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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1ade1ed3683e381ae0cc38b512be128ac35c2ed2
| 27
|
py
|
Python
|
src/inventory_management/models/__init__.py
|
grai-io/inventory-management
|
85b898b4354090ed970f0f61f2a6acd286a085e2
|
[
"MIT"
] | null | null | null |
src/inventory_management/models/__init__.py
|
grai-io/inventory-management
|
85b898b4354090ed970f0f61f2a6acd286a085e2
|
[
"MIT"
] | null | null | null |
src/inventory_management/models/__init__.py
|
grai-io/inventory-management
|
85b898b4354090ed970f0f61f2a6acd286a085e2
|
[
"MIT"
] | null | null | null |
from .order_up_to import *
| 13.5
| 26
| 0.777778
| 5
| 27
| 3.8
| 1
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| 0
| 0
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| 0
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| 1
| 0
|
0
| 6
|
1ae72d863860ad5ab3724754d8a1196f7848277b
| 33
|
py
|
Python
|
evolocity/pl.py
|
samsledje/evolocity
|
2b162ff61d4239ba5af06a601e5bb62f501d4a0f
|
[
"MIT"
] | 25
|
2021-06-03T22:51:24.000Z
|
2022-03-18T09:38:42.000Z
|
evolocity/pl.py
|
samsledje/evolocity
|
2b162ff61d4239ba5af06a601e5bb62f501d4a0f
|
[
"MIT"
] | 7
|
2021-07-11T17:20:24.000Z
|
2022-03-17T02:31:13.000Z
|
evolocity/pl.py
|
samsledje/evolocity
|
2b162ff61d4239ba5af06a601e5bb62f501d4a0f
|
[
"MIT"
] | 5
|
2021-06-08T18:39:14.000Z
|
2022-03-11T12:55:33.000Z
|
from evolocity.plotting import *
| 16.5
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|
0
| 6
|
dcd0745abfd4bc4dca36e61ae99c7c41acf2e01b
| 11,759
|
py
|
Python
|
tests/test_blogs.py
|
NaKolenke/kolenka-backend
|
b8bf0a49b424e088b02bfeb59b7a083e59973b88
|
[
"MIT"
] | null | null | null |
tests/test_blogs.py
|
NaKolenke/kolenka-backend
|
b8bf0a49b424e088b02bfeb59b7a083e59973b88
|
[
"MIT"
] | null | null | null |
tests/test_blogs.py
|
NaKolenke/kolenka-backend
|
b8bf0a49b424e088b02bfeb59b7a083e59973b88
|
[
"MIT"
] | null | null | null |
import datetime
import pytest
from src.model.models import Blog, BlogInvite, BlogParticipiation, Token, User
@pytest.fixture
def other_user_and_token():
user = User.create(
username="other_user",
password="0x:993fadc17393cdfb06dfb7f5dd0d13de",
email="asd",
registration_date=datetime.datetime.now(),
last_active_date=datetime.datetime.now(),
)
token = Token.generate_access_token(user)
from src.model import db
db.db_wrapper.database.close()
return [user, token]
@pytest.fixture
def user_not_in_blog_with_token():
user = User.create(
username="user_not_in_blog",
password="0x:993fadc17393cdfb06dfb7f5dd0d13de",
email="asd",
registration_date=datetime.datetime.now(),
last_active_date=datetime.datetime.now(),
name="name",
birthday=datetime.date.today(),
about="",
avatar=None,
)
token = Token.generate_access_token(user)
from src.model import db
db.db_wrapper.database.close()
return [user, token]
@pytest.fixture
def blogs(user):
for i in range(30):
Blog.create(
title="test_blog" + str(i),
url="test_blog" + str(i),
creator=user,
created_date=datetime.datetime.now(),
updated_date=datetime.datetime.now(),
)
from src.model import db
db.db_wrapper.database.close()
def test_blogs(client, blog):
rv = client.get("/blogs/")
assert rv.json["success"] == 1
assert len(rv.json["blogs"]) == 1, "We should have only one blog"
assert rv.json["blogs"][0]["title"] == "test_blog", "With title test_blog"
assert rv.json["blogs"][0]["readers"] == 3, "Wrong count of readers"
assert rv.json["meta"]["page_count"] == 1, "There should be one page"
def test_blogs_pagination(client, blogs):
rv = client.get("/blogs/")
assert len(rv.json["blogs"]) == 20, "We should have 20 blogs without pagination"
assert rv.json["blogs"][0]["title"] == "test_blog0", "Wrong title"
assert rv.json["meta"]["page_count"] == 2, "There should be two pages"
rv = client.get("/blogs/?page=2")
assert len(rv.json["blogs"]) == 10, "We should have 10 blogs on second page"
assert rv.json["blogs"][0]["title"] == "test_blog20", "Wrong title"
assert rv.json["meta"]["page_count"] == 2, "There should be two pages"
rv = client.get("/blogs/?page=3")
assert len(rv.json["blogs"]) == 0, "We should have 0 blogs on nonexistent page"
def test_empty_blogs(client):
rv = client.get("/blogs/")
assert len(rv.json["blogs"]) == 0, "We should have no blogs"
assert rv.json["meta"]["page_count"] == 0, "There should be no pages"
def test_create_blog(client, user_token):
rv = client.post(
"/blogs/",
headers={"Authorization": user_token[1].token},
json={"url": "blog-url"},
)
assert rv.json["success"] == 1
assert "blog" in rv.json, "No blog in response"
blog = Blog.get()
assert blog.creator == user_token[0], "Wrong creator"
assert blog.id == rv.json["blog"]["id"]
participiation = BlogParticipiation.get()
assert participiation.blog.id == rv.json["blog"]["id"]
assert participiation.user == user_token[0]
assert participiation.role == 1, "Not owner on creation"
def test_edit_blog(client, blog, user_token):
rv = client.put(
"/blogs/" + str(blog.url) + "/",
json={"title": "new title"},
headers={"Authorization": user_token[1].token},
)
assert rv.json["success"] == 1
assert Blog.get().title == "new title", "Blog title not chaged"
def test_edit_blog_wrong_user(client, blog, user_not_in_blog_with_token):
rv = client.put(
"/blogs/" + str(blog.url) + "/",
json={"title": "new title"},
headers={"Authorization": user_not_in_blog_with_token[1].token},
)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 3
assert Blog.get().title == blog.title, "Blog title changed"
assert Blog.get().title != "new title"
def test_edit_blog_reader(client, blog, reader_token):
rv = client.put(
"/blogs/" + str(blog.url) + "/",
json={"title": "new title"},
headers={"Authorization": reader_token[1].token},
)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 3
assert Blog.get().title == blog.title, "Blog title changed"
assert Blog.get().title != "new title"
def test_delete_blog(client, blog, user_token):
rv = client.delete(
"/blogs/" + str(blog.url) + "/", headers={"Authorization": user_token[1].token}
)
assert rv.json["success"] == 1
assert Blog.select().count() == 0, "Blog not deleted"
def test_delete_blog_wrong_user(client, blog, user_not_in_blog_with_token):
rv = client.delete(
"/blogs/" + str(blog.url) + "/",
headers={"Authorization": user_not_in_blog_with_token[1].token},
)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 3
assert Blog.select().count() == 1, "Blog was deleted"
def test_delete_blog_reader(client, blog, reader_token):
rv = client.delete(
"/blogs/" + str(blog.url) + "/",
headers={"Authorization": reader_token[1].token},
)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 3
assert Blog.select().count() == 1, "Blog was deleted"
def test_blog(client, blog):
rv = client.get("/blogs/" + str(blog.url) + "/")
assert rv.json["success"] == 1
assert rv.json["blog"]["title"] == blog.title, "Wrong blog title"
assert rv.json["blog"]["readers"] == 3, "Wrong count of readers"
def test_blog_readers(client, blog):
rv = client.get("/blogs/" + str(blog.url) + "/readers/")
assert rv.json["success"] == 1
assert len(rv.json["readers"]) == 3, "Wrong count of readers"
assert "name" in rv.json["readers"][0], "Readers schema"
user1 = rv.json["readers"][0]
user2 = rv.json["readers"][1]
assert user1["name"] != user2["name"]
assert rv.json["meta"]["page_count"] == 1, "There should be one page"
def test_blog_invite(client, blog, user_token, user_not_in_blog_with_token):
body = {"user": user_not_in_blog_with_token[0].id, "role": "writer"}
rv = client.post(
"/blogs/" + str(blog.url) + "/invite/",
headers={"Authorization": user_token[1].token},
json=body,
)
assert rv.json["success"] == 1
assert "invite" in rv.json
assert BlogInvite.select().count() == 1, "There should be one invite"
invite = BlogInvite.get()
assert invite.blog.id == blog.id
assert invite.user_from.id == user_token[0].id
assert invite.user_to.id == user_not_in_blog_with_token[0].id
assert invite.role == 2
assert not invite.is_accepted
query = BlogParticipiation.select().where(
(BlogParticipiation.user == user_not_in_blog_with_token[0])
& (BlogParticipiation.blog == blog)
)
assert query.count() == 0, "User should not be added to blog"
from src.model import db
db.db_wrapper.database.close()
rv = client.post(
"/blogs/" + str(blog.url) + "/invite/",
headers={"Authorization": user_not_in_blog_with_token[1].token},
json={"invite": rv.json["invite"]},
)
assert rv.json["success"] == 1
assert BlogInvite.select().count() == 1, "There should be one invite"
invite = BlogInvite.get()
assert invite.blog.id == blog.id
assert invite.user_from.id == user_token[0].id
assert invite.user_to.id == user_not_in_blog_with_token[0].id
assert invite.role == 2
assert invite.is_accepted
assert query.count() == 1, "User should be added to blog"
participiation = query.get()
assert participiation.role == 2
def test_blog_invite_cant_accept_other_user(
client, blog, user_token, user_not_in_blog_with_token, other_user_and_token
):
body = {"user": user_not_in_blog_with_token[0].id, "role": "writer"}
rv = client.post(
"/blogs/" + str(blog.url) + "/invite/",
headers={"Authorization": user_token[1].token},
json=body,
)
assert rv.json["success"] == 1
assert "invite" in rv.json
assert BlogInvite.select().count() == 1, "There should be one invite"
invite = BlogInvite.get()
assert invite.blog.id == blog.id
assert invite.user_from.id == user_token[0].id
assert invite.user_to.id == user_not_in_blog_with_token[0].id
assert invite.role == 2
assert not invite.is_accepted
query = BlogParticipiation.select().where(
(BlogParticipiation.user == user_not_in_blog_with_token[0])
& (BlogParticipiation.blog == blog)
)
assert query.count() == 0, "User should not be added to blog"
from src.model import db
db.db_wrapper.database.close()
rv = client.post(
"/blogs/" + str(blog.url) + "/invite/",
headers={"Authorization": other_user_and_token[1].token},
json={"invite": rv.json["invite"]},
)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 3
assert BlogInvite.select().count() == 1, "There should be one invite"
invite = BlogInvite.get()
assert invite.blog.id == blog.id
assert invite.user_from.id == user_token[0].id
assert invite.user_to.id == user_not_in_blog_with_token[0].id
assert invite.role == 2
assert not invite.is_accepted
query = BlogParticipiation.select().where(
(BlogParticipiation.user == user_not_in_blog_with_token[0])
& (BlogParticipiation.blog == blog)
)
assert query.count() == 0, "User should not be added to blog"
query = BlogParticipiation.select().where(
(BlogParticipiation.user == other_user_and_token[0])
& (BlogParticipiation.blog == blog)
)
assert query.count() == 0, "Other user should not be added to blog"
def test_blog_invite_incorrect(client, blog, user_token, user_not_in_blog_with_token):
body = {"user": user_not_in_blog_with_token[0].id, "role": "writer"}
rv = client.post("/blogs/" + str(blog.url) + "/invite/", json=body)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 1
assert BlogInvite.select().count() == 0, "There should be no invites"
from src.model import db
db.db_wrapper.database.close()
rv = client.post(
"/blogs/" + str(blog.url) + "/invite/",
headers={"Authorization": user_not_in_blog_with_token[1].token},
json=body,
)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 3
assert BlogInvite.select().count() == 0, "There should be no invites"
def test_blog_invite_from_reader(
client, blog, reader_token, user_not_in_blog_with_token
):
body = {"user": user_not_in_blog_with_token[0].id, "role": "writer"}
rv = client.post(
"/blogs/" + str(blog.url) + "/invite/",
headers={"Authorization": reader_token[1].token},
json=body,
)
assert rv.json["success"] == 0
assert rv.json["error"]["code"] == 3
assert BlogInvite.select().count() == 0, "There should be no invites"
body = {"user": user_not_in_blog_with_token[0].id, "role": "reader"}
from src.model import db
db.db_wrapper.database.close()
rv = client.post(
"/blogs/" + str(blog.url) + "/invite/",
headers={"Authorization": reader_token[1].token},
json=body,
)
assert rv.json["success"] == 1
assert BlogInvite.select().count() == 1, "There should be one invite"
invite = BlogInvite.get()
assert invite.blog.id == blog.id
assert invite.user_from.id == reader_token[0].id
assert invite.user_to.id == user_not_in_blog_with_token[0].id
assert invite.role == 3
| 31.525469
| 87
| 0.633217
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| 11,759
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| 0
|
0
| 6
|
b48fb0f921f202fd4bd5e90216d0dd595a872ca2
| 164
|
py
|
Python
|
Day2/environment-and-paths-python/python-main-functions/import_this_example.py
|
klimpie94/Python-training
|
7af210126cfe2e9386a8f22075ea0d7eff80daac
|
[
"RSA-MD"
] | null | null | null |
Day2/environment-and-paths-python/python-main-functions/import_this_example.py
|
klimpie94/Python-training
|
7af210126cfe2e9386a8f22075ea0d7eff80daac
|
[
"RSA-MD"
] | 1
|
2021-12-13T20:33:28.000Z
|
2021-12-13T20:33:28.000Z
|
Day2/environment-and-paths-python/python-main-functions/import_this_example.py
|
klimpie94/Python-training
|
7af210126cfe2e9386a8f22075ea0d7eff80daac
|
[
"RSA-MD"
] | 9
|
2020-02-05T10:24:12.000Z
|
2020-02-10T13:08:50.000Z
|
import python_zen
# As the main function is not imported we can only use the function
# from the python_zen module...
python_zen.import_this_without_side_effect()
| 27.333333
| 67
| 0.810976
| 28
| 164
| 4.5
| 0.714286
| 0.214286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.140244
| 164
| 5
| 68
| 32.8
| 0.893617
| 0.579268
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
| 0
| 0
| null | 1
| 0
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| 1
| 0
| 1
| 0
|
0
| 6
|
b4ed2685093b23a5fa2b12e0bcda8f79dc6f414f
| 12,467
|
py
|
Python
|
Data_Collection_Code/Syntax_Analysis/parse_ground_truth.py
|
dimitristaufer/Cloud-NLP-Evaluation
|
201d7721616f4526ae58bed503fc05874cf2e52a
|
[
"MIT"
] | null | null | null |
Data_Collection_Code/Syntax_Analysis/parse_ground_truth.py
|
dimitristaufer/Cloud-NLP-Evaluation
|
201d7721616f4526ae58bed503fc05874cf2e52a
|
[
"MIT"
] | null | null | null |
Data_Collection_Code/Syntax_Analysis/parse_ground_truth.py
|
dimitristaufer/Cloud-NLP-Evaluation
|
201d7721616f4526ae58bed503fc05874cf2e52a
|
[
"MIT"
] | 1
|
2022-03-26T11:47:11.000Z
|
2022-03-26T11:47:11.000Z
|
import sys
sys.path.append('../')
sys.path.append('../..')
import pyconll
from mongo_connect import Connect
# This program parses two POS corpora and stores them in a local mongoDB database #
### Universal Dependencies version of syntax annotations
# from the GUM corpus (https://corpling.uis.georgetown.edu/gum/) ###
### 5.427 sentences, 101.277 Tokens ###
### Copyright: CC BY-NC-SA 4.0 ###
### https://universaldependencies.org ###
### Noun Verb Dataset by Elkahky et al. (https://www.aclweb.org/anthology/D18-1277.pdf) ###
### @InProceedings{NOUNVERB, ###
### title = {A Challenge Set and Methods for Noun-Verb Ambiguity}, ###
### author = {Ali Elkahky and Kellie Webster and Daniel Andor and Emily Pitler}, ###
### booktitle = {Proceedings of EMNLP}, ###
### year = {2018} ###
### } ###
### ###
### Mongo DB Init ###
connection = Connect.get_connection()
db = connection.syntaxAnalysis
udGroundTruth = db.udGroundTruth
udGroundTruthCopy = db.udGroundTruthCopy
udPTGroundTruth = db.udPTGroundTruth
udPTGroundTruthCopy = db.udPTGroundTruthCopy
udEWGroundTruth = db.udEWGroundTruth
udEWGroundTruthCopy = db.udEWGroundTruthCopy
nvGroundTruth = db.nvGroundTruth
nvGroundTruthCopy = db.nvGroundTruthCopy
### ###
UD_EN_DEV = '../../../External_Datasets/Syntax_Analysis/UD_English-GUM-master/en_gum-ud-dev.conllu' # 0.9 MB
UD_EN_TEST = '../../../External_Datasets/Syntax_Analysis/UD_English-GUM-master/en_gum-ud-test.conllu' # 0.9 MB
UD_EN_TRAIN = '../../../External_Datasets/Syntax_Analysis/UD_English-GUM-master/en_gum-ud-train.conllu' # 4.0 MB
UD_PT_EN_DEV = '../../../External_Datasets/Syntax_Analysis/UD_English-ParTUT-master/en_partut-ud-dev.conllu' # 0.2 MB
UD_PT_EN_TEST = '../../../External_Datasets/Syntax_Analysis/UD_English-ParTUT-master/en_partut-ud-test.conllu' # 0.2 MB
UD_PT_EN_TRAIN = '../../../External_Datasets/Syntax_Analysis/UD_English-ParTUT-master/en_partut-ud-train.conllu' # 2.4 MB
UD_EW_EN_DEV = '../../../External_Datasets/Syntax_Analysis/UD_English-EWT-master/en_ewt-ud-dev.conllu' # 1.7 MB
UD_EW_EN_TEST = '../../../External_Datasets/Syntax_Analysis/UD_English-EWT-master/en_ewt-ud-test.conllu' # 1.7 MB
UD_EW_EN_TRAIN = '../../../External_Datasets/Syntax_Analysis/UD_English-EWT-master/en_ewt-ud-train.conllu' # 13.3 MB
'''
ADJ: adjective
ADP: adposition
ADV: adverb
AUX: auxiliary
CCONJ: coordinating conjunction
DET: determiner
INTJ: interjection
NOUN: noun
NUM: numeral
PART: particle
PRON: pronoun
PROPN: proper noun
PUNCT: punctuation
SCONJ: subordinating conjunction
SYM: symbol
VERB: verb
X: other
'''
NV_EN_DEV = '../../../External_Datasets/Syntax_Analysis/noun-verb-master/dev.conll' # 1.2 MB
NV_EN_TEST = '../../../External_Datasets/Syntax_Analysis/noun-verb-master/test.conll' # 3.0 MB
NV_EN_TRAIN = '../../../External_Datasets/Syntax_Analysis/noun-verb-master/train.conll' # 10.6 MB
'''
VERB
NON-VERB
'''
def getTestDataUDDev():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_EN_DEV)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udGroundTruth.insert_one(groundTruthDocument)
udGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDTest():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_EN_TEST)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udGroundTruth.insert_one(groundTruthDocument)
udGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDTrain():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_EN_TRAIN)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udGroundTruth.insert_one(groundTruthDocument)
udGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataNVDev():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(NV_EN_DEV)
for sentence in devSet:
sentenceText = ""
groundTruthDocument = {}
for i, token in enumerate(sentence):
if i == len(sentence)-1:
sentenceText += token.form + ""
else:
sentenceText += token.form + " "
if token.upos == "VERB":
groundTruthDocument["annotation"] = {
"token_text" : token.form,
"pos_tag" : "VERB"
}
elif token.upos == "NON-VERB":
groundTruthDocument["annotation"] = {
"token_text" : token.form,
"pos_tag" : "NON-VERB"
}
groundTruthDocument["sentence"] = sentenceText
nvGroundTruth.insert_one(groundTruthDocument)
nvGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataNVTest():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(NV_EN_TEST)
for sentence in devSet:
sentenceText = ""
groundTruthDocument = {}
for i, token in enumerate(sentence):
if i == len(sentence)-1:
sentenceText += token.form + ""
else:
sentenceText += token.form + " "
if token.upos == "VERB":
groundTruthDocument["annotation"] = {
"token_text" : token.form,
"pos_tag" : "VERB"
}
elif token.upos == "NON-VERB":
groundTruthDocument["annotation"] = {
"token_text" : token.form,
"pos_tag" : "NON-VERB"
}
groundTruthDocument["sentence"] = sentenceText
nvGroundTruth.insert_one(groundTruthDocument)
nvGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDPTDev():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_PT_EN_DEV)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udPTGroundTruth.insert_one(groundTruthDocument)
udPTGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDPTTest():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_PT_EN_TEST)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udPTGroundTruth.insert_one(groundTruthDocument)
udPTGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDPTTrain():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_PT_EN_TRAIN)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udPTGroundTruth.insert_one(groundTruthDocument)
udPTGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDEWDev():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_EW_EN_DEV)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udEWGroundTruth.insert_one(groundTruthDocument)
udEWGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDEWTest():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_EW_EN_TEST)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udEWGroundTruth.insert_one(groundTruthDocument)
udEWGroundTruthCopy.insert_one(groundTruthDocument)
def getTestDataUDEWTrain():
"""
Imports conll type ground-truth data into a local MongoDB for POS-tagging testing.
"""
devSet = pyconll.load_from_file(UD_EW_EN_TRAIN)
for sentence in devSet:
groundTruthDocument = {
"sentence" : sentence.text
}
posTags = []
index = 0
for token in sentence:
posTags.append({
"token_text" : token.form,
"token_begin_offset" : index,
"pos_tag" : token.upos
})
index += (len(token.form)+1)
groundTruthDocument['annotation'] = posTags
udEWGroundTruth.insert_one(groundTruthDocument)
udEWGroundTruthCopy.insert_one(groundTruthDocument)
# Usage Example:
getTestDataUDDev()
getTestDataUDTest()
getTestDataUDTrain()
| 35.518519
| 121
| 0.574477
| 1,208
| 12,467
| 5.771523
| 0.166391
| 0.033563
| 0.088353
| 0.033563
| 0.784423
| 0.784423
| 0.777395
| 0.75043
| 0.742685
| 0.742685
| 0
| 0.008053
| 0.322692
| 12,467
| 350
| 122
| 35.62
| 0.817622
| 0.174862
| 0
| 0.726891
| 0
| 0
| 0.171008
| 0.103161
| 0
| 0
| 0
| 0
| 0
| 1
| 0.046218
| false
| 0
| 0.012605
| 0
| 0.058824
| 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
|
370a5bdc43ab9effc0ca3a17db706337b6f94e46
| 305
|
py
|
Python
|
python/AmcCarrierCore/AppHardware/RtmCryoDet/__init__.py
|
slaclab/amc-carrier-core
|
1ecbd2d2c9215863c200b4404456616dd4ed2e2f
|
[
"BSD-3-Clause-LBNL"
] | 1
|
2019-01-31T17:12:03.000Z
|
2019-01-31T17:12:03.000Z
|
python/AmcCarrierCore/AppHardware/RtmCryoDet/__init__.py
|
slaclab/amc-carrier-core
|
1ecbd2d2c9215863c200b4404456616dd4ed2e2f
|
[
"BSD-3-Clause-LBNL"
] | 33
|
2018-03-25T21:06:15.000Z
|
2022-01-27T17:07:01.000Z
|
python/AmcCarrierCore/AppHardware/RtmCryoDet/__init__.py
|
slaclab/amc-carrier-core
|
1ecbd2d2c9215863c200b4404456616dd4ed2e2f
|
[
"BSD-3-Clause-LBNL"
] | 3
|
2020-04-16T12:37:58.000Z
|
2022-03-31T09:14:13.000Z
|
from AmcCarrierCore.AppHardware.RtmCryoDet._rtmCryoDet import *
from AmcCarrierCore.AppHardware.RtmCryoDet._dacLut import *
from AmcCarrierCore.AppHardware.RtmCryoDet._spiCryo import *
from AmcCarrierCore.AppHardware.RtmCryoDet._spiMax import *
from AmcCarrierCore.AppHardware.RtmCryoDet._spiSr import *
| 43.571429
| 63
| 0.865574
| 30
| 305
| 8.633333
| 0.3
| 0.34749
| 0.559846
| 0.752896
| 0.694981
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068852
| 305
| 6
| 64
| 50.833333
| 0.911972
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
3727cb3a74ffa8549a8cd8b273642a7ffa60c246
| 5,887
|
py
|
Python
|
src/the_tale/the_tale/common/utils/tests/test_xls.py
|
al-arz/the-tale
|
542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5
|
[
"BSD-3-Clause"
] | 85
|
2017-11-21T12:22:02.000Z
|
2022-03-27T23:07:17.000Z
|
src/the_tale/the_tale/common/utils/tests/test_xls.py
|
al-arz/the-tale
|
542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5
|
[
"BSD-3-Clause"
] | 545
|
2017-11-04T14:15:04.000Z
|
2022-03-27T14:19:27.000Z
|
src/the_tale/the_tale/common/utils/tests/test_xls.py
|
al-arz/the-tale
|
542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5
|
[
"BSD-3-Clause"
] | 45
|
2017-11-11T12:36:30.000Z
|
2022-02-25T06:10:44.000Z
|
import smart_imports
smart_imports.all()
class XLSTests(testcase.TestCase):
def setUp(self):
super(XLSTests, self).setUp()
def test_load_table_simple(self):
data = utils_xls.load_table(os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=0)
self.assertEqual(data, [[3.0, 'd', 3.0, 4.0, '', ''],
['', 2.0, 3.0, 4.0, 'df', '+'],
['', 6.0, 7.0, 8.0, '', ''],
[123.0, 23.0, 34.0, 45.0, 'qw', '']])
def test_load_table_with_rows(self):
data = utils_xls.load_table(os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=1,
rows=['row1', 'row2', 'row3', 'row4'])
self.assertEqual(data,
{'row1': [3.0, 'd', 3.0, 4.0, '', ''],
'row2': ['', 2.0, 3.0, 4.0, 'df', '+'],
'row3': ['', 6.0, 7.0, 8.0, '', ''],
'row4': [123.0, 23.0, 34.0, 45.0, 'qw', '']})
def test_load_table_with_rows_duplicate_rows_in_args(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=1,
rows=['row1', 'row2', 'row2', 'row4'])
def test_load_table_with_rows_duplicate_rows(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=2,
rows=['row1', 'row2', 'row3', 'row4'])
def test_load_table_with_rows_undefined_rows(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=2,
rows=['row1', 'row2', 'row3', 'row5'])
def test_load_table_with_columns(self):
data = xls.load_table(os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=3,
columns=['col1', 'col2', 'col3', 'col4', 'col5'])
self.assertEqual(data, [{'col1': 3.0, 'col2': 'd', 'col3': 3.0, 'col4': 4.0, 'col5': ''},
{'col1': '', 'col2': 2.0, 'col3': 3.0, 'col4': 4.0, 'col5': 'df'},
{'col1': '', 'col2': 6.0, 'col3': 7.0, 'col4': 8.0, 'col5': ''},
{'col1': 123.0, 'col2': 23.0, 'col3': 34.0, 'col4': 45.0, 'col5': 'qw'}])
def test_load_table_with_columns_duplicate_column_in_args(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=3,
columns=['col1', 'col2', 'col2', 'col4', 'col5'])
def test_load_table_with_columns_duplicate_column(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=4,
columns=['col1', 'col2', 'col3', 'col4', 'col5'])
def test_load_table_with_columns_too_long_columns(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=5,
columns=['col1', 'col2', 'col3', 'col4', 'col5'])
def test_load_table_with_rows_and_columns(self):
data = xls.load_table(os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=6,
rows=['row1', 'row2', 'row3', 'row4'],
columns=['col1', 'col2', 'col3', 'col4', 'col5'])
self.assertEqual(data, {'row1': {'col1': 3.0, 'col2': 'd', 'col3': 3.0, 'col4': 4.0, 'col5': ''},
'row2': {'col1': '', 'col2': 2.0, 'col3': 3.0, 'col4': 4.0, 'col5': 'df'},
'row3': {'col1': '', 'col2': 6.0, 'col3': 7.0, 'col4': 8.0, 'col5': ''},
'row4': {'col1': 123.0, 'col2': 23.0, 'col3': 34.0, 'col4': 45.0, 'col5': 'qw'}})
def test_load_table_with_rows_and_columns_undefined_row(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=6,
rows=['row1', 'row2', 'row3', 'row5'],
columns=['col1', 'col2', 'col3', 'col4', 'col5'])
def test_load_table_with_rows_and_columns_undefined_column(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=6,
rows=['row1', 'row2', 'row3', 'row5'],
columns=['col1', 'col2', 'col6', 'col4', 'col5'])
def test_load_table_with_rows_and_columns_duplicate_row(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=7,
rows=['row1', 'row2', 'row3', 'row4'],
columns=['col1', 'col2', 'col3', 'col4', 'col5'])
def test_load_table_with_rows_and_columns_duplicate_column(self):
self.assertRaises(xls.XLSException,
xls.load_table, os.path.join(os.path.dirname(__file__), 'fixtures/xls_table_simple.xls'), sheet_index=8,
rows=['row1', 'row2', 'row3', 'row4'],
columns=['col1', 'col2', 'col3', 'col4', 'col5'])
| 58.87
| 130
| 0.530661
| 722
| 5,887
| 4.041551
| 0.094183
| 0.086361
| 0.052776
| 0.076765
| 0.921864
| 0.917752
| 0.906785
| 0.885538
| 0.85024
| 0.822138
| 0
| 0.063799
| 0.289112
| 5,887
| 99
| 131
| 59.464646
| 0.633453
| 0
| 0
| 0.358974
| 0
| 0
| 0.160381
| 0.068977
| 0
| 0
| 0
| 0
| 0.179487
| 1
| 0.192308
| false
| 0
| 0.025641
| 0
| 0.230769
| 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
|
2e960f50a60f96630a19c6b1c7f7adc7990c9fe1
| 43
|
py
|
Python
|
pypro/base/models/__init__.py
|
rodrigoddc/django-advanced-course
|
098507d8111f38f8a6b914575e50861538913f6c
|
[
"MIT"
] | 1
|
2020-06-30T01:30:31.000Z
|
2020-06-30T01:30:31.000Z
|
pypro/base/models/__init__.py
|
rodrigoddc/django-advanced-course
|
098507d8111f38f8a6b914575e50861538913f6c
|
[
"MIT"
] | 102
|
2020-06-30T01:03:27.000Z
|
2021-09-22T19:26:44.000Z
|
pypro/base/models/__init__.py
|
rodrigoddc/django-advanced-course
|
098507d8111f38f8a6b914575e50861538913f6c
|
[
"MIT"
] | null | null | null |
from .user_custom import UserManager, User
| 21.5
| 42
| 0.837209
| 6
| 43
| 5.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116279
| 43
| 1
| 43
| 43
| 0.921053
| 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
|
2e9d2e683a165edaa137a3269842afc8ab17dee2
| 211
|
py
|
Python
|
lang/Python/url-decoding.py
|
ethansaxenian/RosettaDecode
|
8ea1a42a5f792280b50193ad47545d14ee371fb7
|
[
"MIT"
] | null | null | null |
lang/Python/url-decoding.py
|
ethansaxenian/RosettaDecode
|
8ea1a42a5f792280b50193ad47545d14ee371fb7
|
[
"MIT"
] | null | null | null |
lang/Python/url-decoding.py
|
ethansaxenian/RosettaDecode
|
8ea1a42a5f792280b50193ad47545d14ee371fb7
|
[
"MIT"
] | null | null | null |
#Python 2.X
import urllib.request, urllib.parse, urllib.error
print(urllib.parse.unquote("http%3A%2F%2Ffoo%20bar%2F"))
#Python 3.5+
from urllib.parse import unquote
print((unquote('http%3A%2F%2Ffoo%20bar%2F')))
| 30.142857
| 56
| 0.758294
| 36
| 211
| 4.444444
| 0.5
| 0.20625
| 0.1625
| 0.1875
| 0.3375
| 0.3375
| 0.3375
| 0
| 0
| 0
| 0
| 0.076142
| 0.066351
| 211
| 6
| 57
| 35.166667
| 0.736041
| 0.099526
| 0
| 0
| 0
| 0
| 0.265957
| 0.265957
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 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
| 0
| 1
|
0
| 6
|
2c1ee07bb606278d910c7ea9e8cb49c6dcf518db
| 30
|
py
|
Python
|
rocketman/utils/__init__.py
|
Batucho/Rocketman
|
ff93b7e68be269e4b1b04b4d72611e308e73a76b
|
[
"MIT"
] | null | null | null |
rocketman/utils/__init__.py
|
Batucho/Rocketman
|
ff93b7e68be269e4b1b04b4d72611e308e73a76b
|
[
"MIT"
] | null | null | null |
rocketman/utils/__init__.py
|
Batucho/Rocketman
|
ff93b7e68be269e4b1b04b4d72611e308e73a76b
|
[
"MIT"
] | null | null | null |
from rocketman.utils import *
| 15
| 29
| 0.8
| 4
| 30
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 30
| 1
| 30
| 30
| 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
|
25cc4f1d3641dd7fe191eec78b5416a3a202fed5
| 160
|
py
|
Python
|
Python/PycharmProjects/aula 6/main.py
|
MarcelaSamili/Desafios-do-curso-de-Python
|
f331e91821c0c25b3e32d2075254ef650292f280
|
[
"MIT"
] | null | null | null |
Python/PycharmProjects/aula 6/main.py
|
MarcelaSamili/Desafios-do-curso-de-Python
|
f331e91821c0c25b3e32d2075254ef650292f280
|
[
"MIT"
] | null | null | null |
Python/PycharmProjects/aula 6/main.py
|
MarcelaSamili/Desafios-do-curso-de-Python
|
f331e91821c0c25b3e32d2075254ef650292f280
|
[
"MIT"
] | null | null | null |
#Os arquivos main.py das aulas 6,7 e 8 não tem nada, porém os arquivos main.py das outras aulas tem breves explicações da aula na qual foi inspirado o desafio.
| 80
| 159
| 0.78125
| 32
| 160
| 3.90625
| 0.78125
| 0.16
| 0.224
| 0.256
| 0.304
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022901
| 0.18125
| 160
| 1
| 160
| 160
| 0.931298
| 0.9875
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
25ceade12d46713648345cc4a48e0416c43d5c97
| 32
|
py
|
Python
|
acq4/drivers/PatchStar/__init__.py
|
tropp/ACQ4
|
792e05e99cedfc175593d200aeabecd6fa6304ce
|
[
"MIT"
] | null | null | null |
acq4/drivers/PatchStar/__init__.py
|
tropp/ACQ4
|
792e05e99cedfc175593d200aeabecd6fa6304ce
|
[
"MIT"
] | null | null | null |
acq4/drivers/PatchStar/__init__.py
|
tropp/ACQ4
|
792e05e99cedfc175593d200aeabecd6fa6304ce
|
[
"MIT"
] | null | null | null |
from patchstar import PatchStar
| 16
| 31
| 0.875
| 4
| 32
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 32
| 1
| 32
| 32
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
25f16c129e44eeb148c31a847f92c7fe65ad20c1
| 60
|
py
|
Python
|
inception/__init__.py
|
nishaq503/InceptionTime
|
01fa6d2f72e91b27f421ca9bd1cd06381406d4e6
|
[
"MIT"
] | null | null | null |
inception/__init__.py
|
nishaq503/InceptionTime
|
01fa6d2f72e91b27f421ca9bd1cd06381406d4e6
|
[
"MIT"
] | null | null | null |
inception/__init__.py
|
nishaq503/InceptionTime
|
01fa6d2f72e91b27f421ca9bd1cd06381406d4e6
|
[
"MIT"
] | null | null | null |
from . import datagen
from . import net
from . import utils
| 15
| 21
| 0.75
| 9
| 60
| 5
| 0.555556
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 60
| 3
| 22
| 20
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
25f9d69c8977e55156ace5077ef14dbe4fac1bfe
| 20,419
|
py
|
Python
|
mdl-ref-server/test.py
|
joshrl-clearme/mdl-ref-apps
|
4aacf07dc769657497b526c9982e247886fdc019
|
[
"Apache-2.0"
] | 41
|
2020-04-13T12:24:57.000Z
|
2022-01-31T18:51:32.000Z
|
mdl-ref-server/test.py
|
joshrl-clearme/mdl-ref-apps
|
4aacf07dc769657497b526c9982e247886fdc019
|
[
"Apache-2.0"
] | 13
|
2020-05-12T19:20:59.000Z
|
2022-01-05T16:28:55.000Z
|
mdl-ref-server/test.py
|
joshrl-clearme/mdl-ref-apps
|
4aacf07dc769657497b526c9982e247886fdc019
|
[
"Apache-2.0"
] | 27
|
2020-03-26T04:21:59.000Z
|
2022-02-25T07:08:30.000Z
|
#!/usr/bin/python3
import cbor
import hashlib
import tornado.testing
import unittest
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives.asymmetric import ec
import server
import util
class coseTest(unittest.TestCase):
def test_cose_key(self):
private_key = ec.generate_private_key(ec.SECP256R1(), default_backend())
public_key = private_key.public_key()
cose_key = util.to_cose_key(public_key)
extracted_public_key = util.from_cose_key(cose_key)
# Can't just compare the objects, we have to get the raw numbers/bytes
# and compare them
public_key_bytes = public_key.public_bytes(
serialization.Encoding.DER,
serialization.PublicFormat.SubjectPublicKeyInfo)
extracted_public_key_bytes = extracted_public_key.public_bytes(
serialization.Encoding.DER,
serialization.PublicFormat.SubjectPublicKeyInfo)
self.assertEqual(public_key_bytes, extracted_public_key_bytes)
def test_cose_sign(self):
private_key = ec.generate_private_key(ec.SECP256R1(), default_backend())
public_key = private_key.public_key()
data = b"some bytes to sign"
sig = util.cose_sign1_sign(private_key, data)
self.assertEqual(data, util.cose_sign1_get_data(sig))
self.assertTrue(util.cose_sign1_verify(public_key, sig, data))
class certTest(unittest.TestCase):
def test_credential_key_cert(self):
credential_key_private = ec.generate_private_key(ec.SECP256R1(), default_backend())
credential_key_public = credential_key_private.public_key()
cert_chain = util.generate_x509_cert_for_credential_key(credential_key_private)
# Can't just compare the objects, we have to get the raw numbers/bytes
# and compare them
extracted_public_key = util.cert_chain_get_public_key(cert_chain)
credential_key_public_bytes = credential_key_public.public_bytes(
serialization.Encoding.DER,
serialization.PublicFormat.SubjectPublicKeyInfo)
extracted_public_key_bytes = extracted_public_key.public_bytes(
serialization.Encoding.DER,
serialization.PublicFormat.SubjectPublicKeyInfo)
self.assertEqual(credential_key_public_bytes, extracted_public_key_bytes)
# TODO: check Android Attestation Extension stuff
def test_auth_key_cert(self):
credential_key_private = ec.generate_private_key(ec.SECP256R1(), default_backend())
credential_key_public = credential_key_private.public_key()
auth_key_private = ec.generate_private_key(ec.SECP256R1(), default_backend())
auth_key_public = auth_key_private.public_key()
pop_sha256 = b"123"
cert = util.generate_x509_cert_for_auth_key(auth_key_public, credential_key_private,
pop_sha256)
# TODO: check ProofOfBinding at OID
# TODO: check signature
class mdlServerTest(tornado.testing.AsyncHTTPTestCase):
def get_app(self):
self.s = server.Server(":memory:")
util.setup_test_data(self.s.database)
return self.s.get_app()
def test_happy_path(self):
path = "/mdlServer"
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "StartProvisioning",
"provisioningCode": "1001",
# TODO: MCD
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"], "ReadyToProvisionMessage")
session_id = cbor_response["eSessionId"]
self.assertTrue(len(session_id) > 0)
# TODO: insert AdditionalInformationRequired steps here
response = self.fetch(method="POST", path=path, body=cbor.dumps({
"messageType": "com.android.identity_credential.StartProvisioning",
"eSessionId": session_id,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.ProvisioningResponse")
self.assertEqual(session_id, cbor_response["eSessionId"])
challenge = cbor_response["challenge"]
self.assertTrue(isinstance(challenge, bytes) and len(challenge) > 0)
doc_type = cbor_response["docType"]
self.assertEqual(doc_type, "org.iso.18013.5.1.mDL")
credential_key = ec.generate_private_key(ec.SECP256R1(), default_backend())
# TODO: pass challenge and other Android-specific things to
# be included in the Android Attestation Extension
#
cert_chain = util.generate_x509_cert_for_credential_key(credential_key)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.SetCertificateChain",
"eSessionId": session_id,
"credentialKeyCertificateChain": cert_chain,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.DataToProvisionMessage")
self.assertEqual(session_id, cbor_response["eSessionId"])
access_control_profiles = cbor_response["accessControlProfiles"]
self.assertTrue(access_control_profiles and isinstance(access_control_profiles, list))
name_spaces = cbor_response["nameSpaces"]
self.assertTrue(name_spaces and isinstance(name_spaces, dict))
# Build ProofOfProvisioning
#
proof_of_provisioning = cbor.dumps(["ProofOfProvisioning",
doc_type,
access_control_profiles,
name_spaces,
False])
pop_sha256 = hashlib.sha256(proof_of_provisioning).digest()
pop_signature = util.cose_sign1_sign(credential_key, proof_of_provisioning)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.SetProofOfProvisioning",
"eSessionId": session_id,
"proofOfProvisioningSignature": pop_signature,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"], "EndSessionMessage")
self.assertEqual(session_id, cbor_response["eSessionId"])
self.assertEqual("Success", cbor_response["reason"])
# --------------------------------------------------------------------------------
# Now get some auth keys. This is a new flow.
#
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.CertifyAuthKeys",
"credentialKey": util.to_cose_key(credential_key.public_key()),
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.CertifyAuthKeysProveOwnership")
session_id = cbor_response["eSessionId"]
self.assertTrue(len(session_id) > 0)
challenge = cbor_response["challenge"]
self.assertTrue(len(challenge) > 0)
# Identify ourselves
#
proof_of_ownership = cbor.dumps(["ProofOfOwnership",
doc_type,
challenge,
False])
poo_signature = util.cose_sign1_sign(credential_key, proof_of_ownership)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.CertifyAuthKeysProveOwnershipResponse",
"eSessionId": session_id,
"proofOfOwnershipSignature": poo_signature,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.CertifyAuthKeysReady")
self.assertEqual(session_id, cbor_response["eSessionId"])
# Create some auth keys and send them
#
auth_key_certs = []
for n in range(3):
auth_key = ec.generate_private_key(ec.SECP256R1(), default_backend())
cert = util.generate_x509_cert_for_auth_key(auth_key.public_key(), credential_key,
pop_sha256)
auth_key_certs.append(cert)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.CertifyAuthKeysSendCerts",
"eSessionId": session_id,
"authKeyCerts": auth_key_certs,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.CertifyAuthKeysResponse")
self.assertEqual(session_id, cbor_response["eSessionId"])
static_auth_datas = cbor_response["staticAuthDatas"]
# TODO: inspect |staticAuthDatas|
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "RequestEndSession",
"eSessionId": session_id,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"], "EndSessionMessage")
self.assertEqual(session_id, cbor_response["eSessionId"])
self.assertEqual("Success", cbor_response["reason"])
# --------------------------------------------------------------------------------
# Check update. This is a new flow.
#
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.UpdateCredential",
"credentialKey": util.to_cose_key(credential_key.public_key()),
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.UpdateCredentialProveOwnership")
session_id = cbor_response["eSessionId"]
self.assertTrue(len(session_id) > 0)
challenge = cbor_response["challenge"]
self.assertTrue(len(challenge) > 0)
# Identify ourselves expected no updates
#
proof_of_ownership = cbor.dumps(["ProofOfOwnership",
doc_type,
challenge,
False])
poo_signature = util.cose_sign1_sign(credential_key, proof_of_ownership)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.UpdateCredentialProveOwnershipResponse",
"eSessionId": session_id,
"proofOfOwnershipSignature": poo_signature,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.UpdateCredentialResponse")
self.assertEqual(session_id, cbor_response["eSessionId"])
# self.assertEqual("no_update", cbor_response["updateCredentialResult"])
# --------------------------------------------------------------------------------
# Change the document data in the database.
# document_id 11 - Erika
util.update_document_test_data(self.s.database, 11)
# Check update. This is a new flow.
#
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.UpdateCredential",
"credentialKey": util.to_cose_key(credential_key.public_key()),
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.UpdateCredentialProveOwnership")
session_id = cbor_response["eSessionId"]
self.assertTrue(len(session_id) > 0)
challenge = cbor_response["challenge"]
self.assertTrue(len(challenge) > 0)
# Identify ourselves expected update
#
proof_of_ownership = cbor.dumps(["ProofOfOwnership",
doc_type,
challenge,
False])
poo_signature = util.cose_sign1_sign(credential_key, proof_of_ownership)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.UpdateCredentialProveOwnershipResponse",
"eSessionId": session_id,
"proofOfOwnershipSignature": poo_signature,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.UpdateCredentialResponse")
self.assertEqual(session_id, cbor_response["eSessionId"])
self.assertEqual("update", cbor_response["updateCredentialResult"])
# Get data to update (new provisioning)
#
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.UpdateCredentialGetDataToUpdate",
"eSessionId": session_id,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.UpdateCredentialDataToProvisionMessage")
self.assertEqual(session_id, cbor_response["eSessionId"])
access_control_profiles = cbor_response["accessControlProfiles"]
self.assertTrue(access_control_profiles and isinstance(access_control_profiles, list))
name_spaces = cbor_response["nameSpaces"]
self.assertTrue(name_spaces and isinstance(name_spaces, dict))
# Build ProofOfProvisioning for updated data
#
proof_of_provisioning = cbor.dumps(["ProofOfProvisioning",
doc_type,
access_control_profiles,
name_spaces,
False])
pop_sha256 = hashlib.sha256(proof_of_provisioning).digest()
pop_signature = util.cose_sign1_sign(credential_key, proof_of_provisioning)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.UpdateCredentialSetProofOfProvisioning",
"eSessionId": session_id,
"proofOfProvisioningSignature": pop_signature,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"], "EndSessionMessage")
self.assertEqual(session_id, cbor_response["eSessionId"])
self.assertEqual("Success", cbor_response["reason"])
# --------------------------------------------------------------------------------
# Delete credential. This is a new flow.
#
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.DeleteCredential",
"credentialKey": util.to_cose_key(credential_key.public_key()),
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.DeleteCredentialProveOwnership")
session_id = cbor_response["eSessionId"]
self.assertTrue(len(session_id) > 0)
challenge = cbor_response["challenge"]
self.assertTrue(len(challenge) > 0)
# Identify ourselves expected update
#
proof_of_ownership = cbor.dumps(["ProofOfOwnership",
doc_type,
challenge,
False])
poo_signature = util.cose_sign1_sign(credential_key, proof_of_ownership)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.DeleteCredentialProveOwnershipResponse",
"eSessionId": session_id,
"proofOfOwnershipSignature": poo_signature,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"],
"com.android.identity_credential.DeleteCredentialReadyForDeletion")
self.assertEqual(session_id, cbor_response["eSessionId"])
challenge = cbor_response["challenge"]
self.assertTrue(len(challenge) > 0)
# Get data to update (new provisioning)
#
proof_of_deletion = cbor.dumps(["ProofOfDeletion",
doc_type,
challenge,
False])
pod_signature = util.cose_sign1_sign(credential_key, proof_of_deletion)
response = self.fetch(method="POST", path=path,
body=cbor.dumps({
"messageType": "com.android.identity_credential.DeleteCredentialDeleted",
"eSessionId": session_id,
"proofOfDeletionSignature": pod_signature,
}))
self.assertEqual(response.code, 200)
cbor_response = cbor.loads(response.body)
self.assertEqual(cbor_response["messageType"], "EndSessionMessage")
self.assertEqual(session_id, cbor_response["eSessionId"])
self.assertEqual("Success", cbor_response["reason"])
if __name__ == '__main__':
unittest.main()
| 49.802439
| 122
| 0.574122
| 1,780
| 20,419
| 6.343258
| 0.125843
| 0.073333
| 0.050217
| 0.069347
| 0.782924
| 0.774511
| 0.767425
| 0.759277
| 0.759277
| 0.74555
| 0
| 0.011086
| 0.32411
| 20,419
| 409
| 123
| 49.924205
| 0.807043
| 0.069347
| 0
| 0.738019
| 0
| 0
| 0.164961
| 0.099757
| 0
| 0
| 0
| 0.002445
| 0.226837
| 1
| 0.019169
| false
| 0
| 0.028754
| 0
| 0.060703
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 1
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
d31c101f61d00331a4f6b4b7ee5a32e9011c72e0
| 12,962
|
py
|
Python
|
mimic3benchmark/readers.py
|
aureus5/mimiciII
|
0ce15bb67a90b88dbe96e842dbe05d84b6b8ab59
|
[
"MIT"
] | 11
|
2019-10-16T15:34:19.000Z
|
2022-03-31T10:50:44.000Z
|
mimic3benchmark/readers.py
|
aureus5/mimiciII
|
0ce15bb67a90b88dbe96e842dbe05d84b6b8ab59
|
[
"MIT"
] | null | null | null |
mimic3benchmark/readers.py
|
aureus5/mimiciII
|
0ce15bb67a90b88dbe96e842dbe05d84b6b8ab59
|
[
"MIT"
] | 4
|
2019-05-24T11:50:35.000Z
|
2021-02-23T07:01:14.000Z
|
import os
import numpy as np
import random
class Reader(object):
def __init__(self, dataset_dir, listfile=None):
self._dataset_dir = dataset_dir
if listfile is None:
listfile_path = os.path.join(dataset_dir, "listfile.csv")
else:
listfile_path = listfile
with open(listfile_path, "r") as lfile:
self._data = lfile.readlines()
def get_number_of_examples(self):
return len(self._data)
def random_shuffle(self, seed=None):
if (seed is not None):
random.seed(seed)
random.shuffle(self._data)
def read_example(self, index):
raise NotImplementedError()
def read_next(self):
if not hasattr(self, '_current_index'):
self._current_index = 0
to_read_index = self._current_index
self._current_index += 1
if (self._current_index == self.get_number_of_examples()):
self._current_index = 0
return self.read_example(to_read_index)
class DecompensationReader(Reader):
r"""
Reader for decompensation prediction task.
Parameters
----------
dataset_dir : str
Directory where timeseries files are stored.
listilfe : str
Path to a listfile. If this parameter is left `None` then
`dataset_dir/listfile.csv` will be used.
"""
def __init__(self, dataset_dir, listfile=None):
Reader.__init__(self, dataset_dir, listfile)
self._data = [line.split(',') for line in self._data]
self._data = [(x, float(t), int(y)) for (x, t, y) in self._data]
def _read_timeseries(self, ts_filename, time_bound):
ret = []
with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
header = tsfile.readline().strip().split(',')
assert header[0] == "Hours"
for line in tsfile:
mas = line.strip().split(',')
t = float(mas[0])
if t > time_bound + 1e-6:
break
ret.append(np.array(mas))
return (np.stack(ret), header)
def read_example(self, index):
r"""
Reads the example with given index.
Parameters
----------
index : int
Index of the line of the listfile to read (counting starts from 0).
Returns (X, t, y, header)
-------
X : np.array
2D array containing all events. Each row corresponds to a moment.
First coloumn is the time and other columns correspond to different
variables.
t : float
Lenght of the data in hours. Note, in general, it is not eqaul to the
timestamp of last event.
y : int (0 or 1)
Mortality within next 24 hours.
header : array of strings
Names of the columns. The ordering of the columns is always the same.
"""
if (index < 0 or index >= len(self._data)):
raise ValueError("Index must be from 0 (inclusive) to number of examples (exclusive).")
t = self._data[index][1]
(X, header) = self._read_timeseries(self._data[index][0], t)
y = self._data[index][2]
return (X, t, y, header)
class InHospitalMortalityReader(Reader):
r"""
Reader for in-hospital moratality prediction task.
Parameters
----------
dataset_dir : str
Directory where timeseries files are stored.
listilfe : str
Path to a listfile. If this parameter is left `None` then
`dataset_dir/listfile.csv` will be used.
period_length : float
Length of the period (in hours) from which the prediction is done.
"""
def __init__(self, dataset_dir, listfile=None, period_length=48.0):
Reader.__init__(self, dataset_dir, listfile)
self._data = [line.split(',') for line in self._data]
self._data = [(x, int(y)) for (x, y) in self._data]
self._period_length = period_length
def _read_timeseries(self, ts_filename):
ret = []
with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
header = tsfile.readline().strip().split(',')
assert header[0] == "Hours"
for line in tsfile:
mas = line.strip().split(',')
ret.append(np.array(mas))
return (np.stack(ret), header)
def read_example(self, index):
r"""
Reads the example with given index.
Parameters
----------
index : int
Index of the line of the listfile to read (counting starts from 0).
Returns (X, t, y, header)
-------
X : np.array
2D array containing all events. Each row corresponds to a moment.
First coloumn is the time and other columns correspond to different
variables.
t : float
Lenght of the data in hours. Note, in general, it is not eqaul to the
timestamp of last event.
y : int (0 or 1)
In-hospital mortality.
header : array of strings
Names of the columns. The ordering of the columns is always the same.
"""
if (index < 0 or index >= len(self._data)):
raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")
(X, header) = self._read_timeseries(self._data[index][0])
y = self._data[index][1]
return (X, self._period_length, y, header)
class LengthOfStayReader(Reader):
r"""
Reader for length of stay prediction task.
Parameters
----------
dataset_dir : str
Directory where timeseries files are stored.
listilfe : str
Path to a listfile. If this parameter is left `None` then
`dataset_dir/listfile.csv` will be used.
"""
def __init__(self, dataset_dir, listfile=None):
Reader.__init__(self, dataset_dir, listfile)
self._data = [line.split(',') for line in self._data]
self._data = [(x, float(t), float(y)) for (x, t, y) in self._data]
def _read_timeseries(self, ts_filename, time_bound):
ret = []
with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
header = tsfile.readline().strip().split(',')
assert header[0] == "Hours"
for line in tsfile:
mas = line.strip().split(',')
t = float(mas[0])
if t > time_bound + 1e-6:
break
ret.append(np.array(mas))
return (np.stack(ret), header)
def read_example(self, index):
r"""
Reads the example with given index.
Parameters
----------
index : int
Index of the line of the listfile to read (counting starts from 0).
Returns (X, t, y, header)
-------
X : np.array
2D array containing all events. Each row corresponds to a moment.
First coloumn is the time and other columns correspond to different
variables.
t : float
Lenght of the data in hours. Note, in general, it is not eqaul to the
timestamp of last event.
y : float
Remaining time in ICU.
header : array of strings
Names of the columns. The ordering of the columns is always the same.
"""
if (index < 0 or index >= len(self._data)):
raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")
t = self._data[index][1]
(X, header) = self._read_timeseries(self._data[index][0], t)
y = self._data[index][2]
return (X, t, y, header)
class PhenotypingReader(Reader):
r"""
Reader for phenotype classification task.
Parameters
----------
dataset_dir : str
Directory where timeseries files are stored.
listilfe : str
Path to a listfile. If this parameter is left `None` then
`dataset_dir/listfile.csv` will be used.
"""
def __init__(self, dataset_dir, listfile=None):
Reader.__init__(self, dataset_dir, listfile)
self._listfile_header = self._data[0]
self._data = self._data[1:]
self._data = [line.split(',') for line in self._data]
self._data = [(mas[0], float(mas[1]), map(int, mas[2:])) for mas in self._data]
def _read_timeseries(self, ts_filename):
ret = []
with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
header = tsfile.readline().strip().split(',')
assert header[0] == "Hours"
for line in tsfile:
mas = line.strip().split(',')
ret.append(np.array(mas))
return (np.stack(ret), header)
def read_example(self, index):
r"""
Reads the example with given index.
Parameters
----------
index : int
Index of the line of the listfile to read (counting starts from 0).
Returns (X, t, y, header)
-------
X : np.array
2D array containing all events. Each row corresponds to a moment.
First coloumn is the time and other columns correspond to different
variables.
t : float
Lenght of the data in hours. Note, in general, it is not eqaul to the
timestamp of last event.
y : array of ints
Phenotype labels.
header : array of strings
Names of the columns. The ordering of the columns is always the same.
"""
if (index < 0 or index >= len(self._data)):
raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")
(X, header) = self._read_timeseries(self._data[index][0])
y = self._data[index][2]
return (X, self._data[index][1], y, header)
class MultitaskReader(Reader):
r"""
Reader for multitask.
Parameters
----------
dataset_dir : str
Directory where timeseries files are stored.
listilfe : str
Path to a listfile. If this parameter is left `None` then
`dataset_dir/listfile.csv` will be used.
"""
def __init__(self, dataset_dir, listfile=None):
Reader.__init__(self, dataset_dir, listfile)
self._listfile_header = self._data[0]
self._data = self._data[1:]
self._data = [line.split(',') for line in self._data]
def process_ihm(ihm):
return map(int, ihm.split(';'))
def process_los(los):
los = los.split(';')
return (map(int, los[:len(los)/2]), map(float, los[len(los)/2:]))
def process_ph(ph):
return map(int, ph.split(';'))
def process_decomp(decomp):
decomp = decomp.split(';')
return (map(int, decomp[:len(decomp)/2]), map(int, decomp[len(decomp)/2:]))
self._data = [(fname, float(t), process_ihm(ihm), process_los(los),
process_ph(ph), process_decomp(decomp))
for fname, t, ihm, los, ph, decomp in self._data]
def _read_timeseries(self, ts_filename):
ret = []
with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
header = tsfile.readline().strip().split(',')
assert header[0] == "Hours"
for line in tsfile:
mas = line.strip().split(',')
ret.append(np.array(mas))
return (np.stack(ret), header)
def read_example(self, index):
r"""
Reads the example with given index.
Parameters
----------
index : int
Index of the line of the listfile to read (counting starts from 0).
Returns (X, t, ihm, los, ph, decomp, header)
-------
X : np.array
2D array containing all events. Each row corresponds to a moment.
First coloumn is the time and other columns correspond to different
variables.
t : float
Lenght of the data in hours. Note, in general, it is not eqaul to the
timestamp of last event.
ihm : array
Array of 3 integers: [pos, mask, label].
los : array
Array of 2 arrays: [masks, labels].
ph : array
Array of 25 binary integers (phenotype labels).
decomp : array
Array of 2 arrays: [masks, labels].
header : array of strings
Names of the columns. The ordering of the columns is always the same.
"""
if (index < 0 or index >= len(self._data)):
raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")
(X, header) = self._read_timeseries(self._data[index][0])
return [X] + list(self._data[index][1:]) + [header]
| 35.032432
| 99
| 0.569974
| 1,651
| 12,962
| 4.345851
| 0.107813
| 0.052404
| 0.033171
| 0.027596
| 0.803902
| 0.790244
| 0.775192
| 0.757352
| 0.757352
| 0.757352
| 0
| 0.008183
| 0.32117
| 12,962
| 369
| 100
| 35.127371
| 0.807251
| 0.353186
| 0
| 0.611111
| 0
| 0
| 0.05465
| 0
| 0
| 0
| 0
| 0
| 0.030864
| 1
| 0.148148
| false
| 0
| 0.018519
| 0.018519
| 0.302469
| 0
| 0
| 0
| 0
| null | 0
| 0
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| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
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| 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
|
d341084571320f433acd2ecc79500ed615bc51d2
| 84
|
py
|
Python
|
chapter-04/04-04-QSharp-From-Python/host.py
|
Djohnnie/Introducing-Microsoft-Quantum-Computing-For-Developers
|
e437815576bd3576050f7c75b99d47a3c68955c4
|
[
"MIT"
] | null | null | null |
chapter-04/04-04-QSharp-From-Python/host.py
|
Djohnnie/Introducing-Microsoft-Quantum-Computing-For-Developers
|
e437815576bd3576050f7c75b99d47a3c68955c4
|
[
"MIT"
] | null | null | null |
chapter-04/04-04-QSharp-From-Python/host.py
|
Djohnnie/Introducing-Microsoft-Quantum-Computing-For-Developers
|
e437815576bd3576050f7c75b99d47a3c68955c4
|
[
"MIT"
] | null | null | null |
import qsharp
from _04_04_QSharp_From_Python import HelloQ
print(HelloQ.simulate())
| 21
| 44
| 0.857143
| 13
| 84
| 5.153846
| 0.615385
| 0.298507
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.051948
| 0.083333
| 84
| 4
| 45
| 21
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0.333333
| 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
|
d346d29a8413b326e2aa4ef59b53271433f06e25
| 101
|
py
|
Python
|
Beta/Number of Triangles in an Finite Nsized Triangular Tiling.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 6
|
2020-09-03T09:32:25.000Z
|
2020-12-07T04:10:01.000Z
|
Beta/Number of Triangles in an Finite Nsized Triangular Tiling.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 1
|
2021-12-13T15:30:21.000Z
|
2021-12-13T15:30:21.000Z
|
Beta/Number of Triangles in an Finite Nsized Triangular Tiling.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | null | null | null |
# https://oeis.org/A002717
from math import floor
def solve(n):
return floor(n*(n+2)*(2*n+1)//8)
| 20.2
| 36
| 0.643564
| 20
| 101
| 3.25
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114943
| 0.138614
| 101
| 4
| 37
| 25.25
| 0.632184
| 0.237624
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
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| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
d36bb5f72c05b99c68f6aad997c1388a728d6015
| 9,851
|
py
|
Python
|
tests/kovan_test_data.py
|
jannikluhn/tlbc-monitor
|
9d54d40bfed48db5542fd6714946ea27684a918e
|
[
"MIT"
] | null | null | null |
tests/kovan_test_data.py
|
jannikluhn/tlbc-monitor
|
9d54d40bfed48db5542fd6714946ea27684a918e
|
[
"MIT"
] | 61
|
2019-04-08T20:13:47.000Z
|
2020-07-16T09:18:48.000Z
|
tests/kovan_test_data.py
|
jannikluhn/tlbc-monitor
|
9d54d40bfed48db5542fd6714946ea27684a918e
|
[
"MIT"
] | 3
|
2019-02-22T14:15:27.000Z
|
2019-10-23T04:20:47.000Z
|
from hexbytes import HexBytes
from web3.datastructures import AttributeDict
# test vectors retrieved from Kovan via web3.py
KOVAN_GENESIS_BLOCK = AttributeDict(
{
"author": "0x0000000000000000000000000000000000000000",
"difficulty": 131072,
"extraData": HexBytes("0x"),
"gasLimit": 6000000,
"gasUsed": 0,
"hash": HexBytes(
"0xa3c565fc15c7478862d50ccd6561e3c06b24cc509bf388941c25ea985ce32cb9"
),
"logsBloom": HexBytes(
"0x00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000"
),
"miner": "0x0000000000000000000000000000000000000000",
"number": 0,
"parentHash": HexBytes(
"0x0000000000000000000000000000000000000000000000000000000000000000"
),
"receiptsRoot": HexBytes(
"0x56e81f171bcc55a6ff8345e692c0f86e5b48e01b996cadc001622fb5e363b421"
),
"sealFields": [
"0x80",
"0xb8410000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000",
],
"sha3Uncles": HexBytes(
"0x1dcc4de8dec75d7aab85b567b6ccd41ad312451b948a7413f0a142fd40d49347"
),
"signature": "0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000",
"size": 533,
"stateRoot": HexBytes(
"0x2480155b48a1cea17d67dbfdfaafe821c1d19cdd478c5358e8ec56dec24502b2"
),
"step": "0",
"timestamp": 0,
"totalDifficulty": 131072,
"transactions": [],
"transactionsRoot": HexBytes(
"0x56e81f171bcc55a6ff8345e692c0f86e5b48e01b996cadc001622fb5e363b421"
),
"uncles": [],
}
)
KOVAN_BLOCKS = [
AttributeDict(
{
"author": "0x00a0a24b9f0e5ec7aa4c7389b8302fd0123194de",
"difficulty": 131072,
"extraData": HexBytes("0xd5830105048650617269747986312e31352e31826c69"),
"gasLimit": 5994142,
"gasUsed": 0,
"hash": HexBytes(
"0xaa4149067ff439918dc3cd510ca969cb7b710e4c40b66d535a17b88c4591122c"
),
"logsBloom": HexBytes(
"0x00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000"
),
"miner": "0x00A0A24b9f0E5EC7Aa4c7389b8302fd0123194dE",
"number": 1,
"parentHash": HexBytes(
"0xa3c565fc15c7478862d50ccd6561e3c06b24cc509bf388941c25ea985ce32cb9"
),
"receiptsRoot": HexBytes(
"0x56e81f171bcc55a6ff8345e692c0f86e5b48e01b996cadc001622fb5e363b421"
),
"sealFields": [
"0x84162e05a6",
"0xb84147ead3ed26a11937a210dd861dd6df225d275dc917156087425955d8414db27b0882b2a7cd10d9a4c8c2f0242c26794039ecbce77ffe3aebc02d9cce3dfb850a00",
],
"sha3Uncles": HexBytes(
"0x1dcc4de8dec75d7aab85b567b6ccd41ad312451b948a7413f0a142fd40d49347"
),
"signature": "47ead3ed26a11937a210dd861dd6df225d275dc917156087425955d8414db27b0882b2a7cd10d9a4c8c2f0242c26794039ecbce77ffe3aebc02d9cce3dfb850a00",
"size": 563,
"stateRoot": HexBytes(
"0x627211819453eefbd3389bac45eecbbd606ad52042d0e2aa839216c8a6107abc"
),
"step": "372114854",
"timestamp": 1488459416,
"totalDifficulty": 262144,
"transactions": [],
"transactionsRoot": HexBytes(
"0x56e81f171bcc55a6ff8345e692c0f86e5b48e01b996cadc001622fb5e363b421"
),
"uncles": [],
}
),
AttributeDict(
{
"author": "0x00427feae2419c15b89d1c21af10d1b6650a4d3d",
"difficulty": 340282366920938463463374607431768211454,
"extraData": HexBytes("0xd5830107008650617269747986312e31382e30826c69"),
"gasLimit": 6881641,
"gasUsed": 191888,
"hash": HexBytes(
"0xd288a0d5b323db2424d91aca86e79b188bf2f25e0a3d8052bb8e7ccd498b6c21"
),
"logsBloom": HexBytes(
"0x00000000000000000000000000000000000000010000000010000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000008000000000000000000000000000000000000000000000000000000010040040000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000004000000000000002000200000000000000000000000000000002004000000000000000000000000000000000000000000000000000000010004000000000000010000100"
),
"miner": "0x00427FEaE2419C15b89D1C21af10D1b6650A4d3D",
"number": 3000000,
"parentHash": HexBytes(
"0xc59389f641284f042b5395a3f322b49999ba0d0a875d469c610ae0fec0c050d8"
),
"receiptsRoot": HexBytes(
"0xa2e4c63a15a985c91ac778a39a606200aa31e321a15db078908778baf1d0bf0b"
),
"sealFields": [
"0x8416613797",
"0xb841f38ade5d13609fc5216a0b5f14f6258c79af3f508734541341dacd8835a51248470e09f01505cd3d0bce9e2c69aeefdca7efef35edeb2f9abafe725369ea3e7300",
],
"sha3Uncles": HexBytes(
"0x1dcc4de8dec75d7aab85b567b6ccd41ad312451b948a7413f0a142fd40d49347"
),
"signature": "f38ade5d13609fc5216a0b5f14f6258c79af3f508734541341dacd8835a51248470e09f01505cd3d0bce9e2c69aeefdca7efef35edeb2f9abafe725369ea3e7300",
"size": 822,
"stateRoot": HexBytes(
"0xc0da1b25a302b154c835f0fe4c0875b3cef2c3e9138b8d660228c4e929f30ec9"
),
"step": "375469975",
"timestamp": 1501879900,
"totalDifficulty": 970403982972822393504776633864226765894316764,
"transactions": [
HexBytes(
"0x9505d3362925a2a1a0ebdcd09bb83af2b20e969c151a287e13ad5ec090203467"
)
],
"transactionsRoot": HexBytes(
"0x321fc7d3510ad08a447494adcb9e8426f4322aa15eb0713638904a8b5330fa2a"
),
"uncles": [],
}
),
AttributeDict(
{
"author": "0x0010f94b296a852aaac52ea6c5ac72e03afd032d",
"difficulty": 340282366920938463463374607431768211454,
"extraData": HexBytes("0xd5830107068650617269747986312e32302e30826c69"),
"gasLimit": 6999987,
"gasUsed": 30737,
"hash": HexBytes(
"0x8cc5e080b2f13514e94c18d4afc1ec96b020549c0474e7319053497d7a2afa3b"
),
"logsBloom": HexBytes(
"0x00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000400000000000080000000000000000040000000000000000000000000008000000000000080000000000000000000000000000000000000000002000000000000000000000000000000000000000000000000020000000000000000000000000008000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000010000000000000000000000004000000000000000000000000000000000000"
),
"miner": "0x0010f94b296A852aAac52EA6c5Ac72e03afD032D",
"number": 4346794,
"parentHash": HexBytes(
"0x8e35652c5e7e28a9f25d0e74e7b54fa68dd1372c105c79d2afc3bc5a7117b42c"
),
"receiptsRoot": HexBytes(
"0x87f9bdcceec4095ad70ad91eb1a68a9f0a86702d846c9c11ac3ec0edd8e471ec"
),
"sealFields": [
"0x841679b339",
"0xb841aef4a1a5768e41351a9700a44b1130b551308dc8d23bd9617d951c36a4b9b1d757453f9e1ad3150b565a08351cedf34f0979361aa5227919a5060a61f54f258e01",
],
"sha3Uncles": HexBytes(
"0x1dcc4de8dec75d7aab85b567b6ccd41ad312451b948a7413f0a142fd40d49347"
),
"signature": "aef4a1a5768e41351a9700a44b1130b551308dc8d23bd9617d951c36a4b9b1d757453f9e1ad3150b565a08351cedf34f0979361aa5227919a5060a61f54f258e01",
"size": 756,
"stateRoot": HexBytes(
"0xc979c77de1e0be4e8bc40d6bfc5b9f98973d99f85d8e97579c865f4e0cf7c76e"
),
"step": "377074489",
"timestamp": 1508297956,
"totalDifficulty": 1428694233047740790466468774905687602471037510,
"transactions": [
HexBytes(
"0x7fa35ab95c19c10e9e40b5134389353e8f35e27d32c838d09e5c83ce13eb4401"
)
],
"transactionsRoot": HexBytes(
"0x98d614e1cacb50435d43f12e3a86657343ad336de2814ba6f8570790ccad20b4"
),
"uncles": [],
}
),
]
| 51.307292
| 532
| 0.699726
| 234
| 9,851
| 29.444444
| 0.444444
| 0.01103
| 0.048766
| 0.053991
| 0.03135
| 0
| 0
| 0
| 0
| 0
| 0
| 0.618405
| 0.239976
| 9,851
| 191
| 533
| 51.575916
| 0.301857
| 0.004568
| 0
| 0.543478
| 0
| 0
| 0.626275
| 0.541616
| 0
| 0
| 0.492656
| 0
| 0
| 1
| 0
| false
| 0
| 0.01087
| 0
| 0.01087
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
d36c5a68e2ea7701dd9533031acedf4bf739650a
| 105
|
py
|
Python
|
main.py
|
ronaldshaooo/OpenDataJudicial
|
ee55b234276511b824a04d836607741f9189ebd8
|
[
"MIT"
] | null | null | null |
main.py
|
ronaldshaooo/OpenDataJudicial
|
ee55b234276511b824a04d836607741f9189ebd8
|
[
"MIT"
] | null | null | null |
main.py
|
ronaldshaooo/OpenDataJudicial
|
ee55b234276511b824a04d836607741f9189ebd8
|
[
"MIT"
] | null | null | null |
import update_judicial_data
update_judicial_data.refresh()
update_judicial_data.copy_fincial_judgement()
| 26.25
| 45
| 0.904762
| 14
| 105
| 6.214286
| 0.571429
| 0.482759
| 0.62069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.038095
| 105
| 4
| 45
| 26.25
| 0.861386
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
d3713e121f08a00650cddbe78763a428aff511cf
| 222
|
py
|
Python
|
scenario/targeting/__init__.py
|
NREL/K_Road
|
ec8049cf1b81c58dd3b95f8298a362d863cd4a68
|
[
"BSD-3-Clause"
] | 1
|
2021-04-19T23:28:26.000Z
|
2021-04-19T23:28:26.000Z
|
scenario/targeting/__init__.py
|
NREL/K_Road
|
ec8049cf1b81c58dd3b95f8298a362d863cd4a68
|
[
"BSD-3-Clause"
] | null | null | null |
scenario/targeting/__init__.py
|
NREL/K_Road
|
ec8049cf1b81c58dd3b95f8298a362d863cd4a68
|
[
"BSD-3-Clause"
] | null | null | null |
from .target_baseline import TargetBaseline
from .target_observer import TargetObserver
from .target_process import TargetProcess
from .target_rewarder import TargetRewarder
from .target_terminator import TargetTerminator
| 37
| 47
| 0.887387
| 25
| 222
| 7.68
| 0.52
| 0.260417
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09009
| 222
| 5
| 48
| 44.4
| 0.950495
| 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
|
d39af3a729e6e29538c1c2cb75f7f4c1dbdd9667
| 420
|
py
|
Python
|
presqt/targets/gitlab/utilities/__init__.py
|
djordjetrajkovic/presqt
|
8424b61b1c5b8d29de74c7a333889d9e9eb7aee8
|
[
"Apache-2.0"
] | 3
|
2019-01-29T19:45:25.000Z
|
2020-12-01T18:24:51.000Z
|
presqt/targets/gitlab/utilities/__init__.py
|
djordjetrajkovic/presqt
|
8424b61b1c5b8d29de74c7a333889d9e9eb7aee8
|
[
"Apache-2.0"
] | 419
|
2018-09-13T23:11:15.000Z
|
2021-09-22T17:49:00.000Z
|
presqt/targets/gitlab/utilities/__init__.py
|
djordjetrajkovic/presqt
|
8424b61b1c5b8d29de74c7a333889d9e9eb7aee8
|
[
"Apache-2.0"
] | 2
|
2020-04-10T08:19:41.000Z
|
2021-01-04T15:29:42.000Z
|
from presqt.targets.gitlab.utilities.validation_check import validation_check
from presqt.targets.gitlab.utilities.gitlab_paginated_data import gitlab_paginated_data
from presqt.targets.gitlab.utilities.download_content import download_content
from presqt.targets.gitlab.utilities.delete_gitlab_project import delete_gitlab_project
from presqt.targets.gitlab.utilities.extra_metadata_helper import extra_metadata_helper
| 70
| 87
| 0.904762
| 56
| 420
| 6.5
| 0.303571
| 0.137363
| 0.233516
| 0.315934
| 0.43956
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.047619
| 420
| 5
| 88
| 84
| 0.91
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d3a1b5ceb1f7c3703557f14c545723043f438b44
| 20
|
py
|
Python
|
modules/tests/helpers/__init__.py
|
PeterDaveHello/eden
|
26174a9dde2f19cd3bc879694f373ad5f765b6ed
|
[
"MIT"
] | 1
|
2016-12-22T09:31:22.000Z
|
2016-12-22T09:31:22.000Z
|
modules/tests/helpers/__init__.py
|
PeterDaveHello/eden
|
26174a9dde2f19cd3bc879694f373ad5f765b6ed
|
[
"MIT"
] | null | null | null |
modules/tests/helpers/__init__.py
|
PeterDaveHello/eden
|
26174a9dde2f19cd3bc879694f373ad5f765b6ed
|
[
"MIT"
] | 1
|
2019-12-16T15:14:46.000Z
|
2019-12-16T15:14:46.000Z
|
from report import *
| 20
| 20
| 0.8
| 3
| 20
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 20
| 1
| 20
| 20
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d3ac57572a2922aad562812262ffcfda90640f7f
| 98
|
py
|
Python
|
frost/ext/__init__.py
|
Den4200/pyfrost
|
c341eeb1262746d889dcff4f405f7d52f7caf23d
|
[
"MIT"
] | 2
|
2020-03-02T14:29:07.000Z
|
2020-03-04T00:36:35.000Z
|
frost/ext/__init__.py
|
Den4200/pyfrost
|
c341eeb1262746d889dcff4f405f7d52f7caf23d
|
[
"MIT"
] | 16
|
2020-03-08T05:50:48.000Z
|
2020-05-11T04:40:13.000Z
|
frost/ext/__init__.py
|
Den4200/pyfrost
|
c341eeb1262746d889dcff4f405f7d52f7caf23d
|
[
"MIT"
] | 1
|
2020-03-29T00:33:04.000Z
|
2020-03-29T00:33:04.000Z
|
from frost.ext.cog import Cog
from frost.ext.handler import Handler
__all__ = ('Cog', 'Handler')
| 19.6
| 37
| 0.744898
| 15
| 98
| 4.6
| 0.466667
| 0.26087
| 0.347826
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.132653
| 98
| 4
| 38
| 24.5
| 0.811765
| 0
| 0
| 0
| 0
| 0
| 0.102041
| 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
|
6cb7ae5da53d83defcb003178ee1a57595acf20d
| 109
|
py
|
Python
|
python_interface/gecco_modules/string_to_form.py
|
ak-ustutt/GeCCo-public
|
8d43a6c9323aeba7eb54625b95553bfd4b2418c6
|
[
"MIT"
] | null | null | null |
python_interface/gecco_modules/string_to_form.py
|
ak-ustutt/GeCCo-public
|
8d43a6c9323aeba7eb54625b95553bfd4b2418c6
|
[
"MIT"
] | null | null | null |
python_interface/gecco_modules/string_to_form.py
|
ak-ustutt/GeCCo-public
|
8d43a6c9323aeba7eb54625b95553bfd4b2418c6
|
[
"MIT"
] | null | null | null |
from .stf_pack.stf_top import Formula, GenForm, make_formula
__all__=["Formula", "GenForm", "make_formula"]
| 27.25
| 60
| 0.770642
| 15
| 109
| 5.066667
| 0.6
| 0.368421
| 0.473684
| 0.657895
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091743
| 109
| 3
| 61
| 36.333333
| 0.767677
| 0
| 0
| 0
| 0
| 0
| 0.238532
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
9f0de2441af5bcc6147b7c75411bf219db7cc7d4
| 121
|
py
|
Python
|
scipyplot/__init__.py
|
robertocalandra/scipyplot
|
c2f2ffe00207fbb5b78732981d572c031660ed74
|
[
"MIT"
] | 4
|
2016-12-02T20:24:54.000Z
|
2020-07-26T20:54:03.000Z
|
scipyplot/__init__.py
|
robertocalandra/scipyplot
|
c2f2ffe00207fbb5b78732981d572c031660ed74
|
[
"MIT"
] | 6
|
2017-01-17T01:22:14.000Z
|
2021-06-10T09:26:12.000Z
|
scipyplot/__init__.py
|
robertocalandra/scipyplot
|
c2f2ffe00207fbb5b78732981d572c031660ed74
|
[
"MIT"
] | 1
|
2021-04-22T08:46:22.000Z
|
2021-04-22T08:46:22.000Z
|
from __future__ import absolute_import
from .plot import *
from .log import *
from .stats import *
from .utils import *
| 17.285714
| 38
| 0.760331
| 17
| 121
| 5.117647
| 0.470588
| 0.45977
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173554
| 121
| 6
| 39
| 20.166667
| 0.87
| 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
|
9f6cabaceea075866e57c8dd1484f978a9c16360
| 152
|
py
|
Python
|
tests/unit/plugins/settings/conftest.py
|
pauleveritt/kaybee
|
a00a718aaaa23b2d12db30dfacb6b2b6ec84459c
|
[
"Apache-2.0"
] | 2
|
2017-11-08T19:55:57.000Z
|
2018-12-21T12:41:41.000Z
|
tests/unit/plugins/settings/conftest.py
|
pauleveritt/kaybee
|
a00a718aaaa23b2d12db30dfacb6b2b6ec84459c
|
[
"Apache-2.0"
] | null | null | null |
tests/unit/plugins/settings/conftest.py
|
pauleveritt/kaybee
|
a00a718aaaa23b2d12db30dfacb6b2b6ec84459c
|
[
"Apache-2.0"
] | 1
|
2018-10-13T08:59:29.000Z
|
2018-10-13T08:59:29.000Z
|
import dectate
import pytest
@pytest.fixture()
def settings_kb_app():
class settings_kb_app(dectate.App):
pass
yield settings_kb_app
| 13.818182
| 39
| 0.723684
| 21
| 152
| 4.952381
| 0.52381
| 0.288462
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.203947
| 152
| 10
| 40
| 15.2
| 0.859504
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| true
| 0.142857
| 0.285714
| 0
| 0.571429
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
9f7dbeab62b8e4f5d3d4c26f73882e30a04ce9f2
| 6,591
|
py
|
Python
|
test/data/test_dataset_strategy_changes.py
|
CristianPachacama/cartoframes
|
3dc4e10d175069a7d7b734db3d9526127aad9dec
|
[
"BSD-3-Clause"
] | 1
|
2020-11-23T23:44:32.000Z
|
2020-11-23T23:44:32.000Z
|
test/data/test_dataset_strategy_changes.py
|
CristianPachacama/cartoframes
|
3dc4e10d175069a7d7b734db3d9526127aad9dec
|
[
"BSD-3-Clause"
] | null | null | null |
test/data/test_dataset_strategy_changes.py
|
CristianPachacama/cartoframes
|
3dc4e10d175069a7d7b734db3d9526127aad9dec
|
[
"BSD-3-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
"""Unit tests for cartoframes.data.dataset about strategy changes"""
import unittest
import pandas as pd
from cartoframes.auth import Credentials
from cartoframes.utils import load_geojson
from cartoframes.data import StrategiesRegistry
from cartoframes.data.registry.dataframe_dataset import DataFrameDataset
from cartoframes.data.registry.table_dataset import TableDataset
from cartoframes.data.registry.query_dataset import QueryDataset
from cartoframes import context
from ..mocks.dataset_mock import DatasetMock
from ..mocks.context_mock import ContextMock
class TestDatasetStrategyChanges(unittest.TestCase):
def setUp(self):
self.username = 'fake_username'
self.api_key = 'fake_api_key'
self.credentials = Credentials(username=self.username, api_key=self.api_key)
self.test_geojson = {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"properties": {},
"geometry": {
"type": "Point",
"coordinates": [
-3.1640625,
42.032974332441405
]
}
}
]
}
self._context_mock = ContextMock()
# Mock create_context method
self.original_create_context = context.create_context
context.create_context = lambda c: self._context_mock
def tearDown(self):
StrategiesRegistry.instance = None
context.create_context = self.original_create_context
def test_dataset_from_table(self):
table_name = 'fake_table'
dataset = DatasetMock(table_name, credentials=self.credentials)
self.assertTrue(isinstance(dataset._strategy, TableDataset))
self.assertTrue(dataset.is_remote())
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_table_after_download(self):
table_name = 'fake_table'
dataset = DatasetMock(table_name, credentials=self.credentials)
self.assertTrue(isinstance(dataset._strategy, TableDataset))
self.assertTrue(dataset.is_remote())
self.assertTrue(dataset.is_saved_in_carto)
dataset.download()
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_table_after_upload(self):
table_name = 'fake_table'
dataset = DatasetMock(table_name, credentials=self.credentials)
dataset.download()
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertTrue(dataset.is_saved_in_carto)
dataset.upload(table_name='another_table')
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertEqual(dataset.table_name, 'another_table')
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_query(self):
query = "SELECT 1"
dataset = DatasetMock(query, credentials=self.credentials)
self.assertTrue(isinstance(dataset._strategy, QueryDataset))
self.assertTrue(dataset.is_remote())
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_query_after_download(self):
query = "SELECT 1"
dataset = DatasetMock(query, credentials=self.credentials)
self.assertTrue(isinstance(dataset._strategy, QueryDataset))
self.assertTrue(dataset.is_remote())
self.assertTrue(dataset.is_saved_in_carto)
dataset.download()
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_query_and_upload(self):
query = "SELECT 1"
dataset = DatasetMock(query, credentials=self.credentials)
self.assertTrue(isinstance(dataset._strategy, QueryDataset))
self.assertTrue(dataset.is_remote())
self.assertTrue(dataset.is_saved_in_carto)
dataset.upload(table_name='another_table')
self.assertTrue(isinstance(dataset._strategy, TableDataset))
self.assertTrue(dataset.is_remote())
self.assertEqual(dataset.table_name, 'another_table')
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_dataframe(self):
df = pd.DataFrame({'column_name': [2]})
dataset = DatasetMock(df)
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertFalse(dataset.is_saved_in_carto)
def test_dataset_from_dataframe_upload(self):
df = pd.DataFrame({'column_name': [2]})
dataset = DatasetMock(df)
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertFalse(dataset.is_saved_in_carto)
dataset.upload(table_name='another_table', credentials=self.credentials)
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertEqual(dataset.table_name, 'another_table')
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_dataframe_upload_append(self):
df = pd.DataFrame({'column_name': [2]})
dataset = DatasetMock(df)
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertFalse(dataset.is_saved_in_carto)
dataset.upload(table_name='another_table', credentials=self.credentials, if_exists=DatasetMock.APPEND)
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertTrue(dataset.is_saved_in_carto)
def test_dataset_from_geodataframe(self):
gdf = load_geojson(self.test_geojson)
dataset = DatasetMock(gdf)
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertFalse(dataset.is_saved_in_carto)
def test_dataset_from_geojson(self):
geojson = self.test_geojson
dataset = DatasetMock(geojson)
self.assertTrue(isinstance(dataset._strategy, DataFrameDataset))
self.assertTrue(dataset.is_local())
self.assertFalse(dataset.is_saved_in_carto)
| 42.522581
| 110
| 0.693521
| 702
| 6,591
| 6.249288
| 0.132479
| 0.146797
| 0.138819
| 0.15204
| 0.758833
| 0.741509
| 0.723273
| 0.723273
| 0.723273
| 0.723273
| 0
| 0.006163
| 0.212259
| 6,591
| 154
| 111
| 42.798701
| 0.83879
| 0.016993
| 0
| 0.583333
| 0
| 0
| 0.043418
| 0
| 0
| 0
| 0
| 0
| 0.409091
| 1
| 0.098485
| false
| 0
| 0.083333
| 0
| 0.189394
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
9fba0815c5c681bcbd3892fffa18d096896e01a9
| 2,583
|
py
|
Python
|
tests/lib/k8s/test_cli.py
|
ChauffeurPrive/nestor-api
|
364b5f034eeb929932a5a8c3f3b00d1275a7ae5b
|
[
"Apache-2.0"
] | 2
|
2020-08-17T09:59:03.000Z
|
2020-08-17T09:59:23.000Z
|
tests/lib/k8s/test_cli.py
|
ChauffeurPrive/nestor-api
|
364b5f034eeb929932a5a8c3f3b00d1275a7ae5b
|
[
"Apache-2.0"
] | 83
|
2020-06-12T14:37:35.000Z
|
2022-01-26T14:10:10.000Z
|
tests/lib/k8s/test_cli.py
|
ChauffeurPrive/nestor-api
|
364b5f034eeb929932a5a8c3f3b00d1275a7ae5b
|
[
"Apache-2.0"
] | 1
|
2020-07-02T14:33:45.000Z
|
2020-07-02T14:33:45.000Z
|
import os
from unittest import TestCase
from unittest.mock import patch
import nestor_api.lib.k8s.cli as cli
from nestor_api.lib.k8s.enums.k8s_resource_kind import K8sResourceKind
class TestK8sCli(TestCase):
@patch("nestor_api.lib.k8s.cli.K8sConfiguration", autospec=True)
@patch("nestor_api.lib.k8s.cli.io", autospec=True)
def test_fetch_resource_configuration_deployment(self, io_mock, config_mock):
config_mock.get_http_proxy.return_value = "k8s-proxy.my-domain.com"
io_mock.execute.return_value = '{"key": "value"}'
result = cli.fetch_resource_configuration(
"cluster", "namespace", "my-app", [K8sResourceKind.DEPLOYMENT]
)
self.assertEqual(result, {"key": "value"})
io_mock.execute.assert_called_once_with(
(
"kubectl "
"--context cluster "
"--namespace namespace "
"get Deployment "
"--output=json "
"--selector app=my-app"
),
env={**os.environ, "HTTP_PROXY": "k8s-proxy.my-domain.com"},
)
@patch("nestor_api.lib.k8s.cli.K8sConfiguration", autospec=True)
@patch("nestor_api.lib.k8s.cli.io", autospec=True)
def test_fetch_resource_configuration_deployment_and_cronjob(self, io_mock, config_mock):
config_mock.get_http_proxy.return_value = "k8s-proxy.my-domain.com"
io_mock.execute.return_value = '{"key": "value"}'
result = cli.fetch_resource_configuration(
"cluster", "namespace", "my-app", [K8sResourceKind.DEPLOYMENT, K8sResourceKind.CRONJOB],
)
self.assertEqual(result, {"key": "value"})
io_mock.execute.assert_called_once_with(
(
"kubectl "
"--context cluster "
"--namespace namespace "
"get Deployment,CronJob "
"--output=json "
"--selector app=my-app"
),
env={**os.environ, "HTTP_PROXY": "k8s-proxy.my-domain.com"},
)
@patch("nestor_api.lib.k8s.cli.K8sConfiguration", autospec=True)
@patch("nestor_api.lib.k8s.cli.io", autospec=True)
def test_apply_config(self, io_mock, config_mock):
config_mock.get_http_proxy.return_value = "k8s-proxy.my-domain.com"
cli.apply_config("cluster_name", "/path/to/config")
io_mock.execute.assert_called_once_with(
"kubectl --context cluster_name apply -f /path/to/config",
env={**os.environ, "HTTP_PROXY": "k8s-proxy.my-domain.com"},
)
| 38.552239
| 100
| 0.619822
| 299
| 2,583
| 5.133779
| 0.210702
| 0.046906
| 0.062541
| 0.078176
| 0.828013
| 0.816287
| 0.816287
| 0.816287
| 0.816287
| 0.816287
| 0
| 0.011862
| 0.249322
| 2,583
| 66
| 101
| 39.136364
| 0.779783
| 0
| 0
| 0.6
| 0
| 0
| 0.285714
| 0.127758
| 0
| 0
| 0
| 0
| 0.090909
| 1
| 0.054545
| false
| 0
| 0.090909
| 0
| 0.163636
| 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
|
9fce41f39d7199678a591024060bdb8424530065
| 73
|
py
|
Python
|
pytorl/envs/__init__.py
|
kareido/pytorl
|
2f2f5258425166b8bfbde985a229fecdef3752d9
|
[
"MIT"
] | 16
|
2019-03-03T02:00:55.000Z
|
2020-02-13T19:22:32.000Z
|
pytorl/envs/__init__.py
|
kareido/pytorl
|
2f2f5258425166b8bfbde985a229fecdef3752d9
|
[
"MIT"
] | null | null | null |
pytorl/envs/__init__.py
|
kareido/pytorl
|
2f2f5258425166b8bfbde985a229fecdef3752d9
|
[
"MIT"
] | null | null | null |
from .ale_atari import make_atari_env
from .gym_ctrl import make_ctrl_env
| 36.5
| 37
| 0.876712
| 14
| 73
| 4.142857
| 0.571429
| 0.344828
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09589
| 73
| 2
| 38
| 36.5
| 0.878788
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 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
|
4c9ab1a13194d60a313c9ae4656afcd71a2930ea
| 18,449
|
py
|
Python
|
bulbware_model.py
|
fintopo/bulbware
|
638cf34684e86f3a3adf03f999199717748a46fa
|
[
"MIT"
] | null | null | null |
bulbware_model.py
|
fintopo/bulbware
|
638cf34684e86f3a3adf03f999199717748a46fa
|
[
"MIT"
] | 7
|
2015-01-11T00:07:05.000Z
|
2016-06-18T02:33:27.000Z
|
bulbware_model.py
|
fintopo/bulbware
|
638cf34684e86f3a3adf03f999199717748a46fa
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from google.appengine.ext import ndb
from google.appengine.api import users
from google.appengine.api import memcache
import datetime
import calendar
import pickle
import json
import logging
import bulbware_lib
import user_model
class BulbwareProject(ndb.Model):
app_name = ndb.StringProperty()
owner = ndb.KeyProperty(user_model.UserInfo)
name = ndb.StringProperty()
options = ndb.TextProperty()
tags = ndb.StringProperty(repeated=True)
sorttext = ndb.StringProperty()
create_datetime = ndb.DateTimeProperty(auto_now_add=True)
update_datetime = ndb.DateTimeProperty(auto_now=True)
def get_property(self):
return {
'id': self.key.urlsafe(),
'owner_id': self.owner.urlsafe(),
'owner_name': self.owner.get().name,
'name': self.name,
'options': self.options,
'tags': self.tags,
'sorttext': self.sorttext,
'create_datetime': bulbware_lib.jst_date(self.create_datetime).strftime('%Y-%m-%d %H:%M:%S'),
'update_datetime': bulbware_lib.jst_date(self.update_datetime).strftime('%Y-%m-%d %H:%M:%S')
}
def get_option_values(self):
if self.options:
return json.loads(self.options)
else:
return {}
def get_owner(self):
return self.owner.get()
def get_pages(self):
return search_pages_project(self.app_name, self)
def get_items(self):
return search_items_project(self.app_name, self)
def check_edit(self, userinfo):
return (self.owner.urlsafe() == userinfo.key.urlsafe())
def check_delete(self, userinfo):
pages = self.get_pages()
items = self.get_items()
return (pages.count() == 0) and (items.count() == 0) and (self.owner.urlsafe() == userinfo.key.urlsafe())
def save(self, userinfo, name, options, tags, sorttext=None):
self.name = name
self.options = options
self.tags = tags
if sorttext:
self.sorttext = sorttext
else:
self.sorttext = name
self.put()
def inTag(self, tag):
return (tag in self.tags)
def get_project(app_name, key_str):
if key_str:
key = ndb.Key(urlsafe=key_str)
if key.kind() == 'BulbwareProject':
project = key.get()
if project.app_name == app_name:
return project
def add_project(app_name, userinfo, name, options, tags, sorttext=None):
project = BulbwareProject()
project.app_name = app_name
project.owner = userinfo.key
project.save(userinfo, name, options, tags, sorttext)
return project
def search_projects_owner(app_name, userinfo, tags=None):
q = BulbwareProject.query(BulbwareProject.app_name==app_name, BulbwareProject.owner==userinfo.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareProject.tags.IN(tags))
#
return q.order(BulbwareProject.sorttext)
def search_projects_name(app_name, name, tags=None):
q = BulbwareProject.query(BulbwareProject.app_name==app_name, BulbwareProject.name==name)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareProject.tags.IN(tags))
#
return q.order(BulbwareProject.sorttext)
def search_projects_tags(app_name, tags=None):
q = BulbwareProject.query(BulbwareProject.app_name==app_name)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareProject.tags.IN(tags))
#
return q.order(BulbwareProject.sorttext)
class BulbwarePage(ndb.Model):
app_name = ndb.StringProperty()
project = ndb.KeyProperty(BulbwareProject)
owner = ndb.KeyProperty(user_model.UserInfo)
name = ndb.StringProperty()
options = ndb.TextProperty()
tags = ndb.StringProperty(repeated=True)
sorttext = ndb.StringProperty()
create_datetime = ndb.DateTimeProperty(auto_now_add=True)
update_datetime = ndb.DateTimeProperty(auto_now=True)
def get_property(self):
ret = {
'id': self.key.urlsafe(),
'project': '',
'owner_id': self.owner.urlsafe(),
'owner_name': self.owner.get().name,
'name': self.name,
'options': self.options,
'tags': self.tags,
'sorttext': self.sorttext,
'create_datetime': bulbware_lib.jst_date(self.create_datetime).strftime('%Y-%m-%d %H:%M:%S'),
'update_datetime': bulbware_lib.jst_date(self.update_datetime).strftime('%Y-%m-%d %H:%M:%S')
}
if self.project:
ret['project'] = self.project.get().get_property()
return ret
def get_option_values(self):
if self.options:
return json.loads(self.options)
else:
return {}
def get_owner(self):
return self.owner.get()
def check_edit(self, userinfo):
return (self.owner.urlsafe() == userinfo.key.urlsafe())
def check_delete(self, userinfo):
return self.check_edit(userinfo)
def save(self, name, options, tags, sorttext=None, project_key=None):
self.name = name
self.options = options
self.tags = tags
if sorttext:
self.sorttext = sorttext
else:
self.sorttext = name
if project_key:
self.project = ndb.Key(urlsafe=project_key)
self.put()
def get_page(app_name, key_str):
if key_str:
key = ndb.Key(urlsafe=key_str)
if key.kind() == 'BulbwarePage':
page = key.get()
if page.app_name == app_name:
return page
def add_page(app_name, userinfo, name, options, tags, sorttext=None, project_key=None):
page = BulbwarePage()
page.app_name = app_name
page.owner = userinfo.key
page.save(name, options, tags, sorttext, project_key)
return page
def search_pages(app_name, userinfo=None, project=None, tags=None, order=True):
q = BulbwarePage.query(BulbwarePage.app_name==app_name)
#
if userinfo:
q = q.filter(BulbwarePage.owner==bulbware_lib.get_key(userinfo, user_model.UserInfo))
#
if project:
q = q.filter(BulbwarePage.project==bulbware_lib.get_key(project, BulbwareProject))
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwarePage.tags.IN(tags))
#
if order:
return q.order(BulbwarePage.sorttext)
else:
return q
def search_pages_project(app_name, project, tags=None):
q = BulbwarePage.query(BulbwarePage.app_name==app_name, BulbwarePage.project==project.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwarePage.tags.IN(tags))
#
return q.order(BulbwarePage.sorttext)
class BulbwareItem(ndb.Model):
app_name = ndb.StringProperty()
project = ndb.KeyProperty(BulbwareProject)
owner = ndb.KeyProperty(user_model.UserInfo)
name = ndb.StringProperty()
options = ndb.TextProperty()
tags = ndb.StringProperty(repeated=True)
sorttext = ndb.StringProperty()
create_datetime = ndb.DateTimeProperty(auto_now_add=True)
update_datetime = ndb.DateTimeProperty(auto_now=True)
def get_property(self):
ret = {
'id': self.key.urlsafe(),
'project': '',
'owner_id': self.owner.urlsafe(),
'owner_name': self.owner.get().name,
'name': self.name,
'options': self.options,
'tags': self.tags,
'sorttext': self.sorttext,
'create_datetime': bulbware_lib.jst_date(self.create_datetime).strftime('%Y-%m-%d %H:%M:%S'),
'update_datetime': bulbware_lib.jst_date(self.update_datetime).strftime('%Y-%m-%d %H:%M:%S')
}
if self.project:
ret['project'] = self.project.get().get_property()
return ret
def get_option_values(self):
if self.options:
return json.loads(self.options)
else:
return {}
def get_owner(self):
return self.owner.get()
def check_edit(self, userinfo):
return (self.owner.urlsafe() == userinfo.key.urlsafe())
def check_delete(self, userinfo):
return self.check_edit(userinfo)
def save(self, name, options, tags, sorttext=None, project_key=None):
self.name = name
self.options = options
self.tags = tags
if sorttext:
self.sorttext = sorttext
else:
self.sorttext = name
if project_key:
self.project = ndb.Key(urlsafe=project_key)
self.put()
def get_item(app_name, key_str):
if key_str:
key = ndb.Key(urlsafe=key_str)
if key.kind() == 'BulbwareItem':
item = key.get()
if item.app_name == app_name:
return item
def add_item(app_name, userinfo, name, options, tags, sorttext=None, project_key=None):
item = BulbwareItem()
item.app_name = app_name
item.owner = userinfo.key
item.save(name, options, tags, sorttext, project_key)
return item
def search_items(app_name, userinfo=None, project=None, tags=None, order=True):
q = BulbwareItem.query(BulbwareItem.app_name==app_name)
#
if userinfo:
q = q.filter(BulbwareItem.owner==bulbware_lib.get_key(userinfo, user_model.UserInfo))
#
if project:
q = q.filter(BulbwareItem.project==bulbware_lib.get_key(project, BulbwareProject))
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareItem.tags.IN(tags))
#
if order:
return q.order(BulbwareItem.sorttext)
else:
return q
def search_items_project(app_name, project, tags=None):
q = BulbwareItem.query(BulbwareItem.app_name==app_name, BulbwareItem.project==project.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareItem.tags.IN(tags))
#
return q.order(BulbwareItem.sorttext)
class BulbwareAttribute(ndb.Model):
app_name = ndb.StringProperty()
project = ndb.KeyProperty(BulbwareProject)
owner = ndb.KeyProperty(user_model.UserInfo)
name = ndb.StringProperty()
options = ndb.TextProperty()
tags = ndb.StringProperty(repeated=True)
sorttext = ndb.StringProperty()
create_datetime = ndb.DateTimeProperty(auto_now_add=True)
update_datetime = ndb.DateTimeProperty(auto_now=True)
def get_property(self):
ret = {
'id': self.key.urlsafe(),
'project': '',
'owner_id': self.owner.urlsafe(),
'owner_name': self.owner.get().name,
'name': self.name,
'options': self.options,
'tags': self.tags,
'sorttext': self.sorttext,
'create_datetime': bulbware_lib.jst_date(self.create_datetime).strftime('%Y-%m-%d %H:%M:%S'),
'update_datetime': bulbware_lib.jst_date(self.update_datetime).strftime('%Y-%m-%d %H:%M:%S')
}
if self.project:
ret['project'] = self.project.get().get_property()
return ret
def get_option_values(self):
if self.options:
return json.loads(self.options)
else:
return {}
def get_owner(self):
return self.owner.get()
def check_edit(self, userinfo):
return (self.owner.urlsafe() == userinfo.key.urlsafe())
def check_delete(self, userinfo):
return self.check_edit(userinfo)
def save(self, name, options, tags, sorttext=None, project_key=None):
self.name = name
self.options = options
self.tags = tags
if sorttext:
self.sorttext = sorttext
else:
self.sorttext = name
self.project = bulbware_lib.get_key(project_key, BulbwareProject)
self.put()
def get_attribute(app_name, key_str):
if key_str:
key = ndb.Key(urlsafe=key_str)
if key.kind() == 'BulbwareAttribute':
attribute = key.get()
if attribute.app_name == app_name:
return attribute
def add_attribute(app_name, userinfo, name, options, tags, sorttext=None, project_key=None):
attribute = BulbwareAttribute()
attribute.app_name = app_name
attribute.owner = userinfo.key
attribute.save(name, options, tags, sorttext, project_key)
return attribute
def search_attributes_owner(app_name, userinfo, tags=None):
q = BulbwareAttribute.query(BulbwareAttribute.app_name==app_name, BulbwareAttribute.owner==userinfo.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareAttribute.tags.IN(tags))
#
return q.order(BulbwareAttribute.sorttext)
def search_attributes_project(app_name, project, tags=None):
q = BulbwareAttribute.query(BulbwareAttribute.app_name==app_name, BulbwareAttribute.project==project.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareAttribute.tags.IN(tags))
#
return q.order(BulbwareAttribute.sorttext)
class BulbwareElement(ndb.Model):
app_name = ndb.StringProperty()
project = ndb.KeyProperty(BulbwareProject)
owner = ndb.KeyProperty(user_model.UserInfo)
page = ndb.KeyProperty(BulbwarePage)
item = ndb.KeyProperty(BulbwareItem)
attribute = ndb.KeyProperty(BulbwareAttribute)
options = ndb.TextProperty()
tags = ndb.StringProperty(repeated=True)
sorttext = ndb.StringProperty()
element_datetime = ndb.DateTimeProperty()
create_datetime = ndb.DateTimeProperty(auto_now_add=True)
update_datetime = ndb.DateTimeProperty(auto_now=True)
def get_property(self):
ret = {
'id': self.key.urlsafe(),
'project': '',
'page': '',
'item': '',
'attribute': '',
'options': self.options,
'tags': self.tags,
'sorttext': self.sorttext,
'element_datetime': bulbware_lib.jst_date(self.element_datetime).strftime('%Y-%m-%d %H:%M:%S') if self.element_datetime else '',
'create_datetime': bulbware_lib.jst_date(self.create_datetime).strftime('%Y-%m-%d %H:%M:%S'),
'update_datetime': bulbware_lib.jst_date(self.update_datetime).strftime('%Y-%m-%d %H:%M:%S')
}
if self.project:
ret['project'] = self.project.get().get_property()
if self.page:
ret['page'] = self.page.get().get_property()
if self.item:
ret['item'] = self.item.get().get_property()
if self.attribute:
ret['attribute'] = self.attribute.get().get_property()
return ret
def get_option_values(self):
if self.options:
return json.loads(self.options)
else:
return {}
def get_owner(self):
return self.owner.get()
def check_edit(self, userinfo):
return (self.owner.urlsafe() == userinfo.key.urlsafe())
def check_delete(self, userinfo):
return (self.owner.urlsafe() == userinfo.key.urlsafe())
def save(self, options, tags, sorttext=None, element_datetime=None, project=None, page=None, item=None, attribute=None):
self.options = options
self.tags = tags
if sorttext:
self.sorttext = sorttext
else:
self.sorttext = element_datetime
if element_datetime:
self.element_datetime = bulbware_lib.utc_date(bulbware_lib.parse_datetime(element_datetime))
else:
self.element_datetime = None
self.project = bulbware_lib.get_key(project, BulbwareProject)
self.page = bulbware_lib.get_key(page, BulbwarePage)
self.item = bulbware_lib.get_key(item, BulbwareItem)
self.attribute = bulbware_lib.get_key(attribute, BulbwareAttribute)
self.put()
def get_element(app_name, key_str):
if key_str:
key = ndb.Key(urlsafe=key_str)
if key.kind() == 'BulbwareElement':
element = key.get()
if element.app_name == app_name:
return element
def add_element(app_name, userinfo, options, tags, sorttext=None, element_datetime=None, project=None, page=None, item=None, attribute=None):
element = BulbwareElement()
element.app_name = app_name
element.owner = userinfo.key
element.save(options, tags, sorttext, element_datetime, project, page, item, attribute)
return element
def search_elements(app_name, userinfo=None, project=None, page=None, item=None, attribute=None, tags=None, datetime1=None, datetime2=None, order=True):
q = BulbwareElement.query(BulbwareElement.app_name==app_name)
#
if userinfo:
q = q.filter(BulbwareElement.owner==bulbware_lib.get_key(userinfo, user_model.UserInfo))
#
if project:
q = q.filter(BulbwareElement.project==bulbware_lib.get_key(project, BulbwareProject))
#
if page:
q = q.filter(BulbwareElement.page==bulbware_lib.get_key(page, BulbwarePage))
#
if item:
q = q.filter(BulbwareElement.item==bulbware_lib.get_key(item, BulbwareItem))
#
if attribute:
q = q.filter(BulbwareElement.attribute==bulbware_lib.get_key(attribute, BulbwareAttribute))
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareElement.tags.IN(tags))
#
if datetime1:
q = q.filter(BulbwareElement.element_datetime>=bulbware_lib.get_datetime(datetime1))
if datetime2:
q = q.filter(BulbwareElement.element_datetime<=bulbware_lib.get_datetime(datetime2))
#
if order:
return q.order(BulbwareElement.sorttext)
else:
return q
def search_elements_project(app_name, project, tags=None):
q = BulbwareElement.query(BulbwareElement.app_name==app_name, BulbwareElement.project==project.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareElement.tags.IN(tags))
#
return q.order(-BulbwareElement.sorttext)
def search_elements_page(app_name, page, tags=None):
q = BulbwareElement.query(BulbwareElement.app_name==app_name, BulbwareElement.page==page.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareElement.tags.IN(tags))
#
return q.order(-BulbwareElement.sorttext)
def search_elements_item(app_name, item, tags=None):
q = BulbwareElement.query(BulbwareElement.app_name==app_name, BulbwareElement.item==item.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareElement.tags.IN(tags))
#
return q.order(-BulbwareElement.sorttext)
def search_elements_attribute(app_name, attribute, tags=None):
q = BulbwareElement.query(BulbwareElement.app_name==app_name, BulbwareElement.attribute==attribute.key)
#
if tags:
while tags.count('') > 0:
tags.remove('')
if tags and (len(tags) > 0):
q = q.filter(BulbwareElement.tags.IN(tags))
#
return q.order(-BulbwareElement.sorttext)
def delete_elements(app_name, userinfo=None, project=None, page=None, item=None, attribute=None, tags=None, datetime1=None, datetime2=None):
q = search_elements(app_name, userinfo, project, page, item, attribute, tags, datetime1, datetime2)
ndb.delete_multi(q.fetch(keys_only=True))
| 33.422101
| 152
| 0.684319
| 2,419
| 18,449
| 5.081852
| 0.045887
| 0.046124
| 0.02237
| 0.027333
| 0.832018
| 0.791507
| 0.771333
| 0.732856
| 0.700561
| 0.680387
| 0
| 0.00271
| 0.180064
| 18,449
| 551
| 153
| 33.482759
| 0.809942
| 0.002277
| 0
| 0.689076
| 0
| 0
| 0.038493
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.121849
| false
| 0
| 0.021008
| 0.037815
| 0.378151
| 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
|
4cb4048bca3b306d73e88bab9a256a33070c5eba
| 20
|
py
|
Python
|
dopamine/agents/sdqn/__init__.py
|
jdmartin86/dopamine
|
7aefe7dcce4d0f089aa9f5690599dfda0ffbe1a6
|
[
"Apache-2.0"
] | 1
|
2018-12-29T20:02:47.000Z
|
2018-12-29T20:02:47.000Z
|
dopamine/agents/sdqn/__init__.py
|
jdmartin86/dopamine
|
7aefe7dcce4d0f089aa9f5690599dfda0ffbe1a6
|
[
"Apache-2.0"
] | null | null | null |
dopamine/agents/sdqn/__init__.py
|
jdmartin86/dopamine
|
7aefe7dcce4d0f089aa9f5690599dfda0ffbe1a6
|
[
"Apache-2.0"
] | null | null | null |
# ICML 2019 release
| 10
| 19
| 0.75
| 3
| 20
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 0.2
| 20
| 1
| 20
| 20
| 0.6875
| 0.85
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
4cd0fd76435bfe7d04978481f108394ac319c36b
| 34
|
py
|
Python
|
server/__init__.py
|
lclbm/xrx_pvp_server
|
f40cdbdc5de8c04b8113611c3174c84a200402b1
|
[
"MIT"
] | 1
|
2022-03-31T06:27:03.000Z
|
2022-03-31T06:27:03.000Z
|
server/__init__.py
|
lclbm/xrx_pvp_server
|
f40cdbdc5de8c04b8113611c3174c84a200402b1
|
[
"MIT"
] | null | null | null |
server/__init__.py
|
lclbm/xrx_pvp_server
|
f40cdbdc5de8c04b8113611c3174c84a200402b1
|
[
"MIT"
] | 1
|
2022-03-10T07:39:24.000Z
|
2022-03-10T07:39:24.000Z
|
from .d2_pvp_server import DB, API
| 34
| 34
| 0.823529
| 7
| 34
| 3.714286
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.033333
| 0.117647
| 34
| 1
| 34
| 34
| 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
|
4ce9097ca001e06017cae73654c1b4b9623c305d
| 82
|
py
|
Python
|
util/generic_util.py
|
jasoncin/dewarp-deskew-stn
|
050170fadceb9d98508aec6a855ea36b9b06cb74
|
[
"MIT"
] | null | null | null |
util/generic_util.py
|
jasoncin/dewarp-deskew-stn
|
050170fadceb9d98508aec6a855ea36b9b06cb74
|
[
"MIT"
] | null | null | null |
util/generic_util.py
|
jasoncin/dewarp-deskew-stn
|
050170fadceb9d98508aec6a855ea36b9b06cb74
|
[
"MIT"
] | null | null | null |
import random
def decision(probability):
return random.random() < probability
| 20.5
| 40
| 0.768293
| 9
| 82
| 7
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146341
| 82
| 4
| 40
| 20.5
| 0.9
| 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
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
4cfe9318239e87c1ee6a5ee0856b35350ac80799
| 225
|
py
|
Python
|
backend/wallet/admin.py
|
crowdbotics-apps/ezride-28420
|
9dad5e710b3cf0c7d81c01505b400b21a840f268
|
[
"FTL",
"AML",
"RSA-MD"
] | null | null | null |
backend/wallet/admin.py
|
crowdbotics-apps/ezride-28420
|
9dad5e710b3cf0c7d81c01505b400b21a840f268
|
[
"FTL",
"AML",
"RSA-MD"
] | null | null | null |
backend/wallet/admin.py
|
crowdbotics-apps/ezride-28420
|
9dad5e710b3cf0c7d81c01505b400b21a840f268
|
[
"FTL",
"AML",
"RSA-MD"
] | null | null | null |
from django.contrib import admin
from .models import DriverWallet, PaymentMethod, UserWallet
admin.site.register(DriverWallet)
admin.site.register(UserWallet)
admin.site.register(PaymentMethod)
# Register your models here.
| 25
| 59
| 0.831111
| 27
| 225
| 6.925926
| 0.481481
| 0.144385
| 0.272727
| 0.28877
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088889
| 225
| 8
| 60
| 28.125
| 0.912195
| 0.115556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 0
| 1
| 0
| 0
| null | 0
| 1
| 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
|
9815b7031a38e4d3fcff60bedd884581c84a0b7d
| 3,283
|
py
|
Python
|
python/athena/gpu_ops/SwapOut.py
|
DMALab/TSplit
|
8f86f987163aa06521bfeeb174616eb4a0a81b47
|
[
"Apache-2.0"
] | 2
|
2021-05-29T11:18:14.000Z
|
2021-09-09T14:29:21.000Z
|
python/athena/gpu_ops/SwapOut.py
|
DMALab/TSplit
|
8f86f987163aa06521bfeeb174616eb4a0a81b47
|
[
"Apache-2.0"
] | null | null | null |
python/athena/gpu_ops/SwapOut.py
|
DMALab/TSplit
|
8f86f987163aa06521bfeeb174616eb4a0a81b47
|
[
"Apache-2.0"
] | 1
|
2021-05-01T16:34:37.000Z
|
2021-05-01T16:34:37.000Z
|
from __future__ import absolute_import
from .Node import Op, NAME_RULE, PROFILING_MODE
from .. import profiler
import ndarray
import numpy as np
from ..stream import create_event_handle
class SwapOutOp(Op):
def __call__(self, node_A):
new_node = Op.__call__(self)
new_node.inputs = [node_A]
new_node.profiler = None
self.event = create_event_handle(ndarray.gpu(0))
if PROFILING_MODE == 1:
new_node.profiler = profiler.CreateProfiler()
if NAME_RULE == 0:
new_node.name = "SwapOut(%s)" % (node_A.name)
elif NAME_RULE == 1:
new_node.name = "SwapOut"
else:
new_node.name = "SwapOut" + str(new_node.id)
new_node.desc = new_node.name + "(%s)" % (node_A.name)
return new_node
def compute(self, node, input_val, output_val, use_numpy=True, stream_handle=None):
if node.profiler is not None:
import time
start = time.time()
assert len(input_val) == 1
if use_numpy:
raise NotImplementedError
else:
output_val.async_d2h(input_val, stream_handle, self.event)
del input_val
if node.profiler is not None:
node.profiler.time = (time.time() - start) * 1000
def gradient(self, node, output_grad):
return
def infer_shape(self, node, input_shapes):
assert len(input_shapes) == 1
return input_shapes[0]
class SwapInOp(Op):
def __call__(self, node_A):
new_node = Op.__call__(self)
new_node.inputs = [node_A]
new_node.profiler = None
self.event = create_event_handle(ndarray.gpu(0))
if PROFILING_MODE == 1:
new_node.profiler = profiler.CreateProfiler()
if NAME_RULE == 0:
new_node.name = "SwapIn(%s)" % (node_A.name)
elif NAME_RULE == 1:
new_node.name = "SwapIn"
else:
new_node.name = "SwapIn" + str(new_node.id)
new_node.desc = new_node.name + "(%s)" % (node_A.name)
return new_node
def compute(self, node, input_val, output_val, use_numpy=True, stream_handle=None):
if node.profiler is not None:
import time
start = time.time()
assert len(input_val) == 1
if use_numpy:
raise NotImplementedError
else:
output_val.async_h2d(input_val, stream_handle, self.event)
del input_val
if node.profiler is not None:
node.profiler.time = (time.time() - start) * 1000
def gradient(self, node, output_grad):
return
def infer_shape(self, node, input_shapes):
assert len(input_shapes) == 1
return input_shapes[0]
def swap_out_op(node_A):
"""swap out the array from device(GPU) to host(CPU).
Parameters:
----
node_A : Node
The node to be swapped out.
Returns:
----
A new Node instance created by Op.
"""
return SwapOutOp()(node_A)
def swap_in_op(node_A):
"""swap in the array from host(CPU) to device(GPU).
Parameters:
----
node_A : Node
The node to be swapped in.
Returns:
----
A new Node instance created by Op.
"""
return SwapInOp()(node_A)
| 28.798246
| 87
| 0.59671
| 436
| 3,283
| 4.247706
| 0.190367
| 0.090713
| 0.047516
| 0.025918
| 0.791577
| 0.791577
| 0.791577
| 0.791577
| 0.791577
| 0.708423
| 0
| 0.01048
| 0.302467
| 3,283
| 114
| 88
| 28.798246
| 0.798253
| 0.098386
| 0
| 0.736842
| 0
| 0
| 0.019084
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 1
| 0.131579
| false
| 0
| 0.105263
| 0.026316
| 0.368421
| 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
|
e2385f795d0f754603057d2883fd52490dd01902
| 627
|
py
|
Python
|
lib/constants.py
|
seunomosowon/TA-connectivity
|
40244c5fb2ba7f8f32fd250fb1abf85fbfcb9114
|
[
"CC-BY-3.0"
] | 4
|
2016-06-19T11:49:50.000Z
|
2019-10-28T09:18:42.000Z
|
bin/connectivity_lib/constants.py
|
dbreddyAI/TA-connectivity
|
f953318237bdc8b2e16445eb575312145704e483
|
[
"CC-BY-3.0"
] | 8
|
2016-10-21T00:22:29.000Z
|
2021-01-26T13:04:57.000Z
|
bin/connectivity_lib/constants.py
|
dbreddyAI/TA-connectivity
|
f953318237bdc8b2e16445eb575312145704e483
|
[
"CC-BY-3.0"
] | 4
|
2016-06-19T11:49:52.000Z
|
2019-11-14T10:10:49.000Z
|
"""
This includes constants that may be used in the connectivity_libs module.
"""
WEBTIMEOUT = 5
NUM_OF_WORKER_PROCESSES = 4
REGEX_IP = r'(?P<ip_address>(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))'
REGEX_EMAIL = r'[_a-z0-9-]+(\.[_a-z0-9-]+)*@[a-z0-9-]+(\.[a-z0-9-]+)*(\.[a-z]{2,4})'
REGEX_PASSWORD = r'([\w!@#$%-]+)'
REGEX_HOSTNAME = r'((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)(\.(25[0-5]|2[0-4][0-9]|' \
r'[01]?[0-9][0-9]?)){3})$|^((([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\-]*[a-zA-Z0-9])' \
r'\.)*([A-Za-z0-9]|[A-Za-z0-9][A-Za-z0-9\-]*[A-Za-z0-9]))'
| 48.230769
| 119
| 0.4689
| 139
| 627
| 2.035971
| 0.273381
| 0.084806
| 0.141343
| 0.169611
| 0.448763
| 0.448763
| 0.420495
| 0.420495
| 0.388693
| 0.388693
| 0
| 0.16158
| 0.111643
| 627
| 12
| 120
| 52.25
| 0.346499
| 0.116427
| 0
| 0
| 0
| 0.625
| 0.695971
| 0.672161
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
e24c1f15c4c17516ddfb3c32a727c0acae528450
| 9,240
|
py
|
Python
|
api/tests/mathematical_object_list.py
|
Gawaboumga/OEMS
|
1e60fa1f350f4cf1ca2e48072e0b4228eeb15024
|
[
"MIT"
] | null | null | null |
api/tests/mathematical_object_list.py
|
Gawaboumga/OEMS
|
1e60fa1f350f4cf1ca2e48072e0b4228eeb15024
|
[
"MIT"
] | 10
|
2020-02-11T23:18:38.000Z
|
2022-03-11T23:32:40.000Z
|
api/tests/mathematical_object_list.py
|
Gawaboumga/OEMS
|
1e60fa1f350f4cf1ca2e48072e0b4228eeb15024
|
[
"MIT"
] | null | null | null |
from rest_framework import status
from rest_framework.test import APITestCase
from django.test import override_settings
from django.urls import reverse
from oems.settings import PAGINATION_SIZE, TEST_MEDIA_ROOT
from api.models import Function, MathematicalObject, Name, Tag
from api.tests import utils
@override_settings(MEDIA_ROOT=TEST_MEDIA_ROOT)
class MathematicalObjectListTests(APITestCase):
def test_create_small_mathematical_object(self):
utils.log_as(self, utils.UserType.STAFF)
representation = 'test'
type = 'S'
data = {
'latex': representation,
'type': type
}
response = self.client.post(reverse('api:mathematical_objects'), data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), 1)
response_data = response.data
self.assertEqual(representation, response_data['latex'])
self.assertEqual(type, response_data['type'])
mathematical_object = MathematicalObject.objects.get(pk=response_data['id'])
self.assertEqual(mathematical_object.functions.count(), 0)
self.assertEqual(mathematical_object.names.count(), 0)
def test_create_partial_mathematical_object_with_function(self):
utils.log_as(self, utils.UserType.STAFF)
representation = 'test'
type = 'S'
function = 'function'
data = {
'latex': representation,
'type': type,
'functions': [{'function': function}]
}
response = self.client.post(reverse('api:mathematical_objects'), data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), 1)
response_data = response.data
self.assertEqual(representation, response_data['latex'])
self.assertEqual(type, response_data['type'])
mathematical_object = MathematicalObject.objects.get(pk=response_data['id'])
self.assertEqual(mathematical_object.functions.count(), 1)
retrieved_function = Function.objects.get(id__in=mathematical_object.functions.all())
self.assertEqual(function, retrieved_function.function)
self.assertEqual(mathematical_object.names.count(), 0)
def test_create_partial_mathematical_object_with_name(self):
utils.log_as(self, utils.UserType.STAFF)
representation = 'test'
type = 'S'
name = 'name'
data = {
'latex': representation,
'type': type,
'names': [{'name': name}]
}
response = self.client.post(reverse('api:mathematical_objects'), data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), 1)
response_data = response.data
self.assertEqual(representation, response_data['latex'])
self.assertEqual(type, response_data['type'])
mathematical_object = MathematicalObject.objects.get(pk=response_data['id'])
self.assertEqual(mathematical_object.functions.count(), 0)
self.assertEqual(mathematical_object.names.count(), 1)
retrieved_name = Name.objects.get(id__in=mathematical_object.names.all())
self.assertEqual(name, retrieved_name.name)
def test_create_full_mathematical_object(self):
utils.log_as(self, utils.UserType.STAFF)
representation = 'test'
type = 'S'
function = 'function'
name = 'name'
tag = 'tag'
convergence_radius = '|z < 1|'
data = {
'latex': representation,
'type': type,
'functions': [{'function': function}],
'names': [{'name': name}],
'tags': [{'tag': tag}],
'convergence_radius': convergence_radius
}
response = self.client.post(reverse('api:mathematical_objects'), data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), 1)
response_data = response.data
self.assertEqual(representation, response_data['latex'])
self.assertEqual(type, response_data['type'])
self.assertEqual(convergence_radius, response_data['convergence_radius'])
mathematical_object = MathematicalObject.objects.get(pk=response_data['id'])
self.assertEqual(mathematical_object.functions.count(), 1)
self.assertEqual(mathematical_object.names.count(), 1)
retrieved_function = Function.objects.get(id__in=mathematical_object.functions.all())
self.assertEqual(function, retrieved_function.function)
retrieved_name = Name.objects.get(id__in=mathematical_object.names.all())
self.assertEqual(name, retrieved_name.name)
retrieved_tag = Tag.objects.get(id__in=mathematical_object.tags.all())
self.assertEqual(tag, retrieved_tag.tag)
def test_create_mathematical_object_with_description(self):
utils.log_as(self, utils.UserType.STAFF)
representation = 'testcreatemathematicalobjectwithdescription'
type = 'S'
description = 'test_create_mathematical_object_with_description'
data = {
'latex': representation,
'type': type,
'description': description
}
response = self.client.post(reverse('api:mathematical_objects'), data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), 1)
response_data = response.data
self.assertEqual(representation, response_data['latex'])
self.assertEqual(type, response_data['type'])
self.assertEqual(description, response_data['description'])
mathematical_object = MathematicalObject.objects.get(pk=response_data['id'])
self.assertEqual(mathematical_object.functions.count(), 0)
self.assertEqual(mathematical_object.names.count(), 0)
def test_create_invalid_type_mathematical_object(self):
utils.log_as(self, utils.UserType.STAFF)
representation = 'test'
type = 'BUG'
function = 'function'
name = 'name'
data = {
'latex': representation,
'type': type,
'functions': [{'function', function}],
'names': [{'name', name}]
}
response = self.client.post(reverse('api:mathematical_objects'), data, format='json')
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertEqual(MathematicalObject.objects.count(), 0)
def test_get_mathematical_objects(self):
utils.log_as(self, utils.UserType.STAFF)
representation_1 = 'test'
type_1 = 'S'
function_1 = 'function'
name_1 = 'name'
data_1 = {
'latex': representation_1,
'type': type_1,
'functions': [{'function': function_1}],
'names': [{'name': name_1}]
}
response = self.client.post(reverse('api:mathematical_objects'), data_1, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), 1)
representation_2 = 'test'
type_2 = 'S'
function_2 = 'function'
name_2 = 'name'
data_2 = {
'latex': representation_2,
'type': type_2,
'functions': [{'function': function_2}],
'names': [{'name': name_2}]
}
response = self.client.post(reverse('api:mathematical_objects'), data_2, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), 2)
response = self.client.get(reverse('api:mathematical_objects'), format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(len(response.data['results']), 2)
def test_get_paginated_mathematical_objects(self):
utils.log_as(self, utils.UserType.STAFF)
number_of_objects_to_create = PAGINATION_SIZE + 5
for i in range(number_of_objects_to_create):
representation = 'test'
type = 'S'
function = 'function'
name = 'name'
data = {
'latex': representation,
'type': type,
'functions': [{'function': function}],
'names': [{'name': name}]
}
response = self.client.post(reverse('api:mathematical_objects'), data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(MathematicalObject.objects.count(), i + 1)
response = self.client.get(reverse('api:mathematical_objects'), format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(MathematicalObject.objects.count(), number_of_objects_to_create)
self.assertEqual(len(response.data['results']), PAGINATION_SIZE)
| 40.526316
| 97
| 0.654978
| 964
| 9,240
| 6.064315
| 0.094398
| 0.128293
| 0.033869
| 0.054567
| 0.827403
| 0.792166
| 0.761888
| 0.755217
| 0.733664
| 0.7039
| 0
| 0.010516
| 0.228139
| 9,240
| 227
| 98
| 40.704846
| 0.80917
| 0
| 0
| 0.614973
| 0
| 0
| 0.096861
| 0.03842
| 0
| 0
| 0
| 0
| 0.26738
| 1
| 0.042781
| false
| 0
| 0.037433
| 0
| 0.085562
| 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
|
e254a02892e0e122786e75d98d1c2ae455f8cbe1
| 9,500
|
py
|
Python
|
python/dcf-tools/dcf/parse.py
|
tshu/lely-core
|
fd0ceff2db726af6d2a766039a0b5a6d33d056e8
|
[
"Apache-2.0"
] | 4
|
2020-12-27T11:31:57.000Z
|
2022-02-09T11:32:08.000Z
|
python/dcf-tools/dcf/parse.py
|
tshu/lely-core
|
fd0ceff2db726af6d2a766039a0b5a6d33d056e8
|
[
"Apache-2.0"
] | null | null | null |
python/dcf-tools/dcf/parse.py
|
tshu/lely-core
|
fd0ceff2db726af6d2a766039a0b5a6d33d056e8
|
[
"Apache-2.0"
] | 1
|
2022-01-03T01:41:59.000Z
|
2022-01-03T01:41:59.000Z
|
import collections
import configparser
import os
class IniDict(collections.abc.MutableMapping):
def __init__(
self, dict_type=collections.OrderedDict, key_xform=lambda str: str.lower()
):
self.__dict = dict_type()
self.key_xform = key_xform
def __delitem__(self, key):
self.__dict.__delitem__(self.key_xform(key))
def __getitem__(self, key):
key, value = self.__dict.__getitem__(self.key_xform(key))
return value
def __iter__(self):
for value, _ in self.__dict.values():
yield value
def __len__(self):
return self.__dict.__len__()
def __setitem__(self, key, value):
self.__dict.__setitem__(self.key_xform(key), (key, value))
def copy(self):
copy = self.__class__(self.__dict.__class__, self.key_xform)
copy.__dict = self.__dict.copy()
return copy
def parse_file(filename: str) -> dict:
cfg = configparser.ConfigParser(
dict_type=IniDict, inline_comment_prefixes=("#", ";"), interpolation=None
)
cfg.optionxform = str # case sensitive
with open(os.path.expandvars(filename), "r") as f:
cfg.read_file(f, filename)
__add_compact_rpdo(cfg)
__add_compact_tpdo(cfg)
return cfg
def __add_compact_rpdo(cfg: dict):
if "DeviceInfo" not in cfg:
return
section = cfg["DeviceInfo"]
compact_pdo = 0
if "CompactPDO" in section and section["CompactPDO"]:
compact_pdo = int(section["CompactPDO"], 0)
if compact_pdo == 0:
return
# Count the number of explicit Receive-PDOs.
npdo = 0
for i in range(512):
if "{:04X}".format(0x1400 + i) in cfg:
npdo += 1
# Create implicit Receive-PDOs, if necessary.
nr_of_rx_pdo = 0
if "NrOfRxPDO" in section and section["NrOfRxPDO"]:
nr_of_rx_pdo = int(section["NrOfRxPDO"], 0)
for i in range(512):
if nr_of_rx_pdo <= npdo:
break
# Check if the communication parameters already exist.
name = "{:04X}".format(0x1400 + i)
if name in cfg:
continue
npdo += 1
n = int(cfg["OptionalObjects"]["SupportedObjects"], 0)
cfg["OptionalObjects"]["SupportedObjects"] = str(n + 1)
cfg["OptionalObjects"][str(n + 1)] = "0x" + name
cfg[name] = {}
obj = cfg[name]
obj["SubNumber"] = "1"
obj["ParameterName"] = "RPDO communication parameter"
obj["ObjectType"] = "0x09"
cfg[name + "sub0"] = {}
subobj = cfg[name + "sub0"]
subobj["ParameterName"] = "highest sub-index supported"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "const"
sub_number = 1
sub_index = 0
if compact_pdo & 0x01:
sub_number += 1
sub_index = 1
cfg[name + "sub1"] = {}
subobj = cfg[name + "sub1"]
subobj["ParameterName"] = "COB-ID used by RPDO"
subobj["DataType"] = "0x0007"
subobj["AccessType"] = "rw"
if i < 4:
subobj["DefaultValue"] = "$NODEID+0x{:X}".format(
(i + 1) * 0x100 + 0x100
)
else:
subobj["DefaultValue"] = "0x80000000"
if compact_pdo & 0x02:
sub_number += 1
sub_index = 2
cfg[name + "sub2"] = {}
subobj = cfg[name + "sub2"]
subobj["ParameterName"] = "transmission type"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "rw"
if compact_pdo & 0x04:
sub_number += 1
sub_index = 3
cfg[name + "sub3"] = {}
subobj = cfg[name + "sub3"]
subobj["ParameterName"] = "inhibit time"
subobj["DataType"] = "0x0006"
subobj["AccessType"] = "rw"
if compact_pdo & 0x08:
sub_number += 1
sub_index = 4
cfg[name + "sub4"] = {}
subobj = cfg[name + "sub4"]
subobj["ParameterName"] = "compatibility entry"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "rw"
if compact_pdo & 0x10:
sub_number += 1
sub_index = 5
cfg[name + "sub5"] = {}
subobj = cfg[name + "sub5"]
subobj["ParameterName"] = "event-timer"
subobj["DataType"] = "0x0006"
subobj["AccessType"] = "rw"
if compact_pdo & 0x20:
sub_number += 1
sub_index = 6
cfg[name + "sub6"] = {}
subobj = cfg[name + "sub6"]
subobj["ParameterName"] = "SYNC start value"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "rw"
cfg[name]["SubNumber"] = str(sub_number)
cfg[name + "sub0"]["DefaultValue"] = str(sub_index)
# Add the mapping parameters, if necessary.
name = "{:04X}".format(0x1600 + i)
if name not in cfg:
cfg["OptionalObjects"]["SupportedObjects"] = str(n + 2)
cfg["OptionalObjects"][str(n + 2)] = "0x" + name
cfg[name] = {}
obj = cfg[name]
obj["ParameterName"] = "RPDO mapping parameter"
obj["ObjectType"] = "0x09"
obj["DataType"] = "0x0007"
obj["AccessType"] = "rw"
obj["CompactSubObj"] = "0x40"
def __add_compact_tpdo(cfg: dict):
if "DeviceInfo" not in cfg:
return
section = cfg["DeviceInfo"]
compact_pdo = 0
if "CompactPDO" in section and section["CompactPDO"]:
compact_pdo = int(section["CompactPDO"], 0)
if compact_pdo == 0:
return
# Count the number of explicit Transmit-PDOs.
npdo = 0
for i in range(512):
if "{:04X}".format(0x1800 + i) in cfg:
npdo += 1
# Create implicit Transmit-PDOs, if necessary.
nr_of_tx_pdo = 0
if "NrOfTxPDO" in section and section["NrOfTxPDO"]:
nr_of_tx_pdo = int(section["NrOfTxPDO"], 0)
for i in range(512):
if nr_of_tx_pdo <= npdo:
break
# Check if the communication parameters already exist.
name = "{:04X}".format(0x1800 + i)
if name in cfg:
continue
npdo += 1
n = int(cfg["OptionalObjects"]["SupportedObjects"], 0)
cfg["OptionalObjects"]["SupportedObjects"] = str(n + 1)
cfg["OptionalObjects"][str(n + 1)] = "0x" + name
cfg[name] = {}
obj = cfg[name]
obj["SubNumber"] = "1"
obj["ParameterName"] = "TPDO communication parameter"
obj["ObjectType"] = "0x09"
cfg[name + "sub0"] = {}
subobj = cfg[name + "sub0"]
subobj["ParameterName"] = "highest sub-index supported"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "const"
sub_number = 1
sub_index = 0
if compact_pdo & 0x01:
sub_number += 1
sub_index = 1
cfg[name + "sub1"] = {}
subobj = cfg[name + "sub1"]
subobj["ParameterName"] = "COB-ID used by TPDO"
subobj["DataType"] = "0x0007"
subobj["AccessType"] = "rw"
if i < 4:
subobj["DefaultValue"] = "$NODEID+0x{:X}".format((i + 1) * 0x100 + 0x80)
else:
subobj["DefaultValue"] = "0x80000000"
if compact_pdo & 0x02:
sub_number += 1
sub_index = 2
cfg[name + "sub2"] = {}
subobj = cfg[name + "sub2"]
subobj["ParameterName"] = "transmission type"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "rw"
if compact_pdo & 0x04:
sub_number += 1
sub_index = 3
cfg[name + "sub3"] = {}
subobj = cfg[name + "sub3"]
subobj["ParameterName"] = "inhibit time"
subobj["DataType"] = "0x0006"
subobj["AccessType"] = "rw"
if compact_pdo & 0x08:
sub_number += 1
sub_index = 4
cfg[name + "sub4"] = {}
subobj = cfg[name + "sub4"]
subobj["ParameterName"] = "reserved"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "rw"
if compact_pdo & 0x10:
sub_number += 1
sub_index = 5
cfg[name + "sub5"] = {}
subobj = cfg[name + "sub5"]
subobj["ParameterName"] = "event timer"
subobj["DataType"] = "0x0006"
subobj["AccessType"] = "rw"
if compact_pdo & 0x20:
sub_number += 1
sub_index = 6
cfg[name + "sub6"] = {}
subobj = cfg[name + "sub6"]
subobj["ParameterName"] = "SYNC start value"
subobj["DataType"] = "0x0005"
subobj["AccessType"] = "rw"
cfg[name]["SubNumber"] = str(sub_number)
cfg[name + "sub0"]["DefaultValue"] = str(sub_index)
# Add the mapping parameters, if necessary.
name = "{:04X}".format(0x1A00 + i)
if name not in cfg:
cfg["OptionalObjects"]["SupportedObjects"] = str(n + 2)
cfg["OptionalObjects"][str(n + 2)] = "0x" + name
cfg[name] = {}
obj = cfg[name]
obj["ParameterName"] = "TPDO mapping parameter"
obj["ObjectType"] = "0x09"
obj["DataType"] = "0x0007"
obj["AccessType"] = "rw"
obj["CompactSubObj"] = "0x40"
| 33.450704
| 88
| 0.520737
| 1,001
| 9,500
| 4.777223
| 0.162837
| 0.058553
| 0.035132
| 0.038059
| 0.77959
| 0.771644
| 0.771644
| 0.761188
| 0.761188
| 0.752405
| 0
| 0.050747
| 0.344526
| 9,500
| 283
| 89
| 33.568905
| 0.717199
| 0.04
| 0
| 0.73251
| 0
| 0
| 0.197805
| 0
| 0
| 0
| 0.026674
| 0
| 0
| 1
| 0.041152
| false
| 0
| 0.012346
| 0.004115
| 0.090535
| 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
|
e28ebae1e7cc5ceada2fe8c587bb581778154270
| 289
|
py
|
Python
|
lobotomy/_services/__init__.py
|
rocketboosters/lobotomy
|
209817120d3a1fd11c218d2f243acbbdd17d8b0f
|
[
"MIT"
] | null | null | null |
lobotomy/_services/__init__.py
|
rocketboosters/lobotomy
|
209817120d3a1fd11c218d2f243acbbdd17d8b0f
|
[
"MIT"
] | null | null | null |
lobotomy/_services/__init__.py
|
rocketboosters/lobotomy
|
209817120d3a1fd11c218d2f243acbbdd17d8b0f
|
[
"MIT"
] | null | null | null |
from lobotomy._services._definitions import Method # noqa: F401
from lobotomy._services._definitions import Service # noqa: F401
def load_definition(service_name: str) -> "Service":
"""Load client definition data for the associated AWS service."""
return Service(service_name)
| 36.125
| 69
| 0.768166
| 36
| 289
| 5.972222
| 0.583333
| 0.111628
| 0.186047
| 0.288372
| 0.344186
| 0
| 0
| 0
| 0
| 0
| 0
| 0.024292
| 0.145329
| 289
| 7
| 70
| 41.285714
| 0.846154
| 0.283737
| 0
| 0
| 0
| 0
| 0.035
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2c9c386e5cd74b35e63f7566916bc9086f65d625
| 38
|
py
|
Python
|
JarvisAI/JarvisAI/JarvisAI/features/utilities/utilities.py
|
MaroonAngel/Jarvis_AI
|
7757be9672c7eb4119aa8a2d3d8c8769ef9b6101
|
[
"MIT"
] | 1
|
2022-03-16T02:17:14.000Z
|
2022-03-16T02:17:14.000Z
|
JarvisAI/JarvisAI/JarvisAI/features/utilities/utilities.py
|
MaroonAngel/Jarvis_AI
|
7757be9672c7eb4119aa8a2d3d8c8769ef9b6101
|
[
"MIT"
] | null | null | null |
JarvisAI/JarvisAI/JarvisAI/features/utilities/utilities.py
|
MaroonAngel/Jarvis_AI
|
7757be9672c7eb4119aa8a2d3d8c8769ef9b6101
|
[
"MIT"
] | null | null | null |
def set_timer():
# WIP
pass
| 6.333333
| 16
| 0.5
| 5
| 38
| 3.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.394737
| 38
| 5
| 17
| 7.6
| 0.782609
| 0.078947
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
e2c58db4326fdd23d3e9a37d432e858dc3d04e9e
| 59
|
py
|
Python
|
python-analysers/src/test/resources/org/jetbrains/research/lupa/pythonAnalysis/imports/analysis/importStatementsAnalysisExecutorData/in_1_project_with_imports/sample_project/src/common/common.py
|
JetBrains-Research/Lupa
|
c105487621564c60cae17395bf32eb40868ceb89
|
[
"Apache-2.0"
] | 16
|
2022-01-11T00:32:20.000Z
|
2022-03-25T21:40:52.000Z
|
python-analysers/src/test/resources/org/jetbrains/research/lupa/pythonAnalysis/imports/analysis/importStatementsAnalysisExecutorData/in_1_project_with_imports/sample_project/src/common/common.py
|
nbirillo/Kotlin-Analysis
|
73c3b8a59bf40ed932bb512f30b0ff31f251af40
|
[
"Apache-2.0"
] | 12
|
2021-07-05T11:42:01.000Z
|
2021-12-23T07:57:54.000Z
|
python-analysers/src/test/resources/org/jetbrains/research/lupa/pythonAnalysis/imports/analysis/importStatementsAnalysisExecutorData/in_1_project_with_imports/sample_project/src/common/common.py
|
nbirillo/Kotlin-Analysis
|
73c3b8a59bf40ed932bb512f30b0ff31f251af40
|
[
"Apache-2.0"
] | 3
|
2021-09-10T13:21:54.000Z
|
2021-11-23T11:37:55.000Z
|
def common_function():
print('I am a common function')
| 19.666667
| 35
| 0.694915
| 9
| 59
| 4.444444
| 0.777778
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.186441
| 59
| 2
| 36
| 29.5
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0.372881
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 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
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
1aba7a3df39d2d589efd5c2907b4c74942095581
| 192
|
py
|
Python
|
hermes/hooks/mail/admin.py
|
NiekKeijzer/hermes
|
48c2e015ab5b299dbbc488a8934af76cabf144cb
|
[
"MIT"
] | null | null | null |
hermes/hooks/mail/admin.py
|
NiekKeijzer/hermes
|
48c2e015ab5b299dbbc488a8934af76cabf144cb
|
[
"MIT"
] | null | null | null |
hermes/hooks/mail/admin.py
|
NiekKeijzer/hermes
|
48c2e015ab5b299dbbc488a8934af76cabf144cb
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import FormMailSettings
# Register your models here.
@admin.register(FormMailSettings)
class FormMailSettingsAdmin(admin.ModelAdmin):
pass
| 19.2
| 46
| 0.8125
| 21
| 192
| 7.428571
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 192
| 9
| 47
| 21.333333
| 0.928571
| 0.135417
| 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
|
1ac422f579a24cd21c91d7284a26084dedd24b1f
| 36
|
py
|
Python
|
app/user/tests/__init__.py
|
Seiya7/django-api-recipe-app
|
bac4e4d5808990ddd7aca0b399bcebc52a12625d
|
[
"MIT"
] | null | null | null |
app/user/tests/__init__.py
|
Seiya7/django-api-recipe-app
|
bac4e4d5808990ddd7aca0b399bcebc52a12625d
|
[
"MIT"
] | null | null | null |
app/user/tests/__init__.py
|
Seiya7/django-api-recipe-app
|
bac4e4d5808990ddd7aca0b399bcebc52a12625d
|
[
"MIT"
] | null | null | null |
from .test_user_api import * # noqa
| 36
| 36
| 0.75
| 6
| 36
| 4.166667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 36
| 1
| 36
| 36
| 0.833333
| 0.111111
| 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
|
46ea13c4da85c3a0147823593395d25a91361ffa
| 158
|
py
|
Python
|
languages/python/design_closure_example1.py
|
RohitAthithya/learntosolveit
|
fe1df98534d3af2fb3ba87c6540d9d8fa883c244
|
[
"BSD-3-Clause"
] | 136
|
2015-03-06T18:11:21.000Z
|
2022-03-10T22:31:40.000Z
|
languages/python/design_closure_example1.py
|
RohitAthithya/learntosolveit
|
fe1df98534d3af2fb3ba87c6540d9d8fa883c244
|
[
"BSD-3-Clause"
] | 27
|
2015-01-07T01:38:03.000Z
|
2021-12-22T19:20:15.000Z
|
languages/python/design_closure_example1.py
|
RohitAthithya/learntosolveit
|
fe1df98534d3af2fb3ba87c6540d9d8fa883c244
|
[
"BSD-3-Clause"
] | 1,582
|
2015-01-01T20:37:06.000Z
|
2022-03-30T12:29:24.000Z
|
class Constant():
def __init__(self, value):
self._value = value
def __call__(self):
return self._value
y = Constant(5)
print((y()))
| 17.555556
| 30
| 0.601266
| 20
| 158
| 4.25
| 0.55
| 0.317647
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008547
| 0.259494
| 158
| 8
| 31
| 19.75
| 0.717949
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0
| 0.142857
| 0.571429
| 0.142857
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
20266ce69be294ba9ed6c6655d0e39b745667298
| 21
|
py
|
Python
|
__init__.py
|
top-fox/appkit
|
7cd62c1b6c7f68e423e056708db15c505054aff8
|
[
"MIT"
] | null | null | null |
__init__.py
|
top-fox/appkit
|
7cd62c1b6c7f68e423e056708db15c505054aff8
|
[
"MIT"
] | null | null | null |
__init__.py
|
top-fox/appkit
|
7cd62c1b6c7f68e423e056708db15c505054aff8
|
[
"MIT"
] | null | null | null |
import src as appkit
| 10.5
| 20
| 0.809524
| 4
| 21
| 4.25
| 1
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| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0.190476
| 21
| 1
| 21
| 21
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| null | 0
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| null | 0
| 0
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2029d3b27966d5c39881f426c350074f201d64a8
| 17,127
|
py
|
Python
|
client/reference/client_test.py
|
ehohenstein/flack
|
0677080a77a3f5c16cbb208207f15ba5113a5fb1
|
[
"MIT"
] | null | null | null |
client/reference/client_test.py
|
ehohenstein/flack
|
0677080a77a3f5c16cbb208207f15ba5113a5fb1
|
[
"MIT"
] | null | null | null |
client/reference/client_test.py
|
ehohenstein/flack
|
0677080a77a3f5c16cbb208207f15ba5113a5fb1
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
import datetime
import json
import unittest
import client
class FakeTime(object):
def __init__(self):
self.slept_ms = 0
def sleep(self, ms):
self.slept_ms += ms
class FakeWebSocket(object):
def __init__(self):
self.url = None
self.onOpen = None
self.onMessage = None
self.onError = None
self.onClose = None
self.running = False
self.sent_frames = []
def WebSocketApp(self, url, on_open, on_message, on_error, on_close):
self.url = url
self.onOpen = on_open
self.onMessage = on_message
self.onError = on_error
self.onClose = on_close
return self
def run_forever(self):
self.running = True
def send(self, data):
self.sent_frames.append(data)
def close(self):
self.running = False
class ClientTests(unittest.TestCase):
def setUp(self):
self.ws = FakeWebSocket()
self.tm = FakeTime()
self.url = 'ws://foobar:4242/some/path'
self.username = "user1234"
self.chat = "chat5678"
self.count = 1000
self.delay = 0
self.user_id = "someone"
self.now = "2015-11-12T08:15:51Z"
self.client = None
def start_client(self):
self.client = client.Client(url=self.url, username=self.username, chat=self.chat, count=self.count, delay=self.delay, ws=self.ws, tm=self.tm)
self.client.run()
self.client.onOpen()
def send_valid_server_hello(self):
self.start_client()
self.client.onMessage(json.dumps({"record": "server_hello", "protocol_version": "1.0"}))
def send_valid_authenticated(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "authenticated", "user_id": self.user_id}))
def send_valid_chat_state(self):
self.send_valid_authenticated()
self.client.onMessage(json.dumps({"record": "chat_state", "chat_name": self.chat, "users": []}))
def send_valid_joined(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "joined", "chat_name": self.chat, "user_name": self.username, "user_id": self.user_id, "timestamp": self.now, "sequence": 42}))
def send_valid_chat(self):
self.send_valid_joined()
self.client.onMessage(json.dumps({"record": "chat_message", 'chat_name': self.chat, 'user_id': self.user_id, 'mime_type': 'text/plain', 'message': 'I like cats', 'timestamp': self.now, 'sequence': 43}))
def send_valid_ping(self):
self.send_valid_chat()
self.client.onMessage(json.dumps({"record": "ping_reply"}))
def send_valid_left(self):
self.count = 1
self.send_valid_chat()
self.client.onMessage(json.dumps({"record": "left", 'chat_name': self.chat, 'user_id': self.user_id, 'timestamp': self.now, 'sequence': 44}))
def test_connects_to_server(self):
self.start_client()
self.assertEqual(self.url, self.ws.url)
def test_assigns_callbacks(self):
self.start_client()
self.assertEqual(self.client.onOpen, self.ws.onOpen)
self.assertEqual(self.client.onMessage, self.ws.onMessage)
self.assertEqual(self.client.onError, self.ws.onError)
self.assertEqual(self.client.onClose, self.ws.onClose)
def test_runs_forever(self):
self.start_client()
self.assertTrue(self.ws.running)
def test_sends_client_hello_on_open(self):
self.start_client()
client_hello = json.loads(self.ws.sent_frames[0])
self.assertEqual({"record": "client_hello", "protocol_version": "1.0"}, client_hello)
def test_closes_on_bad_json_message(self):
self.start_client()
self.client.onMessage('this is not json')
self.assertFalse(self.ws.running)
def test_closes_on_bad_record(self):
self.start_client()
self.client.onMessage("{}")
self.assertFalse(self.ws.running)
def test_closes_on_protocol_error(self):
self.start_client()
self.client.onMessage(json.dumps({"record": "protocol_error", "code": "ERR-123", "reason": "I don't like you very much"}))
self.assertFalse(self.ws.running)
def test_closes_on_invalid_server_hello(self):
self.start_client()
self.client.onMessage(json.dumps({"record": "server_hello"}))
self.assertFalse(self.ws.running)
def test_sends_authenticate_on_valid_server_hello(self):
self.send_valid_server_hello()
authenticate = json.loads(self.ws.sent_frames[1])
self.assertEqual({"record": "authenticate", "user_name": self.username}, authenticate)
def test_closes_on_invalid_authenticated(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "authenticated"}))
self.assertFalse(self.ws.running)
def test_closes_on_authenticated_in_wrong_state(self):
self.send_valid_authenticated()
self.client.onMessage(json.dumps({"record": "authenticated", "user_id": self.user_id}))
self.assertFalse(self.ws.running)
def test_sends_join_on_valid_authenticated(self):
self.send_valid_authenticated()
join = json.loads(self.ws.sent_frames[2])
self.assertEqual({"record": "join_chat", "chat_name": self.chat}, join)
def test_closes_on_non_server_hello_message_first(self):
self.start_client()
self.client.onMessage(json.dumps({"record": "ping_reply"}))
self.assertFalse(self.ws.running)
def test_closes_on_second_server_hello(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "server_hello", "protocol_version": "1.0", "user_id": "someone_else"}))
self.assertFalse(self.ws.running)
def test_closes_on_bad_chat_state_message(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "chat_state"}))
self.assertFalse(self.ws.running)
def test_closes_on_wrong_chat_name_in_chat_state_message(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "chat_state", "chat_name": "other_chat", "users": []}))
self.assertFalse(self.ws.running)
def test_closes_on_second_chat_state_message(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "chat_state", "chat_name": self.chat, "users": []}))
self.assertFalse(self.ws.running)
def test_closes_on_bad_joined_message(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "joined"}))
self.assertFalse(self.ws.running)
def test_closes_on_joined_before_chat_state(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "joined", "chat_name": self.chat, "user_name": self.username, "user_id": self.user_id, "timestamp": self.now, "sequence": 42}))
self.assertFalse(self.ws.running)
def test_closes_on_wrong_joined_chat_name(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "joined", "chat_name": "other_chat", "user_name": self.username, "user_id": self.user_id, "timestamp": self.now, "sequence": 42}))
self.assertFalse(self.ws.running)
def test_closes_on_other_user_name_before_self_joined(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "joined", "chat_name": self.chat, "user_name": "other_user", "user_id": self.user_id, "timestamp": self.now, "sequence": 42}))
self.assertFalse(self.ws.running)
def test_closes_on_other_user_name_before_self_joined(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "joined", "chat_name": self.chat, "user_name": self.username, "user_id": "other_user", "timestamp": self.now, "sequence": 42}))
self.assertFalse(self.ws.running)
def test_can_receive_other_user_join_after_self_join(self):
self.send_valid_joined()
self.client.onMessage(json.dumps({"record": "joined", "chat_name": self.chat, "user_name": "other_user", "user_id": "other_user_id", "timestamp": self.now, "sequence": 42}))
self.assertTrue(self.ws.running)
def test_sends_chat_message_after_joining(self):
self.send_valid_joined()
chat_message = json.loads(self.ws.sent_frames[3])
self.assertEqual({'record': 'chat_message', 'chat_name': self.chat, 'mime_type': 'text/plain', 'message': 'I like cats'}, chat_message)
def test_closes_on_bad_chat_message(self):
self.send_valid_joined()
self.client.onMessage(json.dumps({"record": "chat_message"}))
self.assertFalse(self.ws.running)
def test_closes_on_chat_message_for_wrong_chat(self):
self.send_valid_joined()
self.client.onMessage(json.dumps({"record": "chat_message", 'chat_name': 'other_chat', 'user_id': self.user_id, 'mime_type': 'text/plain', 'message': 'I like cats', 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_closes_on_chat_message_from_user_not_in_chat(self):
self.send_valid_joined()
self.client.onMessage(json.dumps({"record": "chat_message", 'chat_name': self.chat, 'user_id': 'other_user', 'mime_type': 'text/plain', 'message': 'I like cats', 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_allows_chat_from_user_in_chat(self):
self.send_valid_chat()
self.assertTrue(self.ws.running)
def test_sends_ping_on_chat_from_self_when_not_done(self):
self.send_valid_chat()
ping = json.loads(self.ws.sent_frames[4])
self.assertEqual({"record": "ping"}, ping)
def test_sends_leave_chat_on_chat_from_self_when_done(self):
self.count = 1
self.send_valid_chat()
leave_chat = json.loads(self.ws.sent_frames[4])
self.assertEqual({"record": "leave_chat", "chat_name": self.chat}, leave_chat)
def test_closes_on_bad_left_message(self):
self.send_valid_chat()
self.client.onMessage(json.dumps({"record": "left"}))
self.assertFalse(self.ws.running)
def test_closes_on_wrong_chat_name_in_left_message(self):
self.send_valid_chat()
self.client.onMessage(json.dumps({"record": "left", 'chat_name': 'other_chat', 'user_id': self.user_id, 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_closes_on_left_message_for_user_not_in_chat(self):
self.send_valid_chat()
self.client.onMessage(json.dumps({"record": "left", 'chat_name': self.chat, 'user_id': 'other_user', 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_closes_on_left_message_for_self_when_not_sent_leave(self):
self.send_valid_chat()
self.client.onMessage(json.dumps({"record": "left", 'chat_name': self.chat, 'user_id': self.user_id, 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_removes_user_on_left(self):
self.send_valid_joined()
self.client.onMessage(json.dumps({"record": "joined", "chat_name": self.chat, "user_name": "other_user", "user_id": "someone_else", "timestamp": self.now, "sequence": 43}))
self.client.onMessage(json.dumps({"record": "left", 'chat_name': self.chat, 'user_id': 'someone_else', 'timestamp': self.now, 'sequence': 44}))
self.client.onMessage(json.dumps({"record": "chat_message", 'chat_name': self.chat, 'user_id': 'someone_else', 'mime_type': 'text/plain', 'message': 'I like cats', 'timestamp': self.now, 'sequence': 45}))
self.assertFalse(self.ws.running)
def test_sends_goodbye_when_self_left(self):
self.send_valid_left()
goodbye = json.loads(self.ws.sent_frames[5])
self.assertEqual({"record": "client_goodbye"}, goodbye)
def test_closes_on_message_before_chat_state(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "chat_message", 'chat_name': self.chat, 'user_id': self.user_id, 'mime_type': 'text/plain', 'message': 'I like cats', 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_closes_on_message_before_joined(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "chat_message", 'chat_name': self.chat, 'user_id': self.user_id, 'mime_type': 'text/plain', 'message': 'I like cats', 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_closes_on_left_before_chat_state(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "left", 'chat_name': self.chat, 'user_id': self.user_id, 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_closes_on_left_before_joined(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "left", 'chat_name': self.chat, 'user_id': self.user_id, 'timestamp': self.now, 'sequence': 42}))
self.assertFalse(self.ws.running)
def test_closes_on_bad_ping_reply(self):
self.send_valid_chat()
self.client.onMessage(json.dumps({"record": "ping_reply", "foo": "bar"}))
self.assertFalse(self.ws.running)
def test_closes_on_ping_reply_before_chat_state(self):
self.send_valid_server_hello()
self.client.onMessage(json.dumps({"record": "ping_reply"}))
self.assertFalse(self.ws.running)
def test_closes_on_ping_reply_before_joined(self):
self.send_valid_chat_state()
self.client.onMessage(json.dumps({"record": "ping_reply"}))
self.assertFalse(self.ws.running)
def test_sends_chat_message_on_ping_reply(self):
self.send_valid_ping()
self.client.onMessage(json.dumps({"record": "ping_reply"}))
chat_message = json.loads(self.ws.sent_frames[5])
self.assertEqual({'record': 'chat_message', 'chat_name': self.chat, 'mime_type': 'text/plain', 'message': 'I like cats'}, chat_message)
def test_waits_before_sending_ping_reply(self):
self.delay = 42
self.send_valid_ping()
self.assertEqual(0.042, self.tm.slept_ms)
def test_closes_when_server_goodbye_received(self):
self.send_valid_left()
self.client.onMessage(json.dumps({"record": "server_goodbye"}))
self.assertFalse(self.ws.running)
class MessageValidationTests(unittest.TestCase):
def setUp(self):
self.url = 'ws://foobar:4242/some/path'
self.ws = FakeWebSocket()
self.client = client.Client(url=self.url, username='someone', chat='somechat', count=42, delay=0, ws=self.ws)
def test_message_validation_fails_for_extra_fields(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar'}, {'record': 'foobar', 'foo': 'bar'})
def test_message_validation_fails_for_missing_fields(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar', 'foo': str}, {'record': 'foobar'})
def test_message_validation_fails_for_wrong_field_type(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar', 'foo': str}, {'record': 'foobar', 'foo': 42})
def test_message_validation_fails_for_empty_string_type(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar', 'foo': str}, {'record': 'foobar', 'foo': ''})
def test_message_validation_fails_for_wrong_exact_value(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar', 'foo': 'bar'}, {'record': 'foobar', 'foo': 'baz'})
def test_message_validation_fails_for_non_list_in_list_field(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar', 'foo': [{'record': 'bar'}]}, {'record': 'foobar', 'foo': 'bar'})
def test_message_validation_fails_for_bad_list_field(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar', 'foo': [{'record': 'bar'}]}, {'record': 'foobar', 'foo': [{'record': 'baz'}]})
def test_message_validation_fails_for_bad_timestamp_field(self):
self.assertRaises(client.ProtocolError, self.client.validate, {'record': 'foobar', 'ts': datetime.datetime}, {'record': 'foobar', 'ts': 'foobar'})
def test_message_validation_passes_for_good_timestamp_field(self):
self.client.validate({'record': 'foobar', 'ts': datetime.datetime}, {'record': 'foobar', 'ts': '2015-11-12T08:15:51Z'})
def test_message_validation_passes_for_valid_message(self):
self.client.validate({'record': 'foobar', 'foo': str}, {'record': 'foobar', 'foo': 'baz'})
def test_message_validation_passes_for_valid_list_field(self):
self.client.validate({'record': 'foobar', 'foo': [{'record': 'bar'}]}, {'record': 'foobar', 'foo': [{'record': 'bar'}]})
unittest.main()
| 46.414634
| 213
| 0.681964
| 2,273
| 17,127
| 4.853938
| 0.077871
| 0.051482
| 0.075773
| 0.085471
| 0.820085
| 0.777667
| 0.745491
| 0.709417
| 0.634098
| 0.617602
| 0
| 0.008494
| 0.168272
| 17,127
| 368
| 214
| 46.540761
| 0.766023
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| 0.156301
| 0.00304
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| 0.200704
| 1
| 0.260563
| false
| 0.010563
| 0.014085
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| 0.292254
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| null | 0
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| 0
|
0
| 6
|
20308039b7a10eba9e32069aa5ff03bfd9fe6271
| 187
|
py
|
Python
|
sysvis/shapes/__init__.py
|
signdoubt/sysvis
|
cdff15b1d29e65ebbae0a8eaf1db05b48c9c14bb
|
[
"MIT"
] | null | null | null |
sysvis/shapes/__init__.py
|
signdoubt/sysvis
|
cdff15b1d29e65ebbae0a8eaf1db05b48c9c14bb
|
[
"MIT"
] | null | null | null |
sysvis/shapes/__init__.py
|
signdoubt/sysvis
|
cdff15b1d29e65ebbae0a8eaf1db05b48c9c14bb
|
[
"MIT"
] | null | null | null |
from sysvis.shapes.basic import Rect, Cylinder, Person, Arrow, Shape
from sysvis.shapes.compound import Compound, Group, Zone
from sysvis.shapes.utils import Shape, ShapeAttrs, Gap, Text
| 46.75
| 68
| 0.807487
| 27
| 187
| 5.592593
| 0.62963
| 0.198676
| 0.317881
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| 0
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| 0
| 0
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| 0.112299
| 187
| 3
| 69
| 62.333333
| 0.909639
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| true
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| 1
| 0
| 1
| 0
|
0
| 6
|
6475ae27c4ae5b6aca350a170bd38a7733bab1b5
| 14,949
|
py
|
Python
|
web/transiq/fms/trip_history.py
|
manibhushan05/transiq
|
763fafb271ce07d13ac8ce575f2fee653cf39343
|
[
"Apache-2.0"
] | null | null | null |
web/transiq/fms/trip_history.py
|
manibhushan05/transiq
|
763fafb271ce07d13ac8ce575f2fee653cf39343
|
[
"Apache-2.0"
] | 14
|
2020-06-05T23:06:45.000Z
|
2022-03-12T00:00:18.000Z
|
web/transiq/fms/trip_history.py
|
manibhushan05/transiq
|
763fafb271ce07d13ac8ce575f2fee653cf39343
|
[
"Apache-2.0"
] | null | null | null |
from datetime import datetime, timedelta
from django.contrib.auth.models import User
from api.decorators import api_get
from api.helper import json_response
from broker.models import Broker
from fms.decorators import authenticated_user
from fms.views import get_or_none, get_vehicle_current_data
from owner.models import Vehicle
from owner.vehicle_util import compare_format, display_format
from sme.models import Sme
from team.helper.helper import to_int
from team.models import ManualBooking
def booking_status(amount):
if amount == 0:
return 'paid'
else:
return 'unpaid'
def final_payment_date(booking):
try:
if booking.outward_booking.all():
return booking.outward_booking.all().latest('payment_date').payment_date.strftime('%d-%b-%Y')
else:
return ''
except BaseException:
pass
def supplier_booking_data(bookings):
data = []
for booking in bookings:
if to_int(booking.total_amount_to_owner - booking.total_out_ward_amount) != 0:
data.append(
{
'status': 'unpaid',
'lr_number': '\n'.join(booking.lr_numbers.values_list('lr_number',
flat=True)) if booking.lr_numbers.exists() else booking.booking_id,
'paid': to_int(booking.total_out_ward_amount),
'transaction_id': booking.id,
'id': booking.id,
'vehicle_number': display_format(booking.lorry_number),
'source_city': booking.from_city,
'destination_city': booking.to_city,
'amount': to_int(booking.total_amount_to_owner),
'shipment_date': booking.shipment_date.strftime('%d-%b-%Y'),
'balance': to_int(booking.total_amount_to_owner - booking.total_out_ward_amount),
'booking_id': booking.booking_id,
'pod_status': booking.pod_status,
'pod_docs': [
{'url': pod.s3_url if pod.s3_url else pod.s3_upload.public_url(),
'thumb_url': pod.s3_thumb_url if pod.s3_thumb_url else pod.s3_upload.public_url(),
'lr_number': pod.lr_number.lr_number if pod.lr_number else ''} for pod in
booking.podfile_set.all()
]
}
)
else:
data.append(
{
'status': 'paid',
'lr_number': '\n'.join(booking.lr_numbers.values_list('lr_number',
flat=True)) if booking.lr_numbers.exists() else booking.booking_id,
'paid': to_int(booking.total_out_ward_amount),
'id': booking.id,
'transaction_id': booking.id,
'booking_id': booking.booking_id,
'vehicle_number': display_format(booking.lorry_number),
'source_city': booking.from_city,
'destination_city': booking.to_city,
'amount': to_int(booking.total_amount_to_owner),
'shipment_date': booking.shipment_date.strftime('%d-%b-%Y'),
'balance': to_int(booking.total_amount_to_owner - booking.total_out_ward_amount),
'final_payment_date': final_payment_date(booking=booking),
'pod_status': booking.pod_status,
'pod_docs': [
{'url': pod.s3_url if pod.s3_url else pod.s3_upload.public_url(),
'thumb_url': pod.s3_thumb_url if pod.s3_thumb_url else pod.s3_upload.public_url(),
'lr_number': pod.lr_number.lr_number if pod.lr_number else ''} for pod in
booking.podfile_set.all()
]
}
)
return data
def customer_booking_data(bookings):
data = []
for booking in bookings:
gps_data = get_vehicle_current_data(booking.vehicle.vehicle_number)
current_location = {}
if len(gps_data) > 0:
current_location = gps_data[0]['location']
# print(booking.vehicle.id)
if to_int(booking.total_amount_to_owner - booking.total_out_ward_amount) != 0:
data.append(
{
'status': 'unpaid',
'lr_number': '\n'.join(booking.lr_numbers.values_list('lr_number',
flat=True)) if booking.lr_numbers.exists() else booking.booking_id,
'paid': to_int(booking.total_out_ward_amount),
'transaction_id': booking.id,
'id': booking.vehicle.id,
'vehicle_number': display_format(booking.lorry_number),
'source_city': booking.from_city,
'destination_city': booking.to_city,
'amount': to_int(booking.total_amount_to_owner),
'shipment_date': booking.shipment_date.strftime('%d-%b-%Y'),
'balance': to_int(booking.total_amount_to_owner - booking.total_out_ward_amount),
'booking_id': booking.booking_id,
'pod_status': booking.pod_status,
'pod_docs': [
{'url': pod.s3_url if pod.s3_url else pod.s3_upload.public_url(),
'thumb_url': pod.s3_thumb_url if pod.s3_thumb_url else pod.s3_upload.public_url(),
'lr_number': pod.lr_number.lr_number if pod.lr_number else ''} for pod in
booking.podfile_set.all()
],
'current_location': current_location
}
)
else:
data.append(
{
'status': 'paid',
'lr_number': '\n'.join(booking.lr_numbers.values_list('lr_number',
flat=True)) if booking.lr_numbers.exists() else booking.booking_id,
'paid': to_int(booking.total_out_ward_amount),
'id': booking.vehicle.id,
'transaction_id': booking.id,
'booking_id': booking.booking_id,
'vehicle_number': display_format(booking.lorry_number),
'source_city': booking.from_city,
'destination_city': booking.to_city,
'amount': to_int(booking.total_amount_to_owner),
'shipment_date': booking.shipment_date.strftime('%d-%b-%Y'),
'balance': to_int(booking.total_amount_to_owner - booking.total_out_ward_amount),
'final_payment_date': final_payment_date(booking=booking),
'pod_status': booking.pod_status,
'pod_docs': [
{'url': pod.s3_url if pod.s3_url else pod.s3_upload.public_url(),
'thumb_url': pod.s3_thumb_url if pod.s3_thumb_url else pod.s3_upload.public_url(),
'lr_number': pod.lr_number.lr_number if pod.lr_number else ''} for pod in
booking.podfile_set.all()
],
'current_location': current_location
}
)
return data
@api_get
@authenticated_user
def team_booking_data(request, vehicle_id):
print(request.data)
if to_int(vehicle_id) != 9999999 and isinstance(get_or_none(Vehicle, id=vehicle_id), Vehicle):
vehicle = get_or_none(Vehicle, id=vehicle_id)
bookings = ManualBooking.objects.filter(
lorry_number__in=[display_format(compare_format(vehicle.vehicle_number))]).exclude(
booking_status__icontains='cancelled').order_by('-shipment_date')
else:
broker = Broker.objects.get(name=User.objects.get(username=request.user.username))
bookings = ManualBooking.objects.filter(supplier=broker,
shipment_date__gte=datetime(2017, 12, 1).date()).exclude(
booking_status__icontains='cancelled').order_by('-shipment_date')
return json_response({'status': 'success', 'data': supplier_booking_data(bookings=bookings)})
@api_get
@authenticated_user
def team_customer_booking_data(request):
print(request.data)
customer = Sme.objects.get(name=request.user)
# broker = Broker.objects.get(name=User.objects.get(username=request.user.username))
bookings = ManualBooking.objects.filter(company=customer,
shipment_date__gte=datetime(2017, 12, 1).date()).exclude(
booking_status__icontains='cancelled').order_by('-shipment_date')
return json_response({'status': 'success', 'data': supplier_booking_data(bookings=bookings)})
@api_get
@authenticated_user
def trip_details(request, booking_id):
booking = ManualBooking.objects.get(id=booking_id)
data = {}
data.update({'status': 'success'})
data.update(
{
'basic_details': {
'lr_number': '\n'.join(booking.lr_numbers.values_list('lr_number', flat=True)),
'source_city': booking.from_city,
'destination_city': booking.to_city,
'shipment_date': None if not booking.shipment_date else booking.shipment_date.strftime('%d-%b-%Y'),
'vehicle_number': booking.lorry_number,
'weight': float(booking.supplier_charged_weight),
'rate': float(booking.supplier_rate),
'bill_number': 'OPB-{}'.format(
''.join(booking.outward_payment_bill.values_list('bill_number', flat=True))),
'pod_docs': [
{'url': pod.s3_url, 'thumb_url': pod.s3_thumb_url} for pod in booking.podfile_set.all()
]
}
}
)
data.update(
{
'rates': {
'weight': float(booking.supplier_charged_weight),
'rate': float(booking.supplier_rate),
'freight': int(float(booking.supplier_charged_weight) * float(booking.supplier_rate)),
'loading': booking.loading_charge,
'unloading': booking.unloading_charge,
'detention': booking.detention_charge,
'additional': booking.additional_charges_for_company,
'commission': booking.commission,
'lr_charge': booking.lr_cost,
'deduction_for_advance': booking.deduction_for_advance,
'deduction_for_balance': booking.deduction_for_balance,
'other_deduction': booking.other_deduction,
'total_amount_to_owner': int(booking.total_amount_to_owner),
'total_out_ward_amount': int(booking.total_out_ward_amount),
}
}
)
data.update(
{
'payments': [
{
'amount': float(payment.actual_amount),
'paid_to': payment.paid_to,
'remarks': payment.remarks,
'payment_mode': payment.get_payment_mode_display(),
'account_number': None if not payment.bank_account else payment.bank_account.account_number,
'fuel_card': None if not payment.fuel_card else payment.fuel_card.card_number,
'date': None if not payment.payment_date else payment.payment_date.strftime('%d-%b-%Y')
} for payment in booking.outward_booking.all().exclude(is_refund_amount=True)
]
}
)
return json_response(data=data)
@api_get
@authenticated_user
def customer_trip_details(request, booking_id):
booking = ManualBooking.objects.get(id=booking_id)
data = {}
data.update({'status': 'success'})
data.update(
{
'basic_details': {
'lr_number': '\n'.join(booking.lr_numbers.values_list('lr_number', flat=True)),
'source_city': booking.from_city,
'destination_city': booking.to_city,
'shipment_date': None if not booking.shipment_date else booking.shipment_date.strftime('%d-%b-%Y'),
'vehicle_number': booking.lorry_number,
'weight': float(booking.charged_weight),
'rate': float(booking.party_rate),
'bill_number': 'OPB-{}'.format(
''.join(booking.outward_payment_bill.values_list('bill_number', flat=True))),
'pod_docs': [
{'url': pod.s3_url, 'thumb_url': pod.s3_thumb_url} for pod in booking.podfile_set.all()
]
}
}
)
data.update(
{
'rates': {
'weight': float(booking.charged_weight),
'rate': float(booking.party_rate),
'freight': int(float(booking.charged_weight) * float(booking.party_rate)),
'loading': booking.loading_charge,
'unloading': booking.unloading_charge,
'detention': booking.detention_charge,
'additional': booking.additional_charges_for_company,
'company_remarks': booking.invoice_remarks_for_additional_charges,
'commission': booking.commission,
'lr_charge': booking.lr_cost,
'deductions_for_company': booking.deductions_for_company,
'deduction_remarks_company': booking.invoice_remarks_for_deduction_discount,
'deduction_for_advance': booking.deduction_for_advance,
'deduction_for_balance': booking.deduction_for_balance,
'other_deduction': booking.other_deduction,
'total_amount_to_company': int(booking.total_amount_to_company),
'total_out_ward_amount': int(booking.total_out_ward_amount),
}
}
)
data.update(
{
'payments': [
{
'amount': float(payment.actual_amount),
'paid_to': payment.paid_to,
'remarks': payment.remarks,
'payment_mode': payment.get_payment_mode_display(),
'account_number': None if not payment.bank_account else payment.bank_account.account_number,
'fuel_card': None if not payment.fuel_card else payment.fuel_card.card_number,
'date': None if not payment.payment_date else payment.payment_date.strftime('%d-%b-%Y')
} for payment in booking.outward_booking.all().exclude(is_refund_amount=True)
]
}
)
return json_response(data=data)
| 48.378641
| 141
| 0.569536
| 1,598
| 14,949
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| 0.096996
| 0.027986
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| 0.833458
| 0.822464
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| 0.778736
| 0.771614
| 0
| 0.005361
| 0.326243
| 14,949
| 308
| 142
| 48.535714
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| 0.007225
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| 0.6875
| 0
| 0
| 0.121647
| 0.014625
| 0
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| 0
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| 1
| 0.027778
| false
| 0.003472
| 0.041667
| 0
| 0.104167
| 0.006944
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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|
0
| 6
|
647f499fca0718879f6ce8ce88804820346afbec
| 10,752
|
py
|
Python
|
tests/helpers/test_misc.py
|
praveendhac/nephos
|
f4b43d53c1eebe5c0d4474e4bd5cbbf0a7abcba0
|
[
"Apache-2.0"
] | null | null | null |
tests/helpers/test_misc.py
|
praveendhac/nephos
|
f4b43d53c1eebe5c0d4474e4bd5cbbf0a7abcba0
|
[
"Apache-2.0"
] | null | null | null |
tests/helpers/test_misc.py
|
praveendhac/nephos
|
f4b43d53c1eebe5c0d4474e4bd5cbbf0a7abcba0
|
[
"Apache-2.0"
] | 1
|
2020-12-06T19:55:45.000Z
|
2020-12-06T19:55:45.000Z
|
from subprocess import CalledProcessError
from unittest import mock
from unittest.mock import call
from nephos.helpers.misc import (execute, execute_until_success,
input_data, input_files,
get_response, pretty_print)
class TestExecute:
@mock.patch('nephos.helpers.misc.check_output')
@mock.patch('nephos.helpers.misc.print')
def test_execute(self, mock_print, mock_check_output):
execute('ls')
mock_print.assert_called_once()
mock_print.assert_called_with('ls')
mock_check_output.assert_called_once()
mock_check_output.assert_called_with('ls', shell=True, stderr=-2)
@mock.patch('nephos.helpers.misc.check_output')
@mock.patch('nephos.helpers.misc.print')
def test_execute_quiet(self, mock_print, mock_check_output):
execute('ls', show_command=False)
mock_print.assert_not_called()
mock_check_output.assert_called_once()
mock_check_output.assert_called_with('ls', shell=True, stderr=-2)
@mock.patch('nephos.helpers.misc.check_output')
@mock.patch('nephos.helpers.misc.print')
def test_execute_verbose(self, mock_print, mock_check_output):
# Add some side effects
mock_check_output.side_effect = ['output'.encode('ascii')]
execute('ls', verbose=True)
# First check_output
mock_check_output.assert_called_once()
mock_check_output.assert_called_with('ls', shell=True, stderr=-2)
# Then print
mock_print.assert_has_calls([call('ls'), call('output')])
@mock.patch('nephos.helpers.misc.check_output')
@mock.patch('nephos.helpers.misc.print')
def test_execute_error(self, mock_print, mock_check_output):
# Add some side effects
mock_check_output.side_effect = CalledProcessError(cmd='lst',
returncode=127,
output='/bin/sh: lst: command not found'.encode('ascii'))
execute('lst')
# First check_output
mock_check_output.assert_called_once()
mock_check_output.assert_called_with('lst', shell=True, stderr=-2)
# Then print
mock_print.assert_has_calls([call('lst'),
call('Command failed with CalledProcessError:'),
call('/bin/sh: lst: command not found')])
@mock.patch('nephos.helpers.misc.check_output')
@mock.patch('nephos.helpers.misc.print')
def test_execute_error_quiet(self, mock_print, mock_check_output):
# Add some side effects
mock_check_output.side_effect = CalledProcessError(cmd='lst',
returncode=127,
output='/bin/sh: lst: command not found'.encode('ascii'))
execute('lst', show_command=False, show_errors=False)
# First check_output
mock_check_output.assert_called_once()
mock_check_output.assert_called_with('lst', shell=True, stderr=-2)
# Then print
mock_print.assert_not_called()
class TestExecuteUntilSuccess:
@mock.patch('nephos.helpers.misc.execute')
@mock.patch('nephos.helpers.misc.print')
def test_execute(self, mock_print, mock_execute):
mock_execute.side_effect = ['', '', '<h1>SomeWebsite</h1>']
execute_until_success('curl example.com', delay=0)
mock_print.assert_has_calls([call('.', end='', flush=True)] * 2)
mock_execute.assert_has_calls([call('curl example.com', show_command=True, show_errors=True, verbose=False)] +
[call('curl example.com', show_command=False, show_errors=False, verbose=False)] * 2)
@mock.patch('nephos.helpers.misc.execute')
@mock.patch('nephos.helpers.misc.print')
def test_execute_verbose(self, mock_print, mock_execute):
mock_execute.side_effect = ['', '', '<h1>SomeWebsite</h1>']
execute_until_success('curl example.com', verbose=True, delay=0)
mock_print.assert_has_calls([call('.', end='', flush=True)] * 2 +
[call('<h1>SomeWebsite</h1>')])
mock_execute.assert_has_calls([call('curl example.com', show_command=True, show_errors=True, verbose=True)] +
[call('curl example.com', show_command=False, show_errors=False,
verbose=False)] * 2)
class TestInputData:
@mock.patch('nephos.helpers.misc.get_response')
def test_input_data(self, mock_get_response):
input_data(('hello',))
mock_get_response.assert_called_with('Input hello')
@mock.patch('nephos.helpers.misc.get_response')
def test_input_data_suffix(self, mock_get_response):
input_data(('hello',), text_append='big')
mock_get_response.assert_called_with('Input hello big')
@mock.patch('nephos.helpers.misc.get_response')
def test_input_data_multiple(self, mock_get_response):
input_data(('hello', ('goodbye', {'sensitive': True})))
mock_get_response.assert_has_calls([call('Input hello'), call('Input goodbye', sensitive=True)])
class TestInputFiles:
files = ['./some_folder/some_file&.txt', './another_file.txt']
@mock.patch('nephos.helpers.misc.open')
@mock.patch('nephos.helpers.misc.isfile')
@mock.patch('nephos.helpers.misc.get_response')
@mock.patch('nephos.helpers.misc.print')
def test_input_files(self, mock_print, mock_get_response, mock_isfile, mock_open):
mock_isfile.side_effect = [True]
mock_get_response.side_effect = [self.files[0]]
data = input_files(('hello',))
mock_print.assert_not_called()
mock_get_response.assert_called_with('Input hello')
mock_isfile.assert_called_with(self.files[0])
mock_open.assert_called_with(self.files[0], 'rb')
assert data.keys() == {'hello'}
@mock.patch('nephos.helpers.misc.open')
@mock.patch('nephos.helpers.misc.isfile')
@mock.patch('nephos.helpers.misc.get_response')
@mock.patch('nephos.helpers.misc.print')
def test_input_files_suffix(self, mock_print, mock_get_response, mock_isfile, mock_open):
mock_isfile.side_effect = [True]
mock_get_response.side_effect = [self.files[0]]
data = input_files(('hello',), text_append='big')
mock_print.assert_not_called()
mock_get_response.assert_called_with('Input hello big')
mock_isfile.assert_called_with(self.files[0])
mock_open.assert_called_with(self.files[0], 'rb')
assert data.keys() == {'hello'}
@mock.patch('nephos.helpers.misc.open')
@mock.patch('nephos.helpers.misc.isfile')
@mock.patch('nephos.helpers.misc.get_response')
@mock.patch('nephos.helpers.misc.print')
def test_input_files_multiple(self, mock_print, mock_get_response, mock_isfile, mock_open):
mock_isfile.side_effect = [True, True]
mock_get_response.side_effect = self.files
data = input_files(('hello', 'goodbye'))
mock_print.assert_not_called()
mock_get_response.assert_has_calls([call('Input hello'), call('Input goodbye')])
mock_isfile.assert_has_calls([call(self.files[0]), call(self.files[1])])
mock_open.assert_any_call(self.files[0], 'rb')
mock_open.assert_any_call(self.files[1], 'rb')
assert data.keys() == {'hello', 'goodbye'}
@mock.patch('nephos.helpers.misc.open')
@mock.patch('nephos.helpers.misc.isfile')
@mock.patch('nephos.helpers.misc.get_response')
@mock.patch('nephos.helpers.misc.print')
def test_input_files_mistake(self, mock_print, mock_get_response, mock_isfile, mock_open):
mock_isfile.side_effect = [False, True]
mock_get_response.side_effect = [self.files[0] + 'OOPS', self.files[0]]
data = input_files(('hello',))
mock_print.assert_called_once_with('{} is not a file'.format(self.files[0] + 'OOPS'))
mock_get_response.assert_has_calls([call('Input hello'), call('Input hello')])
mock_isfile.assert_has_calls([call(self.files[0] + 'OOPS'), call(self.files[0])])
mock_open.assert_called_with(self.files[0], 'rb')
assert data.keys() == {'hello'}
@mock.patch('nephos.helpers.misc.open')
@mock.patch('nephos.helpers.misc.isfile')
@mock.patch('nephos.helpers.misc.get_response')
@mock.patch('nephos.helpers.misc.print')
def test_input_files_cleankey(self, mock_print, mock_get_response, mock_isfile, mock_open):
mock_isfile.side_effect = [True]
mock_get_response.side_effect = [self.files[0]]
data = input_files((None,), clean_key=True)
mock_print.assert_called_once_with('Replaced some_file&.txt with some_file_.txt')
mock_get_response.assert_called_with('Input None')
mock_isfile.assert_called_with(self.files[0])
mock_open.assert_called_with(self.files[0], 'rb')
assert data.keys() == {'some_file_.txt'}
class TestGetResponse:
@mock.patch('nephos.helpers.misc.input')
@mock.patch('nephos.helpers.misc.print')
def test_get_response(self, mock_print, mock_input):
mock_input.side_effect = ['An answer']
answer = get_response('A question')
mock_input.assert_called_once()
mock_print.assert_called_with('A question')
assert answer == 'An answer'
@mock.patch('nephos.helpers.misc.getpass')
@mock.patch('nephos.helpers.misc.input')
@mock.patch('nephos.helpers.misc.print')
def test_get_response_password(self, mock_print, mock_input, mock_getpass):
mock_getpass.side_effect = ['A password']
answer = get_response('A question', sensitive=True)
mock_input.assert_not_called()
mock_print.assert_called_with('A question')
mock_getpass.assert_called_once_with('Password:')
assert answer == 'A password'
@mock.patch('nephos.helpers.misc.input')
@mock.patch('nephos.helpers.misc.print')
def test_get_response_options(self, mock_print, mock_input):
mock_input.side_effect = ['mistake', 'y']
get_response('A question', ('y', 'n'))
mock_input.assert_has_calls([call()] * 2)
mock_print.assert_has_calls([call('A question'),
call("Permitted responses: ('y', 'n')"),
call('Invalid response, try again!')])
class TestPrettyPrint:
@mock.patch('nephos.helpers.misc.print')
def test_pretty_print(self, mock_print):
pretty_print('{"some": "json"}')
mock_print.assert_called_with('{\x1b[34;01m"some"\x1b[39;49;00m: \x1b[33m"json"\x1b[39;49;00m}\n')
| 48.432432
| 123
| 0.65532
| 1,372
| 10,752
| 4.855685
| 0.092566
| 0.089763
| 0.117382
| 0.148604
| 0.831432
| 0.805464
| 0.778445
| 0.741219
| 0.708946
| 0.669619
| 0
| 0.007647
| 0.209449
| 10,752
| 221
| 124
| 48.651584
| 0.776118
| 0.014416
| 0
| 0.491892
| 0
| 0.005405
| 0.207708
| 0.124114
| 0
| 0
| 0
| 0
| 0.318919
| 1
| 0.102703
| false
| 0.027027
| 0.021622
| 0
| 0.162162
| 0.275676
| 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
|
648211ea2be32e845d77511739eb62fde10d0dc2
| 268
|
py
|
Python
|
app/blueprint_open_class/__init__.py
|
medsci-tech/mime_analysis_flask_mongodb_2017
|
7856aeee976b6e9fb89aa52ba89a53eb43f5b877
|
[
"MIT"
] | null | null | null |
app/blueprint_open_class/__init__.py
|
medsci-tech/mime_analysis_flask_mongodb_2017
|
7856aeee976b6e9fb89aa52ba89a53eb43f5b877
|
[
"MIT"
] | null | null | null |
app/blueprint_open_class/__init__.py
|
medsci-tech/mime_analysis_flask_mongodb_2017
|
7856aeee976b6e9fb89aa52ba89a53eb43f5b877
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
blueprint_open_class = Blueprint('blueprint_open_class',
__name__,
url_prefix='/open_class',
template_folder='../templates/open_class')
from . import views
| 26.8
| 69
| 0.559701
| 24
| 268
| 5.75
| 0.541667
| 0.26087
| 0.318841
| 0.391304
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.365672
| 268
| 9
| 70
| 29.777778
| 0.811765
| 0
| 0
| 0
| 0
| 0
| 0.202247
| 0.086142
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
b37ff63107f4a0d5fecc4b0b9abccc0b06f70bfa
| 88
|
py
|
Python
|
tests/quectel_ec25/fixtures/urc.py
|
Sinusoidal36/async-gsm-modem
|
d220753a50b61a86faedfcdf4e4c52a959dbe882
|
[
"Apache-2.0"
] | 4
|
2021-07-09T01:24:05.000Z
|
2021-09-02T22:25:13.000Z
|
tests/quectel_ec25/fixtures/urc.py
|
Sinusoidal36/async-gsm-modem
|
d220753a50b61a86faedfcdf4e4c52a959dbe882
|
[
"Apache-2.0"
] | null | null | null |
tests/quectel_ec25/fixtures/urc.py
|
Sinusoidal36/async-gsm-modem
|
d220753a50b61a86faedfcdf4e4c52a959dbe882
|
[
"Apache-2.0"
] | null | null | null |
import pytest
@pytest.fixture
def urc_codes():
return [(b'+CMTI', 1), (b'+CMT', 2)]
| 17.6
| 40
| 0.613636
| 14
| 88
| 3.785714
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027027
| 0.159091
| 88
| 5
| 40
| 17.6
| 0.689189
| 0
| 0
| 0
| 0
| 0
| 0.101124
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.25
| 0.25
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
b3c167078c982e9a689b89abfed7966d1209c947
| 35
|
py
|
Python
|
test/python_rules/custom_runner/custom_runner_single/custom_runner_test.py
|
samwestmoreland/please
|
1616742eeefca3dd0b3194e4c1ec9a8542ec13c7
|
[
"Apache-2.0"
] | 1,992
|
2016-08-08T11:14:10.000Z
|
2022-03-31T08:29:57.000Z
|
test/python_rules/custom_runner/custom_runner_single/custom_runner_test.py
|
samwestmoreland/please
|
1616742eeefca3dd0b3194e4c1ec9a8542ec13c7
|
[
"Apache-2.0"
] | 1,059
|
2016-08-03T17:11:37.000Z
|
2022-03-30T16:27:30.000Z
|
test/python_rules/custom_runner/custom_runner_single/custom_runner_test.py
|
samwestmoreland/please
|
1616742eeefca3dd0b3194e4c1ec9a8542ec13c7
|
[
"Apache-2.0"
] | 213
|
2016-12-09T15:37:00.000Z
|
2022-03-23T23:08:26.000Z
|
def test_custom_runner():
pass
| 11.666667
| 25
| 0.714286
| 5
| 35
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 35
| 2
| 26
| 17.5
| 0.821429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
37343348cd6e5e2264eacf2f39fe2a4aa21e18ce
| 215
|
py
|
Python
|
dlfairness/original_code/nifr/nifr/layers/inn/__init__.py
|
lin-tan/fairness-variance
|
7f6aee23160707ffe78f429e5d960022ea1c9fe4
|
[
"BSD-3-Clause"
] | null | null | null |
dlfairness/original_code/nifr/nifr/layers/inn/__init__.py
|
lin-tan/fairness-variance
|
7f6aee23160707ffe78f429e5d960022ea1c9fe4
|
[
"BSD-3-Clause"
] | null | null | null |
dlfairness/original_code/nifr/nifr/layers/inn/__init__.py
|
lin-tan/fairness-variance
|
7f6aee23160707ffe78f429e5d960022ea1c9fe4
|
[
"BSD-3-Clause"
] | null | null | null |
from .activations import *
from .bijector import *
from .chain import *
from .coupling import *
from .glow import *
from .misc import *
from .normalization import *
from .permutation import *
from .reshape import *
| 21.5
| 28
| 0.748837
| 27
| 215
| 5.962963
| 0.407407
| 0.496894
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.167442
| 215
| 9
| 29
| 23.888889
| 0.899441
| 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
|
376e44a7a1e7b889bbdf9140d9c51758e8bc0bfd
| 3,542
|
py
|
Python
|
pyfronius/tests/web_raw/web_state.py
|
skatsaounis/pyfronius
|
6568a9a496316c2841c7eeafed9d10dc08951c0e
|
[
"MIT"
] | null | null | null |
pyfronius/tests/web_raw/web_state.py
|
skatsaounis/pyfronius
|
6568a9a496316c2841c7eeafed9d10dc08951c0e
|
[
"MIT"
] | null | null | null |
pyfronius/tests/web_raw/web_state.py
|
skatsaounis/pyfronius
|
6568a9a496316c2841c7eeafed9d10dc08951c0e
|
[
"MIT"
] | null | null | null |
GET_POWER_FLOW_REALTIME_DATA = {
'timestamp': {
'value': '2019-01-10T23:33:12+01:00'
},
'status': {
'Code': 0,
'Reason': '',
'UserMessage': ''
},
'energy_day': {
'value': 0,
'unit': 'Wh'
},
'energy_total': {
'value': 26213502,
'unit': 'Wh'
},
'energy_year': {
'value': 12400.100586,
'unit': 'Wh'
},
'meter_location': {
'value': 'load'
},
'meter_mode': {
'value': 'vague-meter'
},
'power_battery': {
'value': None,
'unit': 'W'
},
'power_grid': {
'value': 367.722145,
'unit': 'W'
},
'power_load': {
'value': -367.722145,
'unit': 'W'
},
'power_photovoltaics': {
'value': None,
'unit': 'W'
}
}
GET_METER_REALTIME_DATA_SYSTEM = {
'timestamp': {
'value': '2019-01-10T23:33:13+01:00'
},
'status': {
'Code': 0,
'Reason': '',
'UserMessage': ''
},
'meters': {
'0': {
'power_real': {
'value': -367.722145,
'unit': 'W'
},
'meter_location': {
'value': 1
},
'enable': {
'value': 1
},
'visible': {
'value': 1
},
'manufacturer': {
'value': 'Fronius'
},
'model': {
'value': ''
},
'serial': {
'value': ''
}
}
}
}
GET_METER_REALTIME_DATA_SCOPE_DEVICE = {
'timestamp': {
'value': '2019-01-10T23:33:14+01:00'
},
'status': {
'Code': 0,
'Reason': '',
'UserMessage': ''
},
'power_real': {
'value': -367.722145,
'unit': 'W'
},
'meter_location': {
'value': 1
},
'enable': {
'value': 1
},
'visible': {
'value': 1
},
'manufacturer': {
'value': 'Fronius'
},
'model': {
'value': ''
},
'serial': {
'value': ''
}
}
GET_INVERTER_REALTIME_DATA_SCOPE_DEVICE = {
'timestamp': {
'value': '2019-01-10T23:33:15+01:00'
},
'status': {
'Code': 0,
'Reason': '',
'UserMessage': ''
},
'energy_day': {
'value': 0,
'unit': 'Wh'
},
'energy_total': {
'value': 26213502,
'unit': 'Wh'
},
'energy_year': {
'value': 12400.1,
'unit': 'Wh'
}
}
GET_INVERTER_REALTIME_DATA_SYSTEM = {
'timestamp': {
'value': '2019-01-10T23:33:16+01:00'
},
'status': {
'Code': 0,
'Reason': '',
'UserMessage': ''
},
'energy_day': {
'value': 0,
'unit': 'Wh'
},
'energy_total': {
'value': 26213502,
'unit': 'Wh'
},
'energy_year': {
'value': 12400,
'unit': 'Wh'
},
'power_ac': {
'value': 0,
'unit': 'W'
},
'inverters': {
'1': {
'energy_day': {
'value': 0,
'unit': 'Wh'
},
'energy_total': {
'value': 26213502,
'unit': 'Wh'
},
'energy_year': {
'value': 12400,
'unit': 'Wh'
},
'power_ac': {
'value': 0,
'unit': 'Wh'
}
}
}
}
| 19.145946
| 44
| 0.355167
| 270
| 3,542
| 4.488889
| 0.214815
| 0.064356
| 0.079208
| 0.082508
| 0.820957
| 0.820957
| 0.759076
| 0.706271
| 0.706271
| 0.632013
| 0
| 0.105699
| 0.45511
| 3,542
| 184
| 45
| 19.25
| 0.52228
| 0
| 0
| 0.572222
| 0
| 0
| 0.283173
| 0.035291
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 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
|
37a66c516356314f24ceae2d36e83ebe080f7b17
| 287
|
py
|
Python
|
src/spaceone/notification/info/__init__.py
|
stat-kwon/notification
|
55c4c7e1355636a1fd08e2fde7d16a09c5a04b3e
|
[
"Apache-2.0"
] | 2
|
2021-11-05T06:55:13.000Z
|
2022-01-16T08:58:33.000Z
|
src/spaceone/notification/info/__init__.py
|
stat-kwon/notification
|
55c4c7e1355636a1fd08e2fde7d16a09c5a04b3e
|
[
"Apache-2.0"
] | 5
|
2022-02-18T03:43:35.000Z
|
2022-03-16T12:08:34.000Z
|
src/spaceone/notification/info/__init__.py
|
stat-kwon/notification
|
55c4c7e1355636a1fd08e2fde7d16a09c5a04b3e
|
[
"Apache-2.0"
] | 4
|
2021-05-14T06:27:17.000Z
|
2022-02-28T14:32:49.000Z
|
from spaceone.notification.info.common_info import *
from spaceone.notification.info.protocol_info import *
from spaceone.notification.info.project_channel_info import *
from spaceone.notification.info.user_channel_info import *
from spaceone.notification.info.notification_info import *
| 57.4
| 61
| 0.864111
| 37
| 287
| 6.513514
| 0.27027
| 0.39834
| 0.497925
| 0.580913
| 0.688797
| 0.688797
| 0.373444
| 0
| 0
| 0
| 0
| 0
| 0.066202
| 287
| 5
| 62
| 57.4
| 0.899254
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
805d7eb0144c7c7b0b504b6069791aba6122a9e8
| 233
|
py
|
Python
|
liquepy/element/__init__.py
|
geosharma/liquepy
|
d567f6a7eb93eb76218f718375f2d34355d7edd4
|
[
"MIT"
] | 4
|
2020-04-12T14:49:24.000Z
|
2020-09-22T09:28:10.000Z
|
liquepy/element/__init__.py
|
geosharma/liquepy
|
d567f6a7eb93eb76218f718375f2d34355d7edd4
|
[
"MIT"
] | null | null | null |
liquepy/element/__init__.py
|
geosharma/liquepy
|
d567f6a7eb93eb76218f718375f2d34355d7edd4
|
[
"MIT"
] | 4
|
2019-11-02T02:11:34.000Z
|
2021-04-30T06:06:41.000Z
|
from liquepy.element.models import ShearTest
from liquepy.element.assess import calc_diss_energy_fd, calc_abs_delta_tau_fd, calc_diss_energy_et, \
calc_abs_delta_tau_et, calc_case_et, calc_case_fd, calc_case_peaks_and_indices_fd
| 58.25
| 101
| 0.871245
| 41
| 233
| 4.390244
| 0.463415
| 0.1
| 0.2
| 0.166667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081545
| 233
| 3
| 102
| 77.666667
| 0.841122
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
8061a39ad080c776feca6469cbee4bd9ce9fb1ea
| 31,029
|
py
|
Python
|
test/sarun_test.py
|
Najafova/hls4ml_test
|
21f9951b3f0ca88cc2312a8863977c1477b9233e
|
[
"Apache-2.0"
] | null | null | null |
test/sarun_test.py
|
Najafova/hls4ml_test
|
21f9951b3f0ca88cc2312a8863977c1477b9233e
|
[
"Apache-2.0"
] | null | null | null |
test/sarun_test.py
|
Najafova/hls4ml_test
|
21f9951b3f0ca88cc2312a8863977c1477b9233e
|
[
"Apache-2.0"
] | null | null | null |
import pytest
import hls4ml
import tensorflow as tf
import numpy as np
from tensorflow.keras import optimizers
from tensorflow.keras.layers import Input, Dense, Activation, Conv1D, Conv2D, \
Reshape, ELU, LeakyReLU, ThresholdedReLU, \
PReLU, BatchNormalization, Add, Subtract, \
Multiply, Average, Maximum, Minimum, Concatenate, \
MaxPooling1D, MaxPooling2D, AveragePooling1D, \
AveragePooling2D
import math
from tensorflow.keras import backend as K
from numpy.testing import assert_allclose
# ***Input, ***Reshape,
# ***Dense, BinaryDense, TernaryDense
# ***Activation, ***LeakyReLU, ***ThresholdedReLU, ***ELU, ***PReLU
# ~BatchNormalization
# ***Conv1D, ***Conv2D
# Merge layers - Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate
# ***MaxPooling1D, ***MaxPooling2D, ***AveragePooling1D, ***AveragePooling2D
# TODO there are too much assertion functions. replace them with helper functions
# TODO write models with functional model too
# TODO the other stages of conversion; maybe 4 stages
# TODO save the models in h5py file
# TODO C++ test
# TODO JSON codes
# TODO Focus on Convolutional layers mor
# TODO Convert from h5py file and yml file
# ALMOST DONE
# TODO Consider BinaryDense ve TernaryDense layers
def test_dense():
model = tf.keras.models.Sequential()
model.add(Dense(2,
input_shape=(1,),
name='Dense',
use_bias=True,
kernel_initializer= tf.keras.initializers.RandomUniform(minval=1, maxval=10),
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None))
model.add(Activation(activation='elu', name='Activation'))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(1,)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
# config['Model']['Precision'] = 'ap_fixed<32,12>'
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
assert round(np.average(np.subtract(np.abs(keras_prediction), np.abs(hls_prediction)))) < 3
# actual_num = 2.986982345
# desired_num = 2.98698237
# assert_allclose(actual_num, desired_num, rtol=3e-0)
# assert_allclose(hls_prediction,keras_prediction, rtol=3e-0)
assert len(model.layers) + 1 == len(hls_model.get_layers())
assert list(hls_model.get_layers())[0].attributes['class_name'] == "InputLayer"
assert list(hls_model.get_layers())[1].attributes["class_name"] == model.layers[0]._name
assert list(hls_model.get_layers())[2].attributes['class_name'] == model.layers[1]._name
assert list(hls_model.get_layers())[0].attributes['input_shape'] == list(model.layers[0].input_shape[1:])
assert list(hls_model.get_layers())[1].attributes['n_in'] == model.layers[0].input_shape[1:][0]
assert list(hls_model.get_layers())[1].attributes['n_out'] == model.layers[0].output_shape[1:][0]
assert list(hls_model.get_layers())[2].attributes['activation'] == str(model.layers[1].activation).split()[1]
assert list(hls_model.get_layers())[1].attributes['activation'] == str(model.layers[0].activation).split()[1]
# # DONE
# def test_reshape():
# model = tf.keras.models.Sequential()
# model.add(Dense(12,
# input_shape=(1,),
# name='Dense',
# use_bias=True,
# kernel_initializer= tf.keras.initializers.RandomUniform(minval=1, maxval=10),
# bias_initializer='zeros',
# kernel_regularizer=None,
# bias_regularizer=None,
# activity_regularizer=None,
# kernel_constraint=None,
# bias_constraint=None))
# model.add(Reshape((3,4)))
# model.add(Activation(activation="elu", name='Activation'))
# model.compile(optimizer='adam', loss='mse')
# # X_input = np.random.rand(12,)
# X_input = np.ndarray(shape=(12,), dtype=float)
# keras_prediction = model.predict(X_input)
# config = hls4ml.utils.config_from_keras_model(model)
# hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
# hls_model.compile()
# hls_prediction = hls_model.predict(X_input)
# # assert round(np.average(np.subtract(np.abs(keras_prediction), np.abs(hls_prediction)))) < 3
# # hls_model = hls4ml.converters.convert_from_keras_model(model)
# assert len(model.layers) + 1 == len(hls_model.get_layers())
# assert list(hls_model.get_layers())[2].attributes["name"] == model.layers[1]._name
# assert list(hls_model.get_layers())[2].attributes['target_shape'] == list(model.layers[1].target_shape)
# DONE
keras_activation_functions = [LeakyReLU, ELU]
@pytest.mark.parametrize("activation_functions", keras_activation_functions)
def test_activation_leakyrelu_elu(activation_functions):
model = tf.keras.models.Sequential()
model.add(Dense(64,
input_shape=(1,),
name='Dense',
kernel_initializer='lecun_uniform',
kernel_regularizer=None))
model.add(activation_functions(alpha=1.0))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(1)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
assert round(np.average(np.subtract(np.abs(keras_prediction), np.abs(hls_prediction)))) < 3
assert len(model.layers) + 1 == len(hls_model.get_layers())
if activation_functions == 'ELU':
assert list(hls_model.get_layers())[2].attributes['class_name'] == 'ELU'
elif activation_functions == 'LeakyReLU':
assert list(hls_model.get_layers())[2].attributes['class_name'] == 'LeakyReLU'
# DONE
keras_activation_functions = [ThresholdedReLU]
@pytest.mark.parametrize("activation_functions", keras_activation_functions)
def test_activation_thresholdedrelu(activation_functions):
model = tf.keras.models.Sequential()
model.add(Dense(64,
input_shape=(1,),
name='Dense',
kernel_initializer='lecun_uniform',
kernel_regularizer=None))
model.add(activation_functions(theta=1.0))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(1,)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
assert round(np.average(np.subtract(np.abs(keras_prediction), np.abs(hls_prediction)))) < 3
assert len(model.layers) + 1 == len(hls_model.get_layers())
if activation_functions == 'ThresholdedReLU':
assert list(hls_model.get_layers())[2].attributes['class_name'] == 'ThresholdedReLU'
# DONE
keras_activation_functions = [PReLU]
@pytest.mark.parametrize("activation_functions", keras_activation_functions)
def test_activation_prelu(activation_functions):
model = tf.keras.models.Sequential()
model.add(Dense(64,
input_shape=(1,),
name='Dense',
kernel_initializer='lecun_uniform',
kernel_regularizer=None))
model.add(activation_functions(alpha_initializer="zeros",))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(1,)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
assert round(np.average(np.subtract(np.abs(keras_prediction), np.abs(hls_prediction)))) < 3
assert len(model.layers) + 1 == len(hls_model.get_layers())
if activation_functions == 'PReLU':
assert list(hls_model.get_layers())[2].attributes['class_name'] == 'PReLU'
# DONE
keras_activation_functions = [Activation]
@pytest.mark.parametrize("activation_functions", keras_activation_functions)
def test_activation(activation_functions):
model = tf.keras.models.Sequential()
model.add(Dense(64,
input_shape=(1,),
name='Dense',
kernel_initializer='lecun_uniform',
kernel_regularizer=None))
model.add(Activation(activation='relu', name='Activation'))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(1,)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
assert round(np.average(np.subtract(np.abs(keras_prediction), np.abs(hls_prediction)))) < 3
assert len(model.layers) + 1 == len(hls_model.get_layers())
if activation_functions == 'Activation':
assert list(hls_model.get_layers())[2].attributes["activation"] == str(model.layers[1].activation).split()[1]
# DONE
keras_conv1d = [Conv1D]
padds_options = ['same', 'valid']
# @pytest.mark.parametrize("conv1d", keras_conv1d)
# @pytest.mark.parametrize("padds", padds_options)
def test_conv1d():
model = tf.keras.models.Sequential()
input_shape = (10, 128, 4)
model.add(Conv1D(filters=32,
kernel_size=3,
strides=2,
padding='same',
activation='relu',
input_shape=input_shape[1:],
kernel_initializer='normal',
use_bias=False,
data_format='channels_last'))
model.add(Activation(activation='relu'))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(10, 128,4)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
assert len(model.layers) + 2 == len(hls_model.get_layers())
assert list(hls_model.get_layers())[1].attributes['name'] == model.layers[0]._name
# if conv1d == 'Conv1D':
# assert list(hls_model.get_layers())[1].attributes['class_name'] == 'Conv1D'
assert list(hls_model.get_layers())[1].attributes['activation'] == str(model.layers[0].activation).split()[1]
assert list(hls_model.get_layers())[1].attributes["n_in"] == model.layers[0]._batch_input_shape[1]
assert list(hls_model.get_layers())[1].attributes['filt_width'] == model.layers[0].kernel_size[0]
assert list(hls_model.get_layers())[1].attributes['n_chan'] == model.layers[0].input_shape[2]
assert list(hls_model.get_layers())[1].attributes['n_filt'] == model.layers[0].filters
assert list(hls_model.get_layers())[1].attributes['stride'] == model.layers[0].strides[0]
assert list(hls_model.get_layers())[1].attributes['padding'] == model.layers[0].padding
assert list(hls_model.get_layers())[1].attributes['data_format'] == model.layers[0].data_format
assert list(hls_model.get_layers())[1].attributes["n_out"] == list(model.layers[0].output_shape)[1]
out_width = math.ceil(float(model.layers[0]._batch_input_shape[2]) / float(model.layers[0].strides[0]))
pad_along_width = max((out_width - 1) * model.layers[0].strides[0] + model.layers[0].kernel_size[0] - model.layers[0]._batch_input_shape[2], 0)
pad_left = pad_along_width // 2
pad_right = pad_along_width - pad_left
out_valid = math.ceil(float(model.layers[0]._batch_input_shape[1] - model.layers[0].kernel_size[0] + 1) / float(model.layers[0].strides[0]))
if model.layers[0].padding == 'same':
assert list(hls_model.get_layers())[1].attributes['pad_left'] == pad_left
assert list(hls_model.get_layers())[1].attributes['pad_right'] == pad_right
elif model.layers[0].padding == 'valid':
assert list(hls_model.get_layers())[1].attributes['pad_left'] == 0
assert list(hls_model.get_layers())[1].attributes['pad_right'] == 0
print(model.layers[0].__dict__.items())
print()
print(list(hls_model.get_layers())[1].attributes)
test_conv1d()
# # DONE
# keras_conv2d = [Conv2D]
# padds_options = ['same', 'valid']
# chans_options = ['channels_first', 'channels_last']
# @pytest.mark.parametrize("conv2d", keras_conv2d)
# @pytest.mark.parametrize("chans", chans_options)
# @pytest.mark.parametrize("padds", padds_options)
# def test_conv2d(conv2d, chans, padds):
# model = tf.keras.models.Sequential()
# input_shape = (4, 4, 28, 30)
# model.add(conv2d(filters=32,
# kernel_size=(4,4),
# strides=(4,4),
# padding=padds,
# activation='relu',
# input_shape=input_shape[1:],
# kernel_initializer='normal',
# use_bias=False,
# data_format=chans
# ))
# model.add(Activation(activation='relu'))
# model.compile(optimizer='adam', loss='mse')
# X_input = np.random.rand(4, 4, 28, 30)
# keras_prediction = model.predict(X_input)
# config = hls4ml.utils.config_from_keras_model(model)
# hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
# hls_model.compile()
# hls_prediction = hls_model.predict(X_input)
# # hls_model = hls4ml.converters.convert_from_keras_model(model)
# assert len(model.layers) + 2 == len(hls_model.get_layers())
# assert list(hls_model.get_layers())[1].attributes['name'] == model.layers[0]._name
# if conv2d == 'Conv2D':
# assert list(hls_model.get_layers())[1].attributes['class_name'] == 'Conv2D'
# assert list(hls_model.get_layers())[1].attributes['activation'] == str(model.layers[0].activation).split()[1]
# assert list(hls_model.get_layers())[1].attributes['filt_width'] == model.layers[0].kernel_size[1]
# assert list(hls_model.get_layers())[1].attributes['filt_height'] == model.layers[0].kernel_size[0]
# assert list(hls_model.get_layers())[1].attributes['n_filt'] == model.layers[0].filters
# assert list(hls_model.get_layers())[1].attributes['stride_width'] == model.layers[0].strides[1]
# assert list(hls_model.get_layers())[1].attributes['stride_height'] == model.layers[0].strides[0]
# assert list(hls_model.get_layers())[1].attributes['padding'] == model.layers[0].padding
# assert list(hls_model.get_layers())[1].attributes['data_format'] == model.layers[0].data_format
# if model.layers[0].data_format == 'channels_first':
# assert list(hls_model.get_layers())[1].attributes['n_chan'] == model.layers[0]._batch_input_shape[1]
# assert list(hls_model.get_layers())[1].attributes['in_height'] == model.layers[0]._batch_input_shape[2]
# assert list(hls_model.get_layers())[1].attributes['in_width'] == model.layers[0]._batch_input_shape[3]
# assert list(hls_model.get_layers())[1].attributes['out_height'] == model.layers[0].output_shape[2]
# assert list(hls_model.get_layers())[1].attributes['out_width'] == model.layers[0].output_shape[3]
# elif model.layers[0].data_format == 'channels_last':
# assert list(hls_model.get_layers())[1].attributes['n_chan'] == model.layers[0]._batch_input_shape[3]
# assert list(hls_model.get_layers())[1].attributes['in_height'] == model.layers[0]._batch_input_shape[1]
# assert list(hls_model.get_layers())[1].attributes['in_width'] == model.layers[0]._batch_input_shape[2]
# assert list(hls_model.get_layers())[1].attributes['out_height'] == model.layers[0].output_shape[1]
# assert list(hls_model.get_layers())[1].attributes['out_width'] == model.layers[0].output_shape[2]
# if model.layers[0].padding =='same':
# if model.layers[0].data_format == 'channels_first':
# out_height = model.layers[0].output_shape[2]
# out_width = model.layers[0].output_shape[3]
# pad_along_height = max((out_height - 1) * model.layers[0].strides[0] + model.layers[0].kernel_size[0] - model.layers[0]._batch_input_shape[2], 0)
# pad_along_width = max((out_width - 1) * model.layers[0].strides[1] + model.layers[0].kernel_size[1] - model.layers[0]._batch_input_shape[3], 0)
# elif model.layers[0].data_format == 'channels_last':
# out_height = model.layers[0].output_shape[1]
# out_width = model.layers[0].output_shape[2]
# pad_along_height = max((out_height - 1) * model.layers[0].strides[0] + model.layers[0].kernel_size[0] - model.layers[0]._batch_input_shape[1], 0)
# pad_along_width = max((out_width - 1) * model.layers[0].strides[1] + model.layers[0].kernel_size[1] - model.layers[0]._batch_input_shape[2], 0)
# pad_top = pad_along_height // 2
# pad_bottom = pad_along_height - pad_top
# pad_left = pad_along_width // 2
# pad_right = pad_along_width - pad_left
# assert list(hls_model.get_layers())[1].attributes['pad_top'] == pad_top
# assert list(hls_model.get_layers())[1].attributes['pad_bottom'] == pad_bottom
# assert list(hls_model.get_layers())[1].attributes['pad_left'] == pad_left
# assert list(hls_model.get_layers())[1].attributes['pad_right'] == pad_right
# elif model.layers[0].padding =='valid':
# assert list(hls_model.get_layers())[1].attributes['pad_top'] == 0
# assert list(hls_model.get_layers())[1].attributes['pad_bottom'] == 0
# assert list(hls_model.get_layers())[1].attributes['pad_left'] == 0
# assert list(hls_model.get_layers())[1].attributes['pad_right'] == 0
# DONE
pooling_layers = [MaxPooling1D, MaxPooling2D, AveragePooling1D, AveragePooling2D]
padds_options = ['same', 'valid']
chans_options = ['channels_first', 'channels_last']
@pytest.mark.parametrize("poolings", pooling_layers)
@pytest.mark.parametrize("padds", padds_options)
@pytest.mark.parametrize("chans", chans_options)
def test_pooling(poolings, padds, chans):
model = tf.keras.models.Sequential()
if poolings == 'MaxPooling2D' or poolings == 'AveragePooling2D':
model.add(Conv2D(1, (3,3), activation='relu', input_shape=(8, 8, 1)))
model.add(AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None))
model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(10, 128,4)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
for i in range(2):
assert list(hls_model.get_layers())[i + 3].attributes['name'] == model.layers[i + 1]._name
assert list(hls_model.get_layers())[i + 3].attributes['class_name'][-2] == str(2)
assert list(hls_model.get_layers())[i + 3].attributes['stride_height'] == model.layers[i + 1].strides[0]
assert list(hls_model.get_layers())[i + 3].attributes['stride_width'] == model.layers[i + 1].strides[1]
assert list(hls_model.get_layers())[i + 3].attributes['pool_height'] == model.layers[i + 1].pool_size[1]
assert list(hls_model.get_layers())[i + 3].attributes['pool_width'] == model.layers[i + 1].pool_size[0]
assert list(hls_model.get_layers())[i + 3].attributes['padding'] == model.layers[i + 1].padding
if list(hls_model.get_layers())[i + 3].attributes['data_format'] == 'channels_last':
assert list(hls_model.get_layers())[i + 3].attributes['in_height'] == model.layers[i + 1].input_shape[1]
assert list(hls_model.get_layers())[i + 3].attributes['in_width'] == model.layers[i + 1].input_shape[2]
assert list(hls_model.get_layers())[i + 3].attributes['n_filt'] == model.layers[i + 1].input_shape[3]
elif list(hls_model.get_layers())[i + 3].attributes['data_format'] == 'channels_first':
assert list(hls_model.get_layers())[i + 3].attributes['in_height'] == model.layers[i + 1].input_shape[2]
assert list(hls_model.get_layers())[i + 3].attributes['in_width'] == model.layers[i + 1].input_shape[3]
assert list(hls_model.get_layers())[i + 3].attributes['n_filt'] == model.layers[i + 1].input_shape[1]
if list(hls_model.get_layers())[i + 3].attributes['padding'] == 'same':
# Height
in_height = model.layers[i + 1].input_shape[1]
if model.layers[i + 1].data_format == 'channels_first':
in_height = model.layers[i + 1].input_shape[2]
out_height = int(math.ceil(float(in_height) / float(model.layers[i + 1].strides[0])))
assert out_height == list(hls_model.get_layers())[i + 3].attributes['out_height']
if in_height % model.layers[i + 1].strides[0] == 0:
pad_along_height = max(model.layers[i + 1].pool_size[1] - model.layers[i + 1].strides[0], 0)
else:
pad_along_height = max(model.layers[i + 1].pool_size[1] - (in_height % model.layers[i + 1].strides[0]), 0)
pad_top = pad_along_height // 2
pad_bottom = pad_along_height - pad_top
assert pad_bottom == list(hls_model.get_layers())[i + 3].attributes['pad_bottom']
assert pad_top == list(hls_model.get_layers())[i + 3].attributes['pad_top']
# Width
in_width = model.layers[i + 1].input_shape[2]
if model.layers[i + 1].data_format == 'channels_first':
in_height = model.layers[1].input_shape[i + 3]
out_width = int(math.ceil(float(in_width) / float(model.layers[i + 1].strides[1])))
assert out_width == list(hls_model.get_layers())[i + 3].attributes['out_width']
if in_width % model.layers[i + 1].strides[1] == 0:
pad_along_width = max(model.layers[i + 1].pool_size[0] - model.layers[i + 1].strides[1], 0)
else:
pad_along_width = max(model.layers[i + 1].pool_size[0] - (in_width % model.layers[i + 1].strides[1]), 0)
pad_left = pad_along_width // 2
pad_right = pad_along_width - pad_left
assert pad_left == list(hls_model.get_layers())[i + 3].attributes['pad_left']
assert pad_right == list(hls_model.get_layers())[i + 3].attributes['pad_right']
elif list(hls_model.get_layers())[i + 3].attributes['padding'] == 'valid':
if list(hls_model.get_layers())[i + 3].attributes['data_format'] == 'channels_first':
in_height = model.layers[i + 1].input_shape[2]
in_width = model.layers[i + 1].input_shape[3]
elif list(hls_model.get_layers())[i + 3].attributes['data_format'] == 'channels_last':
in_height = model.layers[i + 1].input_shape[1]
in_width = model.layers[i + 1].input_shape[2]
out_width = int(math.ceil(float(in_width - model.layers[i + 1].pool_size[0] + 1) / float(model.layers[i + 1].strides[1])))
out_height = int(math.ceil(float(in_height - model.layers[i + 1].pool_size[1] + 1) / float(model.layers[i + 1].strides[0])))
assert list(hls_model.get_layers())[i + 3].attributes['out_height'] == out_height
assert list(hls_model.get_layers())[i + 3].attributes['out_width'] == out_width
assert list(hls_model.get_layers())[i + 3].attributes['pad_top'] == 0
assert list(hls_model.get_layers())[i + 3].attributes['pad_bottom'] == 0
assert list(hls_model.get_layers())[i + 3].attributes['pad_left'] == 0
assert list(hls_model.get_layers())[i + 3].attributes['pad_right'] == 0
elif poolings == 'MaxPooling1D' or poolings == 'AveragePooling2D':
input_shape = (10, 128, 4)
model.add(Conv1D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu',
input_shape=input_shape[1:], kernel_initializer='normal', use_bias=False,
data_format='channels_last'))
model.add(MaxPooling1D(pool_size=2, strides=None, padding=padds, data_format=chans))
model.add(AveragePooling1D(pool_size=2, strides=None, padding=padds, data_format=chans))
model.compile(optimizer='adam', loss='mse')
X_input = np.random.rand(10, 128,4)
keras_prediction = model.predict(X_input)
config = hls4ml.utils.config_from_keras_model(model)
hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config)
hls_model.compile()
hls_prediction = hls_model.predict(X_input)
for i in range(2):
assert list(hls_model.get_layers())[i + 3].attributes['name'] == model.layers[i + 1]._name
assert list(hls_model.get_layers())[i + 3].attributes['class_name'][-2] == str(1)
assert list(hls_model.get_layers())[i + 3].attributes['n_in'] == model.layers[i + 1].input_shape[1]
assert list(hls_model.get_layers())[i + 3].attributes['n_filt'] == model.layers[i + 1].input_shape[2]
assert list(hls_model.get_layers())[i + 3].attributes['pool_size'] == model.layers[i + 1].pool_size[0]
assert list(hls_model.get_layers())[i + 3].attributes['stride'] == model.layers[i + 1].strides[0]
assert list(hls_model.get_layers())[i + 3].attributes['padding'] == model.layers[i + 1].padding
out_same = math.ceil(float(model.layers[i + 1].input_shape[1]) / float(model.layers[i + 1].strides[0]))
out_valid = math.ceil(float(model.layers[i + 1].input_shape[1] - model.layers[i + 1].pool_size[0] + 1) / model.layers[i + 1].strides[0])
if list(hls_model.get_layers())[i + 3].attributes['padding'] == 'same':
assert list(hls_model.get_layers())[i + 3].attributes['n_out'] == out_same
if model.layers[i + 1].input_shape[1] % model.layers[i + 1].strides[0] == 0:
pad_along_width = max(model.layers[i + 1].pool_size[0] - model.layers[i + 1].strides[0], 0)
else:
pad_along_width = max(model.layers[i + 1].pool_size[0] - (model.layers[i + 1].input_shape[1] % model.layers[i + 1].strides[0]), 0)
assert list(hls_model.get_layers())[i + 3].attributes['pad_left'] == pad_along_width // 2
assert list(hls_model.get_layers())[i + 3].attributes['pad_right'] == pad_along_width - pad_along_width // 2
elif list(hls_model.get_layers())[i + 3].attributes['padding'] == 'valid':
assert list(hls_model.get_layers())[i + 3].attributes['n_out'] == out_valid
assert list(hls_model.get_layers())[i + 3].attributes['pad_left'] == 0
assert list(hls_model.get_layers())[i + 3].attributes['pad_right'] == 0
# merge_layers = [Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate]
# # @pytest.mark.parametrize('merges', merge_layers)
# def test_merge():
# input1 = tf.keras.layers.Input(shape=(16,))
# x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
# input2 = tf.keras.layers.Input(shape=(32,))
# x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
# added = tf.keras.layers.Concatenate()([x1, x2])
# out = tf.keras.layers.Dense(4)(added)
# model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
# hls_model = hls4ml.converters.convert_from_keras_model(model)
# # assert list(hls_model.get_layers())[6].attributes['op'] == model.layers[4]._name
# if model.layers[4]._name.lower() == 'concatenate':
# # assert list(hls_model.get_layers())[6].attributes['op'] == model.layers[4]._name.lower() + '{}d'.format(rank)
# assert list(hls_model.get_layers())[6].attributes['axis'] == model.layers[4].axis
# else:
# list(hls_model.get_layers())[6].attributes['class_name'] = 'Merge'
# assert len(list(hls_model.get_layers())[6].attributes['inputs']) <= 2
# # print()
# # list(hls_model.get_layers())[6].attributes['op']
# # # print(list(hls_model.get_layers())[6].attributes)
# # print()
# print(model.layers[4].__dict__.items())
# # 'name': 'subtract', 'class_name': 'Merge', 'inputs': ['dense_relu', 'dense_1_relu'], 'op': 'subtract'
# # keras model -> Input, Input, Dense, Dense, Add, Dense
# # hls_model -> Input, Input, Dense, Activation, Dense, Activation, Merge, Dense
# test_merge()
# # merge_layers = [Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate]
# # # @pytest.mark.parametrize("merges", merge_layers)
# # def test_merge():
# # model1 = tf.keras.models.Sequential()
# # model1.add(Dense(1, input_shape=(2,), activation='sigmoid'))
# # model2 = tf.keras.models.Sequential()
# # model2.add(Dense(1, input_shape=(1,), activation='sigmoid'))
# # model = tf.keras.models.Sequential()
# # model.add(Concatenate([model1, model2]))
# # model.add(Activation(activation='relu'))
# # hls_model = hls4ml.converters.convert_from_keras_model(model)
# # # print(model.layers)
# # # print()
# # # print(m.__dict__.keys())
# # # input_shape = (2, 3, 4)
# # # x1 = tf.random.normal(input_shape)
# # # print(x1)
# # # x2 = tf.random.normal(input_shape)
# # # print(x2)
# # # y = tf.keras.layers.Add()([x1, x2])
# # # print(y)
# # BatchNormalization layer dissappears when keras model is converted to hls model
# # keras_normalization_function = [BatchNormalization]
# # @pytest.mark.parametrize("normalization_function", keras_normalization_function)
# # def test_batch_normalization():
# # model = tf.keras.models.Sequential()
# # model.add(Dense(64,
# # input_shape=(16,),
# # name='Dense',
# # use_bias=True,
# # kernel_initializer='lecun_uniform',
# # bias_initializer='zeros'))
# # model.add(BatchNormalization())
# # model.add(Activation(activation='elu', name='Activation'))
# # hls_model = hls4ml.converters.convert_from_keras_model(model)
# # print(hls_model.get_layers())
# # print()
# # print(model.layers)
# # test_batch_normalization()
# # keras_dense_layers = [BinaryDense, TernaryDense]
# # @pytest.mark.parametrize("dense_layers", keras_dense_layers)
# # def test_dense_layer():
# # model = tf.keras.models.Sequential()
# test_dense_layer()
| 50.290113
| 155
| 0.656031
| 4,145
| 31,029
| 4.683715
| 0.056695
| 0.065108
| 0.068559
| 0.105954
| 0.836973
| 0.823632
| 0.80545
| 0.78124
| 0.735243
| 0.718605
| 0
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| 0.185117
| 31,029
| 617
| 156
| 50.290113
| 0.741586
| 0.358084
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| 0.001621
| 0.263699
| 1
| 0.023973
| false
| 0
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| 0.010274
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| 1
| 1
| 1
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| 1
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| 0
| 0
|
0
| 6
|
8064dbe79598dfb44534a14aa5d2935099353134
| 11,167
|
py
|
Python
|
hallo/modules/channel_control/operator.py
|
SpangleLabs/Hallo
|
17145d8f76552ecd4cbc5caef8924bd2cf0cbf24
|
[
"MIT"
] | 1
|
2022-01-27T13:25:01.000Z
|
2022-01-27T13:25:01.000Z
|
hallo/modules/channel_control/operator.py
|
joshcoales/Hallo
|
17145d8f76552ecd4cbc5caef8924bd2cf0cbf24
|
[
"MIT"
] | 75
|
2015-09-26T18:07:18.000Z
|
2022-01-04T07:15:11.000Z
|
hallo/modules/channel_control/operator.py
|
SpangleLabs/Hallo
|
17145d8f76552ecd4cbc5caef8924bd2cf0cbf24
|
[
"MIT"
] | 1
|
2021-04-10T12:02:47.000Z
|
2021-04-10T12:02:47.000Z
|
from hallo.events import EventMode
from hallo.function import Function
import hallo.modules.channel_control.channel_control
from hallo.server import Server
class Operator(Function):
"""
Gives a user on an irc server "operator" status.
"""
def __init__(self):
"""
Constructor
"""
super().__init__()
# Name for use in help listing
self.help_name = "op"
# Names which can be used to address the function
self.names = {
"op",
"operator",
"give op",
"gib op",
"get op",
"give operator",
"gib operator",
"get operator",
}
# Help documentation, if it's just a single line, can be set here
self.help_docs = (
"Op member in given channel, or current channel if no channel given. Or command user if no "
"member given. Format: op <name> <channel>"
)
def run(self, event):
# Get server object
server_obj = event.server
# If server isn't IRC type, we can't give op.
if server_obj.type != Server.TYPE_IRC:
return event.create_response(
"Error, this function is only available for IRC servers."
)
# If 0 arguments, op user who called command.
line_split = event.command_args.split()
if len(line_split) == 0:
# Check that this is a channel
if event.channel is None:
return event.create_response(
"Error, I can't op you in a private message, please provide a channel."
)
# Give op to the user
return event.create_response(self.give_op(event.channel, event.user))
# If 1 argument, see if it's a channel or a user.
if len(line_split) == 1:
# If message was sent in private message, it's referring to a channel
if event.channel is None:
channel = server_obj.get_channel_by_name(event.command_args)
if channel is None:
return event.create_response(
"Error, {} is not known on {}.".format(
event.command_args, server_obj.name
)
)
return event.create_response(self.give_op(channel, event.user))
# See if it's a channel that hallo is in
test_channel = server_obj.get_channel_by_name(event.command_args)
if test_channel is not None and test_channel.in_channel:
return event.create_response(self.give_op(test_channel, event.user))
# Argument must be a user?
target_user = server_obj.get_user_by_name(event.command_args)
if target_user is None:
return event.create_response(
"Error, {} is not known on {}.".format(
event.command_args, server_obj.name
)
)
return event.create_response(self.give_op(event.channel, target_user))
# If 2 arguments, try with first argument as channel
target_channel = server_obj.get_channel_by_name(line_split[0])
if target_channel is not None and target_channel.in_channel:
target_user = server_obj.get_user_by_name(line_split[1])
if target_user is None:
return event.create_response(
"Error, {} is not known on {}.".format(
line_split[1], server_obj.name
)
)
return event.create_response(self.give_op(target_channel, target_user))
# 2 args, try with second argument as channel
target_user = server_obj.get_user_by_name(line_split[0])
if target_user is None:
return event.create_response(
"Error, {} is not known on {}.".format(line_split[0], server_obj.name)
)
target_channel = server_obj.get_channel_by_name(line_split[1])
if target_channel is None:
return event.create_response(
"Error, {} is not known on {}.".format(line_split[1], server_obj.name)
)
return event.create_response(self.give_op(target_channel, target_user))
def give_op(self, channel, user):
"""
Gives op to a user in a given channel, after checks.
:param channel: Channel to give user op in
:type channel: destination.Channel
:param user: User to give op to
:type user: destination.User
:return: Response to send to requester
:rtype: str
"""
# Check if in channel
if not channel.in_channel:
return "Error, I'm not in that channel."
# Check if user is in channel
if user not in channel.get_user_list():
return "Error, {} is not in {}.".format(user.name, channel.name)
# Check if hallo has op in channel
if not hallo.modules.channel_control.channel_control.hallo_has_op(channel):
return "Error, I don't have power to give op in {}.".format(channel.name)
# Check that user does not have op in channel
user_membership = channel.get_membership_by_user(user)
if user_membership.is_op:
return "Error, this user already has op."
mode_evt = EventMode(
channel.server, channel, None, "+o {}".format(user.address), inbound=False
)
channel.server.send(mode_evt)
return "Op status given."
class DeOperator(Function):
"""
Removes a user on an irc server's "operator" status.
"""
def __init__(self):
"""
Constructor
"""
super().__init__()
# Name for use in help listing
self.help_name = "deop"
# Names which can be used to address the function
self.names = {
"deop",
"deoperator",
"unoperator",
"take op",
"del op",
"delete op",
"remove op",
"take operator",
"del operator",
"delete op",
"remove operator",
}
# Help documentation, if it's just a single line, can be set here
self.help_docs = (
"Deop member in given channel, or current channel if no channel given. Or command user if "
"no member given. Format: deop <name> <channel>"
)
def run(self, event):
# Get server object
server_obj = event.server
# If server isn't IRC type, we can't take op.
if server_obj.type != Server.TYPE_IRC:
return event.create_response(
"Error, this function is only available for IRC servers."
)
# If 0 arguments, de-op user who called command.
line_split = event.command_args.split()
if len(line_split) == 0:
# Check that this is a channel
if event.channel is None:
return event.create_response(
"Error, I can't de-op you in a private message, please provide a channel."
)
# Remove op
return event.create_response(self.take_op(event.channel, event.user))
# If 1 argument, see if it's a channel or a user.
if len(line_split) == 1:
# If message was sent in private message, it's referring to a channel
if event.channel is None:
channel = server_obj.get_channel_by_name(event.command_args)
if channel is None:
return event.create_response(
"Error, {} is not known on {}.".format(
event.command_args, server_obj.name
)
)
return event.create_response(self.take_op(channel, event.user))
# See if it's a channel that hallo is in
test_channel = server_obj.get_channel_by_name(event.command_args)
if test_channel is not None and test_channel.in_channel:
return event.create_response(self.take_op(test_channel, event.user))
# Argument must be a user?
target_user = server_obj.get_user_by_name(event.command_args)
if target_user is None:
return event.create_response(
"Error, {} is not known on {}.".format(
event.command_args, server_obj.name
)
)
return event.create_response(self.take_op(event.channel, target_user))
# If 2 arguments, try with first argument as channel
target_channel = server_obj.get_channel_by_name(line_split[0])
if target_channel is not None and target_channel.in_channel:
target_user = server_obj.get_user_by_name(line_split[1])
if target_user is None:
return event.create_response(
"Error, {} is not known on {}.".format(
line_split[1], server_obj.name
)
)
return event.create_response(self.take_op(target_channel, target_user))
# 2 args, try with second argument as channel
target_user = server_obj.get_user_by_name(line_split[0])
if target_user is None:
return event.create_response(
"Error, {} is not known on {}.".format(line_split[0], server_obj.name)
)
target_channel = server_obj.get_channel_by_name(line_split[1])
if target_channel is None:
return event.create_response(
"Error, {} is not known on {}.".format(line_split[1], server_obj.name)
)
return event.create_response(self.take_op(target_channel, target_user))
def take_op(self, channel, user):
"""
Takes op from a user in a given channel, after checks.
:param channel: Channel to take user op in
:type channel: destination.Channel
:param user: User to take op from
:type user: destination.User
:return: Response to send to requester
:rtype: str
"""
# Check if in channel
if not channel.in_channel:
return "Error, I'm not in that channel."
# Check if user is in channel
if user not in channel.get_user_list():
return "Error, {} is not in {}.".format(user.name, channel.name)
# Check if hallo has op in channel
if not hallo.modules.channel_control.channel_control.hallo_has_op(channel):
return "Error, I don't have power to take op in {}.".format(channel.name)
# Check that user does not have op in channel
user_membership = channel.get_membership_by_user(user)
if not user_membership.is_op:
return "Error, this user doesn't have op."
mode_evt = EventMode(
channel.server, channel, None, "-o {}".format(user.address), inbound=False
)
channel.server.send(mode_evt)
return "Op status taken."
| 42.95
| 104
| 0.576251
| 1,417
| 11,167
| 4.371912
| 0.102329
| 0.040678
| 0.071348
| 0.104923
| 0.923648
| 0.923648
| 0.910412
| 0.90912
| 0.897175
| 0.886521
| 0
| 0.003541
| 0.342527
| 11,167
| 259
| 105
| 43.11583
| 0.840234
| 0.179189
| 0
| 0.579235
| 0
| 0
| 0.144075
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032787
| false
| 0
| 0.021858
| 0
| 0.262295
| 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
|
807de119319727e3fffbae808030f1343b4a14da
| 24
|
py
|
Python
|
paypal/standard/ipn/tests/__init__.py
|
Telofy/django-paypal
|
0d7080cbc7552212b5fca7ed5042bd710d094bee
|
[
"Unlicense",
"MIT"
] | 19
|
2015-04-29T16:16:07.000Z
|
2018-08-21T18:18:18.000Z
|
paypal/standard/ipn/tests/__init__.py
|
sdvoynikov/idea-color-themes
|
c770dd14ee6cca58a5078c8885a7d8adac34965b
|
[
"MIT"
] | 2
|
2015-08-14T20:20:12.000Z
|
2015-10-21T17:15:28.000Z
|
paypal/standard/ipn/tests/__init__.py
|
sdvoynikov/idea-color-themes
|
c770dd14ee6cca58a5078c8885a7d8adac34965b
|
[
"MIT"
] | 5
|
2015-07-04T02:29:05.000Z
|
2018-02-21T06:28:35.000Z
|
from .test_ipn import *
| 12
| 23
| 0.75
| 4
| 24
| 4.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 24
| 1
| 24
| 24
| 0.85
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| true
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
808f4f68ece9cc5c42f633e02263205fd7a121a7
| 74
|
py
|
Python
|
null_bot_api/models/experts/__init__.py
|
lordralinc/null_bot_api
|
334b46d48669c7b6c95f27f23b3e7a81a897df2e
|
[
"MIT"
] | 1
|
2021-08-03T15:28:57.000Z
|
2021-08-03T15:28:57.000Z
|
null_bot_api/models/experts/__init__.py
|
lordralinc/null_bot_api
|
334b46d48669c7b6c95f27f23b3e7a81a897df2e
|
[
"MIT"
] | null | null | null |
null_bot_api/models/experts/__init__.py
|
lordralinc/null_bot_api
|
334b46d48669c7b6c95f27f23b3e7a81a897df2e
|
[
"MIT"
] | null | null | null |
from .get_card import ExpertsGetCard
from .get_info import ExpertsGetInfo
| 24.666667
| 36
| 0.864865
| 10
| 74
| 6.2
| 0.7
| 0.225806
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 74
| 2
| 37
| 37
| 0.939394
| 0
| 0
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| 0
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| 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
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
809b20ae6e7c02c9bd12a810cf1fe8ec7d752b4b
| 28
|
py
|
Python
|
lib/junno/nnet/__init__.py
|
LIV4D/JuNNo
|
7358f8344a7c125088e53aa1de0072c4699a9f07
|
[
"BSD-3-Clause"
] | null | null | null |
lib/junno/nnet/__init__.py
|
LIV4D/JuNNo
|
7358f8344a7c125088e53aa1de0072c4699a9f07
|
[
"BSD-3-Clause"
] | 1
|
2019-03-04T09:18:54.000Z
|
2019-03-05T06:15:06.000Z
|
lib/junno/nnet/__init__.py
|
LIV4D/JuNNo
|
7358f8344a7c125088e53aa1de0072c4699a9f07
|
[
"BSD-3-Clause"
] | null | null | null |
from .history import History
| 28
| 28
| 0.857143
| 4
| 28
| 6
| 0.75
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0.107143
| 28
| 1
| 28
| 28
| 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
|
80d3cb44a9b9565e4f5733e0043c03468549d5d1
| 58
|
py
|
Python
|
gtloader/__init__.py
|
geotrellis/python-geotrellis
|
819be71fd9fa1c89954be6a117bfc36a419b23ce
|
[
"Apache-2.0"
] | 13
|
2015-01-01T15:44:08.000Z
|
2021-11-16T13:32:31.000Z
|
gtloader/__init__.py
|
geotrellis/python-geotrellis
|
819be71fd9fa1c89954be6a117bfc36a419b23ce
|
[
"Apache-2.0"
] | null | null | null |
gtloader/__init__.py
|
geotrellis/python-geotrellis
|
819be71fd9fa1c89954be6a117bfc36a419b23ce
|
[
"Apache-2.0"
] | null | null | null |
import commands
def main(args):
commands.main(args)
| 11.6
| 23
| 0.706897
| 8
| 58
| 5.125
| 0.625
| 0.390244
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.189655
| 58
| 4
| 24
| 14.5
| 0.87234
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
80dc6f669c408a086aa65809f9e56c7aee09bb9a
| 3,442
|
py
|
Python
|
tests/terraform/checks/resource/azure/test_StorageAccountName.py
|
antonblr/checkov
|
9415c6593c537945c08f7a19f28bdd8b96966f67
|
[
"Apache-2.0"
] | 4,013
|
2019-12-09T13:16:54.000Z
|
2022-03-31T14:31:01.000Z
|
tests/terraform/checks/resource/azure/test_StorageAccountName.py
|
antonblr/checkov
|
9415c6593c537945c08f7a19f28bdd8b96966f67
|
[
"Apache-2.0"
] | 1,258
|
2019-12-17T09:55:51.000Z
|
2022-03-31T19:17:17.000Z
|
tests/terraform/checks/resource/azure/test_StorageAccountName.py
|
antonblr/checkov
|
9415c6593c537945c08f7a19f28bdd8b96966f67
|
[
"Apache-2.0"
] | 638
|
2019-12-19T08:57:38.000Z
|
2022-03-30T21:38:37.000Z
|
import unittest
import hcl2
from checkov.terraform.checks.resource.azure.StorageAccountName import check
from checkov.common.models.enums import CheckResult
class TestAzureStorageAccountNamingRule(unittest.TestCase):
def test_failure_dash(self):
hcl_res = hcl2.loads("""
resource "azurerm_storage_account" "example" {
name = "this-is-wrong"
resource_group_name = data.azurerm_resource_group.example.name
location = data.azurerm_resource_group.example.location
account_tier = "Standard"
account_replication_type = "GRS"
}
""")
resource_conf = hcl_res['resource'][0]['azurerm_storage_account']['example']
scan_result = check.scan_resource_conf(conf=resource_conf)
self.assertEqual(CheckResult.FAILED, scan_result)
def test_failure_length(self):
hcl_res = hcl2.loads("""
resource "azurerm_storage_account" "example" {
name = "thisiswayyyyyytoooloooong"
resource_group_name = data.azurerm_resource_group.example.name
location = data.azurerm_resource_group.example.location
account_tier = "Standard"
account_replication_type = "GRS"
}
""")
resource_conf = hcl_res['resource'][0]['azurerm_storage_account']['example']
scan_result = check.scan_resource_conf(conf=resource_conf)
self.assertEqual(CheckResult.FAILED, scan_result)
def test_failure_case(self):
hcl_res = hcl2.loads("""
resource "azurerm_storage_account" "example" {
name = "thisIsWrong"
resource_group_name = data.azurerm_resource_group.example.name
location = data.azurerm_resource_group.example.location
account_tier = "Standard"
account_replication_type = "GRS"
}
""")
resource_conf = hcl_res['resource'][0]['azurerm_storage_account']['example']
scan_result = check.scan_resource_conf(conf=resource_conf)
self.assertEqual(CheckResult.FAILED, scan_result)
def test_success(self):
hcl_res = hcl2.loads("""
resource "azurerm_storage_account" "example" {
name = "stomyexample"
resource_group_name = data.azurerm_resource_group.example.name
location = data.azurerm_resource_group.example.location
account_tier = "Standard"
account_replication_type = "GRS"
}
""")
resource_conf = hcl_res['resource'][0]['azurerm_storage_account']['example']
scan_result = check.scan_resource_conf(conf=resource_conf)
self.assertEqual(CheckResult.PASSED, scan_result)
def test_failure_empty_configuration(self):
hcl_res = hcl2.loads("""
resource "azurerm_storage_account" "example" {
}
""")
resource_conf = hcl_res['resource'][0]['azurerm_storage_account']['example']
scan_result = check.scan_resource_conf(conf=resource_conf)
self.assertEqual(CheckResult.FAILED, scan_result)
if __name__ == '__main__':
unittest.main()
| 43.56962
| 85
| 0.601685
| 326
| 3,442
| 6.003067
| 0.180982
| 0.091978
| 0.107307
| 0.143076
| 0.830864
| 0.8186
| 0.8186
| 0.8186
| 0.8186
| 0.8186
| 0
| 0.00462
| 0.308251
| 3,442
| 78
| 86
| 44.128205
| 0.817304
| 0
| 0
| 0.671642
| 0
| 0
| 0.567403
| 0.20308
| 0
| 0
| 0
| 0
| 0.074627
| 1
| 0.074627
| false
| 0.014925
| 0.059701
| 0
| 0.149254
| 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
|
03ed254b9d0e19064f3d26ab53c245fd33ff791a
| 203
|
py
|
Python
|
tests/common/scripts/cleaning/test_code_to_empty.py
|
arkhn/fhir-river
|
a12179c34fad131d16dedc20c61297ed83d805e6
|
[
"Apache-2.0"
] | 42
|
2020-03-25T16:47:30.000Z
|
2022-01-31T21:26:38.000Z
|
tests/common/scripts/cleaning/test_code_to_empty.py
|
arkhn/fhir-river
|
a12179c34fad131d16dedc20c61297ed83d805e6
|
[
"Apache-2.0"
] | 367
|
2020-04-08T12:46:34.000Z
|
2022-02-16T01:15:32.000Z
|
tests/common/scripts/cleaning/test_code_to_empty.py
|
arkhn/fhir-river
|
a12179c34fad131d16dedc20c61297ed83d805e6
|
[
"Apache-2.0"
] | 3
|
2020-05-14T08:24:46.000Z
|
2021-08-04T05:00:16.000Z
|
from common.scripts.cleaning import code_to_empty
def test_code_to_empty():
assert code_to_empty("-1") is None
assert code_to_empty("(sans)") is None
assert code_to_empty("-2") is not None
| 25.375
| 49
| 0.73399
| 35
| 203
| 3.942857
| 0.485714
| 0.217391
| 0.398551
| 0.369565
| 0.333333
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0.011765
| 0.162562
| 203
| 7
| 50
| 29
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0.049261
| 0
| 0
| 0
| 0
| 0
| 0.6
| 1
| 0.2
| true
| 0
| 0.2
| 0
| 0.4
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
ff13cf375534ae942d9d3932fff10c0b92706baa
| 1,516
|
py
|
Python
|
events/permissions.py
|
Strategy-Tap/Novizi-BackEnd
|
536edde68dc79ad5467f2dbb0931a56930a4edea
|
[
"MIT"
] | null | null | null |
events/permissions.py
|
Strategy-Tap/Novizi-BackEnd
|
536edde68dc79ad5467f2dbb0931a56930a4edea
|
[
"MIT"
] | 4
|
2021-04-08T21:23:49.000Z
|
2022-03-12T00:44:54.000Z
|
events/permissions.py
|
Strategy-Tap/Novizi-BackEnd
|
536edde68dc79ad5467f2dbb0931a56930a4edea
|
[
"MIT"
] | 1
|
2020-06-12T16:08:46.000Z
|
2020-06-12T16:08:46.000Z
|
"""Collection permissions."""
from typing import Any
from django.utils import timezone
from rest_framework import permissions
from rest_framework.request import Request
class IsOwnerOrReadOnly(permissions.BasePermission):
"""Allows full access to object owner otherwise read only."""
def has_object_permission(
self: "IsOwnerOrReadOnly", request: Request, view: Any, obj: Any
) -> bool:
"""Checking if user have object level permission.
Args:
request: Request object
view: Any type of view
obj: Any django model
Returns:
If user is owner of the object it return True otherwise it false
"""
if request.method in permissions.SAFE_METHODS:
return True
return obj.hosted_by == request.user and obj.event_date > timezone.now()
class IsProposerOrReadOnly(permissions.BasePermission):
"""Allows full access to object owner otherwise read only."""
def has_object_permission(
self: "IsProposerOrReadOnly", request: Request, view: Any, obj: Any
) -> bool:
"""Checking if user have object level permission.
Args:
request: Request object
view: Any type of view
obj: Any django model
Returns:
If user is owner of the object it return True otherwise it false
"""
if request.method in permissions.SAFE_METHODS:
return True
return obj.proposed_by == request.user
| 29.72549
| 80
| 0.649736
| 178
| 1,516
| 5.47191
| 0.331461
| 0.057495
| 0.034908
| 0.071869
| 0.702259
| 0.702259
| 0.702259
| 0.702259
| 0.702259
| 0.702259
| 0
| 0
| 0.282982
| 1,516
| 50
| 81
| 30.32
| 0.896044
| 0.371372
| 0
| 0.444444
| 0
| 0
| 0.04551
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0
| 0.222222
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
ff1f74155e59f3e43afaee9b00bb1b4628ce9fd7
| 147
|
py
|
Python
|
databird/__init__.py
|
jonas-hagen/databird
|
cfb358e74da62bb9d7ea0e6c7ac984671472120b
|
[
"MIT"
] | 1
|
2021-11-05T00:12:00.000Z
|
2021-11-05T00:12:00.000Z
|
databird/__init__.py
|
jonas-hagen/databird
|
cfb358e74da62bb9d7ea0e6c7ac984671472120b
|
[
"MIT"
] | null | null | null |
databird/__init__.py
|
jonas-hagen/databird
|
cfb358e74da62bb9d7ea0e6c7ac984671472120b
|
[
"MIT"
] | null | null | null |
from databird.base_driver import BaseDriver
from databird.classes import Repository, Profile
from databird.configuration import ConfigurationError
| 36.75
| 53
| 0.884354
| 17
| 147
| 7.588235
| 0.647059
| 0.27907
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088435
| 147
| 3
| 54
| 49
| 0.962687
| 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
|
205f1675b3b88ab291e7e8f46ac758a0b7853e24
| 150
|
py
|
Python
|
stock_management/stock_management/doctype/warehouse/test_warehouse.py
|
rahib-hassan/stock_management
|
9f54f2db45c8bf4e0c904c7d30164d2a81040045
|
[
"MIT"
] | null | null | null |
stock_management/stock_management/doctype/warehouse/test_warehouse.py
|
rahib-hassan/stock_management
|
9f54f2db45c8bf4e0c904c7d30164d2a81040045
|
[
"MIT"
] | null | null | null |
stock_management/stock_management/doctype/warehouse/test_warehouse.py
|
rahib-hassan/stock_management
|
9f54f2db45c8bf4e0c904c7d30164d2a81040045
|
[
"MIT"
] | 3
|
2022-03-11T07:17:25.000Z
|
2022-03-21T04:55:26.000Z
|
# Copyright (c) 2022, Rahib Hassan and Contributors
# See license.txt
# import frappe
import unittest
class TestWarehouse(unittest.TestCase):
pass
| 16.666667
| 51
| 0.78
| 19
| 150
| 6.157895
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.03125
| 0.146667
| 150
| 8
| 52
| 18.75
| 0.882813
| 0.526667
| 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
|
20bc837e60420e0e181cc7d2df78cb50932421da
| 12,288
|
py
|
Python
|
src/utils/feature_extraction.py
|
cpaismz89/DeepFireTopology
|
9cfe7c5ed9997a2a89b7405af47d9991da7d5471
|
[
"MIT"
] | null | null | null |
src/utils/feature_extraction.py
|
cpaismz89/DeepFireTopology
|
9cfe7c5ed9997a2a89b7405af47d9991da7d5471
|
[
"MIT"
] | null | null | null |
src/utils/feature_extraction.py
|
cpaismz89/DeepFireTopology
|
9cfe7c5ed9997a2a89b7405af47d9991da7d5471
|
[
"MIT"
] | null | null | null |
# Importations
from keras.applications import ResNet50, VGG16, imagenet_utils
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.image import img_to_array, load_img
from keras.utils import to_categorical
from imutils import paths
import numpy as np
import pickle
import random
import os
# Extract features for each set VGG
def ExtractFeatures_VGG(TRAIN='training',
TEST='evaluation',
VAL='validation',
BATCH_SIZE=32,
BASE_PATH=os.path.join('TransferLearningKeras', 'dataset'),
BASE_CSV_PATH=os.path.join('TransferLearningKeras', 'output'),
LE_PATH=os.path.join('TransferLearningKeras', 'output'),
):
# load the VGG16 network and initialize the label encoder
print("[INFO] loading network...")
model = VGG16(weights="imagenet", include_top=False) # No top layer
le = None
# Loop over the different sets
for split in (TRAIN, TEST, VAL):
# Get all images (paths)
print("[INFO] processing '{} split'...".format(split))
p = os.path.sep.join([BASE_PATH, split])
imagePaths = list(paths.list_images(p))
# Shuffle them and extract the labels from the paths
random.shuffle(imagePaths)
labels = [p.split(os.path.sep)[-2] for p in imagePaths]
# If the label encoder is None, create it
if le is None:
le = LabelEncoder()
le.fit(labels)
# Open the output CSV file for writing
csvPath = os.path.sep.join([BASE_CSV_PATH, "{}.csv".format(split)])
csv = open(csvPath, "w")
# loop over the images in batches
for (b, i) in enumerate(range(0, len(imagePaths), BATCH_SIZE)):
# extract the batch of images and labels, then initialize the
print("[INFO] processing batch {}/{}".format(b + 1,
int(np.ceil(len(imagePaths) / float(BATCH_SIZE))))
)
# Get a batch of images *paths* to extract features
batchPaths = imagePaths[i:i + BATCH_SIZE]
batchLabels = le.transform(labels[i:i + BATCH_SIZE])
batchImages = []
# loop over the images and labels in the current batch
for imagePath in batchPaths:
# Load image using the Keras helper utility
# Resize to 224x224 pixels (to match VGG16 architecture)
image = load_img(imagePath, target_size=(224, 224))
image = img_to_array(image)
# Preprocess: (1) expand the dimensions and (2) substract the mean RGB intensity
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
# Add the image *array* to the batch
batchImages.append(image)
# Pass the images through the network and use the outputs as
# our actual features, then reshape the features into a
# flattened volume = vector
batchImages = np.vstack(batchImages) # Stacked to match Keras format
features = model.predict(batchImages, batch_size=BATCH_SIZE) # Get features for BS images
features = features.reshape((features.shape[0], 7 * 7 * 512)) # Reshape to create a flattened vector BS, FEAT
# Loop over the class labels and extracted features
for (label, vec) in zip(batchLabels, features):
# construct a row that exists of the class label and extracted features
vec = ",".join([str(v) for v in vec]) # Join features by comma ,
csv.write("{},{}\n".format(label, vec)) # Write label and vector of features to csv separate by ,
# close the CSV file
csv.close()
# Serialize the label encoder to disk
f = open(LE_PATH, "wb")
f.write(pickle.dumps(le))
f.close()
# Extract features for specific Batch
def ExtractFeatures_batch_VGG(image_batch_paths):
# load the VGG16 network and initialize the label encoder
print("[INFO] loading VGG16 network...")
model = VGG16(weights="imagenet", include_top=False) # No top layer
# Containers
batchImages = []
# loop over the images and labels in the current batch
for imagePath in image_batch_paths:
# Load image using the Keras helper utility
# Resize to 224x224 pixels (to match VGG16 architecture)
image = load_img(imagePath, target_size=(224, 224))
image = img_to_array(image)
# Preprocess: (1) expand the dimensions and (2) substract the mean RGB intensity
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
# Add the image *array* to the batch
batchImages.append(image)
# Pass the images through the network and use the outputs as
# our actual features, then reshape the features into a
# flattened volume = vector
batchImages = np.vstack(batchImages) # Stacked to match Keras format
features = model.predict(batchImages, batch_size=len(batchImages)) # Get features for BS images
features = features.reshape((features.shape[0], 7 * 7 * 512)) # Reshape to create a flattened vector BS, FEAT
# Return features
return features
# Extract features for each set ResNet
def ExtractFeatures_ResNet(TRAIN='training',
TEST='evaluation',
VAL='validation',
BATCH_SIZE=32,
BASE_PATH=os.path.join('TransferLearningKeras', 'dataset'),
BASE_CSV_PATH=os.path.join('TransferLearningKeras', 'output'),
LE_PATH=os.path.join('TransferLearningKeras', 'output'),
):
# load the ResNet50 network and initialize the label encoder
print("[INFO] loading ResNet50 network...")
model = ResNet50(weights="imagenet", include_top=False)
le = None
# loop over the data splits
for split in (TRAIN, TEST, VAL):
# grab all image paths in the current split
print("[INFO] processing '{} split'...".format(split))
p = os.path.sep.join([BASE_PATH, split])
imagePaths = list(paths.list_images(p))
# randomly shuffle the image paths and then extract the class
# labels from the file paths
random.shuffle(imagePaths)
labels = [p.split(os.path.sep)[-2] for p in imagePaths]
# if the label encoder is None, create it
if le is None:
le = LabelEncoder()
le.fit(labels)
# open the output CSV file for writing
csvPath = os.path.sep.join([BASE_CSV_PATH,
"{}.csv".format(split)])
csv = open(csvPath, "w")
# loop over the images in batches
for (b, i) in enumerate(range(0, len(imagePaths), BATCH_SIZE)):
# extract the batch of images and labels, then initialize the
# list of actual images that will be passed through the network
# for feature extraction
print("[INFO] processing batch {}/{}".format(b + 1,
int(np.ceil(len(imagePaths) / float(BATCH_SIZE)))))
batchPaths = imagePaths[i:i + BATCH_SIZE]
batchLabels = le.transform(labels[i:i + BATCH_SIZE])
batchImages = []
# loop over the images and labels in the current batch
for imagePath in batchPaths:
# load the input image using the Keras helper utility
# while ensuring the image is resized to 224x224 pixels
image = load_img(imagePath, target_size=(224, 224))
image = img_to_array(image)
# preprocess the image by (1) expanding the dimensions and
# (2) subtracting the mean RGB pixel intensity from the
# ImageNet dataset
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
# add the image to the batch
batchImages.append(image)
# pass the images through the network and use the outputs as
# our actual features, then reshape the features into a
# flattened volume
batchImages = np.vstack(batchImages)
features = model.predict(batchImages, batch_size=BATCH_SIZE)
features = features.reshape((features.shape[0], 7 * 7 * 2048))
# loop over the class labels and extracted features
for (label, vec) in zip(batchLabels, features):
# construct a row that exists of the class label and
# extracted features
vec = ",".join([str(v) for v in vec])
csv.write("{},{}\n".format(label, vec))
# close the CSV file
csv.close()
# serialize the label encoder to disk
f = open(LE_PATH, "wb")
f.write(pickle.dumps(le))
f.close()
# Extract features for specific Batch
def ExtractFeatures_batch_ResNet(image_batch_paths):
# load the VGG16 network and initialize the label encoder
print("[INFO] loading ResNet50 network...")
model = ResNet50(weights="imagenet", include_top=False) # No top layer
# Containers
batchImages = []
# loop over the images and labels in the current batch
for imagePath in image_batch_paths:
# Load image using the Keras helper utility
# Resize to 224x224 pixels (to match VGG16 architecture)
image = load_img(imagePath, target_size=(224, 224))
image = img_to_array(image)
# Preprocess: (1) expand the dimensions and (2) substract the mean RGB intensity
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
# Add the image *array* to the batch
batchImages.append(image)
# Pass the images through the network and use the outputs as
# our actual features, then reshape the features into a
# flattened volume = vector
batchImages = np.vstack(batchImages) # Stacked to match Keras format
features = model.predict(batchImages, batch_size=len(batchImages)) # Get features for BS images
features = features.reshape((features.shape[0], 7 * 7 * 2048)) # Reshape to create a flattened vector BS, FEAT
# Return features
return features
# Custom Data generator: to yield csv features
def csv_feature_generator(inputPath,
bs,
numClasses,
mode="train"):
# open the input file for reading
f = open(inputPath, "r")
# loop indefinitely
while True:
# initialize our batch of data and labels
data = []
labels = []
# keep looping until we reach our batch size
while len(data) < bs:
# attempt to read the next row of the CSV file
row = f.readline()
# check to see if the row is empty, indicating we have
# reached the end of the file
if row == "":
# reset the file pointer to the beginning of the file
# and re-read the row
f.seek(0)
row = f.readline()
# if we are evaluating we should now break from our
# loop to ensure we don't continue to fill up the
# batch from samples at the beginning of the file
if mode == "eval":
break
# extract the class label and features from the row
row = row.strip().split(",")
label = row[0]
label = to_categorical(label, num_classes=numClasses)
features = np.array(row[1:], dtype="float")
# update the data and label lists
data.append(features)
labels.append(label)
# yield the batch to the calling function
yield (np.array(data), np.array(labels))
| 42.082192
| 124
| 0.593913
| 1,486
| 12,288
| 4.851279
| 0.169583
| 0.021223
| 0.015259
| 0.011652
| 0.771119
| 0.753641
| 0.736857
| 0.736857
| 0.728811
| 0.722292
| 0
| 0.015783
| 0.324544
| 12,288
| 292
| 125
| 42.082192
| 0.852771
| 0.345459
| 0
| 0.724832
| 0
| 0
| 0.068774
| 0.015871
| 0
| 0
| 0
| 0
| 0
| 1
| 0.033557
| false
| 0
| 0.060403
| 0
| 0.107383
| 0.053691
| 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
|
20d086b539c89a2a9d6783a8592e42d2d1d6d45f
| 3,033
|
py
|
Python
|
plot_voronoy.py
|
RandallBalestriero/SMASO
|
ad7607928c369a06225a74bdfe93332ebf60c650
|
[
"MIT"
] | null | null | null |
plot_voronoy.py
|
RandallBalestriero/SMASO
|
ad7607928c369a06225a74bdfe93332ebf60c650
|
[
"MIT"
] | null | null | null |
plot_voronoy.py
|
RandallBalestriero/SMASO
|
ad7607928c369a06225a74bdfe93332ebf60c650
|
[
"MIT"
] | null | null | null |
from pylab import *
eps=0.1
def VQ(x,W,ppi,sigma):
print shape(dot(x,W.T))
vq = argmax(dot(x,W.T)+log(ppi).reshape((1,-1))*sigma-0.5*(W*W).sum(1).reshape((1,-1)),axis=1)
print unique(vq)
return vq
def MVQ(x,W,b):
print shape(dot(x,W.T))
vq = argmax(dot(x,W.T)+b.reshape((1,-1)),axis=1)
print unique(vq)
return vq
def fVQ(x,W,ppi,sigma):
# print shape(dot(x,W.T))
H = dot(x,W.T)+log(ppi).reshape((1,-1))*sigma-0.5*(W*W).sum(1).reshape((1,-1))
vq = argmax(H,axis=1)
# print unique(vq)
return H[range(len(vq)),vq]
def fMVQ(x,W,b):
# print shape(dot(x,W.T))
H = dot(x,W.T)+b.reshape((1,-1))
vq = argmax(H,axis=1)
# print unique(vq)
return H[range(len(vq)),vq]
def get_boundary(X):
K=abs(X[:-1,:-1]-X[1:,:-1])+abs(X[:-1,:-1]-X[:-1,1:])+abs(X[1:,:-1]-X[:-1,1:])+abs(X[1:,1:]-X[:-1,:-1])#diff(X,axis=0))+abs(diff(X,axis=0))
K=K+roll(K,1,1)+roll(K,0,1)
return pad(K,[[1,0],[1,0]],'constant')>0
def doplot(W,ppi,sigma):
xx, yy = np.meshgrid(linspace(W[:,0].min()-eps, W[:,0].max()+eps, h),linspace(W[:,1].min()-eps,W[:,1].max()+eps, h))
xxx=xx.flatten()
yyy=yy.flatten()
DD = asarray([xxx,yyy]).astype('float32').T
vq = VQ(DD,W,ppi,sigma)
vq=vq.reshape(xx.shape)
contourf(xx, yy, 1-get_boundary(vq),cmap='gray')
for w in W:
plot(w[0],w[1],'ko')
def doplotm(W,b):
xx, yy = np.meshgrid(linspace(W[:,0].min()-eps, W[:,0].max()+eps, h),linspace(W[:,1].min()-eps,W[:,1].max()+eps, h))
xxx=xx.flatten()
yyy=yy.flatten()
DD = asarray([xxx,yyy]).astype('float32').T
vq = MVQ(DD,W,b)
vq=vq.reshape(xx.shape)
contourf(xx, yy, 1-get_boundary(vq),cmap='gray')
for w in W:
plot(w[0],w[1],'ko')
def fdoplot(W,ppi,sigma):
xx, yy = np.meshgrid(linspace(W[:,0].min()-eps, W[:,0].max()+eps, h),linspace(W[:,1].min()-eps,W[:,1].max()+eps, h))
xxx=xx.flatten()
yyy=yy.flatten()
DD = asarray([xxx,yyy]).astype('float32').T
vq = fVQ(DD,W,ppi,sigma)
vq=vq.reshape(xx.shape)
contourf(xx, yy, vq,20,cmap='jet',alpha=0.5)
for w in W:
plot(w[0],w[1],'ko')
def fdoplotm(W,b):
xx, yy = np.meshgrid(linspace(W[:,0].min()-eps, W[:,0].max()+eps, h),linspace(W[:,1].min()-eps,W[:,1].max()+eps, h))
xxx=xx.flatten()
yyy=yy.flatten()
DD = asarray([xxx,yyy]).astype('float32').T
vq = fMVQ(DD,W,b)
vq=vq.reshape(xx.shape)
contourf(xx, yy, vq,20,cmap='jet',alpha=0.5)
for w in W:
plot(w[0],w[1],'ko')
h=100
K=16
W=randn(K,2)#asarray([[0.5,0.5],[0,0],[-0.5,0.5]])
ppi=ones(K)/K#asarray([0.33,0.33,0.33])
figure(figsize=(25,5))
for i in xrange(5):
subplot(1,5,i+1)
doplot(W,ppi,0+i*2)
fdoplot(W,ppi,0+i*2)
title(r'$\sigma='+str(i*2)+'$',fontsize=18)
xticks([])
yticks([])
tight_layout()
savefig('plotsimplepartition1.png')
close()
figure(figsize=(25,5))
for i in xrange(5):
subplot(1,5,i+1)
b=randn(K)/10
doplotm(W,i*b)
fdoplotm(W,i*b)
xticks([])
yticks([])
title(r'$B \sim \mathcal{N}(0;'+str(i*0.1)+')$',fontsize=18)
tight_layout()
savefig('plotsimplepartition2.png')
close()
#
| 22.301471
| 140
| 0.577646
| 620
| 3,033
| 2.817742
| 0.156452
| 0.017172
| 0.022896
| 0.027476
| 0.753864
| 0.74585
| 0.74585
| 0.74585
| 0.744705
| 0.741271
| 0
| 0.056359
| 0.134191
| 3,033
| 135
| 141
| 22.466667
| 0.608911
| 0.063304
| 0
| 0.615385
| 0
| 0
| 0.049082
| 0.016949
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.010989
| null | null | 0.043956
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
455180fa627d80ce2b9778a763d7b68cd25895c3
| 58
|
py
|
Python
|
spikeforest/sorters/mountainsort4/__init__.py
|
flatironinstitute/spikeforest
|
bbb5e38f35f66b09c327a593012d5468f4c46d30
|
[
"Apache-2.0"
] | 22
|
2019-05-07T18:18:06.000Z
|
2021-11-29T12:03:17.000Z
|
spikeforest/sorters/mountainsort4/__init__.py
|
flatironinstitute/spikeforest
|
bbb5e38f35f66b09c327a593012d5468f4c46d30
|
[
"Apache-2.0"
] | 79
|
2019-03-05T13:04:46.000Z
|
2021-11-05T09:27:09.000Z
|
spikeforest/sorters/mountainsort4/__init__.py
|
flatironinstitute/spikeforest
|
bbb5e38f35f66b09c327a593012d5468f4c46d30
|
[
"Apache-2.0"
] | 8
|
2019-06-04T18:05:28.000Z
|
2021-09-23T01:09:34.000Z
|
from .mountainsort4_wrapper1 import mountainsort4_wrapper1
| 58
| 58
| 0.931034
| 6
| 58
| 8.666667
| 0.666667
| 0.807692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072727
| 0.051724
| 58
| 1
| 58
| 58
| 0.872727
| 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
|
4553b146f87c200babea52ab46274bb2edb78b20
| 626
|
py
|
Python
|
controller/home.py
|
Rudi9719/booksearch-web
|
c48300e04ebdf2f01a990c67d4ff63d282da8168
|
[
"Apache-2.0"
] | null | null | null |
controller/home.py
|
Rudi9719/booksearch-web
|
c48300e04ebdf2f01a990c67d4ff63d282da8168
|
[
"Apache-2.0"
] | null | null | null |
controller/home.py
|
Rudi9719/booksearch-web
|
c48300e04ebdf2f01a990c67d4ff63d282da8168
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
from controller.base import BaseController
from frameworks.bottle import view, static_file, template
class HomeController(BaseController):
def index(self):
return template('home/index')
def login(self):
return template('home/login')
def about(self):
return template('home/about')
def help(self):
return template('home/help')
def discover(self):
return template('home/discover')
def contact(self):
return template('home/contact')
@view('home/suny')
def suny(self):
pass
| 20.193548
| 57
| 0.607029
| 69
| 626
| 5.492754
| 0.42029
| 0.158311
| 0.28496
| 0.348285
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.282748
| 626
| 30
| 58
| 20.866667
| 0.844098
| 0.031949
| 0
| 0
| 0
| 0
| 0.120861
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.388889
| false
| 0.055556
| 0.111111
| 0.333333
| 0.888889
| 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
|
45853906437bf22421624d861e93aa33535bb92a
| 109
|
py
|
Python
|
artificial-intelligence-practice/handwritten-digit-recognition/main.py
|
rileyweber13/learning-sandbox
|
668c13daf8620826cc674f4deacbddff3c477d92
|
[
"MIT"
] | null | null | null |
artificial-intelligence-practice/handwritten-digit-recognition/main.py
|
rileyweber13/learning-sandbox
|
668c13daf8620826cc674f4deacbddff3c477d92
|
[
"MIT"
] | 4
|
2019-11-15T16:36:04.000Z
|
2021-02-01T20:28:17.000Z
|
artificial-intelligence-practice/handwritten-digit-recognition/main.py
|
rileyweber13/learning-sandbox
|
668c13daf8620826cc674f4deacbddff3c477d92
|
[
"MIT"
] | null | null | null |
import plot_mnist_data
import baseline
if __name__ == "__main__":
# plot_mnist_data
baseline.main()
| 15.571429
| 26
| 0.733945
| 14
| 109
| 4.857143
| 0.571429
| 0.264706
| 0.382353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.183486
| 109
| 6
| 27
| 18.166667
| 0.764045
| 0.137615
| 0
| 0
| 0
| 0
| 0.086957
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
45b8cf7b714ef1d6cd4b9dd250223bb4dbc22746
| 45
|
py
|
Python
|
api/schema/branch/__init__.py
|
matchd-ch/matchd-backend
|
84be4aab1b4708cae50a8988301b15df877c8db0
|
[
"Apache-2.0"
] | 1
|
2022-03-03T09:55:57.000Z
|
2022-03-03T09:55:57.000Z
|
api/schema/branch/__init__.py
|
matchd-ch/matchd-backend
|
84be4aab1b4708cae50a8988301b15df877c8db0
|
[
"Apache-2.0"
] | 7
|
2022-02-09T10:44:53.000Z
|
2022-03-28T03:29:43.000Z
|
api/schema/branch/__init__.py
|
matchd-ch/matchd-backend
|
84be4aab1b4708cae50a8988301b15df877c8db0
|
[
"Apache-2.0"
] | null | null | null |
from .schema import BranchInput, BranchQuery
| 22.5
| 44
| 0.844444
| 5
| 45
| 7.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 45
| 1
| 45
| 45
| 0.95
| 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
|
45baf3c0dff1479ed7778746c9934f9289a8780c
| 207
|
py
|
Python
|
Day12/Even_Length_Strings.py
|
tushartrip1010/100_days_code_py
|
ee74b429e98cdd8bdf8661cf987da67c9fee5a3e
|
[
"Apache-2.0"
] | null | null | null |
Day12/Even_Length_Strings.py
|
tushartrip1010/100_days_code_py
|
ee74b429e98cdd8bdf8661cf987da67c9fee5a3e
|
[
"Apache-2.0"
] | null | null | null |
Day12/Even_Length_Strings.py
|
tushartrip1010/100_days_code_py
|
ee74b429e98cdd8bdf8661cf987da67c9fee5a3e
|
[
"Apache-2.0"
] | null | null | null |
def Even_Length(Test_string):
return "\n".join([words for words in Test_string.split() if len(words) % 2 == 0])
Test_string = input("Enter a String: ")
print(f"Output: {Even_Length(Test_string)}")
| 34.5
| 86
| 0.681159
| 33
| 207
| 4.090909
| 0.666667
| 0.296296
| 0.207407
| 0.296296
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011429
| 0.154589
| 207
| 6
| 87
| 34.5
| 0.76
| 0
| 0
| 0
| 0
| 0
| 0.256158
| 0.128079
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.25
| 0.5
| 0.25
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 6
|
45dbcb5f4267102a32936263d291ae0dcaefde15
| 196
|
py
|
Python
|
modules/config/model_path.py
|
cloud441/Procerfa
|
7472aeecc4eb3003e2e0f09bb0ae71f1b33e9ccf
|
[
"MIT"
] | null | null | null |
modules/config/model_path.py
|
cloud441/Procerfa
|
7472aeecc4eb3003e2e0f09bb0ae71f1b33e9ccf
|
[
"MIT"
] | null | null | null |
modules/config/model_path.py
|
cloud441/Procerfa
|
7472aeecc4eb3003e2e0f09bb0ae71f1b33e9ccf
|
[
"MIT"
] | null | null | null |
import os
# Linux dev:
#dir_path = f"data{os.sep}pdf_models{os.sep}"
# Windows release:
dir_path = f"..{os.sep}..{os.sep}data{os.sep}pdf_models{os.sep}"
model_path = dir_path + "new_model.pdf"
| 19.6
| 64
| 0.693878
| 37
| 196
| 3.486486
| 0.405405
| 0.232558
| 0.124031
| 0.186047
| 0.356589
| 0.356589
| 0.356589
| 0
| 0
| 0
| 0
| 0
| 0.112245
| 196
| 9
| 65
| 21.777778
| 0.741379
| 0.362245
| 0
| 0
| 0
| 0.333333
| 0.520661
| 0.413223
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 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
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
2fbd62f5feda17fc27730a66c4c3f1247e55b804
| 267
|
py
|
Python
|
Codewars/8kyu/twice-as-old/Python/test.py
|
RevansChen/online-judge
|
ad1b07fee7bd3c49418becccda904e17505f3018
|
[
"MIT"
] | 7
|
2017-09-20T16:40:39.000Z
|
2021-08-31T18:15:08.000Z
|
Codewars/8kyu/twice-as-old/Python/test.py
|
RevansChen/online-judge
|
ad1b07fee7bd3c49418becccda904e17505f3018
|
[
"MIT"
] | null | null | null |
Codewars/8kyu/twice-as-old/Python/test.py
|
RevansChen/online-judge
|
ad1b07fee7bd3c49418becccda904e17505f3018
|
[
"MIT"
] | null | null | null |
# Python - 3.6.0
Test.describe('Basic Tests')
Test.assert_equals(twice_as_old(36, 7), 22)
Test.assert_equals(twice_as_old(55, 30), 5)
Test.assert_equals(twice_as_old(42, 21), 0)
Test.assert_equals(twice_as_old(22, 1), 20)
Test.assert_equals(twice_as_old(29 ,0), 29)
| 29.666667
| 43
| 0.752809
| 53
| 267
| 3.509434
| 0.45283
| 0.268817
| 0.430108
| 0.564516
| 0.698925
| 0.698925
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 0.082397
| 267
| 8
| 44
| 33.375
| 0.644898
| 0.052434
| 0
| 0
| 0
| 0
| 0.043825
| 0
| 0
| 0
| 0
| 0
| 0.833333
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2fcd40f834c7fee3b01f9bb48cd72afee34cc1a3
| 94
|
py
|
Python
|
src/jt/jcc/__config__.py
|
karpierz/jtypes.jcc
|
6cb029345034ec9ba1d671d5e20b72f5e1a17f8b
|
[
"Apache-2.0"
] | null | null | null |
src/jt/jcc/__config__.py
|
karpierz/jtypes.jcc
|
6cb029345034ec9ba1d671d5e20b72f5e1a17f8b
|
[
"Apache-2.0"
] | null | null | null |
src/jt/jcc/__config__.py
|
karpierz/jtypes.jcc
|
6cb029345034ec9ba1d671d5e20b72f5e1a17f8b
|
[
"Apache-2.0"
] | null | null | null |
__import__("jvm._util", globals(), None, ["make_config"], 2).make_config("jcc.cfg", "jt.jcc")
| 47
| 93
| 0.670213
| 14
| 94
| 4
| 0.785714
| 0.357143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011364
| 0.06383
| 94
| 1
| 94
| 94
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0.351064
| 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
|
2fe9a7acb090960e4d1f8df64661a44695d8bd20
| 31
|
py
|
Python
|
exospoc/__init__.py
|
ceyzeriat/exospoc
|
e6d2ff1efa482222da90a9c89a11b56945c4202e
|
[
"MIT"
] | 1
|
2016-08-16T09:41:27.000Z
|
2016-08-16T09:41:27.000Z
|
exospoc/__init__.py
|
ceyzeriat/exospoc
|
e6d2ff1efa482222da90a9c89a11b56945c4202e
|
[
"MIT"
] | null | null | null |
exospoc/__init__.py
|
ceyzeriat/exospoc
|
e6d2ff1efa482222da90a9c89a11b56945c4202e
|
[
"MIT"
] | null | null | null |
"""
exospoc
"""
import exospoc
| 6.2
| 14
| 0.645161
| 3
| 31
| 6.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16129
| 31
| 5
| 14
| 6.2
| 0.769231
| 0.225806
| 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
|
64094acaaabc62779dfa14cd154dfe7c091c86ab
| 3,438
|
py
|
Python
|
test/unit/test_tasks_key_load.py
|
cyberkov/puppet-st2
|
5985c58323896f37d942590ca97825ec47ad778a
|
[
"Apache-2.0"
] | 21
|
2015-01-14T20:47:01.000Z
|
2022-03-06T09:04:36.000Z
|
test/unit/test_tasks_key_load.py
|
cyberkov/puppet-st2
|
5985c58323896f37d942590ca97825ec47ad778a
|
[
"Apache-2.0"
] | 187
|
2015-01-27T18:36:12.000Z
|
2022-03-23T05:42:41.000Z
|
test/unit/test_tasks_key_load.py
|
cyberkov/puppet-st2
|
5985c58323896f37d942590ca97825ec47ad778a
|
[
"Apache-2.0"
] | 31
|
2015-01-14T15:22:15.000Z
|
2021-11-12T13:20:55.000Z
|
from test.unit.st2_test_case import St2TestCase
import mock
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'files'))
from st2_task_base import St2TaskBase
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'tasks'))
from key_load import St2KeyLoad
class St2KeyLoadTestCase(St2TestCase):
__test__ = True
def test_init(self):
task = St2KeyLoad()
self.assertIsInstance(task, St2TaskBase)
@mock.patch('os.remove')
@mock.patch('os.fdopen')
@mock.patch('tempfile.mkstemp')
@mock.patch('key_load.St2KeyLoad.exec_cmd')
def test_task_impl(self, mock_exec_cmd, mock_mkstemp, mock_fdopen, mock_remove):
args = {
'keys': [
{
'name': 'key1',
'value': 'blah1'
},
{
'name': 'key2',
'value': 'blah2'
},
]
}
mock_mkstemp.return_value = (123, '/tmp/xyz123.json')
mock_temp_file = mock.MagicMock()
mock_context_manager = mock.MagicMock()
mock_context_manager.__enter__ = mock.MagicMock(return_value=mock_temp_file)
mock_context_manager.__exit__ = mock.MagicMock(return_value=None)
mock_fdopen.return_value = mock_context_manager
mock_exec_cmd.return_value = 'expected'
task = St2KeyLoad()
result = task.task(args)
self.assertEquals(result, 'expected')
mock_mkstemp.assert_called_with(suffix='.json')
mock_temp_file.write.assert_called_with(
'[{"name": "key1", "value": "blah1"}, {"name": "key2", "value": "blah2"}]')
mock_exec_cmd.assert_called_with(['st2', 'key', 'load', '--json', '/tmp/xyz123.json'],
'load keys')
mock_remove.assert_called_with('/tmp/xyz123.json')
@mock.patch('os.remove')
@mock.patch('os.fdopen')
@mock.patch('tempfile.mkstemp')
@mock.patch('key_load.St2KeyLoad.exec_cmd')
def test_task_impl_convert(self, mock_exec_cmd, mock_mkstemp, mock_fdopen, mock_remove):
args = {
'keys': [
{
'name': 'key1',
'value': 'blah1'
},
{
'name': 'key2',
'value': 'blah2'
},
],
'convert': True,
}
mock_mkstemp.return_value = (123, '/tmp/xyz123.json')
mock_temp_file = mock.MagicMock()
mock_context_manager = mock.MagicMock()
mock_context_manager.__enter__ = mock.MagicMock(return_value=mock_temp_file)
mock_context_manager.__exit__ = mock.MagicMock(return_value=None)
mock_fdopen.return_value = mock_context_manager
mock_exec_cmd.return_value = 'expected'
task = St2KeyLoad()
result = task.task(args)
self.assertEquals(result, 'expected')
mock_mkstemp.assert_called_with(suffix='.json')
mock_temp_file.write.assert_called_with(
'[{"name": "key1", "value": "blah1"}, {"name": "key2", "value": "blah2"}]')
mock_exec_cmd.assert_called_with(['st2', 'key', 'load', '--json', '--convert',
'/tmp/xyz123.json'],
'load keys')
mock_remove.assert_called_with('/tmp/xyz123.json')
| 36.967742
| 94
| 0.565445
| 364
| 3,438
| 5.002747
| 0.184066
| 0.060406
| 0.079077
| 0.039539
| 0.853377
| 0.853377
| 0.853377
| 0.853377
| 0.853377
| 0.853377
| 0
| 0.022765
| 0.297266
| 3,438
| 92
| 95
| 37.369565
| 0.73096
| 0
| 0
| 0.62963
| 0
| 0.024691
| 0.165794
| 0.016289
| 0
| 0
| 0
| 0
| 0.135802
| 1
| 0.037037
| false
| 0
| 0.074074
| 0
| 0.135802
| 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
|
ff4ecdb302c9374d5fbf9f174f9204bf631762fa
| 270
|
py
|
Python
|
compiler/lexical/token.py
|
danielbibit/COMP1-UFG
|
8c2ffdb4049bedc8e91a6bfe5da65fe5a105db3e
|
[
"MIT"
] | null | null | null |
compiler/lexical/token.py
|
danielbibit/COMP1-UFG
|
8c2ffdb4049bedc8e91a6bfe5da65fe5a105db3e
|
[
"MIT"
] | null | null | null |
compiler/lexical/token.py
|
danielbibit/COMP1-UFG
|
8c2ffdb4049bedc8e91a6bfe5da65fe5a105db3e
|
[
"MIT"
] | null | null | null |
class Token():
def __init__(self, classe, lexema, tipo):
self.classe = classe
self.lexema = lexema
self.tipo = tipo
def __repr__(self) -> str:
return 'classe: ' + self.classe + ', lexema: ' + self.lexema + ', tipo: ' + self.tipo
| 30
| 93
| 0.566667
| 31
| 270
| 4.677419
| 0.354839
| 0.206897
| 0.22069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.288889
| 270
| 8
| 94
| 33.75
| 0.755208
| 0
| 0
| 0
| 0
| 0
| 0.096296
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0
| 0.142857
| 0.571429
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
ff754e85577b46a301fd40bc5aa2ba847d29a7b6
| 8,630
|
py
|
Python
|
datasets/build_dataset.py
|
XiaoyuanGuo/TEND_MedicalNoveltyDetection
|
5c2144f0592373d814540cc0fa8e60197ea51756
|
[
"MIT"
] | null | null | null |
datasets/build_dataset.py
|
XiaoyuanGuo/TEND_MedicalNoveltyDetection
|
5c2144f0592373d814540cc0fa8e60197ea51756
|
[
"MIT"
] | null | null | null |
datasets/build_dataset.py
|
XiaoyuanGuo/TEND_MedicalNoveltyDetection
|
5c2144f0592373d814540cc0fa8e60197ea51756
|
[
"MIT"
] | null | null | null |
import logging
from .MNISTDataset import*
from .CIFAR10Dataset import *
from .IVCFilter import *
from .PneumoniaDataset import *
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
mnist_tsfms = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
cifar10_tsfms = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
def build_ae_dataset(dataset_name, data_path, ae_batch_size, normal_class):
logger = logging.getLogger()
logger.info("Build AutoEncoder's dataset for {}".format(dataset_name))
assert dataset_name in ['mnist', 'cifar10', 'ivc-filter', 'rsna']
if dataset_name == "mnist":
# normal_class = [0]
normal_x_train, normal_y_train, normal_x_val, normal_y_val, outlier_x_test, outlier_y_test = get_mnist_data(normal_class)
train_set = MNISTLabelDataset(normal_x_train, normal_y_train, mnist_tsfms)
validate_set = MNISTLabelDataset(normal_x_val, normal_y_val, mnist_tsfms)
test_set = MNISTLabelDataset(normal_x_val+outlier_x_test, normal_y_val+outlier_y_test, mnist_tsfms)
ae_dataloaders = {'train': DataLoader(train_set, batch_size = ae_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(validate_set, batch_size = ae_batch_size, num_workers = 1),
'test': DataLoader(test_set, batch_size = ae_batch_size, num_workers = 1)}
ae_dataset_sizes = {'train': len(train_set), 'val': len(validate_set), 'test': len(test_set)}
elif dataset_name == "cifar10":
# normal_class = [0]
normal_x_train, normal_y_train, normal_x_val, normal_y_val, outlier_x_test, outlier_y_test = get_cifar10_data(normal_class)
train_set = CIFAR10LabelDataset(normal_x_train, normal_y_train, cifar10_tsfms)
validate_set = CIFAR10LabelDataset(normal_x_val, normal_y_val, cifar10_tsfms)
test_set = CIFAR10LabelDataset(normal_x_val+outlier_x_test, normal_y_val+outlier_y_test, cifar10_tsfms)
ae_dataloaders = {'train': DataLoader(train_set, batch_size = ae_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(validate_set, batch_size = ae_batch_size, num_workers = 1),
'test': DataLoader(test_set, batch_size = ae_batch_size, num_workers = 1)}
ae_dataset_sizes = {'train': len(train_set), 'val': len(validate_set), 'test': len(test_set)}
elif dataset_name == "ivc-filter":
trn_img, trn_lbl, tst_img, tst_lbl = get_ivc_anomaly_dataset(normal_class)
train_set = IVCFilter_Dataset(trn_img[0:int(0.8*len(trn_img))], trn_lbl[0:int(0.8*len(trn_img))])
validate_set = IVCFilter_Dataset(trn_img[int(0.8*len(trn_img))+1:], trn_lbl[int(0.8*len(trn_img))+1:])
test_set = IVCFilter_Dataset(trn_img[int(0.8*len(trn_img))+1:]+tst_img, trn_lbl[int(0.8*len(trn_img))+1:]+tst_lbl)
ae_dataloaders = {'train': DataLoader(train_set, batch_size = ae_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(validate_set, batch_size = ae_batch_size, num_workers = 1),
'test': DataLoader(test_set, batch_size = ae_batch_size, num_workers = 1)}
ae_dataset_sizes = {'train': len(train_set), 'val': len(validate_set), 'test':len(test_set)}
elif dataset_name == "rsna":
#normal: 0, opacity: 1, other: 2
normal_path, opacity_path, other_path = get_rsna_data()
all_path = [normal_path, opacity_path, other_path]
new_normal_path = []
for i in normal_class:
new_normal_path = new_normal_path + all_path[i]
new_abnormal_path = []
for i in range(0, len(all_path)):
if i not in normal_class:
new_abnormal_path = new_abnormal_path + all_path[i]
traindata = new_normal_path[0:int(0.8*(len(new_normal_path)))]
valdata = new_normal_path[int(0.8*(len(new_normal_path)))+1:]
train_set = PneumoniaDataset(traindata, [0]*len(traindata))
validate_set = PneumoniaDataset(valdata, [0]*len(valdata))
test_set = PneumoniaDataset(valdata+new_abnormal_path, [0]*len(valdata)+[1]*len(new_abnormal_path))
ae_dataloaders = {'train': DataLoader(train_set, batch_size = ae_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(validate_set, batch_size = ae_batch_size, num_workers = 1),
'test': DataLoader(test_set, batch_size = ae_batch_size, num_workers = 1)}
ae_dataset_sizes = {'train': len(train_set), 'val': len(validate_set), 'test':len(test_set)}
return ae_dataloaders, ae_dataset_sizes
def build_tend_dateset(dataset_name, data_path, tend_batch_size, normal_class):
logger = logging.getLogger()
logger.info("Build TEND's dataset for {}".format(dataset_name))
assert dataset_name in ['mnist', 'cifar10', 'ivc-filter', 'rsna']
if dataset_name == "mnist":
# normal_class = [0]
normal_x_train, normal_y_train, normal_x_val, normal_y_val, outlier_x_test, outlier_y_test = get_mnist_data(normal_class)
cls_train_set = MNISTtfmDataset(normal_x_train, normal_y_train, mnist_tsfms)
cls_validate_set = MNISTLabelDataset(normal_x_val+outlier_x_test, normal_y_val+outlier_y_test, mnist_tsfms)
cls_dataloaders = {'train': DataLoader(cls_train_set, batch_size = tend_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(cls_validate_set, batch_size = tend_batch_size, num_workers = 1)}
cls_dataset_sizes = {'train': len(cls_train_set), 'val': len(cls_validate_set)}
elif dataset_name == "cifar10":
# normal_class = [0]
normal_x_train, normal_y_train, normal_x_val, normal_y_val, outlier_x_test, outlier_y_test = get_cifar10_data(normal_class)
cls_train_set = CIFAR10TfmDataset(normal_x_train, normal_y_train, cifar10_tsfms)
cls_validate_set = CIFAR10LabelDataset(normal_x_val+outlier_x_test, normal_y_val+outlier_y_test, cifar10_tsfms)
cls_dataloaders = {'train': DataLoader(cls_train_set, batch_size = tend_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(cls_validate_set, batch_size = tend_batch_size, num_workers = 1)}
cls_dataset_sizes = {'train': len(cls_train_set), 'val': len(cls_validate_set)}
elif dataset_name == "ivc-filter":
trn_img, trn_lbl, tst_img, tst_lbl = get_ivc_anomaly_dataset(normal_class)
cls_train_set = IVCFilterTfm_Dataset(trn_img[0:int(0.8*len(trn_img))], trn_lbl[0:int(0.8*len(trn_img))])
cls_validate_set = IVCFilter_Dataset(trn_img[int(0.8*len(trn_img))+1:]+tst_img, trn_lbl[int(0.8*len(trn_img))+1:]+tst_lbl)
cls_dataloaders = {'train': DataLoader(cls_train_set, batch_size = tend_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(cls_validate_set, batch_size = tend_batch_size, num_workers = 1)}
cls_dataset_sizes = {'train': len(cls_train_set), 'val': len(cls_validate_set)}
elif dataset_name == "rsna":
normal_path, opacity_path, other_path = get_rsna_data()
all_path = [normal_path, opacity_path, other_path]
new_normal_path = []
for i in normal_class:
new_normal_path = new_normal_path + all_path[i]
new_abnormal_path = []
for i in range(0, len(all_path)):
if i not in normal_class:
new_abnormal_path = new_abnormal_path + all_path[i]
traindata = new_normal_path[0:int(0.8*(len(new_normal_path)))]
valdata = new_normal_path[int(0.8*(len(new_normal_path)))+1:]
cls_train_set = PneumoniaTfmDataset(traindata, [0]*len(traindata))
cls_validate_set = PneumoniaDataset(valdata+new_abnormal_path, [0]*len(valdata)+[1]*len(new_abnormal_path))
cls_dataloaders = {'train': DataLoader(cls_train_set, batch_size = tend_batch_size, shuffle = True, num_workers = 1),
'val': DataLoader(cls_validate_set, batch_size = tend_batch_size, num_workers = 1)}
cls_dataset_sizes = {'train': len(cls_train_set), 'val': len(cls_validate_set)}
return cls_dataloaders, cls_dataset_sizes
| 53.9375
| 131
| 0.669873
| 1,188
| 8,630
| 4.468013
| 0.08165
| 0.071213
| 0.045215
| 0.0211
| 0.870008
| 0.86266
| 0.834401
| 0.834401
| 0.808026
| 0.804069
| 0
| 0.022808
| 0.217613
| 8,630
| 159
| 132
| 54.27673
| 0.763329
| 0.015064
| 0
| 0.666667
| 0
| 0
| 0.038312
| 0
| 0
| 0
| 0
| 0
| 0.018018
| 1
| 0.018018
| false
| 0
| 0.063063
| 0
| 0.099099
| 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
|
ffa4f2e8b47a1a70aac1422e65ea92af54579853
| 236
|
py
|
Python
|
src/lesson_runtime_features/os_access.py
|
jasonwee/asus-rt-n14uhp-mrtg
|
4fa96c3406e32ea6631ce447db6d19d70b2cd061
|
[
"Apache-2.0"
] | 3
|
2018-08-14T09:33:52.000Z
|
2022-03-21T12:31:58.000Z
|
src/lesson_runtime_features/os_access.py
|
jasonwee/asus-rt-n14uhp-mrtg
|
4fa96c3406e32ea6631ce447db6d19d70b2cd061
|
[
"Apache-2.0"
] | null | null | null |
src/lesson_runtime_features/os_access.py
|
jasonwee/asus-rt-n14uhp-mrtg
|
4fa96c3406e32ea6631ce447db6d19d70b2cd061
|
[
"Apache-2.0"
] | null | null | null |
import os
print('Testing:', __file__)
print('Exists:', os.access(__file__, os.F_OK))
print('Readable:', os.access(__file__, os.R_OK))
print('Writable:', os.access(__file__, os.W_OK))
print('Executable:', os.access(__file__, os.X_OK))
| 26.222222
| 50
| 0.711864
| 37
| 236
| 3.891892
| 0.405405
| 0.222222
| 0.333333
| 0.388889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076271
| 236
| 8
| 51
| 29.5
| 0.66055
| 0
| 0
| 0
| 0
| 0
| 0.187234
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.166667
| 0
| 0.166667
| 0.833333
| 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
| 0
| 0
| 0
| 1
|
0
| 6
|
ffa5ff3afc7a2b2d01cf5789c778ebe4175b7c49
| 32
|
py
|
Python
|
lister/controllers/__init__.py
|
nricklin/gbdx-s3-browser
|
b226e7242911f2893e8c225c77b7900fe2398541
|
[
"MIT"
] | 5
|
2015-10-11T20:49:32.000Z
|
2018-08-07T15:33:18.000Z
|
lister/controllers/__init__.py
|
nricklin/gbdx-s3-browser
|
b226e7242911f2893e8c225c77b7900fe2398541
|
[
"MIT"
] | 4
|
2015-10-16T19:45:06.000Z
|
2017-04-03T20:26:01.000Z
|
lister/controllers/__init__.py
|
nricklin/gbdx-s3-browser
|
b226e7242911f2893e8c225c77b7900fe2398541
|
[
"MIT"
] | 5
|
2015-10-27T19:31:16.000Z
|
2019-09-27T15:15:11.000Z
|
from heartbeat import Heartbeat
| 16
| 31
| 0.875
| 4
| 32
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 32
| 1
| 32
| 32
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ffb29c27b975c1453b7ba42ccf8a334f1cc22a9c
| 20,880
|
py
|
Python
|
uraeus/nmbd/python/engine/numerics/solvers/temp.py
|
khaledghobashy/uraeus-nmbd-python
|
ea2364ed328baeb33693494ec669e992e86c1488
|
[
"BSD-3-Clause"
] | null | null | null |
uraeus/nmbd/python/engine/numerics/solvers/temp.py
|
khaledghobashy/uraeus-nmbd-python
|
ea2364ed328baeb33693494ec669e992e86c1488
|
[
"BSD-3-Clause"
] | 1
|
2021-01-30T19:48:08.000Z
|
2021-01-31T09:15:09.000Z
|
uraeus/nmbd/python/engine/numerics/solvers/temp.py
|
khaledghobashy/uraeus-nmbd-python
|
ea2364ed328baeb33693494ec669e992e86c1488
|
[
"BSD-3-Clause"
] | null | null | null |
# Standard library imports.
import time
# Third party imports.
import numpy as np
import scipy as sc
# Local imports.
from .base import abstract_solver, solve, progress_bar
from .integrators import BDF
################################################################
################################################################
class dds_solver(abstract_solver):
def __init__(self, model):
super().__init__(model)
if self.model.n == self.model.nc:
raise ValueError('Model is fully constrained.')
def solve(self, run_id):
t0 = time.perf_counter()
time_array = self.time_array
dt = self.step_size
bar_length = len(time_array)-1
print('\nStarting System Dynamic Analysis:')
self._extract_independent_coordinates()
pos_t0 = self._pos_history[0]
vel_t0 = self._vel_history[0]
self._newton_raphson(pos_t0)
M, J, Qt, Qd = self._eval_augmented_matricies(pos_t0, vel_t0)
acc_t0, lamda_t0 = self._solve_augmented_system(M, J, Qt, Qd)
self._acc_history[0] = acc_t0
self._lgr_history[0] = lamda_t0
self.integrator = BDF(self.SSODE, 0, 0, time_array[-1], dt)
print('\nRunning System Dynamic Analysis:')
i = 0
while i != bar_length:
t = time_array[i+1]
progress_bar(bar_length, i, t0, t)
self._set_time(t)
self._extract_independent_coordinates(self._jac[:-self.dof])
self._solve_time_step(t, i, dt)
i += 1
print('\n')
self._creat_results_dataframes()
def _solve_time_step(self, t, i, dt):
q = self._pos_history[i]
qd = self._vel_history[i]
qdd = self._acc_history[i]
lamda = self._lgr_history[i]
fi = np.concatenate([qdd, lamda])
soln = self.integrator.step([self._pos_history, self._vel_history], i, fi)
qn, qdn, fn = soln
qddn = fn[:self.model.n]
lamda_n = fn[self.model.n:]
#self._newton_raphson(qn)
self._pos_history[i+1] = qn
self._vel_history[i+1] = qdn
self._acc_history[i+1] = qddn
self._lgr_history[i+1] = lamda_n
# @profile
def SSODE(self, fi, q, qd, t, beta0, h):
self._set_time(t)
acc = fi[:self.model.n]
lamda = fi[self.model.n:]
v = self.get_indpenednt_q(q)
vd = self.get_indpenednt_q(qd)
guess = q + (qd * h) + (0.5 * acc * (h**2))
for c in range(self.dof):
guess[np.argmax(self.independent_cols[:, c]), 0] = v[c,0]
self._newton_raphson(guess)
A = self._jac
q_mod = self._pos
vel_rhs = self._eval_vel_eq(vd)
qd_mod = solve(A, -vel_rhs)
self._set_gen_coordinates(q_mod)
self._set_gen_velocities(qd_mod)
# 3) evaluating the mass matrix and force vector
M = self._eval_mass_eq()
Qt = self._eval_frc_eq()
J = self._jac[:-self.dof,:]
#Qd = self._eval_acc_eq()
A = self.eval_coefficient_matrix(M, J)
A = np.eye(A.shape[0]) - h*beta0*A
constraints = super()._eval_pos_eq()
eq1 = M@acc + J.T@lamda - Qt
eq2 = constraints
b = fi - h*beta0 * np.concatenate([eq1, eq2])
print('Norm b = %s'%np.linalg.norm(b))
print('Norm eq1 = %s'%np.linalg.norm(eq1))
print('Norm eq2 = %s'%np.linalg.norm(eq2))
return A, b
def _extract_independent_coordinates(self, jacobian=None):
A = super()._eval_jac_eq() if jacobian is None else jacobian
rows, cols = A.shape
permutaion_mat = sc.linalg.lu(A.T)[0]
independent_cols = permutaion_mat[:, rows:]
self.dof = dof = independent_cols.shape[1]
independent_cord = [self._coordinates_indicies[np.argmax(independent_cols[:,i])] for i in range(dof) ]
self.permutaion_mat = permutaion_mat.T
self.independent_cols = independent_cols
self.independent_cord = independent_cord
def _eval_augmented_matricies(self, q , qd):
self._set_gen_coordinates(q)
self._set_gen_velocities(qd)
J = super()._eval_jac_eq()
M = self._eval_mass_eq()
Qt = self._eval_frc_eq()
Qd = self._eval_acc_eq()
return M, J, Qt, Qd
def _solve_augmented_system(self, M, J, Qt, Qd):
A = self.eval_coefficient_matrix(M, J)
b = np.concatenate([Qt, -Qd])
x = solve(A, b)
n = self.model.n
accelerations = x[:n]
lamda = x[n:]
return accelerations, lamda
def _eval_pos_eq(self):
A = super()._eval_pos_eq()
Z = np.zeros((self.dof, 1))
A = np.concatenate([A, Z])
return A
def _eval_vel_eq(self,ind_vel_i):
A = super()._eval_vel_eq()
V = np.array(ind_vel_i).reshape((self.dof, 1))
A = np.concatenate([A, -V])
return A
def _eval_jac_eq(self):
A = np.concatenate([super()._eval_jac_eq(), self.independent_cols.T])
return A
def get_indpenednt_q(self, q):
# Boolean matrix (ndof x n)
P = self.independent_cols.T
qv = P@q
return qv
def _eval_lagrange_multipliers(self, i):
self._set_gen_coordinates(self._pos_history[i])
return self._lgr_history[i]
def eval_coefficient_matrix(self, M, J):
z = np.zeros((self.model.nc, self.model.nc))
u = np.concatenate([M, J.T], axis=1)
l = np.concatenate([J, z], axis=1)
A = np.concatenate([u, l], axis=0)
return A
################################################################
class dds_solver4(abstract_solver):
def __init__(self, model):
super().__init__(model)
if self.model.n == self.model.nc:
raise ValueError('Model is fully constrained.')
def solve(self, run_id):
t0 = time.perf_counter()
time_array = self.time_array
dt = self.step_size
bar_length = len(time_array)-1
print('\nStarting System Dynamic Analysis:')
pos_t0 = self._pos_history[0]
vel_t0 = self._vel_history[0]
self.factorize_constraints_jacobian()
self._extract_independent_coordinates()
self._newton_raphson(pos_t0)
M, J, Qt, Qd = self._eval_augmented_matricies(pos_t0, vel_t0)
acc_t0, lamda_t0 = self._solve_augmented_system(M, J, Qt, Qd)
self._acc_history[0] = acc_t0
self._lgr_history[0] = lamda_t0
print('\nRunning System Dynamic Analysis:')
i = 0
while i != bar_length:
t = time_array[i+1]
progress_bar(bar_length, i, t0, t)
self._set_time(t)
self.factorize_constraints_jacobian()
self._solve_time_step(t, dt, i)
i += 1
print('\n')
self._creat_results_dataframes()
def _solve_time_step(self, t, h, i):
print('SOLVING TIME STEP (%s)'%i)
print('======================')
qdd0 = self._acc_history[i]
vdd0 = self.get_indpenednt_q(qdd0)
q, qd, qdd, lamda = self.step(vdd0, t, h, i)
self._pos_history[i+1] = q
self._vel_history[i+1] = qd
self._acc_history[i+1] = qdd
self._lgr_history[i+1] = lamda
print('FINISHED TIME STEP (%s)'%i)
print('=======================\n')
def factorize_constraints_jacobian(self, jacobian=None):
A = super()._eval_jac_eq() if jacobian is None else jacobian
P, L, U = sc.linalg.lu(A.T)
self.permutaion_mat = P.T
def step(self, vdd, t, h, i):
#print('Entring Step Calculation')
soln = self.integrator_rhs(vdd, t, h, i)
residual, q, qd, qdd, lamda, M_hat = soln
delta = solve(M_hat, -residual)
norm = np.linalg.norm(delta)
itr = 0
while norm >1e-3:
print('Itr : %s'%itr)
print('ResNorm = %s' %np.linalg.norm(residual))
print('DelNorm = %s\n' %np.linalg.norm(delta))
vdd = vdd + delta
soln = self.integrator_rhs(vdd, t, h, i)
residual, q, qd, qdd, lamda, _ = soln
delta = solve(M_hat, -residual)
norm = np.linalg.norm(delta)
if itr > 50:
print("Integration Iterations exceded \n")
raise ValueError("Integration Iterations exceded \n")
break
itr += 1
return q, qd, qdd, lamda
def integrator_rhs(self, vdd, t, h, i):
print('Entring Integrator RHS')
self._set_time(t)
# setting some common used values
dof = self.dof
P = self.permutaion_mat
# Getting last time step states
q0 = self._pos_history[i]
qd0 = self._vel_history[i]
qdd0 = self._acc_history[i]
# Getting independent states using premutation matrix
v0 = self.get_indpenednt_q(q0)
vd0 = self.get_indpenednt_q(qd0)
vdd0 = self.get_indpenednt_q(qdd0)
# getting the new (n+1) independent position (v)
v_bar = v0 + (h * vd0) + ((h**2/4) * vdd0)
v = v_bar + (h**2/4) * vdd
# getting the new (n+1) independent velocity (vd)
vd_bar = vd0 + (h * vdd0)
vd = vd_bar + (h/2) * vdd
# Evaluating matrices for the RHS expression
# 1) getting dependent coordinates u from v
# guess = q0 #+ (qd0 * h) + (0.5* qdd0 *(h**2))
# for c in range(self.dof):
# guess[np.argmax(self.independent_cols[:, c]), 0] = v[c,0]
# self._newton_raphson(guess)
# self._set_gen_coordinates(self._pos)
# q = self._pos
# qp = P@q
# u = qp[:-dof, :]
self._set_gen_coordinates(q0)
J = super()._eval_jac_eq()
Jp = J @ P.T
Jv = Jp[:,-dof:]
Ju = Jp[:,:-dof]
H = solve(-Ju, Jv)
u0 = (P@q0)[:-dof, :]
ud0 = (P@qd0)[:-dof, :]
udd0 = (P@qdd0)[:-dof, :]
u_guess = u0 + (h * ud0) + ((h**2/4) * udd0)
constraint_res = super()._eval_pos_eq()
u_delta = solve(Ju, -constraint_res)
itr = 0
while np.linalg.norm(u_delta) >1e-3:
u_guess = u_guess + u_delta
q = solve(P, np.concatenate([u_guess, v]))
self._set_gen_coordinates(q)
constraint_res = super()._eval_pos_eq()
u_delta = solve(Ju, -constraint_res)
if itr > 50:
print("RHS Iterations exceded \n")
raise ValueError("Integration Iterations exceded \n")
break
itr += 1
q = solve(P, np.concatenate([u_guess, v]))
self._set_gen_coordinates(q)
# 2) getting dependent velocities ud from vd
#J = self._jac[:-dof]
ud = H@vd
qd = solve(P, np.concatenate([ud, vd]))
self._set_gen_velocities(qd)
# 3) evaluating the mass matrix and force vector
M = self._eval_mass_eq()
Qt = self._eval_frc_eq()
Qd = self._eval_acc_eq()
Mp = P @ M @ P.T
Qp = P@Qt
Mvv = Mp[-dof:, -dof:]
Mvu = Mp[-dof:, :-dof]
Muu = Mp[:-dof, :-dof]
Muv = Mp[:-dof, -dof:]
Qv = Qp[-dof:]
Qu = Qp[:-dof]
# 4) getting the dependent accelerations (udd)
udd = H@vdd + solve(Ju, -Qd)
qdd = solve(P, np.concatenate([udd, vdd]))
# 5) getting the lagrange multipliers
lamda = solve(Ju.T, (Qu - Muv@vdd - Muu@udd))
# 6) evaluating the error
residual = Mvv@vdd + Mvu@udd + Jv.T@lamda - Qv
M_hat = Mvv + (Mvu @ H) + H.T@(Muv + Muu@H)
return residual, q, qd, qdd, lamda, M_hat
def perform_coordinates_partition(self, M, J, Q):
P = self.permutaion_mat
dof = self.dof
Mp = P @ M @ P.T
Qp = P@Q
Jp = J@P.T
self.Jv = Jp[:,-dof:]
self.Ju = Jp[:,:-dof]
self.H = -solve(Ju, Jv)
self.Mvv = Mp[-dof:, -dof:]
self.Mvu = Mp[-dof:, :-dof]
self.Muu = Mp[:-dof, :-dof]
self.Muv = Mp[:-dof, -dof:]
self.Qv = Qp[-dof:]
self.Qu = Qp[:-dof]
def _extract_independent_coordinates(self):
P = self.permutaion_mat
self.independent_cols = P.T[:, self.model.nc:]
self.dof = self.independent_cols.shape[1]
self.independent_cord = [self._coordinates_indicies[np.argmax(self.independent_cols[:,i])] for i in range(self.dof) ]
def _eval_augmented_matricies(self, q , qd):
self._set_gen_coordinates(q)
self._set_gen_velocities(qd)
J = super()._eval_jac_eq()
M = self._eval_mass_eq()
Qt = self._eval_frc_eq()
Qd = self._eval_acc_eq()
return M, J, Qt, Qd
def _solve_augmented_system(self, M, J, Qt, Qd):
A = self.eval_coefficient_matrix(M, J)
b = np.concatenate([Qt, -Qd])
x = solve(A, b)
n = self.model.n
accelerations = x[:n]
lamda = x[n:]
return accelerations, lamda
def _eval_pos_eq(self):
A = super()._eval_pos_eq()
Z = np.zeros((self.dof, 1))
A = np.concatenate([A, Z])
return A
def _eval_vel_eq(self,ind_vel_i):
A = super()._eval_vel_eq()
V = np.array(ind_vel_i).reshape((self.dof, 1))
A = np.concatenate([A, -V])
return A
def _eval_jac_eq(self):
A = np.concatenate([super()._eval_jac_eq(), self.independent_cols.T])
return A
def get_indpenednt_q(self, q):
dof = self.dof
P = self.permutaion_mat
qp = P@q
qv = qp[-dof:, :]
return qv
def _eval_lagrange_multipliers(self, i):
self._set_gen_coordinates(self._pos_history[i])
return self._lgr_history[i]
def eval_coefficient_matrix(self, M, J):
z = np.zeros((self.model.nc, self.model.nc))
u = np.concatenate([M, J.T], axis=1)
l = np.concatenate([J, z], axis=1)
A = np.concatenate([u, l], axis=0)
return A
################################################################
class dds_solver5(abstract_solver):
def __init__(self, model):
super().__init__(model)
if self.model.n == self.model.nc:
raise ValueError('Model is fully constrained.')
def solve(self, run_id):
t0 = time.perf_counter()
time_array = self.time_array
dt = self.step_size
bar_length = len(time_array)-1
print('\nStarting System Dynamic Analysis:')
pos_t0 = self._pos_history[0]
vel_t0 = self._vel_history[0]
self.factorize_constraints_jacobian()
self._extract_independent_coordinates()
self._newton_raphson(pos_t0)
M, J, Qt, Qd = self._eval_augmented_matricies(pos_t0, vel_t0)
acc_t0, lamda_t0 = self._solve_augmented_system(M, J, Qt, Qd)
self._acc_history[0] = acc_t0
self._lgr_history[0] = lamda_t0
print('\nRunning System Dynamic Analysis:')
i = 0
while i != bar_length:
t = time_array[i+1]
progress_bar(bar_length, i, t0, t)
self._set_time(t)
self.factorize_constraints_jacobian(self._jac[:-self.dof])
self._extract_independent_coordinates()
self._solve_time_step(t, dt, i)
i += 1
print('\n')
self._creat_results_dataframes()
def _solve_time_step(self, t, h, i):
print('SOLVING TIME STEP (%s)'%i)
print('======================')
qdd0 = self._acc_history[i]
lgr0 = self._lgr_history[i]
fi0 = np.concatenate([qdd0, lgr0])
q, qd, qdd, lamda = self.step(fi0, t, h, i)
self._pos_history[i+1] = q
self._vel_history[i+1] = qd
self._acc_history[i+1] = qdd
self._lgr_history[i+1] = lamda
print('FINISHED TIME STEP (%s)'%i)
print('=======================\n')
def factorize_constraints_jacobian(self, jacobian=None):
A = super()._eval_jac_eq() if jacobian is None else jacobian
P, L, U = sc.linalg.lu(A.T)
self.permutaion_mat = P.T
def step(self, fi, t, h, i):
print('Entring Step Calculation')
soln = self.integrator_rhs(fi, t, h, i)
residual, q, qd, qdd, lamda, M_hat = soln
delta = solve(M_hat, -residual)
norm = np.linalg.norm(delta)
itr = 0
while norm >1e-3:
print('Itr : %s'%itr)
print('ResNorm = %s' %np.linalg.norm(residual))
print('DelNorm = %s\n' %np.linalg.norm(delta))
fi = fi + delta
soln = self.integrator_rhs(fi, t, h, i)
residual, q, qd, qdd, lamda, M_hat = soln
delta = solve(M_hat, -residual)
norm = np.linalg.norm(delta)
if itr > 150:
print("Integration Iterations exceded \n")
raise ValueError("Integration Iterations exceded \n")
break
itr += 1
return q, qd, qdd, lamda
def integrator_rhs(self, fi, t, h, i):
print('Entring Integrator RHS')
self._set_time(t+h)
qdd = fi[:self.model.n]
lamda = fi[self.model.n:]
# Getting last time step states
q0 = self._pos_history[i]
qd0 = self._vel_history[i]
qdd0 = self._acc_history[i]
# getting the new (n+1) independent position (v)
q_bar = q0 + (h * qd0) + ((h**2/4) * qdd0)
q = q_bar + (h**2/4) * qdd
# getting the new (n+1) independent velocity (vd)
qd_bar = qd0 + (h * qdd0)
qd = qd_bar + (h/2) * qdd
v = self.get_indpenednt_q(q)
vd = self.get_indpenednt_q(qd)
guess = q + (qd * h) + (0.5 * qdd * (h**2))
for c in range(self.dof):
guess[np.argmax(self.independent_cols[:, c]), 0] = v[c,0]
self._newton_raphson(guess)
A = self._jac
q_mod = self._pos
vel_rhs = self._eval_vel_eq(vd)
qd_mod = solve(A, -vel_rhs)
self._set_gen_coordinates(q_mod)
self._set_gen_velocities(qd_mod)
J = A[:-self.dof]
M = self._eval_mass_eq()
Qt = self._eval_frc_eq()
Qd = self._eval_acc_eq()
M_hat = self.eval_coefficient_matrix(M, J)
residual = (M_hat @ fi) - np.concatenate([Qt, -Qd])
return residual, q_mod, qd_mod, qdd, lamda, M_hat
def _extract_independent_coordinates(self):
P = self.permutaion_mat
self.independent_cols = P.T[:, self.model.nc:]
self.dof = self.independent_cols.shape[1]
self.independent_cord = [self._coordinates_indicies[np.argmax(self.independent_cols[:,i])] for i in range(self.dof) ]
def _eval_augmented_matricies(self, q , qd):
self._set_gen_coordinates(q)
self._set_gen_velocities(qd)
J = super()._eval_jac_eq()
M = self._eval_mass_eq()
Qt = self._eval_frc_eq()
Qd = self._eval_acc_eq()
return M, J, Qt, Qd
def _solve_augmented_system(self, M, J, Qt, Qd):
A = self.eval_coefficient_matrix(M, J)
b = np.concatenate([Qt, -Qd])
x = solve(A, b)
n = self.model.n
accelerations = x[:n]
lamda = x[n:]
return accelerations, lamda
def _eval_pos_eq(self):
A = super()._eval_pos_eq()
Z = np.zeros((self.dof, 1))
A = np.concatenate([A, Z])
return A
def _eval_vel_eq(self,ind_vel_i):
A = super()._eval_vel_eq()
V = np.array(ind_vel_i).reshape((self.dof, 1))
A = np.concatenate([A, -V])
return A
def _eval_jac_eq(self):
A = np.concatenate([super()._eval_jac_eq(), self.independent_cols.T])
return A
def get_indpenednt_q(self, q):
dof = self.dof
P = self.permutaion_mat
qp = P@q
qv = qp[-dof:, :]
return qv
def _eval_lagrange_multipliers(self, i):
self._set_gen_coordinates(self._pos_history[i])
return self._lgr_history[i]
def eval_coefficient_matrix(self, M, J):
z = np.zeros((self.model.nc, self.model.nc))
u = np.concatenate([M, J.T], axis=1)
l = np.concatenate([J, z], axis=1)
A = np.concatenate([u, l], axis=0)
return A
| 31.257485
| 125
| 0.537356
| 2,809
| 20,880
| 3.754361
| 0.080812
| 0.024275
| 0.017068
| 0.006827
| 0.797079
| 0.771193
| 0.755452
| 0.738384
| 0.725583
| 0.720083
| 0
| 0.014631
| 0.322414
| 20,880
| 667
| 126
| 31.304348
| 0.730775
| 0.055412
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| 0.753684
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| 0.043279
| 0.004837
| 0
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| 0
| 0
| 0
| 1
| 0.092632
| false
| 0
| 0.010526
| 0
| 0.170526
| 0.067368
| 0
| 0
| 0
| null | 0
| 0
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| 1
| 1
| 1
| 1
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| 0
| 0
|
0
| 6
|
443d2268196cf3d112bd157e5d9d11131f832791
| 112
|
py
|
Python
|
MathOperations/exponentiation.py
|
mkm99/firstDockerProject
|
9a0190216d9be7c2a01b428923ffb0e1c932991b
|
[
"MIT"
] | null | null | null |
MathOperations/exponentiation.py
|
mkm99/firstDockerProject
|
9a0190216d9be7c2a01b428923ffb0e1c932991b
|
[
"MIT"
] | null | null | null |
MathOperations/exponentiation.py
|
mkm99/firstDockerProject
|
9a0190216d9be7c2a01b428923ffb0e1c932991b
|
[
"MIT"
] | null | null | null |
class Exponentiation:
@staticmethod
def exponentiation(base, exponent):
return base ** exponent
| 22.4
| 39
| 0.696429
| 10
| 112
| 7.8
| 0.7
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| 112
| 5
| 40
| 22.4
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| 1
| 0
|
0
| 6
|
4457652eb6441f727b9226ed645acf084de90136
| 9,640
|
py
|
Python
|
pyscf/lib/test/test_einsum.py
|
robert-anderson/pyscf
|
cdc56e168cb15f47e8cdc791a92d689fa9b655af
|
[
"Apache-2.0"
] | 501
|
2018-12-06T23:48:17.000Z
|
2022-03-31T11:53:18.000Z
|
pyscf/lib/test/test_einsum.py
|
robert-anderson/pyscf
|
cdc56e168cb15f47e8cdc791a92d689fa9b655af
|
[
"Apache-2.0"
] | 710
|
2018-11-26T22:04:52.000Z
|
2022-03-30T03:53:12.000Z
|
pyscf/lib/test/test_einsum.py
|
robert-anderson/pyscf
|
cdc56e168cb15f47e8cdc791a92d689fa9b655af
|
[
"Apache-2.0"
] | 273
|
2018-11-26T10:10:24.000Z
|
2022-03-30T12:25:28.000Z
|
import unittest
import numpy
from pyscf import lib
einsum = lib.einsum
lib.numpy_helper.EINSUM_MAX_SIZE, bak = 0, lib.numpy_helper.EINSUM_MAX_SIZE
def tearDownModule():
lib.numpy_helper.EINSUM_MAX_SIZE = bak
class KnownValues(unittest.TestCase):
def test_d_d(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a, b)
c1 = einsum('abcd,fdea->cebf', a, b)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_c_c(self):
a = numpy.random.random((7,1,3,4)).astype(numpy.float32)
b = numpy.random.random((2,4,5,7)).astype(numpy.float32)
c0 = numpy.einsum('abcd,fdea->cebf', a, b)
c1 = einsum('abcd,fdea->cebf', a, b)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-5)
def test_c_d(self):
a = numpy.random.random((7,1,3,4)).astype(numpy.float32) + 0j
b = numpy.random.random((2,4,5,7)).astype(numpy.float32)
c0 = numpy.einsum('abcd,fdea->cebf', a, b)
c1 = einsum('abcd,fdea->cebf', a, b)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-5)
def test_d_z(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7)) + 0j
c0 = numpy.einsum('abcd,fdea->cebf', a, b)
c1 = einsum('abcd,fdea->cebf', a, b)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_z_z(self):
a = numpy.random.random((7,1,3,4)) + 0j
b = numpy.random.random((2,4,5,7)) + 0j
c0 = numpy.einsum('abcd,fdea->cebf', a, b)
c1 = einsum('abcd,fdea->cebf', a, b)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_d_dslice(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a, b[:,:,1:3,:])
c1 = einsum('abcd,fdea->cebf', a, b[:,:,1:3,:])
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_d_dslice1(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a[:4].copy(), b[:,:,:,2:6])
c1 = einsum('abcd,fdea->cebf', a[:4].copy(), b[:,:,:,2:6])
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_dslice_d(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a[:,:,1:3,:], b)
c1 = einsum('abcd,fdea->cebf', a[:,:,1:3,:], b)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_dslice_dslice(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a[:,:,1:3], b[:,:,:2,:])
c1 = einsum('abcd,fdea->cebf', a[:,:,1:3], b[:,:,:2,:])
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_dslice_dslice1(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a[2:6,:,:1], b[:,:,1:3,2:6])
c1 = einsum('abcd,fdea->cebf', a[2:6,:,:1], b[:,:,1:3,2:6])
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_d_cslice(self):
a = numpy.random.random((7,1,3,4))
b = numpy.random.random((2,4,5,7)).astype(numpy.float32)
c0 = numpy.einsum('abcd,fdea->cebf', a, b[:,:,1:3,:])
c1 = einsum('abcd,fdea->cebf', a, b[:,:,1:3,:])
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_z_cslice(self):
a = numpy.random.random((7,1,3,4)).astype(numpy.float32) + 0j
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a, b[:,:,1:3,:])
c1 = einsum('abcd,fdea->cebf', a, b[:,:,1:3,:])
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_cslice_dslice(self):
a = numpy.random.random((7,1,3,4)).astype(numpy.float32) + 0j
b = numpy.random.random((2,4,5,7))
c0 = numpy.einsum('abcd,fdea->cebf', a[2:6], b[:,:,1:3,2:6])
c1 = einsum('abcd,fdea->cebf', a[2:6], b[:,:,1:3,2:6])
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-14)
def test_3operands(self):
a = numpy.random.random((7,1,3,4)) + 1j
b = numpy.random.random((2,4,5,7))
c = numpy.random.random((2,8,3,6))
c0 = numpy.einsum('abcd,fdea,ficj->iebj', a, b, c)
c1 = einsum('abcd,fdea,ficj->iebj', a, b, c)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_3operands1(self):
a = numpy.random.random((2,2,2,2)) + 1j
b = numpy.random.random((2,2,2,2))
c = numpy.random.random((2,2,2,2))
c0 = numpy.einsum('abcd,acde,adef->ebf', a, b, c)
c1 = einsum('abcd,acde,adef->ebf', a, b, c)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_3operands2(self):
a = numpy.random.random(4)
ab = numpy.random.rand(4, 5)
b = numpy.random.random(5)
c0 = numpy.einsum('i,ij,j->ij', a, ab, b)
c1 = einsum('i,ij,j->ij', a, ab, b)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_1operand(self):
a = numpy.random.random((4,1,3,4)) + 1j
c0 = numpy.einsum('abca->bc', a)
c1 = einsum('abca->bc', a)
self.assertTrue(c0.dtype == c1.dtype)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_wrong_dimension(self):
a = numpy.random.random((5,1,3,4))
b = numpy.random.random((2,4,5,7))
self.assertRaises(ValueError, einsum, 'abcd,fdea->cebf', a, b)
def test_contraction1(self):
a = numpy.random.random((5,2,3,2))
c0 = numpy.einsum('ijkj->k', a)
c1 = einsum('ijkj->k', a)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_contraction2(self):
a = numpy.random.random((5,2,3,2))
b = numpy.random.random((2,4,7))+1j
c0 = numpy.einsum('ijkj,jlp->pk', a, b)
c1 = einsum('ijkj,jlp->pk', a, b)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_contraction3(self):
a = numpy.random.random((5,2,3,2))
b = numpy.random.random((2,4,7))+1j
c0 = numpy.einsum('ijkj,jlp->jpk', a, b)
c1 = einsum('ijkj,jlp->jpk', a, b)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_contraction4(self):
a = numpy.random.random((5,2,3,2))
b = numpy.random.random((2,4,7))
c0 = numpy.einsum('...jkj,jlp->...jp', a, b)
c1 = einsum('...jkj,jlp->...jp', a, b)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_contraction5(self):
x = numpy.random.random((8,6))
y = numpy.random.random((8,6,6))
c0 = numpy.einsum("in,ijj->n", x, y)
c1 = einsum("in,ijj->n", x, y)
self.assertTrue(abs(c0-c1).max() < 1e-13)
x = numpy.random.random((6,6))
y = numpy.random.random((8,8))
c0 = numpy.einsum("ii,jj->", x, y)
c1 = einsum("ii,jj->", x, y)
self.assertTrue(abs(c0-c1).max() < 1e-13)
x = numpy.random.random((6))
y = numpy.random.random((8))
c0 = numpy.einsum("i,j->", x, y)
c1 = einsum("i,j->", x, y)
self.assertTrue(abs(c0-c1).max() < 1e-13)
c0 = numpy.einsum("i,i->", x, x)
c1 = einsum("i,i->", x, x)
self.assertTrue(abs(c0-c1).max() < 1e-13)
x = numpy.random.random((6,8))
y = numpy.random.random((8,6))
c0 = numpy.einsum("ij,ji->", x, y)
c1 = einsum("ij,ji->", x, y)
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_contract(self):
try:
from pyscf.lib import tblis_einsum
tblis_available = True
except (ImportError, OSError):
tblis_available = False
if tblis_available:
a = numpy.random.random((5,4,6))
b = numpy.random.random((4,9,6))
c1 = numpy.ones((9,5), dtype=numpy.complex128)
c0 = tblis_einsum._contract('ijk,jlk->li', a, b, out=c1, alpha=.5j, beta=.2)
c1 = numpy.ones((9,5), dtype=numpy.complex128)
c1 = c1*.2 + numpy.einsum('ijk,jlk->li', a, b)*.5j
self.assertTrue(abs(c0-c1).max() < 1e-13)
def test_4operands(self):
a = numpy.random.random((30,40,5,10))
b = numpy.random.random((10,30,5,20))
c = numpy.random.random((10,20,20))
d = numpy.random.random((20,10))
f = lib.einsum('ijkl,xiky,ayp,px->ajl', a,b,c,d, optimize=True)
ref = lib.einsum('ijkl,xiky->jlxy', a,b)
ref = lib.einsum('jlxy,ayp->jlxap', ref,c)
ref = lib.einsum('jlxap,px->ajl', ref,d)
self.assertAlmostEqual(abs(ref-f).max(), 0, 9)
f = lib.einsum('ijkl,xiky,lyp,px->jl', a,b,c,d, optimize=True)
ref = lib.einsum('ijkl,xiky->jlxy', a, b)
ref = lib.einsum('jlxy,lyp->jlxp', ref, c)
ref = lib.einsum('jlxp,px->jl', ref, d)
self.assertAlmostEqual(abs(ref-f).max(), 0, 9)
if __name__ == '__main__':
unittest.main()
| 38.870968
| 88
| 0.54305
| 1,550
| 9,640
| 3.337419
| 0.085806
| 0.125459
| 0.190605
| 0.093949
| 0.841678
| 0.791224
| 0.732264
| 0.708486
| 0.667311
| 0.6474
| 0
| 0.075552
| 0.243465
| 9,640
| 247
| 89
| 39.02834
| 0.633758
| 0
| 0
| 0.464455
| 0
| 0
| 0.086826
| 0.002178
| 0
| 0
| 0
| 0
| 0.222749
| 1
| 0.123223
| false
| 0
| 0.023697
| 0
| 0.151659
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
928a63649f0e9b55199c806683483e4618f608dd
| 134
|
py
|
Python
|
trialbot/data/iterators/__init__.py
|
zxteloiv/TrialBot
|
145bd4fbfc4866a0bf1895fc0dc549d8b18d434a
|
[
"MIT"
] | 3
|
2019-07-04T10:22:28.000Z
|
2020-02-13T05:39:21.000Z
|
trialbot/data/iterators/__init__.py
|
zxteloiv/TrialBot
|
145bd4fbfc4866a0bf1895fc0dc549d8b18d434a
|
[
"MIT"
] | null | null | null |
trialbot/data/iterators/__init__.py
|
zxteloiv/TrialBot
|
145bd4fbfc4866a0bf1895fc0dc549d8b18d434a
|
[
"MIT"
] | null | null | null |
from .random_iterator import RandomIterator
from .bucket_iterator import BucketIterator
from .cluster_iterator import ClusterIterator
| 33.5
| 45
| 0.88806
| 15
| 134
| 7.733333
| 0.6
| 0.362069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089552
| 134
| 3
| 46
| 44.666667
| 0.95082
| 0
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| 0
| 0
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| 0
| true
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| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2b84fdc1144a7d895266dbe5ed101e7218ebcf03
| 1,986
|
py
|
Python
|
src/api/v1/utils/pdto.py
|
BuildForSDG/Team-183
|
737a42a6d6cc30adfc0cdd7bf3a4558728f966ad
|
[
"MIT"
] | null | null | null |
src/api/v1/utils/pdto.py
|
BuildForSDG/Team-183
|
737a42a6d6cc30adfc0cdd7bf3a4558728f966ad
|
[
"MIT"
] | null | null | null |
src/api/v1/utils/pdto.py
|
BuildForSDG/Team-183
|
737a42a6d6cc30adfc0cdd7bf3a4558728f966ad
|
[
"MIT"
] | 2
|
2020-05-02T04:20:40.000Z
|
2020-05-25T17:18:46.000Z
|
from flask_restx import reqparse
product_parser = reqparse.RequestParser()
product_parser.add_argument('product_name', required=True, type=str, help='product_name should be a string')
product_parser.add_argument('product_category', required=True, type=str, help='product_category should be a string')
update_product_parser = reqparse.RequestParser()
update_product_parser.add_argument('product_name', required=True, type=str, help='product_name should be a string')
update_product_parser.add_argument('product_category', required=True, type=str, help='product_category should be a string')
product_parser_resp = reqparse.RequestParser()
product_parser_resp.add_argument('product_name', required=True, type=str, help='product_name should be a string')
product_parser_resp.add_argument('inventory', required=True, type=str, help='product_category should be a string')
product_parser_resp.add_argument('min_quantity', required=True, type=str, help='product_name should be a string')
product_parser_resp.add_argument('category', required=True, type=str, help='product_category should be a string')
product_parser_resp.add_argument('price', required=True, type=str, help='product_name should be a string')
# product_parser_resp.add_argument('product_category', required=True, type=str, help='product_category should be a string')
update_product_parser_resp = reqparse.RequestParser()
# update_product_parser_resp.add_argument('product_name', required=True, type=str, help='product_name should be a string')
update_product_parser_resp.add_argument('inventory', required=True, type=int, help='product_category should be a number')
update_product_parser_resp.add_argument('min_quantity', required=True, type=int, help='product_name should be a number')
# update_product_parser_resp.add_argument('category', required=True, type=str, help='product_category should be a string')
update_product_parser_resp.add_argument('price', required=True, type=int, help='product_name should be a number')
| 79.44
| 123
| 0.820242
| 292
| 1,986
| 5.30137
| 0.099315
| 0.159561
| 0.155039
| 0.147287
| 0.959948
| 0.919251
| 0.897287
| 0.897287
| 0.897287
| 0.868863
| 0
| 0
| 0.072004
| 1,986
| 24
| 124
| 82.75
| 0.839935
| 0.182779
| 0
| 0
| 0
| 0
| 0.321186
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.058824
| 0
| 0.058824
| 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
|
2bc18c10a74917d4d23af14877e0054576900adc
| 29,876
|
py
|
Python
|
tests/unit/test_msc_tools.py
|
GregDMeyer/dynamite
|
440f0c3674bf12a835b8ad4b3c10c303c2d28265
|
[
"MIT"
] | 16
|
2017-10-11T22:53:30.000Z
|
2021-12-23T01:17:07.000Z
|
tests/unit/test_msc_tools.py
|
GregDMeyer/dynamite
|
440f0c3674bf12a835b8ad4b3c10c303c2d28265
|
[
"MIT"
] | 8
|
2017-10-11T22:57:09.000Z
|
2021-02-03T20:35:44.000Z
|
tests/unit/test_msc_tools.py
|
GregDMeyer/dynamite
|
440f0c3674bf12a835b8ad4b3c10c303c2d28265
|
[
"MIT"
] | 3
|
2018-02-21T19:36:47.000Z
|
2022-01-26T21:38:18.000Z
|
# pylint: disable=W0212
'''
Unit tests for the classmethods of the Operator class in operators.py.
These tests should NOT require MPI.
'''
import unittest as ut
import numpy as np
from dynamite import msc_tools
class ToNumpy(ut.TestCase):
'''
Test the msc_tools.MSC_to_numpy method, whose behavior defines the MSC array.
So in some sense these tests are the definition of the MSC array.
'''
def check_same(self, dnm, npy):
'''
Helper function to check that dynamite and numpy arrays are equal, and
print the differences if not.
'''
self.assertTrue(np.all(dnm == npy),
msg = '\n\n'.join(['\ndnm:\n'+str(dnm), '\nnpy:\n'+str(npy)]))
def test_identity(self):
dnm = msc_tools.msc_to_numpy([(0, 0, 1)], (5,5))
npy = np.identity(5)
self.check_same(dnm, npy)
def test_identity_wide(self):
dnm = msc_tools.msc_to_numpy([(0, 0, 1)], (3,5),
idx_to_state = lambda x: x if x < 3 else -1)
npy = np.identity(5)[:3,:]
self.check_same(dnm, npy)
def test_identity_tall(self):
def state_to_idx(x):
rtn = x.copy()
rtn[rtn >= 3] = -1
return rtn
dnm = msc_tools.msc_to_numpy([(0, 0, 1)], (5,3),
state_to_idx = state_to_idx)
npy = np.identity(5)[:,:3]
self.check_same(dnm, npy)
def test_cut_off(self):
def state_to_idx(x):
rtn = x.copy()
rtn[rtn >= 3] = -1
return rtn
dnm = msc_tools.msc_to_numpy([(0, 0, 1)], (5,5),
state_to_idx = state_to_idx)
npy = np.array(
[
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
]
)
self.check_same(dnm, npy)
def test_nonherm_diag(self):
dnm = msc_tools.msc_to_numpy([(0, 0, 1j)], (2,2))
npy = np.array(
[
[1j, 0],
[0, 1j],
]
)
self.check_same(dnm, npy)
def test_nonherm_offdiag(self):
dnm = msc_tools.msc_to_numpy([(1, 0, 1j)], (2,2))
npy = np.array(
[
[0, 1j],
[1j, 0],
]
)
self.check_same(dnm, npy)
def test_nonherm_sign(self):
dnm = msc_tools.msc_to_numpy([(1, 1, 1j)], (2,2))
npy = np.array(
[
[0, -1j],
[1j, 0],
]
)
self.check_same(dnm, npy)
def test_twoterms_tall(self):
def state_to_idx(x):
rtn = x.copy()
rtn[rtn >= 3] = -1
return rtn
dnm = msc_tools.msc_to_numpy([(0, 0, 1), (2, 0, 2)], (5,3),
state_to_idx = state_to_idx)
npy = np.array(
[
[1, 0, 2],
[0, 1, 0],
[2, 0, 1],
[0, 2, 0],
[0, 0, 0],
]
)
self.check_same(dnm, npy)
def test_allflip(self):
dnm = msc_tools.msc_to_numpy([(15, 0, 1)], (16,16))
npy = np.identity(16)[:,::-1]
self.check_same(dnm, npy)
def test_sign1(self):
dnm = msc_tools.msc_to_numpy([(0, 1, 1)], (16,16))
npy = np.diag([1, -1]*8)
self.check_same(dnm, npy)
def test_sign2(self):
dnm = msc_tools.msc_to_numpy([(0, 3, 1)], (16,16))
npy = np.diag([1, -1, -1, 1]*4)
self.check_same(dnm, npy)
def test_signL(self):
dnm = msc_tools.msc_to_numpy([(0, 8, 1)], (16,16))
npy = np.diag([1]*8 + [-1]*8)
self.check_same(dnm, npy)
def test_signL2(self):
dnm = msc_tools.msc_to_numpy([(0, 9, 1)], (16,16))
npy = np.diag([1, -1]*4 + [-1, 1]*4)
self.check_same(dnm, npy)
def test_full(self):
dnm = msc_tools.msc_to_numpy([(1, 5, 0.5j), (4, 3, -2)], (8, 8))
npy = np.array(
[
[ 0,-0.5j, 0, 0, -2, 0, 0, 0 ],
[ 0.5j, 0, 0, 0, 0, 2, 0, 0 ],
[ 0, 0, 0,-0.5j, 0, 0, 2, 0 ],
[ 0, 0, 0.5j, 0, 0, 0, 0, -2 ],
[ -2, 0, 0, 0, 0, 0.5j, 0, 0 ],
[ 0, 2, 0, 0,-0.5j, 0, 0, 0 ],
[ 0, 0, 2, 0, 0, 0, 0, 0.5j ],
[ 0, 0, 0, -2, 0, 0,-0.5j, 0 ],
]
)
self.check_same(dnm, npy)
def test_dense(self):
dnm = msc_tools.msc_to_numpy([(1, 5, 0.5j), (4, 3, -2)], (8, 8), sparse = False)
npy = np.array(
[
[ 0,-0.5j, 0, 0, -2, 0, 0, 0 ],
[ 0.5j, 0, 0, 0, 0, 2, 0, 0 ],
[ 0, 0, 0,-0.5j, 0, 0, 2, 0 ],
[ 0, 0, 0.5j, 0, 0, 0, 0, -2 ],
[ -2, 0, 0, 0, 0, 0.5j, 0, 0 ],
[ 0, 2, 0, 0,-0.5j, 0, 0, 0 ],
[ 0, 0, 2, 0, 0, 0, 0, 0.5j ],
[ 0, 0, 0, -2, 0, 0,-0.5j, 0 ],
]
)
self.check_same(dnm, npy)
def test_map1(self):
dnm = msc_tools.msc_to_numpy([(1, 5, 0.5j), (4, 3, -2)], (8, 8),
idx_to_state = lambda x: x^4)
npy = np.array(
[
[ -2, 0, 0, 0, 0, 0.5j, 0, 0 ],
[ 0, 2, 0, 0,-0.5j, 0, 0, 0 ],
[ 0, 0, 2, 0, 0, 0, 0, 0.5j ],
[ 0, 0, 0, -2, 0, 0,-0.5j, 0 ],
[ 0,-0.5j, 0, 0, -2, 0, 0, 0 ],
[ 0.5j, 0, 0, 0, 0, 2, 0, 0 ],
[ 0, 0, 0,-0.5j, 0, 0, 2, 0 ],
[ 0, 0, 0.5j, 0, 0, 0, 0, -2 ],
]
)
self.check_same(dnm, npy)
def test_map2(self):
dnm = msc_tools.msc_to_numpy([(1, 5, 0.5j), (4, 3, -2)], (8, 8),
state_to_idx = lambda x: x^4)
npy = np.array(
[
[ -2, 0, 0, 0, 0,-0.5j, 0, 0 ],
[ 0, 2, 0, 0, 0.5j, 0, 0, 0 ],
[ 0, 0, 2, 0, 0, 0, 0,-0.5j ],
[ 0, 0, 0, -2, 0, 0, 0.5j, 0 ],
[ 0, 0.5j, 0, 0, -2, 0, 0, 0 ],
[-0.5j, 0, 0, 0, 0, 2, 0, 0 ],
[ 0, 0, 0, 0.5j, 0, 0, 2, 0 ],
[ 0, 0,-0.5j, 0, 0, 0, 0, -2 ],
]
)
self.check_same(dnm, npy)
def test_map_both(self):
dnm = msc_tools.msc_to_numpy([(1, 5, 0.5j), (4, 3, -2)], (8, 8),
state_to_idx = lambda x: x^4,
idx_to_state = lambda x: x^2)
npy = np.array(
[
[ 0, 0, 2, 0, 0, 0, 0,-0.5j ],
[ 0, 0, 0, -2, 0, 0, 0.5j, 0 ],
[ -2, 0, 0, 0, 0,-0.5j, 0, 0 ],
[ 0, 2, 0, 0, 0.5j, 0, 0, 0 ],
[ 0, 0, 0, 0.5j, 0, 0, 2, 0 ],
[ 0, 0,-0.5j, 0, 0, 0, 0, -2 ],
[ 0, 0.5j, 0, 0, -2, 0, 0, 0 ],
[-0.5j, 0, 0, 0, 0, 2, 0, 0 ],
]
)
self.check_same(dnm, npy)
class IsHermitian(ut.TestCase):
'''
Test the is_hermitian method.
'''
def test_hermitian(self):
check = np.array([(1, 3, 1j)], dtype=msc_tools.msc_dtype)
self.assertTrue(msc_tools.is_hermitian(check))
def test_hermitian_big(self):
check = np.array([( 3, 0, 1. +0.j), ( 3, 3, -1. +0.j),
( 0, 3, 1. +0.j), ( 6, 0, 1. +0.j),
( 6, 6, -1. +0.j), ( 0, 6, 1. +0.j),
(12, 0, 1. +0.j), (12, 12, -1. +0.j),
( 0, 12, 1. +0.j), ( 0, 1, 0.09762701+0.j),
( 0, 2, 0.43037873+0.j), ( 0, 4, 0.20552675+0.j),
( 0, 8, 0.08976637+0.j)], dtype=msc_tools.msc_dtype)
self.assertTrue(msc_tools.is_hermitian(check))
def test_nonhermitian(self):
check = np.array([(1, 3, 3)], dtype=msc_tools.msc_dtype)
self.assertFalse(msc_tools.is_hermitian(check))
def test_nonhermitian_big(self):
check = np.array([( 0, 1, 0.09762701+0.j), ( 0, 2, 0.43037873+0.j),
( 0, 3, 1. +0.j), ( 0, 4, 0.20552675+0.j),
( 0, 6, 1. +0.j), ( 0, 8, 0.08976637+0.j),
( 0, 12, 1. +0.j), ( 3, 0, 1. +0.j),
( 3, 3, -1. +0.j), ( 4, 0, 1. +0.j),
( 4, 4, -1. +0.j), ( 6, 0, 1. +0.j),
( 6, 6, -1. +0.j), (12, 0, 1. +0.j),
(12, 12, -1. +0.j)], dtype=msc_tools.msc_dtype)
self.assertFalse(msc_tools.is_hermitian(check))
class MSCSum(ut.TestCase):
'''
Test the _MSC_sum method.
'''
def setUp(self):
self.dtype = msc_tools.msc_dtype
def check_same(self, check, target):
check = msc_tools.combine_and_sort(check)
target = msc_tools.combine_and_sort(np.array(target, dtype=self.dtype))
self.assertTrue(np.array_equal(check, target),
msg = '\ncheck:\n'+str(check) + '\ntarget:\n'+str(target))
def test_single(self):
check = msc_tools.msc_sum(np.array([(1, 2, 3)], dtype=self.dtype))
target = [(1, 2, 3)]
self.check_same(check, target)
def test_2x1(self):
lst = [
np.array([(3, 2, 1j), (5, 6, 2)], dtype=self.dtype),
np.array([(1, 2, 3)], dtype=self.dtype)
]
check = msc_tools.msc_sum(lst)
target = [(3, 2, 1j), (5, 6, 2), (1, 2, 3)]
self.check_same(check, target)
def test_iterable(self):
check = msc_tools.msc_sum(np.array([(1, 2, i**2)], dtype=self.dtype) for i in range(5))
target = [(1, 2, 30)]
self.check_same(check, target)
def test_empty(self):
check = msc_tools.msc_sum([])
target = []
self.check_same(check, target)
class MSCProduct(ut.TestCase):
'''
Test the _MSC_product method.
'''
def setUp(self):
self.dtype = msc_tools.msc_dtype
def check_same(self, check, target):
check = msc_tools.combine_and_sort(check)
target = msc_tools.combine_and_sort(np.array(target, dtype=self.dtype))
self.assertTrue(np.array_equal(check, target),
msg = '\ncheck:\n'+str(check) + '\ntarget:\n'+str(target))
def test_single(self):
check = msc_tools.msc_product(np.array([(1,2,3)], dtype=self.dtype))
target = [(1,2,3)]
self.check_same(check, target)
def test_XX(self):
lst = [
[(1, 0, 2)],
[(2, 0, 3)]
]
lst = [np.array(x, dtype=self.dtype) for x in lst]
check = msc_tools.msc_product(lst)
target = [(3, 0, 6)]
self.check_same(check, target)
def test_ZZ(self):
lst = [
[(0, 1, 2)],
[(0, 2, 3)]
]
lst = [np.array(x, dtype=self.dtype) for x in lst]
check = msc_tools.msc_product(lst)
target = [(0, 3, 6)]
self.check_same(check, target)
def test_YY(self):
lst = [
[(1, 1, 2)],
[(2, 2, 3)]
]
lst = [np.array(x, dtype=self.dtype) for x in lst]
check = msc_tools.msc_product(lst)
target = [(3, 3, 6)]
self.check_same(check, target)
def test_XZ(self):
lst = [
[(1, 0, 2)],
[(0, 1, 3)]
]
lst = [np.array(x, dtype=self.dtype) for x in lst]
check = msc_tools.msc_product(lst)
target = [(1, 1, 6)]
self.check_same(check, target)
def test_ZX(self):
lst = [
[(0, 1, 2)],
[(1, 0, 3)]
]
lst = [np.array(x, dtype=self.dtype) for x in lst]
check = msc_tools.msc_product(lst)
target = [(1, 1, -6)]
self.check_same(check, target)
def test_1x2x3(self):
lst = [
[(1, 0, 1)],
[(0, 1, 2), (3, 3, 5)],
[(1, 0, 3), (6, 4, 7), (3, 4, 11)]
]
lst = [np.array(x, dtype=self.dtype) for x in lst]
check = msc_tools.msc_product(lst)
target = [(0, 1, -6),
(7, 5, 14),
(2, 5,-22),
(3, 3,-15),
(4, 7,-35),
(1, 7, 55)]
self.check_same(check, target)
def test_one_empty(self):
lst = [
[],
[(0, 1, 2), (3, 3, 5)],
[(1, 0, 3), (6, 4, 7), (3, 4, 11)]
]
lst = [np.array(x, dtype=self.dtype) for x in lst]
check = msc_tools.msc_product(lst)
target = []
self.check_same(check, target)
class ShiftMSC(ut.TestCase):
'''
Tests the shift method.
'''
def setUp(self):
self.dtype = msc_tools.msc_dtype
def test_single_mask(self):
msc = np.array([(1, 0, 0.5j)], dtype = self.dtype)
for i in range(5):
with self.subTest(shift=i):
shifted = msc_tools.shift(msc, i, None)
self.assertEqual(shifted['masks'], 2**i)
self.assertEqual(shifted['signs'], 0)
self.assertEqual(shifted['coeffs'], 0.5j)
# check that we haven't changed it
self.assertTrue(np.all(msc == np.array([(1, 0, 0.5j)], dtype = self.dtype)))
def test_single_sign(self):
msc = np.array([(0, 1, 0.5j)], dtype = self.dtype)
for i in range(5):
with self.subTest(shift=i):
shifted = msc_tools.shift(msc, i, None)
self.assertEqual(shifted['masks'], 0)
self.assertEqual(shifted['signs'], 2**i)
self.assertEqual(shifted['coeffs'], 0.5j)
self.assertTrue(np.all(msc == np.array([(0, 1, 0.5j)], dtype = self.dtype)))
def test_single_mask_wrap(self):
msc = np.array([(16, 0, 0.5j)], dtype = self.dtype)
for i in range(1,5):
with self.subTest(shift=i):
shifted = msc_tools.shift(msc, i, 5)
self.assertEqual(shifted['masks'], 2**(i-1))
self.assertEqual(shifted['signs'], 0)
self.assertEqual(shifted['coeffs'], 0.5j)
self.assertTrue(np.all(msc == np.array([(16, 0, 0.5j)], dtype = self.dtype)))
def test_single_sign_wrap(self):
msc = np.array([(0, 16, 0.5j)], dtype = self.dtype)
for i in range(1,5):
with self.subTest(shift=i):
shifted = msc_tools.shift(msc, i, 5)
self.assertEqual(shifted['masks'], 0)
self.assertEqual(shifted['signs'], 2**(i-1))
self.assertEqual(shifted['coeffs'], 0.5j)
self.assertTrue(np.all(msc == np.array([(0, 16, 0.5j)], dtype = self.dtype)))
def test_multiple(self):
msc = np.array([(3, 4, 0.5),
(4, 1, 1.5),
(1, 3, 4.5j)], dtype = self.dtype)
orig = msc.copy()
shifted = msc_tools.shift(msc, 2, None)
self.assertTrue(np.all(shifted['masks'] == msc['masks']*4))
self.assertTrue(np.all(shifted['signs'] == msc['signs']*4))
self.assertTrue(np.all(shifted['coeffs'] == msc['coeffs']))
self.assertTrue(np.all(msc == orig))
def test_multiple_wrap(self):
msc = np.array([(5, 4, 0.5),
(4, 1, 1.5),
(1, 3, 4.5j)], dtype = self.dtype)
orig = msc.copy()
shifted = msc_tools.shift(msc, 3, 5)
self.assertTrue(np.all(shifted['masks'] == np.array([9, 1, 8])))
self.assertTrue(np.all(shifted['signs'] == np.array([1, 8, 24])))
self.assertTrue(np.all(shifted['coeffs'] == msc['coeffs']))
self.assertTrue(np.all(msc == orig))
class ReduceMSC(ut.TestCase):
'''
Test the _combine_and_sort method.
'''
def setUp(self):
self.dtype = msc_tools.msc_dtype
def check_same(self, check, target):
check = msc_tools.combine_and_sort(check)
self.assertTrue(np.array_equal(check, target),
msg = '\ncheck:\n'+str(check) + '\ntarget:\n'+str(target))
def test_single(self):
check = np.array([(5, 2, -0.5j)], dtype=self.dtype)
target = np.array([(5, 2, -0.5j)], dtype=self.dtype)
self.check_same(check, target)
def test_same(self):
check = np.array([(5, 2, 2), (5, 2, -0.5)], dtype=self.dtype)
target = np.array([(5, 2, 1.5)], dtype=self.dtype)
self.check_same(check, target)
def test_same_zero(self):
check = np.array([(5, 2, 0.5), (5, 2, -0.5)], dtype=self.dtype)
target = np.array([], dtype=self.dtype)
self.check_same(check, target)
def test_different(self):
check = np.array([(5, 3, 2), (5, 2, -0.5)], dtype=self.dtype)
target = np.array([(5, 2, -0.5), (5, 3, 2)], dtype=self.dtype)
self.check_same(check, target)
def test_many(self):
check = np.array([(5, 3, 2),
(5, 2, -0.5),
(0, 0, 0),
(15, 3, 1.5j),
(14, 3, 2j),
(1, 0, 0),
(0, 0, 1.2),
(1, 0, 3),
(1, 0, -2),
(2, 3, 4),
(1, 0, -1)],
dtype=self.dtype)
target = np.array([(0, 0, 1.2),
(2, 3, 4),
(5, 2, -0.5),
(5, 3, 2),
(14, 3, 2j),
(15, 3, 1.5j)],
dtype=self.dtype)
self.check_same(check, target)
class Serialization(ut.TestCase):
'''
Test the msc.serialize and msc.deserialize methods.
'''
def setUp(self):
dtype32 = np.dtype([('masks', np.int32),
('signs', np.int32),
('coeffs', np.complex128)])
dtype64 = np.dtype([('masks', np.int64),
('signs', np.int64),
('coeffs', np.complex128)])
self.test_cases = [
{
'MSC' : np.array([(1, 5, -0.2j), (0, 1, 2)],
dtype = dtype32.newbyteorder('B')),
'serial' : b'2\n32\n' + \
b'\x00\x00\x00\x01\x00\x00\x00\x00' + \
b'\x00\x00\x00\x05\x00\x00\x00\x01' + \
b'\x80\x00\x00\x00\x00\x00\x00\x00\xbf\xc9\x99\x99\x99\x99\x99\x9a' + \
b'@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00',
},
{
'MSC' : np.array([(1, 5, -0.2j), (0, 1, 2)],
dtype = dtype32.newbyteorder('L')),
'serial' : b'2\n32\n' + \
b'\x00\x00\x00\x01\x00\x00\x00\x00' + \
b'\x00\x00\x00\x05\x00\x00\x00\x01' + \
b'\x80\x00\x00\x00\x00\x00\x00\x00\xbf\xc9\x99\x99\x99\x99\x99\x9a' + \
b'@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00',
},
{
'MSC' : np.array([(1, 5, -0.2j), (0, 1, 2)],
dtype = dtype64.newbyteorder('B')),
'serial' : b'2\n64\n' + \
b'\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00' + \
b'\x00\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00\x00\x00\x00\x00\x01' + \
b'\x80\x00\x00\x00\x00\x00\x00\x00\xbf\xc9\x99\x99\x99\x99\x99\x9a' + \
b'@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00',
},
{
'MSC' : np.array([(1, 5, -0.2j), (0, 1, 2)],
dtype = dtype64.newbyteorder('L')),
'serial' : b'2\n64\n' + \
b'\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00' + \
b'\x00\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00\x00\x00\x00\x00\x01' + \
b'\x80\x00\x00\x00\x00\x00\x00\x00\xbf\xc9\x99\x99\x99\x99\x99\x9a' + \
b'@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00',
}
]
def test_serialize(self):
for n, case in enumerate(self.test_cases):
with self.subTest(n = n):
ser = msc_tools.serialize(case['MSC'])
self.assertEqual(ser, case['serial'])
def test_deserialize(self):
for n, case in enumerate(self.test_cases):
with self.subTest(n = n):
msc = msc_tools.deserialize(case['serial'])
self.assertTrue(np.all(msc == case['MSC']),
msg = '\n'+'\n\n'.join([str(msc), str(case['MSC'])]))
class MaxSpinIdx(ut.TestCase):
dtype = np.dtype([('masks', np.int32),
('signs', np.int32),
('coeffs', np.complex128)])
def test_single_zero(self):
check = np.array([(1, 0, 2)], dtype = self.dtype)
self.assertEqual(msc_tools.max_spin_idx(check), 0)
def test_single_mask(self):
check = np.array([(4, 2, 18j)], dtype = self.dtype)
self.assertEqual(msc_tools.max_spin_idx(check), 2)
def test_single_sign(self):
check = np.array([(1, 3, 18j)], dtype = self.dtype)
self.assertEqual(msc_tools.max_spin_idx(check), 1)
def test_multiple_mask(self):
check = np.array([(1, 3, 18j), (9, 2, 1), (2, 5, 18j)], dtype = self.dtype)
self.assertEqual(msc_tools.max_spin_idx(check), 3)
def test_multiple_sign(self):
check = np.array([(1, 3, 18j), (9, 2, 1), (2, 17, 12)], dtype = self.dtype)
self.assertEqual(msc_tools.max_spin_idx(check), 4)
def test_empty(self):
# we want -1 in this case so that for loops based on this terminate correctly
check = np.array([], dtype = self.dtype)
self.assertEqual(msc_tools.max_spin_idx(check), -1)
class NNZ(ut.TestCase):
'''
Test the msc_tools.nnz method.
'''
dtype = np.dtype([('masks', np.int32),
('signs', np.int32),
('coeffs', np.complex128)])
def test_empty(self):
check = np.array([], dtype = self.dtype)
self.assertEqual(msc_tools.nnz(check), 0)
def test_single(self):
check = np.array([(0, 0, 1)], dtype = self.dtype)
self.assertEqual(msc_tools.nnz(check), 1)
def test_single_offdiag(self):
check = np.array([(2, 0, 1)], dtype = self.dtype)
self.assertEqual(msc_tools.nnz(check), 1)
def test_multiple_sign(self):
check = np.array([(2, 3, 1j), (2, 0, 1)], dtype = self.dtype)
self.assertEqual(msc_tools.nnz(check), 1)
def test_multiple(self):
check = np.array([(0, 0, 1),
(1, 0, 2),
(1, 1, 3),
(2, 4, 0.5j)], dtype = self.dtype)
self.assertEqual(msc_tools.nnz(check), 3)
class Table(ut.TestCase):
def test_empty_L5(self):
L = 5
msc = []
correct = ' coeff. | operator \n' +\
'=====================\n'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_empty_L9(self):
L = 9
msc = []
correct = ' coeff. | operator \n' +\
'======================\n'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_empty_L10(self):
L = 10
msc = []
correct = ' coeff. | operator \n' +\
'=======================\n'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_identity(self):
L = 5
msc = [(0, 0, 2.3)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 2.300 | -----'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_sigmax_0(self):
L = 5
msc = [(1, 0, 1)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.000 | X----'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_sigmaz_0(self):
L = 5
L = 5
msc = [(0, 1, 1)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.000 | Z----'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_sigmay_0(self):
L = 5
msc = [(1, 1, 1j)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.000 | Y----'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_sigmay_2(self):
L = 5
msc = [(4, 4, 1j)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.000 | --Y--'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_sigmax_0_coeff(self):
L = 5
msc = [(1, 0, 3.141592)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 3.142 | X----'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_XX(self):
L = 5
msc = [(3, 0, 1)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.000 | XX---'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_ZZ(self):
L = 5
msc = [(0, 3, 1)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.000 | ZZ---'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_XYZ(self):
L = 5
msc = [(3, 6, 0.5j)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 0.500 | XYZ--'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_three(self):
L = 5
msc = [(1, 0, 2.3), (3, 1, 2j), (3, 6, 0.5j)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 2.300 | X----\n' +\
' 2.000 | YX---\n' +\
' 0.500 | XYZ--'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_ZZ_wrap(self):
L = 5
msc = [(0, 3, 0.25), (0, 6, 0.25), (0, 12, 0.25), (0, 24, 0.25), (0, 17, 0.25),]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 0.250 | ZZ---\n' +\
' 0.250 | -ZZ--\n' +\
' 0.250 | --ZZ-\n' +\
' 0.250 | ---ZZ\n' +\
' 0.250 | Z---Z'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_large_coeff(self):
L = 5
msc = [(0, 1, 1E+9)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.00e+09 | Z----'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
def test_small_coeff(self):
L = 5
msc = [(0, 1, 1E-9)]
correct = ' coeff. | operator \n' +\
'=====================\n' +\
' 1.00e-09 | Z----'
check = msc_tools.table(msc, L)
self.assertEqual(check, correct, msg = '\n' + '\n\n'.join([check, correct]))
if __name__ == '__main__':
ut.main()
| 37.113043
| 98
| 0.421308
| 3,906
| 29,876
| 3.127752
| 0.063492
| 0.039781
| 0.035852
| 0.098224
| 0.861341
| 0.820578
| 0.783744
| 0.752558
| 0.707129
| 0.671196
| 0
| 0.104587
| 0.398012
| 29,876
| 804
| 99
| 37.159204
| 0.574701
| 0.023832
| 0
| 0.534848
| 0
| 0.018182
| 0.083589
| 0.043277
| 0.025758
| 0
| 0
| 0
| 0.092424
| 1
| 0.130303
| false
| 0
| 0.004545
| 0
| 0.157576
| 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
|
2bd49fd3b73e8ceeee638e905fe7700fe2196670
| 345
|
py
|
Python
|
chainladder/__init__.py
|
Aborah30/chainladder-python
|
c7d3f4f0a5333b6bd34922cc406f252ab9c47e10
|
[
"MIT"
] | null | null | null |
chainladder/__init__.py
|
Aborah30/chainladder-python
|
c7d3f4f0a5333b6bd34922cc406f252ab9c47e10
|
[
"MIT"
] | null | null | null |
chainladder/__init__.py
|
Aborah30/chainladder-python
|
c7d3f4f0a5333b6bd34922cc406f252ab9c47e10
|
[
"MIT"
] | null | null | null |
from chainladder.utils import * # noqa (API Import)
from chainladder.core import * # noqa (API Import)
from chainladder.development import * # noqa (API Import)
from chainladder.tails import * # noqa (API Import)
from chainladder.methods import * # noqa (API Import)
from chainladder.workflow import * # noqa (API Import)
__version__ = '0.3.1'
| 38.333333
| 57
| 0.747826
| 46
| 345
| 5.521739
| 0.326087
| 0.354331
| 0.307087
| 0.448819
| 0.669291
| 0.669291
| 0
| 0
| 0
| 0
| 0
| 0.010239
| 0.150725
| 345
| 8
| 58
| 43.125
| 0.856655
| 0.310145
| 0
| 0
| 0
| 0
| 0.021645
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.857143
| 0
| 0.857143
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9200ca642003bc8c3561fcc4cf7ec725e98f96ac
| 97
|
py
|
Python
|
test/util/imported_cfg.py
|
dmarnerides/boardom
|
b7eec437b43398ac8dd8d2755dca9bbe7a8ff8a3
|
[
"BSD-2-Clause"
] | 2
|
2021-10-05T15:52:50.000Z
|
2021-11-22T00:05:03.000Z
|
test/util/imported_cfg.py
|
dmarnerides/boardom
|
b7eec437b43398ac8dd8d2755dca9bbe7a8ff8a3
|
[
"BSD-2-Clause"
] | null | null | null |
test/util/imported_cfg.py
|
dmarnerides/boardom
|
b7eec437b43398ac8dd8d2755dca9bbe7a8ff8a3
|
[
"BSD-2-Clause"
] | null | null | null |
from boardom import cfg
def my_bd_cfg_is_same_as_given(other_cfg):
return cfg is other_cfg
| 16.166667
| 42
| 0.804124
| 19
| 97
| 3.684211
| 0.684211
| 0.142857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.164948
| 97
| 5
| 43
| 19.4
| 0.864198
| 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
|
9218cd405a4e23619160455ed609d65ef20b2f2d
| 2,787
|
py
|
Python
|
link/reqi/dsl/semantics.py
|
linkdd/link.reqi
|
e5c133f5a77fb07fad82af199c9bbfaf23e52eba
|
[
"MIT"
] | null | null | null |
link/reqi/dsl/semantics.py
|
linkdd/link.reqi
|
e5c133f5a77fb07fad82af199c9bbfaf23e52eba
|
[
"MIT"
] | null | null | null |
link/reqi/dsl/semantics.py
|
linkdd/link.reqi
|
e5c133f5a77fb07fad82af199c9bbfaf23e52eba
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
class GraphDSLSemantics(object):
def digit(self, ast):
return ast
def symbol(self, ast):
return ast
def characters(self, ast):
return ast
def identifier(self, ast):
return ast
def string(self, ast):
return ast[1]
def natural(self, ast):
return ast
def sign(self, ast):
return ast
def integer(self, ast):
return int(''.join(ast))
def decimal(self, ast):
return ast
def boolean(self, ast):
return ast
def value(self, ast):
return ast
def property_name(self, ast):
return ast
def alias(self, ast):
return ast
def aliased_property(self, ast):
return ast
def aliases(self, ast):
return ast
def cond_operator(self, ast):
return ast
def expression(self, ast):
return ast
def type(self, ast):
return ast
def types(self, ast):
return ast
def property_cond(self, ast):
return ast
def properties_cond(self, ast):
return ast
def elements(self, ast):
return ast
def aliased_elements(self, ast):
return ast
def graph_joint(self, ast):
return ast
def elt_joint(self, ast):
return ast
def backward(self, ast):
return ast
def forward(self, ast):
return ast
def cardinality(self, ast):
c1, c2, c3 = ast
return [
int(''.join(c1)),
c2,
int(''.join(c3))
]
def walk_mode(self, ast):
return ast
def base_joint(self, ast):
return ast
def node_joint(self, ast):
return ast
def relationship_joint(self, ast):
return ast
def joint(self, ast):
return ast
def path(self, ast):
return ast
def walkthrough_stmt(self, ast):
return ast
def walkthrough_stmts(self, ast):
return ast
def condition(self, ast):
return ast
def filter_stmt(self, ast):
return ast
def new_property(self, ast):
return ast
def new_properties(self, ast):
return ast
def new_cardinality(self, ast):
return int(''.join(ast))
def new_data(self, ast):
return ast
def create_stmt(self, ast):
return ast
def read_stmt(self, ast):
return ast
def update_keyword(self, ast):
return ast
def update_alias(self, ast):
return ast
def update_data(self, ast):
return ast
def update_stmt(self, ast):
return ast
def delete_stmt(self, ast):
return ast
def crud_stmt(self, ast):
return ast
def request(self, ast):
return ast
| 17.09816
| 38
| 0.554718
| 354
| 2,787
| 4.29096
| 0.183616
| 0.235023
| 0.427913
| 0.505596
| 0.741935
| 0.516787
| 0.034233
| 0
| 0
| 0
| 0
| 0.004447
| 0.354503
| 2,787
| 162
| 39
| 17.203704
| 0.839911
| 0.007535
| 0
| 0.453704
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.472222
| false
| 0
| 0
| 0.462963
| 0.953704
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
a63bec4d2e5fc3d4c63bd95852f5e73ffc03d800
| 10,194
|
py
|
Python
|
src/som/vmobjects/integer.py
|
SOM-st/RPySOM
|
2dcfc71786a3bd5be5a842c649645f71d6c35f89
|
[
"MIT"
] | 12
|
2016-01-07T14:20:57.000Z
|
2019-10-13T06:56:20.000Z
|
src/som/vmobjects/integer.py
|
SOM-st/RPySOM
|
2dcfc71786a3bd5be5a842c649645f71d6c35f89
|
[
"MIT"
] | 2
|
2016-05-26T06:53:33.000Z
|
2020-09-02T15:58:28.000Z
|
src/som/vmobjects/integer.py
|
SOM-st/RPySOM
|
2dcfc71786a3bd5be5a842c649645f71d6c35f89
|
[
"MIT"
] | 2
|
2016-05-25T06:07:52.000Z
|
2019-10-02T16:52:25.000Z
|
from rlib.arithmetic import ovfcheck, bigint_from_int, divrem, int_type
from rlib.llop import as_32_bit_signed_value, int_mod, Signed
from som.vmobjects.abstract_object import AbstractObject
from som.vm.globals import trueObject, falseObject
class Integer(AbstractObject):
_immutable_fields_ = ["_embedded_integer"]
def __init__(self, value):
AbstractObject.__init__(self)
assert isinstance(value, int_type), "Value: " + str(value)
self._embedded_integer = value
def get_embedded_integer(self):
return self._embedded_integer
def __str__(self):
return str(self._embedded_integer)
def get_class(self, universe):
return universe.integerClass
def quick_add(self, from_method, frame, interpreter, bytecode_index):
right = frame.top()
frame.pop()
frame.pop()
frame.push(self.prim_add(right))
def quick_multiply(self, from_method, frame, interpreter, bytecode_index):
right = frame.top()
frame.pop()
frame.pop()
frame.push(self.prim_multiply(right))
def quick_subtract(self, from_method, frame, interpreter, bytecode_index):
right = frame.top()
frame.pop()
frame.pop()
frame.push(self.prim_subtract(right))
def _to_double(self):
from .double import Double
return Double(float(self._embedded_integer))
def prim_less_than(self, right):
from .double import Double
from .biginteger import BigInteger
# Check second parameter type:
if isinstance(right, BigInteger):
result = bigint_from_int(self._embedded_integer).lt(
right.get_embedded_biginteger())
elif isinstance(right, Double):
return self._to_double().prim_less_than(right)
else:
result = self._embedded_integer < right.get_embedded_integer()
if result:
return trueObject
else:
return falseObject
def prim_less_than_or_equal(self, right):
from .double import Double
from .biginteger import BigInteger
# Check second parameter type:
if isinstance(right, BigInteger):
result = bigint_from_int(self._embedded_integer).le(
right.get_embedded_biginteger())
elif isinstance(right, Double):
return self._to_double().prim_less_than_or_equal(right)
else:
result = self._embedded_integer <= right.get_embedded_integer()
if result:
return trueObject
else:
return falseObject
def prim_greater_than(self, right):
from .double import Double
from .biginteger import BigInteger
# Check second parameter type:
if isinstance(right, BigInteger):
result = bigint_from_int(self._embedded_integer).gt(
right.get_embedded_biginteger())
elif isinstance(right, Double):
return self._to_double().prim_greater_than(right)
else:
result = self._embedded_integer > right.get_embedded_integer()
if result:
return trueObject
else:
return falseObject
def prim_as_string(self):
from .string import String
return String(str(self._embedded_integer))
def prim_abs(self):
return Integer(abs(self._embedded_integer))
def prim_as_32_bit_signed_value(self):
val = as_32_bit_signed_value(self._embedded_integer)
return Integer(val)
def prim_max(self, right):
from .biginteger import BigInteger
if isinstance(right, BigInteger):
left = bigint_from_int(self._embedded_integer)
if right.get_embedded_biginteger().gt(left):
return right
return self
assert isinstance(right, Integer)
if right.get_embedded_integer() > self._embedded_integer:
return right
return self
def prim_add(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
return BigInteger(
right.get_embedded_biginteger().add(
bigint_from_int(self._embedded_integer)))
elif isinstance(right, Double):
return self._to_double().prim_add(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
try:
result = ovfcheck(l + r)
return Integer(result)
except OverflowError:
return BigInteger(
bigint_from_int(l).add(bigint_from_int(r)))
def prim_subtract(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
r = bigint_from_int(self._embedded_integer).sub(
right.get_embedded_biginteger())
return BigInteger(r)
elif isinstance(right, Double):
return self._to_double().prim_subtract(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
try:
result = ovfcheck(l - r)
return Integer(result)
except OverflowError:
return BigInteger(
bigint_from_int(l).sub(bigint_from_int(r)))
def prim_multiply(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
r = bigint_from_int(self._embedded_integer).mul(
right.get_embedded_biginteger())
return BigInteger(r)
elif isinstance(right, Double):
return self._to_double().prim_multiply(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
try:
result = ovfcheck(l * r)
return Integer(result)
except OverflowError:
return BigInteger(
bigint_from_int(l).mul(bigint_from_int(r)))
def prim_double_div(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
r = bigint_from_int(self._embedded_integer).truediv(
right.get_embedded_biginteger())
return Double(r)
elif isinstance(right, Double):
return self._to_double().prim_double_div(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
return Double(l / float(r))
def prim_int_div(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
r = bigint_from_int(self._embedded_integer).floordiv(
right.get_embedded_biginteger())
return BigInteger(r)
elif isinstance(right, Double):
return self._to_double().prim_int_div(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
return Integer(l // r)
def prim_modulo(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
r = bigint_from_int(self._embedded_integer).mod(
right.get_embedded_biginteger())
return BigInteger(r)
elif isinstance(right, Double):
return self._to_double().prim_modulo(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
return Integer(l % r)
def prim_remainder(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
d, r = divrem(bigint_from_int(self._embedded_integer),
right.get_embedded_biginteger())
return BigInteger(r)
elif isinstance(right, Double):
return self._to_double().prim_remainder(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
return Integer(int_mod(Signed, l, r))
def prim_and(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
r = bigint_from_int(self._embedded_integer).and_(
right.get_embedded_biginteger())
return BigInteger(r)
elif isinstance(right, Double):
return self._to_double().prim_and(right)
else:
l = self._embedded_integer
r = right.get_embedded_integer()
return Integer(l & r)
def prim_equals(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
result = bigint_from_int(self._embedded_integer).eq(
right.get_embedded_biginteger())
elif isinstance(right, Double):
result = self._embedded_integer == right.get_embedded_double()
elif isinstance(right, Integer):
l = self._embedded_integer
r = right.get_embedded_integer()
result = l == r
else:
return falseObject
if result:
return trueObject
else:
return falseObject
def prim_unequals(self, right):
from .double import Double
from .biginteger import BigInteger
if isinstance(right, BigInteger):
result = bigint_from_int(self._embedded_integer).ne(
right.get_embedded_biginteger())
elif isinstance(right, Double):
result = self._embedded_integer != right.get_embedded_double()
elif isinstance(right, Integer):
l = self._embedded_integer
r = right.get_embedded_integer()
result = l != r
else:
return trueObject
if result:
return trueObject
else:
return falseObject
| 34.439189
| 78
| 0.609869
| 1,101
| 10,194
| 5.380563
| 0.087193
| 0.1342
| 0.11867
| 0.051992
| 0.812289
| 0.774983
| 0.740716
| 0.73312
| 0.723498
| 0.706786
| 0
| 0.000856
| 0.312733
| 10,194
| 295
| 79
| 34.555932
| 0.844705
| 0.008436
| 0
| 0.624
| 0
| 0
| 0.002375
| 0
| 0
| 0
| 0
| 0
| 0.008
| 1
| 0.1
| false
| 0
| 0.132
| 0.016
| 0.452
| 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
|
a64e55255ef5b0432f8a3e943cb9deb99cf1d429
| 3,771
|
py
|
Python
|
tests/test_g711.py
|
IMRSVDataLabs/aiortc
|
8e601d7c72056c927f2ecbb29c94fcfd6575d13c
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_g711.py
|
IMRSVDataLabs/aiortc
|
8e601d7c72056c927f2ecbb29c94fcfd6575d13c
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_g711.py
|
IMRSVDataLabs/aiortc
|
8e601d7c72056c927f2ecbb29c94fcfd6575d13c
|
[
"BSD-3-Clause"
] | null | null | null |
from aiortc.codecs import PCMA_CODEC, PCMU_CODEC, get_decoder, get_encoder
from aiortc.codecs.g711 import (PcmaDecoder, PcmaEncoder, PcmuDecoder,
PcmuEncoder)
from aiortc.jitterbuffer import JitterFrame
from aiortc.mediastreams import AudioFrame
from .codecs import CodecTestCase
class PcmaTest(CodecTestCase):
def test_decoder(self):
decoder = get_decoder(PCMA_CODEC)
self.assertTrue(isinstance(decoder, PcmaDecoder))
frames = decoder.decode(JitterFrame(data=b'\xd5' * 160, timestamp=0))
self.assertEqual(len(frames), 1)
frame = frames[0]
self.assertEqual(frame.channels, 1)
self.assertEqual(frame.data, b'\x08\x00' * 160)
self.assertEqual(frame.sample_rate, 8000)
self.assertEqual(frame.timestamp, 0)
def test_encoder_mono_8hz(self):
encoder = get_encoder(PCMA_CODEC)
self.assertTrue(isinstance(encoder, PcmaEncoder))
frame = AudioFrame(
channels=1,
data=b'\x00\x00' * 160,
sample_rate=8000,
timestamp=0)
data = encoder.encode(frame)
self.assertEqual(data, b'\xd5' * 160)
def test_encoder_stereo_8khz(self):
encoder = get_encoder(PCMA_CODEC)
self.assertTrue(isinstance(encoder, PcmaEncoder))
frame = AudioFrame(
channels=2,
data=b'\x00\x00' * 2 * 160,
sample_rate=8000,
timestamp=0)
data = encoder.encode(frame)
self.assertEqual(data, b'\xd5' * 160)
def test_encoder_stereo_48khz(self):
encoder = get_encoder(PCMA_CODEC)
self.assertTrue(isinstance(encoder, PcmaEncoder))
frame = AudioFrame(
channels=2,
data=b'\x00\x00' * 2 * 960,
sample_rate=48000,
timestamp=0)
data = encoder.encode(frame)
self.assertEqual(data, b'\xd5' * 160)
def test_roundtrip(self):
self.roundtrip_audio(PCMA_CODEC, output_channels=1, output_sample_rate=8000)
class PcmuTest(CodecTestCase):
def test_decoder(self):
decoder = get_decoder(PCMU_CODEC)
self.assertTrue(isinstance(decoder, PcmuDecoder))
frames = decoder.decode(JitterFrame(data=b'\xff' * 160, timestamp=0))
self.assertEqual(len(frames), 1)
frame = frames[0]
self.assertEqual(frame.channels, 1)
self.assertEqual(frame.data, b'\x00\x00' * 160)
self.assertEqual(frame.sample_rate, 8000)
self.assertEqual(frame.timestamp, 0)
def test_encoder_mono_8hz(self):
encoder = get_encoder(PCMU_CODEC)
self.assertTrue(isinstance(encoder, PcmuEncoder))
frame = AudioFrame(
channels=1,
data=b'\x00\x00' * 160,
sample_rate=8000,
timestamp=0)
data = encoder.encode(frame)
self.assertEqual(data, b'\xff' * 160)
def test_encoder_stereo_8khz(self):
encoder = get_encoder(PCMU_CODEC)
self.assertTrue(isinstance(encoder, PcmuEncoder))
frame = AudioFrame(
channels=2,
data=b'\x00\x00' * 2 * 160,
sample_rate=8000,
timestamp=0)
data = encoder.encode(frame)
self.assertEqual(data, b'\xff' * 160)
def test_encoder_stereo_48khz(self):
encoder = get_encoder(PCMU_CODEC)
self.assertTrue(isinstance(encoder, PcmuEncoder))
frame = AudioFrame(
channels=2,
data=b'\x00\x00' * 2 * 960,
sample_rate=48000,
timestamp=0)
data = encoder.encode(frame)
self.assertEqual(data, b'\xff' * 160)
def test_roundtrip(self):
self.roundtrip_audio(PCMU_CODEC, output_channels=1, output_sample_rate=8000)
| 33.078947
| 84
| 0.623707
| 429
| 3,771
| 5.342657
| 0.137529
| 0.034904
| 0.066318
| 0.101222
| 0.868674
| 0.832461
| 0.80192
| 0.80192
| 0.69808
| 0.69808
| 0
| 0.060044
| 0.271281
| 3,771
| 113
| 85
| 33.371681
| 0.774017
| 0
| 0
| 0.804348
| 0
| 0
| 0.025457
| 0
| 0
| 0
| 0
| 0
| 0.26087
| 1
| 0.108696
| false
| 0
| 0.054348
| 0
| 0.184783
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
a6b63418236241ae79205d5821850877d6ce5dc4
| 84
|
py
|
Python
|
skrl/agents/torch/dqn/__init__.py
|
simonbogh/skrl
|
4ca483c7d24fdf020bdbd9422927ef5737c9dec4
|
[
"MIT"
] | 43
|
2021-12-19T07:47:43.000Z
|
2022-03-31T05:24:42.000Z
|
skrl/agents/torch/dqn/__init__.py
|
simonbogh/skrl
|
4ca483c7d24fdf020bdbd9422927ef5737c9dec4
|
[
"MIT"
] | 5
|
2022-01-05T07:54:13.000Z
|
2022-03-08T21:00:39.000Z
|
skrl/agents/torch/dqn/__init__.py
|
simonbogh/skrl
|
4ca483c7d24fdf020bdbd9422927ef5737c9dec4
|
[
"MIT"
] | 1
|
2022-01-31T17:53:52.000Z
|
2022-01-31T17:53:52.000Z
|
from .dqn import DQN, DQN_DEFAULT_CONFIG
from .ddqn import DDQN, DDQN_DEFAULT_CONFIG
| 42
| 43
| 0.845238
| 14
| 84
| 4.785714
| 0.428571
| 0.38806
| 0
| 0
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| 0
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| 0
| 0.107143
| 84
| 2
| 43
| 42
| 0.893333
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| 1
| 0
| true
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
a6df4c8e7b5163f1308039aa89ecffec01853e74
| 588,082
|
py
|
Python
|
UI/resource_rc.py
|
aneopsy/PokerStats
|
7d0c72f2c0ed20ccb76b31bda560cb2e88465837
|
[
"CC0-1.0"
] | 13
|
2016-10-21T20:28:22.000Z
|
2022-01-21T03:52:59.000Z
|
UI/resource_rc.py
|
aneopsy/PokerStats
|
7d0c72f2c0ed20ccb76b31bda560cb2e88465837
|
[
"CC0-1.0"
] | null | null | null |
UI/resource_rc.py
|
aneopsy/PokerStats
|
7d0c72f2c0ed20ccb76b31bda560cb2e88465837
|
[
"CC0-1.0"
] | 6
|
2018-11-10T16:10:56.000Z
|
2022-01-14T10:11:40.000Z
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.4.1)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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\x89\
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\x00\x01\xb5\x00\x00\x00\xba\x08\x06\x00\x00\x00\x29\x8b\xd3\xbc\
\x00\x00\x00\x09\x70\x48\x59\x73\x00\x00\x0b\x13\x00\x00\x0b\x13\
\x01\x00\x9a\x9c\x18\x00\x00\x0a\x4f\x69\x43\x43\x50\x50\x68\x6f\
\x74\x6f\x73\x68\x6f\x70\x20\x49\x43\x43\x20\x70\x72\x6f\x66\x69\
\x6c\x65\x00\x00\x78\xda\x9d\x53\x67\x54\x53\xe9\x16\x3d\xf7\xde\
\xf4\x42\x4b\x88\x80\x94\x4b\x6f\x52\x15\x08\x20\x52\x42\x8b\x80\
\x14\x91\x26\x2a\x21\x09\x10\x4a\x88\x21\xa1\xd9\x15\x51\xc1\x11\
\x45\x45\x04\x1b\xc8\xa0\x88\x03\x8e\x8e\x80\x8c\x15\x51\x2c\x0c\
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\xca\xff\x06\x2b\x90\xb4\xb2\
"
qt_resource_name = b"\
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\x05\xe2\x59\x27\
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\x00\x6f\x00\x67\x00\x6f\x00\x2e\x00\x70\x00\x6e\x00\x67\
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\x00\x61\x00\x75\x00\x73\x00\x65\x00\x2d\x00\x69\x00\x63\x00\x6f\x00\x6e\x00\x2e\x00\x73\x00\x76\x00\x67\
\x00\x0d\
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\x00\x73\
\x00\x74\x00\x6f\x00\x70\x00\x2d\x00\x69\x00\x63\x00\x6f\x00\x6e\x00\x2e\x00\x70\x00\x6e\x00\x67\
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\x00\x69\
\x00\x63\x00\x6f\x00\x6e\x00\x2e\x00\x69\x00\x63\x00\x6f\
"
qt_resource_struct = b"\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x05\x00\x00\x00\x01\
\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\
\x00\x00\x00\x7c\x00\x01\x00\x00\x00\x01\x00\x00\x96\x38\
\x00\x00\x00\x3a\x00\x00\x00\x00\x00\x01\x00\x00\x92\x80\
\x00\x00\x00\x5c\x00\x01\x00\x00\x00\x01\x00\x00\x94\xdb\
\x00\x00\x00\x16\x00\x00\x00\x00\x00\x01\x00\x00\x90\x31\
"
def qInitResources():
QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
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0
| 6
|
a6e84f7fc5fcc8c5153339a27cedc2a1dafe625e
| 35
|
py
|
Python
|
exceptions/__init__.py
|
edchelstephens/django-rest-utils
|
15cee427149217d1e53384281894f91e9653b6b4
|
[
"BSD-3-Clause"
] | 1
|
2022-02-20T01:37:25.000Z
|
2022-02-20T01:37:25.000Z
|
exceptions/__init__.py
|
edchelstephens/django-rest-utils
|
15cee427149217d1e53384281894f91e9653b6b4
|
[
"BSD-3-Clause"
] | null | null | null |
exceptions/__init__.py
|
edchelstephens/django-rest-utils
|
15cee427149217d1e53384281894f91e9653b6b4
|
[
"BSD-3-Clause"
] | null | null | null |
from exceptions.exceptions import *
| 35
| 35
| 0.857143
| 4
| 35
| 7.5
| 0.75
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| 1
| 0
|
0
| 6
|
a6f782d32c9a728eb128673b26bdb75ad45a4c4a
| 51
|
py
|
Python
|
accelbyte_py_sdk/services/auth/__init__.py
|
encyphered/accelbyte-python-sdk
|
09c1e989d7251de308150fdcd3119d662ca2d205
|
[
"MIT"
] | null | null | null |
accelbyte_py_sdk/services/auth/__init__.py
|
encyphered/accelbyte-python-sdk
|
09c1e989d7251de308150fdcd3119d662ca2d205
|
[
"MIT"
] | null | null | null |
accelbyte_py_sdk/services/auth/__init__.py
|
encyphered/accelbyte-python-sdk
|
09c1e989d7251de308150fdcd3119d662ca2d205
|
[
"MIT"
] | null | null | null |
from ._auth import login
from ._auth import logout
| 17
| 25
| 0.803922
| 8
| 51
| 4.875
| 0.625
| 0.410256
| 0.717949
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| 51
| 2
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|
0
| 6
|
5b5157776e94f524cf6bc306dcbc88669dd3312c
| 259,519
|
py
|
Python
|
instances/passenger_demand/pas-20210422-1717-int6000000000000001e-1/75.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-int6000000000000001e-1/75.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-int6000000000000001e-1/75.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 11416
passenger_arriving = (
(2, 4, 1, 3, 2, 0, 2, 2, 1, 1, 1, 0, 0, 1, 2, 2, 1, 3, 2, 2, 0, 2, 1, 1, 1, 0), # 0
(4, 1, 4, 4, 1, 1, 1, 2, 1, 1, 1, 0, 0, 4, 4, 3, 3, 6, 0, 1, 1, 0, 1, 0, 1, 0), # 1
(2, 3, 3, 0, 2, 0, 2, 1, 1, 0, 0, 0, 0, 1, 7, 3, 5, 1, 2, 3, 0, 2, 0, 0, 0, 0), # 2
(2, 4, 2, 4, 2, 1, 3, 2, 3, 0, 0, 0, 0, 3, 5, 3, 4, 4, 2, 2, 2, 2, 0, 0, 0, 0), # 3
(5, 3, 8, 3, 2, 0, 1, 2, 4, 1, 2, 0, 0, 2, 6, 1, 2, 4, 2, 2, 1, 1, 2, 0, 0, 0), # 4
(4, 2, 3, 4, 2, 1, 1, 3, 2, 1, 1, 1, 0, 1, 5, 1, 1, 2, 3, 1, 2, 4, 2, 1, 1, 0), # 5
(6, 1, 4, 4, 2, 1, 2, 3, 0, 0, 2, 0, 0, 8, 5, 3, 2, 5, 2, 3, 1, 2, 0, 0, 0, 0), # 6
(4, 5, 4, 4, 4, 2, 3, 0, 2, 0, 1, 0, 0, 6, 4, 2, 4, 4, 4, 1, 2, 1, 1, 1, 2, 0), # 7
(3, 6, 1, 5, 0, 0, 1, 1, 1, 0, 0, 1, 0, 7, 4, 2, 9, 4, 1, 6, 1, 2, 1, 0, 0, 0), # 8
(7, 7, 2, 4, 3, 2, 3, 4, 1, 2, 0, 1, 0, 4, 4, 3, 5, 4, 4, 1, 1, 0, 2, 2, 0, 0), # 9
(4, 8, 5, 2, 1, 1, 3, 3, 2, 1, 0, 1, 0, 2, 2, 4, 1, 4, 4, 3, 2, 2, 0, 0, 0, 0), # 10
(5, 7, 7, 2, 3, 2, 2, 2, 1, 1, 1, 0, 0, 5, 6, 4, 5, 7, 1, 0, 1, 2, 1, 1, 1, 0), # 11
(4, 5, 7, 1, 7, 1, 2, 2, 1, 0, 0, 1, 0, 5, 2, 4, 2, 2, 4, 3, 1, 1, 1, 1, 0, 0), # 12
(5, 6, 7, 4, 4, 1, 3, 4, 4, 3, 0, 0, 0, 0, 7, 3, 2, 6, 4, 4, 5, 2, 5, 0, 1, 0), # 13
(10, 6, 9, 3, 7, 0, 4, 1, 0, 1, 1, 1, 0, 3, 2, 4, 2, 4, 4, 1, 1, 2, 0, 1, 0, 0), # 14
(5, 7, 3, 6, 6, 1, 0, 2, 2, 1, 1, 0, 0, 1, 5, 2, 3, 2, 4, 2, 1, 1, 2, 0, 1, 0), # 15
(3, 4, 4, 3, 5, 1, 1, 1, 2, 1, 0, 0, 0, 4, 6, 4, 3, 7, 4, 2, 2, 1, 1, 2, 0, 0), # 16
(5, 3, 5, 5, 4, 0, 4, 0, 5, 1, 2, 0, 0, 3, 5, 3, 2, 2, 3, 3, 0, 1, 4, 0, 1, 0), # 17
(6, 10, 5, 3, 4, 4, 6, 2, 0, 1, 0, 0, 0, 2, 5, 4, 4, 0, 7, 3, 0, 3, 2, 0, 0, 0), # 18
(6, 6, 4, 6, 4, 1, 0, 1, 2, 1, 1, 0, 0, 6, 6, 6, 4, 2, 4, 2, 2, 3, 2, 0, 1, 0), # 19
(7, 9, 3, 6, 6, 4, 2, 1, 4, 1, 2, 0, 0, 7, 2, 8, 6, 4, 2, 2, 0, 2, 2, 0, 1, 0), # 20
(5, 5, 4, 5, 8, 1, 1, 5, 2, 2, 2, 0, 0, 10, 4, 1, 4, 5, 2, 5, 4, 5, 0, 0, 0, 0), # 21
(5, 11, 7, 3, 3, 2, 6, 3, 1, 0, 0, 0, 0, 7, 6, 1, 5, 4, 3, 2, 0, 2, 2, 1, 0, 0), # 22
(5, 4, 5, 5, 5, 0, 2, 2, 3, 0, 2, 1, 0, 1, 3, 4, 7, 4, 4, 4, 1, 2, 1, 1, 0, 0), # 23
(3, 5, 4, 5, 2, 4, 1, 3, 4, 1, 0, 1, 0, 8, 9, 6, 3, 5, 0, 4, 1, 3, 2, 3, 0, 0), # 24
(9, 7, 4, 5, 4, 3, 1, 1, 0, 2, 0, 0, 0, 5, 10, 4, 1, 6, 2, 0, 2, 0, 2, 0, 0, 0), # 25
(9, 5, 6, 1, 2, 2, 3, 2, 0, 2, 0, 1, 0, 7, 2, 3, 5, 4, 0, 1, 3, 2, 2, 1, 0, 0), # 26
(9, 4, 1, 5, 5, 2, 3, 2, 3, 0, 1, 1, 0, 8, 5, 7, 7, 5, 3, 2, 0, 5, 3, 1, 0, 0), # 27
(11, 6, 4, 8, 4, 3, 4, 2, 3, 3, 0, 0, 0, 6, 11, 3, 1, 3, 1, 0, 1, 2, 3, 0, 2, 0), # 28
(5, 7, 5, 6, 5, 2, 3, 3, 2, 0, 1, 0, 0, 7, 7, 1, 6, 7, 2, 0, 3, 4, 4, 0, 0, 0), # 29
(3, 7, 3, 10, 3, 1, 5, 1, 2, 0, 0, 1, 0, 5, 6, 4, 6, 5, 4, 3, 1, 4, 1, 1, 1, 0), # 30
(7, 5, 8, 5, 5, 4, 1, 1, 0, 0, 2, 0, 0, 4, 8, 10, 2, 6, 4, 2, 1, 4, 0, 0, 0, 0), # 31
(6, 4, 7, 2, 5, 2, 1, 3, 2, 0, 1, 1, 0, 6, 6, 2, 4, 3, 2, 3, 2, 1, 1, 1, 1, 0), # 32
(4, 7, 5, 7, 5, 0, 3, 1, 3, 0, 2, 1, 0, 4, 5, 7, 2, 6, 2, 2, 0, 1, 5, 0, 0, 0), # 33
(8, 9, 7, 6, 2, 0, 3, 3, 3, 2, 0, 0, 0, 9, 3, 3, 2, 1, 2, 3, 3, 2, 1, 0, 0, 0), # 34
(3, 4, 5, 6, 2, 2, 2, 3, 1, 2, 2, 0, 0, 6, 3, 5, 4, 6, 4, 4, 0, 4, 0, 2, 2, 0), # 35
(6, 10, 7, 9, 6, 2, 2, 4, 2, 2, 0, 0, 0, 4, 6, 4, 2, 7, 6, 4, 1, 1, 0, 0, 1, 0), # 36
(9, 10, 3, 6, 1, 2, 2, 4, 3, 3, 0, 1, 0, 5, 4, 5, 6, 6, 2, 2, 0, 1, 3, 0, 1, 0), # 37
(0, 6, 5, 2, 9, 0, 1, 1, 5, 1, 0, 0, 0, 4, 5, 5, 6, 4, 3, 5, 2, 4, 0, 0, 1, 0), # 38
(6, 6, 4, 4, 6, 3, 3, 3, 2, 3, 2, 1, 0, 9, 5, 4, 0, 3, 3, 0, 2, 0, 0, 1, 0, 0), # 39
(9, 4, 9, 4, 6, 3, 1, 3, 1, 1, 1, 0, 0, 7, 3, 3, 2, 5, 3, 4, 2, 3, 4, 0, 2, 0), # 40
(2, 3, 4, 5, 2, 3, 3, 2, 1, 1, 1, 1, 0, 4, 3, 2, 3, 6, 6, 4, 3, 5, 1, 2, 0, 0), # 41
(5, 6, 6, 5, 2, 0, 2, 1, 2, 1, 1, 0, 0, 6, 5, 4, 2, 7, 2, 2, 0, 1, 0, 0, 2, 0), # 42
(8, 5, 6, 3, 6, 2, 1, 2, 3, 1, 1, 1, 0, 6, 6, 1, 5, 4, 2, 2, 2, 2, 0, 0, 1, 0), # 43
(7, 4, 5, 12, 1, 3, 2, 3, 3, 1, 2, 0, 0, 6, 5, 3, 2, 3, 2, 2, 1, 5, 3, 3, 1, 0), # 44
(4, 8, 6, 5, 2, 1, 2, 2, 2, 1, 1, 0, 0, 3, 7, 3, 5, 2, 6, 1, 1, 3, 1, 0, 0, 0), # 45
(5, 5, 2, 2, 2, 3, 4, 2, 3, 0, 0, 0, 0, 4, 2, 3, 1, 6, 3, 5, 1, 0, 2, 1, 1, 0), # 46
(6, 8, 5, 5, 3, 3, 1, 2, 3, 4, 1, 0, 0, 7, 5, 7, 2, 3, 1, 3, 0, 5, 1, 2, 0, 0), # 47
(8, 7, 6, 9, 11, 1, 0, 4, 2, 1, 1, 0, 0, 3, 6, 3, 2, 4, 3, 2, 3, 4, 2, 1, 0, 0), # 48
(6, 10, 1, 7, 2, 2, 2, 0, 2, 0, 3, 0, 0, 6, 6, 6, 7, 4, 2, 2, 1, 4, 1, 0, 0, 0), # 49
(5, 3, 5, 9, 6, 1, 0, 1, 1, 1, 0, 0, 0, 4, 5, 1, 4, 5, 2, 1, 1, 1, 3, 0, 1, 0), # 50
(5, 4, 4, 5, 2, 4, 4, 3, 4, 1, 1, 2, 0, 7, 0, 7, 3, 6, 2, 1, 1, 1, 2, 0, 0, 0), # 51
(4, 3, 3, 7, 3, 3, 0, 3, 4, 2, 0, 0, 0, 4, 8, 3, 0, 5, 0, 4, 2, 1, 3, 3, 0, 0), # 52
(5, 3, 6, 4, 4, 4, 2, 4, 0, 1, 1, 0, 0, 3, 5, 4, 4, 6, 4, 3, 4, 4, 4, 0, 1, 0), # 53
(7, 5, 7, 6, 7, 1, 0, 1, 1, 0, 1, 1, 0, 5, 5, 3, 7, 5, 1, 3, 1, 3, 1, 0, 1, 0), # 54
(9, 7, 6, 7, 1, 3, 2, 3, 4, 2, 1, 0, 0, 3, 3, 2, 4, 5, 0, 4, 2, 4, 2, 1, 2, 0), # 55
(5, 10, 5, 12, 0, 2, 0, 0, 0, 3, 0, 0, 0, 5, 5, 4, 9, 4, 4, 4, 4, 1, 0, 0, 0, 0), # 56
(3, 3, 5, 7, 8, 3, 2, 1, 1, 1, 3, 0, 0, 10, 4, 3, 5, 9, 4, 1, 2, 5, 2, 1, 1, 0), # 57
(7, 7, 6, 3, 7, 2, 2, 4, 5, 2, 1, 0, 0, 7, 4, 5, 5, 5, 1, 2, 1, 2, 3, 1, 0, 0), # 58
(5, 7, 3, 8, 5, 0, 4, 3, 1, 1, 0, 0, 0, 6, 4, 6, 7, 5, 2, 2, 0, 3, 2, 0, 0, 0), # 59
(6, 5, 10, 4, 3, 1, 4, 1, 4, 0, 1, 0, 0, 4, 7, 5, 5, 3, 1, 2, 1, 2, 2, 4, 0, 0), # 60
(6, 8, 5, 5, 2, 2, 1, 0, 2, 1, 0, 3, 0, 7, 3, 3, 1, 5, 6, 1, 2, 4, 2, 5, 0, 0), # 61
(8, 5, 3, 4, 7, 0, 3, 3, 0, 0, 1, 0, 0, 8, 9, 2, 3, 7, 3, 1, 1, 1, 2, 1, 0, 0), # 62
(7, 5, 4, 8, 3, 1, 3, 2, 6, 1, 1, 0, 0, 6, 5, 3, 6, 6, 3, 3, 3, 1, 0, 1, 1, 0), # 63
(7, 4, 3, 10, 5, 2, 2, 2, 5, 1, 3, 0, 0, 10, 4, 5, 2, 3, 3, 1, 2, 3, 4, 2, 1, 0), # 64
(6, 7, 8, 3, 1, 1, 5, 3, 4, 1, 1, 2, 0, 9, 4, 6, 3, 3, 0, 1, 1, 2, 5, 0, 0, 0), # 65
(3, 3, 9, 11, 1, 3, 0, 0, 0, 2, 0, 0, 0, 7, 5, 5, 3, 6, 1, 5, 1, 3, 2, 0, 0, 0), # 66
(5, 5, 4, 7, 3, 2, 2, 1, 5, 1, 1, 1, 0, 8, 3, 7, 3, 5, 2, 3, 4, 0, 2, 0, 1, 0), # 67
(6, 4, 7, 8, 3, 2, 3, 0, 2, 2, 3, 1, 0, 7, 8, 1, 2, 4, 1, 1, 0, 0, 2, 0, 0, 0), # 68
(7, 5, 5, 7, 6, 1, 2, 4, 2, 2, 1, 0, 0, 7, 5, 3, 2, 6, 4, 3, 1, 2, 1, 1, 0, 0), # 69
(8, 8, 2, 1, 7, 3, 3, 1, 5, 0, 2, 0, 0, 6, 4, 5, 1, 2, 2, 0, 1, 3, 3, 0, 0, 0), # 70
(3, 5, 3, 10, 6, 3, 2, 0, 3, 0, 0, 1, 0, 11, 5, 6, 3, 5, 1, 2, 3, 3, 1, 0, 0, 0), # 71
(4, 9, 2, 3, 6, 3, 0, 5, 1, 0, 0, 0, 0, 9, 1, 4, 6, 4, 2, 1, 0, 1, 0, 1, 0, 0), # 72
(4, 7, 2, 5, 3, 6, 1, 1, 1, 3, 2, 0, 0, 5, 5, 3, 3, 7, 4, 2, 0, 1, 0, 0, 0, 0), # 73
(7, 1, 6, 2, 6, 1, 1, 2, 4, 1, 0, 0, 0, 3, 3, 2, 4, 4, 4, 4, 0, 3, 1, 1, 0, 0), # 74
(7, 6, 7, 10, 3, 1, 1, 2, 2, 1, 0, 1, 0, 5, 8, 4, 2, 3, 1, 1, 0, 4, 0, 2, 1, 0), # 75
(9, 12, 4, 2, 3, 0, 2, 2, 4, 2, 0, 0, 0, 9, 0, 5, 2, 5, 0, 3, 1, 2, 3, 1, 2, 0), # 76
(9, 7, 8, 6, 9, 1, 2, 4, 0, 0, 1, 0, 0, 8, 3, 2, 5, 3, 1, 1, 3, 3, 1, 0, 0, 0), # 77
(2, 9, 6, 3, 4, 1, 2, 3, 3, 2, 0, 0, 0, 7, 6, 3, 2, 4, 2, 1, 0, 3, 3, 1, 0, 0), # 78
(7, 3, 3, 5, 4, 2, 1, 1, 3, 2, 1, 0, 0, 2, 6, 3, 2, 3, 2, 5, 2, 0, 2, 1, 2, 0), # 79
(5, 3, 5, 3, 1, 3, 2, 3, 2, 1, 1, 0, 0, 6, 5, 3, 1, 6, 4, 3, 2, 0, 1, 3, 0, 0), # 80
(11, 4, 3, 3, 2, 2, 0, 0, 0, 0, 1, 1, 0, 5, 5, 3, 5, 6, 4, 0, 0, 1, 0, 0, 0, 0), # 81
(4, 8, 3, 7, 4, 2, 2, 4, 6, 1, 2, 0, 0, 3, 4, 10, 3, 5, 1, 2, 3, 1, 0, 0, 1, 0), # 82
(5, 5, 5, 4, 5, 2, 0, 1, 2, 0, 2, 0, 0, 3, 6, 4, 7, 1, 1, 2, 3, 1, 0, 1, 1, 0), # 83
(5, 5, 4, 9, 3, 1, 1, 3, 5, 3, 3, 1, 0, 3, 6, 5, 2, 3, 1, 1, 1, 3, 1, 1, 1, 0), # 84
(5, 5, 6, 2, 1, 0, 1, 1, 2, 1, 0, 0, 0, 6, 8, 2, 2, 4, 1, 0, 2, 3, 1, 1, 1, 0), # 85
(6, 1, 4, 8, 1, 0, 1, 1, 0, 2, 0, 1, 0, 5, 5, 2, 4, 5, 3, 6, 2, 2, 1, 1, 1, 0), # 86
(6, 3, 5, 6, 2, 1, 3, 3, 2, 1, 1, 1, 0, 3, 3, 4, 2, 5, 0, 1, 2, 1, 1, 1, 0, 0), # 87
(7, 7, 9, 10, 3, 4, 4, 1, 10, 1, 0, 1, 0, 8, 11, 4, 4, 3, 4, 3, 1, 2, 1, 0, 0, 0), # 88
(5, 3, 3, 9, 4, 1, 2, 2, 2, 2, 1, 0, 0, 10, 3, 5, 2, 6, 6, 3, 1, 2, 2, 2, 1, 0), # 89
(9, 5, 6, 4, 2, 2, 0, 2, 3, 1, 1, 1, 0, 5, 6, 2, 2, 5, 0, 0, 1, 2, 3, 2, 0, 0), # 90
(6, 2, 8, 4, 2, 3, 4, 0, 1, 0, 0, 1, 0, 7, 5, 3, 2, 3, 4, 4, 3, 5, 0, 2, 1, 0), # 91
(11, 4, 5, 5, 6, 1, 3, 5, 3, 0, 1, 0, 0, 6, 6, 5, 3, 5, 3, 4, 1, 0, 2, 2, 0, 0), # 92
(4, 10, 2, 4, 4, 2, 2, 1, 4, 2, 1, 0, 0, 7, 4, 1, 0, 1, 0, 0, 0, 4, 1, 0, 0, 0), # 93
(8, 3, 6, 4, 1, 1, 5, 5, 0, 0, 0, 0, 0, 9, 4, 7, 5, 5, 2, 0, 3, 3, 3, 2, 1, 0), # 94
(7, 7, 8, 5, 5, 1, 1, 0, 3, 0, 1, 0, 0, 6, 3, 1, 3, 2, 4, 1, 1, 3, 1, 0, 0, 0), # 95
(10, 2, 6, 6, 6, 3, 4, 1, 1, 1, 0, 2, 0, 5, 6, 6, 1, 5, 0, 3, 1, 2, 3, 0, 0, 0), # 96
(8, 5, 5, 6, 7, 4, 3, 3, 4, 2, 2, 0, 0, 7, 2, 7, 0, 10, 2, 3, 2, 2, 4, 2, 0, 0), # 97
(3, 3, 3, 8, 4, 3, 2, 0, 0, 0, 0, 0, 0, 4, 3, 0, 2, 1, 1, 2, 1, 4, 2, 0, 0, 0), # 98
(6, 3, 3, 3, 8, 1, 3, 3, 3, 0, 0, 1, 0, 7, 1, 1, 4, 4, 1, 2, 0, 2, 2, 0, 0, 0), # 99
(6, 3, 4, 6, 3, 3, 1, 1, 0, 1, 2, 0, 0, 5, 5, 4, 3, 3, 3, 2, 0, 2, 4, 1, 1, 0), # 100
(3, 2, 2, 9, 9, 3, 0, 1, 0, 2, 1, 0, 0, 8, 1, 4, 4, 2, 0, 4, 2, 2, 0, 0, 0, 0), # 101
(5, 2, 3, 5, 5, 5, 2, 2, 0, 2, 1, 2, 0, 6, 3, 2, 3, 6, 3, 3, 3, 3, 3, 3, 0, 0), # 102
(6, 6, 4, 3, 4, 2, 0, 2, 3, 0, 2, 0, 0, 8, 9, 1, 4, 2, 1, 6, 1, 0, 0, 1, 0, 0), # 103
(8, 5, 3, 4, 7, 3, 3, 1, 3, 1, 0, 0, 0, 7, 4, 7, 3, 5, 2, 0, 1, 4, 3, 0, 1, 0), # 104
(5, 5, 8, 2, 4, 1, 2, 2, 1, 0, 0, 0, 0, 10, 4, 2, 3, 5, 3, 0, 0, 4, 1, 0, 0, 0), # 105
(7, 6, 4, 6, 3, 4, 0, 4, 4, 0, 0, 0, 0, 6, 9, 4, 0, 3, 2, 4, 1, 0, 2, 2, 0, 0), # 106
(5, 4, 6, 9, 9, 0, 0, 3, 6, 1, 1, 1, 0, 4, 5, 3, 2, 0, 0, 3, 1, 1, 6, 1, 1, 0), # 107
(8, 3, 8, 4, 2, 1, 1, 1, 3, 1, 0, 0, 0, 6, 3, 5, 3, 9, 2, 2, 1, 1, 4, 0, 2, 0), # 108
(6, 1, 6, 7, 7, 5, 2, 0, 2, 1, 1, 1, 0, 5, 7, 3, 0, 2, 3, 2, 1, 2, 1, 1, 0, 0), # 109
(4, 7, 6, 2, 4, 0, 3, 1, 5, 0, 1, 0, 0, 7, 5, 2, 0, 5, 0, 5, 0, 2, 3, 0, 0, 0), # 110
(9, 7, 3, 5, 9, 5, 2, 4, 3, 1, 0, 1, 0, 4, 2, 6, 4, 7, 4, 0, 4, 4, 3, 0, 0, 0), # 111
(5, 6, 7, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 8, 6, 6, 4, 1, 1, 2, 2, 3, 4, 2, 1, 0), # 112
(7, 5, 1, 5, 6, 2, 2, 4, 3, 2, 1, 0, 0, 7, 4, 7, 3, 3, 1, 4, 0, 1, 0, 0, 0, 0), # 113
(7, 5, 4, 5, 6, 2, 5, 1, 5, 0, 0, 2, 0, 3, 4, 3, 1, 5, 0, 2, 3, 3, 1, 0, 0, 0), # 114
(5, 2, 3, 5, 5, 3, 2, 2, 1, 1, 0, 0, 0, 7, 2, 4, 3, 3, 2, 4, 6, 1, 4, 0, 0, 0), # 115
(12, 7, 5, 4, 3, 0, 3, 0, 0, 1, 0, 0, 0, 4, 0, 2, 5, 1, 2, 3, 1, 2, 1, 1, 0, 0), # 116
(3, 5, 5, 6, 2, 4, 3, 2, 2, 0, 0, 1, 0, 3, 10, 2, 2, 2, 5, 2, 5, 4, 1, 2, 0, 0), # 117
(2, 0, 6, 2, 6, 2, 3, 2, 5, 0, 0, 1, 0, 2, 5, 6, 2, 2, 2, 0, 1, 1, 1, 2, 1, 0), # 118
(8, 4, 2, 7, 6, 0, 2, 1, 2, 0, 1, 1, 0, 8, 2, 5, 2, 4, 6, 3, 2, 3, 2, 1, 0, 0), # 119
(5, 3, 6, 5, 1, 2, 1, 1, 0, 0, 1, 1, 0, 3, 6, 3, 2, 7, 2, 1, 2, 0, 6, 0, 0, 0), # 120
(2, 3, 8, 5, 6, 0, 1, 1, 0, 3, 1, 0, 0, 6, 5, 1, 1, 4, 4, 3, 0, 2, 2, 0, 0, 0), # 121
(5, 4, 4, 6, 3, 0, 0, 5, 3, 1, 0, 0, 0, 6, 1, 4, 2, 1, 3, 1, 0, 0, 1, 0, 0, 0), # 122
(4, 6, 4, 12, 4, 1, 1, 2, 1, 0, 1, 1, 0, 9, 6, 2, 1, 4, 0, 1, 0, 4, 4, 1, 0, 0), # 123
(3, 3, 7, 3, 3, 1, 3, 1, 1, 1, 2, 0, 0, 2, 4, 1, 3, 3, 0, 1, 0, 4, 2, 2, 2, 0), # 124
(9, 3, 5, 3, 5, 1, 2, 1, 4, 2, 0, 0, 0, 6, 4, 0, 3, 1, 3, 1, 4, 4, 2, 1, 1, 0), # 125
(3, 4, 2, 8, 9, 0, 2, 1, 4, 0, 0, 0, 0, 3, 6, 3, 1, 6, 2, 3, 1, 3, 2, 1, 0, 0), # 126
(9, 4, 7, 3, 3, 3, 1, 1, 2, 0, 0, 0, 0, 3, 8, 3, 1, 6, 5, 1, 1, 1, 3, 2, 2, 0), # 127
(7, 5, 5, 10, 4, 3, 3, 0, 1, 2, 2, 1, 0, 4, 4, 0, 4, 2, 7, 0, 0, 2, 2, 0, 0, 0), # 128
(5, 7, 6, 7, 4, 2, 2, 3, 2, 0, 1, 1, 0, 11, 3, 3, 2, 1, 0, 0, 0, 1, 1, 1, 0, 0), # 129
(6, 3, 5, 6, 2, 2, 1, 3, 0, 1, 0, 0, 0, 4, 2, 3, 2, 3, 2, 2, 4, 1, 2, 0, 0, 0), # 130
(5, 3, 6, 5, 3, 2, 1, 1, 1, 1, 1, 0, 0, 8, 3, 9, 3, 5, 3, 1, 2, 1, 4, 1, 0, 0), # 131
(5, 2, 3, 6, 3, 1, 0, 5, 5, 0, 1, 0, 0, 4, 9, 4, 2, 1, 3, 3, 0, 4, 1, 0, 0, 0), # 132
(3, 6, 2, 9, 6, 1, 1, 0, 3, 0, 0, 1, 0, 6, 2, 2, 2, 5, 3, 0, 1, 2, 1, 1, 1, 0), # 133
(8, 3, 0, 2, 3, 1, 2, 2, 2, 1, 2, 0, 0, 5, 4, 1, 4, 3, 0, 2, 3, 4, 4, 0, 0, 0), # 134
(4, 2, 5, 3, 4, 2, 1, 3, 3, 0, 0, 1, 0, 5, 5, 3, 1, 9, 1, 0, 1, 1, 0, 0, 1, 0), # 135
(5, 4, 5, 2, 5, 1, 1, 1, 3, 2, 0, 1, 0, 8, 4, 5, 4, 4, 1, 1, 3, 4, 2, 3, 0, 0), # 136
(5, 4, 5, 6, 3, 2, 1, 3, 2, 1, 0, 0, 0, 6, 2, 1, 1, 2, 2, 3, 0, 3, 1, 1, 0, 0), # 137
(5, 2, 3, 2, 3, 3, 1, 0, 2, 0, 0, 0, 0, 8, 6, 4, 0, 7, 0, 2, 3, 1, 1, 0, 0, 0), # 138
(7, 2, 7, 5, 8, 1, 3, 0, 1, 0, 1, 0, 0, 6, 2, 3, 1, 2, 0, 0, 1, 3, 0, 0, 1, 0), # 139
(6, 8, 2, 6, 6, 2, 0, 2, 3, 1, 1, 0, 0, 4, 5, 6, 1, 7, 1, 2, 2, 6, 2, 0, 0, 0), # 140
(9, 2, 2, 10, 6, 3, 5, 1, 1, 0, 0, 0, 0, 7, 3, 5, 4, 6, 0, 2, 3, 1, 2, 1, 0, 0), # 141
(4, 4, 2, 3, 4, 3, 1, 1, 1, 0, 0, 0, 0, 4, 4, 3, 0, 6, 2, 2, 0, 2, 1, 2, 0, 0), # 142
(10, 3, 7, 7, 4, 1, 1, 4, 5, 0, 2, 0, 0, 2, 4, 0, 3, 4, 3, 2, 1, 3, 2, 2, 1, 0), # 143
(6, 2, 3, 1, 3, 2, 1, 2, 1, 1, 1, 2, 0, 6, 6, 1, 1, 4, 4, 1, 0, 4, 1, 2, 1, 0), # 144
(5, 7, 3, 3, 3, 3, 4, 1, 4, 0, 0, 0, 0, 8, 6, 2, 2, 3, 2, 0, 1, 2, 3, 0, 0, 0), # 145
(5, 2, 6, 5, 0, 1, 5, 2, 5, 0, 1, 0, 0, 6, 5, 1, 1, 3, 2, 0, 0, 2, 3, 0, 0, 0), # 146
(2, 4, 3, 5, 4, 1, 4, 0, 2, 0, 0, 0, 0, 8, 2, 1, 4, 4, 3, 3, 3, 0, 2, 2, 0, 0), # 147
(8, 1, 4, 1, 1, 0, 1, 1, 2, 0, 3, 0, 0, 6, 6, 4, 2, 5, 1, 2, 2, 4, 1, 0, 0, 0), # 148
(2, 5, 5, 6, 3, 1, 1, 1, 4, 1, 0, 0, 0, 3, 4, 3, 2, 4, 0, 1, 1, 2, 1, 0, 0, 0), # 149
(5, 2, 2, 4, 6, 5, 2, 1, 1, 1, 2, 1, 0, 1, 2, 1, 2, 9, 0, 2, 1, 3, 2, 1, 0, 0), # 150
(4, 3, 3, 8, 5, 0, 2, 0, 4, 1, 1, 0, 0, 4, 2, 5, 3, 5, 2, 4, 2, 2, 2, 0, 0, 0), # 151
(4, 0, 6, 4, 5, 3, 0, 1, 2, 1, 0, 0, 0, 6, 5, 4, 2, 5, 2, 1, 0, 2, 0, 2, 0, 0), # 152
(4, 3, 5, 2, 1, 1, 2, 1, 0, 0, 0, 0, 0, 5, 1, 1, 4, 2, 4, 0, 2, 4, 1, 0, 0, 0), # 153
(3, 0, 6, 5, 4, 2, 2, 1, 1, 0, 3, 0, 0, 5, 3, 1, 1, 1, 0, 1, 3, 3, 0, 0, 0, 0), # 154
(3, 6, 1, 2, 4, 1, 2, 1, 8, 1, 0, 0, 0, 8, 6, 6, 1, 3, 0, 0, 1, 1, 4, 2, 0, 0), # 155
(11, 3, 5, 9, 7, 1, 2, 2, 0, 0, 0, 1, 0, 4, 4, 1, 2, 7, 2, 1, 0, 0, 4, 1, 0, 0), # 156
(4, 2, 6, 4, 3, 0, 0, 0, 1, 0, 0, 0, 0, 6, 2, 2, 5, 4, 2, 2, 2, 0, 0, 3, 1, 0), # 157
(2, 3, 3, 3, 1, 4, 1, 0, 2, 0, 0, 2, 0, 5, 4, 3, 1, 5, 3, 2, 1, 2, 3, 0, 1, 0), # 158
(2, 2, 7, 3, 1, 3, 2, 2, 0, 1, 1, 0, 0, 4, 1, 4, 0, 2, 1, 3, 3, 2, 3, 0, 0, 0), # 159
(4, 2, 7, 5, 8, 2, 3, 1, 3, 0, 0, 0, 0, 7, 4, 5, 2, 3, 3, 1, 0, 2, 1, 1, 0, 0), # 160
(5, 3, 2, 3, 1, 0, 0, 2, 2, 1, 1, 0, 0, 2, 5, 1, 1, 6, 2, 1, 1, 1, 1, 0, 1, 0), # 161
(1, 2, 4, 3, 7, 1, 1, 4, 2, 2, 0, 2, 0, 5, 5, 2, 2, 4, 3, 2, 2, 0, 1, 0, 0, 0), # 162
(4, 2, 5, 1, 4, 3, 4, 1, 2, 0, 0, 0, 0, 3, 0, 7, 4, 6, 1, 1, 1, 1, 1, 0, 0, 0), # 163
(2, 4, 5, 4, 3, 3, 5, 1, 2, 0, 0, 0, 0, 2, 3, 1, 3, 3, 2, 0, 0, 0, 1, 0, 0, 0), # 164
(2, 1, 3, 2, 4, 2, 1, 3, 3, 0, 0, 1, 0, 6, 7, 6, 3, 1, 3, 0, 1, 0, 2, 0, 0, 0), # 165
(3, 5, 2, 6, 2, 4, 0, 3, 3, 0, 1, 0, 0, 3, 5, 3, 2, 2, 1, 0, 1, 1, 1, 2, 0, 0), # 166
(3, 4, 6, 5, 3, 3, 1, 1, 3, 2, 0, 0, 0, 3, 2, 0, 3, 7, 1, 0, 2, 0, 2, 1, 0, 0), # 167
(5, 1, 2, 3, 3, 1, 0, 4, 1, 2, 0, 0, 0, 3, 4, 3, 2, 2, 2, 2, 2, 5, 0, 0, 0, 0), # 168
(5, 1, 5, 5, 4, 2, 0, 0, 4, 1, 1, 0, 0, 3, 2, 3, 1, 5, 3, 1, 1, 0, 1, 0, 0, 0), # 169
(2, 1, 2, 3, 4, 1, 0, 4, 0, 2, 0, 2, 0, 4, 4, 2, 1, 4, 6, 2, 0, 0, 2, 1, 0, 0), # 170
(3, 2, 1, 1, 0, 3, 2, 2, 1, 0, 1, 0, 0, 4, 2, 5, 1, 5, 1, 1, 4, 1, 0, 0, 0, 0), # 171
(3, 3, 5, 3, 5, 1, 1, 3, 1, 0, 0, 1, 0, 3, 1, 5, 1, 3, 0, 0, 2, 1, 0, 0, 0, 0), # 172
(3, 2, 5, 3, 2, 1, 0, 0, 2, 0, 0, 0, 0, 4, 7, 5, 3, 3, 3, 1, 0, 2, 2, 1, 0, 0), # 173
(1, 3, 5, 3, 2, 0, 1, 0, 1, 2, 0, 0, 0, 5, 2, 5, 3, 5, 1, 1, 1, 2, 2, 0, 1, 0), # 174
(5, 2, 0, 1, 4, 3, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 2, 2, 1, 1, 2, 0, 1, 1, 1, 0), # 175
(8, 0, 3, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 4, 1, 5, 0, 2, 0, 1, 1, 1, 2, 0, 0, 0), # 176
(0, 5, 2, 5, 3, 0, 0, 0, 1, 0, 1, 0, 0, 5, 1, 2, 3, 1, 1, 0, 0, 2, 0, 0, 0, 0), # 177
(2, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 1, 3, 1, 0, 0, 0, 0, 1, 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 = (
(3.012519347023061, 3.3151731358159005, 3.1267699377675044, 3.72880066431806, 3.3328113645657673, 1.8831929592224035, 2.487621369224349, 2.7919057037876542, 3.6540594373008046, 2.374809009313422, 2.523159098331362, 2.9387490469837045, 3.050328127547018), # 0
(3.2125962912119848, 3.5340364164868556, 3.333207954798092, 3.9750865519659704, 3.5534942024845426, 2.007590150425534, 2.6516813647582165, 2.975708233743425, 3.895347195799746, 2.53138841536629, 2.689857681573679, 3.1327299964886133, 3.2518749884639133), # 1
(3.412033805387839, 3.752031102534588, 3.5388253713284485, 4.220392622798877, 3.773377784249847, 2.1314910687074673, 2.815091104776423, 3.158775148742652, 4.13567208412653, 2.6873482650123277, 2.855893889265788, 3.3259395912318332, 3.4526188969258147), # 2
(3.6100546758879375, 3.9682923011618687, 3.742806521591913, 4.463745844519245, 3.991597005601142, 2.254404373551068, 2.9772021849887738, 3.3403806944967047, 4.374081096552656, 2.842069619055709, 3.0206090470964297, 3.517611410958277, 3.651763683752536), # 3
(3.8058816890495892, 4.181955119571465, 3.9443357398218226, 4.704173184829543, 4.207286762277896, 2.375838724439201, 3.1373662011050754, 3.51979911671695, 4.609621227349625, 2.9949335383006077, 3.1833444807543456, 3.706979035412859, 3.848513179763891), # 4
(3.9987376312101066, 4.392154664966154, 4.142597360251511, 4.940701611432237, 4.419581950019569, 2.495302780854729, 3.294934748835135, 3.696304661114756, 4.841339470788936, 3.1453210835511984, 3.34344151592828, 3.8932760443404892, 4.042071215779696), # 5
(4.1878452887068, 4.598026044548702, 4.33677571711432, 5.172358092029792, 4.627617464565627, 2.6123052022805187, 3.449259423888758, 3.8691715734014935, 5.068282821142089, 3.2926133156116553, 3.50024147830697, 4.0757360174860855, 4.231641622619764), # 6
(4.372427447876982, 4.798704365521879, 4.526055144643582, 5.398169594324678, 4.830528201655533, 2.726354648199433, 3.5996918219757514, 4.037674099288531, 5.2894982726805875, 3.4361912952861524, 3.6530856935791616, 4.253592534594558, 4.416428231103912), # 7
(4.551706895057961, 4.99332473508846, 4.709619977072638, 5.6171630860193575, 5.027449057028752, 2.836959778094334, 3.745583538805919, 4.201086484487235, 5.504032819675924, 3.575436083378865, 3.801315487433593, 4.426079175410822, 4.595634872051951), # 8
(4.724906416587052, 5.181022260451214, 4.886654548634823, 5.828365534816302, 5.217514926424746, 2.943629251448091, 3.8862861700890723, 4.358682974708978, 5.710933456399605, 3.7097287406939654, 3.9442721855590075, 4.592429519679789, 4.768465376283698), # 9
(4.89124879880156, 5.36093204881291, 5.0563431935634755, 6.030803908417973, 5.399860705582981, 3.045871727743565, 4.0211513115350135, 4.509737815665127, 5.909247177123129, 3.838450328035629, 4.081297113644145, 4.751877147146372, 4.934123574618967), # 10
(5.049956828038804, 5.532189207376325, 5.2178702460919295, 6.2235051745268395, 5.573621290242921, 3.1431958664636213, 4.1495305588535505, 4.653525253067049, 6.098020976117997, 3.960981906208032, 4.21173159737775, 4.903655637555487, 5.091813297877567), # 11
(5.200253290636088, 5.69392884334422, 5.370420040453524, 6.4054963008453685, 5.737931576144027, 3.235110327091122, 4.270775507754488, 4.789319532626113, 6.276301847655708, 4.0767045360153435, 4.3349169624485615, 5.0469985706520415, 5.240738376879321), # 12
(5.341360972930726, 5.845286063919373, 5.513176910881598, 6.575804255076027, 5.891926459025762, 3.321123769108935, 4.384237753947634, 4.91639490005369, 6.44313678600776, 4.184999278261743, 4.4501945345453215, 5.181139526180953, 5.380102642444042), # 13
(5.47250266126003, 5.985395976304554, 5.645325191609489, 6.733456004921276, 6.034740834627595, 3.400744851999921, 4.489268893142797, 5.034025601061148, 6.597572785445654, 4.285247193751401, 4.5569056393567715, 5.305312083887137, 5.509109925391539), # 14
(5.592901141961314, 6.113393687702531, 5.766049216870527, 6.877478518083592, 6.165509598688986, 3.473482235246948, 4.585220521049776, 5.141485881359854, 6.738656840240893, 4.376829343288495, 4.6543916025716525, 5.418749823515504, 5.626964056541629), # 15
(5.701779201371881, 6.22841430531608, 5.874533320898055, 7.006898762265431, 6.283367646949401, 3.538844578332876, 4.671444233378386, 5.238049986661177, 6.865435944664973, 4.4591267876771985, 4.741993749878708, 5.520686324810964, 5.732868866714128), # 16
(5.798359625829046, 6.329592936347969, 5.969961837925412, 7.120743705169268, 6.387449875148303, 3.596340540740572, 4.747291625838427, 5.322992162676491, 6.976957092989391, 4.531520587721681, 4.81905340696668, 5.610355167518434, 5.82602818672885), # 17
(5.881865201670123, 6.416064688000965, 6.051519102185929, 7.218040314497568, 6.476891179025154, 3.645478781952902, 4.812114294139711, 5.395586655117157, 7.072267279485659, 4.593391804226123, 4.8849118995243055, 5.68698993138283, 5.905645847405608), # 18
(5.95151871523242, 6.486964667477848, 6.118389447912948, 7.29781555795279, 6.55082645431942, 3.6857679614527257, 4.865263833992037, 5.4551077096945475, 7.1504134984252685, 4.644121497994697, 4.938910553240327, 5.749824196149059, 5.9709256795642185), # 19
(6.00654295285325, 6.541427981981378, 6.169757209339802, 7.359096403237413, 6.608390596770567, 3.7167167387229116, 4.906091841105218, 5.500829572120031, 7.21044274407972, 4.683090729831576, 4.980390693803491, 5.7980915415620355, 6.021071514024495), # 20
(6.046160700869921, 6.578589738714336, 6.204806720699832, 7.400909818053893, 6.648718502118054, 3.737833773246322, 4.933949911189056, 5.532026488104974, 7.251402010720514, 4.709680560540933, 5.008693646902536, 5.831025547366677, 6.055287181606248), # 21
(6.0695947456197485, 6.597585044879487, 6.222722316226372, 7.422282770104703, 6.670945066101346, 3.7486277245058206, 4.948189639953361, 5.54797270336075, 7.27233829261915, 4.723272050926946, 5.0231607382262045, 5.847859793307895, 6.0727765131292974), # 22
(6.078236018005584, 6.599834156378602, 6.224953909465022, 7.424958487654322, 6.676639233619559, 3.7500000000000004, 4.9498824013556035, 5.549696296296298, 7.274955740740742, 4.724875363511661, 5.024974822297045, 5.8499385459533615, 6.075000000000001), # 23
(6.084607447820493, 6.598522222222224, 6.224588888888889, 7.424629166666668, 6.679864568168912, 3.7500000000000004, 4.948952287581701, 5.547300000000001, 7.274605000000001, 4.723890370370371, 5.024774747474749, 5.849451851851852, 6.075000000000001), # 24
(6.090844338126949, 6.59593621399177, 6.223868312757203, 7.423977623456792, 6.683018967356703, 3.7500000000000004, 4.947119341563787, 5.542592592592594, 7.2739120370370385, 4.7219513031550076, 5.024378039655818, 5.84849108367627, 6.075000000000001), # 25
(6.096946211428821, 6.592115637860084, 6.22280205761317, 7.423011265432099, 6.6861023210526636, 3.7500000000000004, 4.944412030985234, 5.535662962962964, 7.272885740740741, 4.719090425240055, 5.023788290310513, 5.847069410150893, 6.075000000000001), # 26
(6.102912590229983, 6.5871, 6.221400000000001, 7.421737500000001, 6.689114519126529, 3.7500000000000004, 4.940858823529413, 5.526600000000001, 7.271535000000001, 4.71534, 5.023009090909092, 5.845200000000001, 6.075000000000001), # 27
(6.108742997034302, 6.580928806584363, 6.2196720164609065, 7.420163734567902, 6.6920554514480415, 3.7500000000000004, 4.936488186879691, 5.515492592592594, 7.269868703703704, 4.710732290809329, 5.022044032921811, 5.842896021947875, 6.075000000000001), # 28
(6.114436954345651, 6.573641563786009, 6.217627983539096, 7.418297376543211, 6.694925007886933, 3.7500000000000004, 4.93132858871944, 5.5024296296296304, 7.267895740740742, 4.705299561042525, 5.02089670781893, 5.8401706447187935, 6.075000000000001), # 29
(6.119993984667899, 6.565277777777779, 6.215277777777779, 7.416145833333334, 6.697723078312943, 3.7500000000000004, 4.925408496732027, 5.4875, 7.265625000000001, 4.699074074074075, 5.019570707070708, 5.837037037037037, 6.075000000000001), # 30
(6.125413610504916, 6.555876954732512, 6.212631275720166, 7.41371651234568, 6.700449552595806, 3.7500000000000004, 4.918756378600825, 5.470792592592594, 7.263065370370372, 4.692088093278464, 5.018069622147401, 5.833508367626888, 6.075000000000001), # 31
(6.130695354360573, 6.5454786008230466, 6.209698353909465, 7.411016820987656, 6.7031043206052585, 3.7500000000000004, 4.9114007020092, 5.452396296296298, 7.260225740740742, 4.6843738820301795, 5.016397044519267, 5.829597805212622, 6.075000000000001), # 32
(6.135838738738739, 6.534122222222223, 6.20648888888889, 7.408054166666667, 6.705687272211039, 3.7500000000000004, 4.903369934640524, 5.432400000000001, 7.257115000000001, 4.6759637037037045, 5.014556565656566, 5.825318518518519, 6.075000000000001), # 33
(6.140843286143286, 6.521847325102881, 6.2030127572016465, 7.404835956790125, 6.70819829728288, 3.7500000000000004, 4.894692544178167, 5.410892592592593, 7.253742037037039, 4.666889821673526, 5.012551777029555, 5.820683676268862, 6.075000000000001), # 34
(6.145708519078085, 6.508693415637861, 6.199279835390948, 7.401369598765433, 6.710637285690523, 3.7500000000000004, 4.885396998305496, 5.3879629629629635, 7.250115740740743, 4.65718449931413, 5.0103862701084925, 5.81570644718793, 6.075000000000001), # 35
(6.150433960047004, 6.4947, 6.1953000000000005, 7.397662500000001, 6.713004127303702, 3.7500000000000004, 4.875511764705883, 5.3637000000000015, 7.246245, 4.64688, 5.008063636363637, 5.810400000000001, 6.075000000000001), # 36
(6.155019131553915, 6.479906584362141, 6.191083127572017, 7.393722067901235, 6.715298711992154, 3.7500000000000004, 4.865065311062697, 5.338192592592593, 7.242138703703705, 4.636008587105625, 5.005587467265246, 5.8047775034293565, 6.075000000000001), # 37
(6.159463556102686, 6.46435267489712, 6.186639094650206, 7.389555709876545, 6.717520929625615, 3.7500000000000004, 4.8540861050593085, 5.311529629629631, 7.2378057407407415, 4.624602524005488, 5.002961354283578, 5.798852126200276, 6.075000000000001), # 38
(6.163766756197193, 6.448077777777779, 6.181977777777779, 7.385170833333334, 6.719670670073823, 3.7500000000000004, 4.842602614379086, 5.2838, 7.2332550000000015, 4.612694074074075, 5.000188888888889, 5.7926370370370375, 6.075000000000001), # 39
(6.167928254341299, 6.431121399176956, 6.177109053497943, 7.380574845679013, 6.721747823206512, 3.7500000000000004, 4.830643306705399, 5.255092592592594, 7.228495370370371, 4.6003155006858725, 4.997273662551441, 5.786145404663925, 6.075000000000001), # 40
(6.171947573038878, 6.413523045267491, 6.17204279835391, 7.375775154320989, 6.723752278893422, 3.7500000000000004, 4.818236649721618, 5.225496296296297, 7.223535740740742, 4.587499067215364, 4.99421926674149, 5.779390397805213, 6.075000000000001), # 41
(6.175824234793801, 6.395322222222224, 6.166788888888891, 7.370779166666669, 6.725683927004286, 3.7500000000000004, 4.805411111111112, 5.195100000000001, 7.218385000000001, 4.574277037037038, 4.991029292929293, 5.772385185185186, 6.075000000000001), # 42
(6.179557762109936, 6.376558436213993, 6.161357201646092, 7.365594290123458, 6.727542657408843, 3.7500000000000004, 4.79219515855725, 5.163992592592594, 7.213052037037038, 4.560681673525378, 4.987707332585111, 5.7651429355281225, 6.075000000000001), # 43
(6.183147677491157, 6.357271193415639, 6.155757613168726, 7.360227932098767, 6.729328359976828, 3.7500000000000004, 4.778617259743404, 5.132262962962964, 7.207545740740741, 4.546745240054871, 4.9842569771792, 5.7576768175583, 6.075000000000001), # 44
(6.186593503441331, 6.337500000000001, 6.150000000000001, 7.354687500000001, 6.731040924577979, 3.7500000000000004, 4.764705882352942, 5.1000000000000005, 7.201875, 4.532500000000001, 4.980681818181819, 5.75, 6.075000000000001), # 45
(6.18989476246433, 6.317284362139919, 6.144094238683128, 7.348980401234569, 6.732680241082031, 3.7500000000000004, 4.7504894940692335, 5.067292592592594, 7.196048703703704, 4.517978216735254, 4.976985447063225, 5.7421256515775045, 6.075000000000001), # 46
(6.193050977064022, 6.296663786008231, 6.138050205761318, 7.343114043209877, 6.734246199358721, 3.7500000000000004, 4.735996562575648, 5.03422962962963, 7.190075740740742, 4.5032121536351175, 4.973171455293678, 5.73406694101509, 6.075000000000001), # 47
(6.196061669744278, 6.275677777777779, 6.131877777777778, 7.337095833333335, 6.735738689277787, 3.7500000000000004, 4.721255555555556, 5.0009000000000015, 7.1839650000000015, 4.488234074074074, 4.969243434343435, 5.725837037037038, 6.075000000000001), # 48
(6.198926363008972, 6.254365843621399, 6.125586831275721, 7.330933179012347, 6.737157600708965, 3.7500000000000004, 4.7062949406923265, 4.967392592592593, 7.177725370370371, 4.473076241426613, 4.965204975682754, 5.717449108367628, 6.075000000000001), # 49
(6.201644579361971, 6.232767489711936, 6.119187242798356, 7.324633487654322, 6.73850282352199, 3.7500000000000004, 4.691143185669331, 4.933796296296297, 7.171365740740741, 4.457770919067216, 4.961059670781894, 5.70891632373114, 6.075000000000001), # 50
(6.204215841307147, 6.210922222222223, 6.112688888888889, 7.318204166666668, 6.7397742475866, 3.7500000000000004, 4.675828758169935, 4.900200000000001, 7.164895000000001, 4.442350370370371, 4.956811111111112, 5.700251851851854, 6.075000000000001), # 51
(6.206639671348368, 6.188869547325105, 6.1061016460905355, 7.311652623456791, 6.74097176277253, 3.7500000000000004, 4.660380125877512, 4.866692592592592, 7.158322037037038, 4.426846858710563, 4.9524628881406665, 5.691468861454048, 6.075000000000001), # 52
(6.2089155919895065, 6.166648971193417, 6.099435390946504, 7.304986265432099, 6.742095258949519, 3.7500000000000004, 4.644825756475431, 4.833362962962964, 7.151655740740742, 4.411292647462278, 4.948018593340816, 5.682580521262003, 6.075000000000001), # 53
(6.211043125734431, 6.144300000000001, 6.0927, 7.298212500000001, 6.743144625987302, 3.7500000000000004, 4.629194117647059, 4.8003, 7.144905000000001, 4.395720000000001, 4.943481818181818, 5.6736, 6.075000000000001), # 54
(6.213021795087014, 6.121862139917696, 6.08590534979424, 7.291338734567901, 6.744119753755615, 3.7500000000000004, 4.613513677075769, 4.767592592592593, 7.138078703703704, 4.380161179698217, 4.938856154133933, 5.664540466392319, 6.075000000000001), # 55
(6.214851122551123, 6.099374897119342, 6.079061316872429, 7.28437237654321, 6.7450205321241965, 3.7500000000000004, 4.59781290244493, 4.73532962962963, 7.131185740740742, 4.364648449931414, 4.934145192667415, 5.655415089163238, 6.075000000000001), # 56
(6.21653063063063, 6.076877777777779, 6.072177777777779, 7.277320833333334, 6.745846850962781, 3.7500000000000004, 4.582120261437909, 4.703600000000002, 7.124235000000001, 4.349214074074075, 4.929352525252526, 5.646237037037038, 6.075000000000001), # 57
(6.218059841829408, 6.054410288065845, 6.065264609053499, 7.270191512345679, 6.746598600141105, 3.7500000000000004, 4.566464221738078, 4.672492592592593, 7.117235370370372, 4.333890315500687, 4.9244817433595225, 5.637019478737998, 6.075000000000001), # 58
(6.219438278651324, 6.03201193415638, 6.0583316872427995, 7.262991820987655, 6.747275669528908, 3.7500000000000004, 4.550873251028808, 4.642096296296297, 7.110195740740743, 4.318709437585735, 4.91953643845866, 5.627775582990399, 6.075000000000001), # 59
(6.220665463600247, 6.009722222222224, 6.051388888888889, 7.255729166666668, 6.747877948995923, 3.7500000000000004, 4.535375816993464, 4.612500000000001, 7.103125000000002, 4.303703703703705, 4.914520202020203, 5.61851851851852, 6.075000000000001), # 60
(6.22174091918005, 5.987580658436215, 6.044446090534981, 7.248410956790124, 6.748405328411888, 3.7500000000000004, 4.52000038731542, 4.5837925925925935, 7.0960320370370376, 4.2889053772290815, 4.909436625514404, 5.60926145404664, 6.075000000000001), # 61
(6.222664167894603, 5.965626748971194, 6.037513168724281, 7.241044598765434, 6.7488576976465415, 3.7500000000000004, 4.504775429678045, 4.556062962962963, 7.088925740740741, 4.274346721536352, 4.9042893004115236, 5.600017558299041, 6.075000000000001), # 62
(6.223434732247776, 5.9439, 6.030600000000001, 7.233637500000002, 6.749234946569615, 3.7500000000000004, 4.489729411764706, 4.529400000000001, 7.081815000000001, 4.26006, 4.899081818181819, 5.590800000000001, 6.075000000000001), # 63
(6.224052134743439, 5.922439917695474, 6.023716460905351, 7.226197067901236, 6.74953696505085, 3.7500000000000004, 4.474890801258776, 4.503892592592594, 7.074708703703704, 4.246077475994514, 4.893817770295548, 5.581621947873801, 6.075000000000001), # 64
(6.224515897885464, 5.901286008230453, 6.01687242798354, 7.218730709876544, 6.749763642959981, 3.7500000000000004, 4.460288065843622, 4.47962962962963, 7.067615740740742, 4.2324314128943765, 4.888500748222972, 5.572496570644719, 6.075000000000001), # 65
(6.224825544177719, 5.88047777777778, 6.010077777777779, 7.211245833333335, 6.749914870166744, 3.7500000000000004, 4.445949673202615, 4.456700000000001, 7.060545000000001, 4.2191540740740745, 4.883134343434344, 5.563437037037039, 6.075000000000001), # 66
(6.224980596124076, 5.860054732510289, 6.003342386831276, 7.203749845679013, 6.749990536540879, 3.7500000000000004, 4.431904091019124, 4.435192592592594, 7.053505370370371, 4.206277722908094, 4.877722147399926, 5.554456515775034, 6.075000000000001), # 67
(6.224874968688496, 5.839949183592195, 5.996643569958848, 7.196185044283416, 6.749926773178408, 3.7499304069501602, 4.418109116897789, 4.415006310013718, 7.046452709190674, 4.193772264988301, 4.872171593124226, 5.545518013139847, 6.074925090020577), # 68
(6.2238850241545896, 5.819547311827958, 5.989793055555555, 7.188170108695653, 6.749346405228758, 3.749380246913581, 4.4041609088079685, 4.39505925925926, 7.039078703703705, 4.181283834422658, 4.865917703349282, 5.536331384015596, 6.074331597222224), # 69
(6.221931472535338, 5.798755475906014, 5.982761059670782, 7.179652274557167, 6.748199588477366, 3.7482967535436673, 4.389996080736823, 4.375171467764061, 7.031341735253774, 4.168751714677641, 4.858889331915648, 5.526853656775686, 6.073159400720166), # 70
(6.219041796385758, 5.777586101154919, 5.9755500514403295, 7.17064410225443, 6.746500847252483, 3.7466974851394608, 4.375620995723393, 4.355349519890262, 7.023253326474624, 4.156176215605584, 4.851112422470318, 5.51709176232763, 6.07142393261317), # 71
(6.215243478260871, 5.756051612903226, 5.9681625, 7.161158152173914, 6.744264705882354, 3.744600000000001, 4.361042016806723, 4.3356, 7.014825000000002, 4.143557647058824, 4.842612918660288, 5.507052631578948, 6.069140625000001), # 72
(6.210564000715693, 5.734164436479491, 5.960600874485597, 7.151206984702094, 6.741505688695232, 3.7420218564243255, 4.346265507025856, 4.315929492455419, 7.00606827846365, 4.130896318889696, 4.833416764132553, 5.496743195437152, 6.066324909979425), # 73
(6.2050308463052435, 5.711936997212267, 5.952867644032922, 7.140803160225444, 6.7382383200193665, 3.7389806127114777, 4.331297829419834, 4.296344581618656, 6.996994684499315, 4.118192540950537, 4.823549902534114, 5.486170384809761, 6.062992219650208), # 74
(6.198671497584542, 5.689381720430108, 5.944965277777778, 7.129959239130436, 6.734477124183008, 3.7354938271604947, 4.3161453470277005, 4.276851851851853, 6.9876157407407415, 4.105446623093682, 4.813038277511962, 5.475341130604289, 6.059157986111113), # 75
(6.191513437108607, 5.666511031461571, 5.936896244855968, 7.118687781803544, 6.730236625514404, 3.731579058070417, 4.3008144228884975, 4.257457887517147, 6.977942969821674, 4.092658875171468, 4.801907832713096, 5.46426236372825, 6.054837641460906), # 76
(6.183584147432457, 5.643337355635206, 5.928663014403292, 7.10700134863124, 6.725531348341806, 3.727253863740284, 4.2853114200412685, 4.238169272976681, 6.967987894375859, 4.079829607036231, 4.7901845117845125, 5.452941015089165, 6.050046617798356), # 77
(6.174911111111112, 5.619873118279572, 5.920268055555556, 7.094912500000001, 6.7203758169934655, 3.722535802469136, 4.269642701525056, 4.218992592592594, 6.957762037037037, 4.066959128540305, 4.777894258373206, 5.441384015594544, 6.044800347222224), # 78
(6.165521810699589, 5.59613074472322, 5.91171383744856, 7.082433796296297, 6.714784555797629, 3.717442432556013, 4.253814630378901, 4.199934430727024, 6.947276920438958, 4.0540477495360285, 4.765063016126174, 5.429598296151903, 6.039114261831276), # 79
(6.155443728752909, 5.5721226602947045, 5.903002829218107, 7.069577797906604, 6.708772089082548, 3.7119913122999546, 4.237833569641849, 4.181001371742113, 6.936544067215364, 4.041095779875737, 4.751716728690414, 5.417590787668762, 6.033003793724281), # 80
(6.144704347826088, 5.547861290322582, 5.8941375, 7.0563570652173935, 6.702352941176471, 3.7062000000000004, 4.221705882352942, 4.1622, 6.925575, 4.028103529411766, 4.73788133971292, 5.405368421052633, 6.026484375), # 81
(6.133331150474146, 5.523359060135405, 5.885120318930043, 7.042784158615138, 6.6955416364076505, 3.7000860539551907, 4.205437931551223, 4.143536899862826, 6.914381241426613, 4.0150713079964495, 4.723582792840689, 5.392938127211033, 6.019571437757203), # 82
(6.121351619252104, 5.498628395061729, 5.8759537551440335, 7.028871638486313, 6.688352699104334, 3.693667032464564, 4.1890360802757325, 4.12501865569273, 6.902974314128945, 4.001999425482127, 4.708847031720717, 5.380306837051478, 6.0122804140946515), # 83
(6.108793236714976, 5.4736817204301085, 5.866640277777779, 7.014632065217393, 6.680800653594773, 3.686960493827161, 4.172506691565515, 4.106651851851852, 6.891365740740741, 3.988888191721134, 4.693700000000001, 5.367481481481482, 6.004626736111112), # 84
(6.095683485417786, 5.448531461569097, 5.857182355967079, 7.000077999194849, 6.672900024207215, 3.679983996342022, 4.1558561284596145, 4.088443072702333, 6.879567043895747, 3.9757379165658038, 4.678167641325537, 5.354468991408563, 5.996625835905351), # 85
(6.082049847915549, 5.423190043807249, 5.8475824588477385, 6.985222000805155, 6.664665335269911, 3.6727550983081856, 4.139090753997072, 4.0703989026063105, 6.867589746227711, 3.962548909868475, 4.662275899344321, 5.341276297740237, 5.988293145576132), # 86
(6.067919806763285, 5.397669892473119, 5.837843055555556, 6.970076630434783, 6.656111111111113, 3.6652913580246915, 4.122216931216932, 4.052525925925926, 6.855445370370372, 3.9493214814814817, 4.64605071770335, 5.327910331384016, 5.979644097222223), # 87
(6.053320844516015, 5.371983432895261, 5.827966615226338, 6.954654448470211, 6.647251876059067, 3.657610333790581, 4.105241023158235, 4.03483072702332, 6.843145438957477, 3.936055941257161, 4.62951804004962, 5.31437802324742, 5.970694122942389), # 88
(6.0382804437287545, 5.346143090402231, 5.817955606995886, 6.938968015297908, 6.638102154442025, 3.649729583904893, 4.088169392860025, 4.017319890260632, 6.8307014746227726, 3.92275259904785, 4.612703810030127, 5.300686304237962, 5.961458654835392), # 89
(6.022826086956522, 5.320161290322582, 5.807812500000001, 6.923029891304349, 6.628676470588237, 3.6416666666666675, 4.071008403361345, 4.0, 6.818125000000002, 3.909411764705883, 4.595633971291867, 5.286842105263158, 5.951953125000001), # 90
(6.006985256754339, 5.294050457984866, 5.797539763374486, 6.906852636876009, 6.61898934882595, 3.633439140374943, 4.053764417701237, 3.9828776406035673, 6.80542753772291, 3.896033748083596, 4.578334467481836, 5.2728523572305255, 5.942192965534979), # 91
(5.990785435677224, 5.267823018717643, 5.787139866255145, 6.890448812399357, 6.6090553134834185, 3.6250645633287615, 4.036443798918746, 3.9659593964334716, 6.792620610425241, 3.882618859033326, 4.5608312422470325, 5.258723991047579, 5.932193608539095), # 92
(5.974254106280194, 5.2414913978494635, 5.776615277777778, 6.873830978260871, 6.59888888888889, 3.6165604938271607, 4.019052910052911, 3.949251851851853, 6.779715740740742, 3.8691674074074074, 4.543150239234451, 5.244463937621833, 5.921970486111111), # 93
(5.957418751118269, 5.215068020708881, 5.765968467078191, 6.8570116948470226, 6.588504599370613, 3.607944490169182, 4.001598114142777, 3.9327615912208516, 6.766724451303157, 3.8556797030581786, 4.525317402091087, 5.230079127860805, 5.911539030349795), # 94
(5.940306852746467, 5.188565312624453, 5.755201903292182, 6.840003522544285, 6.577916969256839, 3.5992341106538643, 3.984085774227387, 3.916495198902607, 6.753658264746229, 3.842156055837973, 4.507358674463938, 5.2155764926720085, 5.900914673353911), # 95
(5.922945893719809, 5.161995698924733, 5.744318055555557, 6.822819021739131, 6.5671405228758175, 3.590446913580247, 3.966522253345783, 3.9004592592592604, 6.7405287037037045, 3.828596775599129, 4.489300000000001, 5.200962962962964, 5.890112847222223), # 96
(5.90536335659331, 5.135371604938273, 5.733319393004115, 6.805470752818036, 6.556189784555799, 3.581600457247371, 3.9489139145370085, 3.88466035665295, 6.7273472908093295, 3.815002172193981, 4.47116732234627, 5.186245469641182, 5.8791489840535), # 97
(5.8875867239219914, 5.108705455993629, 5.722208384773663, 6.7879712761674735, 6.545079278625032, 3.572712299954276, 3.931267120840106, 3.869105075445817, 6.714125548696845, 3.8013725554748654, 4.452986585149744, 5.17143094361418, 5.868038515946503), # 98
(5.86964347826087, 5.0820096774193555, 5.710987500000001, 6.770333152173914, 6.533823529411766, 3.5638000000000005, 3.9135882352941187, 3.8538000000000006, 6.700875000000001, 3.787708235294118, 4.434783732057417, 5.156526315789474, 5.8567968750000015), # 99
(5.851561102164967, 5.0552966945440065, 5.6996592078189305, 6.752568941223833, 6.5224370612442515, 3.5548811156835853, 3.8958836209380876, 3.8387517146776413, 6.687607167352539, 3.7740095215040754, 4.416584706716287, 5.141538517074581, 5.8454394933127585), # 100
(5.833367078189301, 5.028578932696138, 5.6882259773662565, 6.734691203703705, 6.510934398450739, 3.54597320530407, 3.8781596408110577, 3.823966803840878, 6.674333573388204, 3.760276723957073, 4.398415452773348, 5.126474478377013, 5.83398180298354), # 101
(5.81508888888889, 5.001868817204302, 5.676690277777778, 6.716712500000002, 6.499330065359478, 3.537093827160495, 3.8604226579520704, 3.8094518518518523, 6.661065740740741, 3.7465101525054476, 4.3803019138755985, 5.1113411306042895, 5.822439236111112), # 102
(5.796754016818752, 4.975178773397054, 5.665054578189301, 6.6986453904991965, 6.4876385862987185, 3.5282605395518982, 3.8426790354001685, 3.795213443072703, 6.647815192043897, 3.7327101170015338, 4.362270033670034, 5.0961454046639245, 5.810827224794239), # 103
(5.778389944533907, 4.9485212266029475, 5.653321347736626, 6.680502435587763, 6.475874485596709, 3.519490900777321, 3.824935136194396, 3.78125816186557, 6.634593449931414, 3.7188769272976687, 4.3443457558036505, 5.080894231463433, 5.799161201131688), # 104
(5.760024154589373, 4.921908602150538, 5.641493055555556, 6.662296195652175, 6.4640522875817, 3.510802469135803, 3.8071973233737952, 3.7675925925925933, 6.621412037037038, 3.7050108932461883, 4.326555023923445, 5.0655945419103325, 5.787456597222223), # 105
(5.741684129540169, 4.89535332536838, 5.629572170781894, 6.6440392310789065, 6.452186516581943, 3.5022128029263833, 3.7894719599774076, 3.754223319615913, 6.608282475994513, 3.6911123246994273, 4.308923781676413, 5.050253266912137, 5.775728845164609), # 106
(5.723397351941315, 4.868867821585027, 5.617561162551442, 6.625744102254429, 6.4402916969256845, 3.4937394604481034, 3.7717654090442774, 3.741156927297669, 6.5952162894375865, 3.677181531509724, 4.291477972709552, 5.034877337376363, 5.763993377057614), # 107
(5.705191304347827, 4.842464516129033, 5.605462500000002, 6.607423369565219, 6.428382352941177, 3.4854000000000007, 3.7540840336134456, 3.7284000000000006, 6.582225000000001, 3.6632188235294123, 4.274243540669857, 5.019473684210528, 5.7522656250000015), # 108
(5.687093469314727, 4.816155834328954, 5.5932786522633755, 6.589089593397746, 6.416473008956671, 3.4772119798811163, 3.7364341967239576, 3.715959122085049, 6.569320130315502, 3.649224510610829, 4.257246429204325, 5.0040492383221435, 5.740561021090536), # 109
(5.66913132939703, 4.789954201513343, 5.581012088477368, 6.570755334138488, 6.4045781893004134, 3.4691929583904897, 3.718822261414854, 3.7038408779149523, 6.5565132030178335, 3.6351989026063105, 4.240512581959951, 4.988610930618729, 5.728894997427985), # 110
(5.6513323671497595, 4.763872043010754, 5.56866527777778, 6.552433152173914, 6.392712418300655, 3.4613604938271614, 3.7012545907251795, 3.6920518518518524, 6.543815740740742, 3.6211423093681923, 4.224067942583733, 4.9731656920077985, 5.717282986111112), # 111
(5.633724065127931, 4.737921784149741, 5.556240689300413, 6.5341356078905, 6.380890220285646, 3.45373214449017, 3.6837375476939753, 3.680598628257888, 6.53123926611797, 3.60705504074881, 4.207938454722666, 4.957720453396867, 5.705740419238684), # 112
(5.616302534221828, 4.712159171271572, 5.543770696951242, 6.515900329495225, 6.3691054081042235, 3.4463218615287086, 3.6663155781940624, 3.6695115412683643, 6.518827686755173, 3.592982841967638, 4.192154343691721, 4.942315761902657, 5.6942663405059335), # 113
(5.59888853874004, 4.686838310115322, 5.531427405012972, 6.497873652766403, 6.357236012739796, 3.439112765117941, 3.649210925046348, 3.6589267557030722, 6.506771421427837, 3.57918910074767, 4.176746602267919, 4.927147303267786, 5.682765248496022), # 114
(5.581430941802398, 4.661968319209828, 5.519218159624391, 6.480050703109068, 6.345244606733162, 3.4320861074992592, 3.6324357901149376, 3.648841592567226, 6.495074987201274, 3.5656951967928467, 4.161692706856257, 4.912222549535311, 5.6712039789962265), # 115
(5.5639079239425815, 4.637512968775022, 5.507119313990325, 6.4623996901598435, 6.333113115830268, 3.425225326372259, 3.6159628905167365, 3.6392281877393966, 6.483708803536699, 3.552476015069729, 4.146963558762889, 4.897513918492408, 5.659564355853536), # 116
(5.546297665694264, 4.613436029030833, 5.4951072213155925, 6.444888823555347, 6.320823465777067, 3.4185138594365347, 3.599764943368652, 3.6300586770981513, 6.472643289895323, 3.5395064405448777, 4.132530059293971, 4.882993827926251, 5.647828202914936), # 117
(5.528578347591128, 4.589701270197195, 5.483158234805021, 6.427486312932201, 6.308357582319506, 3.4119351443916837, 3.58381466578759, 3.6213051965220586, 6.461848865738362, 3.526761358184856, 4.118363109755655, 4.86863469562401, 5.635977344027416), # 118
(5.5107281501668455, 4.566272462494038, 5.471248707663435, 6.410160367927024, 6.295697391203533, 3.4054726189373, 3.568084774890454, 3.6129398818896874, 6.451295950527027, 3.5142156529562234, 4.104433611454097, 4.8544089393728616, 5.6239936030379605), # 119
(5.492725253955098, 4.543113376141296, 5.4593549930956575, 6.392879198176438, 6.282824818175099, 3.3991097207729797, 3.5525479877941524, 3.604934869079606, 6.440954963722536, 3.501844209825544, 4.09071246569545, 4.840288976959979, 5.611858803793559), # 120
(5.474547839489562, 4.520187781358898, 5.447453444306514, 6.375611013317061, 6.269721788980152, 3.3928298875983187, 3.5371770216155887, 3.5972622939703847, 6.4307963247861, 3.489621913759378, 4.07717057378587, 4.8262472261725335, 5.599554770141197), # 121
(5.456174087303913, 4.497459448366778, 5.435520414500828, 6.3583240229855145, 6.2563702293646415, 3.386616557112912, 3.521944593471671, 3.5898942924405897, 6.420790453178935, 3.4775236497242865, 4.063778837031511, 4.812256104797701, 5.587063325927863), # 122
(5.437582177931832, 4.474892147384865, 5.423532256883422, 6.340986436818417, 6.242752065074515, 3.3804531670163556, 3.5068234204793036, 3.5828030003687905, 6.410907768362253, 3.4655243026868328, 4.050508156738527, 4.798288030622653, 5.5743662950005435), # 123
(5.418750291906993, 4.452449648633093, 5.411465324659123, 6.323566464452393, 6.228849221855723, 3.374323155008244, 3.491786219755392, 3.575960553633556, 6.40111868979727, 3.453598757613577, 4.037329434213072, 4.784315421434566, 5.561445501206228), # 124
(5.399656609763076, 4.430095722331393, 5.399295971032755, 6.306032315524057, 6.214643625454214, 3.368209958788174, 3.476805708416844, 3.569339088113455, 6.391393636945197, 3.441721899471081, 4.024213570761301, 4.770310695020609, 5.548282768391898), # 125
(5.380279312033758, 4.407794138699697, 5.38700054920914, 6.288352199670033, 6.200117201615937, 3.362097016055741, 3.4618546035805626, 3.562910739687056, 6.381703029267252, 3.429868613225906, 4.011131467689368, 4.756246269167961, 5.534859920404548), # 126
(5.360596579252717, 4.385508667957935, 5.374555412393104, 6.27049432652694, 6.18525187608684, 3.3559677645105395, 3.446905622363457, 3.556647644232928, 6.372017286224645, 3.4180137838446147, 3.9980540263034277, 4.74209456166379, 5.52115878109116), # 127
(5.340586591953628, 4.363203080326041, 5.361936913789471, 6.2524269057314, 6.170029574612875, 3.349805641852167, 3.4319314818824296, 3.5505219376296377, 6.362306827278592, 3.406132296293768, 3.984952147909635, 4.727827990295274, 5.507161174298723), # 128
(5.320227530670169, 4.340841146023945, 5.349121406603064, 6.2341181469200295, 6.154432222939987, 3.343594085780217, 3.4169048992543885, 3.5445057557557567, 6.352542071890306, 3.394199035539928, 3.971796733814143, 4.713418972849582, 5.492848923874224), # 129
(5.29949757593602, 4.3183866352715805, 5.336085244038708, 6.215536259729452, 6.138441746814129, 3.3373165339942856, 3.401798591596238, 3.538571234489851, 6.3426934395210015, 3.3821888865496548, 3.958558685323107, 4.698839927113892, 5.47820385366465), # 130
(5.2783749082848574, 4.295803318288876, 5.322804779301229, 6.196649453796286, 6.122040071981248, 3.330956424193969, 3.386585276024886, 3.5326905097104904, 6.332731349631892, 3.3700767342895115, 3.945208903742681, 4.6840632708753756, 5.463207787516988), # 131
(5.256837708250356, 4.273054965295767, 5.309256365595449, 6.177425938757153, 6.105209124187293, 3.324497194078862, 3.3712376696572357, 3.5268357172962443, 6.322626221684193, 3.3578374637260593, 3.931718290379019, 4.669061421921206, 5.447842549278226), # 132
(5.234864156366198, 4.250105346512182, 5.295416356126191, 6.1578339242486715, 6.087930829178212, 3.3179222813485603, 3.355728489610194, 3.52097899312568, 6.312348475139117, 3.34544595982586, 3.9180577465382767, 4.6538067980385565, 5.432089962795352), # 133
(5.212432433166057, 4.226918232158054, 5.281261104098283, 6.137841619907463, 6.070187112699956, 3.3112151237026595, 3.3400304530006677, 3.5150924730773667, 6.301868529457878, 3.3328771075554746, 3.904198173526608, 4.6382718170146005, 5.41593185191535), # 134
(5.189520719183613, 4.203457392453315, 5.266766962716546, 6.117417235370148, 6.051959900498473, 3.3043591588407555, 3.324116276945561, 3.5091482930298725, 6.291156804101688, 3.320105791881466, 3.890110472650166, 4.622428896636512, 5.399350040485213), # 135
(5.1661071949525414, 4.179686597617896, 5.251910285185806, 6.0965289802733444, 6.033231118319711, 3.2973378244624443, 3.307958678561781, 3.5031185888617666, 6.280183718531765, 3.307106897770394, 3.8757655452151067, 4.606250454691465, 5.382326352351923), # 136
(5.142170041006521, 4.155569617871729, 5.236667424710887, 6.075145064253676, 6.0139826919096215, 3.290134558267319, 3.2915303749662335, 3.496975496451618, 6.26891969220932, 3.2938553101888215, 3.8611342925275838, 4.5897089089666325, 5.364842611362467), # 137
(5.117687437879229, 4.131070223434746, 5.221014734496611, 6.053233696947759, 5.994196547014152, 3.2827327979549787, 3.274804083275823, 3.4906911516779946, 6.25733514459557, 3.2803259141033085, 3.8461876158937516, 4.572776677249188, 5.346880641363835), # 138
(5.092637566104342, 4.106152184526877, 5.204928567747805, 6.030763087992216, 5.9738546093792495, 3.2751159812250163, 3.2577525206074562, 3.484237690419465, 6.245400495151723, 3.2664935944804188, 3.8308964166197645, 4.5554261773263045, 5.328422266203012), # 139
(5.066998606215539, 4.080779271368056, 5.188385277669293, 6.007701447023668, 5.952938804750868, 3.267267545777028, 3.2403484040780386, 3.477587248554598, 6.233086163339, 3.2523332362867117, 3.8152315960117766, 4.537629826985156, 5.30944930972699), # 140
(5.040748738746498, 4.054915254178213, 5.171361217465897, 5.984016983678733, 5.931431058874953, 3.2591709293106095, 3.2225644508044775, 3.4707119619619626, 6.220362568618609, 3.237819724488751, 3.7991640553759436, 4.519360044012916, 5.2899435957827485), # 141
(5.013866144230894, 4.02852390317728, 5.153832740342443, 5.959677907594033, 5.9093132974974525, 3.2508095695253565, 3.2043733779036763, 3.4635839665201273, 6.207200130451766, 3.222927944053097, 3.7826646960184185, 4.500589246196757, 5.26988694821728), # 142
(4.986329003202405, 4.001568988585189, 5.135776199503756, 5.934652428406186, 5.886567446364318, 3.242166904120864, 3.1857479024925417, 3.4561753981076597, 6.193569268299689, 3.207632779946312, 3.765704419245356, 4.481289851323854, 5.249261190877571), # 143
(4.958115496194711, 3.9740142806218723, 5.117167948154657, 5.908908755751814, 5.8631754312214985, 3.2332263707967286, 3.1666607416879797, 3.44845839260313, 6.179440401623586, 3.1919091171349563, 3.748254126362911, 4.46143427718138, 5.228048147610609), # 144
(4.929203803741487, 3.94582354950726, 5.097984339499974, 5.882415099267537, 5.839119177814941, 3.2239714072525447, 3.1470846126068963, 3.4404050858851063, 6.164783949884673, 3.175731840585593, 3.7302847186772374, 4.440994941556509, 5.206229642263381), # 145
(4.899572106376411, 3.916960565461286, 5.07820172674453, 5.855139668589977, 5.814380611890596, 3.214385451187909, 3.1269922323661983, 3.4319876138321566, 6.149570332544164, 3.1590758352647828, 3.71176709749449, 4.419944262236412, 5.183787498682872), # 146
(4.8691985846331605, 3.8873890987038786, 5.057796463093147, 5.827050673355749, 5.788941659194411, 3.204451940302415, 3.106356318082789, 3.4231781123228497, 6.133769969063275, 3.1419159861390877, 3.692672164120822, 4.398254657008267, 5.160703540716072), # 147
(4.838061419045413, 3.857072919454973, 5.036744901750651, 5.798116323201479, 5.7627842454723375, 3.194154312295661, 3.0851495868735763, 3.4139487172357548, 6.117353278903218, 3.124227178175069, 3.67297081986239, 4.375898543659242, 5.136959592209966), # 148
(4.806138790146848, 3.8259757979344986, 5.015023395921867, 5.7683048277637825, 5.735890296470323, 3.18347600486724, 3.0633447558554647, 3.4042715644494406, 6.100290681525204, 3.1059842963392885, 3.652633966025346, 4.352848339976515, 5.112537477011543), # 149
(4.773408878471139, 3.794061504362388, 4.992608298811617, 5.737584396679283, 5.708241737934316, 3.17240045571675, 3.040914542145361, 3.3941187898424743, 6.082552596390452, 3.0871622255983064, 3.631632503915846, 4.329076463747258, 5.087419018967789), # 150
(4.7398498645519656, 3.761293808958573, 4.969475963624726, 5.705923239584599, 5.679820495610267, 3.1609111025437833, 3.0178316628601705, 3.3834625292934266, 6.0641094429601745, 3.067735850918687, 3.609937334840043, 4.304555332758643, 5.0615860419256915), # 151
(4.705439928923006, 3.727636481942984, 4.945602743566021, 5.673289566116352, 5.650608495244122, 3.1489913830479384, 2.994068835116799, 3.372274918680865, 6.044931640695583, 3.04768005726699, 3.5875193601040936, 4.2792573647978465, 5.035020369732239), # 152
(4.670157252117937, 3.6930532935355544, 4.9209649918403215, 5.63965158591116, 5.620587662581834, 3.1366247349288097, 2.969598776032152, 3.3605280938833575, 6.0249896090578945, 3.0269697296097777, 3.5643494810141503, 4.253154977652039, 5.007703826234417), # 153
(4.6339800146704375, 3.657508013956215, 4.895539061652456, 5.604977508605646, 5.589739923369349, 3.1237945958859927, 2.9443942027231373, 3.3481941907794743, 6.00425376750832, 3.005579752913612, 3.5403985988763678, 4.2262205891083955, 4.979618235279215), # 154
(4.596886397114182, 3.6209644134248973, 4.869301306207246, 5.569235543836427, 5.5580472033526185, 3.110484403619083, 2.9184278323066573, 3.335245345247782, 5.982694535508077, 2.983485012145053, 3.5156376149969004, 4.198426616954089, 4.950745420713616), # 155
(4.5588545799828495, 3.5833862621615333, 4.842228078709517, 5.532393901240126, 5.525491428277589, 3.096677595827677, 2.89167238189962, 3.3216536931668514, 5.960282332518377, 2.9606603922706642, 3.490037430681903, 4.169745478976294, 4.9210672063846115), # 156
(4.519862743810118, 3.5447373303860545, 4.814295732364092, 5.494420790453363, 5.492054523890212, 3.0823576102113686, 2.8641005686189316, 3.3073913704152496, 5.936987578000434, 2.937080778257005, 3.4635689472375297, 4.1401495929621825, 4.890565416139187), # 157
(4.478808567843144, 3.5042718724633555, 4.784155172341414, 5.453861748990747, 5.4562086635226, 3.0666146857902663, 2.8350640325567147, 3.291478171409624, 5.910997254959459, 2.9120195497746866, 3.435357451523366, 4.108559738516604, 4.857891515649208), # 158
(4.429372060187042, 3.457839191759687, 4.744042691041793, 5.402386295273073, 5.409114785868978, 3.0442137888042624, 2.80092765803143, 3.26832811965863, 5.8718563567332875, 2.8813685964592475, 3.400450161371397, 4.068817514209865, 4.815256588152117), # 159
(4.370923256942587, 3.405058037124429, 4.693152574773534, 5.339146506245316, 5.349852078808078, 3.0146047776286107, 2.7613462490302707, 3.237359513716408, 5.8184551363371915, 2.8447233911177365, 3.3583557051657245, 4.020301169332709, 4.761852365336149), # 160
(4.303933232751577, 3.3462725208482818, 4.632028183146186, 5.264743502254038, 5.279035874569268, 2.9781463449421888, 2.7166088676041262, 3.1989603198658685, 5.751497860199412, 2.8023949159025627, 3.3094449927598246, 3.963460481344543, 4.698224426891459), # 161
(4.228873062255815, 3.281826755221944, 4.561212875769298, 5.179778403645797, 5.197281505381923, 2.9351971834238735, 2.667004575803886, 3.1535185043899214, 5.671688794748182, 2.7546941529661395, 3.254088934007173, 3.8987452277047767, 4.6249183525082005), # 162
(4.146213820097099, 3.212064852536115, 4.4812500122524215, 5.084852330767161, 5.105204303475413, 2.886115985752544, 2.6128224356804406, 3.1014220335714753, 5.5797322064117445, 2.7019320844608754, 3.1926584387612453, 3.82660518587282, 4.5424797218765285), # 163
(4.056426580917231, 3.1373309250814927, 4.3926829522051065, 4.9805664039646915, 5.003419601079114, 2.831261444607078, 2.55435150928468, 3.043058873693442, 5.476332361618335, 2.644419692539181, 3.125524416875518, 3.7474901333080823, 4.451454114686597), # 164
(3.9599824193580107, 3.0579690851487795, 4.296055055236902, 4.867521743584952, 4.892542730422395, 2.770992252666352, 2.4918808586674936, 2.978816991038728, 5.362193526796188, 2.5824679593534685, 3.0530577782034674, 3.6618498474699726, 4.3523871106285625), # 165
(3.857352410061239, 2.974323445028673, 4.191909680957357, 4.746319469974502, 4.773189023734629, 2.7056671026092456, 2.425699545879772, 2.9090843518902467, 5.238019968373544, 2.5163878670561477, 2.9756294325985677, 3.5701341058179015, 4.245824289392578), # 166
(3.749007627668714, 2.886738117011873, 4.080790188976023, 4.617560703479906, 4.645973813245188, 2.6356446871146355, 2.356096632972405, 2.834248922530906, 5.10451595277864, 2.4464903977996286, 2.8936102899142964, 3.4727926858112754, 4.132311230668798), # 167
(3.6354191468222377, 2.7955572133890776, 3.9632399389024493, 4.4818465644477286, 4.511512431183446, 2.5612836988614, 2.2833611819962827, 2.754698669243616, 4.962385746439714, 2.3730865337363234, 2.8073712600041287, 3.3702753649095074, 4.012393514147377), # 168
(3.5170580421636095, 2.701124846450988, 3.839802290346186, 4.339778173224531, 4.370420209778772, 2.482942830528417, 2.207782255002295, 2.6708215583112875, 4.812333615785003, 2.2964872570186423, 2.717283252721541, 3.2630319205720038, 3.8866167195184715), # 169
(3.3943953883346305, 2.603785128488303, 3.7110206029167814, 4.191956650156873, 4.223312481260541, 2.400980774794564, 2.129648914041332, 2.5830055560168286, 4.655063827242744, 2.217003549798995, 2.62371717792001, 3.1515121302581757, 3.7555264264722337), # 170
(3.2679022599771006, 2.503882171791721, 3.577438236223787, 4.038983115591321, 4.070804577858124, 2.3157562243387195, 2.0492502211642836, 2.49163862864315, 4.491280647241174, 2.1349463942297935, 2.527043945453009, 3.036165771427432, 3.6196682146988195), # 171
(3.1380497317328193, 2.401760088651942, 3.4395985498767523, 3.8814586898744383, 3.913511831800893, 2.2276278718397604, 1.9668752384220396, 2.3971087424731627, 4.321688342208532, 2.050626772463448, 2.4276344651740165, 2.917442621539183, 3.4795876638883825), # 172
(3.0053088782435884, 2.2977629913596656, 3.2980449034852275, 3.719984493352786, 3.7520495753182215, 2.1369544099765654, 1.88281302786549, 2.2998038637897746, 4.146991178573054, 1.9643556666523692, 2.325859646936507, 2.7957924580528353, 3.335830353731078), # 173
(2.8701507741512065, 2.192234992205591, 3.1533206566587615, 3.555161646372925, 3.5870331406394804, 2.0440945314280112, 1.7973526515455256, 2.2001119588758966, 3.967893422762979, 1.8764440589489682, 2.222090400593957, 2.671665058427801, 3.1889418639170604), # 174
(2.7330464940974735, 2.0855202034804172, 3.0059691690069053, 3.3875912692814207, 3.419077859994042, 1.9494069288729774, 1.710783171513035, 2.098420994014438, 3.7850993412065437, 1.7872029315056548, 2.116697635999842, 2.545510200123489, 3.039467774136485), # 175
(2.5944671127241916, 1.977962737474844, 2.8565338001392075, 3.2178744824248353, 3.2487990656112786, 1.8532502949903402, 1.6233936498189092, 1.9951189354883097, 3.5993132003319857, 1.6969432664748405, 2.0100522630076383, 2.4177776605993078, 2.8879536640795047), # 176
(2.45488370467316, 1.8699067064795711, 2.7055579096652185, 3.0466124061497304, 3.076812089720564, 1.7559833224589783, 1.5354731485140378, 1.89059374958042, 3.4112392665675424, 1.605976046008935, 1.9025251914708217, 2.2889172173146677, 2.734945113436275), # 177
(2.3147673445861785, 1.7616962227852973, 2.5535848571944886, 2.87440616080267, 2.9037322645512686, 1.6579647039577698, 1.4473107296493106, 1.7852334025736796, 3.221581806341452, 1.5146122522603502, 1.7944873312428677, 2.1593786477289765, 2.5809877018969516), # 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 = (
(2, 4, 1, 3, 2, 0, 2, 2, 1, 1, 1, 0, 0, 1, 2, 2, 1, 3, 2, 2, 0, 2, 1, 1, 1, 0), # 0
(6, 5, 5, 7, 3, 1, 3, 4, 2, 2, 2, 0, 0, 5, 6, 5, 4, 9, 2, 3, 1, 2, 2, 1, 2, 0), # 1
(8, 8, 8, 7, 5, 1, 5, 5, 3, 2, 2, 0, 0, 6, 13, 8, 9, 10, 4, 6, 1, 4, 2, 1, 2, 0), # 2
(10, 12, 10, 11, 7, 2, 8, 7, 6, 2, 2, 0, 0, 9, 18, 11, 13, 14, 6, 8, 3, 6, 2, 1, 2, 0), # 3
(15, 15, 18, 14, 9, 2, 9, 9, 10, 3, 4, 0, 0, 11, 24, 12, 15, 18, 8, 10, 4, 7, 4, 1, 2, 0), # 4
(19, 17, 21, 18, 11, 3, 10, 12, 12, 4, 5, 1, 0, 12, 29, 13, 16, 20, 11, 11, 6, 11, 6, 2, 3, 0), # 5
(25, 18, 25, 22, 13, 4, 12, 15, 12, 4, 7, 1, 0, 20, 34, 16, 18, 25, 13, 14, 7, 13, 6, 2, 3, 0), # 6
(29, 23, 29, 26, 17, 6, 15, 15, 14, 4, 8, 1, 0, 26, 38, 18, 22, 29, 17, 15, 9, 14, 7, 3, 5, 0), # 7
(32, 29, 30, 31, 17, 6, 16, 16, 15, 4, 8, 2, 0, 33, 42, 20, 31, 33, 18, 21, 10, 16, 8, 3, 5, 0), # 8
(39, 36, 32, 35, 20, 8, 19, 20, 16, 6, 8, 3, 0, 37, 46, 23, 36, 37, 22, 22, 11, 16, 10, 5, 5, 0), # 9
(43, 44, 37, 37, 21, 9, 22, 23, 18, 7, 8, 4, 0, 39, 48, 27, 37, 41, 26, 25, 13, 18, 10, 5, 5, 0), # 10
(48, 51, 44, 39, 24, 11, 24, 25, 19, 8, 9, 4, 0, 44, 54, 31, 42, 48, 27, 25, 14, 20, 11, 6, 6, 0), # 11
(52, 56, 51, 40, 31, 12, 26, 27, 20, 8, 9, 5, 0, 49, 56, 35, 44, 50, 31, 28, 15, 21, 12, 7, 6, 0), # 12
(57, 62, 58, 44, 35, 13, 29, 31, 24, 11, 9, 5, 0, 49, 63, 38, 46, 56, 35, 32, 20, 23, 17, 7, 7, 0), # 13
(67, 68, 67, 47, 42, 13, 33, 32, 24, 12, 10, 6, 0, 52, 65, 42, 48, 60, 39, 33, 21, 25, 17, 8, 7, 0), # 14
(72, 75, 70, 53, 48, 14, 33, 34, 26, 13, 11, 6, 0, 53, 70, 44, 51, 62, 43, 35, 22, 26, 19, 8, 8, 0), # 15
(75, 79, 74, 56, 53, 15, 34, 35, 28, 14, 11, 6, 0, 57, 76, 48, 54, 69, 47, 37, 24, 27, 20, 10, 8, 0), # 16
(80, 82, 79, 61, 57, 15, 38, 35, 33, 15, 13, 6, 0, 60, 81, 51, 56, 71, 50, 40, 24, 28, 24, 10, 9, 0), # 17
(86, 92, 84, 64, 61, 19, 44, 37, 33, 16, 13, 6, 0, 62, 86, 55, 60, 71, 57, 43, 24, 31, 26, 10, 9, 0), # 18
(92, 98, 88, 70, 65, 20, 44, 38, 35, 17, 14, 6, 0, 68, 92, 61, 64, 73, 61, 45, 26, 34, 28, 10, 10, 0), # 19
(99, 107, 91, 76, 71, 24, 46, 39, 39, 18, 16, 6, 0, 75, 94, 69, 70, 77, 63, 47, 26, 36, 30, 10, 11, 0), # 20
(104, 112, 95, 81, 79, 25, 47, 44, 41, 20, 18, 6, 0, 85, 98, 70, 74, 82, 65, 52, 30, 41, 30, 10, 11, 0), # 21
(109, 123, 102, 84, 82, 27, 53, 47, 42, 20, 18, 6, 0, 92, 104, 71, 79, 86, 68, 54, 30, 43, 32, 11, 11, 0), # 22
(114, 127, 107, 89, 87, 27, 55, 49, 45, 20, 20, 7, 0, 93, 107, 75, 86, 90, 72, 58, 31, 45, 33, 12, 11, 0), # 23
(117, 132, 111, 94, 89, 31, 56, 52, 49, 21, 20, 8, 0, 101, 116, 81, 89, 95, 72, 62, 32, 48, 35, 15, 11, 0), # 24
(126, 139, 115, 99, 93, 34, 57, 53, 49, 23, 20, 8, 0, 106, 126, 85, 90, 101, 74, 62, 34, 48, 37, 15, 11, 0), # 25
(135, 144, 121, 100, 95, 36, 60, 55, 49, 25, 20, 9, 0, 113, 128, 88, 95, 105, 74, 63, 37, 50, 39, 16, 11, 0), # 26
(144, 148, 122, 105, 100, 38, 63, 57, 52, 25, 21, 10, 0, 121, 133, 95, 102, 110, 77, 65, 37, 55, 42, 17, 11, 0), # 27
(155, 154, 126, 113, 104, 41, 67, 59, 55, 28, 21, 10, 0, 127, 144, 98, 103, 113, 78, 65, 38, 57, 45, 17, 13, 0), # 28
(160, 161, 131, 119, 109, 43, 70, 62, 57, 28, 22, 10, 0, 134, 151, 99, 109, 120, 80, 65, 41, 61, 49, 17, 13, 0), # 29
(163, 168, 134, 129, 112, 44, 75, 63, 59, 28, 22, 11, 0, 139, 157, 103, 115, 125, 84, 68, 42, 65, 50, 18, 14, 0), # 30
(170, 173, 142, 134, 117, 48, 76, 64, 59, 28, 24, 11, 0, 143, 165, 113, 117, 131, 88, 70, 43, 69, 50, 18, 14, 0), # 31
(176, 177, 149, 136, 122, 50, 77, 67, 61, 28, 25, 12, 0, 149, 171, 115, 121, 134, 90, 73, 45, 70, 51, 19, 15, 0), # 32
(180, 184, 154, 143, 127, 50, 80, 68, 64, 28, 27, 13, 0, 153, 176, 122, 123, 140, 92, 75, 45, 71, 56, 19, 15, 0), # 33
(188, 193, 161, 149, 129, 50, 83, 71, 67, 30, 27, 13, 0, 162, 179, 125, 125, 141, 94, 78, 48, 73, 57, 19, 15, 0), # 34
(191, 197, 166, 155, 131, 52, 85, 74, 68, 32, 29, 13, 0, 168, 182, 130, 129, 147, 98, 82, 48, 77, 57, 21, 17, 0), # 35
(197, 207, 173, 164, 137, 54, 87, 78, 70, 34, 29, 13, 0, 172, 188, 134, 131, 154, 104, 86, 49, 78, 57, 21, 18, 0), # 36
(206, 217, 176, 170, 138, 56, 89, 82, 73, 37, 29, 14, 0, 177, 192, 139, 137, 160, 106, 88, 49, 79, 60, 21, 19, 0), # 37
(206, 223, 181, 172, 147, 56, 90, 83, 78, 38, 29, 14, 0, 181, 197, 144, 143, 164, 109, 93, 51, 83, 60, 21, 20, 0), # 38
(212, 229, 185, 176, 153, 59, 93, 86, 80, 41, 31, 15, 0, 190, 202, 148, 143, 167, 112, 93, 53, 83, 60, 22, 20, 0), # 39
(221, 233, 194, 180, 159, 62, 94, 89, 81, 42, 32, 15, 0, 197, 205, 151, 145, 172, 115, 97, 55, 86, 64, 22, 22, 0), # 40
(223, 236, 198, 185, 161, 65, 97, 91, 82, 43, 33, 16, 0, 201, 208, 153, 148, 178, 121, 101, 58, 91, 65, 24, 22, 0), # 41
(228, 242, 204, 190, 163, 65, 99, 92, 84, 44, 34, 16, 0, 207, 213, 157, 150, 185, 123, 103, 58, 92, 65, 24, 24, 0), # 42
(236, 247, 210, 193, 169, 67, 100, 94, 87, 45, 35, 17, 0, 213, 219, 158, 155, 189, 125, 105, 60, 94, 65, 24, 25, 0), # 43
(243, 251, 215, 205, 170, 70, 102, 97, 90, 46, 37, 17, 0, 219, 224, 161, 157, 192, 127, 107, 61, 99, 68, 27, 26, 0), # 44
(247, 259, 221, 210, 172, 71, 104, 99, 92, 47, 38, 17, 0, 222, 231, 164, 162, 194, 133, 108, 62, 102, 69, 27, 26, 0), # 45
(252, 264, 223, 212, 174, 74, 108, 101, 95, 47, 38, 17, 0, 226, 233, 167, 163, 200, 136, 113, 63, 102, 71, 28, 27, 0), # 46
(258, 272, 228, 217, 177, 77, 109, 103, 98, 51, 39, 17, 0, 233, 238, 174, 165, 203, 137, 116, 63, 107, 72, 30, 27, 0), # 47
(266, 279, 234, 226, 188, 78, 109, 107, 100, 52, 40, 17, 0, 236, 244, 177, 167, 207, 140, 118, 66, 111, 74, 31, 27, 0), # 48
(272, 289, 235, 233, 190, 80, 111, 107, 102, 52, 43, 17, 0, 242, 250, 183, 174, 211, 142, 120, 67, 115, 75, 31, 27, 0), # 49
(277, 292, 240, 242, 196, 81, 111, 108, 103, 53, 43, 17, 0, 246, 255, 184, 178, 216, 144, 121, 68, 116, 78, 31, 28, 0), # 50
(282, 296, 244, 247, 198, 85, 115, 111, 107, 54, 44, 19, 0, 253, 255, 191, 181, 222, 146, 122, 69, 117, 80, 31, 28, 0), # 51
(286, 299, 247, 254, 201, 88, 115, 114, 111, 56, 44, 19, 0, 257, 263, 194, 181, 227, 146, 126, 71, 118, 83, 34, 28, 0), # 52
(291, 302, 253, 258, 205, 92, 117, 118, 111, 57, 45, 19, 0, 260, 268, 198, 185, 233, 150, 129, 75, 122, 87, 34, 29, 0), # 53
(298, 307, 260, 264, 212, 93, 117, 119, 112, 57, 46, 20, 0, 265, 273, 201, 192, 238, 151, 132, 76, 125, 88, 34, 30, 0), # 54
(307, 314, 266, 271, 213, 96, 119, 122, 116, 59, 47, 20, 0, 268, 276, 203, 196, 243, 151, 136, 78, 129, 90, 35, 32, 0), # 55
(312, 324, 271, 283, 213, 98, 119, 122, 116, 62, 47, 20, 0, 273, 281, 207, 205, 247, 155, 140, 82, 130, 90, 35, 32, 0), # 56
(315, 327, 276, 290, 221, 101, 121, 123, 117, 63, 50, 20, 0, 283, 285, 210, 210, 256, 159, 141, 84, 135, 92, 36, 33, 0), # 57
(322, 334, 282, 293, 228, 103, 123, 127, 122, 65, 51, 20, 0, 290, 289, 215, 215, 261, 160, 143, 85, 137, 95, 37, 33, 0), # 58
(327, 341, 285, 301, 233, 103, 127, 130, 123, 66, 51, 20, 0, 296, 293, 221, 222, 266, 162, 145, 85, 140, 97, 37, 33, 0), # 59
(333, 346, 295, 305, 236, 104, 131, 131, 127, 66, 52, 20, 0, 300, 300, 226, 227, 269, 163, 147, 86, 142, 99, 41, 33, 0), # 60
(339, 354, 300, 310, 238, 106, 132, 131, 129, 67, 52, 23, 0, 307, 303, 229, 228, 274, 169, 148, 88, 146, 101, 46, 33, 0), # 61
(347, 359, 303, 314, 245, 106, 135, 134, 129, 67, 53, 23, 0, 315, 312, 231, 231, 281, 172, 149, 89, 147, 103, 47, 33, 0), # 62
(354, 364, 307, 322, 248, 107, 138, 136, 135, 68, 54, 23, 0, 321, 317, 234, 237, 287, 175, 152, 92, 148, 103, 48, 34, 0), # 63
(361, 368, 310, 332, 253, 109, 140, 138, 140, 69, 57, 23, 0, 331, 321, 239, 239, 290, 178, 153, 94, 151, 107, 50, 35, 0), # 64
(367, 375, 318, 335, 254, 110, 145, 141, 144, 70, 58, 25, 0, 340, 325, 245, 242, 293, 178, 154, 95, 153, 112, 50, 35, 0), # 65
(370, 378, 327, 346, 255, 113, 145, 141, 144, 72, 58, 25, 0, 347, 330, 250, 245, 299, 179, 159, 96, 156, 114, 50, 35, 0), # 66
(375, 383, 331, 353, 258, 115, 147, 142, 149, 73, 59, 26, 0, 355, 333, 257, 248, 304, 181, 162, 100, 156, 116, 50, 36, 0), # 67
(381, 387, 338, 361, 261, 117, 150, 142, 151, 75, 62, 27, 0, 362, 341, 258, 250, 308, 182, 163, 100, 156, 118, 50, 36, 0), # 68
(388, 392, 343, 368, 267, 118, 152, 146, 153, 77, 63, 27, 0, 369, 346, 261, 252, 314, 186, 166, 101, 158, 119, 51, 36, 0), # 69
(396, 400, 345, 369, 274, 121, 155, 147, 158, 77, 65, 27, 0, 375, 350, 266, 253, 316, 188, 166, 102, 161, 122, 51, 36, 0), # 70
(399, 405, 348, 379, 280, 124, 157, 147, 161, 77, 65, 28, 0, 386, 355, 272, 256, 321, 189, 168, 105, 164, 123, 51, 36, 0), # 71
(403, 414, 350, 382, 286, 127, 157, 152, 162, 77, 65, 28, 0, 395, 356, 276, 262, 325, 191, 169, 105, 165, 123, 52, 36, 0), # 72
(407, 421, 352, 387, 289, 133, 158, 153, 163, 80, 67, 28, 0, 400, 361, 279, 265, 332, 195, 171, 105, 166, 123, 52, 36, 0), # 73
(414, 422, 358, 389, 295, 134, 159, 155, 167, 81, 67, 28, 0, 403, 364, 281, 269, 336, 199, 175, 105, 169, 124, 53, 36, 0), # 74
(421, 428, 365, 399, 298, 135, 160, 157, 169, 82, 67, 29, 0, 408, 372, 285, 271, 339, 200, 176, 105, 173, 124, 55, 37, 0), # 75
(430, 440, 369, 401, 301, 135, 162, 159, 173, 84, 67, 29, 0, 417, 372, 290, 273, 344, 200, 179, 106, 175, 127, 56, 39, 0), # 76
(439, 447, 377, 407, 310, 136, 164, 163, 173, 84, 68, 29, 0, 425, 375, 292, 278, 347, 201, 180, 109, 178, 128, 56, 39, 0), # 77
(441, 456, 383, 410, 314, 137, 166, 166, 176, 86, 68, 29, 0, 432, 381, 295, 280, 351, 203, 181, 109, 181, 131, 57, 39, 0), # 78
(448, 459, 386, 415, 318, 139, 167, 167, 179, 88, 69, 29, 0, 434, 387, 298, 282, 354, 205, 186, 111, 181, 133, 58, 41, 0), # 79
(453, 462, 391, 418, 319, 142, 169, 170, 181, 89, 70, 29, 0, 440, 392, 301, 283, 360, 209, 189, 113, 181, 134, 61, 41, 0), # 80
(464, 466, 394, 421, 321, 144, 169, 170, 181, 89, 71, 30, 0, 445, 397, 304, 288, 366, 213, 189, 113, 182, 134, 61, 41, 0), # 81
(468, 474, 397, 428, 325, 146, 171, 174, 187, 90, 73, 30, 0, 448, 401, 314, 291, 371, 214, 191, 116, 183, 134, 61, 42, 0), # 82
(473, 479, 402, 432, 330, 148, 171, 175, 189, 90, 75, 30, 0, 451, 407, 318, 298, 372, 215, 193, 119, 184, 134, 62, 43, 0), # 83
(478, 484, 406, 441, 333, 149, 172, 178, 194, 93, 78, 31, 0, 454, 413, 323, 300, 375, 216, 194, 120, 187, 135, 63, 44, 0), # 84
(483, 489, 412, 443, 334, 149, 173, 179, 196, 94, 78, 31, 0, 460, 421, 325, 302, 379, 217, 194, 122, 190, 136, 64, 45, 0), # 85
(489, 490, 416, 451, 335, 149, 174, 180, 196, 96, 78, 32, 0, 465, 426, 327, 306, 384, 220, 200, 124, 192, 137, 65, 46, 0), # 86
(495, 493, 421, 457, 337, 150, 177, 183, 198, 97, 79, 33, 0, 468, 429, 331, 308, 389, 220, 201, 126, 193, 138, 66, 46, 0), # 87
(502, 500, 430, 467, 340, 154, 181, 184, 208, 98, 79, 34, 0, 476, 440, 335, 312, 392, 224, 204, 127, 195, 139, 66, 46, 0), # 88
(507, 503, 433, 476, 344, 155, 183, 186, 210, 100, 80, 34, 0, 486, 443, 340, 314, 398, 230, 207, 128, 197, 141, 68, 47, 0), # 89
(516, 508, 439, 480, 346, 157, 183, 188, 213, 101, 81, 35, 0, 491, 449, 342, 316, 403, 230, 207, 129, 199, 144, 70, 47, 0), # 90
(522, 510, 447, 484, 348, 160, 187, 188, 214, 101, 81, 36, 0, 498, 454, 345, 318, 406, 234, 211, 132, 204, 144, 72, 48, 0), # 91
(533, 514, 452, 489, 354, 161, 190, 193, 217, 101, 82, 36, 0, 504, 460, 350, 321, 411, 237, 215, 133, 204, 146, 74, 48, 0), # 92
(537, 524, 454, 493, 358, 163, 192, 194, 221, 103, 83, 36, 0, 511, 464, 351, 321, 412, 237, 215, 133, 208, 147, 74, 48, 0), # 93
(545, 527, 460, 497, 359, 164, 197, 199, 221, 103, 83, 36, 0, 520, 468, 358, 326, 417, 239, 215, 136, 211, 150, 76, 49, 0), # 94
(552, 534, 468, 502, 364, 165, 198, 199, 224, 103, 84, 36, 0, 526, 471, 359, 329, 419, 243, 216, 137, 214, 151, 76, 49, 0), # 95
(562, 536, 474, 508, 370, 168, 202, 200, 225, 104, 84, 38, 0, 531, 477, 365, 330, 424, 243, 219, 138, 216, 154, 76, 49, 0), # 96
(570, 541, 479, 514, 377, 172, 205, 203, 229, 106, 86, 38, 0, 538, 479, 372, 330, 434, 245, 222, 140, 218, 158, 78, 49, 0), # 97
(573, 544, 482, 522, 381, 175, 207, 203, 229, 106, 86, 38, 0, 542, 482, 372, 332, 435, 246, 224, 141, 222, 160, 78, 49, 0), # 98
(579, 547, 485, 525, 389, 176, 210, 206, 232, 106, 86, 39, 0, 549, 483, 373, 336, 439, 247, 226, 141, 224, 162, 78, 49, 0), # 99
(585, 550, 489, 531, 392, 179, 211, 207, 232, 107, 88, 39, 0, 554, 488, 377, 339, 442, 250, 228, 141, 226, 166, 79, 50, 0), # 100
(588, 552, 491, 540, 401, 182, 211, 208, 232, 109, 89, 39, 0, 562, 489, 381, 343, 444, 250, 232, 143, 228, 166, 79, 50, 0), # 101
(593, 554, 494, 545, 406, 187, 213, 210, 232, 111, 90, 41, 0, 568, 492, 383, 346, 450, 253, 235, 146, 231, 169, 82, 50, 0), # 102
(599, 560, 498, 548, 410, 189, 213, 212, 235, 111, 92, 41, 0, 576, 501, 384, 350, 452, 254, 241, 147, 231, 169, 83, 50, 0), # 103
(607, 565, 501, 552, 417, 192, 216, 213, 238, 112, 92, 41, 0, 583, 505, 391, 353, 457, 256, 241, 148, 235, 172, 83, 51, 0), # 104
(612, 570, 509, 554, 421, 193, 218, 215, 239, 112, 92, 41, 0, 593, 509, 393, 356, 462, 259, 241, 148, 239, 173, 83, 51, 0), # 105
(619, 576, 513, 560, 424, 197, 218, 219, 243, 112, 92, 41, 0, 599, 518, 397, 356, 465, 261, 245, 149, 239, 175, 85, 51, 0), # 106
(624, 580, 519, 569, 433, 197, 218, 222, 249, 113, 93, 42, 0, 603, 523, 400, 358, 465, 261, 248, 150, 240, 181, 86, 52, 0), # 107
(632, 583, 527, 573, 435, 198, 219, 223, 252, 114, 93, 42, 0, 609, 526, 405, 361, 474, 263, 250, 151, 241, 185, 86, 54, 0), # 108
(638, 584, 533, 580, 442, 203, 221, 223, 254, 115, 94, 43, 0, 614, 533, 408, 361, 476, 266, 252, 152, 243, 186, 87, 54, 0), # 109
(642, 591, 539, 582, 446, 203, 224, 224, 259, 115, 95, 43, 0, 621, 538, 410, 361, 481, 266, 257, 152, 245, 189, 87, 54, 0), # 110
(651, 598, 542, 587, 455, 208, 226, 228, 262, 116, 95, 44, 0, 625, 540, 416, 365, 488, 270, 257, 156, 249, 192, 87, 54, 0), # 111
(656, 604, 549, 591, 457, 210, 228, 230, 264, 116, 95, 44, 0, 633, 546, 422, 369, 489, 271, 259, 158, 252, 196, 89, 55, 0), # 112
(663, 609, 550, 596, 463, 212, 230, 234, 267, 118, 96, 44, 0, 640, 550, 429, 372, 492, 272, 263, 158, 253, 196, 89, 55, 0), # 113
(670, 614, 554, 601, 469, 214, 235, 235, 272, 118, 96, 46, 0, 643, 554, 432, 373, 497, 272, 265, 161, 256, 197, 89, 55, 0), # 114
(675, 616, 557, 606, 474, 217, 237, 237, 273, 119, 96, 46, 0, 650, 556, 436, 376, 500, 274, 269, 167, 257, 201, 89, 55, 0), # 115
(687, 623, 562, 610, 477, 217, 240, 237, 273, 120, 96, 46, 0, 654, 556, 438, 381, 501, 276, 272, 168, 259, 202, 90, 55, 0), # 116
(690, 628, 567, 616, 479, 221, 243, 239, 275, 120, 96, 47, 0, 657, 566, 440, 383, 503, 281, 274, 173, 263, 203, 92, 55, 0), # 117
(692, 628, 573, 618, 485, 223, 246, 241, 280, 120, 96, 48, 0, 659, 571, 446, 385, 505, 283, 274, 174, 264, 204, 94, 56, 0), # 118
(700, 632, 575, 625, 491, 223, 248, 242, 282, 120, 97, 49, 0, 667, 573, 451, 387, 509, 289, 277, 176, 267, 206, 95, 56, 0), # 119
(705, 635, 581, 630, 492, 225, 249, 243, 282, 120, 98, 50, 0, 670, 579, 454, 389, 516, 291, 278, 178, 267, 212, 95, 56, 0), # 120
(707, 638, 589, 635, 498, 225, 250, 244, 282, 123, 99, 50, 0, 676, 584, 455, 390, 520, 295, 281, 178, 269, 214, 95, 56, 0), # 121
(712, 642, 593, 641, 501, 225, 250, 249, 285, 124, 99, 50, 0, 682, 585, 459, 392, 521, 298, 282, 178, 269, 215, 95, 56, 0), # 122
(716, 648, 597, 653, 505, 226, 251, 251, 286, 124, 100, 51, 0, 691, 591, 461, 393, 525, 298, 283, 178, 273, 219, 96, 56, 0), # 123
(719, 651, 604, 656, 508, 227, 254, 252, 287, 125, 102, 51, 0, 693, 595, 462, 396, 528, 298, 284, 178, 277, 221, 98, 58, 0), # 124
(728, 654, 609, 659, 513, 228, 256, 253, 291, 127, 102, 51, 0, 699, 599, 462, 399, 529, 301, 285, 182, 281, 223, 99, 59, 0), # 125
(731, 658, 611, 667, 522, 228, 258, 254, 295, 127, 102, 51, 0, 702, 605, 465, 400, 535, 303, 288, 183, 284, 225, 100, 59, 0), # 126
(740, 662, 618, 670, 525, 231, 259, 255, 297, 127, 102, 51, 0, 705, 613, 468, 401, 541, 308, 289, 184, 285, 228, 102, 61, 0), # 127
(747, 667, 623, 680, 529, 234, 262, 255, 298, 129, 104, 52, 0, 709, 617, 468, 405, 543, 315, 289, 184, 287, 230, 102, 61, 0), # 128
(752, 674, 629, 687, 533, 236, 264, 258, 300, 129, 105, 53, 0, 720, 620, 471, 407, 544, 315, 289, 184, 288, 231, 103, 61, 0), # 129
(758, 677, 634, 693, 535, 238, 265, 261, 300, 130, 105, 53, 0, 724, 622, 474, 409, 547, 317, 291, 188, 289, 233, 103, 61, 0), # 130
(763, 680, 640, 698, 538, 240, 266, 262, 301, 131, 106, 53, 0, 732, 625, 483, 412, 552, 320, 292, 190, 290, 237, 104, 61, 0), # 131
(768, 682, 643, 704, 541, 241, 266, 267, 306, 131, 107, 53, 0, 736, 634, 487, 414, 553, 323, 295, 190, 294, 238, 104, 61, 0), # 132
(771, 688, 645, 713, 547, 242, 267, 267, 309, 131, 107, 54, 0, 742, 636, 489, 416, 558, 326, 295, 191, 296, 239, 105, 62, 0), # 133
(779, 691, 645, 715, 550, 243, 269, 269, 311, 132, 109, 54, 0, 747, 640, 490, 420, 561, 326, 297, 194, 300, 243, 105, 62, 0), # 134
(783, 693, 650, 718, 554, 245, 270, 272, 314, 132, 109, 55, 0, 752, 645, 493, 421, 570, 327, 297, 195, 301, 243, 105, 63, 0), # 135
(788, 697, 655, 720, 559, 246, 271, 273, 317, 134, 109, 56, 0, 760, 649, 498, 425, 574, 328, 298, 198, 305, 245, 108, 63, 0), # 136
(793, 701, 660, 726, 562, 248, 272, 276, 319, 135, 109, 56, 0, 766, 651, 499, 426, 576, 330, 301, 198, 308, 246, 109, 63, 0), # 137
(798, 703, 663, 728, 565, 251, 273, 276, 321, 135, 109, 56, 0, 774, 657, 503, 426, 583, 330, 303, 201, 309, 247, 109, 63, 0), # 138
(805, 705, 670, 733, 573, 252, 276, 276, 322, 135, 110, 56, 0, 780, 659, 506, 427, 585, 330, 303, 202, 312, 247, 109, 64, 0), # 139
(811, 713, 672, 739, 579, 254, 276, 278, 325, 136, 111, 56, 0, 784, 664, 512, 428, 592, 331, 305, 204, 318, 249, 109, 64, 0), # 140
(820, 715, 674, 749, 585, 257, 281, 279, 326, 136, 111, 56, 0, 791, 667, 517, 432, 598, 331, 307, 207, 319, 251, 110, 64, 0), # 141
(824, 719, 676, 752, 589, 260, 282, 280, 327, 136, 111, 56, 0, 795, 671, 520, 432, 604, 333, 309, 207, 321, 252, 112, 64, 0), # 142
(834, 722, 683, 759, 593, 261, 283, 284, 332, 136, 113, 56, 0, 797, 675, 520, 435, 608, 336, 311, 208, 324, 254, 114, 65, 0), # 143
(840, 724, 686, 760, 596, 263, 284, 286, 333, 137, 114, 58, 0, 803, 681, 521, 436, 612, 340, 312, 208, 328, 255, 116, 66, 0), # 144
(845, 731, 689, 763, 599, 266, 288, 287, 337, 137, 114, 58, 0, 811, 687, 523, 438, 615, 342, 312, 209, 330, 258, 116, 66, 0), # 145
(850, 733, 695, 768, 599, 267, 293, 289, 342, 137, 115, 58, 0, 817, 692, 524, 439, 618, 344, 312, 209, 332, 261, 116, 66, 0), # 146
(852, 737, 698, 773, 603, 268, 297, 289, 344, 137, 115, 58, 0, 825, 694, 525, 443, 622, 347, 315, 212, 332, 263, 118, 66, 0), # 147
(860, 738, 702, 774, 604, 268, 298, 290, 346, 137, 118, 58, 0, 831, 700, 529, 445, 627, 348, 317, 214, 336, 264, 118, 66, 0), # 148
(862, 743, 707, 780, 607, 269, 299, 291, 350, 138, 118, 58, 0, 834, 704, 532, 447, 631, 348, 318, 215, 338, 265, 118, 66, 0), # 149
(867, 745, 709, 784, 613, 274, 301, 292, 351, 139, 120, 59, 0, 835, 706, 533, 449, 640, 348, 320, 216, 341, 267, 119, 66, 0), # 150
(871, 748, 712, 792, 618, 274, 303, 292, 355, 140, 121, 59, 0, 839, 708, 538, 452, 645, 350, 324, 218, 343, 269, 119, 66, 0), # 151
(875, 748, 718, 796, 623, 277, 303, 293, 357, 141, 121, 59, 0, 845, 713, 542, 454, 650, 352, 325, 218, 345, 269, 121, 66, 0), # 152
(879, 751, 723, 798, 624, 278, 305, 294, 357, 141, 121, 59, 0, 850, 714, 543, 458, 652, 356, 325, 220, 349, 270, 121, 66, 0), # 153
(882, 751, 729, 803, 628, 280, 307, 295, 358, 141, 124, 59, 0, 855, 717, 544, 459, 653, 356, 326, 223, 352, 270, 121, 66, 0), # 154
(885, 757, 730, 805, 632, 281, 309, 296, 366, 142, 124, 59, 0, 863, 723, 550, 460, 656, 356, 326, 224, 353, 274, 123, 66, 0), # 155
(896, 760, 735, 814, 639, 282, 311, 298, 366, 142, 124, 60, 0, 867, 727, 551, 462, 663, 358, 327, 224, 353, 278, 124, 66, 0), # 156
(900, 762, 741, 818, 642, 282, 311, 298, 367, 142, 124, 60, 0, 873, 729, 553, 467, 667, 360, 329, 226, 353, 278, 127, 67, 0), # 157
(902, 765, 744, 821, 643, 286, 312, 298, 369, 142, 124, 62, 0, 878, 733, 556, 468, 672, 363, 331, 227, 355, 281, 127, 68, 0), # 158
(904, 767, 751, 824, 644, 289, 314, 300, 369, 143, 125, 62, 0, 882, 734, 560, 468, 674, 364, 334, 230, 357, 284, 127, 68, 0), # 159
(908, 769, 758, 829, 652, 291, 317, 301, 372, 143, 125, 62, 0, 889, 738, 565, 470, 677, 367, 335, 230, 359, 285, 128, 68, 0), # 160
(913, 772, 760, 832, 653, 291, 317, 303, 374, 144, 126, 62, 0, 891, 743, 566, 471, 683, 369, 336, 231, 360, 286, 128, 69, 0), # 161
(914, 774, 764, 835, 660, 292, 318, 307, 376, 146, 126, 64, 0, 896, 748, 568, 473, 687, 372, 338, 233, 360, 287, 128, 69, 0), # 162
(918, 776, 769, 836, 664, 295, 322, 308, 378, 146, 126, 64, 0, 899, 748, 575, 477, 693, 373, 339, 234, 361, 288, 128, 69, 0), # 163
(920, 780, 774, 840, 667, 298, 327, 309, 380, 146, 126, 64, 0, 901, 751, 576, 480, 696, 375, 339, 234, 361, 289, 128, 69, 0), # 164
(922, 781, 777, 842, 671, 300, 328, 312, 383, 146, 126, 65, 0, 907, 758, 582, 483, 697, 378, 339, 235, 361, 291, 128, 69, 0), # 165
(925, 786, 779, 848, 673, 304, 328, 315, 386, 146, 127, 65, 0, 910, 763, 585, 485, 699, 379, 339, 236, 362, 292, 130, 69, 0), # 166
(928, 790, 785, 853, 676, 307, 329, 316, 389, 148, 127, 65, 0, 913, 765, 585, 488, 706, 380, 339, 238, 362, 294, 131, 69, 0), # 167
(933, 791, 787, 856, 679, 308, 329, 320, 390, 150, 127, 65, 0, 916, 769, 588, 490, 708, 382, 341, 240, 367, 294, 131, 69, 0), # 168
(938, 792, 792, 861, 683, 310, 329, 320, 394, 151, 128, 65, 0, 919, 771, 591, 491, 713, 385, 342, 241, 367, 295, 131, 69, 0), # 169
(940, 793, 794, 864, 687, 311, 329, 324, 394, 153, 128, 67, 0, 923, 775, 593, 492, 717, 391, 344, 241, 367, 297, 132, 69, 0), # 170
(943, 795, 795, 865, 687, 314, 331, 326, 395, 153, 129, 67, 0, 927, 777, 598, 493, 722, 392, 345, 245, 368, 297, 132, 69, 0), # 171
(946, 798, 800, 868, 692, 315, 332, 329, 396, 153, 129, 68, 0, 930, 778, 603, 494, 725, 392, 345, 247, 369, 297, 132, 69, 0), # 172
(949, 800, 805, 871, 694, 316, 332, 329, 398, 153, 129, 68, 0, 934, 785, 608, 497, 728, 395, 346, 247, 371, 299, 133, 69, 0), # 173
(950, 803, 810, 874, 696, 316, 333, 329, 399, 155, 129, 68, 0, 939, 787, 613, 500, 733, 396, 347, 248, 373, 301, 133, 70, 0), # 174
(955, 805, 810, 875, 700, 319, 333, 329, 399, 155, 129, 68, 0, 940, 790, 613, 502, 735, 397, 348, 250, 373, 302, 134, 71, 0), # 175
(963, 805, 813, 876, 701, 319, 333, 329, 399, 155, 131, 68, 0, 944, 791, 618, 502, 737, 397, 349, 251, 374, 304, 134, 71, 0), # 176
(963, 810, 815, 881, 704, 319, 333, 329, 400, 155, 132, 68, 0, 949, 792, 620, 505, 738, 398, 349, 251, 376, 304, 134, 71, 0), # 177
(965, 811, 818, 882, 704, 319, 333, 329, 400, 155, 132, 68, 0, 951, 794, 621, 506, 741, 399, 349, 251, 376, 304, 135, 73, 0), # 178
(965, 811, 818, 882, 704, 319, 333, 329, 400, 155, 132, 68, 0, 951, 794, 621, 506, 741, 399, 349, 251, 376, 304, 135, 73, 0), # 179
)
passenger_arriving_rate = (
(3.012519347023061, 3.038908707831242, 2.605641614806254, 2.796600498238545, 2.2218742430438447, 1.0985292262130688, 1.2438106846121746, 1.163294043244856, 1.2180198124336015, 0.5937022523283556, 0.42052651638856037, 0.24489575391530874, 0.0, 3.050328127547018, 2.6938532930683956, 2.102632581942802, 1.7811067569850665, 2.436039624867203, 1.6286116605427985, 1.2438106846121746, 0.7846637330093348, 1.1109371215219224, 0.9322001660795152, 0.5211283229612508, 0.2762644279846584, 0.0), # 0
(3.2125962912119848, 3.2395333817796175, 2.777673295665077, 2.9813149139744777, 2.368996134989695, 1.1710942544148948, 1.3258406823791082, 1.2398784307264272, 1.2984490652665819, 0.6328471038415726, 0.4483096135956133, 0.2610608330407178, 0.0, 3.2518749884639133, 2.8716691634478955, 2.241548067978066, 1.8985413115247174, 2.5968981305331638, 1.735829803016998, 1.3258406823791082, 0.8364958960106391, 1.1844980674948475, 0.9937716379914928, 0.5555346591330154, 0.294503034707238, 0.0), # 1
(3.412033805387839, 3.439361843990039, 2.949021142773707, 3.165294467099158, 2.5155851894998977, 1.243369790079356, 1.4075455523882114, 1.316156311976105, 1.37855736137551, 0.671837066253082, 0.47598231487763143, 0.2771616326026528, 0.0, 3.4526188969258147, 3.0487779586291803, 2.379911574388157, 2.0155111987592456, 2.75711472275102, 1.842618836766547, 1.4075455523882114, 0.8881212786281114, 1.2577925947499489, 1.0550981556997194, 0.5898042285547415, 0.312669258544549, 0.0), # 2
(3.6100546758879375, 3.637601276065046, 3.1190054346599276, 3.347809383389434, 2.6610646704007612, 1.3150692179047898, 1.4886010924943869, 1.391825289373627, 1.4580270321842186, 0.7105174047639273, 0.5034348411827384, 0.2931342842465231, 0.0, 3.651763683752536, 3.224477126711754, 2.5171742059136917, 2.1315522142917818, 2.916054064368437, 1.9485554051230778, 1.4886010924943869, 0.9393351556462783, 1.3305323352003806, 1.1159364611298115, 0.6238010869319855, 0.3306910250968224, 0.0), # 3
(3.8058816890495892, 3.833458859607176, 3.286946449851519, 3.528129888622157, 2.804857841518597, 1.385905922589534, 1.5686831005525377, 1.4665829652987292, 1.5365404091165416, 0.748733384575152, 0.5305574134590577, 0.30891491961773826, 0.0, 3.848513179763891, 3.3980641157951204, 2.652787067295288, 2.246200153725456, 3.073080818233083, 2.053216151418221, 1.5686831005525377, 0.9899328018496671, 1.4024289207592986, 1.176043296207386, 0.6573892899703039, 0.3484962599642888, 0.0), # 4
(3.9987376312101066, 4.026141776218974, 3.452164466876259, 3.705526208574178, 2.9463879666797124, 1.4555932888319254, 1.6474673744175674, 1.5401269421311483, 1.6137798235963121, 0.7863302708877998, 0.5572402526547134, 0.32443967036170746, 0.0, 4.042071215779696, 3.568836373978782, 2.7862012632735667, 2.3589908126633987, 3.2275596471926242, 2.1561777189836078, 1.6474673744175674, 1.0397094920228038, 1.4731939833398562, 1.2351754028580595, 0.6904328933752518, 0.3660128887471795, 0.0), # 5
(4.1878452887068, 4.214857207502976, 3.613979764261934, 3.8792685690223436, 3.0850783097104175, 1.5238447013303027, 1.724629711944379, 1.6121548222506223, 1.689427607047363, 0.823153328902914, 0.5833735797178284, 0.3396446681238405, 0.0, 4.231641622619764, 3.736091349362245, 2.9168678985891416, 2.4694599867087415, 3.378855214094726, 2.2570167511508714, 1.724629711944379, 1.0884605009502162, 1.5425391548552088, 1.2930895230074482, 0.7227959528523867, 0.3831688370457252, 0.0), # 6
(4.372427447876982, 4.398812335061723, 3.7717126205363183, 4.048627195743508, 3.220352134437022, 1.5903735447830027, 1.7998459109878757, 1.6823642080368881, 1.7631660908935292, 0.8590478238215383, 0.608847615596527, 0.35446604454954656, 0.0, 4.416428231103912, 3.8991264900450116, 3.0442380779826346, 2.5771434714646144, 3.5263321817870583, 2.3553098912516433, 1.7998459109878757, 1.1359811034164304, 1.610176067218511, 1.3495423985811696, 0.7543425241072638, 0.39989203046015664, 0.0), # 7
(4.551706895057961, 4.577214340497755, 3.9246833142271984, 4.212872314514518, 3.3516327046858345, 1.6548932038883617, 1.8727917694029594, 1.7504527018696814, 1.8346776065586412, 0.8938590208447165, 0.6335525812389322, 0.36883993128423526, 0.0, 4.595634872051951, 4.057239244126587, 3.167762906194661, 2.681577062534149, 3.6693552131172824, 2.450633782617554, 1.8727917694029594, 1.1820665742059726, 1.6758163523429173, 1.4042907715048396, 0.7849366628454397, 0.41611039459070503, 0.0), # 8
(4.724906416587052, 4.749270405413613, 4.072212123862353, 4.371274151112226, 3.478343284283164, 1.7171170633447197, 1.9431430850445361, 1.8161179061287411, 1.9036444854665349, 0.9274321851734916, 0.657378697593168, 0.3827024599733158, 0.0, 4.768465376283698, 4.2097270597064735, 3.28689348796584, 2.782296555520474, 3.8072889709330697, 2.5425650685802377, 1.9431430850445361, 1.2265121881033711, 1.739171642141582, 1.4570913837040758, 0.8144424247724705, 0.4317518550376012, 0.0), # 9
(4.89124879880156, 4.914187711411834, 4.213619327969563, 4.52310293131348, 3.5999071370553204, 1.7767585078504131, 2.0105756557675067, 1.8790574231938029, 1.969749059041043, 0.9596125820089074, 0.6802161856073575, 0.3959897622621977, 0.0, 4.934123574618967, 4.355887384884174, 3.4010809280367877, 2.8788377460267216, 3.939498118082086, 2.6306803924713242, 2.0105756557675067, 1.269113219893152, 1.7999535685276602, 1.5077009771044938, 0.8427238655939127, 0.4467443374010759, 0.0), # 10
(5.049956828038804, 5.071173440094964, 4.348225205076608, 4.66762888089513, 3.715747526828614, 1.8335309221037792, 2.0747652794267752, 1.9389688554446038, 2.032673658705999, 0.9902454765520082, 0.7019552662296251, 0.40863796979629063, 0.0, 5.091813297877567, 4.495017667759196, 3.5097763311481254, 2.970736429656024, 4.065347317411998, 2.7145563976224456, 2.0747652794267752, 1.3096649443598423, 1.857873763414307, 1.5558762936317103, 0.8696450410153217, 0.46101576728136046, 0.0), # 11
(5.200253290636088, 5.219434773065535, 4.475350033711271, 4.804122225634027, 3.8252877174293514, 1.8871476908031546, 2.135387753877244, 1.9955498052608804, 2.092100615885236, 1.019176134003836, 0.7224861604080937, 0.42058321422100353, 0.0, 5.240738376879321, 4.626415356431038, 3.612430802040468, 3.0575284020115077, 4.184201231770472, 2.7937697273652327, 2.135387753877244, 1.3479626362879675, 1.9126438587146757, 1.6013740752113426, 0.8950700067422541, 0.47449407027868507, 0.0), # 12
(5.341360972930726, 5.358178891926092, 4.594314092401332, 4.93185319130702, 3.927950972683841, 1.9373221986468787, 2.192118876973817, 2.048497875022371, 2.1477122620025866, 1.046249819565436, 0.741699089090887, 0.43176162718174615, 0.0, 5.380102642444042, 4.749377898999207, 3.7084954454544348, 3.1387494586963074, 4.295424524005173, 2.8678970250313194, 2.192118876973817, 1.3838015704620563, 1.9639754863419205, 1.6439510637690071, 0.9188628184802665, 0.48710717199328113, 0.0), # 13
(5.47250266126003, 5.486612978279174, 4.704437659674574, 5.050092003690957, 4.0231605564183965, 1.9837678303332873, 2.2446344465713985, 2.097510667108812, 2.1991909284818845, 1.0713117984378504, 0.7594842732261287, 0.4421093403239281, 0.0, 5.509109925391539, 4.863202743563209, 3.797421366130643, 3.2139353953135505, 4.398381856963769, 2.9365149339523366, 2.2446344465713985, 1.4169770216666338, 2.0115802782091983, 1.6833640012303195, 0.9408875319349149, 0.49878299802537956, 0.0), # 14
(5.592901141961314, 5.60394421372732, 4.805041014058772, 5.158108888562694, 4.110339732459323, 2.0261979705607196, 2.292610260524888, 2.1422857838999394, 2.246218946746964, 1.094207335822124, 0.7757319337619422, 0.4515624852929587, 0.0, 5.626964056541629, 4.967187338222545, 3.8786596688097106, 3.2826220074663714, 4.492437893493928, 2.999200097459915, 2.292610260524888, 1.4472842646862283, 2.0551698662296616, 1.7193696295208984, 0.9610082028117546, 0.509449473975211, 0.0), # 15
(5.701779201371881, 5.709379779873073, 4.895444434081713, 5.255174071699074, 4.188911764632934, 2.064326004027511, 2.335722116689193, 2.1825208277754906, 2.2884786482216573, 1.1147816969192998, 0.7903322916464515, 0.4600571937342471, 0.0, 5.732868866714128, 5.060629131076717, 3.951661458232257, 3.3443450907578987, 4.5769572964433145, 3.0555291588856868, 2.335722116689193, 1.474518574305365, 2.094455882316467, 1.7517246905663582, 0.9790888868163427, 0.5190345254430068, 0.0), # 16
(5.798359625829046, 5.802126858318971, 4.974968198271177, 5.340557778876951, 4.2582999167655355, 2.0978653154320006, 2.3736458129192135, 2.217913401115205, 2.325652364329797, 1.1328801469304204, 0.8031755678277801, 0.46752959729320287, 0.0, 5.82602818672885, 5.142825570225231, 4.0158778391389, 3.3986404407912607, 4.651304728659594, 3.1050787615612867, 2.3736458129192135, 1.4984752253085718, 2.1291499583827678, 1.7801859262923174, 0.9949936396542355, 0.5274660780289975, 0.0), # 17
(5.881865201670123, 5.881392630667551, 5.042932585154941, 5.413530235873176, 4.317927452683436, 2.1265292894725265, 2.4060571470698555, 2.2481611062988156, 2.3574224264952193, 1.1483479510565309, 0.814151983254051, 0.4739158276152359, 0.0, 5.905645847405608, 5.213074103767594, 4.070759916270255, 3.445043853169592, 4.7148448529904385, 3.1474255488183416, 2.4060571470698555, 1.518949492480376, 2.158963726341718, 1.8045100786243924, 1.0085865170309882, 0.5346720573334138, 0.0), # 18
(5.95151871523242, 5.94638427852136, 5.09865787326079, 5.473361668464593, 4.3672176362129465, 2.1500313108474236, 2.4326319169960184, 2.2729615457060617, 2.383471166141756, 1.1610303744986745, 0.823151758873388, 0.479152016345755, 0.0, 5.9709256795642185, 5.270672179803304, 4.11575879436694, 3.483091123496023, 4.766942332283512, 3.1821461639884863, 2.4326319169960184, 1.5357366506053025, 2.1836088181064732, 1.8244538894881979, 1.0197315746521582, 0.5405803889564874, 0.0), # 19
(6.00654295285325, 5.996308983482929, 5.141464341116502, 5.51932230242806, 4.405593731180378, 2.168084764255032, 2.453045920552609, 2.2920123217166797, 2.40348091469324, 1.1707726824578941, 0.8300651156339153, 0.4831742951301697, 0.0, 6.021071514024495, 5.3149172464318655, 4.150325578169576, 3.5123180473736815, 4.80696182938648, 3.2088172504033516, 2.453045920552609, 1.5486319744678798, 2.202796865590189, 1.8397741008093538, 1.0282928682233006, 0.5451189984984482, 0.0), # 20
(6.046160700869921, 6.030373927154808, 5.17067226724986, 5.550682363540419, 4.432479001412036, 2.180403034393688, 2.466974955594528, 2.305011036710406, 2.4171340035735045, 1.1774201401352336, 0.8347822744837561, 0.4859187956138898, 0.0, 6.055287181606248, 5.345106751752787, 4.173911372418781, 3.5322604204057, 4.834268007147009, 3.2270154513945686, 2.466974955594528, 1.557430738852634, 2.216239500706018, 1.8502274545134736, 1.0341344534499721, 0.548215811559528, 0.0), # 21
(6.0695947456197485, 6.04778629113953, 5.185601930188644, 5.5667120775785275, 4.44729671073423, 2.186699505961729, 2.4740948199766803, 2.311655293066979, 2.424112764206383, 1.1808180127317367, 0.8371934563710342, 0.4873216494423246, 0.0, 6.0727765131292974, 5.36053814386557, 4.185967281855171, 3.5424540381952094, 4.848225528412766, 3.236317410293771, 2.4740948199766803, 1.561928218544092, 2.223648355367115, 1.8555706925261761, 1.037120386037729, 0.5497987537399573, 0.0), # 22
(6.078236018005584, 6.049847976680385, 5.187461591220852, 5.568718865740742, 4.451092822413039, 2.1875000000000004, 2.4749412006778018, 2.312373456790124, 2.4249852469135806, 1.1812188408779154, 0.8374958037161743, 0.48749487882944686, 0.0, 6.075000000000001, 5.362443667123914, 4.187479018580871, 3.5436565226337455, 4.849970493827161, 3.2373228395061737, 2.4749412006778018, 1.5625000000000002, 2.2255464112065195, 1.856239621913581, 1.0374923182441704, 0.549986179698217, 0.0), # 23
(6.084607447820493, 6.048645370370371, 5.187157407407408, 5.568471875000001, 4.453243045445941, 2.1875000000000004, 2.4744761437908505, 2.3113750000000004, 2.4248683333333334, 1.180972592592593, 0.8374624579124582, 0.4874543209876544, 0.0, 6.075000000000001, 5.361997530864198, 4.187312289562291, 3.5429177777777783, 4.849736666666667, 3.235925000000001, 2.4744761437908505, 1.5625000000000002, 2.2266215227229704, 1.8561572916666675, 1.0374314814814818, 0.5498768518518521, 0.0), # 24
(6.090844338126949, 6.046274862825789, 5.186556927297669, 5.567983217592594, 4.455345978237801, 2.1875000000000004, 2.4735596707818934, 2.3094135802469142, 2.4246373456790127, 1.180487825788752, 0.8373963399426364, 0.4873742569730225, 0.0, 6.075000000000001, 5.361116826703247, 4.186981699713182, 3.5414634773662557, 4.849274691358025, 3.23317901234568, 2.4735596707818934, 1.5625000000000002, 2.2276729891189007, 1.8559944058641984, 1.037311385459534, 0.5496613511659809, 0.0), # 25
(6.096946211428821, 6.04277266803841, 5.185668381344309, 5.567258449074075, 4.457401547368442, 2.1875000000000004, 2.472206015492617, 2.306526234567902, 2.42429524691358, 1.179772606310014, 0.8372980483850856, 0.48725578417924115, 0.0, 6.075000000000001, 5.359813625971652, 4.186490241925428, 3.5393178189300416, 4.84859049382716, 3.2291367283950625, 2.472206015492617, 1.5625000000000002, 2.228700773684221, 1.8557528163580252, 1.0371336762688619, 0.5493429698216737, 0.0), # 26
(6.102912590229983, 6.038175, 5.184500000000001, 5.566303125000001, 4.459409679417686, 2.1875000000000004, 2.4704294117647065, 2.3027500000000005, 2.423845, 1.1788350000000003, 0.8371681818181821, 0.48710000000000014, 0.0, 6.075000000000001, 5.358100000000001, 4.18584090909091, 3.536505, 4.84769, 3.223850000000001, 2.4704294117647065, 1.5625000000000002, 2.229704839708843, 1.8554343750000006, 1.0369000000000004, 0.5489250000000001, 0.0), # 27
(6.108742997034302, 6.032518072702333, 5.183060013717422, 5.565122800925927, 4.461370300965361, 2.1875000000000004, 2.4682440934398455, 2.2981219135802475, 2.4232895679012345, 1.1776830727023324, 0.837007338820302, 0.48690800182898963, 0.0, 6.075000000000001, 5.355988020118885, 4.18503669410151, 3.5330492181069966, 4.846579135802469, 3.2173706790123466, 2.4682440934398455, 1.5625000000000002, 2.2306851504826803, 1.855040933641976, 1.0366120027434846, 0.5484107338820303, 0.0), # 28
(6.114436954345651, 6.025838100137175, 5.181356652949247, 5.563723032407408, 4.463283338591289, 2.1875000000000004, 2.46566429435972, 2.2926790123456793, 2.422631913580247, 1.1763248902606314, 0.8368161179698218, 0.48668088705989954, 0.0, 6.075000000000001, 5.353489757658894, 4.184080589849109, 3.5289746707818934, 4.845263827160494, 3.209750617283951, 2.46566429435972, 1.5625000000000002, 2.2316416692956444, 1.8545743441358031, 1.0362713305898494, 0.5478034636488341, 0.0), # 29
(6.119993984667899, 6.018171296296297, 5.179398148148149, 5.562109375, 4.4651487188752945, 2.1875000000000004, 2.4627042483660135, 2.2864583333333335, 2.421875, 1.174768518518519, 0.8365951178451181, 0.4864197530864198, 0.0, 6.075000000000001, 5.350617283950617, 4.18297558922559, 3.5243055555555562, 4.84375, 3.201041666666667, 2.4627042483660135, 1.5625000000000002, 2.2325743594376473, 1.854036458333334, 1.03587962962963, 0.5471064814814817, 0.0), # 30
(6.125413610504916, 6.009553875171469, 5.177192729766805, 5.56028738425926, 4.466966368397204, 2.1875000000000004, 2.4593781893004123, 2.2794969135802474, 2.421021790123457, 1.1730220233196162, 0.8363449370245669, 0.48612569730224076, 0.0, 6.075000000000001, 5.347382670324647, 4.181724685122834, 3.519066069958848, 4.842043580246914, 3.1912956790123466, 2.4593781893004123, 1.5625000000000002, 2.233483184198602, 1.8534291280864204, 1.035438545953361, 0.5463230795610428, 0.0), # 31
(6.130695354360573, 6.000022050754459, 5.174748628257888, 5.558262615740742, 4.468736213736839, 2.1875000000000004, 2.4557003510046, 2.2718317901234575, 2.4200752469135804, 1.171093470507545, 0.8360661740865446, 0.48579981710105186, 0.0, 6.075000000000001, 5.34379798811157, 4.180330870432723, 3.5132804115226346, 4.840150493827161, 3.1805645061728405, 2.4557003510046, 1.5625000000000002, 2.2343681068684194, 1.8527542052469144, 1.0349497256515776, 0.5454565500685873, 0.0), # 32
(6.135838738738739, 5.989612037037038, 5.172074074074075, 5.5560406250000005, 4.470458181474026, 2.1875000000000004, 2.451684967320262, 2.2635000000000005, 2.4190383333333334, 1.1689909259259263, 0.8357594276094278, 0.4854432098765433, 0.0, 6.075000000000001, 5.339875308641975, 4.1787971380471385, 3.5069727777777784, 4.838076666666667, 3.1689000000000007, 2.451684967320262, 1.5625000000000002, 2.235229090737013, 1.8520135416666672, 1.0344148148148151, 0.5445101851851853, 0.0), # 33
(6.140843286143286, 5.978360048010974, 5.169177297668039, 5.553626967592594, 4.472132198188587, 2.1875000000000004, 2.4473462720890833, 2.254538580246914, 2.4179140123456797, 1.1667224554183817, 0.8354252961715926, 0.48505697302240525, 0.0, 6.075000000000001, 5.335626703246457, 4.177126480857963, 3.5001673662551447, 4.835828024691359, 3.1563540123456795, 2.4473462720890833, 1.5625000000000002, 2.2360660990942933, 1.8512089891975316, 1.033835459533608, 0.5434872770919068, 0.0), # 34
(6.145708519078085, 5.966302297668039, 5.166066529492457, 5.551027199074074, 4.473758190460348, 2.1875000000000004, 2.442698499152748, 2.244984567901235, 2.4167052469135806, 1.1642961248285326, 0.8350643783514156, 0.48464220393232754, 0.0, 6.075000000000001, 5.331064243255602, 4.175321891757077, 3.492888374485597, 4.833410493827161, 3.142978395061729, 2.442698499152748, 1.5625000000000002, 2.236879095230174, 1.8503423996913586, 1.0332133058984916, 0.5423911179698219, 0.0), # 35
(6.150433960047004, 5.953475, 5.162750000000001, 5.548246875, 4.475336084869134, 2.1875000000000004, 2.4377558823529415, 2.2348750000000006, 2.415415, 1.1617200000000003, 0.834677272727273, 0.4842000000000002, 0.0, 6.075000000000001, 5.326200000000001, 4.173386363636364, 3.4851600000000005, 4.83083, 3.128825000000001, 2.4377558823529415, 1.5625000000000002, 2.237668042434567, 1.8494156250000007, 1.0325500000000003, 0.5412250000000001, 0.0), # 36
(6.155019131553915, 5.939914368998629, 5.159235939643348, 5.545291550925926, 4.476865807994769, 2.1875000000000004, 2.4325326555313485, 2.2242469135802474, 2.4140462345679015, 1.1590021467764065, 0.8342645778775412, 0.4837314586191131, 0.0, 6.075000000000001, 5.3210460448102435, 4.171322889387706, 3.4770064403292187, 4.828092469135803, 3.113945679012346, 2.4325326555313485, 1.5625000000000002, 2.2384329039973845, 1.8484305169753092, 1.0318471879286697, 0.5399922153635118, 0.0), # 37
(6.159463556102686, 5.925656618655693, 5.155532578875172, 5.542166782407408, 4.478347286417076, 2.1875000000000004, 2.4270430525296542, 2.213137345679013, 2.412601913580247, 1.1561506310013723, 0.8338268923805964, 0.4832376771833564, 0.0, 6.075000000000001, 5.31561444901692, 4.169134461902981, 3.468451893004116, 4.825203827160494, 3.0983922839506186, 2.4270430525296542, 1.5625000000000002, 2.239173643208538, 1.8473889274691366, 1.0311065157750345, 0.5386960562414268, 0.0), # 38
(6.163766756197193, 5.910737962962964, 5.1516481481481495, 5.538878125000001, 4.479780446715882, 2.1875000000000004, 2.421301307189543, 2.2015833333333337, 2.4110850000000004, 1.153173518518519, 0.833364814814815, 0.48271975308641984, 0.0, 6.075000000000001, 5.309917283950617, 4.166824074074075, 3.4595205555555566, 4.822170000000001, 3.082216666666667, 2.421301307189543, 1.5625000000000002, 2.239890223357941, 1.846292708333334, 1.03032962962963, 0.537339814814815, 0.0), # 39
(6.167928254341299, 5.895194615912209, 5.147590877914952, 5.53543113425926, 4.481165215471008, 2.1875000000000004, 2.4153216533526995, 2.1896219135802477, 2.4094984567901236, 1.1500788751714683, 0.8328789437585736, 0.4821787837219938, 0.0, 6.075000000000001, 5.303966620941931, 4.164394718792868, 3.450236625514404, 4.818996913580247, 3.0654706790123467, 2.4153216533526995, 1.5625000000000002, 2.240582607735504, 1.8451437114197538, 1.0295181755829905, 0.5359267832647464, 0.0), # 40
(6.171947573038878, 5.879062791495199, 5.143368998628259, 5.531831365740741, 4.482501519262281, 2.1875000000000004, 2.409118324860809, 2.1772901234567907, 2.4078452469135803, 1.1468747668038413, 0.8323698777902485, 0.4816158664837678, 0.0, 6.075000000000001, 5.297774531321445, 4.161849388951242, 3.440624300411523, 4.8156904938271605, 3.048206172839507, 2.409118324860809, 1.5625000000000002, 2.2412507596311406, 1.8439437885802477, 1.0286737997256519, 0.534460253772291, 0.0), # 41
(6.175824234793801, 5.862378703703705, 5.138990740740742, 5.5280843750000015, 4.483789284669523, 2.1875000000000004, 2.402705555555556, 2.1646250000000005, 2.4061283333333336, 1.1435692592592597, 0.8318382154882157, 0.48103209876543224, 0.0, 6.075000000000001, 5.291353086419754, 4.159191077441078, 3.430707777777778, 4.812256666666667, 3.030475000000001, 2.402705555555556, 1.5625000000000002, 2.2418946423347617, 1.8426947916666676, 1.0277981481481486, 0.5329435185185187, 0.0), # 42
(6.179557762109936, 5.845178566529493, 5.1344643347050765, 5.524195717592594, 4.485028438272561, 2.1875000000000004, 2.396097579278625, 2.151663580246914, 2.404350679012346, 1.1401704183813448, 0.8312845554308519, 0.48042857796067695, 0.0, 6.075000000000001, 5.284714357567445, 4.156422777154259, 3.4205112551440338, 4.808701358024692, 3.01232901234568, 2.396097579278625, 1.5625000000000002, 2.2425142191362806, 1.841398572530865, 1.0268928669410153, 0.5313798696844995, 0.0), # 43
(6.183147677491157, 5.827498593964336, 5.129798010973938, 5.520170949074076, 4.486218906651218, 2.1875000000000004, 2.389308629871702, 2.1384429012345687, 2.4025152469135804, 1.136686310013718, 0.8307094961965334, 0.47980640146319176, 0.0, 6.075000000000001, 5.277870416095109, 4.153547480982667, 3.410058930041153, 4.805030493827161, 2.993820061728396, 2.389308629871702, 1.5625000000000002, 2.243109453325609, 1.8400569830246922, 1.0259596021947879, 0.5297725994513033, 0.0), # 44
(6.186593503441331, 5.809375000000001, 5.125000000000001, 5.5160156250000005, 4.487360616385319, 2.1875000000000004, 2.382352941176471, 2.1250000000000004, 2.400625, 1.1331250000000004, 0.8301136363636366, 0.47916666666666674, 0.0, 6.075000000000001, 5.270833333333333, 4.1505681818181825, 3.3993750000000005, 4.80125, 2.9750000000000005, 2.382352941176471, 1.5625000000000002, 2.2436803081926593, 1.8386718750000006, 1.0250000000000004, 0.5281250000000002, 0.0), # 45
(6.18989476246433, 5.790843998628259, 5.12007853223594, 5.511735300925927, 4.488453494054687, 2.1875000000000004, 2.3752447470346167, 2.1113719135802476, 2.398682901234568, 1.1294945541838137, 0.8294975745105375, 0.4785104709647921, 0.0, 6.075000000000001, 5.263615180612712, 4.147487872552688, 3.3884836625514403, 4.797365802469136, 2.9559206790123467, 2.3752447470346167, 1.5625000000000002, 2.2442267470273434, 1.8372451003086427, 1.0240157064471882, 0.52644036351166, 0.0), # 46
(6.193050977064022, 5.771941803840877, 5.115041838134432, 5.507335532407408, 4.489497466239147, 2.1875000000000004, 2.367998281287824, 2.097595679012346, 2.396691913580247, 1.1258030384087796, 0.8288619092156131, 0.4778389117512576, 0.0, 6.075000000000001, 5.256228029263832, 4.144309546078065, 3.377409115226338, 4.793383827160494, 2.9366339506172845, 2.367998281287824, 1.5625000000000002, 2.2447487331195735, 1.8357785108024698, 1.0230083676268864, 0.5247219821673527, 0.0), # 47
(6.196061669744278, 5.752704629629631, 5.109898148148149, 5.502821875000001, 4.490492459518524, 2.1875000000000004, 2.360627777777778, 2.083708333333334, 2.394655, 1.1220585185185188, 0.8282072390572391, 0.4771530864197532, 0.0, 6.075000000000001, 5.248683950617284, 4.141036195286196, 3.3661755555555555, 4.78931, 2.9171916666666675, 2.360627777777778, 1.5625000000000002, 2.245246229759262, 1.8342739583333343, 1.02197962962963, 0.5229731481481483, 0.0), # 48
(6.198926363008972, 5.733168689986282, 5.104655692729768, 5.49819988425926, 4.491438400472643, 2.1875000000000004, 2.3531474703461632, 2.0697469135802473, 2.3925751234567905, 1.1182690603566534, 0.8275341626137924, 0.4764540923639691, 0.0, 6.075000000000001, 5.240995016003659, 4.137670813068962, 3.3548071810699596, 4.785150246913581, 2.897645679012346, 2.3531474703461632, 1.5625000000000002, 2.2457192002363215, 1.8327332947530872, 1.0209311385459536, 0.5211971536351167, 0.0), # 49
(6.201644579361971, 5.7133701989026076, 5.099322702331963, 5.4934751157407415, 4.492335215681326, 2.1875000000000004, 2.3455715928346654, 2.055748456790124, 2.3904552469135805, 1.1144427297668043, 0.826843278463649, 0.4757430269775951, 0.0, 6.075000000000001, 5.233173296753545, 4.134216392318245, 3.3433281893004123, 4.780910493827161, 2.8780478395061735, 2.3455715928346654, 1.5625000000000002, 2.246167607840663, 1.8311583719135809, 1.0198645404663929, 0.5193972908093281, 0.0), # 50
(6.204215841307147, 5.693345370370371, 5.093907407407408, 5.488653125000001, 4.4931828317244, 2.1875000000000004, 2.3379143790849675, 2.0417500000000004, 2.3882983333333336, 1.110587592592593, 0.8261351851851855, 0.4750209876543212, 0.0, 6.075000000000001, 5.225230864197532, 4.130675925925927, 3.3317627777777785, 4.776596666666667, 2.858450000000001, 2.3379143790849675, 1.5625000000000002, 2.2465914158622, 1.8295510416666674, 1.0187814814814817, 0.517576851851852, 0.0), # 51
(6.206639671348368, 5.673130418381346, 5.08841803840878, 5.483739467592593, 4.493981175181686, 2.1875000000000004, 2.330190062938756, 2.0277885802469138, 2.3861073456790125, 1.106711714677641, 0.8254104813567779, 0.4742890717878374, 0.0, 6.075000000000001, 5.21717978966621, 4.127052406783889, 3.3201351440329225, 4.772214691358025, 2.838904012345679, 2.330190062938756, 1.5625000000000002, 2.246990587590843, 1.8279131558641981, 1.0176836076817561, 0.5157391289437588, 0.0), # 52
(6.2089155919895065, 5.652761556927298, 5.082862825788753, 5.478739699074074, 4.494730172633012, 2.1875000000000004, 2.3224128782377154, 2.0139012345679017, 2.3838852469135805, 1.1028231618655697, 0.8246697655568027, 0.47354837677183365, 0.0, 6.075000000000001, 5.20903214449017, 4.123348827784014, 3.3084694855967083, 4.767770493827161, 2.8194617283950625, 2.3224128782377154, 1.5625000000000002, 2.247365086316506, 1.8262465663580252, 1.0165725651577509, 0.5138874142661182, 0.0), # 53
(6.211043125734431, 5.632275000000001, 5.07725, 5.473659375, 4.495429750658201, 2.1875000000000004, 2.3145970588235296, 2.000125, 2.381635, 1.0989300000000004, 0.8239136363636363, 0.4728000000000001, 0.0, 6.075000000000001, 5.2008, 4.119568181818182, 3.2967900000000006, 4.76327, 2.8001750000000003, 2.3145970588235296, 1.5625000000000002, 2.2477148753291005, 1.8245531250000007, 1.0154500000000002, 0.5120250000000002, 0.0), # 54
(6.213021795087014, 5.611706961591222, 5.0715877914952, 5.468504050925926, 4.496079835837076, 2.1875000000000004, 2.3067568385378845, 1.9864969135802473, 2.3793595679012345, 1.0950402949245546, 0.8231426923556555, 0.47204503886602667, 0.0, 6.075000000000001, 5.192495427526293, 4.115713461778277, 3.2851208847736633, 4.758719135802469, 2.7810956790123464, 2.3067568385378845, 1.5625000000000002, 2.248039917918538, 1.8228346836419758, 1.0143175582990402, 0.5101551783264747, 0.0), # 55
(6.214851122551123, 5.59109365569273, 5.065884430727024, 5.463279282407408, 4.496680354749464, 2.1875000000000004, 2.298906451222465, 1.9730540123456795, 2.3770619135802473, 1.0911621124828537, 0.822357532111236, 0.47128459076360324, 0.0, 6.075000000000001, 5.184130498399635, 4.11178766055618, 3.2734863374485603, 4.754123827160495, 2.7622756172839513, 2.298906451222465, 1.5625000000000002, 2.248340177374732, 1.821093094135803, 1.013176886145405, 0.5082812414266119, 0.0), # 56
(6.21653063063063, 5.570471296296297, 5.06014814814815, 5.457990625000001, 4.497231233975187, 2.1875000000000004, 2.2910601307189546, 1.959833333333334, 2.3747450000000003, 1.087303518518519, 0.8215587542087543, 0.47051975308641986, 0.0, 6.075000000000001, 5.175717283950617, 4.107793771043772, 3.261910555555556, 4.749490000000001, 2.7437666666666676, 2.2910601307189546, 1.5625000000000002, 2.2486156169875935, 1.819330208333334, 1.01202962962963, 0.5064064814814816, 0.0), # 57
(6.218059841829408, 5.549876097393691, 5.054387174211249, 5.4526436342592595, 4.49773240009407, 2.1875000000000004, 2.283232110869039, 1.946871913580247, 2.372411790123457, 1.083472578875172, 0.8207469572265872, 0.46975162322816655, 0.0, 6.075000000000001, 5.167267855509832, 4.103734786132936, 3.2504177366255154, 4.744823580246914, 2.725620679012346, 2.283232110869039, 1.5625000000000002, 2.248866200047035, 1.8175478780864203, 1.01087743484225, 0.5045341906721538, 0.0), # 58
(6.219438278651324, 5.529344272976681, 5.048609739369, 5.447243865740742, 4.498183779685938, 2.1875000000000004, 2.275436625514404, 1.9342067901234572, 2.3700652469135806, 1.079677359396434, 0.8199227397431101, 0.4689812985825333, 0.0, 6.075000000000001, 5.158794284407866, 4.099613698715551, 3.239032078189301, 4.740130493827161, 2.7078895061728403, 2.275436625514404, 1.5625000000000002, 2.249091889842969, 1.8157479552469142, 1.0097219478738, 0.5026676611796984, 0.0), # 59
(6.220665463600247, 5.508912037037038, 5.042824074074074, 5.4417968750000005, 4.498585299330615, 2.1875000000000004, 2.267687908496732, 1.9218750000000004, 2.3677083333333337, 1.0759259259259264, 0.8190867003367005, 0.46820987654321, 0.0, 6.075000000000001, 5.150308641975309, 4.095433501683503, 3.2277777777777787, 4.7354166666666675, 2.6906250000000007, 2.267687908496732, 1.5625000000000002, 2.2492926496653074, 1.8139322916666674, 1.008564814814815, 0.5008101851851854, 0.0), # 60
(6.22174091918005, 5.48861560356653, 5.037038408779151, 5.436308217592593, 4.498936885607925, 2.1875000000000004, 2.26000019365771, 1.909913580246914, 2.365344012345679, 1.0722263443072706, 0.8182394375857341, 0.46743845450388677, 0.0, 6.075000000000001, 5.141822999542754, 4.09119718792867, 3.216679032921811, 4.730688024691358, 2.67387901234568, 2.26000019365771, 1.5625000000000002, 2.2494684428039626, 1.8121027391975315, 1.0074076817558304, 0.4989650548696847, 0.0), # 61
(6.222664167894603, 5.4684911865569275, 5.031260973936901, 5.430783449074076, 4.499238465097694, 2.1875000000000004, 2.2523877148390223, 1.8983595679012348, 2.3629752469135803, 1.0685866803840882, 0.8173815500685874, 0.46666812985825346, 0.0, 6.075000000000001, 5.133349428440788, 4.086907750342936, 3.205760041152264, 4.725950493827161, 2.657703395061729, 2.2523877148390223, 1.5625000000000002, 2.249619232548847, 1.810261149691359, 1.0062521947873804, 0.4971355624142662, 0.0), # 62
(6.223434732247776, 5.448575, 5.025500000000001, 5.425228125000002, 4.499489964379743, 2.1875000000000004, 2.244864705882353, 1.8872500000000003, 2.360605, 1.0650150000000003, 0.8165136363636366, 0.4659000000000001, 0.0, 6.075000000000001, 5.1249, 4.082568181818183, 3.1950450000000004, 4.72121, 2.6421500000000004, 2.244864705882353, 1.5625000000000002, 2.2497449821898714, 1.808409375000001, 1.0051000000000003, 0.49532500000000007, 0.0), # 63
(6.224052134743439, 5.428903257887518, 5.019763717421125, 5.419647800925927, 4.499691310033899, 2.1875000000000004, 2.237445400629388, 1.8766219135802475, 2.3582362345679013, 1.0615193689986286, 0.815636295049258, 0.4651351623228168, 0.0, 6.075000000000001, 5.116486785550984, 4.07818147524629, 3.184558106995885, 4.716472469135803, 2.6272706790123466, 2.237445400629388, 1.5625000000000002, 2.2498456550169497, 1.8065492669753094, 1.0039527434842253, 0.49353665980795625, 0.0), # 64
(6.224515897885464, 5.409512174211248, 5.0140603566529505, 5.414048032407408, 4.499842428639987, 2.1875000000000004, 2.230144032921811, 1.8665123456790127, 2.355871913580247, 1.0581078532235944, 0.8147501247038288, 0.46437471422039334, 0.0, 6.075000000000001, 5.108121856424326, 4.073750623519143, 3.1743235596707824, 4.711743827160494, 2.613117283950618, 2.230144032921811, 1.5625000000000002, 2.2499212143199934, 1.8046826774691365, 1.0028120713305901, 0.49177383401920444, 0.0), # 65
(6.224825544177719, 5.390437962962965, 5.008398148148149, 5.408434375000001, 4.499943246777829, 2.1875000000000004, 2.2229748366013076, 1.856958333333334, 2.3535150000000002, 1.0547885185185188, 0.8138557239057241, 0.46361975308641995, 0.0, 6.075000000000001, 5.099817283950618, 4.06927861952862, 3.164365555555556, 4.7070300000000005, 2.5997416666666675, 2.2229748366013076, 1.5625000000000002, 2.2499716233889147, 1.8028114583333341, 1.00167962962963, 0.49003981481481507, 0.0), # 66
(6.224980596124076, 5.371716838134431, 5.002785322359397, 5.40281238425926, 4.499993691027252, 2.1875000000000004, 2.215952045509562, 1.8479969135802476, 2.3511684567901234, 1.0515694307270238, 0.8129536912333212, 0.46287137631458625, 0.0, 6.075000000000001, 5.091585139460448, 4.064768456166606, 3.1547082921810707, 4.702336913580247, 2.5871956790123467, 2.215952045509562, 1.5625000000000002, 2.249996845513626, 1.8009374614197537, 1.0005570644718795, 0.48833789437585745, 0.0), # 67
(6.224874968688496, 5.353286751626178, 4.997202974965707, 5.3971387832125615, 4.499951182118938, 2.1874594040542603, 2.2090545584488943, 1.8395859625057158, 2.3488175697302243, 1.0484430662470754, 0.8120285988540378, 0.4621265010949873, 0.0, 6.074925090020577, 5.08339151204486, 4.060142994270189, 3.1453291987412255, 4.6976351394604485, 2.575420347508002, 2.2090545584488943, 1.5624710028959001, 2.249975591059469, 1.7990462610708544, 0.9994405949931414, 0.4866624319660163, 0.0), # 68
(6.2238850241545896, 5.334585035842295, 4.991494212962963, 5.39112758152174, 4.499564270152505, 2.187138477366256, 2.2020804544039843, 1.8312746913580251, 2.346359567901235, 1.0453209586056647, 0.8109862838915471, 0.4613609486679664, 0.0, 6.074331597222224, 5.0749704353476295, 4.054931419457736, 3.1359628758169933, 4.69271913580247, 2.5637845679012354, 2.2020804544039843, 1.5622417695473254, 2.2497821350762526, 1.7970425271739137, 0.9982988425925927, 0.4849622759856632, 0.0), # 69
(6.221931472535338, 5.315525852913846, 4.985634216392318, 5.384739205917875, 4.498799725651577, 2.186506439567139, 2.1949980403684113, 1.8229881115683588, 2.3437805784179244, 1.0421879286694105, 0.8098148886526081, 0.46057113806464056, 0.0, 6.073159400720166, 5.066282518711046, 4.049074443263041, 3.126563786008231, 4.687561156835849, 2.5521833561957026, 2.1949980403684113, 1.5617903139765281, 2.2493998628257885, 1.7949130686392922, 0.9971268432784638, 0.4832296229921679, 0.0), # 70
(6.219041796385758, 5.296120592725343, 4.9796250428669415, 5.377983076690823, 4.497667231501655, 2.185573532998019, 2.1878104978616966, 1.8147289666209425, 2.3410844421582078, 1.0390440539013963, 0.8085187370783864, 0.4597576468606359, 0.0, 6.07142393261317, 5.057334115466994, 4.042593685391932, 3.1171321617041885, 4.6821688843164155, 2.54062055326932, 2.1878104978616966, 1.561123952141442, 2.2488336157508275, 1.792661025563608, 0.9959250085733884, 0.4814655084295767, 0.0), # 71
(6.215243478260871, 5.27638064516129, 4.97346875, 5.370868614130435, 4.496176470588235, 2.1843500000000007, 2.1805210084033617, 1.8065000000000002, 2.3382750000000003, 1.0358894117647062, 0.8071021531100481, 0.45892105263157906, 0.0, 6.069140625000001, 5.048131578947369, 4.0355107655502405, 3.107668235294118, 4.676550000000001, 2.5291000000000006, 2.1805210084033617, 1.5602500000000006, 2.2480882352941176, 1.790289538043479, 0.9946937500000002, 0.47967096774193557, 0.0), # 72
(6.210564000715693, 5.2563174001062, 4.9671673954046645, 5.363405238526571, 4.494337125796821, 2.18284608291419, 2.173132753512928, 1.7983039551897582, 2.3353560928212165, 1.0327240797224242, 0.805569460688759, 0.45806193295309605, 0.0, 6.066324909979425, 5.038681262484056, 4.027847303443795, 3.0981722391672717, 4.670712185642433, 2.5176255372656615, 2.173132753512928, 1.5591757735101357, 2.2471685628984104, 1.787801746175524, 0.9934334790809329, 0.47784703637329096, 0.0), # 73
(6.2050308463052435, 5.235942247444578, 4.960723036694102, 5.355602370169082, 4.49215888001291, 2.1810720240816956, 2.165648914709917, 1.79014357567444, 2.3323315614997715, 1.0295481352376346, 0.8039249837556858, 0.4571808654008135, 0.0, 6.062992219650208, 5.028989519408948, 4.019624918778429, 3.0886444057129028, 4.664663122999543, 2.506201005944216, 2.165648914709917, 1.5579085886297825, 2.246079440006455, 1.7852007900563613, 0.9921446073388204, 0.47599474976768896, 0.0), # 74
(6.198671497584542, 5.215266577060932, 4.954137731481482, 5.347469429347827, 4.489651416122005, 2.179038065843622, 2.1580726735138502, 1.782021604938272, 2.32920524691358, 1.0263616557734208, 0.8021730462519937, 0.4562784275503575, 0.0, 6.059157986111113, 5.019062703053931, 4.010865231259968, 3.0790849673202616, 4.65841049382716, 2.494830246913581, 2.1580726735138502, 1.5564557613168728, 2.2448257080610023, 1.7824898097826094, 0.9908275462962965, 0.4741151433691757, 0.0), # 75
(6.191513437108607, 5.194301778839773, 4.9474135373799735, 5.339015836352658, 4.486824417009602, 2.1767544505410767, 2.1504072114442487, 1.7739407864654781, 2.325980989940558, 1.0231647187928672, 0.8003179721188494, 0.4553551969773542, 0.0, 6.054837641460906, 5.0089071667508955, 4.001589860594247, 3.069494156378601, 4.651961979881116, 2.4835171010516697, 2.1504072114442487, 1.5548246075293404, 2.243412208504801, 1.7796719454508865, 0.9894827074759949, 0.47220925262179764, 0.0), # 76
(6.183584147432457, 5.173059242665606, 4.940552512002744, 5.33025101147343, 4.483687565561204, 2.1742314205151656, 2.1426557100206343, 1.765903863740284, 2.3226626314586194, 1.019957401759058, 0.7983640852974189, 0.45441175125743044, 0.0, 6.050046617798356, 4.998529263831734, 3.991820426487094, 3.0598722052771734, 4.645325262917239, 2.472265409236398, 2.1426557100206343, 1.5530224432251183, 2.241843782780602, 1.7767503371578104, 0.9881105024005488, 0.4702781129696006, 0.0), # 77
(6.174911111111112, 5.1515503584229405, 4.933556712962964, 5.321184375000001, 4.48025054466231, 2.171479218106996, 2.134821350762528, 1.7579135802469141, 2.319254012345679, 1.0167397821350765, 0.7963157097288678, 0.453448667966212, 0.0, 6.044800347222224, 4.987935347628332, 3.9815785486443387, 3.050219346405229, 4.638508024691358, 2.46107901234568, 2.134821350762528, 1.5510565843621402, 2.240125272331155, 1.7737281250000008, 0.9867113425925927, 0.4683227598566311, 0.0), # 78
(6.165521810699589, 5.129786515996284, 4.9264281978738005, 5.311825347222223, 4.476523037198419, 2.1685080856576744, 2.1269073151894506, 1.7499726794695933, 2.3157589734796526, 1.0135119373840074, 0.7941771693543623, 0.45246652467932535, 0.0, 6.039114261831276, 4.977131771472578, 3.970885846771812, 3.0405358121520214, 4.631517946959305, 2.4499617512574305, 2.1269073151894506, 1.5489343468983388, 2.2382615185992094, 1.7706084490740748, 0.9852856395747601, 0.46634422872693504, 0.0), # 79
(6.155443728752909, 5.107779105270145, 4.919169024348423, 5.302183348429953, 4.4725147260550315, 2.165328265508307, 2.1189167848209247, 1.7420839048925472, 2.3121813557384545, 1.0102739449689344, 0.7919527881150691, 0.45146589897239686, 0.0, 6.033003793724281, 4.966124888696365, 3.959763940575345, 3.030821834906803, 4.624362711476909, 2.438917466849566, 2.1189167848209247, 1.5466630467916478, 2.2362573630275158, 1.7673944494766514, 0.9838338048696846, 0.46434355502455876, 0.0), # 80
(6.144704347826088, 5.0855395161290335, 4.911781250000001, 5.292267798913045, 4.4682352941176475, 2.1619500000000005, 2.110852941176471, 1.7342500000000003, 2.308525, 1.0070258823529417, 0.7896468899521534, 0.4504473684210528, 0.0, 6.026484375, 4.95492105263158, 3.9482344497607667, 3.0210776470588243, 4.61705, 2.4279500000000005, 2.110852941176471, 1.5442500000000001, 2.2341176470588238, 1.7640892663043488, 0.9823562500000002, 0.4623217741935486, 0.0), # 81
(6.133331150474146, 5.0630791384574545, 4.904266932441702, 5.2820881189613536, 4.463694424271766, 2.1583835314738615, 2.1027189657756113, 1.7264737082761776, 2.304793747142204, 1.0037678269991126, 0.7872637988067815, 0.44941151060091944, 0.0, 6.019571437757203, 4.9435266166101135, 3.936318994033907, 3.011303480997337, 4.609587494284408, 2.417063191586649, 2.1027189657756113, 1.5417025224813294, 2.231847212135883, 1.760696039653785, 0.9808533864883406, 0.4602799216779505, 0.0), # 82
(6.121351619252104, 5.040409362139918, 4.896628129286695, 5.2716537288647345, 4.458901799402889, 2.1546391022709956, 2.0945180401378662, 1.7187577732053043, 2.3009914380429812, 1.000499856370532, 0.7848078386201196, 0.4483589030876232, 0.0, 6.0122804140946515, 4.931947933963855, 3.924039193100598, 3.0014995691115955, 4.6019828760859625, 2.4062608824874263, 2.0945180401378662, 1.5390279301935683, 2.2294508997014444, 1.7572179096215788, 0.9793256258573391, 0.4582190329218108, 0.0), # 83
(6.108793236714976, 5.017541577060933, 4.888866898148149, 5.260974048913045, 4.453867102396515, 2.1507269547325105, 2.0862533457827577, 1.7111049382716053, 2.2971219135802468, 0.9972220479302837, 0.7822833333333336, 0.4472901234567902, 0.0, 6.004626736111112, 4.920191358024692, 3.911416666666667, 2.9916661437908503, 4.5942438271604935, 2.3955469135802474, 2.0862533457827577, 1.5362335390946504, 2.2269335511982575, 1.7536580163043487, 0.97777337962963, 0.45614014336917574, 0.0), # 84
(6.095683485417786, 4.994487173105005, 4.880985296639232, 5.250058499396136, 4.448600016138143, 2.146657331199513, 2.0779280642298072, 1.7035179469593054, 2.2931890146319156, 0.9939344791414512, 0.7796946068875895, 0.446205749284047, 0.0, 5.996625835905351, 4.908263242124516, 3.8984730344379477, 2.981803437424353, 4.586378029263831, 2.3849251257430275, 2.0779280642298072, 1.533326665142509, 2.2243000080690716, 1.7500194997987126, 0.9761970593278466, 0.45404428846409145, 0.0), # 85
(6.082049847915549, 4.971257540156645, 4.872985382373115, 5.2389165006038665, 4.443110223513274, 2.1424404740131084, 2.069545376998536, 1.6959995427526295, 2.2891965820759035, 0.990637227467119, 0.7770459832240536, 0.44510635814501975, 0.0, 5.988293145576132, 4.896169939595216, 3.8852299161202675, 2.9719116824013563, 4.578393164151807, 2.3743993598536814, 2.069545376998536, 1.5303146242950774, 2.221555111756637, 1.7463055002012893, 0.9745970764746232, 0.45193250365060417, 0.0), # 86
(6.067919806763285, 4.947864068100359, 4.864869212962963, 5.227557472826088, 4.437407407407409, 2.1380866255144033, 2.061108465608466, 1.6885524691358027, 2.285148456790124, 0.9873303703703706, 0.7743417862838917, 0.44399252761533475, 0.0, 5.979644097222223, 4.883917803768681, 3.8717089314194584, 2.9619911111111112, 4.570296913580248, 2.363973456790124, 2.061108465608466, 1.5272047325102882, 2.2187037037037043, 1.7425191576086962, 0.9729738425925928, 0.44980582437275995, 0.0), # 87
(6.053320844516015, 4.924318146820656, 4.856638846021949, 5.215990836352658, 4.431501250706044, 2.1336060280445057, 2.0526205115791174, 1.6811794695930502, 2.281048479652492, 0.9840139853142905, 0.77158634000827, 0.4428648352706184, 0.0, 5.970694122942389, 4.871513187976801, 3.85793170004135, 2.952041955942871, 4.562096959304984, 2.35365125743027, 2.0526205115791174, 1.5240043057460755, 2.215750625353022, 1.7386636121175532, 0.9713277692043898, 0.44766528607460515, 0.0), # 88
(6.0382804437287545, 4.900631166202045, 4.848296339163238, 5.2042260114734304, 4.425401436294683, 2.129008923944521, 2.0440846964300126, 1.6738832876085967, 2.276900491540924, 0.9806881497619627, 0.7687839683383546, 0.4417238586864969, 0.0, 5.961458654835392, 4.858962445551465, 3.843919841691773, 2.9420644492858874, 4.553800983081848, 2.3434366026520355, 2.0440846964300126, 1.5207206599603722, 2.2127007181473415, 1.7347420038244774, 0.9696592678326478, 0.445511924200186, 0.0), # 89
(6.022826086956522, 4.876814516129033, 4.839843750000001, 5.192272418478262, 4.419117647058824, 2.1243055555555563, 2.0355042016806726, 1.6666666666666667, 2.272708333333334, 0.977352941176471, 0.7659389952153112, 0.44057017543859656, 0.0, 5.951953125000001, 4.846271929824561, 3.829694976076556, 2.9320588235294123, 4.545416666666668, 2.3333333333333335, 2.0355042016806726, 1.5173611111111116, 2.209558823529412, 1.7307574728260877, 0.9679687500000003, 0.4433467741935485, 0.0), # 90
(6.006985256754339, 4.852879586486127, 4.831283136145405, 5.180139477657006, 4.412659565883967, 2.119506165218717, 2.0268822088506186, 1.6595323502514865, 2.2684758459076364, 0.9740084370208992, 0.7630557445803061, 0.4394043631025438, 0.0, 5.942192965534979, 4.833447994127981, 3.8152787229015304, 2.922025311062697, 4.536951691815273, 2.3233452903520813, 2.0268822088506186, 1.5139329751562263, 2.2063297829419835, 1.7267131592190026, 0.9662566272290811, 0.4411708714987389, 0.0), # 91
(5.990785435677224, 4.8288377671578395, 4.822616555212621, 5.167836609299518, 4.406036875655612, 2.114620995275111, 2.018221899459373, 1.65248308184728, 2.264206870141747, 0.9706547147583318, 0.7601385403745055, 0.43822699925396497, 0.0, 5.932193608539095, 4.820496991793614, 3.8006927018725274, 2.911964144274995, 4.528413740283494, 2.313476314586192, 2.018221899459373, 1.5104435680536508, 2.203018437827806, 1.7226122030998396, 0.9645233110425242, 0.43898525155980367, 0.0), # 92
(5.974254106280194, 4.804700448028675, 4.813846064814816, 5.155373233695654, 4.39925925925926, 2.109660288065844, 2.0095264550264553, 1.6455216049382722, 2.2599052469135805, 0.9672918518518521, 0.7571917065390752, 0.43703866146848613, 0.0, 5.921970486111111, 4.807425276153347, 3.7859585326953757, 2.9018755555555558, 4.519810493827161, 2.303730246913581, 2.0095264550264553, 1.506900205761317, 2.19962962962963, 1.7184577445652183, 0.9627692129629631, 0.4367909498207887, 0.0), # 93
(5.957418751118269, 4.780479018983141, 4.804973722565159, 5.1427587711352665, 4.392336399580408, 2.104634285932023, 2.0007990570713887, 1.6386506630086881, 2.255574817101052, 0.9639199257645449, 0.7542195670151812, 0.4358399273217338, 0.0, 5.911539030349795, 4.794239200539072, 3.771097835075906, 2.891759777293634, 4.511149634202104, 2.2941109282121634, 2.0007990570713887, 1.503310204237159, 2.196168199790204, 1.714252923711756, 0.960994744513032, 0.43458900172574016, 0.0), # 94
(5.940306852746467, 4.7561848699057485, 4.796001586076819, 5.130002641908213, 4.3852779795045596, 2.0995532312147542, 1.9920428871136935, 1.631872999542753, 2.251219421582076, 0.9605390139594935, 0.7512264457439898, 0.4346313743893341, 0.0, 5.900914673353911, 4.780945118282674, 3.7561322287199483, 2.8816170418784797, 4.502438843164152, 2.284622199359854, 1.9920428871136935, 1.49968087943911, 2.1926389897522798, 1.7100008806360716, 0.9592003172153638, 0.43238044271870446, 0.0), # 95
(5.922945893719809, 4.731829390681005, 4.786931712962964, 5.117114266304348, 4.378093681917211, 2.094427366255144, 1.9832611266728915, 1.625191358024692, 2.246842901234568, 0.9571491938997825, 0.7482166666666669, 0.43341358024691373, 0.0, 5.890112847222223, 4.767549382716051, 3.7410833333333344, 2.8714475816993468, 4.493685802469136, 2.2752679012345687, 1.9832611266728915, 1.496019547325103, 2.1890468409586057, 1.7057047554347833, 0.957386342592593, 0.4301663082437278, 0.0), # 96
(5.90536335659331, 4.707423971193417, 4.777766160836763, 5.104103064613527, 4.370793189703866, 2.0892669333943, 1.9744569572685042, 1.6186084819387292, 2.242449096936443, 0.9537505430484955, 0.7451945537243784, 0.4321871224700986, 0.0, 5.8791489840535, 4.7540583471710836, 3.725972768621892, 2.8612516291454857, 4.484898193872886, 2.266051874714221, 1.9744569572685042, 1.4923335238530713, 2.185396594851933, 1.7013676882045095, 0.9555532321673527, 0.4279476337448561, 0.0), # 97
(5.8875867239219914, 4.682980001327493, 4.768506987311386, 5.090978457125605, 4.363386185750021, 2.084082174973328, 1.965633560420053, 1.6121271147690905, 2.238041849565615, 0.9503431388687166, 0.7421644308582908, 0.430952578634515, 0.0, 5.868038515946503, 4.740478364979665, 3.7108221542914532, 2.851029416606149, 4.47608369913123, 2.2569779606767266, 1.965633560420053, 1.4886301249809484, 2.1816930928750105, 1.6969928190418688, 0.9537013974622772, 0.4257254546661358, 0.0), # 98
(5.86964347826087, 4.658508870967743, 4.759156250000001, 5.0777498641304355, 4.355882352941177, 2.0788833333333336, 1.9567941176470594, 1.6057500000000002, 2.233625, 0.9469270588235297, 0.7391306220095696, 0.42971052631578954, 0.0, 5.8567968750000015, 4.726815789473684, 3.6956531100478474, 2.840781176470588, 4.46725, 2.2480500000000005, 1.9567941176470594, 1.4849166666666669, 2.1779411764705885, 1.692583288043479, 0.9518312500000002, 0.42350080645161303, 0.0), # 99
(5.851561102164967, 4.634021969998672, 4.749716006515776, 5.0644267059178745, 4.348291374162834, 2.073680650815425, 1.9479418104690438, 1.5994798811156838, 2.229202389117513, 0.9435023803760191, 0.7360974511193812, 0.4284615430895485, 0.0, 5.8454394933127585, 4.713076973985032, 3.6804872555969057, 2.8305071411280567, 4.458404778235026, 2.2392718335619577, 1.9479418104690438, 1.4812004648681607, 2.174145687081417, 1.6881422353059587, 0.9499432013031552, 0.42127472454533393, 0.0), # 100
(5.833367078189301, 4.609530688304793, 4.74018831447188, 5.051018402777779, 4.340622932300493, 2.0684843697607076, 1.9390798204055288, 1.5933195016003658, 2.224777857796068, 0.9400691809892685, 0.7330692421288914, 0.4272062065314178, 0.0, 5.83398180298354, 4.699268271845595, 3.6653462106444565, 2.820207542967805, 4.449555715592136, 2.230647302240512, 1.9390798204055288, 1.4774888355433624, 2.1703114661502463, 1.6836728009259267, 0.9480376628943762, 0.41904824439134486, 0.0), # 101
(5.81508888888889, 4.58504641577061, 4.730575231481482, 5.037534375000002, 4.332886710239652, 2.063304732510289, 1.9302113289760352, 1.5872716049382718, 2.2203552469135803, 0.9366275381263621, 0.7300503189792665, 0.4259450942170242, 0.0, 5.822439236111112, 4.6853960363872655, 3.650251594896332, 2.809882614379086, 4.440710493827161, 2.2221802469135805, 1.9302113289760352, 1.4737890946502061, 2.166443355119826, 1.6791781250000009, 0.9461150462962965, 0.4168224014336919, 0.0), # 102
(5.796754016818752, 4.560580542280633, 4.720878815157751, 5.023984042874398, 4.325092390865812, 2.058151981405274, 1.9213395177000843, 1.5813389346136262, 2.2159383973479656, 0.9331775292503837, 0.7270450056116725, 0.42467878372199375, 0.0, 5.810827224794239, 4.67146662094193, 3.635225028058362, 2.79953258775115, 4.431876794695931, 2.2138745084590767, 1.9213395177000843, 1.4701085581466242, 2.162546195432906, 1.6746613476247996, 0.9441757630315504, 0.4145982311164212, 0.0), # 103
(5.778389944533907, 4.536144457719368, 4.711101123113855, 5.010376826690822, 4.3172496570644725, 2.0530363587867706, 1.912467568097198, 1.5755242341106543, 2.2115311499771377, 0.9297192318244174, 0.7240576259672752, 0.4234078526219528, 0.0, 5.799161201131688, 4.65748637884148, 3.6202881298363754, 2.7891576954732518, 4.4230622999542755, 2.205733927754916, 1.912467568097198, 1.4664545419905504, 2.1586248285322363, 1.6701256088969412, 0.9422202246227711, 0.412376768883579, 0.0), # 104
(5.760024154589373, 4.5117495519713255, 4.701244212962964, 4.996722146739131, 4.309368191721133, 2.047968106995885, 1.9035986616868976, 1.5698302469135805, 2.2071373456790124, 0.9262527233115473, 0.721092503987241, 0.4221328784925278, 0.0, 5.787456597222223, 4.6434616634178045, 3.6054625199362045, 2.7787581699346413, 4.414274691358025, 2.197762345679013, 1.9035986616868976, 1.462834362139918, 2.1546840958605666, 1.6655740489130442, 0.9402488425925928, 0.4101590501792115, 0.0), # 105
(5.741684129540169, 4.487407214921015, 4.691310142318245, 4.98302942330918, 4.301457677721295, 2.0429574683737237, 1.8947359799887038, 1.5642597165066305, 2.202760825331504, 0.922778081174857, 0.7181539636127356, 0.4208544389093448, 0.0, 5.775728845164609, 4.629398828002793, 3.590769818063678, 2.7683342435245706, 4.405521650663008, 2.1899636031092826, 1.8947359799887038, 1.4592553345526598, 2.1507288388606476, 1.661009807769727, 0.9382620284636491, 0.40794611044736506, 0.0), # 106
(5.723397351941315, 4.463128836452942, 4.681300968792868, 4.9693080766908215, 4.293527797950456, 2.038014685261394, 1.8858827045221387, 1.5588153863740288, 2.198405429812529, 0.9192953828774312, 0.7152463287849253, 0.41957311144803033, 0.0, 5.763993377057614, 4.615304225928333, 3.5762316439246264, 2.7578861486322928, 4.396810859625058, 2.18234154092364, 1.8858827045221387, 1.4557247751867097, 2.146763898975228, 1.6564360255636077, 0.9362601937585737, 0.40573898513208567, 0.0), # 107
(5.705191304347827, 4.438925806451614, 4.671218750000001, 4.955567527173915, 4.285588235294117, 2.0331500000000005, 1.8770420168067228, 1.5535000000000003, 2.194075, 0.9158047058823533, 0.7123739234449762, 0.4182894736842107, 0.0, 5.7522656250000015, 4.601184210526317, 3.5618696172248807, 2.7474141176470592, 4.38815, 2.1749000000000005, 1.8770420168067228, 1.4522500000000003, 2.1427941176470586, 1.6518558423913052, 0.9342437500000004, 0.4035387096774195, 0.0), # 108
(5.687093469314727, 4.414809514801541, 4.661065543552813, 4.941817195048309, 4.27764867263778, 2.0283736549306512, 1.8682170983619788, 1.5483163008687704, 2.189773376771834, 0.9123061276527075, 0.7095410715340542, 0.417004103193512, 0.0, 5.740561021090536, 4.587045135128632, 3.547705357670271, 2.736918382958122, 4.379546753543668, 2.167642821216279, 1.8682170983619788, 1.4488383249504653, 2.13882433631889, 1.6472723983494368, 0.9322131087105627, 0.40134631952741284, 0.0), # 109
(5.66913132939703, 4.3907913513872305, 4.650843407064474, 4.928066500603865, 4.269718792866942, 2.0236958923944526, 1.859411130707427, 1.5432670324645634, 2.1855044010059443, 0.9087997256515778, 0.7067520969933253, 0.4157175775515608, 0.0, 5.728894997427985, 4.572893353067168, 3.533760484966626, 2.726399176954733, 4.371008802011889, 2.160573845450389, 1.859411130707427, 1.4454970659960373, 2.134859396433471, 1.6426888335346224, 0.9301686814128948, 0.3991628501261119, 0.0), # 110
(5.6513323671497595, 4.366882706093191, 4.64055439814815, 4.914324864130435, 4.261808278867103, 2.019126954732511, 1.8506272953625897, 1.5383549382716053, 2.181271913580247, 0.9052855773420483, 0.7040113237639555, 0.4144304743339832, 0.0, 5.717282986111112, 4.558735217673815, 3.5200566188197775, 2.715856732026144, 4.362543827160494, 2.1536969135802475, 1.8506272953625897, 1.4422335390946506, 2.1309041394335515, 1.638108288043479, 0.92811087962963, 0.3969893369175629, 0.0), # 111
(5.633724065127931, 4.343094968803928, 4.630200574417011, 4.900601705917875, 4.253926813523764, 2.0146770842859327, 1.8418687738469877, 1.5335827617741202, 2.1770797553726564, 0.9017637601872027, 0.7013230757871111, 0.4131433711164056, 0.0, 5.705740419238684, 4.544577082280461, 3.5066153789355554, 2.7052912805616076, 4.354159510745313, 2.1470158664837684, 1.8418687738469877, 1.4390550602042376, 2.126963406761882, 1.6335339019726254, 0.9260401148834023, 0.39482681534581177, 0.0), # 112
(5.616302534221828, 4.319479240332274, 4.619808914126035, 4.886925247121419, 4.246070272069482, 2.01035441922508, 1.8331577890970312, 1.528963142195152, 2.172942562251724, 0.8982457104919097, 0.6986923906152869, 0.41185964682522147, 0.0, 5.6942663405059335, 4.530456115077436, 3.493461953076434, 2.6947371314757285, 4.345885124503448, 2.1405483990732126, 1.8331577890970312, 1.4359674423036286, 2.123035136034741, 1.6289750823738067, 0.9239617828252071, 0.3926799309392977, 0.0), # 113
(5.59888853874004, 4.296268450939045, 4.60952283751081, 4.873405239574803, 4.238157341826531, 2.0061491129854656, 1.824605462523174, 1.5245528148762801, 2.1689238071426122, 0.8947972751869178, 0.6961244337113198, 0.41059560860564887, 0.0, 5.682765248496022, 4.5165516946621365, 3.4806221685565992, 2.684391825560753, 4.3378476142852245, 2.134373940826792, 1.824605462523174, 1.4329636521324756, 2.1190786709132654, 1.6244684131916012, 0.9219045675021622, 0.39056985917627685, 0.0), # 114
(5.581430941802398, 4.273470959275675, 4.59934846635366, 4.860038027331801, 4.230163071155441, 2.002050229374568, 1.8162178950574688, 1.5203506635696775, 2.165024995733758, 0.8914237991982119, 0.6936154511427095, 0.40935187912794263, 0.0, 5.6712039789962265, 4.502870670407368, 3.4680772557135473, 2.674271397594635, 4.330049991467516, 2.1284909289975484, 1.8162178950574688, 1.4300358781246914, 2.1150815355777204, 1.6200126757772675, 0.919869693270732, 0.3884973599341524, 0.0), # 115
(5.5639079239425815, 4.251053554710436, 4.589266094991937, 4.846799767619883, 4.222075410553511, 1.9980481070504847, 1.8079814452583682, 1.5163450782247487, 2.1612362678455663, 0.8881190037674325, 0.6911605931271482, 0.4081261598743674, 0.0, 5.659564355853536, 4.489387758618041, 3.455802965635741, 2.6643570113022967, 4.322472535691133, 2.1228831095146483, 1.8079814452583682, 1.4271772193217747, 2.1110377052767557, 1.6155999225399613, 0.9178532189983876, 0.3864594140645852, 0.0), # 116
(5.546297665694264, 4.228983026611597, 4.579256017762994, 4.833666617666511, 4.213882310518045, 1.9941330846713121, 1.799882471684326, 1.5125244487908964, 2.1575477632984406, 0.8848766101362197, 0.6887550098823285, 0.40691615232718764, 0.0, 5.647828202914936, 4.476077675599063, 3.4437750494116424, 2.654629830408658, 4.315095526596881, 2.117534228307255, 1.799882471684326, 1.4243807747652228, 2.1069411552590225, 1.6112222058888372, 0.9158512035525989, 0.38445300241923613, 0.0), # 117
(5.528578347591128, 4.2072261643474285, 4.569298529004184, 4.82061473469915, 4.205571721546337, 1.9902955008951488, 1.791907332893795, 1.5088771652175246, 2.153949621912787, 0.8816903395462142, 0.6863938516259426, 0.40571955796866754, 0.0, 5.635977344027416, 4.462915137655342, 3.4319692581297123, 2.645071018638642, 4.307899243825574, 2.1124280313045345, 1.791907332893795, 1.4216396434965348, 2.1027858607731686, 1.6068715782330507, 0.913859705800837, 0.3824751058497663, 0.0), # 118
(5.5107281501668455, 4.185749757286201, 4.5593739230528625, 4.807620275945268, 4.197131594135689, 1.986525694380092, 1.784042387445227, 1.5053916174540365, 2.1504319835090087, 0.8785539132390561, 0.6840722685756829, 0.40453407828107185, 0.0, 5.6239936030379605, 4.449874861091789, 3.4203613428784148, 2.6356617397171673, 4.300863967018017, 2.107548264435651, 1.784042387445227, 1.4189469245572084, 2.0985657970678444, 1.6025400919817565, 0.9118747846105726, 0.38052270520783654, 0.0), # 119
(5.492725253955098, 4.164520594796188, 4.549462494246382, 4.794659398632328, 4.188549878783399, 1.9828140037842383, 1.7762739938970762, 1.502056195449836, 2.146984987907512, 0.8754610524563862, 0.6817854109492417, 0.4033574147466649, 0.0, 5.611858803793559, 4.436931562213314, 3.4089270547462087, 2.626383157369158, 4.293969975815024, 2.10287867362977, 1.7762739938970762, 1.4162957169887416, 2.0942749393916995, 1.5982197995441099, 0.9098924988492764, 0.37859278134510804, 0.0), # 120
(5.474547839489562, 4.1435054662456565, 4.539544536922095, 4.781708259987796, 4.179814525986767, 1.979150767765686, 1.7685885108077943, 1.498859289154327, 2.1435987749286998, 0.8724054784398447, 0.6795284289643118, 0.40218726884771117, 0.0, 5.599554770141197, 4.424059957324822, 3.3976421448215586, 2.6172164353195333, 4.2871975498573995, 2.098403004816058, 1.7685885108077943, 1.4136791198326328, 2.0899072629933837, 1.5939027533292658, 0.9079089073844191, 0.3766823151132416, 0.0), # 121
(5.456174087303913, 4.122671161002879, 4.529600345417356, 4.768743017239136, 4.170913486243094, 1.9755263249825321, 1.7609722967358354, 1.4957892885169124, 2.140263484392978, 0.8693809124310719, 0.6772964728385852, 0.4010213420664751, 0.0, 5.587063325927863, 4.411234762731225, 3.386482364192926, 2.608142737293215, 4.280526968785956, 2.0941050039236773, 1.7609722967358354, 1.41109023213038, 2.085456743121547, 1.589581005746379, 0.9059200690834714, 0.3747882873638982, 0.0), # 122
(5.437582177931832, 4.101984468436126, 4.519610214069519, 4.755739827613812, 4.161834710049677, 1.9719310140928743, 1.7534117102396518, 1.492834583486996, 2.136969256120751, 0.8663810756717084, 0.6750846927897546, 0.39985733588522115, 0.0, 5.5743662950005435, 4.398430694737432, 3.3754234639487724, 2.5991432270151247, 4.273938512241502, 2.0899684168817947, 1.7534117102396518, 1.4085221529234815, 2.0809173550248383, 1.5852466092046047, 0.9039220428139038, 0.3729076789487388, 0.0), # 123
(5.418750291906993, 4.081412177913668, 4.509554437215936, 4.742674848339295, 4.152566147903816, 1.968355173754809, 1.745893109877696, 1.4899835640139818, 2.1337062299324234, 0.8633996894033945, 0.6728882390355121, 0.39869295178621383, 0.0, 5.561445501206228, 4.385622469648352, 3.3644411951775606, 2.590199068210183, 4.267412459864847, 2.0859769896195743, 1.745893109877696, 1.4059679812534351, 2.076283073951908, 1.5808916161130986, 0.9019108874431874, 0.37103747071942444, 0.0), # 124
(5.399656609763076, 4.060921078803776, 4.4994133091939625, 4.729524236643043, 4.143095750302809, 1.964789142626435, 1.738402854208422, 1.487224620047273, 2.130464545648399, 0.8604304748677705, 0.6707022617935502, 0.3975258912517175, 0.0, 5.548282768391898, 4.372784803768892, 3.353511308967751, 2.581291424603311, 4.260929091296798, 2.082114468066182, 1.738402854208422, 1.4034208161617392, 2.0715478751514045, 1.5765080788810146, 0.8998826618387926, 0.3691746435276161, 0.0), # 125
(5.380279312033758, 4.040477960474722, 4.48916712434095, 4.716264149752525, 4.133411467743958, 1.9612232593658492, 1.7309273017902813, 1.4845461415362735, 2.127234343089084, 0.8574671533064767, 0.6685219112815614, 0.3963538557639968, 0.0, 5.534859920404548, 4.359892413403964, 3.342609556407807, 2.5724014599194294, 4.254468686178168, 2.078364598150783, 1.7309273017902813, 1.4008737566898921, 2.066705733871979, 1.5720880499175087, 0.8978334248681901, 0.3673161782249748, 0.0), # 126
(5.360596579252717, 4.020049612294773, 4.478796176994253, 4.702870744895205, 4.123501250724559, 1.9576478626311482, 1.7234528111817284, 1.4819365184303865, 2.124005762074882, 0.8545034459611539, 0.666342337717238, 0.3951745468053159, 0.0, 5.52115878109116, 4.346920014858474, 3.3317116885861897, 2.563510337883461, 4.248011524149764, 2.0747111258025415, 1.7234528111817284, 1.3983199018793915, 2.0617506253622797, 1.5676235816317354, 0.8957592353988507, 0.3654590556631613, 0.0), # 127
(5.340586591953628, 3.9996028236322045, 4.468280761491226, 4.68932017929855, 4.113353049741916, 1.954053291080431, 1.7159657409412148, 1.4793841406790158, 2.120768942426197, 0.8515330740734423, 0.6641586913182725, 0.39398566585793954, 0.0, 5.507161174298723, 4.3338423244373345, 3.3207934565913626, 2.554599222220326, 4.241537884852394, 2.071137796950622, 1.7159657409412148, 1.3957523507717362, 2.056676524870958, 1.5631067264328504, 0.8936561522982452, 0.36360025669383683, 0.0), # 128
(5.320227530670169, 3.9791043838552826, 4.457601172169221, 4.675588610190022, 4.102954815293325, 1.9504298833717935, 1.7084524496271942, 1.4768773982315653, 2.117514023963435, 0.8485497588849822, 0.6619661223023573, 0.3927849144041319, 0.0, 5.492848923874224, 4.32063405844545, 3.309830611511786, 2.545649276654946, 4.23502804792687, 2.0676283575241916, 1.7084524496271942, 1.3931642024084239, 2.0514774076466624, 1.5585295367300078, 0.8915202344338442, 0.36173676216866213, 0.0), # 129
(5.29949757593602, 3.958521082332282, 4.44673770336559, 4.661652194797089, 4.092294497876086, 1.9467679781633334, 1.700899295798119, 1.4744046810374378, 2.1142311465070005, 0.8455472216374139, 0.6597597808871846, 0.3915699939261577, 0.0, 5.47820385366465, 4.307269933187734, 3.2987989044359227, 2.536641664912241, 4.228462293014001, 2.064166553452413, 1.700899295798119, 1.3905485558309525, 2.046147248938043, 1.5538840649323635, 0.8893475406731182, 0.3598655529392984, 0.0), # 130
(5.2783749082848574, 3.9378197084314697, 4.435670649417691, 4.647487090347214, 4.0813600479874985, 1.9430579141131485, 1.693292638012443, 1.4719543790460377, 2.1109104498772973, 0.8425191835723781, 0.6575348172904469, 0.39033860590628133, 0.0, 5.463207787516988, 4.293724664969094, 3.2876740864522342, 2.5275575507171335, 4.2218208997545945, 2.060736130664453, 1.693292638012443, 1.3878985100808203, 2.0406800239937493, 1.5491623634490719, 0.8871341298835383, 0.3579836098574064, 0.0), # 131
(5.256837708250356, 3.9169670515211195, 4.424380304662874, 4.633069454067865, 4.070139416124862, 1.9392900298793363, 1.6856188348286179, 1.4695148822067685, 2.107542073894731, 0.839459365931515, 0.6552863817298366, 0.38908845182676716, 0.0, 5.447842549278226, 4.2799729700944384, 3.2764319086491827, 2.5183780977945442, 4.215084147789462, 2.057320835089476, 1.6856188348286179, 1.3852071641995258, 2.035069708062431, 1.5443564846892888, 0.8848760609325749, 0.35608791377464727, 0.0), # 132
(5.234864156366198, 3.8959299009695, 4.412846963438493, 4.618375443186504, 4.058620552785475, 1.9354546641199937, 1.677864244805097, 1.4670745804690333, 2.1041161583797052, 0.8363614899564652, 0.6530096244230462, 0.38781723316987976, 0.0, 5.432089962795352, 4.2659895648686765, 3.2650481221152305, 2.509084469869395, 4.2082323167594105, 2.053904412656647, 1.677864244805097, 1.3824676172285668, 2.0293102763927373, 1.5394584810621683, 0.8825693926876986, 0.3541754455426819, 0.0), # 133
(5.212432433166057, 3.874675046144883, 4.401050920081903, 4.6033812149305975, 4.046791408466637, 1.9315421554932182, 1.6700152265003338, 1.4646218637822361, 2.100622843152626, 0.8332192768888689, 0.6506996955877681, 0.3865226514178834, 0.0, 5.41593185191535, 4.251749165596717, 3.25349847793884, 2.4996578306666057, 4.201245686305252, 2.0504706092951306, 1.6700152265003338, 1.3796729682094415, 2.0233957042333186, 1.5344604049768662, 0.8802101840163805, 0.35224318601317123, 0.0), # 134
(5.189520719183613, 3.8531692764155387, 4.388972468930455, 4.588062926527611, 4.034639933665648, 1.9275428426571075, 1.6620581384727806, 1.4621451220957802, 2.097052268033896, 0.8300264479703667, 0.6483517454416944, 0.3852024080530427, 0.0, 5.399350040485213, 4.237226488583469, 3.241758727208472, 2.4900793439110993, 4.194104536067792, 2.0470031709340923, 1.6620581384727806, 1.3768163161836482, 2.017319966832824, 1.5293543088425374, 0.8777944937860911, 0.3502881160377763, 0.0), # 135
(5.1661071949525414, 3.8313793811497376, 4.376591904321505, 4.572396735205008, 4.022154078879807, 1.9234470642697592, 1.6539793392808906, 1.4596327453590694, 2.0933945728439216, 0.8267767244425988, 0.6459609242025178, 0.38385420455762215, 0.0, 5.382326352351923, 4.222396250133843, 3.229804621012589, 2.4803301733277956, 4.186789145687843, 2.0434858435026975, 1.6539793392808906, 1.373890760192685, 2.0110770394399036, 1.5241322450683366, 0.8753183808643011, 0.34830721646815804, 0.0), # 136
(5.142170041006521, 3.8092721497157513, 4.363889520592406, 4.556358798190257, 4.009321794606414, 1.9192451589892696, 1.6457651874831167, 1.4570731235215075, 2.0896398974031065, 0.8234638275472056, 0.6435223820879307, 0.3824757424138861, 0.0, 5.364842611362467, 4.207233166552746, 3.2176119104396532, 2.4703914826416162, 4.179279794806213, 2.0399023729301105, 1.6457651874831167, 1.3708893992780498, 2.004660897303207, 1.5187862660634195, 0.8727779041184812, 0.34629746815597745, 0.0), # 137
(5.117687437879229, 3.78681437148185, 4.3508456120805095, 4.5399252727108195, 3.9961310313427676, 1.9149274654737378, 1.6374020416379116, 1.454454646532498, 2.0857783815318562, 0.8200814785258274, 0.6410312693156254, 0.381064723104099, 0.0, 5.346880641363835, 4.191711954145088, 3.2051563465781263, 2.4602444355774815, 4.1715567630637125, 2.036236505145497, 1.6374020416379116, 1.3678053324812411, 1.9980655156713838, 1.5133084242369401, 0.870169122416102, 0.3442558519528955, 0.0), # 138
(5.092637566104342, 3.763972835816304, 4.337440473123171, 4.5230723159941615, 3.9825697395861663, 1.9104843223812598, 1.6288762603037281, 1.4517657043414438, 2.0818001650505744, 0.8166233986201049, 0.6384827361032942, 0.37961884811052543, 0.0, 5.328422266203012, 4.175807329215779, 3.1924136805164705, 2.449870195860314, 4.163600330101149, 2.032471986078021, 1.6288762603037281, 1.3646316588437568, 1.9912848697930832, 1.5076907719980543, 0.8674880946246343, 0.34217934871057315, 0.0), # 139
(5.066998606215539, 3.7407143320873844, 4.323654398057744, 4.505776085267751, 3.968625869833912, 1.9059060683699331, 1.6201742020390193, 1.4489946868977492, 2.0776953877796664, 0.8130833090716781, 0.6358719326686295, 0.3781358189154297, 0.0, 5.30944930972699, 4.159494008069726, 3.1793596633431473, 2.439249927215034, 4.155390775559333, 2.0285925616568488, 1.6201742020390193, 1.361361477407095, 1.984312934916956, 1.5019253617559174, 0.8647308796115489, 0.34006493928067133, 0.0), # 140
(5.040748738746498, 3.7170056496633617, 4.309467681221581, 4.48801273775905, 3.954287372583302, 1.9011830420978557, 1.6112822254022388, 1.4461299841508177, 2.073454189539536, 0.809454931122188, 0.633194009229324, 0.37661333700107635, 0.0, 5.2899435957827485, 4.142746707011839, 3.1659700461466196, 2.4283647933665633, 4.146908379079072, 2.024581977811145, 1.6112822254022388, 1.357987887212754, 1.977143686291651, 1.4960042459196834, 0.8618935362443163, 0.33790960451485114, 0.0), # 141
(5.013866144230894, 3.692813577912507, 4.294860616952036, 4.469758430695524, 3.939542198331635, 1.8963055822231247, 1.6021866889518381, 1.443159986050053, 2.0690667101505884, 0.8057319860132744, 0.6304441160030698, 0.3750491038497298, 0.0, 5.26988694821728, 4.125540142347027, 3.152220580015349, 2.4171959580398226, 4.138133420301177, 2.0204239804700745, 1.6021866889518381, 1.354503987302232, 1.9697710991658175, 1.4899194768985085, 0.8589721233904073, 0.3357103252647734, 0.0), # 142
(4.986329003202405, 3.6681049062030895, 4.279813499586464, 4.45098932130464, 3.924378297576212, 1.8912640274038375, 1.5928739512462708, 1.4400730825448582, 2.0645230894332296, 0.8019081949865782, 0.6276174032075594, 0.37344082094365455, 0.0, 5.249261190877571, 4.1078490303801996, 3.138087016037797, 2.4057245849597337, 4.129046178866459, 2.0161023155628017, 1.5928739512462708, 1.3509028767170268, 1.962189148788106, 1.4836631071015467, 0.8559626999172927, 0.33346408238209907, 0.0), # 143
(4.958115496194711, 3.6428464239033826, 4.264306623462215, 4.431681566813861, 3.908783620814332, 1.886048716298092, 1.5833303708439899, 1.4368576635846375, 2.059813467207862, 0.7979772792837393, 0.6247090210604852, 0.371786189765115, 0.0, 5.228048147610609, 4.089648087416265, 3.123545105302426, 2.393931837851217, 4.119626934415724, 2.0116007290184927, 1.5833303708439899, 1.347177654498637, 1.954391810407166, 1.4772271889379538, 0.852861324692443, 0.3311678567184894, 0.0), # 144
(4.929203803741487, 3.617004920381655, 4.248320282916645, 4.411811324450653, 3.8927461185432937, 1.8806499875639846, 1.5735423063034482, 1.4335021191187944, 2.054927983294891, 0.7939329601463985, 0.6217141197795396, 0.3700829117963758, 0.0, 5.206229642263381, 4.070912029760133, 3.108570598897698, 2.3817988804391947, 4.109855966589782, 2.006902966766312, 1.5735423063034482, 1.3433214196885603, 1.9463730592716468, 1.4706037748168845, 0.8496640565833291, 0.32881862912560506, 0.0), # 145
(4.899572106376411, 3.5905471850061783, 4.231834772287109, 4.391354751442482, 3.8762537412603972, 1.8750581798596135, 1.5634961161830991, 1.4299948390967319, 2.049856777514721, 0.7897689588161959, 0.618627849582415, 0.3683286885197011, 0.0, 5.183787498682872, 4.051615573716711, 3.093139247912075, 2.369306876448587, 4.099713555029442, 2.0019927747354247, 1.5634961161830991, 1.3393272713282953, 1.9381268706301986, 1.4637849171474944, 0.8463669544574218, 0.3264133804551072, 0.0), # 146
(4.8691985846331605, 3.563440007145222, 4.214830385910956, 4.370288005016811, 3.859294439462941, 1.8692636318430758, 1.5531781590413944, 1.4263242134678542, 2.044589989687758, 0.7854789965347722, 0.6154453606868038, 0.3665212214173556, 0.0, 5.160703540716072, 4.031733435590911, 3.0772268034340184, 2.356436989604316, 4.089179979375516, 1.9968538988549958, 1.5531781590413944, 1.3351883084593397, 1.9296472197314705, 1.4567626683389374, 0.8429660771821912, 0.3239490915586566, 0.0), # 147
(4.838061419045413, 3.5356501761670587, 4.197287418125542, 4.348587242401109, 3.8418561636482247, 1.863256682172469, 1.5425747934367882, 1.4224786321815646, 2.039117759634406, 0.7810567945437674, 0.6121618033103984, 0.3646582119716036, 0.0, 5.136959592209966, 4.011240331687639, 3.0608090165519917, 2.3431703836313016, 4.078235519268812, 1.9914700850541904, 1.5425747934367882, 1.330897630123192, 1.9209280818241123, 1.44952908080037, 0.8394574836251085, 0.3214227432879145, 0.0), # 148
(4.806138790146848, 3.507144481439957, 4.179186163268223, 4.326228620822837, 3.823926864313549, 1.8570276695058903, 1.5316723779277324, 1.418446485187267, 2.0334302271750677, 0.7764960740848224, 0.6087723276708911, 0.36273736166470966, 0.0, 5.112537477011543, 3.9901109783118054, 3.0438616383544552, 2.3294882222544664, 4.066860454350135, 1.9858250792621739, 1.5316723779277324, 1.32644833536135, 1.9119634321567744, 1.4420762069409458, 0.8358372326536446, 0.3188313164945416, 0.0), # 149
(4.773408878471139, 3.477889712332189, 4.160506915676348, 4.303188297509463, 3.805494491956211, 1.8505669325014376, 1.5204572710726805, 1.4142161624343643, 2.0275175321301506, 0.7717905563995768, 0.6052720839859744, 0.36075637197893823, 0.0, 5.087419018967789, 3.96832009176832, 3.0263604199298717, 2.31537166919873, 4.055035064260301, 1.9799026274081102, 1.5204572710726805, 1.3218335232153124, 1.9027472459781054, 1.434396099169821, 0.8321013831352696, 0.316171792030199, 0.0), # 150
(4.7398498645519656, 3.447852658212025, 4.141229969687272, 4.279442429688449, 3.7865469970735113, 1.843864809817207, 1.5089158314300852, 1.4097760538722612, 2.021369814320058, 0.766933962729672, 0.6016562224733406, 0.35871294439655366, 0.0, 5.0615860419256915, 3.9458423883620894, 3.008281112366703, 2.300801888189015, 4.042739628640116, 1.9736864754211656, 1.5089158314300852, 1.3170462927265765, 1.8932734985367556, 1.42648080989615, 0.8282459939374544, 0.31344115074654777, 0.0), # 151
(4.705439928923006, 3.4170001084477355, 4.1213356196383515, 4.2549671745872635, 3.767072330162748, 1.8369116401112975, 1.4970344175583996, 1.4051145494503605, 2.014977213565194, 0.7619200143167477, 0.5979198933506823, 0.3566047803998206, 0.0, 5.035020369732239, 3.9226525843980258, 2.9895994667534116, 2.2857600429502423, 4.029954427130388, 1.9671603692305049, 1.4970344175583996, 1.312079742936641, 1.883536165081374, 1.4183223915290881, 0.8242671239276703, 0.3106363734952487, 0.0), # 152
(4.670157252117937, 3.3852988524075913, 4.100804159866935, 4.22973868943337, 3.7470584417212227, 1.8296977620418058, 1.484799388016076, 1.4002200391180657, 2.008329869685965, 0.7567424324024447, 0.5940582468356918, 0.3544295814710033, 0.0, 5.007703826234417, 3.8987253961810353, 2.970291234178459, 2.270227297207333, 4.01665973937193, 1.960308054765292, 1.484799388016076, 1.306926972887004, 1.8735292208606114, 1.4099128964777903, 0.820160831973387, 0.3077544411279629, 0.0), # 153
(4.6339800146704375, 3.3527156794598634, 4.07961588471038, 4.203733131454235, 3.726493282246232, 1.8222135142668292, 1.4721971013615687, 1.395080912824781, 2.0014179225027733, 0.7513949382284032, 0.5900664331460613, 0.3521850490923663, 0.0, 4.979618235279215, 3.874035540016029, 2.9503321657303068, 2.254184814685209, 4.002835845005547, 1.9531132779546936, 1.4721971013615687, 1.3015810816191637, 1.863246641123116, 1.4012443771514118, 0.8159231769420761, 0.3047923344963513, 0.0), # 154
(4.596886397114182, 3.3192173789728225, 4.057751088506039, 4.1769266578773205, 3.7053648022350787, 1.8144492354444652, 1.4592139161533286, 1.3896855605199092, 1.9942315118360256, 0.7458712530362633, 0.5859396024994835, 0.3498688847461741, 0.0, 4.950745420713616, 3.8485577322079147, 2.929698012497417, 2.2376137591087897, 3.9884630236720513, 1.945559784727873, 1.4592139161533286, 1.296035168174618, 1.8526824011175393, 1.392308885959107, 0.8115502177012078, 0.3017470344520748, 0.0), # 155
(4.5588545799828495, 3.284770740314739, 4.035190065591264, 4.149295425930094, 3.6836609521850594, 1.8063952642328118, 1.44583619094981, 1.3840223721528548, 1.9867607775061256, 0.7401650980676662, 0.5816729051136506, 0.3474787899146912, 0.0, 4.9210672063846115, 3.822266689061603, 2.9083645255682526, 2.220495294202998, 3.973521555012251, 1.9376313210139968, 1.44583619094981, 1.2902823315948655, 1.8418304760925297, 1.3830984753100317, 0.8070380131182529, 0.29861552184679446, 0.0), # 156
(4.519862743810118, 3.249342552853883, 4.01191311030341, 4.120815592840023, 3.6613696825934743, 1.7980419392899651, 1.4320502843094658, 1.3780797376730207, 1.978995859333478, 0.7342701945642514, 0.577261491206255, 0.34501246608018193, 0.0, 4.890565416139187, 3.7951371268820004, 2.886307456031275, 2.2028105836927536, 3.957991718666956, 1.929311632742229, 1.4320502843094658, 1.2843156709214036, 1.8306848412967371, 1.373605197613341, 0.802382622060682, 0.29539477753217125, 0.0), # 157
(4.478808567843144, 3.2122492164247425, 3.9867959769511785, 4.09039631174306, 3.6374724423483995, 1.7888585667109889, 1.4175320162783573, 1.3714492380873433, 1.9703324183198196, 0.7280048874436718, 0.5725595752538944, 0.34237997820971705, 0.0, 4.857891515649208, 3.766179760306887, 2.8627978762694717, 2.184014662331015, 3.9406648366396393, 1.9200289333222809, 1.4175320162783573, 1.2777561190792777, 1.8187362211741998, 1.3634654372476869, 0.7973591953902358, 0.292022656038613, 0.0), # 158
(4.429372060187042, 3.169685925779713, 3.9533689092014943, 4.051789721454805, 3.6060765239126518, 1.7757913768024864, 1.400463829015715, 1.3618033831910958, 1.9572854522444292, 0.720342149114812, 0.5667416935618995, 0.33906812618415544, 0.0, 4.815256588152117, 3.7297493880257093, 2.8337084678094975, 2.161026447344436, 3.9145709044888584, 1.9065247364675344, 1.400463829015715, 1.268422412001776, 1.8030382619563259, 1.3505965738182684, 0.7906737818402989, 0.288153265979974, 0.0), # 159
(4.370923256942587, 3.121303200697393, 3.910960478977945, 4.004359879683987, 3.5665680525387184, 1.7585194536166897, 1.3806731245151354, 1.3488997973818369, 1.9394850454457304, 0.7111808477794342, 0.5597259508609541, 0.3350250974443924, 0.0, 4.761852365336149, 3.685276071888316, 2.7986297543047707, 2.1335425433383026, 3.878970090891461, 1.8884597163345715, 1.3806731245151354, 1.2560853240119212, 1.7832840262693592, 1.3347866265613293, 0.7821920957955891, 0.28375483642703575, 0.0), # 160
(4.303933232751577, 3.067416477444258, 3.860023485955155, 3.948557626690528, 3.5193572497128454, 1.7372520345496103, 1.3583044338020631, 1.3329001332774453, 1.9171659533998038, 0.7005987289756408, 0.5515741654599708, 0.3302883734453786, 0.0, 4.698224426891459, 3.6331721078991643, 2.757870827299854, 2.101796186926922, 3.8343319067996076, 1.8660601865884234, 1.3583044338020631, 1.2408943103925787, 1.7596786248564227, 1.3161858755635096, 0.7720046971910312, 0.27885604340402353, 0.0), # 161
(4.228873062255815, 3.0083411922867818, 3.8010107298077487, 3.8848338027343474, 3.464854336921282, 1.7121983569972596, 1.333502287901943, 1.3139660434958007, 1.8905629315827275, 0.688673538241535, 0.5423481556678622, 0.32489543564206474, 0.0, 4.6249183525082005, 3.573849792062712, 2.7117407783393106, 2.0660206147246045, 3.781125863165455, 1.839552460894121, 1.333502287901943, 1.222998826426614, 1.732427168460641, 1.2949446009114494, 0.7602021459615498, 0.273485562935162, 0.0), # 162
(4.146213820097099, 2.9443927814914383, 3.7343750102103512, 3.8136392480753707, 3.4034695356502755, 1.6835676583556507, 1.3064112178402203, 1.2922591806547814, 1.8599107354705815, 0.675483021115219, 0.5321097397935409, 0.3188837654894017, 0.0, 4.5424797218765285, 3.5077214203834184, 2.6605486989677045, 2.0264490633456567, 3.719821470941163, 1.809162852916694, 1.3064112178402203, 1.2025483273968933, 1.7017347678251378, 1.2712130826917905, 0.7468750020420704, 0.2676720710446763, 0.0), # 163
(4.056426580917231, 2.8758866813247015, 3.660569126837589, 3.735424802973519, 3.3356130673860758, 1.6515691760207956, 1.27717575464234, 1.2679411973722674, 1.8254441205394447, 0.6611049231347954, 0.5209207361459197, 0.31229084444234023, 0.0, 4.451454114686597, 3.435199288865742, 2.6046036807295985, 1.983314769404386, 3.6508882410788894, 1.7751176763211745, 1.27717575464234, 1.1796922685862825, 1.6678065336930379, 1.245141600991173, 0.7321138253675178, 0.2614442437567911, 0.0), # 164
(3.9599824193580107, 2.803138328053048, 3.5800458793640852, 3.650641307688714, 3.26169515361493, 1.6164121473887054, 1.2459404293337468, 1.2411737462661367, 1.787397842265396, 0.6456169898383672, 0.5088429630339113, 0.30515415395583106, 0.0, 4.3523871106285625, 3.3566956935141414, 2.544214815169556, 1.9368509695151013, 3.574795684530792, 1.7376432447725916, 1.2459404293337468, 1.1545801052776468, 1.630847576807465, 1.2168804358962382, 0.7160091758728171, 0.25483075709573166, 0.0), # 165
(3.857352410061239, 2.72646315794295, 3.493258067464464, 3.5597396024808763, 3.1821260158230857, 1.5783058098553933, 1.212849772939886, 1.2121184799542695, 1.7460066561245147, 0.629096966764037, 0.495938238766428, 0.29751117548482514, 0.0, 4.245824289392578, 3.272622930333076, 2.47969119383214, 1.8872909002921108, 3.4920133122490293, 1.6969658719359773, 1.212849772939886, 1.1273612927538523, 1.5910630079115429, 1.1865798674936257, 0.698651613492893, 0.24786028708572277, 0.0), # 166
(3.749007627668714, 2.6461766072608834, 3.4006584908133526, 3.46317052760993, 3.097315875496792, 1.5374594008168707, 1.1780483164862026, 1.1809370510545443, 1.70150531759288, 0.6116225994499073, 0.4822683816523828, 0.289399390484273, 0.0, 4.132311230668798, 3.1833932953270025, 2.411341908261914, 1.8348677983497215, 3.40301063518576, 1.653311871476362, 1.1780483164862026, 1.0981852862977648, 1.548657937748396, 1.1543901758699768, 0.6801316981626705, 0.24056150975098944, 0.0), # 167
(3.6354191468222377, 2.562594112273321, 3.3026999490853743, 3.3613849233357964, 3.0076749541222974, 1.49408215766915, 1.1416805909981413, 1.14779111218484, 1.6541285821465712, 0.593271633434081, 0.4678952100006882, 0.28085628040912564, 0.0, 4.012393514147377, 3.0894190845003817, 2.339476050003441, 1.7798149003022425, 3.3082571642931424, 1.606907557058776, 1.1416805909981413, 1.06720154119225, 1.5038374770611487, 1.1204616411119324, 0.660539989817075, 0.2329631011157565, 0.0), # 168
(3.5170580421636095, 2.476031109246739, 3.1998352419551552, 3.254833629918398, 2.913613473185848, 1.4483833178082435, 1.1038911275011476, 1.1128423159630365, 1.6041112052616677, 0.5741218142546607, 0.4528805421202569, 0.27191932671433366, 0.0, 3.8866167195184715, 2.99111259385767, 2.2644027106012845, 1.7223654427639818, 3.2082224105233355, 1.5579792423482512, 1.1038911275011476, 1.0345595127201739, 1.456806736592924, 1.0849445433061329, 0.639967048391031, 0.22509373720424902, 0.0), # 169
(3.3943953883346305, 2.386803034447611, 3.092517169097318, 3.1439674876176547, 2.8155416541736935, 1.4005721186301625, 1.064824457020666, 1.076252315007012, 1.5516879424142478, 0.5542508874497488, 0.4372861963200017, 0.262626010854848, 0.0, 3.7555264264722337, 2.8888861194033275, 2.1864309816000085, 1.6627526623492461, 3.1033758848284956, 1.5067532410098168, 1.064824457020666, 1.0004086561644019, 1.4077708270868468, 1.0479891625392184, 0.6185034338194636, 0.21698209404069194, 0.0), # 170
(3.2679022599771006, 2.2952253241424105, 2.9811985301864894, 3.029237336693491, 2.7138697185720826, 1.3508577975309197, 1.0246251105821418, 1.038182761934646, 1.4970935490803914, 0.5337365985574485, 0.4211739909088349, 0.25301381428561937, 0.0, 3.6196682146988195, 2.7831519571418126, 2.1058699545441746, 1.6012097956723452, 2.9941870981607828, 1.4534558667085042, 1.0246251105821418, 0.9648984268077998, 1.3569348592860413, 1.0097457788978306, 0.5962397060372979, 0.20865684764931008, 0.0), # 171
(3.1380497317328193, 2.2016134145976136, 2.8663321248972937, 2.9110940174058286, 2.6090078878672616, 1.299449591906527, 0.9834376192110198, 0.9987953093638179, 1.4405627807361772, 0.5126566931158621, 0.40460574419566947, 0.24312021846159862, 0.0, 3.4795876638883825, 2.6743224030775843, 2.0230287209783473, 1.5379700793475861, 2.8811255614723543, 1.398313433109345, 0.9834376192110198, 0.9281782799332335, 1.3045039439336308, 0.9703646724686098, 0.5732664249794588, 0.20014667405432854, 0.0), # 172
(3.0053088782435884, 2.1062827420796935, 2.748370752904356, 2.7899883700145893, 2.5013663835454807, 1.2465567391529966, 0.941406513932745, 0.9582516099124061, 1.3823303928576847, 0.4910889166630924, 0.3876432744894179, 0.2329827048377363, 0.0, 3.335830353731078, 2.562809753215099, 1.938216372447089, 1.4732667499892769, 2.7646607857153693, 1.3415522538773685, 0.941406513932745, 0.8903976708235689, 1.2506831917727403, 0.9299961233381967, 0.5496741505808713, 0.19148024927997215, 0.0), # 173
(2.8701507741512065, 2.009548742855125, 2.627767213882301, 2.6663712347796937, 2.3913554270929867, 1.19238847666634, 0.8986763257727628, 0.9167133161982903, 1.322631140920993, 0.46911101473724215, 0.37034840009899284, 0.22263875486898346, 0.0, 3.1889418639170604, 2.4490263035588176, 1.851742000494964, 1.4073330442117262, 2.645262281841986, 1.2833986426776065, 0.8986763257727628, 0.8517060547616714, 1.1956777135464933, 0.8887904115932315, 0.5255534427764603, 0.18268624935046593, 0.0), # 174
(2.7330464940974735, 1.9117268531903824, 2.5049743075057544, 2.5406934519610656, 2.279385239996028, 1.1371540418425703, 0.8553915857565175, 0.8743420808393492, 1.2616997804021812, 0.4468007328764138, 0.352782939333307, 0.21212585001029077, 0.0, 3.039467774136485, 2.333384350113198, 1.763914696666535, 1.3404021986292411, 2.5233995608043625, 1.2240789131750889, 0.8553915857565175, 0.8122528870304073, 1.139692619998014, 0.8468978173203554, 0.5009948615011509, 0.1737933502900348, 0.0), # 175
(2.5944671127241916, 1.8131325093519404, 2.3804448334493395, 2.4134058618186263, 2.165866043740852, 1.0810626720776986, 0.8116968249094546, 0.8312995564534624, 1.1997710667773285, 0.42423581661871024, 0.33500871050127307, 0.201481471716609, 0.0, 2.8879536640795047, 2.2162961888826986, 1.6750435525063654, 1.2727074498561304, 2.399542133554657, 1.1638193790348474, 0.8116968249094546, 0.7721876229126418, 1.082933021870426, 0.804468620606209, 0.476088966689868, 0.16483022812290368, 0.0), # 176
(2.45488370467316, 1.7140811476062734, 2.254631591387682, 2.284959304612298, 2.0512080598137095, 1.0243236047677373, 0.7677365742570189, 0.7877473956585084, 1.137079755522514, 0.40149401150223385, 0.31708753191180367, 0.190743101442889, 0.0, 2.734945113436275, 2.0981741158717786, 1.585437659559018, 1.204482034506701, 2.274159511045028, 1.1028463539219118, 0.7677365742570189, 0.7316597176912409, 1.0256040299068547, 0.7616531015374328, 0.4509263182775365, 0.1558255588732976, 0.0), # 177
(2.3147673445861785, 1.6148882042198558, 2.1279873809954073, 2.1558046206020025, 1.9358215097008458, 0.9671460773086992, 0.7236553648246553, 0.7438472510723665, 1.0738606021138173, 0.37865306306508767, 0.2990812218738113, 0.1799482206440814, 0.0, 2.5809877018969516, 1.979430427084895, 1.4954061093690565, 1.1359591891952627, 2.1477212042276346, 1.0413861515013132, 0.7236553648246553, 0.6908186266490708, 0.9679107548504229, 0.7186015402006676, 0.4255974761990815, 0.14680801856544146, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
74, # 1
)
| 277.560428
| 500
| 0.770637
| 32,987
| 259,519
| 6.062509
| 0.205475
| 0.356429
| 0.342027
| 0.648052
| 0.383546
| 0.372895
| 0.366114
| 0.362564
| 0.361994
| 0.361739
| 0
| 0.850587
| 0.095307
| 259,519
| 934
| 501
| 277.857602
| 0.001188
| 0.015456
| 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
|
5b5dcc28444866756538a11926b6d489a50ed7d2
| 95
|
py
|
Python
|
build/lib/dsrt/config/__init__.py
|
sbarham/dsrt
|
bc664739f2f52839461d3e72773b71146fd56a9a
|
[
"MIT"
] | 1
|
2019-02-11T10:05:33.000Z
|
2019-02-11T10:05:33.000Z
|
dsrt/config/__init__.py
|
sbarham/dsrt
|
bc664739f2f52839461d3e72773b71146fd56a9a
|
[
"MIT"
] | null | null | null |
dsrt/config/__init__.py
|
sbarham/dsrt
|
bc664739f2f52839461d3e72773b71146fd56a9a
|
[
"MIT"
] | null | null | null |
from . Configuration import Configuration
from . ConfigurationLoader import ConfigurationLoader
| 47.5
| 53
| 0.884211
| 8
| 95
| 10.5
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094737
| 95
| 2
| 53
| 47.5
| 0.976744
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
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