price
threshold is reached
client_extensions (ClientExtensions, optional): The client extensions to add to the
guaranteed stop loss order
"""
super().__init__("GUARANTEED_STOP_LOSS")
if (price is None and distance is None) or (price and distance):
raise OandaError("Only price or distance may be specified")
self.trade_id = trade_id
self.price = price
self.distance = distance
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.trigger_condition = trigger_condition
self.client_trade_id = client_trade_id
self.client_extensions = client_extensions
def as_dict(self):
gslor_dict = {
"type": self.type,
"tradeID": self.trade_id,
"timeInForce": self.time_in_force,
"triggerCondition": self.trigger_condition,
}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
gslor_dict.update({"price": str(self.price)} if self.price else {})
gslor_dict.update({"distance": str(self.distance)} if self.distance else {})
_conditional_update(gslor_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
_conditional_update(gslor_dict, self.client_trade_id, "clientTradeID", self.client_trade_id)
_conditional_update(
gslor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
return gslor_dict
class TrailingStopLossOrderRequest(OrderRequest):
def __init__(
self,
trade_id: int,
distance: float,
time_in_force: Optional[str] = "GTC",
gtd_time: Optional[str] = None,
trigger_condition: Optional[str] = "DEFAULT",
client_trade_id: Optional[str] = None,
client_extensions: Optional[ClientExtensions] = None,
):
"""
Define a trailing stop loss order request
Args:
trade_id (int): The id of the trade to close when the price threshold is breached
distance (float): The distance (in price units) from the trade's open price to use as the stop loss
order price
time_in_force (str, optional): The time in force for the requested trailing stop loss order
NOTE: May only be 'GTC', 'GFD', 'GTD'
see TimeInForce in oanda_guide.txt
gtd_time (str, optional): The date the trailing stop loss order will be canceled on if
time_in_force is 'GTD'
see DateTime in oanda_guide.txt
trigger_condition (str, optional): Specify which price component should be used when determining if an
order should be triggered and filled
This can happen when a filled order opens a trade requiring a trailing stop loss, or when a trade's
dependent trailing stop loss order is modified directly through the trade
see OrderTriggerCondition in oanda_guide.txt
client_trade_id (str, optional): The client trade id of the order to close when the price
threshold is reached
client_extensions (ClientExtensions, optional): The client extensions to add to the trailing stop loss order
"""
super().__init__("TRAILING_STOP_LOSS")
self.trade_id = trade_id
self.distance = distance
self.time_in_force = time_in_force
self.gtd_time = gtd_time
self.trigger_condition = trigger_condition
self.client_trade_id = client_trade_id
self.client_extensions = client_extensions
def as_dict(self):
tslor_dict = {
"type": self.type,
"tradeID": self.trade_id,
"timeInForce": self.time_in_force,
"triggerCondition": self.trigger_condition,
}
if self.time_in_force == "GTD" and self.gtd_time is None:
raise OandaError("Invalid GTD time provided. If time_in_force is GTD, you must specify a proper GTD time")
tslor_dict.update({"distance": str(self.distance)} if self.distance else {})
_conditional_update(tslor_dict, self.time_in_force == "GTD", "gtdTime", self.gtd_time)
_conditional_update(tslor_dict, self.client_trade_id, "clientTradeID", self.client_trade_id)
_conditional_update(
tslor_dict,
self.client_extensions,
"clientExtensions",
self.client_extensions.as_dict(),
)
return tslor_dict
class OandaApi:
def __init__(
self,
auth: str,
live: bool = False,
account_index: Optional[int] = 0,
datetime_format: Optional[str] = "RFC3339",
):
"""
Initialize the API for a specific account under the given api token.
Args:
auth (str): The api authorization token
live (bool, optional): Whether the api should make calls on the live account or not
account_index (int, optional): The account index to use, should the api token govern multiple accounts
datetime_format (str, optional): The datetime format to use
see AcceptDatetimeFormat in oanda_guide.txt
"""
self.auth = auth
self.live = live
self.datetime_format = datetime_format
self.account_id = self.get_accounts()["accounts"][account_index]["id"]
def get_accounts(self) -> dict:
"""
Get a list of accounts for a given api token
"""
return self._oanda_api_call("get", "accounts")
def get_account_details(self) -> dict:
"""
Get account details for the account specified at API initialization
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}")
def get_account_summary(self) -> dict:
"""
Get a summary for the account associated with the API
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/summary")
def get_account_instruments(self, instruments: Optional[List[str]] = None) -> dict:
"""
Get a list of tradable instruments available for a given account
Args:
instruments (List[str], optional): A list of instruments
see InstrumentName in oanda_guide.txt
"""
params = {"instruments": ",".join(instruments)} if instruments else None
return self._oanda_api_call("get", f"accounts/{self.account_id}/instruments", params=params)
def get_account_changes(self, since_transaction: int) -> dict:
"""
Poll an account for its current state and changes since a given transaction id
Args:
since_transaction (int): ID of the transaction to get account changes since
see TransactionID in oanda_guide.txt
"""
params = {"sinceTransactionID": str(since_transaction)}
return self._oanda_api_call("get", f"accounts/{self.account_id}/changes", params=params)
def configure_account(self, alias: Optional[str] = None, margin_rate: Optional[float] = None) -> dict:
"""
Configure the alias and/or the margin rate for the account
Args:
alias (str, optional): Custom name to associate with the account
margin_rate (float, optional): Margin rate to change the account to
ex. A 50:1 margin rate would be represented as 0.02
"""
data = {}
data.update({"alias": alias} if alias else {})
data.update({"marginRate": str(margin_rate)} if margin_rate else {})
return self._oanda_api_call("patch", f"accounts/{self.account_id}/configuration", data=data)
def get_instrument_candles(
self,
instrument: str,
price: Optional[str] = None,
granularity: Optional[str] = None,
count: Optional[int] = None,
from_time: Optional[str] = None,
to_time: Optional[str] = None,
smooth: Optional[bool] = None,
include_first: Optional[bool] = None,
daily_align: Optional[int] = None,
timezone_align: Optional[str] = None,
weekly_align: Optional[str] = None,
units: Optional[float] = None,
) -> dict:
"""
Get candlestick data for an instrument
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
price (str, optional): The price component(s) ot get candlestick data for
default: 'M'
see PricingComponent in oanda_guide.txt
granularity (str, optional): The granularity of the candlesticks to fetch
default: 'S5'
see CandlestickGranularity in oanda_guide.txt
count (int, optional): The number of candlesticks to return
NOTE: count should not be specified if both the from and to time are specified
default: 500, max: 5000
from_time (str, optional): The start of the time range to fetch candlesticks for
see DateTime in oanda_guide.txt
to_time (str, optional): The end of the time range to fetch candlesticks for
see DateTime in oanda_guide.txt
smooth (bool, optional): A flag that controls whether the candlesticks are smoothed
default: False
include_first (bool, optional): A flag that controls whether the candlestick that is covered by the
from time is included in the results
default: True
daily_align (int, optional): The hour of the day (in the specified timezone) to use for granularities
that have daily alignments
min: 0, default: 17, max: 23
timezone_align (str, optional): The timezone to use for the daily_align parameter
timezones are specified in the form America/New_York
default: 'America/New_York'
weekly_align (str, optional): The day of the week used for granularities that have weekly alignment
default: 'Friday'
see WeeklyAlignment in oanda_guide.txt
units (float, optional): Number of units used to calculate the volume-weighted average bid and ask prices
"""
params = {}
params.update({"price": price} if price else {})
params.update({"granularity": granularity} if granularity else {})
params.update({"count": str(count)} if count else {})
params.update({"from": from_time} if from_time else {})
params.update({"to": to_time} if to_time else {})
params.update({"smooth": str(smooth)} if smooth else {})
params.update({"includeFirst": str(include_first)} if include_first else {})
params.update({"dailyAlignment": str(daily_align)} if daily_align else {})
params.update({"alignmentTimezone": timezone_align} if timezone_align else {})
params.update({"weeklyAlignment": weekly_align} if weekly_align else {})
params.update({"units": str(units)} if units else {})
return self._oanda_api_call(
"get",
f"accounts/{self.account_id}/instruments/{instrument}/candles",
params=params,
)
def get_instrument_candles_in_range(
self,
instrument: str,
from_time: str,
to_time: str,
price: Optional[str] = None,
granularity: Optional[str] = None,
smooth: Optional[bool] = None,
include_first: Optional[bool] = None,
daily_align: Optional[int] = None,
timezone_align: Optional[str] = None,
weekly_align: Optional[str] = None,
units: Optional[float] = None,
):
"""
Get candlestick data for an instrument within a given time range
NOTE: This is intended to be used when you need more than 5000 candlesticks for a given time range
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
from_time (str): The start of the time range to fetch candlesticks for
see DateTime in oanda_guide.txt
to_time (str): The end of the time range to fetch candlesticks for
see DateTime in oanda_guide.txt
price (str, optional): The price component(s) ot get candlestick data for
default: 'M'
see PricingComponent in oanda_guide.txt
granularity (str, optional): The granularity of the candlesticks to fetch
default: 'S5'
see CandlestickGranularity in oanda_guide.txt
smooth (bool, optional): A flag that controls whether the candlesticks are smoothed
default: False
include_first (bool, optional): A flag that controls whether the candlestick that is covered by the
from time is included in the results
default: True
daily_align (int, optional): The hour of the day (in the specified timezone) to use for granularities
that have daily alignments
min: 0, default: 17, max: 23
timezone_align (str, optional): The timezone to use for the daily_align parameter
timezones are specified in the form America/New_York
default: 'America/New_York'
weekly_align (str, optional): The day of the week used for granularities that have weekly alignment
default: 'Friday'
see WeeklyAlignment in oanda_guide.txt
units (float, optional): Number of units used to calculate the volume-weighted average bid and ask prices
"""
params = {"count": 5000}
params.update({"price": price} if price else {})
params.update({"granularity": granularity} if granularity else {})
params.update({"from": from_time} if from_time else {})
params.update({"smooth": str(smooth)} if smooth else {})
params.update({"includeFirst": str(include_first)} if include_first else {})
params.update({"dailyAlignment": str(daily_align)} if daily_align else {})
params.update({"alignmentTimezone": timezone_align} if timezone_align else {})
params.update({"weeklyAlignment": weekly_align} if weekly_align else {})
params.update({"units": str(units)} if units else {})
start = self.oanda_time_to_datetime(from_time)
end = self.oanda_time_to_datetime(to_time)
count = 5000
while start < end and count == 5000:
candles = self._oanda_api_call(
"get",
f"accounts/{self.account_id}/instruments/{instrument}/candles",
params=params,
)["candles"]
count = len(candles)
params.update({"from": candles[-1]["time"]})
start = self.oanda_time_to_datetime(candles[-1]["time"])
for candle in candles:
if self.oanda_time_to_datetime(candle["time"]) < end: # Strip Z and last 3 nanosecond digits
yield candle
else:
break
def get_instrument_order_book(self, instrument: str, time: Optional[str] = None) -> dict:
"""
Get an order book for an instrument
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
time (str, optional) The time of the snapshot to fetch
see DateTime in oanda_guide.txt
"""
params = {}
params.update({"time": time} if time else {})
return self._oanda_api_call("get", f"instruments/{instrument}/orderBook", params=params)
def get_instrument_position_book(self, instrument: str, time: Optional[str] = None) -> dict:
"""
Get a position book for an instrument
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
time (str, optional) The time of the snapshot to fetch
see DateTime in oanda_guide.txt
"""
params = {}
params.update({"time": time} if time else {})
return self._oanda_api_call("get", f"instruments/{instrument}/positionBook", params=params)
def get_orders(
self,
ids: Optional[List[int]] = None,
state: Optional[str] = None,
instrument: Optional[str] = None,
count: Optional[int] = None,
before_id: Optional[int] = None,
) -> dict:
"""
Get a list of orders for the account
Args:
ids (list, optional): List of order ids to retrieve
see OrderID in oanda_guide.txt
state (str, optional): The state to filter the requested orders by
see OrderStateFilter in oanda_guide.txt
instrument (str, optional): The instrument to filter the requested orders by
see InstrumentName in oanda_guide.txt
count (int, optional): The maximum number of orders to return
max: 500
before_id (int, optional): The maximum order id to return (if not provided, return the most recent orders)
see OrderId in oanda_guide.txt
"""
params = {}
params.update({"ids": ",".join([str(order_id) for order_id in ids])} if ids else {})
params.update({"state": state} if state else {})
params.update({"instrument": instrument} if instrument else {})
params.update({"count": str(count)} if count else {})
params.update({"beforeID": str(before_id)} if before_id else {})
return self._oanda_api_call("get", f"accounts/{self.account_id}/orders", params=params)
def get_pending_orders(self) -> dict:
"""
Get all pending orders in the account
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/pendingOrders")
def get_order_details(self, order_id: int) -> dict:
"""
Get details for a single order in the account
Args:
order_id (int): The id of the order to retrieve details for
see OrderID in oanda_guide.txt
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/orders/{str(order_id)}")
def create_order(self, order: OrderRequest) -> dict:
"""
Create an order for the account
Args:
order (OrderRequest): An OrderRequest representing the order you wish to create
NOTE: You may use any of the 8 available sub-classes of OrderRequest, but not OrderRequest itself
see OrderRequest in oanda_guide.txt
"""
return self._oanda_api_call("post", f"accounts/{self.account_id}/orders", data=order.as_dict())
def replace_order(self, order_id: int, order: OrderRequest) -> dict:
"""
Replace an order in the account by simultaneously cancelling it and creating a replacement order
Args:
order_id (int): The id of the order to cancel
see OrderID in oanda_guide.txt
order (OrderRequest): An OrderRequest representing the order you wish to create
NOTE: You may use any of the 8 available sub-classes of OrderRequest, but not OrderRequest itself
see OrderRequest in oanda_guide.txt
"""
return self._oanda_api_call(
"put",
f"accounts/{self.account_id}/orders/{str(order_id)}",
data=order.as_dict(),
)
def cancel_order(self, order_id: int) -> dict:
"""
Cancel an order for the account
Args:
order_id (int): The id of the order to cancel
see OrderID in oanda_guide.txt
"""
return self._oanda_api_call("put", f"accounts/{self.account_id}/orders/{str(order_id)}/cancel")
def update_order_client_extensions(
self,
order_id: int,
client_extensions: Optional[ClientExtensions] = None,
trade_client_extensions: Optional[ClientExtensions] = None,
) -> dict:
"""
Update client extensions for an order
Args:
order_id (int): The id of the order to update client extensions for
see OrderID in oanda_guide.txt
client_extensions (ClientExtensions, optional): The client extensions to update the order to
see ClientExtensions in oanda_guide.txt
trade_client_extensions (ClientExtensions, optional): The client extensions to update the trade to
see ClientExtensions in oanda_guide.txt
"""
data = {}
data.update({"clientExtensions": client_extensions.as_dict()} if client_extensions else {})
data.update({"tradeClientExtensions": trade_client_extensions.as_dict()} if trade_client_extensions else {})
return self._oanda_api_call(
"put",
f"accounts/{self.account_id}/orders/{str(order_id)}/clientExtensions",
data=data,
)
def get_trades(
self,
ids: Optional[List[int]] = None,
state: Optional[str] = None,
instrument: Optional[str] = None,
count: Optional[int] = None,
before_id: Optional[int] = None,
) -> dict:
"""
Get a list of trades for the account
Args:
ids (list, optional): List of trade ids to retrieve
see OrderID in oanda_guide.txt
state (str, optional): The state to filter the requested trades by
see OrderStateFilter in oanda_guide.txt
instrument (str, optional): The instrument to filter the requested orders by
see InstrumentName in oanda_guide.txt
count (int, optional): The maximum number of trades to return
max: 500
before_id (int, optional): The maximum trade id to return (if not provided, return the most recent trades)
see TradeId in oanda_guide.txt
"""
params = {}
params.update({"ids": ",".join([str(trade_id) for trade_id in ids])} if ids else {})
params.update({"state": state} if state else {})
params.update({"instrument": instrument} if instrument else {})
params.update({"count": str(count)} if count else {})
params.update({"beforeID": str(before_id)} if before_id else {})
return self._oanda_api_call("get", f"accounts/{self.account_id}/trades", params=params)
def get_open_trades(self) -> dict:
"""
Get a list of open trades for the account
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/openTrades")
def get_trade_details(self, trade_id: int) -> dict:
"""
Get details for a single trade in the account
Args:
trade_id (int): The id of the trade to retrieve details for
see TradeId in oanda_guide.txt
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/trades/{str(trade_id)}")
def close_trade(self, trade_id: int, units: Optional[float] = None) -> dict:
"""
Close (partially or fully) a specific open trade in the account
Args:
trade_id (int): The id of the trade to close
see TradeId in oanda_guide.txt
units (float, optional): The default behavior is to close the trade fully
If units are specified, then the trade will be closed the provided number of units
By default, this will close the trade fully if a number of units is not specified
NOTE: This number must be positive
"""
data = {"units": "ALL"} if units is None else {"units": str(units)}
return self._oanda_api_call("put", f"accounts/{self.account_id}/trades/{str(trade_id)}/close", data=data)
def modify_trade_dependent_orders(
self,
trade_id: int,
take_profit: Optional[Union[str, TakeProfitDetails]] = "NO_CHANGE",
stop_loss: Optional[Union[str, StopLossDetails]] = "NO_CHANGE",
trailing_stop_loss: Optional[Union[str, TrailingStopLossDetails]] = "NO_CHANGE",
guaranteed_stop_loss: Optional[Union[str, GuaranteedStopLossDetails]] = "NO_CHANGE",
) -> dict:
"""
Create, replace, and cancel a trade's dependent orders (take profit, stop loss, and trailing stop loss)
through the trade itself
Args:
trade_id (int): The id of the trade to modify the orders of
see TradeId in oanda_guide.txt
take_profit (str ['NO_CHANGE', 'CANCEL'], TakeProfitDetails, optional): If take_profit is set to 'NO_CHANGE'
the take profit, if it exists, will not be modified. If set to 'CANCEL', the take profit, if it exists,
will be canceled. If take_profit is supplied with TakeProfitDetails, then the take profit will update.
see TakeProfitDetails in oanda_guide.txt
stop_loss (str ['NO_CHANGE', 'CANCEL'], StopLossDetails, optional): If stop_loss is set to 'NO_CHANGE'
the stop loss, if it exists, will not be modified. If set to 'CANCEL', the stop loss, if it exists,
will be canceled. If stop_loss is supplied with StopLossDetails, then the stop loss will update.
see StopLossDetails in oanda_guide.txt
trailing_stop_loss (str ['NO_CHANGE', 'CANCEL'], TrailingStopLossDetails, optional): If trailing_stop_loss
is set to 'NO_CHANGE' the trailing stop loss, if it exists, will not be modified. If set to 'CANCEL',
the trailing stop loss, if it exists, will be canceled. If trailing_stop_loss is supplied with
TrailingStopLossDetails, then the trailing stop loss will update.
see TrailingStopLossDetails in oanda_guide.txt
guaranteed_stop_loss (str ['NO_CHANGE', 'CANCEL'], GuaranteedStopLossDetails, optional): If
guaranteed_stop_loss is set to 'NO_CHANGE' the guaranteed stop loss, if it exists, will not be modified.
If set to 'CANCEL', the guaranteed stop loss, if it exists, will be canceled. If guaranteed_stop_loss is
supplied with GuaranteedStopLossDetails, then the guaranteed stop loss will update.
see GuaranteedStopLossDetails in oanda_guide.txt
"""
data = {}
if type(take_profit) == str and take_profit != "NO_CHANGE":
data.update(
{"takeProfit": None}
if type(take_profit) == str and take_profit == "CANCEL"
else {"takeProfit": take_profit.as_dict()}
)
if type(stop_loss) == str and stop_loss != "NO_CHANGE":
data.update(
{"stopLoss": None}
if type(stop_loss) == str and stop_loss == "CANCEL"
else {"stopLoss": stop_loss.as_dict()}
)
if type(trailing_stop_loss) == str and trailing_stop_loss != "NO_CHANGE":
data.update(
{"trailingStopLoss": None}
if type(trailing_stop_loss) == str and trailing_stop_loss == "CANCEL"
else {"trailingStopLoss": trailing_stop_loss.as_dict()}
)
if type(guaranteed_stop_loss) == str and guaranteed_stop_loss != "NO_CHANGE":
data.update(
{"guaranteedStopLoss": None}
if type(guaranteed_stop_loss) == str and guaranteed_stop_loss == "CANCEL"
else {"guaranteedStopLoss": guaranteed_stop_loss.as_dict()}
)
return self._oanda_api_call(
"put",
f"accounts/{self.account_id}/trades/{str(trade_id)}/orders",
data=data,
)
def update_trade_client_extensions(
self, trade_id: int, client_extensions: Optional[ClientExtensions] = None
) -> dict:
"""
Update client extensions for a trade
Args:
trade_id (int): The id of the order to update client extensions for
see TradeId in oanda_guide.txt
client_extensions (ClientExtensions, optional): The client extensions to update the order to
see ClientExtensions in oanda_guide.txt
"""
data = {}
data.update({"clientExtensions": client_extensions.as_dict()} if client_extensions else {})
return self._oanda_api_call(
"put",
f"accounts/{self.account_id}/orders/{str(trade_id)}/clientExtensions",
data=data,
)
def get_positions(self) -> dict:
"""
Get a list of positions for the account
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/positions")
def get_open_positions(self) -> dict:
"""
Get a list of open positions for the account
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/openPositions")
def get_instrument_position(self, instrument: str) -> dict:
"""
Get the position of a given instrument for the account (Position may be open or closed)
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/positions/{instrument}")
def close_instrument_position(
self,
instrument: str,
long_units: Optional[Union[str, float]] = "ALL",
short_units: Optional[Union[str, float]] = "ALL",
long_client_extensions: Optional[ClientExtensions] = None,
short_client_extensions: Optional[ClientExtensions] = None,
) -> dict:
"""
Close a position for specific instrument, in whole or in part
Args:
instrument (str): Name of the instrument
see InstrumentName in oanda_guide.txt
long_units (str ['ALL', 'NONE'], float, optional): The amount of units of the long position to close
from 'NONE' to 'ALL' or some float value in-between
short_units (str ['ALL', 'NONE'], float, optional): The amount of units of the short position to close
from 'NONE' to 'ALL' or some float value in-between
long_client_extensions (ClientExtensions, optional): the client extensions to add to the market order
created to close the long position
short_client_extensions (ClientExtensions, optional): the client extensions to add to the market order
created to close the short position
"""
data = {}
if type(long_units) == str and long_units != "ALL":
data.update(
{"longUnits": "NONE"}
if type(long_units) == str and long_units == "NONE"
else {"longUnits": str(long_units)}
)
if type(short_units) == str and short_units != "ALL":
data.update(
{"shortUnits": "NONE"}
if type(short_units) == str and short_units == "NONE"
else {"shortUnits": str(short_units)}
)
data.update({"longClientExtensions": long_client_extensions.as_dict()} if long_client_extensions else {})
data.update({"shortClientExtensions": short_client_extensions.as_dict()} if short_client_extensions else {})
return self._oanda_api_call("put", f"accounts/{self.account_id}/positions/{instrument}/close", data=data)
def get_transactions(
self,
from_time: Optional[str] = None,
to_time: Optional[str] = None,
page_size: Optional[int] = None,
transaction_type: Optional[List[str]] = None,
) -> dict:
"""
Get a list of transactions given a set of time based parameters
Args:
from_time (str, optional): The start of the time range to fetch transaction history for
default: account creation
see DateTime in oanda_guide.txt
to_time (str, optional): The end of the time range to fetch transaction history for
default: current time
see DateTime in oanda_guide.txt
page_size (int, optional): The number of transactions to include in each page of the results
max: 1000
transaction_type (List[str], optional): Filters to apply to the transactions returned
see TransactionFilter in oanda_guide.txt
"""
params = {}
params.update({"from": from_time} if from_time else {})
params.update({"to": to_time} if to_time else {})
params.update({"pageSize": str(page_size)} if page_size else {})
params.update({"type": ",".join(transaction_type)} if transaction_type else {})
return self._oanda_api_call("get", f"accounts/{self.account_id}/transactions", params=params)
def get_transaction_details(self, transaction_id: id) -> dict:
"""
Get details for a single transaction in the account
Args:
transaction_id (int): The id of the transaction to retrieve details for
see TransactionID in oanda_guide.txt
"""
return self._oanda_api_call("get", f"accounts/{self.account_id}/transactions/{str(transaction_id)}")
def get_transactions_in_range(self, from_id: int, to_id: int, transaction_type: Optional[List[str]] = None) -> dict:
"""
Get a list of transactions given a range of transaction ids
Args:
from_id (int): The starting transaction id of the range
see TransactionID in oanda_guide.txt
to_id (int): The ending transaction id of the rage
see TransactionID in oanda_guide.txt
transaction_type (List[str], optional): Filters to apply to the transactions returned
see TransactionFilter in oanda_guide.txt
"""
params = {"from": str(from_id), "to": str(to_id)}
params.update({"type": ",".join(transaction_type)} if transaction_type else {})
return self._oanda_api_call("get", f"accounts/{self.account_id}/transactions/idrange", params=params)
def get_transactions_since_id(self, from_id: int, transaction_type: Optional[List[str]] = None) -> dict:
"""
Get a list of transactions since a given transaction id
Args:
from_id (int): The starting transaction id
see TransactionID in oanda_guide.txt
transaction_type (List[str], optional): Filters to apply to the transactions returned
see TransactionFilter in oanda_guide.txt
"""
params = {"id": str(from_id)}
params.update({"type": ",".join(transaction_type)} if transaction_type else {})
return self._oanda_api_call("get", f"accounts/{self.account_id}/transactions/sinceid", params=params)
def transaction_stream(self):
"""
Connect to the transaction stream
NOTE: This returns a generator
--- Usage ---
transaction_stream = API.transaction_stream()
for transaction in transaction_stream:
# Do something with transaction
print(transaction)
# Keep everything within the for loop
# It will produce new transactions as transactions are made
-------------
"""
stream = self._oanda_api_stream_call("get", f"accounts/{self.account_id}/transactions/stream")
with stream as stream:
for transaction in stream.iter_lines():
transaction = json.loads(transaction.decode("utf-8"))
yield transaction
def get_candles(
self,
candle_specs: List[str],
units: Optional[float] = None,
smooth: Optional[bool] = None,
daily_align: Optional[int] = None,
timezone_align: Optional[str] = None,
weekly_align: Optional[str] = None,
) -> dict:
"""
Get recently completed candles for a given combination of instruments/specs
Args:
candle_specs (List[str]): List of candle specifications to get pricing for
see CandleSpecification in oanda_guide.txt
units (float, optional): Number of units used to calculate the volume-weighted average bid and ask prices
smooth (bool, optional): A flag that controls whether the candlesticks are smoothed
default: False
daily_align (int, optional): The hour of the day (in the specified timezone) to use for granularities
that have daily alignments
min: 0, default: 17, max: 23
timezone_align (str, optional): The timezone to use for the daily_align parameter
timezones are specified in the form America/New_York
default: 'America/New_York'
weekly_align (str, optional): The day of the week used for granularities that have weekly alignment
default: 'Friday'
see WeeklyAlignment in oanda_guide.txt
"""
params = {"candleSpecifications": ",".join(candle_specs)}
params.update({"units": str(units)} if units else {})
params.update({"smooth": str(smooth)} if smooth else {})
params.update({"dailyAlignment": str(daily_align)} if daily_align else {})
params.update({"alignmentTimezone": timezone_align} if timezone_align else {})
params.update({"weeklyAlignment": weekly_align} if weekly_align else {})
return self._oanda_api_call("get", f"accounts/{self.account_id}/candles/latest", params=params)
def get_instrument_pricing(
self,
instruments: List[str],
since: Optional[str] = None,
convert: Optional[bool] = None,
) -> dict:
"""
Get pricing for a given list of instruments
Args:
instruments (List[str]): A list of instruments
see InstrumentName in oanda_guide.txt
since (str, optional): Only provide pricing info since the given datetime
see DateTime in oanda_guide.txt
convert (bool, optional): Include home conversions in the returned response
default: True
"""
params = {"instruments": ",".join(instruments)}
params.update({"since": since} if since else {})
params.update({"includeHomeConversion": str(convert)} if convert else {})
return self._oanda_api_call("get", f"accounts/{self.account_id}/pricing", params=params)
def pricing_stream(
self,
instruments: List[str],
snapshot: Optional[bool] = None,
convert: Optional[bool] = None,
):
"""
Connect to the pricing stream
NOTE: This returns a generator
Args:
instruments (List[str]): A list of instruments
see InstrumentName in oanda_guide.txt
snapshot (bool, optional): Flag that enables/disables the sending of a pricing snapshot on connection
default: True
convert (bool, optional): Include home conversions in the returned response
default: True
--- Usage ---
pricing_stream = API.pricing_stream(['EUR_USD', 'GBP_USD'])
for pricing in pricing_stream:
# Do something with transaction
print(pricing)
# Keep everything within the for loop
# It will produce new prices live
-------------
"""
params = {"instruments": ",".join(instruments)}
params.update({"snapshot": str(snapshot)} if snapshot else {})
params.update({"includeHomeConversion": str(convert)} if convert else {})
stream = self._oanda_api_stream_call("get", f"accounts/{self.account_id}/pricing/stream", params=params)
with stream as stream:
for price in stream.iter_lines():
price = json.loads(price.decode("utf-8"))
if price.get("type") and price.get("type") == "PRICE" and price.get("tradeable"):
price.pop("status")
yield price
def oanda_time_to_datetime(self, time_str: str):
if self.datetime_format == "RFC3339":
if time_str[-1] == "Z":
return datetime.fromisoformat(time_str[0:-4])
else:
return datetime.fromisoformat(time_str)
elif self.datetime_format == "UNIX":
return datetime.fromtimestamp(int(float(time_str)))
else:
raise OandaError("Improper datetime format. Must be 'RFC3339' or 'UNIX'")
def datetime_to_oanda_time(self, date: datetime):
if self.datetime_format == "RFC3339":
return date.isoformat("T") + "000Z"
elif self.datetime_format == "UNIX": # TODO TEST
return date.timestamp()
else:
raise OandaError("Improper datetime format. Must be 'RFC3339' or 'UNIX'")
def _oanda_api_call(self, method, endpoint, params=None, data=None):
params = params if params != {} else None
data = data if data != {} else None
base_url = live_url if self.live else practice_url
full_url = f"{base_url}/{api_version}/{endpoint}"
headers = {
"Authorization": f"Bearer {self.auth}",
"Content-Type": "application/json",
"Accept-Datetime-Format": self.datetime_format,
}
response = getattr(requests, method)(full_url, headers=headers, params=params, json=data)
if response.status_code >= 300:
raise OandaError("HTTP Error {}: {}".format(response.status_code, response.json()["errorMessage"]))
return response.json()
def _oanda_api_stream_call(self, method, endpoint, params=None, data=None):
params = params if params != {} else None
data = data if data != {} else None
base_url = live_stream_url if self.live else practice_stream_url
full_url = f"{base_url}/{api_version}/{endpoint}"
headers = {
"Authorization": f"Bearer {self.auth}",
"Content-Type": "application/json",
"Accept-Datetime-Format": self.datetime_format,
}
response = getattr(requests, method)(full_url, headers=headers, params=params, json=data, stream=True)
if response.status_code >= 300:
raise OandaError("HTTP Error {}: {}".format(response.status_code, response.json()["errorMessage"]))
return response
| 47.651591 | 120 | 0.632546 | 10,526 | 85,344 | 4.948793 | 0.047216 | 0.028105 | 0.024496 | 0.029948 | 0.850185 | 0.820929 | 0.795896 | 0.787564 | 0.774049 | 0.762051 | 0 | 0.001621 | 0.291749 | 85,344 | 1,790 | 121 | 47.678212 | 0.86017 | 0.411218 | 0 | 0.659465 | 0 | 0 | 0.12728 | 0.0442 | 0 | 0 | 0 | 0.000559 | 0 | 1 | 0.072016 | false | 0 | 0.005144 | 0.002058 | 0.149177 | 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 |
78cb1942d2bcd385d4fd1f157de0fb5937388fd4 | 35 | py | Python | tests/import.py | MarcoQin/python-lua | 0a93d3841860547a101068d4895bfa743f45c67d | [
"Apache-2.0"
] | 69 | 2020-02-23T11:20:18.000Z | 2022-03-14T06:10:40.000Z | tests/import.py | lumimyrsky/python-lua | 80b41381057a5c01793c1bc5beed0d6a1678349a | [
"Apache-2.0"
] | 5 | 2020-05-27T13:32:18.000Z | 2022-03-19T01:52:28.000Z | tests/import.py | lumimyrsky/python-lua | 80b41381057a5c01793c1bc5beed0d6a1678349a | [
"Apache-2.0"
] | 15 | 2020-03-29T17:54:41.000Z | 2022-03-15T06:22:01.000Z | import foo.bar
import bar as bar_ex | 17.5 | 20 | 0.828571 | 8 | 35 | 3.5 | 0.625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 35 | 2 | 20 | 17.5 | 0.933333 | 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 |
156af6400e11dd64ed6bee827b241623e6d1388d | 30,844 | py | Python | interfaces/rail/convertDESCcat.py | ixkael/PhotoZviaGP | 32967d597a6d8c799235d4f9ea75e7328ce0c7af | [
"MIT"
] | null | null | null | interfaces/rail/convertDESCcat.py | ixkael/PhotoZviaGP | 32967d597a6d8c799235d4f9ea75e7328ce0c7af | [
"MIT"
] | null | null | null | interfaces/rail/convertDESCcat.py | ixkael/PhotoZviaGP | 32967d597a6d8c799235d4f9ea75e7328ce0c7af | [
"MIT"
] | null | null | null | #######################################################################################################
#
# script : convertDESCcat.py
#
# convert DESC catalog to be injected in Delight
# produce files `galaxies-redshiftpdfs.txt` and `galaxies-redshiftpdfs2.txt` for training and target
#
#########################################################################################################
import sys
import os
import numpy as np
from functools import reduce
from delight.io import *
from delight.utils import *
from tables_io import io
import coloredlogs
import logging
logger = logging.getLogger(__name__)
coloredlogs.install(level='DEBUG', logger=logger,fmt='%(asctime)s,%(msecs)03d %(programname)s, %(name)s[%(process)d] %(levelname)s %(message)s')
# option to convert DC2 flux level (in AB units) into internal Delight units
# this option will be removed when optimisation of parameters will be implemented
FLAG_CONVERTFLUX_TODELIGHTUNIT=True
def group_entries(f):
"""
group entries in single numpy array
"""
galid = f['id'][()][:, np.newaxis]
redshift = f['redshift'][()][:, np.newaxis]
mag_err_g_lsst = f['mag_err_g_lsst'][()][:, np.newaxis]
mag_err_i_lsst = f['mag_err_i_lsst'][()][:, np.newaxis]
mag_err_r_lsst = f['mag_err_r_lsst'][()][:, np.newaxis]
mag_err_u_lsst = f['mag_err_u_lsst'][()][:, np.newaxis]
mag_err_y_lsst = f['mag_err_y_lsst'][()][:, np.newaxis]
mag_err_z_lsst = f['mag_err_z_lsst'][()][:, np.newaxis]
mag_g_lsst = f['mag_g_lsst'][()][:, np.newaxis]
mag_i_lsst = f['mag_i_lsst'][()][:, np.newaxis]
mag_r_lsst = f['mag_r_lsst'][()][:, np.newaxis]
mag_u_lsst = f['mag_u_lsst'][()][:, np.newaxis]
mag_y_lsst = f['mag_y_lsst'][()][:, np.newaxis]
mag_z_lsst = f['mag_z_lsst'][()][:, np.newaxis]
full_arr = np.hstack((galid, redshift, mag_u_lsst, mag_g_lsst, mag_r_lsst, mag_i_lsst, mag_z_lsst, mag_y_lsst, \
mag_err_u_lsst, mag_err_g_lsst, mag_err_r_lsst, mag_err_i_lsst, mag_err_z_lsst,
mag_err_y_lsst))
return full_arr
def filter_mag_entries(d,nb=6):
"""
Filter bad data with bad magnitudes
input
- d: array of magnitudes and errors
- nb : number of bands
output :
- indexes of row to be filtered
"""
u = d[:, 2]
idx_u = np.where(u > 31.8)[0]
return idx_u
def mag_to_flux(d,nb=6):
"""
Convert magnitudes to fluxes
input:
-d : array of magnitudes with errors
:return:
array of fluxes with error
"""
fluxes = np.zeros_like(d)
fluxes[:, 0] = d[:, 0] # object index
fluxes[:, 1] = d[:, 1] # redshift
for idx in np.arange(nb):
fluxes[:, 2 + idx] = np.power(10, -0.4 * d[:, 2 + idx]) # fluxes
fluxes[:, 8 + idx] = fluxes[:, 2 + idx] * d[:, 8 + idx] # errors on fluxes
return fluxes
def filter_flux_entries(d,nb=6,nsig=5):
"""
Filter noisy data on the the number SNR
input :
- d: flux and errors array
- nb : number of bands
- nsig : number of sigma
output:
indexes of row to suppress
"""
# collection of indexes
indexes = []
#indexes = np.array(indexes, dtype=np.int)
indexes = np.array(indexes, dtype=int)
for idx in np.arange(nb):
ratio = d[:, 2 + idx] / d[:, 8 + idx] # flux divided by sigma-flux
bad_indexes = np.where(ratio < nsig)[0]
indexes = np.concatenate((indexes, bad_indexes))
indexes = np.unique(indexes)
return np.sort(indexes)
def convertDESCcatChunk(configfilename,data,chunknum,flag_filter_validation = True, snr_cut_validation = 5):
"""
convertDESCcatChunk(configfilename,data,chunknum,flag_filter_validation = True, snr_cut_validation = 5)
Convert files in ascii format to be used by Delight
Input data can be filtered by series of filters. But it is necessary to remember which entries are kept,
which are eliminated
input args:
- configfilename : Delight configuration file containing path for output files (flux variances and redshifts)
- data : the DC2 data
- chunknum : number of the chunk
- filter_validation : Flag to activate quality filter data
- snr_cut_validation : cut on flux SNR
output :
- the target file of the chunk which path is in configuration file
:return:
- the list of selected (unfiltered DC2 data)
"""
msg="--- Convert DESC catalogs chunk {}---".format(chunknum)
logger.info(msg)
if FLAG_CONVERTFLUX_TODELIGHTUNIT:
flux_multiplicative_factor = 2.22e10
else:
flux_multiplicative_factor = 1
# produce a numpy array
magdata = group_entries(data)
# remember the number of entries
Nin = magdata.shape[0]
msg = "Number of objects = {} , in chunk : {}".format(Nin,chunknum)
logger.debug(msg)
# keep indexes to filter data with bad magnitudes
if flag_filter_validation:
indexes_bad_mag = filter_mag_entries(magdata)
#magdata_f = np.delete(magdata, indexes_bad_mag, axis=0)
magdata_f = magdata # filtering will be done later
else:
indexes_bad_mag=np.array([])
magdata_f = magdata
Nbadmag = len(indexes_bad_mag)
msg = "Number of objects with bad magnitudes = {} , in chunk : {}".format(Nbadmag, chunknum)
logger.debug(msg)
#print("indexes_bad_mag = ",indexes_bad_mag)
# convert mag to fluxes
fdata = mag_to_flux(magdata_f)
# keep indexes to filter data with bad SNR
if flag_filter_validation:
indexes_bad_snr = filter_flux_entries(fdata, nsig = snr_cut_validation)
fdata_f = fdata
#fdata_f = np.delete(fdata, indexes_bad, axis=0)
#magdata_f = np.delete(magdata_f, indexes_bad, axis=0)
else:
fdata_f=fdata
indexes_bad_snr = np.array([])
Nbadsnr = len(indexes_bad_snr)
msg = "Number of objects with bad SNR = {} , in chunk : {}".format(Nbadsnr, chunknum)
logger.debug(msg)
#print("indexes_bad_snr = ", indexes_bad_snr)
# make union of indexes (unique id) before removing them for Delight
idxToRemove = reduce(np.union1d,(indexes_bad_mag,indexes_bad_snr))
NtoRemove=len(idxToRemove)
msg = "Number of objects filtered out = {} , in chunk : {}".format(NtoRemove, chunknum)
logger.debug(msg)
#print("indexes_to_remove = ", idxToRemove)
#pprint(idxToRemove)
# fdata_f contains the fluxes and errors to be send to Delight
# indexes of full input dataset
idxInitial = np.arange(Nin)
if NtoRemove>0:
fdata_f = np.delete(fdata_f,idxToRemove, axis=0)
idxFinal=np.delete(idxInitial,idxToRemove, axis=0)
else:
idxFinal = idxInitial
Nkept = len(idxFinal)
msg = "Number of objects kept = {} , in chunk : {}".format(Nkept, chunknum)
logger.debug(msg)
#print("indexes_kept = ", idxFinal)
gid = fdata_f[:, 0]
rs = fdata_f[:, 1]
# 2) parameter file
params = parseParamFile(configfilename, verbose=False, catFilesNeeded=False)
numB = len(params['bandNames'])
numObjects = len(gid)
msg = "get {} objects ".format(numObjects)
logger.debug(msg)
logger.debug(params['bandNames'])
# Generate target data
# -------------------------
# what is fluxes and fluxes variance
fluxes, fluxesVar = np.zeros((numObjects, numB)), np.zeros((numObjects, numB))
# loop on objects to simulate for the target and save in output trarget file
for k in range(numObjects):
# loop on number of bands
for i in range(numB):
trueFlux = fdata_f[k, 2 + i]
noise = fdata_f[k, 8 + i]
# put the DC2 data to the internal units of Delight
trueFlux *= flux_multiplicative_factor
noise *= flux_multiplicative_factor
# fluxes[k, i] = trueFlux + noise * np.random.randn() # noisy flux
fluxes[k, i] = trueFlux
if fluxes[k, i] < 0:
# fluxes[k, i]=np.abs(noise)/10.
fluxes[k, i] = trueFlux
fluxesVar[k, i] = noise ** 2.
# container for target galaxies output
# at some redshift, provides the flux and its variance inside each band
data = np.zeros((numObjects, 1 + len(params['target_bandOrder'])))
bandIndices, bandNames, bandColumns, bandVarColumns, redshiftColumn, refBandColumn = readColumnPositions(params,
prefix="target_")
for ib, pf, pfv in zip(bandIndices, bandColumns, bandVarColumns):
data[:, pf] = fluxes[:, ib]
data[:, pfv] = fluxesVar[:, ib]
data[:, redshiftColumn] = rs
data[:, -1] = 0 # NO TYPE
msg = "write file {}".format(os.path.basename(params['targetFile']))
logger.debug(msg)
msg = "write target file {}".format(params['targetFile'])
logger.debug(msg)
outputdir = os.path.dirname(params['targetFile'])
if not os.path.exists(outputdir): # pragma: no cover
msg = " outputdir not existing {} then create it ".format(outputdir)
logger.info(msg)
os.makedirs(outputdir)
np.savetxt(params['targetFile'], data)
# return the index of selected data
return idxFinal
#def convertDESCcat(configfilename,desctraincatalogfile,desctargetcatalogfile,\ #flag_filter_training=True,flag_filter_validation=True,snr_cut_training=5,snr_cut_validation=5):
# """
# convertDESCcat(configfilename,desctraincatalogfile,desctargetcatalogfile,\
# flag_filter_training=True,flag_filter_validation=True,snr_cut_training=5,snr_cut_validation=5):
# Convert files in ascii format to be used by Delight
# input args:
# - configfilename : Delight configuration file containingg path for output files (flux variances and redshifts)
# - desctraincatalogfile : training file provided by RAIL (hdf5 format)
# - desctargetcatalogfile : target file provided by RAIL (hdf5 format)
# - flag_filter_training : Activate filtering on training data
# - flag_filter_validation : Activate filtering on validation data
# - snr_cut_training : Cut on flux SNR in training data
# - snr_cut_validation : Cut on flux SNR in validation data
# output :
# - the Delight training and target file which path is in configuration file
# :return: nothing
# """
# logger.info("--- Convert DESC training and target catalogs ---")
# if FLAG_CONVERTFLUX_TODELIGHTUNIT:
# flux_multiplicative_factor = 2.22e10
# else:
# flux_multiplicative_factor = 1
# 1) DESC catalog file
# msg="read DESC hdf5 training file {} ".format(desctraincatalogfile)
# logger.debug(msg)
# f = io.readHdf5ToDict(desctraincatalogfile, groupname='photometry')
# produce a numpy array
# magdata = group_entries(f)
# remember the number of entries
# Nin = magdata.shape[0]
# msg = "Number of objects = {} , in training dataset".format(Nin)
# logger.debug(msg)
# keep indexes to filter data with bad magnitudes
# if flag_filter_training:
# indexes_bad_mag = filter_mag_entries(magdata)
# magdata_f = np.delete(magdata, indexes_bad_mag, axis=0)
# magdata_f = magdata # filtering will be done later
# else:
# indexes_bad_mag = np.array([])
# magdata_f = magdata
# Nbadmag = len(indexes_bad_mag)
# msg = "Number of objects with bad magnitudes {} in training dataset".format(Nbadmag)
# logger.debug(msg)
# convert mag to fluxes
# fdata = mag_to_flux(magdata_f)
# keep indexes to filter data with bad SNR
# if flag_filter_training:
# indexes_bad_snr = filter_flux_entries(fdata, nsig=snr_cut_training)
# fdata_f = fdata
# else:
# fdata_f = fdata
# indexes_bad_snr = np.array([])
# Nbadsnr = len(indexes_bad_snr)
# msg = "Number of objects with bad SNR = {} , in training dataset".format(Nbadsnr)
# logger.debug(msg)
# make union of indexes (unique id) before removing them for Delight
# idxToRemove = reduce(np.union1d, (indexes_bad_mag, indexes_bad_snr))
# NtoRemove = len(idxToRemove)
# msg = "Number of objects filtered out = {} , in training dataset".format(NtoRemove)
# logger.debug(msg)
# fdata_f contains the fluxes and errors to be send to Delight
# indexes of full input dataset
# idxInitial = np.arange(Nin)
# if NtoRemove > 0:
# fdata_f = np.delete(fdata_f, idxToRemove, axis=0)
# idxFinal = np.delete(idxInitial, idxToRemove, axis=0)
# else:
# idxFinal = idxInitial
# Nkept = len(idxFinal)
# msg = "Number of objects kept = {} , in training dataset".format(Nkept)
# logger.debug(msg)
# gid = fdata_f[:, 0]
# rs = fdata_f[:, 1]
# 2) parameter file
# params = parseParamFile(configfilename, verbose=False, catFilesNeeded=False)
# numB = len(params['bandNames'])
# numObjects = len(gid)
# msg = "get {} objects ".format(numObjects)
# logger.debug(msg)
# logger.debug(params['bandNames'])
# Generate training data
#-------------------------
# what is fluxes and fluxes variance
# fluxes, fluxesVar = np.zeros((numObjects, numB)), np.zeros((numObjects, numB))
# loop on objects to simulate for the training and save in output training file
# for k in range(numObjects):
#loop on number of bands
# for i in range(numB):
# trueFlux = fdata_f[k,2+i]
# noise = fdata_f[k,8+i]
# put the DC2 data to the internal units of Delight
# trueFlux *= flux_multiplicative_factor
# noise *= flux_multiplicative_factor
#fluxes[k, i] = trueFlux + noise * np.random.randn() # noisy flux
# fluxes[k, i] = trueFlux
# if fluxes[k, i]<0:
#fluxes[k, i]=np.abs(noise)/10.
# fluxes[k, i] = trueFlux
# fluxesVar[k, i] = noise**2.
# container for training galaxies output
# at some redshift, provides the flux and its variance inside each band
# data = np.zeros((numObjects, 1 + len(params['training_bandOrder'])))
# bandIndices, bandNames, bandColumns, bandVarColumns, redshiftColumn,refBandColumn = readColumnPositions(params, prefix="training_")
# for ib, pf, pfv in zip(bandIndices, bandColumns, bandVarColumns):
# data[:, pf] = fluxes[:, ib]
# data[:, pfv] = fluxesVar[:, ib]
# data[:, redshiftColumn] = rs
# data[:, -1] = 0 # NO type
# msg="write training file {}".format(params['trainingFile'])
# logger.debug(msg)
# outputdir=os.path.dirname(params['trainingFile'])
# if not os.path.exists(outputdir):
# msg = " outputdir not existing {} then create it ".format(outputdir)
# logger.info(msg)
# os.makedirs(outputdir)
# np.savetxt(params['trainingFile'], data)
# Generate Target data : procedure similar to the training
#-----------------------------------------------------------
# 1) DESC catalog file
# msg = "read DESC hdf5 validation file {} ".format(desctargetcatalogfile)
# logger.debug(msg)
# f = io.readHdf5ToDict(desctargetcatalogfile, groupname='photometry')
# produce a numpy array
# magdata = group_entries(f)
# remember the number of entries
# Nin = magdata.shape[0]
# msg = "Number of objects = {} , in validation dataset".format(Nin)
# logger.debug(msg)
# filter bad data
# keep indexes to filter data with bad magnitudes
# if flag_filter_validation:
# indexes_bad_mag = filter_mag_entries(magdata)
# magdata_f = np.delete(magdata, indexes_bad_mag, axis=0)
# magdata_f = magdata # filtering will be done later
# else:
# indexes_bad_mag = np.array([])
# magdata_f = magdata
# Nbadmag = len(indexes_bad_mag)
# msg = "Number of objects with bad magnitudes = {} , in validation dataset".format(Nbadmag)
# logger.debug(msg)
# convert mag to fluxes
# fdata = mag_to_flux(magdata_f)
# keep indexes to filter data with bad SNR
# if flag_filter_validation:
# indexes_bad_snr = filter_flux_entries(fdata, nsig=snr_cut_validation)
# fdata_f = fdata
# fdata_f = np.delete(fdata, indexes_bad, axis=0)
# magdata_f = np.delete(magdata_f, indexes_bad, axis=0)
# else:
# fdata_f = fdata
# indexes_bad_snr = np.array([])
# Nbadsnr = len(indexes_bad_snr)
# msg = "Number of objects with bad SNR = {} , in validation dataset".format(Nbadsnr)
# logger.debug(msg)
# make union of indexes (unique id) before removing them for Delight
# idxToRemove = reduce(np.union1d, (indexes_bad_mag, indexes_bad_snr))
# NtoRemove = len(idxToRemove)
# msg = "Number of objects filtered out = {} , in validation dataset".format(NtoRemove)
# logger.debug(msg)
# fdata_f contains the fluxes and errors to be send to Delight
# indexes of full input dataset
# idxInitial = np.arange(Nin)
# if NtoRemove > 0:
# fdata_f = np.delete(fdata_f, idxToRemove, axis=0)
# idxFinal = np.delete(idxInitial, idxToRemove, axis=0)
# else:
# idxFinal = idxInitial
# Nkept = len(idxFinal)
# msg = "Number of objects kept = {} , in validation dataset".format(Nkept)
# logger.debug(msg)
# gid = fdata_f[:, 0]
# rs = fdata_f[:, 1]
# numObjects = len(gid)
# msg = "get {} objects ".format(numObjects)
# logger.debug(msg)
# fluxes, fluxesVar = np.zeros((numObjects, numB)), np.zeros((numObjects, numB))
# loop on objects in target files
# for k in range(numObjects):
# loop on bands
# for i in range(numB):
# compute the flux in that band at the redshift
# trueFlux = fdata_f[k, 2 + i]
# noise = fdata_f[k, 8 + i]
# put the DC2 data to the internal units of Delight
# trueFlux *= flux_multiplicative_factor
# noise *= flux_multiplicative_factor
#fluxes[k, i] = trueFlux + noise * np.random.randn()
# fluxes[k, i] = trueFlux
# if fluxes[k, i]<0:
#fluxes[k, i]=np.abs(noise)/10.
# fluxes[k, i] = trueFlux
# fluxesVar[k, i] = noise**2
# data = np.zeros((numObjects, 1 + len(params['target_bandOrder'])))
# bandIndices, bandNames, bandColumns, bandVarColumns, redshiftColumn,refBandColumn = readColumnPositions(params, prefix="target_")
# for ib, pf, pfv in zip(bandIndices, bandColumns, bandVarColumns):
# data[:, pf] = fluxes[:, ib]
# data[:, pfv] = fluxesVar[:, ib]
# data[:, redshiftColumn] = rs
# data[:, -1] = 0 # NO TYPE
# msg = "write file {}".format(os.path.basename(params['targetFile']))
# logger.debug(msg)
# msg = "write target file {}".format(params['targetFile'])
# logger.debug(msg)
# outputdir = os.path.dirname(params['targetFile'])
# if not os.path.exists(outputdir):
# msg = " outputdir not existing {} then create it ".format(outputdir)
# logger.info(msg)
# os.makedirs(outputdir)
# np.savetxt(params['targetFile'], data)
################################################################################
# New version of RAIL with data structure directly provided: (SDC 2021/10/23) #
################################################################################
def convertDESCcatTrainData(configfilename,descatalogdata,flag_filter=True,snr_cut=5):
"""
convertDESCcatData(configfilename,desccatalogdata,
flag_filter=True,snr_cut=5,s):
Convert files in ascii format to be used by Delight
input args:
- configfilename : Delight configuration file containingg path for output files (flux variances and redshifts)
- desccatalogdata : data provided by RAIL (dictionary format)
- flag_filter : Activate filtering on training data
- snr_cut: Cut on flux SNR in training data
output :
- the Delight training which path is in configuration file
:return: nothing
"""
logger.info("--- Convert DESC training catalogs data ---")
if FLAG_CONVERTFLUX_TODELIGHTUNIT:
flux_multiplicative_factor = 2.22e10
else:
flux_multiplicative_factor = 1
magdata = group_entries(descatalogdata)
# remember the number of entries
Nin = magdata.shape[0]
msg = "Number of objects = {} , in training dataset".format(Nin)
logger.debug(msg)
# keep indexes to filter data with bad magnitudes
if flag_filter:
indexes_bad_mag = filter_mag_entries(magdata)
# magdata_f = np.delete(magdata, indexes_bad_mag, axis=0)
magdata_f = magdata # filtering will be done later
else:
indexes_bad_mag = np.array([])
magdata_f = magdata
Nbadmag = len(indexes_bad_mag)
msg = "Number of objects with bad magnitudes {} in training dataset".format(Nbadmag)
logger.debug(msg)
# convert mag to fluxes
fdata = mag_to_flux(magdata_f)
# keep indexes to filter data with bad SNR
if flag_filter:
indexes_bad_snr = filter_flux_entries(fdata, nsig=snr_cut)
fdata_f = fdata
# fdata_f = np.delete(fdata, indexes_bad, axis=0)
# magdata_f = np.delete(magdata_f, indexes_bad, axis=0)
else:
fdata_f = fdata
indexes_bad_snr = np.array([])
Nbadsnr = len(indexes_bad_snr)
msg = "Number of objects with bad SNR = {} , in training dataset".format(Nbadsnr)
logger.debug(msg)
# make union of indexes (unique id) before removing them for Delight
idxToRemove = reduce(np.union1d, (indexes_bad_mag, indexes_bad_snr))
NtoRemove = len(idxToRemove)
msg = "Number of objects filtered out = {} , in training dataset".format(NtoRemove)
logger.debug(msg)
# fdata_f contains the fluxes and errors to be send to Delight
# indexes of full input dataset
idxInitial = np.arange(Nin)
if NtoRemove > 0:
fdata_f = np.delete(fdata_f, idxToRemove, axis=0)
idxFinal = np.delete(idxInitial, idxToRemove, axis=0)
else:
idxFinal = idxInitial
Nkept = len(idxFinal)
msg = "Number of objects kept = {} , in training dataset".format(Nkept)
logger.debug(msg)
gid = fdata_f[:, 0]
rs = fdata_f[:, 1]
# 2) parameter file
#-------------------
params = parseParamFile(configfilename, verbose=False, catFilesNeeded=False)
numB = len(params['bandNames'])
numObjects = len(gid)
msg = "get {} objects ".format(numObjects)
logger.debug(msg)
logger.debug(params['bandNames'])
# Generate training data
#-------------------------
# what is fluxes and fluxes variance
fluxes, fluxesVar = np.zeros((numObjects, numB)), np.zeros((numObjects, numB))
# loop on objects to simulate for the training and save in output training file
for k in range(numObjects):
#loop on number of bands
for i in range(numB):
trueFlux = fdata_f[k,2+i]
noise = fdata_f[k,8+i]
# put the DC2 data to the internal units of Delight
trueFlux *= flux_multiplicative_factor
noise *= flux_multiplicative_factor
#fluxes[k, i] = trueFlux + noise * np.random.randn() # noisy flux
fluxes[k, i] = trueFlux
if fluxes[k, i]<0:
#fluxes[k, i]=np.abs(noise)/10.
fluxes[k, i] = trueFlux
fluxesVar[k, i] = noise**2.
# container for training galaxies output
# at some redshift, provides the flux and its variance inside each band
data = np.zeros((numObjects, 1 + len(params['training_bandOrder'])))
bandIndices, bandNames, bandColumns, bandVarColumns, redshiftColumn,refBandColumn = readColumnPositions(params, prefix="training_")
for ib, pf, pfv in zip(bandIndices, bandColumns, bandVarColumns):
data[:, pf] = fluxes[:, ib]
data[:, pfv] = fluxesVar[:, ib]
data[:, redshiftColumn] = rs
data[:, -1] = 0 # NO type
msg="write training file {}".format(params['trainingFile'])
logger.debug(msg)
outputdir=os.path.dirname(params['trainingFile'])
if not os.path.exists(outputdir):
msg = " outputdir not existing {} then create it ".format(outputdir)
logger.info(msg)
os.makedirs(outputdir)
np.savetxt(params['trainingFile'], data)
#---
def convertDESCcatTargetFile(configfilename,desctargetcatalogfile,flag_filter=True,snr_cut=5):
"""
convertDESCcatTargetFile(configfilename,desctargetcatalogfile,flag_filter=True,snr_cut)
Convert files in ascii format to be used by Delight
input args:
- configfilename : Delight configuration file containingg path for output files (flux variances and redshifts)
- desctargetcatalogfile : target file provided by RAIL (hdf5 format)
- flag_filter_ : Activate filtering on validation data
- snr_cut: Cut on flux SNR in validation data
output :
- the Delight target file which path is in configuration file
:return: nothing
"""
logger.info("--- Convert DESC target catalogs ---")
if FLAG_CONVERTFLUX_TODELIGHTUNIT:
flux_multiplicative_factor = 2.22e10
else:
flux_multiplicative_factor = 1
# Generate Target data : procedure similar to the training
#-----------------------------------------------------------
# 1) DESC catalog file
#---------------------
msg = "read DESC hdf5 validation file {} ".format(desctargetcatalogfile)
logger.debug(msg)
f = io.readHdf5ToDict(desctargetcatalogfile, groupname='photometry')
# produce a numpy array
magdata = group_entries(f)
# remember the number of entries
Nin = magdata.shape[0]
msg = "Number of objects = {} , in validation dataset".format(Nin)
logger.debug(msg)
# filter bad data
# keep indexes to filter data with bad magnitudes
if flag_filter:
indexes_bad_mag = filter_mag_entries(magdata)
# magdata_f = np.delete(magdata, indexes_bad_mag, axis=0)
magdata_f = magdata # filtering will be done later
else:
indexes_bad_mag = np.array([])
magdata_f = magdata
Nbadmag = len(indexes_bad_mag)
msg = "Number of objects with bad magnitudes = {} , in validation dataset".format(Nbadmag)
logger.debug(msg)
# convert mag to fluxes
fdata = mag_to_flux(magdata_f)
# keep indexes to filter data with bad SNR
if flag_filter:
indexes_bad_snr = filter_flux_entries(fdata, nsig=snr_cut)
fdata_f = fdata
# fdata_f = np.delete(fdata, indexes_bad, axis=0)
# magdata_f = np.delete(magdata_f, indexes_bad, axis=0)
else:
fdata_f = fdata
indexes_bad_snr = np.array([])
Nbadsnr = len(indexes_bad_snr)
msg = "Number of objects with bad SNR = {} , in validation dataset".format(Nbadsnr)
logger.debug(msg)
# make union of indexes (unique id) before removing them for Delight
idxToRemove = reduce(np.union1d, (indexes_bad_mag, indexes_bad_snr))
NtoRemove = len(idxToRemove)
msg = "Number of objects filtered out = {} , in validation dataset".format(NtoRemove)
logger.debug(msg)
# fdata_f contains the fluxes and errors to be send to Delight
# indexes of full input dataset
idxInitial = np.arange(Nin)
if NtoRemove > 0:
fdata_f = np.delete(fdata_f, idxToRemove, axis=0)
idxFinal = np.delete(idxInitial, idxToRemove, axis=0)
else:
idxFinal = idxInitial
Nkept = len(idxFinal)
msg = "Number of objects kept = {} , in validation dataset".format(Nkept)
logger.debug(msg)
gid = fdata_f[:, 0]
rs = fdata_f[:, 1]
# 2) parameter file
#-------------------
params = parseParamFile(configfilename, verbose=False, catFilesNeeded=False)
numB = len(params['bandNames'])
numObjects = len(gid)
msg = "get {} objects ".format(numObjects)
logger.debug(msg)
logger.debug(params['bandNames'])
# 3) Generate target data
#------------------------
numObjects = len(gid)
msg = "get {} objects ".format(numObjects)
logger.debug(msg)
fluxes, fluxesVar = np.zeros((numObjects, numB)), np.zeros((numObjects, numB))
# loop on objects in target files
for k in range(numObjects):
# loop on bands
for i in range(numB):
# compute the flux in that band at the redshift
trueFlux = fdata_f[k, 2 + i]
noise = fdata_f[k, 8 + i]
# put the DC2 data to the internal units of Delight
trueFlux *= flux_multiplicative_factor
noise *= flux_multiplicative_factor
#fluxes[k, i] = trueFlux + noise * np.random.randn()
fluxes[k, i] = trueFlux
if fluxes[k, i]<0:
#fluxes[k, i]=np.abs(noise)/10.
fluxes[k, i] = trueFlux
fluxesVar[k, i] = noise**2
data = np.zeros((numObjects, 1 + len(params['target_bandOrder'])))
bandIndices, bandNames, bandColumns, bandVarColumns, redshiftColumn,refBandColumn = readColumnPositions(params, prefix="target_")
for ib, pf, pfv in zip(bandIndices, bandColumns, bandVarColumns):
data[:, pf] = fluxes[:, ib]
data[:, pfv] = fluxesVar[:, ib]
data[:, redshiftColumn] = rs
data[:, -1] = 0 # NO TYPE
msg = "write file {}".format(os.path.basename(params['targetFile']))
logger.debug(msg)
msg = "write target file {}".format(params['targetFile'])
logger.debug(msg)
outputdir = os.path.dirname(params['targetFile'])
if not os.path.exists(outputdir):
msg = " outputdir not existing {} then create it ".format(outputdir)
logger.info(msg)
os.makedirs(outputdir)
np.savetxt(params['targetFile'], data)
if __name__ == "__main__": # pragma: no cover
# execute only if run as a script
msg="Start convertDESCcat.py"
logger.info(msg)
logger.info("--- convert DESC catalogs ---")
if len(sys.argv) < 4:
raise Exception('Please provide a parameter file and the training and validation and catalog files')
convertDESCcat(sys.argv[1],sys.argv[2],sys.argv[3])
| 30.998995 | 193 | 0.618953 | 3,785 | 30,844 | 4.92074 | 0.084544 | 0.031141 | 0.03157 | 0.024161 | 0.847517 | 0.824698 | 0.808376 | 0.795007 | 0.777933 | 0.777933 | 0 | 0.008314 | 0.25522 | 30,844 | 994 | 194 | 31.030181 | 0.802455 | 0.489171 | 0 | 0.69 | 0 | 0.003333 | 0.126513 | 0.002993 | 0 | 0 | 0 | 0 | 0 | 1 | 0.023333 | false | 0 | 0.03 | 0 | 0.07 | 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 |
15ab521643177805667c3b28ddd62d5b0fd9c70f | 31 | py | Python | mygtestabcde/__init__.py | Adriengith/mygtestabcde | 10a4939290758dbfff923ed4c8705e6729492313 | [
"MIT"
] | null | null | null | mygtestabcde/__init__.py | Adriengith/mygtestabcde | 10a4939290758dbfff923ed4c8705e6729492313 | [
"MIT"
] | null | null | null | mygtestabcde/__init__.py | Adriengith/mygtestabcde | 10a4939290758dbfff923ed4c8705e6729492313 | [
"MIT"
] | null | null | null | from mygtestabcde.Ml import Ml
| 15.5 | 30 | 0.83871 | 5 | 31 | 5.2 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 31 | 1 | 31 | 31 | 0.962963 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 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 |
eceb0bfce03b700e6aa0ed99eb55a90a822590dc | 30 | py | Python | src/test/__init__.py | caveman1234/tpython-kinter | 159879c7c2dcb7f1af1876fe3b76a3466e3ac7b3 | [
"MIT"
] | null | null | null | src/test/__init__.py | caveman1234/tpython-kinter | 159879c7c2dcb7f1af1876fe3b76a3466e3ac7b3 | [
"MIT"
] | null | null | null | src/test/__init__.py | caveman1234/tpython-kinter | 159879c7c2dcb7f1af1876fe3b76a3466e3ac7b3 | [
"MIT"
] | null | null | null | from src.test.test import func | 30 | 30 | 0.833333 | 6 | 30 | 4.166667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 30 | 1 | 30 | 30 | 0.925926 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bf17db4529599b0629e22acadb01d724fb0441ac | 504 | py | Python | vulture-whitelist.py | lschmelzeisen/wikidata-history-analyzer | 8673639b61839d2dca271fbbaf2feb8563b75f2d | [
"ECL-2.0",
"Apache-2.0"
] | 6 | 2021-06-10T09:26:44.000Z | 2021-07-07T13:49:00.000Z | vulture-whitelist.py | lschmelzeisen/wikidated | 299c65b99008a7131a580b21067fab66ac0d8fc0 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | vulture-whitelist.py | lschmelzeisen/wikidated | 299c65b99008a7131a580b21067fab66ac0d8fc0 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | _.error_on_external_run # unused attribute (/home/lschmelzeisen/Repositories/wikidated/noxfile.py:22)
_.reuse_existing_virtualenvs # unused attribute (/home/lschmelzeisen/Repositories/wikidated/noxfile.py:23)
_.stop_on_first_error # unused attribute (/home/lschmelzeisen/Repositories/wikidated/noxfile.py:24)
test # unused function (/home/lschmelzeisen/Repositories/wikidated/noxfile.py:44)
_.isLoggable # unused method (/home/lschmelzeisen/Repositories/wikidated/src/wikidated/_jvm_manager.py:100)
| 84 | 108 | 0.829365 | 61 | 504 | 6.622951 | 0.47541 | 0.210396 | 0.358911 | 0.470297 | 0.576733 | 0.576733 | 0.460396 | 0.460396 | 0 | 0 | 0 | 0.023207 | 0.059524 | 504 | 5 | 109 | 100.8 | 0.829114 | 0.78373 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
bf321f31198cefe44778b98181074b618e168ba2 | 44 | py | Python | tlxzoo/module/bert/__init__.py | tensorlayer/TLXZoo | 8747c090825a6c0f6cd9239b281bfe56852fe2fb | [
"Apache-2.0"
] | 11 | 2022-01-14T07:31:10.000Z | 2022-01-26T08:36:51.000Z | tlxzoo/module/bert/__init__.py | tensorlayer/TLXZoo | 8747c090825a6c0f6cd9239b281bfe56852fe2fb | [
"Apache-2.0"
] | null | null | null | tlxzoo/module/bert/__init__.py | tensorlayer/TLXZoo | 8747c090825a6c0f6cd9239b281bfe56852fe2fb | [
"Apache-2.0"
] | 6 | 2022-01-20T10:15:51.000Z | 2022-01-25T04:58:41.000Z | from .bert import *
from .transform import * | 22 | 24 | 0.75 | 6 | 44 | 5.5 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.159091 | 44 | 2 | 24 | 22 | 0.891892 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bd61433695a8b48c3fc94618ec7d4b7023150061 | 366 | py | Python | jivago/wsgi/request/streaming_request_body.py | keotl/jivago | 892dfb0cae773e36245083c3e56f0f8523145523 | [
"MIT"
] | 12 | 2018-03-19T20:57:44.000Z | 2020-01-27T14:11:24.000Z | jivago/wsgi/request/streaming_request_body.py | keotl/jivago | 892dfb0cae773e36245083c3e56f0f8523145523 | [
"MIT"
] | 73 | 2018-04-20T22:26:00.000Z | 2021-12-01T14:17:37.000Z | jivago/wsgi/request/streaming_request_body.py | keotl/jivago | 892dfb0cae773e36245083c3e56f0f8523145523 | [
"MIT"
] | 1 | 2019-02-28T13:33:45.000Z | 2019-02-28T13:33:45.000Z | import io
class StreamingRequestBody(object):
def __init__(self, content: io.RawIOBase):
self.content = content
def read(self, n: int = 1) -> bytes:
return self.content.read(n)
def readall(self) -> bytes:
return self.content.readall()
def readinto(self, out: bytearray) -> int:
return self.content.readinto(out)
| 21.529412 | 46 | 0.639344 | 45 | 366 | 5.111111 | 0.444444 | 0.23913 | 0.221739 | 0.191304 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00361 | 0.243169 | 366 | 16 | 47 | 22.875 | 0.826715 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0.1 | 0.3 | 0.9 | 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 |
bd8ddd013f4601ae6fab0ec607f64af5cfc5b0c1 | 156 | py | Python | backend/python_service/service.py | tuilagio/amos2021ws07-nft-development | 10e52a1186401e2b27a3d7e4c12667f8e39d654d | [
"MIT"
] | null | null | null | backend/python_service/service.py | tuilagio/amos2021ws07-nft-development | 10e52a1186401e2b27a3d7e4c12667f8e39d654d | [
"MIT"
] | null | null | null | backend/python_service/service.py | tuilagio/amos2021ws07-nft-development | 10e52a1186401e2b27a3d7e4c12667f8e39d654d | [
"MIT"
] | null | null | null | # SPDX-License-Identifier: MIT
# SPDX-FileCopyrightText: 2021 Felix Steinkohl <steinkohl@campus.tu-berlin.de>
def hello_world():
return "hello world"
| 22.285714 | 78 | 0.75641 | 20 | 156 | 5.85 | 0.8 | 0.17094 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.029412 | 0.128205 | 156 | 6 | 79 | 26 | 0.830882 | 0.673077 | 0 | 0 | 0 | 0 | 0.229167 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
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