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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ee09ec9d7463d96deb99037517a5a553b01ae8b8
| 26
|
py
|
Python
|
Air_Ng/__init__.py
|
aunghtet008900/Air-Ng
|
62f907803a3b4c672f7b16b79acebb4157de2458
|
[
"Apache-2.0"
] | 1
|
2020-04-05T18:09:27.000Z
|
2020-04-05T18:09:27.000Z
|
Air_Ng/__init__.py
|
aunghtet008900/Air-Ng
|
62f907803a3b4c672f7b16b79acebb4157de2458
|
[
"Apache-2.0"
] | null | null | null |
Air_Ng/__init__.py
|
aunghtet008900/Air-Ng
|
62f907803a3b4c672f7b16b79acebb4157de2458
|
[
"Apache-2.0"
] | 1
|
2020-04-05T18:09:28.000Z
|
2020-04-05T18:09:28.000Z
|
from .airminum_ng import *
| 26
| 26
| 0.807692
| 4
| 26
| 5
| 1
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| 0
| 0
| 0
| 0
| 0
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| 0.115385
| 26
| 1
| 26
| 26
| 0.869565
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| 1
| 0
|
0
| 6
|
ee67223d4c86b6c1997ab4e7b58e528c1a0a4335
| 6,012
|
py
|
Python
|
tfmiss/preprocessing/preprocessing.py
|
shkarupa-alex/tfmiss
|
4fe1bb3a47327c07711f910ee53319167032b6af
|
[
"MIT"
] | 1
|
2019-06-25T15:58:20.000Z
|
2019-06-25T15:58:20.000Z
|
tfmiss/preprocessing/preprocessing.py
|
shkarupa-alex/tfmiss
|
4fe1bb3a47327c07711f910ee53319167032b6af
|
[
"MIT"
] | 1
|
2021-11-11T12:56:51.000Z
|
2021-11-11T12:56:51.000Z
|
tfmiss/preprocessing/preprocessing.py
|
shkarupa-alex/tfmiss
|
4fe1bb3a47327c07711f910ee53319167032b6af
|
[
"MIT"
] | 2
|
2020-02-11T15:46:58.000Z
|
2021-11-21T02:47:36.000Z
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import random_seed
from tensorflow.python.ops.ragged import ragged_tensor
from tfmiss.ops import tfmiss_ops
def cbow_context(source, window, empty, name=None):
"""Generates `Continuous bag-of-words` contexts for inference from batched list of tokens.
Args:
source: `2-D` string `Tensor` or `RaggedTensor`, batched lists of tokens [sentences, tokens].
window: `int`, size of context before and after target token, must be > 0.
name: `string`, a name for the operation (optional).
Returns:
`2-D` string `RaggedTensor`: context tokens.
`2-D` int32 `RaggedTensor`: context positions.
"""
with tf.name_scope(name or 'cbow_context'):
source = ragged_tensor.convert_to_tensor_or_ragged_tensor(source, name='source')
if source.shape.rank != 2:
raise ValueError('Rank of `source` must equals 2')
if not ragged_tensor.is_ragged(source):
source = ragged_tensor.RaggedTensor.from_tensor(source, ragged_rank=1)
if source.ragged_rank != 1:
raise ValueError('Ragged rank of `source` must equals 1')
context_values, context_splits, context_positions = tfmiss_ops.miss_cbow_context(
source_values=source.values,
source_splits=source.row_splits,
window=window,
empty=empty
)
context = tf.RaggedTensor.from_row_splits(context_values, context_splits)
position = tf.RaggedTensor.from_row_splits(context_positions, context_splits)
return context, position
def cont_bow(source, window, seed=None, name=None):
"""Generates `Continuous bag-of-words` target and context pairs from batched list of tokens.
Args:
source: `2-D` string `Tensor` or `RaggedTensor`, batched lists of tokens [sentences, tokens].
window: `int`, size of context before and after target token, must be > 0.
seed: `int`, used to create a random seed (optional).
See @{tf.random.set_seed} for behavior.
name: `string`, a name for the operation (optional).
Returns:
`1-D` string `Tensor`: target tokens.
`2-D` string `RaggedTensor`: context tokens.
`2-D` int32 `RaggedTensor`: context positions.
"""
with tf.name_scope(name or 'cont_bow'):
source = ragged_tensor.convert_to_tensor_or_ragged_tensor(source, name='source')
if source.shape.rank != 2:
raise ValueError('Rank of `source` must equals 2')
if not ragged_tensor.is_ragged(source):
source = ragged_tensor.RaggedTensor.from_tensor(source, ragged_rank=1)
if source.ragged_rank != 1:
raise ValueError('Ragged rank of `source` must equals 1')
seed1, seed2 = random_seed.get_seed(seed)
target, context_values, context_splits, context_positions = tfmiss_ops.miss_cont_bow(
source_values=source.values,
source_splits=source.row_splits,
window=window,
seed=seed1,
seed2=seed2
)
context = tf.RaggedTensor.from_row_splits(context_values, context_splits)
position = tf.RaggedTensor.from_row_splits(context_positions, context_splits)
return target, context, position
def skip_gram(source, window, seed=None, name=None):
"""Generates `Skip-Gram` target and context pairs from batched list of tokens.
Args:
source: `2-D` string `Tensor` or `RaggedTensor`, batched lists of tokens [sentences, tokens].
window: `int`, size of context before and after target token, must be > 0.
seed: `int`, used to create a random seed (optional).
See @{tf.random.set_seed} for behavior.
name: `string`, a name for the operation (optional).
Returns:
Two `1-D` string `Tensor`s: target and context tokens.
"""
with tf.name_scope(name or 'skip_gram'):
source = ragged_tensor.convert_to_tensor_or_ragged_tensor(source, name='source')
if source.shape.rank != 2:
raise ValueError('Rank of `source` must equals 2')
if not ragged_tensor.is_ragged(source):
source = ragged_tensor.RaggedTensor.from_tensor(source, ragged_rank=1)
if source.ragged_rank != 1:
raise ValueError('Ragged rank of `source` must equals 1')
seed1, seed2 = random_seed.get_seed(seed)
target, context = tfmiss_ops.miss_skip_gram(
source_values=source.values,
source_splits=source.row_splits,
window=window,
seed=seed1,
seed2=seed2
)
return target, context
def spaces_after(source, name=None):
"""Separates spaces from tokens.
Args:
source: `2-D` string `Tensor` or `RaggedTensor`, batched lists of "tokens with spaces" [sentences, tokens].
name: `string`, a name for the operation (optional).
Returns:
`2-D` string `RaggedTensor`: tokens.
`2-D` string `RaggedTensor`: spaces.
"""
with tf.name_scope(name or 'spaces_after'):
source = ragged_tensor.convert_to_tensor_or_ragged_tensor(source, name='source')
if source.shape.rank != 2:
raise ValueError('Rank of `source` must equals 2')
if not ragged_tensor.is_ragged(source):
source = ragged_tensor.RaggedTensor.from_tensor(source, ragged_rank=1)
if source.ragged_rank != 1:
raise ValueError('Ragged rank of `source` must equals 1')
token_values, space_values, common_splits = tfmiss_ops.miss_spaces_after(
source_values=source.values,
source_splits=source.row_splits
)
tokens = tf.RaggedTensor.from_row_splits(token_values, common_splits)
spaces = tf.RaggedTensor.from_row_splits(space_values, common_splits)
return tokens, spaces
| 37.575
| 115
| 0.664005
| 765
| 6,012
| 5.035294
| 0.130719
| 0.05296
| 0.016615
| 0.033229
| 0.814123
| 0.791796
| 0.780893
| 0.746885
| 0.746885
| 0.706906
| 0
| 0.010762
| 0.242681
| 6,012
| 159
| 116
| 37.811321
| 0.835273
| 0.301896
| 0
| 0.607595
| 0
| 0
| 0.082589
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.050633
| false
| 0
| 0.088608
| 0
| 0.189873
| 0.012658
| 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
|
4e9d2267cda10d886c7ec94be901d442aff4bb35
| 145
|
py
|
Python
|
edit_model/encdec/__init__.py
|
neulab/incremental_tree_edit
|
8651f2c75154bd776682726ea7e1d3da8a12924b
|
[
"MIT"
] | 32
|
2021-01-26T06:19:27.000Z
|
2022-02-18T04:02:53.000Z
|
edit_model/encdec/__init__.py
|
sunlab-osu/incremental_tree_edit
|
fba36b7fdff8366c7affc847335a974b4d106547
|
[
"MIT"
] | 4
|
2021-04-11T18:10:38.000Z
|
2021-08-10T20:13:54.000Z
|
edit_model/encdec/__init__.py
|
sunlab-osu/incremental_tree_edit
|
fba36b7fdff8366c7affc847335a974b4d106547
|
[
"MIT"
] | 5
|
2021-03-19T04:57:46.000Z
|
2021-08-07T11:25:36.000Z
|
from .graph_encoder import SyntaxTreeEncoder
from .sequential_decoder import SequentialDecoder
from .transition_decoder import TransitionDecoder
| 36.25
| 49
| 0.896552
| 15
| 145
| 8.466667
| 0.666667
| 0.204724
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.082759
| 145
| 3
| 50
| 48.333333
| 0.954887
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4eb8602e6c346eacd4ef6e909b75e4dba74d5b64
| 48
|
py
|
Python
|
src/main/python/gps/__init__.py
|
BikeAtor/WoMoAtor
|
700cc8b970dcfdd5af2f471df1a223d2a38cb1bf
|
[
"Apache-2.0"
] | null | null | null |
src/main/python/gps/__init__.py
|
BikeAtor/WoMoAtor
|
700cc8b970dcfdd5af2f471df1a223d2a38cb1bf
|
[
"Apache-2.0"
] | null | null | null |
src/main/python/gps/__init__.py
|
BikeAtor/WoMoAtor
|
700cc8b970dcfdd5af2f471df1a223d2a38cb1bf
|
[
"Apache-2.0"
] | null | null | null |
from .gps import GPS
from .gpsmap import GPSMap
| 16
| 26
| 0.791667
| 8
| 48
| 4.75
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 48
| 2
| 27
| 24
| 0.95
| 0
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| null | 0
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| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4ecdc5aed8cf872de513190a6da9b17b5934aaed
| 46
|
py
|
Python
|
src/datasets/__init__.py
|
data-sachez-2511/pl_segmentation
|
78fc15b4a5dfc60eb8e885f0f0af0a0563603e06
|
[
"MIT"
] | 3
|
2021-11-27T15:55:36.000Z
|
2021-12-13T12:49:44.000Z
|
src/datasets/__init__.py
|
data-sachez-2511/pl_segmentation
|
78fc15b4a5dfc60eb8e885f0f0af0a0563603e06
|
[
"MIT"
] | 1
|
2021-11-24T23:07:13.000Z
|
2021-11-24T23:07:13.000Z
|
src/datasets/__init__.py
|
data-sachez-2511/pl_segmentation
|
78fc15b4a5dfc60eb8e885f0f0af0a0563603e06
|
[
"MIT"
] | null | null | null |
from datasets.coco_dataset import CocoDataset
| 23
| 45
| 0.891304
| 6
| 46
| 6.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086957
| 46
| 1
| 46
| 46
| 0.952381
| 0
| 0
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| 0
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| 1
| 0
| true
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| null | 0
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| 1
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
e1264a5b821ca04673119a9862c4730f9566d8d5
| 29
|
py
|
Python
|
actions/__init__.py
|
jroivas/klapi
|
786f614d1bf0c40b6186501ef7fd8e1855d2a811
|
[
"MIT"
] | 3
|
2018-02-21T17:54:14.000Z
|
2019-08-31T21:34:14.000Z
|
actions/__init__.py
|
jroivas/klapi
|
786f614d1bf0c40b6186501ef7fd8e1855d2a811
|
[
"MIT"
] | null | null | null |
actions/__init__.py
|
jroivas/klapi
|
786f614d1bf0c40b6186501ef7fd8e1855d2a811
|
[
"MIT"
] | 4
|
2018-02-21T21:50:23.000Z
|
2021-11-25T06:56:33.000Z
|
import actions
import client
| 9.666667
| 14
| 0.862069
| 4
| 29
| 6.25
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 2
| 15
| 14.5
| 1
| 0
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| 0
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| 0
| 0
| 1
| 0
| true
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0126e4ca4350918e5312497c26d196e9f4dc4a4d
| 40
|
py
|
Python
|
makenew_python_app/server/__init__.py
|
makenew/python-app
|
5f3c6669efe6e80d356d39afb712d72bf0e69916
|
[
"MIT"
] | 2
|
2021-01-10T05:54:37.000Z
|
2021-01-12T01:24:38.000Z
|
makenew_python_app/server/__init__.py
|
makenew/python-app
|
5f3c6669efe6e80d356d39afb712d72bf0e69916
|
[
"MIT"
] | null | null | null |
makenew_python_app/server/__init__.py
|
makenew/python-app
|
5f3c6669efe6e80d356d39afb712d72bf0e69916
|
[
"MIT"
] | null | null | null |
"""
Server.
"""
from .boot import boot
| 6.666667
| 22
| 0.6
| 5
| 40
| 4.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 40
| 5
| 23
| 8
| 0.75
| 0.175
| 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
|
6d66f67de0458087f95c3ed2fde04b7558684251
| 135
|
py
|
Python
|
src/interpreter/functions/find.py
|
b-Development-Team/b-star
|
e1a47e118d0f30f7caca5ecc3ac08fadaf2227c6
|
[
"MIT"
] | 1
|
2021-12-28T22:07:10.000Z
|
2021-12-28T22:07:10.000Z
|
src/interpreter/functions/find.py
|
b-Development-Team/b-star
|
e1a47e118d0f30f7caca5ecc3ac08fadaf2227c6
|
[
"MIT"
] | 6
|
2022-01-07T22:49:19.000Z
|
2022-03-11T05:39:04.000Z
|
src/interpreter/functions/find.py
|
b-Development-Team/b-star
|
e1a47e118d0f30f7caca5ecc3ac08fadaf2227c6
|
[
"MIT"
] | 4
|
2021-11-26T01:38:32.000Z
|
2022-02-27T20:54:08.000Z
|
# TODO: Give better variable names
# TODO: Make v3 and v4 optional arguments
def find(v1, v2, v3, v4):
return v1.index(v2, v3, v4)
| 27
| 41
| 0.688889
| 24
| 135
| 3.875
| 0.708333
| 0.086022
| 0.129032
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.092593
| 0.2
| 135
| 4
| 42
| 33.75
| 0.768519
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
6dac80a1ff51af0cf7a466668618e8d3a05ea7c9
| 117
|
py
|
Python
|
trpg/registration/utility.py
|
jffifa/trpg
|
d808e5ce208a164154d93b177cb0c013a0d5f742
|
[
"MIT"
] | 2
|
2018-08-01T16:02:10.000Z
|
2018-08-11T05:08:14.000Z
|
trpg/registration/utility.py
|
jffifa/trpg
|
d808e5ce208a164154d93b177cb0c013a0d5f742
|
[
"MIT"
] | 5
|
2018-08-01T12:59:03.000Z
|
2018-08-10T19:32:20.000Z
|
trpg/registration/utility.py
|
jffifa/trpg
|
d808e5ce208a164154d93b177cb0c013a0d5f742
|
[
"MIT"
] | null | null | null |
from django.conf import settings
def decrypt(text):
half_len = len(text)//2
return text[half_len:]
| 14.625
| 33
| 0.649573
| 17
| 117
| 4.352941
| 0.705882
| 0.216216
| 0.297297
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011364
| 0.247863
| 117
| 7
| 34
| 16.714286
| 0.829545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
0964af2e7fa47894249aae9537d9732479a5a78c
| 2,690
|
py
|
Python
|
beforeThePackage/cor.py
|
AkiraDemenech/Postimpressionism
|
2a88ed95d2fb108bdddeb1803b4aad8ab6b7d0da
|
[
"MIT"
] | 2
|
2020-10-05T17:31:24.000Z
|
2021-03-23T11:59:52.000Z
|
beforeThePackage/cor.py
|
AkiraDemenech/Postimpressionism
|
2a88ed95d2fb108bdddeb1803b4aad8ab6b7d0da
|
[
"MIT"
] | null | null | null |
beforeThePackage/cor.py
|
AkiraDemenech/Postimpressionism
|
2a88ed95d2fb108bdddeb1803b4aad8ab6b7d0da
|
[
"MIT"
] | null | null | null |
""""""
from scipy import misc
import time
img = 'IMG-20200828-WA0022.'
output0 = img+'..png'
output1 = img+'...png'
img+='jpg'
input0 = misc.imread(img)
input1 = misc.imread(img)
l = input0.shape[0]
c = input1.shape[1]
print('\n %d x %d [%d] \n' %(l, c, (l*c)))
start = time.time()
for x in range(l):
#print(x,end='\t')
for y in range(c):
zero = list(input0[x,y])
um = list(input1[x,y])
if x%2 == y%2:
#verde no 0
zero[0] = zero[2] = (zero[0] + zero[2])//2
# zero[0] = zero[2] = (int(zero[0]) + int(zero[2]))//2
#cinza no 1
um[0]=um[1]=um[2] = (um[0]+um[1]+um[1]+um[2])//4
# um[0]=um[1]=um[2] = (int(um[0])+int(um[1])+int(um[1])+int(um[2]))//4
elif y%2 == 0:
#vermelho no 0
zero[1] = zero[2] = (zero[1] + zero[2])//2
# zero[1] = zero[2] = (int(zero[1]) + int(zero[2]))//2
#verde no 1
um[0] = um[2] = (um[0] + um[2])//2
# um[0] = um[2] = (int(um[0]) + int(um[2]))//2
else:
#azul no 0
zero[1] = zero[0] = (zero[1] + zero[0])//2
# zero[1] = zero[0] = (int(zero[1]) + int(zero[0]))//2
if x%2 == 0:
#vermelho no 1
um[1] = um[2] = (um[1] + um[2])//2
# um[1] = um[2] = (int(um[1]) + int(um[2]))//2
else:
#azul no 1
um[1] = um[0] = (um[1] + um[0])//2
# um[1] = um[0] = (int(um[1]) + int(um[0]))//2
input0[x,y] = zero
input1[x,y] = um
t = time.time() - start
print('%f s \a' %t)
print('%d px \t\t %f px/s \t %f s/px' %(l*c, (l*c)/t, t/(l*c)))
print('%d ln \t\t %f ln/s \t %f s/ln' %(l, l/t, t/l))
print('%d col \t\t %f col/s \t %f s/col' %(c, c/t, t/c))
misc.imsave(output1, input1)
misc.imsave(output0, input0)
#time.sleep(11)
input0 = misc.imread(img)
input1 = misc.imread(img)
start = time.time()
for x in range(l):
#print(x,end='\t')
for y in range(c):
zero = list(input0[x,y])
um = list(input1[x,y])
if x%2 == y%2:
#verde no 0
zero[0] = zero[2] = (int(zero[0]) + int(zero[2]))//2
#cinza no 1
um[0]=um[1]=um[2] = (int(um[0])+int(um[1])+int(um[1])+int(um[2]))//4
elif y%2 == 0:
#vermelho no 0
zero[1] = zero[2] = (int(zero[1]) + int(zero[2]))//2
#verde no 1
um[0] = um[2] = (int(um[0]) + int(um[2]))//2
else:
#azul no 0
zero[1] = zero[0] = (int(zero[1]) + int(zero[0]))//2
if x%2 == 0:
#vermelho no 1
um[1] = um[2] = (int(um[1]) + int(um[2]))//2
else:
#azul no 1
um[1] = um[0] = (int(um[1]) + int(um[0]))//2
input0[x,y] = zero
input1[x,y] = um
t = time.time() - start
print('%f s \a' %t)
print('%d px \t\t %f px/s \t %f s/px' %(l*c, (l*c)/t, t/(l*c)))
print('%d ln \t\t %f ln/s \t %f s/ln' %(l, l/t, t/l))
print('%d col \t\t %f col/s \t %f s/col' %(c, c/t, t/c))
misc.imsave(output1+'.png', input1)
misc.imsave(output0+'.png', input0)
| 26.9
| 72
| 0.500743
| 581
| 2,690
| 2.318417
| 0.092943
| 0.046771
| 0.048255
| 0.035635
| 0.85078
| 0.807721
| 0.804009
| 0.804009
| 0.747587
| 0.747587
| 0
| 0.084428
| 0.207435
| 2,690
| 100
| 73
| 26.9
| 0.547373
| 0.20855
| 0
| 0.584615
| 0
| 0
| 0.120952
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.030769
| 0
| 0.030769
| 0.138462
| 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
|
09daddf511940253cc26d996c34031c10fd00229
| 2,703
|
py
|
Python
|
tests/test_scipy_stats.py
|
miltondp/clustermatch-gene-expr
|
664bcf9032f53e22165ce7aa586dbf11365a5827
|
[
"BSD-2-Clause-Patent"
] | null | null | null |
tests/test_scipy_stats.py
|
miltondp/clustermatch-gene-expr
|
664bcf9032f53e22165ce7aa586dbf11365a5827
|
[
"BSD-2-Clause-Patent"
] | 13
|
2021-08-13T16:02:15.000Z
|
2022-01-31T17:56:57.000Z
|
tests/test_scipy_stats.py
|
miltondp/clustermatch-gene-expr
|
664bcf9032f53e22165ce7aa586dbf11365a5827
|
[
"BSD-2-Clause-Patent"
] | 1
|
2021-08-09T14:57:40.000Z
|
2021-08-09T14:57:40.000Z
|
import numpy as np
from scipy import stats
from clustermatch.scipy.stats import rank
def test_rank_no_duplicates():
data = np.array([0, 10, 1, 5, 7, 8, -5, -2])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group():
data = np.array([0, 10, 1, 5, 7, 8, 1, -2])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group_with_more_elements():
data = np.array([0, 10, 1, 1, 7, 8, 1, -2])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group_at_beginning():
data = np.array([0, 0, 1, -10, 7, 8, 9.4, -2])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group_at_beginning_with_more_elements():
data = np.array([0.13, 0.13, 0.13, 1, -10, 7, 8, 9.4, -2])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group_at_beginning_are_smallest():
data = np.array([0, 10, 1.5, -99.5, -99.5, -99.5, 5, 7, 8, -5, -2])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group_at_end():
data = np.array([0, 1, -10, 7, 8, 9.4, -2.5, -2.5])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group_at_end_with_more_elements():
data = np.array([0, 1, -10, 7, 8, 9.4, -12.5, -12.5, -12.5])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_one_duplicate_group_at_end_is_the_largest():
data = np.array([0, 1, -10, 7, 8, 9.4, 120.5, 120.5, 120.5])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
def test_rank_all_are_duplicates():
data = np.array([1.5, 1.5, 1.5, 1.5])
expected_ranks = stats.rankdata(data, "average")
observed_ranks = rank(data)
np.testing.assert_array_equal(observed_ranks, expected_ranks)
| 28.452632
| 71
| 0.709952
| 425
| 2,703
| 4.221176
| 0.12
| 0.06689
| 0.061316
| 0.144928
| 0.896321
| 0.891304
| 0.88573
| 0.836678
| 0.835563
| 0.818283
| 0
| 0.057068
| 0.157233
| 2,703
| 94
| 72
| 28.755319
| 0.730465
| 0
| 0
| 0.566038
| 0
| 0
| 0.025897
| 0
| 0
| 0
| 0
| 0
| 0.188679
| 1
| 0.188679
| false
| 0
| 0.056604
| 0
| 0.245283
| 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
|
61ead290e283ed9a918c5bff9967a06bc344fa38
| 2,603
|
py
|
Python
|
argos/libs/clients/ontology.py
|
daedafusion/django-argos
|
f28081be13d450baf8a7eaecef1e71e22a6e8833
|
[
"Apache-2.0"
] | null | null | null |
argos/libs/clients/ontology.py
|
daedafusion/django-argos
|
f28081be13d450baf8a7eaecef1e71e22a6e8833
|
[
"Apache-2.0"
] | null | null | null |
argos/libs/clients/ontology.py
|
daedafusion/django-argos
|
f28081be13d450baf8a7eaecef1e71e22a6e8833
|
[
"Apache-2.0"
] | null | null | null |
from django.conf import settings
import requests
from argos.libs.discovery import Discovery
__author__ = 'mphilpot'
class OntologyClient(object):
def __init__(self, token, url=None):
if url is None:
discovery = Discovery()
self.url = discovery.get_url("ontology")
else:
self.url = url
self.token = token
self.cert = getattr(settings, 'REQUESTS_CLIENT_CERT', None)
self.verify = getattr(settings, 'REQUESTS_CLIENT_VERIFY', None)
def get_ontology_description(self, domain, uuids=None):
headers = {
'accept': 'application/json',
'authorization': self.token,
}
params = []
if uuids:
for uuid in uuids:
params.append(('uuid', uuid))
r = requests.get('%s/ontologies/%s' % (self.url, domain), headers=headers, params=params, cert=self.cert, verify=self.verify)
r.raise_for_status()
return r.json()
def get_labels(self, domain, uuids=None):
headers = {
'accept': 'application/json',
'authorization': self.token,
}
params = []
if uuids:
for uuid in uuids:
params.append(('uuid', uuid))
r = requests.get('%s/ontologies/%s/labels' % (self.url, domain), headers=headers, params=params, cert=self.cert, verify=self.verify)
r.raise_for_status()
return r.json()
class OntologyAdminClient(object):
def __init__(self, token, url=None):
if url is None:
discovery = Discovery()
self.url = discovery.get_url("ontology")
else:
self.url = url
self.token = token
self.cert = getattr(settings, 'REQUESTS_CLIENT_CERT', None)
self.verify = getattr(settings, 'REQUESTS_CLIENT_VERIFY', None)
def get_ontology_meta(self, domain):
headers = {
'accept': 'application/json',
'authorization': self.token,
}
r = requests.get('%s/admin/ontologies/meta/%s' % (self.url, domain), headers=headers, cert=self.cert, verify=self.verify)
r.raise_for_status()
return r.json()
def upload_ontology_rdf(self, domain, rdf):
headers = {
'accept': 'application/json',
'content-type': 'text/xml',
'authorization': self.token,
}
r = requests.post('%s/admin/ontology/%s' % (self.url, domain), data=rdf, headers=headers, cert=self.cert, verify=self.verify)
r.raise_for_status()
return r.json()
| 27.114583
| 140
| 0.580484
| 291
| 2,603
| 5.065292
| 0.206186
| 0.048847
| 0.062415
| 0.078697
| 0.786974
| 0.759837
| 0.750339
| 0.716418
| 0.716418
| 0.716418
| 0
| 0
| 0.293123
| 2,603
| 96
| 141
| 27.114583
| 0.801087
| 0
| 0
| 0.707692
| 0
| 0
| 0.139017
| 0.036098
| 0
| 0
| 0
| 0
| 0
| 1
| 0.092308
| false
| 0
| 0.046154
| 0
| 0.230769
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
fef9ad1ce9f1ecfc27f7c2ebfb177c28a8841593
| 12,822
|
py
|
Python
|
tests/test_numbering.py
|
JwoolardAU/pandoc-numbering
|
e505c6bb26ce46c4cb9b53d084cc9aa430327a40
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_numbering.py
|
JwoolardAU/pandoc-numbering
|
e505c6bb26ce46c4cb9b53d084cc9aa430327a40
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_numbering.py
|
JwoolardAU/pandoc-numbering
|
e505c6bb26ce46c4cb9b53d084cc9aa430327a40
|
[
"BSD-3-Clause"
] | null | null | null |
# This Python file uses the following encoding: utf-8
from unittest import TestCase
from pandocfilters import Para, Str, Space, Span, Strong, RawInline, Emph, Header, DefinitionList, Plain
import json
import pandoc_numbering
from helper import init, createMetaList, createMetaMap, createMetaInlines, createListStr, createMetaString, createMetaBool
def getMeta1():
return {
'pandoc-numbering': createMetaList([
createMetaMap({
'category': createMetaInlines(u'exercise'),
'sectioning': createMetaInlines(u'-.+.')
})
])
}
def getMeta2():
return {
'pandoc-numbering': createMetaList([
createMetaMap({
'category': createMetaInlines(u'exercise'),
'first': createMetaString(u'2'),
'last': createMetaString(u'2'),
})
])
}
def getMeta3():
return {
'pandoc-numbering': createMetaList([
createMetaMap({
'category': createMetaInlines(u'exercise'),
'first': createMetaString(u'a'),
'last': createMetaString(u'b'),
})
])
}
def getMeta4():
return {
'pandoc-numbering': createMetaList([
createMetaMap({
'category': createMetaInlines(u'exercise'),
'classes': createMetaList([createMetaInlines(u'my-class')])
})
])
}
def getMeta5():
return {
'pandoc-numbering': createMetaList([
createMetaMap({
'category': createMetaInlines(u'exercise'),
'format': createMetaBool(False)
})
])
}
def test_numbering_none():
init()
src = Para(createListStr(u'Not an exercise'))
dest = src
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering():
init()
src = Para(createListStr(u'Exercise #'))
dest = Para([
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_definitionlist():
init()
src = DefinitionList([
[
createListStr(u'Exercise #'),
[Plain([createListStr(u'Content A')])]
],
[
createListStr(u'Exercise #'),
[Plain([createListStr(u'Content B')])]
]
])
dest = DefinitionList([
[
[Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1'))]
)],
[Plain([createListStr(u'Content A')])]
],
[
[Span(
[u'exercise:2', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 2'))]
)],
[Plain([createListStr(u'Content B')])]
]
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_prefix_single():
init()
src = Para(createListStr(u'Exercise #ex:'))
dest = Para([
Span(
[u'ex:1', ['pandoc-numbering-text', 'ex'], []],
[Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Para(createListStr(u'Exercise #'))
dest = Para([
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_latex():
init()
src = Para(createListStr(u'Exercise #'))
dest = Para([
RawInline(u'tex', u'\\phantomsection\\addcontentsline{exercise}{exercise}{\\protect\\numberline {1}{\\ignorespaces Exercise}}'),
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[RawInline('tex', '\\label{exercise:1}'), Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], 'latex', {}) == dest
init()
src = Para(createListStr(u'Exercise (The title) #'))
dest = Para([
RawInline(u'tex', u'\\phantomsection\\addcontentsline{exercise}{exercise}{\\protect\\numberline {1}{\\ignorespaces The title}}'),
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[
RawInline('tex', '\\label{exercise:1}'),
Strong(createListStr(u'Exercise 1')),
Space(),
Emph(createListStr(u'(') + createListStr(u'The title') + createListStr(u')'))
]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], 'latex', {}) == dest
def test_numbering_double():
init()
src = Para(createListStr(u'Exercise #'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
src = Para(createListStr(u'Exercise #'))
dest = Para([
Span(
[u'exercise:2', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 2'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_title():
init()
src = Para(createListStr(u'Exercise (The title) #'))
dest = Para([
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[
Strong(createListStr(u'Exercise 1')),
Space(),
Emph(createListStr(u'(') + createListStr(u'The title') + createListStr(u')'))
]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_level():
init()
src = Para(createListStr(u'Exercise +.+.#'))
dest = Para([
Span(
[u'exercise:0.0.1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 0.0.1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Header(1, [u'first-chapter', [], []], createListStr(u'First chapter'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
src = Header(2, [u'first-section', [], []], createListStr(u'First section'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
src = Para(createListStr(u'Exercise +.+.#'))
dest = Para([
Span(
[u'exercise:1.1.1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1.1.1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Para(createListStr(u'Exercise +.+.#'))
dest = Para([
Span(
[u'exercise:1.1.2', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1.1.2'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Header(2, [u'second-section', [], []], createListStr(u'Second section'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
src = Para(createListStr(u'Exercise +.+.#'))
dest = Para([
Span(
[u'exercise:1.2.1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1.2.1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_unnumbered():
init()
src = Header(1, [u'unnumbered-chapter', [u'unnumbered'], []], createListStr(u'Unnumbered chapter'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
src = Para(createListStr(u'Exercise +.#'))
dest = Para([
Span(
[u'exercise:0.1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 0.1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_hidden():
init()
src = Header(1, [u'first-chapter', [], []], createListStr(u'First chapter'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
src = Para(createListStr(u'Exercise -.#exercise:one'))
dest = Para([
Span(
[u'exercise:one', ['pandoc-numbering-text', 'exercise'], []],
[
Strong(createListStr(u'Exercise 1'))
]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Para(createListStr(u'Exercise -.#'))
dest = Para([
Span(
[u'exercise:1.2', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 2'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Header(1, [u'second-chapter', [], []], createListStr(u'Second chapter'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
src = Para(createListStr(u'Exercise -.#'))
dest = Para([
Span(
[u'exercise:2.1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Para(createListStr(u'Exercise +.#'))
dest = Para([
Span(
[u'exercise:2.2', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 2.2'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
src = Para([Str(u'Exercise'), Space(), Str(u'#')])
dest = Para([
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', {}) == dest
def test_numbering_sharp_sharp():
init()
src = Para(createListStr(u'Exercise ##'))
dest = Para(createListStr(u'Exercise #'))
pandoc_numbering.numbering(src['t'], src['c'], '', {})
assert src == dest
def sectioning(meta):
src = Header(1, [u'first-chapter', [], []], createListStr(u'First chapter'))
pandoc_numbering.numbering(src['t'], src['c'], '', meta)
src = Header(1, [u'second-chapter', [], []], createListStr(u'Second chapter'))
pandoc_numbering.numbering(src['t'], src['c'], '', meta)
src = Header(2, [u'first-section', [], []], createListStr(u'First section'))
pandoc_numbering.numbering(src['t'], src['c'], '', meta)
src = Header(2, [u'second-section', [], []], createListStr(u'Second section'))
pandoc_numbering.numbering(src['t'], src['c'], '', meta)
def test_numbering_sectioning_string():
init()
meta = getMeta1()
sectioning(meta)
src = Para(createListStr(u'Exercise #'))
dest = Para([
Span(
[u'exercise:2.2.1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 2.1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', meta) == dest
def test_numbering_sectioning_map():
init()
meta = getMeta2()
sectioning(meta)
src = Para([Str(u'Exercise'), Space(), Str(u'#')])
dest = Para([
Span(
[u'exercise:2.2.1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 2.1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', meta) == dest
def test_numbering_sectioning_map_error():
init()
meta = getMeta3()
sectioning(meta)
src = Para(createListStr(u'Exercise #'))
dest = Para([
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercise'], []],
[Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', meta) == dest
def test_classes():
init()
meta = getMeta4()
src = Para(createListStr(u'Exercise #'))
dest = Para([
Span(
[u'exercise:1', ['pandoc-numbering-text', 'my-class'], []],
[Strong(createListStr(u'Exercise 1'))]
)
])
assert pandoc_numbering.numbering(src['t'], src['c'], '', meta) == dest
def test_format():
init()
meta = getMeta5()
src = Para(createListStr(u'Exercise #'))
dest = json.loads(json.dumps(Para([
Span(
[u'exercise:1', ['pandoc-numbering-text', 'exercice'], []],
[
Span(['', ['description'], []], createListStr(u'Exercise')),
Span(['', ['number'], []], createListStr(u'1')),
Span(['', ['title'], []], [])
]
)
])))
json.loads(json.dumps(pandoc_numbering.numbering(src['t'], src['c'], '', meta))) == dest
| 29.207289
| 137
| 0.533224
| 1,276
| 12,822
| 5.301724
| 0.077586
| 0.1051
| 0.15935
| 0.143681
| 0.841833
| 0.823356
| 0.812565
| 0.798374
| 0.782557
| 0.73969
| 0
| 0.010861
| 0.267587
| 12,822
| 438
| 138
| 29.273973
| 0.709509
| 0.003978
| 0
| 0.653061
| 0
| 0
| 0.19563
| 0.051218
| 0
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| 0
| 0
| 0.069971
| 1
| 0.06414
| false
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| 0.014577
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0
| 6
|
3a0f2453cb506aa914604d26d3646a8fea4702eb
| 70
|
py
|
Python
|
Background.py
|
ViktorTrojan/Flappy-Bird
|
a1b2499f42c24bb2b4e8fba34bb0c219e9e399fd
|
[
"MIT"
] | null | null | null |
Background.py
|
ViktorTrojan/Flappy-Bird
|
a1b2499f42c24bb2b4e8fba34bb0c219e9e399fd
|
[
"MIT"
] | null | null | null |
Background.py
|
ViktorTrojan/Flappy-Bird
|
a1b2499f42c24bb2b4e8fba34bb0c219e9e399fd
|
[
"MIT"
] | null | null | null |
from Scrolling import Scrolling
class Background(Scrolling):
pass
| 17.5
| 31
| 0.8
| 8
| 70
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157143
| 70
| 4
| 32
| 17.5
| 0.949153
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| true
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| null | 0
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| 0
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| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
3a1f3bed0cd670ce52f454e878bcf94876e87b65
| 46
|
py
|
Python
|
src/zope/index/topic/__init__.py
|
Recursing/zope.index
|
5d182f2547b8cad270cbc4e1a1e7eddb51697d2c
|
[
"ZPL-2.1"
] | 7
|
2015-05-11T15:38:17.000Z
|
2021-08-25T05:48:03.000Z
|
src/zope/index/topic/__init__.py
|
Recursing/zope.index
|
5d182f2547b8cad270cbc4e1a1e7eddb51697d2c
|
[
"ZPL-2.1"
] | 23
|
2015-08-20T15:15:29.000Z
|
2022-03-29T06:37:46.000Z
|
src/zope/index/topic/__init__.py
|
Recursing/zope.index
|
5d182f2547b8cad270cbc4e1a1e7eddb51697d2c
|
[
"ZPL-2.1"
] | 8
|
2015-04-03T09:04:12.000Z
|
2021-09-29T19:55:22.000Z
|
from zope.index.topic.index import TopicIndex
| 23
| 45
| 0.847826
| 7
| 46
| 5.571429
| 0.857143
| 0
| 0
| 0
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| 0.086957
| 46
| 1
| 46
| 46
| 0.928571
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 6
|
3a384c4401271da02b52b0bc16b658da2f7b4469
| 8,027
|
py
|
Python
|
automol/zmatrix/newzmat/test_.py
|
kevinmooreiii/autochem
|
87f50adc09c3f1170459c629697aadd74154c769
|
[
"Apache-2.0"
] | null | null | null |
automol/zmatrix/newzmat/test_.py
|
kevinmooreiii/autochem
|
87f50adc09c3f1170459c629697aadd74154c769
|
[
"Apache-2.0"
] | null | null | null |
automol/zmatrix/newzmat/test_.py
|
kevinmooreiii/autochem
|
87f50adc09c3f1170459c629697aadd74154c769
|
[
"Apache-2.0"
] | null | null | null |
""" test automol.zmatrix
"""
import automol
from automol.zmatrix.newzmat._bimol_ts import hydrogen_abstraction
from automol.zmatrix.newzmat._bimol_ts import addition
from automol.zmatrix.newzmat._bimol_ts import insertion
from automol.zmatrix.newzmat._bimol_ts import substitution
from automol.zmatrix.newzmat._unimol_ts import hydrogen_migration
from automol.zmatrix.newzmat._unimol_ts import beta_scission
from automol.zmatrix.newzmat._unimol_ts import concerted_unimol_elimination
from automol.zmatrix.newzmat._unimol_ts import ring_forming_scission
from automol.zmatrix.newzmat._util import shifted_standard_zmas_graphs
# ZMA Bank
C2H6_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('CC')))
C2H4_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('C=C')))
CH4_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('C')))
CH2_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('[CH2]')))
OH_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('[OH]')))
H_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('[H]')))
CH3_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('[CH3]')))
H2O_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('O')))
HO2_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('O[O]')))
CH2O_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('C=O')))
CH3CH2O_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('CC[O]')))
H2O2_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('OO')))
CH2COH_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('[CH2]CO')))
CH3CH2CH2CH2_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('CCC[CH2]')))
CH3CHCH2CH3_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('CC[CH]C')))
C3H8_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('CCC')))
CH3CH2OO_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('CCO[O]')))
CH2CH2OOH_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('[CH2]COO')))
cCH2OCH2_ZMA = automol.geom.zmatrix(
automol.inchi.geometry(automol.smiles.inchi('C1CO1')))
# BIMOL TS
def test__ts_hydrogen_abstraction():
""" test zmatrix.ts.hydrogen_abstraction
"""
rct_zmas = [CH4_ZMA, OH_ZMA]
prd_zmas = [CH3_ZMA, H2O_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = hydrogen_abstraction(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
def test__ts_addition():
""" test zmatrix.ts.addition
"""
rct_zmas = [C2H4_ZMA, OH_ZMA]
prd_zmas = [CH2COH_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = addition(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
def test__ts_insertion():
""" test zmatrix.ts.insertion
"""
rct_zmas = [C2H6_ZMA, CH2_ZMA]
prd_zmas = [C3H8_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = insertion(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
def test__ts_substitution():
""" test zmatrix.ts.substitution
"""
rct_zmas = [H2O2_ZMA, H_ZMA]
prd_zmas = [H2O_ZMA, OH_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = substitution(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
# UNIMOL TS
def test__ts_hydrogen_migration():
""" test zmatrix.ts.hydrogen_migration
"""
rct_zmas = [CH3CH2CH2CH2_ZMA]
prd_zmas = [CH3CHCH2CH3_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
rct_zmas = [rct_zmas]
prd_zmas = [prd_zmas]
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = hydrogen_migration(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
def test__ts_beta_scission():
""" test zmatrix.ts.beta_scission
"""
rct_zmas = [CH3CH2O_ZMA]
prd_zmas = [CH3_ZMA, CH2O_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = beta_scission(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
def test__ts_elimination():
""" test zmatrix.ts.elimination
"""
rct_zmas = [CH3CH2OO_ZMA]
prd_zmas = [C2H4_ZMA, HO2_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
rct_zmas = [rct_zmas]
prd_zmas = [prd_zmas]
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = concerted_unimol_elimination(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
def test__ts_ring_forming_scission():
""" test zmatrix.ts.ring_forming_scission
"""
rct_zmas = [CH2CH2OOH_ZMA]
prd_zmas = [cCH2OCH2_ZMA, OH_ZMA]
rct_zmas, rct_gras = shifted_standard_zmas_graphs(
rct_zmas, remove_stereo=True)
prd_zmas, prd_gras = shifted_standard_zmas_graphs(
prd_zmas, remove_stereo=True)
rct_zmas = [rct_zmas]
prd_zmas = [prd_zmas]
tras, _, _, rtyp = automol.graph.reac.classify(rct_gras, prd_gras)
print('\nrtyp', rtyp)
zma_ret = ring_forming_scission(rct_zmas, prd_zmas, tras)
print('zma\n', automol.zmatrix.string(zma_ret['ts_zma']))
print('bnd keys\n', zma_ret['bnd_keys'])
print('const keys\n', zma_ret['const_keys'])
if __name__ == '__main__':
# BIMOL
test__ts_hydrogen_abstraction()
test__ts_addition()
test__ts_substitution()
# test__ts_insertion()
# UNIMOL
test__ts_hydrogen_migration()
test__ts_beta_scission()
test__ts_elimination()
# test__ts_ring_forming_scission()
| 34.012712
| 75
| 0.712595
| 1,135
| 8,027
| 4.693392
| 0.074009
| 0.049934
| 0.049934
| 0.074901
| 0.829923
| 0.767974
| 0.767974
| 0.710156
| 0.710156
| 0.710156
| 0
| 0.010457
| 0.154105
| 8,027
| 235
| 76
| 34.157447
| 0.77408
| 0.05033
| 0
| 0.472727
| 0
| 0
| 0.072213
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.048485
| false
| 0
| 0.060606
| 0
| 0.109091
| 0.193939
| 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
|
e91e58d3aa56c1dfae07be7e5bcb50917b337e09
| 6
|
py
|
Python
|
testsuite/modulegraph-dir/pkg_d/__init__.py
|
xoviat/modulegraph2
|
766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a
|
[
"MIT"
] | 9
|
2020-03-22T14:48:01.000Z
|
2021-05-30T12:18:12.000Z
|
testsuite/modulegraph-dir/pkg_d/__init__.py
|
xoviat/modulegraph2
|
766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a
|
[
"MIT"
] | 15
|
2020-01-06T10:02:32.000Z
|
2021-05-28T12:22:44.000Z
|
testsuite/modulegraph-dir/pkg_d/__init__.py
|
ronaldoussoren/modulegraph2
|
b6ab1766b0098651b51083235ff8a18a5639128b
|
[
"MIT"
] | 4
|
2020-05-10T18:51:41.000Z
|
2021-04-07T14:03:12.000Z
|
e = 1
| 3
| 5
| 0.333333
| 2
| 6
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 0.5
| 6
| 1
| 6
| 6
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
e943a0f7bed4bdb488545417034a35a5dd1fde4c
| 525
|
py
|
Python
|
tests/helpers/random_utils.py
|
aeskincbr/kesher-backend
|
57e0fd7410fc8ae6a6fe438b3633f191bb791072
|
[
"MIT"
] | 1
|
2021-05-13T10:58:06.000Z
|
2021-05-13T10:58:06.000Z
|
tests/helpers/random_utils.py
|
aeskincbr/kesher-backend
|
57e0fd7410fc8ae6a6fe438b3633f191bb791072
|
[
"MIT"
] | 1
|
2021-12-23T13:36:43.000Z
|
2021-12-23T13:36:43.000Z
|
tests/helpers/random_utils.py
|
CyberArkForTheCommunity/jobli-backend
|
2309c9ac33993cb89a8e1581630d99b46f8d55aa
|
[
"MIT"
] | 2
|
2021-04-06T15:28:16.000Z
|
2021-05-13T23:02:59.000Z
|
import random
import string
from ipaddress import IPv4Address
def random_ip_address():
random.seed(random.randint(1, 10001))
return str(IPv4Address(random.getrandbits(32)))
def random_string(n=8):
return ''.join(random.choices(string.ascii_letters, k=n))
def random_password(n=10):
return ''.join(random.choices(string.digits, k=1)+random.choices(string.ascii_lowercase, k=1) +
random.choices(string.ascii_uppercase, k=1)+random.choices(string.ascii_letters + string.digits, k=n-3))
| 27.631579
| 123
| 0.725714
| 75
| 525
| 4.973333
| 0.4
| 0.174263
| 0.254692
| 0.257373
| 0.41555
| 0.209115
| 0
| 0
| 0
| 0
| 0
| 0.037611
| 0.139048
| 525
| 18
| 124
| 29.166667
| 0.787611
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| false
| 0.090909
| 0.272727
| 0.181818
| 0.818182
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 6
|
3a7ac88f94d3eb68abdab7a18f15df6ca2df4cad
| 664
|
py
|
Python
|
leonardo/module/web/models/__init__.py
|
timgates42/django-leonardo
|
c155f97fee9e2be1e0f508d47a1c205028253ecc
|
[
"BSD-3-Clause"
] | 102
|
2015-04-30T12:27:14.000Z
|
2021-10-31T18:21:16.000Z
|
leonardo/module/web/models/__init__.py
|
timgates42/django-leonardo
|
c155f97fee9e2be1e0f508d47a1c205028253ecc
|
[
"BSD-3-Clause"
] | 158
|
2015-04-30T22:42:34.000Z
|
2019-09-07T15:37:22.000Z
|
leonardo/module/web/models/__init__.py
|
timgates42/django-leonardo
|
c155f97fee9e2be1e0f508d47a1c205028253ecc
|
[
"BSD-3-Clause"
] | 64
|
2015-05-10T12:00:39.000Z
|
2021-07-29T19:47:27.000Z
|
from leonardo.module.web.models.page import *
from leonardo.module.web.models.widget import *
from leonardo.module.web.widget.icon.models import IconWidget
from leonardo.module.web.widget.application.models import ApplicationWidget
from leonardo.module.web.widget.markuptext.models import MarkupTextWidget
from leonardo.module.web.widget.feedreader.models import FeedReaderWidget
from leonardo.module.web.widget.pagetitle.models import PageTitleWidget
from leonardo.module.web.widget.table.models import TableWidget
from leonardo.module.web.widget.siteheading.models import SiteHeadingWidget
from leonardo.module.web.widget.htmltext.models import HtmlTextWidget
| 51.076923
| 75
| 0.861446
| 86
| 664
| 6.651163
| 0.27907
| 0.20979
| 0.314685
| 0.367133
| 0.493007
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.063253
| 664
| 12
| 76
| 55.333333
| 0.919614
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 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
|
c9167d5ee1ce85604547220c0f49a2d273e293bc
| 91
|
py
|
Python
|
resources/mgltools_x86_64Linux2_1.5.6/MGLToolsPckgs/Volume/Renderers/UTVolumeLibrary/UTVolumeLibrary.py
|
J-E-J-S/aaRS-Pipeline
|
43f59f28ab06e4b16328c3bc405cdddc6e69ac44
|
[
"MIT"
] | 8
|
2021-12-14T21:30:01.000Z
|
2022-02-14T11:30:03.000Z
|
resources/mgltools_x86_64Linux2_1.5.6/MGLToolsPckgs/Volume/Renderers/UTVolumeLibrary/UTVolumeLibrary.py
|
J-E-J-S/aaRS-Pipeline
|
43f59f28ab06e4b16328c3bc405cdddc6e69ac44
|
[
"MIT"
] | null | null | null |
resources/mgltools_x86_64Linux2_1.5.6/MGLToolsPckgs/Volume/Renderers/UTVolumeLibrary/UTVolumeLibrary.py
|
J-E-J-S/aaRS-Pipeline
|
43f59f28ab06e4b16328c3bc405cdddc6e69ac44
|
[
"MIT"
] | null | null | null |
#from utvollib.UTVolumeLibrary import *
from UTpackages.UTvolrend.UTVolumeLibrary import *
| 30.333333
| 50
| 0.846154
| 9
| 91
| 8.555556
| 0.666667
| 0.545455
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.087912
| 91
| 2
| 51
| 45.5
| 0.927711
| 0.417582
| 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
|
c948d9462e71b957fb9ab098bd572622b49f4e29
| 28
|
py
|
Python
|
libs/__init__.py
|
manishrawat4u/plugin.video.bloimediaplayer
|
d561c095fd0862bbe21620daef80d0c5fde36ca5
|
[
"MIT"
] | 1
|
2019-01-27T23:49:49.000Z
|
2019-01-27T23:49:49.000Z
|
libs/__init__.py
|
manishrawat4u/plugin.video.bloimediaplayer
|
d561c095fd0862bbe21620daef80d0c5fde36ca5
|
[
"MIT"
] | null | null | null |
libs/__init__.py
|
manishrawat4u/plugin.video.bloimediaplayer
|
d561c095fd0862bbe21620daef80d0c5fde36ca5
|
[
"MIT"
] | null | null | null |
print('inside init of libs')
| 28
| 28
| 0.75
| 5
| 28
| 4.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 28
| 1
| 28
| 28
| 0.84
| 0
| 0
| 0
| 0
| 0
| 0.655172
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
a32a284034ea6e3537cb1450fed0266d635c0152
| 66
|
py
|
Python
|
tests/tests.py
|
sasriawesome/django_products
|
a945dbf983748ff558583695c66226d579bfa4a0
|
[
"MIT"
] | 1
|
2021-06-23T07:07:18.000Z
|
2021-06-23T07:07:18.000Z
|
tests/tests.py
|
sasriawesome/django_extra_referrals
|
a8588412b7c25acb5820d6b69bb373356a5ad7e2
|
[
"MIT"
] | 5
|
2020-04-08T15:47:35.000Z
|
2021-06-10T18:57:53.000Z
|
tests/tests.py
|
realnoobs/django_extra_referrals
|
a8588412b7c25acb5820d6b69bb373356a5ad7e2
|
[
"MIT"
] | null | null | null |
from django.utils import timezone
from django.test import TestCase
| 33
| 33
| 0.863636
| 10
| 66
| 5.7
| 0.7
| 0.350877
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.106061
| 66
| 2
| 34
| 33
| 0.966102
| 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
|
a36da5ce1b1bd88749c1491ad3f6e4e58155fb7f
| 50
|
py
|
Python
|
app/app/crud/__init__.py
|
Tall-Programacion-FIME/backend
|
95b6934fd57086ffc2be3d9135732df3d240f694
|
[
"Apache-2.0"
] | null | null | null |
app/app/crud/__init__.py
|
Tall-Programacion-FIME/backend
|
95b6934fd57086ffc2be3d9135732df3d240f694
|
[
"Apache-2.0"
] | 13
|
2021-03-04T22:59:54.000Z
|
2021-05-16T23:24:22.000Z
|
app/app/crud/__init__.py
|
Tall-Programacion-FIME/backend
|
95b6934fd57086ffc2be3d9135732df3d240f694
|
[
"Apache-2.0"
] | 1
|
2021-04-20T14:51:43.000Z
|
2021-04-20T14:51:43.000Z
|
from .crud_book import *
from .crud_user import *
| 16.666667
| 24
| 0.76
| 8
| 50
| 4.5
| 0.625
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 50
| 2
| 25
| 25
| 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
|
6ea6e7bc3ce558c37c262ee176490ddeb40198d8
| 213
|
py
|
Python
|
ml-framework/main.py
|
SANER22-ERA/extract-method-experiments
|
f03591b14216bc623cd85601d3d4e4e52f7bf0e8
|
[
"Apache-2.0"
] | null | null | null |
ml-framework/main.py
|
SANER22-ERA/extract-method-experiments
|
f03591b14216bc623cd85601d3d4e4e52f7bf0e8
|
[
"Apache-2.0"
] | null | null | null |
ml-framework/main.py
|
SANER22-ERA/extract-method-experiments
|
f03591b14216bc623cd85601d3d4e4e52f7bf0e8
|
[
"Apache-2.0"
] | null | null | null |
from src.trainer import train_by_config, test_by_config
import os
# train_by_config(os.path.join('settings', 'training_settings_7.ini'))
#
test_by_config(os.path.join('test_settings', 'test_settings.ini'))
| 30.428571
| 71
| 0.774648
| 34
| 213
| 4.5
| 0.441176
| 0.20915
| 0.169935
| 0.183007
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005181
| 0.093897
| 213
| 6
| 72
| 35.5
| 0.787565
| 0.319249
| 0
| 0
| 0
| 0
| 0.220588
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
6ed0a6f3859812c8d438a487cc744e13d0d8f9d1
| 3,155
|
py
|
Python
|
data/agriculture/FAOSTAT_livestock_product_produced/viz/generate.py
|
ilopezgp/human_impacts
|
b2758245edac0946080a647f1dbfd1098c0f0b27
|
[
"MIT"
] | 4
|
2020-08-25T00:52:01.000Z
|
2020-11-16T16:57:46.000Z
|
data/agriculture/FAOSTAT_livestock_product_produced/viz/generate.py
|
ilopezgp/human_impacts
|
b2758245edac0946080a647f1dbfd1098c0f0b27
|
[
"MIT"
] | 5
|
2020-10-30T21:22:55.000Z
|
2021-12-30T02:07:02.000Z
|
data/agriculture/FAOSTAT_livestock_product_produced/viz/generate.py
|
ilopezgp/human_impacts
|
b2758245edac0946080a647f1dbfd1098c0f0b27
|
[
"MIT"
] | 2
|
2020-08-28T10:11:28.000Z
|
2020-11-11T07:58:46.000Z
|
#%%
import numpy as np
import pandas as pd
import altair as alt
# Load the production data.
data = pd.read_csv('../processed/FAOSTAT_livestock_and_product.csv')
data['year'] = pd.to_datetime(data['year'], format='%Y')
# Generate JSON vis for subcategories
for g, d in data.groupby('subcategory'):
chart= alt.Chart(d).encode(
x=alt.X(field="year", type="temporal", timeUnit='year',
title="year"),
y=alt.Y(field="mass_produced_Mt",
type="quantitative",
title="produced mass [Mt]"),
tooltip=[alt.Tooltip("year:T", timeUnit="year", title="year"),
alt.Tooltip("mass_produced_Mt:Q", format="0.0f", title="produced mass [Mt]")]
).properties(width="container",
height=300
).mark_line(color='dodgerblue')
l = chart.mark_line(color='dodgerblue')
p = chart.mark_point(filled=True, color='dodgerblue')
figure = alt.layer(l, p)
figure.save(f'{g}.json')
# %%
# Generate JSON vis for categories
for g, d in data.groupby('category'):
d = d.groupby(['year']).sum().reset_index()
chart= alt.Chart(d).encode(
x=alt.X(field="year", type="temporal", timeUnit='year',
title="year"),
y=alt.Y(field="mass_produced_Mt",
type="quantitative",
title="produced mass [Mt]"),
tooltip=[alt.Tooltip("year:T", timeUnit="year", title="year"),
alt.Tooltip("mass_produced_Mt:Q", format="0.0f", title="produced mass [Mt]")]
).properties(width="container",
height=300
).mark_line(color='dodgerblue')
l = chart.mark_line(color='dodgerblue')
p = chart.mark_point(filled=True, color='dodgerblue')
figure = alt.layer(l, p)
figure.save(f'{g}.json')
# %%
# Generate JSON vis for subcategories
for g, d in data.groupby('subcategory'):
chart= alt.Chart(d).encode(
x=alt.X(field="year", type="temporal", timeUnit='year',
title="year"),
y=alt.Y(field="yield_kg_per_head",
type="quantitative",
title="production yield [kg / animal]"),
tooltip=[alt.Tooltip("year", type='temporal', timeUnit="year", title="year", format='%Y'),
alt.Tooltip("yield_kg_per_head", type='quantitative', format="0.1f", title="yield [kg / animal]")]
).properties(width="container",
height=300
).mark_line(color='dodgerblue')
l = chart.mark_line(color='dodgerblue')
p = chart.mark_point(filled=True, color='dodgerblue')
figure = alt.layer(l, p)
figure.save(f"{'_'.join(g.lower().split(' '))}_yield.json")
# %%
| 43.819444
| 130
| 0.501743
| 341
| 3,155
| 4.55132
| 0.243402
| 0.086985
| 0.065722
| 0.081186
| 0.798969
| 0.798969
| 0.748711
| 0.724871
| 0.724871
| 0.724871
| 0
| 0.007289
| 0.347702
| 3,155
| 71
| 131
| 44.43662
| 0.746842
| 0.045325
| 0
| 0.777778
| 0
| 0
| 0.219374
| 0.024301
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.055556
| 0
| 0.055556
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
42d8f5960650b518501ea6de7021125d36008264
| 6,553
|
py
|
Python
|
PiCN/Layers/ChunkLayer/Chunkifyer/test/test_SimpleContentChunkifyer.py
|
NikolaiRutz/PiCN
|
7775c61caae506a88af2e4ec34349e8bd9098459
|
[
"BSD-3-Clause"
] | null | null | null |
PiCN/Layers/ChunkLayer/Chunkifyer/test/test_SimpleContentChunkifyer.py
|
NikolaiRutz/PiCN
|
7775c61caae506a88af2e4ec34349e8bd9098459
|
[
"BSD-3-Clause"
] | 5
|
2020-07-15T09:01:42.000Z
|
2020-09-28T08:45:21.000Z
|
PiCN/Layers/ChunkLayer/Chunkifyer/test/test_SimpleContentChunkifyer.py
|
NikolaiRutz/PiCN
|
7775c61caae506a88af2e4ec34349e8bd9098459
|
[
"BSD-3-Clause"
] | null | null | null |
"""Test for Simple Content Chunkifyer"""
import unittest
from PiCN.Layers.ChunkLayer.Chunkifyer import SimpleContentChunkifyer
from PiCN.Packets import Content, Name
class test_SimpleContentChunkifyer(unittest.TestCase):
def setUp(self):
self.chunkifyer = SimpleContentChunkifyer()
def tearDown(self):
pass
def test_generate_metadata_no_next(self):
"""Test generating a simple metadata object"""
name = Name("/test/data")
res = self.chunkifyer.generate_meta_data(2,4,0, 0,name,300)
self.assertEqual(res.name.to_string(), "/test/data")
self.assertEqual(res.content, "mdo:300:/test/data/c2;/test/data/c3:")
def test_generate_metadata_one_next(self):
"""Test generating a simple metadata object with one following"""
name = Name("/test/data")
res = self.chunkifyer.generate_meta_data(2,4,0,1,name,300)
self.assertEqual(res.name.to_string(), "/test/data")
self.assertEqual(res.content, "mdo:300:/test/data/c2;/test/data/c3:/test/data/m1")
def test_generate_metadata_two_next(self):
"""Test generating a simple metadata object with two following"""
name = Name("/test/data")
res = self.chunkifyer.generate_meta_data(2,4,1,2, name,300)
self.assertEqual(res.name.to_string(), "/test/data/m1")
self.assertEqual(res.content, "mdo:300:/test/data/c2;/test/data/c3:/test/data/m2")
def test_chunk_single_metadata(self):
name = Name("/test/data")
string = "A" * 4096 + "B" * 4096 + "C" * 4096
content = Content(name, string)
md, content = self.chunkifyer.chunk_data(content)
md_name_comp = ['/test/data']
md_data_comp = ['mdo:12288:/test/data/c0;/test/data/c1;/test/data/c2:']
content_name_comp = ['/test/data/c0', '/test/data/c1', '/test/data/c2']
content_data_comp = ["A" * 4096, "B" * 4096, "C" * 4096]
for i in range(0, len(md)):
self.assertEqual(md[i].name.to_string(), md_name_comp[i])
self.assertEqual(md[i].content, md_data_comp[i])
for i in range(0, len(content)):
self.assertEqual(content[i].name.to_string(), content_name_comp[i])
self.assertEqual(content[i].content, content_data_comp[i])
def test_chunk_multiple_metadata(self):
"""Test chunking metadata with three metadata objects and 10 chunks"""
name = Name("/test/data")
string = "A"*4096 + "B"*4096 + "C"*4096 + "D"*4096 + "E"*4096 + "F"*4096 + "G"*4096 + "H"*4096 \
+ "I"*4096 + "J"*4000
content = Content(name, string)
md, chunked_content = self.chunkifyer.chunk_data(content)
md_name_comp = ['/test/data', '/test/data/m1', '/test/data/m2']
md_data_comp = ['mdo:40864:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:/test/data/m1',
'mdo:40864:/test/data/c4;/test/data/c5;/test/data/c6;/test/data/c7:/test/data/m2',
'mdo:40864:/test/data/c8;/test/data/c9:']
content_name_comp = ['/test/data/c0', '/test/data/c1', '/test/data/c2', '/test/data/c3', '/test/data/c4',
'/test/data/c5', '/test/data/c6', '/test/data/c7', '/test/data/c8', '/test/data/c9']
content_data_comp = ["A"*4096, "B"*4096, "C"*4096, "D"*4096, "E"*4096, "F"*4096, "G"*4096, "H"*4096,
"I"*4096, "J"*4000]
for i in range(0, len(md)):
self.assertEqual(md[i].name.to_string(), md_name_comp[i])
self.assertEqual(md[i].content, md_data_comp[i])
for i in range(0, len(chunked_content)):
self.assertEqual(chunked_content[i].name.to_string(), content_name_comp[i])
self.assertEqual(chunked_content[i].content, content_data_comp[i])
def test_chunk_multiple_metadata_reassemble(self):
"""Test chunking metadata with three metadata objects and 10 chunks and reassemble"""
name = Name("/test/data")
string = "A" * 4096 + "B" * 4096 + "C" * 4096 + "D" * 4096 + "E" * 4096 + "F" * 4096 + "G" * 4096 + "H" * 4096 \
+ "I" * 4096 + "J" * 4000
content = Content(name, string)
md, chunked_content = self.chunkifyer.chunk_data(content)
md_name_comp = ['/test/data', '/test/data/m1', '/test/data/m2']
md_data_comp = ['mdo:40864:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:/test/data/m1',
'mdo:40864:/test/data/c4;/test/data/c5;/test/data/c6;/test/data/c7:/test/data/m2',
'mdo:40864:/test/data/c8;/test/data/c9:']
content_name_comp = ['/test/data/c0', '/test/data/c1', '/test/data/c2', '/test/data/c3', '/test/data/c4',
'/test/data/c5', '/test/data/c6', '/test/data/c7', '/test/data/c8', '/test/data/c9']
content_data_comp = ["A" * 4096, "B" * 4096, "C" * 4096, "D" * 4096, "E" * 4096, "F" * 4096, "G" * 4096,
"H" * 4096,
"I" * 4096, "J" * 4000]
for i in range(0, len(md)):
self.assertEqual(md[i].name.to_string(), md_name_comp[i])
self.assertEqual(md[i].content, md_data_comp[i])
for i in range(0, len(chunked_content)):
self.assertEqual(chunked_content[i].name.to_string(), content_name_comp[i])
self.assertEqual(chunked_content[i].content, content_data_comp[i])
reassembled_content = self.chunkifyer.reassamble_data(md[0].name, chunked_content)
self.assertEqual(content, reassembled_content)
def test_parse_metadata_next(self):
"""Test parse metadata with next metadata"""
md, names, size = self.chunkifyer.parse_meta_data(
"mdo:300:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:/test/data/m1")
self.assertEqual(Name("/test/data/m1"), md)
names_comp = [Name("/test/data/c0"), Name("/test/data/c1"), Name("/test/data/c2"), Name("/test/data/c3")]
self.assertEqual(names, names_comp)
self.assertEqual(int(size), 300)
def test_parse_metadata(self):
"""Test parse metadata"""
md, names, size = self.chunkifyer.parse_meta_data(
"mdo:300:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:")
self.assertEqual(None, md)
names_comp = [Name("/test/data/c0"), Name("/test/data/c1"), Name("/test/data/c2"), Name("/test/data/c3")]
self.assertEqual(names, names_comp)
self.assertEqual(int(size), 300)
| 44.578231
| 120
| 0.599725
| 914
| 6,553
| 4.175055
| 0.100656
| 0.192872
| 0.04717
| 0.033019
| 0.830189
| 0.81499
| 0.81499
| 0.81499
| 0.803721
| 0.772537
| 0
| 0.069516
| 0.225088
| 6,553
| 147
| 121
| 44.578231
| 0.681961
| 0.060888
| 0
| 0.568421
| 0
| 0.094737
| 0.221786
| 0.117599
| 0
| 0
| 0
| 0
| 0.263158
| 1
| 0.105263
| false
| 0.010526
| 0.031579
| 0
| 0.147368
| 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
|
2865e09ed1270b581757be94c2f99e1add60561b
| 34
|
py
|
Python
|
visionlib/object/detection/__init__.py
|
sumeshmn/Visionlib
|
c543ee038d6d1dcf9d88a8d7a782addd998e6036
|
[
"MIT"
] | 46
|
2020-04-26T23:00:32.000Z
|
2022-02-10T15:38:45.000Z
|
visionlib/object/detection/__init__.py
|
sumeshmn/Visionlib
|
c543ee038d6d1dcf9d88a8d7a782addd998e6036
|
[
"MIT"
] | 3
|
2020-05-18T22:34:32.000Z
|
2021-10-01T03:07:59.000Z
|
visionlib/object/detection/__init__.py
|
sumeshmn/Visionlib
|
c543ee038d6d1dcf9d88a8d7a782addd998e6036
|
[
"MIT"
] | 2
|
2020-04-29T15:15:26.000Z
|
2020-05-01T18:49:00.000Z
|
from .detection import ODetection
| 17
| 33
| 0.852941
| 4
| 34
| 7.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 34
| 1
| 34
| 34
| 0.966667
| 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
|
289a75729e654ce57f733d76a96089abbd768e4f
| 49
|
py
|
Python
|
bot/__main__.py
|
Temmon/Bibliomantic_Oracles
|
b75566f38fffee5937cc2cb00cb309b74d01be98
|
[
"MIT"
] | 11
|
2021-07-31T18:31:34.000Z
|
2022-03-07T16:26:24.000Z
|
bot/__main__.py
|
Temmon/Bibliomantic_Oracles
|
b75566f38fffee5937cc2cb00cb309b74d01be98
|
[
"MIT"
] | 2
|
2021-09-19T23:40:51.000Z
|
2021-09-20T00:41:20.000Z
|
bot/__main__.py
|
Temmon/Bibliomantic_Oracles
|
b75566f38fffee5937cc2cb00cb309b74d01be98
|
[
"MIT"
] | 3
|
2021-07-29T14:28:01.000Z
|
2022-03-16T06:56:19.000Z
|
from bot import bot
bot.bot.run(bot.getToken())
| 12.25
| 27
| 0.734694
| 9
| 49
| 4
| 0.555556
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122449
| 49
| 3
| 28
| 16.333333
| 0.837209
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
95491114b66ca44aba9a90df623e293de3ace47a
| 244
|
py
|
Python
|
bagpy/__init__.py
|
jmscslgroup/rosbagpy
|
128065498ef1992aa3536c9b484e7c8aebd27c92
|
[
"MIT"
] | 107
|
2020-05-06T08:28:41.000Z
|
2022-03-24T07:31:35.000Z
|
bagpy/__init__.py
|
jmscslgroup/rosbagpy
|
128065498ef1992aa3536c9b484e7c8aebd27c92
|
[
"MIT"
] | 22
|
2020-07-07T11:57:14.000Z
|
2022-03-17T10:51:32.000Z
|
bagpy/__init__.py
|
jmscslgroup/rosbagpy
|
128065498ef1992aa3536c9b484e7c8aebd27c92
|
[
"MIT"
] | 24
|
2020-09-21T17:51:45.000Z
|
2022-03-23T08:31:42.000Z
|
# Initial Date: March 2, 2020
# Author: Rahul Bhadani
# Copyright (c) Rahul Bhadani, Arizona Board of Regents
# All rights reserved.
from .bagreader import bagreader
from .bagreader import animate_timeseries
from .bagreader import create_fig
| 27.111111
| 56
| 0.790984
| 33
| 244
| 5.787879
| 0.727273
| 0.204188
| 0.298429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.024155
| 0.151639
| 244
| 8
| 57
| 30.5
| 0.898551
| 0.512295
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
95daa458e4a231f72bcecdb2578ef8cf66ca3848
| 41
|
py
|
Python
|
adelie/train/__init__.py
|
JRBCH/adelie
|
02a12fcb424866635d248c356cc236f4ae5d12af
|
[
"MIT"
] | null | null | null |
adelie/train/__init__.py
|
JRBCH/adelie
|
02a12fcb424866635d248c356cc236f4ae5d12af
|
[
"MIT"
] | null | null | null |
adelie/train/__init__.py
|
JRBCH/adelie
|
02a12fcb424866635d248c356cc236f4ae5d12af
|
[
"MIT"
] | null | null | null |
from .OnlineTrainer import OnlineTrainer
| 20.5
| 40
| 0.878049
| 4
| 41
| 9
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097561
| 41
| 1
| 41
| 41
| 0.972973
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| null | 0
| 0
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| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
95db802f2cb831b635ca73f8a5591bc0d6804f79
| 28
|
py
|
Python
|
Tests/test_testing/_common.py
|
craneworks/srl-python-lib
|
83b24b9dc406f4481d2b5fad814e2eff932cb04f
|
[
"MIT"
] | null | null | null |
Tests/test_testing/_common.py
|
craneworks/srl-python-lib
|
83b24b9dc406f4481d2b5fad814e2eff932cb04f
|
[
"MIT"
] | null | null | null |
Tests/test_testing/_common.py
|
craneworks/srl-python-lib
|
83b24b9dc406f4481d2b5fad814e2eff932cb04f
|
[
"MIT"
] | null | null | null |
from srllib.testing import *
| 28
| 28
| 0.821429
| 4
| 28
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 28
| 1
| 28
| 28
| 0.92
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| true
| 0
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| 1
| 0
| 1
| 0
|
0
| 6
|
255e1078bfe319a1f4e61b4724b9e83a404f08ee
| 38
|
py
|
Python
|
FacebookEnum/__init__.py
|
coldfusion39/FacebookEnum
|
241d2af1c577ab20985cf39fee5b1b44dcd836ea
|
[
"MIT"
] | 3
|
2017-03-15T14:39:23.000Z
|
2020-09-28T07:40:37.000Z
|
FacebookEnum/__init__.py
|
coldfusion39/FacebookEnum
|
241d2af1c577ab20985cf39fee5b1b44dcd836ea
|
[
"MIT"
] | null | null | null |
FacebookEnum/__init__.py
|
coldfusion39/FacebookEnum
|
241d2af1c577ab20985cf39fee5b1b44dcd836ea
|
[
"MIT"
] | 2
|
2019-07-16T02:10:58.000Z
|
2020-09-28T07:40:41.000Z
|
from FacebookEnum import FacebookEnum
| 19
| 37
| 0.894737
| 4
| 38
| 8.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 38
| 1
| 38
| 38
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
256b5d0ca4d9335981abb9b702a599584a97ec39
| 2,058
|
py
|
Python
|
skypy/galaxies/tests/test_schechter.py
|
itrharrison/skypy-itrharrison
|
cea1f02d1b2cd3b689266d7ae9bca1a4cfe986a2
|
[
"BSD-3-Clause"
] | 88
|
2020-04-06T15:48:17.000Z
|
2022-02-16T12:01:54.000Z
|
skypy/galaxies/tests/test_schechter.py
|
itrharrison/skypy-itrharrison
|
cea1f02d1b2cd3b689266d7ae9bca1a4cfe986a2
|
[
"BSD-3-Clause"
] | 332
|
2020-04-04T07:30:08.000Z
|
2022-03-30T14:49:08.000Z
|
skypy/galaxies/tests/test_schechter.py
|
itrharrison/skypy-itrharrison
|
cea1f02d1b2cd3b689266d7ae9bca1a4cfe986a2
|
[
"BSD-3-Clause"
] | 41
|
2020-04-03T13:50:43.000Z
|
2022-03-24T16:10:03.000Z
|
import numpy as np
from astropy.cosmology import default_cosmology
def test_schechter_lf():
from pytest import raises
from skypy.galaxies import schechter_lf
from astropy import units
# redshift and magnitude distributions are tested separately
# only test that output is consistent here
# parameters for the sampling
z = np.linspace(0., 1., 100)
M_star = -20
phi_star = 1e-3
alpha = -0.5
m_lim = 30.
sky_area = 1.0 * units.deg**2
cosmo = default_cosmology.get()
# sample redshifts and magnitudes
z_gal, M_gal = schechter_lf(z, M_star, phi_star, alpha, m_lim, sky_area, cosmo)
# check length
assert len(z_gal) == len(M_gal)
# turn M_star, phi_star, alpha into arrays
z, M_star, phi_star, alpha = np.broadcast_arrays(z, M_star, phi_star, alpha)
# sample s.t. arrays need to be interpolated
# alpha array not yet supported
with raises(NotImplementedError):
z_gal, M_gal = schechter_lf(z, M_star, phi_star, alpha, m_lim, sky_area, cosmo)
def test_schechter_smf():
from pytest import raises
from skypy.galaxies import schechter_smf
from astropy import units
# redshift and magnitude distributions are tested separately
# only test that output is consistent here
# parameters for the sampling
z = np.linspace(0., 1., 100)
m_star = 10 ** 10.67
phi_star = 1e-3
alpha = -1.5
m_min = 1.e7
m_max = 1.e13
sky_area = 1.0 * units.deg**2
cosmo = default_cosmology.get()
# sample redshifts and magnitudes
z_gal, m_gal = schechter_smf(z, m_star, phi_star, alpha, m_min, m_max, sky_area, cosmo)
# check length
assert len(z_gal) == len(m_gal)
# turn m_star, phi_star, alpha into arrays
z, m_star, phi_star, alpha = np.broadcast_arrays(z, m_star, phi_star, alpha)
# sample s.t. arrays need to be interpolated
# alpha array not yet supported
with raises(NotImplementedError):
z_gal, m_gal = schechter_smf(z, m_star, phi_star, alpha, m_min, m_max, sky_area, cosmo)
| 29.4
| 95
| 0.685131
| 325
| 2,058
| 4.135385
| 0.264615
| 0.044643
| 0.059524
| 0.089286
| 0.901786
| 0.879464
| 0.879464
| 0.879464
| 0.879464
| 0.799107
| 0
| 0.024668
| 0.231778
| 2,058
| 69
| 96
| 29.826087
| 0.825427
| 0.278426
| 0
| 0.514286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 1
| 0.057143
| false
| 0
| 0.228571
| 0
| 0.285714
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
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| 0
| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
c2ea35e233a6178befd40f4aea43eab0094d510f
| 1,978
|
py
|
Python
|
docs_build/tutorials_templates/task_workflows/qa/create_a_new_qa_task/mds.py
|
dataloop-ai/sdk_examples
|
422d5629df5af343d2dc275e9570bb83c4e2f49d
|
[
"MIT"
] | 3
|
2022-01-07T20:33:49.000Z
|
2022-03-22T12:41:30.000Z
|
docs_build/tutorials_templates/task_workflows/qa/create_a_new_qa_task/mds.py
|
dataloop-ai/sdk_examples
|
422d5629df5af343d2dc275e9570bb83c4e2f49d
|
[
"MIT"
] | null | null | null |
docs_build/tutorials_templates/task_workflows/qa/create_a_new_qa_task/mds.py
|
dataloop-ai/sdk_examples
|
422d5629df5af343d2dc275e9570bb83c4e2f49d
|
[
"MIT"
] | 3
|
2021-12-29T13:11:30.000Z
|
2022-03-22T12:25:50.000Z
|
def func1():
"""
## Create a QA Task
To reach the tasks and assignments repositories go to <a href="https://sdk-docs.dataloop.ai/en/latest/repositories.html#module-dtlpy.repositories.tasks" target="_blank">tasks</a> and <a href="https://sdk-docs.dataloop.ai/en/latest/repositories.html#module-dtlpy.repositories.assignments" target="_blank">assignments</a>.
To reach the tasks and assignments entities go to <a href="https://sdk-docs.dataloop.ai/en/latest/entities.html#module-dtlpy.entities.task" target="_blank">tasks</a> and <a href="https://sdk-docs.dataloop.ai/en/latest/entities.html#module-dtlpy.entities.assignment" target="_blank">assignments</a>.
In Dataloop there are two ways to create a QA task:
1. You can create a QA task from the annotation task. This will collect all completed Items and create a QA Task.
2. You can create a standalone QA task.
### QA task from the annotation task
#### prep
"""
def func2():
"""
#### 2. Create a QA Task
This action will collect all completed Items and create a QA Task under the annotation task.
<div style="background-color: lightblue; color: black; width: 50%; padding: 10px; border-radius: 15px 5px 5px 5px;"><b>Note</b><br>
Adding filters is <b>optional</b>. Learn all about filters <a href="https://dataloop.ai/docs/sdk-sort-filter" target="_blank">here</a>.</div>
"""
def func3():
"""
### A standalone QA task
#### prep
"""
def func4():
"""
#### 2. Add filter by directory
<div style="background-color: lightblue; color: black; width: 50%; padding: 10px; border-radius: 15px 5px 5px 5px;"><b>Note</b><br>
Adding filters is <b>optional</b>. Learn all about filters <a href="https://dataloop.ai/docs/sdk-sort-filter" target="_blank">here</a>.</div>
"""
def func5():
"""
### Create a QA Task
This action will collect all items on the folder and create a QA Task from them.
"""
| 43.955556
| 324
| 0.671385
| 299
| 1,978
| 4.421405
| 0.287625
| 0.049924
| 0.054463
| 0.078669
| 0.763238
| 0.742814
| 0.658094
| 0.658094
| 0.658094
| 0.612708
| 0
| 0.016667
| 0.180991
| 1,978
| 45
| 325
| 43.955556
| 0.799383
| 0.877149
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| true
| 0
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| 1
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
c2f3aa5ec7e898feb6fba43a98aef8a4631f3244
| 64
|
py
|
Python
|
textclf/tester/__init__.py
|
lswjkllc/textclf
|
e4e7504989dd5d39c9376eafda1abc580c053913
|
[
"MIT"
] | 146
|
2020-02-20T02:29:55.000Z
|
2022-01-21T09:49:40.000Z
|
textclf/tester/__init__.py
|
lswjkllc/textclf
|
e4e7504989dd5d39c9376eafda1abc580c053913
|
[
"MIT"
] | 4
|
2020-03-08T03:24:16.000Z
|
2021-03-26T05:34:09.000Z
|
textclf/tester/__init__.py
|
lswjkllc/textclf
|
e4e7504989dd5d39c9376eafda1abc580c053913
|
[
"MIT"
] | 16
|
2020-02-26T04:45:40.000Z
|
2021-05-08T03:52:38.000Z
|
from .ml_tester import MLTester
from .dl_tester import DLTester
| 21.333333
| 31
| 0.84375
| 10
| 64
| 5.2
| 0.7
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 64
| 2
| 32
| 32
| 0.928571
| 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
|
c2f8ba904f69f2e5b9c3f90624bfe28d890152f4
| 2,545
|
py
|
Python
|
rest_tools/client/session.py
|
dsschult/rest-tools
|
233d2815d7ccde3d243edd6c9d9c013c5f03ebc9
|
[
"MIT"
] | 1
|
2020-08-24T19:05:57.000Z
|
2020-08-24T19:05:57.000Z
|
rest_tools/client/session.py
|
dsschult/rest-tools
|
233d2815d7ccde3d243edd6c9d9c013c5f03ebc9
|
[
"MIT"
] | 32
|
2020-05-15T20:14:31.000Z
|
2022-03-16T15:01:45.000Z
|
rest_tools/client/session.py
|
WIPACrepo/rest-tools
|
01f5822db4c8d2546cf3a8cdfaaae6afd1e96679
|
[
"MIT"
] | null | null | null |
"""Get a `requests`_ Session that fully retries errors.
.. _requests: http://docs.python-requests.org
"""
# fmt:off
# pylint: skip-file
from typing import Iterable
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
from requests_futures.sessions import FuturesSession # type: ignore[import]
def AsyncSession(
retries: int = 10,
backoff_factor: float = 0.3,
allowed_methods: Iterable[str] = ('HEAD', 'TRACE', 'GET', 'POST', 'PUT', 'OPTIONS', 'DELETE'),
status_forcelist: Iterable[int] = (408, 429, 500, 502, 503, 504),
) -> FuturesSession:
"""Return a Session object with full retry capabilities.
Args:
retries (int): number of retries
backoff_factor (float): speed factor for retries (in seconds)
allowed_methods (iterable): http methods to retry on
status_forcelist (iterable): http status codes to retry on
Returns:
:py:class:`requests.Session`: session object
"""
session = FuturesSession()
retry = Retry(
total=retries,
connect=retries,
read=retries,
redirect=retries,
# status=retries,
allowed_methods=allowed_methods,
status_forcelist=status_forcelist,
backoff_factor=backoff_factor,
)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)
return session
def Session(
retries: int = 10,
backoff_factor: float = 0.3,
allowed_methods: Iterable[str] = ('HEAD', 'TRACE', 'GET', 'POST', 'PUT', 'OPTIONS', 'DELETE'),
status_forcelist: Iterable[int] = (408, 429, 500, 502, 503, 504),
) -> requests.Session:
"""Return a Session object with full retry capabilities.
Args:
retries (int): number of retries
backoff_factor (float): speed factor for retries (in seconds)
allowed_methods (iterable): http methods to retry on
status_forcelist (iterable): http status codes to retry on
Returns:
:py:class:`requests.Session`: session object
"""
session = requests.Session()
retry = Retry(
total=retries,
connect=retries,
read=retries,
redirect=retries,
# status=retries,
allowed_methods=allowed_methods,
status_forcelist=status_forcelist,
backoff_factor=backoff_factor,
)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)
return session
| 30.662651
| 98
| 0.660511
| 289
| 2,545
| 5.716263
| 0.290657
| 0.062954
| 0.043584
| 0.023002
| 0.780872
| 0.780872
| 0.780872
| 0.780872
| 0.780872
| 0.780872
| 0
| 0.022866
| 0.226719
| 2,545
| 82
| 99
| 31.036585
| 0.816565
| 0.339882
| 0
| 0.711111
| 0
| 0
| 0.059008
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.044444
| false
| 0
| 0.111111
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
6c5e376b18cb320b5b3a6ad3571b387e102c7812
| 75
|
py
|
Python
|
SimpleShot/architecture/__init__.py
|
mbonto/fewshot_neuroimaging_classification
|
2ff0aab6d2c7991e566200d8e4da4b2cbf025a4a
|
[
"MIT"
] | 4
|
2020-11-25T14:59:56.000Z
|
2021-08-09T05:24:38.000Z
|
SimpleShot/architecture/__init__.py
|
mbonto/fewshot_neuroimaging_classification
|
2ff0aab6d2c7991e566200d8e4da4b2cbf025a4a
|
[
"MIT"
] | null | null | null |
SimpleShot/architecture/__init__.py
|
mbonto/fewshot_neuroimaging_classification
|
2ff0aab6d2c7991e566200d8e4da4b2cbf025a4a
|
[
"MIT"
] | 1
|
2021-04-14T12:32:13.000Z
|
2021-04-14T12:32:13.000Z
|
from .GNN import *
from .Conv import *
from .MLP import *
from .LR import *
| 18.75
| 19
| 0.693333
| 12
| 75
| 4.333333
| 0.5
| 0.576923
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 75
| 4
| 20
| 18.75
| 0.866667
| 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
|
66620238e0453fb3d80afd39c53946058a5a210e
| 78
|
py
|
Python
|
a3000_rom_emulator/python_lib/arcflash/main.py
|
mfkiwl/myelin-acorn-electron-hardware-xc6slx25
|
1215cf39a835f4b22011129f4863c3b11bc184a7
|
[
"Apache-2.0"
] | 42
|
2017-04-22T06:37:00.000Z
|
2022-03-30T07:23:13.000Z
|
a3000_rom_emulator/python_lib/arcflash/main.py
|
mfkiwl/myelin-acorn-electron-hardware-xc6slx25
|
1215cf39a835f4b22011129f4863c3b11bc184a7
|
[
"Apache-2.0"
] | 6
|
2018-01-05T16:28:29.000Z
|
2019-01-15T05:57:49.000Z
|
a3000_rom_emulator/python_lib/arcflash/main.py
|
isabella232/myelin-acorn-electron-hardware
|
d0539b43ff7073ceea2f438aac671c3d81e15e17
|
[
"Apache-2.0"
] | 20
|
2017-04-24T01:35:12.000Z
|
2022-01-06T13:49:09.000Z
|
from __future__ import print_function
def main():
print("Arcflash - TODO")
| 13
| 37
| 0.74359
| 10
| 78
| 5.3
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 78
| 5
| 38
| 15.6
| 0.80303
| 0
| 0
| 0
| 0
| 0
| 0.192308
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0.666667
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 6
|
66d775becf6bc60c781bcd017ca0017368ee4833
| 112
|
py
|
Python
|
yolox/models/losses/__init__.py
|
DDGRCF/YOLOX_OBB
|
27b80953306492b8bc83b86b1353d8cee01ef9b6
|
[
"Apache-2.0"
] | 39
|
2021-11-09T12:12:06.000Z
|
2022-03-28T13:45:20.000Z
|
yolox/models/losses/__init__.py
|
DDGRCF/YOLOX_OBB
|
27b80953306492b8bc83b86b1353d8cee01ef9b6
|
[
"Apache-2.0"
] | 12
|
2021-11-09T11:33:29.000Z
|
2022-03-25T17:00:14.000Z
|
yolox/models/losses/__init__.py
|
DDGRCF/YOLOX_OBB
|
27b80953306492b8bc83b86b1353d8cee01ef9b6
|
[
"Apache-2.0"
] | 1
|
2022-03-24T06:53:39.000Z
|
2022-03-24T06:53:39.000Z
|
from .common_losses import *
from .poly_iou_loss import PolyIoULoss, PolyGIOULoss
from .kld_loss import KLDLoss
| 28
| 52
| 0.839286
| 16
| 112
| 5.625
| 0.6875
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116071
| 112
| 3
| 53
| 37.333333
| 0.909091
| 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
|
66e41252c34d924461625f6ee404e87d3cf71f56
| 78
|
py
|
Python
|
adet/modeling/blendmask/__init__.py
|
manusheoran/AdelaiDet_DA
|
04f0843c6be8e436716783300abcba715d560853
|
[
"BSD-2-Clause"
] | 2,597
|
2020-03-15T06:01:23.000Z
|
2022-03-31T18:21:31.000Z
|
adet/modeling/blendmask/__init__.py
|
manusheoran/AdelaiDet_DA
|
04f0843c6be8e436716783300abcba715d560853
|
[
"BSD-2-Clause"
] | 467
|
2020-03-16T11:31:52.000Z
|
2022-03-31T08:50:15.000Z
|
adet/modeling/blendmask/__init__.py
|
manusheoran/AdelaiDet_DA
|
04f0843c6be8e436716783300abcba715d560853
|
[
"BSD-2-Clause"
] | 584
|
2020-03-15T05:53:40.000Z
|
2022-03-26T02:56:30.000Z
|
from .basis_module import build_basis_module
from .blendmask import BlendMask
| 26
| 44
| 0.871795
| 11
| 78
| 5.909091
| 0.545455
| 0.338462
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 78
| 2
| 45
| 39
| 0.928571
| 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
|
dd1bfee78cd1d3a36fa16871186e8ce6d743646b
| 272
|
py
|
Python
|
snmpagent_unity/unity_impl/DiskQueueLength.py
|
factioninc/snmp-unity-agent
|
3525dc0fac60d1c784dcdd7c41693544bcbef843
|
[
"Apache-2.0"
] | 2
|
2019-03-01T11:14:59.000Z
|
2019-10-02T17:47:59.000Z
|
snmpagent_unity/unity_impl/DiskQueueLength.py
|
factioninc/snmp-unity-agent
|
3525dc0fac60d1c784dcdd7c41693544bcbef843
|
[
"Apache-2.0"
] | 2
|
2019-03-01T11:26:29.000Z
|
2019-10-11T18:56:54.000Z
|
snmpagent_unity/unity_impl/DiskQueueLength.py
|
factioninc/snmp-unity-agent
|
3525dc0fac60d1c784dcdd7c41693544bcbef843
|
[
"Apache-2.0"
] | 1
|
2019-10-03T21:09:17.000Z
|
2019-10-03T21:09:17.000Z
|
class DiskQueueLength(object):
def read_get(self, name, idx_name, unity_client):
return unity_client.get_disk_queue_length(idx_name)
class DiskQueueLengthColumn(object):
def get_idx(self, name, idx, unity_client):
return unity_client.get_disks()
| 30.222222
| 59
| 0.75
| 37
| 272
| 5.189189
| 0.459459
| 0.229167
| 0.114583
| 0.229167
| 0.322917
| 0.322917
| 0
| 0
| 0
| 0
| 0
| 0
| 0.161765
| 272
| 8
| 60
| 34
| 0.842105
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 1
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
dd3bb905e0080255b675fd83e6d169087a001385
| 113
|
py
|
Python
|
memegencli/__init__.py
|
MineRobber9000/memegen-cli
|
7038046c0e87aaf70d457a390c5f624b2e808098
|
[
"MIT"
] | null | null | null |
memegencli/__init__.py
|
MineRobber9000/memegen-cli
|
7038046c0e87aaf70d457a390c5f624b2e808098
|
[
"MIT"
] | null | null | null |
memegencli/__init__.py
|
MineRobber9000/memegen-cli
|
7038046c0e87aaf70d457a390c5f624b2e808098
|
[
"MIT"
] | null | null | null |
# grab memegencli.memegrab.Meme and memegencli.memegrab.CustomMeme
from memegen.memegrab import Meme, CustomMeme
| 37.666667
| 66
| 0.849558
| 14
| 113
| 6.857143
| 0.642857
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088496
| 113
| 2
| 67
| 56.5
| 0.932039
| 0.566372
| 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
|
dd405cb4257ed29732ae5d55a72b83be96af07c0
| 116
|
py
|
Python
|
example/myapp.py
|
pawnhearts/configuru
|
9ed06324079e0a92fe9bb0da97b90c192d2eebb7
|
[
"BSD-3-Clause"
] | 1
|
2021-05-02T14:51:29.000Z
|
2021-05-02T14:51:29.000Z
|
example/myapp.py
|
pawnhearts/configuru
|
9ed06324079e0a92fe9bb0da97b90c192d2eebb7
|
[
"BSD-3-Clause"
] | null | null | null |
example/myapp.py
|
pawnhearts/configuru
|
9ed06324079e0a92fe9bb0da97b90c192d2eebb7
|
[
"BSD-3-Clause"
] | null | null | null |
from config import config
import os
print('env DBURL:', os.environ.get('DB_URL'))
print('config:', config.db_url)
| 16.571429
| 45
| 0.724138
| 19
| 116
| 4.315789
| 0.578947
| 0.292683
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.112069
| 116
| 6
| 46
| 19.333333
| 0.796117
| 0
| 0
| 0
| 0
| 0
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 6
|
06b47ceb72b699312d411a12bbd4db5be125e889
| 9,215
|
py
|
Python
|
Code_Plot.py
|
Chenwithcats/Data-Collection-and-Analysis-of-Rental-Real-Estate-in-Shanghai
|
eb0669f63d4a99462018463b1455722814826162
|
[
"CC0-1.0"
] | null | null | null |
Code_Plot.py
|
Chenwithcats/Data-Collection-and-Analysis-of-Rental-Real-Estate-in-Shanghai
|
eb0669f63d4a99462018463b1455722814826162
|
[
"CC0-1.0"
] | null | null | null |
Code_Plot.py
|
Chenwithcats/Data-Collection-and-Analysis-of-Rental-Real-Estate-in-Shanghai
|
eb0669f63d4a99462018463b1455722814826162
|
[
"CC0-1.0"
] | null | null | null |
import pandas as pd
import re
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
#from bokeh.plotting import figure, output_file, show
houses = pd.read_csv("out_4.1.csv",index_col=0)
houses.sort_values('具体日期',inplace=True)
houses = houses.iloc[1:]
print(re.search(r"(\d+-\d+)(-\d+)", houses['具体日期'][40]).group(1))
#houses['具体日期'] = houses.apply(lambda x: re.search(r"(\d+-\d+)(-\d+)", x['具体日期']).group(1),axis=1)
#houses.price = houses.price.where(houses.price < 20000)
#houses['具体日期'] = houses.apply(lambda x: re.search(r"(\d+-\d+)(-\d+)", x['具体日期']).group(1),axis=1)
#houses['具体日期'] = houses['具体日期'].mask(houses['具体日期'].duplicated())
#plt.gca().xaxis.set_major_locator(ticker.MultipleLocator(50))
plt.figure(0,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['电视'] == '有']
houses["租金2"] = houses["租金"].loc[houses['电视'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Television')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Television')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure0.png')
##plt.show()
plt.figure(1,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['冰箱'] == '有']
houses["租金2"] = houses["租金"].loc[houses['冰箱'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Refrigerator')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Refrigerator')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure1.png')
#plt.show()
plt.figure(2,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['洗衣机'] == '有']
houses["租金2"] = houses["租金"].loc[houses['洗衣机'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Washing Machine')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Washing Machine')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure2.png')
#plt.show()
plt.figure(3,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['空调'] == '有']
houses["租金2"] = houses["租金"].loc[houses['空调'] == '无']
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Air Conditioner')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Air Conditioner')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure3.png')
#plt.show()
plt.figure(4,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['热水器'] == '有']
houses["租金2"] = houses["租金"].loc[houses['热水器'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Water Heater')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Water Heater')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure4.png')
#plt.show()
plt.figure(5,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['床'] == '有']
houses["租金2"] = houses["租金"].loc[houses['床'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Bed')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Bed')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure5.png')
#plt.show()
plt.figure(6,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['暖气'] == '有']
houses["租金2"] = houses["租金"].loc[houses['暖气'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Heating')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Heating')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure6.png')
#plt.show()
plt.figure(7,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['宽带'] == '有']
houses["租金2"] = houses["租金"].loc[houses['宽带'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Wifi')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Wifi')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure7.png')
#plt.show()
plt.figure(8,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['衣柜'] == '有']
houses["租金2"] = houses["租金"].loc[houses['衣柜'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Wardrobe')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Wardrobe')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure8.png')
#plt.show()
plt.figure(9,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['天然气'] == '有']
houses["租金2"] = houses["租金"].loc[houses['天然气'] == '无']
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Natural Gas')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Natural Gas')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
#plt.savefig('./house_img/figure9.png')
##plt.show()
plt.figure(10,figsize=(10,10))
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金'],s=3,color='red', label='With Natural Gas')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
#plt.savefig('./house_img/figure10.png')
##plt.show()
plt.figure(11,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['距离地铁站距离'] == houses['距离地铁站距离']]
houses["租金2"] = houses["租金"].loc[houses['距离地铁站距离'] != houses['距离地铁站距离']]
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Station Nearby')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Station Nearby')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
plt.savefig('./house_img/figure_subway.png')
plt.show()
plt.figure(10,figsize=(10,10))
x = range(1,180,18)
houses.sort_values('面积',inplace=True)
plt.scatter(houses['面积'],houses['租金'],s=3,color='red', label='With Natural Gas')
plt.xticks(x,('10','60','90','120','150','180','210','240','270','310'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.savefig('./house_img/figure_area.png')
plt.show()
plt.figure(11,figsize=(10,10))
houses["租金1"] = houses["租金"].loc[houses['距离地铁站距离'] == houses['距离地铁站距离']]
houses["租金2"] = houses["租金"].loc[houses['距离地铁站距离'] != houses['距离地铁站距离']]
x = range(1,340,34)
plt.scatter(houses['具体日期'],houses['租金1'],s=3,color='red', label='With Station Nearby')
plt.scatter(houses['具体日期'],houses['租金2'],s=3,color='blue', label='Without Station Nearby')
plt.xticks(x,('2018-01','2018-03','2018-05','2018-07','2018-09','2018-11','2019-01','2019-03','2019-05','2019-07'),rotation=30,ha='right')
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Rent of Apartment")
plt.legend(loc='upper right')
plt.savefig('./house_img/figure11.png')
| 45.171569
| 139
| 0.64612
| 1,515
| 9,215
| 3.914191
| 0.10363
| 0.052277
| 0.075548
| 0.084317
| 0.879427
| 0.841147
| 0.839123
| 0.793592
| 0.793592
| 0.729511
| 0
| 0.127911
| 0.072708
| 9,215
| 204
| 140
| 45.171569
| 0.566062
| 0.103744
| 0
| 0.584906
| 0
| 0
| 0.319586
| 0.009968
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.025157
| 0
| 0.025157
| 0.006289
| 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
|
06d4bb2d0b854be14ba8b7005826ba4dc1f7c494
| 14,065
|
py
|
Python
|
standingssync/tests/test_views.py
|
buahaha/aa-standingssync
|
7cad43ff986bb25af1a8afe7ffb06a3acdd50567
|
[
"MIT"
] | null | null | null |
standingssync/tests/test_views.py
|
buahaha/aa-standingssync
|
7cad43ff986bb25af1a8afe7ffb06a3acdd50567
|
[
"MIT"
] | null | null | null |
standingssync/tests/test_views.py
|
buahaha/aa-standingssync
|
7cad43ff986bb25af1a8afe7ffb06a3acdd50567
|
[
"MIT"
] | null | null | null |
from unittest.mock import Mock, patch
from django.contrib.auth.models import User
from django.contrib.sessions.middleware import SessionMiddleware
from django.test import RequestFactory, TestCase
from django.urls import reverse
from esi.models import Token
from allianceauth.authentication.models import CharacterOwnership
from allianceauth.eveonline.models import EveCharacter
from allianceauth.tests.auth_utils import AuthUtils
from app_utils.testing import NoSocketsTestCase
from .. import views
from ..models import EveContact, EveEntity, SyncedCharacter, SyncManager
from . import ALLIANCE_CONTACTS, LoadTestDataMixin, create_test_user
MODULE_PATH = "standingssync.views"
class TestMainScreen(LoadTestDataMixin, TestCase):
@classmethod
def setUpClass(cls):
super().setUpClass()
# user 1 is the manager
cls.user_1 = create_test_user(cls.character_1)
cls.main_ownership_1 = CharacterOwnership.objects.get(
character=cls.character_1, user=cls.user_1
)
# sync manager with contacts
cls.sync_manager = SyncManager.objects.create(
alliance=cls.alliance_1,
character_ownership=cls.main_ownership_1,
version_hash="new",
)
for contact in ALLIANCE_CONTACTS:
EveContact.objects.create(
manager=cls.sync_manager,
eve_entity=EveEntity.objects.get(id=contact["contact_id"]),
standing=contact["standing"],
is_war_target=False,
)
# user 2 is a normal user and has two alts and permission
cls.user_2 = create_test_user(cls.character_2)
cls.alt_ownership_1 = CharacterOwnership.objects.create(
character=cls.character_4, owner_hash="x4", user=cls.user_2
)
AuthUtils.add_permission_to_user_by_name(
"standingssync.add_syncedcharacter", cls.user_2
)
cls.user_2 = User.objects.get(pk=cls.user_2.pk)
cls.sync_char = SyncedCharacter.objects.create(
manager=cls.sync_manager, character_ownership=cls.alt_ownership_1
)
# user 3 has no permission
cls.user_3 = create_test_user(cls.character_3)
cls.factory = RequestFactory()
def test_user_with_permission_can_open_app(self):
request = self.factory.get(reverse("standingssync:index"))
request.user = self.user_2
response = views.index(request)
self.assertEqual(response.status_code, 200)
def test_user_wo_permission_can_not_open_app(self):
request = self.factory.get(reverse("standingssync:index"))
request.user = self.user_3
response = views.index(request)
self.assertEqual(response.status_code, 302)
@patch(MODULE_PATH + ".messages_plus")
def test_user_can_remove_sync_char(self, mock_messages_plus):
request = self.factory.get(
reverse("standingssync:remove_character", args=(self.sync_char.pk,))
)
request.user = self.user_2
response = views.remove_character(request, self.sync_char.pk)
self.assertEqual(response.status_code, 302)
self.assertTrue(mock_messages_plus.success.called)
self.assertFalse(SyncedCharacter.objects.filter(pk=self.sync_char.pk).exists())
def test_user_with_permission_can_set_alliance_char(self):
pass
def test_user_wo_permission_can_not_set_alliance_char(self):
pass
@patch(MODULE_PATH + ".tasks.run_character_sync")
@patch(MODULE_PATH + ".messages_plus")
class TestAddSyncChar(LoadTestDataMixin, NoSocketsTestCase):
@classmethod
def setUpClass(cls):
super().setUpClass()
# user 1 is the manager
cls.user_1 = create_test_user(cls.character_1)
cls.main_ownership_1 = CharacterOwnership.objects.get(
character=cls.character_1, user=cls.user_1
)
# sync manager with contacts
cls.sync_manager = SyncManager.objects.create(
alliance=cls.alliance_1,
character_ownership=cls.main_ownership_1,
version_hash="new",
)
for contact in ALLIANCE_CONTACTS:
EveContact.objects.create(
manager=cls.sync_manager,
eve_entity=EveEntity.objects.get(id=contact["contact_id"]),
standing=contact["standing"],
is_war_target=False,
)
# user 2 is a normal user and has three alts
cls.user_2 = create_test_user(cls.character_2)
cls.alt_ownership_1 = CharacterOwnership.objects.create(
character=cls.character_4, owner_hash="x4", user=cls.user_2
)
cls.alt_ownership_2 = CharacterOwnership.objects.create(
character=cls.character_5, owner_hash="x5", user=cls.user_2
)
CharacterOwnership.objects.create(
character=cls.character_6, owner_hash="x6", user=cls.user_2
)
AuthUtils.add_permission_to_user_by_name(
"standingssync.add_syncedcharacter", cls.user_2
)
cls.factory = RequestFactory()
def make_request(self, user, character):
token = Mock(spec=Token)
token.character_id = character.character_id
request = self.factory.get(reverse("standingssync:add_character"))
request.user = user
request.token = token
middleware = SessionMiddleware()
middleware.process_request(request)
orig_view = views.add_character.__wrapped__.__wrapped__.__wrapped__
return orig_view(request, token)
def test_users_can_not_add_alliance_members(
self, mock_messages_plus, mock_run_character_sync
):
response = self.make_request(self.user_2, self.character_2)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.warning.called)
self.assertFalse(mock_run_character_sync.delay.called)
def test_user_can_add_blue_alt(self, mock_messages_plus, mock_run_character_sync):
response = self.make_request(self.user_2, self.character_4)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.success.called)
self.assertTrue(mock_run_character_sync.delay.called)
self.assertTrue(
SyncedCharacter.objects.filter(manager=self.sync_manager)
.filter(character_ownership__character=self.character_4)
.exists()
)
@patch(MODULE_PATH + ".STANDINGSSYNC_CHAR_MIN_STANDING", 0)
def test_user_can_add_neutral_alt(
self, mock_messages_plus, mock_run_character_sync
):
response = self.make_request(self.user_2, self.character_6)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.success.called)
self.assertTrue(mock_run_character_sync.delay.called)
self.assertTrue(
SyncedCharacter.objects.filter(manager=self.sync_manager)
.filter(character_ownership__character=self.character_6)
.exists()
)
def test_user_can_not_add_non_blue_alt(
self, mock_messages_plus, mock_run_character_sync
):
response = self.make_request(self.user_2, self.character_5)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.warning.called)
self.assertFalse(mock_run_character_sync.delay.called)
self.assertFalse(
SyncedCharacter.objects.filter(manager=self.sync_manager)
.filter(character_ownership__character=self.character_5)
.exists()
)
def test_user_can_not_add_char_users_down_not_own(
self, mock_messages_plus, mock_run_character_sync
):
response = self.make_request(self.user_2, self.character_3)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.warning.called)
self.assertFalse(mock_run_character_sync.delay.called)
self.assertFalse(
SyncedCharacter.objects.filter(manager=self.sync_manager)
.filter(character_ownership__character=self.character_3)
.exists()
)
def test_raises_exception_if_alliance_not_found(
self, mock_messages_plus, mock_run_character_sync
):
my_char = EveCharacter.objects.create(
character_id=1098,
character_name="Joker",
corporation_id=2098,
corporation_name="Joker Corp",
alliance_id=3098,
alliance_name="Joker Alliance",
)
my_user = create_test_user(my_char)
with self.assertRaises(RuntimeError):
self.make_request(my_user, self.character_4)
def test_raises_exception_if_no_sync_manager_for_alliance(
self, mock_messages_plus, mock_run_character_sync
):
my_user = create_test_user(self.character_3)
with self.assertRaises(RuntimeError):
self.make_request(my_user, self.character_4)
@patch(MODULE_PATH + ".tasks.run_manager_sync")
@patch(MODULE_PATH + ".messages_plus")
class TestAddAllianceManager(LoadTestDataMixin, NoSocketsTestCase):
@classmethod
def setUpClass(cls):
super().setUpClass()
# user 1 is the manager
cls.user_1 = create_test_user(cls.character_1)
cls.main_ownership_1 = CharacterOwnership.objects.get(
character=cls.character_1, user=cls.user_1
)
# sync manager with contacts
cls.sync_manager = SyncManager.objects.create(
alliance=cls.alliance_1,
character_ownership=cls.main_ownership_1,
version_hash="new",
)
for contact in ALLIANCE_CONTACTS:
EveContact.objects.create(
manager=cls.sync_manager,
eve_entity=EveEntity.objects.get(id=contact["contact_id"]),
standing=contact["standing"],
is_war_target=False,
)
AuthUtils.add_permission_to_user_by_name(
"standingssync.add_syncedcharacter", cls.user_1
)
AuthUtils.add_permission_to_user_by_name(
"standingssync.add_syncmanager", cls.user_1
)
# user 2 is a normal user and has two alts
cls.user_2 = create_test_user(cls.character_2)
cls.alt_ownership_1 = CharacterOwnership.objects.create(
character=cls.character_4, owner_hash="x4", user=cls.user_2
)
cls.alt_ownership_2 = CharacterOwnership.objects.create(
character=cls.character_5, owner_hash="x5", user=cls.user_2
)
AuthUtils.add_permission_to_user_by_name(
"standingssync.add_syncedcharacter", cls.user_2
)
cls.factory = RequestFactory()
def make_request(self, user, character):
token = Mock(spec=Token)
token.character_id = character.character_id
request = self.factory.get(reverse("standingssync:add_alliance_manager"))
request.user = user
request.token = token
middleware = SessionMiddleware()
middleware.process_request(request)
orig_view = views.add_alliance_manager.__wrapped__.__wrapped__.__wrapped__
return orig_view(request, token)
def test_user_with_permission_can_add_alliance_manager(
self, mock_messages_plus, mock_run_manager_sync
):
response = self.make_request(self.user_1, self.character_1)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.success.called)
self.assertTrue(mock_run_manager_sync.delay.called)
self.assertTrue(
SyncManager.objects.filter(alliance=self.alliance_1)
.filter(character_ownership__character=self.character_1)
.exists()
)
"""
def test_user_wo_permission_can_not_add_alliance_manager(
self, mock_messages_plus, mock_run_manager_sync
):
response = self.make_request(self.user_2, self.character_2)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse('standingssync:index'))
self.assertFalse(mock_run_manager_sync.delay.called)
self.assertFalse(
SyncManager.objects
.filter(alliance=self.alliance_1)
.filter(character_ownership__character=self.character_2)
.exists()
)
"""
def test_character_for_manager_must_be_alliance_member(
self, mock_messages_plus, mock_run_manager_sync
):
response = self.make_request(self.user_1, self.character_5)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.warning.called)
self.assertFalse(mock_run_manager_sync.delay.called)
self.assertFalse(
SyncManager.objects.filter(alliance=self.alliance_1)
.filter(character_ownership__character=self.character_5)
.exists()
)
def test_character_for_manager_must_be_owned_by_user(
self, mock_messages_plus, mock_run_manager_sync
):
response = self.make_request(self.user_1, self.character_3)
self.assertEqual(response.status_code, 302)
self.assertEqual(response.url, reverse("standingssync:index"))
self.assertTrue(mock_messages_plus.warning.called)
self.assertFalse(mock_run_manager_sync.delay.called)
self.assertFalse(
SyncManager.objects.filter(alliance=self.alliance_1)
.filter(character_ownership__character=self.character_3)
.exists()
)
| 40.768116
| 87
| 0.685958
| 1,637
| 14,065
| 5.570556
| 0.100794
| 0.013708
| 0.052966
| 0.038162
| 0.845816
| 0.81566
| 0.794385
| 0.756443
| 0.742845
| 0.715429
| 0
| 0.014009
| 0.228582
| 14,065
| 344
| 88
| 40.886628
| 0.826452
| 0.022112
| 0
| 0.633803
| 0
| 0
| 0.052243
| 0.025247
| 0
| 0
| 0
| 0
| 0.161972
| 1
| 0.070423
| false
| 0.007042
| 0.045775
| 0
| 0.133803
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
662a384987abf01873b12c37f8760a97dc06129c
| 114
|
py
|
Python
|
utils.py
|
tuxskar/trending-highlighter
|
40579962d67b1e428f88e2f5a5fefc157e5e7b83
|
[
"MIT"
] | 1
|
2021-07-05T00:54:17.000Z
|
2021-07-05T00:54:17.000Z
|
utils.py
|
tuxskar/trending-highlighter
|
40579962d67b1e428f88e2f5a5fefc157e5e7b83
|
[
"MIT"
] | null | null | null |
utils.py
|
tuxskar/trending-highlighter
|
40579962d67b1e428f88e2f5a5fefc157e5e7b83
|
[
"MIT"
] | null | null | null |
def json_dates_handler(obj):
if hasattr(obj, 'isoformat'):
return obj.isoformat()
return str(obj)
| 22.8
| 33
| 0.657895
| 15
| 114
| 4.866667
| 0.666667
| 0.328767
| 0.493151
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.219298
| 114
| 4
| 34
| 28.5
| 0.820225
| 0
| 0
| 0
| 0
| 0
| 0.078947
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
66433ccb69109a131dfc7e783ddd1f71d9c3faf1
| 306
|
py
|
Python
|
tests/cli-args/examples/example.py
|
looking-for-a-job/listify.py
|
c205dd12f400fca1e795b341b058ca6ac7a3c845
|
[
"Unlicense"
] | 1
|
2019-08-19T09:46:37.000Z
|
2019-08-19T09:46:37.000Z
|
tests/cli-args/examples/example.py
|
looking-for-a-job/listify.py
|
c205dd12f400fca1e795b341b058ca6ac7a3c845
|
[
"Unlicense"
] | null | null | null |
tests/cli-args/examples/example.py
|
looking-for-a-job/listify.py
|
c205dd12f400fca1e795b341b058ca6ac7a3c845
|
[
"Unlicense"
] | 1
|
2021-09-01T02:47:52.000Z
|
2021-09-01T02:47:52.000Z
|
#!/usr/bin/env python
import listify
@listify.listify
def func():
return
@listify.listify
def func2():
yield "value"
@listify.listify
def func3():
return [1, 2, 3]
assert isinstance(func(), list) == True
assert isinstance(func2(), list) == True
assert isinstance(func3(), list) == True
| 13.304348
| 40
| 0.666667
| 40
| 306
| 5.1
| 0.5
| 0.27451
| 0.25
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027888
| 0.179739
| 306
| 22
| 41
| 13.909091
| 0.784861
| 0.065359
| 0
| 0.230769
| 0
| 0
| 0.017544
| 0
| 0
| 0
| 0
| 0
| 0.230769
| 1
| 0.230769
| true
| 0
| 0.076923
| 0.153846
| 0.461538
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
|
0
| 6
|
b08ed0da0512bbc109c6cdfb5f296d1119746860
| 35
|
py
|
Python
|
utils/__init__.py
|
Rafiatu/weekly_order_stats
|
a3ae71ebc6aa2d8ae93cae8c3ee06f38f99ef7f5
|
[
"MIT"
] | null | null | null |
utils/__init__.py
|
Rafiatu/weekly_order_stats
|
a3ae71ebc6aa2d8ae93cae8c3ee06f38f99ef7f5
|
[
"MIT"
] | null | null | null |
utils/__init__.py
|
Rafiatu/weekly_order_stats
|
a3ae71ebc6aa2d8ae93cae8c3ee06f38f99ef7f5
|
[
"MIT"
] | null | null | null |
from .auxillary_functions import *
| 17.5
| 34
| 0.828571
| 4
| 35
| 7
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 35
| 1
| 35
| 35
| 0.903226
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
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| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 1
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| null | 0
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| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
b0f81d1e5d5c5ac82d2059786e343cd0a73dee6e
| 170
|
py
|
Python
|
utilities/color_traceback.py
|
petermchale/trfermik
|
8f1bdd49ec3700619aaa6557d4660f302951ec3c
|
[
"MIT"
] | 9
|
2021-11-24T13:10:34.000Z
|
2022-01-11T17:46:34.000Z
|
utilities/color_traceback.py
|
petermchale/trfermikit
|
8f1bdd49ec3700619aaa6557d4660f302951ec3c
|
[
"MIT"
] | null | null | null |
utilities/color_traceback.py
|
petermchale/trfermikit
|
8f1bdd49ec3700619aaa6557d4660f302951ec3c
|
[
"MIT"
] | null | null | null |
import colored_traceback
# TODO: create a custom pygments style using: https://pygments.org/docs/styles/
colored_traceback.add_hook(always=True, style='solarized-dark')
| 34
| 79
| 0.805882
| 24
| 170
| 5.583333
| 0.833333
| 0.238806
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.082353
| 170
| 4
| 80
| 42.5
| 0.858974
| 0.452941
| 0
| 0
| 0
| 0
| 0.153846
| 0
| 0
| 0
| 0
| 0.25
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
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| 0
| 0
| null | 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 1
| 0
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| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
9fe2197ee34bae70570a22a957feb9b5a108cf31
| 64
|
py
|
Python
|
model/__init__.py
|
limberc/SelectiveBackprop
|
4f5e24a321267f4969cd29395a3b13e31961d13b
|
[
"Apache-2.0"
] | null | null | null |
model/__init__.py
|
limberc/SelectiveBackprop
|
4f5e24a321267f4969cd29395a3b13e31961d13b
|
[
"Apache-2.0"
] | null | null | null |
model/__init__.py
|
limberc/SelectiveBackprop
|
4f5e24a321267f4969cd29395a3b13e31961d13b
|
[
"Apache-2.0"
] | null | null | null |
from .resnet import ResNet18
from .wide_resnet import WideResNet
| 32
| 35
| 0.859375
| 9
| 64
| 6
| 0.666667
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.035088
| 0.109375
| 64
| 2
| 35
| 32
| 0.912281
| 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
|
c6ab25f444aec3055d60523efd8b58814450fc0b
| 108
|
py
|
Python
|
user/__init__.py
|
sshikshu/app.cavill.in
|
4e9472ea9640dad920f17d29b9625c8485022a5e
|
[
"MIT"
] | null | null | null |
user/__init__.py
|
sshikshu/app.cavill.in
|
4e9472ea9640dad920f17d29b9625c8485022a5e
|
[
"MIT"
] | null | null | null |
user/__init__.py
|
sshikshu/app.cavill.in
|
4e9472ea9640dad920f17d29b9625c8485022a5e
|
[
"MIT"
] | null | null | null |
"""
user exports
"""
from user.resources import UsersResource
from user.resources_auth import AuthResource
| 15.428571
| 44
| 0.805556
| 13
| 108
| 6.615385
| 0.615385
| 0.186047
| 0.395349
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12037
| 108
| 6
| 45
| 18
| 0.905263
| 0.111111
| 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
|
c6b84fc9562be5f74a50a7971a323b220039b023
| 28
|
py
|
Python
|
test/fixtures/python/corpus/assignment.B.py
|
matsubara0507/semantic
|
67899f701abc0f1f0cb4374d8d3c249afc33a272
|
[
"MIT"
] | 8,844
|
2019-05-31T15:47:12.000Z
|
2022-03-31T18:33:51.000Z
|
test/fixtures/python/corpus/assignment.B.py
|
matsubara0507/semantic
|
67899f701abc0f1f0cb4374d8d3c249afc33a272
|
[
"MIT"
] | 401
|
2019-05-31T18:30:26.000Z
|
2022-03-31T16:32:29.000Z
|
test/fixtures/python/corpus/assignment.B.py
|
matsubara0507/semantic
|
67899f701abc0f1f0cb4374d8d3c249afc33a272
|
[
"MIT"
] | 504
|
2019-05-31T17:55:03.000Z
|
2022-03-30T04:15:04.000Z
|
a, b = 2, 1
c = 1
b, = 1, 2
| 7
| 11
| 0.321429
| 9
| 28
| 1
| 0.555556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.3125
| 0.428571
| 28
| 3
| 12
| 9.333333
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
c6c9457f9d804831900e14128276987087337ac0
| 179
|
py
|
Python
|
cython_helper/setup.py
|
omron-sinicx/jaxmapp
|
3c11c5b0865bdc29a89186a79b55649a483da68f
|
[
"MIT"
] | 15
|
2022-02-08T02:18:02.000Z
|
2022-03-10T00:54:11.000Z
|
cython_helper/setup.py
|
omron-sinicx/jaxmapp
|
3c11c5b0865bdc29a89186a79b55649a483da68f
|
[
"MIT"
] | 1
|
2022-02-22T11:53:20.000Z
|
2022-02-22T11:53:20.000Z
|
cython_helper/setup.py
|
omron-sinicx/jaxmapp
|
3c11c5b0865bdc29a89186a79b55649a483da68f
|
[
"MIT"
] | 1
|
2022-03-11T11:40:54.000Z
|
2022-03-11T11:40:54.000Z
|
from distutils.core import setup
from Cython.Build import cythonize
setup(
name="check_continuous_collision",
ext_modules=cythonize("check_continuous_collision.pyx"),
)
| 19.888889
| 60
| 0.793296
| 22
| 179
| 6.227273
| 0.681818
| 0.218978
| 0.350365
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122905
| 179
| 8
| 61
| 22.375
| 0.872611
| 0
| 0
| 0
| 0
| 0
| 0.312849
| 0.312849
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
c6e587735a0354f57dbc522586d88320626024d1
| 137
|
py
|
Python
|
tests/python-reference/bool/bool-float.py
|
jpolitz/lambda-py-paper
|
746ef63fc1123714b4adaf78119028afbea7bd76
|
[
"Apache-2.0"
] | 25
|
2015-04-16T04:31:49.000Z
|
2022-03-10T15:53:28.000Z
|
tests/python-reference/bool/bool-float.py
|
jpolitz/lambda-py-paper
|
746ef63fc1123714b4adaf78119028afbea7bd76
|
[
"Apache-2.0"
] | 1
|
2018-11-21T22:40:02.000Z
|
2018-11-26T17:53:11.000Z
|
tests/python-reference/bool/bool-float.py
|
jpolitz/lambda-py-paper
|
746ef63fc1123714b4adaf78119028afbea7bd76
|
[
"Apache-2.0"
] | 1
|
2021-03-26T03:36:19.000Z
|
2021-03-26T03:36:19.000Z
|
___assertEqual(float(False), 0.0)
___assertIsNot(float(False), False)
___assertEqual(float(True), 1.0)
___assertIsNot(float(True), True)
| 27.4
| 35
| 0.781022
| 18
| 137
| 5.277778
| 0.388889
| 0.336842
| 0.357895
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.031008
| 0.058394
| 137
| 4
| 36
| 34.25
| 0.705426
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
05c39dda2fb0d663fe6874d82237711cd8e548f0
| 117
|
py
|
Python
|
core/scan/factory.py
|
abdallah-elsharif/WRock
|
7cfd4bf29e932bf0048ee357c16cf6c021e7fb81
|
[
"MIT"
] | 14
|
2022-03-13T19:51:24.000Z
|
2022-03-18T07:36:39.000Z
|
core/scan/factory.py
|
abdallah-elsharif/WRock
|
7cfd4bf29e932bf0048ee357c16cf6c021e7fb81
|
[
"MIT"
] | null | null | null |
core/scan/factory.py
|
abdallah-elsharif/WRock
|
7cfd4bf29e932bf0048ee357c16cf6c021e7fb81
|
[
"MIT"
] | 3
|
2022-03-14T05:58:06.000Z
|
2022-03-14T11:46:47.000Z
|
from core.scan.executor import *
def executorFactory(config: ScannerConfig):
return GeneralScanExecutor(config)
| 23.4
| 43
| 0.803419
| 12
| 117
| 7.833333
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119658
| 117
| 5
| 44
| 23.4
| 0.912621
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
05e48d70961326f50eee523556e9ff548655b0fa
| 126
|
py
|
Python
|
debian/_sysconfigdata.py
|
Hadron/python
|
73137f499ed658169f49273eee46845e3b53e800
|
[
"PSF-2.0"
] | 2
|
2018-12-11T16:35:20.000Z
|
2019-01-23T16:42:17.000Z
|
debian/_sysconfigdata.py
|
Hadron/python
|
73137f499ed658169f49273eee46845e3b53e800
|
[
"PSF-2.0"
] | 12
|
2020-03-24T17:39:25.000Z
|
2022-03-12T00:01:24.000Z
|
debian/_sysconfigdata.py
|
Hadron/python
|
73137f499ed658169f49273eee46845e3b53e800
|
[
"PSF-2.0"
] | 3
|
2018-01-21T17:53:17.000Z
|
2021-09-08T10:22:05.000Z
|
import sys
if hasattr(sys, 'gettotalrefcount'):
from _sysconfigdata_dm import *
else:
from _sysconfigdata_m import *
| 18
| 36
| 0.746032
| 15
| 126
| 6
| 0.666667
| 0.377778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.18254
| 126
| 6
| 37
| 21
| 0.873786
| 0
| 0
| 0
| 0
| 0
| 0.126984
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 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
|
af023a036781382230e9facc24702b968d2fe1a0
| 134
|
py
|
Python
|
bgheatmaps/__init__.py
|
MathieuBo/bg-heatmaps
|
eaf2856bc31a69a16eeda7b7fad9d37acd4aa0a5
|
[
"MIT"
] | 10
|
2022-01-21T14:19:56.000Z
|
2022-03-14T09:49:50.000Z
|
bgheatmaps/__init__.py
|
MathieuBo/bg-heatmaps
|
eaf2856bc31a69a16eeda7b7fad9d37acd4aa0a5
|
[
"MIT"
] | 6
|
2022-01-21T13:17:47.000Z
|
2022-03-10T16:24:41.000Z
|
bgheatmaps/__init__.py
|
MathieuBo/bg-heatmaps
|
eaf2856bc31a69a16eeda7b7fad9d37acd4aa0a5
|
[
"MIT"
] | 2
|
2022-01-22T01:08:59.000Z
|
2022-03-09T13:48:28.000Z
|
from bgheatmaps.heatmaps import heatmap
from bgheatmaps.planner import plan
from bgheatmaps.slicer import get_structures_slice_coords
| 33.5
| 57
| 0.88806
| 18
| 134
| 6.444444
| 0.666667
| 0.362069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089552
| 134
| 3
| 58
| 44.666667
| 0.95082
| 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
|
af34f3fdaf9e789a5fd0ba252af9c2916fa641cc
| 11,207
|
py
|
Python
|
modules/lmi.py
|
YNedderhoff/sentiment-classifier
|
13d28217f81c21562e5b79f0f85309a968ee534a
|
[
"MIT"
] | 1
|
2017-06-26T15:43:23.000Z
|
2017-06-26T15:43:23.000Z
|
modules/lmi.py
|
YNedderhoff/sentiment-classifier
|
13d28217f81c21562e5b79f0f85309a968ee534a
|
[
"MIT"
] | null | null | null |
modules/lmi.py
|
YNedderhoff/sentiment-classifier
|
13d28217f81c21562e5b79f0f85309a968ee534a
|
[
"MIT"
] | null | null | null |
from math import log
class lmi(object):
def __init__(self, tokens, feat_vec):
self.tokens = tokens
self.feat_vec = feat_vec
self.top_x = [2, 3, 4, 5]
def compute_lmi(self):
pos_tags = {}
lmi_dict = {}
for feat in self.feat_vec:
lmi_dict[feat] = {}
for token in self.tokens:
if token.gold_tag_1 in pos_tags:
pos_tags[token.gold_tag_1] += 1
else:
pos_tags[token.gold_tag_1] = 1
# Uppercase
if token.uppercase_1:
if token.gold_tag_1 in lmi_dict["uppercase_1"]:
lmi_dict["uppercase_1"][token.gold_tag_1] += 1
else:
lmi_dict["uppercase_1"][token.gold_tag_1] = 1
if token.capitalized_1:
upper_char = False
for char in token.form_1[1:]:
if char.isupper():
upper_char = True
if not upper_char:
if token.gold_tag_1 in lmi_dict["capitalized_1"]:
lmi_dict["capitalized_1"][token.gold_tag_1] += 1
else:
lmi_dict["capitalized_1"][token.gold_tag_1] = 1
# form
# the current form:
if token.gold_tag_1 in lmi_dict["current_form_token_1_" + token.form_1]:
lmi_dict["current_form_token_1_" + token.form_1][token.gold_tag_1] += 1
else:
lmi_dict["current_form_token_1_" + token.form_1][token.gold_tag_1] = 1
# if applicable, the previous form:
if token.previous_token:
if token.gold_tag_1 in lmi_dict["prev_form_token_1_" + token.previous_token.form_1]:
lmi_dict["prev_form_token_1_" + token.previous_token.form_1][token.gold_tag_1] += 1
else:
lmi_dict["prev_form_token_1_" + token.previous_token.form_1][token.gold_tag_1] = 1
# if applicable, the next token form:
if token.next_token:
if token.gold_tag_1 in lmi_dict["next_form_token_1_" + token.next_token.form_1]:
lmi_dict["next_form_token_1_" + token.next_token.form_1][token.gold_tag_1] += 1
else:
lmi_dict["next_form_token_1_" + token.next_token.form_1][token.gold_tag_1] = 1
# form length
# the current form length:
if token.gold_tag_1 in lmi_dict["current_word_len_token_1_" + str(len(token.form_1))]:
lmi_dict["current_word_len_token_1_" + str(len(token.form_1))][token.gold_tag_1] += 1
else:
lmi_dict["current_word_len_token_1_" + str(len(token.form_1))][token.gold_tag_1] = 1
# if applicable, the previous form length:
if token.previous_token:
if token.gold_tag_1 in lmi_dict["prev_word_len_token_1_" + str(len(token.previous_token.form_1))]:
lmi_dict["prev_word_len_token_1_" + str(len(token.previous_token.form_1))][token.gold_tag_1] += 1
else:
lmi_dict["prev_word_len_token_1_" + str(len(token.previous_token.form_1))][token.gold_tag_1] = 1
# if applicable, the next token form length:
if token.next_token:
if token.gold_tag_1 in lmi_dict["next_word_len_token_1_" + str(len(token.next_token.form_1))]:
lmi_dict["next_word_len_token_1_" + str(len(token.next_token.form_1))][token.gold_tag_1] += 1
else:
lmi_dict["next_word_len_token_1_" + str(len(token.next_token.form_1))][token.gold_tag_1] = 1
# position in sentence
if token.gold_tag_1 in lmi_dict["position_in_sentence_token_1_" + str(token.t_id_1)]:
lmi_dict["position_in_sentence_token_1_" + str(token.t_id_1)][token.gold_tag_1] += 1
else:
lmi_dict["position_in_sentence_token_1_" + str(token.t_id_1)][token.gold_tag_1] = 1
for i in self.top_x:
if "prefix_token_1_" + token.form_1[:i] in lmi_dict:
if token.gold_tag_1 in lmi_dict["prefix_token_1_" + token.form_1[:i]]:
lmi_dict["prefix_token_1_" + token.form_1[:i]][token.gold_tag_1] += 1
else:
lmi_dict["prefix_token_1_" + token.form_1[:i]][token.gold_tag_1] = 1
if "suffix_token_1_" + token.form_1[-i:] in lmi_dict:
if token.gold_tag_1 in lmi_dict["suffix_token_1_" + token.form_1[-i:]]:
lmi_dict["suffix_token_1_" + token.form_1[-i:]][token.gold_tag_1] += 1
else:
lmi_dict["suffix_token_1_" + token.form_1[-i:]][token.gold_tag_1] = 1
if len(token.form_1) > i+1 and i > 2:
# letter combinations in the word
# if they don't overlap with pre- or suffixes
for j in range(i, len(token.form_1)-(i*2-1)):
if "lettercombs_token_1_" + token.form_1[j:j+i] in lmi_dict:
if token.gold_tag_1 in lmi_dict["lettercombs_token_1_" + token.form_1[j:j+i]]:
lmi_dict["lettercombs_token_1_" + token.form_1[j:j+i]][token.gold_tag_1] += 1
else:
lmi_dict["lettercombs_token_1_" + token.form_1[j:j+i]][token.gold_tag_1] = 1
if token.gold_tag_2 in pos_tags:
pos_tags[token.gold_tag_2] += 1
else:
pos_tags[token.gold_tag_2] = 1
# Uppercase
if token.uppercase_2:
if token.gold_tag_2 in lmi_dict["uppercase_2"]:
lmi_dict["uppercase_2"][token.gold_tag_2] += 1
else:
lmi_dict["uppercase_2"][token.gold_tag_2] = 1
if token.capitalized_2:
upper_char = False
for char in token.form_2[1:]:
if char.isupper():
upper_char = True
if not upper_char:
if token.gold_tag_2 in lmi_dict["capitalized_2"]:
lmi_dict["capitalized_2"][token.gold_tag_2] += 1
else:
lmi_dict["capitalized_2"][token.gold_tag_2] = 1
# form
# the current form:
if token.gold_tag_2 in lmi_dict["current_form_token_2_" + token.form_2]:
lmi_dict["current_form_token_2_" + token.form_2][token.gold_tag_2] += 1
else:
lmi_dict["current_form_token_2_" + token.form_2][token.gold_tag_2] = 1
# if applicable, the previous form:
if token.previous_token:
if token.gold_tag_2 in lmi_dict["prev_form_token_2_" + token.previous_token.form_2]:
lmi_dict["prev_form_token_2_" + token.previous_token.form_2][token.gold_tag_2] += 1
else:
lmi_dict["prev_form_token_2_" + token.previous_token.form_2][token.gold_tag_2] = 1
# if applicable, the next token form:
if token.next_token:
if token.gold_tag_2 in lmi_dict["next_form_token_2_" + token.next_token.form_2]:
lmi_dict["next_form_token_2_" + token.next_token.form_2][token.gold_tag_2] += 1
else:
lmi_dict["next_form_token_2_" + token.next_token.form_2][token.gold_tag_2] = 1
# form length
# the current form length:
if token.gold_tag_2 in lmi_dict["current_word_len_token_2_" + str(len(token.form_2))]:
lmi_dict["current_word_len_token_2_" + str(len(token.form_2))][token.gold_tag_2] += 1
else:
lmi_dict["current_word_len_token_2_" + str(len(token.form_2))][token.gold_tag_2] = 1
# if applicable, the previous form length:
if token.previous_token:
if token.gold_tag_2 in lmi_dict["prev_word_len_token_2_" + str(len(token.previous_token.form_2))]:
lmi_dict["prev_word_len_token_2_" + str(len(token.previous_token.form_2))][token.gold_tag_2] += 1
else:
lmi_dict["prev_word_len_token_2_" + str(len(token.previous_token.form_2))][token.gold_tag_2] = 1
# if applicable, the next token form length:
if token.next_token:
if token.gold_tag_2 in lmi_dict["next_word_len_token_2_" + str(len(token.next_token.form_2))]:
lmi_dict["next_word_len_token_2_" + str(len(token.next_token.form_2))][token.gold_tag_2] += 1
else:
lmi_dict["next_word_len_token_2_" + str(len(token.next_token.form_2))][token.gold_tag_2] = 1
# position in sentence
if token.gold_tag_2 in lmi_dict["position_in_sentence_token_2_" + str(token.t_id_2)]:
lmi_dict["position_in_sentence_token_2_" + str(token.t_id_2)][token.gold_tag_2] += 1
else:
lmi_dict["position_in_sentence_token_2_" + str(token.t_id_2)][token.gold_tag_2] = 1
for i in self.top_x:
if "prefix_token_2_" + token.form_2[:i] in lmi_dict:
if token.gold_tag_2 in lmi_dict["prefix_token_2_" + token.form_2[:i]]:
lmi_dict["prefix_token_2_" + token.form_2[:i]][token.gold_tag_2] += 1
else:
lmi_dict["prefix_token_2_" + token.form_2[:i]][token.gold_tag_2] = 1
if "suffix_token_2_" + token.form_2[-i:] in lmi_dict:
if token.gold_tag_2 in lmi_dict["suffix_token_2_" + token.form_2[-i:]]:
lmi_dict["suffix_token_2_" + token.form_2[-i:]][token.gold_tag_2] += 1
else:
lmi_dict["suffix_token_2_" + token.form_2[-i:]][token.gold_tag_2] = 1
if len(token.form_2) > i+1 and i > 2:
# letter combinations in the word
# if they don't overlap with pre- or suffixes
for j in range(i, len(token.form_2)-(i*2-1)):
if "lettercombs_token_2_" + token.form_2[j:j+i] in lmi_dict:
if token.gold_tag_2 in lmi_dict["lettercombs_token_2_" + token.form_2[j:j+i]]:
lmi_dict["lettercombs_token_2_" + token.form_2[j:j+i]][token.gold_tag_2] += 1
else:
lmi_dict["lettercombs_token_2_" + token.form_2[j:j+i]][token.gold_tag_2] = 1
# compute lmi
for feature in lmi_dict:
temp = sum(lmi_dict[feature].values())
for pos_tag in lmi_dict[feature]:
lmi_dict[feature][pos_tag] = round(float(lmi_dict[feature][pos_tag])*log(float(lmi_dict[feature][pos_tag])/(float(pos_tags[pos_tag])*float(temp)), 2), 2)
return lmi_dict
| 50.481982
| 169
| 0.555367
| 1,593
| 11,207
| 3.49027
| 0.048336
| 0.109532
| 0.168345
| 0.091187
| 0.929856
| 0.91205
| 0.897122
| 0.882374
| 0.835971
| 0.732014
| 0
| 0.040584
| 0.340412
| 11,207
| 221
| 170
| 50.710407
| 0.71158
| 0.058267
| 0
| 0.283871
| 0
| 0
| 0.140008
| 0.067819
| 0
| 0
| 0
| 0
| 0
| 1
| 0.012903
| false
| 0
| 0.006452
| 0
| 0.032258
| 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
|
af36a8eee033a656c55100c247107b33f5493e33
| 29
|
py
|
Python
|
spry/__init__.py
|
Ofekmeister/spry
|
0a4ea14236938c07bfade59147f7dc169c537663
|
[
"MIT"
] | 1
|
2017-09-24T12:23:52.000Z
|
2017-09-24T12:23:52.000Z
|
spry/__init__.py
|
Ofekmeister/spry
|
0a4ea14236938c07bfade59147f7dc169c537663
|
[
"MIT"
] | null | null | null |
spry/__init__.py
|
Ofekmeister/spry
|
0a4ea14236938c07bfade59147f7dc169c537663
|
[
"MIT"
] | 1
|
2017-06-10T14:15:58.000Z
|
2017-06-10T14:15:58.000Z
|
from spry.api import httpget
| 14.5
| 28
| 0.827586
| 5
| 29
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 1
| 29
| 29
| 0.96
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
af91401421ee6a57f1c306a8e72a8c8ecbeb9108
| 22
|
py
|
Python
|
strawberry_django/legacy/hooks.py
|
NeoLight1010/strawberry-graphql-django
|
86d0dbb606a1dd0d96bb79a4cdd6902c6a515b2f
|
[
"MIT"
] | 18
|
2020-11-10T10:12:11.000Z
|
2021-03-10T18:51:01.000Z
|
strawberry_django/legacy/hooks.py
|
NeoLight1010/strawberry-graphql-django
|
86d0dbb606a1dd0d96bb79a4cdd6902c6a515b2f
|
[
"MIT"
] | 8
|
2020-11-19T18:05:14.000Z
|
2021-03-10T19:06:33.000Z
|
strawberry_django/legacy/hooks.py
|
NeoLight1010/strawberry-graphql-django
|
86d0dbb606a1dd0d96bb79a4cdd6902c6a515b2f
|
[
"MIT"
] | 2
|
2021-02-20T11:18:03.000Z
|
2021-03-10T07:14:34.000Z
|
from ..hooks import *
| 11
| 21
| 0.681818
| 3
| 22
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 22
| 1
| 22
| 22
| 0.833333
| 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
|
afb59c8ca9b8130fe0d3146c8803c05e66308845
| 24
|
py
|
Python
|
pyis/__init__.py
|
BlackIQ/pyis
|
11c57fbb3abbf8d667b662c7451d7d3bcd66d097
|
[
"MIT"
] | 13
|
2021-07-10T09:54:54.000Z
|
2022-01-16T11:59:28.000Z
|
pyis/__init__.py
|
BlackIQ/pyis
|
11c57fbb3abbf8d667b662c7451d7d3bcd66d097
|
[
"MIT"
] | null | null | null |
pyis/__init__.py
|
BlackIQ/pyis
|
11c57fbb3abbf8d667b662c7451d7d3bcd66d097
|
[
"MIT"
] | null | null | null |
from pyis.pyis import *
| 12
| 23
| 0.75
| 4
| 24
| 4.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 24
| 1
| 24
| 24
| 0.9
| 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
|
afc01d80f2b4cdc0095002d3bd1bcf4f33b4356d
| 194
|
py
|
Python
|
eppy/utils.py
|
infonetworks-global/eppy
|
d16d796a532455f8aca21c09ff0d0aef3293d806
|
[
"MIT"
] | 22
|
2016-04-16T01:09:07.000Z
|
2021-06-09T12:16:20.000Z
|
eppy/utils.py
|
infonetworks-global/eppy
|
d16d796a532455f8aca21c09ff0d0aef3293d806
|
[
"MIT"
] | 19
|
2016-04-30T11:00:08.000Z
|
2019-05-22T16:41:51.000Z
|
eppy/utils.py
|
infonetworks-global/eppy
|
d16d796a532455f8aca21c09ff0d0aef3293d806
|
[
"MIT"
] | 22
|
2016-03-02T10:55:00.000Z
|
2020-12-14T16:10:11.000Z
|
from random import choice
TRID_CSET = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
def gen_trid(length=12):
return ''.join(choice(TRID_CSET) for _i in range(length))
| 21.555556
| 76
| 0.798969
| 22
| 194
| 6.863636
| 0.772727
| 0.13245
| 0.18543
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.070175
| 0.118557
| 194
| 8
| 77
| 24.25
| 0.812866
| 0
| 0
| 0
| 0
| 0
| 0.319588
| 0.319588
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 0.75
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
bba027a7ef7742a702bca3a96716b9dfaf586369
| 8,099
|
py
|
Python
|
lib/tests/test_storage_upgrade.py
|
johnlito123/electrum-xuez
|
4eb35889f95e31f0a08d5488082df9ab94b4c3ca
|
[
"MIT"
] | null | null | null |
lib/tests/test_storage_upgrade.py
|
johnlito123/electrum-xuez
|
4eb35889f95e31f0a08d5488082df9ab94b4c3ca
|
[
"MIT"
] | null | null | null |
lib/tests/test_storage_upgrade.py
|
johnlito123/electrum-xuez
|
4eb35889f95e31f0a08d5488082df9ab94b4c3ca
|
[
"MIT"
] | 4
|
2018-07-07T16:35:50.000Z
|
2018-12-25T16:02:52.000Z
|
import shutil
import tempfile
from lib.storage import WalletStorage
from lib.wallet import Wallet
from lib.tests.test_wallet import WalletTestCase
# TODO add other wallet types: 2fa, xpub-only
# TODO hw wallet with client version 2.6.x (single-, and multiacc)
class TestStorageUpgrade(WalletTestCase):
def test_upgrade_from_client_1_9_8_seeded(self):
wallet_str = "{'addr_history':{'177hEYTccmuYH8u68pYfaLteTxwJrVgvJj':[],'15V7MsQK2vjF5aEXLVG11qi2eZPZsXdnYc':[],'1DgrwN2JCDZ6uPMSvSz8dPeUtaxLxWM2kf':[],'1H3mPXHFzA8UbvhQVabcDjYw3CPb3djvxs':[],'1DjtUCcQwwzA3GSPA7Kd79PMnri7tLDPYC':[],'1PGEgaPG1XJqmuSj68GouotWeYkCtwo4wm':[],'1PAgpPxnL42Hp3cWxmSfdChPqqGiM8g7zj':[],'1HocPduHmQUJerpdaLG8DnmxvnDCVQwWsa':[]},'accounts_expanded':{},'master_public_key':'756d1fe6ded28d43d4fea902a9695feb785447514d6e6c3bdf369f7c3432fdde4409e4efbffbcf10084d57c5a98d1f34d20ac1f133bdb64fa02abf4f7bde1dfb','use_encryption':False,'seed':'2605aafe50a45bdf2eb155302437e678','accounts':{0:{0:['1DjtUCcQwwzA3GSPA7Kd79PMnri7tLDPYC','1PAgpPxnL42Hp3cWxmSfdChPqqGiM8g7zj','177hEYTccmuYH8u68pYfaLteTxwJrVgvJj','1PGEgaPG1XJqmuSj68GouotWeYkCtwo4wm','15V7MsQK2vjF5aEXLVG11qi2eZPZsXdnYc'],1:['1H3mPXHFzA8UbvhQVabcDjYw3CPb3djvxs','1HocPduHmQUJerpdaLG8DnmxvnDCVQwWsa','1DgrwN2JCDZ6uPMSvSz8dPeUtaxLxWM2kf']}},'seed_version':4}"
self._upgrade_storage(wallet_str)
# TODO pre-2.0 mixed wallets are not split currently
#def test_upgrade_from_client_1_9_8_mixed(self):
# wallet_str = "{'addr_history':{'15V7MsQK2vjF5aEXLVG11qi2eZPZsXdnYc':[],'177hEYTccmuYH8u68pYfaLteTxwJrVgvJj':[],'1DjtUCcQwwzA3GSPA7Kd79PMnri7tLDPYC':[],'1PGEgaPG1XJqmuSj68GouotWeYkCtwo4wm':[],'1PAgpPxnL42Hp3cWxmSfdChPqqGiM8g7zj':[],'1DgrwN2JCDZ6uPMSvSz8dPeUtaxLxWM2kf':[],'1H3mPXHFzA8UbvhQVabcDjYw3CPb3djvxs':[],'1HocPduHmQUJerpdaLG8DnmxvnDCVQwWsa':[]},'accounts_expanded':{},'master_public_key':'756d1fe6ded28d43d4fea902a9695feb785447514d6e6c3bdf369f7c3432fdde4409e4efbffbcf10084d57c5a98d1f34d20ac1f133bdb64fa02abf4f7bde1dfb','use_encryption':False,'seed':'2605aafe50a45bdf2eb155302437e678','accounts':{0:{0:['1DjtUCcQwwzA3GSPA7Kd79PMnri7tLDPYC','1PAgpPxnL42Hp3cWxmSfdChPqqGiM8g7zj','177hEYTccmuYH8u68pYfaLteTxwJrVgvJj','1PGEgaPG1XJqmuSj68GouotWeYkCtwo4wm','15V7MsQK2vjF5aEXLVG11qi2eZPZsXdnYc'],1:['1H3mPXHFzA8UbvhQVabcDjYw3CPb3djvxs','1HocPduHmQUJerpdaLG8DnmxvnDCVQwWsa','1DgrwN2JCDZ6uPMSvSz8dPeUtaxLxWM2kf'],'mpk':'756d1fe6ded28d43d4fea902a9695feb785447514d6e6c3bdf369f7c3432fdde4409e4efbffbcf10084d57c5a98d1f34d20ac1f133bdb64fa02abf4f7bde1dfb'}},'imported_keys':{'15CyDgLffJsJgQrhcyooFH4gnVDG82pUrA':'5JyVyXU1LiRXATvRTQvR9Kp8Rx1X84j2x49iGkjSsXipydtByUq','1Exet2BhHsFxKTwhnfdsBMkPYLGvobxuW6':'L3Gi6EQLvYw8gEEUckmqawkevfj9s8hxoQDFveQJGZHTfyWnbk1U','1364Js2VG66BwRdkaoxAaFtdPb1eQgn8Dr':'L2sED74axVXC4H8szBJ4rQJrkfem7UMc6usLCPUoEWxDCFGUaGUM'},'seed_version':4}"
# self._upgrade_storage(wallet_str, accounts=2)
def test_upgrade_from_client_2_0_4_seeded(self):
wallet_str = '{"accounts":{"0":{"change":["03d8e267e8de7769b52a8727585b3c44b4e148b86b2c90e3393f78a75bd6aab83f","03f09b3562bec870b4eb8626c20d449ee85ef17ea896a6a82b454e092eef91b296","02df953880df9284715e8199254edcf3708c635adc92a90dbf97fbd64d1eb88a36"],"receiving":["02cd4d73d5e335dafbf5c9338f88ceea3d7511ab0f9b8910745ac940ff40913a30","0243ed44278a178101e0fb14d36b68e6e13d00fe3434edb56e4504ea6f5db2e467","0367c0aa3681ec3635078f79f8c78aa339f19e38d9e1c9e2853e30e66ade02cac3","0237d0fe142cff9d254a3bdd3254f0d5f72676b0099ba799764a993a0d0ba80111","020a899fd417527b3929c8f625c93b45392244bab69ff91b582ed131977d5cd91e","039e84264920c716909b88700ef380336612f48237b70179d0b523784de28101f7","03125452df109a51be51fe21e71c3a4b0bba900c9c0b8d29b4ee2927b51f570848","0291fa554217090bab96eeff63e1c6fdec37358ed597d18fa32c60c02a48878c8c","030b6354a4365bab55e86269fb76241fd69716f02090ead389e1fce13d474aa569","023dcba431d8887ab63595f0df1e978e4a5f1c3aac6670e43d03956448a229f740","0332a61cbe04fe027033369ce7569b860c24462878bdd8c0332c22a3f5fdcc1790","021249480422d93dba2aafcd4575e6f630c4e3a2a832dd8a15f884e1052b6836e4","02516e91dede15d3a15dd648591bb92e107b3a53d5bc34b286ab389ce1af3130aa","02e1da3dddd81fa6e4895816da9d4b8ab076d6ea8034b1175169c0f247f002f4cf","0390ef1e3fdbe137767f8b5abad0088b105eee8c39e075305545d405be3154757a","03fca30eb33c6e1ffa071d204ccae3060680856ae9b93f31f13dd11455e67ee85d","034f6efdbbe1bfa06b32db97f16ff3a0dd6cf92769e8d9795c465ff76d2fbcb794","021e2901009954f23d2bf3429d4a531c8ca3f68e9598687ef816f20da08ff53848","02d3ccf598939ff7919ee23d828d229f85e3e58842582bf054491c59c8b974aa6e","03a1daffa39f42c1aaae24b859773a170905c6ee8a6dab8c1bfbfc93f09b88f4db"],"xpub":"xpub661MyMwAqRbcFsrzES8RWNiD7RxDqT4p8NjvTY9mLi8xdphQ9x1TiY8GnqCpQx4LqJBdcGeXrsAa2b2G7ZcjJcest9wHcqYfTqXmQja6vfV"}},"accounts_expanded":{},"master_private_keys":{"x/":"xprv9s21ZrQH143K3PnX8QbR9EmUZQ7jRzLxm9pKf9k9nNbym2NFcQhDAjonwZ39jtWLYp6qk5UHotj13p2y7w1ZhhvvyV5eCcaPUrKofs9CXQ9"},"master_public_keys":{"x/":"xpub661MyMwAqRbcFsrzES8RWNiD7RxDqT4p8NjvTY9mLi8xdphQ9x1TiY8GnqCpQx4LqJBdcGeXrsAa2b2G7ZcjJcest9wHcqYfTqXmQja6vfV"},"seed":"seven direct thunder glare prevent please fatal blush buzz artefact gate vendor above","seed_version":11,"use_encryption":false,"wallet_type":"standard"}'
self._upgrade_storage(wallet_str)
def test_upgrade_from_client_2_0_4_importedkeys(self):
wallet_str = '{"accounts":{"/x":{"imported":{"1364Js2VG66BwRdkaoxAaFtdPb1eQgn8Dr":["0344b1588589958b0bcab03435061539e9bcf54677c104904044e4f8901f4ebdf5","L2sED74axVXC4H8szBJ4rQJrkfem7UMc6usLCPUoEWxDCFGUaGUM"],"15CyDgLffJsJgQrhcyooFH4gnVDG82pUrA":["04575f52b82f159fa649d2a4c353eb7435f30206f0a6cb9674fbd659f45082c37d559ffd19bea9c0d3b7dcc07a7b79f4cffb76026d5d4dff35341efe99056e22d2","5JyVyXU1LiRXATvRTQvR9Kp8Rx1X84j2x49iGkjSsXipydtByUq"],"1Exet2BhHsFxKTwhnfdsBMkPYLGvobxuW6":["0389508c13999d08ffae0f434a085f4185922d64765c0bff2f66e36ad7f745cc5f","L3Gi6EQLvYw8gEEUckmqawkevfj9s8hxoQDFveQJGZHTfyWnbk1U"]}}},"accounts_expanded":{},"use_encryption":false,"wallet_type":"imported"}'
self._upgrade_storage(wallet_str)
def test_upgrade_from_client_2_0_4_watchaddresses(self):
wallet_str = '{"accounts":{"/x":{"imported":{"1DgrwN2JCDZ6uPMSvSz8dPeUtaxLxWM2kf":[null,null],"1H3mPXHFzA8UbvhQVabcDjYw3CPb3djvxs":[null,null],"1HocPduHmQUJerpdaLG8DnmxvnDCVQwWsa":[null,null]}}},"accounts_expanded":{},"wallet_type":"imported"}'
self._upgrade_storage(wallet_str)
##########
@classmethod
def setUpClass(cls):
super().setUpClass()
from lib.plugins import Plugins
from lib.simple_config import SimpleConfig
cls.electrum_path = tempfile.mkdtemp()
config = SimpleConfig({'electrum_path': cls.electrum_path})
gui_name = 'cmdline'
# TODO it's probably wasteful to load all plugins... only need Trezor
Plugins(config, True, gui_name)
@classmethod
def tearDownClass(cls):
super().tearDownClass()
shutil.rmtree(cls.electrum_path)
def _upgrade_storage(self, wallet_json, accounts=1):
storage = self._load_storage_from_json_string(wallet_json, manual_upgrades=True)
if accounts == 1:
self.assertFalse(storage.requires_split())
if storage.requires_upgrade():
storage.upgrade()
self._sanity_check_upgraded_storage(storage)
else:
self.assertTrue(storage.requires_split())
new_paths = storage.split_accounts()
self.assertEqual(accounts, len(new_paths))
for new_path in new_paths:
new_storage = WalletStorage(new_path, manual_upgrades=False)
self._sanity_check_upgraded_storage(new_storage)
def _sanity_check_upgraded_storage(self, storage):
self.assertFalse(storage.requires_split())
self.assertFalse(storage.requires_upgrade())
w = Wallet(storage)
def _load_storage_from_json_string(self, wallet_json, manual_upgrades=True):
with open(self.wallet_path, "w") as f:
f.write(wallet_json)
storage = WalletStorage(self.wallet_path, manual_upgrades=manual_upgrades)
return storage
| 97.578313
| 2,245
| 0.827016
| 517
| 8,099
| 12.690522
| 0.34236
| 0.013717
| 0.010669
| 0.013717
| 0.2713
| 0.221003
| 0.211858
| 0.211858
| 0.178022
| 0.178022
| 0
| 0.236383
| 0.079639
| 8,099
| 82
| 2,246
| 98.768293
| 0.643815
| 0.208297
| 0
| 0.148148
| 0
| 0.074074
| 0.632468
| 0.617454
| 0
| 0
| 0
| 0.012195
| 0.092593
| 1
| 0.166667
| false
| 0
| 0.185185
| 0
| 0.388889
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
bbda435bf0db2f20c5b247213e5644fbd9b60d92
| 5,602
|
py
|
Python
|
simian/test/test_patch.py
|
movermeyer/python-simian
|
c5870e4c5a81554bd37c835981cc9d22e720e9bd
|
[
"MIT"
] | 1
|
2018-11-24T16:44:49.000Z
|
2018-11-24T16:44:49.000Z
|
simian/test/test_patch.py
|
movermeyer/python-simian
|
c5870e4c5a81554bd37c835981cc9d22e720e9bd
|
[
"MIT"
] | 3
|
2015-11-27T19:02:14.000Z
|
2018-03-05T17:27:39.000Z
|
simian/test/test_patch.py
|
movermeyer/python-simian
|
c5870e4c5a81554bd37c835981cc9d22e720e9bd
|
[
"MIT"
] | 1
|
2018-03-05T17:23:25.000Z
|
2018-03-05T17:23:25.000Z
|
from mock import call
from nose.tools import eq_, raises
from simian import patch
from simian.test.my_package import internal_module
from simian.test.my_package import external_module
def test_patch_with_multiple_arguments():
@patch(
module=internal_module,
external=(
'simian.test.my_package.external_module.external_fn_a',
'simian.test.my_package.external_module.external_fn_b'),
internal=(
'internal_fn_a',
'internal_fn_b'))
def inner(master_mock):
internal_module.my_fn()
eq_(master_mock.mock_calls, [
call.external_fn_a(),
call.external_fn_b(),
call.internal_fn_a(),
call.internal_fn_b()])
inner() # pylint: disable=E1120
@raises(RuntimeError)
def test_patch_with_no_external():
@patch(
module=internal_module,
internal=(
'internal_fn_a',
'internal_fn_b'))
def inner(master_mock):
try:
internal_module.my_fn()
except RuntimeError as e:
eq_(str(e), 'called external_fn_a()')
eq_(master_mock.mock_calls, [])
raise
inner() # pylint: disable=E1120
def test_patch_with_no_external_does_not_reload():
@patch(
module=internal_module,
internal=(
'internal_fn_a',
'internal_fn_b'))
def inner(master_mock):
assert master_mock
ne_(internal_fn_a, internal_module.internal_fn_a)
ne_(internal_fn_b, internal_module.internal_fn_b)
eq_(external_fn_a, external_module.external_fn_a)
eq_(external_fn_b, external_module.external_fn_b)
internal_fn_a = internal_module.internal_fn_a
internal_fn_b = internal_module.internal_fn_b
external_fn_a = external_module.external_fn_a
external_fn_b = external_module.external_fn_b
inner() # pylint: disable=E1120
eq_(internal_fn_a, internal_module.internal_fn_a)
eq_(internal_fn_b, internal_module.internal_fn_b)
eq_(external_fn_a, external_module.external_fn_a)
eq_(external_fn_b, external_module.external_fn_b)
@raises(RuntimeError)
def test_patch_with_no_internal():
@patch(
module=internal_module,
external=(
'simian.test.my_package.external_module.external_fn_a',
'simian.test.my_package.external_module.external_fn_b'))
def inner(master_mock):
try:
internal_module.my_fn()
except RuntimeError as e:
eq_(str(e), 'called internal_fn_a()')
eq_(master_mock.mock_calls, [
call.external_fn_a(),
call.external_fn_b()])
raise
inner() # pylint: disable=E1120
def test_patch_with_internal_restores_targets():
@patch(
module=internal_module,
external=(
'simian.test.my_package.external_module.external_fn_a',
'simian.test.my_package.external_module.external_fn_b'),
internal=(
'internal_fn_a',
'internal_fn_b'))
def inner(master_mock):
internal_module.my_fn()
eq_(master_mock.mock_calls, [
call.external_fn_a(),
call.external_fn_b(),
call.internal_fn_a(),
call.internal_fn_b()])
inner() # pylint: disable=E1120
@raises(RuntimeError)
def ensure_target_unpatched(target):
target()
ensure_target_unpatched(external_module.external_fn_a)
ensure_target_unpatched(external_module.external_fn_b)
ensure_target_unpatched(internal_module.internal_fn_a)
ensure_target_unpatched(internal_module.internal_fn_b)
def test_patch_with_test_generator_targets():
@patch(
module=internal_module,
external=(
'simian.test.my_package.external_module.external_fn_a',
'simian.test.my_package.external_module.external_fn_b'),
internal=(
'internal_fn_a',
'internal_fn_b'))
def inner(master_mock):
internal_module.my_fn()
eq_(master_mock.mock_calls, [
call.external_fn_a(),
call.external_fn_b(),
call.internal_fn_a(),
call.internal_fn_b()])
yield inner
yield inner
@raises(RuntimeError)
def test_patch_with_no_internal_no_external():
@patch(module=internal_module)
def inner(master_mock):
try:
internal_module.my_fn()
except RuntimeError as e:
eq_(str(e), 'called external_fn_a()')
eq_(master_mock.mock_calls, [])
raise
inner() # pylint: disable=E1120
def test_patch_with_generated_targets():
external_format = 'simian.test.my_package.external_module.external_fn_{c}'
internal_format = 'internal_fn_{c}'
# noinspection PyUnresolvedReferences
@patch(
module=internal_module,
external=(external_format.format(c=c) for c in 'ab'),
internal=(internal_format.format(c=c) for c in 'ab'))
def inner(master_mock):
internal_module.my_fn()
eq_(master_mock.mock_calls, [
call.external_fn_a(),
call.external_fn_b(),
call.internal_fn_a(),
call.internal_fn_b()])
inner() # pylint: disable=E1120
@raises(RuntimeError)
def test_no_patch():
try:
internal_module.my_fn()
except RuntimeError as e:
eq_(str(e), 'called external_fn_a()')
raise
#
# Test Helpers
#
def ne_(a, b, msg=None):
if a == b:
raise AssertionError(
msg or "{a!r} == {b!r}".format(a=a, b=b)) # pragma: no cover
| 29.797872
| 78
| 0.644234
| 707
| 5,602
| 4.678925
| 0.094767
| 0.108827
| 0.06318
| 0.123337
| 0.856106
| 0.844921
| 0.804414
| 0.745768
| 0.62757
| 0.614268
| 0
| 0.006728
| 0.257051
| 5,602
| 187
| 79
| 29.957219
| 0.788083
| 0.039093
| 0
| 0.733766
| 0
| 0
| 0.134264
| 0.087523
| 0
| 0
| 0
| 0
| 0.012987
| 1
| 0.123377
| false
| 0
| 0.032468
| 0
| 0.155844
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 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
|
bbed4189a66b6477f57ac5aae7360696939bdb26
| 42
|
py
|
Python
|
lagom/core/utils/__init__.py
|
lkylych/lagom
|
64777be7f09136072a671c444b5b3fbbcb1b2f18
|
[
"MIT"
] | null | null | null |
lagom/core/utils/__init__.py
|
lkylych/lagom
|
64777be7f09136072a671c444b5b3fbbcb1b2f18
|
[
"MIT"
] | null | null | null |
lagom/core/utils/__init__.py
|
lkylych/lagom
|
64777be7f09136072a671c444b5b3fbbcb1b2f18
|
[
"MIT"
] | null | null | null |
from lagom.core.utils.logger import Logger
| 42
| 42
| 0.857143
| 7
| 42
| 5.142857
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071429
| 42
| 1
| 42
| 42
| 0.923077
| 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
|
a520e628de18070a740b9610243ffe25520d9581
| 34
|
py
|
Python
|
panelexpr/__init__.py
|
actcwlf/panelexpr
|
a13a01981daab965b314b328f346b641634c7de1
|
[
"MIT"
] | null | null | null |
panelexpr/__init__.py
|
actcwlf/panelexpr
|
a13a01981daab965b314b328f346b641634c7de1
|
[
"MIT"
] | null | null | null |
panelexpr/__init__.py
|
actcwlf/panelexpr
|
a13a01981daab965b314b328f346b641634c7de1
|
[
"MIT"
] | null | null | null |
from panelexpr.interface import *
| 17
| 33
| 0.823529
| 4
| 34
| 7
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 34
| 1
| 34
| 34
| 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
|
a5ac85fff1cb89e33ef3e666c4021f6000372c7d
| 54
|
py
|
Python
|
Basic/HelloWorld.py
|
lishan1047/PythonTeachingSample
|
30e8675eeb370a27107b3be90e78a9e8d28c8020
|
[
"MIT"
] | null | null | null |
Basic/HelloWorld.py
|
lishan1047/PythonTeachingSample
|
30e8675eeb370a27107b3be90e78a9e8d28c8020
|
[
"MIT"
] | null | null | null |
Basic/HelloWorld.py
|
lishan1047/PythonTeachingSample
|
30e8675eeb370a27107b3be90e78a9e8d28c8020
|
[
"MIT"
] | null | null | null |
# Print Hello World in Screen.
print("Hello World!")
| 13.5
| 30
| 0.703704
| 8
| 54
| 4.75
| 0.625
| 0.526316
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 54
| 3
| 31
| 18
| 0.844444
| 0.518519
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
3c0a9049c14b5758cd150ee8dbeb45c5c4d546fb
| 71
|
py
|
Python
|
asgard/api/__main__.py
|
pabarros/asgard-api
|
3c10d5f99f584df5e8011558cf42e8b201d567e9
|
[
"MIT"
] | 3
|
2020-01-10T02:16:09.000Z
|
2020-02-19T18:42:37.000Z
|
asgard/api/__main__.py
|
pabarros/asgard-api
|
3c10d5f99f584df5e8011558cf42e8b201d567e9
|
[
"MIT"
] | 13
|
2020-01-15T18:22:35.000Z
|
2021-03-31T19:21:54.000Z
|
asgard/api/__main__.py
|
rockerbacon/asgard-api
|
1c1eb19225ace4bbecb06b65b1b9c4ab131eb24a
|
[
"MIT"
] | 6
|
2020-03-07T09:49:19.000Z
|
2021-07-25T03:14:10.000Z
|
from asgard.app import app
from asgard.handlers import http
app.run()
| 14.2
| 32
| 0.788732
| 12
| 71
| 4.666667
| 0.583333
| 0.357143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.140845
| 71
| 4
| 33
| 17.75
| 0.918033
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 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
|
3c31468a41da1d56ad03605bd7e6d0df665a8e48
| 160
|
py
|
Python
|
tests/unicode/unicode_index.py
|
learnforpractice/micropython-cpp
|
004bc8382f74899e7b876cc29bfa6a9cc976ba10
|
[
"MIT"
] | 13,648
|
2015-01-01T01:34:51.000Z
|
2022-03-31T16:19:53.000Z
|
tests/unicode/unicode_index.py
|
learnforpractice/micropython-cpp
|
004bc8382f74899e7b876cc29bfa6a9cc976ba10
|
[
"MIT"
] | 7,092
|
2015-01-01T07:59:11.000Z
|
2022-03-31T23:52:18.000Z
|
tests/unicode/unicode_index.py
|
learnforpractice/micropython-cpp
|
004bc8382f74899e7b876cc29bfa6a9cc976ba10
|
[
"MIT"
] | 4,942
|
2015-01-02T11:48:50.000Z
|
2022-03-31T19:57:10.000Z
|
print("Привет".find("т"))
print("Привет".find("П"))
print("Привет".rfind("т"))
print("Привет".rfind("П"))
print("Привет".index("т"))
print("Привет".index("П"))
| 22.857143
| 26
| 0.625
| 24
| 160
| 4.166667
| 0.291667
| 0.66
| 0.36
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0375
| 160
| 6
| 27
| 26.666667
| 0.649351
| 0
| 0
| 0
| 0
| 0
| 0.2625
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
3c5b4b55c71694dd990e5db1c257a943c75ba20d
| 69
|
py
|
Python
|
lrp/__init__.py
|
nielsrolf/tensorflow-lrp
|
8262f2a2170cfeff9047ae88345a15d46c8ac34e
|
[
"Apache-2.0"
] | 9
|
2018-05-21T03:29:43.000Z
|
2022-02-01T19:36:09.000Z
|
lrp/__init__.py
|
nielsrolf/tensorflow-lrp
|
8262f2a2170cfeff9047ae88345a15d46c8ac34e
|
[
"Apache-2.0"
] | 1
|
2019-01-22T13:23:46.000Z
|
2019-01-23T00:05:51.000Z
|
lrp/__init__.py
|
nielsrolf/tensorflow-lrp
|
8262f2a2170cfeff9047ae88345a15d46c8ac34e
|
[
"Apache-2.0"
] | 1
|
2020-07-28T08:43:25.000Z
|
2020-07-28T08:43:25.000Z
|
from .train import *
from .evaluate_rule import *
from . import data
| 17.25
| 28
| 0.753623
| 10
| 69
| 5.1
| 0.6
| 0.392157
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 69
| 3
| 29
| 23
| 0.894737
| 0
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| 0
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| 0
| 0
| 0
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| 0
| 1
| 0
| true
| 0
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| null | 1
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| 0
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| null | 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
3c7180c4d4c342fcc4a98911ec149a8bb00601f7
| 55
|
py
|
Python
|
swagger-4-es development/images/swagger/source/ingest_operations/__init__.py
|
swarmee/swagger-4-es
|
8ee367267c9a4afd9abba5964570d32c44c7ce34
|
[
"MIT"
] | 3
|
2021-12-28T08:43:00.000Z
|
2022-02-09T14:51:07.000Z
|
swagger-4-es development/images/swagger/source/ingest_operations/__init__.py
|
swarmee/swagger-4-es
|
8ee367267c9a4afd9abba5964570d32c44c7ce34
|
[
"MIT"
] | null | null | null |
swagger-4-es development/images/swagger/source/ingest_operations/__init__.py
|
swarmee/swagger-4-es
|
8ee367267c9a4afd9abba5964570d32c44c7ce34
|
[
"MIT"
] | null | null | null |
from .ingest_operations import api as ingest_operations
| 55
| 55
| 0.890909
| 8
| 55
| 5.875
| 0.75
| 0.680851
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.090909
| 55
| 1
| 55
| 55
| 0.94
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| true
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| null | 1
| 0
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| 0
| 0
| 0
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| 0
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| 1
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| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
b1d85ba4a9bbf8c2a46affd61b5603d54be6cfa1
| 15,361
|
py
|
Python
|
datasets.py
|
aseldawy/deep-spatial-join
|
ce0e8e08f49b05e972287c6f4f50272735e04e60
|
[
"Apache-2.0"
] | null | null | null |
datasets.py
|
aseldawy/deep-spatial-join
|
ce0e8e08f49b05e972287c6f4f50272735e04e60
|
[
"Apache-2.0"
] | null | null | null |
datasets.py
|
aseldawy/deep-spatial-join
|
ce0e8e08f49b05e972287c6f4f50272735e04e60
|
[
"Apache-2.0"
] | null | null | null |
import numpy as np
import pandas as pd
from sklearn import preprocessing
import math
def load_datasets_feature(filename):
features_df = pd.read_csv(filename, delimiter='\\s*,\\s*', header=0)
return features_df
def load_join_data3(features_df, result_file, histograms_path, num_rows, num_columns):
cols = ['dataset1', 'dataset2', 'result_size', 'mbr_tests', 'duration']
# Result DF contains dataset names, result cardinality, # of MBR tests, and duration in seconds
result_df = pd.read_csv(result_file, delimiter='\\s*,\\s*', header=None, names=cols)
# result_df = result_df.sample(frac=1)
# Add dataset information of the first (left) dataset
result_df = pd.merge(result_df, features_df, left_on='dataset1', right_on='dataset_name')
# Add dataset information for the second (right) dataset
result_df = pd.merge(result_df, features_df, left_on='dataset2', right_on='dataset_name')
# Load histograms
ds1_histograms, ds2_histograms, ds1_original_histograms, ds2_original_histograms, ds_all_histogram, ds_bops_histogram = load_histograms(
result_df, histograms_path, num_rows, num_columns)
#print(ds1_histograms.shape)
#print(result_df.shape)
#exit(0)
# Compute BOPS
# First, do an element-wise multiplication of the two histograms
bops = np.multiply(ds1_original_histograms, ds2_original_histograms)
# Reshape into a two dimensional array. First dimension represents the dataset number, e.g., first entry
# represents the first dataset of each. Second dimension represents the values in the multiplied histograms
bops = bops.reshape((bops.shape[0], num_rows * num_columns))
# Sum the values in each row to compute the final BOPS value
bops_values = np.sum(bops, axis=1)
# The final reshape puts each BOPS value in an array with a single value. Thus it produces a 2D array.
bops_values = bops_values.reshape((bops_values.shape[0], 1))
result_df['bops'] = bops_values
cardinality_x = result_df['cardinality_x']
cardinality_y = result_df['cardinality_y']
result_size = result_df['result_size']
mbr_tests = result_df['mbr_tests']
# Compute the join selectivity as result_cardinality/(cardinality x * cardinality y)
result_df['join_selectivity'] = result_size / (cardinality_x * cardinality_y)
# Compute the MBR selectivity in the same way
result_df['mbr_tests_selectivity'] = mbr_tests / (cardinality_x * cardinality_y)
# Apply MinMaxScaler to normalize numeric columns used in either training or testing to the range [0, 1]
# The following transformation tries to adjust relevant columns to be scaled together
column_groups = [
['duration'],
['AVG area_x', 'AVG area_y'],
['AVG x_x', 'AVG y_x', 'AVG x_y', 'AVG y_y'],
['E0_x', 'E2_x', 'E0_y', 'E2_y'],
['join_selectivity'],
['mbr_tests_selectivity'],
['cardinality_x', 'cardinality_y', 'result_size'],
['bops', 'mbr_tests']
]
for column_group in column_groups:
input_data = result_df[column_group].to_numpy()
original_shape = input_data.shape
reshaped = input_data.reshape(input_data.size, 1)
reshaped = preprocessing.minmax_scale(reshaped)
result_df[column_group] = reshaped.reshape(original_shape)
#result_df[column_group] = scaler.fit_transform(result_df[column_group])
return result_df, ds1_histograms, ds2_histograms, ds_all_histogram, ds_bops_histogram
def load_join_data(features_df, result_file, histograms_path, num_rows, num_columns):
cols = ['dataset1', 'dataset2', 'result_size', 'mbr_tests', 'duration']
# Result DF contains dataset names, result cardinality, # of MBR tests, and duration in seconds
result_df = pd.read_csv(result_file, delimiter=',', header=None, names=cols)
# result_df = result_df.sample(frac=1)
# Add dataset information of the first (left) dataset
result_df = pd.merge(result_df, features_df, left_on='dataset1', right_on='dataset_name')
# Add dataset information for the second (right) dataset
result_df = pd.merge(result_df, features_df, left_on='dataset2', right_on='dataset_name')
# Load histograms
ds1_histograms, ds2_histograms, ds1_original_histograms, ds2_original_histograms, ds_all_histogram, ds_bops_histogram = load_histograms(
result_df, histograms_path, num_rows, num_columns)
# Compute BOPS
# First, do an element-wise multiplication of the two histograms
bops = np.multiply(ds1_original_histograms, ds2_original_histograms)
# Reshape into a two dimensional array. First dimension represents the dataset number, e.g., first entry
# represents the first dataset of each. Second dimension represents the values in the multiplied histograms
bops = bops.reshape((bops.shape[0], num_rows * num_columns))
# Sum the values in each row to compute the final BOPS value
bops_values = np.sum(bops, axis=1)
# The final reshape puts each BOPS value in an array with a single value. Thus it produces a 2D array.
bops_values = bops_values.reshape((bops_values.shape[0], 1))
# result_df['bops'] = bops_values
cardinality_x = result_df[' cardinality_x']
cardinality_y = result_df[' cardinality_y']
result_size = result_df['result_size']
mbr_tests = result_df['mbr_tests']
# Compute the join selectivity as result_cardinality/(cardinality x * cardinality y) * 10E+9
join_selectivity = result_size / (cardinality_x * cardinality_y)
join_selectivity = join_selectivity * 1E5
# Compute the MBR selectivity in the same way
mbr_tests_selectivity = mbr_tests / (cardinality_x * cardinality_y)
mbr_tests_selectivity = mbr_tests_selectivity * 1E5
duration = result_df['duration']
dataset1 = result_df['dataset1']
dataset2 = result_df['dataset2']
# result_df = result_df.drop(columns=['result_size', 'dataset1', 'dataset2', 'dataset_name_x', 'dataset_name_y', ' cardinality_x', ' cardinality_y'])
# result_df = result_df.drop(
# columns=['result_size', 'dataset1', 'dataset2', 'dataset_name_x', 'dataset_name_y'])
result_df = result_df.drop(
columns=['result_size', 'dataset1', 'dataset2', 'dataset_name_x', 'dataset_name_y', ' cardinality_x',
' cardinality_y', 'mbr_tests', 'duration'])
# Normalize all the values using MinMax scaler
# These values are [AVG area_x, AVG x_x, AVG y_x, E0_x, E2_x, AVG area_y, AVG x_y, AVG y_y, E0_y, E2_y]
x = result_df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
result_df = pd.DataFrame(x_scaled, columns=result_df.columns)
result_df['cardinality_x'] = cardinality_x
result_df['cardinality_y'] = cardinality_y
result_df['bops'] = bops_values
result_df['dataset1'] = dataset1
result_df['dataset2'] = dataset2
result_df.insert(len(result_df.columns), 'result_size', result_size, True)
result_df.insert(len(result_df.columns), 'join_selectivity', join_selectivity, True)
result_df.insert(len(result_df.columns), 'mbr_tests', mbr_tests, True)
result_df.insert(len(result_df.columns), 'mbr_tests_selectivity', mbr_tests_selectivity, True)
result_df.insert(len(result_df.columns), 'duration', duration, True)
return result_df, ds1_histograms, ds2_histograms, ds_all_histogram, ds_bops_histogram
def load_join_data2(features_df, result_file, histograms_path, num_rows, num_columns):
cols = ['count', 'dataset1', 'dataset2', 'result_size', 'mbr_tests', 'duration']
result_df = pd.read_csv(result_file, delimiter=',', header=None, names=cols)
# result_df = result_df.sample(frac=1)
result_df = pd.merge(result_df, features_df, left_on='dataset1', right_on='dataset_name')
result_df = pd.merge(result_df, features_df, left_on='dataset2', right_on='dataset_name')
# Load histograms
ds1_histograms, ds2_histograms, ds1_original_histograms, ds2_original_histograms, ds_all_histogram, ds_bops_histogram = load_histograms2(
result_df, histograms_path, num_rows, num_columns)
# Compute BOPS
bops = np.multiply(ds1_original_histograms, ds2_original_histograms)
# print (bops)
bops = bops.reshape((bops.shape[0], num_rows * num_columns))
bops_values = np.sum(bops, axis=1)
bops_values = bops_values.reshape((bops_values.shape[0], 1))
# result_df['bops'] = bops_values
cardinality_x = result_df[' cardinality_x']
cardinality_y = result_df[' cardinality_y']
result_size = result_df['result_size']
mbr_tests = result_df['mbr_tests']
join_selectivity = result_size / (cardinality_x * cardinality_y)
join_selectivity = join_selectivity * math.pow(10, 9)
dataset1 = result_df['dataset1']
dataset2 = result_df['dataset2']
# result_df = result_df.drop(columns=['result_size', 'dataset1', 'dataset2', 'dataset_name_x', 'dataset_name_y', ' cardinality_x', ' cardinality_y'])
# result_df = result_df.drop(
# columns=['result_size', 'dataset1', 'dataset2', 'dataset_name_x', 'dataset_name_y'])
result_df = result_df.drop(
columns=['count', 'result_size', 'dataset1', 'dataset2', 'dataset_name_x', 'dataset_name_y', ' cardinality_x',
' cardinality_y', 'mbr_tests', 'duration'])
x = result_df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
result_df = pd.DataFrame(x_scaled)
result_df['cardinality_x'] = cardinality_x
result_df['cardinality_y'] = cardinality_y
result_df['bops'] = bops_values
result_df['dataset1'] = dataset1
result_df['dataset2'] = dataset2
result_df.insert(len(result_df.columns), 'result_size', result_size, True)
result_df.insert(len(result_df.columns), 'join_selectivity', join_selectivity, True)
result_df.insert(len(result_df.columns), 'mbr_tests', join_selectivity, True)
# print (len(result_df))
# result_df.to_csv('result_df.csv')
return result_df, ds1_histograms, ds2_histograms, ds_all_histogram, ds_bops_histogram
def load_histogram(histograms_path, num_rows, num_columns, dataset):
hist = np.genfromtxt('{}/{}x{}/{}'.format(histograms_path, num_rows, num_columns, dataset), delimiter=',')
normalized_hist = hist / hist.max()
normalized_hist = normalized_hist.reshape((hist.shape[0], hist.shape[1], 1))
hist = hist.reshape((hist.shape[0], hist.shape[1], 1))
return normalized_hist, hist
def load_histogram2(histograms_path, num_rows, num_columns, count, dataset):
hist = np.genfromtxt('{}/{}x{}/{}/{}'.format(histograms_path, num_rows, num_columns, count, dataset), delimiter=',')
normalized_hist = hist / hist.max()
normalized_hist = normalized_hist.reshape((hist.shape[0], hist.shape[1], 1))
hist = hist.reshape((hist.shape[0], hist.shape[1], 1))
return normalized_hist, hist
def load_histograms(result_df, histograms_path, num_rows, num_columns):
ds1_histograms = []
ds2_histograms = []
ds1_original_histograms = []
ds2_original_histograms = []
ds_all_histogram = []
ds_bops_histogram = []
for dataset in result_df['dataset1']:
normalized_hist, hist = load_histogram(histograms_path, num_rows, num_columns, dataset)
ds1_histograms.append(normalized_hist)
ds1_original_histograms.append(hist)
for dataset in result_df['dataset2']:
normalized_hist, hist = load_histogram(histograms_path, num_rows, num_columns, dataset)
ds2_histograms.append(normalized_hist)
ds2_original_histograms.append(hist)
for i in range(len(ds1_histograms)):
hist1 = ds1_original_histograms[i]
hist2 = ds2_original_histograms[i]
combined_hist = np.dstack((hist1, hist2))
combined_hist = combined_hist / combined_hist.max()
ds_all_histogram.append(combined_hist)
for i in range(len(ds1_histograms)):
hist1 = ds1_original_histograms[i]
hist2 = ds2_original_histograms[i]
bops_hist = np.multiply(hist1, hist2)
if bops_hist.max() > 0:
bops_hist = bops_hist / bops_hist.max()
ds_bops_histogram.append(bops_hist)
return np.array(ds1_histograms), np.array(ds2_histograms), np.array(ds1_original_histograms), np.array(
ds2_original_histograms), np.array(ds_all_histogram), np.array(ds_bops_histogram)
def load_histograms2(result_df, histograms_path, num_rows, num_columns):
ds1_histograms = []
ds2_histograms = []
ds1_original_histograms = []
ds2_original_histograms = []
ds_all_histogram = []
ds_bops_histogram = []
for index, row in result_df.iterrows():
count = row['count']
dataset1 = row['dataset1']
dataset2 = row['dataset2']
normalized_hist, hist = load_histogram2(histograms_path, num_rows, num_columns, count, dataset1)
ds1_histograms.append(normalized_hist)
ds1_original_histograms.append(hist)
normalized_hist, hist = load_histogram2(histograms_path, num_rows, num_columns, count, dataset2)
ds2_histograms.append(normalized_hist)
ds2_original_histograms.append(hist)
# count = 0
# for dataset in result_df['dataset1']:
# count += 1
# normalized_hist, hist = load_histogram2(histograms_path, num_rows, num_columns, count, dataset)
# ds1_histograms.append(normalized_hist)
# ds1_original_histograms.append(hist)
#
# count = 0
# for dataset in result_df['dataset2']:
# count += 1
# normalized_hist, hist = load_histogram2(histograms_path, num_rows, num_columns, count, dataset)
# ds2_histograms.append(normalized_hist)
# ds2_original_histograms.append(hist)
for i in range(len(ds1_histograms)):
hist1 = ds1_original_histograms[i]
hist2 = ds2_original_histograms[i]
combined_hist = np.dstack((hist1, hist2))
combined_hist = combined_hist / combined_hist.max()
ds_all_histogram.append(combined_hist)
for i in range(len(ds1_histograms)):
hist1 = ds1_original_histograms[i]
hist2 = ds2_original_histograms[i]
bops_hist = np.multiply(hist1, hist2)
if bops_hist.max() > 0:
bops_hist = bops_hist / bops_hist.max()
ds_bops_histogram.append(bops_hist)
return np.array(ds1_histograms), np.array(ds2_histograms), np.array(ds1_original_histograms), np.array(
ds2_original_histograms), np.array(ds_all_histogram), np.array(ds_bops_histogram)
def main():
print('Dataset utils')
# features_df = load_datasets_feature('data/uniform_datasets_features.csv')
# load_join_data(features_df, 'data/uniform_result_size.csv', 'data/histogram_uniform_values', 16, 16)
features_df = load_datasets_feature('data/data_aligned/aligned_small_datasets_features.csv')
join_data, ds1_histograms, ds2_histograms, ds_all_histogram = load_join_data(features_df,
'data/data_aligned/join_results_small_datasets.csv',
'data/data_aligned/histograms/small_datasets', 32,
32)
print (join_data)
if __name__ == '__main__':
main()
| 46.975535
| 153
| 0.706595
| 2,043
| 15,361
| 4.99021
| 0.091043
| 0.085532
| 0.020598
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0
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|
59334df846841bf038223cbc73d93bed2962747c
| 79
|
py
|
Python
|
bolt/utils/__init__.py
|
arahmanhamdy/bolt
|
8f5d9b8149db833b54a7b353162b2c28a53c8aff
|
[
"MIT"
] | 15
|
2016-10-21T14:30:38.000Z
|
2021-10-12T04:50:48.000Z
|
bolt/utils/__init__.py
|
arahmanhamdy/bolt
|
8f5d9b8149db833b54a7b353162b2c28a53c8aff
|
[
"MIT"
] | 51
|
2016-02-05T01:24:32.000Z
|
2019-12-09T16:52:20.000Z
|
bolt/utils/__init__.py
|
arahmanhamdy/bolt
|
8f5d9b8149db833b54a7b353162b2c28a53c8aff
|
[
"MIT"
] | 6
|
2016-10-17T13:48:16.000Z
|
2021-03-28T20:40:14.000Z
|
"""
"""
from ._utfiles import *
from ._utconfig import *
from ._uttime import *
| 15.8
| 24
| 0.683544
| 9
| 79
| 5.666667
| 0.555556
| 0.392157
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.164557
| 79
| 5
| 25
| 15.8
| 0.772727
| 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
|
59490332404059607a7feed225859f4f28aa0745
| 258,449
|
py
|
Python
|
instances/passenger_demand/pas-20210422-1717-int1/11.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-int1/11.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-int1/11.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 19423
passenger_arriving = (
(5, 4, 5, 6, 7, 2, 3, 1, 0, 1, 2, 0, 0, 6, 6, 2, 2, 3, 1, 1, 1, 2, 2, 1, 1, 0), # 0
(5, 7, 2, 5, 4, 2, 2, 1, 5, 1, 1, 1, 0, 10, 5, 2, 0, 6, 2, 0, 3, 3, 3, 1, 2, 0), # 1
(3, 6, 7, 3, 4, 4, 0, 1, 2, 0, 0, 1, 0, 5, 2, 7, 2, 4, 3, 1, 2, 1, 5, 0, 0, 0), # 2
(6, 3, 7, 6, 5, 2, 3, 2, 3, 0, 1, 0, 0, 5, 5, 4, 7, 6, 1, 4, 2, 1, 1, 1, 0, 0), # 3
(4, 6, 3, 4, 8, 0, 2, 4, 2, 0, 3, 0, 0, 9, 5, 6, 4, 9, 4, 2, 1, 5, 4, 0, 0, 0), # 4
(9, 7, 4, 2, 5, 3, 9, 2, 5, 2, 0, 2, 0, 10, 7, 9, 5, 4, 6, 0, 1, 4, 2, 0, 2, 0), # 5
(6, 14, 9, 7, 5, 4, 1, 3, 3, 1, 2, 1, 0, 13, 6, 1, 1, 4, 6, 1, 1, 5, 3, 3, 0, 0), # 6
(10, 11, 7, 12, 5, 2, 4, 1, 4, 0, 0, 0, 0, 9, 8, 7, 4, 4, 3, 4, 3, 3, 4, 1, 3, 0), # 7
(4, 10, 8, 7, 7, 2, 2, 2, 2, 1, 1, 0, 0, 6, 8, 10, 6, 4, 3, 2, 1, 4, 3, 2, 1, 0), # 8
(11, 10, 6, 7, 4, 2, 1, 4, 4, 2, 4, 0, 0, 4, 4, 8, 4, 6, 8, 4, 3, 2, 2, 1, 1, 0), # 9
(13, 8, 1, 8, 9, 2, 1, 4, 3, 0, 1, 1, 0, 10, 8, 7, 3, 4, 3, 3, 3, 8, 2, 3, 0, 0), # 10
(9, 6, 13, 12, 4, 6, 1, 2, 6, 2, 1, 0, 0, 9, 4, 4, 2, 3, 7, 7, 1, 3, 3, 0, 1, 0), # 11
(7, 10, 7, 3, 5, 5, 1, 6, 3, 2, 1, 0, 0, 12, 6, 5, 6, 5, 6, 3, 1, 2, 2, 3, 1, 0), # 12
(9, 7, 14, 8, 5, 2, 4, 3, 3, 0, 0, 1, 0, 6, 9, 11, 3, 5, 5, 2, 1, 4, 2, 0, 2, 0), # 13
(8, 5, 4, 12, 7, 2, 0, 4, 8, 0, 0, 0, 0, 10, 10, 5, 4, 10, 2, 3, 0, 5, 5, 2, 0, 0), # 14
(11, 10, 12, 12, 4, 4, 4, 5, 3, 4, 2, 0, 0, 13, 5, 7, 11, 3, 6, 3, 2, 4, 5, 0, 1, 0), # 15
(12, 8, 7, 4, 3, 5, 6, 4, 1, 0, 2, 1, 0, 6, 11, 9, 2, 8, 12, 3, 2, 3, 3, 1, 0, 0), # 16
(7, 9, 13, 9, 4, 3, 4, 4, 4, 0, 2, 0, 0, 9, 10, 11, 4, 6, 12, 4, 2, 3, 3, 2, 1, 0), # 17
(11, 14, 9, 8, 6, 0, 3, 8, 7, 2, 0, 2, 0, 10, 11, 12, 6, 4, 3, 2, 5, 3, 3, 3, 0, 0), # 18
(6, 9, 10, 10, 6, 4, 7, 4, 6, 3, 0, 0, 0, 11, 6, 10, 9, 8, 4, 6, 2, 7, 3, 3, 1, 0), # 19
(4, 6, 10, 9, 3, 4, 8, 3, 4, 0, 1, 0, 0, 10, 9, 4, 9, 7, 8, 5, 5, 5, 3, 1, 1, 0), # 20
(10, 5, 9, 12, 9, 5, 7, 5, 4, 1, 1, 0, 0, 7, 2, 9, 3, 6, 3, 5, 0, 4, 2, 2, 2, 0), # 21
(13, 13, 9, 8, 10, 2, 2, 4, 4, 4, 1, 1, 0, 9, 7, 7, 6, 4, 6, 5, 3, 6, 0, 1, 1, 0), # 22
(10, 12, 13, 6, 6, 3, 4, 2, 6, 1, 2, 0, 0, 8, 5, 7, 4, 5, 4, 4, 2, 5, 2, 0, 1, 0), # 23
(7, 12, 8, 4, 9, 4, 3, 1, 4, 3, 2, 1, 0, 13, 13, 8, 5, 9, 10, 4, 4, 2, 5, 2, 0, 0), # 24
(11, 7, 9, 10, 10, 0, 3, 5, 2, 0, 0, 1, 0, 13, 9, 13, 4, 13, 2, 5, 0, 3, 5, 1, 3, 0), # 25
(12, 7, 11, 7, 7, 2, 7, 4, 5, 1, 0, 1, 0, 7, 12, 7, 10, 6, 8, 2, 3, 5, 4, 1, 0, 0), # 26
(14, 9, 11, 9, 9, 4, 5, 6, 8, 3, 0, 1, 0, 9, 5, 5, 5, 12, 4, 5, 2, 1, 6, 1, 1, 0), # 27
(10, 6, 4, 5, 7, 2, 3, 1, 5, 3, 2, 0, 0, 13, 6, 7, 3, 4, 5, 2, 2, 4, 1, 3, 1, 0), # 28
(15, 9, 9, 12, 6, 3, 6, 2, 0, 2, 2, 0, 0, 12, 7, 10, 5, 10, 8, 4, 2, 5, 2, 0, 0, 0), # 29
(2, 12, 8, 11, 8, 0, 4, 3, 4, 5, 3, 1, 0, 4, 10, 7, 6, 6, 10, 6, 7, 7, 4, 1, 2, 0), # 30
(9, 12, 8, 6, 9, 4, 1, 3, 2, 3, 1, 1, 0, 7, 4, 9, 8, 9, 9, 4, 6, 2, 6, 3, 2, 0), # 31
(12, 10, 7, 10, 6, 5, 4, 3, 5, 1, 0, 1, 0, 10, 7, 3, 8, 7, 4, 4, 3, 3, 4, 2, 0, 0), # 32
(11, 10, 11, 19, 12, 4, 6, 2, 2, 3, 4, 0, 0, 10, 11, 5, 8, 7, 6, 8, 3, 1, 4, 0, 1, 0), # 33
(9, 9, 5, 14, 5, 4, 1, 6, 4, 1, 0, 0, 0, 7, 10, 8, 8, 14, 2, 2, 3, 3, 5, 3, 0, 0), # 34
(10, 5, 10, 9, 8, 3, 5, 4, 1, 6, 0, 1, 0, 11, 18, 6, 4, 10, 2, 2, 5, 2, 2, 2, 0, 0), # 35
(15, 9, 7, 9, 6, 3, 5, 4, 5, 0, 1, 0, 0, 10, 8, 14, 3, 11, 4, 1, 1, 6, 2, 1, 1, 0), # 36
(7, 10, 11, 9, 7, 4, 6, 3, 9, 2, 0, 0, 0, 7, 11, 3, 3, 6, 5, 5, 4, 2, 2, 0, 1, 0), # 37
(7, 4, 5, 7, 4, 3, 4, 6, 4, 2, 0, 0, 0, 13, 2, 4, 10, 8, 3, 1, 2, 0, 5, 2, 1, 0), # 38
(11, 8, 8, 6, 5, 3, 7, 3, 7, 2, 2, 1, 0, 9, 6, 12, 4, 8, 6, 2, 4, 4, 3, 3, 2, 0), # 39
(13, 12, 10, 11, 8, 4, 2, 7, 3, 0, 2, 1, 0, 11, 7, 7, 6, 8, 5, 4, 3, 4, 5, 2, 3, 0), # 40
(9, 7, 8, 11, 7, 7, 7, 4, 5, 0, 2, 2, 0, 10, 11, 6, 7, 4, 7, 4, 4, 2, 4, 3, 0, 0), # 41
(12, 13, 18, 11, 8, 0, 4, 4, 3, 1, 1, 1, 0, 13, 9, 8, 7, 11, 6, 6, 1, 2, 6, 4, 1, 0), # 42
(12, 6, 12, 13, 6, 6, 6, 4, 4, 2, 4, 0, 0, 13, 12, 4, 4, 9, 3, 2, 5, 4, 2, 2, 1, 0), # 43
(11, 15, 6, 4, 10, 4, 4, 3, 3, 0, 3, 2, 0, 14, 5, 11, 6, 11, 7, 3, 4, 4, 0, 2, 1, 0), # 44
(18, 14, 14, 7, 7, 3, 1, 4, 7, 0, 0, 0, 0, 8, 6, 9, 9, 7, 9, 7, 1, 2, 4, 1, 2, 0), # 45
(9, 9, 9, 6, 12, 5, 5, 9, 5, 3, 1, 1, 0, 11, 7, 9, 8, 7, 6, 4, 3, 2, 3, 1, 2, 0), # 46
(13, 13, 6, 7, 4, 3, 4, 0, 7, 2, 3, 0, 0, 5, 8, 6, 4, 11, 2, 5, 5, 6, 3, 2, 2, 0), # 47
(15, 10, 9, 11, 8, 5, 2, 2, 2, 3, 1, 1, 0, 13, 4, 5, 5, 5, 4, 3, 6, 2, 0, 0, 2, 0), # 48
(11, 10, 6, 5, 8, 1, 4, 7, 6, 2, 0, 3, 0, 12, 9, 5, 11, 7, 4, 2, 2, 6, 0, 0, 0, 0), # 49
(12, 14, 11, 8, 10, 5, 4, 3, 3, 1, 0, 0, 0, 2, 12, 6, 3, 10, 6, 7, 1, 6, 3, 2, 1, 0), # 50
(6, 13, 3, 8, 4, 10, 2, 5, 3, 2, 4, 0, 0, 11, 15, 10, 7, 8, 4, 3, 1, 1, 4, 0, 1, 0), # 51
(7, 9, 10, 12, 8, 7, 6, 5, 5, 1, 1, 0, 0, 7, 14, 7, 7, 7, 6, 6, 5, 2, 2, 2, 0, 0), # 52
(6, 7, 5, 9, 5, 2, 3, 3, 6, 5, 1, 4, 0, 7, 9, 7, 7, 12, 8, 5, 0, 4, 4, 0, 1, 0), # 53
(8, 8, 10, 4, 8, 4, 2, 3, 0, 4, 1, 3, 0, 18, 9, 8, 5, 6, 6, 1, 0, 3, 1, 4, 2, 0), # 54
(3, 9, 7, 11, 6, 2, 7, 1, 2, 0, 2, 0, 0, 9, 7, 10, 8, 10, 5, 2, 3, 9, 1, 2, 2, 0), # 55
(6, 13, 9, 6, 15, 3, 5, 6, 2, 1, 0, 1, 0, 12, 6, 4, 5, 6, 3, 6, 2, 5, 1, 1, 1, 0), # 56
(10, 11, 9, 8, 4, 3, 2, 2, 5, 4, 0, 1, 0, 9, 9, 10, 7, 11, 7, 4, 2, 9, 3, 1, 0, 0), # 57
(12, 7, 8, 10, 3, 5, 4, 1, 3, 1, 0, 1, 0, 9, 14, 7, 4, 8, 5, 2, 2, 4, 1, 3, 0, 0), # 58
(12, 12, 9, 6, 3, 1, 5, 3, 5, 2, 1, 1, 0, 13, 8, 7, 7, 9, 5, 2, 0, 7, 1, 2, 2, 0), # 59
(13, 8, 16, 9, 5, 4, 2, 2, 3, 2, 0, 1, 0, 14, 7, 5, 3, 6, 3, 4, 5, 5, 5, 1, 0, 0), # 60
(8, 16, 9, 12, 8, 7, 9, 6, 4, 2, 0, 0, 0, 9, 7, 7, 4, 5, 4, 4, 2, 0, 3, 6, 1, 0), # 61
(9, 6, 9, 6, 10, 2, 7, 1, 6, 1, 1, 1, 0, 11, 12, 8, 5, 8, 7, 3, 3, 3, 0, 0, 1, 0), # 62
(12, 10, 5, 9, 3, 2, 1, 3, 6, 3, 2, 0, 0, 12, 9, 4, 7, 8, 3, 5, 3, 3, 1, 1, 2, 0), # 63
(10, 15, 9, 8, 7, 3, 4, 2, 4, 3, 3, 0, 0, 12, 8, 8, 6, 10, 2, 2, 1, 3, 5, 2, 0, 0), # 64
(9, 6, 5, 9, 8, 2, 3, 6, 5, 6, 1, 3, 0, 10, 9, 6, 7, 3, 3, 3, 3, 2, 4, 2, 0, 0), # 65
(14, 9, 5, 10, 9, 1, 6, 2, 8, 1, 2, 0, 0, 13, 8, 5, 4, 10, 7, 1, 2, 5, 4, 3, 1, 0), # 66
(6, 8, 12, 8, 6, 6, 8, 5, 1, 0, 2, 0, 0, 8, 7, 12, 4, 7, 1, 1, 3, 6, 4, 2, 2, 0), # 67
(11, 18, 11, 16, 7, 2, 4, 3, 3, 4, 1, 1, 0, 10, 4, 7, 3, 4, 3, 2, 2, 4, 0, 0, 1, 0), # 68
(9, 2, 10, 8, 9, 6, 6, 1, 3, 1, 1, 3, 0, 7, 7, 9, 4, 6, 1, 3, 3, 7, 8, 1, 0, 0), # 69
(9, 5, 11, 8, 3, 1, 5, 2, 2, 3, 3, 1, 0, 11, 9, 6, 7, 7, 5, 3, 1, 3, 3, 2, 2, 0), # 70
(15, 9, 8, 12, 7, 4, 3, 1, 3, 1, 1, 2, 0, 10, 10, 9, 12, 7, 2, 2, 2, 4, 4, 5, 3, 0), # 71
(11, 10, 13, 13, 12, 3, 0, 5, 5, 1, 2, 2, 0, 11, 6, 6, 9, 4, 4, 3, 4, 4, 3, 2, 2, 0), # 72
(16, 14, 10, 12, 6, 2, 3, 2, 4, 1, 2, 1, 0, 4, 8, 5, 7, 6, 2, 5, 1, 3, 2, 2, 1, 0), # 73
(13, 4, 6, 7, 10, 3, 7, 2, 3, 1, 2, 2, 0, 14, 6, 5, 5, 7, 6, 3, 1, 4, 2, 1, 0, 0), # 74
(15, 13, 6, 11, 6, 6, 4, 2, 4, 2, 2, 3, 0, 6, 4, 7, 4, 12, 4, 2, 1, 2, 3, 2, 1, 0), # 75
(11, 13, 9, 12, 6, 5, 4, 1, 2, 2, 0, 2, 0, 17, 6, 5, 3, 5, 5, 3, 1, 1, 4, 2, 3, 0), # 76
(15, 10, 7, 9, 5, 3, 3, 2, 3, 3, 1, 3, 0, 10, 4, 6, 3, 7, 4, 5, 2, 6, 3, 1, 2, 0), # 77
(12, 13, 12, 4, 5, 2, 0, 3, 3, 3, 0, 0, 0, 12, 10, 2, 4, 8, 5, 6, 2, 4, 1, 3, 1, 0), # 78
(10, 10, 6, 12, 6, 6, 5, 3, 4, 0, 3, 1, 0, 9, 12, 4, 5, 8, 6, 6, 3, 4, 6, 5, 0, 0), # 79
(15, 7, 7, 6, 16, 4, 4, 2, 3, 2, 3, 1, 0, 8, 11, 7, 9, 5, 3, 4, 0, 2, 3, 0, 1, 0), # 80
(10, 8, 11, 12, 7, 2, 3, 4, 4, 1, 0, 1, 0, 14, 12, 6, 2, 8, 8, 1, 0, 0, 3, 0, 1, 0), # 81
(11, 11, 4, 11, 10, 4, 1, 3, 3, 1, 3, 0, 0, 13, 9, 7, 6, 8, 5, 0, 5, 3, 2, 1, 1, 0), # 82
(14, 10, 10, 13, 10, 2, 3, 3, 7, 0, 2, 0, 0, 8, 14, 4, 1, 16, 5, 1, 2, 3, 4, 1, 0, 0), # 83
(9, 11, 2, 12, 15, 2, 4, 4, 2, 4, 3, 2, 0, 11, 8, 9, 6, 3, 2, 5, 5, 5, 7, 0, 0, 0), # 84
(4, 5, 7, 9, 9, 2, 9, 4, 4, 2, 4, 2, 0, 13, 3, 5, 9, 12, 1, 4, 2, 7, 1, 0, 0, 0), # 85
(11, 18, 10, 8, 9, 1, 3, 3, 3, 2, 1, 0, 0, 12, 14, 13, 4, 9, 1, 6, 3, 5, 1, 1, 2, 0), # 86
(11, 10, 8, 5, 6, 2, 2, 1, 5, 2, 0, 1, 0, 16, 11, 2, 6, 6, 5, 2, 0, 4, 2, 1, 0, 0), # 87
(6, 8, 3, 6, 6, 4, 4, 0, 0, 2, 0, 0, 0, 8, 8, 8, 5, 7, 4, 0, 2, 8, 5, 0, 0, 0), # 88
(16, 15, 5, 11, 12, 4, 2, 3, 2, 4, 2, 0, 0, 12, 8, 3, 2, 5, 3, 3, 1, 3, 5, 1, 0, 0), # 89
(8, 11, 6, 8, 11, 1, 5, 3, 5, 1, 1, 1, 0, 10, 8, 5, 6, 10, 3, 1, 4, 2, 3, 1, 0, 0), # 90
(12, 6, 8, 4, 9, 1, 0, 4, 2, 2, 1, 0, 0, 8, 7, 7, 6, 8, 2, 4, 3, 4, 4, 2, 0, 0), # 91
(13, 6, 5, 6, 4, 1, 4, 2, 1, 0, 1, 0, 0, 5, 8, 5, 2, 7, 6, 3, 5, 3, 7, 0, 0, 0), # 92
(7, 9, 6, 14, 6, 4, 5, 0, 3, 3, 0, 0, 0, 10, 9, 4, 7, 6, 3, 3, 3, 5, 4, 1, 0, 0), # 93
(10, 6, 12, 9, 12, 1, 2, 2, 7, 7, 0, 1, 0, 10, 10, 7, 14, 9, 1, 6, 4, 2, 5, 1, 1, 0), # 94
(4, 7, 10, 5, 9, 4, 2, 3, 12, 0, 1, 1, 0, 11, 6, 10, 5, 10, 3, 3, 6, 6, 5, 1, 1, 0), # 95
(13, 9, 8, 11, 8, 6, 2, 3, 7, 1, 0, 1, 0, 8, 8, 6, 3, 7, 3, 7, 4, 5, 0, 0, 0, 0), # 96
(13, 9, 5, 12, 4, 9, 3, 1, 1, 2, 0, 2, 0, 13, 11, 7, 3, 10, 4, 5, 0, 4, 1, 1, 1, 0), # 97
(13, 8, 6, 9, 8, 1, 6, 5, 2, 1, 2, 1, 0, 12, 14, 9, 2, 2, 1, 3, 3, 0, 3, 0, 0, 0), # 98
(13, 12, 7, 9, 6, 4, 3, 2, 7, 4, 1, 0, 0, 10, 7, 5, 4, 12, 0, 4, 1, 2, 7, 3, 1, 0), # 99
(10, 10, 4, 14, 4, 6, 4, 0, 1, 2, 1, 0, 0, 14, 9, 6, 4, 5, 0, 6, 0, 10, 5, 0, 0, 0), # 100
(7, 4, 10, 8, 7, 2, 0, 2, 4, 1, 1, 1, 0, 10, 10, 9, 9, 6, 3, 3, 1, 3, 3, 2, 1, 0), # 101
(9, 9, 8, 6, 6, 2, 6, 4, 3, 1, 2, 0, 0, 7, 8, 6, 3, 6, 1, 4, 2, 4, 1, 0, 0, 0), # 102
(13, 9, 7, 13, 5, 4, 0, 4, 6, 3, 0, 0, 0, 6, 4, 10, 0, 5, 2, 4, 2, 2, 1, 1, 0, 0), # 103
(13, 8, 9, 7, 12, 1, 2, 1, 1, 2, 2, 2, 0, 7, 5, 3, 5, 6, 5, 4, 3, 10, 3, 0, 0, 0), # 104
(14, 9, 8, 16, 15, 8, 6, 2, 1, 1, 3, 1, 0, 9, 10, 5, 2, 14, 5, 2, 4, 4, 2, 2, 0, 0), # 105
(12, 4, 10, 9, 5, 1, 4, 1, 5, 1, 0, 0, 0, 6, 14, 4, 3, 8, 3, 3, 3, 0, 7, 1, 1, 0), # 106
(5, 6, 9, 5, 2, 1, 1, 2, 1, 0, 2, 1, 0, 11, 4, 4, 8, 5, 4, 3, 4, 1, 1, 1, 1, 0), # 107
(7, 7, 15, 7, 13, 4, 4, 3, 2, 0, 0, 2, 0, 9, 12, 6, 4, 11, 5, 2, 1, 6, 2, 2, 0, 0), # 108
(10, 12, 9, 4, 5, 6, 4, 5, 2, 2, 1, 1, 0, 8, 6, 6, 5, 4, 3, 4, 6, 4, 3, 2, 0, 0), # 109
(11, 9, 7, 7, 9, 7, 1, 1, 3, 1, 2, 0, 0, 4, 6, 8, 4, 7, 5, 5, 2, 5, 1, 0, 2, 0), # 110
(14, 4, 6, 11, 10, 3, 4, 1, 2, 4, 1, 0, 0, 14, 2, 7, 8, 8, 4, 2, 3, 6, 1, 1, 0, 0), # 111
(10, 7, 7, 7, 9, 5, 3, 3, 6, 1, 5, 0, 0, 11, 6, 10, 3, 11, 0, 4, 2, 2, 1, 2, 1, 0), # 112
(13, 5, 11, 7, 2, 2, 1, 5, 3, 0, 2, 1, 0, 4, 12, 10, 4, 7, 3, 5, 4, 4, 7, 2, 0, 0), # 113
(8, 0, 5, 7, 3, 2, 2, 4, 3, 1, 1, 2, 0, 7, 9, 7, 0, 6, 0, 1, 4, 4, 3, 3, 1, 0), # 114
(9, 6, 10, 13, 8, 8, 0, 1, 9, 1, 1, 1, 0, 10, 4, 5, 1, 6, 4, 2, 3, 4, 3, 3, 1, 0), # 115
(5, 5, 9, 18, 5, 4, 7, 3, 6, 0, 1, 1, 0, 8, 10, 10, 4, 8, 7, 7, 7, 4, 0, 1, 1, 0), # 116
(12, 4, 7, 8, 11, 3, 5, 5, 3, 1, 2, 0, 0, 7, 11, 6, 5, 7, 10, 2, 3, 7, 2, 1, 1, 0), # 117
(7, 4, 9, 7, 7, 6, 3, 1, 7, 1, 3, 0, 0, 14, 5, 4, 3, 6, 3, 2, 0, 4, 3, 4, 1, 0), # 118
(5, 3, 8, 5, 13, 1, 2, 1, 1, 0, 1, 1, 0, 10, 13, 10, 4, 6, 4, 10, 3, 2, 0, 3, 0, 0), # 119
(9, 7, 7, 10, 8, 3, 6, 3, 4, 0, 3, 1, 0, 9, 6, 6, 8, 9, 3, 2, 2, 2, 2, 3, 1, 0), # 120
(10, 4, 5, 6, 4, 1, 2, 7, 1, 2, 0, 0, 0, 10, 5, 8, 2, 5, 3, 4, 4, 2, 4, 1, 1, 0), # 121
(8, 11, 8, 7, 7, 5, 0, 3, 3, 4, 1, 1, 0, 10, 9, 2, 6, 10, 5, 3, 4, 4, 5, 1, 0, 0), # 122
(12, 8, 10, 8, 13, 1, 1, 1, 6, 2, 2, 0, 0, 6, 8, 5, 3, 8, 4, 4, 1, 5, 2, 1, 2, 0), # 123
(6, 7, 10, 7, 10, 3, 3, 2, 7, 0, 0, 0, 0, 10, 6, 6, 1, 7, 1, 2, 1, 1, 1, 1, 0, 0), # 124
(16, 5, 8, 3, 8, 5, 2, 0, 6, 0, 0, 0, 0, 7, 5, 9, 1, 4, 2, 5, 1, 3, 3, 1, 0, 0), # 125
(10, 6, 11, 12, 5, 0, 4, 3, 7, 3, 0, 0, 0, 12, 6, 7, 4, 4, 4, 1, 2, 4, 4, 3, 0, 0), # 126
(8, 7, 13, 1, 5, 4, 6, 5, 3, 4, 0, 0, 0, 6, 9, 10, 3, 4, 4, 2, 5, 3, 4, 0, 0, 0), # 127
(7, 7, 7, 6, 11, 4, 3, 1, 4, 3, 0, 2, 0, 5, 9, 6, 3, 4, 2, 6, 1, 5, 3, 1, 0, 0), # 128
(6, 4, 2, 11, 8, 2, 1, 6, 4, 1, 0, 0, 0, 9, 10, 5, 6, 9, 2, 6, 2, 2, 5, 0, 2, 0), # 129
(8, 9, 14, 9, 9, 4, 5, 1, 4, 0, 0, 1, 0, 4, 2, 4, 5, 9, 4, 3, 0, 0, 1, 3, 0, 0), # 130
(12, 3, 9, 7, 8, 4, 5, 4, 4, 2, 2, 0, 0, 4, 3, 7, 3, 6, 2, 2, 1, 2, 0, 2, 0, 0), # 131
(11, 5, 15, 8, 4, 2, 4, 1, 6, 2, 1, 0, 0, 12, 4, 1, 3, 6, 5, 5, 1, 0, 5, 1, 1, 0), # 132
(10, 4, 8, 7, 4, 3, 4, 0, 3, 2, 2, 1, 0, 6, 9, 6, 1, 6, 6, 4, 3, 1, 6, 2, 1, 0), # 133
(10, 7, 7, 8, 5, 4, 3, 1, 0, 0, 2, 0, 0, 10, 9, 2, 3, 5, 5, 1, 1, 5, 1, 3, 0, 0), # 134
(9, 7, 5, 8, 6, 3, 4, 4, 1, 1, 1, 1, 0, 12, 9, 8, 3, 3, 3, 5, 2, 4, 2, 2, 0, 0), # 135
(7, 13, 8, 5, 8, 1, 4, 2, 7, 4, 2, 2, 0, 12, 7, 5, 3, 6, 3, 1, 3, 3, 1, 1, 0, 0), # 136
(10, 7, 14, 9, 8, 6, 2, 2, 1, 1, 0, 0, 0, 7, 10, 6, 2, 7, 4, 6, 2, 3, 3, 0, 0, 0), # 137
(13, 6, 7, 8, 7, 4, 1, 3, 3, 0, 2, 0, 0, 7, 8, 4, 5, 7, 2, 6, 3, 4, 3, 0, 0, 0), # 138
(15, 2, 9, 10, 9, 2, 2, 4, 3, 2, 1, 1, 0, 8, 6, 4, 5, 6, 5, 4, 1, 4, 3, 3, 0, 0), # 139
(13, 5, 7, 9, 10, 6, 3, 1, 7, 2, 2, 0, 0, 6, 6, 2, 3, 10, 4, 2, 1, 4, 3, 1, 1, 0), # 140
(5, 8, 6, 8, 8, 5, 2, 3, 6, 0, 0, 1, 0, 4, 7, 4, 6, 10, 5, 1, 0, 1, 1, 1, 0, 0), # 141
(10, 2, 12, 10, 10, 5, 2, 2, 5, 0, 0, 0, 0, 12, 8, 7, 3, 5, 1, 2, 1, 1, 2, 0, 1, 0), # 142
(6, 7, 6, 4, 4, 2, 2, 3, 2, 2, 0, 2, 0, 10, 3, 6, 4, 5, 2, 1, 1, 5, 3, 2, 1, 0), # 143
(12, 4, 7, 6, 2, 3, 2, 0, 4, 2, 3, 1, 0, 15, 6, 8, 5, 6, 4, 4, 0, 8, 4, 0, 0, 0), # 144
(12, 4, 4, 8, 8, 5, 4, 0, 2, 3, 2, 0, 0, 8, 1, 9, 5, 7, 1, 0, 2, 4, 1, 1, 0, 0), # 145
(8, 4, 8, 6, 8, 4, 4, 3, 4, 2, 2, 0, 0, 7, 8, 6, 4, 4, 2, 4, 2, 4, 3, 2, 0, 0), # 146
(9, 4, 8, 10, 7, 3, 3, 2, 1, 2, 2, 0, 0, 9, 8, 5, 7, 5, 3, 1, 3, 3, 2, 3, 1, 0), # 147
(11, 5, 6, 5, 5, 1, 1, 1, 3, 1, 0, 2, 0, 10, 15, 3, 1, 9, 4, 4, 1, 5, 2, 1, 1, 0), # 148
(8, 9, 7, 13, 9, 2, 6, 1, 2, 0, 2, 0, 0, 7, 5, 2, 4, 6, 7, 4, 2, 3, 1, 4, 0, 0), # 149
(8, 7, 5, 6, 7, 7, 3, 7, 7, 2, 1, 0, 0, 10, 10, 5, 7, 12, 4, 0, 0, 0, 1, 3, 1, 0), # 150
(5, 10, 2, 3, 6, 5, 2, 1, 1, 1, 3, 0, 0, 8, 12, 6, 3, 7, 1, 2, 1, 5, 7, 3, 0, 0), # 151
(15, 7, 5, 7, 8, 1, 2, 1, 3, 2, 0, 1, 0, 12, 8, 3, 4, 6, 2, 0, 0, 2, 1, 1, 0, 0), # 152
(9, 5, 9, 6, 8, 3, 1, 1, 5, 2, 0, 0, 0, 8, 5, 4, 3, 8, 2, 2, 4, 5, 3, 2, 1, 0), # 153
(8, 12, 4, 5, 10, 7, 4, 0, 2, 2, 4, 1, 0, 7, 7, 5, 4, 10, 7, 1, 3, 3, 5, 1, 1, 0), # 154
(11, 7, 5, 5, 5, 3, 2, 4, 4, 1, 1, 1, 0, 8, 9, 1, 4, 6, 0, 1, 1, 1, 3, 0, 0, 0), # 155
(6, 5, 4, 7, 6, 2, 2, 0, 4, 0, 1, 2, 0, 11, 9, 5, 1, 6, 3, 0, 0, 1, 1, 1, 0, 0), # 156
(10, 4, 9, 8, 7, 6, 1, 2, 3, 0, 1, 0, 0, 12, 7, 3, 2, 6, 1, 1, 0, 4, 4, 2, 0, 0), # 157
(9, 4, 7, 8, 7, 5, 5, 0, 2, 0, 1, 0, 0, 11, 1, 5, 4, 4, 4, 1, 1, 3, 6, 0, 0, 0), # 158
(10, 8, 8, 10, 5, 0, 4, 4, 3, 1, 1, 1, 0, 8, 5, 4, 4, 8, 3, 1, 2, 5, 0, 1, 1, 0), # 159
(7, 3, 3, 9, 7, 1, 2, 3, 5, 1, 2, 0, 0, 10, 6, 9, 2, 6, 3, 3, 5, 1, 2, 2, 2, 0), # 160
(9, 7, 7, 6, 10, 2, 1, 5, 6, 0, 0, 2, 0, 3, 11, 7, 4, 5, 0, 1, 3, 1, 1, 0, 0, 0), # 161
(10, 4, 4, 4, 9, 7, 1, 2, 5, 0, 1, 0, 0, 7, 3, 3, 2, 5, 4, 2, 6, 2, 0, 0, 1, 0), # 162
(8, 4, 7, 7, 8, 3, 1, 1, 1, 0, 0, 0, 0, 6, 2, 4, 3, 6, 1, 3, 0, 3, 2, 3, 2, 0), # 163
(4, 2, 8, 5, 5, 4, 3, 3, 6, 0, 2, 1, 0, 7, 10, 2, 6, 7, 5, 3, 0, 2, 2, 1, 2, 0), # 164
(5, 6, 4, 2, 6, 4, 3, 2, 7, 0, 0, 0, 0, 9, 3, 6, 3, 5, 6, 2, 0, 3, 2, 1, 0, 0), # 165
(7, 2, 9, 6, 2, 3, 1, 1, 2, 1, 0, 0, 0, 8, 7, 4, 5, 7, 4, 0, 1, 3, 2, 0, 0, 0), # 166
(5, 4, 4, 7, 7, 2, 1, 3, 4, 0, 2, 0, 0, 8, 8, 2, 4, 6, 4, 4, 1, 2, 2, 1, 1, 0), # 167
(5, 8, 3, 7, 3, 3, 1, 4, 4, 4, 0, 0, 0, 7, 5, 5, 3, 5, 1, 0, 3, 2, 1, 1, 0, 0), # 168
(7, 3, 5, 7, 4, 1, 2, 1, 2, 0, 0, 1, 0, 7, 3, 4, 3, 7, 2, 3, 1, 2, 4, 0, 0, 0), # 169
(3, 0, 8, 2, 5, 5, 3, 0, 1, 2, 0, 1, 0, 6, 12, 2, 3, 4, 2, 0, 0, 2, 1, 1, 1, 0), # 170
(8, 5, 7, 6, 5, 2, 1, 0, 4, 1, 2, 0, 0, 8, 3, 4, 3, 2, 4, 5, 2, 4, 3, 2, 1, 0), # 171
(9, 5, 4, 2, 1, 3, 0, 0, 0, 2, 0, 0, 0, 7, 3, 2, 2, 3, 3, 1, 1, 2, 1, 1, 0, 0), # 172
(5, 2, 1, 4, 3, 3, 0, 0, 4, 2, 1, 0, 0, 2, 3, 8, 2, 3, 3, 3, 1, 3, 1, 0, 0, 0), # 173
(5, 7, 4, 7, 12, 0, 1, 2, 3, 2, 1, 0, 0, 5, 7, 3, 2, 5, 5, 2, 2, 1, 3, 1, 1, 0), # 174
(4, 1, 6, 0, 0, 1, 2, 4, 6, 0, 0, 0, 0, 4, 7, 5, 2, 6, 3, 2, 0, 1, 1, 2, 0, 0), # 175
(2, 0, 4, 5, 2, 1, 4, 0, 0, 1, 0, 1, 0, 4, 5, 1, 1, 9, 1, 0, 0, 1, 2, 1, 0, 0), # 176
(2, 7, 4, 8, 5, 3, 1, 1, 1, 0, 1, 0, 0, 3, 4, 2, 1, 4, 3, 1, 1, 4, 0, 1, 0, 0), # 177
(2, 1, 6, 2, 4, 1, 0, 3, 2, 2, 0, 0, 0, 5, 6, 5, 2, 3, 2, 0, 0, 2, 0, 0, 2, 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 = (
(5.020865578371768, 5.525288559693166, 5.211283229612507, 6.214667773863432, 5.554685607609612, 3.1386549320373387, 4.146035615373915, 4.653176172979423, 6.090099062168007, 3.9580150155223697, 4.205265163885603, 4.897915078306173, 5.083880212578363), # 0
(5.354327152019974, 5.890060694144759, 5.555346591330152, 6.625144253276616, 5.922490337474237, 3.3459835840425556, 4.419468941263694, 4.959513722905708, 6.492245326332909, 4.21898069227715, 4.483096135956131, 5.221216660814354, 5.419791647439855), # 1
(5.686723008979731, 6.253385170890979, 5.8980422855474135, 7.033987704664794, 6.288962973749744, 3.5524851145124448, 4.691818507960704, 5.264625247904419, 6.892786806877549, 4.478913775020546, 4.759823148776313, 5.543232652053055, 5.75436482820969), # 2
(6.016757793146562, 6.613820501936447, 6.238010869319854, 7.439576407532074, 6.652661676001902, 3.757340622585113, 4.962003641647955, 5.567301157494507, 7.290135160921093, 4.736782698426181, 5.0343484118273825, 5.862685684930461, 6.086272806254225), # 3
(6.343136148415981, 6.9699251992857745, 6.573892899703036, 7.840288641382569, 7.012144603796492, 3.9597312073986677, 5.2289436685084585, 5.866331861194915, 7.682702045582707, 4.991555897167679, 5.305574134590575, 6.178298392354764, 6.414188632939817), # 4
(6.66456271868351, 7.320257774943588, 6.9043289337525175, 8.234502685720393, 7.36596991669928, 4.158837968091214, 5.491557914725224, 6.160507768524592, 8.068899117981559, 5.242201805918663, 5.572402526547132, 6.488793407234148, 6.736785359632827), # 5
(6.979742147844666, 7.663376740914501, 7.227959528523866, 8.620596820049652, 7.712695774276043, 4.353842003800864, 5.7487657064812625, 6.4486192890024885, 8.447138035236815, 5.487688859352758, 5.833735797178282, 6.792893362476808, 7.052736037699606), # 6
(7.2873790797949685, 7.997840609203132, 7.543425241072635, 8.996949323874462, 8.050880336092554, 4.543924413665721, 5.999486369959585, 6.729456832147552, 8.815830454467644, 5.726985492143586, 6.088476155965268, 7.089320890990929, 7.360713718506519), # 7
(7.586178158429934, 8.322207891814099, 7.849366628454396, 9.361938476698928, 8.379081761714586, 4.7282662968238895, 6.2426392313431975, 7.001810807478725, 9.173388032793206, 5.959060138964774, 6.335525812389321, 7.376798625684702, 7.659391453419917), # 8
(7.874844027645085, 8.635037100752022, 8.144424247724704, 9.713942558027169, 8.69585821070791, 4.906048752413484, 6.47714361681512, 7.264471624514963, 9.518222427332674, 6.182881234489941, 6.573786975931678, 7.654049199466313, 7.947442293806162), # 9
(8.152081331335932, 8.934886748021516, 8.427238655939124, 10.051339847363288, 8.9997678426383, 5.076452879572607, 6.701918852558355, 7.516229692775211, 9.848745295205214, 6.397417213392714, 6.802161856073574, 7.919795245243952, 8.22353929103161), # 10
(8.416594713398005, 9.220315345627206, 8.696450410153215, 10.372508624211397, 9.289368817071534, 5.238659777439368, 6.915884264755916, 7.7558754217784145, 10.163368293529993, 6.601636510346719, 7.019552662296249, 8.17275939592581, 8.486355496462611), # 11
(8.667088817726812, 9.489881405573698, 8.95070006742254, 10.675827168075612, 9.563219293573377, 5.391850545151869, 7.1179591795908115, 7.982199221043521, 10.460503079426179, 6.794507560025572, 7.224861604080934, 8.411664284420068, 8.734563961465534), # 12
(8.902268288217876, 9.74214343986562, 9.188628184802662, 10.959673758460044, 9.819877431709601, 5.5352062818482235, 7.307062923246056, 8.193991500089481, 10.738561310012932, 6.974998797102904, 7.416990890908869, 8.63523254363492, 8.966837737406735), # 13
(9.120837768766716, 9.975659960507588, 9.408875319349146, 11.222426674868792, 10.05790139104599, 5.667908086666534, 7.482114821904661, 8.390042668435246, 10.995954642409421, 7.142078656252334, 7.594842732261284, 8.84218680647856, 9.181849875652563), # 14
(9.321501903268855, 10.188989479504217, 9.610082028117542, 11.462464196805985, 10.275849331148308, 5.789137058744912, 7.642034201749626, 8.569143135599756, 11.23109473373482, 7.29471557214749, 7.757319337619419, 9.031249705859171, 9.37827342756938), # 15
(9.5029653356198, 10.380690508860132, 9.790888868163425, 11.678164603775716, 10.472279411582333, 5.898074297221459, 7.785740388963976, 8.73008331110196, 11.442393241108286, 7.431877979461996, 7.9033229164645125, 9.20114387468494, 9.554781444523545), # 16
(9.663932709715075, 10.549321560579946, 9.949936396542352, 11.867906175282112, 10.645749791913838, 5.993900901234285, 7.9121527097307105, 8.871653604460818, 11.628261821648984, 7.552534312869467, 8.031755678277799, 9.350591945864055, 9.710046977881415), # 17
(9.803108669450204, 10.693441146668274, 10.08586517030988, 12.030067190829278, 10.794818631708589, 6.075797969921503, 8.020190490232851, 8.99264442519526, 11.787112132476096, 7.6556530070435365, 8.141519832540508, 9.478316552304715, 9.842743079009345), # 18
(9.919197858720699, 10.811607779129744, 10.197315746521578, 12.163025929921314, 10.918044090532366, 6.142946602421208, 8.108773056653394, 9.091846182824245, 11.917355830708779, 7.740202496657828, 8.231517588733878, 9.583040326915096, 9.951542799273696), # 19
(10.010904921422082, 10.902379969968962, 10.282928682233003, 12.265160672062354, 11.013984327950944, 6.194527897871518, 8.176819735175362, 9.168049286866717, 12.017404573466198, 7.805151216385958, 8.30065115633915, 9.66348590260339, 10.035119190040824), # 20
(10.076934501449866, 10.964316231190558, 10.341344534499719, 12.334849696756486, 11.081197503530088, 6.229722955410535, 8.223249851981759, 9.220044146841623, 12.085670017867521, 7.849467600901555, 8.34782274483756, 9.718375912277793, 10.092145302677078), # 21
(10.115991242699579, 10.995975074799144, 10.371203860377285, 12.370471283507836, 11.118241776835575, 6.247712874176367, 8.2469827332556, 9.246621172267915, 12.120563821031915, 7.872120084878242, 8.37193456371034, 9.74643298884649, 10.121294188548827), # 22
(10.13039336334264, 10.999723593964335, 10.374923182441702, 12.374930812757203, 11.127732056032597, 6.25, 8.249804002259339, 9.249493827160494, 12.124926234567901, 7.874792272519433, 8.37495803716174, 9.749897576588934, 10.125), # 23
(10.141012413034153, 10.997537037037038, 10.374314814814815, 12.374381944444446, 11.133107613614852, 6.25, 8.248253812636166, 9.2455, 12.124341666666666, 7.87315061728395, 8.37462457912458, 9.749086419753086, 10.125), # 24
(10.15140723021158, 10.993227023319616, 10.373113854595337, 12.373296039094651, 11.138364945594503, 6.25, 8.24519890260631, 9.237654320987655, 12.123186728395062, 7.869918838591678, 8.373963399426362, 9.747485139460448, 10.125), # 25
(10.161577019048034, 10.986859396433472, 10.371336762688616, 12.37168544238683, 11.143503868421105, 6.25, 8.240686718308721, 9.226104938271606, 12.1214762345679, 7.865150708733425, 8.372980483850855, 9.745115683584821, 10.125), # 26
(10.171520983716636, 10.978499999999999, 10.369, 12.369562499999999, 11.148524198544214, 6.25, 8.234764705882354, 9.211, 12.119225, 7.858899999999999, 8.371681818181818, 9.742, 10.125), # 27
(10.181238328390501, 10.968214677640603, 10.366120027434842, 12.366939557613168, 11.153425752413401, 6.25, 8.22748031146615, 9.192487654320988, 12.116447839506172, 7.851220484682213, 8.370073388203018, 9.73816003657979, 10.125), # 28
(10.19072825724275, 10.95606927297668, 10.362713305898492, 12.36382896090535, 11.15820834647822, 6.25, 8.218880981199066, 9.170716049382715, 12.113159567901235, 7.842165935070874, 8.368161179698216, 9.733617741197987, 10.125), # 29
(10.199989974446497, 10.94212962962963, 10.358796296296296, 12.360243055555555, 11.162871797188236, 6.25, 8.209014161220043, 9.145833333333332, 12.109375, 7.83179012345679, 8.365951178451178, 9.728395061728394, 10.125), # 30
(10.209022684174858, 10.926461591220852, 10.354385459533608, 12.356194187242798, 11.167415920993008, 6.25, 8.19792729766804, 9.117987654320988, 12.105108950617284, 7.820146822130773, 8.363449370245666, 9.722513946044812, 10.125), # 31
(10.217825590600954, 10.909131001371742, 10.349497256515773, 12.35169470164609, 11.171840534342095, 6.25, 8.185667836681999, 9.087327160493828, 12.100376234567902, 7.807289803383631, 8.360661740865444, 9.715996342021034, 10.125), # 32
(10.226397897897897, 10.890203703703703, 10.344148148148149, 12.346756944444444, 11.176145453685063, 6.25, 8.172283224400871, 9.054, 12.095191666666667, 7.793272839506173, 8.357594276094275, 9.708864197530863, 10.125), # 33
(10.23473881023881, 10.869745541838133, 10.338354595336076, 12.341393261316872, 11.180330495471466, 6.25, 8.15782090696361, 9.018154320987653, 12.089570061728397, 7.778149702789209, 8.354252961715924, 9.701139460448102, 10.125), # 34
(10.242847531796807, 10.847822359396433, 10.332133058984912, 12.335615997942385, 11.18439547615087, 6.25, 8.142328330509159, 8.979938271604938, 12.083526234567902, 7.761974165523548, 8.350643783514153, 9.692844078646548, 10.125), # 35
(10.250723266745005, 10.824499999999999, 10.3255, 12.3294375, 11.188340212172836, 6.25, 8.12585294117647, 8.9395, 12.077074999999999, 7.7448, 8.346772727272727, 9.684000000000001, 10.125), # 36
(10.258365219256524, 10.799844307270233, 10.318471879286694, 12.322870113168724, 11.192164519986921, 6.25, 8.108442185104494, 8.896987654320988, 12.070231172839506, 7.726680978509374, 8.34264577877541, 9.674629172382259, 10.125), # 37
(10.265772593504476, 10.773921124828533, 10.311065157750342, 12.315926183127573, 11.19586821604269, 6.25, 8.09014350843218, 8.85254938271605, 12.063009567901235, 7.707670873342479, 8.33826892380596, 9.664753543667125, 10.125), # 38
(10.272944593661986, 10.746796296296296, 10.303296296296297, 12.308618055555556, 11.199451116789703, 6.25, 8.071004357298476, 8.806333333333333, 12.055425000000001, 7.687823456790124, 8.333648148148148, 9.654395061728394, 10.125), # 39
(10.279880423902163, 10.718535665294924, 10.295181755829903, 12.300958076131687, 11.202913038677519, 6.25, 8.05107217784233, 8.758487654320989, 12.047492283950618, 7.667192501143119, 8.328789437585733, 9.643575674439873, 10.125), # 40
(10.286579288398128, 10.689205075445816, 10.286737997256516, 12.29295859053498, 11.206253798155702, 6.25, 8.030394416202695, 8.709160493827161, 12.0392262345679, 7.645831778692272, 8.323698777902482, 9.632317329675354, 10.125), # 41
(10.293040391323, 10.658870370370371, 10.277981481481483, 12.284631944444445, 11.209473211673808, 6.25, 8.009018518518518, 8.6585, 12.030641666666668, 7.623795061728395, 8.318382154882155, 9.620641975308642, 10.125), # 42
(10.299262936849892, 10.627597393689987, 10.268928669410151, 12.275990483539095, 11.212571095681403, 6.25, 7.98699193092875, 8.606654320987655, 12.021753395061728, 7.601136122542296, 8.312845554308517, 9.608571559213535, 10.125), # 43
(10.305246129151927, 10.595451989026063, 10.259596021947875, 12.267046553497943, 11.215547266628045, 6.25, 7.964362099572339, 8.553771604938273, 12.0125762345679, 7.577908733424783, 8.307094961965332, 9.596128029263832, 10.125), # 44
(10.310989172402216, 10.5625, 10.25, 12.2578125, 11.218401540963296, 6.25, 7.9411764705882355, 8.5, 12.003124999999999, 7.554166666666667, 8.301136363636363, 9.583333333333332, 10.125), # 45
(10.31649127077388, 10.528807270233196, 10.240157064471878, 12.24830066872428, 11.221133735136716, 6.25, 7.917482490115388, 8.445487654320988, 11.993414506172838, 7.529963694558756, 8.294975745105374, 9.57020941929584, 10.125), # 46
(10.321751628440035, 10.49443964334705, 10.230083676268862, 12.238523405349794, 11.223743665597867, 6.25, 7.893327604292747, 8.390382716049382, 11.983459567901235, 7.505353589391861, 8.288619092156129, 9.55677823502515, 10.125), # 47
(10.326769449573796, 10.459462962962963, 10.219796296296296, 12.228493055555557, 11.22623114879631, 6.25, 7.868759259259259, 8.334833333333334, 11.973275000000001, 7.4803901234567896, 8.28207239057239, 9.543061728395061, 10.125), # 48
(10.331543938348286, 10.42394307270233, 10.209311385459534, 12.218221965020577, 11.228596001181607, 6.25, 7.8438249011538765, 8.278987654320987, 11.96287561728395, 7.455127069044353, 8.275341626137923, 9.529081847279379, 10.125), # 49
(10.336074298936616, 10.387945816186559, 10.198645404663925, 12.207722479423868, 11.230838039203315, 6.25, 7.81857197611555, 8.222993827160494, 11.9522762345679, 7.429618198445358, 8.268432784636488, 9.514860539551899, 10.125), # 50
(10.34035973551191, 10.351537037037037, 10.187814814814814, 12.197006944444444, 11.232957079310998, 6.25, 7.793047930283224, 8.167, 11.941491666666668, 7.403917283950617, 8.261351851851853, 9.50041975308642, 10.125), # 51
(10.344399452247279, 10.314782578875173, 10.176836076817558, 12.186087705761317, 11.234952937954214, 6.25, 7.767300209795852, 8.111154320987653, 11.930536728395062, 7.3780780978509375, 8.254104813567777, 9.485781435756746, 10.125), # 52
(10.348192653315843, 10.27774828532236, 10.165725651577505, 12.174977109053497, 11.23682543158253, 6.25, 7.741376260792383, 8.055604938271605, 11.919426234567903, 7.3521544124371285, 8.246697655568026, 9.470967535436671, 10.125), # 53
(10.351738542890716, 10.2405, 10.154499999999999, 12.1636875, 11.238574376645502, 6.25, 7.715323529411765, 8.000499999999999, 11.908175, 7.3262, 8.239136363636362, 9.456, 10.125), # 54
(10.355036325145022, 10.203103566529492, 10.143175582990398, 12.152231224279834, 11.24019958959269, 6.25, 7.689189461792948, 7.945987654320987, 11.896797839506172, 7.300268632830361, 8.231426923556553, 9.44090077732053, 10.125), # 55
(10.358085204251871, 10.165624828532236, 10.131768861454047, 12.140620627572016, 11.241700886873659, 6.25, 7.663021504074881, 7.892216049382716, 11.885309567901235, 7.274414083219022, 8.223575321112358, 9.425691815272062, 10.125), # 56
(10.360884384384383, 10.12812962962963, 10.120296296296297, 12.128868055555555, 11.243078084937967, 6.25, 7.636867102396514, 7.839333333333334, 11.873725, 7.24869012345679, 8.215587542087542, 9.410395061728394, 10.125), # 57
(10.36343306971568, 10.090683813443073, 10.108774348422497, 12.116985853909464, 11.244331000235174, 6.25, 7.610773702896797, 7.787487654320987, 11.862058950617284, 7.223150525834477, 8.20746957226587, 9.395032464563329, 10.125), # 58
(10.36573046441887, 10.053353223593964, 10.097219478737998, 12.104986368312757, 11.245459449214845, 6.25, 7.584788751714678, 7.736827160493827, 11.850326234567902, 7.197849062642891, 8.1992273974311, 9.379625971650663, 10.125), # 59
(10.367775772667077, 10.016203703703704, 10.085648148148147, 12.092881944444445, 11.246463248326537, 6.25, 7.558959694989106, 7.6875, 11.838541666666668, 7.172839506172839, 8.190867003367003, 9.364197530864198, 10.125), # 60
(10.369568198633415, 9.97930109739369, 10.0740768175583, 12.080684927983539, 11.247342214019811, 6.25, 7.533333978859033, 7.639654320987654, 11.826720061728395, 7.148175628715135, 8.182394375857339, 9.348769090077733, 10.125), # 61
(10.371106946491004, 9.942711248285322, 10.062521947873801, 12.068407664609055, 11.248096162744234, 6.25, 7.507959049463406, 7.5934382716049384, 11.814876234567901, 7.123911202560586, 8.17381550068587, 9.333362597165067, 10.125), # 62
(10.37239122041296, 9.9065, 10.051, 12.056062500000001, 11.248724910949356, 6.25, 7.482882352941176, 7.549, 11.803025, 7.100099999999999, 8.165136363636364, 9.318, 10.125), # 63
(10.373420224572397, 9.870733196159122, 10.039527434842249, 12.043661779835391, 11.249228275084748, 6.25, 7.458151335431292, 7.506487654320988, 11.791181172839506, 7.076795793324188, 8.156362950492579, 9.302703246456334, 10.125), # 64
(10.374193163142438, 9.835476680384087, 10.0281207133059, 12.031217849794238, 11.249606071599967, 6.25, 7.433813443072703, 7.466049382716049, 11.779359567901235, 7.054052354823959, 8.147501247038285, 9.287494284407863, 10.125), # 65
(10.374709240296196, 9.800796296296298, 10.016796296296297, 12.018743055555555, 11.249858116944573, 6.25, 7.409916122004357, 7.427833333333334, 11.767575, 7.031923456790123, 8.138557239057238, 9.272395061728396, 10.125), # 66
(10.374967660206792, 9.766757887517146, 10.005570644718793, 12.006249742798353, 11.24998422756813, 6.25, 7.386506818365206, 7.391987654320989, 11.755842283950617, 7.010462871513489, 8.12953691233321, 9.257427526291723, 10.125), # 67
(10.374791614480825, 9.733248639320323, 9.994405949931412, 11.993641740472357, 11.249877955297345, 6.2498840115836, 7.363515194829646, 7.358343850022862, 11.744087848651121, 6.989620441647166, 8.120285988540376, 9.242530021899743, 10.124875150034294), # 68
(10.373141706924315, 9.699245519713262, 9.982988425925925, 11.980283514492752, 11.248910675381262, 6.248967078189301, 7.340268181346613, 7.325098765432099, 11.731797839506173, 6.968806390704429, 8.10986283891547, 9.227218973359324, 10.12388599537037), # 69
(10.369885787558895, 9.664592459843355, 9.971268432784635, 11.966087124261943, 11.246999314128942, 6.247161255906112, 7.31666013456137, 7.291952446273434, 11.718902892089622, 6.947919524462734, 8.09814888652608, 9.211422761292809, 10.121932334533609), # 70
(10.365069660642929, 9.62931016859153, 9.959250085733881, 11.951073503757382, 11.244168078754136, 6.244495808565767, 7.292701659538988, 7.258915866483768, 11.705422210791038, 6.926960359342639, 8.085187370783862, 9.195152937212715, 10.119039887688615), # 71
(10.358739130434783, 9.593419354838709, 9.946937499999999, 11.935263586956522, 11.240441176470588, 6.2410000000000005, 7.268403361344538, 7.226, 11.691375, 6.905929411764705, 8.07102153110048, 9.17842105263158, 10.115234375), # 72
(10.35094000119282, 9.556940727465816, 9.934334790809327, 11.918678307836823, 11.23584281449205, 6.236703094040542, 7.243775845043092, 7.193215820759031, 11.676780464106082, 6.884827198149493, 8.055694606887588, 9.161238659061919, 10.110541516632374), # 73
(10.341718077175404, 9.519894995353777, 9.921446073388202, 11.901338600375738, 11.230397200032275, 6.231634354519128, 7.218829715699722, 7.160574302697759, 11.661657807498857, 6.863654234917561, 8.039249837556856, 9.143617308016267, 10.104987032750344), # 74
(10.331119162640901, 9.482302867383511, 9.908275462962962, 11.883265398550725, 11.224128540305012, 6.22582304526749, 7.1935755783795, 7.128086419753086, 11.6460262345679, 6.84241103848947, 8.021730462519935, 9.125568551007147, 10.098596643518519), # 75
(10.319189061847677, 9.44418505243595, 9.894827074759945, 11.864479636339238, 11.217061042524005, 6.219298430117361, 7.168024038147495, 7.095763145861912, 11.629904949702789, 6.821098125285779, 8.003179721188491, 9.107103939547082, 10.091396069101508), # 76
(10.305973579054093, 9.40556225939201, 9.881105024005485, 11.845002247718732, 11.209218913903008, 6.212089772900472, 7.142185700068779, 7.063615454961135, 11.613313157293096, 6.7997160117270505, 7.983640852974187, 9.088235025148606, 10.083411029663925), # 77
(10.291518518518519, 9.366455197132618, 9.867113425925925, 11.824854166666666, 11.200626361655774, 6.204226337448559, 7.116071169208425, 7.031654320987655, 11.596270061728394, 6.7782652142338415, 7.9631570972886765, 9.068973359324238, 10.074667245370371), # 78
(10.275869684499314, 9.326884574538697, 9.8528563957476, 11.804056327160493, 11.191307592996047, 6.195737387593354, 7.089691050631501, 6.9998907178783725, 11.578794867398262, 6.756746249226714, 7.941771693543622, 9.049330493586504, 10.065190436385459), # 79
(10.259072881254847, 9.286871100491172, 9.838338048696844, 11.782629663177671, 11.181286815137579, 6.18665218716659, 7.063055949403081, 6.968335619570188, 11.560906778692273, 6.7351596331262265, 7.919527881150688, 9.029317979447935, 10.0550063228738), # 80
(10.241173913043479, 9.246435483870968, 9.8235625, 11.760595108695654, 11.170588235294117, 6.177, 7.036176470588235, 6.937, 11.542625, 6.713505882352941, 7.8964688995215315, 9.008947368421053, 10.044140624999999), # 81
(10.222218584123576, 9.205598433559008, 9.808533864883403, 11.737973597691894, 11.159236060679415, 6.166810089925317, 7.009063219252036, 6.90589483310471, 11.52396873571102, 6.691785513327416, 7.872637988067813, 8.988230212018387, 10.03261906292867), # 82
(10.202252698753504, 9.164380658436214, 9.793256258573388, 11.714786064143853, 11.147254498507221, 6.156111720774272, 6.981726800459553, 6.875031092821216, 11.504957190214906, 6.669999042470211, 7.848078386201194, 8.967178061752461, 10.020467356824417), # 83
(10.181322061191626, 9.122802867383513, 9.777733796296296, 11.691053442028986, 11.134667755991286, 6.144934156378601, 6.954177819275858, 6.844419753086419, 11.485609567901234, 6.648146986201889, 7.822833333333333, 8.945802469135803, 10.007711226851852), # 84
(10.159472475696308, 9.080885769281826, 9.761970593278463, 11.666796665324746, 11.121500040345357, 6.133306660570035, 6.926426880766024, 6.814071787837221, 11.465945073159578, 6.626229860943005, 7.796946068875894, 8.924114985680937, 9.994376393175584), # 85
(10.136749746525913, 9.03865007301208, 9.745970764746229, 11.64203666800859, 11.107775558783183, 6.121258497180309, 6.89848458999512, 6.783998171010516, 11.445982910379517, 6.604248183114124, 7.770459832240534, 8.902127162900394, 9.98048857596022), # 86
(10.113199677938807, 8.996116487455197, 9.729738425925925, 11.61679438405797, 11.09351851851852, 6.108818930041152, 6.870361552028219, 6.75420987654321, 11.425742283950619, 6.582202469135802, 7.743417862838915, 8.879850552306692, 9.96607349537037), # 87
(10.088868074193357, 8.9533057214921, 9.713277692043896, 11.59109074745035, 11.07875312676511, 6.096017222984301, 6.842068371930391, 6.724717878372199, 11.40524239826246, 6.560093235428601, 7.715863400082698, 8.857296705412365, 9.951156871570646), # 88
(10.063800739547922, 8.910238484003717, 9.696592678326475, 11.564946692163177, 11.063503590736707, 6.082882639841488, 6.813615654766708, 6.695533150434385, 11.384502457704619, 6.537920998413083, 7.687839683383544, 8.834477173729935, 9.935764424725651), # 89
(10.03804347826087, 8.866935483870968, 9.6796875, 11.538383152173914, 11.04779411764706, 6.069444444444445, 6.785014005602241, 6.666666666666666, 11.363541666666668, 6.515686274509804, 7.65938995215311, 8.81140350877193, 9.919921875), # 90
(10.011642094590563, 8.823417429974777, 9.662566272290809, 11.511421061460013, 11.031648914709915, 6.055731900624904, 6.756274029502062, 6.638129401005944, 11.342379229538182, 6.4933895801393255, 7.63055744580306, 8.788087262050874, 9.903654942558298), # 91
(9.984642392795372, 8.779705031196071, 9.64523311042524, 11.484081353998926, 11.015092189139029, 6.041774272214601, 6.727406331531242, 6.609932327389118, 11.321034350708734, 6.471031431722209, 7.601385403745053, 8.764539985079297, 9.886989347565157), # 92
(9.957090177133654, 8.735818996415771, 9.62769212962963, 11.456384963768118, 10.998148148148148, 6.027600823045267, 6.69842151675485, 6.582086419753087, 11.299526234567901, 6.448612345679011, 7.57191706539075, 8.74077322936972, 9.869950810185184), # 93
(9.92903125186378, 8.691780034514801, 9.609947445130317, 11.428352824745035, 10.98084099895102, 6.0132408169486355, 6.669330190237961, 6.554602652034752, 11.277874085505259, 6.426132838430297, 7.54219567015181, 8.716798546434674, 9.85256505058299), # 94
(9.90051142124411, 8.647608854374088, 9.592003172153635, 11.400005870907139, 10.963194948761398, 5.9987235177564395, 6.640142957045644, 6.527491998171011, 11.25609710791038, 6.403593426396621, 7.512264457439896, 8.69262748778668, 9.834857788923182), # 95
(9.871576489533012, 8.603326164874554, 9.573863425925927, 11.371365036231884, 10.945234204793028, 5.984078189300411, 6.610870422242971, 6.500765432098766, 11.234214506172838, 6.3809946259985475, 7.482166666666667, 8.668271604938273, 9.816854745370371), # 96
(9.842272260988848, 8.558952674897121, 9.555532321673525, 11.342451254696725, 10.926982974259664, 5.969334095412284, 6.581523190895013, 6.474433927754916, 11.212245484682214, 6.358336953656634, 7.451945537243782, 8.64374244940197, 9.798581640089164), # 97
(9.812644539869984, 8.514509093322713, 9.53701397462277, 11.31328546027912, 10.908465464375052, 5.954520499923793, 6.552111868066842, 6.44850845907636, 11.190209247828074, 6.335620925791441, 7.421644308582906, 8.619051572690298, 9.78006419324417), # 98
(9.782739130434782, 8.470016129032258, 9.5183125, 11.283888586956522, 10.889705882352942, 5.939666666666667, 6.52264705882353, 6.423, 11.168125, 6.312847058823529, 7.391306220095694, 8.59421052631579, 9.761328125), # 99
(9.752601836941611, 8.425494490906676, 9.49943201303155, 11.254281568706388, 10.870728435407084, 5.924801859472641, 6.493139368230145, 6.3979195244627345, 11.146011945587563, 6.290015869173458, 7.36097451119381, 8.569230861790967, 9.742399155521262), # 100
(9.722278463648834, 8.380964887826895, 9.480376628943759, 11.224485339506174, 10.85155733075123, 5.909955342173449, 6.463599401351762, 6.3732780064014625, 11.123889288980338, 6.267127873261788, 7.330692421288912, 8.544124130628353, 9.723303004972564), # 101
(9.691814814814816, 8.336448028673836, 9.461150462962962, 11.194520833333334, 10.832216775599129, 5.895156378600824, 6.43403776325345, 6.349086419753086, 11.1017762345679, 6.244183587509078, 7.300503189792663, 8.518901884340481, 9.704065393518519), # 102
(9.661256694697919, 8.291964622328422, 9.4417576303155, 11.164408984165325, 10.812730977164529, 5.880434232586496, 6.40446505900028, 6.325355738454504, 11.079691986739826, 6.221183528335889, 7.270450056116723, 8.493575674439873, 9.68471204132373), # 103
(9.63064990755651, 8.247535377671579, 9.422202246227709, 11.134170725979603, 10.79312414266118, 5.865818167962201, 6.374891893657326, 6.302096936442616, 11.057655749885688, 6.19812821216278, 7.24057625967275, 8.468157052439054, 9.665268668552812), # 104
(9.600040257648953, 8.203181003584229, 9.402488425925926, 11.103826992753623, 10.773420479302832, 5.851337448559671, 6.345328872289658, 6.279320987654321, 11.035686728395062, 6.175018155410313, 7.210925039872408, 8.442657569850553, 9.64576099537037), # 105
(9.569473549233614, 8.158922208947299, 9.382620284636488, 11.073398718464842, 10.753644194303236, 5.837021338210638, 6.315786599962345, 6.25703886602652, 11.01380412665752, 6.151853874499045, 7.181539636127355, 8.417088778186894, 9.626214741941014), # 106
(9.538995586568856, 8.11477970264171, 9.362601937585735, 11.042906837090714, 10.733819494876139, 5.822899100746838, 6.286275681740461, 6.235261545496114, 10.992027149062643, 6.128635885849539, 7.152463287849252, 8.391462228960604, 9.606655628429355), # 107
(9.508652173913044, 8.070774193548388, 9.3424375, 11.012372282608696, 10.713970588235293, 5.809, 6.256806722689075, 6.214, 10.970375, 6.105364705882353, 7.1237392344497605, 8.365789473684211, 9.587109375), # 108
(9.478489115524543, 8.026926390548255, 9.322131087105625, 10.98181598899624, 10.69412168159445, 5.795353299801859, 6.227390327873262, 6.193265203475081, 10.948866883859168, 6.082040851018047, 7.09541071534054, 8.340082063870238, 9.567601701817559), # 109
(9.448552215661715, 7.983257002522237, 9.301686814128946, 10.951258890230811, 10.674296982167354, 5.7819882639841484, 6.198037102358089, 6.173068129858253, 10.92752200502972, 6.058664837677183, 7.06752096993325, 8.314351551031214, 9.54815832904664), # 110
(9.41888727858293, 7.9397867383512555, 9.281108796296298, 10.920721920289855, 10.654520697167756, 5.768934156378601, 6.168757651208631, 6.153419753086419, 10.906359567901236, 6.035237182280319, 7.040113237639553, 8.288609486679663, 9.528804976851852), # 111
(9.38954010854655, 7.896536306916234, 9.26040114883402, 10.890226013150832, 10.634817033809409, 5.756220240816949, 6.139562579489958, 6.134331047096479, 10.885398776863282, 6.011758401248016, 7.013230757871109, 8.26286742232811, 9.509567365397805), # 112
(9.360504223703044, 7.853598618785952, 9.239617828252069, 10.85983388249204, 10.615175680173705, 5.7438697692145135, 6.1105259636567695, 6.115852568780606, 10.86471281125862, 5.988304736612729, 6.9869239061528665, 8.237192936504428, 9.490443900843221), # 113
(9.331480897900065, 7.811397183525536, 9.219045675021619, 10.829789421277336, 10.595393354566326, 5.731854608529901, 6.082018208410579, 6.09821125950512, 10.84461903571306, 5.965315167912783, 6.961244337113197, 8.211912172112974, 9.471275414160035), # 114
(9.302384903003995, 7.769947198683046, 9.198696932707318, 10.800084505181779, 10.5754076778886, 5.7201435124987645, 6.054059650191562, 6.081402654278709, 10.82512497866879, 5.942825327988077, 6.936154511427094, 8.187037582558851, 9.452006631660376), # 115
(9.273179873237634, 7.729188281291702, 9.178532189983873, 10.770666150266404, 10.555188526383779, 5.708708877287098, 6.026604817527893, 6.065380312898993, 10.80618133922783, 5.920793358449547, 6.911605931271481, 8.162523197487346, 9.43260725975589), # 116
(9.243829442823772, 7.689060048384721, 9.158512035525986, 10.741481372592244, 10.53470577629511, 5.6975230990608905, 5.9996082389477525, 6.050097795163585, 10.787738816492203, 5.899177400908129, 6.887550098823283, 8.13832304654375, 9.413047004858225), # 117
(9.214297245985211, 7.649502116995324, 9.138597058008367, 10.712477188220333, 10.513929303865842, 5.686558573986138, 5.973024442979315, 6.0355086608700965, 10.769748109563935, 5.877935596974759, 6.863938516259424, 8.11439115937335, 9.393295573379024), # 118
(9.184546916944742, 7.610454104156729, 9.118747846105723, 10.683600613211706, 10.492828985339221, 5.675787698228833, 5.946807958150756, 6.021566469816145, 10.752159917545043, 5.857026088260372, 6.840722685756828, 8.090681565621434, 9.373322671729932), # 119
(9.154542089925162, 7.571855626902158, 9.098924988492762, 10.654798663627394, 10.471374696958497, 5.665182867954965, 5.920913312990253, 6.008224781799343, 10.734924939537558, 5.836407016375905, 6.817854109492416, 8.067148294933297, 9.353098006322597), # 120
(9.124246399149268, 7.533646302264829, 9.079089073844187, 10.626018355528434, 10.449536314966918, 5.6547164793305305, 5.89529503602598, 5.995437156617307, 10.717993874643499, 5.816036522932296, 6.795284289643116, 8.043745376954222, 9.33259128356866), # 121
(9.093623478839854, 7.495765747277961, 9.059200690834711, 10.597206704975855, 10.427283715607734, 5.644360928521519, 5.869907655786117, 5.983157154067649, 10.70131742196489, 5.795872749540477, 6.772964728385851, 8.0204268413295, 9.31177220987977), # 122
(9.062636963219719, 7.458153578974774, 9.039220428139036, 10.568310728030694, 10.40458677512419, 5.634088611693925, 5.844705700798839, 5.971338333947983, 10.684846280603754, 5.775873837811387, 6.750846927897544, 7.997146717704421, 9.290610491667572), # 123
(9.031250486511654, 7.420749414388487, 9.01910887443187, 10.539277440753986, 10.381415369759537, 5.623871925013739, 5.819643699592319, 5.959934256055926, 10.668531149662115, 5.755997929355961, 6.728882390355119, 7.973859035724275, 9.269075835343711), # 124
(8.999427682938459, 7.38349287055232, 8.998826618387923, 10.51005385920676, 10.357739375757022, 5.613683264646956, 5.794676180694739, 5.948898480189091, 10.652322728241993, 5.736203165785134, 6.707022617935501, 7.950517825034348, 9.247137947319828), # 125
(8.967132186722928, 7.346323564499494, 8.978334248681898, 10.480586999450054, 10.333528669359893, 5.603495026759568, 5.76975767263427, 5.938184566145092, 10.636171715445418, 5.7164476887098425, 6.685219112815613, 7.927077115279934, 9.224766534007578), # 126
(8.93432763208786, 7.309181113263224, 8.957592353988504, 10.450823877544899, 10.308753126811398, 5.593279607517565, 5.744842703939094, 5.927746073721545, 10.620028810374407, 5.696689639741024, 6.6634233771723785, 7.903490936106316, 9.201931301818599), # 127
(8.900977653256046, 7.272005133876735, 8.93656152298245, 10.420711509552332, 10.28338262435479, 5.583009403086944, 5.719885803137382, 5.917536562716062, 10.603844712130984, 5.6768871604896125, 6.641586913182724, 7.879713317158788, 9.178601957164537), # 128
(8.867045884450281, 7.234735243373241, 8.91520234433844, 10.390196911533382, 10.257387038233311, 5.572656809633695, 5.694841498757313, 5.90750959292626, 10.587570119817174, 5.656998392566545, 6.619661223023571, 7.855698288082636, 9.154748206457038), # 129
(8.832495959893366, 7.197311058785966, 8.893475406731179, 10.359227099549086, 10.230736244690213, 5.562194223323808, 5.669664319327063, 5.89761872414975, 10.571155732535, 5.636981477582757, 6.5975978088718445, 7.831399878523152, 9.130339756107748), # 130
(8.797291513808094, 7.159672197148127, 8.87134129883538, 10.327749089660475, 10.203400119968745, 5.55159404032328, 5.644308793374809, 5.88781751618415, 10.554552249386486, 5.616794557149185, 6.575348172904468, 7.806772118125624, 9.105346312528312), # 131
(8.76139618041726, 7.121758275492944, 8.848760609325746, 10.295709897928587, 10.175348540312154, 5.540828656798102, 5.618729449428725, 5.878059528827073, 10.537710369473654, 5.596395772876765, 6.552863817298364, 7.781769036535342, 9.079737582130376), # 132
(8.724773593943663, 7.083508910853635, 8.825693926876983, 10.263056540414452, 10.146551381963686, 5.529870468914266, 5.592880816016989, 5.868298321876132, 10.520580791898526, 5.575743266376432, 6.53009624423046, 7.756344663397592, 9.053483271325586), # 133
(8.687387388610095, 7.044863720263423, 8.802101840163804, 10.229736033179103, 10.116978521166592, 5.518691872837765, 5.566717421667779, 5.858487455128944, 10.503114215763128, 5.5547951792591235, 6.506996955877678, 7.730453028357666, 9.026553086525583), # 134
(8.649201198639354, 7.005762320755524, 8.777944937860909, 10.195695392283579, 10.08659983416412, 5.507265264734592, 5.540193794909268, 5.84858048838312, 10.48526134016948, 5.533509653135776, 6.483517454416942, 7.704048161060852, 8.99891673414202), # 135
(8.610178658254235, 6.966144329363159, 8.753183808643008, 10.160881633788906, 10.055385197199517, 5.495563040770739, 5.513264464269635, 5.838530981436277, 10.466972864219606, 5.511844829617322, 6.459609242025177, 7.677084091152441, 8.970543920586536), # 136
(8.570283401677534, 6.925949363119547, 8.72777904118481, 10.125241773756125, 10.023304486516034, 5.483557597112198, 5.485883958277055, 5.828292494086029, 10.448199487015533, 5.4897588503147015, 6.435223820879306, 7.649514848277719, 8.941404352270776), # 137
(8.529479063132047, 6.885117039057908, 8.701691224161017, 10.088722828246263, 9.990327578356919, 5.471221329924964, 5.458006805459704, 5.81781858612999, 10.428891907659281, 5.4672098568388465, 6.410312693156252, 7.621294462081978, 8.91146773560639), # 138
(8.487729276840568, 6.843586974211461, 8.67488094624634, 10.051271813320358, 9.956424348965415, 5.458526635375026, 5.429587534345759, 5.807062817365774, 10.409000825252871, 5.444155990800697, 6.38482736103294, 7.592376962210506, 8.880703777005019), # 139
(8.444997677025897, 6.801298785613425, 8.647308796115487, 10.012835745039444, 9.92156467458478, 5.445445909628379, 5.400580673463397, 5.795978747590996, 10.388476938898332, 5.420555393811186, 6.358719326686294, 7.562716378308592, 8.849082182878314), # 140
(8.40124789791083, 6.758192090297021, 8.61893536244316, 9.973361639464553, 9.885718431458253, 5.431951548851015, 5.370940751340795, 5.78451993660327, 10.36727094769768, 5.396366207481251, 6.331940092293238, 7.532266740021525, 8.816572659637913), # 141
(8.356443573718156, 6.714206505295466, 8.58972123390407, 9.93279651265672, 9.848855495829087, 5.418015949208927, 5.340622296506126, 5.772639944200211, 10.345333550752942, 5.371546573421828, 6.304441160030697, 7.500982076994594, 8.783144913695466), # 142
(8.310548338670674, 6.669281647641981, 8.559626999172925, 9.891087380676975, 9.810945743940529, 5.403611506868106, 5.3095798374875685, 5.760292330179432, 10.322615447166147, 5.3460546332438525, 6.276174032075593, 7.4688164188730894, 8.748768651462617), # 143
(8.263525826991184, 6.623357134369786, 8.528613246924428, 9.848181259586356, 9.771959052035829, 5.388710617994547, 5.277767902813299, 5.747430654338549, 10.29906733603931, 5.31984852855826, 6.247090210604851, 7.435723795302299, 8.713413579351014), # 144
(8.215339672902477, 6.576372582512099, 8.496640565833289, 9.804025165445895, 9.731865296358233, 5.3732856787542405, 5.245141021011493, 5.734008476475176, 10.274639916474454, 5.292886400975988, 6.217141197795395, 7.401658235927513, 8.6770494037723), # 145
(8.16595351062735, 6.528267609102142, 8.463669544574216, 9.758566114316626, 9.690634353150992, 5.35730908531318, 5.21165372061033, 5.719979356386927, 10.249283887573606, 5.2651263921079705, 6.186278495824149, 7.3665737703940195, 8.639645831138118), # 146
(8.1153309743886, 6.47898183117313, 8.42966077182191, 9.71175112225958, 9.648236098657351, 5.340753233837358, 5.177260530137981, 5.705296853871415, 10.22294994843879, 5.236526643565146, 6.154453606868036, 7.3304244283471105, 8.601172567860118), # 147
(8.063435698409021, 6.428454865758288, 8.394574836251083, 9.663527205335797, 9.604640409120561, 5.323590520492767, 5.1419159781226265, 5.689914528726257, 10.195588798172029, 5.207045296958447, 6.1216180331039824, 7.29316423943207, 8.561599320349941), # 148
(8.010231316911412, 6.37662632989083, 8.358372326536443, 9.613841379606303, 9.55981716078387, 5.3057933414453995, 5.105574593092441, 5.673785940749067, 10.167151135875338, 5.176640493898813, 6.08772327670891, 7.254747233294191, 8.520895795019237), # 149
(7.955681464118564, 6.323435840603979, 8.321013831352694, 9.562640661132138, 9.513736229890526, 5.287334092861249, 5.0681909035756005, 5.656864649737456, 10.137587660650752, 5.1452703759971765, 6.0527208398597425, 7.215127439578763, 8.479031698279647), # 150
(7.899749774253275, 6.268823014930954, 8.282459939374542, 9.50987206597433, 9.466367492683776, 5.268185170906305, 5.029719438100283, 5.639104215489043, 10.106849071600289, 5.112893084864478, 6.016562224733405, 7.174258887931072, 8.435976736542818), # 151
(7.842399881538343, 6.212727469904973, 8.242671239276701, 9.455482610193918, 9.417680825406869, 5.2483189717465635, 4.9901147251946645, 5.620458197801441, 10.07488606782597, 5.079466762111649, 5.979198933506821, 7.132095607996409, 8.391700616220398), # 152
(7.78359542019656, 6.155088822559256, 8.201608319733868, 9.399419309851933, 9.367646104303056, 5.2277078915480155, 4.949331293386919, 5.600880156472262, 10.041649348429823, 5.044949549349629, 5.940582468356916, 7.088591629420064, 8.346173043724027), # 153
(7.723300024450729, 6.095846689927024, 8.159231769420758, 9.34162918100941, 9.31623320561558, 5.206324326476654, 4.907323671205228, 5.580323651299123, 10.007089612513866, 5.009299588189353, 5.900664331460612, 7.043700981847325, 8.299363725465357), # 154
(7.6614773285236355, 6.034940689041495, 8.115502177012075, 9.282059239727378, 9.263412005587696, 5.184140672698471, 4.864046387177761, 5.558742242079636, 9.971157559180128, 4.972475020241754, 5.859396024994833, 6.997377694923482, 8.251242367856026), # 155
(7.598090966638081, 5.972310436935888, 8.070380131182526, 9.220656502066875, 9.209152380462648, 5.161129326379461, 4.8194539698327, 5.5360894886114185, 9.933803887530626, 4.934433987117773, 5.816729051136504, 6.949575798293822, 8.201778677307685), # 156
(7.533104573016862, 5.907895550643423, 8.023826220606818, 9.157367984088937, 9.153424206483685, 5.137262683685614, 4.773500947698219, 5.512318950692082, 9.894979296667389, 4.895134630428341, 5.772614912062549, 6.900249321603637, 8.150942360231976), # 157
(7.464680946405239, 5.840453120772258, 7.973591953902355, 9.089769581651243, 9.093681105870997, 5.11102447631711, 4.725106720927857, 5.485796952349372, 9.851662091599097, 4.8533659162911436, 5.7255957525389425, 6.847599564194339, 8.096485859415345), # 158
(7.382286766978402, 5.763065319599478, 7.906737818402988, 9.003977158788453, 9.015191309781628, 5.073689648007103, 4.668212763385716, 5.4472135327643825, 9.786427261222144, 4.802280994098745, 5.667416935618994, 6.781362523683108, 8.025427646920194), # 159
(7.284872094904309, 5.675096728540714, 7.821920957955888, 8.89857751040886, 8.916420131346795, 5.024341296047684, 4.602243748383784, 5.3955991895273465, 9.697425227228651, 4.741205651862893, 5.59725950860954, 6.700501948887847, 7.93642060889358), # 160
(7.17322205458596, 5.577120868080469, 7.720046971910309, 8.774572503756728, 8.798393124282113, 4.963577241570314, 4.527681446006876, 5.33160053310978, 9.585829766999018, 4.6706581931709374, 5.515741654599707, 6.605767468907571, 7.830374044819097), # 161
(7.048121770426357, 5.469711258703239, 7.602021459615496, 8.632964006076326, 8.662135842303204, 4.891995305706455, 4.445007626339809, 5.255864173983202, 9.452814657913637, 4.5911569216102315, 5.42348155667862, 6.497908712841293, 7.708197254180333), # 162
(6.9103563668284975, 5.353441420893524, 7.468750020420702, 8.474753884611934, 8.508673839125688, 4.810193309587572, 4.354704059467401, 5.169036722619125, 9.299553677352906, 4.503220140768125, 5.321097397935408, 6.3776753097880325, 7.570799536460879), # 163
(6.760710968195384, 5.228884875135821, 7.321138253675176, 8.300944006607818, 8.339032668465189, 4.718769074345129, 4.257252515474466, 5.071764789489069, 9.127220602697223, 4.407366154231968, 5.209207361459196, 6.245816888846803, 7.419090191144328), # 164
(6.599970698930017, 5.096615141914632, 7.160091758728169, 8.112536239308252, 8.154237884037324, 4.618320421110586, 4.153134764445822, 4.964694985064546, 8.93698921132698, 4.3041132655891134, 5.088429630339111, 6.10308307911662, 7.25397851771427), # 165
(6.428920683435397, 4.957205741714454, 6.9865161349289275, 7.910532449957501, 7.955315039557714, 4.509445171015408, 4.042832576466286, 4.848473919817077, 8.730033280622573, 4.193979778426912, 4.959382387664279, 5.950223509696501, 7.0763738156542955), # 166
(6.248346046114523, 4.811230195019787, 6.801316981626704, 7.695934505799843, 7.74328968874198, 4.392741145191058, 3.9268277216206746, 4.723748204218176, 8.5075265879644, 4.077483996332714, 4.822683816523827, 5.7879878096854585, 6.887185384447996), # 167
(6.059031911370395, 4.659262022315128, 6.605399898170748, 7.469744274079546, 7.519187385305742, 4.268806164768999, 3.805601969993804, 4.5911644487393595, 8.270642910732855, 3.955144222893872, 4.678952100006881, 5.617125608182511, 6.6873225235789615), # 168
(5.861763403606015, 4.501874744084979, 6.399670483910309, 7.232963622040883, 7.28403368296462, 4.138238050880695, 3.6796370916704917, 4.451369263852145, 8.020556026308338, 3.8274787616977366, 4.528805421202568, 5.438386534286672, 6.477694532530785), # 169
(5.657325647224384, 4.339641880813837, 6.185034338194635, 6.98659441692812, 7.038854135434233, 4.001634624657607, 3.549414856735553, 4.305009260028047, 7.7584397120712385, 3.6950059163316578, 4.372861963200016, 5.252520217096959, 6.259210710787055), # 170
(5.4465037666285, 4.173136952986201, 5.962397060372978, 6.731638525985535, 6.784674296430206, 3.8595937072311983, 3.4154170352738054, 4.152731047738583, 7.485467745401956, 3.5582439903829886, 4.211739909088348, 5.060276285712386, 6.032780357831365), # 171
(5.230082886221365, 4.002933481086569, 5.7326642497945866, 6.4690978164573965, 6.5225197196681535, 3.7127131197329337, 3.2781253973700655, 3.9951812374552707, 7.202813903680886, 3.41771128743908, 4.046057441956694, 4.862404369231971, 5.799312773147303), # 172
(5.00884813040598, 3.8296049855994423, 5.4967415058087115, 6.1999741555879755, 6.253415958863702, 3.5615906832942748, 3.1380217131091497, 3.8330064396496235, 6.911651964288422, 3.2739261110872815, 3.8764327448941778, 4.659654096754725, 5.5597172562184625), # 173
(4.783584623585344, 3.653724987009318, 5.2555344277646014, 5.9252694106215404, 5.978388567732466, 3.406824219046685, 2.9955877525758754, 3.6668532647931604, 6.613155704604964, 3.1274067649149466, 3.7034840009899277, 4.452775097379668, 5.314903106528433), # 174
(4.555077490162455, 3.4758670058006946, 5.009948615011508, 5.645985448802367, 5.698463099990069, 3.2490115481216284, 2.851305285855058, 3.497368323357396, 6.308498902010905, 2.9786715525094243, 3.5278293933330693, 4.242517000205814, 5.0657796235608075), # 175
(4.324111854540319, 3.296604562458073, 4.760889666898678, 5.363124137374725, 5.41466510935213, 3.0887504916505666, 2.705656083031515, 3.325198225813849, 5.998855333886642, 2.828238777458067, 3.35008710501273, 4.029629434332179, 4.813256106799174), # 176
(4.0914728411219325, 3.1165111774659513, 4.5092631827753635, 5.077687343582883, 5.128020149534273, 2.9266388707649633, 2.5591219141900625, 3.1509895826340326, 5.68539877761257, 2.6766267433482245, 3.1708753191180357, 3.8148620288577786, 4.5582418557271245), # 177
(3.8579455743102966, 2.9361603713088282, 4.255974761990814, 4.790676934671116, 4.8395537742521135, 2.7632745065962827, 2.4121845494155174, 2.9753890042894655, 5.3693030105690855, 2.52435375376725, 2.9908122187381125, 3.598964412881627, 4.301646169828252), # 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 = (
(5, 4, 5, 6, 7, 2, 3, 1, 0, 1, 2, 0, 0, 6, 6, 2, 2, 3, 1, 1, 1, 2, 2, 1, 1, 0), # 0
(10, 11, 7, 11, 11, 4, 5, 2, 5, 2, 3, 1, 0, 16, 11, 4, 2, 9, 3, 1, 4, 5, 5, 2, 3, 0), # 1
(13, 17, 14, 14, 15, 8, 5, 3, 7, 2, 3, 2, 0, 21, 13, 11, 4, 13, 6, 2, 6, 6, 10, 2, 3, 0), # 2
(19, 20, 21, 20, 20, 10, 8, 5, 10, 2, 4, 2, 0, 26, 18, 15, 11, 19, 7, 6, 8, 7, 11, 3, 3, 0), # 3
(23, 26, 24, 24, 28, 10, 10, 9, 12, 2, 7, 2, 0, 35, 23, 21, 15, 28, 11, 8, 9, 12, 15, 3, 3, 0), # 4
(32, 33, 28, 26, 33, 13, 19, 11, 17, 4, 7, 4, 0, 45, 30, 30, 20, 32, 17, 8, 10, 16, 17, 3, 5, 0), # 5
(38, 47, 37, 33, 38, 17, 20, 14, 20, 5, 9, 5, 0, 58, 36, 31, 21, 36, 23, 9, 11, 21, 20, 6, 5, 0), # 6
(48, 58, 44, 45, 43, 19, 24, 15, 24, 5, 9, 5, 0, 67, 44, 38, 25, 40, 26, 13, 14, 24, 24, 7, 8, 0), # 7
(52, 68, 52, 52, 50, 21, 26, 17, 26, 6, 10, 5, 0, 73, 52, 48, 31, 44, 29, 15, 15, 28, 27, 9, 9, 0), # 8
(63, 78, 58, 59, 54, 23, 27, 21, 30, 8, 14, 5, 0, 77, 56, 56, 35, 50, 37, 19, 18, 30, 29, 10, 10, 0), # 9
(76, 86, 59, 67, 63, 25, 28, 25, 33, 8, 15, 6, 0, 87, 64, 63, 38, 54, 40, 22, 21, 38, 31, 13, 10, 0), # 10
(85, 92, 72, 79, 67, 31, 29, 27, 39, 10, 16, 6, 0, 96, 68, 67, 40, 57, 47, 29, 22, 41, 34, 13, 11, 0), # 11
(92, 102, 79, 82, 72, 36, 30, 33, 42, 12, 17, 6, 0, 108, 74, 72, 46, 62, 53, 32, 23, 43, 36, 16, 12, 0), # 12
(101, 109, 93, 90, 77, 38, 34, 36, 45, 12, 17, 7, 0, 114, 83, 83, 49, 67, 58, 34, 24, 47, 38, 16, 14, 0), # 13
(109, 114, 97, 102, 84, 40, 34, 40, 53, 12, 17, 7, 0, 124, 93, 88, 53, 77, 60, 37, 24, 52, 43, 18, 14, 0), # 14
(120, 124, 109, 114, 88, 44, 38, 45, 56, 16, 19, 7, 0, 137, 98, 95, 64, 80, 66, 40, 26, 56, 48, 18, 15, 0), # 15
(132, 132, 116, 118, 91, 49, 44, 49, 57, 16, 21, 8, 0, 143, 109, 104, 66, 88, 78, 43, 28, 59, 51, 19, 15, 0), # 16
(139, 141, 129, 127, 95, 52, 48, 53, 61, 16, 23, 8, 0, 152, 119, 115, 70, 94, 90, 47, 30, 62, 54, 21, 16, 0), # 17
(150, 155, 138, 135, 101, 52, 51, 61, 68, 18, 23, 10, 0, 162, 130, 127, 76, 98, 93, 49, 35, 65, 57, 24, 16, 0), # 18
(156, 164, 148, 145, 107, 56, 58, 65, 74, 21, 23, 10, 0, 173, 136, 137, 85, 106, 97, 55, 37, 72, 60, 27, 17, 0), # 19
(160, 170, 158, 154, 110, 60, 66, 68, 78, 21, 24, 10, 0, 183, 145, 141, 94, 113, 105, 60, 42, 77, 63, 28, 18, 0), # 20
(170, 175, 167, 166, 119, 65, 73, 73, 82, 22, 25, 10, 0, 190, 147, 150, 97, 119, 108, 65, 42, 81, 65, 30, 20, 0), # 21
(183, 188, 176, 174, 129, 67, 75, 77, 86, 26, 26, 11, 0, 199, 154, 157, 103, 123, 114, 70, 45, 87, 65, 31, 21, 0), # 22
(193, 200, 189, 180, 135, 70, 79, 79, 92, 27, 28, 11, 0, 207, 159, 164, 107, 128, 118, 74, 47, 92, 67, 31, 22, 0), # 23
(200, 212, 197, 184, 144, 74, 82, 80, 96, 30, 30, 12, 0, 220, 172, 172, 112, 137, 128, 78, 51, 94, 72, 33, 22, 0), # 24
(211, 219, 206, 194, 154, 74, 85, 85, 98, 30, 30, 13, 0, 233, 181, 185, 116, 150, 130, 83, 51, 97, 77, 34, 25, 0), # 25
(223, 226, 217, 201, 161, 76, 92, 89, 103, 31, 30, 14, 0, 240, 193, 192, 126, 156, 138, 85, 54, 102, 81, 35, 25, 0), # 26
(237, 235, 228, 210, 170, 80, 97, 95, 111, 34, 30, 15, 0, 249, 198, 197, 131, 168, 142, 90, 56, 103, 87, 36, 26, 0), # 27
(247, 241, 232, 215, 177, 82, 100, 96, 116, 37, 32, 15, 0, 262, 204, 204, 134, 172, 147, 92, 58, 107, 88, 39, 27, 0), # 28
(262, 250, 241, 227, 183, 85, 106, 98, 116, 39, 34, 15, 0, 274, 211, 214, 139, 182, 155, 96, 60, 112, 90, 39, 27, 0), # 29
(264, 262, 249, 238, 191, 85, 110, 101, 120, 44, 37, 16, 0, 278, 221, 221, 145, 188, 165, 102, 67, 119, 94, 40, 29, 0), # 30
(273, 274, 257, 244, 200, 89, 111, 104, 122, 47, 38, 17, 0, 285, 225, 230, 153, 197, 174, 106, 73, 121, 100, 43, 31, 0), # 31
(285, 284, 264, 254, 206, 94, 115, 107, 127, 48, 38, 18, 0, 295, 232, 233, 161, 204, 178, 110, 76, 124, 104, 45, 31, 0), # 32
(296, 294, 275, 273, 218, 98, 121, 109, 129, 51, 42, 18, 0, 305, 243, 238, 169, 211, 184, 118, 79, 125, 108, 45, 32, 0), # 33
(305, 303, 280, 287, 223, 102, 122, 115, 133, 52, 42, 18, 0, 312, 253, 246, 177, 225, 186, 120, 82, 128, 113, 48, 32, 0), # 34
(315, 308, 290, 296, 231, 105, 127, 119, 134, 58, 42, 19, 0, 323, 271, 252, 181, 235, 188, 122, 87, 130, 115, 50, 32, 0), # 35
(330, 317, 297, 305, 237, 108, 132, 123, 139, 58, 43, 19, 0, 333, 279, 266, 184, 246, 192, 123, 88, 136, 117, 51, 33, 0), # 36
(337, 327, 308, 314, 244, 112, 138, 126, 148, 60, 43, 19, 0, 340, 290, 269, 187, 252, 197, 128, 92, 138, 119, 51, 34, 0), # 37
(344, 331, 313, 321, 248, 115, 142, 132, 152, 62, 43, 19, 0, 353, 292, 273, 197, 260, 200, 129, 94, 138, 124, 53, 35, 0), # 38
(355, 339, 321, 327, 253, 118, 149, 135, 159, 64, 45, 20, 0, 362, 298, 285, 201, 268, 206, 131, 98, 142, 127, 56, 37, 0), # 39
(368, 351, 331, 338, 261, 122, 151, 142, 162, 64, 47, 21, 0, 373, 305, 292, 207, 276, 211, 135, 101, 146, 132, 58, 40, 0), # 40
(377, 358, 339, 349, 268, 129, 158, 146, 167, 64, 49, 23, 0, 383, 316, 298, 214, 280, 218, 139, 105, 148, 136, 61, 40, 0), # 41
(389, 371, 357, 360, 276, 129, 162, 150, 170, 65, 50, 24, 0, 396, 325, 306, 221, 291, 224, 145, 106, 150, 142, 65, 41, 0), # 42
(401, 377, 369, 373, 282, 135, 168, 154, 174, 67, 54, 24, 0, 409, 337, 310, 225, 300, 227, 147, 111, 154, 144, 67, 42, 0), # 43
(412, 392, 375, 377, 292, 139, 172, 157, 177, 67, 57, 26, 0, 423, 342, 321, 231, 311, 234, 150, 115, 158, 144, 69, 43, 0), # 44
(430, 406, 389, 384, 299, 142, 173, 161, 184, 67, 57, 26, 0, 431, 348, 330, 240, 318, 243, 157, 116, 160, 148, 70, 45, 0), # 45
(439, 415, 398, 390, 311, 147, 178, 170, 189, 70, 58, 27, 0, 442, 355, 339, 248, 325, 249, 161, 119, 162, 151, 71, 47, 0), # 46
(452, 428, 404, 397, 315, 150, 182, 170, 196, 72, 61, 27, 0, 447, 363, 345, 252, 336, 251, 166, 124, 168, 154, 73, 49, 0), # 47
(467, 438, 413, 408, 323, 155, 184, 172, 198, 75, 62, 28, 0, 460, 367, 350, 257, 341, 255, 169, 130, 170, 154, 73, 51, 0), # 48
(478, 448, 419, 413, 331, 156, 188, 179, 204, 77, 62, 31, 0, 472, 376, 355, 268, 348, 259, 171, 132, 176, 154, 73, 51, 0), # 49
(490, 462, 430, 421, 341, 161, 192, 182, 207, 78, 62, 31, 0, 474, 388, 361, 271, 358, 265, 178, 133, 182, 157, 75, 52, 0), # 50
(496, 475, 433, 429, 345, 171, 194, 187, 210, 80, 66, 31, 0, 485, 403, 371, 278, 366, 269, 181, 134, 183, 161, 75, 53, 0), # 51
(503, 484, 443, 441, 353, 178, 200, 192, 215, 81, 67, 31, 0, 492, 417, 378, 285, 373, 275, 187, 139, 185, 163, 77, 53, 0), # 52
(509, 491, 448, 450, 358, 180, 203, 195, 221, 86, 68, 35, 0, 499, 426, 385, 292, 385, 283, 192, 139, 189, 167, 77, 54, 0), # 53
(517, 499, 458, 454, 366, 184, 205, 198, 221, 90, 69, 38, 0, 517, 435, 393, 297, 391, 289, 193, 139, 192, 168, 81, 56, 0), # 54
(520, 508, 465, 465, 372, 186, 212, 199, 223, 90, 71, 38, 0, 526, 442, 403, 305, 401, 294, 195, 142, 201, 169, 83, 58, 0), # 55
(526, 521, 474, 471, 387, 189, 217, 205, 225, 91, 71, 39, 0, 538, 448, 407, 310, 407, 297, 201, 144, 206, 170, 84, 59, 0), # 56
(536, 532, 483, 479, 391, 192, 219, 207, 230, 95, 71, 40, 0, 547, 457, 417, 317, 418, 304, 205, 146, 215, 173, 85, 59, 0), # 57
(548, 539, 491, 489, 394, 197, 223, 208, 233, 96, 71, 41, 0, 556, 471, 424, 321, 426, 309, 207, 148, 219, 174, 88, 59, 0), # 58
(560, 551, 500, 495, 397, 198, 228, 211, 238, 98, 72, 42, 0, 569, 479, 431, 328, 435, 314, 209, 148, 226, 175, 90, 61, 0), # 59
(573, 559, 516, 504, 402, 202, 230, 213, 241, 100, 72, 43, 0, 583, 486, 436, 331, 441, 317, 213, 153, 231, 180, 91, 61, 0), # 60
(581, 575, 525, 516, 410, 209, 239, 219, 245, 102, 72, 43, 0, 592, 493, 443, 335, 446, 321, 217, 155, 231, 183, 97, 62, 0), # 61
(590, 581, 534, 522, 420, 211, 246, 220, 251, 103, 73, 44, 0, 603, 505, 451, 340, 454, 328, 220, 158, 234, 183, 97, 63, 0), # 62
(602, 591, 539, 531, 423, 213, 247, 223, 257, 106, 75, 44, 0, 615, 514, 455, 347, 462, 331, 225, 161, 237, 184, 98, 65, 0), # 63
(612, 606, 548, 539, 430, 216, 251, 225, 261, 109, 78, 44, 0, 627, 522, 463, 353, 472, 333, 227, 162, 240, 189, 100, 65, 0), # 64
(621, 612, 553, 548, 438, 218, 254, 231, 266, 115, 79, 47, 0, 637, 531, 469, 360, 475, 336, 230, 165, 242, 193, 102, 65, 0), # 65
(635, 621, 558, 558, 447, 219, 260, 233, 274, 116, 81, 47, 0, 650, 539, 474, 364, 485, 343, 231, 167, 247, 197, 105, 66, 0), # 66
(641, 629, 570, 566, 453, 225, 268, 238, 275, 116, 83, 47, 0, 658, 546, 486, 368, 492, 344, 232, 170, 253, 201, 107, 68, 0), # 67
(652, 647, 581, 582, 460, 227, 272, 241, 278, 120, 84, 48, 0, 668, 550, 493, 371, 496, 347, 234, 172, 257, 201, 107, 69, 0), # 68
(661, 649, 591, 590, 469, 233, 278, 242, 281, 121, 85, 51, 0, 675, 557, 502, 375, 502, 348, 237, 175, 264, 209, 108, 69, 0), # 69
(670, 654, 602, 598, 472, 234, 283, 244, 283, 124, 88, 52, 0, 686, 566, 508, 382, 509, 353, 240, 176, 267, 212, 110, 71, 0), # 70
(685, 663, 610, 610, 479, 238, 286, 245, 286, 125, 89, 54, 0, 696, 576, 517, 394, 516, 355, 242, 178, 271, 216, 115, 74, 0), # 71
(696, 673, 623, 623, 491, 241, 286, 250, 291, 126, 91, 56, 0, 707, 582, 523, 403, 520, 359, 245, 182, 275, 219, 117, 76, 0), # 72
(712, 687, 633, 635, 497, 243, 289, 252, 295, 127, 93, 57, 0, 711, 590, 528, 410, 526, 361, 250, 183, 278, 221, 119, 77, 0), # 73
(725, 691, 639, 642, 507, 246, 296, 254, 298, 128, 95, 59, 0, 725, 596, 533, 415, 533, 367, 253, 184, 282, 223, 120, 77, 0), # 74
(740, 704, 645, 653, 513, 252, 300, 256, 302, 130, 97, 62, 0, 731, 600, 540, 419, 545, 371, 255, 185, 284, 226, 122, 78, 0), # 75
(751, 717, 654, 665, 519, 257, 304, 257, 304, 132, 97, 64, 0, 748, 606, 545, 422, 550, 376, 258, 186, 285, 230, 124, 81, 0), # 76
(766, 727, 661, 674, 524, 260, 307, 259, 307, 135, 98, 67, 0, 758, 610, 551, 425, 557, 380, 263, 188, 291, 233, 125, 83, 0), # 77
(778, 740, 673, 678, 529, 262, 307, 262, 310, 138, 98, 67, 0, 770, 620, 553, 429, 565, 385, 269, 190, 295, 234, 128, 84, 0), # 78
(788, 750, 679, 690, 535, 268, 312, 265, 314, 138, 101, 68, 0, 779, 632, 557, 434, 573, 391, 275, 193, 299, 240, 133, 84, 0), # 79
(803, 757, 686, 696, 551, 272, 316, 267, 317, 140, 104, 69, 0, 787, 643, 564, 443, 578, 394, 279, 193, 301, 243, 133, 85, 0), # 80
(813, 765, 697, 708, 558, 274, 319, 271, 321, 141, 104, 70, 0, 801, 655, 570, 445, 586, 402, 280, 193, 301, 246, 133, 86, 0), # 81
(824, 776, 701, 719, 568, 278, 320, 274, 324, 142, 107, 70, 0, 814, 664, 577, 451, 594, 407, 280, 198, 304, 248, 134, 87, 0), # 82
(838, 786, 711, 732, 578, 280, 323, 277, 331, 142, 109, 70, 0, 822, 678, 581, 452, 610, 412, 281, 200, 307, 252, 135, 87, 0), # 83
(847, 797, 713, 744, 593, 282, 327, 281, 333, 146, 112, 72, 0, 833, 686, 590, 458, 613, 414, 286, 205, 312, 259, 135, 87, 0), # 84
(851, 802, 720, 753, 602, 284, 336, 285, 337, 148, 116, 74, 0, 846, 689, 595, 467, 625, 415, 290, 207, 319, 260, 135, 87, 0), # 85
(862, 820, 730, 761, 611, 285, 339, 288, 340, 150, 117, 74, 0, 858, 703, 608, 471, 634, 416, 296, 210, 324, 261, 136, 89, 0), # 86
(873, 830, 738, 766, 617, 287, 341, 289, 345, 152, 117, 75, 0, 874, 714, 610, 477, 640, 421, 298, 210, 328, 263, 137, 89, 0), # 87
(879, 838, 741, 772, 623, 291, 345, 289, 345, 154, 117, 75, 0, 882, 722, 618, 482, 647, 425, 298, 212, 336, 268, 137, 89, 0), # 88
(895, 853, 746, 783, 635, 295, 347, 292, 347, 158, 119, 75, 0, 894, 730, 621, 484, 652, 428, 301, 213, 339, 273, 138, 89, 0), # 89
(903, 864, 752, 791, 646, 296, 352, 295, 352, 159, 120, 76, 0, 904, 738, 626, 490, 662, 431, 302, 217, 341, 276, 139, 89, 0), # 90
(915, 870, 760, 795, 655, 297, 352, 299, 354, 161, 121, 76, 0, 912, 745, 633, 496, 670, 433, 306, 220, 345, 280, 141, 89, 0), # 91
(928, 876, 765, 801, 659, 298, 356, 301, 355, 161, 122, 76, 0, 917, 753, 638, 498, 677, 439, 309, 225, 348, 287, 141, 89, 0), # 92
(935, 885, 771, 815, 665, 302, 361, 301, 358, 164, 122, 76, 0, 927, 762, 642, 505, 683, 442, 312, 228, 353, 291, 142, 89, 0), # 93
(945, 891, 783, 824, 677, 303, 363, 303, 365, 171, 122, 77, 0, 937, 772, 649, 519, 692, 443, 318, 232, 355, 296, 143, 90, 0), # 94
(949, 898, 793, 829, 686, 307, 365, 306, 377, 171, 123, 78, 0, 948, 778, 659, 524, 702, 446, 321, 238, 361, 301, 144, 91, 0), # 95
(962, 907, 801, 840, 694, 313, 367, 309, 384, 172, 123, 79, 0, 956, 786, 665, 527, 709, 449, 328, 242, 366, 301, 144, 91, 0), # 96
(975, 916, 806, 852, 698, 322, 370, 310, 385, 174, 123, 81, 0, 969, 797, 672, 530, 719, 453, 333, 242, 370, 302, 145, 92, 0), # 97
(988, 924, 812, 861, 706, 323, 376, 315, 387, 175, 125, 82, 0, 981, 811, 681, 532, 721, 454, 336, 245, 370, 305, 145, 92, 0), # 98
(1001, 936, 819, 870, 712, 327, 379, 317, 394, 179, 126, 82, 0, 991, 818, 686, 536, 733, 454, 340, 246, 372, 312, 148, 93, 0), # 99
(1011, 946, 823, 884, 716, 333, 383, 317, 395, 181, 127, 82, 0, 1005, 827, 692, 540, 738, 454, 346, 246, 382, 317, 148, 93, 0), # 100
(1018, 950, 833, 892, 723, 335, 383, 319, 399, 182, 128, 83, 0, 1015, 837, 701, 549, 744, 457, 349, 247, 385, 320, 150, 94, 0), # 101
(1027, 959, 841, 898, 729, 337, 389, 323, 402, 183, 130, 83, 0, 1022, 845, 707, 552, 750, 458, 353, 249, 389, 321, 150, 94, 0), # 102
(1040, 968, 848, 911, 734, 341, 389, 327, 408, 186, 130, 83, 0, 1028, 849, 717, 552, 755, 460, 357, 251, 391, 322, 151, 94, 0), # 103
(1053, 976, 857, 918, 746, 342, 391, 328, 409, 188, 132, 85, 0, 1035, 854, 720, 557, 761, 465, 361, 254, 401, 325, 151, 94, 0), # 104
(1067, 985, 865, 934, 761, 350, 397, 330, 410, 189, 135, 86, 0, 1044, 864, 725, 559, 775, 470, 363, 258, 405, 327, 153, 94, 0), # 105
(1079, 989, 875, 943, 766, 351, 401, 331, 415, 190, 135, 86, 0, 1050, 878, 729, 562, 783, 473, 366, 261, 405, 334, 154, 95, 0), # 106
(1084, 995, 884, 948, 768, 352, 402, 333, 416, 190, 137, 87, 0, 1061, 882, 733, 570, 788, 477, 369, 265, 406, 335, 155, 96, 0), # 107
(1091, 1002, 899, 955, 781, 356, 406, 336, 418, 190, 137, 89, 0, 1070, 894, 739, 574, 799, 482, 371, 266, 412, 337, 157, 96, 0), # 108
(1101, 1014, 908, 959, 786, 362, 410, 341, 420, 192, 138, 90, 0, 1078, 900, 745, 579, 803, 485, 375, 272, 416, 340, 159, 96, 0), # 109
(1112, 1023, 915, 966, 795, 369, 411, 342, 423, 193, 140, 90, 0, 1082, 906, 753, 583, 810, 490, 380, 274, 421, 341, 159, 98, 0), # 110
(1126, 1027, 921, 977, 805, 372, 415, 343, 425, 197, 141, 90, 0, 1096, 908, 760, 591, 818, 494, 382, 277, 427, 342, 160, 98, 0), # 111
(1136, 1034, 928, 984, 814, 377, 418, 346, 431, 198, 146, 90, 0, 1107, 914, 770, 594, 829, 494, 386, 279, 429, 343, 162, 99, 0), # 112
(1149, 1039, 939, 991, 816, 379, 419, 351, 434, 198, 148, 91, 0, 1111, 926, 780, 598, 836, 497, 391, 283, 433, 350, 164, 99, 0), # 113
(1157, 1039, 944, 998, 819, 381, 421, 355, 437, 199, 149, 93, 0, 1118, 935, 787, 598, 842, 497, 392, 287, 437, 353, 167, 100, 0), # 114
(1166, 1045, 954, 1011, 827, 389, 421, 356, 446, 200, 150, 94, 0, 1128, 939, 792, 599, 848, 501, 394, 290, 441, 356, 170, 101, 0), # 115
(1171, 1050, 963, 1029, 832, 393, 428, 359, 452, 200, 151, 95, 0, 1136, 949, 802, 603, 856, 508, 401, 297, 445, 356, 171, 102, 0), # 116
(1183, 1054, 970, 1037, 843, 396, 433, 364, 455, 201, 153, 95, 0, 1143, 960, 808, 608, 863, 518, 403, 300, 452, 358, 172, 103, 0), # 117
(1190, 1058, 979, 1044, 850, 402, 436, 365, 462, 202, 156, 95, 0, 1157, 965, 812, 611, 869, 521, 405, 300, 456, 361, 176, 104, 0), # 118
(1195, 1061, 987, 1049, 863, 403, 438, 366, 463, 202, 157, 96, 0, 1167, 978, 822, 615, 875, 525, 415, 303, 458, 361, 179, 104, 0), # 119
(1204, 1068, 994, 1059, 871, 406, 444, 369, 467, 202, 160, 97, 0, 1176, 984, 828, 623, 884, 528, 417, 305, 460, 363, 182, 105, 0), # 120
(1214, 1072, 999, 1065, 875, 407, 446, 376, 468, 204, 160, 97, 0, 1186, 989, 836, 625, 889, 531, 421, 309, 462, 367, 183, 106, 0), # 121
(1222, 1083, 1007, 1072, 882, 412, 446, 379, 471, 208, 161, 98, 0, 1196, 998, 838, 631, 899, 536, 424, 313, 466, 372, 184, 106, 0), # 122
(1234, 1091, 1017, 1080, 895, 413, 447, 380, 477, 210, 163, 98, 0, 1202, 1006, 843, 634, 907, 540, 428, 314, 471, 374, 185, 108, 0), # 123
(1240, 1098, 1027, 1087, 905, 416, 450, 382, 484, 210, 163, 98, 0, 1212, 1012, 849, 635, 914, 541, 430, 315, 472, 375, 186, 108, 0), # 124
(1256, 1103, 1035, 1090, 913, 421, 452, 382, 490, 210, 163, 98, 0, 1219, 1017, 858, 636, 918, 543, 435, 316, 475, 378, 187, 108, 0), # 125
(1266, 1109, 1046, 1102, 918, 421, 456, 385, 497, 213, 163, 98, 0, 1231, 1023, 865, 640, 922, 547, 436, 318, 479, 382, 190, 108, 0), # 126
(1274, 1116, 1059, 1103, 923, 425, 462, 390, 500, 217, 163, 98, 0, 1237, 1032, 875, 643, 926, 551, 438, 323, 482, 386, 190, 108, 0), # 127
(1281, 1123, 1066, 1109, 934, 429, 465, 391, 504, 220, 163, 100, 0, 1242, 1041, 881, 646, 930, 553, 444, 324, 487, 389, 191, 108, 0), # 128
(1287, 1127, 1068, 1120, 942, 431, 466, 397, 508, 221, 163, 100, 0, 1251, 1051, 886, 652, 939, 555, 450, 326, 489, 394, 191, 110, 0), # 129
(1295, 1136, 1082, 1129, 951, 435, 471, 398, 512, 221, 163, 101, 0, 1255, 1053, 890, 657, 948, 559, 453, 326, 489, 395, 194, 110, 0), # 130
(1307, 1139, 1091, 1136, 959, 439, 476, 402, 516, 223, 165, 101, 0, 1259, 1056, 897, 660, 954, 561, 455, 327, 491, 395, 196, 110, 0), # 131
(1318, 1144, 1106, 1144, 963, 441, 480, 403, 522, 225, 166, 101, 0, 1271, 1060, 898, 663, 960, 566, 460, 328, 491, 400, 197, 111, 0), # 132
(1328, 1148, 1114, 1151, 967, 444, 484, 403, 525, 227, 168, 102, 0, 1277, 1069, 904, 664, 966, 572, 464, 331, 492, 406, 199, 112, 0), # 133
(1338, 1155, 1121, 1159, 972, 448, 487, 404, 525, 227, 170, 102, 0, 1287, 1078, 906, 667, 971, 577, 465, 332, 497, 407, 202, 112, 0), # 134
(1347, 1162, 1126, 1167, 978, 451, 491, 408, 526, 228, 171, 103, 0, 1299, 1087, 914, 670, 974, 580, 470, 334, 501, 409, 204, 112, 0), # 135
(1354, 1175, 1134, 1172, 986, 452, 495, 410, 533, 232, 173, 105, 0, 1311, 1094, 919, 673, 980, 583, 471, 337, 504, 410, 205, 112, 0), # 136
(1364, 1182, 1148, 1181, 994, 458, 497, 412, 534, 233, 173, 105, 0, 1318, 1104, 925, 675, 987, 587, 477, 339, 507, 413, 205, 112, 0), # 137
(1377, 1188, 1155, 1189, 1001, 462, 498, 415, 537, 233, 175, 105, 0, 1325, 1112, 929, 680, 994, 589, 483, 342, 511, 416, 205, 112, 0), # 138
(1392, 1190, 1164, 1199, 1010, 464, 500, 419, 540, 235, 176, 106, 0, 1333, 1118, 933, 685, 1000, 594, 487, 343, 515, 419, 208, 112, 0), # 139
(1405, 1195, 1171, 1208, 1020, 470, 503, 420, 547, 237, 178, 106, 0, 1339, 1124, 935, 688, 1010, 598, 489, 344, 519, 422, 209, 113, 0), # 140
(1410, 1203, 1177, 1216, 1028, 475, 505, 423, 553, 237, 178, 107, 0, 1343, 1131, 939, 694, 1020, 603, 490, 344, 520, 423, 210, 113, 0), # 141
(1420, 1205, 1189, 1226, 1038, 480, 507, 425, 558, 237, 178, 107, 0, 1355, 1139, 946, 697, 1025, 604, 492, 345, 521, 425, 210, 114, 0), # 142
(1426, 1212, 1195, 1230, 1042, 482, 509, 428, 560, 239, 178, 109, 0, 1365, 1142, 952, 701, 1030, 606, 493, 346, 526, 428, 212, 115, 0), # 143
(1438, 1216, 1202, 1236, 1044, 485, 511, 428, 564, 241, 181, 110, 0, 1380, 1148, 960, 706, 1036, 610, 497, 346, 534, 432, 212, 115, 0), # 144
(1450, 1220, 1206, 1244, 1052, 490, 515, 428, 566, 244, 183, 110, 0, 1388, 1149, 969, 711, 1043, 611, 497, 348, 538, 433, 213, 115, 0), # 145
(1458, 1224, 1214, 1250, 1060, 494, 519, 431, 570, 246, 185, 110, 0, 1395, 1157, 975, 715, 1047, 613, 501, 350, 542, 436, 215, 115, 0), # 146
(1467, 1228, 1222, 1260, 1067, 497, 522, 433, 571, 248, 187, 110, 0, 1404, 1165, 980, 722, 1052, 616, 502, 353, 545, 438, 218, 116, 0), # 147
(1478, 1233, 1228, 1265, 1072, 498, 523, 434, 574, 249, 187, 112, 0, 1414, 1180, 983, 723, 1061, 620, 506, 354, 550, 440, 219, 117, 0), # 148
(1486, 1242, 1235, 1278, 1081, 500, 529, 435, 576, 249, 189, 112, 0, 1421, 1185, 985, 727, 1067, 627, 510, 356, 553, 441, 223, 117, 0), # 149
(1494, 1249, 1240, 1284, 1088, 507, 532, 442, 583, 251, 190, 112, 0, 1431, 1195, 990, 734, 1079, 631, 510, 356, 553, 442, 226, 118, 0), # 150
(1499, 1259, 1242, 1287, 1094, 512, 534, 443, 584, 252, 193, 112, 0, 1439, 1207, 996, 737, 1086, 632, 512, 357, 558, 449, 229, 118, 0), # 151
(1514, 1266, 1247, 1294, 1102, 513, 536, 444, 587, 254, 193, 113, 0, 1451, 1215, 999, 741, 1092, 634, 512, 357, 560, 450, 230, 118, 0), # 152
(1523, 1271, 1256, 1300, 1110, 516, 537, 445, 592, 256, 193, 113, 0, 1459, 1220, 1003, 744, 1100, 636, 514, 361, 565, 453, 232, 119, 0), # 153
(1531, 1283, 1260, 1305, 1120, 523, 541, 445, 594, 258, 197, 114, 0, 1466, 1227, 1008, 748, 1110, 643, 515, 364, 568, 458, 233, 120, 0), # 154
(1542, 1290, 1265, 1310, 1125, 526, 543, 449, 598, 259, 198, 115, 0, 1474, 1236, 1009, 752, 1116, 643, 516, 365, 569, 461, 233, 120, 0), # 155
(1548, 1295, 1269, 1317, 1131, 528, 545, 449, 602, 259, 199, 117, 0, 1485, 1245, 1014, 753, 1122, 646, 516, 365, 570, 462, 234, 120, 0), # 156
(1558, 1299, 1278, 1325, 1138, 534, 546, 451, 605, 259, 200, 117, 0, 1497, 1252, 1017, 755, 1128, 647, 517, 365, 574, 466, 236, 120, 0), # 157
(1567, 1303, 1285, 1333, 1145, 539, 551, 451, 607, 259, 201, 117, 0, 1508, 1253, 1022, 759, 1132, 651, 518, 366, 577, 472, 236, 120, 0), # 158
(1577, 1311, 1293, 1343, 1150, 539, 555, 455, 610, 260, 202, 118, 0, 1516, 1258, 1026, 763, 1140, 654, 519, 368, 582, 472, 237, 121, 0), # 159
(1584, 1314, 1296, 1352, 1157, 540, 557, 458, 615, 261, 204, 118, 0, 1526, 1264, 1035, 765, 1146, 657, 522, 373, 583, 474, 239, 123, 0), # 160
(1593, 1321, 1303, 1358, 1167, 542, 558, 463, 621, 261, 204, 120, 0, 1529, 1275, 1042, 769, 1151, 657, 523, 376, 584, 475, 239, 123, 0), # 161
(1603, 1325, 1307, 1362, 1176, 549, 559, 465, 626, 261, 205, 120, 0, 1536, 1278, 1045, 771, 1156, 661, 525, 382, 586, 475, 239, 124, 0), # 162
(1611, 1329, 1314, 1369, 1184, 552, 560, 466, 627, 261, 205, 120, 0, 1542, 1280, 1049, 774, 1162, 662, 528, 382, 589, 477, 242, 126, 0), # 163
(1615, 1331, 1322, 1374, 1189, 556, 563, 469, 633, 261, 207, 121, 0, 1549, 1290, 1051, 780, 1169, 667, 531, 382, 591, 479, 243, 128, 0), # 164
(1620, 1337, 1326, 1376, 1195, 560, 566, 471, 640, 261, 207, 121, 0, 1558, 1293, 1057, 783, 1174, 673, 533, 382, 594, 481, 244, 128, 0), # 165
(1627, 1339, 1335, 1382, 1197, 563, 567, 472, 642, 262, 207, 121, 0, 1566, 1300, 1061, 788, 1181, 677, 533, 383, 597, 483, 244, 128, 0), # 166
(1632, 1343, 1339, 1389, 1204, 565, 568, 475, 646, 262, 209, 121, 0, 1574, 1308, 1063, 792, 1187, 681, 537, 384, 599, 485, 245, 129, 0), # 167
(1637, 1351, 1342, 1396, 1207, 568, 569, 479, 650, 266, 209, 121, 0, 1581, 1313, 1068, 795, 1192, 682, 537, 387, 601, 486, 246, 129, 0), # 168
(1644, 1354, 1347, 1403, 1211, 569, 571, 480, 652, 266, 209, 122, 0, 1588, 1316, 1072, 798, 1199, 684, 540, 388, 603, 490, 246, 129, 0), # 169
(1647, 1354, 1355, 1405, 1216, 574, 574, 480, 653, 268, 209, 123, 0, 1594, 1328, 1074, 801, 1203, 686, 540, 388, 605, 491, 247, 130, 0), # 170
(1655, 1359, 1362, 1411, 1221, 576, 575, 480, 657, 269, 211, 123, 0, 1602, 1331, 1078, 804, 1205, 690, 545, 390, 609, 494, 249, 131, 0), # 171
(1664, 1364, 1366, 1413, 1222, 579, 575, 480, 657, 271, 211, 123, 0, 1609, 1334, 1080, 806, 1208, 693, 546, 391, 611, 495, 250, 131, 0), # 172
(1669, 1366, 1367, 1417, 1225, 582, 575, 480, 661, 273, 212, 123, 0, 1611, 1337, 1088, 808, 1211, 696, 549, 392, 614, 496, 250, 131, 0), # 173
(1674, 1373, 1371, 1424, 1237, 582, 576, 482, 664, 275, 213, 123, 0, 1616, 1344, 1091, 810, 1216, 701, 551, 394, 615, 499, 251, 132, 0), # 174
(1678, 1374, 1377, 1424, 1237, 583, 578, 486, 670, 275, 213, 123, 0, 1620, 1351, 1096, 812, 1222, 704, 553, 394, 616, 500, 253, 132, 0), # 175
(1680, 1374, 1381, 1429, 1239, 584, 582, 486, 670, 276, 213, 124, 0, 1624, 1356, 1097, 813, 1231, 705, 553, 394, 617, 502, 254, 132, 0), # 176
(1682, 1381, 1385, 1437, 1244, 587, 583, 487, 671, 276, 214, 124, 0, 1627, 1360, 1099, 814, 1235, 708, 554, 395, 621, 502, 255, 132, 0), # 177
(1684, 1382, 1391, 1439, 1248, 588, 583, 490, 673, 278, 214, 124, 0, 1632, 1366, 1104, 816, 1238, 710, 554, 395, 623, 502, 255, 134, 0), # 178
(1684, 1382, 1391, 1439, 1248, 588, 583, 490, 673, 278, 214, 124, 0, 1632, 1366, 1104, 816, 1238, 710, 554, 395, 623, 502, 255, 134, 0), # 179
)
passenger_arriving_rate = (
(5.020865578371768, 5.064847846385402, 4.342736024677089, 4.661000830397574, 3.7031237384064077, 1.8308820436884476, 2.0730178076869574, 1.938823405408093, 2.030033020722669, 0.9895037538805926, 0.7008775273142672, 0.4081595898588478, 0.0, 5.083880212578363, 4.489755488447325, 3.5043876365713356, 2.968511261641777, 4.060066041445338, 2.7143527675713304, 2.0730178076869574, 1.3077728883488913, 1.8515618692032039, 1.5536669434658585, 0.8685472049354179, 0.4604407133077639, 0.0), # 0
(5.354327152019974, 5.399222302966028, 4.629455492775127, 4.968858189957462, 3.948326891649491, 1.9518237573581576, 2.209734470631847, 2.066464051210712, 2.164081775444303, 1.0547451730692876, 0.7471826893260219, 0.4351013884011963, 0.0, 5.419791647439855, 4.786115272413158, 3.73591344663011, 3.164235519207862, 4.328163550888606, 2.8930496716949965, 2.209734470631847, 1.3941598266843982, 1.9741634458247455, 1.6562860633191545, 0.9258910985550255, 0.49083839117872996, 0.0), # 1
(5.686723008979731, 5.732269739983398, 4.915035237956178, 5.275490778498595, 4.192641982499829, 2.072282983465593, 2.345909253980352, 2.193593853293508, 2.297595602292516, 1.1197284437551367, 0.7933038581293855, 0.46193605433775464, 0.0, 5.75436482820969, 5.0812965977153, 3.9665192906469278, 3.3591853312654094, 4.595191204585032, 3.0710313946109116, 2.345909253980352, 1.480202131046852, 2.0963209912499146, 1.758496926166199, 0.9830070475912357, 0.5211154309075817, 0.0), # 2
(6.016757793146562, 6.062668793441743, 5.198342391099879, 5.579682305649055, 4.435107784001268, 2.191782029841316, 2.4810018208239777, 2.3197088156227115, 2.430045053640364, 1.1841956746065454, 0.8390580686378972, 0.4885571404108718, 0.0, 6.086272806254225, 5.374128544519589, 4.195290343189486, 3.5525870238196355, 4.860090107280728, 3.247592341871796, 2.4810018208239777, 1.5655585927437972, 2.217553892000634, 1.8598941018830188, 1.0396684782199759, 0.551151708494704, 0.0), # 3
(6.343136148415981, 6.389098099345293, 5.478244083085864, 5.880216481036927, 4.674763069197661, 2.3098432043158894, 2.6144718342542292, 2.444304942164548, 2.560900681860902, 1.24788897429192, 0.8842623557650959, 0.514858199362897, 0.0, 6.414188632939817, 5.6634401929918665, 4.42131177882548, 3.743666922875759, 5.121801363721804, 3.422026919030367, 2.6144718342542292, 1.6498880030827783, 2.3373815345988307, 1.9600721603456428, 1.095648816617173, 0.5808270999404813, 0.0), # 4
(6.66456271868351, 6.710236293698289, 5.753607444793765, 6.175877014290295, 4.910646611132853, 2.4259888147198754, 2.745778957362612, 2.566878236885247, 2.689633039327186, 1.310550451479666, 0.9287337544245222, 0.5407327839361791, 0.0, 6.736785359632827, 5.948060623297969, 4.64366877212261, 3.9316513544389973, 5.379266078654372, 3.593629531639346, 2.745778957362612, 1.7328491533713395, 2.4553233055664263, 2.058625671430099, 1.1507214889587531, 0.6100214812452991, 0.0), # 5
(6.979742147844666, 7.024762012504959, 6.023299607103222, 6.465447615037239, 5.141797182850695, 2.5397411688838374, 2.8743828532406313, 2.686924703751037, 2.8157126784122717, 1.3719222148381898, 0.9722892995297139, 0.5660744468730674, 0.0, 7.052736037699606, 6.22681891560374, 4.8614464976485685, 4.115766644514569, 5.631425356824543, 3.761694585251452, 2.8743828532406313, 1.8141008349170267, 2.5708985914253475, 2.1551492050124135, 1.2046599214206444, 0.6386147284095418, 0.0), # 6
(7.2873790797949685, 7.331353891769537, 6.286187700893863, 6.747711992905847, 5.367253557395036, 2.650622574638337, 2.9997431849797924, 2.8039403467281465, 2.9386101514892147, 1.4317463730358968, 1.0147460259942116, 0.5907767409159108, 0.0, 7.360713718506519, 6.498544150075018, 5.073730129971057, 4.2952391191076895, 5.877220302978429, 3.9255164854194056, 2.9997431849797924, 1.8933018390273837, 2.683626778697518, 2.249237330968616, 1.2572375401787725, 0.6664867174335943, 0.0), # 7
(7.586178158429934, 7.628690567496257, 6.54113885704533, 7.021453857524196, 5.586054507809724, 2.7581553398139356, 3.1213196156715988, 2.917421169782802, 3.0577960109310682, 1.4897650347411937, 1.0559209687315536, 0.6147332188070586, 0.0, 7.659391453419917, 6.762065406877643, 5.279604843657768, 4.469295104223581, 6.1155920218621365, 4.084389637695923, 3.1213196156715988, 1.970110957009954, 2.793027253904862, 2.3404846191747324, 1.3082277714090662, 0.6935173243178416, 0.0), # 8
(7.874844027645085, 7.915450675689353, 6.787020206437253, 7.285456918520376, 5.797238807138606, 2.861861772241199, 3.23857180840756, 3.0268631768812346, 3.1727408091108913, 1.5457203086224858, 1.0956311626552797, 0.6378374332888596, 0.0, 7.947442293806162, 7.016211766177453, 5.478155813276398, 4.637160925867456, 6.345481618221783, 4.237608447633728, 3.23857180840756, 2.044186980172285, 2.898619403569303, 2.4284856395067926, 1.3574040412874508, 0.7195864250626686, 0.0), # 9
(8.152081331335932, 8.190312852353056, 7.022698879949271, 7.538504885522466, 5.999845228425533, 2.961264179750688, 3.3509594262791773, 3.1317623719896712, 3.282915098401738, 1.599354303348179, 1.133693642678929, 0.6599829371036627, 0.0, 8.22353929103161, 7.259812308140289, 5.668468213394645, 4.798062910044536, 6.565830196803476, 4.384467320785539, 3.3509594262791773, 2.11518869982192, 2.9999226142127666, 2.5128349618408223, 1.4045397759898541, 0.7445738956684597, 0.0), # 10
(8.416594713398005, 8.451955733491605, 7.247042008461013, 7.779381468158547, 6.192912544714355, 3.055884870172965, 3.457942132377958, 3.2316147590743394, 3.3877894311766643, 1.6504091275866801, 1.1699254437160416, 0.6810632829938176, 0.0, 8.486355496462611, 7.491696112931993, 5.849627218580208, 4.951227382760039, 6.775578862353329, 4.524260662704076, 3.457942132377958, 2.1827749072664036, 3.0964562723571776, 2.5931271560528497, 1.4494084016922026, 0.7683596121356006, 0.0), # 11
(8.667088817726812, 8.699057955109222, 7.458916722852117, 8.006870376056709, 6.375479529048918, 3.1452461513385908, 3.5589795897954057, 3.325916342101467, 3.486834359808726, 1.6986268900063934, 1.2041436006801558, 0.7009720237016724, 0.0, 8.734563961465534, 7.710692260718395, 6.020718003400779, 5.095880670019179, 6.973668719617452, 4.656282878942054, 3.5589795897954057, 2.246604393813279, 3.187739764524459, 2.6689567920189035, 1.4917833445704234, 0.7908234504644749, 0.0), # 12
(8.902268288217876, 8.93029815321015, 7.657190154002218, 8.219755318845033, 6.546584954473067, 3.2288703310781304, 3.653531461623028, 3.414163125037284, 3.579520436670977, 1.7437496992757264, 1.2361651484848115, 0.7196027119695768, 0.0, 8.966837737406735, 7.915629831665344, 6.180825742424058, 5.2312490978271775, 7.159040873341954, 4.7798283750521975, 3.653531461623028, 2.306335950770093, 3.2732924772365335, 2.7399184396150114, 1.5314380308004438, 0.8118452866554684, 0.0), # 13
(9.120837768766716, 9.144354963798623, 7.840729432790956, 8.416820006151594, 6.705267594030659, 3.306279717222145, 3.7410574109523305, 3.4958511118480193, 3.6653182141364735, 1.785519664063084, 1.2658071220435476, 0.7368489005398801, 0.0, 9.181849875652563, 8.10533790593868, 6.329035610217737, 5.3565589921892505, 7.330636428272947, 4.894191556587227, 3.7410574109523305, 2.3616283694443894, 3.3526337970153297, 2.8056066687171985, 1.5681458865581912, 0.8313049967089657, 0.0), # 14
(9.321501903268855, 9.339907022878865, 8.008401690097953, 8.59684814760449, 6.850566220765538, 3.376996617601199, 3.821017100874813, 3.5704763064998986, 3.743698244578273, 1.823678893036873, 1.2928865562699035, 0.752604142154931, 0.0, 9.37827342756938, 8.27864556370424, 6.464432781349516, 5.471036679110618, 7.487396489156546, 4.998666829099858, 3.821017100874813, 2.4121404411437135, 3.425283110382769, 2.865616049201497, 1.6016803380195905, 0.8490824566253515, 0.0), # 15
(9.5029653356198, 9.51563296645512, 8.159074056802854, 8.758623452831788, 6.981519607721555, 3.4405433400458514, 3.892870194481988, 3.6375347129591504, 3.8141310803694286, 1.8579694948654994, 1.3172204860774188, 0.7667619895570784, 0.0, 9.554781444523545, 8.434381885127861, 6.586102430387094, 5.5739084845964975, 7.628262160738857, 5.092548598142811, 3.892870194481988, 2.4575309571756083, 3.4907598038607777, 2.9195411509439295, 1.6318148113605708, 0.8650575424050111, 0.0), # 16
(9.663932709715075, 9.670211430531618, 8.291613663785293, 8.900929631461583, 7.097166527942559, 3.4964421923866666, 3.9560763548653552, 3.6965223351920073, 3.8760872738829946, 1.8881335782173672, 1.3386259463796333, 0.7792159954886714, 0.0, 9.710046977881415, 8.571375950375383, 6.693129731898166, 5.6644007346521, 7.752174547765989, 5.17513126926881, 3.9560763548653552, 2.4974587088476192, 3.5485832639712793, 2.9669765438205284, 1.6583227327570589, 0.8791101300483289, 0.0), # 17
(9.803108669450204, 9.802321051112584, 8.404887641924901, 9.022550393121959, 7.1965457544723925, 3.5442154824542103, 4.010095245116426, 3.746935177164692, 3.929037377492032, 1.9139132517608846, 1.3569199720900849, 0.7898597126920597, 0.0, 9.842743079009345, 8.688456839612655, 6.784599860450424, 5.741739755282652, 7.858074754984064, 5.245709248030569, 4.010095245116426, 2.531582487467293, 3.5982728772361963, 3.0075167977073205, 1.6809775283849802, 0.8911200955556896, 0.0), # 18
(9.919197858720699, 9.910640464202265, 8.497763122101317, 9.122269447440985, 7.2786960603549105, 3.5833855180790386, 4.054386528326697, 3.7882692428434357, 3.9724519435695926, 1.9350506241644574, 1.3719195981223131, 0.7985866939095915, 0.0, 9.951542799273696, 8.784453633005505, 6.859597990611565, 5.80515187249337, 7.944903887139185, 5.30357693998081, 4.054386528326697, 2.55956108434217, 3.6393480301774552, 3.0407564824803295, 1.6995526244202632, 0.9009673149274788, 0.0), # 19
(10.010904921422082, 9.993848305804882, 8.569107235194169, 9.198870504046766, 7.342656218633962, 3.613474607091719, 4.088409867587681, 3.8200205361944657, 4.005801524488732, 1.95128780409649, 1.3834418593898585, 0.805290491883616, 0.0, 10.035119190040824, 8.858195410719775, 6.9172092969492915, 5.853863412289469, 8.011603048977465, 5.348028750672252, 4.088409867587681, 2.5810532907797996, 3.671328109316981, 3.0662901680155894, 1.713821447038834, 0.9085316641640803, 0.0), # 20
(10.076934501449866, 10.050623211924679, 8.6177871120831, 9.251137272567364, 7.387465002353392, 3.6340050573228124, 4.1116249259908795, 3.84168506118401, 4.028556672622507, 1.9623669002253892, 1.39130379080626, 0.8098646593564828, 0.0, 10.092145302677078, 8.90851125292131, 6.9565189540313, 5.887100700676166, 8.057113345245014, 5.378359085657614, 4.1116249259908795, 2.5957178980877234, 3.693732501176696, 3.0837124241891223, 1.72355742241662, 0.91369301926588, 0.0), # 21
(10.115991242699579, 10.079643818565883, 8.642669883647738, 9.277853462630876, 7.41216118455705, 3.644499176602881, 4.1234913666278, 3.852758821778298, 4.040187940343971, 1.968030021219561, 1.3953224272850568, 0.8122027490705409, 0.0, 10.121294188548827, 8.934230239775948, 6.976612136425284, 5.904090063658682, 8.080375880687942, 5.393862350489617, 4.1234913666278, 2.6032136975734863, 3.706080592278525, 3.09261782087696, 1.7285339767295478, 0.9163312562332622, 0.0), # 22
(10.13039336334264, 10.083079961133974, 8.645769318701419, 9.281198109567903, 7.418488037355065, 3.6458333333333335, 4.124902001129669, 3.8539557613168727, 4.0416420781893, 1.9686980681298587, 1.3958263395269568, 0.8124914647157445, 0.0, 10.125, 8.93740611187319, 6.9791316976347835, 5.906094204389575, 8.0832841563786, 5.395538065843622, 4.124902001129669, 2.604166666666667, 3.7092440186775324, 3.0937327031893016, 1.729153863740284, 0.9166436328303613, 0.0), # 23
(10.141012413034153, 10.08107561728395, 8.645262345679013, 9.280786458333335, 7.422071742409901, 3.6458333333333335, 4.124126906318083, 3.852291666666667, 4.041447222222222, 1.968287654320988, 1.39577076318743, 0.8124238683127573, 0.0, 10.125, 8.936662551440328, 6.978853815937151, 5.904862962962962, 8.082894444444443, 5.393208333333334, 4.124126906318083, 2.604166666666667, 3.7110358712049507, 3.0935954861111123, 1.7290524691358027, 0.9164614197530866, 0.0), # 24
(10.15140723021158, 10.077124771376313, 8.644261545496114, 9.279972029320987, 7.4255766303963355, 3.6458333333333335, 4.122599451303155, 3.8490226337448563, 4.041062242798354, 1.96747970964792, 1.3956605665710604, 0.8122904282883707, 0.0, 10.125, 8.935194711172077, 6.978302832855302, 5.902439128943758, 8.082124485596708, 5.388631687242799, 4.122599451303155, 2.604166666666667, 3.7127883151981678, 3.0933240097736636, 1.728852309099223, 0.9161022519433014, 0.0), # 25
(10.161577019048034, 10.071287780064015, 8.642780635573846, 9.278764081790122, 7.429002578947403, 3.6458333333333335, 4.120343359154361, 3.8442103909465026, 4.0404920781893, 1.9662876771833566, 1.3954967473084758, 0.8120929736320684, 0.0, 10.125, 8.933022709952752, 6.977483736542379, 5.898863031550069, 8.0809841563786, 5.381894547325103, 4.120343359154361, 2.604166666666667, 3.7145012894737013, 3.0929213605967085, 1.7285561271147696, 0.915571616369456, 0.0), # 26
(10.171520983716636, 10.063624999999998, 8.640833333333333, 9.277171874999999, 7.432349465696142, 3.6458333333333335, 4.117382352941177, 3.837916666666667, 4.039741666666666, 1.9647250000000003, 1.3952803030303031, 0.8118333333333335, 0.0, 10.125, 8.930166666666667, 6.976401515151515, 5.894175, 8.079483333333332, 5.373083333333334, 4.117382352941177, 2.604166666666667, 3.716174732848071, 3.0923906250000006, 1.7281666666666669, 0.914875, 0.0), # 27
(10.181238328390501, 10.054196787837219, 8.638433356195703, 9.275204668209877, 7.4356171682756, 3.6458333333333335, 4.113740155733075, 3.830203189300412, 4.038815946502057, 1.9628051211705537, 1.3950122313671698, 0.8115133363816492, 0.0, 10.125, 8.926646700198141, 6.9750611568358485, 5.88841536351166, 8.077631893004114, 5.3622844650205765, 4.113740155733075, 2.604166666666667, 3.7178085841378, 3.091734889403293, 1.7276866712391405, 0.9140178898033837, 0.0), # 28
(10.19072825724275, 10.043063500228623, 8.635594421582077, 9.272871720679012, 7.438805564318813, 3.6458333333333335, 4.109440490599533, 3.821131687242798, 4.037719855967078, 1.9605414837677189, 1.3946935299497027, 0.811134811766499, 0.0, 10.125, 8.922482929431489, 6.973467649748514, 5.881624451303155, 8.075439711934155, 5.349584362139917, 4.109440490599533, 2.604166666666667, 3.7194027821594067, 3.0909572402263383, 1.7271188843164156, 0.9130057727480568, 0.0), # 29
(10.199989974446497, 10.03028549382716, 8.63233024691358, 9.270182291666666, 7.441914531458824, 3.6458333333333335, 4.104507080610022, 3.8107638888888884, 4.036458333333333, 1.957947530864198, 1.39432519640853, 0.8106995884773662, 0.0, 10.125, 8.917695473251028, 6.9716259820426485, 5.873842592592593, 8.072916666666666, 5.335069444444444, 4.104507080610022, 2.604166666666667, 3.720957265729412, 3.0900607638888897, 1.7264660493827162, 0.9118441358024693, 0.0), # 30
(10.209022684174858, 10.01592312528578, 8.62865454961134, 9.267145640432098, 7.444943947328672, 3.6458333333333335, 4.09896364883402, 3.799161522633745, 4.035036316872428, 1.9550367055326936, 1.3939082283742779, 0.8102094955037343, 0.0, 10.125, 8.912304450541077, 6.969541141871389, 5.865110116598079, 8.070072633744855, 5.318826131687243, 4.09896364883402, 2.604166666666667, 3.722471973664336, 3.0890485468107003, 1.7257309099222682, 0.910538465935071, 0.0), # 31
(10.217825590600954, 10.00003675125743, 8.624581047096479, 9.263771026234568, 7.447893689561397, 3.6458333333333335, 4.092833918340999, 3.7863863168724285, 4.033458744855967, 1.951822450845908, 1.3934436234775742, 0.8096663618350862, 0.0, 10.125, 8.906329980185948, 6.96721811738787, 5.8554673525377225, 8.066917489711933, 5.3009408436214, 4.092833918340999, 2.604166666666667, 3.7239468447806985, 3.0879236754115236, 1.7249162094192958, 0.909094250114312, 0.0), # 32
(10.226397897897897, 9.98268672839506, 8.620123456790123, 9.260067708333333, 7.450763635790041, 3.6458333333333335, 4.086141612200436, 3.7725000000000004, 4.031730555555555, 1.9483182098765437, 1.392932379349046, 0.8090720164609053, 0.0, 10.125, 8.899792181069957, 6.96466189674523, 5.84495462962963, 8.06346111111111, 5.2815, 4.086141612200436, 2.604166666666667, 3.7253818178950207, 3.086689236111112, 1.724024691358025, 0.9075169753086421, 0.0), # 33
(10.23473881023881, 9.963933413351622, 8.615295496113397, 9.256044945987654, 7.453553663647644, 3.6458333333333335, 4.078910453481805, 3.7575643004115222, 4.029856687242798, 1.9445374256973027, 1.3923754936193207, 0.8084282883706753, 0.0, 10.125, 8.892711172077426, 6.961877468096604, 5.833612277091907, 8.059713374485597, 5.260590020576132, 4.078910453481805, 2.604166666666667, 3.726776831823822, 3.085348315329219, 1.7230590992226795, 0.9058121284865113, 0.0), # 34
(10.242847531796807, 9.943837162780063, 8.610110882487428, 9.25171199845679, 7.456263650767246, 3.6458333333333335, 4.071164165254579, 3.741640946502058, 4.0278420781893, 1.9404935413808875, 1.3917739639190256, 0.807737006553879, 0.0, 10.125, 8.88510707209267, 6.958869819595128, 5.821480624142661, 8.0556841563786, 5.238297325102881, 4.071164165254579, 2.604166666666667, 3.728131825383623, 3.0839039994855972, 1.7220221764974855, 0.9039851966163696, 0.0), # 35
(10.250723266745005, 9.922458333333331, 8.604583333333334, 9.247078125, 7.45889347478189, 3.6458333333333335, 4.062926470588235, 3.724791666666667, 4.025691666666666, 1.9362000000000004, 1.391128787878788, 0.8070000000000002, 0.0, 10.125, 8.877, 6.95564393939394, 5.8086, 8.051383333333332, 5.214708333333334, 4.062926470588235, 2.604166666666667, 3.729446737390945, 3.0823593750000007, 1.7209166666666669, 0.9020416666666666, 0.0), # 36
(10.258365219256524, 9.89985728166438, 8.598726566072246, 9.242152584876543, 7.4614430133246135, 3.6458333333333335, 4.054221092552247, 3.707078189300412, 4.023410390946502, 1.931670244627344, 1.3904409631292352, 0.8062190976985216, 0.0, 10.125, 8.868410074683737, 6.952204815646175, 5.79501073388203, 8.046820781893004, 5.189909465020577, 4.054221092552247, 2.604166666666667, 3.7307215066623067, 3.080717528292182, 1.7197453132144491, 0.8999870256058529, 0.0), # 37
(10.265772593504476, 9.876094364426155, 8.592554298125286, 9.23694463734568, 7.46391214402846, 3.6458333333333335, 4.04507175421609, 3.6885622427983544, 4.021003189300411, 1.92691771833562, 1.3897114873009937, 0.8053961286389272, 0.0, 10.125, 8.859357415028198, 6.948557436504967, 5.780753155006859, 8.042006378600822, 5.163987139917697, 4.04507175421609, 2.604166666666667, 3.73195607201423, 3.078981545781894, 1.7185108596250571, 0.8978267604023779, 0.0), # 38
(10.272944593661986, 9.851229938271604, 8.586080246913582, 9.231463541666667, 7.466300744526468, 3.6458333333333335, 4.035502178649238, 3.6693055555555554, 4.0184750000000005, 1.9219558641975314, 1.3889413580246914, 0.8045329218106996, 0.0, 10.125, 8.849862139917693, 6.944706790123457, 5.765867592592593, 8.036950000000001, 5.137027777777778, 4.035502178649238, 2.604166666666667, 3.733150372263234, 3.07715451388889, 1.7172160493827164, 0.8955663580246914, 0.0), # 39
(10.279880423902163, 9.82532435985368, 8.579318129858253, 9.225718557098766, 7.468608692451679, 3.6458333333333335, 4.025536088921165, 3.649369855967079, 4.015830761316872, 1.9167981252857802, 1.3881315729309558, 0.8036313062033228, 0.0, 10.125, 8.83994436823655, 6.940657864654778, 5.750394375857339, 8.031661522633744, 5.1091177983539104, 4.025536088921165, 2.604166666666667, 3.7343043462258394, 3.0752395190329227, 1.7158636259716507, 0.8932113054412438, 0.0), # 40
(10.286579288398128, 9.79843798582533, 8.57228166438043, 9.219718942901235, 7.4708358654371345, 3.6458333333333335, 4.015197208101347, 3.628816872427984, 4.0130754115226335, 1.9114579446730684, 1.3872831296504138, 0.8026931108062796, 0.0, 10.125, 8.829624218869075, 6.936415648252069, 5.734373834019204, 8.026150823045267, 5.0803436213991775, 4.015197208101347, 2.604166666666667, 3.7354179327185673, 3.073239647633746, 1.7144563328760862, 0.8907670896204848, 0.0), # 41
(10.293040391323, 9.770631172839506, 8.564984567901236, 9.213473958333335, 7.472982141115872, 3.6458333333333335, 4.004509259259259, 3.6077083333333335, 4.010213888888889, 1.9059487654320992, 1.3863970258136926, 0.8017201646090536, 0.0, 10.125, 8.818921810699589, 6.931985129068463, 5.717846296296297, 8.020427777777778, 5.050791666666667, 4.004509259259259, 2.604166666666667, 3.736491070557936, 3.0711579861111122, 1.7129969135802474, 0.8882391975308643, 0.0), # 42
(10.299262936849892, 9.741964277549155, 8.557440557841794, 9.206992862654321, 7.475047397120935, 3.6458333333333335, 3.993495965464375, 3.58610596707819, 4.007251131687243, 1.9002840306355744, 1.3854742590514195, 0.800714296601128, 0.0, 10.125, 8.807857262612407, 6.927371295257098, 5.700852091906722, 8.014502263374485, 5.020548353909466, 3.993495965464375, 2.604166666666667, 3.7375236985604676, 3.0689976208847747, 1.7114881115683587, 0.8856331161408324, 0.0), # 43
(10.305246129151927, 9.712497656607225, 8.549663351623229, 9.200284915123458, 7.477031511085363, 3.6458333333333335, 3.9821810497861696, 3.564071502057614, 4.0041920781893, 1.8944771833561962, 1.3845158269942222, 0.7996773357719861, 0.0, 10.125, 8.796450693491845, 6.92257913497111, 5.683431550068587, 8.0083841563786, 4.98970010288066, 3.9821810497861696, 2.604166666666667, 3.7385157555426813, 3.0667616383744867, 1.709932670324646, 0.8829543324188387, 0.0), # 44
(10.310989172402216, 9.682291666666666, 8.541666666666668, 9.193359375, 7.478934360642197, 3.6458333333333335, 3.9705882352941178, 3.541666666666667, 4.001041666666666, 1.8885416666666672, 1.3835227272727273, 0.798611111111111, 0.0, 10.125, 8.784722222222221, 6.917613636363637, 5.665625, 8.002083333333331, 4.958333333333334, 3.9705882352941178, 2.604166666666667, 3.7394671803210984, 3.064453125000001, 1.7083333333333335, 0.8802083333333335, 0.0), # 45
(10.31649127077388, 9.65140666438043, 8.533464220393233, 9.186225501543209, 7.480755823424477, 3.6458333333333335, 3.958741245057694, 3.518953189300412, 3.997804835390946, 1.8824909236396894, 1.3824959575175624, 0.7975174516079867, 0.0, 10.125, 8.772691967687852, 6.912479787587812, 5.647472770919067, 7.995609670781892, 4.926534465020577, 3.958741245057694, 2.604166666666667, 3.7403779117122387, 3.062075167181071, 1.7066928440786466, 0.8774006058527665, 0.0), # 46
(10.321751628440035, 9.619903006401461, 8.525069730224052, 9.178892554012345, 7.482495777065244, 3.6458333333333335, 3.9466638021463734, 3.4959927983539094, 3.994486522633745, 1.8763383973479657, 1.3814365153593549, 0.7963981862520958, 0.0, 10.125, 8.760380048773053, 6.9071825767967745, 5.629015192043896, 7.98897304526749, 4.894389917695474, 3.9466638021463734, 2.604166666666667, 3.741247888532622, 3.0596308513374493, 1.7050139460448106, 0.8745366369455876, 0.0), # 47
(10.326769449573796, 9.587841049382716, 8.516496913580248, 9.171369791666667, 7.48415409919754, 3.6458333333333335, 3.9343796296296296, 3.4728472222222226, 3.9910916666666667, 1.8700975308641978, 1.3803453984287317, 0.7952551440329219, 0.0, 10.125, 8.74780658436214, 6.901726992143659, 5.610292592592592, 7.982183333333333, 4.861986111111112, 3.9343796296296296, 2.604166666666667, 3.74207704959877, 3.05712326388889, 1.7032993827160496, 0.871621913580247, 0.0), # 48
(10.331543938348286, 9.555281149977136, 8.507759487882945, 9.163666473765433, 7.485730667454405, 3.6458333333333335, 3.9219124505769383, 3.4495781893004116, 3.987625205761317, 1.8637817672610888, 1.3792236043563206, 0.7940901539399483, 0.0, 10.125, 8.73499169333943, 6.896118021781603, 5.5913453017832655, 7.975250411522634, 4.829409465020577, 3.9219124505769383, 2.604166666666667, 3.7428653337272024, 3.054555491255145, 1.7015518975765893, 0.8686619227251944, 0.0), # 49
(10.336074298936616, 9.522283664837678, 8.49887117055327, 9.155791859567902, 7.4872253594688765, 3.6458333333333335, 3.909285988057775, 3.4262474279835393, 3.9840920781893, 1.85740454961134, 1.3780721307727481, 0.7929050449626583, 0.0, 10.125, 8.72195549458924, 6.89036065386374, 5.572213648834019, 7.9681841563786, 4.796746399176955, 3.909285988057775, 2.604166666666667, 3.7436126797344382, 3.051930619855968, 1.6997742341106543, 0.86566215134888, 0.0), # 50
(10.34035973551191, 9.488908950617283, 8.489845679012346, 9.147755208333333, 7.488638052873998, 3.6458333333333335, 3.896523965141612, 3.4029166666666666, 3.9804972222222226, 1.8509793209876546, 1.3768919753086422, 0.7917016460905352, 0.0, 10.125, 8.708718106995885, 6.884459876543211, 5.552937962962963, 7.960994444444445, 4.764083333333334, 3.896523965141612, 2.604166666666667, 3.744319026436999, 3.049251736111112, 1.6979691358024693, 0.8626280864197532, 0.0), # 51
(10.344399452247279, 9.455217363968908, 8.480696730681299, 9.139565779320987, 7.489968625302809, 3.6458333333333335, 3.883650104897926, 3.3796476337448556, 3.976845576131687, 1.8445195244627348, 1.3756841355946297, 0.7904817863130622, 0.0, 10.125, 8.695299649443683, 6.878420677973147, 5.533558573388203, 7.953691152263374, 4.731506687242798, 3.883650104897926, 2.604166666666667, 3.7449843126514044, 3.04652192644033, 1.69613934613626, 0.8595652149062645, 0.0), # 52
(10.348192653315843, 9.421269261545497, 8.471438042981255, 9.131232831790122, 7.491216954388353, 3.6458333333333335, 3.8706881303961915, 3.3565020576131688, 3.9731420781893005, 1.8380386031092826, 1.3744496092613379, 0.7892472946197227, 0.0, 10.125, 8.681720240816947, 6.872248046306688, 5.514115809327846, 7.946284156378601, 4.699102880658437, 3.8706881303961915, 2.604166666666667, 3.7456084771941764, 3.043744277263375, 1.694287608596251, 0.8564790237768635, 0.0), # 53
(10.351738542890716, 9.387125000000001, 8.462083333333332, 9.122765625, 7.492382917763668, 3.6458333333333335, 3.8576617647058824, 3.333541666666666, 3.9693916666666667, 1.8315500000000005, 1.3731893939393938, 0.788, 0.0, 10.125, 8.668, 6.865946969696969, 5.49465, 7.938783333333333, 4.666958333333333, 3.8576617647058824, 2.604166666666667, 3.746191458881834, 3.040921875000001, 1.6924166666666667, 0.8533750000000002, 0.0), # 54
(10.355036325145022, 9.352844935985367, 8.452646319158665, 9.114173418209877, 7.493466393061793, 3.6458333333333335, 3.844594730896474, 3.3108281893004117, 3.9655992798353905, 1.8250671582075908, 1.3719044872594257, 0.7867417314433777, 0.0, 10.125, 8.654159045877153, 6.859522436297127, 5.4752014746227715, 7.931198559670781, 4.6351594650205765, 3.844594730896474, 2.604166666666667, 3.7467331965308963, 3.0380578060699595, 1.6905292638317333, 0.8502586305441244, 0.0), # 55
(10.358085204251871, 9.31848942615455, 8.443140717878373, 9.105465470679011, 7.4944672579157725, 3.6458333333333335, 3.8315107520374405, 3.288423353909465, 3.961769855967078, 1.818603520804756, 1.3705958868520598, 0.7854743179393385, 0.0, 10.125, 8.640217497332722, 6.852979434260299, 5.455810562414267, 7.923539711934156, 4.603792695473251, 3.8315107520374405, 2.604166666666667, 3.7472336289578863, 3.035155156893005, 1.6886281435756747, 0.8471354023776865, 0.0), # 56
(10.360884384384383, 9.284118827160494, 8.433580246913582, 9.096651041666666, 7.495385389958644, 3.6458333333333335, 3.818433551198257, 3.2663888888888892, 3.957908333333333, 1.812172530864198, 1.369264590347924, 0.7841995884773663, 0.0, 10.125, 8.626195473251027, 6.8463229517396185, 5.436517592592593, 7.915816666666666, 4.572944444444445, 3.818433551198257, 2.604166666666667, 3.747692694979322, 3.0322170138888898, 1.6867160493827165, 0.844010802469136, 0.0), # 57
(10.36343306971568, 9.24979349565615, 8.423978623685414, 9.087739390432098, 7.496220666823449, 3.6458333333333335, 3.8053868514483984, 3.2447865226337447, 3.954019650205761, 1.8057876314586196, 1.367911595377645, 0.7829193720469442, 0.0, 10.125, 8.612113092516385, 6.8395579768882255, 5.417362894375858, 7.908039300411522, 4.5427011316872425, 3.8053868514483984, 2.604166666666667, 3.7481103334117245, 3.029246463477367, 1.684795724737083, 0.8408903177869229, 0.0), # 58
(10.36573046441887, 9.215573788294467, 8.414349565614998, 9.078739776234567, 7.49697296614323, 3.6458333333333335, 3.792394375857339, 3.2236779835390945, 3.9501087448559673, 1.799462265660723, 1.3665378995718502, 0.7816354976375554, 0.0, 10.125, 8.597990474013107, 6.83268949785925, 5.398386796982168, 7.900217489711935, 4.513149176954733, 3.792394375857339, 2.604166666666667, 3.748486483071615, 3.02624659207819, 1.6828699131229998, 0.8377794352994972, 0.0), # 59
(10.367775772667077, 9.181520061728396, 8.404706790123456, 9.069661458333334, 7.497642165551024, 3.6458333333333335, 3.779479847494553, 3.203125, 3.946180555555556, 1.7932098765432103, 1.3651445005611673, 0.7803497942386832, 0.0, 10.125, 8.583847736625515, 6.825722502805837, 5.37962962962963, 7.892361111111112, 4.484375, 3.779479847494553, 2.604166666666667, 3.748821082775512, 3.023220486111112, 1.6809413580246915, 0.8346836419753088, 0.0), # 60
(10.369568198633415, 9.147692672610884, 8.395064014631917, 9.060513695987654, 7.498228142679874, 3.6458333333333335, 3.7666669894295164, 3.183189300411523, 3.9422400205761314, 1.7870439071787843, 1.3637323959762233, 0.7790640908398111, 0.0, 10.125, 8.56970499923792, 6.818661979881115, 5.361131721536351, 7.884480041152263, 4.456465020576132, 3.7666669894295164, 2.604166666666667, 3.749114071339937, 3.0201712319958856, 1.6790128029263836, 0.8316084247828076, 0.0), # 61
(10.371106946491004, 9.114151977594878, 8.385434956561502, 9.051305748456791, 7.498730775162823, 3.6458333333333335, 3.753979524731703, 3.1639326131687247, 3.9382920781893, 1.7809778006401469, 1.3623025834476452, 0.7777802164304223, 0.0, 10.125, 8.555582380734645, 6.811512917238226, 5.3429334019204395, 7.8765841563786, 4.429505658436215, 3.753979524731703, 2.604166666666667, 3.7493653875814115, 3.0171019161522645, 1.6770869913123003, 0.8285592706904436, 0.0), # 62
(10.37239122041296, 9.080958333333333, 8.375833333333334, 9.042046875, 7.499149940632904, 3.6458333333333335, 3.741441176470588, 3.1454166666666667, 3.9343416666666666, 1.7750250000000003, 1.360856060606061, 0.7765000000000001, 0.0, 10.125, 8.5415, 6.804280303030303, 5.325075, 7.868683333333333, 4.403583333333334, 3.741441176470588, 2.604166666666667, 3.749574970316452, 3.014015625000001, 1.675166666666667, 0.8255416666666667, 0.0), # 63
(10.373420224572397, 9.048172096479195, 8.366272862368541, 9.032746334876544, 7.4994855167231655, 3.6458333333333335, 3.729075667715646, 3.127703189300412, 3.9303937242798352, 1.7691989483310475, 1.3593938250820965, 0.7752252705380279, 0.0, 10.125, 8.527477975918305, 6.796969125410483, 5.307596844993141, 7.8607874485596705, 4.378784465020577, 3.729075667715646, 2.604166666666667, 3.7497427583615828, 3.0109154449588487, 1.6732545724737085, 0.822561099679927, 0.0), # 64
(10.374193163142438, 9.015853623685413, 8.35676726108825, 9.023413387345679, 7.499737381066645, 3.6458333333333335, 3.7169067215363514, 3.1108539094650207, 3.9264531893004113, 1.7635130887059902, 1.357916874506381, 0.7739578570339887, 0.0, 10.125, 8.513536427373873, 6.7895843725319045, 5.290539266117969, 7.852906378600823, 4.355195473251029, 3.7169067215363514, 2.604166666666667, 3.7498686905333223, 3.0078044624485605, 1.67135345221765, 0.819623056698674, 0.0), # 65
(10.374709240296196, 8.984063271604938, 8.34733024691358, 9.014057291666667, 7.499905411296382, 3.6458333333333335, 3.7049580610021784, 3.094930555555556, 3.9225250000000003, 1.7579808641975312, 1.3564262065095398, 0.7726995884773664, 0.0, 10.125, 8.499695473251029, 6.782131032547699, 5.273942592592592, 7.8450500000000005, 4.332902777777778, 3.7049580610021784, 2.604166666666667, 3.749952705648191, 3.0046857638888897, 1.6694660493827165, 0.8167330246913582, 0.0), # 66
(10.374967660206792, 8.952861396890716, 8.337975537265661, 9.004687307098765, 7.499989485045419, 3.6458333333333335, 3.693253409182603, 3.0799948559670787, 3.9186140946502057, 1.7526157178783728, 1.3549228187222018, 0.7714522938576437, 0.0, 10.125, 8.485975232434079, 6.774614093611008, 5.257847153635117, 7.837228189300411, 4.31199279835391, 3.693253409182603, 2.604166666666667, 3.7499947425227096, 3.001562435699589, 1.6675951074531323, 0.8138964906264289, 0.0), # 67
(10.374791614480825, 8.922144586043629, 8.328671624942844, 8.995231305354269, 7.499918636864896, 3.645765673423767, 3.681757597414823, 3.0659766041761927, 3.9146959495503735, 1.747405110411792, 1.3533809980900628, 0.770210835158312, 0.0, 10.124875150034294, 8.47231918674143, 6.766904990450313, 5.242215331235375, 7.829391899100747, 4.29236724584667, 3.681757597414823, 2.604118338159833, 3.749959318432448, 2.99841043511809, 1.6657343249885688, 0.8111040532766937, 0.0), # 68
(10.373141706924315, 8.890975059737157, 8.319157021604937, 8.985212635869564, 7.499273783587508, 3.6452307956104257, 3.6701340906733066, 3.052124485596708, 3.910599279835391, 1.7422015976761076, 1.3516438064859118, 0.7689349144466104, 0.0, 10.12388599537037, 8.458284058912714, 6.758219032429559, 5.226604793028321, 7.821198559670782, 4.272974279835391, 3.6701340906733066, 2.6037362825788755, 3.749636891793754, 2.9950708786231885, 1.6638314043209876, 0.8082704599761052, 0.0), # 69
(10.369885787558895, 8.859209754856408, 8.309390360653863, 8.974565343196456, 7.497999542752628, 3.6441773992785653, 3.658330067280685, 3.0383135192805977, 3.9063009640298736, 1.736979881115684, 1.3496914810876801, 0.7676185634410675, 0.0, 10.121932334533609, 8.44380419785174, 6.7484574054383994, 5.210939643347051, 7.812601928059747, 4.253638926992837, 3.658330067280685, 2.6029838566275467, 3.748999771376314, 2.991521781065486, 1.6618780721307727, 0.8053827049869463, 0.0), # 70
(10.365069660642929, 8.826867654542236, 8.299375071444901, 8.963305127818035, 7.496112052502757, 3.6426225549966977, 3.646350829769494, 3.0245482777015704, 3.9018074035970125, 1.7317400898356603, 1.347531228463977, 0.7662627447677263, 0.0, 10.119039887688615, 8.428890192444989, 6.737656142319885, 5.195220269506979, 7.803614807194025, 4.234367588782199, 3.646350829769494, 2.6018732535690696, 3.7480560262513785, 2.987768375939346, 1.6598750142889804, 0.8024425140492942, 0.0), # 71
(10.358739130434783, 8.793967741935482, 8.289114583333333, 8.95144769021739, 7.493627450980392, 3.6405833333333337, 3.634201680672269, 3.0108333333333333, 3.897125, 1.7264823529411768, 1.3451702551834133, 0.7648684210526316, 0.0, 10.115234375, 8.413552631578947, 6.7258512759170666, 5.179447058823529, 7.79425, 4.215166666666667, 3.634201680672269, 2.600416666666667, 3.746813725490196, 2.983815896739131, 1.6578229166666667, 0.7994516129032258, 0.0), # 72
(10.35094000119282, 8.760529000176998, 8.27861232567444, 8.939008730877617, 7.490561876328034, 3.638076804856983, 3.621887922521546, 2.9971732586495965, 3.8922601547020275, 1.7212067995373737, 1.3426157678145982, 0.7634365549218266, 0.0, 10.110541516632374, 8.397802104140093, 6.71307883907299, 5.163620398612119, 7.784520309404055, 4.196042562109435, 3.621887922521546, 2.598626289183559, 3.745280938164017, 2.979669576959206, 1.655722465134888, 0.7964117272888181, 0.0), # 73
(10.341718077175404, 8.726570412407629, 8.267871727823502, 8.926003950281803, 7.486931466688183, 3.6351200401361585, 3.609414857849861, 2.9835726261240665, 3.8872192691662857, 1.7159135587293908, 1.3398749729261428, 0.7619681090013557, 0.0, 10.104987032750344, 8.38164919901491, 6.699374864630713, 5.147740676188171, 7.774438538332571, 4.177001676573693, 3.609414857849861, 2.5965143143829703, 3.7434657333440917, 2.975334650093935, 1.6535743455647005, 0.7933245829461482, 0.0), # 74
(10.331119162640901, 8.692110961768218, 8.256896219135802, 8.912449048913043, 7.482752360203341, 3.6317301097393697, 3.59678778918975, 2.9700360082304527, 3.8820087448559666, 1.7106027596223679, 1.336955077086656, 0.7604640459172624, 0.0, 10.098596643518519, 8.365104505089885, 6.684775385433279, 5.131808278867102, 7.764017489711933, 4.158050411522634, 3.59678778918975, 2.594092935528121, 3.7413761801016703, 2.9708163496376816, 1.6513792438271604, 0.7901919056152927, 0.0), # 75
(10.319189061847677, 8.65716963139962, 8.245689228966622, 8.898359727254428, 7.478040695016003, 3.6279240842351275, 3.5840120190737474, 2.956567977442463, 3.876634983234263, 1.7052745313214452, 1.3338632868647486, 0.7589253282955902, 0.0, 10.091396069101508, 8.348178611251491, 6.669316434323743, 5.115823593964334, 7.753269966468526, 4.139195168419449, 3.5840120190737474, 2.5913743458822336, 3.7390203475080015, 2.96611990908481, 1.6491378457933243, 0.7870154210363293, 0.0), # 76
(10.305973579054093, 8.621765404442675, 8.234254186671238, 8.883751685789049, 7.472812609268672, 3.6237190341919425, 3.5710928500343897, 2.9431731062338065, 3.871104385764365, 1.699929002931763, 1.3306068088290313, 0.7573529187623839, 0.0, 10.083411029663925, 8.330882106386222, 6.653034044145156, 5.099787008795288, 7.74220877152873, 4.120442348727329, 3.5710928500343897, 2.58837073870853, 3.736406304634336, 2.9612505619296834, 1.6468508373342476, 0.7837968549493343, 0.0), # 77
(10.291518518518519, 8.585917264038233, 8.222594521604938, 8.868640625, 7.467084241103849, 3.6191320301783265, 3.5580355846042124, 2.9298559670781894, 3.8654233539094642, 1.6945663035584608, 1.327192849548113, 0.7557477799436866, 0.0, 10.074667245370371, 8.313225579380552, 6.635964247740564, 5.083698910675381, 7.7308467078189285, 4.101798353909466, 3.5580355846042124, 2.585094307270233, 3.7335421205519244, 2.956213541666667, 1.6445189043209878, 0.7805379330943849, 0.0), # 78
(10.275869684499314, 8.549644193327138, 8.210713663123, 8.85304224537037, 7.460871728664031, 3.61418014276279, 3.5448455253157505, 2.916621132449322, 3.859598289132754, 1.6891865623066789, 1.3236286155906039, 0.7541108744655421, 0.0, 10.065190436385459, 8.295219619120962, 6.618143077953018, 5.067559686920035, 7.719196578265508, 4.083269585429051, 3.5448455253157505, 2.5815572448305644, 3.7304358643320157, 2.951014081790124, 1.6421427326246, 0.7772403812115581, 0.0), # 79
(10.259072881254847, 8.51296517545024, 8.198615040580703, 8.836972247383253, 7.454191210091719, 3.6088804425138448, 3.5315279747015405, 2.9034731748209115, 3.853635592897424, 1.683789908281557, 1.3199213135251149, 0.7524431649539947, 0.0, 10.0550063228738, 8.27687481449394, 6.599606567625574, 5.05136972484467, 7.707271185794848, 4.064862444749276, 3.5315279747015405, 2.577771744652746, 3.7270956050458595, 2.945657415794418, 1.639723008116141, 0.7739059250409311, 0.0), # 80
(10.241173913043479, 8.475899193548386, 8.186302083333333, 8.82044633152174, 7.447058823529411, 3.60325, 3.5180882352941176, 2.890416666666667, 3.8475416666666664, 1.6783764705882358, 1.3160781499202554, 0.7507456140350878, 0.0, 10.044140624999999, 8.258201754385965, 6.580390749601277, 5.035129411764706, 7.695083333333333, 4.046583333333333, 3.5180882352941176, 2.57375, 3.7235294117647055, 2.940148777173914, 1.6372604166666667, 0.7705362903225808, 0.0), # 81
(10.222218584123576, 8.438465230762423, 8.17377822073617, 8.803480198268922, 7.43949070711961, 3.5973058857897686, 3.504531609626018, 2.8774561804602956, 3.841322911903673, 1.6729463783318543, 1.3121063313446355, 0.7490191843348656, 0.0, 10.03261906292867, 8.23921102768352, 6.560531656723177, 5.018839134995561, 7.682645823807346, 4.0284386526444145, 3.504531609626018, 2.5695042041355487, 3.719745353559805, 2.934493399422974, 1.634755644147234, 0.767133202796584, 0.0), # 82
(10.202252698753504, 8.400682270233196, 8.16104688214449, 8.78608954810789, 7.431502999004814, 3.591065170451659, 3.4908634002297765, 2.8645962886755068, 3.8349857300716352, 1.6674997606175532, 1.3080130643668657, 0.7472648384793719, 0.0, 10.020467356824417, 8.219913223273089, 6.540065321834328, 5.002499281852659, 7.6699714601432705, 4.01043480414571, 3.4908634002297765, 2.5650465503226134, 3.715751499502407, 2.9286965160359637, 1.632209376428898, 0.7636983882030178, 0.0), # 83
(10.181322061191626, 8.362569295101553, 8.14811149691358, 8.768290081521739, 7.423111837327523, 3.584544924554184, 3.477088909637929, 2.851841563786008, 3.8285365226337444, 1.6620367465504726, 1.3038055555555557, 0.7454835390946503, 0.0, 10.007711226851852, 8.200318930041153, 6.519027777777778, 4.986110239651417, 7.657073045267489, 3.9925781893004113, 3.477088909637929, 2.5603892318244172, 3.7115559186637617, 2.922763360507247, 1.629622299382716, 0.7602335722819594, 0.0), # 84
(10.159472475696308, 8.32414528850834, 8.13497549439872, 8.75009749899356, 7.414333360230238, 3.577762218665854, 3.463213440383012, 2.8391965782655086, 3.8219816910531925, 1.6565574652357518, 1.2994910114793157, 0.7436762488067449, 0.0, 9.994376393175584, 8.180438736874192, 6.497455057396579, 4.969672395707254, 7.643963382106385, 3.9748752095717124, 3.463213440383012, 2.5555444419041815, 3.707166680115119, 2.916699166331187, 1.626995098879744, 0.7567404807734855, 0.0), # 85
(10.136749746525913, 8.285429233594407, 8.121642303955191, 8.731527501006443, 7.405183705855455, 3.57073412335518, 3.44924229499756, 2.826665904587715, 3.815327636793172, 1.6510620457785314, 1.2950766387067558, 0.7418439302416996, 0.0, 9.98048857596022, 8.160283232658694, 6.475383193533778, 4.953186137335593, 7.630655273586344, 3.9573322664228017, 3.44924229499756, 2.550524373825129, 3.7025918529277275, 2.910509167002148, 1.6243284607910382, 0.7532208394176735, 0.0), # 86
(10.113199677938807, 8.246440113500597, 8.10811535493827, 8.712595788043478, 7.3956790123456795, 3.563477709190672, 3.4351807760141093, 2.8142541152263374, 3.8085807613168727, 1.645550617283951, 1.290569643806486, 0.7399875460255577, 0.0, 9.96607349537037, 8.139863006281134, 6.452848219032429, 4.936651851851852, 7.6171615226337455, 3.9399557613168725, 3.4351807760141093, 2.54534122085048, 3.6978395061728397, 2.904198596014493, 1.6216230709876542, 0.7496763739545999, 0.0), # 87
(10.088868074193357, 8.207196911367758, 8.094398076703246, 8.693318060587762, 7.385835417843406, 3.5560100467408424, 3.4210341859651954, 2.801965782655083, 3.8017474660874866, 1.6400233088571508, 1.2859772333471164, 0.7381080587843638, 0.0, 9.951156871570646, 8.119188646628, 6.429886166735582, 4.9200699265714505, 7.603494932174973, 3.9227520957171165, 3.4210341859651954, 2.540007176243459, 3.692917708921703, 2.897772686862588, 1.6188796153406495, 0.7461088101243417, 0.0), # 88
(10.063800739547922, 8.16771861033674, 8.080493898605397, 8.673710019122383, 7.375669060491138, 3.5483482065742016, 3.406807827383354, 2.7898054793476605, 3.794834152568206, 1.634480249603271, 1.2813066138972575, 0.7362064311441613, 0.0, 9.935764424725651, 8.098270742585774, 6.4065330694862865, 4.903440748809812, 7.589668305136412, 3.905727671086725, 3.406807827383354, 2.534534433267287, 3.687834530245569, 2.891236673040795, 1.6160987797210793, 0.7425198736669765, 0.0), # 89
(10.03804347826087, 8.128024193548386, 8.06640625, 8.653787364130435, 7.365196078431373, 3.5405092592592595, 3.3925070028011204, 2.7777777777777777, 3.7878472222222226, 1.6289215686274514, 1.2765649920255184, 0.7342836257309943, 0.0, 9.919921875, 8.077119883040936, 6.382824960127592, 4.886764705882353, 7.575694444444445, 3.888888888888889, 3.3925070028011204, 2.5289351851851856, 3.6825980392156863, 2.884595788043479, 1.6132812500000002, 0.7389112903225807, 0.0), # 90
(10.011642094590563, 8.088132644143545, 8.05213856024234, 8.63356579609501, 7.35443260980661, 3.532510275364528, 3.378137014751031, 2.7658872504191434, 3.780793076512727, 1.6233473950348318, 1.2717595743005101, 0.7323406051709063, 0.0, 9.903654942558298, 8.055746656879968, 6.35879787150255, 4.870042185104494, 7.561586153025454, 3.872242150586801, 3.378137014751031, 2.5232216252603767, 3.677216304903305, 2.8778552653650036, 1.6104277120484682, 0.7352847858312315, 0.0), # 91
(9.984642392795372, 8.048062945263066, 8.0376942586877, 8.613061015499195, 7.343394792759352, 3.524368325458518, 3.363703165765621, 2.754138469745466, 3.773678116902911, 1.6177578579305527, 1.2668975672908422, 0.7303783320899415, 0.0, 9.886989347565157, 8.034161652989356, 6.334487836454211, 4.853273573791657, 7.547356233805822, 3.8557938576436523, 3.363703165765621, 2.517405946756084, 3.671697396379676, 2.871020338499732, 1.6075388517375402, 0.7316420859330061, 0.0), # 92
(9.957090177133654, 8.00783408004779, 8.023076774691358, 8.592288722826089, 7.332098765432098, 3.5161004801097393, 3.349210758377425, 2.742536008230453, 3.766508744855967, 1.6121530864197533, 1.261986177565125, 0.7283977691141434, 0.0, 9.869950810185184, 8.012375460255576, 6.309930887825625, 4.836459259259259, 7.533017489711934, 3.839550411522634, 3.349210758377425, 2.5115003429355283, 3.666049382716049, 2.86409624094203, 1.6046153549382718, 0.727984916367981, 0.0), # 93
(9.92903125186378, 7.967465031638567, 8.008289537608597, 8.571264618558777, 7.320560665967347, 3.5077238098867043, 3.3346650951189805, 2.7310844383478132, 3.759291361835086, 1.6065332096075746, 1.2570326116919686, 0.7263998788695563, 0.0, 9.85256505058299, 7.990398667565118, 6.285163058459842, 4.819599628822722, 7.518582723670172, 3.823518213686939, 3.3346650951189805, 2.5055170070619317, 3.6602803329836733, 2.8570882061862592, 1.6016579075217197, 0.7243150028762335, 0.0), # 94
(9.90051142124411, 7.926974783176247, 7.993335976794697, 8.550004403180354, 7.308796632507598, 3.499255385357923, 3.320071478522822, 2.719788332571255, 3.7520323693034596, 1.6008983565991557, 1.2520440762399827, 0.7243856239822234, 0.0, 9.834857788923182, 7.968241863804456, 6.260220381199914, 4.8026950697974655, 7.504064738606919, 3.8077036655997567, 3.320071478522822, 2.4994681323985164, 3.654398316253799, 2.850001467726785, 1.5986671953589393, 0.7206340711978407, 0.0), # 95
(9.871576489533012, 7.886382317801674, 7.978219521604939, 8.528523777173913, 7.296822803195352, 3.4907122770919066, 3.3054352111214853, 2.708652263374486, 3.7447381687242793, 1.5952486564996373, 1.247027777777778, 0.7223559670781895, 0.0, 9.816854745370371, 7.945915637860083, 6.23513888888889, 4.785745969498911, 7.489476337448559, 3.7921131687242804, 3.3054352111214853, 2.4933659122085046, 3.648411401597676, 2.8428412590579715, 1.595643904320988, 0.7169438470728796, 0.0), # 96
(9.842272260988848, 7.845706618655694, 7.962943601394604, 8.506838441022543, 7.284655316173109, 3.482111555657166, 3.2907615954475067, 2.697680803231215, 3.7374151615607376, 1.589584238414159, 1.2419909228739638, 0.7203118707834976, 0.0, 9.798581640089164, 7.923430578618472, 6.209954614369819, 4.768752715242476, 7.474830323121475, 3.7767531245237014, 3.2907615954475067, 2.4872225397551184, 3.6423276580865545, 2.8356128136741816, 1.5925887202789208, 0.7132460562414268, 0.0), # 97
(9.812644539869984, 7.804966668879153, 7.947511645518976, 8.48496409520934, 7.272310309583368, 3.4734702916222124, 3.276055934033421, 2.68687852461515, 3.7300697492760246, 1.5839052314478608, 1.236940718097151, 0.7182542977241916, 0.0, 9.78006419324417, 7.900797274966106, 6.184703590485755, 4.751715694343581, 7.460139498552049, 3.7616299344612103, 3.276055934033421, 2.48105020830158, 3.636155154791684, 2.8283213650697805, 1.589502329103795, 0.7095424244435595, 0.0), # 98
(9.782739130434782, 7.764181451612902, 7.931927083333334, 8.462916440217391, 7.259803921568627, 3.464805555555556, 3.261323529411765, 2.67625, 3.7227083333333333, 1.5782117647058826, 1.2318843700159492, 0.7161842105263159, 0.0, 9.761328125, 7.878026315789473, 6.159421850079745, 4.734635294117647, 7.445416666666667, 3.7467500000000005, 3.261323529411765, 2.474861111111111, 3.6299019607843137, 2.820972146739131, 1.5863854166666669, 0.7058346774193549, 0.0), # 99
(9.752601836941611, 7.723369949997786, 7.916193344192958, 8.44071117652979, 7.247152290271389, 3.4561344180257074, 3.2465696841150726, 2.665799801859473, 3.715337315195854, 1.572503967293365, 1.2268290851989685, 0.714102571815914, 0.0, 9.742399155521262, 7.8551282899750525, 6.134145425994841, 4.717511901880093, 7.430674630391708, 3.732119722603262, 3.2465696841150726, 2.468667441446934, 3.6235761451356945, 2.8135703921765973, 1.5832386688385918, 0.7021245409088898, 0.0), # 100
(9.722278463648834, 7.682551147174654, 7.900313857453133, 8.41836400462963, 7.234371553834153, 3.4474739496011786, 3.231799700675881, 2.6555325026672763, 3.7079630963267793, 1.5667819683154474, 1.2217820702148188, 0.7120103442190294, 0.0, 9.723303004972564, 7.832113786409323, 6.108910351074094, 4.7003459049463405, 7.415926192653559, 3.7177455037341867, 3.231799700675881, 2.4624813925722706, 3.6171857769170765, 2.806121334876544, 1.5800627714906266, 0.6984137406522414, 0.0), # 101
(9.691814814814816, 7.641744026284349, 7.884292052469135, 8.395890625, 7.221477850399419, 3.4388412208504806, 3.217018881626725, 2.645452674897119, 3.7005920781893, 1.56104589687727, 1.2167505316321108, 0.7099084903617069, 0.0, 9.704065393518519, 7.808993393978774, 6.083752658160553, 4.683137690631809, 7.4011841563786, 3.703633744855967, 3.217018881626725, 2.4563151577503435, 3.6107389251997093, 2.798630208333334, 1.5768584104938272, 0.6947040023894864, 0.0), # 102
(9.661256694697919, 7.60096757046772, 7.8681313585962505, 8.373306738123993, 7.208487318109686, 3.430253302342123, 3.20223252950014, 2.63556489102271, 3.6932306622466085, 1.5552958820839726, 1.211741676019454, 0.7077979728699895, 0.0, 9.68471204132373, 7.785777701569883, 6.058708380097269, 4.6658876462519165, 7.386461324493217, 3.689790847431794, 3.20223252950014, 2.4501809302443736, 3.604243659054843, 2.7911022460413317, 1.5736262717192502, 0.6909970518607019, 0.0), # 103
(9.63064990755651, 7.560240762865614, 7.851835205189758, 8.350628044484703, 7.195416095107452, 3.421727264644617, 3.187445946828663, 2.6258737235177567, 3.685885249961896, 1.5495320530406955, 1.2067627099454585, 0.7056797543699213, 0.0, 9.665268668552812, 7.762477298069133, 6.033813549727292, 4.648596159122086, 7.371770499923792, 3.6762232129248593, 3.187445946828663, 2.4440909033175835, 3.597708047553726, 2.783542681494901, 1.5703670410379515, 0.687294614805965, 0.0), # 104
(9.600040257648953, 7.519582586618876, 7.835407021604938, 8.327870244565217, 7.182280319535221, 3.4132801783264752, 3.172664436144829, 2.6163837448559675, 3.6785622427983538, 1.5437545388525786, 1.201820839978735, 0.7035547974875461, 0.0, 9.64576099537037, 7.739102772363006, 6.009104199893674, 4.631263616557734, 7.3571244855967075, 3.662937242798354, 3.172664436144829, 2.4380572702331964, 3.5911401597676105, 2.775956748188406, 1.5670814043209877, 0.6835984169653525, 0.0), # 105
(9.569473549233614, 7.479012024868357, 7.818850237197074, 8.305049038848631, 7.1690961295354905, 3.404929113956206, 3.1578932999811724, 2.6070995275110502, 3.6712680422191735, 1.5379634686247616, 1.1969232726878927, 0.701424064848908, 0.0, 9.626214741941014, 7.715664713337986, 5.9846163634394625, 4.613890405874283, 7.342536084438347, 3.6499393385154706, 3.1578932999811724, 2.4320922242544327, 3.5845480647677452, 2.768349679616211, 1.5637700474394147, 0.6799101840789417, 0.0), # 106
(9.538995586568856, 7.438548060754901, 7.802168281321446, 8.282180127818036, 7.155879663250759, 3.3966911421023225, 3.1431378408702306, 2.5980256439567144, 3.6640090496875475, 1.532158971462385, 1.1920772146415421, 0.6992885190800504, 0.0, 9.606655628429355, 7.692173709880553, 5.96038607320771, 4.596476914387154, 7.328018099375095, 3.6372359015394005, 3.1431378408702306, 2.426207958644516, 3.5779398316253794, 2.760726709272679, 1.5604336562642893, 0.6762316418868093, 0.0), # 107
(9.508652173913044, 7.398209677419356, 7.785364583333334, 8.259279211956523, 7.1426470588235285, 3.3885833333333335, 3.1284033613445374, 2.589166666666667, 3.656791666666667, 1.5263411764705888, 1.1872898724082936, 0.6971491228070177, 0.0, 9.587109375, 7.668640350877193, 5.936449362041468, 4.579023529411765, 7.313583333333334, 3.624833333333334, 3.1284033613445374, 2.4204166666666667, 3.5713235294117642, 2.7530930706521746, 1.557072916666667, 0.6725645161290325, 0.0), # 108
(9.478489115524543, 7.358015858002567, 7.768442572588021, 8.23636199174718, 7.129414454396299, 3.3806227582177515, 3.113695163936631, 2.580527168114617, 3.6496222946197223, 1.5205102127545123, 1.1825684525567568, 0.6950068386558532, 0.0, 9.567601701817559, 7.645075225214384, 5.9128422627837836, 4.561530638263536, 7.299244589239445, 3.612738035360464, 3.113695163936631, 2.4147305415841083, 3.5647072271981495, 2.7454539972490606, 1.5536885145176043, 0.668910532545688, 0.0), # 109
(9.448552215661715, 7.317985585645383, 7.751405678440788, 8.213444167673108, 7.116197988111569, 3.3728264873240867, 3.0990185511790447, 2.5721117207742723, 3.6425073350099066, 1.5146662094192962, 1.177920161655542, 0.6928626292526012, 0.0, 9.54815832904664, 7.621488921778612, 5.8896008082777085, 4.543998628257887, 7.285014670019813, 3.600956409083981, 3.0990185511790447, 2.409161776660062, 3.5580989940557846, 2.737814722557703, 1.5502811356881578, 0.6652714168768531, 0.0), # 110
(9.41888727858293, 7.278137843488651, 7.7342573302469155, 8.190541440217391, 7.103013798111837, 3.365211591220851, 3.0843788256043156, 2.5639248971193416, 3.635453189300412, 1.5088092955700803, 1.173352206273259, 0.6907174572233054, 0.0, 9.528804976851852, 7.597892029456357, 5.866761031366295, 4.526427886710239, 7.270906378600824, 3.5894948559670783, 3.0843788256043156, 2.4037225651577505, 3.5515068990559184, 2.7301804800724643, 1.546851466049383, 0.6616488948626047, 0.0), # 111
(9.38954010854655, 7.238491614673214, 7.717000957361684, 8.167669509863124, 7.089878022539605, 3.357795140476554, 3.069781289744979, 2.5559712696235333, 3.628466258954427, 1.5029396003120044, 1.1688717929785184, 0.6885722851940093, 0.0, 9.509567365397805, 7.574295137134101, 5.844358964892591, 4.5088188009360115, 7.256932517908854, 3.5783597774729463, 3.069781289744979, 2.3984251003403956, 3.5449390112698027, 2.7225565032877084, 1.543400191472337, 0.6580446922430195, 0.0), # 112
(9.360504223703044, 7.1991320672204555, 7.699681523543391, 8.14487541186903, 7.076783786782469, 3.3505906987084666, 3.0552629818283847, 2.548271903658586, 3.6215709370862066, 1.4970761841531826, 1.1644873176921446, 0.6864327447087024, 0.0, 9.490443900843221, 7.550760191795725, 5.8224365884607225, 4.491228552459547, 7.243141874172413, 3.5675806651220205, 3.0552629818283847, 2.3932790705060474, 3.5383918933912346, 2.7149584706230105, 1.5399363047086783, 0.654466551565496, 0.0), # 113
(9.331480897900065, 7.16044741823174, 7.682538062518016, 8.122342065958001, 7.063595569710884, 3.343581854975776, 3.0410091042052896, 2.5409213581271333, 3.6148730119043533, 1.491328791978196, 1.1602073895188663, 0.684326014342748, 0.0, 9.471275414160035, 7.5275861577702265, 5.801036947594331, 4.473986375934587, 7.229746023808707, 3.557289901377987, 3.0410091042052896, 2.3882727535541255, 3.531797784855442, 2.7074473553193346, 1.5365076125036032, 0.6509497652937947, 0.0), # 114
(9.302384903003995, 7.122451598792792, 7.665580777256098, 8.100063378886334, 7.050271785259067, 3.3367503822909463, 3.027029825095781, 2.533917772616129, 3.6083749928895963, 1.4857063319970194, 1.1560257519045158, 0.6822531318799043, 0.0, 9.452006631660376, 7.5047844506789465, 5.7801287595225785, 4.457118995991058, 7.216749985779193, 3.5474848816625806, 3.027029825095781, 2.3833931302078186, 3.5251358926295335, 2.700021126295445, 1.5331161554512198, 0.647495599890254, 0.0), # 115
(9.273179873237634, 7.0850892578507265, 7.648776824986561, 8.077999612699802, 7.036792350922519, 3.330080178417474, 3.0133024087639466, 2.5272417970412473, 3.6020604464092765, 1.480198339612387, 1.1519343218785802, 0.6802102664572789, 0.0, 9.43260725975589, 7.482312931030067, 5.7596716093929015, 4.44059501883716, 7.204120892818553, 3.5381385158577463, 3.0133024087639466, 2.3786286988696244, 3.5183961754612594, 2.6926665375666015, 1.5297553649973124, 0.6440990234409752, 0.0), # 116
(9.243829442823772, 7.04830504435266, 7.632093362938321, 8.056111029444182, 7.02313718419674, 3.323555141118853, 2.9998041194738763, 2.5208740813181603, 3.5959129388307343, 1.4747943502270324, 1.1479250164705472, 0.6781935872119792, 0.0, 9.413047004858225, 7.46012945933177, 5.739625082352736, 4.424383050681096, 7.1918258776614685, 3.5292237138454245, 2.9998041194738763, 2.3739679579420376, 3.51156859209837, 2.6853703431480613, 1.5264186725876645, 0.6407550040320601, 0.0), # 117
(9.214297245985211, 7.0120436072457135, 7.615497548340306, 8.03435789116525, 7.009286202577227, 3.317159168158581, 2.9865122214896576, 2.51479527536254, 3.5899160365213114, 1.46948389924369, 1.143989752709904, 0.6761992632811126, 0.0, 9.393295573379024, 7.438191896092237, 5.71994876354952, 4.40845169773107, 7.179832073042623, 3.5207133855075567, 2.9865122214896576, 2.369399405827558, 3.5046431012886137, 2.678119297055084, 1.5230995096680613, 0.6374585097496104, 0.0), # 118
(9.184546916944742, 6.976249595477001, 7.598956538421437, 8.012700459908778, 6.99521932355948, 3.3108761573001524, 2.973403979075378, 2.5089860290900607, 3.5840533058483475, 1.4642565220650932, 1.1401204476261382, 0.6742234638017862, 0.0, 9.373322671729932, 7.416458101819647, 5.70060223813069, 4.392769566195279, 7.168106611696695, 3.5125804407260848, 2.973403979075378, 2.3649115409286803, 3.49760966177974, 2.670900153302927, 1.5197913076842873, 0.6342045086797276, 0.0), # 119
(9.154542089925162, 6.940867657993644, 7.582437490410635, 7.991098997720545, 6.980916464638998, 3.304690006307063, 2.9604566564951265, 2.5034269924163928, 3.578308313179186, 1.4591017540939766, 1.136309018248736, 0.6722623579111081, 0.0, 9.353098006322597, 7.394885937022188, 5.68154509124368, 4.377305262281929, 7.156616626358372, 3.50479778938295, 2.9604566564951265, 2.360492861647902, 3.490458232319499, 2.663699665906849, 1.516487498082127, 0.6309879689085133, 0.0), # 120
(9.124246399149268, 6.90584244374276, 7.565907561536823, 7.969513766646325, 6.966357543311279, 3.29858461294281, 2.94764751801299, 2.4980988152572112, 3.572664624881166, 1.4540091307330743, 1.1325473816071863, 0.6703121147461852, 0.0, 9.33259128356866, 7.373433262208036, 5.662736908035931, 4.362027392199222, 7.145329249762332, 3.497338341360096, 2.94764751801299, 2.356131866387721, 3.4831787716556395, 2.656504588882109, 1.5131815123073646, 0.6278038585220692, 0.0), # 121
(9.093623478839854, 6.871118601671464, 7.549333909028926, 7.947905028731892, 6.951522477071823, 3.292543874970886, 2.9349538278930587, 2.492982147528187, 3.5671058073216297, 1.4489681873851195, 1.1288274547309753, 0.6683689034441251, 0.0, 9.31177220987977, 7.352057937885375, 5.644137273654876, 4.346904562155357, 7.1342116146432595, 3.490175006539462, 2.9349538278930587, 2.351817053550633, 3.4757612385359113, 2.6493016762439643, 1.5098667818057854, 0.6246471456064968, 0.0), # 122
(9.062636963219719, 6.836640780726876, 7.532683690115864, 7.92623304602302, 6.936391183416127, 3.28655169015479, 2.9223528503994194, 2.4880576391449933, 3.5616154268679177, 1.443968459452847, 1.1251411546495909, 0.6664288931420351, 0.0, 9.290610491667572, 7.330717824562385, 5.625705773247954, 4.33190537835854, 7.123230853735835, 3.4832806948029904, 2.9223528503994194, 2.3475369215391355, 3.4681955917080636, 2.642077682007674, 1.5065367380231727, 0.621512798247898, 0.0), # 123
(9.031250486511654, 6.802353629856113, 7.515924062026559, 7.90445808056549, 6.920943579839691, 3.2805919562580144, 2.9098218497961597, 2.483305940023303, 3.5561770498873715, 1.4389994823389904, 1.1214803983925201, 0.664488252977023, 0.0, 9.269075835343711, 7.309370782747252, 5.6074019919625995, 4.316998447016971, 7.112354099774743, 3.476628316032624, 2.9098218497961597, 2.3432799687557244, 3.4604717899198456, 2.634819360188497, 1.5031848124053118, 0.618395784532374, 0.0), # 124
(8.999427682938459, 6.768201798006293, 7.499022181989936, 7.88254039440507, 6.905159583838015, 3.274648571044058, 2.8973380903473696, 2.478707700078788, 3.5507742427473308, 1.4340507914462837, 1.1178371029892504, 0.6625431520861957, 0.0, 9.247137947319828, 7.2879746729481525, 5.5891855149462515, 4.30215237433885, 7.1015484854946616, 3.470190780110303, 2.8973380903473696, 2.3390346936028985, 3.4525797919190073, 2.6275134648016905, 1.4998044363979874, 0.6152910725460268, 0.0), # 125
(8.967132186722928, 6.734129934124536, 7.481945207234916, 7.8604402495875405, 6.889019112906595, 3.2687054322764144, 2.884878836317135, 2.474243569227122, 3.545390571815139, 1.4291119221774609, 1.1142031854692689, 0.6605897596066612, 0.0, 9.224766534007578, 7.266487355673273, 5.571015927346345, 4.287335766532382, 7.090781143630278, 3.463940996917971, 2.884878836317135, 2.334789594483153, 3.4445095564532977, 2.620146749862514, 1.4963890414469831, 0.6121936303749579, 0.0), # 126
(8.93432763208786, 6.7000826871579555, 7.464660294990421, 7.838117908158674, 6.8725020845409315, 3.26274643771858, 2.872421351969547, 2.469894197383977, 3.5400096034581354, 1.4241724099352562, 1.1105705628620632, 0.6586242446755264, 0.0, 9.201931301818599, 7.244866691430789, 5.552852814310316, 4.272517229805768, 7.080019206916271, 3.457851876337568, 2.872421351969547, 2.3305331697989855, 3.4362510422704657, 2.612705969386225, 1.4929320589980841, 0.6090984261052688, 0.0), # 127
(8.900977653256046, 6.666004706053673, 7.447134602485375, 7.815533632164248, 6.855588416236526, 3.2567554851340508, 2.859942901568691, 2.465640234465026, 3.534614904043661, 1.4192217901224033, 1.1069311521971208, 0.6566427764298991, 0.0, 9.178601957164537, 7.223070540728888, 5.534655760985604, 4.257665370367209, 7.069229808087322, 3.4518963282510366, 2.859942901568691, 2.3262539179528936, 3.427794208118263, 2.6051778773880834, 1.4894269204970751, 0.6060004278230613, 0.0), # 128
(8.867045884450281, 6.631840639758805, 7.4293352869486995, 7.792647683650037, 6.838258025488874, 3.250716472286322, 2.8474207493786565, 2.4614623303859418, 3.529190039939058, 1.4142495981416365, 1.1032768705039286, 0.6546415240068865, 0.0, 9.154748206457038, 7.20105676407575, 5.516384352519642, 4.242748794424909, 7.058380079878116, 3.4460472625403185, 2.8474207493786565, 2.321940337347373, 3.419129012744437, 2.597549227883346, 1.4858670573897401, 0.6028946036144368, 0.0), # 129
(8.832495959893366, 6.5975351372204685, 7.411229505609316, 7.769420324661814, 6.820490829793475, 3.2446132969388883, 2.8348321596635313, 2.457341135062396, 3.5237185775116666, 1.4092453693956895, 1.0995996348119743, 0.6526166565435961, 0.0, 9.130339756107748, 7.178783221979556, 5.4979981740598705, 4.2277361081870675, 7.047437155023333, 3.4402775890873545, 2.8348321596635313, 2.3175809263849203, 3.4102454148967376, 2.589806774887272, 1.4822459011218634, 0.5997759215654973, 0.0), # 130
(8.797291513808094, 6.563032847385783, 7.392784415696151, 7.7458118172453565, 6.802266746645829, 3.238429856855247, 2.8221543966874045, 2.4532572984100627, 3.5181840831288285, 1.4041986392872965, 1.0958913621507447, 0.6505643431771354, 0.0, 9.105346312528312, 7.156207774948489, 5.479456810753724, 4.212595917861889, 7.036368166257657, 3.4345602177740875, 2.8221543966874045, 2.3131641834680337, 3.4011333733229145, 2.5819372724151193, 1.4785568831392302, 0.596639349762344, 0.0), # 131
(8.76139618041726, 6.528278419201865, 7.373967174438122, 7.72178242344644, 6.783565693541435, 3.2321500497988933, 2.8093647247143627, 2.449191470344614, 3.5125701231578845, 1.3990989432191914, 1.0921439695497275, 0.6484807530446118, 0.0, 9.079737582130376, 7.13328828349073, 5.460719847748638, 4.1972968296575734, 7.025140246315769, 3.4288680584824593, 2.8093647247143627, 2.3086786069992096, 3.3917828467707176, 2.573927474482147, 1.4747934348876244, 0.5934798562910787, 0.0), # 132
(8.724773593943663, 6.493216501615832, 7.354744939064153, 7.697292405310838, 6.764367587975791, 3.225757773533322, 2.7964404080084946, 2.445124300781722, 3.5068602639661752, 1.3939358165941083, 1.0883493740384103, 0.6463620552831327, 0.0, 9.053483271325586, 7.10998260811446, 5.44174687019205, 4.181807449782324, 7.0137205279323505, 3.4231740210944106, 2.7964404080084946, 2.3041126953809443, 3.3821837939878954, 2.5657641351036133, 1.4709489878128308, 0.590292409237803, 0.0), # 133
(8.687387388610095, 6.457791743574804, 7.33508486680317, 7.672302024884328, 6.7446523474443945, 3.2192369258220297, 2.7833587108338893, 2.44103643963706, 3.5010380719210428, 1.388698794814781, 1.0844994926462799, 0.6442044190298056, 0.0, 9.026553086525583, 7.0862486093278605, 5.422497463231399, 4.166096384444343, 7.0020761438420855, 3.417451015491884, 2.7833587108338893, 2.2994549470157355, 3.3723261737221972, 2.557434008294776, 1.4670169733606342, 0.5870719766886187, 0.0), # 134
(8.649201198639354, 6.421948794025897, 7.314954114884091, 7.646771544212684, 6.724399889442747, 3.212571404428512, 2.770096897454634, 2.4369085368263, 3.4950871133898262, 1.3833774132839443, 1.0805862424028239, 0.6420040134217377, 0.0, 8.99891673414202, 7.0620441476391145, 5.402931212014119, 4.150132239851832, 6.9901742267796525, 3.41167195155682, 2.770096897454634, 2.2946938603060802, 3.3621999447213735, 2.548923848070895, 1.4629908229768183, 0.583813526729627, 0.0), # 135
(8.610178658254235, 6.385632301916229, 7.294319840535841, 7.62066122534168, 6.703590131466344, 3.205745107116265, 2.7566322321348173, 2.4327212422651154, 3.4889909547398688, 1.3779612074043308, 1.0766015403375297, 0.6397570075960368, 0.0, 8.970543920586536, 7.037327083556404, 5.383007701687648, 4.133883622212991, 6.9779819094797375, 3.4058097391711617, 2.7566322321348173, 2.289817933654475, 3.351795065733172, 2.540220408447227, 1.4588639681071682, 0.58051202744693, 0.0), # 136
(8.570283401677534, 6.348786916192918, 7.273149200987342, 7.593931330317094, 6.682202991010689, 3.1987419316487826, 2.7429419791385277, 2.428455205869179, 3.4827331623385107, 1.3724397125786756, 1.0725373034798844, 0.63745957068981, 0.0, 8.941404352270776, 7.012055277587909, 5.362686517399421, 4.117319137736026, 6.965466324677021, 3.3998372882168506, 2.7429419791385277, 2.284815665463416, 3.3411014955053444, 2.5313104434390317, 1.4546298401974684, 0.577162446926629, 0.0), # 137
(8.529479063132047, 6.311357285803083, 7.251409353467515, 7.566542121184698, 6.660218385571278, 3.1915457757895624, 2.729003402729852, 2.4240910775541624, 3.4762973025530934, 1.3668024642097119, 1.0683854488593754, 0.6351078718401649, 0.0, 8.91146773560639, 6.986186590241813, 5.341927244296877, 4.100407392629135, 6.952594605106187, 3.3937275085758274, 2.729003402729852, 2.2796755541354017, 3.330109192785639, 2.5221807070615663, 1.450281870693503, 0.5737597532548258, 0.0), # 138
(8.487729276840568, 6.273288059693839, 7.229067455205284, 7.538453859990269, 6.63761623264361, 3.184140537302099, 2.7147937671728797, 2.4196095072357395, 3.469666941750957, 1.3610389977001744, 1.0641378935054902, 0.6326980801842089, 0.0, 8.880703777005019, 6.959678882026297, 5.32068946752745, 4.083116993100523, 6.939333883501914, 3.3874533101300353, 2.7147937671728797, 2.274386098072928, 3.318808116321805, 2.51281795333009, 1.4458134910410567, 0.5702989145176218, 0.0), # 139
(8.444997677025897, 6.234523886812306, 7.206090663429573, 7.509626808779583, 6.614376449723186, 3.176510113949888, 2.7002903367316984, 2.4149911448295818, 3.462825646299444, 1.3551388484527966, 1.0597865544477159, 0.6302263648590494, 0.0, 8.849082182878314, 6.932490013449542, 5.298932772238579, 4.0654165453583895, 6.925651292598888, 3.3809876027614147, 2.7002903367316984, 2.2689357956784915, 3.307188224861593, 2.5032089362598615, 1.4412181326859146, 0.5667748988011189, 0.0), # 140
(8.40124789791083, 6.195009416105602, 7.1824461353693, 7.480021229598415, 6.590478954305501, 3.1686384034964257, 2.6854703756703975, 2.4102166402513627, 3.455756982565893, 1.349091551870313, 1.0553233487155398, 0.6276888950017938, 0.0, 8.816572659637913, 6.904577845019731, 5.276616743577699, 4.047274655610939, 6.911513965131786, 3.3743032963519077, 2.6854703756703975, 2.26331314535459, 3.2952394771527507, 2.4933404098661387, 1.4364892270738603, 0.5631826741914184, 0.0), # 141
(8.356443573718156, 6.154689296520844, 7.158101028253392, 7.44959738449254, 6.565903663886058, 3.1605093037052074, 2.670311148253063, 2.4052666434167547, 3.448444516917647, 1.3428866433554572, 1.0507401933384497, 0.6250818397495496, 0.0, 8.783144913695466, 6.875900237245045, 5.253700966692247, 4.028659930066371, 6.896889033835294, 3.3673733007834565, 2.670311148253063, 2.2575066455037196, 3.282951831943029, 2.4831991281641805, 1.4316202056506786, 0.5595172087746222, 0.0), # 142
(8.310548338670674, 6.113508177005149, 7.133022499310772, 7.418315535507731, 6.540630495960352, 3.152106712339729, 2.6547899187437842, 2.4001218042414303, 3.4408718157220486, 1.3365136583109634, 1.0460290053459322, 0.6224013682394242, 0.0, 8.748768651462617, 6.846415050633665, 5.230145026729661, 4.009540974932889, 6.881743631444097, 3.360170525938002, 2.6547899187437842, 2.251504794528378, 3.270315247980176, 2.472771845169244, 1.4266044998621543, 0.5557734706368318, 0.0), # 143
(8.263525826991184, 6.071410706505636, 7.107177705770357, 7.386135944689768, 6.514639368023886, 3.1434145271634857, 2.6388839514066493, 2.3947627726410623, 3.4330224453464364, 1.3299621321395652, 1.0411817017674754, 0.619643649608525, 0.0, 8.713413579351014, 6.816080145693774, 5.205908508837376, 3.9898863964186946, 6.866044890692873, 3.3526678816974873, 2.6388839514066493, 2.245296090831061, 3.257319684011943, 2.4620453148965895, 1.4214355411540713, 0.5519464278641489, 0.0), # 144
(8.215339672902477, 6.0283415339694235, 7.080533804861075, 7.353018874084421, 6.487910197572155, 3.134416645939974, 2.6225705105057466, 2.3891701985313234, 3.424879972158151, 1.3232216002439972, 1.036190199632566, 0.6168048529939595, 0.0, 8.6770494037723, 6.784853382933553, 5.180950998162829, 3.969664800731991, 6.849759944316302, 3.344838277943853, 2.6225705105057466, 2.238869032814267, 3.2439550987860777, 2.451006291361474, 1.4161067609722149, 0.548031048542675, 0.0), # 145
(8.16595351062735, 5.984245308343629, 7.053057953811847, 7.318924585737469, 6.460422902100661, 3.1250969664326886, 2.605826860305165, 2.3833247318278863, 3.4164279625245353, 1.3162815980269928, 1.0310464159706916, 0.6138811475328351, 0.0, 8.639645831138118, 6.7526926228611845, 5.155232079853457, 3.948844794080978, 6.832855925049071, 3.3366546245590407, 2.605826860305165, 2.2322121188804918, 3.2302114510503306, 2.439641528579157, 1.4106115907623695, 0.5440223007585119, 0.0), # 146
(8.1153309743886, 5.93906667857537, 7.024717309851591, 7.283813341694685, 6.4321573991049, 3.1154393864051255, 2.5886302650689905, 2.3772070224464232, 3.40764998281293, 1.3091316608912866, 1.0257422678113395, 0.6108687023622593, 0.0, 8.601172567860118, 6.719555725984851, 5.1287113390566965, 3.9273949826738592, 6.81529996562586, 3.3280898314249923, 2.5886302650689905, 2.2253138474322327, 3.21607869955245, 2.4279377805648954, 1.4049434619703185, 0.5399151525977609, 0.0), # 147
(8.063435698409021, 5.892750293611764, 6.9954790302092364, 7.247645404001847, 6.403093606080374, 3.105427803620781, 2.5709579890613132, 2.3707977203026074, 3.398529599390676, 1.301761324239612, 1.0202696721839972, 0.6077636866193392, 0.0, 8.561599320349941, 6.68540055281273, 5.101348360919985, 3.905283972718835, 6.797059198781352, 3.3191168084236504, 2.5709579890613132, 2.2181627168719866, 3.201546803040187, 2.4158818013339496, 1.3990958060418472, 0.535704572146524, 0.0), # 148
(8.010231316911412, 5.845240802399927, 6.965310272113703, 7.210381034704727, 6.37321144052258, 3.0950461158431497, 2.5527872965462204, 2.3640774753121114, 3.3890503786251127, 1.2941601234747035, 1.0146205461181517, 0.6045622694411826, 0.0, 8.520895795019237, 6.650184963853008, 5.073102730590758, 3.88248037042411, 6.778100757250225, 3.3097084654369557, 2.5527872965462204, 2.21074722560225, 3.18660572026129, 2.403460344901576, 1.3930620544227408, 0.5313855274909026, 0.0), # 149
(7.955681464118564, 5.796482853886981, 6.934178192793912, 7.171980495849104, 6.342490819927017, 3.0842782208357287, 2.5340954517878003, 2.3570269373906068, 3.3791958868835836, 1.2863175939992944, 1.0087868066432906, 0.601260619964897, 0.0, 8.479031698279647, 6.6138668196138655, 5.043934033216452, 3.8589527819978824, 6.758391773767167, 3.2998377123468496, 2.5340954517878003, 2.2030558720255207, 3.1712454099635083, 2.390660165283035, 1.3868356385587826, 0.5269529867169983, 0.0), # 150
(7.899749774253275, 5.746421097020041, 6.902049949478785, 7.132404049480748, 6.310911661789184, 3.0731080163620113, 2.5148597190501416, 2.3496267564537683, 3.3689496905334293, 1.2782232712161197, 1.002760370788901, 0.5978549073275894, 0.0, 8.435976736542818, 6.576403980603482, 5.013801853944504, 3.8346698136483583, 6.737899381066859, 3.2894774590352753, 2.5148597190501416, 2.1950771545442938, 3.155455830894592, 2.377468016493583, 1.3804099898957571, 0.5224019179109128, 0.0), # 151
(7.842399881538343, 5.6950001807462245, 6.868892699397251, 7.091611957645439, 6.278453883604579, 3.0615194001854955, 2.4950573625973322, 2.3418575824172674, 3.3582953559419897, 1.2698666905279126, 0.9965331555844703, 0.5943413006663675, 0.0, 8.391700616220398, 6.537754307330042, 4.982665777922351, 3.809600071583737, 6.716590711883979, 3.2786006153841742, 2.4950573625973322, 2.1867995715610684, 3.1392269418022893, 2.36387065254848, 1.3737785398794504, 0.5177272891587478, 0.0), # 152
(7.78359542019656, 5.642164754012652, 6.834673599778224, 7.049564482388949, 6.245097402868703, 3.049496270069676, 2.4746656466934596, 2.333700065196776, 3.3472164494766075, 1.2612373873374074, 0.9900970780594861, 0.5907159691183387, 0.0, 8.346173043724027, 6.497875660301725, 4.95048539029743, 3.783712162012222, 6.694432898953215, 3.2671800912754865, 2.4746656466934596, 2.17821162147834, 3.1225487014343516, 2.3498548274629836, 1.3669347199556448, 0.5129240685466048, 0.0), # 153
(7.723300024450729, 5.587859465766439, 6.7993598078506325, 7.006221885757057, 6.210822137077053, 3.0370225237780484, 2.453661835602614, 2.325134854707968, 3.3356965375046217, 1.2523248970473384, 0.9834440552434354, 0.5869750818206104, 0.0, 8.299363725465357, 6.456725900026714, 4.917220276217177, 3.7569746911420143, 6.671393075009243, 3.2551887965911552, 2.453661835602614, 2.169301802698606, 3.1054110685385266, 2.335407295252353, 1.3598719615701265, 0.5079872241605854, 0.0), # 154
(7.6614773285236355, 5.532028964954703, 6.762918480843396, 6.961544429795533, 6.175608003725131, 3.0240820590741087, 2.4320231935888805, 2.316142600866515, 3.323719186393376, 1.2431187550604388, 0.9765660041658056, 0.5831148079102902, 0.0, 8.251242367856026, 6.414262887013191, 4.882830020829028, 3.7293562651813157, 6.647438372786752, 3.242599641213121, 2.4320231935888805, 2.160058613624363, 3.0878040018625654, 2.320514809931845, 1.3525836961686795, 0.5029117240867913, 0.0), # 155
(7.598090966638081, 5.474617900524564, 6.725316775985439, 6.915492376550157, 6.139434920308432, 3.0106587737213526, 2.40972698491635, 2.3067039535880913, 3.3112679625102084, 1.2336084967794434, 0.9694548418560842, 0.5791313165244852, 0.0, 8.201778677307685, 6.370444481769337, 4.84727420928042, 3.7008254903383295, 6.622535925020417, 3.2293855350233276, 2.40972698491635, 2.150470552658109, 3.069717460154216, 2.3051641255167192, 1.3450633551970879, 0.49769253641132405, 0.0), # 156
(7.533104573016862, 5.415570921423138, 6.686521850505682, 6.868025988066703, 6.102282804322456, 2.9967365654832747, 2.3867504738491094, 2.2967995627883675, 3.2983264322224626, 1.2237836576070855, 0.9621024853437583, 0.5750207768003032, 0.0, 8.150942360231976, 6.325228544803333, 4.810512426718791, 3.671350972821256, 6.596652864444925, 3.2155193879037145, 2.3867504738491094, 2.140526118202339, 3.051141402161228, 2.2893419960222348, 1.3373043701011365, 0.4923246292202853, 0.0), # 157
(7.464680946405239, 5.353748694041236, 6.644659961585297, 6.817327186238432, 6.062454070580665, 2.9814309445183143, 2.3625533604639286, 2.285748730145572, 3.2838873638663655, 1.213341479072786, 0.9542659587564906, 0.570633297016195, 0.0, 8.096485859415345, 6.276966267178143, 4.771329793782452, 3.640024437218358, 6.567774727732731, 3.200048222203801, 2.3625533604639286, 2.129593531798796, 3.0312270352903323, 2.2724423954128112, 1.3289319923170593, 0.48670442673102154, 0.0), # 158
(7.382286766978402, 5.282809876299521, 6.58894818200249, 6.7529828690913405, 6.010127539854418, 2.95965229467081, 2.334106381692858, 2.2696723053184926, 3.2621424204073812, 1.2005702485246865, 0.9445694892698324, 0.5651135436402591, 0.0, 8.025427646920194, 6.216248980042849, 4.722847446349162, 3.601710745574059, 6.5242848408147625, 3.17754122744589, 2.334106381692858, 2.114037353336293, 3.005063769927209, 2.250994289697114, 1.3177896364004982, 0.4802554432999565, 0.0), # 159
(7.284872094904309, 5.202172001162321, 6.51826746496324, 6.673933132806645, 5.94428008756453, 2.9308657560278157, 2.301121874191892, 2.248166328969728, 3.2324750757428835, 1.1853014129657236, 0.9328765847682567, 0.5583751624073207, 0.0, 7.93642060889358, 6.142126786480525, 4.664382923841283, 3.55590423889717, 6.464950151485767, 3.147432860557619, 2.301121874191892, 2.0934755400198686, 2.972140043782265, 2.2246443776022153, 1.3036534929926482, 0.47292472737839286, 0.0), # 160
(7.17322205458596, 5.11236079574043, 6.4333724765919245, 6.5809293778175455, 5.865595416188075, 2.895420057582683, 2.263840723003438, 2.2215002221290754, 3.1952765889996724, 1.1676645482927346, 0.9192902757666179, 0.5504806224089643, 0.0, 7.830374044819097, 6.055286846498606, 4.596451378833089, 3.5029936448782033, 6.390553177999345, 3.1101003109807053, 2.263840723003438, 2.0681571839876307, 2.9327977080940375, 2.1936431259391824, 1.2866744953183848, 0.46476007234003913, 0.0), # 161
(7.048121770426357, 5.013901987144635, 6.335017883012913, 6.474723004557244, 5.7747572282021356, 2.853663928328766, 2.2225038131699044, 2.1899434058263343, 3.150938219304545, 1.147789230402558, 0.9039135927797701, 0.5414923927367745, 0.0, 7.708197254180333, 5.956416320104519, 4.519567963898851, 3.4433676912076736, 6.30187643860909, 3.065920768156868, 2.2225038131699044, 2.03833137737769, 2.8873786141010678, 2.158241001519082, 1.2670035766025827, 0.4558092715586033, 0.0), # 162
(6.9103563668284975, 4.90732130248573, 6.223958350350585, 6.35606541345895, 5.672449226083792, 2.8059460972594175, 2.1773520297337003, 2.153765301091302, 3.0998512257843016, 1.1258050351920315, 0.8868495663225682, 0.5314729424823361, 0.0, 7.570799536460879, 5.846202367305696, 4.43424783161284, 3.3774151055760937, 6.199702451568603, 3.015271421527823, 2.1773520297337003, 2.0042472123281554, 2.836224613041896, 2.118688471152984, 1.2447916700701172, 0.4461201184077937, 0.0), # 163
(6.760710968195384, 4.793144468874502, 6.100948544729314, 6.225708004955863, 5.559355112310126, 2.752615293367992, 2.128626257737233, 2.113235328953779, 3.0424068675657407, 1.1018415385579923, 0.8682012269098661, 0.5204847407372336, 0.0, 7.419090191144328, 5.725332148109569, 4.34100613454933, 3.305524615673976, 6.0848137351314815, 2.9585294605352903, 2.128626257737233, 1.9661537809771372, 2.779677556155063, 2.075236001651955, 1.2201897089458629, 0.43574040626131844, 0.0), # 164
(6.599970698930017, 4.671897213421746, 5.966743132273474, 6.084402179481189, 5.436158589358215, 2.694020245647842, 2.076567382222911, 2.068622910443561, 2.9789964037756596, 1.0760283163972786, 0.8480716050565187, 0.5085902565930517, 0.0, 7.25397851771427, 5.594492822523568, 4.2403580252825925, 3.2280849491918353, 5.957992807551319, 2.8960720746209856, 2.076567382222911, 1.9243001754627442, 2.7180792946791077, 2.0281340598270634, 1.1933486264546949, 0.42471792849288603, 0.0), # 165
(6.428920683435397, 4.54410526323825, 5.82209677910744, 5.932899337468126, 5.3035433597051425, 2.630509683092322, 2.021416288233143, 2.020197466590449, 2.9100110935408576, 1.0484949446067282, 0.8265637312773799, 0.49585195914137514, 0.0, 7.0763738156542955, 5.454371550555126, 4.1328186563869, 3.145484833820184, 5.820022187081715, 2.8282764532266285, 2.021416288233143, 1.8789354879230868, 2.6517716798525712, 1.9776331124893758, 1.1644193558214881, 0.41310047847620457, 0.0), # 166
(6.248346046114523, 4.410294345434805, 5.667764151355587, 5.771950879349882, 5.1621931258279865, 2.562432334694784, 1.9634138608103373, 1.9682284184242402, 2.835842195988133, 1.0193709990831787, 0.8037806360873045, 0.48233231747378824, 0.0, 6.887185384447996, 5.30565549221167, 4.0189031804365225, 3.058112997249536, 5.671684391976266, 2.755519785793936, 1.9634138608103373, 1.8303088104962744, 2.5810965629139933, 1.9239836264499612, 1.1335528302711175, 0.4009358495849823, 0.0), # 167
(6.059031911370395, 4.270990187122201, 5.50449991514229, 5.60230820555966, 5.012791590203827, 2.490136929448583, 1.902800984996902, 1.9129851869747332, 2.7568809702442847, 0.9887860557234682, 0.7798253500011468, 0.468093800681876, 0.0, 6.6873225235789615, 5.149031807500635, 3.8991267500057343, 2.9663581671704042, 5.513761940488569, 2.6781792617646265, 1.902800984996902, 1.7786692353204163, 2.5063957951019136, 1.867436068519887, 1.100899983028458, 0.3882718351929274, 0.0), # 168
(5.861763403606015, 4.1267185154112305, 5.333058736591924, 5.4247227165306615, 4.856022455309747, 2.413972196347072, 1.8398185458352458, 1.8547371932717271, 2.6735186754361124, 0.9568696904244344, 0.7548009035337614, 0.45319887785722274, 0.0, 6.477694532530785, 4.985187656429449, 3.774004517668807, 2.8706090712733023, 5.347037350872225, 2.596632070580418, 1.8398185458352458, 1.724265854533623, 2.4280112276548733, 1.808240905510221, 1.066611747318385, 0.3751562286737483, 0.0), # 169
(5.657325647224384, 3.978005057412684, 5.154195281828863, 5.23994581269609, 4.692569423622822, 2.334286864383604, 1.7747074283677764, 1.7937538583450197, 2.5861465706904125, 0.9237514790829147, 0.7288103272000027, 0.4377100180914133, 0.0, 6.259210710787055, 4.814810199005545, 3.6440516360000137, 2.7712544372487433, 5.172293141380825, 2.5112554016830275, 1.7747074283677764, 1.6673477602740028, 2.346284711811411, 1.7466486042320304, 1.0308390563657726, 0.36163682340115316, 0.0), # 170
(5.4465037666285, 3.82537554023735, 4.968664216977482, 5.048728894489152, 4.523116197620137, 2.2514296625515327, 1.7077085176369027, 1.7303046032244096, 2.495155915133985, 0.8895609975957474, 0.7019566515147247, 0.4216896904760322, 0.0, 6.032780357831365, 4.638586595236354, 3.509783257573624, 2.6686829927872413, 4.99031183026797, 2.4224264445141737, 1.7077085176369027, 1.6081640446796661, 2.2615580988100685, 1.6829096314963843, 0.9937328433954964, 0.3477614127488501, 0.0), # 171
(5.230082886221365, 3.6693556909960217, 4.777220208162156, 4.851823362343048, 4.348346479778769, 2.1657493198442115, 1.6390626986850327, 1.664658848939696, 2.4009379678936282, 0.8544278218597702, 0.6743429069927823, 0.4052003641026643, 0.0, 5.799312773147303, 4.457204005129307, 3.3717145349639117, 2.56328346557931, 4.8018759357872565, 2.3305223885155746, 1.6390626986850327, 1.5469637998887225, 2.1741732398893845, 1.6172744541143496, 0.9554440416324312, 0.3335777900905475, 0.0), # 172
(5.00884813040598, 3.510471236799489, 4.58061792150726, 4.649980616690982, 4.168943972575801, 2.077594565254994, 1.5690108565545748, 1.5970860165206766, 2.303883988096141, 0.8184815277718206, 0.6460721241490297, 0.3883045080628938, 0.0, 5.5597172562184625, 4.271349588691831, 3.2303606207451483, 2.4554445833154612, 4.607767976192282, 2.235920423128947, 1.5690108565545748, 1.483996118039281, 2.0844719862879004, 1.5499935388969943, 0.916123584301452, 0.31913374879995354, 0.0), # 173
(4.783584623585344, 3.349247904758541, 4.3796120231371685, 4.443952057966156, 3.9855923784883105, 1.987314127777233, 1.4977938762879377, 1.5278555269971503, 2.204385234868321, 0.7818516912287369, 0.6172473334983214, 0.37106459144830567, 0.0, 5.314903106528433, 4.081710505931362, 3.0862366674916064, 2.34555507368621, 4.408770469736642, 2.1389977377960103, 1.4977938762879377, 1.4195100912694523, 1.9927961892441552, 1.4813173526553853, 0.8759224046274336, 0.3044770822507765, 0.0), # 174
(4.555077490162455, 3.18621142198397, 4.174957179176257, 4.2344890866017755, 3.7989753999933793, 1.8952567364042834, 1.425652642927529, 1.457236801398915, 2.102832967336968, 0.7446678881273562, 0.5879715655555117, 0.35354308335048457, 0.0, 5.0657796235608075, 3.8889739168553294, 2.939857827777558, 2.234003664382068, 4.205665934673936, 2.040131521958481, 1.425652642927529, 1.3537548117173452, 1.8994876999966896, 1.411496362200592, 0.8349914358352515, 0.28965558381672457, 0.0), # 175
(4.324111854540319, 3.0218875155865668, 3.9674080557488987, 4.0223431030310435, 3.609776739568087, 1.8017711201294973, 1.3528280415157574, 1.3854992607557703, 1.9996184446288805, 0.7070596943645169, 0.558347850835455, 0.33580245286101496, 0.0, 4.813256106799174, 3.693826981471164, 2.791739254177275, 2.1211790830935504, 3.999236889257761, 1.9396989650580787, 1.3528280415157574, 1.2869793715210696, 1.8048883697840434, 1.3407810343436815, 0.7934816111497798, 0.2747170468715061, 0.0), # 176
(4.0914728411219325, 2.856801912677122, 3.7577193189794698, 3.808265507687162, 3.4186800996895155, 1.7072060079462288, 1.2795609570950313, 1.3129123260975137, 1.8951329258708567, 0.6691566858370562, 0.528479219853006, 0.3179051690714816, 0.0, 4.5582418557271245, 3.496956859786297, 2.6423960992650297, 2.0074700575111684, 3.7902658517417134, 1.838077256536519, 1.2795609570950313, 1.2194328628187348, 1.7093400498447577, 1.269421835895721, 0.751543863795894, 0.25970926478882933, 0.0), # 177
(3.8579455743102966, 2.6914803403664256, 3.5466456349923448, 3.593007701003337, 3.226369182834742, 1.6119101288478317, 1.2060922747077587, 1.239745418453944, 1.7897676701896952, 0.6310884384418126, 0.49846870312301883, 0.299913701073469, 0.0, 4.301646169828252, 3.299050711808158, 2.4923435156150937, 1.8932653153254375, 3.5795353403793904, 1.7356435858355217, 1.2060922747077587, 1.1513643777484512, 1.613184591417371, 1.1976692336677792, 0.7093291269984691, 0.24468003094240237, 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
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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), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 132
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 133
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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), # 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
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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), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
10, # 1
)
| 276.416043
| 494
| 0.769688
| 32,987
| 258,449
| 6.030072
| 0.217692
| 0.358346
| 0.343867
| 0.651538
| 0.37483
| 0.367134
| 0.364369
| 0.363956
| 0.363795
| 0.363795
| 0
| 0.849903
| 0.095702
| 258,449
| 934
| 495
| 276.711991
| 0.001194
| 0.01552
| 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
|
a72cfb8f31586da727a5e0af66f6a276985ba077
| 47
|
py
|
Python
|
performance_model/__init__.py
|
lorenz0890/pytorch-admm-pruning
|
85f15d86e6d9037fe4016ebcd435065ecba823b5
|
[
"BSD-3-Clause"
] | null | null | null |
performance_model/__init__.py
|
lorenz0890/pytorch-admm-pruning
|
85f15d86e6d9037fe4016ebcd435065ecba823b5
|
[
"BSD-3-Clause"
] | null | null | null |
performance_model/__init__.py
|
lorenz0890/pytorch-admm-pruning
|
85f15d86e6d9037fe4016ebcd435065ecba823b5
|
[
"BSD-3-Clause"
] | null | null | null |
from .performance_model import PerformanceModel
| 47
| 47
| 0.914894
| 5
| 47
| 8.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06383
| 47
| 1
| 47
| 47
| 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
|
59659a617177bdcc72fe7b6bbcc89823b05223b7
| 65
|
py
|
Python
|
model/__init__.py
|
WonhoZhung/HEPATOTOXICITY_PREDICTION
|
32abb08c3f99f9b148e5f4d3ee397ed1b7e8921f
|
[
"MIT"
] | null | null | null |
model/__init__.py
|
WonhoZhung/HEPATOTOXICITY_PREDICTION
|
32abb08c3f99f9b148e5f4d3ee397ed1b7e8921f
|
[
"MIT"
] | null | null | null |
model/__init__.py
|
WonhoZhung/HEPATOTOXICITY_PREDICTION
|
32abb08c3f99f9b148e5f4d3ee397ed1b7e8921f
|
[
"MIT"
] | null | null | null |
from .dataset import *
from .utils import *
from .model import *
| 16.25
| 22
| 0.723077
| 9
| 65
| 5.222222
| 0.555556
| 0.425532
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.184615
| 65
| 3
| 23
| 21.666667
| 0.886792
| 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
|
5988581c2b65622f6e509f07a7c64e2b8fd7b888
| 26
|
py
|
Python
|
branch1.py
|
hahaharsh7/lab-2-test-repo
|
360a799eac40e0cd23fd3b1b1c59e8892720e638
|
[
"MIT"
] | null | null | null |
branch1.py
|
hahaharsh7/lab-2-test-repo
|
360a799eac40e0cd23fd3b1b1c59e8892720e638
|
[
"MIT"
] | null | null | null |
branch1.py
|
hahaharsh7/lab-2-test-repo
|
360a799eac40e0cd23fd3b1b1c59e8892720e638
|
[
"MIT"
] | null | null | null |
print("Hello main branch")
| 26
| 26
| 0.769231
| 4
| 26
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 26
| 1
| 26
| 26
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0.62963
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
59bd1aae72e79aa76e012fc0d5cae2d68675eece
| 103
|
py
|
Python
|
sergeant/encoder/compressor/__init__.py
|
adir-intsights/sergeant
|
76229b045309a3d795ac760d9f08da04b5e0a750
|
[
"MIT"
] | 152
|
2020-04-05T08:45:37.000Z
|
2022-02-24T06:10:06.000Z
|
sergeant/encoder/compressor/__init__.py
|
adir-intsights/sergeant
|
76229b045309a3d795ac760d9f08da04b5e0a750
|
[
"MIT"
] | 29
|
2020-05-26T10:13:20.000Z
|
2022-03-31T10:52:39.000Z
|
sergeant/encoder/compressor/__init__.py
|
adir-intsights/sergeant
|
76229b045309a3d795ac760d9f08da04b5e0a750
|
[
"MIT"
] | 10
|
2020-06-03T12:30:26.000Z
|
2022-02-24T06:10:08.000Z
|
from . import _compressor
from . import bzip2
from . import gzip
from . import lzma
from . import zlib
| 17.166667
| 25
| 0.757282
| 15
| 103
| 5.133333
| 0.466667
| 0.649351
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012048
| 0.194175
| 103
| 5
| 26
| 20.6
| 0.915663
| 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
|
59c94c13d91d0f052a6dffb9735c6118156f8c57
| 29
|
py
|
Python
|
modules/models/encoder/language/__init__.py
|
kaylode/tern
|
a85b7568c574515031a2a41e8c21df1002c05c64
|
[
"MIT"
] | 3
|
2021-12-22T14:42:40.000Z
|
2022-01-07T03:19:56.000Z
|
modules/models/encoder/language/__init__.py
|
kaylode/tern
|
a85b7568c574515031a2a41e8c21df1002c05c64
|
[
"MIT"
] | null | null | null |
modules/models/encoder/language/__init__.py
|
kaylode/tern
|
a85b7568c574515031a2a41e8c21df1002c05c64
|
[
"MIT"
] | null | null | null |
from .bert import EncoderBERT
| 29
| 29
| 0.862069
| 4
| 29
| 6.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.103448
| 29
| 1
| 29
| 29
| 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
|
e6129f0945ad5118f40c983a8ef5ba8ee3798659
| 41
|
py
|
Python
|
wot-bans/utils/__init__.py
|
Buster-2002/WoT-Bans
|
0678925804607e958d0d2af3327974901c620f74
|
[
"MIT"
] | null | null | null |
wot-bans/utils/__init__.py
|
Buster-2002/WoT-Bans
|
0678925804607e958d0d2af3327974901c620f74
|
[
"MIT"
] | null | null | null |
wot-bans/utils/__init__.py
|
Buster-2002/WoT-Bans
|
0678925804607e958d0d2af3327974901c620f74
|
[
"MIT"
] | null | null | null |
from .utils import *
from .enums import *
| 20.5
| 20
| 0.731707
| 6
| 41
| 5
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170732
| 41
| 2
| 21
| 20.5
| 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
|
e618e35bc3141ef53b80097fd3fbeac13fb1f67d
| 137
|
py
|
Python
|
aplpy/tests/setup_package.py
|
nbrunett/aplpy
|
f5d128faf3568adea753d52c11ba43014d25d90a
|
[
"MIT"
] | null | null | null |
aplpy/tests/setup_package.py
|
nbrunett/aplpy
|
f5d128faf3568adea753d52c11ba43014d25d90a
|
[
"MIT"
] | null | null | null |
aplpy/tests/setup_package.py
|
nbrunett/aplpy
|
f5d128faf3568adea753d52c11ba43014d25d90a
|
[
"MIT"
] | 1
|
2018-10-04T18:16:05.000Z
|
2018-10-04T18:16:05.000Z
|
def get_package_data():
return {
_ASTROPY_PACKAGE_NAME_ + '.tests': ['coveragerc', 'data/*/*.hdr', 'baseline_images/*.png']}
| 34.25
| 99
| 0.635036
| 15
| 137
| 5.333333
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.160584
| 137
| 3
| 100
| 45.666667
| 0.695652
| 0
| 0
| 0
| 0
| 0
| 0.357664
| 0.153285
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0
| 0.333333
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
053f5adc7afbbe0cbd87fd4e9ac1380959a92efe
| 21
|
py
|
Python
|
python/testData/resolve/multiFile/relativeAndSameDirectoryImports/sameDirectoryImportsNotCached/dir/main.py
|
Tasemo/intellij-community
|
50aeaf729b7073e91c7c77487a1f155e0dfe3fcd
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/resolve/multiFile/relativeAndSameDirectoryImports/sameDirectoryImportsNotCached/dir/main.py
|
Tasemo/intellij-community
|
50aeaf729b7073e91c7c77487a1f155e0dfe3fcd
|
[
"Apache-2.0"
] | null | null | null |
python/testData/resolve/multiFile/relativeAndSameDirectoryImports/sameDirectoryImportsNotCached/dir/main.py
|
Tasemo/intellij-community
|
50aeaf729b7073e91c7c77487a1f155e0dfe3fcd
|
[
"Apache-2.0"
] | null | null | null |
import os
print(os)
| 5.25
| 9
| 0.714286
| 4
| 21
| 3.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 21
| 3
| 10
| 7
| 0.882353
| 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
|
0546fab8bcdc974461ad7d21968b35205765876b
| 33
|
py
|
Python
|
bsmindwave/__init__.py
|
thekerrlab/brainstaves
|
9e61be264fc21c45f7533be96a4c3bc1f06356ea
|
[
"MIT"
] | null | null | null |
bsmindwave/__init__.py
|
thekerrlab/brainstaves
|
9e61be264fc21c45f7533be96a4c3bc1f06356ea
|
[
"MIT"
] | null | null | null |
bsmindwave/__init__.py
|
thekerrlab/brainstaves
|
9e61be264fc21c45f7533be96a4c3bc1f06356ea
|
[
"MIT"
] | null | null | null |
from .bs_mindwave import Mindwave
| 33
| 33
| 0.878788
| 5
| 33
| 5.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 33
| 1
| 33
| 33
| 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
|
056ee04195b0645467d9fbe502d39605997d4bb6
| 213
|
py
|
Python
|
pava/implementation/natives/java/lang/ClassLoaderHelper.py
|
laffra/pava
|
54d10cf7f8def2f96e254c0356623d08f221536f
|
[
"MIT"
] | 4
|
2017-03-30T16:51:16.000Z
|
2020-10-05T12:25:47.000Z
|
pava/implementation/natives/java/lang/ClassLoaderHelper.py
|
laffra/pava
|
54d10cf7f8def2f96e254c0356623d08f221536f
|
[
"MIT"
] | null | null | null |
pava/implementation/natives/java/lang/ClassLoaderHelper.py
|
laffra/pava
|
54d10cf7f8def2f96e254c0356623d08f221536f
|
[
"MIT"
] | null | null | null |
def add_native_methods(clazz):
def mapAlternativeName__java_io_File__(a0):
raise NotImplementedError()
clazz.mapAlternativeName__java_io_File__ = staticmethod(mapAlternativeName__java_io_File__)
| 30.428571
| 95
| 0.816901
| 23
| 213
| 6.695652
| 0.565217
| 0.428571
| 0.467532
| 0.545455
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005376
| 0.126761
| 213
| 6
| 96
| 35.5
| 0.822581
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| 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
|
05868f335d82e0b05c10533b4b0cc9a10ca77ab5
| 257
|
py
|
Python
|
python/hidebound/server/__init__.py
|
theNewFlesh/nerve
|
4e430db20cf69cb065318806edf13656b5b035c0
|
[
"MIT"
] | 3
|
2020-05-18T11:56:50.000Z
|
2021-12-27T17:23:34.000Z
|
python/hidebound/server/__init__.py
|
theNewFlesh/nerve
|
4e430db20cf69cb065318806edf13656b5b035c0
|
[
"MIT"
] | 8
|
2021-05-17T23:44:44.000Z
|
2021-07-13T00:58:04.000Z
|
python/hidebound/server/__init__.py
|
theNewFlesh/nerve
|
4e430db20cf69cb065318806edf13656b5b035c0
|
[
"MIT"
] | 1
|
2020-12-04T20:04:00.000Z
|
2020-12-04T20:04:00.000Z
|
# app needs to come before api
import hidebound.server.app # noqa F401
import hidebound.server.api # noqa F401
import hidebound.server.components # noqa F401
import hidebound.server.progress # noqa F401
import hidebound.server.server_tools # noqa F401
| 36.714286
| 49
| 0.793774
| 37
| 257
| 5.486486
| 0.378378
| 0.369458
| 0.517241
| 0.453202
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068182
| 0.143969
| 257
| 6
| 50
| 42.833333
| 0.854545
| 0.303502
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
059094f76b825e33464a4b6f11c4cd08ea387697
| 25
|
py
|
Python
|
{{cookiecutter.project_slug}}/app/admin/views.py
|
zxyle/hello-flask
|
36f7c03f18c93beda1a8802178f2b2ef7ad19b35
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_slug}}/app/admin/views.py
|
zxyle/hello-flask
|
36f7c03f18c93beda1a8802178f2b2ef7ad19b35
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_slug}}/app/admin/views.py
|
zxyle/hello-flask
|
36f7c03f18c93beda1a8802178f2b2ef7ad19b35
|
[
"MIT"
] | null | null | null |
from . import admin_blue
| 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
|
553e06f6d8a67cbef9050d7a15df53183e6ad647
| 140
|
py
|
Python
|
app/cms/__init__.py
|
ningwenyan/Flask_Todo_Demo
|
77cca93287ae6b84d1324873fd0813ba74816c44
|
[
"MIT"
] | 2
|
2021-01-02T10:01:09.000Z
|
2021-06-15T08:08:26.000Z
|
app/cms/__init__.py
|
ningwenyan/Flask_Todo_Demo
|
77cca93287ae6b84d1324873fd0813ba74816c44
|
[
"MIT"
] | null | null | null |
app/cms/__init__.py
|
ningwenyan/Flask_Todo_Demo
|
77cca93287ae6b84d1324873fd0813ba74816c44
|
[
"MIT"
] | 1
|
2021-05-03T15:06:21.000Z
|
2021-05-03T15:06:21.000Z
|
#!/usr/bin/env python
# coding=utf-8
from flask import Blueprint
cms_bp = Blueprint('cms', __name__, subdomain='cms')
from . import views
| 17.5
| 52
| 0.728571
| 21
| 140
| 4.619048
| 0.761905
| 0.247423
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008264
| 0.135714
| 140
| 8
| 53
| 17.5
| 0.793388
| 0.235714
| 0
| 0
| 0
| 0
| 0.056604
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0.666667
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
5550be4fefab76641d0c71a1c0523f2ebe031301
| 76
|
py
|
Python
|
src/models/__init__.py
|
ngocphucck/IR-pose-classification
|
f02b09bd8c7a9a1d8d3ae1743d07338a6732f4d9
|
[
"MIT"
] | null | null | null |
src/models/__init__.py
|
ngocphucck/IR-pose-classification
|
f02b09bd8c7a9a1d8d3ae1743d07338a6732f4d9
|
[
"MIT"
] | null | null | null |
src/models/__init__.py
|
ngocphucck/IR-pose-classification
|
f02b09bd8c7a9a1d8d3ae1743d07338a6732f4d9
|
[
"MIT"
] | null | null | null |
from .efficientnet import *
from .convnext import *
from .helpers import *
| 15.2
| 27
| 0.75
| 9
| 76
| 6.333333
| 0.555556
| 0.350877
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.171053
| 76
| 4
| 28
| 19
| 0.904762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 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
|
55612fbaa44a7d8d2e87be7cdb28da1db7174a96
| 95
|
py
|
Python
|
metaconnectors/__init__.py
|
analyticsdept/py-firestoreauth
|
ae2f88e92a3129a647746d50b81f11584ecf42e5
|
[
"MIT"
] | null | null | null |
metaconnectors/__init__.py
|
analyticsdept/py-firestoreauth
|
ae2f88e92a3129a647746d50b81f11584ecf42e5
|
[
"MIT"
] | null | null | null |
metaconnectors/__init__.py
|
analyticsdept/py-firestoreauth
|
ae2f88e92a3129a647746d50b81f11584ecf42e5
|
[
"MIT"
] | null | null | null |
from metaconnectors.firestore import MetaFirestore
from metaconnectors.pubsub import MetaPubSub
| 47.5
| 50
| 0.905263
| 10
| 95
| 8.6
| 0.7
| 0.418605
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.073684
| 95
| 2
| 51
| 47.5
| 0.977273
| 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
|
e974a37246a09d5ad52517e749bba30a2deec572
| 30
|
py
|
Python
|
examples/examples.py
|
TaitoUnited/tool-template
|
b8796788c5a10c4578f5f6ade590a5f140b42e09
|
[
"MIT"
] | null | null | null |
examples/examples.py
|
TaitoUnited/tool-template
|
b8796788c5a10c4578f5f6ade590a5f140b42e09
|
[
"MIT"
] | null | null | null |
examples/examples.py
|
TaitoUnited/tool-template
|
b8796788c5a10c4578f5f6ade590a5f140b42e09
|
[
"MIT"
] | null | null | null |
print('Running the examples')
| 15
| 29
| 0.766667
| 4
| 30
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 30
| 1
| 30
| 30
| 0.851852
| 0
| 0
| 0
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
e9cde38e8d6431169d2ba02ba5f9aa2e8ad344ec
| 108
|
py
|
Python
|
DSA/Python/src/dsa/container/lists/tests/fixture.py
|
JackieMa000/problems
|
c521558830a0bbf67f94109af92d7be4397d0a43
|
[
"BSD-3-Clause"
] | null | null | null |
DSA/Python/src/dsa/container/lists/tests/fixture.py
|
JackieMa000/problems
|
c521558830a0bbf67f94109af92d7be4397d0a43
|
[
"BSD-3-Clause"
] | 1
|
2020-10-23T04:06:56.000Z
|
2020-10-23T04:06:56.000Z
|
DSA/Python/src/dsa/container/lists/tests/fixture.py
|
JackieMa000/problems
|
c521558830a0bbf67f94109af92d7be4397d0a43
|
[
"BSD-3-Clause"
] | null | null | null |
from dsa.container.tests.fixture import ContainerTestCase
class ListTestCase(ContainerTestCase):
pass
| 18
| 57
| 0.824074
| 11
| 108
| 8.090909
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12037
| 108
| 5
| 58
| 21.6
| 0.936842
| 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
|
759e4e1a86152dfcf45d2db00c5ef5d285349802
| 187
|
py
|
Python
|
home/views.py
|
bilal-yousuf/django_local_library
|
18dbf298253a097412a2bf365dc6b3a557a634a2
|
[
"MIT"
] | null | null | null |
home/views.py
|
bilal-yousuf/django_local_library
|
18dbf298253a097412a2bf365dc6b3a557a634a2
|
[
"MIT"
] | 7
|
2020-02-12T00:31:18.000Z
|
2022-03-12T00:34:07.000Z
|
home/views.py
|
bilal-yousuf/django_local_library
|
18dbf298253a097412a2bf365dc6b3a557a634a2
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
# Create your views here.
def index(request):
return render(request, 'home/index.html')
def cv(request):
return render(request, 'home/cv.html')
| 17
| 42
| 0.737968
| 27
| 187
| 5.111111
| 0.592593
| 0.188406
| 0.275362
| 0.376812
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.139037
| 187
| 11
| 43
| 17
| 0.857143
| 0.122995
| 0
| 0
| 0
| 0
| 0.165644
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.2
| 0.4
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
f93b01127f46e237004226ecf7fe04b58f6fbaa6
| 86
|
py
|
Python
|
philter_ucsf/__init__.py
|
data2health/philter-ucsf
|
19c1e80efb003e0ced48e63f72d2ab3e5b96ba6f
|
[
"BSD-3-Clause"
] | 48
|
2020-01-17T16:34:55.000Z
|
2022-03-31T13:14:30.000Z
|
philter_ucsf/__init__.py
|
data2health/philter-ucsf
|
19c1e80efb003e0ced48e63f72d2ab3e5b96ba6f
|
[
"BSD-3-Clause"
] | 8
|
2020-04-19T18:49:13.000Z
|
2022-01-02T23:43:07.000Z
|
philter_ucsf/__init__.py
|
data2health/philter-ucsf
|
19c1e80efb003e0ced48e63f72d2ab3e5b96ba6f
|
[
"BSD-3-Clause"
] | 26
|
2019-08-30T15:10:32.000Z
|
2022-03-24T17:43:27.000Z
|
__version__ = "1.0.0"
import philter_ucsf.philter
import philter_ucsf.coordinate_map
| 17.2
| 34
| 0.825581
| 13
| 86
| 4.923077
| 0.615385
| 0.40625
| 0.53125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.038462
| 0.093023
| 86
| 4
| 35
| 21.5
| 0.782051
| 0
| 0
| 0
| 0
| 0
| 0.05814
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f93c9c49bc5513a46965bbad43e7fb421c47d914
| 157
|
py
|
Python
|
wealthbot/ria/forms/__init__.py
|
jliev/wealthbot_chatterbot
|
f6681da8e5f833990ccd321e166994f31253353f
|
[
"MIT"
] | 1
|
2019-04-22T07:20:31.000Z
|
2019-04-22T07:20:31.000Z
|
wealthbot/ria/forms/__init__.py
|
jliev/wealthbot_chatterbot
|
f6681da8e5f833990ccd321e166994f31253353f
|
[
"MIT"
] | null | null | null |
wealthbot/ria/forms/__init__.py
|
jliev/wealthbot_chatterbot
|
f6681da8e5f833990ccd321e166994f31253353f
|
[
"MIT"
] | null | null | null |
from .riskQuestions import *
from .riaSearchClients import *
from .inviteProspect import *
from .suggestedPortfolio import *
from .riaClientAccount import *
| 26.166667
| 33
| 0.808917
| 15
| 157
| 8.466667
| 0.466667
| 0.314961
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.127389
| 157
| 5
| 34
| 31.4
| 0.927007
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
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| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f97d39a82d4002bc18657432fca60b02005208c1
| 171
|
py
|
Python
|
src/publisher/admin.py
|
kostinbrodorg/open-library
|
bbceb953b2d78d7eb0f2c64b81c6deac13d73531
|
[
"MIT"
] | null | null | null |
src/publisher/admin.py
|
kostinbrodorg/open-library
|
bbceb953b2d78d7eb0f2c64b81c6deac13d73531
|
[
"MIT"
] | null | null | null |
src/publisher/admin.py
|
kostinbrodorg/open-library
|
bbceb953b2d78d7eb0f2c64b81c6deac13d73531
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from publisher.models import Publisher
class AdminPublisher(admin.ModelAdmin):
pass
admin.site.register(Publisher, AdminPublisher)
| 19
| 46
| 0.818713
| 20
| 171
| 7
| 0.65
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116959
| 171
| 8
| 47
| 21.375
| 0.927152
| 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
|
f99fa3cef357d1bf12c964181b17104e526cc94a
| 422
|
py
|
Python
|
src/misc/fab/pysshUpload.py
|
igabriel85/DICE-Project
|
eb1c43fcef4bdd9ca7e75c51c0b58d0d1fdc1c29
|
[
"Apache-2.0"
] | 1
|
2015-06-09T22:03:21.000Z
|
2015-06-09T22:03:21.000Z
|
src/misc/fab/pysshUpload.py
|
igabriel85/IeAT-DICE-Repository
|
eb1c43fcef4bdd9ca7e75c51c0b58d0d1fdc1c29
|
[
"Apache-2.0"
] | 165
|
2015-08-13T23:00:33.000Z
|
2017-03-09T20:25:23.000Z
|
src/misc/fab/pysshUpload.py
|
igabriel85/DICE-Monitoring
|
eb1c43fcef4bdd9ca7e75c51c0b58d0d1fdc1c29
|
[
"Apache-2.0"
] | 3
|
2016-03-03T14:20:13.000Z
|
2016-11-09T19:57:42.000Z
|
from pssh import *
hostlist = ['109.231.122.228' ,'109.231.122.187' ,'109.231.122.173' ,'109.231.122.164' ,'109.231.122.233' ,'109.231.122.201' ,'109.231.122.130'
,'109.231.122.231' ,'109.231.122.194' ,'109.231.122.182' ,'109.231.122.207' ,'109.231.122.156' ,'109.231.122.240' ,'109.231.122.127']
client = ParallelSSHClient(hostlist, user=' ',password=' ')
client.copy_file('nodeBootstrapper.sh','nodeBootstrapper.sh')
| 52.75
| 144
| 0.672986
| 72
| 422
| 3.930556
| 0.388889
| 0.29682
| 0.44523
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.428571
| 0.07109
| 422
| 8
| 145
| 52.75
| 0.293367
| 0
| 0
| 0
| 0
| 0
| 0.592417
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.2
| 0.2
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
f9d5736ce972a50694d6137bbf9f58790b0c45f3
| 3,642
|
py
|
Python
|
src/lib/mine/src_gen/test_structure.py
|
rdw20170120/workstation
|
ed19aa930a83885c2a8cb58eb0bb5afe58f95df3
|
[
"MIT"
] | null | null | null |
src/lib/mine/src_gen/test_structure.py
|
rdw20170120/workstation
|
ed19aa930a83885c2a8cb58eb0bb5afe58f95df3
|
[
"MIT"
] | 2
|
2021-04-06T18:07:32.000Z
|
2021-06-02T01:50:40.000Z
|
src/lib/mine/src_gen/test_structure.py
|
rdw20170120/workstation
|
ed19aa930a83885c2a8cb58eb0bb5afe58f95df3
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env false
"""TODO: Write
TODO: Generate tests
NOTE: There is little value in testing "composed" methods,
e.g., those consisting of 'return [...]'.
"""
# Internal packages (absolute references, distributed with Python)
# External packages (absolute references, NOT distributed with Python)
from pytest import raises
# Library modules (absolute references, NOT packaged, in project)
from utility import my_assert as is_
from src_gen.renderer import Renderer
from src_gen.source import my_visitor_map
from src_gen.structure import *
# Project modules (relative references, NOT packaged, in project)
s = Renderer(my_visitor_map)._serialize
will_squash = (None, "", (), [])
wont_squash = (False, True, 0, 0.0, 0j, " ", "Test", {})
def test_bt():
# TODO: Expand tests for full pattern
# TODO: Break up tests into individual test methods
assert is_.equal(s(bt()), "``")
assert is_.equal(s(bt(None)), "``")
assert is_.equal(s(bt("")), "``")
assert is_.equal(s(bt("Test")), "`Test`")
assert is_.equal(s(bt("Test", "123")), "`Test123`")
def test_dq():
# TODO: Expand tests for full pattern
# TODO: Break up tests into individual test methods
assert is_.equal(s(dq()), '""')
assert is_.equal(s(dq(None)), '""')
assert is_.equal(s(dq("")), '""')
assert is_.equal(s(dq("Test")), '"Test"')
assert is_.equal(s(dq("Test", "123")), '"Test123"')
def test_eol():
# TODO: Break up tests into individual test methods
assert is_.equal(s(eol()), "\n")
with raises(TypeError):
eol(None)
def test_line():
# TODO: Expand tests for full pattern
# TODO: Break up tests into individual test methods
assert is_.equal(s(line()), "\n")
assert is_.equal(s(line(None)), "\n")
assert is_.equal(s(line("Test")), "Test\n")
def test_sq():
# TODO: Expand tests for full pattern
# TODO: Break up tests into individual test methods
assert is_.equal(s(sq()), "''")
assert is_.equal(s(sq(None)), "''")
assert is_.equal(s(sq("")), "''")
assert is_.equal(s(sq("Test")), "'Test'")
assert is_.equal(s(sq("Test", "123")), "'Test123'")
def test_squashed_01():
assert is_.equal(squashed(False), False)
def test_squashed_02():
assert is_.equal(squashed(True), True)
def test_squashed_03():
assert is_.equal(squashed(0), 0)
def test_squashed_04():
assert is_.equal(squashed(0.0), 0.0)
def test_squashed_05():
assert is_.equal(squashed(0j), 0j)
def test_squashed_06():
assert is_.equal(squashed(" "), " ")
def test_squashed_07():
assert is_.equal(squashed("Test"), "Test")
def test_squashed_08():
assert is_.equal(squashed({}), {})
def test_squashed_09():
assert is_.equal(squashed(None), None)
def test_squashed_10():
assert is_.equal(squashed(""), None)
def test_squashed_11():
assert is_.equal(squashed(()), None)
def test_squashed_12():
assert is_.equal(squashed((None)), None)
def test_squashed_13():
assert is_.equal(squashed((None,)), None)
def test_squashed_14():
assert is_.equal(squashed((None, None)), None)
def test_squashed_15():
assert is_.equal(squashed([None]), None)
def test_squashed_16():
assert is_.equal(squashed([None, None]), None)
def test_squashed_17():
for i in will_squash:
for j in wont_squash:
assert is_.equal(squashed((i, j)), j)
assert is_.equal(squashed([i, j]), j)
def test_squashed_18():
for i in wont_squash:
for j in will_squash:
assert is_.equal(squashed((i, j)), i)
assert is_.equal(squashed([i, j]), i)
"""DisabledContent
"""
| 23.960526
| 70
| 0.646623
| 522
| 3,642
| 4.327586
| 0.197318
| 0.138114
| 0.224436
| 0.185923
| 0.619743
| 0.549358
| 0.474989
| 0.395308
| 0.359894
| 0.281983
| 0
| 0.022327
| 0.188358
| 3,642
| 151
| 71
| 24.119205
| 0.741881
| 0.223504
| 0
| 0
| 1
| 0
| 0.045585
| 0
| 0
| 0
| 0
| 0.019868
| 0.526316
| 1
| 0.302632
| false
| 0
| 0.065789
| 0
| 0.368421
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
ddd76dbd56c6786121b6342110b9d5c11da7264a
| 35
|
py
|
Python
|
Model/ReadFile/__init__.py
|
nevoit/Information-Retrieval
|
fd7199e04d2a8ff67045b63f4cc43ac469776f3a
|
[
"MIT"
] | 1
|
2018-07-24T18:02:57.000Z
|
2018-07-24T18:02:57.000Z
|
Model/ReadFile/__init__.py
|
nevoit/Information-Retrieval
|
fd7199e04d2a8ff67045b63f4cc43ac469776f3a
|
[
"MIT"
] | null | null | null |
Model/ReadFile/__init__.py
|
nevoit/Information-Retrieval
|
fd7199e04d2a8ff67045b63f4cc43ac469776f3a
|
[
"MIT"
] | 2
|
2019-11-05T09:24:25.000Z
|
2019-11-15T10:32:09.000Z
|
from Model.ReadFile import Reader
| 17.5
| 34
| 0.828571
| 5
| 35
| 5.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 35
| 1
| 35
| 35
| 0.966667
| 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
|
dde716d4d34b5c19788736da10f11eb9cdf63f43
| 26
|
py
|
Python
|
fbnotify/__init__.py
|
devArtoria/fbnotify
|
982fc55db5577275bfdc74de06f60f828a7ba13c
|
[
"BSD-2-Clause"
] | null | null | null |
fbnotify/__init__.py
|
devArtoria/fbnotify
|
982fc55db5577275bfdc74de06f60f828a7ba13c
|
[
"BSD-2-Clause"
] | null | null | null |
fbnotify/__init__.py
|
devArtoria/fbnotify
|
982fc55db5577275bfdc74de06f60f828a7ba13c
|
[
"BSD-2-Clause"
] | null | null | null |
from .main import fbnotify
| 26
| 26
| 0.846154
| 4
| 26
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 26
| 1
| 26
| 26
| 0.956522
| 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
|
fb035cc101aa25c7c2f84ace11c8524ad07f45d9
| 19,777
|
py
|
Python
|
application/pages/fast_gallery/fast_gallery.py
|
slamer59/awesome-panel
|
91c30bd6d6859eadf9c65b1e143952f7e64d5290
|
[
"Apache-2.0"
] | null | null | null |
application/pages/fast_gallery/fast_gallery.py
|
slamer59/awesome-panel
|
91c30bd6d6859eadf9c65b1e143952f7e64d5290
|
[
"Apache-2.0"
] | null | null | null |
application/pages/fast_gallery/fast_gallery.py
|
slamer59/awesome-panel
|
91c30bd6d6859eadf9c65b1e143952f7e64d5290
|
[
"Apache-2.0"
] | null | null | null |
"""The Awesome Panel Gallery based on the Fast Components"""
# pylint: disable=line-too-long
from awesome_panel_extensions.frameworks.fast.templates.fast_gallery_template import (
FastGalleryTemplate,
)
from awesome_panel_extensions.models.resource import Application, Author
ASSETS = (
"https://github.com/MarcSkovMadsen/awesome-panel-assets/blob/master/awesome-panel/applications/"
)
def get_applications():
"""Returns a list of all applications"""
jochem_smit = Author(
name="Jochem Smit",
url="https://github.com/Jhsmit",
avatar_url="https://avatars1.githubusercontent.com/u/7881506?s=400&u=bdf7b6635bf57e7022763ce3b002649fe80ef6a8&v=40",
)
marc_skov_madsen = Author(
name="Marc Skov Madsen",
url="https://datamodelsanalytics.com",
avatar_url="https://avatars0.githubusercontent.com/u/42288570",
)
return sorted([
Application(
name="Async Tasks",
description="We show case how to start a background thread that updates a progressbar while the rest of the application remains responsive.",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/async_tasks.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/async_tasks/async_tasks.py",
youtube_url="https://www.youtube.com/watch?v=Ohr29FJjBi0",
mp4_url=ASSETS + "async_tasks.mp4",
gif_url=ASSETS + "async_tasks.gif",
documentation_url="https://awesome-panel.readthedocs.org",
author=jochem_smit,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Bootstrap Dashboard",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/bootstrap_dashboard.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/bootstrap_dashboard/main.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Custom Bokeh Model",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/custom_bokeh_model.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/custom_bokeh_model/custom.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Dashboard",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/dashboard.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/dashboard/dashboard.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="DataExplorer - Loading...",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/dataexplorer_loading.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/dataexplorer_loading/dataexplorer_loading.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="DE:TR: Object Detection",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/detr.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/detr/detr.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Image Classifier",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/image_classifier.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/image_classifier/image_classifier.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="JS Actions",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/js_actions.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/js_actions/js_actions.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Kickstarter Dashboard",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/kickstarter_dashboard.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/kickstarter_dashboard/main.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Owid Choropleth Map",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/owid_choropleth_map.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/owid_choropleth_map/main.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Pandas Profiling",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/pandas_profiling_app.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/pandas_profiling_app/pandas_profiling_app.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Param Reference Example",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/param_reference_example.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/param_reference_example/param_reference_example.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Yahoo Query",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/yahooquery_app.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/yahooquery_app/yahooquery_app.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Test Bootstrap Alerts",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_bootstrap_alerts.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_bootstrap_alerts.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Bootstrap Card",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_bootstrap_card.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_bootstrap_card.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Code",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_code.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_code.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test DataFrame",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_dataframe.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_dataframe.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Divider",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_divider.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_divider.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test ECharts",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_echarts.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_echarts.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test FontAwesome",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_fontawesome.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_fontawesome.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Headings",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_headings.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_headings.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Markdown",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_markdown.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_markdown.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Material Components",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_material_components.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_material.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Test Model Viewer",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_model_viewer.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_model_viewer.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Test Perspective Viewer",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_perspective.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_perspective.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
],
),
Application(
name="Test Progress Extension",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_progress_ext.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_progress_ext.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Share Links",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_share_links.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_share_links.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Spinners",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_spinners.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_spinners.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Test Wired",
description="",
url="https://awesome-panel.org",
thumbnail_url="https://github.com/MarcSkovMadsen/awesome-panel/raw/master/assets/images/thumbnails/test_wired.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/awesome_panel_express_tests/test_wired.py",
author=marc_skov_madsen,
tags=[
"Code",
"App In Gallery",
"awesome_panel.express",
],
),
Application(
name="Dialog Template",
description="An example of a custom Panel Template with a Modal",
url="dialog_template",
thumbnail_url="https://raw.githubusercontent.com/MarcSkovMadsen/awesome-panel/master/application/pages/dialog_template/assets/thumbnail.png",
code_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/dialog_template",
mp4_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/dialog_template/assets/dialog_template.mp4",
gif_url="https://github.com/MarcSkovMadsen/awesome-panel/blob/master/application/pages/dialog_template/assets/dialog_template.gif",
author=marc_skov_madsen,
tags=[
"Code",
"Awesome Panel",
"Shoelace",
"Modal",
],
),
])
def get_fast_gallery():
"""Return a FastGalleryTemplate"""
return FastGalleryTemplate(
site_name="Awesome Panel",
site_url="https://awesome-panel.org",
name="Gallery",
url="https://awesome-panel.org/gallery",
description="""The purpose of the Awesome Panel Gallery is to inspire and help you create awesome analytics apps in <fast-anchor href="https://panel.holoviz.org" target="_blank" appearance="hypertext">Panel</fast-anchor> using the tools you know and love.""",
background_image_url="https://ih1.redbubble.net/image.875683605.8623/ur,mug_lifestyle,tall_portrait,750x1000.jpg",
items=get_applications(),
target="_blank",
)
if __name__.startswith("bokeh"):
get_fast_gallery().servable()
| 46.424883
| 268
| 0.583708
| 1,945
| 19,777
| 5.777892
| 0.098715
| 0.139883
| 0.078484
| 0.162573
| 0.768464
| 0.764371
| 0.754583
| 0.751468
| 0.751468
| 0.751468
| 0
| 0.005384
| 0.295596
| 19,777
| 425
| 269
| 46.534118
| 0.801306
| 0.007534
| 0
| 0.671498
| 0
| 0.161836
| 0.517746
| 0.016417
| 0
| 0
| 0
| 0
| 0
| 1
| 0.004831
| false
| 0
| 0.004831
| 0
| 0.014493
| 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
|
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